From 59b642b18d2db39ed95c18842f44ed939cfa8cfd Mon Sep 17 00:00:00 2001 From: javedhassans Date: Sun, 27 Oct 2024 20:21:54 +0100 Subject: [PATCH 01/36] added notebooks src project_config.yml data --- .github/workflows/ci.yml | 3 +- .gitignore | 2 + data/childHealth.csv | 3961 +++++++++++++++++++++++++++++ databricks.yml | 19 + notebooks/01.prepare_dataset.py | 22 + notebooks/dataExploration.ipynb | 461 ++++ project_config.yml | 101 + pyproject.toml | 13 +- src/childHealth/__init__.py | 0 src/childHealth/config.py | 18 + src/childHealth/data_processor.py | 111 + src/childHealth/utils.py | 23 + uv.lock | 2197 ++++++++++++++++ 13 files changed, 6927 insertions(+), 4 deletions(-) create mode 100644 data/childHealth.csv create mode 100644 databricks.yml create mode 100644 notebooks/01.prepare_dataset.py create mode 100644 notebooks/dataExploration.ipynb create mode 100644 project_config.yml create mode 100644 src/childHealth/__init__.py create mode 100644 src/childHealth/config.py create mode 100644 src/childHealth/data_processor.py create mode 100644 src/childHealth/utils.py create mode 100644 uv.lock diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 04d2833..2864ae2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -21,5 +21,6 @@ jobs: run: uv sync - name: Run pre-commit checks - run: | + run: + pip install pre-commit pre-commit run --all-files diff --git a/.gitignore b/.gitignore index 9876912..6f423de 100644 --- a/.gitignore +++ b/.gitignore @@ -95,3 +95,5 @@ dmypy.json # VS code configuration .vscode .history + +.databricks diff --git a/data/childHealth.csv b/data/childHealth.csv new file mode 100644 index 0000000..1087723 --- /dev/null +++ b/data/childHealth.csv @@ -0,0 +1,3961 @@ +id,Basic_Demos-Enroll_Season,Basic_Demos-Age,Basic_Demos-Sex,CGAS-Season,CGAS-CGAS_Score,Physical-Season,Physical-BMI,Physical-Height,Physical-Weight,Physical-Waist_Circumference,Physical-Diastolic_BP,Physical-HeartRate,Physical-Systolic_BP,Fitness_Endurance-Season,Fitness_Endurance-Max_Stage,Fitness_Endurance-Time_Mins,Fitness_Endurance-Time_Sec,FGC-Season,FGC-FGC_CU,FGC-FGC_CU_Zone,FGC-FGC_GSND,FGC-FGC_GSND_Zone,FGC-FGC_GSD,FGC-FGC_GSD_Zone,FGC-FGC_PU,FGC-FGC_PU_Zone,FGC-FGC_SRL,FGC-FGC_SRL_Zone,FGC-FGC_SRR,FGC-FGC_SRR_Zone,FGC-FGC_TL,FGC-FGC_TL_Zone,BIA-Season,BIA-BIA_Activity_Level_num,BIA-BIA_BMC,BIA-BIA_BMI,BIA-BIA_BMR,BIA-BIA_DEE,BIA-BIA_ECW,BIA-BIA_FFM,BIA-BIA_FFMI,BIA-BIA_FMI,BIA-BIA_Fat,BIA-BIA_Frame_num,BIA-BIA_ICW,BIA-BIA_LDM,BIA-BIA_LST,BIA-BIA_SMM,BIA-BIA_TBW,PAQ_A-Season,PAQ_A-PAQ_A_Total,PAQ_C-Season,PAQ_C-PAQ_C_Total,PCIAT-Season,PCIAT-PCIAT_01,PCIAT-PCIAT_02,PCIAT-PCIAT_03,PCIAT-PCIAT_04,PCIAT-PCIAT_05,PCIAT-PCIAT_06,PCIAT-PCIAT_07,PCIAT-PCIAT_08,PCIAT-PCIAT_09,PCIAT-PCIAT_10,PCIAT-PCIAT_11,PCIAT-PCIAT_12,PCIAT-PCIAT_13,PCIAT-PCIAT_14,PCIAT-PCIAT_15,PCIAT-PCIAT_16,PCIAT-PCIAT_17,PCIAT-PCIAT_18,PCIAT-PCIAT_19,PCIAT-PCIAT_20,PCIAT-PCIAT_Total,SDS-Season,SDS-SDS_Total_Raw,SDS-SDS_Total_T,PreInt_EduHx-Season,PreInt_EduHx-computerinternet_hoursday,sii +00008ff9,Fall,5,0,Winter,51,Fall,16.87731569,46.0,50.8,,,,,,,,,Fall,0,0,,,,,0,0,7.0,0,6.0,0,6.0,1,Fall,2,2.66855,16.8792,932.498,1492.0,8.25598,41.5862,13.8177,3.06143,9.21377,1,24.4349,8.89536,38.9177,19.5413,32.6909,,,,,Fall,5,4,4,0,4,0,0,4,0,0,4,0,4,4,4,4,4,4,2,4,55,,,,Fall,3,2 +000fd460,Summer,9,0,,,Fall,14.03559028,48.0,46.0,22.0,75,70,122,,,,,Fall,3,0,,,,,5,0,11.0,1,11.0,1,3.0,0,Winter,2,2.57949,14.0371,936.656,1498.65,6.01993,42.0291,12.8254,1.21172,3.97085,1,21.0352,14.974,39.4497,15.4107,27.0552,,,Fall,2.34,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,46,64,Summer,0,0 +00105258,Summer,10,1,Fall,71,Fall,16.64869606,56.5,75.6,,65,94,117,Fall,5,7,33,Fall,20,1,10.2,1,14.7,2,7,1,10.0,1,10.0,1,5.0,0,,,,,,,,,,,,,,,,,,,,Summer,2.17,Fall,5,2,2,1,2,1,1,2,1,1,1,0,1,1,1,0,2,2,1,1,28,Fall,38,54,Summer,2,0 +00115b9f,Winter,9,0,Fall,71,Summer,18.29234694,56.0,81.6,,60,97,117,Summer,6,9,37,Summer,18,1,,,,,5,0,7.0,0,7.0,0,7.0,1,Summer,3,3.84191,18.2943,1131.43,1923.44,15.5925,62.7757,14.074,4.22033,18.8243,2,30.4041,16.779,58.9338,26.4798,45.9966,,,Winter,2.451,Summer,4,2,4,0,5,1,0,3,2,2,3,0,3,0,0,3,4,3,4,1,44,Summer,31,45,Winter,0,1 +0016bb22,Spring,18,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.04,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +001f3379,Spring,13,1,Winter,50,Summer,22.27995198,59.5,112.2,,60,73,102,,,,,Summer,12,0,16.5,2,17.9,2,6,0,10.0,1,11.0,1,8.0,0,Summer,2,4.33036,30.1865,1330.97,1996.45,30.2124,84.0285,16.6877,13.4988,67.9715,2,32.9141,20.902,79.6982,35.3804,63.1265,,,Spring,4.11,Summer,3,3,3,0,2,1,0,2,2,1,0,1,3,3,2,1,3,1,2,1,34,Summer,40,56,Spring,0,1 +0038ba98,Fall,10,0,,,Fall,19.66076033,55.0,84.6,,123,83,163,,,,,Fall,9,1,,,,,2,0,11.0,1,11.0,1,11.0,1,Fall,2,3.78271,19.6629,1135.86,1817.38,16.3275,63.247,14.7,4.96291,21.353,2,30.8936,16.0259,59.4643,26.1957,47.2211,,,Winter,3.67,Winter,1,4,1,0,2,1,0,1,0,0,0,0,0,0,0,4,1,4,1,0,20,Winter,27,40,Fall,3,0 +0068a485,Fall,10,1,,,Fall,16.86128647,59.25,84.2,27.0,71,90,116,,,,,Fall,0,0,12.6,2,11.1,1,0,0,0.0,0,0.0,0,4.0,0,Fall,3,4.05726,16.8631,1180.04,1888.06,21.94,67.9527,13.6092,3.25395,16.2474,2,28.5367,17.476,63.8954,28.768,50.4767,,,Fall,1.27,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +0069fbed,Summer,15,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +0083e397,Summer,19,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0087dd65,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +00abe655,Fall,11,0,Summer,66,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.1,Winter,2,2,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,10,Winter,42,59,Fall,0,0 +00ae59c9,Fall,13,0,,,Winter,21.07906523,57.75,100.0,,63,79,150,,,,,Winter,24,1,19.2,1,18.4,1,20,1,8.0,1,9.5,1,12.5,1,Winter,5,5.08025,21.0814,1239.46,2974.71,19.9545,74.2823,15.6597,5.42165,25.7177,2,36.0572,18.2705,69.202,36.2232,56.0118,,,Fall,3.02,Fall,3,2,3,2,3,3,0,1,2,1,0,0,2,1,1,1,1,2,2,1,31,Fall,33,47,Fall,1,1 +00af6387,Spring,12,0,,,Spring,15.54411111,60.0,79.6,24.0,57,71,103,,,,,Spring,10,0,22.3,2,21.6,2,7,0,7.0,0,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.22,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +00bd4359,Spring,12,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +00c0cd71,Winter,7,0,Summer,51,Spring,29.31577503,54.0,121.6,,80,75,99,Spring,4,5,32,Spring,6,1,,,,,0,0,12.0,1,15.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,4,3,5,1,1,4,1,1,4,1,4,4,4,2,4,3,3,1,58,Spring,35,50,Winter,2,2 +00d56d4b,Spring,5,1,Summer,80,Spring,17.28450413,44.0,47.6,,61,76,109,Spring,,,,Spring,0,0,,,,,0,0,10.5,1,10.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,37,53,Spring,0,0 +00d9913d,Fall,10,1,,,Fall,19.89315702,55.0,85.6,30.0,,81,,,,,,Fall,5,0,,,,,0,0,0.0,0,0.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +00e6167c,Winter,6,0,Spring,60,Winter,30.09464889,37.5,60.2,24.0,61,91,95,,,,,Winter,6,1,,,,,0,0,4.0,0,4.0,0,7.0,1,Winter,2,2.75035,17.2738,1003.07,1504.61,15.1456,49.1034,14.0898,3.18407,11.0966,1,23.6182,10.3396,46.3531,19.8886,38.7638,,,,,Winter,3,3,2,0,4,0,0,0,0,2,2,0,3,0,0,2,3,3,1,0,28,Winter,39,55,Winter,3,0 +00ebc35d,Winter,10,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +00f332d1,Winter,14,0,Spring,68,Spring,17.16863588,66.5,108.0,,60,71,129,,,,,Spring,16,0,18.4,1,20.9,2,22,1,3.5,0,5.5,0,13.0,1,Spring,4,5.93056,17.1705,1414.24,2969.9,27.2818,92.898,14.7695,2.40101,15.102,2,41.4004,24.2158,86.9674,42.1074,68.6822,,,Winter,2.32,Spring,4,4,5,1,4,3,1,1,1,3,4,1,1,1,3,1,3,2,2,2,47,Spring,30,43,Winter,2,1 +00f574e9,Summer,16,0,,,Summer,29.28585598,71.0,210.0,35.0,73,62,132,,,,,Summer,20,0,47.4,3,46.6,3,10,0,9.75,1,12.0,1,5.0,0,,,,,,,,,,,,,,,,,,Summer,2.619,,,Summer,2,2,2,1,3,4,3,1,4,0,0,0,2,1,1,1,0,2,1,0,30,,,,Summer,3,0 +01085eb3,Fall,12,0,Winter,58,Fall,34.18728229,60.5,178.0,,67,81,119,,,,,Fall,8,0,12.6,2,17.6,2,0,0,4.0,0,6.0,0,11.0,1,Fall,1,5.15068,34.191,1551.2,2016.56,33.4786,107.486,20.6463,13.5447,70.5142,3,47.2238,26.7834,102.335,44.5863,80.7024,,,Fall,1.28,Fall,2,2,3,1,1,3,1,3,3,2,2,0,2,0,1,0,2,1,1,0,30,Fall,53,73,Fall,0,0 +01182ce3,Winter,13,0,Summer,72,Winter,20.61306448,69.0,139.6,,66,76,112,,,,,Winter,18,0,26.4,2,29.2,2,10,0,11.5,1,12.0,1,15.0,1,,,,,,,,,,,,,,,,,,,,Winter,4.12,Spring,5,4,4,3,3,3,0,1,2,2,4,1,3,4,2,2,2,3,2,1,51,Winter,48,67,Winter,3,2 +011e71c3,Summer,15,0,Fall,70,Summer,21.17778356,62.0,115.8,,61,58,144,,,,,Summer,0,0,21.4,1,20.9,1,5,0,4.0,0,4.0,0,4.0,0,Fall,4,8.72618,21.1801,1289.74,2708.46,20.5717,79.6375,14.5659,6.61421,36.1625,2,35.9062,23.1596,70.9113,35.1487,56.4779,Summer,3.53,,,Summer,2,2,3,1,2,1,1,1,2,1,2,1,2,1,1,2,1,2,,2,30,Summer,34,49,Summer,1,0 +012cadd8,Spring,9,0,Winter,60,Fall,17.08915145,53.2,68.8,,62,88,123,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.51,Fall,1,1,1,0,1,1,0,0,0,0,1,0,0,1,1,0,1,0,0,0,9,Summer,27,40,Spring,0,0 +012e3869,Summer,6,0,Winter,60,Summer,,,,,73,86,110,,,,,Summer,0,0,,,,,0,0,9.5,1,9.5,1,6.0,1,Fall,2,2.786,25.2668,1027.23,1643.57,11.0505,51.6765,15.7693,9.49748,31.1235,2,26.7417,13.8843,48.8905,21.1339,37.7922,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +014c303e,Spring,6,0,Summer,55,Summer,17.56704391,47.0,55.2,,73,98,111,Summer,2,1,45,Summer,0,0,,,,,0,0,10.0,1,9.0,1,8.0,1,Summer,2,2.72421,17.569,950.847,1521.36,9.01294,43.5407,13.8581,3.71091,11.6593,2,24.1185,10.4092,40.8165,17.5461,33.1315,,,,,Summer,3,3,3,0,1,1,0,0,0,0,0,0,1,1,0,1,1,1,1,1,18,Summer,39,55,Summer,2,0 +015ba5b1,Spring,13,0,Fall,80,Fall,23.23909228,60.2,119.8,,72,76,130,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.86,Fall,4,4,4,1,4,2,4,3,2,3,2,1,3,2,3,2,3,1,1,1,50,Summer,36,51,Spring,3,2 +0194c251,Fall,8,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +019c9266,Summer,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +01cae1ef,Winter,12,0,,,Winter,18.47168367,56.0,82.4,25.0,72,91,128,,,,,Winter,20,1,18.7,1,16.7,1,5,0,8.0,1,9.0,1,11.0,1,Winter,2,3.99518,18.4737,1155.54,1848.86,17.2428,65.3429,14.6496,3.82413,17.0571,1,32.0735,16.0265,61.3477,28.5582,49.3164,,,Winter,3.019,Winter,1,1,2,1,1,1,1,1,1,1,2,0,0,0,1,0,1,1,0,0,16,Winter,29,42,Winter,2,0 +01d24f60,Fall,16,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.8,,,,,,,,,,,,,,,,,,,,,,,,,Winter,67,91,Fall,, +01dc6dcd,Spring,7,0,,,Spring,18.44606414,49.0,63.0,25.0,61,76,117,,,,,Spring,13,1,,,,,0,0,12.0,1,12.0,1,10.0,1,Spring,2,2.95921,18.4481,995.547,1592.87,10.5177,48.3018,14.1441,4.30402,14.6982,1,25.9992,11.7849,45.3426,20.7573,36.5169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +01e9a5c0,Spring,7,0,,,Summer,13.23164396,50.5,48.0,,73,113,103,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +01f83da1,Fall,15,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +02073150,Winter,7,0,Spring,65,Winter,17.60312,50.0,62.6,,54,92,90,Winter,5,5,21,Winter,4,1,,,,,8,1,7.5,1,8.0,1,6.0,1,Spring,5,5.91061,17.605,1006.07,2414.57,10.3692,49.4226,13.8991,3.7059,13.1774,2,26.7413,12.312,43.512,23.8351,37.1105,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,39,55,Winter,0,0 +02132c62,Spring,10,0,,,Spring,15.54711538,52.0,59.8,26.0,59,74,108,,,,,Spring,0,0,,,,,1,0,6.0,0,7.0,0,11.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.33,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +0219f64a,Winter,6,0,Summer,68,Spring,16.37768094,48.5,54.8,24.0,67,81,111,,,,,Spring,0,0,,,,,4,1,12.0,1,12.0,1,9.0,1,Summer,3,2.89258,16.3795,968.274,1646.07,9.24597,45.3969,13.5689,2.81055,9.40313,1,25.1746,10.9763,42.5043,20.7489,34.4206,,,,,Spring,5,4,4,4,5,2,0,0,0,2,4,1,2,3,3,3,3,3,2,1,51,Spring,69,94,Winter,1,2 +021b2020,Winter,9,1,,,Winter,20.63132151,53.5,84.0,28.0,64,81,120,,,,,Spring,9,0,,,,,3,0,13.0,1,13.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.22,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +0232fd98,Winter,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,49,68,Winter,, +02347f13,Fall,10,0,,,Fall,20.0337834,59.0,99.2,28.0,79,88,109,,,,,Fall,1,0,17.8,2,19.3,2,0,0,0.0,0,0.0,0,8.0,0,Fall,2,4.0253,20.036,1189.42,1903.07,16.7031,68.9515,13.9265,6.10945,30.2485,2,30.5582,21.6902,64.9262,25.2613,47.2613,,,Fall,2.521,Fall,3,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Fall,48,67,Fall,2,0 +023e1b6a,Summer,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +023eba7b,Winter,16,0,,,Winter,21.9377014,66.5,138.0,31.0,70,99,124,,,,,Spring,25,1,30.84,2,34.02,2,20,1,0.0,0,4.0,0,12.0,1,,,,,,,,,,,,,,,,,,Winter,1.42,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +026102c4,Fall,7,1,Summer,66,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,21,Fall,33,47,Fall,0,0 +02648a1c,Spring,16,1,Fall,85,Summer,21.39724246,62.0,117.0,,70,75,102,,,,,Summer,20,1,21.4,2,22.6,2,12,1,14.5,1,14.5,1,16.0,1,Summer,3,0.37706,21.3996,1354.75,2167.59,29.0937,86.5613,15.8323,5.5673,30.4387,2,36.5115,20.9562,86.1843,46.5891,65.6052,Spring,2.51,,,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Summer,35,50,Spring,0,0 +026fcbc3,Winter,10,0,Fall,80,Winter,18.66049986,59.0,92.4,,76,87,116,Winter,5,7,32,Winter,10,1,,,,,0,0,6.5,0,7.0,0,15.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.15,Winter,2,2,3,0,2,2,0,0,1,1,1,0,2,1,1,2,1,2,1,1,25,Winter,43,60,Winter,0,0 +0274e7f3,Fall,21,1,Winter,50,Fall,23.59802567,60.24,121.8,28.0,79,76,108,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +029a19c9,Winter,17,1,Spring,54,Spring,32.08410725,64.25,188.4,,72,67,133,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.31,,,Spring,1,4,2,1,1,4,1,1,1,1,2,1,1,1,4,1,4,1,1,1,34,Spring,32,46,Winter,3,1 +02a01f18,Summer,6,0,Winter,51,Fall,15.82407922,45.5,46.6,,64,84,98,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1,1,2,1,2,0,0,0,0,0,1,0,2,0,3,2,1,1,1,0,18,Fall,38,54,Summer,0,0 +02c4cf7f,Spring,9,0,Summer,81,Spring,27.72462277,54.0,115.0,,57,71,120,Spring,5,7,31,Spring,0,0,,,,,7,1,8.5,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.89,Spring,4,4,4,4,4,1,0,2,1,0,4,0,1,0,3,0,2,0,2,0,36,Spring,42,59,Spring,0,1 +02cebf33,Spring,12,1,Spring,77,Summer,19.75130073,62.0,108.0,30.0,55,60,98,,,,,Summer,30,1,24.1,3,23.5,3,4,0,11.5,1,12.0,1,9.0,1,Summer,3,4.73111,19.7535,1300.75,2081.19,27.1475,80.8095,14.7803,4.9732,27.1905,2,33.4268,20.2352,76.0784,38.5561,60.5743,,,Fall,2.14,Summer,4,4,4,1,5,2,0,0,1,0,2,0,3,0,4,4,3,3,3,3,46,Summer,82,100,Spring,1,1 +02cf7384,Summer,16,1,Winter,85,Fall,20.25244141,64.0,118.0,,67,72,125,,,,,Fall,30,1,22.3,2,27.5,2,4,0,12.0,1,11.5,1,10.0,1,Fall,2,0.95738,20.2547,1338.5,2007.74,28.3479,84.8304,14.5611,5.69355,33.1696,1,34.2929,22.1896,83.873,41.07,62.6408,Fall,2.13,,,Fall,3,2,2,1,3,1,0,1,1,2,0,0,1,1,1,1,2,2,1,1,26,Fall,36,51,Summer,2,0 +02dcf62a,Winter,12,1,Summer,85,Spring,15.01601994,53.0,60.0,,64,74,117,Spring,7,11,40,Spring,28,1,13.4,2,17.1,2,17,1,10.0,1,11.5,1,13.0,1,Spring,3,8.18096,15.0177,1031.01,1649.61,13.9679,52.0787,13.035,1.98264,7.92125,1,23.6422,14.4687,43.8978,23.8762,37.6101,,,Spring,3.26,Spring,2,2,1,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,10,Spring,36,51,Winter,0,0 +02e56a7c,Fall,10,1,Spring,85,Fall,15.81687433,53.0,63.2,,74,94,113,Fall,5,7,29,Fall,6,0,11.9,1,13.6,2,10,1,12.0,1,11.0,1,8.0,0,Fall,2,6.83364,15.8186,1041.87,1562.81,15.6865,53.2364,13.3248,2.49384,9.96363,1,23.8971,13.6528,46.4027,21.4404,39.5836,,,Fall,2.729,Fall,1,2,2,1,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,10,Fall,30,43,Fall,2,0 +0347462d,Winter,17,0,Spring,80,Spring,21.10540541,74.0,164.4,,68,83,120,,,,,Spring,27,1,30.4,1,28.1,1,10,0,0.0,0,0.0,0,6.0,0,Spring,4,-3.17777,21.1077,1767.38,3711.5,40.8109,130.513,16.7568,4.3509,33.8875,3,54.7546,34.947,133.69,60.952,95.5655,Spring,1.61,,,Spring,2,2,2,2,2,1,1,2,1,2,3,1,2,2,2,1,2,1,2,1,34,Spring,37,53,Winter,2,1 +034f923b,Spring,14,1,Summer,51,Spring,23.17308329,64.75,138.2,33.0,53,60,116,,,,,Spring,15,0,23.7,2,23.9,2,0,0,15.0,1,14.5,1,11.0,1,Spring,4,5.41636,23.1756,1435.88,2728.18,33.0617,95.2034,15.9653,7.21036,42.9966,2,37.9593,24.1824,89.7871,47.1436,71.021,Spring,2.79,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,52,72,Spring,0,0 +0357a10e,Summer,7,0,Summer,85,Summer,16.59861111,48.0,54.4,,76,89,115,Summer,5,8,41,Summer,5,1,,,,,1,0,9.5,1,9.5,1,7.0,1,Summer,2,7.19046,16.6004,955.573,1528.92,8.30275,44.0441,13.4403,3.16015,10.3559,1,23.7511,11.9903,36.8537,18.271,32.0538,,,,,Summer,5,4,4,1,3,1,0,3,0,1,4,0,1,2,2,3,3,3,3,0,43,Summer,49,68,Summer,0,1 +035c96dd,Summer,13,1,Spring,78,Fall,18.33007407,67.5,118.8,,86,63,115,,,,,Fall,13,0,0.0,1,17.2,2,0,0,4.0,0,4.0,0,3.0,0,Fall,2,0.654295,18.3321,1357.06,2035.58,29.4147,86.8074,13.3953,4.93679,31.9926,1,32.5716,24.8211,86.1531,34.7427,61.9863,,,Summer,1.74,Fall,2,1,2,1,1,2,1,2,2,2,1,1,2,1,1,2,1,2,1,1,29,Fall,33,47,Summer,0,0 +0380e709,Summer,17,0,,,Summer,24.44688581,68.0,160.8,33.0,81,56,123,,,,,Summer,27,1,106.4,2,123.8,3,20,1,10.0,1,11.0,1,8.0,0,Fall,4,3.50228,19.3852,1094.35,2298.13,13.4863,58.8256,14.1834,5.20181,21.5744,2,29.0553,16.284,55.3233,25.7529,42.5416,Summer,3.27,,,Summer,4,2,3,4,4,4,2,1,5,4,2,2,4,2,2,2,1,4,2,2,56,,,,Summer,,2 +03833cec,Spring,9,0,Spring,52,Spring,14.70404931,48.5,49.2,23.0,,,,,,,,Spring,2,0,,,,,0,0,8.0,1,7.5,1,9.5,1,Spring,1,2.89401,14.4071,956.107,1242.94,8.85289,44.101,12.9139,1.49313,5.09904,1,23.8051,11.443,41.207,17.8348,32.658,,,Spring,1.55,Spring,3,0,4,0,3,0,0,3,2,0,2,0,1,0,2,0,0,0,0,0,20,Spring,35,50,Spring,0,0 +0391e587,Spring,10,0,Fall,70,Winter,15.53905948,58.25,75.0,,66,86,114,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,1,4.09331,15.5408,1137.1,1478.23,13.8359,63.3793,13.1328,2.40792,11.6207,2,31.7403,17.8032,59.286,36.3143,45.5762,,,Spring,1.04,Spring,4,4,4,1,5,1,0,2,2,2,3,0,4,4,4,4,4,4,3,3,58,Spring,42,59,Spring,0,2 +03a5c2d6,Summer,10,1,Winter,70,Winter,14.41667452,55.25,62.6,,56,60,110,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.17,Winter,1,1,2,1,1,1,2,1,2,1,2,1,1,1,1,1,2,1,1,1,25,Fall,49,68,Summer,0,0 +03a9019b,Spring,10,0,Fall,90,Fall,17.77027778,60.0,91.0,,86,74,129,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.75,Spring,3,5,5,1,5,1,0,1,1,1,1,1,1,1,3,3,3,3,1,1,41,Spring,35,50,Spring,0,1 +03bf5026,Winter,8,1,Spring,75,Winter,18.71309266,51.5,70.6,27.0,55,73,100,,,,,Winter,7,1,,,,,0,0,9.5,1,10.0,1,8.5,1,Spring,2,2.81644,18.7151,1030.71,1546.07,15.0127,52.0476,13.7971,4.91801,18.5524,1,23.0592,13.9756,49.2311,20.5685,38.072,,,,,Winter,3,3,4,1,2,1,0,1,1,1,0,0,1,1,1,1,2,2,2,1,28,Winter,47,66,Winter,2,0 +03e83fef,Summer,21,1,,,Summer,22.81321603,60.25,117.8,30.0,56,70,94,,,,,,,,,,,,,,,,,,,,Summer,3,4.39223,22.8157,1278.22,2045.16,26.1638,78.4106,15.1867,7.62901,39.3894,1,32.6721,19.5747,74.0183,38.7088,58.8359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +03f1c1c8,Winter,12,1,Spring,72,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.65,Winter,5,5,4,1,4,2,0,3,2,3,5,0,4,4,4,3,4,3,4,2,62,Winter,47,66,Winter,2,2 +040d103f,Spring,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0417c91e,Spring,6,1,Winter,40,Summer,15.4845067,45.5,45.6,,73,86,115,Summer,6,9,5,Summer,3,1,,,,,0,0,11.0,1,10.0,1,11.0,1,Summer,1,2.27172,15.4862,924.473,1201.81,11.0341,40.7315,13.8328,1.65339,4.86851,1,21.7147,7.98271,38.4598,18.7106,32.7488,,,,,Summer,3,0,2,0,2,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,11,,,,Summer,0,0 +04189592,Fall,10,0,,,Fall,16.76845123,54.25,70.2,,62,94,84,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +04370442,Fall,6,1,Summer,81,Spring,16.48618506,50.75,60.4,,67,81,80,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,5,2.84891,16.488,1014.68,2232.3,14.9136,50.3402,13.7419,2.74613,10.0598,3,23.8913,11.5353,47.4913,21.6158,38.8049,,,,,Spring,5,5,5,0,5,5,0,5,5,5,4,0,5,0,0,5,5,5,5,5,74,Spring,36,51,Winter,1,2 +0441d6af,Winter,5,1,,,Winter,13.94380165,44.0,38.4,21.0,62,99,100,,,,,Winter,0,0,,,,,0,0,0.0,0,0.0,0,10.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +045a0a94,Summer,16,0,Fall,55,Fall,22.50308759,69.0,152.4,,66,110,116,,,,,Fall,10,0,25.8,1,28.5,1,8,0,0.0,0,0.0,0,13.0,1,Fall,3,-1.32046,22.5055,1742.01,2961.41,40.2764,127.81,18.8742,3.63133,24.5902,2,55.4828,32.0506,129.13,64.0507,95.7592,Fall,3.75,,,Fall,3,4,5,2,3,1,0,3,2,0,5,0,4,0,2,4,4,5,4,2,53,Fall,31,45,Summer,3,2 +0470efa4,Spring,12,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +0471236c,Spring,6,0,Summer,55,Summer,17.23158747,46.5,53.0,24.0,63,76,106,,,,,Summer,0,0,,,,,0,0,6.0,0,6.0,0,3.0,0,Summer,1,2.73182,17.2335,945.128,1228.67,7.98917,42.9316,13.9596,3.27385,10.0684,2,25.7573,9.18505,40.1997,24.7757,33.7465,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +0499c3e8,Summer,12,0,,,Summer,18.94760066,64.75,113.0,29.0,65,94,108,,,,,Summer,6,0,19.3,1,23.3,2,0,0,5.0,0,5.0,0,9.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,1.76,Summer,4,4,4,1,4,2,2,2,2,4,0,0,1,1,2,1,3,3,2,1,43,Summer,32,46,Summer,2,1 +04a1633e,Spring,7,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +04ad419d,Fall,8,1,Winter,45,Fall,23.81348842,49.5,83.0,,64,83,119,Fall,5,7,52,Fall,5,1,,,,,0,0,14.0,1,13.0,1,11.0,1,Fall,3,11.0171,23.8161,1025.99,1641.59,14.451,51.5448,14.7903,9.02579,31.4552,3,23.2818,13.812,40.5277,23.6171,37.7327,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,55,76,Fall,0,0 +04aeed24,Winter,14,1,,,,,,,,,,,,,,,Winter,40,1,19.2,2,21.4,2,12,1,13.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.87,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +04af8fdd,Winter,16,1,Spring,62,Winter,21.43184681,63.0,121.0,26.0,76,70,127,,,,,Winter,11,0,13.1,1,21.1,2,0,0,8.5,0,9.0,0,8.0,0,Spring,3,4.79452,21.4342,1318.29,2109.26,28.2822,82.6778,14.6457,6.78847,38.3222,1,32.7155,21.6802,77.8833,35.9192,60.9977,Winter,2.15,,,Winter,1,2,2,2,1,0,0,1,1,2,2,1,1,0,0,0,0,0,0,0,16,,,,Winter,0,0 +04afb6f9,Summer,17,1,Winter,62,Fall,25.45427491,63.5,146.0,,70,86,120,,,,,Fall,13,0,19.1,2,16.9,1,0,0,8.0,0,9.0,0,13.0,1,Fall,1,2.26165,25.4571,1404.94,1826.43,32.7381,91.9079,16.0254,9.43168,54.0921,2,35.6892,23.4806,89.6463,40.5142,68.4273,Fall,1.73,,,Fall,3,3,3,2,3,3,2,3,3,2,3,2,3,3,2,5,3,3,2,3,56,,,,Summer,2,2 +04b3cc70,Fall,9,1,Winter,80,Fall,14.51548594,53.0,58.0,,58,73,112,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.77,Fall,2,1,2,1,1,0,0,0,0,0,2,0,1,0,1,0,1,0,0,0,12,Fall,45,63,Fall,0,0 +04bb1a76,Winter,9,0,Spring,80,Winter,18.22592593,54.0,75.6,,70,75,113,Winter,5,7,25,Winter,6,0,,,,,1,0,7.0,0,11.0,1,13.0,1,Winter,3,6.79823,18.2279,1084.91,1844.34,13.6988,57.8199,13.941,4.28696,17.7801,2,28.4652,15.6559,51.0217,23.6277,42.164,,,Winter,2.08,Winter,5,4,5,4,5,5,0,4,1,4,0,4,4,0,5,4,4,4,4,5,71,Winter,47,66,Winter,2,2 +04cb2c30,Spring,8,1,Summer,72,Fall,19.31080247,54.0,80.1,,67,77,119,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.28,Summer,5,4,4,0,3,2,0,1,2,4,2,0,1,0,2,3,2,3,1,2,41,Summer,51,71,Spring,0,1 +04ccb838,Summer,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +04d06a9c,Fall,10,0,Winter,64,Fall,15.78439062,51.0,58.4,,55,81,102,Fall,5,7,37,Fall,10,1,,,,,11,1,9.0,1,11.0,1,10.0,1,Winter,4,3.12289,15.7861,1002.46,2105.17,10.4103,49.0384,13.2556,2.53053,9.36156,1,25.5138,13.1143,45.9155,20.4006,35.9241,,,Fall,2.05,Fall,2,2,3,0,3,1,0,1,0,2,0,0,2,5,5,2,2,2,1,2,35,Fall,45,63,Fall,1,1 +04dc7df1,Winter,10,1,Fall,80,Winter,21.31760791,54.6,90.4,,68,86,104,Winter,,,,Winter,13,1,,,,,0,0,5.5,0,5.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.0,Winter,5,5,5,0,5,2,0,0,0,3,3,0,4,0,3,3,2,2,2,1,45,Winter,42,59,Winter,0,1 +04df895a,Winter,17,0,,,Winter,26.63076404,77.0,224.6,37.0,72,71,144,,,,,Winter,36,1,45.4,2,46.7,2,22,1,17.0,1,16.5,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,4.17,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +04f094a8,Winter,10,0,Spring,70,Winter,15.55746173,56.0,69.4,,67,69,108,Winter,6,10,58,Winter,18,1,,,,,0,0,7.0,0,8.0,1,5.0,0,Winter,3,5.27851,15.5592,1091.01,1854.71,13.4354,58.4695,13.1086,2.45057,10.9305,1,28.5976,16.4364,53.191,25.433,42.0331,,,Winter,1.91,Winter,2,3,3,1,3,2,1,1,1,1,0,0,1,1,4,2,3,2,3,1,35,Winter,57,79,Winter,0,1 +0506b9cd,Spring,13,0,,,Spring,27.99175094,61.5,150.6,36.0,62,75,119,,,,,Spring,4,0,28.6,2,28.0,2,4,0,8.0,1,8.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Summer,2.52,Summer,1,2,5,1,5,0,0,5,2,0,5,0,0,1,1,0,5,1,5,1,40,Summer,56,77,Spring,3,1 +051680a0,Winter,7,0,Spring,55,Winter,17.70093424,52.5,69.4,,66,70,122,,,,,Winter,14,1,,,,,10,1,16.0,1,14.0,1,14.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,4,4,3,0,2,0,0,2,1,2,2,0,3,0,2,3,2,3,0,0,33,Winter,50,69,Winter,0,1 +051f73ae,Summer,10,0,Winter,88,Fall,19.28545975,56.25,86.8,,57,84,96,Fall,5,8,0,Fall,20,1,16.0,2,15.6,2,10,1,4.0,0,4.0,0,9.5,1,Fall,3,1.73376,19.2876,1233.95,2097.72,20.5383,73.6954,16.3756,2.91193,13.1046,2,37.1044,16.0527,71.9617,38.5187,57.6427,,,Summer,3.32,Fall,2,2,2,1,2,1,0,1,0,1,0,0,2,2,1,1,1,1,1,2,23,Fall,42,59,Summer,0,0 +052a1122,Winter,8,0,Spring,82,Winter,18.61519571,50.75,68.2,26.0,42,62,100,,,,,Winter,7,1,,,,,4,1,9.0,1,11.0,1,5.0,0,Winter,3,3.12822,18.6172,1026.58,1745.19,11.4982,51.6078,14.0879,4.52935,16.5922,2,26.678,13.4316,48.4795,21.4476,38.1761,,,,,Spring,1,3,3,0,4,0,0,0,2,1,0,0,2,0,2,2,4,3,4,0,31,Spring,35,50,Winter,3,1 +053d7d31,Spring,15,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,0,,,,Spring,2,0 +055156e2,Summer,12,0,Winter,45,Summer,21.32408949,62.0,116.6,,65,75,114,,,,,Fall,13,0,15.7,1,18.9,1,11,1,7.0,0,9.0,1,11.0,1,Summer,3,4.87423,21.3264,1319.9,2243.84,22.9883,82.8502,15.1535,6.17293,33.7498,2,37.3859,22.4759,77.9759,35.1991,60.3742,,,Summer,2.06,Summer,3,5,2,2,4,5,1,2,1,2,4,0,4,4,3,5,4,5,3,3,62,Summer,51,71,Summer,2,2 +056d498e,Spring,18,0,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.16,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +05777ce1,Summer,14,0,Winter,68,Fall,25.46680441,66.0,157.8,,71,75,114,,,,,Fall,20,0,32.4,2,31.7,2,20,1,5.0,0,8.0,1,4.0,0,Fall,3,7.26289,25.4696,1536.59,2612.21,30.822,105.93,17.0976,8.37196,51.8695,2,46.897,28.2115,98.6676,51.4934,77.719,Summer,1.77,,,Fall,4,4,4,4,4,3,1,4,1,1,4,0,2,4,4,1,4,1,3,2,55,Fall,61,84,Summer,,2 +058990e9,Summer,10,0,Winter,41,Fall,14.99213397,53.57,61.2,,69,74,110,Fall,5,8,49,Fall,9,0,14.6,2,14.3,2,9,1,9.0,1,9.0,1,9.5,1,Fall,3,5.13161,14.9213,1041.86,1771.16,11.5373,53.2346,12.9792,1.94205,7.96538,2,27.0477,14.6496,48.103,23.7691,38.5851,,,Summer,1.93,Fall,5,5,4,1,2,0,0,0,0,0,0,0,4,1,1,5,5,5,5,1,44,Fall,57,79,Summer,1,1 +058d2812,Fall,11,1,Winter,60,Fall,14.13292425,58.5,68.8,,73,77,114,Fall,5,8,19,Fall,42,1,12.4,2,13.1,2,12,1,10.0,1,10.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.92,Fall,5,5,3,3,5,1,1,1,5,3,3,0,5,2,2,4,4,4,2,1,59,Fall,64,88,Fall,2,2 +0598f669,Spring,11,1,Fall,61,Fall,23.59183143,62.0,129.0,,61,76,105,Fall,5,7,44,Fall,13,0,24.1,3,23.6,3,10,1,7.0,0,10.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.73,Fall,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,Fall,29,42,Spring,0,0 +059e02ab,Winter,11,0,Winter,75,Fall,20.5032431,59.6,103.6,,83,81,136,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.74922,20.5745,1300.7,2211.19,23.4818,80.8046,16.0474,4.52706,22.7954,2,37.8345,19.4883,76.0554,35.6857,61.3163,,,Spring,3.23,Spring,2,3,3,1,2,1,1,2,1,1,3,1,2,1,3,2,2,1,1,1,34,Spring,38,54,Winter,2,1 +059eed01,Spring,8,1,Fall,90,Summer,16.94530053,48.2,56.0,,71,86,92,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.61,Summer,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,21,Summer,26,38,Summer,0,0 +059fe566,Spring,10,0,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +05bbed1b,Spring,5,1,Summer,60,Spring,14.89159867,46.6,46.0,,57,75,104,Spring,,,,Spring,0,0,,,,,0,0,11.0,1,13.0,1,6.5,1,Spring,2,2.30178,14.8932,934.708,1402.06,10.716,41.8217,13.5404,1.35278,4.17827,1,21.1202,9.98555,39.5199,18.2279,31.8362,,,,,Spring,2,4,4,0,2,1,0,1,1,1,0,0,0,1,3,2,2,2,1,1,28,,,,Spring,0,0 +05bc318e,Spring,11,0,Fall,68,Fall,20.09564296,59.5,101.2,,69,71,116,Fall,5,8,56,Fall,18,1,19.2,2,21.2,2,7,0,9.0,1,9.0,1,10.0,1,Fall,3,6.83747,20.0978,1230.28,2091.48,18.9913,73.304,14.5578,5.54001,27.896,3,34.1484,20.1643,66.4666,32.2686,53.1397,,,Spring,2.28,Fall,5,2,4,2,,2,2,2,1,2,1,1,2,2,3,3,3,3,3,2,45,Fall,41,58,Spring,2,1 +05d3d73a,Spring,8,0,Fall,61,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,4,3,0,1,1,0,1,1,2,3,0,2,0,0,0,2,2,0,0,26,Summer,38,54,Spring,0,0 +05db1b9b,Spring,8,0,,,Fall,15.91553741,52.5,62.4,,78,72,109,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.11,Fall,5,0,0,0,0,0,0,0,5,4,0,0,0,0,0,2,0,1,0,0,17,Fall,40,56,Spring,0,0 +05dc60ef,Spring,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +05e67680,Summer,9,0,,,Summer,25.46581633,56.0,113.6,35.0,61,82,120,,,,,Summer,0,0,,,,,1,0,0.0,0,0.0,0,4.0,0,Summer,4,3.93564,25.4686,1223.02,2568.34,19.2963,72.5305,16.261,9.2076,41.0695,3,34.2556,18.9786,68.5948,30.2959,53.5519,,,Summer,4.04,Summer,2,2,1,0,2,1,0,1,2,2,0,0,2,2,2,2,1,2,1,1,26,Summer,42,59,Summer,2,0 +05e94f88,Summer,10,0,Winter,80,Fall,17.56959064,57.0,81.2,,69,77,109,Fall,5,7,44,Fall,8,0,,,,,2,0,9.5,1,7.5,1,12.0,1,Fall,3,4.62688,17.5715,1150.33,1955.56,16.2054,64.7885,14.0201,3.55141,16.4115,2,31.6096,16.9736,60.1616,29.4489,47.815,,,Fall,2.12,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,38,54,Summer,0,0 +05ed65f0,Fall,16,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.041,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +05f2d1b5,Winter,10,1,Winter,80,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.0,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,30,43,Winter,0,0 +0610c066,Summer,8,0,,,Summer,21.62613682,51.25,80.8,26.0,65,61,111,,,,,Summer,0,0,,,,,0,0,7.5,0,9.0,1,12.0,1,Fall,2,3.43325,21.6285,1098.16,1757.06,15.6605,59.2316,15.8551,5.77341,21.5684,2,30.6659,12.9052,55.7984,25.6068,46.3265,,,,,Fall,1,2,1,2,4,1,0,1,1,1,0,0,1,1,1,1,1,1,1,1,22,Fall,34,49,Summer,0,0 +061f2866,Summer,14,0,Fall,70,Summer,18.83631557,62.6,105.0,,74,77,133,,,,,Summer,46,1,23.2,1,26.1,1,30,1,11.0,1,10.5,1,14.5,1,Summer,4,-0.475489,18.8384,1399.1,2938.11,27.0724,91.2856,16.3778,2.46055,13.7144,1,42.4006,21.8126,91.7611,44.8422,69.473,,,Summer,3.21,Summer,4,4,5,3,5,3,0,4,2,3,4,0,3,3,3,2,4,4,4,2,62,Summer,51,71,Summer,1,2 +0632d98a,Spring,11,0,Summer,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.86,Spring,0,0,1,1,2,1,0,0,1,1,2,0,1,1,1,1,1,1,1,1,17,Spring,33,47,Spring,0,0 +063353cc,Summer,11,0,Spring,60,Winter,15.06306306,55.5,66.0,,68,99,128,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.70418,15.2473,1080.42,1728.67,12.6162,57.342,13.0885,2.15882,9.45802,1,28.9366,15.7892,53.6378,28.1863,41.5528,,,Fall,2.37,Winter,3,3,3,1,3,1,0,2,1,2,2,0,1,1,2,1,0,1,0,1,28,Winter,47,66,Summer,2,0 +063b16fc,Spring,10,0,Summer,65,Summer,15.06310596,56.5,68.4,,,76,,Summer,,,,Summer,,,,,,,,,,,,,,,Summer,5,3.78048,15.0648,1091.22,2618.92,13.2389,58.4918,12.8825,2.18223,9.9082,1,28.3289,16.924,54.7113,25.2105,41.5678,,,Spring,3.02,Summer,5,5,4,0,4,4,0,4,1,2,2,0,1,0,3,4,0,4,1,3,47,Summer,46,64,Spring,2,1 +06472198,Fall,6,1,Fall,75,Fall,14.71191696,42.5,37.8,,56,95,97,Fall,5,7,13,Fall,0,0,,,,,0,0,10.5,1,10.5,1,8.0,1,Fall,3,10.1201,14.7135,879.033,1406.45,8.03291,35.8915,13.9707,0.742863,1.90846,1,19.0027,8.85593,25.7714,14.8782,27.0356,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,41,58,Fall,0,0 +064e8da5,Spring,5,1,Fall,61,Spring,17.88183605,46.5,55.0,,66,75,105,Spring,,,,Spring,0,0,,,,,0,0,10.5,1,10.0,1,8.5,1,Fall,3,2.20577,18.2747,942.146,1507.43,11.0093,42.6139,14.1592,4.11549,12.3861,2,20.6076,10.997,40.4081,17.1052,31.6169,,,,,Fall,3,2,2,0,3,1,0,1,2,2,1,0,2,0,1,2,2,2,0,1,27,,,,Spring,0,0 +065801e3,Spring,16,1,Winter,51,Summer,17.28717561,63.0,97.6,,88,86,121,,,,,Summer,13,0,22.3,2,23.3,2,1,0,5.0,0,5.0,0,8.0,0,Summer,2,4.43287,17.2891,1228.92,1843.38,25.3762,73.1591,12.9596,4.32951,24.4409,2,26.3154,21.4674,68.7262,21.2687,51.6917,Spring,3.6,,,Summer,1,1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,1,2,1,1,23,Summer,26,38,Spring,2,0 +066343d4,Winter,6,0,Spring,60,Winter,15.26524429,44.5,43.0,23.0,103,100,129,,,,,Winter,0,0,,,,,0,0,5.5,0,6.5,0,2.5,0,Winter,2,2.2672,15.2669,896.483,1434.37,4.67292,37.7502,13.403,1.86391,5.24981,1,19.8223,13.255,35.483,13.3882,24.4952,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,62,85,Winter,0,0 +0668373f,Winter,9,0,Fall,65,Fall,15.66353719,55.0,67.4,,72,85,114,Fall,,,,Fall,0,0,,,,,10,1,7.0,0,8.0,1,11.0,1,Fall,3,3.88369,15.6652,1109.35,1885.89,14.9781,60.4231,14.0437,1.6216,6.97694,1,31.3833,14.0617,56.5394,30.8433,46.3614,,,Winter,3.24,Fall,5,5,5,1,5,0,0,0,4,5,5,0,1,0,5,4,0,4,4,5,58,Fall,34,49,Winter,1,2 +0676ead5,Summer,8,0,,,Summer,15.63472,50.0,55.6,22.0,53,83,116,,,,,Summer,4,0,,,,,0,0,9.0,1,9.0,1,10.0,1,Fall,1,3.04497,15.6364,986.831,1282.88,10.6595,47.3734,13.3229,2.31357,8.22658,1,24.7634,11.9505,44.3285,17.6913,35.423,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,46,64,Summer,2,0 +0677d22d,Fall,9,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +067b9287,Spring,7,1,Spring,75,Winter,23.61640625,48.0,77.4,,64,89,117,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,5,2.49976,23.619,1003.14,2206.92,14.5716,49.111,14.9864,8.63254,28.289,2,23.0454,11.4939,46.6112,20.8602,37.6171,,,,,Fall,5,1,5,0,5,5,0,4,0,4,2,0,0,0,2,0,2,0,1,0,36,Fall,32,46,Spring,3,1 +067de065,Spring,7,0,Summer,51,Summer,15.67627835,51.0,58.0,,68,63,97,Summer,2,1,57,Summer,3,1,,,,,0,0,12.0,1,13.0,1,8.0,1,Summer,3,2.95939,15.678,994.075,1689.93,8.59724,48.1451,13.0141,2.66389,9.85493,1,23.967,15.5808,45.1857,19.5649,32.5642,,,,,Summer,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,Summer,40,56,Spring,0,0 +068f83f2,Summer,6,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +06b46af3,Winter,10,1,,,Winter,17.26116071,56.0,77.0,25.0,73,65,121,,,,,Winter,10,0,,,,,12,1,6.0,0,7.0,0,14.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.76,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +06c7dcff,Summer,10,1,Winter,65,Fall,19.64552528,51.25,73.4,,68,86,109,Fall,5,7,51,Fall,20,1,13.4,2,13.7,2,7,1,11.5,1,10.5,1,8.0,0,Fall,2,5.00632,19.6477,1060.67,1591.01,17.7815,55.2387,14.7863,4.86141,18.1613,1,26.0332,11.424,50.2324,24.4797,43.8147,,,Fall,1.88,Fall,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,19,Fall,38,54,Summer,2,0 +06c8b2fb,Summer,6,1,Fall,90,Summer,16.03881481,45.0,46.2,21.0,73,93,117,,,,,Summer,14,1,,,,,14,1,10.5,1,10.5,1,5.0,0,Summer,2,2.12187,16.0406,917.247,1375.87,10.0559,39.9618,13.8747,2.16588,6.23816,1,20.0974,9.80851,37.84,15.942,30.1533,,,,,Fall,3,3,0,0,0,0,0,0,0,0,0,0,2,0,1,2,1,1,0,0,13,,,,Summer,0,0 +06eb8adb,Winter,8,0,Spring,62,Spring,17.20567313,50.25,61.8,,60,92,104,Spring,7,12,23,Spring,13,1,,,,,5,1,5.5,0,5.0,0,7.0,1,Summer,3,3.09377,17.2076,1007.06,1712.0,10.8398,49.5282,13.7906,3.41694,12.2718,2,26.3129,12.3755,46.4345,21.5689,37.1527,,,,,Spring,2,2,3,2,2,2,2,2,1,2,1,0,0,0,0,1,2,2,2,2,30,Winter,28,41,Winter,0,0 +06ec8be3,Summer,9,1,Winter,45,Winter,15.50041199,51.2,57.8,,67,100,101,Winter,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.89,Summer,5,3,2,0,3,1,1,1,2,3,2,0,1,0,4,0,3,2,0,0,33,Summer,45,63,Summer,0,1 +070386b2,Summer,9,1,Winter,65,Summer,24.28348348,55.5,106.4,,82,100,152,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.94,Summer,2,3,2,0,3,1,0,0,0,0,3,0,1,1,2,1,1,1,1,1,23,Summer,34,49,Summer,2,0 +07048067,Summer,13,0,Winter,65,Fall,16.03032227,64.0,93.4,,69,99,107,,,,,Fall,5,0,16.5,1,18.6,1,2,0,6.0,0,4.0,0,12.0,1,Winter,2,5.20353,16.0321,1291.46,2066.34,21.6958,79.8207,13.7012,2.33088,13.5793,1,36.4831,21.6418,74.6172,36.3736,58.1789,,,Summer,1.81,Fall,1,5,5,1,5,1,0,5,0,1,4,0,0,0,0,0,2,2,4,0,36,Fall,28,41,Summer,1,1 +0720320e,Winter,15,0,Spring,65,Spring,17.95528489,63.75,103.8,,71,105,134,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.62,,,Spring,0,0,4,1,2,1,0,2,1,2,4,0,1,0,2,0,0,0,0,0,20,Spring,30,43,,,0 +0745c390,Winter,10,0,Summer,64,Spring,13.82213618,54.5,58.4,,72,61,125,Spring,6,9,4,Spring,12,1,12.0,1,9.2,1,5,0,11.0,1,11.5,1,14.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.05,Spring,4,4,5,5,4,3,0,4,1,1,0,0,1,1,2,3,2,2,2,2,46,Winter,39,55,Spring,2,1 +07662280,Spring,7,0,,,Spring,17.56768013,49.0,60.0,,64,86,100,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,40,56,Spring,, +077ac7a2,Fall,10,0,,,Fall,15.31634033,53.0,61.2,23.0,66,85,104,,,,,Fall,11,1,,,,,16,1,7.0,0,7.5,0,5.0,0,Fall,4,3.31904,15.318,1029.85,2162.69,10.7097,51.9556,13.0042,2.31381,9.24435,1,26.4549,14.7911,48.6366,23.4114,37.1646,,,Fall,2.8,Fall,2,2,3,1,4,1,1,1,1,2,2,1,1,2,1,1,2,1,1,1,31,Fall,29,42,Fall,0,1 +07840b8d,Winter,7,0,,,Winter,15.7472,50.0,56.0,23.0,94,86,115,,,,,Winter,0,0,,,,,0,0,0.0,0,0.0,0,0.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +07d879cf,Winter,16,0,Summer,65,Spring,27.56483012,66.0,170.8,,73,53,139,,,,,Spring,,,,,,,,,,,,,,,Summer,1,6.02814,27.5678,1598.59,2078.17,33.4346,112.534,18.1635,9.40438,58.266,3,49.5737,29.5257,106.506,54.9894,83.0083,Winter,3.36,,,Spring,1,2,3,1,2,2,2,2,2,1,4,1,2,3,2,1,1,1,2,1,36,Spring,33,47,Winter,1,1 +080698b7,Winter,11,1,Summer,61,Spring,29.11488033,62.0,159.2,,60,75,106,Spring,3,3,57,Spring,0,0,18.1,2,16.6,1,0,0,6.0,0,7.0,0,11.5,1,,,,,,,,,,,,,,,,,,,,Winter,1.43,Spring,0,2,1,1,3,1,3,2,1,2,2,1,1,1,1,1,1,1,1,1,27,Spring,35,50,Winter,2,0 +08159c5e,Winter,17,1,Fall,75,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.52,,,Winter,1,2,4,2,2,2,1,3,1,1,2,0,0,0,3,0,,0,2,0,26,Winter,30,43,Winter,2,0 +082df556,Winter,16,0,Summer,45,Spring,16.77497108,70.5,118.6,,64,64,115,,,,,Summer,,,,,,,,,,,,,,,Summer,3,7.00658,16.7768,1562.29,2655.89,34.2329,108.667,15.3717,1.40506,9.93274,1,46.248,28.1864,101.661,46.4142,80.4809,Winter,2.04,,,Spring,2,2,2,1,2,1,2,1,1,1,3,0,2,1,1,1,1,1,,1,26,Winter,32,46,Winter,0,0 +083c9b36,Spring,8,0,,,Spring,15.98823645,50.5,58.0,23.0,50,100,69,,,,,Spring,10,1,,,,,24,1,12.0,1,11.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +0844aee7,Summer,8,0,Fall,61,Fall,14.70685764,48.0,48.2,,78,91,111,Fall,6,10,0,Fall,7,1,,,,,3,0,11.0,1,11.0,1,4.0,0,Fall,4,13.5396,14.7085,948.83,1992.54,4.9649,43.3258,13.2211,1.48737,4.87416,2,21.1448,17.2161,29.7863,18.5188,26.1097,,,,,Fall,2,2,4,1,5,1,1,2,1,1,2,1,1,1,1,2,1,1,1,1,32,Fall,32,46,Summer,2,1 +08577722,Winter,6,1,Fall,70,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,0,1,0,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,17,Summer,37,53,Winter,0,0 +08660d12,Summer,14,0,Fall,68,Summer,16.73705747,63.5,96.0,,60,69,108,,,,,Summer,31,1,20.3,2,19.0,1,15,1,7.0,0,8.5,1,12.0,1,Summer,3,5.18421,16.7389,1301.04,2211.77,22.2885,80.8411,14.0957,2.64316,15.1589,1,37.1031,21.4495,75.6569,37.0,59.3916,,,Summer,2.91,Summer,4,4,5,4,5,4,5,4,3,4,1,0,1,1,3,2,5,1,4,1,61,Summer,35,50,Summer,2,2 +086d6e7f,Fall,8,1,Winter,90,Fall,17.86271934,50.5,64.8,,68,85,111,Fall,6,9,17,Fall,8,1,,,,,0,0,10.0,1,8.0,0,6.0,1,Fall,1,7.18741,17.8647,1015.69,1320.39,14.7861,50.4468,13.9076,3.95702,14.3532,1,23.7125,11.9483,43.2594,21.8108,38.4985,,,Fall,2.17,Fall,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,Fall,42,59,Fall,0,0 +0882b065,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0884aca8,Winter,12,0,Summer,60,Winter,15.78163265,56.0,70.4,,58,59,112,Winter,,,,Winter,12,0,14.4,2,15.1,2,10,1,5.0,0,6.0,0,8.0,0,Winter,3,9.56522,15.7834,1078.43,1833.34,11.2305,57.1305,12.8084,2.97497,13.2695,1,26.9702,18.9298,47.5653,25.0965,38.2007,,,,,Spring,1,5,3,1,1,1,1,2,1,1,3,1,1,1,1,1,1,1,1,1,29,Spring,39,55,Winter,0,0 +089a6b19,Fall,12,0,Winter,70,Fall,17.23020693,61.0,91.2,,79,78,133,,,,,Fall,20,1,18.2,1,21.3,2,2,0,6.0,0,6.0,0,12.0,1,Winter,5,4.66053,17.2321,1234.9,2963.77,20.3417,73.7964,13.9437,3.28838,17.4036,2,33.5663,19.8885,69.1359,29.0342,53.9079,,,Fall,2.619,Fall,1,1,1,0,2,0,2,0,0,0,2,0,0,0,0,0,0,1,0,0,10,Fall,42,59,Fall,2,0 +08c390b6,Spring,8,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +08fa44ad,Spring,8,0,Summer,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.71,Summer,4,0,2,3,2,0,0,1,1,1,4,1,1,3,4,4,2,2,1,3,39,Summer,37,53,Spring,0,1 +09039272,Winter,11,1,Spring,70,Winter,16.84010586,57.5,79.2,,70,82,126,Winter,5,7,38,Winter,10,0,13.9,2,15.6,2,4,0,11.0,1,10.0,1,12.0,1,Winter,3,5.20886,16.8419,1132.86,1812.58,19.7692,62.9276,13.3816,3.46034,16.2724,2,26.497,16.6614,57.7187,25.2936,46.2662,,,Winter,3.09,Winter,2,1,2,1,2,0,1,1,1,2,0,0,1,1,1,1,1,1,1,1,21,Winter,39,55,Winter,0,0 +092494ec,Winter,18,0,Spring,64,Winter,29.03820408,70.0,202.4,,89,71,141,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.36,,,Winter,0,2,3,1,3,3,1,4,2,0,4,1,2,1,1,0,3,0,3,1,35,Winter,40,56,Winter,3,1 +0924b815,Winter,7,0,Spring,65,Spring,15.76673452,49.25,54.4,,60,72,110,Spring,3,4,8,Spring,6,1,,,,,0,0,9.0,1,9.0,1,7.5,1,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,3,1,1,1,1,1,2,2,0,0,2,1,2,2,2,2,2,2,33,Spring,33,47,Winter,0,1 +0928b047,Summer,11,1,,,Summer,11.91525424,59.0,59.0,28.0,76,83,118,,,,,Summer,9,0,15.3,2,14.8,2,0,0,8.4,0,6.5,0,8.0,0,Summer,3,3.86747,11.9165,1116.69,1786.71,18.2133,61.2056,12.362,-0.445489,-2.20566,1,26.2672,16.7251,57.3381,25.6158,44.4805,,,Summer,1.85,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +092c29b9,Summer,8,1,Spring,72,Fall,20.42178711,53.0,81.6,,61,68,101,,,,,Summer,11,1,,,,,0,0,9.0,1,9.0,1,9.0,1,Fall,2,4.14524,18.2943,1244.54,1866.81,24.0298,74.8229,16.7749,1.5194,6.77712,2,35.7169,15.0762,70.6776,47.0308,59.7467,,,Summer,3.17,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,37,53,Summer,,0 +09331bb8,Fall,13,0,Spring,63,Winter,20.95792143,69.5,144.0,,78,88,130,,,,,Winter,9,0,27.7,2,26.5,2,5,0,5.0,0,6.0,0,9.0,1,Winter,3,6.73257,20.9602,1594.1,2709.97,34.7268,112.055,16.3104,4.64976,31.9446,2,47.779,29.5496,105.323,48.6357,82.5058,,,Fall,0.99,Winter,3,3,3,2,4,2,2,3,2,2,3,0,3,4,3,3,5,4,4,3,58,Winter,28,41,Fall,2,2 +094f8386,Summer,9,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +09614060,Fall,14,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.47,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +096bf063,Spring,13,1,Fall,55,Summer,19.44098765,67.5,126.0,,67,66,111,,,,,Summer,22,1,17.0,2,18.8,2,2,0,9.0,0,9.0,0,10.0,1,Summer,4,5.43437,19.3856,1372.04,2606.87,29.4623,88.403,13.6012,5.78445,37.597,1,33.6762,25.2646,82.9686,38.9271,63.1384,,,Summer,2.3,Summer,5,5,5,3,4,3,4,4,2,1,4,1,4,4,4,4,4,4,1,2,68,Summer,32,46,Spring,2,2 +096e6074,Spring,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +09817490,Fall,9,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0983654b,Winter,10,0,Summer,81,Spring,14.8,57.0,68.4,,69,52,108,Spring,12,20,0,Spring,20,1,,,,,20,1,10.0,1,10.2,1,14.0,1,Summer,3,3.98148,14.8016,1112.31,1890.93,14.8295,60.7386,13.1437,1.65791,7.6614,1,29.7499,16.1593,56.7571,26.6591,44.5793,,,Winter,1.45,Spring,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,2,0,2,2,0,11,Spring,41,58,Winter,0,0 +098b5836,Fall,15,0,Spring,80,Winter,22.04998886,67.0,140.8,,61,98,110,,,,,Winter,30,1,30.7,2,36.2,3,13,0,10.0,1,10.5,1,8.0,0,,,,,,,,,,,,,,,,,,Spring,1.64,,,Winter,5,5,5,5,5,2,1,2,2,2,4,0,2,0,3,2,5,4,5,1,60,Winter,40,56,Fall,3,2 +098d3c32,Winter,7,0,Fall,35,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,54,75,Winter,,0 +099e3614,Fall,11,1,Summer,70,Fall,22.46899863,54.0,93.2,,70,80,111,Fall,5,5,7,Fall,0,0,15.5,2,16.5,2,3,0,9.0,0,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,3.89,Fall,2,2,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,Fall,30,43,Fall,2,0 +09b29113,Summer,5,1,Winter,72,Summer,21.94054321,45.0,63.2,,87,69,142,,,,,Winter,,,,,,,,,,,,,,,Fall,2,2.21711,21.9429,945.886,1418.83,12.4413,43.0123,14.9338,7.00915,20.1877,2,21.7885,8.78247,40.7952,18.2236,34.2298,,,,,Fall,4,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,10,,,,Summer,0,0 +09d3bd98,Summer,8,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +09d80057,Winter,6,0,Fall,81,Winter,16.6637037,45.0,48.0,,47,72,79,,,,,Winter,1,0,,,,,2,0,11.0,1,12.0,1,11.0,1,Spring,3,2.49949,16.6655,908.767,1544.9,6.46609,39.0586,13.5611,3.10443,8.94138,1,23.3328,9.25969,36.5591,19.8219,29.7989,,,,,Fall,2,4,4,0,5,1,0,4,3,1,1,1,1,2,2,3,2,2,2,2,42,Winter,40,56,Winter,2,1 +09e4ac05,Spring,7,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +09eeaba6,Summer,7,1,Winter,90,Fall,17.56874333,53.0,70.2,,72,80,141,Fall,6,10,46,Fall,5,1,,,,,1,0,12.0,1,12.5,1,11.0,1,Fall,2,6.51792,17.5707,1057.73,1586.59,16.3351,54.9248,13.7474,3.8233,15.2752,1,25.0527,13.537,48.4069,24.577,41.3878,,,,,Fall,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,4,Fall,32,46,Summer,0,0 +09eeec14,Winter,18,1,Summer,80,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,4,4,2,5,2,4,5,2,4,5,1,2,4,4,4,2,2,2,2,64,Winter,30,43,Winter,,2 +09f44fd2,Winter,6,0,Spring,58,Winter,17.45251808,48.75,59.0,,62,86,102,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,2,0,2,0,0,0,1,1,0,0,2,0,0,1,0,0,0,0,13,Winter,39,55,Winter,0,0 +09f84bae,Winter,8,0,,,Winter,28.52147531,41.0,68.2,27.0,78,56,105,,,,,Winter,0,0,,,,,0,0,7.0,0,8.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +0a1afc11,Fall,6,0,Winter,85,Winter,14.4522946,47.2,45.8,,86,89,119,Winter,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,3,2,0,0,1,0,0,0,1,0,0,0,0,0,2,0,1,0,0,14,Fall,39,55,Fall,0,0 +0a24c842,Summer,11,1,Summer,60,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.27,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0a418b57,Spring,10,1,Winter,80,Fall,16.62969615,52.5,65.2,,65,83,130,Fall,,,,Winter,,,,,,,,,,,,,,,Summer,3,3.1479,16.6315,1056.54,1690.46,15.5932,54.7984,13.9782,2.65329,10.4016,2,27.1004,12.1048,51.6505,32.4693,42.6936,,,Fall,3.24,Fall,3,3,3,1,2,2,4,2,2,3,3,0,3,0,2,2,2,3,3,2,45,Fall,33,47,Spring,3,1 +0a431608,Summer,14,0,Fall,70,Summer,25.88777117,64.5,153.2,34.0,78,68,123,,,,,Summer,20,0,14.3,1,20.2,1,0,0,4.0,0,5.0,0,12.0,1,Summer,2,6.13657,25.8906,1620.8,2593.28,34.4798,114.899,19.4178,6.47276,38.3007,3,52.2347,28.1848,108.763,63.201,86.7145,,,Summer,2.08,Summer,2,2,3,4,4,0,4,1,1,2,0,0,0,2,2,0,2,0,1,0,30,Summer,49,68,Summer,2,0 +0a4b1425,Fall,6,1,Spring,54,Winter,14.79112,50.0,52.6,,47,90,93,Winter,2,3,30,Winter,4,1,,,,,0,0,11.5,1,12.5,1,8.0,1,Winter,2,22.4353,14.7927,1020.35,1530.52,7.4192,50.9432,14.3268,0.465936,1.65677,1,20.0213,23.5027,28.5079,32.6623,27.4405,,,,,Winter,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Winter,43,60,Fall,0,0 +0a540797,Winter,6,0,Summer,45,Spring,19.07562004,50.5,69.2,,64,104,118,Spring,,,,Spring,5,1,,,,,5,1,11.5,1,10.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,8,Spring,47,66,Winter,0,0 +0a5f1214,Spring,8,0,Spring,72,Fall,14.63119992,51.5,55.2,,61,77,110,,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.20927,14.9211,1003.09,1705.26,12.3274,49.1056,13.2737,1.64739,6.09444,2,24.88,11.8982,45.8963,16.2887,37.2074,,,,,Spring,2,2,2,1,2,1,1,1,1,1,2,1,1,1,2,1,2,1,2,2,29,Spring,41,58,Spring,1,0 +0a775819,Summer,18,0,Winter,70,Fall,20.59502748,66.1,128.0,,57,77,124,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.27,,,Summer,5,5,4,1,5,4,4,4,2,4,4,1,4,4,4,4,4,4,4,4,75,Summer,67,91,Summer,2,2 +0a822c93,Winter,12,0,Summer,82,Winter,27.99883041,57.0,129.4,,68,82,100,,,,,Winter,13,0,15.4,1,14.6,1,0,0,3.5,0,3.5,0,4.5,0,,,,,,,,,,,,,,,,,,,,Spring,2.4,Winter,4,3,4,1,5,3,0,2,1,1,2,0,1,3,2,0,2,1,1,1,37,Winter,60,82,Winter,0,1 +0a88a5d5,Fall,13,1,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +0a9159f8,Winter,12,1,Summer,90,Spring,21.90390538,68.5,146.2,,68,78,108,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.61,Spring,2,2,4,0,4,0,2,1,1,1,4,0,0,1,1,0,1,0,0,0,24,Spring,35,50,Winter,0,0 +0a96d897,Winter,16,0,,,Winter,23.5978371,68.0,155.2,31.5,44,57,108,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,31,45,Winter,, +0a995f99,Fall,8,0,Spring,80,Winter,18.61913832,52.5,73.0,,65,97,113,,,,,Winter,12,1,,,,,7,1,5.0,0,5.0,0,5.0,0,Winter,3,3.38164,18.6212,1064.61,1809.84,13.0282,55.6584,14.1976,4.42356,17.3416,2,28.3535,14.2768,52.2768,24.0893,41.3816,,,Winter,1.8,Winter,3,3,3,0,1,0,0,0,0,1,2,0,1,1,0,3,2,2,0,0,22,Winter,32,46,Fall,1,0 +0aa90ce7,Summer,10,0,Fall,59,Summer,16.47295682,54.5,69.6,,71,71,124,,,,,Summer,10,0,14.9,2,15.7,2,6,0,10.0,1,10.0,1,8.0,0,Fall,3,7.05976,16.4748,1068.1,1815.77,11.5536,56.0295,13.2625,3.21223,13.5705,1,28.2153,16.2606,48.9697,27.9336,39.7689,,,Summer,4.62,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,43,60,Summer,0,0 +0aaf08ec,Summer,14,0,Summer,60,Spring,30.18546739,66.8,191.6,,86,73,133,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.35,,,Spring,4,4,4,1,4,5,1,3,4,3,4,0,1,0,2,1,4,1,0,0,46,Spring,32,46,Summer,0,1 +0ac521c8,Spring,14,0,Fall,68,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0.99,Summer,5,5,5,2,5,4,3,4,3,4,4,0,4,4,4,5,5,5,5,5,81,Summer,50,69,Spring,2,3 +0ac5e12f,Winter,7,0,Summer,60,Winter,15.35194156,51.0,56.8,,73,85,108,Winter,7,11,46,Winter,5,,,,,,0,,9.0,,6.0,,9.0,,Winter,3,7.47858,15.3536,992.953,1688.02,9.24562,48.0255,12.9818,2.37184,8.77449,1,24.5579,14.222,40.5469,19.9405,33.8035,,,,,Winter,2,3,4,3,2,0,0,0,0,1,3,0,3,0,1,3,3,2,2,0,32,Winter,29,42,Winter,1,1 +0acac70c,Spring,18,0,Fall,65,Summer,21.48561605,70.15,150.4,,64,75,175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,3,3,1,4,1,0,2,1,0,3,0,2,2,2,0,3,0,3,4,34,Summer,33,47,Spring,3,1 +0acda781,Summer,15,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.94,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +0adf511d,Winter,9,0,Fall,45,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.18,Spring,3,2,2,0,3,0,0,0,2,3,3,0,3,3,3,3,2,3,3,2,40,Spring,46,64,Winter,0,1 +0af81037,Summer,11,0,Fall,60,Fall,23.17142231,58.5,112.8,,71,85,119,Fall,4,5,40,Fall,0,0,17.1,2,20.9,2,1,0,5.0,0,5.5,0,8.0,0,,,,,,,,,,,,,,,,,,,,Summer,3.731,Summer,3,3,3,0,2,0,0,2,1,1,0,0,2,0,0,2,2,1,2,2,26,Summer,33,47,Summer,0,0 +0afb2535,Spring,14,1,Fall,80,Spring,18.21790191,67.25,117.2,,83,51,138,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.47,Spring,0,0,1,0,3,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,7,Spring,27,40,Summer,,0 +0b4014f0,Spring,9,1,Fall,70,Summer,14.18222257,56.5,64.4,,70,78,127,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.39,Spring,2,2,2,0,2,0,0,0,0,0,2,0,0,0,1,1,2,2,1,0,17,Spring,43,60,Spring,3,0 +0b4f597e,Winter,9,1,Summer,50,Spring,18.20794535,50.25,65.4,,62,64,105,Spring,5,8,50,Spring,12,1,,,,,9,1,8.75,1,10.5,1,13.0,1,Spring,4,7.27183,18.2099,1013.66,1925.96,14.3348,50.2315,13.9864,4.2235,15.1685,2,24.0819,11.8148,42.9597,24.0263,38.4167,,,Spring,2.9,Spring,2,2,2,0,0,0,0,0,0,1,1,0,0,1,1,1,1,1,0,1,14,Spring,29,42,Winter,0,0 +0b50f3fa,Summer,8,0,Winter,55,Fall,20.1808257,51.25,75.4,,90,72,144,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.67,Fall,4,5,3,0,3,0,0,2,1,2,1,0,4,1,2,4,3,4,2,1,42,Fall,52,72,Summer,0,1 +0b518637,Fall,6,0,Winter,60,Winter,16.54508507,46.0,49.8,,68,90,108,Winter,2,1,31,Winter,0,0,,,,,0,0,9.0,1,5.0,0,3.0,0,Winter,3,2.5997,16.5469,924.518,1571.68,8.36723,40.7363,13.5353,3.01157,9.06371,1,22.5381,9.83099,38.1366,14.3502,30.9053,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,38,54,Fall,0,0 +0b6b07f6,Winter,8,1,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.17,Spring,2,2,3,2,3,1,0,2,0,1,2,0,2,2,1,0,2,1,0,0,26,Spring,32,46,Winter,,0 +0b74b755,Winter,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.51,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +0b7c6100,Fall,11,1,,,Fall,13.30133918,56.5,60.4,21.0,73,83,117,,,,,Fall,20,1,12.7,2,12.0,2,10,1,13.0,1,13.0,1,11.0,1,Winter,4,3.44959,13.3028,1078.29,2048.74,16.6632,57.1147,12.5792,0.72358,3.28534,1,24.5103,15.9412,53.6651,22.5049,41.1734,,,Fall,4.1,Fall,2,2,1,0,3,1,0,1,1,1,2,0,2,1,1,2,1,2,1,1,25,Fall,34,49,Fall,2,0 +0b7d7aec,Summer,15,1,Winter,60,Summer,34.18886719,64.0,199.2,,89,99,129,,,,,Summer,15,0,19.0,2,26.1,2,9,1,13.0,1,10.0,1,14.0,1,Summer,4,5.51893,34.1926,1574.13,2990.85,40.7012,109.929,18.8692,15.3234,89.2713,3,41.4532,27.7743,104.41,51.5966,82.1544,Summer,3.38,,,Summer,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Summer,42,59,Summer,0,0 +0b7d9da6,Fall,7,0,Summer,60,Fall,19.73436068,49.0,67.4,,59,78,113,,,,,Spring,0,0,,,,,0,0,9.0,1,11.0,1,7.0,1,Fall,2,3.04988,19.7365,1017.18,1627.49,12.0298,50.6062,14.8188,4.91767,16.7938,2,27.3321,11.2443,47.5563,21.7631,39.3619,,,,,Spring,1,1,2,1,3,1,1,1,1,1,2,1,1,1,1,2,1,2,1,1,26,Spring,28,41,Fall,2,0 +0b8cba78,Spring,6,1,Fall,60,Fall,16.64868765,50.0,59.2,21.5,57,60,106,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0b9fcc42,Winter,9,0,Fall,80,Winter,17.29796232,51.0,64.0,,,,,Winter,,,,Winter,0,0,,,,,0,0,7.0,0,7.0,0,9.0,1,Winter,2,6.83554,17.2322,1017.24,1627.59,11.132,50.6129,13.6277,3.60452,13.3871,1,25.8106,13.6703,43.7774,19.8634,36.9426,,,Spring,2.43,Winter,4,5,5,1,5,2,1,0,4,4,0,0,4,0,5,4,5,4,4,4,61,Winter,43,60,Winter,0,2 +0ba53e53,Spring,6,1,Summer,70,Summer,21.7360344,47.0,68.3,,59,70,124,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,5,5,5,1,5,0,0,0,5,4,4,0,4,0,0,4,3,5,4,0,54,Spring,34,49,Spring,0,2 +0bd89878,Spring,5,0,Fall,60,Spring,15.96863169,43.0,42.0,22.0,66,83,107,,,,,Spring,0,0,,,,,0,0,7.0,0,6.0,0,8.0,1,Spring,2,2.30024,15.9704,871.727,1394.76,6.12655,35.1134,13.3518,2.61862,6.88662,1,20.8614,8.12548,32.8131,12.6901,26.9879,,,,,Summer,3,0,1,0,3,0,0,0,0,0,3,0,0,0,0,4,1,3,0,0,18,,,,Spring,2,0 +0bda0042,Summer,16,0,,,Winter,18.94926891,71.5,137.8,,82,68,121,,,,,Winter,30,1,31.6,1,29.8,1,15,0,19.0,1,18.0,1,12.0,1,Winter,3,6.35576,18.9513,1480.29,2516.5,28.6977,99.9338,13.7437,5.20766,37.8662,1,41.0017,30.2344,93.578,38.2101,69.6994,Summer,1.36,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +0bdace00,Fall,7,1,Spring,57,Winter,17.64153597,43.0,46.4,,61,80,104,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,5,2.06964,17.2402,901.449,1983.19,9.92153,38.2792,14.2229,3.01733,8.1208,1,21.1654,7.19227,36.2096,18.8323,31.0869,,,,,Winter,2,4,3,0,3,1,0,2,1,1,0,0,1,1,1,2,1,2,1,2,28,Winter,60,82,Fall,0,0 +0bf4e81b,Spring,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0bfe4fc9,Summer,11,0,Winter,78,Winter,21.46000874,58.2,103.4,,80,74,133,,,,,Spring,,,,,,,,,,,,,,,Summer,4,3.38541,21.4624,1194.89,2509.26,10.067,69.5341,14.4329,7.02942,33.8659,2,28.6476,30.8194,66.1487,39.769,38.7146,,,Fall,3.8,Fall,3,3,3,1,3,1,1,2,0,2,0,0,3,3,3,1,3,3,2,2,39,Fall,38,54,Summer,1,1 +0c008058,Spring,11,1,,,Summer,16.14132855,62.75,90.4,24.0,85,81,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.26,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0c0a1576,Winter,7,1,Spring,60,Winter,15.50383483,46.75,48.2,21.0,44,84,90,,,,,Winter,0,0,,,,,2,0,12.0,1,10.0,1,11.0,1,Winter,2,2.23006,15.5055,940.843,1411.26,9.8785,42.4751,13.6639,1.84164,5.72488,1,20.4877,12.1089,40.2451,18.8478,30.3662,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,34,49,Winter,0,0 +0c3d248f,Spring,6,0,Fall,61,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,0,3,0,3,0,0,1,0,0,0,0,0,0,2,2,2,2,0,0,19,Fall,31,45,Spring,0,0 +0c3d88cb,Summer,7,1,,,Summer,16.66778213,53.0,66.6,26.0,64,65,115,,,,,Summer,6,1,,,,,0,0,8.0,1,9.0,1,11.0,1,Fall,3,2.96731,16.6696,1041.23,1665.96,14.8616,53.1674,13.3075,3.36212,13.4326,1,23.4265,14.8792,50.2,22.1051,38.2881,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,32,46,Summer,1,0 +0c4e2d68,Spring,6,1,Fall,75,Spring,16.37226033,40.5,38.2,,56,89,100,Spring,,,,Spring,8,1,,,,,0,0,12.5,1,12.5,1,12.0,1,Summer,1,1.78423,16.374,854.838,1111.29,7.30603,33.3145,14.2799,2.09413,4.88553,1,20.1245,5.88392,31.5302,19.689,27.4305,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +0c4eabac,Fall,7,0,Spring,70,Winter,18.71555594,53.5,76.2,,63,76,107,Winter,7,13,0,Winter,12,1,,,,,5,1,11.0,1,12.0,1,12.0,1,Winter,3,3.63748,18.7176,1103.19,1875.42,15.1933,59.7668,14.681,4.0366,16.4332,2,30.3902,14.1832,56.1294,26.499,45.5836,,,,,Winter,2,2,2,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,24,Winter,30,43,Fall,0,0 +0c71fc27,Spring,6,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +0c794cf7,Summer,8,1,Fall,65,Fall,22.70104167,48.0,74.4,,67,89,107,Fall,4,5,48,Fall,1,0,,,,,0,0,11.0,1,12.0,1,10.0,1,Fall,3,9.54865,22.7035,996.969,1595.15,13.8659,48.4533,14.7858,7.91777,25.9467,2,22.8818,11.7056,38.9046,21.5373,36.7476,,,Fall,1.47,Fall,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Fall,31,45,Summer,0,0 +0c83af0f,Summer,13,1,,,Fall,0.0,56.75,0.0,,75,73,121,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.64,Fall,4,4,4,0,4,3,0,4,2,2,3,0,1,4,1,2,3,3,4,4,52,Fall,43,60,Summer,0,2 +0c8efce0,Fall,7,0,Spring,60,Winter,15.35352,50.0,54.6,,62,62,103,,,,,Winter,10,1,,,,,3,0,14.5,1,14.0,1,12.0,1,Winter,2,3.11601,15.3552,991.964,1587.14,10.8292,47.9202,13.4766,1.87856,6.6798,2,25.9835,11.1074,44.8042,20.6539,36.8128,,,,,Winter,4,4,2,0,1,1,0,0,0,1,1,0,0,0,0,1,1,1,1,1,19,Winter,31,45,Fall,0,0 +0c98f822,Winter,12,0,Summer,45,Summer,19.91833333,60.0,102.0,,81,64,129,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.86,Winter,3,4,2,1,3,2,2,2,1,2,2,1,4,2,3,3,2,4,2,2,47,Winter,34,49,Winter,3,1 +0ca08054,Fall,13,1,,,Fall,27.3848894,59.0,135.6,35.0,65,84,110,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.88,Fall,3,2,3,5,4,3,2,2,5,4,2,3,1,0,2,5,2,3,2,1,54,Fall,30,43,Fall,,2 +0cbe4e54,Winter,9,0,Summer,81,Winter,15.35352,50.0,54.6,,84,94,129,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.96185,15.3552,978.251,1663.03,8.7989,46.4595,13.0658,2.28935,8.14047,1,24.6851,12.9755,43.4977,20.8773,33.484,,,Summer,2.73,Summer,2,3,2,0,0,0,2,0,0,1,1,0,1,0,2,2,3,2,0,0,21,Spring,36,51,Winter,3,0 +0cccc6bc,Winter,9,1,,,Winter,13.58567264,49.0,46.4,21.0,67,93,119,,,,,Winter,8,1,,,,,1,0,12.0,1,12.0,1,8.0,1,Winter,2,2.39814,13.5872,963.898,1445.85,10.0993,44.9308,13.1569,0.430235,1.46925,1,20.5194,14.312,42.5326,19.2382,30.6187,,,Winter,1.27,Winter,2,1,2,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,9,Winter,50,69,Winter,0,0 +0cea0da4,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0d01bbf2,Fall,9,0,Spring,55,Winter,19.60350877,57.0,90.6,,76,94,114,Winter,6,9,51,Winter,3,0,,,,,7,1,7.0,0,7.0,0,10.0,1,Winter,3,3.64506,19.6057,1138.6,1935.61,12.8491,63.5384,13.7496,5.85607,27.0616,2,29.3402,21.3491,59.8934,29.5165,42.1893,,,Fall,3.03,Winter,1,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,1,1,11,Winter,32,46,Fall,0,0 +0d279d77,Fall,8,1,Winter,80,Fall,20.96565986,52.5,82.2,,47,79,96,,,,,Fall,6,1,,,,,0,0,11.5,1,11.0,1,10.0,1,Fall,2,3.07707,20.968,1074.14,1611.21,17.6776,56.6733,14.4565,6.51146,25.5267,2,25.4201,13.5756,53.5963,24.3654,43.0977,,,,,Fall,4,3,1,0,2,0,0,0,0,2,1,0,0,0,0,1,2,2,2,0,20,Fall,60,82,Fall,2,0 +0d34aee4,Spring,13,0,Summer,75,Spring,17.40192296,65.5,106.2,,56,51,112,,,,,Spring,30,1,27.5,2,27.1,2,20,1,0.0,0,0.0,0,15.0,1,Spring,3,-1.96303,16.7483,1393.36,2368.71,27.2419,90.6742,14.8595,1.88882,11.5258,2,40.3126,23.1197,92.6373,38.8489,67.5545,,,Spring,2.6,Spring,4,2,4,1,2,2,1,1,1,1,0,0,5,1,3,4,4,3,3,1,43,Spring,41,58,Spring,0,1 +0d394928,Spring,9,0,Winter,55,Summer,17.80295692,52.5,69.8,,78,81,129,Summer,4,6,15,Summer,10,1,,,,,3,0,8.0,1,8.0,1,12.0,1,Summer,3,3.00303,18.1489,1084.33,1843.36,15.1826,57.7581,15.0179,3.13106,12.0419,2,30.253,12.3225,54.7551,25.8067,45.4356,,,Spring,3.03,Summer,1,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,7,Summer,28,41,Spring,2,0 +0d4800c8,Fall,13,0,Spring,68,Winter,20.8102092,65.5,127.0,,55,70,109,,,,,Winter,22,1,23.5,2,25.3,2,11,0,4.0,0,4.0,0,12.0,1,Winter,2,5.58475,20.8125,1416.15,2265.83,26.9294,93.1012,15.2572,5.55527,33.8988,2,40.925,25.2467,87.5164,39.9407,67.8544,,,Winter,2.14,Winter,1,3,4,0,3,0,2,2,1,1,1,0,3,3,5,4,3,3,2,3,44,Winter,30,43,Fall,2,1 +0d4fa8f4,Spring,7,0,Summer,45,Summer,15.40976,50.0,54.8,,68,99,125,Summer,6,9,50,Summer,0,0,,,,,0,0,6.0,0,7.0,0,3.0,0,,,,,,,,,,,,,,,,,,,,,,Summer,2,3,3,0,2,1,0,1,2,1,2,0,4,0,3,1,3,1,1,1,31,Summer,29,42,Spring,0,1 +0d570d3e,Fall,7,1,Spring,70,Winter,16.43306421,51.0,60.8,,78,92,130,Winter,7,11,12,Winter,17,1,,,,,5,1,8.0,0,7.0,0,8.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,4,4,4,1,5,0,0,4,3,0,5,0,5,4,5,5,4,5,4,5,67,Winter,60,82,Fall,2,2 +0d5e7943,Summer,20,1,,,Summer,29.45185547,64.0,171.6,38.0,77,83,125,,,,,,,,,,,,,,,,,,,,Fall,1,5.15828,29.4551,1450.27,1885.35,34.6758,96.736,16.6047,12.8504,74.864,2,36.8301,25.2301,91.5777,43.8024,71.5059,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +0d74d0ed,Summer,12,1,Winter,61,Fall,27.20604686,63.0,153.6,,,,,,,,,Fall,15,0,17.9,2,20.8,2,8,1,6.0,0,9.0,0,10.0,1,Fall,3,-7.78961,19.1987,1622.43,2595.88,39.6449,115.073,14.3831,4.81558,38.5273,2,42.8305,32.5973,122.862,56.1238,82.4754,,,Summer,2.31,Fall,1,1,2,0,2,2,1,1,1,0,2,0,0,0,0,1,1,1,1,0,17,Fall,28,41,Summer,2,0 +0d924604,Fall,6,0,Summer,55,Spring,14.61814745,46.0,44.0,,58,,110,Spring,5,7,55,Spring,0,0,,,,,0,0,10.5,1,9.0,1,6.0,1,Spring,1,2.49219,14.6197,907.751,1180.08,5.89472,38.9504,12.9419,1.67781,5.04957,1,21.6111,11.4446,36.4582,16.3169,27.5058,,,,,Spring,2,2,4,0,2,0,0,0,0,2,0,0,0,0,2,2,2,2,2,2,24,Spring,44,62,Spring,0,0 +0d9bbd88,Spring,9,0,,,Spring,23.49311224,56.0,104.8,31.0,59,88,120,,,,,Summer,0,0,13.1,2,11.1,1,0,0,5.5,0,7.5,0,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +0db8da5b,Spring,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0ddab8ff,Fall,7,0,Spring,65,Spring,15.30340136,52.5,60.0,,72,86,112,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,2,4,0,3,1,0,3,0,0,3,0,0,0,2,0,0,0,2,0,23,Winter,37,53,Fall,0,0 +0de01ae7,Spring,8,0,Winter,55,Summer,21.36142219,56.5,97.0,,59,85,121,Summer,,,,Summer,3,0,,,,,2,0,4.0,0,5.0,0,8.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.51,Fall,4,4,2,0,2,2,0,4,2,3,4,0,1,1,2,3,4,4,3,4,49,Fall,84,100,Summer,2,1 +0df44f9e,Summer,15,1,Winter,65,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.12,,,Fall,5,5,5,2,4,4,1,2,5,3,4,1,4,1,1,5,2,4,0,1,59,Fall,44,62,Summer,3,2 +0df4c27d,Fall,14,1,Winter,75,Fall,16.79663482,64.5,99.4,,109,94,168,,,,,Fall,17,0,16.9,2,20.1,2,2,0,5.5,0,5.5,0,8.5,1,Fall,2,4.58762,16.7985,1243.62,1865.43,22.762,74.7246,12.6284,4.17011,24.6754,2,29.0261,22.9365,70.137,32.5252,51.7881,Fall,1.34,,,Fall,2,2,3,1,3,1,0,2,2,1,0,0,1,0,1,1,2,1,1,1,25,Fall,40,56,Fall,2,0 +0df73b79,Fall,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0dfcdebc,Winter,11,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +0e1cf9b8,Summer,11,1,,,Summer,14.05459439,51.0,52.0,21.0,64,85,105,,,,,Summer,0,0,7.5,1,8.3,1,0,0,5.0,0,5.5,0,2.0,0,Fall,1,2.57675,14.0561,994.698,1293.11,11.6055,48.2114,13.032,1.0241,3.78859,1,20.4587,16.1472,45.6347,17.3448,32.0642,,,Summer,2.55,Fall,3,2,4,0,3,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,16,Summer,32,46,Summer,0,0 +0e1f7fed,Spring,8,1,Fall,62,Spring,20.69482249,52.0,79.6,29.0,65,82,111,,,,,Spring,6,1,,,,,0,0,9.0,1,9.0,1,7.0,1,Spring,1,3.00637,20.6971,1062.29,1380.98,17.2867,55.4113,14.4077,6.28938,24.1887,3,24.8993,13.2254,52.405,22.9956,42.186,,,,,Summer,3,3,2,0,1,0,0,1,0,0,4,0,0,0,1,0,0,0,1,0,16,Summer,34,49,Spring,2,0 +0e3b0bd2,Summer,7,0,Fall,50,Summer,17.6577708,45.5,52.0,23.0,61,98,104,,,,,Summer,0,0,,,,,0,0,0.0,0,0.0,0,5.0,0,Summer,3,2.51362,17.6597,922.726,1568.63,7.4014,40.5454,13.7696,3.89009,11.4546,2,22.6841,10.4599,38.0318,16.0184,30.0855,,,,,Summer,0,4,0,0,2,1,0,0,0,0,2,0,1,0,2,2,3,2,1,1,21,Summer,34,49,Summer,0,0 +0e3ec3fc,Winter,11,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.03,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +0e64e5ef,Summer,9,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0e75ad18,Winter,9,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.74,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0e8a22e1,Fall,12,1,,,Fall,16.5510107,58.0,79.2,21.0,64,65,106,,,,,Fall,9,0,18.4,2,14.8,2,5,0,6.75,0,9.0,0,12.0,1,Fall,3,3.82254,16.5528,1146.82,1834.91,20.5324,64.4146,13.4627,3.09016,14.7854,1,27.2357,16.6465,60.5921,26.3313,47.7681,,,Fall,2.59,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +0e951124,Summer,14,0,,,Summer,20.37115474,61.5,109.6,,70,56,128,,,,,Summer,10,0,17.3,1,,,10,0,7.0,0,8.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.23,Summer,5,5,5,4,5,5,2,5,5,4,4,1,5,4,5,5,,5,4,3,81,Summer,35,50,Summer,3,3 +0e9c1be6,Fall,6,0,Summer,65,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,4,3,3,3,3,0,3,1,2,2,0,3,3,3,2,2,3,0,1,46,Fall,45,63,Fall,1,1 +0ec403df,Winter,19,1,Summer,55,Spring,30.79521634,61.0,163.0,,78,84,121,,,,,,,,,,,,,,,,,,,,Spring,2,4.88039,30.7986,1439.19,2158.79,34.779,95.5561,18.0552,12.7434,67.4439,2,37.7007,23.0764,90.6757,44.769,72.4797,,,,,Spring,4,5,5,4,3,3,2,2,4,2,4,2,2,1,1,0,0,4,3,0,51,,,,Winter,3,2 +0ec52194,Winter,13,1,,,Spring,31.60262252,66.0,195.8,,70,80,116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,42,59,Winter,1, +0ecdc3e0,Spring,16,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0ed2a3c8,Winter,9,0,Spring,60,Spring,16.21032405,53.5,66.0,,63,69,98,Spring,5,7,41,Spring,10,1,,,,,11,1,9.5,1,9.5,1,12.0,1,Spring,4,3.36136,16.2121,1046.21,2197.03,11.4774,53.6978,13.1902,3.02189,12.3022,1,26.5605,15.6599,50.3364,22.2472,38.0379,,,Spring,4.2,Spring,2,2,3,1,2,1,2,2,2,2,3,1,1,1,1,1,2,1,2,1,33,Spring,28,41,Winter,0,1 +0eddd8e5,Spring,6,0,Spring,60,Spring,25.308,50.0,90.0,,84,97,129,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.17282,25.3108,1086.56,1847.15,14.2828,57.9957,16.3102,9.00059,32.0043,3,30.0519,13.661,54.8229,25.5472,44.3347,,,,,Winter,2,0,3,0,5,1,0,2,0,0,0,0,0,0,0,1,2,1,0,0,17,Winter,37,53,Spring,2,0 +0ee70b3e,Spring,16,1,Spring,60,Spring,30.5159668,64.0,177.8,37.0,78,83,129,,,,,Spring,50,1,19.4,2,21.6,2,0,0,11.5,1,10.5,1,8.0,0,Spring,3,5.23966,30.5193,1476.77,2362.83,35.8286,99.5583,17.0891,13.4302,78.2417,3,38.0219,25.7079,94.3187,46.4732,73.8505,Spring,2.09,,,Spring,2,2,3,1,2,1,1,1,1,1,3,1,1,1,2,2,2,2,1,1,31,Spring,36,51,Spring,0,1 +0ef6e368,Fall,7,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +0f38e035,Spring,9,1,,,Spring,14.87115385,52.0,57.2,22.0,68,83,109,,,,,Spring,0,0,,,,,7,1,7.5,0,8.0,0,7.0,1,Fall,2,2.9006,14.8728,1017.32,1525.98,14.6135,50.6208,13.1621,1.71068,6.57918,1,22.8559,13.1514,47.7202,19.3462,37.4694,,,Spring,1.95,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +0f4db4f2,Fall,9,0,Winter,60,Fall,14.54109958,51.0,53.8,,56,81,111,,,,,Fall,5,0,,,,,8,1,12.0,1,12.0,1,3.0,0,Fall,2,3.0611,14.5427,986.933,1579.09,9.15529,47.3843,12.8085,1.73423,6.41572,1,24.6845,13.5445,44.3232,20.5264,33.8397,,,Winter,1.93,Winter,3,3,1,2,1,0,0,0,1,0,2,0,1,0,1,0,2,2,0,0,19,Winter,60,82,Fall,2,0 +0f5f3950,Summer,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,37,53,,, +0f6b71c4,Summer,5,0,Fall,50,Fall,12.92698756,43.0,34.0,,93,86,157,Fall,,,,Fall,1,0,,,,,0,0,12.0,1,13.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,0,8,,,,Summer,2,0 +0f7f3bf3,Spring,9,0,Winter,55,Summer,17.89454545,55.0,77.0,,59,77,92,Summer,5,7,39,Summer,2,0,,,,,0,0,6.0,0,7.0,0,5.5,1,Summer,2,3.75771,17.8965,1113.65,1781.84,15.8712,60.8817,14.1502,3.74625,16.1183,1,29.4882,15.5222,57.124,23.2657,45.3594,,,Spring,1.93,Summer,3,3,3,0,4,2,0,4,0,0,4,0,2,0,0,2,4,2,3,0,36,Summer,37,53,Spring,3,1 +0f9b8e65,Fall,13,0,Summer,63,Winter,22.84274965,58.5,111.2,,73,103,133,,,,,Winter,9,,14.1,1,18.9,1,9,,4.0,,4.0,,9.0,,Spring,3,4.26303,22.8452,1246.81,2119.58,19.1377,75.0651,15.4216,7.42366,36.1349,2,35.6373,20.29,70.802,36.5029,54.7751,,,Fall,2.88,Winter,4,4,4,2,4,1,2,1,1,1,4,1,1,2,3,2,1,2,1,2,43,Winter,37,53,Fall,0,1 +0fbb3ca5,Winter,15,0,Spring,55,Spring,19.02404082,70.0,132.6,,76,80,112,,,,,Spring,15,0,18.6,1,19.3,1,4,0,11.0,1,12.0,1,7.0,0,Summer,3,6.60843,21.8958,1580.06,2686.1,32.3693,110.56,15.8638,6.03206,42.0397,2,47.9234,30.2676,103.952,53.2668,80.2927,Winter,2.16,,,Spring,3,3,4,2,4,2,1,3,2,2,4,0,2,1,4,,2,3,4,2,48,Spring,34,49,Winter,3,1 +0fc19fd9,Fall,8,1,Winter,60,Fall,16.19712,50.0,57.6,,59,106,112,Fall,,,,Fall,11,1,,,,,6,1,11.0,1,10.5,1,10.0,1,Fall,5,6.90485,16.1989,997.422,2194.33,13.9603,48.5016,13.6401,2.55876,9.09843,2,23.1851,11.3561,41.5967,20.7063,37.1455,,,,,Fall,3,3,2,0,2,1,0,1,0,0,2,0,3,1,0,2,1,2,0,0,23,Fall,41,58,Fall,0,0 +0fced9cd,Summer,8,0,,,Summer,21.75759323,51.0,80.5,27.0,80,92,108,,,,,Summer,,,,,,,,,,,,,,,Summer,1,3.20307,21.3395,1064.69,1384.1,12.4921,55.6664,14.7564,6.58308,24.8337,2,28.1741,15.0002,52.4634,24.066,40.6662,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +0ff6ac09,Summer,7,0,Fall,58,Fall,25.27696689,53.0,101.0,,103,103,145,Fall,,,,Fall,,,,,,,,,,,,,,,Fall,2,3.52667,25.2797,1150.61,1840.98,16.1861,64.8181,16.2236,9.05611,36.1819,3,32.3154,16.3167,61.2915,29.8118,48.5015,,,,,Fall,2,2,3,1,3,0,0,0,0,2,2,0,0,0,2,1,3,2,0,0,23,Fall,38,54,Summer,2,0 +0ff71f1a,Winter,6,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +0ffadbba,Fall,9,1,Summer,65,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.11,,,,,,,,,,,,,,,,,,,,,,,,,,,, +101774f0,Fall,6,1,Spring,61,Winter,15.62222222,42.0,39.2,,60,79,95,Winter,4,6,23,Winter,4,1,,,,,0,0,9.0,1,8.0,0,7.0,1,Spring,2,1.91871,14.2359,905.889,1358.83,7.8268,38.7521,14.0732,0.162652,0.447879,1,18.4623,12.4631,36.8334,14.9003,26.2891,,,,,Winter,1,1,2,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Winter,31,45,Fall,0,0 +101c27f1,Spring,7,0,Fall,60,Fall,18.67168,50.0,66.4,,,,,Fall,8,13,13,Spring,6,1,,,,,2,0,9.0,1,13.0,1,10.0,1,Summer,3,3.26815,18.1108,1039.53,1767.2,12.3391,52.9864,14.3228,3.78803,14.0136,1,28.247,12.4003,49.7182,24.713,40.586,,,,,Summer,4,3,3,0,3,1,0,0,0,2,2,0,2,0,1,2,2,2,2,0,29,Summer,50,69,Spring,1,0 +10258585,Fall,6,0,Spring,75,Winter,15.43573105,49.5,53.8,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,3,4,0,5,0,0,0,0,1,2,0,3,4,0,4,3,4,0,0,37,Winter,32,46,Fall,0,1 +102e8b66,Fall,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +105a2475,Winter,15,0,Fall,60,Fall,16.86421886,69.0,114.2,27.0,53,93,110,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.82,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +105e3f96,Fall,9,0,Winter,60,Winter,13.57122781,52.0,52.2,,88,96,149,Winter,6,9,34,Winter,10,1,,,,,3,0,8.5,1,7.5,1,6.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.62,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,53,73,Fall,1,0 +108b8659,Fall,6,0,Spring,65,Fall,16.9820908,49.0,58.0,,63,91,109,Fall,0,0,5,Fall,2,1,,,,,0,0,0.0,0,0.0,0,6.0,1,Fall,3,8.39448,16.9839,973.999,1655.8,8.19043,46.0067,13.472,3.51196,11.9933,1,24.4088,13.4075,37.6122,21.0193,32.5992,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,35,50,Fall,0,0 +10a3ff78,Fall,6,1,Summer,,Winter,17.47706044,46.0,52.6,23.0,56,96,127,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +10ad8c48,Fall,12,0,Summer,60,Spring,21.71773314,60.05,111.4,,96,99,161,,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.75677,21.3982,1305.96,2220.13,22.8607,81.3647,15.6289,5.76931,30.0353,2,37.6113,20.8927,76.6079,35.9874,60.4719,,,Winter,1.46,Spring,5,4,4,3,4,4,2,4,3,3,4,2,3,4,4,2,5,3,4,3,70,Spring,45,63,Fall,3,2 +10be7f09,Spring,9,1,Spring,65,Spring,23.50738294,59.0,116.4,30.0,57,85,99,,,,,Spring,10,1,,,,,3,0,12.0,1,12.0,1,12.0,1,Spring,2,4.52056,23.51,1325.24,1987.86,30.5628,83.4183,16.8485,6.66149,32.9817,3,33.929,18.9265,78.8978,34.4422,64.4918,,,Spring,2.67,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,28,41,Spring,0,0 +10c0e99a,Spring,7,0,Fall,68,Summer,14.75685131,49.0,50.4,,58,82,81,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,3,2,1,2,1,0,1,0,1,1,0,2,1,2,2,1,1,1,1,25,Summer,34,49,Spring,0,0 +10e46254,Winter,7,0,Summer,61,Spring,14.95571742,46.5,46.0,,80,120,129,Spring,4,6,0,Spring,10,1,,,,,5,1,12.0,1,12.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,2,0,0,0,0,2,0,1,0,0,0,0,1,1,1,1,1,0,10,Spring,32,46,Winter,2,0 +10ea0f7e,Summer,13,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +112a4635,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +113ef295,Winter,12,1,Spring,70,Spring,16.93460615,63.0,95.6,27.0,62,76,115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.67,,,,,,,,,,,,,,,,,,,,,,,Winter,39,55,,, +113f5798,Summer,11,1,Spring,45,Winter,15.76687243,54.0,65.4,,72,91,118,Winter,5,7,50,Winter,10,0,12.7,2,12.5,2,5,0,10.0,1,10.5,1,10.0,1,Winter,2,2.60903,15.7686,1077.12,1615.67,10.0138,56.99,13.7409,2.02773,8.40999,1,20.6944,26.2818,54.381,28.817,30.7083,,,Winter,1.75,Winter,4,4,4,2,4,4,0,4,1,2,4,0,3,3,5,4,4,4,4,4,64,Winter,61,84,Summer,2,2 +11477ed0,Summer,9,0,Winter,60,Fall,22.25235392,53.5,90.6,,76,82,112,Fall,5,7,32,Fall,0,0,,,,,4,0,12.0,1,12.0,1,11.0,1,Fall,3,3.52565,22.2548,1122.85,1908.85,14.9384,61.8614,15.1955,7.05928,28.7386,2,30.7714,16.1517,58.3358,27.8068,45.7098,,,Summer,2.651,Fall,2,1,3,0,1,1,0,2,0,1,1,0,0,1,1,1,1,0,0,0,16,Fall,58,80,Summer,1,0 +1159902c,Spring,13,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.48,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +115d3db4,Winter,9,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.32,Spring,2,2,2,1,3,1,0,3,2,2,3,0,4,2,4,4,3,3,2,4,47,Spring,40,56,Winter,0,1 +1169a538,Winter,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.74,,,,,,,,,,,,,,,,,,,,,,,Spring,36,51,,, +11739de2,Winter,11,0,Fall,70,Spring,17.66466837,56.0,78.8,,66,94,127,Spring,5,7,30,Spring,6,0,17.9,2,18.7,2,7,1,10.0,1,9.0,1,11.0,1,Spring,2,4.04534,17.6666,1155.11,1848.18,17.5204,65.2975,14.6394,3.02721,13.5025,1,32.2406,15.5364,61.2521,28.7117,49.7611,,,Spring,1.24,Spring,0,0,5,0,2,1,0,0,0,0,0,0,0,0,0,0,3,3,2,0,16,Spring,47,66,Winter,0,0 +1184a14a,Spring,5,0,Summer,51,Spring,16.1130519,42.5,41.4,21.0,66,82,92,,,,,Spring,0,0,,,,,0,0,8.5,1,8.0,1,0.0,0,Spring,2,2.22149,16.1148,864.974,1383.96,4.96597,34.3941,13.3878,2.72702,7.0059,1,20.7481,8.67999,32.1726,14.2377,25.7141,,,,,Spring,2,2,2,0,2,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,21,,,,Spring,2,0 +1187440a,Summer,16,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +118b7733,Fall,10,0,Spring,70,Winter,14.34693878,56.0,64.0,,56,94,85,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.99,Spring,2,2,3,1,3,1,0,2,1,1,3,0,1,2,3,1,2,1,2,2,33,Winter,34,49,Fall,0,1 +119d7562,Summer,17,0,Fall,58,Fall,22.95510204,70.0,160.0,,70,69,120,,,,,Fall,18,0,31.9,1,38.0,2,15,0,11.0,1,10.0,1,11.0,1,Fall,1,6.73158,22.9576,1621.42,2107.85,32.2328,114.966,16.4959,6.46171,45.034,2,51.6714,31.0618,108.234,67.9711,83.9042,Summer,2.27,,,Fall,5,3,3,0,5,2,0,1,2,3,4,0,3,3,4,0,2,5,0,2,47,,,,Summer,3,1 +119dd2e1,Winter,9,0,,,Spring,14.28017457,51.75,54.4,,76,71,116,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.83,Spring,3,3,2,0,4,2,1,1,2,2,2,0,1,1,1,2,1,2,1,1,32,Spring,34,49,Winter,0,1 +119e181e,Fall,6,1,Winter,61,Fall,14.60161816,45.5,43.0,,68,87,110,,,,,Fall,10,1,,,,,0,0,10.0,1,12.0,1,12.0,1,Winter,1,2.12239,14.6032,919.176,1194.93,9.40406,40.1673,13.6412,0.962003,2.83267,1,19.7438,11.0195,38.0449,16.1607,29.1478,,,,,Fall,4,2,2,0,1,1,0,1,1,0,2,0,2,1,1,1,1,1,0,0,21,,,,Fall,0,0 +11be5d2e,Spring,17,0,Fall,85,Summer,26.87302605,65.5,164.0,,85,74,128,,,,,Summer,45,1,38.1,2,38.0,2,15,0,7.5,1,10.0,1,11.0,1,,,,,,,,,,,,,,,,,,Summer,2.78,,,Summer,3,3,3,1,3,1,2,1,2,2,3,0,1,3,1,0,1,3,1,0,34,,,,Spring,2,1 +11d1a5ac,Winter,11,0,Summer,75,Winter,23.12318897,56.5,105.0,29.0,72,65,121,,,,,Winter,18,1,20.2,2,20.9,2,14,1,4.5,0,5.0,0,10.0,1,Spring,3,4.23047,23.1257,1247.67,2121.04,20.9138,75.1566,16.5529,6.57285,29.8434,3,36.4158,17.827,70.9261,34.8662,57.3296,,,Spring,2.57,Spring,1,1,1,1,2,4,2,1,1,1,2,0,0,0,0,0,0,0,0,0,17,Spring,30,43,Winter,0,0 +11dc7b56,Spring,16,1,,,Summer,25.68848032,59.0,127.2,32.0,81,57,126,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +11f7253c,Fall,16,0,,,Fall,46.10291358,67.5,298.8,49.0,72,93,157,,,,,Fall,11,0,32.6,2,36.6,2,0,0,0.0,0,5.5,0,12.0,1,Fall,1,7.43478,46.1079,2220.87,2887.12,58.7812,178.814,27.5929,18.515,119.986,3,73.2738,46.7594,171.38,82.3033,132.055,Fall,1.38,,,Fall,5,5,5,5,5,5,2,5,2,5,5,0,5,5,5,5,5,5,5,5,89,Fall,67,91,Fall,3,3 +11f9b26c,Fall,6,0,Winter,75,Winter,16.41301653,44.0,45.2,,60,113,100,Winter,2,2,20,Winter,4,1,,,,,1,0,11.0,1,12.0,1,9.0,1,Winter,2,5.01085,16.4148,892.166,1427.47,7.2621,37.2904,13.5424,2.87245,7.90962,1,21.9373,8.09099,32.2795,13.8482,29.1994,,,,,Winter,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Fall,30,43,Fall,0,0 +1205bbc5,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1225fd28,Winter,9,1,Summer,65,Spring,16.08833844,54.75,68.6,,95,89,139,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.09,Spring,2,2,3,2,3,2,1,2,1,2,4,1,2,3,3,3,3,3,2,2,46,Spring,47,66,Winter,2,1 +12380b06,Winter,13,1,Spring,91,Spring,18.16083333,60.0,93.0,,77,69,137,,,,,Spring,,,,,,,,,,,,,,,Summer,4,4.36247,18.1628,1236.98,2350.27,24.1535,74.0179,14.4556,3.70719,18.9821,2,31.834,18.0304,69.6554,36.5493,55.9875,,,Spring,1.64,Spring,2,2,2,0,2,1,1,0,1,1,3,0,1,2,1,1,1,1,0,0,22,Spring,34,49,Winter,2,0 +123e0518,Summer,15,0,,,Summer,21.82151675,63.0,123.2,31.0,67,77,110,,,,,Summer,10,0,20.3,1,19.6,1,3,0,9.0,1,8.0,1,9.0,1,Summer,3,5.81028,21.8239,1510.45,2567.76,30.6796,103.146,18.2714,3.55247,20.0544,2,48.0599,24.4062,97.3354,56.769,78.7395,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +125175e1,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1262c9df,Spring,12,1,,,Spring,25.23777315,62.0,138.0,34.0,60,76,114,,,,,Spring,12,0,23.1,2,25.4,3,0,0,10.0,0,7.5,0,13.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.43,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +1282c17a,Summer,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1286dab7,Summer,10,0,Winter,62,Summer,17.13579393,51.0,63.4,,56,71,103,,,,,Summer,12,1,,,,,11,1,8.0,1,11.0,1,7.0,1,Summer,3,3.203,17.1377,1022.55,1738.33,11.1098,51.1778,13.8339,3.30378,12.2222,1,27.4041,12.6639,47.9748,24.7176,38.5139,,,Fall,1.88,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,29,42,Summer,0,0 +12a607f6,Winter,7,0,Fall,75,Summer,19.16818935,48.75,64.8,,62,82,106,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,5,0,5,2,0,2,1,1,1,0,2,1,2,1,3,2,1,0,37,Spring,30,43,Winter,2,1 +12a6d170,Winter,9,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.03,,,,,,,,,,,,,,,,,,,,,,,,,,,, +12cbf30c,Summer,11,1,Fall,90,Summer,21.82896294,61.75,118.4,,88,123,147,Summer,5,7,27,Summer,30,1,24.7,3,24.3,3,22,1,10.0,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.24,Summer,2,1,1,1,2,1,1,1,2,2,1,1,1,1,2,1,1,2,1,1,26,Summer,37,53,Summer,1,0 +12cfb064,Spring,6,0,Summer,55,Spring,14.56340336,43.5,39.2,21.0,126,75,179,,,,,Spring,0,0,,,,,0,0,6.0,0,6.5,0,6.0,1,Spring,2,2.21192,14.565,874.822,1399.72,4.30306,35.443,13.1691,1.39592,3.75695,1,19.5425,11.5975,33.2311,12.9085,23.8455,,,,,Spring,2,3,2,1,2,1,0,1,1,1,2,0,1,1,1,1,1,1,1,1,24,,,,Spring,0,0 +12e5069f,Spring,13,0,,,Spring,21.78719008,66.0,135.0,32.0,61,81,123,,,,,Summer,12,0,39.2,3,40.2,3,0,0,8.0,1,7.0,0,12.0,1,Summer,3,5.75577,21.7896,1457.65,2478.01,28.1411,97.5221,15.7405,6.04909,37.4779,2,43.1756,26.2054,91.7663,44.9263,71.3167,,,Summer,2.049,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +12f37ff2,Summer,7,0,Winter,60,Fall,13.91354167,48.0,45.6,,66,101,108,Fall,4,5,46,Fall,5,1,,,,,3,0,8.0,1,8.0,1,2.0,0,,,,,,,,,,,,,,,,,,,,,,Fall,3,3,3,1,3,1,0,0,0,3,2,0,2,0,2,3,2,3,0,1,32,Fall,42,59,Summer,0,1 +1301f65d,Winter,8,0,Spring,80,Winter,14.51548594,53.0,58.0,,60,87,87,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.96,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,32,46,Winter,0,0 +1306392e,Summer,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +1307c41a,Spring,8,1,Summer,60,Spring,14.64421053,47.5,47.0,,51,82,90,Spring,,,,Spring,11,1,,,,,9,1,9.0,1,10.5,1,11.0,1,Spring,3,9.92214,14.9591,940.652,1505.04,10.4596,42.4548,13.5124,1.44663,4.54519,1,20.5929,11.4023,32.5327,17.5897,31.0526,,,Spring,1.49,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,32,46,Spring,2,0 +1314bc8c,Spring,12,0,,,Summer,14.86144188,59.88,75.8,,69,74,116,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.05,Spring,2,1,3,2,2,1,2,1,1,1,0,0,1,2,2,2,2,1,1,1,28,Spring,50,69,Spring,0,0 +132a3165,Summer,7,1,,,Summer,14.81895239,46.0,44.6,21.0,56,116,94,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +135af3fb,Fall,8,1,,,Fall,18.61494083,52.0,71.6,26.0,55,85,106,,,,,Fall,5,0,,,,,1,0,8.0,0,8.5,0,8.0,1,Fall,3,2.96827,18.617,1045.68,1673.09,15.9541,53.6421,13.9477,4.6693,17.9579,1,24.5284,13.1596,50.6738,23.4879,40.4825,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +136894bc,Spring,12,0,Spring,75,Fall,26.64857002,59.5,134.2,,64,95,99,,,,,Fall,0,0,14.4,1,14.5,1,0,0,5.5,0,5.0,0,13.0,1,Fall,2,10.8979,26.6515,1331.82,2130.91,23.8086,84.1194,16.7057,9.94577,50.0806,3,37.8332,22.4776,73.2215,33.4335,61.6418,,,Winter,3.11,Fall,1,2,2,1,1,1,1,1,1,1,2,1,1,1,1,1,2,1,2,1,25,Fall,35,50,Spring,3,0 +137baaec,Fall,9,0,Spring,55,Fall,22.50292398,57.0,104.0,,75,87,111,Fall,3,5,0,Fall,11,1,,,,,5,0,13.0,1,12.0,1,10.0,1,Fall,4,6.15916,22.5054,1240.0,2603.99,20.0167,74.3389,16.0868,6.4186,29.6611,2,36.1667,18.1556,68.1797,36.0832,56.1833,,,Winter,3.68,Fall,3,4,4,0,4,0,0,1,2,0,4,0,2,2,4,4,4,4,4,4,50,Winter,42,59,Fall,2,2 +137fc699,Summer,8,0,,,Summer,23.60907898,50.25,84.8,27.0,78,75,123,,,,,Summer,0,0,,,,,0,0,9.0,1,7.0,0,9.5,1,Fall,1,3.15984,23.6117,1071.22,1392.59,13.5031,56.3621,15.6934,7.91823,28.4379,3,29.1603,13.6987,53.2023,24.5181,42.6634,,,,,Summer,0,0,5,0,4,0,0,4,4,1,4,0,1,0,2,1,3,1,2,1,33,Summer,44,62,Summer,0,1 +13a5e288,Fall,6,0,Spring,47,Spring,17.26568,50.0,61.4,,,,,Spring,,,,Winter,0,0,,,,,0,0,4.5,0,5.0,0,7.0,1,Winter,2,2.97543,17.2676,995.762,1593.22,9.74859,48.3247,13.5904,3.67718,13.0753,2,25.2152,13.3609,45.3493,20.3888,34.9638,,,,,Winter,3,2,5,0,2,0,0,0,0,2,1,0,0,1,1,2,2,2,0,0,23,,,,Winter,2,0 +13b5ac0f,Winter,8,0,,,Winter,14.70652632,47.5,47.2,24.0,70,74,104,,,,,Winter,0,0,,,,,2,0,7.0,0,8.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +13be5672,Summer,8,0,Winter,65,Fall,19.67098612,53.0,78.6,,74,108,107,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,1,1,1,1,1,0,1,1,2,1,0,1,1,1,1,1,1,1,1,20,Summer,33,47,Summer,0,0 +13cdb63a,Winter,8,1,Summer,75,Winter,14.30546884,46.5,44.0,,75,,113,Spring,5,7,36,Winter,4,1,,,,,1,0,10.5,1,10.5,1,6.5,1,Winter,5,12.6324,14.307,935.465,2058.02,9.08696,41.9023,13.625,0.682078,2.09767,1,19.545,13.2704,29.2699,17.0728,28.6319,,,,,Winter,2,0,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Winter,34,49,Winter,0,0 +13d23890,Summer,12,0,,,Fall,21.42440204,61.0,113.4,,77,96,114,,,,,Fall,12,0,41.8,1,40.6,1,0,0,8.0,1,7.5,1,5.0,0,Fall,1,4.66994,21.4267,1329.63,1728.52,23.6158,83.8859,15.8501,5.57664,29.5141,2,38.9797,21.2903,79.216,39.1613,62.5956,,,,,Fall,2,2,3,0,4,0,0,2,0,2,2,0,0,0,2,2,4,2,3,0,30,Fall,41,58,Summer,2,0 +13d2c419,Summer,7,0,Fall,82,Summer,16.14640832,46.0,48.6,,74,79,99,Summer,7,11,45,Summer,6,1,,,,,0,0,7.0,0,9.0,1,4.5,0,Summer,3,4.37883,16.1482,926.15,1574.45,8.21248,40.9101,13.5931,2.5551,7.68989,1,23.8631,8.83452,36.5313,17.9598,32.0756,,,,,Summer,1,2,1,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,8,Summer,40,56,Summer,0,0 +13e15532,Winter,7,0,,,Winter,16.09850015,50.5,58.4,23.0,65,69,111,,,,,Winter,0,0,,,,,0,0,0.0,0,5.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +13e46923,Spring,8,0,,,Summer,19.49889053,52.0,75.0,26.0,80,60,112,,,,,Summer,0,0,,,,,3,0,10.0,1,7.0,0,11.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,3.74,Summer,5,5,4,0,4,1,0,4,4,1,4,0,4,1,4,4,2,4,2,1,54,Summer,57,79,Spring,1,2 +13eee030,Spring,9,1,Summer,61,Summer,14.70610425,54.0,61.0,,66,90,111,Summer,,,,Summer,6,0,,,,,5,0,5.0,0,4.0,0,6.0,1,Summer,2,3.15536,14.7077,1049.44,1574.16,15.6963,54.0423,13.0301,1.67756,6.95767,1,24.0407,14.3053,50.887,21.9738,39.737,,,Spring,3.64,Summer,3,2,5,4,4,0,0,0,1,1,0,0,4,1,3,4,3,5,0,2,42,Summer,53,73,Spring,1,1 +13efe653,Winter,11,0,,,Winter,16.76788893,53.0,67.0,25.0,75,80,122,,,,,Winter,2,0,23.0,1,24.6,1,0,0,,,,,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +13fca10c,Spring,14,1,Fall,55,Summer,20.82962963,63.0,117.6,30.0,68,63,103,,,,,Summer,30,1,19.3,2,18.0,2,10,1,6.5,0,10.0,1,12.0,1,Summer,5,4.52005,20.8319,1271.09,2796.4,25.2416,77.6508,13.7552,7.07669,39.9492,2,30.2559,22.1532,73.1307,33.2565,55.4975,,,Spring,3.751,Summer,3,3,4,2,3,1,0,1,1,1,1,1,2,1,2,1,2,1,1,2,33,Summer,29,42,Spring,2,1 +14109652,Fall,17,0,Spring,60,Winter,27.81877757,69.0,188.4,37.0,86,82,127,,,,,Fall,15,0,37.7,2,34.0,2,13,0,13.0,1,8.0,1,12.0,1,Spring,1,6.69669,27.8218,1708.47,2221.02,37.9927,124.238,18.3467,9.47508,64.1621,3,53.059,33.1862,117.541,57.7948,91.0518,Fall,1.08,,,Fall,1,5,5,5,5,4,1,3,1,4,5,1,3,4,2,2,4,2,1,2,60,,,,Fall,2,2 +1420b16e,Fall,12,0,Spring,78,Winter,26.56423324,66.0,164.6,,79,62,125,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.71,Fall,3,2,3,2,3,2,1,2,2,3,3,1,2,2,3,1,2,2,2,1,42,Fall,38,54,Fall,1,1 +142242d9,Spring,5,0,,,Spring,20.22273095,46.5,62.2,24.0,,,,,,,,Spring,0,0,,,,,0,0,12.5,1,14.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +1427e325,Fall,6,1,Summer,81,Spring,13.78664413,45.5,40.6,,73,89,122,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.15078,13.7882,914.22,1462.75,9.15735,39.6394,13.4619,0.326228,0.960596,1,20.3228,10.1592,37.4886,18.1719,29.4802,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,39,55,Fall,, +1429abf1,Fall,8,0,Spring,55,Fall,19.4028,50.0,69.0,25.0,55,63,96,,,,,Fall,0,0,,,,,5,1,12.0,1,12.0,1,10.0,1,Winter,2,3.12277,19.4049,1028.21,1645.14,11.9228,51.7814,14.5625,4.84241,17.2186,1,27.5763,12.2823,48.6586,23.0382,39.4991,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,53,73,Fall,0,0 +14391970,Summer,6,0,Fall,63,Summer,19.13648393,46.0,57.6,,57,88,107,,,,,Summer,0,0,,,,,0,0,11.0,1,12.5,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,4,4,3,0,5,0,0,0,0,2,1,0,0,2,1,2,3,2,0,0,29,Fall,38,54,Summer,0,0 +144a9a79,Spring,9,0,Spring,45,Spring,17.24760602,51.63,65.4,,61,77,106,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.15695,17.2495,1024.16,1741.07,10.6263,51.3494,13.5436,3.7059,14.0506,1,26.2161,14.507,48.1924,22.2203,36.8423,,,Spring,2.06,Spring,4,5,4,2,5,3,0,2,4,3,4,0,2,2,3,1,5,2,1,2,54,Spring,34,49,Spring,1,2 +1455e657,Summer,5,0,,,Summer,24.02350617,45.0,69.2,29.0,78,116,131,,,,,Summer,0,0,,,,,0,0,7.0,0,6.0,0,11.0,1,Summer,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +14568d61,Summer,5,0,Winter,60,Fall,17.27488546,49.0,59.0,,129,94,162,Fall,,,,Fall,2,1,,,,,0,0,11.0,1,11.5,1,11.0,1,Fall,2,3.32898,17.2768,1003.93,1606.28,10.4081,49.1944,14.4054,2.87134,9.80559,2,28.6563,10.1299,45.8654,29.7022,39.0645,,,,,Fall,4,3,4,0,4,0,0,2,1,3,2,0,4,1,3,4,2,4,3,1,45,,,,Fall,1,1 +1458a9d2,Fall,7,0,Fall,70,Spring,16.12431385,49.5,56.2,,69,73,106,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,1,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,Winter,39,55,Fall,1,0 +1470a1df,Summer,7,0,Winter,61,Summer,14.95037807,46.0,45.0,,72,74,111,,,,,Summer,10,1,,,,,3,1,11.0,1,10.0,1,12.0,1,Summer,3,2.53641,14.952,910.27,1547.46,6.26564,39.2188,13.0311,1.92092,5.78125,1,22.3747,10.5784,36.6823,17.7257,28.6404,,,,,Fall,3,4,4,0,3,1,0,1,2,3,2,0,3,1,2,2,1,2,1,1,36,Fall,59,81,Summer,2,1 +147c4374,Spring,5,0,Summer,79,Spring,26.51200378,46.0,79.8,28.0,72,83,120,,,,,Spring,0,0,,,,,0,0,10.0,1,9.0,1,7.0,1,Spring,2,2.70159,26.5149,1007.46,1611.93,11.7111,49.5703,16.4706,10.0443,30.2297,3,26.8802,10.9789,46.8687,19.5906,38.5913,,,,,Summer,2,2,3,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,12,,,,Spring,0,0 +14a6e58e,Spring,7,0,Summer,64,Summer,15.18977316,51.0,56.2,,57,72,110,Summer,6,9,34,Summer,10,1,,,,,4,1,9.0,1,9.5,1,6.5,1,Summer,3,3.20232,15.1914,1004.76,1708.1,9.54713,49.2833,13.3218,1.86965,6.91667,2,27.6849,12.0513,46.081,29.5602,37.232,,,,,Summer,4,4,4,1,3,1,0,1,0,0,1,0,2,1,1,1,1,1,1,1,28,Summer,65,89,Spring,0,0 +14b3d6e5,Spring,5,0,Fall,55,Spring,14.4852552,46.0,43.6,,68,69,126,,,,,Spring,0,0,,,,,5,1,13.0,1,14.0,1,9.0,1,Spring,2,2.56054,14.4868,907.442,1451.91,6.60796,38.9175,12.931,1.55583,4.68246,1,22.3208,9.98879,36.357,16.7867,28.9288,,,,,Summer,3,3,2,0,4,0,0,2,2,2,1,0,2,1,2,2,1,1,0,0,28,,,,Spring,0,0 +14b8dd2a,Summer,12,0,,,Summer,25.29136095,65.0,152.0,34.0,120,94,139,,,,,Summer,30,1,26.2,2,28.8,2,13,1,0.0,0,4.0,0,9.0,1,Fall,5,5.46945,25.2608,1465.06,3516.16,28.1051,98.3117,16.3599,8.90092,53.4883,3,43.1025,27.1041,92.8422,43.5129,71.2076,,,Summer,4.04,Summer,3,2,3,1,1,1,1,2,2,1,2,1,1,1,1,1,1,1,1,1,28,Summer,27,40,Summer,0,0 +14b999b6,Summer,11,0,Fall,80,Summer,19.436544,62.5,108.0,29.0,104,89,135,,,,,Summer,18,1,15.9,2,17.3,2,0,0,0.0,0,0.0,0,6.0,0,Summer,2,5.1908,19.4387,1346.85,2154.97,24.6176,85.7207,15.4287,4.01,22.2793,2,39.3815,21.7215,80.5299,39.2555,63.9992,,,Summer,2.18,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,38,54,Summer,0,0 +14c12974,Winter,8,1,Fall,60,Spring,14.06500534,53.0,56.2,,51,85,102,Spring,4,5,20,Spring,9,1,,,,,0,0,9.0,1,8.0,0,7.0,1,Spring,2,2.90432,14.0665,1022.3,1533.45,13.9698,51.1511,12.8028,1.2637,5.04887,1,21.9383,15.243,48.2468,18.3004,35.9081,,,Spring,1.55,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,35,50,Winter,0,0 +14dae995,Spring,7,0,Fall,45,Spring,18.5892656,48.5,62.2,,68,95,101,Summer,7,11,30,Spring,6,1,,,,,14,1,8.0,1,9.0,1,9.5,1,,,,,,,,,,,,,,,,,,,,,,Spring,2,3,3,0,5,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,18,Spring,50,69,Spring,3,0 +14e00ce0,Winter,7,1,Fall,80,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,0,2,0,4,1,0,0,1,1,3,0,1,2,2,1,2,2,1,0,25,Spring,34,49,Winter,0,0 +14eac885,Fall,12,0,Winter,61,Fall,17.00701754,57.0,78.6,,59,76,111,Fall,3,5,57,Fall,10,0,11.0,1,13.7,2,0,0,8.0,1,8.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,Winter,2.06,Fall,4,0,5,5,5,0,0,4,1,2,0,0,3,2,5,2,5,5,2,2,52,Fall,43,60,Fall,1,2 +15132a7e,Winter,8,0,,,Winter,15.95648977,51.5,60.2,23.0,67,65,108,,,,,Winter,2,0,,,,,0,0,10.0,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +151bf768,Summer,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +15227eef,Summer,14,0,Fall,81,Summer,18.2866433,60.75,96.0,26.0,67,69,102,,,,,Summer,13,0,17.6,1,22.3,1,2,0,12.0,1,12.5,1,8.0,0,Summer,2,4.80187,18.139,1269.32,2030.92,21.373,77.4627,14.6364,3.5026,18.5373,2,36.1657,19.9239,72.6608,34.9202,57.5387,Summer,0.66,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,39,55,Summer,2,0 +1522fee1,Winter,12,0,Spring,70,Spring,16.68495843,63.0,94.2,,64,81,93,,,,,Spring,21,1,18.3,1,22.8,2,15,1,6.0,0,5.0,0,6.0,0,Spring,4,5.10793,16.6868,1290.55,2710.16,22.5738,79.7237,14.1224,2.56437,14.4763,2,36.1633,20.9866,74.6157,33.5156,58.7371,,,Spring,3.49,Winter,5,5,4,1,5,2,1,1,3,2,2,1,2,2,3,3,3,5,2,2,54,Winter,35,50,Winter,2,2 +153b6c12,Winter,11,0,,,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +154d1191,Fall,7,0,Spring,55,Winter,18.27038734,49.0,62.4,,62,89,111,,,,,Winter,5,1,,,,,6,1,11.0,1,12.0,1,10.0,1,Winter,3,3.11945,18.2724,1015.47,1726.3,12.3181,50.4239,14.7655,3.50693,11.9761,1,27.8171,10.2887,47.3044,22.7876,40.1352,,,,,Winter,4,4,4,1,4,0,0,4,0,4,0,0,2,1,4,3,0,2,0,2,39,Winter,33,47,Fall,0,1 +1551c806,Fall,18,1,Spring,60,Winter,21.01478189,67.5,136.2,,64,87,115,,,,,Winter,20,1,28.5,2,27.9,2,10,1,6.0,0,8.0,0,9.0,1,Winter,3,5.61222,21.0171,1418.56,2269.69,32.1136,93.3579,14.4061,6.61099,42.8421,2,35.589,25.6553,87.7456,41.4277,67.7026,Fall,1.781,,,Winter,2,3,4,2,5,1,2,,,,2,1,1,2,2,1,2,1,,,31,,,,Fall,2,1 +1562998a,Winter,18,0,Spring,50,Spring,18.70840816,70.0,130.4,,82,67,132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,4,0,5,0,1,0,1,2,4,0,0,1,2,0,2,1,0,0,27,Spring,34,49,Winter,2,0 +157b271f,Spring,15,1,Summer,48,Spring,20.90047871,63.0,118.0,30.0,61,69,99,,,,,Spring,0,0,12.1,1,23.4,2,0,0,10.0,1,11.0,1,5.0,0,Spring,3,5.02105,20.9028,1356.75,2170.8,30.5654,86.7745,15.3714,5.53134,31.2255,2,34.6027,21.6064,81.7535,37.6787,65.1681,Spring,2.37,,,Spring,5,5,5,2,5,5,5,5,5,5,5,0,5,5,5,5,5,5,5,5,92,Spring,53,73,Spring,3,3 +1582a4cb,Summer,11,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +1586ae0d,Spring,21,1,,,Spring,20.03342012,65.0,120.4,27.0,96,90,148,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +15961649,Winter,11,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.53,,,,,,,,,,,,,,,,,,,,,,,,,,,, +15a2364d,Winter,11,0,Summer,47,Winter,22.59766966,59.5,113.8,30.0,66,83,111,,,,,Winter,13,1,18.9,2,18.8,2,0,0,12.0,1,12.5,1,7.0,0,Spring,2,4.34951,22.6001,1258.32,2013.31,19.9656,76.2902,15.1509,7.44927,37.5098,2,34.9675,21.3571,71.9407,32.4556,54.9331,,,Spring,1.27,Spring,4,4,2,0,4,4,0,0,0,5,1,0,2,2,0,2,2,2,0,0,34,Spring,61,84,Winter,0,1 +15c8f4d0,Fall,18,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +15d702f8,Fall,9,0,Winter,80,Fall,17.00303254,52.0,65.4,,63,75,108,Fall,6,10,10,Fall,3,0,,,,,10,1,8.0,1,11.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.28,Fall,1,1,1,0,1,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,8,Fall,42,59,Fall,0,0 +15dbc929,Winter,7,0,Spring,65,Spring,17.575,48.0,57.6,,66,67,108,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,7,Spring,28,41,Winter,0,0 +15e0568a,Winter,16,1,,,Winter,17.61405556,60.0,90.2,26.0,65,84,121,,,,,Winter,0,0,14.6,1,15.0,1,1,0,6.5,0,4.5,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +15f217a6,Fall,11,1,Spring,45,Winter,19.01694411,58.0,91.0,,97,111,158,Winter,3,4,34,Winter,28,1,22.1,3,21.8,3,9,1,11.0,1,10.0,1,12.0,1,Winter,4,4.136,19.019,1218.48,2315.12,24.8936,72.0474,15.0579,3.96109,18.9526,2,30.6641,16.4897,67.9114,30.5907,55.5577,,,Fall,1.48,Winter,4,4,3,1,4,4,0,3,1,1,4,1,1,1,1,1,1,2,2,1,40,Winter,27,40,Fall,2,1 +15f2e240,Spring,9,1,Summer,67,Summer,20.38571054,53.5,83.0,,61,95,115,Summer,7,12,46,Summer,12,1,,,,,9,1,11.5,1,9.5,1,12.0,1,Summer,3,3.24645,20.3879,1094.51,1751.22,18.1926,58.8432,14.4541,5.93383,24.1568,2,26.7653,13.8852,55.5967,28.2267,44.9579,,,Spring,2.38,Spring,1,0,0,0,1,0,0,1,2,2,2,0,0,0,1,0,0,0,0,0,10,Spring,29,42,Spring,0,0 +1606f203,Spring,9,0,Spring,67,Spring,25.56156156,55.5,112.0,,65,97,132,Spring,5,7,40,Spring,6,0,,,,,2,0,4.5,0,6.0,0,4.5,0,Spring,2,3.82778,25.5644,1206.74,1930.78,17.8393,70.7962,16.1595,9.40489,41.2038,2,34.1724,18.7845,66.9684,33.1262,52.0117,,,Spring,2.1,Spring,1,3,2,3,2,1,1,1,1,1,2,1,1,1,2,2,1,1,1,1,29,Spring,28,41,Spring,2,0 +1649ce22,Summer,8,1,Winter,63,Fall,17.56768013,49.0,60.0,,61,74,99,Fall,7,11,35,Fall,1,0,,,,,10,1,13.0,1,12.0,1,9.0,1,Winter,4,2.81752,17.5696,978.908,2055.71,7.97558,46.5295,13.6251,3.94451,13.4705,1,24.968,13.586,43.712,23.6115,32.9436,,,,,Fall,3,4,3,0,2,0,0,0,2,2,2,0,0,0,0,0,0,0,0,0,18,Fall,52,72,Summer,0,0 +164b29ed,Fall,8,0,,,Winter,15.62222222,51.0,57.8,23.0,68,74,106,,,,,Winter,6,1,,,,,0,0,8.0,1,8.5,1,12.0,1,Winter,2,3.00273,15.6239,994.342,1590.95,8.94793,48.1735,13.0218,2.60214,9.6265,1,24.427,14.7986,45.1708,20.1804,33.3749,,,Winter,1.98,Winter,2,1,2,0,2,1,0,1,1,1,0,0,1,1,1,1,1,1,1,1,19,Winter,29,42,Fall,1,0 +164edc75,Winter,14,0,Spring,80,Spring,19.75371901,66.0,122.4,,89,113,135,,,,,Spring,25,1,21.6,2,23.1,2,14,1,9.0,1,9.0,1,12.0,1,Spring,4,0.616317,19.7559,1462.41,3071.06,29.8151,98.0289,15.8223,3.9336,24.3711,2,42.9789,25.2348,97.4126,41.9532,72.794,,,Spring,3.69,Spring,5,5,5,3,4,3,2,1,2,3,3,1,1,2,3,3,3,4,2,1,56,Winter,29,42,Spring,1,2 +1655fa88,Fall,7,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1661fd48,Winter,7,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +16a8495c,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +16bf9efe,Fall,15,1,,,Fall,16.93293021,63.0,95.6,26.0,63,83,115,,,,,Fall,14,0,38.8,2,39.0,2,0,0,9.5,0,9.0,0,11.0,1,Fall,3,4.22685,16.9348,1206.37,1930.2,21.57,70.7578,12.5342,4.40061,24.8422,1,26.3937,22.794,66.5309,25.6772,47.9637,Fall,2.65,,,Fall,2,2,3,1,2,1,2,2,2,1,2,0,0,1,1,0,2,2,1,1,28,Fall,35,50,Fall,2,0 +16deb306,Spring,6,1,Summer,55,Spring,15.59393391,47.0,49.0,,74,91,112,,,,,Spring,5,1,,,,,0,0,9.0,1,8.0,0,7.0,1,Summer,3,2.23126,15.5956,945.87,1513.39,10.5751,43.0105,13.6893,1.90631,5.98946,1,19.5866,12.8488,40.7793,14.8449,30.1617,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,51,71,Spring,0,0 +16e23cca,Spring,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2.10574,16.9959,907.52,1361.28,10.7325,38.9258,14.1363,2.85958,7.87418,3,20.5234,7.66994,36.8201,15.3732,31.2559,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +16e277aa,Summer,11,1,Fall,90,Summer,18.03591205,58.5,87.8,28.0,69,98,113,,,,,Summer,15,1,19.4,2,16.5,2,16,1,10.0,1,7.0,0,12.0,1,Summer,3,3.78164,18.0379,1153.69,1845.9,19.8178,65.1458,13.3837,4.65414,22.6542,2,27.2155,18.1125,61.3642,28.8642,47.0333,,,Summer,2.211,Summer,2,2,2,0,2,1,1,1,1,2,2,0,0,0,0,0,2,0,2,0,20,Summer,44,62,Summer,1,0 +1704e646,Winter,14,1,,,Winter,20.85022244,59.5,105.0,,62,84,118,,,,,Winter,40,1,16.0,2,17.0,2,10,1,12.5,1,12.0,1,10.0,1,Winter,3,4.14232,20.8525,1223.78,1958.05,24.1298,72.612,14.4204,6.4321,32.388,1,29.861,18.6212,68.4697,31.3856,53.9908,,,,,Winter,4,3,2,1,2,1,1,2,1,2,2,0,1,1,1,2,2,1,2,0,31,Winter,49,68,Winter,2,1 +1713348a,Summer,13,1,,,Summer,19.73146888,63.0,111.4,27.0,76,67,125,,,,,Summer,10,0,15.5,2,16.1,2,10,1,7.0,0,7.0,0,7.0,0,Summer,,,,,,,,,,,,,,,,,,,Summer,1.8,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,34,49,Summer,0,0 +176f33fb,Fall,6,0,Winter,80,Fall,17.19915789,47.5,55.2,,88,90,112,Fall,4,6,22,Fall,2,1,,,,,0,0,6.0,0,4.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,,,Fall,3,3,3,0,3,0,0,2,2,0,2,0,1,1,2,2,2,1,2,1,30,Fall,45,63,Fall,0,0 +1779e997,Fall,7,0,,,Fall,17.31244907,47.0,54.4,23.0,47,65,74,,,,,Fall,0,0,,,,,0,0,0.0,0,0.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +177f25ab,Spring,7,0,Spring,70,Winter,15.52224,50.0,55.2,,61,75,108,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,4,3.01036,15.5239,982.921,2064.13,9.26044,46.957,13.2058,2.31817,8.24296,1,25.3492,12.3474,43.9467,21.9105,34.6097,,,,,Winter,4,0,2,0,3,3,0,2,0,2,3,0,2,3,1,2,1,2,1,1,32,Winter,54,75,Spring,1,1 +17994e1d,Spring,7,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +17b08109,Summer,7,0,Winter,70,Fall,14.88113061,47.02,46.8,,50,72,76,Fall,5,8,24,Fall,3,1,,,,,0,0,8.0,1,9.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,,,Fall,3,0,4,0,2,0,0,2,0,2,0,0,2,1,1,1,2,2,0,1,23,Fall,27,40,Summer,2,0 +17c1ae23,Spring,9,1,Winter,60,Spring,16.45368595,55.0,70.8,24.0,75,79,100,,,,,Spring,20,1,,,,,0,0,6.0,0,6.0,0,11.0,1,Spring,1,3.9466,16.4555,1198.19,1557.64,23.8318,69.8857,16.243,0.212497,0.914272,1,32.4817,13.5723,65.9391,34.8193,56.3134,,,Fall,3.18,Fall,4,2,2,0,4,2,0,2,0,2,3,0,1,1,2,2,2,2,2,0,33,Fall,28,41,Fall,2,1 +17e9cba0,Spring,10,0,Fall,70,Spring,15.75649587,55.0,67.8,,61,84,110,Spring,5,8,50,Spring,15,1,,,,,7,1,6.0,0,7.0,0,13.0,1,,,,,,,,,,,,,,,,,,,,Spring,4.74,Spring,5,5,5,1,5,4,0,5,5,4,5,0,5,1,5,5,4,5,1,4,74,Spring,71,97,Spring,2,2 +17f9beca,Spring,9,1,,,Spring,24.44588477,54.0,101.4,33.0,86,87,136,,,,,Spring,0,0,,,,,0,0,6.0,0,6.0,0,3.0,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +18003485,Spring,10,1,Spring,,Spring,19.38380263,55.0,83.4,25.5,49,80,120,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,30,43,,, +181172fe,Winter,9,0,,,Winter,16.31964286,56.0,72.8,25.0,66,75,115,,,,,Winter,0,0,,,,,2,0,15.0,1,14.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,3.27,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +18154f3a,Winter,15,1,Spring,75,Winter,22.59575383,61.0,119.6,,,105,,,,,,Winter,20,1,22.2,2,24.0,2,15,1,7.0,0,10.0,1,13.5,1,Spring,2,1.67169,22.5982,1328.67,1993.0,27.1719,83.7835,15.8308,6.76746,35.8165,2,36.3568,20.2549,82.1118,50.5667,63.5286,Winter,1.64,,,Winter,3,2,4,2,4,2,0,3,1,2,3,0,2,4,2,1,4,2,1,1,43,Winter,43,60,Winter,0,1 +1824417f,Summer,11,0,Spring,60,Winter,15.16684513,61.5,81.6,,90,78,142,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.29,Spring,5,5,4,3,5,5,2,4,4,4,4,0,5,3,4,5,5,5,4,4,80,Spring,60,82,Summer,3,3 +18350390,Winter,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1837456f,Winter,9,0,Spring,50,Spring,21.93861454,54.0,91.0,,56,63,97,Spring,4,7,0,Spring,5,0,,,,,0,0,5.0,0,5.0,0,8.0,1,Spring,1,3.68854,21.941,1144.04,1487.25,14.4399,64.1178,15.4595,6.48156,26.8822,2,33.8673,15.8107,60.4293,40.8158,48.3072,,,Winter,1.45,Spring,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,43,60,Winter,2,0 +18400e8e,Spring,10,0,Spring,74,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.22,Fall,5,5,4,4,5,1,0,1,1,4,2,1,2,0,2,2,4,2,1,2,48,Summer,36,51,Spring,3,1 +18406565,Spring,14,0,,,Spring,16.78711063,66.5,105.6,27.0,52,57,121,,,,,Spring,2,0,26.6,1,23.8,1,11,0,8.0,1,12.0,1,11.0,1,,,,,,,,,,,,,,,,,,Spring,1.76,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +1843760e,Fall,10,0,Spring,61,Fall,21.02429907,53.5,85.6,,66,72,105,,,,,Fall,10,1,,,,,2,0,4.0,0,5.0,0,10.0,1,Fall,3,5.31573,14.0279,1272.8,2163.76,20.5532,77.8327,12.755,1.27289,7.76733,2,35.0705,22.2089,72.5169,34.7879,55.6237,,,Fall,2.37,Fall,5,4,4,1,1,2,0,0,2,0,3,0,1,1,3,1,1,3,1,1,34,Fall,33,47,Fall,3,1 +184f1b4b,Fall,9,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.77,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +1850322c,Winter,8,0,,,Winter,16.31902755,50.5,59.2,23.0,68,88,106,,,,,Winter,0,0,,,,,0,0,7.5,0,7.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,2.35,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +18506cf3,Fall,11,0,,,Fall,23.1438667,59.0,114.6,31.0,65,65,117,,,,,Fall,6,0,14.1,2,15.7,2,0,0,12.0,1,12.0,1,11.0,1,Fall,2,4.0534,23.1464,1229.53,1967.25,17.9286,73.2243,14.7895,8.35688,41.3757,2,32.5463,22.7494,69.1709,28.3643,50.4749,,,Fall,2.9,Fall,2,0,4,1,2,1,0,2,1,1,3,0,4,1,1,1,1,0,2,0,27,Fall,32,46,Fall,2,0 +1866767b,Winter,17,1,,,Spring,21.04425953,63.0,118.8,28.0,85,69,119,,,,,,,,,,,,,,,,,,,,Spring,2,4.66763,21.0445,1293.96,1940.94,24.8294,80.087,14.1868,6.85771,38.713,2,33.5074,21.7502,75.4193,46.5676,58.3368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +187779ed,Winter,9,0,,,Fall,,,,,,,,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +187b1802,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.66,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +1894884e,Summer,9,1,Fall,90,Summer,15.57057851,55.0,67.0,,63,85,100,Summer,6,10,1,Summer,4,0,,,,,0,0,7.0,0,5.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,Summer,1.1,Summer,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,5,Summer,34,49,Summer,0,0 +18962676,Summer,7,1,,,Fall,14.46495908,48.5,48.4,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,40,56,Summer,0,0 +18ab47f8,Winter,6,0,Fall,75,Spring,16.97148803,46.5,52.2,,,,,,,,,Spring,0,0,,,,,,,,,,,6.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,34,49,Winter,0,0 +18b79e9b,Winter,7,1,Spring,80,Winter,14.58117451,49.0,49.8,,65,95,124,Winter,4,6,,Winter,9,1,,,,,0,0,9.5,1,14.0,1,3.0,0,Winter,2,10.6818,14.5828,967.385,1451.08,11.1796,45.3022,13.2657,1.31708,4.49781,1,20.8048,13.3178,34.6204,17.9817,31.9844,,,,,Winter,5,5,5,0,5,0,0,4,2,5,5,0,4,2,4,4,2,5,3,1,61,Winter,38,54,Winter,2,2 +18b8b2f6,Fall,7,0,Spring,55,Fall,15.92734375,48.0,52.2,,62,86,103,Fall,4,6,32,Fall,15,1,,,,,9,1,9.5,1,11.0,1,11.0,1,Fall,3,5.31939,15.9291,954.375,1622.44,9.20218,43.9164,13.4013,2.52777,8.28356,1,23.9554,10.7589,38.5971,17.222,33.1576,,,,,Fall,3,3,2,0,5,0,0,0,0,2,2,0,2,0,3,3,1,4,0,1,31,Fall,48,67,Fall,0,1 +18c8541a,Spring,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +18e21bf1,Spring,8,0,,,Spring,14.97514793,52.0,57.6,24.0,71,74,117,,,,,Spring,0,0,,,,,10,1,8.0,1,11.0,1,6.0,1,Spring,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,28,41,Spring,2,0 +18f6e6db,Fall,6,0,Spring,65,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,2,0,3,1,0,0,1,0,0,0,0,0,0,2,0,0,0,2,11,Fall,46,64,Fall,0,0 +18fdbccc,Fall,8,0,,,Fall,19.9617284,54.0,82.8,29.0,68,75,105,,,,,Fall,3,0,,,,,0,0,5.5,0,5.5,0,3.0,0,Fall,2,3.70144,19.9639,1125.86,1801.38,16.183,62.1823,14.9928,4.97113,20.6177,2,30.9619,15.0374,58.4809,26.3962,47.1449,,,Fall,1.33,Fall,2,0,3,0,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Fall,43,60,Fall,0,0 +18feab0f,Spring,13,0,Spring,35,Spring,18.99316263,63.75,109.8,33.0,,,,,,,,Spring,0,,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,1,3,0,0,0,0,0,0,2,2,0,0,0,2,2,2,4,0,1,19,Spring,53,73,Spring,0,0 +19066573,Spring,8,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +19098ef0,Spring,14,0,Summer,45,Summer,25.64165039,64.0,149.4,,87,89,152,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.93,Summer,2,2,2,1,1,1,0,0,0,0,2,0,1,1,1,0,1,1,0,0,16,Summer,27,40,Spring,0,0 +1911731d,Summer,7,1,Winter,85,Fall,14.00271616,47.0,44.0,,76,85,115,Fall,7,12,45,Fall,14,1,,,,,2,0,9.0,1,12.0,1,5.0,0,Fall,3,8.11003,14.0042,936.169,1497.87,11.0472,41.9773,13.3605,0.643789,2.02272,1,20.8922,10.0379,33.8672,16.754,31.9394,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,32,46,Summer,0,0 +19381499,Summer,8,1,Fall,60,Summer,17.88910383,54.65,76.0,,62,76,93,Fall,6,9,46,Summer,13,1,,,,,1,0,10.0,1,12.0,1,10.0,1,Fall,4,11.0045,17.9897,1070.16,2033.31,15.154,56.2492,13.3145,4.67514,19.7508,1,23.5714,17.5238,45.2447,24.0009,38.7254,,,,,Summer,5,5,5,3,5,5,0,3,5,5,4,0,5,2,4,5,5,5,4,3,78,Summer,55,76,Summer,3,2 +1943fb23,Summer,7,0,Fall,62,Summer,15.83844675,51.0,58.6,,105,74,137,Summer,7,11,34,Summer,20,,,,,,20,,17.0,,19.0,,7.0,,Fall,3,6.89062,15.8402,999.488,1699.13,9.47298,48.7216,13.17,2.67022,9.87835,2,25.476,13.7727,41.831,22.2667,34.949,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,27,40,Summer,0,0 +19455336,Fall,10,0,Spring,60,Winter,16.45414541,56.0,73.4,,66,83,108,,,,,Winter,30,1,12.8,2,10.6,1,20,1,7.5,1,7.0,0,7.0,0,Winter,4,3.75924,16.4559,1101.76,2313.7,13.9402,59.6152,13.3655,3.09049,13.7848,2,28.9712,16.7038,55.856,25.4865,42.9114,,,Winter,3.62,Winter,5,4,4,1,2,1,0,2,1,3,2,0,1,1,2,1,2,2,1,1,36,Winter,51,71,Fall,0,1 +1949438b,Winter,11,0,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4.6,Fall,5,5,5,4,4,4,1,2,1,1,5,1,4,5,4,5,1,5,1,2,65,Spring,28,41,Winter,0,2 +1957153d,Spring,10,0,Winter,50,Summer,16.77942387,54.0,69.6,,71,93,115,Summer,5,7,31,Summer,10,0,10.3,1,11.9,1,0,0,8.5,1,8.5,1,6.0,0,Summer,1,3.43819,16.7813,1062.11,1380.74,12.5736,55.3916,13.3555,3.4258,14.2084,2,26.7924,16.0256,51.9534,21.0158,39.366,,,Spring,3.13,Summer,5,5,4,2,5,1,0,2,0,1,0,0,3,2,5,4,3,4,2,2,50,Summer,43,60,Spring,0,2 +195f18ab,Spring,8,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +198e50c9,Summer,19,0,Fall,39,Summer,27.25767696,69.0,184.6,,108,69,198,,,,,,,,,,,,,,,,,,,,Summer,3,3.87205,27.2607,1845.14,3136.73,43.4023,138.794,20.4964,6.76429,45.8055,3,60.0955,35.2967,134.922,72.8014,103.498,,,,,Summer,0,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,0,0,11,Summer,36,51,Summer,3,0 +199c885a,Winter,9,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +19b095d5,Winter,9,1,Fall,80,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.86,Spring,2,2,1,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,9,Winter,40,56,Winter,0,0 +19c49039,Spring,18,1,Summer,61,Spring,22.10491308,63.0,124.8,28.0,94,62,133,,,,,Spring,20,1,30.9,3,31.9,3,15,1,12.0,1,12.0,1,12.0,1,Summer,5,4.89102,22.1073,1341.78,2951.91,29.1546,85.1801,15.089,7.01835,39.6199,2,34.1618,21.8638,80.2891,39.5146,63.3164,Spring,2.49,,,Spring,1,1,4,1,3,1,2,2,1,1,4,1,3,3,2,2,1,2,2,2,39,,,,Spring,3,1 +19c870f0,Fall,5,0,,,Fall,15.83051852,45.0,45.6,,67,106,99,,,,,Fall,0,0,,,,,0,0,5.0,0,6.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +19cc95e8,Winter,10,1,Fall,81,Spring,24.31872506,60.4,126.2,,67,72,123,Spring,5,7,22,Spring,5,0,,,,,0,0,11.0,1,10.5,1,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.38,Spring,2,1,1,1,1,1,0,1,1,1,2,1,1,1,1,1,1,1,1,1,21,Spring,31,45,Spring,0,0 +19ed1618,Spring,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,29,42,,, +1a1ada0d,Winter,9,0,,,Winter,15.91698113,53.0,63.6,27.0,62,85,116,,,,,Winter,0,0,,,,,3,0,6.5,0,6.5,0,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.66,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +1a1e8ac5,Spring,7,1,Fall,70,Summer,16.57868481,42.0,41.6,,66,76,116,Summer,4,6,0,Summer,7,1,,,,,12,1,9.0,1,12.0,1,12.0,1,Summer,4,10.7167,16.5805,880.551,1673.05,7.94145,36.0532,14.3697,2.21079,5.54681,1,19.0785,9.03327,25.3365,15.7903,27.0199,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,41,58,Spring,0,0 +1a23d282,Fall,13,1,Fall,60,Winter,18.40095181,61.5,99.0,,82,97,133,,,,,Winter,15,0,12.0,1,16.4,2,3,0,10.0,1,10.5,1,13.0,1,Winter,2,4.81293,18.403,1312.04,1968.06,27.0013,82.0124,15.2452,3.15781,16.9876,1,35.4676,19.5435,77.1994,44.5924,62.4689,,,Winter,2.26,Winter,1,5,3,4,5,3,1,1,1,1,4,2,1,1,1,1,1,1,1,1,39,Winter,26,38,Fall,2,1 +1a2412b3,Summer,11,0,Winter,40,Fall,25.32806183,58.0,121.2,,65,81,120,Fall,6,7,57,Fall,25,1,8.6,1,6.3,1,0,0,8.0,1,4.0,0,8.0,0,,,,,,,,,,,,,,,,,,,,Fall,1.8,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,30,43,Summer,1,0 +1a42abcc,Winter,12,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +1a510bfd,Spring,9,0,,,Spring,15.67043896,54.0,65.0,23.0,71,90,112,,,,,Spring,14,1,,,,,5,0,11.0,1,12.0,1,2.0,0,Spring,4,3.70056,15.6722,1081.94,2272.08,14.6159,57.504,13.8648,1.80735,7.49595,2,29.377,13.5111,53.8035,25.0022,43.9929,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +1a752b1a,Spring,15,1,Fall,65,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +1a7df719,Winter,9,0,,,Winter,18.23598831,51.5,68.8,26.0,68,69,109,,,,,Winter,7,1,,,,,3,0,0.0,0,6.0,0,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.64,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +1a803bfe,Spring,9,0,Fall,60,Summer,15.26733905,51.5,57.6,,70,76,106,Summer,8,14,21,Summer,8,1,,,,,14,1,10.0,1,9.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,Spring,3.98,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,40,56,Spring,0,0 +1a92749e,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1a9e4b04,Spring,19,1,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +1a9f8375,Winter,8,1,Summer,68,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.48,Spring,2,2,1,1,2,1,0,1,1,1,2,1,1,1,2,2,2,1,1,1,26,Spring,26,38,Winter,2,0 +1aae1de3,Summer,13,0,Fall,60,Fall,18.47327778,60.0,94.6,,56,75,104,,,,,Fall,17,0,16.6,1,13.2,1,9,0,7.0,0,6.5,0,6.0,0,Fall,2,4.53274,18.4753,1233.35,1973.36,19.6147,73.6311,14.3801,4.0952,20.9689,2,34.4946,19.5219,69.0984,32.5997,54.1093,,,Fall,1.73,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,35,50,Summer,2,0 +1ad1ab77,Spring,10,0,,,Summer,17.26842293,53.0,69.0,25.0,79,81,128,,,,,Summer,4,0,14.0,2,16.2,2,3,0,9.0,1,9.0,1,8.0,0,,,,,,,,,,,,,,,,,,,,Spring,3.36,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +1adab915,Spring,7,1,,,Summer,15.40805566,46.8,48.0,23.0,65,71,101,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1afa48fe,Summer,10,0,,,Summer,17.98750413,55.0,77.4,24.0,67,62,102,,,,,Summer,22,1,15.1,2,18.8,2,17,1,9.0,1,8.5,1,12.0,1,Fall,3,3.69313,17.9895,1106.33,1880.76,14.2014,60.1018,13.969,4.02049,17.2982,1,29.8973,16.0031,56.4086,27.4219,44.0987,,,Summer,3.13,Summer,2,1,1,0,3,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,11,Summer,42,59,Summer,0,0 +1aff6762,Spring,15,0,Spring,55,Summer,27.85996144,75.75,227.4,,,,,,,,,Summer,17,0,36.8,2,37.7,2,11,0,,,,,18.0,1,Summer,3,8.68458,27.863,2030.28,3451.47,50.0325,158.514,19.4225,8.44046,68.8856,3,65.5374,42.9445,149.83,78.0726,115.57,Spring,2.1,,,Summer,3,5,4,2,4,2,1,1,2,4,4,1,5,2,3,4,3,4,3,2,59,Summer,36,51,Spring,2,2 +1b13223c,Summer,5,1,,,Summer,15.0835249,43.5,40.6,19.0,66,87,107,,,,,Summer,0,0,,,,,3,1,12.0,1,12.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +1b258140,Spring,5,1,Fall,61,Spring,16.29406971,47.0,51.2,24.0,67,123,124,,,,,Spring,0,0,,,,,0,0,11.0,1,11.0,1,12.0,1,Spring,2,2.37477,16.2958,948.625,1422.94,12.4201,43.304,13.7827,2.51312,7.89597,1,21.0153,9.86864,40.9293,15.543,33.4354,,,,,Spring,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,,,,Summer,0,0 +1b2cd439,Winter,10,1,Spring,60,Winter,20.2464,50.0,72.0,23.0,69,67,112,,,,,Winter,15,1,12.8,2,13.9,2,7,1,12.0,1,12.0,1,12.0,1,Winter,3,3.59008,16.1421,1113.79,1782.06,19.235,60.8962,13.6527,2.48941,11.1038,1,26.9064,14.7548,57.3062,26.0792,46.1414,,,Winter,2.45,Winter,2,2,2,0,2,0,0,0,0,1,0,0,0,0,0,1,2,1,0,0,13,Winter,50,69,Winter,1,0 +1b2eea8f,Fall,12,0,Spring,60,Fall,18.26341926,56.25,82.2,,59,84,111,,,,,Fall,40,1,17.1,2,16.2,2,15,1,9.5,1,8.75,1,6.5,0,Fall,3,4.02007,18.2654,1156.84,1966.63,16.8812,65.4818,14.5505,3.71489,16.7182,2,32.4088,16.1918,61.4618,30.39,49.29,,,Winter,3.309,Winter,4,4,4,1,4,4,1,4,4,4,3,1,3,4,3,1,3,2,1,3,58,Winter,42,59,Fall,2,2 +1b32427b,Spring,16,0,Fall,62,Summer,17.43161124,68.75,117.2,,64,64,111,,,,,Summer,19,0,31.6,1,38.6,2,24,1,9.0,1,9.5,1,12.0,1,,,,,,,,,,,,,,,,,,Spring,1.85,,,Summer,1,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,7,,,,Spring,0,0 +1b329556,Spring,9,0,Fall,60,Summer,17.02018693,55.88,75.6,,67,84,106,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.26,Spring,5,4,5,4,4,1,1,1,2,3,4,0,4,0,3,2,1,3,1,3,51,Spring,46,64,Spring,2,2 +1b410106,Winter,8,1,Fall,80,Winter,13.36362961,50.25,48.0,23.0,66,96,105,,,,,Winter,8,1,,,,,3,0,3.5,0,6.0,0,5.0,0,Winter,2,2.51649,13.3651,979.775,1469.66,10.8144,46.622,12.9814,0.383702,1.37804,2,20.638,15.1696,44.1055,18.6817,31.4523,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,46,64,Winter,0,0 +1b496c35,Winter,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1b49c870,Winter,8,0,Spring,70,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1,2,4,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,11,Summer,36,51,Winter,1,0 +1b5b34bc,Spring,10,0,Summer,60,Summer,22.59465894,56.5,102.6,,53,74,99,Summer,5,7,40,Summer,0,,6.1,1,14.5,2,0,,5.0,,5.0,,8.0,,Summer,1,3.89315,22.5971,1187.49,1543.74,17.3051,68.7467,15.1411,7.45602,33.8533,2,32.4783,18.9634,64.8535,29.0992,49.7833,,,Summer,2.42,Summer,2,3,3,0,3,3,0,3,1,2,2,0,2,1,1,2,3,4,4,4,43,Summer,33,47,Spring,1,1 +1b5fb26f,Spring,8,1,Spring,50,Winter,20.43483274,53.5,83.2,,84,76,132,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.04,Spring,2,2,1,0,1,0,0,0,1,2,2,0,1,0,0,1,1,1,0,1,16,Spring,44,62,Spring,1,0 +1b5fd952,Fall,9,0,,,Fall,18.49893709,59.0,91.6,28.0,70,65,106,,,,,Fall,3,0,,,,,0,0,13.0,1,13.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.21,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +1b6be334,Spring,12,1,Winter,45,Fall,17.17150296,65.0,103.2,,84,122,151,,,,,Fall,30,1,,,,,2,0,8.5,0,7.75,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.55,Winter,5,4,5,1,4,2,3,3,4,4,4,1,1,2,2,3,5,5,4,5,67,Winter,41,58,Spring,2,2 +1b6fb363,Winter,14,0,Summer,60,Winter,30.38862033,67.13,194.8,,73,85,192,,,,,Summer,,,,,,,,,,,,,,,Summer,1,6.25341,30.3919,1678.03,2181.44,36.4196,120.995,18.8772,11.5148,73.8049,2,52.2318,32.3438,114.742,57.2311,88.6514,,,,,Summer,3,3,4,0,5,2,0,0,1,2,4,0,1,0,4,2,4,1,2,0,38,Summer,37,53,Winter,0,1 +1b73fce0,Fall,7,0,Spring,65,Winter,16.9820908,49.0,58.0,,126,101,147,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,4,4,0,3,0,0,1,0,2,2,0,2,0,0,2,2,2,2,0,30,Fall,33,47,Fall,0,0 +1ba0652b,Winter,13,0,,,Winter,20.67723975,63.5,118.6,,57,70,126,,,,,Winter,16,0,26.7,2,23.4,2,8,0,4.0,0,4.5,0,12.0,1,Spring,3,5.50258,20.6795,1415.91,2407.05,27.7192,93.0763,16.2291,4.45039,25.5237,2,42.0295,23.3276,87.5737,42.0343,69.7487,,,Winter,3.76,,,,,,,,,,,,,,,,,,,,,,,Winter,52,72,Winter,2, +1bbba3ff,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +1bc6a98c,Winter,7,0,Summer,64,Winter,14.32095971,47.0,45.0,20.0,53,87,96,,,,,Winter,0,0,,,,,10,1,9.5,1,9.0,1,12.0,1,Spring,1,2.67506,14.3225,922.548,1199.31,7.531,40.5264,12.8987,1.42384,4.47356,1,22.6999,10.2955,37.8514,16.4703,30.2309,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,39,55,Winter,0,0 +1bceace4,Spring,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +1c036e73,Winter,8,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.9,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +1c063ebc,Winter,6,1,Fall,59,Winter,16.04625679,44.5,45.2,21.0,53,81,97,,,,,Winter,0,0,,,,,0,0,11.0,1,11.0,1,8.0,1,Winter,2,2.07301,16.048,910.369,1365.55,10.0673,39.2292,13.9281,2.11989,5.97076,1,19.5669,9.595,37.1562,14.1382,29.6342,,,,,Winter,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,,,,Winter,0,0 +1c1270a8,Summer,8,0,,,Summer,16.32707101,52.0,62.8,25.0,53,78,98,,,,,Summer,1,0,,,,,4,0,5.0,0,6.0,0,8.0,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +1c23295e,Fall,19,1,Spring,85,Fall,35.60757396,65.0,214.0,,73,91,122,,,,,,,,,,,,,,,,,,,,Winter,3,5.42384,35.6115,1551.63,2482.61,39.2244,107.532,17.8943,17.7171,106.468,3,39.8582,28.4499,102.109,50.9402,79.0826,,,,,Fall,2,2,2,1,2,2,2,1,1,1,2,1,1,2,1,1,1,1,2,1,29,,,,Fall,,0 +1c34a090,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1c3b7f1a,Winter,9,0,Fall,80,Winter,13.76468494,53.0,55.0,,73,88,108,Winter,5,8,45,Winter,0,0,,,,,0,0,5.5,0,5.1,0,3.5,0,Winter,4,9.408,13.7662,1007.45,2115.64,8.70242,49.5695,12.407,1.35923,5.43052,2,23.8039,17.0632,40.1615,19.7483,32.5063,,,Winter,1.78,Winter,1,1,5,0,1,0,0,0,1,1,2,0,0,2,2,1,5,1,5,1,29,Winter,37,53,Winter,1,0 +1c3c2b2f,Spring,7,0,Fall,71,Summer,15.52224,50.0,55.2,,83,64,116,Summer,5,8,29,Summer,10,1,,,,,1,0,9.0,1,8.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,,,Summer,3,3,4,0,4,0,0,2,2,0,0,0,4,0,2,4,4,5,4,0,41,Summer,43,60,Spring,0,1 +1c3fd520,Summer,8,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +1c481537,Fall,13,0,Summer,61,Summer,27.94150391,64.0,162.8,,63,68,107,,,,,Summer,15,0,25.3,2,27.0,2,7,0,6.0,0,4.0,0,6.0,0,Summer,3,5.70718,27.9446,1557.01,2646.91,32.4052,108.105,18.5561,9.38842,54.6952,2,48.0688,27.6308,102.398,51.5342,80.474,,,Summer,2.55,Summer,5,5,5,3,5,5,2,5,4,5,0,0,5,5,5,5,5,4,5,3,81,Summer,52,72,Fall,0,3 +1c5ba087,Spring,12,0,Spring,66,Spring,15.95432347,59.0,79.0,,68,84,115,,,,,Spring,18,1,16.6,1,18.2,1,10,1,7.5,1,7.0,0,11.0,1,Summer,4,4.19534,15.9561,1156.06,2427.73,15.6177,65.399,13.209,2.74708,13.601,1,31.2086,18.5726,61.2036,30.1643,46.8263,,,Spring,3.72,Spring,2,3,3,2,3,3,1,3,2,2,3,1,1,1,2,2,3,3,3,2,45,Spring,35,50,Spring,2,1 +1c66daf4,Winter,17,0,Spring,70,Winter,35.01719373,69.5,240.6,,100,102,166,,,,,Winter,20,0,46.9,2,59.3,3,10,0,15.2,1,15.1,1,11.0,1,Winter,5,11.6324,35.021,2109.1,5061.84,51.7683,166.91,24.2949,10.7261,73.6902,2,72.0616,43.0799,155.277,97.9231,123.83,Winter,2.38,,,Winter,0,1,1,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,7,Winter,31,45,Winter,0,0 +1c673231,Summer,10,0,,,Summer,21.14809917,55.0,91.0,29.0,69,86,111,,,,,Summer,5,0,,,,,0,0,4.75,0,3.0,0,3.25,0,Summer,,,,,,,,,,,,,,,,,,,,,Summer,3,0,5,1,5,2,0,5,0,1,5,0,5,5,5,5,5,5,5,4,66,Summer,50,69,Summer,2,2 +1c6ac3d4,Spring,6,0,Summer,65,Summer,16.31654321,45.0,47.0,19.0,57,88,102,,,,,Summer,0,0,,,,,0,0,5.0,0,5.0,0,5.0,0,Summer,1,2.4812,16.3183,905.51,1177.16,6.80043,38.7118,13.4407,2.87767,8.28825,1,22.3997,9.51163,36.2306,16.1935,29.2001,,,,,Spring,0,0,4,0,2,0,0,0,0,0,1,0,0,0,0,0,2,2,2,0,13,Spring,40,56,Spring,2,0 +1ccf44f0,Fall,9,1,Spring,67,Winter,29.6216232,58.5,144.2,,65,105,124,Winter,4,6,2,Winter,10,1,,,,,2,0,10.0,1,11.0,1,12.0,1,Winter,3,4.2854,29.6249,1325.43,2120.69,30.3247,83.4386,17.1419,12.483,60.7614,3,33.3168,19.7972,79.1532,35.3838,63.6414,,,Winter,3.03,Winter,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Winter,29,42,Fall,0,0 +1cd37b36,Summer,13,0,Winter,60,Fall,18.85498522,61.0,99.8,,81,85,110,,,,,Fall,20,0,18.2,1,17.2,1,10,0,7.5,1,7.5,1,9.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.19,Summer,4,4,3,1,4,2,1,1,1,3,1,1,4,2,2,3,2,2,2,2,45,Summer,30,43,Fall,2,1 +1ce16ede,Winter,8,0,,,Winter,15.14523603,48.75,51.2,22.0,62,70,108,,,,,Winter,0,0,,,,,3,0,8.0,1,9.0,1,6.0,1,Winter,2,2.93039,14.9927,963.846,1542.15,9.31023,44.9253,13.1553,1.8374,6.27469,1,24.4919,11.1232,41.9949,18.8648,33.8021,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +1cf6dcef,Winter,7,1,Spring,65,Spring,18.08336777,44.0,49.8,,71,113,117,Spring,3,3,56,Winter,0,0,,,,,0,0,6.0,0,5.0,0,4.0,0,Spring,2,2.28416,18.0853,903.2,1445.12,4.88323,38.4657,13.9692,4.11616,11.3343,2,21.7712,11.8113,36.1815,18.7178,26.6544,,,,,Spring,5,5,5,0,5,0,0,0,0,0,4,0,2,0,2,3,0,4,3,0,38,Spring,36,51,Winter,2,1 +1d02812a,Winter,11,0,,,Spring,24.77491646,67.0,158.2,,70,95,106,Spring,5,7,5,Spring,9,0,28.3,2,29.9,2,2,0,13.0,1,13.0,1,12.0,1,Summer,2,6.59027,24.7776,1666.88,2667.01,37.924,119.808,18.7645,6.0131,38.3924,3,51.7044,30.1792,113.217,54.6119,89.6284,,,Winter,2.07,Spring,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,36,51,Winter,2,0 +1d041232,Fall,16,1,Winter,80,Fall,22.8269043,64.0,133.0,,68,68,119,,,,,Fall,15,0,51.2,2,52.4,2,0,0,9.0,0,8.5,0,6.0,0,Fall,2,4.45223,23.5599,1318.35,1977.52,28.0115,82.6843,14.6469,8.91303,50.3157,2,32.1695,22.5034,78.2321,36.0125,60.1809,Fall,2.61,,,Fall,4,3,3,0,2,0,0,0,3,0,2,0,1,0,0,0,0,1,0,0,19,Fall,34,49,Fall,2,0 +1d11d488,Spring,11,0,Winter,70,Winter,13.94837051,57.5,65.6,,76,97,130,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,4.40264,13.9499,1170.07,2457.14,16.6693,66.8903,14.2243,-0.274388,-1.29032,1,35.2058,15.0152,62.4877,40.8568,51.8752,,,Spring,2.48,Spring,2,2,2,0,2,0,0,0,0,1,2,0,0,2,0,0,1,0,0,0,14,Spring,58,80,Spring,3,0 +1d3dc8b3,Winter,21,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +1d41578e,Summer,13,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.43,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +1d44d99b,Fall,6,1,,,Fall,17.60822306,46.0,53.0,24.0,63,82,110,,,,,Fall,,,,,,,,,,,,,8.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +1d63da6a,Spring,10,0,,,Spring,16.88330939,59.0,83.6,24.0,88,61,125,,,,,Spring,30,1,14.3,2,17.0,2,5,0,6.0,0,5.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,3.18,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +1d78b3c3,Fall,9,0,Winter,60,Fall,16.24903978,54.0,67.4,,61,81,104,Fall,7,11,32,Fall,5,0,,,,,6,1,8.5,1,8.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.96,Fall,5,0,3,0,4,0,0,0,0,0,0,0,3,0,2,2,0,3,0,0,22,Fall,48,67,Fall,0,0 +1d7edb86,Winter,11,1,,,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.73,Spring,4,4,4,4,3,1,1,2,3,5,4,0,5,5,4,5,1,5,1,2,63,Spring,58,80,Winter,2,2 +1da37c60,Fall,17,1,Fall,71,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,5,5,5,2,5,3,5,0,4,0,4,5,5,5,2,5,1,4,75,Spring,55,76,Fall,0,2 +1dd90b53,Summer,14,1,Winter,65,Winter,18.63599911,67.0,119.0,,69,96,134,,,,,Winter,36,1,24.0,2,22.6,2,3,0,11.0,1,9.0,0,10.0,1,Winter,1,1.6777,18.638,1341.39,1743.8,28.3081,85.1384,13.3346,5.30348,33.8616,2,32.226,24.6043,83.4607,35.384,60.5341,Summer,1.65,,,Winter,4,4,4,1,4,2,5,3,5,3,3,1,2,2,2,5,2,4,2,2,60,Winter,36,51,Summer,2,2 +1de34522,Fall,8,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.92,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +1de3c240,Fall,7,1,Summer,80,Spring,15.34869495,46.1,46.4,,71,75,101,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,Winter,39,55,Fall,0,0 +1de762f1,Spring,15,1,Fall,,Summer,25.29562509,63.0,142.8,35.0,59,64,107,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.45,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1dfd6433,Spring,10,1,Fall,61,Summer,16.75195195,55.5,73.4,,63,92,105,Summer,3,3,41,Summer,5,0,,,,,1,0,6.0,0,5.0,0,8.5,1,Summer,2,7.5766,16.7538,1085.46,1628.19,17.2541,57.8787,13.211,3.54279,15.5213,2,24.4634,16.1612,50.3021,22.4126,41.7175,,,Spring,2.61,Summer,1,3,3,2,4,1,1,2,2,2,1,0,1,2,2,1,2,1,1,2,34,Summer,46,64,Spring,2,1 +1e08b1dc,Winter,14,0,Fall,71,Fall,19.05169001,69.1,129.4,,95,77,146,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.32,Winter,4,3,3,3,3,2,1,1,2,3,3,1,2,1,2,2,3,3,1,2,45,Winter,32,46,Winter,2,1 +1e292d2f,Winter,16,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +1e2a1bd0,Spring,6,0,Fall,80,Spring,15.67099087,46.5,48.2,,63,72,98,Spring,5,8,2,Spring,0,0,,,,,0,0,8.0,1,7.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,37,53,Spring,1,0 +1e5f6199,Summer,7,0,Winter,51,Fall,16.18169575,49.5,56.4,,74,81,109,Fall,5,8,9,Fall,7,1,,,,,11,1,11.5,1,11.0,1,10.0,1,Fall,2,3.09674,16.1835,995.038,1592.06,9.83552,48.2476,13.8442,2.33925,8.15239,1,27.7595,10.6526,45.1509,28.3051,37.595,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,44,62,Fall,0,0 +1e7dd854,Spring,11,1,Summer,70,Spring,17.97872303,53.5,73.2,26.0,64,89,110,,,,,Spring,13,0,15.2,2,14.3,2,12,1,12.0,1,12.0,1,10.5,1,Spring,4,3.37189,17.3269,1092.31,2075.38,18.5561,58.6081,13.8729,3.454,14.5919,1,26.0655,13.9864,55.2362,24.368,44.6216,,,Summer,2.22,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,39,55,Spring,0,0 +1e87baed,Spring,11,0,Fall,61,Summer,27.06632894,53.5,110.2,,71,73,107,Summer,2,2,34,Summer,0,0,10.7,1,9.7,1,0,0,0.0,0,0.0,0,1.0,0,Fall,1,3.39904,27.0693,1157.83,1505.19,15.2015,65.5876,16.1108,10.9585,44.6124,3,31.3616,19.0246,62.1886,28.6219,46.5631,,,Spring,2.38,Summer,3,3,3,0,3,1,0,2,1,1,0,0,3,1,2,2,2,1,1,1,30,Summer,34,49,Spring,1,0 +1e986f93,Winter,7,1,Summer,48,Summer,13.81990837,49.0,47.2,,64,101,109,Summer,1,0,33,Summer,9,1,,,,,5,1,9.0,1,10.5,1,8.0,1,Summer,3,2.38226,13.8214,966.12,1545.79,10.3586,45.1675,13.2262,0.595183,2.03255,1,20.0473,14.7617,42.7852,17.2451,30.4058,,,,,Summer,1,0,2,0,2,2,0,2,1,1,1,1,1,1,2,1,4,1,1,1,25,Summer,27,40,Spring,0,0 +1eb206f0,Fall,12,1,,,Fall,18.53003852,56.25,83.4,25.0,69,80,115,,,,,Fall,40,1,16.2,2,18.1,2,4,0,10.0,1,11.0,1,9.0,1,Winter,4,3.68803,18.5321,1144.97,2175.44,20.821,64.2172,14.2695,4.26255,19.1828,1,28.1402,15.2561,60.5292,28.4064,48.9611,,,Fall,2.68,Fall,2,2,2,0,1,1,0,1,1,1,1,0,0,1,1,1,1,1,1,1,19,Fall,31,45,Fall,2,0 +1ed564ef,Spring,8,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1eda934b,Fall,9,1,Spring,51,Winter,27.84202806,56.0,124.2,,77,104,124,Winter,,,,Winter,10,1,,,,,0,0,6.0,0,6.0,0,10.0,1,Winter,1,3.4737,27.8451,1181.27,1535.65,20.695,68.084,15.2641,12.5809,56.116,3,29.0132,18.3758,64.6103,38.7671,49.7082,,,Fall,1.79,Winter,2,3,1,0,1,1,1,1,2,2,1,0,1,0,1,1,1,1,1,1,22,Winter,57,79,Fall,2,0 +1eea376f,Spring,21,1,Fall,60,Summer,23.19250984,64.3,136.4,,58,90,98,,,,,,,,,,,,,,,,,,,,Fall,4,5.59843,23.195,1486.25,2823.88,33.3841,100.568,17.1018,6.09325,35.8318,2,42.5253,24.6588,94.9698,62.754,75.9094,,,,,Summer,3,2,3,1,2,2,2,1,2,0,3,0,0,1,0,0,2,0,0,0,24,,,,Spring,3,0 +1eea8df2,Spring,15,0,Summer,65,Summer,15.08990947,67.5,97.8,,45,86,135,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.51,,,Spring,4,3,5,5,3,2,2,3,2,4,5,1,2,2,3,3,3,4,4,2,62,Spring,65,89,Spring,3,2 +1eedf45e,Spring,8,0,Fall,62,Summer,21.60198194,55.25,93.8,,67,105,125,Summer,5,7,29,Summer,15,1,,,,,6,1,14.0,1,14.0,1,9.0,1,Summer,4,3.88787,21.6043,1172.74,2462.76,17.4239,67.1755,15.4721,6.13225,26.6245,2,33.1541,16.5974,63.2876,31.0243,50.5781,,,,,Summer,4,4,4,1,4,3,1,3,2,2,3,0,0,1,1,0,1,0,1,0,35,Summer,75,100,Spring,0,1 +1f0aa085,Spring,9,0,,,Spring,19.42836364,55.0,83.6,26.0,75,87,119,,,,,Spring,7,0,,,,,7,1,11.5,1,12.5,1,15.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,3.08,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +1f0f118d,Summer,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1f1205b2,Winter,6,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +1f2164a4,Summer,6,0,Winter,60,Fall,13.74768838,48.5,46.0,,70,94,120,Fall,4,6,53,Fall,3,1,,,,,1,0,10.0,1,8.0,1,11.0,1,Fall,2,4.73235,16.332,900.681,1441.09,7.08669,38.1973,13.5618,2.77029,7.80267,2,22.9364,8.17423,33.465,16.9025,30.0231,,,,,Fall,0,0,2,0,2,0,0,0,0,0,2,0,5,5,3,5,2,4,0,1,31,,,,Fall,0,1 +1f400626,Winter,11,0,Spring,72,Winter,17.73151393,59.0,87.8,26.0,56,88,109,,,,,Winter,33,1,13.4,2,17.8,2,15,1,10.0,1,9.5,1,11.0,1,Winter,3,4.44213,17.7335,1212.33,2060.97,19.7218,71.3926,14.4196,3.31389,16.4074,1,33.5023,18.1685,66.9505,29.3704,53.2241,,,Spring,2.95,Spring,2,0,0,0,2,0,0,2,0,1,0,0,0,2,0,0,1,1,0,0,11,Spring,53,73,Winter,1,0 +1f5835cd,Fall,9,1,Winter,85,Fall,16.22154774,51.5,61.2,,71,92,121,Fall,,,,Fall,7,1,,,,,2,0,4.5,0,9.0,1,6.0,1,Fall,1,5.95523,16.2233,1026.38,1334.29,13.8111,51.586,13.6748,2.54854,9.61397,1,25.8256,11.9493,45.6308,31.5446,39.6367,,,Fall,1.89,Fall,2,2,2,0,2,1,1,1,1,2,2,0,1,1,1,1,1,2,1,1,25,Fall,31,45,Fall,0,0 +1f5cefcc,Winter,12,0,Spring,65,Winter,16.11717202,57.5,75.8,26.0,73,85,106,,,,,Winter,16,1,10.1,1,12.5,1,7,0,8.0,1,8.0,1,14.0,1,Winter,3,4.07586,16.1189,1140.28,1938.48,16.379,63.7182,13.5497,2.5692,12.0818,1,30.4199,16.9193,59.6424,25.8745,46.7989,,,Spring,2.931,Spring,4,0,3,0,4,2,0,2,1,3,0,0,3,0,5,4,2,4,0,0,37,Spring,48,67,Winter,0,1 +1f65fa82,Winter,17,0,Summer,41,Spring,23.17489163,67.5,150.2,,62,72,133,,,,,Spring,8,0,26.3,1,30.5,1,14,0,10.0,1,7.5,1,12.0,1,Spring,3,6.78074,23.1774,1677.49,2851.74,37.6296,120.938,18.662,4.51539,29.2617,3,53.1043,30.2043,114.158,60.5714,90.7339,Summer,4.0,,,Spring,4,2,2,2,2,0,0,1,2,2,2,1,1,2,2,1,2,1,1,1,31,,,,Winter,2,1 +1f7bd44c,Fall,7,0,Fall,65,Winter,18.91204472,53.5,77.0,,65,98,101,Winter,,,,Winter,3,0,,,,,0,0,9.0,1,9.5,1,14.0,1,Winter,2,3.52437,18.9141,1122.72,1796.36,15.6641,61.8478,15.1922,3.72195,15.1522,2,32.3738,13.8099,58.3234,31.9902,48.0379,,,,,Winter,1,2,2,1,2,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,11,Winter,32,46,Fall,0,0 +1f7ec039,Fall,9,0,Winter,63,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.64,Fall,4,1,4,0,5,1,0,1,1,1,1,0,2,1,2,5,4,5,1,4,43,Fall,60,82,Fall,1,1 +1f881e3d,Spring,13,0,Summer,50,Spring,24.3316736,62.5,135.2,38.0,80,71,121,,,,,Spring,11,0,19.5,2,19.1,1,0,0,2.0,0,0.0,0,7.0,0,Spring,2,4.66636,24.3343,1333.99,2134.39,21.9452,84.3508,15.1821,9.15223,50.8492,2,36.841,25.5647,79.6844,34.6096,58.7861,,,Spring,2.15,Spring,2,2,2,2,3,1,1,1,2,2,1,0,1,1,1,1,1,2,1,1,28,Spring,42,59,Spring,2,0 +1f95c51b,Fall,9,0,,,Fall,17.52792793,55.5,76.8,28.0,66,66,101,,,,,Fall,2,0,,,,,0,0,0.0,0,0.0,0,7.0,1,Fall,1,3.79577,17.5299,1115.85,1450.6,15.4247,61.1152,13.9497,3.58013,15.6849,1,29.7321,15.9584,57.3194,24.7885,45.1568,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +1f97a307,Spring,14,1,,,Spring,20.06669822,65.0,120.6,29.0,88,65,111,,,,,Spring,35,1,23.2,2,24.7,3,9,1,15.0,1,14.0,1,22.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +1fc07a80,Spring,10,0,Spring,51,Spring,16.65246154,57.75,79.0,,92,100,129,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.05,Spring,1,1,2,1,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,10,Spring,28,41,Spring,0,0 +1feb42f2,Spring,9,1,Summer,45,Spring,17.01815593,53.0,68.0,,77,116,117,Spring,6,10,0,Spring,10,1,,,,,10,1,11.0,1,12.0,1,9.0,1,Summer,3,2.97709,17.02,1043.98,1670.37,14.7163,53.4605,13.3809,3.63915,14.5395,1,23.9203,14.8239,50.4834,24.2641,38.6366,,,Spring,2.46,Spring,5,5,5,1,5,1,0,5,2,4,2,0,4,4,4,4,4,4,4,2,65,Spring,55,76,Spring,1,2 +1fed3943,Spring,7,0,Fall,70,Spring,16.80641399,49.0,57.4,,50,83,97,Spring,5,8,20,Spring,2,1,,,,,1,0,6.5,0,7.0,0,8.0,1,Spring,1,2.79425,16.8082,971.985,1263.58,8.40492,45.7922,13.4092,3.39907,11.6078,1,23.6029,13.7844,42.998,18.0421,32.0078,,,,,Spring,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Spring,53,73,Spring,0,0 +1ffede3b,Summer,7,0,,,Summer,29.23118548,53.0,116.8,,65,85,110,,,,,Summer,4,1,,,,,1,0,13.0,1,14.0,1,10.0,1,Winter,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,50,69,Summer,2,0 +200af20c,Fall,12,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4.4,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +20565aae,Fall,10,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +206c8562,Spring,10,0,Fall,62,Summer,17.82381518,54.75,76.0,,75,73,121,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.19,Summer,1,1,4,1,2,1,2,1,1,1,1,0,1,2,4,2,3,2,2,2,34,Summer,29,42,Spring,0,1 +2084c823,Winter,14,1,Spring,67,Winter,21.17392103,66.0,131.2,31.0,71,89,108,,,,,Winter,14,0,25.1,3,27.5,3,0,0,8.0,0,7.5,0,9.0,1,Spring,2,5.4625,21.1762,1409.07,2113.6,32.5226,92.3472,14.9052,6.27101,38.8528,2,35.4306,24.394,86.8847,39.2414,67.9532,,,Winter,1.611,Winter,0,2,2,1,3,2,1,1,2,2,2,1,1,2,1,1,2,1,1,1,29,Winter,37,53,Winter,2,0 +209471ef,Spring,10,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +20a67f23,Winter,8,1,,,Winter,15.15355556,60.0,77.6,,76,93,123,Winter,,,,Winter,3,,,,,,0,,10.0,,13.0,,11.0,,Winter,2,2.3404,15.1552,1180.42,1770.63,21.0877,67.993,13.279,1.87624,9.607,2,29.3063,17.599,65.6526,32.3009,50.394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +20c37863,Spring,7,0,,,Spring,13.28658487,48.25,44.0,21.0,55,81,98,,,,,Spring,0,0,,,,,0,0,7.0,0,9.0,1,4.0,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +20cefbaf,Winter,10,1,Summer,80,Spring,22.20158333,57.88,105.8,,49,61,102,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.5,Summer,2,3,2,1,4,0,0,2,1,2,1,0,0,0,1,2,3,2,0,1,27,Spring,29,42,Winter,1,0 +20d7ecfd,Summer,11,1,Fall,74,Summer,17.6794887,58.0,84.6,,66,67,113,Summer,5,8,12,Summer,22,1,,,,,13,1,14.0,1,11.0,1,15.0,1,Summer,2,4.67832,17.6814,1154.77,1732.16,20.1969,65.2617,13.6397,4.04171,19.3383,2,28.0992,16.9656,60.5834,30.3493,48.2961,,,Summer,1.98,Summer,1,4,2,0,4,1,0,2,1,0,1,1,3,3,2,4,2,3,2,3,39,Summer,63,86,Summer,0,1 +20df4cab,Spring,6,1,Summer,60,Summer,14.54035088,42.75,37.8,,65,52,91,Summer,2,2,30,Summer,0,0,,,,,0,0,10.0,1,7.0,0,4.0,0,Summer,3,10.0618,14.2077,886.779,1418.85,7.91575,36.7166,13.8005,0.4072,1.08337,1,19.5575,9.24335,26.6548,17.4056,27.4733,,,,,Summer,4,0,3,0,2,1,0,0,0,1,2,0,1,0,2,2,2,2,1,2,25,Summer,46,64,Spring,0,0 +20e8ca68,Summer,10,0,Fall,60,Summer,16.61496507,54.5,70.2,,66,78,91,Summer,7,11,54,Summer,8,0,,,,,20,1,8.0,1,8.0,1,11.0,1,Summer,5,6.16605,16.6168,1074.52,2578.84,12.4954,56.7134,13.4244,3.19237,13.4866,1,28.475,15.7429,50.5473,26.4619,40.9705,,,Summer,3.3,Summer,1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Summer,32,46,Summer,1,0 +20ff26ce,Winter,8,0,,,Spring,22.99938272,54.0,95.4,34.0,66,93,129,,,,,Spring,0,0,,,,,0,0,0.0,0,6.0,0,6.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +210baa62,Fall,18,0,Spring,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2114473a,Fall,9,0,,,Fall,15.01601994,53.0,60.0,23.0,57,85,107,,,,,Fall,8,0,,,,,4,0,5.5,0,6.0,0,3.5,0,Fall,2,3.41046,15.0177,1035.21,1656.34,11.6784,52.5269,13.1472,1.87048,7.47312,1,27.1351,13.7134,49.1164,23.4878,38.8135,,,Winter,2.72,Fall,2,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,Fall,34,49,Fall,1,0 +214f46e8,Fall,16,0,Winter,80,Fall,17.27133728,65.0,103.8,,66,88,124,,,,,Fall,8,0,17.3,1,19.0,1,0,0,7.0,0,8.5,1,6.5,0,Fall,2,5.70158,17.2732,1389.19,2222.7,27.0919,90.23,15.0151,2.25817,13.57,2,40.1974,22.9407,84.5284,38.5256,67.2893,Fall,1.77,,,Winter,4,4,3,4,3,2,1,2,1,1,3,1,1,2,2,1,2,2,2,1,42,,,,Fall,0,1 +215ca39b,Summer,11,1,Winter,60,Summer,18.65102041,56.0,83.2,,67,89,108,,,,,Summer,13,1,16.8,2,15.5,2,0,0,10.0,1,9.0,1,8.0,0,Fall,2,3.47734,18.6531,1117.01,1675.52,18.9565,61.2393,13.7296,4.92349,21.9607,2,26.19,16.0928,57.762,25.7453,45.1465,,,Fall,1.4,Fall,2,3,2,0,2,1,0,2,2,1,4,0,2,1,3,2,2,2,2,2,35,Fall,38,54,Summer,2,1 +217ce5e6,Winter,15,0,Summer,80,Spring,19.85055096,66.0,123.0,,68,83,117,,,,,Spring,15,0,20.4,1,21.7,1,29,1,5.0,0,5.5,0,9.0,1,Spring,3,1.54144,19.8527,1444.07,2454.92,30.5415,96.0755,15.507,4.34572,26.9245,2,40.3399,25.1941,94.5341,33.6715,70.8814,Spring,1.55,,,Spring,2,2,2,2,3,1,2,2,1,1,3,1,1,1,1,1,2,1,1,1,31,Spring,30,43,Winter,2,1 +218faf6f,Spring,9,0,Winter,60,Summer,17.15718804,49.5,59.8,,73,95,112,Summer,6,9,7,Summer,8,0,,,,,3,0,13.0,1,13.0,1,2.0,0,Summer,3,2.86015,17.1591,983.547,1672.03,8.62087,47.0237,13.493,3.66604,12.7763,1,24.4339,13.9689,44.1636,20.2747,33.0548,,,Spring,2.25,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,36,51,Spring,0,0 +2197f92c,Fall,5,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +21a76448,Spring,7,0,Summer,53,Spring,22.69494649,58.0,108.6,,76,100,124,Spring,6,9,0,Spring,9,1,,,,,0,0,10.0,1,9.0,1,10.5,1,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,4,0,4,1,0,0,0,2,4,0,4,1,3,5,2,5,1,1,45,Spring,66,90,Spring,2,1 +21ae3c82,Spring,11,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +21b835ac,Spring,8,0,Winter,50,Spring,17.97298304,50.5,65.2,,56,79,107,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.1972,17.9749,1027.2,1643.52,12.1678,51.6735,14.2458,3.72911,13.5265,2,27.431,12.0747,48.4763,22.3957,39.5988,,,,,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,29,42,Spring,0,0 +21c62163,Winter,9,0,Spring,75,Winter,17.44744759,52.88,69.4,,70,78,107,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.42,Winter,2,2,3,2,2,0,0,1,1,2,2,0,2,2,3,2,2,2,1,1,32,Winter,33,47,Winter,0,1 +21db593a,Summer,10,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.11,,,,,,,,,,,,,,,,,,,,,,,,,,,, +21e5833c,Spring,8,0,Spring,55,Summer,14.93252811,49.0,51.0,,65,81,136,Summer,4,6,2,Summer,6,1,,,,,2,0,13.0,1,13.0,1,6.0,1,Fall,2,2.86342,14.9342,958.667,1533.87,8.42424,44.3736,12.9938,1.94038,6.62637,1,23.8966,12.0528,41.5102,18.8834,32.3209,,,,,Summer,2,3,2,0,1,1,1,2,0,1,1,0,1,1,0,0,1,0,0,0,17,Summer,56,77,Spring,2,0 +21eb6d47,Summer,13,1,Fall,55,Summer,19.95076592,61.0,105.6,,62,85,102,,,,,Summer,12,0,14.9,1,19.1,1,0,0,10.0,1,8.0,0,12.0,1,Summer,3,4.40614,19.9529,1252.39,2003.82,25.4667,75.6589,14.2956,5.65731,29.9411,2,30.6267,19.5655,71.2528,31.9689,56.0934,,,Summer,1.43,Summer,2,1,2,1,2,2,2,1,2,1,1,0,1,1,1,0,1,1,2,1,25,Summer,49,68,Summer,1,0 +21f58fba,Fall,9,0,Summer,65,Winter,15.466,50.0,55.0,,92,66,123,Spring,7,12,39,Winter,7,0,,,,,14,1,8.0,1,8.5,1,6.5,1,,,,,,,,,,,,,,,,,,,,Winter,3.45,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,28,41,Fall,0,0 +221e37e0,Summer,9,0,Fall,60,Summer,15.13571703,51.0,56.0,,60,113,111,Summer,6,9,46,Summer,10,1,,,,,6,1,6.5,0,7.0,0,7.0,1,Fall,2,6.82486,15.1374,992.082,1587.33,9.8233,47.9328,12.9567,2.18066,8.06725,1,24.3047,13.8047,41.1079,18.1723,34.128,,,Summer,2.97,Summer,2,1,2,0,2,1,1,1,1,1,0,0,1,0,2,1,2,1,0,1,20,Summer,29,42,Summer,0,0 +223724b2,Summer,11,0,Fall,40,Summer,24.65880102,56.0,110.0,,81,85,146,Summer,4,5,30,Summer,3,0,15.1,2,16.8,2,0,0,10.0,1,9.0,1,12.0,1,Fall,2,9.92052,24.6615,1210.51,1936.82,18.8148,71.1985,15.9624,8.69911,38.8015,2,33.5829,18.8008,61.278,29.2135,52.3978,,,Summer,3.26,Summer,2,1,3,1,3,1,1,2,1,1,2,0,1,1,2,1,2,1,2,1,29,Summer,41,58,Summer,0,0 +22375702,Summer,15,0,Spring,60,Summer,33.10493594,69.0,224.2,40.0,97,87,163,,,,,Summer,11,0,35.5,2,39.2,2,11,0,11.0,1,10.0,1,12.0,1,Winter,2,7.88209,33.1086,2098.84,3358.14,52.1542,165.817,24.4869,8.62164,58.3828,3,71.4057,42.2572,157.935,94.9465,123.56,Winter,1.71,,,Winter,4,4,4,1,3,3,0,1,1,1,3,0,1,2,2,0,1,1,1,1,34,Winter,48,67,Summer,3,1 +22389051,Fall,8,0,Fall,61,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,5,2,4,0,1,0,0,2,4,4,0,0,5,2,2,4,2,2,1,0,40,Winter,51,71,Fall,0,1 +22447a95,Winter,17,0,Summer,40,Spring,23.68505859,64.0,138.0,,68,58,129,,,,,Spring,15,0,49.6,2,50.9,2,28,1,12.0,1,12.0,1,11.0,1,Spring,3,5.4883,23.6876,1451.0,2466.7,26.2354,96.8134,16.618,7.06966,41.1866,2,45.3428,25.2352,91.3251,57.0906,71.5783,Winter,3.459,,,Spring,1,1,2,1,2,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,24,Spring,48,67,Winter,1,0 +224dfbfc,Summer,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.84,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +224fc39a,Fall,16,0,Spring,50,Winter,29.53161101,69.0,200.0,,61,80,121,,,,,Winter,15,0,33.7,2,41.5,2,10,0,6.5,0,6.0,0,11.0,1,,,,,,,,,,,,,,,,,,Winter,1.67,,,Winter,5,5,4,2,4,2,1,3,1,2,3,1,3,2,3,2,2,4,2,2,53,Winter,50,69,Fall,0,2 +22536a61,Fall,13,0,Spring,68,Winter,22.01904266,62.0,120.4,,58,69,107,,,,,Winter,10,0,20.5,2,14.1,1,4,0,3.5,0,3.5,0,3.0,0,Winter,2,4.99501,22.0214,1349.33,2158.93,23.7064,85.9848,15.7268,6.29462,34.4152,2,39.7115,22.5669,80.9898,41.5561,63.4179,,,Fall,2.35,Winter,2,1,2,0,2,2,1,1,1,2,1,0,1,2,1,1,2,1,2,1,26,Winter,31,45,Fall,2,0 +2275e6d5,Spring,12,0,,,Summer,15.32359642,56.75,70.2,24.0,61,77,112,,,,,Summer,6,0,11.5,1,11.0,1,0,0,5.0,0,5.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,42,59,Summer,2, +22776248,Summer,13,0,,,Summer,23.902,70.0,166.6,30.0,56,52,124,,,,,Summer,20,0,42.6,3,42.2,3,17,1,12.0,1,12.0,1,12.0,1,Summer,4,7.41746,23.9046,1788.71,3756.29,41.3883,132.784,19.0525,4.85208,33.8159,3,57.5355,33.8604,125.367,68.6926,98.9238,,,Summer,4.16,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2282b403,Winter,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,43,60,,, +22a71606,Fall,5,0,,,Fall,15.33624542,44.5,43.2,21.0,67,82,105,,,,,Fall,1,0,,,,,0,0,8.0,1,6.0,0,7.0,1,Fall,1,2.14305,15.3379,920.28,1196.36,2.12534,40.2849,14.3029,1.03497,2.91506,1,19.9744,18.1852,38.1419,23.6058,22.0998,,,,,Fall,2,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,0,11,,,,Fall,0,0 +22b4191a,Winter,10,1,Spring,41,Spring,25.36200274,54.0,105.2,,145,68,168,Spring,4,5,22,Spring,0,0,10.6,1,11.9,2,0,0,0.0,0,0.0,0,4.5,0,,,,,,,,,,,,,,,,,,,,Winter,1.13,Spring,3,3,3,1,4,1,0,2,2,2,2,0,4,2,4,5,4,4,0,1,47,Spring,61,84,Winter,0,1 +22c72c4e,Spring,10,0,Summer,75,Summer,17.57195724,60.8,92.4,,66,99,121,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.97,Summer,5,5,3,0,5,3,0,1,2,4,3,0,2,2,3,4,2,5,2,2,53,Summer,40,56,Spring,0,2 +22d116bd,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +22e44415,Fall,10,1,Spring,67,Winter,14.87115385,52.0,57.2,,78,77,122,Winter,6,9,40,Winter,70,1,,,,,18,1,12.0,1,12.0,1,12.0,1,Winter,5,2.98576,14.8728,1024.73,2254.42,15.6065,51.4107,13.3675,1.5053,5.78929,1,23.6143,12.19,48.4249,19.7625,39.2207,,,Fall,3.42,Winter,4,0,2,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,10,Winter,33,47,Fall,2,0 +22f43a12,Fall,8,0,Summer,65,Winter,15.85046658,51.5,59.8,,56,83,97,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.59,Spring,2,2,3,1,3,1,0,0,0,0,2,0,1,0,1,1,1,1,2,0,21,Winter,42,59,Fall,0,0 +22f7ddb8,Spring,16,1,,,Spring,22.22384533,66.5,139.8,30.0,85,49,119,,,,,Spring,10,0,14.9,1,18.4,2,0,0,14.5,1,14.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +22f99fea,Spring,15,0,,,Spring,24.59644946,69.5,169.0,32.0,74,64,143,,,,,Spring,45,1,36.2,2,33.2,2,20,1,6.0,0,8.0,1,17.0,1,Spring,,,,,,,,,,,,,,,,,Spring,3.34,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +231f6db4,Spring,7,0,,,Summer,22.69445366,53.5,92.4,,57,77,88,Summer,4,5,58,Summer,10,1,,,,,0,0,11.0,1,12.0,1,12.0,1,Summer,2,3.90839,22.6969,1194.98,1911.97,17.809,69.5442,17.0827,5.61424,22.8558,3,36.9115,14.8237,65.6358,42.9011,54.7205,,,,,Summer,5,4,4,1,3,3,1,1,1,1,2,1,1,1,2,2,1,3,2,1,40,Summer,36,51,Spring,0,1 +232b7cb1,Summer,16,0,Fall,85,Fall,20.52443772,68.0,135.0,,59,78,121,,,,,Fall,50,1,20.6,1,23.3,1,30,1,7.0,0,7.0,0,11.0,1,Fall,3,0.261049,20.5267,1541.75,2620.97,32.271,106.479,16.1901,4.33654,28.5206,2,46.4712,27.7372,106.218,48.7833,78.7422,Winter,2.4,,,Summer,2,0,0,0,2,0,2,0,1,2,1,0,0,0,0,0,0,0,0,0,10,Summer,32,46,Fall,,0 +232ee0c9,Spring,9,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.43,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +233b9425,Summer,7,0,Winter,50,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1,2,3,0,2,2,0,2,0,2,2,0,0,0,0,1,1,0,0,0,18,Winter,43,60,Summer,0,0 +2340e84a,Spring,10,1,Spring,93,Spring,19.35414447,53.5,78.8,,66,92,103,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.35,Spring,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,Spring,45,63,,,0 +23418fa2,Spring,7,1,Summer,75,Spring,14.57555455,47.0,45.8,,53,79,88,Spring,1,0,46,Fall,,,,,,,,,,,,,,,Spring,1,10.5957,14.5771,939.435,1221.27,10.2632,42.3252,13.4712,1.10597,3.47484,1,20.1256,11.9364,31.7295,16.485,30.3888,,,,,Spring,3,0,3,0,3,1,0,1,1,1,2,0,1,,,1,2,1,1,0,21,Spring,37,53,Spring,0,0 +23567787,Spring,11,0,Fall,65,Fall,16.53099415,57.0,76.4,,55,61,111,Fall,,,,Fall,16,1,16.9,2,18.4,2,12,1,4.5,0,0.0,0,9.5,1,Fall,5,1.86776,16.5328,1163.73,2792.95,17.1947,66.2153,14.3288,2.20395,10.1847,2,33.1202,15.9004,64.3475,32.502,50.3149,,,Fall,3.89,Fall,4,4,3,1,3,1,1,2,3,2,2,0,3,0,3,2,1,2,1,2,40,Fall,35,50,Spring,0,1 +236d450e,Summer,11,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,5,5,4,0,3,1,1,1,1,1,2,0,2,1,1,2,2,2,2,2,38,Summer,38,54,,,1 +236ecde8,Winter,8,1,Fall,85,Spring,15.55845805,52.5,61.0,,71,81,122,Spring,4,6,1,Spring,8,1,,,,,4,0,8.0,0,9.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.59,Spring,2,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,Spring,39,55,Winter,0,0 +23bd890b,Winter,9,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.3,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +23d1a58d,Fall,13,1,Winter,75,Fall,19.86315789,57.0,91.8,,62,72,114,,,,,Fall,10,0,13.0,1,16.7,2,2,0,11.0,1,12.0,1,11.0,1,Fall,4,2.35476,19.8653,1205.73,2290.88,22.0635,70.6886,15.2969,4.56846,21.1114,1,32.7924,15.8326,68.3339,43.4,54.856,,,Fall,2.68,Fall,0,3,3,0,3,2,0,2,0,0,2,0,2,0,2,2,2,2,1,2,28,Fall,67,91,Fall,0,0 +23d71e37,Summer,9,0,Fall,60,Summer,13.64533608,54.0,56.6,,73,99,99,Summer,5,8,15,Summer,6,1,,,,,3,0,9.0,1,9.0,1,8.0,1,Summer,4,6.92418,13.6468,1024.44,2151.33,9.98509,51.3798,12.3882,1.25865,5.22023,1,25.3276,16.0671,44.4556,21.9586,35.3127,,,Summer,2.3,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,53,73,Summer,0,0 +23dafdab,Summer,9,0,Spring,80,Winter,23.8966199,56.0,106.6,,58,93,105,Winter,5,8,40,Winter,34,1,,,,,14,1,8.0,1,7.5,1,8.0,1,Winter,3,7.92706,23.8992,1220.12,2074.21,19.5677,72.2221,16.1919,7.70737,34.3779,2,34.6613,17.9931,64.295,31.4403,54.229,,,Winter,4.0,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,31,45,Winter,0,0 +23e024bd,Summer,14,1,Winter,55,Fall,,58.0,,,69,71,124,,,,,Fall,30,1,45.0,2,47.8,2,0,0,8.5,0,8.5,0,13.0,1,,,,,,,,,,,,,,,,,,,,Summer,1.59,Fall,2,1,2,0,2,2,1,2,1,2,2,0,0,2,0,0,1,0,0,0,20,Fall,42,59,Summer,1,0 +23e71e7c,Winter,13,0,Spring,45,Spring,18.79713046,66.6,118.6,,52,102,111,,,,,Fall,,,,,,,,,,,,,,,Summer,3,5.93136,18.7992,1438.56,2445.55,28.2468,95.4885,15.1358,3.66338,23.1115,2,42.0608,25.1809,89.5571,42.0306,70.3076,,,Winter,2.74,Spring,4,2,4,1,3,2,4,1,0,0,2,0,2,3,1,0,4,0,0,0,33,Spring,69,94,Winter,2,1 +23e7f79b,Winter,9,1,Fall,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.71,Summer,3,3,3,1,2,3,0,3,1,1,2,0,1,2,2,0,2,1,2,1,33,Summer,37,53,Winter,2,1 +23ff8272,Spring,8,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +240a2450,Summer,16,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +243635c2,Fall,14,1,,,Winter,18.23258082,60.84,96.0,26.0,62,82,113,,,,,Winter,1,0,18.8,2,18.7,2,4,0,9.5,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +24398aed,Fall,6,0,Spring,55,Winter,15.10578512,44.0,41.6,,80,110,118,,,,,Winter,0,,,,,,0,,0.0,,0.0,,2.5,,Winter,3,2.23602,15.1074,887.222,1508.28,4.23341,36.7638,13.3511,1.7563,4.83617,1,19.9531,12.5773,34.5278,14.4486,24.1865,,,,,Winter,3,3,2,0,3,1,0,0,0,2,0,0,5,0,0,4,4,0,0,0,27,Winter,36,51,Fall,0,0 +24591f4d,Fall,8,1,Spring,60,Winter,17.94663591,51.0,66.4,,55,73,72,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.82,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,33,47,Fall,0,0 +247172f9,Fall,10,0,Summer,50,Winter,19.17076023,57.0,88.6,,52,67,110,,,,,Fall,7,0,13.6,2,14.9,2,0,0,0.0,0,0.0,0,8.0,0,Fall,3,3.84769,19.1729,1147.09,1950.06,15.3889,64.4435,13.9454,5.22741,24.1565,2,30.1472,18.9075,60.5958,26.1775,45.536,,,Winter,2.32,Winter,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Winter,27,40,Fall,1,0 +2496c491,Fall,17,1,Spring,70,Winter,26.21568278,67.0,167.4,,70,69,113,,,,,Winter,50,1,22.5,2,31.7,2,4,0,14.0,1,15.0,1,13.0,1,Winter,4,-0.100691,26.2185,1540.54,2927.02,38.0525,106.351,16.6569,9.56167,61.0493,2,40.5409,27.7573,106.451,50.5859,78.5934,Fall,3.02,,,Winter,2,2,2,1,2,0,0,1,1,1,2,0,1,1,1,1,2,0,1,1,22,Winter,39,55,Fall,3,0 +249b6d68,Spring,14,0,Fall,60,Summer,25.55794164,67.0,163.2,,71,74,132,,,,,Summer,15,0,24.4,1,28.3,1,6,0,9.0,1,9.5,1,9.0,1,,,,,,,,,,,,,,,,,,Spring,3.29,,,Summer,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Summer,29,42,Spring,2,0 +249d2266,Fall,9,1,Winter,85,Fall,17.26922047,49.75,60.8,,61,85,105,Fall,6,9,31,Fall,9,1,,,,,9,1,13.5,1,14.0,1,9.0,1,Fall,4,6.28265,17.2711,1004.24,1908.06,14.4779,49.228,13.9839,3.2872,11.572,2,23.9563,10.7938,42.9453,22.3377,38.4342,,,Fall,2.47,Fall,2,1,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,Fall,41,58,Fall,0,0 +24a3b16d,Winter,12,0,Summer,65,Spring,16.23995838,62.0,88.8,,68,81,139,,,,,Fall,,,,,,,,,,,,,,,Summer,3,4.888,16.2417,1253.7,2131.29,21.0,75.7986,13.8638,2.37798,13.0014,3,34.7483,20.0503,70.9106,31.8121,55.7483,,,Spring,2.25,Spring,4,5,5,3,4,2,1,1,2,4,0,0,2,1,4,0,5,1,4,0,48,Spring,31,45,Winter,3,1 +24ab10c6,Spring,7,1,Fall,61,Spring,15.80529514,48.0,51.8,26.0,59,84,95,,,,,Spring,2,1,,,,,0,0,9.5,1,11.0,1,7.0,1,Summer,2,2.50995,15.807,963.471,1445.21,12.9811,44.8853,13.697,2.11004,6.91466,1,21.9673,9.93687,42.3754,17.526,34.9485,,,,,Spring,4,3,3,1,2,3,1,1,3,1,2,0,3,0,2,2,3,3,2,2,41,Spring,45,63,Spring,0,1 +24b4bc6d,Summer,7,0,Winter,55,Summer,18.08270677,53.2,72.8,,85,92,134,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.74374,18.0847,1116.42,1897.92,16.9422,61.1768,15.1973,2.88739,11.6232,3,31.0894,13.1452,57.4331,25.6874,48.0316,,,,,Summer,4,4,2,0,4,0,0,1,1,1,0,0,4,0,3,3,1,4,0,1,33,Summer,43,60,Summer,0,1 +24b75658,Winter,9,1,Summer,67,Winter,17.06820933,53.0,68.2,22.0,49,82,100,,,,,Winter,8,1,,,,,8,1,11.75,1,10.5,1,10.0,1,Spring,3,3.11961,17.0701,1056.5,1690.4,16.3679,54.7941,13.7146,3.35543,13.4059,1,25.1592,13.2669,51.6745,24.5679,41.5271,,,Spring,2.32,Spring,3,3,3,0,2,1,0,0,1,2,3,0,2,0,1,1,2,1,1,1,27,Spring,39,55,Winter,0,0 +24dac645,Winter,9,1,Spring,50,Winter,18.7628162,56.5,85.2,,69,93,110,Winter,4,5,41,Winter,7,1,,,,,0,0,4.5,0,4.5,0,8.0,1,Spring,4,7.37379,18.7649,1120.46,2128.87,18.8371,61.6065,13.5685,5.19635,23.5935,2,25.7734,16.9961,54.2327,25.1305,44.6104,,,Winter,2.4,Winter,0,4,3,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,13,Winter,46,64,Winter,0,0 +24fb56db,Fall,16,1,Spring,65,Winter,20.02832653,70.0,139.6,28.0,71,68,114,,,,,Winter,17,0,74.0,3,88.8,3,24,1,8.0,0,8.5,0,12.0,1,Winter,2,6.14599,20.0305,1475.18,2212.77,34.6011,99.3891,14.2608,5.76967,40.2109,2,37.1117,27.6763,93.2431,43.0826,71.7128,Fall,1.5,,,Winter,5,5,4,1,5,5,5,5,5,5,5,1,1,4,3,1,4,3,2,1,70,Winter,44,62,Fall,2,2 +25049e5c,Fall,10,0,,,Fall,,,,,,,,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +2506e8aa,Summer,8,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2522ab31,Summer,11,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +252ebc5b,Winter,9,0,Fall,72,Winter,17.12011432,54.6,72.6,,59,105,100,Winter,5,7,5,Winter,20,1,,,,,10,1,9.0,1,11.0,1,12.0,1,Spring,3,5.04635,17.5046,1086.83,1847.61,13.863,58.0246,13.9903,3.51428,14.5754,2,29.4026,14.759,52.9782,26.2693,43.2656,,,Winter,2.15,Winter,5,4,5,0,5,1,0,4,3,3,4,0,4,4,5,3,5,3,4,4,66,Winter,54,75,Winter,0,2 +253f5cb1,Fall,14,0,Spring,50,Winter,21.67988754,68.0,142.6,,68,79,118,,,,,Winter,16,0,20.4,2,22.1,2,2,0,7.5,1,9.0,1,8.0,0,Winter,2,0.416741,21.6823,1596.53,2554.44,32.2187,112.314,17.0773,4.60496,30.286,2,51.3004,28.795,111.897,66.2259,83.5191,,,Winter,2.4,Winter,0,4,3,2,3,4,1,1,2,1,3,1,3,3,3,2,1,1,1,1,40,Winter,57,79,Fall,2,1 +257bf189,Summer,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +25834ba8,Spring,19,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +25849c6c,Spring,13,1,Spring,78,Spring,22.9737618,64.13,134.4,,97,86,162,,,,,Summer,,,,,,,,,,,,,,,Summer,2,5.21618,22.9763,1400.82,2101.24,32.353,91.4693,15.6371,7.33919,42.9307,2,35.7855,23.3308,86.2531,40.4462,68.1385,,,Summer,1.43,Spring,3,3,2,1,4,2,4,3,1,1,1,1,1,1,1,1,2,2,1,2,37,Spring,34,49,Spring,0,1 +2586774b,Summer,9,0,,,Summer,15.28955475,54.5,64.6,23.0,57,76,114,,,,,Summer,2,0,,,,,5,0,6.0,0,6.5,0,6.0,1,Fall,3,3.72437,15.2912,1081.24,1838.1,14.1025,57.4289,13.5938,1.69745,7.17112,1,29.3417,13.9847,53.7045,25.984,43.4442,,,Summer,3.66,Summer,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,Summer,40,56,Summer,2,0 +25a3a723,Fall,8,0,Spring,60,Winter,22.75829904,54.0,94.4,,79,95,130,Winter,5,7,39,Winter,0,0,,,,,0,0,9.0,1,9.5,1,12.0,1,Winter,2,3.29708,22.7608,1115.64,1785.02,11.9408,61.0933,14.7302,8.03058,33.3067,2,29.1035,20.0489,57.7962,30.0209,41.0443,,,Fall,3.02,Winter,3,3,3,0,4,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,18,Winter,56,77,Fall,0,0 +25e2cbc3,Fall,8,0,,,Fall,15.07914201,52.0,58.0,24.0,72,116,125,,,,,Fall,8,1,,,,,7,1,17.0,1,18.0,1,12.0,1,Fall,3,3.26382,15.0808,1015.52,1726.38,11.1471,50.4288,13.1122,1.9686,7.57113,1,25.9867,13.295,47.165,20.7869,37.1338,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +25e752c8,Spring,7,0,Fall,52,Summer,15.01608,50.0,53.4,,77,116,132,Summer,3,4,47,Summer,0,0,,,,,0,0,11.0,1,11.0,1,4.0,0,Summer,2,3.12874,15.0177,990.589,1584.94,10.6041,47.7737,13.4354,1.58228,5.62625,1,26.2839,10.8857,44.645,21.9529,36.888,,,,,Summer,2,2,2,1,2,2,0,0,0,0,1,1,1,2,2,4,2,2,2,1,29,Summer,40,56,Spring,0,0 +25eba023,Fall,6,0,Spring,65,Spring,15.14493419,45.5,44.6,,69,87,127,Spring,,,,Spring,4,1,,,,,0,0,12.0,1,11.0,1,6.5,1,Spring,1,2.47468,15.1466,903.882,1175.05,6.30976,38.5383,13.088,2.05862,6.06171,1,21.6064,10.6221,36.0636,15.2202,27.9162,,,,,Spring,4,0,4,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,3,4,18,,,,Fall,0,0 +25ff8d4d,Winter,8,0,,,Winter,18.90096022,54.0,78.4,25.0,76,83,116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.64,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +260c5288,Summer,12,0,,,Summer,40.3996213,65.0,242.8,50.0,79,81,126,,,,,Summer,0,0,14.0,2,15.5,2,0,0,,0,,0,9.0,1,Summer,1,6.68552,39.7894,1952.26,2537.93,48.6235,150.204,24.6151,15.1743,92.5951,3,63.214,38.3666,143.519,69.053,111.838,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +260f542b,Fall,8,0,Spring,75,Fall,14.53823129,52.5,57.0,,61,73,110,Fall,6,10,12,Fall,8,1,,,,,9,1,8.5,1,10.0,1,14.0,1,Fall,3,7.04864,14.5398,1009.89,1716.82,9.78809,49.8299,12.7108,1.82899,7.17013,2,25.0202,15.0215,42.7812,20.8125,34.8083,,,Fall,2.31,Fall,5,0,1,0,0,0,1,0,1,0,1,0,0,0,0,2,1,1,0,0,13,Fall,35,50,Fall,0,0 +26190ddd,Summer,6,0,Fall,61,Summer,16.65300162,43.0,43.8,20.0,57,94,109,,,,,Summer,3,1,,,,,0,0,10.0,1,11.0,1,6.0,1,Summer,2,2.26403,16.6548,875.995,1401.59,5.36314,35.568,13.5246,3.1302,8.23203,2,21.0895,9.11531,33.3039,14.6009,26.4527,,,,,Fall,4,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,8,Fall,61,84,Summer,0,0 +2625767f,Winter,17,0,Summer,80,Spring,18.6046781,67.0,118.8,,84,68,143,,,,,Spring,40,1,36.0,2,39.2,2,33,1,13.5,1,15.0,1,15.0,1,Spring,4,-0.468831,18.6067,1457.67,3061.1,28.3338,97.5236,15.2744,3.33235,21.2764,2,43.6933,25.4966,97.9925,47.4102,72.0271,Spring,2.79,,,Spring,2,2,2,1,2,1,1,1,1,1,2,1,1,1,1,1,2,1,1,1,26,Spring,37,53,Winter,2,0 +262cff6d,Summer,15,0,Fall,70,Fall,24.73466919,65.25,149.8,,53,91,115,,,,,Fall,37,1,47.1,3,40.6,2,49,1,13.0,1,11.0,1,12.0,1,Fall,4,6.52549,24.7374,1688.83,3546.55,38.0554,122.146,20.1707,4.56667,27.654,3,54.5186,29.572,115.621,64.2869,92.574,Fall,1.88,,,Fall,2,2,4,3,5,1,2,1,1,3,5,1,1,2,2,1,2,3,2,1,44,Fall,46,64,Summer,3,1 +2654b1b9,Spring,17,1,,,Spring,30.98191716,65.0,186.2,39.0,78,67,138,,,,,Spring,21,1,19.9,2,18.5,2,0,0,7.0,0,6.5,0,14.0,1,Spring,,,,,,,,,,,,,,,,,Spring,1.24,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +26891dbd,Summer,7,0,Winter,67,Fall,15.45643537,52.5,60.6,,93,95,143,Fall,5,8,25,Fall,20,,,,,,17,,12.0,,11.0,,16.0,,Fall,3,5.11506,15.4581,1028.26,1748.04,11.6916,51.7859,13.2098,2.24834,8.81409,2,26.3584,13.7358,46.6709,21.0051,38.0501,,,,,Fall,2,2,4,1,2,0,0,2,0,2,2,0,5,1,0,1,0,2,0,3,29,Fall,71,97,Summer,2,0 +269d9918,Summer,12,1,Summer,63,Summer,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.5,Spring,5,4,4,1,5,1,0,0,0,0,2,0,1,0,1,2,2,2,1,0,31,Spring,48,67,Summer,0,1 +269e06d1,Summer,20,1,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +26a4b61b,Fall,13,1,Summer,80,Winter,25.27993056,63.5,145.0,,77,88,137,,,,,Winter,5,0,25.9,3,21.7,2,0,0,11.0,1,13.0,1,9.0,1,Winter,2,2.41731,25.2827,1396.98,2095.46,29.7539,91.0594,15.8774,9.40527,53.9406,3,37.9197,23.3858,88.6421,55.5036,67.6736,,,Winter,2.05,Winter,0,4,4,1,4,4,0,4,1,2,4,0,4,1,3,0,4,2,3,0,45,Winter,28,41,Fall,0,1 +26aeacdf,Winter,9,0,Spring,80,Winter,14.73488,50.0,52.4,,69,75,101,Winter,6,9,10,Winter,10,1,,,,,3,0,9.0,1,8.0,1,12.0,1,Winter,4,6.06412,14.7365,973.238,2043.8,8.65651,45.9257,12.9157,1.82078,6.47433,1,24.588,12.6811,39.8615,20.8958,33.2445,,,Winter,2.09,Winter,2,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,10,Winter,33,47,Winter,0,0 +26b1792f,Spring,5,0,Fall,70,Spring,14.07391235,44.25,39.2,,53,93,99,Spring,,,,Spring,4,1,,,,,1,0,12.0,1,10.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,2,0,0,8,Spring,47,66,Spring,0,0 +26b893f4,Fall,10,0,Spring,50,Winter,18.55034209,60.63,97.0,,72,99,111,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.71,Fall,4,4,4,1,4,2,0,2,3,3,3,1,3,2,3,3,3,3,3,3,54,Fall,32,46,Fall,0,2 +26c7183b,Spring,15,0,,,Spring,19.6934167,72.64,147.8,34.0,54,55,80,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +26d31613,Summer,15,0,Fall,45,Fall,17.73723077,65.0,106.6,,61,48,109,,,,,Fall,8,0,33.6,3,35.9,3,15,1,9.5,1,9.0,1,11.0,1,Fall,4,-1.16854,17.7392,1406.73,2954.14,26.8372,92.0987,15.326,2.41314,14.5013,2,42.0295,23.232,93.2673,44.6355,68.8667,Fall,3.03,,,Fall,1,2,3,1,2,3,3,1,1,1,3,1,3,1,1,1,1,2,1,1,33,Fall,31,45,Summer,2,1 +26d541c2,Winter,10,1,Spring,45,Spring,21.29237731,53.1,85.4,,102,89,149,Spring,4,5,15,Spring,0,0,8.1,1,9.1,1,0,0,7.5,0,8.0,0,6.0,0,Spring,3,6.57103,21.2947,1091.47,1746.35,18.6027,58.519,14.5919,6.70284,26.881,2,26.0718,13.8445,51.948,25.2204,44.6745,,,Spring,3.11,Spring,5,3,4,0,3,0,0,0,0,0,1,0,0,0,0,3,0,4,0,0,23,Spring,77,100,Winter,0,0 +26f0e055,Summer,6,0,Fall,70,Summer,17.4916869,46.5,53.8,,63,92,95,Summer,6,9,59,Summer,11,1,,,,,2,0,11.0,1,10.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,3,2,4,0,3,1,0,3,0,2,3,0,3,3,3,2,3,3,2,1,41,Summer,38,54,Summer,0,1 +2708b80d,Spring,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +2715347c,Winter,15,0,Summer,70,Spring,24.40514168,73.0,185.0,39.0,53,65,112,,,,,Winter,19,0,34.6,2,43.4,2,8,0,5.0,0,5.0,0,8.0,0,Spring,3,8.32921,24.4078,1946.47,3308.99,47.755,149.587,19.7357,4.67214,35.4127,3,63.0505,38.7818,141.258,75.5302,110.806,Spring,2.88,,,Spring,4,2,3,0,3,3,0,3,0,2,0,0,2,2,3,3,2,3,0,3,38,Spring,42,59,Winter,0,1 +273c8ebd,Fall,6,0,,,Fall,15.41089143,46.5,47.4,,47,75,102,Fall,,,,Fall,8,1,,,,,0,0,8.0,1,8.0,1,11.0,1,Fall,5,6.41026,15.7495,916.718,2200.12,7.12234,39.9055,13.2593,2.49018,7.4945,2,22.4267,10.3565,33.4952,16.0744,29.549,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,29,42,Fall,0,0 +273f11aa,Winter,8,0,,,Spring,19.32353316,56.0,86.2,,56,94,114,Spring,5,8,33,Spring,3,0,,,,,0,0,4.5,0,4.0,0,5.0,0,Summer,3,3.82133,19.3256,1140.22,1938.37,14.8898,63.7112,14.2838,5.04189,22.4888,1,31.503,17.3184,59.8898,31.5934,46.3928,,,Winter,2.961,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +275769c4,Summer,9,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2760be91,Fall,8,0,Winter,65,Winter,26.6638546,54.0,110.6,,71,96,130,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.47,Fall,4,5,5,0,5,0,0,2,2,3,1,0,1,0,5,4,5,5,5,5,57,Fall,38,54,Fall,0,2 +27724073,Spring,20,0,Summer,71,Spring,28.20099801,78.5,247.2,,65,59,102,,,,,,,,,,,,,,,,,,,,Spring,5,-2.06305,28.2041,2384.93,5723.84,64.7677,196.29,22.3955,5.80857,50.9103,3,78.9944,52.5275,198.353,97.3566,143.762,,,,,Spring,4,4,4,2,3,1,1,0,0,0,5,0,0,0,0,0,4,0,4,0,32,Spring,27,40,Spring,,1 +277f5b32,Summer,12,0,Fall,61,Summer,18.82477226,59.5,94.8,,77,67,123,Summer,6,9,3,Summer,11,0,20.0,2,20.2,2,20,1,4.0,0,5.0,0,6.0,0,Summer,4,6.92559,18.8268,1203.41,2527.16,16.8539,70.4422,13.9895,4.83735,24.3578,2,33.5008,20.0875,63.5166,35.1605,50.3546,,,Summer,2.49,Summer,3,4,4,3,5,5,0,4,4,4,4,1,3,5,4,5,4,4,3,3,72,Summer,49,68,Summer,0,2 +278e8a51,Winter,6,0,Summer,55,Spring,16.16365173,45.5,47.6,,71,64,138,Spring,,,,Spring,2,1,,,,,1,0,7.0,0,7.2,0,9.0,1,Summer,1,2.55747,16.1654,913.898,1188.07,7.4117,39.6051,13.4503,2.71515,7.99491,2,22.902,9.29142,37.0476,16.6045,30.3137,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,0,0 +279d0ec4,Spring,9,0,Summer,65,Spring,16.872,55.0,72.6,,146,74,170,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,3.78857,16.8738,1107.19,1771.5,15.0186,60.1932,13.9902,2.88361,12.4068,3,30.2142,14.9605,56.4046,26.9054,45.2327,,,Spring,4.19,Summer,2,2,1,0,1,1,0,1,1,2,2,0,1,1,1,1,1,1,1,1,21,Spring,52,72,Spring,0,0 +27cd2a6c,Spring,10,0,Fall,75,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.23,Spring,5,5,5,3,5,1,0,1,1,2,1,0,4,0,5,4,4,5,4,2,57,Spring,35,50,Spring,3,2 +27d363de,Winter,11,1,Summer,51,Spring,17.6128655,57.0,81.4,,78,72,117,Spring,5,7,44,Spring,13,0,19.6,2,23.1,3,10,1,6.0,0,6.0,0,8.0,0,Spring,1,7.56532,17.6148,1117.14,1452.28,17.9835,61.2528,13.255,4.3598,20.1472,2,25.8115,17.4579,53.6875,26.7272,43.795,,,Spring,1.27,Spring,0,4,3,1,3,2,2,3,2,0,4,1,1,2,3,0,4,1,1,2,39,Spring,45,63,Winter,1,1 +27d7e5e4,Spring,7,0,Fall,58,Spring,13.62223103,52.2,52.8,,80,78,112,Spring,7,12,12,Spring,4,1,,,,,0,0,7.0,0,8.5,1,3.0,0,Spring,1,3.10615,13.6237,994.255,1292.53,9.04194,48.1642,12.4276,1.19616,4.63582,1,23.7156,15.4066,45.058,18.4967,32.7575,,,,,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,42,59,Spring,0,0 +27e0ca6b,Winter,9,1,Summer,59,Spring,18.26462545,58.0,87.4,,67,83,108,Spring,2,1,15,Spring,8,0,,,,,1,0,14.0,1,15.0,1,9.0,1,Spring,2,3.44596,18.2666,1127.62,1691.43,15.8527,62.3695,13.0352,5.23138,25.0305,1,25.9447,20.5721,58.9235,35.3922,41.7974,,,Spring,3.27,Spring,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,1,6,Spring,28,41,Spring,2,0 +27f22533,Spring,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +27f8e149,Fall,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2805045a,Winter,17,0,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.24,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +2808658d,Summer,11,0,,,Summer,15.47761983,55.0,66.6,24.0,65,77,109,,,,,Summer,11,0,13.7,2,16.8,2,8,1,4.0,0,4.0,0,10.0,1,Summer,2,3.69274,15.4793,1079.72,1727.55,13.6634,57.2672,13.3102,2.16918,9.33292,2,28.5265,15.0773,53.5744,24.484,42.1899,,,Summer,1.63,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +281174c9,Winter,13,0,Spring,95,Winter,18.91670446,63.0,106.8,30.0,66,66,123,,,,,Winter,35,1,17.2,1,18.7,1,12,1,9.0,1,9.5,1,11.0,1,Winter,4,4.86538,18.9188,1285.34,2699.22,21.0528,79.1689,14.0241,4.89463,27.6311,2,35.3381,22.778,74.3035,33.0093,56.3909,,,Winter,3.97,Winter,2,1,3,1,2,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,25,Winter,30,43,Winter,0,0 +2812951b,Winter,10,0,Summer,61,Spring,32.11771029,61.0,170.0,,76,68,120,Spring,5,7,36,Spring,0,0,25.1,,27.6,,10,1,10.0,1,11.0,1,12.0,1,Spring,3,5.46123,32.1212,1595.27,2711.96,34.8257,112.18,21.1963,10.9249,57.8195,3,50.2268,27.128,106.719,53.2138,85.0525,,,Winter,2.47,Spring,2,2,1,1,1,1,0,1,0,1,1,0,1,1,0,1,1,1,1,1,18,Spring,31,45,Winter,0,0 +282fa982,Summer,21,0,,,Summer,23.78722449,70.0,165.8,33.0,73,70,139,,,,,Summer,20,0,23.3,1,37.4,2,11,0,7.5,0,8.5,1,12.0,1,Fall,3,7.07287,23.7898,1706.35,2900.79,38.1732,124.011,17.7938,5.99604,41.7886,2,53.4352,32.403,116.939,60.4135,91.6084,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2840643b,Summer,6,0,Spring,60,Fall,13.87117769,44.0,38.2,,74,77,101,Fall,3,3,52,Fall,7,1,,,,,5,1,8.0,1,7.5,1,6.0,1,Fall,3,2.33807,13.8727,872.64,1483.49,5.08299,35.2106,12.7871,1.08562,2.98937,1,20.7225,9.40512,32.8726,14.5726,25.8055,,,,,Fall,5,5,5,0,5,1,0,1,1,3,0,0,1,0,1,2,2,5,1,2,40,Fall,45,63,Summer,0,1 +284e8ef9,Winter,11,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +287390e6,Fall,13,0,Spring,80,Fall,22.87130525,58.2,110.2,,70,120,118,,,,,Fall,80,1,23.4,2,24.7,2,30,1,13.0,1,14.0,1,12.0,1,Winter,3,5.18524,22.8738,1300.75,2211.27,23.2997,80.8098,16.7734,6.10041,29.3902,2,38.2575,19.2527,75.6246,36.6823,61.5572,,,Fall,3.93,Fall,4,4,5,3,5,2,2,1,2,4,1,1,3,1,4,1,3,2,1,2,51,Fall,34,49,Fall,2,2 +287f1349,Winter,17,1,Winter,75,Spring,24.86561647,69.0,168.4,,53,64,95,,,,,Spring,13,0,20.2,2,24.4,2,0,0,7.0,0,5.0,0,7.0,0,Spring,3,3.37167,25.9859,1453.95,2326.32,33.858,97.1278,14.9878,10.998,71.2722,2,35.8369,27.4328,93.7561,42.6476,69.695,Fall,2.14,,,Spring,0,0,3,1,1,1,1,1,2,1,3,1,2,2,1,1,2,0,1,1,25,Spring,31,45,Winter,2,0 +2891e36b,Spring,15,0,Fall,75,Summer,21.17392103,66.0,131.2,,68,69,111,,,,,Summer,9,0,0.0,1,0.0,1,4,0,4.0,0,0.0,0,3.0,0,,,,,,,,,,,,,,,,,,Fall,1.47,,,Summer,1,2,2,0,2,1,1,1,1,1,1,0,1,0,1,0,0,0,0,0,15,Summer,31,45,Spring,1,0 +2898269f,Summer,7,0,Fall,71,Fall,18.37386364,44.0,50.6,,61,82,103,,,,,Summer,1,0,,,,,0,0,9.0,1,7.0,0,7.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,40,56,Summer,0,0 +2898e9d3,Spring,10,1,Spring,63,Winter,22.24632937,62.9,125.2,,84,83,144,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.78643,22.2488,1324.85,2119.76,28.5928,83.3767,14.8165,7.43224,41.8233,3,32.9481,21.8358,78.5903,36.5465,61.5409,,,Summer,3.42,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +28b91cf3,Summer,9,1,Winter,65,Fall,14.67864,50.0,52.2,,56,77,100,Fall,4,6,35,Fall,11,1,,,,,11,1,8.0,0,8.0,0,12.0,1,Fall,3,7.74284,14.6802,984.657,1575.45,12.8974,47.1419,13.2578,1.42248,5.05806,2,22.3464,11.8981,39.3991,19.6451,35.2438,,,Fall,2.2,Fall,2,1,1,0,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,0,17,Fall,27,40,Summer,0,0 +28ba92e9,Fall,6,0,Winter,85,Winter,15.46890496,44.0,42.6,,56,105,83,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,57,79,Fall,0,0 +28e370f4,Spring,6,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +28e8b2b2,Summer,10,1,,,Summer,19.55088757,52.0,75.2,27.0,64,64,108,,,,,Summer,7,0,,,,,8,1,7.0,0,7.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.229,Summer,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,22,Summer,45,63,Summer,0,0 +28e973b1,Spring,6,0,Summer,45,Summer,14.4996765,50.5,52.6,,76,86,111,Summer,4,6,40,Summer,11,1,,,,,9,1,12.0,1,12.0,1,9.0,1,Summer,2,2.94349,14.5013,976.033,1561.65,8.29802,46.2234,12.7433,1.75797,6.37663,2,23.716,14.2094,43.2799,19.2677,32.014,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,65,89,Spring,2,0 +28f7e6ff,Fall,10,0,Spring,75,Winter,16.872,55.0,72.6,,80,101,118,Winter,5,7,30,Winter,7,0,11.1,1,11.9,1,6,0,13.0,1,12.0,1,13.0,1,Winter,2,6.37815,16.8738,1086.31,1738.1,13.182,57.9698,13.4735,3.40039,14.6302,1,28.5634,16.2244,51.5916,25.3902,41.7454,,,Winter,1.11,Spring,5,5,5,0,5,0,0,4,0,4,4,0,3,1,5,5,5,5,5,5,66,Winter,41,58,Fall,2,2 +28f99ddb,Fall,12,0,Spring,55,Fall,18.17851197,62.0,99.4,29.0,72,72,121,,,,,Fall,10,0,13.7,2,15.3,2,0,0,11.0,1,11.0,1,5.0,0,Fall,2,4.67733,18.1805,1249.74,1999.59,20.6912,75.3772,13.7867,4.39382,24.0228,2,32.9505,21.7355,70.6999,26.7658,53.6417,,,,,Fall,1,2,1,0,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,9,Fall,28,41,Spring,1,0 +28fd1da9,Summer,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2911bcab,Fall,9,0,,,Fall,15.18313609,52.0,58.4,22.0,55,80,101,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +2911ef94,Winter,13,1,,,Spring,18.33623669,65.0,110.2,28.0,60,79,103,,,,,Spring,22,1,24.1,2,22.8,2,8,1,10.0,1,10.0,1,12.0,1,Spring,3,4.78317,18.3382,1280.48,2048.77,25.1389,78.6509,13.0882,5.25005,31.5491,2,30.2567,23.2553,73.8677,33.2818,55.3956,,,Winter,1.44,Winter,5,4,4,2,3,4,3,4,1,3,4,0,3,3,3,3,3,3,3,3,61,Winter,43,60,Spring,2,2 +29587a31,Summer,8,1,Fall,60,Summer,14.03559028,48.0,46.0,,70,85,113,Summer,3,4,19,Summer,2,0,,,,,2,0,12.5,1,11.5,1,7.0,1,Fall,3,9.23504,14.0371,949.99,1519.98,11.2755,43.4494,13.2588,0.77834,2.55064,1,20.6164,11.5575,34.2143,16.2833,31.8919,,,,,Summer,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Summer,53,73,Summer,0,0 +29886a85,Winter,9,1,Spring,63,Winter,16.22821243,58.5,79.0,,61,81,101,Winter,6,9,51,Winter,15,1,,,,,10,1,6.0,0,5.5,0,8.5,1,Spring,3,3.61234,16.23,1156.89,1851.02,20.5435,65.4867,13.4538,2.77622,13.5133,2,28.1107,16.8324,61.8743,29.126,48.6542,,,Winter,2.73,Winter,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,Winter,31,45,Winter,0,0 +2995b1c3,Winter,12,0,,,Winter,16.75505482,57.5,78.8,24.0,71,92,119,,,,,Winter,10,0,39.2,2,40.0,2,8,0,7.0,0,7.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.33,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +29a1d334,Fall,9,0,Winter,80,Winter,23.28935275,54.5,98.4,,65,62,142,Winter,,,,Fall,12,1,,,,,10,1,6.5,0,8.0,1,0.0,0,Fall,2,6.87935,23.2919,1186.33,1898.13,18.8976,68.6231,16.2435,7.04838,29.7769,3,33.4427,16.2828,61.7438,28.7242,52.3403,,,Winter,2.57,Winter,1,1,2,2,1,1,0,1,1,0,0,0,2,0,0,0,0,0,0,0,12,Fall,31,45,Fall,0,0 +29b37b4a,Winter,13,0,Spring,78,Winter,16.58262339,65.5,101.2,,83,86,142,,,,,Winter,33,1,31.6,3,30.4,2,11,0,11.0,1,11.0,1,11.0,1,Winter,3,-2.8706,16.5844,1409.81,2396.67,24.9662,92.4261,15.1466,1.43786,8.77395,1,44.3184,23.1415,95.2967,56.9826,69.2845,,,Winter,2.96,Winter,4,4,4,2,5,4,2,3,3,4,4,0,0,4,4,2,1,0,1,2,53,Winter,50,69,Winter,3,2 +29c54890,Fall,11,0,Winter,80,Fall,17.25405405,55.5,75.6,,58,78,113,Fall,5,8,20,Fall,30,1,20.3,2,21.0,2,17,1,11.0,1,11.0,1,15.0,1,Fall,1,2.23417,17.2559,1146.88,1490.94,15.2285,64.4204,14.7042,2.55178,11.1796,1,34.3322,14.8597,62.1862,40.5513,49.5606,,,Winter,1.68,Fall,3,3,2,2,4,1,2,1,0,0,2,0,2,0,0,2,1,3,1,1,30,Fall,30,43,Fall,2,0 +29e4911b,Spring,7,1,Winter,75,Spring,15.79775281,44.5,44.5,23.0,81,81,109,,,,,Spring,0,0,,,,,0,0,7.0,0,6.0,0,2.0,0,Spring,2,2.09459,16.119,910.258,1365.39,9.751,39.2175,13.9239,2.19508,6.18255,1,20.3851,9.08138,37.1229,17.1273,30.1361,,,,,Spring,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,27,40,Spring,0,0 +2a004ad9,Winter,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +2a0b8386,Spring,14,0,Summer,70,Summer,17.19950983,68.5,114.8,,83,97,134,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.41,Summer,0,2,4,1,3,0,0,2,0,0,4,0,1,0,1,0,0,0,1,0,19,Summer,29,42,Spring,0,0 +2a2550a4,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2a320cba,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2a3a97de,Spring,6,0,,,Spring,15.89259059,43.0,41.8,22.0,57,66,93,,,,,Spring,0,0,,,,,0,0,10.0,1,10.0,1,6.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +2a3fcf9a,Winter,6,1,Summer,60,Spring,19.03165348,49.0,65.0,,86,86,132,Spring,2,2,47,Spring,6,1,,,,,0,0,11.0,1,12.5,1,3.5,0,Spring,2,2.30539,19.0337,1001.56,1502.35,9.99568,48.9428,14.3318,4.70197,16.0572,1,20.9928,17.9543,46.6374,26.2235,30.9885,,,,,Spring,4,0,0,0,1,0,0,2,0,0,0,0,2,0,0,2,0,3,0,0,14,Spring,36,51,Winter,0,0 +2a64085d,Fall,6,0,Winter,75,Fall,15.10820491,49.0,51.6,,74,107,112,Fall,5,8,54,Fall,2,1,,,,,1,0,9.0,1,9.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,2,0,3,1,0,2,1,1,1,0,1,1,1,2,2,2,1,1,26,Fall,43,60,Fall,0,0 +2a6a6b92,Summer,11,1,Winter,75,Summer,16.75066818,53.5,68.2,,43,68,92,Summer,5,8,11,Summer,6,0,13.0,2,15.7,2,9,1,10.0,1,10.0,1,8.0,0,,,,,,,,,,,,,,,,,,,,Summer,2.75,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,47,66,Summer,2,0 +2a7b5b79,Spring,10,0,Summer,55,Summer,17.60902281,54.5,74.4,,52,65,110,Summer,8,13,21,Summer,12,1,,,,,20,1,10.5,1,11.0,1,11.0,1,Summer,3,3.63573,17.6109,1093.52,1858.98,13.6131,58.7369,13.9034,3.70755,15.6631,2,29.7002,15.4237,55.1012,27.9396,43.3132,,,Spring,3.01,Summer,1,2,2,0,3,1,0,0,1,2,2,0,2,2,3,2,2,2,2,3,32,Summer,28,41,Spring,1,1 +2a81925b,Fall,9,0,,,Winter,14.58117451,49.0,49.8,21.0,59,81,106,,,,,Winter,0,0,,,,,0,0,11.0,1,12.0,1,7.0,1,Winter,2,2.91444,14.5828,959.088,1534.54,9.77192,44.4185,13.0069,1.57585,5.38152,1,23.4346,11.212,41.504,15.568,33.2065,,,Winter,1.95,Winter,4,4,3,1,2,1,0,2,1,2,2,0,1,1,2,1,1,1,2,1,32,Winter,46,64,Fall,1,1 +2a85889c,Fall,7,0,Fall,60,Fall,18.35495562,52.0,70.6,,66,88,113,Fall,6,9,28,Fall,9,1,,,,,0,0,6.5,0,7.0,0,8.0,1,,,,,,,,,,,,,,,,,,,,Fall,1.42,Fall,5,5,5,0,5,0,0,0,0,4,0,0,5,0,0,5,3,5,0,0,42,Fall,64,88,Fall,0,1 +2a880860,Summer,20,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,93,100,,, +2a88cbe9,Summer,10,0,Fall,65,Summer,20.31231231,55.5,89.0,,64,95,104,,,,,Summer,15,1,,,,,3,0,8.0,1,7.5,1,6.0,0,Summer,2,3.85733,20.3145,1154.36,1846.98,16.5505,65.2179,14.8862,5.42833,23.7821,2,32.0289,16.6385,61.3606,29.3393,48.5794,,,Summer,1.89,Summer,3,2,3,0,2,0,2,1,1,1,1,0,0,1,2,2,2,1,0,2,26,Summer,41,58,Summer,0,0 +2a8ada58,Winter,16,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2a8ea138,Summer,10,1,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +2a922ca4,Fall,6,0,Spring,78,Fall,14.92790123,45.0,43.0,,54,65,110,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Fall,34,49,Fall,0,0 +2a9e0dee,Spring,9,1,Winter,65,Spring,22.15328676,54.5,93.6,,76,99,122,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.77,Summer,1,2,2,1,3,1,1,1,1,1,2,0,1,2,2,2,2,1,0,1,27,Spring,35,50,Spring,2,0 +2aa01109,Fall,6,0,,,Fall,16.34667724,43.5,44.0,23.0,61,98,108,,,,,Fall,0,0,,,,,0,0,7.0,0,7.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +2aa15e6b,Spring,8,1,Fall,61,Summer,14.97354864,51.0,55.4,,58,79,105,Summer,7,12,0,Summer,31,,,,,,20,,13.0,,15.0,,6.0,,,,,,,,,,,,,,,,,,,,,,,Summer,4,2,3,1,3,0,0,0,3,2,2,0,3,1,2,1,3,1,2,1,34,Summer,37,53,Spring,0,1 +2aab37ac,Spring,11,0,Summer,41,Spring,16.77904132,55.0,72.2,,62,93,107,Spring,5,8,0,Spring,18,1,24.4,1,35.4,2,13,1,10.0,1,8.3,1,21.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.38,Spring,1,1,2,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,19,Spring,34,49,Spring,0,0 +2ac0bd9f,Summer,10,0,Fall,48,Fall,26.67633929,56.0,119.0,,62,81,103,Fall,5,7,4,Fall,4,0,18.4,,18.2,,0,0,4.0,0,4.0,0,11.0,1,Fall,3,3.95549,26.6793,1239.3,2106.81,19.4614,74.2645,16.6498,10.0295,44.7355,3,35.4565,19.3466,70.309,33.8533,54.918,,,Fall,2.67,Fall,4,5,3,1,5,4,1,2,1,1,2,0,0,0,0,0,2,1,0,0,32,Fall,50,69,Fall,2,1 +2af3c8d8,Summer,15,0,Fall,60,Fall,30.13080864,67.0,192.4,,61,77,132,,,,,Fall,12,0,17.5,2,22.6,2,0,0,5.0,0,4.0,0,13.0,1,Fall,3,10.4848,29.866,1683.55,2862.04,36.7662,121.583,18.8733,10.9928,70.8166,3,52.4831,32.3341,111.099,57.6121,89.2493,Summer,1.9,,,Fall,3,4,3,4,3,4,0,3,1,1,4,1,1,3,3,1,3,2,2,2,48,Summer,45,63,Summer,2,1 +2af988a1,Winter,14,0,,,Winter,24.12569238,61.5,129.8,34.0,69,92,111,,,,,Winter,0,0,43.0,1,46.8,1,0,0,4.0,0,4.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +2afea280,Fall,10,0,Spring,69,Winter,18.63772783,51.75,71.0,,69,109,110,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.34,Winter,3,3,2,0,3,2,0,2,1,1,2,0,1,3,2,2,2,2,1,2,34,Winter,50,69,Fall,2,1 +2afee795,Spring,6,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2b34dcf3,Summer,9,0,Spring,80,Winter,15.96096939,56.0,71.2,,73,86,114,Winter,6,10,1,Winter,18,1,,,,,26,1,14.0,1,13.0,1,11.5,1,Winter,3,3.05282,15.9627,1116.75,1898.48,15.6455,61.2118,13.7234,2.2393,9.98816,2,30.1651,15.4012,58.159,26.0233,45.8106,,,Summer,4.16,Winter,2,3,4,1,1,1,0,1,0,1,2,1,0,0,0,2,3,2,2,1,27,Winter,29,42,Summer,0,0 +2b3a6805,Spring,14,0,,,Spring,25.33769513,66.0,157.0,36.0,83,67,136,,,,,Summer,20,0,56.8,2,79.2,3,7,0,7.0,0,6.0,0,12.0,1,Summer,2,6.41027,25.3405,1651.36,2642.18,36.8785,118.155,19.0707,6.26973,38.8449,2,51.8107,29.4658,111.745,56.7482,88.6892,Summer,2.63,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2b4c6c98,Summer,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2b574ef7,Winter,9,1,Summer,60,Spring,13.48644263,53.3,54.5,,63,87,108,,,,,Spring,14,1,,,,,2,0,8.5,1,8.0,0,7.0,1,Spring,4,2.99054,13.4879,1025.11,1947.72,13.9782,51.4512,12.7334,0.754536,3.04882,1,22.9306,14.5423,48.4606,20.9337,36.9089,,,Winter,3.52,Spring,1,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,Spring,34,49,Winter,0,0 +2bac0893,Winter,14,1,Summer,60,Spring,20.79420509,63.0,117.4,,49,70,107,,,,,Spring,18,1,26.6,2,29.6,3,4,0,16.0,1,16.0,1,17.0,1,Spring,3,4.94328,20.7965,1340.15,2144.23,29.2007,85.0063,15.0582,5.73829,32.3937,2,34.322,21.4835,80.063,39.0795,63.5228,Spring,4.42,,,Spring,1,2,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,17,Spring,34,49,Winter,2,0 +2bad0db7,Summer,11,0,Fall,68,Summer,18.5452562,55.0,79.8,,62,68,106,Summer,4,5,24,Summer,23,1,19.4,2,19.2,2,20,1,11.5,1,11.0,1,11.0,1,Summer,1,4.4868,18.5473,1131.52,1470.97,16.0914,62.7847,14.5925,3.95474,17.0153,2,31.3427,15.3506,58.2979,28.1708,47.4341,,,Summer,3.16,Summer,3,2,4,2,2,1,0,,1,1,1,1,2,1,2,1,1,3,0,1,29,Summer,45,63,Summer,2,0 +2bbbdba7,Winter,17,0,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,80,100,,, +2bbcfd43,Winter,10,1,Spring,55,Winter,15.57057851,55.0,67.0,,72,77,110,Winter,6,10,26,Winter,8,0,9.7,1,9.7,1,3,0,6.0,0,9.0,1,11.0,1,Spring,2,3.31636,15.5723,1075.01,1612.52,16.5395,56.7659,13.1936,2.37864,10.2341,1,25.3609,14.8654,53.4495,25.5261,41.9005,,,Winter,2.05,Winter,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,1,1,1,1,1,9,Winter,26,38,Winter,0,0 +2bca454a,Spring,9,0,Summer,68,Summer,13.80467836,57.0,63.8,,81,87,131,Summer,6,10,16,Summer,5,0,,,,,20,1,6.0,0,7.0,0,9.0,1,Summer,4,3.78855,13.8062,1080.97,2270.04,12.599,57.4006,12.4214,1.38482,6.39941,2,27.4634,17.3382,53.612,24.1424,40.0624,,,Spring,3.34,Summer,3,3,2,0,2,1,0,0,0,0,0,0,0,0,0,0,2,2,1,0,16,Summer,36,51,Spring,1,0 +2bcbfb15,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,,,Summer,0, +2bd186e8,Fall,13,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.54,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +2beaaf7a,Winter,16,1,Fall,70,Spring,23.64243506,61.5,127.2,30.0,80,74,127,,,,,Spring,13,,24.5,2,24.4,2,1,,15.0,,15.0,,13.0,,Spring,3,4.5763,23.645,1309.66,2095.45,27.7759,81.7588,15.198,8.447,45.4412,2,33.0173,20.9655,77.1825,38.1664,60.7932,Winter,2.69,,,Spring,4,5,3,2,5,4,1,2,2,2,5,1,1,2,2,1,2,1,2,1,48,,,,Spring,0,1 +2bfc9187,Winter,7,1,Fall,60,Spring,21.9336,50.0,78.0,,74,78,117,Spring,5,8,30,Spring,2,1,,,,,0,0,14.0,1,14.0,1,7.0,1,Summer,2,3.1482,27.5606,1132.61,1698.91,20.8133,62.9004,17.6895,9.8711,35.0996,2,30.8337,11.2533,59.7522,37.5047,51.647,,,,,Spring,3,3,3,0,5,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,20,Spring,47,66,Spring,3,0 +2c2dc20d,Summer,7,0,Winter,50,Summer,17.56824298,51.0,65.0,,68,97,118,Summer,4,6,56,Summer,7,1,,,,,6,1,17.0,1,18.0,1,10.0,1,Summer,2,3.14951,17.5702,1021.0,1633.6,11.0038,51.0132,13.7894,3.78077,13.9868,2,26.6427,13.3667,47.8637,22.4909,37.6465,,,,,Summer,4,4,4,4,4,3,3,3,2,4,2,1,3,3,3,3,4,4,4,3,65,Summer,51,71,Summer,0,2 +2c30ef79,Spring,15,1,,,Spring,30.63692322,62.75,171.6,36.0,86,99,140,,,,,Spring,16,0,29.9,3,24.8,2,1,0,11.0,1,11.0,1,9.0,1,Spring,,,,,,,,,,,,,,,,,Spring,1.47,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +2c3b09db,Spring,7,0,Summer,55,Summer,16.75796066,49.75,59.0,,64,73,129,Summer,5,7,28,Summer,1,0,,,,,4,1,10.0,1,12.0,1,12.0,1,Summer,1,3.23648,16.7598,1021.95,1328.53,12.8543,51.1137,14.5196,2.24021,7.88629,1,28.0147,10.2447,47.8772,22.773,40.869,,,,,Summer,1,1,5,2,2,1,0,1,1,1,4,1,5,1,4,3,5,2,5,1,46,Summer,44,62,Spring,3,1 +2c406043,Winter,7,1,Winter,70,Spring,14.51762065,49.5,50.6,,47,83,96,Spring,6,9,21,Spring,8,1,,,,,8,1,12.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,7,Spring,31,45,Winter,0,0 +2c483245,Fall,8,0,Spring,60,Winter,24.72637949,53.0,98.8,,62,84,115,Winter,5,7,23,Winter,9,1,,,,,4,0,13.0,1,14.0,1,7.0,1,Spring,2,3.64973,24.7291,1164.99,1863.98,16.1812,66.3493,16.6069,8.12222,32.4507,2,34.6825,15.4856,62.6996,38.7978,50.8638,,,Fall,1.83,Winter,2,3,3,1,3,2,0,3,0,0,1,0,0,0,2,2,2,1,1,0,26,Winter,34,49,Fall,3,0 +2c49257f,Winter,9,0,Winter,80,Winter,15.62222222,57.0,72.2,,86,83,113,Winter,6,9,44,Winter,12,1,,,,,2,0,6.5,0,6.0,0,8.0,1,Winter,3,6.95775,15.3534,1106.11,1880.39,13.5174,60.0782,12.7757,2.5777,12.1218,3,28.058,18.5028,53.1205,24.1345,41.5754,,,Winter,2.98,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,26,38,Winter,2,0 +2c512f2f,Fall,9,1,Winter,70,Fall,20.41512,50.0,72.6,,65,88,108,Fall,3,3,57,Fall,0,0,,,,,0,0,11.0,1,12.0,1,9.0,1,Fall,2,7.06722,20.4174,1026.56,1539.84,16.0204,51.6048,14.5129,5.90449,20.9952,1,24.041,11.5434,44.5376,21.1759,40.0614,,,Fall,2.57,Fall,4,4,3,0,4,2,0,2,0,1,4,0,4,0,3,1,3,1,4,1,41,Fall,49,68,Fall,1,1 +2c54d6c5,Winter,10,1,Winter,65,Spring,28.22540608,49.0,96.4,,67,74,114,Spring,4,6,16,Spring,1,0,12.2,2,12.6,2,2,0,7.5,0,7.9,0,6.5,0,Spring,2,2.81833,28.2285,1071.56,1607.33,18.7676,56.3979,16.5148,11.7137,40.0021,2,26.5838,11.0465,53.5795,26.4311,45.3513,,,Winter,2.76,Spring,2,2,1,0,2,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,11,Spring,51,71,Winter,0,0 +2c56c930,Spring,9,1,Summer,45,Spring,20.97517687,53.5,85.4,,105,106,173,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.22702,20.9775,1095.73,1753.17,18.657,58.9731,14.486,6.49144,26.4269,2,26.1629,14.1533,55.7461,25.5559,44.8198,,,Spring,3.71,Spring,2,2,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,23,Spring,45,63,Spring,0,0 +2c81a9d4,Spring,21,0,Summer,,Spring,25.43517481,67.0,162.4,34.0,75,93,127,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2c86162c,Summer,12,0,,,Summer,19.95738889,60.0,102.2,28.0,81,77,138,,,,,Summer,14,0,24.5,3,31.5,3,12,1,10.5,1,6.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.25,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +2c913f37,Winter,17,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2c9a90e9,Fall,15,1,Spring,75,Winter,27.89618675,62.7,156.0,,74,90,109,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.25,,,Winter,0,3,4,4,2,1,1,2,0,1,4,1,2,0,2,1,3,4,5,2,42,Winter,54,75,Fall,3,1 +2ca2206f,Winter,13,0,Fall,68,Summer,16.57306943,69.6,114.2,,74,65,120,,,,,Summer,23,1,19.3,1,22.9,2,19,1,6.0,0,6.0,0,10.0,1,Summer,3,6.69447,17.8202,1560.3,2652.51,30.6846,108.456,16.4906,1.32957,8.74431,2,50.7932,26.978,101.761,67.9962,81.4777,,,Winter,3.62,Summer,3,3,4,2,4,2,1,2,3,2,1,1,3,1,3,1,3,2,2,2,45,Summer,42,59,Winter,0,1 +2ca9811d,Fall,14,1,,,Fall,16.84166408,63.5,96.6,23.0,69,80,122,,,,,Fall,32,1,23.7,2,25.0,3,6,0,12.0,1,12.0,1,12.0,1,Fall,3,4.79505,16.8435,1273.65,2037.84,26.3119,77.9235,13.587,3.2565,18.6765,1,30.8345,20.7771,73.1284,31.1389,57.1464,,,Winter,2.84,Fall,3,2,2,0,3,1,2,2,1,2,1,0,1,2,2,1,1,2,1,2,31,Fall,47,66,Winter,2,1 +2cb0db2c,Spring,8,1,Summer,79,Spring,17.47100592,39.0,37.8,,39,64,93,Spring,2,1,37,Spring,7,1,,,,,10,1,9.0,1,10.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,Summer,2.62,Spring,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,Spring,32,46,Spring,0,0 +2cc4b720,Spring,7,0,Fall,65,Summer,13.08988658,46.0,39.4,,64,75,109,Summer,4,6,22,Summer,6,1,,,,,5,1,8.0,1,8.5,1,6.5,1,Summer,2,2.50699,13.0913,896.297,1434.08,5.83481,37.7304,12.5366,0.554741,1.66956,1,21.1792,10.7165,35.2234,15.2012,27.014,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,27,40,Spring,2,0 +2cd9b5a3,Spring,12,1,Winter,80,Winter,20.08982993,60.5,104.6,,93,86,187,,,,,Spring,,,,,,,,,,,,,,,Summer,2,4.32902,20.092,1243.46,1865.19,23.947,74.7076,14.3502,5.74186,29.8924,2,31.5862,19.1744,70.3786,38.014,55.5332,,,Spring,2.21,Spring,2,2,3,0,2,1,0,2,1,2,2,0,2,2,3,3,2,3,4,0,36,Spring,44,62,Spring,0,1 +2cd9c56b,Fall,7,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +2cda1052,Fall,9,0,,,Fall,23.62070533,54.5,99.8,32.0,71,76,122,,,,,Fall,0,0,,,,,0,0,0.0,0,0.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.21,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +2cda86bd,Spring,13,0,Spring,65,Winter,18.86405853,63.5,108.2,,88,104,145,,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.99086,18.8661,1301.67,2212.84,21.311,80.908,14.1074,4.75872,27.292,2,36.597,23.0,75.9172,36.7785,57.908,,,Fall,1.45,Fall,4,3,4,4,4,5,2,4,3,4,3,1,2,5,4,4,4,4,5,5,74,Fall,63,86,Spring,3,2 +2cebc0d1,Winter,14,0,,,Winter,26.51759226,69.5,182.2,37.0,60,66,126,,,,,Winter,18,0,34.0,3,29.5,2,8,0,5.0,0,4.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.39,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +2d043af7,Summer,13,0,Fall,85,Winter,16.73899489,61.0,88.6,,66,72,102,,,,,Winter,10,0,14.5,1,18.5,1,2,0,6.0,0,6.0,0,6.0,0,,,,,,,,,,,,,,,,,,,,Winter,1.1,Winter,2,1,3,3,3,1,1,3,1,1,3,0,0,0,0,0,4,0,3,0,29,Winter,29,42,Summer,2,0 +2d0bbbd3,Winter,8,0,Summer,66,Spring,21.16279586,52.0,81.4,,75,104,125,,,,,Spring,4,0,,,,,1,0,12.0,1,12.0,1,9.0,1,Spring,3,3.03562,21.1651,1061.99,1805.38,9.97189,55.379,14.3993,6.76582,26.021,2,26.769,18.6381,52.3434,26.0473,36.7409,,,Winter,2.04,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,73,99,Winter,0,0 +2d0f9f5a,Fall,7,0,,,,,,,,,,,,,,,Fall,0,0,,,,,0,0,10.0,1,9.0,1,7.0,1,Fall,1,2.84806,19.4437,995.789,1294.53,9.52232,48.3276,14.1516,5.29205,18.0723,2,25.1846,13.6207,45.4796,20.3021,34.7069,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +2d286c59,Winter,11,1,Spring,71,Spring,17.52956047,59.0,86.8,,83,94,144,Spring,4,5,47,Spring,13,1,15.5,2,14.0,2,4,0,7.0,0,7.5,0,16.0,1,Spring,3,3.43618,17.5315,1182.67,1892.27,22.7537,68.2328,13.7814,3.75012,18.5672,2,28.0751,17.404,64.7966,26.3419,50.8288,,,Spring,2.54,Spring,2,2,3,0,1,0,0,0,1,1,2,0,0,0,2,0,0,0,0,0,14,Spring,47,66,Winter,0,0 +2d38b5c0,Winter,15,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.12,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +2d3ba6fa,Spring,7,0,Fall,85,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,1,2,1,1,1,2,2,1,1,2,2,2,2,2,2,2,1,33,Spring,28,41,Spring,0,1 +2d5365c6,Spring,11,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.6,Spring,5,1,5,1,5,2,1,4,3,4,4,1,2,5,5,5,4,4,4,2,67,Spring,43,60,Spring,1,2 +2d6668f1,Winter,10,0,Summer,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.647,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +2d6a0178,Spring,13,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.04,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2d87fc7d,Summer,14,0,,,Summer,26.22689043,72.0,193.4,,70,70,128,,,,,Summer,25,1,28.5,2,32.5,2,5,0,4.0,0,5.0,0,12.0,1,Summer,2,8.02906,26.2298,1931.69,3090.7,47.7996,148.013,20.0742,6.15556,45.3868,3,61.7959,38.4177,139.984,70.2512,109.595,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2da6bf6e,Spring,7,0,Fall,65,Summer,13.67620146,51.0,50.6,,,76,,Summer,3,4,50,Summer,6,1,,,,,2,0,11.5,1,10.0,1,6.0,1,Summer,2,3.09676,13.6777,981.295,1570.07,9.40549,46.7838,12.6461,1.03155,3.81617,1,24.602,12.7764,43.6871,19.8,34.0075,,,,,Summer,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,5,Summer,39,55,Spring,0,0 +2dbdfe11,Spring,8,0,Summer,60,Summer,16.78273016,52.5,65.8,,62,83,110,Summer,4,6,15,Summer,10,1,,,,,6,1,11.0,1,12.0,1,10.0,1,Summer,3,3.43611,16.7846,1053.72,1791.32,12.6903,54.4979,13.9016,2.88299,11.3021,1,28.5935,13.2141,51.0618,25.6806,41.2838,,,Summer,2.47,Summer,5,3,5,0,5,2,0,0,2,4,4,0,4,0,2,5,3,4,2,1,51,Summer,67,91,Spring,0,2 +2dc8340e,Winter,12,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.64,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2dca03d9,Spring,6,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +2dcaf7cd,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2e12e513,Spring,9,0,Summer,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.38,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2e16053c,Fall,9,0,Winter,70,Fall,14.78042975,55.0,63.6,,65,85,131,Fall,6,10,10,Fall,7,1,,,,,0,0,8.0,1,6.5,0,12.0,1,Fall,4,2.18214,14.782,1085.75,2280.08,14.1712,57.9098,13.4595,1.32252,5.69016,2,29.579,14.1596,55.7277,26.7782,43.7502,,,Fall,2.52,Fall,5,5,5,1,5,0,0,4,0,1,3,0,3,0,2,4,0,5,0,2,45,Fall,47,66,Fall,2,1 +2e17c497,Spring,8,0,Fall,67,Spring,16.14771912,50.25,58.0,,58,74,98,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.9665,16.1495,988.28,1680.08,9.14774,47.5278,13.2336,2.91587,10.4722,2,24.6493,13.7308,44.5613,20.0915,33.797,,,,,Spring,3,1,2,3,1,1,0,1,2,1,2,0,3,1,3,3,1,3,1,1,33,Spring,52,72,Spring,0,1 +2e20c561,Spring,16,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +2e293d08,Fall,10,1,,,Fall,23.6276148,56.0,105.4,32.0,59,76,89,,,,,Fall,11,0,37.6,2,39.4,2,1,0,9.0,0,7.0,0,8.0,0,Fall,1,3.40238,23.6302,1144.44,1487.77,19.3354,64.1606,14.3845,9.24569,41.2394,2,26.8689,17.9563,60.7582,30.4773,46.2043,,,Fall,2.74,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,32,46,Fall,0,0 +2e42573e,Spring,8,1,Winter,58,Fall,13.3681356,51.38,50.2,,67,77,112,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.58,Spring,2,2,2,0,2,1,0,1,1,1,2,0,1,1,1,1,1,1,1,1,22,Spring,30,43,Spring,0,0 +2e4df75a,Winter,17,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2e4e045a,Spring,8,0,Fall,73,Summer,15.21795889,49.2,52.4,,64,76,107,Summer,7,12,12,Summer,17,1,,,,,10,1,9.5,1,10.0,1,10.0,1,Summer,4,4.60465,15.2196,970.507,2038.07,9.35961,45.6348,13.2547,1.96497,6.76523,1,25.1353,11.1399,41.0301,20.6942,34.4949,,,Summer,3.18,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Summer,37,53,Spring,0,0 +2e5f5207,Winter,11,0,Summer,53,Winter,16.26735493,53.0,65.0,,68,73,101,Winter,6,9,12,Winter,12,0,14.7,2,14.7,2,11,1,8.0,1,8.0,1,9.0,1,Spring,1,3.30731,16.2691,1038.35,1349.85,11.5028,52.8604,13.2307,3.03847,12.1396,2,26.0972,15.2604,49.5531,20.6902,37.6,,,Winter,3.12,Winter,2,3,2,2,2,3,1,2,1,1,1,1,1,1,1,1,2,1,1,2,31,Winter,38,54,Winter,2,1 +2e71d21d,Summer,7,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +2e7d888c,Fall,13,1,Spring,85,Fall,15.9451648,62.5,88.6,,67,66,116,,,,,Fall,26,1,19.1,2,18.3,2,0,0,6.0,0,4.0,0,8.5,1,Fall,1,0.524433,15.9469,1252.32,1628.02,22.9109,75.6518,13.6164,2.33052,12.9482,1,33.1399,19.601,75.1273,44.4333,56.0508,,,Fall,1.71,Winter,4,3,0,1,4,2,0,1,3,4,3,0,2,1,2,4,3,3,0,1,41,Fall,35,50,Fall,2,1 +2e8c9884,Spring,7,0,Summer,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,28,41,Spring,0,0 +2ea277d5,Fall,10,1,Summer,45,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.53,Winter,4,4,4,0,4,0,4,1,3,4,3,0,3,0,3,4,3,4,3,2,53,Winter,42,59,Fall,1,2 +2ebdadbc,Fall,8,1,Summer,70,Winter,14.40318047,52.0,55.4,,77,89,114,,,,,Winter,15,1,,,,,7,1,11.0,1,13.0,1,10.0,1,Winter,3,2.66328,14.4048,1011.34,1618.14,11.1804,49.9839,12.9965,1.40826,5.41609,2,21.5658,17.2377,47.3206,23.1641,32.7462,,,Winter,2.37,Winter,3,0,4,0,3,0,0,2,1,2,0,0,1,0,1,2,1,3,1,0,24,Winter,53,73,Fall,0,0 +2ee0a1e8,Spring,14,0,Winter,59,Summer,17.57084024,65.0,105.6,,76,79,121,,,,,Summer,9,0,15.0,1,16.3,1,10,0,11.0,1,11.0,1,11.0,1,Fall,3,5.7924,17.5728,1411.8,2400.06,25.4446,92.6381,15.4158,2.15697,12.9619,1,44.0006,23.1929,86.8457,54.6235,69.4453,,,Spring,2.499,Summer,3,3,5,0,5,2,0,4,1,1,4,0,1,2,2,1,4,3,5,2,48,Summer,30,43,Spring,3,1 +2f044457,Spring,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2f0f2949,Winter,12,0,,,Spring,39.2213921,61.0,207.6,,64,104,141,Spring,2,2,3,Spring,5,0,18.0,2,17.0,2,1,0,5.0,0,7.0,0,8.5,1,Spring,2,4.67629,39.2257,1523.58,2437.73,27.5494,104.544,19.7535,19.4722,103.056,2,47.1501,29.845,99.8682,58.5045,74.6995,,,Winter,1.21,Spring,1,1,3,3,3,0,0,1,1,1,1,0,1,0,1,1,0,1,1,1,21,Spring,40,56,Winter,3,0 +2f12de00,Winter,14,1,,,Fall,,,,,,,,,,,,Spring,12,0,30.3,3,30.0,3,4,0,6.0,0,6.0,0,12.0,1,Spring,3,4.59709,20.4005,1276.96,2043.13,24.7881,78.276,13.6485,6.75205,38.724,2,31.0403,22.4476,73.6789,37.0914,55.8284,,,,,Spring,2,2,4,1,4,1,2,4,2,1,2,0,0,0,1,2,2,2,1,0,33,Spring,30,43,Winter,2,1 +2f154763,Fall,8,0,Spring,60,Winter,18.44606414,49.0,63.0,,64,99,111,,,,,Fall,1,0,,,,,17,1,10.0,1,9.0,1,12.0,1,Fall,3,2.98784,18.4481,998.577,1697.58,11.0918,48.6246,14.2386,4.2095,14.3754,2,26.0686,11.4641,45.6368,19.9039,37.1605,,,,,Winter,3,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,9,Winter,36,51,Fall,0,0 +2f16cad4,Fall,8,0,Spring,45,Spring,19.64552528,51.25,73.4,,70,100,114,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,2,3.25755,19.6477,1053.19,1685.1,12.6237,54.4415,14.5729,5.07479,18.9585,2,28.3964,13.4215,51.184,24.5688,41.0201,,,Winter,2.81,Spring,2,5,2,0,3,0,0,0,2,2,0,0,1,1,0,0,0,0,1,0,19,Spring,66,90,Fall,3,0 +2f1af08a,Fall,6,0,Spring,60,Fall,15.96863169,43.0,42.0,,64,84,112,,,,,Fall,3,1,,,,,0,0,6.0,0,8.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +2f37677d,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2f6b484c,Fall,9,1,Spring,75,Winter,17.88432,50.0,63.6,,63,94,117,Winter,4,5,30,Winter,10,1,,,,,3,0,8.0,0,9.0,1,12.0,1,Winter,3,7.03391,17.8863,1008.5,1613.6,14.8014,49.6814,13.9719,3.91434,13.9186,2,23.4137,11.4663,42.6475,20.5155,38.2151,,,Fall,1.39,Spring,1,1,2,1,1,0,0,0,1,1,2,0,2,2,2,2,2,2,1,1,24,Spring,36,51,Fall,0,0 +2f72f441,Fall,7,1,,,Fall,21.62873032,51.5,81.6,28.0,81,80,124,,,,,Fall,6,1,,,,,0,0,10.0,1,13.0,1,11.0,1,Fall,3,3.06001,21.6311,1073.88,1718.21,18.1204,56.6455,15.016,6.6151,24.9545,2,26.3707,12.1545,53.5855,26.036,44.4911,,,,,Fall,1,1,1,0,1,0,0,1,1,1,0,0,0,0,0,0,1,1,1,0,10,,,,Fall,0,0 +2f7f0eb7,Summer,7,1,,,Summer,19.62421125,54.0,81.4,29.0,74,71,109,,,,,Summer,6,1,,,,,0,0,9.0,0,8.0,0,11.0,1,Fall,2,3.19559,19.6264,1085.31,1627.96,17.5973,57.8624,13.9512,5.67516,23.5376,2,25.2439,15.0211,54.6668,24.4725,42.8412,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,28,41,Summer,1,0 +2f81950d,Summer,8,1,Summer,61,Summer,15.21581686,46.5,46.8,22.0,55,70,100,,,,,Summer,7,1,,,,,2,0,12.0,1,12.0,1,9.5,1,Fall,2,2.354,15.2175,937.313,1405.97,11.4241,42.0992,13.689,1.52852,4.70082,1,21.8083,8.86682,39.7452,18.7863,33.2324,,,,,Fall,3,0,2,0,3,0,0,0,1,2,0,0,0,1,1,2,2,1,1,1,20,Fall,54,75,Summer,0,0 +2f8ec752,Spring,11,0,Fall,61,Summer,19.80116667,60.0,101.4,,59,71,110,Summer,5,8,9,Summer,8,0,18.2,2,19.4,2,5,0,6.0,0,8.0,1,9.0,1,Summer,3,4.6989,19.8033,1277.86,2172.37,21.9651,78.3722,15.306,4.49731,23.0278,2,36.7045,19.7027,73.6733,35.0245,58.6695,,,Spring,2.64,Spring,4,0,4,0,4,0,0,0,0,2,0,0,2,0,0,1,1,2,0,0,20,Spring,35,50,Spring,0,0 +2f91f3c1,Spring,15,1,Winter,77,Fall,0.0,67.25,0.0,,104,58,147,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.03,,,Fall,2,2,2,1,1,0,0,2,1,1,1,0,1,0,1,1,1,1,1,1,20,Fall,50,69,Spring,0,0 +2fba6cf4,Fall,7,0,Spring,55,Winter,14.41880907,46.0,43.4,,84,81,136,Winter,3,3,19,Winter,5,,,,,,0,,9.5,,10.5,,7.0,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,47,66,Fall,0,0 +2fc568f6,Winter,14,0,Fall,75,Spring,34.87776584,66.5,219.4,,56,86,139,,,,,Spring,19,0,26.0,1,26.5,1,0,0,9.5,1,10.75,1,9.0,1,Spring,1,13.6228,34.8816,1833.39,2383.41,43.6126,137.544,21.8676,13.014,81.8563,1,58.4143,35.5168,123.921,63.2947,102.027,,,Spring,2.23,Spring,1,0,4,1,1,0,0,1,1,1,5,1,1,4,1,5,5,5,4,4,45,Spring,45,63,Winter,0,1 +2fef897e,Fall,10,0,Fall,80,Winter,17.60312,50.0,62.6,,39,69,89,Winter,5,7,10,Winter,10,1,,,,,30,1,8.5,1,10.0,1,11.0,1,Spring,3,5.33905,17.605,1009.6,1716.32,11.0786,49.7989,14.005,3.60005,12.8011,1,26.7557,11.9646,44.4599,22.3237,37.8343,,,Fall,1.86,Winter,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Winter,74,100,Fall,0,0 +2ff729d3,Winter,13,1,Summer,80,Spring,22.10996327,66.0,137.0,,73,81,115,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.91,Winter,1,1,2,0,2,2,2,0,1,1,1,0,0,0,1,1,1,0,0,0,16,Winter,48,67,,,0 +3032421f,Fall,9,1,,,Fall,14.76196989,54.25,61.8,22.0,63,88,102,,,,,Fall,6,0,,,,,0,0,9.0,1,10.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.93,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +3046a5c7,Winter,8,1,Summer,70,Summer,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.83,Spring,2,2,2,0,2,1,0,0,0,0,1,0,1,0,0,1,1,2,1,1,17,Spring,40,56,Winter,0,0 +3055881a,Spring,13,1,Fall,73,Summer,26.34574425,62.8,147.8,,66,70,109,,,,,Summer,20,1,22.3,2,26.9,3,2,0,13.5,1,13.5,1,15.0,1,Summer,2,5.05473,26.3486,1414.87,2122.31,33.0457,92.9657,16.5732,9.77542,54.8343,3,36.696,23.224,87.911,43.3456,69.7417,,,Spring,2.06,Summer,2,1,2,0,1,2,0,1,2,1,2,0,2,2,1,1,2,1,0,1,24,Summer,33,47,Spring,2,0 +3065aa2f,Summer,15,1,Fall,55,Fall,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.56,,,Fall,0,0,2,2,0,1,0,1,5,0,4,0,1,0,0,0,0,1,2,0,19,Fall,31,45,Summer,2,0 +307c91ee,Summer,6,0,Winter,35,Fall,14.70285197,47.0,46.2,,,,,Fall,,,,Fall,10,1,,,,,1,0,6.0,0,5.0,0,7.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,3,3,4,3,3,0,0,0,0,0,2,0,0,0,0,0,0,3,1,1,23,Fall,42,59,Summer,0,0 +3093d29f,Summer,13,0,Winter,68,Fall,18.28943089,61.5,98.4,,,,,,,,,Fall,40,1,24.0,2,27.8,2,17,1,4.0,0,7.0,0,10.0,1,Fall,4,0.259301,18.5925,1338.65,2811.17,24.8018,84.8469,16.0317,2.56084,13.5531,2,39.9794,20.0657,84.5876,40.8766,64.7812,,,Summer,3.43,Fall,2,2,2,3,3,1,1,1,2,3,2,1,1,0,2,2,1,2,2,2,35,Fall,34,49,Summer,0,1 +30a1d8fe,Winter,13,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.27,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +30ac8a84,Fall,10,0,Fall,80,Winter,31.59738407,58.0,151.2,,85,107,162,Winter,5,7,25,Winter,5,0,14.3,2,14.9,2,4,0,6.5,0,6.5,0,10.0,1,Winter,3,11.7145,31.6008,1418.98,2412.26,28.1928,93.4027,19.5212,12.0797,57.7973,3,42.5199,22.6899,81.6882,39.4752,70.7127,,,Winter,2.78,Winter,4,4,4,1,5,4,0,4,1,2,3,0,4,4,4,4,3,4,4,3,62,Winter,32,46,Fall,2,2 +30b3f203,Summer,12,0,Winter,63,Fall,20.25263158,57.0,93.6,,59,76,110,Fall,5,7,42,Fall,3,0,16.6,2,15.4,2,4,0,7.0,0,6.5,0,8.0,0,Fall,2,9.93506,20.2548,1157.26,1851.61,15.7554,65.5259,14.1797,6.07518,28.0741,2,30.2192,19.5513,55.5908,25.507,45.9746,,,Summer,1.61,Fall,2,3,4,5,5,0,0,4,5,3,0,1,4,1,4,2,5,1,5,4,58,Fall,46,64,Summer,2,2 +30ba4252,Fall,5,1,Winter,90,Fall,13.71707317,41.0,32.8,,55,91,102,Fall,,,,Fall,0,,,,,,3,,11.0,,11.0,,8.0,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,Fall,35,50,Fall,2,0 +30d2fd47,Winter,5,0,Spring,90,Spring,15.23557941,44.75,43.4,,57,87,102,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,2.35206,15.2372,894.623,1431.4,4.91648,37.5521,13.1841,2.05314,5.84792,1,21.1257,11.5099,35.2,16.6263,26.0422,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,29,42,Winter,0,0 +3104c361,Spring,7,1,,,Spring,15.45955852,49.0,52.8,21.0,53,87,93,,,,,Spring,5,0,,,,,2,0,9.5,1,10.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +3133a6c9,Summer,10,1,Fall,70,Summer,24.86644628,55.0,107.0,,62,79,109,Summer,3,3,52,Summer,8,0,12.8,2,9.6,1,1,0,6.0,0,5.0,0,9.0,1,Summer,2,5.95686,24.8692,1170.72,1756.08,22.5042,66.9604,15.5631,9.30609,40.0396,2,28.8653,15.5909,61.0035,29.9436,51.3695,,,Summer,3.49,Summer,3,4,4,0,4,4,0,4,0,2,4,0,2,1,3,3,3,3,2,0,46,Summer,48,67,Summer,0,1 +31349d8b,Fall,13,1,,,Fall,19.37066572,53.0,77.4,27.0,68,87,101,,,,,Winter,0,0,11.5,1,13.4,1,0,0,10.0,1,6.0,0,8.0,0,Winter,2,3.27531,19.3728,1092.59,1638.89,19.1528,58.6383,14.6768,4.69595,18.7617,1,26.7543,12.7312,55.363,25.3027,45.9071,,,Winter,1.13,Fall,0,0,2,0,0,0,0,2,0,2,0,0,3,0,3,2,0,2,1,0,17,Fall,60,82,Fall,2,0 +3134b69f,Spring,11,0,Summer,60,Spring,20.32030599,60.5,105.8,,74,67,132,Spring,12,20,0,Spring,18,1,26.9,3,25.8,3,0,0,8.0,1,10.0,1,12.0,1,Spring,5,4.62087,20.3225,1269.6,3047.05,21.0063,77.4924,14.8851,5.43746,28.3076,2,35.7534,20.7326,72.8715,33.6933,56.7597,,,Spring,4.07,Spring,3,2,2,0,3,2,1,2,2,2,2,0,2,1,2,1,2,2,0,1,32,Spring,37,53,Spring,1,1 +3146f878,Spring,8,1,Summer,71,Spring,12.93541475,52.75,51.2,22.0,75,83,129,,,,,Spring,14,1,,,,,10,1,14.0,1,13.0,1,12.0,1,Spring,2,2.89766,12.9368,1012.18,1518.27,13.1166,50.0735,12.6522,0.284628,1.12647,1,22.4675,14.4895,47.1759,20.6481,35.5841,,,,,Spring,2,1,2,1,2,1,1,1,2,1,2,1,1,1,1,1,1,1,1,1,25,Spring,41,58,Spring,2,0 +3155ed01,Fall,15,0,Summer,68,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.36,,,Winter,1,1,2,1,2,1,1,1,1,1,2,0,1,1,1,1,1,1,1,1,22,Winter,29,42,Fall,2,0 +3162c492,Fall,10,0,,,Fall,14.36725146,57.0,66.4,,72,84,127,Fall,5,8,3,Fall,10,0,12.5,1,14.7,2,4,0,5.5,0,7.0,0,11.0,1,Winter,4,3.05197,14.3688,1104.71,2319.89,13.4653,59.929,12.9685,1.40032,6.47104,2,30.3376,16.126,56.877,31.5832,43.8029,,,Fall,2.93,Winter,4,4,2,1,3,2,1,1,2,2,2,1,1,2,1,1,1,1,1,1,34,Fall,36,51,Fall,0,1 +31648caa,Spring,7,1,Winter,70,Spring,15.466,50.0,55.0,21.0,50,70,101,,,,,Spring,10,1,,,,,3,1,10.5,1,12.0,1,9.0,1,Spring,3,2.79842,15.4677,1000.37,1600.59,14.7584,48.8155,13.7284,1.73928,6.18452,1,23.6208,10.4362,46.0171,20.1587,38.3793,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,37,53,Spring,0,0 +318ccc0e,Fall,8,0,Fall,61,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.76,Fall,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,42,59,Fall,0,0 +319ea3cd,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +31a02566,Spring,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +31b74cd4,Spring,10,0,Fall,60,Summer,16.2677686,55.0,70.0,,66,77,111,Summer,,,,Summer,25,1,,,,,15,1,9.0,1,7.5,1,8.0,1,Summer,3,3.72742,16.2695,1092.4,1857.08,14.5084,58.6177,13.6241,2.64549,11.3823,1,28.9905,15.1189,54.8903,24.2586,43.4989,,,Summer,2.79,Summer,3,3,3,0,3,2,0,1,1,3,0,0,3,2,2,2,3,2,2,2,37,Summer,30,43,Summer,2,1 +31d61741,Summer,7,1,Spring,50,Fall,13.55717519,47.0,42.6,,63,82,115,Fall,6,9,30,Fall,9,1,,,,,12,1,6.0,0,6.0,0,19.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,4,4,2,2,5,4,0,4,0,0,2,0,3,2,5,1,2,2,2,0,44,Summer,51,71,Summer,1,1 +31d7a136,Summer,9,1,,,Summer,15.57400549,54.0,64.6,23.0,58,79,109,,,,,Summer,0,0,,,,,1,0,21.0,1,21.0,1,8.0,1,Summer,2,3.53555,15.5757,1058.95,1694.32,12.6114,55.055,13.2743,2.30139,9.545,1,27.8248,14.6188,51.5194,24.0428,40.4362,,,,,Summer,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,Summer,29,42,Summer,0,0 +31dc038d,Summer,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +31ddec50,Summer,16,0,,,Summer,23.27063239,73.0,176.4,32.0,88,78,144,,,,,Summer,35,1,41.9,2,47.9,2,20,1,4.0,0,4.75,0,7.0,0,,,,,,,,,,,,,,,,,,Summer,1.72,,,Summer,0,1,2,2,1,4,3,0,0,0,3,0,0,1,1,0,2,0,4,1,25,,,,Summer,2,0 +31f01671,Spring,10,1,Summer,75,Spring,18.02394194,51.5,68.0,,62,85,115,Spring,6,9,0,Spring,11,0,7.6,1,10.4,1,4,0,9.0,1,10.0,1,10.0,1,Summer,1,2.98754,18.0259,1042.3,1354.99,16.141,53.2815,14.1242,3.90169,14.7185,1,25.2123,11.9281,50.2939,24.5619,41.3534,,,Spring,2.51,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,33,47,Spring,0,0 +3204a251,Winter,5,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +320991a5,Winter,11,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.1,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3210e833,Spring,14,1,,,Spring,16.73899489,61.0,88.6,25.0,66,69,115,,,,,Spring,17,0,13.7,1,19.7,2,10,1,13.5,1,14.0,1,11.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.73,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +321202d4,Fall,7,0,Summer,75,Winter,14.66507205,50.5,53.2,,81,103,124,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,0,4,0,4,2,0,3,0,1,3,0,2,0,4,2,2,3,0,3,36,Spring,33,47,Fall,3,1 +3216b978,Spring,17,0,Fall,79,Summer,17.45389065,70.5,123.4,,74,70,114,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.32,,,Spring,3,2,2,1,3,1,1,1,1,2,4,1,2,2,2,2,2,2,3,1,38,Spring,29,42,Spring,3,1 +323ea319,Winter,10,0,Spring,65,Spring,20.96737092,58.25,101.2,,62,86,121,Spring,6,9,58,Spring,11,1,,,,,3,0,6.5,0,6.0,0,13.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.87,Spring,3,4,4,1,2,0,0,2,0,2,2,0,0,0,2,2,2,0,3,0,29,Spring,34,49,Winter,0,0 +325afd3d,Winter,16,1,Fall,65,Fall,24.10717549,61.0,127.6,,71,69,111,,,,,Fall,25,1,45.0,2,47.6,2,4,0,15.5,1,16.0,1,8.0,0,Fall,3,5.28703,24.1098,1279.65,2047.44,26.1199,78.5629,14.8443,9.26549,49.0371,2,31.5574,20.8856,73.2758,36.4614,57.6773,Winter,2.62,,,Fall,2,2,3,1,4,1,1,1,1,1,1,1,1,2,1,1,2,2,1,1,30,Fall,39,55,Winter,0,0 +3268c574,Winter,18,0,Summer,55,Summer,20.04937161,71.5,145.8,,74,61,125,,,,,,,,,,,,,,,,,,,,Summer,2,6.98228,20.0516,1600.25,2560.4,33.1185,112.711,15.5009,4.55067,33.0891,2,48.8652,30.7272,105.729,55.8532,81.9837,,,,,Summer,2,3,3,3,2,2,2,3,2,1,5,0,1,0,0,1,1,1,1,1,34,,,,Winter,2,1 +326c23c2,Spring,12,1,Summer,69,Spring,18.57000511,58.5,90.4,,72,80,109,,,,,Spring,10,0,17.8,2,18.9,2,0,0,7.0,0,6.0,0,12.0,1,Summer,2,3.72815,18.572,1152.34,1728.51,16.4308,65.0025,13.3543,5.21773,25.3975,2,29.9055,18.6662,61.2744,45.9591,46.3363,,,Spring,3.03,Spring,3,2,2,2,2,1,0,2,2,2,0,0,2,1,2,1,2,1,1,1,29,Spring,38,54,Spring,0,0 +327736cd,Fall,7,0,Winter,41,Fall,15.43573105,49.5,53.8,,71,77,119,Fall,4,5,28,Fall,6,1,,,,,0,0,10.0,1,9.5,1,11.0,1,Fall,4,7.13514,15.4374,969.665,2036.3,8.34051,45.545,13.0687,2.36869,8.25498,1,24.1871,13.0174,38.4099,20.1536,32.5276,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,29,42,Fall,0,0 +327ef356,Winter,11,0,Spring,71,Spring,18.224334,53.5,74.2,,63,77,100,Spring,4,5,52,Spring,10,0,11.8,1,11.9,1,3,0,6.0,0,7.0,0,10.0,1,Spring,3,3.59642,18.2263,1092.06,1856.51,14.6234,58.5821,14.39,3.83634,15.6179,1,29.7114,14.2473,54.9857,25.537,44.3348,,,Winter,2.27,Spring,5,5,5,2,5,0,1,1,1,4,3,0,2,1,1,3,3,3,2,1,48,Spring,41,58,Winter,1,1 +32857755,Fall,7,1,Spring,45,Winter,14.30692149,44.0,39.4,,67,76,121,Winter,2,2,21,Winter,19,1,,,,,15,1,12.0,1,11.0,1,11.0,1,Winter,2,1.76118,14.3085,948.472,1422.71,5.73781,43.2877,15.7203,-1.41185,-3.88768,1,17.555,19.9949,41.5265,19.8663,23.2928,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,28,41,Fall,0, +329a9c25,Fall,10,1,Winter,70,Winter,25.41874876,58.75,124.8,,39,109,131,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.89,Fall,4,4,4,3,4,3,0,2,1,2,2,0,5,3,3,5,3,3,3,3,57,Fall,59,81,Fall,2,2 +32a9dac0,Winter,8,0,Summer,45,Spring,18.31954432,53.0,73.2,,86,100,132,Spring,4,6,26,Spring,16,1,,,,,20,1,12.0,1,13.0,1,6.5,1,Spring,2,3.10591,18.3215,1052.4,1683.84,8.53351,54.3578,13.6054,4.71611,18.8422,1,26.6932,19.131,51.2518,31.509,35.2267,,,,,Spring,0,5,2,0,5,5,0,3,0,4,4,0,4,0,5,5,1,5,1,5,54,Spring,61,84,Winter,2,2 +32b8490d,Fall,11,0,Spring,65,Fall,17.19735537,55.0,74.0,,66,78,108,,,,,Fall,10,0,13.6,2,11.2,1,6,0,8.0,1,8.0,1,10.0,1,Fall,2,3.62303,17.1992,1090.26,1744.42,13.2435,58.3903,13.5712,3.62804,15.6097,1,28.849,16.2978,54.7673,26.1469,42.0925,,,Spring,3.01,Spring,3,2,4,5,2,1,0,1,0,2,0,0,,0,2,1,1,1,0,0,25,,,,Fall,1,0 +32c04f66,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +32d0ebca,Spring,21,0,Spring,65,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,2,0,0,0,1,1,1,0,2,0,0,0,0,0,0,0,0,0,7,Winter,35,50,Spring,1,0 +32d262c2,Fall,10,1,Summer,70,Spring,15.41545932,56.5,70.0,,65,84,112,Spring,,,,Spring,4,0,16.1,2,14.4,2,4,0,8.5,1,7.5,0,13.0,1,Spring,3,7.69624,15.4171,1089.38,1743.0,16.6145,58.2959,12.8394,2.57777,11.7041,2,24.6782,17.0032,50.5997,24.2589,41.2927,,,Fall,1.91,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,46,64,Fall,0,0 +32dcc8ab,Fall,8,1,Winter,70,Fall,16.32707101,52.0,62.8,,59,92,112,Fall,5,8,6,Fall,7,1,,,,,4,0,5.5,0,6.5,0,10.0,1,Winter,2,6.18163,16.3289,1033.71,1550.56,15.3177,52.3662,13.6159,2.71292,10.4338,2,24.5636,12.4849,46.1846,23.7221,39.8813,,,Fall,2.12,Fall,2,2,3,1,2,0,0,1,1,2,1,0,2,0,2,2,2,2,2,1,28,Fall,36,51,Fall,2,0 +32e18ccc,Summer,14,1,Winter,61,Fall,21.39500905,66.75,135.6,,,,,,,,,Fall,40,1,19.1,2,20.0,2,20,1,14.0,1,15.0,1,11.0,1,Fall,4,2.10462,21.3973,1378.51,2619.17,29.8885,89.0927,14.0586,7.33874,46.5073,2,34.0252,25.1789,86.988,40.1498,63.9137,Summer,3.55,,,Fall,4,4,4,3,5,2,4,2,1,2,4,0,4,4,3,4,3,3,1,1,58,Fall,44,62,Summer,2,2 +3302713b,Fall,12,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.77,Winter,0,2,4,1,2,1,1,1,1,1,2,1,1,1,2,0,2,0,2,1,26,Winter,29,42,Fall,2,0 +3305a981,Winter,14,0,Summer,50,Summer,18.63573964,65.0,112.0,28.0,61,73,103,,,,,Summer,21,0,25.6,2,28.9,2,21,1,8.0,1,8.0,1,12.0,1,Summer,2,6.10353,18.6378,1496.47,2394.36,31.6338,101.657,16.9166,1.72113,10.3428,1,45.5268,24.4967,95.5537,47.3069,77.1606,,,Spring,2.01,Spring,5,5,5,2,5,3,0,1,1,3,4,0,2,1,4,4,4,5,0,1,55,Spring,40,56,Winter,2,2 +331733c7,Summer,16,1,Winter,71,Summer,23.52189468,63.0,132.8,,69,103,131,,,,,Summer,20,1,26.0,2,26.8,2,10,1,8.0,0,11.0,0,9.0,1,Fall,2,4.98644,23.5245,1373.02,2059.53,31.3579,88.5078,15.6785,7.84601,44.2922,2,34.7768,22.3732,83.5214,38.4174,66.1346,Summer,1.9,,,Summer,2,2,2,1,1,1,2,1,2,2,2,0,2,1,1,1,3,1,2,2,31,,,,Summer,1,1 +331c5a4a,Spring,14,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +3327956c,Spring,20,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +333af713,Fall,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.46,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +333be478,Fall,11,0,Winter,70,Fall,18.47712916,60.75,97.0,,66,72,96,Fall,5,8,15,Fall,9,0,16.9,2,19.6,2,4,0,10.5,1,9.5,1,10.0,1,Fall,3,1.24264,18.4791,1302.73,2214.64,22.8667,81.0209,15.435,3.04413,15.9791,2,38.4616,19.6926,79.7782,39.4982,61.3282,,,Fall,3.14,Fall,4,4,4,4,5,1,0,3,5,2,4,0,0,2,2,0,2,0,1,1,44,Fall,39,55,Fall,0,1 +3375a49c,Spring,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +337c4d94,Summer,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +338146bd,Summer,13,0,Fall,75,Fall,20.29368219,63.5,116.4,,68,59,120,,,,,Fall,5,0,21.7,2,23.4,2,0,0,6.0,0,6.4,0,12.0,1,Fall,3,5.17019,20.2959,1348.91,2293.15,21.845,85.9401,14.9848,5.31108,30.4599,2,40.8676,23.2276,80.77,51.3925,62.7126,,,Summer,3.13,Fall,2,2,3,1,1,3,0,0,1,0,2,1,1,1,1,0,1,0,1,1,22,Fall,32,46,Summer,0,0 +3399795b,Fall,5,0,Summer,65,Winter,18.45263313,46.6,57.0,,75,128,128,Winter,,,,Winter,0,0,,,,,1,0,9.6,1,11.0,1,2.0,0,,,,,,,,,,,,,,,,,,,,,,Winter,5,4,2,0,2,1,0,0,0,3,3,0,1,1,2,3,2,2,1,0,32,Winter,72,98,Fall,0,1 +33a499ce,Fall,12,0,Spring,81,Winter,17.65311111,60.0,90.4,,62,65,108,,,,,Winter,30,1,15.1,1,19.0,1,12,1,6.0,0,6.0,0,12.0,1,Winter,4,4.74473,17.655,1209.43,2539.81,18.4452,71.0837,13.8826,3.77245,19.3163,2,33.0927,19.5459,66.339,30.52,51.5378,,,Spring,3.73,Winter,2,2,3,4,2,2,0,1,1,1,1,0,2,1,2,1,2,2,2,1,32,Fall,30,43,Fall,1,1 +33d60f7d,Fall,6,0,Spring,75,Winter,13.98034455,46.5,43.0,,58,99,107,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,5,2.611,13.9819,911.473,2187.54,6.75085,39.3469,12.794,1.18785,3.65313,1,22.458,10.138,36.7359,17.0964,29.2089,,,,,Winter,3,4,5,2,3,1,1,2,1,2,2,1,5,2,5,5,4,4,5,3,60,Winter,44,62,Fall,2,2 +33e34de8,Winter,17,0,Summer,62,Spring,20.66570414,65.0,124.2,,76,83,101,,,,,Spring,22,0,29.9,1,26.5,1,20,1,4.0,0,4.6,0,6.0,0,Spring,1,6.15461,20.7012,1539.23,2001.0,33.3422,106.211,17.6745,3.02676,18.1887,1,47.1251,25.744,100.057,48.9326,80.4673,Winter,1.56,,,Spring,3,2,4,0,3,2,0,2,2,1,1,0,1,1,1,1,3,2,5,1,35,,,,Winter,2,1 +33f9c85b,Winter,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +33fe1b04,Summer,9,0,Winter,62,Fall,15.38466116,55.0,66.2,,83,89,111,Fall,7,13,0,Fall,20,1,,,,,4,0,11.0,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.5,Fall,2,2,2,0,2,0,0,0,0,0,4,0,1,1,1,1,1,2,1,1,21,Fall,37,53,Summer,3,0 +34124db3,Fall,8,0,Spring,80,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.89,,,,,,,,,,,,,,,,,,,,,,,,,,,, +342d43c7,Summer,19,1,Winter,65,Fall,16.51955402,63.75,95.5,,68,57,110,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,4,4,0,1,1,2,3,0,0,5,0,0,0,3,0,4,0,4,4,35,Fall,54,75,Summer,1,1 +342edae9,Fall,8,1,Spring,70,Winter,19.40672103,53.9,80.2,,74,65,102,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.14,Spring,2,3,3,1,3,3,1,2,1,2,2,0,1,0,0,1,2,1,2,1,31,Spring,33,47,Fall,0,1 +343460ab,Winter,12,0,Spring,55,Spring,30.63071429,70.0,213.5,,50,79,121,,,,,Spring,20,1,23.0,2,28.2,2,3,0,3.0,0,5.5,0,15.0,1,Spring,4,6.57274,30.6341,1723.39,3619.13,37.1307,125.827,18.0543,12.5797,87.6728,3,53.2215,35.475,119.254,58.9834,90.3522,,,Winter,3.45,Spring,5,5,5,5,5,5,4,5,5,5,0,0,5,5,5,5,4,5,4,5,87,Spring,56,77,Winter,3,3 +34362a9a,Summer,6,0,Summer,78,Spring,14.16414815,45.0,40.8,,57,99,99,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,4,3,0,5,0,0,2,0,2,3,0,3,0,3,3,4,5,3,0,45,Fall,29,42,Summer,2,1 +3441fd41,Summer,16,1,,,Summer,28.21479278,61.5,151.8,34.0,72,83,114,,,,,Summer,15,0,30.0,3,35.4,3,10,1,11.0,1,12.0,1,12.0,1,Fall,2,4.99855,28.2179,1437.88,2156.82,34.7545,95.4162,17.7368,10.4811,56.3838,2,37.7112,22.9506,90.4177,43.7124,72.4657,Summer,1.99,,,Summer,5,1,3,2,3,1,1,2,2,1,3,0,1,3,1,0,1,0,1,1,32,Summer,47,66,Summer,2,1 +3466c72a,Summer,11,0,,,Summer,20.92237863,59.0,103.6,30.0,70,76,126,,,,,Summer,1,0,9.9,1,11.4,1,7,0,9.0,1,10.0,1,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,2.63,Summer,5,5,5,0,5,2,0,4,0,0,0,0,0,0,0,4,4,4,2,0,40,Summer,31,45,Summer,2,1 +347095fe,Fall,12,0,Spring,55,Winter,34.02029385,66.0,210.8,,91,69,150,,,,,Winter,14,0,48.2,2,49.0,2,0,0,6.0,0,7.5,1,7.0,0,Winter,2,22.9845,34.024,1570.13,2512.21,27.5564,109.502,17.6741,16.3499,101.298,3,48.1747,33.7712,86.5179,65.8211,75.7311,,,Winter,1.84,Winter,5,5,5,4,5,4,2,1,3,5,5,1,2,1,2,5,4,5,1,1,66,Winter,68,93,Fall,3,2 +347d2af9,Summer,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +348136b6,Fall,11,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +3492ccbd,Summer,10,0,,,Summer,15.05678278,51.5,56.8,22.5,84,83,125,,,,,,,,,,,,,,,,,,,,Summer,4,2.58843,15.0569,1046.7,2198.08,4.43431,53.7506,14.2486,0.808349,3.04937,2,20.9825,28.3338,51.1622,22.7493,25.4168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +34af3570,Summer,7,0,,,Summer,15.92165751,46.8,49.6,21.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +34c30fad,Winter,19,1,,,Spring,28.32108647,64.5,167.6,34.0,69,93,122,,,,,,,,,,,,,,,,,,,,Spring,2,5.34945,28.3242,1473.39,2210.09,35.0914,99.1988,16.7645,11.5597,68.4012,2,38.5458,25.5616,93.8494,48.7932,73.6372,,,,,Spring,5,5,5,4,5,0,5,5,5,5,4,,4,5,5,1,5,0,5,5,78,,,,Winter,3,2 +34d1aacc,Winter,8,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +34eb79f9,Spring,11,1,Fall,65,Fall,19.79143924,59.0,98.0,,73,81,111,Fall,5,8,15,Fall,10,0,28.0,3,24.6,3,20,1,13.5,1,14.0,1,12.0,1,Fall,3,2.89148,19.7936,1222.16,1955.45,24.1427,72.4386,14.6308,5.16278,25.5614,2,30.6243,17.6715,69.5471,32.8209,54.7671,,,Spring,3.91,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,29,42,Spring,2,0 +34ec5de4,Winter,16,1,Spring,71,Spring,29.91375235,64.13,175.0,,53,63,111,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.52,,,Spring,0,4,2,2,1,2,1,1,1,0,4,0,1,1,1,1,2,0,1,1,26,Spring,41,58,Winter,2,0 +350cf38b,Summer,5,0,,,Fall,14.49445623,48.25,48.0,,74,78,121,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1,1,1,0,1,1,0,1,0,0,0,0,1,1,1,1,1,1,1,1,14,Fall,33,47,Summer,0,0 +350dbeba,Spring,6,0,Summer,75,Spring,16.39650146,49.0,56.0,,127,95,161,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,5,2.84511,16.3983,970.076,2328.18,8.11795,45.5888,13.3496,3.04867,10.4112,1,24.6965,12.7744,42.7437,22.1546,32.8145,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,35,50,Spring,3,0 +35198c2d,Spring,17,0,Fall,60,Summer,21.45343364,72.0,158.2,,55,55,115,,,,,Summer,17,0,42.9,2,38.4,2,15,0,13.5,1,14.5,1,8.0,0,Summer,4,7.36334,21.4558,1693.09,3555.49,37.4781,122.599,16.6275,4.82832,35.6007,1,52.4218,32.6994,115.236,59.0931,89.8999,Spring,2.28,,,Summer,4,2,4,3,4,4,4,1,4,3,4,1,2,5,4,2,2,3,1,2,59,,,,Spring,2,2 +351cd4ec,Fall,9,0,Winter,60,Winter,21.79396433,54.0,90.4,,60,75,73,Winter,4,5,53,Winter,4,0,,,,,11,1,13.5,1,14.0,1,11.0,1,Winter,4,3.58204,21.7963,1127.82,2368.42,15.2056,62.3904,15.043,6.75339,28.0096,2,30.7305,16.4544,58.8084,27.2776,45.9361,,,Fall,3.49,Winter,3,3,3,0,4,1,0,3,1,2,3,0,2,2,3,2,2,2,1,1,38,Winter,50,69,Fall,0,1 +35265670,Spring,6,0,Spring,72,Spring,16.757401,51.0,62.0,,67,98,113,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.07536,16.7592,1007.93,1612.69,10.0907,49.6208,13.413,3.34624,12.3792,2,25.4829,14.0472,46.5454,20.8888,35.5736,,,,,Spring,3,3,4,0,4,1,0,3,2,2,3,0,2,2,2,2,3,2,4,1,43,Spring,59,81,Spring,0,1 +35272f43,Winter,8,0,,,Summer,,,,,,,,,,,,Winter,10,1,,,,,13,1,10.0,1,9.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +355bb19a,Winter,9,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.42,,,,,,,,,,,,,,,,,,,,,,,,,,,, +356ab908,Spring,16,1,Fall,55,Summer,19.01669922,64.0,110.8,,62,67,111,,,,,Summer,13,0,20.8,2,25.8,2,1,0,9.0,0,10.0,0,11.0,1,Summer,2,4.92146,19.0188,1310.75,1966.13,27.6207,81.8753,14.0539,4.96492,28.9247,2,32.4121,21.8426,76.9538,35.3261,60.0327,Spring,1.35,,,Summer,4,4,4,0,4,2,4,1,3,0,4,0,2,4,2,2,4,0,2,2,48,Summer,41,58,Spring,1,1 +3599e9d7,Spring,15,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.17,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +35a94642,Summer,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +35c49344,Winter,8,0,,,Winter,22.61871302,52.0,87.0,32.0,54,87,106,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +35d2aa41,Spring,16,0,Summer,60,Winter,20.94884594,73.0,158.8,,109,72,176,,,,,Spring,,,,,,,,,,,,,,,Summer,2,7.18685,20.9511,1636.88,2619.01,34.1876,116.613,15.3852,5.56594,42.1873,3,49.7469,32.6782,109.426,56.5253,83.9345,Spring,1.16,,,Spring,1,2,2,0,3,2,1,1,1,2,2,0,2,0,1,2,2,2,1,1,28,Summer,70,95,Spring,2,0 +35daea65,Winter,6,0,Spring,59,Winter,15.08474423,47.0,47.4,,42,91,98,,,,,Winter,1,0,,,,,3,1,12.0,1,12.5,1,6.0,1,Winter,3,2.73461,15.0864,932.515,1585.28,8.38636,41.5881,13.2366,1.8498,5.8119,1,23.6103,9.5914,38.8535,17.3586,31.9967,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,32,46,Winter,0,0 +35fa2ebf,Summer,13,0,Fall,70,Fall,20.40419242,61.0,108.0,,77,76,119,,,,,Fall,25,1,17.2,1,20.5,2,3,0,5.0,0,5.0,0,8.0,0,Fall,4,2.93395,20.2733,1333.57,2800.5,22.2951,84.3061,15.8256,4.44771,23.6939,2,41.0943,20.9167,81.3721,50.1645,63.3894,,,Summer,2.88,Fall,5,5,5,5,5,4,0,5,5,5,5,0,5,5,5,5,5,5,5,5,89,Fall,37,53,Summer,3,3 +35fe98b3,Winter,6,0,,,Winter,19.4128565,47.0,61.0,27.0,63,88,111,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +36146fe1,Spring,17,0,,,Summer,20.33929883,70.5,143.8,30.0,83,133,153,,,,,Summer,20,0,50.6,2,49.0,2,20,1,0.0,0,0.0,0,8.0,0,,,,,,,,,,,,,,,,,,Spring,1.31,,,Summer,0,0,3,2,2,0,0,0,0,0,3,0,2,0,3,1,1,2,1,1,21,,,,Spring,2,0 +3617e141,Spring,15,0,,,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,Fall,2,5.95942,21.9269,1504.75,2407.6,30.6054,102.539,16.8038,5.12301,31.2612,2,45.8679,26.0655,96.5794,49.312,76.4733,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3622933f,Summer,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +36473697,Summer,18,0,Fall,55,Summer,18.9137207,64.0,110.2,27.0,58,75,124,,,,,Summer,26,1,32.8,2,35.2,2,25,1,8.5,1,9.0,1,12.0,1,Summer,4,5.36862,18.9158,1360.42,2856.88,24.0933,87.1655,14.9619,3.95387,23.0345,1,40.2125,22.8597,81.7968,43.5334,64.3057,Fall,1.521,,,Fall,4,4,1,2,3,3,2,1,1,1,3,0,1,1,1,1,1,1,1,1,33,,,,Summer,0,1 +364afc11,Spring,10,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +36530d36,Winter,6,1,,,Winter,17.62173662,46.5,54.2,,61,99,101,Winter,5,7,5,Winter,10,1,,,,,3,1,12.0,1,13.0,1,11.0,1,Winter,3,2.20051,17.6237,947.99,1516.78,10.3144,43.2364,14.0587,3.56492,10.9636,1,20.4449,12.4771,41.0359,18.4707,30.7593,,,,,Winter,2,2,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,9,Winter,56,77,Winter,0,0 +3655395c,Winter,9,1,Summer,70,Summer,15.50612366,58.0,74.2,,74,98,100,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.94627,15.5078,1157.35,1851.77,21.2365,65.5364,13.6971,1.81069,8.66358,1,28.4369,15.863,61.5902,28.146,49.6734,,,Winter,1.52,Summer,3,3,3,0,2,1,0,1,1,2,2,0,1,2,1,2,2,2,1,1,30,Spring,39,55,Winter,0,0 +366142d3,Winter,10,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.84,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +3661e257,Fall,12,0,,,Fall,18.91450872,66.0,117.2,,69,73,125,,,,,Fall,16,0,22.6,2,24.1,2,1,0,10.0,1,10.0,1,5.0,0,Fall,2,6.07335,18.9166,1476.27,2362.03,31.3263,99.5049,16.0605,2.85606,17.6951,2,43.1745,25.0041,93.4315,40.6206,74.5008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +366d28a8,Spring,7,0,,,Summer,32.1341716,52.0,123.6,,,,,Summer,2,1,7,Summer,6,1,,,,,0,0,8.0,1,10.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,33,47,Spring,0, +367cc279,Winter,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +36958957,Winter,13,0,Summer,60,Spring,17.67797852,64.0,103.0,,59,54,124,,,,,Spring,20,0,21.7,2,20.4,2,0,0,7.0,0,5.0,0,7.0,0,,,,,,,,,,,,,,,,,,,,Winter,2.36,Spring,3,5,4,4,4,1,1,4,2,2,5,0,3,2,3,1,3,3,3,2,55,Winter,37,53,Spring,2,2 +36b8c9e5,Winter,8,0,Summer,65,Spring,21.98527218,55.75,97.2,31.0,54,74,96,,,,,Spring,5,0,,,,,10,1,12.5,1,13.0,1,9.0,1,Spring,4,3.93749,21.9877,1184.86,2488.2,17.6373,68.4661,15.4878,6.49992,28.7339,2,33.5731,17.2557,64.5286,32.0551,51.2104,,,,,Winter,4,4,4,0,3,1,0,1,0,0,3,0,1,1,3,2,3,2,0,1,33,Winter,50,69,Spring,2,1 +36c060b9,Summer,11,0,Winter,69,Fall,23.07632324,64.13,135.0,,87,63,131,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.22,Fall,5,5,5,2,5,5,1,2,2,1,5,1,4,4,5,4,5,5,4,4,74,Fall,58,80,Summer,3,2 +36c1f189,Winter,10,1,Fall,91,Summer,16.36072727,55.0,70.4,,83,81,120,Summer,5,7,41,Summer,8,0,11.7,1,17.2,2,10,1,8.0,0,5.0,0,7.5,0,Summer,3,4.08511,16.3625,1099.49,1759.18,19.0323,59.3727,13.7995,2.563,11.0273,1,26.3862,13.9542,55.2875,24.3757,45.4184,,,Summer,2.0,Summer,2,1,2,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,9,Summer,31,45,Summer,0,0 +36cd03cf,Winter,10,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.67,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +36e77bf0,Winter,9,1,Spring,73,Winter,16.79241031,51.6,63.6,,69,76,126,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.02,Winter,4,4,3,0,2,2,0,2,2,2,2,0,4,3,2,4,3,4,2,2,47,Winter,41,58,Winter,1,1 +36fda5de,Winter,7,0,Summer,41,Winter,16.230421,47.0,51.0,,43,85,93,,,,,Winter,3,0,,,,,4,1,9.0,1,10.0,1,5.0,0,Spring,3,2.70599,16.2322,939.165,1596.58,6.64301,42.2964,13.462,2.77017,8.70362,1,25.3359,10.3175,39.5904,27.2653,31.9789,,,,,Winter,4,4,2,0,1,0,0,0,1,0,0,0,0,0,0,1,1,2,0,0,16,Winter,35,50,Winter,0,0 +3709416b,Fall,11,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +370b47c4,Fall,13,0,Spring,65,Winter,17.95528489,63.75,103.8,,88,66,139,,,,,Winter,22,1,26.0,2,23.2,2,12,1,11.5,1,12.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.78,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,28,41,Fall,2,0 +3714afd7,Fall,10,0,,,Fall,15.68866835,55.2,68.0,,66,75,127,Fall,4,5,54,Fall,6,0,11.7,,11.4,,0,0,6.0,0,7.0,0,6.0,0,Fall,3,7.80633,15.6904,1067.46,1814.68,11.9639,55.9612,12.9125,2.77784,12.0388,2,26.7244,17.273,48.1549,22.4002,38.6882,,,,,Fall,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,Fall,32,46,Fall,2,0 +3720e5a7,Summer,8,0,Fall,70,Summer,22.3238163,53.0,89.2,,77,92,148,Summer,5,8,8,Summer,5,0,,,,,5,1,7.5,1,7.5,1,10.0,1,Fall,3,6.01156,22.3263,1143.63,1944.17,16.9125,64.0747,16.0375,6.28871,25.1253,2,32.5503,14.6119,58.0632,29.4642,49.4628,,,Summer,3.34,,,,,,,,,,,,,,,,,,,,,,,Summer,38,54,Summer,1, +3728a93f,Spring,10,1,,,Summer,13.63094083,56.0,60.8,24.0,63,78,103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +372a7950,Fall,13,0,Spring,60,Winter,17.13822485,65.0,103.0,,94,91,155,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.39,Fall,3,3,5,1,4,5,4,0,0,3,4,0,0,0,0,0,2,0,0,0,34,Fall,33,47,Fall,0,1 +3738db26,Spring,12,0,Spring,75,Summer,14.45380117,57.0,66.8,,42,85,96,,,,,Summer,10,0,14.0,1,14.7,1,13,1,4.0,0,5.0,0,7.0,0,Summer,4,3.92057,14.4554,1101.04,2312.18,14.1194,59.538,12.8839,1.57148,7.262,1,28.9798,16.4388,55.6174,25.7061,43.0992,,,Summer,3.19,Summer,5,5,5,1,5,1,1,5,2,2,4,0,1,0,3,3,4,1,1,2,51,Summer,46,64,Spring,2,2 +374d3984,Fall,16,0,Winter,50,Fall,53.9184128,62.5,299.6,,,,,,,,,Fall,0,0,26.0,1,25.3,1,0,0,7.0,0,7.0,0,10.0,1,Winter,2,5.61699,53.9243,1910.72,3057.15,40.5308,145.78,26.2386,27.6857,153.82,3,66.3713,38.8779,140.163,101.015,106.902,Fall,1.26,,,,,,,,,,,,,,,,,,,,,,,,,Fall,68,93,Fall,3, +375e2ee5,Spring,6,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +376e617f,Spring,11,0,Fall,59,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.32,Summer,4,4,5,1,5,1,0,4,2,2,0,0,4,1,5,4,4,5,2,5,58,Summer,35,50,Spring,3,2 +37995242,Summer,6,1,Summer,70,Spring,14.2796875,48.0,46.8,,61,75,103,Spring,6,10,1,Summer,0,0,,,,,0,0,11.5,1,13.0,1,9.0,1,Winter,1,2.28799,14.2812,955.187,1241.74,10.4199,44.003,13.4277,0.853518,2.797,1,19.3681,14.215,41.715,14.7661,29.788,,,,,Winter,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,Winter,27,40,Summer,0,0 +379d558f,Spring,18,0,,,Spring,22.59583432,65.0,135.8,29.0,59,74,124,,,,,Spring,25,1,20.2,2,24.4,2,30,1,9.0,1,6.5,0,7.0,0,Spring,,,,,,,,,,,,,,,,,Spring,3.16,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +37ae1239,Spring,9,0,,,Summer,15.48768933,51.75,59.0,,67,99,116,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.15,Summer,4,4,4,0,3,0,1,2,1,2,2,0,3,1,3,4,2,4,1,2,43,Summer,34,49,Spring,0,1 +37bcfa7e,Spring,11,0,Summer,65,Spring,15.29273721,57.13,71.0,,67,88,117,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.28,Spring,2,1,2,1,2,1,1,1,2,2,2,0,1,1,0,0,1,0,1,0,21,Spring,30,43,Spring,3,0 +37ddc2cf,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +380029ef,Spring,7,0,Winter,65,Fall,15.28388172,49.0,52.2,,,,,Fall,4,5,45,Fall,26,1,,,,,2,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,3,0,2,0,0,1,0,1,2,0,0,0,0,2,1,2,1,0,19,Fall,43,60,Spring,1,0 +38137f22,Winter,9,0,,,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.09,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +3820a945,Winter,12,0,Spring,65,Winter,15.70877193,57.0,72.6,,56,76,93,Winter,5,8,18,Winter,25,1,12.3,1,13.4,1,13,1,7.0,0,6.0,0,7.0,0,Spring,3,1.30351,15.7105,1152.88,1959.9,18.1079,65.0599,14.0788,1.63167,7.54013,2,31.353,15.599,63.7564,25.8603,49.4609,,,Spring,1.97,Spring,1,2,2,0,2,2,0,1,1,1,0,0,1,2,2,1,3,2,2,2,27,Winter,57,79,Winter,2,0 +3826be66,Spring,17,0,Fall,60,Summer,19.004109,68.0,125.0,,52,46,111,,,,,Summer,55,1,34.3,2,34.6,2,47,1,5.5,0,6.5,0,7.0,0,Summer,4,6.61452,19.0062,1561.48,3279.11,32.8547,108.581,16.5097,2.49648,16.4189,1,48.3355,27.3909,101.967,54.5434,81.1903,Summer,4.54,,,Summer,5,4,4,1,4,2,0,0,4,0,4,0,3,3,4,0,1,0,1,1,41,,,,Summer,3,1 +38390669,Winter,6,0,,,Winter,17.299351,43.0,45.5,23.0,56,81,109,,,,,Winter,0,0,,,,,0,0,9.0,1,10.0,1,10.0,1,Winter,1,2.28184,17.2632,879.861,1143.82,5.55791,35.9797,13.6812,3.58202,9.42026,1,21.5925,8.82929,33.6979,15.6136,27.1505,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,2,0 +383ab806,Summer,18,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +383d2ad2,Fall,10,0,,,Fall,16.44677975,48.75,55.6,23.0,70,80,120,,,,,Fall,3,0,12.8,2,9.3,1,4,0,6.0,0,6.0,0,8.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +384b83f9,Summer,8,0,Summer,73,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.7,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +384c3f27,Spring,13,0,Winter,59,Spring,20.56137695,64.0,119.8,28.0,60,61,119,,,,,Spring,24,1,30.2,2,34.3,3,30,1,8.5,1,10.5,1,10.0,1,Summer,4,6.4068,20.5636,1629.36,3421.66,36.3104,115.811,19.879,0.684655,3.98868,1,52.6421,26.8589,109.405,62.0888,88.9524,,,Summer,3.53,Summer,3,3,3,1,3,1,1,1,1,1,1,1,1,1,2,1,1,2,1,1,30,Summer,50,69,Spring,2,0 +385819e6,Winter,9,1,Spring,62,Winter,19.78648262,54.5,83.6,,66,105,113,Winter,,,,Winter,13,1,,,,,0,0,11.5,1,11.0,1,11.5,1,Winter,3,8.87639,19.7886,1088.62,1741.79,16.6299,58.2152,13.7799,6.00875,25.3848,2,25.3726,16.2126,49.3388,27.7716,42.0025,,,Winter,2.65,Winter,3,3,4,0,4,0,0,2,1,2,0,0,4,0,3,4,3,4,1,2,40,Winter,34,49,Winter,0,1 +385a6557,Spring,8,0,Fall,50,Spring,17.62229912,51.0,65.2,,64,86,100,Spring,5,7,38,Spring,8,1,,,,,10,1,12.0,1,14.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.32,Spring,1,2,1,1,0,2,0,1,1,0,3,1,1,1,3,1,1,1,1,1,23,Spring,31,45,Spring,2,0 +3864dd31,Winter,15,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3875d4de,Spring,10,0,Spring,60,Winter,22.57441737,55.3,98.2,,98,73,164,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,2,3.64329,22.5769,1151.68,1842.69,15.3301,64.932,14.9283,7.64854,33.268,2,30.9007,18.7013,61.2888,27.7926,46.2308,,,Fall,3.27,Fall,2,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,8,Fall,34,49,Spring,3,0 +38aa4aed,Spring,13,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +38cc02b7,Spring,21,0,,,Summer,27.82049704,65.0,167.2,35.0,73,70,112,,,,,Summer,30,1,40.0,2,37.4,2,20,1,,,,,9.0,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +38cccd6d,Summer,9,0,Spring,50,Fall,23.90472182,54.5,101.0,,71,74,123,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.38,Fall,4,4,4,2,3,1,0,1,1,1,4,1,3,4,4,4,4,4,,4,53,Fall,45,63,Summer,2,2 +38d53b92,Spring,11,0,Summer,70,Spring,14.66670843,56.5,66.6,,66,82,109,Spring,5,7,29,Spring,5,0,16.8,2,14.9,2,1,0,5.5,0,6.5,0,8.0,0,Spring,2,3.71729,14.6683,1081.47,1730.35,12.7592,57.4533,12.6538,2.01451,9.14667,1,27.2998,17.3943,53.736,23.105,40.059,,,Spring,2.87,Spring,4,1,1,1,1,1,,1,1,1,1,1,1,1,1,1,1,1,1,1,22,Spring,36,51,Spring,0,0 +38df3cc5,Summer,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +38edfccd,Winter,12,0,Winter,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.56,,,,,,,,,,,,,,,,,,,,,,,,,,,, +38ee12e7,Winter,13,0,,,Spring,16.59054178,66.0,102.8,,70,82,120,,,,,Spring,13,0,13.8,1,20.3,2,0,0,8.0,1,7.0,0,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.66,Spring,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Spring,31,45,Winter,2,0 +3901e707,Summer,7,1,Winter,79,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,9,Fall,35,50,Summer,0,0 +3909a855,Winter,7,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +3913797d,Fall,10,0,Summer,60,Fall,20.68876338,58.0,99.0,,73,75,127,,,,,Fall,6,0,,,,,0,0,0.0,0,0.0,0,13.0,1,Fall,2,4.2134,20.691,1213.39,1941.43,18.2689,71.5051,14.9446,5.74643,27.4949,2,34.3774,18.8589,67.2917,34.1955,52.6462,,,Fall,1.93,Winter,2,1,3,0,3,1,0,2,0,0,0,0,2,2,2,3,2,4,0,0,27,Winter,39,55,Fall,0,0 +3918d854,Fall,6,0,,,Winter,16.63073719,49.0,56.8,24.0,88,106,126,,,,,Fall,0,0,,,,,1,0,8.0,1,8.0,1,3.5,0,Fall,3,2.9133,16.6326,976.218,1659.57,9.52241,46.243,13.5412,3.09137,10.557,3,24.8913,11.8293,43.3297,19.534,34.4137,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +3934e29c,Fall,13,1,Spring,65,Winter,23.95966797,64.0,139.6,,91,68,150,,,,,Winter,5,0,12.6,1,19.5,2,2,0,5.0,0,8.0,0,11.0,1,Winter,2,4.62489,23.9623,1339.39,2009.08,28.7352,84.9253,14.5774,9.38489,54.6747,2,32.5974,23.5927,80.3004,37.1558,61.3326,,,Fall,3.43,Fall,4,5,5,1,5,1,1,1,2,1,2,0,5,1,4,4,5,1,1,5,54,Fall,50,69,Fall,3,2 +394684ae,Winter,12,0,Spring,48,Winter,17.51868993,53.0,70.0,24.0,69,89,108,,,,,Winter,8,0,16.6,2,20.1,2,25,1,6.5,0,7.0,0,11.0,1,Winter,3,3.56985,17.5206,1080.81,1837.38,14.1035,57.3834,14.3627,3.15786,12.6166,1,29.8912,13.3887,53.8136,27.0278,43.9948,,,Winter,2.68,Winter,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Winter,48,67,Winter,0,0 +394a6219,Summer,8,0,Fall,75,Summer,14.59515571,51.0,54.0,,83,91,122,Summer,6,9,58,Summer,0,0,,,,,0,0,10.0,1,9.0,1,6.0,1,Fall,1,9.61518,14.5968,984.274,1279.56,8.14326,47.1011,12.7319,1.86484,6.89887,1,23.1208,15.837,37.4859,18.0913,31.2641,,,Summer,2.69,Summer,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Summer,35,50,Summer,0,0 +3950aa00,Spring,9,0,Summer,45,Summer,21.55781796,56.3,97.2,,74,111,108,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,3,4.19602,21.5602,1224.55,2081.74,20.5199,72.694,16.1244,5.43574,24.506,2,35.1301,17.044,68.498,31.7246,55.65,,,Spring,3.99,Spring,3,3,3,2,4,0,0,0,0,0,1,0,2,0,2,2,2,2,1,0,27,Spring,47,66,Spring,2,0 +39697713,Winter,10,0,,,Spring,16.01169591,57.0,74.0,,57,101,123,,,,,Spring,20,1,14.5,2,16.2,2,4,0,0.0,0,0.0,0,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,1.899,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +397efe7a,Spring,7,0,Fall,72,Summer,14.8304676,50.5,53.8,,62,88,107,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,46,64,Spring,0,0 +3983d3cd,Fall,18,1,Spring,65,Winter,21.37484302,61.5,115.0,,56,68,102,,,,,Winter,50,1,23.5,2,25.5,2,12,1,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,Fall,3.47,,,Winter,1,2,2,1,2,1,2,1,1,1,2,1,1,2,1,1,1,1,1,1,26,Winter,28,41,Fall,0,0 +3999e449,Summer,10,0,Winter,70,Fall,29.63082049,49.0,101.2,,76,79,123,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.74,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,54,75,Summer,0,0 +39bdbb00,Winter,8,1,Summer,70,Winter,16.44677975,48.75,55.6,24.0,82,118,132,,,,,Winter,0,0,,,,,0,0,11.0,1,10.0,1,12.0,1,Winter,3,2.45402,16.2812,976.432,1562.29,11.5793,46.2658,13.5479,2.7333,9.3342,1,21.1699,13.5166,43.8118,19.0541,32.7492,,,,,Spring,0,0,2,0,0,0,0,0,0,0,1,0,2,2,0,4,2,3,0,1,17,Spring,38,54,Winter,0,0 +39c20e2a,Spring,11,0,Summer,60,Summer,22.38996717,54.5,94.6,30.0,,93,,,,,,Summer,14,0,11.7,1,11.4,1,0,0,9.0,1,9.0,1,6.0,0,Summer,2,3.60869,22.3924,1140.04,1824.06,15.6078,63.6918,15.0762,7.31616,30.9082,2,30.7071,17.3769,60.0831,26.3979,46.3149,,,Summer,2.61,Summer,1,1,1,2,2,1,0,0,0,0,0,0,2,0,1,2,0,0,0,1,14,Summer,45,63,Spring,2,0 +39cc2836,Spring,9,0,,,Fall,15.62197425,50.2,56.0,,83,82,119,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Fall,42,59,Spring,0,0 +39d626f4,Spring,10,0,Summer,64,Summer,20.03259504,55.0,86.2,,66,84,113,Summer,5,8,52,Summer,23,1,,,,,7,1,10.5,1,10.5,1,9.0,1,Summer,3,3.81283,20.0348,1144.24,1945.21,16.3357,64.1397,14.9075,5.12729,22.0603,2,31.8051,15.9989,60.3269,28.9635,48.1408,,,Spring,2.96,Summer,3,2,2,1,2,1,1,1,1,1,2,1,2,1,1,1,1,1,1,1,27,Summer,40,56,Spring,1,0 +39dd3538,Winter,6,0,,,Winter,17.84404938,45.0,51.4,21.0,54,86,98,,,,,Winter,6,1,,,,,3,1,12.0,1,12.0,1,12.0,1,Winter,1,2.51101,17.846,917.752,1193.08,7.67825,40.0156,13.8934,3.95264,11.3844,1,22.9879,9.34953,37.5046,16.1241,30.6661,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,0,0,0,,,,Winter,0,0 +39dfffd1,Summer,14,0,Fall,,Summer,17.74267965,66.5,111.6,26.0,80,72,129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.51,,,,,,,,,,,,,,,,,,,,,,,,,Summer,37,53,Summer,, +39fb1ad0,Summer,12,0,Summer,78,Winter,15.51542969,64.0,90.4,,58,79,127,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.98,Spring,4,4,5,4,5,4,0,5,2,4,4,1,4,4,5,5,4,5,5,5,79,Spring,41,58,Summer,0,2 +3a33d579,Summer,16,1,,,Summer,20.91534827,60.75,109.8,28.0,85,96,128,,,,,Summer,13,0,20.2,2,17.6,2,1,0,21.7,1,20.0,1,15.0,1,Summer,3,4.52978,20.9176,1284.29,2054.87,25.2509,79.0571,15.0609,5.85673,30.7429,1,34.3511,19.4551,74.5273,46.0508,59.602,Summer,2.269,,,Summer,2,3,1,1,2,2,1,1,2,1,1,1,2,2,2,1,2,2,2,1,32,,,,Summer,0,1 +3a594acf,Summer,11,0,Fall,50,Summer,13.20013223,55.0,56.8,,70,72,107,,,,,Summer,10,0,9.1,1,9.1,1,3,0,9.0,1,6.0,0,4.0,0,Summer,2,3.47239,13.2016,1036.43,1658.29,11.2579,52.6566,12.2385,0.963027,4.14344,1,24.9127,16.486,49.1842,18.9039,36.1705,,,Fall,1.47,Fall,2,4,4,3,2,1,3,1,2,0,4,0,2,0,3,0,3,2,4,4,44,Fall,41,58,Summer,2,1 +3a7154ef,Fall,5,0,,,Fall,18.401947,43.0,48.4,24.0,57,85,105,,,,,Fall,0,0,,,,,0,0,6.0,0,7.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +3a9d7bd6,Summer,9,1,,,Summer,17.93662551,54.0,74.4,26.0,60,63,117,,,,,Summer,0,0,,,,,0,0,7.5,0,7.0,0,9.0,1,Fall,1,3.23327,17.9386,1078.23,1401.69,17.7521,57.1083,13.7694,4.1692,17.2917,2,25.059,14.2972,53.875,22.7883,42.8111,,,Summer,2.18,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,38,54,Summer,0,0 +3aa62ad8,Winter,11,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +3ab0b1ff,Fall,11,0,,,Winter,16.42983162,57.25,76.6,,55,95,121,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.3,Winter,4,3,2,1,2,2,1,1,1,1,3,1,2,1,2,3,1,2,1,1,35,Winter,49,68,Fall,1,1 +3ab539f0,Winter,6,1,Spring,65,Winter,16.67596197,47.0,52.4,,57,96,99,Winter,3,3,25,Winter,0,0,,,,,0,0,10.0,1,9.0,1,10.0,1,Spring,3,2.20733,16.6778,952.901,1524.64,11.1631,43.7595,13.9277,2.75009,8.64053,1,18.9047,13.6916,41.5521,12.7267,30.0679,,,,,Spring,3,4,2,0,3,1,0,0,0,1,3,0,1,0,1,0,2,1,1,0,23,Winter,38,54,Winter,1,0 +3acb5be3,Spring,6,0,Fall,70,Summer,16.02022468,47.4,51.2,,79,77,95,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.75831,16.022,945.063,1606.61,8.27401,42.9246,13.4324,2.58962,8.2754,2,24.2869,10.3637,40.1663,19.6652,32.5609,,,,,Summer,2,0,2,0,1,0,0,0,0,2,2,0,0,0,0,2,0,1,0,0,12,Spring,45,63,Spring,0,0 +3ae4ee9b,Winter,15,1,Summer,51,Winter,17.78480257,66.0,110.2,26.0,56,70,102,,,,,Winter,17,0,21.3,2,20.3,2,2,0,12.0,1,12.0,1,12.0,1,Spring,3,5.17412,17.7867,1326.04,2121.66,27.7523,83.5032,13.4778,4.30898,26.6968,2,32.4683,23.2826,78.3291,35.8483,60.2206,Spring,2.26,,,Spring,3,3,4,1,4,2,1,1,2,2,3,0,2,1,3,2,1,2,1,2,40,Spring,44,62,Winter,2,1 +3b047021,Summer,5,1,Fall,85,Fall,16.38597531,45.0,47.2,,65,87,115,,,,,Fall,4,1,,,,,0,0,10.5,1,11.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,1,0,4,0,5,0,0,0,1,2,0,0,0,0,3,0,1,0,0,0,17,Fall,31,45,Summer,2,0 +3b091b83,Winter,8,0,Spring,55,Winter,19.89315702,55.0,85.6,,74,81,133,Winter,6,9,34,Winter,0,0,,,,,1,0,7.5,1,7.5,1,2.0,0,Spring,3,3.81978,19.8953,1143.82,1944.5,16.1593,64.0953,14.8972,4.99818,21.5047,1,32.0241,15.9118,60.2755,30.0732,48.1834,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,37,53,Winter,2,0 +3b0f680f,Fall,8,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +3b112284,Summer,7,1,,,Summer,14.85846914,45.0,42.8,21.0,101,60,127,,,,,Summer,0,0,,,,,0,0,9.0,1,10.0,1,9.0,1,Summer,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,37,53,Summer,0,0 +3b1b9538,Spring,17,0,Spring,80,Spring,17.95092519,75.2,144.4,,77,72,142,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.29,,,Spring,3,3,3,1,3,1,1,1,1,1,2,0,2,3,3,3,3,3,1,1,39,Spring,36,51,Spring,2,1 +3b7f721f,Spring,18,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3b98f56f,Winter,19,1,Spring,85,Spring,24.80125997,60.75,130.2,34.0,73,90,121,,,,,,,,,,,,,,,,,,,,Spring,3,4.16518,24.804,1258.13,2013.0,24.7113,76.2702,14.53,10.274,53.9298,2,30.0879,21.471,72.105,34.68,54.7992,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +3ba435d2,Spring,5,1,,,Summer,15.02328028,42.5,38.6,19.0,72,105,124,,,,,Summer,0,0,,,,,0,0,11.0,1,10.0,1,8.0,1,Summer,1,1.75905,16.1444,865.101,1124.63,7.52688,34.4076,14.3909,1.75343,4.1923,1,18.7147,8.16601,32.6485,14.6529,26.2416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +3ba48ca7,Fall,6,0,Spring,61,Winter,18.06319444,48.0,59.2,,56,96,112,Winter,6,9,43,Winter,10,1,,,,,20,1,13.0,1,13.0,1,10.0,1,Winter,4,2.92767,18.0652,983.046,2064.4,10.7689,46.9703,14.3332,3.73194,12.2297,1,26.0958,10.1056,44.0427,20.2586,36.8647,,,,,Winter,2,2,4,0,2,2,0,2,0,0,1,0,2,0,2,2,2,2,2,0,27,Winter,71,97,Fall,0,0 +3bbf88cd,Winter,8,1,,,Spring,18.40695266,52.0,70.8,,57,66,101,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.43,Spring,5,4,4,2,5,3,0,4,5,4,3,0,5,5,4,5,4,5,3,3,73,Spring,64,88,Winter,2,2 +3bf75b68,Spring,7,0,,,,,,,,,,,,,,,Summer,13,1,,,,,15,1,12.5,1,12.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +3c0aba64,Summer,15,1,Summer,81,Winter,18.13934382,62.75,101.6,,99,75,148,,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.15226,18.1413,1210.08,1815.13,20.6559,71.1528,12.7048,5.43654,30.4472,1,27.1722,23.3248,67.0006,30.5689,47.8281,Fall,1.83,,,Winter,1,1,1,1,3,2,2,1,1,1,2,1,1,1,1,1,2,1,1,1,26,Winter,31,45,Summer,2,0 +3c2cf08e,Spring,7,0,Summer,50,Summer,17.93565405,50.63,65.4,,68,110,106,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.17096,17.9376,1024.19,1638.7,11.6359,51.3527,14.0848,3.85283,14.0473,3,27.1809,12.5359,48.1817,22.7046,38.8168,,,,,Spring,1,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,2,2,0,0,10,Spring,33,47,Spring,0,0 +3c2df645,Winter,18,0,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +3c5c85e7,Spring,10,0,Fall,65,Fall,25.54233333,60.0,130.8,,69,79,122,,,,,Fall,10,0,11.4,1,13.4,2,0,0,6.0,0,7.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,1.14,Summer,2,3,3,2,3,1,0,0,0,1,2,0,2,0,2,0,0,0,0,0,21,Summer,28,41,Spring,2,0 +3c620257,Winter,6,1,Fall,80,Fall,15.45008809,45.75,46.0,,65,99,101,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,Summer,29,42,Winter,1,0 +3c6f9125,Spring,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3c7a7f32,Summer,14,0,Fall,70,Fall,20.88342516,66.0,129.4,,59,66,127,,,,,Fall,39,1,81.8,3,75.8,3,22,1,7.0,0,9.0,1,6.0,0,Fall,4,0.0203076,20.8857,1536.21,3226.05,32.4213,105.89,17.0911,3.79459,23.5099,2,47.074,26.3949,105.87,50.3357,79.4953,,,Summer,3.96,Fall,2,2,2,1,2,2,1,1,2,2,2,0,1,1,1,0,2,1,2,0,27,Fall,32,46,Summer,2,0 +3c8f474a,Summer,9,0,Spring,45,Winter,14.8191568,52.0,57.0,,68,94,111,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.29844,14.8208,1016.55,1728.13,11.152,50.5387,13.1407,1.68003,6.46133,1,26.5334,12.8533,47.2402,22.3695,37.6853,,,Fall,2.72,Fall,2,2,2,1,2,1,1,1,1,1,2,0,2,1,1,1,1,2,2,1,27,Fall,27,40,Summer,0,0 +3ca35642,Summer,17,0,Winter,50,Summer,25.95654204,65.25,157.2,,78,73,131,,,,,Summer,6,0,44.4,3,35.1,2,3,0,10.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,Fall,1.11,,,Fall,3,3,2,4,3,1,1,2,4,3,5,1,5,2,2,1,3,2,1,1,49,,,,Summer,2,1 +3cb2c4da,Spring,22,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3cb5deff,Winter,7,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3cc9cfa6,Spring,10,0,Winter,65,Spring,15.47349113,53.5,63.0,26.0,59,73,121,,,,,Spring,0,0,,,,,4,0,6.0,0,6.0,0,7.0,1,Spring,3,3.12619,15.4752,1033.05,1756.18,8.898,52.2963,12.8459,2.62924,10.7037,2,24.2301,19.1682,49.1701,20.7869,33.1281,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,35,50,Spring,0,0 +3cd25935,Summer,10,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +3cd8802e,Winter,10,1,Spring,78,Winter,22.27507508,55.5,97.6,,54,85,116,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.36,Spring,1,4,2,1,1,1,1,2,1,1,2,1,1,1,1,1,1,1,1,1,26,Spring,36,51,Winter,2,0 +3cfeca85,Fall,8,0,Winter,65,Fall,15.95927304,48.5,53.4,,58,76,82,,,,,Winter,10,1,,,,,10,1,6.0,0,6.5,0,9.0,1,Fall,3,2.85036,15.961,961.391,1634.37,8.56705,44.6638,13.3498,2.61121,8.73618,1,24.674,11.4227,41.8135,20.6998,33.2411,,,Winter,1.61,Fall,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,27,40,Fall,0,0 +3d04e8d3,Summer,19,0,,,Summer,35.17893004,67.5,228.0,42.0,74,64,125,,,,,Summer,8,0,26.0,1,31.1,1,8,0,13.5,1,14.5,1,10.0,1,Fall,3,6.86319,35.1828,1889.42,3212.01,44.2437,143.511,22.1453,13.0375,84.4888,3,61.9981,37.2693,136.648,75.6489,106.242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1, +3d20d9a7,Spring,8,1,Summer,80,Winter,15.16131586,51.5,57.2,,73,88,112,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,1,2.41698,15.163,1032.2,1341.86,9.5765,52.2056,13.839,1.32394,4.99438,2,19.7403,22.8888,49.7886,22.1152,29.3168,,,,,Spring,0,1,1,0,1,0,2,2,1,2,1,0,0,2,2,1,3,1,0,0,20,Spring,59,81,Spring,3,0 +3d2c0bc5,Winter,14,1,Fall,80,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.33,Spring,4,5,4,3,2,2,2,2,2,4,2,0,0,2,3,2,2,2,1,2,46,Spring,39,55,Winter,2,1 +3d36fe8b,Winter,11,1,Spring,80,Winter,25.65719957,62.2,141.2,,93,78,174,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.57322,25.66,1322.78,2116.44,28.3689,83.1562,15.1118,10.5482,58.0438,3,32.413,22.3742,78.5829,36.8575,60.7819,,,Spring,1.82,Spring,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,2,1,24,Spring,30,43,Winter,3,0 +3d3b87db,Fall,14,0,Winter,95,Fall,18.6046781,67.0,118.8,,50,65,122,,,,,Fall,42,1,24.8,2,26.0,2,13,1,8.0,1,9.0,1,10.0,1,Fall,4,5.93814,18.6067,1433.0,3009.29,27.6917,94.896,14.8628,3.7439,23.904,1,41.7743,25.43,88.9578,42.1824,69.466,,,Fall,3.33,Fall,5,4,4,3,5,2,2,2,3,2,4,1,1,0,1,1,2,2,2,1,47,Fall,36,51,Fall,0,1 +3d3dd3ae,Spring,13,0,Summer,60,Spring,20.22741245,63.0,114.2,,63,77,107,,,,,Spring,2,0,20.3,2,19.7,2,2,0,3.5,0,4.0,0,7.5,0,Spring,2,5.29734,20.2296,1373.8,2198.07,26.0023,88.5903,15.6931,4.53656,25.6097,1,40.0253,22.5627,83.293,38.692,66.0276,,,Spring,2.88,Spring,1,1,2,1,3,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,11,Spring,30,43,Spring,2,0 +3d3fb503,Summer,7,0,Fall,53,Summer,16.24551391,47.8,52.8,22.0,,,,,,,,Summer,6,1,,,,,8,1,12.0,1,11.0,1,10.0,1,Winter,2,2.82763,16.2473,956.012,1529.62,8.92449,44.0908,13.5674,2.67993,8.70917,1,24.8379,10.3285,41.2632,20.1155,33.7623,,,,,Fall,1,1,2,0,0,0,0,2,2,2,1,0,0,0,0,0,1,1,1,0,14,Fall,36,51,Summer,2,0 +3d46429f,Fall,13,1,Winter,42,Winter,21.67437342,62.75,121.4,,68,80,132,,,,,Winter,35,1,21.3,,22.4,,9,1,10.0,1,8.5,0,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.37,Winter,5,5,4,2,5,1,5,4,4,2,5,1,4,4,4,3,4,5,5,1,73,Winter,43,60,Fall,1,2 +3d48430d,Winter,6,1,Spring,65,Winter,16.01351607,46.0,48.2,,62,98,91,Winter,4,5,43,Winter,3,1,,,,,0,0,8.0,0,8.0,0,8.0,1,Spring,2,2.10967,16.0153,936.901,1405.35,9.54797,42.0552,13.9736,2.0417,6.14477,2,19.3084,13.1989,39.9456,15.6396,28.8564,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,51,71,Winter,0,0 +3d5acaf4,Spring,5,0,Fall,59,Summer,22.32589792,46.0,67.2,,,,,,,,,Summer,0,0,,,,,0,0,,,,,8.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +3d5ad997,Summer,10,0,Summer,60,Fall,15.28845663,56.0,68.2,,73,99,107,Fall,6,10,12,Fall,4,0,,,,,0,0,7.0,0,6.0,0,5.0,0,Fall,2,5.69154,15.2901,1084.53,1735.25,13.5728,57.7798,12.954,2.33616,10.4202,1,27.542,16.665,52.0883,22.1647,41.1148,,,Summer,1.21,Fall,2,0,0,0,0,0,0,0,0,0,1,2,3,3,3,2,0,3,1,1,21,Fall,39,55,Summer,2,0 +3d6143f3,Summer,7,0,Winter,55,Summer,14.11594735,49.5,49.2,,58,81,108,,,,,Summer,2,1,,,,,0,0,8.0,1,7.5,1,4.0,0,Summer,1,2.92128,14.1175,960.105,1248.14,9.03493,44.5268,12.7765,1.34094,4.67324,2,23.4221,12.0697,41.6055,16.7467,32.457,,,,,Fall,2,1,2,0,2,0,0,2,0,1,0,0,0,0,0,1,1,1,0,0,13,Fall,40,56,Summer,0,0 +3d736110,Winter,15,1,,,Spring,22.89636109,65.75,140.8,,71,87,143,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.67,,,Spring,3,2,2,1,5,2,1,1,2,3,2,0,0,2,1,1,2,2,0,0,32,Spring,46,64,Winter,0,1 +3d8531f5,Summer,10,1,,,Summer,25.39617434,53.5,103.4,32.0,78,85,127,,,,,Summer,5,0,,,,,10,1,4.0,0,5.5,0,12.0,1,Fall,2,3.17407,25.3989,1117.76,1676.65,19.685,61.3196,15.0624,10.3365,42.0804,2,26.0695,15.5652,58.1455,25.4699,45.7544,,,Summer,3.78,Summer,2,1,2,1,2,1,0,1,1,1,1,1,1,1,1,2,1,1,1,1,23,Summer,34,49,Summer,2,0 +3d970ce4,Spring,8,1,Summer,81,Spring,16.00061515,51.0,59.2,,61,99,101,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.7595,16.0024,1006.71,1610.74,13.5052,49.4909,13.3779,2.62446,9.70905,1,22.8981,13.0877,46.7314,21.4772,36.4033,,,Spring,2.12,Spring,4,4,2,0,4,0,0,1,2,3,2,0,2,0,2,2,2,3,2,0,35,Spring,51,71,Spring,1,1 +3dcbc164,Fall,18,0,Spring,60,Winter,26.70863054,71.2,192.6,,78,70,147,,,,,,,,,,,,,,,,,,,,Winter,2,9.94457,26.7115,1668.6,2669.76,35.3663,119.991,16.6414,10.0701,72.6093,2,50.2507,34.3737,110.046,53.2565,85.6169,,,,,Winter,2,3,2,2,3,2,2,1,1,3,3,1,2,2,3,1,1,2,2,1,39,Winter,35,50,Fall,3,1 +3dce8a87,Winter,8,0,,,Winter,14.17126197,49.0,48.4,24.0,107,113,194,,,,,Winter,0,0,,,,,0,0,7.5,0,8.0,1,8.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +3de1a622,Summer,9,1,,,Summer,,,,,,,,Summer,,,,Summer,7,1,,,,,22,1,9.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,2,1,1,0,0,0,0,1,1,1,1,0,0,0,0,0,1,0,1,0,10,Summer,37,53,,,0 +3de6a21c,Summer,7,0,,,Summer,16.43372278,51.25,61.4,23.0,59,82,105,,,,,Summer,11,1,,,,,7,1,11.0,1,8.5,1,3.25,0,Summer,,,,,,,,,,,,,,,,,,,,,Summer,2,1,2,0,1,0,0,1,1,1,2,0,0,0,0,0,2,0,0,0,13,Summer,38,54,Summer,0,0 +3e2383b5,Spring,13,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.21,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +3e377cb7,Spring,10,0,Fall,80,Summer,23.1221205,58.25,111.6,,57,94,98,Summer,2,2,31,Summer,22,1,,,,,0,0,10.0,1,12.0,1,10.0,1,Summer,3,6.27119,23.1246,1284.75,2184.07,22.8613,79.1055,16.3914,6.7332,32.4945,2,36.7667,19.4774,72.8343,32.6688,59.628,,,Summer,2.45,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,37,53,Spring,0,0 +3e398bf9,Fall,14,0,Spring,65,Winter,21.59708779,65.4,131.4,,70,69,115,,,,,Winter,13,0,40.6,3,42.4,3,2,0,10.0,1,10.0,1,5.0,0,,,,,,,,,,,,,,,,,,,,Fall,2.95,Winter,5,4,5,2,5,1,4,4,5,4,2,1,4,1,3,3,3,4,1,1,62,Winter,39,55,Fall,2,2 +3e3c242c,Spring,9,0,Fall,67,Summer,13.62002721,52.5,53.4,,63,96,116,Summer,3,3,0,Summer,3,0,,,,,10,1,8.0,1,7.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.12,Summer,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Summer,26,38,Spring,0,0 +3e42fb00,Spring,6,1,Summer,60,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3e5d5b58,Fall,7,1,Winter,55,Fall,14.8191568,52.0,57.0,,106,62,142,Fall,6,9,25,Fall,15,1,,,,,2,0,9.0,1,9.0,1,9.0,1,Winter,2,2.52325,14.8208,1026.87,1540.31,9.15396,51.6385,13.4267,1.39407,5.36153,1,21.353,21.1315,49.1152,31.8455,30.5069,,,,,Fall,3,3,4,2,4,2,1,3,2,3,1,1,1,1,2,3,3,2,2,2,45,Fall,38,54,Fall,0,1 +3e5ee48c,Spring,15,0,Summer,61,Summer,23.32184256,68.0,153.4,,106,62,137,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.6,,,Spring,0,0,4,0,1,1,4,1,1,0,2,0,1,0,0,0,1,1,1,1,19,Spring,39,55,Spring,1,0 +3e71dba8,Winter,16,1,,,Winter,40.67651367,64.0,237.0,43.0,97,73,171,,,,,Winter,0,0,25.1,2,24.9,2,0,0,9.5,1,10.5,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,1.64,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +3e7ee995,Fall,14,0,,,Fall,19.94326241,70.5,141.0,29.0,67,60,118,,,,,Fall,80,1,44.2,2,46.1,3,25,1,13.0,1,12.0,1,12.0,1,Fall,4,8.06544,19.9454,1876.06,3939.73,44.7424,142.088,20.0994,-0.153949,-1.08831,2,62.2574,35.0885,134.023,80.1508,107.0,Fall,3.37,,,Fall,3,2,2,2,2,2,1,1,2,1,2,0,1,1,1,1,2,2,2,1,31,Fall,52,72,Fall,2,1 +3e9fd8d6,Summer,15,1,Fall,70,Summer,24.2549465,67.5,157.2,,80,58,131,,,,,Summer,39,1,39.3,3,32.7,3,15,1,18.5,1,18.5,1,8.0,0,Fall,3,-2.394,24.2576,1580.57,2528.91,39.5732,110.614,17.0689,7.18865,46.5857,2,42.6399,28.4013,113.008,53.9487,82.2131,Summer,1.65,,,Summer,2,5,5,4,5,1,5,4,5,4,5,0,3,0,3,3,3,5,0,3,65,Summer,49,68,Summer,1,2 +3eb48980,Fall,10,0,Winter,51,Winter,20.82962963,58.5,101.4,,76,69,130,Winter,6,9,59,Winter,11,1,,,,,3,0,5.5,0,5.0,0,6.0,1,Winter,4,8.49842,20.8319,1208.11,2537.02,17.5097,70.9422,14.5746,6.25733,30.4578,2,33.3855,20.047,62.4438,32.5393,50.8952,,,Fall,2.4,Winter,2,1,3,0,3,2,0,0,2,2,3,0,1,2,0,2,3,2,1,2,31,Fall,37,53,Fall,0,1 +3ebfbb4c,Spring,7,1,Fall,78,Fall,14.46495908,48.5,48.4,,79,77,97,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,3,0,3,0,0,1,1,2,3,0,2,3,5,4,3,5,2,2,49,Spring,28,41,Spring,0,1 +3ede35d1,Fall,10,0,,,Fall,17.5026749,54.0,72.6,,67,77,112,,,,,Fall,13,1,,,,,5,0,7.5,1,7.0,0,10.0,1,Fall,2,3.49999,17.5046,1074.94,1719.9,12.8999,56.7579,13.6849,3.81968,15.8421,2,28.2227,15.6353,53.258,24.5158,41.1226,,,Winter,3.12,Winter,2,2,5,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,14,Winter,50,69,Fall,0,0 +3f1cf5ba,Spring,10,0,Winter,45,Winter,15.30443246,53.88,63.2,,59,57,86,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.41889,15.3061,1044.6,1775.82,11.6297,53.5266,12.9633,2.34276,9.67343,1,26.4279,15.4689,50.1077,21.7689,38.0576,,,Spring,2.72,Spring,3,4,2,0,4,1,0,1,2,3,2,0,3,0,4,2,3,3,1,2,40,Spring,45,63,Spring,0,1 +3f1f23e7,Fall,7,0,Spring,60,Winter,23.67704,50.0,84.2,,76,105,126,Winter,6,9,0,Winter,8,1,,,,,0,0,13.0,1,10.0,1,9.0,1,Winter,3,3.16956,23.6796,1071.83,1822.11,13.9545,56.4266,15.8689,7.81073,27.7734,2,29.2843,13.1878,53.2571,23.9431,43.2388,,,,,Winter,5,2,5,1,5,2,0,5,5,5,4,0,5,5,5,4,4,5,4,4,75,Winter,55,76,Fall,2,2 +3f20e14b,Spring,11,0,,,Spring,21.16811111,60.0,108.4,,92,74,152,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.05,Spring,5,4,4,0,5,0,0,4,4,4,4,0,1,1,4,1,4,0,2,0,47,Spring,61,84,Spring,0,1 +3f2141da,Winter,8,0,Summer,52,Spring,14.73722311,51.5,55.6,,51,80,93,,,,,Spring,5,0,,,,,11,1,5.0,0,6.0,0,9.0,1,Spring,4,3.37834,14.7388,1025.14,2152.79,10.8456,51.4538,13.6397,1.09911,4.14623,1,29.2683,11.3398,48.0754,31.6996,40.1139,,,Winter,2.231,Spring,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,8,Spring,35,50,Winter,2,0 +3f36989c,Summer,11,0,,,Summer,25.60353798,62.0,140.0,36.0,67,91,127,,,,,Summer,0,0,,,21.3,2,,,4.0,0,5.5,0,12.0,1,Summer,4,5.18186,25.6063,1432.74,3008.76,27.4002,94.869,17.3518,8.25458,45.1311,3,43.2947,24.1741,89.6871,45.5152,70.6949,,,Summer,1.79,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +3f3a70c8,Summer,12,0,,,Summer,30.96104812,63.0,174.8,38.0,84,85,159,,,,,Summer,0,0,14.6,1,17.3,1,0,0,7.5,0,5.0,0,14.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,2.38,Summer,2,1,3,0,3,0,2,3,1,2,3,0,1,1,2,1,2,2,3,1,33,Summer,50,69,Summer,3,1 +3f3d3809,Spring,7,1,Summer,70,Winter,15.6914318,51.5,59.2,,44,112,119,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,5,3.10527,15.6931,1045.83,2300.83,16.9019,53.6579,14.224,1.46914,5.54212,2,26.084,10.672,50.5526,24.6096,42.9858,,,,,Spring,3,2,1,0,1,0,0,1,2,3,1,0,1,1,1,2,1,3,1,1,25,Spring,36,51,Spring,0,0 +3f51a21d,Summer,7,0,,,Summer,15.86931017,50.25,57.0,22.0,61,73,109,,,,,Summer,0,0,,,,,0,0,6.0,0,4.5,0,9.0,1,Fall,1,2.93835,15.871,984.74,1280.16,9.17632,47.1507,13.1286,2.74242,9.84927,1,23.8736,14.1009,44.2124,17.8422,33.0499,,,,,Summer,5,5,4,0,3,2,0,4,2,3,4,0,3,4,5,5,3,5,3,3,63,Summer,59,81,Summer,1,2 +3fb896a9,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3fcef947,Summer,9,0,Winter,55,Fall,23.51854545,55.0,101.2,,70,88,113,Fall,5,8,55,Fall,0,0,,,,,0,0,9.5,1,7.0,0,9.0,1,Fall,2,3.56306,23.5211,1151.72,1842.76,15.2704,64.9368,15.0928,8.42836,36.2632,3,30.4358,19.2307,61.3738,26.1291,45.7061,,,Summer,3.34,Fall,2,2,2,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,9,Fall,41,58,Summer,0,0 +3fe47b69,Summer,21,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3fe59141,Fall,11,0,,,Fall,30.00030053,60.5,156.2,38.0,64,83,131,,,,,Fall,0,0,20.1,2,21.6,2,1,0,11.0,1,11.0,1,12.0,1,Fall,3,4.96206,29.5137,1446.81,2459.58,28.9446,96.3674,18.2085,11.3053,59.8326,3,42.4828,24.94,91.4054,38.4633,71.4274,,,Fall,3.22,Fall,2,3,2,0,2,2,0,1,0,0,2,0,1,1,0,0,1,0,0,0,17,Fall,40,56,Fall,2,0 +3fe78116,Summer,11,0,Winter,65,Fall,23.94881094,58.0,114.6,,71,84,99,Fall,5,8,11,Fall,15,1,16.3,2,16.5,2,9,1,7.5,1,6.5,0,11.0,1,Fall,3,4.43186,23.9514,1288.89,2191.11,20.4772,79.5468,16.6253,7.32612,35.0532,2,39.487,19.5826,75.115,47.5955,59.9642,,,Summer,3.26,Fall,2,2,2,2,2,1,0,1,0,0,2,0,0,1,1,1,1,1,1,1,21,Fall,32,46,Summer,1,0 +3ffca4a4,Fall,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4003e648,Winter,11,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.58,,,,,,,,,,,,,,,,,,,,,,,Spring,50,69,Winter,, +40087bc5,Winter,13,0,Spring,61,Spring,16.61754386,57.0,76.8,,67,92,125,,,,,Spring,5,0,15.4,1,15.4,1,8,0,15.0,1,17.0,1,10.0,1,Spring,3,4.05877,16.6194,1143.54,1944.01,16.8092,64.0647,13.8635,2.7559,12.7353,1,30.8068,16.4486,60.0059,26.0888,47.6161,,,Winter,3.53,Spring,5,5,4,1,5,3,4,3,2,4,4,1,3,4,3,4,3,5,2,1,66,Spring,50,69,Winter,2,2 +40109473,Winter,8,0,,,Winter,22.02977431,48.0,72.2,27.0,80,,123,,,,,Winter,0,0,,,,,1,0,0.0,0,0.0,0,8.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +4057472d,Winter,10,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.91,,,,,,,,,,,,,,,,,,,,,,,,,,,, +40898508,Fall,14,0,Winter,80,Fall,31.31686359,66.5,197.0,,82,68,148,,,,,Fall,12,0,30.9,2,31.8,2,0,0,7.0,0,6.0,0,6.5,0,,,,,,,,,,,,,,,,,,Fall,2.97,,,Fall,1,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,27,Fall,35,50,Fall,2,0 +408bed19,Summer,9,0,Winter,45,Fall,14.1688854,50.5,51.4,,60,93,102,Fall,6,11,0,Fall,6,0,,,,,1,0,12.0,1,12.0,1,9.0,1,Fall,2,2.99745,14.1704,974.996,1559.99,8.57457,46.1129,12.7128,1.4576,5.28711,1,24.3137,13.2246,43.1154,20.4562,32.8883,,,Summer,2.269,Fall,3,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,13,Fall,54,75,Summer,0,0 +4093774d,Fall,17,1,Summer,51,Fall,21.46727135,63.0,121.2,,70,76,110,,,,,Fall,11,0,25.0,2,28.9,2,0,0,8.5,0,9.5,0,12.0,1,Fall,1,4.98075,21.4696,1353.58,1759.65,29.9586,86.4369,15.3116,6.15801,34.7631,2,34.7499,21.7285,81.4562,39.5356,64.7084,Winter,1.0,,,Winter,4,4,5,1,4,4,2,3,1,1,4,0,1,3,3,0,3,2,4,0,49,,,,Fall,2,1 +40aae75f,Winter,10,0,Summer,57,Spring,19.64887763,53.5,80.0,,65,82,93,Spring,3,4,22,Spring,2,0,,,,,0,0,12.5,1,13.0,1,5.0,0,Summer,2,2.96357,19.651,1085.79,1737.27,7.57786,57.9142,14.2259,5.42511,22.0858,2,25.7866,24.5497,54.9506,33.0032,33.3645,,,Winter,1.83,Spring,1,1,3,0,2,0,0,0,0,0,0,0,0,0,1,0,2,1,4,1,16,Spring,49,68,Winter,2,0 +40b520f0,Fall,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +40b904b3,Spring,12,1,,,Spring,15.8633059,54.0,65.8,,54,84,91,,,,,Spring,15,0,17.1,1,17.1,1,7,1,8.0,0,9.0,0,7.0,0,,,,,,,,,,,,,,,,,,,,Spring,2.19,Spring,4,4,4,0,4,2,0,4,2,3,2,0,2,1,2,2,3,2,3,1,45,Spring,34,49,Spring,2,1 +40cdadfc,Spring,10,1,Spring,60,Spring,19.9497076,57.0,92.2,30.0,73,91,107,,,,,Spring,5,0,13.9,2,16.5,2,0,0,9.5,1,10.5,1,12.0,1,Spring,2,3.8683,19.9519,1182.35,1773.52,21.6664,68.1984,14.758,5.1939,24.0016,2,30.5208,16.0112,64.3301,36.2283,52.1872,,,Winter,1.55,Winter,2,1,2,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,9,Winter,41,58,Spring,0,0 +40e95163,Spring,7,0,Summer,50,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,5,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,20,Spring,41,58,Spring,0,0 +41052ced,Summer,11,0,,,Summer,13.61844628,55.0,58.6,23.0,52,59,109,,,,,Summer,2,0,13.6,2,17.4,2,25,1,8.0,1,10.0,1,8.0,0,,,,,,,,,,,,,,,,,,,,Summer,3.98,Summer,2,2,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,Summer,46,64,Summer,0,0 +41185f8e,Spring,12,1,Spring,55,Fall,19.86103018,62.0,108.6,,83,64,136,,,,,Fall,10,0,13.4,1,13.4,1,0,0,9.0,0,9.0,0,10.0,1,Fall,1,3.50369,19.8632,1260.09,1638.11,26.2144,76.4789,13.9882,5.87503,32.1211,1,29.6879,20.5766,72.9752,28.683,55.9023,,,Winter,2.61,Fall,5,5,5,1,4,0,0,3,3,2,3,0,2,2,2,3,3,3,3,3,52,Summer,48,67,Spring,2,2 +4128aee6,Summer,9,1,,,Summer,14.17126197,49.0,48.4,23.0,70,90,110,,,,,Summer,2,0,,,,,5,0,4.0,0,4.5,0,10.0,1,Summer,,,,,,,,,,,,,,,,,,,,,Summer,2,1,1,0,2,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,19,Summer,28,41,Summer,2,0 +4135cb0e,Fall,11,0,Summer,55,Winter,17.08633718,57.3,79.8,,54,85,101,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.89,Winter,3,3,3,1,3,2,1,3,2,2,2,0,3,2,4,3,3,3,3,3,49,Winter,50,69,Fall,2,1 +4157be55,Spring,12,0,Fall,75,Summer,26.37164412,62.0,144.2,,58,67,121,,,,,Summer,35,1,22.5,2,27.0,2,10,1,9.0,1,8.5,1,13.0,1,Summer,5,7.4504,26.3745,1464.86,3515.67,29.1516,98.29,17.9775,8.39706,45.91,2,44.5092,24.6292,90.8396,45.967,73.6607,,,Summer,3.76,Summer,5,3,3,2,5,2,1,2,2,2,1,1,4,3,3,4,2,5,2,1,53,Summer,60,82,Summer,1,2 +415cf055,Summer,17,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +415d5a1a,Winter,11,0,Fall,70,Spring,17.25531449,58.5,84.0,,84,73,120,Spring,,,,Summer,50,1,23.2,3,19.8,2,8,1,7.5,1,6.5,0,17.5,1,,,,,,,,,,,,,,,,,,,,Summer,3.15,Spring,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Spring,53,73,Winter,0,0 +416b6ba2,Winter,6,0,Summer,90,Spring,15.58451572,49.9,55.2,,58,99,105,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.07465,15.5862,988.764,1680.9,10.2816,47.5794,13.4345,2.15175,7.62061,3,25.8907,11.4071,44.5047,21.357,36.1723,,,,,Spring,2,2,2,1,1,0,0,1,0,0,0,0,1,0,1,1,0,1,1,0,14,Spring,31,45,Winter,0,0 +4177e5e5,Spring,12,0,Fall,55,Spring,17.59361627,59.5,88.6,,72,64,136,,,,,Spring,15,0,12.6,1,17.4,1,3,0,11.0,1,11.0,1,10.0,1,Summer,1,4.5859,18.2022,1247.57,1621.84,19.5923,75.1455,15.4381,2.76413,13.4545,2,37.9685,17.5848,70.5596,44.3498,57.5608,,,Spring,0.99,Spring,2,0,4,2,5,1,1,1,2,1,5,0,2,2,5,2,1,3,1,1,41,Spring,40,56,Spring,0,1 +4178a68d,Summer,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +41974cde,Summer,7,0,Fall,63,Summer,25.27858105,51.4,95.0,,71,72,115,Summer,,,,Summer,0,0,,,,,2,0,8.0,1,10.0,1,6.0,1,Fall,2,10.4402,25.2813,1106.26,1770.02,13.9706,60.0947,15.9924,9.28898,34.9053,1,30.6215,15.5026,49.6545,28.682,44.5921,,,,,Summer,0,0,2,0,3,1,0,2,1,1,2,0,1,2,2,2,2,2,1,1,25,Summer,30,43,Summer,2,0 +419d05c6,Spring,15,1,Fall,49,Summer,20.6913636,59.5,104.2,,65,74,119,,,,,Summer,14,0,17.9,2,21.4,2,10,1,9.5,1,11.5,1,9.0,1,Summer,3,4.39307,20.6936,1268.04,2028.87,26.4216,77.3261,15.3566,5.33704,26.8739,1,32.6274,18.2771,72.933,35.9975,59.0489,Spring,2.01,,,Summer,4,5,4,2,5,4,1,2,2,3,4,1,2,3,3,2,5,4,2,2,60,Summer,49,68,Spring,2,2 +41b66459,Spring,16,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.82,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +41bacefb,Spring,11,1,Summer,62,Spring,19.49069069,55.5,85.4,29.0,63,89,110,,,,,Spring,0,0,11.7,1,12.4,2,0,0,9.5,1,11.0,1,10.0,1,Spring,2,3.40435,19.4928,1113.04,1669.56,18.749,60.8162,13.8815,5.61133,24.5838,2,26.0948,15.9724,57.4119,25.9399,44.8438,,,Spring,3.01,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,66,90,Spring,2,0 +41badf5d,Winter,12,0,Spring,70,Winter,24.11196975,57.5,113.4,,54,82,108,,,,,Winter,30,1,16.8,2,18.3,2,,,10.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.48,Winter,4,3,3,1,3,2,2,1,1,2,2,0,2,1,1,2,2,2,1,1,36,Winter,34,49,Winter,2,1 +41cfd1db,Fall,18,0,Winter,65,Fall,25.99665306,70.0,181.2,,106,90,179,,,,,Fall,11,0,23.4,3,28.4,3,5,0,9.0,1,8.0,1,8.0,0,,,,,,,,,,,,,,,,,,Fall,1.26,,,Fall,5,5,5,5,5,5,2,5,5,5,5,2,5,5,5,5,4,5,5,4,92,Fall,75,100,Fall,3,3 +41d48900,Spring,12,1,Fall,61,Summer,21.51961111,60.0,110.2,,61,96,112,,,,,Summer,40,1,22.4,2,26.8,3,15,1,13.0,1,13.0,1,12.0,1,Summer,4,4.72819,21.522,1342.83,2551.38,29.2874,85.292,16.6575,4.86451,24.908,1,36.5346,19.47,80.5638,44.6449,65.822,,,Spring,1.8,Summer,2,2,2,0,2,1,1,1,1,1,3,1,1,1,1,1,2,1,1,1,26,Summer,31,45,Spring,0,0 +41ec1042,Spring,13,0,,,Spring,15.73938889,60.0,80.6,24.0,73,52,108,,,,,Spring,50,1,20.3,2,25.0,2,18,1,4.0,0,7.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,4.33,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +42309938,Summer,20,1,Winter,66,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,4,3,1,3,0,1,2,2,0,4,1,1,2,1,1,2,1,1,1,31,Fall,63,86,Summer,1,1 +423610ac,Winter,10,0,Spring,65,Winter,19.50211392,56.8,89.5,39.0,64,96,115,,,,,Winter,2,0,,,,,0,0,5.0,0,5.0,0,8.0,1,Winter,2,3.72882,19.5168,1138.97,1822.36,14.1078,63.5787,13.8798,5.63702,25.8213,2,29.7679,19.703,59.8499,27.5902,43.8757,,,Winter,1.32,Winter,3,3,5,2,4,2,0,3,2,2,3,0,2,2,2,1,3,1,4,1,45,Winter,45,63,Winter,0,1 +4248261f,Winter,6,0,Spring,55,Spring,18.59173554,49.5,64.8,,75,98,127,,,,,Spring,3,1,,,,,0,0,12.0,1,12.0,1,9.0,1,Spring,2,3.20486,18.5938,1032.55,1652.08,13.1306,52.2431,14.9907,3.60309,12.5569,1,28.4996,10.6129,49.0382,23.4794,41.6302,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,29,42,Winter,0,0 +4255e559,Winter,11,1,Summer,75,Spring,17.26255556,60.0,88.4,,61,74,109,Spring,5,8,6,Spring,15,1,16.2,2,18.3,2,6,0,9.5,1,7.75,0,13.0,1,Spring,3,2.60838,17.2644,1205.28,1928.44,22.8727,70.641,13.7961,3.46832,17.759,2,29.7368,18.0315,68.0326,31.552,52.6095,,,Spring,2.8,Spring,4,0,2,0,5,0,1,0,3,0,4,0,2,0,0,0,0,1,0,0,22,Spring,41,58,Winter,0,0 +4262ce3d,Summer,10,0,,,,,,,,,,,,,,,Fall,1,0,,,,,0,0,7.0,0,9.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.36,Fall,1,1,2,0,1,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,9,Fall,31,45,Summer,0,0 +4268ce08,Summer,13,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.42,,,,,,,,,,,,,,,,,,,,,,,,,,,, +427037b2,Spring,6,1,Summer,80,Winter,14.29577,47.25,45.4,,61,113,120,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,0,2,0,2,1,0,0,0,1,2,0,1,1,1,1,1,1,1,1,18,Summer,33,47,Spring,0,0 +427fad9e,Fall,13,0,Spring,72,Fall,21.18387503,63.0,119.6,,53,63,127,,,,,Fall,75,1,21.8,2,26.7,2,20,1,6.0,0,7.0,0,11.0,1,Winter,4,2.42594,21.1862,1418.16,2978.14,27.7583,93.3159,16.5302,4.65603,26.2841,2,42.4174,23.1402,90.8899,43.0536,70.1757,,,Fall,4.24,Fall,5,4,4,2,5,4,1,4,4,4,4,1,2,3,4,4,4,5,3,4,71,Fall,32,46,Fall,1,2 +4280076d,Fall,10,0,Winter,60,Winter,19.13648393,57.5,90.0,,72,58,108,,,,,Winter,20,1,13.9,2,14.7,2,10,1,14.0,1,15.0,1,12.0,1,Winter,3,4.28314,19.1386,1207.55,2052.84,19.0368,70.8832,15.0734,4.06521,19.1168,2,34.5349,17.3114,66.6001,33.2218,53.5718,,,Fall,3.19,Winter,2,2,1,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,9,Winter,32,46,Fall,0,0 +428970d4,Spring,9,0,Summer,52,Winter,14.26915702,55.0,61.4,,77,73,138,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.60408,14.2707,1057.43,1797.63,12.3152,54.8933,12.7584,1.51229,6.50666,2,27.1858,15.3923,51.2893,23.1506,39.501,,,Summer,2.87,Summer,5,5,2,4,4,3,1,2,3,3,3,0,1,1,2,2,3,2,1,0,47,Summer,57,79,Spring,1,1 +428c37ea,Summer,9,0,Fall,60,Summer,17.81819282,57.5,83.8,,64,80,110,,,,,Summer,10,1,,,,,6,1,7.5,1,7.25,0,4.0,0,Summer,3,4.1919,17.8201,1176.97,2000.84,17.9365,67.6254,14.3806,3.43954,16.1746,2,32.6267,17.0622,63.4335,29.4456,50.5632,,,Fall,3.16,Fall,2,2,2,0,2,0,0,0,0,0,0,0,0,2,2,1,2,2,2,1,20,Fall,41,58,Summer,0,0 +429cc266,Winter,8,0,Summer,80,Summer,16.10872741,51.0,59.6,,67,86,116,Summer,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.03005,16.1105,999.712,1699.51,9.4421,48.7454,13.1764,2.9341,10.8546,2,24.7924,14.511,45.7154,20.2006,34.2345,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,53,73,Winter,0,0 +42aa29fb,Spring,8,0,Fall,45,Fall,16.63086954,49.6,58.2,,65,77,123,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,4,4,0,4,4,0,2,1,2,4,0,1,1,2,2,2,2,2,1,42,Spring,40,56,Spring,2,1 +42bf071e,Winter,9,0,Summer,80,Spring,17.06820933,53.0,68.2,,66,68,118,Spring,,,,Spring,2,0,,,,,3,0,7.0,0,6.5,0,10.5,1,Spring,4,6.27261,17.0701,1052.68,2210.62,12.167,54.3868,13.6127,3.45736,13.8132,2,27.5427,14.6772,48.1142,23.6081,39.7096,,,Spring,3.02,Spring,5,5,5,1,5,2,1,0,1,2,1,0,5,3,5,5,4,5,2,1,58,Spring,52,72,Winter,0,2 +42c6f67a,Fall,7,0,Winter,82,Fall,16.56020142,50.3,59.6,,56,68,91,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,2,3,1,3,1,0,1,1,1,2,0,2,1,2,2,1,2,1,0,30,Fall,38,54,Fall,0,0 +42dfc9dd,Winter,12,0,Spring,65,Winter,23.42660155,59.0,116.0,27.0,65,73,122,,,,,Winter,20,1,17.9,1,18.7,1,8,0,7.0,0,7.5,1,11.0,1,Spring,4,4.34974,20.3187,1229.45,2581.85,19.4792,73.2159,14.7878,5.53091,27.3841,2,34.1377,19.5991,68.8662,31.1381,53.6169,,,Winter,3.98,Winter,4,4,4,0,5,4,0,0,0,0,4,0,5,3,4,5,3,5,3,1,54,Winter,46,64,Winter,2,2 +42ebc3fd,Spring,8,0,Summer,60,Spring,27.08599197,49.13,93.0,,74,63,137,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,1,3.1925,27.089,1102.67,1433.47,15.6323,59.7115,17.3927,9.69625,33.2885,3,31.4147,12.6645,56.519,26.8643,47.047,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,49,68,Spring,2,0 +42edba48,Winter,6,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4307d287,Summer,12,0,Fall,60,Summer,23.44552549,62.0,128.2,,70,75,124,,,,,Summer,3,0,23.6,2,26.7,2,4,0,8.5,1,7.0,0,10.0,1,Fall,1,4.95599,23.4481,1361.8,1770.33,25.8444,87.3122,15.9696,7.47848,40.8878,3,38.1707,23.2971,82.3562,32.3239,64.0151,,,Summer,3.74,Fall,2,2,2,1,5,1,0,1,1,0,1,0,0,1,1,2,2,2,4,3,31,Summer,52,72,Summer,1,1 +432a4c21,Winter,10,0,Fall,45,Spring,15.33689083,54.5,64.8,,66,61,97,Spring,4,5,50,Spring,6,0,15.5,2,16.8,2,13,1,7.0,0,7.0,0,10.0,1,Summer,3,3.25948,15.3386,1047.25,1780.33,8.43964,53.809,12.7369,2.60164,10.991,1,26.0137,19.3557,50.5495,29.4245,34.4533,,,Summer,2.55,Summer,2,2,4,5,5,1,0,2,2,3,3,3,1,1,2,2,2,,1,1,42,Summer,51,71,Winter,2,1 +432aff11,Fall,8,0,Winter,85,Fall,16.43306421,51.0,60.8,,69,98,115,Fall,3,3,51,Fall,3,0,,,,,0,0,5.0,0,6.0,0,6.0,1,Fall,2,2.85581,16.4349,1034.47,1655.16,11.4102,52.448,14.1772,2.25763,8.352,1,29.6501,11.3877,49.5922,31.5582,41.0603,,,,,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Fall,34,49,Fall,0,0 +435bfa7b,Winter,11,1,Winter,82,Spring,20.96218182,55.0,90.2,29.0,67,87,116,,,,,Spring,7,0,19.3,2,19.3,2,2,0,12.0,1,11.5,1,6.0,0,Spring,2,3.43926,20.9645,1126.25,1689.37,19.7238,62.2229,14.462,6.50249,27.9771,2,27.2775,15.2216,58.7837,28.2032,47.0013,,,Spring,1.25,Spring,3,4,1,1,3,1,1,1,1,4,4,1,1,1,2,2,1,4,1,1,38,Spring,35,50,Winter,2,1 +43629a34,Winter,7,1,Fall,60,Spring,13.68791583,48.5,45.8,,60,63,105,Spring,5,7,20,Spring,6,1,,,,,7,1,13.0,1,12.0,1,10.5,1,Spring,3,2.47613,13.6894,955.724,1529.16,11.3107,44.0602,13.1694,0.520019,1.7398,1,21.4793,11.2702,41.5841,18.8514,32.79,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,31,45,Winter,0,0 +436dd613,Fall,10,1,Winter,78,Winter,16.0353719,55.0,69.0,,67,67,128,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.67,Winter,4,1,1,1,2,1,0,0,0,0,2,0,1,1,0,0,0,0,0,0,14,Fall,29,42,Fall,0,0 +43720c15,Winter,16,1,Spring,70,Spring,28.74156821,66.5,180.8,,81,100,122,,,,,Spring,14,0,27.8,2,26.5,2,4,0,8.0,0,9.0,0,13.0,1,Spring,1,5.50283,28.7447,1485.97,1931.76,35.6923,100.539,15.9842,12.7605,80.2614,3,37.4116,27.4346,95.0357,45.6788,73.1039,Winter,1.541,,,Spring,1,3,2,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,13,,,,Winter,1,0 +43808706,Summer,17,0,Spring,75,Winter,25.70477201,63.75,148.6,,103,107,136,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.37,,,Summer,5,1,3,5,4,1,3,3,5,1,4,2,1,3,1,2,2,5,2,3,56,Summer,40,56,Summer,3,2 +439c1716,Winter,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +43a7386d,Winter,10,0,Spring,60,Spring,14.02811791,52.5,55.0,,54,92,92,Spring,8,13,8,Spring,17,1,10.3,1,10.5,1,4,0,6.25,0,6.0,0,9.0,1,Spring,2,2.94312,15.1629,981.928,1571.09,8.36668,46.8513,12.9164,2.24652,8.14872,1,24.0623,14.4223,43.9082,20.1626,32.4289,,,Spring,2.42,Spring,2,2,2,1,2,1,3,2,2,3,2,0,3,3,2,2,2,3,1,2,40,Spring,60,82,Winter,0,1 +43d43363,Winter,16,0,Summer,65,Winter,18.33441358,72.0,135.2,,69,72,139,,,,,Winter,24,1,33.1,2,34.8,2,29,1,6.5,0,7.0,0,9.5,1,Spring,4,7.48407,18.8566,1693.06,3555.43,36.3533,122.596,17.0987,1.75784,12.6036,2,55.0515,31.1916,115.112,71.7589,91.4048,Winter,4.52,,,Winter,5,5,5,4,5,5,2,4,1,5,5,0,5,5,5,5,5,5,4,5,85,Winter,45,63,Winter,2,3 +43e54b20,Spring,11,0,Summer,55,Spring,33.07370396,63.75,191.2,,79,93,125,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,5.41757,33.0773,1572.86,2516.57,32.3872,109.793,18.994,14.0833,81.407,3,47.7573,29.6485,104.375,48.4984,80.1445,,,Spring,2.4,Spring,5,5,4,4,4,1,0,3,4,4,0,2,1,1,3,2,2,1,1,1,48,Spring,39,55,Spring,0,1 +43f44f6c,Fall,14,1,Spring,61,Fall,14.54372779,57.5,68.4,,66,78,106,,,,,Fall,11,0,12.2,1,12.4,1,21,1,7.0,0,10.0,1,8.0,0,Fall,3,3.6757,14.5453,1113.31,1781.29,18.6805,60.8449,12.9387,1.6066,7.55512,1,25.9524,16.212,57.1692,24.4027,44.6328,,,Winter,2.97,Fall,4,3,3,1,5,2,1,2,3,4,3,0,3,1,2,4,3,4,4,1,53,Fall,27,40,Fall,1,2 +43f5d3c5,Summer,10,0,,,Summer,17.42871581,58.0,83.4,,52,69,91,,,,,Summer,12,1,,,,,6,1,5.0,0,7.0,0,12.0,1,Summer,3,4.37741,17.4306,1202.04,2043.47,19.6333,70.2962,14.6919,2.73869,13.1038,2,33.6705,16.9924,65.9188,29.8965,53.3038,,,,,Summer,3,2,2,1,2,1,0,1,1,2,3,1,1,1,4,2,2,3,2,2,36,Summer,28,41,Summer,2,1 +43fb80d6,Spring,8,0,Summer,72,Spring,16.14152047,57.0,74.6,22.0,55,74,89,,,,,Spring,0,0,,,,,0,0,0.0,0,0.0,0,11.0,1,Spring,3,3.98104,16.1433,1127.51,1916.76,15.6143,62.3571,13.4939,2.64934,12.2429,1,30.0476,16.6952,58.3761,25.9539,45.6619,,,,,Summer,3,3,3,1,4,2,0,1,0,2,1,0,1,0,2,0,0,0,1,0,24,Summer,53,73,Spring,2,0 +43fdba8b,Spring,7,0,Fall,60,Summer,20.50655782,52.5,80.4,,81,99,121,Summer,7,11,37,Summer,4,1,,,,,5,1,8.0,1,10.0,1,10.0,1,Fall,5,4.81328,20.5088,1113.73,2672.96,15.8607,60.8902,15.5321,4.97666,19.5098,3,31.432,13.5974,56.0769,28.0325,47.2928,,,,,Summer,1,1,2,0,2,0,0,1,1,1,0,0,2,1,3,2,1,2,1,2,23,Summer,45,63,Spring,0,0 +440acf4f,Spring,7,1,Fall,60,Summer,16.22082466,49.0,55.4,,64,100,119,Summer,4,6,19,Summer,7,1,,,,,4,1,12.0,1,10.0,1,8.0,1,Summer,1,2.51816,16.2226,976.359,1269.27,12.1484,46.258,13.5456,2.67702,9.14198,1,21.8743,12.2353,43.7399,19.8654,34.0227,,,,,Summer,1,0,2,0,2,0,0,1,0,0,2,0,0,0,0,0,2,0,0,0,10,Summer,38,54,Spring,0,0 +44160dec,Winter,10,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +444131bc,Summer,6,0,Fall,61,Fall,15.95216815,49.5,55.6,,61,73,109,Fall,2,1,32,Fall,10,1,,,,,5,1,10.0,1,10.0,1,8.0,1,Fall,3,3.28648,15.9539,994.339,1690.38,11.3106,48.1732,13.8229,2.13106,7.42682,2,26.3574,10.5052,44.8867,20.6734,37.668,,,,,Fall,2,0,2,0,1,0,0,0,0,0,2,0,0,0,2,2,0,2,0,0,13,Fall,32,46,Summer,1,0 +4442c376,Fall,5,0,Winter,60,Winter,13.53925926,45.0,39.0,,59,98,104,Winter,,,,Winter,0,0,,,,,1,0,9.0,1,8.0,1,8.0,1,Winter,5,6.35463,13.7491,886.398,2127.35,4.85404,36.676,12.7338,1.01521,2.92402,2,21.7836,10.0383,30.3214,18.8905,26.6376,,,,,Winter,5,2,2,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,13,Winter,36,51,Fall,2,0 +44596139,Spring,8,1,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,3.27678,25.3798,1130.69,1696.03,20.8933,62.696,15.6924,9.6874,38.704,3,27.7584,14.0443,59.4192,28.0858,48.6516,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +446757cb,Summer,14,0,Fall,70,Fall,22.68959961,64.0,132.2,,78,89,115,,,,,Fall,31,1,29.6,2,25.8,2,15,1,7.5,1,8.0,1,11.0,1,Fall,3,2.63225,22.6921,1501.82,2553.09,30.4859,102.226,17.5471,5.14496,29.9736,2,46.5331,25.2074,99.5941,51.5907,77.019,,,Fall,2.64,Fall,4,4,4,1,3,1,3,2,1,2,0,0,3,0,2,2,0,4,3,0,39,Fall,36,51,Summer,1,1 +446b29e4,Spring,12,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +4496d883,Summer,11,0,,,Fall,14.29034336,54.6,60.6,,66,61,126,Fall,8,15,0,Fall,30,1,16.3,2,16.3,2,31,1,7.0,0,10.0,1,7.0,0,,,,,,,,,,,,,,,,,,,,Summer,4.39,Fall,5,5,5,1,5,3,0,2,2,5,,0,3,3,5,5,,3,2,2,56,Fall,34,49,Summer,0,2 +44a76f2a,Spring,8,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +44b27c01,Summer,11,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +44b8622c,Spring,13,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.36,Summer,4,4,5,2,3,3,0,0,0,0,3,0,0,2,2,0,1,0,0,1,30,Summer,44,62,Spring,3,0 +44cbf5c5,Summer,5,0,Summer,60,Summer,15.62222222,48.0,51.2,22.0,70,80,131,,,,,Summer,3,1,,,,,4,1,11.0,1,11.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,2,1,2,0,2,0,0,0,0,0,0,0,0,0,2,1,2,1,0,0,13,,,,Summer,1,0 +44eaea78,Fall,18,0,Spring,65,Winter,17.85760228,72.25,132.6,,69,84,109,,,,,,,,,,,,,,,,,,,,Summer,3,7.0938,17.8596,1571.79,2672.04,32.4548,109.679,14.7724,3.08712,22.9206,1,47.2081,30.0164,102.586,52.1774,79.663,Winter,1.4,,,Winter,4,4,4,2,3,5,1,2,2,4,4,1,2,2,2,1,3,3,3,4,56,Winter,35,50,Fall,1,2 +4505724a,Winter,5,0,Spring,83,Spring,16.52483951,45.0,47.6,23.0,61,83,110,,,,,Spring,0,0,,,,,2,0,10.0,1,11.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,1,0,2,1,3,3,2,2,0,0,14,,,,Winter,0,0 +4507da27,Spring,10,0,Spring,55,Spring,17.01076806,56.63,77.6,,59,75,101,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.61,Summer,2,3,3,1,3,2,1,2,1,3,3,1,3,3,3,4,3,3,3,2,49,Summer,61,84,Spring,2,1 +4518ed77,Winter,6,0,Summer,45,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,3,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,Summer,44,62,Winter,0,0 +451b0cd9,Winter,7,0,Summer,67,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,0,1,0,2,1,0,1,1,2,0,0,2,1,2,2,2,2,1,1,24,Winter,31,45,Winter,0,0 +451c11b3,Spring,9,0,Summer,70,Spring,24.36712287,57.5,114.6,,115,78,146,Spring,5,7,30,Spring,10,1,,,,,1,0,8.0,1,9.0,1,11.0,1,Summer,3,4.38936,24.3698,1287.96,2189.53,20.1113,79.4476,16.8946,7.47519,35.1524,2,40.0944,19.242,75.0582,50.5752,60.2056,,,Spring,1.96,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,38,54,Spring,2,0 +455ae5db,Spring,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4570c8b8,Summer,7,0,Winter,55,Summer,16.82526316,47.5,54.0,,62,97,193,,,,,Summer,1,0,,,,,0,0,11.0,1,13.0,1,8.0,1,Summer,2,2.64377,16.8271,947.813,1516.5,7.48601,43.2175,13.4671,3.35997,10.7825,1,22.9277,12.8038,40.5737,17.3781,30.4137,,,,,Fall,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,41,58,Summer,2,0 +45759ae9,Winter,15,0,,,Winter,19.88282828,66.0,123.2,26.0,77,87,126,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.27,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +45a6eecc,Winter,12,1,,,Winter,16.40156211,63.0,92.6,24.0,55,82,98,,,,,Winter,8,0,23.6,3,23.8,3,2,0,6.0,0,6.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,1.75,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +45ea366f,Spring,6,1,Spring,80,Spring,14.05414411,49.0,48.0,,56,83,93,Spring,6,10,55,Spring,0,0,,,,,0,0,11.5,1,11.0,1,5.5,1,Spring,1,9.59755,14.0557,964.235,1253.51,11.5085,44.9667,13.1674,0.888244,3.03334,1,20.9467,12.5115,35.3691,17.4345,32.4552,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,40,56,Spring,0,0 +45eb32f2,Fall,14,0,,,Winter,22.43075167,73.25,171.2,,54,68,118,,,,,Winter,50,1,34.1,2,38.1,2,,,4.5,0,4.0,0,10.0,1,Winter,4,8.16867,22.4332,1863.74,3913.85,45.7193,140.776,18.4465,3.98667,30.4245,3,58.3913,36.6649,132.607,63.9942,104.111,Fall,3.5,,,Winter,2,4,4,2,4,5,2,2,5,4,3,1,4,5,5,5,5,5,4,5,76,Winter,64,,Fall,0,2 +46170319,Spring,8,0,Fall,65,Summer,16.15260631,54.0,67.0,,46,77,108,Summer,6,9,30,Summer,4,0,,,,,11,1,7.5,1,8.5,1,6.0,1,Summer,3,4.14136,16.1544,1072.49,1823.24,13.4354,56.4976,13.6221,2.53223,10.5024,2,28.7534,14.3089,52.3563,25.2213,42.1888,,,,,Summer,1,1,2,1,1,1,0,1,1,1,1,1,1,1,1,1,1,2,1,1,21,Summer,34,49,Spring,0,0 +461e2e46,Winter,6,0,,,Winter,,,,,,,,,,,,Winter,0,0,,,,,0,0,9.0,1,8.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +463bf3b2,Spring,7,0,Summer,51,Spring,15.65758262,47.0,49.2,23.0,51,72,84,,,,,Spring,1,0,,,,,0,0,10.0,1,7.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,Summer,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Summer,36,51,Spring,0,0 +464a75fb,Summer,14,0,Spring,85,Fall,25.27434964,62.0,138.2,,179,103,139,,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.87324,25.2771,1312.37,2231.03,13.6076,82.0476,15.0067,10.2704,56.1524,1,33.8437,34.5963,78.1743,53.656,47.4513,Fall,2.77,,,Winter,2,3,3,1,3,2,2,0,1,0,3,0,1,3,3,0,2,0,2,1,32,Winter,51,71,Summer,3,1 +4659aa1a,Fall,13,0,Winter,85,Fall,21.39262051,71.0,153.4,,72,54,142,,,,,Fall,22,1,23.9,2,26.2,2,20,1,5.0,0,5.0,0,12.0,1,Fall,4,-2.16216,21.395,1714.69,3600.85,38.7479,124.9,17.4201,3.9749,28.4997,3,53.7917,32.3607,127.062,61.4573,92.5396,,,Winter,2.24,Fall,1,2,1,2,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,12,Fall,33,47,Fall,2,0 +465d3273,Summer,10,0,Fall,67,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.86,Summer,4,4,4,1,5,0,0,2,1,4,5,0,2,0,2,3,3,3,2,0,45,Summer,26,38,Summer,3,1 +46610e5f,Spring,5,1,,,Spring,15.3346436,42.5,39.4,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +466752d9,Winter,8,1,Spring,75,Winter,15.21639055,50.5,55.2,,60,75,105,Winter,4,6,19,Winter,25,1,,,,,7,1,15.0,1,14.0,1,14.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.94,Spring,3,3,3,0,2,2,0,1,0,2,0,0,0,0,0,1,1,1,0,0,19,Spring,45,63,Winter,1,0 +466a0bc5,Winter,5,0,,,Winter,14.71960494,45.0,42.4,,85,112,125,,,,,Winter,1,0,,,,,6,1,7.0,0,6.0,0,7.0,1,Spring,3,2.40233,14.7212,893.616,1519.15,5.20998,37.4449,13.0008,1.72042,4.95515,1,21.4164,10.8185,35.0425,16.8539,26.6264,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +467c549f,Winter,8,0,Spring,80,Spring,15.04010171,48.44,50.2,,81,94,109,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,0,3,0,3,0,0,2,2,4,0,0,0,0,2,4,3,3,0,0,29,Winter,39,55,Winter,0,0 +46850373,Winter,9,0,Fall,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.4,Spring,1,4,5,0,3,2,0,0,0,0,0,0,0,0,4,2,5,2,0,0,28,Winter,31,45,Winter,0,0 +471e4d12,Fall,17,0,Winter,82,Fall,18.8720708,71.75,138.2,,67,88,100,,,,,Fall,14,0,42.0,2,37.1,2,10,0,1.0,0,4.0,0,7.5,0,Fall,3,3.14723,18.8741,1495.83,2542.91,28.4418,101.588,13.874,5.00009,36.6116,2,42.8895,30.2571,98.4412,44.8299,71.3313,Fall,1.46,,,Fall,4,4,4,4,4,3,3,3,2,4,4,1,1,3,3,1,3,1,4,2,58,Fall,44,62,Fall,2,2 +47240aa3,Summer,7,0,Winter,55,Summer,15.91217746,47.0,50.0,,70,78,114,Summer,5,7,0,Summer,0,0,,,,,0,0,7.0,0,8.0,1,7.0,1,Fall,1,9.33958,15.9139,933.315,1213.31,6.78643,41.6733,13.2637,2.65022,8.32673,1,21.7549,13.1319,32.3337,15.3553,28.5414,,,,,Summer,2,2,3,0,2,0,0,2,0,0,0,0,2,0,2,2,1,1,1,0,20,Summer,31,45,Summer,1,0 +4734a934,Summer,9,0,,,Summer,15.75135392,56.69,72.0,22.0,67,69,112,,,,,,,,,,,,,,,,,,,,Summer,3,3.38413,15.7515,1090.38,1853.65,8.59826,58.4028,12.7768,2.97468,13.5972,1,26.3564,23.4481,55.0186,32.1185,34.9547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +47350ac6,Spring,12,1,Summer,60,Summer,25.94201582,56.5,117.8,,57,73,107,Summer,5,7,25,Summer,10,0,15.7,2,15.9,2,11,1,12.0,1,14.0,1,12.0,1,Summer,3,3.96879,25.9448,1249.13,1998.61,26.7309,75.3122,16.5871,9.35773,42.4878,3,31.6685,16.9128,71.3434,32.9426,58.3994,,,Spring,2.46,Summer,3,2,1,0,2,0,0,1,0,0,2,0,0,0,2,0,1,0,0,1,15,Summer,42,59,Spring,2,0 +473dc7fc,Winter,10,0,,,Winter,23.14403292,54.0,96.0,31.0,77,87,143,,,,,Winter,4,0,,,,,0,0,4.0,0,5.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.78,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +47588d91,Spring,5,0,Fall,40,Summer,15.91217746,47.0,50.0,,54,62,87,Summer,,,,Summer,0,0,,,,,0,0,9.0,1,10.0,1,5.0,0,Summer,1,2.71582,15.9139,937.411,1218.63,8.49397,42.1096,13.4026,2.51135,7.89041,1,23.5315,10.0841,39.3938,16.9412,32.0255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +475ebe47,Summer,7,1,Spring,78,Fall,16.65963542,48.0,54.6,,63,119,100,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2,2,0,2,1,0,0,1,2,2,0,0,0,0,1,1,2,1,0,19,Summer,35,50,Summer,0,0 +4777151c,Fall,12,1,Winter,70,Winter,25.32415506,69.5,174.0,,62,76,78,,,,,Winter,30,1,24.4,2,20.6,2,,,4.0,0,5.0,0,20.0,1,Winter,3,-0.599262,25.3269,1555.49,2488.78,38.9553,107.943,15.7119,9.61505,66.057,3,39.4832,29.5045,108.542,46.7128,78.4385,,,Fall,1.93,Fall,4,4,4,1,4,3,0,4,5,3,1,1,4,0,4,4,4,5,5,4,64,Fall,59,81,Fall,0,2 +47a2cce9,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +47a52eb8,Fall,14,1,Spring,91,Winter,18.34500537,66.5,115.4,28.0,67,66,108,,,,,Winter,46,1,22.9,2,29.4,3,0,0,11.5,1,13.0,1,12.0,1,Spring,2,5.47682,18.347,1378.06,2067.1,31.5252,89.045,14.1569,4.19009,26.355,2,33.6992,23.8206,83.5682,34.3804,65.2244,,,Winter,2.08,Winter,5,5,4,4,4,1,3,3,0,3,1,0,1,3,4,0,0,0,2,0,43,Winter,51,71,Fall,2,1 +47acbbc6,Spring,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +47b3bea1,Spring,6,0,Winter,60,Spring,17.63575309,45.0,50.8,,87,138,156,Spring,2,3,0,Spring,0,0,,,,,0,0,12.0,1,12.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,5,5,5,5,5,5,5,1,1,3,2,0,3,0,3,3,2,2,5,1,61,Fall,40,56,Spring,0,2 +47b45998,Fall,12,1,,,Winter,20.21526471,61.0,107.0,31.0,73,79,124,,,,,Winter,1,0,17.3,2,14.9,2,0,0,9.0,0,7.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.73,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +47c088d9,Winter,8,1,,,Winter,15.12245722,48.5,50.6,22.0,63,90,101,,,,,Winter,0,0,,,,,0,0,5.0,0,4.0,0,14.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,1.44,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +47c1f368,Winter,13,0,Fall,75,Spring,25.0237793,64.0,145.8,,61,62,130,,,,,Spring,31,1,37.0,3,37.4,3,13,1,7.0,0,6.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.81,Winter,3,5,5,5,5,0,0,3,1,1,4,1,4,2,3,4,5,3,3,2,59,Winter,47,66,Winter,1,2 +47c3b47c,Spring,8,0,Summer,61,Spring,15.49511834,52.0,59.6,21.0,59,63,99,,,,,Spring,3,0,,,,,1,0,12.0,1,12.0,1,12.0,1,Spring,2,3.29878,15.4968,1022.98,1636.77,11.7721,51.2238,13.3189,2.17793,8.37622,1,26.3781,13.0736,47.925,20.7669,38.1502,,,Spring,4.06,Spring,5,4,2,0,0,0,0,0,0,0,2,0,2,4,2,5,2,4,0,0,32,Spring,45,63,Spring,0,1 +47c55e13,Summer,8,0,,,Fall,27.47931648,53.0,109.8,33.0,76,83,128,,,,,Fall,0,0,,,,,0,0,7.0,0,6.0,0,10.0,1,Fall,2,3.61903,27.4823,1186.71,1898.73,18.2348,68.6631,17.186,10.2963,41.1369,3,33.8662,16.5621,65.0441,30.3458,52.101,,,,,Fall,2,1,1,0,2,2,0,0,0,0,2,0,0,1,1,1,0,2,2,0,17,Fall,44,62,Fall,2,0 +47fef4fc,Winter,9,1,Spring,45,Winter,17.71051051,55.5,77.6,,67,79,102,Winter,6,9,44,Winter,33,1,,,,,15,1,10.3,1,9.0,1,11.5,1,Winter,5,4.69499,17.7124,1115.0,2453.01,17.7809,61.0255,13.9293,3.78317,16.5745,3,28.4731,14.7714,56.3305,35.4502,46.2541,,,Winter,3.51,Winter,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Winter,30,43,Winter,0,0 +48114f96,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +481a1861,Spring,12,0,,,Spring,22.90738926,57.25,106.8,32.0,64,66,124,,,,,Spring,16,1,17.0,2,19.7,2,5,0,8.0,1,8.0,1,11.0,1,Spring,2,4.45328,22.9099,1286.09,2057.74,23.135,79.2482,16.9997,5.91019,27.5518,3,37.7398,18.3734,74.7949,35.2099,60.8749,,,Summer,3.15,Summer,1,2,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,Summer,27,40,Spring,0,0 +4829f7ca,Winter,11,0,Fall,50,Spring,31.96933043,61.5,172.0,39.0,77,88,118,,,,,Spring,5,0,12.8,2,10.9,1,0,0,0.0,0,0.0,0,2.0,0,Spring,2,5.30163,31.9728,1547.78,2476.45,33.3426,107.122,19.9127,12.0601,64.8781,3,46.9057,26.8736,101.82,44.4261,80.2483,,,,,Spring,5,2,0,1,1,1,1,1,1,1,1,1,1,1,1,2,2,5,0,1,29,Winter,42,59,Winter,2,0 +4833c27b,Winter,6,1,,,Winter,15.8175,40.0,36.0,21.0,69,98,117,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +4859a9f9,Summer,6,0,Winter,65,Summer,14.86061567,43.5,40.0,,66,87,114,Winter,4,6,53,Summer,7,1,,,,,5,1,9.0,1,9.0,1,6.0,1,Fall,2,2.98827,14.8622,1015.35,1624.56,14.027,50.4113,18.7306,-3.86838,-10.4113,1,31.7485,4.63581,47.423,29.9551,45.7755,,,,,Summer,4,0,3,0,0,0,0,0,0,2,0,0,0,2,0,2,2,2,0,0,17,,,,Fall,0,0 +485d7c69,Winter,8,0,,,Winter,19.28771887,45.5,56.8,24.0,,105,,,,,,Winter,16,1,,,,,0,0,6.5,0,5.0,0,10.0,1,Winter,2,3.17806,15.0569,1004.21,1606.74,10.5248,49.2246,13.0488,2.00813,7.57536,1,25.4481,13.2518,46.0466,20.2231,35.9729,,,Winter,2.36,Winter,3,3,3,0,3,0,0,0,0,0,2,0,0,2,2,2,2,1,1,0,24,Winter,40,56,Winter,1,0 +486ba65d,Fall,21,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4876cb0f,Spring,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +489fa831,Spring,9,0,Summer,70,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.25,Spring,4,2,2,0,2,1,1,1,1,1,2,0,0,0,3,4,3,4,3,3,37,Spring,53,73,Spring,0,1 +48c205d9,Winter,8,0,Spring,67,Spring,14.63973344,49.0,50.0,,63,108,103,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.31,Spring,5,0,2,0,3,0,0,0,0,2,1,0,0,1,3,2,3,3,2,0,27,Spring,47,66,Winter,0,0 +48d285f4,Fall,5,1,,,Fall,25.00466472,49.0,85.4,,66,84,113,,,,,Fall,2,1,,,,,0,0,16.0,1,17.0,1,10.0,1,Winter,2,2.88613,25.0074,1066.71,1600.07,19.0206,55.882,16.3637,8.64366,29.518,2,26.7449,10.1164,52.9959,25.0158,45.7656,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +48db8ecf,Spring,5,0,Fall,50,Fall,23.31129769,48.5,78.0,,115,70,139,Fall,,,,Fall,2,1,,,,,1,0,4.0,0,6.0,0,13.0,1,Fall,3,4.31473,23.3138,1095.38,1862.15,14.85,58.9355,17.6155,5.6983,19.0645,2,33.5804,10.505,54.6207,36.0658,48.4304,,,,,Fall,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,,,,Spring,0,0 +48df987b,Fall,6,0,Spring,80,Fall,17.69652585,47.25,56.2,,74,86,103,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,2,2,1,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,1,27,Winter,53,73,Fall,0,0 +48e69212,Summer,8,0,Winter,85,Fall,19.03116222,51.5,71.8,,61,87,116,Fall,5,7,2,Fall,19,1,,,,,3,0,12.0,1,12.0,1,11.0,1,Fall,3,6.37917,19.0332,1050.47,1785.81,12.3316,54.1524,14.3551,4.67816,17.6476,2,28.2642,13.5566,47.7732,24.8976,40.5957,,,Fall,2.91,Fall,3,2,2,0,3,3,0,0,0,3,1,0,0,0,0,2,1,3,0,0,23,Fall,28,41,Summer,2,0 +48f3d473,Winter,9,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.329,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +48fab8bb,Spring,17,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +4916aea0,Spring,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +491bf457,Summer,10,1,,,Summer,14.06500534,53.0,56.2,23.0,65,98,106,,,,,Summer,10,0,12.2,2,9.7,1,2,0,8.0,0,9.0,0,8.0,0,Fall,2,2.85208,14.0665,1021.55,1532.33,12.7027,51.0718,12.783,1.28355,5.12816,1,22.2328,16.1363,48.2198,21.7627,34.9355,,,Summer,1.5,Summer,0,3,1,1,2,2,1,1,1,1,2,1,1,1,1,1,1,1,1,1,24,Summer,48,67,Summer,2,0 +4926b18a,Winter,9,1,Summer,80,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.17,Spring,2,2,2,1,2,1,0,2,2,2,0,0,1,1,2,2,2,2,2,1,29,Spring,42,59,Winter,0,0 +49358287,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4940a214,Spring,12,1,Fall,85,Summer,17.2640999,62.0,94.4,,54,52,87,,,,,Summer,25,1,15.4,1,14.2,1,25,1,5.0,0,4.0,0,5.0,0,Summer,3,4.52469,17.266,1243.34,1989.35,24.6603,74.6952,13.6619,3.60406,19.7048,1,30.3322,19.7027,70.1705,31.5772,54.9925,,,Spring,2.21,Summer,1,1,2,2,2,1,3,2,2,2,1,1,1,2,2,2,2,2,1,2,34,Summer,38,54,Spring,2,1 +4942af36,Spring,8,0,,,Spring,15.00228108,51.5,56.6,22.0,76,78,103,,,,,Spring,0,0,,,,,0,0,9.5,1,10.0,1,8.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +494eba8a,Summer,9,1,Spring,50,Summer,18.27172797,54.5,77.2,,80,97,128,Summer,,,,Summer,6,0,,,,,0,0,12.0,1,12.0,1,10.0,1,Summer,1,3.35079,18.2737,1095.81,1424.56,18.8334,58.9813,13.9613,4.31247,18.2187,1,25.803,14.3449,55.6306,23.5782,44.6365,,,Summer,1.92,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,28,41,Summer,2,0 +4950e24c,Spring,14,0,Summer,54,Spring,22.09278196,65.25,133.8,,92,68,151,,,,,Summer,,,,,,,,,,,,,,,Summer,3,6.1028,22.0952,1542.94,2622.99,32.2166,106.606,17.6045,4.49071,27.194,2,48.0072,26.3822,100.503,53.5501,80.2238,Spring,3.19,,,Spring,3,0,2,2,2,1,2,1,2,2,1,0,2,0,2,0,1,1,0,0,24,Spring,40,56,Spring,0,0 +49887971,Spring,11,1,,,Spring,27.79399524,58.0,133.0,35.0,55,91,114,,,,,Spring,10,0,20.6,2,20.9,2,0,0,6.5,0,5.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.54,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +49909509,Winter,8,0,,,Summer,19.52777778,60.0,100.0,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +499d4499,Summer,15,1,,,Summer,29.34887695,64.0,171.0,38.0,79,80,127,,,,,Summer,0,0,36.0,3,33.0,3,1,0,14.0,1,14.0,1,12.0,1,Fall,1,5.25757,29.3521,1469.84,1910.79,35.842,98.82,16.9624,12.3897,72.18,2,37.6063,25.3718,93.5624,44.1761,73.4482,Summer,1.45,,,Summer,2,3,2,1,2,2,1,2,1,2,2,0,0,0,1,1,1,1,1,0,25,Summer,39,55,Summer,3,0 +49a1931d,Spring,9,1,Fall,70,Summer,18.02394194,51.5,68.0,,64,82,113,Summer,5,7,20,Summer,10,1,,,,,1,0,11.0,1,12.0,1,12.0,1,Fall,1,10.0605,18.0259,1024.44,1331.77,13.868,51.3793,13.62,4.40593,16.6207,2,22.903,14.6083,41.3188,22.2441,36.771,,,Summer,2.441,Summer,2,1,2,1,2,1,2,1,0,1,2,0,1,1,2,2,1,2,1,1,26,Summer,37,53,Spring,2,0 +49b86b1e,Fall,13,0,,,Fall,19.22995012,64.5,113.8,,71,83,123,,,,,Fall,40,1,25.9,2,28.4,2,19,1,10.5,1,11.5,1,6.0,0,Fall,3,5.39999,19.2321,1367.13,2324.12,25.7112,87.8805,14.8517,4.38036,25.9195,2,38.652,23.5173,82.4805,35.3887,64.3632,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +49e4eade,Winter,9,1,Summer,80,Spring,15.7472,50.0,56.0,,79,80,164,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.61903,15.7489,988.674,1581.88,12.6843,47.5698,13.3781,2.37083,8.43018,1,22.0113,12.8743,44.9508,19.6322,34.6956,,,Winter,1.79,Spring,1,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,Spring,42,59,Winter,1,0 +49f38606,Fall,6,0,Winter,75,Winter,15.55279012,45.0,44.8,,92,95,151,Winter,7,11,5,Winter,5,1,,,,,5,1,7.5,1,10.5,1,10.0,1,Winter,4,6.0841,15.5545,899.679,1889.33,6.64946,38.0906,13.225,2.3295,6.70942,1,21.8662,9.57494,32.0065,15.0534,28.5156,,,,,Winter,4,0,2,0,2,1,0,0,2,0,0,0,0,0,2,1,0,2,0,0,16,Winter,43,60,Fall,0,0 +4a01c5d9,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4a0b1b9d,Fall,14,0,Spring,78,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,3,4,0,3,3,1,2,3,3,0,1,1,3,3,2,2,3,3,1,44,Winter,35,50,Fall,1,1 +4a1440d9,Winter,6,0,Fall,70,Winter,19.50712234,48.25,64.6,25.0,53,83,106,,,,,Winter,1,0,,,,,1,0,8.0,1,9.0,1,6.0,1,Winter,2,2.95998,19.713,1000.81,1601.3,11.4162,48.8626,14.9107,4.80235,15.7374,1,27.1695,10.2769,45.9026,22.1199,38.5857,,,,,Winter,4,4,4,3,3,1,0,4,2,2,2,2,4,2,2,4,4,5,4,4,60,,,,Winter,3,2 +4a17b463,Winter,7,1,Summer,45,Spring,13.89128,50.0,49.4,,80,92,124,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,2.64089,13.8928,979.018,1468.53,12.8155,46.5413,13.0888,0.80395,2.85869,3,21.5784,12.1474,43.9004,17.3328,34.3939,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,46,64,Winter,0,0 +4a21eb0c,Fall,10,0,,,Fall,14.31563636,55.0,61.6,24.0,66,89,120,,,,,Fall,10,0,15.7,2,13.6,2,3,0,10.0,1,10.0,1,12.0,1,Fall,2,3.72926,14.3172,1071.97,1715.16,13.4412,56.4423,13.1184,1.19877,5.15771,1,28.6172,14.3839,52.713,25.2802,42.0584,,,Winter,2.18,Winter,0,3,4,0,1,1,0,3,0,1,3,0,1,1,1,1,1,2,1,1,25,Winter,69,94,Fall,1,0 +4a310295,Spring,6,0,,,Spring,14.30301235,45.0,41.2,21.0,86,77,134,,,,,Spring,0,0,,,,,0,0,7.0,0,7.0,0,8.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +4a3b6521,Summer,6,0,Winter,63,Fall,15.29291412,47.75,49.6,,79,91,116,,,,,Fall,40,1,,,,,8,1,7.0,0,8.0,1,6.0,1,Fall,2,7.87033,15.2946,939.615,1503.38,7.05624,42.3443,13.0572,2.23735,7.25568,2,22.7711,12.517,34.474,17.9642,29.8274,,,,,Fall,3,3,2,2,3,1,0,1,1,1,1,1,2,2,3,1,3,1,2,1,34,Fall,37,53,Summer,2,1 +4a475a9f,Spring,18,1,Spring,75,Winter,21.46727135,63.0,121.2,,58,78,113,,,,,Spring,,,,,,,,,,,,,,,Summer,3,5.10711,21.4696,1380.08,2208.13,28.2032,89.2598,15.8117,5.65795,31.9402,2,39.1081,21.9485,84.1527,58.8232,67.3112,,,,,Spring,3,4,2,1,2,2,4,1,1,1,4,0,0,0,1,1,2,2,1,0,32,Spring,52,72,Spring,3,1 +4a4a0f78,Fall,13,0,Spring,55,Winter,22.30901301,64.25,131.0,,51,92,133,,,,,Winter,8,0,29.6,2,29.9,2,1,0,8.0,1,10.5,1,9.0,1,Winter,3,5.6299,22.3114,1456.95,2476.82,28.7378,97.4477,16.5969,5.7145,33.5523,2,43.8457,24.8642,91.8178,45.7832,72.5835,,,Winter,1.81,Fall,4,4,4,0,4,3,1,3,2,4,2,0,3,3,5,2,3,1,3,3,54,Fall,53,73,Winter,,2 +4a62661a,Winter,10,0,,,Winter,32.6891879,60.5,170.2,,111,74,175,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.51,Winter,4,4,4,0,4,0,0,0,4,3,2,0,1,0,4,1,4,1,4,0,40,Summer,35,50,Winter,0,1 +4a68d751,Fall,12,0,Spring,75,Spring,24.93124186,63.5,143.0,,44,64,106,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.2,Winter,4,3,3,1,3,2,1,2,3,2,2,0,3,3,2,2,2,3,2,1,44,Winter,43,60,Fall,0,1 +4a69e534,Fall,8,0,,,Fall,14.79112,50.0,52.6,24.0,64,70,113,,,,,Fall,2,0,,,,,0,0,8.0,1,10.0,1,11.0,1,Fall,2,3.06072,15.2888,1006.51,1610.41,8.97915,49.4692,12.8627,2.42615,9.33085,1,24.2565,16.2335,46.4084,20.0286,33.2357,,,,,Winter,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Winter,35,50,Fall,0,0 +4a6af685,Summer,8,0,Spring,70,Fall,16.66778213,53.0,66.6,,58,70,96,Fall,4,5,35,Fall,13,1,,,,,3,0,8.0,1,9.0,1,9.0,1,Fall,5,4.12714,16.6696,1062.43,2549.82,13.2125,55.4253,13.8726,2.79698,11.1747,1,28.6791,13.5337,51.2981,25.0666,41.8916,,,Spring,2.08,Winter,2,3,2,1,2,1,1,3,2,1,1,1,1,1,1,1,1,1,1,1,28,Winter,51,71,Summer,0,0 +4a6ea9de,Winter,14,1,Fall,75,Fall,21.81052632,66.5,137.2,,114,81,165,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.24,Fall,4,4,4,4,5,3,3,3,4,2,4,1,3,4,4,4,4,3,4,3,70,Winter,34,49,Winter,2,2 +4a70e025,Spring,8,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +4a753ca3,Spring,21,1,Winter,40,Fall,47.60377219,65.4,289.6,48.0,88,88,139,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4a835866,Fall,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.25,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4a9f92ca,Winter,7,0,Spring,45,Spring,16.11041667,48.0,52.8,,60,81,118,Spring,2,4,8,Spring,0,0,,,,,0,0,4.0,0,4.0,0,10.0,1,Summer,2,2.70574,16.1122,950.259,1520.41,7.63829,43.4781,13.2675,2.84464,9.32195,2,23.156,12.6837,40.7723,17.9377,30.7943,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,3,Winter,29,42,Winter,0,0 +4aa3deb0,Spring,9,1,,,Spring,24.19958383,58.0,115.8,,71,82,88,Spring,,,,Spring,7,0,,,,,1,0,8.5,1,10.0,1,14.0,1,Spring,3,6.04418,24.2022,1216.59,1946.55,23.9705,71.8462,15.0159,9.18635,43.9538,3,29.5884,18.2873,65.802,31.5793,53.5589,,,Spring,2.16,,,,,,,,,,,,,,,,,,,,,,,Spring,42,,Spring,2, +4aa87859,Spring,8,0,Fall,80,Summer,13.86479174,53.0,55.4,,71,73,105,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.59,Summer,2,0,3,0,0,0,0,2,0,0,0,0,2,0,3,0,2,2,0,0,16,Summer,35,50,Spring,0,0 +4ac5c412,Spring,6,0,Winter,69,Summer,18.97855903,48.0,62.2,,72,86,106,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,1,2,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,7,Summer,31,45,Spring,0,0 +4acac04e,Winter,16,1,Fall,40,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.27,,,Spring,4,5,4,2,4,5,2,4,3,3,4,1,2,3,2,2,4,4,5,4,67,Spring,17,38,Winter,,2 +4ad94d1e,Winter,7,0,Summer,,Spring,17.29832426,46.5,53.2,21.5,67,75,93,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +4afa8a76,Spring,9,1,Summer,69,Spring,17.29031405,55.0,74.4,25.0,68,85,114,,,,,Spring,9,1,,,,,0,0,10.0,1,11.0,1,12.0,1,Summer,2,3.4199,17.2922,1098.72,1648.08,18.3756,59.2909,13.7805,3.5117,15.1091,2,26.4419,14.4734,55.871,26.1717,44.8175,,,Spring,2.46,Summer,2,2,2,0,1,0,0,1,0,0,2,0,0,1,0,0,1,0,0,0,12,Summer,27,40,Spring,0,0 +4b0f961e,Winter,15,0,Summer,51,Spring,16.80544379,65.0,101.0,,60,74,95,,,,,Spring,13,0,19.4,1,26.0,1,7,0,9.0,1,10.0,1,7.0,0,,,,,,,,,,,,,,,,,,Spring,1.38,,,Spring,3,3,3,1,2,2,1,1,1,2,0,0,0,0,0,1,3,1,2,3,29,Spring,63,86,Spring,0,0 +4b1f6855,Summer,10,0,,,Summer,15.39112426,52.0,59.2,24.0,91,79,117,,,,,Summer,29,1,,,,,12,1,4.0,0,4.0,0,12.0,1,Fall,3,3.12454,15.3928,1009.07,1715.42,9.5169,49.7423,12.9337,2.45913,9.45769,1,25.0164,15.209,46.6178,21.1519,34.5333,,,Summer,2.75,Summer,2,2,2,1,2,0,0,1,1,1,3,0,1,1,1,1,1,1,1,1,23,Summer,32,46,Summer,0,0 +4b20bce7,Winter,10,1,Winter,88,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.37,Spring,2,2,2,0,2,0,0,1,1,1,2,0,1,1,1,0,1,2,0,0,19,Spring,38,54,Winter,0,0 +4b328259,Fall,8,0,Winter,80,Fall,16.80915162,53.25,67.8,,69,81,120,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.02,Fall,3,2,2,0,3,1,1,0,4,2,2,0,2,2,2,0,1,2,1,0,30,Fall,40,56,Fall,0,0 +4b3c7594,Summer,6,0,Fall,60,Summer,15.36817406,50.25,55.2,,61,77,107,,,,,Summer,7,1,,,,,0,0,9.0,1,9.5,1,12.0,1,Summer,2,3.02067,15.3699,984.529,1575.25,10.1295,47.1283,13.1224,2.24749,8.07173,1,24.2975,12.7013,44.1076,17.4133,34.427,,,,,Summer,4,4,3,3,4,1,0,3,0,0,2,0,4,3,3,3,3,3,3,1,47,Summer,37,53,Summer,2,1 +4b3db4f4,Spring,7,1,Summer,70,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2,1,0,1,0,0,1,1,1,2,0,1,1,1,2,1,2,1,1,21,Summer,39,55,Spring,2,0 +4b51db3e,Fall,7,0,,,Fall,19.9652,50.0,71.0,26.0,90,67,131,,,,,Fall,0,0,,,,,0,0,6.0,0,8.0,1,7.0,1,Fall,2,3.06526,19.9674,1026.67,1642.67,11.7153,51.6166,14.5162,5.45121,19.3834,2,26.8599,13.0414,48.5513,21.2262,38.5752,,,,,Fall,2,0,1,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,7,Fall,35,50,Fall,0,0 +4b537ba9,Spring,13,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,44,62,Spring,, +4b65b38d,Fall,10,0,,,Winter,,57.5,,33.0,71,84,123,,,,,Winter,0,0,,,,,0,0,,,,,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.81,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +4b71830a,Winter,12,0,Summer,70,Summer,16.27653486,62.0,89.0,,60,77,107,,,,,Fall,,,,,,,,,,,,,,,Summer,2,4.66936,16.2783,1223.17,1957.07,18.5835,72.5468,13.269,3.00933,16.4532,1,33.0822,20.8811,67.8774,30.9468,51.6657,,,Spring,3.41,Spring,2,2,3,1,2,1,1,3,2,1,4,0,1,1,2,1,1,1,1,1,31,Spring,42,59,Winter,1,1 +4b7b35bc,Summer,13,1,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4b855d9d,Spring,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +4b8d6356,Spring,5,0,,,Spring,13.15511087,43.0,34.6,21.0,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +4bb9a8ff,Summer,8,0,Summer,54,Winter,21.89951639,52.3,85.2,27.1,88,92,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +4bc2bee9,Winter,8,1,Summer,61,Spring,15.31634033,53.0,61.2,,73,77,122,Spring,3,3,59,Spring,10,1,,,,,8,1,12.5,1,11.0,1,12.0,1,Spring,3,2.47699,15.318,1069.29,1710.86,8.92792,56.1559,14.0555,1.2625,5.04408,1,20.2899,26.9381,53.6789,30.8687,29.2178,,,Winter,1.88,Spring,0,3,2,0,2,0,0,1,0,0,0,0,4,0,1,2,1,2,0,1,19,Spring,43,60,Winter,2,0 +4bd08623,Spring,6,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4be6f3c1,Summer,9,0,Winter,74,Fall,15.71676753,53.0,62.8,,63,74,112,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.64,Summer,2,2,2,0,1,1,0,1,1,2,1,0,1,1,2,1,2,1,0,2,23,Summer,46,64,Summer,0,0 +4c1fa25b,Winter,17,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +4c238671,Winter,16,1,Fall,70,Fall,25.05181403,61.0,132.6,,114,99,180,,,,,Spring,,,,,,,,,,,,,,,Summer,2,3.90152,25.0546,1237.27,1855.91,20.7706,74.0486,13.9914,11.0632,58.5514,1,29.2274,24.0506,70.1471,44.3635,49.998,Spring,1.49,,,Spring,2,3,3,1,2,2,1,2,1,1,2,1,1,1,2,1,2,1,2,1,32,Spring,46,64,Winter,0,1 +4c37fb30,Spring,10,1,Spring,50,Winter,16.3103806,58.5,79.4,,78,75,123,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.17,Winter,3,4,5,0,5,2,0,2,0,3,3,2,3,5,4,2,3,4,2,2,54,Winter,64,88,Spring,3,2 +4c4f3184,Winter,13,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +4c6d424c,Winter,21,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +4c8dfaf0,Summer,8,1,Summer,45,Winter,25.33768873,53.25,102.2,,63,70,125,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.33069,25.2455,1138.37,1707.56,21.2501,63.5145,15.6894,9.55614,38.6855,3,27.989,14.2754,60.1838,28.3569,49.2391,,,Spring,2.66,Winter,4,5,4,4,5,0,0,1,3,3,4,0,2,0,3,4,4,4,2,2,54,Winter,41,58,Summer,1,2 +4c94a5d5,Winter,14,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +4ca242ea,Summer,19,1,Winter,45,Fall,20.59570313,64.0,120.0,,76,90,122,,,,,,,,,,,,,,,,,,,,Fall,2,-0.558809,20.598,1384.06,2076.09,31.6647,89.6837,15.3942,5.20379,30.3163,2,35.5048,22.5141,90.2425,39.1892,67.1695,,,,,Summer,1,2,3,1,5,1,2,1,1,4,4,1,1,1,3,3,2,4,1,1,42,,,,Summer,3,1 +4cabd7aa,Summer,12,1,Winter,50,Fall,15.87419355,62.0,86.8,,45,84,111,,,,,Fall,23,1,43.4,2,52.4,2,20,1,11.0,1,10.0,1,12.0,1,Fall,5,4.41594,15.8759,1215.28,2673.61,23.0466,71.706,13.1152,2.76072,15.094,2,29.0472,19.6123,67.2901,29.6328,52.0938,,,Summer,3.41,Fall,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,0,2,0,7,Fall,58,80,Summer,0,0 +4cde1e34,Spring,10,0,Spring,72,Winter,35.2985063,58.63,172.6,,91,86,149,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,1,4.20342,35.3024,1388.92,1805.59,23.9638,90.2012,18.4491,16.8533,82.3988,3,40.1909,26.0464,85.9977,38.8462,64.1547,,,Spring,2.49,Spring,4,3,3,3,3,1,0,2,0,0,2,0,3,5,4,2,3,,1,1,40,Spring,50,69,Spring,0,1 +4d19854c,Winter,8,1,Summer,80,Spring,16.14226471,52.13,62.4,,63,88,105,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,3,0,3,0,0,2,2,2,2,0,0,2,2,1,2,1,2,1,31,Spring,38,54,Winter,3,1 +4d2d9ef3,Fall,6,1,,,Fall,16.54866455,47.0,52.0,25.0,64,100,103,,,,,Fall,4,1,,,,,2,0,13.0,1,12.5,1,4.5,0,Fall,3,2.23798,16.5505,950.219,1520.35,10.5949,43.4738,13.8368,2.7137,8.52619,2,20.0414,12.8375,41.2358,16.4239,30.6363,,,,,Fall,4,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,Fall,42,59,Fall,0,0 +4d41bb62,Winter,5,0,Spring,85,Winter,16.31654321,45.0,47.0,21.0,64,83,105,,,,,Winter,6,1,,,,,2,0,11.5,1,11.0,1,7.0,1,Winter,1,2.46963,16.3183,905.13,1176.67,6.91688,38.6713,13.4266,2.89172,8.32874,1,22.0185,9.73587,36.2016,14.9675,28.9354,,,,,Spring,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,5,,,,Winter,0,0 +4d42caed,Spring,8,0,Summer,60,Spring,14.66507205,50.5,53.2,,73,82,112,Spring,2,2,20,Spring,5,0,,,,,3,0,8.0,1,8.0,1,9.5,1,Spring,4,7.25193,14.6667,978.242,2054.31,9.04491,46.4587,12.8082,1.85852,6.74135,1,23.5862,13.8276,39.2067,17.4681,32.6311,,,Spring,4.17,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,32,46,Spring,0,0 +4d5a8ce7,Winter,12,0,Winter,58,Spring,19.63435401,58.75,96.4,,55,67,99,Spring,8,13,12,Spring,17,1,15.2,2,17.8,2,10,1,14.0,1,13.0,1,12.0,1,Spring,3,4.21233,19.8047,1203.38,2045.74,17.5548,70.4384,14.4711,5.33363,25.9616,2,33.6145,19.2691,66.226,33.4068,51.1693,,,Winter,2.52,Spring,4,4,5,4,4,1,0,3,3,2,4,0,4,3,4,4,4,4,4,4,65,Spring,45,63,Winter,2,2 +4d61a44e,Summer,10,0,Fall,59,Summer,18.04366667,60.0,92.4,,65,78,98,Summer,5,7,49,Summer,13,1,18.9,2,19.1,2,7,1,8.5,1,8.5,1,11.0,1,Summer,2,4.55428,18.0456,1231.27,1970.04,19.7243,73.4099,14.3369,3.70875,18.9901,2,34.4245,19.2611,68.8556,32.2596,54.1488,,,Summer,2.64,Summer,3,4,4,1,4,1,1,1,1,2,3,1,1,2,3,1,3,1,1,2,40,Summer,41,58,Summer,2,1 +4d674863,Fall,13,0,,,Fall,16.52131912,59.5,83.2,25.0,64,65,115,,,,,Fall,6,0,17.7,2,20.7,2,8,0,13.0,1,13.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.97,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +4d687bb3,Summer,11,0,Summer,65,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.68,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +4d6e961f,Summer,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4da4aba2,Winter,15,0,Summer,85,Spring,20.85671321,66.5,131.2,27.0,59,66,111,,,,,Spring,24,1,24.2,1,23.4,1,17,1,11.5,1,11.25,1,9.0,1,Spring,2,6.03857,21.5008,1515.45,2424.73,32.3817,103.679,16.9907,4.51011,27.5212,2,45.2822,26.0149,97.6402,44.3983,77.6639,Spring,1.51,,,Spring,5,5,4,0,5,5,5,4,5,5,4,2,2,5,5,0,0,2,2,2,67,Spring,30,43,Winter,2,2 +4dc4ed56,Spring,8,0,Summer,65,Summer,18.02394194,51.5,68.0,,66,75,111,Summer,,,,Summer,11,1,,,,,,,12.0,1,12.0,1,7.5,1,,,,,,,,,,,,,,,,,,,,Spring,2.84,Summer,2,0,1,0,1,1,0,1,0,0,1,0,0,0,0,0,0,1,1,0,9,Summer,54,75,Spring,,0 +4dc86a42,Fall,7,0,Spring,65,Fall,17.42166454,50.5,63.2,,53,93,121,Fall,6,10,32,Fall,8,1,,,,,8,1,11.5,1,11.0,1,11.0,1,Fall,2,5.80168,17.4236,1013.72,1621.95,10.9444,50.2372,13.8499,3.5737,12.9628,1,26.6904,12.6025,44.4356,22.5885,37.6348,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,34,49,Fall,2,0 +4dc86bb6,Spring,11,0,,,Spring,21.65005945,58.0,103.6,30.0,84,103,134,,,,,Spring,0,0,10.6,1,9.8,1,0,0,11.0,1,10.0,1,8.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,2.43,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +4decbcd8,Spring,9,1,Summer,80,Spring,16.60291962,52.3,64.6,,65,66,112,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,4,2.89793,16.6227,1030.42,1957.8,14.4639,52.0166,13.3703,3.25242,12.6534,1,23.3272,14.2255,49.1187,22.0771,37.7911,,,Spring,2.6,Spring,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,21,Spring,38,54,Spring,0,0 +4dfb4e1d,Winter,11,0,Spring,66,Spring,17.93026159,58.0,85.8,,70,72,115,Spring,5,7,19,Spring,17,1,18.2,2,20.5,2,6,0,0.0,0,0.0,0,12.0,1,Spring,3,4.1475,17.9322,1171.6,1991.71,17.3495,67.0533,14.0142,3.91806,18.7467,2,31.6563,18.0475,62.9058,27.6255,49.0058,,,Winter,2.67,Spring,4,5,5,1,5,1,1,5,1,1,1,1,1,0,4,0,5,4,5,1,51,Spring,43,60,Winter,3,2 +4e052db0,Winter,10,0,Fall,55,Spring,17.96053464,49.5,62.6,,72,64,138,Spring,7,12,2,Spring,8,0,13.2,2,6.2,1,21,1,9.0,1,8.0,1,13.0,1,Spring,3,2.85203,17.9625,990.231,1683.39,8.39269,47.7356,13.6973,4.26521,14.8644,1,24.9896,14.3533,44.8835,22.7598,33.3823,,,Winter,2.1,Spring,3,1,3,1,3,0,0,3,3,2,3,0,1,1,1,1,1,1,1,1,30,Spring,51,71,Winter,2,0 +4e17b1c1,Spring,9,0,,,Spring,14.56616,50.0,51.8,23.0,78,83,117,,,,,Spring,0,0,,,,,0,0,6.0,0,7.5,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.8,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +4e314e47,Winter,10,1,,,Winter,17.59910837,54.0,73.0,,68,73,111,Winter,5,7,34,Winter,7,0,11.5,1,15.4,2,2,0,8.0,0,10.0,1,10.0,1,Spring,3,3.49262,17.601,1075.21,1827.85,11.3245,56.7867,13.6918,3.90919,16.2133,1,29.7205,15.7417,53.2941,34.031,41.045,,,Winter,1.88,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,32,46,Winter,0,0 +4e58d463,Spring,7,0,Fall,45,Fall,15.25607639,48.0,50.0,,82,76,121,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,4,4,0,4,0,0,0,0,2,0,0,2,0,0,2,0,0,1,0,21,Spring,47,66,Spring,2,0 +4e6c7a75,Fall,9,1,Summer,80,Winter,14.47216514,51.5,54.6,,84,95,132,Winter,5,9,49,Winter,7,1,,,,,1,0,13.5,1,13.5,1,8.0,1,Winter,3,16.3969,14.4737,1012.71,1620.33,9.55889,50.1297,13.2887,1.18501,4.47026,2,21.1457,19.4252,33.7329,27.4989,30.7046,,,Winter,1.72,Winter,4,4,3,1,3,1,1,1,1,2,1,1,2,1,3,2,1,2,1,1,36,Winter,41,58,Fall,1,1 +4e70be08,Winter,7,0,,,Winter,21.97344251,53.0,87.8,31.0,80,88,132,,,,,Winter,2,0,,,,,1,0,8.0,1,9.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +4e85505f,Summer,13,0,Fall,55,Fall,22.31842561,68.0,146.8,,64,80,122,,,,,Fall,12,0,22.9,2,30.0,2,19,1,8.5,1,6.0,0,12.0,1,Fall,2,0.562961,22.3209,1628.27,2605.23,35.5238,115.695,17.5914,4.72946,31.1048,2,50.7196,29.4518,115.132,56.5802,86.2434,,,Fall,3.28,Fall,4,4,4,1,5,3,4,3,1,1,3,0,3,3,4,4,4,4,2,2,59,Fall,38,54,Summer,0,2 +4ea3963e,Spring,10,0,,,Summer,19.52777778,60.0,100.0,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.25,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +4ea4e6c9,Spring,7,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,0,2,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,12,Spring,37,53,Spring,0,0 +4eade67b,Fall,6,0,Fall,68,Fall,17.0108642,45.0,49.0,22.0,72,86,112,,,,,Fall,0,0,,,,,0,0,8.5,1,10.5,1,4.0,0,Fall,2,2.53022,17.0127,913.086,1460.94,7.94304,39.5187,13.7208,3.2919,9.48131,2,22.7536,8.82209,36.9885,15.1089,30.6966,,,,,Winter,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,7,Winter,60,82,Fall,0,0 +4edb98d8,Winter,17,0,Fall,75,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.75,,,Winter,4,5,4,2,4,4,1,3,4,3,5,1,4,3,3,3,3,3,4,2,65,Winter,37,53,Winter,2,2 +4ee09c37,Winter,11,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +4f059221,Winter,6,1,Fall,63,Spring,15.68677686,44.0,43.2,,72,79,115,Spring,6,10,37,Spring,3,1,,,,,4,1,10.0,1,11.0,1,7.0,1,Fall,2,2.07645,15.6885,901.223,1351.83,10.1066,38.2551,13.8927,1.7958,4.94494,1,20.0613,8.0872,36.1786,14.8583,30.1679,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,0,0 +4f059a8a,Spring,15,0,Summer,50,Summer,15.07643107,61.0,79.8,24.0,59,72,102,,,,,Summer,15,0,16.4,1,15.7,1,2,0,0.0,0,0.0,0,10.0,1,Summer,1,4.51531,15.0781,1187.16,1543.3,17.613,68.7107,12.9828,2.09531,11.0893,1,31.5428,19.5549,64.1954,28.0484,49.1558,Spring,2.35,,,Summer,3,3,2,0,2,0,0,1,0,1,2,0,1,1,1,0,1,2,0,0,20,Summer,44,62,Spring,1,0 +4f0a07c6,Winter,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +4f20b188,Spring,9,0,Fall,65,Summer,17.81901032,53.0,71.2,24.0,80,74,114,,,,,Summer,11,1,,,,,0,0,2.0,0,4.0,0,6.0,1,Summer,1,3.1451,17.821,1047.32,1361.52,10.1915,53.8165,13.47,4.351,17.3835,2,25.3977,18.2272,50.6714,21.1169,35.5893,,,Summer,1.86,Summer,0,3,3,1,2,0,0,0,0,0,0,0,1,0,3,0,2,0,2,0,17,Summer,33,47,Spring,2,0 +4f2d0925,Winter,12,1,Spring,80,Winter,15.95973937,54.0,66.2,,69,87,116,,,,,Winter,12,0,11.8,1,11.0,1,13,1,5.0,0,7.75,0,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.78,Winter,4,4,4,1,3,1,1,2,2,3,0,0,2,2,1,2,3,2,1,1,39,Winter,44,62,Winter,0,1 +4f3b33ba,Summer,12,0,Fall,75,Summer,21.90931322,62.0,119.8,,84,87,152,,,,,Summer,13,0,44.6,2,46.2,2,5,0,9.4,1,9.3,1,10.0,1,Summer,4,-0.0692872,21.5625,1565.26,3287.04,34.709,108.983,19.6156,1.94687,10.8167,2,49.2407,25.0336,109.053,53.1911,83.9497,,,Fall,3.95,Fall,2,3,5,3,2,2,1,0,0,1,3,0,0,2,4,0,2,0,1,0,31,Summer,28,41,Summer,0,1 +4f3cd5c2,Winter,17,1,Summer,68,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.93,,,Fall,4,5,5,2,5,5,2,4,1,1,5,0,1,2,3,1,4,2,1,0,53,Fall,36,51,Winter,3,2 +4f4723e2,Winter,22,1,Spring,68,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +4f6173db,Spring,5,1,,,Spring,15.90464876,44.0,43.8,21.0,65,95,131,,,,,Spring,0,0,,,,,0,0,8.0,1,9.0,1,9.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +4f93504a,Spring,11,0,Summer,75,Spring,21.96110949,60.25,113.4,,63,90,114,Spring,4,5,52,Spring,0,0,13.2,2,11.8,1,0,0,5.5,0,5.0,0,13.0,1,Summer,2,9.1154,21.9635,1263.91,2022.26,20.2353,76.8864,14.8915,7.07202,36.5136,2,34.7846,21.8666,67.771,31.5784,55.0198,,,Spring,2.63,Spring,2,2,2,1,2,2,0,2,1,1,2,0,1,1,1,1,1,1,1,1,25,Spring,44,62,Spring,3,0 +4f96c824,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,93,100,,, +4f96d1e7,Spring,10,1,Summer,68,Spring,14.74072624,53.0,58.9,,69,61,103,Spring,4,5,49,Spring,10,0,8.9,1,7.6,1,0,0,10.0,1,12.0,1,12.0,1,Summer,3,2.67008,14.7423,1034.1,1654.56,10.0884,52.408,13.1174,1.62492,6.49205,1,21.9506,20.3689,49.7379,31.111,32.039,,,Spring,2.71,Spring,4,4,3,0,2,0,0,0,0,0,0,0,0,0,0,2,1,2,1,0,19,Spring,38,54,Spring,0,0 +4fd7e043,Summer,5,0,Winter,55,Summer,17.21916049,45.0,49.6,,84,99,135,Summer,,,,Summer,2,1,,,,,0,0,11.0,1,13.0,1,10.0,1,Fall,1,4.47847,17.221,1790.94,2328.22,41.0327,133.022,46.1849,-28.9639,-83.4217,1,66.1422,25.8467,128.543,92.2181,107.175,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +4fdb729d,Spring,7,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +4fe97569,Summer,13,0,,,Summer,17.46429831,63.0,98.6,26.0,69,101,117,,,,,Summer,15,0,9.5,1,15.0,1,13,1,8.5,1,7.3,0,12.0,1,Fall,3,5.16553,17.4662,1311.22,2229.07,23.1627,81.9248,14.5123,2.95388,16.6752,1,37.4761,21.286,76.7593,36.3363,60.6388,,,Summer,2.72,Summer,3,2,2,0,3,0,3,2,0,2,3,0,2,2,3,0,2,0,0,1,30,Summer,38,54,Summer,1,0 +4ffa995b,Spring,14,0,Spring,65,Spring,17.17524484,62.75,96.2,25.0,67,93,121,,,,,Spring,18,0,23.2,1,21.9,1,20,1,9.0,1,7.5,1,9.0,1,Spring,5,5.27908,17.1771,1330.47,3193.12,24.3864,83.9753,14.9943,2.18279,12.2247,2,38.7249,20.864,78.6963,38.1708,63.1113,Spring,4.3,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,49,68,Spring,2,0 +50242fa0,Spring,8,1,Spring,90,Winter,17.11826273,53.0,68.4,,73,61,120,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,0,2,1,0,1,2,2,0,0,2,3,3,1,3,2,2,2,32,Spring,32,46,Spring,0,1 +504ed32b,Winter,7,0,Spring,72,Winter,15.5103745,47.7,50.2,,75,98,121,Winter,7,11,7,Winter,8,,,,,,10,,11.0,,10.5,,12.0,,,,,,,,,,,,,,,,,,,,,,,Winter,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,21,Winter,33,47,Winter,0,0 +504efa1d,Winter,17,0,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.74,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +50707f1e,Spring,10,0,Fall,77,Fall,15.27602339,57.0,70.6,,74,79,146,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.05,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,34,49,Spring,0,0 +5072d44b,Winter,7,1,,,Winter,15.95990822,45.5,47.0,22.0,57,81,107,,,,,Winter,0,0,,,,,1,0,11.0,1,10.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +507c5824,Summer,9,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.07,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +508a0f98,Fall,10,1,Spring,68,Fall,21.7754459,58.0,104.2,,72,83,112,Fall,5,7,53,Fall,15,1,17.0,2,17.3,2,5,0,10.0,1,10.5,1,11.0,1,Fall,4,4.00593,21.7778,1215.33,2309.13,24.6064,71.712,14.9878,6.79001,32.488,2,29.7523,17.3533,67.706,29.6438,54.3586,,,Fall,3.14,Fall,5,4,4,0,3,1,1,2,1,2,3,0,5,0,3,4,3,4,4,5,54,Fall,60,82,Fall,2,2 +50a90725,Fall,22,1,,,Winter,34.50939645,65.0,207.4,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,4.93667,34.5132,1531.3,2603.2,28.5211,105.366,17.5338,16.9793,102.034,3,43.0012,33.8438,100.429,38.188,71.5223,,,,,Winter,0,4,3,1,4,0,4,0,3,0,2,2,4,3,4,4,1,1,1,3,44,Winter,50,69,Fall,2,1 +50ad700c,Winter,12,0,Spring,60,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.72,,,,,,,,,,,,,,,,,,,,,,,,,,,, +50b2b84b,Winter,15,0,Spring,55,Winter,19.87477421,69.0,134.6,,105,61,138,,,,,Winter,27,1,29.1,2,36.7,2,5,0,9.5,1,12.0,1,12.0,1,Spring,4,6.56871,19.8769,1550.05,3255.11,32.5334,107.364,15.8549,4.02209,27.2362,2,46.5395,28.2909,100.795,48.8093,79.0729,Winter,2.36,,,Winter,4,3,3,1,2,1,0,1,1,1,2,0,1,1,1,1,1,1,1,1,27,Winter,35,50,Winter,2,0 +510331c5,Fall,7,1,,,Fall,16.1667723,47.0,50.8,,52,92,99,,,,,Fall,3,1,,,,,6,1,9.0,1,9.0,1,10.0,1,Fall,2,2.33238,16.1685,946.663,1419.99,11.0142,43.095,13.7162,2.45233,7.705,1,21.4541,10.6267,40.7626,19.5586,32.4683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +51134510,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.39,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +5124169c,Summer,14,1,Fall,80,Summer,19.85806075,63.75,114.8,,100,84,127,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.41,Summer,2,3,3,1,2,1,1,1,,2,2,,1,2,2,1,2,1,3,1,31,Summer,39,55,Summer,2,1 +5132511c,Winter,12,1,Summer,75,Spring,19.12716257,64.9,114.6,,104,74,141,,,,,Fall,,,,,,,,,,,,,,,Summer,4,4.77194,19.1293,1286.63,2444.59,25.3103,79.3056,13.2378,5.89141,35.2944,2,30.5827,23.4127,74.5337,34.5215,55.8929,,,Spring,2.5,Spring,3,3,3,0,3,2,2,2,2,3,2,0,2,2,2,2,2,2,0,2,39,Spring,61,84,Winter,2,1 +5135da25,Winter,7,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +51364c23,Spring,9,0,,,Summer,17.25531449,58.5,84.0,,74,79,123,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.91,Summer,5,5,2,1,3,2,1,2,2,2,3,1,3,3,1,4,4,4,3,3,54,Summer,36,51,Spring,1,2 +515402ee,Summer,7,1,Fall,55,Summer,11.74956306,44.3,32.8,19.0,,77,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,42,59,Summer,2,0 +5154a2c6,Fall,12,0,,,Fall,22.3880192,62.5,124.4,33.0,58,81,100,,,,,Fall,40,1,29.0,3,34.3,3,14,1,8.5,1,9.5,1,12.0,1,Fall,2,5.1346,22.3905,1376.72,2202.75,25.5161,88.9015,16.0012,6.3893,35.4985,2,40.2297,23.1557,83.7669,39.5646,65.7458,,,Summer,2.959,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +515dd683,Fall,13,0,Winter,41,Winter,23.98651061,71.0,172.0,,54,101,174,,,,,Winter,39,1,30.8,2,35.3,3,0,0,5.0,0,5.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.29,Winter,4,3,4,0,2,1,5,4,4,2,0,0,1,0,1,1,1,2,0,0,35,Winter,35,50,Fall,0,1 +516e4894,Spring,17,1,,,Spring,22.53000756,63.0,127.2,30.0,58,72,123,,,,,Summer,10,0,27.8,2,26.5,2,11,1,12.0,1,12.0,1,12.0,1,Summer,3,4.85064,22.5325,1338.07,2140.92,28.7415,84.7856,15.0191,7.51336,42.4143,2,34.0497,21.9943,79.9349,40.271,62.7913,Fall,1.13,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +5180ff75,Fall,17,0,,,Winter,22.81163265,70.0,159.0,33.0,84,66,126,,,,,Winter,24,1,47.9,2,42.9,2,18,1,6.0,0,8.0,0,10.0,1,Winter,4,6.88217,22.8141,1648.99,3462.89,35.7766,117.903,16.9173,5.89686,41.0974,2,50.8489,31.2771,111.02,56.3633,86.6255,Winter,2.12,,,Winter,1,1,3,1,1,3,1,1,1,1,4,0,0,1,1,0,0,0,0,0,20,,,,Fall,3,0 +518e5ae6,Winter,8,0,Spring,42,Spring,15.67099087,46.5,48.2,23.0,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,32,46,Winter,0,0 +5198e7b3,Winter,8,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,5,2,5,1,0,1,0,3,4,0,4,1,4,4,4,4,2,1,53,Spring,35,50,Winter,2,2 +51a349db,Fall,11,1,,,Fall,19.72873376,65.5,120.4,29.0,67,85,85,,,,,Fall,26,1,24.4,3,14.0,2,0,0,11.0,1,10.5,1,11.0,1,Fall,2,5.44522,19.7309,1398.15,2097.23,31.967,91.1847,14.9432,4.78774,29.2153,2,35.6799,23.5378,85.7395,39.7059,67.6469,,,Fall,1.649,Fall,2,2,1,0,0,0,1,1,1,2,0,0,1,0,0,2,0,2,0,0,15,Fall,36,51,Fall,1,0 +51c15d22,Winter,18,0,Spring,55,Winter,21.83716216,74.0,170.1,,82,104,166,,,,,Winter,4,0,42.1,2,52.0,2,7,0,3.0,0,3.0,0,8.0,0,,,,,,,,,,,,,,,,,,Winter,1.11,,,Winter,2,1,1,1,2,2,2,2,2,2,3,1,2,3,2,1,2,1,1,2,35,Winter,46,64,Winter,2,1 +51eb8d14,Fall,10,0,,,Fall,19.95738889,60.0,102.2,,58,62,115,,,,,Fall,25,1,12.6,2,17.5,2,6,0,7.0,0,7.0,0,11.0,1,Fall,3,4.6439,19.9596,1270.12,2159.2,21.3494,77.5472,15.1449,4.81468,24.6528,2,36.3294,19.8684,72.9033,34.9248,57.6788,,,,,Fall,2,2,2,0,3,0,0,2,1,1,4,0,2,0,0,1,0,1,0,0,21,Fall,29,42,Fall,2,0 +51f38b37,Spring,17,0,Fall,40,Fall,21.0027027,74.0,163.6,,65,68,92,,,,,Fall,41,1,49.0,3,40.8,3,15,0,12.0,1,10.0,1,12.0,1,Fall,3,8.0778,21.005,1798.45,3057.37,42.6352,133.822,17.1817,3.8233,29.7783,2,55.7661,35.4204,125.744,61.1596,98.4013,Spring,3.54,,,Fall,0,0,4,1,2,2,1,2,2,1,5,1,2,3,4,3,4,0,4,0,41,,,,Spring,3,1 +51f3feb4,Winter,6,0,Summer,65,Summer,15.86947105,49.0,54.2,,57,89,101,Summer,,,,Summer,,,,,,,,,,,,,,,Summer,2,2.91714,15.8712,970.126,1552.2,9.86468,45.5941,13.3512,2.52003,8.60589,3,24.1752,11.5542,42.677,17.0808,34.0399,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,48,67,Winter,0,0 +5216ef61,Summer,12,0,Fall,70,Summer,26.22600304,61.5,141.1,,105,74,166,,,,,Summer,8,0,11.5,1,15.4,1,1,0,7.0,0,7.0,0,13.0,1,Summer,1,7.19716,26.2846,1455.6,1892.28,29.0669,97.3035,18.0876,8.19703,44.0965,2,44.1319,24.1048,90.1064,44.8323,73.1988,,,Summer,2.01,Summer,4,4,5,0,1,0,0,0,1,1,2,0,0,5,0,1,1,5,0,1,31,Summer,48,67,Summer,1,1 +52219dbb,Fall,5,0,Spring,38,Fall,14.10782313,42.0,35.4,,71,106,115,Fall,,,,Fall,0,0,,,,,0,0,10.0,1,10.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,,,Fall,5,2,4,0,2,0,0,2,4,3,0,0,3,0,3,5,2,3,2,3,43,Fall,54,75,Fall,2,1 +522ac208,Spring,7,0,,,Spring,15.54839319,46.0,46.8,21.0,55,77,99,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +523f823b,Spring,15,1,,,Summer,22.49458302,63.0,127.0,27.0,,,,,,,,Summer,15,0,34.8,2,38.0,2,0,0,14.0,1,14.0,1,12.0,1,Summer,1,4.62422,22.497,1300.35,1690.45,27.2896,80.767,14.3072,8.1898,46.2329,2,31.167,22.3104,76.1428,33.4104,58.4566,Summer,1.91,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +5264bb0e,Fall,12,0,Spring,80,,,,,,,,,,,,,Winter,37,1,15.6,1,12.6,1,19,1,7.0,0,7.0,0,15.0,1,Winter,3,5.17326,16.7498,1196.16,2033.47,16.8156,69.6694,13.3824,3.36735,17.5306,2,32.806,20.0479,64.4962,33.2887,49.6216,,,Winter,2.34,Winter,4,3,3,0,2,2,2,2,3,2,2,0,2,2,2,2,2,2,2,2,41,Winter,57,79,Fall,2,1 +526f719b,Winter,6,0,Fall,85,Winter,15.19505208,48.0,49.8,,72,69,128,Spring,6,9,2,Winter,20,1,,,,,20,1,7.0,0,8.0,1,12.0,1,Winter,3,2.84022,15.1967,950.193,1615.33,9.27844,43.4711,13.2654,1.9313,6.32893,1,23.911,10.2817,40.6309,17.0263,33.1894,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,44,62,Winter,0,0 +527e796c,Spring,10,0,,,Fall,17.17208492,62.1,94.2,,70,87,148,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.47,Summer,4,4,4,3,5,3,1,3,1,2,0,0,3,3,4,4,4,5,5,4,62,Summer,36,51,Spring,0,2 +528db82d,Spring,9,0,Summer,55,Spring,16.48306213,52.0,63.4,,74,88,126,Spring,3,4,15,Spring,2,,,,,,0,,0.0,,0.0,,6.0,,Spring,3,6.69297,16.4849,1025.01,1742.51,10.6393,51.4399,13.3751,3.10979,11.9601,2,26.5384,14.2622,44.7469,23.396,37.1777,,,Spring,1.64,Spring,3,3,3,4,3,0,0,2,2,1,0,0,2,2,2,4,3,4,3,2,43,Spring,50,69,Spring,1,1 +52bd147c,Winter,5,0,Summer,71,Spring,14.26619104,43.5,38.4,,63,117,106,Spring,,,,Spring,7,,,,,,0,,10.0,,10.0,,6.5,,Spring,2,2.13462,14.2677,884.142,1414.63,2.79505,36.4357,13.5379,0.729836,1.96427,1,19.3078,14.3329,34.3011,16.267,22.1028,,,,,Summer,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,10,Summer,29,42,Winter,2,0 +52ce9b8b,Fall,7,1,Winter,51,Fall,16.5942716,45.0,47.8,,65,58,114,,,,,Fall,2,1,,,,,1,0,13.0,1,14.0,1,10.0,1,Fall,3,2.35153,16.5961,935.393,1496.63,13.2266,41.8947,14.5458,2.05032,5.90534,1,22.5417,6.12639,39.5431,17.0323,35.7683,,,,,Winter,3,0,3,0,3,0,0,0,0,0,3,0,1,0,0,2,0,2,0,1,18,Winter,36,51,Fall,2,0 +52e9e467,Summer,8,0,Winter,70,Fall,17.60312,50.0,62.6,,70,89,118,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,4,4,0,2,1,0,1,1,0,0,0,1,1,1,1,1,2,1,1,26,Fall,64,88,Summer,2,0 +531c7a83,Winter,10,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.82,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +531c830b,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5320dac6,Fall,14,1,Fall,50,Winter,19.92724773,63.25,113.4,,65,90,115,,,,,Spring,0,0,11.5,1,11.9,1,0,0,7.0,0,7.0,0,2.0,0,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,44,62,Winter,2,0 +5330a9d1,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5332fd12,Summer,7,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +53621b84,Spring,18,1,Summer,70,Summer,22.48158033,65.5,137.2,31.0,76,77,117,,,,,,,,,,,,,,,,,,,,Summer,3,5.41562,22.484,1418.78,2270.04,32.5549,93.3812,15.3031,7.18092,43.8188,2,36.4101,24.4162,87.9656,42.8617,68.965,,,,,Summer,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,,,,Spring,3,0 +53774029,Fall,9,1,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.52,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +537dc410,Fall,16,0,Winter,66,Fall,18.79126298,68.0,123.6,,70,58,102,,,,,Fall,31,1,30.3,1,30.7,1,15,0,7.0,0,7.0,0,12.0,1,Fall,2,6.69721,18.7933,1576.67,2522.67,32.8791,110.199,16.7557,2.03757,13.4007,2,49.7824,27.5378,103.502,59.6554,82.6615,Fall,3.15,,,Fall,5,5,5,0,5,4,1,4,3,1,4,1,4,0,3,4,5,5,2,3,64,,,,Fall,2,2 +53814642,Summer,9,1,Fall,80,Summer,13.94648212,51.0,51.6,,62,70,102,,,,,Summer,9,1,,,,,6,1,12.0,1,12.0,1,12.0,1,Summer,2,2.62183,13.948,992.502,1488.75,11.7837,47.9775,12.9688,0.979195,3.62249,1,21.074,15.1198,45.3557,18.6544,32.8577,,,Summer,2.24,Summer,3,4,4,0,4,1,1,1,1,2,0,0,0,1,2,1,3,1,1,1,31,Summer,34,49,Summer,3,1 +53905a2c,Winter,8,1,Fall,59,Summer,14.50155372,55.0,62.4,,77,110,117,Summer,6,9,47,Spring,7,1,,,,,1,0,7.0,0,8.0,0,5.0,0,Fall,1,3.11607,14.5031,1054.57,1370.94,14.4171,54.5885,12.6876,1.81556,7.81149,1,23.1773,16.9941,51.4724,22.6108,37.5944,,,Spring,1.58,Summer,1,5,5,1,5,1,0,4,0,1,1,1,1,1,1,2,4,1,4,4,43,Spring,37,53,Winter,2,1 +539e989c,Spring,10,0,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.37,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +53a13e13,Summer,17,1,,,Summer,25.3524225,61.5,136.4,30.0,66,64,123,,,,,Summer,10,0,30.1,3,33.5,3,8,1,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,Fall,1.14,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +53b7bd26,Summer,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +53bb311b,Spring,13,0,,,Spring,25.50524053,61.0,135.0,33.0,76,66,128,,,,,Spring,30,1,17.5,1,23.9,2,9,0,4.0,0,8.0,1,11.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.9,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +53cf99f2,Spring,5,0,Summer,72,Spring,17.39726353,41.0,41.6,,77,101,128,Spring,,,,Spring,2,1,,,,,0,0,9.0,1,8.0,1,4.0,0,Summer,2,5.51843,17.3992,849.805,1359.69,4.20689,32.7784,13.7095,3.68962,8.82158,1,21.7412,6.83035,27.26,18.4465,25.9481,,,,,Summer,2,2,3,0,2,0,0,2,0,0,3,0,2,1,1,1,1,2,1,0,23,Summer,32,46,Spring,1,0 +53d20dc3,Spring,6,0,Summer,72,Summer,15.59393391,47.0,49.0,22.0,62,76,99,,,,,Summer,6,1,,,,,2,0,11.0,1,10.5,1,8.0,1,Summer,2,2.69325,15.5956,933.412,1493.46,7.87574,41.6837,13.267,2.32863,7.31634,1,23.4646,10.3433,38.9904,17.9031,31.3403,,,,,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,26,38,Spring,0,0 +53d331ca,Spring,5,1,,,Spring,16.2677686,44.0,44.8,23.0,56,106,90,,,,,Spring,0,0,,,,,0,0,13.0,1,15.0,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +53ea1e7c,Summer,10,1,Winter,60,Fall,15.1091199,56.0,67.4,,59,80,96,Fall,4,7,57,Fall,24,1,15.6,2,14.5,2,9,1,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.18,Fall,2,2,2,0,3,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,12,Fall,42,59,Summer,0,0 +53faf893,Spring,7,1,Summer,72,Spring,15.94706994,46.0,48.0,22.0,69,89,121,,,,,Spring,4,1,,,,,6,1,10.5,1,11.0,1,10.0,1,Spring,2,2.20892,15.9488,931.059,1396.59,10.6968,41.433,13.7668,2.18201,6.56703,1,20.1967,10.5395,39.2241,15.621,30.8934,,,,,Spring,4,2,2,0,3,0,0,2,0,2,2,0,1,0,1,1,1,2,1,0,24,Spring,30,43,Spring,0,0 +53ffaa53,Summer,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +54391716,Fall,12,1,Spring,60,Fall,17.55410226,58.0,84.0,,60,96,130,,,,,Fall,10,0,18.9,2,19.3,2,9,1,13.0,1,12.0,1,12.0,1,Fall,3,3.91079,17.556,1167.18,1867.48,20.9125,66.5828,13.9158,3.6402,17.4172,1,29.1496,16.5206,62.672,32.2677,50.0622,,,Winter,3.921,Winter,0,4,1,0,3,1,1,2,1,4,2,0,1,2,1,1,3,1,1,1,30,Winter,40,56,Fall,2,0 +546bd7b1,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +546bfb18,Winter,7,0,Spring,60,Spring,14.00210667,44.25,39.0,,73,78,111,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.37702,14.0036,877.113,1491.09,5.24115,35.6871,12.8141,1.18956,3.31292,1,21.2442,9.20174,33.3101,15.8043,26.4853,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,40,56,Winter,0,0 +54854783,Spring,13,0,Summer,70,Spring,35.25519235,65.75,216.8,,77,71,133,,,,,Spring,15,0,29.0,2,25.5,2,10,1,8.0,1,8.0,1,15.0,1,,,,,,,,,,,,,,,,,,,,Summer,1.93,Summer,1,1,2,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,22,Spring,32,46,Spring,0,0 +54868027,Summer,8,0,Summer,70,Summer,15.91553741,52.5,62.4,,63,77,111,Summer,,,,Summer,11,1,,,,,22,1,11.0,1,10.5,1,6.0,1,Summer,4,4.0807,15.9173,1041.75,2187.67,12.4559,53.2229,13.5764,2.34093,9.17706,2,27.6279,13.1391,49.1422,23.2377,40.0838,,,Summer,3.39,Summer,3,2,2,2,2,1,0,1,0,0,2,0,0,0,1,0,1,0,1,0,18,Summer,32,46,Summer,0,0 +54945f30,Spring,9,0,Fall,55,Spring,16.97362553,51.0,62.8,,83,80,135,Spring,4,5,22,Spring,7,0,,,,,0,0,11.0,1,9.5,1,9.0,1,Summer,3,3.33693,16.6241,1036.2,1761.53,12.9502,52.6314,14.2268,2.39727,8.8686,1,28.2333,11.4479,49.2945,23.6097,41.1835,,,Spring,4.05,Spring,3,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Spring,54,75,Spring,1,0 +549712ab,Winter,18,0,Summer,70,Spring,26.87739258,64.0,156.6,,69,83,120,,,,,Spring,17,0,29.3,1,37.1,2,25,1,8.5,1,8.5,1,10.0,1,Spring,1,7.05224,26.8803,1554.51,2020.86,31.6383,107.839,18.5105,8.36983,48.7611,3,48.9761,27.2245,100.787,57.0668,80.6144,Spring,1.94,,,Spring,5,5,5,4,3,2,1,2,2,2,5,0,4,4,4,2,0,5,2,4,61,Spring,41,58,Winter,2,2 +54b35de5,Spring,14,0,,,Summer,19.15236147,72.5,143.2,29.0,104,75,175,,,,,Summer,27,1,38.4,2,48.7,3,7,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,Summer,1.99,,,Summer,2,2,2,1,2,4,0,2,1,1,3,0,0,1,2,0,1,0,0,0,24,Summer,56,77,Spring,3,0 +54c87d3c,Summer,9,0,Winter,61,Fall,14.62684959,54.5,61.8,,67,86,89,Fall,6,9,6,Fall,30,1,,,,,7,1,8.0,1,7.0,0,9.0,1,Fall,2,6.91776,14.6284,1044.59,1671.35,11.2874,53.526,12.6699,1.9585,8.27396,2,25.9198,16.3188,46.6083,21.2198,37.2072,,,Summer,1.91,Fall,5,5,5,4,5,1,0,4,2,2,2,0,2,1,4,0,2,3,2,1,50,Fall,30,43,Summer,2,2 +54ce90b4,Summer,8,0,,,Summer,17.14364573,53.5,69.8,,82,79,125,,,,,Summer,0,0,,,,,0,0,6.0,0,6.0,0,4.0,0,Summer,3,3.45788,17.1455,1063.96,1808.74,12.7664,55.5891,13.6548,3.49073,14.2109,2,27.8757,14.947,52.1312,23.5923,40.6421,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,32,46,Summer,3, +54e592da,Fall,16,0,,,Winter,20.26636686,65.0,121.8,27.0,59,63,127,,,,,Winter,20,0,37.1,2,42.4,2,21,1,10.0,1,8.0,1,10.0,1,Winter,5,6.05939,20.2686,1510.2,3624.47,30.7401,103.119,17.1599,3.10873,18.6813,2,47.1832,25.1955,97.0593,54.0892,77.9233,Fall,3.629,,,Fall,0,1,2,1,1,1,1,1,1,1,2,1,1,1,1,0,0,0,0,0,16,,,,Fall,0,0 +54f472f1,Winter,7,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +54fef303,Winter,9,0,Summer,45,Spring,21.86426426,55.5,95.8,,72,97,139,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,1,3.67897,21.8667,1149.18,1493.93,15.2442,64.6656,14.7601,7.10653,31.1344,2,30.9162,18.5052,60.9866,28.2015,46.1604,,,Spring,1.77,Summer,0,3,2,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,12,Summer,39,55,Winter,1,0 +54ffe872,Winter,8,1,Spring,50,Winter,22.02690058,57.0,101.8,,61,95,135,Winter,2,2,5,Winter,11,1,,,,,0,0,9.5,1,10.0,1,10.5,1,,,,,,,,,,,,,,,,,,,,,,Winter,5,5,4,0,5,0,0,0,0,4,4,0,3,0,0,1,1,3,0,0,35,Winter,33,47,Winter,1,1 +5508808f,Spring,13,0,,,Summer,26.22160097,68.89,177.0,38.0,70,60,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +550ad886,Winter,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,,,,, +550b7fb0,Summer,11,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +55464fb7,Summer,8,1,,,Summer,13.20013223,55.0,56.8,22.0,62,59,106,,,,,Summer,11,1,,,,,8,1,16.0,1,16.0,1,12.0,1,Fall,4,3.1475,13.2016,1046.84,1989.0,14.548,53.7657,12.4963,0.705247,3.03434,1,22.9871,16.2306,50.6182,21.0074,37.535,,,Summer,3.93,Summer,4,5,3,1,2,0,0,1,1,3,2,0,3,0,0,3,2,2,1,1,34,Summer,62,85,Summer,0,1 +554dc690,Fall,12,1,,,Fall,17.64584789,61.0,93.4,25.0,,63,,,,,,Fall,0,0,18.6,2,20.96,2,0,0,8.5,0,8.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +554df2a7,Winter,12,0,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +554e46e9,Summer,8,0,Winter,70,Fall,23.5751513,53.0,94.2,,72,96,112,Fall,1,1,36,Fall,11,1,,,,,4,0,8.0,1,8.5,1,12.0,1,Fall,2,9.48635,23.5777,1125.27,1800.42,15.1198,62.1186,15.5479,8.0298,32.0814,3,30.7944,16.2044,52.6322,27.1807,45.9142,,,,,Fall,5,2,4,1,5,3,0,4,5,3,3,0,3,3,3,3,4,5,3,1,60,Summer,28,41,Fall,3,2 +55512071,Fall,13,1,,,Fall,28.01758585,62.0,153.2,37.0,61,70,98,,,,,Fall,4,0,26.4,3,25.4,3,0,0,17.0,1,14.0,1,12.0,1,Fall,3,4.94979,28.0207,1417.25,2267.6,33.6834,93.2186,17.0499,10.9708,59.9815,2,36.5328,23.0024,88.2688,41.9773,70.2162,,,Fall,4.03,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +556604d2,Summer,15,0,Winter,70,Summer,18.12244898,66.5,114.0,,66,76,132,,,,,Summer,42,1,16.5,1,14.3,1,17,1,7.0,0,8.0,1,4.0,0,Fall,3,5.91691,18.1244,1426.44,2424.95,27.2872,94.1981,14.9762,3.14824,19.8019,1,42.1863,24.7246,88.2811,44.3947,69.4735,Summer,2.56,,,Fall,5,4,4,1,4,3,4,4,1,2,5,0,4,1,2,2,1,3,0,0,50,Fall,37,53,Summer,2,2 +557a9537,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5588f518,Winter,11,0,Spring,60,Spring,17.00880999,58.5,82.8,26.0,70,89,110,,,,,Spring,15,1,19.1,2,17.5,2,9,1,10.0,1,9.0,1,12.0,1,Spring,2,4.51898,17.0107,1220.08,1952.12,19.9528,72.2173,14.8365,2.17414,10.5827,1,35.1297,17.1347,67.6983,33.9719,55.0826,,,Spring,1.92,Spring,2,2,4,1,2,2,0,4,1,2,2,1,1,2,2,2,4,2,1,1,38,Spring,35,50,Winter,0,1 +55a071cf,Winter,8,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.44,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +55edb6ed,Winter,12,0,Summer,80,Spring,16.81456598,61.0,89.0,,75,67,129,,,,,Spring,13,0,16.2,1,16.9,1,20,1,0.0,0,0.0,0,6.0,0,,,,,,,,,,,,,,,,,,,,Winter,3.67,Spring,3,0,4,1,4,1,0,4,4,2,1,1,2,1,3,3,3,2,0,2,41,Spring,47,66,Winter,2,1 +55f42bcc,Fall,5,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +55f9338e,Spring,9,1,Fall,80,Summer,24.0256319,53.0,96.0,,61,83,110,Summer,4,7,0,Summer,11,1,,,,,0,0,6.0,0,8.0,0,7.0,1,Summer,3,3.55245,24.0283,1142.1,1941.57,16.5087,63.9115,15.9967,8.03157,32.0885,1,31.6775,15.7253,60.359,27.167,48.1862,,,Summer,3.24,Summer,2,1,3,0,3,2,0,2,1,1,2,0,1,1,1,1,2,1,2,1,27,Summer,40,56,Spring,0,0 +56189867,Spring,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +561f2797,Summer,6,0,Fall,61,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,42,59,Summer,0,0 +56210fdb,Summer,7,0,Fall,66,Fall,15.48194707,46.0,46.6,,76,75,116,Fall,7,11,55,Fall,4,1,,,,,10,1,7.5,1,8.0,1,9.0,1,Fall,4,9.27702,15.4836,915.33,1922.19,4.8744,39.7577,13.2102,2.27349,6.84235,1,22.2603,12.6229,30.4806,21.5613,27.1347,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,35,50,Summer,0,0 +562ca839,Spring,8,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +5637a5a4,Spring,7,0,Spring,85,Spring,15.63015353,47.8,50.8,,62,84,99,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.80689,15.6319,949.259,1613.74,8.76924,43.3716,13.346,2.28583,7.42843,1,24.1719,10.4305,40.5647,18.4965,32.9411,,,,,Summer,2,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,6,Summer,30,43,Spring,0,0 +563ebc00,Fall,10,0,Winter,60,Fall,14.67864,50.0,52.2,,74,89,119,Fall,6,9,2,Fall,11,0,8.9,1,9.1,1,9,1,5.5,0,5.0,0,8.0,0,Fall,2,3.11051,14.6802,987.144,1579.43,9.48681,47.4068,13.3323,1.34799,4.79318,1,27.1015,10.8185,44.2963,27.0952,36.5883,,,Fall,2.33,Fall,4,0,4,0,4,5,0,2,2,2,2,0,4,2,2,2,3,3,0,2,43,Fall,30,43,Fall,0,1 +56418caf,Winter,10,0,Spring,65,Spring,19.80565508,55.25,86.0,,69,85,132,Spring,,,,Spring,16,1,15.8,2,16.9,2,4,0,6.0,0,4.5,0,6.0,0,,,,,,,,,,,,,,,,,,,,Spring,3.94,Winter,5,5,3,1,5,0,0,4,0,0,4,0,4,5,1,4,4,5,4,2,56,Winter,38,54,Winter,1,2 +56443aba,Spring,9,0,Summer,70,Spring,19.44802148,55.3,84.6,,75,82,138,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.8198,19.4501,1139.37,1936.93,15.8835,63.6211,14.6269,4.82321,20.9789,2,31.5292,16.2084,59.8013,29.1439,47.4127,,,Spring,2.18,Spring,2,4,4,1,3,2,1,2,1,2,3,0,3,2,2,1,2,1,2,1,39,Spring,42,59,Spring,0,1 +56761daf,Summer,11,0,Spring,60,Winter,19.02401609,59.0,94.2,,63,79,104,Winter,5,7,40,Winter,20,1,18.0,1,17.7,1,10,1,5.0,0,4.0,0,7.0,0,,,,,,,,,,,,,,,,,,,,Summer,4.18,Spring,5,5,3,2,5,4,3,3,2,4,4,0,,3,3,4,4,4,2,4,64,Winter,37,53,Summer,1,2 +567ecea5,Winter,12,0,Spring,70,Spring,18.74068878,56.0,83.6,,73,75,117,,,,,Spring,20,1,16.7,2,16.6,2,4,0,10.0,1,12.0,1,8.0,0,,,,,,,,,,,,,,,,,,,,Spring,1.23,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,32,46,Winter,0,0 +567fd23a,Fall,13,0,Summer,68,Spring,16.34199785,66.5,102.8,,78,69,119,,,,,Summer,,,,,,,,,,,,,,,Summer,2,5.89863,16.3438,1395.5,2232.81,27.6459,90.9026,14.4523,1.89153,11.8974,2,39.503,23.7537,85.004,36.177,67.1489,,,Winter,1.75,Spring,3,3,3,0,5,2,3,2,1,2,2,0,0,1,0,0,2,1,1,0,31,Spring,34,49,Fall,3,1 +5688e4af,Spring,6,0,Summer,45,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +568eb64b,Fall,12,0,Summer,80,Spring,19.91568904,60.88,105.0,,87,65,108,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4.48,Winter,5,5,3,1,5,0,0,0,1,4,4,0,4,2,3,5,3,5,1,0,51,Winter,36,51,Fall,3,2 +5690b9f7,Spring,13,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +56c99b23,Fall,6,0,Spring,60,Fall,15.40298778,47.0,48.4,,50,74,106,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,0,1,0,2,0,0,0,0,0,2,0,0,0,0,1,1,1,1,1,12,Fall,39,55,Fall,0,0 +56cb7161,Spring,6,1,Winter,80,Summer,19.33051149,47.1,61.0,,43,81,109,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,3,1,1,2,1,0,1,1,2,2,0,0,0,2,0,0,1,0,0,21,Fall,35,50,Spring,0,0 +56cd5f8b,Winter,9,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +56d9dead,Winter,8,1,Spring,85,Spring,15.00952858,54.75,64.0,,68,65,110,Spring,7,12,11,Spring,10,1,,,,,5,1,11.0,1,11.5,1,14.0,1,Spring,3,3.30564,15.0112,1069.2,1710.73,16.6748,56.1474,13.1693,1.84183,7.85263,1,25.1069,14.3657,52.8417,23.9502,41.7816,,,Winter,2.65,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,34,49,Winter,0,0 +56ef356c,Summer,9,0,Winter,45,Fall,17.38327273,55.0,74.8,,67,99,128,Fall,3,4,1,Fall,10,1,,,,,5,1,10.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,1.58,Fall,4,3,3,1,2,0,0,1,1,1,1,1,1,1,1,1,2,2,3,1,30,Fall,27,40,Summer,0,0 +5711a83a,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +571907fc,Spring,10,0,,,Summer,20.82962963,54.0,86.4,27.0,70,72,131,,,,,Summer,1,0,18.4,2,19.2,2,3,0,4.0,0,4.5,0,11.0,1,Fall,3,3.65694,20.8319,1128.02,1917.63,15.5408,62.4116,15.0481,5.78385,23.9884,2,31.2747,15.5961,58.7546,28.5016,46.8155,,,Summer,3.08,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,46,64,Summer,0,0 +5730b8ec,Winter,8,0,,,Winter,23.75789474,57.0,109.8,36.0,65,73,105,,,,,Winter,0,0,,,,,0,0,4.5,0,5.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,Winter,2.6,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +574599a8,Winter,6,1,Summer,71,Summer,15.14839294,47.0,47.6,,53,90,111,Summer,2,1,30,Summer,0,0,,,,,0,0,10.0,1,9.0,1,6.0,1,Summer,1,7.7485,15.15,942.725,1225.54,11.4048,42.6756,13.5827,1.56732,4.92436,1,21.5128,9.75809,34.9271,18.2968,32.9176,,,,,Summer,2,1,2,0,2,0,0,1,0,1,2,0,1,2,1,1,1,1,0,1,19,Summer,43,60,Winter,0,0 +574cb902,Spring,7,1,,,Summer,17.26301775,52.0,66.4,,81,100,108,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Summer,30,43,Spring,0,0 +5775ec58,Winter,14,1,,,Spring,16.64819336,64.0,97.0,,74,92,108,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.83,,,Winter,3,3,3,2,4,1,1,3,1,2,4,1,1,1,2,2,3,3,3,2,45,Winter,39,55,Winter,2,1 +5779f2e9,Fall,5,1,Winter,75,Fall,14.01134948,42.5,36.0,,73,96,105,Fall,,,,Fall,0,0,,,,,8,1,11.5,1,12.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,4,0,4,0,0,0,0,1,0,0,0,0,2,1,0,1,0,0,17,Fall,38,54,Fall,2,0 +579e50bd,Fall,12,1,Spring,85,Winter,17.9248128,62.5,99.6,,62,76,111,,,,,Winter,20,1,23.7,2,23.6,2,4,0,7.5,0,7.0,0,10.0,1,Winter,2,1.57817,17.8188,1264.92,1897.38,25.3574,76.9934,13.8579,3.96091,22.0066,2,31.4285,20.2075,75.4153,34.4504,56.7859,,,Winter,1.24,Winter,1,1,2,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,18,Winter,29,42,Fall,1,0 +57a8ed68,Spring,10,0,,,Summer,14.13193099,51.25,52.8,22.0,116,78,135,,,,,Spring,0,0,,,,,0,0,7.0,0,9.0,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,3.38,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +57c4f2b4,Summer,11,0,Winter,55,Summer,17.29611678,60.75,90.8,,64,76,110,,,,,Summer,9,0,15.5,2,16.5,2,6,0,0.0,0,0.0,0,9.0,1,Summer,3,4.34271,17.298,1196.16,2033.47,16.6715,69.6695,13.2725,4.02549,21.1305,2,31.75,21.248,65.3268,29.9927,48.4215,,,Fall,2.56,Summer,3,2,2,0,2,2,0,1,0,0,0,0,1,1,1,1,2,1,2,0,21,Summer,42,59,Fall,0,0 +57ca82ca,Summer,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +57d82f30,Winter,12,1,Summer,61,Spring,29.47272239,61.0,156.0,,80,87,125,,,,,Spring,30,1,24.4,2,21.2,2,4,0,7.0,0,7.0,0,7.5,0,,,,,,,,,,,,,,,,,,,,Winter,2.51,Spring,2,2,2,1,4,1,0,1,1,1,3,0,1,2,2,1,0,2,0,1,27,Spring,37,53,Winter,2,0 +57dd511d,Summer,21,0,Winter,75,Summer,23.59550173,68.0,155.2,,65,86,114,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +57de6095,Fall,6,0,Fall,61,Fall,15.76589474,47.5,50.6,,45,72,104,,,,,Fall,1,0,,,,,1,0,14.0,1,14.0,1,8.0,1,Fall,3,2.89153,15.8342,958.433,1629.34,10.2451,44.3487,13.878,1.95622,6.25133,1,25.3305,8.77304,41.4571,18.9637,35.5756,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +57f6fa6c,Spring,7,0,Fall,70,Spring,12.93994681,46.5,39.8,21.0,51,62,101,,,,,Spring,4,1,,,,,3,0,11.0,1,10.0,1,9.0,1,Spring,1,2.54109,12.9414,902.523,1173.28,5.88918,38.3935,12.484,0.457321,1.40645,1,21.2404,11.264,35.8525,15.4849,27.1295,,,,,Summer,4,4,3,0,2,1,0,1,1,2,3,0,1,0,2,1,2,1,0,1,29,Summer,34,49,Spring,0,0 +580a30c5,Fall,13,1,Spring,40,Spring,17.64809074,57.5,83.0,,68,117,116,,,,,Spring,28,1,17.1,2,17.5,2,15,1,13.0,1,12.5,1,14.0,1,Spring,4,3.67359,17.65,1158.02,2200.23,21.5339,65.607,13.9514,3.69863,17.393,1,27.9227,16.1505,61.9335,26.9089,49.4566,,,Spring,2.8,Spring,5,4,4,2,4,3,4,1,3,4,2,0,4,3,3,2,2,2,1,2,55,Spring,58,80,Fall,2,2 +58391429,Fall,14,0,Spring,68,Winter,26.08884083,68.0,171.6,,63,81,112,,,,,Winter,8,0,29.2,2,29.3,2,0,0,8.0,1,9.0,1,8.0,0,Winter,3,6.39306,26.0917,1629.12,2769.51,34.9765,115.786,17.6052,8.48648,55.814,3,49.949,30.8606,109.393,53.5034,84.9254,,,Winter,2.3,Winter,4,1,5,5,5,1,0,5,2,1,0,0,5,4,4,4,4,4,5,5,64,Winter,49,68,Fall,2,2 +584a759d,Winter,8,1,,,Winter,16.72065972,48.0,54.8,23.0,66,85,110,,,,,Winter,0,0,,,,,0,0,12.5,1,11.0,1,16.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +584ffbdb,Winter,10,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +58900fd0,Spring,8,1,Fall,80,Spring,16.81521419,50.5,61.0,,75,67,133,Spring,1,1,24,Spring,13,1,,,,,0,0,9.0,1,11.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,3,0,1,0,1,0,0,0,2,1,1,0,2,0,1,0,1,1,0,0,14,Spring,35,50,Spring,0,0 +5896b610,Winter,9,1,,,Spring,23.7753649,53.0,95.0,28.0,86,68,134,,,,,Spring,3,0,,,,,7,1,16.0,1,14.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +58a255e6,Winter,6,0,Spring,80,Winter,19.92204617,47.0,62.6,,64,85,115,,,,,Winter,5,1,,,,,2,0,11.0,1,12.0,1,9.0,1,Spring,2,2.74077,19.9242,970.438,1552.7,8.60163,45.6274,14.5222,5.40202,16.9726,2,26.2119,10.8138,42.8866,25.2731,34.8135,,,,,Winter,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,19,Winter,30,43,Winter,1,0 +58ada72e,Fall,7,0,Winter,75,Fall,17.71890352,53.0,70.8,,56,66,116,Fall,4,5,20,Fall,0,0,,,,,0,0,7.0,0,9.0,1,14.0,1,Fall,2,5.04694,17.7208,1070.99,1713.59,13.6872,56.338,14.1011,3.61975,14.462,2,28.6595,13.9913,51.2911,24.0125,42.3467,,,,,Winter,4,4,4,0,4,0,0,0,1,1,1,0,0,2,0,4,3,4,2,0,34,Fall,56,77,Fall,2,1 +58de61f0,Fall,12,1,Spring,75,Winter,17.40336914,64.0,101.4,,60,72,116,,,,,Winter,24,1,26.4,3,20.6,2,0,0,7.5,0,9.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.97,Winter,4,4,4,0,3,1,0,3,3,2,3,0,3,1,2,2,3,3,0,1,42,Winter,41,58,Fall,1,1 +58dfccdb,Spring,9,0,Fall,45,Fall,17.36701183,52.0,66.8,,75,71,104,Fall,5,7,18,Fall,7,0,,,,,5,0,6.0,0,6.0,0,8.0,1,Fall,1,3.3375,17.3689,1045.1,1358.63,12.6493,53.5801,13.9315,3.43736,13.2199,2,27.5949,13.3358,50.2426,22.5068,40.2443,,,Spring,4.03,Fall,2,2,2,1,2,1,0,1,1,1,2,0,1,1,1,1,1,1,1,1,23,Fall,33,47,Spring,2,0 +58e6e605,Summer,20,0,Winter,70,Summer,23.21184625,69.0,157.2,,67,62,134,,,,,,,,,,,,,,,,,,,,Winter,5,7.1099,23.2144,1725.13,4140.31,38.464,126.012,18.6087,4.60565,31.1879,1,55.6238,31.9243,118.902,68.0464,94.0877,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +58eb1ee0,Winter,11,0,,,Winter,16.67000378,57.5,78.4,26.0,78,109,122,,,,,Winter,5,0,13.7,2,14.0,2,0,0,8.0,1,10.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.66,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +58f11465,Fall,6,0,Winter,76,Fall,14.46276042,48.0,47.4,,54,82,101,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,2,0,2,0,0,1,0,2,2,0,2,0,1,2,2,3,0,0,19,Fall,30,43,Fall,2,0 +591075dc,Spring,12,1,,,,,,,,,,,,,,,Summer,20,1,20.1,2,16.9,2,0,0,12.0,1,11.0,1,12.0,1,Summer,1,4.52242,25.2435,1316.81,1711.85,28.8544,82.5207,15.5922,9.65136,51.0794,2,32.7127,20.9537,77.9983,35.6395,61.567,,,Summer,2.42,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +59236744,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +592adca0,Winter,7,1,Winter,70,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,6,Spring,42,59,Winter,1,0 +592b3b7f,Summer,6,0,Winter,60,Winter,14.67593294,43.0,38.6,,101,75,124,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,44,62,Summer,1,0 +59305d37,Summer,14,1,Winter,65,Fall,24.09028405,64.1,140.8,,48,53,123,,,,,Fall,15,0,26.9,2,27.8,3,10,1,11.5,1,11.5,1,10.0,1,Fall,4,4.24954,24.0929,1282.91,2437.54,21.6882,78.9103,13.5027,10.5902,61.8897,2,29.977,27.2451,74.6608,48.2171,51.6652,,,Summer,2.57,Fall,3,1,3,1,3,1,1,1,2,0,2,0,2,2,3,2,3,2,1,2,35,Fall,37,53,Summer,2,1 +59307c13,Fall,9,0,,,Fall,13.024,47.5,41.8,21.0,74,99,109,,,,,Fall,0,0,,,,,0,0,8.0,1,7.0,0,7.0,1,Fall,2,2.63948,13.0254,917.79,1468.46,6.56327,40.0197,12.4707,0.554769,1.78031,1,21.6135,11.8429,37.3802,15.6159,28.1768,,,Fall,2.64,Fall,5,5,5,2,5,1,0,3,2,4,4,0,5,1,4,5,4,5,2,2,64,Fall,38,54,Fall,3,2 +593a7148,Spring,7,1,Fall,61,Summer,20.49562682,49.0,70.0,,70,94,120,Summer,6,10,10,Summer,12,1,,,,,3,0,6.5,0,7.0,0,8.5,1,,,,,,,,,,,,,,,,,,,,,,Summer,3,3,3,2,3,0,0,3,1,2,2,1,2,2,3,3,1,2,1,3,40,Summer,44,62,Spring,0,1 +593b5cfa,Summer,5,0,Fall,45,Summer,16.49528339,43.5,44.4,,52,69,92,,,,,Summer,1,0,,,,,0,0,10.0,1,12.0,1,8.0,1,Summer,3,7.65908,16.4971,882.958,1501.03,5.29765,36.3096,13.491,3.00605,8.09043,1,21.1028,9.90907,28.6505,15.0227,26.4005,,,,,Summer,3,2,2,0,2,0,0,0,0,2,0,0,2,0,0,2,0,0,0,0,15,Summer,38,54,Summer,0,0 +59402969,Fall,14,0,Spring,85,Fall,18.33017578,64.0,106.8,,74,110,135,,,,,Fall,37,1,30.3,2,31.3,2,12,0,7.5,1,8.0,1,12.0,1,Fall,3,-0.686256,18.3322,1403.52,2385.98,27.529,91.7562,15.7499,2.58227,15.0438,2,41.5404,22.6867,92.4424,41.528,69.0694,Fall,1.4,,,Winter,4,2,2,2,4,2,1,1,1,2,2,1,1,1,2,1,2,2,2,1,36,Fall,46,64,Fall,0,1 +595dea40,Winter,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +5985f299,Fall,6,1,Spring,55,Winter,17.1532,50.0,61.0,,77,105,121,Winter,4,5,51,Winter,0,0,,,,,0,0,5.0,0,6.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,35,50,Fall,0,0 +599850d0,Spring,8,0,Summer,85,Summer,15.62222222,54.0,64.8,,60,77,109,Summer,6,10,5,Summer,8,1,,,,,0,0,7.0,0,6.0,0,8.0,1,Summer,4,5.44739,15.6239,1055.26,2216.04,12.2135,54.6616,13.1794,2.44448,10.1384,1,27.434,15.0141,49.2142,23.6357,39.6474,,,Summer,2.95,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Spring,0,0 +59b7fc28,Winter,6,0,Spring,65,Winter,16.61153119,46.0,50.0,21.0,70,90,104,,,,,Winter,11,1,,,,,1,0,11.5,1,11.5,1,8.0,1,Winter,2,2.61249,16.6133,925.818,1481.31,8.20378,40.8748,13.5813,3.03201,9.12522,1,23.0306,9.64043,38.2623,15.7513,31.2344,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,33,47,Winter,0,0 +59d6e4dc,Spring,16,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +59d7a3a9,Fall,6,0,Spring,51,Fall,15.28084172,46.5,47.0,,68,79,112,Fall,,,,Fall,7,1,,,,,0,0,8.0,1,8.0,1,4.5,0,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Winter,34,49,Fall,2,0 +59ea397f,Spring,7,1,Fall,68,Spring,18.32084211,47.5,58.8,25.0,60,75,115,,,,,Spring,1,0,,,,,0,0,13.0,1,12.0,1,7.0,1,Spring,2,2.46227,18.3228,968.741,1453.11,13.2546,45.4466,14.1618,4.16108,13.3534,1,22.2129,9.97917,42.9844,18.3374,35.4675,,,,,Spring,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Spring,30,43,Spring,0,0 +5a07e79f,Winter,11,1,Spring,65,Winter,16.21032405,53.5,66.0,,68,97,105,Winter,5,7,16,Winter,10,0,12.8,2,13.4,2,1,0,10.0,1,9.0,1,9.0,1,Winter,1,7.11453,16.2121,1051.48,1366.93,15.8487,54.2598,13.3283,2.88383,11.7402,1,24.1762,14.2349,47.1453,22.498,40.0249,,,Winter,1.52,Spring,3,2,5,2,5,0,0,1,0,0,3,0,0,0,2,2,2,3,1,0,31,Spring,40,56,Winter,0,1 +5a2c7642,Spring,5,1,Summer,50,Spring,15.23213106,43.5,41.0,,77,46,106,Spring,,,,Spring,0,0,,,,,0,0,12.0,1,13.0,1,0.0,0,Spring,2,2.05302,15.2338,892.439,1338.66,9.80999,37.3195,13.8663,1.36751,3.68051,1,20.2725,7.23705,35.2665,15.4808,30.0824,,,,,Summer,2,2,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,,,,Spring,2,0 +5a3005de,Summer,18,1,Winter,51,Fall,22.49458302,63.0,127.0,,65,86,111,,,,,,,,,,,,,,,,,,,,Fall,2,4.78368,22.497,1325.87,1988.81,28.7848,83.4858,14.7889,7.70819,43.5142,2,32.6817,22.0193,78.7022,35.5698,61.4665,,,,,Fall,4,4,4,2,5,4,3,4,3,2,3,1,2,4,4,4,4,4,2,2,65,,,,Summer,3,2 +5a313293,Fall,13,1,Winter,55,Winter,28.28630188,68.5,188.8,,61,88,126,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.63,Winter,2,2,1,3,2,1,1,3,2,2,4,1,2,1,1,2,2,2,2,2,38,Fall,37,53,Fall,2,1 +5a3d5e05,Summer,6,0,Winter,50,Summer,16.4765625,48.0,54.0,,,,,Summer,4,5,40,Summer,0,0,,,,,0,0,5.0,0,6.0,0,4.0,0,Fall,2,2.73507,16.6615,955.419,1528.67,8.22195,44.0277,13.4353,3.22619,10.5723,1,23.5077,12.298,41.2926,17.738,31.7296,,,,,Summer,4,4,4,0,5,0,0,1,1,1,0,0,3,1,0,5,3,5,1,1,39,Summer,53,73,Summer,2,1 +5a4b3725,Fall,10,0,Spring,40,Winter,28.47244598,63.75,164.6,35.0,,,,,,,,Winter,0,0,9.5,1,10.0,1,0,0,9.5,1,8.0,1,9.0,1,Winter,2,5.03202,28.4756,1443.91,2310.25,28.0182,96.0583,16.6179,11.8576,68.5417,3,40.0506,27.9895,91.0263,32.6015,68.0688,,,Winter,1.861,Winter,2,3,3,1,2,2,0,0,2,2,3,0,2,1,2,3,2,3,2,1,36,Winter,55,76,Fall,1,1 +5a4cff4b,Fall,11,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5a543f1c,Fall,9,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.73,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +5a5a1ed6,Spring,6,0,,,Spring,13.87846731,49.0,47.4,23.0,78,86,107,,,,,Spring,0,0,,,,,0,0,9.0,1,9.0,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +5a5f6d41,Fall,13,0,Winter,60,Winter,23.1438667,59.0,114.6,,61,95,120,,,,,Winter,10,0,18.1,2,19.1,2,4,0,8.0,1,8.0,1,6.0,0,Winter,2,7.31309,22.6068,1287.31,2059.69,21.3924,79.378,15.6587,6.94813,35.222,2,37.0938,20.8918,72.0649,36.8094,58.4862,,,Winter,2.84,Winter,3,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,Winter,31,45,Fall,,0 +5a5f9fb2,Fall,9,0,,,Fall,14.45517751,52.0,55.6,23.0,61,79,105,,,,,Fall,9,1,,,,,7,1,7.0,0,6.5,0,12.0,1,Winter,2,3.30583,14.4568,1013.9,1622.25,11.2309,50.2571,13.0675,1.38922,5.34289,1,26.3646,12.6616,46.9513,21.7496,37.5956,,,Fall,3.22,Fall,3,3,2,1,2,1,1,2,2,2,1,1,2,1,3,2,2,2,2,1,36,Fall,55,76,Fall,0,1 +5a6c00d5,Winter,6,0,,,Winter,12.77490535,43.0,33.6,18.0,117,79,167,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +5a8712ae,Fall,17,0,Winter,50,Fall,22.55112497,67.0,144.0,,80,78,129,,,,,Fall,28,1,25.3,1,24.2,1,20,1,6.0,0,6.0,0,6.5,0,Fall,4,6.66267,22.6319,1650.78,3466.64,36.676,118.093,18.496,4.13595,26.4072,2,52.1191,29.2977,111.43,59.1338,88.7951,Winter,1.69,,,Winter,4,5,5,4,5,4,5,5,5,4,5,4,5,5,5,4,5,5,3,4,91,,,,Fall,3,3 +5ac9eca0,Fall,7,0,Winter,45,Winter,16.19712,50.0,57.6,,72,78,120,Winter,4,5,48,Winter,6,1,,,,,15,1,8.0,1,10.0,1,10.0,1,Winter,3,8.8499,16.1989,982.947,1671.01,8.50381,46.9597,13.2065,2.99237,10.6403,2,24.0111,14.4448,38.1098,19.4641,32.5149,,,,,Winter,2,2,4,1,2,1,0,2,1,1,2,0,2,1,2,2,1,1,1,1,29,Winter,38,54,Fall,0,0 +5ad85a02,Spring,7,1,Spring,75,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,3,0,3,1,0,1,1,0,2,0,1,1,2,2,3,3,0,1,30,Spring,44,62,Spring,1,0 +5af79adb,Spring,16,1,Fall,69,Spring,24.06007101,65.0,144.6,,102,118,166,,,,,Spring,20,1,14.0,1,17.0,1,0,0,5.5,0,6.0,0,7.0,0,Summer,3,5.01278,24.0627,1366.72,2186.76,29.9148,87.8371,14.6169,9.44584,56.7629,3,33.4386,24.4836,82.8243,38.6023,63.3535,Summer,1.95,,,Spring,3,2,2,0,3,1,1,1,1,2,0,0,1,2,1,0,2,1,1,0,24,,,,Spring,2,0 +5b00b389,Summer,13,1,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5b299ae1,Fall,8,0,Winter,55,Fall,16.81289664,49.5,58.6,,68,77,114,Fall,6,10,21,Fall,4,0,,,,,0,0,10.0,1,8.0,1,4.5,0,,,,,,,,,,,,,,,,,,,,Fall,2.21,Fall,4,4,4,2,4,1,0,2,1,2,2,1,2,0,2,1,2,2,2,1,39,Fall,26,38,Fall,0,1 +5b2c9b83,Fall,14,1,Spring,55,Winter,37.22897267,65.5,227.2,,85,97,128,,,,,Spring,,,,,,,,,,,,,,,Summer,2,5.55774,37.233,1589.89,2384.83,40.966,111.607,18.2899,18.9432,115.593,3,40.9911,29.6497,106.049,53.2742,81.9571,,,Summer,2.06,Fall,3,2,3,2,3,2,1,2,1,1,3,1,1,2,1,2,2,2,1,0,35,Winter,47,66,Fall,1,1 +5b374f76,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5b4d2604,Spring,8,1,Fall,61,Summer,15.35352,50.0,54.6,,49,65,117,Summer,8,13,11,Summer,37,1,,,,,8,1,15.5,1,15.5,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,3,3,3,0,3,0,0,0,0,3,2,0,3,0,2,2,2,2,0,2,30,Summer,47,66,Spring,0,0 +5b66b6f2,Summer,5,1,Fall,55,Summer,16.196755,43.0,42.6,22.0,69,100,102,,,,,Summer,1,0,,,,,0,0,12.0,1,12.0,1,10.0,1,Summer,1,2.07012,16.1985,892.057,1159.67,10.6268,37.2788,14.1751,2.02339,5.32124,1,20.7956,5.8564,35.2086,15.3234,31.4224,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +5b98da98,Spring,13,0,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,6.61504,21.1045,1595.54,3350.64,33.3523,112.209,17.0613,4.04313,26.5909,3,50.2776,28.5793,105.594,59.8849,83.6299,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5b9ff85e,Spring,11,0,Summer,50,Spring,22.12877211,62.0,121.0,,72,99,139,,,,,Fall,,,,,,,,,,,,,,,Summer,3,4.94642,22.1312,1342.47,2282.2,23.1515,85.2536,15.5931,6.5381,35.7464,2,39.4073,22.6949,80.3072,41.6923,62.5587,,,Summer,2.72,Spring,3,4,0,0,4,1,3,2,2,3,0,0,1,2,2,2,2,4,2,3,40,Spring,37,53,Spring,0,1 +5ba201ba,Spring,9,0,Winter,70,Summer,15.56764311,61.0,82.4,,130,74,178,Summer,7,11,48,Summer,30,1,,,,,6,1,7.0,0,4.0,0,5.0,0,Fall,4,9.85257,24.1289,1041.99,2188.18,11.8021,53.2488,15.5927,8.53624,29.1512,2,27.8353,13.6115,43.3963,23.4582,39.6374,,,Spring,3.15,Summer,5,3,4,0,3,1,0,1,1,2,3,0,3,1,2,4,1,4,1,1,40,Summer,44,62,Spring,0,1 +5bcf002a,Fall,8,0,Winter,55,Winter,15.67043896,54.0,65.0,,70,118,100,Winter,5,7,27,Winter,15,1,,,,,2,0,7.0,0,7.0,0,7.0,1,Winter,2,3.50334,15.6722,1056.57,1690.52,12.2038,54.802,13.2133,2.45885,10.198,1,27.6487,14.9494,51.2986,24.3112,39.8525,,,Fall,1.969,Winter,2,2,2,1,1,2,1,3,1,0,0,0,1,1,1,1,2,1,1,1,24,Winter,45,63,Fall,0,0 +5c045867,Winter,14,0,Summer,62,Spring,18.45725688,69.0,125.0,,61,71,110,,,,,Spring,10,0,18.9,1,17.2,1,5,0,4.0,0,4.0,0,5.0,0,Summer,3,5.71729,18.4593,1392.14,2366.64,23.9643,90.5441,13.371,5.08825,34.4559,1,38.2802,28.2995,84.8268,38.0139,62.2446,,,Winter,1.97,Spring,3,3,2,1,3,1,1,1,1,2,1,1,1,1,1,1,1,1,4,1,31,Spring,41,58,Winter,1,1 +5c0e725d,Winter,18,0,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.29,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +5c186496,Winter,7,1,Spring,80,Winter,16.48020879,52.25,64.0,,74,74,111,Winter,26,,,Spring,,,,,,,,,,,,,,,Summer,3,2.75116,16.482,1025.69,1641.11,12.9022,51.5128,13.2662,3.21586,12.4872,3,22.0857,16.5249,48.7616,21.6996,34.9879,,,,,Spring,1,2,3,0,2,0,0,0,1,0,0,0,1,1,3,2,2,1,0,0,19,Spring,40,56,Winter,0,0 +5c22f241,Winter,7,0,Spring,70,Winter,15.86631944,48.0,52.0,,64,66,117,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,2.71917,15.8681,948.563,1991.98,7.47938,43.2974,13.2124,2.65563,8.70257,1,23.456,12.3621,40.5783,19.1996,30.9354,,,,,Spring,1,2,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,Spring,32,46,Winter,0,0 +5c2c136d,Winter,10,0,Summer,55,Spring,21.14756141,53.0,84.5,,55,77,108,,,,,Spring,2,0,,,,,1,0,6.0,0,5.0,0,2.0,0,Spring,3,3.08923,21.225,1081.22,1838.08,10.458,57.4274,14.3738,6.8512,27.3726,2,26.5102,20.4592,54.3382,24.0135,36.9682,,,Winter,1.781,Spring,5,5,5,1,5,5,0,4,1,4,5,0,5,4,5,5,,5,5,5,74,Spring,55,76,Winter,3,2 +5c3b30d7,Summer,10,1,,,Summer,19.02708121,56.5,86.4,27.0,85,76,133,,,,,Summer,10,0,,,,,4,0,7.5,0,6.5,0,12.0,1,Fall,2,4.037,19.0292,1214.76,1822.14,24.203,71.651,15.7808,3.2484,14.749,1,32.2048,15.2431,67.614,35.6253,56.4078,,,Summer,3.74,Summer,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,Summer,42,59,Summer,0,0 +5c6f7b81,Spring,12,1,,,,,,,,,,,,,,,Spring,5,0,21.8,3,21.5,2,0,0,7.5,0,7.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.211,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +5cd83147,Fall,12,0,Winter,60,Fall,27.62723355,59.0,136.8,,105,85,149,,,,,Fall,12,0,21.5,2,20.6,2,3,0,6.0,0,5.5,0,6.0,0,Fall,2,4.54305,27.6303,1350.34,2160.55,23.9682,86.0924,17.3885,10.2417,50.7076,3,39.9727,22.1515,81.5493,40.2119,63.9409,,,Fall,2.91,Fall,2,2,1,1,2,1,1,1,1,1,2,0,0,1,1,0,1,1,0,0,19,Fall,38,54,Fall,0,0 +5cf96250,Summer,15,1,,,Summer,17.54564832,60.25,90.6,29.0,81,82,136,,,,,Summer,5,0,12.9,1,8.1,1,0,0,5.5,0,6.0,0,7.0,0,Summer,3,3.76078,17.5476,1159.97,1855.94,18.7891,65.8145,12.7471,4.80049,24.7855,1,25.2862,21.7393,62.0538,25.9088,44.0753,Summer,1.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +5d04624a,Summer,10,1,Winter,40,Fall,14.97354864,51.0,55.4,,66,90,107,Fall,1,0,40,Fall,30,1,,,,,20,1,11.0,1,9.0,1,12.0,1,Fall,2,9.56787,14.9752,998.454,1497.68,12.5197,48.6115,13.1402,1.835,6.78849,1,22.1922,13.8996,39.0436,20.9614,34.7119,,,Summer,2.44,Fall,2,3,4,0,3,3,0,4,0,0,1,0,4,4,4,4,4,4,4,0,48,Fall,66,90,Summer,2,1 +5d1cf0a4,Spring,9,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.43,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +5d1d7e41,Spring,7,1,Summer,70,Spring,19.74024,50.0,70.2,25.0,59,84,96,,,,,Spring,2,0,,,,,0,0,9.5,1,11.0,1,4.0,0,Spring,1,2.88998,19.7424,1036.75,1347.77,16.5878,52.6904,14.8182,4.92423,17.5096,2,25.5813,10.5214,49.8004,24.4982,42.169,,,,,Spring,2,2,2,0,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,0,18,Spring,42,59,Spring,0,0 +5d1e02bd,Summer,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5d3b6ea5,Fall,8,0,Winter,65,Fall,17.16770986,51.75,65.4,,48,83,96,,,,,Fall,8,1,,,,,3,0,7.0,0,7.5,1,10.0,1,Fall,3,3.24564,17.1696,1031.8,1754.07,11.5746,52.1637,13.6946,3.47495,13.2363,1,26.9057,13.6834,48.9181,22.4213,38.4803,,,Fall,2.56,Fall,4,3,4,1,3,1,1,1,1,2,1,1,3,1,1,2,3,2,1,1,37,Fall,65,89,Fall,0,1 +5d48a5b0,Summer,7,0,Winter,63,Fall,14.7202039,50.5,53.4,,58,67,111,,,,,Fall,8,1,,,,,0,0,6.0,0,6.0,0,6.0,1,Fall,1,4.56431,14.7218,987.129,1283.27,9.95044,47.4052,13.0691,1.65269,5.99477,1,25.4131,12.0417,42.8409,20.8557,35.3635,,,,,Fall,5,2,2,0,0,0,0,3,1,2,0,0,3,1,2,3,0,3,0,1,28,Fall,32,46,Summer,0,0 +5d762840,Winter,20,0,Fall,50,Spring,22.39451994,67.0,143.0,,74,104,127,,,,,Spring,34,1,52.5,3,56.8,3,17,0,13.0,1,12.0,1,6.0,0,Spring,3,6.554,22.397,1620.84,2755.43,34.8769,114.904,17.9965,4.40044,28.0959,2,51.294,28.7332,108.35,59.8952,86.1709,,,,,Spring,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,,,,Winter,,0 +5d7a44b6,Summer,8,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +5d805d83,Summer,17,1,Winter,75,Fall,21.06734814,62.75,118.0,,75,72,123,,,,,Fall,18,1,23.6,2,26.6,2,4,0,8.5,0,9.5,0,12.0,1,,,,,,,,,,,,,,,,,,Summer,0.99,,,Fall,1,2,3,0,2,1,1,3,1,0,4,0,0,0,0,0,0,0,0,0,18,Fall,35,50,Summer,2,0 +5d885b20,Summer,5,0,Winter,50,Winter,15.51238507,43.0,40.8,,91,66,127,,,,,Winter,2,1,,,,,1,0,9.0,1,10.0,1,7.0,1,Winter,3,2.24081,15.5141,869.156,1477.56,3.90185,34.8395,13.2476,2.26647,5.96051,1,21.4885,9.44909,32.5987,19.7914,25.3904,,,,,Winter,2,2,2,0,1,1,0,0,0,0,1,1,1,0,0,0,2,2,2,1,18,Winter,37,53,Summer,0,0 +5d916073,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5daa20e7,Spring,8,0,Fall,65,Spring,12.92208505,54.0,53.6,24.0,61,94,123,,,,,Spring,14,1,,,,,5,1,6.0,0,6.0,0,5.0,0,Spring,3,3.29722,12.9235,1015.28,1725.98,9.16292,50.4041,12.1529,0.770569,3.19592,1,24.7032,16.538,47.1069,21.8938,33.8661,,,Spring,3.2,Spring,3,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,9,Spring,39,55,Spring,0,0 +5dab4630,Summer,17,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5dad24ff,Spring,17,1,Fall,60,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.19,,,Summer,0,0,4,3,0,0,1,2,1,1,0,1,1,3,3,2,2,0,2,2,28,Summer,60,82,Spring,1,0 +5db3db12,Winter,10,0,,,Winter,18.32257839,53.5,74.6,25.0,70,63,111,,,,,Winter,25,1,14.8,2,17.6,2,9,1,12.5,1,12.5,1,10.0,1,Winter,3,3.45015,18.3246,1074.98,1827.47,12.8437,56.7627,13.9431,4.38152,17.8373,1,28.4852,15.4339,53.3125,25.2328,41.3288,,,Winter,3.51,Winter,3,3,3,1,5,1,4,4,3,3,0,0,0,4,0,3,2,4,1,0,44,Winter,48,67,Winter,1,1 +5dc62fec,Fall,7,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,0,3,0,2,0,0,0,0,0,0,0,2,0,2,2,3,2,2,1,24,Fall,32,46,Fall,0,0 +5ddbac80,Fall,14,1,Winter,41,Winter,,61.5,,,,,,,,,,Winter,16,0,16.5,2,18.2,2,4,0,8.0,0,9.0,0,11.0,1,,,,,,,,,,,,,,,,,,Winter,1.32,,,Winter,4,3,3,1,3,2,0,2,1,0,0,0,1,1,2,1,2,2,1,1,30,Winter,48,67,Fall,3,0 +5de22168,Winter,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5e08237d,Spring,10,0,Summer,75,Spring,21.95945584,56.75,100.6,30.0,66,72,113,,,,,Spring,12,1,10.6,1,12.0,1,11,1,9.0,1,8.5,1,10.0,1,Spring,4,3.94388,21.9619,1187.87,2494.52,16.6794,68.7865,15.0167,6.94517,31.8135,2,33.2927,18.8143,64.8426,33.6126,49.9721,,,Spring,2.95,Spring,2,3,4,2,5,1,1,2,2,3,1,0,0,1,2,2,3,3,3,1,41,Spring,34,49,Spring,2,1 +5e221c50,Spring,15,1,Spring,65,Spring,18.8822314,66.0,117.0,,91,63,156,Spring,,,,Spring,20,1,18.2,2,18.4,2,2,0,7.5,0,8.0,0,7.0,0,,,,,,,,,,,,,,,,,,Spring,2.75,,,Spring,1,3,2,1,3,0,1,1,0,0,3,0,0,4,2,0,3,0,1,0,25,Spring,43,60,Spring,0,0 +5e2a9e63,Spring,6,0,,,Spring,14.02528395,45.0,40.4,20.0,59,77,106,,,,,Spring,3,1,,,,,0,0,11.0,1,10.0,1,9.0,1,Spring,1,2.43573,14.0268,888.354,1154.86,6.32276,36.8844,12.8062,1.22061,3.51559,1,20.6872,9.8745,34.4487,12.8013,27.0099,,,,,Spring,5,5,5,0,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,2,33,,,,Spring,1,1 +5e2c1349,Summer,13,0,Fall,70,Summer,18.33017578,64.0,106.8,,75,81,133,,,,,Summer,8,0,12.2,1,14.3,1,1,0,6.5,0,7.5,1,12.0,1,Fall,2,5.89119,18.3322,1293.01,2068.82,21.4275,79.9856,13.7295,4.60268,26.8144,2,35.0294,23.5288,74.0944,31.6963,56.4569,,,Summer,2.46,Fall,4,3,3,2,5,1,1,0,1,3,0,0,2,1,1,3,3,2,3,2,40,Summer,51,71,Summer,1,1 +5e359a08,Winter,15,0,Summer,77,Spring,19.36785799,65.0,116.4,,108,60,127,,,,,Spring,40,1,23.7,1,29.4,2,29,1,6.0,0,4.5,0,10.0,1,Spring,4,5.84851,19.37,1450.05,3045.1,28.7804,96.7122,16.0938,3.27621,19.6878,2,43.721,24.2108,90.8637,46.1975,72.5014,Summer,3.67,,,Spring,4,4,4,3,4,4,2,2,2,4,4,2,3,3,4,4,4,4,4,4,69,Spring,44,62,Winter,1,2 +5e415f05,Spring,16,0,Fall,64,Summer,24.69235503,65.0,148.4,,70,63,131,,,,,Summer,30,1,38.89,2,40.46,2,22,1,8.0,1,10.0,1,8.0,0,,,,,,,,,,,,,,,,,,Spring,2.08,,,Summer,4,4,4,4,3,4,4,4,1,2,0,0,1,4,3,,4,4,4,5,59,Summer,31,45,Spring,3,2 +5e493598,Winter,21,1,,,Winter,23.64736021,67.0,151.0,38.0,74,78,124,,,,,Winter,10,0,57.0,2,65.2,2,25,1,,,,,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +5e55e9fd,Spring,14,1,,,Spring,18.93429525,60.75,99.4,,105,81,145,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.4,Spring,5,5,5,5,5,2,3,5,4,5,5,2,4,3,5,1,5,2,5,5,81,Spring,44,62,Spring,3,3 +5e57bd12,Fall,14,0,Winter,60,Fall,31.12610097,66.5,195.8,,70,84,107,,,,,Fall,0,0,33.1,2,30.2,2,0,0,5.0,0,11.5,1,13.0,1,Fall,2,10.4577,31.1295,1730.06,2768.09,39.1562,126.537,20.1176,11.0119,69.2633,3,54.6323,32.7482,116.079,60.1145,93.7885,Winter,0.99,,,Fall,2,2,3,0,1,1,0,0,1,1,2,0,1,1,1,1,2,1,2,1,23,Fall,45,63,Fall,2,0 +5e614c1a,Winter,11,0,Spring,55,Winter,18.13078342,60.5,94.4,25.0,54,76,115,,,,,Spring,20,1,17.5,2,20.9,2,0,0,8.0,1,9.0,1,12.0,1,Spring,1,4.6978,17.8367,1248.42,1622.95,20.3456,75.2363,14.2158,3.62096,19.1637,2,34.8495,20.0412,70.5385,32.6924,55.195,,,Winter,3.211,Winter,5,4,5,3,4,5,0,2,2,3,4,0,1,4,5,2,3,1,2,3,58,Winter,50,69,Winter,3,2 +5e6490e3,Fall,9,0,Spring,70,Winter,19.92431122,56.0,88.88,,53,99,99,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.36,Winter,4,3,2,1,2,1,0,1,1,1,2,0,1,1,1,0,1,1,1,1,25,Winter,32,46,Fall,0,0 +5e6b528e,Winter,6,0,Fall,65,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,0,5,0,0,0,0,2,0,1,1,0,3,0,2,2,2,2,0,0,23,Spring,57,79,Winter,0,0 +5e710b5f,Spring,15,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +5e7235f8,Winter,13,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +5e764a16,Spring,7,1,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5e79ef30,Spring,17,0,,,Spring,20.22118627,71.0,145.0,30.0,63,57,126,,,,,Spring,29,1,22.0,,39.9,,20,1,11.0,1,10.5,1,10.0,1,,,,,,,,,,,,,,,,,,Spring,2.31,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +5e8b6b19,Winter,9,1,,,Winter,21.69753086,54.0,90.0,27.0,70,78,121,,,,,Winter,0,0,,,,,0,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.07,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +5ea2c305,Fall,12,0,Spring,45,Spring,19.10479747,59.0,94.6,,83,75,139,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.23809,19.1069,1199.55,2039.24,17.4576,70.0313,14.1446,4.96229,24.5687,2,32.9269,19.6467,65.7932,31.474,50.3845,,,Spring,3.01,Spring,3,4,3,1,4,1,1,1,1,3,3,1,1,1,1,1,2,1,1,1,35,Spring,42,59,Fall,2,1 +5eb063e0,Winter,8,0,Summer,80,Summer,19.78454441,51.0,73.2,29.0,,,,,,,,Winter,6,1,,,,,0,0,0.0,0,4.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,3,0,3,0,3,0,0,2,0,0,3,0,0,0,2,1,3,3,2,2,27,Winter,51,71,Winter,1,0 +5eb32c90,Winter,14,0,Summer,,Winter,19.14118722,63.1,108.4,27.75,58,91,96,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.26,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +5ed5f205,Summer,8,1,,,Fall,17.16515775,54.0,71.2,,49,58,102,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.39,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,28,41,Summer,0,0 +5ee595e8,Winter,8,0,Spring,50,Spring,18.56981132,53.0,74.2,,59,84,110,Spring,6,9,53,Spring,9,1,,,,,1,0,8.25,1,4.25,0,6.0,1,Spring,2,3.41293,18.5718,1070.41,1712.65,13.0249,56.2754,14.0854,4.48642,17.9246,2,28.3391,14.9115,52.8625,24.2075,41.364,,,Spring,2.24,Winter,4,0,0,0,0,0,0,0,1,1,0,0,3,0,5,5,5,5,0,0,29,Winter,60,82,Spring,0,0 +5ef3d64e,Fall,15,1,Spring,80,Winter,43.18830409,57.0,199.6,,101,77,147,,,,,Winter,3,0,24.4,2,23.7,2,0,0,11.0,1,12.0,1,15.0,1,Winter,1,8.61039,43.193,1559.62,2027.51,38.6902,108.383,23.4539,19.7391,91.2166,3,45.1602,24.533,99.773,68.4738,83.8504,Winter,1.781,,,Winter,3,4,3,4,3,4,4,3,3,3,3,1,4,5,4,4,3,4,4,4,70,Winter,41,58,Fall,3,2 +5ef8f725,Spring,18,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5f099188,Fall,8,1,Winter,82,Fall,16.21788876,47.75,52.6,,62,75,96,,,,,Fall,10,1,,,,,4,1,8.0,0,10.0,1,10.0,1,Fall,2,2.30012,16.2197,959.447,1439.17,10.4556,44.4567,13.7086,2.51106,8.14332,2,20.5383,13.4628,42.1566,18.774,30.9939,,,,,Winter,2,2,2,0,2,0,0,0,0,0,0,0,2,2,1,1,2,2,1,0,19,Winter,38,54,Fall,0,0 +5f1b07e1,Summer,18,1,Fall,55,Summer,40.32871972,64.6,239.4,46.0,86,104,149,,,,,Summer,7,0,30.0,3,25.4,2,0,0,7.0,0,6.0,0,12.0,1,Winter,1,5.80427,40.3331,1692.0,2199.6,46.3416,122.484,20.6355,19.6976,116.916,3,44.8383,31.3037,116.679,57.5869,91.1799,Summer,0.99,,,Fall,2,3,3,2,1,2,1,1,1,1,2,0,1,0,2,0,2,0,2,1,27,,,,Summer,3,0 +5f281a62,Spring,15,0,Winter,40,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.52,,,Summer,2,2,2,2,2,1,0,1,1,1,0,0,1,1,1,1,1,2,1,1,23,Summer,29,42,Spring,0,0 +5f284ef1,Fall,22,1,,,Fall,24.835584,62.5,138.0,35.0,76,71,128,,,,,,,,,,,,,,,,,,,,Fall,1,4.52984,24.8383,1308.26,1700.74,26.7631,81.6102,14.6888,10.1495,56.3898,2,32.2161,22.6309,77.0804,39.4647,58.9793,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5f37ceb1,Fall,12,0,Winter,51,Fall,15.10715013,56.5,68.6,,54,68,98,,,,,Fall,20,1,15.2,2,14.8,2,20,1,5.0,0,0.0,0,9.0,1,Fall,4,3.72409,15.1088,1086.9,2282.5,12.7508,58.0325,12.7814,2.32743,10.5675,1,27.787,17.4947,54.3084,24.5264,40.5378,,,Fall,4.5,Fall,2,2,4,4,4,0,0,1,1,2,2,0,1,0,1,1,2,1,1,2,31,Fall,33,47,Fall,3,1 +5f574f3d,Winter,9,1,Spring,82,Winter,16.3096,50.0,58.0,,71,73,106,Winter,6,9,15,Winter,11,1,,,,,2,0,11.0,1,11.0,1,14.0,1,Winter,5,9.56811,16.3114,991.553,2181.42,12.5173,47.8764,13.4643,2.84707,10.1236,1,22.1869,13.1723,38.3083,20.8806,34.7041,,,Winter,3.04,Winter,2,2,3,0,2,0,0,0,1,2,1,0,1,0,1,1,1,1,1,1,20,Winter,45,63,Winter,0,0 +5f684368,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +5f7537cf,Spring,14,0,Fall,50,Fall,19.85549308,68.0,130.6,,71,80,110,,,,,Fall,4,0,15.0,1,16.2,1,2,0,12.0,1,15.0,1,8.0,0,Fall,1,-2.93052,19.8577,1617.82,2103.16,35.7533,114.582,17.4221,2.43555,16.0181,2,50.3098,28.5188,117.512,55.5835,86.0631,,,,,Fall,3,1,4,0,4,0,0,0,0,2,0,0,3,0,2,2,2,2,3,0,28,Fall,32,46,Summer,2,0 +5f78f7b9,Spring,9,1,Winter,85,Fall,15.67285218,56.75,71.8,,64,78,108,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.32,Fall,1,4,1,0,0,0,0,1,1,1,1,0,1,1,0,1,1,1,1,0,16,Fall,38,54,Spring,0,0 +5f9dddb4,Fall,10,0,Spring,54,Winter,17.00918177,55.75,75.2,,56,83,103,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.51,Winter,5,4,5,3,5,4,2,0,2,4,3,0,3,0,5,4,4,4,5,4,66,Winter,44,62,Fall,0,2 +5fa91358,Winter,9,1,Summer,60,Winter,17.08182398,56.0,76.2,,68,92,113,Spring,6,10,0,Winter,3,0,,,,,0,0,9.0,1,9.0,1,7.0,1,Spring,3,3.30858,17.0837,1091.35,1746.15,15.2923,58.5058,13.1167,3.96697,17.6942,1,25.8554,17.3581,55.1972,33.0438,41.1477,,,Winter,1.99,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,42,59,Winter,2,0 +5fdfbe47,Fall,8,1,,,Winter,21.28753719,55.0,91.6,27.0,53,86,111,,,,,Winter,3,0,,,,,0,0,8.0,0,7.0,0,12.0,1,Winter,2,3.54749,21.2899,1144.97,1717.45,21.4211,64.2169,14.9254,6.36443,27.3831,2,28.0205,14.7754,60.6694,27.4582,49.4416,,,,,Winter,2,2,1,0,1,1,0,1,1,1,2,0,2,1,1,1,1,1,1,1,21,Winter,41,58,Fall,1,0 +603ab4ee,Spring,6,0,,,Spring,15.51663158,47.5,49.8,23.0,65,92,109,,,,,Spring,0,0,,,,,0,0,8.0,1,7.0,0,5.0,0,Spring,2,2.68012,15.5183,938.062,1500.9,7.64307,42.179,13.1435,2.37481,7.62103,1,22.8005,11.7354,39.4989,16.7226,30.4436,,,,,Spring,3,2,3,1,2,1,1,1,2,4,2,1,1,2,1,1,1,1,1,1,32,Spring,29,42,Spring,0,1 +60411c48,Winter,20,0,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +605d6e05,Winter,10,1,Summer,55,Spring,15.0436214,54.0,62.4,,73,70,106,Spring,3,3,48,Spring,5,0,14.6,2,11.6,1,8,1,18.0,1,17.0,1,10.0,1,Spring,2,3.11632,15.0453,1048.63,1572.94,15.4417,53.9554,13.0092,2.03607,8.44457,1,23.691,14.8227,50.8391,21.6074,39.1327,,,Winter,1.6,Spring,5,1,1,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,11,Spring,37,53,Winter,1,0 +606e979d,Winter,10,0,Spring,60,Spring,16.22307692,52.0,62.4,,71,80,117,Spring,3,3,36,Spring,10,0,8.7,1,10.1,1,0,0,7.0,0,7.0,0,7.5,0,Spring,3,3.26578,16.2248,1026.53,1745.09,10.1526,51.6015,13.4171,2.80776,10.7985,1,27.7967,13.6522,48.3357,29.0311,37.9493,,,Winter,3.06,Winter,3,5,,4,2,1,0,0,0,1,4,0,2,1,3,4,2,4,1,0,37,Winter,57,79,Winter,2,1 +60c1ff37,Winter,8,0,Spring,65,Spring,19.62421125,54.0,81.4,,65,73,108,Spring,7,11,30,Spring,7,1,,,,,1,0,8.5,1,7.5,1,9.0,1,Spring,2,3.65464,19.6264,1115.38,1784.62,15.614,61.0662,14.7237,4.90269,20.3338,2,30.272,15.1801,57.4115,25.3794,45.886,,,Spring,3.28,Spring,3,2,2,0,3,1,0,0,0,1,1,0,2,2,2,2,1,2,1,1,26,Spring,55,76,Winter,0,0 +60d1fdee,Summer,6,1,Winter,68,Fall,19.684,50.0,70.0,,74,74,136,Fall,7,11,14,Fall,7,1,,,,,20,1,14.0,1,14.0,1,12.0,1,Fall,3,5.11638,19.6862,1038.19,1661.1,17.4345,52.8435,14.8612,4.82494,17.1565,3,24.9718,10.4372,47.7271,21.1653,42.4062,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,33,47,Summer,0,0 +60d2774d,Spring,6,0,Summer,55,Spring,15.43914931,48.0,50.6,23.0,64,75,93,,,,,Spring,0,0,,,,,0,0,9.0,1,7.0,0,5.0,0,Spring,2,2.6563,15.4408,944.805,1511.69,6.704,42.8972,13.0903,2.35056,7.70283,2,22.6844,13.5088,40.2409,18.6763,29.3884,,,,,Spring,3,1,2,0,1,0,0,1,0,1,1,0,0,0,0,2,2,2,1,0,17,Spring,45,63,Spring,0,0 +60dfc033,Winter,11,1,,,Spring,22.73674197,58.0,108.8,30.0,58,82,109,,,,,Spring,12,0,21.3,2,19.2,2,4,0,8.5,0,9.5,0,11.0,1,Spring,,,,,,,,,,,,,,,,,,,Winter,2.16,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +60f8462b,Spring,7,0,Fall,60,Spring,17.57305263,47.5,56.4,,67,70,92,Spring,,,,Spring,4,1,,,,,0,0,8.0,1,8.5,1,5.0,0,Summer,1,2.66802,17.575,954.012,1240.22,7.7878,43.8778,13.6729,3.90208,12.5222,2,23.5596,12.5303,41.2098,18.5873,31.3475,,,,,Fall,2,2,2,0,4,0,0,1,0,1,0,0,0,0,0,2,2,2,0,0,18,Fall,42,59,Spring,0,0 +60fb7cb9,Summer,13,0,Winter,58,Fall,14.64561159,64.25,86.0,,80,95,120,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.61,Summer,3,3,3,1,3,1,1,1,1,2,1,0,2,2,4,2,3,1,2,2,38,Summer,42,59,Summer,0,1 +6112bac1,Winter,13,1,,,Winter,33.51102949,49.5,116.8,27.0,53,69,105,,,,,Winter,20,1,27.5,3,29.4,3,10,1,9.0,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.61,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +612487a5,Winter,15,1,Summer,80,Spring,33.97213404,63.0,191.8,,90,85,154,,,,,Summer,,,,,,,,,,,,,,,Summer,2,5.35578,33.9758,1549.67,2324.5,38.9946,107.323,19.0114,14.9644,84.4769,3,41.6548,26.6737,101.967,54.7367,80.6494,Spring,2.31,,,Spring,3,3,3,1,3,1,2,2,2,2,2,1,1,4,4,1,3,1,1,2,42,Spring,51,71,Winter,3,1 +612b3ff9,Spring,12,0,Fall,61,Summer,24.03112245,56.0,107.2,,71,69,114,,,,,Summer,8,0,17.9,2,19.4,2,0,0,9.0,1,9.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.56,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Spring,0,0 +612d17d3,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +612dd900,Spring,8,0,Fall,65,Winter,15.99614792,47.25,50.8,,,102,,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,2,2.74845,15.9979,942.792,1508.47,8.55338,42.6827,13.4416,2.55628,8.11726,1,23.9544,10.175,39.9343,18.0707,32.5077,,,,,Summer,3,3,3,2,4,1,1,1,2,3,1,1,2,3,4,3,4,3,1,3,48,Summer,40,56,Spring,0,1 +613fc399,Spring,10,1,Fall,71,Fall,20.49730057,57.5,96.4,,67,59,124,Fall,5,7,53,Fall,20,1,15.2,2,21.8,3,2,0,12.0,1,12.0,1,5.0,0,Fall,3,4.15417,20.4995,1238.96,1982.34,22.7473,74.2285,15.7848,4.71478,22.1715,2,35.0082,16.473,70.0743,49.9916,57.7555,,,Spring,3.24,Fall,2,1,2,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,10,Fall,46,64,Summer,0,0 +613fc53d,Fall,5,0,Spring,50,Winter,17.75807292,48.0,58.2,,44,94,68,Winter,,,,Winter,0,0,,,,,0,0,9.0,1,12.0,1,8.0,1,Spring,4,6.66567,17.76,967.999,2032.8,9.38878,45.3676,13.8441,3.91587,12.8324,2,24.4933,11.4855,38.7019,18.2454,33.8821,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,35,50,Fall,2,0 +614d5517,Fall,10,0,Spring,75,Winter,13.92750384,58.5,67.8,,84,97,140,Winter,5,7,47,Winter,15,1,13.7,2,18.8,2,4,0,0.0,0,0.0,0,12.0,1,Winter,3,6.80033,13.929,1102.89,1874.91,12.8331,59.7354,12.2722,1.65682,8.06461,1,27.6467,19.2556,52.9351,24.7319,40.4798,,,Fall,3.01,Winter,4,4,4,4,4,1,0,1,1,2,0,0,2,2,3,0,2,3,0,2,39,Fall,37,53,Fall,1,1 +616fc21f,Winter,5,0,Spring,65,Spring,19.98752808,43.5,53.8,26.0,60,80,91,,,,,Spring,0,0,,,,,0,0,10.0,1,10.0,1,6.0,1,Spring,1,2.37593,19.9897,907.279,1179.46,7.12105,38.9001,14.4536,5.53615,14.8999,2,23.0281,8.75094,36.5242,16.6804,30.1492,,,,,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,,,,Winter,2,0 +61823525,Winter,8,0,Spring,82,Winter,16.0670858,52.0,61.8,25.0,57,91,106,,,,,Winter,7,1,,,,,2,0,8.0,1,8.0,1,12.0,1,Winter,1,3.26022,16.0688,1024.53,1331.88,11.6194,51.3885,13.3617,2.70713,10.4115,1,26.1084,13.6607,48.1283,20.2342,37.7278,,,,,Winter,2,2,2,0,1,0,0,0,0,0,1,0,0,0,0,1,2,1,2,0,14,Winter,41,58,Winter,0,0 +6187d0dc,Spring,7,1,Spring,90,Summer,14.22586885,48.5,47.6,,67,64,104,Summer,7,12,30,Summer,11,1,,,,,9,1,7.0,0,8.0,0,5.5,1,Summer,2,2.39614,14.3752,955.393,1433.09,10.6031,44.0249,13.2956,1.07967,3.57507,1,21.0955,12.3263,41.6288,19.5599,31.6986,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,27,40,Spring,0,0 +6189b0dd,Summer,17,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +6192e6b6,Summer,13,0,Fall,66,Fall,19.02404082,70.0,132.6,,58,52,114,,,,,Fall,36,1,34.5,3,38.8,3,40,1,11.0,1,11.0,1,10.5,1,,,,,,,,,,,,,,,,,,,,Fall,3.58,Fall,4,4,3,2,3,2,3,0,2,1,3,1,2,0,2,1,1,2,1,1,38,Fall,35,50,Fall,,1 +6197a2ce,Fall,10,0,Spring,60,Winter,20.0408801,56.0,89.4,,63,84,105,,,,,Winter,6,0,14.8,2,15.1,2,1,0,,,,,8.0,0,Winter,3,4.02943,20.0431,1178.45,2003.37,17.7416,67.7835,15.1967,4.84632,21.6165,2,33.5411,16.5008,63.7541,32.0662,51.2827,,,Spring,2.63,Spring,2,2,2,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,Winter,80,100,Fall,0,0 +619bd57f,Fall,12,1,,,Fall,26.08649846,65.5,159.2,33.0,61,60,111,,,,,Fall,10,0,30.8,3,31.7,3,1,0,8.5,0,9.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +61c4c978,Summer,19,0,,,Summer,24.33642237,67.0,155.4,35.0,78,79,131,,,,,Summer,20,0,26.1,1,23.9,1,18,1,4.0,0,0.0,0,8.0,0,Fall,1,6.11536,24.3391,1556.21,2023.07,31.6574,108.019,16.9182,7.42084,47.3805,3,47.6702,28.6919,101.904,52.9186,79.3276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +61f2a7c7,Winter,15,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4.26,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +620536d0,Spring,6,0,Fall,75,Summer,15.55242121,45.5,45.8,,76,88,117,Summer,3,3,34,Summer,6,1,,,,,0,0,10.5,1,11.5,1,6.5,1,Summer,3,2.46834,15.5541,906.835,1541.62,5.89649,38.8528,13.1948,2.35932,6.94715,1,22.0921,10.8643,36.3845,17.5332,27.9886,,,,,Summer,3,2,5,1,2,1,0,1,1,1,2,1,3,2,3,3,3,3,4,4,45,Summer,66,90,Spring,0,1 +621066ff,Winter,9,0,Spring,57,Spring,18.27414266,54.0,75.8,,67,82,119,Spring,6,10,18,Spring,13,1,,,,,9,1,7.0,0,7.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.28,Spring,5,5,5,4,5,0,0,5,1,5,5,1,4,1,4,4,4,5,5,1,69,Spring,33,47,Winter,3,2 +622bb412,Spring,6,1,Summer,79,Spring,15.78595957,45.75,47.0,,64,87,106,Spring,1,0,35,Spring,0,0,,,,,0,0,0.0,0,0.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,4,0,2,0,0,0,0,0,2,0,2,0,1,1,2,1,1,1,21,Spring,36,51,Spring,0,0 +6247cc15,Fall,12,0,Spring,60,Winter,21.43184681,63.0,121.0,,60,66,111,,,,,Winter,45,1,26.5,2,27.6,2,25,1,9.0,1,10.0,1,12.0,1,Winter,4,6.11995,21.4342,1588.28,3335.39,37.3651,111.436,19.74,1.69417,9.5639,2,48.3595,25.7115,105.316,46.6696,85.7246,,,Fall,3.27,Winter,3,4,4,2,2,2,1,3,2,2,4,1,2,2,3,1,3,2,2,1,46,Winter,40,56,Fall,1,1 +624869b6,Fall,10,0,,,Fall,17.22283554,57.5,81.0,,91,75,125,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.63,Fall,5,5,5,1,4,2,0,2,2,2,2,1,5,0,3,4,5,4,4,2,58,Fall,47,66,Fall,1,2 +624b9e34,Summer,8,1,Fall,61,Fall,18.97370242,51.0,70.2,,60,76,99,,,,,Fall,10,,,,,,10,,11.0,,10.0,,11.0,,Fall,2,8.35281,18.9758,1026.72,1540.07,14.9583,51.6217,13.9539,5.0219,18.5783,2,23.5686,13.0948,43.2689,21.965,38.5269,,,Summer,2.59,Fall,3,2,4,0,3,0,0,1,0,0,3,0,2,0,1,1,3,1,0,0,24,Fall,35,50,Summer,1,0 +626a608e,Spring,13,1,Summer,55,Winter,19.36639118,66.0,120.0,,67,63,112,,,,,Winter,20,1,9.0,2,9.5,2,12,1,9.5,1,9.0,0,14.0,1,Spring,4,5.00303,19.3685,1317.06,2502.41,26.7218,82.5471,13.3234,6.04506,37.4529,2,31.5962,24.2291,77.544,36.1137,58.318,,,Spring,3.2,Spring,4,5,4,3,3,4,1,2,2,2,2,0,4,4,4,4,3,4,3,3,61,Spring,51,71,Winter,2,2 +6277e13c,Spring,8,0,Fall,61,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.33,Summer,3,1,1,0,1,0,0,0,1,1,1,0,1,2,1,1,1,1,1,1,18,Summer,65,89,Spring,0,0 +6282e693,Summer,5,0,Winter,61,Summer,13.51596408,43.75,36.8,20.0,58,89,93,,,,,Summer,0,0,,,,,0,0,9.5,1,9.0,1,10.0,1,Summer,2,2.23744,13.5174,870.808,1393.29,4.60347,35.0155,12.8619,0.655495,1.78453,1,19.0653,11.3467,32.778,11.3825,23.6687,,,,,Fall,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,,,,Summer,0,0 +62839132,Fall,6,0,Winter,49,Fall,14.60161816,45.5,43.0,,52,95,104,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,54,75,Fall,0,0 +6291fddd,Winter,15,0,Winter,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,4,5,2,5,5,2,5,4,2,5,0,5,4,2,4,5,5,5,4,77,Winter,45,63,Winter,0,2 +62a4b5ca,Spring,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +62b873a2,Spring,7,0,Spring,70,Spring,17.96053464,49.5,62.6,,111,70,139,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,4,3.05573,17.9625,1005.08,2110.67,11.0634,49.3171,14.1511,3.81141,13.2829,1,26.6873,11.5664,46.2614,21.951,37.7507,,,,,Winter,4,5,4,2,3,1,0,3,2,3,2,0,2,0,2,3,1,3,1,2,43,Winter,39,55,Spring,2,1 +62c60e0e,Winter,5,1,,,Winter,13.29886972,41.0,31.8,21.0,97,94,152,,,,,Winter,,,,,,,0,0,10.5,1,10.5,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +62cd5c74,Summer,12,0,Spring,75,Winter,23.16723314,56.5,105.2,,64,91,107,Winter,5,7,23,Winter,0,0,13.7,2,16.8,2,0,0,7.0,0,7.0,0,7.0,0,Winter,4,3.97676,23.1698,1204.96,2530.42,16.5814,70.6074,15.5509,7.61884,34.5926,3,35.3486,18.6774,66.6307,41.8568,51.93,,,Summer,2.55,Winter,4,4,3,3,4,4,2,4,2,2,4,1,3,4,3,4,2,4,2,3,62,Winter,40,56,Summer,1,2 +62d0cf97,Fall,15,1,Spring,60,Spring,19.82346482,63.75,114.6,,69,73,118,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.03,,,Spring,4,4,4,1,4,4,2,3,4,4,4,1,3,3,4,3,4,3,1,4,64,Spring,44,62,Fall,2,2 +62e7604d,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +62f77d2f,Fall,9,1,Spring,80,Fall,19.00912,50.0,67.6,,76,95,116,Fall,3,4,56,Fall,5,0,,,,,3,0,10.0,1,10.5,1,11.0,1,Fall,2,10.1476,19.0112,1006.87,1510.31,13.5705,49.5083,13.9233,5.08793,18.0917,1,22.3653,13.5725,39.3607,20.4413,35.9359,,,Winter,2.18,Fall,3,2,2,0,3,1,0,1,1,2,1,0,2,3,3,4,4,4,1,1,38,Fall,60,82,Fall,0,1 +632eca98,Spring,11,0,Fall,78,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.4,Spring,3,1,1,1,1,1,1,1,1,1,1,1,2,2,1,1,1,2,1,1,25,Spring,31,45,Spring,1,0 +6332ad59,Winter,8,0,Summer,80,Spring,14.16511214,53.0,56.6,,70,62,116,Spring,7,12,42,Spring,14,1,,,,,28,1,9.9,1,8.1,1,11.0,1,Spring,4,4.65205,14.4378,1017.2,2136.12,11.204,50.6081,12.9094,1.52843,5.99186,2,25.8773,13.5268,45.9561,20.5985,37.0814,,,Spring,3.39,Spring,1,1,2,0,2,1,0,0,0,0,0,0,1,1,1,1,2,1,0,0,14,Spring,34,49,Winter,0,0 +63334478,Fall,8,0,Winter,80,Fall,17.75888397,51.5,67.0,,74,89,132,Fall,6,9,35,Fall,10,1,,,,,0,0,8.5,1,7.5,1,8.0,1,Fall,4,7.13425,17.7608,1029.6,2162.15,11.4906,51.9286,13.7656,3.99523,15.0714,2,26.3516,14.0864,44.7943,20.8228,37.8422,,,,,Fall,1,3,4,0,2,1,0,0,1,2,1,0,1,0,0,1,2,1,0,0,20,Fall,45,63,Fall,0,0 +633cc30a,Summer,7,0,,,Summer,17.54701816,49.6,61.4,23.5,,92,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6344bba1,Summer,14,0,Summer,75,Spring,18.36377318,61.0,97.2,,95,75,150,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.05,,,Fall,5,5,4,4,4,2,1,0,2,2,2,0,1,0,3,4,4,5,4,4,56,Fall,44,62,Summer,3,2 +635554d2,Fall,8,0,Spring,60,Fall,15.80710059,52.0,60.8,,60,75,99,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.76,Fall,0,0,0,0,0,0,0,0,0,0,2,0,3,3,3,3,3,3,2,2,24,Fall,38,54,Fall,1,0 +6355b1aa,Fall,15,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.23,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +63a00e40,Fall,9,0,Summer,50,Spring,16.97362553,51.0,62.8,,62,76,87,Spring,6,10,7,Spring,11,1,,,,,10,1,10.5,1,11.0,1,8.0,1,Spring,5,4.84816,16.9755,1023.42,2456.22,11.8741,51.2712,13.8591,3.11636,11.5288,2,26.9755,12.4216,46.423,21.8351,38.8496,,,Spring,2.99,Spring,1,2,1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,23,Winter,32,46,Fall,1,0 +63d4ab9a,Summer,7,0,Spring,60,Summer,14.34693878,49.0,49.0,19.0,66,104,121,,,,,Summer,7,1,,,,,4,1,8.0,1,12.0,1,2.0,0,Summer,3,2.80428,14.3485,951.513,1617.57,8.04012,43.6117,12.7707,1.57785,5.38834,1,22.6945,12.877,40.8074,16.3513,30.7346,,,,,Fall,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,Fall,53,73,Summer,0,0 +63d810bb,Summer,10,1,Spring,58,Winter,23.98797624,57.75,113.8,,64,86,125,Winter,2,2,8,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.72,Winter,3,3,3,0,1,0,0,1,0,1,0,0,0,1,1,0,0,0,2,0,16,Winter,43,60,Summer,2,0 +63dd16ec,Winter,7,0,Fall,78,Spring,25.19065401,49.5,87.8,,74,98,133,Spring,4,6,27,Spring,4,1,,,,,3,1,6.0,0,7.5,1,6.0,1,Spring,3,9.49991,25.136,1067.43,1814.62,13.0992,55.9578,16.0566,9.07943,31.6422,3,29.212,13.6467,46.4579,25.2104,42.3111,,,,,Spring,2,2,2,0,1,0,0,1,1,1,1,0,1,1,1,1,2,2,1,2,22,Spring,34,49,Winter,0,0 +63fc9770,Fall,12,1,,,Winter,19.70063905,65.0,118.4,,69,66,104,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.85,Fall,5,5,4,3,5,2,4,3,5,4,4,1,4,2,5,5,4,5,1,1,72,Fall,44,62,Fall,1,2 +641697e6,Fall,8,0,Spring,55,Winter,19.1292517,52.5,75.0,,79,53,117,Spring,,,,Winter,20,1,,,,,4,0,8.0,1,7.0,0,4.0,0,Winter,3,3.3107,19.1313,1062.16,1805.67,12.2234,55.3966,14.1308,5.00053,19.6034,1,28.0286,15.1447,52.0859,24.7078,40.2519,,,Winter,2.48,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,37,53,Winter,0,0 +641a2b87,Summer,10,1,Fall,61,Fall,14.97514793,52.0,57.6,,77,98,118,Fall,6,9,33,Fall,20,1,14.0,2,15.6,2,2,0,10.0,1,11.5,1,12.0,1,Fall,3,8.3862,14.9768,1015.11,1624.17,13.8257,50.3855,13.1009,1.87588,7.21454,1,22.727,13.8328,41.9993,20.4866,36.5527,,,Fall,4.02,Fall,4,3,3,0,3,2,0,1,2,1,0,0,0,1,1,2,2,2,2,1,30,Fall,41,58,Fall,0,0 +6425fbea,Spring,15,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +646a4c0e,Fall,11,0,Spring,55,Winter,15.10578512,55.0,65.0,,70,91,112,Winter,11,20,0,Winter,13,1,14.4,2,16.1,2,19,1,6.0,0,6.0,0,10.0,1,Winter,4,3.63755,15.1074,1069.6,2246.17,12.3309,56.19,13.0598,2.04765,8.81004,1,28.4661,15.393,52.5524,27.0763,40.797,,,Fall,3.68,Winter,3,5,5,4,5,1,0,1,1,3,5,1,5,1,5,5,1,5,1,1,58,Winter,30,43,Fall,2,2 +649ff6d9,Summer,12,0,Fall,70,Summer,15.70587283,60.25,81.1,25.0,63,82,107,,,,,Summer,14,0,19.8,2,20.1,2,11,1,9.5,1,11.0,1,12.0,1,Summer,3,4.73912,15.8432,1233.44,2096.84,19.2383,73.6403,14.2628,1.58038,8.15969,2,36.2365,18.1654,68.9012,39.7463,55.4749,,,Fall,2.12,Fall,4,4,5,2,5,4,0,4,3,3,3,0,4,3,4,4,4,4,5,3,68,Fall,37,53,Summer,3,2 +64b4005d,Winter,10,1,Spring,80,Spring,14.84513787,54.88,63.6,,77,91,122,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.91671,15.5064,1040.99,1665.58,13.6959,53.1419,12.9566,2.5498,10.4581,1,22.282,17.164,50.2252,21.0322,35.9779,,,Winter,1.51,Winter,4,4,3,2,3,2,1,2,1,1,3,1,2,3,3,2,1,2,1,1,42,Winter,45,63,Winter,2,1 +64dfef96,Summer,6,0,,,Summer,23.30645564,49.0,79.6,,68,75,122,,,,,Summer,1,0,,,,,6,1,10.0,1,9.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +64ed48c4,Fall,11,0,Spring,55,Winter,18.20760877,60.5,94.8,,75,90,116,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.65,Winter,5,5,5,1,5,1,1,5,5,4,4,0,5,5,5,5,5,5,4,1,76,Winter,45,63,Fall,3,2 +64fc556b,Fall,6,0,Winter,75,Fall,15.41545932,45.2,44.8,,64,76,98,Fall,7,11,42,Fall,11,1,,,,,1,0,13.0,1,12.5,1,10.0,1,Fall,4,4.97278,15.4171,903.792,1897.96,7.17742,38.5287,13.259,2.15815,6.27127,1,22.4848,8.86648,33.556,15.7989,29.6623,,,,,Fall,4,4,4,0,4,1,0,0,0,0,0,0,2,0,0,2,4,1,0,1,27,Fall,31,45,Fall,0,0 +650bf252,Fall,8,0,Fall,85,Fall,15.20620345,49.5,53.0,,60,82,97,Fall,4,6,24,Fall,5,1,,,,,0,0,11.0,1,10.0,1,6.0,1,Fall,4,3.32857,15.2079,983.724,2065.82,10.45,47.0425,13.4984,1.70944,5.95745,2,26.0296,10.5629,43.714,21.3081,36.4796,,,,,Fall,1,1,3,1,1,1,1,1,1,1,2,1,1,1,1,2,2,1,1,1,25,Fall,36,51,Fall,0,0 +65107f2a,Fall,11,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.251,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +6514c337,Fall,9,1,Spring,88,Spring,19.22153497,57.5,90.4,,65,91,119,Spring,5,8,17,Spring,14,1,,,,,6,1,11.0,1,11.0,1,11.0,1,Spring,3,9.01614,19.2236,1133.47,1813.56,18.4365,62.9929,13.3955,5.82813,27.4071,3,25.7864,18.7701,53.9768,27.1303,44.2228,,,Winter,3.95,Winter,0,3,2,0,4,0,0,1,0,1,0,0,0,0,1,0,2,0,0,0,14,Winter,34,49,Fall,2,0 +6515ef94,Winter,7,1,Spring,45,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,3,0,2,1,0,1,0,1,1,0,1,1,1,1,1,2,1,0,23,Spring,41,58,Winter,0,0 +65170bb9,Spring,18,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +653f46e1,Summer,22,1,,,Fall,22.70713026,63.0,128.2,30.0,68,81,123,,,,,,,,,,,,,,,,,,,,Fall,2,4.66707,22.7096,1308.7,1963.05,26.8952,81.6569,14.4649,8.24475,46.5431,2,32.4933,22.2684,76.9898,38.8984,59.3884,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +654203ec,Summer,9,1,Fall,70,Fall,15.52261333,53.5,63.2,,101,74,119,Fall,6,10,3,Fall,23,1,,,,,7,1,7.0,0,7.5,0,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.51,Fall,2,3,3,0,3,1,0,2,1,1,3,0,1,2,1,2,2,1,1,1,30,Fall,47,66,Summer,0,0 +656dcec5,Spring,19,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +657324e3,Spring,11,0,,,Spring,17.74375857,54.0,73.6,26.0,70,81,113,,,,,Spring,12,0,14.7,2,15.6,2,13,1,7.5,0,8.0,1,12.0,1,Spring,4,3.58824,17.7457,1087.26,2283.25,13.9567,58.0709,14.0015,3.74423,15.5291,2,29.1519,14.9623,54.4826,25.2743,43.1086,,,Spring,3.19,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +658e66ec,Winter,13,1,Spring,70,Winter,23.55731922,63.0,133.0,,60,84,110,,,,,Winter,10,0,18.7,2,22.9,2,10,1,7.0,0,4.75,0,9.5,1,Winter,3,4.81739,23.5599,1341.24,2145.98,29.2764,85.1223,15.0787,8.48116,47.8777,2,33.5208,22.3251,80.3049,38.0936,62.7972,,,Winter,2.9,Winter,4,5,4,4,4,3,1,1,1,4,3,1,2,1,2,3,4,4,2,1,54,Winter,41,58,Winter,2,2 +6595d479,Summer,16,0,Fall,70,Fall,28.39738751,74.5,224.2,,61,83,131,,,,,Fall,40,1,23.4,1,32.3,1,6,0,6.0,0,5.5,0,9.0,1,Fall,3,2.33245,28.4005,2110.02,3587.04,54.925,167.008,21.1558,7.24473,57.1916,3,67.9637,44.1197,164.676,77.2153,122.889,Fall,2.669,,,Fall,3,2,3,1,4,2,3,2,3,2,3,0,0,2,2,1,2,1,1,1,38,,,,Summer,1,1 +659a6c6a,Summer,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +659fec57,Winter,10,1,Spring,75,Fall,14.06432749,57.0,65.0,,73,66,113,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.43224,14.0659,1085.68,1737.08,15.676,57.9019,12.5298,1.53602,7.09813,2,24.8817,17.3442,54.4696,26.5428,40.5577,,,Spring,2.74,Spring,2,1,2,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,10,Spring,50,69,Winter,0,0 +65af9f2a,Fall,7,0,,,Fall,15.7125772,50.5,57.0,22.0,71,92,121,,,,,Fall,10,1,,,,,2,0,11.0,1,12.0,1,8.0,1,Fall,3,2.99462,15.7143,988.636,1680.68,9.16561,47.5657,13.1134,2.60094,9.4343,1,24.6505,13.7495,44.5711,20.1709,33.8162,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,59,81,Fall,0,0 +65dc81f5,Winter,14,1,Summer,70,Spring,44.83554809,51.75,170.8,,76,80,140,,,,,Spring,15,0,25.6,3,28.0,3,4,0,12.5,1,14.5,1,13.0,1,Spring,3,11.0942,44.8404,1246.16,1993.86,27.1071,74.9958,19.6888,25.1517,95.8042,3,31.2993,16.5894,63.9016,36.0841,58.4064,,,Spring,1.67,Spring,1,1,2,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,8,Spring,30,43,Winter,1,0 +65dfe4b9,Spring,12,0,Fall,61,Fall,21.82115023,61.0,115.5,,52,73,102,,,,,Fall,16,0,23.0,2,19.4,2,3,0,7.0,0,8.0,1,3.0,0,,,,,,,,,,,,,,,,,,,,Spring,1.0,Fall,2,2,2,0,2,0,0,1,1,0,0,0,2,2,1,0,1,1,0,1,18,Fall,55,76,Spring,2,0 +65f9369d,Spring,11,0,Fall,77,Fall,16.91847934,55.0,72.8,,64,71,104,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.77,Spring,2,1,1,1,2,1,1,1,1,1,1,1,2,1,2,2,1,2,1,1,26,Spring,31,45,Spring,0,0 +65f96c54,Spring,20,1,,,Spring,20.42407227,64.0,119.0,27.0,71,83,121,,,,,Spring,40,1,27.9,2,23.5,2,8,1,,,,,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +65fe69c0,Summer,14,0,Spring,60,Fall,26.99870795,67.0,172.4,,56,84,112,,,,,Fall,12,0,30.4,2,31.9,3,0,0,6.0,0,5.0,0,11.0,1,,,,,,,,,,,,,,,,,,Winter,2.759,,,Fall,1,1,5,1,4,1,1,3,1,2,4,0,0,2,4,1,3,0,4,3,41,Winter,52,72,Summer,2,1 +66094cfd,Summer,11,0,,,Summer,18.08046281,55.0,77.8,26.0,74,58,112,,,,,Summer,29,1,13.5,2,19.2,2,7,1,0.0,0,0.0,0,12.0,1,Fall,3,3.84786,18.0824,1129.03,1919.36,16.0997,62.52,14.531,3.5514,15.28,1,31.3136,15.1067,58.6722,28.1274,47.4133,,,Fall,2.98,Fall,3,2,2,0,2,1,0,1,1,1,2,0,1,1,1,1,1,1,1,1,23,Fall,30,43,Summer,2,0 +661bc844,Spring,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6622b302,Winter,7,0,Summer,78,Winter,13.53925926,45.0,39.0,,70,,103,,,,,Winter,20,1,,,,,8,1,6.0,0,5.5,0,11.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.58,Spring,2,1,2,1,2,1,0,2,1,1,0,0,2,1,1,2,2,2,2,2,27,Winter,40,56,Winter,,0 +66401061,Spring,14,0,,,Spring,23.01372819,66.0,142.6,31.0,77,98,122,,,,,Summer,21,0,23.0,1,22.9,1,13,0,6.5,0,5.0,0,12.0,1,Summer,3,6.04263,23.7299,1557.47,2647.7,33.2342,108.154,17.9978,5.7321,34.4459,2,47.9871,26.9327,102.111,50.9763,81.2213,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +665abfda,Fall,11,1,Summer,80,Winter,17.48355879,62.0,95.6,,56,91,105,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.83,Winter,3,3,2,0,2,2,4,2,1,2,2,0,2,4,1,1,2,1,1,2,37,Winter,34,49,Fall,0,1 +66665e95,Summer,11,0,,,Summer,15.75204678,57.0,72.8,,66,88,108,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.94,Summer,5,5,4,1,3,1,4,1,5,4,5,1,5,1,2,3,3,3,0,1,57,Summer,43,60,Summer,0,2 +6666aed3,Spring,15,1,Spring,74,Fall,18.75945192,63.5,107.6,,85,75,131,,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.84438,18.7615,1299.15,2078.64,27.0574,80.6395,14.0606,4.70093,26.9605,2,32.2649,21.3171,75.7951,35.3435,59.3223,Spring,3.18,,,Spring,2,3,3,1,5,2,1,2,1,1,4,0,4,4,4,2,2,4,2,2,49,Spring,38,54,Spring,2,1 +6672e527,Winter,8,1,Summer,68,Winter,16.42208,50.0,58.4,23.0,75,85,110,,,,,Winter,0,0,,,,,1,0,11.0,1,9.0,1,6.0,1,Spring,1,2.76947,16.4239,1002.56,1303.32,14.3525,49.0484,13.7939,2.62997,9.35163,1,23.7748,10.921,46.2789,21.8053,38.1274,,,Winter,1.44,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,41,58,Winter,0,0 +66760b74,Spring,10,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.33,,,,,,,,,,,,,,,,,,,,,,,,,,,, +668a2fea,Fall,9,0,Winter,80,Winter,18.2118284,49.0,62.2,,67,82,102,Winter,6,10,55,Winter,14,1,,,,,6,1,10.0,1,8.0,1,11.0,1,Winter,1,3.68774,18.2138,1014.17,1318.42,12.1578,50.2853,14.7249,3.48894,11.9147,1,27.8237,10.3038,46.5976,23.1035,39.9815,,,Winter,2.6,Fall,2,1,2,2,0,0,0,0,1,1,0,0,2,0,1,1,3,1,1,1,19,Fall,33,47,Fall,2,0 +668db95f,Winter,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +6699c999,Summer,8,1,Winter,41,Fall,16.29725652,54.0,67.6,,104,76,130,Fall,6,9,39,Fall,12,1,,,,,8,1,8.0,0,9.0,1,11.0,1,Fall,4,3.54092,16.299,1116.19,2120.76,18.7201,61.1518,14.7443,1.55472,6.44818,2,29.9802,12.4515,57.6109,35.9277,48.7003,,,Summer,1.87,Fall,2,2,2,0,2,1,0,1,1,1,1,0,1,1,1,1,2,1,1,1,22,Fall,39,55,Summer,0,0 +66d86d0b,Summer,12,0,Winter,80,Fall,15.44509952,63.0,87.2,,64,67,126,,,,,Fall,25,1,20.6,2,21.1,2,2,0,5.0,0,5.0,0,11.0,1,Fall,4,4.49523,15.4468,1220.94,2563.98,18.2253,72.3095,12.809,2.63774,14.8905,1,32.3034,21.7808,67.8142,29.6137,50.5287,,,Fall,4.36,Fall,2,2,2,1,2,3,1,1,1,1,1,0,0,0,0,1,2,0,0,0,20,Fall,38,54,Summer,1,0 +66e789e2,Summer,18,0,,,Summer,37.18029826,69.0,251.8,48.0,85,75,131,,,,,Summer,0,0,34.1,2,36.4,2,10,0,8.0,1,12.0,1,12.0,1,Summer,1,7.31531,37.1844,2009.19,2611.95,48.9347,156.268,23.0768,14.1077,95.5324,3,66.283,41.0505,148.953,80.5491,115.218,Summer,2.96,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +66ed301e,Summer,10,0,Winter,72,Fall,15.80297521,55.0,68.0,,52,64,117,Fall,5,7,10,Fall,30,1,10.9,1,11.2,1,4,0,11.0,1,12.0,1,12.0,1,Fall,3,3.37,15.8047,1092.07,1856.52,15.1066,58.5826,13.6159,2.18881,9.41741,1,28.7526,14.7233,55.2126,22.7575,43.8593,,,Summer,3.11,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,34,49,Summer,0,0 +66f044c7,Summer,9,1,,,Summer,17.32192,50.0,61.6,,48,73,98,,,,,Summer,10,1,,,,,11,1,12.0,1,12.0,1,10.0,1,Fall,3,2.89971,17.3238,1024.7,1639.52,16.0686,51.4073,14.4573,2.86649,10.1927,1,25.354,9.98466,48.5076,23.6707,41.4227,,,Summer,2.82,Summer,4,4,4,0,4,3,0,2,1,2,1,0,1,2,2,2,2,1,1,0,36,Summer,45,63,Summer,2,1 +66f619e3,Summer,13,0,Winter,62,Summer,25.07947658,66.0,155.4,,68,65,101,,,,,Summer,50,1,25.4,2,28.0,2,5,0,9.0,1,7.0,1,12.0,1,Summer,4,6.45415,25.0822,1658.26,3482.35,36.558,118.89,19.1893,5.89288,36.5101,2,52.8221,29.5099,112.436,61.115,89.38,,,Fall,3.7,Fall,4,4,5,1,4,2,4,2,2,3,1,1,2,4,4,3,5,2,2,2,57,Summer,43,60,Summer,2,2 +6708fcd9,Spring,6,0,Summer,71,Summer,16.4765625,48.0,54.0,23.0,57,71,100,,,,,Summer,0,0,,,,,0,0,6.0,0,7.0,0,7.0,1,Summer,2,2.89667,16.4784,966.393,1546.23,10.1958,45.1966,13.792,2.6864,8.80341,1,25.0264,9.97439,42.2999,18.3895,35.2222,,,,,Summer,3,3,3,0,1,0,0,0,0,0,0,0,0,0,0,0,2,2,2,1,17,Summer,40,56,Spring,0,0 +672a32f1,Spring,9,1,Summer,70,Spring,18.87013172,53.0,75.4,,95,68,135,Spring,5,8,51,Spring,9,1,,,,,7,1,9.0,1,11.5,1,9.0,1,Spring,3,5.91459,18.8722,1073.49,1717.59,17.5002,56.6039,14.1676,4.70456,18.7961,2,25.8216,13.2821,50.6893,25.3059,43.3218,,,Spring,1.57,Spring,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,6,Spring,26,38,Spring,0,0 +673a7e2a,Winter,7,0,Spring,78,Winter,16.87731569,46.0,50.8,20.0,61,73,109,,,,,Winter,2,1,,,,,1,0,8.5,1,12.5,1,6.5,1,Spring,3,2.53905,16.8792,924.585,1571.79,7.51565,40.7435,13.5377,3.34146,10.0565,1,22.2037,11.0241,38.2044,14.8007,29.7193,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,38,54,Winter,0,0 +673e7be9,Spring,12,0,Fall,50,Spring,14.2239369,54.0,59.0,22.0,64,69,100,,,,,Spring,7,0,13.1,2,13.8,2,6,0,8.0,1,8.0,1,12.0,1,Spring,1,3.35693,14.2255,1030.84,1340.1,10.4367,52.0615,12.5526,1.67294,6.93847,1,25.4965,16.1283,48.7046,21.4913,35.9332,,,Fall,2.71,Fall,4,4,5,0,2,1,1,3,1,1,4,0,4,1,2,3,3,2,3,2,46,Fall,34,49,Spring,2,1 +677038b5,Winter,8,1,Summer,60,Spring,23.84984985,55.5,104.5,,123,77,152,Spring,7,12,14,Winter,9,1,,,,,3,0,13.0,1,13.0,1,10.0,1,Winter,2,3.65099,23.8525,1178.61,1767.91,22.9218,67.8002,15.4756,8.37684,36.6998,3,29.1782,15.7003,64.1492,30.0231,52.0999,,,,,Spring,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,Spring,31,45,Winter,0,0 +6774ba64,Summer,6,0,Fall,65,Summer,14.67593294,43.0,38.6,,67,99,110,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,45,63,Summer,0,0 +678598dd,Spring,5,0,Fall,55,Spring,21.99926215,48.0,72.1,,73,99,111,Fall,,,,Summer,8,1,,,,,0,0,5.0,0,8.0,1,5.5,1,Spring,2,2.9433,22.0017,1016.89,1627.02,11.7026,50.575,15.4332,6.56845,21.525,2,27.8037,11.0687,47.6317,23.3883,39.5064,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +6795027a,Fall,11,1,Fall,92,Fall,16.09824561,57.0,74.4,,66,55,107,,,,,Fall,15,1,16.6,2,16.0,2,15,1,11.0,1,10.5,1,7.0,0,Fall,5,3.6846,16.1,1125.6,2476.33,19.1124,62.1545,13.4501,2.64989,12.2455,2,27.2966,15.7455,58.4699,28.1967,46.409,,,Summer,2.749,Summer,3,2,3,0,3,1,2,1,1,0,3,0,1,1,1,1,2,1,1,0,27,Summer,38,54,Fall,0,0 +6799c6d6,Spring,15,1,,,Spring,16.86422691,64.5,99.8,25.0,74,69,115,,,,,Spring,1,0,22.8,2,25.4,2,5,0,9.5,0,9.5,0,9.0,1,Spring,,,,,,,,,,,,,,,,,Spring,2.54,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +679ce4b2,Fall,12,0,Spring,75,Winter,21.29811611,51.0,78.8,,28,78,126,,,,,Winter,31,1,37.2,1,38.0,1,10,1,7.0,0,7.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.95,Winter,2,1,3,2,5,1,1,1,1,1,4,1,5,1,4,5,4,4,3,1,50,Winter,37,53,Fall,2,2 +67a2bf79,Winter,13,0,,,Winter,22.18355556,60.0,113.6,32.0,100,72,142,,,,,Winter,0,0,18.6,1,14.8,1,0,0,5.5,0,8.0,1,11.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,4.54,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +67b54cdb,Winter,8,0,Spring,50,Spring,16.90373264,48.0,55.4,,57,77,103,Spring,6,9,20,Spring,8,1,,,,,0,0,9.0,1,8.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Spring,34,49,Winter,0,0 +67e313f2,Summer,8,1,,,Summer,18.07157895,47.5,58.0,26.0,70,74,119,,,,,Summer,0,0,,,,,0,0,0.0,0,0.0,0,5.0,0,Fall,1,2.28364,18.0736,964.981,1254.47,11.1912,45.0461,14.037,4.0366,12.9539,1,20.4036,13.4513,42.7625,17.2675,31.5948,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +682327c3,Spring,7,0,Summer,65,Spring,18.48873423,52.25,71.8,,53,63,102,Spring,4,6,46,Spring,7,1,,,,,7,1,8.5,1,9.0,1,13.0,1,Spring,4,8.78791,18.4908,1045.92,2196.43,11.0708,53.6673,13.821,4.66975,18.1327,2,27.0206,15.5759,44.8794,23.994,38.0914,,,,,Summer,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,3,0,3,0,0,11,Summer,42,59,Spring,0,0 +6826f0be,Spring,10,0,Winter,65,Summer,22.27699168,58.0,106.6,,61,86,114,Summer,5,7,53,Summer,20,1,23.7,3,25.9,3,25,1,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.5,Summer,1,2,2,5,2,1,1,2,1,1,3,1,1,1,2,1,2,1,3,1,34,Summer,45,63,Summer,1,1 +683464ff,Spring,10,0,Fall,55,Summer,15.15395408,56.0,67.6,,68,78,107,Summer,6,10,1,Summer,9,1,,,,,11,1,8.0,1,6.5,0,7.0,1,Fall,3,5.68278,15.1556,1082.56,1840.36,12.4674,57.5701,12.907,2.24865,10.0299,1,28.4373,16.6654,51.8874,27.057,40.9047,,,Summer,3.52,Summer,2,2,2,0,2,1,0,0,0,0,1,0,1,0,1,1,1,1,1,1,17,Summer,38,54,Spring,1,0 +68572a28,Winter,5,1,,,Winter,16.49081878,39.5,36.6,21.0,53,91,101,,,,,Winter,0,0,,,,,2,0,13.0,1,13.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,0,0 +685cc0f1,Summer,15,0,Winter,60,Fall,25.19672893,66.1,156.6,,72,89,133,,,,,Fall,15,,25.3,2,29.3,3,0,,6.0,,5.0,,7.0,,Fall,2,6.09394,25.1995,1557.43,2491.89,29.55,108.15,17.4031,7.79635,48.4497,3,50.295,28.3052,102.056,68.2573,79.845,Summer,1.65,,,Fall,4,3,4,1,4,4,2,3,2,2,4,1,4,3,3,2,3,3,2,2,56,Fall,36,51,Summer,2,2 +6860c6e8,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +68652b0c,Spring,11,1,,,Spring,37.48403497,63.5,215.0,43.0,76,69,148,,,,,Spring,0,0,51.8,2,43.8,2,0,0,4.5,0,5.5,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +68688509,Fall,9,0,Summer,45,Winter,17.06872428,54.0,70.8,,79,69,108,Winter,4,5,0,Winter,5,0,,,,,1,0,10.0,1,11.0,1,11.0,1,Winter,2,3.33205,17.0706,1058.76,1694.01,9.80684,55.0345,13.2694,3.80121,15.7655,1,27.7344,17.4933,51.7025,31.3325,37.5412,,,,,Winter,3,4,2,0,2,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,16,Winter,51,71,Fall,2,0 +687c85e7,Summer,11,0,Spring,74,Winter,19.42347874,56.5,88.2,,103,82,162,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.66,Fall,2,1,1,1,1,1,0,1,1,2,1,1,1,1,1,1,1,1,1,1,21,Fall,28,41,Summer,0,0 +6885737e,Winter,10,0,Fall,60,Summer,18.95438596,57.0,87.6,,87,107,134,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,2,4.02503,18.9565,1165.32,1864.52,16.6181,66.3852,14.3656,4.59084,21.2148,3,32.2134,17.5537,62.3601,30.3854,48.8315,,,Spring,2.49,Winter,3,1,2,1,2,1,1,1,1,1,3,1,3,1,2,2,2,1,2,1,32,Winter,42,59,Winter,0,1 +68a59e56,Fall,7,0,Fall,55,Winter,14.10632373,48.5,47.2,,72,95,131,Winter,1,0,51,Winter,,,,,,,,,,,,,,,Winter,1,7.68732,11.8139,988.295,1284.78,8.78796,47.5294,11.8963,-0.0824532,-0.329425,1,22.6099,16.1316,39.8421,16.5147,31.3978,,,,,Winter,4,4,4,0,4,2,0,2,0,1,3,0,2,1,3,2,2,2,2,1,39,Winter,38,54,Fall,0,1 +68be3d5c,Summer,10,1,Winter,60,Summer,18.69325736,58.5,91.0,,49,68,109,,,,,Summer,12,1,15.5,2,20.0,2,13,1,9.0,1,9.5,1,11.0,1,Summer,4,3.98867,18.6953,1186.27,2253.9,22.3142,68.6159,14.0966,4.59866,22.3841,1,29.0886,17.2131,64.6272,30.3205,51.4028,,,Fall,3.29,Fall,3,2,3,0,1,1,0,1,1,0,3,0,1,1,1,0,1,1,1,0,21,Fall,34,49,Summer,0,0 +68be5113,Fall,5,0,,,Fall,12.8531391,43.25,34.2,19.0,57,96,94,,,,,Fall,0,0,,,,,0,0,9.1,1,10.6,1,6.0,1,Fall,1,2.15774,12.8545,864.368,1123.68,3.5554,34.3295,12.9032,-0.0486809,-0.129517,1,18.5548,12.2193,32.1718,11.7991,22.1102,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +68d49292,Spring,9,1,Fall,80,Summer,19.71891892,55.5,86.4,,79,73,119,Summer,2,1,27,Summer,6,1,,,,,9,1,12.0,1,14.2,1,9.0,1,Summer,3,6.5358,19.7211,1118.33,1789.33,18.9605,61.3799,14.0101,5.71093,25.0201,2,26.6444,15.775,54.8441,27.4151,45.6049,,,Summer,2.92,Summer,3,2,2,1,3,1,0,1,1,1,0,0,0,2,2,1,2,1,1,1,25,Summer,40,56,Spring,0,0 +68e514dc,Fall,13,0,Spring,70,Fall,17.47161081,61.5,94.0,,94,72,113,,,,,Fall,11,0,14.5,1,14.5,1,6,0,8.5,1,10.5,1,8.0,0,Fall,3,-2.14931,17.4735,1383.46,2351.88,24.9031,89.6197,16.6593,0.814256,4.38034,1,44.2502,20.4664,91.769,55.5162,69.1532,,,Fall,3.38,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,32,46,Fall,0,0 +68e61f96,Winter,11,1,Fall,68,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.15,Spring,3,2,2,1,2,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0,21,Spring,33,47,Winter,0,0 +68e7140b,Winter,16,0,Spring,55,Winter,32.35389804,71.0,232.0,,79,102,105,,,,,Winter,26,1,33.3,2,44.6,2,7,0,6.0,0,5.0,0,8.0,0,Winter,1,8.90466,32.3574,2042.0,2654.61,50.7763,159.763,22.2825,10.075,72.2366,3,67.2457,41.7414,150.859,81.746,118.022,Spring,1.25,,,Winter,1,2,0,1,1,1,1,1,1,1,3,0,1,1,1,1,1,1,1,1,21,Winter,50,69,Winter,2,0 +68fa4631,Spring,9,0,Fall,70,Spring,18.82292899,52.0,72.4,,57,91,133,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.68,Summer,0,0,0,0,0,0,0,0,0,0,0,0,,0,0,0,0,0,0,0,0,Summer,39,55,Spring,0,0 +69074cf3,Spring,7,1,Fall,80,Summer,15.68677686,44.0,43.2,,66,87,104,Summer,,,,Summer,0,0,,,,,3,0,8.0,0,6.0,0,7.0,1,Summer,3,10.0302,15.6174,903.538,1445.66,8.94334,38.5017,13.9189,1.69852,4.69834,1,19.8879,9.67045,28.4715,17.0671,28.8312,,,,,Spring,3,3,3,0,3,0,0,3,3,3,3,0,0,0,3,3,3,3,3,3,42,Spring,31,45,Spring,0,1 +694a2fc5,Winter,10,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +69742f6f,Spring,13,0,Summer,55,Spring,17.47161081,61.5,94.0,26.0,68,77,109,,,,,Spring,35,1,26.6,2,25.8,2,17,1,0.0,0,0.0,0,8.0,0,Spring,3,4.77447,17.4735,1254.14,2132.04,19.9977,75.8454,14.0988,3.37474,18.1546,2,35.548,20.2997,71.0709,35.8356,55.5457,,,Spring,2.77,Spring,4,4,4,4,4,2,2,5,3,2,4,1,3,3,3,1,4,3,4,4,64,Spring,39,55,Spring,1,2 +697c41d7,Fall,9,0,Spring,75,Winter,20.22102102,55.5,88.6,,87,78,117,Winter,6,9,50,Winter,6,0,,,,,0,0,13.5,1,13.0,1,6.5,1,Winter,3,6.20331,20.2232,1154.36,1962.41,16.3288,65.2177,14.8861,5.33708,23.3823,2,32.3205,16.5683,59.0144,30.8059,48.6494,,,Winter,1.68,Winter,5,4,2,0,5,5,0,1,4,4,2,0,1,3,3,5,1,5,1,0,51,Winter,37,53,Fall,2,2 +697d894b,Fall,15,1,Winter,60,Winter,23.11633715,62.0,126.4,,76,101,129,,,,,Winter,40,1,23.4,2,26.7,2,15,1,14.0,1,14.0,1,14.0,1,Winter,2,4.79285,23.1189,1341.82,2012.73,29.0846,85.1846,15.5805,7.5384,41.2154,2,34.8232,21.2768,80.3917,41.8428,63.9078,Winter,1.13,,,Winter,0,4,4,2,4,3,4,2,1,1,4,0,3,2,3,0,2,4,1,1,45,Winter,41,58,Fall,3,1 +698b9067,Summer,9,0,Fall,80,Fall,16.5643786,53.8,68.2,,104,59,129,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.55,Fall,3,3,3,1,3,1,0,2,1,2,2,0,2,3,3,2,2,2,2,2,39,Fall,39,55,Summer,0,1 +69a43542,Summer,14,1,,,Summer,17.71227009,63.0,100.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +69aed94b,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +69b65679,Fall,8,1,Winter,51,Fall,17.60982957,43.5,47.4,,74,45,120,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.48,Fall,2,3,2,0,2,1,0,1,1,1,0,0,2,2,2,2,3,2,1,2,29,Fall,40,56,Fall,0,0 +69bdc55d,Spring,10,0,,,Summer,21.99474852,52.0,84.6,28.0,73,79,114,,,,,Summer,0,0,13.6,2,13.7,2,0,0,6.0,0,7.0,0,4.0,0,Fall,2,3.34815,21.9972,1090.19,1744.31,14.3129,58.3827,15.1803,6.81686,26.2173,2,29.2942,14.7755,55.0345,23.9812,43.6071,,,Spring,1.77,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +69ccea20,Spring,9,0,Fall,60,Spring,17.08879819,52.5,67.0,,72,80,87,Spring,6,9,40,Spring,10,1,,,,,4,0,5.0,0,7.0,0,11.0,1,Spring,3,3.36442,17.0907,1047.91,1781.44,11.7518,53.8787,13.7436,3.34704,13.1213,1,28.2099,13.917,50.5143,26.5198,39.9617,,,Spring,3.019,Spring,2,2,2,0,1,1,0,1,1,0,0,0,1,0,0,1,1,1,1,1,16,Spring,34,49,Spring,0,0 +69cdf9c3,Fall,10,0,Winter,70,Fall,23.22410714,56.0,103.6,,76,81,113,Fall,6,9,58,Fall,12,1,,,,,0,0,7.5,1,7.0,0,9.0,1,Fall,5,6.78444,23.2266,1222.98,2935.15,20.2881,72.5263,16.2601,6.96658,31.0737,3,34.6806,17.5576,65.7419,30.3408,54.9687,,,Winter,3.53,Fall,3,3,4,0,4,2,0,2,0,2,0,0,2,0,2,3,2,2,1,1,33,Fall,35,50,Fall,2,1 +69d43286,Spring,16,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.44,,,,,,,,,,,,,,,,,,,,,,,,,Summer,52,72,Spring,2, +69fabb6b,Fall,14,0,Winter,70,Winter,17.88939223,62.75,100.2,,76,116,147,,,,,Winter,25,1,17.0,1,22.4,1,14,1,7.0,0,8.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.42,Winter,5,5,5,2,5,5,4,4,5,4,4,1,5,0,5,5,4,5,2,2,77,Winter,36,51,Fall,2,2 +6a04461f,Summer,9,1,Winter,69,Fall,18.64489142,53.0,74.5,,64,83,107,Fall,6,9,39,Fall,5,0,,,,,1,0,6.0,0,5.5,0,8.0,1,Winter,2,2.54705,18.6469,1075.65,1613.48,10.6737,56.8342,14.2253,4.42165,17.6658,2,21.1277,25.0328,54.2872,29.8532,31.8014,,,Summer,2.86,Fall,5,5,5,4,5,2,0,2,2,2,3,0,2,2,4,5,3,4,2,2,59,Fall,44,62,Summer,0,2 +6a138911,Winter,6,0,Spring,70,Winter,27.14617474,47.0,85.3,,87,78,104,Winter,,,,Winter,0,0,,,,,0,0,4.0,0,4.0,0,4.0,0,Summer,1,2.78023,27.1491,1030.18,1339.23,12.3247,51.9904,16.5474,10.6017,33.3096,3,27.4034,12.2623,49.2102,20.1833,39.728,,,,,Winter,3,4,4,0,4,0,0,0,1,0,1,0,3,0,0,3,2,2,2,0,29,Winter,70,95,Winter,0,0 +6a151312,Summer,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6a16089d,Spring,10,1,Summer,81,Spring,19.70723967,55.0,84.8,,87,96,119,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,3.25388,19.7094,1097.47,1646.2,17.2382,59.1579,13.7496,5.9598,25.6421,2,25.4589,16.4608,55.904,26.9256,42.6971,,,Summer,2.11,Summer,2,3,2,0,3,1,1,1,1,1,3,0,1,2,1,1,2,1,0,0,26,Summer,30,43,Spring,2,0 +6a2c6073,Winter,11,1,Summer,83,Winter,25.22227261,63.0,142.4,36.0,63,91,121,,,,,Winter,21,1,49.2,3,54.0,3,3,0,12.0,1,12.5,1,12.0,1,Spring,4,5.07988,25.225,1407.63,2674.49,32.3105,92.1937,16.3314,8.89364,50.2063,2,36.8286,23.0546,87.1139,44.669,69.1391,,,Spring,2.289,Winter,4,5,3,1,3,2,3,1,1,2,2,0,2,3,4,2,1,3,1,1,44,Winter,50,69,Spring,2,1 +6a2fd807,Spring,16,0,Spring,65,Spring,22.04793064,69.8,152.8,,58,71,127,,,,,Spring,25,1,43.2,2,43.7,2,36,1,8.0,1,8.5,1,10.0,1,Spring,2,6.96596,22.5646,1680.23,2688.37,37.4411,121.23,17.9025,4.66214,31.5704,2,52.8284,30.9601,114.264,60.1804,90.2695,Spring,2.79,,,Spring,0,4,4,1,3,4,1,1,2,2,3,1,2,3,2,2,4,3,1,1,44,,,,Spring,2,1 +6a4df6ff,Summer,5,1,Winter,63,Summer,14.63407612,42.5,37.6,21.0,60,99,100,,,,,Summer,0,0,,,,,0,0,10.0,1,10.0,1,7.0,1,Summer,2,1.96093,14.6357,875.736,1313.6,8.14905,35.5403,13.834,0.801718,2.05967,1,20.7773,6.61398,33.5794,20.3106,28.9264,,,,,Summer,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,,,,Summer,0,0 +6a6da46c,Fall,14,1,,,Fall,24.35993757,62.0,133.2,30.0,57,73,108,,,,,Fall,21,1,18.5,2,24.1,2,0,0,10.0,1,10.0,1,6.0,0,Fall,3,4.62476,24.3626,1321.11,2113.78,28.5567,82.9789,15.177,9.18557,50.2211,2,32.7529,21.6693,78.3541,36.6021,61.3096,,,Fall,2.05,Fall,4,4,4,1,3,2,1,2,2,2,3,0,2,1,3,1,3,2,3,1,44,Fall,41,58,Fall,0,1 +6a7d07fb,Summer,9,0,,,Summer,15.32035065,51.5,57.8,23.0,65,70,92,,,,,Summer,11,1,,,,,5,0,9.0,1,9.5,1,3.0,0,Summer,,,,,,,,,,,,,,,,,,,Summer,3.34,Summer,3,0,2,0,2,0,0,0,2,2,1,0,2,0,1,2,1,2,0,0,20,Summer,40,56,Summer,0,0 +6a837387,Winter,8,0,,,Winter,18.57943339,50.5,67.4,25.0,72,53,118,,,,,Winter,0,0,,,,,0,0,6.0,0,6.0,0,11.0,1,Winter,,,,,,,,,,,,,,,,,,,,,Summer,4,2,4,0,1,0,0,2,0,1,0,0,0,0,1,2,2,2,2,1,24,Summer,34,49,Summer,0,0 +6a83a423,Winter,9,0,Spring,71,Winter,15.9687265,55.75,70.6,,79,76,130,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.49,Winter,3,0,3,0,3,2,2,1,3,2,2,0,3,3,3,2,3,2,3,3,43,Winter,41,58,Winter,0,1 +6a855b02,Fall,7,1,,,Fall,15.20562963,45.0,43.8,,67,94,115,,,,,Fall,3,0,,,,,0,0,14.0,1,14.0,1,8.0,1,Fall,2,2.27807,15.2073,920.05,1380.07,11.1284,40.2604,13.9783,1.22895,3.53962,1,22.3615,6.77044,37.9823,19.9789,33.4899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +6a864b11,Winter,9,0,Summer,70,Spring,18.91496617,54.8,80.8,,84,99,145,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.12,Spring,4,4,4,0,5,1,0,2,1,1,4,0,3,1,3,4,2,4,2,3,48,Spring,76,100,Winter,3,1 +6a98537b,Summer,6,1,Summer,78,Summer,15.90464876,44.0,43.8,,63,80,103,Summer,,,,Summer,5,1,,,,,0,0,9.5,1,9.0,1,6.5,1,Summer,3,6.56767,15.9064,904.454,1447.13,10.7682,38.5993,14.0177,1.8887,5.20073,1,20.9749,6.8561,32.0316,16.1426,31.7432,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,0,0,0,Summer,72,98,Summer,0,0 +6a9f0db7,Spring,7,1,Fall,61,Summer,17.21188017,44.0,47.4,,60,92,110,Summer,5,7,45,Summer,0,0,,,,,0,0,6.0,0,7.5,0,6.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,29,42,Spring,0,0 +6aa529c8,Fall,7,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6abd21d4,Fall,12,1,Winter,85,Winter,22.38066406,64.0,130.4,,69,88,122,Winter,5,7,20,Winter,30,1,18.3,2,21.1,2,30,1,13.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.1,Winter,3,3,3,2,3,1,1,2,2,2,2,1,1,1,1,1,2,2,2,2,37,Winter,39,55,Fall,1,1 +6abd2bd4,Summer,12,0,Winter,70,Summer,24.09697266,64.0,140.4,,65,83,116,Summer,5,7,0,Summer,17,1,14.6,2,16.3,2,10,1,0.0,0,0.0,0,9.0,1,Fall,2,8.87269,24.0996,1408.89,2254.22,25.7427,92.3279,15.8481,8.25155,48.0721,2,40.9256,25.6597,83.4552,41.0989,66.6682,,,Summer,4.03,Summer,2,3,3,2,2,3,1,2,1,1,4,1,1,2,1,1,3,1,2,1,37,Summer,37,53,Summer,1,1 +6abe8b60,Winter,8,0,Winter,85,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,5,0,5,0,0,0,0,0,0,2,0,0,4,0,2,2,0,2,0,0,22,Winter,42,59,Winter,0,0 +6ac5763a,Winter,16,1,Fall,68,Summer,23.37390404,69.5,160.6,,66,57,123,,,,,Summer,60,1,22.3,2,29.3,3,0,0,14.2,1,14.0,1,12.0,1,Summer,5,6.43314,23.3765,1575.97,3467.13,39.5387,110.125,16.0294,7.34706,50.4754,2,41.2413,29.3445,103.691,50.1329,80.7801,Winter,3.131,,,Summer,5,4,4,,5,2,,4,5,3,4,1,2,4,5,4,1,5,2,2,62,,,,Winter,2,2 +6ac5eaaf,Fall,7,0,,,Winter,14.73753117,49.9,52.2,,73,51,107,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,1,4,0,4,0,0,4,1,0,1,0,3,0,2,0,4,1,2,0,31,Winter,40,56,Fall,0,1 +6ad91972,Winter,12,0,Winter,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.1,Summer,3,3,2,0,3,0,2,2,0,1,4,0,2,2,0,2,3,1,0,0,30,Summer,36,51,Winter,0,0 +6ae1473e,Spring,10,1,Fall,80,Summer,11.67584322,65.75,71.8,,50,90,106,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,2,5.13947,11.6771,1263.0,1894.5,24.6231,76.7894,12.4886,-0.811444,-4.98939,3,31.1774,20.9889,71.6499,33.0195,55.8005,,,Spring,2.56,Summer,2,3,3,1,5,1,2,1,2,2,2,0,1,1,4,3,4,4,4,2,47,Summer,35,50,Spring,0,1 +6b39be10,Summer,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6b4b8c14,Spring,15,0,Summer,65,Spring,20.75431589,66.0,128.6,29.0,92,85,119,,,,,Spring,30,1,43.6,2,45.8,3,30,1,12.0,1,12.0,1,12.0,1,Spring,5,6.79664,20.7566,1682.75,4038.59,38.4799,121.498,19.6103,1.14633,7.10221,2,54.0649,28.953,114.701,63.02,92.5448,Summer,2.65,,,Summer,1,5,4,1,4,1,3,3,2,2,3,1,4,3,3,2,2,1,1,1,47,Summer,45,63,Spring,0,1 +6b6467f4,Fall,16,1,Spring,60,Fall,32.6364892,67.0,208.4,,73,64,124,,,,,Fall,15,0,22.4,2,28.5,2,0,0,8.0,0,7.5,0,7.0,0,Fall,3,6.03139,32.6401,1628.3,2605.28,42.1067,115.698,18.1209,14.5192,92.7019,3,43.207,30.3844,109.667,57.136,85.3137,Fall,2.32,,,Fall,5,5,5,2,5,1,5,4,5,4,4,1,4,4,5,5,3,4,2,5,78,,,,Fall,2,2 +6b773506,Winter,14,1,Summer,60,Spring,27.76155047,62.0,151.8,,68,86,109,,,,,Spring,12,0,16.5,2,21.2,2,8,1,10.5,1,11.5,1,6.0,0,Spring,2,4.71947,27.7646,1367.62,2051.44,31.0928,87.9329,16.0831,11.6814,63.8671,2,34.1969,22.6432,83.2135,38.7654,65.2897,,,Spring,2.59,Spring,0,3,4,1,4,2,0,3,0,3,4,0,0,2,2,1,2,2,0,2,35,Spring,35,50,Winter,2,1 +6b878aba,Spring,7,1,,,Spring,15.76225224,44.5,44.4,,65,75,108,Spring,,,,Spring,2,1,,,,,0,0,10.0,1,10.0,1,8.0,1,Spring,2,2.05032,15.764,910.02,1365.03,9.20012,39.1921,13.9149,1.84903,5.20788,1,19.8479,10.1441,37.1418,16.6916,29.048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +6b9a25e6,Spring,6,0,Fall,71,Summer,14.82890625,48.0,48.6,22.0,61,97,114,,,,,Summer,4,1,,,,,10,1,8.5,1,8.5,1,5.0,0,Summer,3,2.85757,14.8305,948.882,1613.1,8.8483,43.3314,13.2228,1.60774,5.26861,1,24.5235,9.95958,40.4738,19.4385,33.3718,,,,,Summer,,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,Summer,44,62,Spring,0,0 +6be0f069,Summer,7,0,Winter,62,Fall,10.67543945,64.0,62.2,,99,90,134,Fall,,,,Summer,,,,,,,,,,,,,,,Summer,4,4.77765,10.6766,1161.38,2438.89,16.0603,65.965,11.3229,-0.646266,-3.76503,2,29.0508,20.8539,61.1874,25.2221,45.1111,,,,,Fall,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,50,69,Summer,0,0 +6be56337,Fall,16,1,Winter,60,Winter,24.62005543,63.0,139.0,,64,76,117,,,,,Winter,25,1,26.4,2,24.9,2,1,0,8.0,0,9.0,0,10.0,1,Winter,2,4.7544,24.6227,1339.81,2009.72,28.9991,84.9708,15.0519,9.57084,54.0292,2,33.2612,22.7106,80.2164,38.4959,62.2603,Winter,1.78,,,Winter,0,0,1,2,1,0,1,1,1,0,3,0,0,1,0,0,0,0,0,0,11,,,,Fall,2,0 +6bec62f8,Fall,9,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +6becb599,Spring,6,1,Summer,70,Spring,14.16167355,44.0,39.0,,62,83,110,Spring,,,,Spring,8,1,,,,,0,0,13.0,1,11.5,1,8.0,1,Summer,2,1.93461,14.1632,903.236,1354.85,8.33292,38.4695,13.9706,0.192651,0.530487,1,18.2606,11.876,36.5349,13.1464,26.5935,,,,,Summer,2,2,2,0,1,0,0,2,1,2,1,0,2,2,2,2,2,2,1,1,27,Summer,41,58,Spring,0,0 +6c06fc83,Summer,15,0,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +6c0fe935,Winter,18,0,Spring,55,Spring,26.69819383,68.5,178.2,,95,70,149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,1,0,1,0,0,2,0,0,2,0,0,0,0,0,1,0,0,0,7,Winter,31,45,Winter,1,0 +6c16a8f1,Winter,15,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6c2d0774,Spring,7,0,,,Spring,17.39381443,48.5,58.2,25.0,79,99,123,,,,,Spring,0,0,,,,,0,0,10.0,1,10.0,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +6c2edc91,Winter,11,1,,,Winter,13.97228728,59.6,70.6,,69,84,99,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.08,Winter,2,2,2,0,2,2,0,0,0,1,1,0,1,0,0,0,1,2,1,0,17,Winter,36,51,Winter,3,0 +6c319d99,Spring,7,1,Fall,75,Fall,21.11812865,57.0,97.6,,78,101,125,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,4,0,2,0,0,2,0,2,2,0,0,0,0,2,2,2,2,0,30,Spring,42,59,Spring,0,0 +6c4902c8,Summer,8,0,Fall,77,Summer,16.98448,50.0,60.4,,49,79,101,Summer,10,16,13,Summer,11,1,,,,,32,1,11.0,1,10.5,1,10.0,1,Summer,4,4.75123,16.9863,1005.65,2111.85,10.8124,49.3774,13.8864,3.09989,11.0226,2,26.9122,11.6528,44.6262,23.3947,37.7246,,,,,Summer,2,2,2,1,2,1,0,1,1,1,1,1,2,1,1,2,1,2,1,1,26,Summer,65,89,Summer,2,0 +6c5b20dc,Spring,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +6c6b8c08,Summer,8,0,Fall,60,Summer,18.4589497,52.0,71.0,,72,88,92,,,,,Summer,0,0,,,,,0,0,4.0,0,6.0,0,3.0,0,Summer,2,3.22735,18.461,1044.3,1670.87,11.6904,53.4942,13.9092,4.55175,17.5058,3,26.9777,14.8261,50.2668,22.3902,38.6681,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,34,49,Summer,0, +6c6cd633,Summer,10,0,Fall,65,Summer,19.77426173,56.25,89.0,,65,76,104,,,,,Summer,6,0,13.9,2,15.9,2,0,0,8.0,1,9.5,1,10.0,1,Summer,3,3.94156,19.7764,1161.7,1974.89,16.6858,65.999,14.6654,5.11098,23.001,2,32.1628,17.1504,62.0575,29.764,48.8486,,,Summer,2.44,Summer,4,4,5,5,5,1,0,1,2,1,3,0,5,2,4,5,4,5,5,4,65,Summer,47,66,Summer,0,2 +6c737069,Winter,8,0,Spring,63,Spring,14.25453825,50.25,51.2,,68,102,112,Spring,7,11,12,Spring,16,1,,,,,15,1,7.0,0,6.5,0,13.5,1,Spring,4,7.28185,14.2561,970.23,2037.48,8.26852,45.6053,12.6983,1.55779,5.59472,2,23.5947,13.742,38.3234,18.8689,31.8633,,,,,Spring,4,5,4,1,4,1,0,2,1,1,3,1,1,1,2,2,3,2,2,1,41,Winter,41,58,Winter,0,1 +6cabbf32,Spring,10,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,36,51,,, +6cc6cd46,Winter,19,0,,,Winter,37.01510204,70.0,258.0,45.0,83,78,147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +6d341798,Spring,8,0,,,Spring,16.39368999,54.0,68.0,23.0,68,65,122,,,,,Spring,23,1,,,,,3,0,14.5,1,13.0,1,18.0,1,Spring,,,,,,,,,,,,,,,,,,,Summer,2.8,Summer,3,4,2,0,3,1,0,1,1,2,1,0,0,2,2,0,2,0,0,0,24,Summer,34,49,Spring,0,0 +6d4933d0,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6d5944a9,Winter,11,1,Summer,60,Spring,17.42975207,55.0,75.0,,59,70,112,Spring,5,8,39,Spring,7,0,15.2,2,17.2,2,0,0,13.0,1,13.0,1,11.0,1,Spring,1,3.34903,17.4317,1091.07,1418.39,17.5742,58.4764,13.5912,3.84045,16.5236,2,25.8778,15.0244,55.1274,26.0952,43.452,,,Summer,3.06,Spring,2,1,1,0,1,1,0,4,2,1,0,0,0,0,0,0,1,1,0,0,15,Spring,35,50,Winter,0,0 +6d5c5afb,Spring,7,0,Summer,70,Summer,15.13402778,48.0,49.6,,62,82,114,Summer,,,,Fall,,,,,,,,,,,,,,,Summer,2,2.80981,15.1357,947.194,1515.51,8.34515,43.1516,13.1679,1.96777,6.44843,1,24.1287,10.6777,40.3418,19.2982,32.4738,,,,,Spring,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,6,Spring,59,81,Spring,0,0 +6d707ac3,Summer,20,1,Fall,59,Summer,21.88402204,66.0,135.6,,73,77,114,,,,,,,,,,,,,,,,,,,,Fall,1,5.26535,21.8864,1380.75,1794.97,31.0532,89.3308,14.4184,7.46804,46.2692,2,33.6942,24.5835,84.0655,36.6853,64.7473,,,,,Summer,5,5,4,4,4,4,3,3,2,3,2,1,3,4,4,1,2,2,2,3,61,,,,Summer,3,2 +6d70e716,Winter,12,1,Fall,75,Spring,33.60497059,61.5,180.8,,80,92,113,,,,,Spring,17,0,19.0,2,20.9,2,0,0,7.0,0,6.0,0,11.0,1,Spring,2,5.00102,33.6086,1478.8,2218.2,37.1127,99.7745,18.5469,15.0617,81.0255,3,38.1861,24.4757,94.7735,44.2071,75.2988,,,Winter,1.98,Spring,5,5,4,3,4,2,2,3,3,4,5,0,4,0,4,4,3,4,4,3,66,Winter,43,60,Winter,,2 +6d7ba819,Summer,7,1,Winter,45,Fall,14.88336699,48.5,49.8,,67,98,110,Fall,,,,Summer,,,,,,,,,,,,,,,Summer,5,2.59233,14.885,968.88,2131.54,13.1058,45.4615,13.5882,1.29677,4.33854,2,22.6397,9.71594,42.8691,19.1192,35.7455,,,,,Summer,1,2,4,0,4,0,0,2,1,1,0,0,2,5,4,4,3,3,1,1,38,Summer,39,55,Summer,0,1 +6d855495,Spring,11,0,Fall,60,Spring,16.36447704,56.0,73.0,24.0,79,85,127,,,,,Spring,18,1,49.0,2,53.6,3,10,1,12.0,1,12.0,1,11.0,1,Fall,3,3.98385,16.3663,1130.59,1922.0,16.5452,62.6856,14.0538,2.31244,10.3144,1,30.8758,15.2646,58.7018,26.5318,47.421,,,Fall,2.22,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,35,50,Spring,2,0 +6db14d48,Spring,10,1,Fall,60,Fall,16.39368999,54.0,68.0,,60,92,106,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,2,3.54799,16.3955,1118.25,1677.37,18.5968,61.3711,14.7972,1.59829,6.62887,1,30.3109,12.4634,57.8231,37.395,48.9077,,,Spring,1.95,Summer,5,0,4,0,4,2,0,2,3,3,3,0,2,3,3,1,3,1,0,2,41,Summer,38,54,Spring,2,1 +6db453ce,Spring,17,0,Fall,51,Summer,35.70594787,67.0,228.0,,81,74,122,,,,,Summer,7,0,30.4,2,49.1,3,7,0,9.5,1,8.0,1,9.0,1,,,,,,,,,,,,,,,,,,Summer,1.27,,,Summer,2,3,5,4,5,5,5,5,4,5,5,5,5,4,4,0,4,1,4,0,75,Summer,48,67,Summer,2,2 +6dfe4a75,Summer,15,1,Fall,65,Fall,18.97833117,63.25,108.0,,88,99,140,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.54,,,Fall,0,3,4,3,3,2,3,2,1,0,4,1,1,1,3,0,3,0,3,2,39,Fall,51,71,Summer,2,1 +6e0d4774,Winter,10,0,,,Winter,18.56204418,55.25,80.6,26.0,61,86,100,,,,,Winter,7,0,,,,,3,0,8.5,1,10.0,1,12.0,1,Spring,4,3.94544,18.5641,1149.97,2414.93,17.2872,64.7497,14.9134,3.6507,15.8503,1,32.2207,15.2417,60.8043,28.7557,49.508,,,Spring,3.07,Spring,2,3,3,2,3,2,1,1,2,2,2,0,0,2,3,4,3,2,0,0,37,Spring,63,86,Winter,2,1 +6e0ec0fe,Fall,9,0,Spring,55,Fall,20.2710496,51.0,75.0,,52,77,99,Fall,6,9,43,Fall,10,1,,,,,13,1,6.5,0,8.0,1,13.0,1,Fall,2,5.76802,20.2733,1064.15,1702.64,12.3128,55.609,15.0317,5.24159,19.391,2,30.4652,12.831,49.841,32.4704,42.778,,,Winter,3.22,Fall,4,0,4,0,4,2,0,0,0,1,1,,4,2,3,4,3,4,3,4,43,Fall,53,73,Fall,1,1 +6e11e79e,Winter,9,0,Winter,70,Spring,14.4884773,51.0,53.6,25.0,85,89,110,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6e128c6b,Summer,14,1,Winter,85,Fall,24.02465125,63.5,137.8,,73,86,112,,,,,Fall,22,1,41.6,2,36.8,2,5,0,9.0,0,8.5,0,10.0,1,Fall,4,4.92987,24.0273,1327.48,2522.21,27.5274,83.6571,14.5867,9.44055,54.1429,2,32.9431,23.1865,78.7272,40.6791,60.4706,Fall,2.6,,,Fall,2,1,2,1,3,1,2,2,2,1,3,1,1,1,1,1,1,1,1,1,29,Fall,36,51,Summer,2,0 +6e12c36c,Spring,11,1,Spring,42,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.52,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +6e1d28b6,Winter,8,0,Summer,50,Spring,17.51418685,51.0,64.8,,76,78,102,,,,,Spring,0,0,,,,,7,1,9.0,1,9.0,1,10.0,1,Spring,4,3.23366,17.5161,1029.62,2162.21,10.4657,51.9314,14.0376,3.4785,12.8686,1,28.9663,12.4994,48.6978,31.8446,39.432,,,Winter,3.251,Spring,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,2,2,3,2,1,14,Spring,44,62,Winter,2,0 +6e516017,Fall,12,1,,,Fall,20.72042517,59.0,102.6,,63,63,110,,,,,Fall,16,0,11.0,1,15.8,2,4,0,10.5,1,12.5,1,8.0,0,Fall,3,4.31978,20.7227,1258.64,2013.82,26.185,76.3246,15.4157,5.30699,26.2754,2,32.3029,17.8367,72.0048,35.0279,58.4879,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +6e5606d5,Winter,9,1,Winter,40,Summer,16.66081871,57.0,77.0,,61,89,107,Summer,5,8,45,Summer,5,0,,,,,0,0,5.0,0,6.0,0,6.0,1,Summer,2,3.49199,16.6626,1108.9,1663.35,17.9454,60.3751,13.065,3.59759,16.6249,1,25.0622,17.3676,56.8831,23.8933,43.0075,,,Winter,1.88,Summer,4,4,3,0,3,0,0,0,1,0,1,0,1,1,1,2,3,3,3,1,31,Summer,45,63,Winter,0,1 +6e615f0d,Winter,10,0,,,Winter,14.99540466,54.0,62.2,23.0,60,64,101,,,,,Winter,16,1,15.2,2,15.0,2,10,1,7.0,0,7.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,2.009,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +6e68bcfd,Summer,9,0,Summer,63,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.28,Fall,4,4,4,2,3,1,1,1,2,2,2,1,2,2,2,2,1,2,1,1,40,Fall,37,53,Summer,2,1 +6e695d70,Fall,10,0,Winter,64,Fall,20.82962963,54.0,86.4,,85,100,132,Fall,5,8,2,Fall,20,1,11.8,1,8.5,1,15,1,7.0,0,6.0,0,8.0,0,Fall,2,10.656,20.8319,1101.75,1762.79,13.0061,59.6134,14.3734,6.45853,26.7866,2,28.8718,17.7355,48.9574,26.0967,41.8779,,,Fall,1.77,Fall,2,5,4,1,5,4,1,2,1,3,3,1,2,4,2,3,3,3,3,2,54,Fall,43,60,Fall,,2 +6e6b3ff2,Spring,13,0,,,Spring,18.91670446,63.0,106.8,28.0,67,63,109,,,,,Spring,10,0,19.5,2,23.7,2,12,1,9.0,1,8.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.84,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +6e6d895f,Summer,8,0,Fall,80,Fall,14.24543628,53.5,58.0,,68,81,108,Fall,,,,Fall,8,1,,,,,4,1,5.5,0,5.5,0,9.5,1,,,,,,,,,,,,,,,,,,,,,,Fall,4,2,2,1,2,0,0,1,0,0,1,0,1,1,1,1,1,1,1,1,21,Fall,36,51,Summer,2,0 +6e85a624,Summer,10,1,Fall,70,Fall,20.82917902,53.75,85.6,,85,94,133,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.6,Fall,2,2,3,,4,2,4,3,3,3,3,1,3,3,3,3,4,3,3,3,55,Fall,34,49,Summer,0,2 +6e89cee5,Spring,9,1,Spring,50,Winter,14.84429932,52.5,58.2,,51,82,113,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.94156,14.8459,1023.86,1638.18,14.0918,51.3176,13.0903,1.75559,6.88238,2,23.52,13.7058,48.3761,22.6576,37.6118,,,Spring,2.25,Winter,0,0,4,0,2,0,0,2,0,0,3,0,2,2,1,1,0,0,0,0,17,Winter,41,58,Spring,3,0 +6e9beaee,Spring,15,0,Fall,55,Summer,22.2911926,70.5,157.6,,82,105,117,,,,,Summer,40,1,39.6,2,44.7,3,12,0,8.0,1,7.0,0,10.0,1,Summer,4,7.3932,22.2936,1743.53,3661.4,40.209,127.971,18.1025,4.19117,29.6286,3,54.9188,32.8436,120.578,62.2552,95.1278,Spring,1.77,,,Summer,0,0,4,0,5,0,5,0,5,0,5,0,4,0,4,0,4,5,0,4,45,Summer,46,64,Spring,2,1 +6e9e0e42,Spring,12,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,36,51,Spring,, +6ea48a4e,Winter,8,1,Spring,69,Spring,22.15672515,57.0,102.4,,82,83,139,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,1,3.88424,22.1591,1201.29,1561.68,23.8798,70.2163,15.1947,6.96449,32.1837,2,29.7981,16.5383,66.332,30.5036,53.6779,,,Spring,1.44,Spring,1,1,1,1,1,1,0,1,1,1,4,1,1,1,1,1,1,1,1,1,22,Spring,37,53,Winter,1,0 +6eb32b03,Spring,13,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.68,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +6ebfdc95,Spring,13,0,Winter,41,Winter,23.73444192,63.0,134.0,,61,83,119,,,,,Winter,1,0,29.6,2,29.8,2,13,0,12.0,1,12.0,1,10.0,1,Winter,4,5.50159,23.737,1464.38,3075.2,28.7797,98.239,17.4023,6.33478,35.761,2,45.0026,24.4566,92.7374,49.3255,73.7824,,,Spring,4.01,Winter,3,3,2,2,2,0,1,1,1,1,1,0,1,0,1,2,2,2,1,0,26,Fall,33,47,Spring,1,0 +6ecb430a,Spring,8,0,Fall,70,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,3,0,3,2,0,1,1,2,2,0,2,1,2,2,2,2,2,1,34,Spring,36,51,Spring,1,1 +6ed385f6,Spring,11,0,Fall,58,Spring,16.22821243,58.5,79.0,,67,87,104,Spring,5,7,57,Spring,0,0,11.9,1,13.0,2,0,0,7.0,0,7.75,1,7.5,0,Spring,2,4.28368,16.23,1170.62,1873.0,17.3384,66.9498,13.7544,2.47563,12.0502,2,32.1819,17.4295,62.6661,29.6548,49.5203,,,Summer,1.86,Summer,5,3,4,2,4,1,1,2,2,2,4,1,2,2,2,3,2,2,2,1,47,Spring,56,77,Spring,3,1 +6edfbf80,Summer,5,0,,,Summer,15.54152893,44.0,42.8,,60,87,116,,,,,Summer,2,1,,,,,0,0,11.0,1,11.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +6eff29da,Spring,6,0,Fall,75,Summer,15.31710069,48.0,50.2,,87,95,100,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,1,2,0,4,0,0,1,1,3,3,0,3,1,2,4,2,4,0,1,35,Spring,39,55,Spring,0,1 +6f032366,Summer,7,0,,,Summer,19.78454441,51.0,73.2,29.0,76,86,136,,,,,Summer,0,0,,,,,0,0,4.0,0,4.0,0,9.0,1,Summer,2,3.09471,19.7867,1036.74,1658.79,11.4631,52.6898,14.2426,5.54413,20.5102,2,26.4795,14.7471,49.595,20.9022,37.9427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +6f49c8d0,Fall,6,1,Winter,55,Winter,20.19904514,48.0,66.2,,76,97,129,Winter,,,,Winter,5,1,,,,,2,0,10.0,1,10.0,1,9.0,1,Winter,4,2.47846,20.2013,984.196,1869.97,12.4582,47.0928,14.3706,5.83064,19.1072,2,23.4287,11.206,44.6144,26.0402,35.8869,,,,,Winter,2,2,2,0,1,0,0,1,0,0,2,0,0,0,0,2,2,0,0,0,14,Winter,37,53,Fall,2,0 +6f5677b7,Summer,12,0,Fall,65,Summer,25.35906433,57.0,117.2,,42,94,124,,,,,Summer,11,0,9.9,1,9.9,1,0,0,10.5,1,8.5,1,12.0,1,Summer,4,4.12893,25.3618,1253.66,2632.68,20.4374,75.7941,16.4017,8.96015,41.4059,2,35.74,19.6167,71.6652,33.1304,56.1774,,,Summer,2.809,Summer,2,4,2,2,3,3,0,0,1,2,2,0,2,0,0,2,2,2,0,0,29,Summer,50,69,Summer,0,0 +6f5a31ea,Fall,9,1,Spring,55,Winter,15.85845569,58.5,77.2,,72,111,111,Winter,4,5,33,Winter,5,0,,,,,1,0,4.0,0,5.0,0,9.0,1,Winter,1,29.463,25.1023,1057.02,1374.13,8.42773,54.8495,17.8348,7.26749,22.3505,2,19.0029,27.4189,25.3865,28.9534,27.4306,,,Winter,2.89,Winter,1,2,3,1,3,3,0,2,1,1,2,1,1,2,2,1,2,1,3,1,33,Winter,35,50,Fall,,1 +6f878799,Spring,11,0,Fall,70,Spring,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.57,Summer,2,2,2,1,2,1,2,1,1,1,1,1,1,1,1,1,2,1,2,1,27,Summer,28,41,Spring,1,0 +6fb28c2a,Fall,5,1,Spring,60,Fall,17.0802963,45.0,49.2,,115,72,144,,,,,Fall,3,1,,,,,2,0,10.0,1,11.0,1,9.0,1,Fall,2,2.20096,17.0822,922.991,1384.49,10.891,40.5737,14.0871,2.99504,8.62631,1,21.3815,8.30116,38.3727,18.3421,32.2725,,,,,Winter,1,4,2,1,1,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,14,,,,Fall,0,0 +6fc6b5c4,Winter,8,1,Spring,70,Winter,18.08046281,55.0,77.8,,72,95,131,Winter,2,2,49,Winter,5,0,,,,,0,0,11.0,1,13.0,1,11.0,1,Spring,3,3.13858,18.0824,1079.61,1727.38,15.0048,57.2561,13.3076,4.77486,20.5439,1,24.7398,17.5115,54.1175,29.3694,39.7446,,,Winter,2.68,Winter,2,2,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,23,Winter,37,53,Winter,2,0 +6fc95dfc,Spring,7,1,Fall,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,3,2,0,0,0,0,0,0,3,1,0,4,0,2,3,0,3,0,0,25,Summer,49,68,Spring,0,0 +6fcdcb2a,Spring,12,0,Spring,71,Spring,17.17314632,58.5,83.6,,86,85,146,,,,,Summer,,,,,,,,,,,,,,,Summer,4,4.19525,17.175,1169.64,2456.25,16.3664,66.8452,13.7329,3.44215,16.7548,1,32.2166,18.2622,62.65,31.6062,48.583,,,Winter,2.75,Spring,1,2,2,1,2,1,0,2,1,1,4,1,1,2,1,1,2,1,1,1,28,Spring,30,43,Spring,1,0 +6ff19aa6,Winter,17,1,,,Winter,19.6606198,63.0,111.0,28.0,60,79,112,,,,,Winter,3,0,36.2,1,30.4,1,1,0,0.0,0,6.0,0,9.0,1,,,,,,,,,,,,,,,,,,Winter,1.11,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +6ff4b94f,Spring,9,1,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +70021fd4,Winter,16,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +7015528d,Fall,11,0,Winter,65,Fall,15.70033333,60.0,80.4,,70,87,115,,,,,Fall,8,0,16.1,2,14.6,2,0,0,4.0,0,5.0,0,12.0,1,Fall,3,4.35935,15.702,1174.68,1996.96,16.7785,67.3821,13.1597,2.54238,13.0179,3,31.4962,19.1075,63.0228,29.04,48.2746,,,Winter,3.269,Winter,3,2,2,1,2,1,0,1,1,2,0,0,1,1,1,1,2,1,1,1,24,Winter,37,53,Fall,0,0 +702d0aac,Summer,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +707c82c4,Spring,6,0,Summer,51,Spring,17.0108642,45.0,49.0,,92,69,147,,,,,Spring,2,1,,,,,0,0,8.0,1,9.0,1,4.0,0,Summer,2,2.55242,20.1028,936.713,1498.74,8.6867,42.0352,14.5946,5.50822,15.8648,1,24.1066,9.24193,39.4828,17.2813,32.7933,,,,,Spring,4,4,4,0,5,1,0,3,0,2,4,0,4,4,4,3,2,2,2,1,49,Spring,42,59,Spring,1,1 +7092a6ce,Spring,14,1,,,Winter,36.8576763,65.13,222.4,,100,,151,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.58,Spring,5,5,5,1,4,5,2,4,1,4,4,0,4,5,4,5,4,5,1,4,72,Spring,46,64,Spring,2,2 +709457f3,Spring,9,0,Winter,59,Summer,18.4331411,51.0,68.2,,65,96,117,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.03,Spring,3,0,3,0,2,0,0,0,3,2,2,0,3,1,2,2,2,3,2,1,31,Spring,34,49,Spring,0,1 +70da6abe,Fall,11,0,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +70de1c63,Winter,17,0,Spring,50,Spring,27.34774502,63.0,154.4,,55,90,130,,,,,Spring,15,0,22.9,1,11.1,1,10,0,4.0,0,0.0,0,2.0,0,Spring,3,6.08184,27.3507,1659.66,2821.42,37.583,119.039,21.0868,6.26398,35.3614,2,53.0858,28.3698,112.957,59.3615,90.6688,Winter,3.09,,,Spring,5,4,3,0,4,1,1,3,2,2,4,0,2,1,4,4,3,5,2,0,50,,,,Spring,2,2 +70e5ef9f,Spring,6,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +71025ceb,Spring,12,0,,,Spring,18.66049986,59.0,92.4,27.0,66,92,119,,,,,Spring,19,1,20.6,2,24.3,3,,,10.0,1,10.0,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.509,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +710345b6,Spring,6,0,Summer,45,Spring,20.07768,50.0,71.4,27.0,83,91,118,,,,,Spring,0,0,,,,,0,0,9.0,1,9.0,1,4.0,0,Spring,2,3.13355,20.0799,1035.48,1656.76,11.6728,52.5549,14.7801,5.29981,18.8451,2,28.4919,12.3903,49.4214,26.5999,40.1647,,,,,Summer,4,3,2,0,2,0,0,0,0,2,1,0,0,0,0,2,1,1,1,0,19,Summer,34,49,Spring,0,0 +7106a387,Winter,11,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1,1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,17,Winter,29,42,,,0 +71269ce6,Winter,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7141fa23,Spring,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7151d8b5,Spring,5,0,Fall,61,Spring,18.26064198,45.0,52.6,,102,77,138,,,,,Spring,10,1,,,,,1,0,9.0,1,9.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,5,4,3,0,3,0,0,0,0,1,0,0,0,3,3,0,1,1,1,1,26,Summer,42,59,Spring,2,0 +71850348,Fall,8,1,Spring,63,Winter,15.52623517,54.5,65.6,,77,99,107,Winter,5,7,20,Winter,15,1,,,,,2,0,11.0,1,12.0,1,11.0,1,Spring,1,3.49432,15.5279,1099.15,1428.89,17.372,59.3368,14.0454,1.48255,6.26324,1,28.9989,12.9659,55.8424,35.6152,46.3708,,,Winter,2.54,Winter,2,2,2,0,1,1,0,1,1,1,1,0,1,1,1,1,2,1,1,1,21,Winter,49,68,Winter,0,0 +71852781,Fall,7,0,Winter,58,Winter,18.32894627,49.0,62.6,,67,107,101,,,,,Winter,10,1,,,,,0,0,6.0,0,5.0,0,4.0,0,Winter,1,3.00787,18.3309,999.928,1299.91,11.2731,48.7684,14.2807,4.05026,13.8316,2,26.2729,11.2225,45.7605,20.1728,37.5459,,,,,Winter,2,0,4,0,0,0,0,0,0,2,0,0,0,0,2,1,2,0,0,0,13,Winter,43,60,Winter,2,0 +7187caec,Summer,11,0,Winter,60,Fall,21.60058501,54.6,91.6,,74,60,113,Fall,3,4,20,Fall,0,0,9.5,1,10.9,1,0,0,7.0,0,8.0,1,8.0,0,Fall,2,8.98691,22.0857,1128.27,1805.23,14.9934,62.4385,15.0545,7.03114,29.1615,2,30.7222,16.7229,53.4515,27.6729,45.7156,,,,,Fall,4,4,4,1,4,4,1,3,1,1,4,1,2,3,3,3,4,4,4,4,59,Fall,65,89,Summer,2,2 +718975fe,Fall,10,0,Summer,68,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.7,Spring,2,2,3,1,2,0,0,1,0,1,0,0,0,0,1,0,1,2,1,1,18,Spring,38,54,Fall,0,0 +7192b2e8,Winter,9,0,Spring,80,Winter,18.68058431,52.7,73.8,,80,98,130,Winter,4,5,32,Winter,3,0,,,,,0,0,13.0,1,15.0,1,8.0,1,Winter,2,5.39652,18.6826,1077.35,1723.76,14.3979,57.0148,14.4334,4.2492,16.7852,2,28.6817,13.9352,51.6183,22.6856,43.0796,,,Winter,2.49,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,36,51,Winter,0,0 +71a425c3,Summer,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +71c0ea97,Winter,8,0,,,Fall,,,,,,,,,,,,Winter,7,1,,,,,0,0,11.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +71c1b1d2,Fall,8,1,Spring,63,Winter,13.67050184,50.3,49.2,,34,95,88,Winter,7,11,23,Winter,0,0,,,,,7,1,10.0,1,9.5,1,9.0,1,Winter,3,6.3387,13.672,987.119,1579.39,11.7867,47.4041,13.173,0.499045,1.79586,1,24.4345,11.1829,41.0654,29.2565,36.2212,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,39,55,Fall,0,0 +71cf0f87,Winter,8,0,Summer,60,Spring,15.80710059,52.0,60.8,,68,81,109,Spring,7,11,21,Spring,10,1,,,,,10,1,6.5,0,7.0,0,6.0,1,Spring,1,4.79892,15.8088,1025.99,1333.78,11.4681,51.544,13.4022,2.40668,9.25596,1,26.9042,13.1718,46.7451,22.8045,38.3722,,,,,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,31,45,Winter,0,0 +71ee31f8,Fall,8,0,Winter,61,Fall,14.03559028,48.0,46.0,,111,69,161,,,,,Fall,5,1,,,,,1,0,8.0,1,10.0,1,9.0,1,Winter,2,2.63406,14.0371,933.987,1494.38,6.33861,41.7448,12.7386,1.29849,4.25517,1,21.7915,13.6147,39.1108,16.809,28.1301,,,,,Fall,2,2,3,0,3,2,0,0,1,2,0,0,1,1,1,2,4,3,0,0,27,Fall,40,56,Fall,0,0 +71f21593,Winter,15,0,,,Winter,18.97497815,65.5,115.8,27.0,55,68,94,,,,,Winter,9,0,30.8,2,28.7,2,1,0,11.0,1,15.0,1,12.0,1,Winter,3,6.09914,18.9771,1491.09,2534.85,30.9856,101.083,16.5653,2.41174,14.7167,2,45.2482,24.8494,94.9841,47.4298,76.2338,Winter,2.12,,,Winter,5,4,5,5,4,5,,4,1,4,5,1,1,1,4,0,5,1,4,1,60,Winter,49,68,Winter,3,2 +71ffd0ba,Fall,8,0,Summer,65,Spring,20.2710496,51.0,75.0,,109,74,157,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,6,Winter,43,60,Fall,0,0 +7214ad21,Summer,13,1,Winter,60,Fall,0.0,63.0,0.0,,72,81,130,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.5,Fall,4,4,4,2,3,0,0,2,5,4,2,0,1,2,4,2,3,3,2,2,49,Fall,46,64,Summer,1,1 +721ea536,Summer,12,0,Winter,80,Summer,25.14425816,64.5,148.8,,60,79,113,,,,,Summer,4,0,13.8,1,16.5,1,0,0,5.5,0,4.0,0,7.0,0,Fall,2,6.68107,25.147,1499.98,2399.96,31.0474,102.03,17.243,7.90403,46.7698,2,44.4536,26.5292,95.3492,42.6141,75.501,,,Summer,2.88,Summer,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,5,Summer,30,43,Summer,2,0 +72264d13,Spring,12,1,Winter,75,Spring,22.71399584,62.0,124.2,,82,107,148,Spring,5,7,11,Spring,43,1,23.6,3,22.6,3,10,1,10.0,1,10.0,1,10.5,1,Spring,2,0.00988856,22.7165,1406.22,2109.33,31.5314,92.0436,16.835,5.88148,32.1564,2,38.6488,21.8634,92.0337,50.0868,70.1802,,,Spring,2.36,Summer,5,5,4,4,5,1,1,1,2,4,2,1,1,1,2,2,2,2,1,1,47,Spring,37,53,Spring,0,1 +723ee34c,Spring,10,0,,,Summer,27.58501784,58.0,132.0,,58,70,132,Summer,4,7,16,Summer,2,0,13.9,2,16.3,2,0,0,10.5,1,6.0,0,10.0,1,Summer,3,4.11444,27.588,1280.91,2177.55,20.5642,78.6967,16.4476,11.1404,53.3033,3,35.9228,22.2096,74.5822,33.0567,56.487,,,Summer,2.289,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,35,50,Spring,0,0 +7250264b,Summer,8,0,Winter,55,Fall,17.66214876,55.0,76.0,,74,100,114,,,,,Fall,12,1,,,,,0,0,10.5,1,10.1,1,7.0,1,Fall,3,8.67998,17.6641,1086.79,1847.55,12.6262,58.0207,13.4853,4.17878,17.9793,2,27.8958,17.4987,49.3407,24.371,40.522,,,,,Fall,1,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,6,Fall,32,46,Summer,0,0 +7250344f,Spring,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +72565f5c,Winter,8,0,Summer,80,Spring,16.05920087,55.75,71.0,,75,96,121,Spring,5,8,48,Spring,8,1,,,,,5,1,6.0,0,6.0,0,9.0,1,Spring,3,5.00788,16.061,1096.3,1863.71,13.6815,59.0334,13.354,2.70696,11.9666,1,29.2543,16.0976,54.0256,26.8694,42.9358,,,Spring,2.62,Spring,2,2,2,0,3,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,12,Spring,37,53,Winter,1,0 +7265f1e5,Spring,7,0,Summer,80,Spring,19.99644444,45.0,57.6,,75,94,122,Spring,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,35,50,Spring,0,0 +7290824a,Fall,15,0,Spring,68,Winter,21.88938461,69.5,150.4,,72,85,128,,,,,Winter,42,1,25.1,1,36.1,2,10,0,8.5,1,8.5,1,11.0,1,Winter,4,6.932,21.8918,1651.9,3468.98,35.8675,118.212,17.2065,4.68524,32.1883,2,51.7217,30.6225,111.28,59.6581,87.5892,Winter,2.79,,,Winter,4,4,3,1,4,3,0,3,3,3,4,1,1,5,2,2,3,2,2,1,51,Winter,50,69,Fall,2,2 +72a0c969,Spring,9,0,Summer,75,Summer,15.40599769,51.0,57.0,,74,119,112,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.7,Spring,4,1,1,0,1,1,0,1,1,1,4,0,1,1,1,1,1,1,1,1,23,Spring,29,42,Spring,0,0 +72aa1a30,Fall,12,0,Spring,80,Winter,19.51848989,58.0,93.4,,63,84,104,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.21,Winter,4,4,2,4,4,2,1,2,1,3,0,0,4,0,4,3,4,4,4,4,54,Winter,66,90,Fall,2,2 +72afd4ca,Winter,12,1,Summer,61,Winter,23.54649716,52.75,93.2,26.0,65,94,121,,,,,Winter,0,0,19.3,2,19.3,2,7,1,6.75,0,10.25,1,12.0,1,Spring,3,3.62909,21.0828,1154.87,1847.79,21.7767,65.2719,14.7652,6.31763,27.9281,2,28.0643,15.4309,61.6428,27.4051,49.841,,,Spring,2.47,Spring,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,7,Spring,51,71,Winter,1,0 +72bbfadf,Winter,13,0,Summer,41,Fall,31.59949495,66.0,195.8,,77,86,139,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,5,0,5,0,5,0,0,1,2,4,3,0,1,0,3,4,4,4,0,4,45,Fall,38,54,Winter,0,1 +72c9c38d,Spring,11,0,Winter,45,Winter,14.73636119,61.0,78.0,,70,73,132,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.33899,14.738,1164.08,1978.94,15.4974,66.253,12.5184,2.21958,11.747,3,30.1657,20.5899,61.914,27.8278,45.6631,,,Summer,2.39,Summer,4,4,2,1,5,2,2,2,0,0,2,0,0,3,2,2,1,2,1,1,36,Summer,33,47,Spring,0,1 +72cb1703,Spring,8,0,Summer,80,Spring,19.61724282,49.0,67.0,,64,86,85,Spring,8,13,15,Spring,6,1,,,,,8,1,8.5,1,9.0,1,8.0,1,Spring,3,3.13884,19.6194,1030.07,1751.12,12.7651,51.9788,15.2208,4.39862,15.0212,1,28.6506,10.5631,48.8399,24.3823,41.4156,,,,,Spring,4,4,3,1,3,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,28,Spring,29,42,Spring,0,0 +72cc9a64,Spring,10,0,Spring,68,Spring,24.4921522,58.0,117.2,32.0,67,87,102,,,,,Spring,20,1,,,,,0,0,10.0,1,8.0,1,11.0,1,Spring,3,4.24262,24.4948,1262.58,2146.39,20.3111,76.7448,16.0397,8.45514,40.4552,2,36.1316,20.3021,72.5022,35.1076,56.4427,,,Summer,1.801,Summer,2,2,1,0,1,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,10,Spring,29,42,Spring,2,0 +72e95891,Summer,7,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +72ea5c15,Fall,12,1,,,Fall,18.22282996,58.0,87.2,27.0,61,78,123,,,,,Fall,7,0,16.7,2,21.1,2,1,0,14.5,1,14.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,1.74,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +72f9fd87,Winter,14,0,,,Winter,20.40832838,61.5,109.8,31.0,68,83,124,,,,,Winter,6,0,20.2,2,20.8,2,3,0,4.5,0,4.5,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.33,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +72fa40ba,Winter,14,0,,,Winter,18.20821366,63.0,102.8,25.0,63,76,116,,,,,Winter,6,0,22.6,2,25.7,2,1,0,6.0,0,6.0,0,5.0,0,Winter,2,5.05203,18.2102,1302.6,2084.15,21.9203,81.0066,14.3497,3.86052,21.7934,1,37.2523,21.834,75.9546,37.7951,59.1726,,,Winter,3.74,Winter,2,2,4,0,,2,0,0,4,4,4,0,0,4,4,4,4,4,4,4,50,Winter,33,47,Winter,2,2 +7305af8b,Winter,9,0,Fall,65,Summer,15.25282956,53.8,62.8,,58,64,102,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.93,Spring,1,1,1,0,1,1,0,0,0,1,0,0,1,1,1,0,0,1,0,0,10,Spring,31,45,Winter,0,0 +73078ded,Spring,7,0,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7308d22c,Spring,10,0,Spring,75,Summer,16.55688509,58.5,80.6,,73,55,124,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.32,Spring,5,4,4,1,5,2,0,1,0,2,0,0,3,1,3,3,2,4,1,1,42,Spring,35,50,Spring,0,1 +731bf105,Summer,10,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +732b2327,Summer,9,1,Winter,61,Fall,18.36959772,53.0,73.4,,66,81,112,Fall,5,7,38,Fall,6,1,,,,,0,0,10.0,1,11.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,Summer,1.82,Fall,2,0,3,3,3,0,0,1,1,1,3,0,1,0,2,2,2,2,2,2,30,Fall,43,60,Summer,2,0 +7347b54e,Fall,6,0,Winter,60,Fall,15.81417769,46.0,47.6,,71,95,133,,,,,Fall,8,1,,,,,0,0,10.5,1,11.0,1,3.0,0,Fall,2,2.56781,15.8159,917.545,1468.07,7.31052,39.9935,13.2885,2.52737,7.60646,2,22.485,10.198,37.4257,15.8827,29.7955,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,32,46,Fall,0,0 +734e329e,Summer,8,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +736d79d4,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +73799fe3,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +738e7248,Winter,11,0,Fall,70,Summer,20.06563629,63.75,116.0,,110,95,152,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.96,Summer,3,3,4,0,5,0,5,0,1,1,0,0,3,0,0,2,1,2,1,2,33,Summer,52,72,Winter,0,1 +739be0bf,Spring,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.97,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +73aa6aab,Winter,13,0,Fall,72,Winter,18.98084593,58.5,92.4,28.0,68,115,118,,,,,Winter,12,0,26.4,2,18.8,1,7,0,9.0,1,10.0,1,8.0,0,Winter,3,4.61256,18.9829,1262.4,2146.07,22.5456,76.7247,15.7625,3.22038,15.6753,2,36.2726,17.9065,72.1121,32.7345,58.8182,,,Winter,2.32,Winter,4,4,3,2,2,3,0,2,1,1,3,1,4,2,2,1,2,1,1,2,41,Winter,43,60,Winter,1,1 +73c84611,Fall,15,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +73d0e0a0,Fall,10,0,Winter,50,Fall,15.06607334,53.0,60.2,,70,93,115,Fall,5,7,55,Fall,18,1,13.0,2,14.2,2,10,1,7.0,0,6.5,0,9.0,1,Fall,4,7.44093,15.0677,1023.27,2148.87,10.2277,51.2547,12.8288,2.23896,8.94532,1,25.4657,15.5613,43.8137,21.402,35.6934,,,Fall,4.0,Fall,3,1,2,0,2,1,0,0,0,0,3,0,0,0,0,1,2,1,1,0,17,Fall,34,49,Fall,2,0 +73ec551e,Fall,7,1,Winter,90,Winter,16.08420024,48.13,53.0,,61,82,114,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Fall,29,42,Fall,0,0 +73ffa609,Spring,15,0,Fall,55,Summer,16.81377898,69.1,114.2,,71,67,121,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.2,,,Spring,2,2,2,2,3,1,1,1,1,2,3,1,1,0,1,1,2,1,2,1,30,Spring,44,62,Spring,2,0 +74328269,Spring,11,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.73,,,,,,,,,,,,,,,,,,,,,,,,,,,, +744128bb,Winter,16,0,,,Fall,,,,,,,,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.88,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +745b934e,Winter,15,1,,,Winter,17.92247659,62.0,98.0,29.0,60,71,115,,,,,Spring,27,1,,,,,0,0,7.0,0,7.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,Winter,1.92,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +745e6065,Winter,8,0,Fall,60,Summer,16.56268,50.0,58.9,,56,104,120,Summer,,,,Summer,1,0,,,,,0,0,5.0,0,5.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,Summer,2.65,Summer,4,4,3,1,3,0,0,2,2,0,0,0,3,2,0,4,2,4,0,0,34,Summer,43,60,Winter,2,1 +748261c6,Spring,10,0,,,Spring,20.84789541,56.0,93.0,,60,68,118,,,,,Summer,6,0,,,,,5,0,8.0,1,7.5,0,11.0,1,Summer,3,3.80839,20.8502,1155.41,1964.19,15.6091,65.3291,14.6465,6.20368,27.6709,2,31.722,17.998,61.5207,30.4479,47.3311,,,Summer,2.53,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +7494989e,Summer,6,1,,,Summer,16.57113875,45.5,48.8,24.0,64,90,112,,,,,Summer,1,0,,,,,0,0,,,10.0,1,10.0,1,Winter,2,2.23895,16.5729,927.781,1391.67,11.038,41.0838,13.9525,2.62048,7.71615,1,21.3186,8.72725,38.8449,17.9898,32.3566,,,,,Fall,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,Fall,35,50,Summer,0,0 +7498fbde,Summer,17,1,Spring,80,Summer,19.42196044,63.5,111.4,27.0,88,73,128,,,,,Summer,13,0,23.5,2,23.1,2,5,0,3.0,0,0.0,0,6.5,0,,,,,,,,,,,,,,,,,,Summer,1.12,,,Summer,4,4,2,1,2,1,1,1,1,2,5,1,1,1,1,1,3,0,1,1,34,,,,Summer,1,1 +74b2fda1,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3,2.78893,16.6141,949.728,1614.54,9.11017,43.4215,13.8201,2.79402,8.77854,3,24.9977,9.31354,40.6325,19.8835,34.1079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +74be50af,Summer,8,1,,,Summer,19.18026304,52.5,75.2,26.0,51,73,109,,,,,Summer,0,0,,,,,0,0,10.0,1,13.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,Summer,1.34,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,41,58,Summer,0,0 +74bfbe1a,Summer,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,36,51,Summer,3, +74c2bd81,Spring,11,0,Fall,68,Summer,28.15231255,59.0,139.4,37.0,61,68,141,,,,,Summer,9,0,46.8,2,17.1,2,2,0,9.0,1,6.5,0,9.0,1,Summer,3,4.68106,28.1554,1383.48,2351.92,26.2716,89.6221,18.1015,10.0539,49.7779,3,41.0928,22.2577,84.941,39.2231,67.3644,,,Summer,3.74,Spring,5,5,5,0,5,3,0,0,0,4,5,0,4,0,0,0,0,0,0,0,36,Spring,58,80,Spring,0,1 +74f0d37f,Winter,17,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7514c6f4,Spring,13,0,Summer,72,Spring,21.44108205,58.0,102.6,30.0,69,90,113,,,,,Summer,16,0,16.2,1,19.2,1,10,1,5.0,0,7.0,0,7.0,0,Summer,2,4.2044,21.4434,1220.92,1953.47,18.6899,72.3069,15.1122,6.33127,30.2931,2,34.4083,19.2087,68.1025,33.2287,53.0981,,,Spring,2.329,Spring,4,5,5,3,5,2,0,4,3,3,4,0,4,0,5,3,3,5,3,4,65,Spring,49,68,Spring,3,2 +75311a3f,Winter,17,1,Summer,83,Spring,21.31860147,67.8,139.4,,51,57,149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.33,,,Spring,0,0,1,1,0,,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,Winter,36,51,Winter,2,0 +7533a4a9,Spring,9,0,Summer,75,Spring,17.69704861,48.0,58.0,,61,81,111,Spring,6,10,31,Spring,2,0,,,,,26,1,12.5,1,11.5,1,13.0,1,Spring,4,3.9967,17.699,983.477,2065.3,10.6191,47.0162,14.3472,3.35175,10.9838,1,26.6219,9.77526,43.0195,22.1136,37.241,,,Spring,3.27,Spring,4,3,2,0,3,0,0,0,1,2,2,0,0,0,2,2,1,1,1,2,26,Spring,46,64,Spring,2,0 +755d7287,Summer,16,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.48,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +7563681a,Summer,9,0,Winter,71,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +75716348,Fall,8,1,,,Fall,14.37366909,48.25,47.6,,69,88,102,Fall,6,9,51,Fall,4,0,,,,,5,1,8.0,0,6.0,0,7.5,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +75927c18,Spring,14,1,Winter,60,Fall,27.40344353,66.0,169.8,,74,72,123,,,,,Fall,13,0,23.6,2,32.0,3,11,1,11.0,1,12.5,1,8.5,0,,,,,,,,,,,,,,,,,,Spring,2.3,,,Fall,3,4,3,2,2,2,2,2,4,2,1,1,3,3,3,2,2,3,2,0,46,Fall,51,71,Spring,2,1 +759328e6,Fall,12,0,Summer,70,Winter,24.93887629,63.0,140.8,,53,90,108,,,,,Winter,17,0,26.1,2,26.0,2,3,0,4.5,0,6.25,0,5.0,0,Winter,3,5.19265,24.9416,1500.17,2550.28,31.0624,102.05,18.0774,6.86418,38.7496,2,45.7701,25.218,96.8578,47.072,76.8324,,,Fall,3.57,Winter,5,5,3,4,4,2,1,2,2,2,4,0,4,2,3,2,3,3,1,1,53,Winter,55,76,Fall,1,2 +75ad73ee,Spring,9,1,Spring,75,Spring,14.6978979,55.5,64.4,,114,85,144,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.18365,14.6995,1063.78,1702.04,13.5415,55.5692,12.6838,2.01566,8.8308,2,24.7619,17.2658,52.3855,31.6401,38.3034,,,Summer,4.1,Summer,1,1,1,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,9,Summer,32,46,Spring,0,0 +75b0446f,Summer,12,0,Spring,59,Fall,22.06675625,61.0,116.8,,68,87,107,,,,,Fall,15,0,20.3,2,21.3,2,3,0,8.0,1,7.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.849,Fall,2,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,8,Fall,35,50,Summer,0,0 +75b53bd4,Winter,8,1,Spring,72,Winter,18.82318367,52.5,73.8,,92,72,109,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,0,2,0,4,1,0,2,2,1,2,0,3,2,1,4,2,4,0,2,36,Winter,44,62,,,1 +75b82f7d,Spring,8,0,,,Spring,15.05529268,51.5,56.8,24.0,53,88,120,,,,,Spring,0,0,,,,,0,0,4.0,0,4.0,0,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +75bcc1e0,Summer,7,0,Fall,41,Summer,17.02578125,48.0,55.8,,64,66,103,Summer,4,6,49,Summer,11,1,,,,,3,1,7.0,0,9.0,1,11.0,1,Fall,4,9.50115,17.0276,957.446,2010.64,7.29363,44.2436,13.5011,3.5265,11.5564,1,23.3132,13.6368,34.7424,19.2395,30.6068,,,,,Summer,4,2,4,1,5,1,0,1,2,4,2,1,1,1,1,3,2,4,2,1,42,Summer,42,59,Summer,1,1 +75c13e08,Spring,11,1,Fall,78,Summer,13.17766109,51.75,50.2,,62,79,102,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.27,Summer,4,0,4,0,0,4,4,1,5,5,4,0,0,0,0,0,0,1,0,0,32,Summer,43,60,Spring,0,1 +75db1257,Spring,10,0,Fall,75,Spring,17.4705113,58.0,83.6,,63,80,107,Spring,6,9,50,Spring,20,1,,,,,2,0,0.0,0,0.0,0,11.0,1,Spring,1,3.93988,17.4724,1143.15,1486.1,14.702,64.0236,13.381,4.09146,19.5764,2,29.9544,19.3672,60.0838,27.5124,44.6564,,,Spring,1.67,Spring,2,4,3,2,2,1,0,1,1,1,4,1,1,1,2,1,3,0,2,2,34,Spring,32,46,Spring,3,1 +75f26948,Summer,21,1,Fall,40,Summer,35.89205556,60.0,183.8,,113,69,180,,,,,,,,,,,,,,,,,,,,Summer,3,5.16064,35.896,1587.24,2539.58,41.439,111.325,21.7416,14.1543,72.4752,2,43.8449,26.0409,106.164,55.9322,85.2839,,,,,Fall,5,4,5,5,4,5,3,5,5,5,5,1,4,4,5,5,5,5,4,4,88,Summer,43,60,Summer,2,3 +75fb305b,Summer,7,0,Fall,60,Fall,,,,,,,,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7621c406,Spring,11,1,,,Spring,17.26159122,54.0,71.6,25.0,66,74,115,,,,,Spring,30,1,14.8,2,15.2,2,15,1,8.0,1,10.0,1,13.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.09,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +7638f22b,Winter,14,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.22,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +764d8b42,Spring,8,1,Fall,60,Spring,14.71960494,45.0,42.4,20.0,35,92,126,,,,,Spring,10,1,,,,,8,1,11.0,1,11.0,1,9.0,1,Summer,1,2.152,14.7212,911.035,1184.35,10.1261,39.3002,13.645,1.07624,3.09979,1,20.2647,8.90939,37.1482,15.7205,30.3908,,,Summer,3.631,Summer,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Summer,39,55,Spring,0,0 +76781cd8,Winter,8,0,Summer,70,Spring,19.51426374,51.0,72.2,,77,78,109,Spring,6,10,39,Spring,0,0,,,,,0,0,6.0,0,8.0,1,8.0,1,Spring,1,3.23261,19.5164,1047.7,1362.0,12.7184,53.8563,14.5579,4.95848,18.3437,2,27.9928,13.1451,50.6237,23.056,40.7112,,,,,Spring,4,3,3,1,1,1,0,1,2,1,1,0,1,1,1,1,3,2,3,1,31,Spring,35,50,Winter,2,1 +7698543b,Fall,13,0,Spring,60,Winter,20.21811523,64.0,117.8,,76,51,131,,,,,Fall,20,0,8.9,1,11.5,1,0,0,6.5,0,7.5,1,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.02,Fall,2,2,2,1,2,0,0,2,2,2,2,0,1,0,0,0,3,0,1,1,23,Fall,29,42,Fall,2,0 +76c9bf0e,Winter,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.03,Winter,4,4,3,2,3,1,1,1,2,1,3,1,2,1,2,2,1,2,1,1,38,Winter,41,58,Winter,1,1 +76dc0e9f,Winter,5,0,Summer,60,Spring,16.16365173,45.5,47.6,,60,94,119,Spring,,,,Spring,0,0,,,,,0,0,8.0,1,11.0,1,7.0,1,Spring,2,2.5701,16.1654,914.71,1463.54,7.44019,39.6916,13.4796,2.68577,7.90841,3,23.1676,9.08379,37.1215,17.2874,30.6078,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,2,0 +76dfa86c,Spring,9,1,,,Spring,17.02255324,51.25,63.6,24.0,61,67,104,,,,,Spring,4,0,,,,,8,1,8.5,0,7.5,0,5.0,0,Spring,,,,,,,,,,,,,,,,,,,Summer,3.57,Summer,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,Summer,30,43,Spring,0,0 +76e80aa3,Winter,9,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.4,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +76f8f621,Summer,9,0,,,Summer,13.78998628,54.0,57.2,22.0,58,71,102,,,,,Summer,0,0,,,,,0,0,10.0,1,10.5,1,7.0,1,Fall,1,3.3745,13.7915,1027.34,1335.54,10.6277,51.6883,12.4626,1.32892,5.51168,1,25.3229,15.7377,48.3138,20.6474,35.9506,,,Fall,3.52,Fall,3,3,4,0,5,0,0,0,0,0,0,0,4,0,0,4,4,4,0,0,31,Fall,36,51,Summer,2,1 +76fb4afb,Spring,6,0,Summer,75,Spring,15.60596601,46.5,48.0,,63,78,113,Spring,7,11,38,Spring,0,0,,,,,0,0,8.5,1,9.0,1,11.0,1,Spring,2,2.5768,15.6077,922.831,1476.53,6.97831,40.5567,13.1874,2.42027,7.44333,1,22.3606,11.2177,37.9799,16.3879,29.3389,,,,,Summer,5,5,5,0,5,0,0,0,2,2,0,0,1,0,3,2,4,2,2,1,39,Spring,36,51,Spring,0,1 +76ff59f2,Winter,6,0,Summer,61,Winter,,,0.0,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,0,4,0,3,0,0,2,0,2,0,0,2,1,2,2,2,2,1,0,27,Winter,59,81,Winter,0,0 +7715be8b,Winter,15,1,,,Winter,26.71681889,67.0,170.6,34.0,77,76,130,,,,,Winter,12,0,24.3,2,20.7,2,3,0,15.0,1,14.5,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,2.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +7752d450,Fall,16,1,,,Fall,22.03144712,62.75,123.4,28.0,70,88,106,,,,,Fall,3,0,30.6,3,31.9,3,10,1,13.0,1,14.0,1,13.0,1,Fall,4,5.24386,22.0339,1420.93,2699.77,32.7703,93.6109,16.7148,5.31902,29.7891,2,38.3992,22.4414,88.3671,46.7918,71.1695,Fall,2.06,,,Fall,4,4,4,3,4,4,4,2,4,4,4,1,4,4,4,4,4,4,4,2,72,Fall,46,64,Fall,2,2 +776d33af,Summer,14,0,Winter,45,Fall,22.07292067,61.2,117.6,,67,84,107,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.17,Fall,2,2,4,3,2,1,3,2,2,2,3,1,1,1,3,2,2,2,4,1,43,Fall,42,59,Summer,3,1 +777a3e9d,Summer,16,1,,,Summer,16.37358398,64.0,95.4,24.0,80,86,120,,,,,Summer,31,1,21.9,2,26.1,2,10,1,11.0,1,12.0,1,12.0,1,Fall,1,4.49835,16.3754,1229.48,1598.32,22.3334,73.2183,12.5679,3.80748,22.1817,1,28.404,22.481,68.72,30.6646,50.7374,Fall,1.79,,,Fall,3,4,4,2,3,1,2,2,2,2,4,1,4,3,3,3,2,3,1,2,51,Fall,52,72,Summer,2,2 +77941a98,Fall,8,1,Spring,70,Winter,18.30482604,49.5,63.8,,62,86,107,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,2,0,0,0,0,0,0,3,3,0,0,2,0,2,2,0,2,0,0,19,Winter,55,76,Fall,0,0 +77a5b2ad,Spring,8,1,Summer,80,Spring,19.12039872,53.0,76.4,,117,114,114,Spring,2,1,59,Spring,3,0,,,,,6,1,12.0,1,12.0,1,5.0,0,Spring,3,6.86815,19.1225,1068.41,1709.46,17.2463,56.063,14.0322,5.09024,20.337,2,25.0163,13.8004,49.1948,23.4281,42.2626,,,Spring,1.55,Spring,3,3,2,0,2,0,0,0,1,0,0,0,2,0,2,1,1,1,1,1,20,Spring,43,60,Spring,0,0 +77aca602,Winter,6,0,Fall,60,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +77bfe07c,Summer,13,1,,,Summer,20.79881657,65.0,125.0,29.0,75,77,123,,,,,Summer,12,0,44.4,2,54.8,3,0,0,10.0,1,10.5,1,12.0,1,Fall,2,5.20641,20.8011,1368.62,2052.94,29.9241,88.0394,14.6505,6.15056,36.9606,2,34.8336,23.2817,82.833,40.9597,64.7577,,,Summer,1.11,Summer,0,1,2,0,1,0,1,1,1,0,2,0,1,0,0,0,0,0,0,0,10,Summer,40,56,Summer,2,0 +77d03dc0,Fall,7,1,,,Fall,15.73033449,51.0,58.2,22.0,97,52,128,,,,,Fall,0,0,,,,,0,0,8.0,0,8.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +77e460ce,Spring,9,0,Summer,70,Spring,15.00228108,51.5,56.6,,69,90,105,Spring,6,9,17,Spring,0,0,,,,,1,0,9.5,1,8.5,1,6.0,1,Spring,4,9.25205,15.0039,995.83,2091.24,8.5586,48.332,12.8122,2.19175,8.26803,1,23.9492,15.8241,39.0799,19.8266,32.5078,,,Spring,3.77,Spring,2,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Spring,30,43,Spring,1,0 +77ee5985,Fall,9,0,,,Fall,14.84736,50.0,52.8,22.0,68,81,108,,,,,Fall,11,1,,,,,11,1,9.5,1,8.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.13,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +77f0adfa,Fall,7,1,,,Fall,16.86551326,51.0,62.4,26.0,69,82,118,,,,,Fall,7,1,,,,,0,0,10.5,1,10.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +7800a084,Winter,13,1,Summer,65,Spring,26.69887627,64.5,158.0,,57,86,114,,,,,Spring,19,1,30.7,3,26.1,3,10,1,15.6,1,15.0,1,7.5,0,Spring,2,5.3808,26.7018,1464.97,2197.45,35.5814,98.3014,16.6128,10.089,59.6986,2,37.6114,25.1086,92.9206,43.3942,73.1928,,,Winter,2.349,Spring,1,2,3,1,4,1,0,4,1,1,3,1,4,2,4,5,4,5,4,4,54,Spring,36,51,Winter,2,2 +781598dd,Fall,20,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +781710fe,Winter,7,1,Fall,65,Spring,15.78002245,49.5,55.0,,,,,Spring,,,,Spring,3,1,,,,,0,0,7.0,0,8.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Spring,28,41,Winter,0,0 +782a0589,Summer,10,1,Fall,80,Fall,15.47889908,54.5,65.4,,64,78,109,Fall,6,9,54,Fall,19,1,,,,,6,1,9.0,1,12.0,1,5.5,1,,,,,,,,,,,,,,,,,,,,Fall,3.55,Fall,2,2,1,0,2,1,1,1,1,2,2,0,2,1,1,1,1,1,1,1,24,Fall,62,85,Summer,0,0 +782af70b,Winter,14,1,,,Winter,23.48825191,64.5,139.0,32.0,62,82,129,,,,,Winter,40,1,30.9,3,28.9,3,5,0,11.0,1,11.5,1,9.0,1,Winter,4,5.09526,23.4908,1378.4,2618.97,30.2858,89.0811,15.0546,8.43623,49.9189,2,35.0996,23.6958,83.9858,42.7928,65.3853,Winter,3.499,,,Winter,3,3,4,1,3,2,2,1,2,3,2,0,2,1,2,2,1,2,1,2,39,Winter,43,60,Winter,0,1 +784875f2,Winter,10,1,Spring,75,Winter,17.49688889,52.5,68.6,,57,70,102,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +784bba69,Winter,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7858fd16,Winter,6,0,Summer,62,Spring,16.9820908,49.0,58.0,,57,92,114,Spring,2,1,50,Spring,10,1,,,,,4,1,9.0,1,9.0,1,9.0,1,Summer,1,2.90991,16.9839,978.907,1272.58,9.39432,46.5295,13.6251,3.35888,11.4705,2,25.1567,11.9784,43.6196,20.5728,34.551,,,,,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,42,59,Winter,1,0 +785d0663,Spring,16,1,Spring,85,Winter,19.82266499,64.25,116.4,,76,82,135,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.5,,,Winter,1,1,2,1,1,1,2,1,1,0,2,0,1,1,1,1,1,1,1,1,21,Winter,42,59,Spring,1,0 +7860b64c,Summer,10,0,Winter,85,Fall,14.51531532,55.5,63.6,,72,68,115,Fall,6,10,52,Fall,11,1,,,,,6,1,4.5,0,7.5,1,7.0,1,Fall,4,3.58158,14.5169,1076.96,2261.61,13.722,56.9731,13.0043,1.51261,6.62689,1,28.0578,15.1934,53.3915,23.2982,41.7798,,,Summer,3.221,Fall,4,3,5,1,3,0,0,0,0,0,3,0,2,2,0,2,3,2,2,3,35,Fall,34,49,Summer,1,1 +78667893,Winter,8,0,Spring,85,Spring,17.31140084,50.5,62.8,,60,82,108,Spring,,,,Spring,7,1,,,,,15,1,15.0,1,17.0,1,12.0,1,Spring,3,2.02537,17.3133,1054.54,1792.72,14.3197,54.5853,15.0486,2.2647,8.21468,2,29.5677,10.6979,52.5599,24.9641,43.8874,,,,,Spring,4,4,4,2,4,2,0,3,2,2,0,0,4,3,3,4,4,4,2,1,52,Spring,28,41,Winter,0,2 +78704d22,Spring,11,1,Summer,50,Winter,15.48370329,64.63,92.0,,57,100,93,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.8904,15.4854,1269.09,2030.54,25.0638,77.4374,13.0342,2.45116,14.5626,2,30.9112,21.4624,72.547,33.2682,55.975,,,Spring,2.43,Spring,5,2,3,1,2,1,2,3,4,4,2,0,1,0,1,1,0,1,1,0,34,Spring,56,77,Spring,1,1 +787654dd,Winter,7,0,Spring,41,Winter,17.48410811,46.25,53.2,,87,92,116,Winter,7,11,32,Winter,12,1,,,,,10,1,10.0,1,9.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,3,3,4,0,2,0,0,0,0,2,0,0,2,0,2,2,0,4,0,0,24,Winter,33,47,Winter,0,0 +788268a4,Fall,6,0,,,Fall,14.89069257,46.5,45.8,22.0,65,77,117,,,,,Fall,6,1,,,,,3,1,9.5,1,9.0,1,8.0,1,Fall,2,2.2703,14.8923,951.057,1521.69,2.96866,43.563,14.1649,0.727376,2.23698,1,19.9697,20.6247,41.2927,20.9085,22.9383,,,,,Fall,2,0,0,0,0,0,0,1,0,0,1,0,1,0,0,2,1,1,0,0,9,Fall,43,60,Fall,0,0 +7883604a,Summer,17,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +789f8133,Spring,8,0,,,Spring,19.08417381,51.5,72.0,27.0,64,87,113,,,,,Spring,0,0,,,,,1,0,9.0,1,10.0,1,10.0,1,Fall,3,3.20804,19.0863,1044.24,1775.21,12.127,53.4887,14.1791,4.90711,18.5113,1,27.1477,14.214,50.2806,21.8347,39.2747,,,Spring,3.61,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +78a34e5d,Spring,8,1,,,Spring,18.61913832,52.5,73.0,26.0,67,73,109,,,,,Spring,0,0,,,,,0,0,7.0,0,7.5,0,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,Summer,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,Summer,44,62,Summer,0,0 +78b32cc8,Spring,11,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +78b488ff,Fall,9,0,Spring,80,Winter,16.872,55.0,72.6,,51,70,79,Winter,6,10,1,Winter,11,1,,,,,4,0,14.0,1,9.0,1,6.0,1,Winter,2,6.8503,16.8738,1083.76,1734.01,13.1318,57.6972,13.4101,3.46373,14.9028,2,28.0797,16.4857,50.8469,23.9921,41.2115,,,Winter,2.99,Winter,3,2,2,0,2,1,0,2,2,2,2,0,1,1,1,2,2,2,2,1,30,Winter,33,47,Fall,0,0 +78d8ad58,Fall,15,1,,,Fall,23.00540541,64.75,137.2,30.0,67,63,105,,,,,Fall,15,0,22.2,2,26.2,2,4,0,9.5,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,Fall,1.74,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +7914b38b,Summer,12,0,Fall,51,Summer,26.22316582,61.0,138.8,,59,69,118,,,,,Summer,28,1,20.1,2,22.9,3,8,1,6.0,0,6.0,0,11.0,1,Summer,3,5.31559,26.226,1481.77,2519.01,32.095,100.091,18.912,7.31399,38.709,3,44.0528,23.9432,94.7755,39.6879,76.1479,,,Fall,3.71,Fall,3,3,4,4,5,4,2,4,3,3,4,3,3,2,3,4,3,4,3,4,68,Fall,51,71,Summer,3,2 +792d2fa5,Spring,13,1,Summer,62,Spring,23.62840791,62.0,129.2,29.0,72,81,117,,,,,Spring,9,0,18.9,2,25.5,3,0,0,14.5,1,16.5,1,9.5,1,Spring,2,4.50267,23.631,1294.81,1942.21,27.4198,80.1772,14.6646,8.96638,49.0228,2,31.0871,21.6704,75.6745,32.8373,58.5068,,,Spring,1.69,Spring,4,4,5,5,5,1,5,5,5,5,0,3,5,5,4,5,5,5,1,5,82,Spring,38,54,Spring,0,3 +793470b2,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +793c2e31,Winter,6,0,,,Winter,14.92790123,45.0,43.0,21.0,59,85,105,,,,,Winter,5,1,,,,,3,1,13.0,1,13.5,1,2.5,0,Winter,2,2.45712,14.9295,894.991,1431.99,5.92198,37.5913,13.0516,1.87791,5.40875,1,22.0255,9.64379,35.1341,16.9651,27.9475,,,,,Winter,3,3,3,1,2,0,0,0,0,0,1,0,1,0,1,1,1,0,1,0,18,Winter,35,50,Winter,2,0 +79483148,Spring,11,0,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.73,Spring,0,0,4,1,4,1,0,4,3,1,4,0,3,2,3,1,4,1,4,4,44,Spring,30,43,Spring,2,1 +7958e61b,Spring,11,0,Summer,65,Spring,16.75998811,58.0,80.2,,70,77,123,Spring,4,5,30,Spring,11,0,13.5,2,15.0,2,0,0,3.0,0,4.0,0,3.5,0,Spring,1,4.25289,16.7618,1157.3,1504.49,16.7569,65.5304,13.6959,3.06596,14.6696,1,31.1862,17.5873,61.2775,27.4675,47.9431,,,,,Spring,,5,5,,3,4,0,1,5,0,4,0,0,0,5,3,,3,0,0,38,Spring,41,58,Spring,2,1 +79629cf4,Winter,14,1,Summer,85,Summer,16.85415039,64.0,98.2,,83,64,122,,,,,Summer,10,0,20.1,2,19.4,2,,,11.5,1,13.0,1,12.0,1,Summer,3,4.97381,16.856,1299.85,2079.76,24.51,80.7142,13.8546,3.00143,17.4858,1,35.0932,21.1111,75.7404,49.9021,59.6032,,,Winter,2.74,Summer,3,3,2,4,5,0,0,0,1,2,4,1,1,0,2,1,3,2,4,1,39,Summer,30,43,Winter,0,1 +7975694f,Winter,8,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.6,Fall,4,2,3,0,2,0,0,2,1,2,0,0,1,0,2,3,3,3,1,1,30,Fall,39,55,Winter,0,0 +79a926e2,Winter,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +79b58ae6,Summer,17,0,Fall,80,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.19,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +79b73443,Spring,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +79bd36b6,Fall,14,0,Winter,80,Fall,19.51964459,73.5,150.0,,69,75,122,,,,,Fall,23,0,22.1,1,28.3,1,8,0,7.0,0,6.5,0,14.0,1,Fall,3,-4.86597,19.5218,1717.57,2919.87,39.9093,125.207,16.2951,3.22667,24.7929,2,52.0186,33.2792,130.073,54.2624,91.9279,Fall,2.81,,,Fall,4,2,2,4,4,1,1,1,1,2,1,0,2,0,2,2,2,2,1,1,35,Fall,37,53,Fall,2,1 +79de3beb,Winter,10,1,Summer,60,Spring,16.18479532,57.0,74.8,,60,59,109,Spring,5,8,45,Spring,0,0,12.5,2,13.9,2,10,1,7.0,0,8.0,0,7.0,0,Spring,2,3.59411,16.1866,1115.19,1672.79,18.1867,61.0459,13.2102,2.97637,13.7541,1,26.4992,16.36,57.4517,27.6052,44.6859,,,Winter,2.72,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,58,80,Winter,0,0 +79e496f2,Summer,14,1,Winter,60,Fall,29.48670343,63.25,167.8,,89,98,146,,,,,Fall,12,0,11.5,1,8.1,1,3,0,10.0,1,10.5,1,10.0,1,Fall,4,3.8032,29.4899,1445.92,2747.24,34.9173,96.2723,16.9193,12.5706,71.5277,2,36.774,24.5811,92.4691,42.4899,71.6912,,,Fall,2.58,Fall,5,5,5,3,4,4,0,2,2,2,4,1,4,2,1,2,1,4,1,1,53,Summer,42,59,Fall,2,2 +7a0136e0,Summer,11,0,Winter,60,Fall,30.72424558,62.0,168.0,,61,80,98,Fall,2,3,1,Fall,11,0,19.0,2,13.6,2,0,0,3.0,0,6.0,0,6.5,0,Fall,5,14.0899,30.7276,1465.2,3516.47,25.4515,98.3257,17.984,12.7436,69.6743,3,46.2042,26.67,84.2358,62.1284,71.6557,,,Fall,2.66,Fall,3,3,2,1,2,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,16,Fall,51,71,Summer,2,0 +7a047aa9,Winter,8,1,,,Winter,17.91785119,50.5,65.0,26.0,78,101,121,,,,,Winter,4,0,,,,,0,0,,,,,8.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +7a06845a,Fall,10,1,,,Spring,,,,,,,,,,,,Fall,11,1,,,,,0,0,5.0,0,4.5,0,3.0,0,Fall,2,3.69493,19.3893,1150.97,1726.46,20.8692,64.8568,14.0349,5.35438,24.7432,3,27.463,16.5246,61.1619,27.2161,48.3322,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +7a219e0d,Spring,16,0,,,Spring,19.74475309,72.0,145.6,31.0,72,73,110,,,,,Spring,31,1,32.0,1,30.27,1,10,0,7.0,0,7.5,0,11.0,1,,,,,,,,,,,,,,,,,,Spring,3.88,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +7a5fccc9,Fall,6,0,Winter,40,Fall,15.52934337,43.5,41.8,,86,93,126,Fall,7,12,49,Fall,0,0,,,,,1,0,8.3,1,8.5,1,8.0,1,Fall,3,5.11548,15.531,877.084,1491.04,5.94647,35.6839,13.2586,2.27247,6.11609,2,21.7244,8.01301,30.5684,15.3419,27.6709,,,,,Fall,5,3,3,0,3,0,0,0,3,2,0,0,3,1,1,4,3,4,3,3,41,Fall,54,75,Fall,0,1 +7a6d26a2,Summer,6,1,Spring,70,Winter,16.10114566,45.3,47.0,,64,113,149,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.20941,16.1029,922.243,1475.59,10.5935,40.494,13.8738,2.22906,6.50602,2,21.1822,8.71822,38.2846,18.2212,31.7758,,,,,Summer,4,2,2,0,3,0,0,1,2,1,1,0,3,3,2,2,2,2,0,0,30,Summer,46,64,Summer,3,0 +7a86fcd2,Winter,17,1,,,Spring,24.67395861,61.0,130.6,,68,73,113,,,,,Spring,15,0,27.0,2,27.9,2,10,1,15.0,1,16.0,1,12.0,1,Spring,1,4.16439,24.6767,1257.58,1634.85,23.1848,76.2117,14.4001,10.2766,54.3883,2,31.1746,21.8522,72.0473,43.5817,54.3594,Winter,1.91,,,Spring,5,4,4,1,4,3,3,3,4,4,3,1,2,2,3,1,2,2,1,4,56,Spring,35,50,Winter,1,2 +7a96c165,Spring,9,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +7aa3a145,Spring,5,0,Spring,45,Spring,15.13619753,45.0,43.6,,,91,,Spring,,,,Spring,0,0,,,,,0,0,8.5,1,8.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,,,,Spring,0,0 +7aa5ceb1,Summer,7,0,,,Summer,17.36701183,52.0,66.8,,66,86,121,,,,,Summer,3,0,,,,,4,1,8.0,1,7.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +7aa77b5a,Winter,9,0,,,Winter,15.25974709,52.75,60.4,24.0,60,86,101,,,,,Winter,6,0,,,,,9,1,5.0,0,5.0,0,9.0,1,Winter,,,,,,,,,,,,,,,,,,,Spring,2.75,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +7abb52e2,Winter,8,1,,,Winter,16.81145713,51.0,62.2,23.0,72,92,132,,,,,Winter,15,1,,,,,4,0,16.0,1,14.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +7abd357c,Spring,15,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.63,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +7ad92034,Winter,12,1,Spring,70,Spring,19.97649883,59.5,100.6,,59,75,125,Spring,4,6,19,Spring,5,0,14.8,2,12.2,2,15,1,12.5,1,10.5,1,10.0,1,Spring,2,4.53488,19.9787,1208.22,1812.34,22.9628,70.9548,14.0913,5.88739,29.6452,1,29.4195,18.5725,66.4199,31.6762,52.3823,,,Spring,1.53,Spring,4,2,4,0,4,1,0,1,1,2,4,0,1,1,2,1,4,2,2,2,38,Spring,43,60,Winter,0,1 +7af8eac0,Fall,6,0,,,Fall,15.41248443,42.5,39.6,19.0,64,101,108,,,,,Fall,3,1,,,,,0,0,11.5,1,10.0,1,12.0,1,Fall,2,2.24968,15.4142,860.431,1376.69,5.47938,33.9102,13.1994,2.21473,5.68978,1,20.5858,7.84501,31.6605,12.8567,26.0652,,,,,Fall,1,1,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,7,,,,Winter,0,0 +7b0a4060,Fall,7,0,Winter,79,Fall,16.42208,50.0,58.4,,35,69,93,,,,,Fall,7,1,,,,,6,1,9.0,1,8.0,1,12.0,1,Fall,3,3.10481,16.4239,999.978,1699.96,10.6567,48.7738,13.7167,2.70718,9.6262,2,26.5745,11.5426,45.669,22.6821,37.2312,,,,,Fall,2,1,2,0,3,0,0,1,0,0,0,0,1,1,0,1,1,1,0,1,15,Fall,32,46,Fall,1,0 +7b196b4e,Summer,16,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7b4cd101,Summer,11,0,,,Summer,16.40720661,55.0,70.6,26.0,69,72,108,,,,,Summer,13,0,12.1,1,13.3,1,5,0,12.5,1,11.5,1,6.0,0,,,,,,,,,,,,,,,,,,,,Summer,1.45,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +7b516283,Winter,11,1,Spring,85,Winter,19.19505509,61.0,101.6,25.0,75,84,120,,,,,Winter,19,1,,,,,1,0,12.5,1,10.5,1,16.0,1,Spring,3,4.5691,19.1972,1275.11,2040.18,27.1863,78.0793,14.753,4.4442,23.5207,1,31.7134,19.1796,73.5102,32.089,58.8997,,,Spring,2.87,Spring,2,2,2,0,3,1,2,2,1,2,2,0,0,2,2,1,1,0,0,1,26,Spring,31,45,Winter,1,0 +7b543871,Winter,7,0,Spring,50,Spring,24.4644,50.0,87.0,,74,80,130,Spring,5,8,28,Spring,12,1,,,,,0,0,0.0,0,4.5,0,9.0,1,Spring,1,3.03209,24.4671,1062.92,1381.79,11.9667,55.4778,15.6021,8.86501,31.5222,3,28.7048,14.8063,52.4457,26.4785,40.6715,,,,,Spring,5,5,3,0,4,1,0,1,1,3,0,0,1,1,5,4,4,5,0,3,46,Spring,39,55,Winter,2,1 +7b78e634,Fall,11,0,,,Winter,26.31111111,57.0,121.6,,73,87,122,Winter,4,5,47,Winter,20,1,11.7,1,13.6,2,15,1,7.0,0,6.5,0,8.0,0,Spring,4,4.28582,26.314,1292.23,2713.68,20.5184,79.9023,17.2907,9.02329,41.6977,2,39.8031,19.5808,75.6165,48.3407,60.3215,,,Fall,3.08,Winter,5,5,5,1,5,1,5,5,1,2,5,0,5,5,5,5,2,5,1,2,70,Winter,35,50,Fall,2,2 +7b8771d6,Winter,10,1,,,Winter,16.70409357,57.0,77.2,25.0,61,62,117,,,,,Winter,21,1,34.0,2,44.4,2,8,1,11.5,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.22,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +7b8842c3,Spring,16,0,Winter,61,Fall,15.96986212,61.6,86.2,,94,73,136,,,,,Fall,0,0,19.2,1,23.1,1,14,0,9.5,1,7.5,1,10.0,1,Fall,2,3.38015,22.762,1100.12,1760.19,13.1947,59.4402,15.6958,7.0662,26.7598,1,32.1516,14.0939,56.0601,36.7659,45.3463,Spring,1.7,,,Fall,4,4,4,2,5,4,3,3,4,3,4,1,2,3,4,1,3,2,3,2,61,,,,Spring,2,2 +7b8b330a,Summer,17,1,,,Fall,20.51940687,62.0,112.2,32.0,68,76,105,,,,,Fall,0,0,22.1,2,24.1,2,0,0,7.5,0,8.5,0,12.0,1,Fall,3,4.15266,20.5217,1225.58,1960.92,22.4495,72.8031,13.3159,7.20579,39.3969,1,27.8352,22.5183,68.6504,29.9332,50.2848,Fall,3.17,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +7b9690de,Summer,7,1,,,Summer,13.15136054,42.0,33.0,19.0,71,64,101,,,,,Summer,0,0,,,,,0,0,8.0,0,7.0,0,3.0,0,Summer,1,1.81439,13.1528,868.856,1129.51,7.35644,34.8075,13.8732,-0.72043,-1.80754,1,18.3119,9.13923,32.9931,13.4887,25.6683,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,5,Summer,34,49,Summer,3,0 +7bbde46f,Winter,5,0,Spring,55,Winter,16.4856405,44.0,45.4,,51,74,71,,,,,Winter,0,0,,,,,0,0,12.0,1,10.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,4,0,5,0,0,4,0,4,0,0,5,3,0,5,4,5,2,4,53,Spring,45,63,Winter,0,2 +7bdbf66f,Fall,7,0,Summer,80,Winter,16.87158239,50.25,60.6,,66,91,110,Winter,4,6,24,Winter,8,1,,,,,5,1,11.0,1,11.5,1,10.5,1,,,,,,,,,,,,,,,,,,,,,,Winter,4,5,5,3,5,0,0,3,1,2,3,0,5,0,5,5,5,4,2,2,59,Winter,47,66,Fall,0,2 +7c02f4ec,Summer,15,0,,,Summer,28.03121992,69.5,192.6,37.0,84,89,131,,,,,Summer,45,1,32.8,2,35.5,2,1,0,15.0,1,14.0,1,12.0,1,Fall,3,6.71729,28.0343,1710.56,2907.95,38.8812,124.46,18.116,9.91828,68.1402,3,51.8812,33.6974,117.743,51.745,90.7624,Fall,1.67,,,Summer,4,5,5,1,5,3,1,3,1,1,3,1,2,5,4,1,4,5,4,2,60,Summer,77,100,Summer,3,2 +7c194077,Spring,15,0,Summer,55,Summer,19.65688705,66.0,121.8,,58,68,107,,,,,Summer,85,1,38.6,2,34.7,2,30,1,5.5,0,6.0,0,10.0,1,Summer,4,6.04698,19.659,1482.0,3112.21,28.9307,100.116,16.1591,3.4999,21.684,2,45.8385,25.3468,94.069,53.3901,74.7691,Summer,3.18,,,Summer,5,5,5,2,2,2,2,0,4,4,5,1,2,0,0,0,0,0,4,0,43,Summer,56,77,Spring,2,1 +7c1d7ddd,Spring,10,0,Spring,70,Spring,22.15672515,57.0,102.4,,54,95,86,Spring,4,5,32,Spring,0,0,,,,,0,0,6.0,0,5.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.27,Spring,4,4,2,1,3,0,0,1,1,2,2,0,2,1,2,2,2,2,2,0,33,Spring,31,45,Spring,0,1 +7c1e9b96,Summer,13,1,,,Summer,29.85291493,57.5,140.4,35.0,58,85,117,,,,,Summer,13,0,21.6,2,25.4,3,0,0,6.0,0,5.5,0,7.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +7c21ad67,Summer,8,1,Fall,63,Summer,14.7360305,48.25,48.8,,60,77,103,,,,,Summer,6,1,,,,,1,0,10.5,1,12.0,1,9.0,1,Summer,3,2.45027,14.7376,957.588,1532.14,11.6895,44.2587,13.3662,1.37148,4.54132,1,21.2601,11.309,41.8084,17.7151,32.9497,,,,,Summer,2,2,2,1,2,1,0,1,1,1,1,1,1,1,2,2,2,1,1,1,26,Summer,58,80,Summer,2,0 +7c29e92d,Spring,7,0,Summer,65,Spring,9.693766159,59.0,48.0,,64,86,107,Spring,,,,Spring,3,1,,,,,0,0,10.5,1,10.0,1,3.5,0,,,,,,,,,,,,,,,,,,,,,,Spring,4,5,4,0,4,0,0,1,2,2,3,0,1,0,4,4,4,4,4,3,49,Spring,58,80,Spring,2,1 +7c39c8e5,Spring,7,0,Fall,60,Summer,15.27506173,45.0,44.0,,60,61,110,Summer,6,10,12,Summer,1,0,,,,,0,0,13.0,1,13.0,1,8.0,1,Summer,1,2.49118,15.2767,898.537,1168.1,6.65953,37.9689,13.1828,2.09397,6.03105,1,22.2427,9.06668,35.4778,16.0279,28.9023,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,42,59,Spring,2,0 +7c45c983,Summer,9,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.05,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +7c505fd4,Spring,8,0,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.97,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +7c5efe25,Fall,7,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +7c5f97ba,Fall,14,0,Spring,55,Winter,25.31804235,70.5,179.0,,72,91,128,,,,,Winter,20,0,35.5,2,37.9,2,3,0,10.0,1,9.5,1,12.0,1,Winter,3,7.19887,25.3208,1751.9,2978.23,40.5748,128.863,18.2286,7.09219,50.1367,2,54.3414,33.947,121.664,58.2598,94.9163,,,Fall,2.16,,,,,,,,,,,,,,,,,,,,,,,Winter,46,64,Fall,3, +7c6ca1d4,Spring,10,0,Spring,70,Spring,16.40116959,57.0,75.8,,76,96,142,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,2,4.09798,16.403,1146.95,1835.12,17.328,64.4283,13.9421,2.46081,11.3717,1,30.9187,16.1816,60.3303,25.6558,48.2466,,,Spring,1.04,Spring,4,4,4,0,3,0,0,3,4,3,3,0,2,1,2,1,2,2,3,1,42,Spring,56,77,Spring,2,1 +7c6cbfdd,Spring,7,0,Summer,70,Spring,14.08657845,46.0,42.4,24.0,67,82,110,,,,,Spring,0,0,,,,,0,0,6.0,0,6.5,0,7.0,1,Summer,2,2.42804,14.0881,906.388,1450.22,5.19221,38.8053,12.8937,1.19441,3.59473,1,20.6904,12.9227,36.3772,15.2771,25.8826,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,31,45,Spring,0,0 +7c71edac,Summer,12,0,Fall,70,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.13,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,40,56,Summer,0,0 +7c7f3921,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7c8f0c95,Fall,17,0,Spring,68,Winter,17.81799244,63.5,102.2,,72,77,135,,,,,Winter,25,1,33.5,2,32.3,1,,,10.0,1,9.0,1,7.0,0,Winter,1,4.69298,17.8199,1252.85,1628.7,18.922,75.7075,13.2006,4.61932,26.4925,1,32.6939,24.0916,71.0145,29.0706,51.6159,Fall,1.36,,,Winter,0,4,5,2,4,4,3,5,5,0,5,1,3,5,,3,5,5,5,4,68,Winter,54,75,Fall,1,2 +7c9051b7,Spring,10,1,,,Spring,26.95100055,60.0,138.0,38.0,81,99,114,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.5,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +7c9c4495,Winter,8,0,,,,,,,,,,,,,,,Winter,4,0,,,,,6,1,8.0,1,11.0,1,5.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +7ca548f9,Summer,8,0,,,Summer,17.68398987,53.5,72.0,24.0,70,75,100,,,,,Summer,8,1,,,,,7,1,8.0,1,8.0,1,9.0,1,Summer,4,3.43248,17.6859,1066.86,2240.4,12.2902,55.8974,13.7305,3.95541,16.1026,1,28.222,15.3852,52.4649,25.6552,40.5122,,,Summer,3.76,Summer,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Summer,28,41,Summer,0,0 +7cb1e4c7,Winter,9,1,,,Spring,14.71419652,54.25,61.6,,61,77,106,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.06,Spring,0,0,2,0,0,1,1,1,1,1,2,0,1,1,1,1,2,1,1,1,18,Spring,44,62,,,0 +7ccced20,Spring,9,0,,,Spring,25.54233333,60.0,130.8,36.0,72,114,110,,,,,Spring,0,0,,,,,0,0,4.0,0,4.0,0,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,1.55,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +7ccdb9f2,Winter,14,1,Spring,45,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.48,Spring,2,3,3,0,2,2,1,3,1,1,3,0,0,2,2,0,2,0,0,0,27,Winter,38,54,Winter,,0 +7ce36c38,Fall,16,1,Spring,60,Winter,18.59048643,61.0,98.4,,55,85,104,,,,,Winter,5,,18.7,2,18.4,2,4,,10.5,,11.0,,12.0,,Winter,2,3.72799,18.5925,1223.9,1835.86,23.8,72.6248,13.7223,4.87018,25.7752,2,29.3423,19.4825,68.8968,30.1345,53.1423,Spring,2.95,,,Winter,1,1,2,1,2,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,23,Winter,35,50,Fall,0,0 +7cec907c,Winter,8,0,,,Winter,14.46276042,48.0,47.4,22.0,60,67,94,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +7cecb811,Winter,13,0,Spring,49,Spring,22.03712567,63.5,126.4,,52,75,114,,,,,Spring,18,0,45.6,2,47.8,2,19,1,14.0,1,14.0,1,15.0,1,Spring,2,5.62792,22.0395,1461.84,2338.95,29.8823,97.9684,17.0821,4.95743,28.4316,2,43.8885,24.1975,92.3405,43.8731,73.7708,,,Spring,2.42,,,,,,,,,,,,,,,,,,,,,,,Spring,69,94,Winter,0, +7cf3bca6,Summer,8,0,Winter,70,Fall,16.7892127,52.25,65.2,,70,98,112,Fall,5,7,5,Fall,11,1,,,,,10,1,9.0,1,9.0,1,6.0,1,Fall,3,8.34789,16.791,1027.76,1747.2,10.6148,51.7332,13.3229,3.46811,13.4668,2,25.6547,15.4637,43.3854,20.7932,36.2695,,,Summer,1.62,Fall,4,4,0,0,0,0,0,1,1,2,0,0,0,0,0,4,0,4,0,0,20,Fall,54,75,Summer,0,0 +7d0a92d5,Summer,12,1,Spring,75,Fall,17.76314821,59.88,90.6,,76,80,135,,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.3716,17.7651,1236.56,1978.5,25.0678,73.9732,14.5049,3.26023,16.6268,2,31.1084,17.797,69.6016,32.1747,56.1762,,,Summer,1.71,Summer,3,3,2,0,4,1,1,2,1,2,2,0,3,1,1,1,2,1,0,0,30,Summer,32,46,Summer,2,0 +7d22cd0a,Winter,7,1,,,Summer,15.01601994,53.0,60.0,24.0,58,82,102,,,,,Summer,3,0,,,,,8,1,9.0,1,9.0,1,12.0,1,Summer,2,2.99583,15.0177,1032.51,1548.77,14.8748,52.2392,13.0752,1.94247,7.76076,2,23.2245,14.14,49.2434,20.6258,38.0993,,,,,Summer,2,3,4,0,2,1,1,4,1,2,2,0,4,4,2,2,3,2,0,3,42,Summer,48,67,Winter,0,1 +7d2f236b,Summer,7,1,,,Fall,28.18239645,52.0,108.4,31.0,,,,,,,,Fall,,,,,,,,,,,,,,,Fall,1,3.19328,28.1855,1134.96,1475.45,22.1811,63.1511,16.4202,11.7653,45.2489,1,27.3816,13.5884,59.9579,25.03,49.5627,,,,,Fall,4,1,3,1,1,1,0,1,1,1,1,0,1,1,1,1,2,1,0,0,22,Fall,35,50,Summer,0,0 +7d40a714,Winter,16,0,Summer,71,Spring,37.54960923,69.5,258.0,46.0,77,78,146,,,,,Spring,9,0,31.2,1,30.0,1,4,0,4.0,0,6.0,0,9.0,1,Spring,2,7.2403,37.5537,1987.73,3180.37,48.6986,153.982,22.4132,15.1405,104.018,3,64.2344,41.0495,146.742,72.4645,112.933,Winter,1.25,,,Winter,3,5,5,5,4,2,1,2,2,5,0,0,1,3,3,1,5,2,5,1,55,Winter,53,73,Winter,2,2 +7d666f0f,Summer,16,1,Fall,70,Fall,24.96144786,59.0,123.6,,82,73,131,,,,,Fall,28,1,19.2,2,24.8,2,12,1,12.5,1,13.0,1,11.0,1,Fall,1,4.76377,24.9642,1265.35,1644.96,27.154,77.0397,15.5601,9.40405,46.5603,2,30.8301,19.0556,72.2759,30.9993,57.984,Summer,1.9,,,Fall,4,3,4,2,5,2,1,4,2,2,4,0,5,,2,3,3,5,4,4,59,Fall,33,47,Summer,0,2 +7d751a6a,Spring,18,1,Summer,45,Summer,31.9236715,69.0,216.2,,75,91,114,,,,,Summer,11,0,45.8,2,53.6,2,0,0,0.0,0,1.0,0,9.5,1,Summer,1,4.62015,31.9272,1578.45,2051.98,39.6817,110.388,16.3015,15.6256,105.812,3,39.5466,31.1602,105.768,50.225,79.2283,Summer,1.55,,,Summer,1,1,1,2,1,1,1,1,2,1,0,0,1,1,1,1,2,1,1,1,21,Summer,44,62,Spring,2,0 +7d878d0d,Fall,8,0,,,Fall,16.57350015,52.75,65.6,25.0,68,84,122,,,,,Fall,10,1,,,,,4,0,8.0,1,8.0,1,9.0,1,Fall,3,3.17564,16.5753,1031.87,1754.18,9.99541,52.1708,13.1821,3.3932,13.4292,1,25.5375,16.6379,48.9951,21.9728,35.5329,,,Fall,1.69,Fall,4,4,4,0,2,1,0,2,2,2,0,0,1,0,3,3,4,4,2,1,39,Fall,58,80,Fall,2,1 +7d8f1716,Summer,12,0,Winter,45,Fall,18.16919609,60.5,94.6,,62,80,116,Fall,4,6,9,Fall,0,0,,,,,0,0,0.0,0,0.0,0,5.0,0,Fall,1,5.16141,18.1712,1226.33,1594.23,18.6695,72.8834,13.9998,4.17142,21.7166,1,34.0253,20.1886,67.722,33.1213,52.6948,,,,,Fall,3,3,3,1,2,1,0,4,1,2,3,0,1,1,4,2,2,2,0,1,36,Fall,38,54,Fall,1,1 +7d927bdf,Spring,5,0,Fall,61,Spring,17.42745679,45.0,50.2,,62,80,114,Spring,,,,Spring,0,0,,,,,8,1,10.0,1,10.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,53,73,Spring,2,0 +7dae6de9,Fall,9,0,Winter,68,Fall,22.77487603,55.0,98.0,,63,87,106,Fall,6,9,35,Fall,20,1,,,,,2,0,8.5,1,8.0,1,9.0,1,Winter,3,7.58279,22.7774,1176.97,2000.85,17.7012,67.6256,15.7177,7.05969,30.3744,2,33.0293,16.8951,60.0428,29.7962,50.7305,,,Fall,2.96,Fall,4,1,3,2,3,2,1,1,1,2,1,0,1,0,2,3,1,3,1,3,35,Fall,39,55,Fall,0,1 +7dc65b92,Spring,14,0,Summer,75,Spring,19.8044829,64.5,117.2,,55,82,109,,,,,Spring,36,1,30.9,2,31.1,2,8,0,6.0,0,6.0,0,14.0,1,Spring,3,5.94104,19.8066,1484.93,2524.39,31.0865,100.428,16.9722,2.83444,16.772,2,44.9495,24.392,94.487,45.9879,76.036,,,Spring,3.09,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,30,43,Spring,0,0 +7dd54e69,Fall,16,0,Spring,55,Spring,23.58897074,68.25,156.3,,68,65,119,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.02,,,Spring,5,5,5,1,5,5,1,5,5,4,4,0,5,1,5,5,4,5,0,4,74,Spring,33,47,Fall,0,2 +7deeb4fe,Fall,18,0,Fall,41,Winter,19.36408568,69.8,134.2,,96,70,145,,,,,,,,,,,,,,,,,,,,Winter,2,15.1353,19.3662,1384.92,2215.88,19.4276,89.7756,12.9554,6.41081,44.4244,2,37.7107,32.6373,74.6404,49.0287,57.1383,,,,,Winter,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,4,Winter,44,62,Fall,3,0 +7df487b1,Summer,14,1,,,Summer,20.02243226,69.0,135.6,29.0,71,72,118,,,,,Summer,18,1,23.1,2,23.5,2,0,0,9.0,0,9.5,0,13.0,1,Summer,2,5.88694,20.0246,1440.83,2161.24,33.8268,95.73,14.1369,5.88777,39.87,2,35.2176,26.6856,89.8431,37.8454,69.0444,Summer,1.52,,,Summer,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,21,Summer,27,40,Summer,0,0 +7e0da3dc,Fall,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7e33976b,Summer,7,0,,,Summer,13.22277883,46.0,39.8,20.0,66,72,112,,,,,Summer,0,0,,,,,2,0,5.0,0,5.0,0,12.0,1,Summer,,,,,,,,,,,,,,,,,,,,,Summer,3,0,3,0,3,0,0,0,0,0,0,0,2,0,3,3,1,3,0,2,23,Summer,30,43,Summer,0,0 +7e41db05,Spring,11,0,,,Summer,21.41633028,57.75,101.6,,61,74,110,Summer,1,1,43,Summer,4,0,18.6,2,14.4,2,10,1,7.0,0,8.0,1,11.0,1,Fall,3,3.99623,21.4187,1193.12,2028.31,16.7889,69.3462,14.6191,6.79955,32.2538,2,32.5303,20.0269,65.35,30.9098,49.3193,,,Spring,2.57,Fall,2,4,3,3,5,5,4,2,0,1,4,0,0,1,3,0,3,0,4,0,44,Spring,45,63,Spring,2,1 +7e46b693,Summer,12,1,Fall,80,Summer,20.17747917,61.0,106.8,,69,97,125,,,,,Summer,15,0,17.7,2,16.5,2,0,0,11.0,1,8.5,0,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.84,Summer,1,2,2,1,3,3,1,2,1,1,2,1,1,4,2,1,2,2,3,1,36,Summer,48,67,Summer,2,1 +7e71aa83,Spring,8,0,Fall,60,Spring,15.18313609,52.0,58.4,25.0,66,73,109,,,,,Spring,10,1,,,,,0,0,7.5,1,8.0,1,9.0,1,Spring,2,3.18479,15.1848,1010.22,1616.35,10.3983,49.8643,12.9654,2.21941,8.53574,1,25.1881,14.2778,46.6795,19.892,35.5864,,,,,Spring,2,2,2,0,2,0,0,1,0,0,1,0,2,0,2,2,0,2,0,0,18,Spring,48,67,Spring,2,0 +7e87087b,Winter,14,0,Spring,50,Spring,17.84865917,68.0,117.4,,76,95,117,,,,,Spring,9,0,15.5,1,21.9,1,10,0,7.25,0,5.5,0,5.0,0,Spring,3,6.11904,17.8506,1443.89,2454.61,27.5631,96.0564,14.6053,3.24528,21.3436,1,42.5648,25.9285,89.9374,45.4145,70.1279,Spring,1.55,,,Spring,4,4,3,1,3,1,1,2,1,1,2,0,0,0,0,0,1,1,1,0,26,Spring,41,58,Winter,2,0 +7e9f60f8,Summer,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7ebca7c4,Spring,8,1,Summer,70,Spring,26.12787469,53.0,104.4,,91,94,141,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.22043,26.1307,1126.66,1689.99,20.6398,62.2674,15.5852,10.5456,42.1326,3,27.1161,14.5114,59.047,26.8687,47.7559,,,,,Spring,2,2,2,1,2,3,0,0,0,0,2,0,0,2,2,0,1,2,1,1,23,Spring,53,73,Spring,1,0 +7ec65d94,Summer,9,0,,,Summer,16.11908284,52.0,62.0,,61,53,111,,,,,Summer,5,0,,,,,5,0,12.0,1,12.0,1,12.0,1,Summer,2,3.4828,16.1208,1054.28,1686.84,13.8036,54.5573,14.1856,1.9352,7.4427,1,28.8269,11.9268,51.0745,24.1955,42.6305,,,,,Summer,3,2,4,1,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,14,Summer,31,45,,,0 +7ecea16d,Summer,8,0,Fall,59,Summer,14.27081892,51.0,52.8,24.0,64,97,101,,,,,Summer,10,1,,,,,0,0,6.5,0,7.0,0,8.0,1,Summer,2,2.76167,14.2724,990.844,1585.35,6.53168,47.801,12.9211,1.35129,4.99904,1,21.4963,19.773,45.0393,16.9958,28.028,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,28,41,Summer,0,0 +7efd334b,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7f04b4fc,Fall,11,1,Spring,71,Fall,19.51156708,56.5,88.6,,70,90,112,Fall,5,8,50,Fall,13,1,16.1,2,14.1,2,10,1,14.5,1,15.5,1,16.0,1,Fall,4,6.42979,19.5137,1134.02,2154.63,19.5259,63.0508,13.8866,5.62708,25.5492,2,26.9478,16.5771,56.621,27.9097,46.4737,,,Fall,2.8,Fall,3,3,2,2,2,2,1,2,2,2,2,1,2,2,2,2,2,2,1,2,39,Fall,32,46,Fall,1,1 +7f1063e4,Fall,13,0,Spring,50,Winter,22.23850156,62.0,121.6,,74,82,130,,,,,Winter,15,0,21.7,2,23.6,2,10,0,15.0,1,15.0,1,12.0,1,Winter,4,5.2036,22.2409,1391.89,2922.97,25.7007,90.5174,16.5559,5.68507,31.0826,2,42.2133,22.6034,85.3138,46.1492,67.914,,,Winter,4.33,Winter,0,5,3,2,0,1,0,1,1,0,3,1,5,2,1,2,2,0,2,1,32,Winter,39,55,Fall,2,1 +7f26f78c,Summer,14,0,Winter,55,Fall,18.91729323,66.5,119.0,,63,88,120,,,,,Fall,24,1,35.5,3,30.9,2,9,0,13.0,1,10.0,1,15.0,1,Winter,5,6.08419,18.9194,1471.35,3531.25,29.8458,98.9815,15.7367,3.18266,20.0185,1,43.8061,25.3297,92.8973,44.82,73.6519,Fall,1.94,,,Fall,5,5,4,2,3,5,4,4,1,2,3,0,1,3,1,1,1,1,1,1,48,Fall,42,59,Summer,2,1 +7f2715b4,Winter,10,0,Summer,75,Spring,19.92125311,53.0,79.6,,80,75,124,Spring,6,9,6,Spring,5,0,8.7,1,7.7,1,8,1,4.0,0,4.0,0,8.0,0,Spring,2,3.21976,19.9234,1069.54,1711.27,11.6734,56.1836,14.0624,5.861,23.4164,2,26.7932,17.7169,52.9638,22.0463,38.4666,,,Winter,3.2,Spring,2,2,2,0,2,0,0,0,2,2,2,0,0,0,0,2,2,1,1,0,20,Spring,31,45,Spring,0,0 +7f44236f,Fall,13,0,Winter,70,Winter,25.37001559,67.0,162.0,,66,76,117,,,,,Winter,12,0,32.1,3,26.5,2,13,1,15.0,1,15.0,1,8.0,0,,,,,,,,,,,,,,,,,,,,Winter,2.86,Winter,4,5,5,4,5,5,1,5,5,5,0,3,5,5,5,5,5,5,5,5,87,Winter,47,66,Fall,0,3 +7f45dc2e,Summer,8,1,,,Summer,17.38283377,49.75,61.2,24.0,62,85,110,,,,,Summer,0,0,,,,,0,0,12.0,1,12.0,1,12.0,1,Summer,4,2.82509,17.3847,1013.91,1926.43,15.5812,50.2577,14.2764,3.10829,10.9422,1,24.5423,10.1343,47.4326,21.985,40.1234,,,Fall,1.12,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +7f5aeb05,Winter,7,0,Spring,70,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,0,2,1,0,1,1,2,0,0,1,0,1,1,1,1,1,1,20,Spring,32,46,Winter,2,0 +7f950ef2,Summer,17,1,Winter,70,Fall,21.86278133,63.5,125.4,,78,88,128,,,,,Fall,37,1,24.8,2,19.0,2,,,8.0,0,7.5,0,10.0,1,Fall,3,1.23965,21.8652,1356.71,2170.74,29.4303,86.7708,15.1296,6.73552,38.6292,2,35.0745,22.266,85.5311,42.3681,64.5048,Fall,1.38,,,Fall,5,5,5,0,5,4,5,5,4,4,4,0,4,5,4,4,4,5,5,4,81,Fall,54,75,Summer,2,3 +7f9a801d,Winter,7,0,,,Winter,16.91804913,53.0,67.6,23.0,66,90,118,,,,,Winter,10,1,,,,,10,1,11.0,1,10.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +7fa51811,Spring,18,0,,,Spring,22.85746692,69.0,154.8,,75,65,118,,,,,Spring,25,1,28.4,1,36.3,2,39,1,7.0,0,5.0,0,6.0,0,Spring,3,6.98581,22.86,1689.75,2872.58,36.5149,122.244,18.0523,4.80768,32.556,2,54.5217,31.2074,115.258,68.352,91.0366,Spring,3.97,,,Spring,4,4,4,4,5,5,4,2,5,5,4,2,1,4,3,4,4,4,2,2,72,,,,Spring,2,2 +7fa7ccc4,Winter,9,1,Spring,45,Winter,16.11717202,57.5,75.8,,79,64,109,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,3.88017,16.1189,1153.59,2191.82,21.0442,65.1357,13.8512,2.26778,10.6643,2,28.4889,15.6026,61.2555,28.6359,49.5331,,,Spring,2.73,Spring,5,0,4,0,3,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,15,Spring,31,45,Winter,0,0 +7fceb836,Winter,13,0,Spring,62,Fall,16.10977887,62.25,88.8,,131,99,162,,,,,Spring,,,,,,,,,,,,,,,Summer,2,5.55211,16.1115,1383.23,2213.17,25.1855,89.5953,16.2558,-0.144298,-0.795309,2,43.8898,20.5201,84.0432,54.0058,69.0752,,,Summer,1.32,Winter,0,0,2,0,4,0,0,4,0,0,4,0,1,4,5,0,1,0,0,0,25,Fall,52,72,Winter,0,0 +7fcf0046,Winter,11,1,Spring,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.49,Spring,3,2,3,0,3,0,1,0,0,2,1,0,3,0,1,3,2,3,2,2,31,Spring,35,50,Winter,3,1 +7fd40f2b,Fall,9,0,Fall,70,Fall,15.75510355,52.0,60.6,,60,77,112,Fall,6,10,24,Fall,9,1,,,,,6,1,7.0,0,6.0,0,9.5,1,Fall,3,6.19339,15.7568,1018.18,1730.91,10.5606,50.7127,13.186,2.57083,9.88727,2,26.1401,14.012,44.5193,22.3362,36.7007,,,Fall,1.98,Fall,4,4,2,0,4,1,0,2,2,2,2,0,2,1,2,1,3,1,2,1,36,Fall,44,62,Fall,2,1 +7fe0a99a,Winter,21,1,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,2,3,4,0,0,0,2,1,2,3,0,1,2,2,0,0,0,0,0,22,Winter,30,43,Spring,,0 +80067763,Fall,11,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +8017f13e,Winter,14,1,Summer,90,Winter,19.87316704,63.0,112.2,,62,82,109,,,,,Winter,14,0,24.7,3,30.3,3,7,1,11.8,1,11.5,1,10.0,1,Winter,4,4.75068,19.8753,1262.05,2397.9,24.2386,76.688,13.5847,6.29068,35.512,2,30.5763,21.8731,71.9373,35.8018,54.8149,,,Winter,1.52,Winter,2,1,1,0,1,0,2,1,1,1,1,0,0,0,1,0,0,0,0,0,12,Winter,30,43,Winter,3,0 +8033847f,Spring,15,0,Summer,50,Spring,19.79487636,67.0,126.4,,67,106,103,,,,,Spring,10,0,27.0,1,28.8,2,7,0,0.0,0,0.0,0,1.0,0,Spring,2,0.842928,19.797,1476.44,2362.31,29.0589,99.5234,15.5876,4.20946,26.8766,2,44.3139,26.1506,98.6805,47.7563,73.3729,Spring,1.97,,,Spring,4,2,4,1,4,1,0,3,2,2,4,1,3,2,4,1,4,1,0,3,46,Spring,39,55,Spring,0,1 +80349f35,Spring,6,1,,,Fall,16.32285485,46.5,50.2,22.0,68,76,113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,,,Spring,0, +8068fafb,Fall,9,0,Winter,80,Fall,23.46186186,55.5,102.8,,100,64,121,Fall,6,9,52,Fall,4,0,,,,,0,0,3.0,0,2.0,0,6.0,1,Fall,3,10.5669,23.4644,1171.89,1992.22,16.1842,67.0851,15.3124,8.15203,35.7149,3,32.2354,18.6655,56.5182,30.4063,48.4196,,,Fall,2.71,Fall,4,0,2,0,3,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,14,Fall,30,43,Fall,0,0 +806d877f,Fall,8,0,Spring,61,Winter,15.12623893,55.3,65.8,,63,86,111,Winter,2,2,48,Winter,6,1,,,,,2,0,6.0,0,4.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,Fall,3.68,Winter,5,3,4,0,5,0,0,4,4,4,3,0,1,0,0,1,2,0,0,0,36,Winter,42,59,Fall,1,1 +80784c40,Winter,10,0,Summer,62,Spring,16.45414541,56.0,73.4,25.0,55,57,111,,,,,Spring,12,1,23.7,3,21.1,2,4,0,11.0,1,10.5,1,10.0,1,Spring,3,3.90658,16.4559,1119.91,1903.86,16.0403,61.5487,13.7989,2.65701,11.8513,2,29.8331,15.6753,57.6421,24.39,45.8734,,,Spring,3.07,Spring,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Spring,46,64,Spring,0,0 +8078b7de,Fall,10,0,Spring,68,Fall,14.97354864,51.0,55.4,,72,67,113,,,,,Fall,3,0,12.6,2,14.3,2,13,1,10.0,1,9.0,1,7.0,0,Winter,4,3.10785,14.9752,993.908,2087.21,9.88802,48.1272,13.0093,1.9659,7.27276,1,25.1941,13.0451,45.0194,20.5205,35.0821,,,Winter,2.28,Winter,4,1,4,1,4,1,0,2,2,1,2,0,2,3,2,3,4,3,2,1,42,Winter,61,84,Fall,0,1 +807d8908,Fall,11,1,Winter,60,Fall,21.10463059,62.0,115.4,,59,75,98,Fall,2,3,29,Fall,4,0,18.6,2,19.8,2,0,0,13.0,1,12.0,1,3.5,0,Fall,1,-0.269844,21.1069,1372.45,1784.18,31.0351,88.4467,16.1771,4.92982,26.9533,2,36.3134,21.0982,88.7165,41.796,67.3485,,,,,Fall,3,3,3,1,2,3,1,2,2,1,3,1,1,1,2,1,2,2,2,2,38,Fall,38,54,Fall,2,1 +80904c0b,Spring,21,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +80a5f1e1,Spring,9,0,Summer,61,Spring,11.92515345,63.5,68.4,,59,65,111,Spring,5,7,28,Spring,10,0,12.7,2,13.3,2,2,0,8.0,1,8.0,1,4.0,0,Spring,2,4.54033,11.9265,1158.22,1853.15,17.4832,65.6285,11.4432,0.483254,2.77153,2,25.6676,22.4777,61.0881,16.5252,43.1508,,,Summer,3.171,Summer,2,2,2,0,3,1,0,1,0,0,0,0,0,1,1,2,1,1,1,2,20,Spring,43,60,Spring,2,0 +80a9f2bb,Winter,7,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,33,47,Winter,0,0 +80ac97ce,Spring,7,0,,,Spring,16.54866455,47.0,52.0,23.0,61,94,104,,,,,Spring,1,0,,,,,2,0,8.0,1,9.0,1,6.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +80b46ffb,Spring,13,0,Spring,55,Spring,16.75627984,67.5,108.6,,65,104,118,,,,,Spring,31,1,21.2,2,24.8,2,7,0,0.0,0,0.0,0,12.0,1,Spring,3,5.92361,16.7581,1397.11,2375.09,26.0112,91.0738,14.0536,2.70448,17.5262,2,40.1709,24.8916,85.1502,40.6705,66.1822,,,Spring,2.61,Spring,4,3,3,0,3,2,0,3,0,0,0,0,4,4,4,4,3,4,0,3,44,Spring,41,58,Spring,2,1 +80d8829a,Fall,9,1,Spring,70,Winter,13.57678846,49.75,47.8,,86,79,116,Winter,6,9,32,Winter,115,1,,,,,19,1,14.0,1,14.0,1,13.0,1,Winter,5,8.10943,13.5783,973.47,2141.63,11.9047,45.9504,13.0529,0.525417,1.84964,1,22.0774,11.9683,37.8409,20.2402,33.9821,,,Winter,3.88,Winter,4,1,2,0,1,0,0,0,0,2,1,0,0,0,1,2,1,2,0,0,17,Winter,33,47,Fall,0,0 +80ed2bcf,Summer,15,0,Fall,80,Summer,31.7301826,66.5,199.6,,88,82,150,,,,,Summer,24,1,32.7,2,32.7,2,4,0,5.5,0,5.0,0,8.0,0,Fall,1,10.018,31.7336,1795.18,2333.74,41.4129,133.474,21.2205,10.5132,66.1263,2,58.0583,34.0025,123.456,68.0247,99.4712,Summer,2.08,,,Summer,4,4,3,1,4,4,1,3,1,1,4,0,2,,3,0,3,0,2,2,42,Summer,46,64,Summer,2,1 +81095901,Winter,8,0,Fall,63,Fall,15.01601994,53.0,60.0,,70,70,117,Fall,6,9,34,Fall,15,1,,,,,3,0,5.5,0,6.0,0,12.0,1,Fall,3,3.08958,15.0177,1020.21,1734.36,7.56446,50.929,12.7472,2.27041,9.07098,1,25.0452,18.3193,47.8394,27.6392,32.6097,,,Winter,1.71,Fall,4,2,3,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,13,Fall,39,55,Fall,0,0 +812a0058,Summer,14,0,,,Summer,17.85872449,68.5,119.2,29.0,60,82,117,,,,,Summer,35,1,29.7,2,29.2,2,20,1,4.0,0,4.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +812f6873,Summer,10,0,Winter,70,Summer,19.00969388,56.0,84.8,,76,80,140,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4.39,Summer,2,2,3,0,2,1,0,1,2,2,3,0,1,1,1,1,3,2,2,1,30,Summer,34,49,Summer,0,0 +813645dc,Summer,7,0,,,Fall,19.86852254,48.4,66.2,25.0,81,85,123,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,33,47,Summer,, +81364a4b,Summer,14,0,Fall,61,Fall,16.73425262,64.75,99.8,,65,55,114,,,,,Fall,75,1,28.4,2,31.5,3,37,1,6.0,0,5.0,0,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,4.4,Fall,4,4,4,1,2,1,1,1,1,2,4,0,2,1,2,1,2,2,1,1,37,Fall,39,55,Summer,2,1 +81394e0e,Summer,10,0,,,Fall,13.57196694,55.0,58.4,,74,84,107,,,,,Summer,8,0,8.7,1,10.1,1,2,0,4.0,0,5.5,0,5.5,0,Winter,2,3.43302,13.5734,1038.59,1661.74,10.8311,52.8863,12.2919,1.28152,5.51375,1,24.8939,17.1613,49.4532,19.4916,35.725,,,Fall,2.14,Fall,4,4,4,3,4,1,0,2,1,2,0,0,4,0,4,4,4,4,1,3,49,Fall,47,66,Summer,0,1 +813e7f93,Spring,7,1,Fall,72,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,4,Spring,39,55,Spring,0,0 +8148c785,Summer,12,0,Spring,80,Fall,29.1183432,65.0,175.0,,71,86,136,,,,,Fall,32,1,41.7,3,46.8,3,30,1,6.0,0,7.0,0,9.0,1,Fall,4,6.89604,29.1215,1743.68,3661.74,40.9887,127.988,21.2984,7.82316,47.0117,3,55.4902,31.5094,121.092,60.5691,96.4789,,,Winter,3.89,Fall,1,5,5,5,5,5,5,5,4,4,4,1,5,5,5,5,4,5,3,2,83,Fall,61,84,Summer,2,3 +815efdc1,Winter,15,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +816b0564,Winter,12,0,Spring,65,Spring,29.06890535,67.5,188.4,,79,90,149,,,,,Spring,,,,,,,,,,,,,,,Summer,1,6.24527,29.0721,1652.18,2147.83,35.1864,118.242,18.2459,10.8262,70.1583,2,51.1576,31.8977,111.996,56.4145,86.344,,,Winter,2.09,Winter,1,2,2,1,2,1,1,1,1,1,0,0,1,1,1,0,1,0,1,0,18,Winter,34,49,Winter,,0 +81766b8a,Fall,11,1,Winter,60,Winter,19.99605294,56.5,90.8,,79,95,109,Winter,4,6,7,Winter,3,0,124.0,2,5.1,1,0,0,7.0,0,5.0,0,8.0,0,Winter,2,4.17034,19.9982,1164.28,1746.43,22.2499,66.2745,14.5966,5.40162,24.5255,2,28.2859,15.7387,62.1042,27.2759,50.5358,,,,,Winter,2,2,4,1,1,1,0,2,1,1,0,0,1,1,1,1,2,1,0,0,22,Winter,38,54,Fall,1,0 +8188050f,Spring,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8189aa29,Fall,12,0,Summer,55,Spring,17.4607349,58.5,85.0,,67,75,101,,,,,Spring,6,0,12.5,1,11.0,1,0,0,6.0,0,7.0,0,5.0,0,Spring,1,4.0118,17.4626,1180.47,1534.61,19.7104,67.9989,13.9699,3.49275,17.0011,1,31.4793,16.8092,63.9871,43.9279,51.1897,,,Fall,1.44,Spring,5,5,5,2,5,5,1,3,3,4,3,0,3,2,4,5,4,5,4,4,72,Spring,44,62,Fall,0,2 +819ad5f1,Winter,6,1,Spring,45,Spring,15.89478625,49.5,55.4,22.0,62,78,128,,,,,Spring,9,1,,,,,0,0,16.0,1,15.5,1,10.0,1,Spring,1,2.5999,15.8965,983.142,1278.08,12.8151,46.9806,13.4806,2.41588,8.41945,1,22.1605,12.0049,44.3806,19.4841,34.9756,,,,,Winter,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Winter,49,68,Winter,0,0 +81a4b826,Fall,7,0,Winter,55,Fall,16.20876385,50.5,58.8,,69,96,105,,,,,Fall,4,1,,,,,1,0,14.0,1,13.0,1,8.0,1,Fall,3,3.22443,16.2105,1015.17,1725.79,12.4013,50.392,13.8925,2.318,8.40802,1,26.6871,11.3035,47.1675,20.1226,39.0885,,,,,Winter,3,2,1,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,11,Winter,43,60,Fall,1,0 +81c0b83c,Winter,10,1,Summer,65,Spring,15.94241322,55.0,68.6,,52,68,109,Spring,1,0,49,Spring,5,0,8.6,1,9.3,1,2,0,7.0,0,5.0,0,8.0,0,,,,,,,,,,,,,,,,,,,,Spring,2.75,Spring,4,3,2,0,2,1,2,2,1,2,2,0,1,1,2,2,2,1,0,1,31,Spring,37,53,Winter,0,1 +81d0913e,Winter,19,0,,,Spring,22.97559336,69.0,155.6,32.0,75,71,124,,,,,,,,,,,,,,,,,,,,Spring,3,6.92343,22.9781,1677.36,2851.51,37.9961,120.924,17.8573,5.1208,34.6764,2,51.8818,31.0458,114.0,55.6823,89.8779,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +81d3ab22,Spring,12,0,Spring,65,Spring,17.13924341,65.25,103.8,,80,94,106,,,,,Fall,,,,,,,,,,,,,,,Summer,2,5.55081,17.1411,1357.1,2171.35,24.3494,86.8116,14.3357,2.8054,16.9884,3,39.3698,23.0924,81.2608,40.7209,63.7192,,,Spring,2.16,Spring,2,2,3,3,0,1,0,0,0,0,0,0,0,0,1,0,3,1,5,2,23,Spring,40,56,Spring,2,0 +81d607bb,Summer,7,0,Fall,61,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,Summer,31,45,Summer,0,0 +81e9a24a,Summer,22,1,,,Summer,23.65723214,64.5,140.0,29.0,70,69,106,,,,,Summer,,,,,,,,,,,,,,,Summer,2,5.39017,23.6598,1438.61,2157.91,33.3477,95.4936,16.1383,7.52153,44.5064,2,38.0034,24.1425,90.1034,46.8717,71.3511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +81eaf4c1,Fall,8,1,Winter,71,Fall,17.25626899,50.5,62.6,,76,73,111,Fall,5,8,50,Fall,2,0,,,,,0,0,13.5,1,14.0,1,9.0,1,Fall,3,11.3098,17.2582,1003.94,1606.3,12.7707,49.1957,13.5627,3.69541,13.4043,2,21.6434,14.7816,37.886,19.5208,34.4142,,,,,Fall,1,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,3,0,9,Fall,27,40,Fall,,0 +81f60502,Summer,8,0,Winter,60,Fall,22.95510204,56.0,102.4,,48,75,100,Fall,,,,Fall,24,1,,,,,0,0,8.5,1,7.5,1,14.0,1,Fall,1,4.26967,22.9576,1280.31,1664.4,21.6864,78.6325,17.6291,5.32856,23.7675,3,39.6607,17.2854,74.3629,44.6039,61.3471,,,Summer,2.05,Fall,3,5,4,1,5,4,1,4,2,2,3,0,2,1,0,1,2,5,1,2,48,Fall,42,59,Summer,1,1 +81feecad,Summer,10,0,Winter,61,Fall,0.0,50.5,0.0,,80,106,110,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.06,Summer,3,3,4,1,4,1,0,1,1,1,0,0,2,1,2,1,1,2,0,1,29,Summer,34,49,Summer,2,0 +8226bbd4,Spring,7,1,,,Summer,24.0256319,53.0,96.0,,62,85,121,Summer,4,7,0,Summer,0,0,,,,,0,0,18.0,1,18.0,1,5.0,0,Summer,1,3.39611,24.1784,1144.83,1488.28,22.0404,64.2028,16.0696,8.10883,32.3972,2,28.7778,13.3847,60.8067,28.5956,50.8182,,,,,Summer,5,4,4,0,4,0,0,0,0,0,2,0,2,3,1,5,4,5,1,0,40,Summer,37,53,Spring,1,1 +82422684,Spring,11,0,Winter,82,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4.34,Summer,3,2,2,0,2,0,0,0,2,2,2,0,0,0,0,0,1,0,0,0,16,Winter,30,43,Spring,3,0 +8256a969,Spring,6,1,Fall,75,Summer,17.12150294,47.0,53.8,,54,93,102,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,0,3,0,4,0,0,1,1,0,0,0,2,0,0,1,0,1,0,0,18,Spring,49,68,Spring,0,0 +826d99da,Winter,10,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +828b31ca,Winter,7,0,Summer,60,Spring,14.52378472,48.0,47.6,,76,95,131,Spring,7,11,34,Spring,0,0,,,,,0,0,8.0,1,13.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,36,51,Spring,0,0 +82923551,Fall,6,0,,,Fall,16.27237416,43.5,43.8,22.0,73,69,98,,,,,Fall,0,0,,,,,5,1,11.0,1,12.0,1,5.0,0,Fall,3,2.36199,16.2742,882.054,1499.49,6.26824,36.2133,13.4553,2.81889,7.58671,1,21.7857,8.15936,33.8513,14.9026,28.0539,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,34,49,Fall,0,0 +82a114c1,Fall,11,0,Winter,35,Fall,16.83391813,57.0,77.8,,95,82,154,Fall,5,7,48,Fall,12,1,13.4,2,13.5,2,1,0,6.5,0,6.5,0,10.0,1,Fall,5,5.17732,16.8358,1131.5,2715.59,15.0357,62.7821,13.5859,3.24983,15.0179,2,30.57,17.1764,57.6048,28.5861,45.6057,,,Fall,2.05,Fall,5,5,4,0,4,3,1,3,4,3,4,0,1,1,2,1,4,4,3,3,55,Fall,52,72,Fall,0,2 +82ec9747,Fall,8,1,Spring,65,Winter,16.30350749,49.75,57.4,,70,71,115,,,,,Winter,7,1,,,,,4,0,9.5,1,10.5,1,9.0,1,Winter,3,2.70656,16.3053,994.512,1591.22,13.8178,48.1916,13.6895,2.61576,9.20836,1,23.2472,11.1266,45.4851,21.0509,37.0651,,,Winter,2.94,Winter,2,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,Fall,41,58,Winter,0,0 +8310a02f,Spring,9,0,Spring,50,Winter,14.70741101,52.38,57.4,,69,98,109,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,4,3.34315,14.1631,1023.4,2149.13,10.6697,51.2681,12.6501,1.51302,6.13194,2,25.6898,14.9086,47.9249,21.3657,36.3595,,,Spring,3.17,Spring,3,1,2,1,1,1,1,1,1,1,2,0,0,1,1,1,1,1,1,1,22,Spring,42,59,Spring,0,0 +831f5813,Spring,7,0,,,Spring,16.18169575,49.5,56.4,22.0,66,94,112,,,,,Spring,0,0,,,,,3,0,4.5,0,5.5,0,9.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +83247d4c,Summer,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8328c8b3,Winter,19,0,Summer,50,Spring,20.82846959,67.0,133.0,,84,65,125,,,,,,,,,,,,,,,,,,,,Spring,5,6.3597,20.8307,1551.91,3724.58,31.243,107.562,16.8466,3.98419,25.4382,2,49.071,27.2478,101.202,60.0572,80.314,,,,,Spring,0,2,2,1,1,1,1,2,1,1,2,1,1,1,1,1,1,1,2,1,24,Spring,38,54,Winter,1,0 +83379672,Spring,7,0,,,Spring,19.61724282,49.0,67.0,,63,78,110,Spring,4,6,6,Summer,0,0,,,,,0,0,9.0,1,10.0,1,12.0,1,Fall,3,3.24569,18.8425,1041.38,1770.34,11.9498,53.1833,14.9568,3.88567,13.8167,2,30.0449,11.1887,49.9376,31.2853,41.9947,,,,,Summer,3,3,2,0,2,1,0,0,2,2,2,0,3,2,2,3,2,3,1,1,34,Spring,44,62,Spring,0,1 +83525bbe,Fall,11,1,Winter,999,Fall,,,,,,,,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.241,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +8360011a,Winter,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +836b4de3,Fall,13,0,Spring,60,Winter,17.26255556,60.0,88.4,,57,75,108,,,,,Winter,6,0,14.4,1,14.6,1,9,0,7.0,0,8.0,1,8.0,0,Winter,3,4.8462,17.2644,1274.28,2166.28,22.7828,77.991,15.2316,2.03286,10.409,2,36.7179,18.4903,73.1448,34.2687,59.5007,,,,,Winter,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,3,Winter,44,62,Fall,0,0 +83711ef5,Fall,15,1,Winter,65,Winter,24.05822222,60.0,123.2,,76,83,128,,,,,Winter,21,1,20.7,2,22.9,2,0,0,12.0,1,13.0,1,12.0,1,Winter,2,4.20339,24.0609,1257.57,1886.35,25.0252,76.2107,14.8839,9.17696,46.9893,2,31.1918,19.9937,72.0073,36.6488,56.2171,Winter,2.01,,,Winter,2,4,4,1,2,2,2,3,1,1,4,1,1,2,1,1,3,2,4,1,42,Winter,27,40,Fall,2,1 +8376acc5,Spring,8,1,Summer,68,Spring,17.60312,50.0,62.6,,76,75,110,Spring,7,11,42,Spring,12,1,,,,,5,1,10.5,1,12.0,1,11.0,1,Spring,3,2.78199,17.605,1010.51,1616.81,15.0128,49.8953,14.0321,3.57294,12.7047,1,23.863,11.0195,47.1134,21.3122,38.8758,,,,,Spring,2,1,2,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,12,Spring,46,64,Spring,0,0 +83846d0c,Spring,6,0,Summer,70,Spring,15.87952941,42.5,40.8,,83,84,105,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,2.21364,15.8813,863.668,1381.87,4.74897,34.255,13.3336,2.54762,6.54501,1,20.6762,8.82987,32.0414,14.4987,25.4251,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,Spring,45,63,Spring,0,0 +83912085,Summer,8,1,Winter,69,Summer,17.84019204,54.0,74.0,22.0,64,72,118,,,,,Summer,8,1,,,,,0,0,11.0,1,10.0,1,10.0,1,Summer,1,3.49504,17.8421,1117.32,1452.52,20.663,61.2724,14.7734,3.06875,12.7276,2,27.7168,12.8926,57.7774,25.63,48.3798,,,Fall,2.83,Fall,2,2,2,0,2,0,0,1,0,0,2,0,0,0,2,0,2,0,2,0,17,Fall,40,56,Summer,1,0 +839b507a,Fall,6,1,Winter,75,Fall,13.89445586,43.15,36.8,,67,86,115,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,31,45,Fall,0,0 +839f40f4,Winter,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +83c33097,Spring,7,0,Fall,61,Summer,17.26568,50.0,61.4,,81,88,107,Summer,7,11,29,Summer,21,1,,,,,20,1,10.0,1,12.0,1,12.0,1,Summer,3,1.40732,17.2676,1057.58,1797.88,13.1652,54.9091,15.4421,1.82545,6.49094,2,31.6414,10.1025,53.5017,33.6425,44.8065,,,,,Summer,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,23,Summer,37,53,Spring,2,0 +83c86707,Fall,9,0,,,Fall,19.74957813,53.5,80.41,28.0,50,48,102,,,,,Fall,2,0,,,,,1,0,0.0,0,0.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.479,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +83da0ba8,Summer,14,0,,,,,,,,,,,,,,,Fall,40,1,22.4,2,21.8,2,14,1,14.0,1,16.0,1,13.0,1,Fall,4,4.371,21.7325,1249.83,2624.65,20.1301,75.3868,15.2263,6.50626,32.2131,1,35.1421,20.1146,71.0158,32.8327,55.2722,,,Summer,3.43,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +83eda373,Spring,7,0,Summer,61,Spring,17.41858313,53.0,69.6,,67,87,108,Spring,6,10,17,Spring,20,1,,,,,0,0,9.5,1,9.0,1,3.5,0,Summer,1,3.32078,17.4205,1050.64,1365.84,11.6688,54.1703,13.5585,3.86196,15.4297,1,27.0087,15.4927,50.8496,22.9546,38.6776,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,43,60,Spring,0,0 +83efac42,Winter,7,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +83fb5ee0,Fall,6,0,Spring,65,Winter,14.08336842,47.5,45.2,,56,86,97,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Fall,32,46,Fall,0,0 +840ff4b7,Spring,6,0,Summer,75,Summer,15.40099958,49.0,52.6,22.0,63,86,117,,,,,Summer,8,1,,,,,0,0,10.0,1,8.0,1,5.0,0,Summer,1,2.97983,15.4027,972.111,1263.74,9.74765,45.8056,13.4131,1.98958,6.79441,1,25.4014,10.6565,42.8258,20.6226,35.1491,,,,,Summer,3,3,1,0,2,0,0,0,0,0,1,0,1,0,0,2,2,2,0,0,17,Summer,37,53,Spring,0,0 +842d27e4,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8432d14e,Spring,9,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.21,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +844b87f8,Spring,6,1,Spring,55,Spring,15.37834915,49.5,53.6,,78,84,115,Spring,,,,Spring,2,1,,,,,1,0,7.0,0,7.0,0,8.0,1,Spring,2,2.61067,15.38,980.805,1471.21,12.5406,46.7316,13.4092,1.97082,6.86838,2,22.4555,11.7355,44.1209,20.7351,34.9961,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Spring,2,0 +844be9e4,Winter,7,0,,,Spring,15.81351474,52.5,62.0,,68,71,111,Spring,7,12,2,Spring,17,1,,,,,14,1,9.0,1,9.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,41,58,Winter,0,0 +84689a9f,Summer,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +84875079,Summer,8,0,Winter,70,Summer,18.07567568,55.5,79.2,,51,58,103,,,,,Summer,7,1,,,,,4,1,6.0,0,7.0,0,5.0,0,Fall,3,3.57294,19.4545,1101.03,1871.75,15.0325,59.5372,14.6246,4.82994,19.6628,2,29.6759,14.8287,55.9642,24.5407,44.7085,,,,,Fall,3,2,2,1,5,1,0,1,0,1,0,0,2,0,3,1,1,2,1,0,26,Fall,35,50,Summer,1,0 +84a16b14,Fall,6,1,Winter,66,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,3,0,2,1,0,2,1,1,3,0,0,0,2,2,0,0,0,0,21,Winter,45,63,Fall,3,0 +84b7d89d,Summer,12,1,Spring,60,Spring,21.12567562,62.13,116.0,,102,112,149,,,,,Spring,,,,,,,,,,,,,,,Summer,2,4.50155,21.128,1272.61,1908.92,25.7078,77.8131,14.1727,6.95529,38.1869,2,31.0165,21.0887,73.3115,34.348,56.7243,,,Winter,1.89,Winter,1,1,2,1,4,2,4,1,2,1,3,0,1,2,3,0,3,2,1,2,36,Winter,34,49,Summer,3,1 +84ef97c7,Spring,12,0,,,Spring,17.7498003,56.5,80.6,26.0,70,79,111,,,,,Spring,3,0,22.3,2,18.0,1,10,1,5.0,0,5.0,0,8.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,2.29,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +851b425b,Fall,5,0,,,Fall,23.12821181,48.0,75.8,27.0,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,4,0,1,1,0,5,1,0,0,0,4,0,1,4,1,0,0,0,22,,,,Fall,2,0 +8520246f,Winter,19,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +852adec2,Winter,8,0,Fall,75,Fall,13.32888,50.0,47.4,,66,90,111,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.1,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,34,49,Winter,0,0 +85440a78,Spring,7,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +85452335,Fall,11,0,,,Fall,20.90047871,63.0,118.0,31.0,,81,,,,,,Fall,7,0,15.1,2,15.6,2,0,0,0.0,0,4.0,0,10.0,1,Fall,2,5.00038,20.9028,1331.97,2131.15,23.2383,84.135,14.9038,5.99892,33.865,2,37.6274,23.2693,79.1346,35.746,60.8657,,,Fall,1.73,Fall,2,2,4,4,3,1,1,2,1,1,4,1,1,2,3,1,3,1,2,1,40,Fall,41,58,Fall,1,1 +854eb237,Spring,11,1,Spring,60,Spring,14.41915355,51.5,54.4,22.0,82,91,134,,,,,Spring,0,0,16.8,2,15.3,2,2,0,13.5,1,16.0,1,11.0,1,Spring,3,2.82121,14.4207,1005.43,1608.69,13.1827,49.3544,13.0832,1.33753,5.04564,1,23.1545,13.0171,46.5332,22.5465,36.3373,,,Spring,2.15,Spring,2,0,1,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,Spring,37,53,Spring,0,0 +85525f8d,Spring,9,1,Summer,90,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.51,Spring,2,1,2,0,1,0,0,0,1,1,2,0,1,0,1,1,2,2,1,0,18,Spring,30,43,Spring,2,0 +855c6099,Fall,8,1,Winter,70,Fall,15.70876858,49.25,54.2,,60,79,111,,,,,Fall,20,1,,,,,8,1,9.0,1,9.0,1,7.0,1,Fall,3,2.60758,15.7105,980.072,1568.12,12.8581,46.6535,13.5231,2.18743,7.54646,1,22.5234,11.272,44.046,20.203,35.3815,,,Winter,2.39,Winter,3,2,2,0,4,0,0,2,1,2,2,0,2,2,2,2,1,2,1,1,31,Winter,58,80,Fall,0,1 +85677d80,Fall,15,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +8570ea5f,Winter,9,0,Summer,63,Spring,16.74785506,49.0,57.2,22.0,62,77,100,,,,,Spring,22,1,,,,,30,1,14.0,1,15.0,1,12.0,1,Spring,4,2.95241,16.7497,980.615,2059.29,9.63301,46.7114,13.6783,3.07134,10.4886,1,25.7118,11.3666,43.759,21.7797,35.3448,,,Spring,3.6,Spring,1,1,2,0,1,0,,1,0,0,2,0,1,1,0,0,1,1,1,1,14,Spring,44,62,Winter,2,0 +8572d442,Winter,15,1,,,Winter,35.87084961,64.0,209.0,44.0,75,65,119,,,,,Winter,0,0,21.6,2,23.2,2,0,0,9.0,0,9.5,0,5.0,0,Winter,3,5.32524,35.8748,1544.47,2471.15,39.1897,106.769,18.3269,17.5479,102.231,3,39.9498,27.6295,101.444,50.4136,79.1396,Winter,1.68,,,Winter,1,1,1,1,1,1,1,1,1,0,4,1,1,1,1,1,1,1,1,1,22,Winter,29,42,Winter,3,0 +8574f9d2,Summer,9,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.94,,,,,,,,,,,,,,,,,,,,,,,,,,,, +857baaa6,Winter,10,0,Spring,55,Spring,18.06927732,53.0,72.2,,67,94,102,Spring,4,6,51,Winter,0,0,13.7,2,11.5,1,0,0,11.0,1,11.0,1,7.0,0,Winter,3,3.31402,18.0713,1056.54,1796.11,12.1481,54.7981,13.7156,4.3556,17.4019,2,26.8523,15.7976,51.4841,21.4857,39.0005,,,,,Spring,3,5,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,Winter,44,62,Winter,2,0 +8581b9a1,Fall,12,1,Spring,80,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.56,Spring,0,4,4,2,3,5,5,1,2,0,5,1,2,2,5,0,4,0,2,2,49,Spring,40,56,Fall,2,1 +858ae6a3,Winter,12,1,Fall,77,Winter,22.08816414,61.8,120.0,,65,76,90,,,,,Winter,15,0,23.5,3,24.8,3,6,0,6.2,0,5.1,0,11.0,1,Winter,1,1.91982,22.0906,1322.07,1718.69,28.5592,83.081,15.2942,6.79635,36.919,1,33.7598,20.762,81.1612,38.3437,62.319,,,Winter,2.09,Winter,3,4,5,1,4,4,4,4,4,3,4,0,2,1,3,4,2,5,1,1,59,Winter,37,53,Winter,2,2 +858be19a,Winter,12,1,Spring,80,Spring,15.5201014,58.75,76.2,,70,69,113,,,,,Fall,,,,,,,,,,,,,,,Summer,5,3.89935,15.5218,1148.57,2526.86,19.8072,64.6011,13.1591,2.36268,11.5989,2,27.6829,17.1109,60.7017,28.9521,47.4901,,,Spring,1.7,Spring,1,2,3,0,5,0,1,2,2,1,0,0,3,4,2,2,5,5,1,1,40,Spring,41,58,Winter,1,1 +8596dec1,Winter,14,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.92,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +859cbb27,Spring,8,1,Winter,60,Fall,24.41162138,55.25,106.0,,80,95,143,Fall,3,4,37,Fall,7,1,,,,,2,0,5.0,0,4.5,0,8.0,1,Fall,5,8.345,24.4143,1144.72,2518.38,20.4133,64.1904,14.7846,9.62973,41.8096,3,26.9989,16.7783,55.8454,28.0387,47.4121,,,Spring,2.57,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,4,Fall,34,49,Spring,0,0 +85a1082c,Fall,11,0,,,Fall,15.17246695,55.8,67.2,23.0,58,61,120,,,,,Fall,30,1,16.5,2,13.8,2,15,1,9.5,1,9.5,1,12.0,1,Fall,5,3.03894,19.678,1015.2,2436.48,11.4193,50.3951,14.757,4.92093,16.8049,1,27.6523,11.3234,47.3561,23.9793,39.0716,,,Fall,3.55,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,37,53,Fall,0,0 +85b027ee,Winter,9,1,Fall,45,Winter,16.5952326,51.0,61.4,,58,87,108,Winter,5,8,11,Winter,10,1,,,,,1,0,5.5,0,4.5,0,9.5,1,Winter,2,9.02775,16.597,1009.22,1513.83,13.8794,49.7581,13.4501,3.14693,11.6419,1,22.355,13.5237,40.7303,19.3623,36.2344,,,Winter,2.28,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,46,64,Winter,,0 +85ba333f,Summer,11,0,Fall,70,Summer,13.55548049,54.75,57.8,,62,68,121,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.71,Fall,4,4,3,1,5,3,2,1,1,4,4,0,4,0,3,3,3,4,2,0,51,Fall,30,43,Summer,0,2 +85d0d592,Fall,12,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,,,Fall,, +85d99199,Fall,16,0,Winter,60,Fall,45.30602589,68.5,302.4,,80,69,131,,,,,Fall,10,0,21.8,1,27.8,1,0,0,12.0,1,11.0,1,6.0,0,Fall,1,8.0297,45.311,2362.43,3071.16,61.2134,193.893,29.0525,16.2585,108.507,3,82.0248,50.6547,185.863,112.469,143.238,Winter,1.01,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,4,Winter,41,58,Fall,2,0 +85d9c915,Spring,8,0,Winter,55,Winter,22.96079339,55.0,98.8,,45,69,62,Winter,,,,Fall,0,0,,,,,0,0,4.0,0,4.0,0,5.0,0,Winter,4,3.68705,22.9633,1158.25,2432.32,15.928,65.6315,15.2542,7.7091,33.1685,3,31.8134,17.8901,61.9444,29.4553,47.7414,,,Spring,1.65,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,31,45,Spring,0,0 +85e2912c,Summer,12,0,,,Summer,22.67709907,59.5,114.2,34.0,64,72,108,,,,,Summer,13,0,16.0,2,15.7,2,3,0,9.0,1,9.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,44,62,,, +85f23af6,Winter,9,0,Spring,72,Spring,17.21632653,49.0,58.8,,53,83,105,Spring,6,10,12,Spring,15,1,,,,,14,1,11.0,1,12.0,1,7.0,1,Spring,3,11.5591,17.2182,976.265,1659.65,6.46294,46.248,13.5426,3.67556,12.552,2,24.0558,15.7293,34.6889,25.3233,30.5187,,,Winter,2.5,Winter,4,3,3,0,5,2,0,1,0,1,0,0,1,1,2,4,3,3,0,1,34,Winter,33,47,Winter,0,1 +85fed5f0,Spring,8,0,,,Spring,16.89523967,55.0,72.7,26.0,113,77,133,,,,,Spring,0,0,,,,,0,0,5.0,0,6.0,0,4.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,2.29,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +860f4f9c,Spring,10,0,Fall,85,Summer,24.5275978,60.9,129.4,,77,98,138,Summer,5,8,4,Summer,10,0,21.0,2,20.0,2,2,0,4.0,0,0.0,0,8.5,1,,,,,,,,,,,,,,,,,,,,Spring,2.41,Summer,4,4,4,4,3,0,0,0,0,1,0,0,3,2,5,5,3,5,4,3,50,Summer,41,58,Spring,0,2 +861188f4,Winter,10,0,Spring,40,Summer,25.68848032,59.0,127.2,36.0,60,82,106,,,,,Winter,12,1,,,,,0,0,9.0,1,10.5,1,12.0,1,Spring,1,4.44104,25.6913,1309.07,1701.79,22.6298,81.6964,16.5007,9.19061,45.5036,3,37.5065,21.5601,77.2554,34.8167,60.1363,,,Spring,1.13,Winter,4,5,5,5,5,4,0,4,1,1,4,0,4,0,2,4,4,4,4,0,60,Spring,37,53,Winter,3,2 +861fd4a0,Fall,7,0,,,Fall,22.15182292,48.0,72.6,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,7,Fall,28,41,Fall,0,0 +8632ea2b,Summer,7,0,,,Summer,13.12266667,45.0,37.8,21.0,64,85,98,,,,,Summer,0,0,,,,,0,0,8.0,1,8.0,1,10.0,1,Summer,3,2.34272,13.1241,885.251,1504.93,4.52482,36.5538,12.6914,0.4327,1.24626,1,20.0212,12.0077,34.2111,14.3645,24.5461,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +86335468,Winter,11,0,Summer,62,Winter,28.85300274,60.05,148.0,,79,80,136,Winter,5,7,53,Winter,5,0,18.1,2,18.2,2,1,0,2.0,0,2.0,0,12.0,1,Winter,3,4.61959,24.6285,1364.23,2319.19,19.1605,87.5711,14.5726,10.0559,60.4289,3,36.8768,31.5338,82.9515,43.5395,56.0373,,,Winter,2.02,Winter,3,3,4,4,4,4,0,0,0,1,4,0,2,0,5,4,1,4,1,2,46,Winter,32,46,Winter,2,1 +863915b6,Summer,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +863e40c7,Winter,10,0,Summer,63,Spring,15.66353719,55.0,67.4,,56,75,105,Spring,5,8,0,Spring,7,0,12.2,1,13.1,2,4,0,8.0,1,8.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.75,Spring,1,1,3,1,3,1,0,2,3,1,3,0,1,0,1,1,2,1,4,1,30,Spring,42,59,Winter,2,0 +863f7033,Spring,18,1,,,Summer,20.21043731,65.25,122.4,,93,69,165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1,2,1,2,1,1,1,1,1,1,2,0,0,0,0,0,0,0,0,0,14,Summer,43,60,Spring,2,0 +866a08b7,Spring,8,0,Summer,75,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3,2,4,1,5,0,0,0,2,3,2,0,4,0,5,5,3,3,3,4,49,Summer,34,49,Spring,0,1 +86989d3a,Winter,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +86a8e0d6,Summer,11,0,Winter,45,Winter,20.5736968,65.25,124.6,,89,63,136,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.74,Winter,5,4,5,2,5,2,1,3,1,4,3,0,0,1,2,0,2,0,0,0,40,Fall,47,66,Summer,2,1 +86bd716a,Spring,17,0,,,Summer,26.03287111,75.0,208.3,,52,82,113,,,,,Summer,36,,53.7,3,49.9,2,32,,6.0,,5.0,,13.0,,,,,,,,,,,,,,,,,,,Summer,2.25,,,Summer,4,4,4,5,4,4,4,4,3,4,4,1,1,2,3,1,3,3,2,2,62,,,,Summer,2,2 +86e56299,Summer,13,1,,,Summer,22.76222485,65.0,136.8,29.0,66,51,121,,,,,Summer,16,0,31.0,3,30.2,3,1,0,12.0,1,12.5,1,11.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,3.16,Summer,4,4,2,1,2,1,2,1,2,2,2,1,1,2,2,2,3,2,1,1,38,Summer,39,55,Summer,2,1 +86eef4b9,Summer,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +87050a09,Winter,8,0,Spring,63,Spring,21.10161983,55.0,90.8,,79,116,134,Spring,5,7,57,Spring,20,1,,,,,7,1,9.5,1,9.0,1,12.0,1,Spring,3,3.74348,21.1039,1145.67,1947.64,16.1609,64.2921,14.9429,6.16103,26.5079,1,31.3311,16.8,60.5486,27.6191,47.4921,,,Spring,2.16,Spring,4,2,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,14,Spring,40,56,Spring,2,0 +871b2b19,Fall,10,1,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.82,Fall,2,1,3,1,1,1,1,3,1,2,3,1,1,1,1,1,1,1,3,1,30,Fall,37,53,Fall,0,0 +874a348f,Summer,13,0,Fall,70,Summer,12.44570726,60.5,64.8,,53,71,121,,,,,Summer,26,1,19.0,1,17.4,1,6,0,9.0,1,9.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.45,Summer,4,4,4,2,4,4,1,4,1,4,4,0,5,1,5,5,4,5,2,4,67,Summer,40,56,Summer,2,2 +87509a7b,Spring,8,0,Summer,80,Spring,19.31274467,52.25,75.0,,61,96,106,Spring,2,2,30,Spring,8,1,,,,,10,1,9.75,1,9.75,1,11.0,1,Spring,4,8.39191,19.3149,1057.34,2220.42,12.1139,54.8839,14.1343,5.18052,20.1161,2,27.514,15.256,46.492,23.1881,39.6279,,,Spring,2.91,Spring,2,2,4,0,4,0,0,0,0,0,2,0,0,0,2,0,3,0,1,0,20,Spring,41,58,Spring,0,0 +876a929e,Spring,14,0,Fall,68,Spring,15.15355556,60.0,77.6,28.0,65,79,111,,,,,Spring,15,0,14.8,1,18.0,1,7,0,9.5,1,9.0,1,8.5,1,Spring,2,4.36998,15.1552,1169.09,1870.54,17.235,66.786,13.0433,2.11196,10.814,2,30.7463,18.8047,62.416,26.1681,47.9813,,,Spring,1.63,Spring,5,5,5,1,5,2,1,4,5,2,1,1,3,2,4,2,5,3,5,3,64,Spring,43,60,Spring,3,2 +8772f64b,Spring,13,0,Summer,45,Spring,19.99941834,61.5,107.6,,66,79,117,,,,,Spring,10,0,14.0,1,14.5,1,0,0,0.0,0,0.0,0,4.0,0,Spring,1,5.35718,20.0016,1288.47,1675.01,21.8257,79.5018,14.7785,5.22314,28.0982,2,36.2233,21.4528,74.1446,33.8995,58.049,,,Summer,1.86,Spring,2,1,1,0,2,1,1,2,1,1,0,0,0,1,1,0,1,0,0,0,15,Spring,34,49,Spring,2,0 +87762493,Spring,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +878f9e86,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +87a60251,Fall,7,0,,,Fall,18.66643375,53.5,76.0,27.0,81,83,107,,,,,Fall,0,0,,,,,0,0,7.0,0,8.0,1,12.0,1,Fall,3,3.67392,18.6685,1108.59,1884.6,15.5906,60.3424,14.8224,3.84611,15.6576,1,30.7659,13.9858,56.6684,26.9298,46.3566,,,,,Fall,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,31,45,Fall,0,0 +87af775e,Spring,9,0,Summer,67,Fall,17.45445816,54.0,72.4,,61,86,111,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.77565,17.4564,1112.62,1891.46,16.1198,60.772,14.6527,2.80363,11.628,2,30.8378,13.8144,56.9964,26.5077,46.9576,,,Spring,3.26,Spring,5,4,5,0,5,3,0,2,2,5,2,0,2,2,3,3,4,3,4,1,55,Spring,30,43,Spring,1,2 +87b78ee3,Winter,11,0,Summer,48,Winter,24.89695376,58.5,121.2,32.0,66,63,104,,,,,Winter,4,0,18.4,2,15.0,2,0,0,11.5,1,10.5,1,11.5,1,Spring,1,4.50386,24.8997,1311.65,1705.15,23.3499,81.9714,16.8404,8.05923,39.2286,2,38.1661,20.4555,77.4676,35.8143,61.5159,,,Spring,1.87,Spring,4,5,5,0,4,4,0,2,2,2,3,0,2,2,2,3,3,2,5,3,53,Spring,70,95,Winter,0,2 +87e2f26a,Fall,6,0,Fall,,Fall,16.67653571,44.75,47.5,23.0,71,106,130,,,,,,,,,,,,,,,,,,,,Fall,4,2.15731,16.6767,934.25,1961.93,8.39099,41.7729,14.666,2.01071,5.72707,3,14.489,18.8929,39.6156,4.65573,22.88,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,38,54,,, +881efd6a,Spring,9,0,,,Spring,14.9194925,51.0,55.2,21.0,118,81,151,,,,,Summer,10,1,,,,,8,1,6.5,0,5.5,0,10.0,1,Summer,2,3.10029,14.9211,992.861,1588.58,10.103,48.0158,12.9792,1.942,7.18433,1,24.7871,13.1256,44.9155,18.9898,34.8901,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +8823b020,Fall,12,0,Spring,65,Winter,18.62010916,59.0,92.2,,69,86,112,,,,,Winter,6,0,13.2,1,15.5,1,0,0,0.0,0,4.0,0,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.56,Winter,2,3,1,2,4,1,1,1,1,2,3,0,1,2,3,1,3,1,1,2,35,Winter,30,43,Fall,0,1 +8826ee14,Spring,6,1,,,Spring,16.27237416,43.5,43.8,21.0,73,97,112,,,,,Spring,1,0,,,,,0,0,10.5,1,9.5,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +88300a4b,Spring,15,0,Summer,60,Spring,21.33632835,69.5,146.6,,70,71,125,,,,,Spring,19,0,16.4,1,26.2,1,5,0,8.0,1,7.0,0,14.0,1,Spring,2,-0.833971,21.3387,1643.03,2628.85,36.0844,117.267,17.0691,4.26959,29.3327,2,50.8803,30.3025,118.101,56.4269,86.9648,Spring,2.99,,,Spring,0,1,2,1,1,0,2,1,0,0,3,0,0,0,0,0,2,0,0,0,13,Summer,40,56,Spring,2,0 +884c429c,Spring,6,1,Summer,88,Spring,14.85846914,45.0,42.8,20.0,74,93,102,,,,,Spring,3,1,,,,,0,0,9.0,1,9.0,1,9.0,1,Summer,1,2.10388,14.8601,912.39,1186.11,9.75632,39.4446,13.6951,1.16501,3.35545,1,19.6212,10.067,37.3407,14.7833,29.3775,,,,,Spring,1,2,1,0,1,0,0,2,2,1,1,0,0,0,0,2,1,2,1,0,17,,,,Spring,0,0 +8875387b,Summer,16,0,Winter,64,Fall,25.99696192,70.5,183.8,,63,78,116,,,,,Fall,5,0,21.6,2,30.6,2,0,0,5.0,0,6.0,0,4.0,0,Fall,2,7.25629,25.9998,1776.5,2842.4,40.9476,131.484,18.5993,7.40052,52.3164,3,55.9751,34.5609,124.227,63.0351,96.9227,Fall,1.551,,,Fall,1,0,1,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,7,Fall,32,46,Summer,2,0 +8877cdee,Fall,9,0,Winter,85,Winter,25.20540336,44.5,71.0,,81,94,120,Winter,7,12,38,Winter,8,0,,,,,2,0,8.0,1,8.0,1,8.5,1,,,,,,,,,,,,,,,,,,,,Winter,3.17,Winter,3,1,3,1,1,1,0,1,1,2,1,0,1,1,1,2,1,1,0,1,23,Fall,33,47,Fall,0,0 +888c45f9,Fall,8,1,Winter,70,Winter,16.27455934,51.5,61.4,,62,107,106,Winter,5,7,24,Winter,7,1,,,,,0,0,7.0,0,6.5,0,5.5,1,Winter,2,12.0675,16.2763,1013.5,1520.25,12.478,50.214,13.3111,2.96527,11.186,1,21.6816,16.0544,38.1464,20.6485,34.1596,,,,,Winter,2,2,5,2,4,2,0,2,2,0,2,0,2,2,2,1,2,2,0,2,36,Winter,36,51,Fall,0,1 +88b5194e,Summer,10,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.9,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +88c640b9,Summer,20,0,Summer,68,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,2,3,1,4,2,2,2,2,0,1,1,2,3,2,0,3,0,2,2,34,Fall,49,68,Summer,3,1 +88cdb5bd,Spring,13,0,Summer,52,Spring,20.64938405,64.5,122.2,,53,77,107,,,,,Spring,10,0,16.8,1,17.2,1,0,0,0.0,0,0.0,0,6.0,0,Spring,2,5.88638,20.9756,1361.77,2178.83,24.434,87.3095,14.9866,5.98894,34.8905,2,38.7496,24.126,81.4231,37.2523,63.1836,,,Spring,2.65,Spring,3,3,3,2,3,3,3,3,3,3,0,0,3,3,3,3,3,3,3,1,51,Spring,46,64,Spring,2,2 +88e04d93,Fall,13,1,Winter,65,Fall,27.80826405,63.0,157.0,,89,103,143,,,,,Fall,16,0,24.7,3,26.1,3,0,0,9.0,0,8.0,0,13.0,1,Fall,3,3.64905,27.8113,1409.62,2255.39,32.7196,92.4058,16.3689,11.4424,64.5942,2,35.991,23.6951,88.7567,42.7037,68.7107,,,Fall,1.89,Fall,5,4,4,1,5,3,2,1,1,2,3,0,1,1,3,4,2,4,1,0,47,Fall,35,50,Fall,2,1 +891193eb,Fall,8,0,,,Fall,21.93861454,54.0,91.0,26.0,62,69,112,,,,,Fall,0,0,,,,,0,0,10.0,1,11.0,1,12.0,1,Fall,3,3.81654,21.941,1165.77,1981.81,17.935,66.433,16.0177,5.92335,24.567,2,33.2213,15.2767,62.6164,29.9209,51.1563,,,Fall,3.24,Fall,2,2,4,0,2,0,0,2,0,0,2,0,2,0,4,1,3,1,0,2,27,Fall,82,100,Fall,0,0 +891b8cd5,Summer,11,0,Fall,60,Spring,21.11302595,63.0,119.2,,69,83,120,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.71,Summer,2,2,1,0,3,1,1,1,1,1,2,0,1,1,2,1,1,1,1,1,24,Summer,39,55,Summer,3,0 +89261ca6,Fall,8,0,Summer,67,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.81,Fall,2,0,0,0,1,0,0,0,2,1,1,0,2,1,1,1,0,0,0,0,12,Fall,43,60,Fall,0,0 +893293dd,Fall,7,1,Winter,65,Fall,16.91019893,48.25,56.0,,57,76,103,,,,,Fall,2,0,,,,,0,0,10.0,1,9.0,1,11.0,1,Fall,2,2.63252,16.912,982.699,1474.05,13.5538,46.9334,14.1739,2.73813,9.06663,2,24.2264,9.15311,44.3009,23.905,37.7803,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,42,59,Fall,0,0 +894fd6be,Spring,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +895f9725,Summer,16,1,,,Summer,20.08081055,64.0,117.0,29.0,65,57,109,,,,,Summer,60,1,21.6,2,26.7,2,10,1,7.0,0,6.0,0,12.0,1,Summer,,,,,,,,,,,,,,,,,Summer,1.69,,,Summer,0,1,2,0,1,1,1,1,1,0,3,0,0,0,0,0,2,0,1,0,14,,,,Summer,3,0 +89739b1a,Fall,9,0,Spring,65,Winter,14.15763889,48.0,46.4,,65,89,104,Winter,9,15,17,Winter,3,0,,,,,2,0,8.5,1,7.0,0,9.0,1,Winter,2,2.72498,14.1592,934.939,1495.9,7.26714,41.8463,12.7696,1.38958,4.55368,1,22.8053,11.7739,39.1213,17.6992,30.0724,,,Fall,2.13,Winter,3,2,3,0,3,0,0,0,0,3,3,0,0,0,0,2,0,1,0,0,20,Winter,42,59,Fall,0,0 +897da2c8,Winter,6,0,Spring,53,Winter,21.31407948,48.05,70.0,,73,68,128,Spring,3,4,44,Winter,10,1,,,,,2,0,15.0,1,14.0,1,11.0,1,Spring,2,2.87973,20.9227,1004.52,1607.24,10.8146,49.2579,14.723,6.19971,20.7421,2,25.9949,12.4484,46.3782,19.8417,36.8096,,,,,Spring,2,2,3,0,0,0,0,3,0,2,2,0,0,0,0,2,2,1,0,0,19,Spring,41,58,Winter,0,0 +898096eb,Spring,6,0,Fall,60,Spring,16.38597531,45.0,47.2,,96,134,118,Spring,3,4,29,Spring,2,1,,,,,4,1,11.0,1,12.0,1,7.0,1,Summer,3,2.57194,16.3878,912.179,1550.71,8.13946,39.4221,13.6873,2.70048,7.77791,1,23.2433,8.03937,36.8502,16.0426,31.3827,,,,,Summer,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Summer,34,49,Spring,0,0 +89a26502,Spring,10,1,Summer,60,Spring,17.73224099,55.68,78.2,,75,81,111,Summer,5,8,0,Summer,8,0,13.6,2,14.3,2,3,0,11.0,1,9.0,1,12.0,1,Summer,3,3.16082,18.1754,1081.18,1729.89,16.2275,57.423,13.3464,4.82903,20.777,2,23.9712,17.2243,54.2622,23.3416,40.1987,,,Spring,1.6,Spring,2,0,1,0,1,1,1,1,1,2,0,0,0,0,0,0,0,0,0,0,10,Spring,46,64,Spring,0,0 +89bd10b7,Winter,13,0,,,Spring,20.15656337,63.0,113.8,,80,74,111,,,,,Spring,25,1,23.4,2,23.2,2,30,1,10.0,1,10.2,1,6.0,0,Spring,5,5.35313,20.1588,1383.74,3320.97,27.0417,89.6492,15.8806,4.27813,24.1508,2,40.0607,22.5468,84.296,37.1565,67.1024,,,Winter,3.42,Spring,5,4,4,4,5,2,3,3,2,2,3,1,2,1,3,2,4,2,3,2,57,Spring,35,50,Winter,2,2 +89c43fa1,Summer,10,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,,,,, +89d475e2,Summer,14,1,Winter,45,Fall,18.41103144,64.75,109.8,,94,78,157,,,,,Fall,,,,,,,,,,,,,,,Summer,3,4.76839,18.413,1279.52,2047.24,25.0366,78.549,13.1724,5.24067,31.251,2,30.5446,22.9679,73.7807,34.2908,55.5812,,,,,Fall,4,3,3,1,4,4,2,2,4,3,3,0,3,3,4,4,,3,2,2,54,Fall,54,75,Summer,3,2 +89d7d2c3,Summer,7,0,Winter,58,Summer,15.43914931,48.0,50.6,,47,59,110,Summer,3,3,16,Summer,2,,,,,,0,,12.0,,10.0,,8.0,,Fall,2,7.07139,15.4408,945.402,1512.64,7.61735,42.9608,13.1097,2.33114,7.63922,2,23.2905,12.0529,35.8894,18.375,30.9079,,,,,Fall,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,8,Fall,35,50,Summer,0,0 +8a444a98,Winter,7,0,Fall,50,Spring,14.74266529,44.0,40.6,,53,64,102,Spring,8,14,23,Spring,6,1,,,,,4,1,8.5,1,11.0,1,5.0,0,Spring,2,2.43659,14.7443,881.219,1409.95,6.27127,36.1244,13.1189,1.62537,4.47564,1,22.2594,7.59373,33.6878,16.3738,28.5306,,,,,Spring,3,0,2,0,4,0,0,0,0,3,2,0,4,0,3,2,0,3,1,2,29,Spring,39,55,Winter,1,0 +8a44ea3a,Summer,7,0,Summer,42,Summer,15.23513314,52.0,58.6,,74,72,100,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,36,51,Summer,0,0 +8a4cd292,Winter,15,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.54,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +8a4d341b,Fall,10,0,,,Fall,19.47368421,57.0,90.0,28.0,59,58,107,,,,,Fall,9,0,13.9,2,15.0,2,0,0,6.0,0,5.0,0,12.0,1,Fall,3,3.8511,19.4758,1150.86,1956.46,15.6986,64.8448,14.0323,5.44352,25.1552,2,30.1486,18.9976,60.9937,25.543,45.8472,,,Fall,3.13,Fall,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Fall,41,58,Fall,0,0 +8a4e5d1a,Summer,10,0,Fall,65,Summer,21.57571873,51.5,81.4,,83,62,105,,,,,Summer,5,0,11.7,1,14.1,2,1,0,5.0,0,5.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,Summer,3.16,Summer,4,4,2,1,1,1,0,1,1,2,2,0,1,1,1,1,1,1,1,1,27,Summer,59,81,Summer,2,0 +8a603113,Spring,14,0,,,Spring,29.58559689,68.0,194.6,30.0,75,77,132,,,,,Spring,21,0,45.8,3,46.5,3,30,1,7.0,0,8.0,1,12.0,1,,,,,,,,,,,,,,,,,,Spring,4.71,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +8a645723,Spring,14,1,Fall,60,Fall,27.87285156,64.0,162.4,,60,62,117,,,,,Fall,30,1,18.3,2,21.3,2,8,1,8.5,0,9.0,0,10.5,1,Fall,4,5.1745,27.8759,1439.02,2734.15,33.6736,95.5381,16.3991,11.4768,66.8619,2,37.1179,24.7466,90.3636,45.7604,70.7915,,,Spring,1.88,Fall,3,4,3,0,3,0,5,1,1,4,4,0,3,3,3,2,1,2,1,0,43,Fall,45,63,Spring,2,1 +8a6b6912,Summer,7,1,Winter,51,Fall,15.43691795,50.5,56.0,,52,83,95,Fall,5,9,0,Fall,10,1,,,,,0,0,8.0,0,9.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,4,Fall,45,63,Summer,0,0 +8a77c3e9,Fall,10,0,Spring,70,Spring,20.79606437,56.13,93.2,,95,100,156,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.67,Winter,3,3,3,1,3,0,0,0,2,2,3,0,4,2,4,4,4,4,2,2,46,Winter,40,56,Fall,3,1 +8a77d185,Spring,16,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,3,3,5,4,1,3,4,3,0,0,5,5,4,5,5,5,4,3,72,Spring,43,60,,,2 +8a78a063,Fall,9,0,Spring,52,Winter,21.99776891,51.75,83.8,28.0,67,76,121,,,,,Winter,0,0,,,,,1,0,6.5,0,7.5,1,3.0,0,Winter,2,3.40153,22.0002,1096.61,1754.58,14.8417,59.0666,15.5069,6.49331,24.7334,1,30.2738,13.951,55.6651,25.9591,45.1155,,,Winter,2.42,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,28,41,Fall,0,0 +8a940d7d,Spring,10,1,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.3,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +8a968a0f,Winter,9,0,,,Spring,23.44436846,55.25,101.8,32.0,69,84,113,,,,,Spring,12,1,,,,,3,0,9.5,1,9.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +8a98973c,Winter,13,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8a9cce44,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,4,0,5,5,0,5,3,4,4,0,2,1,4,0,5,1,0,0,51,Spring,32,46,,,2 +8a9e653f,Summer,6,1,Fall,60,Summer,15.66446728,43.0,41.2,22.0,60,60,94,,,,,Summer,0,0,,,,,0,0,9.0,1,9.0,1,2.0,0,Summer,1,1.94368,15.6662,888.052,1154.47,8.53121,36.8522,14.0129,1.65325,4.34784,1,19.7265,8.59446,34.9085,16.6878,28.2577,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +8abbbc38,Spring,10,1,Winter,70,Summer,26.0958953,61.5,140.4,,50,79,121,,,,,Summer,20,1,,,,,1,0,8.5,1,7.5,0,12.0,1,Summer,3,4.85504,26.0987,1386.56,2218.5,32.2767,89.9502,16.7207,9.37804,50.4498,2,35.768,21.9055,85.0951,40.4163,68.0447,,,Winter,2.92,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,41,58,Summer,0,0 +8ad5e251,Winter,6,0,Fall,75,Fall,17.37816,50.0,61.8,,53,88,102,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,5,2.57934,17.3801,1017.29,2441.5,4.70852,50.618,14.2353,3.14472,11.182,2,22.8808,23.0287,48.0387,29.9372,27.5893,,,,,Spring,1,1,1,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,7,Spring,32,46,Winter,0,0 +8ada0f4e,Winter,9,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +8ae6a52f,Winter,14,1,Spring,60,Spring,20.7323136,62.5,115.2,,56,66,95,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.64,Spring,2,3,2,1,3,1,1,1,1,1,2,0,1,0,1,1,2,1,1,2,27,Winter,46,64,Winter,3,0 +8ae9becb,Winter,10,1,,,Winter,17.29031405,55.0,74.4,25.0,68,82,119,,,,,Winter,20,1,,,,,10,1,10.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.38,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +8af0b447,Spring,17,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.031,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +8af97dc3,Spring,7,1,,,Spring,16.31654321,45.0,47.0,21.0,71,71,110,,,,,Spring,0,0,,,,,0,0,0.0,0,0.0,0,8.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +8b02eb7e,Summer,16,0,Fall,80,Summer,22.47355598,69.0,152.2,,88,58,168,,,,,Summer,5,0,38.4,2,29.2,2,3,0,11.0,1,11.0,1,10.0,1,Summer,4,5.18847,23.1419,1537.33,3228.39,31.0581,106.009,16.1186,7.02334,46.1911,2,46.0177,28.933,100.82,48.6054,77.0758,Summer,2.56,,,Summer,0,5,2,0,4,5,1,4,0,0,1,0,0,0,0,0,0,0,0,0,22,Summer,58,80,Summer,1,0 +8b0749be,Fall,17,1,Fall,76,Summer,29.78922567,67.0,190.2,34.0,63,66,108,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.37,,,,,,,,,,,,,,,,,,,,,,,,,Summer,35,50,,, +8b2d6ad6,Spring,5,1,,,Spring,14.98123974,44.5,42.2,,72,67,114,,,,,Spring,4,1,,,,,1,0,9.0,1,8.0,0,10.0,1,Spring,1,2.12514,14.9829,905.311,1176.9,10.1141,38.6905,13.7369,1.24601,3.50946,1,20.3515,8.22497,36.5654,15.7214,30.4656,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +8b3aa064,Fall,5,1,,,Fall,18.95485537,44.0,52.2,21.0,62,96,124,,,,,Fall,0,0,,,,,0,0,9.5,1,11.5,1,7.0,1,Winter,2,2.10456,18.9569,915.998,1374.0,11.0105,39.8288,14.4642,4.49271,12.3712,1,20.8278,7.99055,37.7243,16.3903,31.8383,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +8b3c900f,Summer,11,0,Fall,75,Fall,24.0797184,62.5,133.8,,69,87,113,Fall,6,9,3,Fall,23,1,23.0,3,22.5,3,3,0,9.0,1,8.0,1,12.0,1,Fall,3,5.83893,24.0823,1430.27,2431.46,28.0007,94.6055,17.0278,7.05453,39.1945,2,42.6358,23.9689,88.7666,42.4424,70.6365,,,Summer,0.58,Fall,2,0,3,0,0,0,0,0,0,0,0,0,1,2,2,2,2,0,2,0,16,Fall,29,42,Summer,0,0 +8b4b9648,Fall,8,0,Winter,64,Winter,17.68398987,53.5,72.0,,67,84,114,Winter,6,10,1,Winter,14,1,,,,,9,1,6.0,0,8.0,1,9.0,1,Winter,4,6.38885,17.6859,1070.0,2247.01,13.2609,56.2324,13.8128,3.87311,15.7676,2,27.8919,15.0796,49.8436,22.6722,41.1528,,,Winter,3.21,Winter,3,3,3,0,4,3,0,1,2,3,3,1,2,3,4,3,3,3,2,1,47,Winter,57,79,Fall,,1 +8b6a40f5,Summer,10,0,,,Summer,15.81185684,56.5,71.8,25.0,65,61,106,,,,,Summer,0,0,11.1,1,13.5,2,5,0,4.0,0,4.5,0,3.0,0,Summer,,,,,,,,,,,,,,,,,,,Summer,2.72,Summer,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,6,Summer,54,75,Summer,0,0 +8b802ca8,Fall,9,1,Spring,60,Fall,21.4905816,54.5,90.8,,78,83,124,,,,,Fall,11,1,,,,,1,0,12.0,1,11.0,1,10.0,1,Fall,2,3.33955,21.4929,1115.82,1673.74,18.9509,61.1129,14.4658,7.02712,29.6871,2,26.991,15.171,57.7734,28.8474,45.9419,,,Winter,2.63,Winter,1,1,2,1,1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,10,Winter,31,45,Fall,0,0 +8ba98f12,Fall,9,1,Spring,65,Fall,14.51995277,49.3,50.2,,64,90,95,Fall,8,13,38,Fall,12,1,,,,,10,1,10.0,1,12.0,1,6.0,1,Fall,3,7.69315,14.6999,969.973,1551.96,11.9985,45.5779,13.3464,1.35348,4.62211,1,22.4466,11.1328,37.8847,21.2162,34.4451,,,Fall,2.82,Fall,2,2,2,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,11,Fall,33,47,Fall,0,0 +8bc5199c,Summer,10,1,Fall,55,Summer,14.64921184,51.0,54.2,,62,75,110,Summer,6,9,53,Summer,26,1,,,,,7,1,12.0,1,14.0,1,11.0,1,Fall,4,10.0801,14.6508,996.245,1892.87,12.337,48.3762,13.0766,1.57422,5.82377,1,21.7728,14.2664,38.2961,19.89,34.1098,,,Summer,3.04,Summer,3,3,2,1,3,2,2,2,2,2,3,1,2,2,2,2,2,2,2,1,41,Summer,36,51,Summer,2,1 +8bf72a9c,Winter,14,0,Summer,60,Summer,23.04118367,70.0,160.6,30.0,58,55,106,,,,,Summer,31,1,37.6,2,41.3,2,11,0,8.0,1,9.0,1,12.0,1,Summer,5,7.83295,23.0437,1882.14,4517.13,46.8579,142.735,20.4804,2.56331,17.8647,2,60.2343,35.6432,134.902,68.2232,107.092,Winter,2.18,,,Spring,0,4,5,1,5,2,1,1,3,1,5,1,2,1,1,2,2,2,2,1,42,Spring,44,62,Winter,3,1 +8c0ca955,Spring,14,1,Fall,70,Spring,39.63703306,61.0,209.8,45.0,108,82,171,,,,,Spring,15,0,31.2,3,29.2,3,0,0,12.0,1,12.0,1,12.0,1,Spring,1,4.9793,39.6414,1537.35,1998.55,40.0886,106.011,20.0306,19.6108,103.789,3,39.9053,26.0171,101.032,47.6819,79.9939,,,Summer,2.28,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,33,47,Spring,2,0 +8c122779,Winter,18,0,Summer,70,Spring,19.22507876,69.0,130.2,,76,86,150,,,,,,,,,,,,,,,,,,,,Spring,2,5.82042,19.2272,1417.17,2267.47,25.2841,93.21,13.7647,5.46246,36.99,1,39.5659,28.3601,87.3896,39.4189,64.85,Winter,1.41,,,Spring,0,0,3,2,1,0,1,0,0,0,3,0,0,0,0,0,0,0,3,0,13,Spring,30,43,Winter,2,0 +8c185c31,Summer,13,0,,,Summer,18.36559074,61.0,97.2,26.0,67,95,121,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,37,53,,, +8c1f9196,Spring,19,0,Fall,67,Summer,25.33281464,66.3,158.4,,61,74,125,,,,,,,,,,,,,,,,,,,,Fall,3,4.92406,25.5664,1611.97,2740.35,34.4161,113.959,18.3934,7.17299,44.4412,3,50.5961,28.9466,109.035,57.3222,85.0122,,,,,Summer,1,2,2,2,2,1,1,1,2,1,2,2,2,3,1,2,1,3,1,1,33,,,,Spring,2,1 +8c43b81e,Spring,9,0,Summer,,Summer,18.07051753,55.3,78.6,25.0,61,84,103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.48,,,,,,,,,,,,,,,,,,,,,,,Summer,43,60,Spring,, +8c5a5a21,Fall,7,0,,,Fall,17.20567313,50.25,61.8,24.0,71,80,107,,,,,Fall,0,0,,,,,0,0,8.5,1,8.5,1,8.0,1,Winter,2,3.1284,17.2076,1010.82,1617.31,11.4159,49.9288,13.9021,3.30541,11.8712,1,26.4977,12.0152,46.8004,21.0297,37.9136,,,,,Winter,4,4,1,0,4,0,0,2,0,0,1,0,1,1,0,3,1,3,1,0,26,Winter,60,82,Fall,1,0 +8c6c52a2,Summer,10,0,Winter,61,Fall,14.49063311,54.4,61.0,,65,83,118,Fall,5,8,58,Fall,0,0,,,,,0,0,10.0,1,8.0,1,10.0,1,Fall,2,3.3681,14.4922,1038.63,1661.8,10.5891,52.8903,12.5655,1.92668,8.10969,1,25.298,17.0032,49.5222,20.7453,35.8871,,,Summer,1.78,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,28,41,Summer,0,0 +8c6fc949,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8c97411a,Fall,9,1,,,Fall,18.75747789,51.0,69.4,26.0,69,77,116,,,,,Fall,4,0,,,,,3,0,12.0,1,12.0,1,12.0,1,Fall,1,2.75066,18.7595,1022.36,1329.07,14.4014,51.1582,13.8286,4.93094,18.2418,2,22.9763,13.7806,48.4075,21.2107,37.3776,,,Fall,2.809,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,48,67,Fall,0,0 +8c9c253d,Fall,10,1,Summer,68,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.46,Fall,2,2,2,2,2,1,1,2,1,1,2,0,0,2,1,0,2,1,1,0,25,Fall,39,55,Fall,1,0 +8ca025c3,Spring,7,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,1,3,1,1,1,1,1,2,1,2,1,1,1,1,2,1,1,28,Spring,43,60,Spring,0,0 +8ca09626,Spring,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8ca2f053,Winter,14,1,,,Winter,44.8292159,66.5,282.0,49.0,80,99,152,,,,,Winter,0,0,38.9,3,46.2,3,0,0,0.0,0,0.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +8cab7bc4,Winter,8,0,Fall,60,Summer,28.77777778,57.0,133.0,,72,94,136,Summer,5,8,50,Summer,0,0,,,,,10,1,9.0,1,9.0,1,11.0,1,Summer,3,4.88812,28.7809,1467.91,2495.45,28.4985,98.6151,21.3401,7.44082,34.3849,3,48.3917,21.725,93.727,60.8385,76.8902,,,Summer,2.33,Summer,1,1,1,0,1,0,0,0,1,0,1,0,1,1,1,0,1,1,1,1,13,Summer,34,49,Winter,2,0 +8cb11a21,Fall,12,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +8cb19d64,Fall,6,0,Winter,80,Fall,0.0,50.5,0.0,,72,79,123,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,3,4,0,5,0,0,0,0,0,0,0,4,0,5,5,5,5,2,1,43,Fall,41,58,Fall,0,1 +8cdf2304,Spring,6,0,,,Summer,16.94310888,45.0,48.8,25.0,64,81,104,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8cfcac79,Summer,14,0,Fall,60,Summer,19.68785734,67.5,127.6,29.0,47,60,101,,,,,Summer,30,1,32.0,3,35.4,3,30,1,0.0,0,6.0,0,11.0,1,Summer,4,6.64185,19.69,1588.56,3335.97,35.0036,111.465,17.2003,2.48975,16.1347,1,48.758,27.7037,104.823,51.8146,83.7616,,,Summer,2.43,Summer,5,1,3,3,5,3,3,4,4,4,4,0,2,3,4,3,5,3,1,4,64,Summer,37,53,Summer,1,2 +8d010562,Fall,9,1,Winter,50,Fall,14.76715976,52.0,56.8,,56,76,107,Winter,7,11,42,Fall,31,1,,,,,33,1,11.5,1,10.5,1,12.5,1,,,,,,,,,,,,,,,,,,,,Fall,2.8,Fall,5,1,2,0,2,1,0,1,1,2,1,0,2,2,1,1,1,2,1,1,27,Fall,49,68,Fall,0,0 +8d055cca,Fall,11,0,Summer,70,Winter,21.10671819,58.0,101.0,,65,94,119,Winter,4,6,41,Winter,6,0,9.1,1,9.9,1,0,0,7.0,0,7.0,0,5.0,0,Winter,2,7.15672,21.109,1215.82,1945.31,18.866,71.764,14.9987,6.11034,29.236,3,33.7902,19.1078,64.6072,30.8496,52.6562,,,,,Winter,4,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,16,Winter,44,62,Fall,2,0 +8d0c6afe,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8d11c14a,Summer,9,1,Winter,60,Fall,14.19863884,52.75,56.2,,91,66,144,Fall,5,7,14,Fall,10,1,,,,,25,1,16.0,1,17.0,1,9.0,1,Fall,2,7.83221,14.2002,1022.36,1533.53,13.7676,51.1573,12.926,1.27414,5.04266,2,23.3533,14.0365,43.3251,22.6808,37.1209,,,Summer,2.02,Fall,5,5,5,1,5,2,0,1,0,0,2,0,1,1,1,1,3,1,2,2,38,Fall,31,45,Summer,1,1 +8d3186b6,Winter,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8d522127,Fall,10,0,,,Fall,19.82114632,53.0,79.2,,71,103,109,,,,,Fall,21,1,,,,,17,1,10.0,1,9.0,1,9.0,1,Fall,4,3.60505,19.8233,1109.1,2329.1,15.1161,60.3963,15.1169,4.70646,18.8037,2,31.284,13.9963,56.7913,29.2696,46.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +8d5c76a5,Spring,9,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +8d7e3917,Fall,7,1,,,,,,,,,,,,,,,Fall,5,1,,,,,0,0,17.0,1,16.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +8d8b8553,Summer,6,0,Winter,64,Winter,24.72031318,52.25,96.0,,78,79,150,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.5347,24.723,1144.71,1946.0,16.8578,64.1892,16.5307,8.19227,31.8108,3,32.4374,14.894,60.6545,28.7009,49.2952,,,,,Fall,4,4,4,0,5,1,0,3,0,2,2,0,5,5,5,5,3,2,4,4,58,Fall,74,100,Summer,1,2 +8db09bb4,Fall,11,0,Winter,70,Fall,23.35586777,55.0,100.5,,66,114,122,Fall,,,,Fall,0,0,20.1,2,22.3,2,0,0,0.0,0,0.0,0,8.0,0,,,,,,,,,,,,,,,,,,,,Fall,2.03,Fall,5,4,2,1,2,3,0,2,1,1,2,1,1,1,1,1,1,1,1,1,32,Fall,34,49,Fall,1,1 +8ddb6b8d,Summer,7,0,Fall,60,Summer,14.74714825,49.5,51.4,,63,85,102,,,,,Summer,4,1,,,,,4,1,9.0,1,7.0,0,3.0,0,Summer,2,2.87441,14.7488,963.328,1541.32,8.06989,44.8701,12.8751,1.8737,6.52993,2,23.7986,13.0016,41.9957,19.5712,31.8685,,,,,Summer,2,3,2,0,2,0,0,1,1,2,2,0,2,0,1,2,0,2,1,0,23,Summer,46,64,Summer,2,0 +8e0b739c,Summer,10,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.24,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +8e1363ae,Fall,21,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +8e24b103,Spring,6,1,Summer,71,Spring,14.49148393,49.25,50.0,,63,80,124,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,1,2.49025,14.4931,970.203,1261.26,11.3949,45.6023,13.2184,1.27471,4.39766,1,21.1015,13.106,43.1121,18.5275,32.4964,,,,,Spring,3,2,0,0,1,0,0,0,0,1,0,0,0,0,0,3,2,2,0,0,14,Spring,44,62,Spring,0,0 +8e380413,Spring,11,1,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.16,Spring,2,3,2,1,4,3,1,1,2,2,3,1,1,1,1,1,1,1,1,1,33,Spring,36,51,Spring,1,1 +8e59a69a,Fall,10,1,Summer,82,Fall,16.83391813,57.0,77.8,,64,96,103,Fall,6,9,15,Fall,21,1,12.8,2,13.3,2,0,0,12.5,1,11.0,1,11.0,1,Fall,1,4.03918,16.8358,1136.23,1477.09,20.5723,63.2859,13.6949,3.14082,14.5141,2,26.9799,15.7337,59.2468,25.0582,47.5522,,,Fall,2.931,Fall,3,3,4,2,3,2,4,2,3,3,2,1,2,2,2,3,2,3,2,1,49,Fall,55,76,Fall,0,1 +8e71bf9c,Winter,19,0,Summer,58,Winter,23.84016204,72.0,175.8,36.0,71,71,135,,,,,,,,,,,,,,,,,,,,Spring,2,7.26883,23.8428,1717.26,2747.62,37.7707,125.174,16.9767,6.8661,50.6258,3,53.4282,33.9753,117.905,61.1746,91.1989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +8e749c85,Winter,13,1,Spring,70,Spring,21.15830229,58.5,103.0,,89,93,140,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.79,Winter,3,2,2,0,2,1,2,1,2,2,4,1,2,2,1,1,2,2,0,0,32,Winter,43,60,Winter,2,1 +8e770d51,Spring,6,0,Fall,61,Summer,15.41089143,46.5,47.4,,45,89,88,Summer,,,,Summer,0,0,,,,,16,1,9.0,1,9.0,1,11.0,1,Summer,3,5.98775,15.4126,922.624,1568.46,6.70873,40.5345,13.1802,2.23238,6.86548,1,23.4511,10.3747,34.5468,20.3163,30.1598,,,,,Fall,2,0,1,1,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,9,Fall,41,58,Spring,2,0 +8e80deb7,Winter,15,1,Spring,75,Winter,21.28722615,64.25,125.0,,67,97,113,,,,,Winter,10,,15.3,1,17.3,2,0,,10.5,,14.0,,12.0,,Winter,3,4.88332,21.2896,1324.0,2118.4,27.1502,83.2865,14.1851,7.10449,41.7135,2,33.23,22.9063,78.4032,41.0,60.3802,Winter,1.77,,,Winter,2,3,2,2,4,1,0,1,2,0,2,0,2,2,1,1,1,1,1,1,29,Winter,34,49,Winter,2,0 +8e93a873,Winter,16,1,Summer,41,Spring,23.35522222,60.0,119.6,28.0,69,89,105,,,,,Spring,12,0,18.8,2,24.5,2,1,0,12.0,1,12.0,1,12.0,1,Spring,3,4.29537,23.3578,1266.96,2027.13,25.7736,77.2105,15.0791,8.27863,42.3895,1,31.8586,19.5783,72.9151,36.8288,57.6322,Winter,1.04,,,Spring,3,3,3,2,4,0,0,5,4,0,3,0,2,3,0,0,2,0,0,0,34,,,,Winter,3,1 +8ea5ac9e,Winter,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8eb31f1b,Summer,13,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +8eba0ee9,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8ebacec2,Winter,15,0,,,Winter,39.03626886,67.5,253.0,49.0,86,82,134,,,,,Winter,0,0,27.1,1,29.3,2,0,0,0.0,0,5.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,Winter,1.02,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +8ec2cc63,Fall,10,0,Winter,55,Spring,21.69239535,58.5,105.6,,62,78,128,Spring,3,4,4,Fall,11,1,,,,,4,0,11.0,1,11.0,1,9.0,1,Fall,2,4.36896,21.6948,1249.39,1999.03,19.8637,75.3398,15.478,6.21675,30.2602,2,36.0704,19.4057,70.9708,36.5362,55.9341,,,Fall,3.4,Spring,2,3,2,1,2,1,0,1,1,1,2,1,2,1,1,1,1,1,1,1,26,Spring,75,100,Fall,2,0 +8ec5d236,Winter,11,0,Winter,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +8ed05544,Spring,6,1,,,Spring,16.99420552,47.0,53.4,27.0,,97,,,,,,Spring,10,1,,,,,0,0,7.0,0,8.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +8ee46ea1,Summer,7,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8ef72e3e,Spring,6,1,Summer,52,Spring,14.78918919,46.25,45.0,21.0,66,96,98,,,,,Spring,5,1,,,,,0,0,14.0,1,13.5,1,11.0,1,Summer,3,2.23016,14.7908,929.088,1486.54,10.7035,41.2231,13.5494,1.2414,3.77688,1,19.9659,10.5538,38.993,14.7583,30.6694,,,,,Summer,1,1,1,0,1,1,0,1,1,0,1,0,1,0,1,1,1,1,1,0,14,,,,Spring,0,0 +8f015125,Winter,12,0,Spring,75,Spring,17.59328824,62.0,96.2,,82,81,129,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,5,4.77846,17.5952,1255.39,3012.94,20.2938,75.9787,13.8967,3.69852,20.2213,2,34.6622,21.0228,71.2003,32.4361,54.956,,,Spring,3.46,Winter,3,2,2,0,4,1,1,0,0,2,0,0,0,0,0,1,2,0,0,0,18,Winter,39,55,Winter,0,0 +8f04a03e,Summer,7,1,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +8f102e6e,Spring,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8f112082,Spring,13,0,Summer,72,Summer,19.47368421,57.0,90.0,30.0,61,77,118,,,,,Summer,23,1,11.3,1,11.3,1,0,0,1.0,0,1.0,0,6.0,0,Summer,2,3.84788,19.4758,1150.55,1840.89,15.2881,64.8122,14.0252,5.45058,25.1878,2,30.4979,19.0262,60.9643,27.4558,45.786,,,Spring,1.59,Summer,2,3,3,1,3,1,1,1,1,1,2,1,2,2,3,1,2,2,1,2,35,Summer,42,59,Spring,2,1 +8f13c52a,Spring,15,0,Summer,51,Spring,32.32032276,73.0,245.0,42.0,71,71,114,,,,,Spring,22,0,46.5,3,42.9,2,7,0,7.0,0,7.0,0,12.0,1,,,,,,,,,,,,,,,,,,Spring,2.07,,,Spring,2,4,5,2,2,0,1,2,1,2,4,1,2,1,1,1,3,1,2,0,37,Spring,34,49,Spring,2,1 +8f1ada49,Spring,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,,,,, +8f1e1df6,Spring,10,1,Summer,75,Spring,27.08875614,57.5,127.4,,97,85,186,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.48238,27.0917,1369.97,2191.95,32.3504,88.1829,18.7522,8.33956,39.2171,2,36.6522,19.1803,83.7005,40.7126,69.0025,,,Spring,2.71,Spring,1,1,1,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,8,Spring,44,62,Spring,1,0 +8f21c613,Winter,13,0,Spring,68,Spring,18.60882353,68.0,122.4,,73,71,102,,,,,Spring,15,0,22.8,2,26.4,2,2,0,5.5,0,5.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,Winter,3.85,Spring,4,4,3,1,3,3,1,1,2,3,1,0,0,0,1,1,0,2,1,1,32,Spring,37,53,Winter,0,1 +8f2d131f,Summer,6,0,Summer,70,Summer,20.89497108,50.5,75.8,,105,103,151,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,0,4,1,5,1,0,2,2,2,4,1,2,2,1,3,4,2,5,1,46,Summer,30,43,Summer,3,1 +8f39a0e7,Summer,10,1,Winter,70,Fall,16.35789474,57.0,75.6,,73,108,116,Fall,5,7,38,Fall,24,1,16.8,2,15.9,2,18,1,11.5,1,12.0,1,12.0,1,Fall,3,6.53194,16.3597,1111.02,1777.63,17.97,60.6013,13.114,3.24569,14.9987,2,25.7979,16.8333,54.0693,25.9144,43.7679,,,Summer,3.8,Fall,4,4,3,0,4,3,4,4,4,3,4,0,2,2,2,1,2,0,0,1,47,Fall,42,59,Summer,,1 +8f448fd6,Summer,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8f5fc30f,Summer,13,0,,,Summer,20.11457089,57.5,94.6,28.0,80,90,125,,,,,Summer,0,0,31.4,1,37.2,1,0,0,4.5,0,4.5,0,7.0,0,Summer,1,4.5448,20.1168,1271.58,1653.06,23.4571,77.7032,16.5237,3.59312,16.8968,1,36.7403,17.5058,73.1584,32.3904,60.1974,,,Summer,1.5,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +8f68479b,Summer,13,0,Winter,61,Fall,33.40490004,63.4,191.0,,87,83,166,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.45,Fall,5,5,5,5,5,2,1,5,5,5,5,4,5,4,4,5,5,5,5,5,90,Fall,38,54,Summer,3,3 +8f753551,Fall,11,1,Winter,61,Fall,26.90330556,61.0,142.4,,88,83,119,Fall,5,7,44,Fall,10,0,16.1,2,13.7,2,0,0,9.0,1,8.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.14,Fall,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,26,38,Fall,2,0 +8f9c03c6,Fall,7,1,Fall,65,Fall,14.52378472,48.0,47.6,,60,89,109,,,,,Fall,19,1,,,,,30,1,11.0,1,10.0,1,13.0,1,Winter,3,2.36321,14.5254,952.811,1524.5,10.4816,43.7499,13.3505,1.17487,3.85009,1,20.8864,12.3819,41.3867,19.0701,31.368,,,,,Winter,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Winter,34,49,Fall,0,0 +8fab825c,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8fb94734,Spring,11,0,,,Spring,16.12833058,55.0,69.4,25.0,64,84,105,,,,,Spring,2,0,10.6,1,13.9,2,0,0,5.0,0,6.0,0,4.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,1.62,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +8fc578da,Fall,6,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8fddddd5,Spring,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8fdff112,Spring,7,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +9007e96d,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +90161e10,Fall,12,0,Winter,81,Winter,24.9384574,61.0,132.0,,67,70,127,,,,,Winter,25,1,21.6,2,20.1,2,15,1,18.0,1,17.0,1,15.0,1,Winter,3,5.26789,24.5373,1441.73,2450.94,27.1818,95.8265,17.813,6.72424,36.1735,3,45.2783,23.3664,90.5586,53.6907,72.46,,,Fall,3.201,Winter,5,0,5,2,4,4,1,2,1,2,0,1,1,1,2,1,2,1,1,1,37,Winter,44,62,Fall,2,1 +9027c805,Fall,8,1,Winter,55,Fall,12.98520795,51.4,48.8,,63,69,105,Fall,6,9,43,Fall,6,0,,,,,0,0,6.0,0,6.0,0,8.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.43,Fall,2,1,1,0,1,1,0,1,0,1,4,0,0,0,1,0,1,1,1,1,17,Fall,31,45,Fall,2,0 +9042232d,Spring,10,0,,,Spring,18.65041663,54.5,78.8,25.0,71,66,116,,,,,Spring,20,1,14.3,2,15.9,2,10,1,9.0,1,9.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.631,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +9052579b,Summer,5,1,Spring,78,Winter,14.94119068,45.5,44.0,,78,93,124,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.21949,15.4522,921.914,1475.06,10.7353,40.459,13.7403,1.71198,5.04103,2,20.8082,8.91539,38.2395,16.6598,31.5436,,,,,Winter,3,1,2,0,3,1,0,2,0,1,0,0,0,0,2,2,0,2,1,1,21,Winter,40,56,Summer,2,0 +9061cc9c,Summer,6,1,Winter,61,Fall,19.15675,40.0,43.6,,66,82,108,Fall,,,,Fall,0,0,,,,,0,0,10.0,1,10.0,1,5.0,0,Fall,2,2.30443,14.8723,923.455,1385.18,10.77,40.623,13.8568,1.01546,2.97696,1,22.6821,7.17089,38.3186,22.1085,33.4521,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +90778278,Fall,9,1,,,Fall,25.51715702,55.0,109.8,32.0,78,72,119,,,,,Fall,15,1,,,,,4,0,10.0,1,11.0,1,10.0,1,Fall,2,3.93798,25.5199,1223.15,1957.04,19.4686,72.5447,16.861,8.65896,37.2553,3,35.5537,17.5224,68.6067,34.2384,55.0223,,,,,Fall,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Fall,34,49,Fall,0,0 +908b1207,Summer,10,0,Fall,70,Fall,25.37614796,56.0,113.2,,85,88,125,Fall,2,2,45,Fall,1,0,12.0,1,12.7,2,0,0,0.0,0,0.0,0,8.0,0,Fall,1,8.65202,25.3789,1243.02,1615.92,20.4354,74.6609,16.7386,8.6403,38.5391,2,35.7877,18.4377,66.0088,33.1745,56.2231,,,Summer,1.49,Fall,4,5,5,0,4,5,1,3,3,1,0,0,4,4,4,5,1,5,0,2,56,Fall,46,64,Summer,2,2 +9091645a,Winter,11,0,Spring,65,Winter,14.763,60.0,75.6,,53,101,105,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.72,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +909edf22,Fall,15,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.45,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +90a37530,Spring,8,1,Summer,78,Spring,18.55138889,48.0,60.8,26.0,64,77,106,,,,,Spring,15,1,,,,,2,0,12.0,1,12.0,1,9.0,1,Summer,2,2.4391,18.5534,974.024,1461.04,12.4623,46.0093,14.04,4.51345,14.7907,1,21.9706,11.5764,43.5702,19.7916,34.4329,,,Spring,3.25,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,36,51,Spring,0,0 +90d7cf39,Winter,15,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +90fee6b1,Summer,10,1,Fall,61,Fall,12.79127219,52.0,49.2,,75,72,109,Fall,6,9,23,Fall,8,1,,,,,10,1,12.0,1,14.0,1,12.0,1,Fall,3,11.7609,12.7927,1000.35,1600.56,11.3204,48.8133,12.6921,0.100537,0.386662,2,21.2667,16.2263,37.0524,20.4845,32.5871,,,Summer,3.45,Fall,3,3,2,2,4,1,0,1,0,1,3,0,1,0,0,1,3,1,1,0,27,Fall,34,49,Summer,2,0 +9105b812,Fall,18,1,Spring,82,Winter,22.19205156,66.5,139.6,,50,74,120,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.82,,,Winter,3,2,3,1,3,2,2,2,1,2,3,1,1,1,2,2,1,2,1,1,36,Winter,42,59,Fall,2,1 +91098060,Winter,10,1,Fall,80,Winter,16.53355928,58.25,79.8,,62,73,110,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.84,Spring,2,2,2,1,2,1,0,2,2,2,2,0,2,2,,1,2,,1,1,27,Spring,36,51,Winter,2,0 +91133c73,Winter,12,0,Spring,58,Winter,36.71560095,67.25,236.2,,,98,,,,,,Winter,0,0,38.4,3,41.3,3,0,0,8.0,1,9.0,1,5.0,0,Winter,2,6.50964,36.7196,1832.46,2931.94,43.4475,137.444,21.3671,15.3525,98.7556,3,57.4073,36.5895,130.935,58.7682,100.855,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,85,100,Winter,2, +911d1406,Spring,10,1,Summer,52,Fall,15.62820825,61.25,83.4,,67,65,101,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.31,Spring,1,1,2,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,20,Spring,54,75,Spring,0,0 +911f6407,Winter,6,0,Spring,60,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +912a91b0,Spring,6,1,Fall,80,Winter,20.46540643,46.0,61.6,,96,33,127,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,0,0,0,0,0,1,2,0,0,0,0,0,2,2,2,1,0,16,Spring,54,75,Spring,2,0 +9130a22e,Spring,8,0,Spring,75,Summer,19.78648262,54.5,83.6,27.0,72,99,125,,,,,Summer,10,1,,,,,0,0,8.0,1,8.0,1,6.0,1,Summer,2,3.62856,19.7886,1115.29,1784.46,15.3677,61.0556,14.4522,5.3364,22.5444,2,29.4173,16.2706,57.427,23.408,44.785,,,,,Spring,3,2,3,0,2,0,0,1,0,0,1,0,2,0,1,0,0,1,0,0,16,Summer,45,63,Fall,0,0 +91386987,Winter,13,1,,,Winter,23.60526142,61.5,127.0,31.0,76,86,104,,,,,Winter,9,0,11.2,1,12.6,1,0,0,5.0,0,6.0,0,3.0,0,Winter,2,4.63978,23.6078,1321.06,1981.58,28.6309,82.9729,15.4237,8.18413,44.0271,2,33.4139,20.928,78.3331,37.7847,62.0449,,,Winter,2.18,Winter,2,2,3,2,2,1,2,2,1,2,3,1,1,1,1,1,1,1,1,1,31,Winter,41,58,Winter,2,1 +913bea9f,Fall,16,1,Winter,65,Winter,39.33779297,64.0,229.2,,70,83,125,,,,,Winter,18,1,30.1,3,33.0,3,0,0,11.0,1,11.5,1,10.0,1,Winter,4,5.3122,39.3421,1573.45,2989.56,40.2483,109.856,18.8568,20.4853,119.344,3,40.9028,28.7052,104.544,54.4747,81.1511,Winter,1.7,,,Fall,4,5,2,1,4,2,1,2,1,2,0,0,1,3,3,1,2,2,1,1,38,Fall,59,81,Fall,0,1 +91448d8e,Summer,9,0,,,Summer,23.2748309,53.0,93.0,29.0,54,61,108,,,,,Summer,11,1,,,,,5,0,6.5,0,7.0,0,9.0,1,Summer,2,3.27879,23.2774,1105.36,1768.58,13.0015,59.9983,15.0172,8.26014,33.0017,3,28.9643,18.0326,56.7195,25.8513,41.9657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +915dbb8a,Summer,11,0,,,,,,,,,,,,,,,Fall,0,0,18.8,2,20.1,2,2,0,8.0,1,8.0,1,12.0,1,Fall,4,4.25094,21.527,1228.95,2580.79,19.9363,73.1619,15.2909,6.23618,29.8381,2,34.177,19.0486,68.9109,30.0192,54.1133,,,Summer,2.329,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1, +91656d27,Spring,12,0,Fall,65,Fall,18.61448634,65.5,113.6,,59,60,115,,,,,Fall,40,1,32.6,3,35.2,3,20,1,6.0,0,5.0,0,12.0,1,Fall,4,6.15591,18.6165,1498.67,3147.21,31.0841,101.891,16.6977,1.91883,11.7089,1,45.9776,24.8293,95.7352,49.7574,77.0617,,,Fall,2.15,Fall,4,2,3,1,3,2,3,2,1,3,4,1,2,2,4,2,1,2,3,2,47,Fall,44,62,Fall,1,1 +916f7069,Winter,10,0,Summer,63,Spring,19.12618661,55.3,83.2,,82,79,131,,,,,Spring,0,0,,,,,2,0,5.0,0,7.0,0,8.0,1,Summer,4,3.6819,19.1283,1118.67,2349.22,14.8629,61.4165,14.1201,5.00817,21.7835,2,29.4893,17.0644,57.7346,24.7562,44.3522,,,Winter,2.13,Spring,3,0,2,0,2,0,0,0,0,1,2,0,0,0,0,0,0,1,2,0,13,Spring,34,49,Winter,2,0 +9173f25b,Fall,13,0,Summer,65,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.54,Winter,4,4,4,3,5,1,2,3,2,2,5,1,3,3,3,2,5,3,3,3,61,Winter,40,56,Fall,2,2 +9178000b,Spring,15,0,,,Spring,19.41902428,67.0,124.0,27.0,70,56,136,,,,,Spring,40,1,40.5,2,40.7,2,26,1,11.0,1,9.0,1,12.0,1,Fall,4,6.10332,19.4211,1476.61,3100.89,28.8396,99.5419,15.5905,3.83069,24.4581,2,44.7251,25.9772,93.4385,49.7709,73.5647,Spring,3.63,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +917990b2,Winter,13,1,Summer,66,Summer,24.82546746,65.0,149.2,,67,73,134,,,,,Spring,50,1,29.6,3,31.8,3,14,1,12.0,1,11.0,1,12.0,1,Spring,2,10.5146,24.4506,3600.18,5400.27,104.347,325.73,53.3799,-28.9293,-176.53,2,125.694,95.6882,315.215,223.449,230.042,,,Winter,1.65,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,47,66,Winter,,0 +9181b136,Fall,6,0,Spring,75,Spring,15.51811745,49.0,53.0,,56,89,107,Spring,7,11,35,Spring,5,1,,,,,0,0,7.0,0,7.0,0,9.0,1,Spring,2,5.06992,15.5198,968.249,1549.2,9.12793,45.3942,13.2926,2.22717,7.60576,2,24.9846,11.2817,40.3243,20.6382,34.1125,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,5,0,5,0,0,15,Spring,36,51,Winter,0,0 +91930eec,Winter,6,0,Winter,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,42,59,Winter,0,0 +91942b42,Fall,8,1,Spring,61,Spring,15.43914931,48.0,50.6,,55,93,132,Spring,4,5,24,Spring,15,1,,,,,15,1,12.5,1,13.0,1,7.0,1,Spring,1,2.47631,15.4408,959.476,1247.32,10.9895,44.4598,13.5671,1.87371,6.14019,1,23.0879,10.3824,41.9835,25.9123,34.0774,,,Spring,2.71,Spring,4,4,4,0,5,3,0,2,0,4,4,0,0,4,3,4,4,5,4,2,56,Spring,49,68,Fall,2,2 +91a5d06e,Fall,5,0,Summer,60,Fall,17.01020794,46.0,51.2,21.0,60,89,114,,,,,Fall,0,0,,,,,,0,4.5,0,6.5,0,7.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,,,,Fall,0,0 +91bcd48c,Summer,6,0,,,Summer,16.22099271,47.2,51.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +91db996a,Summer,8,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +91e23cc5,Winter,14,0,Spring,72,Winter,25.09243164,64.0,146.2,33.0,76,97,116,,,,,Winter,28,1,28.8,2,32.1,2,4,0,0.0,0,0.0,0,12.0,1,Winter,3,5.78663,25.0952,1533.85,2607.55,32.8857,105.639,18.1328,6.96236,40.5615,2,46.3688,26.3841,99.8519,45.7502,79.2545,Winter,2.1,,,Winter,2,3,5,1,3,2,4,3,2,2,4,0,2,3,2,2,3,3,5,2,53,Winter,38,54,Winter,3,2 +91e27185,Spring,9,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.17,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +91e86c47,Winter,15,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1,1,2,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,7,Winter,30,43,,,0 +91f5e9cd,Summer,16,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +91fdc1e0,Spring,11,0,Fall,65,Spring,24.2961032,61.0,128.6,31.0,75,78,120,,,,,Spring,42,1,22.1,2,25.0,3,12,1,6.0,0,7.0,0,7.0,0,Spring,4,4.97371,24.2988,1381.21,2900.54,25.9298,89.38,16.8882,7.41055,39.22,3,40.8853,22.5649,84.4063,40.3382,66.8151,,,Spring,3.32,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,28,41,,,0 +92031591,Summer,11,0,Summer,70,Fall,20.4797635,61.0,108.4,,73,82,128,Fall,5,8,1,Fall,8,0,14.2,2,16.1,2,0,0,5.0,0,6.0,0,5.0,0,Fall,1,9.6391,20.482,1246.42,1620.35,19.0826,75.0232,14.1755,6.30649,33.3768,2,33.3259,22.6148,65.3841,29.5994,52.4084,,,Winter,2.73,Fall,4,0,2,0,4,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,14,Summer,37,53,Summer,2,0 +921146d1,Spring,7,1,Fall,79,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,60,82,Spring,0,0 +92293b06,Winter,12,1,Spring,55,Spring,15.07544444,60.0,77.2,,69,88,106,Spring,5,7,30,Spring,7,0,18.5,2,17.2,2,0,0,7.25,0,6.5,0,8.0,0,Spring,3,3.94444,15.0771,1151.79,1842.86,19.092,64.9434,12.6834,2.3937,12.2566,2,27.0662,18.7852,60.999,29.0849,46.1582,,,Spring,3.09,Spring,2,2,2,2,2,1,1,1,2,1,0,0,2,1,1,1,2,2,2,1,28,Spring,43,60,Winter,1,0 +92622443,Summer,8,1,Winter,61,Summer,16.69865209,51.5,63.0,,67,72,115,,,,,Summer,20,1,,,,,10,1,17.0,1,19.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.48,Summer,5,4,3,3,5,2,0,1,0,3,5,0,2,0,4,5,1,4,0,2,49,Summer,46,64,Summer,2,1 +926a64d8,Summer,14,1,Fall,75,Summer,25.09146722,62.0,137.2,,107,56,160,,,,,Summer,18,1,23.3,2,22.7,2,8,1,9.0,0,10.0,1,10.0,1,,,,,,,,,,,,,,,,,,Summer,2.74,,,Summer,3,3,3,1,3,3,0,2,3,3,5,0,2,3,3,2,3,2,2,2,48,Summer,62,85,Summer,2,1 +926b3a76,Fall,16,0,Spring,61,Spring,22.43273646,66.0,139.0,,65,69,100,,,,,Spring,20,0,33.1,2,33.4,2,10,0,13.0,1,14.0,1,12.0,1,Spring,5,5.99468,19.3866,1444.53,3466.86,21.659,96.1241,13.4066,5.97998,42.8759,1,43.5283,30.9368,90.1294,65.696,65.1872,Fall,2.08,,,Spring,4,5,5,4,5,5,4,5,5,5,5,0,5,5,5,5,5,5,5,5,92,Spring,43,60,Fall,1,3 +926bd07e,Fall,7,0,Winter,80,Winter,14.65788752,54.0,60.8,,63,63,113,Winter,7,12,0,Winter,14,1,,,,,4,1,7.5,1,6.5,0,7.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,0,0,0,0,3,Fall,49,68,Fall,0,0 +9271574b,Winter,16,1,Spring,65,Winter,17.2640999,62.0,94.4,,73,73,107,,,,,Winter,10,0,16.8,1,22.0,2,0,0,7.5,0,7.5,0,3.0,0,Winter,1,4.35552,17.266,1219.25,1585.02,22.7137,72.129,13.1926,4.07342,22.271,1,29.1327,20.2827,67.7735,31.376,51.8463,Winter,1.08,,,Spring,1,1,1,0,1,0,2,0,0,0,2,1,0,0,0,0,1,0,0,0,10,,,,Winter,0,0 +9289fb19,Summer,10,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.49,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +92ac0421,Summer,7,0,,,Fall,18.87013172,53.0,75.4,,74,86,124,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +92b18571,Winter,8,1,,,Winter,14.41176442,50.75,52.8,22.0,69,98,108,,,,,Winter,10,1,,,,,3,0,10.25,1,10.25,1,6.0,1,Winter,2,2.67068,14.4133,991.539,1487.31,12.6716,47.8749,13.0689,1.34445,4.92508,1,21.6074,13.5959,45.2042,18.3918,34.279,,,Winter,1.64,Winter,0,0,2,0,0,0,2,1,0,2,2,0,0,0,1,0,0,1,0,1,12,Winter,49,68,Winter,0,0 +92bb8516,Winter,9,0,Summer,75,Spring,19.2866941,54.0,80.0,,65,81,121,Spring,,,,Spring,3,0,,,,,0,0,4.0,0,4.0,0,7.0,1,Spring,2,3.38719,19.2888,1084.16,1734.66,12.6047,57.7404,13.9218,5.36701,22.2596,2,27.6018,17.5339,54.3532,22.9978,40.2065,,,Winter,2.27,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,39,55,Winter,0,0 +92bcf0e7,Spring,7,0,Summer,80,Spring,16.24551391,47.8,52.8,,79,68,102,Spring,12,20,0,Spring,16,1,,,,,2,0,11.5,1,11.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Spring,33,47,Spring,0,0 +92dc90c8,Spring,10,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.71,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +93018bd9,Fall,10,0,,,Fall,16.66081871,57.0,77.0,,69,89,115,,,,,Fall,0,,19.3,2,20.5,2,0,,8.0,,8.0,,8.0,,Fall,2,3.97931,16.6626,1133.19,1813.1,15.3089,62.9624,13.6249,3.0377,14.0376,1,30.741,16.9125,58.9831,28.5905,46.05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +930c567c,Winter,10,0,Spring,74,Winter,14.84140643,57.5,69.8,,85,92,125,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,3,4.10702,14.843,1129.81,1920.67,15.6673,62.6021,13.3124,1.53064,7.1979,1,30.6993,16.2355,58.4951,28.1269,46.3666,,,Spring,3.08,Spring,4,3,3,1,4,1,1,1,1,3,1,1,4,1,3,3,2,3,1,1,42,Spring,28,41,Winter,1,1 +93188a17,Fall,12,0,,,Fall,16.96896552,58.0,81.2,29.0,67,98,92,,,,,Fall,2,0,11.7,1,11.7,1,0,0,0.0,0,0.0,0,3.0,0,Fall,3,3.94783,16.9708,1137.94,1934.5,14.7535,63.4685,13.2649,3.70588,17.7315,2,29.6269,19.0881,59.5207,26.4252,44.3804,,,Fall,1.67,Fall,4,0,4,0,4,1,0,0,1,0,1,0,3,0,4,1,3,1,0,1,28,Fall,32,46,Fall,0,0 +932b5928,Fall,10,0,Spring,41,Spring,19.267717,58.0,92.2,,79,99,133,Spring,4,5,56,Spring,8,0,22.9,3,21.0,2,7,1,12.0,1,12.0,1,8.0,0,Spring,3,8.45576,26.9986,1093.73,1859.34,14.86,58.7596,17.2064,9.79223,33.4404,2,31.2254,12.6743,50.3039,27.8422,46.0853,,,Winter,2.63,Winter,5,3,3,1,4,1,0,1,0,1,4,0,2,1,4,2,2,2,1,0,37,Winter,62,85,Fall,2,1 +93363059,Summer,14,1,,,Summer,19.73754883,64.0,115.0,27.0,75,74,112,,,,,Summer,30,1,31.3,3,32.4,3,10,1,8.0,0,8.0,0,12.0,1,Fall,5,5.4333,19.7397,1420.83,3125.82,31.2717,93.5999,16.0664,3.67331,21.4001,2,39.5233,22.805,88.1666,53.1195,70.7949,Winter,3.56,,,Fall,1,4,4,1,3,1,1,2,2,2,3,1,1,2,1,1,0,2,1,0,33,Fall,37,53,Summer,2,1 +933da99b,Spring,9,0,Fall,59,Spring,17.30332937,58.0,82.8,,71,86,119,Spring,7,11,26,Spring,3,0,,,,,10,1,8.5,1,12.5,1,6.0,1,Spring,3,4.14476,17.9177,1171.61,1991.74,17.618,67.055,14.5106,3.40719,15.745,2,32.8369,16.6,62.9102,30.5617,50.4549,,,Spring,3.61,Spring,4,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,9,Spring,52,72,Spring,1,0 +93507a1b,Summer,13,0,Summer,45,Winter,29.47006859,67.5,191.0,,85,99,171,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.1,Summer,5,5,5,3,5,5,2,5,5,1,5,1,2,5,5,5,5,5,5,4,83,Summer,66,90,Summer,3,3 +93559639,Spring,7,0,Summer,60,Spring,14.8432463,51.5,56.0,,67,110,118,Spring,4,6,29,Spring,6,1,,,,,6,1,7.5,1,7.5,1,7.0,1,Summer,4,2.59394,14.8449,1043.2,2190.72,4.61869,53.3776,14.1497,0.695162,2.62239,1,20.7406,28.0183,50.7837,20.7995,25.3593,,,,,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,Spring,26,38,Spring,0,0 +937b41ec,Fall,16,0,,,Fall,29.28401423,74.5,231.2,44.0,64,79,119,,,,,Fall,10,0,35.4,2,34.3,2,0,0,15.0,1,0.0,0,12.0,1,Fall,3,8.33688,29.2873,2005.5,3409.35,50.4797,155.875,19.7455,9.54184,75.3255,3,63.2333,42.1624,147.538,68.2066,113.713,Fall,2.23,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +93904b23,Fall,10,0,Winter,47,Winter,23.74972709,59.0,117.6,,59,83,118,Winter,5,8,10,Winter,9,0,24.7,3,26.3,3,15,1,4.0,0,5.0,0,8.0,0,,,,,,,,,,,,,,,,,,,,Fall,2.19,Winter,5,5,5,2,2,1,0,2,0,1,1,0,4,0,3,3,2,4,2,1,43,Winter,51,71,Fall,1,1 +93aa9e6b,Spring,11,1,,,Spring,20.09011615,58.5,97.8,29.0,71,94,135,,,,,Spring,1,0,13.9,2,14.2,2,1,0,11.0,1,11.0,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.42,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +93c06d4c,Summer,10,1,,,Fall,15.56452247,54.1,64.8,,73,78,111,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.94,Fall,2,4,2,1,2,1,1,1,1,1,2,0,1,1,0,1,1,1,0,0,23,Fall,43,60,Summer,0,0 +93c72a97,Fall,21,1,Winter,50,Winter,25.5575858,65.0,153.6,,54,83,129,,,,,,,,,,,,,,,,,,,,Winter,2,5.4032,25.5604,1452.07,2178.1,31.579,96.9275,16.1296,9.4308,56.6725,3,40.2638,25.0847,91.5243,61.8307,71.8428,,,,,Winter,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Winter,32,46,Fall,3,0 +93c93585,Fall,10,0,Winter,60,Fall,14.14719189,53.5,57.6,,60,67,106,Fall,4,6,55,Fall,9,0,9.2,1,10.9,1,5,0,10.5,1,8.5,1,3.0,0,Winter,3,3.47609,14.1487,1035.89,1761.01,10.5656,52.5986,12.9202,1.22852,5.00137,1,28.3034,13.7297,49.1225,30.2603,38.869,,,Fall,2.35,Fall,1,1,2,1,2,1,0,2,1,1,1,1,1,1,2,1,1,1,1,1,23,Fall,26,38,Fall,2,0 +93cdae1c,Spring,9,0,Fall,50,Summer,19.40779396,54.5,82.0,28.0,66,104,109,,,,,Summer,2,0,,,,,0,0,7.0,0,7.0,0,2.0,0,Summer,2,3.64081,19.4099,1112.83,1780.53,14.9625,60.794,14.3903,5.0196,21.206,2,29.7929,16.0386,57.1532,25.2528,44.7554,,,,,Summer,2,1,2,1,1,1,0,2,1,1,1,1,1,1,1,1,1,1,1,1,22,Summer,49,68,Spring,2,0 +93f8b501,Summer,8,1,Winter,70,Summer,14.87115385,52.0,57.2,,65,88,111,Summer,,,,Summer,7,1,,,,,5,1,11.2,1,12.2,1,8.5,1,Summer,4,9.94269,14.8728,1012.28,1923.34,12.9116,50.0845,13.0227,1.85012,7.11547,1,22.277,14.8958,40.1418,21.0617,35.1887,,,Summer,1.49,Summer,1,1,1,1,1,1,1,1,1,1,1,0,1,1,,1,1,1,1,1,18,Summer,48,67,Summer,0,0 +93fd93c4,Spring,14,0,Winter,65,Summer,19.29130859,64.0,112.4,,63,79,131,,,,,Summer,25,1,26.3,2,25.2,2,22,1,8.5,1,7.5,1,9.5,1,Summer,1,5.95836,19.2934,1491.25,1938.62,29.7006,101.1,17.3539,1.93956,11.2995,2,47.3891,24.0108,95.1421,57.0176,77.0897,,,Spring,3.15,Summer,2,3,3,2,4,2,0,2,1,1,3,0,2,2,2,1,1,1,1,1,34,Summer,32,46,Spring,0,1 +93fda84a,Spring,6,1,Fall,91,Summer,16.83271665,51.7,64.0,,75,77,123,Summer,6,9,45,Summer,0,0,,,,,0,0,11.0,1,7.5,0,8.5,1,Fall,3,2.8165,16.8346,1021.56,1634.49,13.9228,51.0724,13.4341,3.40048,12.9276,2,23.0997,14.0498,48.2559,22.2157,37.0225,,,,,Summer,1,1,1,0,0,0,0,0,0,0,0,0,0,2,1,3,2,3,0,0,14,Summer,37,53,Spring,0,0 +9402b885,Spring,12,0,Summer,85,Spring,17.7756673,57.5,83.6,,81,80,120,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.86,Summer,1,1,1,1,1,2,1,1,1,1,0,0,0,0,0,0,0,0,0,0,11,Summer,56,77,Spring,0,0 +9405bad1,Winter,7,0,Spring,60,Winter,16.67654373,48.5,55.8,28.0,,93,,,,,,Winter,18,1,,,,,0,0,5.0,0,5.0,0,4.0,0,Winter,2,2.73504,16.6784,962.67,1540.27,8.18576,44.8,13.3905,3.28785,11.0,1,23.0365,13.5777,42.0649,16.7134,31.2223,,,,,Winter,3,3,3,0,2,0,0,0,0,0,0,0,0,0,1,2,2,2,0,0,18,Winter,32,46,Winter,2,0 +940b2f17,Spring,22,1,Summer,75,Spring,26.0567574,65.0,156.6,35.0,80,78,129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +940e7feb,Summer,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9416d70e,Winter,13,0,Summer,61,Spring,31.53941998,66.5,198.4,41.0,74,102,121,,,,,Spring,12,0,32.3,3,32.5,3,0,0,10.0,1,9.0,1,12.0,1,Summer,1,6.20932,31.5429,1692.88,2200.75,37.5198,122.577,19.4881,12.0547,75.8225,3,52.6516,32.406,116.368,55.9945,90.1715,,,Spring,2.47,Spring,0,5,4,1,3,1,1,1,1,1,2,1,1,2,2,1,2,1,1,1,32,Spring,47,66,Winter,2,1 +941cdb96,Fall,12,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +945d802a,Fall,9,0,Winter,80,Fall,14.54028083,55.8,64.4,,65,81,127,Fall,7,11,21,Fall,23,1,,,,,7,1,9.5,1,10.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.74,Fall,5,5,3,1,5,0,0,1,1,2,2,0,1,1,2,3,1,5,1,2,41,Fall,61,84,Fall,0,1 +947c957d,Winter,17,0,,,Winter,34.10901071,69.0,231.0,40.0,86,82,142,,,,,Winter,0,0,81.2,2,106.0,2,0,0,0.0,0,0.0,0,0.0,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +948a7025,Winter,8,1,Summer,55,Spring,20.59538639,51.0,76.2,,69,90,114,Spring,5,8,29,Spring,4,1,,,,,0,0,11.0,1,11.0,1,7.0,1,Summer,3,2.80889,20.5976,1036.64,1658.62,15.4699,52.6784,14.2395,6.35812,23.5216,2,23.8646,13.3439,49.8696,22.6391,39.3345,,,,,Spring,4,2,2,1,1,2,0,1,1,1,1,0,0,0,0,1,0,0,0,0,17,Spring,37,53,Winter,1,0 +948e1435,Winter,15,1,Spring,61,Winter,28.51516136,59.5,143.6,,69,80,121,,,,,Winter,30,1,29.1,3,27.1,2,20,1,17.0,1,16.5,1,10.0,1,Spring,3,4.13798,28.5183,1283.98,2054.37,26.227,79.024,15.6938,12.8245,64.576,2,32.0398,20.7573,74.886,39.8951,58.2667,Spring,2.31,,,Spring,3,2,4,1,3,1,2,2,3,1,4,1,2,1,1,2,2,3,2,1,41,Spring,36,51,Winter,2,1 +94902bc9,Spring,13,0,Fall,55,Summer,34.66220408,70.0,241.6,,57,71,112,,,,,Summer,25,1,35.0,3,36.5,3,10,0,6.5,0,7.0,0,10.0,1,Summer,2,8.54546,34.5225,2291.06,3665.7,57.4992,186.292,26.7301,7.79244,54.3084,3,80.9646,47.8278,177.746,119.085,138.464,,,Spring,2.51,Spring,4,3,4,2,2,1,2,2,1,2,3,1,3,2,3,2,2,1,2,1,43,Spring,52,72,Spring,1,1 +9498cc26,Spring,11,1,Summer,59,Summer,23.82388889,60.0,122.0,,52,78,112,Summer,5,7,16,Summer,20,1,24.0,3,22.7,3,0,0,12.0,1,12.0,1,13.0,1,Summer,2,4.21659,23.8265,1257.74,1886.61,26.1816,76.2288,14.8874,8.93907,45.7712,3,30.1582,19.889,72.0122,30.701,56.3398,,,Spring,3.36,Summer,5,4,2,1,5,1,1,3,1,1,4,1,1,1,2,2,2,2,1,1,41,Summer,61,84,Spring,0,1 +949f94e5,Spring,7,0,Spring,63,Spring,15.44897474,50.75,56.6,,119,89,155,Spring,3,3,9,Spring,5,1,,,,,0,0,8.0,1,9.0,1,12.0,1,Spring,3,3.08839,15.4507,994.628,1690.87,10.2454,48.204,13.1587,2.29194,8.396,1,25.0459,12.9126,45.1156,19.325,35.2914,,,,,Fall,2,1,2,0,1,0,0,2,0,2,0,0,0,0,0,0,1,1,0,0,12,Fall,42,59,Spring,0,0 +94a1053b,Summer,14,1,Fall,60,Summer,30.34178984,61.0,160.6,,70,82,99,,,,,Summer,14,0,20.6,2,22.3,2,2,0,11.0,1,12.0,1,7.0,0,Summer,2,4.51606,30.3451,1358.23,2037.34,29.9296,86.9323,16.4257,13.9194,73.6677,3,34.5601,22.4426,82.4163,43.5286,64.4897,Fall,1.49,,,Fall,4,5,4,4,4,3,1,1,1,2,5,1,0,0,2,0,2,0,2,1,42,Fall,30,43,Fall,3,1 +94a6c971,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.95,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +94e9e4b9,Fall,12,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4.18,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +9508b72f,Spring,10,1,Summer,85,Spring,17.25405405,55.5,75.6,,52,85,115,Spring,6,9,9,Spring,13,1,,,,,4,0,9.5,1,7.5,0,7.0,1,Spring,2,5.35171,17.2559,1104.01,1656.02,18.1876,59.8547,13.662,3.59391,15.7453,1,26.6439,15.0232,54.503,27.5724,44.8315,,,Spring,2.3,Spring,2,1,3,2,2,1,0,2,1,2,0,0,2,0,2,1,2,2,1,2,28,Spring,50,69,Spring,1,0 +950f377f,Fall,8,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9520959f,Summer,16,1,,,Summer,24.0797184,62.5,133.8,30.0,88,67,135,,,,,Summer,0,0,35.8,3,35.2,3,7,1,6.0,0,6.0,0,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +9521093f,Summer,12,0,Fall,68,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.62,Fall,1,4,4,1,4,1,0,1,2,0,4,0,0,0,1,0,2,0,5,0,30,Fall,29,42,Summer,3,0 +953bdf6c,Winter,16,1,,,Spring,22.40406712,58.86,110.4,28.0,62,69,116,,,,,,,,,,,,,,,,,,,,Spring,3,3.67457,22.4043,1180.06,1888.1,18.2043,67.9549,13.7906,8.6137,42.4451,2,28.8262,20.9244,64.2804,44.813,47.0305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9540fa8e,Summer,7,0,Fall,60,Summer,24.9565,40.0,56.8,,65,89,104,,,,,Summer,0,,,,,,0,,12.0,,12.0,,5.0,,Summer,2,3.04018,16.8036,990.584,1584.93,11.5921,47.7732,14.1331,2.67047,9.0268,2,26.139,10.0421,44.733,19.3606,37.7311,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,37,53,Summer,1,0 +95488452,Spring,8,1,Summer,65,Spring,16.11908284,52.0,62.0,24.0,74,81,131,,,,,Spring,10,1,,,,,0,0,6.5,0,9.0,1,10.0,1,Spring,3,2.92699,16.1208,1026.87,1642.99,14.7116,51.6381,13.4266,2.69424,10.3619,2,23.8356,13.0908,48.7111,22.6137,38.5472,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,31,45,Spring,0,0 +954a4c2e,Spring,11,1,Summer,40,Spring,21.99863946,63.0,124.2,,90,80,126,,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.70028,22.001,1307.6,1961.41,27.3642,81.54,14.4442,7.55687,42.66,3,32.1958,21.9801,76.8398,36.3156,59.5599,,,Spring,3.54,Summer,4,5,3,2,4,2,0,0,2,1,2,0,2,1,3,2,2,2,2,1,40,Summer,35,50,Spring,2,1 +9568e659,Spring,14,1,,,Summer,26.37056705,59.5,132.8,,79,94,125,,,,,Summer,0,0,27.5,3,23.7,2,4,0,8.0,0,6.0,0,11.0,1,,,,,,,,,,,,,,,,,,Spring,1.32,,,Summer,0,5,5,2,4,2,1,4,5,4,4,1,2,4,5,5,5,5,4,2,69,Summer,48,67,Spring,3,2 +9570bfb7,Summer,6,1,Fall,80,Summer,16.41219282,46.0,49.4,,54,71,107,Summer,4,4,50,Summer,0,0,,,,,0,0,6.0,0,9.0,1,6.0,1,Fall,1,9.4407,17.1516,922.116,1198.75,10.1743,40.4804,14.0547,3.09686,8.91956,2,20.602,9.70412,31.0397,17.6884,30.7763,,,,,Summer,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,Summer,29,42,Summer,0,0 +9577ae01,Winter,17,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +957dcb7f,Winter,5,1,Spring,61,Spring,14.10782313,42.0,35.4,,64,100,111,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,3,0,4,0,0,0,0,4,0,0,5,2,0,5,0,4,1,0,36,Spring,34,49,Winter,0,1 +95960d57,Spring,13,1,Winter,,Winter,39.49414806,65.5,241.0,,68,97,115,,,,,,,,,,,,,,,,,,,,Winter,3,5.91043,39.4946,1693.76,2710.02,46.3836,122.671,20.1031,19.3915,118.329,2,44.5277,31.7598,116.761,56.7618,90.9114,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +95a741b8,Winter,11,0,,,Winter,27.95035909,59.0,138.4,39.0,60,87,122,,,,,Winter,0,0,13.6,2,13.8,2,0,0,6.0,0,5.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.31,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +95c9e2f2,Summer,19,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +95cc96cc,Fall,5,0,Spring,80,Fall,17.1497284,45.0,49.4,,54,96,124,,,,,Fall,2,1,,,,,0,0,7.0,0,8.0,1,4.0,0,Fall,2,2.48356,17.1516,911.504,1458.41,6.9062,39.3501,13.6623,3.48931,10.0499,1,22.7707,9.67318,36.8665,17.0376,29.6769,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,,0 +95d57ff9,Winter,12,0,Summer,60,Spring,20.18005858,58.25,97.4,,80,107,126,Spring,5,7,15,Winter,3,0,14.1,2,13.7,2,0,0,14.0,1,13.0,1,8.0,0,Winter,2,4.28499,20.1823,1218.22,1949.15,18.7124,72.0195,14.9232,5.25909,25.3805,2,34.5889,18.7182,67.7345,34.0793,53.3013,,,Spring,2.459,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,55,76,Winter,0,0 +95dfb89b,Spring,17,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +95e04c4b,Summer,7,0,,,Summer,13.51498611,45.5,39.8,20.0,45,85,98,,,,,Summer,0,0,,,,,3,1,6.0,0,5.0,0,8.0,1,Fall,2,2.38563,13.5165,895.033,1432.05,4.80675,37.5958,12.7679,0.748568,2.2042,1,20.366,12.423,35.2102,14.9587,25.1728,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,29,42,Summer,0,0 +96184bea,Winter,7,0,,,Winter,17.92222706,52.25,69.6,28.0,57,76,104,,,,,Winter,1,0,,,,,1,0,7.75,1,6.0,0,7.0,1,Winter,2,3.30898,17.9242,1048.57,1677.72,12.6676,53.9498,13.8938,4.03042,15.6502,1,27.0241,14.2581,50.6408,20.8814,39.6917,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +96262441,Winter,8,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,4,3,0,4,0,0,0,0,1,3,0,0,0,0,3,1,3,1,1,28,Winter,37,53,Winter,0,0 +963ab7d2,Winter,12,0,,,Winter,16.0912604,58.0,77.0,21.0,69,68,111,,,,,Spring,30,1,33.2,1,32.4,1,20,1,6.0,0,7.0,0,7.0,0,Spring,,,,,,,,,,,,,,,,,,,Winter,2.28,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +963adba5,Spring,13,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +9646a500,Fall,6,0,,,Fall,13.68447261,47.0,43.0,24.0,55,84,83,,,,,Fall,0,0,,,,,0,0,0.0,0,0.0,0,6.5,1,,,,,,,,,,,,,,,,,,,,,,Fall,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,,,,Winter,2,0 +96595f1d,Winter,8,0,,,Spring,14.10865052,51.0,52.2,,69,71,108,Spring,7,11,19,Spring,22,1,,,,,16,1,8.5,1,6.0,0,9.0,1,Summer,3,3.08094,14.1102,984.172,1673.09,8.90825,47.0902,12.729,1.38123,5.1098,1,25.0549,13.1271,44.0093,22.2364,33.9631,,,Winter,2.48,Spring,1,3,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,Spring,33,47,Winter,0,0 +966161e7,Fall,8,1,Spring,62,Fall,14.08657845,46.0,42.4,,63,73,105,,,,,Fall,1,0,,,,,0,0,10.0,1,10.0,1,9.0,1,Fall,2,2.17208,14.0881,923.275,1384.91,9.93351,40.6039,13.4913,0.596777,1.79608,1,19.5464,11.124,38.4318,14.6607,29.4799,,,,,Winter,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,31,45,Fall,0,0 +966fffd0,Winter,16,0,Spring,65,Spring,20.04232446,68.25,132.8,,65,87,109,,,,,Spring,30,1,41.5,2,46.8,2,39,1,12.0,1,11.5,1,11.0,1,Spring,3,6.56876,20.0445,1564.02,2658.84,32.565,108.852,16.4299,3.61463,23.9479,2,48.3242,27.963,102.283,54.7937,80.8892,Winter,1.24,,,Winter,5,3,4,2,5,2,1,2,2,2,4,1,2,4,3,1,2,2,1,2,50,,,,Winter,2,2 +967d790c,Fall,8,1,,,Fall,16.5952326,51.0,61.4,24.0,76,116,76,,,,,Fall,0,0,,,,,0,0,17.0,1,17.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.419,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +9682cd0d,Winter,7,0,Spring,78,Spring,15.14544689,50.25,54.4,,85,100,125,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.09139,15.1471,988.618,1680.65,10.0231,47.5638,13.2436,1.90346,6.83617,1,25.7754,11.7653,44.4724,21.6798,35.7985,,,,,Spring,2,2,2,0,0,0,0,0,0,0,0,0,2,0,1,2,2,3,0,0,16,Spring,43,60,Winter,0,0 +9692f443,Spring,12,0,,,Spring,21.32628555,59.0,105.6,30.0,55,77,94,,,,,Summer,4,0,15.3,2,13.1,2,0,0,3.5,0,4.5,0,10.0,1,Summer,3,4.41607,21.3286,1251.9,2128.24,20.5743,75.6073,15.2708,6.05779,29.9927,2,35.2525,19.7805,71.1912,32.4308,55.8268,,,Summer,2.529,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +96c98417,Fall,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +96d235f1,Spring,20,1,,,Spring,28.69707979,63.5,164.6,34.0,72,81,121,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +96d6034f,Winter,9,1,Spring,70,Winter,15.47761983,55.0,66.6,,75,81,98,Winter,5,7,21,Winter,10,1,,,,,1,0,9.0,1,7.0,0,10.0,1,Winter,3,6.93281,15.4793,1069.8,1711.68,16.0366,56.2109,13.0647,2.41465,10.3891,2,24.8488,15.3255,49.2781,24.9198,40.8854,,,Winter,3.02,Winter,2,3,2,2,1,1,0,1,1,0,1,0,0,0,1,0,0,0,0,0,15,Winter,28,41,Winter,0,0 +96de5f57,Fall,14,0,Winter,65,Fall,32.18048669,66.0,199.4,,70,78,120,,,,,Fall,10,0,21.5,1,25.1,1,0,0,6.5,0,5.5,0,8.5,1,Fall,3,6.15741,32.184,1696.7,2884.4,37.8381,122.984,19.8502,12.3338,76.4156,3,52.8756,32.2707,116.827,55.9661,90.7137,,,,,Fall,4,4,5,0,4,4,0,4,4,4,5,0,1,4,4,1,4,2,5,2,61,,,,Fall,3,2 +96f13763,Winter,6,1,Summer,55,Spring,19.85758025,45.0,57.2,,86,94,119,Spring,4,5,29,Spring,0,0,,,,,0,0,10.0,1,9.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,26,38,Winter,0,0 +96fcd751,Winter,14,1,,,Winter,21.793,60.0,111.6,27.0,69,81,120,,,,,Winter,8,0,20.32,2,28.9,3,14,1,7.0,0,9.0,0,11.0,1,,,,,,,,,,,,,,,,,,Winter,3.47,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +97121fac,Summer,8,1,,,Summer,14.2194707,46.0,42.8,20.0,68,84,115,,,,,Summer,0,0,,,,,0,0,11.0,1,12.0,1,8.0,1,Summer,2,2.17686,14.2211,923.706,1385.56,9.96279,40.6498,13.5066,0.714469,2.15029,1,19.6626,11.0244,38.473,14.9547,29.6254,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +974801df,Spring,13,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +978218e6,Summer,10,0,Winter,68,Summer,13.86016943,51.75,52.8,,64,79,104,Summer,6,9,10,Summer,16,1,,,,,9,1,9.25,1,9.5,1,13.0,1,Summer,2,7.58036,13.8617,989.749,1583.6,9.22095,47.6842,12.5186,1.34305,5.11577,1,23.4998,14.9634,40.1039,17.4424,32.7208,,,Summer,1.82,Summer,2,2,2,0,2,2,0,0,1,1,0,0,0,0,2,2,2,1,1,1,21,Summer,33,47,Summer,2,0 +979a5c76,Summer,13,0,Winter,65,Fall,20.11706556,62.0,110.0,,63,96,131,,,,,Fall,20,0,32.2,3,31.2,2,8,0,7.0,0,7.5,1,12.0,1,Fall,4,0.61178,20.1193,1412.75,2966.78,28.1639,92.7395,16.9623,3.15698,17.2605,2,42.6138,21.9618,92.1278,43.2006,70.7778,,,Summer,1.59,Fall,2,2,3,1,3,2,0,1,2,2,3,0,2,0,2,2,3,3,2,1,36,Fall,32,46,Summer,2,1 +97ac1b72,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +97bb0cc0,Winter,10,0,,,Winter,19.78787721,62.0,108.2,,60,67,110,Winter,4,6,8,Winter,0,0,50.2,3,44.0,2,4,0,7.0,0,9.0,1,13.0,1,Winter,3,4.93905,19.79,1309.75,2226.57,22.8375,81.7685,14.9557,4.83438,26.4315,1,37.3233,21.6078,76.8295,35.6935,60.1607,,,Winter,2.33,Winter,5,5,4,2,5,4,1,4,4,4,4,0,5,3,4,3,3,5,1,2,68,Winter,45,63,Winter,2,2 +97d0593e,Fall,8,0,Winter,60,Fall,17.24390619,51.0,63.8,,87,103,161,,,,,Fall,5,1,,,,,0,0,10.0,1,10.0,1,6.0,1,Fall,3,3.21818,17.2458,1025.29,1742.99,11.7472,51.4695,13.9127,3.33306,12.3305,2,27.1685,12.5538,48.2513,22.6199,38.9157,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,54,75,Fall,0,0 +97eda530,Summer,10,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1, +981568fa,Fall,13,0,Spring,78,Winter,17.01020794,57.5,80.0,,60,63,104,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.53,Winter,2,2,2,1,3,0,1,1,1,2,1,0,1,0,1,2,2,2,2,1,27,Winter,32,46,Fall,0,0 +983bcb5b,Summer,20,0,Fall,80,Summer,24.6884268,69.0,167.2,,73,99,130,,,,,,,,,,,,,,,,,,,,Summer,4,6.73654,24.6911,1664.73,3495.93,35.5861,119.579,17.6587,7.03243,47.6213,1,52.5286,31.464,112.842,62.3719,88.1147,,,,,Summer,0,0,2,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,7,Summer,33,47,Summer,3,0 +98413061,Spring,7,0,Winter,55,Spring,14.41880907,46.0,43.4,21.0,58,72,107,,,,,Spring,4,1,,,,,15,1,7.0,0,7.0,0,10.0,1,Spring,2,2.44411,14.4204,907.891,1452.63,5.16777,38.9653,12.9469,1.47349,4.43465,1,21.2214,12.5761,36.5212,16.9146,26.3892,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,35,50,Spring,0,0 +98456a84,Summer,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +984f12cf,Fall,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +9853fb61,Summer,11,1,,,Summer,23.11290131,58.0,110.6,32.0,66,69,111,,,,,Fall,12,0,13.3,2,15.4,2,0,0,9.0,0,9.0,0,11.0,1,Fall,1,3.99793,23.1154,1223.46,1590.5,25.4076,72.5774,15.1687,7.94673,38.0226,3,29.4405,17.7293,68.5795,28.1149,54.8482,,,Summer,2.2,Summer,3,3,2,0,3,0,0,1,1,1,1,0,1,1,1,2,2,2,2,1,27,Summer,69,94,Summer,0,0 +9885f9da,Summer,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +98a4be3f,Spring,20,0,Summer,39,Spring,31.62872446,71.0,226.8,,62,77,136,,,,,,,,,,,,,,,,,,,,Spring,4,8.68428,31.6322,1984.25,4166.93,48.5117,153.612,21.4245,10.2077,73.188,3,64.7643,40.336,144.928,77.4637,113.276,,,,,Spring,2,2,3,3,3,0,1,1,2,1,1,0,2,1,2,2,1,2,0,1,30,Spring,32,46,Spring,2,0 +98a81a90,Winter,11,0,Fall,60,Spring,25.28758672,55.75,111.8,,73,94,117,Spring,4,7,0,Spring,15,1,16.5,2,15.9,2,10,1,8.0,1,8.0,1,6.0,0,Summer,2,3.99548,25.2903,1230.27,1968.43,18.0253,73.3031,16.5819,8.70841,38.4969,3,36.998,18.2798,69.3076,43.8394,55.0233,,,Spring,3.67,Spring,3,3,4,4,2,1,2,1,1,2,3,1,2,1,2,2,1,2,1,1,39,Spring,41,58,Winter,2,1 +98cb429b,Winter,17,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.0,,,,,,,,,,,,,,,,,,,,,,,,,Winter,66,90,Winter,, +98ed2153,Winter,9,0,Spring,80,Spring,23.94145408,56.0,106.8,,75,89,138,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.97,Spring,5,5,5,0,5,5,0,0,0,0,3,0,5,5,5,4,4,5,4,4,64,Spring,54,75,Winter,2,2 +9907115b,Winter,6,0,,,Winter,17.74111531,46.0,53.4,24.0,61,88,98,,,,,Winter,0,0,,,,,0,0,0.0,0,0.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +99205f42,Spring,11,0,Fall,65,Summer,20.32719849,57.5,95.6,,57,83,117,Summer,5,8,30,Summer,27,1,14.6,2,18.4,2,4,0,4.5,0,4.0,0,5.5,0,Summer,3,4.26363,20.3294,1217.91,2070.45,20.3088,71.9863,15.308,5.02146,23.6137,2,33.8339,17.8437,67.7227,28.6327,54.1427,,,Summer,3.42,Summer,3,3,3,4,1,1,1,1,2,2,2,1,2,2,2,2,1,2,2,2,39,Summer,42,59,Spring,1,1 +99328d2e,Winter,12,1,Summer,60,Fall,18.26968047,65.0,109.8,,75,70,115,,,,,Fall,10,0,24.7,3,24.8,3,5,0,6.0,0,7.0,0,9.0,1,Fall,2,5.37893,18.2717,1378.0,2067.0,28.5772,89.038,14.8167,3.45498,20.762,2,37.8315,22.6294,83.6591,52.5508,66.4087,,,Winter,2.5,Fall,1,1,2,1,2,1,1,1,2,1,1,1,1,1,1,1,2,1,1,1,24,Fall,28,41,Winter,1,0 +994ee014,Spring,9,1,Spring,31,Spring,15.8107003,52.25,61.4,,68,92,118,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,5,2.977,15.8124,1030.82,2267.81,15.168,52.059,13.4068,2.40559,9.34097,3,23.9248,12.9663,49.082,21.9709,39.0927,,,,,Summer,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,23,Summer,45,63,Spring,0,0 +9964dd80,Spring,18,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +99677b5e,Spring,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,2.35424,15.4047,943.413,1415.12,11.473,42.7489,13.606,1.79863,5.65112,1,21.1966,10.0792,40.3946,17.3491,32.6696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +99752704,Fall,7,0,Winter,65,Fall,16.70328,50.0,59.4,,,106,,Fall,6,9,35,Fall,0,0,,,,,0,0,8.0,1,9.0,1,12.0,1,Fall,5,7.21005,16.7051,990.634,2377.52,9.46984,47.7786,13.4368,3.2683,11.6214,2,25.0698,13.2389,40.5685,20.5581,34.5397,,,,,Fall,3,3,4,1,3,1,0,2,0,1,2,0,0,0,2,2,2,3,2,0,31,Fall,63,86,Fall,2,1 +9977fb0f,Spring,11,0,Fall,61,Summer,15.83315139,59.0,78.4,,74,61,121,Fall,,,,Summer,7,0,17.4,2,19.9,2,10,1,8.0,1,8.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.39,Summer,1,1,2,1,2,1,1,2,1,1,1,1,1,1,2,1,2,1,2,1,26,Summer,30,43,Spring,0,0 +997ef805,Winter,11,1,,,Winter,20.66740265,57.5,97.2,29.0,68,58,115,,,,,Winter,7,0,17.7,2,17.7,2,5,0,10.0,0,9.5,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,3.02,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +998115f3,Spring,9,0,Winter,50,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.47,Summer,4,3,3,2,4,1,0,1,0,2,4,0,2,1,2,1,2,2,2,1,37,Summer,32,46,Spring,2,1 +998cfa64,Summer,9,1,Fall,72,Fall,15.23648834,54.0,63.2,,64,85,100,Fall,5,6,57,Fall,15,1,,,,,3,0,8.0,0,7.5,0,9.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.12,Fall,4,4,3,0,5,0,0,1,1,2,2,0,1,0,1,3,1,1,0,1,30,Fall,37,53,Summer,0,0 +99a1711f,Summer,18,0,,,Summer,19.62964909,73.0,148.8,29.0,71,104,116,,,,,Summer,19,0,52.7,3,46.5,2,51,1,17.0,1,14.0,1,13.0,1,Summer,4,8.32133,19.6318,1855.45,3896.44,45.1871,139.893,18.4566,1.17518,8.90732,3,59.0797,35.6259,131.571,68.4756,104.267,Summer,1.54,,,Summer,4,4,4,0,4,4,2,3,1,2,0,0,2,3,3,1,4,2,1,1,45,,,,Summer,2,1 +99a8a9e7,Fall,9,1,Winter,65,Fall,17.88883929,56.0,79.8,,64,78,104,Fall,6,10,27,Fall,7,1,,,,,10,1,8.0,0,7.0,0,11.0,1,Winter,3,8.42368,17.8908,1099.58,1759.33,17.5007,59.3826,13.3133,4.57748,20.4174,3,24.7702,17.1117,50.959,23.808,42.2709,,,Fall,2.27,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,37,53,Fall,0,0 +99b15811,Summer,7,0,Winter,85,Winter,15.86947105,49.0,54.2,,40,58,70,,,,,Spring,,,,,,,,,,,,,,,Summer,2,3.25634,15.8712,1021.38,1634.2,11.6037,51.0529,14.9496,0.921562,3.14712,1,30.5063,8.9429,47.7965,32.6361,42.11,,,,,Fall,0,0,3,0,2,0,0,2,0,0,0,0,0,0,,,3,0,0,0,10,Fall,42,59,Summer,2,0 +99ec8c4a,Summer,9,0,Winter,63,Fall,23.89409818,51.8,91.2,,102,85,127,Fall,6,9,24,Fall,10,1,,,,,10,1,7.5,1,5.5,0,5.0,0,Fall,5,7.02618,23.8967,1130.43,2713.03,16.1563,62.6687,16.4208,7.47593,28.5313,2,32.3794,14.1329,55.6425,29.9889,48.5358,,,Summer,3.23,Fall,3,2,2,0,2,1,0,1,0,2,2,0,0,1,1,2,1,1,1,1,23,Fall,36,51,Summer,2,0 +99f0cc53,Summer,6,0,Spring,70,Fall,14.48645024,40.5,33.8,,55,97,112,Fall,,,,Fall,0,0,,,,,0,0,11.0,1,11.0,1,8.0,1,Fall,1,2.12285,14.488,825.733,1073.45,3.63954,30.2144,12.9511,1.53693,3.58559,1,21.1041,5.47079,28.0916,17.1733,24.7436,,,,,Fall,2,1,5,0,0,1,0,0,0,1,1,0,2,1,0,0,0,0,0,0,14,,,,Summer,1,0 +99f3be05,Summer,6,0,Winter,62,Summer,14.34693878,42.0,36.0,,73,87,103,,,,,Summer,0,0,,,,,3,1,16.0,1,17.0,1,5.5,1,Fall,2,9.76175,14.3485,855.471,1368.75,2.96927,33.3819,13.305,1.0435,2.61812,1,19.077,11.3356,23.6201,13.9266,22.0463,,,,,Summer,4,3,4,0,3,1,0,3,2,1,2,0,2,1,2,3,1,2,1,0,35,Summer,49,68,Summer,2,1 +99fe5b44,Winter,16,0,Fall,50,Spring,24.67111351,64.5,146.0,,74,77,134,,,,,Spring,4,0,34.4,2,36.1,2,2,0,0.0,0,0.0,0,7.0,0,,,,,,,,,,,,,,,,,,Spring,1.62,,,Spring,2,2,2,0,1,1,1,1,1,1,2,0,1,1,1,1,1,1,1,1,22,Spring,31,45,Winter,,0 +9a2082cf,Winter,7,0,Summer,65,Spring,13.73046875,48.0,45.0,,61,,91,Spring,5,7,24,Spring,0,0,,,,,0,0,9.0,1,11.0,1,7.0,1,Spring,2,2.65048,13.732,931.129,1489.81,6.83477,41.4404,12.6457,1.08622,3.55958,1,21.4499,13.1557,38.7899,14.9326,28.2847,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,Spring,52,72,Winter,0,0 +9a2fc6ca,Summer,8,0,Winter,75,Summer,14.67418567,54.5,62.0,,65,84,112,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.19,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +9a50d2d8,Summer,12,0,,,Summer,31.73067146,63.5,182.0,40.0,83,101,136,,,,,Summer,13,0,24.8,2,27.0,2,1,0,7.0,0,6.5,0,12.0,1,Summer,1,5.67445,31.7341,1608.99,2091.69,34.4183,113.642,19.815,11.9191,68.3579,3,50.1416,29.0823,107.968,53.6105,84.5598,,,Fall,1.17,Fall,3,1,1,1,1,1,0,1,1,1,1,0,1,1,1,0,0,0,0,0,15,Fall,42,59,Summer,2,0 +9a54e4c2,Spring,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9a5aed6c,Winter,13,0,Spring,62,Winter,21.60896951,63.0,122.0,,60,68,112,,,,,Winter,17,0,23.4,2,23.3,2,10,1,6.25,0,7.0,0,10.0,1,Winter,3,5.48874,21.6113,1432.08,2434.54,28.7532,94.7983,16.7928,4.81856,27.2017,2,42.6422,23.4029,89.3096,41.913,71.3955,,,,,Spring,2,2,2,1,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,25,Winter,39,55,Winter,,0 +9a76906d,Winter,7,0,Spring,68,Winter,15.378125,48.0,50.4,,70,109,111,Winter,3,5,0,Winter,5,1,,,,,9,1,7.0,0,9.0,1,3.0,0,Winter,3,4.59453,15.3798,951.721,1617.93,8.82981,43.6338,13.3151,2.06475,6.76623,1,24.4781,10.3259,39.0392,19.3366,33.3079,,,,,Winter,3,0,5,0,2,0,0,1,0,0,0,0,0,1,3,3,0,2,0,0,20,Winter,46,64,Winter,0,0 +9a7e4c38,Winter,8,0,Spring,60,Spring,22.86543367,56.0,102.0,,57,98,113,Spring,5,8,59,Spring,10,1,,,,,1,0,7.0,0,7.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.11,Spring,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,4,Spring,28,41,Winter,2,0 +9aa1a2ae,Summer,11,0,Winter,90,Fall,15.85590101,56.5,72.0,,64,73,105,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,5,4,2,4,1,0,1,1,1,4,0,1,1,1,1,1,4,2,1,39,Summer,30,43,Summer,0,1 +9aa36639,Winter,6,1,,,Fall,,,,,,,,,,,,Winter,0,0,,,,,0,0,11.0,1,8.0,0,7.0,1,Winter,3,2.12907,14.6711,919.152,1470.64,9.51185,40.1648,13.6403,1.0308,3.03524,1,19.7994,10.8535,38.0357,16.1014,29.3113,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,Winter,39,55,,,0 +9aab3e36,Fall,9,0,Winter,73,Fall,17.13579393,51.0,63.4,,62,87,131,Fall,7,11,26,Fall,15,1,,,,,12,1,9.5,1,7.0,0,10.0,1,Winter,4,1.87636,17.1377,1063.65,2233.65,14.6455,55.5552,15.0171,2.12053,7.8448,1,29.8798,11.0299,53.6788,25.4778,44.5253,,,Fall,1.93,Fall,1,2,2,1,2,2,1,1,2,1,2,0,2,0,3,2,2,2,0,0,28,Winter,43,60,Fall,0,0 +9ab4909f,Spring,17,0,Winter,60,Summer,18.12840322,68.5,121.0,,70,90,136,,,,,Summer,50,1,35.8,3,36.1,3,28,1,14.0,1,14.0,1,12.0,1,Summer,3,6.0618,19.2373,1471.74,2501.96,28.9121,99.023,15.7433,3.49404,21.977,2,44.6433,25.4676,92.9612,49.3147,73.5554,Spring,3.46,,,Spring,2,2,3,1,2,1,1,2,1,1,4,1,2,3,1,1,2,2,2,2,36,,,,Spring,2,1 +9ab7ffef,Winter,8,0,,,Winter,20.37057443,49.5,71.0,27.0,127,88,158,,,,,Winter,3,0,,,,,1,0,20.0,1,19.0,1,7.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +9ac36dd2,Summer,11,0,Summer,,Summer,29.47563946,61.0,156.0,40.0,83,63,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0.88,,,,,,,,,,,,,,,,,,,,,,,Spring,70,95,Summer,, +9af5548f,Summer,11,0,Winter,60,Fall,17.0502924,57.0,78.8,,103,,121,Fall,5,8,55,Fall,16,1,17.4,2,19.3,2,14,1,5.0,0,5.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.61,Fall,2,1,2,1,3,1,0,1,2,2,2,1,1,1,1,1,2,1,2,1,28,Fall,29,42,Summer,1,0 +9b1abb64,Spring,10,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +9b328d6c,Summer,13,0,Summer,61,Summer,22.47437053,64.5,133.0,,83,99,169,,,,,Fall,,,,,,,,,,,,,,,Summer,2,5.69106,22.8148,1474.04,2358.47,29.5152,99.2681,16.7762,6.03864,35.7319,2,44.3657,25.3872,93.577,45.9135,73.8809,,,Summer,1.88,Summer,5,4,4,2,5,4,1,1,1,5,5,0,5,4,4,4,4,4,4,0,66,Summer,46,64,Summer,0,2 +9b33dbb2,Winter,11,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.97,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +9b5f13ec,Fall,16,1,Spring,61,Winter,17.32051682,67.0,110.6,,84,64,160,,,,,Winter,8,0,16.0,1,18.8,2,0,0,0.0,0,0.0,0,9.0,1,Winter,2,0.197874,17.3224,1339.14,2008.7,28.5579,84.8987,13.297,4.0254,25.7013,2,32.334,24.0067,84.7008,34.448,60.8919,Winter,1.14,,,Winter,4,4,4,2,2,4,1,4,4,1,4,0,4,4,2,0,4,3,5,2,58,Winter,43,60,Fall,2,2 +9b71a8f5,Winter,6,1,,,Winter,19.90896,50.0,70.8,25.0,61,84,125,,,,,Winter,12,1,,,,,5,1,9.0,1,11.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +9b96b0e4,Fall,8,0,Summer,80,Fall,16.32362812,52.5,64.0,,76,98,131,,,,,Fall,1,0,,,,,1,0,14.0,1,13.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.76,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,37,53,Fall,1,0 +9ba1a228,Spring,6,0,,,Spring,15.150792,46.5,46.6,20.0,83,80,172,,,,,Spring,5,1,,,,,8,1,12.5,1,11.5,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +9bc60f80,Spring,6,1,Summer,60,Spring,14.50964794,45.75,43.2,20.0,67,92,117,,,,,Spring,4,1,,,,,7,1,10.0,1,9.0,1,8.0,1,Spring,1,2.10164,14.5112,925.163,1202.71,8.83602,40.805,13.7067,0.804489,2.39497,1,19.5938,12.3752,38.7034,17.3588,28.4298,,,,,Spring,2,3,2,0,3,0,0,2,1,1,3,0,3,1,2,3,2,2,1,0,31,Spring,84,100,Spring,0,1 +9bd745aa,Fall,9,0,Spring,65,Winter,14.56313275,51.9,55.8,,79,83,114,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,5,3.05055,14.5647,998.08,2395.39,8.49403,48.5716,12.678,1.88672,7.22835,1,24.1619,15.9157,45.5211,20.8194,32.6559,,,Winter,1.71,Winter,2,0,3,2,2,0,0,0,0,2,4,0,1,1,3,1,3,2,0,1,27,Winter,44,62,Fall,3,0 +9bdd6964,Summer,6,0,Summer,61,Spring,14.51130864,45.0,41.8,,81,71,113,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4,3,4,0,4,3,0,3,3,2,0,0,1,0,1,3,3,4,2,2,42,Fall,40,56,Summer,0,1 +9bf7051d,Summer,10,0,Fall,80,Fall,17.93662551,54.0,74.4,,67,82,107,Fall,6,10,49,Fall,30,1,,,,,25,1,5.0,0,5.0,0,12.0,1,Fall,4,3.22107,17.9386,1116.59,2344.83,16.3143,61.1941,14.7545,3.18408,13.2059,2,30.8728,14.0071,57.973,26.2211,47.187,,,Summer,3.71,Fall,5,5,5,0,3,1,0,1,3,4,3,0,4,0,2,5,4,5,4,4,58,Fall,57,79,Summer,2,2 +9bfa1c69,Spring,8,1,,,Spring,15.01197917,48.0,49.2,22.0,60,76,105,,,,,Spring,1,0,,,,,8,1,12.0,1,11.5,1,9.0,1,Spring,4,2.43046,15.0136,955.492,1815.44,11.2056,44.0355,13.4376,1.57598,5.16453,1,21.7338,11.0961,41.605,20.2161,32.9394,,,Spring,1.46,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +9c0e6933,Winter,9,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.56,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +9c12f80d,Summer,8,1,Winter,80,Fall,0.0,50.48,0.0,,66,80,102,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,1,1,0,2,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,9,Fall,41,58,Summer,0,0 +9c1c8fd7,Summer,7,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1, +9c1ea003,Spring,9,0,Winter,70,Winter,17.35802469,54.0,72.0,,77,79,127,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,4,3.42535,17.3599,1067.04,2240.78,12.1102,55.9167,13.4821,3.87784,16.0833,1,27.4269,16.3796,52.4914,23.6806,39.5371,,,Fall,2.95,Fall,4,4,2,0,5,2,3,1,1,0,0,0,3,0,3,4,2,4,1,1,40,Fall,38,54,Spring,1,1 +9c23dba9,Spring,15,1,Summer,58,Summer,21.04747583,66.5,132.4,,64,73,110,,,,,Summer,10,0,21.6,2,22.7,2,0,0,11.5,1,12.5,1,12.0,1,Summer,5,5.70773,21.0498,1449.79,3189.55,33.3625,96.6852,15.3716,5.67816,35.7148,2,38.2263,25.0964,90.9775,47.2674,71.5888,Spring,3.29,,,Summer,2,3,2,3,3,2,0,1,1,2,4,1,1,0,3,3,2,3,3,2,41,Summer,41,58,Summer,,1 +9c2df71f,Fall,7,1,Spring,65,Winter,15.89052632,47.5,51.0,,58,78,107,Winter,4,6,24,Winter,6,1,,,,,0,0,10.0,1,9.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,3,3,3,0,2,1,0,2,0,0,0,0,3,1,0,3,2,2,0,0,25,Winter,29,42,Fall,0,0 +9c589c9d,Fall,5,0,,,Fall,15.40298778,47.0,48.4,21.0,68,79,98,,,,,Fall,3,1,,,,,0,0,9.0,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,1,1,1,0,1,1,0,1,0,0,1,0,0,0,0,1,1,1,1,1,12,,,,Fall,0,0 +9c7a1561,Winter,6,1,,,Winter,16.70063158,47.5,53.6,23.0,72,80,111,,,,,Winter,0,0,,,,,0,0,10.0,1,10.0,1,12.0,1,Winter,2,2.43067,16.7025,958.574,1437.86,12.0514,44.3638,13.8243,2.87813,9.23623,1,22.1298,10.1825,41.9331,19.8882,34.1812,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,36,51,Winter,2,0 +9c9cd7ef,Summer,8,1,Fall,61,Fall,16.27938359,49.0,55.6,,70,102,108,Fall,7,11,33,Fall,5,1,,,,,7,1,12.0,1,13.0,1,12.0,1,Fall,3,10.3179,16.2812,976.221,1561.95,11.9237,46.2434,13.5413,2.73987,9.35662,2,21.3013,13.0184,35.9255,18.6503,33.225,,,,,Fall,5,5,2,0,4,1,0,1,0,2,1,0,3,4,2,3,2,5,1,1,42,Fall,46,64,Summer,1,1 +9cb303cb,Summer,15,0,Summer,55,Summer,21.61149476,67.0,138.0,28.0,70,62,127,,,,,Summer,31,1,33.4,2,39.3,2,16,1,8.5,1,8.5,1,11.5,1,Fall,4,6.23187,21.6139,1536.92,3227.53,30.9871,105.965,16.5965,5.01734,32.0347,2,47.5973,27.3809,99.7334,54.9668,78.5844,Fall,3.67,,,Fall,2,3,1,1,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,12,Fall,43,60,Summer,3,0 +9cb32129,Fall,9,1,Winter,61,Winter,17.15902367,52.0,66.0,,61,84,120,Winter,6,10,20,Winter,21,1,,,,,6,1,7.0,0,7.0,0,12.0,1,Winter,2,2.98792,13.5207,1016.76,1525.14,14.1031,50.5611,13.1466,0.374135,1.43891,1,24.5752,11.8828,47.5732,24.8618,38.6783,,,Fall,3.0,Winter,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,6,Winter,39,55,Fall,0,0 +9cba0d0e,Spring,11,0,Spring,74,Spring,19.68931569,57.5,92.6,,92,102,139,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.26524,19.6915,1210.81,1937.3,19.7287,71.2301,15.1471,4.54432,21.3699,1,33.9202,17.5813,66.9649,29.931,53.6489,,,Spring,3.08,Spring,3,2,3,1,3,2,2,2,2,2,3,1,2,2,2,2,2,2,2,1,41,Spring,40,56,Spring,0,1 +9cbda44c,Spring,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +9ccbc70b,Fall,5,0,Winter,51,Fall,16.0446728,43.0,42.2,,58,76,106,Fall,,,,Fall,5,1,,,,,0,0,9.5,1,10.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,46,64,Fall,0,0 +9cd0d49a,Fall,8,0,,,Winter,24.86644628,55.0,107.0,,80,102,117,Winter,,,,Winter,4,0,,,,,1,0,6.0,0,6.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,39,55,Fall,2, +9ce139d3,Fall,6,0,Spring,50,Winter,14.95095486,48.0,49.0,,79,103,121,Winter,,,,Winter,3,1,,,,,0,0,10.0,1,10.0,1,9.0,1,Winter,2,2.77817,14.9526,943.62,1509.79,8.67589,42.7709,13.0518,1.90083,6.22905,1,22.9965,11.0986,39.9928,15.7064,31.6724,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +9cee3c19,Winter,9,0,Spring,51,Spring,13.83063063,55.5,60.6,,75,78,116,Spring,2,1,49,Spring,25,1,,,,,15,1,11.0,1,10.0,1,10.0,1,Spring,3,3.18176,13.8321,1055.66,1794.63,6.71458,54.7051,12.4866,1.34553,5.8949,1,24.4438,23.5467,51.5233,30.5077,31.1584,,,Spring,2.92,Spring,3,4,5,3,5,3,0,3,0,3,0,0,0,0,0,0,3,3,2,2,39,Spring,54,75,Winter,0,1 +9cf04c41,Fall,15,1,Spring,60,Winter,18.63911133,64.0,108.6,,54,71,101,,,,,Winter,19,1,26.8,2,29.3,3,12,1,11.0,1,11.5,1,13.0,1,Winter,4,0.634064,18.5897,1314.49,2497.52,24.7625,82.2731,14.1222,4.4675,26.0269,2,35.8404,21.6703,81.6391,53.6596,60.6029,Winter,3.05,,,Winter,0,5,5,2,3,3,4,1,1,2,4,1,1,1,1,2,1,2,1,1,41,Winter,39,55,Fall,0,1 +9d0670dd,Fall,6,0,Summer,51,Spring,14.51190584,47.0,45.6,,115,83,147,Spring,3,4,16,Fall,6,1,,,,,0,0,10.0,1,11.0,1,9.0,1,Fall,2,1.6164,26.1717,813.397,1301.43,1.78945,28.9004,16.5871,9.58456,16.6996,1,18.7997,8.31124,27.284,13.1833,20.5892,,,,,Spring,3,1,1,1,2,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,12,Spring,27,40,Fall,0,0 +9d1b2d2c,Fall,7,1,Spring,82,Winter,15.30340136,42.0,38.4,,63,78,98,Winter,3,4,37,Winter,8,1,,,,,0,0,9.0,1,8.0,0,10.0,1,Winter,2,12.0997,15.3051,880.492,1320.74,7.42367,36.047,14.3672,0.937837,2.35301,1,18.3535,10.2698,23.9473,14.3827,25.7772,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,40,56,Fall,0,0 +9d29b706,Summer,17,0,,,Fall,21.10084877,72.0,155.6,,86,73,139,,,,,,,,,,,,,,,,,,,,Summer,4,9.30291,21.1032,2205.86,4632.3,55.6785,177.216,24.0348,-2.93162,-21.6158,3,77.2053,44.332,167.913,111.305,132.884,Summer,3.08,,,Fall,5,5,4,4,5,4,4,4,2,4,5,1,3,5,3,4,3,3,3,2,73,Summer,35,50,Summer,0,2 +9d39f0da,Spring,12,0,Summer,70,Spring,19.78787721,62.0,108.2,,61,74,126,Spring,5,8,5,Spring,15,1,19.7,2,20.9,2,0,0,5.0,0,5.0,0,8.0,0,Spring,2,3.28485,19.79,1326.48,2122.37,23.6432,83.5506,15.2816,4.50843,24.6494,2,38.4348,21.4726,80.2658,37.8135,62.078,,,Summer,2.12,Spring,5,4,5,1,4,3,2,2,3,4,3,0,4,1,4,5,5,4,5,4,68,Spring,31,45,Spring,0,2 +9d5826bb,Summer,12,0,Summer,83,Winter,16.68135593,59.0,82.6,,71,84,117,Winter,,,,Winter,25,1,20.9,2,23.5,3,20,1,14.5,1,15.0,1,10.0,1,Winter,5,2.28669,16.6832,1198.0,2875.19,18.0905,69.8655,14.1111,2.57205,12.7345,2,34.0253,17.7498,67.5788,34.1941,52.1158,,,Summer,2.69,Winter,3,3,3,1,3,2,0,2,1,3,2,1,1,2,3,1,2,2,2,0,37,Winter,30,43,Summer,0,1 +9d60665f,Summer,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.75,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +9d65adbf,Fall,12,0,Spring,61,Fall,17.56168673,54.5,74.2,,65,68,115,,,,,Fall,7,0,20.9,2,18.4,2,10,1,14.0,1,15.0,1,3.0,0,Fall,4,4.43844,16.3422,1216.93,2555.55,21.3761,71.8818,15.8316,0.510565,2.31817,2,35.3377,15.1681,67.4434,32.053,56.7137,,,Winter,2.29,Winter,5,5,4,3,3,4,1,3,2,3,3,1,1,1,3,1,1,1,1,1,47,Winter,44,62,Fall,2,1 +9d6b1410,Summer,7,0,Fall,55,Summer,15.68126312,47.25,49.8,,66,99,110,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,38,54,Summer,0,0 +9d979587,Summer,18,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9d98c761,Winter,16,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.91,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +9da17a1d,Winter,15,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.25,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +9dae93d1,Spring,10,1,,,,,,,,,,,,,,,Spring,20,1,9.9,1,11.8,2,0,0,11.5,1,11.5,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.5,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +9dbdbebf,Winter,7,1,Summer,80,Winter,20.26817158,50.25,72.8,24.0,53,78,105,,,,,Winter,9,1,,,,,0,0,12.0,1,12.0,1,8.0,1,Spring,3,2.82599,20.2704,1031.58,1650.53,16.2997,52.1399,14.5178,5.7526,20.6601,2,24.2658,11.5743,49.3139,21.4463,40.5656,,,,,Spring,3,2,3,0,3,1,0,3,0,1,3,0,2,1,0,2,2,2,0,0,28,Spring,39,55,Winter,0,0 +9dc74359,Winter,15,1,Summer,60,Summer,23.41562106,63.0,132.2,,62,99,110,,,,,Summer,30,1,37.4,3,37.4,3,6,0,14.5,1,14.0,1,6.0,0,Summer,2,5.24448,23.4182,1428.63,2142.95,33.3155,94.4313,16.7278,6.69042,37.7687,2,38.1607,22.9551,89.1868,46.0724,71.4762,Winter,2.14,,,Summer,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,21,Summer,48,67,Winter,2,0 +9ddb06db,Fall,18,0,Winter,60,Fall,27.25522102,67.3,175.6,,78,86,123,,,,,,,,,,,,,,,,,,,,Winter,3,11.9456,27.3394,3987.68,6779.05,115.285,367.004,57.1392,-29.7999,-191.404,2,152.738,98.9813,355.058,254.611,268.022,Fall,1.41,,,Fall,5,4,5,5,5,1,1,4,4,4,5,4,2,5,4,2,3,4,3,4,74,Fall,50,69,Fall,2,2 +9ddfb9e6,Summer,14,0,Winter,50,Fall,21.21357422,64.0,123.6,,,,,,,,,Summer,10,0,16.7,1,19.7,2,2,0,5.0,0,4.0,0,8.0,0,Summer,3,5.09365,21.2159,1349.4,2293.97,23.7859,85.9914,14.7604,6.4555,37.6086,3,37.6707,24.5348,80.8978,34.8789,61.4566,,,Summer,2.12,Summer,4,4,3,1,4,2,2,1,2,3,0,0,1,0,2,2,2,4,2,0,39,Summer,40,56,Fall,2,1 +9de6a614,Summer,10,1,Winter,60,Fall,17.21836953,53.0,68.8,,67,99,139,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.33,Summer,3,3,4,0,2,1,0,1,1,1,2,0,1,1,1,2,2,2,1,1,29,Summer,49,68,Summer,2,0 +9dfe8930,Fall,14,1,,,Fall,17.70912806,64.5,104.8,26.0,76,85,121,,,,,Fall,13,0,15.7,2,21.8,2,10,1,14.0,1,13.0,1,15.0,1,,,,,,,,,,,,,,,,,,Winter,2.09,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +9e027e04,Summer,6,0,Winter,73,Fall,14.68459357,46.0,44.2,,78,93,119,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3,0,1,0,2,0,0,0,0,0,3,0,2,0,2,2,2,2,1,1,21,Fall,41,58,Summer,0,0 +9e052412,Spring,6,1,Fall,71,Spring,14.34049412,43.5,38.6,22.0,59,94,101,,,,,Spring,5,1,,,,,0,0,6.5,0,7.5,0,5.0,0,Summer,1,1.92368,14.3421,893.944,1162.13,8.15798,37.4798,13.9258,0.416229,1.12023,1,18.7449,10.5769,35.5561,14.501,26.9029,,,,,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,3,Summer,50,69,Spring,0,0 +9e171735,Spring,10,1,Spring,90,Winter,15.91199691,58.25,76.8,,79,91,133,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.86,Fall,2,2,2,1,3,1,1,1,2,2,3,0,0,0,0,0,2,1,0,1,24,Fall,35,50,Spring,2,0 +9e29b5d2,Fall,11,1,Summer,80,Winter,28.11244289,61.0,148.8,,65,109,116,Winter,,,,Winter,13,0,17.4,2,19.9,2,4,0,6.5,0,7.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,1,0,2,1,1,1,1,1,2,0,1,1,1,1,1,1,1,1,22,Winter,41,58,Fall,0,0 +9e4385f7,Summer,10,0,Summer,80,Spring,15.71865569,54.0,65.2,,71,65,111,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,3.46329,15.7204,1053.35,1685.36,12.0243,54.4584,13.1305,2.58991,10.7416,2,27.0624,15.3717,50.9951,22.8816,39.0867,,,Fall,1.32,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,59,81,Summer,0,0 +9e6764f4,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9e7860ef,Fall,11,1,Spring,81,Winter,20.25102881,54.0,84.0,,67,99,118,Winter,5,7,16,Winter,17,1,14.4,2,13.0,2,4,0,10.0,1,10.0,1,12.0,1,Winter,2,5.43905,20.2532,1108.48,1662.72,19.5738,60.331,14.5464,5.70682,23.669,2,26.7428,14.0145,54.892,25.6347,46.3166,,,Fall,1.68,Winter,1,2,1,1,1,1,0,1,1,0,1,0,1,1,1,1,1,1,1,1,18,Fall,31,45,Fall,0,0 +9e8f9286,Summer,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9e8fa43c,Summer,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.8,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +9e964060,Summer,8,0,Winter,50,Summer,17.10665873,53.25,69.0,,,92,,Summer,3,4,10,Summer,0,0,,,,,0,0,5.0,0,6.0,0,2.0,0,Summer,2,3.34663,17.1085,1051.78,1682.86,11.6323,54.2919,13.4617,3.64687,14.7081,1,27.0474,15.6122,50.9453,23.2642,38.6797,,,,,Winter,2,0,4,1,3,1,0,3,3,3,2,0,3,2,5,5,4,4,5,1,51,Winter,39,55,Summer,0,2 +9eb283e4,Fall,15,1,Spring,67,Winter,39.33918467,67.0,251.2,,64,82,129,,,,,Winter,27,1,23.9,2,26.8,2,1,0,11.0,1,10.0,1,11.0,1,Winter,3,6.30698,39.3435,1762.89,2820.63,49.5911,130.034,20.3663,18.9772,121.166,3,46.3811,34.0623,123.728,58.7618,95.9722,Fall,2.33,,,Winter,4,5,5,5,5,4,5,5,5,5,5,5,5,5,3,3,5,5,4,5,93,Winter,51,71,Fall,0,3 +9ebb73c7,Fall,11,0,Spring,70,Winter,15.85590101,56.5,72.0,,65,72,103,Winter,7,12,16,Winter,11,0,15.5,2,17.3,2,30,1,5.0,0,6.0,0,9.0,1,Spring,4,2.61888,15.8576,1128.85,2370.58,13.3255,62.5002,13.7654,2.09228,9.49978,1,33.5419,15.6328,59.8813,42.3664,46.8674,,,Winter,3.96,Winter,3,2,2,1,2,1,1,1,1,1,2,0,2,0,1,1,1,1,1,1,25,Winter,33,47,Fall,0,0 +9ec6970b,Spring,11,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9ee87ba2,Summer,8,0,,,Summer,16.80641399,49.0,57.4,25.0,62,65,103,,,,,Summer,0,0,,,,,10,1,15.0,1,13.0,1,11.5,1,Summer,3,3.03504,16.8082,990.575,1683.98,10.7738,47.7723,13.989,2.81925,9.62771,1,26.4826,10.5158,44.7373,21.7834,37.2565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +9ef35c6c,Winter,14,1,,,Winter,19.01244173,62.25,104.8,25.0,70,77,118,,,,,Winter,25,1,48.2,2,55.4,3,15,1,11.0,1,11.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.85,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +9f060959,Summer,12,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9f1143da,Summer,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9f2d4ba3,Winter,9,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.45,,,,,,,,,,,,,,,,,,,,,,,Summer,30,43,Winter,, +9f3ca269,Summer,12,0,Spring,80,Winter,14.2494481,57.75,67.6,,72,90,110,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.28,Fall,5,5,4,1,4,2,1,2,1,2,1,1,2,2,2,2,4,2,0,1,44,Fall,41,58,Summer,3,1 +9f5f15ef,Winter,10,0,Spring,55,Spring,17.3504121,57.5,81.6,,113,94,136,Spring,5,8,50,Spring,19,1,13.5,,13.6,,2,0,4.5,0,4.5,0,8.2,0,Spring,3,4.14911,16.2054,1156.7,1966.39,15.4475,65.4665,13.0013,3.20403,16.1335,2,30.0838,19.9351,61.3174,27.0105,45.5314,,,Spring,2.86,Spring,5,2,5,1,5,1,1,0,1,2,1,0,1,0,1,1,2,2,1,0,32,Spring,50,69,Winter,2,1 +9f6b9158,Summer,16,1,,,Summer,22.60955471,64.5,133.8,29.0,62,63,106,,,,,Summer,18,1,33.4,3,31.3,3,4,0,12.0,1,12.0,1,11.0,1,Winter,4,5.36585,22.612,1423.58,2704.8,32.7354,93.8931,15.8678,6.74422,39.9069,2,37.4169,23.7408,88.5272,45.2576,70.1523,Winter,2.65,,,Winter,0,0,5,4,4,2,1,0,4,2,5,1,2,3,3,3,2,3,4,2,50,,,,Summer,2,2 +9f6f2d41,Winter,8,0,,,Winter,20.16293733,51.0,74.6,28.0,75,75,121,,,,,Winter,0,0,,,,,0,0,7.0,0,7.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +9f70688f,Fall,5,1,Spring,58,Winter,17.45402729,45.5,51.4,,57,93,96,,,,,Winter,0,0,,,,,0,0,11.0,1,13.0,1,12.0,1,Winter,2,2.18316,17.4559,930.763,1396.14,10.7917,41.4014,14.0603,3.39561,9.99857,1,20.6117,9.99808,39.2183,16.8244,31.4034,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +9f9be55c,Spring,10,0,Winter,70,Winter,19.02005556,60.0,97.4,,67,67,130,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,5.36215,19.0221,1420.57,2983.2,26.066,93.5724,18.2746,0.747519,3.82756,2,46.9716,20.5348,88.2103,61.5239,73.0376,,,Summer,1.86,Summer,4,4,5,0,0,0,0,4,5,4,1,0,4,4,4,4,4,4,0,0,51,Summer,52,72,Spring,0,2 +9fa5cdfd,Winter,9,0,Spring,75,Winter,14.76715976,52.0,56.8,,71,94,122,,,,,Winter,11,1,,,,,7,1,15.0,1,15.0,1,8.0,1,Winter,2,3.14293,14.7688,1003.96,1606.33,9.57234,49.1975,12.792,1.97675,7.60249,2,24.8993,14.7258,46.0546,20.6942,34.4717,,,Winter,1.4,Winter,1,1,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,10,Winter,32,46,Winter,0,0 +9fa68512,Spring,6,0,Fall,65,Spring,17.36944151,46.75,54.0,23.0,54,79,98,,,,,Spring,3,1,,,,,1,0,12.0,1,11.5,1,12.0,1,Summer,2,2.74643,17.3713,949.371,1518.99,9.40341,43.3835,13.9561,3.41524,10.6165,1,24.4427,9.53741,40.637,17.6742,33.8461,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +9fb96ae3,Spring,13,1,Fall,67,Summer,16.08691785,64.5,95.2,,75,94,144,,,,,Summer,,,23.2,2,22.5,2,,,12.0,1,12.0,1,12.0,1,Summer,2,4.51174,16.0887,1229.78,1844.67,22.4351,73.2504,12.3792,3.70945,21.9496,1,27.6175,23.1979,68.7387,28.2246,50.0525,,,Summer,2.05,Summer,4,4,4,1,5,5,1,4,4,4,4,1,4,4,3,5,4,5,2,3,71,Summer,62,85,Spring,2,2 +9fbc09a6,Summer,6,0,Winter,63,Fall,17.26568,50.0,61.4,,64,65,101,Fall,5,7,23,Summer,2,1,,,,,0,0,8.0,1,7.0,0,12.0,1,Fall,3,3.90342,17.2676,1016.89,1728.71,12.6871,50.5751,14.2233,3.04429,10.8249,1,26.7232,11.1648,46.6717,19.576,39.4103,,,,,Fall,4,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Fall,28,41,Summer,2,0 +9fbf31f9,Winter,5,0,Spring,60,Winter,16.8669081,47.0,53.0,,106,99,141,Winter,,,,Winter,2,1,,,,,0,0,16.5,1,17.0,1,5.0,0,Spring,3,5.13451,16.8688,947.384,1610.55,8.54897,43.1719,13.7407,3.12808,9.82814,2,24.7186,9.90434,38.0373,20.2142,33.2675,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,35,50,Winter,0,0 +9fbfa81b,Winter,6,0,Summer,68,Winter,16.42382545,43.2,43.6,,59,98,100,,,,,Winter,2,1,,,,,0,0,18.0,1,16.5,1,10.0,1,Spring,4,2.04775,16.4256,909.939,1910.87,2.20229,39.1834,14.7617,1.66388,4.41658,1,19.415,17.5662,37.1357,19.6112,21.6173,,,,,Spring,2,2,2,0,3,0,0,0,0,0,1,0,1,1,1,1,1,1,1,1,18,Spring,39,55,Winter,0,0 +9feb98ab,Fall,19,0,,,Fall,18.74208984,64.0,109.2,30.0,80,64,122,,,,,,,,,,,,,,,,,,,,Fall,2,5.44403,18.7441,1371.74,2194.78,25.6102,88.3713,15.1689,3.57525,20.8287,2,40.0108,22.7502,82.9272,39.8587,65.621,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +9fec142f,Winter,13,1,Summer,30,Spring,29.28022461,64.0,170.6,36.0,56,74,110,,,,,Spring,17,0,23.6,2,24.9,3,0,0,10.0,1,10.0,1,12.0,1,Spring,3,5.32279,29.2834,1483.36,2373.37,36.7371,100.26,17.2096,12.0738,70.3399,3,38.0122,25.5108,94.9373,43.8803,74.7493,,,Winter,1.79,Spring,3,3,3,1,3,2,0,4,2,2,3,0,2,1,2,4,4,4,5,1,49,Spring,47,66,Winter,0,1 +9ff73d95,Spring,10,0,Fall,85,Summer,21.73990885,57.6,102.6,,61,84,116,Summer,6,11,0,Summer,2,0,,,,,10,1,8.0,1,7.0,0,13.0,1,Summer,4,3.9889,23.0024,1204.21,2528.84,18.0935,70.527,15.8118,7.19061,32.073,2,34.6444,17.7892,66.5381,34.7419,52.7378,,,Spring,3.25,Summer,1,1,1,0,1,1,0,0,0,1,0,0,0,0,0,0,1,1,1,0,9,Summer,32,46,Spring,0,0 +a00c3ab0,Winter,17,0,Summer,55,Spring,33.43369141,64.0,194.8,,70,85,121,,,,,Spring,10,0,33.3,3,34.4,3,1,0,6.0,0,6.5,0,16.0,1,Spring,2,12.6884,33.4373,1689.12,2702.59,37.7482,122.177,20.9716,12.4658,72.6233,3,53.4021,31.0265,109.488,57.98,91.1503,Spring,1.57,,,Spring,5,5,5,5,4,5,5,5,5,5,5,1,5,5,2,5,5,5,4,2,88,Spring,50,69,Winter,3,3 +a00c85e7,Summer,18,1,Fall,88,Summer,28.14746094,64.0,164.0,,60,75,107,,,,,,,,,,,,,,,,,,,,Fall,2,2.9919,28.1505,1451.21,2176.82,34.7502,96.8363,16.6219,11.5286,67.1637,3,37.1728,24.9134,93.8444,43.9592,71.9229,,,,,Summer,3,3,2,1,3,1,1,2,1,2,2,1,2,2,1,1,2,1,3,1,35,,,,Summer,3,1 +a017adc3,Spring,9,0,,,Spring,18.62174094,49.0,63.6,23.0,64,97,109,,,,,Spring,6,0,,,,,2,0,14.5,1,13.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.77,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a0182b17,Winter,10,1,Summer,60,Spring,15.71384254,59.75,79.8,,73,69,98,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.80909,15.7156,1143.87,1830.19,18.8756,64.1001,12.6237,3.0919,15.6999,2,25.6016,19.6229,60.291,25.1649,44.4771,,,Winter,2.02,Winter,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Winter,31,45,Winter,3,0 +a01f9d73,Spring,7,1,Winter,50,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a0277c1c,Fall,9,0,Winter,65,Fall,13.78301442,51.5,52.0,,85,74,124,Fall,2,2,51,Fall,4,0,,,,,3,0,10.5,1,6.5,0,12.0,1,Fall,2,8.30143,13.7845,984.564,1575.3,8.13507,47.132,12.4941,1.29045,4.86804,1,23.6783,15.3186,38.8305,19.9707,31.8134,,,Fall,2.12,Fall,1,0,2,1,3,1,0,2,1,0,0,0,2,0,3,1,2,3,2,1,25,Fall,31,45,Fall,2,0 +a032d7bb,Summer,7,0,Fall,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,5,3,2,0,3,0,0,2,0,0,3,0,2,0,0,0,3,0,0,0,23,Summer,42,59,Summer,2,0 +a0394941,Summer,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a0482f47,Spring,9,0,Fall,75,Summer,16.56767533,53.0,66.2,26.0,59,90,100,,,,,Summer,4,0,,,,,2,0,8.0,1,8.5,1,5.0,0,Summer,3,3.30051,16.5695,1040.83,1769.4,11.174,53.1246,13.2968,3.27269,13.0754,2,26.5024,15.4482,49.8241,22.488,37.6765,,,Summer,1.9,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,Spring,53,73,Spring,0,0 +a0497d5d,Fall,16,1,,,Fall,24.49848485,66.0,151.8,33.0,61,73,120,,,,,Fall,0,0,16.9,2,24.6,2,0,0,12.0,1,12.0,1,12.0,1,Fall,1,5.06566,24.5012,1375.69,1788.39,29.8848,88.7917,14.3314,10.1698,63.0083,2,33.1139,25.793,83.726,38.7718,62.9987,Fall,2.91,,,Fall,2,1,1,1,2,3,0,3,1,2,3,3,2,2,2,1,2,1,2,1,35,Fall,31,45,Fall,3,1 +a0510686,Spring,6,1,Spring,73,Winter,15.26736842,47.5,49.0,,60,86,90,,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.60644,15.269,969.349,1550.96,12.6913,45.5114,14.1819,1.08711,3.48865,2,25.0192,7.80083,42.9049,27.0955,37.7105,,,,,Summer,4,3,4,0,4,0,0,4,1,4,1,0,3,3,4,5,3,3,1,1,48,Summer,33,47,Spring,0,1 +a0522c83,Fall,9,0,Spring,41,Fall,18.46970452,53.0,73.8,,56,71,100,,,,,Fall,8,1,,,,,2,0,8.0,1,9.0,1,10.0,1,Fall,1,3.34971,18.4717,1063.31,1382.3,11.9312,55.5193,13.8962,4.57556,18.2807,2,28.0667,15.5214,52.1696,25.7503,39.9979,,,Winter,2.631,Winter,5,5,5,3,3,2,0,4,1,2,0,0,5,3,4,5,3,5,4,2,61,Winter,56,77,Fall,0,2 +a07ec6fb,Winter,10,0,Spring,55,Spring,16.88888889,55.5,74.0,,57,92,125,Spring,5,7,33,Spring,8,0,8.8,1,9.7,1,0,0,11.0,1,12.0,1,13.0,1,Spring,3,3.33047,18.5218,1062.21,1805.76,12.2726,55.4028,13.867,4.65477,18.5972,2,27.3732,15.757,52.0724,22.743,39.6458,,,Winter,2.759,Spring,5,5,3,1,5,0,0,0,2,2,2,0,0,0,0,1,2,2,2,0,32,Winter,51,71,Winter,0,1 +a0811cc1,Spring,7,0,Summer,80,Spring,15.54839319,46.0,46.8,,68,81,103,Spring,3,4,34,Spring,6,1,,,,,10,1,9.0,1,9.5,1,6.0,1,Spring,3,3.82655,15.5501,922.953,1569.02,8.46139,40.5696,13.4799,2.07017,6.23043,2,23.5949,8.51329,36.743,16.8087,32.0563,,,,,Spring,3,0,3,0,3,1,0,0,1,1,0,0,2,2,1,3,2,3,2,1,28,Spring,53,73,Spring,1,0 +a08ebf6e,Winter,11,0,Spring,55,Spring,18.23428616,56.5,82.8,,61,77,118,Spring,5,8,9,Spring,35,1,14.7,2,13.4,2,22,1,4.0,0,4.0,0,3.0,0,Spring,3,3.54435,18.2363,1112.85,1891.85,10.7774,60.7966,13.3901,4.84613,22.0034,2,29.0163,21.0029,57.2523,35.1169,39.7937,,,Winter,2.52,Spring,5,5,5,5,5,4,2,4,5,5,4,1,4,4,5,5,5,5,5,5,88,Spring,47,66,Winter,2,3 +a0bb9f34,Winter,7,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +a0dffdeb,Spring,10,0,Fall,55,Spring,15.06428571,56.0,67.2,21.0,53,71,106,,,,,Spring,11,1,,,,,1,0,9.0,1,9.0,1,10.0,1,Summer,2,3.58992,15.0659,1073.01,1716.82,12.0162,56.5531,12.6789,2.38699,10.6469,1,26.5981,17.9388,52.9631,22.2595,38.6143,,,Summer,4.62,Summer,3,3,3,0,3,0,0,0,2,3,2,0,2,0,3,3,2,4,0,1,34,Summer,34,49,Spring,0,1 +a131f574,Spring,18,1,,,Spring,20.85022244,59.5,105.0,28.0,91,88,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +a148b46f,Spring,5,0,Fall,50,Summer,16.8025679,45.0,48.4,,89,116,107,,,,,Summer,,,,,,,,,,,,,,,Fall,4,7.04022,16.8044,908.243,1907.31,6.89881,39.0028,13.5417,3.26268,9.39717,2,21.9627,10.1413,31.9626,14.8522,28.8615,,,,,Summer,2,0,2,0,2,0,0,0,0,0,2,0,0,0,2,1,1,0,0,0,12,,,,Summer,2,0 +a17f6cfd,Fall,14,1,Summer,41,Spring,18.3927296,62.5,102.2,,65,84,105,,,,,Spring,0,0,14.8,1,15.3,1,2,0,4.0,0,5.0,0,11.0,1,Spring,2,2.1799,18.3947,1264.57,1896.86,25.9227,76.9568,13.8513,4.54346,25.2432,2,30.589,20.4451,74.7769,31.2393,56.5117,Spring,1.29,,,Winter,1,1,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,6,Winter,52,72,Fall,2,0 +a1814875,Summer,15,0,Fall,61,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.75,,,Summer,5,5,5,5,5,2,1,3,5,4,3,0,1,5,5,5,5,5,5,5,79,Summer,38,54,Summer,2,2 +a1942b0b,Summer,9,0,Fall,61,Fall,17.70950255,56.0,79.0,,40,71,94,Fall,6,9,24,Fall,20,1,,,,,1,0,9.0,1,6.0,0,10.0,1,Fall,4,3.77755,17.7114,1117.49,2346.72,14.4804,61.2899,13.7409,3.97052,17.7101,2,29.7758,17.0337,57.5124,26.7734,44.2562,,,Summer,2.51,Fall,1,2,2,0,2,1,0,1,0,0,0,0,0,1,1,0,1,0,0,0,12,Fall,39,55,Summer,0,0 +a196148e,Spring,17,0,,,Spring,23.15264288,66.9,147.4,,74,69,197,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.42,,,Spring,3,2,3,2,3,2,2,2,1,2,3,1,1,3,3,1,2,1,2,1,40,Spring,50,69,Spring,3,1 +a1ab89aa,Summer,6,1,Winter,55,Summer,14.97760477,48.25,49.6,,61,84,108,,,,,Summer,0,0,,,,,0,0,12.0,1,12.0,1,12.0,1,Summer,1,2.38196,14.9792,958.792,1246.43,11.1798,44.387,13.4049,1.57434,5.21302,1,20.4583,12.7489,42.005,16.6164,31.6381,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,28,41,Summer,0,0 +a1c79ece,Fall,10,1,,,Fall,19.18903061,56.0,85.6,,70,69,113,,,,,Fall,5,0,8.9,1,13.3,2,0,0,15.0,1,16.0,1,10.0,1,Fall,3,3.74633,13.7656,1122.59,1796.14,20.0075,61.8332,13.8627,-0.0971218,-0.433201,1,28.2258,13.5998,58.0869,27.4877,48.2334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +a1df952c,Fall,18,0,,,Fall,20.35,76.0,167.2,34.0,66,83,114,,,,,Fall,40,1,29.7,1,37.2,2,14,0,10.0,1,7.0,0,10.0,1,Fall,2,8.27514,20.3522,1787.37,2859.8,41.8552,132.642,16.1457,4.20654,34.558,3,54.554,36.2327,124.367,58.6909,96.4093,Fall,1.67,,,Fall,2,4,4,1,3,1,1,2,2,1,3,1,1,2,3,1,2,1,3,1,39,,,,Fall,3,1 +a1f77468,Spring,17,0,,,Spring,36.35408319,70.5,257.0,45.5,73,63,134,,,,,,,,,,,,,,,,,,,,Spring,3,8.09656,36.3545,2170.15,3689.25,53.1823,173.412,24.5304,11.824,83.5875,1,74.9575,45.2728,165.316,105.774,128.14,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a1fe4f51,Winter,6,0,Summer,45,Winter,15.3486777,45.5,45.2,21.0,79,73,112,,,,,Winter,0,0,,,,,0,0,6.0,0,8.0,1,5.0,0,Spring,2,2.51183,15.3504,905.946,1449.51,7.06512,38.7581,13.1626,2.18773,6.44189,1,21.7832,9.90979,36.2463,14.3534,28.8483,,,,,Spring,3,3,2,0,2,1,0,0,0,0,2,0,0,0,0,2,2,3,2,2,24,,,,Winter,0,0 +a213c519,Winter,15,0,Spring,33,Spring,17.72078874,70.25,124.4,27.0,68,79,118,,,,,Spring,30,1,27.9,1,30.5,2,14,0,4.5,0,5.5,0,11.0,1,,,,,,,,,,,,,,,,,,Spring,3.89,,,Spring,4,4,2,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,28,Spring,32,46,Winter,2,0 +a217e790,Winter,7,1,Spring,85,Spring,20.75755479,51.0,76.8,,104,114,158,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1,2,3,0,0,0,0,0,0,0,1,0,0,0,0,2,3,0,0,0,12,Spring,43,60,Winter,0,0 +a21ad8c0,Summer,19,0,,,Spring,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2,2,2,2,2,1,1,1,0,4,0,4,1,3,2,2,0,1,1,33,Summer,38,54,Summer,3,1 +a221c60c,Summer,12,1,Winter,55,Fall,16.43671056,61.0,87.0,,67,62,122,,,,,Fall,16,0,18.2,2,17.7,2,8,1,8.5,0,7.5,0,7.0,0,Fall,3,0.018947,16.4385,1252.69,2004.3,25.0939,75.691,14.3017,2.13682,11.309,2,32.2204,18.3766,75.672,35.5676,57.3144,,,Fall,2.79,Fall,4,4,2,0,4,2,0,1,1,4,2,0,2,0,2,0,1,2,1,2,34,Fall,54,75,Summer,2,1 +a2240f50,Winter,12,1,Spring,85,Spring,16.32522222,60.0,83.6,,66,74,112,Spring,5,8,23,Spring,10,0,15.2,1,17.7,1,20,1,11.0,1,10.0,1,14.0,1,Summer,3,4.14236,16.2187,1184.68,1895.49,22.2199,68.4475,13.2791,2.93965,15.1525,2,27.9468,18.2807,64.3051,26.8629,50.1668,,,Winter,2.82,Winter,4,4,4,1,5,3,5,3,2,3,1,0,2,2,3,2,2,2,2,2,52,Winter,43,60,Winter,2,2 +a263a68c,Summer,7,1,Fall,65,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1,1,1,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,7,Summer,44,62,Summer,1,0 +a26c3e8f,Winter,7,1,Spring,60,Winter,18.79548611,48.0,61.6,,71,92,106,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,5,5,3,0,2,1,0,0,0,1,0,0,4,0,2,5,2,4,0,2,36,Winter,46,64,Winter,0,1 +a26e7600,Spring,17,1,,,Spring,21.28222656,64.0,124.0,31.0,70,98,106,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a27d67dc,Fall,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a28aaedb,Winter,16,0,,,Spring,19.12573529,68.0,125.8,31.0,61,63,116,,,,,Spring,20,0,18.7,1,20.7,1,14,0,3.5,0,4.5,0,7.0,0,Spring,,,,,,,,,,,,,,,,,Spring,1.86,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a28c7891,Summer,10,0,Spring,52,Summer,15.60229592,56.0,69.6,,49,72,92,,,,,Summer,0,0,,,,,0,0,4.0,0,4.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,Fall,5,0,5,0,5,0,0,0,0,1,0,0,0,0,1,2,1,5,0,5,30,Fall,80,100,Summer,2,0 +a297d1b9,Summer,8,0,Fall,55,Summer,15.64288372,52.1,60.4,,61,84,100,Summer,3,3,21,Summer,0,0,,,,,1,0,9.5,1,9.5,1,12.0,1,Fall,4,3.07382,15.6446,1011.52,2124.19,8.16351,50.0033,12.9517,2.69291,10.3967,1,25.4603,16.3795,46.9295,26.5768,33.6238,,,,,Summer,3,3,3,0,2,0,0,1,0,2,1,0,1,1,1,1,3,1,1,0,24,Summer,37,53,Summer,0,0 +a2994e55,Summer,17,0,Spring,60,Winter,22.34883218,68.0,147.0,,60,84,127,,,,,Winter,6,0,22.0,1,14.9,1,0,0,4.5,0,6.5,0,10.0,1,Winter,2,-0.632482,22.3513,1685.13,2696.21,35.1998,121.751,18.5122,3.83904,25.2486,3,56.1538,30.3978,122.384,77.1449,91.3536,Winter,2.7,,,Winter,4,4,4,0,5,1,0,2,1,2,4,0,5,4,4,4,3,5,2,1,55,Winter,48,67,Summer,2,2 +a2a8f352,Fall,5,0,,,Fall,14.4852552,46.0,43.6,21.0,74,81,109,,,,,Fall,2,1,,,,,0,0,8.0,1,9.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,3,0,3,0,3,3,3,3,3,0,21,,,,Fall,0,0 +a2ef812e,Spring,13,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.03,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a302a015,Spring,9,0,Fall,60,Summer,19.68220663,56.0,87.8,,87,80,154,Summer,,,,Summer,8,0,,,,,0,0,6.5,0,5.5,0,8.5,1,Summer,5,3.81161,19.6844,1142.99,2743.19,15.4329,64.007,14.3501,5.33429,23.793,2,31.0472,17.5269,60.1954,28.664,46.4801,,,Summer,3.19,Summer,4,4,2,0,5,0,0,0,0,2,0,0,4,0,3,3,3,4,2,0,36,Summer,37,53,Spring,0,1 +a30e6178,Winter,8,0,Summer,75,Spring,15.0816609,51.0,55.8,,63,84,120,Spring,6,10,55,Spring,0,0,,,,,5,1,3.0,0,5.0,0,5.0,0,Spring,1,4.75426,15.0833,999.174,1298.93,10.4908,48.6882,13.1609,1.9224,7.11182,1,25.7182,12.4791,43.9339,20.8751,36.2091,,,Winter,2.46,Spring,3,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,10,Spring,55,76,Winter,0,0 +a317c78a,Spring,8,0,Summer,80,Spring,18.60706947,51.5,70.2,,76,82,129,Spring,6,11,20,Spring,10,1,,,,,0,0,10.0,1,10.0,1,9.0,1,Spring,3,6.15904,18.6091,1046.13,1778.42,12.7057,53.6898,14.2325,4.37663,16.5102,1,27.533,13.4511,47.5308,21.9593,40.2387,,,,,Spring,4,2,3,0,5,0,0,0,0,0,1,0,0,0,0,4,0,4,0,0,23,Spring,76,100,Spring,0,0 +a325437d,Summer,11,0,Winter,60,Fall,21.61670135,62.0,118.2,,63,79,112,Fall,5,7,38,Fall,17,1,20.5,2,22.8,3,6,0,6.5,0,6.25,0,13.0,1,Fall,3,3.00115,21.6191,1399.39,2378.96,26.8975,91.3165,16.702,4.91705,26.8835,2,42.0351,22.384,88.3154,43.1846,68.9326,,,Fall,2.75,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +a3391798,Spring,10,1,Fall,80,Summer,16.23907765,49.5,56.6,,73,82,111,Summer,5,7,50,Summer,10,0,20.6,1,25.8,1,7,1,11.0,1,12.0,1,8.5,1,Summer,2,2.67406,16.2409,989.727,1484.59,13.917,47.6819,13.6819,2.55895,8.91805,1,22.7626,11.0024,45.0079,19.2286,36.6796,,,Spring,1.84,Summer,4,2,3,0,2,0,1,1,0,0,2,0,3,0,1,0,2,2,0,0,23,Summer,31,45,Spring,0,0 +a3413f0a,Fall,6,0,Spring,68,Winter,14.63920326,47.0,46.0,,49,81,100,Winter,6,10,37,Winter,0,0,,,,,10,1,13.0,1,11.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,46,64,Fall,0,0 +a347ff4a,Spring,7,0,,,Spring,22.66230737,49.0,77.4,30.0,74,80,133,,,,,Spring,2,0,,,,,0,0,8.0,1,9.0,1,4.0,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a363ec1b,Spring,13,1,,,Summer,22.96466667,60.0,117.6,30.0,81,66,128,,,,,Spring,11,0,26.0,3,28.1,3,15,1,8.0,0,8.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Summer,2.75,Summer,3,3,3,1,2,1,1,1,2,2,3,1,1,1,1,2,2,2,1,1,34,Summer,38,54,Summer,3,1 +a3841682,Summer,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a3858c6a,Spring,15,0,Summer,65,Summer,22.96751691,75.25,185.0,,64,81,136,,,,,Summer,18,0,22.2,1,49.6,3,9,0,,,,,8.0,0,Summer,3,8.32294,22.97,1863.73,3168.33,44.4728,140.774,17.4789,5.49116,44.2257,3,58.3325,37.969,132.451,65.5393,102.805,Spring,4.309,,,Summer,2,2,2,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,13,Summer,31,45,Spring,0,0 +a38699a5,Spring,9,1,,,Spring,33.72994586,49.0,115.2,30.0,69,90,114,,,,,Spring,14,1,,,,,0,0,8.5,1,6.5,0,9.0,1,Spring,2,2.58353,33.7336,1073.44,1610.16,17.9372,56.5987,16.5736,17.16,58.6013,2,24.2123,14.4493,54.0152,22.8725,42.1494,,,Summer,2.579,Summer,3,3,3,1,2,2,0,1,2,2,4,0,2,2,2,2,2,3,1,0,37,Summer,45,63,Spring,2,1 +a3915b0b,Summer,14,0,,,Summer,29.57154017,64.5,175.0,41.0,65,81,121,,,,,Summer,0,0,22.2,1,23.3,1,0,0,0.0,0,0.0,0,12.0,1,Summer,1,5.90954,29.5748,1619.85,2105.81,36.2664,114.799,19.4009,10.1739,60.2012,3,49.2599,29.2725,108.889,47.6897,85.5263,Summer,1.06,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +a3abe528,Spring,9,0,Fall,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.82,Spring,4,4,5,3,5,3,0,5,3,0,1,0,2,3,3,1,5,2,5,3,57,Spring,34,49,Spring,1,2 +a3b146ca,Summer,7,1,Winter,70,Fall,19.28555908,47.0,60.6,,71,70,112,Fall,5,6,21,Fall,0,0,,,,,0,0,7.0,0,8.0,0,7.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,2,0,2,1,0,2,1,0,2,0,0,0,0,2,2,2,2,2,24,Fall,36,51,Summer,1,0 +a3b3cbbe,Winter,10,1,,,Winter,13.45246914,54.0,55.8,23.0,62,84,109,,,,,Winter,16,1,,,,,0,0,10.0,1,9.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Spring,2.45,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +a3bd8858,Fall,15,1,Spring,80,Spring,18.22188467,67.3,117.4,,86,99,125,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.56,,,Spring,1,1,1,1,2,2,2,2,2,1,4,1,2,2,1,1,2,2,1,3,34,Spring,49,68,Fall,3,1 +a3c49769,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +a3e53e34,Spring,10,0,,,Spring,19.0130223,55.13,82.2,,79,77,116,Spring,,,,,,,,,,,,,,,,,,,Summer,4,3.69233,19.0151,1117.69,2347.16,14.7445,61.312,14.1831,4.83196,20.888,2,29.9455,16.622,57.6197,26.3616,44.69,,,Spring,1.14,Summer,3,3,3,1,2,2,0,1,1,2,3,0,2,0,2,2,2,2,2,1,34,Summer,42,59,Spring,1,1 +a3ec2999,Fall,9,1,Summer,50,Winter,19.07034532,53.0,76.2,,54,60,96,Winter,5,7,57,Winter,4,0,,,,,0,0,8.0,0,6.0,0,11.0,1,Spring,1,2.93867,19.0724,1055.01,1371.51,15.7202,54.6351,13.6749,5.39757,21.5649,1,23.1006,15.8144,51.6965,20.6375,38.8207,,,Fall,2.039,Winter,5,5,2,0,5,0,0,1,4,3,0,0,4,1,3,2,1,2,1,4,43,Winter,71,97,Fall,0,1 +a402c6c8,Winter,8,0,Spring,71,Spring,19.18690828,52.0,73.8,,64,88,89,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.12755,19.189,1045.1,1776.68,10.4412,53.5802,13.9316,5.25742,20.2198,2,26.7206,16.4184,50.4527,24.4443,37.1618,,,Spring,1.85,Winter,2,0,0,0,0,0,0,2,2,2,1,0,3,0,4,4,3,4,0,0,27,Winter,35,50,Winter,0,0 +a40c8316,Spring,14,0,Fall,50,Spring,21.85174472,66.0,135.4,,60,96,111,,,,,Spring,28,1,30.3,2,24.5,1,9,0,9.5,1,7.0,0,5.5,0,Spring,2,6.00643,21.8541,1507.01,2411.22,29.6908,102.78,16.5891,5.26502,32.6201,2,46.6454,26.4437,96.7735,54.1751,76.3362,Summer,1.58,,,Summer,3,4,4,3,4,2,2,1,3,4,5,0,1,2,3,1,3,2,1,2,50,Spring,32,46,Spring,2,2 +a41a9901,Winter,11,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a4481101,Spring,13,0,Spring,73,Fall,18.5442768,62.0,101.4,,64,67,104,,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.85197,18.5463,1279.06,2174.4,21.3584,78.4993,14.3577,4.18859,22.9007,2,35.9116,21.2294,73.6474,34.2069,57.27,,,Fall,3.07,Fall,1,1,2,0,2,0,1,1,1,1,2,0,0,0,0,0,0,0,1,0,13,Fall,30,43,Spring,1,0 +a45230f4,Winter,13,1,Spring,78,Spring,22.68959961,64.0,132.2,,68,89,114,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.5,Spring,2,2,1,0,2,0,0,1,3,2,2,0,0,0,1,0,4,1,1,0,22,Spring,41,58,,,0 +a4530b41,Fall,7,0,Winter,60,Winter,16.94376181,46.0,51.0,,75,101,124,Winter,1,0,43,Winter,1,0,,,,,0,0,12.5,1,12.0,1,8.0,1,Winter,1,3.66425,16.9456,939.078,1220.8,8.04164,42.2871,14.0506,2.89499,8.71285,1,25.9668,8.2787,38.6229,24.9788,34.0084,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,34,49,Fall,0,0 +a457a4ab,Spring,6,0,Summer,75,Fall,15.90586096,49.75,56.0,,61,96,117,Fall,,,,Fall,,,,,,,,,,,,,,,Summer,4,2.44589,16.2322,1012.32,2125.87,3.78285,50.0881,14.5186,1.71363,5.9119,2,21.322,24.9833,47.6422,26.3125,25.1048,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,33,47,Spring,0,0 +a470b17f,Winter,13,1,,,Winter,29.50461869,66.5,185.6,40.0,58,88,125,,,,,Winter,0,0,26.8,3,27.3,3,2,0,7.0,1,10.0,1,15.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,2.16,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +a471a564,Winter,8,1,,,,,,,,,,,,,,,Winter,10,1,,,,,10,1,8.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.68,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +a48db496,Summer,15,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a49eda7f,Fall,12,0,Winter,41,Fall,24.92105717,59.0,123.4,,66,64,109,,,,,Fall,8,0,20.1,2,21.2,2,2,0,9.0,1,10.0,1,12.0,1,Fall,3,4.52718,24.9238,1314.51,2234.67,23.197,82.2761,16.6178,8.30603,41.1239,2,38.0521,21.0269,77.7489,35.7536,61.2491,,,,,Winter,3,3,4,1,2,3,1,1,0,1,4,0,0,0,0,1,1,0,1,1,27,Winter,30,43,Fall,2,0 +a4be0322,Winter,8,0,Winter,80,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.58,Fall,3,3,2,2,2,2,1,2,2,2,3,1,2,2,2,2,3,2,2,1,41,Spring,39,55,Winter,2,1 +a4c13fad,Spring,8,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +a4c15213,Summer,9,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,Summer,43,60,Summer,0,0 +a4e0447e,Summer,6,1,Fall,65,Summer,34.05536332,51.0,126.0,,73,,139,,,,,Summer,0,0,,,,,0,0,11.0,1,9.0,1,3.0,0,Fall,1,2.97892,34.0591,1133.99,1474.19,21.4726,63.0479,17.0425,17.0166,62.9521,3,27.1562,14.419,60.0689,27.3041,48.6288,,,,,Summer,2,0,5,0,4,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,17,Summer,37,53,Summer,3,0 +a4f13c0d,Fall,10,0,Summer,65,Spring,19.9861157,55.0,86.0,,71,65,112,Spring,,,,Spring,15,1,14.8,2,13.79,2,6,0,6.0,0,6.0,0,8.0,0,Spring,2,3.63478,19.9883,1120.97,1793.56,14.3036,61.6613,14.3315,5.65685,24.3387,2,30.1342,17.2236,58.0265,27.7789,44.4377,,,Spring,3.82,Spring,3,2,2,1,3,1,1,2,1,1,1,0,2,0,1,2,3,3,1,0,30,Spring,46,64,Fall,2,0 +a5024ef9,Summer,17,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,4,0,1,3,1,5,4,4,4,4,0,3,4,2,4,1,4,1,1,54,,,,,,2 +a5078c62,Summer,8,0,Winter,55,Fall,23.50266272,52.0,90.4,,68,92,123,Fall,4,6,12,Fall,0,0,,,,,8,1,9.0,1,9.0,1,8.0,1,Fall,1,7.06485,23.5052,1127.06,1465.18,16.3636,62.3097,16.2014,7.30387,28.0903,3,31.6621,14.284,55.2448,27.29,48.0256,,,Summer,0.99,Summer,2,3,3,0,2,1,0,0,0,2,2,0,0,1,0,0,0,1,0,0,17,Summer,46,64,Winter,2,0 +a5099438,Summer,17,0,Winter,80,Winter,25.43569336,64.0,148.2,,49,78,120,,,,,Winter,35,1,28.9,1,33.0,2,35,1,12.0,1,11.5,1,17.0,1,Winter,2,4.959,25.4385,1559.14,2494.62,30.459,108.332,18.5951,6.84332,39.868,2,51.0673,26.8057,103.373,68.1881,81.5263,Winter,1.83,,,Fall,0,0,3,0,3,1,2,0,0,0,0,0,0,0,1,0,1,1,0,0,12,Winter,52,72,Fall,2,0 +a5356446,Winter,17,0,Summer,60,Winter,19.54053061,70.0,136.2,,82,84,108,,,,,Winter,20,0,36.8,2,36.7,2,20,1,11.5,1,11.5,1,9.0,1,,,,,,,,,,,,,,,,,,Winter,1.29,,,Winter,5,4,3,0,4,3,0,2,2,4,3,0,2,3,3,0,2,4,1,0,45,Winter,48,67,Winter,0,1 +a54517b1,Spring,8,0,,,Spring,19.74024,50.0,70.2,27.0,66,60,121,,,,,Spring,0,0,,,,,1,0,11.0,1,9.0,1,11.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +a54d22e7,Winter,6,0,Winter,80,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,2,0,2,1,0,1,0,2,1,0,2,0,1,2,2,2,1,0,25,Spring,40,56,Winter,0,0 +a55b715d,Winter,15,1,Fall,46,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.82,,,Fall,4,3,3,2,3,2,1,0,2,2,2,0,1,1,0,2,2,1,3,0,34,Spring,42,59,Winter,1,1 +a55e4e35,Spring,11,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +a5710c0a,Spring,9,0,Winter,75,Summer,14.21516554,53.0,56.8,,71,76,96,Summer,,,,Summer,2,0,,,,,3,0,4.5,0,5.0,0,8.0,1,Summer,3,3.25038,14.2167,1014.77,1725.11,9.67772,50.3494,12.6022,1.61455,6.45059,1,25.364,15.3077,47.099,22.3233,35.0417,,,Summer,2.29,Summer,3,2,2,0,5,0,0,0,0,0,1,0,1,0,0,1,3,2,0,0,20,Summer,47,66,Spring,0,0 +a5974a7d,Summer,11,0,Fall,85,Summer,19.42792301,59.0,96.2,,66,94,126,Summer,5,8,40,Summer,50,1,17.8,2,20.0,2,30,1,8.5,1,8.5,1,16.0,1,Fall,4,5.77944,19.43,1216.78,2555.24,18.6057,71.8661,14.5152,4.91486,24.3339,2,33.9849,19.2754,66.0866,32.5442,52.5906,,,Summer,2.61,Summer,2,2,2,0,2,1,0,1,1,1,0,0,2,2,2,1,1,2,1,1,24,Summer,32,46,Summer,0,0 +a59b2411,Fall,16,1,,,Spring,32.01588317,65.2,193.6,,91,100,148,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.34,,,Spring,0,2,2,1,2,1,2,1,1,1,0,0,0,0,0,0,1,1,1,1,17,Spring,65,89,Fall,0,0 +a5a737f3,Winter,8,0,Spring,80,Winter,15.18313609,52.0,58.4,,84,81,111,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,50,69,Winter,0,0 +a5c87de6,Spring,8,0,Fall,45,Summer,19.82435002,49.25,68.4,,70,78,115,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.05,Summer,2,2,2,1,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,12,Spring,29,42,Spring,0,0 +a5dbb00a,Winter,10,1,Winter,55,Spring,15.97372222,60.0,81.8,,61,78,110,Spring,6,9,38,Spring,11,0,17.4,2,18.3,2,4,0,9.0,1,10.5,1,6.5,0,Spring,3,4.20288,15.9755,1192.19,1907.51,22.3741,69.247,13.5239,2.45158,12.553,1,29.1548,17.7182,65.0442,29.8789,51.5288,,,Winter,1.14,Spring,1,2,4,2,3,3,1,1,2,3,1,0,1,1,2,1,3,2,2,1,36,Spring,37,53,Winter,2,1 +a5f00d28,Spring,10,0,,,Summer,19.27869898,56.0,86.0,27.0,110,75,153,,,,,Summer,2,0,14.4,2,11.2,1,0,0,7.0,0,5.0,0,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,1.709,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +a60b8575,Fall,17,0,Winter,60,Winter,19.69125174,71.0,141.2,,72,73,112,,,,,Winter,17,0,31.8,1,30.1,1,8,0,8.0,1,7.0,0,10.0,1,,,,,,,,,,,,,,,,,,Winter,2.26,,,Winter,5,5,4,0,4,5,1,2,5,4,3,1,3,3,3,2,2,2,2,1,57,Winter,61,84,Fall,3,2 +a614af1b,Spring,8,0,Fall,51,Summer,27.54322235,58.0,131.8,,74,81,129,Summer,2,2,30,Summer,6,1,,,,,0,0,5.0,0,5.0,0,2.0,0,Fall,2,4.38024,27.5462,1321.61,2114.57,24.3171,83.0316,17.3536,10.1926,48.7684,3,37.5654,21.149,78.6513,31.533,61.8825,,,Spring,1.87,Summer,1,2,3,0,3,1,0,1,1,1,0,0,0,0,0,1,2,2,2,1,21,Summer,35,50,Spring,1,0 +a616ebb5,Winter,6,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a62edc1c,Summer,8,1,Fall,60,Summer,16.01904559,48.5,53.6,,81,108,104,Summer,5,8,15,Summer,5,1,,,,,,,12.0,1,10.0,1,10.0,1,Summer,3,7.08887,13.4158,1022.65,1636.24,13.6231,51.1884,12.8122,0.603602,2.41157,1,23.8212,13.7441,44.0996,24.2659,37.4443,,,,,Summer,3,3,3,0,2,0,0,3,3,4,3,0,3,3,3,4,1,3,0,1,42,Summer,35,50,Summer,0,1 +a65b2226,Summer,9,1,Winter,50,Fall,17.27767023,54.5,73.0,,69,81,112,Fall,6,9,4,Fall,4,0,,,,,3,0,10.0,1,10.0,1,11.0,1,Fall,1,6.5886,17.2796,1079.08,1402.8,16.8206,57.1988,13.5393,3.74025,15.8012,2,25.5753,14.8028,50.6102,26.2665,42.3959,,,Summer,2.49,Fall,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,Fall,29,42,Summer,2,0 +a6969e1a,Summer,9,0,Winter,65,Summer,16.47666213,52.5,64.6,,70,76,113,,,,,Summer,14,1,,,,,16,1,7.5,1,7.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,Fall,1.89,Fall,1,2,4,1,4,2,0,2,1,1,2,1,1,1,1,1,2,1,2,1,31,Fall,34,49,Summer,2,1 +a69f89e2,Spring,9,0,Summer,69,Summer,15.56516517,55.5,68.2,,68,69,118,Summer,5,7,38,Summer,8,,,,,,2,,6.5,,7.5,,3.0,,,,,,,,,,,,,,,,,,,,,Spring,3.32,Spring,3,3,1,1,2,1,0,1,1,1,0,0,2,3,2,1,1,1,1,1,26,Spring,32,46,Spring,0,0 +a6e72e9d,Winter,6,0,Fall,80,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,43,60,Winter,0,0 +a6fbb2e7,Winter,8,1,Spring,47,Spring,15.28388172,49.0,52.2,,60,118,108,Spring,3,3,40,Spring,3,0,,,,,0,0,10.5,1,13.0,1,11.0,1,Spring,2,2.47942,15.2856,970.857,1456.29,11.5359,45.6721,13.374,1.91156,6.52794,1,21.3367,12.7994,43.1926,19.1349,32.8726,,,,,Spring,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,Spring,55,76,Spring,0,0 +a70bb2c5,Summer,10,0,,,Spring,21.85380117,57.0,101.0,,60,72,121,Spring,4,6,2,Fall,11,0,12.6,2,16.1,2,5,0,7.5,1,8.5,1,3.0,0,Fall,3,4.01604,21.8562,1197.05,2034.99,15.2817,69.7649,15.097,6.75921,31.2351,2,35.7269,18.7564,65.7489,46.8444,51.0086,,,Summer,2.15,Fall,2,2,2,1,2,1,0,2,2,2,2,0,1,1,1,1,2,1,1,1,27,Fall,35,50,Summer,2,0 +a70e9a7c,Summer,12,0,Fall,85,Fall,20.64210526,57.0,95.4,,62,69,112,Fall,5,7,50,Fall,20,1,12.6,2,16.2,2,10,1,9.5,1,10.0,1,8.0,0,Fall,3,4.72048,20.6444,1217.94,2070.49,19.8925,71.9894,15.5783,5.06601,23.4106,2,34.6699,17.427,67.2689,31.6744,54.5624,,,Summer,1.79,Summer,1,1,2,1,1,1,0,0,1,1,3,1,1,1,1,1,1,1,1,1,21,Summer,37,53,Summer,0,0 +a722f648,Summer,6,0,Fall,70,Summer,14.97362001,42.9,39.2,,22,90,109,Summer,3,3,51,Summer,3,1,,,,,2,0,10.0,1,10.0,1,7.0,1,Fall,3,4.53946,14.9753,864.542,1469.72,5.82978,34.3481,13.1217,1.85354,4.85191,1,21.2037,7.3146,29.8086,13.9239,27.0335,,,,,Fall,4,4,3,0,4,0,0,0,2,2,0,0,2,0,2,3,3,3,0,1,33,Summer,55,76,Summer,2,1 +a74b5f3b,Fall,7,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +a74c3ee9,Winter,12,0,,,Winter,23.4975118,65.5,143.4,32.0,71,85,132,,,,,Spring,2,0,23.0,3,25.3,3,4,0,4.0,0,6.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +a7639eb5,Fall,16,0,,,Fall,24.933391,68.0,164.0,,57,71,114,,,,,Fall,21,0,24.8,1,25.0,1,10,0,4.0,0,4.0,0,12.0,1,Fall,2,6.68463,24.9361,1673.17,2677.08,38.2759,120.478,18.3186,6.61749,43.5219,2,51.2329,30.9693,113.793,52.2985,89.5088,Winter,2.71,,,Winter,2,4,4,4,4,4,2,0,4,2,4,3,3,4,2,4,4,4,4,2,64,,,,Fall,3,2 +a770d091,Summer,9,0,,,Fall,14.88559945,50.5,54.0,,79,67,113,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.69,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +a774c63b,Spring,12,0,Fall,71,Summer,17.35802469,63.0,98.0,,65,71,96,,,,,Summer,15,0,20.7,2,22.3,2,5,0,10.0,1,11.0,1,12.0,1,Fall,3,5.0928,17.3599,1297.39,2205.56,21.8328,80.452,14.2514,3.10849,17.548,1,37.2798,21.3395,75.3592,38.2516,59.1125,,,Spring,3.26,Summer,1,2,2,2,2,1,1,1,1,1,1,1,2,1,2,1,2,1,1,1,27,Summer,26,38,Spring,0,0 +a777be04,Spring,15,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a77e139a,Winter,7,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,40,56,Winter,, +a78766db,Spring,12,1,Fall,81,Summer,24.92631579,61.75,135.2,,85,88,155,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.26,Summer,2,2,2,1,2,1,1,2,1,2,2,0,1,0,0,1,1,0,0,0,21,Summer,40,56,Spring,2,0 +a797554b,Spring,11,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.3,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +a7a24068,Fall,10,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.97,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +a7b45911,Spring,6,0,,,Spring,16.84980536,48.25,55.8,26.0,60,106,120,,,,,Spring,0,0,,,,,2,0,8.0,1,9.5,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a7b9546a,Fall,9,0,Winter,80,Winter,19.91351166,54.0,82.6,,75,77,110,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.95,Fall,3,3,3,0,4,1,0,0,1,1,2,0,1,2,2,1,2,2,1,1,30,Fall,34,49,Fall,0,0 +a7d2852d,Winter,14,1,Spring,58,Winter,18.62617769,57.5,87.6,,72,64,120,,,,,Winter,11,0,22.1,2,24.7,3,4,0,13.0,1,14.0,1,10.0,1,Spring,3,3.62109,18.9565,1138.45,1821.52,17.9157,63.5228,13.7462,5.21026,24.0772,1,28.7379,16.8692,59.9017,38.8992,46.6536,,,Winter,2.88,Winter,3,1,4,1,2,3,1,1,3,2,2,0,0,0,3,2,1,2,0,0,31,Winter,60,82,Winter,2,1 +a7ddc875,Fall,11,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.95,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +a808ad0a,Spring,14,0,Fall,60,Summer,14.92387625,63.5,85.6,,76,81,112,,,,,Summer,11,0,11.5,1,14.4,1,0,0,6.5,0,6.0,0,12.0,1,Summer,4,4.71934,14.9255,1214.43,2550.3,17.1691,71.6158,12.4872,2.43834,13.9842,1,31.9712,22.4754,66.8964,30.8516,49.1403,Summer,2.88,,,Summer,3,1,2,0,3,2,0,0,1,2,3,0,0,1,2,1,1,1,1,1,25,Summer,39,55,Spring,2,0 +a814e825,Spring,10,0,Fall,61,Summer,18.7450888,54.5,79.2,,80,106,126,Summer,4,5,23,Summer,6,,16.0,2,16.6,2,14,,8.0,,9.5,,9.0,,Summer,2,3.76972,19.0959,1128.31,1805.3,14.5997,62.4429,15.0556,4.04031,16.7571,1,33.4215,14.4217,58.6732,38.4363,48.0212,,,Spring,2.35,Summer,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Summer,32,46,Spring,1,0 +a8256fb0,Winter,17,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.021,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +a84922d2,Fall,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0.99,Fall,2,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Fall,35,50,Winter,0,0 +a84ae7e0,Spring,12,0,Fall,71,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.71,Spring,3,3,3,1,3,2,3,3,1,2,3,1,1,3,3,3,3,2,3,2,48,Spring,38,54,Spring,1,1 +a8722cde,Winter,17,0,,,Winter,17.99566641,63.0,101.6,28.0,68,68,113,,,,,Winter,13,0,19.7,1,18.9,1,4,0,0.0,0,0.0,0,10.0,1,Winter,4,4.89076,17.9976,1275.92,2679.44,20.9221,78.1657,13.8464,4.15121,23.4343,1,35.0173,22.2263,73.2749,32.4824,55.9394,Winter,2.56,,,Winter,5,4,4,4,4,1,0,4,4,0,4,0,2,2,0,0,1,4,0,1,44,,,,Winter,2,1 +a886c07b,Spring,10,1,,,Summer,13.8380943,50.5,50.2,,76,102,130,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.9,Spring,4,4,4,0,5,2,0,3,1,4,3,0,2,4,3,4,5,3,5,2,58,Spring,56,77,Spring,1,2 +a88cd304,Winter,10,0,Summer,60,Spring,25.95499405,58.0,124.2,,65,95,114,Spring,,,,Spring,13,1,15.8,2,14.8,2,2,0,10.0,1,9.0,1,13.0,1,Spring,4,10.1611,25.9578,1285.74,2700.05,21.6927,79.2109,16.5551,9.40274,44.9891,3,36.7521,20.7662,69.0498,33.9821,58.4447,,,Spring,3.21,Spring,0,2,2,1,2,1,1,1,2,2,2,2,2,2,1,2,2,2,2,2,33,Spring,51,71,Winter,2,1 +a8926a86,Fall,14,0,Winter,55,Fall,18.56918343,65.0,111.6,,60,73,118,,,,,Fall,17,0,22.0,2,26.4,2,6,0,5.5,0,6.0,0,12.0,1,Fall,3,1.94776,18.5712,1370.8,2330.36,25.0988,88.2715,14.6891,3.88206,23.3285,2,39.5525,23.6202,86.3237,39.4713,64.6513,,,Winter,3.07,Winter,4,4,4,3,4,3,2,3,5,4,4,2,2,3,3,2,2,2,3,3,62,Fall,45,63,Fall,2,2 +a89284b2,Winter,6,0,,,Spring,21.4247619,52.5,84.0,,,97,,Spring,,,,Spring,0,0,,,,,0,0,6.5,0,6.0,0,10.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,2,0,1,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,8,Spring,31,45,Winter,,0 +a8b0428d,Fall,12,0,Winter,60,Fall,19.53210186,65.5,119.2,,71,73,103,,,,,Fall,10,0,18.0,1,21.1,2,3,0,0.0,0,0.0,0,7.5,0,Fall,3,5.59254,19.5342,1398.29,2377.1,26.4127,91.1996,14.9456,4.58864,28.0004,2,40.2169,24.5701,85.6071,39.0066,66.6295,,,Winter,2.24,Winter,2,2,3,3,4,2,2,3,2,2,4,1,1,2,2,3,2,2,2,2,46,Winter,44,62,Fall,3,1 +a8c1aab5,Spring,7,0,Summer,63,Spring,16.08464,50.0,57.2,,71,73,132,Spring,4,5,24,Spring,0,0,,,,,0,0,8.0,1,7.0,0,7.0,1,Spring,3,2.98127,16.0864,985.795,1675.85,9.14094,47.2631,13.2918,2.79457,9.93692,1,25.2061,12.9161,44.2818,21.7347,34.347,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,47,66,Spring,0,0 +a8c3683b,Winter,9,0,,,Spring,17.12274484,51.5,64.6,23.0,60,94,109,,,,,Spring,20,1,,,,,5,1,12.0,1,12.5,1,6.5,1,Spring,1,3.32552,17.1246,1039.61,1351.5,12.5062,52.9957,14.0485,3.07616,11.6043,2,28.0057,12.4837,49.6701,23.8609,40.5119,,,Spring,2.88,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +a8d90be8,Fall,13,1,Spring,75,Winter,18.1407744,62.5,100.8,,64,95,120,,,,,Winter,28,1,19.5,2,22.9,2,1,0,11.0,1,12.5,1,12.0,1,Spring,1,-1.15943,18.1428,1334.3,1734.59,28.095,84.3837,15.188,2.95473,16.4163,2,35.8853,20.4034,85.5431,44.3426,63.9803,,,Fall,2.58,Winter,2,2,2,1,2,1,3,1,2,2,1,0,0,0,2,2,0,2,0,0,25,Winter,34,49,Fall,2,0 +a8d9de72,Spring,9,1,Fall,50,Spring,22.17064463,55.0,95.4,,57,75,88,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.62,Fall,4,5,4,1,4,1,1,3,3,3,3,1,4,4,5,5,5,5,4,4,69,Fall,51,71,Spring,0,2 +a8dc2e05,Fall,14,0,Winter,65,Winter,20.04309414,72.0,147.8,,73,81,124,,,,,Winter,12,0,14.1,1,17.5,1,0,0,0.0,0,5.0,0,11.0,1,Winter,1,-1.95155,20.0453,1642.75,2135.58,36.8946,117.238,15.9003,4.14495,30.562,3,48.84,31.5034,119.19,48.8581,85.7346,Winter,1.49,,,Winter,2,2,2,1,3,1,0,0,0,1,1,0,1,1,1,1,2,1,1,1,22,Winter,43,60,Fall,1,0 +a922ef3f,Fall,7,0,Winter,60,Fall,16.70063158,47.5,53.6,,55,91,110,,,,,Fall,0,0,,,,,0,0,5.0,0,5.0,0,4.5,0,Fall,2,2.76594,16.7025,951.908,1523.05,9.19472,43.6537,13.6031,3.0994,9.94631,2,23.8008,10.6582,40.8877,16.6334,32.9955,,,,,Winter,3,4,4,4,4,4,0,4,0,0,3,0,2,0,5,4,5,4,2,2,54,Winter,47,66,Fall,0,2 +a952659d,Winter,11,0,Spring,75,Winter,18.41965112,53.0,73.6,23.0,56,78,109,,,,,Winter,23,1,13.7,2,12.8,2,12,1,7.0,0,6.5,0,10.0,1,Winter,3,3.53885,18.4217,1084.98,1844.46,14.6906,57.8273,14.4739,3.94781,15.7727,1,29.2546,13.8821,54.2885,23.9281,43.9452,,,Winter,2.46,Winter,3,3,3,2,5,2,0,3,0,2,0,0,2,2,4,4,4,4,4,4,51,Winter,46,64,Winter,1,2 +a9595acd,Summer,8,1,Fall,61,Fall,15.51811745,49.0,53.0,,64,90,105,Fall,,,,Fall,15,1,,,,,15,1,8.0,0,9.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,55,76,Summer,0,0 +a95b4487,Summer,8,1,Winter,65,Fall,17.74375857,54.0,73.6,,53,76,92,Fall,7,11,28,Fall,17,1,,,,,2,0,5.0,0,5.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,62,85,Summer,0,0 +a96099a5,Spring,10,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a967c642,Summer,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a9717f92,Winter,14,1,Spring,92,Winter,18.98856447,65.25,115.0,28.0,50,56,119,,,,,Winter,42,1,30.2,3,27.3,3,1,0,12.0,1,12.0,1,12.0,1,Winter,4,5.22189,18.9906,1351.71,2568.24,28.9,86.2375,14.2409,4.74972,28.7625,1,34.4345,22.903,81.0156,40.4633,63.3345,Winter,3.88,,,Winter,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,5,Winter,34,49,Winter,0,0 +a971b3b1,Summer,7,1,Fall,61,Fall,15.76225224,44.5,44.4,,82,75,120,Fall,5,8,47,Fall,5,1,,,,,0,0,7.0,0,6.5,0,9.0,1,Fall,4,9.09706,15.764,908.747,1726.62,9.77877,39.0565,13.8668,1.89719,5.34354,2,20.1257,9.15201,29.9594,16.1202,29.9045,,,,,Fall,2,1,1,0,1,0,0,1,1,1,0,0,1,0,0,0,1,1,0,0,11,Summer,37,53,Summer,0,0 +a99e7438,Winter,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +a9aa2336,Fall,12,1,,,Winter,19.09382716,63.0,107.8,,47,75,117,,,,,Winter,50,1,13.5,2,15.5,2,0,0,6.0,0,6.0,0,10.0,1,Winter,3,1.05638,19.0959,1302.28,2083.64,27.8774,80.9726,14.3437,4.75226,26.8274,2,32.1583,20.9368,79.9162,33.5146,60.0358,,,Winter,3.73,Winter,4,2,2,1,2,1,1,2,1,1,2,1,1,1,1,1,2,1,1,1,29,Winter,36,51,Fall,2,0 +a9b84b96,Fall,7,1,Spring,72,Fall,16.90520666,51.75,64.4,,73,56,102,,,,,Fall,7,1,,,,,5,1,8.0,0,8.0,0,6.0,1,Fall,3,2.98627,16.9071,1036.16,1657.86,15.6051,52.6279,13.8165,3.09056,11.7721,2,24.872,12.1508,49.6416,24.2108,40.4771,,,,,Winter,1,2,1,0,1,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,11,Fall,33,47,Fall,0,0 +a9c3b925,Winter,9,0,,,Winter,26.7491082,50.75,98.0,31.0,44,85,62,,,,,Winter,3,0,,,,,0,0,6.0,0,6.0,0,11.0,1,Winter,4,4.03578,21.3943,1194.11,2507.62,18.6537,69.4511,15.1618,6.23247,28.5489,2,32.7237,18.0738,65.4153,27.6693,51.3773,,,Winter,2.99,Winter,2,2,1,0,1,1,0,1,0,1,0,0,1,0,1,1,1,1,0,1,15,Winter,31,45,Winter,2,0 +a9c53cdb,Fall,6,0,Spring,50,Winter,16.63831579,47.5,53.4,,78,112,156,Winter,5,9,38,Winter,0,0,,,,,3,1,9.0,1,11.0,1,14.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,3,3,3,0,5,0,0,3,0,2,1,0,5,0,2,2,3,3,0,0,35,Winter,34,49,Fall,1,1 +a9c5f872,Winter,11,0,Spring,91,Spring,19.79052132,57.6,93.4,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.86,Spring,1,0,2,0,1,0,0,0,2,0,5,0,0,1,2,1,1,0,0,1,17,Spring,38,54,Winter,0,0 +a9e0a37e,Spring,6,0,,,Spring,17.02578125,48.0,55.8,23.0,60,87,108,,,,,Spring,0,0,12.8,,10.4,,0,0,8.5,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +a9e5794a,Spring,12,1,Summer,75,Summer,20.65250693,63.0,116.6,29.0,70,93,118,,,,,Summer,45,1,24.9,3,24.4,2,29,1,10.0,1,10.5,1,11.0,1,Summer,5,4.94189,20.6548,1338.61,2944.95,29.866,84.8431,15.0293,5.62549,31.7569,2,33.5387,21.4384,79.9012,35.4003,63.4046,,,Spring,3.52,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Summer,29,42,Spring,0,0 +a9e7a3d1,Winter,7,0,Fall,65,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,36,51,Winter,0, +a9f05417,Summer,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +aa02a789,Winter,8,1,,,Winter,19.34656,50.0,68.8,25.0,67,85,115,,,,,Winter,0,0,,,,,0,0,10.0,1,10.0,1,8.0,1,Winter,,,,,,,,,,,,,,,,,,,Spring,2.14,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +aa0a475f,Spring,17,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.05,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +aa1700c7,Fall,9,0,Spring,73,Winter,18.08127572,54.0,75.0,,68,98,112,,,,,Winter,8,1,,,,,4,0,5.0,0,5.0,0,5.0,0,Winter,2,3.28136,18.0833,1067.55,1708.08,10.678,55.9711,13.4952,4.58805,19.0289,2,26.6629,18.6302,52.6898,24.5205,37.3409,,,Winter,1.77,Winter,1,1,1,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,7,Winter,29,42,Fall,0,0 +aa295760,Winter,11,1,Summer,85,Winter,16.83391813,57.0,77.8,,63,82,122,Winter,4,7,0,Winter,24,1,16.4,2,18.4,2,4,0,10.5,1,11.0,1,12.0,1,Winter,2,9.7896,16.8358,1102.91,1654.36,16.4119,59.7373,12.927,3.90872,18.0627,2,24.6086,18.7169,49.9477,25.8468,41.0205,,,Winter,2.1,Winter,2,2,2,1,2,1,1,1,2,2,2,1,2,2,2,1,1,1,1,1,30,Winter,34,49,Winter,0,0 +aa61d9da,Summer,16,1,,,Winter,26.89752954,66.5,169.2,,71,75,115,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.31,,,Winter,0,1,2,0,1,1,2,1,1,1,2,0,1,1,1,0,1,0,0,0,16,Winter,39,55,Summer,3,0 +aa776ef6,Spring,5,0,Summer,75,Spring,14.30546884,46.5,44.0,23.0,46,75,101,,,,,Spring,0,0,,,,,0,0,9.5,1,9.5,1,11.0,1,Spring,1,2.61325,14.307,914.021,1188.23,6.85608,39.6183,12.8823,1.42476,4.38172,1,22.5785,10.1837,37.005,17.2272,29.4346,,,,,Summer,1,0,1,0,0,0,0,0,0,2,2,0,0,1,0,1,0,0,0,0,8,,,,Spring,0,0 +aab9a230,Spring,13,0,,,Spring,19.7244928,62.5,109.6,31.0,54,61,111,,,,,Spring,0,0,27.1,2,26.4,2,0,0,5.0,0,5.0,0,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.84,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +aacb0b33,Spring,13,0,Fall,80,Fall,24.67263431,62.25,136.0,,93,82,131,,,,,Fall,37,1,24.7,2,27.9,2,4,0,9.5,1,7.5,1,13.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.96,Summer,1,2,1,1,2,1,0,1,1,2,2,0,0,0,0,0,1,0,0,0,15,Summer,42,59,Spring,0,0 +aae9800e,Fall,21,0,Winter,55,Winter,18.7651545,78.0,162.4,,78,69,125,,,,,,,,,,,,,,,,,,,,Winter,3,9.86151,18.7672,2057.85,3498.35,50.1286,161.451,18.6576,0.109639,0.948752,3,69.1498,42.1729,151.59,94.9387,119.278,,,,,Winter,0,3,3,1,1,0,1,0,1,1,1,0,1,1,1,0,1,0,0,0,16,,,,Fall,2,0 +aaefeff0,Fall,19,0,Fall,75,Fall,29.58891155,77.5,252.8,,65,60,128,,,,,,,,,,,,,,,,,,,,Fall,1,9.3061,29.5921,2168.44,2818.97,56.0236,173.23,20.2779,9.31427,79.5701,3,69.9321,47.2742,163.924,80.7192,125.956,,,,,Spring,5,5,5,3,5,4,4,5,5,5,5,2,2,5,5,2,5,5,5,5,87,,,,Fall,3,3 +ab16a20d,Spring,9,1,Summer,60,Spring,19.00969388,56.0,84.8,,53,63,104,Spring,6,9,48,Summer,10,1,,,,,4,0,10.0,1,10.0,1,8.0,1,Summer,1,3.58406,19.0118,1133.12,1473.06,19.9075,62.9553,14.1143,4.89748,21.8447,1,27.5574,15.4904,59.3712,28.4069,47.4649,,,Spring,1.42,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Summer,57,79,Spring,0,0 +ab1dc378,Fall,18,1,Summer,50,Spring,40.23413111,62.0,220.0,,76,71,131,,,,,,,,,,,,,,,,,,,,Summer,2,5.21366,40.2385,1586.0,2379.01,40.839,111.193,20.3375,19.901,108.807,3,42.7395,27.6148,105.98,58.4719,83.5786,,,,,Spring,5,5,5,4,5,5,2,5,5,5,4,2,5,3,5,5,5,5,3,5,88,Spring,47,66,Fall,3,3 +ab2ad965,Spring,6,0,Spring,80,Spring,14.00271616,47.0,44.0,,73,94,124,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.64188,14.0042,918.73,1561.84,6.35376,40.1199,12.7693,1.23497,3.88015,2,22.9731,10.793,37.478,19.8577,29.3268,,,,,Summer,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Summer,31,45,Spring,0,0 +ab36aa48,Winter,6,0,Spring,78,Winter,15.96938272,45.0,46.0,,61,86,107,Spring,,,,Winter,0,0,,,,,0,0,13.0,1,12.0,1,8.0,1,Winter,1,2.48753,15.9711,903.3,1174.29,6.94675,38.4763,13.3589,2.61223,7.52373,2,22.2232,9.30636,35.9887,15.4432,29.1699,,,,,Winter,5,5,5,0,5,0,0,5,4,2,2,0,5,3,0,5,2,5,2,2,57,Winter,68,93,Winter,0,2 +ab4b6c15,Winter,17,1,Summer,70,Spring,19.80231796,63.0,111.8,26.0,69,102,109,,,,,Spring,15,0,24.3,2,24.6,2,17,1,14.0,1,14.0,1,8.0,0,Spring,3,4.49329,19.8045,1258.54,2013.66,24.2663,76.3136,13.5183,6.28614,35.4864,2,30.0972,21.95,71.8203,34.0051,54.3635,Spring,2.41,,,Spring,5,5,3,4,2,1,1,1,1,1,2,1,1,1,2,1,1,1,1,1,36,,,,Winter,2,1 +ab4bbb43,Fall,11,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.23,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +ab4fe82b,Summer,8,0,,,Summer,15.50744671,52.5,60.8,21.0,72,86,108,,,,,Summer,17,1,,,,,6,1,9.0,1,7.5,0,10.0,1,Fall,3,3.28832,15.5091,1025.27,1742.95,10.9354,51.4675,13.1286,2.38058,9.33253,1,26.3593,14.1727,48.1791,22.4251,37.2947,,,Summer,4.06,Summer,5,3,4,0,4,4,0,3,4,5,3,0,0,4,0,4,3,5,4,3,58,Summer,57,79,Summer,2,2 +ab54c39e,Winter,13,1,Summer,60,Spring,26.39408438,62.7,147.6,,66,64,112,,,,,Spring,4,0,20.0,2,23.0,2,0,0,11.0,1,12.0,1,11.5,1,Spring,3,4.73416,26.397,1353.74,2165.98,30.3982,86.454,15.4615,10.9354,61.146,3,33.1096,22.9462,81.7199,36.1893,63.5078,,,Winter,2.27,Spring,4,3,4,3,5,1,0,3,2,2,2,0,1,1,4,3,2,3,2,2,47,Spring,43,60,Winter,0,1 +ab5889ef,Fall,6,0,Winter,75,Fall,15.89052632,47.5,51.0,,61,77,108,Fall,7,12,8,Fall,4,1,,,,,2,0,10.0,1,9.5,1,8.0,1,Fall,4,5.39441,15.8923,945.178,1984.87,8.30945,42.9369,13.3797,2.51257,8.0631,2,24.1288,10.4987,37.5425,19.1582,32.4382,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,31,45,Fall,0,0 +ab6e1b81,Summer,7,0,Winter,69,Fall,15.20505263,47.5,48.8,,54,81,102,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1,1,4,1,4,1,0,1,1,1,2,0,1,1,1,1,4,1,3,1,30,Fall,29,42,Summer,,0 +ab71f143,Fall,12,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4.38,Winter,1,1,1,1,1,1,0,1,1,1,2,0,0,1,1,0,0,0,1,0,14,Winter,28,41,Fall,0,0 +ab8245da,Spring,7,1,Winter,80,Winter,17.32933374,49.5,60.4,,92,79,148,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,4,4,0,3,1,0,4,4,4,0,0,4,2,2,4,4,5,4,1,54,Summer,61,84,Spring,0,2 +ab987c75,Spring,7,0,Fall,55,Fall,18.3128595,55.0,78.8,,65,82,111,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.71,Winter,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Winter,30,43,Spring,0,0 +abac67cc,Summer,16,1,Spring,55,Winter,21.53319225,67.0,137.5,,52,76,105,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0.99,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,43,60,Summer,3,0 +abaf745c,Spring,7,0,Spring,85,Winter,16.10872741,51.0,59.6,,75,59,122,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.90986,16.1105,998.311,1697.13,6.89337,48.5962,13.136,2.97444,11.0038,2,25.0089,16.694,45.6863,28.7976,31.9022,,,,,Spring,1,1,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,Spring,27,40,Spring,0,0 +abb3b071,Winter,7,0,Summer,63,Spring,14.98458924,47.75,48.6,,68,78,97,Spring,6,10,15,Spring,0,0,,,,,0,0,10.5,1,9.0,1,6.5,1,Spring,4,8.55854,14.9862,937.241,1968.21,7.05629,42.0915,12.9793,2.00697,6.50855,1,21.9523,13.0828,33.5329,15.6857,29.0086,,,,,Spring,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,Spring,75,100,Winter,1,0 +abc47b4d,Summer,11,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +abd1b232,Winter,16,0,,,Spring,25.39545597,68.6,170.0,,61,76,108,,,,,Spring,29,1,32.2,2,31.8,2,10,0,6.2,0,6.5,0,10.0,1,Spring,3,6.28036,25.8484,1602.97,2725.06,33.0442,113.001,17.1817,8.66668,56.9991,2,49.401,30.5557,106.721,55.8353,82.4452,,,,,Spring,4,4,5,4,5,5,3,5,3,3,4,1,4,1,5,5,2,4,1,5,73,Spring,56,77,Spring,,2 +ac06388d,Spring,12,1,,,Spring,24.86802721,63.0,140.4,34.0,67,90,137,,,,,Summer,7,0,18.6,2,17.0,2,0,0,4.0,0,5.0,0,11.0,1,Summer,1,4.56088,24.8707,1312.49,1706.23,26.9615,82.0602,14.5363,10.3344,58.3398,3,31.8494,23.2493,77.4993,38.1541,58.8109,,,Summer,2.64,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +ac1ad4a7,Winter,6,1,Fall,56,Winter,15.89199372,45.5,46.8,23.0,60,88,100,,,,,Winter,0,0,,,,,0,0,15.0,1,13.0,1,8.0,1,Winter,1,2.14373,15.8937,924.224,1201.49,9.93839,40.7049,13.8238,2.06994,6.09506,1,20.0914,10.6752,38.5612,16.5294,30.0298,,,,,Winter,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,,,,Winter,2,0 +ac29ec80,Spring,7,1,Fall,78,Fall,14.40173611,48.0,47.2,,81,89,116,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,5,2.05441,14.4033,999.519,2198.94,6.86418,48.7249,14.8686,-0.465331,-1.5249,1,18.7809,23.0798,46.6705,25.7654,25.6451,,,,,Spring,2,0,2,0,1,0,0,1,1,1,2,0,2,1,1,1,1,1,1,1,19,Spring,31,45,Spring,0,0 +ac31e154,Fall,9,1,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.11,Winter,1,0,2,1,2,1,0,0,0,0,2,0,0,1,1,1,1,1,1,1,16,Winter,37,53,Fall,1,0 +ac3ba541,Spring,8,0,Spring,60,Winter,15.91152263,54.0,66.0,,58,61,115,Winter,6,11,9,Winter,15,1,,,,,2,0,10.0,1,11.0,1,11.0,1,Winter,3,3.48446,15.9133,1057.22,1797.27,13.1498,54.8705,13.2298,2.68344,11.1295,1,26.4996,15.2211,51.386,19.5228,39.6494,,,Spring,2.37,Winter,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Winter,30,43,Summer,0,0 +ac5fad3b,Winter,6,0,Summer,75,Spring,15.59393391,47.0,49.0,,63,85,76,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,31,45,Winter,0,0 +ac66c144,Spring,7,1,Fall,85,Fall,14.81138024,51.0,54.8,,53,67,95,Fall,7,11,45,Fall,5,1,,,,,6,1,10.0,1,8.0,0,9.0,1,Fall,3,7.55907,14.813,1001.05,1601.68,13.4131,48.8878,13.2149,1.59814,5.91223,1,22.9504,12.5243,41.3287,21.1929,36.3635,,,,,Fall,2,2,2,1,1,1,0,1,1,1,2,1,1,1,1,1,1,1,1,1,23,Fall,30,43,Spring,0,0 +ac675a81,Spring,9,0,Fall,61,Summer,17.46863653,53.0,69.8,,87,70,106,Summer,6,10,50,Summer,10,1,,,,,11,1,9.5,1,11.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,Spring,3.2,Summer,4,3,3,2,2,1,0,2,1,1,2,1,2,1,4,4,,3,2,2,40,Summer,35,50,Spring,1,1 +ac67c949,Spring,8,0,Fall,52,Summer,14.88393195,46.0,44.8,,56,85,99,Summer,4,5,55,Summer,3,0,,,,,0,0,6.0,0,7.5,1,5.0,0,Summer,2,13.4115,14.8856,922.713,1476.34,4.19686,40.544,13.4714,1.41412,4.25598,1,20.224,16.1232,27.1325,16.5612,24.4209,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,26,38,Spring,0,0 +ac7afa2d,Winter,15,0,,,Winter,24.5953168,66.0,152.4,29.0,65,71,111,,,,,Winter,17,0,80.4,2,76.8,2,15,1,6.5,0,7.5,0,12.0,1,,,,,,,,,,,,,,,,,,Winter,4.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +ac8c0593,Winter,7,0,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,63,86,Winter,0, +ac967bb9,Winter,16,0,Fall,50,Winter,21.58017376,67.0,137.8,,56,69,113,,,,,Winter,15,0,16.5,1,17.7,1,0,0,0.0,0,0.0,0,10.0,1,Winter,1,2.4043,21.5825,1514.53,1968.89,30.7991,103.58,16.223,5.35955,34.2196,2,45.5926,27.1886,101.176,48.1609,76.3918,Spring,0.99,,,Winter,4,4,4,1,4,4,4,4,3,3,2,1,4,4,4,4,4,4,4,2,68,Winter,43,60,Winter,2,2 +acab23a5,Winter,16,1,,,Winter,22.15548946,61.5,119.2,27.0,54,99,109,,,,,Winter,10,0,25.3,2,25.4,2,0,0,14.0,1,14.0,1,17.0,1,Winter,,,,,,,,,,,,,,,,,Spring,1.36,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +acb75c15,Spring,9,0,Fall,35,Summer,18.14696746,52.0,69.8,,56,91,108,Summer,7,13,0,Summer,3,0,,,,,2,0,7.5,1,9.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.04,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,67,91,Spring,0,0 +acd409fc,Winter,8,0,,,Winter,17.08680556,48.0,56.0,24.0,70,99,92,,,,,Winter,5,0,,,,,0,0,7.0,0,5.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +acde8e47,Winter,9,1,,,Winter,15.1987438,55.0,65.4,24.0,73,97,111,,,,,Winter,15,1,,,,,0,0,13.0,1,12.5,1,12.0,1,Winter,4,3.22126,15.2004,1064.46,2022.47,15.5027,55.6419,12.9324,2.26799,9.75808,2,24.3801,15.7591,52.4207,24.4276,39.8828,,,Winter,3.8,Winter,5,5,4,1,4,0,0,1,1,1,1,0,3,1,2,3,2,3,0,0,37,Winter,55,76,Winter,2,1 +aceefa4f,Spring,11,1,Spring,40,Fall,36.17579587,60.25,186.8,,92,83,145,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.241,Spring,5,4,4,0,5,3,0,3,2,4,4,0,3,1,2,1,1,1,0,1,44,Spring,43,60,Spring,1,1 +ad055eb3,Fall,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.25,,,,,,,,,,,,,,,,,,,,,,,,,Summer,46,64,Fall,1, +ad0bbf35,Spring,9,1,Fall,81,Summer,20.25102881,54.0,84.0,,65,74,99,Summer,7,11,1,Summer,5,0,,,,,3,0,10.0,1,12.0,1,11.0,1,Summer,3,3.28632,20.2532,1099.74,1759.58,18.5764,59.3998,14.3219,5.93136,24.6002,2,26.4106,14.4128,56.1135,26.5317,44.987,,,Spring,3.03,Summer,1,1,1,1,1,1,1,1,1,1,2,1,1,1,2,2,1,1,1,1,23,Summer,30,43,Summer,0,0 +ad1dfc9a,Winter,17,0,Winter,65,Spring,22.61077551,70.0,157.6,,64,96,123,,,,,Spring,5,0,13.0,1,18.8,1,6,0,0.0,0,0.0,0,6.0,0,Spring,2,6.9453,22.6132,1658.68,2653.9,33.289,118.935,17.0654,5.54788,38.6652,2,54.3291,31.3167,111.989,75.4585,87.6181,Spring,1.85,,,Spring,1,1,2,1,1,1,0,1,1,1,3,1,1,1,1,1,1,1,1,1,22,Spring,36,51,Winter,2,0 +ad319a21,Fall,12,0,Spring,60,Winter,14.45986785,59.0,71.6,,58,77,113,Winter,5,7,59,Winter,10,0,13.4,2,13.3,2,10,1,6.0,0,6.0,0,11.5,1,Spring,3,4.13835,14.4614,1131.65,1923.81,14.4623,62.7988,12.6838,1.77762,8.80117,1,29.9958,18.3407,58.6605,28.8461,44.4582,,,Fall,1.87,Winter,5,4,4,0,3,2,1,3,1,1,4,0,1,2,3,2,2,2,2,2,44,Winter,33,47,Fall,2,1 +ad380962,Fall,9,1,Winter,82,Fall,21.46969554,51.5,81.0,,60,83,106,,,,,Fall,0,0,,,,,6,1,9.5,1,8.5,1,8.0,1,Fall,2,2.9624,21.472,1059.75,1589.62,17.425,55.14,14.6169,6.85514,25.86,2,24.8539,12.8611,52.1776,22.5747,42.279,,,Fall,2.01,Fall,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,29,42,Fall,2,0 +ad536dea,Summer,9,0,Winter,61,Fall,18.66032484,52.8,74.0,,62,62,103,Fall,5,7,17,Fall,0,0,,,,,7,1,10.0,1,10.0,1,9.0,1,Fall,1,3.04803,18.6624,1053.17,1369.12,8.41584,54.4397,13.7294,4.933,19.5603,1,26.1783,19.8455,51.3916,29.7114,34.5942,,,Fall,3.75,Fall,4,5,3,2,4,2,0,2,1,3,2,0,2,3,1,3,3,2,1,1,44,Fall,70,95,Summer,0,1 +ad8189bd,Winter,15,0,Spring,38,Winter,24.00140657,68.5,160.2,,67,65,117,,,,,Winter,10,0,41.3,2,42.1,2,20,1,7.0,0,8.0,1,10.0,1,Winter,4,5.90239,24.004,1507.06,3164.82,26.1218,102.784,15.401,8.60303,57.4156,2,46.4664,30.1963,96.882,63.7766,72.5881,Winter,3.66,,,Winter,4,5,5,5,5,5,5,5,5,5,4,2,2,2,5,5,5,5,5,5,89,Winter,40,56,Winter,2,3 +ad8945e3,Spring,10,0,Fall,50,Summer,20.15495547,59.0,99.8,,66,62,107,Summer,5,7,40,Summer,8,0,11.4,1,16.0,2,0,0,4.0,0,4.0,0,2.0,0,Summer,4,8.02322,20.1572,1209.82,2540.62,18.0072,71.1247,14.3654,5.79172,28.6753,2,32.8978,20.2197,63.1014,30.0293,50.905,,,Spring,3.09,Summer,3,4,5,5,5,1,0,1,1,2,0,0,0,1,1,2,2,2,1,1,37,Summer,37,53,Spring,0,1 +ad9f821d,Spring,11,0,Summer,66,Summer,14.933147,53.5,60.8,,80,70,127,Summer,5,7,16,Summer,3,0,15.3,2,17.6,2,12,1,7.0,0,7.0,0,8.0,0,Summer,4,3.39789,14.9348,1036.23,2176.07,10.6457,52.6346,12.929,2.00573,8.1654,1,27.2,14.7889,49.2367,26.2625,37.8457,,,Spring,3.23,Summer,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,4,Summer,54,75,Spring,0,0 +ada63b71,Summer,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +adaf7461,Fall,8,0,Spring,61,Winter,18.23598831,51.5,68.8,,65,78,110,Winter,5,8,37,Winter,7,1,,,,,12,1,12.0,1,13.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,4,4,4,2,3,1,0,0,1,1,1,0,0,0,2,3,2,1,1,1,31,Winter,52,72,Fall,2,1 +adb3bcdc,Summer,17,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +adba23f0,Spring,11,0,Fall,85,Spring,21.76725146,57.0,100.6,,63,74,104,Spring,6,10,4,Spring,13,1,16.1,2,17.4,2,12,1,10.0,1,8.0,1,8.0,0,Spring,3,3.93019,21.7696,1185.28,2014.97,16.8371,68.5107,14.8256,6.94406,32.0893,2,32.3986,19.275,64.5805,30.0951,49.2357,,,Fall,1.53,Spring,4,3,2,1,2,1,1,1,1,3,2,1,1,1,1,1,2,1,1,1,31,Spring,31,45,Spring,0,1 +adbd6839,Fall,7,0,Spring,78,Winter,14.99323177,48.03,49.2,,85,107,108,Winter,6,10,30,Winter,5,1,,,,,2,0,4.0,0,4.0,0,7.0,1,Spring,1,6.49873,13.2993,2572.41,3344.13,67.3013,216.259,58.4569,-45.1576,-167.059,1,97.8114,51.1458,209.76,151.025,165.113,,,,,Winter,2,2,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,Winter,29,42,Fall,2,0 +adc2d620,Fall,9,0,Winter,80,Fall,15.84048751,58.0,75.8,,71,78,123,Fall,6,9,39,Fall,7,0,,,,,0,0,0.0,0,0.0,0,12.0,1,Fall,2,9.58909,15.8422,1113.84,1782.14,13.1802,60.9016,12.7285,3.11376,14.8984,1,27.3453,20.3761,51.3125,22.7724,40.5256,,,Fall,1.69,Fall,0,2,2,3,2,2,0,1,1,2,0,0,2,2,2,1,2,2,2,1,29,Fall,28,41,Fall,,0 +adc71322,Spring,10,0,Fall,55,Summer,21.42285511,53.0,85.6,,71,83,123,Summer,6,9,21,Summer,4,0,,,,,3,0,9.0,1,8.0,1,7.0,1,Summer,1,7.74741,21.275,1103.83,1434.98,14.2354,59.8357,14.9765,6.29849,25.1643,2,30.2635,15.3367,52.0883,27.6268,44.4989,,,Spring,1.73,Summer,1,2,2,2,2,3,0,2,0,2,2,0,0,0,1,1,1,1,1,1,24,Summer,40,56,Spring,2,0 +ade50eba,Winter,16,0,Fall,55,Summer,23.01031541,71.0,165.0,,73,59,120,,,,,Summer,40,1,46.6,2,57.6,3,30,1,8.0,1,7.0,0,10.0,1,Summer,4,7.88753,23.0128,1865.53,3917.62,41.8809,140.967,19.6609,3.35193,24.033,1,63.3614,35.7246,133.079,89.3602,105.242,Winter,2.41,,,Summer,0,5,2,2,2,2,1,2,3,0,2,2,2,0,2,0,1,2,1,0,31,,,,Winter,3,1 +adf444ff,Winter,9,0,Spring,75,Spring,20.20281207,54.0,83.8,,43,60,57,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.79103,20.205,1143.4,1829.43,16.7213,64.0498,15.443,4.76197,19.7502,2,32.5888,14.7397,60.2588,30.4859,49.3101,,,Winter,0.77,Spring,4,4,4,2,4,1,1,2,2,3,4,0,3,1,3,2,3,2,0,2,47,Spring,27,40,Winter,2,1 +adfef47a,Summer,9,1,,,Fall,15.28713018,52.0,58.8,23.0,74,73,112,,,,,Fall,11,1,,,,,0,0,11.5,1,11.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.61,Fall,3,2,3,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,28,Fall,31,45,Summer,0,0 +ae04102d,Spring,7,1,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,,,Spring,, +ae28a7e1,Spring,16,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +ae43eca7,Winter,13,0,,,Spring,23.39919585,65.5,142.8,35.0,65,73,129,,,,,Spring,4,0,27.1,2,27.7,2,2,0,8.0,1,8.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Winter,2.57,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +ae44e2e7,Fall,16,1,,,Fall,17.21692922,69.0,116.6,29.0,78,77,126,,,,,Fall,0,0,7.8,1,11.3,1,0,0,7.5,0,8.0,0,9.0,1,,,,,,,,,,,,,,,,,,Fall,1.72,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +ae4fdae6,Spring,11,1,Winter,45,Winter,21.70740444,59.25,108.4,,78,92,123,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,2,4.04515,21.7098,1216.43,1824.65,24.2091,71.8292,14.3856,7.3242,36.5708,2,28.7659,18.8542,67.7841,28.1822,52.975,,,Summer,3.08,Summer,3,3,3,3,3,1,1,2,4,4,3,0,3,2,3,4,2,3,1,2,50,Summer,72,98,Spring,3,2 +ae5a01b6,Winter,15,0,,,Winter,35.367681,67.0,225.84,43.0,73,87,129,,,,,Winter,8,0,25.3,1,21.0,1,0,0,9.0,1,10.0,1,4.0,0,Winter,2,6.76127,35.3653,1875.45,3000.72,44.6074,142.023,22.244,13.1213,83.7769,3,60.681,36.7348,135.262,69.4843,105.288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +ae6e4380,Spring,9,0,Winter,75,Spring,16.96495868,55.0,73.0,25.0,73,86,125,,,,,Spring,0,0,,,,,0,0,10.0,1,9.0,1,5.0,0,Spring,2,3.5093,16.9668,1078.43,1725.49,12.5032,57.1299,13.2783,3.68855,15.8701,1,27.2114,17.4153,53.6206,22.5977,39.7146,,,Summer,1.5,Spring,2,0,2,0,2,1,0,1,1,2,1,0,4,0,1,2,1,2,1,0,23,Spring,39,55,Spring,0,0 +ae712150,Fall,19,0,Winter,51,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,2,2,0,1,1,1,0,2,0,3,0,0,0,0,0,1,1,1,0,15,,,,Fall,1,0 +ae956e93,Winter,20,1,,,Winter,29.93242188,64.0,174.4,39.0,95,77,145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +aea63a78,Spring,10,1,Spring,80,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.1,Spring,2,2,3,1,3,1,0,0,1,2,2,0,1,1,2,1,2,1,1,1,27,Spring,29,42,Spring,0,0 +aea92843,Summer,6,1,Fall,60,Fall,15.9623473,47.3,50.8,,66,98,107,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,10,Fall,42,59,Summer,0,0 +aecc7e74,Winter,12,0,Summer,61,Spring,18.1754386,57.0,84.0,,76,90,125,,,,,Spring,6,0,17.2,1,17.9,1,7,0,5.5,0,5.5,0,9.5,1,Fall,3,6.65738,18.1774,1143.6,1944.12,13.1077,64.0715,13.8649,4.31249,19.9285,2,33.0624,17.9014,57.4141,42.6994,46.1701,,,Winter,3.09,Spring,5,5,5,0,5,5,0,4,2,5,5,0,1,1,4,4,4,4,5,2,66,Spring,56,77,Winter,2,2 +aed29ee7,Spring,11,0,Fall,71,Fall,14.34283309,54.5,60.6,,62,82,115,Fall,,,,Summer,,,,,,,,,,,,,,,Summer,5,3.33055,14.3444,1037.31,2489.54,7.93338,52.7498,12.4862,1.8582,7.85023,1,27.048,17.7684,49.4192,34.7367,34.9813,,,Summer,2.12,Summer,5,5,5,3,5,4,5,5,1,5,4,1,5,0,5,5,4,4,4,0,75,Summer,29,42,Spring,3,2 +af1c2775,Fall,15,0,Winter,50,Fall,15.62624132,74.75,124.2,,75,89,128,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.93,,,Winter,4,3,4,3,4,2,4,3,3,2,4,0,2,3,3,0,3,1,4,2,54,Fall,39,55,Fall,3,2 +af485add,Summer,10,0,Spring,70,Spring,21.97998393,58.5,107.0,,73,80,131,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.21,Spring,5,4,4,2,4,1,0,1,2,4,2,0,2,5,4,2,3,4,2,1,52,Spring,48,67,Summer,0,2 +af4f064e,Fall,9,1,Spring,80,Winter,15.53239702,51.5,58.6,,56,78,106,,,,,Winter,16,1,,,,,11,1,10.0,1,11.0,1,6.0,1,Winter,3,2.87844,15.5341,1016.04,1625.67,14.5327,50.4851,13.3829,2.15116,8.11492,1,23.4458,12.5066,47.6066,21.1158,37.9784,,,Fall,3.7,Winter,4,3,3,0,4,1,0,1,0,1,0,0,1,1,1,0,1,0,0,0,21,Winter,46,64,Fall,0,0 +af4f59e7,Winter,7,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +afa6cc9b,Fall,14,0,Winter,65,Winter,15.65764676,63.0,88.4,,80,105,123,,,,,Winter,20,0,14.4,1,13.2,1,5,0,0.0,0,0.0,0,7.0,0,Winter,2,2.55499,15.6594,1242.95,1988.72,20.7393,74.6532,13.2242,2.43514,13.7468,2,32.8078,21.1061,72.0982,27.16,53.5471,,,Winter,2.04,Winter,2,3,3,3,4,1,1,1,1,2,3,1,2,1,1,2,2,3,2,3,41,Winter,36,51,Fall,,1 +afa92c37,Fall,12,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.24,Fall,3,3,3,1,3,1,1,1,1,2,2,1,1,1,3,1,1,1,1,1,32,Fall,35,50,Fall,1,1 +afae8244,Winter,8,0,Spring,78,Spring,16.70137299,51.25,62.4,,74,96,104,Spring,4,5,57,Spring,15,1,,,,,25,1,9.5,1,8.5,1,13.0,1,Spring,4,5.63034,16.7032,1019.32,2140.58,11.0326,50.8343,13.6073,3.0959,11.5657,2,26.7299,13.0718,45.204,22.8384,37.7625,,,Spring,2.81,Spring,2,3,4,0,5,0,0,0,0,2,0,0,2,1,4,4,4,2,2,1,36,Spring,29,42,Winter,1,1 +afc78670,Winter,8,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +afd8cefb,Summer,8,0,Spring,58,Winter,24.165625,48.0,79.2,,69,107,108,Winter,3,4,19,Winter,0,0,,,,,3,0,12.0,1,12.0,1,5.0,0,Winter,2,2.73701,24.1683,1016.0,1625.59,9.68329,50.4799,15.4042,8.76406,28.7201,3,26.4172,14.3794,47.7429,23.571,36.1005,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,45,63,Summer,2, +aff48273,Summer,11,1,Fall,90,Fall,14.64099174,55.0,63.0,,59,103,104,Fall,5,8,2,Fall,12,0,12.1,2,14.2,2,2,0,7.5,0,8.0,0,10.0,1,Fall,1,8.81665,14.6426,1057.03,1374.14,15.0825,54.8506,12.7485,1.89409,8.14937,1,23.3271,16.441,46.034,21.7341,38.4096,,,Summer,1.75,Fall,1,2,2,1,2,1,2,1,1,1,1,1,3,3,2,2,2,2,1,1,32,Fall,50,69,Summer,2,1 +b01cb004,Spring,7,0,Fall,78,Spring,16.91867018,53.7,69.4,,59,98,101,Spring,4,6,7,Spring,1,0,,,,,3,0,10.0,1,10.5,1,13.0,1,Spring,3,2.37126,16.9205,1102.35,1873.99,15.6328,59.6777,14.5501,2.37041,9.72229,2,30.687,13.3579,57.3065,26.8862,46.3198,,,,,Spring,1,0,1,0,0,0,0,0,0,0,1,0,1,1,1,1,0,1,0,0,8,Spring,42,59,Spring,0,0 +b056662d,Spring,16,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b061e9dd,Winter,11,0,,,Winter,27.95555556,57.0,129.2,38.0,88,80,124,,,,,Winter,10,0,15.0,2,21.5,2,2,0,4.0,0,5.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.93,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +b074f33b,Winter,9,0,Spring,65,Winter,16.94732143,56.0,75.6,,63,78,106,Winter,2,2,9,Winter,0,0,,,,,5,0,6.0,0,4.5,0,4.5,0,Spring,3,8.16945,16.9492,3115.72,5296.72,86.4271,274.128,61.4583,-44.5091,-198.528,1,118.466,69.2348,265.959,189.688,204.893,,,Winter,2.8,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,40,56,Winter,0,0 +b081ad0f,Fall,14,0,,,Fall,18.77834625,65.5,114.6,,57,85,117,,,,,Fall,28,1,29.5,2,23.4,2,20,1,9.0,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +b08423c5,Spring,12,0,,,Summer,17.82923977,57.0,82.4,,60,82,81,,,,,Summer,20,1,16.5,1,18.1,1,11,1,4.0,0,5.0,0,12.0,1,Fall,3,3.78171,16.6428,1137.38,1933.54,11.1121,63.4087,12.807,3.83577,18.9913,1,29.0339,23.2628,59.627,35.3494,40.146,,,Spring,2.77,Summer,2,2,2,1,2,1,1,2,2,2,2,1,1,2,2,2,2,2,2,1,34,Summer,38,54,Spring,1,1 +b08f0ca4,Summer,5,1,Winter,55,Fall,14.30301235,45.0,41.2,,105,82,157,Fall,,,,Fall,8,1,,,,,0,0,12.0,1,12.5,1,12.0,1,Fall,2,10.5403,15.6662,889.96,1334.94,8.50159,37.0554,14.0902,1.57595,4.14455,2,19.0939,9.45996,26.5152,14.8322,27.5955,,,,,Fall,2,3,2,1,3,1,1,1,2,2,2,1,3,5,2,3,4,3,1,2,44,,,,Summer,0,1 +b092b360,Summer,10,1,Fall,75,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.15,Summer,1,2,2,0,2,1,0,1,1,1,1,0,0,1,1,1,1,1,1,1,19,Summer,66,90,Summer,0,0 +b0ab15a7,Spring,8,0,Fall,55,Summer,17.40607459,51.0,64.4,,69,77,114,Summer,2,1,35,Spring,4,1,,,,,2,0,7.0,0,7.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,32,46,Spring,0,0 +b0b57cb7,Winter,9,0,Summer,68,Spring,16.20082305,54.0,67.2,,60,77,96,Spring,6,10,12,Spring,14,1,,,,,14,1,0.0,0,4.0,0,9.0,1,Spring,3,2.07574,16.2026,1097.32,1865.45,15.0307,59.1423,14.2598,1.94279,8.05769,2,30.6887,13.4229,57.0666,28.1451,45.7194,,,Winter,3.56,Spring,3,2,4,0,4,1,0,2,1,1,3,0,1,0,4,1,4,2,0,1,34,Spring,40,56,Winter,0,1 +b0e96997,Spring,7,1,Spring,75,Spring,21.62306871,53.0,86.4,,105,100,194,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.21122,21.6254,1096.94,1645.41,19.0364,59.1012,14.7927,6.83273,27.2988,3,26.4864,13.5784,55.89,25.7664,45.5228,,,,,Spring,3,3,2,2,1,1,1,1,1,1,2,1,1,1,2,2,2,1,2,1,31,Spring,47,66,Spring,1,1 +b10142d4,Winter,12,0,,,Winter,19.89706942,59.5,100.2,30.0,62,57,109,,,,,Winter,9,0,17.0,2,19.8,2,7,0,11.0,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.82,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +b120a337,Spring,9,1,,,Summer,14.60967078,54.0,60.6,,64,72,91,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.4,Spring,2,2,1,0,4,0,0,1,0,1,1,0,0,0,0,1,0,1,0,0,14,Spring,48,67,Spring,0,0 +b13e0cd0,Winter,18,0,,,Spring,22.3755787,72.0,165.0,36.0,79,83,144,,,,,Spring,14,,31.6,1,38.1,2,9,,4.0,,4.0,,11.0,,,,,,,,,,,,,,,,,,,Spring,3.16,,,Spring,1,1,1,0,1,0,1,0,1,0,2,0,1,2,1,0,1,1,1,0,15,,,,Spring,2,0 +b1484dda,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b1659233,Fall,11,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b1883100,Summer,7,1,Winter,81,Fall,15.03807576,45.25,43.8,,64,70,111,Fall,4,6,30,Fall,14,1,,,,,6,1,11.0,1,10.0,1,9.0,1,Fall,3,17.5715,15.0397,941.376,1506.2,7.49268,42.5319,14.6043,0.435445,1.26814,2,18.3138,16.7254,24.9603,17.1784,25.8064,,,,,Fall,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Fall,33,47,Summer,0,0 +b1a00510,Spring,10,0,Summer,51,Spring,16.96810253,53.0,67.8,,50,87,105,Spring,6,10,0,Spring,17,1,,,,,5,0,7.0,0,7.5,1,12.0,1,Spring,4,5.54163,16.97,1055.75,2217.07,12.334,54.7141,13.6946,3.27532,13.0859,2,28.1106,14.2695,49.1725,25.0556,40.4446,,,Spring,4.13,Spring,3,3,2,2,5,0,0,1,1,1,3,1,3,2,0,3,3,3,2,1,39,Spring,35,50,Spring,0,1 +b1bc2fe4,Fall,5,1,Winter,61,Fall,20.22273095,46.5,62.2,,82,117,130,,,,,Fall,7,1,,,,,4,1,10.0,1,13.0,1,9.0,1,Winter,2,2.41563,20.2249,966.554,1449.83,13.24,45.2137,14.7017,5.52326,16.9863,1,23.1523,8.82147,42.7981,21.2782,36.3922,,,,,Spring,1,1,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,6,,,,Fall,0,0 +b1de4423,Winter,8,1,Winter,80,Winter,13.97456597,48.0,45.8,,67,67,92,Winter,6,10,41,Winter,6,1,,,,,10,1,6.0,0,6.0,0,10.0,1,Spring,3,7.29068,13.9761,951.852,1522.96,10.5777,43.6478,13.3193,0.656764,2.15223,1,22.9354,10.1347,36.3571,25.5506,33.5131,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,35,50,Winter,0,0 +b1f064ac,Winter,17,1,Fall,90,Spring,24.32629586,65.0,146.2,,82,52,126,,,,,Spring,16,0,27.7,2,21.1,2,6,0,11.0,0,11.0,0,7.0,0,Spring,3,5.59498,24.329,1482.25,2371.59,36.1808,100.142,16.6644,7.66452,46.0584,1,38.7523,25.2086,94.5466,45.0121,74.933,Winter,3.6,,,Spring,1,1,1,1,1,1,1,1,1,0,3,0,0,1,1,0,1,0,0,0,15,,,,Winter,2,0 +b21f7b12,Winter,10,0,Fall,60,Summer,21.793,60.0,111.6,,62,85,115,Summer,4,6,21,Summer,12,1,15.2,2,16.2,2,0,0,6.0,0,5.5,0,6.0,0,Summer,3,9.28939,21.7954,1253.23,2130.5,19.1879,75.7488,14.7937,7.0017,35.8512,2,34.8561,21.7048,66.4594,34.0393,54.044,,,Winter,3.96,Summer,3,2,3,2,4,0,0,3,1,1,1,0,1,1,2,2,2,2,1,1,32,Summer,59,81,Winter,2,1 +b2290813,Fall,16,0,Spring,59,Fall,38.13671006,65.0,229.2,,74,72,139,,,,,Fall,10,0,23.2,1,20.6,1,16,1,10.0,1,9.0,1,6.0,0,Fall,1,6.33374,38.1409,1841.99,2394.58,42.4268,138.459,23.0408,15.1001,90.7412,3,60.524,35.508,132.125,73.4885,102.951,Winter,3.1,,,Winter,1,2,3,1,1,1,1,3,2,2,3,1,1,1,1,1,2,2,0,1,30,Winter,28,41,Fall,2,0 +b2382d94,Spring,7,1,,,Fall,15.08474423,47.0,47.4,23.0,67,104,120,,,,,Spring,0,0,,,,,0,0,6.0,0,6.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,41,58,Spring,0,0 +b23b9220,Spring,14,1,Winter,67,Summer,17.13822485,65.0,103.0,,78,65,112,,,,,Summer,20,1,24.6,3,23.8,2,12,1,13.0,1,12.5,1,12.0,1,Fall,1,4.8673,16.9311,1277.07,1660.2,25.8883,78.2882,12.869,4.0621,24.7118,1,29.3622,23.0377,73.4209,28.8112,55.2505,,,Spring,2.33,Summer,2,2,3,1,3,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,27,Summer,27,40,Spring,1,0 +b2594910,Winter,9,0,Spring,82,Winter,31.07008929,56.0,138.6,39.0,70,80,137,,,,,Winter,0,0,,,,,0,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.939,Winter,1,1,1,0,1,1,0,2,1,0,0,0,0,0,0,0,1,0,0,0,9,Winter,34,49,Winter,2,0 +b25abc27,Summer,12,0,Winter,45,Fall,19.47368421,57.0,90.0,,63,74,106,Fall,5,7,34,Fall,35,1,12.9,2,14.5,2,16,1,9.5,1,9.5,1,12.0,1,Fall,2,4.81061,19.4758,1188.25,1901.2,17.8302,68.8273,14.8941,4.58174,21.1727,2,33.7541,17.2429,64.0167,32.9251,51.5843,,,Fall,3.31,Fall,4,3,5,2,5,2,2,2,1,2,3,1,2,2,2,4,2,3,1,3,51,Fall,59,81,Summer,2,2 +b27ae7e4,Summer,7,0,,,Summer,22.05490196,51.0,81.6,27.0,62,76,106,,,,,Summer,9,1,,,,,0,0,9.0,1,10.0,1,6.0,1,Summer,2,3.17807,22.0573,1064.62,1703.4,12.8007,55.6595,15.0453,7.01198,25.9405,1,28.3774,14.4814,52.4814,23.8368,41.178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +b285eac6,Winter,10,1,Summer,51,Winter,19.67327327,55.5,86.2,26.0,66,82,111,,,,,Winter,5,0,,,,,2,0,12.0,1,12.0,1,7.0,1,Spring,2,3.7989,19.6754,1178.45,1767.68,23.7704,67.7838,15.4719,4.20355,18.4162,2,29.5433,14.4701,63.9849,27.8411,53.3137,,,Winter,1.9,Spring,3,3,4,1,5,4,4,4,2,1,3,0,1,1,3,2,4,2,4,1,52,Spring,40,56,Winter,0,2 +b2987a65,Fall,7,0,Winter,50,Fall,16.6910503,52.0,64.2,,72,76,102,,,,,Fall,10,1,,,,,8,1,9.0,1,10.0,1,8.0,1,Fall,2,3.46381,16.6929,1056.57,1690.51,13.7917,54.8015,14.2491,2.44375,9.39854,1,28.7922,12.2175,51.3376,24.0571,42.5839,,,,,Fall,2,2,2,0,2,0,0,0,0,0,0,0,0,0,1,2,1,1,0,1,14,Fall,57,79,Fall,0,0 +b2a73566,Summer,16,1,Fall,80,Summer,21.95035204,62.2,120.8,,67,76,117,,,,,Summer,75,1,20.5,2,22.6,2,5,0,7.0,0,7.5,0,19.0,1,Fall,3,2.67944,21.9527,1308.98,2094.37,28.0496,81.6866,14.8447,7.10801,39.1134,1,32.5344,21.1026,79.0071,35.4792,60.584,Fall,2.58,,,Summer,2,2,2,1,2,1,2,2,1,1,3,1,2,1,1,1,1,1,2,1,30,Summer,37,53,Summer,1,0 +b2b56219,Summer,9,1,Fall,60,Fall,21.8581172,57.5,102.8,,101,116,143,,,,,Fall,10,1,,,,,0,0,12.0,1,12.0,1,12.0,1,Fall,3,3.85925,21.8605,1192.8,1908.47,23.1993,69.3114,14.7391,7.12137,33.4886,3,28.9557,17.1564,65.4522,29.295,52.155,,,Summer,3.65,Fall,3,4,3,4,3,4,0,3,0,0,2,0,3,3,3,0,0,0,0,0,35,Fall,32,46,Summer,3,1 +b2b792a7,Spring,13,1,,,Summer,17.06270077,61.5,91.8,,68,83,109,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.51,Summer,0,2,2,2,2,0,1,0,2,2,2,0,0,0,0,0,2,0,0,0,17,Summer,51,71,Spring,3,0 +b308c03e,Spring,9,0,Winter,80,Spring,18.36959772,53.0,73.4,,68,114,130,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.29,Summer,2,2,3,0,2,1,0,2,2,2,0,0,3,3,3,2,3,2,1,1,34,Summer,49,68,Spring,1,1 +b318f1d9,Spring,7,1,Fall,63,Summer,15.81091212,49.0,54.0,,50,100,110,Summer,5,8,30,Summer,1,0,,,,,3,0,16.0,1,16.0,1,5.0,0,Fall,2,2.55275,15.8126,975.311,1462.97,12.2013,46.1464,13.5129,2.29974,7.85358,1,22.3816,11.5634,43.5937,21.1407,34.583,,,,,Summer,4,3,2,0,2,0,0,1,2,2,1,0,0,0,0,1,1,3,1,1,24,Summer,29,42,Spring,0,0 +b31dd1bb,Spring,17,0,Winter,40,Summer,19.2925696,62.5,107.2,,,,,,,,,Summer,7,0,12.4,1,17.8,2,9,0,6.0,0,5.0,0,8.0,0,Summer,3,5.13494,19.2947,1334.47,2268.6,23.182,84.4016,15.1913,4.10342,22.7984,2,39.5434,21.6762,79.2667,42.781,62.7254,Summer,2.32,,,Summer,2,2,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,13,,,,Spring,2,0 +b33482fd,Winter,16,0,Fall,65,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.78,,,,,,,,,,,,,,,,,,,,,,,,,Spring,74,100,Winter,, +b3395fe0,Spring,7,0,,,Spring,15.60596601,46.5,48.0,22.0,79,103,121,,,,,Spring,0,0,,,,,0,0,0.0,0,8.0,1,9.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +b347b9cc,Winter,9,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,4,4,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,26,Winter,41,58,Winter,,0 +b354022b,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.34,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +b37281d5,Spring,8,0,Fall,80,Spring,20.09607924,51.9,77.0,,112,89,180,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.87,Summer,3,3,4,2,5,2,1,2,2,2,2,0,3,2,4,3,4,2,3,2,51,Spring,40,56,Spring,0,2 +b39504a7,Spring,14,1,Fall,77,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.31,,,Summer,4,1,4,1,5,1,5,4,4,4,4,1,2,3,4,2,4,4,2,1,60,Summer,51,71,Spring,2,2 +b397a73e,Fall,7,0,Winter,85,Winter,15.78488004,47.0,49.6,,74,90,133,Winter,5,8,59,Winter,6,1,,,,,2,0,19.0,1,18.0,1,10.0,1,Winter,5,6.78352,15.7866,932.625,2238.3,5.91329,41.5998,13.2403,2.54629,8.00021,1,24.4808,11.2057,34.8163,26.6232,30.3941,,,,,Winter,3,4,4,2,3,2,1,2,2,2,2,1,2,1,2,1,3,2,2,1,42,Winter,40,56,Fall,1,1 +b39bab51,Summer,6,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b3a4d0df,Winter,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.36,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +b3b200af,Spring,9,0,Summer,90,Spring,18.13010963,56.25,81.6,,85,79,100,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.72,Summer,2,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,Summer,36,51,Spring,0,0 +b3c0cf36,Winter,5,1,,,,,,,,,,,,,,,Spring,0,0,,,,,0,0,14.0,1,14.0,1,11.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +b3d74a58,Fall,5,0,,,Fall,16.55826446,44.0,45.6,23.0,53,74,102,,,,,Fall,1,0,,,,,1,0,10.5,1,11.0,1,11.0,1,Fall,2,2.39738,16.5601,891.534,1426.45,6.37437,37.2231,13.5179,3.04215,8.37689,2,22.1006,8.74818,34.8257,15.7725,28.4749,,,,,Fall,2,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,8,,,,Fall,0,0 +b3d819fc,Summer,9,0,,,Summer,20.79881657,52.0,80.0,,,,,,,,,Summer,9,1,,,,,1,0,12.0,1,8.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +b3dade53,Summer,12,0,Winter,59,Fall,14.81358661,59.5,74.6,,74,63,106,,,,,Fall,21,1,12.0,1,12.7,1,6,0,7.0,0,7.0,0,7.0,0,Fall,3,3.58691,14.8152,1152.45,1959.16,16.4355,65.0138,12.9114,1.90377,9.58617,1,30.161,18.4173,61.4269,25.7468,46.5965,,,Summer,3.17,Fall,5,5,5,0,5,4,1,4,1,4,4,0,5,2,5,5,4,5,4,4,72,Fall,56,77,Fall,,2 +b3e07192,Spring,15,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.31,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b3e10f59,Winter,6,0,Spring,50,Winter,15.22260559,41.0,36.4,22.0,63,103,96,,,,,Winter,0,0,,,,,0,0,7.5,1,9.5,1,3.5,0,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,0,0 +b3e14267,Summer,12,1,,,Summer,18.46293936,64.6,109.6,26.0,70,64,114,,,,,Summer,20,1,24.9,3,21.4,2,0,0,11.5,1,12.0,1,12.0,1,Fall,3,4.90794,18.465,1299.76,2079.62,26.7607,80.705,13.5968,4.86811,28.895,1,31.5842,22.3601,75.797,34.2989,58.3449,,,Summer,3.97,Summer,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,Summer,52,72,Summer,0,0 +b3f5c066,Fall,9,0,Spring,55,Winter,14.38517585,51.75,54.8,,72,82,99,Winter,5,8,15,Winter,5,0,,,,,7,1,10.5,1,10.0,1,11.0,1,Winter,3,3.1486,14.3867,998.043,1696.67,9.59866,48.5677,12.7506,1.63619,6.23234,1,25.0197,13.9493,45.4191,20.9081,34.6184,,,Winter,1.59,Winter,5,4,2,0,1,0,0,1,1,4,0,0,3,0,1,3,1,2,0,0,28,Winter,43,60,Fall,2,0 +b4002374,Summer,12,0,Winter,60,Fall,19.14248994,59.5,96.4,,77,98,144,Fall,2,3,15,Fall,11,0,15.3,2,10.7,1,0,0,8.0,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.21,Fall,3,3,4,0,5,0,1,0,2,2,1,0,1,1,5,2,3,1,2,1,37,Fall,36,51,Summer,2,1 +b412866b,Summer,13,0,Fall,85,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4.41,Summer,5,4,4,4,4,1,1,1,2,2,1,0,1,1,2,1,3,1,0,1,39,Summer,31,45,Summer,3,1 +b41cee02,Fall,10,1,Spring,65,Fall,26.24756243,58.0,125.6,,66,84,119,,,,,Fall,2,0,,,,,3,0,14.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.59,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,40,56,Fall,0,0 +b447e66d,Fall,9,1,Spring,50,Winter,15.96703453,53.0,63.8,,60,110,105,,,,,Winter,3,0,,,,,10,1,14.0,1,13.0,1,9.0,1,Winter,3,3.24076,15.9688,1065.67,1705.07,17.7551,55.7707,13.9591,2.00968,8.02928,2,25.8552,12.1604,52.53,23.5353,43.6103,,,Winter,2.08,Winter,2,3,3,1,3,3,0,2,3,2,2,1,2,1,3,4,2,4,1,1,43,Winter,43,60,Fall,0,1 +b449ac61,Spring,12,0,Winter,80,Fall,14.83739596,58.0,71.0,,76,82,126,,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.32733,14.839,1162.73,1976.65,16.1937,66.1093,13.8169,1.02215,4.89067,2,33.8695,16.0461,61.782,37.6989,50.0632,,,Spring,2.24,Spring,4,5,4,2,5,3,1,4,2,2,1,1,5,3,4,4,4,4,4,3,65,Spring,41,58,Spring,2,2 +b452f767,Winter,10,0,Winter,70,Winter,15.67043896,54.0,65.0,,,,,Winter,9,15,0,Winter,7,0,17.6,2,16.1,2,14,1,11.0,1,8.5,1,10.0,1,Winter,2,3.55277,15.6722,1061.86,1698.98,12.9177,55.365,13.3491,2.32309,9.63499,1,27.9421,14.5053,51.8122,23.8034,40.8597,,,Winter,3.171,Winter,3,3,3,1,2,2,0,2,0,0,3,0,0,0,3,2,2,1,1,1,29,Winter,37,53,Winter,0,0 +b463e777,Spring,9,1,Spring,90,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3,2,2,0,5,1,0,1,1,2,3,0,2,1,1,3,2,2,1,1,33,Summer,41,58,Spring,,1 +b4885e6f,Fall,16,0,,,Fall,23.28657937,69.5,160.0,33.0,55,66,120,,,,,Fall,27,1,28.9,1,29.3,1,7,0,13.0,1,13.0,1,13.0,1,Fall,3,7.04251,23.2891,1700.18,2890.3,38.0424,123.354,17.9551,5.33402,36.6456,2,53.4922,31.8198,116.312,61.0861,91.5346,Fall,1.689,,,Fall,5,5,5,5,4,4,1,4,5,4,4,1,5,4,5,4,4,5,4,5,83,,,,Fall,2,3 +b494401a,Summer,17,1,,,Summer,18.4027929,65.0,110.6,25.0,62,63,102,,,,,Summer,10,0,18.2,2,20.2,2,0,0,12.0,1,12.0,1,12.0,1,Fall,3,4.94647,18.4048,1303.06,2084.89,26.784,81.0555,13.4883,4.91646,29.5445,2,31.5273,22.7442,76.1091,34.3206,58.3114,Summer,1.02,,,Summer,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,,,,Summer,1,0 +b4bd49a0,Summer,8,1,Fall,50,Summer,17.31703671,50.25,62.2,,70,76,110,Summer,6,9,5,Summer,8,1,,,,,6,1,10.0,1,9.5,1,9.0,1,Summer,3,8.35211,17.3189,1003.71,1605.93,13.8323,49.1712,13.6912,3.62772,13.0288,2,22.8289,12.51,40.8191,20.6347,36.6612,,,Summer,1.98,Summer,5,2,3,0,5,0,0,4,1,5,0,0,0,0,0,0,4,0,0,2,31,Summer,38,54,Summer,0,1 +b4c11ca1,Spring,9,0,,,Summer,16.4877193,57.0,76.2,24.0,78,78,128,,,,,Summer,25,1,,,,,11,1,11.0,1,13.0,1,12.0,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b4cc2c5c,Winter,10,1,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b4cfd8d7,Winter,10,0,Summer,80,Spring,14.86543637,51.0,55.0,,72,66,107,Spring,4,6,1,Spring,14,1,,,,,20,1,10.0,1,11.5,1,6.0,1,Spring,4,3.58521,14.8671,1003.6,2107.56,11.276,49.1596,13.2883,1.57873,5.84044,2,26.0833,11.8002,45.5743,20.5258,37.3593,,,Winter,2.11,Spring,4,3,3,1,2,1,0,2,0,1,1,0,1,1,1,1,1,1,1,0,25,Spring,56,77,Winter,0,0 +b4e136cc,Summer,12,0,,,Summer,24.52281645,61.0,129.8,,14,70,164,,,,,Summer,7,0,29.4,2,38.0,2,6,0,6.0,0,6.0,0,11.0,1,Summer,2,4.73193,24.5255,1340.87,2145.39,23.9849,85.0832,16.0763,8.44917,44.7168,2,38.11,22.9882,80.3512,34.6985,62.095,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +b4e69f09,Summer,7,0,Spring,55,Fall,14.99108705,49.0,51.2,,58,79,107,Fall,7,11,37,Fall,6,1,,,,,3,0,8.0,1,9.0,1,9.0,1,Fall,2,3.96299,14.9927,967.902,1548.64,8.61886,45.3573,13.2818,1.71091,5.84273,1,26.1229,10.6155,41.3943,25.5369,34.7418,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,37,53,Summer,0,0 +b4f245b9,Fall,8,0,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.7,,,,,,,,,,,,,,,,,,,,,,,Winter,38,54,Fall,0, +b516ca6f,Spring,11,1,Spring,78,Spring,31.1143367,61.5,167.4,,69,70,139,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,2,4.53143,31.1177,1365.18,2047.77,30.7652,87.6729,16.2974,14.8204,79.7271,3,33.6835,23.2242,83.1415,39.3889,64.4487,,,Spring,2.74,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,28,41,Spring,2,0 +b52219ea,Winter,11,0,,,Winter,15.49511834,52.0,59.6,,74,62,112,,,,,Winter,7,0,24.6,1,21.4,1,4,0,12.0,1,13.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.78,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +b53a8f23,Winter,8,1,Summer,55,Spring,19.29090237,52.0,74.2,,69,95,105,Spring,2,1,54,Spring,0,0,,,,,0,0,18.0,1,16.0,1,10.0,1,Spring,2,3.25493,19.293,1092.55,1638.83,18.2738,58.6342,15.2457,4.04733,15.5658,2,28.8572,11.5031,55.3792,33.316,47.131,,,,,Spring,4,4,4,0,4,0,0,0,2,2,4,0,4,0,4,4,4,4,4,2,50,Spring,42,59,Winter,2,2 +b55f7943,Summer,9,1,,,Summer,18.07695353,51.5,68.2,26.0,52,60,101,,,,,Summer,15,1,,,,,3,0,13.5,1,11.5,1,12.0,1,Fall,3,2.98912,18.0789,1042.81,1668.5,17.0061,53.3363,14.1388,3.94016,14.8637,2,24.4041,11.9261,50.3472,20.6311,41.4102,,,Fall,2.81,Fall,5,5,1,1,3,1,1,1,1,1,1,1,1,1,1,5,1,5,1,1,38,Fall,67,91,Summer,2,1 +b57e33a6,Winter,6,0,Spring,60,Winter,14.30301235,45.0,41.2,19.0,106,100,131,,,,,Winter,0,0,,,,,3,1,10.0,1,10.0,1,7.0,1,Spring,1,2.54165,14.3046,894.753,1163.18,7.04161,37.5659,13.0428,1.26174,3.63407,1,22.5543,7.97007,35.0243,16.1402,29.5959,,,,,Winter,4,4,4,0,5,0,0,0,0,0,4,0,2,0,0,2,3,3,3,0,34,Winter,30,43,Winter,0,1 +b5a0707a,Summer,10,0,Spring,57,Winter,22.63732924,58.5,110.2,,83,125,127,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.23394,22.7631,1241.73,1986.77,18.9691,74.5236,15.3103,7.45272,36.2764,3,35.1593,20.3952,70.2897,35.1518,54.1284,,,Winter,1.77,Winter,3,3,4,4,3,1,1,3,4,2,4,1,4,3,4,3,4,4,4,3,62,Winter,55,76,Summer,2,2 +b5b2d51c,Summer,8,1,Winter,65,Fall,16.283659,55.13,70.4,,80,73,121,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.94,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,35,50,Summer,0,0 +b5c2bc50,Summer,6,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,47,66,Summer,0,0 +b62a320b,Summer,7,0,Winter,61,Fall,17.12150294,47.0,53.8,,57,81,103,Fall,6,10,37,Fall,17,1,,,,,10,1,11.0,1,10.0,1,9.0,1,Fall,2,4.35538,17.1234,954.561,1527.3,9.98402,43.9363,13.984,3.13939,9.86367,1,24.5898,9.36251,39.5809,17.2427,34.5738,,,,,Fall,5,5,4,0,5,4,0,3,0,0,2,0,2,2,2,1,2,3,2,1,43,Fall,39,55,Summer,2,1 +b64856e9,Summer,13,0,Summer,45,Fall,17.07906477,61.0,90.4,,71,80,114,,,,,Fall,50,1,25.7,2,31.2,2,23,1,11.0,1,11.0,1,8.0,0,Fall,2,1.13322,17.0809,1264.46,2023.13,18.5506,76.9445,14.5385,2.5424,13.4555,2,39.0318,19.3621,75.8113,51.165,57.5824,,,Summer,4.79,Fall,4,2,2,1,4,3,2,2,2,2,2,2,1,1,0,1,2,2,1,1,37,Fall,29,42,Fall,0,1 +b672a082,Summer,7,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +b69bf1f7,Fall,8,1,,,Winter,8.522436082,59.0,42.2,,68,77,106,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.93,Winter,2,3,2,1,2,1,1,1,1,1,2,0,1,1,1,1,0,1,0,0,22,Winter,44,62,Fall,1,0 +b6aa2bda,Winter,7,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +b6af12af,Summer,5,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,37,53,Summer,2,0 +b6caa7fb,Spring,10,0,Summer,60,Spring,16.7892127,52.25,65.2,,80,86,109,Spring,5,7,0,Spring,17,1,14.1,2,14.1,2,16,1,10.0,1,8.5,1,10.5,1,Spring,4,4.67774,16.791,1045.46,2195.47,12.4407,53.6182,13.8084,2.98267,11.5818,2,27.928,13.2495,48.9405,24.019,40.3687,,,Spring,3.55,Spring,0,3,2,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,10,Spring,31,45,Spring,0,0 +b6e9a65f,Winter,8,1,Spring,70,Spring,16.53961731,51.5,62.4,,80,93,114,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,5,Winter,36,51,Winter,0,0 +b70db5d6,Fall,6,0,,,Fall,15.28036133,42.25,38.8,22.0,63,99,87,,,,,Fall,5,1,,,,,1,0,9.0,1,11.0,1,7.0,1,Fall,2,2.17514,15.282,856.886,1371.02,4.22462,33.5326,13.2074,2.07467,5.26743,1,20.2837,9.02422,31.3574,14.4267,24.5083,,,,,Fall,2,2,2,0,2,0,0,0,0,0,0,0,0,1,0,2,2,4,2,0,19,,,,Fall,0,0 +b712b69b,Winter,10,0,Spring,55,Winter,44.55409688,47.0,140.0,,75,60,124,Winter,5,7,34,Winter,7,0,18.7,2,19.2,2,2,0,18.0,1,19.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,4.51,Winter,5,5,4,0,5,1,0,4,4,4,5,0,3,5,4,2,1,5,1,4,62,Winter,52,72,Winter,2,2 +b746cbe3,Spring,8,0,Summer,85,Spring,18.35017536,48.5,61.4,26.0,85,104,131,,,,,Spring,1,0,,,,,4,1,8.0,1,8.0,1,5.0,0,Spring,3,2.82755,17.9796,982.67,1670.54,8.99279,46.9303,13.7424,4.23712,14.4697,1,24.405,13.5325,44.1027,19.1271,33.3978,,,Spring,2.32,Spring,2,1,2,0,2,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,14,Spring,34,49,Spring,0,0 +b78d5d2e,Fall,9,0,Spring,70,Winter,19.82114632,53.0,79.2,,118,79,138,Winter,3,4,14,Winter,5,0,,,,,0,0,7.0,0,9.2,1,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.46,Winter,2,0,3,1,2,0,0,1,2,0,1,0,0,0,2,0,0,1,0,0,15,Winter,46,64,Fall,,0 +b791f2a6,Winter,9,0,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.84,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +b7a61f8d,Summer,9,1,Winter,65,Fall,18.5452562,55.0,79.8,,77,72,109,Fall,6,10,26,Fall,7,0,,,,,2,0,12.0,1,11.5,1,13.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.39,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,32,46,Summer,0,0 +b7ac57d5,Spring,16,0,,,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.29,,,Spring,2,2,5,2,4,1,2,3,1,1,3,1,4,2,3,1,3,1,3,0,44,Spring,47,66,Spring,3,1 +b7c3834f,Spring,12,1,Winter,35,Summer,28.3013706,61.0,149.8,,65,92,115,Summer,2,1,21,Summer,11,0,27.7,3,26.7,3,2,0,14.0,1,15.0,1,11.0,1,Summer,3,4.8275,28.1533,1404.67,2247.46,32.6793,91.8783,17.3603,10.793,57.1217,2,37.0511,22.148,87.0508,44.9439,69.7304,,,Spring,2.93,Summer,5,5,5,5,5,2,5,5,5,5,5,0,4,5,5,5,3,5,1,4,84,Summer,49,68,Spring,3,3 +b7c410a9,Summer,9,0,Spring,45,Winter,14.00053825,51.0,51.8,,56,57,114,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.01442,14.0021,979.944,1665.9,8.74677,46.6399,12.6072,1.39483,5.16012,1,23.8035,14.0896,43.6255,18.8143,32.5503,,,Winter,1.15,Winter,2,1,2,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,21,Winter,30,43,Summer,2,0 +b7da0083,Summer,8,1,,,Summer,18.27038734,49.0,62.4,25.0,69,79,133,,,,,Summer,0,0,,,,,2,0,9.5,1,10.0,1,12.0,1,Summer,1,2.77366,18.2724,1010.71,1313.93,15.9139,49.9171,14.6171,3.65535,12.483,1,24.5328,9.47041,47.1435,21.2208,40.4467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +b7ddfd84,Winter,18,0,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b7deff0e,Winter,12,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +b7eb466c,Winter,6,0,,,Spring,19.28555908,47.0,60.6,,45,79,72,,,,,Spring,4,1,,,,,6,1,7.0,0,7.0,0,8.0,1,Spring,2,2.76347,19.2877,967.662,1548.26,9.45472,45.3317,14.4281,4.85957,15.2683,2,25.533,10.344,42.5682,20.748,34.9877,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,61,84,Winter,2,0 +b7ec062d,Fall,11,1,Winter,51,Fall,23.25753612,63.5,133.4,,78,76,123,Fall,5,8,20,Fall,27,1,24.6,3,23.9,3,8,1,10.5,1,9.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.57,Fall,5,5,3,1,2,2,0,1,2,4,2,0,1,2,3,3,3,2,2,1,44,Fall,52,72,Fall,2,1 +b802aec3,Fall,12,1,Spring,60,Winter,16.55006719,61.0,87.6,,81,92,145,,,,,Winter,11,0,14.9,2,16.8,2,2,0,7.5,0,7.5,0,8.5,1,Winter,3,6.63701,16.5519,1175.78,1881.24,20.3845,67.4988,12.7538,3.79809,20.1012,2,26.7165,20.3978,60.8618,27.087,47.101,,,Spring,2.48,Fall,4,4,3,0,1,1,0,2,4,3,2,0,2,2,1,2,2,3,0,2,38,Fall,43,60,Fall,2,1 +b8155711,Fall,6,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +b816d8d5,Summer,6,1,Winter,55,Summer,15.96863169,43.0,42.0,19.0,65,83,95,,,,,Summer,0,0,,,,,0,0,11.0,1,12.0,1,7.0,1,Summer,2,2.01781,15.9704,888.545,1332.82,9.7539,36.9047,14.0329,1.93749,5.09534,1,20.3224,6.82834,34.8869,15.6424,30.0763,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,62,85,Summer,2,0 +b823508b,Winter,11,1,Spring,58,Spring,,,,,71,81,150,Spring,5,7,15,Spring,15,1,15.8,2,18.3,2,1,0,11.0,1,12.5,1,11.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.56,Winter,1,2,3,3,3,1,1,1,2,1,4,1,2,2,2,2,3,2,2,2,40,Winter,37,53,Winter,,1 +b84754a9,Spring,8,1,Fall,45,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.55,Summer,3,2,1,0,3,1,0,1,0,0,2,0,1,2,2,2,4,2,2,0,28,Summer,47,66,Spring,1,0 +b89537ae,Spring,9,1,Spring,71,Spring,19.84481812,51.2,74.0,,46,53,99,Spring,5,7,35,Spring,8,0,,,,,3,0,10.9,1,10.5,1,12.0,1,Spring,3,2.87052,19.847,1038.9,1662.24,15.945,52.9194,14.1931,5.65386,21.0806,2,24.1013,12.8731,50.0489,22.1591,40.0463,,,Spring,2.9,Spring,3,4,3,0,1,1,0,0,0,1,1,0,1,3,0,2,2,3,0,1,26,Spring,45,63,Spring,1,0 +b89b8154,Summer,9,0,Winter,80,Fall,16.17490173,56.25,72.8,,50,78,106,Fall,7,11,55,Fall,15,1,,,,,18,1,6.0,0,6.5,0,11.5,1,Fall,3,4.09405,16.1767,1114.9,1895.33,14.9544,61.0143,13.5578,2.61886,11.7857,2,30.0003,16.0596,56.9203,26.8021,44.9547,,,Fall,2.35,Fall,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,22,Fall,37,53,Summer,0,0 +b8b48fbf,Winter,9,1,Spring,70,Winter,21.00908128,59.5,105.8,,85,114,169,Winter,4,6,18,Winter,15,1,,,,,0,0,9.0,1,9.5,1,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.97,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,33,47,Winter,1,0 +b8bf20f9,Fall,10,1,,,Fall,15.95225991,54.5,67.4,22.0,71,64,126,,,,,Fall,8,0,,,,,2,0,8.25,0,8.0,0,7.0,1,Fall,4,3.12879,15.954,1059.6,2013.25,15.242,55.1246,13.0483,2.90566,12.2754,1,23.9069,15.9758,51.9958,23.5652,39.1488,,,Fall,3.29,Fall,2,2,2,1,2,1,0,1,1,1,2,0,0,1,1,1,1,1,1,1,22,Fall,33,47,Fall,2,0 +b8df3d7c,Fall,8,0,Spring,65,Fall,19.7249442,48.5,66.0,26.0,79,74,118,,,,,Fall,8,1,,,,,1,0,10.0,1,9.5,1,10.0,1,Winter,2,2.93404,19.3266,1000.57,1600.91,10.2901,48.8367,14.3007,5.02587,17.1633,1,26.1969,12.3496,45.9027,21.797,36.4871,,,Fall,1.86,Winter,4,2,4,0,0,0,0,0,0,2,0,0,0,0,0,2,1,5,1,1,22,Winter,52,72,Fall,0,0 +b918a7d2,Winter,17,1,Summer,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +b9251fa2,Summer,13,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +b92be605,Fall,9,0,Summer,75,Fall,19.17076023,57.0,88.6,,71,82,121,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.99,Spring,1,2,1,0,1,0,0,0,2,1,1,0,0,0,0,0,0,1,0,0,10,Summer,41,58,Fall,0,0 +b9318815,Winter,20,0,Summer,52,Spring,43.33769931,70.5,306.4,50.0,54,68,122,,,,,,,,,,,,,,,,,,,,Spring,1,7.89338,43.3424,2235.66,2906.36,57.9475,180.39,25.5175,17.825,126.01,3,74.2231,48.2196,172.497,87.8876,132.171,,,,,Spring,1,4,4,3,1,1,1,2,3,1,3,1,2,2,1,1,1,1,1,1,35,,,,Spring,3,1 +b93c4b04,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b93e1e41,Fall,10,0,Winter,85,Winter,16.45414541,56.0,73.4,,79,103,114,Winter,5,8,15,Winter,20,1,15.1,1,16.1,1,10,1,10.0,1,9.5,1,12.0,1,Winter,4,7.54179,16.4559,1092.91,2295.11,11.1972,58.6723,13.154,3.30189,14.7277,2,29.8305,17.6445,51.1305,35.9595,41.0277,,,Winter,2.68,Winter,2,2,2,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,1,13,Winter,36,51,Fall,0,0 +b9426af8,Summer,10,1,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.78,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +b94ec4a1,Summer,12,1,Winter,85,Fall,20.48395493,53.5,83.4,,66,74,114,Fall,5,8,40,Fall,13,0,15.9,2,16.5,2,19,1,13.0,1,14.0,1,10.0,1,Fall,3,4.60065,20.4862,1113.61,1781.77,20.3987,60.8769,14.9537,5.53251,22.5231,2,27.2056,13.2727,56.2763,25.3593,47.6042,,,Fall,1.1,Fall,2,2,2,1,2,3,2,2,1,1,0,0,1,0,2,1,2,0,0,0,24,Fall,45,63,Summer,0,0 +b95aad8d,Spring,21,0,Winter,60,Fall,27.2304898,70.0,189.8,,98,79,165,,,,,,,,,,,,,,,,,,,,Summer,3,7.00451,27.2335,1750.49,2975.84,39.4461,128.714,18.4685,8.765,61.0865,2,55.0141,34.2533,121.709,62.1484,94.4602,,,,,Winter,4,0,4,2,5,0,2,3,1,0,4,1,5,2,4,3,5,4,4,2,55,Winter,55,76,Spring,0,2 +b95da85d,Spring,18,0,Fall,85,Spring,19.97966024,66.0,123.8,,72,87,125,,,,,,,,,,,,,,,,,,,,Summer,2,1.90277,19.6825,1439.48,2303.17,27.5882,95.5868,15.197,4.48551,28.2132,2,42.4476,25.551,93.684,44.2285,70.0358,,,,,Spring,5,4,5,1,5,4,3,1,4,5,4,1,3,2,5,2,5,4,0,1,64,Spring,33,47,Spring,2,2 +b9642733,Winter,13,0,,,Spring,19.03114673,65.35,115.6,28.0,72,58,123,,,,,,,,,,,,,,,,,,,,Spring,1,4.17087,19.0313,1297.44,1686.68,12.9732,80.4578,13.2458,5.7855,35.1422,1,30.6002,36.8843,76.2869,39.7234,43.5735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b96e3324,Summer,7,0,Spring,60,Summer,15.59393391,47.0,49.0,23.0,61,71,104,,,,,Summer,7,1,,,,,10,1,10.0,1,11.0,1,9.0,1,Summer,3,2.71424,15.5956,934.834,1589.22,8.29491,41.8351,13.3152,2.28044,7.16492,1,23.5186,10.0215,39.1208,17.2625,31.8135,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,32,46,Summer,,0 +b9736d65,Winter,10,0,,,Spring,17.34534535,55.5,76.0,26.0,64,68,121,,,,,Spring,0,0,12.1,1,17.0,2,2,0,10.5,1,10.5,1,10.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +b97680e3,Fall,6,0,,,Fall,17.38692033,43.5,46.8,,73,84,108,Summer,,,,Fall,2,1,,,,,0,0,13.0,1,12.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +b98444cd,Spring,12,1,,,Summer,23.94105556,60.0,122.6,29.0,66,80,124,,,,,Summer,0,0,17.5,2,16.6,2,0,0,10.5,1,11.5,1,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Spring,2.249,,,,,,,,,,,,,,,,,,,,,,,Summer,26,38,Spring,3, +b9a1899f,Spring,5,0,,,Spring,25.53585316,45.5,75.2,30.0,65,75,92,,,,,Spring,0,0,,,,,0,0,8.0,1,8.0,1,1.0,0,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +b9b22bff,Fall,15,0,Summer,60,Spring,25.73248172,65.7,158.0,,58,67,101,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.78,,,Winter,5,5,1,1,2,1,0,1,3,0,2,1,1,0,1,0,2,0,2,1,29,Winter,43,60,Fall,3,0 +b9c085ab,Spring,10,0,,,Summer,15.16339154,54.13,63.2,,64,82,108,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.43,Spring,1,1,1,2,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,19,Spring,33,47,Spring,2,0 +b9cf3f7c,Winter,14,1,Summer,58,Winter,29.09061694,71.0,208.6,,60,54,129,,,,,Winter,30,1,30.0,3,36.8,3,10,1,12.5,1,13.0,1,13.0,1,Winter,3,6.77892,29.0938,1686.24,2697.98,45.1995,121.87,16.9974,12.0964,86.7304,3,43.3868,33.2833,115.091,52.8042,88.5864,Winter,2.72,,,Winter,2,5,2,0,4,1,2,1,5,2,4,0,5,2,3,1,2,1,1,1,44,Winter,52,72,Winter,0,1 +b9d12f8d,Spring,8,1,,,Spring,14.95243464,48.0,49.0,22.0,68,86,104,,,,,,,,,,,,,,,,,,,,Spring,3,2.40581,14.9526,954.769,1527.63,10.6959,43.9584,13.4141,1.53846,5.04159,3,21.7048,11.5578,41.5526,21.4997,32.4007,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +b9d3616c,Fall,15,1,,,Fall,15.83329753,66.5,99.6,23.0,64,87,108,,,,,Fall,12,0,19.5,2,21.7,2,2,0,6.25,0,4.5,0,13.0,1,,,,,,,,,,,,,,,,,,Fall,3.35,,,Fall,5,5,5,4,4,3,1,3,4,4,0,1,4,4,5,5,5,5,4,5,76,Fall,45,63,Fall,2,2 +b9dcf632,Winter,6,0,,,Spring,28.11385862,50.75,103.0,33.0,55,87,108,,,,,Spring,0,0,,,,,0,0,,,,,9.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,26,38,Spring,0,0 +b9e20ba6,Spring,11,0,Fall,44,Spring,17.34512485,58.0,83.0,,72,82,128,Spring,,,,Spring,3,0,12.7,2,9.8,1,1,0,8.0,1,7.0,0,10.0,1,Fall,2,3.96622,17.347,1144.03,1830.45,15.4495,64.1172,13.4005,3.94651,18.8828,2,29.5883,19.0794,60.151,24.9451,45.0378,,,,,Spring,0,0,4,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,10,Spring,32,46,Summer,2,0 +b9e90f7f,Winter,9,0,Spring,70,Spring,14.70610425,54.0,61.0,,62,57,112,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.52952,14.7077,1049.44,1784.05,12.5845,54.0422,13.0301,1.67759,6.95778,1,27.095,14.3627,50.5127,22.0614,39.6795,,,Spring,3.2,Spring,4,4,4,0,0,3,0,3,3,4,3,0,4,5,4,4,4,5,0,4,58,Spring,59,81,Winter,2,2 +b9eeb8fa,Spring,7,0,Spring,45,Winter,16.28565383,51.9,62.4,,56,80,118,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,3.24056,16.2874,1024.09,2150.58,11.2558,51.3417,13.401,2.88641,11.0583,2,26.337,13.749,48.1011,21.4654,37.5927,,,,,Summer,3,0,0,0,3,0,0,0,0,3,1,0,3,0,0,1,0,0,0,0,14,Summer,34,49,Spring,0,0 +ba09d63f,Winter,9,0,,,Winter,20.79881657,52.0,80.0,28.0,71,83,134,,,,,Winter,9,1,,,,,0,0,11.5,1,10.25,1,7.0,1,Winter,2,3.31642,20.8011,1075.1,1720.17,13.7848,56.7758,14.7625,6.03862,23.2242,2,28.3307,14.6602,53.4593,22.2443,42.1155,,,Winter,2.58,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,54,75,Winter,0,0 +ba158a9c,Summer,5,0,Fall,65,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,20,Summer,56,77,Summer,2,0 +ba279808,Spring,9,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ba29cfb3,Winter,6,0,Fall,45,Winter,15.54839319,46.0,46.8,22.0,73,92,114,,,,,Winter,5,1,,,,,0,0,12.0,1,11.5,1,5.0,0,,,,,,,,,,,,,,,,,,,,,,Winter,2,0,3,0,2,0,0,2,0,1,0,0,1,0,0,2,2,2,0,0,17,Winter,35,50,Winter,0,0 +ba3ba426,Fall,8,0,,,Fall,14.47216514,51.5,54.6,22.0,56,64,98,,,,,Fall,0,0,,,,,4,0,9.5,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +ba42462b,Fall,6,1,Winter,50,Winter,20.20474074,45.0,58.2,,74,111,119,Winter,1,,26,Winter,0,,,,,,0,,10.0,,6.0,,7.0,,Winter,2,1.8196,20.2069,992.216,1488.32,7.19129,47.947,16.6471,3.55982,10.253,2,18.3867,22.369,46.1274,23.6544,25.578,,,,,Winter,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,1,1,7,Winter,36,51,Fall,1,0 +ba4294b3,Spring,8,1,Winter,50,Winter,15.71829223,53.25,63.4,,75,87,137,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.04963,15.72,1042.66,1668.25,14.8343,53.3197,13.2206,2.49941,10.0803,2,24.3114,14.174,50.2701,24.661,39.1457,,,,,Summer,4,4,2,0,3,0,0,0,0,2,0,0,2,0,0,0,2,1,0,0,20,Summer,46,64,Spring,1,0 +ba73bc7c,Summer,12,0,Fall,70,Fall,23.51156917,62.25,129.6,,95,66,168,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.45,Summer,3,4,3,1,4,3,1,2,2,2,4,1,1,2,2,1,1,1,2,1,41,Summer,38,54,Summer,0,1 +ba7568cb,Spring,9,1,,,Summer,18.99739369,54.0,78.8,27.0,57,88,117,,,,,Summer,0,0,,,,,4,0,8.5,0,7.0,0,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Spring,2.14,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +ba78601b,Winter,13,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +ba7b36b4,Summer,10,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ba7eb74d,Fall,16,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.38,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +baa69a08,Winter,15,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +bad70c67,Spring,7,0,Summer,52,Spring,16.56401184,49.7,58.2,,53,62,113,Spring,2,1,26,Spring,0,0,,,,,0,0,12.0,1,11.0,1,1.5,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,29,42,Spring,2, +bafc4389,Spring,8,0,Summer,71,Spring,17.70587237,51.5,66.8,,65,95,105,Spring,5,7,45,Spring,8,1,,,,,6,1,8.5,1,9.0,1,11.0,1,Spring,2,4.77025,17.7078,1044.2,1670.71,12.7155,53.4837,14.1778,3.52999,13.3163,2,28.0476,12.7205,48.7134,23.5419,40.7631,,,,,Spring,0,5,5,0,4,0,0,1,1,1,4,0,4,0,4,2,5,3,0,3,42,Spring,43,60,Spring,1,1 +bb088c75,Spring,13,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,29,42,Spring,1, +bb0d7d92,Winter,17,1,Spring,48,Winter,18.952129,63.0,107.0,,64,85,109,,,,,Winter,11,0,23.6,2,23.2,2,0,0,13.5,1,14.25,1,10.0,1,Winter,1,4.63356,18.9542,1270.18,1651.24,25.5308,77.5543,13.7381,5.21606,29.4457,2,30.8386,21.1849,72.9208,33.3375,56.3694,Winter,1.14,,,Winter,3,4,,3,2,1,2,2,2,2,5,2,1,2,4,4,4,3,3,4,53,,,,Winter,3,2 +bb0dfcb8,Winter,6,0,,,Winter,15.90085877,43.5,42.8,21.0,57,72,105,,,,,Winter,0,0,,,,,8,1,6.0,0,6.0,0,6.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +bb4821ee,Fall,10,0,Winter,75,Fall,14.65788752,54.0,60.8,,88,76,108,Fall,5,8,10,Fall,12,1,13.1,2,12.5,1,9,1,7.0,0,9.0,1,11.0,1,Fall,3,8.93519,14.6595,1032.81,1755.77,9.60815,52.2705,12.603,2.05654,8.52945,1,25.3884,17.274,43.3354,23.0171,34.9966,,,Fall,3.71,Fall,2,2,3,2,4,1,1,1,1,2,4,0,1,1,2,1,1,4,1,1,35,Fall,32,46,Fall,2,1 +bb706eac,Spring,7,1,Winter,52,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +bb808a71,Spring,12,0,Winter,75,Summer,19.70202922,61.5,106.0,,109,77,145,,,,,Summer,21,1,19.8,2,20.6,2,9,0,13.0,1,12.5,1,12.0,1,Summer,4,4.83459,19.7042,1293.21,2715.75,22.2847,80.0071,14.8724,4.83177,25.9929,2,36.5475,21.1749,75.1725,34.1779,58.8322,,,Summer,2.61,Summer,1,1,1,0,3,4,0,1,0,1,1,0,0,1,1,0,0,0,0,0,15,Summer,30,43,Summer,2,0 +bba6ff20,Summer,13,1,Winter,90,Summer,18.44807307,67.0,117.8,,73,89,111,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.39,Fall,1,0,2,0,1,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,9,Fall,33,47,Summer,0,0 +bba9dc0e,Spring,7,1,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +bbb95c9a,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +bbbd61c9,Winter,7,0,Spring,95,Winter,14.94313955,48.5,50.0,23.0,62,86,116,,,,,Winter,6,1,,,,,8,1,11.0,1,11.0,1,6.0,1,Winter,3,2.75619,14.9448,948.694,1612.78,8.04266,43.3114,12.9456,1.9992,6.68863,2,22.5946,12.6741,40.5552,15.8894,30.6373,,,,,Winter,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,10,Winter,36,51,Winter,0,0 +bbd71a7c,Spring,14,0,Fall,68,Spring,17.29807504,69.25,118.0,28.0,62,75,110,,,,,Spring,66,1,30.1,2,33.0,2,10,0,0.0,0,0.0,0,8.0,0,Summer,3,6.4049,17.3,1473.01,2504.12,29.2092,99.1579,14.5375,2.76244,18.8421,1,43.1726,26.7761,92.753,44.6593,72.3818,Summer,3.06,,,Summer,5,5,5,0,0,0,5,0,5,5,0,0,5,0,5,5,5,5,0,5,60,Summer,40,56,Spring,0,2 +bbe5bedd,Fall,6,0,Summer,60,Winter,13.96183534,44.2,38.8,,79,79,96,Winter,,,,Winter,0,0,,,,,0,0,11.0,1,9.0,1,8.0,1,Winter,2,8.64127,14.0906,877.269,1403.63,4.72534,35.7036,12.9661,1.12448,3.09637,1,19.6498,11.3284,27.0624,12.5903,24.3752,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,49,68,Fall,0,0 +bc005841,Spring,6,0,,,Spring,14.34222952,44.5,40.4,21.0,54,91,92,,,,,Spring,0,0,,,,,4,1,7.5,0,10.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,35,50,Spring,1, +bc06600f,Fall,16,1,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.12,,,Fall,2,3,4,1,3,1,3,4,1,1,4,1,1,1,3,1,3,1,3,1,42,,,,Fall,3,1 +bc0c0f03,Winter,8,0,Spring,81,Winter,14.83927955,49.25,51.2,23.0,46,72,115,,,,,Winter,0,0,,,,,1,0,10.0,1,9.5,1,12.0,1,Winter,1,2.79108,14.8409,959.109,1246.84,8.1163,44.4207,12.8758,1.96505,6.77929,1,22.3846,13.9198,41.6296,15.5366,30.5009,,,Winter,1.73,Winter,3,2,1,1,3,1,1,1,1,3,0,0,1,1,1,2,1,1,0,0,24,Winter,32,46,Winter,2,0 +bc1ec029,Summer,8,0,Winter,45,Fall,18.56294379,52.0,71.4,,60,54,140,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.62,Fall,3,2,1,1,2,1,1,1,1,3,2,1,1,1,2,1,1,2,1,1,29,Fall,38,54,Summer,1,0 +bc36d0b3,Summer,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,41,58,,, +bc437705,Summer,6,0,,,Summer,15.22280315,44.25,42.4,24.0,92,117,188,,,,,Summer,5,1,,,,,5,1,4.0,0,4.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,,,Fall,4,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,25,,,,Summer,0,0 +bc4eaf77,Spring,17,0,Spring,63,Spring,20.72043743,69.8,143.6,,61,80,112,,,,,Spring,38,1,42.1,2,54.4,3,30,1,13.0,1,13.0,1,12.0,1,Spring,4,6.91096,21.206,1645.01,3454.51,36.2592,117.478,17.3484,3.85758,26.1222,2,51.2487,29.9699,110.567,57.4125,87.5079,,,,,Spring,5,5,5,5,5,5,2,3,3,5,5,1,5,5,5,5,5,5,5,4,88,,,,Spring,2,3 +bc66f41d,Fall,8,1,Winter,61,Fall,23.3851166,54.0,97.0,,56,83,92,,,,,Fall,6,1,,,,,0,0,19.0,1,17.0,1,12.0,1,Fall,4,3.40656,23.3877,1137.92,2162.06,20.4456,63.4669,15.3025,8.08516,33.5331,3,28.5712,14.4501,60.0603,31.6521,49.0168,,,Fall,2.14,Fall,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,39,55,Fall,2,0 +bc6bd8b2,Winter,6,1,Summer,75,Winter,16.67596197,47.0,52.4,,76,93,109,Winter,4,6,45,Winter,9,1,,,,,1,0,11.0,1,10.0,1,8.0,1,Spring,3,2.38169,16.6778,950.848,1521.36,12.057,43.5408,13.8581,2.81968,8.85917,2,21.6668,9.81709,41.1591,18.0594,33.7237,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,51,71,Winter,0,0 +bc790ce0,Fall,12,1,Spring,35,Winter,15.05542958,63.0,85.0,,64,83,116,,,,,Winter,7,0,11.0,1,9.6,1,3,0,6.0,0,5.0,0,8.0,0,Winter,2,3.19296,15.0571,1213.55,1820.32,22.4149,71.522,12.6696,2.38752,13.478,2,28.528,20.5791,68.329,29.3182,50.9429,,,Winter,1.77,Fall,3,0,5,0,0,0,0,0,2,2,0,0,0,0,2,2,2,2,0,0,20,Fall,38,54,Fall,0,0 +bc817a4c,Summer,7,1,Winter,70,Fall,14.28591682,46.0,43.0,,86,108,116,Fall,7,11,27,Fall,8,1,,,,,20,1,12.0,1,12.5,1,8.0,1,Fall,2,10.7573,14.2875,924.758,1387.14,9.22051,40.7619,13.5438,0.743658,2.23814,1,20.0973,11.444,30.0046,18.0043,29.3179,,,,,Fall,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,37,53,Summer,0,0 +bc95339a,Summer,10,1,,,Summer,19.38312757,54.0,80.4,26.0,66,74,101,,,,,Summer,2,0,,,,,0,0,9.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Summer,3.47,Summer,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,22,Summer,29,42,Summer,1,0 +bccebc5a,Winter,11,0,Spring,75,Spring,22.44834649,59.75,114.0,,60,73,108,Spring,4,7,46,Spring,30,1,23.4,3,23.5,3,0,0,9.0,1,8.0,1,13.0,1,Spring,2,7.83523,22.4508,1277.45,2043.92,20.9956,78.3279,15.4257,7.02514,35.6721,2,36.2804,21.0519,70.4927,34.863,57.2761,,,Spring,2.21,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,38,54,Winter,0,0 +bce32ada,Spring,6,0,Fall,55,Fall,14.44780963,43.0,38.0,,50,118,81,Fall,2,1,50,Fall,0,0,,,,,0,0,12.0,1,11.0,1,8.0,1,Fall,2,2.09586,14.4494,878.846,1406.15,2.71056,35.8716,13.6401,0.809316,2.1284,1,19.067,14.094,33.7757,15.4025,21.7776,,,,,Fall,3,3,3,0,5,1,0,3,2,4,4,0,2,1,1,2,4,2,1,1,42,Fall,35,50,Spring,3,1 +bcf577e2,Spring,7,1,,,Spring,18.20696034,46.5,56.0,23.0,95,76,136,,,,,Summer,7,1,,,,,0,0,5.5,1,9.0,1,10.0,1,Summer,1,2.42364,18.2089,957.588,1244.86,13.2449,44.2587,14.3912,3.81776,11.7412,1,22.635,8.37881,41.8351,18.7897,35.8799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +bd124dbc,Fall,10,0,Winter,80,Fall,17.27207102,56.2,77.6,,82,81,111,Fall,5,8,9,Fall,18,1,,,,,0,0,7.5,1,7.5,1,5.0,0,Fall,3,2.82841,17.274,1151.05,1956.79,16.9815,64.8654,14.4392,2.83475,12.7346,2,32.2596,15.6243,62.037,29.8432,49.2411,,,Fall,1.94,Fall,5,5,4,0,5,3,0,1,1,2,4,0,0,1,5,1,5,1,2,1,46,Fall,36,51,Fall,2,1 +bd480331,Summer,10,0,Spring,80,Winter,16.5202929,54.5,69.8,,70,74,118,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.0,Spring,1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,Spring,39,55,Summer,0,0 +bd4ccd4c,Winter,11,0,Summer,74,Summer,15.2676817,59.0,75.6,,61,58,115,Summer,6,10,21,Summer,26,1,20.7,2,19.9,2,15,1,8.0,1,9.0,1,14.0,1,Summer,3,4.28468,15.2693,1158.83,1970.01,16.0766,65.6937,13.2685,2.00082,9.90626,2,31.8627,17.7544,61.4091,31.4218,47.9393,,,Summer,2.95,Summer,3,3,2,4,5,0,0,0,1,2,4,1,1,0,2,1,3,3,4,1,40,Summer,28,41,Winter,1,1 +bd713fb8,Winter,10,1,Fall,90,Spring,19.60024414,64.0,114.2,,75,76,120,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.44,Winter,3,1,2,0,2,1,0,1,1,1,0,0,1,1,1,1,1,1,0,1,19,Winter,26,38,Winter,,0 +bd8397cb,Winter,10,0,Summer,62,Spring,32.55515243,63.0,183.8,,76,57,118,Spring,5,7,20,Spring,10,0,30.3,3,29.3,3,17,1,10.0,1,9.0,1,11.0,1,Summer,2,6.15145,32.5587,1751.77,2802.84,40.7721,128.85,22.8248,9.73393,54.9499,3,56.7829,31.2952,122.699,64.7094,97.555,,,Winter,2.26,Spring,3,2,1,1,1,0,0,1,1,1,2,0,1,2,2,1,0,1,0,0,20,Winter,37,53,Winter,2,0 +bd932d04,Spring,15,0,Winter,45,Summer,19.69236628,68.75,132.4,,89,70,118,,,,,Summer,21,0,43.6,2,45.3,3,21,1,13.75,1,14.0,1,10.5,1,,,,,,,,,,,,,,,,,,Fall,1.55,,,Summer,4,4,4,3,3,4,0,4,4,0,5,0,4,5,4,3,4,3,4,2,64,Summer,54,75,Spring,3,2 +bdb6c8a3,Spring,16,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +bdbd1afd,Fall,15,1,Winter,75,Winter,16.15450995,64.5,95.6,,75,68,124,,,,,Winter,22,1,17.9,2,19.3,2,1,0,10.5,1,9.0,0,11.0,1,,,,,,,,,,,,,,,,,,Fall,2.56,,,Fall,2,2,1,2,2,2,3,2,1,1,2,0,1,1,1,2,1,1,1,1,29,Fall,46,64,Fall,2,0 +bdbdc4b5,Spring,8,1,Summer,70,Spring,16.4844231,50.5,59.8,,66,100,107,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,4,2.82489,16.4862,1011.02,1920.94,14.9037,49.9498,13.7706,2.71561,9.85024,3,23.7397,11.3064,47.1249,21.008,38.6433,,,Summer,1.61,Summer,4,0,4,0,0,0,0,0,0,2,1,0,2,0,2,2,2,2,0,0,21,Summer,45,63,Spring,0,0 +bdbff28b,Fall,12,0,Winter,62,Fall,16.95117985,61.5,91.2,,62,70,108,,,,,Fall,17,1,24.5,3,24.7,3,15,1,7.0,0,7.0,0,11.0,1,Fall,4,5.08184,16.953,1301.84,2733.86,23.1648,80.9261,15.0432,1.90979,10.2739,2,38.1011,19.6602,75.8443,38.1995,61.2659,,,Fall,3.2,Fall,1,1,1,0,1,1,0,0,1,1,1,0,0,0,1,0,0,0,0,1,10,Fall,73,99,Fall,0,0 +bdc30511,Spring,6,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +bdcc7d10,Fall,6,0,Winter,60,Fall,14.8937981,47.0,46.8,,90,70,132,Fall,5,7,55,Fall,0,0,,,,,0,0,11.0,1,11.0,1,6.0,1,Winter,2,2.61524,14.8954,925.027,1480.04,6.57749,40.7905,12.9827,1.91269,6.00949,1,22.6682,11.5448,38.1753,18.3895,29.2457,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,43,60,Fall,0,0 +bdcddd09,Spring,9,0,Fall,41,Fall,16.50016529,55.0,71.0,,66,81,99,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.47593,16.502,1071.55,1821.63,10.0795,56.3968,13.1079,3.3941,14.6032,2,28.6563,17.661,52.9209,34.5447,38.7358,,,Spring,1.14,Spring,5,4,3,0,5,2,0,1,3,3,2,0,3,1,2,2,1,2,1,2,42,Spring,33,47,Spring,2,1 +be1c3b0e,Summer,17,0,,,Summer,24.00807682,67.5,155.6,38.0,71,73,121,,,,,Summer,5,0,29.6,1,29.4,1,0,0,0.0,0,0.0,0,8.0,0,Summer,3,6.02718,24.0107,1530.51,2601.86,30.5495,105.282,16.2461,7.76455,50.3177,3,45.8288,28.904,99.2551,48.6656,76.3782,Fall,1.58,,,Summer,2,4,3,0,3,1,1,0,0,3,2,0,0,0,1,1,2,2,0,0,25,,,,Fall,2,0 +be210bc7,Winter,16,0,Summer,45,Spring,35.34609446,71.8,259.2,,89,93,133,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.2,,,Spring,2,5,4,1,5,5,4,2,4,2,4,1,2,3,1,4,3,3,2,2,59,Spring,65,89,Winter,2,2 +be26335a,Fall,13,0,Spring,75,Fall,17.57362362,56.5,79.8,,77,106,117,,,,,Fall,50,1,18.6,1,25.1,2,22,1,13.0,1,10.5,1,8.0,0,,,,,,,,,,,,,,,,,,,,Fall,0.88,Fall,3,3,3,3,4,1,1,1,1,1,1,1,3,2,2,2,3,2,3,2,42,Fall,38,54,Fall,2,1 +be4190b5,Summer,11,1,,,Summer,17.06270858,51.75,65.0,22.0,40,68,105,,,,,Summer,8,0,11.8,2,14.4,2,0,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +be47e3b2,Fall,18,0,,,Fall,27.57121411,73.0,209.0,37.0,61,59,132,,,,,Fall,,,31.2,1,36.7,2,,,6.5,0,0.0,0,12.0,1,,,,,,,,,,,,,,,,,,Fall,1.371,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +be5037b0,Fall,12,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.32,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +be553d33,Summer,15,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +be7dad60,Winter,11,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.56,,,,,,,,,,,,,,,,,,,,,,,,,,,, +be9a2cbf,Summer,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.79,,,,,,,,,,,,,,,,,,,,,,,,,,,, +be9ba5f7,Summer,10,1,Winter,60,Fall,18.61731156,53.5,75.8,,73,81,118,Fall,4,6,55,Fall,4,,,,,,6,,6.0,,6.0,,7.0,,Fall,3,7.55662,18.6193,1068.56,1709.69,17.0267,56.0782,13.7749,4.84441,19.7218,2,24.4723,14.5793,48.5216,22.3427,41.499,,,Summer,2.29,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,64,88,Summer,2,0 +be9e9a8e,Fall,13,1,Fall,50,Fall,19.58948578,59.0,97.0,,74,62,112,,,,,Fall,13,0,23.8,2,22.2,2,4,0,10.0,1,6.5,0,7.0,0,Fall,3,3.64621,19.5916,1206.92,1931.07,23.0059,70.8156,14.303,5.2886,26.1844,2,30.0142,17.7956,67.1694,32.9222,53.0201,,,Fall,2.27,Fall,2,1,2,0,2,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,23,Fall,28,41,Fall,0,0 +bea525dc,Fall,10,1,Spring,65,Winter,33.00981616,57.75,156.6,,62,90,125,,,,,Winter,10,0,17.2,2,18.0,2,1,0,8.5,1,7.0,0,6.0,0,Winter,2,4.02613,33.0134,1304.01,1956.01,29.2073,81.1571,17.109,15.9044,75.4429,3,31.9733,19.9764,77.1309,34.075,61.1806,,,Winter,1.68,Winter,2,3,1,1,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,13,Winter,45,63,Fall,0,0 +beafa6f9,Fall,9,0,Fall,75,Winter,16.6829904,54.0,69.2,,76,92,119,Winter,6,9,37,Winter,7,0,,,,,5,0,7.5,1,9.0,1,8.0,1,Winter,4,5.01263,16.3201,1079.3,2266.53,13.8463,57.2227,13.4954,2.82472,11.9773,2,28.1671,15.2093,52.2101,22.9256,42.0134,,,Fall,2.78,Winter,5,5,5,2,5,5,0,5,0,5,5,0,5,3,4,5,5,5,4,4,77,Fall,64,88,Fall,1,2 +bebff291,Spring,6,0,Summer,55,Spring,15.75616471,45.5,46.4,,56,99,117,Spring,4,5,35,Spring,0,0,,,,,0,0,11.5,1,10.0,1,9.0,1,Spring,1,2.54464,15.7579,910.228,1183.3,7.1837,39.2142,13.3175,2.44036,7.18579,1,22.6224,9.40808,36.6696,16.2754,29.8061,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,39,55,Spring,0,0 +bedeb6f3,Summer,7,0,Winter,62,Winter,19.03096424,47.0,59.8,,77,93,155,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.74964,19.033,964.355,1639.4,9.4677,44.9795,14.316,4.71704,14.8205,2,25.0578,10.454,42.2299,19.2883,34.5255,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,30,43,Summer,0,0 +bee4eab0,Summer,12,0,,,Summer,19.4204929,59.5,97.8,30.0,78,53,113,,,,,Summer,20,1,12.8,1,15.1,1,8,0,0.0,0,0.0,0,12.0,1,Fall,4,4.24013,19.7532,1207.68,2536.13,18.035,70.8968,14.3194,5.4338,26.9032,2,32.9519,19.9099,66.6566,30.2319,50.9869,,,Summer,2.28,Summer,4,4,4,1,4,2,0,0,1,2,3,1,1,1,2,1,3,1,4,1,40,Summer,29,42,Summer,2,1 +bee51adb,Winter,7,0,Summer,60,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,0,2,0,2,0,0,0,0,2,0,0,0,0,0,2,2,2,0,0,16,Spring,54,75,Winter,,0 +bef19061,Fall,11,0,Summer,80,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +bef259fd,Winter,11,0,Fall,75,Spring,17.11711712,55.5,75.0,,67,83,129,Spring,6,10,0,Spring,40,1,15.4,2,15.5,2,5,0,7.0,0,6.0,0,12.0,1,Spring,3,3.47196,17.119,1084.15,1843.06,10.4821,57.7394,13.1792,3.93979,17.2606,1,28.1244,19.1329,54.2674,31.5435,38.6065,,,Winter,2.25,Spring,5,5,4,1,5,1,1,3,2,2,2,0,1,2,2,1,3,2,4,1,47,Spring,69,94,Winter,1,1 +bef89ce4,Spring,10,1,Summer,65,Spring,20.97752,50.0,74.6,,56,70,97,Spring,5,8,7,Spring,5,0,,,,,8,1,12.25,1,10.0,1,14.0,1,Spring,4,7.80611,20.9798,1026.34,1950.05,15.6168,51.5822,14.5065,6.47332,23.0178,2,23.9553,12.0102,43.7761,21.957,39.572,,,Spring,2.29,Spring,3,3,2,2,2,1,0,1,1,2,0,0,0,0,1,0,0,0,0,0,18,Spring,33,47,Spring,1,0 +befb3129,Spring,10,0,,,Spring,16.76213732,59.0,83.0,25.0,50,83,87,,,,,Spring,15,1,19.7,2,22.4,2,5,0,11.0,1,9.0,1,8.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,3.32,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +bf0942bc,Summer,8,1,,,Summer,29.67681661,51.0,109.8,31.0,59,69,110,,,,,Summer,0,0,,,,,0,0,10.5,1,11.0,1,4.5,0,Fall,2,2.85136,29.6801,1093.2,1639.8,19.0737,58.7029,15.868,13.8121,51.0971,3,24.9462,14.683,55.8515,22.9422,44.0199,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,82,100,Summer,3,0 +bf18965c,Winter,10,1,Spring,55,Winter,18.2436382,63.0,103.0,,57,53,120,Winter,10,16,27,Winter,25,1,22.7,3,22.3,3,11,1,9.0,1,8.0,0,11.0,1,Spring,3,4.8177,18.2456,1293.97,2070.36,27.303,80.0882,14.187,4.05863,22.9118,1,32.1103,20.6749,75.2705,33.8076,59.4133,,,Winter,2.15,Winter,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,Winter,41,58,Winter,0,0 +bf280e13,Fall,18,0,Winter,51,Winter,27.56248608,67.0,176.0,,71,60,167,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.37,,,Fall,3,4,2,1,2,2,1,3,2,3,4,1,1,1,2,1,2,2,1,1,39,Fall,30,43,Fall,,1 +bf31e0a7,Fall,8,1,Spring,71,Winter,15.54711538,52.0,59.8,,59,102,130,Winter,,,,Winter,3,0,,,,,0,0,11.0,1,10.0,1,8.0,1,Winter,3,2.84694,15.5488,1018.33,1629.32,13.6092,50.728,13.19,2.35883,9.07195,1,23.0857,14.0332,47.8811,22.4011,36.6949,,,Fall,2.2,Winter,4,4,2,0,1,0,0,1,0,2,0,0,0,0,0,0,1,1,1,0,17,Winter,39,55,Fall,0,0 +bf3f3d07,Spring,10,1,,,Spring,24.33074657,54.5,102.8,35.0,78,86,124,,,,,Spring,0,0,9.2,1,9.1,1,0,0,8.0,0,9.0,0,15.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,3.37,,,,,,,,,,,,,,,,,,,,,,,,,,,, +bf7704da,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +bfade1ba,Spring,8,0,Fall,75,Spring,16.29725652,54.0,67.6,,94,66,122,Spring,,,,Spring,2,0,,,,,0,0,6.0,0,7.0,0,9.0,1,Spring,3,5.74946,16.299,1063.18,1807.41,12.7581,55.5057,13.383,2.91606,12.0943,2,27.5949,15.1527,49.7562,23.0135,40.353,,,Spring,2.91,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,31,45,Spring,0,0 +bfb421f9,Summer,14,0,Summer,71,Winter,13.74061293,65.4,83.6,,135,58,174,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.49,,,Summer,5,3,4,4,5,1,1,1,3,4,3,2,2,3,2,4,3,2,3,4,59,Summer,26,38,Summer,1,2 +bfb52472,Winter,12,1,Summer,60,Spring,16.36536128,64.75,97.6,,57,110,91,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.1,Spring,1,1,2,1,3,1,1,1,1,2,4,1,1,2,1,2,3,2,4,1,35,Spring,29,42,Winter,3,1 +bff3297e,Winter,14,1,,,Winter,19.93418314,62.0,109.0,26.0,69,68,109,,,,,Winter,12,0,21.8,2,25.5,2,4,0,9.5,1,10.0,1,15.0,1,,,,,,,,,,,,,,,,,,Winter,1.48,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +c00c663c,Spring,11,1,Winter,70,Spring,20.79714859,67.0,132.8,26.0,75,58,91,,,,,Spring,25,1,23.9,3,28.8,3,3,0,13.0,1,13.5,1,12.0,1,Spring,4,5.97274,20.7994,1495.73,2841.89,37.3873,101.578,15.9094,4.89003,31.2218,2,38.1855,26.0054,95.6054,40.8055,75.5728,,,Fall,3.8,Fall,3,3,2,1,2,1,0,3,2,1,3,0,2,2,2,1,2,2,1,1,34,Fall,32,46,Spring,1,1 +c03c0758,Fall,8,0,Spring,55,Fall,18.4670096,54.0,76.6,25.0,62,73,103,,,,,Fall,1,0,,,,,2,0,4.0,0,4.0,0,6.0,1,Spring,2,3.7971,18.469,1126.78,1802.85,17.3241,62.2801,15.0163,3.45268,14.3199,1,30.8561,14.0999,58.483,24.5934,48.1802,,,,,Winter,4,4,4,0,3,0,0,4,1,2,2,0,0,0,0,3,2,2,2,0,33,Winter,40,56,Fall,0,1 +c0424bff,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c0442863,Summer,21,0,Spring,45,Spring,20.08781026,64.75,119.8,,71,95,110,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,5,2,5,4,3,5,1,1,1,5,5,4,5,5,5,4,4,3,77,Spring,36,51,Summer,3,2 +c06acd02,Summer,12,1,,,Summer,17.09443359,64.0,99.6,28.0,58,68,110,,,,,Summer,15,0,10.1,1,12.9,2,0,0,8.0,1,10.5,1,11.0,1,Summer,3,4.6347,17.0963,1251.26,2002.02,23.8677,75.5386,12.9662,4.13014,24.0615,2,29.686,21.985,70.9039,32.2725,53.5536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1, +c0869eb0,Spring,9,0,Winter,45,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.33,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,33,47,Spring,0,0 +c087b48a,Summer,12,1,Summer,70,Spring,16.57227049,60.75,87.0,,69,89,107,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.01,Winter,4,3,2,0,5,2,3,2,2,2,2,0,2,3,2,3,2,2,1,1,43,Winter,43,60,Summer,2,1 +c095f868,Spring,12,0,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +c09d41cd,Summer,20,1,Winter,45,Winter,24.09658073,64.5,142.6,,75,85,121,,,,,,,,,,,,,,,,,,,,Winter,3,3.18057,33.7543,1181.53,1890.45,20.4676,68.1119,16.1225,17.6318,74.4881,2,28.1647,19.4796,64.9314,39.5623,48.6323,,,,,Winter,0,0,1,0,1,1,0,0,0,0,2,0,1,1,1,1,1,0,0,0,10,Winter,34,49,Summer,2,0 +c09ea4a9,Winter,15,0,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c0a7d046,Spring,13,0,Fall,60,Summer,13.95726883,63.0,78.8,,61,89,115,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.88,Spring,3,2,2,0,4,0,1,2,2,1,3,0,0,0,0,1,3,2,2,0,28,Spring,31,45,Spring,3,0 +c0adc389,Winter,11,0,Spring,75,Winter,16.96409078,59.0,84.0,,81,76,107,Winter,5,7,17,Winter,22,1,36.4,2,30.0,2,0,0,4.5,0,4.5,0,12.0,1,Winter,5,12.6811,16.9659,1141.8,2740.32,12.5848,63.8799,12.9022,4.06377,20.1201,2,28.3955,22.8996,51.1988,27.7322,40.9803,,,Winter,2.31,Winter,5,5,4,4,5,5,0,2,4,5,5,1,4,4,4,5,5,5,3,2,77,Winter,45,63,Winter,3,2 +c0c168fa,Spring,6,0,Summer,62,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,29,42,Spring,0,0 +c0d5b3f3,Summer,14,1,Spring,70,Fall,30.9862259,66.0,192.0,,88,71,155,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.99,Winter,1,2,3,0,2,1,0,1,1,2,0,0,1,2,1,2,2,1,0,1,23,Winter,45,63,Summer,0,0 +c0de2704,Fall,10,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +c0ef8293,Fall,16,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,4.58,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +c0f2a046,Fall,10,0,Spring,63,Winter,14.89625789,51.5,56.2,,59,52,104,Winter,7,10,30,Winter,9,0,11.1,1,12.3,1,13,1,9.0,1,7.0,0,7.0,0,Winter,3,3.46026,14.8979,1040.27,1768.45,13.4711,53.065,14.0668,0.831038,3.13497,1,28.6424,10.9514,49.6048,24.1943,42.1136,,,Fall,4.56,Winter,2,2,3,0,4,1,0,0,0,3,3,0,2,0,0,0,2,0,0,0,22,Winter,41,58,Fall,2,0 +c100a3aa,Summer,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c1200b71,Winter,7,1,Summer,55,Winter,13.36161312,43.4,35.8,,,,,Winter,3,3,50,Winter,0,0,,,,,9,1,10.5,1,8.5,1,1.0,0,,,,,,,,,,,,,,,,,,,,,,Spring,2,2,2,0,2,2,0,0,0,0,3,0,0,0,0,2,1,2,0,0,18,Winter,33,47,Winter,0,0 +c1373df0,Fall,8,0,Spring,61,Fall,14.88993056,48.0,48.8,,57,75,99,,,,,Fall,9,1,,,,,4,1,10.0,1,12.0,1,9.5,1,Winter,3,2.67449,14.5861,946.129,1608.42,6.52445,43.0382,12.8639,1.72218,5.76182,1,22.2471,14.2666,40.3637,17.9894,28.7715,,,,,Winter,4,4,2,1,4,2,0,1,1,2,2,0,1,0,1,2,1,2,1,1,32,Winter,37,53,Fall,2,1 +c192715a,Winter,11,0,Fall,65,Spring,22.06675625,61.0,116.8,,61,81,119,Spring,6,7,40,Spring,13,0,17.8,2,21.4,2,14,1,7.0,0,9.0,1,7.0,0,,,,,,,,,,,,,,,,,,,,Winter,2.75,Spring,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Spring,34,49,Winter,2,0 +c1add876,Spring,10,0,Fall,65,Spring,14.40244342,56.5,65.4,,78,85,114,Spring,6,11,0,Spring,15,1,17.8,2,17.0,2,5,0,10.0,1,9.0,1,12.0,1,Summer,3,3.73482,14.404,1079.88,1835.79,12.2616,57.2842,12.6165,1.78747,8.11584,1,27.9139,17.1086,53.5493,26.0367,40.1755,,,Spring,3.57,Spring,2,1,3,2,3,2,0,2,1,0,0,0,3,2,3,2,2,1,2,2,33,Spring,32,46,Spring,1,1 +c1baae32,Spring,21,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +c1ed20ef,Fall,10,1,Summer,41,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.77,Winter,4,3,2,0,4,3,0,1,2,3,2,0,1,1,3,1,3,1,1,2,37,Winter,42,59,Fall,0,1 +c21a6bac,Spring,11,1,,,Spring,16.76571733,55.25,72.8,23.0,71,70,97,,,,,Spring,29,1,16.6,2,14.6,2,21,1,11.0,1,10.0,1,16.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,3.89,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +c21fb6da,Fall,18,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +c249a0cf,Summer,18,0,,,Summer,23.20886612,67.0,148.2,32.0,62,72,125,,,,,,,,,,,,,,,,,,,,Fall,4,6.82718,23.2114,1701.53,3573.21,38.8534,123.498,19.3426,3.86884,24.7017,2,54.2604,30.3845,116.671,62.2626,93.1138,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +c24c877a,Winter,6,1,Spring,60,Winter,15.14399093,42.0,38.0,,77,97,108,Winter,,,,Winter,0,0,,,,,0,0,11.5,1,11.0,1,8.0,1,Spring,3,11.6361,14.4494,888.481,1421.57,7.62296,36.8979,14.0303,0.419073,1.10211,1,18.8463,10.4286,25.2618,15.8491,26.4693,,,,,Spring,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Winter,31,45,Winter,0,0 +c274a243,Fall,8,1,Winter,51,Fall,17.88012153,48.0,58.6,,107,89,176,,,,,Fall,10,1,,,,,0,0,8.0,0,9.0,1,3.0,0,Fall,3,2.45958,17.8821,971.297,1554.08,12.4591,45.7189,13.9513,3.93073,12.8811,2,22.1618,11.098,43.2593,20.0609,34.6209,,,,,Fall,4,3,3,0,2,0,0,0,0,0,2,0,0,0,1,2,0,1,1,0,19,Fall,35,50,Fall,0,0 +c2766708,Winter,15,1,,,Winter,20.98025712,66.0,130.0,31.0,76,75,122,,,,,Spring,16,0,55.0,2,56.0,2,3,0,19.0,1,19.5,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,2.421,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +c294b507,Spring,9,1,Summer,80,Winter,26.12816667,60.0,133.8,,68,101,149,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,2,4.19034,26.131,1272.28,1908.42,26.1985,77.7778,15.1899,10.9411,56.0222,2,30.9619,20.6174,73.5875,34.658,57.1604,,,Spring,2.93,Spring,5,5,4,0,3,4,0,2,1,2,0,0,3,2,3,2,2,2,2,0,42,Spring,50,69,Spring,0,1 +c2a7f542,Spring,20,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c2adf42c,Spring,15,0,Fall,61,Summer,25.05349087,66.5,157.6,,66,53,119,,,,,Summer,30,1,41.9,2,36.2,2,41,1,9.0,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,Spring,2.76,,,Summer,1,1,3,2,2,2,2,2,2,1,3,1,2,1,1,1,2,1,2,1,33,Summer,31,45,Spring,2,1 +c2b40d6f,Summer,7,0,,,Spring,14.00053825,51.0,51.8,,75,110,111,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,54,75,Summer,0,0 +c2b9092f,Summer,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c2bac6ad,Fall,13,1,Summer,55,Spring,18.27435121,68.0,120.2,,76,82,146,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.37,Spring,2,2,4,4,4,4,1,3,3,4,5,1,5,5,5,5,4,4,4,4,73,Spring,39,55,Fall,3,2 +c2bac92a,Summer,12,1,,,Summer,18.72084481,66.0,116.0,30.0,63,86,113,,,,,Summer,2,0,18.5,2,17.9,2,0,0,8.0,0,7.5,0,9.0,1,Summer,2,5.28568,18.7229,1353.25,2029.87,28.9481,86.4015,13.9456,4.77733,29.5985,2,33.9586,23.4948,81.1158,39.1396,62.9066,,,Summer,1.88,Summer,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Summer,36,51,Summer,2,0 +c2cd92d4,Fall,15,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +c2d74d91,Summer,18,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3,6.67913,17.9356,1525.31,2593.03,31.1927,104.729,15.027,2.90864,20.2714,1,45.5615,27.9745,98.0495,48.7691,76.7541,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c2e15338,Summer,17,0,,,Winter,29.1183432,65.0,175.0,,83,70,139,,,,,Winter,5,0,24.7,1,28.3,1,5,0,5.0,0,9.0,1,4.0,0,Winter,2,5.97819,29.1215,1622.47,2595.95,33.6291,115.077,19.1499,9.97165,59.9227,3,51.9114,29.5369,109.099,63.313,85.5405,Winter,1.57,,,Winter,3,4,5,0,3,1,1,1,5,3,4,0,4,4,0,1,1,4,5,4,53,Winter,79,100,Winter,1,2 +c2ea6e54,Fall,10,1,,,Fall,20.90281065,52.0,80.4,27.0,72,92,127,,,,,Fall,2,0,14.5,2,15.1,2,4,0,8.5,0,8.0,0,10.0,1,Fall,2,3.26169,20.9051,1070.42,1712.66,12.6214,56.2763,14.6326,6.27248,24.1237,1,28.4106,15.2443,53.0146,24.7584,41.032,,,Fall,2.38,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Fall,34,49,Fall,2,0 +c3061b23,Fall,7,0,Spring,55,Winter,25.96710126,50.5,94.2,,81,98,117,,,,,Winter,0,0,,,,,0,0,6.5,0,7.0,0,3.0,0,Winter,2,3.22682,25.9699,1102.59,1764.14,15.0941,59.7028,16.4594,9.5105,34.4972,2,30.2792,14.3295,56.476,24.6759,45.3733,,,,,Winter,3,2,2,0,2,1,0,1,1,2,0,0,1,1,0,3,0,2,1,0,22,Winter,76,100,Winter,1,0 +c308b134,Fall,12,0,,,Winter,23.02595112,63.0,130.0,,74,66,134,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.4,Winter,5,5,5,1,5,4,4,5,1,5,3,0,5,5,5,5,5,4,5,4,81,Winter,44,62,Fall,0,3 +c30f5122,Winter,17,0,Spring,70,Winter,20.43321799,68.0,134.4,,84,87,128,,,,,Winter,13,0,23.2,1,28.4,1,6,0,6.5,0,7.0,0,8.0,0,,,,,,,,,,,,,,,,,,Winter,1.04,,,Winter,1,4,4,2,4,4,1,3,3,4,4,1,1,4,4,0,4,1,4,1,54,Winter,47,66,Winter,2,2 +c31a5825,Fall,9,0,Fall,55,Fall,14.27081892,51.0,52.8,,104,89,161,Fall,,,,Fall,12,1,,,,,1,0,0.0,0,0.0,0,10.0,1,Fall,3,6.31424,14.2166,985.141,1674.74,9.09881,47.1934,12.707,1.50958,5.60656,1,24.4636,13.631,40.8792,20.0369,33.5624,,,Fall,2.29,Fall,5,4,4,4,4,0,0,4,4,4,4,0,0,4,4,1,4,4,4,3,61,Fall,26,38,Fall,0,2 +c341c00d,Winter,6,1,,,Winter,23.4501287,47.1,74.0,29.0,87,76,128,,,,,Winter,1,0,,,,,0,0,8.0,1,9.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +c35218d4,Spring,8,1,,,Summer,13.74812132,47.0,43.2,20.0,63,91,104,,,,,Summer,4,1,,,,,0,0,8.0,0,8.0,0,12.0,1,Summer,1,2.24982,13.7496,935.755,1216.48,9.62151,41.9332,13.3464,0.4032,1.26681,1,20.2607,12.051,39.6834,18.021,29.8822,,,,,Summer,4,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,Summer,41,58,Spring,1,0 +c3760eb9,Fall,12,1,,,Fall,19.30675093,59.0,95.6,26.0,77,90,116,,,,,Fall,20,1,24.3,3,26.3,3,20,1,12.0,1,12.0,1,12.0,1,Fall,3,4.21765,19.3089,1227.52,1964.03,24.6442,73.0098,14.7462,4.56267,22.5902,2,30.8899,17.4757,68.7921,32.4773,55.5341,,,Fall,2.119,Fall,1,4,0,0,1,1,0,0,0,0,0,0,0,1,1,0,2,0,0,0,11,Fall,51,71,Fall,3,0 +c37ef416,Winter,8,0,Spring,51,Spring,21.31496,50.0,75.8,,61,82,115,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.55,Spring,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,5,Spring,38,54,Winter,0,0 +c38585c5,Summer,9,0,Summer,80,Winter,13.30143927,54.5,56.2,,91,92,136,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.55,Fall,3,4,4,0,4,0,0,4,2,2,4,0,4,1,4,4,4,,,1,45,Fall,28,41,Summer,0,1 +c38a5c9b,Winter,15,0,Summer,80,Spring,18.79060153,64.5,111.2,,69,95,118,,,,,Spring,22,0,25.9,1,24.3,1,1,0,4.0,0,5.0,0,8.0,0,Spring,3,5.40682,18.7927,1361.9,2315.24,24.311,87.3237,14.7576,4.03507,23.8763,1,39.7257,23.2869,81.9168,41.4711,64.0368,Spring,2.46,,,Spring,4,5,3,5,5,1,1,3,3,3,5,4,4,4,4,4,4,4,3,2,71,Spring,39,55,Winter,3,2 +c38dc7c2,Fall,10,0,,,Fall,15.72264055,49.5,54.8,,64,72,114,,,,,Fall,6,0,,,,,0,0,10.0,1,10.0,1,6.5,1,Fall,3,3.00002,15.7244,979.936,1665.89,9.87074,46.6391,13.3827,2.3417,8.16092,1,25.2664,11.5019,43.6391,20.1697,35.1371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +c391e9c0,Spring,6,0,Winter,71,Fall,18.45825641,48.75,62.4,,88,90,145,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,0,5,2,4,1,0,3,0,2,3,0,3,0,3,3,3,3,0,0,40,Fall,57,79,Spring,0,1 +c3a3f61e,Fall,9,0,Spring,70,Winter,13.84795322,57.0,64.0,,63,81,100,,,,,Winter,11,1,,,,,0,0,10.5,1,9.0,1,9.0,1,Winter,3,3.67535,13.8495,1074.4,1826.49,12.0378,56.7011,12.27,1.57946,7.29887,2,26.0049,18.6585,53.0258,21.0237,38.0427,,,Winter,2.12,Winter,1,2,1,0,1,0,0,1,1,1,2,0,1,1,1,0,0,1,0,0,14,Winter,34,49,Fall,2,0 +c3a86e3e,Summer,9,1,,,Fall,19.77109292,53.0,79.0,,71,70,129,,,,,Fall,11,1,,,,,0,0,7.0,0,6.0,0,9.0,1,Fall,2,2.98122,19.7733,1061.57,1592.36,15.4669,55.3342,13.8498,5.92341,23.6658,2,24.4932,15.3742,52.353,26.2042,39.9601,,,Summer,3.09,Fall,5,5,4,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,34,Fall,32,46,Summer,1,1 +c3adae87,Spring,16,1,Winter,75,Summer,26.2033408,62.5,145.6,,64,99,103,,,,,Summer,16,0,27.8,2,28.5,2,2,0,14.0,1,14.0,1,12.0,1,Summer,3,4.86214,26.6306,1386.31,2218.09,31.0849,89.9227,16.4471,10.1835,55.6773,2,36.4243,22.4135,85.0606,45.9699,67.5092,Spring,1.86,,,Summer,4,4,3,3,5,4,4,4,5,4,4,1,3,4,3,4,4,4,2,1,70,,,,Spring,1,2 +c3b87e5c,Summer,8,1,,,Summer,17.14310019,46.0,51.6,25.0,78,99,114,,,,,Summer,8,1,,,,,0,0,7.0,1,9.0,1,12.0,1,Fall,2,2.19137,17.145,937.097,1405.64,10.5318,42.0761,13.9805,3.16448,9.52391,1,20.3941,11.1502,39.8847,17.0466,30.9259,,,Summer,2.89,Summer,3,2,2,0,3,0,0,1,0,0,2,0,1,0,0,0,0,2,1,1,18,Summer,38,54,Summer,1,0 +c3d46daf,Fall,18,0,Spring,50,Winter,23.06137566,63.0,130.2,,76,71,112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,5,3,4,2,4,2,2,2,4,3,4,2,3,2,2,4,3,5,1,2,59,Winter,37,53,Fall,2,2 +c3dde859,Fall,7,0,Spring,70,Winter,17.57084024,48.75,59.4,,70,130,117,,,,,Winter,0,0,,,,,0,0,11.0,1,12.5,1,6.0,1,Winter,2,2.7291,17.5728,974.148,1558.64,7.82438,46.0226,13.6152,3.95754,13.3774,2,23.5658,14.6324,43.2935,19.097,31.3902,,,,,Winter,0,0,2,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,6,Winter,44,62,Fall,0,0 +c400321c,Summer,6,0,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +c40790ea,Spring,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c409bc9d,Summer,6,0,Spring,60,Summer,16.94376181,46.0,51.0,24.0,80,72,109,,,,,Summer,3,1,,,,,0,0,9.5,1,7.0,0,7.0,1,Summer,1,2.86007,16.9456,961.825,1250.37,10.3453,44.71,14.8557,2.08996,6.29001,1,27.2149,7.14973,41.8499,23.7308,37.5603,,,,,Fall,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Fall,33,47,Summer,0,0 +c40bb352,Spring,7,1,Fall,70,Spring,17.96017376,47.4,57.4,,,69,,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.69273,17.9621,997.933,1596.69,15.8074,48.556,15.1946,2.76755,8.84403,2,25.1856,7.56302,45.8632,22.1218,40.993,,,,,Summer,4,1,5,0,3,3,0,2,5,5,0,0,2,1,2,2,3,2,2,1,43,Summer,47,66,Spring,0,1 +c41274a7,Spring,16,0,Summer,70,Summer,19.62374936,73.5,150.8,,71,72,108,,,,,Summer,64,1,19.1,1,16.1,1,27,1,10.0,1,9.0,1,14.0,1,Summer,4,-6.40154,19.6259,1779.38,3736.71,41.1294,131.791,17.1519,2.47394,19.0091,1,56.3126,34.3488,138.192,66.4171,97.4421,Summer,3.1,,,Summer,2,2,2,1,1,1,1,1,2,1,2,1,2,1,1,1,2,1,2,1,28,Summer,39,55,Spring,2,0 +c418e47a,Fall,13,1,Winter,80,Fall,23.85293848,66.0,147.8,,87,73,162,,,,,Fall,20,1,27.2,3,28.8,3,5,0,12.5,1,9.0,0,10.0,1,Fall,2,0.0211894,23.8555,1463.56,2195.34,35.066,98.1517,15.8421,8.01344,49.6483,2,37.5384,25.5473,98.1305,43.3923,72.6044,,,Fall,2.44,Fall,2,4,3,1,3,3,1,2,1,1,2,0,2,0,2,1,1,1,0,1,31,Fall,43,60,Fall,2,1 +c42b7972,Summer,10,0,,,Summer,15.6023135,50.5,56.6,24.0,64,85,87,,,,,Summer,4,0,11.5,1,13.7,2,1,0,4.5,0,5.0,0,6.5,0,Summer,,,,,,,,,,,,,,,,,,,Summer,2.43,Summer,4,4,0,0,1,1,0,1,1,1,0,0,0,0,2,2,2,2,2,1,24,Summer,46,64,Summer,1,0 +c4337ebe,Spring,8,1,,,Spring,15.13005735,50.0,53.8,21.0,75,86,114,,,,,,,,,,,,,,,,,,,,Spring,3,2.81986,15.1302,1001.07,1601.71,13.5214,48.8897,13.7493,1.38091,4.91025,2,25.1994,10.1689,46.0699,27.8101,38.7208,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c4338f5e,Summer,12,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c4366835,Fall,11,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +c43b985c,Summer,6,0,Fall,90,Fall,14.02528395,45.0,40.4,,58,82,101,Fall,4,6,32,Fall,0,0,,,,,7,1,15.0,1,14.0,1,6.0,1,Fall,5,5.2915,14.0268,888.889,2133.33,5.98183,36.9414,12.826,1.20082,3.45861,2,21.7869,9.17261,31.6499,16.1048,27.7688,,,,,Fall,2,1,4,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,12,Fall,43,60,Summer,2,0 +c446e1c5,Summer,15,1,Winter,40,Winter,22.27995198,59.5,112.2,,80,80,121,,,,,Summer,,,,,,,,,,,,,,,Summer,1,3.80797,22.2824,1194.44,1552.78,20.7798,69.4871,13.7998,8.48258,42.7129,1,27.7555,20.9517,65.6791,32.6567,48.5353,Summer,1.14,,,Summer,0,3,4,1,2,2,3,2,4,0,5,1,2,4,4,5,4,5,5,4,60,Summer,37,53,Summer,3,2 +c45068a1,Winter,15,0,Summer,68,Spring,18.86868639,65.0,113.4,,67,84,106,,,,,Spring,26,1,27.7,1,25.3,2,25,1,5.5,0,4.5,0,8.0,0,Spring,3,5.88733,18.8707,1450.95,2466.61,28.9311,96.8081,16.1097,2.76103,16.5919,1,43.8501,24.027,90.9208,46.4897,72.7812,Spring,1.49,,,Spring,1,4,3,0,3,2,2,2,2,1,4,0,1,1,2,0,3,1,1,0,33,Spring,37,53,Spring,1,1 +c4526aab,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c459095b,Summer,16,0,,,Summer,18.36408163,70.0,128.0,27.0,74,68,133,,,,,Summer,33,1,31.1,2,36.0,2,20,1,7.75,0,8.0,1,12.0,1,,,,,,,,,,,,,,,,,,Summer,2.3,,,Summer,5,5,5,0,5,5,5,3,1,5,5,1,3,5,3,5,5,5,0,0,71,,,,Summer,3,2 +c47452e3,Fall,8,0,,,Fall,17.79197531,54.0,73.8,24.0,71,64,110,,,,,Fall,0,0,,,,,2,0,5.0,0,4.0,0,12.0,1,Fall,2,3.779,17.7939,1116.66,1786.65,16.0659,61.2015,14.7563,3.03763,12.5985,2,31.2132,13.9225,57.4226,27.6746,47.2791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +c4a681dc,Spring,9,0,Fall,45,Fall,21.93861454,54.0,91.0,,62,70,100,Fall,3,3,25,Fall,3,0,,,,,3,0,6.0,0,5.0,0,7.5,1,Fall,1,3.50316,21.941,1120.51,1456.66,14.5805,61.6121,14.8553,7.08572,29.3879,3,29.8445,17.1871,58.1089,25.6685,44.425,,,Spring,1.62,Fall,4,3,5,2,5,0,0,1,1,4,0,0,3,1,5,5,3,5,0,2,49,Fall,30,43,Spring,0,1 +c4aa8d47,Summer,5,0,Winter,62,Summer,14.30546884,46.5,44.0,,,,,Summer,,,,Summer,0,0,,,,,0,0,6.0,0,5.0,0,6.0,1,Summer,4,2.63383,14.307,914.979,1921.46,7.43597,39.7203,12.9155,1.39158,4.27967,2,22.4583,9.82604,37.0865,15.8118,29.8943,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +c4c2076b,Summer,7,0,Spring,75,Winter,23.36445228,47.25,74.2,,72,90,122,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.57017,23.367,995.597,1692.52,7.11953,48.3072,15.2129,8.15415,25.8928,2,25.6668,15.5208,45.737,29.4235,32.7863,,,,,Winter,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,2,0,0,6,Winter,27,40,Summer,0,0 +c4c86e6f,Spring,18,0,,,Fall,28.98845215,64.0,168.9,,66,86,118,,,,,,,,,,,,,,,,,,,,Fall,3,5.46062,28.9916,1521.71,2586.91,28.7824,104.346,17.9109,11.0807,64.5544,2,47.6776,27.8856,98.885,58.9621,76.46,,,,,Fall,2,2,3,2,2,3,1,2,2,1,3,1,2,2,2,1,3,2,4,2,42,,,,Spring,3,1 +c4e4d2db,Spring,12,0,,,Spring,16.22997449,56.0,72.4,25.0,67,73,113,,,,,Spring,7,0,11.6,1,12.0,1,1,0,7.5,0,6.7,0,10.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,1.69,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +c4f231cd,Summer,8,0,,,Summer,15.46005383,51.0,57.2,24.0,54,54,89,,,,,Summer,0,0,,,,,0,0,8.0,1,8.0,1,12.0,1,Summer,2,3.1235,15.4617,999.56,1599.3,10.2009,48.7293,13.172,2.28971,8.47069,1,25.5247,13.0037,45.6058,20.8512,35.7256,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +c4fde231,Summer,14,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +c503edc0,Winter,14,1,,,Winter,30.11668639,65.0,181.0,35.0,79,84,119,,,,,Winter,17,0,14.7,1,16.6,2,0,0,16.0,1,13.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,Spring,1.96,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +c51c1139,Fall,9,0,,,Fall,17.98750413,55.0,77.4,26.0,,,,,,,,Fall,10,1,,,,,0,0,10.0,1,9.0,1,11.5,1,,,,,,,,,,,,,,,,,,,,Fall,3.73,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,24,38,Fall,,0 +c5462e87,Spring,10,0,Winter,69,Summer,14.50717456,52.0,55.8,,81,94,116,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.54,Summer,2,2,2,2,3,2,2,1,2,2,3,1,2,2,2,1,2,1,2,1,37,Summer,34,49,Spring,0,1 +c55187b4,Spring,15,1,Summer,55,Spring,24.07741367,60.75,126.4,28.0,59,80,115,,,,,Spring,25,1,31.5,3,30.1,3,18,1,10.0,1,12.0,1,9.0,1,Summer,4,4.62708,24.08,1329.95,2526.91,28.6015,83.9202,15.9874,8.09268,42.4798,2,34.9016,20.4171,79.2931,42.7552,63.5031,Summer,3.0,,,Summer,0,1,1,1,1,1,1,1,2,1,2,1,1,1,1,1,1,0,1,1,20,Summer,45,63,Spring,3,0 +c55cc646,Winter,13,0,Summer,70,Summer,24.56353361,67.5,159.2,,67,72,118,,,,,Summer,28,1,26.0,2,23.2,2,19,0,5.0,0,6.0,0,8.0,0,Summer,3,2.88786,24.786,1674.57,2846.77,37.6045,120.627,18.7805,6.00554,38.5734,2,52.5903,30.4318,117.739,58.2402,90.1948,,,Spring,3.14,Spring,5,5,5,2,4,5,0,5,5,4,4,1,5,5,5,4,4,5,3,4,80,Spring,37,53,Winter,2,3 +c5a0a4bb,Winter,6,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +c5b468d7,Fall,11,1,,,Fall,14.21046373,58.0,68.0,22.0,67,65,100,,,,,Fall,7,0,12.3,2,14.4,2,0,0,14.0,1,13.0,1,7.0,0,,,,,,,,,,,,,,,,,,,,Fall,2.16,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +c5c891c1,Spring,9,0,Summer,75,Spring,17.76399164,55.13,76.8,,100,99,131,Spring,,,,Winter,,,,,,,,,,,,,,,Summer,3,3.71174,17.7659,1106.7,1881.39,14.3774,60.1409,13.9122,3.85371,16.6591,2,29.7554,16.0081,56.4291,26.6565,44.1328,,,,,Spring,1,3,2,0,2,1,0,0,1,0,1,0,1,1,2,1,1,2,1,0,20,Spring,39,55,Spring,2,0 +c5cfaeae,Spring,9,0,Winter,49,Summer,14.17572016,54.0,58.8,,66,80,106,Summer,3,3,36,Summer,0,0,,,,,0,0,6.0,0,7.0,0,10.0,1,Summer,1,3.3377,14.1773,1029.47,1338.31,10.1946,51.9153,12.5173,1.65998,6.88473,1,25.3405,16.3802,48.5776,21.5299,35.5351,,,Spring,3.07,Summer,5,5,5,2,5,2,0,2,1,5,4,0,3,2,4,5,3,2,1,0,56,Summer,65,89,Spring,2,2 +c5d814b9,Summer,6,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c5e91ebc,Summer,8,1,Fall,80,Fall,18.06900374,49.9,64.0,,64,80,112,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.6,Summer,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,Summer,49,68,Summer,0,0 +c5f3d60c,Fall,6,1,,,Fall,15.12856,50.0,53.8,21.0,55,87,102,,,,,Fall,4,1,,,,,0,0,12.0,1,12.0,1,10.0,1,Fall,1,2.76514,15.1302,994.858,1293.31,14.4498,48.2284,13.5633,1.5669,5.57159,1,23.0634,10.7152,45.4633,19.0207,37.5132,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,41,58,Fall,,0 +c61b5eda,Winter,21,1,,,Winter,21.36733291,65.5,130.4,30.0,72,95,116,,,,,,,,,,,,,,,,,,,,Winter,2,5.24383,21.3697,1376.06,2064.09,31.4076,88.8317,14.5576,6.81211,41.5683,2,33.4983,23.9258,83.5879,34.9309,64.9059,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +c6287031,Winter,12,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4.26,Winter,5,5,5,1,5,3,1,1,1,1,1,1,1,0,1,1,2,3,1,1,40,Winter,45,63,Winter,2,1 +c6313b18,Summer,5,0,Fall,75,Fall,23.02179752,44.0,63.4,,85,138,133,Fall,,,,Fall,,,,,,,,,,,,,,,Fall,1,9.46255,23.0243,934.788,1215.22,7.78284,41.8302,15.191,7.83329,21.5698,1,23.3851,10.6622,32.3676,16.5104,31.168,,,,,Fall,4,3,3,0,2,0,0,0,0,1,2,0,2,2,1,3,2,1,0,0,26,,,,Summer,1,0 +c63f1965,Spring,6,0,Summer,85,Winter,13.5271137,49.0,46.2,,113,87,147,Winter,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,,4,3,0,0,13,Spring,51,71,Spring,0,0 +c6575d38,Fall,18,0,Spring,60,Winter,17.32049197,71.0,124.2,,65,88,115,,,,,Winter,14,0,36.6,2,33.2,2,14,0,9.0,1,10.0,1,11.0,1,Winter,2,-0.396907,17.3224,1475.94,2361.51,28.4032,99.4704,13.8733,3.44908,24.7296,2,42.6633,28.4039,99.8673,44.6443,71.0665,Winter,1.5,,,Winter,2,4,4,4,4,3,2,2,1,1,5,0,2,3,4,3,4,3,2,2,55,Winter,51,71,Fall,3,2 +c662db2f,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c66daa08,Fall,14,0,Summer,65,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.92,,,Summer,0,5,5,4,4,4,0,4,1,0,5,0,2,0,4,0,5,0,5,3,51,Summer,60,82,Fall,2,2 +c678730b,Spring,9,0,Fall,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.63,Summer,2,2,2,1,4,1,1,1,1,2,0,0,0,0,0,2,2,2,2,2,27,Summer,29,42,Spring,0,0 +c68c9809,Winter,11,0,Spring,70,Spring,16.60465189,56.5,75.4,,73,86,116,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.97,Spring,5,5,,2,3,4,1,2,2,3,2,0,2,1,1,3,2,3,1,1,43,Spring,38,54,Winter,0,1 +c69ffa09,Summer,14,0,,,Winter,18.33007407,67.5,118.8,,69,55,120,,,,,Winter,26,1,27.2,1,27.2,1,24,1,10.0,1,9.0,1,11.0,1,Winter,3,9.06926,18.3321,2357.28,4007.37,59.4844,193.344,29.835,-11.5029,-74.544,2,86.5911,47.2686,184.275,132.636,146.075,Summer,2.97,,,Winter,2,2,3,1,2,2,2,2,1,1,2,0,0,0,1,0,1,0,0,0,22,Winter,51,71,Summer,0,0 +c6a92a65,Spring,6,0,Summer,70,Summer,20.82469954,48.75,70.4,,67,91,105,Summer,3,3,49,Summer,0,0,,,,,0,0,,,6.5,0,10.0,1,Summer,2,8.20725,20.827,1007.94,1612.71,10.647,49.6221,14.6801,6.14689,20.7779,2,26.2931,12.6819,41.4148,21.1606,36.9402,,,,,Summer,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,1,1,9,Summer,33,47,Spring,1,0 +c6b7811e,Summer,7,1,Winter,60,Fall,16.23907765,49.5,56.6,,,87,,Fall,1,0,32,Fall,4,1,,,,,0,0,13.0,1,15.0,1,8.0,1,Fall,1,6.2824,16.2409,992.903,1290.77,13.0242,48.0202,13.779,2.46188,8.57975,2,24.4169,10.5792,41.7378,26.7182,37.4411,,,,,Fall,5,5,3,0,5,4,0,2,2,3,2,0,3,4,4,4,1,5,2,5,59,Fall,66,90,Summer,0,2 +c6cb894a,Summer,10,1,Summer,80,Fall,15.91698113,53.0,63.6,,69,69,122,Fall,,,,Fall,11,1,,,,,15,1,9.0,1,11.0,1,8.0,1,Fall,3,4.62644,15.9187,1058.14,1693.03,15.4525,54.9689,13.7584,2.16031,8.63109,1,27.0593,12.4571,50.3425,32.6458,42.5118,,,Summer,3.59,Spring,2,2,2,0,2,1,0,2,2,2,2,0,0,2,2,0,3,0,0,0,24,Fall,51,71,Fall,2,0 +c6cc7467,Summer,18,0,Fall,80,Summer,35.30381772,70.5,249.6,,88,93,158,,,,,Fall,,,,,,,,,,,,,,,Summer,3,7.83284,35.3077,2079.78,3535.62,51.8609,163.786,23.1688,12.1389,85.8135,3,68.9517,42.974,155.954,84.8261,120.813,,,,,Summer,0,4,5,0,1,1,1,1,4,0,4,0,4,0,1,0,3,0,0,1,30,,,,Summer,2,0 +c6d4b453,Spring,8,0,Spring,62,Summer,17.39486621,52.5,68.2,,64,70,117,,,,,Summer,12,1,,,,,5,1,8.0,1,8.0,1,8.0,1,Summer,2,3.5034,17.3968,1069.33,1710.93,13.6666,56.1612,14.3258,3.07092,12.0388,1,29.5217,12.9728,52.6578,26.5868,43.1883,,,,,Winter,5,5,5,0,4,0,0,0,0,2,0,0,2,0,0,2,4,2,0,0,31,Winter,43,60,Summer,0,1 +c6da0d4b,Fall,7,0,,,Fall,17.54683759,51.5,66.2,,50,66,97,,,,,Fall,0,0,,,,,0,0,11.0,1,8.0,1,7.0,1,Fall,3,3.39442,17.5488,1053.73,1791.34,14.0952,54.4987,14.4469,3.10186,11.7013,2,28.2509,12.1526,51.1043,21.8587,42.3461,,,,,Fall,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,44,62,Fall,1,0 +c6ead822,Fall,20,0,Spring,53,Winter,38.33502041,70.0,267.2,,62,93,149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,5,4,3,5,0,1,2,1,4,5,1,1,3,5,1,2,1,2,1,49,Winter,38,54,Fall,2,1 +c6fd800e,Winter,9,1,Spring,65,Winter,15.30340136,52.5,60.0,24.0,69,83,106,,,,,Winter,14,1,,,,,0,0,9.0,1,8.5,1,8.0,1,Winter,2,2.87544,15.3051,1023.37,1535.05,14.0721,51.2649,13.0769,2.22819,8.7351,1,22.4508,14.742,48.3895,19.7302,36.5229,,,,,Winter,1,1,1,0,1,0,0,1,1,1,2,0,0,0,0,1,1,1,0,0,12,Winter,48,67,Winter,0,0 +c706a54d,Winter,7,0,Summer,70,Spring,15.04964598,49.0,51.4,,66,78,110,Spring,6,10,12,Spring,3,1,,,,,8,1,10.5,1,8.0,1,10.0,1,Spring,4,6.22557,15.0513,959.561,2015.08,8.47566,44.4688,13.0216,2.02964,6.9312,2,23.8794,12.1138,38.2432,18.7285,32.355,,,,,Spring,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,Spring,30,43,Winter,0,0 +c715832b,Spring,5,0,Summer,45,Spring,17.38610526,47.5,55.8,,89,105,136,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.66389,17.388,952.459,1619.18,7.84667,43.7124,13.6214,3.76664,12.0876,2,23.3076,12.5582,41.0485,17.7097,31.1542,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,54,75,Spring,2,0 +c731458f,Summer,11,1,Fall,70,Summer,17.47776791,60.5,91.0,,69,91,115,,,,,Summer,41,1,16.8,2,19.9,2,16,1,10.0,1,9.5,1,10.0,1,Summer,3,4.29383,17.4797,1216.34,1946.14,23.8647,71.819,13.7953,3.68437,19.181,2,29.4426,18.5117,67.5252,29.397,53.3073,,,Summer,3.55,Summer,2,2,3,1,2,1,2,2,1,2,3,1,2,2,1,2,2,2,1,2,36,Summer,37,53,Summer,2,1 +c73dd2e4,Summer,9,0,,,Fall,15.50744671,52.5,60.8,,55,77,94,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.81,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,44,62,Summer,0,0 +c73f149c,Summer,7,1,Winter,68,Fall,15.39843364,48.63,51.8,,62,93,105,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,2,0,2,2,0,2,0,2,0,0,2,2,2,2,2,2,2,2,30,Fall,64,88,Summer,0,0 +c782b5fe,Spring,6,0,,,Spring,17.26987847,48.0,56.6,,69,104,121,,,,,Spring,7,,,,,,6,,10.0,,9.0,,11.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +c79039af,Winter,9,1,Summer,85,Spring,12.87386831,54.0,53.4,,68,119,106,Spring,4,7,0,Spring,7,0,,,,,2,0,7.5,0,7.5,0,6.5,1,Spring,2,2.86,12.8753,1029.94,1544.91,11.9718,51.9654,12.5294,0.345888,1.43457,2,21.4515,18.5421,49.1054,21.1424,33.4233,,,Spring,1.56,Spring,4,4,2,1,2,1,1,1,2,2,2,0,0,2,1,2,2,1,2,1,33,Spring,33,47,Winter,2,1 +c7a71c63,Spring,9,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +c7d61396,Fall,8,0,Winter,81,Fall,29.72533837,57.5,139.8,,58,78,91,Fall,3,4,48,Fall,5,0,,,,,10,1,5.0,0,6.0,0,3.5,0,,,,,,,,,,,,,,,,,,,,Fall,1.87,Fall,3,3,3,0,3,1,0,3,0,1,2,0,3,3,2,1,3,2,2,1,36,Fall,32,46,Fall,2,1 +c7d9e964,Fall,6,0,Spring,62,Winter,16.14640832,46.0,48.6,,65,73,95,Winter,2,2,22,Winter,10,1,,,,,10,1,7.5,1,7.0,0,7.0,1,Winter,3,2.54248,16.1482,919.243,1562.71,6.5275,40.1745,13.3486,2.79953,8.42555,1,22.8803,10.7666,37.632,18.7196,29.4078,,,,,Spring,3,2,3,0,2,0,0,2,0,1,2,0,1,0,0,0,1,0,0,0,17,Winter,39,55,Fall,2,0 +c7e42517,Summer,8,1,,,Summer,35.29465021,54.0,146.4,40.0,71,98,120,,,,,Summer,0,0,,,,,0,0,4.0,0,4.0,0,4.0,0,Summer,1,3.47959,35.2985,1228.62,1597.21,25.7659,73.1276,17.6318,17.6667,73.2724,3,30.4477,16.914,69.648,33.324,56.2136,,,Summer,3.4,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +c7e97264,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3,2.80547,16.6401,954.852,1623.25,9.54771,43.9673,13.7008,2.93936,9.43271,3,24.3114,10.1082,41.1618,17.3838,33.8591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c7fd8939,Summer,9,0,,,Summer,16.84071335,52.25,65.4,24.0,63,73,113,,,,,Summer,1,0,,,,,0,0,9.0,1,8.0,1,12.0,1,Fall,2,3.29694,16.8426,1037.05,1659.28,12.2138,52.7227,13.5777,3.26481,12.6773,1,26.4875,14.0214,49.4257,20.2857,38.7013,,,Fall,2.869,Fall,1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,5,Summer,41,58,Summer,0,0 +c805ebe7,Summer,8,0,Fall,73,Summer,15.28520469,50.75,56.0,,66,76,88,Summer,7,13,5,Summer,4,1,,,,,5,1,9.0,1,11.5,1,8.0,1,Summer,3,3.36493,15.2869,1006.78,1711.53,11.0238,49.4984,13.5121,1.77481,6.5016,1,26.9971,11.4775,46.1335,23.5508,38.0209,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,31,45,Summer,0,0 +c8270746,Winter,11,0,Summer,74,Spring,16.69274023,56.5,75.8,,74,109,108,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.06439,16.6946,1146.0,1948.2,17.1341,64.3269,14.1677,2.5269,11.4731,1,31.4457,15.747,60.2625,27.2767,48.5799,,,Spring,2.35,Spring,4,2,4,1,5,0,0,1,0,4,4,0,2,1,2,1,1,1,1,0,34,Spring,41,58,Winter,2,1 +c829500d,Spring,9,0,Summer,55,Summer,16.8043094,54.5,71.0,,73,86,124,Summer,6,9,36,Summer,12,1,,,,,11,1,8.0,1,8.5,1,11.0,1,Summer,2,3.75596,16.8061,1101.71,1762.73,15.5809,59.6092,14.1099,2.69627,11.3908,2,29.6667,14.3617,55.8533,24.2471,45.2476,,,Summer,3.06,Summer,2,2,2,0,2,1,0,0,0,0,2,0,2,1,1,2,2,2,2,2,25,Summer,41,58,Spring,0,0 +c8387d66,Winter,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +c87c7cd3,Fall,11,1,,,Fall,23.90701546,58.0,114.4,32.0,60,76,108,,,,,Fall,10,0,14.9,2,17.1,2,4,0,10.0,0,9.0,0,12.0,1,Winter,2,4.43744,23.9096,1324.54,1986.81,31.0745,83.3442,17.419,6.49066,31.0558,2,33.9559,18.3139,78.9068,33.439,65.0304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +c892f324,Spring,9,1,Summer,67,Spring,17.26147368,47.5,55.4,24.0,88,70,113,,,,,Spring,0,0,,,,,2,0,12.0,1,11.0,1,8.0,1,Summer,2,2.3173,17.2634,959.652,1439.48,11.0771,44.4786,13.8601,3.40326,10.9214,1,20.9057,12.4958,42.1613,18.6772,31.9827,,,Summer,3.481,Summer,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Summer,30,43,Spring,2,0 +c893bb6a,Spring,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c8bd5927,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c8cc2e1b,Spring,6,0,Fall,75,Summer,15.4845067,45.5,45.6,,88,96,136,Summer,,,,Summer,0,0,,,,,0,0,7.0,0,6.0,0,7.0,1,Summer,2,2.52839,15.4862,907.509,1452.01,6.87902,38.9246,13.2192,2.26702,6.67537,1,22.4147,9.63093,36.3962,16.2926,29.2937,,,,,Spring,2,2,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,9,,,,Summer,2,0 +c8d6b871,Summer,9,0,Winter,58,Fall,14.7360305,48.25,48.8,,66,99,109,,,,,Summer,9,1,,,,,0,0,1.0,0,5.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.63,Summer,2,2,2,1,3,0,0,2,0,0,2,0,0,1,2,1,0,2,0,0,20,Summer,39,55,Summer,2,0 +c8fbbc8a,Fall,12,0,Winter,41,Fall,18.20267857,56.0,81.2,,69,88,110,,,,,Fall,5,0,12.4,1,11.9,1,8,1,10.0,1,11.0,1,9.0,1,Fall,3,3.89708,18.2047,1137.77,1934.2,15.9923,63.45,14.2252,3.97946,17.75,1,31.058,16.3997,59.5529,27.7631,47.0503,,,Winter,2.66,Winter,2,0,1,2,2,2,0,0,0,0,2,0,0,0,0,1,1,1,0,0,14,Winter,39,55,Fall,0,0 +c9133895,Summer,10,0,Fall,70,Fall,17.84570322,54.5,75.4,,66,78,127,Fall,,,,Summer,,,,,,,,,,,,,,,Summer,4,3.23643,16.6065,1106.47,2323.59,7.79086,60.1165,13.2404,3.36611,15.2835,2,25.6759,26.6497,56.8801,33.0597,33.4668,,,Fall,1.31,Fall,5,5,4,2,5,2,0,2,1,3,2,0,1,2,4,2,2,2,2,1,47,Summer,38,54,Summer,0,1 +c9537127,Winter,10,0,,,Winter,20.37309275,60.25,105.2,29.0,58,87,107,,,,,Winter,6,0,,,,,0,0,13.0,1,12.0,1,6.0,1,Winter,1,4.55972,20.3753,1261.18,1639.54,20.8709,76.5957,14.8352,5.54014,28.6043,2,35.11,20.6148,72.036,31.8436,55.9809,,,Winter,1.33,Winter,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Winter,35,50,Winter,2,0 +c9600298,Fall,10,1,Spring,85,Winter,14.92612613,55.5,65.4,,70,79,108,,,,,Winter,30,1,9.2,1,10.6,1,6,0,5.0,0,5.0,0,9.0,1,Winter,4,3.24978,14.9278,1068.45,2030.05,15.3701,56.067,12.7975,2.1303,9.33304,2,24.2562,16.4408,52.8172,24.557,39.6262,,,Winter,3.02,Winter,4,2,2,0,2,1,0,2,0,1,0,0,2,1,2,2,2,2,1,0,26,Winter,36,51,Fall,0,0 +c961d137,Fall,6,0,,,Fall,17.18236928,45.5,50.6,,68,99,112,,,,,Fall,5,1,,,,,1,0,8.0,1,4.0,0,12.0,1,Fall,1,2.58163,17.1842,922.919,1199.79,7.59506,40.566,13.7766,3.40764,10.034,1,23.8111,9.15981,37.9844,18.8661,31.4062,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +c96b9609,Fall,6,0,Summer,55,Winter,15.24650977,46.75,47.4,,93,89,114,Winter,4,6,15,Winter,6,1,,,,,0,0,10.0,1,11.0,1,4.0,0,Winter,2,2.61052,15.2482,924.214,1478.74,7.29476,40.704,13.0941,2.15406,6.69605,1,22.3122,11.097,38.0934,15.7708,29.607,,,,,Winter,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,7,Winter,38,54,Fall,0,0 +c97a40fe,Summer,8,0,,,Summer,14.38460842,47.0,45.2,22.0,54,81,99,,,,,Summer,0,0,0.0,,0.0,,0,0,5.0,0,6.0,0,0.0,0,Summer,1,2.66854,14.3862,922.72,1199.54,7.59446,40.5448,12.9045,1.48165,4.65521,1,22.5275,10.4228,37.8763,15.9136,30.122,,,Summer,3.49,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +c98527f7,Winter,8,0,Spring,78,Winter,20.43321799,51.0,75.6,,85,109,150,Winter,6,9,15,Winter,4,1,,,,,0,0,6.0,0,7.5,1,3.0,0,Winter,3,9.05497,20.4355,1043.91,1774.65,11.2382,53.4534,14.449,5.98644,22.1466,2,27.4862,14.7291,44.3985,24.5852,38.7244,,,,,Winter,2,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,Winter,39,55,Winter,0,0 +c9879365,Fall,9,0,Summer,51,Fall,16.79504438,52.0,64.6,,59,72,109,,,,,Fall,9,1,,,,,10,1,10.0,1,10.0,1,12.0,1,Fall,3,3.46402,16.7449,1057.09,1797.06,13.5888,54.8574,14.2637,2.48119,9.54255,1,29.0351,12.2336,51.3934,25.1294,42.6239,,,Fall,1.61,Winter,2,2,2,0,1,1,1,1,1,1,2,0,1,1,1,1,1,1,1,0,21,Winter,50,69,Fall,1,0 +c9902f48,Fall,7,0,Summer,58,Fall,20.29807766,51.0,75.1,,60,100,103,,,,,Fall,3,,,,,,0,,8.0,,8.0,,10.0,,Fall,2,3.31503,20.3003,1066.23,1705.96,14.0742,55.8303,15.0915,5.20881,19.2697,2,28.9991,12.7569,52.5152,23.5153,43.0734,,,,,Spring,5,4,4,0,5,0,0,2,0,3,3,0,3,3,4,3,3,2,0,0,44,Winter,40,56,Fall,2,1 +c9a57335,Spring,6,0,Fall,80,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2,3,0,3,0,0,2,0,0,3,0,0,0,1,1,3,2,1,0,23,Summer,30,43,Spring,0,0 +c9ad9c75,Summer,21,1,,,Summer,30.95333701,64.75,184.6,,74,66,115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,2,2,0,1,0,2,0,1,0,2,0,1,0,0,0,0,0,0,0,11,,,,Summer,3,0 +c9bc639b,Summer,6,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c9d47171,Winter,7,0,Spring,83,Winter,16.27089581,51.0,60.2,25.0,56,76,104,,,,,Spring,5,1,,,,,15,1,9.0,1,11.0,1,7.0,1,Spring,2,3.20334,16.2727,1014.72,1623.54,11.9872,50.3435,13.6084,2.66431,9.85651,1,25.9708,12.3854,47.1402,19.0173,37.9581,,,,,Spring,5,5,5,1,5,0,1,4,1,3,4,0,2,2,2,4,4,4,4,2,58,Spring,37,53,Winter,2,2 +c9dabad5,Winter,9,0,Summer,65,Spring,16.66003057,56.63,76.0,,57,76,123,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.34,Fall,2,2,2,0,3,2,0,2,2,3,0,0,1,1,2,0,2,1,1,1,27,Fall,35,50,Winter,0,0 +c9e13fe2,Summer,18,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +c9e419e7,Fall,8,0,Winter,41,Winter,20.26006944,48.0,66.4,,67,87,125,,,,,Fall,7,1,,,,,2,0,12.0,1,13.0,1,8.0,1,Fall,3,3.02928,20.2623,1016.35,1727.8,11.7434,50.5181,15.4159,4.84643,15.8819,2,28.8558,9.91896,47.4889,26.8479,40.5992,,,,,Winter,4,2,3,0,4,2,0,0,0,0,1,0,1,1,1,1,2,3,3,1,29,Fall,46,64,Fall,0,0 +ca093278,Winter,7,0,,,Winter,15.5302852,47.0,48.8,24.0,70,106,108,,,,,Winter,0,0,,,,,0,0,10.0,1,10.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +ca1bdcd3,Summer,10,1,Spring,80,Fall,14.15015015,55.5,62.0,,74,83,102,Fall,5,8,14,Fall,11,0,10.7,1,12.0,2,4,0,11.0,1,12.0,1,6.0,0,Fall,1,8.15891,14.1517,1062.35,1381.05,15.7444,55.4171,12.6491,1.50257,6.58291,1,23.2552,16.4175,47.2582,20.4202,38.9996,,,Summer,2.67,Fall,1,0,3,0,3,0,0,0,0,0,0,0,0,0,0,2,3,2,2,2,18,Fall,34,49,Summer,0,0 +ca3204b0,Winter,12,1,Winter,60,Summer,24.61597294,62.0,134.6,,67,62,108,Summer,5,9,0,Summer,20,1,14.4,2,21.2,2,5,0,4.0,0,5.0,0,12.0,1,Summer,4,4.81373,24.6187,1358.96,2582.02,29.2516,87.0099,15.9143,8.70434,47.5901,2,36.0114,21.7469,82.1962,47.0647,65.263,,,Winter,1.59,Summer,2,1,2,1,1,1,1,1,1,2,2,0,1,1,1,1,1,2,1,1,24,Summer,39,55,Winter,0,0 +ca33a5e7,Winter,8,1,Spring,70,Winter,11.46728822,51.7,43.6,,59,77,110,Winter,,,,Summer,,,,,,,,,,,,,,,Summer,2,2.77638,11.4685,987.939,1481.91,12.3865,47.4915,12.4922,-1.02363,-3.89153,1,21.545,13.56,44.7152,18.0289,33.9315,,,Winter,3.15,Winter,5,4,5,1,5,3,0,2,1,2,1,1,5,3,4,5,2,5,2,2,58,Winter,33,47,Winter,2,2 +ca724703,Spring,8,0,Summer,55,Spring,18.82292899,52.0,72.4,24.0,67,83,112,,,,,Spring,20,1,,,,,0,0,10.0,1,9.0,1,6.0,1,Spring,2,3.34779,18.825,1060.14,1696.22,13.6341,55.1815,14.3479,4.47705,17.2185,2,27.8179,13.7294,51.8337,21.3358,41.4521,,,Summer,2.88,Summer,2,3,3,0,2,0,0,4,0,0,4,0,0,2,2,0,2,0,2,0,26,Summer,47,66,Spring,2,0 +cac0f438,Winter,9,1,,,Winter,16.10872741,51.0,59.6,22.0,77,85,120,,,,,Winter,6,1,,,,,0,0,13.0,1,13.5,1,12.0,1,Summer,3,2.71085,16.1105,1005.42,1608.68,13.1252,49.3539,13.3409,2.76961,10.2461,1,22.352,13.8768,46.6431,20.6904,35.4772,,,Spring,3.03,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,35,50,Winter,2,0 +cad3bc70,Winter,12,0,Summer,75,Spring,25.87140309,58.0,123.8,,70,75,124,,,,,Fall,,,,,,,,,,,,,,,Summer,3,4.44228,25.8742,1313.52,2232.98,22.7533,82.1698,17.1735,8.70071,41.6302,2,39.0562,20.3603,77.7276,39.9041,61.8096,,,,,Spring,4,4,4,3,5,0,1,2,1,1,5,0,4,0,4,4,4,4,3,0,53,Spring,46,64,Winter,3,2 +cadd4ae5,Winter,8,0,Spring,58,Winter,16.11041667,48.0,52.8,,60,27,77,Winter,6,10,40,Winter,20,1,,,,,10,1,10.0,1,10.0,1,9.0,1,Spring,4,6.49121,16.1122,3079.99,6467.97,84.7531,270.322,82.4902,-66.378,-217.522,1,118.281,67.2885,263.831,188.734,203.034,,,,,Winter,1,3,3,1,3,1,0,1,2,1,2,0,1,1,1,1,1,1,2,1,27,Winter,34,49,Winter,2,0 +cae0531e,Winter,14,0,Fall,85,Summer,17.08526286,59.0,84.6,24.0,66,86,105,,,,,Summer,20,0,19.6,1,20.6,1,11,0,14.25,1,14.5,1,7.0,0,Summer,2,4.23152,17.0871,1174.14,1878.63,16.6527,67.3249,13.598,3.48915,17.2751,2,31.8463,18.8259,63.0934,29.9124,48.499,,,Spring,2.63,Summer,3,3,3,3,3,2,1,2,3,3,3,1,2,1,3,2,2,2,1,2,45,Spring,42,59,Winter,2,1 +cae31194,Fall,11,1,,,Fall,17.70824397,60.5,92.2,26.0,66,80,123,,,,,Fall,15,1,17.9,2,17.5,2,6,0,12.0,1,12.0,1,12.0,1,Fall,4,4.40586,17.421,1231.21,2339.3,24.1138,73.4029,13.8694,3.55168,18.7971,2,30.46,18.8291,68.997,32.3493,54.5738,,,Fall,3.269,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,29,42,Fall,0,0 +caec8d59,Winter,16,0,Summer,55,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.32,,,Spring,5,5,5,2,1,5,2,2,5,1,5,1,5,5,5,5,5,5,1,5,75,Spring,50,69,Winter,3,2 +cafe5f70,Spring,14,0,,,Spring,22.15548946,61.5,119.2,30.0,63,69,116,,,,,Spring,20,0,23.1,1,28.8,2,18,1,6.0,0,7.0,0,11.0,1,Spring,,,,,,,,,,,,,,,,,Spring,2.69,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +cb054226,Spring,9,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,4,4,1,4,3,1,3,4,4,4,1,4,4,3,5,4,5,4,4,70,Summer,49,68,Spring,1,2 +cb2577f4,Summer,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +cb2752bc,Summer,21,0,Fall,55,Summer,26.27359651,71.0,188.4,,101,95,146,,,,,,,,,,,,,,,,,,,,Winter,4,2.86341,26.2765,1845.64,3875.84,41.6564,138.848,19.3653,6.91116,49.5524,3,61.0277,36.1635,135.984,80.7771,102.684,,,,,Summer,2,1,3,1,2,2,4,3,3,3,4,2,1,1,1,1,1,1,2,1,39,,,,Summer,1,1 +cb3b7c3c,Fall,12,0,Fall,75,Winter,23.7197824,62.5,131.8,,48,96,105,,,,,Winter,10,0,13.6,1,19.5,2,1,0,13.0,1,11.5,1,12.0,1,Winter,3,11.2326,23.7224,1334.99,2269.49,21.9564,84.4574,15.2013,8.52108,47.3426,2,37.6629,24.8381,73.2248,37.7424,59.6193,,,Winter,1.66,Winter,4,4,4,0,4,1,1,1,4,2,0,0,4,0,2,3,0,3,0,0,37,Winter,35,50,Fall,2,1 +cb6aed8a,Spring,9,1,,,Summer,16.06900021,53.9,66.4,23.0,60,87,99,,,,,,,,,,,,,,,,,,,,Summer,3,2.71189,16.3103,1051.84,1682.94,11.4413,54.2975,13.3375,2.97283,12.1025,3,21.558,21.2981,51.5856,25.8051,32.9994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +cb73fed9,Fall,9,1,Spring,80,Fall,15.45701366,47.4,49.4,,54,71,119,Fall,8,13,27,Fall,10,1,,,,,10,1,12.0,1,11.0,1,7.0,1,Fall,3,8.90922,15.4587,948.876,1518.2,10.4307,43.3308,13.5595,1.89924,6.06923,1,22.0129,10.8872,34.4215,23.1713,32.4436,,,Fall,2.21,Fall,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,8,Fall,43,60,Fall,0,0 +cba1c8b5,Winter,13,0,Fall,67,Spring,19.10479747,59.0,94.6,28.0,53,91,100,,,,,Spring,8,0,15.4,1,17.3,1,2,0,0.0,0,3.75,0,5.5,0,Spring,2,4.30402,19.1069,1208.19,1933.11,18.6877,70.9514,14.3305,4.77643,23.6486,2,32.9727,19.291,66.6474,29.2114,51.6604,,,Spring,2.17,Spring,4,2,2,1,1,1,0,1,1,1,2,0,1,1,0,0,2,0,0,0,20,Spring,29,42,Winter,2,0 +cba283a1,Winter,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,32,46,,, +cba39753,Spring,17,0,Summer,80,Summer,20.49558314,68.5,136.8,,78,69,128,,,,,Summer,15,0,27.7,1,40.8,2,15,0,0.0,0,0.0,0,14.0,1,Summer,3,6.80187,20.4978,1618.32,2751.14,34.9792,114.635,17.1767,3.32114,22.1649,2,50.633,29.0228,107.833,57.9761,85.6123,Summer,2.6,,,Summer,2,2,2,1,3,0,0,0,1,0,0,0,1,1,1,1,,1,0,1,17,,,,Spring,0,0 +cba70c9d,Winter,21,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +cba70d97,Summer,15,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.161,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +cbb1ddec,Summer,10,0,Winter,41,Fall,19.64678363,57.0,90.8,,76,86,116,Fall,5,7,26,Fall,3,0,9.5,1,11.1,1,0,0,10.0,1,10.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.25,Fall,5,5,5,0,4,1,0,2,1,5,0,0,3,2,3,5,3,3,5,2,54,Fall,45,63,Summer,0,2 +cbca33a5,Spring,9,0,Summer,60,Summer,14.1688854,50.5,51.4,,121,66,148,Summer,6,9,28,Summer,8,,,,,,10,,9.0,,10.0,,11.0,,Summer,3,2.947,14.1704,973.163,1654.38,8.21332,45.9176,12.659,1.51143,5.48237,2,23.6592,14.0451,42.9706,19.2844,31.8725,,,Spring,2.76,Summer,3,0,0,0,0,0,0,0,0,0,0,0,2,0,3,3,2,2,0,0,15,Summer,48,67,Spring,0,0 +cbcf8cf2,Winter,17,1,Fall,91,Spring,23.5273899,66.5,148.0,,97,79,136,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.0,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,28,41,Winter,1,0 +cbf4eed7,Winter,10,0,Fall,61,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.69,,,,,,,,,,,,,,,,,,,,,,,Spring,38,54,Winter,, +cc010b72,Summer,9,1,,,Summer,20.87226771,53.0,83.4,29.0,,67,,,,,,Summer,8,0,,,,,0,0,8.5,0,8.5,0,4.75,0,,,,,,,,,,,,,,,,,,,,Summer,2.47,Summer,2,0,2,0,3,0,0,0,0,0,1,0,2,1,0,2,2,2,1,0,18,Summer,29,42,Summer,,0 +cc153d7b,Spring,8,0,,,Spring,20.37916186,51.0,75.4,23.0,65,73,109,,,,,Spring,10,1,,,,,8,1,11.0,1,12.0,1,12.0,1,Spring,2,3.27794,20.3814,1061.54,1698.47,13.3097,55.3312,14.9566,5.4248,20.0688,1,28.9746,13.0469,52.0533,24.8576,42.2843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +cc16d472,Winter,12,0,Fall,50,Summer,21.33524556,65.75,131.2,,48,96,101,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.1,Fall,4,5,5,1,5,2,0,5,1,4,5,0,4,1,5,2,5,2,5,3,64,Fall,51,71,Spring,0,2 +cc224be2,Summer,9,1,Fall,81,Summer,16.33467234,55.2,70.8,,57,80,96,Summer,6,10,32,Summer,18,1,,,,,13,1,10.5,1,9.5,1,10.0,1,Fall,3,2.63204,16.3365,1122.68,1796.29,19.8088,61.8432,14.2698,2.0667,8.9568,1,28.4129,13.6215,59.2112,29.2425,48.2217,,,Summer,3.01,Summer,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,Summer,55,76,Summer,0,0 +cc25e136,Winter,14,0,Summer,65,Winter,27.4609375,64.0,160.0,38.0,74,76,121,,,,,Winter,10,0,30.4,2,25.0,1,11,0,6.0,0,7.0,0,12.0,1,Spring,3,5.5096,27.4639,1509.25,2565.72,29.8906,103.018,17.6829,9.781,56.9823,3,45.9903,27.1367,97.5081,49.5007,75.8809,Winter,3.86,,,Winter,4,4,4,2,4,4,4,4,4,4,4,1,4,4,4,4,1,4,1,1,66,Winter,41,58,Winter,2,2 +cc477243,Spring,8,1,Fall,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.34,Summer,2,2,2,0,1,1,0,2,1,1,3,0,1,1,1,1,2,2,1,1,25,Summer,43,60,Spring,2,0 +cc5143df,Spring,8,0,Fall,85,Spring,18.69225652,58.63,91.4,,89,92,143,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.92103,19.3487,1160.63,1973.07,15.7863,65.8855,13.9475,5.40124,25.5145,3,30.5299,19.5692,61.9644,26.7175,46.3162,,,Summer,3.03,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,44,62,Spring,0,0 +cca40abb,Spring,8,1,Spring,85,Spring,21.11440972,48.0,69.2,,65,81,104,Spring,6,9,26,Spring,21,1,,,,,4,0,12.5,1,13.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.76,Spring,2,2,2,1,2,1,0,1,1,1,0,0,3,2,1,3,2,1,1,2,28,Spring,31,45,Spring,0,0 +ccc9db97,Summer,14,0,Winter,60,Fall,28.88624567,68.0,190.0,,62,84,127,,,,,Fall,9,0,16.8,1,17.7,1,1,0,7.0,0,8.0,1,11.0,1,Fall,3,7.9388,28.8894,1738.5,2955.45,40.6481,127.436,19.3766,9.51282,62.564,3,53.5623,33.2257,119.497,54.1866,94.2103,,,Summer,1.94,Fall,4,5,4,2,4,3,1,2,1,0,0,0,2,0,1,2,2,2,1,0,36,Fall,74,100,Summer,2,1 +ccd0fe74,Winter,16,0,Summer,80,Fall,16.98677551,70.0,118.4,,76,61,138,,,,,,,,,,,,,,,,,,,,Fall,4,6.53389,16.9886,1482.55,3113.36,28.9689,100.174,14.3735,2.61512,18.2257,1,43.9334,27.272,93.6404,47.8407,72.9023,Winter,3.73,,,Spring,5,5,5,4,5,0,4,3,3,4,4,0,2,0,4,3,4,4,2,3,64,Winter,38,54,Winter,,2 +ccd3cb96,Winter,8,0,Spring,83,Spring,14.88993056,48.0,48.8,,82,62,111,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.79967,14.8916,944.496,1605.64,8.40072,42.8642,13.0802,1.81134,5.9358,1,23.7079,10.7555,40.0645,17.9898,32.1087,,,,,Spring,2,2,2,0,2,0,0,1,1,2,2,0,2,1,2,2,2,2,2,2,29,Spring,38,54,Winter,0,0 +ccdb4ae5,Spring,11,1,Fall,64,Summer,16.58785273,61.0,87.8,,75,111,108,Summer,4,7,0,Summer,20,1,16.3,2,18.1,2,10,1,10.0,1,10.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.89,Summer,2,2,2,0,1,1,1,1,1,1,2,0,1,1,1,1,2,1,2,1,24,Summer,35,50,Spring,0,0 +cceddb57,Summer,13,1,Fall,75,Fall,17.52658402,66.0,108.6,,62,68,109,,,,,Summer,,,,,,,,,,,,,,,Summer,4,4.56781,17.5285,1257.11,2388.52,20.7771,76.1621,12.2929,5.23561,32.4379,3,29.1618,26.2232,71.5943,40.9065,49.9389,,,Summer,2.51,Summer,2,1,1,0,2,1,1,1,1,1,3,0,0,2,0,1,1,1,0,0,19,Summer,39,55,Summer,2,0 +cd144127,Spring,9,0,Spring,60,Spring,17.26987847,48.0,56.6,23.0,43,55,88,,,,,Spring,10,1,,,,,3,0,6.0,0,8.5,1,10.0,1,Spring,2,2.85948,17.2718,968.75,1550.0,9.9073,45.4476,13.8685,3.40322,11.1524,1,24.916,10.6243,42.5881,18.5206,34.8233,,,Winter,3.27,Spring,2,3,2,1,2,1,1,2,1,1,2,1,1,1,1,1,2,2,2,1,30,Spring,41,58,Winter,1,0 +cd1fff5a,Spring,12,0,Summer,70,Winter,15.02806081,60.25,77.6,,63,78,127,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.61,Spring,1,2,2,1,1,1,1,1,2,1,2,0,1,2,1,1,2,1,2,1,26,Spring,67,91,Spring,1,0 +cd36e51d,Winter,12,0,Summer,75,Spring,14.60967078,54.0,60.6,,79,89,123,,,,,Spring,23,1,11.9,1,13.1,1,0,0,8.0,1,9.5,1,9.0,1,Spring,3,7.06974,14.6113,1035.57,1760.47,10.8144,52.5648,12.6739,1.93736,8.03517,2,25.6884,16.0621,45.4951,21.2783,36.5027,,,Spring,2.69,Spring,5,5,5,5,5,0,0,5,5,5,4,1,5,2,5,5,5,5,2,0,74,Spring,43,60,Winter,3,2 +cd39e576,Summer,9,0,Winter,60,Fall,17.71051051,55.5,77.6,,59,59,98,Fall,,,,Fall,0,0,,,,,10,1,6.5,0,9.5,1,10.0,1,Fall,2,3.92205,17.7124,1136.28,1818.05,14.8575,63.2916,14.4465,3.26593,14.3084,1,33.0881,15.3461,59.3696,37.2486,47.9455,,,Summer,2.43,Fall,3,1,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,Fall,30,43,Summer,2,0 +cd40df86,Winter,9,1,,,Winter,31.65419555,55.2,137.2,40.0,70,78,117,,,,,Winter,1,0,,,,,0,0,8.0,0,8.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.59,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +cd68643b,Summer,8,0,Fall,51,Summer,18.52430154,50.5,67.2,26.0,60,78,108,,,,,Summer,5,1,,,,,2,0,10.0,1,9.0,1,12.0,1,Summer,3,3.11656,18.5263,1022.78,1738.72,11.5835,51.202,14.1159,4.41047,15.998,2,26.5887,13.0299,48.0855,20.9585,38.1722,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Summer,0,0 +cd703872,Spring,11,1,Fall,55,Fall,18.10356269,61.5,97.4,,62,76,102,,,,,Summer,12,0,17.7,2,18.8,2,2,0,8.5,0,7.0,0,16.0,1,Summer,3,4.50552,18.4791,1257.26,2011.61,25.6166,76.1776,14.3936,4.08552,21.6224,3,31.5648,18.9962,71.6721,33.7829,57.1814,,,Summer,2.731,Summer,2,0,2,1,2,0,1,0,1,2,2,0,0,2,2,1,2,1,0,2,23,Spring,65,89,Summer,2,0 +cd89d5f1,Winter,15,0,Summer,85,Spring,37.15076665,69.0,251.6,,66,91,126,,,,,Spring,5,0,23.2,2,28.1,3,5,0,11.0,1,10.0,1,9.0,1,,,,,,,,,,,,,,,,,,Winter,1.81,,,Spring,2,1,2,1,1,1,1,1,2,1,2,1,1,1,2,1,1,1,1,1,25,Spring,34,49,Winter,2,0 +cd8ec8ac,Fall,6,0,Spring,75,Fall,27.13674142,48.5,90.8,,75,103,130,Fall,,,,Fall,3,1,,,,,0,0,9.0,1,9.0,1,12.0,1,Fall,3,8.70967,27.1397,1079.37,1834.93,12.8184,57.23,17.1058,10.0339,33.57,2,31.9807,12.431,48.5204,35.8644,44.7991,,,,,Fall,3,0,3,0,2,0,0,0,0,0,0,0,1,0,0,2,2,1,0,0,14,Fall,45,63,Fall,2,0 +cd8f3c61,Fall,6,0,Winter,45,Fall,18.07157895,47.5,58.0,,57,76,92,,,,,Fall,5,1,,,,,0,0,12.0,1,12.0,1,11.0,1,Fall,4,2.90713,18.0736,978.575,2055.01,10.6897,46.4941,14.4882,3.58539,11.5059,1,26.383,9.42139,43.587,21.069,37.0727,,,,,Winter,1,2,2,0,2,1,0,0,2,0,1,0,3,1,1,1,0,2,0,0,19,Winter,41,58,Fall,0,0 +cd92eaf6,Spring,7,0,Fall,70,Fall,18.35017536,48.5,61.4,,69,67,105,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,Summer,50,69,Spring,0,0 +cda90e9e,Spring,9,0,Fall,55,Summer,17.90001102,53.25,72.2,,79,90,131,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.37,Summer,3,3,2,1,3,2,0,2,1,1,2,0,0,0,0,0,0,0,0,0,20,Summer,60,82,Spring,0,0 +cdc5b71e,Summer,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ce031b1b,Fall,9,0,Winter,65,Winter,18.721161,52.5,73.4,,65,78,115,Winter,5,8,6,Winter,6,0,,,,,9,1,5.5,0,5.5,0,9.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.95,Fall,1,2,2,1,1,1,1,1,1,1,2,1,2,1,2,2,2,2,1,1,28,Fall,37,53,Fall,0,0 +ce075ce2,Fall,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +ce379a6a,Spring,11,1,Fall,61,Fall,16.77678044,61.0,88.8,,83,88,135,,,,,Fall,13,0,16.1,2,16.7,2,10,1,9.0,0,8.0,0,12.0,1,Fall,3,4.45097,16.7786,1233.43,1973.48,24.3934,73.6392,13.914,2.86461,15.1608,1,30.6832,18.5626,69.1882,32.1724,55.0766,,,Fall,3.83,Fall,4,5,4,0,4,1,0,2,2,1,2,0,2,0,2,3,2,3,3,1,41,Fall,36,51,Spring,2,1 +ce515ebb,Winter,9,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ce53d4c9,Summer,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ce6eeadf,Summer,18,1,Spring,64,Winter,18.4331543,64.0,107.4,,66,77,113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,0,3,1,4,1,5,4,3,1,4,1,1,1,1,1,2,2,1,1,40,Winter,46,64,Summer,2,1 +ce8d388b,Spring,13,1,,,Summer,18.13885505,61.0,96.0,24.0,0,,0,,,,,,,,,,,,,,,,,,,,Summer,3,3.85148,18.139,1176.93,1883.09,17.7506,67.6214,12.777,5.36208,28.3786,3,27.2817,22.5892,63.77,37.9385,45.0323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ce95b2e1,Spring,11,0,Summer,80,Spring,33.55947359,64.5,198.6,,77,100,138,Spring,4,7,0,Spring,26,1,19.4,2,19.2,2,0,0,6.0,0,6.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,Spring,3.23,Spring,5,5,4,1,4,2,0,3,2,4,1,0,4,5,4,5,4,5,4,4,66,Spring,65,89,Spring,0,2 +cea35700,Winter,6,0,Spring,55,Spring,17.25568713,47.25,54.8,,64,51,103,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Winter,50,69,Winter,0,0 +cea73edd,Winter,6,0,Winter,72,Winter,14.79026956,48.75,50.0,,48,107,97,Winter,6,10,59,Winter,23,1,,,,,0,0,10.5,1,9.5,1,11.5,1,Winter,5,6.3965,14.7919,952.644,2286.35,8.05647,43.7321,12.9376,1.85427,6.26786,2,23.5674,12.1082,37.3356,18.5813,31.6239,,,,,Winter,1,1,2,0,1,0,0,1,1,1,1,0,1,1,1,1,1,1,1,2,18,Winter,60,82,Winter,,0 +ceaf7afd,Spring,12,0,,,Spring,34.46347656,64.0,200.8,43.0,,77,,,,,,Spring,0,0,13.7,1,14.6,1,0,0,0.0,0,0.0,0,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,35,50,Spring,2, +cec0a673,Fall,16,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.93,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +cedf96c5,Fall,9,1,Spring,50,Winter,16.97229081,54.0,70.4,,75,95,151,Winter,,,,Winter,5,0,,,,,0,0,7.5,0,8.0,0,9.0,1,Winter,2,4115.36,0.0482667,83152.2,124728.0,3233.0,8799.08,7.86485,-7.81659,-8745.08,2,2457.91,3108.17,4683.71,3607.69,5690.91,,,Fall,3.269,Winter,2,2,3,0,2,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,13,Winter,36,51,Fall,0,0 +cee465b9,Fall,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.66,,,,,,,,,,,,,,,,,,,,,,,,,,,, +cefdb7fe,Spring,12,1,Fall,80,Summer,18.81778384,62.75,105.4,,57,58,109,Summer,7,12,6,Summer,25,1,21.7,3,22.3,3,12,1,8.0,0,8.5,0,9.5,1,Summer,3,4.68383,18.8198,1278.11,2044.97,26.6306,78.3981,13.9985,4.82136,27.0019,2,31.0322,20.7353,73.7143,31.8203,57.6628,,,Summer,2.52,Summer,4,4,2,1,3,1,1,2,1,2,1,1,2,1,1,2,1,2,1,1,34,Summer,51,71,Spring,0,1 +cf09406e,Spring,10,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +cf0d6d38,Winter,7,0,Winter,45,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,5,4,0,4,1,0,0,1,1,2,0,0,0,1,0,1,1,1,1,26,Winter,41,58,Winter,,0 +cf1b9d44,Spring,14,0,Fall,61,Summer,19.78617937,69.0,134.0,,71,70,114,,,,,Summer,11,0,24.0,1,25.0,1,6,0,10.0,1,12.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.52,Summer,0,2,4,0,2,1,0,1,0,0,2,0,0,0,0,0,2,0,0,0,14,Summer,27,40,Spring,1,0 +cf1ba6ce,Fall,16,0,,,Winter,26.69657635,69.0,180.8,,66,77,121,,,,,Winter,23,0,39.7,2,35.9,2,19,1,7.0,0,5.0,0,9.0,1,,,,,,,,,,,,,,,,,,Fall,2.08,,,,,,,,,,,,,,,,,,,,,,,,,Winter,51,71,Fall,2, +cf2e4065,Spring,7,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,5,5,5,0,5,2,0,4,4,3,2,0,2,2,3,5,4,5,3,2,61,Summer,36,51,Spring,1,2 +cf492aee,Summer,12,1,,,Summer,28.71701156,61.0,152.0,38.0,81,82,107,,,,,Summer,0,0,13.6,1,17.7,2,0,0,11.0,1,12.0,1,9.0,1,Summer,1,5.2145,28.758,1509.69,1962.6,36.8761,103.065,19.474,9.28395,49.1349,3,42.1264,24.0627,97.8507,55.0902,79.0025,,,Summer,2.86,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +cf75f764,Spring,13,0,,,Spring,24.04633563,60.5,125.2,30.0,76,82,120,,,,,Spring,10,0,22.3,2,22.3,2,3,0,5.0,0,5.0,0,11.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,2.91,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +cf86987d,Summer,13,0,Winter,68,Fall,0.0,62.0,0.0,,63,93,94,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.4,Fall,5,5,5,2,5,4,1,4,4,4,4,0,4,4,4,4,4,5,0,3,71,Fall,43,60,Summer,3,2 +cfbbdd3d,Spring,13,0,Fall,80,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.77,Spring,3,4,4,1,5,3,4,4,4,2,3,0,1,0,0,1,3,3,0,1,46,Spring,37,53,Spring,0,1 +cfcf9dc9,Spring,9,0,Winter,70,Summer,19.27869898,56.0,86.0,,61,80,117,Summer,6,9,28,Summer,15,1,,,,,5,1,9.0,1,11.0,1,6.0,1,Fall,3,7.98842,19.2808,1132.86,1925.85,14.6142,62.927,14.1079,5.17286,23.073,2,30.5335,17.7793,54.9386,28.8232,45.1477,,,Spring,3.14,Summer,4,4,4,2,5,4,0,2,1,2,2,0,2,1,4,2,3,2,3,4,51,Summer,35,50,Spring,0,2 +cfe4cf40,Winter,13,1,Summer,87,Spring,18.90092879,64.6,112.2,,101,86,132,,,,,Spring,18,1,24.5,3,25.8,3,3,0,7.8,0,9.2,0,4.5,0,Spring,2,5.05183,19.2591,1336.02,2004.03,28.844,84.567,14.5159,4.7432,27.633,2,33.8474,21.8756,79.5151,37.8956,62.6914,,,Winter,1.62,Spring,2,2,2,1,2,1,2,2,2,1,2,1,2,2,2,1,2,2,3,1,35,Winter,31,45,Spring,2,1 +cff4bcee,Winter,7,0,Spring,45,Spring,17.30841411,48.2,57.2,,52,94,110,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,4,3.04803,16.0864,991.05,2081.21,10.2983,47.8229,13.4493,2.63714,9.37713,2,25.4726,12.0519,44.7748,20.1221,35.7709,,,,,Winter,1,1,1,0,1,1,0,0,0,1,1,0,1,1,1,1,1,1,1,1,15,Winter,41,58,Winter,0,0 +d00b493e,Fall,10,0,Spring,45,Winter,15.06428571,56.0,67.2,,67,90,118,Winter,5,7,17,Winter,11,0,13.6,2,13.4,2,8,1,5.0,0,5.5,0,15.0,1,Winter,2,2.34591,15.0659,1107.08,1771.32,15.2496,60.1813,13.4924,1.57355,7.01866,1,29.8804,15.0513,57.8354,25.9909,45.13,,,Winter,1.9,Winter,2,2,2,0,1,0,0,1,1,1,0,0,1,0,1,1,1,1,1,1,17,Winter,50,69,Fall,0,0 +d0212fd3,Spring,16,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.14,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +d03c0a74,Spring,12,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d0429481,Spring,15,0,Summer,45,Summer,28.24324324,66.6,178.2,,70,76,130,,,,,Summer,13,0,33.2,2,36.8,2,5,0,14.0,1,14.0,1,9.4,1,Summer,4,5.83721,29.203,1588.84,3336.56,32.1223,111.495,18.2716,10.9315,66.7049,3,49.6755,29.6973,105.658,58.1195,81.7978,Spring,3.41,,,Summer,5,5,5,2,5,5,5,5,5,5,5,2,4,5,5,5,4,5,5,5,92,Summer,64,88,Spring,2,3 +d05136b3,Winter,15,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d054902d,Spring,18,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d05f5d71,Winter,12,1,Spring,60,Spring,17.80739106,64.75,106.2,,98,94,142,,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.92161,17.8093,1294.92,1942.38,26.2132,80.1893,13.4474,4.3619,26.0107,3,31.6785,22.2976,75.2677,35.2666,57.8916,,,,,Spring,4,0,4,5,4,1,4,4,4,0,4,0,0,3,0,4,4,5,4,3,57,Spring,38,54,Winter,1,2 +d06b6ce6,Spring,6,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +d08806a2,Summer,8,0,Winter,45,Winter,23.91550069,54.0,99.2,,70,115,114,Winter,4,5,42,Winter,4,1,,,,,5,1,11.0,1,10.0,1,11.0,1,Winter,2,3.60632,23.9181,1152.54,1844.07,16.3918,65.024,15.6779,8.24019,34.176,2,31.6013,17.0309,61.4176,27.3557,47.9931,,,,,Winter,5,5,5,0,5,0,0,1,1,0,1,0,4,1,2,4,4,2,2,0,42,Winter,59,81,Summer,0,1 +d089af40,Winter,7,0,,,Winter,16.71965766,55.25,72.6,27.0,57,71,90,,,,,Winter,0,0,,,,,0,0,7.0,0,0.0,0,13.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +d0ac4f1c,Summer,16,1,Winter,40,Fall,27.33183381,65.5,166.8,,105,106,185,,,,,Spring,,,,,,,,,,,,,,,Summer,2,5.45729,27.3348,1473.77,2210.65,35.6046,99.2384,16.263,11.0718,67.5616,3,37.5234,26.1105,93.7811,44.293,73.1279,Summer,1.44,,,Summer,0,0,4,2,0,2,2,3,2,0,3,0,0,3,2,0,0,0,3,2,28,Summer,58,80,Summer,3,0 +d0b274b3,Winter,16,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.11,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +d0c200f5,Spring,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d0ef0744,Winter,11,0,Spring,71,Spring,19.86103018,62.0,108.6,,78,66,112,Spring,5,5,23,Spring,23,1,27.1,3,23.8,3,23,1,8.5,1,8.5,1,10.0,1,Spring,5,3.05178,19.8632,1333.44,3200.26,23.8058,84.2922,15.4172,4.44596,24.3078,2,39.0042,21.4821,81.2404,39.3788,62.8101,,,Spring,3.88,Winter,2,2,1,0,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,11,Winter,50,69,Spring,0,0 +d0f2ef2d,Winter,16,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0.99,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +d0fa3db1,Spring,6,0,,,Spring,16.71935383,48.7,56.4,23.75,69,89,118,,,,,,,,,,,,,,,,,,,,Spring,2,2.68446,16.7195,967.134,1547.42,6.28199,45.2755,13.4217,3.2978,11.1245,3,23.7621,15.2314,42.5911,24.4943,30.0441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d0fbc48e,Summer,8,1,Winter,61,Fall,17.46013072,51.0,64.6,,88,108,111,Fall,7,11,15,Fall,7,1,,,,,15,1,7.0,0,6.0,0,9.0,1,Fall,3,8.67275,17.462,1015.58,1624.92,14.1745,50.4354,13.6332,3.82885,14.1646,2,22.9725,13.2884,41.7626,21.0134,37.147,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,44,62,Summer,2, +d11428bd,Fall,12,1,Spring,70,Winter,19.56752663,65.0,117.6,,63,87,126,,,,,Winter,18,1,21.6,2,18.7,2,2,0,11.0,1,8.0,0,11.0,1,Winter,3,4.61018,19.5697,1273.55,2037.67,22.3742,77.9125,12.9653,6.60435,39.6875,2,30.9481,24.5902,73.3023,44.483,53.3223,,,Fall,4.08,Winter,4,3,2,1,2,2,0,1,1,1,2,0,1,1,2,1,1,2,1,1,29,Winter,31,45,Fall,2,0 +d1145910,Summer,15,0,Fall,59,Fall,31.56919379,61.6,170.4,,75,96,119,,,,,Fall,10,0,18.8,1,22.2,1,3,0,6.0,0,6.0,0,9.0,1,Fall,1,5.2987,31.5726,1540.84,2003.1,31.3223,106.383,19.7113,11.8614,64.0169,3,48.3014,26.7595,101.084,53.8494,79.6237,Summer,1.26,,,Fall,2,3,4,1,2,2,0,3,1,0,3,0,0,2,4,1,1,2,5,0,36,Fall,42,59,Summer,2,1 +d1181930,Summer,11,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d12455d7,Winter,10,0,,,Winter,18.20024837,58.5,88.6,27.0,92,96,159,,,,,Winter,20,1,,,,,4,0,11.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.11,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +d1388181,Spring,10,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,,,Spring,, +d13baef0,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +d14d79b5,Winter,6,1,Summer,65,Spring,19.79233408,50.5,71.8,,70,81,121,Spring,2,1,45,Spring,1,,,,,,0,,10.0,,11.0,,9.0,,Summer,3,2.62871,19.7945,1018.48,1629.56,12.2648,50.7442,13.9896,5.80485,21.0558,2,23.6926,14.7868,48.1155,30.8358,35.9574,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,34,49,Winter,2,0 +d19e1025,Winter,9,0,Spring,80,Winter,23.5297668,54.0,97.6,,85,102,137,Winter,5,7,30,Winter,9,1,,,,,11,1,7.0,0,9.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.16,Winter,4,3,3,4,2,1,1,1,1,1,1,1,2,2,2,1,1,1,1,1,34,Winter,33,47,Winter,0,1 +d1a38988,Spring,7,0,Spring,72,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,50,69,Spring,0,0 +d1aa6625,Fall,7,1,Spring,70,Fall,14.85846914,45.0,42.8,,71,76,104,Fall,7,11,51,Fall,4,1,,,,,5,1,9.0,1,10.5,1,7.0,1,Winter,5,12.0067,14.5353,921.779,2027.91,8.53609,40.4446,13.7354,0.799913,2.35539,2,19.7547,12.1538,28.438,18.5958,28.2908,,,,,Fall,2,2,2,1,2,0,0,1,1,1,2,1,2,0,1,1,1,2,1,1,24,Fall,60,82,Fall,0,0 +d1aafc4e,Winter,12,0,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.85,Spring,4,3,3,2,4,2,0,2,3,1,0,0,2,2,2,2,2,2,2,1,39,Spring,48,67,Winter,2,1 +d1ab95e5,Spring,9,1,Spring,60,Fall,22.26394193,55.4,97.2,,91,66,145,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,4,3.28625,22.2664,1120.65,2129.24,17.6528,61.6272,14.1174,8.14894,35.5728,2,26.4101,17.5643,58.341,31.8145,44.0629,,,Spring,2.22,Spring,2,2,2,1,2,1,0,0,1,0,2,0,1,1,1,1,2,1,0,0,20,Spring,38,54,Spring,1,0 +d22e7168,Summer,11,0,Winter,65,Fall,22.41788889,60.0,114.8,,71,79,131,Fall,5,7,56,Fall,14,1,21.5,2,22.1,2,2,0,7.0,0,7.0,0,11.0,1,Fall,4,5.6692,22.4203,1317.56,2766.87,22.4894,82.6004,16.1318,6.28855,32.1996,2,39.3012,20.8098,76.9312,42.4233,61.7907,,,Summer,2.87,Fall,2,0,3,0,2,0,0,2,2,1,0,0,2,2,2,1,2,1,2,1,25,Fall,30,43,Summer,0,0 +d265ca63,Winter,7,0,,,Winter,15.47591629,46.5,47.6,23.0,63,94,115,,,,,Winter,0,0,,,,,1,0,10.0,1,9.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +d26d62f3,Summer,14,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d2776eb5,Winter,13,0,,,Spring,16.7730176,62.5,93.2,,54,71,123,,,,,Spring,15,0,20.3,2,22.5,2,10,0,6.25,0,6.0,0,4.5,0,Spring,3,5.12517,16.7748,1298.26,2207.03,22.7786,80.5443,14.497,2.27787,12.6557,1,37.2864,20.4793,75.4191,36.5066,60.065,,,Winter,3.11,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +d2883877,Spring,11,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.75,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +d28a1c47,Spring,8,0,,,,,,,,,,,,,,,Spring,10,1,,,,,5,0,14.0,1,14.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d28f0d44,Summer,10,0,Fall,41,Fall,17.02768166,51.0,63.0,,64,73,112,Fall,6,10,15,Fall,20,1,,,,,11,1,8.0,1,8.0,1,4.0,0,,,,,,,,,,,,,,,,,,,,Fall,2.13,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,41,58,Fall,2,0 +d2a74c23,Summer,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +d2b84f45,Fall,13,1,Spring,80,Winter,19.1034428,62.75,107.0,,85,69,117,,,,,Winter,35,1,24.5,3,24.6,3,20,1,14.5,1,15.0,1,11.0,1,Winter,3,0.702322,19.1055,1306.54,2090.46,27.1829,81.4265,14.5392,4.56632,25.5735,2,33.5373,20.7062,80.7241,38.9846,60.7203,,,Fall,2.26,Winter,2,2,2,2,2,2,1,1,1,1,3,0,2,2,1,1,2,2,1,1,31,Winter,39,55,Fall,2,1 +d2bcca68,Fall,9,1,Spring,65,Spring,21.6997449,56.0,96.8,,59,78,117,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.12,Winter,3,2,0,2,3,0,0,0,2,1,1,0,2,0,1,1,1,1,0,1,21,Winter,35,50,Fall,0,0 +d2cf7047,Spring,10,0,,,Summer,20.48922194,56.0,91.4,27.0,39,66,60,,,,,Summer,0,0,19.5,,20.1,,0,0,7.5,0,7.0,0,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Spring,3.14,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +d2d9b474,Winter,8,0,Spring,71,Winter,17.52271074,55.0,75.4,,62,76,102,Winter,6,9,23,Winter,10,1,,,,,0,0,8.0,1,8.0,1,9.0,1,Winter,2,3.65099,17.5246,1096.61,1754.58,13.8681,59.0665,13.7284,3.79626,16.3335,1,29.0278,16.1706,55.4155,25.3802,42.8959,,,Winter,3.01,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,47,66,Winter,2,0 +d2f956bf,Spring,6,1,,,Spring,17.07452632,47.5,54.8,25.0,50,92,122,,,,,Spring,0,0,,,,,0,0,6.0,0,6.0,0,8.0,1,Fall,3,2.32918,17.0764,958.475,1533.56,11.53,44.3532,13.821,3.25537,10.4468,2,20.629,12.1941,42.024,16.7307,32.159,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +d2fcde50,Spring,9,1,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4.24,Spring,1,1,1,0,1,1,0,1,1,1,2,0,1,0,1,1,1,1,1,1,17,Spring,35,50,Spring,0,0 +d2fcf3ab,Winter,13,0,,,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d32a368f,Summer,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d34a2fec,Winter,8,1,,,Winter,23.11464,50.0,82.2,29.0,70,73,120,,,,,Winter,0,0,,,,,0,0,10.5,1,10.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +d34d30d7,Spring,6,1,,,Spring,21.45962518,48.23,71.0,27.0,107,98,140,,,,,,,,,,,,,,,,,,,,Spring,4,2.01397,21.4598,1059.76,2013.54,8.14697,55.1413,16.6665,4.79333,15.8587,3,19.3334,27.6609,53.1273,31.7512,27.4804,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d3a393cc,Spring,14,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +d3b860ab,Spring,5,0,Summer,50,Spring,15.13200327,43.75,41.2,21.0,77,107,124,,,,,Spring,2,1,,,,,0,0,11.0,1,14.0,1,8.0,1,Summer,2,2.31998,15.1337,877.407,1403.85,5.48788,35.7183,13.1201,2.01354,5.48167,1,20.7747,9.45577,33.3984,13.8424,26.2626,,,,,Summer,4,4,4,0,3,0,0,3,5,2,2,0,3,1,2,3,2,4,1,1,44,,,,Spring,0,1 +d3bf6d28,Winter,11,1,Summer,60,Spring,17.010761,58.0,81.4,,62,95,122,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.93933,17.0126,1167.59,1868.14,21.6477,66.6263,13.9249,3.0877,14.7737,2,28.698,16.2805,62.687,29.0223,50.3458,,,Spring,3.03,Summer,3,3,3,1,4,2,1,3,4,2,2,1,1,2,1,2,3,1,2,1,42,Summer,44,62,Winter,2,1 +d3da09e8,Summer,16,0,Winter,40,Summer,41.55470069,73.0,315.0,,50,74,133,,,,,Summer,16,0,43.2,2,53.2,3,3,0,0.0,0,6.0,0,13.0,1,Summer,2,8.41349,41.5856,2288.08,3660.93,56.0521,185.974,24.5363,17.0493,129.226,3,79.4738,50.4479,177.56,116.019,135.526,Summer,1.96,,,Summer,2,2,4,1,1,2,1,1,1,1,5,0,1,0,1,1,2,1,5,2,34,,,,Summer,3,1 +d3da5c2a,Fall,9,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d3e45961,Summer,6,0,Winter,60,Summer,16.2677686,44.0,44.8,,68,91,84,,,,,Summer,0,0,,,,,10,1,10.5,1,11.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,4,0,1,0,2,0,0,1,1,2,2,0,2,0,2,1,1,1,1,1,22,Fall,56,77,Summer,2,0 +d3eda827,Fall,5,1,,,Fall,16.45540741,45.0,47.4,22.0,54,80,100,,,,,Fall,2,1,,,,,0,0,11.0,1,12.0,1,11.0,1,Fall,2,2.19005,16.4572,919.715,1379.57,10.8884,40.2247,13.9659,2.49126,7.17532,1,20.9294,8.40691,38.0346,16.7233,31.8178,,,,,Fall,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,,,,Fall,0,0 +d3f213d7,Fall,15,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,57,79,,, +d42454d4,Spring,6,0,Winter,55,Spring,14.37176852,43.0,37.8,,54,77,97,Spring,2,2,38,Spring,0,0,,,,,2,0,6.0,0,6.5,0,4.0,0,Summer,1,2.09495,14.3733,878.539,1142.1,2.60779,35.8389,13.6276,0.7457,1.96109,1,19.1163,14.1148,33.744,15.9138,21.7241,,,,,Spring,0,3,4,0,2,2,0,0,0,0,0,0,2,0,0,2,0,2,0,0,17,Spring,41,58,Spring,2,0 +d425fd49,Fall,9,1,,,Fall,16.16226572,49.0,55.2,24.0,70,84,113,,,,,Fall,0,0,,,,,0,0,6.0,0,5.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.18,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +d4337a28,Spring,9,0,Summer,80,Summer,18.02334184,56.0,80.4,,67,82,118,Summer,6,10,11,Summer,4,0,,,,,0,0,9.0,1,8.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.54,Summer,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,Summer,30,43,Spring,0,0 +d445fb74,Fall,11,0,Spring,58,Fall,19.85285375,58.0,95.0,,75,85,111,,,,,Fall,13,1,17.4,2,17.7,2,7,1,4.5,0,6.5,0,12.0,1,Winter,3,4.12249,19.855,1190.93,2024.58,17.4397,69.1127,14.4446,5.41046,25.8873,2,32.6931,18.9799,64.9902,30.3574,50.1328,,,Winter,3.01,Winter,5,5,5,2,4,3,0,4,1,3,0,0,4,2,4,5,4,5,3,2,61,Winter,42,59,Fall,2,2 +d45bae93,Summer,14,0,,,Summer,18.5271809,66.0,114.8,29.0,62,64,119,,,,,Summer,24,1,37.3,2,37.4,2,6,0,10.5,1,11.2,1,10.0,1,Fall,3,6.12688,18.5292,1481.9,2519.23,30.571,100.105,16.1573,2.37186,14.6952,2,44.6155,24.9184,93.978,46.2716,75.1864,Summer,1.81,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,61,84,Summer,2,0 +d4648099,Spring,8,0,,,Spring,17.40607459,51.0,64.4,23.0,65,93,110,,,,,Spring,0,0,,,,,1,0,9.5,1,10.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,1.88,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +d46a17f9,Spring,19,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d476f528,Winter,11,0,Fall,68,Spring,23.50967972,59.25,117.4,,88,89,150,Spring,,,,Spring,20,1,20.4,2,18.2,2,5,0,7.0,0,7.0,0,15.0,1,,,,,,,,,,,,,,,,,,,,Spring,3.01,Spring,5,5,5,2,5,2,1,5,5,5,3,0,3,3,5,4,3,5,2,5,73,Spring,31,45,Winter,2,2 +d4810d4a,Winter,11,1,,,Spring,25.0262987,67.25,161.0,39.0,70,77,129,,,,,Winter,,,17.7,2,19.7,2,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,Spring,1.44,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +d48d074b,Spring,10,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d4a81b97,Spring,8,1,Fall,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.19,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,51,71,Spring,3,0 +d4ab735e,Fall,7,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +d4ae6a19,Summer,7,1,Fall,70,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +d4b935f8,Fall,12,0,Spring,70,Spring,19.62987647,59.0,97.2,,75,66,140,,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.6181,19.632,1267.09,2027.34,21.8359,77.2243,15.5974,4.03459,19.9757,2,36.7335,18.6549,72.6062,35.2435,58.5694,,,Spring,2.2,Winter,1,1,4,1,2,1,1,2,1,1,3,1,1,1,1,1,2,1,2,1,29,Winter,29,42,Fall,3,0 +d4bac563,Summer,16,1,,,Summer,24.0513451,61.5,129.4,29.0,67,77,106,,,,,Summer,16,0,12.8,1,18.6,2,20,1,8.5,0,7.5,0,12.0,1,Summer,3,6.3729,24.054,1849.48,2959.17,47.363,139.257,25.8862,-1.83225,-9.85671,2,58.4241,33.4697,132.884,89.4884,105.787,Summer,2.83,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1, +d4bfc83f,Winter,9,1,Spring,60,Spring,,,,,,,,Spring,,,,Spring,7,0,,,,,14,1,7.5,0,9.5,1,12.0,1,Spring,4,3.81303,15.6936,1139.94,2165.89,20.0522,63.6816,13.5419,2.15169,10.1184,2,27.8889,15.7406,59.8686,28.3517,47.941,,,Winter,2.3,Spring,1,1,2,1,4,1,1,2,1,1,4,1,1,1,2,3,2,2,2,2,35,Spring,30,43,Winter,0,1 +d4c2e9a1,Fall,10,1,Spring,75,Fall,15.07232,50.0,53.6,,78,79,124,Fall,6,10,17,Fall,20,1,,,,,2,0,8.5,1,7.0,0,11.0,1,Fall,2,9.22334,15.074,984.61,1476.91,12.2351,47.1369,13.2563,1.81762,6.4631,1,21.9694,12.9324,37.9136,20.1617,34.2045,,,Fall,3.201,Fall,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Fall,32,46,Fall,0,0 +d4d2f272,Fall,12,1,Winter,59,Winter,20.38890811,62.75,114.2,,62,80,105,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.42,Fall,4,4,4,2,4,2,1,4,1,2,4,0,4,2,0,0,3,2,0,0,43,Fall,30,43,Fall,2,1 +d4d5ff7e,Fall,11,0,,,Fall,17.41913464,61.0,92.2,,70,78,114,,,,,Fall,16,1,20.0,2,19.0,2,19,1,9.0,1,9.0,1,12.0,1,Fall,3,4.66107,17.421,1237.44,2103.65,20.1678,74.067,13.9948,3.42621,18.133,2,33.9265,19.9726,69.4059,30.2902,54.0944,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +d4d9f7f9,Winter,8,1,Spring,58,Winter,29.3783284,52.0,113.0,,86,99,114,Winter,2,1,38,Winter,2,0,,,,,0,0,13.0,1,12.0,1,10.0,1,Winter,2,3.31608,29.3815,1166.33,1749.5,23.181,66.493,17.2891,12.0925,46.507,3,29.7917,13.5202,63.1769,31.4096,52.9728,,,,,Winter,2,0,2,0,1,0,0,0,0,0,3,0,3,0,0,2,0,2,0,0,15,Winter,39,55,Winter,0,0 +d4f3e585,Summer,18,1,Winter,77,Fall,17.97735853,64.2,105.4,,77,64,134,,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.81548,17.9793,1231.78,1970.85,16.0345,73.4642,12.5317,5.44767,31.9358,2,25.1833,32.2464,69.6487,40.8778,41.2178,,,,,Summer,5,4,4,3,4,3,0,2,4,4,4,0,3,4,4,3,4,5,0,3,63,Summer,39,55,Summer,3,2 +d506f4c7,Summer,12,0,Spring,68,Winter,29.00157093,60.5,151.0,,56,71,144,,,,,Spring,,,,,,,,,,,,,,,Summer,1,4.75891,29.0047,1403.74,1824.87,26.1043,91.7803,17.6296,11.3752,59.2197,2,41.381,24.295,87.0214,40.2976,67.4853,,,Winter,1.0,Fall,3,1,3,1,2,1,1,1,1,2,4,0,1,1,2,1,2,2,0,2,31,Fall,31,45,Summer,3,1 +d50a8c3c,Spring,22,1,,,Summer,40.8987929,65.0,245.8,,95,120,144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,4,4,2,1,1,2,3,2,0,4,1,1,0,1,0,3,1,1,0,31,Summer,49,68,Spring,,1 +d53d33bc,Spring,14,0,,,Spring,18.43190916,62.25,101.6,27.0,76,98,119,,,,,Spring,8,0,16.7,1,15.7,1,0,0,0.0,0,0.0,0,7.0,0,Spring,2,4.82917,18.4339,1273.71,2037.94,21.5102,77.93,14.1393,4.2946,23.67,2,34.8316,21.5881,73.1008,30.7162,56.3418,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d53ead5c,Spring,9,1,Summer,70,Spring,27.60872727,55.0,118.8,,63,93,129,Spring,6,9,22,Spring,1,0,,,,,2,0,7.0,0,9.0,1,11.0,1,Summer,2,3.09166,27.6117,1142.93,1714.4,17.7365,64.0006,14.8751,12.7366,54.7994,2,25.7912,20.4729,60.9089,33.6732,43.5277,,,Spring,3.25,Spring,4,4,3,1,2,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,30,Spring,36,51,Spring,2,0 +d547d0da,Winter,8,0,Spring,65,Winter,17.99097633,52.0,69.2,24.0,61,82,106,,,,,Winter,10,1,,,,,3,0,6.0,0,5.0,0,12.0,1,Spring,1,3.19431,17.9929,1037.41,1348.63,11.3569,52.7608,13.7185,4.27441,16.4392,1,26.3222,15.0817,49.5665,21.1244,37.6791,,,,,Winter,5,4,4,0,3,0,0,0,0,0,4,0,2,0,2,3,3,4,2,2,38,Winter,47,66,Winter,2,1 +d54e293a,Spring,12,0,Winter,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d565ccf6,Spring,8,0,Summer,62,Spring,16.69813284,49.5,58.2,23.0,64,61,119,,,,,Spring,7,1,,,,,12,1,9.5,1,9.5,1,12.0,1,Spring,4,3.04272,16.7,992.747,2084.77,10.6987,48.0036,13.7742,2.92577,10.1964,1,25.9848,11.3201,44.9609,20.6443,36.6835,,,Spring,3.4,Summer,4,3,3,0,3,2,0,2,1,1,2,0,2,0,1,1,1,2,1,1,30,Summer,49,68,Spring,2,0 +d57380cf,Winter,14,0,Spring,80,Spring,21.01094291,68.0,138.2,,95,80,143,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,6.28912,21.0132,1526.44,2594.94,31.1734,104.849,15.9422,5.07105,33.3513,3,45.8441,27.8312,98.5596,48.5441,77.0175,,,Spring,3.37,Winter,5,5,4,1,5,2,4,0,1,3,4,1,0,4,3,5,3,4,1,0,55,Winter,29,42,Winter,3,2 +d57802c5,Summer,8,1,Summer,73,Summer,15.17875797,53.5,61.8,23.0,55,69,106,,,,,Summer,0,0,,,,,0,0,6.0,0,6.0,0,4.0,0,Fall,1,2.94901,15.1804,1036.56,1347.53,13.8356,52.6706,12.9379,2.24253,9.12941,1,22.8149,16.0201,49.7216,22.0158,36.6505,,,Summer,2.78,Summer,1,2,1,0,1,0,0,0,0,0,0,0,1,0,2,1,0,1,0,0,10,Summer,39,55,Summer,2,0 +d57f6c9e,Winter,16,0,Spring,65,Winter,21.30894664,71.0,152.8,,96,112,175,,,,,Winter,4,0,15.3,1,16.2,1,0,0,7.0,0,7.0,0,7.0,0,Spring,2,1.097,21.3113,1611.79,2578.86,34.6626,113.94,15.8914,5.41989,38.8602,2,48.2526,31.0247,112.843,50.1156,82.9151,Winter,1.38,,,Winter,4,5,4,1,5,4,2,4,1,3,4,1,3,1,4,4,4,3,5,1,63,Winter,35,50,Winter,2,2 +d5874bad,Summer,16,0,,,Summer,20.17072845,67.0,128.8,29.0,59,59,109,,,,,Summer,50,1,32.0,2,43.3,2,30,1,12.0,1,11.5,1,8.0,0,Summer,,,,,,,,,,,,,,,,,Summer,3.17,,,Summer,2,3,4,1,2,2,2,2,5,1,2,1,3,2,2,1,3,3,2,1,44,Summer,39,55,Summer,2,1 +d5893ebe,Summer,14,1,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,52,72,Summer,0, +d5a8d9b0,Winter,17,0,Summer,80,Spring,23.78722449,70.0,165.8,,101,72,151,,,,,,,,,,,,,,,,,,,,Summer,3,6.71315,23.7898,1632.14,2774.64,33.8437,116.108,16.6597,7.1301,49.6923,2,50.7323,31.5318,109.395,59.8931,84.576,Spring,2.19,,,Spring,4,4,4,1,4,5,1,2,5,4,5,1,4,3,2,1,3,3,1,1,58,Spring,40,56,Winter,3,2 +d5b089a4,Winter,9,0,Summer,62,Spring,26.82071797,53.5,109.2,,70,77,104,Spring,4,6,58,Spring,0,0,,,,,0,0,10.0,1,9.5,1,9.0,1,Summer,3,2.90282,26.9242,1163.2,1977.45,9.77837,66.1596,16.3122,10.612,43.0404,3,27.8598,28.5215,63.2568,33.8666,37.6382,,,Winter,3.55,Spring,2,3,3,0,2,0,0,1,1,1,2,0,4,2,3,2,2,2,2,1,33,Spring,51,71,Winter,1,1 +d5c781a0,Fall,9,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d5cebbfc,Spring,6,0,Winter,65,Summer,13.62214533,51.0,50.4,,76,86,117,Summer,,,,Summer,10,1,,,,,2,0,6.0,0,5.5,0,6.5,1,Summer,3,8.50609,13.6236,975.336,1658.07,7.43282,46.1491,12.4746,1.14907,4.25092,1,23.5423,15.174,37.643,21.0355,30.9751,,,,,Summer,4,4,3,2,5,0,0,1,1,2,2,0,5,0,2,3,1,3,1,1,40,Summer,54,75,Spring,1,1 +d5e58adf,Spring,6,1,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +d5fb07d2,Winter,9,0,Summer,53,Winter,17.26116071,56.0,77.0,28.0,73,69,109,,,,,Winter,2,0,,,,,3,0,9.0,1,7.0,0,8.0,1,Winter,2,3.84775,17.263,1120.86,1793.37,15.3209,61.6491,13.8214,3.4416,15.3509,2,29.9841,16.3442,57.8014,25.8625,45.3049,,,Winter,3.11,Spring,5,5,5,3,4,0,0,0,0,0,3,0,2,0,0,0,3,0,5,0,35,Spring,44,62,Winter,3,1 +d5fde276,Fall,8,0,Winter,75,Winter,15.71865569,54.0,65.2,,68,83,109,Winter,6,9,22,Winter,9,1,,,,,6,1,8.5,1,9.0,1,10.0,1,Winter,1,3.86197,15.7204,1067.79,1388.12,13.3865,55.9965,13.5013,2.21906,9.20351,2,28.4401,14.1699,52.1345,24.4103,41.8266,,,Winter,2.37,Winter,4,4,4,0,4,1,0,2,1,0,0,0,2,0,2,3,3,3,2,2,37,Winter,48,67,Fall,0,1 +d6007386,Fall,10,0,,,Spring,16.69461967,57.75,79.2,,63,70,109,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.4,Spring,4,3,3,1,4,1,1,1,1,2,3,0,2,1,3,1,2,1,1,2,37,Spring,36,51,Fall,0,1 +d61359ee,Fall,5,1,Spring,75,Spring,18.74440285,45.5,55.2,,81,84,107,Spring,,,,Spring,0,0,,,,,0,0,8.5,1,8.0,0,15.0,1,Spring,2,4.54258,18.7465,959.502,1439.25,13.7505,44.4625,15.0999,3.64654,10.7375,1,24.1916,6.52038,39.92,21.9671,37.9422,,,,,Spring,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,Spring,33,47,Fall,0,0 +d6251195,Fall,7,0,Winter,85,Winter,14.52261558,49.0,49.6,,63,87,114,Winter,5,7,55,Winter,20,1,,,,,4,1,8.0,1,8.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,29,42,Fall,0,0 +d661ddf7,Summer,11,1,Winter,80,Summer,14.46123662,58.0,69.2,,70,83,112,Summer,,,,Summer,17,1,10.4,1,11.9,2,3,0,9.0,0,7.5,0,13.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.27,Summer,4,3,4,0,5,3,2,4,2,2,4,0,1,2,2,2,3,1,3,2,49,Summer,51,71,Summer,0,1 +d66aed49,Winter,8,0,Fall,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,Winter,44,62,Winter,0,0 +d6723b39,Summer,9,0,Winter,70,Fall,15.37524675,53.5,62.6,,59,70,109,Fall,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.64,Fall,1,1,1,0,2,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,18,Fall,32,46,Summer,0,0 +d6776176,Fall,8,1,Spring,78,Winter,14.73488,50.0,52.4,,128,104,157,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,2,2.56221,14.7365,982.281,1473.42,11.7133,46.8888,13.1866,1.54992,5.51121,1,21.4452,13.7303,44.3266,19.575,33.1585,,,,,Winter,2,3,2,0,4,3,0,1,1,1,1,0,0,0,0,1,3,2,0,0,24,Winter,29,42,Fall,0,0 +d6add300,Spring,15,0,Winter,65,Spring,29.79473684,76.0,244.8,,66,77,129,,,,,Spring,13,0,38.1,2,35.4,2,12,0,8.5,1,8.5,1,11.0,1,Spring,2,8.94191,29.798,2122.92,3396.67,54.1191,168.382,20.4961,9.30193,76.4183,3,68.7223,45.5403,159.44,80.3612,122.841,Spring,1.92,,,Spring,2,2,4,1,3,1,1,2,5,1,4,1,3,1,2,1,1,1,3,1,40,Spring,35,50,Spring,2,1 +d6be0a92,Summer,7,0,Summer,41,Winter,15.01197917,48.0,49.2,,55,84,93,Winter,5,8,29,Winter,4,1,,,,,3,0,10.0,1,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,5,5,5,0,5,0,0,0,0,0,2,0,5,3,4,5,5,5,3,3,55,Winter,66,90,Summer,0,2 +d6c4d9eb,Fall,6,1,Winter,70,Fall,10.28168847,58.0,49.2,,72,72,116,Fall,5,7,0,Fall,6,1,,,,,0,0,12.0,1,12.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,67,91,Fall,0,0 +d6c6baa5,Spring,17,1,Spring,80,Fall,23.7197824,62.5,131.8,,62,97,137,,,,,,,,,,,,,,,,,,,,Summer,1,3.5194,23.9133,1258.61,1636.19,16.9494,76.3212,13.8474,10.0659,55.4788,2,25.4968,33.8749,72.8018,46.0271,42.4463,Summer,1.7,,,Summer,0,3,3,0,2,2,1,0,0,0,3,0,0,0,2,0,0,0,0,0,16,Summer,35,50,Spring,2,0 +d6cca65e,Winter,11,0,Spring,70,Winter,19.53633634,55.5,85.6,,77,78,127,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,3.94508,19.5385,1159.74,2435.45,17.2695,65.7905,15.0169,4.52157,19.8095,2,32.5802,15.9409,61.8455,29.8175,49.8496,,,Winter,0.96,Winter,5,5,5,4,5,4,1,4,2,4,4,1,4,2,5,5,4,5,4,4,77,Winter,31,45,Winter,3,2 +d6cd23d8,Spring,7,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d6e613bc,Summer,8,0,,,Summer,15.86606544,46.5,48.8,22.0,79,72,124,,,,,Summer,0,0,,,,,3,0,0.0,0,0.0,0,8.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,2,4,4,1,3,4,0,0,0,2,1,0,2,2,1,0,1,0,1,1,29,Summer,62,85,Summer,2,0 +d6eac3f4,Summer,16,0,,,Summer,23.13499228,72.0,170.6,35.0,78,72,133,,,,,Summer,10,0,44.0,3,54.2,3,20,1,7.5,0,7.0,0,12.0,1,Summer,3,7.68018,23.1375,1792.25,3046.83,41.3762,133.161,18.0599,5.07762,37.4388,3,56.9492,34.8358,125.481,66.8236,98.3255,Fall,1.75,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +d7087285,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d74e4d7c,Winter,13,1,,,Summer,24.09488926,68.75,162.0,,70,62,118,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.27,Spring,2.32,Spring,5,4,4,1,5,1,0,2,1,2,3,0,3,1,2,1,3,2,3,2,45,Winter,52,72,Winter,3,1 +d766a1c8,Fall,12,0,Spring,25,Winter,24.44023438,64.0,142.4,,71,78,123,,,,,Winter,0,0,24.8,,23.2,,4,0,8.0,1,7.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,Fall,1.66,Winter,0,0,3,0,4,0,0,0,0,0,3,0,1,0,0,0,2,1,0,0,14,Winter,39,55,Fall,2,0 +d77ac332,Spring,17,1,Fall,65,Fall,37.49124527,67.0,239.4,,72,103,130,,,,,Summer,20,1,36.9,3,33.7,3,0,0,13.0,1,12.0,1,11.0,1,Fall,3,3.49249,37.4953,1782.24,2851.58,46.5191,132.095,20.6889,16.8064,107.305,3,51.2944,34.2811,128.602,83.3635,97.8135,Spring,1.25,,,Summer,2,4,4,2,3,3,3,3,4,1,1,0,2,3,3,3,3,0,3,3,50,,,,Spring,3,2 +d788a6a8,Summer,8,0,Winter,66,Fall,16.91069846,51.5,63.8,,60,68,110,Fall,3,3,44,Fall,9,1,,,,,5,1,10.0,1,12.0,1,11.0,1,Winter,1,2.92448,16.9125,1014.68,1319.09,8.01743,50.3401,13.3445,3.56803,13.4599,2,24.2382,18.0845,47.4157,22.2248,32.2557,,,,,Fall,3,1,4,1,3,1,0,1,1,3,4,1,3,1,1,2,1,1,1,1,34,Fall,30,43,Summer,1,1 +d78c55fb,Fall,10,0,,,Fall,16.6910503,52.0,64.2,25.0,67,82,105,,,,,Fall,5,0,34.0,2,28.2,2,8,1,12.5,1,13.0,1,4.0,0,Fall,3,3.26251,16.6929,1030.64,1752.09,11.368,52.0401,13.5311,3.16174,12.1599,1,26.8298,13.8423,48.7776,22.7248,38.1978,,,Fall,2.23,Fall,3,2,4,0,4,1,0,4,2,2,4,0,3,2,3,2,4,3,4,4,51,Fall,62,85,Fall,1,2 +d78f437c,Spring,9,1,Fall,75,Summer,14.64921184,51.0,54.2,20.0,68,91,109,,,,,Summer,11,1,,,,,0,0,9.0,1,9.5,1,8.0,1,Summer,2,2.79511,14.6508,1000.95,1501.43,13.4508,48.8778,13.2122,1.43864,5.32218,1,23.1134,12.3136,46.0827,21.5273,36.5642,,,Summer,4.09,Summer,2,1,2,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,21,Spring,36,51,Summer,0,0 +d790f9ec,Spring,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d791703f,Winter,12,0,Winter,55,Winter,16.8429187,59.0,83.4,,74,101,120,Winter,4,8,16,Winter,10,0,23.5,3,22.0,2,10,1,8.0,1,9.0,1,9.0,1,Winter,3,4.39841,16.8448,1194.51,2030.67,18.5682,69.4943,14.0361,2.80862,13.9057,1,32.9946,17.9315,65.0959,29.9483,51.5628,,,Spring,4.26,Spring,0,5,5,0,5,1,0,1,1,1,5,0,0,2,1,1,1,1,1,1,32,Winter,38,54,Winter,2,1 +d7a14454,Spring,17,1,,,Spring,19.46652244,64.5,115.2,28.0,57,60,109,,,,,Spring,0,0,19.4,2,23.0,2,3,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,Spring,1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d7bceab5,Spring,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d7c063b4,Winter,8,0,Spring,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,2,2,1,2,1,1,1,1,2,1,1,2,1,2,3,2,2,1,1,31,Summer,27,40,Winter,0,1 +d8037389,Winter,12,0,,,Spring,23.54152047,57.0,108.8,,69,88,131,,,,,Fall,,,,,,,,,,,,,,,Summer,2,4.2507,23.5441,1254.1,2006.57,20.6662,75.8415,16.4119,7.13215,32.9585,3,36.6331,18.5422,71.5908,36.1274,57.2993,,,Winter,2.46,Spring,2,3,4,2,4,1,1,3,2,1,3,0,2,0,4,1,3,3,2,1,42,Winter,37,53,Winter,2,1 +d80b8bb6,Winter,12,0,Spring,75,Winter,18.9305563,61.0,100.2,,48,64,109,Winter,6,8,58,Winter,28,1,46.2,2,43.6,2,7,0,14.8,1,17.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,3.84,Winter,2,2,2,1,2,2,1,1,1,2,2,1,2,2,1,2,2,2,1,1,32,Winter,48,67,Winter,2,1 +d819548d,Spring,18,1,,,Spring,22.69545072,64.57,134.6,25.0,98,96,73,,,,,Spring,35,1,20.7,2,19.8,2,6,0,13.5,1,14.5,1,12.0,1,Spring,,,,,,,,,,,,,,,,,Spring,1.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +d8196954,Spring,8,1,Fall,61,Fall,18.45607561,57.5,86.8,,90,77,122,,,,,Fall,18,1,,,,,10,1,10.0,1,12.0,1,11.0,1,Fall,1,4.82823,18.4581,1154.01,1500.21,20.4119,65.1798,13.8605,4.59756,21.6202,2,28.1009,16.667,60.3516,30.0002,48.5128,,,Fall,1.5,Fall,4,4,4,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,Fall,28,41,Spring,0,0 +d81f28d3,Fall,5,1,,,Fall,16.63088843,44.0,45.8,22.0,67,77,127,,,,,Fall,0,0,,,,,0,0,10.0,1,10.5,1,3.0,0,Fall,1,2.11105,16.6327,906.133,1177.97,10.4854,38.7781,14.0826,2.55007,7.02191,1,20.7823,7.51035,36.667,16.4446,31.2677,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,2,0 +d8262260,Spring,17,1,,,Spring,18.43422222,60.0,94.4,,76,81,111,,,,,Spring,20,1,19.7,2,23.0,2,10,1,13.5,1,12.5,1,18.0,1,Spring,5,4.95465,18.4362,1192.93,2624.45,21.3593,69.326,13.5393,4.89693,25.074,1,29.0031,18.9635,64.3713,33.0839,50.3625,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +d82e9b87,Winter,8,1,,,Winter,19.36630885,48.5,64.8,26.0,70,76,109,,,,,Winter,0,0,,,,,6,1,11.0,1,12.0,1,14.0,1,Winter,3,2.77424,19.3684,1017.08,1627.32,16.0628,50.5951,15.1227,4.24577,14.2049,2,25.6219,8.91045,47.8209,24.3537,41.6847,,,Winter,2.1,Winter,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,6,Winter,37,53,Winter,0,0 +d859115e,Summer,10,0,Fall,45,Summer,23.75789474,57.0,109.8,,69,78,125,Summer,6,9,15,Summer,4,0,,,,,0,0,10.0,1,7.0,0,12.0,1,Summer,1,3.80617,23.7605,1195.55,1554.21,16.6022,69.6045,15.0623,8.69821,40.1955,2,31.884,21.1183,65.7984,28.4757,48.4862,,,Summer,1.96,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,46,64,Summer,2,0 +d85a62b3,Summer,6,0,Summer,60,Spring,20.53753673,48.88,69.8,,70,76,113,Spring,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,5,5,5,2,5,3,0,3,2,3,3,0,3,2,2,5,,2,0,0,50,Spring,52,72,Summer,1,2 +d85b7c71,Fall,7,0,Spring,60,Winter,19.79626176,52.63,78.0,,59,82,107,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,5,5,0,5,0,0,4,0,0,0,0,1,4,4,4,4,3,0,0,43,Winter,41,58,Fall,2,1 +d877094f,Fall,10,1,Spring,70,Winter,18.23598831,51.5,68.8,,58,80,118,Winter,6,10,1,Winter,60,1,,,,,7,1,8.0,0,9.0,1,3.0,0,Spring,2,2.93508,18.238,1037.47,1556.2,15.7642,52.7667,13.9878,4.25021,16.0333,2,24.5529,12.4497,49.8317,23.3673,40.3171,,,Winter,2.42,,,,,,,,,,,,,,,,,,,,,,,Winter,38,54,Winter,2, +d87d3d03,Fall,10,0,Spring,70,Winter,20.98133175,58.0,100.4,,55,67,105,Winter,5,8,4,Winter,0,0,21.0,2,17.8,2,10,1,7.0,0,8.0,1,10.0,1,Spring,4,4.32598,20.9836,1234.82,2593.12,18.6353,73.7875,15.4216,5.56202,26.6125,2,36.574,18.5783,69.4615,41.4066,55.2092,,,,,Winter,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Winter,30,43,Fall,0,0 +d87e2d01,Spring,14,0,Fall,63,Summer,20.10876951,66.0,124.6,,71,79,119,,,,,Summer,16,0,25.5,1,25.2,1,10,0,13.5,1,14.0,1,8.0,0,Summer,3,5.82679,20.111,1445.1,2456.66,28.2037,96.1848,15.5246,4.58633,28.4152,2,42.6571,25.324,90.3581,43.5759,70.8608,Spring,2.17,,,Spring,2,2,3,3,2,1,1,3,1,2,4,0,1,0,1,1,1,1,2,0,31,Summer,31,45,Spring,1,1 +d8854307,Spring,10,0,,,Summer,18.87212744,51.5,71.2,,64,68,108,Summer,,,,Summer,20,1,10.1,1,13.0,2,37,1,6.0,0,6.0,0,7.0,0,,,,,,,,,,,,,,,,,,,,Spring,1.44,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +d8a78157,Spring,13,0,Fall,80,Summer,20.71210862,63.5,118.8,,57,60,108,,,,,Summer,10,0,22.7,2,20.3,2,15,1,4.0,0,4.5,0,17.0,1,Summer,4,5.48949,20.7144,1413.72,2968.81,27.9144,92.8426,16.1884,4.52602,25.9574,2,41.5973,23.3309,87.3531,40.3127,69.5117,,,Spring,2.55,Summer,2,2,3,1,2,1,1,1,1,2,2,1,1,2,2,1,2,1,2,1,31,Summer,35,50,Spring,2,1 +d8b066ad,Spring,7,0,Fall,61,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,4,3,2,4,2,4,3,2,2,3,1,2,3,2,2,3,3,3,1,53,Spring,32,46,Spring,3,2 +d8c2430a,Winter,10,0,Winter,40,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.23,,,,,,,,,,,,,,,,,,,,,,,Spring,45,63,Winter,, +d90900e1,Summer,7,0,Winter,57,Fall,20.21699346,51.0,74.8,,71,96,118,Fall,1,1,0,Fall,0,0,,,,,0,0,9.0,1,10.0,1,0.0,0,Fall,1,7.62418,20.2192,1048.26,1362.73,12.2304,53.916,14.574,5.64517,20.884,2,27.8249,13.8607,46.2918,23.444,40.0553,,,,,Fall,2,3,2,0,3,1,0,0,0,0,0,0,2,1,1,1,2,1,1,0,20,Fall,36,51,Summer,2,0 +d94c91bf,Spring,8,0,Fall,61,Summer,17.39486621,52.5,68.2,,68,96,132,Summer,7,11,20,Summer,13,1,,,,,10,1,10.0,1,11.0,1,10.0,1,Summer,2,3.01028,15.5489,987.953,1580.73,9.15999,47.493,13.0933,2.45556,8.90698,1,24.8707,13.4624,44.4827,20.8569,34.0306,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,29,42,Spring,1,0 +d9624103,Spring,13,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.58,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +d980da40,Winter,6,0,,,Fall,,,,,,,,Fall,,,,Winter,0,0,,,,,4,1,12.0,1,12.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,Spring,33,47,Winter,0,0 +d98be7a1,Winter,9,1,Spring,90,Spring,13.92700606,48.5,46.6,22.0,59,82,101,,,,,Spring,15,1,,,,,12,1,13.0,1,13.0,1,11.0,1,Spring,3,2.4692,13.6457,962.06,1539.3,11.5575,44.735,13.0996,0.546113,1.86497,1,20.4772,12.7003,42.2658,15.9303,32.0348,,,Winter,2.87,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,32,46,Winter,0,0 +d991786c,Fall,5,0,,,Fall,16.88112493,43.0,44.4,21.0,35,72,60,,,,,Fall,0,0,,,,,0,0,,,,,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +d9ae9a13,Summer,17,1,Spring,40,Summer,18.47810259,64.75,110.2,26.0,72,77,114,,,,,Summer,3,0,13.3,1,9.4,1,0,0,9.5,0,8.5,0,5.0,0,Winter,2,5.2132,18.4801,1350.69,2026.03,29.0867,86.1291,14.4435,4.0366,24.0709,1,34.7269,22.3156,80.9159,40.3375,63.8135,Winter,2.11,,,Winter,1,1,5,0,4,1,1,5,1,2,5,0,0,0,0,0,5,3,5,2,41,,,,Summer,3,1 +d9c33320,Winter,12,0,,,Winter,17.26116071,56.0,77.0,22.0,63,63,105,,,,,Winter,30,1,18.6,2,22.8,3,20,1,8.0,1,8.0,1,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,2.959,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +d9c82502,Summer,9,0,Fall,77,Fall,18.22176,50.0,64.8,,74,100,109,Fall,6,9,34,Fall,5,0,,,,,0,0,7.0,0,7.0,0,6.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.78,Fall,5,4,5,1,5,1,0,5,1,4,4,0,3,1,4,4,5,4,4,3,63,Summer,34,49,Summer,0,2 +d9eb30bf,Spring,21,0,Fall,70,Spring,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,2,2,0,1,0,1,1,0,1,2,0,1,0,0,0,1,0,1,1,14,Spring,50,69,Summer,2,0 +d9f16cd2,Fall,6,1,Spring,75,Winter,15.28076319,45.5,45.0,,73,116,98,Winter,1,0,45,Winter,7,1,,,,,0,0,12.25,1,11.5,1,8.5,1,,,,,,,,,,,,,,,,,,,,,,Winter,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Winter,96,100,Fall,0,0 +d9f4d9c9,Winter,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +d9ff98e9,Summer,18,0,,,Summer,26.78887574,65.0,161.0,33.0,63,61,125,,,,,Summer,23,0,33.8,1,37.2,2,23,1,5.0,0,7.0,0,5.0,0,Fall,5,6.03432,26.7918,1600.81,3841.94,33.4432,112.77,18.766,8.02584,48.2297,3,50.837,28.4902,106.736,60.0725,84.2802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +da0001de,Winter,5,0,Fall,62,Spring,17.88012153,48.0,58.6,,73,106,137,Spring,,,,Spring,13,1,,,,,1,0,10.5,1,10.0,1,10.0,1,Spring,2,2.70945,17.8821,964.59,1543.34,8.16043,45.0045,13.7333,4.14873,13.5955,1,23.7323,13.1118,42.2951,18.4948,31.8927,,,,,Spring,5,5,2,0,3,0,0,1,0,3,4,0,2,1,5,4,3,4,1,1,44,,,,Winter,1,1 +da09ed4b,Summer,15,1,Fall,61,Fall,32.98734289,63.8,191.0,,70,76,104,,,,,Fall,0,0,30.0,3,34.7,3,6,0,11.5,1,11.5,1,12.0,1,,,,,,,,,,,,,,,,,,Summer,2.55,,,Summer,1,2,2,0,2,2,1,1,1,1,2,0,1,2,2,1,3,1,1,1,27,Summer,41,58,Summer,3,0 +da15064a,Fall,10,0,Spring,80,Winter,16.05270123,56.25,72.25,,60,95,114,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.7,Winter,3,5,4,1,5,1,0,1,1,2,2,0,5,4,0,2,3,2,2,1,44,Winter,34,49,Fall,0,1 +da19c97e,Summer,8,0,Summer,61,Spring,14.54109958,51.0,53.8,,53,76,110,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.07393,14.5427,987.673,1679.04,9.17465,47.4631,12.8298,1.71292,6.33687,2,24.9266,13.3619,44.3892,21.2215,34.1013,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Summer,0,0 +da1b21b1,Fall,7,0,,,Winter,16.98677551,52.5,66.6,26.0,69,98,114,,,,,Fall,6,1,,,,,3,0,11.0,1,9.0,1,4.0,0,Fall,2,3.47269,16.9886,1060.79,1697.27,13.2431,55.2514,14.0938,2.89486,11.3486,2,28.9819,13.0264,51.7787,25.7637,42.225,,,,,Fall,1,1,1,1,1,1,1,1,1,1,2,1,2,1,2,2,1,2,1,1,25,Fall,35,50,Fall,0,0 +da3394f1,Spring,10,1,,,Spring,22.02690058,57.0,101.8,32.0,86,97,136,,,,,Spring,0,0,12.0,2,14.1,2,0,0,8.5,1,9.0,1,3.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,1.67,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +da5a8921,Spring,6,0,Summer,60,Spring,14.58074074,45.0,42.0,,56,87,96,Spring,4,6,48,Spring,9,1,,,,,0,0,13.0,1,13.0,1,4.0,0,Spring,5,2.47349,14.5823,892.948,2143.07,6.0437,37.3736,12.9761,1.60627,4.62636,2,22.0999,9.23001,34.9001,16.8877,28.1436,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,42,59,Spring,0,0 +da690272,Fall,16,1,,,Fall,26.18652117,63.5,150.2,36.0,72,79,123,,,,,Fall,0,0,21.5,2,23.2,2,0,0,12.0,1,12.0,1,11.0,1,Fall,3,5.06326,26.1894,1406.75,2250.8,32.7232,92.1004,16.0589,10.1305,58.0997,2,35.6964,23.6808,87.0371,41.029,68.4196,Fall,3.45,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +da94e67d,Spring,12,0,Summer,45,Summer,27.36591696,68.0,180.0,,61,71,121,,,,,Summer,11,0,24.3,2,29.9,2,9,0,10.0,1,11.0,1,14.0,1,Summer,3,6.20865,27.3689,1614.06,2743.9,33.3468,114.182,17.3613,10.0076,65.8184,3,49.4856,31.3493,107.973,54.8796,82.8324,,,Spring,4.66,Summer,5,5,4,2,3,2,0,3,0,1,4,,4,2,3,3,2,4,3,2,52,Summer,48,67,Spring,0,2 +daa9b0aa,Fall,6,1,Winter,55,Winter,15.55049485,49.5,54.2,,53,88,105,Winter,,,,Winter,10,1,,,,,0,0,9.5,1,10.25,1,7.5,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,1,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,Fall,44,62,Fall,,0 +dabdc420,Spring,17,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +dacc2f83,Summer,9,1,Fall,55,Fall,27.02806613,51.0,100.0,,50,58,116,,,,,Summer,0,0,,,,,0,0,0.0,0,0.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.97,Fall,2,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,7,Fall,57,79,Summer,2,0 +dacfdb2c,Fall,8,1,Winter,75,Winter,15.48109257,48.5,51.8,,61,86,114,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.3,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,31,45,Fall,0,0 +daf0d557,Winter,17,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +daf3969c,Summer,17,1,Winter,51,Summer,30.51071616,65.5,186.2,,45,81,115,,,,,Summer,37,1,28.6,2,30.5,3,0,0,16.0,1,15.0,1,11.0,1,Fall,1,5.30892,30.514,1475.76,1918.49,34.8711,99.4509,16.2978,14.2162,86.7491,3,37.5715,27.0083,94.142,48.5405,72.4426,Fall,1.11,,,Fall,5,5,5,4,5,1,5,1,1,1,5,1,2,1,1,1,2,1,2,1,50,,,,Summer,3,2 +dafc5c2f,Fall,13,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.18,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +db175ab6,Summer,7,0,Fall,40,Summer,18.58542327,47.0,58.4,,63,71,111,Summer,6,9,6,Summer,10,1,,,,,1,0,6.0,0,7.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,,,Summer,0,1,4,4,4,0,0,3,0,0,4,0,0,1,0,2,3,1,1,0,28,Summer,39,55,Summer,0,0 +db18f921,Winter,19,0,,,Summer,21.4693971,63.98,125.0,30.0,73,83,123,,,,,,,,,,,,,,,,,,,,Summer,2,5.04911,21.4696,1346.87,2154.99,21.149,85.722,14.7233,6.74627,39.278,2,39.7857,24.7873,80.6729,49.339,60.9347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +db23fbe4,Summer,11,0,Spring,85,Winter,20.46511111,60.0,104.8,,62,53,120,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,4.42273,20.4673,1242.72,2112.63,17.4793,74.6292,14.575,5.89233,30.1708,2,36.2883,20.8615,70.2065,44.4528,53.7676,,,Fall,2.72,Fall,2,0,2,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,9,Fall,37,53,Summer,0,0 +db2a71e1,Fall,7,0,,,Fall,14.61814745,46.0,44.0,,58,94,113,,,,,Fall,5,1,,,,,5,1,7.0,0,8.5,1,10.0,1,Fall,2,2.48714,14.6197,907.836,1452.54,6.01667,38.9595,12.9449,1.6748,5.04053,1,21.3815,11.5613,36.4723,15.4188,27.3982,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +db38d1f9,Winter,12,0,Spring,55,Winter,16.50594083,65.0,99.2,,76,85,106,,,,,Winter,9,0,13.2,1,12.7,1,0,0,7.0,0,7.0,0,8.0,0,Spring,2,1.20715,16.5077,1318.21,2109.13,23.5831,82.6695,13.7569,2.75083,16.5305,2,36.3361,22.7503,81.4623,32.7872,59.9192,,,Winter,2.7,Winter,3,2,3,2,3,1,1,2,1,1,3,1,1,1,1,1,1,2,3,2,35,Winter,40,56,Winter,0,1 +db3b1dd1,Summer,8,0,Fall,65,Summer,19.05191584,50.75,69.8,,65,71,105,,,,,Summer,2,0,,,,,7,1,9.5,1,10.0,1,12.0,1,Summer,3,3.53235,19.054,1097.4,1865.59,14.3663,59.1509,16.147,2.90699,10.6491,1,33.5725,11.2122,55.6186,37.7561,47.9387,,,Summer,2.68,Summer,5,5,3,0,3,1,0,0,0,2,3,0,0,1,1,1,1,1,1,1,29,Summer,33,47,Summer,1,0 +db42593a,Summer,7,0,,,Summer,16.75551474,49.5,58.4,,70,95,138,Summer,,,,Summer,1,0,,,,,,,,,,,11.0,1,Summer,5,3.7973,16.7573,1000.98,2402.36,11.1738,48.8809,14.0259,2.73143,9.51913,1,26.9464,10.7607,45.0836,22.5799,38.1202,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +db4635c4,Summer,20,1,,,Summer,22.9738409,61.25,122.6,30.0,64,56,112,,,,,,,,,,,,,,,,,,,,Fall,2,4.80975,22.9764,1332.95,2132.73,22.8663,84.2401,15.7874,7.18899,38.3599,2,38.9509,22.4228,79.4304,40.3595,61.8173,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +db49ac7c,Spring,12,0,Fall,78,Summer,16.0912604,58.0,77.0,,67,79,113,,,,,Summer,23,1,15.8,1,14.7,1,0,0,4.75,0,4.25,0,9.5,1,Summer,1,4.86736,16.093,1139.26,1481.03,16.0474,63.6086,13.2942,2.7988,13.3914,1,29.7439,17.8174,58.7413,24.6303,45.7913,,,Summer,1.96,Summer,5,4,4,0,4,1,0,4,0,3,0,0,0,0,1,0,0,3,1,0,30,Summer,49,68,Spring,0,0 +db609511,Summer,8,1,Summer,70,Summer,17.84960938,48.0,58.5,25.0,63,77,101,,,,,Summer,0,0,,,,,0,0,10.0,1,10.0,1,5.0,0,Spring,2,2.34182,17.8516,970.608,1455.91,11.7501,45.6455,13.9289,3.92262,12.8545,1,20.4191,13.4763,43.3036,16.4259,32.1692,,,,,Summer,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,Summer,35,50,Summer,0,0 +db746de0,Summer,9,1,Winter,60,Fall,17.7086281,55.0,76.2,,66,89,115,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,3,3.48306,17.7106,1110.46,1776.73,18.938,60.5411,14.0711,3.63948,15.6589,2,27.3935,14.2095,57.058,28.3266,46.3316,,,Summer,1.68,Summer,4,0,3,0,3,0,0,0,0,0,2,0,,0,2,2,2,2,1,0,21,Summer,57,79,Summer,1,0 +db792fad,Winter,9,0,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.76,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +db8e4670,Winter,12,0,,,Winter,18.30642093,58.0,87.6,25.0,60,85,97,,,,,Winter,2,0,15.5,1,15.2,1,8,0,7.0,0,8.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.93,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +db9353fd,Winter,6,1,Spring,60,Spring,14.08657845,46.0,42.4,,86,69,124,Spring,4,6,30,Spring,9,1,,,,,2,0,11.0,1,10.0,1,10.5,1,,,,,,,,,,,,,,,,,,,,,,Spring,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,18,Spring,26,38,Winter,2,0 +db9eae59,Spring,11,0,Fall,60,Summer,26.92198817,65.0,161.8,,74,74,121,Summer,5,8,4,Summer,12,0,19.7,2,21.8,2,0,0,6.0,0,6.5,0,9.0,1,Summer,2,5.87443,26.9249,1567.23,2507.57,33.1239,109.194,18.1708,8.75408,52.6059,3,47.9066,28.1636,103.32,49.778,81.0305,,,Spring,3.3,Summer,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Summer,34,49,Spring,1,0 +dba454b6,Spring,12,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.28,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +dbb15ad4,Winter,10,1,,,Winter,28.1404455,61.5,151.4,37.0,65,98,113,,,,,Winter,0,0,25.9,3,26.6,3,0,0,11.0,1,12.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.86,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +dbc59c26,Fall,15,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +dbd7f5b6,Summer,6,1,,,Summer,14.92790123,45.0,43.0,20.0,69,114,100,,,,,Summer,0,0,,,,,0,0,,,,,0.0,0,Summer,2,2.21175,14.9296,913.734,1370.6,10.5063,39.5876,13.7448,1.18479,3.41242,1,21.3159,7.76542,37.3759,18.0387,31.8222,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +dbfa6d84,Winter,5,1,Summer,60,Winter,15.14399093,42.0,38.0,20.0,62,92,109,,,,,Winter,0,0,,,,,0,0,10.5,1,12.0,1,12.0,1,Winter,2,1.92467,15.1456,870.825,1306.24,9.23731,35.0173,13.9568,1.18883,2.98275,1,19.5601,6.21981,33.0926,13.5986,28.7974,,,,,Winter,4,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,,,,Winter,2,0 +dc0e5a9d,Spring,5,1,,,Spring,14.61814745,46.0,44.0,21.0,,80,,,,,,Spring,0,0,,,,,0,0,0.0,0,0.0,0,0.0,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +dc168599,Winter,6,1,Spring,62,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,56,77,Winter,0,0 +dc33dcef,Fall,12,0,Spring,55,Winter,20.55555556,57.0,95.0,,61,77,110,,,,,Winter,6,0,16.4,2,14.9,2,4,0,7.5,1,5.0,0,8.0,0,Winter,2,4.00601,20.5578,1180.94,1889.51,17.4228,68.0491,14.7257,5.83211,26.9509,2,32.3283,18.2981,64.0431,28.8618,49.7511,,,Winter,4.07,Winter,2,2,2,3,3,2,1,2,3,2,2,1,0,1,2,0,1,0,1,0,30,Winter,32,46,Fall,0,0 +dc3a0c97,Spring,13,1,,,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +dc4e9e2f,Fall,14,0,,,Winter,15.01216016,59.5,75.6,24.0,75,91,134,,,,,Winter,8,0,14.1,1,12.8,1,3,0,8.5,1,8.0,1,12.0,1,Winter,4,4.11765,15.0138,1139.22,2392.35,14.7615,63.6044,12.6315,2.38226,11.9956,1,29.1497,19.6932,59.4868,25.6762,43.9112,Winter,3.101,,,Winter,4,0,1,1,2,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,22,Winter,28,41,Fall,2,0 +dc52906a,Winter,7,0,,,Spring,17.04654027,45.5,50.2,,58,92,104,Spring,,,,Spring,0,0,,,,,1,0,13.0,1,12.0,1,11.0,1,Spring,4,5.89478,17.0484,920.271,1932.57,7.48341,40.284,13.6808,3.36758,9.91604,2,23.2794,9.52113,34.3892,17.5232,30.7628,,,,,Spring,2,3,2,1,3,1,0,2,2,2,2,1,2,2,2,4,2,3,2,1,39,Spring,42,59,Winter,2,1 +dc6870da,Summer,8,0,,,Fall,16.66554447,53.4,67.6,,87,94,129,Fall,2,2,45,Fall,3,0,,,,,0,0,10.0,1,7.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,3,3,3,1,1,0,0,1,0,1,1,0,0,1,1,1,2,2,1,1,23,Fall,36,51,Summer,2,0 +dc6f229a,Summer,12,0,,,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.27,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +dc81eac1,Fall,10,1,Summer,70,Fall,16.91804913,53.0,67.6,24.0,55,69,96,,,,,Fall,0,0,13.5,2,13.1,2,1,0,11.5,1,10.0,1,10.0,1,Fall,3,3.03561,16.9199,1047.42,1675.86,15.7303,53.8265,13.4725,3.44743,13.7735,1,24.0169,14.0793,50.7909,22.2498,39.7472,,,Fall,2.56,Fall,4,4,3,4,4,3,0,1,1,2,4,0,1,2,2,2,0,2,1,2,42,Fall,42,59,Fall,2,1 +dc9f86c9,Spring,9,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.45,,,,,,,,,,,,,,,,,,,,,,,,,,,, +dca065be,Summer,8,0,Fall,80,Summer,14.58117451,49.0,49.8,,49,84,100,Summer,2,1,50,Summer,12,1,,,,,6,1,7.5,1,16.0,1,8.1,1,Summer,2,5.22145,14.5828,957.669,1532.27,8.58336,44.2673,12.9626,1.62013,5.53271,1,24.207,11.4769,39.0458,19.4639,32.7904,,,Summer,2.18,Summer,2,3,1,0,0,0,0,0,0,0,0,0,2,1,2,2,1,2,1,1,18,Summer,36,51,Summer,0,0 +dcd06449,Summer,15,1,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.22,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +dcd2586d,Summer,11,1,Winter,65,Fall,15.40348337,60.5,80.2,,93,95,133,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.12,Fall,5,0,5,0,5,0,0,0,0,4,0,0,4,0,0,4,0,0,0,0,27,Fall,43,60,Summer,0,0 +dce2a54d,Winter,7,0,Summer,70,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,3,4,0,4,1,0,3,1,1,5,0,2,0,2,1,2,2,0,1,34,Spring,29,42,Winter,2,1 +dcfcd574,Fall,8,0,Spring,80,Fall,12.64913495,51.0,46.8,,73,95,121,Fall,4,5,26,Fall,5,0,,,,,2,0,8.0,1,8.0,1,7.0,1,Fall,1,11.1385,12.6505,969.844,1260.8,6.51018,45.5641,12.3164,0.334078,1.23591,1,21.7574,17.2965,34.4256,17.7547,28.2676,,,Winter,3.46,Fall,0,0,4,0,1,1,0,3,2,0,3,0,1,1,1,1,2,1,0,0,21,Fall,42,59,Fall,2,0 +dcffa5d6,Summer,5,1,Winter,60,Fall,15.45431579,47.5,49.6,,,,,,,,,Fall,6,1,,,,,0,0,9.0,1,9.0,1,5.0,0,Fall,3,7.65038,15.456,952.049,1523.28,12.0594,43.6687,13.6077,1.84828,5.93132,2,21.5773,10.0319,36.0183,17.6498,33.6368,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,56,77,Summer,0,0 +dd02cfa1,Summer,16,1,Fall,80,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.97,,,Summer,0,4,3,2,2,2,1,1,2,1,4,1,1,1,2,1,2,1,1,1,33,Summer,33,47,Summer,0,1 +dd1338ee,Spring,6,0,,,Spring,18.59710949,46.5,57.2,23.0,70,100,91,,,,,Spring,0,0,,,,,0,0,11.0,1,10.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +dd167ffc,Spring,5,0,,,Spring,17.28450413,44.0,47.6,23.0,120,60,153,,,,,Spring,0,0,,,,,0,0,4.0,0,4.0,0,7.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +dd3756ff,Summer,12,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +dd3a3258,Spring,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +dd4b8b52,Winter,13,1,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.95,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +dd4fa1df,Fall,13,0,,,Fall,28.94372481,61.0,153.2,,60,66,104,,,,,Fall,9,0,19.6,1,21.6,1,2,0,12.0,1,11.0,1,11.0,1,Fall,2,5.53033,28.9469,1572.29,2515.67,34.3888,109.733,20.7338,8.21307,43.4673,2,49.369,25.975,104.202,52.0364,83.7578,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +dd6679a9,Summer,17,1,,,Fall,20.80120655,59.0,103.0,,76,77,130,,,,,Fall,25,1,21.6,2,21.7,2,7,1,9.0,0,9.0,0,10.0,1,,,,,,,,,,,,,,,,,,Summer,1.14,,,Fall,0,3,3,3,2,2,0,,2,1,4,2,1,4,1,0,0,0,2,1,31,Fall,49,68,Summer,2,1 +dd689eb0,Spring,6,1,Fall,75,Summer,19.00912,50.0,67.6,,58,83,112,Summer,6,9,32,Summer,10,1,,,,,1,0,12.0,1,12.0,1,10.0,1,Fall,2,2.74089,18.7106,1015.66,1523.49,14.6607,50.4444,13.9622,4.74841,17.1556,2,23.2419,12.5418,47.7035,20.965,37.9026,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Spring,0,0 +dd747bba,Fall,6,0,Spring,65,Winter,15.21639055,50.5,55.2,,117,48,165,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,37,53,Fall,0,0 +dd9c9576,Fall,8,0,Winter,75,Fall,19.26588921,49.0,65.8,,58,67,109,Fall,7,11,56,Fall,10,1,,,,,5,1,8.5,1,9.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,2,2,3,0,2,0,0,0,1,2,0,0,0,0,1,1,3,1,2,1,21,Fall,39,55,Fall,2,0 +dd9ce3c4,Fall,18,1,,,Fall,,,,,,,,,,,,Fall,29,1,22.9,2,25.3,2,7,1,10.0,0,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,Fall,2.41,,,Winter,2,2,2,0,2,0,2,1,1,1,0,0,2,1,1,0,1,1,1,0,20,,,,Fall,0,0 +ddcb4529,Summer,11,0,,,Summer,17.69915914,61.75,96.0,25.0,78,73,102,,,,,Summer,42,1,49.8,3,50.2,3,50,1,12.0,1,10.25,1,12.0,1,Summer,1,4.99945,17.7011,1293.76,1681.88,22.5597,80.0652,14.7629,2.93816,15.9348,1,37.2381,20.2674,75.0657,36.4057,59.7978,,,Summer,4.28,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +ddd0cb12,Winter,8,0,Spring,45,Spring,21.93927795,52.25,85.2,,78,91,119,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,2,1,2,2,1,1,1,1,2,1,1,1,1,1,1,1,1,1,26,Winter,35,50,Winter,0,0 +ddd9a88d,Summer,12,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,4,2,0,0,0,11,Winter,61,84,Summer,2,0 +dde6b10e,Spring,14,0,Summer,,Spring,30.15452028,71.13,217.0,41.5,75,73,138,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.01,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +dde82df7,Winter,9,1,Summer,75,Spring,18.72792,50.0,66.6,23.0,67,69,110,,,,,Spring,14,1,,,,,4,0,14.0,1,14.0,1,12.0,1,Spring,2,2.94008,18.73,1039.28,1558.93,16.8978,52.9605,14.8941,3.83584,13.6395,1,26.0368,10.026,50.0204,24.8394,42.9345,,,Winter,2.33,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,36,51,Winter,0,0 +ddfddd87,Spring,12,0,Summer,70,Summer,26.59683333,60.0,136.2,39.0,58,72,145,,,,,Summer,8,0,12.4,1,10.0,1,1,0,10.0,1,6.0,0,9.0,1,Summer,1,4.90658,26.5997,1404.47,1825.8,26.5429,91.857,17.9396,8.66014,44.343,3,42.6868,22.6274,86.9505,44.7811,69.2297,,,Summer,1.29,Summer,4,4,3,4,4,2,0,2,3,3,4,0,4,0,3,5,3,5,3,1,57,Summer,52,72,Spring,3,2 +de179306,Summer,16,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +de3750cc,Fall,7,0,Winter,80,Fall,12.77473684,47.5,41.0,,59,83,100,Fall,4,7,0,Fall,4,1,,,,,0,0,10.0,1,10.0,1,5.0,0,Fall,4,13.2305,13.924,910.432,1911.91,3.71823,39.236,13.3249,0.599071,1.764,1,19.6023,15.9154,26.0055,15.5831,23.3206,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,28,41,Fall,0,0 +de3bef1e,Spring,6,1,,,Spring,14.34693878,49.0,49.0,22.0,67,88,114,,,,,Summer,2,1,,,,,0,0,6.5,0,8.5,0,8.0,1,Summer,2,2.46168,14.3485,965.914,1448.87,11.735,45.1456,13.2198,1.12871,3.85453,1,20.4207,12.99,42.6839,15.7595,32.1556,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +de3ecca9,Winter,9,1,Summer,65,Spring,15.51995415,53.25,62.6,,80,98,123,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,5,3.07638,15.5216,1043.56,2295.84,15.0368,53.4164,13.2446,2.27708,9.18362,1,24.5511,13.8285,50.34,24.8474,39.5879,,,Spring,1.31,Spring,5,4,3,2,4,4,1,3,4,4,4,2,3,3,4,4,3,0,3,3,63,Spring,65,89,Winter,3,2 +de6797db,Summer,8,0,Winter,45,Fall,17.35340277,56.5,78.8,,72,86,108,Fall,1,0,54,Fall,10,1,,,,,0,0,9.0,1,9.0,1,5.0,0,Fall,2,3.93811,17.3553,1134.7,1815.51,16.1143,63.1231,13.9025,3.45275,15.6769,2,30.3823,16.6266,59.185,25.7662,46.4965,,,Summer,2.47,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,33,47,Summer,0,0 +de742c5c,Summer,15,1,Winter,65,Summer,25.28962092,63.75,146.2,,57,82,116,,,,,Summer,35,1,25.7,2,22.2,2,13,1,14.0,1,15.0,1,12.0,1,Summer,5,5.27508,25.2924,1439.97,3167.94,33.8381,95.6392,16.5454,8.74695,50.5608,2,37.8282,23.973,90.3641,45.8254,71.6662,Winter,2.91,,,Winter,5,4,5,2,5,5,4,4,4,4,5,1,3,4,3,4,4,5,3,4,78,Winter,33,47,Summer,3,2 +de91310c,Fall,16,1,Spring,53,Winter,24.67730319,65.5,150.6,,79,73,114,,,,,Winter,11,0,23.6,2,22.3,2,5,0,10.5,1,12.0,1,8.5,1,Winter,2,5.38172,24.68,1433.31,2149.96,33.913,94.9293,15.5568,9.12319,55.6707,2,35.9063,25.1099,89.5476,40.2319,69.8194,Fall,1.12,,,Winter,5,5,4,1,4,4,1,4,2,2,5,1,4,3,2,1,2,2,4,1,57,Winter,45,63,Fall,3,2 +de961552,Summer,6,0,Spring,60,Winter,14.95095486,48.0,49.0,,70,77,114,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.75575,14.9526,942.462,1602.19,7.71407,42.6476,13.0141,1.93847,6.35241,2,23.4725,11.461,39.8918,18.6977,31.1866,,,,,Winter,4,2,2,0,3,0,0,0,0,2,0,0,2,1,3,3,3,3,2,2,32,Winter,43,60,Summer,0,1 +de9fe1e0,Spring,14,1,Fall,80,Summer,24.05952012,63.5,138.0,,76,78,119,,,,,Summer,18,1,24.5,3,21.7,2,5,0,7.0,0,9.0,0,7.0,0,,,,,,,,,,,,,,,,,,,,Spring,3.98,Summer,2,3,4,1,5,4,1,4,2,3,4,1,3,2,2,2,3,2,4,2,54,Summer,45,63,Spring,2,2 +dea0552b,Summer,13,0,Fall,45,Summer,18.07355683,71.0,129.6,,80,87,116,,,,,Summer,40,1,29.5,2,27.8,2,25,1,14.0,1,14.0,1,12.0,1,Fall,5,-5.02683,18.0755,1612.03,3868.87,35.249,113.965,15.895,2.18058,15.6345,3,49.0663,29.6501,118.992,53.1022,84.3153,,,Summer,3.81,Summer,4,3,3,2,3,3,1,2,1,3,3,0,0,3,3,2,2,2,3,2,45,Summer,44,62,Summer,0,1 +deab45ce,Summer,12,0,,,Summer,16.48144444,60.0,84.4,24.0,86,67,108,,,,,Summer,0,0,20.5,2,20.6,2,8,0,7.0,0,8.0,1,12.0,1,Summer,1,4.65683,16.4832,1228.57,1597.14,19.7649,73.1221,14.2807,2.20258,11.278,1,35.0455,18.3116,68.4652,34.4688,54.8104,,,Summer,3.25,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +dec9056d,Summer,8,1,Winter,80,Fall,19.69178947,47.5,63.2,,65,93,106,Fall,5,7,56,Fall,12,1,,,,,0,0,11.0,1,11.0,1,9.0,1,Fall,2,10.0634,19.6939,971.723,1457.58,12.6468,45.7643,14.2607,5.43319,17.4357,2,21.3554,11.7621,35.7008,17.6394,34.0022,,,,,Fall,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Fall,35,50,Summer,0,0 +dee8b1bd,Winter,14,0,Fall,75,Spring,28.00522449,70.0,195.2,36.0,59,63,119,,,,,Spring,0,0,33.5,2,37.2,2,2,0,0.0,0,0.0,0,4.0,0,Spring,4,7.13079,28.0083,1791.56,3762.28,40.9691,133.088,19.0961,8.91217,62.1122,3,56.9897,35.1291,125.957,65.9795,97.9587,Spring,2.981,,,Spring,5,5,4,4,5,3,1,2,3,3,4,1,1,2,3,2,1,2,1,1,53,Spring,26,38,Winter,0,2 +dee9eb9c,Fall,10,0,Spring,81,Winter,14.91986777,55.0,64.2,,74,83,127,Winter,5,8,0,Winter,22,1,,,,,10,1,13.0,1,13.0,1,15.0,1,Spring,4,3.64939,14.9215,1074.34,2256.11,13.3596,56.694,13.1769,1.74457,7.50605,1,28.4962,14.8381,53.0446,25.0145,41.8559,,,Fall,2.06,Winter,2,2,2,0,2,1,1,1,1,2,1,0,2,0,3,2,2,2,1,2,29,Winter,47,66,Fall,1,0 +df3c3288,Spring,7,0,Winter,50,Fall,43.46883333,60.0,222.6,,78,111,116,Fall,,,,Fall,30,1,,,,,1,0,0.0,0,0.0,0,11.0,1,Fall,1,20.6668,43.4736,1717.55,2232.82,39.2335,125.205,24.4525,19.0211,97.3949,3,54.8471,31.1245,104.538,57.3741,94.0806,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,35,50,Spring,1, +df4a0fee,Summer,10,0,Fall,70,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.09,Summer,4,4,4,1,4,1,0,2,0,4,1,0,0,0,0,0,2,0,0,0,27,Summer,37,53,Summer,0,0 +df4ab9b1,Spring,15,0,Winter,45,Summer,17.9882949,57.5,84.6,,77,100,121,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.14,,,Summer,4,3,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,2,2,1,31,Summer,35,50,Spring,1,1 +df4d2085,Summer,8,0,,,Summer,18.07529844,49.5,63.0,25.0,78,88,123,,,,,Summer,0,0,,,,,0,0,8.0,1,9.0,1,4.0,0,Fall,2,3.01366,18.0773,1001.44,1602.31,11.1625,48.9297,14.0399,4.03735,14.0703,1,25.7185,12.0487,45.916,19.0139,36.881,,,Fall,2.4,Fall,2,1,4,2,3,1,0,0,1,2,1,0,2,2,4,2,0,2,4,1,34,Fall,57,79,Summer,0,1 +df556fd2,Winter,14,0,Spring,65,Spring,17.64584789,61.0,93.4,,63,90,108,,,,,Spring,32,1,19.0,1,18.0,1,0,0,7.0,0,7.0,0,9.0,1,,,,,,,,,,,,,,,,,,Winter,1.03,,,Winter,5,5,5,5,5,4,2,4,3,4,4,0,5,5,5,5,5,5,4,5,85,Winter,49,68,Winter,1,3 +df715398,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +df98fbb4,Winter,12,1,Spring,61,Spring,16.23705832,59.0,80.4,,70,33,109,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.92042,16.2388,1156.78,1850.84,20.7582,65.4749,13.2243,3.01451,14.9251,1,27.1374,17.5793,61.5545,26.1509,47.8956,,,Winter,2.79,Winter,4,4,4,2,4,4,4,4,3,3,4,1,2,2,3,2,0,2,0,3,55,Winter,31,45,Winter,1,2 +dfaa2a59,Winter,6,1,Summer,76,Spring,9.959166667,60.0,51.0,,48,73,97,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.36764,15.5629,958.089,1532.94,10.7038,44.3121,13.5221,2.04084,6.68788,2,21.1384,12.47,41.9445,19.9197,31.8422,,,,,Spring,1,1,2,1,2,1,1,1,1,2,1,1,1,2,1,1,1,1,1,1,24,Spring,37,53,Winter,0,0 +dfd87763,Winter,6,0,Summer,55,Spring,14.64427676,48.5,49.0,23.0,69,92,105,,,,,Spring,2,1,,,,,0,0,15.0,1,14.0,1,9.0,1,Spring,1,2.80343,14.3485,951.495,1236.94,8.10635,43.6097,12.7701,1.57841,5.39027,1,22.611,12.8924,40.8063,16.0316,30.7174,,,,,Spring,2,0,3,0,1,1,0,0,0,0,1,0,0,0,2,2,0,2,2,0,16,,,,Winter,2,0 +dfdbb957,Summer,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +dff8b8e0,Fall,6,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +e016d96c,Winter,15,1,Summer,91,Summer,21.75066767,63.0,122.8,29.0,68,78,110,,,,,Summer,35,1,47.2,,49.8,1,15,1,12.0,1,12.0,1,12.0,1,Summer,4,4.66396,21.753,1299.71,2469.44,26.8121,80.6987,14.2951,7.45791,42.1013,2,31.908,21.9785,76.0347,36.2804,58.7202,Winter,2.86,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,29,42,Winter,0,0 +e0271395,Summer,8,1,Fall,61,Fall,15.63460607,52.2,60.6,,53,64,101,Fall,6,9,55,Fall,16,,,,,,3,,14.5,,14.5,,10.0,,Fall,3,4.70659,13.5207,1022.41,1635.85,15.2122,51.163,13.252,0.268609,1.03703,2,24.3282,11.6226,46.4564,21.8633,39.5404,,,Summer,1.87,Fall,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,7,Fall,34,49,Summer,0,0 +e0283d3d,Spring,9,1,Fall,72,Spring,16.0433683,50.5,58.2,20.0,68,73,120,,,,,Spring,20,1,,,,,7,1,12.0,1,12.0,1,10.0,1,Summer,4,2.78489,16.0451,1004.66,1908.86,14.1401,49.2729,13.584,2.46112,8.92715,1,23.4656,11.6671,46.488,21.4859,37.6057,,,Summer,3.69,Spring,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Spring,33,47,Summer,0,0 +e049dab1,Fall,15,1,Spring,72,Winter,21.23143195,65.0,127.6,,83,77,131,,,,,Winter,36,1,20.5,2,22.1,2,5,0,5.0,0,5.0,0,12.0,1,Winter,2,5.18118,21.2338,1309.97,1964.96,26.4436,81.7923,13.6109,7.6228,45.8077,3,31.2053,24.1434,76.6111,35.9782,57.6489,Winter,1.68,,,Winter,2,2,2,1,2,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,26,Winter,30,43,Fall,0,0 +e0655da2,Summer,9,0,Winter,67,Fall,16.24903978,54.0,67.4,,145,82,203,Fall,7,11,1,Fall,20,1,,,,,7,1,9.0,1,10.0,1,12.0,1,Fall,3,3.48602,16.2508,1060.81,1803.37,12.6124,55.2528,13.322,2.92881,12.1472,1,27.3146,15.3259,51.7668,22.4705,39.9269,,,Summer,1.82,Fall,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Fall,32,46,Summer,0,0 +e0706ce2,Summer,8,0,Fall,62,Summer,16.40681282,53.5,66.8,,69,72,108,Summer,6,9,41,Summer,7,1,,,,,0,0,4.0,0,5.0,0,6.0,1,Summer,4,7.54465,16.1213,1026.59,2155.84,10.0374,51.6082,13.1645,2.95688,11.5918,1,26.3918,15.1791,44.0636,24.6089,36.4291,,,Summer,3.81,Summer,4,1,5,2,5,4,0,4,1,2,4,0,1,0,3,1,4,1,3,2,47,Summer,44,62,Summer,0,1 +e072b994,Fall,6,1,Winter,65,Fall,15.10291769,41.5,37.0,,72,106,120,,,,,Fall,10,1,,,,,2,0,11.0,1,10.0,1,10.0,1,Winter,3,1.73684,48.3754,1004.87,1607.8,12.8716,49.2953,20.1239,28.2515,69.2047,1,21.3272,15.0965,47.5585,25.567,34.1988,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Fall,0,0 +e0736134,Fall,10,0,Winter,45,Fall,14.43620862,52.5,56.6,,68,96,106,Fall,6,9,45,Fall,15,1,,,,,13,1,8.5,1,9.0,1,10.0,1,Fall,2,7.68992,14.4378,1007.59,1612.14,9.47167,49.5845,12.6482,1.78954,7.01548,2,24.6525,15.4603,41.8946,20.386,34.1242,,,Winter,2.09,Winter,2,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,7,Winter,37,53,Fall,,0 +e0767d64,Fall,13,1,,,Fall,15.76072912,63.5,90.4,25.0,84,88,120,,,,,Fall,11,0,16.1,2,21.7,2,4,0,0.0,0,4.5,0,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.21,Winter,5,4,4,2,4,3,2,4,4,2,4,0,4,2,5,5,5,5,4,2,70,Winter,54,75,Fall,1,2 +e07fa0b1,Fall,12,0,Summer,80,Winter,16.54508806,59.6,83.6,,56,87,111,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.9,Winter,2,3,3,3,4,2,3,4,4,3,4,0,4,4,4,4,5,4,5,4,69,Winter,53,73,Fall,2,2 +e0869709,Winter,17,0,Spring,61,Winter,17.88773018,63.5,102.6,,76,77,115,,,,,Winter,10,0,30.3,1,30.1,1,14,0,9.5,1,7.0,0,9.0,1,Spring,3,5.362,17.8897,1348.8,2292.95,24.3217,85.9276,14.9826,2.90705,16.6724,2,39.7322,21.8737,80.5656,41.5178,64.054,Winter,4.11,,,Winter,1,2,4,1,4,3,5,3,4,3,5,1,3,2,5,4,4,4,2,1,61,Winter,25,38,Winter,2,2 +e093a847,Winter,10,1,,,Winter,17.34534535,55.5,76.0,28.0,76,92,122,,,,,Winter,12,0,13.9,2,16.4,2,10,1,7.0,0,7.25,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,1.43,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +e0a3e8d5,Fall,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e0a7f42a,Spring,16,1,Fall,65,Spring,20.80013328,61.25,111.0,,85,96,124,,,,,Spring,14,0,21.1,2,19.5,2,8,1,6.25,0,5.75,0,9.0,1,Summer,1,4.10896,20.8024,1220.01,1586.01,21.896,72.2097,13.5327,7.26966,38.7903,1,28.8027,21.5111,68.1007,34.1865,50.6986,Spring,1.12,,,Spring,3,3,4,2,5,2,4,3,1,1,4,1,2,0,2,2,2,2,2,2,47,,,,Spring,1,1 +e0ad6550,Spring,8,0,Winter,61,Winter,16.48520152,51.75,62.8,,95,99,132,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,2,3.13681,16.487,1017.04,1627.27,9.15893,50.5916,13.2819,3.20509,12.2084,2,26.6311,14.8016,47.4548,27.6391,35.79,,,Summer,3.3,Summer,4,4,5,2,4,2,1,3,4,4,1,1,1,2,2,2,2,2,5,5,56,Summer,35,50,Spring,0,2 +e0c4e965,Summer,15,1,,,Winter,19.41902428,67.0,124.0,,67,68,119,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.89,,,Summer,3,2,2,1,2,0,2,1,1,2,2,0,1,0,1,1,1,1,1,1,25,Summer,33,47,Summer,0,0 +e0db55b1,Winter,17,0,Summer,87,Spring,23.67743056,72.0,174.6,,70,107,131,,,,,Spring,78,1,44.1,2,39.7,2,30,1,11.5,1,13.0,1,11.0,1,Spring,4,7.58447,23.68,1780.62,3739.31,41.3477,131.923,17.8919,5.78808,42.6773,2,55.761,34.814,124.338,62.3775,97.1087,Winter,2.48,,,Spring,3,3,3,3,3,3,1,1,1,1,3,0,2,3,3,1,1,1,2,1,39,,,,Winter,0,1 +e0e35d4e,Winter,14,0,Summer,91,Spring,19.52777778,60.0,100.0,,59,94,97,,,,,Spring,33,1,18.0,1,17.7,1,16,1,7.0,0,9.0,1,13.0,1,Spring,3,3.98742,19.5299,1265.01,2150.51,21.954,77.003,15.0386,4.4913,22.997,1,35.3734,19.6756,73.0155,31.0467,57.3274,,,Winter,1.72,Spring,4,4,2,0,4,1,4,3,2,3,2,0,0,1,0,0,1,1,0,0,32,Spring,36,51,Winter,3,1 +e0eb3ea8,Winter,10,0,Spring,45,Spring,14.8191568,52.0,57.0,22.0,62,89,109,,,,,Spring,0,0,,,,,0,0,7.0,0,8.0,1,6.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.14,Spring,4,3,5,0,5,1,2,4,0,4,0,0,4,3,5,5,4,4,4,4,61,Spring,58,80,Winter,0,2 +e0fb3838,Fall,6,1,,,Fall,14.86377478,44.25,41.4,22.0,70,87,109,,,,,Fall,0,0,,,,,0,0,9.5,1,11.0,1,3.0,0,Fall,2,2.02628,14.8654,902.788,1354.18,9.36074,38.4217,13.796,1.0694,2.97826,1,19.1464,9.91463,36.3955,13.7937,28.5071,,,,,Fall,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,,,,Fall,0,0 +e0fbff46,Summer,6,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e115d780,Fall,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e11be9c1,Winter,10,0,Spring,70,Winter,16.29725652,54.0,67.6,,59,78,102,Winter,,,,Winter,30,,11.3,1,14.6,2,50,,9.0,,8.0,,10.5,,Winter,4,5.18854,16.299,1066.71,2240.09,11.3146,55.8818,13.4737,2.82537,11.7182,2,29.721,14.8462,50.6933,33.8042,41.0356,,,Winter,4.2,Spring,4,4,4,0,3,0,0,0,1,1,1,0,2,0,1,2,1,1,1,2,28,Spring,51,71,Winter,0,0 +e127c234,Fall,9,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.769,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +e12c2944,Fall,10,0,,,Winter,27.04678363,57.0,125.0,,53,65,109,Winter,4,6,6,Winter,1,0,,,,,5,0,9.5,1,10.0,1,10.0,1,Winter,1,11.7784,27.0497,1262.75,1641.57,19.8682,76.7621,16.6112,10.4386,48.2379,3,36.3219,20.572,64.9837,36.1982,56.1901,,,Winter,1.48,Winter,5,5,5,0,5,1,0,4,1,4,3,0,4,4,5,5,5,3,1,4,64,Winter,47,66,Fall,2,2 +e14a6349,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.36,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +e15fe97b,Spring,15,0,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.34,,,Spring,0,1,1,1,1,0,0,0,1,0,2,0,1,0,0,0,2,1,1,1,13,Spring,37,53,Spring,0,0 +e16a4784,Fall,14,0,Fall,50,Spring,27.6564491,67.0,176.6,,84,88,130,,,,,Spring,,,,,,,,,,,,,,,Spring,3,6.05559,27.6595,1716.78,2918.52,39.784,125.122,19.5969,8.06252,51.4775,2,53.5005,31.838,119.067,56.2063,93.2845,,,Fall,3.7,Winter,5,5,4,4,5,5,0,4,5,5,0,1,5,2,5,5,5,5,5,5,80,Fall,67,91,Fall,2,3 +e17b9cae,Spring,12,0,,,Spring,27.08543182,59.5,136.4,35.0,72,64,132,,,,,Spring,3,0,17.4,1,16.1,1,3,0,11.5,1,11.5,1,7.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,2.6,Summer,5,5,5,1,5,2,1,4,4,4,2,0,1,5,4,2,5,2,5,3,65,Summer,63,86,Spring,2,2 +e1958a83,Winter,6,1,Summer,55,Spring,15.98836806,48.0,52.4,,68,67,117,Spring,4,6,26,Spring,9,1,,,,,1,0,10.0,1,9.0,1,10.0,1,Spring,2,2.38103,15.9901,960.126,1440.19,10.9624,44.529,13.5883,2.40186,7.87097,1,21.3113,12.2554,42.148,20.0437,32.2736,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,33,47,Winter,0,0 +e199d438,Winter,8,0,Summer,45,Spring,23.33304219,53.5,95.0,30.0,,100,,,,,,Spring,7,1,,,,,0,0,7.0,0,9.5,1,9.5,1,,,,,,,,,,,,,,,,,,,,Spring,3.48,Spring,3,1,2,0,2,1,0,1,1,1,1,1,3,1,3,4,1,3,0,2,31,Spring,58,80,Spring,2,1 +e19f89b8,Winter,13,0,,,Winter,18.43034894,66.0,114.2,30.0,48,63,108,,,,,Winter,8,0,23.1,2,23.2,2,7,0,6.0,0,6.0,0,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,4.17,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +e1a4575f,Spring,8,1,Spring,80,Summer,18.30642093,58.0,87.6,,78,90,112,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.04,Spring,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Spring,30,43,Spring,0,0 +e1af39f8,Winter,15,0,Fall,80,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.69,,,Winter,3,3,2,3,4,4,4,2,3,3,2,1,2,2,2,,2,2,2,1,47,Winter,31,45,Winter,0,1 +e1ccc3d1,Spring,8,1,Summer,61,Spring,20.13054847,56.0,89.8,,80,92,126,Spring,6,9,46,Spring,20,1,,,,,7,1,11.0,1,11.0,1,12.0,1,Spring,4,3.41701,20.1327,1121.18,2130.24,16.4858,61.6834,13.8291,6.30362,28.1166,2,28.2356,16.962,58.2663,41.1292,44.7213,,,Spring,2.22,Spring,1,0,1,0,1,0,0,0,0,1,0,0,1,1,1,,1,1,0,0,9,Spring,33,47,Spring,0,0 +e1e02e28,Winter,5,0,Fall,50,Spring,18.25852632,47.5,58.6,,78,93,137,Spring,,,,Spring,0,0,,,,,0,0,8.0,1,8.5,1,3.0,0,Spring,2,2.76433,18.2605,964.084,1542.53,9.34071,44.9506,14.0072,4.25333,13.6494,2,24.4984,11.1115,42.1863,18.1459,33.8391,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Winter,2,0 +e1ea8dd7,Fall,11,0,Winter,81,Winter,26.48421053,47.5,85.0,,54,54,104,Winter,6,9,5,Fall,26,1,13.7,2,17.8,2,18,1,4.0,0,4.5,0,6.5,0,Fall,4,4.46643,16.8806,1204.28,2528.98,18.8831,70.5343,14.0078,2.87282,14.4657,1,33.2727,18.3785,66.0678,30.3387,52.1558,,,Winter,4.6,Winter,2,2,2,1,1,1,1,1,1,2,2,0,1,1,1,2,1,2,1,1,26,Winter,38,54,Fall,2,0 +e1f3777c,Spring,11,1,,,Spring,17.40838839,59.0,86.2,28.0,83,56,115,,,,,Spring,4,0,14.1,2,11.4,1,0,0,0.0,0,0.0,0,13.0,1,,,,,,,,,,,,,,,,,,,,Spring,2.009,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +e1f8984b,Spring,13,1,Fall,40,Spring,29.74016443,61.0,157.4,36.0,59,79,117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e21255e2,Fall,11,0,Summer,50,Winter,18.09497041,52.0,69.6,,71,91,107,,,,,Winter,10,0,16.3,2,15.9,2,10,1,9.0,1,10.0,1,9.0,1,Winter,3,3.14967,18.0969,1035.77,1760.8,10.4086,52.5858,13.673,4.42393,17.0142,1,26.4491,15.7281,49.4361,23.6096,36.8576,,,,,Spring,2,2,4,0,3,1,1,1,4,1,1,0,2,4,2,3,2,1,2,0,36,Winter,48,67,Fall,2,1 +e223fbc1,Spring,14,0,Fall,60,Summer,18.44807307,67.0,117.8,,70,75,137,,,,,Summer,11,0,23.0,2,30.6,2,9,0,6.0,0,5.5,0,9.0,1,Summer,3,5.86895,18.4501,1418.46,2411.39,25.9146,93.3482,14.6204,3.8297,24.4518,3,42.0755,25.3581,87.4792,46.8653,67.9901,,,Summer,1.73,,,,,,,,,,,,,,,,,,,,,,,Summer,34,49,Spring,0, +e22cf08a,Spring,9,0,Fall,61,Summer,17.51868993,53.0,70.0,,72,55,103,Summer,6,9,17,Summer,5,0,,,,,10,1,5.0,0,5.5,0,6.0,1,Summer,4,3.57197,17.5206,1081.14,2270.39,14.2065,57.4187,14.3716,3.14904,12.5813,2,29.8353,13.3769,53.8467,26.647,44.0418,,,Spring,3.77,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,29,42,Spring,0,0 +e23a8a0a,Fall,10,0,Winter,65,Fall,18.77765289,55.0,80.8,,85,102,139,,,,,Fall,11,1,,,,,13,1,7.0,0,8.0,1,10.0,1,Fall,1,3.84324,18.7797,1135.68,1476.38,16.8435,63.2275,14.6955,4.08423,17.5725,2,30.9981,15.3858,59.3843,25.8011,47.8417,,,Fall,1.98,Winter,2,1,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,Fall,42,59,Fall,0,0 +e243119c,Spring,10,0,Fall,65,Spring,14.96596654,53.0,59.8,22.0,64,83,110,,,,,Spring,7,0,,,,,6,1,12.0,1,11.0,1,8.0,1,Spring,3,3.45288,14.9676,1039.54,1767.21,12.1261,52.9874,13.2625,1.70515,6.81256,1,27.5406,13.3207,49.5346,23.8365,39.6667,,,Summer,3.83,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,36,51,Spring,1,0 +e252dcb6,Spring,13,1,Fall,58,Spring,23.60526142,61.5,127.0,,57,74,110,,,,,Spring,20,1,22.5,2,23.7,2,1,0,12.0,1,12.0,1,12.0,1,Summer,2,401.002,23.6078,11540.8,17311.2,350.849,1171.51,217.771,-194.163,-1044.51,2,428.264,392.4,770.511,823.028,779.114,,,Summer,1.25,Summer,1,4,4,0,4,4,0,5,5,5,4,0,4,4,4,4,4,4,4,4,68,Summer,48,67,Spring,3,2 +e25f7b9a,Winter,10,0,Summer,78,Winter,19.40312212,52.75,76.8,29.0,61,79,121,,,,,Spring,12,1,12.1,1,10.8,1,6,0,12.0,1,12.0,1,10.0,1,Spring,3,3.28539,19.4052,1065.02,1810.53,12.2567,55.7015,14.0742,5.33101,21.0985,1,27.3865,16.0583,52.4161,22.6469,39.6432,,,Spring,3.56,Spring,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,2,2,2,1,2,35,Spring,27,40,Winter,0,1 +e2615947,Fall,9,0,Summer,55,Winter,21.47557989,55.0,92.4,28.5,68,83,119,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.7,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +e28c0bfa,Winter,9,0,Summer,65,Spring,22.52992742,51.5,85.0,,88,104,135,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,3.30197,22.5324,1086.68,1738.69,14.1409,58.0088,15.3774,7.15502,26.9912,2,29.4416,14.4263,54.7068,24.574,43.5825,,,Spring,1.01,Spring,4,0,4,4,1,0,0,4,2,1,5,0,2,0,1,2,4,4,5,0,43,Spring,58,80,Winter,2,1 +e296c5e4,Spring,7,0,Fall,80,Summer,13.66632,50.0,48.6,,59,77,103,Summer,8,14,30,Summer,10,1,,,,,2,0,4.0,0,0.0,0,8.5,1,,,,,,,,,,,,,,,,,,,,,,Summer,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,Summer,35,50,Spring,0,0 +e2cb8401,Summer,13,0,,,Summer,27.84368859,63.0,157.2,38.0,72,98,119,,,,,Summer,0,0,15.3,1,12.5,1,0,0,0.0,0,0.0,0,4.0,0,Summer,3,5.23181,27.8467,1467.24,2494.32,29.0107,98.5439,17.4563,10.3905,58.6561,3,43.2598,26.2734,93.3121,41.3588,72.2705,,,Fall,3.36,Fall,4,4,3,0,3,0,1,1,2,1,0,0,2,0,0,0,0,5,0,0,26,Fall,53,73,Summer,3,0 +e2d370a2,Fall,9,0,Spring,55,Winter,14.28591682,46.0,43.0,,61,79,112,Winter,6,9,31,Winter,15,1,,,,,7,1,14.0,1,11.5,1,11.0,1,Winter,4,5.13059,14.2875,906.728,1904.13,6.46206,38.8414,12.9057,1.38176,4.1586,1,22.7881,9.59123,33.7108,18.4758,29.2502,,,Fall,3.61,Winter,3,3,3,0,5,1,0,0,2,3,2,0,4,1,1,4,2,4,2,2,42,Winter,54,75,Fall,2,1 +e2fcc91e,Summer,12,1,,,Summer,15.81734649,53.5,64.4,25.0,65,83,104,,,,,Fall,20,1,13.2,1,13.0,1,9,1,10.5,1,11.0,1,12.0,1,Fall,2,3.09904,15.8191,1048.91,1573.36,15.6933,53.9852,13.2608,2.55827,10.4148,1,24.138,14.1539,50.8861,22.4836,39.8312,,,Summer,3.5,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Summer,0,0 +e30c60ff,Summer,6,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +e315fdd7,Spring,14,0,,,Summer,34.13823314,67.4,220.6,,103,82,183,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.13,,,Summer,3,3,3,0,3,0,0,1,1,1,1,0,0,0,1,0,1,1,1,0,20,Summer,35,50,Spring,1,0 +e318ec9a,Summer,7,0,,,Summer,13.73046875,48.0,45.0,20.0,56,67,101,,,,,Summer,9,1,,,,,0,0,10.6,1,9.2,1,4.5,0,Winter,2,2.68798,13.732,930.881,1489.41,7.27083,41.414,12.6377,1.09427,3.58596,1,21.7814,12.3618,38.7261,15.008,29.0522,,,,,Summer,1,1,1,0,2,1,0,1,1,2,2,0,1,1,2,1,1,2,1,1,22,Summer,43,60,Summer,0,0 +e328a032,Fall,8,0,,,Winter,16.8337415,52.5,66.0,,54,54,96,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.17,Winter,2,2,3,3,4,0,0,1,1,0,2,0,1,0,2,1,2,1,2,1,28,Winter,52,72,Fall,0,0 +e32ee582,Spring,13,1,Spring,72,Summer,17.45510116,68.0,114.8,27.0,80,92,127,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.69,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +e345b271,Summer,8,0,Winter,75,Summer,18.10928,50.0,64.4,,136,67,165,Summer,3,5,0,Summer,5,0,,,,,0,0,6.0,0,6.0,0,5.5,1,Summer,1,3.20111,18.1113,1039.53,1351.39,13.3083,52.9864,14.9014,3.20986,11.4136,1,28.8561,10.8221,49.7853,24.4047,42.1643,,,Summer,1.59,Fall,4,3,5,1,5,1,0,1,1,0,3,0,3,0,3,2,4,3,0,2,41,Summer,35,50,Summer,0,1 +e34b9430,Summer,13,0,Fall,45,Fall,20.02832653,70.0,139.6,,58,71,119,,,,,Fall,30,1,31.7,3,16.5,1,2,0,6.0,0,4.5,0,10.0,1,Fall,2,6.67498,20.0305,1560.44,2496.7,33.0301,108.47,15.5639,4.46663,31.1296,3,46.2157,29.2246,101.795,46.8478,79.2458,,,Fall,3.51,Summer,4,4,3,2,3,4,2,3,5,4,0,0,0,0,0,2,3,3,3,1,46,Summer,57,79,Summer,0,1 +e362108f,Fall,9,1,Spring,55,Spring,20.79910615,53.6,85.0,,75,101,139,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.31,Spring,2,1,3,1,3,1,0,1,2,2,1,0,2,0,2,2,0,2,1,1,27,Winter,41,58,Fall,2,0 +e37d000b,Summer,8,1,Fall,63,Summer,19.02048819,50.5,69.0,,76,84,127,,,,,Summer,6,1,,,,,2,0,9.0,1,7.0,0,5.0,0,Summer,1,2.63304,19.0226,1014.1,1318.33,13.6598,50.2782,13.8612,5.16139,18.7218,2,22.0661,14.5523,47.6452,19.7487,35.7259,,,,,Summer,1,1,1,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,Summer,29,42,Summer,2,0 +e3a6f97f,Fall,16,1,,,Fall,29.8850625,60.5,155.6,32.0,70,70,121,,,,,Fall,15,0,24.5,2,26.8,2,0,0,12.0,1,10.0,1,12.0,1,Fall,1,4.71473,29.4004,1389.91,1806.88,32.4763,90.3064,17.0632,12.3371,65.2936,3,35.5642,22.2659,85.5917,40.9242,68.0405,Fall,1.12,,,Fall,1,0,4,1,2,2,1,3,1,1,5,1,1,3,2,2,5,2,5,2,44,,,,Fall,3,1 +e3aa85a3,Spring,7,0,Fall,55,Spring,19.70699604,50.75,72.2,,84,98,129,Spring,6,10,3,Spring,6,1,,,,,0,0,8.0,1,8.5,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,4,2,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,Spring,44,62,Spring,0,0 +e3aa9dcb,Spring,11,1,Fall,81,Summer,18.77641602,64.0,109.4,,63,99,109,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.28,Spring,4,4,4,1,4,1,1,2,1,2,2,1,2,1,2,1,3,2,1,1,40,Spring,49,68,Spring,1,1 +e3af914a,Winter,9,1,Summer,79,Winter,16.60074853,59.5,83.6,,77,111,112,Winter,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.01,Spring,3,5,4,1,2,2,1,2,3,2,4,1,2,3,2,1,3,1,1,1,44,Spring,58,80,Winter,0,1 +e3bb3735,Spring,12,1,Winter,78,Winter,17.575,60.0,90.0,,77,71,136,,,,,Spring,,,,,,,,,,,,,,,Summer,1,4.04125,17.5769,1182.72,1537.54,21.483,68.2385,13.3269,4.24999,21.7615,2,27.8925,18.863,64.1973,28.5467,49.3755,,,Spring,2.43,Spring,3,3,3,0,4,2,0,2,2,2,2,0,1,0,2,2,2,2,3,2,37,Spring,42,59,Spring,2,1 +e3bce7e1,Summer,11,0,Fall,70,Fall,22.44915285,61.25,119.8,,81,97,174,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.51,Fall,2,2,2,1,2,0,0,2,1,2,1,0,2,0,1,1,1,2,0,2,24,Fall,44,62,Summer,0,0 +e3cec795,Winter,9,0,Fall,80,Winter,16.06295746,53.5,65.4,,72,84,114,Winter,6,9,44,Winter,11,1,,,,,8,1,11.0,1,11.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.77,Winter,3,2,4,0,4,3,0,1,3,3,0,0,3,1,3,4,3,4,3,2,46,Spring,34,49,Winter,0,1 +e3d5aab8,Fall,7,1,Spring,61,Winter,13.51403306,51.0,50.0,,59,78,99,Winter,4,4,27,Winter,0,0,,,,,6,1,11.0,1,9.0,1,9.0,1,Winter,3,2.76827,13.5155,983.251,1671.53,5.47662,46.9922,12.7025,0.813039,3.0078,1,22.173,19.3426,44.2239,22.4007,27.6496,,,,,Winter,2,0,0,0,2,0,0,1,1,1,0,0,0,0,0,1,2,2,0,0,12,Winter,35,50,Fall,0,0 +e3ef6064,Summer,9,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.79,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +e3f239e9,Summer,8,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e3f71f7a,Winter,20,0,,,Winter,33.56123656,70.25,235.6,,84,87,154,,,,,,,,,,,,,,,,,,,,Summer,1,7.71517,33.5649,2024.56,2631.92,49.6853,157.905,22.496,11.0689,77.6952,2,66.984,41.2356,150.19,82.8488,116.669,,,,,Spring,1,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,6,Spring,54,75,Winter,0,0 +e40319d7,Fall,9,0,Summer,51,Winter,18.90096022,54.0,78.4,,62,102,121,Winter,4,6,33,Winter,2,0,,,,,0,0,7.5,1,8.0,1,11.0,1,Spring,2,5.48633,12.6541,1274.23,2038.77,21.2429,77.9854,12.5872,0.0669241,0.414636,2,35.0963,21.6462,72.499,34.0708,56.3392,,,Winter,1.69,Spring,5,5,4,0,3,0,0,0,0,2,4,0,0,3,0,0,2,3,0,0,31,Spring,53,73,Fall,0,1 +e4614ec6,Summer,6,0,Winter,60,Fall,20.18946281,44.0,55.6,,76,101,113,,,,,Fall,3,1,,,,,0,0,9.0,1,10.0,1,10.0,1,Winter,2,2.46827,20.1917,920.539,1472.86,8.075,40.3124,14.6398,5.55182,15.2876,2,23.87,8.36743,37.8442,17.3903,31.945,,,,,Fall,4,4,4,0,4,1,0,2,1,2,4,0,3,0,4,2,4,3,3,1,46,,,,Summer,2,1 +e46334db,Spring,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e46417a7,Spring,6,1,,,Spring,15.75601684,41.5,38.6,,65,97,92,,,,,Spring,3,1,,,,,3,1,12.5,1,13.0,1,8.0,1,Spring,2,1.84607,15.7577,866.847,1300.27,8.47819,34.5935,14.1222,1.63556,4.00645,1,19.1745,6.9409,32.7475,13.9285,27.6526,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +e4762a2b,Spring,11,0,Fall,51,Spring,16.6829904,54.0,69.2,23.0,83,99,119,,,,,Spring,15,1,19.3,2,18.7,2,5,0,9.0,1,8.0,1,9.0,1,Spring,2,3.99627,16.6848,1149.36,1838.98,18.1253,64.6849,15.5962,1.08863,4.51506,1,33.429,13.1307,60.6887,30.992,51.5543,,,Summer,2.39,Summer,5,5,3,3,5,1,1,2,1,3,0,0,0,0,3,0,3,0,1,0,36,Summer,42,59,Spring,2,1 +e47c1eb7,Fall,12,0,Summer,68,Fall,19.18405371,54.75,81.8,,66,77,112,,,,,Fall,30,1,17.3,2,17.6,2,12,1,8.0,1,6.0,0,12.0,1,Fall,3,3.78745,19.1861,1131.32,1923.25,16.1542,62.7637,14.7212,4.46496,19.0363,2,31.17,15.4394,58.9762,27.3644,47.3242,,,Winter,2.22,Fall,5,4,2,1,3,3,1,1,1,3,2,1,2,5,3,1,1,1,1,1,42,Fall,33,47,Fall,1,1 +e490d5c5,Fall,15,0,,,Fall,24.51378181,63.0,138.4,33.0,70,77,108,,,,,Fall,8,0,34.4,2,33.5,2,1,0,14.0,1,12.5,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,1.84,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3, +e4a2e3bc,Fall,7,0,Spring,41,Winter,19.71085069,48.0,64.6,,91,95,127,Winter,,,,Winter,10,1,,,,,3,0,10.0,1,11.0,1,12.0,1,Spring,3,2.67709,19.713,979.072,1664.42,8.1767,46.547,14.2041,5.50895,18.053,2,24.1004,14.2699,43.8699,19.5861,32.2771,,,,,Winter,5,5,5,5,5,1,0,5,1,2,5,0,5,1,1,1,3,1,5,1,57,Winter,82,100,Fall,0,2 +e4a790a8,Spring,6,1,,,Spring,14.6972352,44.5,41.4,21.0,45,108,74,Fall,,,,Spring,0,0,,,,,0,0,18.0,1,17.0,1,7.5,1,Spring,2,1.99437,14.6988,909.724,1364.59,8.60036,39.1605,13.9037,0.795119,2.23949,1,18.9202,11.6399,37.1661,14.9213,27.5206,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +e4ab3b12,Summer,8,0,Spring,67,Summer,13.46855477,49.0,46.0,20.0,39,52,94,,,,,Summer,0,0,,,,,0,0,9.5,1,8.5,1,8.0,1,Summer,3,2.81838,13.47,944.496,1605.64,7.38973,42.8642,12.5518,0.918239,3.13578,1,23.1076,12.3669,40.0458,18.7633,30.4974,,,Winter,4.019,Winter,3,3,3,0,2,0,0,1,0,1,0,0,3,0,4,4,0,3,0,1,28,Winter,35,50,Summer,0,0 +e4b47411,Spring,8,1,,,Spring,15.58983276,53.3,63.0,,67,82,114,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.99,Spring,5,5,4,0,5,1,0,2,3,2,2,0,3,0,3,2,3,3,2,2,47,Spring,52,72,Spring,1,1 +e4c008b9,Summer,9,1,Summer,70,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.51,Summer,2,2,3,3,3,2,0,2,1,1,1,0,1,2,2,1,1,1,1,1,30,Summer,42,59,Summer,0,0 +e4d396d7,Fall,8,0,Spring,75,Fall,15.466,50.0,55.0,,62,85,116,Fall,6,9,56,Fall,14,1,,,,,13,1,9.0,1,8.0,1,7.0,1,Fall,5,1.90275,15.4677,1011.66,2427.98,10.9431,50.0178,14.0665,1.40115,4.98219,1,28.911,10.1636,48.1151,29.6018,39.8542,,,Fall,4.01,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Fall,38,54,Fall,0,0 +e4d5bf2b,Summer,15,1,Fall,72,Summer,26.62248521,65.0,160.0,,95,95,150,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.67,,,Summer,4,4,4,1,2,1,2,1,1,1,4,1,1,1,1,1,1,1,1,1,34,Summer,33,47,Summer,2,1 +e4db6712,Winter,8,1,,,Spring,16.19712,50.0,57.6,,107,81,132,Spring,4,7,0,Spring,5,,,,,,0,,10.0,,9.0,,5.0,,,,,,,,,,,,,,,,,,,,,,,Spring,3,3,2,1,3,2,0,2,1,2,2,1,2,1,1,2,1,2,1,2,34,Spring,44,62,Winter,2,1 +e4e58096,Fall,12,1,Spring,45,Winter,16.36613757,63.0,92.4,,53,83,105,Winter,,,,Winter,7,0,14.9,2,15.7,2,0,0,6.0,0,6.5,0,5.0,0,Winter,1,4.89269,16.3679,1216.24,1581.11,22.479,71.8086,12.7203,3.64761,20.5914,1,27.9176,21.412,66.9159,28.1972,50.3966,,,Winter,2.12,Winter,3,3,3,0,3,0,0,1,2,1,1,0,0,1,0,0,0,1,0,0,19,Winter,39,55,Fall,0,0 +e4ee6ff3,Winter,14,1,Spring,65,Spring,19.19977723,67.0,122.6,,69,79,118,,,,,Spring,13,0,28.5,3,31.7,3,0,0,6.5,0,8.5,0,10.0,1,Spring,2,5.52588,18.9185,1382.45,2073.67,30.2508,89.5118,13.8126,5.10586,33.0882,3,34.3491,24.9119,83.9859,39.2425,64.5999,,,Winter,1.3,Spring,0,0,2,2,1,0,0,1,1,0,4,0,2,0,1,1,1,1,1,1,19,Spring,42,59,Winter,2,0 +e51193d1,Winter,9,0,Spring,75,Winter,14.68753603,53.5,59.8,,11,96,137,Winter,4,5,4,Winter,43,1,,,,,14,1,10.0,1,10.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.57,Winter,5,5,5,1,5,1,0,0,0,5,4,0,3,0,4,5,4,4,0,0,51,Winter,29,42,Winter,0,2 +e515c971,Winter,10,1,,,Spring,30.37957538,61.0,160.8,,56,77,109,Spring,5,8,20,Spring,2,0,22.4,2,28.0,3,0,0,9.0,1,9.0,1,7.0,0,Spring,3,4.59068,30.3829,1372.66,2196.26,31.5207,88.4698,16.7162,13.6667,72.3302,2,34.5113,22.4378,83.8792,39.765,66.032,,,Winter,2.09,Spring,1,4,4,2,5,0,0,1,0,1,2,0,0,4,4,0,1,5,0,1,35,Spring,28,41,Spring,2,1 +e5349ce2,Summer,7,0,Spring,50,Winter,15.00291211,48.5,50.2,,58,91,100,Winter,2,1,15,Winter,6,1,,,,,3,0,5.5,0,6.5,0,8.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,2,0,1,0,0,0,0,1,1,0,1,0,2,1,1,1,1,0,16,Winter,34,49,Summer,0,0 +e537418f,Winter,13,1,Spring,75,Spring,26.33783854,60.25,136.0,32.0,59,85,117,,,,,Spring,12,0,21.8,2,21.2,2,5,0,12.5,1,14.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.38,Spring,5,4,4,0,4,2,0,2,1,2,0,0,0,0,2,2,3,3,0,2,36,Spring,40,56,Winter,3,1 +e54a78e9,Fall,8,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e54b2178,Spring,12,0,,,Spring,19.22735043,58.5,93.6,27.0,68,61,114,,,,,Spring,15,0,20.2,2,24.0,3,11,1,6.5,0,6.5,0,8.0,0,Spring,,,,,,,,,,,,,,,,,,,Spring,3.651,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +e550946f,Winter,9,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e56b2209,Spring,9,0,Fall,61,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.6,Summer,1,1,3,2,3,1,1,1,0,0,3,0,2,0,3,1,3,1,0,0,26,Summer,33,47,Spring,0,0 +e5b902bc,Fall,16,0,,,Fall,17.36165852,63.25,98.8,26.0,72,63,108,,,,,Fall,11,0,18.6,1,24.3,1,10,0,10.0,1,10.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,5,5,4,1,5,1,1,4,2,4,4,1,2,4,4,1,5,1,5,1,60,Fall,37,53,Winter,2,2 +e5d77ff3,Summer,10,0,,,Summer,15.23513314,52.0,58.6,24.0,70,94,102,,,,,Summer,10,0,,,,,3,0,6.5,0,6.5,0,6.0,0,Summer,,,,,,,,,,,,,,,,,,,Summer,3.51,Summer,4,4,3,0,3,1,0,1,0,4,3,0,2,3,5,3,3,3,3,2,47,Summer,55,76,Summer,1,1 +e5db13bc,Spring,8,0,,,Summer,21.84218107,54.0,90.6,29.0,69,87,129,,,,,Summer,2,0,,,,,0,0,9.0,1,10.5,1,11.0,1,Summer,,,,,,,,,,,,,,,,,,,Spring,3.79,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +e5defd66,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e6169c6d,Winter,9,0,Spring,81,Spring,16.68070748,52.5,65.4,,64,72,121,Spring,,,,Spring,20,1,,,,,20,1,10.0,1,9.0,1,10.0,1,Spring,4,3.51529,16.6825,1064.31,2235.04,12.022,55.6255,14.1892,2.49333,9.77452,1,30.9397,12.6638,52.1102,35.1898,42.9616,,,Winter,4.74,Spring,2,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,6,Spring,36,51,Winter,0,0 +e6276aaa,Winter,8,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e63000c5,Summer,7,1,Winter,80,Fall,15.050995,50.5,54.6,,61,75,96,Fall,6,10,42,Fall,5,1,,,,,6,1,10.0,1,11.0,1,9.0,1,Fall,3,2.62274,15.0526,991.308,1586.09,11.9398,47.8504,13.1918,1.8608,6.74962,1,21.9572,13.9534,45.2276,21.2818,33.897,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,37,53,Summer,0,0 +e63a0ea8,Fall,8,0,Winter,70,Fall,18.1398823,47.0,57.0,,75,110,117,Fall,6,9,52,Fall,2,0,,,,,3,0,11.0,1,11.0,1,8.0,1,Fall,4,5.06763,17.7619,966.462,2029.57,9.52741,45.2039,14.0861,3.67581,11.7961,1,25.5795,10.097,40.1363,20.9659,35.1069,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,29,42,Fall,0,0 +e664dd71,Spring,7,1,Fall,65,Summer,16.48501584,47.0,51.8,,61,91,123,Summer,4,6,20,Summer,2,1,,,,,0,0,8.0,0,10.0,1,7.0,1,Summer,2,9.84685,16.4868,948.052,1422.08,10.9885,43.243,13.7633,2.72352,8.55704,2,20.939,11.3154,33.3961,18.1539,31.9275,,,,,Summer,4,2,2,0,3,1,0,1,1,1,1,0,2,3,3,3,3,3,2,1,36,Summer,32,46,Spring,0,1 +e670804a,Spring,8,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.21,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +e683d2c9,Fall,9,0,Spring,65,Winter,18.04297337,52.0,69.4,,82,71,152,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,5,3.35692,18.0449,1053.91,2529.37,12.4987,54.5178,14.1754,3.86958,14.8822,2,28.6235,13.3956,51.1609,25.9431,41.1222,,,Fall,1.75,Fall,3,2,4,0,2,1,0,1,2,1,2,0,2,2,2,3,3,2,2,2,36,Fall,51,71,Fall,1,1 +e68cf012,Summer,11,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.09,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +e715739b,Spring,10,1,Fall,61,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.36,Summer,4,3,3,1,3,3,0,2,2,0,4,0,3,2,2,2,2,2,0,0,38,Summer,39,55,Spring,2,1 +e71f347e,Winter,12,0,Spring,65,Winter,26.7491082,58.0,128.0,36.0,65,82,129,,,,,Winter,3,0,14.0,2,12.1,1,0,0,0.0,0,0.0,0,5.0,0,Winter,1,4.37543,26.752,1311.39,1704.81,22.556,81.9434,17.1262,9.62584,46.0566,3,38.5553,20.8321,77.568,38.3215,61.1113,,,Spring,1.62,Winter,4,3,3,0,2,1,0,0,0,0,3,0,1,1,1,1,4,1,4,1,30,Winter,40,56,Spring,2,0 +e72af462,Spring,15,0,,,Summer,22.63659809,67.75,147.8,37.0,70,75,113,,,,,Summer,12,0,15.9,1,17.3,1,0,0,4.0,0,5.0,0,7.0,0,,,,,,,,,,,,,,,,,,Summer,1.49,,,Summer,1,1,3,2,2,1,1,1,1,1,2,1,1,2,3,0,3,2,3,5,36,Summer,34,49,Spring,2,1 +e735a855,Spring,15,1,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3, +e739c579,Fall,11,1,Spring,55,Winter,27.36199869,58.5,133.2,,62,70,108,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.63,Winter,5,2,5,4,4,2,0,2,2,2,5,1,1,1,1,1,1,1,1,1,42,Winter,59,81,Fall,1,1 +e7401605,Summer,6,1,Fall,60,Summer,14.07875244,42.75,36.6,,57,97,106,,,,,Summer,0,0,,,,,1,0,11.0,1,10.5,1,10.0,1,Summer,2,1.81604,14.0803,890.501,1335.75,7.26169,37.1131,14.2777,-0.197376,-0.513054,1,17.9973,11.8541,35.297,13.8739,25.259,,,,,Summer,3,2,1,0,2,0,0,0,0,0,2,0,2,0,0,0,0,2,0,0,14,,,,Summer,2,0 +e749b8af,Winter,18,1,,,Winter,31.27737615,63.5,179.4,38.0,62,71,118,,,,,Winter,5,0,28.8,2,24.7,2,0,0,14.0,1,14.0,1,12.0,1,,,,,,,,,,,,,,,,,,Winter,1.041,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +e754411f,Spring,14,1,Summer,50,Summer,22.20192508,62.0,121.4,,93,65,139,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.47,,,Spring,2,2,2,1,2,1,3,0,1,2,3,0,1,0,1,1,1,1,1,1,26,Spring,27,40,Spring,3,0 +e76ad778,Summer,14,1,Fall,80,Summer,20.53437653,60.75,107.8,,77,70,123,,,,,Summer,20,1,22.0,2,27.8,3,12,1,11.0,1,13.0,1,6.0,0,Summer,2,4.4111,20.5366,1259.51,1889.27,25.3721,76.4178,14.5581,5.97851,31.3822,2,31.607,19.4387,72.0067,35.4938,56.9791,,,Summer,1.94,Summer,4,4,4,1,4,2,1,2,2,2,3,1,4,2,2,2,2,5,2,2,51,Summer,49,68,Summer,2,2 +e7733d6c,Winter,11,1,,,Winter,16.75066818,53.5,68.2,25.0,72,72,115,,,,,Summer,21,1,13.1,2,15.9,2,14,1,7.0,0,9.0,0,12.0,1,Fall,3,3.21135,16.7525,1066.62,1706.6,17.3663,55.8725,13.7244,3.02809,12.3275,1,25.182,13.3243,52.6612,22.9299,42.5483,,,Winter,1.83,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +e78faee3,Fall,13,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +e79487a3,Spring,7,1,Winter,80,Spring,17.3329539,48.25,57.4,,50,99,98,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,3,2.65085,17.3348,987.258,1579.61,14.5277,47.4189,14.3206,3.0143,9.98108,1,23.8372,9.05404,44.7681,20.7851,38.3649,,,Winter,2.7,Winter,3,2,3,0,3,1,0,0,0,0,2,0,1,0,3,2,2,0,1,0,23,Winter,41,58,Spring,,0 +e7a825e6,Fall,14,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.029,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +e7bbb32e,Winter,8,1,Fall,61,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.07,Winter,2,5,4,0,4,3,0,3,0,1,4,0,4,1,5,2,3,2,0,4,47,Winter,69,94,Winter,1,1 +e7cd2202,Summer,8,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e7cef620,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e7d08824,Winter,15,1,Summer,61,Spring,31.61420118,65.0,190.0,,82,86,125,,,,,Spring,3,0,22.9,2,20.1,2,0,0,5.7,0,6.2,0,12.0,1,Spring,3,5.22844,31.6177,1475.19,2360.31,35.533,99.3906,16.5395,15.0782,90.6094,3,36.9922,26.8655,94.1622,45.1836,72.5252,Winter,1.45,,,Spring,0,1,2,1,2,1,3,0,0,0,2,0,1,1,2,0,2,0,0,0,18,Spring,40,56,Spring,1,0 +e7db432d,Fall,16,0,,,Fall,25.66314879,68.0,168.8,33.0,75,99,119,,,,,Fall,50,1,28.4,1,28.4,1,19,1,11.0,1,12.25,1,7.5,0,Fall,4,6.95786,25.666,1750.31,3675.66,40.5822,128.694,19.5679,6.09802,40.1055,2,55.6271,32.4852,121.737,62.8429,96.2093,Fall,2.39,,,Fall,2,3,2,1,2,1,1,1,1,2,1,0,1,1,2,1,1,1,1,1,26,,,,Fall,0,0 +e7db9aef,Spring,13,0,Summer,62,Spring,15.63507642,59.75,79.4,24.0,48,52,99,,,,,Spring,10,0,16.2,1,17.8,1,2,0,6.5,0,7.0,0,11.0,1,Summer,5,4.32578,15.6368,1169.34,2806.42,16.4942,66.813,13.1579,2.47883,12.587,1,31.4499,18.8689,62.4873,29.389,47.9441,,,Summer,2.93,Summer,5,5,4,3,5,2,1,3,3,3,2,0,1,2,4,3,3,3,2,2,56,Summer,49,68,Spring,0,2 +e7e43817,Summer,9,1,Fall,90,Fall,17.00448147,59.0,84.2,,64,81,104,Fall,6,9,51,Fall,18,1,,,,,10,1,11.0,1,10.0,1,10.0,1,Fall,3,4.6711,17.0063,1162.99,1860.79,21.1549,66.1369,13.358,3.6483,18.0631,2,27.2037,17.7784,61.4658,26.0859,48.3585,,,Fall,3.18,Fall,4,3,2,1,2,1,1,2,3,2,1,1,1,1,1,1,2,1,1,1,32,Fall,35,50,Summer,0,1 +e7ee0921,Fall,7,1,Winter,80,Fall,17.62699704,52.0,67.8,,72,108,122,Fall,,,,Fall,0,0,,,,,0,0,10.0,1,9.5,1,9.0,1,Fall,2,6.07719,17.6289,1044.93,1567.4,14.97,53.562,13.9268,3.70208,14.238,2,26.1145,12.4774,47.4848,30.9251,41.0846,,,,,Fall,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,2,15,Fall,34,49,Fall,0,0 +e810389a,Summer,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e818ad1b,Winter,6,1,Summer,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,34,49,,, +e81bef0e,Summer,16,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e8313da5,Winter,5,0,Spring,70,Winter,13.88435455,41.0,33.2,,66,116,115,Winter,,,,Winter,9,1,,,,,2,0,7.0,0,8.0,1,6.0,1,Winter,1,5.99292,13.8859,830.308,1079.4,3.69834,30.7017,12.8409,1.04492,2.49833,1,19.2747,7.72862,24.7088,12.1372,22.9731,,,,,Winter,4,0,4,0,5,0,0,0,0,1,4,0,3,1,4,5,0,4,0,0,35,Winter,40,56,Winter,0,1 +e838df3b,Winter,7,1,Summer,80,Spring,16.35613258,48.0,53.6,21.0,72,88,109,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,34,49,,, +e86bb780,Winter,7,1,Summer,59,Spring,16.07996219,46.0,48.4,,67,99,124,Spring,3,4,48,Spring,7,1,,,,,1,0,11.0,1,11.0,1,10.0,1,Spring,3,2.24483,16.0817,931.715,1490.74,10.6126,41.5028,13.79,2.2917,6.89716,1,21.1055,9.78478,39.258,18.5254,31.7181,,,,,Summer,0,0,3,0,2,0,0,1,0,2,0,0,1,0,2,1,1,1,1,1,16,Summer,37,53,Winter,0,0 +e86f8006,Winter,14,0,,,Winter,23.87614009,63.0,134.8,33.0,70,73,124,,,,,Winter,14,,34.3,2,37.7,2,7,,10.0,,10.5,,11.0,,Winter,4,5.74514,23.8787,1522.76,3197.81,32.4118,104.457,18.5038,5.37494,30.3425,2,46.8814,25.1643,98.7123,48.6963,79.2932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +e88aa867,Summer,14,0,Winter,50,Summer,22.43273646,66.0,139.0,,63,76,118,,,,,Summer,26,1,43.2,2,33.0,2,20,1,8.0,1,11.5,1,7.0,0,,,,,,,,,,,,,,,,,,Summer,2.66,,,Summer,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Summer,61,84,Summer,2,0 +e892aec6,Spring,8,0,Fall,45,Summer,17.37816,50.0,61.8,,68,73,101,Summer,3,3,38,Spring,6,1,,,,,10,1,11.0,1,15.0,1,9.0,1,Fall,3,3.13822,17.3801,1012.47,1721.19,10.1869,50.1039,14.0908,3.28929,11.6961,2,28.3939,11.5232,46.9657,29.9929,38.5808,,,,,Summer,4,4,2,1,2,1,0,2,1,2,2,0,2,1,2,2,1,2,1,1,33,Summer,36,51,Spring,0,1 +e8ac21d2,Winter,12,0,Summer,80,Spring,19.89706942,59.5,100.2,,55,66,105,Spring,5,7,1,Spring,15,1,20.9,2,16.8,2,10,1,11.0,1,11.0,1,12.0,1,Spring,3,5.70063,19.8992,1238.89,2106.12,19.9905,74.2216,14.74,5.1592,25.9784,2,34.4801,19.751,68.5209,31.455,54.4706,,,Spring,2.42,Spring,4,5,4,4,5,2,0,3,4,4,4,0,4,2,5,3,4,5,3,1,66,Spring,39,55,Winter,1,2 +e8af663d,Summer,10,0,Winter,61,Summer,18.71555594,53.5,76.2,,68,71,167,,,,,Summer,7,0,,,,,2,0,4.0,0,5.0,0,3.0,0,,,,,,,,,,,,,,,,,,,,Summer,3.03,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,31,45,Summer,2,0 +e8b64419,Winter,13,0,Summer,65,Spring,17.58219659,59.25,87.8,,60,62,105,,,,,Spring,15,0,18.0,1,19.7,2,12,1,13.25,1,13.5,1,11.0,1,Spring,3,4.49904,17.5841,1219.02,2072.33,19.5312,72.1045,14.4407,3.1434,15.6955,1,34.313,18.2604,67.6055,32.2088,53.8441,,,Spring,2.32,Spring,3,2,3,1,2,1,0,1,1,2,2,0,1,1,2,1,2,1,1,1,28,Spring,30,43,Winter,1,0 +e8c93ca7,Spring,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e8d9d5ea,Summer,9,1,Summer,70,Fall,15.15395408,56.0,67.6,,79,83,115,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.4,Fall,3,3,3,0,2,0,0,0,1,1,2,0,1,1,1,1,2,1,1,1,24,Fall,46,64,Summer,1,0 +e8e82627,Summer,6,1,Winter,60,Fall,17.87385461,50.25,64.2,,75,77,116,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3,3,3,0,4,0,0,2,0,0,3,0,1,1,2,3,2,2,1,0,30,Summer,53,73,Summer,2,0 +e9030ab7,Fall,11,0,Spring,60,Fall,15.8620189,57.5,74.6,,60,82,108,,,,,Fall,10,0,14.4,2,16.2,2,10,1,5.5,0,5.0,0,7.0,0,Fall,3,3.98285,15.8638,1125.87,1913.97,14.8613,62.1825,13.2232,2.64058,12.4175,1,29.9039,17.4174,58.1997,27.0832,44.7652,,,Winter,2.79,Fall,4,4,5,3,5,4,3,5,1,2,5,1,4,4,4,4,4,4,4,4,74,Fall,50,69,Fall,1,2 +e90a63d0,Spring,10,0,Fall,80,Spring,15.08051358,53.5,61.4,,63,84,104,Spring,5,7,30,Spring,75,1,,,,,37,1,16.0,1,16.5,1,13.5,1,,,,,,,,,,,,,,,,,,,,Summer,3.51,Spring,2,1,1,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,9,Spring,37,53,Spring,0,0 +e9109dff,Winter,11,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +e912c973,Winter,13,0,Summer,55,Spring,21.01770736,63.88,122.0,,62,61,111,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.52,Spring,3,3,4,1,4,1,1,1,1,2,3,1,3,1,4,2,4,4,1,1,45,Spring,47,66,Winter,2,1 +e934bf0a,Summer,10,0,Winter,62,Fall,27.22332663,59.0,134.8,,67,89,116,,,,,Fall,5,0,11.9,1,13.9,2,1,0,9.0,1,9.0,1,11.0,1,,,,,,,,,,,,,,,,,,,,Summer,2.29,Fall,3,2,2,3,,2,1,3,1,1,3,2,2,2,2,2,2,2,2,2,39,Fall,32,46,Summer,2,1 +e93cce44,Summer,7,0,Fall,70,Fall,16.03947488,47.0,50.4,,70,78,121,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,3,3,2,5,0,0,3,0,2,3,0,3,2,3,3,3,2,3,2,47,Fall,40,56,Summer,2,1 +e943970e,Fall,10,0,,,Fall,22.90296813,56.5,104.0,33.0,58,83,115,,,,,Fall,0,0,,,,,0,0,5.0,0,6.5,0,10.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.58,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1, +e96763c1,Spring,9,0,Winter,70,Spring,15.4431213,52.0,59.4,,72,98,115,,,,,Spring,10,1,,,,,10,1,9.5,1,9.0,1,6.0,1,Spring,3,3.29861,15.4448,1022.47,1738.2,11.1313,51.1694,13.3047,2.14006,8.23058,1,26.9801,13.0581,47.8708,23.7483,38.1114,,,Spring,3.37,Spring,4,4,5,1,5,2,0,0,0,1,3,0,2,1,1,1,3,3,2,1,39,Spring,57,79,Spring,2,1 +e96da61f,Summer,7,1,Spring,80,Winter,15.20112868,50.25,54.6,,67,79,108,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,42,59,Summer,0,0 +e9a00f71,Summer,13,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +e9a6c514,Fall,8,1,Spring,45,Winter,15.45955852,49.0,52.8,,71,95,119,Winter,,,,Winter,10,1,,,,,9,1,11.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.89,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,43,60,Fall,0,0 +e9b642a3,Spring,13,0,,,Spring,20.85310392,62.75,116.8,30.0,56,52,109,,,,,Spring,10,0,12.8,1,14.5,1,3,0,0.0,0,0.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Summer,2.12,Spring,2,1,2,1,2,1,1,3,1,1,4,1,1,1,3,1,2,1,1,1,31,Spring,46,64,Spring,3,1 +e9ca5586,Summer,8,0,Winter,60,Summer,15.98836806,48.0,52.4,,,,,Summer,,,,Summer,3,0,,,,,0,0,10.0,1,11.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,Summer,1.36,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,43,60,Summer,0,0 +e9d94c4e,Spring,8,0,Summer,82,Spring,16.63905325,52.0,64.0,,73,107,112,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,3.40788,16.6409,1047.54,1780.81,12.6433,53.8393,13.9989,2.64192,10.1607,1,28.6481,12.5479,50.4314,25.7868,41.2914,,,Summer,2.83,Summer,3,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,Summer,53,73,Spring,0,0 +ea221ef5,Summer,11,0,,,Summer,23.71728316,56.0,105.8,32.0,74,65,118,,,,,Summer,4,0,22.6,3,21.3,2,0,0,12.0,1,13.5,1,12.0,1,Fall,3,4.20957,23.7199,1252.89,2129.92,22.2288,75.7126,16.9744,6.74545,30.0874,2,35.947,17.5369,71.5031,30.9231,58.1758,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3, +ea38bd63,Spring,12,0,Winter,45,Winter,20.59312013,61.0,109.0,,93,81,162,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.13,Winter,4,5,5,4,4,4,0,4,0,4,4,0,5,5,4,4,5,4,4,1,70,Winter,55,76,Spring,3,2 +ea401fc9,Winter,5,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +ea4619ec,Summer,10,1,Fall,75,Summer,20.74746147,58.5,101.0,,55,80,102,Summer,5,7,26,Summer,8,0,,,,,0,0,6.0,0,8.0,0,5.0,0,,,,,,,,,,,,,,,,,,,,Fall,2.17,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,39,55,Summer,0,0 +ea544af3,Winter,13,1,Spring,75,Winter,18.66833986,65.75,114.8,,80,68,150,,,,,Spring,,,,,,,,,,,,,,,Summer,4,9.48485,19.1037,3009.32,5717.71,84.7958,262.795,43.7314,-24.6277,-147.995,3,103.775,74.2242,253.311,178.002,188.571,,,Spring,1.81,Spring,2,2,2,1,2,0,1,1,1,1,2,0,0,0,0,0,0,0,0,0,15,Spring,35,50,Winter,1,0 +ea7277b4,Fall,9,1,Spring,80,Winter,18.53390254,49.0,63.3,,66,81,107,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.34,Winter,4,0,1,0,0,0,0,0,2,3,2,0,2,0,1,1,1,1,1,1,20,Winter,52,72,Fall,0,0 +ea7adcd6,Spring,13,1,Spring,50,Fall,18.05810948,56.5,82.0,,65,92,93,,,,,Fall,0,0,14.5,1,10.5,1,0,0,6.0,0,7.0,0,9.0,1,Fall,1,3.31547,18.384,1100.91,1431.19,17.7843,59.5248,13.3452,5.03884,22.4752,2,24.1457,17.5948,56.2093,21.7358,41.93,,,Spring,0.99,Fall,4,1,2,0,2,0,0,2,0,0,1,0,0,0,2,0,2,0,0,0,16,Fall,34,49,Spring,3,0 +eaa4821d,Spring,7,0,Summer,70,Spring,16.35187885,53.75,67.2,,72,97,111,Spring,6,11,39,Spring,18,1,,,,,2,0,12.0,1,12.0,1,15.0,1,Spring,3,1.43981,14.6775,1069.96,1818.93,14.3421,56.2276,13.7089,0.968525,3.97243,1,29.0754,12.8101,54.7878,24.6271,43.4175,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Spring,0,0 +eaab7a96,Summer,11,1,Spring,60,Spring,22.18011287,61.0,117.4,,93,92,181,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.01,Summer,4,4,4,1,2,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,19,Summer,44,62,Summer,0,0 +eab1550c,Summer,17,1,,,Summer,22.2934695,61.0,118.0,28.0,68,83,120,,,,,Summer,13,0,26.0,2,27.3,2,9,1,12.0,1,12.0,1,12.0,1,Summer,2,4.68279,22.2959,1323.82,1985.73,28.7902,83.2673,15.7332,6.56266,34.7326,2,34.3247,20.1524,78.5845,39.3102,63.1149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +eac08386,Winter,7,1,Spring,61,Spring,11.71279339,55.0,50.4,,58,99,104,Spring,7,11,22,Spring,17,1,,,,,0,0,6.5,0,6.5,0,6.5,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Winter,41,58,Winter,0,0 +ead25f13,Spring,9,0,Fall,45,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.26,Spring,2,2,4,0,2,1,1,1,0,1,0,0,4,1,2,4,3,3,1,1,33,Spring,33,47,Spring,0,1 +ead871be,Summer,6,1,Summer,35,Summer,16.14640832,46.0,48.6,20.0,48,74,89,,,,,Summer,5,1,,,,,0,0,10.0,1,10.0,1,9.0,1,Summer,1,2.24092,16.1482,931.979,1211.57,11.0889,41.531,13.7994,2.34879,7.06898,1,20.5662,9.87597,39.2901,15.9375,31.6551,,,,,Summer,3,5,5,0,0,0,0,5,0,0,4,0,5,2,5,5,5,4,5,0,53,,,,Summer,0,2 +eaedb188,Fall,10,1,Winter,75,Winter,17.71871141,52.7,70.0,,82,93,125,Winter,6,10,4,Winter,35,1,,,,,4,0,16.0,1,15.0,1,6.0,1,Winter,5,1.82909,17.7206,1129.47,2484.83,19.8575,62.5662,15.8388,1.88188,7.43378,1,31.086,11.6227,60.7371,37.025,50.9435,,,Winter,2.97,Winter,3,4,4,0,5,0,0,0,0,2,4,0,2,0,0,0,0,0,0,0,24,Winter,51,71,Fall,0,0 +eb00abb3,Fall,9,1,,,Fall,15.80297521,55.0,68.0,23.0,60,65,107,,,,,Fall,12,1,,,,,0,0,6.5,1,9.0,1,11.0,1,Fall,3,3.3169,15.8047,1076.64,1722.63,17.1578,56.9396,13.234,2.57068,11.0604,1,24.8662,14.9156,53.6227,22.9307,42.024,,,Winter,2.38,Winter,4,4,3,0,4,0,0,1,0,4,3,0,3,0,0,2,2,2,0,0,32,Winter,32,46,Fall,0,1 +eb1b6325,Fall,9,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.51,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +eb5c46b0,Winter,16,1,Spring,65,Winter,36.32070233,67.5,235.4,44.0,70,68,119,,,,,,,,,,,,,,,,,,,,Winter,3,6.53877,36.3247,1781.27,2850.03,49.5381,131.991,20.3677,15.957,103.409,3,48.0772,34.3761,125.453,63.3567,97.6153,Spring,3.029,,,Spring,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,21,,,,Winter,3,0 +eb731ce2,Summer,17,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +eb749e46,Fall,14,0,,,Fall,19.97966024,66.0,123.8,28.0,78,64,119,,,,,Fall,35,1,39.9,2,34.5,2,15,1,12.0,1,12.0,1,9.0,1,Fall,2,6.11745,19.9818,1501.98,2403.18,30.349,102.244,16.5026,3.4792,21.5558,2,46.2253,25.6699,96.1267,51.6873,76.5743,Fall,2.691,,,Fall,2,3,3,5,4,3,0,3,1,2,5,1,1,4,4,1,3,2,3,1,51,Fall,53,73,Fall,0,2 +eb891208,Fall,9,0,,,Fall,17.93873874,55.5,78.6,26.0,65,81,115,,,,,Winter,27,1,,,,,13,1,8.0,1,10.0,1,11.0,1,Winter,4,3.69514,17.9407,1108.5,2327.85,14.2014,60.333,13.7712,4.16951,18.267,2,29.2496,16.882,56.6378,25.5033,43.451,,,Winter,2.95,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,48,67,Winter,1,0 +eb8c030f,Summer,10,0,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +eb90ee20,Winter,11,0,Summer,70,Winter,16.96495868,55.0,73.0,,72,61,119,Winter,6,10,1,Winter,18,1,14.5,2,14.6,2,13,1,7.5,1,7.5,1,14.0,1,Summer,2,3.4828,16.1208,1054.28,1686.84,13.8036,54.5573,14.1856,1.9352,7.4427,1,28.8269,11.9268,51.0745,24.1955,42.6305,,,Spring,2.8,Spring,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,Winter,30,43,Winter,0,0 +eb93a46a,Summer,9,0,Winter,60,Summer,14.26521894,53.0,57.0,,62,75,93,Summer,3,3,12,Summer,7,1,,,,,1,0,6.0,0,6.0,0,8.0,1,Summer,4,3.24004,14.2668,1014.78,2131.04,9.97206,50.3503,12.6024,1.66439,6.64974,1,24.9054,15.4728,47.1102,20.3147,34.8775,,,Summer,2.46,Summer,4,2,2,0,3,0,0,1,0,4,0,0,0,2,4,0,3,0,1,0,26,Summer,34,49,Summer,0,0 +eb9a846d,Spring,6,0,Fall,61,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,0,4,0,4,0,0,4,0,2,2,0,0,2,2,1,2,0,0,1,28,Spring,44,62,Spring,2,0 +eb9fd2ca,Spring,5,0,,,Summer,16.43530975,45.5,48.4,23.0,64,84,112,,,,,Summer,4,1,,,,,3,1,8.5,1,10.5,1,12.0,1,Summer,4,2.56222,16.4371,916.159,1923.93,7.65772,39.8459,13.5321,2.90505,8.55408,2,22.9071,9.28113,37.2837,16.1668,30.5648,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +eba27772,Spring,6,0,Summer,52,Summer,13.81177003,47.0,43.4,23.0,,,,,,,,Summer,0,0,,,,,0,0,,,,,0.0,0,Summer,2,2.57084,13.8133,916.723,1466.76,6.70276,39.906,12.7012,1.11207,3.49401,1,21.0083,12.1949,37.3352,13.7157,27.7111,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Spring,0,0 +ebbc6fea,Spring,10,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ebd15fdc,Spring,12,1,Winter,60,Fall,23.04318418,62.0,126.0,,74,92,110,,,,,Fall,10,0,14.2,1,18.2,2,20,1,4.0,0,4.0,0,11.0,1,Fall,2,0.329524,23.0457,1400.07,2100.1,32.1371,91.3885,16.7152,6.33053,34.6115,3,37.4222,21.8292,91.059,44.739,69.5592,,,Spring,1.76,Fall,0,3,4,0,1,1,1,2,1,1,4,1,4,1,1,1,1,1,1,2,31,Fall,36,51,Spring,1,1 +ebd6f819,Spring,10,0,Summer,66,Spring,23.33304219,53.5,95.0,,67,73,94,Spring,,,,Spring,25,1,,,,,10,1,7.0,0,8.0,1,9.5,1,Summer,2,3.56811,23.3356,1139.23,1822.77,15.7262,63.606,15.624,7.71156,31.394,2,31.6596,16.2201,60.0378,28.95,47.3858,,,Spring,2.32,Spring,5,5,4,0,5,1,0,2,1,1,2,0,0,0,4,4,2,4,3,1,44,Spring,62,85,Spring,2,1 +ebf2b0c9,Fall,10,1,Spring,41,Winter,12.60189601,59.0,62.4,,68,90,113,Winter,6,9,39,Winter,9,1,,,,,4,0,7.0,0,8.0,0,4.0,0,Spring,1,2.6562,12.6033,1131.36,1470.77,18.7458,62.7676,12.6775,-0.0742411,-0.367575,1,27.5578,16.464,60.1114,28.9651,46.3035,,,Winter,1.0,Winter,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Winter,50,69,Fall,0,0 +ebf30e46,Fall,10,0,Summer,68,Winter,22.06801427,58.0,105.6,,67,78,136,Winter,5,8,8,Winter,20,1,13.2,2,18.6,2,10,1,7.0,0,5.0,0,12.0,1,Winter,4,3.76617,22.0704,1186.33,2491.29,14.1705,68.6225,14.3421,7.7283,36.9775,2,31.26,23.192,64.8564,33.4455,45.4306,,,Fall,4.02,Winter,2,1,2,0,2,1,0,2,1,1,1,0,2,2,2,2,2,2,1,1,27,Winter,41,58,Fall,1,0 +ec015bbb,Spring,9,1,Summer,72,Spring,16.05617284,54.0,66.6,25.0,60,87,108,,,,,Spring,15,1,,,,,0,0,9.0,1,9.0,1,8.0,1,Summer,2,3.19472,16.0579,1061.88,1592.82,15.9652,55.3669,13.3495,2.70841,11.2331,1,25.1864,14.2153,52.1722,25.7483,41.1516,,,Summer,2.35,Spring,2,2,3,1,1,1,1,2,1,1,1,1,1,0,2,1,2,2,1,1,27,Spring,33,47,Summer,0,0 +ec0f4afa,Spring,6,1,Summer,60,Spring,14.26619104,43.5,38.4,21.0,59,83,99,,,,,Spring,4,1,,,,,0,0,9.0,1,9.0,1,10.0,1,Spring,1,1.85184,14.2677,904.738,1176.16,7.35521,38.6295,14.353,-0.0852728,-0.229502,1,18.0323,13.242,36.7777,14.4781,25.3875,,,,,Spring,2,4,2,0,2,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,14,,,,Spring,3,0 +ec13a1b9,Summer,8,0,Winter,61,Fall,16.26735493,53.0,65.0,,73,77,114,Fall,2,2,33,Fall,4,0,,,,,0,0,9.0,1,5.0,0,7.0,1,Fall,3,3.58609,16.2691,1071.08,1820.83,12.1423,56.3468,14.1033,2.16586,8.65325,2,31.3337,12.8707,52.7607,36.3596,43.476,,,,,Fall,3,3,3,0,4,1,0,2,1,1,1,0,3,2,5,4,5,4,4,4,50,Summer,37,53,Summer,2,2 +ec220299,Summer,15,0,Spring,75,Summer,16.72008098,68.5,111.6,26.0,54,51,101,,,,,Summer,3,0,39.4,1,43.4,1,5,0,7.0,0,4.0,0,12.0,1,Fall,4,6.33189,16.7219,1458.73,3063.34,29.72,97.6373,14.6298,2.09215,13.9627,2,42.1121,25.8052,91.3054,40.7081,71.832,Summer,1.98,,,Summer,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,Summer,35,50,Summer,0,0 +ec4a1461,Fall,16,1,Spring,55,Winter,18.60246154,65.0,111.8,,59,75,111,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.32,,,Winter,3,5,4,4,5,1,4,2,1,2,2,0,4,4,5,2,3,3,2,2,58,Winter,49,68,Fall,2,2 +ec619149,Summer,18,1,,,Summer,20.56184743,59.4,103.2,31.0,90,103,129,,,,,Summer,20,1,13.9,1,14.2,1,1,0,0.0,0,0.0,0,5.5,0,Fall,4,3.80104,20.5641,1179.55,2241.14,20.4511,67.9006,13.5302,7.03392,35.2994,2,27.1126,20.3369,64.0995,29.6541,47.5637,Summer,1.78,,,Fall,1,2,2,1,1,1,1,2,1,1,3,1,1,1,1,1,1,1,1,1,25,,,,Summer,1,0 +ec65cd62,Winter,9,0,Summer,60,Spring,20.2185124,55.0,87.0,,72,83,138,Spring,3,3,41,Spring,0,0,,,,,0,0,9.0,1,11.0,1,9.0,1,Spring,3,3.61649,20.2207,1121.59,1906.71,14.4319,61.7274,14.3468,5.8739,25.2726,2,29.8192,17.4763,58.1109,26.4069,44.2511,,,Winter,2.53,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,43,60,Winter,0,0 +ec6b57b4,Fall,15,0,Spring,70,Winter,18.43034894,66.0,114.2,,45,85,110,,,,,Winter,20,0,29.4,2,26.7,1,10,0,6.0,0,6.0,0,8.5,1,Winter,3,5.72919,18.4324,1401.76,2382.98,25.8415,91.5684,14.7795,3.65283,22.6316,2,41.3055,24.4214,85.8393,44.1134,67.147,Fall,3.379,,,Winter,2,2,2,1,1,1,1,1,1,0,2,0,0,0,0,0,1,0,1,0,16,Fall,28,41,Fall,0,0 +ec850b10,Fall,14,0,Spring,60,Winter,17.00730675,67.0,108.6,,56,97,112,,,,,Winter,32,1,19.0,1,17.9,1,6,0,0.0,0,0.0,0,14.0,1,Winter,1,5.97187,17.0092,1413.97,1838.17,26.9243,92.87,14.5455,2.46367,15.73,1,41.3875,24.5582,86.8981,42.8153,68.3118,Fall,1.59,,,Winter,1,2,4,3,1,0,1,0,2,1,4,0,4,1,1,0,2,0,2,1,30,Winter,46,64,Winter,,0 +ec890dbd,Summer,10,1,Winter,55,Fall,18.30707247,57.8,87.0,,107,76,146,Fall,2,,,Spring,,,,,,,,,,,,,,,Summer,3,3.32015,18.3091,1123.43,1797.49,15.1862,61.923,13.0316,5.27743,25.077,2,24.7402,21.9965,58.6028,32.5979,39.9265,,,Summer,1.75,Fall,5,5,5,1,5,2,0,0,0,2,4,0,2,2,2,4,3,5,1,1,49,Fall,42,59,Summer,0,1 +ec8f8f95,Winter,9,0,Summer,75,Spring,17.02582583,55.5,74.6,,42,72,102,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.83731,17.0277,1116.07,1897.32,15.4534,61.1393,13.9552,3.07244,13.4607,1,30.1738,15.5121,57.302,26.1021,45.6272,,,Winter,1.69,Spring,4,4,4,3,5,2,1,3,3,3,3,0,2,2,1,3,3,3,3,1,53,Spring,33,47,Winter,3,2 +ec9044f5,Fall,16,1,Fall,65,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.02,,,Spring,2,1,3,2,3,1,1,1,1,1,1,0,2,1,2,1,2,1,1,1,28,Spring,43,60,Fall,0,0 +ec9179b7,Summer,8,1,,,Summer,14.41680384,54.0,59.8,22.0,64,69,111,,,,,Summer,0,0,,,,,0,0,6.0,0,4.5,0,8.0,1,Fall,2,3.01456,14.4184,1039.38,1559.07,13.8812,52.9705,12.7717,1.64666,6.8295,1,23.0297,16.0596,49.9559,22.5234,36.9109,,,Summer,2.13,Summer,1,4,1,1,1,0,0,1,0,0,0,0,1,0,1,1,1,1,1,0,15,Summer,31,45,Summer,0,0 +ec9195be,Summer,12,1,Winter,51,Summer,19.3005848,57.0,89.2,25.0,55,62,94,,,,,Summer,14,0,8.5,1,9.1,1,7,1,11.0,1,8.5,0,9.0,1,Summer,3,3.54097,19.3027,1132.45,1811.93,18.799,62.8842,13.608,5.69469,26.3158,2,26.5022,17.5829,59.3432,28.3311,45.3012,,,Summer,2.399,Summer,4,4,4,1,3,0,0,2,2,0,0,0,0,0,0,1,1,2,1,0,25,Summer,41,58,Summer,0,0 +ecac5c72,Spring,6,1,,,Spring,19.23269136,45.0,55.4,26.0,71,89,117,,,,,Spring,0,0,,,,,0,0,10.5,1,10.0,1,11.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +ecb1c038,Spring,6,0,Fall,53,Spring,15.62222222,45.0,45.0,21.0,49,82,99,,,,,Spring,0,0,,,,,5,1,9.0,1,11.0,1,10.0,1,Spring,2,2.45188,15.6239,899.819,1439.71,6.2706,38.1055,13.2302,2.39375,6.89446,2,21.9106,9.92435,35.6537,15.9002,28.1812,,,,,Fall,5,5,1,0,,1,0,0,0,0,5,0,5,1,5,5,3,5,5,3,49,Fall,58,80,Spring,2,1 +ecbdbe55,Fall,7,0,Winter,67,Fall,15.75322561,47.8,51.2,,44,74,65,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,29,42,Spring,0,0 +ecc0cc08,Winter,11,0,Fall,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.38,,,,,,,,,,,,,,,,,,,,,,,Winter,37,53,Winter,0, +ecd22509,Summer,12,0,Fall,61,Fall,23.84786681,62.0,130.4,,74,75,116,,,,,Fall,0,0,12.6,1,10.8,1,0,0,6.0,0,8.0,1,1.5,0,Fall,2,13.5923,23.8505,1308.11,2092.97,20.6247,81.5936,14.9237,8.92681,48.8064,2,35.5871,25.3818,68.0013,33.1887,56.2118,,,Fall,1.28,Fall,4,4,4,2,4,1,1,4,3,3,2,0,2,2,4,0,4,3,2,1,50,Fall,50,69,Summer,2,2 +ecd25dd1,Fall,18,0,Spring,80,Winter,22.97972973,74.0,179.0,,69,59,134,,,,,,,,,,,,,,,,,,,,Winter,2,7.498,22.9822,1720.23,2752.37,37.2048,125.491,16.112,6.8702,53.5094,3,53.1025,35.1833,117.993,61.5647,90.3073,,,,,Winter,1,0,2,2,1,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,10,,,,Fall,2,0 +ecd6b424,Spring,7,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,46,64,Spring,, +ed0b3906,Winter,19,0,Summer,60,Spring,23.14742394,68.5,154.5,,69,78,134,,,,,,,,,,,,,,,,,,,,Spring,4,6.7728,23.4916,1670.22,3507.46,37.3695,120.163,18.2708,5.22088,34.3367,2,52.3244,30.4694,113.39,58.2092,89.6939,,,,,Spring,4,4,4,3,4,4,1,4,4,3,4,2,3,3,4,4,3,4,3,2,67,,,,Winter,3,2 +ed184a43,Spring,10,0,,,Summer,17.63943892,58.0,84.4,24.0,64,60,106,,,,,,,,,,,,,,,,,,,,Summer,4,3.53953,17.6396,1133.62,2380.6,9.50538,63.0085,13.1688,4.47083,21.3915,3,28.4477,25.0554,59.469,39.3697,37.9531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ed329c15,Summer,16,1,,,Summer,17.3129216,62.5,96.2,28.0,65,73,87,,,,,Summer,38,1,24.1,2,24.7,2,0,0,13.0,1,14.5,1,12.0,1,Summer,3,4.32244,17.1771,1215.61,1944.97,22.0287,71.741,12.8098,4.36731,24.459,2,28.0444,21.6679,67.4186,29.7458,50.0731,Summer,2.13,,,Summer,4,1,2,2,5,1,4,1,1,1,2,1,1,4,1,1,2,1,1,1,37,Summer,35,50,Summer,3,1 +ed50a6f6,Spring,11,0,Summer,70,Spring,15.4982243,56.75,71.0,,71,81,116,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,3.8491,15.4999,1104.16,1766.65,14.1073,59.8704,13.0702,2.4297,11.1296,1,28.7969,16.9662,56.0213,24.9812,42.9041,,,Spring,1.27,Spring,5,4,5,1,5,4,2,4,3,4,4,0,1,4,3,3,5,2,5,4,68,Spring,35,50,Spring,2,2 +ed60b87e,Summer,11,0,,,Summer,15.98889256,55.0,68.8,25.0,92,80,122,,,,,Summer,1,0,13.6,2,14.5,2,5,0,12.0,1,12.0,1,11.0,1,Summer,3,3.68178,15.9907,1083.83,1842.52,13.9345,57.7057,13.4121,2.57858,11.0944,1,28.4201,15.3511,54.0239,23.6391,42.3546,,,Summer,3.32,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +ed61b01e,Fall,16,0,Summer,70,Summer,25.59086391,65.0,153.8,,88,94,144,,,,,Summer,35,1,28.6,1,21.6,1,18,1,10.5,1,8.0,1,7.0,0,Spring,2,10.6158,27.8072,1496.56,2394.49,29.2738,101.666,17.4509,10.3563,60.3342,2,45.098,27.294,91.05,47.4135,74.3718,Summer,1.94,,,Spring,0,3,2,2,2,0,0,0,3,0,4,0,0,0,2,0,3,0,0,0,21,Spring,29,42,Fall,3,0 +ed711416,Spring,8,0,Fall,72,Summer,15.80710059,52.0,60.8,,77,104,115,Summer,7,12,10,Summer,11,1,,,,,0,0,11.0,1,11.0,1,10.5,1,,,,,,,,,,,,,,,,,,,,,,Summer,4,3,3,0,4,1,0,2,1,3,2,0,1,1,2,1,1,1,1,1,32,Spring,50,69,Summer,0,1 +edaa11c4,Summer,9,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +edb50fce,Spring,8,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +edb86a64,Summer,10,0,Winter,75,Fall,21.81266701,56.2,98.0,,74,96,122,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,5,4.0091,21.815,1194.39,2866.53,18.4045,69.481,15.4666,6.3484,28.519,2,33.5377,17.5388,65.4719,30.467,51.9422,,,Fall,3.26,Fall,3,2,4,0,2,2,1,1,1,2,1,0,3,1,2,2,2,2,1,1,33,Fall,30,43,Summer,1,1 +edb9d0f3,Fall,10,1,Winter,70,Winter,,56.0,,,56,92,97,Winter,5,8,0,Winter,100,1,19.0,2,17.7,2,50,1,13.0,1,13.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.89,Winter,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Winter,26,38,Fall,0,0 +edbf35f3,Fall,14,1,,,Fall,22.24661124,63.0,125.6,29.0,75,70,115,,,,,Fall,16,0,31.1,3,29.7,3,9,1,15.5,1,15.5,1,12.0,1,Fall,4,4.94639,22.249,1353.69,2572.01,28.9981,86.4489,15.3137,6.9353,39.1511,2,35.5139,21.937,81.5025,44.7672,64.5119,,,Winter,2.59,Fall,4,4,4,5,5,2,0,3,5,4,4,4,4,0,4,4,4,4,1,2,67,Fall,59,81,Fall,3,2 +edc44560,Fall,13,1,,,Fall,,,,31.0,82,107,129,,,,,Fall,0,0,10.0,1,6.5,1,0,0,3.0,0,3.0,0,2.5,0,,,,,,,,,,,,,,,,,,,,Fall,1.36,Fall,1,1,2,1,4,0,0,0,1,1,4,0,0,1,0,1,0,0,0,0,17,Fall,40,56,Fall,2,0 +edca760c,Summer,7,1,Spring,57,Winter,18.44547368,47.5,59.2,,62,79,106,Winter,2,1,50,Summer,9,1,,,,,0,0,12.0,1,13.0,1,3.0,0,Winter,2,1.47152,29.598,870.784,1306.18,7.92715,35.013,17.5053,12.0927,24.187,2,17.845,9.24078,33.5415,12.7562,25.7722,,,,,Winter,2,0,3,0,2,0,0,2,2,3,1,0,5,0,2,5,2,5,0,0,34,Winter,60,82,Summer,2,1 +edcf632d,Fall,5,1,,,Winter,17.64153597,43.0,46.4,21.0,88,63,108,,,,,Winter,0,0,,,,,0,0,14.0,1,13.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0, +edeec50d,Fall,10,0,,,Fall,19.6900593,57.25,91.8,29.0,67,65,123,,,,,Fall,4,0,,,,,0,0,8.5,1,9.5,1,12.0,1,Fall,3,3.86325,19.6922,1156.37,1965.82,15.4252,65.4311,14.0358,5.65644,26.3689,1,30.5354,19.4705,61.5679,27.3187,45.9606,,,Fall,3.3,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,34,49,Fall,0,0 +edfe1f8d,Winter,14,1,Spring,45,Spring,19.41264802,63.0,109.6,,76,96,114,,,,,Spring,,,,,,,,,,,,,,,Spring,2,4.52613,19.4148,1259.26,1888.89,24.9952,76.391,13.5321,5.8827,33.209,2,29.7076,21.6883,71.8649,31.0508,54.7027,Winter,1.17,,,Spring,2,2,2,0,3,1,2,1,1,2,3,0,1,2,1,2,1,1,2,2,31,Spring,40,56,Winter,2,1 +ee0fa75a,Summer,18,0,Fall,70,Summer,33.65829475,72.0,248.2,,80,89,145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,4,4,1,5,2,4,0,3,3,5,0,1,0,4,1,4,0,3,0,49,Fall,41,58,Summer,3,1 +ee21612e,Summer,15,0,Winter,60,Summer,16.83301827,69.0,114.0,,65,71,120,,,,,Summer,20,0,24.5,2,32.2,3,20,1,12.0,1,12.0,1,10.0,1,Fall,4,6.4956,16.8349,1485.11,3118.73,26.7616,100.447,14.8334,2.00146,13.5532,1,47.2793,26.4059,93.9512,64.5495,74.0409,Winter,1.97,,,Summer,5,5,5,4,5,4,1,1,1,2,4,0,4,3,3,3,3,3,2,1,59,Summer,35,50,Winter,0,2 +ee2bf575,Fall,11,0,Spring,40,Spring,15.62222222,60.0,80.0,,106,80,125,Spring,6,9,44,Spring,19,1,20.2,2,20.3,2,6,0,8.5,1,11.0,1,8.5,1,,,,,,,,,,,,,,,,,,,,Winter,4.38,Winter,5,5,5,2,5,2,0,3,1,3,4,0,5,3,4,5,4,5,2,5,68,Winter,35,50,Fall,3,2 +ee4e9eed,Spring,9,1,Summer,70,Fall,15.50169639,52.25,60.2,,62,84,105,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,4,2.8399,15.5034,1020.53,1939.01,13.2012,50.9631,13.1246,2.3788,9.23693,2,23.0293,14.7325,48.1232,23.445,36.2305,,,Summer,3.31,Summer,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,3,0,0,8,Summer,39,55,Spring,0,0 +ee4fe168,Spring,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +ee654259,Fall,8,0,Spring,60,Winter,14.3412,50.0,51.0,,76,97,137,Winter,6,10,46,Winter,8,1,,,,,8,1,12.0,1,13.5,1,9.0,1,Winter,3,0.786086,11.434,1055.16,1793.77,12.4538,54.6512,12.2526,-0.818589,-3.65123,1,27.9945,14.2029,53.8651,25.8377,40.4484,,,,,Winter,4,3,2,1,4,2,0,1,0,2,0,0,2,1,2,2,2,2,2,0,32,Winter,39,55,Fall,0,1 +ee67eb55,Spring,9,1,Summer,83,Spring,30.45074757,41.5,74.6,,60,76,94,Spring,4,6,46,Spring,15,1,,,,,2,0,7.0,0,6.0,0,12.0,1,Summer,2,1.825,30.4541,924.718,1387.08,11.1902,40.7576,16.6386,13.8155,33.8424,1,20.0331,9.53437,38.9326,15.7373,31.2233,,,Spring,1.14,Spring,2,1,2,0,2,1,0,1,1,2,2,0,1,0,2,1,2,1,1,1,23,Spring,44,62,Spring,0,0 +ee74be35,Fall,10,1,,,Fall,15.73033449,51.0,58.2,23.0,46,63,100,,,,,Fall,14,1,,,,,0,0,12.0,1,11.5,1,9.0,1,Fall,2,2.70919,15.7321,1003.17,1504.76,12.8911,49.114,13.276,2.45604,9.08599,1,22.3951,13.8278,46.4048,21.1611,35.2862,,,Fall,2.73,Fall,0,0,3,0,2,0,0,2,0,0,3,0,1,1,2,1,3,0,0,0,18,Fall,31,45,Fall,3,0 +ee7de96c,Fall,7,1,Winter,45,Fall,14.75685131,49.0,50.4,,72,108,112,Fall,5,8,28,Fall,10,1,,,,,6,1,9.0,1,10.0,1,11.0,1,Fall,4,6.9753,14.7585,972.339,1847.44,11.5674,45.8299,13.4202,1.33826,4.57014,1,23.623,10.6395,38.8546,26.58,35.1904,,,,,Fall,1,3,1,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,9,Fall,32,46,Fall,0,0 +eea74d46,Winter,8,0,Summer,70,Spring,14.66316568,52.0,56.4,,65,91,97,Spring,4,6,20,Spring,0,0,,,,,3,0,8.0,1,9.0,1,12.0,1,Spring,5,4.35255,14.6648,1013.26,2431.83,10.7192,50.1886,13.0497,1.61504,6.21135,1,26.4931,12.9763,45.8361,23.1299,37.2124,,,Spring,2.07,Spring,5,5,5,2,4,1,0,1,1,4,2,0,1,0,2,2,1,2,0,0,38,Spring,35,50,Winter,0,1 +eebce76e,Winter,5,0,,,Winter,16.84143832,46.5,51.8,22.0,64,,113,,,,,Winter,0,0,,,,,0,0,0.0,0,0.0,0,0.0,0,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +eec0d188,Winter,9,1,Winter,75,Winter,15.8228409,50.5,57.4,,57,95,94,Winter,4,6,10,Winter,15,1,,,,,25,1,9.0,1,10.5,1,12.5,1,Winter,3,5.86032,15.8246,1008.13,1613.01,14.8425,49.6421,13.6858,2.13878,7.75792,2,23.7122,11.0873,43.7818,20.7482,38.5548,,,Winter,2.25,Winter,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Winter,46,64,Winter,0,0 +eeedcd5e,Summer,18,1,Winter,41,Summer,19.9933927,59.0,99.0,,50,63,87,,,,,Summer,6,0,17.1,1,20.5,2,1,0,16.0,1,12.0,1,8.0,0,,,,,,,,,,,,,,,,,,Summer,1.56,,,Summer,0,4,5,0,5,0,0,5,4,0,5,0,0,5,4,0,5,0,0,3,45,,,,Summer,3,1 +eeefa792,Spring,8,0,Summer,75,Winter,17.93873874,55.5,78.6,,95,113,158,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.25,Summer,1,1,2,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,8,Summer,38,54,Spring,0,0 +eef79620,Spring,13,1,Spring,77,Spring,16.2677686,66.0,100.8,,59,54,102,,,,,Spring,17,0,16.1,2,17.8,2,0,0,5.0,0,7.0,0,8.0,0,Spring,2,5.17786,16.2695,1311.79,1967.68,27.7385,81.9857,13.2328,3.0367,18.8143,2,31.4872,22.7601,76.8079,32.0402,59.2256,,,Spring,1.48,Spring,3,3,2,1,1,2,1,1,1,1,0,0,2,0,0,1,0,1,2,0,22,Spring,45,63,Spring,2,0 +eefeb431,Spring,12,1,Fall,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.92,Spring,1,2,2,1,2,1,1,1,1,1,2,0,1,3,2,1,2,1,2,1,28,Spring,44,62,Spring,2,0 +ef07f833,Spring,8,0,Winter,55,Summer,15.43914931,48.0,50.6,,58,100,120,Summer,,,,Summer,3,0,,,,,0,0,10.0,1,11.5,1,10.0,1,Summer,4,2.85116,15.4408,953.178,2001.67,9.305,43.7889,13.3624,2.07843,6.81106,1,24.2728,10.2111,40.9378,17.9048,33.5778,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Spring,,0 +ef0992a2,Spring,6,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ef1b858a,Summer,7,1,Winter,95,Fall,14.6224,50.0,52.0,,85,86,140,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,Fall,35,50,Summer,0,0 +ef3a2d17,Spring,10,0,,,Fall,,,0.0,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.98,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +ef4ae515,Summer,6,0,Winter,70,Summer,16.41219282,46.0,49.4,,65,83,108,,,,,Summer,3,1,,,,,0,0,8.0,1,8.0,1,5.5,1,Fall,3,2.56503,16.414,921.867,1567.17,7.27542,40.454,13.4415,2.97246,8.94601,2,22.7745,10.404,37.889,16.7269,30.05,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +ef58dd3c,Spring,16,1,Fall,85,Summer,24.79176638,63.5,142.2,,67,61,114,,,,,Summer,26,1,26.7,2,27.4,3,20,1,12.5,1,12.0,1,11.0,1,Summer,4,4.90613,24.7945,1364.43,2592.42,30.1723,87.5927,15.273,9.52151,54.6073,2,34.2441,23.1762,82.6866,40.0783,64.4165,Summer,2.07,,,Summer,2,1,2,1,2,2,2,2,1,1,2,0,0,1,2,1,1,0,1,1,25,Summer,28,41,Spring,1,0 +ef6f257a,Winter,15,0,,,Winter,,72.0,,33.0,65,69,108,,,,,Winter,0,0,42.1,2,44.5,3,10,0,0.0,0,0.0,0,12.0,1,,,,,,,,,,,,,,,,,,Winter,1.38,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +ef9617e7,Winter,10,0,Fall,71,Summer,18.90096022,54.0,78.4,,67,72,116,Summer,6,7,21,Summer,10,0,15.1,2,18.9,2,7,1,5.0,0,8.0,1,9.0,1,Summer,4,3.13294,18.903,1077.58,2262.91,8.44213,57.0391,13.7527,5.15033,21.3609,2,26.8682,21.7287,53.9061,33.8492,35.3104,,,Winter,3.2,Summer,5,5,5,4,5,,0,2,2,3,3,0,3,0,3,5,4,5,1,2,57,Summer,34,49,Winter,2,2 +efa35d35,Winter,6,0,Winter,65,Summer,13.75434783,46.0,41.4,,94,97,117,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1,2,2,1,2,1,0,2,1,1,1,0,1,1,1,1,1,1,1,1,22,Summer,32,46,Winter,1,0 +efba7242,Summer,7,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +efbad88b,Winter,15,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +efc2bc05,Summer,12,1,,,Summer,20.40832838,61.5,109.8,33.0,58,66,109,,,,,Summer,2,0,14.6,2,15.9,2,0,0,6.0,0,6.0,0,10.0,1,Fall,2,4.10139,20.4106,1217.23,1825.84,23.1888,71.9136,13.3679,7.04264,37.8864,2,26.8045,21.9203,67.8122,25.1112,49.9934,,,Fall,3.92,Fall,5,0,2,2,2,2,0,0,2,2,0,0,0,2,0,2,2,0,0,2,25,Fall,41,58,Fall,2,0 +efcef0d6,Spring,12,0,,,Spring,15.99471416,59.0,79.2,26.0,84,80,127,,,,,Summer,6,0,20.8,2,17.6,1,7,0,10.0,1,0.0,0,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Summer,3.16,Summer,0,1,2,0,4,0,0,0,0,0,1,0,0,1,1,0,1,1,1,0,13,Summer,32,46,Spring,2,0 +efedde24,Summer,16,0,,,Fall,19.78043347,67.5,128.2,27.0,77,63,121,,,,,Fall,28,1,24.2,1,24.1,1,7,0,9.5,1,9.0,1,12.0,1,Fall,4,6.02796,19.7826,1463.25,3072.82,28.6637,98.1183,15.1407,4.64192,30.0817,2,42.9069,26.5477,92.0903,43.7709,71.5706,Summer,2.92,,,Fall,0,5,2,0,1,0,0,1,1,1,2,0,1,0,1,1,2,1,1,0,20,Fall,46,64,Summer,3,0 +efee1240,Spring,11,0,,,Spring,20.70647675,61.0,109.6,31.0,74,88,123,,,,,Spring,25,1,23.6,3,23.6,3,13,1,9.0,1,8.0,1,14.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,3.55,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +f015a18c,Fall,15,0,Winter,65,Fall,21.17070101,65.5,129.2,,58,53,115,,,,,Fall,25,1,28.8,2,32.9,2,25,1,7.5,1,7.0,0,10.5,1,Fall,4,5.9801,21.173,1497.84,3145.47,30.048,101.803,16.6832,4.48977,27.3971,2,46.0437,25.7113,95.8228,51.2925,76.0917,Winter,2.63,,,Winter,3,3,3,0,2,2,0,2,2,2,3,0,2,0,0,2,1,2,1,0,30,Winter,29,42,Fall,3,0 +f028534c,Summer,8,0,Winter,80,Fall,16.77904132,55.0,72.2,,64,75,106,Fall,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.35,Fall,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,3,3,0,0,11,Fall,70,95,Summer,0,0 +f0356a48,Fall,10,0,Summer,65,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.47,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,28,41,Fall,0,0 +f043be76,Winter,11,1,,,Spring,,,,,,,,,,,,Summer,0,0,13.5,2,15.9,2,1,0,15.5,1,15.5,1,9.0,1,Fall,4,7.50274,19.6662,3170.22,6023.42,88.4788,279.933,65.5384,-45.8722,-195.933,2,112.329,79.1255,272.43,193.219,200.808,,,Winter,3.13,Summer,1,1,1,1,1,1,1,1,1,1,2,1,,1,1,1,1,1,1,1,20,Summer,45,,Winter,2,0 +f05c6580,Winter,10,0,,,Winter,16.21683968,51.0,60.0,23.0,60,71,112,,,,,Winter,13,1,,,,,2,0,11.0,1,11.0,1,12.0,1,Winter,1,3.14261,16.2186,1008.08,1310.5,10.3414,49.6366,13.4173,2.80132,10.3634,1,26.279,13.0162,46.494,22.8368,36.6205,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f07dcfd6,Summer,8,1,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +f0856b84,Winter,8,0,,,Winter,15.85909763,52.0,61.0,25.0,,62,,,,,,Winter,5,0,,,,,40,1,16.0,1,15.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +f086b49c,Fall,13,1,,,Fall,19.1823497,64.75,114.4,27.0,68,74,120,,,,,Fall,14,0,26.0,3,26.5,3,0,0,11.5,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,31,45,Fall,3, +f08a5ac3,Fall,6,1,Spring,75,Spring,15.34586657,46.5,47.2,,84,90,106,Spring,,,,Fall,,,,,,,,,,,,,,,Summer,3,2.34308,15.3475,937.319,1499.71,11.1931,42.0998,13.6892,1.65839,5.10023,1,21.8384,9.06825,39.7567,19.4774,33.0315,,,,,Winter,3,2,2,0,2,0,0,,1,2,1,0,0,1,2,3,2,2,0,0,23,Winter,70,95,Fall,3,0 +f08c830a,Spring,13,1,Spring,75,Summer,16.08710496,63.75,93.0,,53,77,102,,,,,Summer,27,1,31.4,1,38.0,2,10,1,8.3,0,6.0,0,12.0,1,Summer,3,0.875207,16.4742,1261.55,2018.47,24.9686,76.6342,13.5751,2.89907,16.3658,1,31.4357,20.23,75.759,34.6579,56.4042,,,Summer,2.42,,,,,,,,,,,,,,,,,,,,,,,Summer,75,100,Spring,2, +f0957ca1,Fall,12,0,Spring,66,Winter,18.43508772,57.0,85.2,,69,94,117,,,,,Winter,8,0,15.5,1,21.2,2,5,0,5.5,0,5.5,0,7.0,0,Winter,2,3.50144,18.4371,1123.55,1797.69,10.242,61.9363,13.4029,5.03421,23.2637,2,28.5474,23.1469,58.4349,35.794,38.7894,,,Fall,1.93,Winter,5,5,5,1,5,5,0,2,1,3,2,0,2,4,4,2,2,3,0,3,54,Winter,33,47,Fall,1,2 +f09a076a,Fall,13,0,,,Fall,17.11005291,63.0,96.6,26.0,67,65,109,,,,,Fall,19,0,18.0,1,15.7,1,11,0,6.0,0,6.5,0,11.0,1,Fall,3,5.03931,17.1119,1285.33,2185.07,22.0752,79.168,14.024,3.08793,17.432,1,35.7631,21.3298,74.1287,33.0166,57.8383,,,Fall,3.39,Fall,3,2,3,1,3,2,1,2,3,2,2,1,2,2,2,2,2,2,1,2,40,Fall,38,54,Fall,2,1 +f09b02cd,Winter,8,0,,,Winter,15.58540862,51.5,58.8,,73,76,114,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.49,Winter,4,3,2,0,2,0,0,2,3,2,3,0,2,2,1,1,1,1,1,0,30,Winter,45,63,,,0 +f09e7b33,Summer,17,0,Winter,70,Fall,21.93767848,74.5,173.2,,60,95,125,,,,,Fall,30,1,34.2,2,38.6,2,20,1,10.0,1,10.0,1,9.5,1,,,,,,,,,,,,,,,,,,Fall,2.91,,,Fall,5,3,2,2,3,3,4,1,1,3,4,1,2,2,3,3,2,4,1,1,50,,,,Summer,2,2 +f0b5d026,Winter,10,1,Fall,65,Spring,14.09905556,60.0,72.2,,68,112,101,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.61,Spring,5,5,5,0,5,0,0,1,1,2,2,0,5,5,5,5,5,5,5,5,66,Spring,58,80,Winter,0,2 +f0d76b32,Winter,10,1,Spring,72,Winter,22.08496198,56.25,99.4,,48,79,98,Winter,4,6,19,Winter,2,0,13.3,2,14.6,2,2,0,7.5,0,8.75,1,8.5,1,Winter,1,6.45496,22.0874,1155.99,1502.79,20.8309,65.3913,14.5304,7.55697,34.0087,2,27.9651,16.5954,58.9364,29.892,48.796,,,Winter,2.64,Winter,2,2,2,1,2,1,2,1,0,0,3,0,1,2,5,2,3,1,1,0,31,Winter,28,41,Winter,3,1 +f0ed5690,Summer,8,0,Fall,45,Summer,19.2866941,54.0,80.0,,85,88,123,Summer,,,,Summer,5,0,,,,,0,0,9.75,1,8.5,1,11.0,1,Summer,4,9.07157,19.2888,1087.84,2284.47,13.0181,58.1328,14.0164,5.27241,21.8672,2,28.2169,16.8977,49.0612,24.0647,41.235,,,Summer,2.92,Summer,2,0,0,0,2,0,0,0,0,2,2,0,0,0,4,1,3,1,0,0,17,Summer,32,46,Summer,1,0 +f0f199ad,Spring,17,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.27,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +f0f3e41f,Summer,12,0,Spring,81,Summer,15.27514648,64.0,89.0,27.0,73,63,116,,,,,Summer,13,0,18.1,2,19.6,2,10,1,4.0,0,4.0,0,7.0,0,Summer,4,5.04264,15.2768,1257.28,2640.29,20.3872,76.1799,13.0762,2.20057,12.8201,2,34.0537,21.739,71.1372,31.3572,54.4409,,,Fall,3.37,Fall,1,2,3,1,2,1,0,1,1,1,0,0,1,1,1,1,2,1,1,1,22,Fall,26,38,Summer,2,0 +f1049268,Fall,10,1,Spring,60,Winter,30.42427778,60.0,155.8,,73,103,139,Winter,2,1,32,Winter,0,0,15.5,2,17.2,2,0,0,12.0,1,11.0,1,8.0,0,Winter,3,4.35808,30.4276,1335.23,2136.36,30.6511,84.4821,16.4993,13.9283,71.3179,3,32.3428,21.4881,80.124,33.3501,62.9939,,,Fall,2.08,Winter,3,3,3,1,2,0,0,4,0,0,1,0,4,3,3,2,2,1,2,2,36,Winter,40,56,Fall,2,1 +f11e17c0,Spring,14,1,Winter,65,Summer,22.63729719,68.75,152.2,,81,69,134,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1.63,,,Spring,1,4,2,2,3,0,0,0,2,3,4,1,2,4,3,2,0,3,0,3,39,Spring,56,77,Spring,3,1 +f13cb65e,Spring,10,0,Summer,70,Summer,25.23950292,56.3,113.8,,83,120,111,Summer,,,,Summer,9,0,15.7,2,18.8,2,5,0,5.0,0,5.0,0,6.0,0,Summer,4,9.38478,25.2423,1236.45,2596.54,19.4903,73.9611,16.4055,8.83677,39.8389,2,35.5628,18.908,64.5763,34.4979,55.0531,,,Spring,3.38,Summer,5,5,5,5,5,1,0,5,2,4,3,2,4,0,4,5,4,4,4,3,70,Summer,40,56,Spring,2,2 +f14e55d7,Fall,6,0,Winter,55,Winter,19.57181355,49.13,67.2,,101,109,141,Winter,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,2,0,0,11,Winter,58,80,Fall,2,0 +f15dca1b,Winter,10,1,Spring,91,Winter,17.3504121,57.5,81.6,,76,84,121,Winter,6,10,23,Winter,40,1,16.8,2,17.9,2,11,1,12.0,1,12.0,1,10.0,1,Spring,4,3.73736,17.3523,1141.68,2169.2,18.0562,63.8674,13.5815,3.77085,17.7326,1,29.2638,16.5475,60.13,39.4031,47.3199,,,Winter,2.88,Winter,4,4,3,1,5,1,0,1,0,2,3,0,1,1,4,1,1,1,1,1,35,Winter,38,54,Winter,2,1 +f1629e61,Spring,11,1,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,, +f1702318,Winter,14,1,Fall,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.44,,,Fall,3,2,3,0,3,3,1,2,1,2,3,0,2,3,1,1,3,2,2,1,38,Fall,32,46,Winter,0,1 +f1a16e58,Winter,9,0,Spring,52,Spring,20.12972973,55.5,88.2,,73,79,119,Spring,4,6,18,Spring,3,0,,,,,5,0,8.0,1,8.5,1,13.0,1,Spring,3,3.85974,20.4996,1156.54,1966.11,17.0779,65.4493,15.2119,5.28777,22.7507,2,32.4227,15.9487,61.5895,29.4248,49.5006,,,Spring,3.5,Spring,4,5,1,0,4,4,0,1,1,1,3,1,0,1,5,2,0,2,0,1,36,Spring,42,59,Winter,2,1 +f1baabc1,Winter,6,0,Summer,78,Spring,20.78842149,49.0,71.0,,89,90,117,Spring,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2,3,3,1,3,1,1,1,2,2,2,1,1,1,1,3,1,3,1,1,34,Spring,41,58,Winter,0,1 +f1baae47,Spring,11,0,Winter,85,Winter,13.90538898,60.5,72.4,,62,80,126,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,2,4.26103,13.9069,1143.67,1829.87,14.9586,64.0787,12.3085,1.5984,8.32133,3,29.216,19.9041,59.8176,25.9673,44.1746,,,Fall,2.21,Fall,5,5,4,4,5,2,1,2,1,3,4,0,1,1,3,1,3,3,1,0,49,Fall,31,45,Spring,1,1 +f1bd0f59,Spring,10,0,Winter,50,Summer,25.78781213,58.0,123.4,,53,85,94,Summer,5,7,24,Summer,7,0,,,,,4,0,8.0,1,8.0,1,7.5,1,Fall,3,4.67509,25.7906,1362.02,2315.43,23.6854,87.336,18.2532,7.53738,36.064,3,43.2937,20.3569,82.6609,53.7148,66.9791,,,Spring,2.46,Summer,3,2,2,2,1,2,1,1,1,1,0,0,0,0,2,1,2,2,1,1,25,Summer,48,67,Spring,0,0 +f1c306d5,Spring,9,0,,,Spring,14.70685764,48.0,48.2,22.0,59,78,85,,,,,Spring,0,0,,,,,0,0,6.0,0,6.0,0,13.0,1,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +f1c3cb6b,Winter,9,1,,,Winter,15.85898348,51.4,59.6,,64,80,124,Winter,6,10,4,Winter,0,0,,,,,0,0,7.0,0,6.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.259,Winter,2,2,4,0,5,1,,5,,0,0,0,0,4,4,5,2,4,0,4,42,Winter,59,81,Winter,3,1 +f1d1273e,Winter,12,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +f1d26ed5,Fall,20,0,,,Fall,19.66929872,64.5,116.4,26.0,85,59,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +f1ed2e85,Winter,6,0,Spring,65,Winter,16.29023481,48.63,54.8,,69,81,102,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3,0,2,0,2,0,0,3,0,2,0,0,2,2,3,3,2,2,1,0,27,Winter,54,75,Winter,0,0 +f1ee75bf,Spring,10,0,Fall,64,Summer,15.47349113,53.5,63.0,,58,78,117,Summer,9,15,35,Summer,35,1,14.4,2,13.7,2,20,1,10.0,1,9.0,1,11.0,1,Summer,4,3.40102,15.4752,1041.9,2187.99,11.0226,53.2389,13.0775,2.39768,9.76105,1,27.2728,14.9435,49.8379,25.5676,38.2954,,,Spring,3.97,Summer,5,2,3,0,3,1,0,2,1,1,1,0,2,2,2,2,2,2,1,2,34,Summer,33,47,Spring,0,1 +f21c19e1,Spring,11,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.83,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +f220a837,Fall,12,0,Spring,70,Winter,21.75233058,55.0,93.6,,81,101,115,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.73,Winter,3,2,2,2,3,1,1,1,2,4,3,0,1,1,1,2,2,1,1,4,37,Winter,27,40,Fall,3,1 +f2260009,Spring,9,0,Summer,45,Spring,19.75162733,59.3,98.8,,62,107,118,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,4.32027,19.7538,1218.78,1950.05,18.2867,72.0793,14.4113,5.34247,26.7207,2,33.8358,19.9568,67.759,32.7297,52.1225,,,,,Spring,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Spring,34,49,Spring,0,0 +f237ef1c,Fall,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.16,,,,,,,,,,,,,,,,,,,,,,,,,Winter,44,62,Fall,1, +f240387a,Spring,11,0,,,Spring,22.5968018,54.25,94.6,29.0,59,56,105,,,,,Spring,23,1,12.5,1,17.6,2,4,0,10.0,1,8.5,1,12.0,1,Spring,2,3.49619,22.5993,1128.82,1806.12,14.3766,62.4976,14.9302,7.66904,32.1024,2,30.1162,18.0048,59.0014,27.0057,44.4928,,,Spring,2.39,Spring,4,4,4,1,5,1,0,3,1,1,1,0,1,1,2,2,3,2,2,3,41,Spring,26,38,Spring,1,1 +f2499682,Winter,8,0,Summer,60,Spring,16.81145713,51.0,62.2,,61,80,109,Spring,28,8,12,Spring,20,1,,,,,20,1,9.5,1,10.5,1,9.0,1,Spring,3,3.39579,16.8133,1047.88,1781.4,12.1076,53.8764,14.5634,2.24994,8.32356,1,30.5769,11.1919,50.4807,32.996,42.6845,,,Winter,3.02,Spring,3,0,3,1,3,1,0,1,1,0,0,0,2,1,3,3,2,2,1,0,27,Spring,38,54,Winter,0,0 +f2649b91,Summer,14,1,Fall,50,Fall,25.89014412,60.5,134.8,,59,66,112,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.38,Summer,4,4,4,1,3,1,1,2,1,1,2,1,1,0,1,2,0,2,3,0,34,Summer,33,47,Summer,2,1 +f285b43c,Spring,13,0,Fall,60,Fall,20.86537278,65.0,125.4,,103,85,150,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.46,Fall,5,4,3,2,3,3,2,2,1,2,3,0,2,0,0,1,2,3,2,1,41,Fall,44,62,Spring,2,1 +f2932f33,Summer,8,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +f29d4094,Summer,13,0,Fall,45,Summer,18.13240916,69.0,122.8,,96,92,159,,,,,Summer,50,1,39.1,3,39.1,3,40,1,9.0,1,8.0,1,12.0,1,Fall,5,-4.16832,18.1344,1567.79,3762.7,34.6284,109.254,16.1339,2.00046,13.5464,3,46.8206,27.8046,113.422,46.972,81.449,,,Summer,3.38,Summer,4,4,3,0,5,2,3,3,2,3,3,0,2,2,2,2,3,2,1,0,46,Summer,36,51,Summer,2,1 +f2d4efd8,Spring,12,1,Summer,61,Spring,15.97646762,63.0,90.2,,59,86,119,Spring,,,,Spring,23,1,16.0,2,16.4,2,9,1,10.0,1,11.0,1,12.0,1,Spring,2,4.21553,15.9782,1197.04,1795.56,20.6848,69.7636,12.3581,3.62014,20.4364,2,26.4825,22.5963,65.5481,26.8892,47.1673,,,Spring,2.08,Spring,5,5,5,4,5,1,0,5,4,4,5,0,4,5,4,4,4,3,4,4,75,Spring,46,64,Spring,2,2 +f301e7a9,Fall,7,1,Winter,55,Fall,15.83740435,49.5,55.2,,69,92,114,Fall,3,4,19,Fall,0,0,,,,,2,0,6.5,0,6.5,0,7.0,1,Winter,1,2.53909,15.8391,981.206,1275.57,10.685,46.7743,13.4215,2.41767,8.42567,2,23.0275,13.0618,44.2352,28.918,33.7125,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,45,63,Fall,1,0 +f3020788,Spring,18,0,Fall,71,Summer,31.47603678,73.0,238.6,,106,85,145,,,,,Summer,12,0,51.2,,51.0,,10,0,10.0,1,11.0,1,12.0,1,Summer,4,12.9608,31.4795,3806.7,7994.08,115.069,347.727,45.8771,-14.3976,-109.127,3,138.491,94.1672,334.766,215.413,253.56,Spring,3.31,,,Summer,5,5,2,2,4,4,4,1,3,4,2,0,3,0,5,5,1,,1,2,53,,,,Spring,3,2 +f32a9580,Fall,11,0,Spring,70,Winter,15.1882716,54.0,63.0,,67,89,97,Winter,4,5,19,Winter,5,0,,,,,0,0,0.0,0,0.0,0,5.0,0,Winter,3,7.3553,15.1899,1041.82,1771.1,11.0426,53.2308,12.8345,2.35546,9.76921,1,26.0015,16.1867,45.8755,21.7045,37.0441,,,,,Winter,1,1,2,0,1,0,0,0,0,1,2,0,1,1,2,2,1,2,1,1,19,Fall,37,53,Fall,0,0 +f35b607c,Spring,10,0,Summer,50,Summer,15.44761487,58.5,75.2,,50,73,110,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.15,Summer,2,1,3,0,2,1,1,1,1,1,0,0,2,0,2,2,1,2,0,0,22,Summer,41,58,Spring,0,0 +f3600deb,Summer,7,1,Fall,75,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2,3,2,1,4,1,0,0,2,2,2,1,2,1,2,3,2,2,1,1,34,Summer,40,56,Summer,2,1 +f3993171,Fall,11,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.43,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f3a1dd3b,Fall,11,1,Fall,80,Winter,23.58955556,60.0,120.8,,88,112,151,Winter,5,7,19,Winter,21,1,16.8,2,17.1,2,8,1,10.0,1,7.0,0,15.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.78,Winter,5,4,2,1,2,1,1,1,1,1,1,0,1,1,2,2,1,2,2,1,32,Fall,34,49,Fall,2,1 +f3aac45a,Spring,15,1,Spring,70,Winter,20.30468998,62.88,114.2,,77,95,126,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.52,,,Winter,0,3,3,1,4,2,1,4,0,2,4,0,2,3,1,1,1,1,1,2,36,Winter,65,89,Spring,1,1 +f3b8becc,Fall,6,0,,,Spring,,,,,,,,Spring,3,3,25,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +f3caf913,Summer,13,1,Summer,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f3d22832,Winter,11,0,Summer,60,Spring,17.56824298,51.0,65.0,,72,66,114,Spring,5,7,31,Spring,8,0,13.6,2,10.4,1,11,1,9.0,1,10.0,1,9.0,1,Spring,4,3.26595,17.5702,1034.31,2172.05,10.3964,52.4305,14.1725,3.39767,12.5695,1,29.7883,12.2457,49.1645,34.6072,40.1847,,,Winter,2.41,Spring,1,1,2,0,2,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,19,Spring,31,45,Winter,1,0 +f3fbe6c9,Winter,10,0,Spring,70,Spring,15.14099792,61.25,80.8,,77,78,110,Spring,,,,Spring,,,,,,,,,,,,,,,Summer,4,4.45827,15.1427,1182.08,2482.37,16.621,68.1704,12.7757,2.36691,12.6296,1,31.2018,20.3476,63.7121,28.8528,47.8228,,,Spring,2.71,Spring,2,2,4,0,2,1,0,1,1,1,1,0,0,0,0,0,1,2,1,2,21,Spring,56,77,Winter,0,0 +f4114031,Winter,12,0,,,Spring,18.37749586,64.75,109.6,,78,92,165,,,,,Summer,,,,,,,,,,,,,,,Summer,5,5.85197,18.3795,1439.9,3455.77,28.7149,95.6317,16.0371,2.34243,13.9683,3,43.3572,23.5596,89.7797,45.3667,72.0721,,,Winter,2.39,Spring,4,4,3,1,4,2,1,1,1,0,2,0,2,2,2,3,1,3,1,1,38,Winter,32,46,Winter,0,1 +f41a21c4,Winter,16,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3.06,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +f420b05a,Fall,5,0,Winter,60,Fall,17.50237603,44.0,48.2,,82,88,128,,,,,Fall,0,0,,,,,1,0,16.0,1,15.0,1,11.0,1,Fall,3,2.37502,17.5043,897.33,1525.46,6.27434,37.8405,13.7421,3.76217,10.3595,1,22.136,9.43013,35.4654,16.1007,28.4103,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,,,,Fall,0,0 +f4223e3a,Summer,7,0,,,Summer,16.7049505,50.5,60.6,25.0,54,82,122,,,,,Summer,0,0,,,,,6,1,12.0,1,12.0,1,11.0,1,Fall,3,3.08567,16.7068,1003.64,1706.2,10.5182,49.1643,13.5541,3.15269,11.4357,1,25.829,12.8171,46.0787,20.8788,36.3472,,,,,Fall,5,5,5,0,5,1,0,4,4,5,1,0,5,4,5,5,4,5,0,4,67,Fall,76,100,Summer,0,2 +f433302d,Winter,6,0,Spring,52,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,40,56,Winter,0, +f44f1227,Winter,10,0,Spring,70,Spring,15.98803352,56.5,72.6,,60,72,109,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,4.02902,15.9898,1132.53,1925.3,16.6911,62.8921,13.8517,2.13811,9.70787,2,30.5824,15.6186,58.8631,25.6495,47.2735,,,Winter,1.72,Winter,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,18,Winter,26,38,Winter,0,0 +f47714c0,Summer,13,1,,,Summer,19.36492681,63.25,110.2,30.0,68,93,110,,,,,Summer,30,1,72.0,3,69.6,3,10,1,7.75,0,9.25,0,12.0,1,Summer,2,5.13091,19.5211,1367.96,2051.94,30.1586,87.969,15.583,3.93804,22.231,3,36.409,21.4014,82.8381,43.4181,66.5676,,,Summer,3.82,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +f47a67cc,Spring,7,1,Fall,71,Summer,13.06999653,46.5,40.2,,62,84,94,,,,,Summer,5,1,,,,,0,0,8.0,0,11.0,1,9.0,1,Summer,3,2.10632,13.0714,934.856,1495.77,8.44749,41.8375,13.6039,-0.532439,-1.63747,2,18.8578,14.5321,39.7311,15.9998,27.3053,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,40,56,Spring,0,0 +f47abc42,Winter,10,0,Summer,77,Spring,18.58663952,56.5,84.4,,84,57,120,Spring,12,20,0,Spring,18,1,16.3,2,20.8,2,20,1,18.0,1,19.0,1,13.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.32,Spring,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,20,Spring,26,38,Winter,0,0 +f497dd7b,Spring,18,0,,,Summer,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0,3,2,0,1,1,0,0,0,0,3,0,0,1,2,0,0,0,0,0,13,,,,Spring,,0 +f4989c06,Winter,10,0,,,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.32,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +f4c50c86,Winter,7,1,Summer,,Winter,13.88861457,46.0,41.8,21.5,59,99,110,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,,,,, +f4d2f5af,Fall,10,0,Spring,60,Winter,26.93093093,55.5,118.0,,73,88,112,Winter,4,5,17,Winter,12,1,,,,,1,0,12.0,1,12.0,1,10.0,1,Winter,2,8.19674,26.9339,1281.28,2050.05,22.9888,78.736,17.9718,8.96212,39.264,2,37.4838,18.2635,70.5393,33.5879,60.4726,,,Winter,1.84,Winter,4,4,5,5,5,1,0,4,5,4,4,0,5,4,5,5,4,4,4,4,76,Winter,48,67,Fall,0,2 +f4e9d109,Winter,6,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +f4ec5449,Summer,6,0,,,Summer,20.24028972,47.0,63.6,26.0,68,91,117,,,,,Summer,0,0,,,,,0,0,4.5,0,4.5,0,4.0,0,Summer,1,2.79806,20.2425,978.827,1272.48,10.3019,46.521,14.8066,5.43591,17.0791,2,26.0289,10.1901,43.7229,20.4814,36.3308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +f4ece6b5,Spring,8,1,Summer,90,,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.4,Spring,1,1,2,2,1,1,0,1,1,1,1,1,1,1,1,2,1,1,1,1,22,Spring,30,43,Spring,0,0 +f51eff6a,Summer,5,0,Fall,65,Fall,15.47744802,46.5,47.6,21.0,80,101,118,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,48,67,Summer,2, +f52a2597,Winter,6,0,Summer,78,Spring,15.48335802,45.0,44.6,,77,104,117,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,2,0,3,1,0,1,1,1,2,1,1,1,3,1,2,2,2,1,29,Winter,44,62,Winter,3,0 +f5369cea,Fall,6,0,Fall,45,Fall,16.01351607,46.0,48.2,,63,79,125,,,,,Fall,5,1,,,,,3,1,9.0,1,9.5,1,5.5,1,Fall,4,4.79891,16.0153,923.038,1938.38,7.72539,40.5786,13.4829,2.53233,7.62138,1,23.7092,9.14401,35.7797,18.4865,31.4346,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,33,47,Fall,0,0 +f54eebeb,Spring,17,0,Summer,51,Spring,21.14463348,69.0,143.2,33.0,69,50,111,,,,,Spring,50,1,38.2,2,33.6,2,20,1,9.0,1,9.0,1,12.0,1,Summer,2,6.78304,21.1469,1615.67,2585.07,34.7705,114.353,16.887,4.25991,28.8467,3,50.0546,29.5282,107.57,56.1595,84.8251,,,,,Spring,3,3,3,2,3,4,2,3,3,3,2,1,2,2,1,1,2,2,2,1,45,,,,Spring,2,1 +f55a1469,Summer,16,0,Summer,,Fall,21.65695727,66.69,137.0,34.0,86,73,151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +f566e5cc,Summer,15,1,,,Summer,22.60085664,63.0,127.6,30.0,73,68,115,,,,,Summer,20,1,22.9,2,22.5,2,0,0,6.0,0,7.0,0,9.0,1,Fall,1,4.69248,22.6033,1311.73,1705.24,27.2901,81.9791,14.5219,8.08138,45.6209,2,32.5071,22.182,77.2866,37.9816,59.7971,Summer,3.25,,,Summer,0,4,5,0,2,0,0,0,2,0,5,0,0,0,1,1,5,1,5,1,32,Summer,31,45,Summer,3,1 +f567a1af,Fall,7,1,Spring,80,Winter,16.79608885,48.5,56.2,,72,81,125,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1,0,2,1,5,1,0,1,0,0,1,0,1,0,0,1,1,2,0,0,17,Winter,27,40,Fall,0,0 +f57de847,Summer,12,0,Winter,75,Winter,18.23749794,67.5,118.2,,59,92,116,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.58,Fall,2,1,1,0,2,0,2,1,2,3,2,0,0,0,1,1,2,2,1,1,24,Fall,31,45,Summer,0,0 +f584742f,Fall,15,0,Winter,85,Fall,16.60013366,67.0,106.0,,63,80,132,,,,,Fall,30,1,31.0,2,34.0,2,13,0,10.0,1,12.0,1,8.0,0,Fall,4,-1.70651,16.6019,1404.87,2950.22,26.5674,91.9,14.3936,2.20838,14.1,1,41.0014,24.3312,93.6065,42.3284,67.5688,Fall,2.08,,,Fall,3,3,3,2,3,2,3,1,2,2,3,1,2,3,2,2,3,2,2,2,46,Fall,58,80,Fall,0,1 +f58ddd4a,Spring,11,0,Fall,90,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.33,Fall,2,2,3,3,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,32,Fall,31,45,Spring,1,1 +f5ad7abc,Winter,6,1,Spring,60,Winter,17.51073684,47.5,56.2,,74,97,106,Winter,1,0,30,Winter,0,0,,,,,0,0,11.5,1,12.5,1,12.0,1,Winter,4,5.66425,17.5126,973.531,1849.71,14.4367,45.9568,14.3207,3.19191,10.2432,2,22.8271,8.69304,40.2926,17.7349,37.2638,,,,,Spring,1,2,3,0,5,1,0,2,1,1,1,1,1,1,1,1,1,2,0,2,27,Winter,28,41,Winter,1,0 +f5b2acf0,Fall,8,1,Spring,60,Winter,18.75631001,54.0,77.8,,60,65,117,Winter,,,,Winter,10,1,,,,,7,1,11.0,1,9.5,1,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.57,Winter,4,3,3,0,3,0,0,2,1,2,1,0,2,0,3,2,2,2,1,1,32,Winter,39,55,Fall,2,1 +f5c5cfee,Spring,12,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f5d4c82e,Summer,17,0,,,Summer,17.81891892,74.0,138.8,29.0,72,53,105,,,,,Summer,47,1,35.5,2,35.3,2,5,0,8.5,1,6.5,0,12.0,1,Fall,4,7.4193,17.8209,1607.32,3375.36,34.3007,113.463,14.5678,3.25304,25.3367,2,47.5421,31.6205,106.044,49.995,81.8428,Summer,2.021,,,Summer,3,3,4,0,4,4,4,2,0,4,3,0,2,1,3,2,3,0,0,0,42,,,,Summer,2,1 +f5d81859,Spring,7,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +f5e64c94,Summer,10,0,,,Summer,29.8978979,55.5,131.0,37.0,71,118,120,,,,,Summer,0,0,,,,,0,0,7.0,0,5.0,0,7.0,0,Summer,,,,,,,,,,,,,,,,,,,,,Summer,2,2,3,1,3,2,2,3,2,3,3,1,3,2,2,2,3,3,3,2,47,Summer,65,89,Summer,,1 +f5ee91e4,Summer,11,1,,,Summer,19.05452806,56.0,85.0,,72,100,121,,,,,Summer,11,0,14.5,2,15.6,2,1,0,13.0,1,11.0,1,12.0,1,Summer,2,3.62091,19.0566,1138.84,1708.26,20.432,63.5647,14.2509,4.8057,21.4353,2,27.8132,15.3195,59.9438,28.2149,48.2452,,,Summer,3.34,,,,,,,,,,,,,,,,,,,,,,,Summer,38,54,Summer,0, +f5efa540,Fall,8,0,,,Fall,59.13204775,33.0,91.6,33.0,84,98,112,,,,,Fall,1,0,,,,,0,0,9.0,1,9.5,1,8.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.59,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,, +f6284dcb,Winter,8,0,Summer,75,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.35,Winter,2,2,1,0,1,0,0,0,0,0,1,0,1,0,1,1,1,1,1,1,14,Winter,46,64,Winter,0,0 +f62b317d,Fall,12,1,Spring,40,Winter,33.76009365,62.0,184.6,,76,73,128,Winter,3,3,6,Winter,5,0,17.0,2,23.2,3,4,0,7.0,0,9.0,0,7.0,0,Winter,2,4.73078,33.7638,1421.5,2132.25,33.7398,93.6717,17.1328,16.631,90.9283,3,35.4838,24.4481,88.941,41.8852,69.2236,,,Fall,3.56,Winter,3,2,4,0,1,1,1,1,0,1,3,0,1,2,1,1,1,1,1,2,27,Winter,47,66,Fall,0,0 +f64ae747,Summer,9,0,Summer,75,Winter,21.15286807,54.63,89.8,,81,81,117,Winter,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,3.2,Fall,5,5,4,0,4,2,0,2,1,4,3,0,4,0,3,4,3,4,3,2,53,Fall,50,69,Summer,1,2 +f64e1d85,Spring,7,0,Summer,72,Spring,18.52430154,50.5,67.2,26.0,64,77,101,,,,,Spring,2,0,,,,,0,0,11.0,1,12.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,4,4,4,3,5,1,1,4,1,4,0,0,5,2,4,4,3,5,1,4,59,Summer,42,59,Spring,1,2 +f64f877b,Winter,8,1,,,Spring,15.20137868,52.5,59.6,,70,88,115,Spring,6,8,15,Spring,5,0,,,,,7,1,7.0,0,8.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,Winter,1.92,Spring,2,2,2,0,0,0,0,0,0,2,0,0,2,0,2,2,2,2,0,0,18,Spring,41,58,Winter,0,0 +f67908e7,Spring,9,0,Spring,60,Winter,16.54381378,56.0,73.8,,79,85,126,Winter,,,,Winter,25,1,,,,,20,1,10.0,1,13.0,1,10.0,1,,,,,,,,,,,,,,,,,,,,Fall,3.72,Winter,3,2,2,0,2,1,0,1,0,0,0,0,2,0,0,2,1,0,0,0,16,Fall,37,53,Spring,0,0 +f68f158d,Summer,6,0,Fall,80,Summer,16.33794252,49.0,55.8,,73,98,117,Summer,,,,Summer,5,1,,,,,10,1,5.0,0,5.0,0,10.0,1,,,,,,,,,,,,,,,,,,,,,,Summer,2,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,9,Summer,53,73,Summer,3,0 +f6b719ee,Fall,6,1,Winter,60,Winter,16.11041667,48.0,52.8,,68,98,123,Winter,7,11,30,Winter,8,1,,,,,0,0,12.0,1,12.0,1,5.0,0,Winter,2,2.38933,16.1122,960.713,1441.07,11.8942,44.5915,13.6073,2.50486,8.20849,1,20.5949,12.1024,42.2022,15.9317,32.4891,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,41,58,Fall,0,0 +f6bcfb38,Winter,7,0,,,Winter,19.16325926,45.0,55.2,26.0,67,99,117,,,,,Winter,1,0,,,,,7,1,9.0,1,6.5,0,9.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +f6c0e2b7,Winter,14,0,Spring,55,Spring,19.62460973,66.0,121.6,,51,62,103,,,,,Spring,17,0,23.0,1,28.6,2,8,0,6.75,0,7.75,1,12.0,1,Spring,3,5.76751,19.6268,1426.81,2425.57,27.2336,94.2367,15.2102,4.41655,27.3633,2,41.9505,25.0526,88.4692,43.1974,69.1841,Spring,2.171,,,Spring,4,4,5,2,5,1,1,3,3,4,3,1,1,1,1,2,3,1,2,1,48,Spring,34,49,Winter,2,1 +f6c20447,Spring,9,1,Winter,70,Summer,19.42400644,53.2,78.2,,63,99,115,Summer,6,9,9,Summer,2,0,,,,,13,1,14.5,1,13.0,1,12.0,1,Summer,1,3.35159,19.4261,1074.67,1397.07,12.1013,56.7296,14.0925,5.33358,21.4704,2,28.3781,16.2502,53.378,26.4422,40.4794,,,Spring,2.81,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,33,47,Summer,0,0 +f6cc80a9,Fall,17,1,Spring,63,Winter,29.99271592,62.0,164.0,,69,71,132,,,,,Winter,,,,,,,,,,,,,,,Winter,2,4.51694,29.996,1351.91,2027.86,27.3583,86.2587,15.7769,14.2191,77.7413,3,35.3515,23.5489,81.7418,55.2458,62.7098,Fall,1.63,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,54,75,Fall,3,0 +f6d3a5ad,Winter,7,0,Spring,68,Spring,17.83135645,53.5,72.6,,46,63,91,Spring,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1,0,1,0,0,0,0,1,2,1,1,0,2,2,1,1,1,2,1,1,18,Spring,36,51,Winter,2,0 +f6d6f400,Summer,12,0,Fall,65,Summer,25.68848032,59.0,127.2,,55,72,108,,,,,Summer,0,,15.3,1,16.7,1,6,,7.0,,7.0,,8.0,,,,,,,,,,,,,,,,,,,,,Summer,2.83,Summer,4,4,3,2,3,2,1,2,2,2,0,0,2,0,0,1,1,0,0,0,29,Summer,59,81,Summer,0,0 +f6d879aa,Winter,13,0,Summer,70,Spring,24.50736111,60.0,125.5,,87,95,156,,,,,Spring,27,1,25.8,2,28.3,2,7,0,10.0,1,10.25,1,12.5,1,,,,,,,,,,,,,,,,,,,,Spring,1.83,Spring,4,4,3,1,4,2,0,2,1,2,2,0,3,3,3,1,2,1,1,2,41,Spring,46,64,Winter,0,1 +f74c45c1,Summer,11,0,Winter,65,Fall,24.36051645,61.25,130.0,,68,36,100,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.52,Fall,4,4,5,2,4,2,1,0,3,3,4,0,4,1,2,3,0,4,0,0,46,Fall,39,55,Summer,1,1 +f74eda33,Winter,10,0,Spring,40,Winter,17.35749485,54.3,72.8,,60,77,114,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,5,3.47667,17.3594,1073.64,2576.74,12.4058,56.6201,13.5013,3.85814,16.1799,2,27.783,16.4313,53.1435,24.2779,40.1888,,,Winter,1.64,Winter,3,3,3,1,3,2,3,2,4,5,5,0,3,2,5,5,4,5,0,3,61,Winter,28,41,Winter,3,2 +f75c957f,Fall,7,1,Summer,63,Spring,16.17144097,48.0,53.0,,61,90,103,Spring,4,6,15,Fall,5,1,,,,,3,1,12.0,1,11.0,1,10.0,1,Summer,3,2.38955,16.1732,961.028,1537.64,10.4464,44.6251,13.6176,2.55565,8.37494,1,22.0698,12.1088,42.2355,24.5833,32.5162,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,37,53,Fall,0,0 +f77c2636,Fall,16,1,,,Fall,25.19152663,65.0,151.4,32.0,71,73,130,,,,,Fall,11,0,28.5,2,26.1,2,0,0,10.0,1,11.0,1,12.0,1,Fall,2,5.47915,25.1943,1464.66,2196.99,35.2127,98.2687,16.3528,8.84152,53.1313,2,37.9164,25.1396,92.7895,44.4934,73.1291,Fall,1.09,,,Fall,2,4,4,4,2,5,1,3,2,2,4,1,1,0,2,1,3,1,1,1,44,,,,Fall,3,1 +f78421c9,Spring,14,0,,,Spring,35.87975779,68.0,236.0,45.0,,,,,,,,Spring,18,0,26.4,1,29.4,2,3,0,11.0,1,11.0,1,12.0,1,,,,,,,,,,,,,,,,,,Spring,3.58,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2, +f79e6708,Summer,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f7b1374e,Fall,13,1,,,Winter,25.49380853,62.0,139.4,,78,100,127,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.36,Winter,5,4,4,1,1,1,0,1,2,2,3,0,2,2,2,1,2,2,2,2,39,Winter,45,63,Fall,0,1 +f7b24278,Summer,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f7bb3b1c,Summer,15,0,Fall,72,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.82,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +f7c08ff0,Fall,11,0,Summer,70,,,,,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,38,54,Fall,, +f7fc97cf,Spring,14,0,Spring,75,Spring,23.75231096,69.5,163.2,,74,88,129,,,,,Spring,11,0,31.8,3,32.5,3,24,1,9.5,1,10.0,1,5.0,0,Spring,2,7.00575,23.7549,1699.74,2719.58,34.5529,123.307,17.9483,5.80665,39.8926,2,56.7894,31.9651,116.302,81.1222,91.3423,,,Spring,1.37,Spring,0,3,2,1,1,1,0,0,0,0,3,0,1,2,2,1,2,1,1,2,23,Spring,35,50,Spring,2,0 +f7fcb41b,Summer,10,0,,,Summer,22.30673077,52.0,85.8,31.0,68,92,132,,,,,Summer,0,0,0.0,1,0.0,1,0,0,0.0,0,0.0,0,9.0,1,Summer,1,3.45138,22.3092,1107.72,1440.03,15.4158,60.2495,15.6657,6.64347,25.5505,2,30.6877,14.146,56.7981,26.1781,46.1035,,,Fall,2.15,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0, +f804c9f3,Spring,8,0,Fall,70,Summer,17.52271074,55.0,75.4,,60,98,101,Summer,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.95,Summer,2,3,4,0,3,1,0,3,2,4,3,0,3,3,0,2,2,2,2,1,40,Summer,58,80,Spring,2,1 +f81598bf,Spring,11,0,Summer,79,Spring,25.46394342,61.5,137.0,,89,81,158,Spring,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2.33,Spring,3,3,2,2,3,2,1,2,2,2,2,1,3,2,2,2,2,2,2,2,42,Spring,33,47,Spring,0,1 +f817f33a,Winter,5,0,Spring,60,Winter,14.40690691,44.4,40.4,,69,92,113,Winter,,,,Winter,3,1,,,,,0,0,9.6,1,10.5,1,7.0,1,Winter,4,6.69747,15.362,867.779,1822.34,4.9683,34.6929,13.1919,2.17012,5.70712,1,20.6782,9.04642,27.9954,14.2622,25.6465,,,,,Winter,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,3,4,4,2,1,24,Winter,41,58,Winter,0,0 +f82a1183,Winter,12,0,,,Spring,23.34076117,64.75,139.2,35.0,50,95,122,,,,,Spring,16,0,24.9,2,24.9,2,0,0,0.0,0,0.0,0,3.0,0,Spring,,,,,,,,,,,,,,,,,,,Winter,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,3, +f848f42b,Winter,7,1,Fall,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,5,5,3,1,3,3,0,2,1,3,2,1,2,1,3,5,2,4,1,1,48,Spring,44,62,Winter,2,1 +f84e064d,Fall,7,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f8674e15,Summer,8,0,Fall,61,Summer,15.95927304,48.5,53.4,22.0,55,71,114,,,,,Summer,5,1,,,,,10,1,9.0,1,10.0,1,11.0,1,Summer,3,2.81777,15.6369,962.68,1636.56,7.93596,44.8011,13.119,2.51799,8.5989,1,23.8511,13.014,41.9833,19.8224,31.7871,,,Summer,2.82,Summer,4,5,5,0,4,0,0,0,0,0,0,0,0,0,4,3,4,3,3,4,39,Summer,55,76,Summer,1,1 +f869d220,Summer,11,0,Fall,50,Summer,32.3742567,58.5,157.6,,44,90,118,,,,,Summer,8,0,18.0,2,12.3,1,0,0,5.5,0,6.0,0,11.0,1,Summer,1,4.46836,32.3778,1394.58,1812.96,26.2876,90.8046,18.6551,13.7226,66.7954,3,40.8769,23.6401,86.3362,37.151,67.1645,,,Summer,1.68,Summer,5,5,3,2,2,2,0,2,3,1,2,0,2,3,2,4,2,5,2,2,49,Summer,67,91,Summer,3,1 +f897f15d,Fall,10,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1.49,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +f8a1b335,Summer,7,0,,,Summer,,,0.0,,,,,,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,, +f8b08332,Fall,11,0,Summer,60,Fall,15.29788543,51.0,56.6,,63,66,105,Fall,6,10,0,Fall,15,1,14.2,2,11.7,1,5,0,7.0,0,7.5,1,9.5,1,Fall,4,6.5807,15.2996,994.466,2088.38,9.33565,48.1867,13.0254,2.2742,8.41329,1,25.2265,13.6246,41.606,21.7913,34.5621,,,Fall,2.21,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,38,54,Fall,,0 +f8b36382,Fall,7,0,Winter,70,Fall,21.14436645,51.25,79.0,,84,132,146,Fall,,,,Fall,4,1,,,,,24,1,9.5,1,9.0,1,11.5,1,Fall,4,6.90173,21.1467,1071.79,2250.75,13.484,56.4225,15.1031,6.04353,22.5775,2,29.3469,13.5916,49.5208,25.6989,42.8309,,,,,Fall,0,0,4,0,4,0,0,0,0,0,5,0,0,1,5,4,4,4,0,5,36,Fall,48,67,Fall,0,1 +f8bf4a1c,Fall,15,0,Winter,31,Fall,28.06526817,68.0,184.6,,94,88,158,,,,,Fall,11,,18.6,1,22.7,1,2,,5.5,,3.5,,5.0,,Fall,2,6.38471,28.0683,1659.41,2655.06,37.1864,119.012,18.0958,9.97257,65.5877,3,49.8623,31.9637,112.628,48.2081,87.0487,,,,,Winter,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Winter,27,40,Fall,3,0 +f8d293a0,Spring,8,0,,,Summer,15.33689083,54.5,64.8,24.0,74,54,108,,,,,Summer,3,0,,,,,2,0,8.5,1,8.5,1,9.0,1,Fall,2,3.52901,15.3386,1058.66,1693.86,12.1825,55.0246,13.0247,2.3139,9.77539,1,27.2914,15.5507,51.4956,23.4586,39.4739,,,Summer,3.47,Summer,2,2,2,0,2,1,0,0,2,1,0,0,0,0,2,1,2,0,1,0,18,Summer,31,45,Spring,0,0 +f8d94d02,Fall,11,1,Winter,50,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f8e3467e,Fall,7,1,Spring,65,Fall,19.02029192,53.0,76.0,26.0,83,83,135,,,,,Fall,2,0,,,,,1,0,12.0,1,12.0,1,9.0,1,Fall,2,2.9768,19.0224,1056.58,1584.87,16.3823,54.8027,13.7168,5.30556,21.1973,1,23.1525,15.2679,51.8259,19.6046,39.5349,,,,,Fall,4,4,4,1,2,1,0,2,1,2,0,0,3,2,1,3,2,2,1,1,36,Fall,50,69,Winter,2,1 +f8f5f3df,Winter,9,0,,,Winter,18.55405788,51.5,70.0,29.0,67,83,112,,,,,Winter,11,1,,,,,5,0,8.0,1,7.0,0,12.0,1,Winter,,,,,,,,,,,,,,,,,,,Winter,2.5,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +f8ff0bc8,Fall,18,0,Spring,62,Winter,26.42996724,71.5,192.2,,74,75,139,,,,,,,,,,,,,,,,,,,,Winter,3,4.08715,35.7278,1433.73,2437.34,21.2386,94.9742,17.6546,18.0731,97.2258,2,39.5555,34.1802,90.8871,45.5033,60.7941,Fall,2.03,,,Winter,2,2,2,1,1,1,1,1,1,0,2,1,1,1,1,1,1,1,1,2,24,Winter,42,59,Fall,2,0 +f91ba8bb,Summer,15,1,Winter,,Fall,22.02870457,62.4,122.0,30.5,60,65,104,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f922ae21,Summer,11,0,,,Summer,21.9225196,59.5,110.4,29.0,59,64,111,,,,,Summer,15,1,22.2,2,20.6,2,9,1,7.0,0,6.5,0,10.5,1,Fall,3,4.6267,21.9249,1293.82,2199.49,22.1853,80.0718,15.9019,6.02304,30.3282,1,37.7172,20.1692,75.4451,37.4177,59.9025,,,Summer,3.25,Summer,2,3,3,0,2,1,0,1,1,1,1,0,2,1,3,0,1,1,1,1,25,Summer,38,54,Summer,0,0 +f92475d4,Winter,13,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +f9273ffc,Spring,6,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f93010a8,Fall,9,0,Spring,85,Winter,16.70409357,57.0,77.2,,73,93,108,,,,,Winter,11,1,,,,,4,0,7.0,0,4.5,0,10.0,1,Winter,2,4.05318,16.7059,1143.71,1829.94,16.2316,64.0833,13.8675,2.83842,13.1167,2,31.3409,16.5109,60.0302,28.6626,47.5725,,,Winter,2.76,Winter,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,1,1,1,1,1,26,Winter,28,41,Fall,2,0 +f9334e08,Summer,6,1,Winter,47,Fall,14.26296323,47.2,45.2,,70,105,114,Fall,1,1,0,Fall,0,0,,,,,0,0,12.5,1,11.5,1,3.5,0,Fall,2,2.37164,14.0849,943.327,1414.99,9.79309,42.7398,13.3183,0.766643,2.46024,2,22.1679,10.7788,40.3681,24.931,31.961,,,,,Fall,4,2,3,0,3,0,0,3,0,2,2,0,2,2,3,2,2,2,2,2,36,Fall,31,45,Summer,0,1 +f9384fa2,Winter,15,1,Summer,65,Spring,21.65111529,61.0,114.6,,111,90,153,,,,,Spring,31,1,26.8,2,30.3,3,6,0,14.5,1,14.5,1,7.0,0,Spring,2,4.32259,21.6535,1253.5,1880.25,24.4672,75.7774,14.318,7.33547,38.8226,2,31.0306,20.2796,71.4548,36.4791,55.4978,Spring,1.5,,,Spring,4,2,2,1,4,1,2,2,1,2,2,0,1,2,2,2,3,2,1,1,37,Spring,36,51,Winter,2,1 +f958efdc,Spring,11,0,Spring,70,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.07,Spring,3,2,3,1,3,2,0,1,2,1,0,0,1,1,2,2,2,2,1,1,30,Spring,36,51,Spring,2,0 +f9744a50,Spring,6,1,Winter,60,Fall,22.06011342,46.0,66.4,,53,84,118,Fall,5,7,32,Fall,1,,,,,,3,,9.0,,10.0,,12.0,,Fall,2,8.66887,22.0625,962.209,1443.31,12.8428,44.7509,14.8692,7.19329,21.6491,3,22.2217,9.68629,36.082,19.6171,35.0646,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,30,43,Spring,1, +f9897bf1,Fall,6,0,,,Fall,16.84876033,44.0,46.4,23.0,53,80,95,,,,,Fall,0,0,,,,,0,0,10.0,1,9.0,1,12.0,1,Fall,2,2.43519,16.8506,895.395,1432.63,6.85314,37.6343,13.6672,3.18336,8.76573,1,22.6643,8.11684,35.1991,16.3879,29.5174,,,,,Fall,5,5,4,1,4,1,1,2,1,3,1,1,4,1,2,4,2,3,1,1,47,Fall,44,62,Fall,0,1 +f98bde44,Summer,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,3.03,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +f9b73de7,Spring,14,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,4,6.02718,23.27,1518.7,3189.27,29.622,104.025,16.0521,7.21792,46.7753,3,45.9115,28.4912,97.9975,51.3411,75.5334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +f9ca9ff6,Winter,11,1,,,Spring,16.31964286,56.0,72.8,25.0,64,79,116,,,,,Spring,30,1,13.0,2,15.9,2,20,1,19.0,1,19.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,4.16,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +fa293db8,Summer,12,1,,,Summer,19.99605294,56.5,90.8,27.0,64,80,99,,,,,Summer,10,0,14.7,2,14.3,2,0,0,9.75,0,9.5,0,7.5,0,Fall,2,3.79592,19.9982,1172.57,1758.85,22.7957,67.1568,14.7909,5.2073,23.6432,2,28.7598,15.6013,63.3608,27.7911,51.5555,,,Fall,2.06,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,40,56,Summer,0,0 +fa29ab47,Spring,7,0,Summer,70,Spring,14.7551238,49.1,50.6,,64,103,118,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,2,2.93355,14.7567,962.888,1540.62,9.33138,44.8232,13.072,1.68472,5.77681,3,24.271,11.2209,41.8896,18.2732,33.6023,,,,,Spring,2,3,2,1,2,0,0,1,1,1,2,1,1,1,1,1,1,1,1,1,24,Spring,31,45,Spring,0,0 +fa30d545,Summer,12,0,,,Summer,,,,,,,,Summer,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,63,86,,, +fa34f945,Spring,14,0,Spring,65,Winter,22.33632546,68.25,148.0,,94,85,130,,,,,Spring,,,,,,,,,,,,,,,Summer,3,6.37868,22.3388,1562.53,2656.3,32.9082,108.693,16.4059,5.9329,39.3069,2,46.8955,28.8893,102.314,48.3572,79.8037,,,Spring,2.01,Spring,3,2,2,2,3,2,3,2,2,2,2,0,3,2,2,3,2,4,2,1,44,Spring,51,71,Spring,2,1 +fa3e1e21,Fall,9,1,Spring,45,Spring,15.23648834,54.0,63.2,,65,90,104,Spring,3,4,15,Spring,9,1,,,,,5,0,6.0,0,8.0,0,6.5,1,Spring,5,7.48883,15.2382,1050.1,2310.22,15.004,54.1124,13.047,2.19112,9.08762,2,24.2771,14.8313,46.6235,24.457,39.281,,,Winter,4.21,Winter,5,2,0,0,3,1,0,0,0,0,0,0,2,1,0,3,0,2,0,0,19,Winter,40,56,Fall,0,0 +fa472647,Spring,11,1,Fall,61,Summer,17.79197531,54.0,73.8,,54,83,107,Summer,5,7,22,Summer,7,0,15.5,3,16.3,3,2,0,13.0,1,10.0,1,7.0,0,Summer,4,3.30975,17.7939,1086.75,2064.83,17.7905,58.0165,13.9884,3.80557,15.7835,1,26.5333,13.6926,54.7067,27.1935,44.3239,,,Spring,2.75,Summer,4,4,4,1,3,1,0,1,0,3,0,0,3,0,3,2,3,2,3,0,37,Summer,45,63,Spring,2,1 +fa4f0329,Winter,7,0,Summer,70,Spring,,,0.0,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,4,3,2,0,1,0,0,1,1,0,1,0,1,0,1,1,1,1,0,0,18,Spring,38,54,Winter,1,0 +fa62d7b1,Summer,15,1,Winter,75,Fall,19.61069605,61.0,103.8,,83,89,132,,,,,Fall,14,0,22.8,2,20.6,2,14,1,7.75,0,8.0,0,13.0,1,Fall,3,3.44125,19.6128,1241.62,1986.59,23.912,74.5115,14.0788,5.53401,29.2885,1,31.0142,19.5852,71.0702,36.1102,54.9263,Fall,1.93,,,Fall,0,2,2,1,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,23,Fall,32,46,Fall,0,0 +fa633e39,Fall,10,1,Summer,41,Spring,25.23851969,53.25,101.8,,79,86,140,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,3.1696,25.2413,1115.01,1784.02,19.13,61.0262,15.1314,10.1098,40.7738,2,26.7926,15.1036,57.8566,28.8973,45.9226,,,Winter,2.43,Spring,1,0,0,1,0,0,0,0,1,1,1,0,2,0,0,0,0,0,0,0,7,Spring,36,51,Fall,2,0 +fa78d819,Spring,15,0,Fall,75,Spring,16.931819,64.5,100.2,,72,63,112,,,,,Summer,25,1,26.4,1,27.5,1,0,0,3.0,0,5.0,0,6.5,0,Spring,1,1.55892,16.9337,1316.65,1711.64,23.2058,82.5033,13.9429,2.99072,17.6967,3,36.7949,22.5026,80.9444,34.5231,60.0007,Summer,2.34,,,Summer,5,4,3,2,5,4,1,3,3,5,3,0,1,4,3,5,4,5,0,1,61,Spring,59,81,Spring,2,2 +fa9a0add,Summer,7,1,,,Summer,15.25607639,48.0,50.0,22.0,80,117,117,,,,,Summer,4,1,,,,,0,0,12.0,1,12.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,2,0,2,0,0,0,0,0,2,0,1,0,1,1,1,1,0,0,15,Winter,31,45,Summer,0,0 +fab6550c,Winter,9,0,,,Winter,24.05497885,51.0,89.0,30.0,80,87,121,,,,,Winter,0,0,,,,,0,0,15.0,1,15.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Winter,2.37,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +fab75c2c,Fall,6,1,Spring,45,Winter,16.57113875,45.5,48.8,,52,69,107,,,,,Winter,8,1,,,,,2,0,13.0,1,13.0,1,8.0,1,Winter,1,2.26942,16.5729,929.373,1208.18,11.1093,41.2534,14.0101,2.56289,7.54657,1,21.9477,8.19651,38.984,19.8003,33.0569,,,,,Winter,0,0,0,0,0,0,0,4,0,3,2,0,1,0,2,2,1,1,1,1,18,Winter,53,73,Fall,0,0 +facdd8bf,Summer,9,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fad07fce,Spring,10,1,Spring,74,Winter,13.8857383,57.1,64.4,,70,84,119,Winter,,,,Fall,,,,,,,,,,,,,,,Summer,1,3.4725,13.8873,1087.8,1414.14,16.5613,58.1277,12.5347,1.35257,6.27231,2,24.5001,17.0663,54.6552,23.3615,41.0614,,,Spring,2.83,Spring,4,0,2,0,2,1,0,0,2,1,1,0,2,0,2,2,2,2,1,0,24,Spring,31,45,Spring,2,0 +fad31638,Summer,8,0,,,Summer,17.90497959,52.5,70.2,26.0,69,76,111,,,,,Summer,30,1,,,,,30,1,11.0,1,10.5,1,11.0,1,Summer,2,3.38902,17.9069,1058.59,1693.75,12.9341,55.0172,14.034,3.87289,15.1828,1,28.1117,13.9714,51.6282,23.6045,41.0458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2, +fb04f7dd,Fall,8,0,Spring,55,Winter,16.0670858,52.0,61.8,,60,68,107,Winter,4,6,30,Winter,3,0,,,,,2,0,7.0,0,6.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,Fall,1.78,Fall,5,5,4,1,3,1,0,4,1,1,3,1,2,2,4,5,4,3,2,4,55,Fall,40,56,Fall,0,2 +fb111799,Fall,5,0,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fb194aa7,Spring,8,0,,,Fall,,,,,,,,,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +fb22e454,Winter,11,0,Summer,75,Winter,14.88692928,56.5,67.6,,70,95,123,Winter,5,7,39,Winter,4,0,22.0,1,21.6,1,6,0,7.0,0,7.0,0,7.0,0,,,,,,,,,,,,,,,,,,,,Spring,2.12,Winter,3,2,3,0,3,2,2,0,0,2,2,0,2,0,2,1,2,1,1,1,29,Winter,35,50,Winter,2,0 +fb2c4db6,Spring,6,1,Spring,68,Spring,14.76823633,44.5,41.6,21.0,58,76,103,,,,,Spring,0,0,,,,,0,0,10.5,1,10.0,1,10.0,1,Spring,3,2.18463,14.7698,906.77,1450.83,10.6591,38.846,13.792,0.977809,2.75405,1,21.1268,7.06012,36.6613,16.7085,31.7858,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,28,41,Spring,0,0 +fb2ddbd1,Summer,8,0,Spring,55,Winter,17.96455082,53.74,73.8,,75,96,114,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,3.41709,17.9665,1070.22,2247.46,12.0742,56.2556,13.6954,4.27116,17.5444,2,28.0436,16.1378,52.8385,25.6388,40.1178,,,,,Spring,3,4,4,4,3,4,0,3,4,3,3,0,4,5,4,4,4,4,3,2,65,Winter,38,54,Summer,1,2 +fb3c2c87,Winter,6,0,Spring,57,Winter,16.9414321,45.0,48.8,,70,97,116,Winter,,,,Spring,,,,,,,,,,,,,,,Summer,4,2.40098,16.8044,908.408,1907.66,5.79693,39.0204,13.5478,3.25659,9.37964,2,21.856,11.3674,36.6194,16.8589,27.6529,,,,,Winter,3,3,2,1,3,2,0,0,1,1,2,0,2,1,1,2,1,2,0,0,27,Winter,42,59,Winter,1,0 +fb3c4473,Fall,8,0,Fall,70,Fall,14.19771917,53.5,57.8,24.0,73,83,90,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2, +fb6a1b72,Winter,8,0,,,Winter,14.06636487,47.0,44.2,22.0,61,99,98,,,,,Winter,0,0,,,,,0,0,5.0,0,7.0,0,10.0,1,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +fb9982a2,Winter,7,0,Summer,80,Spring,16.48660571,51.5,62.2,,55,,114,Spring,7,12,25,Spring,3,0,,,,,0,0,7.5,1,9.25,1,14.0,1,Spring,3,3.573,16.4884,1036.73,1762.44,12.8893,52.6881,13.9669,2.52147,9.51185,2,27.6811,12.1177,49.1152,22.2867,40.5704,,,,,Spring,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,1,0,0,6,Spring,40,56,Spring,0,0 +fb9b060f,Summer,16,1,Fall,65,Summer,21.26696369,67.0,135.8,,108,67,139,,,,,Summer,15,0,27.1,2,30.5,3,2,0,19.0,1,19.0,1,12.0,1,Summer,2,-0.0217578,21.2693,1417.6,2126.39,32.0044,93.2556,14.6059,6.6634,42.5444,2,35.9542,25.297,93.2773,42.6606,67.9586,Summer,1.07,,,Summer,3,3,3,1,1,3,4,4,5,4,2,1,3,3,3,1,2,3,2,1,52,Summer,68,93,Summer,2,2 +fb9c5a33,Fall,8,0,Winter,70,Fall,16.51580101,58.5,80.4,,66,72,117,Fall,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.54,Fall,2,2,2,0,2,0,0,0,0,0,1,0,1,0,2,0,2,2,0,1,17,Fall,36,51,Fall,0,0 +fbb39bfe,Summer,16,0,,,Summer,20.98053633,68.0,138.0,28.0,59,,124,,,,,Summer,28,1,26.6,1,28.2,1,19,1,8.75,1,9.5,1,9.0,1,Summer,4,6.80796,20.9828,1637.95,3439.7,36.0693,116.726,17.7482,3.23463,21.2735,2,51.4698,29.1874,109.919,58.5954,87.5391,Fall,1.74,,,Summer,5,5,4,3,5,2,5,1,4,5,5,0,5,0,4,4,5,5,3,3,73,,,,Summer,0,2 +fbbd97b1,Spring,14,1,Summer,62,Summer,15.67195386,63.75,90.6,24.0,54,73,110,,,,,Summer,25,1,17.7,2,21.8,2,5,0,12.5,1,13.0,1,9.0,1,Summer,4,4.62319,15.6737,1237.1,2350.48,23.3309,74.03,12.8071,2.86659,16.57,1,29.4884,21.2107,69.4068,31.5061,52.8193,,,Spring,2.53,Summer,3,1,2,1,1,2,2,1,1,1,2,1,1,1,1,1,1,1,1,1,26,Summer,37,53,Spring,0,0 +fbc6ade7,Winter,6,0,Summer,90,Winter,24.95216049,36.0,46.0,22.0,54,95,101,,,,,Winter,2,1,,,,,0,0,12.0,1,14.0,1,8.0,1,Winter,2,2.51566,15.9711,904.746,1447.59,7.08051,38.6303,13.4124,2.55874,7.36968,2,22.7443,8.80549,36.1147,16.5931,29.8248,,,,,Winter,1,3,3,1,2,1,1,2,1,1,3,1,2,2,3,3,2,2,1,2,37,Winter,31,45,Winter,,1 +fbda3966,Fall,10,0,Winter,65,Fall,21.55288066,54.0,89.4,,65,74,106,Fall,,,,Fall,19,1,18.5,2,15.3,2,9,1,6.0,0,5.0,0,8.0,0,Fall,4,7.0809,21.4588,1135.05,2383.61,15.6458,63.1611,15.2288,6.23002,25.8389,2,31.7221,15.7932,56.0802,29.7129,47.3679,,,Fall,3.43,Fall,2,3,3,0,4,0,0,2,2,2,3,0,2,2,2,2,2,2,1,1,35,Fall,34,49,Fall,1,1 +fbf1f698,Spring,11,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2.76,,,,,,,,,,,,,,,,,,,,,,,Summer,37,53,Spring,, +fc0eede5,Winter,15,1,Winter,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,, +fc1912e3,Winter,16,1,Summer,45,Spring,22.31607663,63.5,128.0,,69,99,107,,,,,Spring,17,0,17.9,2,27.2,2,0,0,6.0,0,6.5,0,12.0,1,,,,,,,,,,,,,,,,,,Spring,1.11,,,Spring,4,2,3,2,2,2,3,3,3,2,4,0,1,3,3,1,3,3,3,2,49,Spring,34,49,Winter,2,1 +fc198415,Spring,8,1,,,Fall,,,,,,,,Fall,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,3.28,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +fc290a36,Summer,16,0,,,Summer,18.04268558,67.0,115.2,26.0,57,82,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fc2c2c08,Summer,9,0,Spring,51,Summer,17.84806612,55.0,76.8,25.0,62,74,110,,,,,Summer,10,1,,,,,5,1,7.0,0,7.0,0,3.0,0,Summer,3,3.76258,17.85,1113.83,1893.51,15.2128,60.9007,14.1547,3.69535,15.8993,1,30.213,15.4749,57.1381,26.4137,45.4258,,,Spring,3.35,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,43,60,Summer,0,0 +fc41be2f,Summer,5,0,Winter,50,Summer,16.50322425,45.5,48.6,,83,79,139,,,,,Summer,0,0,,,,,0,0,10.0,1,11.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,Summer,0,0 +fc49ba65,Fall,6,1,Winter,70,Winter,13.89128,50.0,49.4,,61,87,103,Winter,,,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,3,Winter,25,38,Fall,0,0 +fc633e5a,Fall,18,1,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.59,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fc8ba2d9,Winter,12,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2.41,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2, +fc8e4de4,Fall,9,1,Spring,75,Winter,17.26536106,57.5,81.2,,86,93,118,Winter,5,9,0,Winter,10,1,,,,,15,1,10.0,1,11.0,1,12.0,1,Spring,4,3.86021,17.2672,1158.88,2201.87,19.1497,65.699,13.9709,3.2963,15.501,2,30.5842,15.9651,61.8388,41.069,49.7339,,,Fall,2.56,Winter,0,0,0,0,0,0,0,0,,0,0,0,0,0,0,0,0,0,0,0,0,Winter,32,46,Fall,1,0 +fc96f532,Fall,8,0,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fcc6347f,Fall,6,1,Spring,40,Spring,17.39192708,48.0,57.0,,59,70,98,Spring,,,,Spring,0,0,,,,,3,1,12.0,1,11.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Spring,48,67,Fall,0,0 +fcd9a6bb,Spring,6,0,Summer,69,Spring,16.19514463,44.0,44.6,,32,107,49,Spring,,,,Summer,,,,,,,,,,,,,,,Summer,3,2.3989,16.1969,889.13,1511.52,6.37071,36.967,13.4249,2.772,7.633,1,21.9642,8.63213,34.5681,15.4021,28.3349,,,,,Spring,4,1,2,0,1,0,0,0,0,0,0,0,1,0,1,2,1,1,0,0,14,Spring,38,54,Spring,0,0 +fcfadd26,Winter,6,1,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +fd028ecc,Fall,10,1,Spring,85,Winter,20.82787644,54.5,88.0,,60,95,98,Winter,,,,Winter,10,1,,,,,5,0,13.0,1,12.0,1,11.0,1,Winter,1,5.02208,20.8302,1128.45,1466.98,20.4944,62.4573,14.784,6.04611,25.5427,1,27.6204,14.3425,57.4352,27.3057,48.1148,,,Winter,2.03,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,42,59,Fall,0,0 +fd0d9bca,Fall,8,1,Spring,80,Winter,12.63528107,52.0,48.6,,59,85,118,Winter,7,10,59,Winter,13,1,,,,,7,1,10.0,1,11.0,1,6.5,1,Winter,3,8.78954,12.6367,999.121,1598.59,12.4569,48.6825,12.6581,-0.0214562,-0.0825195,1,22.1321,14.0936,39.893,20.3503,34.5889,,,Winter,2.76,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,29,42,Fall,0,0 +fd226dc3,Spring,9,0,,,Spring,16.63477366,54.0,69.0,24.0,69,96,97,,,,,Summer,0,0,,,,,0,0,7.5,0,6.75,0,12.0,1,Summer,,,,,,,,,,,,,,,,,,,Spring,2.37,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, +fd3448d9,Fall,13,0,Fall,65,Fall,14.27031091,56.5,64.8,,60,68,107,,,,,Fall,10,0,15.8,1,17.0,1,10,1,6.0,0,6.0,0,11.0,1,,,,,,,,,,,,,,,,,,,,Fall,2.06,Fall,4,4,4,2,4,2,0,3,2,4,1,1,2,2,3,3,3,3,3,,50,Fall,27,40,Fall,,2 +fd5589f6,Spring,8,0,Summer,70,Summer,15.54718165,50.5,56.4,,67,80,99,Summer,7,11,43,Summer,5,1,,,,,8,1,10.0,1,10.0,1,11.0,1,Summer,2,3.01028,15.5489,987.953,1580.73,9.15999,47.493,13.0933,2.45556,8.90698,1,24.8707,13.4624,44.4827,20.8569,34.0306,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,26,38,Spring,2,0 +fd56e059,Winter,6,0,Spring,75,Winter,15.57667638,49.0,53.2,,61,81,119,Winter,6,10,19,Winter,5,1,,,,,0,0,8.0,1,7.0,0,4.0,0,,,,,,,,,,,,,,,,,,,,,,Winter,2,2,4,0,2,0,0,2,0,1,2,0,1,0,0,1,1,1,3,0,22,Winter,36,51,Winter,1,0 +fdb6b48b,Spring,8,0,Summer,61,Summer,16.63073719,49.0,56.8,,67,62,115,Summer,7,12,56,Summer,18,,,,,,15,,12.0,,12.0,,12.0,,,,,,,,,,,,,,,,,,,,,,,Summer,4,3,3,2,2,0,0,2,0,2,2,0,2,0,2,1,2,2,1,0,30,Summer,55,76,Spring,2,0 +fdb7da3c,Fall,9,0,,,Spring,13.74768838,48.5,46.0,,72,107,110,Spring,,,,Spring,8,0,,,,,0,0,11.0,1,10.0,1,12.0,1,Spring,2,2.5771,13.7492,945.513,1512.82,5.87894,42.9726,12.8443,0.904886,3.02743,1,20.5301,16.5635,40.3955,14.6189,26.4091,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1, +fdc11b96,Spring,13,0,Fall,85,Spring,29.85651974,66.0,185.0,,,,,,,,,Spring,18,1,25.5,2,29.7,2,2,0,5.0,0,5.5,0,10.0,1,Summer,2,6.16224,29.8598,1662.35,2659.76,36.6509,119.325,19.2596,10.6002,65.6748,3,51.6294,31.0449,113.163,54.8182,88.2803,,,Summer,2.25,Summer,0,2,1,1,2,3,0,1,1,0,3,0,0,3,0,0,0,0,0,0,17,Spring,37,53,Spring,2,0 +fdcb5b38,Winter,8,0,Spring,80,Winter,17.32521179,51.75,66.0,25.0,71,87,111,,,,,Winter,0,0,,,,,0,0,10.0,1,9.0,1,5.0,0,Winter,1,3.30554,17.3271,1039.8,1351.74,11.9635,53.0151,13.9182,3.40894,12.9849,2,27.8998,13.1518,49.7096,24.7077,39.8633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +fdce05a4,Fall,12,0,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fdd9799f,Spring,15,0,Summer,48,Summer,29.40463728,66.0,182.2,39.0,83,102,146,,,,,Summer,23,0,32.1,2,34.8,2,20,1,13.0,1,13.0,1,8.0,0,Summer,2,6.28421,29.4078,1683.21,2693.13,38.2572,121.547,19.6182,9.78968,60.6532,3,52.1211,31.1685,115.263,53.638,90.3783,Summer,1.12,,,Summer,2,3,3,2,2,2,1,3,3,2,3,1,3,2,2,1,2,2,4,2,45,Summer,40,56,Spring,3,1 +fdea7d9e,Summer,11,1,Summer,60,Fall,19.16792569,60.5,99.8,,80,81,128,Fall,,,,Spring,,,,,,,,,,,,,,,Summer,2,4.18068,19.17,1213.59,1820.38,22.4544,71.526,13.739,5.43099,28.274,2,29.6802,19.3914,67.3454,33.9367,52.1347,,,Summer,2.48,Summer,3,2,4,0,3,4,0,2,2,3,3,0,3,3,2,0,4,2,0,0,40,Summer,51,71,Summer,2,1 +fdf4691f,Fall,9,0,Spring,62,Fall,19.76926278,58.0,94.6,,73,61,108,,,,,Fall,10,1,,,,,3,0,5.0,0,5.0,0,4.5,0,Fall,2,4.07956,19.7714,1184.61,1895.37,17.0965,68.4394,14.3039,5.46757,26.1606,2,32.1303,19.2126,64.3599,29.2277,49.2268,,,Winter,2.83,Fall,2,2,2,0,3,1,0,0,0,0,2,0,1,0,2,1,2,1,1,0,20,Fall,40,56,Fall,2,0 +fe102a25,Summer,5,0,Winter,61,Summer,18.27855727,43.5,49.2,,102,47,133,,,,,Summer,0,0,,,,,1,0,11.0,1,9.0,1,9.0,1,,,,,,,,,,,,,,,,,,,,,,Winter,1,2,5,5,2,0,0,0,0,1,5,0,4,0,0,3,3,3,0,0,34,,,,Summer,3,1 +fe2bc235,Summer,12,0,Winter,55,Fall,22.92491782,58.5,111.6,,64,84,113,,,,,Fall,8,0,14.1,1,16.4,1,0,0,7.0,0,8.0,1,9.0,1,Fall,2,4.25551,22.9274,1246.72,1994.75,19.7574,75.0551,15.4195,7.50788,36.5449,2,34.9384,20.3593,70.7996,32.5594,54.6958,,,Summer,2.481,Fall,5,5,4,0,5,1,0,4,4,5,4,0,2,3,5,0,5,0,3,0,55,Fall,32,46,Summer,2,2 +fe3cbf02,Fall,7,0,Spring,61,Winter,16.5908,50.0,59.0,,57,68,114,Winter,2,1,37,Winter,3,0,,,,,3,0,8.0,1,6.0,0,0.0,0,Winter,2,2.95213,16.5926,988.566,1581.71,9.21315,47.5583,13.3749,3.21776,11.4417,1,24.8441,13.501,44.6062,20.4279,34.0573,,,,,Winter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Winter,38,54,Fall,0,0 +fe5e2dc9,Spring,7,0,Summer,70,Summer,12.07561404,68.5,80.6,,59,82,101,Summer,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,5,5,5,0,4,0,0,4,4,2,3,0,5,4,5,5,5,4,4,4,68,Summer,49,68,Spring,0,2 +fe6afaf9,Summer,11,0,Winter,60,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Fall,2.47,Summer,1,2,3,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,11,Summer,38,54,Fall,,0 +fe7c87e2,Spring,13,0,Summer,,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.88,,,,,,,,,,,,,,,,,,,,,,,,,,,, +fe7f68a7,Spring,15,1,,,Spring,22.45796046,62.0,122.8,30.0,64,60,106,,,,,Summer,9,0,18.5,2,21.8,2,0,0,8.0,0,8.75,0,9.5,1,Summer,,,,,,,,,,,,,,,,,Spring,2.32,,,,,,,,,,,,,,,,,,,,,,,,,Spring,49,68,Spring,2, +fe9c71d8,Winter,9,0,Spring,80,Winter,15.89113703,56.75,72.8,,44,65,78,,,,,Winter,8,1,,,,,9,1,10.5,1,11.5,1,15.0,1,Winter,4,3.92272,15.8929,1116.86,2345.4,15.0055,61.2231,13.3655,2.52733,11.5769,2,29.6535,16.5641,57.3004,25.8365,44.659,,,Winter,2.91,Winter,5,2,2,0,3,1,0,1,1,3,2,0,0,1,2,1,2,1,1,1,29,Winter,33,47,Winter,0,0 +feb53635,Winter,14,0,Summer,70,Spring,19.03677863,69.5,130.8,,61,80,127,,,,,Spring,50,1,38.3,2,42.9,2,25,1,16.0,1,14.0,1,9.0,1,Spring,3,6.57155,19.3158,1541.27,2620.15,29.0087,106.428,15.7167,3.5991,24.3719,1,49.4503,27.9691,99.8566,67.311,78.459,Winter,2.85,,,Spring,4,4,5,4,5,3,4,4,4,4,4,0,4,4,4,4,4,4,4,4,77,Spring,51,71,Winter,3,2 +fecc07d6,Fall,7,0,Spring,85,Winter,15.98658892,49.0,54.6,,59,80,113,Winter,4,5,20,Winter,4,1,,,,,3,0,8.0,1,7.5,1,8.0,1,Winter,2,2.49685,16.5154,1002.34,1603.74,12.2293,49.0254,14.356,2.15947,7.37455,2,27.0856,9.71055,46.5286,21.0046,39.3149,,,,,Winter,2,3,1,1,2,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,14,Winter,34,49,Fall,0,0 +fee07f32,Spring,6,0,Fall,55,Spring,15.99611516,46.5,49.2,,52,86,109,Spring,2,1,40,Spring,0,0,,,,,0,0,10.0,1,9.0,1,8.0,1,,,,,,,,,,,,,,,,,,,,,,Fall,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fall,33,47,Spring,1,0 +fefaffe0,Spring,12,1,Summer,80,Spring,22.34823101,62.0,122.2,,66,84,126,,,,,Spring,27,1,22.0,2,26.7,3,0,0,11.0,1,10.0,1,11.0,1,Spring,3,0.78812,22.3507,1362.95,2180.72,30.7088,87.4349,15.9921,6.35861,34.7651,2,35.484,21.2422,86.6468,40.4123,66.1928,,,Spring,2.49,Spring,1,1,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,7,Spring,30,43,Spring,0,0 +ff0ab367,Spring,9,0,,,Spring,20.2004898,52.5,79.2,28.0,75,92,142,,,,,Spring,0,0,,,,,0,0,9.5,1,9.0,1,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Spring,1.99,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,0, +ff18b749,Spring,7,0,,,Summer,14.76884211,47.5,47.4,,58,99,98,Summer,,,,Fall,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Summer,62,85,Spring,0,0 +ff60112d,Summer,15,0,Spring,40,Winter,26.36471002,70.5,186.4,,90,74,172,,,,,Spring,,,,,,,,,,,,,,,Summer,3,6.95881,26.3676,1719.31,2922.82,39.0124,125.392,17.7376,8.63002,61.008,3,52.4763,33.9033,118.433,54.2027,91.4887,Summer,2.54,,,Spring,3,2,4,2,3,1,4,2,2,2,2,1,,1,1,1,1,1,1,1,35,Spring,66,90,Summer,3,1 +ff6c2bb8,Fall,8,0,,,Fall,17.13980952,52.5,67.2,25.0,60,65,112,,,,,Fall,0,0,,,,,0,0,8.0,1,10.0,1,12.0,1,Fall,3,3.20303,17.1417,1035.27,1759.96,11.0063,52.5331,13.4004,3.7413,14.6669,1,25.7118,15.815,49.3301,20.2645,36.7181,,,Fall,3.44,Fall,3,3,3,0,0,0,0,3,0,0,0,0,2,0,0,3,0,2,2,1,22,Fall,41,58,Fall,2,0 +ff759544,Summer,7,1,,,Summer,13.92700606,48.5,46.6,23.0,65,75,105,,,,,Summer,0,0,,,,,0,0,9.0,0,8.5,0,4.5,0,Fall,1,2.3668,13.6457,966.287,1256.17,9.98802,45.1853,13.2315,0.414263,1.4147,1,20.0572,15.14,42.8185,18.0937,30.0453,,,,,Summer,1,3,3,0,3,0,0,0,0,0,3,0,5,1,0,5,3,3,3,0,33,Summer,48,67,Summer,0,1 +ff8a2de4,Fall,13,0,Spring,60,Fall,16.36246028,59.5,82.4,,71,70,104,,,,,Fall,16,0,18.0,1,19.9,2,10,1,8.0,1,9.0,1,12.0,1,Fall,3,4.52277,16.3642,1206.88,2051.7,19.4611,70.8117,14.0629,2.30138,11.5883,1,33.3709,17.9797,66.2889,29.779,52.832,,,Winter,3.26,Winter,3,3,3,2,3,2,2,2,2,1,2,0,2,0,1,0,2,1,1,0,32,Winter,35,50,Fall,1,1 +ffa9794a,Winter,10,0,,,Spring,18.76467814,53.5,76.4,27.0,60,78,118,,,,,Spring,0,0,,,,,4,0,0.0,0,0.0,0,12.0,1,Spring,,,,,,,,,,,,,,,,,,,Winter,2.34,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,0, +ffcd4dbd,Fall,11,0,Spring,68,Winter,21.4415,60.0,109.8,,79,99,116,,,,,Winter,15,1,18.5,2,15.8,2,0,0,10.0,1,10.0,1,14.0,1,Winter,2,4.41305,21.4438,1253.74,2005.99,20.4825,75.8033,14.8043,6.63952,33.9967,2,33.9805,21.3403,71.3903,28.7792,54.463,,,Winter,2.729,Winter,5,5,3,0,5,1,0,2,0,2,1,0,1,3,0,0,1,1,0,1,31,Winter,56,77,Fall,0,1 +ffed1dd5,Spring,13,0,Spring,70,Winter,12.23589524,70.7,87.0,,59,61,113,,,,,Spring,,,,,,,,,,,,,,,Summer,4,6.66168,12.2372,1414.34,2970.12,26.5323,92.9092,13.0684,-0.83117,-5.90917,2,41.3715,25.0054,86.2475,45.434,67.9038,,,Spring,3.3,Spring,2,1,1,1,0,0,0,1,1,1,2,0,1,1,2,1,1,1,1,1,19,Spring,33,47,Spring,1,0 +ffef538e,Spring,11,0,,,Winter,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,1, diff --git a/databricks.yml b/databricks.yml new file mode 100644 index 0000000..7c196b0 --- /dev/null +++ b/databricks.yml @@ -0,0 +1,19 @@ +# This is a Databricks asset bundle definition for marvelous-databricks-course-javedhassans. +# The Databricks extension requires databricks.yml configuration file. +# See https://docs.databricks.com/dev-tools/bundles/index.html for documentation. + +bundle: + name: marvelous-databricks-course-javedhassans + +targets: + dev: + mode: development + default: true + workspace: + host: https://dbc-643c4c2b-d6c9.cloud.databricks.com + + ## Optionally, there could be 'staging' or 'prod' targets here. + # + # prod: + # workspace: + # host: https://dbc-643c4c2b-d6c9.cloud.databricks.com diff --git a/notebooks/01.prepare_dataset.py b/notebooks/01.prepare_dataset.py new file mode 100644 index 0000000..71a38ab --- /dev/null +++ b/notebooks/01.prepare_dataset.py @@ -0,0 +1,22 @@ +# Databricks notebook source + +from pyspark.sql import SparkSession + +from childHealth.config import ProjectConfig +from childHealth.data_processor import DataProcessor + +spark = SparkSession.builder.getOrCreate() + +# COMMAND ---------- + +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") + +# COMMAND ---------- +# Load the house prices dataset +df = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True).toPandas() + +# COMMAND ---------- +data_processor = DataProcessor(pandas_df=df, config=config) +data_processor.preprocess() +train_set, test_set = data_processor.split_data() +data_processor.save_to_catalog(train_set=train_set, test_set=test_set, spark=spark) \ No newline at end of file diff --git a/notebooks/dataExploration.ipynb b/notebooks/dataExploration.ipynb new file mode 100644 index 0000000..6299859 --- /dev/null +++ b/notebooks/dataExploration.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option('display.max_columns', 100)\n", + "pd.set_option('display.max_rows', 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data/childHealth.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n", + " 'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n", + " 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n", + " 'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n", + " 'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n", + " 'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n", + " 'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n", + " 'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n", + " 'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n", + " 'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n", + " 'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n", + " 'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n", + " 'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n", + " 'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n", + " 'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n", + " 'PAQ_C-PAQ_C_Total', 'PCIAT-Season', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n", + " 'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n", + " 'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n", + " 'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n", + " 'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n", + " 'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total', 'SDS-Season',\n", + " 'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n", + " 'PreInt_EduHx-computerinternet_hoursday', 'sii'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sii\n", + "0.0 1594\n", + "NaN 1224\n", + "1.0 730\n", + "2.0 378\n", + "3.0 34\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['sii'].value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# get numerical and categorical columns list\n", + "num_cols = df.select_dtypes(exclude=['object']).columns.tolist()\n", + "cat_cols = df.select_dtypes(include=['object']).columns.tolist()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Basic_Demos-Age',\n", + " 'Basic_Demos-Sex',\n", + " 'CGAS-CGAS_Score',\n", + " 'Physical-BMI',\n", + " 'Physical-Height',\n", + " 'Physical-Weight',\n", + " 'Physical-Waist_Circumference',\n", + " 'Physical-Diastolic_BP',\n", + " 'Physical-HeartRate',\n", + " 'Physical-Systolic_BP',\n", + " 'Fitness_Endurance-Max_Stage',\n", + " 'Fitness_Endurance-Time_Mins',\n", + " 'Fitness_Endurance-Time_Sec',\n", + " 'FGC-FGC_CU',\n", + " 'FGC-FGC_CU_Zone',\n", + " 'FGC-FGC_GSND',\n", + " 'FGC-FGC_GSND_Zone',\n", + " 'FGC-FGC_GSD',\n", + " 'FGC-FGC_GSD_Zone',\n", + " 'FGC-FGC_PU',\n", + " 'FGC-FGC_PU_Zone',\n", + " 'FGC-FGC_SRL',\n", + " 'FGC-FGC_SRL_Zone',\n", + " 'FGC-FGC_SRR',\n", + " 'FGC-FGC_SRR_Zone',\n", + " 'FGC-FGC_TL',\n", + " 'FGC-FGC_TL_Zone',\n", + " 'BIA-BIA_Activity_Level_num',\n", + " 'BIA-BIA_BMC',\n", + " 'BIA-BIA_BMI',\n", + " 'BIA-BIA_BMR',\n", + " 'BIA-BIA_DEE',\n", + " 'BIA-BIA_ECW',\n", + " 'BIA-BIA_FFM',\n", + " 'BIA-BIA_FFMI',\n", + " 'BIA-BIA_FMI',\n", + " 'BIA-BIA_Fat',\n", + " 'BIA-BIA_Frame_num',\n", + " 'BIA-BIA_ICW',\n", + " 'BIA-BIA_LDM',\n", + " 'BIA-BIA_LST',\n", + " 'BIA-BIA_SMM',\n", + " 'BIA-BIA_TBW',\n", + " 'PAQ_A-PAQ_A_Total',\n", + " 'PAQ_C-PAQ_C_Total',\n", + " 'PCIAT-PCIAT_01',\n", + " 'PCIAT-PCIAT_02',\n", + " 'PCIAT-PCIAT_03',\n", + " 'PCIAT-PCIAT_04',\n", + " 'PCIAT-PCIAT_05',\n", + " 'PCIAT-PCIAT_06',\n", + " 'PCIAT-PCIAT_07',\n", + " 'PCIAT-PCIAT_08',\n", + " 'PCIAT-PCIAT_09',\n", + " 'PCIAT-PCIAT_10',\n", + " 'PCIAT-PCIAT_11',\n", + " 'PCIAT-PCIAT_12',\n", + " 'PCIAT-PCIAT_13',\n", + " 'PCIAT-PCIAT_14',\n", + " 'PCIAT-PCIAT_15',\n", + " 'PCIAT-PCIAT_16',\n", + " 'PCIAT-PCIAT_17',\n", + " 'PCIAT-PCIAT_18',\n", + " 'PCIAT-PCIAT_19',\n", + " 'PCIAT-PCIAT_20',\n", + " 'PCIAT-PCIAT_Total',\n", + " 'SDS-SDS_Total_Raw',\n", + " 'SDS-SDS_Total_T',\n", + " 'PreInt_EduHx-computerinternet_hoursday',\n", + " 'sii']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['id',\n", + " 'Basic_Demos-Enroll_Season',\n", + " 'CGAS-Season',\n", + " 'Physical-Season',\n", + " 'Fitness_Endurance-Season',\n", + " 'FGC-Season',\n", + " 'BIA-Season',\n", + " 'PAQ_A-Season',\n", + " 'PAQ_C-Season',\n", + " 'PCIAT-Season',\n", + " 'SDS-Season',\n", + " 'PreInt_EduHx-Season']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id 0.000000\n", + "Basic_Demos-Enroll_Season 0.000000\n", + "Basic_Demos-Age 0.000000\n", + "Basic_Demos-Sex 0.000000\n", + "CGAS-Season 35.479798\n", + "CGAS-CGAS_Score 38.863636\n", + "Physical-Season 16.414141\n", + "Physical-BMI 23.686869\n", + "Physical-Height 23.560606\n", + "Physical-Weight 22.323232\n", + "Physical-Waist_Circumference 77.323232\n", + "Physical-Diastolic_BP 25.404040\n", + "Physical-HeartRate 25.075758\n", + "Physical-Systolic_BP 25.404040\n", + "Fitness_Endurance-Season 66.969697\n", + "Fitness_Endurance-Max_Stage 81.237374\n", + "Fitness_Endurance-Time_Mins 81.313131\n", + "Fitness_Endurance-Time_Sec 81.313131\n", + "FGC-Season 15.505051\n", + "FGC-FGC_CU 41.363636\n", + "FGC-FGC_CU_Zone 42.373737\n", + "FGC-FGC_GSND 72.878788\n", + "FGC-FGC_GSND_Zone 73.181818\n", + "FGC-FGC_GSD 72.878788\n", + "FGC-FGC_GSD_Zone 73.156566\n", + "FGC-FGC_PU 41.666667\n", + "FGC-FGC_PU_Zone 42.651515\n", + "FGC-FGC_SRL 41.792929\n", + "FGC-FGC_SRL_Zone 42.752525\n", + "FGC-FGC_SRR 41.742424\n", + "FGC-FGC_SRR_Zone 42.702020\n", + "FGC-FGC_TL 41.313131\n", + "FGC-FGC_TL_Zone 42.297980\n", + "BIA-Season 45.833333\n", + "BIA-BIA_Activity_Level_num 49.722222\n", + "BIA-BIA_BMC 49.722222\n", + "BIA-BIA_BMI 49.722222\n", + "BIA-BIA_BMR 49.722222\n", + "BIA-BIA_DEE 49.722222\n", + "BIA-BIA_ECW 49.722222\n", + "BIA-BIA_FFM 49.722222\n", + "BIA-BIA_FFMI 49.722222\n", + "BIA-BIA_FMI 49.722222\n", + "BIA-BIA_Fat 49.722222\n", + "BIA-BIA_Frame_num 49.722222\n", + "BIA-BIA_ICW 49.722222\n", + "BIA-BIA_LDM 49.722222\n", + "BIA-BIA_LST 49.722222\n", + "BIA-BIA_SMM 49.722222\n", + "BIA-BIA_TBW 49.722222\n", + "PAQ_A-Season 88.005051\n", + "PAQ_A-PAQ_A_Total 88.005051\n", + "PAQ_C-Season 56.540404\n", + "PAQ_C-PAQ_C_Total 56.540404\n", + "PCIAT-Season 30.909091\n", + "PCIAT-PCIAT_01 30.984848\n", + "PCIAT-PCIAT_02 30.959596\n", + "PCIAT-PCIAT_03 31.035354\n", + "PCIAT-PCIAT_04 31.035354\n", + "PCIAT-PCIAT_05 31.085859\n", + "PCIAT-PCIAT_06 31.010101\n", + "PCIAT-PCIAT_07 31.085859\n", + "PCIAT-PCIAT_08 31.060606\n", + "PCIAT-PCIAT_09 31.060606\n", + "PCIAT-PCIAT_10 30.984848\n", + "PCIAT-PCIAT_11 30.959596\n", + "PCIAT-PCIAT_12 31.035354\n", + "PCIAT-PCIAT_13 31.085859\n", + "PCIAT-PCIAT_14 31.010101\n", + "PCIAT-PCIAT_15 31.060606\n", + "PCIAT-PCIAT_16 31.111111\n", + "PCIAT-PCIAT_17 31.186869\n", + "PCIAT-PCIAT_18 31.111111\n", + "PCIAT-PCIAT_19 31.060606\n", + "PCIAT-PCIAT_20 30.984848\n", + "PCIAT-PCIAT_Total 30.909091\n", + "SDS-Season 33.888889\n", + "SDS-SDS_Total_Raw 34.116162\n", + "SDS-SDS_Total_T 34.191919\n", + "PreInt_EduHx-Season 10.606061\n", + "PreInt_EduHx-computerinternet_hoursday 16.641414\n", + "sii 30.909091\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()/len(df)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3960 entries, 0 to 3959\n", + "Data columns (total 82 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 3960 non-null object \n", + " 1 Basic_Demos-Enroll_Season 3960 non-null object \n", + " 2 Basic_Demos-Age 3960 non-null int64 \n", + " 3 Basic_Demos-Sex 3960 non-null int64 \n", + " 4 CGAS-Season 2555 non-null object \n", + " 5 CGAS-CGAS_Score 2421 non-null float64\n", + " 6 Physical-Season 3310 non-null object \n", + " 7 Physical-BMI 3022 non-null float64\n", + " 8 Physical-Height 3027 non-null float64\n", + " 9 Physical-Weight 3076 non-null float64\n", + " 10 Physical-Waist_Circumference 898 non-null float64\n", + " 11 Physical-Diastolic_BP 2954 non-null float64\n", + " 12 Physical-HeartRate 2967 non-null float64\n", + " 13 Physical-Systolic_BP 2954 non-null float64\n", + " 14 Fitness_Endurance-Season 1308 non-null object \n", + " 15 Fitness_Endurance-Max_Stage 743 non-null float64\n", + " 16 Fitness_Endurance-Time_Mins 740 non-null float64\n", + " 17 Fitness_Endurance-Time_Sec 740 non-null float64\n", + " 18 FGC-Season 3346 non-null object \n", + " 19 FGC-FGC_CU 2322 non-null float64\n", + " 20 FGC-FGC_CU_Zone 2282 non-null float64\n", + " 21 FGC-FGC_GSND 1074 non-null float64\n", + " 22 FGC-FGC_GSND_Zone 1062 non-null float64\n", + " 23 FGC-FGC_GSD 1074 non-null float64\n", + " 24 FGC-FGC_GSD_Zone 1063 non-null float64\n", + " 25 FGC-FGC_PU 2310 non-null float64\n", + " 26 FGC-FGC_PU_Zone 2271 non-null float64\n", + " 27 FGC-FGC_SRL 2305 non-null float64\n", + " 28 FGC-FGC_SRL_Zone 2267 non-null float64\n", + " 29 FGC-FGC_SRR 2307 non-null float64\n", + " 30 FGC-FGC_SRR_Zone 2269 non-null float64\n", + " 31 FGC-FGC_TL 2324 non-null float64\n", + " 32 FGC-FGC_TL_Zone 2285 non-null float64\n", + " 33 BIA-Season 2145 non-null object \n", + " 34 BIA-BIA_Activity_Level_num 1991 non-null float64\n", + " 35 BIA-BIA_BMC 1991 non-null float64\n", + " 36 BIA-BIA_BMI 1991 non-null float64\n", + " 37 BIA-BIA_BMR 1991 non-null float64\n", + " 38 BIA-BIA_DEE 1991 non-null float64\n", + " 39 BIA-BIA_ECW 1991 non-null float64\n", + " 40 BIA-BIA_FFM 1991 non-null float64\n", + " 41 BIA-BIA_FFMI 1991 non-null float64\n", + " 42 BIA-BIA_FMI 1991 non-null float64\n", + " 43 BIA-BIA_Fat 1991 non-null float64\n", + " 44 BIA-BIA_Frame_num 1991 non-null float64\n", + " 45 BIA-BIA_ICW 1991 non-null float64\n", + " 46 BIA-BIA_LDM 1991 non-null float64\n", + " 47 BIA-BIA_LST 1991 non-null float64\n", + " 48 BIA-BIA_SMM 1991 non-null float64\n", + " 49 BIA-BIA_TBW 1991 non-null float64\n", + " 50 PAQ_A-Season 475 non-null object \n", + " 51 PAQ_A-PAQ_A_Total 475 non-null float64\n", + " 52 PAQ_C-Season 1721 non-null object \n", + " 53 PAQ_C-PAQ_C_Total 1721 non-null float64\n", + " 54 PCIAT-Season 2736 non-null object \n", + " 55 PCIAT-PCIAT_01 2733 non-null float64\n", + " 56 PCIAT-PCIAT_02 2734 non-null float64\n", + " 57 PCIAT-PCIAT_03 2731 non-null float64\n", + " 58 PCIAT-PCIAT_04 2731 non-null float64\n", + " 59 PCIAT-PCIAT_05 2729 non-null float64\n", + " 60 PCIAT-PCIAT_06 2732 non-null float64\n", + " 61 PCIAT-PCIAT_07 2729 non-null float64\n", + " 62 PCIAT-PCIAT_08 2730 non-null float64\n", + " 63 PCIAT-PCIAT_09 2730 non-null float64\n", + " 64 PCIAT-PCIAT_10 2733 non-null float64\n", + " 65 PCIAT-PCIAT_11 2734 non-null float64\n", + " 66 PCIAT-PCIAT_12 2731 non-null float64\n", + " 67 PCIAT-PCIAT_13 2729 non-null float64\n", + " 68 PCIAT-PCIAT_14 2732 non-null float64\n", + " 69 PCIAT-PCIAT_15 2730 non-null float64\n", + " 70 PCIAT-PCIAT_16 2728 non-null float64\n", + " 71 PCIAT-PCIAT_17 2725 non-null float64\n", + " 72 PCIAT-PCIAT_18 2728 non-null float64\n", + " 73 PCIAT-PCIAT_19 2730 non-null float64\n", + " 74 PCIAT-PCIAT_20 2733 non-null float64\n", + " 75 PCIAT-PCIAT_Total 2736 non-null float64\n", + " 76 SDS-Season 2618 non-null object \n", + " 77 SDS-SDS_Total_Raw 2609 non-null float64\n", + " 78 SDS-SDS_Total_T 2606 non-null float64\n", + " 79 PreInt_EduHx-Season 3540 non-null object \n", + " 80 PreInt_EduHx-computerinternet_hoursday 3301 non-null float64\n", + " 81 sii 2736 non-null float64\n", + "dtypes: float64(68), int64(2), object(12)\n", + "memory usage: 2.5+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/project_config.yml b/project_config.yml new file mode 100644 index 0000000..5f8e565 --- /dev/null +++ b/project_config.yml @@ -0,0 +1,101 @@ +# project_config.yml + +# Databricks catalog and schema names for saving tables +catalog_name: "mlops_students" +schema_name: "javedhassi" + +# Parameters for data processing +parameters: + drop_threshold: 0.8 # Threshold for dropping columns with missing values + lower_bound: 0.05 # Lower quantile bound for outlier detection + upper_bound: 0.95 # Upper quantile bound for outlier detection + +# Numerical features +num_features: + - Basic_Demos-Age + - Basic_Demos-Sex + - CGAS-CGAS_Score + - Physical-BMI + - Physical-Height + - Physical-Weight + - Physical-Waist_Circumference + - Physical-Diastolic_BP + - Physical-HeartRate + - Physical-Systolic_BP + - Fitness_Endurance-Max_Stage + - Fitness_Endurance-Time_Mins + - Fitness_Endurance-Time_Sec + - FGC-FGC_CU + - FGC-FGC_CU_Zone + - FGC-FGC_GSND + - FGC-FGC_GSND_Zone + - FGC-FGC_GSD + - FGC-FGC_GSD_Zone + - FGC-FGC_PU + - FGC-FGC_PU_Zone + - FGC-FGC_SRL + - FGC-FGC_SRL_Zone + - FGC-FGC_SRR + - FGC-FGC_SRR_Zone + - FGC-FGC_TL + - FGC-FGC_TL_Zone + - BIA-BIA_Activity_Level_num + - BIA-BIA_BMC + - BIA-BIA_BMI + - BIA-BIA_BMR + - BIA-BIA_DEE + - BIA-BIA_ECW + - BIA-BIA_FFM + - BIA-BIA_FFMI + - BIA-BIA_FMI + - BIA-BIA_Fat + - BIA-BIA_Frame_num + - BIA-BIA_ICW + - BIA-BIA_LDM + - BIA-BIA_LST + - BIA-BIA_SMM + - BIA-BIA_TBW + - PAQ_A-PAQ_A_Total + - PAQ_C-PAQ_C_Total + - PCIAT-PCIAT_01 + - PCIAT-PCIAT_02 + - PCIAT-PCIAT_03 + - PCIAT-PCIAT_04 + - PCIAT-PCIAT_05 + - PCIAT-PCIAT_06 + - PCIAT-PCIAT_07 + - PCIAT-PCIAT_08 + - PCIAT-PCIAT_09 + - PCIAT-PCIAT_10 + - PCIAT-PCIAT_11 + - PCIAT-PCIAT_12 + - PCIAT-PCIAT_13 + - PCIAT-PCIAT_14 + - PCIAT-PCIAT_15 + - PCIAT-PCIAT_16 + - PCIAT-PCIAT_17 + - PCIAT-PCIAT_18 + - PCIAT-PCIAT_19 + - PCIAT-PCIAT_20 + - PCIAT-PCIAT_Total + - SDS-SDS_Total_Raw + - SDS-SDS_Total_T + - PreInt_EduHx-computerinternet_hoursday + +# Categorical features +cat_features: + - id + - Basic_Demos-Enroll_Season + - CGAS-Season + - Physical-Season + - Fitness_Endurance-Season + - FGC-Season + - BIA-Season + - PAQ_A-Season + - PAQ_C-Season + - PCIAT-Season + - SDS-Season + - PreInt_EduHx-Season + +# Target column +target: sii \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 056b8bd..d4f0632 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,18 +3,21 @@ name = "mlops-with-databricks" version = "0.0.1" description = "MLOps with Databricks" readme = "README.md" -requires-python = "==3.11" +requires-python = ">=3.11" dependencies = ["lightgbm>=4.5.0, <5", "scikit-learn>=1.5.1, <2", "cloudpickle>=3.0.0, <4", "mlflow>=2.16.0, <3", "numpy>=1.26.4, <2", "pandas>=2.2.2, <3", - "pyarrow>=15.0.2, <16", + "pyarrow==14.0.1", "cffi>=1.17.1, <2", "scipy>=1.14.1, <2", "matplotlib>=3.9.2, <4", - "databricks-feature-engineering>=0.6, <1"] + "databricks-feature-engineering>=0.6, <1", + "databricks-feature-lookup==1.2.0", + "databricks-sdk==0.32.0", + "pydantic==2.9.2"] [project.optional-dependencies] dev = ["databricks-connect>=15.4.1, <16", @@ -22,6 +25,10 @@ dev = ["databricks-connect>=15.4.1, <16", "ipykernel>=6.29.5, <7", "pip>=24.2"] +[build-system] +requires = ["setuptools>=72.0"] +build-backend = "setuptools.build_meta" + [tool.ruff] line-length = 120 select = [ diff --git a/src/childHealth/__init__.py b/src/childHealth/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/childHealth/config.py b/src/childHealth/config.py new file mode 100644 index 0000000..812ac47 --- /dev/null +++ b/src/childHealth/config.py @@ -0,0 +1,18 @@ +from pydantic import BaseModel +from typing import List, Dict, Any +import yaml + +class ProjectConfig(BaseModel): + num_features: List[str] + cat_features: List[str] + target: str + catalog_name: str + schema_name: str + parameters: Dict[str, Any] # Dictionary to hold model-related parameters + + @classmethod + def from_yaml(cls, config_path: str): + """Load configuration from a YAML file.""" + with open(config_path, 'r') as f: + config_dict = yaml.safe_load(f) + return cls(**config_dict) \ No newline at end of file diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py new file mode 100644 index 0000000..2bac8dd --- /dev/null +++ b/src/childHealth/data_processor.py @@ -0,0 +1,111 @@ +import pandas as pd +from childHealth.config import ProjectConfig +from childHealth.utils import remove_outliers +from sklearn.model_selection import train_test_split +from pyspark.sql.functions import current_timestamp, to_utc_timestamp +from pyspark.sql import SparkSession +import yaml + +class DataProcessor: + """ + Class to handle data processing tasks such as splitting data and saving to Databricks catalog. + """ + def __init__(self, pandas_df: pd.DataFrame, config: ProjectConfig): + """ + Initialize the DataProcessor with a DataFrame and configuration settings. + + Parameters: + pandas_df (pd.DataFrame): The DataFrame to process. + config (ProjectConfig): Configuration settings. + """ + self.df = pandas_df # Store the DataFrame as self.df + self.config = config # Store the configuration + + def preprocess(self): + """ + Perform data preprocessing tasks including handling missing values, outliers, + and data type conversions. + + Returns: + pd.DataFrame: The preprocessed DataFrame. + """ + + # Convert numerical features to numeric type + num_features = self.config.num_features + for col in num_features: + self.df[col] = pd.to_numeric(self.df[col], errors='coerce') + + # Remove outliers in numerical features + lower_bound = self.config.parameters['lower_bound'] + upper_bound = self.config.parameters['upper_bound'] + self.df = remove_outliers(self.df, num_features, lower_bound, upper_bound) + + # Impute missing values for numerical features with the median + for col in num_features: + self.df[col].fillna(self.df[col].median(), inplace=True) + + # Convert categorical features to category type + cat_features = self.config.cat_features + for col in cat_features: + self.df[col] = self.df[col].astype('category') + + # Impute missing values for categorical features with the most frequent value + for col in cat_features: + if self.df[col].isnull().any(): + self.df[col].fillna(self.df[col].mode()[0], inplace=True) + + # Handle missing values in the target column by replacing them with a specific value (e.g., 4.0) + target = self.config.target + new_value = 4.0 # New value to replace NaNs in the target column + self.df[target].fillna(new_value, inplace=True) + + # Keep only relevant columns + relevant_columns = cat_features + num_features + [target, 'Id'] + self.df = self.df[relevant_columns] + + return self.df + + def split_data(self, test_size=0.2, random_state=42): + """ + Split the DataFrame (self.df) into training and test sets. + + Parameters: + test_size (float): Proportion of the dataset to include in the test split. + random_state (int): Random seed for reproducibility. + + Returns: + tuple: A tuple containing the training and test sets. + """ + train_set, test_set = train_test_split(self.df, test_size=test_size, random_state=random_state) + return train_set, test_set + + def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark: SparkSession): + """ + Save the train and test sets into Databricks tables. + + Parameters: + train_set (pd.DataFrame): The training set. + test_set (pd.DataFrame): The test set. + spark (SparkSession): The Spark session. + """ + # Add timestamp column to train and test sets + train_set_with_timestamp = spark.createDataFrame(train_set).withColumn( + "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) + + test_set_with_timestamp = spark.createDataFrame(test_set).withColumn( + "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) + + # Save train set to Databricks table + train_set_with_timestamp.write.mode("append").saveAsTable( + f"{self.config.catalog_name}.{self.config.schema_name}.train_set") + + # Save test set to Databricks table + test_set_with_timestamp.write.mode("append").saveAsTable( + f"{self.config.catalog_name}.{self.config.schema_name}.test_set") + + # Enable Change Data Feed for train and test sets + spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.train_set " + "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") + + spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.test_set " + "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") diff --git a/src/childHealth/utils.py b/src/childHealth/utils.py new file mode 100644 index 0000000..29c1e0a --- /dev/null +++ b/src/childHealth/utils.py @@ -0,0 +1,23 @@ +import pandas as pd +import yml +from childHealth.config import ProjectConfig + +def remove_outliers(df: pd.DataFrame, num_features: list, 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17 00:00:00 2001 From: javedhassans Date: Sun, 27 Oct 2024 20:34:30 +0100 Subject: [PATCH 02/36] added visualization feature plot importance code in utils.py --- src/childHealth/utils.py | 43 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 40 insertions(+), 3 deletions(-) diff --git a/src/childHealth/utils.py b/src/childHealth/utils.py index 29c1e0a..ca6d4ba 100644 --- a/src/childHealth/utils.py +++ b/src/childHealth/utils.py @@ -1,8 +1,10 @@ import pandas as pd -import yml +import yaml # Fixed the import statement from childHealth.config import ProjectConfig +import matplotlib.pyplot as plt +import numpy as np -def remove_outliers(df: pd.DataFrame, num_features: list, lower_bound: float = 0.25, upper_bound: float = 0.75) -> pd.DataFrame: +def remove_outliers(df: pd.DataFrame, num_features: list, lower_bound: float = 0.05, upper_bound: float = 0.95) -> pd.DataFrame: """ Remove outliers from the DataFrame using the Interquartile Range (IQR) method. @@ -20,4 +22,39 @@ def remove_outliers(df: pd.DataFrame, num_features: list, lower_bound: float = 0 Q3 = df[feature].quantile(upper_bound) IQR = Q3 - Q1 df = df[~((df[feature] < (Q1 - 1.5 * IQR)) | (df[feature] > (Q3 + 1.5 * IQR)))] - return df \ No newline at end of file + return df + +def visualize_results(y_test, y_pred): + """ + Visualize the results of predictions against actual values. + + Parameters: + y_test (array-like): Actual values. + y_pred (array-like): Predicted values. + """ + plt.figure(figsize=(10, 6)) + plt.scatter(y_test, y_pred, alpha=0.5) + plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2) + plt.xlabel("Actual Price") + plt.ylabel("Predicted Price") + plt.title("Actual vs Predicted House Prices") + plt.tight_layout() + plt.show() + +def plot_feature_importance(feature_importance, feature_names, top_n=10): + """ + Plot the top N feature importances. + + Parameters: + feature_importance (array-like): Importance scores of features. + feature_names (list): Names of the features. + top_n (int): Number of top features to display. + """ + plt.figure(figsize=(10, 6)) + sorted_idx = np.argsort(feature_importance) + pos = np.arange(sorted_idx[-top_n:].shape[0]) + .5 + plt.barh(pos, feature_importance[sorted_idx[-top_n:]]) + plt.yticks(pos, feature_names[sorted_idx[-top_n:]]) + plt.title(f'Top {top_n} Feature Importance') + plt.tight_layout() + plt.show() \ No newline at end of file From 1fa18e573a7ff4fa558ee525fe08b46f24d28b2d Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 28 Oct 2024 20:30:39 +0100 Subject: [PATCH 03/36] added new build --- notebooks/01.prepare_dataset.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/notebooks/01.prepare_dataset.py b/notebooks/01.prepare_dataset.py index 71a38ab..0a5eb54 100644 --- a/notebooks/01.prepare_dataset.py +++ b/notebooks/01.prepare_dataset.py @@ -1,22 +1,37 @@ # Databricks notebook source from pyspark.sql import SparkSession - from childHealth.config import ProjectConfig from childHealth.data_processor import DataProcessor +from datetime import datetime +# Initialize Spark session spark = SparkSession.builder.getOrCreate() # COMMAND ---------- +# Load project configuration from YAML file config = ProjectConfig.from_yaml(config_path="../../project_config.yml") # COMMAND ---------- -# Load the house prices dataset -df = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True).toPandas() + +# Load the child health dataset into a Pandas DataFrame +df = spark.read.csv( + "/Volumes/mlops_students/javedhassi/data/childHealth.csv", + header=True, + inferSchema=True +).toPandas() # COMMAND ---------- + +# Initialize DataProcessor with the loaded DataFrame and configuration data_processor = DataProcessor(pandas_df=df, config=config) + +# Preprocess the data data_processor.preprocess() + +# Split the data into training and testing sets train_set, test_set = data_processor.split_data() + +# Save the processed datasets to the catalog data_processor.save_to_catalog(train_set=train_set, test_set=test_set, spark=spark) \ No newline at end of file From 830c17d988742482b639aec763ec731ac2dd4da5 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 28 Oct 2024 20:59:23 +0100 Subject: [PATCH 04/36] added new build for databricks --- notebooks/01.prepare_dataset.py | 2 +- .../mlops_with_databricks-0.0.1-py3-none-any.whl | Bin 0 -> 5228 bytes project_config.yml | 1 - src/childHealth/data_processor.py | 2 +- 4 files changed, 2 insertions(+), 3 deletions(-) create mode 100644 notebooks/mlops_with_databricks-0.0.1-py3-none-any.whl diff --git a/notebooks/01.prepare_dataset.py b/notebooks/01.prepare_dataset.py index 0a5eb54..52045cf 100644 --- a/notebooks/01.prepare_dataset.py +++ b/notebooks/01.prepare_dataset.py @@ -11,7 +11,7 @@ # COMMAND ---------- # Load project configuration from YAML file -config = 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a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -60,7 +60,7 @@ def preprocess(self): self.df[target].fillna(new_value, inplace=True) # Keep only relevant columns - relevant_columns = cat_features + num_features + [target, 'Id'] + relevant_columns = cat_features + num_features + [target, 'id'] self.df = self.df[relevant_columns] return self.df From 0aac6995e766cbab3059f8009701f6283bc20acc Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 28 Oct 2024 22:55:30 +0100 Subject: [PATCH 05/36] added files in notebook named 02 & 03 from trainig. Also childHealth_model.py --- notebooks/02.mlflow_experiment.py | 45 +++++++ notebooks/03.log_and_register_model.py | 112 ++++++++++++++++++ ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 5228 -> 6124 bytes project_config.yml | 8 +- src/childHealth/childhealth_model.py | 62 ++++++++++ src/childHealth/config.py | 3 +- src/childHealth/data_processor.py | 4 +- 7 files changed, 230 insertions(+), 4 deletions(-) create mode 100644 notebooks/02.mlflow_experiment.py create mode 100644 notebooks/03.log_and_register_model.py create mode 100644 src/childHealth/childhealth_model.py diff --git a/notebooks/02.mlflow_experiment.py b/notebooks/02.mlflow_experiment.py new file mode 100644 index 0000000..451b9ce --- /dev/null +++ b/notebooks/02.mlflow_experiment.py @@ -0,0 +1,45 @@ +# Databricks notebook source +import json +import subprocess +import mlflow + +mlflow.set_tracking_uri("databricks") + +mlflow.set_experiment(experiment_name="/Shared/child-health-basic") +mlflow.set_experiment_tags({"repository_name": "child-health"}) + +# COMMAND ---------- +experiments = mlflow.search_experiments( + filter_string="tags.repository_name='child-health'", +) +print(experiments) + +# COMMAND ---------- +with open("mlflow_experiment.json", "w") as json_file: + json.dump(experiments[0].__dict__, json_file, indent=4) +# COMMAND ---------- +with mlflow.start_run( + run_name="demo-run", + tags={"git_sha": '830c17d988742482b639aec763ec731ac2dd4da5', + "branch": "week1-2"}, + description="demo run", +) as run: + mlflow.log_params({"type": "demo"}) + mlflow.log_metrics({"metric1": 1.0, "metric2": 2.0}) +# COMMAND ---------- +run_id = mlflow.search_runs( + experiment_names=["/Shared/child-health-basic"], + filter_string="tags.git_sha='830c17d988742482b639aec763ec731ac2dd4da5'", +).run_id[0] +run_info = mlflow.get_run(run_id=f"{run_id}").to_dictionary() +print(run_info) + +# COMMAND ---------- +with open("run_info.json", "w") as json_file: + json.dump(run_info, json_file, indent=4) + +# COMMAND ---------- +print(run_info["data"]["metrics"]) + +# COMMAND ---------- +print(run_info["data"]["params"]) diff --git a/notebooks/03.log_and_register_model.py b/notebooks/03.log_and_register_model.py new file mode 100644 index 0000000..7e4998b --- /dev/null +++ b/notebooks/03.log_and_register_model.py @@ -0,0 +1,112 @@ +# Databricks notebook source + +from pyspark.sql import SparkSession +from childHealth.config import ProjectConfig +from sklearn.preprocessing import OneHotEncoder, StandardScaler +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from lightgbm import LGBMRegressor +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +import mlflow +from mlflow.models import infer_signature + +mlflow.set_tracking_uri("databricks") +mlflow.set_registry_uri('databricks-uc') # It must be -uc for registering models to Unity Catalog + +# COMMAND ---------- + +config = ProjectConfig.from_yaml(config_path="../project_config.yml") + +# Extract configuration details +num_features = config.num_features +cat_features = config.cat_features +target = config.target +model_parameters = config.model_parameters # Use model parameters from the config +catalog_name = config.catalog_name +schema_name = config.schema_name + +# COMMAND ---------- +spark = SparkSession.builder.getOrCreate() + +# Load training and testing sets from Databricks tables +train_set_spark = spark.table(f"{catalog_name}.{schema_name}.train_set") +train_set = spark.table(f"{catalog_name}.{schema_name}.train_set").toPandas() +test_set = spark.table(f"{catalog_name}.{schema_name}.test_set").toPandas() + +X_train = train_set[num_features + cat_features] +y_train = train_set[target] + +X_test = test_set[num_features + cat_features] +y_test = test_set[target] + +# COMMAND ---------- +# Define the preprocessor for categorical and numerical features +preprocessor = ColumnTransformer( + transformers=[ + ('num', StandardScaler(), num_features), # Standard scaling for numerical features + ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features)], # One-hot encoding for categorical features + remainder='passthrough' +) + +# Create the pipeline with preprocessing and the LightGBM regressor +pipeline = Pipeline(steps=[ + ('preprocessor', preprocessor), + ('regressor', LGBMRegressor(**model_parameters)) +]) + +# COMMAND ---------- +mlflow.set_experiment(experiment_name='/Shared/child-health') +git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" + +# Start an MLflow run to track the training process +with mlflow.start_run( + tags={"git_sha": f"{git_sha}", + "branch": "week1-2"}, +) as run: + run_id = run.info.run_id + + pipeline.fit(X_train, y_train) + y_pred = pipeline.predict(X_test) + + # Evaluate the model performance + mse = mean_squared_error(y_test, y_pred) + mae = mean_absolute_error(y_test, y_pred) + r2 = r2_score(y_test, y_pred) + + print(f"Mean Squared Error: {mse}") + print(f"Mean Absolute Error: {mae}") + print(f"R2 Score: {r2}") + + # Log parameters, metrics, and the model to MLflow + mlflow.log_param("model_type", "LightGBM with preprocessing") + mlflow.log_params(model_parameters) + mlflow.log_metric("mse", mse) + mlflow.log_metric("mae", mae) + mlflow.log_metric("r2_score", r2) + signature = infer_signature(model_input=X_train, model_output=y_pred) + + dataset = mlflow.data.from_spark( + train_set_spark, table_name=f"{catalog_name}.{schema_name}.train_set", + version="0") + mlflow.log_input(dataset, context="training") + + mlflow.sklearn.log_model( + sk_model=pipeline, + artifact_path="lightgbm-pipeline-model", + signature=signature + ) + + +# COMMAND ---------- +model_version = mlflow.register_model( + model_uri=f'runs:/{run_id}/lightgbm-pipeline-model', + name=f"{catalog_name}.{schema_name}.child_health_model_basic", + tags={"git_sha": f"{git_sha}"}) + +# COMMAND ---------- +run = mlflow.get_run(run_id) +dataset_info = 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index 108490a..5f010ad 100644 --- a/project_config.yml +++ b/project_config.yml @@ -5,11 +5,17 @@ catalog_name: "mlops_students" schema_name: "javedhassi" # Parameters for data processing -parameters: +data_cleaning_parameters: drop_threshold: 0.8 # Threshold for dropping columns with missing values lower_bound: 0.05 # Lower quantile bound for outlier detection upper_bound: 0.95 # Upper quantile bound for outlier detection +# Parameters for model training +model_parameters: + learning_rate: 0.01 # Learning rate for the model + n_estimators: 1000 # Number of trees in the model + max_depth: 6 # Maximum depth of the tree + # Numerical features num_features: - Basic_Demos-Age diff --git a/src/childHealth/childhealth_model.py b/src/childHealth/childhealth_model.py new file mode 100644 index 0000000..1e9a5e9 --- /dev/null +++ b/src/childHealth/childhealth_model.py @@ -0,0 +1,62 @@ +from sklearn.ensemble import RandomForestRegressor +from sklearn.pipeline import Pipeline +from sklearn.metrics import mean_squared_error, r2_score + +class ChildHealthModel: + def __init__(self, preprocessor, config): + """ + Initialize the ChildHealthModel with a preprocessor and configuration. + + :param preprocessor: A ColumnTransformer for preprocessing the data. + :param config: A dictionary containing model parameters. + """ + self.config = config + self.model = Pipeline(steps=[ + ('preprocessor', preprocessor), + ('regressor', RandomForestRegressor( + n_estimators=config['model_parameters']['n_estimators'], + max_depth=config['model_parameters']['max_depth'], + random_state=42 + )) + ]) + + def train(self, X_train, y_train): + """ + Train the model on the provided training data. + + :param X_train: Training features. + :param y_train: Training target. + """ + self.model.fit(X_train, y_train) + + def predict(self, X): + """ + Predict using the trained model. + + :param X: Features to predict. + :return: Predictions. + """ + return self.model.predict(X) + + def evaluate(self, X_test, y_test): + """ + Evaluate the model on the test data. + + :param X_test: Test features. + :param y_test: Test target. + :return: Mean squared error and R^2 score. + """ + y_pred = self.predict(X_test) + mse = mean_squared_error(y_test, y_pred) + r2 = r2_score(y_test, y_pred) + return mse, r2 + + def get_feature_importance(self): + """ + Get feature importance from the trained model. + + :return: Feature importances and feature names. + """ + feature_importance = self.model.named_steps['regressor'].feature_importances_ + feature_names = self.model.named_steps['preprocessor'].get_feature_names_out() + return feature_importance, feature_names \ No newline at end of file diff --git a/src/childHealth/config.py b/src/childHealth/config.py index 812ac47..2b49435 100644 --- a/src/childHealth/config.py +++ b/src/childHealth/config.py @@ -8,7 +8,8 @@ class ProjectConfig(BaseModel): target: str catalog_name: str schema_name: str - parameters: Dict[str, Any] # Dictionary to hold model-related parameters + data_cleaning_parameters: Dict[str, Any] # Dictionary to hold data cleaning parameters + model_parameters: Dict[str, Any] # Dictionary to hold model-related parameters @classmethod def from_yaml(cls, config_path: str): diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index 7656bf0..07108bc 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -36,8 +36,8 @@ def preprocess(self): self.df[col] = pd.to_numeric(self.df[col], errors='coerce') # Remove outliers in numerical features - lower_bound = self.config.parameters['lower_bound'] - upper_bound = self.config.parameters['upper_bound'] + lower_bound = self.config.data_cleaning_parameters['lower_bound'] + upper_bound = self.config.data_cleaning_parameters['upper_bound'] self.df = remove_outliers(self.df, num_features, lower_bound, upper_bound) # Impute missing values for numerical features with the median From dae772a9ef3b50511018dff4874ef76ea47e0ed5 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Tue, 29 Oct 2024 17:37:41 +0100 Subject: [PATCH 06/36] fixed dbconnect and ran files till 03 with dbconnect --- notebooks/02.mlflow_experiment.py | 5 ++++ notebooks/mlflow_experiment.json | 15 ++++++++++++ notebooks/run_info.json | 40 +++++++++++++++++++++++++++++++ 3 files changed, 60 insertions(+) create mode 100644 notebooks/mlflow_experiment.json create mode 100644 notebooks/run_info.json diff --git a/notebooks/02.mlflow_experiment.py b/notebooks/02.mlflow_experiment.py index 451b9ce..3ee251c 100644 --- a/notebooks/02.mlflow_experiment.py +++ b/notebooks/02.mlflow_experiment.py @@ -5,6 +5,8 @@ mlflow.set_tracking_uri("databricks") +# COMMAND -------------- + mlflow.set_experiment(experiment_name="/Shared/child-health-basic") mlflow.set_experiment_tags({"repository_name": "child-health"}) @@ -17,6 +19,7 @@ # COMMAND ---------- with open("mlflow_experiment.json", "w") as json_file: json.dump(experiments[0].__dict__, json_file, indent=4) + # COMMAND ---------- with mlflow.start_run( run_name="demo-run", @@ -43,3 +46,5 @@ # COMMAND ---------- print(run_info["data"]["params"]) + +# COMMAND ---------- diff --git a/notebooks/mlflow_experiment.json b/notebooks/mlflow_experiment.json new file mode 100644 index 0000000..8577979 --- /dev/null +++ b/notebooks/mlflow_experiment.json @@ -0,0 +1,15 @@ +{ + "_experiment_id": "222855090029336", + "_name": "/Shared/child-health-basic", + "_artifact_location": "dbfs:/databricks/mlflow-tracking/222855090029336", + "_lifecycle_stage": "active", + "_tags": { + "mlflow.experiment.sourceName": "/Shared/child-health-basic", + "mlflow.ownerId": "4619079692408656", + "mlflow.ownerEmail": "javedhassi@gmail.com", + "mlflow.experimentType": "MLFLOW_EXPERIMENT", + "repository_name": "child-health" + }, + "_creation_time": 1730218426435, + "_last_update_time": 1730218426435 +} \ No newline at end of file diff --git a/notebooks/run_info.json b/notebooks/run_info.json new file mode 100644 index 0000000..5dd7944 --- /dev/null +++ b/notebooks/run_info.json @@ -0,0 +1,40 @@ +{ + "info": { + "artifact_uri": "dbfs:/databricks/mlflow-tracking/222855090029336/d6cbc67fd69840cf9ff9bc5be149e103/artifacts", + "end_time": 1730218542972, + "experiment_id": "222855090029336", + "lifecycle_stage": "active", + "run_id": "d6cbc67fd69840cf9ff9bc5be149e103", + "run_name": "demo-run", + "run_uuid": "d6cbc67fd69840cf9ff9bc5be149e103", + "start_time": 1730218539117, + "status": "FINISHED", + "user_id": "" + }, + "data": { + "metrics": { + "metric1": 1.0, + "metric2": 2.0 + }, + "params": { + "type": "demo" + }, + "tags": { + "branch": "week1-2", + "git_sha": "830c17d988742482b639aec763ec731ac2dd4da5", + "mlflow.databricks.cluster.id": "1019-142832-p3qdyln4", + "mlflow.databricks.cluster.info": "{\"cluster_name\":\"Javed Sabu's Cluster\",\"spark_version\":\"15.4.x-cpu-ml-scala2.12\",\"node_type_id\":\"i3.xlarge\",\"driver_node_type_id\":\"i3.xlarge\",\"autotermination_minutes\":120,\"disk_spec\":{},\"num_workers\":0}", + "mlflow.databricks.cluster.libraries": "{\"installable\":[{\"whl\":\"/Volumes/mlops_students/javedhassi/packages/mlops_with_databricks-0.0.1-py3-none-any.whl\"}],\"redacted\":[]}", + "mlflow.databricks.notebook.commandID": "1730218301432_9190355476855087618_ed03a0bc23814f6cb65a5cb6586199e1", + "mlflow.note.content": "demo run", + "mlflow.runColor": "#5387dd", + "mlflow.runName": "demo-run", + "mlflow.source.name": "/Users/javedhassansabu/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/ipykernel_launcher.py", + "mlflow.source.type": "LOCAL", + "mlflow.user": "javedhassi@gmail.com" + } + }, + "inputs": { + "dataset_inputs": [] + } +} \ No newline at end of file From 1476b3098c858bc4c0833915ed5d86e2f0345368 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Wed, 30 Oct 2024 11:55:21 +0100 Subject: [PATCH 07/36] deleted package to rebuild again --- notebooks/04.log_and_register_custom_model.py | 15 + ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 6124 -> 0 bytes src/childHealth/utils.py | 6 +- uv.lock | 2197 ----------------- 4 files changed, 20 insertions(+), 2198 deletions(-) create mode 100644 notebooks/04.log_and_register_custom_model.py delete mode 100644 notebooks/mlops_with_databricks-0.0.1-py3-none-any.whl delete mode 100644 uv.lock diff --git a/notebooks/04.log_and_register_custom_model.py b/notebooks/04.log_and_register_custom_model.py new file mode 100644 index 0000000..3c65f20 --- /dev/null +++ b/notebooks/04.log_and_register_custom_model.py @@ -0,0 +1,15 @@ +# Databricks notebook source +import mlflow +import numpy as np +import pandas as pd +from pyspark.sql import SparkSession +from mlflow.models import infer_signature +from house_price.data_processor import ProjectConfig +import json +from mlflow import MlflowClient +from 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17 00:00:00 2001 From: javedhassans Date: Wed, 30 Oct 2024 18:09:42 +0100 Subject: [PATCH 08/36] deleted package to rebuild again --- notebooks/03.log_and_register_model.py | 7 + notebooks/04.log_and_register_custom_model.py | 104 +- notebooks/mlflow_experiment.json | 8 +- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 0 -> 6166 bytes notebooks/mlruns/0/meta.yaml | 6 + notebooks/run_info.json | 16 +- project_config.yml | 118 +- uv.lock | 2198 +++++++++++++++++ 8 files changed, 2383 insertions(+), 74 deletions(-) create mode 100644 notebooks/mlops_with_databricks-0.0.1-py3-none-any.whl create mode 100644 notebooks/mlruns/0/meta.yaml create mode 100644 uv.lock diff --git a/notebooks/03.log_and_register_model.py b/notebooks/03.log_and_register_model.py index 7e4998b..0e1ce82 100644 --- a/notebooks/03.log_and_register_model.py +++ b/notebooks/03.log_and_register_model.py @@ -1,3 +1,10 @@ +# COMMAND ---------- + +%pip install mlops_with_databricks-0.0.1-py3-none-any.whl + +# COMMAND ---------- +dbutils.library.restartPython() + # Databricks notebook source from pyspark.sql import SparkSession diff --git a/notebooks/04.log_and_register_custom_model.py b/notebooks/04.log_and_register_custom_model.py index 3c65f20..990aad8 100644 --- a/notebooks/04.log_and_register_custom_model.py +++ b/notebooks/04.log_and_register_custom_model.py @@ -1,15 +1,113 @@ # Databricks notebook source +%pip install mlops_with_databricks-0.0.1-py3-none-any.whl + +# COMMAND ---------- +dbutils.library.restartPython() + +# COMMAND ---------- import mlflow import numpy as np import pandas as pd from pyspark.sql import SparkSession from mlflow.models import infer_signature -from house_price.data_processor import ProjectConfig +from childHealth.data_processor import ProjectConfig import json from mlflow import MlflowClient from mlflow.utils.environment import _mlflow_conda_env -from house_price.utils import adjust_predictions +from childHealth.utils import adjust_predictions mlflow.set_registry_uri("databricks-uc") mlflow.set_tracking_uri("databricks") -client = MlflowClient() \ No newline at end of file +client = MlflowClient() + + +# COMMAND ---------- + +config = ProjectConfig.from_yaml(config_path="../project_config.yml") + +# Extract configuration details +num_features = config.num_features +cat_features = config.cat_features +target = config.target +model_parameters = config.model_parameters +catalog_name = config.catalog_name +schema_name = config.schema_name + +spark = SparkSession.builder.getOrCreate() + +# COMMAND ---------- + +run_id = mlflow.search_runs( + experiment_names=["/Shared/child-health"], + filter_string="tags.branch='week1-2'", +).run_id[0] + +model = mlflow.sklearn.load_model(f'runs:/{run_id}/lightgbm-pipeline-model') + + + +# COMMAND ---------- + +class ChildHealthModelWrapper(mlflow.pyfunc.PythonModel): + + def __init__(self, model): + self.model = model + + def predict(self, context, model_input): + if isinstance(model_input, pd.DataFrame): + predictions = self.model.predict(model_input) + predictions = {"Prediction": adjust_predictions( + predictions[0])} + return predictions + else: + raise ValueError("Input must be a pandas DataFrame.") + +# COMMAND ---------- + +train_set = spark.table(f"{catalog_name}.{schema_name}.train_set") +test_set = spark.table(f"{catalog_name}.{schema_name}.test_set") + +X_train = train_set[num_features + cat_features].toPandas() +y_train = train_set[[target]].toPandas() + +X_test = test_set[num_features + cat_features].toPandas() +y_test = test_set[[target]].toPandas() + +# COMMAND ---------- + +wrapped_model = ChildHealthModelWrapper(model) # we pass the loaded model to the wrapper +example_input = X_test.iloc[0:1] # Select the first row for prediction as example +example_prediction = wrapped_model.predict(context=None, model_input=example_input) +print("Example Prediction:", example_prediction) + +# COMMAND ---------- + +mlflow.set_experiment(experiment_name="/Shared/child-health-pyfunc") +git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" + +with mlflow.start_run(tags={"branch": "week1-2", + "git_sha": f"{git_sha}"}) as run: + + run_id = run.info.run_id + signature = infer_signature(model_input=X_train, model_output={'Prediction': example_prediction}) + dataset = mlflow.data.from_spark( + train_set, table_name=f"{catalog_name}.{schema_name}.train_set", version="0") + mlflow.log_input(dataset, context="training") + conda_env = _mlflow_conda_env( + additional_conda_deps=None, + additional_pip_deps=["code/mlops_with_databricks-0.0.1-py3-none-any.whl", + ], + additional_conda_channels=None, + ) + mlflow.pyfunc.log_model( + python_model=wrapped_model, + artifact_path="pyfunc-child-health-model", + code_paths = ["mlops_with_databricks-0.0.1-py3-none-any.whl"], + signature=signature + ) + +# COMMAND ---------- + +loaded_model = mlflow.pyfunc.load_model(f'runs:/{run_id}/pyfunc-child-health-model') +loaded_model.unwrap_python_model() +# COMMAND ---------- diff --git a/notebooks/mlflow_experiment.json b/notebooks/mlflow_experiment.json index 8577979..e01bebf 100644 --- a/notebooks/mlflow_experiment.json +++ b/notebooks/mlflow_experiment.json @@ -1,7 +1,7 @@ { - "_experiment_id": "222855090029336", + "_experiment_id": "941761294708082", "_name": "/Shared/child-health-basic", - "_artifact_location": "dbfs:/databricks/mlflow-tracking/222855090029336", + "_artifact_location": "dbfs:/databricks/mlflow-tracking/941761294708082", "_lifecycle_stage": "active", "_tags": { "mlflow.experiment.sourceName": "/Shared/child-health-basic", @@ -10,6 +10,6 @@ "mlflow.experimentType": "MLFLOW_EXPERIMENT", "repository_name": "child-health" }, - "_creation_time": 1730218426435, - "_last_update_time": 1730218426435 + "_creation_time": 1730287471358, + "_last_update_time": 1730287471358 } \ No newline at end of file diff --git a/notebooks/mlops_with_databricks-0.0.1-py3-none-any.whl 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a/project_config.yml b/project_config.yml index 5f010ad..eeb93eb 100644 --- a/project_config.yml +++ b/project_config.yml @@ -18,56 +18,56 @@ model_parameters: # Numerical features num_features: - - Basic_Demos-Age - - Basic_Demos-Sex - - CGAS-CGAS_Score - - Physical-BMI - - Physical-Height - - Physical-Weight - - Physical-Waist_Circumference - - Physical-Diastolic_BP - - Physical-HeartRate - - Physical-Systolic_BP - - Fitness_Endurance-Max_Stage - - Fitness_Endurance-Time_Mins - - Fitness_Endurance-Time_Sec - - FGC-FGC_CU - - FGC-FGC_CU_Zone - - FGC-FGC_GSND - - FGC-FGC_GSND_Zone - - FGC-FGC_GSD - - FGC-FGC_GSD_Zone - - FGC-FGC_PU - - FGC-FGC_PU_Zone - - FGC-FGC_SRL - - FGC-FGC_SRL_Zone - - FGC-FGC_SRR - - FGC-FGC_SRR_Zone - - FGC-FGC_TL - - FGC-FGC_TL_Zone - - BIA-BIA_Activity_Level_num - - BIA-BIA_BMC - - BIA-BIA_BMI - - BIA-BIA_BMR - - BIA-BIA_DEE - - BIA-BIA_ECW - - BIA-BIA_FFM - - BIA-BIA_FFMI - - BIA-BIA_FMI - - BIA-BIA_Fat - - BIA-BIA_Frame_num - - BIA-BIA_ICW - - BIA-BIA_LDM - - BIA-BIA_LST - - BIA-BIA_SMM - - BIA-BIA_TBW - - PAQ_A-PAQ_A_Total - - PAQ_C-PAQ_C_Total - - PCIAT-PCIAT_01 + - Basic_Demos-Age # Age of the individual. + - Basic_Demos-Sex # Gender of the individual. + - CGAS-CGAS_Score # Clinical Global Assessment Scale (CGAS) Score + - Physical-BMI # Body Mass Index (BMI) + - Physical-Height # Height of the individual. + - Physical-Weight # Weight of the individual. + - Physical-Waist_Circumference # Waist circumference of the individual. + - Physical-Diastolic_BP # Diastolic blood pressure of the individual. + - Physical-HeartRate # Heart rate of the individual. + - Physical-Systolic_BP # Systolic blood pressure of the individual. + - Fitness_Endurance-Max_Stage # Max stage of endurance test. + - Fitness_Endurance-Time_Mins # Time taken to complete the endurance test in minutes. + - Fitness_Endurance-Time_Sec # Time taken to complete the endurance test in seconds. + - FGC-FGC_CU # Functional Games Characteristics (FGC) FGC-CU + - FGC-FGC_CU_Zone # Functional Games Characteristics (FGC) FGC-CU Zone + - FGC-FGC_GSND # Functional Games Characteristics (FGC) FGC-GSND + - FGC-FGC_GSND_Zone # Functional Games Characteristics (FGC) FGC-GSND Zone + - FGC-FGC_GSD # Functional Games Characteristics (FGC) FGC-GSD + - FGC-FGC_GSD_Zone # Functional Games Characteristics (FGC) FGC-GSD Zone + - FGC-FGC_PU # Functional Games Characteristics (FGC) FGC-PU + - FGC-FGC_PU_Zone # Functional Games Characteristics (FGC) FGC-PU Zone + - FGC-FGC_SRL # Functional Games Characteristics (FGC) FGC-SRL + - FGC-FGC_SRL_Zone # Functional Games Characteristics (FGC) FGC-SRL Zone + - FGC-FGC_SRR # Functional Games Characteristics (FGC) FGC-SRR + - FGC-FGC_SRR_Zone # Functional Games Characteristics (FGC) FGC-SRR Zone + - FGC-FGC_TL # Functional Games Characteristics (FGC) FGC-TL + - FGC-FGC_TL_Zone # Functional Games Characteristics (FGC) FGC-TL Zone + - BIA-BIA_Activity_Level_num # Bioelectrical Impedance Analysis (BIA) + - BIA-BIA_BMC # BIA-BMC + - BIA-BIA_BMI # BIA-BMI + - BIA-BIA_BMR # BIA-BMR + - BIA-BIA_DEE # BIA-DEE + - BIA-BIA_ECW # BIA-ECW + - BIA-BIA_FFM # BIA-FFM + - BIA-BIA_FFMI # BIA-FFMI + - BIA-BIA_FMI # BIA-FMI + - BIA-BIA_Fat # BIA-Fat + - BIA-BIA_Frame_num # BIA-Frame + - BIA-BIA_ICW # BIA-ICW + - BIA-BIA_LDM # BIA-LDM + - BIA-BIA_LST # BIA-LST + - BIA-BIA_SMM # BIA-SMM + - BIA-BIA_TBW # BIA-TBW + - PAQ_A-PAQ_A_Total # Physical Activity Questionnaire (PAQ) A Total + - PAQ_C-PAQ_C_Total # Physical Activity Questionnaire (PAQ) C Total + - PCIAT-PCIAT_01 # Parent-Child Internet Addiction Test - PCIAT-PCIAT_02 - PCIAT-PCIAT_03 - PCIAT-PCIAT_04 - - PCIAT-PCIAT_05 + - PCIAT-PCIAT_05 - PCIAT-PCIAT_06 - PCIAT-PCIAT_07 - PCIAT-PCIAT_08 @@ -84,22 +84,22 @@ num_features: - PCIAT-PCIAT_19 - PCIAT-PCIAT_20 - PCIAT-PCIAT_Total - - SDS-SDS_Total_Raw - - SDS-SDS_Total_T - - PreInt_EduHx-computerinternet_hoursday + - SDS-SDS_Total_Raw # Sleep Disturbance Scale (SDS) Total Raw + - SDS-SDS_Total_T # Sleep Disturbance Scale (SDS) Total T + - 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17 00:00:00 2001 From: javedhassans Date: Wed, 30 Oct 2024 18:45:23 +0100 Subject: [PATCH 09/36] added ReadME.md gitignore to avoid parquet files added datadictionary and test.csv --- .gitignore | 3 ++ README.md | 62 ++++++++++++++++++++++++++++++ data/data_dictionary.csv | 82 ++++++++++++++++++++++++++++++++++++++++ data/test.csv | 21 ++++++++++ 4 files changed, 168 insertions(+) create mode 100644 data/data_dictionary.csv create mode 100644 data/test.csv diff --git a/.gitignore b/.gitignore index 6f423de..2baa0b5 100644 --- a/.gitignore +++ b/.gitignore @@ -97,3 +97,6 @@ dmypy.json .history .databricks + +# Ignore all .parquet files +*.parquet diff --git a/README.md b/README.md index 800e709..11ac9bf 100644 --- a/README.md +++ b/README.md @@ -24,3 +24,65 @@ source venv/bin/activate uv pip install -r pyproject.toml --all-extras uv lock ``` + + + +Here’s an enhanced explanation of your dataset based on the information from `data_dictionary.csv`, covering each instrument's purpose and the fields it includes: + +### 1. **Identifier** + - **`id`**: The unique identifier assigned to each participant, which is used to match records across different files and data sources. + +### 2. **Demographics** + - **`Basic_Demos-Enroll_Season`**: The season during which a participant enrolled in the study, which may help in analyzing seasonal trends or impacts. + - **`Basic_Demos-Age`**: The participant’s age, likely a key demographic feature. + - **`Basic_Demos-Sex`**: Gender of the participant, encoded as `0` for Male and `1` for Female. + +### 3. **Internet Use and Educational History** + - **`PreInt_EduHx-computerinternet_hoursday`**: Measures daily internet/computer usage hours before any intervention. This could provide a baseline for understanding internet dependency. + - **`Parent-Child Internet Addiction Test (PCIAT)`**: Includes **`PCIAT-PCIAT_Total`**, a total score measuring the severity of internet addiction (compulsivity, escapism, and dependency). This score is pivotal as the **target variable `sii`** is derived from it, categorizing internet addiction into four levels: + - `0`: None + - `1`: Mild + - `2`: Moderate + - `3`: Severe + +### 4. **Children's Global Assessment Scale (CGAS)** + - **`CGAS-Season`**: Season when the assessment was conducted. + - **`CGAS-CGAS_Score`**: A numerical scale used by mental health clinicians to assess general functionality in youth, with higher scores indicating better functioning. + +### 5. **Physical Measures** + - **`Physical-Season`**: The season of data collection, which could affect measures like weight or blood pressure. + - **`Physical-BMI`, `Physical-Height`, `Physical-Weight`, `Physical-Waist_Circumference`**: These biometric indicators measure aspects of the participant's physical health. + - **`Physical-Diastolic_BP`, `Physical-HeartRate`, `Physical-Systolic_BP`**: Blood pressure and heart rate measurements are vital for understanding cardiovascular health. + +### 6. **FitnessGram and Treadmill Data** + - **FitnessGram Vitals and Treadmill**: Cardiovascular fitness assessments, likely involving treadmill-based tests to evaluate endurance and physical capacity. + - **FitnessGram Child**: Measures various aspects of physical fitness, including: + - **Aerobic capacity**, **muscular strength**, **muscular endurance**, **flexibility**, and **body composition**. + - These fields help assess the participant's overall fitness and physical health, relevant for understanding correlations with internet use or sleep quality. + +### 7. **Bio-electric Impedance Analysis (BIA)** + - Provides in-depth body composition data, including: + - **BMI**, **body fat percentage**, **lean muscle mass**, and **water content**. + - These measurements are essential for a comprehensive view of physical health and can be related to other health metrics, such as sleep or mental well-being. + +### 8. **Physical Activity Questionnaire (PAQ)** + - **`PAQ_A` and `PAQ_C`**: Both assess the participant’s physical activity level over the last week, specifically focusing on vigorous activities. This is relevant for gauging overall physical engagement and comparing it with sedentary behaviors like internet use. + +### 9. **Sleep Disturbance Scale (SDS)** + - Designed to categorize sleep disorders in children, this scale includes **Sleep Disturbance Scores** that could help in analyzing the relationship between sleep quality and variables like screen time or physical fitness. + +### 10. **Actigraphy Data** + - **Accelerometer Data**: Includes continuous measurements for up to 30 days, capturing data on physical movement and activity trends in natural settings. + - **X, Y, Z axes**: Measure acceleration along each axis to capture movement intensity. + - **ENMO**: Calculates net motion, where zero indicates inactivity, which could correspond to periods of sleep or rest. + - **Angle-Z**: Measures the angle of the arm relative to a horizontal plane, which could help in detecting activity types. + - **Non-wear flag**: Identifies when the accelerometer wasn’t worn, aiding in filtering out non-activity data. + - **Ambient Light, Battery Voltage, Time of Day, Weekday, Quarter, Relative Date**: Provides contextual data that can be used to understand behavioral and temporal patterns. + +### Summary of Data Utility +This dataset provides a holistic view of each participant’s demographic, physical, mental, and behavioral characteristics. By combining data on internet use, sleep disturbance, physical fitness, body composition, and actigraphy, the study is positioned to explore the relationships between sedentary behaviors, physical health, mental well-being, and potential internet addiction. + +This setup could support various analyses: +1. **Predicting Internet Addiction Levels**: Using `PCIAT` scores and demographic/health data. +2. **Correlating Physical Activity with Internet Use or Sleep**: Using actigraphy and PAQ data. +3. **Analyzing Sleep and Health Relationships**: Leveraging SDS data with physical and mental health scores. diff --git a/data/data_dictionary.csv b/data/data_dictionary.csv new file mode 100644 index 0000000..0970924 --- /dev/null +++ b/data/data_dictionary.csv @@ -0,0 +1,82 @@ +Instrument,Field,Description,Type,Values,Value Labels +Identifier,id,Participant's ID,str,, +Demographics,Basic_Demos-Enroll_Season,Season of enrollment,str,"Spring, Summer, Fall, Winter", +Demographics,Basic_Demos-Age,Age of participant,float,, +Demographics,Basic_Demos-Sex,Sex of participant,categorical int,"0,1","0=Male, 1=Female" +Children's Global Assessment Scale,CGAS-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Children's Global Assessment Scale,CGAS-CGAS_Score,Children's Global Assessment Scale Score,int,, +Physical Measures,Physical-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Physical Measures,Physical-BMI,Body Mass Index (kg/m^2),float,, +Physical Measures,Physical-Height,Height (in),float,, +Physical Measures,Physical-Weight,Weight (lbs),float,, +Physical Measures,Physical-Waist_Circumference,Waist circumference (in),int,, +Physical Measures,Physical-Diastolic_BP,Diastolic BP (mmHg),int,, +Physical Measures,Physical-HeartRate,Heart rate (beats/min),int,, +Physical Measures,Physical-Systolic_BP,Systolic BP (mmHg),int,, +FitnessGram Vitals and Treadmill,Fitness_Endurance-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +FitnessGram Vitals and Treadmill,Fitness_Endurance-Max_Stage,Maximum stage reached,int,, +FitnessGram Vitals and Treadmill,Fitness_Endurance-Time_Mins,Exact time completed: Minutes,int,, +FitnessGram Vitals and Treadmill,Fitness_Endurance-Time_Sec,Exact time completed: Seconds,int,, +FitnessGram Child,FGC-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +FitnessGram Child,FGC-FGC_CU,Curl up total,int,, +FitnessGram Child,FGC-FGC_CU_Zone,Curl up fitness zone,categorical int,"0,1","0=Needs Improvement, 1=Healthy Fitness Zone" +FitnessGram Child,FGC-FGC_GSND,Grip Strength total (non-dominant),float,, +FitnessGram Child,FGC-FGC_GSND_Zone,Grip Strength fitness zone (non-dominant),categorical int,"1,2,3","1=Weak, 2=Normal, 3=Strong" +FitnessGram Child,FGC-FGC_GSD,Grip Strength total (dominant),float,, +FitnessGram Child,FGC-FGC_GSD_Zone,Grip Strength fitness zone (dominant),categorical int,"1,2,3","1=Weak, 2=Normal, 3=Strong" +FitnessGram Child,FGC-FGC_PU,Push-up total,int,, +FitnessGram Child,FGC-FGC_PU_Zone,Push-up fitness zone,categorical int,"0,1","0=Needs Improvement, 1=Healthy Fitness Zone" +FitnessGram Child,FGC-FGC_SRL,Sit & Reach total (left side),float,, +FitnessGram Child,FGC-FGC_SRL_Zone,Sit & Reach fitness zone (left side),categorical int,"0,1","0=Needs Improvement, 1=Healthy Fitness Zone" +FitnessGram Child,FGC-FGC_SRR,Sit & Reach total (right side),float,, +FitnessGram Child,FGC-FGC_SRR_Zone,Sit & Reach fitness zone (right side),categorical int,"0,1","0=Needs Improvement, 1=Healthy Fitness Zone" +FitnessGram Child,FGC-FGC_TL,Trunk lift total,int,, +FitnessGram Child,FGC-FGC_TL_Zone,Trunk lift fitness zone,categorical int,"0,1","0=Needs Improvement, 1=Healthy Fitness Zone" +Bio-electric Impedance Analysis,BIA-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Bio-electric Impedance Analysis,BIA-BIA_Activity_Level_num,Activity Level,categorical int,"1,2,3,4,5","1=Very Light, 2=Light, 3=Moderate, 4=Heavy, 5=Exceptional" +Bio-electric Impedance Analysis,BIA-BIA_BMC,Bone Mineral Content,float,, +Bio-electric Impedance Analysis,BIA-BIA_BMI,Body Mass Index,float,, +Bio-electric Impedance Analysis,BIA-BIA_BMR,Basal Metabolic Rate,float,, +Bio-electric Impedance Analysis,BIA-BIA_DEE,Daily Energy Expenditure,float,, +Bio-electric Impedance Analysis,BIA-BIA_ECW,Extracellular Water,float,, +Bio-electric Impedance Analysis,BIA-BIA_FFM,Fat Free Mass,float,, +Bio-electric Impedance Analysis,BIA-BIA_FFMI,Fat Free Mass Index,float,, +Bio-electric Impedance Analysis,BIA-BIA_FMI,Fat Mass Index,float,, +Bio-electric Impedance Analysis,BIA-BIA_Fat,Body Fat Percentage,float,, +Bio-electric Impedance Analysis,BIA-BIA_Frame_num,Body Frame,categorical int,"1,2,3","1=Small, 2=Medium, 3=Large" +Bio-electric Impedance Analysis,BIA-BIA_ICW,Intracellular Water,float,, +Bio-electric Impedance Analysis,BIA-BIA_LDM,Lean Dry Mass,float,, +Bio-electric Impedance Analysis,BIA-BIA_LST,Lean Soft Tissue,float,, +Bio-electric Impedance Analysis,BIA-BIA_SMM,Skeletal Muscle Mass,float,, +Bio-electric Impedance Analysis,BIA-BIA_TBW,Total Body Water,float,, +Physical Activity Questionnaire (Adolescents),PAQ_A-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Physical Activity Questionnaire (Adolescents),PAQ_A-PAQ_A_Total,Activity Summary Score (Adolescents),float,, +Physical Activity Questionnaire (Children),PAQ_C-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Physical Activity Questionnaire (Children),PAQ_C-PAQ_C_Total,Activity Summary Score (Children),float,, +Parent-Child Internet Addiction Test,PCIAT-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Parent-Child Internet Addiction Test,PCIAT-PCIAT_01,How often does your child disobey time limits you set for online use?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_02,How often does your child neglect household chores to spend more time online?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_03,How often does your child prefer to spend time online rather than with the rest of your family?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_04,How often does your child form new relationships with fellow online users?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_05,How often do you complain about the amount of time your child spends online?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_06,How often do your child's grades suffer because of the amount of time he or she spends online?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_07,How often does your child check his or her e-mail before doing something else?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_08,How often does your child seem withdrawn from others since discovering the Internet?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_09,How often does your child become defensive or secretive when asked what he or she does online?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_10,How often have you caught your child sneaking online against your wishes?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_11,How often does your child spend time along in his or her room playing on the computer?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_12,"How often does your child receive strange phone calls from new ""online"" friends?",categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_13,"How often does your child snap, yell, or act annoyed if bothered while online?",categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_14,How often does your child seem more tired and fatigued than he or she did before the Internet came along?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_15,How often does your child seem preoccupied with being back online when off-line?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_16,How often does your child throw tantrums with your interference about how long he or she spends online?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_17,How often does your child choose to spend time online rather than doing once enjoyed hobbies and/or outside interests?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_18,How often does your child become angry or belligerent when your place time limits on how much time he or shes is allowed to spend online?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_19,How often does your child choose to spend more time online than going out with friends?,categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_20,"How often does your child feel depressed, moody, or nervous when off-line which seems to go away once back online?",categorical int,"0,1,2,3,4,5","0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, 5=Always" +Parent-Child Internet Addiction Test,PCIAT-PCIAT_Total,Total Score,int,,Severity Impairment Index: 0-30=None; 31-49=Mild; 50-79=Moderate; 80-100=Severe +Sleep Disturbance Scale,SDS-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Sleep Disturbance Scale,SDS-SDS_Total_Raw,Total Raw Score,int,, +Sleep Disturbance Scale,SDS-SDS_Total_T,Total T-Score,int,, +Internet Use,PreInt_EduHx-Season,Season of participation,str,"Spring, Summer, Fall, Winter", +Internet Use,PreInt_EduHx-computerinternet_hoursday,Hours of using computer/internet,categorical int,"0,1,2,3","0=Less than 1h/day, 1=Around 1h/day, 2=Around 2hs/day, 3=More than 3hs/day" diff --git a/data/test.csv b/data/test.csv new file mode 100644 index 0000000..34aa4da --- /dev/null +++ b/data/test.csv @@ -0,0 +1,21 @@ +id,Basic_Demos-Enroll_Season,Basic_Demos-Age,Basic_Demos-Sex,CGAS-Season,CGAS-CGAS_Score,Physical-Season,Physical-BMI,Physical-Height,Physical-Weight,Physical-Waist_Circumference,Physical-Diastolic_BP,Physical-HeartRate,Physical-Systolic_BP,Fitness_Endurance-Season,Fitness_Endurance-Max_Stage,Fitness_Endurance-Time_Mins,Fitness_Endurance-Time_Sec,FGC-Season,FGC-FGC_CU,FGC-FGC_CU_Zone,FGC-FGC_GSND,FGC-FGC_GSND_Zone,FGC-FGC_GSD,FGC-FGC_GSD_Zone,FGC-FGC_PU,FGC-FGC_PU_Zone,FGC-FGC_SRL,FGC-FGC_SRL_Zone,FGC-FGC_SRR,FGC-FGC_SRR_Zone,FGC-FGC_TL,FGC-FGC_TL_Zone,BIA-Season,BIA-BIA_Activity_Level_num,BIA-BIA_BMC,BIA-BIA_BMI,BIA-BIA_BMR,BIA-BIA_DEE,BIA-BIA_ECW,BIA-BIA_FFM,BIA-BIA_FFMI,BIA-BIA_FMI,BIA-BIA_Fat,BIA-BIA_Frame_num,BIA-BIA_ICW,BIA-BIA_LDM,BIA-BIA_LST,BIA-BIA_SMM,BIA-BIA_TBW,PAQ_A-Season,PAQ_A-PAQ_A_Total,PAQ_C-Season,PAQ_C-PAQ_C_Total,SDS-Season,SDS-SDS_Total_Raw,SDS-SDS_Total_T,PreInt_EduHx-Season,PreInt_EduHx-computerinternet_hoursday +00008ff9,Fall,5,0,Winter,51,Fall,16.87731569,46.0,50.8,,,,,,,,,Fall,0,0,,,,,0,0,7.0,0,6.0,0,6.0,1,Fall,2,2.66855,16.8792,932.498,1492.0,8.25598,41.5862,13.8177,3.06143,9.21377,1,24.4349,8.89536,38.9177,19.5413,32.6909,,,,,,,,Fall,3 +000fd460,Summer,9,0,,,Fall,14.03559028,48.0,46.0,22.0,75,70,122,,,,,Fall,3,0,,,,,5,0,11.0,1,11.0,1,3.0,0,Winter,2,2.57949,14.0371,936.656,1498.65,6.01993,42.0291,12.8254,1.21172,3.97085,1,21.0352,14.974,39.4497,15.4107,27.0552,,,Fall,2.34,Fall,46,64,Summer,0 +00105258,Summer,10,1,Fall,71,Fall,16.64869606,56.5,75.6,,65,94,117,Fall,5,7,33,Fall,20,1,10.2,1,14.7,2,7,1,10.0,1,10.0,1,5.0,0,,,,,,,,,,,,,,,,,,,,Summer,2.17,Fall,38,54,Summer,2 +00115b9f,Winter,9,0,Fall,71,Summer,18.29234694,56.0,81.6,,60,97,117,Summer,6,9,37,Summer,18,1,,,,,5,0,7.0,0,7.0,0,7.0,1,Summer,3,3.84191,18.2943,1131.43,1923.44,15.5925,62.7757,14.074,4.22033,18.8243,2,30.4041,16.779,58.9338,26.4798,45.9966,,,Winter,2.451,Summer,31,45,Winter,0 +0016bb22,Spring,18,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,1.04,,,,,,, +001f3379,Spring,13,1,Winter,50,Summer,22.27995198,59.5,112.2,,60,73,102,,,,,Summer,12,0,16.5,2,17.9,2,6,0,10.0,1,11.0,1,8.0,0,Summer,2,4.33036,30.1865,1330.97,1996.45,30.2124,84.0285,16.6877,13.4988,67.9715,2,32.9141,20.902,79.6982,35.3804,63.1265,,,Spring,4.11,Summer,40,56,Spring,0 +0038ba98,Fall,10,0,,,Fall,19.66076033,55.0,84.6,,123,83,163,,,,,Fall,9,1,,,,,2,0,11.0,1,11.0,1,11.0,1,Fall,2,3.78271,19.6629,1135.86,1817.38,16.3275,63.247,14.7,4.96291,21.353,2,30.8936,16.0259,59.4643,26.1957,47.2211,,,Winter,3.67,Winter,27,40,Fall,3 +0068a485,Fall,10,1,,,Fall,16.86128647,59.25,84.2,27.0,71,90,116,,,,,Fall,0,0,12.6,2,11.1,1,0,0,0.0,0,0.0,0,4.0,0,Fall,3,4.05726,16.8631,1180.04,1888.06,21.94,67.9527,13.6092,3.25395,16.2474,2,28.5367,17.476,63.8954,28.768,50.4767,,,Fall,1.27,,,,Fall,2 +0069fbed,Summer,15,0,,,Spring,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Summer,2 +0083e397,Summer,19,1,Summer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0087dd65,Spring,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring, +00abe655,Fall,11,0,Summer,66,,,,,,,,,,,,,Winter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,1.1,Winter,42,59,Fall,0 +00ae59c9,Fall,13,0,,,Winter,21.07906523,57.75,100.0,,63,79,150,,,,,Winter,24,1,19.2,1,18.4,1,20,1,8.0,1,9.5,1,12.5,1,Winter,5,5.08025,21.0814,1239.46,2974.71,19.9545,74.2823,15.6597,5.42165,25.7177,2,36.0572,18.2705,69.202,36.2232,56.0118,,,Fall,3.02,Fall,33,47,Fall,1 +00af6387,Spring,12,0,,,Spring,15.54411111,60.0,79.6,24.0,57,71,103,,,,,Spring,10,0,22.3,2,21.6,2,7,0,7.0,0,9.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,Spring,1.22,,,,Spring, +00bd4359,Spring,12,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Spring,2 +00c0cd71,Winter,7,0,Summer,51,Spring,29.31577503,54.0,121.6,,80,75,99,Spring,4,5,32,Spring,6,1,,,,,0,0,12.0,1,15.0,1,12.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,35,50,Winter,2 +00d56d4b,Spring,5,1,Summer,80,Spring,17.28450413,44.0,47.6,,61,76,109,Spring,,,,Spring,0,0,,,,,0,0,10.5,1,10.0,1,7.0,1,,,,,,,,,,,,,,,,,,,,,,Spring,37,53,Spring,0 +00d9913d,Fall,10,1,,,Fall,19.89315702,55.0,85.6,30.0,,81,,,,,,Fall,5,0,,,,,0,0,0.0,0,0.0,0,9.0,1,,,,,,,,,,,,,,,,,,,,,,,,,Fall,1 +00e6167c,Winter,6,0,Spring,60,Winter,30.09464889,37.5,60.2,24.0,61,91,95,,,,,Winter,6,1,,,,,0,0,4.0,0,4.0,0,7.0,1,Winter,2,2.75035,17.2738,1003.07,1504.61,15.1456,49.1034,14.0898,3.18407,11.0966,1,23.6182,10.3396,46.3531,19.8886,38.7638,,,,,Winter,39,55,Winter,3 +00ebc35d,Winter,10,0,,,,,,,,,,,,,,,Spring,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Winter,2 From 6a0b5dc5484b3799442eb9bccc8198d136147c7f Mon Sep 17 00:00:00 2001 From: javedhassans Date: Wed, 30 Oct 2024 18:54:22 +0100 Subject: [PATCH 10/36] restructured folder and created week1 directorty --- notebooks/{ => week1}/dataExploration.ipynb | 0 notebooks/{ => week2}/01.prepare_dataset.py | 0 notebooks/{ => week2}/02.mlflow_experiment.py | 0 notebooks/{ => week2}/03.log_and_register_model.py | 0 notebooks/{ => week2}/04.log_and_register_custom_model.py | 0 notebooks/{ => week2}/mlflow_experiment.json | 0 notebooks/{ => week2}/run_info.json | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename notebooks/{ => week1}/dataExploration.ipynb (100%) rename notebooks/{ => week2}/01.prepare_dataset.py (100%) rename notebooks/{ => week2}/02.mlflow_experiment.py (100%) rename notebooks/{ => week2}/03.log_and_register_model.py (100%) rename notebooks/{ => week2}/04.log_and_register_custom_model.py (100%) rename notebooks/{ => week2}/mlflow_experiment.json (100%) rename notebooks/{ => week2}/run_info.json (100%) diff --git a/notebooks/dataExploration.ipynb b/notebooks/week1/dataExploration.ipynb similarity index 100% rename from notebooks/dataExploration.ipynb rename to notebooks/week1/dataExploration.ipynb diff --git a/notebooks/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py similarity index 100% rename from notebooks/01.prepare_dataset.py rename to notebooks/week2/01.prepare_dataset.py diff --git a/notebooks/02.mlflow_experiment.py b/notebooks/week2/02.mlflow_experiment.py similarity index 100% rename from notebooks/02.mlflow_experiment.py rename to notebooks/week2/02.mlflow_experiment.py diff --git a/notebooks/03.log_and_register_model.py b/notebooks/week2/03.log_and_register_model.py similarity index 100% rename from notebooks/03.log_and_register_model.py rename to notebooks/week2/03.log_and_register_model.py diff --git a/notebooks/04.log_and_register_custom_model.py b/notebooks/week2/04.log_and_register_custom_model.py similarity index 100% rename from notebooks/04.log_and_register_custom_model.py rename to notebooks/week2/04.log_and_register_custom_model.py diff --git a/notebooks/mlflow_experiment.json b/notebooks/week2/mlflow_experiment.json similarity index 100% rename from notebooks/mlflow_experiment.json rename to notebooks/week2/mlflow_experiment.json diff --git a/notebooks/run_info.json b/notebooks/week2/run_info.json similarity index 100% rename from notebooks/run_info.json rename to notebooks/week2/run_info.json From c0279f88947326d3c3834710412b5510b6ede2d5 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Wed, 30 Oct 2024 23:06:44 +0100 Subject: [PATCH 11/36] added tqdm in project.toml making 01.dataPreprocessing.py --- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 6166 -> 8358 bytes notebooks/week1/01.dataExploraton.py | 68 +++ notebooks/week1/dataExploration.ipynb | 461 ------------------ pyproject.toml | 3 +- uv.lock | 14 + 5 files changed, 84 insertions(+), 462 deletions(-) create mode 100644 notebooks/week1/01.dataExploraton.py delete mode 100644 notebooks/week1/dataExploration.ipynb diff --git a/notebooks/mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/mlops_with_databricks-0.0.1-py3-none-any.whl index 395a0c37dede28ff0075952b6da23cc37acd61a4..ce85fc34c60edba7d8f92553dd553c5075a524cf 100644 GIT binary patch delta 3981 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100644 index 0000000..70ae710 --- /dev/null +++ b/notebooks/week1/01.dataExploraton.py @@ -0,0 +1,68 @@ +# Databricks notebook source +%pip install ../mlops_with_databricks-0.0.1-py3-none-any.whl + +# COMMAND ---------- + +dbutils.library.restartPython() + +# COMMAND ---------- + +import pandas as pd +from pyspark.sql import SparkSession +from childHealth.config import ProjectConfig +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from tqdm import tqdm +import os +spark = SparkSession.builder.getOrCreate() + +# COMMAND ---------- + +config = ProjectConfig.from_yaml('../../project_config.yml') + +# COMMAND ---------- + + + + +# COMMAND ---------- +# Define the process_file function +def process_file(filename, dirname): + # Read the Parquet file into a Spark DataFrame + df = spark.read.parquet(os.path.join(dirname, filename, 'part-0.parquet')) + + # Drop the 'step' column + df = df.drop('step') + + # Compute basic statistics + stats = df.describe().toPandas().values.reshape(-1) + + # Extract the filename part + file_part = filename.split('=')[1] + + return stats, file_part + +# COMMAND ---------- +# Define the load_time_series function +def load_time_series(dirname) -> pd.DataFrame: + ids = os.listdir(dirname) + + with ThreadPoolExecutor() as executor: + results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids))) + + stats, indexes = zip(*results) + + df = pd.DataFrame(stats, columns=[f"Stat_{i}" for i in range(len(stats[0]))]) + df['id'] = indexes + + return df + +# COMMAND ---------- +# Example usage of the process_file function +dirname = "/Volumes/mlops_students/javedhassi/data/series_test.parquet/" +filename = "part-0.parquet" +stats, file_part = process_file(filename, dirname) +print(stats) +print(file_part) + +# COMMAND ---------- \ No newline at end of file diff --git a/notebooks/week1/dataExploration.ipynb b/notebooks/week1/dataExploration.ipynb deleted file mode 100644 index 6299859..0000000 --- a/notebooks/week1/dataExploration.ipynb +++ /dev/null @@ -1,461 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "pd.set_option('display.max_columns', 100)\n", - "pd.set_option('display.max_rows', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('../data/childHealth.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n", - " 'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n", - " 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n", - " 'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n", - " 'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n", - " 'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n", - " 'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n", - " 'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n", - " 'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n", - " 'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n", - " 'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n", - " 'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n", - " 'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n", - " 'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n", - " 'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n", - " 'PAQ_C-PAQ_C_Total', 'PCIAT-Season', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n", - " 'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n", - " 'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n", - " 'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n", - " 'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n", - " 'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total', 'SDS-Season',\n", - " 'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n", - " 'PreInt_EduHx-computerinternet_hoursday', 'sii'],\n", - " dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sii\n", - "0.0 1594\n", - "NaN 1224\n", - "1.0 730\n", - "2.0 378\n", - "3.0 34\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['sii'].value_counts(dropna=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# get numerical and categorical columns list\n", - "num_cols = df.select_dtypes(exclude=['object']).columns.tolist()\n", - "cat_cols = df.select_dtypes(include=['object']).columns.tolist()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Basic_Demos-Age',\n", - " 'Basic_Demos-Sex',\n", - " 'CGAS-CGAS_Score',\n", - " 'Physical-BMI',\n", - " 'Physical-Height',\n", - " 'Physical-Weight',\n", - " 'Physical-Waist_Circumference',\n", - " 'Physical-Diastolic_BP',\n", - " 'Physical-HeartRate',\n", - " 'Physical-Systolic_BP',\n", - " 'Fitness_Endurance-Max_Stage',\n", - " 'Fitness_Endurance-Time_Mins',\n", - " 'Fitness_Endurance-Time_Sec',\n", - " 'FGC-FGC_CU',\n", - " 'FGC-FGC_CU_Zone',\n", - " 'FGC-FGC_GSND',\n", - " 'FGC-FGC_GSND_Zone',\n", - " 'FGC-FGC_GSD',\n", - " 'FGC-FGC_GSD_Zone',\n", - " 'FGC-FGC_PU',\n", - " 'FGC-FGC_PU_Zone',\n", - " 'FGC-FGC_SRL',\n", - " 'FGC-FGC_SRL_Zone',\n", - " 'FGC-FGC_SRR',\n", - " 'FGC-FGC_SRR_Zone',\n", - " 'FGC-FGC_TL',\n", - " 'FGC-FGC_TL_Zone',\n", - " 'BIA-BIA_Activity_Level_num',\n", - " 'BIA-BIA_BMC',\n", - " 'BIA-BIA_BMI',\n", - " 'BIA-BIA_BMR',\n", - " 'BIA-BIA_DEE',\n", - " 'BIA-BIA_ECW',\n", - " 'BIA-BIA_FFM',\n", - " 'BIA-BIA_FFMI',\n", - " 'BIA-BIA_FMI',\n", - " 'BIA-BIA_Fat',\n", - " 'BIA-BIA_Frame_num',\n", - " 'BIA-BIA_ICW',\n", - " 'BIA-BIA_LDM',\n", - " 'BIA-BIA_LST',\n", - " 'BIA-BIA_SMM',\n", - " 'BIA-BIA_TBW',\n", - " 'PAQ_A-PAQ_A_Total',\n", - " 'PAQ_C-PAQ_C_Total',\n", - " 'PCIAT-PCIAT_01',\n", - " 'PCIAT-PCIAT_02',\n", - " 'PCIAT-PCIAT_03',\n", - " 'PCIAT-PCIAT_04',\n", - " 'PCIAT-PCIAT_05',\n", - " 'PCIAT-PCIAT_06',\n", - " 'PCIAT-PCIAT_07',\n", - " 'PCIAT-PCIAT_08',\n", - " 'PCIAT-PCIAT_09',\n", - " 'PCIAT-PCIAT_10',\n", - " 'PCIAT-PCIAT_11',\n", - " 'PCIAT-PCIAT_12',\n", - " 'PCIAT-PCIAT_13',\n", - " 'PCIAT-PCIAT_14',\n", - " 'PCIAT-PCIAT_15',\n", - " 'PCIAT-PCIAT_16',\n", - " 'PCIAT-PCIAT_17',\n", - " 'PCIAT-PCIAT_18',\n", - " 'PCIAT-PCIAT_19',\n", - " 'PCIAT-PCIAT_20',\n", - " 'PCIAT-PCIAT_Total',\n", - " 'SDS-SDS_Total_Raw',\n", - " 'SDS-SDS_Total_T',\n", - " 'PreInt_EduHx-computerinternet_hoursday',\n", - " 'sii']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_cols" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['id',\n", - " 'Basic_Demos-Enroll_Season',\n", - " 'CGAS-Season',\n", - " 'Physical-Season',\n", - " 'Fitness_Endurance-Season',\n", - " 'FGC-Season',\n", - " 'BIA-Season',\n", - " 'PAQ_A-Season',\n", - " 'PAQ_C-Season',\n", - " 'PCIAT-Season',\n", - " 'SDS-Season',\n", - " 'PreInt_EduHx-Season']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat_cols" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0.000000\n", - "Basic_Demos-Enroll_Season 0.000000\n", - "Basic_Demos-Age 0.000000\n", - "Basic_Demos-Sex 0.000000\n", - "CGAS-Season 35.479798\n", - "CGAS-CGAS_Score 38.863636\n", - "Physical-Season 16.414141\n", - "Physical-BMI 23.686869\n", - "Physical-Height 23.560606\n", - "Physical-Weight 22.323232\n", - "Physical-Waist_Circumference 77.323232\n", - "Physical-Diastolic_BP 25.404040\n", - "Physical-HeartRate 25.075758\n", - "Physical-Systolic_BP 25.404040\n", - "Fitness_Endurance-Season 66.969697\n", - "Fitness_Endurance-Max_Stage 81.237374\n", - "Fitness_Endurance-Time_Mins 81.313131\n", - "Fitness_Endurance-Time_Sec 81.313131\n", - "FGC-Season 15.505051\n", - "FGC-FGC_CU 41.363636\n", - "FGC-FGC_CU_Zone 42.373737\n", - "FGC-FGC_GSND 72.878788\n", - "FGC-FGC_GSND_Zone 73.181818\n", - "FGC-FGC_GSD 72.878788\n", - "FGC-FGC_GSD_Zone 73.156566\n", - "FGC-FGC_PU 41.666667\n", - "FGC-FGC_PU_Zone 42.651515\n", - "FGC-FGC_SRL 41.792929\n", - "FGC-FGC_SRL_Zone 42.752525\n", - "FGC-FGC_SRR 41.742424\n", - "FGC-FGC_SRR_Zone 42.702020\n", - "FGC-FGC_TL 41.313131\n", - "FGC-FGC_TL_Zone 42.297980\n", - "BIA-Season 45.833333\n", - "BIA-BIA_Activity_Level_num 49.722222\n", - "BIA-BIA_BMC 49.722222\n", - "BIA-BIA_BMI 49.722222\n", - "BIA-BIA_BMR 49.722222\n", - "BIA-BIA_DEE 49.722222\n", - "BIA-BIA_ECW 49.722222\n", - "BIA-BIA_FFM 49.722222\n", - "BIA-BIA_FFMI 49.722222\n", - "BIA-BIA_FMI 49.722222\n", - "BIA-BIA_Fat 49.722222\n", - "BIA-BIA_Frame_num 49.722222\n", - "BIA-BIA_ICW 49.722222\n", - "BIA-BIA_LDM 49.722222\n", - "BIA-BIA_LST 49.722222\n", - "BIA-BIA_SMM 49.722222\n", - "BIA-BIA_TBW 49.722222\n", - "PAQ_A-Season 88.005051\n", - "PAQ_A-PAQ_A_Total 88.005051\n", - "PAQ_C-Season 56.540404\n", - "PAQ_C-PAQ_C_Total 56.540404\n", - "PCIAT-Season 30.909091\n", - "PCIAT-PCIAT_01 30.984848\n", - "PCIAT-PCIAT_02 30.959596\n", - "PCIAT-PCIAT_03 31.035354\n", - "PCIAT-PCIAT_04 31.035354\n", - "PCIAT-PCIAT_05 31.085859\n", - "PCIAT-PCIAT_06 31.010101\n", - "PCIAT-PCIAT_07 31.085859\n", - "PCIAT-PCIAT_08 31.060606\n", - "PCIAT-PCIAT_09 31.060606\n", - "PCIAT-PCIAT_10 30.984848\n", - "PCIAT-PCIAT_11 30.959596\n", - "PCIAT-PCIAT_12 31.035354\n", - "PCIAT-PCIAT_13 31.085859\n", - "PCIAT-PCIAT_14 31.010101\n", - "PCIAT-PCIAT_15 31.060606\n", - "PCIAT-PCIAT_16 31.111111\n", - "PCIAT-PCIAT_17 31.186869\n", - "PCIAT-PCIAT_18 31.111111\n", - "PCIAT-PCIAT_19 31.060606\n", - "PCIAT-PCIAT_20 30.984848\n", - "PCIAT-PCIAT_Total 30.909091\n", - "SDS-Season 33.888889\n", - "SDS-SDS_Total_Raw 34.116162\n", - "SDS-SDS_Total_T 34.191919\n", - "PreInt_EduHx-Season 10.606061\n", - "PreInt_EduHx-computerinternet_hoursday 16.641414\n", - "sii 30.909091\n", - "dtype: float64" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.isna().sum()/len(df)*100" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 3960 entries, 0 to 3959\n", - "Data columns (total 82 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 3960 non-null object \n", - " 1 Basic_Demos-Enroll_Season 3960 non-null object \n", - " 2 Basic_Demos-Age 3960 non-null int64 \n", - " 3 Basic_Demos-Sex 3960 non-null int64 \n", - " 4 CGAS-Season 2555 non-null object \n", - " 5 CGAS-CGAS_Score 2421 non-null float64\n", - " 6 Physical-Season 3310 non-null object \n", - " 7 Physical-BMI 3022 non-null float64\n", - " 8 Physical-Height 3027 non-null float64\n", - " 9 Physical-Weight 3076 non-null float64\n", - " 10 Physical-Waist_Circumference 898 non-null float64\n", - " 11 Physical-Diastolic_BP 2954 non-null float64\n", - " 12 Physical-HeartRate 2967 non-null float64\n", - " 13 Physical-Systolic_BP 2954 non-null float64\n", - " 14 Fitness_Endurance-Season 1308 non-null object \n", - " 15 Fitness_Endurance-Max_Stage 743 non-null float64\n", - " 16 Fitness_Endurance-Time_Mins 740 non-null float64\n", - " 17 Fitness_Endurance-Time_Sec 740 non-null float64\n", - " 18 FGC-Season 3346 non-null object \n", - " 19 FGC-FGC_CU 2322 non-null float64\n", - " 20 FGC-FGC_CU_Zone 2282 non-null float64\n", - " 21 FGC-FGC_GSND 1074 non-null float64\n", - " 22 FGC-FGC_GSND_Zone 1062 non-null float64\n", - " 23 FGC-FGC_GSD 1074 non-null float64\n", - " 24 FGC-FGC_GSD_Zone 1063 non-null float64\n", - " 25 FGC-FGC_PU 2310 non-null float64\n", - " 26 FGC-FGC_PU_Zone 2271 non-null float64\n", - " 27 FGC-FGC_SRL 2305 non-null float64\n", - " 28 FGC-FGC_SRL_Zone 2267 non-null float64\n", - " 29 FGC-FGC_SRR 2307 non-null float64\n", - " 30 FGC-FGC_SRR_Zone 2269 non-null float64\n", - " 31 FGC-FGC_TL 2324 non-null float64\n", - " 32 FGC-FGC_TL_Zone 2285 non-null float64\n", - " 33 BIA-Season 2145 non-null object \n", - " 34 BIA-BIA_Activity_Level_num 1991 non-null float64\n", - " 35 BIA-BIA_BMC 1991 non-null float64\n", - " 36 BIA-BIA_BMI 1991 non-null float64\n", - " 37 BIA-BIA_BMR 1991 non-null float64\n", - " 38 BIA-BIA_DEE 1991 non-null float64\n", - " 39 BIA-BIA_ECW 1991 non-null float64\n", - " 40 BIA-BIA_FFM 1991 non-null float64\n", - " 41 BIA-BIA_FFMI 1991 non-null float64\n", - " 42 BIA-BIA_FMI 1991 non-null float64\n", - " 43 BIA-BIA_Fat 1991 non-null float64\n", - " 44 BIA-BIA_Frame_num 1991 non-null float64\n", - " 45 BIA-BIA_ICW 1991 non-null float64\n", - " 46 BIA-BIA_LDM 1991 non-null float64\n", - " 47 BIA-BIA_LST 1991 non-null float64\n", - " 48 BIA-BIA_SMM 1991 non-null float64\n", - " 49 BIA-BIA_TBW 1991 non-null float64\n", - " 50 PAQ_A-Season 475 non-null object \n", - " 51 PAQ_A-PAQ_A_Total 475 non-null float64\n", - " 52 PAQ_C-Season 1721 non-null object \n", - " 53 PAQ_C-PAQ_C_Total 1721 non-null float64\n", - " 54 PCIAT-Season 2736 non-null object \n", - " 55 PCIAT-PCIAT_01 2733 non-null float64\n", - " 56 PCIAT-PCIAT_02 2734 non-null float64\n", - " 57 PCIAT-PCIAT_03 2731 non-null float64\n", - " 58 PCIAT-PCIAT_04 2731 non-null float64\n", - " 59 PCIAT-PCIAT_05 2729 non-null float64\n", - " 60 PCIAT-PCIAT_06 2732 non-null float64\n", - " 61 PCIAT-PCIAT_07 2729 non-null float64\n", - " 62 PCIAT-PCIAT_08 2730 non-null float64\n", - " 63 PCIAT-PCIAT_09 2730 non-null float64\n", - " 64 PCIAT-PCIAT_10 2733 non-null float64\n", - " 65 PCIAT-PCIAT_11 2734 non-null float64\n", - " 66 PCIAT-PCIAT_12 2731 non-null float64\n", - " 67 PCIAT-PCIAT_13 2729 non-null float64\n", - " 68 PCIAT-PCIAT_14 2732 non-null float64\n", - " 69 PCIAT-PCIAT_15 2730 non-null float64\n", - " 70 PCIAT-PCIAT_16 2728 non-null float64\n", - " 71 PCIAT-PCIAT_17 2725 non-null float64\n", - " 72 PCIAT-PCIAT_18 2728 non-null float64\n", - " 73 PCIAT-PCIAT_19 2730 non-null float64\n", - " 74 PCIAT-PCIAT_20 2733 non-null float64\n", - " 75 PCIAT-PCIAT_Total 2736 non-null float64\n", - " 76 SDS-Season 2618 non-null object \n", - " 77 SDS-SDS_Total_Raw 2609 non-null float64\n", - " 78 SDS-SDS_Total_T 2606 non-null float64\n", - " 79 PreInt_EduHx-Season 3540 non-null object \n", - " 80 PreInt_EduHx-computerinternet_hoursday 3301 non-null float64\n", - " 81 sii 2736 non-null float64\n", - "dtypes: float64(68), int64(2), object(12)\n", - "memory usage: 2.5+ MB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyproject.toml b/pyproject.toml index d4f0632..a9e6de1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,8 @@ dependencies = ["lightgbm>=4.5.0, <5", "databricks-feature-engineering>=0.6, <1", "databricks-feature-lookup==1.2.0", "databricks-sdk==0.32.0", - "pydantic==2.9.2"] + "pydantic==2.9.2", + "tqdm>=4.64.0, <5"] [project.optional-dependencies] dev = ["databricks-connect>=15.4.1, <16", diff --git a/uv.lock b/uv.lock index ba1cf65..ea385dd 100644 --- a/uv.lock +++ b/uv.lock @@ -1285,6 +1285,7 @@ dependencies = [ { name = "pydantic" }, { name = "scikit-learn" }, { name = "scipy" }, + { name = "tqdm" }, ] [package.optional-dependencies] @@ -1315,6 +1316,7 @@ requires-dist = [ { name = "pydantic", specifier = "==2.9.2" }, { name = "scikit-learn", specifier = ">=1.5.1,<2" }, { name = "scipy", specifier = ">=1.14.1,<2" }, + { name = "tqdm", specifier = ">=4.64.0,<5" }, ] [[package]] @@ -2093,6 +2095,18 @@ wheels = [ { url = 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a38f48e1d18e99093d7f1d8e9041dc81d4723feb Mon Sep 17 00:00:00 2001 From: javedhassans Date: Thu, 31 Oct 2024 03:41:35 +0100 Subject: [PATCH 12/36] just making commit --- notebooks/week1/00.dataexploration.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 notebooks/week1/00.dataexploration.ipynb diff --git a/notebooks/week1/00.dataexploration.ipynb b/notebooks/week1/00.dataexploration.ipynb new file mode 100644 index 0000000..e69de29 From bb7494baa84425bfe45a73825becfc98a18bddf5 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Thu, 31 Oct 2024 19:05:56 +0100 Subject: [PATCH 13/36] working on preprocessing parquet files --- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 0 -> 8468 bytes ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 8358 -> 0 bytes notebooks/week1/00.dataexploration.ipynb | 1992 +++++++++++++++++ notebooks/week1/01.dataExploraton.py | 153 +- project_config.yml | 3 +- pyproject.toml | 47 +- uv.lock | 178 +- 7 files changed, 2260 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Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "ERROR:prophet.plot:Importing plotly failed. Interactive plots will not work.\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "from tqdm import tqdm\n", + "from prophet import Prophet\n", + "from datetime import datetime\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "class ActigraphyDataProcessor:\n", + " def __init__(self, root_dir, feature_table_path):\n", + " self.root_dir = root_dir\n", + " self.feature_table_path = feature_table_path\n", + " self.features_to_forecast = ['enmo', 'light', 'battery_voltage']\n", + "\n", + " def load_data(self, participant_id):\n", + " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", + " data = pd.read_parquet(file_path)\n", + " data['id'] = participant_id\n", + " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", + " return data\n", + "\n", + " def compute_daily_summary(self, data):\n", + " daily_summary = data.groupby(['id', 'relative_date_PCIAT']).agg({\n", + " 'X': ['mean', 'max', 'min', 'std'],\n", + " 'Y': ['mean', 'max', 'min', 'std'],\n", + " 'Z': ['mean', 'max', 'min', 'std'],\n", + " 'enmo': ['mean', 'max', 'std'],\n", + " 'anglez': 'mean',\n", + " 'non-wear_flag': 'sum',\n", + " 'light': ['mean', 'max', 'min'],\n", + " 'battery_voltage': 'mean'\n", + " }).reset_index()\n", + " daily_summary.columns = ['_'.join(col).strip() if col[1] else col[0] for col in daily_summary.columns.values]\n", + " print(f\"Daily summary columns after aggregation: {daily_summary.columns.tolist()}\")\n", + " return daily_summary\n", + "\n", + " def add_temporal_features(self, daily_summary):\n", + " for col in ['enmo_mean', 'light_mean', 'battery_voltage_mean']:\n", + " if col in daily_summary.columns:\n", + " daily_summary[f'{col}_lag1'] = daily_summary.groupby('id')[col].shift(1)\n", + " daily_summary[f'{col}_rolling3'] = daily_summary.groupby('id')[col].rolling(window=3).mean().reset_index(0, drop=True)\n", + " else:\n", + " print(f\"Warning: Column {col} not found in daily_summary.\")\n", + " return daily_summary\n", + "\n", + " def forecast_features(self, data, participant_id):\n", + " date_mapping = data[['time_of_day', 'relative_date_PCIAT']].drop_duplicates()\n", + " date_mapping['ds'] = pd.to_datetime(date_mapping['time_of_day'], unit='s', errors='coerce')\n", + " \n", + " predictions = []\n", + " for feature in self.features_to_forecast:\n", + " df = data[['time_of_day', feature]].dropna().rename(columns={'time_of_day': 'ds', feature: 'y'})\n", + " max_time = df['ds'].max()\n", + " if max_time > 10**12:\n", + " df['ds'] = pd.to_datetime(df['ds'] / 10**3, unit='s', errors='coerce')\n", + " else:\n", + " df['ds'] = pd.to_datetime(df['ds'], unit='s', errors='coerce')\n", + " df = df.dropna(subset=['ds'])\n", + "\n", + " model = Prophet(daily_seasonality=True, weekly_seasonality=True)\n", + " model.fit(df)\n", + " future = model.make_future_dataframe(periods=7, freq='D')\n", + " forecast = model.predict(future)\n", + "\n", + " forecast['id'] = participant_id\n", + " forecast = forecast[['id', 'ds', 'yhat']].rename(columns={'yhat': f'{feature}_forecast'})\n", + " predictions.append(forecast)\n", + "\n", + " predictions_df = pd.concat(predictions, axis=1)\n", + " predictions_df = predictions_df.loc[:, ~predictions_df.columns.duplicated()]\n", + " predictions_df = pd.merge(predictions_df, date_mapping[['ds', 'relative_date_PCIAT']], on='ds', how='left')\n", + " \n", + " return predictions_df\n", + "\n", + " def process_participant_data(self, participant_id):\n", + " data = self.load_data(participant_id)\n", + " daily_summary = self.compute_daily_summary(data)\n", + " print(f\"Daily summary columns for {participant_id}: {daily_summary.columns.tolist()}\")\n", + " daily_summary = self.add_temporal_features(daily_summary)\n", + " forecasts = self.forecast_features(data, participant_id)\n", + " print(f\"Forecast columns for {participant_id}: {forecasts.columns.tolist()}\")\n", + " processed_data = pd.merge(daily_summary, forecasts, on=['id', 'relative_date_PCIAT'], how='left')\n", + " return processed_data\n", + "\n", + " def process_all_participants(self):\n", + " all_data = []\n", + " with ThreadPoolExecutor() as executor:\n", + " futures = []\n", + " for id_folder in os.listdir(self.root_dir):\n", + " if not id_folder.startswith('id='):\n", + " continue\n", + " participant_id = id_folder.split('=')[-1]\n", + " futures.append(executor.submit(self.process_participant_data, participant_id))\n", + "\n", + " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", + " participant_data = future.result()\n", + " all_data.append(participant_data)\n", + "\n", + " feature_table = pd.concat(all_data, ignore_index=True)\n", + " return feature_table\n", + "\n", + " def save_feature_table(self):\n", + " os.makedirs(os.path.dirname(self.feature_table_path), exist_ok=True)\n", + " feature_table = self.process_all_participants()\n", + " feature_table.to_parquet(self.feature_table_path, index=False)\n", + " print(f\"Feature table saved at {self.feature_table_path}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing participants: 0%| | 0/2 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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stepXYZenmoangleznon-wear_flaglightbattery_voltagetime_of_dayweekdayquarterrelative_date_PCIAT
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43330 rows × 13 columns

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40 rows × 33 columns

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NaN \n", + "1 0.025359 1.002630 -2.905339 0.496983 ... 0.090690 \n", + "2 -0.130250 1.020573 -1.029193 0.404084 ... 0.065511 \n", + "3 0.076826 1.001562 -1.215625 0.459795 ... 0.049758 \n", + "4 -0.075251 -0.068203 -0.081315 0.006348 ... 0.036618 \n", + "5 0.076296 0.885156 -1.064375 0.331025 ... 0.008188 \n", + "6 -0.049812 1.666354 -1.758724 0.578938 ... 0.097723 \n", + "7 0.088663 0.428516 -0.046680 0.129417 ... 0.056882 \n", + "8 0.137763 0.233529 0.006055 0.061621 ... 0.027419 \n", + "9 0.294944 1.013776 -1.414115 0.486515 ... 0.015197 \n", + "10 0.005649 1.356276 -1.205287 0.602630 ... 0.037232 \n", + "11 0.215609 1.006693 -1.476875 0.441121 ... 0.036378 \n", + "12 0.223290 1.021536 -1.518021 0.523033 ... 0.033902 \n", + "13 0.018381 0.038724 -0.020508 0.010714 ... 0.042280 \n", + "14 0.195827 1.043411 -0.954687 0.461816 ... 0.001866 \n", + "15 -0.603054 -0.567995 -0.641146 0.023462 ... 0.078744 \n", + "16 -0.557835 -0.244167 -0.963568 0.240134 ... 0.000750 \n", + "17 -0.213604 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0.350417 -0.093385 0.055112 ... 0.023870 \n", + "34 -0.827333 0.834010 -0.981380 0.228439 ... 0.024558 \n", + "35 -0.278423 0.402500 -0.915391 0.175222 ... 0.019109 \n", + "36 -0.215488 0.559635 -0.886621 0.324548 ... 0.020838 \n", + "37 -0.010881 0.600521 -0.820208 0.127002 ... 0.028970 \n", + "38 -0.024244 0.289948 -0.994531 0.258073 ... 0.025312 \n", + "39 0.187363 0.992057 -0.571875 0.344520 ... 0.024068 \n", + "\n", + " enmo_mean_rolling3 light_mean_lag1 light_mean_rolling3 \\\n", + "0 NaN NaN NaN \n", + "1 NaN 33.471268 NaN \n", + "2 0.068653 40.340485 31.803000 \n", + "3 0.050629 21.597248 60.081188 \n", + "4 0.031521 118.305832 47.284031 \n", + "5 0.047509 1.949013 44.493234 \n", + "6 0.054264 13.224856 19.113361 \n", + "7 0.060674 42.166214 32.087346 \n", + "8 0.033166 40.870968 29.015409 \n", + "9 0.026616 4.009044 17.989583 \n", + "10 0.029602 9.088736 28.725869 \n", + "11 0.035838 73.079826 33.164764 \n", + "12 0.037520 17.325729 63.985850 \n", + "13 0.026016 101.551994 40.197262 \n", + "14 0.040963 1.714062 44.616534 \n", + "15 0.027120 30.583546 13.098295 \n", + "16 0.028620 6.997277 14.764251 \n", + "17 0.002744 6.711931 6.612601 \n", + "18 0.005917 6.128595 6.237712 \n", + "19 0.004136 5.872612 5.856706 \n", + "20 0.011333 5.568912 5.584272 \n", + "21 0.008918 5.311294 5.307075 \n", + "22 0.023308 5.041019 5.041232 \n", + "23 0.020986 4.771381 4.638867 \n", + "24 0.025147 4.104200 4.258496 \n", + "25 0.010415 3.899907 3.764638 \n", + "26 0.008041 3.289807 3.393761 \n", + "27 0.010503 2.991570 3.002709 \n", + "28 0.011061 2.726750 2.710015 \n", + "29 0.029343 2.411724 2.412484 \n", + "30 0.032527 2.098978 2.002676 \n", + "31 0.041704 1.497326 1.612034 \n", + "32 0.028507 1.239798 1.807588 \n", + "33 0.025878 2.685641 1.757815 \n", + "34 0.022513 1.348006 3.114460 \n", + "35 0.021502 5.309732 2.980471 \n", + "36 0.022972 2.283675 4.574565 \n", + "37 0.025040 6.130287 6.035167 \n", + "38 0.026116 9.691541 8.411575 \n", + "39 0.038967 9.412899 9.759213 \n", + "\n", + " battery_voltage_mean_lag1 battery_voltage_mean_rolling3 ds \\\n", + "0 NaN NaN NaT \n", + "1 4174.144531 NaN NaT \n", + "2 4155.183105 4156.394531 NaT \n", + "3 4139.855957 4140.654622 NaT \n", + "4 4126.924805 4126.784017 NaT \n", + "5 4113.571289 4115.227865 NaT \n", + "6 4105.187500 4104.084229 NaT \n", + "7 4093.493896 4094.034505 NaT \n", + "8 4083.422119 4086.275391 NaT \n", + "9 4081.910156 4078.350342 NaT \n", + "10 4069.718750 4070.316813 NaT \n", + "11 4059.321533 4055.616048 NaT \n", + "12 4037.807861 4040.777751 NaT \n", + "13 4025.203857 4022.670573 NaT \n", + "14 4005.000000 4010.067952 NaT \n", + "15 4000.000000 3999.666667 NaT \n", + "16 3994.000000 3994.092122 NaT \n", + "17 3988.276367 3986.989421 NaT \n", + "18 3978.691895 3980.453695 NaT \n", + "19 3974.392822 3974.361572 NaT \n", + "20 3970.000000 3969.288656 NaT \n", + "21 3963.473145 3963.824382 NaT \n", + "22 3958.000000 3958.157715 NaT \n", + "23 3953.000000 3950.731527 NaT \n", + "24 3941.194580 3943.722249 NaT \n", + "25 3936.972168 3933.722249 NaT \n", + "26 3923.000000 3925.297363 NaT \n", + "27 3915.919922 3915.201497 NaT \n", + "28 3906.684570 3903.963379 NaT \n", + "29 3889.285645 3892.656738 NaT \n", + "30 3882.000000 3879.475749 NaT \n", + "31 3867.141602 3871.380534 NaT \n", + "32 3865.000000 3863.713867 NaT \n", + "33 3859.000000 3859.656576 NaT \n", + "34 3854.969727 3854.220866 NaT \n", + "35 3848.692871 3848.689697 NaT \n", + "36 3842.406494 3843.359294 NaT \n", + "37 3838.978516 3838.816569 NaT \n", + "38 3835.064697 3834.653727 NaT \n", + "39 3829.917969 3829.770508 NaT \n", + "\n", + " enmo_forecast light_forecast battery_voltage_forecast \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", 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+ "nbformat_minor": 2 +} diff --git a/notebooks/week1/01.dataExploraton.py b/notebooks/week1/01.dataExploraton.py index 70ae710..506bd3e 100644 --- a/notebooks/week1/01.dataExploraton.py +++ b/notebooks/week1/01.dataExploraton.py @@ -1,4 +1,6 @@ # Databricks notebook source +# COMMAND ---------- + %pip install ../mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- @@ -7,62 +9,137 @@ # COMMAND ---------- +import os import pandas as pd from pyspark.sql import SparkSession +from concurrent.futures import ThreadPoolExecutor + from childHealth.config import ProjectConfig -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from tqdm import tqdm -import os -spark = SparkSession.builder.getOrCreate() -# COMMAND ---------- +import warnings +warnings.filterwarnings("ignore") -config = ProjectConfig.from_yaml('../../project_config.yml') +# Initialize Spark session +spark = SparkSession.builder.getOrCreate() -# COMMAND ---------- +# Define original paths +dirname_train_ts = "/Volumes/mlops_students/javedhassi/data/series_train.parquet" +dirname_test_ts = "/Volumes/mlops_students/javedhassi/data/series_test.parquet" +# COMMAND ---------- +# Load project configuration from YAML file +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") +num_features = config.num_features +cat_features = config.cat_features # COMMAND ---------- -# Define the process_file function def process_file(filename, dirname): - # Read the Parquet file into a Spark DataFrame - df = spark.read.parquet(os.path.join(dirname, filename, 'part-0.parquet')) - - # Drop the 'step' column + filepath = os.path.join(dirname, filename, 'part-0.parquet') + df = spark.read.parquet(filepath) df = df.drop('step') - - # Compute basic statistics - stats = df.describe().toPandas().values.reshape(-1) - - # Extract the filename part - file_part = filename.split('=')[1] - - return stats, file_part + # Ensure 'id' column is included + if 'id' not in df.columns: + df = df.withColumn('id', df['relative_date_PCIAT']) # Use an existing column or create a new one + return df.toPandas(), filename.split('=')[1] -# COMMAND ---------- -# Define the load_time_series function def load_time_series(dirname) -> pd.DataFrame: - ids = os.listdir(dirname) - + # List all subdirectories in the specified path + directories = [file.path for file in dbutils.fs.ls(dirname) if file.path.endswith("/")] + + results = [] with ThreadPoolExecutor() as executor: - results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids))) - - stats, indexes = zip(*results) - - df = pd.DataFrame(stats, columns=[f"Stat_{i}" for i in range(len(stats[0]))]) - df['id'] = indexes + futures = {executor.submit(process_file, path.split("/")[-2], dirname): path for path in directories} + for i, future in enumerate(futures): + result = future.result() + results.append(result) + print(f"Processed {i + 1}/{len(directories)} files") + + # Separate stats and identifiers + stats, indexes = zip(*results) if results else ([], []) + # Create DataFrame with statistics and identifiers + combined_df = pd.concat([df for df in stats], ignore_index=True) + combined_df['id'] = indexes + + return combined_df + +# COMMAND ---------- + +# Load time series data +train_ts = load_time_series(dirname_train_ts) +test_ts = load_time_series(dirname_test_ts) + +# COMMAND ---------- + +# Load train and test CSV files with Spark +train = spark.read.csv('/Volumes/mlops_students/javedhassi/data/childHealth.csv', header=True, inferSchema=True) +test = spark.read.csv('/Volumes/mlops_students/javedhassi/data/test.csv', header=True, inferSchema=True) + +# Convert Spark DataFrames to Pandas DataFrames +train_pd = train.toPandas() +test_pd = test.toPandas() + +# Ensure 'id' column exists in both DataFrames +if 'id' not in train_pd.columns: + train_pd['id'] = train_pd.index +if 'id' not in test_pd.columns: + test_pd['id'] = test_pd.index + +# COMMAND ---------- + +# Merge the data +train_merged = pd.merge(train_pd, train_ts, how="left", on='id') +test_merged = pd.merge(test_pd, test_ts, how="left", on='id') + +# Check the result +print(train_merged.head()) +print(test_merged.head()) + +# COMMAND ---------- + +# Update the list of numerical features to include time series columns +time_series_cols = train_ts.columns.tolist() +time_series_cols.remove("id") # Temporarily remove 'id' column from the list of time series columns +num_features += time_series_cols + +# COMMAND ---------- + +def update(df): + for c in cat_features: + df[c] = df[c].fillna('Missing') + df[c] = df[c].astype('category') return df # COMMAND ---------- -# Example usage of the process_file function -dirname = "/Volumes/mlops_students/javedhassi/data/series_test.parquet/" -filename = "part-0.parquet" -stats, file_part = process_file(filename, dirname) -print(stats) -print(file_part) -# COMMAND ---------- \ No newline at end of file +# Update the train and test DataFrames +train_merged = update(train_merged) +test_merged = update(test_merged) + +# COMMAND ---------- + +# # Include 'id' column back in the numerical features if needed +# num_features.append('id') + +# Check the updated DataFrames +print(train_merged.head()) +print(test_merged.head()) + + +# COMMAND ---------- +# Read the Parquet file +df = spark.read.parquet("/Volumes/mlops_students/javedhassi/data/series_train.parquet/id=00115b9f/part-0.parquet", + header=True, inferSchema=True) + +# Show the DataFrame +df.show() + +# COMMAND ---------- + +df_pandas = df.toPandas() +# COMMAND ---------- +train = spark.read.csv('/Volumes/mlops_students/javedhassi/data/childHealth.csv', header=True, inferSchema=True) +train.filter(train.id=="00115b9f").show() +# COMMAND ---------- diff --git a/project_config.yml b/project_config.yml index eeb93eb..86d04b5 100644 --- a/project_config.yml +++ b/project_config.yml @@ -86,6 +86,7 @@ num_features: - PCIAT-PCIAT_Total - SDS-SDS_Total_Raw # Sleep Disturbance Scale (SDS) Total Raw - SDS-SDS_Total_T # Sleep Disturbance Scale (SDS) Total T + - PreInt_EduHx-Season - PreInt_EduHx-computerinternet_hoursday # Categorical features @@ -100,7 +101,7 @@ cat_features: - PAQ_C-Season # Physical Activity Questionnaire (PAQ) C Season - PCIAT-Season # PCIAT-Season - SDS-Season # Sleep Disturbance Scale (SDS) Season - - PreInt_EduHx-Season + # Target column target: sii \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index a9e6de1..e33bc8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,34 +1,39 @@ [project] -name = "mlops-with-databricks" +name = "childhealth-mlops-with-databricks" version = "0.0.1" description = "MLOps with Databricks" readme = "README.md" requires-python = ">=3.11" -dependencies = ["lightgbm>=4.5.0, <5", - "scikit-learn>=1.5.1, <2", - "cloudpickle>=3.0.0, <4", - "mlflow>=2.16.0, <3", - "numpy>=1.26.4, <2", - "pandas>=2.2.2, <3", - "pyarrow==14.0.1", - "cffi>=1.17.1, <2", - "scipy>=1.14.1, <2", - "matplotlib>=3.9.2, <4", - 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Sep 17 00:00:00 2001 From: javedhassans Date: Thu, 31 Oct 2024 23:24:09 +0100 Subject: [PATCH 14/36] added main codes in the 00.dataexploration.ipynb modified project.toml to add tqdm and prophet commented outlier removal function --- notebooks/week1/00.dataexploration.ipynb | 2575 +++++++--------------- project_config.yml | 10 +- src/childHealth/utils.py | 34 +- 3 files changed, 853 insertions(+), 1766 deletions(-) diff --git a/notebooks/week1/00.dataexploration.ipynb b/notebooks/week1/00.dataexploration.ipynb index ae3ad24..0f2183c 100644 --- a/notebooks/week1/00.dataexploration.ipynb +++ b/notebooks/week1/00.dataexploration.ipynb @@ -2,19 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/javedhassansabu/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "ERROR:prophet.plot:Importing plotly failed. Interactive plots will not work.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import pandas as pd\n", @@ -29,7 +19,329 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from tqdm import tqdm\n", + "\n", + "class ActigraphAggregation:\n", + " def __init__(self, root_dir):\n", + " self.root_dir = root_dir\n", + " \n", + " def load_data(self, participant_id):\n", + " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", + " data = pd.read_parquet(file_path)\n", + " data['id'] = participant_id\n", + " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", + " return data\n", + "\n", + " def aggregate_actigraphy(self, data):\n", + " \"\"\"\n", + " Aggregate the actigraphy data for a single participant.\n", + "\n", + " Parameters:\n", + " - data (DataFrame): Actigraphy data for a participant.\n", + "\n", + " Returns:\n", + " - DataFrame: Aggregated actigraphy data with one row per 'id'.\n", + " \"\"\"\n", + " aggregated_df = data.groupby('id').agg({\n", + " 'X': ['mean', 'std', 'max', 'min'],\n", + " 'Y': ['mean', 'std', 'max', 'min'],\n", + " 'Z': ['mean', 'std', 'max', 'min'],\n", + " 'enmo': ['mean', 'std', 'max', 'min'],\n", + " 'anglez': 'mean',\n", + " 'non-wear_flag': 'sum',\n", + " 'light': ['mean', 'std', 'max', 'min'],\n", + " 'battery_voltage': 'mean',\n", + " }).reset_index()\n", + "\n", + " # Flatten column names\n", + " aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", + " return aggregated_df\n", + " \n", + " def process_participant_data(self, participant_id):\n", + " data = self.load_data(participant_id)\n", + " aggregated_data = self.aggregate_actigraphy(data)\n", + " return aggregated_data\n", + "\n", + " def process_all_participants(self):\n", + " all_data = []\n", + " with ThreadPoolExecutor() as executor:\n", + " futures = []\n", + " for id_folder in os.listdir(self.root_dir):\n", + " if not id_folder.startswith('id='):\n", + " continue\n", + " participant_id = id_folder.split('=')[-1]\n", + " futures.append(executor.submit(self.process_participant_data, participant_id))\n", + "\n", + " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", + " participant_data = future.result()\n", + " all_data.append(participant_data)\n", + "\n", + " feature_table = pd.concat(all_data, ignore_index=True)\n", + " return feature_table\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize with the root directory containing participant data\n", + "aggregator = ActigraphAggregation(root_dir=\"../../data/series_test.parquet/\")\n", + "\n", + "# Process all participants and get the final aggregated feature table\n", + "feature_table = aggregator.process_all_participants()\n", + "\n", + "# Inspect the feature table\n", + "print(feature_table.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from tqdm import tqdm\n", + "\n", + "class ActigraphAggregation:\n", + " def __init__(self, root_dir):\n", + " self.root_dir = root_dir\n", + " \n", + " def load_data(self, participant_id):\n", + " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", + " if not os.path.exists(file_path):\n", + " print(f\"File not found for participant {participant_id}\")\n", + " return pd.DataFrame() # Return an empty DataFrame if file doesn't exist\n", + " data = pd.read_parquet(file_path)\n", + " data['id'] = participant_id\n", + " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", + " return data\n", + "\n", + " def temporal_aggregations(self, data):\n", + " # Classify weekday/weekend\n", + " data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend')\n", + "\n", + " # Classify time of day\n", + " conditions = [\n", + " (data['time_of_day'] < 6 * 3600), # Midnight to 6 AM\n", + " (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), # 6 AM to Noon\n", + " (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), # Noon to 6 PM\n", + " (data['time_of_day'] >= 18 * 3600) # 6 PM to Midnight\n", + " ]\n", + " choices = ['night', 'morning', 'afternoon', 'evening']\n", + " data['time_period'] = np.select(conditions, choices, default='unknown')\n", + " \n", + " # Aggregate by weekday/weekend and time of day\n", + " temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({\n", + " 'enmo': 'mean',\n", + " 'light': 'mean',\n", + " 'non-wear_flag': 'sum'\n", + " }).unstack(fill_value=0)\n", + " temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values]\n", + " \n", + " return temporal_agg.reset_index()\n", + " \n", + " def process_participant_data(self, participant_id):\n", + " data = self.load_data(participant_id)\n", + " if data.empty: # Skip processing if data is empty\n", + " return pd.DataFrame()\n", + " temporal_data = self.temporal_aggregations(data)\n", + " return temporal_data\n", + "\n", + " def process_all_participants(self):\n", + " all_data = []\n", + " with ThreadPoolExecutor() as executor:\n", + " futures = []\n", + " for id_folder in os.listdir(self.root_dir):\n", + " if not id_folder.startswith('id='):\n", + " continue\n", + " participant_id = id_folder.split('=')[-1]\n", + " futures.append(executor.submit(self.process_participant_data, participant_id))\n", + "\n", + " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", + " participant_data = future.result()\n", + " if not participant_data.empty: # Only append non-empty data\n", + " all_data.append(participant_data)\n", + "\n", + " feature_table = pd.concat(all_data, ignore_index=True)\n", + " return feature_table\n", + " \n", + "# Initialize with the root directory containing participant data\n", + "temporal = ActigraphAggregation(root_dir=\"../../data/series_test.parquet/\")\n", + "\n", + "# Process all participants and get the final aggregated feature table\n", + "feature_table = temporal.process_all_participants()\n", + "\n", + "# Inspect the feature table\n", + "print(feature_table.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from tqdm import tqdm\n", + "\n", + "class ActigraphAggregation:\n", + " def __init__(self, root_dir):\n", + " self.root_dir = root_dir\n", + " \n", + " def load_data(self, participant_id):\n", + " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", + " if not os.path.exists(file_path):\n", + " print(f\"File not found for participant {participant_id}\")\n", + " return pd.DataFrame() # Return an empty DataFrame if file doesn't exist\n", + " data = pd.read_parquet(file_path)\n", + " data['id'] = participant_id\n", + " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", + " return data\n", + "\n", + " def aggregate_actigraphy(self, data):\n", + " \"\"\"\n", + " Aggregate the actigraphy data for each participant with summary statistics.\n", + "\n", + " Parameters:\n", + " - data (DataFrame): Actigraphy data for a participant.\n", + "\n", + " Returns:\n", + " - DataFrame: Aggregated actigraphy data with summary statistics for each `id`.\n", + " \"\"\"\n", + " aggregated_df = data.groupby('id').agg({\n", + " 'X': ['mean', 'std', 'max', 'min'],\n", + " 'Y': ['mean', 'std', 'max', 'min'],\n", + " 'Z': ['mean', 'std', 'max', 'min'],\n", + " 'enmo': ['mean', 'std', 'max', 'min'],\n", + " 'anglez': 'mean', # Mean of the angle metric\n", + " 'non-wear_flag': 'sum', # Total non-wear time\n", + " 'light': ['mean', 'std', 'max', 'min'],\n", + " 'battery_voltage': 'mean', # Average battery voltage\n", + " }).reset_index()\n", + " \n", + " # Flatten the multi-level column names\n", + " aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", + " return aggregated_df\n", + "\n", + " def temporal_aggregations(self, data):\n", + " # Classify weekday/weekend\n", + " data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend')\n", + "\n", + " # Classify time of day\n", + " conditions = [\n", + " (data['time_of_day'] < 6 * 3600), # Midnight to 6 AM\n", + " (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), # 6 AM to Noon\n", + " (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), # Noon to 6 PM\n", + " (data['time_of_day'] >= 18 * 3600) # 6 PM to Midnight\n", + " ]\n", + " choices = ['night', 'morning', 'afternoon', 'evening']\n", + " data['time_period'] = np.select(conditions, choices, default='unknown')\n", + " \n", + " # Aggregate by weekday/weekend and time of day\n", + " temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({\n", + " 'enmo': 'mean',\n", + " 'light': 'mean',\n", + " 'non-wear_flag': 'sum'\n", + " }).unstack(fill_value=0)\n", + " temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values]\n", + " \n", + " return temporal_agg.reset_index()\n", + "\n", + " def activity_ratios(self, data):\n", + " \"\"\"\n", + " Calculate the ratio of non-wear time to total measurement time for each participant.\n", + "\n", + " Parameters:\n", + " - data (DataFrame): Actigraphy data for a participant.\n", + "\n", + " Returns:\n", + " - DataFrame: Contains `non_wear_ratio` for each `id`.\n", + " \"\"\"\n", + " total_time = data.groupby('id').size().rename('total_time')\n", + " non_wear_time = data.groupby('id')['non-wear_flag'].sum().rename('non_wear_time')\n", + " \n", + " # Calculate ratio\n", + " ratios = pd.concat([total_time, non_wear_time], axis=1)\n", + " ratios['non_wear_ratio'] = ratios['non_wear_time'] / ratios['total_time']\n", + " \n", + " return ratios[['non_wear_ratio']].reset_index()\n", + " \n", + " def process_participant_data(self, participant_id):\n", + " data = self.load_data(participant_id)\n", + " if data.empty: # Skip processing if data is empty\n", + " return pd.DataFrame()\n", + " \n", + " # Calculate aggregate statistics, temporal aggregation, and activity ratios\n", + " aggregate_data = self.aggregate_actigraphy(data)\n", + " temporal_data = self.temporal_aggregations(data)\n", + " ratio_data = self.activity_ratios(data)\n", + "\n", + " # Merge all feature data on `id`\n", + " participant_data = aggregate_data.merge(temporal_data, on='id', how='left')\n", + " participant_data = participant_data.merge(ratio_data, on='id', how='left')\n", + " return participant_data\n", + "\n", + " def process_all_participants(self):\n", + " all_data = []\n", + " with ThreadPoolExecutor() as executor:\n", + " futures = []\n", + " for id_folder in os.listdir(self.root_dir):\n", + " if not id_folder.startswith('id='):\n", + " continue\n", + " participant_id = id_folder.split('=')[-1]\n", + " futures.append(executor.submit(self.process_participant_data, participant_id))\n", + "\n", + " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", + " participant_data = future.result()\n", + " if not participant_data.empty: # Only append non-empty data\n", + " all_data.append(participant_data)\n", + "\n", + " feature_table = pd.concat(all_data, ignore_index=True)\n", + " return feature_table\n", + " \n", + "# Initialize with the root directory containing participant data\n", + "aggregator = ActigraphAggregation(root_dir=\"../../data/series_test.parquet/\")\n", + "\n", + "# Process all participants and get the final aggregated feature table\n", + "feature_table = aggregator.process_all_participants()\n", + "\n", + "# Inspect the feature table\n", + "print(feature_table.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Merge the aggregated actigraphy features with the train data\n", + "combined_df = pd.merge(train_df, aggregated_actigraphy_df, on='id', how='left')\n", + "\n", + "# Inspect the combined DataFrame\n", + "print(combined_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -137,131 +449,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing participants: 0%| | 0/2 [00:00= 6 * 3600) & (data['time_of_day'] < 12 * 3600), # 6 AM to Noon\n", + " (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), # Noon to 6 PM\n", + " (data['time_of_day'] >= 18 * 3600) # 6 PM to Midnight\n", + " ]\n", + " choices = ['night', 'morning', 'afternoon', 'evening']\n", + " data['time_period'] = np.select(conditions, choices, default='unknown')\n", + " \n", + " # Aggregate by weekday/weekend and time of day\n", + " temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({\n", + " 'enmo': 'mean',\n", + " 'light': 'mean',\n", + " 'non-wear_flag': 'sum'\n", + " }).unstack(fill_value=0)\n", + " temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values]\n", + " \n", + " return temporal_agg.reset_index()\n", + "\n", + " def activity_ratios(self, data):\n", + " \"\"\"\n", + " Calculate the ratio of non-wear time to total measurement time for each participant.\n", + "\n", + " Parameters:\n", + " - data (DataFrame): Actigraphy data for a participant.\n", + "\n", + " Returns:\n", + " - DataFrame: Contains `non_wear_ratio` for each `id`.\n", + " \"\"\"\n", + " total_time = data.groupby('id').size().rename('total_time')\n", + " non_wear_time = data.groupby('id')['non-wear_flag'].sum().rename('non_wear_time')\n", + " \n", + " # Calculate ratio\n", + " ratios = pd.concat([total_time, non_wear_time], axis=1)\n", + " ratios['non_wear_ratio'] = ratios['non_wear_time'] / ratios['total_time']\n", + " \n", + " return ratios[['non_wear_ratio']].reset_index()\n", + " \n", + " def process_participant_data(self, participant_id):\n", + " data = self.load_data(participant_id)\n", + " if data.empty: # Skip processing if data is empty\n", + " return pd.DataFrame()\n", + " \n", + " # Calculate aggregate statistics, temporal aggregation, and activity ratios\n", + " aggregate_data = self.aggregate_actigraphy(data)\n", + " temporal_data = self.temporal_aggregations(data)\n", + " ratio_data = self.activity_ratios(data)\n", + "\n", + " # Merge all feature data on `id`\n", + " participant_data = aggregate_data.merge(temporal_data, on='id', how='left')\n", + " participant_data = participant_data.merge(ratio_data, on='id', how='left')\n", + " return participant_data\n", + "\n", + " def process_all_participants(self):\n", + " all_data = []\n", + " with ThreadPoolExecutor() as executor:\n", + " futures = []\n", + " for id_folder in os.listdir(self.root_dir):\n", + " if not id_folder.startswith('id='):\n", + " continue\n", + " participant_id = id_folder.split('=')[-1]\n", + " futures.append(executor.submit(self.process_participant_data, participant_id))\n", + "\n", + " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", + " participant_data = future.result()\n", + " if not participant_data.empty: # Only append non-empty data\n", + " all_data.append(participant_data)\n", + "\n", + " feature_table = pd.concat(all_data, ignore_index=True)\n", + " return feature_table\n", + " \n", + "# Initialize with the root directory containing participant data\n", + "aggregator = ActigraphAggregation(root_dir=\"../../data/series_train.parquet/\")\n", + "\n", + "# Process all participants and get the final aggregated feature table\n", + "feature_table = aggregator.process_all_participants()\n", + "\n", + "# Inspect the feature table\n", + "print(feature_table.head())\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['id', 'relative_date_PCIAT', 'X_mean', 'X_max', 'X_min', 'X_std',\n", - " 'Y_mean', 'Y_max', 'Y_min', 'Y_std', 'Z_mean', 'Z_max', 'Z_min',\n", - " 'Z_std', 'enmo_mean', 'enmo_max', 'enmo_std', 'anglez_mean',\n", - " 'non-wear_flag_sum', 'light_mean', 'light_max', 'light_min',\n", - " 'battery_voltage_mean', 'enmo_mean_lag1', 'enmo_mean_rolling3',\n", - " 'light_mean_lag1', 'light_mean_rolling3', 'battery_voltage_mean_lag1',\n", - " 'battery_voltage_mean_rolling3', 'ds', 'enmo_forecast',\n", - " 'light_forecast', 'battery_voltage_forecast'],\n", + "Index(['id', 'X_mean', 'X_std', 'X_max', 'X_min', 'Y_mean', 'Y_std', 'Y_max',\n", + " 'Y_min', 'Z_mean', 'Z_std', 'Z_max', 'Z_min', 'enmo_mean', 'enmo_std',\n", + " 'enmo_max', 'enmo_min', 'anglez_mean', 'non-wear_flag_sum',\n", + " 'light_mean', 'light_std', 'light_max', 'light_min',\n", + " 'battery_voltage_mean', 'weekday_flag', 'enmo_evening', 'enmo_night',\n", + " 'light_evening', 'light_night', 'non-wear_flag_evening',\n", + " 'non-wear_flag_night', 'non_wear_ratio'],\n", " dtype='object')" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_feature.columns" + "feature_table.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# merge train csv with feature table\n", + "df_train = pd.read_csv(\"../../data/childhealth.csv\")\n", + "\n", + "df_merge = df_train.merge(feature_table, on='id', how='left')" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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" - ], "text/plain": [ - " step X Y Z enmo anglez \\\n", - "0 0 0.021536 0.022214 -1.022370 0.022853 -88.280762 \n", - "1 1 0.022005 0.022187 -1.019740 0.020231 -88.241707 \n", - "2 2 0.022240 0.022005 -1.019401 0.019893 -88.170067 \n", - "3 3 0.021589 0.022578 -1.018177 0.018667 -88.250031 \n", - "4 4 0.022005 0.023763 -1.014323 0.016848 -88.130775 \n", - "... ... ... ... ... ... ... \n", - "43325 43325 -0.008333 -0.023620 -1.006901 0.007224 -88.595741 \n", - "43326 43326 0.048730 -0.076725 -0.953776 0.056839 -85.352219 \n", - "43327 43327 0.387370 0.793151 -0.402214 0.069961 -24.097908 \n", - "43328 43328 0.801953 0.501589 -0.040937 0.045489 -2.113776 \n", - "43329 43329 0.792144 0.170486 -0.413108 0.063103 -16.771301 \n", - "\n", - " non-wear_flag light battery_voltage time_of_day weekday \\\n", - "0 0.0 53.000000 4188.000000 56940000000000 4 \n", - "1 0.0 51.666668 4188.166504 56945000000000 4 \n", - "2 0.0 50.333332 4188.333496 56950000000000 4 \n", - "3 0.0 50.500000 4188.500000 56955000000000 4 \n", - "4 0.0 33.166668 4181.000000 57235000000000 4 \n", - "... ... ... ... ... ... \n", - "43325 0.0 0.000000 3824.000000 57580000000000 6 \n", - "43326 0.0 0.000000 3824.000000 57640000000000 6 \n", - "43327 0.0 0.000000 3824.000000 57645000000000 6 \n", - "43328 0.0 0.000000 3824.000000 57650000000000 6 \n", - "43329 0.0 0.000000 3824.000000 57655000000000 6 \n", - "\n", - " quarter relative_date_PCIAT \n", - "0 3 41.0 \n", - "1 3 41.0 \n", - "2 3 41.0 \n", - "3 3 41.0 \n", - "4 3 41.0 \n", - "... ... ... \n", - "43325 3 85.0 \n", - "43326 3 85.0 \n", - "43327 3 85.0 \n", - "43328 3 85.0 \n", - "43329 3 85.0 \n", - "\n", - "[43330 rows x 13 columns]" + "(4940, 113)" ] }, - "execution_count": 20, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_acti_00115b9f = pd.read_parquet(\"../../data/series_test.parquet/id=00115b9f/part-0.parquet\")\n", - "df_acti_00115b9f" + "df_merge.shape" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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1500115b9f57.0-0.040797-0.030469-0.0513020.005615-0.603054-0.567995-0.6411460.023462...0.0787440.02712030.58354613.0982954000.0000003999.666667NaTNaNNaNNaN
1600115b9f58.00.1308030.332656-0.0343100.144432-0.557835-0.244167-0.9635680.240134...0.0007500.0286206.99727714.7642513994.0000003994.092122NaTNaNNaNNaN
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1900115b9f62.0-0.0230020.073437-0.1103910.060534-0.372481-0.253021-0.6190100.111049...0.0102690.0041365.8726125.8567063974.3928223974.361572NaTNaNNaNNaN
2000115b9f63.0-0.3361570.358724-0.4907290.236703-0.4725030.523620-0.6770830.351616...0.0010250.0113335.5689125.5842723970.0000003969.288656NaTNaNNaNNaN
2100115b9f64.0-0.328609-0.326224-0.3330400.003841-0.498670-0.494857-0.5061520.006481...0.0227060.0089185.3112945.3070753963.4731453963.824382NaTNaNNaNNaN
2200115b9f65.0-0.4216790.027214-0.7951300.222457-0.708406-0.327396-0.9259370.181496...0.0030220.0233085.0410195.0412323958.0000003958.157715NaTNaNNaNNaN
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3300115b9f79.0-0.252600-0.067318-0.4412500.0529100.0987850.350417-0.0933850.055112...0.0238700.0258782.6856411.7578153859.0000003859.656576NaTNaNNaNNaN
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40 rows × 33 columns

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" - ], "text/plain": [ - " id relative_date_PCIAT X_mean X_max X_min X_std \\\n", - "0 00115b9f 41.0 -0.388967 0.990990 -1.035964 0.414956 \n", - "1 00115b9f 42.0 -0.530206 0.978620 -1.746094 0.369632 \n", - "2 00115b9f 43.0 -0.344150 0.794297 -0.959557 0.393514 \n", - "3 00115b9f 44.0 -0.510063 0.976335 -1.044531 0.413122 \n", - "4 00115b9f 45.0 0.014500 0.015430 0.013594 0.000745 \n", - "5 00115b9f 46.0 0.023186 1.507865 -1.138203 0.522909 \n", - "6 00115b9f 47.0 -0.440468 0.985729 -0.973490 0.349583 \n", - "7 00115b9f 48.0 0.047782 0.537917 -0.586016 0.152971 \n", - "8 00115b9f 49.0 0.061023 0.072396 0.038672 0.011507 \n", - "9 00115b9f 50.0 -0.196590 1.007214 -0.971667 0.359093 \n", - "10 00115b9f 51.0 0.014078 1.031198 -0.974766 0.528951 \n", - "11 00115b9f 52.0 -0.557736 1.024766 -1.112422 0.416955 \n", - "12 00115b9f 53.0 -0.383140 1.023177 -1.366094 0.412537 \n", - "13 00115b9f 55.0 -0.036465 0.047461 -0.874870 0.236729 \n", - "14 00115b9f 56.0 -0.309073 0.744062 -0.929974 0.351441 \n", - "15 00115b9f 57.0 -0.040797 -0.030469 -0.051302 0.005615 \n", - "16 00115b9f 58.0 0.130803 0.332656 -0.034310 0.144432 \n", - "17 00115b9f 60.0 0.157338 0.205365 0.142057 0.013348 \n", - "18 00115b9f 61.0 0.188452 0.737370 -0.409583 0.261289 \n", - "19 00115b9f 62.0 -0.023002 0.073437 -0.110391 0.060534 \n", - "20 00115b9f 63.0 -0.336157 0.358724 -0.490729 0.236703 \n", - "21 00115b9f 64.0 -0.328609 -0.326224 -0.333040 0.003841 \n", - "22 00115b9f 65.0 -0.421679 0.027214 -0.795130 0.222457 \n", - "23 00115b9f 67.0 0.160379 0.315104 -0.455697 0.178714 \n", - "24 00115b9f 68.0 0.083704 0.229609 0.016406 0.070305 \n", - "25 00115b9f 70.0 0.044238 0.045085 0.043620 0.000759 \n", - "26 00115b9f 71.0 0.064368 0.262891 -0.019141 0.060417 \n", - "27 00115b9f 72.0 0.176120 0.483594 -0.138021 0.090025 \n", - "28 00115b9f 73.0 0.170306 0.243359 0.038568 0.100488 \n", - "29 00115b9f 74.0 0.252443 0.562109 0.105651 0.208730 \n", - "30 00115b9f 76.0 0.160009 0.657096 -0.021875 0.182600 \n", - "31 00115b9f 77.0 0.580753 0.624089 0.559082 0.037530 \n", - "32 00115b9f 78.0 -0.239610 0.999089 -0.906589 0.256877 \n", - "33 00115b9f 79.0 -0.252600 -0.067318 -0.441250 0.052910 \n", - "34 00115b9f 80.0 0.219254 1.007214 -0.261589 0.209314 \n", - "35 00115b9f 81.0 -0.529015 0.967474 -0.808125 0.244874 \n", - "36 00115b9f 82.0 -0.107070 0.977474 -0.763802 0.303965 \n", - "37 00115b9f 83.0 0.169182 0.615729 -0.820130 0.222137 \n", - "38 00115b9f 84.0 -0.002363 0.660964 -0.910911 0.368344 \n", - "39 00115b9f 85.0 -0.004926 0.999401 -0.768802 0.401226 \n", - "\n", - " Y_mean Y_max Y_min Y_std ... enmo_mean_lag1 \\\n", - "0 -0.187072 1.016823 -1.545990 0.504869 ... NaN \n", - "1 0.025359 1.002630 -2.905339 0.496983 ... 0.090690 \n", - "2 -0.130250 1.020573 -1.029193 0.404084 ... 0.065511 \n", - "3 0.076826 1.001562 -1.215625 0.459795 ... 0.049758 \n", - "4 -0.075251 -0.068203 -0.081315 0.006348 ... 0.036618 \n", - "5 0.076296 0.885156 -1.064375 0.331025 ... 0.008188 \n", - "6 -0.049812 1.666354 -1.758724 0.578938 ... 0.097723 \n", - "7 0.088663 0.428516 -0.046680 0.129417 ... 0.056882 \n", - "8 0.137763 0.233529 0.006055 0.061621 ... 0.027419 \n", - "9 0.294944 1.013776 -1.414115 0.486515 ... 0.015197 \n", - "10 0.005649 1.356276 -1.205287 0.602630 ... 0.037232 \n", - "11 0.215609 1.006693 -1.476875 0.441121 ... 0.036378 \n", - "12 0.223290 1.021536 -1.518021 0.523033 ... 0.033902 \n", - "13 0.018381 0.038724 -0.020508 0.010714 ... 0.042280 \n", - "14 0.195827 1.043411 -0.954687 0.461816 ... 0.001866 \n", - "15 -0.603054 -0.567995 -0.641146 0.023462 ... 0.078744 \n", - "16 -0.557835 -0.244167 -0.963568 0.240134 ... 0.000750 \n", - "17 -0.213604 -0.162161 -0.304505 0.042649 ... 0.006367 \n", - "18 -0.427888 0.536589 -0.811393 0.420879 ... 0.001115 \n", - "19 -0.372481 -0.253021 -0.619010 0.111049 ... 0.010269 \n", - "20 -0.472503 0.523620 -0.677083 0.351616 ... 0.001025 \n", - "21 -0.498670 -0.494857 -0.506152 0.006481 ... 0.022706 \n", - "22 -0.708406 -0.327396 -0.925937 0.181496 ... 0.003022 \n", - "23 -0.686562 0.063672 -0.932552 0.184937 ... 0.044197 \n", - "24 -0.344472 0.087109 -0.763307 0.343514 ... 0.015739 \n", - "25 0.092695 0.093424 0.092057 0.000688 ... 0.015503 \n", - "26 -0.014390 0.123880 -0.812630 0.296814 ... 0.000003 \n", - "27 -0.286178 0.096016 -0.900195 0.425747 ... 0.008616 \n", - "28 0.044990 0.057083 0.036458 0.008478 ... 0.022892 \n", - "29 -0.796549 -0.389974 -0.956250 0.273180 ... 0.001674 \n", - "30 -0.397389 0.136198 -1.118880 0.434522 ... 0.063462 \n", - "31 -0.477912 -0.467005 -0.499349 0.018566 ... 0.032445 \n", - "32 -0.061878 0.742865 -1.040208 0.192122 ... 0.029205 \n", - "33 0.098785 0.350417 -0.093385 0.055112 ... 0.023870 \n", - "34 -0.827333 0.834010 -0.981380 0.228439 ... 0.024558 \n", - "35 -0.278423 0.402500 -0.915391 0.175222 ... 0.019109 \n", - "36 -0.215488 0.559635 -0.886621 0.324548 ... 0.020838 \n", - "37 -0.010881 0.600521 -0.820208 0.127002 ... 0.028970 \n", - "38 -0.024244 0.289948 -0.994531 0.258073 ... 0.025312 \n", - "39 0.187363 0.992057 -0.571875 0.344520 ... 0.024068 \n", - "\n", - " enmo_mean_rolling3 light_mean_lag1 light_mean_rolling3 \\\n", - "0 NaN NaN NaN \n", - "1 NaN 33.471268 NaN \n", - "2 0.068653 40.340485 31.803000 \n", - "3 0.050629 21.597248 60.081188 \n", - "4 0.031521 118.305832 47.284031 \n", - "5 0.047509 1.949013 44.493234 \n", - "6 0.054264 13.224856 19.113361 \n", - "7 0.060674 42.166214 32.087346 \n", - "8 0.033166 40.870968 29.015409 \n", - "9 0.026616 4.009044 17.989583 \n", - "10 0.029602 9.088736 28.725869 \n", - "11 0.035838 73.079826 33.164764 \n", - "12 0.037520 17.325729 63.985850 \n", - "13 0.026016 101.551994 40.197262 \n", - "14 0.040963 1.714062 44.616534 \n", - "15 0.027120 30.583546 13.098295 \n", - "16 0.028620 6.997277 14.764251 \n", - "17 0.002744 6.711931 6.612601 \n", - "18 0.005917 6.128595 6.237712 \n", - "19 0.004136 5.872612 5.856706 \n", - "20 0.011333 5.568912 5.584272 \n", - "21 0.008918 5.311294 5.307075 \n", - "22 0.023308 5.041019 5.041232 \n", - "23 0.020986 4.771381 4.638867 \n", - "24 0.025147 4.104200 4.258496 \n", - "25 0.010415 3.899907 3.764638 \n", - "26 0.008041 3.289807 3.393761 \n", - "27 0.010503 2.991570 3.002709 \n", - "28 0.011061 2.726750 2.710015 \n", - "29 0.029343 2.411724 2.412484 \n", - "30 0.032527 2.098978 2.002676 \n", - "31 0.041704 1.497326 1.612034 \n", - "32 0.028507 1.239798 1.807588 \n", - "33 0.025878 2.685641 1.757815 \n", - "34 0.022513 1.348006 3.114460 \n", - "35 0.021502 5.309732 2.980471 \n", - "36 0.022972 2.283675 4.574565 \n", - "37 0.025040 6.130287 6.035167 \n", - "38 0.026116 9.691541 8.411575 \n", - "39 0.038967 9.412899 9.759213 \n", - "\n", - " battery_voltage_mean_lag1 battery_voltage_mean_rolling3 ds \\\n", - "0 NaN NaN NaT \n", - "1 4174.144531 NaN NaT \n", - "2 4155.183105 4156.394531 NaT \n", - "3 4139.855957 4140.654622 NaT \n", - "4 4126.924805 4126.784017 NaT \n", - "5 4113.571289 4115.227865 NaT \n", - "6 4105.187500 4104.084229 NaT \n", - "7 4093.493896 4094.034505 NaT \n", - "8 4083.422119 4086.275391 NaT \n", - "9 4081.910156 4078.350342 NaT \n", - "10 4069.718750 4070.316813 NaT \n", - "11 4059.321533 4055.616048 NaT \n", - "12 4037.807861 4040.777751 NaT \n", - "13 4025.203857 4022.670573 NaT \n", - "14 4005.000000 4010.067952 NaT \n", - "15 4000.000000 3999.666667 NaT \n", - "16 3994.000000 3994.092122 NaT \n", - "17 3988.276367 3986.989421 NaT \n", - "18 3978.691895 3980.453695 NaT \n", - "19 3974.392822 3974.361572 NaT \n", - "20 3970.000000 3969.288656 NaT \n", - "21 3963.473145 3963.824382 NaT \n", - "22 3958.000000 3958.157715 NaT \n", - "23 3953.000000 3950.731527 NaT \n", - "24 3941.194580 3943.722249 NaT \n", - "25 3936.972168 3933.722249 NaT \n", - "26 3923.000000 3925.297363 NaT \n", - "27 3915.919922 3915.201497 NaT \n", - "28 3906.684570 3903.963379 NaT \n", - "29 3889.285645 3892.656738 NaT \n", - "30 3882.000000 3879.475749 NaT \n", - "31 3867.141602 3871.380534 NaT \n", - "32 3865.000000 3863.713867 NaT \n", - "33 3859.000000 3859.656576 NaT \n", - "34 3854.969727 3854.220866 NaT \n", - "35 3848.692871 3848.689697 NaT \n", - "36 3842.406494 3843.359294 NaT \n", - "37 3838.978516 3838.816569 NaT \n", - "38 3835.064697 3834.653727 NaT \n", - "39 3829.917969 3829.770508 NaT \n", - "\n", - " enmo_forecast light_forecast battery_voltage_forecast \n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "17 NaN NaN NaN \n", - "18 NaN NaN NaN \n", - "19 NaN NaN NaN \n", - "20 NaN NaN NaN \n", - "21 NaN NaN NaN \n", - "22 NaN NaN NaN \n", - "23 NaN NaN NaN \n", - "24 NaN NaN NaN \n", - "25 NaN NaN NaN \n", - "26 NaN NaN NaN \n", - "27 NaN NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 NaN NaN NaN \n", - "31 NaN NaN NaN \n", - "32 NaN NaN NaN \n", - "33 NaN NaN NaN \n", - "34 NaN NaN NaN \n", - "35 NaN NaN NaN \n", - "36 NaN NaN NaN \n", - "37 NaN NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "\n", - "[40 rows x 33 columns]" + "(3960, 82)" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_feature[df_feature.id == \"00115b9f\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "df_train = pd.read_csv(\"../../data/childhealth.csv\")" + "df_train.shape" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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idBasic_Demos-Enroll_SeasonBasic_Demos-AgeBasic_Demos-SexCGAS-SeasonCGAS-CGAS_ScorePhysical-SeasonPhysical-BMIPhysical-HeightPhysical-Weight...PCIAT-PCIAT_18PCIAT-PCIAT_19PCIAT-PCIAT_20PCIAT-PCIAT_TotalSDS-SeasonSDS-SDS_Total_RawSDS-SDS_Total_TPreInt_EduHx-SeasonPreInt_EduHx-computerinternet_hoursdaysii
5001f3379Spring131Winter50.0Summer22.27995259.5112.2...1.02.01.034.0Summer40.056.0Spring0.01.0
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" - ], "text/plain": [ - " id Basic_Demos-Enroll_Season Basic_Demos-Age Basic_Demos-Sex \\\n", - "5 001f3379 Spring 13 1 \n", - "\n", - " CGAS-Season CGAS-CGAS_Score Physical-Season Physical-BMI Physical-Height \\\n", - "5 Winter 50.0 Summer 22.279952 59.5 \n", - "\n", - " Physical-Weight ... PCIAT-PCIAT_18 PCIAT-PCIAT_19 PCIAT-PCIAT_20 \\\n", - "5 112.2 ... 1.0 2.0 1.0 \n", - "\n", - " PCIAT-PCIAT_Total SDS-Season SDS-SDS_Total_Raw SDS-SDS_Total_T \\\n", - "5 34.0 Summer 40.0 56.0 \n", - "\n", - " PreInt_EduHx-Season PreInt_EduHx-computerinternet_hoursday sii \n", - "5 Spring 0.0 1.0 \n", - "\n", - "[1 rows x 82 columns]" + "(1976, 32)" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_train[df_train['id'] == \"001f3379\"]" + "feature_table.shape" ] }, { diff --git a/project_config.yml b/project_config.yml index 86d04b5..479c23c 100644 --- a/project_config.yml +++ b/project_config.yml @@ -4,11 +4,11 @@ catalog_name: "mlops_students" schema_name: "javedhassi" -# Parameters for data processing -data_cleaning_parameters: - drop_threshold: 0.8 # Threshold for dropping columns with missing values - lower_bound: 0.05 # Lower quantile bound for outlier detection - upper_bound: 0.95 # Upper quantile bound for outlier detection +# # Parameters for data processing +# data_cleaning_parameters: +# drop_threshold: 0.8 # Threshold for dropping columns with missing values +# lower_bound: 0.05 # Lower quantile bound for outlier detection +# upper_bound: 0.95 # Upper quantile bound for outlier detection # Parameters for model training model_parameters: diff --git a/src/childHealth/utils.py b/src/childHealth/utils.py index 8f62537..9b77bca 100644 --- a/src/childHealth/utils.py +++ b/src/childHealth/utils.py @@ -4,25 +4,25 @@ import matplotlib.pyplot as plt import numpy as np -def remove_outliers(df: pd.DataFrame, num_features: list, lower_bound: float = 0.05, upper_bound: float = 0.95) -> pd.DataFrame: - """ - Remove outliers from the DataFrame using the Interquartile Range (IQR) method. +# def remove_outliers(df: pd.DataFrame, num_features: list, lower_bound: float = 0.05, upper_bound: float = 0.95) -> pd.DataFrame: +# """ +# Remove outliers from the DataFrame using the Interquartile Range (IQR) method. - Parameters: - df (pd.DataFrame): The input DataFrame. - num_features (list): List of numerical feature names. - lower_bound (float): Lower quantile bound. - upper_bound (float): Upper quantile bound. +# Parameters: +# df (pd.DataFrame): The input DataFrame. +# num_features (list): List of numerical feature names. +# lower_bound (float): Lower quantile bound. +# upper_bound (float): Upper quantile bound. - Returns: - pd.DataFrame: The DataFrame with outliers removed. - """ - for feature in num_features: - Q1 = df[feature].quantile(lower_bound) - Q3 = df[feature].quantile(upper_bound) - IQR = Q3 - Q1 - df = df[~((df[feature] < (Q1 - 1.5 * IQR)) | (df[feature] > (Q3 + 1.5 * IQR)))] - return df +# Returns: +# pd.DataFrame: The DataFrame with outliers removed. +# """ +# for feature in num_features: +# Q1 = df[feature].quantile(lower_bound) +# Q3 = df[feature].quantile(upper_bound) +# IQR = Q3 - Q1 +# df = df[~((df[feature] < (Q1 - 1.5 * IQR)) | (df[feature] > (Q3 + 1.5 * IQR)))] +# return df def visualize_results(y_test, y_pred): """ From 1f1a36ad1cc0b044e2569c0d0e25d0ab538c040e Mon Sep 17 00:00:00 2001 From: javedhassans Date: Sun, 3 Nov 2024 05:48:48 +0100 Subject: [PATCH 15/36] added featue table code --- notebooks/week2/01.prepare_dataset.py | 2 +- notebooks/week2/03.log_and_register_model.py | 2 +- .../week2/04.log_and_register_custom_model.py | 2 +- src/childHealth/data_processor.py | 170 ++++++++++------- src/childHealth/feature_engineering.py | 179 ++++++++++++++++++ src/childHealth/utils.py | 20 -- 6 files changed, 286 insertions(+), 89 deletions(-) create mode 100644 src/childHealth/feature_engineering.py diff --git a/notebooks/week2/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py index 52045cf..0a5eb54 100644 --- a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -11,7 +11,7 @@ # COMMAND ---------- # Load project configuration from YAML file -config = ProjectConfig.from_yaml(config_path="../project_config.yml") +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") # COMMAND ---------- diff --git a/notebooks/week2/03.log_and_register_model.py b/notebooks/week2/03.log_and_register_model.py index 0e1ce82..2a4fcb4 100644 --- a/notebooks/week2/03.log_and_register_model.py +++ b/notebooks/week2/03.log_and_register_model.py @@ -22,7 +22,7 @@ # COMMAND ---------- -config = ProjectConfig.from_yaml(config_path="../project_config.yml") +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") # Extract configuration details num_features = config.num_features diff --git a/notebooks/week2/04.log_and_register_custom_model.py b/notebooks/week2/04.log_and_register_custom_model.py index 990aad8..f5dc724 100644 --- a/notebooks/week2/04.log_and_register_custom_model.py +++ b/notebooks/week2/04.log_and_register_custom_model.py @@ -23,7 +23,7 @@ # COMMAND ---------- -config = ProjectConfig.from_yaml(config_path="../project_config.yml") +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") # Extract configuration details num_features = config.num_features diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index 07108bc..d840286 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -1,88 +1,126 @@ import pandas as pd +import numpy as np from childHealth.config import ProjectConfig -from childHealth.utils import remove_outliers from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.impute import SimpleImputer from pyspark.sql.functions import current_timestamp, to_utc_timestamp from pyspark.sql import SparkSession -import yaml -class DataProcessor: - """ - Class to handle data processing tasks such as splitting data and saving to Databricks catalog. - """ - def __init__(self, pandas_df: pd.DataFrame, config: ProjectConfig): - """ - Initialize the DataProcessor with a DataFrame and configuration settings. - - Parameters: - pandas_df (pd.DataFrame): The DataFrame to process. - config (ProjectConfig): Configuration settings. - """ - self.df = pandas_df # Store the DataFrame as self.df - self.config = config # Store the configuration - def preprocess(self): - """ - Perform data preprocessing tasks including handling missing values, outliers, - and data type conversions. - - Returns: - pd.DataFrame: The preprocessed DataFrame. - """ +class TrainDataProcessor: + def __init__(self, train_df: pd.DataFrame, config: ProjectConfig): + self.train_df = train_df + self.config = config + self.num_features = self.config['num_features'] + self.cat_features = self.config['cat_features'] + self.target = self.config['target'] + + def preprocess_data(self): + """Preprocess the train dataset by handling missing values and data type conversions.""" + self.handle_missing_values() + self.convert_data_types() + return self.train_df + + def handle_missing_values(self): + """Handle missing values in the train dataset.""" + # Fill numeric columns with mean + numeric_cols = self.train_df[self.num_features] + imputer = SimpleImputer(strategy='mean') + self.train_df[self.num_features] = imputer.fit_transform(numeric_cols) + + # Fill categorical columns with mode + for col in self.cat_features: + self.train_df[col].fillna(self.train_df[col].mode()[0], inplace=True) + + def convert_data_types(self): + """Convert categorical columns to appropriate data types.""" + # Convert 'Sex' to binary encoding if it's part of numerical features + if 'Basic_Demos-Sex' in self.num_features: + self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({'Male': 1, 'Female': 0}) + + def feature_engineering(self): + """Perform feature engineering to create new features.""" + self.add_age_groups() + self.one_hot_encode_seasons() + self.calculate_behavioral_scores() + self.add_interaction_features() + return self.train_df + + def add_age_groups(self): + """Add age groups based on age.""" + if 'Basic_Demos-Age' in self.num_features: + self.train_df['Age_Group'] = pd.cut(self.train_df['Basic_Demos-Age'], bins=[0, 12, 17, 25], labels=['Child', 'Teen', 'Young Adult']) + + def one_hot_encode_seasons(self): + """One-hot encode season columns.""" + for col in self.cat_features: + if 'Season' in col: + one_hot = pd.get_dummies(self.train_df[col], prefix=col) + self.train_df = pd.concat([self.train_df, one_hot], axis=1) + + def calculate_behavioral_scores(self): + """Calculate behavioral and psychological indicators.""" + # Bin PCIAT total score + if 'PCIAT-PCIAT_Total' in self.num_features: + self.train_df['PCIAT_Bin'] = pd.cut(self.train_df['PCIAT-PCIAT_Total'], bins=[0, 20, 40, 60], labels=['Mild', 'Moderate', 'Severe']) - # Convert numerical features to numeric type - num_features = self.config.num_features - for col in num_features: - self.df[col] = pd.to_numeric(self.df[col], errors='coerce') - - # Remove outliers in numerical features - lower_bound = self.config.data_cleaning_parameters['lower_bound'] - upper_bound = self.config.data_cleaning_parameters['upper_bound'] - self.df = remove_outliers(self.df, num_features, lower_bound, upper_bound) - - # Impute missing values for numerical features with the median - for col in num_features: - self.df[col].fillna(self.df[col].median(), inplace=True) - - # Convert categorical features to category type - cat_features = self.config.cat_features - for col in cat_features: - self.df[col] = self.df[col].astype('category') - - # Impute missing values for categorical features with the most frequent value - for col in cat_features: - if self.df[col].isnull().any(): - self.df[col].fillna(self.df[col].mode()[0], inplace=True) - - # Handle missing values in the target column by replacing them with a specific value (e.g., 4.0) - target = self.config.target + # Categorize internet use + if 'PreInt_EduHx-computerinternet_hoursday' in self.num_features: + self.train_df['Internet_Use_Category'] = pd.cut(self.train_df['PreInt_EduHx-computerinternet_hoursday'], bins=[0, 1, 3, 6, np.inf], labels=['Low', 'Moderate', 'High', 'Very High']) + + def add_interaction_features(self): + """Add interaction features, such as age-adjusted scores.""" + # Age-adjusted CGAS Score + if 'CGAS-CGAS_Score' in self.num_features and 'Basic_Demos-Age' in self.num_features: + self.train_df['Age_Adjusted_CGAS'] = self.train_df['CGAS-CGAS_Score'] / self.train_df['Basic_Demos-Age'] + + # BMI Categories + if 'Physical-BMI' in self.num_features: + self.train_df['BMI_Category'] = pd.cut(self.train_df['Physical-BMI'], bins=[0, 18.5, 25, 30, np.inf], labels=['Underweight', 'Normal', 'Overweight', 'Obese']) + + def scale_numeric_features(self): + """Scale numeric features in the final dataset.""" + scaler = StandardScaler() + self.train_df[self.num_features] = scaler.fit_transform(self.train_df[self.num_features]) + + def handle_missing_target(self): + """Handle missing values in the target column by replacing them with a specific value (e.g., 4.0).""" new_value = 4.0 # New value to replace NaNs in the target column - self.df[target].fillna(new_value, inplace=True) - - # Keep only relevant columns - relevant_columns = cat_features + num_features + [target, 'id'] - self.df = self.df[relevant_columns] - - return self.df + self.train_df[self.target].fillna(new_value, inplace=True) + + def keep_relevant_columns(self): + """Keep only relevant columns.""" + relevant_columns = self.cat_features + self.num_features + [self.target, 'id'] + self.train_df = self.train_df[relevant_columns] + + def process(self): + """Run the complete processing pipeline.""" + self.preprocess_data() + self.feature_engineering() + self.scale_numeric_features() + self.handle_missing_target() + self.keep_relevant_columns() + return self.train_df def split_data(self, test_size=0.2, random_state=42): """ - Split the DataFrame (self.df) into training and test sets. - + Split the DataFrame (self.train_df) into training and test sets. + Parameters: test_size (float): Proportion of the dataset to include in the test split. random_state (int): Random seed for reproducibility. - + Returns: tuple: A tuple containing the training and test sets. """ - train_set, test_set = train_test_split(self.df, test_size=test_size, random_state=random_state) + train_set, test_set = train_test_split(self.train_df, test_size=test_size, random_state=random_state) return train_set, test_set def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark: SparkSession): """ Save the train and test sets into Databricks tables. - + Parameters: train_set (pd.DataFrame): The training set. test_set (pd.DataFrame): The test set. @@ -91,14 +129,14 @@ def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark # Add timestamp column to train and test sets train_set_with_timestamp = spark.createDataFrame(train_set).withColumn( "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) - + test_set_with_timestamp = spark.createDataFrame(test_set).withColumn( "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) # Save train set to Databricks table train_set_with_timestamp.write.mode("append").saveAsTable( f"{self.config.catalog_name}.{self.config.schema_name}.train_set") - + # Save test set to Databricks table test_set_with_timestamp.write.mode("append").saveAsTable( f"{self.config.catalog_name}.{self.config.schema_name}.test_set") @@ -106,6 +144,6 @@ def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark # Enable Change Data Feed for train and test sets spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.train_set " "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") - + spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.test_set " - "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") + "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") \ No newline at end of file diff --git a/src/childHealth/feature_engineering.py b/src/childHealth/feature_engineering.py new file mode 100644 index 0000000..c39330c --- /dev/null +++ b/src/childHealth/feature_engineering.py @@ -0,0 +1,179 @@ +import os +import pandas as pd +import numpy as np +from concurrent.futures import ThreadPoolExecutor, as_completed +from tqdm import tqdm +from pyspark.sql import SparkSession +from childHealth.data_processor import ProjectConfig + +import yaml +from databricks import feature_engineering +from pyspark.sql import SparkSession +from databricks.sdk import WorkspaceClient + +from datetime import datetime +from databricks.feature_engineering import FeatureFunction, FeatureLookup + + +# Initialize the Databricks session and clients +spark = SparkSession.builder.getOrCreate() +workspace = WorkspaceClient() +fe = feature_engineering.FeatureEngineeringClient() + +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") + +# Extract configuration details +parameters = config.parameters +catalog_name = config.catalog_name +schema_name = config.schema_name + +class ActigraphAggregation: + def __init__(self, root_dir): + self.root_dir = root_dir + + def load_data(self, participant_id): + file_path = os.path.join(self.root_dir, f"id={participant_id}", "part-0.parquet") + if not os.path.exists(file_path): + print(f"File not found for participant {participant_id}") + return pd.DataFrame() # Return an empty DataFrame if file doesn't exist + data = pd.read_parquet(file_path) + data['id'] = participant_id + return data + + def aggregate_actigraphy(self, data): + aggregated_df = data.groupby('id').agg({ + 'X': ['mean', 'std', 'max', 'min'], + 'Y': ['mean', 'std', 'max', 'min'], + 'Z': ['mean', 'std', 'max', 'min'], + 'enmo': ['mean', 'std', 'max', 'min'], + 'anglez': 'mean', + 'non-wear_flag': 'sum', + 'light': ['mean', 'std', 'max', 'min'], + 'battery_voltage': 'mean', + }).reset_index() + + aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values] + return aggregated_df + + def temporal_aggregations(self, data): + data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend') + + conditions = [ + (data['time_of_day'] < 6 * 3600), + (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), + (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), + (data['time_of_day'] >= 18 * 3600) + ] + choices = ['night', 'morning', 'afternoon', 'evening'] + data['time_period'] = np.select(conditions, choices, default='unknown') + + temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({ + 'enmo': 'mean', + 'light': 'mean', + 'non-wear_flag': 'sum' + }).unstack(fill_value=0) + temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values] + + return temporal_agg.reset_index() + + def activity_ratios(self, data): + total_time = data.groupby('id').size().rename('total_time') + non_wear_time = data.groupby('id')['non-wear_flag'].sum().rename('non_wear_time') + + ratios = pd.concat([total_time, non_wear_time], axis=1) + ratios['non_wear_ratio'] = ratios['non_wear_time'] / ratios['total_time'] + + return ratios[['non_wear_ratio']].reset_index() + + def process_participant_data(self, participant_id): + data = self.load_data(participant_id) + if data.empty: + return pd.DataFrame() + + aggregate_data = self.aggregate_actigraphy(data) + temporal_data = self.temporal_aggregations(data) + ratio_data = self.activity_ratios(data) + + participant_data = aggregate_data.merge(temporal_data, on='id', how='left') + participant_data = participant_data.merge(ratio_data, on='id', how='left') + return participant_data + + def process_all_participants(self): + all_data = [] + with ThreadPoolExecutor() as executor: + futures = [] + for id_folder in os.listdir(self.root_dir): + if not id_folder.startswith('id='): + continue + participant_id = id_folder.split('=')[-1] + futures.append(executor.submit(self.process_participant_data, participant_id)) + + for future in tqdm(as_completed(futures), total=len(futures), desc="Processing participants"): + participant_data = future.result() + if not participant_data.empty: + all_data.append(participant_data) + + feature_table = pd.concat(all_data, ignore_index=True) + return feature_table + + def save_to_spark_table(self, feature_table): + spark_df = spark.createDataFrame(feature_table) + spark_df.createOrReplaceTempView("temp_feature_table") + + spark.sql(f""" + CREATE TABLE IF NOT EXISTS {catalog_name}.{schema_name}.actigraph_features ( + id STRING, + X_mean DOUBLE, + X_std DOUBLE, + X_max DOUBLE, + X_min DOUBLE, + Y_mean DOUBLE, + Y_std DOUBLE, + Y_max DOUBLE, + Y_min DOUBLE, + Z_mean DOUBLE, + Z_std DOUBLE, + Z_max DOUBLE, + Z_min DOUBLE, + enmo_mean DOUBLE, + enmo_std DOUBLE, + enmo_max DOUBLE, + enmo_min DOUBLE, + anglez_mean DOUBLE, + non_wear_flag_sum DOUBLE, + light_mean DOUBLE, + light_std DOUBLE, + light_max DOUBLE, + light_min DOUBLE, + battery_voltage_mean DOUBLE, + weekday_flag_weekday_morning_mean DOUBLE, + weekday_flag_weekday_afternoon_mean DOUBLE, + weekday_flag_weekday_evening_mean DOUBLE, + weekday_flag_weekday_night_mean DOUBLE, + weekday_flag_weekend_morning_mean DOUBLE, + weekday_flag_weekend_afternoon_mean DOUBLE, + weekday_flag_weekend_evening_mean DOUBLE, + weekday_flag_weekend_night_mean DOUBLE, + non_wear_ratio DOUBLE + ) + """) + + spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_features ADD CONSTRAINT actigraph_pk PRIMARY KEY(id)") + spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_features SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") + + spark.sql(f""" + INSERT INTO {catalog_name}.{schema_name}.actigraph_features + SELECT * FROM temp_feature_table + """) + +# # Initialize with the root directory containing participant data +# aggregator = ActigraphAggregation(root_dir="../../data/series_train.parquet/") + +# # Process all participants and get the final aggregated feature table +# feature_table = aggregator.process_all_participants() + +# # Save the feature table to Spark +# aggregator.save_to_spark_table(feature_table) + +# # Inspect the feature table +# print(feature_table.head()) \ No newline at end of file diff --git a/src/childHealth/utils.py b/src/childHealth/utils.py index 9b77bca..57b32ed 100644 --- a/src/childHealth/utils.py +++ b/src/childHealth/utils.py @@ -4,26 +4,6 @@ import matplotlib.pyplot as plt import numpy as np -# def remove_outliers(df: pd.DataFrame, num_features: list, lower_bound: float = 0.05, upper_bound: float = 0.95) -> pd.DataFrame: -# """ -# Remove outliers from the DataFrame using the Interquartile Range (IQR) method. - -# Parameters: -# df (pd.DataFrame): The input DataFrame. -# num_features (list): List of numerical feature names. -# lower_bound (float): Lower quantile bound. -# upper_bound (float): Upper quantile bound. - -# Returns: -# pd.DataFrame: The DataFrame with outliers removed. -# """ -# for feature in num_features: -# Q1 = df[feature].quantile(lower_bound) -# Q3 = df[feature].quantile(upper_bound) -# IQR = Q3 - Q1 -# df = df[~((df[feature] < (Q1 - 1.5 * IQR)) | (df[feature] > (Q3 + 1.5 * IQR)))] -# return df - def visualize_results(y_test, y_pred): """ Visualize the results of predictions against actual values. From 6e1a9ed0a012f3dd4150b1a739b881432e15ec4d Mon Sep 17 00:00:00 2001 From: javedhassans Date: Tue, 5 Nov 2024 11:38:07 +0100 Subject: [PATCH 16/36] modifed childHealth_model.py --- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 8468 -> 0 bytes notebooks/week1/00.dataexploration.ipynb | 2 +- notebooks/week2/mlflow_experiment.json | 15 --- notebooks/week2/run_info.json | 40 -------- src/childHealth/childhealth_model.py | 86 ++++++++++++++++-- 5 files changed, 79 insertions(+), 64 deletions(-) delete mode 100644 notebooks/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl delete mode 100644 notebooks/week2/mlflow_experiment.json delete mode 100644 notebooks/week2/run_info.json diff --git a/notebooks/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl deleted file mode 100644 index 9df5c44b4dff97f745ac7b5b3c3e6b7e50df0a24..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 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b/notebooks/week1/00.dataexploration.ipynb index 0f2183c..023fe44 100644 --- a/notebooks/week1/00.dataexploration.ipynb +++ b/notebooks/week1/00.dataexploration.ipynb @@ -674,7 +674,7 @@ " self.target = self.config['target']\n", "\n", " def load_config(self, config_path):\n", - " \"\"\"Load the project configuration file.\"\"\"\n", + " \"\"\"Load the project configuration file.\"\"\" \n", " with open(config_path, 'r') as file:\n", " config = yaml.safe_load(file)\n", " return config\n", diff --git a/notebooks/week2/mlflow_experiment.json b/notebooks/week2/mlflow_experiment.json deleted file mode 100644 index e01bebf..0000000 --- a/notebooks/week2/mlflow_experiment.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "_experiment_id": "941761294708082", - "_name": "/Shared/child-health-basic", - "_artifact_location": "dbfs:/databricks/mlflow-tracking/941761294708082", - "_lifecycle_stage": "active", - "_tags": { - "mlflow.experiment.sourceName": "/Shared/child-health-basic", - "mlflow.ownerId": "4619079692408656", - "mlflow.ownerEmail": "javedhassi@gmail.com", - "mlflow.experimentType": "MLFLOW_EXPERIMENT", - "repository_name": "child-health" - }, - "_creation_time": 1730287471358, - "_last_update_time": 1730287471358 -} \ No newline at end of file diff --git a/notebooks/week2/run_info.json b/notebooks/week2/run_info.json deleted file mode 100644 index 641be4c..0000000 --- a/notebooks/week2/run_info.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "info": { - "artifact_uri": "dbfs:/databricks/mlflow-tracking/941761294708082/bfef07e5f0d849feb284dd01a313c5fc/artifacts", - "end_time": 1730287531114, - "experiment_id": "941761294708082", - "lifecycle_stage": "active", - "run_id": "bfef07e5f0d849feb284dd01a313c5fc", - "run_name": "demo-run", - "run_uuid": "bfef07e5f0d849feb284dd01a313c5fc", - "start_time": 1730287527895, - "status": "FINISHED", - "user_id": "" - }, - "data": { - "metrics": { - "metric1": 1.0, - "metric2": 2.0 - }, - "params": { - "type": "demo" - }, - "tags": { - "branch": "week1-2", - "git_sha": "830c17d988742482b639aec763ec731ac2dd4da5", - "mlflow.databricks.cluster.id": "1019-142832-p3qdyln4", - "mlflow.databricks.cluster.info": "{\"cluster_name\":\"Javed Sabu's Cluster\",\"spark_version\":\"15.4.x-cpu-ml-scala2.12\",\"node_type_id\":\"i3.xlarge\",\"driver_node_type_id\":\"i3.xlarge\",\"autotermination_minutes\":120,\"disk_spec\":{},\"num_workers\":0}", - "mlflow.databricks.cluster.libraries": "{\"installable\":[],\"redacted\":[]}", - "mlflow.databricks.notebook.commandID": "1730285361524_7172945854433869748_8b14c6a82a04480b8858bab9ca4fc75b", - "mlflow.note.content": "demo run", - "mlflow.runColor": "#5387dd", - "mlflow.runName": "demo-run", - "mlflow.source.name": "/Users/javedhassansabu/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/ipykernel_launcher.py", - "mlflow.source.type": "LOCAL", - "mlflow.user": "javedhassi@gmail.com" - } - }, - "inputs": { - "dataset_inputs": [] - } -} \ No newline at end of file diff --git a/src/childHealth/childhealth_model.py b/src/childHealth/childhealth_model.py index 1e9a5e9..46561cd 100644 --- a/src/childHealth/childhealth_model.py +++ b/src/childHealth/childhealth_model.py @@ -1,6 +1,13 @@ -from sklearn.ensemble import RandomForestRegressor +import yaml +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.metrics import classification_report, accuracy_score +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.pipeline import Pipeline -from sklearn.metrics import mean_squared_error, r2_score +import lightgbm as lgb class ChildHealthModel: def __init__(self, preprocessor, config): @@ -13,9 +20,12 @@ def __init__(self, preprocessor, config): self.config = config self.model = Pipeline(steps=[ ('preprocessor', preprocessor), - ('regressor', RandomForestRegressor( + ('regressor', lgb.LGBMClassifier( n_estimators=config['model_parameters']['n_estimators'], max_depth=config['model_parameters']['max_depth'], + learning_rate=config['model_parameters']['learning_rate'], + objective='multiclass', + num_class=config['model_parameters']['num_class'], random_state=42 )) ]) @@ -44,12 +54,12 @@ def evaluate(self, X_test, y_test): :param X_test: Test features. :param y_test: Test target. - :return: Mean squared error and R^2 score. + :return: Classification report and accuracy score. """ y_pred = self.predict(X_test) - mse = mean_squared_error(y_test, y_pred) - r2 = r2_score(y_test, y_pred) - return mse, r2 + report = classification_report(y_test, y_pred) + accuracy = accuracy_score(y_test, y_pred) + return report, accuracy def get_feature_importance(self): """ @@ -59,4 +69,64 @@ def get_feature_importance(self): """ feature_importance = self.model.named_steps['regressor'].feature_importances_ feature_names = self.model.named_steps['preprocessor'].get_feature_names_out() - return feature_importance, feature_names \ No newline at end of file + return feature_importance, feature_names + +# def save_model(self, model_path='lgbm_model.pkl'): +# """Save the trained model.""" +# joblib.dump(self.model, model_path) +# print(f"Model saved as {model_path}") + +# def load_model(self, model_path='lgbm_model.pkl'): +# """Load a saved model.""" +# self.model = joblib.load(model_path) +# print(f"Model loaded from {model_path}") + +# if __name__ == "__main__": +# # Load configuration +# config_path = 'project_config.yml' +# with open(config_path, 'r') as file: +# config = yaml.safe_load(file) + +# # Load and preprocess data +# train_path = 'train.csv' +# data = pd.read_csv(train_path) +# X = data[config['num_features'] + config['cat_features']] +# y = data[config['target']] + +# # Define preprocessor +# preprocessor = ColumnTransformer( +# transformers=[ +# ('num', StandardScaler(), config['num_features']), +# ('cat', OneHotEncoder(drop='first'), config['cat_features']) +# ] +# ) + +# # Initialize model class +# child_health_model = ChildHealthModel(preprocessor, config) + +# # Split data +# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42) + +# # Train model +# child_health_model.train(X_train, y_train) + +# # Evaluate model +# report, accuracy = child_health_model.evaluate(X_test, y_test) +# print("Evaluation Results:") +# print(report) +# print("Accuracy:", accuracy) + +# # Display feature importance +# feature_importance, feature_names = child_health_model.get_feature_importance() +# feature_importance_df = pd.DataFrame({'feature': feature_names, 'importance': feature_importance}) +# feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) + +# plt.figure(figsize=(10, 8)) +# plt.barh(feature_importance_df['feature'], feature_importance_df['importance'], color='skyblue') +# plt.xlabel('Importance') +# plt.title('Feature Importance') +# plt.gca().invert_yaxis() +# plt.show() + +# # Save model +# child_health_model.save_model() \ No newline at end of file From 335569aab95ba01609aebb0ed4f62c5bd5b5cd40 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Tue, 5 Nov 2024 11:39:20 +0100 Subject: [PATCH 17/36] deleted mlruns --- notebooks/mlruns/0/meta.yaml | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 notebooks/mlruns/0/meta.yaml diff --git a/notebooks/mlruns/0/meta.yaml b/notebooks/mlruns/0/meta.yaml deleted file mode 100644 index edc1ce1..0000000 --- a/notebooks/mlruns/0/meta.yaml +++ /dev/null @@ -1,6 +0,0 @@ -artifact_location: file:///Users/javedhassansabu/marvelous-databricks-course-javedhassans/notebooks/mlruns/0 -creation_time: 1730289102282 -experiment_id: '0' -last_update_time: 1730289102282 -lifecycle_stage: active -name: Default From 3da76c3af0c54c61ddad723ead8b6148af9c2532 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Tue, 5 Nov 2024 12:35:53 +0100 Subject: [PATCH 18/36] made uv build --- notebooks/week1/00.dataexploration.ipynb | 169 +++++++++++++++++- notebooks/week2/01.prepare_dataset.py | 11 ++ ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 0 -> 11840 bytes project_config.yml | 11 +- 4 files changed, 184 insertions(+), 7 deletions(-) create mode 100644 notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl diff --git a/notebooks/week1/00.dataexploration.ipynb b/notebooks/week1/00.dataexploration.ipynb index 023fe44..46eedbf 100644 --- a/notebooks/week1/00.dataexploration.ipynb +++ b/notebooks/week1/00.dataexploration.ipynb @@ -1,5 +1,57 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Exploration code" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from childHealth import data_processor\n", + "import pandas as pd\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../../data/childHealth.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'spark'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspark\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sparksession\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'spark'" + ] + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "\n", + "spark = SparkSession.builder.appName('childHealth').getOrCreate()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1052,7 +1104,122 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import yaml\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, accuracy_score\n", + "import lightgbm as lgb\n", + "import joblib\n", + "\n", + "class ChildHealthModel:\n", + " def __init__(self, config_path):\n", + " self.config = self.load_config(config_path)\n", + " self.model = None\n", + "\n", + " def load_config(self, config_path):\n", + " \"\"\"Load configuration file.\"\"\"\n", + " with open(config_path, 'r') as file:\n", + " config = yaml.safe_load(file)\n", + " return config\n", + "\n", + " def load_data(self, filepath):\n", + " \"\"\"Load dataset and split into features and target based on config.\"\"\"\n", + " data = pd.read_csv(filepath)\n", + " X = data[self.config['num_features'] + self.config['cat_features']]\n", + " y = data[self.config['target']]\n", + " return X, y\n", + "\n", + " def preprocess_data(self, X):\n", + " \"\"\"Preprocess the data by encoding categorical variables.\"\"\"\n", + " X = pd.get_dummies(X, columns=self.config['cat_features'], drop_first=True)\n", + " return X\n", + "\n", + " def train(self, X, y):\n", + " \"\"\"Train the LightGBM model.\"\"\"\n", + " X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n", + "\n", + " # LightGBM dataset\n", + " train_data = lgb.Dataset(X_train, label=y_train)\n", + " val_data = lgb.Dataset(X_val, label=y_val, reference=train_data)\n", + "\n", + " # Set LightGBM parameters for multiclass classification\n", + " params = {\n", + " 'objective': 'multiclass',\n", + " 'num_class': len(y.unique()),\n", + " 'learning_rate': self.config['model_parameters']['learning_rate'],\n", + " 'n_estimators': self.config['model_parameters']['n_estimators'],\n", + " 'max_depth': self.config['model_parameters']['max_depth'],\n", + " 'metric': 'multi_logloss'\n", + " }\n", + "\n", + " # Train the model\n", + " self.model = lgb.train(params, train_data, valid_sets=[train_data, val_data], early_stopping_rounds=50, verbose_eval=10)\n", + "\n", + " # Save the model\n", + " joblib.dump(self.model, 'lgbm_model.pkl')\n", + " print(\"Model training complete and saved as 'lgbm_model.pkl'\")\n", + "\n", + " def evaluate(self, X, y):\n", + " \"\"\"Evaluate the model on the test set.\"\"\"\n", + " y_pred = self.model.predict(X, num_iteration=self.model.best_iteration)\n", + " y_pred = y_pred.argmax(axis=1) # Get class with max probability\n", + "\n", + " # Print classification report\n", + " print(\"Evaluation Results:\")\n", + " print(classification_report(y, y_pred))\n", + " print(\"Accuracy:\", accuracy_score(y, y_pred))\n", + "\n", + " def feature_importance(self, X):\n", + " \"\"\"Display the feature importance.\"\"\"\n", + " importance = self.model.feature_importance(importance_type='gain')\n", + " feature_names = X.columns\n", + " feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importance})\n", + " feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n", + "\n", + " # Plotting feature importance\n", + " plt.figure(figsize=(10, 8))\n", + " plt.barh(feature_importance['feature'], feature_importance['importance'], color='skyblue')\n", + " plt.xlabel('Importance')\n", + " plt.title('Feature Importance')\n", + " plt.gca().invert_yaxis()\n", + " plt.show()\n", + "\n", + " def load_model(self, model_path='lgbm_model.pkl'):\n", + " \"\"\"Load a saved model.\"\"\"\n", + " self.model = joblib.load(model_path)\n", + " print(\"Model loaded from\", model_path)\n", + "\n", + " def predict(self, X):\n", + " \"\"\"Make predictions with the trained model.\"\"\"\n", + " X_preprocessed = self.preprocess_data(X)\n", + " y_pred = self.model.predict(X_preprocessed, num_iteration=self.model.best_iteration)\n", + " return y_pred.argmax(axis=1)\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " # Configuration and paths\n", + " config_path = 'project_config.yml'\n", + " train_path = 'train.csv'\n", + "\n", + " # Initialize model class\n", + " child_health_model = ChildHealthModel(config_path)\n", + "\n", + " # Load and preprocess data\n", + " X, y = child_health_model.load_data(train_path)\n", + " X = child_health_model.preprocess_data(X)\n", + "\n", + " # Train model\n", + " child_health_model.train(X, y)\n", + "\n", + " # Evaluate model\n", + " child_health_model.evaluate(X, y)\n", + "\n", + " # Display feature importance\n", + " child_health_model.feature_importance(X)" + ] } ], "metadata": { diff --git a/notebooks/week2/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py index 0a5eb54..07afdd3 100644 --- a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -1,10 +1,21 @@ # Databricks notebook source +%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl + + +# COMMAND ---------- + +dbutils.library.restartPython() + +# COMMAND ---------- from pyspark.sql import SparkSession from childHealth.config import ProjectConfig from childHealth.data_processor import DataProcessor from datetime import datetime +import warnings +warnings.filterwarnings("ignore") + # Initialize Spark session spark = SparkSession.builder.getOrCreate() diff --git 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a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -1,4 +1,5 @@ # Databricks notebook source + %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl @@ -10,7 +11,7 @@ from pyspark.sql import SparkSession from childHealth.config import ProjectConfig -from childHealth.data_processor import DataProcessor +from childHealth.data_processor import TrainDataProcessor from datetime import datetime import warnings @@ -36,13 +37,14 @@ # COMMAND ---------- # Initialize DataProcessor with the loaded DataFrame and configuration -data_processor = DataProcessor(pandas_df=df, config=config) +data_processor = TrainDataProcessor(train_df=df, config=config) # Preprocess the data -data_processor.preprocess() +data_processor.process() # Split the data into training and testing sets train_set, test_set = data_processor.split_data() # Save the processed datasets to the catalog -data_processor.save_to_catalog(train_set=train_set, test_set=test_set, spark=spark) \ No newline at end of file +data_processor.save_to_catalog(train_set=train_set, test_set=test_set, spark=spark) +# COMMAND ---------- diff --git a/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl deleted file mode 100644 index 93ea075ad08586dbefd461f36fd226d97780877b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11840 zcmbVyW0)n|w&hOSnU%K9O53(=+qP}H(zaP?yD}?n+gYiu^IqRR_f)_B(Kq6Yh`oQT zG1p$-+9T#1bI419fT98b07w9~iiyVW4*=l5Zod!o-001~-0D$0+VgChT{$CJ!Hg?7)*1yN+sBJ{8u_OA;=n>9C zaA`AiJ0ghnA#~fc(l%+gUknIB3f3P8SHA@mS)WnAZ*dW8w7ezUvp3e&AHK3NXVz0> zX=2VdDAokOZq!kkW%lVMy*8dIveM$I#PCsNSQy(Bv6IO}^;QM5R0=MU&gqeuRY;C= z9wl1Rd6)|8B*?B%kWpE-u`07EN{6&55G~|#BuQH-ghv-ZSFq>WLQOP88EQzms8aL( zh+iV8XkWHhn~>7*QLLI5sg|r6UWY4Fb{X!Mbh%~nQ+u=?h&b)&NwrC}~-tQtvj+DfZGy>GD 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@@ -1,19 +1,28 @@ -from pydantic import BaseModel +from pydantic import BaseModel, ValidationError from typing import List, Dict, Any import yaml class ProjectConfig(BaseModel): catalog_name: str schema_name: str - # data_cleaning_parameters: Dict[str, Any] # Dictionary to hold data cleaning parameters model_parameters: Dict[str, Any] # Dictionary to hold model-related parameters num_features: List[str] cat_features: List[str] target: str @classmethod - def from_yaml(cls, config_path: str): + def from_yaml(cls, config_path: str) -> 'ProjectConfig': """Load configuration from a YAML file.""" - with open(config_path, 'r') as f: - config_dict = yaml.safe_load(f) - return cls(**config_dict) \ No newline at end of file + try: + with open(config_path, 'r') as f: + config_dict = yaml.safe_load(f) + return cls(**config_dict) + except FileNotFoundError: + raise FileNotFoundError(f"Configuration file not found: {config_path}") + except yaml.YAMLError as e: + raise ValueError(f"Error parsing YAML file: {e}") + except ValidationError as e: + raise ValueError(f"Validation error: {e}") + +# Example usage: +# config = ProjectConfig.from_yaml('path/to/project_config.yml') \ No newline at end of file diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index ae450a8..26bdea1 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -12,9 +12,9 @@ class TrainDataProcessor: def __init__(self, train_df: pd.DataFrame, config: ProjectConfig): self.train_df = train_df self.config = config - self.num_features = self.config.num_features - self.cat_features = self.config.cat_features - self.target = self.config.target + self.num_features = config.num_features + self.cat_features = config.cat_features + self.target = config.target def preprocess_data(self): """Preprocess the train dataset by handling missing values and data type conversions.""" @@ -24,18 +24,27 @@ def preprocess_data(self): def handle_missing_values(self): """Handle missing values in the train dataset.""" - # Fill numeric columns with mean + self._fill_numeric_missing_values() + self._fill_categorical_missing_values() + + def _fill_numeric_missing_values(self): + """Fill numeric columns with mean.""" numeric_cols = self.train_df[self.num_features] + numeric_cols = numeric_cols.apply(pd.to_numeric, errors='coerce') imputer = SimpleImputer(strategy='mean') self.train_df[self.num_features] = imputer.fit_transform(numeric_cols) - # Fill categorical columns with mode + def _fill_categorical_missing_values(self): + """Fill categorical columns with mode.""" for col in self.cat_features: self.train_df[col].fillna(self.train_df[col].mode()[0], inplace=True) def convert_data_types(self): """Convert categorical columns to appropriate data types.""" - # Convert 'Sex' to binary encoding if it's part of numerical features + self._convert_sex_to_binary() + + def _convert_sex_to_binary(self): + """Convert 'Sex' to binary encoding if it's part of numerical features.""" if 'Basic_Demos-Sex' in self.num_features: self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({'Male': 1, 'Female': 0}) @@ -61,21 +70,31 @@ def one_hot_encode_seasons(self): def calculate_behavioral_scores(self): """Calculate behavioral and psychological indicators.""" - # Bin PCIAT total score + self._bin_pciat_total_score() + self._categorize_internet_use() + + def _bin_pciat_total_score(self): + """Bin PCIAT total score.""" if 'PCIAT-PCIAT_Total' in self.num_features: self.train_df['PCIAT_Bin'] = pd.cut(self.train_df['PCIAT-PCIAT_Total'], bins=[0, 20, 40, 60], labels=['Mild', 'Moderate', 'Severe']) - # Categorize internet use + def _categorize_internet_use(self): + """Categorize internet use.""" if 'PreInt_EduHx-computerinternet_hoursday' in self.num_features: self.train_df['Internet_Use_Category'] = pd.cut(self.train_df['PreInt_EduHx-computerinternet_hoursday'], bins=[0, 1, 3, 6, np.inf], labels=['Low', 'Moderate', 'High', 'Very High']) def add_interaction_features(self): """Add interaction features, such as age-adjusted scores.""" - # Age-adjusted CGAS Score + self._add_age_adjusted_cgas() + self._add_bmi_categories() + + def _add_age_adjusted_cgas(self): + """Add age-adjusted CGAS Score.""" if 'CGAS-CGAS_Score' in self.num_features and 'Basic_Demos-Age' in self.num_features: self.train_df['Age_Adjusted_CGAS'] = self.train_df['CGAS-CGAS_Score'] / self.train_df['Basic_Demos-Age'] - # BMI Categories + def _add_bmi_categories(self): + """Add BMI Categories.""" if 'Physical-BMI' in self.num_features: self.train_df['BMI_Category'] = pd.cut(self.train_df['Physical-BMI'], bins=[0, 18.5, 25, 30, np.inf], labels=['Underweight', 'Normal', 'Overweight', 'Obese']) @@ -126,24 +145,19 @@ def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark test_set (pd.DataFrame): The test set. spark (SparkSession): The Spark session. """ - # Add timestamp column to train and test sets - train_set_with_timestamp = spark.createDataFrame(train_set).withColumn( - "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) + self._add_timestamp_and_save(train_set, 'train_set', spark) + self._add_timestamp_and_save(test_set, 'test_set', spark) + self._enable_change_data_feed(spark) - test_set_with_timestamp = spark.createDataFrame(test_set).withColumn( + def _add_timestamp_and_save(self, df: pd.DataFrame, table_name: str, spark: SparkSession): + """Add timestamp column and save DataFrame to Databricks table.""" + df_with_timestamp = spark.createDataFrame(df).withColumn( "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) - - # Save train set to Databricks table - train_set_with_timestamp.write.mode("append").saveAsTable( - f"{self.config.catalog_name}.{self.config.schema_name}.train_set") - - # Save test set to Databricks table - test_set_with_timestamp.write.mode("append").saveAsTable( - f"{self.config.catalog_name}.{self.config.schema_name}.test_set") - - # Enable Change Data Feed for train and test sets - spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.train_set " - "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") - - spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.test_set " - "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") \ No newline at end of file + df_with_timestamp.write.mode("append").saveAsTable( + f"{self.config.catalog_name}.{self.config.schema_name}.{table_name}") + + def _enable_change_data_feed(self, spark: SparkSession): + """Enable Change Data Feed for train and test sets.""" + for table in ['train_set', 'test_set']: + spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.{table} " + "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") \ No newline at end of file diff --git a/uv.lock b/uv.lock index 7f0697e..038cd1f 100644 --- a/uv.lock +++ 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numeric_cols = self.train_df[self.num_features].apply(pd.to_numeric, errors='coerce') imputer = SimpleImputer(strategy='mean') - self.train_df[self.num_features] = imputer.fit_transform(numeric_cols) + imputed_data = imputer.fit_transform(numeric_cols) + + self.train_df[self.num_features] = pd.DataFrame(imputed_data, columns=self.num_features, index=self.train_df.index) def _fill_categorical_missing_values(self): """Fill categorical columns with mode.""" 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znYpq(oI1_2Zg8;>X_kEsRmPPyWvN{nEaWF#K7l2Q7`vsWP`la9^0Q%CDVH*~HD@r+ z6q+HmKl?i^&7h@r^z$#h8(37Jx)FlNh zWarx&ykpE~1$U{sw5zd(EH%(Ow9!lQCFiZHS7O!Pp`Hq+)WA{u$Hu~UN=X0*++m3% zc#3n@x#tohrmI!vQ=0L~kz8IsN{rw}Kt5tWqaw63lW`-A9cg2f$GHX0qq}Y6?bE~T zd6I8fu2P6zB3_qo$ZC*`0-al4Sku@+=k z^cOPx# From 3a5d926350b571b29a558cf701e7b39cf88c6a95 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 11 Nov 2024 15:29:30 +0100 Subject: [PATCH 23/36] rebuild the wheel --- src/childHealth/data_processor.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index 1f451ec..a23d5e5 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -29,11 +29,18 @@ def handle_missing_values(self): def _fill_numeric_missing_values(self): """Fill numeric columns with mean.""" - numeric_cols = self.train_df[self.num_features].apply(pd.to_numeric, errors='coerce') - imputer = SimpleImputer(strategy='mean') - imputed_data = imputer.fit_transform(numeric_cols) - - self.train_df[self.num_features] = pd.DataFrame(imputed_data, columns=self.num_features, index=self.train_df.index) + try: + numeric_cols = self.train_df[self.num_features].apply(pd.to_numeric, errors='coerce') + imputer = SimpleImputer(strategy='mean') + imputed_data = imputer.fit_transform(numeric_cols) + + # Ensure the imputed data has the same columns as the original DataFrame + imputed_df = pd.DataFrame(imputed_data, columns=self.num_features, index=self.train_df.index) + self.train_df[self.num_features] = imputed_df + except KeyError as e: + missing_cols = list(set(self.num_features) - set(self.train_df.columns)) + print(f"Missing columns: {missing_cols}") + raise e def _fill_categorical_missing_values(self): """Fill categorical columns with mode.""" From 3dba4f02b17c430eac1bbdae0781f9fe6599fdeb Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 11 Nov 2024 19:05:35 +0100 Subject: [PATCH 24/36] added create feature table --- notebooks/week2/01.prepare_dataset.py | 6 +- notebooks/week2/02.prepare_featureTable.py | 54 + ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 0 -> 12182 bytes notebooks/week2/mlruns/0/meta.yaml | 6 + project_config.yml | 14 +- src/childHealth/data_processor.py | 22 +- src/childHealth/feature_engineering.py | 198 +- uv.lock | 2287 ----------------- 8 files changed, 170 insertions(+), 2417 deletions(-) create mode 100644 notebooks/week2/02.prepare_featureTable.py create mode 100644 notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl create mode 100644 notebooks/week2/mlruns/0/meta.yaml delete mode 100644 uv.lock diff --git a/notebooks/week2/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py index 910686e..d8ffc7b 100644 --- a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -1,6 +1,6 @@ # Databricks notebook source -%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl +%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- @@ -20,6 +20,8 @@ # Initialize Spark session spark = SparkSession.builder.getOrCreate() + + # COMMAND ---------- # Load project configuration from YAML file @@ -47,4 +49,6 @@ # Save the processed datasets to the catalog data_processor.save_to_catalog(train_set=train_set, test_set=test_set, spark=spark) + + # COMMAND ---------- diff --git a/notebooks/week2/02.prepare_featureTable.py b/notebooks/week2/02.prepare_featureTable.py new file mode 100644 index 0000000..7b249f0 --- /dev/null +++ b/notebooks/week2/02.prepare_featureTable.py @@ -0,0 +1,54 @@ +# Databricks notebook source + +%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall + + +# COMMAND ---------- + +dbutils.library.restartPython() + +# COMMAND ---------- + +from pyspark.sql import SparkSession +from childHealth.config import ProjectConfig +from childHealth.feature_engineering import ActigraphAggregation +from datetime import datetime +from databricks.sdk import WorkspaceClient +from databricks import feature_engineering + + +import warnings +warnings.filterwarnings("ignore") + +# Initialize Spark session +spark = SparkSession.builder.getOrCreate() + + + +# COMMAND ---------- + +# Load project configuration from YAML file +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") + + +# COMMAND ---------- + +# create feature table for artigraph tables + +# Initialize the Databricks session and clients +spark = SparkSession.builder.getOrCreate() +workspace = WorkspaceClient() +fe = feature_engineering.FeatureEngineeringClient() + + +# COMMAND ---------- +aggregator = ActigraphAggregation( + root_dir = "/Volumes/mlops_students/javedhassi/data/series_test.parquet/", + config = config) + + + + +# COMMAND ---------- +feature_table = aggregator.process_all_participants() +# COMMAND ---------- diff --git a/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl new file mode 100644 index 0000000000000000000000000000000000000000..61130ea356a99b1d05cd439d05142076a234948f GIT binary patch literal 12182 zcmbVyV|ZmtyX}h2j?=Ntj&0k)ltW`t&XkE>F+!DyZh|E=ls#1Z zIO*%t+qr`Q6#mwXn!a4A1Ofm+Km`B@|7iAa2=jkK=zp~ZnpppCpQXMLxyFI)JEc!F z3&pL&*yVsE-iy@rwS}%xr|rC72wJG_K&0x;ztH-O=6#EsM6>xV{*I%ew(jtioh7}F zGD8b{wqB_^=yju(+AO_SKk>EUREdoaUp1PaI?V$3wUC2cHnOKOh_ym!fow*f)T~@; zlRsLK>Y?0jN{XRlkYTqB< zGDjjqq!sTE`CKmXsj1}59Jx}$;&|}XX>d{6xZ%=z4rJ1Ap@J-0h_CiL36mnEu%wIu z)yoNeJvclgr{%~c(R)K?U3!l_TmG2_N^fJ~oVRH(H>%%Q3`wHFLC=yvv!U(lPw&rh ziZwWQx8hSB1z_Gtj6cu#Ou|bSk7m-TB*JN<%QU%}=aMT-FT&S#uG9v`x z>easJD<9E>S|v^TYL@^JPk82$9NA(q&9Q~3tD1`((hYiM8eGz7<= 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O{wV*}X*$|}{rWG5^*)#Y literal 0 HcmV?d00001 diff --git a/notebooks/week2/mlruns/0/meta.yaml b/notebooks/week2/mlruns/0/meta.yaml new file mode 100644 index 0000000..c97cd8f --- /dev/null +++ b/notebooks/week2/mlruns/0/meta.yaml @@ -0,0 +1,6 @@ +artifact_location: file:///Users/javedhassansabu/marvelous-databricks-course-javedhassans/notebooks/week2/mlruns/0 +creation_time: 1731342200055 +experiment_id: '0' +last_update_time: 1731342200055 +lifecycle_stage: active +name: Default diff --git a/project_config.yml b/project_config.yml index 2a5a832..af4af56 100644 --- a/project_config.yml +++ b/project_config.yml @@ -4,18 +4,18 @@ catalog_name: "mlops_students" schema_name: "javedhassi" -# Parameters for data processing -data_cleaning_parameters: - drop_threshold: 0.8 # Threshold for dropping columns with missing values - lower_bound: 0.05 # Lower quantile bound for outlier detection - upper_bound: 0.95 # Upper quantile bound for outlier detection - # Parameters for model training model_parameters: learning_rate: 0.01 # Learning rate for the model n_estimators: 1000 # Number of trees in the model max_depth: 6 # Maximum depth of the tree +pipeline_steps: + feature_engineering: true + scaling: true + handle_outliers: true + handle_missing_values: true + # Numerical features num_features: - Basic_Demos-Age # Age of the individual. @@ -86,7 +86,7 @@ num_features: - PCIAT-PCIAT_Total - SDS-SDS_Total_Raw # Sleep Disturbance Scale (SDS) Total Raw - SDS-SDS_Total_T # Sleep Disturbance Scale (SDS) Total T - - PreInt_EduHx-Season + # - PreInt_EduHx-Season - PreInt_EduHx-computerinternet_hoursday # Categorical features diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index a23d5e5..e7e7e09 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -26,20 +26,32 @@ def handle_missing_values(self): """Handle missing values in the train dataset.""" self._fill_numeric_missing_values() self._fill_categorical_missing_values() - + def _fill_numeric_missing_values(self): """Fill numeric columns with mean.""" try: numeric_cols = self.train_df[self.num_features].apply(pd.to_numeric, errors='coerce') imputer = SimpleImputer(strategy='mean') - imputed_data = imputer.fit_transform(numeric_cols) + + # Identify and add any missing columns + missing_columns = set(self.num_features) - set(numeric_cols.columns) + if missing_columns: + for col in missing_columns: + numeric_cols[col] = np.nan - # Ensure the imputed data has the same columns as the original DataFrame + # Ensure the columns in numeric_cols match the order in self.num_features + numeric_cols = numeric_cols[self.num_features] + + # Impute and create a DataFrame + imputed_data = imputer.fit_transform(numeric_cols) imputed_df = pd.DataFrame(imputed_data, columns=self.num_features, index=self.train_df.index) self.train_df[self.num_features] = imputed_df + except KeyError as e: - missing_cols = list(set(self.num_features) - set(self.train_df.columns)) - print(f"Missing columns: {missing_cols}") + print(f"Missing columns: {missing_columns}") + raise e + except ValueError as e: + print(f"ValueError: {e}") raise e def _fill_categorical_missing_values(self): diff --git a/src/childHealth/feature_engineering.py b/src/childHealth/feature_engineering.py index c39330c..f761f01 100644 --- a/src/childHealth/feature_engineering.py +++ b/src/childHealth/feature_engineering.py @@ -3,171 +3,135 @@ import numpy as np from concurrent.futures import ThreadPoolExecutor, as_completed from tqdm import tqdm -from pyspark.sql import SparkSession -from childHealth.data_processor import ProjectConfig +from pyspark.sql import DataFrame -import yaml +from childHealth.config import ProjectConfig from databricks import feature_engineering -from pyspark.sql import SparkSession -from databricks.sdk import WorkspaceClient - from datetime import datetime from databricks.feature_engineering import FeatureFunction, FeatureLookup +from pyspark.sql.functions import current_timestamp, to_utc_timestamp +from pyspark.dbutils import DBUtils +from pyspark.sql import SparkSession +from pyspark.sql import functions as F +# Initialize Spark session +spark = SparkSession.builder \ + .appName("ActigraphAggregation") \ + .getOrCreate() + +# Initialize DBUtils +dbutils = DBUtils(spark) -# Initialize the Databricks session and clients -spark = SparkSession.builder.getOrCreate() -workspace = WorkspaceClient() -fe = feature_engineering.FeatureEngineeringClient() - -config = ProjectConfig.from_yaml(config_path="../../project_config.yml") - -# Extract configuration details -parameters = config.parameters -catalog_name = config.catalog_name -schema_name = config.schema_name class ActigraphAggregation: - def __init__(self, root_dir): + def __init__(self, root_dir, config): self.root_dir = root_dir - + self.config = config + self.catalog_name = config.catalog_name + self.schema_name = config.schema_name + def load_data(self, participant_id): - file_path = os.path.join(self.root_dir, f"id={participant_id}", "part-0.parquet") - if not os.path.exists(file_path): + file_path = f"{self.root_dir}/id={participant_id}/part-0.parquet" + if not dbutils.fs.ls(file_path): print(f"File not found for participant {participant_id}") - return pd.DataFrame() # Return an empty DataFrame if file doesn't exist - data = pd.read_parquet(file_path) - data['id'] = participant_id + return spark.createDataFrame([], schema="id STRING") # Return an empty Spark DataFrame + data = spark.read.parquet(file_path) + data = data.withColumn("id", F.lit(participant_id)) return data def aggregate_actigraphy(self, data): - aggregated_df = data.groupby('id').agg({ - 'X': ['mean', 'std', 'max', 'min'], - 'Y': ['mean', 'std', 'max', 'min'], - 'Z': ['mean', 'std', 'max', 'min'], - 'enmo': ['mean', 'std', 'max', 'min'], - 'anglez': 'mean', - 'non-wear_flag': 'sum', - 'light': ['mean', 'std', 'max', 'min'], - 'battery_voltage': 'mean', - }).reset_index() - - aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values] + aggregated_df = data.groupBy("id").agg( + F.mean("X").alias("X_mean"), F.stddev("X").alias("X_std"), F.max("X").alias("X_max"), F.min("X").alias("X_min"), + F.mean("Y").alias("Y_mean"), F.stddev("Y").alias("Y_std"), F.max("Y").alias("Y_max"), F.min("Y").alias("Y_min"), + F.mean("Z").alias("Z_mean"), F.stddev("Z").alias("Z_std"), F.max("Z").alias("Z_max"), F.min("Z").alias("Z_min"), + F.mean("enmo").alias("enmo_mean"), F.stddev("enmo").alias("enmo_std"), F.max("enmo").alias("enmo_max"), F.min("enmo").alias("enmo_min"), + F.mean("anglez").alias("anglez_mean"), + F.sum("non-wear_flag").alias("non_wear_flag_sum"), + F.mean("light").alias("light_mean"), F.stddev("light").alias("light_std"), F.max("light").alias("light_max"), F.min("light").alias("light_min"), + F.mean("battery_voltage").alias("battery_voltage_mean") + ) return aggregated_df def temporal_aggregations(self, data): - data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend') - - conditions = [ - (data['time_of_day'] < 6 * 3600), - (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), - (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), - (data['time_of_day'] >= 18 * 3600) - ] - choices = ['night', 'morning', 'afternoon', 'evening'] - data['time_period'] = np.select(conditions, choices, default='unknown') + data = data.withColumn("weekday_flag", F.when(F.col("weekday") < 5, "weekday").otherwise("weekend")) + data = data.withColumn( + "time_period", + F.when(F.col("time_of_day") < 6 * 3600, "night") + .when((F.col("time_of_day") >= 6 * 3600) & (F.col("time_of_day") < 12 * 3600), "morning") + .when((F.col("time_of_day") >= 12 * 3600) & (F.col("time_of_day") < 18 * 3600), "afternoon") + .otherwise("evening") + ) - temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({ - 'enmo': 'mean', - 'light': 'mean', - 'non-wear_flag': 'sum' - }).unstack(fill_value=0) - temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values] - - return temporal_agg.reset_index() + temporal_agg = data.groupBy("id", "weekday_flag", "time_period").agg( + F.mean("enmo").alias("enmo_mean"), + F.mean("light").alias("light_mean"), + F.sum("non-wear_flag").alias("non_wear_flag_sum") + ).groupBy("id").pivot("weekday_flag_time_period").agg( + F.first("enmo_mean"), F.first("light_mean"), F.first("non_wear_flag_sum") + ) + return temporal_agg def activity_ratios(self, data): - total_time = data.groupby('id').size().rename('total_time') - non_wear_time = data.groupby('id')['non-wear_flag'].sum().rename('non_wear_time') - - ratios = pd.concat([total_time, non_wear_time], axis=1) - ratios['non_wear_ratio'] = ratios['non_wear_time'] / ratios['total_time'] - - return ratios[['non_wear_ratio']].reset_index() + total_time = data.groupBy("id").count().alias("total_time") + non_wear_time = data.groupBy("id").agg(F.sum("non-wear_flag").alias("non_wear_time")) + ratios = total_time.join(non_wear_time, "id", "left_outer").withColumn( + "non_wear_ratio", F.col("non_wear_time") / F.col("total_time") + ) + return ratios.select("id", "non_wear_ratio") + def process_participant_data(self, participant_id): data = self.load_data(participant_id) - if data.empty: - return pd.DataFrame() + if data.rdd.isEmpty(): + return None aggregate_data = self.aggregate_actigraphy(data) temporal_data = self.temporal_aggregations(data) ratio_data = self.activity_ratios(data) - participant_data = aggregate_data.merge(temporal_data, on='id', how='left') - participant_data = participant_data.merge(ratio_data, on='id', how='left') + participant_data = aggregate_data.join(temporal_data, "id", "left").join(ratio_data, "id", "left") return participant_data - def process_all_participants(self): + + + def process_all_participants(self) -> DataFrame: all_data = [] with ThreadPoolExecutor() as executor: futures = [] - for id_folder in os.listdir(self.root_dir): - if not id_folder.startswith('id='): + for id_folder in dbutils.fs.ls(self.root_dir): + if not id_folder.name.startswith('id='): continue - participant_id = id_folder.split('=')[-1] + participant_id = id_folder.name.split('=')[-1] futures.append(executor.submit(self.process_participant_data, participant_id)) for future in tqdm(as_completed(futures), total=len(futures), desc="Processing participants"): participant_data = future.result() - if not participant_data.empty: + if participant_data is not None and not participant_data.rdd.isEmpty(): all_data.append(participant_data) - feature_table = pd.concat(all_data, ignore_index=True) - return feature_table + # Check if all_data is empty + if not all_data: + print("No participant data was loaded. Returning an empty DataFrame.") + return spark.createDataFrame([], schema="id STRING") - def save_to_spark_table(self, feature_table): - spark_df = spark.createDataFrame(feature_table) - spark_df.createOrReplaceTempView("temp_feature_table") + # Concatenate all Spark DataFrames using union + feature_table = all_data[0] + for df in all_data[1:]: + feature_table = feature_table.union(df) - spark.sql(f""" - CREATE TABLE IF NOT EXISTS {catalog_name}.{schema_name}.actigraph_features ( - id STRING, - X_mean DOUBLE, - X_std DOUBLE, - X_max DOUBLE, - X_min DOUBLE, - Y_mean DOUBLE, - Y_std DOUBLE, - Y_max DOUBLE, - Y_min DOUBLE, - Z_mean DOUBLE, - Z_std DOUBLE, - Z_max DOUBLE, - Z_min DOUBLE, - enmo_mean DOUBLE, - enmo_std DOUBLE, - enmo_max DOUBLE, - enmo_min DOUBLE, - anglez_mean DOUBLE, - non_wear_flag_sum DOUBLE, - light_mean DOUBLE, - light_std DOUBLE, - light_max DOUBLE, - light_min DOUBLE, - battery_voltage_mean DOUBLE, - weekday_flag_weekday_morning_mean DOUBLE, - weekday_flag_weekday_afternoon_mean DOUBLE, - weekday_flag_weekday_evening_mean DOUBLE, - weekday_flag_weekday_night_mean DOUBLE, - weekday_flag_weekend_morning_mean DOUBLE, - weekday_flag_weekend_afternoon_mean DOUBLE, - weekday_flag_weekend_evening_mean DOUBLE, - weekday_flag_weekend_night_mean DOUBLE, - non_wear_ratio DOUBLE - ) - """) + return feature_table - spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_features ADD CONSTRAINT actigraph_pk PRIMARY KEY(id)") - spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_features SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") + def save_to_spark_table(self, feature_table): + feature_table.createOrReplaceTempView("temp_feature_table") spark.sql(f""" - INSERT INTO {catalog_name}.{schema_name}.actigraph_features + CREATE TABLE IF NOT EXISTS {self.catalog_name}.{self.schema_name}.actigraph_features AS SELECT * FROM temp_feature_table """) + spark.sql(f"ALTER TABLE {self.catalog_name}.{self.schema_name}.actigraph_features SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") # # Initialize with the root directory containing participant data -# aggregator = ActigraphAggregation(root_dir="../../data/series_train.parquet/") +# aggregator = ActigraphAggregation(root_dir="dbfs:/Volumes/mlops_students/javedhassi/data/series_train.parquet/", config=config) # # Process all participants and get the final aggregated feature table # feature_table = aggregator.process_all_participants() @@ -176,4 +140,4 @@ def save_to_spark_table(self, feature_table): # aggregator.save_to_spark_table(feature_table) # # Inspect the feature table -# print(feature_table.head()) \ No newline at end of file +# feature_table.show() diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 038cd1f..0000000 --- a/uv.lock +++ /dev/null @@ -1,2287 +0,0 @@ -version = 1 -requires-python = ">=3.11" -resolution-markers = [ - 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.when((F.col("time_of_day") >= 12 * 3600) & (F.col("time_of_day") < 18 * 3600), "afternoon") - .otherwise("evening") - ) - - temporal_agg = data.groupBy("id", "weekday_flag", "time_period").agg( - F.mean("enmo").alias("enmo_mean"), - F.mean("light").alias("light_mean"), - F.sum("non-wear_flag").alias("non_wear_flag_sum") - ).groupBy("id").pivot("weekday_flag_time_period").agg( - F.first("enmo_mean"), F.first("light_mean"), F.first("non_wear_flag_sum") - ) - return temporal_agg - - def activity_ratios(self, data): - total_time = data.groupBy("id").count().alias("total_time") - non_wear_time = data.groupBy("id").agg(F.sum("non-wear_flag").alias("non_wear_time")) - - ratios = total_time.join(non_wear_time, "id", "left_outer").withColumn( - "non_wear_ratio", F.col("non_wear_time") / F.col("total_time") - ) - return ratios.select("id", "non_wear_ratio") - - def process_participant_data(self, participant_id): - data = self.load_data(participant_id) - if data.rdd.isEmpty(): + def _read_participant_file(self, file_path: str, participant_id: str) -> Optional[DataFrame]: + """ + Reads a single participant file and adds the participant ID as a column. + """ + try: + data = self.spark.read.parquet(file_path) + data = data.withColumn("id", F.lit(participant_id)) + print(f"Successfully read file for participant ID: {participant_id}") + return data + except Exception as e: + print(f"Error reading file {file_path} for participant {participant_id}: {e}") return None - - aggregate_data = self.aggregate_actigraphy(data) - temporal_data = self.temporal_aggregations(data) - ratio_data = self.activity_ratios(data) - - participant_data = aggregate_data.join(temporal_data, "id", "left").join(ratio_data, "id", "left") - return participant_data - - - def process_all_participants(self) -> DataFrame: - all_data = [] + def load_all_files(self) -> Optional[DataFrame]: + """ + Loads all `part-0.parquet` files from participant directories within the root directory. + """ + all_data: List[DataFrame] = [] with ThreadPoolExecutor() as executor: futures = [] - for id_folder in dbutils.fs.ls(self.root_dir): - if not id_folder.name.startswith('id='): - continue - participant_id = id_folder.name.split('=')[-1] - futures.append(executor.submit(self.process_participant_data, participant_id)) - - for future in tqdm(as_completed(futures), total=len(futures), desc="Processing participants"): - participant_data = future.result() - if participant_data is not None and not participant_data.rdd.isEmpty(): - all_data.append(participant_data) - - # Check if all_data is empty - if not all_data: - print("No participant data was loaded. Returning an empty DataFrame.") - return spark.createDataFrame([], schema="id STRING") - # Concatenate all Spark DataFrames using union - feature_table = all_data[0] - for df in all_data[1:]: - feature_table = feature_table.union(df) + try: + participant_dirs = self.dbutils.fs.ls(self.root_dir) + except Exception as e: + print(f"Failed to list directory {self.root_dir}: {e}") + return None - return feature_table + for dir_info in participant_dirs: + if not dir_info.path.endswith('/'): + continue # Skip if it's not a directory + participant_id = dir_info.path.split('=')[-1].strip('/') + participant_file_path = f"{dir_info.path}part-0.parquet" + print(f"Looking for file: {participant_file_path}") - def save_to_spark_table(self, feature_table): - feature_table.createOrReplaceTempView("temp_feature_table") - spark.sql(f""" - CREATE TABLE IF NOT EXISTS {self.catalog_name}.{self.schema_name}.actigraph_features AS - SELECT * FROM temp_feature_table - """) - spark.sql(f"ALTER TABLE {self.catalog_name}.{self.schema_name}.actigraph_features SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") + futures.append(executor.submit(self._read_participant_file, participant_file_path, participant_id)) -# # Initialize with the root directory containing participant data -# aggregator = ActigraphAggregation(root_dir="dbfs:/Volumes/mlops_students/javedhassi/data/series_train.parquet/", config=config) - -# # Process all participants and get the final aggregated feature table -# feature_table = aggregator.process_all_participants() + for future in tqdm(as_completed(futures), total=len(futures), desc="Reading participant files"): + participant_data = future.result() + if participant_data is not None: + all_data.append(participant_data) -# # Save the feature table to Spark -# aggregator.save_to_spark_table(feature_table) + if all_data: + feature_table = all_data[0] + for df in all_data[1:]: + feature_table = feature_table.union(df) + return feature_table + else: + print("No data was loaded. Returning an empty DataFrame.") + return self.spark.createDataFrame([], schema="id STRING") -# # Inspect the feature table -# feature_table.show() From 22032b14a361f5ac066b801f4c867f2ad4e66a15 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 11 Nov 2024 21:52:54 +0100 Subject: [PATCH 26/36] modififed create feature table code --- notebooks/week2/02.prepare_featureTable.py | 36 +---- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 12182 -> 11398 bytes src/childHealth/data_processor.py | 119 ++++----------- src/childHealth/feature_engineering.py | 136 ++++++++++++++++-- 4 files changed, 160 insertions(+), 131 deletions(-) diff --git a/notebooks/week2/02.prepare_featureTable.py b/notebooks/week2/02.prepare_featureTable.py index 7b249f0..559dc66 100644 --- a/notebooks/week2/02.prepare_featureTable.py +++ b/notebooks/week2/02.prepare_featureTable.py @@ -11,20 +11,10 @@ from pyspark.sql import SparkSession from childHealth.config import ProjectConfig -from childHealth.feature_engineering import ActigraphAggregation -from datetime import datetime -from databricks.sdk import WorkspaceClient -from databricks import feature_engineering - - +from childHealth.feature_engineering import ActigraphFileReader import warnings warnings.filterwarnings("ignore") -# Initialize Spark session -spark = SparkSession.builder.getOrCreate() - - - # COMMAND ---------- # Load project configuration from YAML file @@ -32,23 +22,11 @@ # COMMAND ---------- - -# create feature table for artigraph tables - -# Initialize the Databricks session and clients -spark = SparkSession.builder.getOrCreate() -workspace = WorkspaceClient() -fe = feature_engineering.FeatureEngineeringClient() - - -# COMMAND ---------- -aggregator = ActigraphAggregation( - root_dir = "/Volumes/mlops_students/javedhassi/data/series_test.parquet/", - config = config) - - - - +Actigraph = ActigraphFileReader( + app_name = "ActigraphAggregation", + root_dir = "/Volumes/mlops_students/javedhassi/data/series_train.parquet/", + catalog_name = config.catalog_name, + schema_name = config.schema_name) # COMMAND ---------- -feature_table = aggregator.process_all_participants() +Actigraph.save_feature_table() # COMMAND ---------- diff --git a/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl index 5b7a140cb1394f209cac54713523af60414eebc7..fb55b88516fb72ecb5021d6f34c62120d0101102 100644 GIT binary patch delta 4520 zcmZWtbyO5w(_a>(OC*;Dk!I=c?pSJ3DUoF<0YQ*hmQo&C*d?VKq(izvQdkg_kQSu7 z5%};s=Q;2Do$sD=&zw7V=6CNOGiPRgN!ITzhz)hHaVP-*06w6zAf8wZ*jZ4mvijg& z)N+pGxerbfxs8eSW7fbJ#a7%oXS7#sdp-GVVPV_SO z#G$7ABRk_D%m{8;yJKi}iASC&m!F!T*5tsrS#E|@f-N?y7`U{XvDE20C#v+3m|P%R zO=>!9>$x}@Z1^PfOlJ=uc{$@e*ml}d_)WF>`J?;7M!}?D7AS940HymLLSV-vr#iRR?j_ZEH6K4$2%354=VH?r#`K0m>jI(2A#rHSez z^2ST5*F*Nox|dVucje0HF^6%BkS!uhLo|xLZ&2GGKPiZp88%aUC$~N)K*d9xd#tLH 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format='%(asctime)s - %(levelname)s - %(message)s') class TrainDataProcessor: def __init__(self, train_df: pd.DataFrame, config: ProjectConfig): @@ -15,18 +20,22 @@ def __init__(self, train_df: pd.DataFrame, config: ProjectConfig): self.num_features = config.num_features self.cat_features = config.cat_features self.target = config.target + logging.info("Initialized TrainDataProcessor with config and dataset") def preprocess_data(self): """Preprocess the train dataset by handling missing values and data type conversions.""" + logging.info("Starting data preprocessing") self.handle_missing_values() self.convert_data_types() + logging.info("Data preprocessing completed") return self.train_df def handle_missing_values(self): """Handle missing values in the train dataset.""" + logging.info("Handling missing values") self._fill_numeric_missing_values() self._fill_categorical_missing_values() - + def _fill_numeric_missing_values(self): """Fill numeric columns with mean.""" try: @@ -38,7 +47,8 @@ def _fill_numeric_missing_values(self): if missing_columns: for col in missing_columns: numeric_cols[col] = np.nan - + logging.warning(f"Column {col} missing from numeric_cols, filled with NaN") + # Ensure the columns in numeric_cols match the order in self.num_features numeric_cols = numeric_cols[self.num_features] @@ -46,138 +56,65 @@ def _fill_numeric_missing_values(self): imputed_data = imputer.fit_transform(numeric_cols) imputed_df = pd.DataFrame(imputed_data, columns=self.num_features, index=self.train_df.index) self.train_df[self.num_features] = imputed_df + logging.info("Numeric missing values filled") except KeyError as e: - print(f"Missing columns: {missing_columns}") + logging.error(f"Missing columns: {missing_columns}") raise e except ValueError as e: - print(f"ValueError: {e}") + logging.error(f"ValueError: {e}") raise e def _fill_categorical_missing_values(self): """Fill categorical columns with mode.""" + logging.info("Filling categorical missing values") for col in self.cat_features: self.train_df[col].fillna(self.train_df[col].mode()[0], inplace=True) + logging.info(f"Filled missing values in {col} with mode") def convert_data_types(self): """Convert categorical columns to appropriate data types.""" + logging.info("Converting data types") self._convert_sex_to_binary() def _convert_sex_to_binary(self): """Convert 'Sex' to binary encoding if it's part of numerical features.""" if 'Basic_Demos-Sex' in self.num_features: self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({'Male': 1, 'Female': 0}) + logging.info("Converted 'Basic_Demos-Sex' to binary") def feature_engineering(self): """Perform feature engineering to create new features.""" + logging.info("Starting feature engineering") self.add_age_groups() self.one_hot_encode_seasons() self.calculate_behavioral_scores() self.add_interaction_features() + logging.info("Feature engineering completed") return self.train_df - def add_age_groups(self): - """Add age groups based on age.""" - if 'Basic_Demos-Age' in self.num_features: - self.train_df['Age_Group'] = pd.cut(self.train_df['Basic_Demos-Age'], bins=[0, 12, 17, 25], labels=['Child', 'Teen', 'Young Adult']) - - def one_hot_encode_seasons(self): - """One-hot encode season columns.""" - for col in self.cat_features: - if 'Season' in col: - one_hot = pd.get_dummies(self.train_df[col], prefix=col) - self.train_df = pd.concat([self.train_df, one_hot], axis=1) - - def calculate_behavioral_scores(self): - """Calculate behavioral and psychological indicators.""" - self._bin_pciat_total_score() - self._categorize_internet_use() - - def _bin_pciat_total_score(self): - """Bin PCIAT total score.""" - if 'PCIAT-PCIAT_Total' in self.num_features: - self.train_df['PCIAT_Bin'] = pd.cut(self.train_df['PCIAT-PCIAT_Total'], bins=[0, 20, 40, 60], labels=['Mild', 'Moderate', 'Severe']) - - def _categorize_internet_use(self): - """Categorize internet use.""" - if 'PreInt_EduHx-computerinternet_hoursday' in self.num_features: - self.train_df['Internet_Use_Category'] = pd.cut(self.train_df['PreInt_EduHx-computerinternet_hoursday'], bins=[0, 1, 3, 6, np.inf], labels=['Low', 'Moderate', 'High', 'Very High']) - - def add_interaction_features(self): - """Add interaction features, such as age-adjusted scores.""" - self._add_age_adjusted_cgas() - self._add_bmi_categories() - - def _add_age_adjusted_cgas(self): - """Add age-adjusted CGAS Score.""" - if 'CGAS-CGAS_Score' in self.num_features and 'Basic_Demos-Age' in self.num_features: - self.train_df['Age_Adjusted_CGAS'] = self.train_df['CGAS-CGAS_Score'] / self.train_df['Basic_Demos-Age'] - - def _add_bmi_categories(self): - """Add BMI Categories.""" - if 'Physical-BMI' in self.num_features: - self.train_df['BMI_Category'] = pd.cut(self.train_df['Physical-BMI'], bins=[0, 18.5, 25, 30, np.inf], labels=['Underweight', 'Normal', 'Overweight', 'Obese']) - - def scale_numeric_features(self): - """Scale numeric features in the final dataset.""" - scaler = StandardScaler() - self.train_df[self.num_features] = scaler.fit_transform(self.train_df[self.num_features]) - - def handle_missing_target(self): - """Handle missing values in the target column by replacing them with a specific value (e.g., 4.0).""" - new_value = 4.0 # New value to replace NaNs in the target column - self.train_df[self.target].fillna(new_value, inplace=True) - - def keep_relevant_columns(self): - """Keep only relevant columns.""" - relevant_columns = self.cat_features + self.num_features + [self.target, 'id'] - self.train_df = self.train_df[relevant_columns] + # Rest of the methods... def process(self): """Run the complete processing pipeline.""" + logging.info("Starting full processing pipeline") self.preprocess_data() self.feature_engineering() self.scale_numeric_features() self.handle_missing_target() self.keep_relevant_columns() + logging.info("Processing pipeline completed") return self.train_df def split_data(self, test_size=0.2, random_state=42): - """ - Split the DataFrame (self.train_df) into training and test sets. - - Parameters: - test_size (float): Proportion of the dataset to include in the test split. - random_state (int): Random seed for reproducibility. - - Returns: - tuple: A tuple containing the training and test sets. - """ + logging.info("Splitting data into training and test sets") train_set, test_set = train_test_split(self.train_df, test_size=test_size, random_state=random_state) + logging.info("Data split completed") return train_set, test_set def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark: SparkSession): - """ - Save the train and test sets into Databricks tables. - - Parameters: - train_set (pd.DataFrame): The training set. - test_set (pd.DataFrame): The test set. - spark (SparkSession): The Spark session. - """ + logging.info("Saving datasets to catalog") self._add_timestamp_and_save(train_set, 'train_set', spark) self._add_timestamp_and_save(test_set, 'test_set', spark) self._enable_change_data_feed(spark) - - def _add_timestamp_and_save(self, df: pd.DataFrame, table_name: str, spark: SparkSession): - """Add timestamp column and save DataFrame to Databricks table.""" - df_with_timestamp = spark.createDataFrame(df).withColumn( - "update_timestamp_utc", to_utc_timestamp(current_timestamp(), "UTC")) - df_with_timestamp.write.mode("append").saveAsTable( - f"{self.config.catalog_name}.{self.config.schema_name}.{table_name}") - - def _enable_change_data_feed(self, spark: SparkSession): - """Enable Change Data Feed for train and test sets.""" - for table in ['train_set', 'test_set']: - spark.sql(f"ALTER TABLE {self.config.catalog_name}.{self.config.schema_name}.{table} " - "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") \ No newline at end of file + logging.info("Datasets saved to catalog and change data feed enabled") \ No newline at end of file diff --git a/src/childHealth/feature_engineering.py b/src/childHealth/feature_engineering.py index feb9dfd..762d31f 100644 --- a/src/childHealth/feature_engineering.py +++ b/src/childHealth/feature_engineering.py @@ -1,3 +1,4 @@ +from pyspark.sql.types import StructType, StructField, StringType, FloatType, IntegerType, LongType import os from datetime import datetime from typing import Optional, List @@ -13,37 +14,76 @@ from childHealth.config import ProjectConfig from databricks.feature_engineering import FeatureFunction, FeatureLookup +import logging +from pyspark.sql.utils import AnalysisException + + +# Configure the logger +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + class ActigraphFileReader: - def __init__(self, app_name: str, root_dir: str): - self.spark = SparkSession.builder.appName(app_name).getOrCreate() + def __init__(self, app_name: str, root_dir: str, catalog_name: str, schema_name: str): + """ + Initializes the ActigraphFileReader class. + """ + self.spark = SparkSession.builder \ + .appName(app_name) \ + .config("spark.databricks.delta.schema.autoMerge.enabled", "true") \ + .getOrCreate() self.dbutils = DBUtils(self.spark) self.root_dir = root_dir + self.catalog_name = catalog_name + self.schema_name = schema_name + + # Define the schema for the feature table + self.schema = StructType([ + StructField("step", LongType(), True), + StructField("X", FloatType(), True), + StructField("Y", FloatType(), True), + StructField("Z", FloatType(), True), + StructField("enmo", FloatType(), True), + StructField("anglez", FloatType(), True), + StructField("non_wear_flag", FloatType(), True), + StructField("light", FloatType(), True), + StructField("battery_voltage", FloatType(), True), + StructField("time_of_day", LongType(), True), + StructField("weekday", IntegerType(), True), + StructField("quarter", IntegerType(), True), + StructField("relative_date_PCIAT", FloatType(), True), + StructField("id", StringType(), True), + ]) + logger.info("Initialized ActigraphFileReader with app name %s, root directory %s, catalog %s, and schema %s.", + app_name, root_dir, catalog_name, schema_name) def _read_participant_file(self, file_path: str, participant_id: str) -> Optional[DataFrame]: """ Reads a single participant file and adds the participant ID as a column. """ try: - data = self.spark.read.parquet(file_path) + data = self.spark.read.schema(self.schema).parquet(file_path) data = data.withColumn("id", F.lit(participant_id)) - print(f"Successfully read file for participant ID: {participant_id}") + logger.info("Successfully read file for participant ID: %s from path: %s", participant_id, file_path) return data + except AnalysisException as e: + logger.warning("File not found or error reading file %s for participant %s: %s", file_path, participant_id, e) + return None except Exception as e: - print(f"Error reading file {file_path} for participant {participant_id}: {e}") + logger.error("Unexpected error reading file %s for participant %s: %s", file_path, participant_id, e) return None def load_all_files(self) -> Optional[DataFrame]: """ Loads all `part-0.parquet` files from participant directories within the root directory. + Combines them into a single DataFrame. """ all_data: List[DataFrame] = [] with ThreadPoolExecutor() as executor: futures = [] - try: participant_dirs = self.dbutils.fs.ls(self.root_dir) except Exception as e: - print(f"Failed to list directory {self.root_dir}: {e}") + logger.error("Failed to list directory %s: %s", self.root_dir, e) return None for dir_info in participant_dirs: @@ -52,8 +92,6 @@ def load_all_files(self) -> Optional[DataFrame]: participant_id = dir_info.path.split('=')[-1].strip('/') participant_file_path = f"{dir_info.path}part-0.parquet" - print(f"Looking for file: {participant_file_path}") - futures.append(executor.submit(self._read_participant_file, participant_file_path, participant_id)) for future in tqdm(as_completed(futures), total=len(futures), desc="Reading participant files"): @@ -65,8 +103,84 @@ def load_all_files(self) -> Optional[DataFrame]: feature_table = all_data[0] for df in all_data[1:]: feature_table = feature_table.union(df) + logger.info("Successfully loaded and combined all participant files.") return feature_table else: - print("No data was loaded. Returning an empty DataFrame.") - return self.spark.createDataFrame([], schema="id STRING") + logger.warning("No data was loaded from the files. Returning an empty DataFrame.") + return self.spark.createDataFrame([], schema=self.schema) + + def save_feature_table(self): + """ + Creates and updates the feature table in Delta format with constraints and change data feed enabled. + """ + # Ensure auto-merge is enabled for schema changes + self.spark.conf.set("spark.databricks.delta.schema.autoMerge.enabled", "true") + logger.info("Auto-merge enabled for schema changes.") + + # Load all files into a DataFrame + feature_table = self.load_all_files() + + # Define the table name + table_name = f"{self.catalog_name}.{self.schema_name}.actigraph_features" + logger.info("Preparing to create or replace the Delta table: %s", table_name) + + # Step 1: Create or replace the table with the defined schema + try: + self.spark.sql(f""" + CREATE OR REPLACE TABLE {table_name} ( + step BIGINT, + X FLOAT, + Y FLOAT, + Z FLOAT, + enmo FLOAT, + anglez FLOAT, + non_wear_flag FLOAT, + light FLOAT, + battery_voltage FLOAT, + time_of_day BIGINT, + weekday TINYINT, + quarter TINYINT, + relative_date_PCIAT FLOAT, + id STRING NOT NULL + ) + USING DELTA + """) + logger.info("Table %s created or replaced successfully.", table_name) + except Exception as e: + logger.error("Error creating or replacing table %s: %s", table_name, e) + return + + # Step 2: Add a primary key constraint to the table + try: + self.spark.sql(f"ALTER TABLE {table_name} ADD CONSTRAINT actigraph_pk PRIMARY KEY (id)") + logger.info("Primary key constraint added to table %s.", table_name) + except Exception as e: + logger.error("Error adding primary key constraint to table %s: %s", table_name, e) + + # Step 3: Enable change data feed for the table + try: + self.spark.sql(f"ALTER TABLE {table_name} SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") + logger.info("Change data feed enabled for table %s.", table_name) + except Exception as e: + logger.error("Error enabling change data feed for table %s: %s", table_name, e) + + # Step 4: Write data to the feature table if there is any + if feature_table.count() > 0: + try: + # Ensure consistent column names and types + feature_table = feature_table.withColumnRenamed("non-wear_flag", "non_wear_flag") \ + .withColumn("weekday", F.col("weekday").cast("TINYINT")) \ + .withColumn("quarter", F.col("quarter").cast("TINYINT")) \ + .withColumn("id", F.col("id").cast("STRING")) + + # Write data with schema merge enabled + feature_table.write.format("delta") \ + .mode("append") \ + .option("mergeSchema", "true") \ + .saveAsTable(table_name) + logger.info("Feature table %s saved and updated successfully.", table_name) + except Exception as e: + logger.error("Error writing data to the feature table %s: %s", table_name, e) + else: + logger.info("Feature table is empty; nothing was saved.") From 6a42c6591e964d1bee3271cacfbe7f1eaa70d291 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Mon, 11 Nov 2024 22:31:40 +0100 Subject: [PATCH 27/36] updated feature table --- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 11398 -> 11830 bytes src/childHealth/feature_engineering.py | 15 +++++++-------- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl index fb55b88516fb72ecb5021d6f34c62120d0101102..6fc093d1511f90586b8b906c53213d352764b397 100644 GIT binary patch delta 3243 zcmY+GcQhO9+s7mJ9wlmz5-TN$O;NkHM2*HMqQojisS#T9u}VtqU9{Auwopp!J!*?S zREsJtLiNXc&hNbMd!KWi&$+MrzOKK%e|%zWnvry7hNNVy004jr0Ix`;lLNvl>a+;t zS7;EWI3l?w50yRz3%tU^rulTk#grNVIOGQa1OZsIxDck(=E0LCc~JO#4Gv0>R&s8@ z@85AB$TRnRJ6k0lB80M?|5Xd6zE>fZ2F59(!GFWXNpLD(tyetHUdHi{^|Xd=Q^eZT zi+t5Ag@yR{!?%XChR8O~VR#{%VaUlBij1+51CB@Lh4w)mvwW`RcSrMsA&VI(A}V!< 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feature_table.withColumnRenamed("non-wear-flag", "non_wear_flag") \ .withColumn("weekday", F.col("weekday").cast("TINYINT")) \ .withColumn("quarter", F.col("quarter").cast("TINYINT")) \ .withColumn("id", F.col("id").cast("STRING")) @@ -179,8 +179,7 @@ def save_feature_table(self): .option("mergeSchema", "true") \ .saveAsTable(table_name) logger.info("Feature table %s saved and updated successfully.", table_name) - except Exception as e: - logger.error("Error writing data to the feature table %s: %s", table_name, e) - else: - logger.info("Feature table is empty; nothing was saved.") - + else: + logger.info("Feature table is empty; nothing was saved.") + except Exception as e: + logger.error("Error writing data to the feature table %s: %s", table_name, e) \ No newline at end of file From 0871415f1b4b9733bb1922ba30433a9f55abba83 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Tue, 12 Nov 2024 14:47:09 +0100 Subject: [PATCH 28/36] updated feature table --- ..._experiment.py => 03.mlflow_experiment.py} | 0 ..._model.py => 04.log_and_register_model.py} | 90 +- ...py => 05.log_and_register_custom_model.py} | 0 .../catboost_info/catboost_training.json | 204 ++ .../catboost_info/learn/events.out.tfevents | Bin 0 -> 9670 bytes notebooks/week2/catboost_info/learn_error.tsv | 201 ++ notebooks/week2/catboost_info/time_left.tsv | 201 ++ ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 11830 -> 11830 bytes notebooks/week2/run_info.json | 40 + project_config.yml | 12 +- src/childHealth/config.py | 2 +- uv.lock | 2289 +++++++++++++++++ 12 files changed, 2988 insertions(+), 51 deletions(-) rename notebooks/week2/{02.mlflow_experiment.py => 03.mlflow_experiment.py} (100%) rename notebooks/week2/{03.log_and_register_model.py => 04.log_and_register_model.py} (53%) rename notebooks/week2/{04.log_and_register_custom_model.py => 05.log_and_register_custom_model.py} (100%) create mode 100644 notebooks/week2/catboost_info/catboost_training.json create mode 100644 notebooks/week2/catboost_info/learn/events.out.tfevents create mode 100644 notebooks/week2/catboost_info/learn_error.tsv create mode 100644 notebooks/week2/catboost_info/time_left.tsv create mode 100644 notebooks/week2/run_info.json create mode 100644 uv.lock diff --git a/notebooks/week2/02.mlflow_experiment.py b/notebooks/week2/03.mlflow_experiment.py similarity index 100% rename from notebooks/week2/02.mlflow_experiment.py rename to notebooks/week2/03.mlflow_experiment.py diff --git a/notebooks/week2/03.log_and_register_model.py b/notebooks/week2/04.log_and_register_model.py similarity index 53% rename from notebooks/week2/03.log_and_register_model.py rename to notebooks/week2/04.log_and_register_model.py index 2a4fcb4..e146724 100644 --- a/notebooks/week2/03.log_and_register_model.py +++ b/notebooks/week2/04.log_and_register_model.py @@ -1,38 +1,38 @@ -# COMMAND ---------- +# Databricks notebook source -%pip install mlops_with_databricks-0.0.1-py3-none-any.whl +%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- dbutils.library.restartPython() # Databricks notebook source - -from pyspark.sql import SparkSession +# Import necessary libraries +from sklearn.ensemble import RandomForestClassifier +from sklearn.pipeline import Pipeline from childHealth.config import ProjectConfig -from sklearn.preprocessing import OneHotEncoder, StandardScaler +from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import ColumnTransformer -from sklearn.pipeline import Pipeline -from lightgbm import LGBMRegressor -from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +from sklearn.metrics import classification_report, accuracy_score import mlflow from mlflow.models import infer_signature +from pyspark.sql import SparkSession +# Set up MLflow tracking mlflow.set_tracking_uri("databricks") -mlflow.set_registry_uri('databricks-uc') # It must be -uc for registering models to Unity Catalog +mlflow.set_registry_uri('databricks-uc') # For registering models to Unity Catalog -# COMMAND ---------- +# COMMAND ---------- +# Load your custom configuration and extract necessary settings config = ProjectConfig.from_yaml(config_path="../../project_config.yml") - -# Extract configuration details num_features = config.num_features cat_features = config.cat_features target = config.target -model_parameters = config.model_parameters # Use model parameters from the config catalog_name = config.catalog_name schema_name = config.schema_name +random_forest_parameters = config.random_forest_parameters -# COMMAND ---------- +# Spark session setup spark = SparkSession.builder.getOrCreate() # Load training and testing sets from Databricks tables @@ -46,74 +46,72 @@ X_test = test_set[num_features + cat_features] y_test = test_set[target] + # COMMAND ---------- -# Define the preprocessor for categorical and numerical features +# Define preprocessing for numerical and categorical features preprocessor = ColumnTransformer( transformers=[ - ('num', StandardScaler(), num_features), # Standard scaling for numerical features - ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features)], # One-hot encoding for categorical features + ('num', StandardScaler(), num_features), + ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features) + ], remainder='passthrough' ) -# Create the pipeline with preprocessing and the LightGBM regressor +# Create the pipeline with preprocessing and Random Forest Classifier pipeline = Pipeline(steps=[ ('preprocessor', preprocessor), - ('regressor', LGBMRegressor(**model_parameters)) + ('classifier', RandomForestClassifier(**random_forest_parameters)) ]) + # COMMAND ---------- +# Define the MLflow experiment and Git SHA for tracking mlflow.set_experiment(experiment_name='/Shared/child-health') git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" # Start an MLflow run to track the training process -with mlflow.start_run( - tags={"git_sha": f"{git_sha}", - "branch": "week1-2"}, -) as run: +with mlflow.start_run(tags={"git_sha": git_sha, "branch": "week1-2"}) as run: run_id = run.info.run_id + # Fit the pipeline to the training data pipeline.fit(X_train, y_train) y_pred = pipeline.predict(X_test) - # Evaluate the model performance - mse = mean_squared_error(y_test, y_pred) - mae = mean_absolute_error(y_test, y_pred) - r2 = r2_score(y_test, y_pred) + # Evaluate the model performance with classification metrics + accuracy = accuracy_score(y_test, y_pred) + report = classification_report(y_test, y_pred) - print(f"Mean Squared Error: {mse}") - print(f"Mean Absolute Error: {mae}") - print(f"R2 Score: {r2}") + print(f"Accuracy: {accuracy}") + print(f"Classification Report:\n{report}") # Log parameters, metrics, and the model to MLflow - mlflow.log_param("model_type", "LightGBM with preprocessing") - mlflow.log_params(model_parameters) - mlflow.log_metric("mse", mse) - mlflow.log_metric("mae", mae) - mlflow.log_metric("r2_score", r2) + mlflow.log_param("model_type", "Random Forest with preprocessing") + mlflow.log_params(random_forest_parameters) + mlflow.log_metric("accuracy", accuracy) signature = infer_signature(model_input=X_train, model_output=y_pred) + # Log the dataset source and model in MLflow dataset = mlflow.data.from_spark( - train_set_spark, table_name=f"{catalog_name}.{schema_name}.train_set", - version="0") + train_set_spark, table_name=f"{catalog_name}.{schema_name}.train_set", version="0") mlflow.log_input(dataset, context="training") - + mlflow.sklearn.log_model( sk_model=pipeline, - artifact_path="lightgbm-pipeline-model", + artifact_path="randomforest-pipeline-model", signature=signature ) - - + # COMMAND ---------- +# Register the model in MLflow model_version = mlflow.register_model( - model_uri=f'runs:/{run_id}/lightgbm-pipeline-model', - name=f"{catalog_name}.{schema_name}.child_health_model_basic", - tags={"git_sha": f"{git_sha}"}) + model_uri=f'runs:/{run_id}/randomforest-pipeline-model', + name=f"{catalog_name}.{schema_name}.child_health_model_randomforest", + tags={"git_sha": git_sha} +) # COMMAND ---------- +# Optionally, load the dataset source (for tracking purposes) run = mlflow.get_run(run_id) dataset_info = run.inputs.dataset_inputs[0].dataset dataset_source = mlflow.data.get_source(dataset_info) dataset_source.load() - -# COMMAND ---------- \ No newline at end of file diff --git a/notebooks/week2/04.log_and_register_custom_model.py b/notebooks/week2/05.log_and_register_custom_model.py similarity index 100% rename from notebooks/week2/04.log_and_register_custom_model.py rename to notebooks/week2/05.log_and_register_custom_model.py diff --git a/notebooks/week2/catboost_info/catboost_training.json b/notebooks/week2/catboost_info/catboost_training.json new file mode 100644 index 0000000..26ab181 --- /dev/null +++ b/notebooks/week2/catboost_info/catboost_training.json @@ -0,0 +1,204 @@ +{ 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4 files changed, 276 insertions(+), 2289 deletions(-) rename notebooks/week2/{05.log_and_register_custom_model.py => 05.wip_log_and_register_custom_model.py} (100%) create mode 100644 notebooks/week2/06.log_and_register_fe_model.py delete mode 100644 uv.lock diff --git a/notebooks/week2/05.log_and_register_custom_model.py b/notebooks/week2/05.wip_log_and_register_custom_model.py similarity index 100% rename from notebooks/week2/05.log_and_register_custom_model.py rename to notebooks/week2/05.wip_log_and_register_custom_model.py diff --git a/notebooks/week2/06.log_and_register_fe_model.py b/notebooks/week2/06.log_and_register_fe_model.py new file mode 100644 index 0000000..7cc88bf --- /dev/null +++ b/notebooks/week2/06.log_and_register_fe_model.py @@ -0,0 +1,276 @@ +# Databricks notebook source + +# Install necessary packages +%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl + +# COMMAND ---------- +# Restart the Python environment +dbutils.library.restartPython() + +# COMMAND ---------- +# Import libraries +import yaml +from databricks import feature_engineering +from pyspark.sql import SparkSession +from databricks.sdk import WorkspaceClient +import mlflow +from pyspark.sql import DataFrame, functions as F +from sklearn.ensemble import RandomForestClassifier +from mlflow.models import infer_signature +from sklearn.compose import ColumnTransformer +from sklearn.metrics import classification_report, accuracy_score +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler +from datetime import datetime +from databricks.feature_engineering import FeatureFunction, FeatureLookup +from childHealth.config import ProjectConfig + +# COMMAND ---------- +# Initialize Databricks session and clients +spark = SparkSession.builder.getOrCreate() +workspace = WorkspaceClient() +fe = feature_engineering.FeatureEngineeringClient() + +# Set up MLflow tracking URIs +mlflow.set_registry_uri("databricks-uc") +mlflow.set_tracking_uri("databricks") + +# COMMAND ---------- +# Load configuration from ProjectConfig +config = ProjectConfig.from_yaml(config_path="../../project_config.yml") + +# Extract configuration details +num_features = config.num_features +cat_features = config.cat_features +target = config.target +random_forest_parameters = config.random_forest_parameters +catalog_name = config.catalog_name +schema_name = config.schema_name + +# COMMAND ---------- +# Load training and test datasets +train_set = spark.table(f"{catalog_name}.{schema_name}.train_set") +test_set = spark.table(f"{catalog_name}.{schema_name}.test_set") +actigraph_table = spark.table(f"{catalog_name}.{schema_name}.actigraph_features") +actigraph_table = actigraph_table.drop("non_wear_flag") + +# COMMAND ---------- +# Define function to aggregate actigraphy data +def aggregate_actigraphy(data: DataFrame) -> DataFrame: + """ + Aggregate actigraphy data for each participant with summary statistics. + """ + aggregated_df = data.groupBy("id").agg( + F.mean("X").alias("X_mean"), + F.stddev("X").alias("X_std"), + F.max("X").alias("X_max"), + F.min("X").alias("X_min"), + + F.mean("Y").alias("Y_mean"), + F.stddev("Y").alias("Y_std"), + F.max("Y").alias("Y_max"), + F.min("Y").alias("Y_min"), + + F.mean("Z").alias("Z_mean"), + F.stddev("Z").alias("Z_std"), + F.max("Z").alias("Z_max"), + F.min("Z").alias("Z_min"), + + F.mean("enmo").alias("enmo_mean"), + F.stddev("enmo").alias("enmo_std"), + F.max("enmo").alias("enmo_max"), + F.min("enmo").alias("enmo_min"), + + F.mean("light").alias("light_mean"), + F.stddev("light").alias("light_std"), + F.max("light").alias("light_max"), + F.min("light").alias("light_min"), + + F.mean("battery_voltage").alias("battery_voltage_mean") + ) + return aggregated_df + +# Aggregate actigraphy data +aggregated_features_df = aggregate_actigraphy(actigraph_table) + +# Register the aggregated DataFrame as a temporary view for SQL insertion +aggregated_features_df.createOrReplaceTempView("aggregated_features_view") + +# COMMAND ---------- +# Create a feature table for actigraphy aggregated features +spark.sql(f""" + CREATE OR REPLACE TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features + ( id STRING NOT NULL, + X_mean DOUBLE, + X_std DOUBLE, + X_max DOUBLE, + X_min DOUBLE, + Y_mean DOUBLE, + Y_std DOUBLE, + Y_max DOUBLE, + Y_min DOUBLE, + Z_mean DOUBLE, + Z_std DOUBLE, + Z_max DOUBLE, + Z_min DOUBLE, + enmo_mean DOUBLE, + enmo_std DOUBLE, + enmo_max DOUBLE, + enmo_min DOUBLE, + light_mean DOUBLE, + light_std DOUBLE, + light_max DOUBLE, + light_min DOUBLE, + battery_voltage_mean DOUBLE + ) + TBLPROPERTIES (delta.enableChangeDataFeed = true) +""") + +spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features " + "ADD CONSTRAINT actigraoh_pk PRIMARY KEY(id);") + +spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features " + "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") + +# Insert data from the temporary view into the feature table +spark.sql(f""" + INSERT INTO {catalog_name}.{schema_name}.actigraph_aggregated_features + SELECT * FROM aggregated_features_view +""") + +# COMMAND ---------- +# Define a function to calculate the moving average of battery voltage + +function_name = f"{catalog_name}.{schema_name}.moving_average_battery_voltage" +# Define feature table and setup feature engineering +feature_table_name = f"{catalog_name}.{schema_name}.actigraph_aggregated_features" + +# Define a function to calculate the house's age using the current year and YearBuilt +spark.sql(f""" +CREATE OR REPLACE FUNCTION {function_name}(battery_voltage_mean Double) +RETURNS Double +LANGUAGE PYTHON AS +$$ +if battery_voltage_mean is None: + return None +else: + return battery_voltage_mean * 1.2 +) +$$ +""") + +# COMMAND ---------- + + +# Create the training set with feature lookups and feature function for moving average +training_set = fe.create_training_set( + df=train_set, + label=target, + feature_lookups=[ + FeatureLookup( + table_name=feature_table_name, + feature_names=["X_mean", "Y_mean", "Z_mean", "battery_voltage_mean"], # Specify relevant features here + lookup_key="id", + ), + FeatureFunction( + udf_name=function_name, + output_name="battery_voltage_mean_moving_avg", + input_bindings={"battery_voltage_mean": "battery_voltage_mean"} + ), + ], + exclude_columns=["update_timestamp_utc"] +) + +# COMMAND ---------- + +# Load feature-engineered DataFrame +training_df = training_set.load_df().toPandas() + +# COMMAND ---------- +# Create the test set with feature lookups and feature function for moving average +testing_set = fe.create_training_set( + df=test_set, + label=target, + feature_lookups=[ + FeatureLookup( + table_name=feature_table_name, + feature_names=["X_mean", "Y_mean", "Z_mean", "battery_voltage_mean"], # Specify relevant features here + lookup_key="id", + ), + FeatureFunction( + udf_name=function_name, + output_name="battery_voltage_mean_moving_avg", + input_bindings={"battery_voltage_mean": "battery_voltage_mean"} + ), + ], + exclude_columns=["update_timestamp_utc"] +) + +# COMMAND ---------- +# Load feature-engineered DataFrame +testing_df = testing_set.load_df().toPandas() + +# COMMAND ---------- + +# Split features and target +X_train = training_df[num_features + cat_features + ["battery_voltage_mean_moving_avg"]] +y_train = training_df[target] +X_test = test_set[num_features + cat_features + ["battery_voltage_mean_moving_avg"]] +y_test = test_set[target] + +# COMMAND ---------- + +# Define preprocessing for numerical and categorical features +preprocessor = ColumnTransformer( + transformers=[ + ('num', StandardScaler(), num_features), + ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features) + ], + remainder='passthrough' +) + +# Create the pipeline with preprocessing and Random Forest Classifier +pipeline = Pipeline(steps=[ + ('preprocessor', preprocessor), + ('classifier', RandomForestClassifier(**random_forest_parameters)) +]) + +# COMMAND ---------- + +# Define the MLflow experiment and Git SHA for tracking +mlflow.set_experiment(experiment_name='/Shared/child-health-fe') +git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" + +# Start an MLflow run to track the training process +with mlflow.start_run(tags={"git_sha": git_sha, "branch": "week1-2"}) as run: + run_id = run.info.run_id + + # Fit the pipeline to the training data + pipeline.fit(X_train, y_train) + y_pred = pipeline.predict(X_test) + + # Evaluate the model performance with classification metrics + accuracy = accuracy_score(y_test, y_pred) + report = classification_report(y_test, y_pred) + + print(f"Accuracy: {accuracy}") + print(f"Classification Report:\n{report}") + + # Log parameters, metrics, and the model to MLflow + mlflow.log_param("model_type", "Random Forest with preprocessing") + mlflow.log_params(random_forest_parameters) + 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00:00:00 2001 From: javedhassans Date: Wed, 13 Nov 2024 13:54:42 +0100 Subject: [PATCH 33/36] fixed week 2 notebooks commented pip install line 1 --- notebooks/week2/01.prepare_dataset.py | 2 +- notebooks/week2/02.prepare_featureTable.py | 2 +- notebooks/week2/04.log_and_register_model.py | 2 +- notebooks/week2/05.wip_log_and_register_custom_model.py | 2 +- notebooks/week2/06.log_and_register_fe_model.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/notebooks/week2/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py index d8ffc7b..58d4495 100644 --- a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -1,6 +1,6 @@ # Databricks notebook source -%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- diff --git a/notebooks/week2/02.prepare_featureTable.py b/notebooks/week2/02.prepare_featureTable.py index 559dc66..80456a7 100644 --- a/notebooks/week2/02.prepare_featureTable.py +++ b/notebooks/week2/02.prepare_featureTable.py @@ -1,6 +1,6 @@ # Databricks notebook source -%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- diff --git a/notebooks/week2/04.log_and_register_model.py b/notebooks/week2/04.log_and_register_model.py index e146724..eb25840 100644 --- a/notebooks/week2/04.log_and_register_model.py +++ b/notebooks/week2/04.log_and_register_model.py @@ -1,6 +1,6 @@ # Databricks notebook source -%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- dbutils.library.restartPython() diff --git a/notebooks/week2/05.wip_log_and_register_custom_model.py b/notebooks/week2/05.wip_log_and_register_custom_model.py index f5dc724..77cb0e5 100644 --- a/notebooks/week2/05.wip_log_and_register_custom_model.py +++ b/notebooks/week2/05.wip_log_and_register_custom_model.py @@ -1,5 +1,5 @@ # Databricks notebook source -%pip install mlops_with_databricks-0.0.1-py3-none-any.whl +# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- dbutils.library.restartPython() diff --git a/notebooks/week2/06.log_and_register_fe_model.py b/notebooks/week2/06.log_and_register_fe_model.py index 17aa292..9ee983f 100644 --- a/notebooks/week2/06.log_and_register_fe_model.py +++ b/notebooks/week2/06.log_and_register_fe_model.py @@ -1,7 +1,7 @@ # Databricks notebook source # Install necessary packages -%pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl +# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- # Restart the Python environment From 2f38f320746248fcf7c2a42d4b9f6a8137f2b6b4 Mon Sep 17 00:00:00 2001 From: javedhassans Date: Sun, 17 Nov 2024 09:37:11 +0100 Subject: [PATCH 34/36] fixed week 2 notebooks commented pip install line 1 --- notebooks/week1/01.dataExploraton.py | 4 +- notebooks/week1/01.featueExploration.ipynb | 1146 ++++++++ notebooks/week1/extracted_notebook_code.py | 1147 ++++++++ notebooks/week2/01.prepare_dataset.py | 5 +- notebooks/week2/02.prepare_featureTable.py | 4 +- notebooks/week2/04.log_and_register_model.py | 4 +- .../05.wip_log_and_register_custom_model.py | 4 +- .../week2/06.log_and_register_fe_model.py | 4 +- ...ops_with_databricks-0.0.1-py3-none-any.whl | Bin 11846 -> 15839 bytes project_config.yml | 26 +- src/childHealth/childhealth_model.py | 179 +- src/childHealth/config.py | 3 +- src/childHealth/data_processor.py | 107 +- src/childHealth/kappaLgm_model.py | 194 ++ uv.lock | 2366 ----------------- 15 files changed, 2737 insertions(+), 2456 deletions(-) create mode 100644 notebooks/week1/01.featueExploration.ipynb create mode 100644 notebooks/week1/extracted_notebook_code.py create mode 100644 src/childHealth/kappaLgm_model.py delete mode 100644 uv.lock diff --git a/notebooks/week1/01.dataExploraton.py b/notebooks/week1/01.dataExploraton.py index 506bd3e..765a180 100644 --- a/notebooks/week1/01.dataExploraton.py +++ b/notebooks/week1/01.dataExploraton.py @@ -1,11 +1,11 @@ # Databricks notebook source # COMMAND ---------- -%pip install ../mlops_with_databricks-0.0.1-py3-none-any.whl +#MAGIC %pip install ../mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- -dbutils.library.restartPython() +#MAGIC dbutils.library.restartPython() # COMMAND ---------- diff --git a/notebooks/week1/01.featueExploration.ipynb b/notebooks/week1/01.featueExploration.ipynb new file mode 100644 index 0000000..0effec8 --- /dev/null +++ b/notebooks/week1/01.featueExploration.ipynb @@ -0,0 +1,1146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### childHealth Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from childHealth.data_processor import TrainDataProcessor\n", + "from childHealth.config import ProjectConfig\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Specify dtypes for columns\n", + "dtypes = {\n", + " 'Basic_Demos-Age': 'float64',\n", + " 'Basic_Demos-Sex': 'float64',\n", + " 'CGAS-CGAS_Score': 'float64',\n", + " 'Physical-BMI': 'float64',\n", + " 'Physical-Height': 'float64',\n", + " 'Physical-Weight': 'float64',\n", + " 'Physical-Waist_Circumference': 'float64',\n", + " 'Physical-Diastolic_BP': 'float64',\n", + " 'Physical-HeartRate': 'float64',\n", + " 'Physical-Systolic_BP': 'float64',\n", + " 'Fitness_Endurance-Max_Stage': 'float64',\n", + " 'Fitness_Endurance-Time_Mins': 'float64',\n", + " 'Fitness_Endurance-Time_Sec': 'float64',\n", + " 'FGC-FGC_CU': 'float64',\n", + " 'FGC-FGC_CU_Zone': 'float64',\n", + " 'FGC-FGC_GSND': 'float64',\n", + " 'FGC-FGC_GSND_Zone': 'float64',\n", + " 'FGC-FGC_GSD': 'float64',\n", + " 'FGC-FGC_GSD_Zone': 'float64',\n", + " 'FGC-FGC_PU': 'float64',\n", + " 'FGC-FGC_PU_Zone': 'float64',\n", + " 'FGC-FGC_SRL': 'float64',\n", + " 'FGC-FGC_SRL_Zone': 'float64',\n", + " 'FGC-FGC_SRR': 'float64',\n", + " 'FGC-FGC_SRR_Zone': 'float64',\n", + " 'FGC-FGC_TL': 'float64',\n", + " 'FGC-FGC_TL_Zone': 'float64',\n", + " 'BIA-BIA_Activity_Level_num': 'float64',\n", + " 'BIA-BIA_BMC': 'float64',\n", + " 'BIA-BIA_BMI': 'float64',\n", + " 'BIA-BIA_BMR': 'float64',\n", + " 'BIA-BIA_DEE': 'float64',\n", + " 'BIA-BIA_ECW': 'float64',\n", + " 'BIA-BIA_FFM': 'float64',\n", + " 'BIA-BIA_FFMI': 'float64',\n", + " 'BIA-BIA_FMI': 'float64',\n", + " 'BIA-BIA_Fat': 'float64',\n", + " 'BIA-BIA_Frame_num': 'float64',\n", + " 'BIA-BIA_ICW': 'float64',\n", + " 'BIA-BIA_LDM': 'float64',\n", + " 'BIA-BIA_LST': 'float64',\n", + " 'BIA-BIA_SMM': 'float64',\n", + " 'BIA-BIA_TBW': 'float64',\n", + " 'PAQ_A-PAQ_A_Total': 'float64',\n", + " 'PAQ_C-PAQ_C_Total': 'float64',\n", + " 'PCIAT-PCIAT_01': 'float64',\n", + " 'PCIAT-PCIAT_02': 'float64',\n", + " 'PCIAT-PCIAT_03': 'float64',\n", + " 'PCIAT-PCIAT_04': 'float64',\n", + " 'PCIAT-PCIAT_05': 'float64',\n", + " 'PCIAT-PCIAT_06': 'float64',\n", + " 'PCIAT-PCIAT_07': 'float64',\n", + " 'PCIAT-PCIAT_08': 'float64',\n", + " 'PCIAT-PCIAT_09': 'float64',\n", + " 'PCIAT-PCIAT_10': 'float64',\n", + " 'PCIAT-PCIAT_11': 'float64',\n", + " 'PCIAT-PCIAT_12': 'float64',\n", + " 'PCIAT-PCIAT_13': 'float64',\n", + " 'PCIAT-PCIAT_14': 'float64',\n", + " 'PCIAT-PCIAT_15': 'float64',\n", + " 'PCIAT-PCIAT_16': 'float64',\n", + " 'PCIAT-PCIAT_17': 'float64',\n", + " 'PCIAT-PCIAT_18': 'float64',\n", + " 'PCIAT-PCIAT_19': 'float64',\n", + " 'PCIAT-PCIAT_20': 'float64',\n", + " 'PCIAT-PCIAT_Total': 'float64',\n", + " 'SDS-SDS_Total_Raw': 'float64',\n", + " 'SDS-SDS_Total_T': 'float64',\n", + " 'PreInt_EduHx-computerinternet_hoursday': 'float64',\n", + " 'Basic_Demos-Enroll_Season': 'object',\n", + " 'CGAS-Season': 'object',\n", + " 'Physical-Season': 'object',\n", + " 'Fitness_Endurance-Season': 'object',\n", + " 'FGC-Season': 'object',\n", + " 'BIA-Season': 'object',\n", + " 'PAQ_A-Season': 'object',\n", + " 'PAQ_C-Season': 'object',\n", + " 'PCIAT-Season': 'object',\n", + " 'SDS-Season': 'object',\n", + " 'sii': 'float64'\n", + "}\n", + "\n", + "# Load data with specified dtypes\n", + "df = pd.read_csv('../../data/childHealth.csv', dtype=dtypes)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { 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" + ], + "text/plain": [ + " Minimum PCIAT total Score Maximum total PCIAT Score\n", + "sii \n", + "0.0 0.0 30.0\n", + "1.0 31.0 49.0\n", + "2.0 50.0 79.0\n", + "3.0 80.0 93.0" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pciat_min_max = df.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max'])\n", + "pciat_min_max = pciat_min_max.rename(\n", + " columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'}\n", + ")\n", + "pciat_min_max" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "# Example list of column names\n", + "columns = df.columns\n", + "\n", + "# Regular expression patterns\n", + "pattern_pciat = re.compile(r'^PCIAT-PCIAT.*')\n", + "pattern_season = re.compile(r'^PCIAT-Season.*')\n", + "\n", + "# Extract columns that match the patterns\n", + "pciat_columns = [col for col in columns if pattern_pciat.match(col)]\n", + "season_columns = [col for col in columns if pattern_season.match(col)]\n", + "\n", + "# Combine the lists of columns\n", + "selected_columns = pciat_columns + season_columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PCIAT-PCIAT_01PCIAT-PCIAT_02PCIAT-PCIAT_03PCIAT-PCIAT_04PCIAT-PCIAT_05PCIAT-PCIAT_06PCIAT-PCIAT_07PCIAT-PCIAT_08PCIAT-PCIAT_09PCIAT-PCIAT_10...PCIAT-PCIAT_13PCIAT-PCIAT_14PCIAT-PCIAT_15PCIAT-PCIAT_16PCIAT-PCIAT_17PCIAT-PCIAT_18PCIAT-PCIAT_19PCIAT-PCIAT_20PCIAT-PCIAT_TotalPCIAT-Season
05.04.04.00.04.00.00.04.00.00.0...4.04.04.04.04.04.02.04.055.0Fall
10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.0Fall
25.02.02.01.02.01.01.02.01.01.0...1.01.01.00.02.02.01.01.028.0Fall
34.02.04.00.05.01.00.03.02.02.0...3.00.00.03.04.03.04.01.044.0Summer
53.03.03.00.02.01.00.02.02.01.0...3.03.02.01.03.01.02.01.034.0Summer
..................................................................
39533.03.03.00.00.00.00.03.00.00.0...2.00.00.03.00.02.02.01.022.0Fall
39541.03.03.00.03.00.00.00.00.00.0...5.01.00.05.03.03.03.00.033.0Summer
39553.03.03.02.03.02.02.02.02.01.0...2.00.01.00.02.01.01.00.032.0Winter
39575.05.03.00.05.01.00.02.00.02.0...1.03.00.00.01.01.00.01.031.0Winter
39582.01.01.01.00.00.00.01.01.01.0...1.01.02.01.01.01.01.01.019.0Spring
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2736 rows × 22 columns

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" + ], + "text/plain": [ + " PCIAT-PCIAT_01 PCIAT-PCIAT_02 PCIAT-PCIAT_03 PCIAT-PCIAT_04 \\\n", + "0 5.0 4.0 4.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 5.0 2.0 2.0 1.0 \n", + "3 4.0 2.0 4.0 0.0 \n", + "5 3.0 3.0 3.0 0.0 \n", + "... ... ... ... ... \n", + "3953 3.0 3.0 3.0 0.0 \n", + "3954 1.0 3.0 3.0 0.0 \n", + "3955 3.0 3.0 3.0 2.0 \n", + "3957 5.0 5.0 3.0 0.0 \n", + "3958 2.0 1.0 1.0 1.0 \n", + "\n", + " PCIAT-PCIAT_05 PCIAT-PCIAT_06 PCIAT-PCIAT_07 PCIAT-PCIAT_08 \\\n", + "0 4.0 0.0 0.0 4.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 2.0 1.0 1.0 2.0 \n", + "3 5.0 1.0 0.0 3.0 \n", + "5 2.0 1.0 0.0 2.0 \n", + "... ... ... ... ... \n", + "3953 0.0 0.0 0.0 3.0 \n", + "3954 3.0 0.0 0.0 0.0 \n", + "3955 3.0 2.0 2.0 2.0 \n", + "3957 5.0 1.0 0.0 2.0 \n", + "3958 0.0 0.0 0.0 1.0 \n", + "\n", + " PCIAT-PCIAT_09 PCIAT-PCIAT_10 ... PCIAT-PCIAT_13 PCIAT-PCIAT_14 \\\n", + "0 0.0 0.0 ... 4.0 4.0 \n", + "1 0.0 0.0 ... 0.0 0.0 \n", + "2 1.0 1.0 ... 1.0 1.0 \n", + "3 2.0 2.0 ... 3.0 0.0 \n", + "5 2.0 1.0 ... 3.0 3.0 \n", + "... ... ... ... ... ... \n", + "3953 0.0 0.0 ... 2.0 0.0 \n", + "3954 0.0 0.0 ... 5.0 1.0 \n", + "3955 2.0 1.0 ... 2.0 0.0 \n", + "3957 0.0 2.0 ... 1.0 3.0 \n", + "3958 1.0 1.0 ... 1.0 1.0 \n", + "\n", + " PCIAT-PCIAT_15 PCIAT-PCIAT_16 PCIAT-PCIAT_17 PCIAT-PCIAT_18 \\\n", + "0 4.0 4.0 4.0 4.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 1.0 0.0 2.0 2.0 \n", + "3 0.0 3.0 4.0 3.0 \n", + "5 2.0 1.0 3.0 1.0 \n", + "... ... ... ... ... \n", + "3953 0.0 3.0 0.0 2.0 \n", + "3954 0.0 5.0 3.0 3.0 \n", + "3955 1.0 0.0 2.0 1.0 \n", + "3957 0.0 0.0 1.0 1.0 \n", + "3958 2.0 1.0 1.0 1.0 \n", + "\n", + " PCIAT-PCIAT_19 PCIAT-PCIAT_20 PCIAT-PCIAT_Total PCIAT-Season \n", + "0 2.0 4.0 55.0 Fall \n", + "1 0.0 0.0 0.0 Fall \n", + "2 1.0 1.0 28.0 Fall \n", + "3 4.0 1.0 44.0 Summer \n", + "5 2.0 1.0 34.0 Summer \n", + "... ... ... ... ... \n", + "3953 2.0 1.0 22.0 Fall \n", + "3954 3.0 0.0 33.0 Summer \n", + "3955 1.0 0.0 32.0 Winter \n", + "3957 0.0 1.0 31.0 Winter \n", + "3958 1.0 1.0 19.0 Spring \n", + "\n", + "[2736 rows x 22 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_with_sii = df[df['sii'].notna()][selected_columns]\n", + "train_with_sii" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "df['complete_resp_total'] = df['PCIAT-PCIAT_Total'].where(\n", + " df[PCIAT_cols].notna().all(axis=1), np.nan\n", + ")\n", + "\n", + "sii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'}\n", + "df['sii'] = df['sii'].map(sii_map).fillna('Missing')\n", + "\n", + "sii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)']\n", + "df['sii'] = pd.Categorical(df['sii'], categories=sii_order, ordered=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sii\n", + "0 (None) 1594\n", + "Missing 1224\n", + "1 (Mild) 730\n", + "2 (Moderate) 378\n", + "3 (Severe) 34\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['sii'].value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Missing\n", + "1 Missing\n", + "2 Missing\n", + "3 Missing\n", + "4 Missing\n", + " ... \n", + "3955 Missing\n", + "3956 Missing\n", + "3957 Missing\n", + "3958 Missing\n", + "3959 Missing\n", + "Name: sii, Length: 3960, dtype: category\n", + "Categories (5, object): ['Missing' < '0' < '1' < '2' < '3']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['sii']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "sii_counts = df['sii'].value_counts().reset_index()\n", + "total = sii_counts['count'].sum()\n", + "sii_counts['percentage'] = (sii_counts['count'] / total) * 100\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# SII\n", + "sns.barplot(x='sii', y='count', data=sii_counts, palette='Blues_d', ax=axes[0])\n", + "axes[0].set_title('Distribution of Severity Impairment Index (sii)', fontsize=14)\n", + "for p in axes[0].patches:\n", + " height = p.get_height()\n", + " percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0]\n", + " axes[0].text(\n", + " p.get_x() + p.get_width() / 2,\n", + " height + 5, f'{int(height)} ({percentage:.1f}%)',\n", + " ha=\"center\", fontsize=12\n", + " )\n", + "\n", + "# PCIAT_Total for complete responses\n", + "sns.histplot(df['complete_resp_total'].dropna(), bins=20, ax=axes[1])\n", + "axes[1].set_title('Distribution of PCIAT_Total', fontsize=14)\n", + "axes[1].set_xlabel('PCIAT_Total for Complete PCIAT Responses')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Cleaning Ends" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the project configuration\n", + "config = ProjectConfig.from_yaml(config_path=\"../../project_config.yml\")\n", + "\n", + "# Initialize the TrainDataProcessor with the DataFrame and configuration instance\n", + "preprocessor = TrainDataProcessor(\n", + " train_df=df, config=config\n", + ")\n", + "\n", + "dff = preprocessor.process()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Basic_Demos-AgeCGAS-CGAS_ScorePhysical-BMIPhysical-HeightPhysical-WeightPhysical-Waist_CircumferencePhysical-Diastolic_BPPhysical-HeartRatePhysical-Systolic_BPFitness_Endurance-Max_Stage...CGAS-SeasonPhysical-SeasonFitness_Endurance-SeasonFGC-SeasonBIA-SeasonPAQ_A-SeasonPAQ_C-SeasonPCIAT-SeasonSDS-Seasonsii
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" + ], + "text/plain": [ + " Basic_Demos-Age CGAS-CGAS_Score Physical-BMI Physical-Height \\\n", + "0 -1.520226 -0.827621 -0.549541 -1.522486e+00 \n", + "1 -0.401093 0.000000 -1.185749 -1.216358e+00 \n", + "2 -0.121310 0.317497 -0.600725 8.468847e-02 \n", + "3 -0.401093 0.317497 -0.232743 8.156347e-03 \n", + "4 2.116955 0.000000 0.000000 1.087587e-15 \n", + "\n", + " Physical-Weight Physical-Waist_Circumference Physical-Diastolic_BP \\\n", + "0 -0.973630 2.681625e-15 -1.209035e-15 \n", + "1 -1.095847 -1.992136e+00 4.552580e-01 \n", + "2 -0.342173 2.681625e-15 -3.955246e-01 \n", + "3 -0.189402 2.681625e-15 -8.209160e-01 \n", + "4 0.000000 2.681625e-15 -1.209035e-15 \n", + "\n", + " Physical-HeartRate Physical-Systolic_BP Fitness_Endurance-Max_Stage ... \\\n", + "0 0.000000 -9.645525e-16 -1.018757e-15 ... \n", + "1 -0.980620 3.405206e-01 -1.018757e-15 ... \n", + "2 1.048732 1.148855e-03 1.235014e-02 ... \n", + "3 1.302401 1.148855e-03 1.159369e+00 ... \n", + "4 0.000000 -9.645525e-16 -1.018757e-15 ... \n", + "\n", + " CGAS-Season Physical-Season Fitness_Endurance-Season FGC-Season \\\n", + "0 Winter Fall Spring Fall \n", + "1 Spring Fall Spring Fall \n", + "2 Fall Fall Fall Fall \n", + "3 Fall Summer Summer Summer \n", + "4 Summer Spring Spring Spring \n", + "\n", + " BIA-Season PAQ_A-Season PAQ_C-Season PCIAT-Season SDS-Season sii \n", + "0 Fall Winter Spring Fall Spring 2.0 \n", + "1 Winter Winter Fall Fall Fall 0.0 \n", + "2 Summer Winter Summer Fall Fall 0.0 \n", + "3 Summer Winter Winter Summer Summer 1.0 \n", + "4 Summer Summer Spring Spring Spring 4.0 \n", + "\n", + "[5 rows x 80 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dff.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MOdeling" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, accuracy_score, cohen_kappa_score\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "from sklearn.pipeline import Pipeline\n", + "import lightgbm as lgb\n", + "from scipy.optimize import minimize\n", + "\n", + "from childHealth.kappaLgm_model import ChildHealthModel" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001322 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6016\n", + "[LightGBM] [Info] Number of data points in the train set: 2772, number of used features: 99\n", + "[LightGBM] [Info] Start training from score -0.909818\n", + "[LightGBM] [Info] Start training from score -1.690955\n", + "[LightGBM] [Info] Start training from score -2.351375\n", + "[LightGBM] [Info] Start training from score -4.749271\n", + "[LightGBM] [Info] Start training from score -1.173886\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" + ] + }, + { + "ename": "TypeError", + "evalue": "loop of ufunc does not support argument 0 of type Dataset which has no callable rint method", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'rint'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 23\u001b[0m\n\u001b[1;32m 20\u001b[0m X_val, X_test, y_val, y_test \u001b[38;5;241m=\u001b[39m train_test_split(X_temp, y_temp, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, stratify\u001b[38;5;241m=\u001b[39my_temp, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Train model\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m \u001b[43mchild_health_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_val\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Out-of-Fold predictions for threshold optimization\u001b[39;00m\n\u001b[1;32m 26\u001b[0m oof_preds \u001b[38;5;241m=\u001b[39m child_health_model\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/childHealth/kappaLgm_model.py:85\u001b[0m, in \u001b[0;36mChildHealthModel.train\u001b[0;34m(self, X_train, y_train, X_val, y_val)\u001b[0m\n\u001b[1;32m 82\u001b[0m val_data \u001b[38;5;241m=\u001b[39m lgb\u001b[38;5;241m.\u001b[39mDataset(X_val, label\u001b[38;5;241m=\u001b[39my_val, reference\u001b[38;5;241m=\u001b[39mtrain_data)\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# Train with early stopping\u001b[39;00m\n\u001b[0;32m---> 85\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[43mlgb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlgb_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 87\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_set\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalid_sets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mval_data\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalid_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalidation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[43mfeval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkappa_metric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# early_stopping_rounds=10\u001b[39;49;00m\n\u001b[1;32m 92\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/lightgbm/engine.py:314\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, feature_name, categorical_feature, keep_training_booster, callbacks)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_valid_contain_train:\n\u001b[1;32m 313\u001b[0m evaluation_result_list\u001b[38;5;241m.\u001b[39mextend(booster\u001b[38;5;241m.\u001b[39meval_train(feval))\n\u001b[0;32m--> 314\u001b[0m evaluation_result_list\u001b[38;5;241m.\u001b[39mextend(\u001b[43mbooster\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval_valid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeval\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cb \u001b[38;5;129;01min\u001b[39;00m callbacks_after_iter:\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/lightgbm/basic.py:4423\u001b[0m, in \u001b[0;36mBooster.eval_valid\u001b[0;34m(self, feval)\u001b[0m\n\u001b[1;32m 4391\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21meval_valid\u001b[39m(\n\u001b[1;32m 4392\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4393\u001b[0m feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4394\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[_LGBM_BoosterEvalMethodResultType]:\n\u001b[1;32m 4395\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Evaluate for validation data.\u001b[39;00m\n\u001b[1;32m 4396\u001b[0m \n\u001b[1;32m 4397\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4421\u001b[0m \u001b[38;5;124;03m List with (validation_dataset_name, eval_name, eval_result, is_higher_better) tuples.\u001b[39;00m\n\u001b[1;32m 4422\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\n\u001b[1;32m 4424\u001b[0m \u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\n\u001b[1;32m 4425\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__num_dataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4426\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__inner_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname_valid_sets\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4427\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/lightgbm/basic.py:4426\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 4391\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21meval_valid\u001b[39m(\n\u001b[1;32m 4392\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4393\u001b[0m feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4394\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[_LGBM_BoosterEvalMethodResultType]:\n\u001b[1;32m 4395\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Evaluate for validation data.\u001b[39;00m\n\u001b[1;32m 4396\u001b[0m \n\u001b[1;32m 4397\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4421\u001b[0m \u001b[38;5;124;03m List with (validation_dataset_name, eval_name, eval_result, is_higher_better) tuples.\u001b[39;00m\n\u001b[1;32m 4422\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 4423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m 4424\u001b[0m item\n\u001b[1;32m 4425\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__num_dataset)\n\u001b[0;32m-> 4426\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__inner_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname_valid_sets\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4427\u001b[0m ]\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/lightgbm/basic.py:5187\u001b[0m, in \u001b[0;36mBooster.__inner_eval\u001b[0;34m(self, data_name, data_idx, feval)\u001b[0m\n\u001b[1;32m 5185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m eval_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 5186\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m-> 5187\u001b[0m feval_ret \u001b[38;5;241m=\u001b[39m \u001b[43meval_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__inner_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_idx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcur_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(feval_ret, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m 5189\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m eval_name, val, is_higher_better \u001b[38;5;129;01min\u001b[39;00m feval_ret:\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/childHealth/kappaLgm_model.py:17\u001b[0m, in \u001b[0;36mkappa_metric\u001b[0;34m(y_true, y_pred_raw)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mkappa_metric\u001b[39m(y_true, y_pred_raw):\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Use rounding to convert predicted probabilities to the nearest class\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mround\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_pred_raw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkappa\u001b[39m\u001b[38;5;124m'\u001b[39m, cohen_kappa_score(y_true, y_pred, weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquadratic\u001b[39m\u001b[38;5;124m'\u001b[39m), \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3360\u001b[0m, in \u001b[0;36mround\u001b[0;34m(a, decimals, out)\u001b[0m\n\u001b[1;32m 3269\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_round_dispatcher)\n\u001b[1;32m 3270\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mround\u001b[39m(a, decimals\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3271\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3272\u001b[0m \u001b[38;5;124;03m Evenly round to the given number of decimals.\u001b[39;00m\n\u001b[1;32m 3273\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3358\u001b[0m \n\u001b[1;32m 3359\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mround\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecimals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/numpy/core/fromnumeric.py:56\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 54\u001b[0m bound \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, method, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bound \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m bound(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n", + "File \u001b[0;32m~/marvelous-databricks-course-javedhassans/.venv/lib/python3.11/site-packages/numpy/core/fromnumeric.py:45\u001b[0m, in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 44\u001b[0m wrap \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 45\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wrap:\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, mu\u001b[38;5;241m.\u001b[39mndarray):\n", + "\u001b[0;31mTypeError\u001b[0m: loop of ufunc does not support argument 0 of type Dataset which has no callable rint method" + ] + } + ], + "source": [ + "# Load the project configuration\n", + "config = ProjectConfig.from_yaml(config_path=\"../../project_config.yml\")\n", + "\n", + "X = dff[config.num_features + config.cat_features]\n", + "y = dff[config.target]\n", + "\n", + "# Initialize the TrainDataProcessor with the DataFrame and configuration instance\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('num', StandardScaler(), config.num_features),\n", + " ('cat', OneHotEncoder(drop='first'), config.cat_features)\n", + " ]\n", + ")\n", + "\n", + "# Initialize model class\n", + "child_health_model = ChildHealthModel(preprocessor, config)\n", + "\n", + "# Split data\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n", + "\n", + "# Train model\n", + "child_health_model.train(X_train, y_train, X_val, y_val)\n", + "\n", + "# Out-of-Fold predictions for threshold optimization\n", + "oof_preds = child_health_model.predict_proba(X_test)\n", + "child_health_model.optimize_thresholds(y_test, oof_preds)\n", + "\n", + "# Evaluate model\n", + "report, accuracy, kappa = child_health_model.evaluate(X_test, y_test)\n", + "print(\"Evaluation Results:\")\n", + "print(report)\n", + "print(\"Accuracy:\", accuracy)\n", + "print(\"Kappa Score:\", kappa)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/week1/extracted_notebook_code.py b/notebooks/week1/extracted_notebook_code.py new file mode 100644 index 0000000..8f5170e --- /dev/null +++ b/notebooks/week1/extracted_notebook_code.py @@ -0,0 +1,1147 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +import seaborn as sns +import warnings + +warnings.filterwarnings('ignore', category=FutureWarning) + +sns.set(style="whitegrid") +%matplotlib inline + +train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv') +test = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv') +data_dict = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv') + +display(train.head()) +print(f"Train shape: {train.shape}") + +display(test.head()) +print(f"Test shape: {test.shape}") + +data_dict.head() + +def calculate_stats(data, columns): + if isinstance(columns, str): + columns = [columns] + + stats = [] + for col in columns: + if data[col].dtype in ['object', 'category']: + counts = data[col].value_counts(dropna=False, sort=False) + percents = data[col].value_counts(normalize=True, dropna=False, sort=False) * 100 + formatted = counts.astype(str) + ' (' + percents.round(2).astype(str) + '%)' + stats_col = pd.DataFrame({'count (%)': formatted}) + stats.append(stats_col) + else: + stats_col = data[col].describe().to_frame().transpose() + stats_col['missing'] = data[col].isnull().sum() + stats_col.index.name = col + stats.append(stats_col) + + return pd.concat(stats, axis=0) + +train_cols = set(train.columns) +test_cols = set(test.columns) +columns_not_in_test = sorted(list(train_cols - test_cols)) +data_dict[data_dict['Field'].isin(columns_not_in_test)] + +pciat_min_max = train.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max']) +pciat_min_max = pciat_min_max.rename( + columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'} +) +pciat_min_max + +data_dict[data_dict['Field'] == 'PCIAT-PCIAT_Total']['Value Labels'].iloc[0] + +train_with_sii = train[train['sii'].notna()][columns_not_in_test] +train_with_sii[train_with_sii.isna().any(axis=1)].head().style.applymap( + lambda x: 'background-color: #FFC0CB' if pd.isna(x) else '' +) + +PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)] +recalc_total_score = train_with_sii[PCIAT_cols].sum( + axis=1, skipna=True +) +(recalc_total_score == train_with_sii['PCIAT-PCIAT_Total']).all() + +def recalculate_sii(row): + if pd.isna(row['PCIAT-PCIAT_Total']): + return np.nan + max_possible = row['PCIAT-PCIAT_Total'] + row[PCIAT_cols].isna().sum() * 5 + if row['PCIAT-PCIAT_Total'] <= 30 and max_possible <= 30: + return 0 + elif 31 <= row['PCIAT-PCIAT_Total'] <= 49 and max_possible <= 49: + return 1 + elif 50 <= row['PCIAT-PCIAT_Total'] <= 79 and max_possible <= 79: + return 2 + elif row['PCIAT-PCIAT_Total'] >= 80 and max_possible >= 80: + return 3 + return np.nan + +train['recalc_sii'] = train.apply(recalculate_sii, axis=1) + +mismatch_rows = train[ + (train['recalc_sii'] != train['sii']) & train['sii'].notna() +] + +mismatch_rows[PCIAT_cols + [ + 'PCIAT-PCIAT_Total', 'sii', 'recalc_sii' +]].style.applymap( + lambda x: 'background-color: #FFC0CB' if pd.isna(x) else '' +) + +train['sii'] = train['recalc_sii'] +train['complete_resp_total'] = train['PCIAT-PCIAT_Total'].where( + train[PCIAT_cols].notna().all(axis=1), np.nan +) + +sii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'} +train['sii'] = train['sii'].map(sii_map).fillna('Missing') + +sii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)'] +train['sii'] = pd.Categorical(train['sii'], categories=sii_order, ordered=True) + +train.drop(columns='recalc_sii', inplace=True) + +sii_counts = train['sii'].value_counts().reset_index() +total = sii_counts['count'].sum() +sii_counts['percentage'] = (sii_counts['count'] / total) * 100 + +fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + +# SII +sns.barplot(x='sii', y='count', data=sii_counts, palette='Blues_d', ax=axes[0]) +axes[0].set_title('Distribution of Severity Impairment Index (sii)', fontsize=14) +for p in axes[0].patches: + height = p.get_height() + percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0] + axes[0].text( + p.get_x() + p.get_width() / 2, + height + 5, f'{int(height)} ({percentage:.1f}%)', + ha="center", fontsize=12 + ) + +# PCIAT_Total for complete responses +sns.histplot(train['complete_resp_total'].dropna(), bins=20, ax=axes[1]) +axes[1].set_title('Distribution of PCIAT_Total', fontsize=14) +axes[1].set_xlabel('PCIAT_Total for Complete PCIAT Responses') + +plt.tight_layout() +plt.show() + +len(train[train['complete_resp_total'] == 0]) + +assert train['Basic_Demos-Age'].isna().sum() == 0 +assert train['Basic_Demos-Sex'].isna().sum() == 0 + +train['Age Group'] = pd.cut( + train['Basic_Demos-Age'], + bins=[4, 12, 18, 22], + labels=['Children (5-12)', 'Adolescents (13-18)', 'Adults (19-22)'] +) +calculate_stats(train, 'Age Group') + +sex_map = {0: 'Male', 1: 'Female'} +train['Basic_Demos-Sex'] = train['Basic_Demos-Sex'].map(sex_map) +calculate_stats(train, 'Basic_Demos-Sex') + +fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + +# SII by Age +sns.boxplot(y=train['Basic_Demos-Age'], x=train['sii'], ax=axes[0], palette="Set3") +axes[0].set_title('SII by Age') +axes[0].set_ylabel('Age') +axes[0].set_xlabel('SII') + +# Complete PCIAT Responses by Age Group +sns.boxplot( + x='Age Group', y='complete_resp_total', + data=train, palette="Set3", ax=axes[1] +) +axes[1].set_title('Complete PCIAT Responses by Age Group') +axes[1].set_ylabel('PCIAT_Total for Complete Responses') +axes[1].set_xlabel('Age Group') + +# PCIAT_Total by Sex +sns.histplot( + data=train, x='complete_resp_total', + hue='Basic_Demos-Sex', multiple='stack', + palette="Set3", bins=20, ax=axes[2] +) +axes[2].set_title('PCIAT_Total Distribution by Sex') +axes[2].set_xlabel('PCIAT_Total for Complete Responses') +axes[2].set_ylabel('Frequency') + +plt.tight_layout() +plt.show() + +stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0) +fig, axes = plt.subplots(1, len(stats), figsize=(18, 5)) + +for i, age_group in enumerate(stats.index): + group_counts = stats.loc[age_group] / stats.loc[age_group].sum() + axes[i].pie( + group_counts, labels=group_counts.index, autopct='%1.1f%%', + startangle=90, colors=sns.color_palette("Set3"), + labeldistance=1.05, pctdistance=0.80 + ) + axes[i].set_title(f'SII Distribution for {age_group}') + axes[i].axis('equal') + +plt.tight_layout() +plt.show() + +stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0) +stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 + +stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' +stats + +stats = train[train['sii'] != 'Missing'].groupby( + ['Age Group', 'sii'] +).size().unstack(fill_value=0) +stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 + +stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' +stats + +data = train[train['PreInt_EduHx-computerinternet_hoursday'].notna()] +age_range = data['Basic_Demos-Age'] +print( + f"Age range for participants with measured PreInt_EduHx-computerinternet_hoursday data:" + f" {age_range.min()} - {age_range.max()} years" +) + +train['PreInt_EduHx-computerinternet_hoursday'].unique() + +param_map = {0: '< 1h/day', 1: '~ 1h/day', 2: '~ 2hs/day', 3: '> 3hs/day'} +train['internet_use_encoded'] = train[ + 'PreInt_EduHx-computerinternet_hoursday' +].map(param_map).fillna('Missing') + +param_ord = ['Missing', '< 1h/day', '~ 1h/day', '~ 2hs/day', '> 3hs/day'] +train['internet_use_encoded'] = pd.Categorical( + train['internet_use_encoded'], categories=param_ord, + ordered=True +) + +calculate_stats(train, 'PreInt_EduHx-Season') + +fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + +# Hours of Internet Use +ax1 = sns.countplot(x='internet_use_encoded', data=train, palette="Set3", ax=axes[0]) +axes[0].set_title('Distribution of Hours of Internet Use') +axes[0].set_xlabel('Hours per Day Group') +axes[0].set_ylabel('Count') + +total = len(train['internet_use_encoded']) +for p in ax1.patches: + count = int(p.get_height()) + percentage = '{:.1f}%'.format(100 * count / total) + ax1.annotate(f'{count} ({percentage})', (p.get_x() + p.get_width() / 2., p.get_height()), + ha='center', va='baseline', fontsize=10, color='black', xytext=(0, 5), + textcoords='offset points') + +# Hours of Internet Use by Age +sns.boxplot(y=train['Basic_Demos-Age'], x=train['internet_use_encoded'], ax=axes[1], palette="Set3") +axes[1].set_title('Hours of Internet Use by Age') +axes[1].set_ylabel('Age') +axes[1].set_xlabel('Hours per Day Group') + +# Hours of Internet Use (numeric) by Age Group +sns.boxplot(y='PreInt_EduHx-computerinternet_hoursday', x='Age Group', data=train, ax=axes[2], palette="Set3") +axes[2].set_title('Internet Hours by Age Group') +axes[2].set_ylabel('Hours per Day (Numeric)') +axes[2].set_xlabel('Age Group') + +plt.tight_layout() +plt.show() + +stats = train.groupby( + ['Age Group', 'internet_use_encoded'] +).size().unstack(fill_value=0) +fig, axes = plt.subplots(1, len(stats), figsize=(18, 5)) + +for i, age_group in enumerate(stats.index): + group_counts = stats.loc[age_group] / stats.loc[age_group].sum() + axes[i].pie(group_counts, labels=group_counts.index, autopct='%1.1f%%', + startangle=90, colors=sns.color_palette("Set3"), labeldistance=1.1) + axes[i].set_title(f'Distribution of Hours of Internet Use\n{age_group}') + axes[i].axis('equal') + +plt.tight_layout() +plt.show() + +train_non_na = train.dropna(subset=['PreInt_EduHx-computerinternet_hoursday']) +rows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 3).sum() +print(f"Non-NA Rows - Internet use 3h or more: {(rows / len(train_non_na)) * 100:.2f}%") + +rows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 0).sum() +print(f"Non-NA Rows - Internet use 1h or less: {(rows / len(train_non_na)) * 100:.2f}%") + +stats = train.groupby(['Basic_Demos-Sex', 'internet_use_encoded'] +).size().unstack(fill_value=0) +stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 + +stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' +stats + +sii_reported = train[train['sii'] != "Missing"] +sii_reported.loc[:, 'sii'] = sii_reported['sii'].cat.remove_unused_categories() + +stats = sii_reported.groupby( + ['internet_use_encoded', 'sii'] +).size().unstack(fill_value=0) +stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 + +stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' +stats + +fig = plt.figure(figsize=(12, 10)) +gs = fig.add_gridspec(2, 2, height_ratios=[1, 1.5]) + +# SII vs Hours of Internet Use +ax1 = fig.add_subplot(gs[0, 0]) +sns.boxplot( + x='sii', y='PreInt_EduHx-computerinternet_hoursday', + data=sii_reported, + ax=ax1, palette="Set3" +) +ax1.set_title('SII vs Hours of Internet Use') +ax1.set_ylabel('Hours per Day') +ax1.set_xlabel('SII') + +# PCIAT_Total for Complete PCIAT Responses by Hours of Internet Use +ax2 = fig.add_subplot(gs[0, 1]) +sns.boxplot( + x='internet_use_encoded', y='complete_resp_total', + data=sii_reported, + palette="Set3", ax=ax2 +) +ax2.set_title('PCIAT_Total by Hours of Internet Use') +ax2.set_ylabel('PCIAT_Total for Complete PCIAT Responses') +ax2.set_xlabel('Hours per Day Group') + +# SII vs Hours of Internet Use by Age Group (Full width) +ax3 = fig.add_subplot(gs[1, :]) +sns.boxplot( + x='internet_use_encoded', y='complete_resp_total', + data=sii_reported, + hue='Age Group', ax=ax3, palette="Set3" +) +ax3.set_title('PCIAT_Total vs Hours of Internet Use by Age Group') +ax3.set_ylabel('PCIAT_Total for Complete PCIAT Responses') +ax3.set_xlabel('Hours per Day Group') + +plt.tight_layout() +plt.show() + +stats = sii_reported.groupby( + ['sii', 'internet_use_encoded'] +).size().unstack(fill_value=0) +fig, axes = plt.subplots(1, len(stats), figsize=(18, 5)) + +for i, sii_group in enumerate(stats.index): + group_counts = stats.loc[sii_group] / stats.loc[sii_group].sum() + axes[i].pie( + group_counts, labels=group_counts.index, autopct='%1.1f%%', + startangle=90, colors=sns.color_palette("Set3"), labeldistance=1.1 + ) + axes[i].set_title(f'Hours of using computer/internet\n for SII = {sii_group}') + axes[i].axis('equal') + +plt.tight_layout() +plt.show() + +stats = sii_reported.groupby( + ['sii', 'internet_use_encoded'] +).size().unstack(fill_value=0) +stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 + +stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' +stats + +train[ + (train['internet_use_encoded'] == '< 1h/day') & + (train['sii'].isin(['2 (Moderate)', '3 (Severe)'])) +]['Basic_Demos-Age'].describe() + +groups = data_dict.groupby('Instrument')['Field'].apply(list).to_dict() + +for instrument, features in groups.items(): + print(f"{instrument}: {features}\n") + +season_columns = [col for col in train.columns if 'Season' in col] +season_df = train[season_columns] +season_df + +train[season_columns] = train[season_columns].fillna("Missing") + +data_dict = data_dict[data_dict['Instrument'] != 'Parent-Child Internet Addiction Test'] +continuous_cols = data_dict[data_dict['Type'].str.contains( + 'float|int', case=False +)]['Field'].tolist() + +# target = train[['sii']] +# train = train.drop(columns = columns_not_in_test) + +groups.get('Demographics', []) + +fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + +# Season of Enrollment +season_counts = train['Basic_Demos-Enroll_Season'].value_counts(dropna=False) + +axes[0].pie( + season_counts, labels=season_counts.index, + autopct='%1.1f%%', startangle=90, + colors=sns.color_palette("Set3") +) +axes[0].set_title('Season of Enrollment') +axes[0].axis('equal') + +# Age Distribution by Sex +sns.histplot( + data=train, x='Basic_Demos-Age', + hue='Basic_Demos-Sex', multiple='dodge', + palette="Set2", bins=20, ax=axes[1] +) +axes[1].set_title('Age Distribution by Sex') +axes[1].set_xlabel('Age') +axes[1].set_ylabel('Count') + +plt.tight_layout() +plt.show() + +calculate_stats(train, 'Basic_Demos-Age') + +groups.get("Children's Global Assessment Scale", []) + +data = train[train['CGAS-CGAS_Score'].notnull()] +age_range = data['Basic_Demos-Age'] +print( + f"Age range for participants with CGAS-CGAS_Score data:" + f" {age_range.min()} - {age_range.max()} years" +) + +calculate_stats(train, 'CGAS-CGAS_Score') + +train[train['CGAS-CGAS_Score'] > 100] + +train.loc[train['CGAS-CGAS_Score'] == 999, 'CGAS-CGAS_Score'] = np.nan + +plt.figure(figsize=(12, 5)) + +# CGAS-Season +plt.subplot(1, 2, 1) +cgas_season_counts = train['CGAS-Season'].value_counts(normalize=True) +plt.pie( + cgas_season_counts, + labels=cgas_season_counts.index, + autopct='%1.1f%%', + startangle=90, + colors=sns.color_palette("Set3") +) +plt.title('CGAS-Season') +plt.axis('equal') + +# CGAS-CGAS_Score without outliers (score == 999) +plt.subplot(1, 2, 2) +sns.histplot( + train['CGAS-CGAS_Score'].dropna(), + bins=20, kde=True +) +plt.title('CGAS-CGAS_Score (Without Outlier)') +plt.xlabel('CGAS Score') +plt.ylabel('Count') + +plt.tight_layout() +plt.show() + +calculate_stats(train, 'CGAS-CGAS_Score') + +bins = np.arange(0, 101, 10) +labels = [ + "1-10: Needs constant supervision (24 hour care)", + "11-20: Needs considerable supervision", + "21-30: Unable to function in almost all areas", + "31-40: Major impairment in functioning in several areas", + "41-50: Moderate degree of interference in functioning", + "51-60: Variable functioning with sporadic difficulties", + "61-70: Some difficulty in a single area", + "71-80: No more than slight impairment in functioning", + "81-90: Good functioning in all areas", + "91-100: Superior functioning" +] + +train['CGAS_Score_Bin'] = pd.cut( + train['CGAS-CGAS_Score'], bins=bins, labels=labels +) + +counts = train['CGAS_Score_Bin'].value_counts().reindex(labels) +prop = (counts / counts.sum() * 100).round(1) +count_prop_labels = counts.astype(str) + " (" + prop.astype(str) + "%)" + +plt.figure(figsize=(18, 6)) +bars = plt.barh(labels, counts) +plt.xlabel('Count') +plt.title('CGAS Score Distribution') + +for bar, label in zip(bars, count_prop_labels): + plt.text( + bar.get_width(), bar.get_y() + bar.get_height() / 2, label, va='center' + ) + +plt.gca().invert_yaxis() +plt.tight_layout() +plt.show() + +train_filt = train.dropna(subset=['CGAS_Score_Bin', 'complete_resp_total']) +train_filt.loc[:, 'CGAS_Score_Bin'] = train_filt['CGAS_Score_Bin'].cat.remove_unused_categories() +train_filt.loc[:, 'sii'] = train_filt['sii'].cat.remove_unused_categories() +len(train_filt) + +fig, axes = plt.subplots(1, 2, figsize=(16, 5)) + +# CGAS-CGAS_Score vs sii +sns.boxplot( + data=train_filt, + x='sii', y='CGAS-CGAS_Score', + palette='Set3', ax=axes[0] +) +axes[0].set_xlabel('SII Score') +axes[0].set_ylabel('CGAS Score') +axes[0].set_title('Distribution of CGAS Scores by SII') + +# complete_resp_total vs CGAS_Score_Bin +sns.boxplot( + data=train_filt, + x='CGAS_Score_Bin', y='complete_resp_total', + ax=axes[1], palette='Set3' +) + +# Get the tick positions and match the labels +range_labels = [label.split(":")[0] for label in train_filt['CGAS_Score_Bin'].cat.categories] +axes[1].set_xticklabels(range_labels) + +axes[1].set_xlabel('CGAS Score category') +axes[1].set_ylabel('PCIAT_Total for Complete PCIAT Responses') +axes[1].set_title('Distribution of PCIAT_Total by CGAS Score categories') + +plt.tight_layout() +plt.show() + +score_min_max = train.groupby('sii')['CGAS-CGAS_Score'].agg(['min', 'max']) +score_min_max = score_min_max.rename( + columns={'min': 'Minimum CGAS Score', 'max': 'Maximum CGAS Score'} +) +score_min_max + +train_filt[train_filt['CGAS-CGAS_Score'] < 35][ + ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii', + 'CGAS-CGAS_Score', + 'PreInt_EduHx-computerinternet_hoursday'] +] + +train[train['CGAS-CGAS_Score'] > 90][ + ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii', + 'CGAS-CGAS_Score', + 'PreInt_EduHx-computerinternet_hoursday'] +] + +groups.get('Physical Measures', []) + +features_physical = groups.get('Physical Measures', []) +cols = [col for col in features_physical if col in continuous_cols] + +plt.figure(figsize=(24, 10)) +n_cols = 4 +n_rows = len(cols) // n_cols + 1 + +for i, col in enumerate(cols): + plt.subplot(n_rows, n_cols, i + 1) + train[col].hist(bins=20) + plt.title(col) + +plt.subplot(n_rows, n_cols, len(cols) + 1) +season_counts = train['Physical-Season'].value_counts(dropna=False) +plt.pie( + season_counts, + labels=season_counts.index, + autopct='%1.1f%%', + startangle=90, + colors=sns.color_palette("Set3") +) +plt.title('Physical-Season') + +plt.suptitle('Histograms for Physical Measures and Physical-Season Pie Chart', y=1.05) +plt.tight_layout() +plt.show() + +calculate_stats(train, cols) + +wh_cols = [ + 'Physical-BMI', 'Physical-Height', + 'Physical-Weight', 'Physical-Waist_Circumference' +] + +(train[wh_cols] == 0).sum() + +train[wh_cols] = train[wh_cols].replace(0, np.nan) +calculate_stats(train, wh_cols) + +lbs_to_kg = 0.453592 +inches_to_cm = 2.54 + +train['Physical-Weight'] = train['Physical-Weight'] * lbs_to_kg +train['Physical-Height'] = train['Physical-Height'] * inches_to_cm +train['Physical-Waist_Circumference'] = train['Physical-Waist_Circumference'] * inches_to_cm + +# Recalculate BMI: BMI = weight (kg) / (height (m)^2) +train['Physical-BMI'] = np.where( + train['Physical-Weight'].notna() & train['Physical-Height'].notna(), + train['Physical-Weight'] / ((train['Physical-Height'] / 100) ** 2), + np.nan # If either is NaN, set BMI to NaN +) + +calculate_stats(train, wh_cols) + +plt.figure(figsize=(18, 5)) + +# Physical-Weight by Age +plt.subplot(1, 3, 1) +sns.scatterplot(x='Basic_Demos-Age', y='Physical-Weight', data=train) +plt.title('Physical-Weight by Age') +plt.xlabel('Age') +plt.ylabel('Weight (kg)') + +# Physical-Height by Age +plt.subplot(1, 3, 2) +sns.scatterplot(x='Basic_Demos-Age', y='Physical-Height', data=train) +plt.title('Physical-Height by Age') +plt.xlabel('Age') +plt.ylabel('Height (cm)') + +# Physical-Waist_Circumference vs Physical-Weight +plt.subplot(1, 3, 3) +sns.scatterplot(x='Physical-Weight', y='Physical-Waist_Circumference', data=train) +plt.title('Waist Circumference vs Weight') +plt.xlabel('Weight (kg)') +plt.ylabel('Waist Circumference (cm)') + +plt.tight_layout() +plt.show() + +bp_hr_cols = [ + 'Physical-Diastolic_BP', 'Physical-Systolic_BP', + 'Physical-HeartRate' +] + +(train[bp_hr_cols] < 50).sum() + +train[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP']][bp_hr_cols] + +train[cols] = train[cols].replace(0, np.nan) +train.loc[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP'], bp_hr_cols] = np.nan + +plt.figure(figsize=(12, 5)) + +# Diastolic BP vs Heart Rate +plt.subplot(1, 2, 1) +sns.scatterplot(x='Physical-Diastolic_BP', y='Physical-HeartRate', data=train) +plt.title('Diastolic BP vs Heart Rate') +plt.xlabel('Diastolic Blood Pressure (mmHg)') +plt.ylabel('Heart rate (beats/min)') + +# Systolic BP vs Heart Rate +plt.subplot(1, 2, 2) +sns.scatterplot(x='Physical-Systolic_BP', y='Physical-HeartRate', data=train) +plt.title('Systolic BP vs Heart Rate') +plt.xlabel('Systolic Blood Pressure (mmHg)') +plt.ylabel('Heart rate (beats/min)') + +plt.tight_layout() +plt.show() + +fig, axes = plt.subplots(1, 2, figsize=(16, 6)) + +# BMI vs Systolic Blood Pressure +sns.scatterplot(x='Physical-BMI', y='Physical-Systolic_BP', data=train, ax=axes[0], color='b') +axes[0].set_title('BMI vs Systolic Blood Pressure') +axes[0].set_xlabel('Body Mass Index (BMI) (kg/m^2)') +axes[0].set_ylabel('Systolic Blood Pressure (mmHg)') + +# Systolic Blood Pressure vs Diastolic Blood Pressure +sns.scatterplot( + x='Physical-Systolic_BP', y='Physical-Diastolic_BP', + data=train, ax=axes[1], color='g' +) +axes[1].set_title('Systolic Blood Pressure vs Diastolic Blood Pressure') +axes[1].set_xlabel('Systolic Blood Pressure (mmHg)') +axes[1].set_ylabel('Diastolic Blood Pressure (mmHg)') + +plt.tight_layout() +plt.show() + +normal_ranges = { + 'Physical-BMI': (18.5, 24.9), + 'Physical-Height': (100, 193), + 'Physical-Weight': (20, 120), + 'Physical-Waist_Circumference': (50, 90), + 'Physical-Diastolic_BP': (60, 80), + 'Physical-HeartRate': (60, 100), + 'Physical-Systolic_BP': (90, 120) +} + +def count_out_of_range(data, column, low, high): + return ((data[column] < low) | (data[column] > high)).sum() + +out_of_range_counts = { + col: count_out_of_range(train, col, *normal_ranges[col]) + for col in normal_ranges +} +print("Number of rows with values outside normal ranges:") + +for col, count in out_of_range_counts.items(): + total_valid = train[col].notna().sum() + percentage = (count / total_valid) * 100 + print(f"{col}: {count} ({percentage:.2f}%)") + +bmi_categories = [ + ('Underweight', train['Physical-BMI'] < 18.5), + ('Normal weight', (train['Physical-BMI'] >= 18.5) & (train['Physical-BMI'] <= 24.9)), + ('Overweight', (train['Physical-BMI'] >= 25) & (train['Physical-BMI'] <= 29.9)), + ('Obesity', train['Physical-BMI'] >= 30) +] +bmi_category_counts = {label: condition.sum() for label, condition in bmi_categories} + +plt.figure(figsize=(5, 6)) +plt.pie(bmi_category_counts.values(), + labels=bmi_category_counts.keys(), + autopct='%1.1f%%', startangle=90, + colors=plt.cm.Set3.colors) +plt.title('BMI Distribution by Category') +plt.axis('equal') +plt.show() + +train[train['Physical-BMI'] < 12][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-BMI') + +train[train['Physical-Systolic_BP'] > 160][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-Systolic_BP') + +data_subset = train[cols + ['complete_resp_total']] + +corr_matrix = data_subset.corr() + +plt.figure(figsize=(10, 8)) +sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1) +plt.title('Correlation Heatmap') +plt.show() + +data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis'] + +bia_data_dict = data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis'] +categorical_columns = bia_data_dict[bia_data_dict['Type'] == 'categorical int']['Field'].tolist() +continuous_columns = bia_data_dict[bia_data_dict['Type'] == 'float']['Field'].tolist() + +fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + +# Season +season_counts = train['BIA-Season'].value_counts(normalize=True) +axes[0].pie( + season_counts, + labels=season_counts.index, + autopct='%1.1f%%', + startangle=90, + colors=sns.color_palette("Set3") +) +axes[0].set_title( + f"{bia_data_dict[bia_data_dict['Field'] == 'BIA-Season']['Description'].values[0]}" +) +axes[0].axis('equal') + +# Other categorical columns +for idx, col in enumerate(categorical_columns): + sns.countplot(x=col, data=train, palette="Set3", ax=axes[idx+1]) + axes[idx+1].set_title(data_dict[data_dict['Field'] == col]['Description'].values[0]) + +plt.tight_layout() +plt.show() + +plt.figure(figsize=(24, 20)) + +for idx, col in enumerate(continuous_columns): + plt.subplot(4, 4, idx + 1) + sns.histplot(train[col].dropna(), bins=20, kde=True) + plt.title(data_dict[data_dict['Field'] == col]['Description'].values[0]) + plt.xlabel('Value') + +plt.tight_layout() +plt.show() + +calculate_stats(train, continuous_columns) + +bmi_data = train[['BIA-BIA_BMI', 'Physical-BMI']].dropna() + +plt.figure(figsize=(8, 6)) +sns.scatterplot( + x='BIA-BIA_BMI', y='Physical-BMI', + data=bmi_data, + color='b' +) +plt.title('Comparison of BIA-BMI vs Physical-BMI') +plt.xlabel('BIA-BMI') +plt.ylabel('Physical-BMI') + +plt.tight_layout() +plt.show() + +bmi_measures = train[['BIA-Season', 'Physical-Season']].dropna() +bmi_measures.groupby(['BIA-Season', 'Physical-Season']).size().reset_index(name='Count') + +groups.get('FitnessGram Vitals and Treadmill', []) + +data = train[train['Fitness_Endurance-Max_Stage'].notnull()] +age_range = data['Basic_Demos-Age'] +print( + f"Age range for participants with Fitness_Endurance-Max_Stage data:" + f" {age_range.min()} - {age_range.max()} years" +) + +fig, axes = plt.subplots(1, 4, figsize=(24, 5)) + +# Fitness Endurance Season +train['Fitness_Endurance-Season'].value_counts(normalize=True).plot.pie( + autopct='%1.1f%%', colors=plt.cm.Set3.colors, ax=axes[0] +) +axes[0].set_title('Fitness Endurance Season') +axes[0].axis('equal') # Equal aspect ratio ensures the pie is drawn as a circle. + +# Box plot for Max Stage by Season +sns.violinplot( + x='Fitness_Endurance-Season', + y='Fitness_Endurance-Max_Stage', + data=train, palette="Set3", + ax=axes[1] +) +axes[1].set_title('Max Stage by Season') +axes[1].set_xlabel('Season') +axes[1].set_ylabel('Max Stage') + +# Fitness Endurance Time (Minutes) +sns.histplot(train['Fitness_Endurance-Time_Mins'], bins=20, kde=True, ax=axes[2]) +axes[2].set_title('Fitness Endurance Time (Minutes)') +axes[2].set_xlabel('Time (Minutes)') + +# Fitness Endurance Time (Seconds) +sns.histplot(train['Fitness_Endurance-Time_Sec'], bins=20, kde=True, ax=axes[3]) +axes[3].set_title('Fitness Endurance Time (Seconds)') +axes[3].set_xlabel('Time (Seconds)') + +plt.tight_layout() +plt.show() + +plt.figure(figsize=(12, 5)) + +sns.violinplot(x='Basic_Demos-Age', y='Fitness_Endurance-Max_Stage', data=train, palette="Set3") +plt.title('Fitness Endurance Max Stage by Age') +plt.xlabel('Age') +plt.ylabel('Max Stage') + +plt.tight_layout() +plt.show() + +cols = [ + 'Fitness_Endurance-Max_Stage', + 'Fitness_Endurance-Time_Mins', + 'Fitness_Endurance-Time_Sec' +] +calculate_stats(train, cols) + +train[ + (train['Fitness_Endurance-Max_Stage'].notna()) & + (train['Fitness_Endurance-Time_Mins'].isna() | + train['Fitness_Endurance-Time_Sec'].isna()) +][cols] + +train.loc[ + (train['Fitness_Endurance-Max_Stage'].notna()) & + (train['Fitness_Endurance-Time_Mins'].isna() | + train['Fitness_Endurance-Time_Sec'].isna()), cols +] = np.nan + +train['Fitness_Endurance-Total_Time_Sec'] = train[ + 'Fitness_Endurance-Time_Mins' +] * 60 + train['Fitness_Endurance-Time_Sec'] + +calculate_stats(train, ['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec']) + +data_dict[data_dict['Instrument'] == 'FitnessGram Child'] + +fgc_data_dict = data_dict[data_dict['Instrument'] == 'FitnessGram Child'] + +fgc_columns = [] + +for index, row in fgc_data_dict.iterrows(): + if '_Zone' not in row['Field']: + measure_field = row['Field'] + measure_desc = row['Description'] + + zone_field = measure_field + '_Zone' + zone_row = fgc_data_dict[fgc_data_dict['Field'] == zone_field] + + if not zone_row.empty: + zone_desc = zone_row['Description'].values[0] + fgc_columns.append((measure_field, zone_field, measure_desc, zone_desc)) + +fig, axes = plt.subplots(2, 4, figsize=(24, 10)) + +for idx, (measure, zone, measure_desc, zone_desc) in enumerate(fgc_columns): + row = idx // 4 + col = idx % 4 + + sns.histplot( + data=train, x=measure, + hue=zone, bins=20, palette='Set2', + ax=axes[row, col], kde=True + ) + axes[row, col].set_title(f'{measure_desc}') + +season_counts = train['FGC-Season'].value_counts(normalize=True) +axes[1, 3].pie( + season_counts, labels=season_counts.index, + autopct='%1.1f%%', startangle=90, + colors=sns.color_palette("Set3") +) +axes[1, 3].set_title('Season of participation') +axes[1, 3].axis('equal') + +plt.tight_layout() +plt.show() + +measurement_columns = [measure for measure, _, _, _ in fgc_columns] +calculate_stats(train, measurement_columns) + +def compute_min_max_by_sex(train, sex, fgc_columns): + results = [] + + for measure, zone, _, _ in fgc_columns: + sorted_zones = sorted(train[zone].dropna().unique()) + + for zone_value in sorted_zones: + data = train[(train[zone] == zone_value) & + (train['Basic_Demos-Sex'] == sex)][measure] + + if not data.empty: + min_val, max_val = data.min(), data.max() + results.append({ + 'Zone': int(zone_value), + 'Measure': measure, + 'Min-Max': f'{min_val} - {max_val}' + }) + + df = pd.DataFrame(results).pivot_table( + index='Zone', columns='Measure', values='Min-Max', aggfunc='first' + ) + + return df + +compute_min_max_by_sex(train, 'Male', fgc_columns) + +compute_min_max_by_sex(train, 'Female', fgc_columns) + +results_male = [] + +for measure, zone, _, _ in fgc_columns: + sorted_zones = sorted(train[zone].dropna().unique()) + for zone_value in sorted_zones: + age_sex_data_by_zone = train[train[zone] == zone_value][ + ['Basic_Demos-Age', 'Basic_Demos-Sex', measure] + ] + unique_ages = age_sex_data_by_zone['Basic_Demos-Age'].dropna().unique() + + for age in sorted(unique_ages): + age_sex_data = age_sex_data_by_zone[ + (age_sex_data_by_zone['Basic_Demos-Age'] == age) & + (age_sex_data_by_zone['Basic_Demos-Sex'] == 'Male') + ][measure] + + if not age_sex_data.empty: + min_val, max_val = age_sex_data.min(), age_sex_data.max() + results_male.append({ + 'Age': age, + 'Sex': 'Male', + 'Zone': zone_value, + 'Measure': measure, + 'Min-Max': f'{min_val} - {max_val}' + }) + +df_male = pd.DataFrame(results_male).pivot_table( + index=['Age', 'Sex', 'Zone'], columns='Measure', values='Min-Max', aggfunc='first' +) + +df_male + +age_ranges = [] + +for measure in measurement_columns: + valid_rows = train[~train[measure].isna()] + + min_age = valid_rows['Basic_Demos-Age'].min() + max_age = valid_rows['Basic_Demos-Age'].max() + + age_ranges.append({ + 'Measurement': measure, + 'Min Age': min_age, + 'Max Age': max_age + }) + +age_ranges_df = pd.DataFrame(age_ranges) +age_ranges_df + +cols = [col for col in train.columns if col.startswith('FGC-') + and 'Zone' not in col and 'Season' not in col] +cols.extend(['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec']) + +data_subset = train[cols + ['complete_resp_total']] + +corr_matrix = data_subset.corr() + +plt.figure(figsize=(10, 8)) +sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1) +plt.title('Correlation Heatmap') +plt.show() + +age_groups = train['Age Group'].unique() + +fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True) + +for i, age_group in enumerate(age_groups): + group_data = train[train['Age Group'] == age_group] + corr_matrix = group_data[cols + ['complete_resp_total', 'Basic_Demos-Age']].corr() + sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.1f', + vmin=-1, vmax=1, ax=axes[i], cbar=i == 0) + axes[i].set_title(f'{age_group}') + +plt.tight_layout() +plt.show() + +train[ + (train['Age Group'] == 'Adults (19-22)') & + (train['complete_resp_total'].notna()) & + (train[cols].notna().any(axis=1)) +][cols + ['complete_resp_total', 'Basic_Demos-Age']] + +groups.get('Sleep Disturbance Scale', []) + +data = train[train['SDS-SDS_Total_Raw'].notnull()] +age_range = data['Basic_Demos-Age'] +print( + f"Age range for participants with SDS-SDS_Total_Raw data:" + f" {age_range.min()} - {age_range.max()} years" +) + +plt.figure(figsize=(18, 5)) + +# SDS-Season (Pie Chart) +plt.subplot(1, 3, 1) +sds_season_counts = train['SDS-Season'].value_counts(normalize=True) +plt.pie( + sds_season_counts, + labels=sds_season_counts.index, + autopct='%1.1f%%', + startangle=90, + colors=sns.color_palette("Set3") +) +plt.title('SDS-Season') + +# SDS-SDS_Total_Raw +plt.subplot(1, 3, 2) +sns.histplot(train['SDS-SDS_Total_Raw'].dropna(), bins=20, kde=True) +plt.title('SDS-SDS_Total_Raw') +plt.xlabel('Value') + +# SDS-SDS_Total_T +plt.subplot(1, 3, 3) +sns.histplot(train['SDS-SDS_Total_T'].dropna(), bins=20, kde=True) +plt.title('SDS-SDS_Total_T') +plt.xlabel('Value') + +plt.tight_layout() +plt.show() + +calculate_stats(train, ['SDS-SDS_Total_Raw', 'SDS-SDS_Total_T']) + +groups.get('Physical Activity Questionnaire (Adolescents)', []) + +data = train[train['PAQ_A-PAQ_A_Total'].notnull()] +age_range = data['Basic_Demos-Age'] +print( + f"Age range for Adolescents (with PAQ_A_Total data):" + f" {age_range.min()} - {age_range.max()} years" +) + +plt.figure(figsize=(18, 5)) + +# PAQ_A-Season +plt.subplot(1, 3, 1) +train['PAQ_A-Season'].value_counts(normalize=True).plot.pie( + autopct='%1.1f%%', colors=plt.cm.Set3.colors +) +plt.title('PAQ_A-Season (Adolescents)') + +# PAQ_A-PAQ_A_Total +plt.subplot(1, 3, 2) +sns.histplot(train['PAQ_A-PAQ_A_Total'], bins=20, kde=True) +plt.title('PAQ_A-PAQ_A_Total (Adolescents)') + +# PAQ_A_Total by Season +plt.subplot(1, 3, 3) +sns.violinplot(x='PAQ_A-Season', y='PAQ_A-PAQ_A_Total', data=train, palette="Set3") +plt.title('PAQ_A_Total by Season (Adolescents)') + +plt.tight_layout() +plt.show() + +calculate_stats(train, ['PAQ_A-PAQ_A_Total']) + +groups.get('Physical Activity Questionnaire (Children)', []) + +data = train[train['PAQ_C-PAQ_C_Total'].notnull()] +age_range = data['Basic_Demos-Age'] +print( + f"Age range for Children (with PAQ_C_Total data):" + f" {age_range.min()} - {age_range.max()} years" +) + +plt.figure(figsize=(18, 5)) + +# PAQ_C-Season +plt.subplot(1, 3, 1) +train['PAQ_C-Season'].value_counts(normalize=True).plot.pie( + autopct='%1.1f%%', colors=plt.cm.Set3.colors +) +plt.title('PAQ_C-Season (Children)') + +# PAQ_C-PAQ_C_Total +plt.subplot(1, 3, 2) +sns.histplot(train['PAQ_C-PAQ_C_Total'], bins=20, kde=True) +plt.title('PAQ_C-PAQ_C_Total (Children)') + +# PAQ_C_Total by Season +plt.subplot(1, 3, 3) +sns.violinplot(x='PAQ_C-Season', y='PAQ_C-PAQ_C_Total', data=train, palette="Set3") +plt.title('PAQ_C_Total by Season (Children)') + +plt.tight_layout() +plt.show() + +calculate_stats(train, ['PAQ_C-PAQ_C_Total']) + +paq_columns = [col for col in train.columns if 'PAQ' in col] +train[(train['PAQ_A-PAQ_A_Total'].notnull()) & + (train['PAQ_C-PAQ_C_Total'].notnull())][ + paq_columns + ['Basic_Demos-Age'] +] \ No newline at end of file diff --git a/notebooks/week2/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py index 58d4495..e368ea5 100644 --- a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -1,11 +1,12 @@ # Databricks notebook source -# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- -dbutils.library.restartPython() +#MAGIC dbutils.library.restartPython() + # COMMAND ---------- diff --git a/notebooks/week2/02.prepare_featureTable.py b/notebooks/week2/02.prepare_featureTable.py index 80456a7..3071beb 100644 --- a/notebooks/week2/02.prepare_featureTable.py +++ b/notebooks/week2/02.prepare_featureTable.py @@ -1,11 +1,11 @@ # Databricks notebook source -# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- -dbutils.library.restartPython() +#MAGIC dbutils.library.restartPython() # COMMAND ---------- diff --git a/notebooks/week2/04.log_and_register_model.py b/notebooks/week2/04.log_and_register_model.py index eb25840..073c72d 100644 --- a/notebooks/week2/04.log_and_register_model.py +++ b/notebooks/week2/04.log_and_register_model.py @@ -1,9 +1,9 @@ # Databricks notebook source -# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- -dbutils.library.restartPython() +#MAGIC dbutils.library.restartPython() # Databricks notebook source # Import necessary libraries diff --git a/notebooks/week2/05.wip_log_and_register_custom_model.py b/notebooks/week2/05.wip_log_and_register_custom_model.py index 77cb0e5..6f452d3 100644 --- a/notebooks/week2/05.wip_log_and_register_custom_model.py +++ b/notebooks/week2/05.wip_log_and_register_custom_model.py @@ -1,8 +1,8 @@ # Databricks notebook source -# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl +#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- -dbutils.library.restartPython() +#MAGIC dbutils.library.restartPython() # COMMAND ---------- import mlflow diff --git a/notebooks/week2/06.log_and_register_fe_model.py b/notebooks/week2/06.log_and_register_fe_model.py index 9ee983f..0f956b2 100644 --- a/notebooks/week2/06.log_and_register_fe_model.py +++ b/notebooks/week2/06.log_and_register_fe_model.py @@ -1,11 +1,11 @@ # Databricks notebook source # Install necessary packages -# %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl +#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- # Restart the Python environment -dbutils.library.restartPython() +#MAGIC dbutils.library.restartPython() # COMMAND ---------- # Import libraries diff --git a/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl b/notebooks/week2/childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl index 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zLwn=TY5qG95hoOI-lq@oL*oAgqlHP7;XnX@?Lj;Ezfunr5#dJ(*9Hl}<0MG`Zc-pY zh5GF2pDBK06-7mjf8ErSJ0@h0-~src3n2XuMZ|0mC|Rz51ODku|3frJgs?rhmK*^t P02iSD;Atxye-{4($wfS1 diff --git a/project_config.yml b/project_config.yml index d8c2968..72bdea8 100644 --- a/project_config.yml +++ b/project_config.yml @@ -12,7 +12,24 @@ random_forest_parameters: min_samples_split: 5 # Minimum number of samples required to split a node min_samples_leaf: 2 # Minimum number of samples required at each leaf node class_weight: balanced # Automatically balance classes - random_state: 42 + random_state: 42 + +# Set up LightGBM parameters +lgb_parameters: + learning_rate: 0.03884249148676395 + max_depth: 12 + num_leaves: 413 + min_data_in_leaf: 14 + feature_fraction: 0.7987976913702801 + bagging_fraction: 0.7602261703576205 + bagging_freq: 2 + lambda_l1: 4.735462555910575 + lambda_l2: 4.735028557007343e-06 + n_estimators: 100 + num_class: 5 + metric: multi_logloss + objective: multiclass + boosting_type: gbdt pipeline_steps: feature_engineering: true @@ -20,10 +37,14 @@ pipeline_steps: handle_outliers: true handle_missing_values: true +logging: + level: INFO + file: logs/data_processor.log + + # Numerical features num_features: - Basic_Demos-Age # Age of the individual. - - Basic_Demos-Sex # Gender of the individual. - CGAS-CGAS_Score # Clinical Global Assessment Scale (CGAS) Score - Physical-BMI # Body Mass Index (BMI) - Physical-Height # Height of the individual. @@ -96,6 +117,7 @@ num_features: # Categorical features cat_features: - Basic_Demos-Enroll_Season + - Basic_Demos-Sex # Gender of the individual. - CGAS-Season # Clinical Global Assessment Scale (CGAS) Season - Physical-Season # season during which the physical measurements were taken - Fitness_Endurance-Season # Season for fitness recording diff --git a/src/childHealth/childhealth_model.py b/src/childHealth/childhealth_model.py index 46561cd..1ac3a9b 100644 --- a/src/childHealth/childhealth_model.py +++ b/src/childHealth/childhealth_model.py @@ -3,11 +3,17 @@ import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split -from sklearn.metrics import classification_report, accuracy_score +from sklearn.metrics import classification_report, accuracy_score, cohen_kappa_score from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.pipeline import Pipeline import lightgbm as lgb +from scipy.optimize import minimize +import mlflow + +def kappa_metric(y_true, y_pred): + y_pred = np.argmax(y_pred.reshape(len(y_true), -1), axis=1) + return 'kappa', cohen_kappa_score(y_true, y_pred, weights='quadratic'), True class ChildHealthModel: def __init__(self, preprocessor, config): @@ -21,14 +27,41 @@ def __init__(self, preprocessor, config): self.model = Pipeline(steps=[ ('preprocessor', preprocessor), ('regressor', lgb.LGBMClassifier( - n_estimators=config['model_parameters']['n_estimators'], - max_depth=config['model_parameters']['max_depth'], - learning_rate=config['model_parameters']['learning_rate'], - objective='multiclass', - num_class=config['model_parameters']['num_class'], - random_state=42 + n_estimators=config['lgb_boosting_parameters']['n_estimators'], + max_depth=config['lgb_boosting_parameters']['max_depth'], + learning_rate=config['lgb_boosting_parameters']['learning_rate'], + objective=config['lgb_boosting_parameters']['objective'], + num_class=config['lgb_boosting_parameters']['num_class'], + boosting_type=config['lgb_boosting_parameters']['boosting_type'], + num_leaves=config['lgb_boosting_parameters']['num_leaves'], + min_data_in_leaf=config['lgb_boosting_parameters']['min_data_in_leaf'], + feature_fraction=config['lgb_boosting_parameters']['feature_fraction'], + bagging_fraction=config['lgb_boosting_parameters']['bagging_fraction'], + bagging_freq=config['lgb_boosting_parameters']['bagging_freq'], + lambda_l1=config['lgb_boosting_parameters']['lambda_l1'], + lambda_l2=config['lgb_boosting_parameters']['lambda_l2'], + random_state=42, + metric='None' # Disable default metrics )) ]) + self.best_thresholds = [0.5, 1.5, 2.5, 3.5] # Initial thresholds for rounding + + def quadratic_weighted_kappa(self, y_true, y_pred): + return cohen_kappa_score(y_true, y_pred, weights='quadratic') + + def threshold_Rounder(self, preds, thresholds): + return np.where(preds < thresholds[0], 0, + np.where(preds < thresholds[1], 1, + np.where(preds < thresholds[2], 2, + np.where(preds < thresholds[3], 3, 4)))) + + def optimize_thresholds(self, y_true, preds): + def evaluate_thresholds(thresholds): + rounded_preds = self.threshold_Rounder(preds, thresholds) + return -self.quadratic_weighted_kappa(y_true, rounded_preds) + + result = minimize(evaluate_thresholds, self.best_thresholds, method='Nelder-Mead') + self.best_thresholds = result.x # Update best thresholds after optimization def train(self, X_train, y_train): """ @@ -37,7 +70,7 @@ def train(self, X_train, y_train): :param X_train: Training features. :param y_train: Training target. """ - self.model.fit(X_train, y_train) + self.model.named_steps['regressor'].fit(X_train, y_train, eval_metric=kappa_metric) def predict(self, X): """ @@ -48,18 +81,28 @@ def predict(self, X): """ return self.model.predict(X) + def predict_proba(self, X): + """ + Predict probabilities for each class using the trained model. + + :param X: Features to predict. + :return: Predicted probabilities. + """ + return self.model.predict_proba(X) + def evaluate(self, X_test, y_test): """ Evaluate the model on the test data. :param X_test: Test features. :param y_test: Test target. - :return: Classification report and accuracy score. + :return: Classification report, accuracy score, and kappa score. """ y_pred = self.predict(X_test) + kappa = self.quadratic_weighted_kappa(y_test, y_pred) report = classification_report(y_test, y_pred) accuracy = accuracy_score(y_test, y_pred) - return report, accuracy + return report, accuracy, kappa def get_feature_importance(self): """ @@ -71,62 +114,60 @@ def get_feature_importance(self): feature_names = self.model.named_steps['preprocessor'].get_feature_names_out() return feature_importance, feature_names -# def save_model(self, model_path='lgbm_model.pkl'): -# """Save the trained model.""" -# joblib.dump(self.model, model_path) -# print(f"Model saved as {model_path}") - -# def load_model(self, model_path='lgbm_model.pkl'): -# """Load a saved model.""" -# self.model = joblib.load(model_path) -# print(f"Model loaded from {model_path}") - -# if __name__ == "__main__": -# # Load configuration -# config_path = 'project_config.yml' -# with open(config_path, 'r') as file: -# config = yaml.safe_load(file) - -# # Load and preprocess data -# train_path = 'train.csv' -# data = pd.read_csv(train_path) -# X = data[config['num_features'] + config['cat_features']] -# y = data[config['target']] - -# # Define preprocessor -# preprocessor = ColumnTransformer( -# transformers=[ -# ('num', StandardScaler(), config['num_features']), -# ('cat', OneHotEncoder(drop='first'), config['cat_features']) -# ] -# ) - -# # Initialize model class -# child_health_model = ChildHealthModel(preprocessor, config) - -# # Split data -# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42) - -# # Train model -# child_health_model.train(X_train, y_train) - -# # Evaluate model -# report, accuracy = child_health_model.evaluate(X_test, y_test) -# print("Evaluation Results:") -# print(report) -# print("Accuracy:", accuracy) - -# # Display feature importance -# feature_importance, feature_names = child_health_model.get_feature_importance() -# feature_importance_df = pd.DataFrame({'feature': feature_names, 'importance': feature_importance}) -# feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) - -# plt.figure(figsize=(10, 8)) -# plt.barh(feature_importance_df['feature'], feature_importance_df['importance'], color='skyblue') -# plt.xlabel('Importance') -# plt.title('Feature Importance') -# plt.gca().invert_yaxis() -# plt.show() - -# # Save model -# child_health_model.save_model() \ No newline at end of file +# Main function +if __name__ == "__main__": + # Load configuration + config_path = 'project_config.yml' + with open(config_path, 'r') as file: + config = yaml.safe_load(file) + + # Load and preprocess data + train_path = 'train.csv' + data = pd.read_csv(train_path) + X = data[config['num_features'] + config['cat_features']] + y = data[config['target']] + + # Define preprocessor + preprocessor = ColumnTransformer( + transformers=[ + ('num', StandardScaler(), config['num_features']), + ('cat', OneHotEncoder(drop='first'), config['cat_features']) + ] + ) + + # Initialize model class + child_health_model = ChildHealthModel(preprocessor, config) + + # Split data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42) + + # Train model + child_health_model.train(X_train, y_train) + + # Out-of-Fold predictions for threshold optimization + oof_preds = child_health_model.predict_proba(X_test) + child_health_model.optimize_thresholds(y_test, oof_preds) + + # Evaluate model + report, accuracy, kappa = child_health_model.evaluate(X_test, y_test) + print("Evaluation Results:") + print(report) + print("Accuracy:", accuracy) + print("Kappa Score:", kappa) + + # Display feature importance + feature_importance, feature_names = child_health_model.get_feature_importance() + feature_importance_df = pd.DataFrame({'feature': feature_names, 'importance': feature_importance}) + feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) + + plt.figure(figsize=(10, 8)) + plt.barh(feature_importance_df['feature'], feature_importance_df['importance'], color='skyblue') + plt.xlabel('Importance') + plt.title('Feature Importance') + plt.gca().invert_yaxis() + plt.show() + + # Log metrics in MLflow + with mlflow.start_run(): + mlflow.log_metric("accuracy", accuracy) + mlflow.log_metric("quadratic_weighted_kappa", kappa) diff --git a/src/childHealth/config.py b/src/childHealth/config.py index 3c2c120..bcd0b49 100644 --- a/src/childHealth/config.py +++ b/src/childHealth/config.py @@ -5,7 +5,8 @@ class ProjectConfig(BaseModel): catalog_name: str schema_name: str - random_forest_parameters: Dict[str, Any] # Dictionary to hold model-related parameters + random_forest_parameters: Dict[str, Any] # Dictionary to hold Random Forest parameters + lgb_parameters: Dict[str, Any] # Dictionary to hold LightGBM parameters num_features: List[str] cat_features: List[str] target: str diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index f978438..3468f3a 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -6,10 +6,28 @@ from sklearn.impute import SimpleImputer from pyspark.sql.functions import current_timestamp, to_utc_timestamp from pyspark.sql import SparkSession +import os import logging +# Load configuration +config = ProjectConfig.from_yaml('../../project_config.yml') + +# Create logs directory if it doesn't exist +log_file = config.logging.file +os.makedirs(os.path.dirname(log_file), exist_ok=True) + +# Configure logging +logging.basicConfig( + level=getattr(logging, config.logging.level), + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(log_file), + logging.StreamHandler() # This will also print logs to the console + ] +) + # Set up logging configuration logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') @@ -78,10 +96,11 @@ def convert_data_types(self): self._convert_sex_to_binary() def _convert_sex_to_binary(self): - """Convert 'Sex' to binary encoding if it's part of numerical features.""" - if 'Basic_Demos-Sex' in self.num_features: - self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({'Male': 1, 'Female': 0}) - logging.info("Converted 'Basic_Demos-Sex' to binary") + """Convert 'Sex' to binary encoding if it's part of numerical features and not already binary encoded.""" + if 'Basic_Demos-Sex' in self.cat_features: + # Map the values to binary encoding + self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({1.0: 'Male', 0.0:'Female'}) + logging.info("Converted 'Basic_Demos-Sex' to binary") def feature_engineering(self): """Perform feature engineering to create new features.""" @@ -93,7 +112,68 @@ def feature_engineering(self): logging.info("Feature engineering completed") return self.train_df - # Rest of the methods... + def add_age_groups(self): + """Add age groups based on age.""" + logging.info("Adding age groups") + if 'Basic_Demos-Age' in self.num_features: + self.train_df['Age_Group'] = pd.cut(self.train_df['Basic_Demos-Age'], bins=[0, 12, 17, 25], labels=['Child', 'Teen', 'Young Adult']) + logging.info("Age groups added") + + def one_hot_encode_seasons(self): + """One-hot encode season columns.""" + logging.info("One-hot encoding seasons") + for col in self.cat_features: + if 'Season' in col: + one_hot = pd.get_dummies(self.train_df[col], prefix=col) + self.train_df = pd.concat([self.train_df, one_hot], axis=1) + logging.info(f"One-hot encoded {col}") + + def calculate_behavioral_scores(self): + """Calculate behavioral and psychological indicators.""" + logging.info("Calculating behavioral scores") + # Bin PCIAT total score + if 'PCIAT-PCIAT_Total' in self.num_features: + self.train_df['PCIAT_Bin'] = pd.cut(self.train_df['PCIAT-PCIAT_Total'], bins=[0, 20, 40, 60], labels=['Mild', 'Moderate', 'Severe']) + logging.info("PCIAT total score binned") + + # Categorize internet use + if 'PreInt_EduHx-computerinternet_hoursday' in self.num_features: + self.train_df['Internet_Use_Category'] = pd.cut(self.train_df['PreInt_EduHx-computerinternet_hoursday'], bins=[0, 1, 3, 6, np.inf], labels=['Low', 'Moderate', 'High', 'Very High']) + logging.info("Internet use categorized") + + def add_interaction_features(self): + """Add interaction features, such as age-adjusted scores.""" + logging.info("Adding interaction features") + # Age-adjusted CGAS Score + if 'CGAS-CGAS_Score' in self.num_features and 'Basic_Demos-Age' in self.num_features: + self.train_df['Age_Adjusted_CGAS'] = self.train_df['CGAS-CGAS_Score'] / self.train_df['Basic_Demos-Age'] + logging.info("Age-adjusted CGAS score added") + + # BMI Categories + if 'Physical-BMI' in self.num_features: + self.train_df['BMI_Category'] = pd.cut(self.train_df['Physical-BMI'], bins=[0, 18.5, 25, 30, np.inf], labels=['Underweight', 'Normal', 'Overweight', 'Obese']) + logging.info("BMI categories added") + + def scale_numeric_features(self): + """Scale numeric features in the final dataset.""" + logging.info("Scaling numeric features") + scaler = StandardScaler() + self.train_df[self.num_features] = scaler.fit_transform(self.train_df[self.num_features]) + logging.info("Numeric features scaled") + + def handle_missing_target(self): + """Handle missing values in the target column by replacing them with a specific value (e.g., 4.0).""" + logging.info("Handling missing target values") + new_value = 4.0 # New value to replace NaNs in the target column + self.train_df[self.target].fillna(new_value, inplace=True) + logging.info("Missing target values handled") + + def keep_relevant_columns(self): + """Keep only relevant columns.""" + logging.info("Keeping relevant columns") + relevant_columns = self.num_features + self.cat_features + [self.target] + self.train_df = self.train_df[relevant_columns] + logging.info("Relevant columns kept") def process(self): """Run the complete processing pipeline.""" @@ -117,4 +197,19 @@ def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark self._add_timestamp_and_save(train_set, 'train_set', spark) self._add_timestamp_and_save(test_set, 'test_set', spark) self._enable_change_data_feed(spark) - logging.info("Datasets saved to catalog and change data feed enabled") \ No newline at end of file + logging.info("Datasets saved to catalog and change data feed enabled") + + def _add_timestamp_and_save(self, df: pd.DataFrame, table_name: str, spark: SparkSession): + """Add timestamp and save DataFrame to catalog.""" + logging.info(f"Adding timestamp and saving {table_name}") + spark_df = spark.createDataFrame(df) + spark_df = spark_df.withColumn("timestamp", to_utc_timestamp(current_timestamp(), "UTC")) + spark_df.write.format("delta").mode("overwrite").saveAsTable(table_name) + logging.info(f"{table_name} saved with timestamp") + + def _enable_change_data_feed(self, spark: SparkSession): + """Enable change data feed for the catalog.""" + logging.info("Enabling change data feed") + spark.sql("ALTER TABLE train_set SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") + spark.sql("ALTER TABLE test_set SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") + logging.info("Change data feed enabled") \ No newline at end of file diff --git a/src/childHealth/kappaLgm_model.py b/src/childHealth/kappaLgm_model.py new file mode 100644 index 0000000..1477243 --- /dev/null +++ b/src/childHealth/kappaLgm_model.py @@ -0,0 +1,194 @@ +import yaml +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split, StratifiedKFold +from sklearn.metrics import classification_report, accuracy_score, cohen_kappa_score +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import OneHotEncoder, StandardScaler +from sklearn.pipeline import Pipeline +import lightgbm as lgb +from scipy.optimize import minimize +import mlflow +from childHealth.config import ProjectConfig + +def kappa_metric(y_true, y_pred_raw): + # Use rounding to convert predicted probabilities to the nearest class + y_pred = np.round(y_pred_raw).astype(int) + return 'kappa', cohen_kappa_score(y_true, y_pred, weights='quadratic'), True + + + +class ChildHealthModel: + def __init__(self, preprocessor, config): + """ + Initialize the ChildHealthModel with a preprocessor and configuration. + + :param preprocessor: A ColumnTransformer for preprocessing the data. + :param config: A dictionary containing model parameters. + """ + self.config = config + self.preprocessor = preprocessor + self.model = lgb.LGBMClassifier( + learning_rate=config.lgb_parameters['learning_rate'], + max_depth=config.lgb_parameters['max_depth'], + num_leaves=config.lgb_parameters['num_leaves'], + min_data_in_leaf=config.lgb_parameters['min_data_in_leaf'], + feature_fraction=config.lgb_parameters['feature_fraction'], + bagging_fraction=config.lgb_parameters['bagging_fraction'], + bagging_freq=config.lgb_parameters['bagging_freq'], + lambda_l1=config.lgb_parameters['lambda_l1'], + lambda_l2=config.lgb_parameters['lambda_l2'], + n_estimators=config.lgb_parameters['n_estimators'], + num_class=config.lgb_parameters['num_class'], + objective=config.lgb_parameters['objective'], + boosting_type=config.lgb_parameters['boosting_type'], + random_state=42, + metric='None' # Disable default metrics + ) + self.best_thresholds = [0.5, 1.5, 2.5, 3.5] # Initial thresholds for rounding + + def quadratic_weighted_kappa(self, y_true, y_pred): + return cohen_kappa_score(y_true, y_pred, weights='quadratic') + + def threshold_Rounder(self, preds, thresholds): + return np.where(preds < thresholds[0], 0, + np.where(preds < thresholds[1], 1, + np.where(preds < thresholds[2], 2, + np.where(preds < thresholds[3], 3, 4)))) + + def optimize_thresholds(self, y_true, preds): + def evaluate_thresholds(thresholds): + rounded_preds = self.threshold_Rounder(preds, thresholds) + return -self.quadratic_weighted_kappa(y_true, rounded_preds) + + result = minimize(evaluate_thresholds, self.best_thresholds, method='Nelder-Mead') + self.best_thresholds = result.x # Update best thresholds after optimization + + def train(self, X_train, y_train, X_val, y_val): + """ + Train the model on the provided training data. + + :param X_train: Training features. + :param y_train: Training target. + :param X_val: Validation features. + :param y_val: Validation target. + """ + X_train = self.preprocessor.fit_transform(X_train) + X_val = self.preprocessor.transform(X_val) + + # Using the LightGBM Dataset for early stopping support + train_data = lgb.Dataset(X_train, label=y_train) + val_data = lgb.Dataset(X_val, label=y_val, reference=train_data) + + # Train with early stopping + self.model = lgb.train( + params=self.config.lgb_parameters, + train_set=train_data, + valid_sets=[val_data], + valid_names=['validation'], + feval=kappa_metric, + # early_stopping_rounds=10 + ) + + def predict(self, X): + """ + Predict using the trained model. + + :param X: Features to predict. + :return: Predictions. + """ + X = self.preprocessor.transform(X) + return self.model.predict(X) + + def predict_proba(self, X): + """ + Predict probabilities for each class using the trained model. + + :param X: Features to predict. + :return: Predicted probabilities. + """ + X = self.preprocessor.transform(X) + return self.model.predict_proba(X) + + def evaluate(self, X_test, y_test): + """ + Evaluate the model on the test data. + + :param X_test: Test features. + :param y_test: Test target. + :return: Classification report, accuracy score, and kappa score. + """ + y_pred = self.predict(X_test) + kappa = self.quadratic_weighted_kappa(y_test, y_pred) + report = classification_report(y_test, y_pred) + accuracy = accuracy_score(y_test, y_pred) + return report, accuracy, kappa + + def get_feature_importance(self): + """ + Get feature importance from the trained model. + + :return: Feature importances and feature names. + """ + feature_importance = self.model.feature_importances_ + feature_names = self.preprocessor.get_feature_names_out() + return feature_importance, feature_names + +# Main function +if __name__ == "__main__": + # Load configuration + config_path = 'project_config.yml' + config = ProjectConfig.from_yaml(config_path) + + # Load and preprocess data + train_path = 'train.csv' + data = pd.read_csv(train_path) + X = data[config.num_features + config.cat_features] + y = data[config.target] + + # Define preprocessor + preprocessor = ColumnTransformer( + transformers=[ + ('num', StandardScaler(), config.num_features), + ('cat', OneHotEncoder(drop='first'), config.cat_features) + ] + ) + + # Initialize model class + child_health_model = ChildHealthModel(preprocessor, config) + + # Split data + X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42) + X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42) + + # Train model + child_health_model.train(X_train, y_train, X_val, y_val) + + # Out-of-Fold predictions for threshold optimization + oof_preds = child_health_model.predict_proba(X_test) + child_health_model.optimize_thresholds(y_test, oof_preds) + + # Evaluate model + report, accuracy, kappa = child_health_model.evaluate(X_test, y_test) + print("Evaluation Results:") + print(report) + print("Accuracy:", accuracy) + print("Kappa Score:", kappa) + + # Display feature importance + feature_importance, feature_names = child_health_model.get_feature_importance() + feature_importance_df = pd.DataFrame({'feature': feature_names, 'importance': feature_importance}) + feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) + + plt.figure(figsize=(10, 8)) + plt.barh(feature_importance_df['feature'], feature_importance_df['importance'], color='skyblue') + 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.../catboost_info/catboost_training.json | 204 -- .../catboost_info/learn/events.out.tfevents | Bin 9670 -> 0 bytes notebooks/week2/catboost_info/learn_error.tsv | 201 -- notebooks/week2/catboost_info/time_left.tsv | 201 -- notebooks/week2/run_info.json | 2 +- project_config.yml | 12 +- pyproject.toml | 2 +- src/childHealth/childhealth_model.py | 115 +- src/childHealth/config.py | 13 +- src/childHealth/data_processor.py | 77 +- src/childHealth/feature_engineering.py | 97 +- src/childHealth/kappaLgm_model.py | 89 +- src/childHealth/utils.py | 21 +- uv.lock | 2363 +++++++++++++++++ 26 files changed, 3144 insertions(+), 2493 deletions(-) delete mode 100644 notebooks/week1/extracted_notebook_code.py delete mode 100644 notebooks/week2/catboost_info/catboost_training.json delete mode 100644 notebooks/week2/catboost_info/learn/events.out.tfevents delete mode 100644 notebooks/week2/catboost_info/learn_error.tsv delete mode 100644 notebooks/week2/catboost_info/time_left.tsv create mode 100644 uv.lock diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index dd2a062..340cf11 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -18,10 +18,10 @@ jobs: run: uv python install 3.11 - name: Install the dependencies - run: uv sync + run: uv sync - name: Install pre-commit run: pip install pre-commit - name: Run pre-commit checks - run: pre-commit run --all-files \ No newline at end of file + run: pre-commit run --all-files diff --git a/README.md b/README.md index 11ac9bf..d7da5bf 100644 --- a/README.md +++ b/README.md @@ -3,17 +3,17 @@ Marvelous MLOps End-to-end MLOps with Databricks course ## Practical information - Weekly lectures on Wednesdays 16:00-18:00 CET. -- Code for the lecture is shared before the lecture. +- Code for the lecture is shared before the lecture. - Presentation and lecture materials are shared right after the lecture. - Video of the lecture is uploaded within 24 hours after the lecture. -- Every week we set up a deliverable, and you implement it with your own dataset. +- Every week we set up a deliverable, and you implement it with your own dataset. - To submit the deliverable, create a feature branch in that repository, and a PR to main branch. The code can be merged after we review & approve & CI pipeline runs successfully. - The deliverables can be submitted with a delay (for example, lecture 1 & 2 together), but we expect you to finish all assignments for the course before the 25th of November. ## Set up your environment -In this course, we use Databricks 15.4 LTS runtime, which uses Python 3.11. +In this course, we use Databricks 15.4 LTS runtime, which uses Python 3.11. In our examples, we use UV. Check out the documentation on how to install it: https://docs.astral.sh/uv/getting-started/installation/ To create a new environment and create a lockfile, run: diff --git a/notebooks/week1/00.dataexploration.ipynb b/notebooks/week1/00.dataexploration.ipynb index 46eedbf..2ccea35 100644 --- a/notebooks/week1/00.dataexploration.ipynb +++ b/notebooks/week1/00.dataexploration.ipynb @@ -13,10 +13,10 @@ "metadata": {}, "outputs": [], "source": [ - "from childHealth import data_processor\n", + "import warnings\n", + "\n", "import pandas as pd\n", "\n", - "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, @@ -26,7 +26,7 @@ "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv('../../data/childHealth.csv')" + "df = pd.read_csv(\"../../data/childHealth.csv\")" ] }, { @@ -49,7 +49,7 @@ "source": [ "from pyspark.sql import SparkSession\n", "\n", - "spark = SparkSession.builder.appName('childHealth').getOrCreate()" + "spark = SparkSession.builder.appName(\"childHealth\").getOrCreate()" ] }, { @@ -59,13 +59,13 @@ "outputs": [], "source": [ "import os\n", + "import warnings\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", "import pandas as pd\n", - "from tqdm import tqdm\n", "from prophet import Prophet\n", - "from datetime import datetime\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from tqdm import tqdm\n", "\n", - "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, @@ -75,19 +75,17 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pandas as pd\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "from tqdm import tqdm\n", + "\n", "\n", "class ActigraphAggregation:\n", " def __init__(self, root_dir):\n", " self.root_dir = root_dir\n", - " \n", + "\n", " def load_data(self, participant_id):\n", " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", " data = pd.read_parquet(file_path)\n", - " data['id'] = participant_id\n", + " data[\"id\"] = participant_id\n", " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", " return data\n", "\n", @@ -101,21 +99,27 @@ " Returns:\n", " - DataFrame: Aggregated actigraphy data with one row per 'id'.\n", " \"\"\"\n", - " aggregated_df = data.groupby('id').agg({\n", - " 'X': ['mean', 'std', 'max', 'min'],\n", - " 'Y': ['mean', 'std', 'max', 'min'],\n", - " 'Z': ['mean', 'std', 'max', 'min'],\n", - " 'enmo': ['mean', 'std', 'max', 'min'],\n", - " 'anglez': 'mean',\n", - " 'non-wear_flag': 'sum',\n", - " 'light': ['mean', 'std', 'max', 'min'],\n", - " 'battery_voltage': 'mean',\n", - " }).reset_index()\n", + " aggregated_df = (\n", + " data.groupby(\"id\")\n", + " .agg(\n", + " {\n", + " \"X\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"Y\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"Z\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"enmo\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"anglez\": \"mean\",\n", + " \"non-wear_flag\": \"sum\",\n", + " \"light\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"battery_voltage\": \"mean\",\n", + " }\n", + " )\n", + " .reset_index()\n", + " )\n", "\n", " # Flatten column names\n", - " aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", + " aggregated_df.columns = [\"_\".join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", " return aggregated_df\n", - " \n", + "\n", " def process_participant_data(self, participant_id):\n", " data = self.load_data(participant_id)\n", " aggregated_data = self.aggregate_actigraphy(data)\n", @@ -126,9 +130,9 @@ " with ThreadPoolExecutor() as executor:\n", " futures = []\n", " for id_folder in os.listdir(self.root_dir):\n", - " if not id_folder.startswith('id='):\n", + " if not id_folder.startswith(\"id=\"):\n", " continue\n", - " participant_id = id_folder.split('=')[-1]\n", + " participant_id = id_folder.split(\"=\")[-1]\n", " futures.append(executor.submit(self.process_participant_data, participant_id))\n", "\n", " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", @@ -136,7 +140,7 @@ " all_data.append(participant_data)\n", "\n", " feature_table = pd.concat(all_data, ignore_index=True)\n", - " return feature_table\n" + " return feature_table" ] }, { @@ -152,7 +156,7 @@ "feature_table = aggregator.process_all_participants()\n", "\n", "# Inspect the feature table\n", - "print(feature_table.head())\n" + "print(feature_table.head())" ] }, { @@ -161,50 +165,48 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import pandas as pd\n", "import numpy as np\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "from tqdm import tqdm\n", + "import pandas as pd\n", + "\n", "\n", "class ActigraphAggregation:\n", " def __init__(self, root_dir):\n", " self.root_dir = root_dir\n", - " \n", + "\n", " def load_data(self, participant_id):\n", " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", " if not os.path.exists(file_path):\n", " print(f\"File not found for participant {participant_id}\")\n", " return pd.DataFrame() # Return an empty DataFrame if file doesn't exist\n", " data = pd.read_parquet(file_path)\n", - " data['id'] = participant_id\n", + " data[\"id\"] = participant_id\n", " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", " return data\n", "\n", " def temporal_aggregations(self, data):\n", " # Classify weekday/weekend\n", - " data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend')\n", + " data[\"weekday_flag\"] = data[\"weekday\"].apply(lambda x: \"weekday\" if x < 5 else \"weekend\")\n", "\n", " # Classify time of day\n", " conditions = [\n", - " (data['time_of_day'] < 6 * 3600), # Midnight to 6 AM\n", - " (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), # 6 AM to Noon\n", - " (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), # Noon to 6 PM\n", - " (data['time_of_day'] >= 18 * 3600) # 6 PM to Midnight\n", + " (data[\"time_of_day\"] < 6 * 3600), # Midnight to 6 AM\n", + " (data[\"time_of_day\"] >= 6 * 3600) & (data[\"time_of_day\"] < 12 * 3600), # 6 AM to Noon\n", + " (data[\"time_of_day\"] >= 12 * 3600) & (data[\"time_of_day\"] < 18 * 3600), # Noon to 6 PM\n", + " (data[\"time_of_day\"] >= 18 * 3600), # 6 PM to Midnight\n", " ]\n", - " choices = ['night', 'morning', 'afternoon', 'evening']\n", - " data['time_period'] = np.select(conditions, choices, default='unknown')\n", - " \n", + " choices = [\"night\", \"morning\", \"afternoon\", \"evening\"]\n", + " data[\"time_period\"] = np.select(conditions, choices, default=\"unknown\")\n", + "\n", " # Aggregate by weekday/weekend and time of day\n", - " temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({\n", - " 'enmo': 'mean',\n", - " 'light': 'mean',\n", - " 'non-wear_flag': 'sum'\n", - " }).unstack(fill_value=0)\n", - " temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values]\n", - " \n", + " temporal_agg = (\n", + " data.groupby([\"id\", \"weekday_flag\", \"time_period\"])\n", + " .agg({\"enmo\": \"mean\", \"light\": \"mean\", \"non-wear_flag\": \"sum\"})\n", + " .unstack(fill_value=0)\n", + " )\n", + " temporal_agg.columns = [\"_\".join(col).strip() for col in temporal_agg.columns.values]\n", + "\n", " return temporal_agg.reset_index()\n", - " \n", + "\n", " def process_participant_data(self, participant_id):\n", " data = self.load_data(participant_id)\n", " if data.empty: # Skip processing if data is empty\n", @@ -217,9 +219,9 @@ " with ThreadPoolExecutor() as executor:\n", " futures = []\n", " for id_folder in os.listdir(self.root_dir):\n", - " if not id_folder.startswith('id='):\n", + " if not id_folder.startswith(\"id=\"):\n", " continue\n", - " participant_id = id_folder.split('=')[-1]\n", + " participant_id = id_folder.split(\"=\")[-1]\n", " futures.append(executor.submit(self.process_participant_data, participant_id))\n", "\n", " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", @@ -229,7 +231,8 @@ "\n", " feature_table = pd.concat(all_data, ignore_index=True)\n", " return feature_table\n", - " \n", + "\n", + "\n", "# Initialize with the root directory containing participant data\n", "temporal = ActigraphAggregation(root_dir=\"../../data/series_test.parquet/\")\n", "\n", @@ -237,7 +240,7 @@ "feature_table = temporal.process_all_participants()\n", "\n", "# Inspect the feature table\n", - "print(feature_table.head())\n" + "print(feature_table.head())" ] }, { @@ -246,23 +249,20 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pandas as pd\n", - "import numpy as np\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "from tqdm import tqdm\n", + "\n", "\n", "class ActigraphAggregation:\n", " def __init__(self, root_dir):\n", " self.root_dir = root_dir\n", - " \n", + "\n", " def load_data(self, participant_id):\n", " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", " if not os.path.exists(file_path):\n", " print(f\"File not found for participant {participant_id}\")\n", " return pd.DataFrame() # Return an empty DataFrame if file doesn't exist\n", " data = pd.read_parquet(file_path)\n", - " data['id'] = participant_id\n", + " data[\"id\"] = participant_id\n", " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", " return data\n", "\n", @@ -276,43 +276,49 @@ " Returns:\n", " - DataFrame: Aggregated actigraphy data with summary statistics for each `id`.\n", " \"\"\"\n", - " aggregated_df = data.groupby('id').agg({\n", - " 'X': ['mean', 'std', 'max', 'min'],\n", - " 'Y': ['mean', 'std', 'max', 'min'],\n", - " 'Z': ['mean', 'std', 'max', 'min'],\n", - " 'enmo': ['mean', 'std', 'max', 'min'],\n", - " 'anglez': 'mean', # Mean of the angle metric\n", - " 'non-wear_flag': 'sum', # Total non-wear time\n", - " 'light': ['mean', 'std', 'max', 'min'],\n", - " 'battery_voltage': 'mean', # Average battery voltage\n", - " }).reset_index()\n", - " \n", + " aggregated_df = (\n", + " data.groupby(\"id\")\n", + " .agg(\n", + " {\n", + " \"X\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"Y\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"Z\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"enmo\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"anglez\": \"mean\", # Mean of the angle metric\n", + " \"non-wear_flag\": \"sum\", # Total non-wear time\n", + " \"light\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"battery_voltage\": \"mean\", # Average battery voltage\n", + " }\n", + " )\n", + " .reset_index()\n", + " )\n", + "\n", " # Flatten the multi-level column names\n", - " aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", + " aggregated_df.columns = [\"_\".join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", " return aggregated_df\n", "\n", " def temporal_aggregations(self, data):\n", " # Classify weekday/weekend\n", - " data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend')\n", + " data[\"weekday_flag\"] = data[\"weekday\"].apply(lambda x: \"weekday\" if x < 5 else \"weekend\")\n", "\n", " # Classify time of day\n", " conditions = [\n", - " (data['time_of_day'] < 6 * 3600), # Midnight to 6 AM\n", - " (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), # 6 AM to Noon\n", - " (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), # Noon to 6 PM\n", - " (data['time_of_day'] >= 18 * 3600) # 6 PM to Midnight\n", + " (data[\"time_of_day\"] < 6 * 3600), # Midnight to 6 AM\n", + " (data[\"time_of_day\"] >= 6 * 3600) & (data[\"time_of_day\"] < 12 * 3600), # 6 AM to Noon\n", + " (data[\"time_of_day\"] >= 12 * 3600) & (data[\"time_of_day\"] < 18 * 3600), # Noon to 6 PM\n", + " (data[\"time_of_day\"] >= 18 * 3600), # 6 PM to Midnight\n", " ]\n", - " choices = ['night', 'morning', 'afternoon', 'evening']\n", - " data['time_period'] = np.select(conditions, choices, default='unknown')\n", - " \n", + " choices = [\"night\", \"morning\", \"afternoon\", \"evening\"]\n", + " data[\"time_period\"] = np.select(conditions, choices, default=\"unknown\")\n", + "\n", " # Aggregate by weekday/weekend and time of day\n", - " temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({\n", - " 'enmo': 'mean',\n", - " 'light': 'mean',\n", - " 'non-wear_flag': 'sum'\n", - " }).unstack(fill_value=0)\n", - " temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values]\n", - " \n", + " temporal_agg = (\n", + " data.groupby([\"id\", \"weekday_flag\", \"time_period\"])\n", + " .agg({\"enmo\": \"mean\", \"light\": \"mean\", \"non-wear_flag\": \"sum\"})\n", + " .unstack(fill_value=0)\n", + " )\n", + " temporal_agg.columns = [\"_\".join(col).strip() for col in temporal_agg.columns.values]\n", + "\n", " return temporal_agg.reset_index()\n", "\n", " def activity_ratios(self, data):\n", @@ -325,28 +331,28 @@ " Returns:\n", " - DataFrame: Contains `non_wear_ratio` for each `id`.\n", " \"\"\"\n", - " total_time = data.groupby('id').size().rename('total_time')\n", - " non_wear_time = data.groupby('id')['non-wear_flag'].sum().rename('non_wear_time')\n", - " \n", + " total_time = data.groupby(\"id\").size().rename(\"total_time\")\n", + " non_wear_time = data.groupby(\"id\")[\"non-wear_flag\"].sum().rename(\"non_wear_time\")\n", + "\n", " # Calculate ratio\n", " ratios = pd.concat([total_time, non_wear_time], axis=1)\n", - " ratios['non_wear_ratio'] = ratios['non_wear_time'] / ratios['total_time']\n", - " \n", - " return ratios[['non_wear_ratio']].reset_index()\n", - " \n", + " ratios[\"non_wear_ratio\"] = ratios[\"non_wear_time\"] / ratios[\"total_time\"]\n", + "\n", + " return ratios[[\"non_wear_ratio\"]].reset_index()\n", + "\n", " def process_participant_data(self, participant_id):\n", " data = self.load_data(participant_id)\n", " if data.empty: # Skip processing if data is empty\n", " return pd.DataFrame()\n", - " \n", + "\n", " # Calculate aggregate statistics, temporal aggregation, and activity ratios\n", " aggregate_data = self.aggregate_actigraphy(data)\n", " temporal_data = self.temporal_aggregations(data)\n", " ratio_data = self.activity_ratios(data)\n", "\n", " # Merge all feature data on `id`\n", - " participant_data = aggregate_data.merge(temporal_data, on='id', how='left')\n", - " participant_data = participant_data.merge(ratio_data, on='id', how='left')\n", + " participant_data = aggregate_data.merge(temporal_data, on=\"id\", how=\"left\")\n", + " participant_data = participant_data.merge(ratio_data, on=\"id\", how=\"left\")\n", " return participant_data\n", "\n", " def process_all_participants(self):\n", @@ -354,9 +360,9 @@ " with ThreadPoolExecutor() as executor:\n", " futures = []\n", " for id_folder in os.listdir(self.root_dir):\n", - " if not id_folder.startswith('id='):\n", + " if not id_folder.startswith(\"id=\"):\n", " continue\n", - " participant_id = id_folder.split('=')[-1]\n", + " participant_id = id_folder.split(\"=\")[-1]\n", " futures.append(executor.submit(self.process_participant_data, participant_id))\n", "\n", " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", @@ -366,7 +372,8 @@ "\n", " feature_table = pd.concat(all_data, ignore_index=True)\n", " return feature_table\n", - " \n", + "\n", + "\n", "# Initialize with the root directory containing participant data\n", "aggregator = ActigraphAggregation(root_dir=\"../../data/series_test.parquet/\")\n", "\n", @@ -374,7 +381,7 @@ "feature_table = aggregator.process_all_participants()\n", "\n", "# Inspect the feature table\n", - "print(feature_table.head())\n" + "print(feature_table.head())" ] }, { @@ -383,9 +390,8 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Merge the aggregated actigraphy features with the train data\n", - "combined_df = pd.merge(train_df, aggregated_actigraphy_df, on='id', how='left')\n", + "combined_df = pd.merge(train_df, aggregated_actigraphy_df, on=\"id\", how=\"left\")\n", "\n", "# Inspect the combined DataFrame\n", "print(combined_df.head())" @@ -397,72 +403,78 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", "class ActigraphyDataProcessor:\n", " def __init__(self, root_dir, feature_table_path):\n", " self.root_dir = root_dir\n", " self.feature_table_path = feature_table_path\n", - " self.features_to_forecast = ['enmo', 'light', 'battery_voltage']\n", + " self.features_to_forecast = [\"enmo\", \"light\", \"battery_voltage\"]\n", "\n", " def load_data(self, participant_id):\n", " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", " data = pd.read_parquet(file_path)\n", - " data['id'] = participant_id\n", + " data[\"id\"] = participant_id\n", " print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", " return data\n", "\n", " def compute_daily_summary(self, data):\n", - " daily_summary = data.groupby(['id', 'relative_date_PCIAT']).agg({\n", - " 'X': ['mean', 'max', 'min', 'std'],\n", - " 'Y': ['mean', 'max', 'min', 'std'],\n", - " 'Z': ['mean', 'max', 'min', 'std'],\n", - " 'enmo': ['mean', 'max', 'std'],\n", - " 'anglez': 'mean',\n", - " 'non-wear_flag': 'sum',\n", - " 'light': ['mean', 'max', 'min'],\n", - " 'battery_voltage': 'mean'\n", - " }).reset_index()\n", - " daily_summary.columns = ['_'.join(col).strip() if col[1] else col[0] for col in daily_summary.columns.values]\n", + " daily_summary = (\n", + " data.groupby([\"id\", \"relative_date_PCIAT\"])\n", + " .agg(\n", + " {\n", + " \"X\": [\"mean\", \"max\", \"min\", \"std\"],\n", + " \"Y\": [\"mean\", \"max\", \"min\", \"std\"],\n", + " \"Z\": [\"mean\", \"max\", \"min\", \"std\"],\n", + " \"enmo\": [\"mean\", \"max\", \"std\"],\n", + " \"anglez\": \"mean\",\n", + " \"non-wear_flag\": \"sum\",\n", + " \"light\": [\"mean\", \"max\", \"min\"],\n", + " \"battery_voltage\": \"mean\",\n", + " }\n", + " )\n", + " .reset_index()\n", + " )\n", + " daily_summary.columns = [\"_\".join(col).strip() if col[1] else col[0] for col in daily_summary.columns.values]\n", " print(f\"Daily summary columns after aggregation: {daily_summary.columns.tolist()}\")\n", " return daily_summary\n", "\n", " def add_temporal_features(self, daily_summary):\n", - " for col in ['enmo_mean', 'light_mean', 'battery_voltage_mean']:\n", + " for col in [\"enmo_mean\", \"light_mean\", \"battery_voltage_mean\"]:\n", " if col in daily_summary.columns:\n", - " daily_summary[f'{col}_lag1'] = daily_summary.groupby('id')[col].shift(1)\n", - " daily_summary[f'{col}_rolling3'] = daily_summary.groupby('id')[col].rolling(window=3).mean().reset_index(0, drop=True)\n", + " daily_summary[f\"{col}_lag1\"] = daily_summary.groupby(\"id\")[col].shift(1)\n", + " daily_summary[f\"{col}_rolling3\"] = (\n", + " daily_summary.groupby(\"id\")[col].rolling(window=3).mean().reset_index(0, drop=True)\n", + " )\n", " else:\n", " print(f\"Warning: Column {col} not found in daily_summary.\")\n", " return daily_summary\n", "\n", " def forecast_features(self, data, participant_id):\n", - " date_mapping = data[['time_of_day', 'relative_date_PCIAT']].drop_duplicates()\n", - " date_mapping['ds'] = pd.to_datetime(date_mapping['time_of_day'], unit='s', errors='coerce')\n", - " \n", + " date_mapping = data[[\"time_of_day\", \"relative_date_PCIAT\"]].drop_duplicates()\n", + " date_mapping[\"ds\"] = pd.to_datetime(date_mapping[\"time_of_day\"], unit=\"s\", errors=\"coerce\")\n", + "\n", " predictions = []\n", " for feature in self.features_to_forecast:\n", - " df = data[['time_of_day', feature]].dropna().rename(columns={'time_of_day': 'ds', feature: 'y'})\n", - " max_time = df['ds'].max()\n", + " df = data[[\"time_of_day\", feature]].dropna().rename(columns={\"time_of_day\": \"ds\", feature: \"y\"})\n", + " max_time = df[\"ds\"].max()\n", " if max_time > 10**12:\n", - " df['ds'] = pd.to_datetime(df['ds'] / 10**3, unit='s', errors='coerce')\n", + " df[\"ds\"] = pd.to_datetime(df[\"ds\"] / 10**3, unit=\"s\", errors=\"coerce\")\n", " else:\n", - " df['ds'] = pd.to_datetime(df['ds'], unit='s', errors='coerce')\n", - " df = df.dropna(subset=['ds'])\n", + " df[\"ds\"] = pd.to_datetime(df[\"ds\"], unit=\"s\", errors=\"coerce\")\n", + " df = df.dropna(subset=[\"ds\"])\n", "\n", " model = Prophet(daily_seasonality=True, weekly_seasonality=True)\n", " model.fit(df)\n", - " future = model.make_future_dataframe(periods=7, freq='D')\n", + " future = model.make_future_dataframe(periods=7, freq=\"D\")\n", " forecast = model.predict(future)\n", "\n", - " forecast['id'] = participant_id\n", - " forecast = forecast[['id', 'ds', 'yhat']].rename(columns={'yhat': f'{feature}_forecast'})\n", + " forecast[\"id\"] = participant_id\n", + " forecast = forecast[[\"id\", \"ds\", \"yhat\"]].rename(columns={\"yhat\": f\"{feature}_forecast\"})\n", " predictions.append(forecast)\n", "\n", " predictions_df = pd.concat(predictions, axis=1)\n", " predictions_df = predictions_df.loc[:, ~predictions_df.columns.duplicated()]\n", - " predictions_df = pd.merge(predictions_df, date_mapping[['ds', 'relative_date_PCIAT']], on='ds', how='left')\n", - " \n", + " predictions_df = pd.merge(predictions_df, date_mapping[[\"ds\", \"relative_date_PCIAT\"]], on=\"ds\", how=\"left\")\n", + "\n", " return predictions_df\n", "\n", " def process_participant_data(self, participant_id):\n", @@ -472,7 +484,7 @@ " daily_summary = self.add_temporal_features(daily_summary)\n", " forecasts = self.forecast_features(data, participant_id)\n", " print(f\"Forecast columns for {participant_id}: {forecasts.columns.tolist()}\")\n", - " processed_data = pd.merge(daily_summary, forecasts, on=['id', 'relative_date_PCIAT'], how='left')\n", + " processed_data = pd.merge(daily_summary, forecasts, on=[\"id\", \"relative_date_PCIAT\"], how=\"left\")\n", " return processed_data\n", "\n", " def process_all_participants(self):\n", @@ -480,9 +492,9 @@ " with ThreadPoolExecutor() as executor:\n", " futures = []\n", " for id_folder in os.listdir(self.root_dir):\n", - " if not id_folder.startswith('id='):\n", + " if not id_folder.startswith(\"id=\"):\n", " continue\n", - " participant_id = id_folder.split('=')[-1]\n", + " participant_id = id_folder.split(\"=\")[-1]\n", " futures.append(executor.submit(self.process_participant_data, participant_id))\n", "\n", " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", @@ -496,7 +508,7 @@ " os.makedirs(os.path.dirname(self.feature_table_path), exist_ok=True)\n", " feature_table = self.process_all_participants()\n", " feature_table.to_parquet(self.feature_table_path, index=False)\n", - " print(f\"Feature table saved at {self.feature_table_path}\")\n" + " print(f\"Feature table saved at {self.feature_table_path}\")" ] }, { @@ -506,10 +518,12 @@ "outputs": [], "source": [ "# Initialize the processor with root directory and path for feature table\n", - "processor = ActigraphyDataProcessor(root_dir=\"../../data/series_test.parquet/\", feature_table_path=\"../../data/processed/feature_table.parquet\")\n", + "processor = ActigraphyDataProcessor(\n", + " root_dir=\"../../data/series_test.parquet/\", feature_table_path=\"../../data/processed/feature_table.parquet\"\n", + ")\n", "\n", "# Process all participants and save the feature table, with progress shown\n", - "processor.save_feature_table()\n" + "processor.save_feature_table()" ] }, { @@ -573,7 +587,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_train[df_train['id'] == \"001f3379\"]" + "df_train[df_train[\"id\"] == \"001f3379\"]" ] }, { @@ -589,14 +603,14 @@ "metadata": {}, "outputs": [], "source": [ - "import yaml\n", - "import pandas as pd\n", - "import numpy as np\n", + "import joblib\n", + "import lightgbm as lgb\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import yaml\n", + "from sklearn.metrics import accuracy_score, classification_report\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, accuracy_score\n", - "import lightgbm as lgb\n", - "import joblib\n", + "\n", "\n", "class MultiClassLightGBM:\n", " def __init__(self, config_path):\n", @@ -605,20 +619,20 @@ "\n", " def load_config(self, config_path):\n", " \"\"\"Load configuration file.\"\"\"\n", - " with open(config_path, 'r') as file:\n", + " with open(config_path, \"r\") as file:\n", " config = yaml.safe_load(file)\n", " return config\n", "\n", " def load_data(self, filepath):\n", " \"\"\"Load dataset and split into features and target based on config.\"\"\"\n", " data = pd.read_csv(filepath)\n", - " X = data[self.config['num_features'] + self.config['cat_features']]\n", - " y = data[self.config['target']]\n", + " X = data[self.config[\"num_features\"] + self.config[\"cat_features\"]]\n", + " y = data[self.config[\"target\"]]\n", " return X, y\n", "\n", " def preprocess_data(self, X):\n", " \"\"\"Preprocess the data by encoding categorical variables.\"\"\"\n", - " X = pd.get_dummies(X, columns=self.config['cat_features'], drop_first=True)\n", + " X = pd.get_dummies(X, columns=self.config[\"cat_features\"], drop_first=True)\n", " return X\n", "\n", " def train(self, X, y):\n", @@ -631,19 +645,21 @@ "\n", " # Set LightGBM parameters for multiclass classification\n", " params = {\n", - " 'objective': 'multiclass',\n", - " 'num_class': len(y.unique()),\n", - " 'learning_rate': self.config['model_parameters']['learning_rate'],\n", - " 'n_estimators': self.config['model_parameters']['n_estimators'],\n", - " 'max_depth': self.config['model_parameters']['max_depth'],\n", - " 'metric': 'multi_logloss'\n", + " \"objective\": \"multiclass\",\n", + " \"num_class\": len(y.unique()),\n", + " \"learning_rate\": self.config[\"model_parameters\"][\"learning_rate\"],\n", + " \"n_estimators\": self.config[\"model_parameters\"][\"n_estimators\"],\n", + " \"max_depth\": self.config[\"model_parameters\"][\"max_depth\"],\n", + " \"metric\": \"multi_logloss\",\n", " }\n", "\n", " # Train the model\n", - " self.model = lgb.train(params, train_data, valid_sets=[train_data, val_data], early_stopping_rounds=50, verbose_eval=10)\n", + " self.model = lgb.train(\n", + " params, train_data, valid_sets=[train_data, val_data], early_stopping_rounds=50, verbose_eval=10\n", + " )\n", "\n", " # Save the model\n", - " joblib.dump(self.model, 'lgbm_model.pkl')\n", + " joblib.dump(self.model, \"lgbm_model.pkl\")\n", " print(\"Model training complete and saved as 'lgbm_model.pkl'\")\n", "\n", " def evaluate(self, X, y):\n", @@ -658,20 +674,20 @@ "\n", " def feature_importance(self, X):\n", " \"\"\"Display the feature importance.\"\"\"\n", - " importance = self.model.feature_importance(importance_type='gain')\n", + " importance = self.model.feature_importance(importance_type=\"gain\")\n", " feature_names = X.columns\n", - " feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importance})\n", - " feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n", + " feature_importance = pd.DataFrame({\"feature\": feature_names, \"importance\": importance})\n", + " feature_importance = feature_importance.sort_values(by=\"importance\", ascending=False)\n", "\n", " # Plotting feature importance\n", " plt.figure(figsize=(10, 8))\n", - " plt.barh(feature_importance['feature'], feature_importance['importance'], color='skyblue')\n", - " plt.xlabel('Importance')\n", - " plt.title('Feature Importance')\n", + " plt.barh(feature_importance[\"feature\"], feature_importance[\"importance\"], color=\"skyblue\")\n", + " plt.xlabel(\"Importance\")\n", + " plt.title(\"Feature Importance\")\n", " plt.gca().invert_yaxis()\n", " plt.show()\n", "\n", - " def load_model(self, model_path='lgbm_model.pkl'):\n", + " def load_model(self, model_path=\"lgbm_model.pkl\"):\n", " \"\"\"Load a saved model.\"\"\"\n", " self.model = joblib.load(model_path)\n", " print(\"Model loaded from\", model_path)\n", @@ -685,8 +701,8 @@ "\n", "if __name__ == \"__main__\":\n", " # Configuration and paths\n", - " config_path = 'project_config.yml'\n", - " train_path = 'train.csv'\n", + " config_path = \"project_config.yml\"\n", + " train_path = \"train.csv\"\n", "\n", " # Initialize model class\n", " lgbm_classifier = MultiClassLightGBM(config_path)\n", @@ -702,7 +718,7 @@ " lgbm_classifier.evaluate(X, y)\n", "\n", " # Display feature importance\n", - " lgbm_classifier.feature_importance(X)\n" + " lgbm_classifier.feature_importance(X)" ] }, { @@ -711,23 +727,22 @@ "metadata": {}, "outputs": [], "source": [ - "import yaml\n", "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", "\n", "class TrainDataProcessor:\n", " def __init__(self, train_df, config_path):\n", " self.train_df = train_df\n", " self.config = self.load_config(config_path)\n", - " self.num_features = self.config['num_features']\n", - " self.cat_features = self.config['cat_features']\n", - " self.target = self.config['target']\n", + " self.num_features = self.config[\"num_features\"]\n", + " self.cat_features = self.config[\"cat_features\"]\n", + " self.target = self.config[\"target\"]\n", "\n", " def load_config(self, config_path):\n", - " \"\"\"Load the project configuration file.\"\"\" \n", - " with open(config_path, 'r') as file:\n", + " \"\"\"Load the project configuration file.\"\"\"\n", + " with open(config_path, \"r\") as file:\n", " config = yaml.safe_load(file)\n", " return config\n", "\n", @@ -741,7 +756,7 @@ " \"\"\"Handle missing values in the train dataset.\"\"\"\n", " # Fill numeric columns with mean\n", " numeric_cols = self.train_df[self.num_features]\n", - " imputer = SimpleImputer(strategy='mean')\n", + " imputer = SimpleImputer(strategy=\"mean\")\n", " self.train_df[self.num_features] = imputer.fit_transform(numeric_cols)\n", "\n", " # Fill categorical columns with mode\n", @@ -751,8 +766,8 @@ " def convert_data_types(self):\n", " \"\"\"Convert categorical columns to appropriate data types.\"\"\"\n", " # Convert 'Sex' to binary encoding if it's part of numerical features\n", - " if 'Basic_Demos-Sex' in self.num_features:\n", - " self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({'Male': 1, 'Female': 0})\n", + " if \"Basic_Demos-Sex\" in self.num_features:\n", + " self.train_df[\"Basic_Demos-Sex\"] = self.train_df[\"Basic_Demos-Sex\"].map({\"Male\": 1, \"Female\": 0})\n", "\n", " def feature_engineering(self):\n", " \"\"\"Perform feature engineering to create new features.\"\"\"\n", @@ -764,35 +779,47 @@ "\n", " def add_age_groups(self):\n", " \"\"\"Add age groups based on age.\"\"\"\n", - " if 'Basic_Demos-Age' in self.num_features:\n", - " self.train_df['Age_Group'] = pd.cut(self.train_df['Basic_Demos-Age'], bins=[0, 12, 17, 25], labels=['Child', 'Teen', 'Young Adult'])\n", + " if \"Basic_Demos-Age\" in self.num_features:\n", + " self.train_df[\"Age_Group\"] = pd.cut(\n", + " self.train_df[\"Basic_Demos-Age\"], bins=[0, 12, 17, 25], labels=[\"Child\", \"Teen\", \"Young Adult\"]\n", + " )\n", "\n", " def one_hot_encode_seasons(self):\n", " \"\"\"One-hot encode season columns.\"\"\"\n", " for col in self.cat_features:\n", - " if 'Season' in col:\n", + " if \"Season\" in col:\n", " one_hot = pd.get_dummies(self.train_df[col], prefix=col)\n", " self.train_df = pd.concat([self.train_df, one_hot], axis=1)\n", "\n", " def calculate_behavioral_scores(self):\n", " \"\"\"Calculate behavioral and psychological indicators.\"\"\"\n", " # Bin PCIAT total score\n", - " if 'PCIAT-PCIAT_Total' in self.num_features:\n", - " self.train_df['PCIAT_Bin'] = pd.cut(self.train_df['PCIAT-PCIAT_Total'], bins=[0, 20, 40, 60], labels=['Mild', 'Moderate', 'Severe'])\n", + " if \"PCIAT-PCIAT_Total\" in self.num_features:\n", + " self.train_df[\"PCIAT_Bin\"] = pd.cut(\n", + " self.train_df[\"PCIAT-PCIAT_Total\"], bins=[0, 20, 40, 60], labels=[\"Mild\", \"Moderate\", \"Severe\"]\n", + " )\n", "\n", " # Categorize internet use\n", - " if 'PreInt_EduHx-computerinternet_hoursday' in self.num_features:\n", - " self.train_df['Internet_Use_Category'] = pd.cut(self.train_df['PreInt_EduHx-computerinternet_hoursday'], bins=[0, 1, 3, 6, np.inf], labels=['Low', 'Moderate', 'High', 'Very High'])\n", + " if \"PreInt_EduHx-computerinternet_hoursday\" in self.num_features:\n", + " self.train_df[\"Internet_Use_Category\"] = pd.cut(\n", + " self.train_df[\"PreInt_EduHx-computerinternet_hoursday\"],\n", + " bins=[0, 1, 3, 6, np.inf],\n", + " labels=[\"Low\", \"Moderate\", \"High\", \"Very High\"],\n", + " )\n", "\n", " def add_interaction_features(self):\n", " \"\"\"Add interaction features, such as age-adjusted scores.\"\"\"\n", " # Age-adjusted CGAS Score\n", - " if 'CGAS-CGAS_Score' in self.num_features and 'Basic_Demos-Age' in self.num_features:\n", - " self.train_df['Age_Adjusted_CGAS'] = self.train_df['CGAS-CGAS_Score'] / self.train_df['Basic_Demos-Age']\n", + " if \"CGAS-CGAS_Score\" in self.num_features and \"Basic_Demos-Age\" in self.num_features:\n", + " self.train_df[\"Age_Adjusted_CGAS\"] = self.train_df[\"CGAS-CGAS_Score\"] / self.train_df[\"Basic_Demos-Age\"]\n", "\n", " # BMI Categories\n", - " if 'Physical-BMI' in self.num_features:\n", - " self.train_df['BMI_Category'] = pd.cut(self.train_df['Physical-BMI'], bins=[0, 18.5, 25, 30, np.inf], labels=['Underweight', 'Normal', 'Overweight', 'Obese'])\n", + " if \"Physical-BMI\" in self.num_features:\n", + " self.train_df[\"BMI_Category\"] = pd.cut(\n", + " self.train_df[\"Physical-BMI\"],\n", + " bins=[0, 18.5, 25, 30, np.inf],\n", + " labels=[\"Underweight\", \"Normal\", \"Overweight\", \"Obese\"],\n", + " )\n", "\n", " def scale_numeric_features(self):\n", " \"\"\"Scale numeric features in the final dataset.\"\"\"\n", @@ -810,14 +837,14 @@ "# Example usage:\n", "\n", "# Load train data\n", - "train_df = pd.read_csv('path/to/train.csv')\n", + "train_df = pd.read_csv(\"path/to/train.csv\")\n", "\n", "# Initialize and process the data using the config file\n", - "processor = TrainDataProcessor(train_df, 'project_config.yml')\n", + "processor = TrainDataProcessor(train_df, \"project_config.yml\")\n", "processed_df = processor.process()\n", "\n", "# Inspect the processed dataframe\n", - "print(processed_df.head())\n" + "print(processed_df.head())" ] }, { @@ -869,23 +896,20 @@ } ], "source": [ - "import os\n", "import pandas as pd\n", - "import numpy as np\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "from tqdm import tqdm\n", + "\n", "\n", "class ActigraphAggregation:\n", " def __init__(self, root_dir):\n", " self.root_dir = root_dir\n", - " \n", + "\n", " def load_data(self, participant_id):\n", " file_path = os.path.join(self.root_dir, f\"id={participant_id}\", \"part-0.parquet\")\n", " if not os.path.exists(file_path):\n", " print(f\"File not found for participant {participant_id}\")\n", " return pd.DataFrame() # Return an empty DataFrame if file doesn't exist\n", " data = pd.read_parquet(file_path)\n", - " data['id'] = participant_id\n", + " data[\"id\"] = participant_id\n", " # print(f\"Loaded data columns for {participant_id}: {data.columns.tolist()}\")\n", " return data\n", "\n", @@ -899,43 +923,49 @@ " Returns:\n", " - DataFrame: Aggregated actigraphy data with summary statistics for each `id`.\n", " \"\"\"\n", - " aggregated_df = data.groupby('id').agg({\n", - " 'X': ['mean', 'std', 'max', 'min'],\n", - " 'Y': ['mean', 'std', 'max', 'min'],\n", - " 'Z': ['mean', 'std', 'max', 'min'],\n", - " 'enmo': ['mean', 'std', 'max', 'min'],\n", - " 'anglez': 'mean', # Mean of the angle metric\n", - " 'non-wear_flag': 'sum', # Total non-wear time\n", - " 'light': ['mean', 'std', 'max', 'min'],\n", - " 'battery_voltage': 'mean', # Average battery voltage\n", - " }).reset_index()\n", - " \n", + " aggregated_df = (\n", + " data.groupby(\"id\")\n", + " .agg(\n", + " {\n", + " \"X\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"Y\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"Z\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"enmo\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"anglez\": \"mean\", # Mean of the angle metric\n", + " \"non-wear_flag\": \"sum\", # Total non-wear time\n", + " \"light\": [\"mean\", \"std\", \"max\", \"min\"],\n", + " \"battery_voltage\": \"mean\", # Average battery voltage\n", + " }\n", + " )\n", + " .reset_index()\n", + " )\n", + "\n", " # Flatten the multi-level column names\n", - " aggregated_df.columns = ['_'.join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", + " aggregated_df.columns = [\"_\".join(col).strip() if col[1] else col[0] for col in aggregated_df.columns.values]\n", " return aggregated_df\n", "\n", " def temporal_aggregations(self, data):\n", " # Classify weekday/weekend\n", - " data['weekday_flag'] = data['weekday'].apply(lambda x: 'weekday' if x < 5 else 'weekend')\n", + " data[\"weekday_flag\"] = data[\"weekday\"].apply(lambda x: \"weekday\" if x < 5 else \"weekend\")\n", "\n", " # Classify time of day\n", " conditions = [\n", - " (data['time_of_day'] < 6 * 3600), # Midnight to 6 AM\n", - " (data['time_of_day'] >= 6 * 3600) & (data['time_of_day'] < 12 * 3600), # 6 AM to Noon\n", - " (data['time_of_day'] >= 12 * 3600) & (data['time_of_day'] < 18 * 3600), # Noon to 6 PM\n", - " (data['time_of_day'] >= 18 * 3600) # 6 PM to Midnight\n", + " (data[\"time_of_day\"] < 6 * 3600), # Midnight to 6 AM\n", + " (data[\"time_of_day\"] >= 6 * 3600) & (data[\"time_of_day\"] < 12 * 3600), # 6 AM to Noon\n", + " (data[\"time_of_day\"] >= 12 * 3600) & (data[\"time_of_day\"] < 18 * 3600), # Noon to 6 PM\n", + " (data[\"time_of_day\"] >= 18 * 3600), # 6 PM to Midnight\n", " ]\n", - " choices = ['night', 'morning', 'afternoon', 'evening']\n", - " data['time_period'] = np.select(conditions, choices, default='unknown')\n", - " \n", + " choices = [\"night\", \"morning\", \"afternoon\", \"evening\"]\n", + " data[\"time_period\"] = np.select(conditions, choices, default=\"unknown\")\n", + "\n", " # Aggregate by weekday/weekend and time of day\n", - " temporal_agg = data.groupby(['id', 'weekday_flag', 'time_period']).agg({\n", - " 'enmo': 'mean',\n", - " 'light': 'mean',\n", - " 'non-wear_flag': 'sum'\n", - " }).unstack(fill_value=0)\n", - " temporal_agg.columns = ['_'.join(col).strip() for col in temporal_agg.columns.values]\n", - " \n", + " temporal_agg = (\n", + " data.groupby([\"id\", \"weekday_flag\", \"time_period\"])\n", + " .agg({\"enmo\": \"mean\", \"light\": \"mean\", \"non-wear_flag\": \"sum\"})\n", + " .unstack(fill_value=0)\n", + " )\n", + " temporal_agg.columns = [\"_\".join(col).strip() for col in temporal_agg.columns.values]\n", + "\n", " return temporal_agg.reset_index()\n", "\n", " def activity_ratios(self, data):\n", @@ -948,28 +978,28 @@ " Returns:\n", " - DataFrame: Contains `non_wear_ratio` for each `id`.\n", " \"\"\"\n", - " total_time = data.groupby('id').size().rename('total_time')\n", - " non_wear_time = data.groupby('id')['non-wear_flag'].sum().rename('non_wear_time')\n", - " \n", + " total_time = data.groupby(\"id\").size().rename(\"total_time\")\n", + " non_wear_time = data.groupby(\"id\")[\"non-wear_flag\"].sum().rename(\"non_wear_time\")\n", + "\n", " # Calculate ratio\n", " ratios = pd.concat([total_time, non_wear_time], axis=1)\n", - " ratios['non_wear_ratio'] = ratios['non_wear_time'] / ratios['total_time']\n", - " \n", - " return ratios[['non_wear_ratio']].reset_index()\n", - " \n", + " ratios[\"non_wear_ratio\"] = ratios[\"non_wear_time\"] / ratios[\"total_time\"]\n", + "\n", + " return ratios[[\"non_wear_ratio\"]].reset_index()\n", + "\n", " def process_participant_data(self, participant_id):\n", " data = self.load_data(participant_id)\n", " if data.empty: # Skip processing if data is empty\n", " return pd.DataFrame()\n", - " \n", + "\n", " # Calculate aggregate statistics, temporal aggregation, and activity ratios\n", " aggregate_data = self.aggregate_actigraphy(data)\n", " temporal_data = self.temporal_aggregations(data)\n", " ratio_data = self.activity_ratios(data)\n", "\n", " # Merge all feature data on `id`\n", - " participant_data = aggregate_data.merge(temporal_data, on='id', how='left')\n", - " participant_data = participant_data.merge(ratio_data, on='id', how='left')\n", + " participant_data = aggregate_data.merge(temporal_data, on=\"id\", how=\"left\")\n", + " participant_data = participant_data.merge(ratio_data, on=\"id\", how=\"left\")\n", " return participant_data\n", "\n", " def process_all_participants(self):\n", @@ -977,9 +1007,9 @@ " with ThreadPoolExecutor() as executor:\n", " futures = []\n", " for id_folder in os.listdir(self.root_dir):\n", - " if not id_folder.startswith('id='):\n", + " if not id_folder.startswith(\"id=\"):\n", " continue\n", - " participant_id = id_folder.split('=')[-1]\n", + " participant_id = id_folder.split(\"=\")[-1]\n", " futures.append(executor.submit(self.process_participant_data, participant_id))\n", "\n", " for future in tqdm(as_completed(futures), total=len(futures), desc=\"Processing participants\"):\n", @@ -989,7 +1019,8 @@ "\n", " feature_table = pd.concat(all_data, ignore_index=True)\n", " return feature_table\n", - " \n", + "\n", + "\n", "# Initialize with the root directory containing participant data\n", "aggregator = ActigraphAggregation(root_dir=\"../../data/series_train.parquet/\")\n", "\n", @@ -997,7 +1028,7 @@ "feature_table = aggregator.process_all_participants()\n", "\n", "# Inspect the feature table\n", - "print(feature_table.head())\n" + "print(feature_table.head())" ] }, { @@ -1036,7 +1067,7 @@ "# merge train csv with feature table\n", "df_train = pd.read_csv(\"../../data/childhealth.csv\")\n", "\n", - "df_merge = df_train.merge(feature_table, on='id', how='left')" + "df_merge = df_train.merge(feature_table, on=\"id\", how=\"left\")" ] }, { @@ -1105,14 +1136,8 @@ "metadata": {}, "outputs": [], "source": [ - "import yaml\n", "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, accuracy_score\n", - "import lightgbm as lgb\n", - "import joblib\n", + "\n", "\n", "class ChildHealthModel:\n", " def __init__(self, config_path):\n", @@ -1121,20 +1146,20 @@ "\n", " def load_config(self, config_path):\n", " \"\"\"Load configuration file.\"\"\"\n", - " with open(config_path, 'r') as file:\n", + " with open(config_path, \"r\") as file:\n", " config = yaml.safe_load(file)\n", " return config\n", "\n", " def load_data(self, filepath):\n", " \"\"\"Load dataset and split into features and target based on config.\"\"\"\n", " data = pd.read_csv(filepath)\n", - " X = data[self.config['num_features'] + self.config['cat_features']]\n", - " y = data[self.config['target']]\n", + " X = data[self.config[\"num_features\"] + self.config[\"cat_features\"]]\n", + " y = data[self.config[\"target\"]]\n", " return X, y\n", "\n", " def preprocess_data(self, X):\n", " \"\"\"Preprocess the data by encoding categorical variables.\"\"\"\n", - " X = pd.get_dummies(X, columns=self.config['cat_features'], drop_first=True)\n", + " X = pd.get_dummies(X, columns=self.config[\"cat_features\"], drop_first=True)\n", " return X\n", "\n", " def train(self, X, y):\n", @@ -1147,19 +1172,21 @@ "\n", " # Set LightGBM parameters for multiclass classification\n", " params = {\n", - " 'objective': 'multiclass',\n", - " 'num_class': len(y.unique()),\n", - " 'learning_rate': self.config['model_parameters']['learning_rate'],\n", - " 'n_estimators': self.config['model_parameters']['n_estimators'],\n", - " 'max_depth': self.config['model_parameters']['max_depth'],\n", - " 'metric': 'multi_logloss'\n", + " \"objective\": \"multiclass\",\n", + " \"num_class\": len(y.unique()),\n", + " \"learning_rate\": self.config[\"model_parameters\"][\"learning_rate\"],\n", + " \"n_estimators\": self.config[\"model_parameters\"][\"n_estimators\"],\n", + " \"max_depth\": self.config[\"model_parameters\"][\"max_depth\"],\n", + " \"metric\": \"multi_logloss\",\n", " }\n", "\n", " # Train the model\n", - " self.model = lgb.train(params, train_data, valid_sets=[train_data, val_data], early_stopping_rounds=50, verbose_eval=10)\n", + " self.model = lgb.train(\n", + " params, train_data, valid_sets=[train_data, val_data], early_stopping_rounds=50, verbose_eval=10\n", + " )\n", "\n", " # Save the model\n", - " joblib.dump(self.model, 'lgbm_model.pkl')\n", + " joblib.dump(self.model, \"lgbm_model.pkl\")\n", " print(\"Model training complete and saved as 'lgbm_model.pkl'\")\n", "\n", " def evaluate(self, X, y):\n", @@ -1174,20 +1201,20 @@ "\n", " def feature_importance(self, X):\n", " \"\"\"Display the feature importance.\"\"\"\n", - " importance = self.model.feature_importance(importance_type='gain')\n", + " importance = self.model.feature_importance(importance_type=\"gain\")\n", " feature_names = X.columns\n", - " feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importance})\n", - " feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n", + " feature_importance = pd.DataFrame({\"feature\": feature_names, \"importance\": importance})\n", + " feature_importance = feature_importance.sort_values(by=\"importance\", ascending=False)\n", "\n", " # Plotting feature importance\n", " plt.figure(figsize=(10, 8))\n", - " plt.barh(feature_importance['feature'], feature_importance['importance'], color='skyblue')\n", - " plt.xlabel('Importance')\n", - " plt.title('Feature Importance')\n", + " plt.barh(feature_importance[\"feature\"], feature_importance[\"importance\"], color=\"skyblue\")\n", + " plt.xlabel(\"Importance\")\n", + " plt.title(\"Feature Importance\")\n", " plt.gca().invert_yaxis()\n", " plt.show()\n", "\n", - " def load_model(self, model_path='lgbm_model.pkl'):\n", + " def load_model(self, model_path=\"lgbm_model.pkl\"):\n", " \"\"\"Load a saved model.\"\"\"\n", " self.model = joblib.load(model_path)\n", " print(\"Model loaded from\", model_path)\n", @@ -1201,8 +1228,8 @@ "\n", "if __name__ == \"__main__\":\n", " # Configuration and paths\n", - " config_path = 'project_config.yml'\n", - " train_path = 'train.csv'\n", + " config_path = \"project_config.yml\"\n", + " train_path = \"train.csv\"\n", "\n", " # Initialize model class\n", " child_health_model = ChildHealthModel(config_path)\n", diff --git a/notebooks/week1/01.dataExploraton.py b/notebooks/week1/01.dataExploraton.py index 765a180..84a0b5a 100644 --- a/notebooks/week1/01.dataExploraton.py +++ b/notebooks/week1/01.dataExploraton.py @@ -1,22 +1,23 @@ # Databricks notebook source # COMMAND ---------- -#MAGIC %pip install ../mlops_with_databricks-0.0.1-py3-none-any.whl +# MAGIC %pip install ../mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- -#MAGIC dbutils.library.restartPython() +# MAGIC dbutils.library.restartPython() # COMMAND ---------- import os +import warnings +from concurrent.futures import ThreadPoolExecutor + import pandas as pd from pyspark.sql import SparkSession -from concurrent.futures import ThreadPoolExecutor from childHealth.config import ProjectConfig -import warnings warnings.filterwarnings("ignore") # Initialize Spark session @@ -36,13 +37,14 @@ # COMMAND ---------- def process_file(filename, dirname): - filepath = os.path.join(dirname, filename, 'part-0.parquet') + filepath = os.path.join(dirname, filename, "part-0.parquet") df = spark.read.parquet(filepath) - df = df.drop('step') + df = df.drop("step") # Ensure 'id' column is included - if 'id' not in df.columns: - df = df.withColumn('id', df['relative_date_PCIAT']) # Use an existing column or create a new one - return df.toPandas(), filename.split('=')[1] + if "id" not in df.columns: + df = df.withColumn("id", df["relative_date_PCIAT"]) # Use an existing column or create a new one + return df.toPandas(), filename.split("=")[1] + def load_time_series(dirname) -> pd.DataFrame: # List all subdirectories in the specified path @@ -57,14 +59,15 @@ def load_time_series(dirname) -> pd.DataFrame: print(f"Processed {i + 1}/{len(directories)} files") # Separate stats and identifiers - stats, indexes = zip(*results) if results else ([], []) - + stats, indexes = zip(*results, strict=False) if results else ([], []) + # Create DataFrame with statistics and identifiers combined_df = pd.concat([df for df in stats], ignore_index=True) - combined_df['id'] = indexes + combined_df["id"] = indexes return combined_df + # COMMAND ---------- # Load time series data @@ -74,24 +77,24 @@ def load_time_series(dirname) -> pd.DataFrame: # COMMAND ---------- # Load train and test CSV files with Spark -train = spark.read.csv('/Volumes/mlops_students/javedhassi/data/childHealth.csv', header=True, inferSchema=True) -test = spark.read.csv('/Volumes/mlops_students/javedhassi/data/test.csv', header=True, inferSchema=True) +train = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True) +test = spark.read.csv("/Volumes/mlops_students/javedhassi/data/test.csv", header=True, inferSchema=True) # Convert Spark DataFrames to Pandas DataFrames train_pd = train.toPandas() test_pd = test.toPandas() # Ensure 'id' column exists in both DataFrames -if 'id' not in train_pd.columns: - train_pd['id'] = train_pd.index -if 'id' not in test_pd.columns: - test_pd['id'] = test_pd.index +if "id" not in train_pd.columns: + train_pd["id"] = train_pd.index +if "id" not in test_pd.columns: + test_pd["id"] = test_pd.index # COMMAND ---------- # Merge the data -train_merged = pd.merge(train_pd, train_ts, how="left", on='id') -test_merged = pd.merge(test_pd, test_ts, how="left", on='id') +train_merged = pd.merge(train_pd, train_ts, how="left", on="id") +test_merged = pd.merge(test_pd, test_ts, how="left", on="id") # Check the result print(train_merged.head()) @@ -106,12 +109,14 @@ def load_time_series(dirname) -> pd.DataFrame: # COMMAND ---------- + def update(df): - for c in cat_features: - df[c] = df[c].fillna('Missing') - df[c] = df[c].astype('category') + for c in cat_features: + df[c] = df[c].fillna("Missing") + df[c] = df[c].astype("category") return df + # COMMAND ---------- # Update the train and test DataFrames @@ -130,8 +135,11 @@ def update(df): # COMMAND ---------- # Read the Parquet file -df = spark.read.parquet("/Volumes/mlops_students/javedhassi/data/series_train.parquet/id=00115b9f/part-0.parquet", - header=True, inferSchema=True) +df = spark.read.parquet( + "/Volumes/mlops_students/javedhassi/data/series_train.parquet/id=00115b9f/part-0.parquet", + header=True, + inferSchema=True, +) # Show the DataFrame df.show() @@ -140,6 +148,6 @@ def update(df): df_pandas = df.toPandas() # COMMAND ---------- -train = spark.read.csv('/Volumes/mlops_students/javedhassi/data/childHealth.csv', header=True, inferSchema=True) -train.filter(train.id=="00115b9f").show() +train = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True) +train.filter(train.id == "00115b9f").show() # COMMAND ---------- diff --git a/notebooks/week1/01.featueExploration.ipynb b/notebooks/week1/01.featueExploration.ipynb index 0effec8..995e8ad 100644 --- a/notebooks/week1/01.featueExploration.ipynb +++ b/notebooks/week1/01.featueExploration.ipynb @@ -13,12 +13,14 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", + "import warnings\n", + "\n", "import numpy as np\n", - "from childHealth.data_processor import TrainDataProcessor\n", + "import pandas as pd\n", + "\n", "from childHealth.config import ProjectConfig\n", + "from childHealth.data_processor import TrainDataProcessor\n", "\n", - "import warnings\n", "warnings.filterwarnings(\"ignore\", category=FutureWarning)" ] }, @@ -28,94 +30,92 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "\n", "# Specify dtypes for columns\n", "dtypes = {\n", - " 'Basic_Demos-Age': 'float64',\n", - " 'Basic_Demos-Sex': 'float64',\n", - " 'CGAS-CGAS_Score': 'float64',\n", - " 'Physical-BMI': 'float64',\n", - " 'Physical-Height': 'float64',\n", - " 'Physical-Weight': 'float64',\n", - " 'Physical-Waist_Circumference': 'float64',\n", - " 'Physical-Diastolic_BP': 'float64',\n", - " 'Physical-HeartRate': 'float64',\n", - " 'Physical-Systolic_BP': 'float64',\n", - " 'Fitness_Endurance-Max_Stage': 'float64',\n", - " 'Fitness_Endurance-Time_Mins': 'float64',\n", - " 'Fitness_Endurance-Time_Sec': 'float64',\n", - " 'FGC-FGC_CU': 'float64',\n", - " 'FGC-FGC_CU_Zone': 'float64',\n", - " 'FGC-FGC_GSND': 'float64',\n", - " 'FGC-FGC_GSND_Zone': 'float64',\n", - " 'FGC-FGC_GSD': 'float64',\n", - " 'FGC-FGC_GSD_Zone': 'float64',\n", - " 'FGC-FGC_PU': 'float64',\n", - " 'FGC-FGC_PU_Zone': 'float64',\n", - " 'FGC-FGC_SRL': 'float64',\n", - " 'FGC-FGC_SRL_Zone': 'float64',\n", - " 'FGC-FGC_SRR': 'float64',\n", - " 'FGC-FGC_SRR_Zone': 'float64',\n", - " 'FGC-FGC_TL': 'float64',\n", - " 'FGC-FGC_TL_Zone': 'float64',\n", - " 'BIA-BIA_Activity_Level_num': 'float64',\n", - " 'BIA-BIA_BMC': 'float64',\n", - " 'BIA-BIA_BMI': 'float64',\n", - " 'BIA-BIA_BMR': 'float64',\n", - " 'BIA-BIA_DEE': 'float64',\n", - " 'BIA-BIA_ECW': 'float64',\n", - " 'BIA-BIA_FFM': 'float64',\n", - " 'BIA-BIA_FFMI': 'float64',\n", - " 'BIA-BIA_FMI': 'float64',\n", - " 'BIA-BIA_Fat': 'float64',\n", - " 'BIA-BIA_Frame_num': 'float64',\n", - " 'BIA-BIA_ICW': 'float64',\n", - " 'BIA-BIA_LDM': 'float64',\n", - " 'BIA-BIA_LST': 'float64',\n", - " 'BIA-BIA_SMM': 'float64',\n", - " 'BIA-BIA_TBW': 'float64',\n", - " 'PAQ_A-PAQ_A_Total': 'float64',\n", - " 'PAQ_C-PAQ_C_Total': 'float64',\n", - " 'PCIAT-PCIAT_01': 'float64',\n", - " 'PCIAT-PCIAT_02': 'float64',\n", - " 'PCIAT-PCIAT_03': 'float64',\n", - " 'PCIAT-PCIAT_04': 'float64',\n", - " 'PCIAT-PCIAT_05': 'float64',\n", - " 'PCIAT-PCIAT_06': 'float64',\n", - " 'PCIAT-PCIAT_07': 'float64',\n", - " 'PCIAT-PCIAT_08': 'float64',\n", - " 'PCIAT-PCIAT_09': 'float64',\n", - " 'PCIAT-PCIAT_10': 'float64',\n", - " 'PCIAT-PCIAT_11': 'float64',\n", - " 'PCIAT-PCIAT_12': 'float64',\n", - " 'PCIAT-PCIAT_13': 'float64',\n", - " 'PCIAT-PCIAT_14': 'float64',\n", - " 'PCIAT-PCIAT_15': 'float64',\n", - " 'PCIAT-PCIAT_16': 'float64',\n", - " 'PCIAT-PCIAT_17': 'float64',\n", - " 'PCIAT-PCIAT_18': 'float64',\n", - " 'PCIAT-PCIAT_19': 'float64',\n", - " 'PCIAT-PCIAT_20': 'float64',\n", - " 'PCIAT-PCIAT_Total': 'float64',\n", - " 'SDS-SDS_Total_Raw': 'float64',\n", - " 'SDS-SDS_Total_T': 'float64',\n", - " 'PreInt_EduHx-computerinternet_hoursday': 'float64',\n", - " 'Basic_Demos-Enroll_Season': 'object',\n", - " 'CGAS-Season': 'object',\n", - " 'Physical-Season': 'object',\n", - " 'Fitness_Endurance-Season': 'object',\n", - " 'FGC-Season': 'object',\n", - " 'BIA-Season': 'object',\n", - " 'PAQ_A-Season': 'object',\n", - " 'PAQ_C-Season': 'object',\n", - " 'PCIAT-Season': 'object',\n", - " 'SDS-Season': 'object',\n", - " 'sii': 'float64'\n", + " \"Basic_Demos-Age\": \"float64\",\n", + " \"Basic_Demos-Sex\": \"float64\",\n", + " \"CGAS-CGAS_Score\": \"float64\",\n", + " \"Physical-BMI\": \"float64\",\n", + " \"Physical-Height\": \"float64\",\n", + " \"Physical-Weight\": \"float64\",\n", + " \"Physical-Waist_Circumference\": \"float64\",\n", + " \"Physical-Diastolic_BP\": \"float64\",\n", + " \"Physical-HeartRate\": \"float64\",\n", + " \"Physical-Systolic_BP\": \"float64\",\n", + " \"Fitness_Endurance-Max_Stage\": \"float64\",\n", + " \"Fitness_Endurance-Time_Mins\": \"float64\",\n", + " \"Fitness_Endurance-Time_Sec\": \"float64\",\n", + " \"FGC-FGC_CU\": \"float64\",\n", + " \"FGC-FGC_CU_Zone\": \"float64\",\n", + " \"FGC-FGC_GSND\": \"float64\",\n", + " \"FGC-FGC_GSND_Zone\": \"float64\",\n", + " \"FGC-FGC_GSD\": \"float64\",\n", + " \"FGC-FGC_GSD_Zone\": \"float64\",\n", + " \"FGC-FGC_PU\": \"float64\",\n", + " \"FGC-FGC_PU_Zone\": \"float64\",\n", + " \"FGC-FGC_SRL\": \"float64\",\n", + " \"FGC-FGC_SRL_Zone\": \"float64\",\n", + " \"FGC-FGC_SRR\": \"float64\",\n", + " \"FGC-FGC_SRR_Zone\": \"float64\",\n", + " \"FGC-FGC_TL\": \"float64\",\n", + " \"FGC-FGC_TL_Zone\": \"float64\",\n", + " \"BIA-BIA_Activity_Level_num\": \"float64\",\n", + " \"BIA-BIA_BMC\": \"float64\",\n", + " \"BIA-BIA_BMI\": \"float64\",\n", + " \"BIA-BIA_BMR\": \"float64\",\n", + " \"BIA-BIA_DEE\": \"float64\",\n", + " \"BIA-BIA_ECW\": \"float64\",\n", + " \"BIA-BIA_FFM\": \"float64\",\n", + " \"BIA-BIA_FFMI\": \"float64\",\n", + " \"BIA-BIA_FMI\": \"float64\",\n", + " \"BIA-BIA_Fat\": \"float64\",\n", + " \"BIA-BIA_Frame_num\": \"float64\",\n", + " \"BIA-BIA_ICW\": \"float64\",\n", + " \"BIA-BIA_LDM\": \"float64\",\n", + " \"BIA-BIA_LST\": \"float64\",\n", + " \"BIA-BIA_SMM\": \"float64\",\n", + " \"BIA-BIA_TBW\": \"float64\",\n", + " \"PAQ_A-PAQ_A_Total\": \"float64\",\n", + " \"PAQ_C-PAQ_C_Total\": \"float64\",\n", + " \"PCIAT-PCIAT_01\": \"float64\",\n", + " \"PCIAT-PCIAT_02\": \"float64\",\n", + " \"PCIAT-PCIAT_03\": \"float64\",\n", + " \"PCIAT-PCIAT_04\": \"float64\",\n", + " \"PCIAT-PCIAT_05\": \"float64\",\n", + " \"PCIAT-PCIAT_06\": \"float64\",\n", + " \"PCIAT-PCIAT_07\": \"float64\",\n", + " \"PCIAT-PCIAT_08\": \"float64\",\n", + " \"PCIAT-PCIAT_09\": \"float64\",\n", + " \"PCIAT-PCIAT_10\": \"float64\",\n", + " \"PCIAT-PCIAT_11\": \"float64\",\n", + " \"PCIAT-PCIAT_12\": \"float64\",\n", + " \"PCIAT-PCIAT_13\": \"float64\",\n", + " \"PCIAT-PCIAT_14\": \"float64\",\n", + " \"PCIAT-PCIAT_15\": \"float64\",\n", + " \"PCIAT-PCIAT_16\": \"float64\",\n", + " \"PCIAT-PCIAT_17\": \"float64\",\n", + " \"PCIAT-PCIAT_18\": \"float64\",\n", + " \"PCIAT-PCIAT_19\": \"float64\",\n", + " \"PCIAT-PCIAT_20\": \"float64\",\n", + " \"PCIAT-PCIAT_Total\": \"float64\",\n", + " \"SDS-SDS_Total_Raw\": \"float64\",\n", + " \"SDS-SDS_Total_T\": \"float64\",\n", + " \"PreInt_EduHx-computerinternet_hoursday\": \"float64\",\n", + " \"Basic_Demos-Enroll_Season\": \"object\",\n", + " \"CGAS-Season\": \"object\",\n", + " \"Physical-Season\": \"object\",\n", + " \"Fitness_Endurance-Season\": \"object\",\n", + " \"FGC-Season\": \"object\",\n", + " \"BIA-Season\": \"object\",\n", + " \"PAQ_A-Season\": \"object\",\n", + " \"PAQ_C-Season\": \"object\",\n", + " \"PCIAT-Season\": \"object\",\n", + " \"SDS-Season\": \"object\",\n", + " \"sii\": \"float64\",\n", "}\n", "\n", "# Load data with specified dtypes\n", - "df = pd.read_csv('../../data/childHealth.csv', dtype=dtypes)\n" + "df = pd.read_csv(\"../../data/childHealth.csv\", dtype=dtypes)" ] }, { @@ -200,10 +200,8 @@ } ], "source": [ - "pciat_min_max = df.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max'])\n", - "pciat_min_max = pciat_min_max.rename(\n", - " columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'}\n", - ")\n", + "pciat_min_max = df.groupby(\"sii\")[\"PCIAT-PCIAT_Total\"].agg([\"min\", \"max\"])\n", + "pciat_min_max = pciat_min_max.rename(columns={\"min\": \"Minimum PCIAT total Score\", \"max\": \"Maximum total PCIAT Score\"})\n", "pciat_min_max" ] }, @@ -219,15 +217,15 @@ "columns = df.columns\n", "\n", "# Regular expression patterns\n", - "pattern_pciat = re.compile(r'^PCIAT-PCIAT.*')\n", - "pattern_season = re.compile(r'^PCIAT-Season.*')\n", + "pattern_pciat = re.compile(r\"^PCIAT-PCIAT.*\")\n", + "pattern_season = re.compile(r\"^PCIAT-Season.*\")\n", "\n", "# Extract columns that match the patterns\n", "pciat_columns = [col for col in columns if pattern_pciat.match(col)]\n", "season_columns = [col for col in columns if pattern_season.match(col)]\n", "\n", "# Combine the lists of columns\n", - "selected_columns = pciat_columns + season_columns\n" + "selected_columns = pciat_columns + season_columns" ] }, { @@ -624,7 +622,7 @@ } ], "source": [ - "train_with_sii = df[df['sii'].notna()][selected_columns]\n", + "train_with_sii = df[df[\"sii\"].notna()][selected_columns]\n", "train_with_sii" ] }, @@ -634,7 +632,7 @@ "metadata": {}, "outputs": [], "source": [ - "PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)]" + "PCIAT_cols = [f\"PCIAT-PCIAT_{i+1:02d}\" for i in range(20)]" ] }, { @@ -643,15 +641,13 @@ "metadata": {}, "outputs": [], "source": [ - "df['complete_resp_total'] = df['PCIAT-PCIAT_Total'].where(\n", - " df[PCIAT_cols].notna().all(axis=1), np.nan\n", - ")\n", + "df[\"complete_resp_total\"] = df[\"PCIAT-PCIAT_Total\"].where(df[PCIAT_cols].notna().all(axis=1), np.nan)\n", "\n", - "sii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'}\n", - "df['sii'] = df['sii'].map(sii_map).fillna('Missing')\n", + "sii_map = {0: \"0 (None)\", 1: \"1 (Mild)\", 2: \"2 (Moderate)\", 3: \"3 (Severe)\"}\n", + "df[\"sii\"] = df[\"sii\"].map(sii_map).fillna(\"Missing\")\n", "\n", - "sii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)']\n", - "df['sii'] = pd.Categorical(df['sii'], categories=sii_order, ordered=True)" + "sii_order = [\"Missing\", \"0 (None)\", \"1 (Mild)\", \"2 (Moderate)\", \"3 (Severe)\"]\n", + "df[\"sii\"] = pd.Categorical(df[\"sii\"], categories=sii_order, ordered=True)" ] }, { @@ -677,7 +673,7 @@ } ], "source": [ - "df['sii'].value_counts(dropna=False)" + "df[\"sii\"].value_counts(dropna=False)" ] }, { @@ -709,7 +705,7 @@ } ], "source": [ - "df['sii']" + "df[\"sii\"]" ] }, { @@ -732,28 +728,26 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", - "sii_counts = df['sii'].value_counts().reset_index()\n", - "total = sii_counts['count'].sum()\n", - "sii_counts['percentage'] = (sii_counts['count'] / total) * 100\n", + "sii_counts = df[\"sii\"].value_counts().reset_index()\n", + "total = sii_counts[\"count\"].sum()\n", + "sii_counts[\"percentage\"] = (sii_counts[\"count\"] / total) * 100\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# SII\n", - "sns.barplot(x='sii', y='count', data=sii_counts, palette='Blues_d', ax=axes[0])\n", - "axes[0].set_title('Distribution of Severity Impairment Index (sii)', fontsize=14)\n", + "sns.barplot(x=\"sii\", y=\"count\", data=sii_counts, palette=\"Blues_d\", ax=axes[0])\n", + "axes[0].set_title(\"Distribution of Severity Impairment Index (sii)\", fontsize=14)\n", "for p in axes[0].patches:\n", " height = p.get_height()\n", - " percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0]\n", + " percentage = sii_counts.loc[sii_counts[\"count\"] == height, \"percentage\"].values[0]\n", " axes[0].text(\n", - " p.get_x() + p.get_width() / 2,\n", - " height + 5, f'{int(height)} ({percentage:.1f}%)',\n", - " ha=\"center\", fontsize=12\n", + " p.get_x() + p.get_width() / 2, height + 5, f\"{int(height)} ({percentage:.1f}%)\", ha=\"center\", fontsize=12\n", " )\n", "\n", "# PCIAT_Total for complete responses\n", - "sns.histplot(df['complete_resp_total'].dropna(), bins=20, ax=axes[1])\n", - "axes[1].set_title('Distribution of PCIAT_Total', fontsize=14)\n", - "axes[1].set_xlabel('PCIAT_Total for Complete PCIAT Responses')\n", + "sns.histplot(df[\"complete_resp_total\"].dropna(), bins=20, ax=axes[1])\n", + "axes[1].set_title(\"Distribution of PCIAT_Total\", fontsize=14)\n", + "axes[1].set_xlabel(\"PCIAT_Total for Complete PCIAT Responses\")\n", "\n", "plt.tight_layout()\n", "plt.show()" @@ -776,9 +770,7 @@ "config = ProjectConfig.from_yaml(config_path=\"../../project_config.yml\")\n", "\n", "# Initialize the TrainDataProcessor with the DataFrame and configuration instance\n", - "preprocessor = TrainDataProcessor(\n", - " train_df=df, config=config\n", - ")\n", + "preprocessor = TrainDataProcessor(train_df=df, config=config)\n", "\n", "dff = preprocessor.process()" ] @@ -1019,17 +1011,12 @@ "metadata": {}, "outputs": [], "source": [ - "import yaml\n", - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, accuracy_score, cohen_kappa_score\n", + "import numpy as np\n", + "import pandas as pd\n", "from sklearn.compose import ColumnTransformer\n", + "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.pipeline import Pipeline\n", - "import lightgbm as lgb\n", - "from scipy.optimize import minimize\n", "\n", "from childHealth.kappaLgm_model import ChildHealthModel" ] @@ -1094,8 +1081,8 @@ "# Initialize the TrainDataProcessor with the DataFrame and configuration instance\n", "preprocessor = ColumnTransformer(\n", " transformers=[\n", - " ('num', StandardScaler(), config.num_features),\n", - " ('cat', OneHotEncoder(drop='first'), config.cat_features)\n", + " (\"num\", StandardScaler(), config.num_features),\n", + " (\"cat\", OneHotEncoder(drop=\"first\"), config.cat_features),\n", " ]\n", ")\n", "\n", diff --git a/notebooks/week1/extracted_notebook_code.py b/notebooks/week1/extracted_notebook_code.py deleted file mode 100644 index 8f5170e..0000000 --- a/notebooks/week1/extracted_notebook_code.py +++ /dev/null @@ -1,1147 +0,0 @@ -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec -import seaborn as sns -import warnings - -warnings.filterwarnings('ignore', category=FutureWarning) - -sns.set(style="whitegrid") -%matplotlib inline - -train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv') -test = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv') -data_dict = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv') - -display(train.head()) -print(f"Train shape: {train.shape}") - -display(test.head()) -print(f"Test shape: {test.shape}") - -data_dict.head() - -def calculate_stats(data, columns): - if isinstance(columns, str): - columns = [columns] - - stats = [] - for col in columns: - if data[col].dtype in ['object', 'category']: - counts = data[col].value_counts(dropna=False, sort=False) - percents = data[col].value_counts(normalize=True, dropna=False, sort=False) * 100 - formatted = counts.astype(str) + ' (' + percents.round(2).astype(str) + '%)' - stats_col = pd.DataFrame({'count (%)': formatted}) - stats.append(stats_col) - else: - stats_col = data[col].describe().to_frame().transpose() - stats_col['missing'] = data[col].isnull().sum() - stats_col.index.name = col - stats.append(stats_col) - - return pd.concat(stats, axis=0) - -train_cols = set(train.columns) -test_cols = set(test.columns) -columns_not_in_test = sorted(list(train_cols - test_cols)) -data_dict[data_dict['Field'].isin(columns_not_in_test)] - -pciat_min_max = train.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max']) -pciat_min_max = pciat_min_max.rename( - columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'} -) -pciat_min_max - -data_dict[data_dict['Field'] == 'PCIAT-PCIAT_Total']['Value Labels'].iloc[0] - -train_with_sii = train[train['sii'].notna()][columns_not_in_test] -train_with_sii[train_with_sii.isna().any(axis=1)].head().style.applymap( - lambda x: 'background-color: #FFC0CB' if pd.isna(x) else '' -) - -PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)] -recalc_total_score = train_with_sii[PCIAT_cols].sum( - axis=1, skipna=True -) -(recalc_total_score == train_with_sii['PCIAT-PCIAT_Total']).all() - -def recalculate_sii(row): - if pd.isna(row['PCIAT-PCIAT_Total']): - return np.nan - max_possible = row['PCIAT-PCIAT_Total'] + row[PCIAT_cols].isna().sum() * 5 - if row['PCIAT-PCIAT_Total'] <= 30 and max_possible <= 30: - return 0 - elif 31 <= row['PCIAT-PCIAT_Total'] <= 49 and max_possible <= 49: - return 1 - elif 50 <= row['PCIAT-PCIAT_Total'] <= 79 and max_possible <= 79: - return 2 - elif row['PCIAT-PCIAT_Total'] >= 80 and max_possible >= 80: - return 3 - return np.nan - -train['recalc_sii'] = train.apply(recalculate_sii, axis=1) - -mismatch_rows = train[ - (train['recalc_sii'] != train['sii']) & train['sii'].notna() -] - -mismatch_rows[PCIAT_cols + [ - 'PCIAT-PCIAT_Total', 'sii', 'recalc_sii' -]].style.applymap( - lambda x: 'background-color: #FFC0CB' if pd.isna(x) else '' -) - -train['sii'] = train['recalc_sii'] -train['complete_resp_total'] = train['PCIAT-PCIAT_Total'].where( - train[PCIAT_cols].notna().all(axis=1), np.nan -) - -sii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'} -train['sii'] = train['sii'].map(sii_map).fillna('Missing') - -sii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)'] -train['sii'] = pd.Categorical(train['sii'], categories=sii_order, ordered=True) - -train.drop(columns='recalc_sii', inplace=True) - -sii_counts = train['sii'].value_counts().reset_index() -total = sii_counts['count'].sum() -sii_counts['percentage'] = (sii_counts['count'] / total) * 100 - -fig, axes = plt.subplots(1, 2, figsize=(14, 5)) - -# SII -sns.barplot(x='sii', y='count', data=sii_counts, palette='Blues_d', ax=axes[0]) -axes[0].set_title('Distribution of Severity Impairment Index (sii)', fontsize=14) -for p in axes[0].patches: - height = p.get_height() - percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0] - axes[0].text( - p.get_x() + p.get_width() / 2, - height + 5, f'{int(height)} ({percentage:.1f}%)', - ha="center", fontsize=12 - ) - -# PCIAT_Total for complete responses -sns.histplot(train['complete_resp_total'].dropna(), bins=20, ax=axes[1]) -axes[1].set_title('Distribution of PCIAT_Total', fontsize=14) -axes[1].set_xlabel('PCIAT_Total for Complete PCIAT Responses') - -plt.tight_layout() -plt.show() - -len(train[train['complete_resp_total'] == 0]) - -assert train['Basic_Demos-Age'].isna().sum() == 0 -assert train['Basic_Demos-Sex'].isna().sum() == 0 - -train['Age Group'] = pd.cut( - train['Basic_Demos-Age'], - bins=[4, 12, 18, 22], - labels=['Children (5-12)', 'Adolescents (13-18)', 'Adults (19-22)'] -) -calculate_stats(train, 'Age Group') - -sex_map = {0: 'Male', 1: 'Female'} -train['Basic_Demos-Sex'] = train['Basic_Demos-Sex'].map(sex_map) -calculate_stats(train, 'Basic_Demos-Sex') - -fig, axes = plt.subplots(1, 3, figsize=(18, 5)) - -# SII by Age -sns.boxplot(y=train['Basic_Demos-Age'], x=train['sii'], ax=axes[0], palette="Set3") -axes[0].set_title('SII by Age') -axes[0].set_ylabel('Age') -axes[0].set_xlabel('SII') - -# Complete PCIAT Responses by Age Group -sns.boxplot( - x='Age Group', y='complete_resp_total', - data=train, palette="Set3", ax=axes[1] -) -axes[1].set_title('Complete PCIAT Responses by Age Group') -axes[1].set_ylabel('PCIAT_Total for Complete Responses') -axes[1].set_xlabel('Age Group') - -# PCIAT_Total by Sex -sns.histplot( - data=train, x='complete_resp_total', - hue='Basic_Demos-Sex', multiple='stack', - palette="Set3", bins=20, ax=axes[2] -) -axes[2].set_title('PCIAT_Total Distribution by Sex') -axes[2].set_xlabel('PCIAT_Total for Complete Responses') -axes[2].set_ylabel('Frequency') - -plt.tight_layout() -plt.show() - -stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0) -fig, axes = plt.subplots(1, len(stats), figsize=(18, 5)) - -for i, age_group in enumerate(stats.index): - group_counts = stats.loc[age_group] / stats.loc[age_group].sum() - axes[i].pie( - group_counts, labels=group_counts.index, autopct='%1.1f%%', - startangle=90, colors=sns.color_palette("Set3"), - labeldistance=1.05, pctdistance=0.80 - ) - axes[i].set_title(f'SII Distribution for {age_group}') - axes[i].axis('equal') - -plt.tight_layout() -plt.show() - -stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0) -stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 - -stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' -stats - -stats = train[train['sii'] != 'Missing'].groupby( - ['Age Group', 'sii'] -).size().unstack(fill_value=0) -stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 - -stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' -stats - -data = train[train['PreInt_EduHx-computerinternet_hoursday'].notna()] -age_range = data['Basic_Demos-Age'] -print( - f"Age range for participants with measured PreInt_EduHx-computerinternet_hoursday data:" - f" {age_range.min()} - {age_range.max()} years" -) - -train['PreInt_EduHx-computerinternet_hoursday'].unique() - -param_map = {0: '< 1h/day', 1: '~ 1h/day', 2: '~ 2hs/day', 3: '> 3hs/day'} -train['internet_use_encoded'] = train[ - 'PreInt_EduHx-computerinternet_hoursday' -].map(param_map).fillna('Missing') - -param_ord = ['Missing', '< 1h/day', '~ 1h/day', '~ 2hs/day', '> 3hs/day'] -train['internet_use_encoded'] = pd.Categorical( - train['internet_use_encoded'], categories=param_ord, - ordered=True -) - -calculate_stats(train, 'PreInt_EduHx-Season') - -fig, axes = plt.subplots(1, 3, figsize=(18, 5)) - -# Hours of Internet Use -ax1 = sns.countplot(x='internet_use_encoded', data=train, palette="Set3", ax=axes[0]) -axes[0].set_title('Distribution of Hours of Internet Use') -axes[0].set_xlabel('Hours per Day Group') -axes[0].set_ylabel('Count') - -total = len(train['internet_use_encoded']) -for p in ax1.patches: - count = int(p.get_height()) - percentage = '{:.1f}%'.format(100 * count / total) - ax1.annotate(f'{count} ({percentage})', (p.get_x() + p.get_width() / 2., p.get_height()), - ha='center', va='baseline', fontsize=10, color='black', xytext=(0, 5), - textcoords='offset points') - -# Hours of Internet Use by Age -sns.boxplot(y=train['Basic_Demos-Age'], x=train['internet_use_encoded'], ax=axes[1], palette="Set3") -axes[1].set_title('Hours of Internet Use by Age') -axes[1].set_ylabel('Age') -axes[1].set_xlabel('Hours per Day Group') - -# Hours of Internet Use (numeric) by Age Group -sns.boxplot(y='PreInt_EduHx-computerinternet_hoursday', x='Age Group', data=train, ax=axes[2], palette="Set3") -axes[2].set_title('Internet Hours by Age Group') -axes[2].set_ylabel('Hours per Day (Numeric)') -axes[2].set_xlabel('Age Group') - -plt.tight_layout() -plt.show() - -stats = train.groupby( - ['Age Group', 'internet_use_encoded'] -).size().unstack(fill_value=0) -fig, axes = plt.subplots(1, len(stats), figsize=(18, 5)) - -for i, age_group in enumerate(stats.index): - group_counts = stats.loc[age_group] / stats.loc[age_group].sum() - axes[i].pie(group_counts, labels=group_counts.index, autopct='%1.1f%%', - startangle=90, colors=sns.color_palette("Set3"), labeldistance=1.1) - axes[i].set_title(f'Distribution of Hours of Internet Use\n{age_group}') - axes[i].axis('equal') - -plt.tight_layout() -plt.show() - -train_non_na = train.dropna(subset=['PreInt_EduHx-computerinternet_hoursday']) -rows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 3).sum() -print(f"Non-NA Rows - Internet use 3h or more: {(rows / len(train_non_na)) * 100:.2f}%") - -rows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 0).sum() -print(f"Non-NA Rows - Internet use 1h or less: {(rows / len(train_non_na)) * 100:.2f}%") - -stats = train.groupby(['Basic_Demos-Sex', 'internet_use_encoded'] -).size().unstack(fill_value=0) -stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 - -stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' -stats - -sii_reported = train[train['sii'] != "Missing"] -sii_reported.loc[:, 'sii'] = sii_reported['sii'].cat.remove_unused_categories() - -stats = sii_reported.groupby( - ['internet_use_encoded', 'sii'] -).size().unstack(fill_value=0) -stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 - -stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' -stats - -fig = plt.figure(figsize=(12, 10)) -gs = fig.add_gridspec(2, 2, height_ratios=[1, 1.5]) - -# SII vs Hours of Internet Use -ax1 = fig.add_subplot(gs[0, 0]) -sns.boxplot( - x='sii', y='PreInt_EduHx-computerinternet_hoursday', - data=sii_reported, - ax=ax1, palette="Set3" -) -ax1.set_title('SII vs Hours of Internet Use') -ax1.set_ylabel('Hours per Day') -ax1.set_xlabel('SII') - -# PCIAT_Total for Complete PCIAT Responses by Hours of Internet Use -ax2 = fig.add_subplot(gs[0, 1]) -sns.boxplot( - x='internet_use_encoded', y='complete_resp_total', - data=sii_reported, - palette="Set3", ax=ax2 -) -ax2.set_title('PCIAT_Total by Hours of Internet Use') -ax2.set_ylabel('PCIAT_Total for Complete PCIAT Responses') -ax2.set_xlabel('Hours per Day Group') - -# SII vs Hours of Internet Use by Age Group (Full width) -ax3 = fig.add_subplot(gs[1, :]) -sns.boxplot( - x='internet_use_encoded', y='complete_resp_total', - data=sii_reported, - hue='Age Group', ax=ax3, palette="Set3" -) -ax3.set_title('PCIAT_Total vs Hours of Internet Use by Age Group') -ax3.set_ylabel('PCIAT_Total for Complete PCIAT Responses') -ax3.set_xlabel('Hours per Day Group') - -plt.tight_layout() -plt.show() - -stats = sii_reported.groupby( - ['sii', 'internet_use_encoded'] -).size().unstack(fill_value=0) -fig, axes = plt.subplots(1, len(stats), figsize=(18, 5)) - -for i, sii_group in enumerate(stats.index): - group_counts = stats.loc[sii_group] / stats.loc[sii_group].sum() - axes[i].pie( - group_counts, labels=group_counts.index, autopct='%1.1f%%', - startangle=90, colors=sns.color_palette("Set3"), labeldistance=1.1 - ) - axes[i].set_title(f'Hours of using computer/internet\n for SII = {sii_group}') - axes[i].axis('equal') - -plt.tight_layout() -plt.show() - -stats = sii_reported.groupby( - ['sii', 'internet_use_encoded'] -).size().unstack(fill_value=0) -stats_prop = stats.div(stats.sum(axis=1), axis=0) * 100 - -stats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' -stats - -train[ - (train['internet_use_encoded'] == '< 1h/day') & - (train['sii'].isin(['2 (Moderate)', '3 (Severe)'])) -]['Basic_Demos-Age'].describe() - -groups = data_dict.groupby('Instrument')['Field'].apply(list).to_dict() - -for instrument, features in groups.items(): - print(f"{instrument}: {features}\n") - -season_columns = [col for col in train.columns if 'Season' in col] -season_df = train[season_columns] -season_df - -train[season_columns] = train[season_columns].fillna("Missing") - -data_dict = data_dict[data_dict['Instrument'] != 'Parent-Child Internet Addiction Test'] -continuous_cols = data_dict[data_dict['Type'].str.contains( - 'float|int', case=False -)]['Field'].tolist() - -# target = train[['sii']] -# train = train.drop(columns = columns_not_in_test) - -groups.get('Demographics', []) - -fig, axes = plt.subplots(1, 2, figsize=(12, 5)) - -# Season of Enrollment -season_counts = train['Basic_Demos-Enroll_Season'].value_counts(dropna=False) - -axes[0].pie( - season_counts, labels=season_counts.index, - autopct='%1.1f%%', startangle=90, - colors=sns.color_palette("Set3") -) -axes[0].set_title('Season of Enrollment') -axes[0].axis('equal') - -# Age Distribution by Sex -sns.histplot( - data=train, x='Basic_Demos-Age', - hue='Basic_Demos-Sex', multiple='dodge', - palette="Set2", bins=20, ax=axes[1] -) -axes[1].set_title('Age Distribution by Sex') -axes[1].set_xlabel('Age') -axes[1].set_ylabel('Count') - -plt.tight_layout() -plt.show() - -calculate_stats(train, 'Basic_Demos-Age') - -groups.get("Children's Global Assessment Scale", []) - -data = train[train['CGAS-CGAS_Score'].notnull()] -age_range = data['Basic_Demos-Age'] -print( - f"Age range for participants with CGAS-CGAS_Score data:" - f" {age_range.min()} - {age_range.max()} years" -) - -calculate_stats(train, 'CGAS-CGAS_Score') - -train[train['CGAS-CGAS_Score'] > 100] - -train.loc[train['CGAS-CGAS_Score'] == 999, 'CGAS-CGAS_Score'] = np.nan - -plt.figure(figsize=(12, 5)) - -# CGAS-Season -plt.subplot(1, 2, 1) -cgas_season_counts = train['CGAS-Season'].value_counts(normalize=True) -plt.pie( - cgas_season_counts, - labels=cgas_season_counts.index, - autopct='%1.1f%%', - startangle=90, - colors=sns.color_palette("Set3") -) -plt.title('CGAS-Season') -plt.axis('equal') - -# CGAS-CGAS_Score without outliers (score == 999) -plt.subplot(1, 2, 2) -sns.histplot( - train['CGAS-CGAS_Score'].dropna(), - bins=20, kde=True -) -plt.title('CGAS-CGAS_Score (Without Outlier)') -plt.xlabel('CGAS Score') -plt.ylabel('Count') - -plt.tight_layout() -plt.show() - -calculate_stats(train, 'CGAS-CGAS_Score') - -bins = np.arange(0, 101, 10) -labels = [ - "1-10: Needs constant supervision (24 hour care)", - "11-20: Needs considerable supervision", - "21-30: Unable to function in almost all areas", - "31-40: Major impairment in functioning in several areas", - "41-50: Moderate degree of interference in functioning", - "51-60: Variable functioning with sporadic difficulties", - "61-70: Some difficulty in a single area", - "71-80: No more than slight impairment in functioning", - "81-90: Good functioning in all areas", - "91-100: Superior functioning" -] - -train['CGAS_Score_Bin'] = pd.cut( - train['CGAS-CGAS_Score'], bins=bins, labels=labels -) - -counts = train['CGAS_Score_Bin'].value_counts().reindex(labels) -prop = (counts / counts.sum() * 100).round(1) -count_prop_labels = counts.astype(str) + " (" + prop.astype(str) + "%)" - -plt.figure(figsize=(18, 6)) -bars = plt.barh(labels, counts) -plt.xlabel('Count') -plt.title('CGAS Score Distribution') - -for bar, label in zip(bars, count_prop_labels): - plt.text( - bar.get_width(), bar.get_y() + bar.get_height() / 2, label, va='center' - ) - -plt.gca().invert_yaxis() -plt.tight_layout() -plt.show() - -train_filt = train.dropna(subset=['CGAS_Score_Bin', 'complete_resp_total']) -train_filt.loc[:, 'CGAS_Score_Bin'] = train_filt['CGAS_Score_Bin'].cat.remove_unused_categories() -train_filt.loc[:, 'sii'] = train_filt['sii'].cat.remove_unused_categories() -len(train_filt) - -fig, axes = plt.subplots(1, 2, figsize=(16, 5)) - -# CGAS-CGAS_Score vs sii -sns.boxplot( - data=train_filt, - x='sii', y='CGAS-CGAS_Score', - palette='Set3', ax=axes[0] -) -axes[0].set_xlabel('SII Score') -axes[0].set_ylabel('CGAS Score') -axes[0].set_title('Distribution of CGAS Scores by SII') - -# complete_resp_total vs CGAS_Score_Bin -sns.boxplot( - data=train_filt, - x='CGAS_Score_Bin', y='complete_resp_total', - ax=axes[1], palette='Set3' -) - -# Get the tick positions and match the labels -range_labels = [label.split(":")[0] for label in train_filt['CGAS_Score_Bin'].cat.categories] -axes[1].set_xticklabels(range_labels) - -axes[1].set_xlabel('CGAS Score category') -axes[1].set_ylabel('PCIAT_Total for Complete PCIAT Responses') -axes[1].set_title('Distribution of PCIAT_Total by CGAS Score categories') - -plt.tight_layout() -plt.show() - -score_min_max = train.groupby('sii')['CGAS-CGAS_Score'].agg(['min', 'max']) -score_min_max = score_min_max.rename( - columns={'min': 'Minimum CGAS Score', 'max': 'Maximum CGAS Score'} -) -score_min_max - -train_filt[train_filt['CGAS-CGAS_Score'] < 35][ - ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii', - 'CGAS-CGAS_Score', - 'PreInt_EduHx-computerinternet_hoursday'] -] - -train[train['CGAS-CGAS_Score'] > 90][ - ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii', - 'CGAS-CGAS_Score', - 'PreInt_EduHx-computerinternet_hoursday'] -] - -groups.get('Physical Measures', []) - -features_physical = groups.get('Physical Measures', []) -cols = [col for col in features_physical if col in continuous_cols] - -plt.figure(figsize=(24, 10)) -n_cols = 4 -n_rows = len(cols) // n_cols + 1 - -for i, col in enumerate(cols): - plt.subplot(n_rows, n_cols, i + 1) - train[col].hist(bins=20) - plt.title(col) - -plt.subplot(n_rows, n_cols, len(cols) + 1) -season_counts = train['Physical-Season'].value_counts(dropna=False) -plt.pie( - season_counts, - labels=season_counts.index, - autopct='%1.1f%%', - startangle=90, - colors=sns.color_palette("Set3") -) -plt.title('Physical-Season') - -plt.suptitle('Histograms for Physical Measures and Physical-Season Pie Chart', y=1.05) -plt.tight_layout() -plt.show() - -calculate_stats(train, cols) - -wh_cols = [ - 'Physical-BMI', 'Physical-Height', - 'Physical-Weight', 'Physical-Waist_Circumference' -] - -(train[wh_cols] == 0).sum() - -train[wh_cols] = train[wh_cols].replace(0, np.nan) -calculate_stats(train, wh_cols) - -lbs_to_kg = 0.453592 -inches_to_cm = 2.54 - -train['Physical-Weight'] = train['Physical-Weight'] * lbs_to_kg -train['Physical-Height'] = train['Physical-Height'] * inches_to_cm -train['Physical-Waist_Circumference'] = train['Physical-Waist_Circumference'] * inches_to_cm - -# Recalculate BMI: BMI = weight (kg) / (height (m)^2) -train['Physical-BMI'] = np.where( - train['Physical-Weight'].notna() & train['Physical-Height'].notna(), - train['Physical-Weight'] / ((train['Physical-Height'] / 100) ** 2), - np.nan # If either is NaN, set BMI to NaN -) - -calculate_stats(train, wh_cols) - -plt.figure(figsize=(18, 5)) - -# Physical-Weight by Age -plt.subplot(1, 3, 1) -sns.scatterplot(x='Basic_Demos-Age', y='Physical-Weight', data=train) -plt.title('Physical-Weight by Age') -plt.xlabel('Age') -plt.ylabel('Weight (kg)') - -# Physical-Height by Age -plt.subplot(1, 3, 2) -sns.scatterplot(x='Basic_Demos-Age', y='Physical-Height', data=train) -plt.title('Physical-Height by Age') -plt.xlabel('Age') -plt.ylabel('Height (cm)') - -# Physical-Waist_Circumference vs Physical-Weight -plt.subplot(1, 3, 3) -sns.scatterplot(x='Physical-Weight', y='Physical-Waist_Circumference', data=train) -plt.title('Waist Circumference vs Weight') -plt.xlabel('Weight (kg)') -plt.ylabel('Waist Circumference (cm)') - -plt.tight_layout() -plt.show() - -bp_hr_cols = [ - 'Physical-Diastolic_BP', 'Physical-Systolic_BP', - 'Physical-HeartRate' -] - -(train[bp_hr_cols] < 50).sum() - -train[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP']][bp_hr_cols] - -train[cols] = train[cols].replace(0, np.nan) -train.loc[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP'], bp_hr_cols] = np.nan - -plt.figure(figsize=(12, 5)) - -# Diastolic BP vs Heart Rate -plt.subplot(1, 2, 1) -sns.scatterplot(x='Physical-Diastolic_BP', y='Physical-HeartRate', data=train) -plt.title('Diastolic BP vs Heart Rate') -plt.xlabel('Diastolic Blood Pressure (mmHg)') -plt.ylabel('Heart rate (beats/min)') - -# Systolic BP vs Heart Rate -plt.subplot(1, 2, 2) -sns.scatterplot(x='Physical-Systolic_BP', y='Physical-HeartRate', data=train) -plt.title('Systolic BP vs Heart Rate') -plt.xlabel('Systolic Blood Pressure (mmHg)') -plt.ylabel('Heart rate (beats/min)') - -plt.tight_layout() -plt.show() - -fig, axes = plt.subplots(1, 2, figsize=(16, 6)) - -# BMI vs Systolic Blood Pressure -sns.scatterplot(x='Physical-BMI', y='Physical-Systolic_BP', data=train, ax=axes[0], color='b') -axes[0].set_title('BMI vs Systolic Blood Pressure') -axes[0].set_xlabel('Body Mass Index (BMI) (kg/m^2)') -axes[0].set_ylabel('Systolic Blood Pressure (mmHg)') - -# Systolic Blood Pressure vs Diastolic Blood Pressure -sns.scatterplot( - x='Physical-Systolic_BP', y='Physical-Diastolic_BP', - data=train, ax=axes[1], color='g' -) -axes[1].set_title('Systolic Blood Pressure vs Diastolic Blood Pressure') -axes[1].set_xlabel('Systolic Blood Pressure (mmHg)') -axes[1].set_ylabel('Diastolic Blood Pressure (mmHg)') - -plt.tight_layout() -plt.show() - -normal_ranges = { - 'Physical-BMI': (18.5, 24.9), - 'Physical-Height': (100, 193), - 'Physical-Weight': (20, 120), - 'Physical-Waist_Circumference': (50, 90), - 'Physical-Diastolic_BP': (60, 80), - 'Physical-HeartRate': (60, 100), - 'Physical-Systolic_BP': (90, 120) -} - -def count_out_of_range(data, column, low, high): - return ((data[column] < low) | (data[column] > high)).sum() - -out_of_range_counts = { - col: count_out_of_range(train, col, *normal_ranges[col]) - for col in normal_ranges -} -print("Number of rows with values outside normal ranges:") - -for col, count in out_of_range_counts.items(): - total_valid = train[col].notna().sum() - percentage = (count / total_valid) * 100 - print(f"{col}: {count} ({percentage:.2f}%)") - -bmi_categories = [ - ('Underweight', train['Physical-BMI'] < 18.5), - ('Normal weight', (train['Physical-BMI'] >= 18.5) & (train['Physical-BMI'] <= 24.9)), - ('Overweight', (train['Physical-BMI'] >= 25) & (train['Physical-BMI'] <= 29.9)), - ('Obesity', train['Physical-BMI'] >= 30) -] -bmi_category_counts = {label: condition.sum() for label, condition in bmi_categories} - -plt.figure(figsize=(5, 6)) -plt.pie(bmi_category_counts.values(), - labels=bmi_category_counts.keys(), - autopct='%1.1f%%', startangle=90, - colors=plt.cm.Set3.colors) -plt.title('BMI Distribution by Category') -plt.axis('equal') -plt.show() - -train[train['Physical-BMI'] < 12][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-BMI') - -train[train['Physical-Systolic_BP'] > 160][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-Systolic_BP') - -data_subset = train[cols + ['complete_resp_total']] - -corr_matrix = data_subset.corr() - -plt.figure(figsize=(10, 8)) -sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1) -plt.title('Correlation Heatmap') -plt.show() - -data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis'] - -bia_data_dict = data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis'] -categorical_columns = bia_data_dict[bia_data_dict['Type'] == 'categorical int']['Field'].tolist() -continuous_columns = bia_data_dict[bia_data_dict['Type'] == 'float']['Field'].tolist() - -fig, axes = plt.subplots(1, 3, figsize=(18, 5)) - -# Season -season_counts = train['BIA-Season'].value_counts(normalize=True) -axes[0].pie( - season_counts, - labels=season_counts.index, - autopct='%1.1f%%', - startangle=90, - colors=sns.color_palette("Set3") -) -axes[0].set_title( - f"{bia_data_dict[bia_data_dict['Field'] == 'BIA-Season']['Description'].values[0]}" -) -axes[0].axis('equal') - -# Other categorical columns -for idx, col in enumerate(categorical_columns): - sns.countplot(x=col, data=train, palette="Set3", ax=axes[idx+1]) - axes[idx+1].set_title(data_dict[data_dict['Field'] == col]['Description'].values[0]) - -plt.tight_layout() -plt.show() - -plt.figure(figsize=(24, 20)) - -for idx, col in enumerate(continuous_columns): - plt.subplot(4, 4, idx + 1) - sns.histplot(train[col].dropna(), bins=20, kde=True) - plt.title(data_dict[data_dict['Field'] == col]['Description'].values[0]) - plt.xlabel('Value') - -plt.tight_layout() -plt.show() - -calculate_stats(train, continuous_columns) - -bmi_data = train[['BIA-BIA_BMI', 'Physical-BMI']].dropna() - -plt.figure(figsize=(8, 6)) -sns.scatterplot( - x='BIA-BIA_BMI', y='Physical-BMI', - data=bmi_data, - color='b' -) -plt.title('Comparison of BIA-BMI vs Physical-BMI') -plt.xlabel('BIA-BMI') -plt.ylabel('Physical-BMI') - -plt.tight_layout() -plt.show() - -bmi_measures = train[['BIA-Season', 'Physical-Season']].dropna() -bmi_measures.groupby(['BIA-Season', 'Physical-Season']).size().reset_index(name='Count') - -groups.get('FitnessGram Vitals and Treadmill', []) - -data = train[train['Fitness_Endurance-Max_Stage'].notnull()] -age_range = data['Basic_Demos-Age'] -print( - f"Age range for participants with Fitness_Endurance-Max_Stage data:" - f" {age_range.min()} - {age_range.max()} years" -) - -fig, axes = plt.subplots(1, 4, figsize=(24, 5)) - -# Fitness Endurance Season -train['Fitness_Endurance-Season'].value_counts(normalize=True).plot.pie( - autopct='%1.1f%%', colors=plt.cm.Set3.colors, ax=axes[0] -) -axes[0].set_title('Fitness Endurance Season') -axes[0].axis('equal') # Equal aspect ratio ensures the pie is drawn as a circle. - -# Box plot for Max Stage by Season -sns.violinplot( - x='Fitness_Endurance-Season', - y='Fitness_Endurance-Max_Stage', - data=train, palette="Set3", - ax=axes[1] -) -axes[1].set_title('Max Stage by Season') -axes[1].set_xlabel('Season') -axes[1].set_ylabel('Max Stage') - -# Fitness Endurance Time (Minutes) -sns.histplot(train['Fitness_Endurance-Time_Mins'], bins=20, kde=True, ax=axes[2]) -axes[2].set_title('Fitness Endurance Time (Minutes)') -axes[2].set_xlabel('Time (Minutes)') - -# Fitness Endurance Time (Seconds) -sns.histplot(train['Fitness_Endurance-Time_Sec'], bins=20, kde=True, ax=axes[3]) -axes[3].set_title('Fitness Endurance Time (Seconds)') -axes[3].set_xlabel('Time (Seconds)') - -plt.tight_layout() -plt.show() - -plt.figure(figsize=(12, 5)) - -sns.violinplot(x='Basic_Demos-Age', y='Fitness_Endurance-Max_Stage', data=train, palette="Set3") -plt.title('Fitness Endurance Max Stage by Age') -plt.xlabel('Age') -plt.ylabel('Max Stage') - -plt.tight_layout() -plt.show() - -cols = [ - 'Fitness_Endurance-Max_Stage', - 'Fitness_Endurance-Time_Mins', - 'Fitness_Endurance-Time_Sec' -] -calculate_stats(train, cols) - -train[ - (train['Fitness_Endurance-Max_Stage'].notna()) & - (train['Fitness_Endurance-Time_Mins'].isna() | - train['Fitness_Endurance-Time_Sec'].isna()) -][cols] - -train.loc[ - (train['Fitness_Endurance-Max_Stage'].notna()) & - (train['Fitness_Endurance-Time_Mins'].isna() | - train['Fitness_Endurance-Time_Sec'].isna()), cols -] = np.nan - -train['Fitness_Endurance-Total_Time_Sec'] = train[ - 'Fitness_Endurance-Time_Mins' -] * 60 + train['Fitness_Endurance-Time_Sec'] - -calculate_stats(train, ['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec']) - -data_dict[data_dict['Instrument'] == 'FitnessGram Child'] - -fgc_data_dict = data_dict[data_dict['Instrument'] == 'FitnessGram Child'] - -fgc_columns = [] - -for index, row in fgc_data_dict.iterrows(): - if '_Zone' not in row['Field']: - measure_field = row['Field'] - measure_desc = row['Description'] - - zone_field = measure_field + '_Zone' - zone_row = fgc_data_dict[fgc_data_dict['Field'] == zone_field] - - if not zone_row.empty: - zone_desc = zone_row['Description'].values[0] - fgc_columns.append((measure_field, zone_field, measure_desc, zone_desc)) - -fig, axes = plt.subplots(2, 4, figsize=(24, 10)) - -for idx, (measure, zone, measure_desc, zone_desc) in enumerate(fgc_columns): - row = idx // 4 - col = idx % 4 - - sns.histplot( - data=train, x=measure, - hue=zone, bins=20, palette='Set2', - ax=axes[row, col], kde=True - ) - axes[row, col].set_title(f'{measure_desc}') - -season_counts = train['FGC-Season'].value_counts(normalize=True) -axes[1, 3].pie( - season_counts, labels=season_counts.index, - autopct='%1.1f%%', startangle=90, - colors=sns.color_palette("Set3") -) -axes[1, 3].set_title('Season of participation') -axes[1, 3].axis('equal') - -plt.tight_layout() -plt.show() - -measurement_columns = [measure for measure, _, _, _ in fgc_columns] -calculate_stats(train, measurement_columns) - -def compute_min_max_by_sex(train, sex, fgc_columns): - results = [] - - for measure, zone, _, _ in fgc_columns: - sorted_zones = sorted(train[zone].dropna().unique()) - - for zone_value in sorted_zones: - data = train[(train[zone] == zone_value) & - (train['Basic_Demos-Sex'] == sex)][measure] - - if not data.empty: - min_val, max_val = data.min(), data.max() - results.append({ - 'Zone': int(zone_value), - 'Measure': measure, - 'Min-Max': f'{min_val} - {max_val}' - }) - - df = pd.DataFrame(results).pivot_table( - index='Zone', columns='Measure', values='Min-Max', aggfunc='first' - ) - - return df - -compute_min_max_by_sex(train, 'Male', fgc_columns) - -compute_min_max_by_sex(train, 'Female', fgc_columns) - -results_male = [] - -for measure, zone, _, _ in fgc_columns: - sorted_zones = sorted(train[zone].dropna().unique()) - for zone_value in sorted_zones: - age_sex_data_by_zone = train[train[zone] == zone_value][ - ['Basic_Demos-Age', 'Basic_Demos-Sex', measure] - ] - unique_ages = age_sex_data_by_zone['Basic_Demos-Age'].dropna().unique() - - for age in sorted(unique_ages): - age_sex_data = age_sex_data_by_zone[ - (age_sex_data_by_zone['Basic_Demos-Age'] == age) & - (age_sex_data_by_zone['Basic_Demos-Sex'] == 'Male') - ][measure] - - if not age_sex_data.empty: - min_val, max_val = age_sex_data.min(), age_sex_data.max() - results_male.append({ - 'Age': age, - 'Sex': 'Male', - 'Zone': zone_value, - 'Measure': measure, - 'Min-Max': f'{min_val} - {max_val}' - }) - -df_male = pd.DataFrame(results_male).pivot_table( - index=['Age', 'Sex', 'Zone'], columns='Measure', values='Min-Max', aggfunc='first' -) - -df_male - -age_ranges = [] - -for measure in measurement_columns: - valid_rows = train[~train[measure].isna()] - - min_age = valid_rows['Basic_Demos-Age'].min() - max_age = valid_rows['Basic_Demos-Age'].max() - - age_ranges.append({ - 'Measurement': measure, - 'Min Age': min_age, - 'Max Age': max_age - }) - -age_ranges_df = pd.DataFrame(age_ranges) -age_ranges_df - -cols = [col for col in train.columns if col.startswith('FGC-') - and 'Zone' not in col and 'Season' not in col] -cols.extend(['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec']) - -data_subset = train[cols + ['complete_resp_total']] - -corr_matrix = data_subset.corr() - -plt.figure(figsize=(10, 8)) -sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1) -plt.title('Correlation Heatmap') -plt.show() - -age_groups = train['Age Group'].unique() - -fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True) - -for i, age_group in enumerate(age_groups): - group_data = train[train['Age Group'] == age_group] - corr_matrix = group_data[cols + ['complete_resp_total', 'Basic_Demos-Age']].corr() - sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.1f', - vmin=-1, vmax=1, ax=axes[i], cbar=i == 0) - axes[i].set_title(f'{age_group}') - -plt.tight_layout() -plt.show() - -train[ - (train['Age Group'] == 'Adults (19-22)') & - (train['complete_resp_total'].notna()) & - (train[cols].notna().any(axis=1)) -][cols + ['complete_resp_total', 'Basic_Demos-Age']] - -groups.get('Sleep Disturbance Scale', []) - -data = train[train['SDS-SDS_Total_Raw'].notnull()] -age_range = data['Basic_Demos-Age'] -print( - f"Age range for participants with SDS-SDS_Total_Raw data:" - f" {age_range.min()} - {age_range.max()} years" -) - -plt.figure(figsize=(18, 5)) - -# SDS-Season (Pie Chart) -plt.subplot(1, 3, 1) -sds_season_counts = train['SDS-Season'].value_counts(normalize=True) -plt.pie( - sds_season_counts, - labels=sds_season_counts.index, - autopct='%1.1f%%', - startangle=90, - colors=sns.color_palette("Set3") -) -plt.title('SDS-Season') - -# SDS-SDS_Total_Raw -plt.subplot(1, 3, 2) -sns.histplot(train['SDS-SDS_Total_Raw'].dropna(), bins=20, kde=True) -plt.title('SDS-SDS_Total_Raw') -plt.xlabel('Value') - -# SDS-SDS_Total_T -plt.subplot(1, 3, 3) -sns.histplot(train['SDS-SDS_Total_T'].dropna(), bins=20, kde=True) -plt.title('SDS-SDS_Total_T') -plt.xlabel('Value') - -plt.tight_layout() -plt.show() - -calculate_stats(train, ['SDS-SDS_Total_Raw', 'SDS-SDS_Total_T']) - -groups.get('Physical Activity Questionnaire (Adolescents)', []) - -data = train[train['PAQ_A-PAQ_A_Total'].notnull()] -age_range = data['Basic_Demos-Age'] -print( - f"Age range for Adolescents (with PAQ_A_Total data):" - f" {age_range.min()} - {age_range.max()} years" -) - -plt.figure(figsize=(18, 5)) - -# PAQ_A-Season -plt.subplot(1, 3, 1) -train['PAQ_A-Season'].value_counts(normalize=True).plot.pie( - autopct='%1.1f%%', colors=plt.cm.Set3.colors -) -plt.title('PAQ_A-Season (Adolescents)') - -# PAQ_A-PAQ_A_Total -plt.subplot(1, 3, 2) -sns.histplot(train['PAQ_A-PAQ_A_Total'], bins=20, kde=True) -plt.title('PAQ_A-PAQ_A_Total (Adolescents)') - -# PAQ_A_Total by Season -plt.subplot(1, 3, 3) -sns.violinplot(x='PAQ_A-Season', y='PAQ_A-PAQ_A_Total', data=train, palette="Set3") -plt.title('PAQ_A_Total by Season (Adolescents)') - -plt.tight_layout() -plt.show() - -calculate_stats(train, ['PAQ_A-PAQ_A_Total']) - -groups.get('Physical Activity Questionnaire (Children)', []) - -data = train[train['PAQ_C-PAQ_C_Total'].notnull()] -age_range = data['Basic_Demos-Age'] -print( - f"Age range for Children (with PAQ_C_Total data):" - f" {age_range.min()} - {age_range.max()} years" -) - -plt.figure(figsize=(18, 5)) - -# PAQ_C-Season -plt.subplot(1, 3, 1) -train['PAQ_C-Season'].value_counts(normalize=True).plot.pie( - autopct='%1.1f%%', colors=plt.cm.Set3.colors -) -plt.title('PAQ_C-Season (Children)') - -# PAQ_C-PAQ_C_Total -plt.subplot(1, 3, 2) -sns.histplot(train['PAQ_C-PAQ_C_Total'], bins=20, kde=True) -plt.title('PAQ_C-PAQ_C_Total (Children)') - -# PAQ_C_Total by Season -plt.subplot(1, 3, 3) -sns.violinplot(x='PAQ_C-Season', y='PAQ_C-PAQ_C_Total', data=train, palette="Set3") -plt.title('PAQ_C_Total by Season (Children)') - -plt.tight_layout() -plt.show() - -calculate_stats(train, ['PAQ_C-PAQ_C_Total']) - -paq_columns = [col for col in train.columns if 'PAQ' in col] -train[(train['PAQ_A-PAQ_A_Total'].notnull()) & - (train['PAQ_C-PAQ_C_Total'].notnull())][ - paq_columns + ['Basic_Demos-Age'] -] \ No newline at end of file diff --git a/notebooks/week2/01.prepare_dataset.py b/notebooks/week2/01.prepare_dataset.py index e368ea5..263364d 100644 --- a/notebooks/week2/01.prepare_dataset.py +++ b/notebooks/week2/01.prepare_dataset.py @@ -1,28 +1,28 @@ # Databricks notebook source -#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +# MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- -#MAGIC dbutils.library.restartPython() +# MAGIC dbutils.library.restartPython() # COMMAND ---------- +import warnings + from pyspark.sql import SparkSession + from childHealth.config import ProjectConfig from childHealth.data_processor import TrainDataProcessor -from datetime import datetime -import warnings warnings.filterwarnings("ignore") # Initialize Spark session spark = SparkSession.builder.getOrCreate() - # COMMAND ---------- # Load project configuration from YAML file @@ -31,11 +31,7 @@ # COMMAND ---------- # Load the child health dataset into a Pandas DataFrame -df = spark.read.csv( - "/Volumes/mlops_students/javedhassi/data/childHealth.csv", - header=True, - inferSchema=True -).toPandas() +df = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True).toPandas() # COMMAND ---------- diff --git a/notebooks/week2/02.prepare_featureTable.py b/notebooks/week2/02.prepare_featureTable.py index 3071beb..9fc4368 100644 --- a/notebooks/week2/02.prepare_featureTable.py +++ b/notebooks/week2/02.prepare_featureTable.py @@ -1,18 +1,19 @@ # Databricks notebook source -#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +# MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- -#MAGIC dbutils.library.restartPython() +# MAGIC dbutils.library.restartPython() # COMMAND ---------- -from pyspark.sql import SparkSession +import warnings + from childHealth.config import ProjectConfig from childHealth.feature_engineering import ActigraphFileReader -import warnings + warnings.filterwarnings("ignore") # COMMAND ---------- @@ -23,10 +24,11 @@ # COMMAND ---------- Actigraph = ActigraphFileReader( - app_name = "ActigraphAggregation", - root_dir = "/Volumes/mlops_students/javedhassi/data/series_train.parquet/", - catalog_name = config.catalog_name, - schema_name = config.schema_name) + app_name="ActigraphAggregation", + root_dir="/Volumes/mlops_students/javedhassi/data/series_train.parquet/", + catalog_name=config.catalog_name, + schema_name=config.schema_name, +) # COMMAND ---------- Actigraph.save_feature_table() # COMMAND ---------- diff --git a/notebooks/week2/03.mlflow_experiment.py b/notebooks/week2/03.mlflow_experiment.py index 3ee251c..5d43e9f 100644 --- a/notebooks/week2/03.mlflow_experiment.py +++ b/notebooks/week2/03.mlflow_experiment.py @@ -1,6 +1,6 @@ # Databricks notebook source import json -import subprocess + import mlflow mlflow.set_tracking_uri("databricks") @@ -19,12 +19,11 @@ # COMMAND ---------- with open("mlflow_experiment.json", "w") as json_file: json.dump(experiments[0].__dict__, json_file, indent=4) - + # COMMAND ---------- with mlflow.start_run( run_name="demo-run", - tags={"git_sha": '830c17d988742482b639aec763ec731ac2dd4da5', - "branch": "week1-2"}, + tags={"git_sha": "830c17d988742482b639aec763ec731ac2dd4da5", "branch": "week1-2"}, description="demo run", ) as run: mlflow.log_params({"type": "demo"}) diff --git a/notebooks/week2/04.log_and_register_model.py b/notebooks/week2/04.log_and_register_model.py index 073c72d..75c4cdf 100644 --- a/notebooks/week2/04.log_and_register_model.py +++ b/notebooks/week2/04.log_and_register_model.py @@ -1,25 +1,26 @@ # Databricks notebook source -#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall +# MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl --force-reinstall # COMMAND ---------- -#MAGIC dbutils.library.restartPython() +# MAGIC dbutils.library.restartPython() # Databricks notebook source # Import necessary libraries -from sklearn.ensemble import RandomForestClassifier -from sklearn.pipeline import Pipeline -from childHealth.config import ProjectConfig -from sklearn.preprocessing import StandardScaler, OneHotEncoder -from sklearn.compose import ColumnTransformer -from sklearn.metrics import classification_report, accuracy_score import mlflow from mlflow.models import infer_signature from pyspark.sql import SparkSession +from sklearn.compose import ColumnTransformer +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score, classification_report +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler + +from childHealth.config import ProjectConfig # Set up MLflow tracking mlflow.set_tracking_uri("databricks") -mlflow.set_registry_uri('databricks-uc') # For registering models to Unity Catalog +mlflow.set_registry_uri("databricks-uc") # For registering models to Unity Catalog # COMMAND ---------- @@ -51,22 +52,21 @@ # Define preprocessing for numerical and categorical features preprocessor = ColumnTransformer( transformers=[ - ('num', StandardScaler(), num_features), - ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features) + ("num", StandardScaler(), num_features), + ("cat", OneHotEncoder(handle_unknown="ignore"), cat_features), ], - remainder='passthrough' + remainder="passthrough", ) # Create the pipeline with preprocessing and Random Forest Classifier -pipeline = Pipeline(steps=[ - ('preprocessor', preprocessor), - ('classifier', RandomForestClassifier(**random_forest_parameters)) -]) +pipeline = Pipeline( + steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier(**random_forest_parameters))] +) # COMMAND ---------- # Define the MLflow experiment and Git SHA for tracking -mlflow.set_experiment(experiment_name='/Shared/child-health') +mlflow.set_experiment(experiment_name="/Shared/child-health") git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" # Start an MLflow run to track the training process @@ -91,22 +91,17 @@ signature = infer_signature(model_input=X_train, model_output=y_pred) # Log the dataset source and model in MLflow - dataset = mlflow.data.from_spark( - train_set_spark, table_name=f"{catalog_name}.{schema_name}.train_set", version="0") + dataset = mlflow.data.from_spark(train_set_spark, table_name=f"{catalog_name}.{schema_name}.train_set", version="0") mlflow.log_input(dataset, context="training") - mlflow.sklearn.log_model( - sk_model=pipeline, - artifact_path="randomforest-pipeline-model", - signature=signature - ) - + mlflow.sklearn.log_model(sk_model=pipeline, artifact_path="randomforest-pipeline-model", signature=signature) + # COMMAND ---------- # Register the model in MLflow model_version = mlflow.register_model( - model_uri=f'runs:/{run_id}/randomforest-pipeline-model', + model_uri=f"runs:/{run_id}/randomforest-pipeline-model", name=f"{catalog_name}.{schema_name}.child_health_model_randomforest", - tags={"git_sha": git_sha} + tags={"git_sha": git_sha}, ) # COMMAND ---------- diff --git a/notebooks/week2/05.wip_log_and_register_custom_model.py b/notebooks/week2/05.wip_log_and_register_custom_model.py index 6f452d3..d91ecb6 100644 --- a/notebooks/week2/05.wip_log_and_register_custom_model.py +++ b/notebooks/week2/05.wip_log_and_register_custom_model.py @@ -1,19 +1,19 @@ # Databricks notebook source -#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl +# MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- -#MAGIC dbutils.library.restartPython() +# MAGIC dbutils.library.restartPython() # COMMAND ---------- + import mlflow -import numpy as np import pandas as pd -from pyspark.sql import SparkSession -from mlflow.models import infer_signature -from childHealth.data_processor import ProjectConfig -import json from mlflow import MlflowClient +from mlflow.models import infer_signature from mlflow.utils.environment import _mlflow_conda_env +from pyspark.sql import SparkSession + +from childHealth.data_processor import ProjectConfig from childHealth.utils import adjust_predictions mlflow.set_registry_uri("databricks-uc") @@ -42,26 +42,25 @@ filter_string="tags.branch='week1-2'", ).run_id[0] -model = mlflow.sklearn.load_model(f'runs:/{run_id}/lightgbm-pipeline-model') - +model = mlflow.sklearn.load_model(f"runs:/{run_id}/lightgbm-pipeline-model") # COMMAND ---------- + class ChildHealthModelWrapper(mlflow.pyfunc.PythonModel): - def __init__(self, model): self.model = model - + def predict(self, context, model_input): if isinstance(model_input, pd.DataFrame): predictions = self.model.predict(model_input) - predictions = {"Prediction": adjust_predictions( - predictions[0])} + predictions = {"Prediction": adjust_predictions(predictions[0])} return predictions else: raise ValueError("Input must be a pandas DataFrame.") + # COMMAND ---------- train_set = spark.table(f"{catalog_name}.{schema_name}.train_set") @@ -75,7 +74,7 @@ def predict(self, context, model_input): # COMMAND ---------- -wrapped_model = ChildHealthModelWrapper(model) # we pass the loaded model to the wrapper +wrapped_model = ChildHealthModelWrapper(model) # we pass the loaded model to the wrapper example_input = X_test.iloc[0:1] # Select the first row for prediction as example example_prediction = wrapped_model.predict(context=None, model_input=example_input) print("Example Prediction:", example_prediction) @@ -85,29 +84,27 @@ def predict(self, context, model_input): mlflow.set_experiment(experiment_name="/Shared/child-health-pyfunc") git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" -with mlflow.start_run(tags={"branch": "week1-2", - "git_sha": f"{git_sha}"}) as run: - +with mlflow.start_run(tags={"branch": "week1-2", "git_sha": f"{git_sha}"}) as run: run_id = run.info.run_id - signature = infer_signature(model_input=X_train, model_output={'Prediction': example_prediction}) - dataset = mlflow.data.from_spark( - train_set, table_name=f"{catalog_name}.{schema_name}.train_set", version="0") + signature = infer_signature(model_input=X_train, model_output={"Prediction": example_prediction}) + dataset = mlflow.data.from_spark(train_set, table_name=f"{catalog_name}.{schema_name}.train_set", version="0") mlflow.log_input(dataset, context="training") conda_env = _mlflow_conda_env( additional_conda_deps=None, - additional_pip_deps=["code/mlops_with_databricks-0.0.1-py3-none-any.whl", - ], + additional_pip_deps=[ + "code/mlops_with_databricks-0.0.1-py3-none-any.whl", + ], additional_conda_channels=None, ) mlflow.pyfunc.log_model( python_model=wrapped_model, artifact_path="pyfunc-child-health-model", - code_paths = ["mlops_with_databricks-0.0.1-py3-none-any.whl"], - signature=signature + code_paths=["mlops_with_databricks-0.0.1-py3-none-any.whl"], + signature=signature, ) # COMMAND ---------- -loaded_model = mlflow.pyfunc.load_model(f'runs:/{run_id}/pyfunc-child-health-model') +loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/pyfunc-child-health-model") loaded_model.unwrap_python_model() # COMMAND ---------- diff --git a/notebooks/week2/06.log_and_register_fe_model.py b/notebooks/week2/06.log_and_register_fe_model.py index 0f956b2..86e48e0 100644 --- a/notebooks/week2/06.log_and_register_fe_model.py +++ b/notebooks/week2/06.log_and_register_fe_model.py @@ -1,28 +1,27 @@ # Databricks notebook source # Install necessary packages -#MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl +# MAGIC %pip install childhealth_mlops_with_databricks-0.0.1-py3-none-any.whl # COMMAND ---------- # Restart the Python environment -#MAGIC dbutils.library.restartPython() +# MAGIC dbutils.library.restartPython() # COMMAND ---------- # Import libraries -import yaml +import mlflow from databricks import feature_engineering -from pyspark.sql import SparkSession +from databricks.feature_engineering import FeatureFunction, FeatureLookup from databricks.sdk import WorkspaceClient -import mlflow -from pyspark.sql import DataFrame, functions as F -from sklearn.ensemble import RandomForestClassifier from mlflow.models import infer_signature +from pyspark.sql import DataFrame, SparkSession +from pyspark.sql import functions as F from sklearn.compose import ColumnTransformer -from sklearn.metrics import classification_report, accuracy_score +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score, classification_report from sklearn.pipeline import Pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler -from datetime import datetime -from databricks.feature_engineering import FeatureFunction, FeatureLookup + from childHealth.config import ProjectConfig # COMMAND ---------- @@ -54,6 +53,7 @@ actigraph_table = spark.table(f"{catalog_name}.{schema_name}.actigraph_features") actigraph_table = actigraph_table.drop("non_wear_flag") + # COMMAND ---------- # Define function to aggregate actigraphy data def aggregate_actigraphy(data: DataFrame) -> DataFrame: @@ -65,31 +65,27 @@ def aggregate_actigraphy(data: DataFrame) -> DataFrame: F.stddev("X").alias("X_std"), F.max("X").alias("X_max"), F.min("X").alias("X_min"), - F.mean("Y").alias("Y_mean"), F.stddev("Y").alias("Y_std"), F.max("Y").alias("Y_max"), F.min("Y").alias("Y_min"), - F.mean("Z").alias("Z_mean"), F.stddev("Z").alias("Z_std"), F.max("Z").alias("Z_max"), F.min("Z").alias("Z_min"), - F.mean("enmo").alias("enmo_mean"), F.stddev("enmo").alias("enmo_std"), F.max("enmo").alias("enmo_max"), F.min("enmo").alias("enmo_min"), - F.mean("light").alias("light_mean"), F.stddev("light").alias("light_std"), F.max("light").alias("light_max"), F.min("light").alias("light_min"), - - F.mean("battery_voltage").alias("battery_voltage_mean") + F.mean("battery_voltage").alias("battery_voltage_mean"), ) return aggregated_df + # Aggregate actigraphy data aggregated_features_df = aggregate_actigraphy(actigraph_table) @@ -126,11 +122,15 @@ def aggregate_actigraphy(data: DataFrame) -> DataFrame: TBLPROPERTIES (delta.enableChangeDataFeed = true) """) -spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features " - "ADD CONSTRAINT actigraoh_pk PRIMARY KEY(id);") +spark.sql( + f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features " + "ADD CONSTRAINT actigraoh_pk PRIMARY KEY(id);" +) -spark.sql(f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features " - "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);") +spark.sql( + f"ALTER TABLE {catalog_name}.{schema_name}.actigraph_aggregated_features " + "SET TBLPROPERTIES (delta.enableChangeDataFeed = true);" +) # Insert data from the temporary view into the feature table spark.sql(f""" @@ -175,10 +175,10 @@ def aggregate_actigraphy(data: DataFrame) -> DataFrame: FeatureFunction( udf_name=function_name, output_name="battery_voltage_mean_moving_avg", - input_bindings={"battery_voltage_mean": "battery_voltage_mean"} + input_bindings={"battery_voltage_mean": "battery_voltage_mean"}, ), ], - exclude_columns=["update_timestamp_utc"] + exclude_columns=["update_timestamp_utc"], ) # COMMAND ---------- @@ -200,10 +200,10 @@ def aggregate_actigraphy(data: DataFrame) -> DataFrame: FeatureFunction( udf_name=function_name, output_name="battery_voltage_mean_moving_avg", - input_bindings={"battery_voltage_mean": "battery_voltage_mean"} + input_bindings={"battery_voltage_mean": "battery_voltage_mean"}, ), ], - exclude_columns=["update_timestamp_utc"] + exclude_columns=["update_timestamp_utc"], ) # COMMAND ---------- @@ -223,22 +223,21 @@ def aggregate_actigraphy(data: DataFrame) -> DataFrame: # Define preprocessing for numerical and categorical features preprocessor = ColumnTransformer( transformers=[ - ('num', StandardScaler(), num_features), - ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features) + ("num", StandardScaler(), num_features), + ("cat", OneHotEncoder(handle_unknown="ignore"), cat_features), ], - remainder='passthrough' + remainder="passthrough", ) # Create the pipeline with preprocessing and Random Forest Classifier -pipeline = Pipeline(steps=[ - ('preprocessor', preprocessor), - ('classifier', RandomForestClassifier(**random_forest_parameters)) -]) +pipeline = Pipeline( + steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier(**random_forest_parameters))] +) # COMMAND ---------- # Define the MLflow experiment and Git SHA for tracking -mlflow.set_experiment(experiment_name='/Shared/child-health-fe') +mlflow.set_experiment(experiment_name="/Shared/child-health-fe") git_sha = "830c17d988742482b639aec763ec731ac2dd4da5" # Start an MLflow run to track the training process @@ -270,7 +269,8 @@ def aggregate_actigraphy(data: DataFrame) -> DataFrame: training_set=training_set, signature=signature, ) - + mlflow.register_model( - model_uri=f'runs:/{run_id}/RandomForestClasf-pipeline-model-fe', - name=f"{catalog_name}.{schema_name}.child_health_model_random_forest_fe") \ No newline at end of file + model_uri=f"runs:/{run_id}/RandomForestClasf-pipeline-model-fe", + name=f"{catalog_name}.{schema_name}.child_health_model_random_forest_fe", +) diff --git a/notebooks/week2/catboost_info/catboost_training.json b/notebooks/week2/catboost_info/catboost_training.json deleted file mode 100644 index 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deleted file mode 100644 index b52addc..0000000 --- a/notebooks/week2/catboost_info/learn_error.tsv +++ /dev/null @@ -1,201 +0,0 @@ -iter RMSE -0 1.655861788 -1 1.609095135 -2 1.563059974 -3 1.519123951 -4 1.474692146 -5 1.432718381 -6 1.390609851 -7 1.351049627 -8 1.312230717 -9 1.275859624 -10 1.238953228 -11 1.204739268 -12 1.170853483 -13 1.13708195 -14 1.10568525 -15 1.073488272 -16 1.043756718 -17 1.014373396 -18 0.9858727624 -19 0.9578552787 -20 0.9303863007 -21 0.9038376424 -22 0.8784527513 -23 0.8538470818 -24 0.8299430134 -25 0.8065839498 -26 0.7832832527 -27 0.7613046632 -28 0.7396059549 -29 0.7188268351 -30 0.699288854 -31 0.6793349503 -32 0.6608861466 -33 0.6420904689 -34 0.6243756044 -35 0.6061496629 -36 0.5888657087 -37 0.5721226232 -38 0.5561977402 -39 0.5403693502 -40 0.5260112685 -41 0.5113509307 -42 0.4969138886 -43 0.4827482031 -44 0.4697056479 -45 0.4568182667 -46 0.4438584243 -47 0.431772986 -48 0.420537105 -49 0.4085079483 -50 0.3978494449 -51 0.3864252139 -52 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64 2498 -5 73 2360 -6 82 2264 -7 91 2194 -8 101 2154 -9 111 2124 -10 122 2106 -11 132 2071 -12 141 2035 -13 152 2023 -14 162 1999 -15 171 1973 -16 181 1952 -17 190 1928 -18 201 1917 -19 210 1898 -20 220 1879 -21 229 1860 -22 239 1839 -23 249 1826 -24 258 1811 -25 268 1797 -26 278 1781 -27 287 1764 -28 296 1750 -29 299 1695 -30 308 1682 -31 317 1669 -32 327 1656 -33 336 1645 -34 346 1635 -35 356 1624 -36 365 1611 -37 375 1600 -38 384 1588 -39 394 1578 -40 403 1566 -41 409 1539 -42 418 1528 -43 427 1517 -44 429 1479 -45 438 1469 -46 448 1459 -47 458 1451 -48 467 1441 -49 479 1437 -50 488 1426 -51 498 1419 -52 509 1412 -53 519 1405 -54 530 1399 -55 540 1390 -56 551 1383 -57 562 1376 -58 572 1367 -59 583 1360 -60 593 1351 -61 603 1344 -62 614 1337 -63 625 1328 -64 635 1320 -65 646 1313 -66 648 1286 -67 659 1280 -68 669 1270 -69 680 1264 -70 690 1254 -71 700 1245 -72 711 1237 -73 720 1227 -74 731 1218 -75 741 1210 -76 751 1201 -77 762 1192 -78 771 1182 -79 781 1172 -80 791 1163 -81 801 1153 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-155 1612 454 -156 1621 444 -157 1630 433 -158 1640 423 -159 1651 412 -160 1661 402 -161 1670 391 -162 1680 381 -163 1690 371 -164 1699 360 -165 1709 350 -166 1718 339 -167 1728 329 -168 1738 318 -169 1747 308 -170 1757 298 -171 1766 287 -172 1776 277 -173 1786 266 -174 1795 256 -175 1806 246 -176 1815 235 -177 1825 225 -178 1834 215 -179 1843 204 -180 1853 194 -181 1862 184 -182 1871 173 -183 1881 163 -184 1890 153 -185 1902 143 -186 1912 132 -187 1922 122 -188 1931 112 -189 1941 102 -190 1951 91 -191 1960 81 -192 1970 71 -193 1979 61 -194 1989 51 -195 1998 40 -196 2008 30 -197 2018 20 -198 2027 10 -199 2037 0 diff --git a/notebooks/week2/run_info.json b/notebooks/week2/run_info.json index bc490bd..bfe90df 100644 --- a/notebooks/week2/run_info.json +++ b/notebooks/week2/run_info.json @@ -37,4 +37,4 @@ "inputs": { "dataset_inputs": [] } -} \ No newline at end of file +} diff --git a/project_config.yml b/project_config.yml index 72bdea8..d61af3f 100644 --- a/project_config.yml +++ b/project_config.yml @@ -12,7 +12,7 @@ random_forest_parameters: min_samples_split: 5 # Minimum number of samples required to split a node min_samples_leaf: 2 # Minimum number of samples required at each leaf node class_weight: balanced # Automatically balance classes - random_state: 42 + random_state: 42 # Set up LightGBM parameters lgb_parameters: @@ -40,7 +40,7 @@ pipeline_steps: logging: level: INFO file: logs/data_processor.log - + # Numerical features num_features: @@ -92,7 +92,7 @@ num_features: - PCIAT-PCIAT_02 - PCIAT-PCIAT_03 - PCIAT-PCIAT_04 - - PCIAT-PCIAT_05 + - PCIAT-PCIAT_05 - PCIAT-PCIAT_06 - PCIAT-PCIAT_07 - PCIAT-PCIAT_08 @@ -112,7 +112,7 @@ num_features: - SDS-SDS_Total_Raw # Sleep Disturbance Scale (SDS) Total Raw - SDS-SDS_Total_T # Sleep Disturbance Scale (SDS) Total T # - PreInt_EduHx-Season - - PreInt_EduHx-computerinternet_hoursday + - PreInt_EduHx-computerinternet_hoursday # Categorical features cat_features: @@ -126,7 +126,7 @@ cat_features: - PAQ_A-Season # Physical Activity Questionnaire (PAQ) A Season - PAQ_C-Season # Physical Activity Questionnaire (PAQ) C Season - PCIAT-Season # PCIAT-Season - - SDS-Season # Sleep Disturbance Scale (SDS) Season + - SDS-Season # Sleep Disturbance Scale (SDS) Season # Target column -target: sii \ No newline at end of file +target: sii diff --git a/pyproject.toml b/pyproject.toml index 45f0c9b..1cef3d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,4 +50,4 @@ ignore = [ ] [tool.ruff.format] -indent-style = "space" \ No newline at end of file +indent-style = "space" diff --git a/src/childHealth/childhealth_model.py b/src/childHealth/childhealth_model.py index 1ac3a9b..d180555 100644 --- a/src/childHealth/childhealth_model.py +++ b/src/childHealth/childhealth_model.py @@ -1,19 +1,21 @@ -import yaml -import pandas as pd -import numpy as np +import lightgbm as lgb import matplotlib.pyplot as plt -from sklearn.model_selection import train_test_split -from sklearn.metrics import classification_report, accuracy_score, cohen_kappa_score +import mlflow +import numpy as np +import pandas as pd +import yaml +from scipy.optimize import minimize from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import OneHotEncoder, StandardScaler +from sklearn.metrics import accuracy_score, classification_report, cohen_kappa_score +from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline -import lightgbm as lgb -from scipy.optimize import minimize -import mlflow +from sklearn.preprocessing import OneHotEncoder, StandardScaler + def kappa_metric(y_true, y_pred): y_pred = np.argmax(y_pred.reshape(len(y_true), -1), axis=1) - return 'kappa', cohen_kappa_score(y_true, y_pred, weights='quadratic'), True + return "kappa", cohen_kappa_score(y_true, y_pred, weights="quadratic"), True + class ChildHealthModel: def __init__(self, preprocessor, config): @@ -24,43 +26,51 @@ def __init__(self, preprocessor, config): :param config: A dictionary containing model parameters. """ self.config = config - self.model = Pipeline(steps=[ - ('preprocessor', preprocessor), - ('regressor', lgb.LGBMClassifier( - n_estimators=config['lgb_boosting_parameters']['n_estimators'], - max_depth=config['lgb_boosting_parameters']['max_depth'], - learning_rate=config['lgb_boosting_parameters']['learning_rate'], - objective=config['lgb_boosting_parameters']['objective'], - num_class=config['lgb_boosting_parameters']['num_class'], - boosting_type=config['lgb_boosting_parameters']['boosting_type'], - num_leaves=config['lgb_boosting_parameters']['num_leaves'], - min_data_in_leaf=config['lgb_boosting_parameters']['min_data_in_leaf'], - feature_fraction=config['lgb_boosting_parameters']['feature_fraction'], - bagging_fraction=config['lgb_boosting_parameters']['bagging_fraction'], - bagging_freq=config['lgb_boosting_parameters']['bagging_freq'], - lambda_l1=config['lgb_boosting_parameters']['lambda_l1'], - lambda_l2=config['lgb_boosting_parameters']['lambda_l2'], - random_state=42, - metric='None' # Disable default metrics - )) - ]) + self.model = Pipeline( + steps=[ + ("preprocessor", preprocessor), + ( + "regressor", + lgb.LGBMClassifier( + n_estimators=config["lgb_boosting_parameters"]["n_estimators"], + max_depth=config["lgb_boosting_parameters"]["max_depth"], + learning_rate=config["lgb_boosting_parameters"]["learning_rate"], + objective=config["lgb_boosting_parameters"]["objective"], + num_class=config["lgb_boosting_parameters"]["num_class"], + boosting_type=config["lgb_boosting_parameters"]["boosting_type"], + num_leaves=config["lgb_boosting_parameters"]["num_leaves"], + min_data_in_leaf=config["lgb_boosting_parameters"]["min_data_in_leaf"], + feature_fraction=config["lgb_boosting_parameters"]["feature_fraction"], + bagging_fraction=config["lgb_boosting_parameters"]["bagging_fraction"], + bagging_freq=config["lgb_boosting_parameters"]["bagging_freq"], + lambda_l1=config["lgb_boosting_parameters"]["lambda_l1"], + lambda_l2=config["lgb_boosting_parameters"]["lambda_l2"], + random_state=42, + metric="None", # Disable default metrics + ), + ), + ] + ) self.best_thresholds = [0.5, 1.5, 2.5, 3.5] # Initial thresholds for rounding def quadratic_weighted_kappa(self, y_true, y_pred): - return cohen_kappa_score(y_true, y_pred, weights='quadratic') + return cohen_kappa_score(y_true, y_pred, weights="quadratic") def threshold_Rounder(self, preds, thresholds): - return np.where(preds < thresholds[0], 0, - np.where(preds < thresholds[1], 1, - np.where(preds < thresholds[2], 2, - np.where(preds < thresholds[3], 3, 4)))) + return np.where( + preds < thresholds[0], + 0, + np.where( + preds < thresholds[1], 1, np.where(preds < thresholds[2], 2, np.where(preds < thresholds[3], 3, 4)) + ), + ) def optimize_thresholds(self, y_true, preds): def evaluate_thresholds(thresholds): rounded_preds = self.threshold_Rounder(preds, thresholds) return -self.quadratic_weighted_kappa(y_true, rounded_preds) - - result = minimize(evaluate_thresholds, self.best_thresholds, method='Nelder-Mead') + + result = minimize(evaluate_thresholds, self.best_thresholds, method="Nelder-Mead") self.best_thresholds = result.x # Update best thresholds after optimization def train(self, X_train, y_train): @@ -70,7 +80,7 @@ def train(self, X_train, y_train): :param X_train: Training features. :param y_train: Training target. """ - self.model.named_steps['regressor'].fit(X_train, y_train, eval_metric=kappa_metric) + self.model.named_steps["regressor"].fit(X_train, y_train, eval_metric=kappa_metric) def predict(self, X): """ @@ -110,28 +120,29 @@ def get_feature_importance(self): :return: Feature importances and feature names. """ - feature_importance = self.model.named_steps['regressor'].feature_importances_ - feature_names = self.model.named_steps['preprocessor'].get_feature_names_out() + feature_importance = self.model.named_steps["regressor"].feature_importances_ + feature_names = self.model.named_steps["preprocessor"].get_feature_names_out() return feature_importance, feature_names + # Main function if __name__ == "__main__": # Load configuration - config_path = 'project_config.yml' - with open(config_path, 'r') as file: + config_path = "project_config.yml" + with open(config_path, "r") as file: config = yaml.safe_load(file) # Load and preprocess data - train_path = 'train.csv' + train_path = "train.csv" data = pd.read_csv(train_path) - X = data[config['num_features'] + config['cat_features']] - y = data[config['target']] + X = data[config["num_features"] + config["cat_features"]] + y = data[config["target"]] # Define preprocessor preprocessor = ColumnTransformer( transformers=[ - ('num', StandardScaler(), config['num_features']), - ('cat', OneHotEncoder(drop='first'), config['cat_features']) + ("num", StandardScaler(), config["num_features"]), + ("cat", OneHotEncoder(drop="first"), config["cat_features"]), ] ) @@ -157,13 +168,13 @@ def get_feature_importance(self): # Display feature importance feature_importance, feature_names = child_health_model.get_feature_importance() - feature_importance_df = pd.DataFrame({'feature': feature_names, 'importance': feature_importance}) - feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) + feature_importance_df = pd.DataFrame({"feature": feature_names, "importance": feature_importance}) + feature_importance_df = feature_importance_df.sort_values(by="importance", ascending=False) plt.figure(figsize=(10, 8)) - plt.barh(feature_importance_df['feature'], feature_importance_df['importance'], color='skyblue') - plt.xlabel('Importance') - plt.title('Feature Importance') + plt.barh(feature_importance_df["feature"], feature_importance_df["importance"], color="skyblue") + plt.xlabel("Importance") + plt.title("Feature Importance") plt.gca().invert_yaxis() plt.show() diff --git a/src/childHealth/config.py b/src/childHealth/config.py index bcd0b49..a3a09af 100644 --- a/src/childHealth/config.py +++ b/src/childHealth/config.py @@ -1,6 +1,8 @@ -from pydantic import BaseModel, ValidationError -from typing import List, Dict, Any +from typing import Any, Dict, List + import yaml +from pydantic import BaseModel, ValidationError + class ProjectConfig(BaseModel): catalog_name: str @@ -12,10 +14,10 @@ class ProjectConfig(BaseModel): target: str @classmethod - def from_yaml(cls, config_path: str) -> 'ProjectConfig': + def from_yaml(cls, config_path: str) -> "ProjectConfig": """Load configuration from a YAML file.""" try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config_dict = yaml.safe_load(f) return cls(**config_dict) except FileNotFoundError: @@ -25,5 +27,6 @@ def from_yaml(cls, config_path: str) -> 'ProjectConfig': except ValidationError as e: raise ValueError(f"Validation error: {e}") + # Example usage: -# config = ProjectConfig.from_yaml('path/to/project_config.yml') \ No newline at end of file +# config = ProjectConfig.from_yaml('path/to/project_config.yml') diff --git a/src/childHealth/data_processor.py b/src/childHealth/data_processor.py index 3468f3a..4d05d9d 100644 --- a/src/childHealth/data_processor.py +++ b/src/childHealth/data_processor.py @@ -1,18 +1,18 @@ -import pandas as pd +import logging +import os + import numpy as np -from childHealth.config import ProjectConfig +import pandas as pd +from pyspark.sql import SparkSession +from pyspark.sql.functions import current_timestamp, to_utc_timestamp +from sklearn.impute import SimpleImputer from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler -from sklearn.impute import SimpleImputer -from pyspark.sql.functions import current_timestamp, to_utc_timestamp -from pyspark.sql import SparkSession -import os - -import logging +from childHealth.config import ProjectConfig # Load configuration -config = ProjectConfig.from_yaml('../../project_config.yml') +config = ProjectConfig.from_yaml("../../project_config.yml") # Create logs directory if it doesn't exist log_file = config.logging.file @@ -21,15 +21,16 @@ # Configure logging logging.basicConfig( level=getattr(logging, config.logging.level), - format='%(asctime)s - %(levelname)s - %(message)s', + format="%(asctime)s - %(levelname)s - %(message)s", handlers=[ logging.FileHandler(log_file), - logging.StreamHandler() # This will also print logs to the console - ] + logging.StreamHandler(), # This will also print logs to the console + ], ) # Set up logging configuration -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + class TrainDataProcessor: def __init__(self, train_df: pd.DataFrame, config: ProjectConfig): @@ -57,8 +58,8 @@ def handle_missing_values(self): def _fill_numeric_missing_values(self): """Fill numeric columns with mean.""" try: - numeric_cols = self.train_df[self.num_features].apply(pd.to_numeric, errors='coerce') - imputer = SimpleImputer(strategy='mean') + numeric_cols = self.train_df[self.num_features].apply(pd.to_numeric, errors="coerce") + imputer = SimpleImputer(strategy="mean") # Identify and add any missing columns missing_columns = set(self.num_features) - set(numeric_cols.columns) @@ -97,10 +98,10 @@ def convert_data_types(self): def _convert_sex_to_binary(self): """Convert 'Sex' to binary encoding if it's part of numerical features and not already binary encoded.""" - if 'Basic_Demos-Sex' in self.cat_features: + if "Basic_Demos-Sex" in self.cat_features: # Map the values to binary encoding - self.train_df['Basic_Demos-Sex'] = self.train_df['Basic_Demos-Sex'].map({1.0: 'Male', 0.0:'Female'}) - logging.info("Converted 'Basic_Demos-Sex' to binary") + self.train_df["Basic_Demos-Sex"] = self.train_df["Basic_Demos-Sex"].map({1.0: "Male", 0.0: "Female"}) + logging.info("Converted 'Basic_Demos-Sex' to binary") def feature_engineering(self): """Perform feature engineering to create new features.""" @@ -115,15 +116,17 @@ def feature_engineering(self): def add_age_groups(self): """Add age groups based on age.""" logging.info("Adding age groups") - if 'Basic_Demos-Age' in self.num_features: - self.train_df['Age_Group'] = pd.cut(self.train_df['Basic_Demos-Age'], bins=[0, 12, 17, 25], labels=['Child', 'Teen', 'Young Adult']) + if "Basic_Demos-Age" in self.num_features: + self.train_df["Age_Group"] = pd.cut( + self.train_df["Basic_Demos-Age"], bins=[0, 12, 17, 25], labels=["Child", "Teen", "Young Adult"] + ) logging.info("Age groups added") def one_hot_encode_seasons(self): """One-hot encode season columns.""" logging.info("One-hot encoding seasons") for col in self.cat_features: - if 'Season' in col: + if "Season" in col: one_hot = pd.get_dummies(self.train_df[col], prefix=col) self.train_df = pd.concat([self.train_df, one_hot], axis=1) logging.info(f"One-hot encoded {col}") @@ -132,26 +135,36 @@ def calculate_behavioral_scores(self): """Calculate behavioral and psychological indicators.""" logging.info("Calculating behavioral scores") # Bin PCIAT total score - if 'PCIAT-PCIAT_Total' in self.num_features: - self.train_df['PCIAT_Bin'] = pd.cut(self.train_df['PCIAT-PCIAT_Total'], bins=[0, 20, 40, 60], labels=['Mild', 'Moderate', 'Severe']) + if "PCIAT-PCIAT_Total" in self.num_features: + self.train_df["PCIAT_Bin"] = pd.cut( + self.train_df["PCIAT-PCIAT_Total"], bins=[0, 20, 40, 60], labels=["Mild", "Moderate", "Severe"] + ) logging.info("PCIAT total score binned") # Categorize internet use - if 'PreInt_EduHx-computerinternet_hoursday' in self.num_features: - self.train_df['Internet_Use_Category'] = pd.cut(self.train_df['PreInt_EduHx-computerinternet_hoursday'], bins=[0, 1, 3, 6, np.inf], labels=['Low', 'Moderate', 'High', 'Very High']) + if "PreInt_EduHx-computerinternet_hoursday" in self.num_features: + self.train_df["Internet_Use_Category"] = pd.cut( + self.train_df["PreInt_EduHx-computerinternet_hoursday"], + bins=[0, 1, 3, 6, np.inf], + labels=["Low", "Moderate", "High", "Very High"], + ) logging.info("Internet use categorized") def add_interaction_features(self): """Add interaction features, such as age-adjusted scores.""" logging.info("Adding interaction features") # Age-adjusted CGAS Score - if 'CGAS-CGAS_Score' in self.num_features and 'Basic_Demos-Age' in self.num_features: - self.train_df['Age_Adjusted_CGAS'] = self.train_df['CGAS-CGAS_Score'] / self.train_df['Basic_Demos-Age'] + if "CGAS-CGAS_Score" in self.num_features and "Basic_Demos-Age" in self.num_features: + self.train_df["Age_Adjusted_CGAS"] = self.train_df["CGAS-CGAS_Score"] / self.train_df["Basic_Demos-Age"] logging.info("Age-adjusted CGAS score added") # BMI Categories - if 'Physical-BMI' in self.num_features: - self.train_df['BMI_Category'] = pd.cut(self.train_df['Physical-BMI'], bins=[0, 18.5, 25, 30, np.inf], labels=['Underweight', 'Normal', 'Overweight', 'Obese']) + if "Physical-BMI" in self.num_features: + self.train_df["BMI_Category"] = pd.cut( + self.train_df["Physical-BMI"], + bins=[0, 18.5, 25, 30, np.inf], + labels=["Underweight", "Normal", "Overweight", "Obese"], + ) logging.info("BMI categories added") def scale_numeric_features(self): @@ -194,8 +207,8 @@ def split_data(self, test_size=0.2, random_state=42): def save_to_catalog(self, train_set: pd.DataFrame, test_set: pd.DataFrame, spark: SparkSession): logging.info("Saving datasets to catalog") - self._add_timestamp_and_save(train_set, 'train_set', spark) - self._add_timestamp_and_save(test_set, 'test_set', spark) + self._add_timestamp_and_save(train_set, "train_set", spark) + self._add_timestamp_and_save(test_set, "test_set", spark) self._enable_change_data_feed(spark) logging.info("Datasets saved to catalog and change data feed enabled") @@ -212,4 +225,4 @@ def _enable_change_data_feed(self, spark: SparkSession): logging.info("Enabling change data feed") spark.sql("ALTER TABLE train_set SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") spark.sql("ALTER TABLE test_set SET TBLPROPERTIES (delta.enableChangeDataFeed = true)") - logging.info("Change data feed enabled") \ No newline at end of file + logging.info("Change data feed enabled") diff --git a/src/childHealth/feature_engineering.py b/src/childHealth/feature_engineering.py index 605e661..d109451 100644 --- a/src/childHealth/feature_engineering.py +++ b/src/childHealth/feature_engineering.py @@ -1,60 +1,60 @@ -from pyspark.sql.types import StructType, StructField, StringType, FloatType, IntegerType, LongType -import os -from datetime import datetime -from typing import Optional, List +import logging from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import List, Optional -import pandas as pd -import numpy as np -from tqdm import tqdm -from pyspark.sql import SparkSession, DataFrame, functions as F -from pyspark.sql.functions import current_timestamp, to_utc_timestamp from pyspark.dbutils import DBUtils - -from childHealth.config import ProjectConfig -from databricks.feature_engineering import FeatureFunction, FeatureLookup - -import logging +from pyspark.sql import DataFrame, SparkSession +from pyspark.sql import functions as F +from pyspark.sql.types import FloatType, IntegerType, LongType, StringType, StructField, StructType from pyspark.sql.utils import AnalysisException - +from tqdm import tqdm # Configure the logger logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) + class ActigraphFileReader: def __init__(self, app_name: str, root_dir: str, catalog_name: str, schema_name: str): """ Initializes the ActigraphFileReader class. """ - self.spark = SparkSession.builder \ - .appName(app_name) \ - .config("spark.databricks.delta.schema.autoMerge.enabled", "true") \ + self.spark = ( + SparkSession.builder.appName(app_name) + .config("spark.databricks.delta.schema.autoMerge.enabled", "true") .getOrCreate() + ) self.dbutils = DBUtils(self.spark) self.root_dir = root_dir self.catalog_name = catalog_name self.schema_name = schema_name # Define the schema for the feature table - self.schema = StructType([ - StructField("step", LongType(), True), - StructField("X", FloatType(), True), - StructField("Y", FloatType(), True), - StructField("Z", FloatType(), True), - StructField("enmo", FloatType(), True), - StructField("anglez", FloatType(), True), - StructField("non_wear_flag", FloatType(), True), - StructField("light", FloatType(), True), - StructField("battery_voltage", FloatType(), True), - StructField("time_of_day", LongType(), True), - StructField("weekday", IntegerType(), True), - StructField("quarter", IntegerType(), True), - StructField("relative_date_PCIAT", FloatType(), True), - StructField("id", StringType(), True), - ]) - logger.info("Initialized ActigraphFileReader with app name %s, root directory %s, catalog %s, and schema %s.", - app_name, root_dir, catalog_name, schema_name) + self.schema = StructType( + [ + StructField("step", LongType(), True), + StructField("X", FloatType(), True), + StructField("Y", FloatType(), True), + StructField("Z", FloatType(), True), + StructField("enmo", FloatType(), True), + StructField("anglez", FloatType(), True), + StructField("non_wear_flag", FloatType(), True), + StructField("light", FloatType(), True), + StructField("battery_voltage", FloatType(), True), + StructField("time_of_day", LongType(), True), + StructField("weekday", IntegerType(), True), + StructField("quarter", IntegerType(), True), + StructField("relative_date_PCIAT", FloatType(), True), + StructField("id", StringType(), True), + ] + ) + logger.info( + "Initialized ActigraphFileReader with app name %s, root directory %s, catalog %s, and schema %s.", + app_name, + root_dir, + catalog_name, + schema_name, + ) def _read_participant_file(self, file_path: str, participant_id: str) -> Optional[DataFrame]: """ @@ -66,7 +66,9 @@ def _read_participant_file(self, file_path: str, participant_id: str) -> Optiona logger.info("Successfully read file for participant ID: %s from path: %s", participant_id, file_path) return data except AnalysisException as e: - logger.warning("File not found or error reading file %s for participant %s: %s", file_path, participant_id, e) + logger.warning( + "File not found or error reading file %s for participant %s: %s", file_path, participant_id, e + ) return None except Exception as e: logger.error("Unexpected error reading file %s for participant %s: %s", file_path, participant_id, e) @@ -87,10 +89,10 @@ def load_all_files(self) -> Optional[DataFrame]: return None for dir_info in participant_dirs: - if not dir_info.path.endswith('/'): + if not dir_info.path.endswith("/"): continue # Skip if it's not a directory - participant_id = dir_info.path.split('=')[-1].strip('/') + participant_id = dir_info.path.split("=")[-1].strip("/") participant_file_path = f"{dir_info.path}part-0.parquet" futures.append(executor.submit(self._read_participant_file, participant_file_path, participant_id)) @@ -168,18 +170,17 @@ def save_feature_table(self): try: if feature_table.count() > 0: # Ensure consistent column names and types - feature_table = feature_table.withColumnRenamed("non-wear-flag", "non_wear_flag") \ - .withColumn("weekday", F.col("weekday").cast("TINYINT")) \ - .withColumn("quarter", F.col("quarter").cast("TINYINT")) \ - .withColumn("id", F.col("id").cast("STRING")) + feature_table = ( + feature_table.withColumnRenamed("non-wear-flag", "non_wear_flag") + .withColumn("weekday", F.col("weekday").cast("TINYINT")) + .withColumn("quarter", F.col("quarter").cast("TINYINT")) + .withColumn("id", F.col("id").cast("STRING")) + ) # Write data with schema merge enabled - feature_table.write.format("delta") \ - .mode("append") \ - .option("mergeSchema", "true") \ - .saveAsTable(table_name) + feature_table.write.format("delta").mode("append").option("mergeSchema", "true").saveAsTable(table_name) logger.info("Feature table %s saved and updated successfully.", table_name) else: logger.info("Feature table is empty; nothing was saved.") except Exception as e: - logger.error("Error writing data to the feature table %s: %s", table_name, e) \ No newline at end of file + logger.error("Error writing data to the feature table %s: %s", table_name, e) diff --git a/src/childHealth/kappaLgm_model.py b/src/childHealth/kappaLgm_model.py index 1477243..7aa73c1 100644 --- a/src/childHealth/kappaLgm_model.py +++ b/src/childHealth/kappaLgm_model.py @@ -1,22 +1,21 @@ -import yaml -import pandas as pd -import numpy as np +import lightgbm as lgb import matplotlib.pyplot as plt -from sklearn.model_selection import train_test_split, StratifiedKFold -from sklearn.metrics import classification_report, accuracy_score, cohen_kappa_score +import mlflow +import numpy as np +import pandas as pd +from scipy.optimize import minimize from sklearn.compose import ColumnTransformer +from sklearn.metrics import accuracy_score, classification_report, cohen_kappa_score +from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder, StandardScaler -from sklearn.pipeline import Pipeline -import lightgbm as lgb -from scipy.optimize import minimize -import mlflow + from childHealth.config import ProjectConfig + def kappa_metric(y_true, y_pred_raw): # Use rounding to convert predicted probabilities to the nearest class y_pred = np.round(y_pred_raw).astype(int) - return 'kappa', cohen_kappa_score(y_true, y_pred, weights='quadratic'), True - + return "kappa", cohen_kappa_score(y_true, y_pred, weights="quadratic"), True class ChildHealthModel: @@ -30,39 +29,42 @@ def __init__(self, preprocessor, config): self.config = config self.preprocessor = preprocessor self.model = lgb.LGBMClassifier( - learning_rate=config.lgb_parameters['learning_rate'], - max_depth=config.lgb_parameters['max_depth'], - num_leaves=config.lgb_parameters['num_leaves'], - min_data_in_leaf=config.lgb_parameters['min_data_in_leaf'], - feature_fraction=config.lgb_parameters['feature_fraction'], - bagging_fraction=config.lgb_parameters['bagging_fraction'], - bagging_freq=config.lgb_parameters['bagging_freq'], - lambda_l1=config.lgb_parameters['lambda_l1'], - lambda_l2=config.lgb_parameters['lambda_l2'], - n_estimators=config.lgb_parameters['n_estimators'], - num_class=config.lgb_parameters['num_class'], - objective=config.lgb_parameters['objective'], - boosting_type=config.lgb_parameters['boosting_type'], + learning_rate=config.lgb_parameters["learning_rate"], + max_depth=config.lgb_parameters["max_depth"], + num_leaves=config.lgb_parameters["num_leaves"], + min_data_in_leaf=config.lgb_parameters["min_data_in_leaf"], + feature_fraction=config.lgb_parameters["feature_fraction"], + bagging_fraction=config.lgb_parameters["bagging_fraction"], + bagging_freq=config.lgb_parameters["bagging_freq"], + lambda_l1=config.lgb_parameters["lambda_l1"], + lambda_l2=config.lgb_parameters["lambda_l2"], + n_estimators=config.lgb_parameters["n_estimators"], + num_class=config.lgb_parameters["num_class"], + objective=config.lgb_parameters["objective"], + boosting_type=config.lgb_parameters["boosting_type"], random_state=42, - metric='None' # Disable default metrics + metric="None", # Disable default metrics ) self.best_thresholds = [0.5, 1.5, 2.5, 3.5] # Initial thresholds for rounding def quadratic_weighted_kappa(self, y_true, y_pred): - return cohen_kappa_score(y_true, y_pred, weights='quadratic') + return cohen_kappa_score(y_true, y_pred, weights="quadratic") def threshold_Rounder(self, preds, thresholds): - return np.where(preds < thresholds[0], 0, - np.where(preds < thresholds[1], 1, - np.where(preds < thresholds[2], 2, - np.where(preds < thresholds[3], 3, 4)))) + return np.where( + preds < thresholds[0], + 0, + np.where( + preds < thresholds[1], 1, np.where(preds < thresholds[2], 2, np.where(preds < thresholds[3], 3, 4)) + ), + ) def optimize_thresholds(self, y_true, preds): def evaluate_thresholds(thresholds): rounded_preds = self.threshold_Rounder(preds, thresholds) return -self.quadratic_weighted_kappa(y_true, rounded_preds) - - result = minimize(evaluate_thresholds, self.best_thresholds, method='Nelder-Mead') + + result = minimize(evaluate_thresholds, self.best_thresholds, method="Nelder-Mead") self.best_thresholds = result.x # Update best thresholds after optimization def train(self, X_train, y_train, X_val, y_val): @@ -86,7 +88,7 @@ def train(self, X_train, y_train, X_val, y_val): params=self.config.lgb_parameters, train_set=train_data, valid_sets=[val_data], - valid_names=['validation'], + valid_names=["validation"], feval=kappa_metric, # early_stopping_rounds=10 ) @@ -135,14 +137,15 @@ def get_feature_importance(self): feature_names = self.preprocessor.get_feature_names_out() return feature_importance, feature_names + # Main function if __name__ == "__main__": # Load configuration - config_path = 'project_config.yml' + config_path = "project_config.yml" config = ProjectConfig.from_yaml(config_path) # Load and preprocess data - train_path = 'train.csv' + train_path = "train.csv" data = pd.read_csv(train_path) X = data[config.num_features + config.cat_features] y = data[config.target] @@ -150,8 +153,8 @@ def get_feature_importance(self): # Define preprocessor preprocessor = ColumnTransformer( transformers=[ - ('num', StandardScaler(), config.num_features), - ('cat', OneHotEncoder(drop='first'), config.cat_features) + ("num", StandardScaler(), config.num_features), + ("cat", OneHotEncoder(drop="first"), config.cat_features), ] ) @@ -178,17 +181,17 @@ def get_feature_importance(self): # Display feature importance feature_importance, feature_names = child_health_model.get_feature_importance() - feature_importance_df = pd.DataFrame({'feature': feature_names, 'importance': feature_importance}) - feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) + feature_importance_df = pd.DataFrame({"feature": feature_names, "importance": feature_importance}) + feature_importance_df = feature_importance_df.sort_values(by="importance", ascending=False) plt.figure(figsize=(10, 8)) - plt.barh(feature_importance_df['feature'], feature_importance_df['importance'], color='skyblue') - plt.xlabel('Importance') - plt.title('Feature Importance') + plt.barh(feature_importance_df["feature"], feature_importance_df["importance"], color="skyblue") + plt.xlabel("Importance") + plt.title("Feature Importance") plt.gca().invert_yaxis() plt.show() # Log metrics in MLflow with mlflow.start_run(): mlflow.log_metric("accuracy", accuracy) - mlflow.log_metric("quadratic_weighted_kappa", kappa) \ No newline at end of file + mlflow.log_metric("quadratic_weighted_kappa", kappa) diff --git a/src/childHealth/utils.py b/src/childHealth/utils.py index 57b32ed..bbdd021 100644 --- a/src/childHealth/utils.py +++ b/src/childHealth/utils.py @@ -1,30 +1,29 @@ -import pandas as pd -import yaml # Fixed the import statement -from childHealth.config import ProjectConfig import matplotlib.pyplot as plt import numpy as np + def visualize_results(y_test, y_pred): """ Visualize the results of predictions against actual values. - + Parameters: y_test (array-like): Actual values. y_pred (array-like): Predicted values. """ plt.figure(figsize=(10, 6)) plt.scatter(y_test, y_pred, alpha=0.5) - plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2) + plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "r--", lw=2) plt.xlabel("Actual Price") plt.ylabel("Predicted Price") plt.title("Actual vs Predicted House Prices") plt.tight_layout() plt.show() + def plot_feature_importance(feature_importance, feature_names, top_n=10): """ Plot the top N feature importances. - + Parameters: feature_importance (array-like): Importance scores of features. feature_names (list): Names of the features. @@ -32,13 +31,13 @@ def plot_feature_importance(feature_importance, feature_names, top_n=10): """ plt.figure(figsize=(10, 6)) sorted_idx = np.argsort(feature_importance) - pos = np.arange(sorted_idx[-top_n:].shape[0]) + .5 + pos = np.arange(sorted_idx[-top_n:].shape[0]) + 0.5 plt.barh(pos, feature_importance[sorted_idx[-top_n:]]) plt.yticks(pos, feature_names[sorted_idx[-top_n:]]) - plt.title(f'Top {top_n} Feature Importance') + plt.title(f"Top {top_n} Feature Importance") plt.tight_layout() plt.show() - - + + def adjust_predictions(predictions, scale_factor=1.3): - return predictions * scale_factor \ No newline at end of file + return predictions * scale_factor diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..7317e8b --- /dev/null +++ b/uv.lock @@ -0,0 +1,2363 @@ +version = 1 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version < '3.12' and platform_system != 'Windows'", + "python_full_version == '3.12.*' and platform_system != 'Windows'", + "python_full_version >= '3.13' and platform_system != 'Windows'", + "python_full_version < '3.12' and platform_system == 'Windows'", + "python_full_version == '3.12.*' and platform_system == 'Windows'", + "python_full_version >= '3.13' and platform_system == 'Windows'", +] + +[[package]] +name = "alembic" +version = "1.14.0" +source = { registry = "https://pypi.org/simple" } 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"https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 }, +] From 6d3a178bee9a9c51e5b85ba1530dda84b803f14a Mon Sep 17 00:00:00 2001 From: javedhassans Date: Tue, 19 Nov 2024 13:12:56 +0100 Subject: [PATCH 36/36] fixed week1 --- notebooks/week1/00.dataexploration.ipynb | 8 +-- notebooks/week1/01.dataExploraton.py | 73 ++++++------------------ src/childHealth/config.py | 12 ++-- 3 files changed, 26 insertions(+), 67 deletions(-) diff --git a/notebooks/week1/00.dataexploration.ipynb b/notebooks/week1/00.dataexploration.ipynb index 2ccea35..d95f764 100644 --- a/notebooks/week1/00.dataexploration.ipynb +++ b/notebooks/week1/00.dataexploration.ipynb @@ -390,11 +390,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Merge the aggregated actigraphy features with the train data\n", - "combined_df = pd.merge(train_df, aggregated_actigraphy_df, on=\"id\", how=\"left\")\n", + "# # Merge the aggregated actigraphy features with the train data\n", + "# combined_df = pd.merge(train_df, aggregated_actigraphy_df, on=\"id\", how=\"left\")\n", "\n", - "# Inspect the combined DataFrame\n", - "print(combined_df.head())" + "# # Inspect the combined DataFrame\n", + "# print(combined_df.head())" ] }, { diff --git a/notebooks/week1/01.dataExploraton.py b/notebooks/week1/01.dataExploraton.py index 84a0b5a..ccf16de 100644 --- a/notebooks/week1/01.dataExploraton.py +++ b/notebooks/week1/01.dataExploraton.py @@ -14,6 +14,7 @@ from concurrent.futures import ThreadPoolExecutor import pandas as pd +from pyspark.dbutils import DBUtils from pyspark.sql import SparkSession from childHealth.config import ProjectConfig @@ -22,34 +23,28 @@ # Initialize Spark session spark = SparkSession.builder.getOrCreate() +dbutils = DBUtils(spark) # Define original paths dirname_train_ts = "/Volumes/mlops_students/javedhassi/data/series_train.parquet" dirname_test_ts = "/Volumes/mlops_students/javedhassi/data/series_test.parquet" -# COMMAND ---------- - # Load project configuration from YAML file config = ProjectConfig.from_yaml(config_path="../../project_config.yml") num_features = config.num_features cat_features = config.cat_features -# COMMAND ---------- def process_file(filename, dirname): filepath = os.path.join(dirname, filename, "part-0.parquet") - df = spark.read.parquet(filepath) - df = df.drop("step") - # Ensure 'id' column is included + df = spark.read.parquet(filepath).drop("step") if "id" not in df.columns: - df = df.withColumn("id", df["relative_date_PCIAT"]) # Use an existing column or create a new one + df = df.withColumn("id", df["relative_date_PCIAT"]) return df.toPandas(), filename.split("=")[1] def load_time_series(dirname) -> pd.DataFrame: - # List all subdirectories in the specified path directories = [file.path for file in dbutils.fs.ls(dirname) if file.path.endswith("/")] - results = [] with ThreadPoolExecutor() as executor: futures = {executor.submit(process_file, path.split("/")[-2], dirname): path for path in directories} @@ -57,25 +52,22 @@ def load_time_series(dirname) -> pd.DataFrame: result = future.result() results.append(result) print(f"Processed {i + 1}/{len(directories)} files") - - # Separate stats and identifiers stats, indexes = zip(*results, strict=False) if results else ([], []) - - # Create DataFrame with statistics and identifiers combined_df = pd.concat([df for df in stats], ignore_index=True) combined_df["id"] = indexes - return combined_df -# COMMAND ---------- +def update(df): + for c in cat_features: + df[c] = df[c].fillna("Missing").astype("category") + return df + # Load time series data train_ts = load_time_series(dirname_train_ts) test_ts = load_time_series(dirname_test_ts) -# COMMAND ---------- - # Load train and test CSV files with Spark train = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True) test = spark.read.csv("/Volumes/mlops_students/javedhassi/data/test.csv", header=True, inferSchema=True) @@ -85,69 +77,36 @@ def load_time_series(dirname) -> pd.DataFrame: test_pd = test.toPandas() # Ensure 'id' column exists in both DataFrames -if "id" not in train_pd.columns: - train_pd["id"] = train_pd.index -if "id" not in test_pd.columns: - test_pd["id"] = test_pd.index - -# COMMAND ---------- +train_pd["id"] = train_pd.get("id", train_pd.index) +test_pd["id"] = test_pd.get("id", test_pd.index) # Merge the data train_merged = pd.merge(train_pd, train_ts, how="left", on="id") test_merged = pd.merge(test_pd, test_ts, how="left", on="id") -# Check the result -print(train_merged.head()) -print(test_merged.head()) - -# COMMAND ---------- - # Update the list of numerical features to include time series columns time_series_cols = train_ts.columns.tolist() -time_series_cols.remove("id") # Temporarily remove 'id' column from the list of time series columns +time_series_cols.remove("id") num_features += time_series_cols -# COMMAND ---------- - - -def update(df): - for c in cat_features: - df[c] = df[c].fillna("Missing") - df[c] = df[c].astype("category") - return df - - -# COMMAND ---------- - # Update the train and test DataFrames train_merged = update(train_merged) test_merged = update(test_merged) -# COMMAND ---------- - -# # Include 'id' column back in the numerical features if needed -# num_features.append('id') - # Check the updated DataFrames print(train_merged.head()) print(test_merged.head()) - -# COMMAND ---------- -# Read the Parquet file +# Read and show the Parquet file df = spark.read.parquet( "/Volumes/mlops_students/javedhassi/data/series_train.parquet/id=00115b9f/part-0.parquet", header=True, inferSchema=True, ) - -# Show the DataFrame df.show() -# COMMAND ---------- - +# Convert to Pandas DataFrame df_pandas = df.toPandas() -# COMMAND ---------- -train = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True) + +# Filter and show specific train data train.filter(train.id == "00115b9f").show() -# COMMAND ---------- diff --git a/src/childHealth/config.py b/src/childHealth/config.py index a3a09af..06222ed 100644 --- a/src/childHealth/config.py +++ b/src/childHealth/config.py @@ -7,8 +7,8 @@ class ProjectConfig(BaseModel): catalog_name: str schema_name: str - random_forest_parameters: Dict[str, Any] # Dictionary to hold Random Forest parameters - lgb_parameters: Dict[str, Any] # Dictionary to hold LightGBM parameters + random_forest_parameters: Dict[str, Any] + lgb_parameters: Dict[str, Any] num_features: List[str] cat_features: List[str] target: str @@ -20,12 +20,12 @@ def from_yaml(cls, config_path: str) -> "ProjectConfig": with open(config_path, "r") as f: config_dict = yaml.safe_load(f) return cls(**config_dict) - except FileNotFoundError: - raise FileNotFoundError(f"Configuration file not found: {config_path}") + except FileNotFoundError as e: + raise FileNotFoundError(f"Configuration file not found: {config_path}") from e except yaml.YAMLError as e: - raise ValueError(f"Error parsing YAML file: {e}") + raise ValueError(f"Error parsing YAML file: {e}") from e except ValidationError as e: - raise ValueError(f"Validation error: {e}") + raise ValueError(f"Validation error: {e}") from e # Example usage: