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box_burn.py
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# boxplots
import matplotlib
from scipy import stats as stats
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import pandas as pd
target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/Himalayan_front/'
name = 'mchi_pandas_output.csv'
#target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/Himalayan_front/precip_driven_chi_20_35k/himalaya_all/'
#name = 'precipitation_output_MChiSegmented_burn.csv'
lithology = True
precipitation = False
export = True
def counter(df):
list_a = df["m_chi"]
list_a = list_a.tolist()
count = len(list_a)
print count
return str(count)
with open(target+name,'r') as csvfile:
#reading target
df = pd.read_csv(csvfile,delimiter=',')
#separate into pandasDF for each bin
# I only want the column m_chi
column = "m_chi"
#column = "burned_data"
#column = "secondary_burned_data"
bins = 500
#bins = 1000
if precipitation:
#starting with precipitation values. 0 to 10000
#start with bins of 500
if bins == 500:
df_1 = df[df["secondary_burned_data"].isin(range(0,500))]
df_2 = df[df["secondary_burned_data"].isin(range(501,1000))]
df_3 = df[df["secondary_burned_data"].isin(range(1001,1501))]
df_4 = df[df["secondary_burned_data"].isin(range(1501,2000))]
df_5 = df[df["secondary_burned_data"].isin(range(2001,2500))]
df_6 = df[df["secondary_burned_data"].isin(range(2501,3000))]
df_7 = df[df["secondary_burned_data"].isin(range(3001,3500))]
df_8 = df[df["secondary_burned_data"].isin(range(3501,4000))]
df_9 = df[df["secondary_burned_data"].isin(range(4001,4500))]
df_10 = df[df["secondary_burned_data"].isin(range(4501,5000))]
df_11 = df[df["secondary_burned_data"].isin(range(5001,5500))]
df_12 = df[df["secondary_burned_data"].isin(range(5501,6000))]
df_13 = df[df["secondary_burned_data"].isin(range(6001,6500))]
df_14 = df[df["secondary_burned_data"].isin(range(6501,7000))]
df_15 = df[df["secondary_burned_data"].isin(range(7001,7500))]
df_16 = df[df["secondary_burned_data"].isin(range(7501,8000))]
df_17 = df[df["secondary_burned_data"].isin(range(8001,8500))]
df_18 = df[df["secondary_burned_data"].isin(range(8501,9000))]
df_19 = df[df["secondary_burned_data"].isin(range(9001,9500))]
df_20 = df[df["secondary_burned_data"].isin(range(9501,10000))]
if bins == 1000:
df_1 = df[df["secondary_burned_data"].isin(range(0,1000))]
df_2 = df[df["secondary_burned_data"].isin(range(1001,2000))]
df_3 = df[df["secondary_burned_data"].isin(range(2001,3000))]
df_4 = df[df["secondary_burned_data"].isin(range(3001,4000))]
df_5 = df[df["secondary_burned_data"].isin(range(4001,5000))]
df_6 = df[df["secondary_burned_data"].isin(range(5001,6000))]
df_7 = df[df["secondary_burned_data"].isin(range(6001,7000))]
if export:
#df_1 = df_1["m_chi"]
#df_1 = df_1.rename(columns={'m_chi' : '1000'},inplace=True)
if bins ==1000:
df_1.rename(columns={column:'0_1000'},inplace=True)
df_1=df_1["0_1000"]
df_2.rename(columns={column:'1000_2000'},inplace=True)
df_2 = df_2["1000_2000"]
df_3.rename(columns={column:'2000_3000'},inplace=True)
df_3 = df_3["2000_3000"]
df_4.rename(columns={column:'3000_4000'},inplace=True)
df_4 = df_4["3000_4000"]
df_5.rename(columns={column:'4000_5000'},inplace=True)
df_5 = df_5["4000_5000"]
df_6.rename(columns={column:'5000_6000'},inplace=True)
df_6 = df_6["5000_6000"]
df_7.rename(columns={column:'6000_7000'},inplace=True)
df_7 = df_7["6000_7000"]
df_1.reset_index(drop=True, inplace=True)
df_2.reset_index(drop=True, inplace=True)
df_3.reset_index(drop=True, inplace=True)
df_4.reset_index(drop=True, inplace=True)
df_5.reset_index(drop=True, inplace=True)
df_6.reset_index(drop=True, inplace=True)
df_7.reset_index(drop=True, inplace=True)
export_df = pd.concat([df_1,df_2,df_3,df_4,df_5,df_6,df_7],axis=1)
#export_df = export_df.join(df_3)
#export_df = export_df.join(df_4)
#export_df = export_df.join(df_5)
#export_df = export_df.join(df_6)
#export_df = export_df.join(df_7)
export_df.to_csv(target+column+"precipitation_bins_MChiSegmented_pandas.csv", mode="w",header=True,index=False)
if bins ==500:
df_1.rename(columns={column:'0_500'},inplace=True)
df_1=df_1["0_500"]
df_2.rename(columns={column:'500_1000'},inplace=True)
df_2 = df_2["500_1000"]
df_3.rename(columns={column:'1000_1500'},inplace=True)
df_3 = df_3["1000_1500"]
df_4.rename(columns={column:'1500_2000'},inplace=True)
df_4 = df_4["1500_2000"]
df_5.rename(columns={column:'2000_2500'},inplace=True)
df_5 = df_5["2000_2500"]
df_6.rename(columns={column:'2500_3000'},inplace=True)
df_6 = df_6["2500_3000"]
df_7.rename(columns={column:'3000_3500'},inplace=True)
df_7 = df_7["3000_3500"]
df_8.rename(columns={column:'3500_4000'},inplace=True)
df_8=df_8["3500_4000"]
df_9.rename(columns={column:'4000_4500'},inplace=True)
df_9 = df_9["4000_4500"]
df_10.rename(columns={column:'4500_5000'},inplace=True)
df_10 = df_10["4500_5000"]
df_11.rename(columns={column:'5000_5500'},inplace=True)
df_11 = df_11["5000_5500"]
df_12.rename(columns={column:'5500_6000'},inplace=True)
df_12 = df_12["5500_6000"]
df_13.rename(columns={column:'6000_6500'},inplace=True)
df_13 = df_13["6000_6500"]
df_14.rename(columns={column:'6500_7000'},inplace=True)
df_14 = df_14["6500_7000"]
df_1.reset_index(drop=True, inplace=True)
df_2.reset_index(drop=True, inplace=True)
df_3.reset_index(drop=True, inplace=True)
df_4.reset_index(drop=True, inplace=True)
df_5.reset_index(drop=True, inplace=True)
df_6.reset_index(drop=True, inplace=True)
df_7.reset_index(drop=True, inplace=True)
df_8.reset_index(drop=True, inplace=True)
df_9.reset_index(drop=True, inplace=True)
df_10.reset_index(drop=True, inplace=True)
df_11.reset_index(drop=True, inplace=True)
df_12.reset_index(drop=True, inplace=True)
df_13.reset_index(drop=True, inplace=True)
df_14.reset_index(drop=True, inplace=True)
export_df = pd.concat([df_1,df_2,df_3,df_4,df_5,df_6,df_7,df_8,df_9,df_10,df_11,df_12,df_13,df_14],axis=1)
#export_df = export_df.join(df_3)
#export_df = export_df.join(df_4)
#export_df = export_df.join(df_5)
#export_df = export_df.join(df_6)
#export_df = export_df.join(df_7)
export_df.to_csv(target+column+"precipitation_500_bins_MChiSegmented.csv", mode="w",header=True,index=False)
#df_1.to_csv(target+"0_1000_MChiSegmented.csv", mode="w",header=True,index=False)
#df_2.to_csv(target+"1000_2000_MChiSegmented.csv", mode="w",header=True,index=False)
#df_3.to_csv(target+"2000_3000_MChiSegmented.csv", mode="w",header=True,index=False)
#read all precipitation bins
### This didn't work because the isin method cannot select float values, so these were all missed out.
#removing isolated outlying results
#df_3 = df_3[df_3["m_chi"].isin(range(0,4000))]
#data_to_plot = [df_1[column], df_2[column], df_3[column],df_4[column],
# df_5[column], df_6[column],df_7[column],df_8[column],
# df_9[column], df_10[column],df_11[column], df_12[column],
# df_13[column], df_14[column]]
#, df_15[column], df_16[column],
#df_17[column],df_18[column], df_19[column], df_20[column]]
names = ["0-500","500-1000","1000-1500","1500-2000","2000-2500","2500-3000",
"3000-3500","3500-4000","4000-4500","4500-5000","5000-5500",
"5500-6000","6000-6500","6500-7000"]
#,"15","16","17","18","19","20"]
if lithology:
#reading into lithology bins
lf_1 = df[df["burned_data"].isin(range(10000,19999))]
lf_2 = df[df["burned_data"].isin(range(20000,29999))]
lf_3 = df[df["burned_data"].isin(range(30000,39999))]
lf_4 = df[df["burned_data"].isin(range(40000,49999))]
lf_5 = df[df["burned_data"].isin(range(50000,59999))]
lf_6 = df[df["burned_data"].isin(range(60000,69999))]
lf_7 = df[df["burned_data"].isin(range(70000,79999))]
lf_8 = df[df["burned_data"].isin(range(80000,89999))]
lf_9 = df[df["burned_data"].isin(range(90000,99999))]
lf_10 = df[df["burned_data"].isin(range(100000,109999))]
lf_11 = df[df["burned_data"].isin(range(110000,119999))]
lf_12 = df[df["burned_data"].isin(range(120000,129999))]
lf_13 = df[df["burned_data"].isin(range(130000,139999))]
lf_14 = df[df["burned_data"].isin(range(140000,149999))]
lf_15 = df[df["burned_data"].isin(range(150000,159999))]
lf_16 = df[df["burned_data"].isin(range(160000,169999))]
#if export:
#lf_1.to_csv(target+"Evaporites_MChiSegmented.csv", mode="w",header=True,index=False)
#lf_2.to_csv(target+"IceandGlaciers_MChiSegmented.csv", mode="w",header=True,index=False)
#lf_3.to_csv(target+"Metamorphics_MChiSegmented.csv", mode="w",header=True,index=False)
#removing isolated outlying results
#lf_3 = lf_3[lf_3["m_chi"].isin(range(0,4000))]
data_to_plot = [lf_1[column], lf_2[column], lf_3[column],lf_4[column],
lf_5[column], lf_6[column],lf_7[column],lf_8[column],
lf_9[column], lf_10[column],lf_11[column], lf_12[column],
lf_13[column], lf_14[column],lf_15[column], lf_16[column]]
names = ["Evaporites","Ice \n and \n Glaciers","Metamorphics","NoData",
"Acid \n Plutonic \n Rocks","Basin \n Plutonic \n Rocks",
"Intermediate \n Plutonic \n Rocks","Pyroclastics","Carbonate \n Sedimentary \n Rocks",
"Mixed \n Sedimentary \n Rocks","Siliciclastic \n Sedimentary \n Rocks",
"Unconsolidated \n Sediments","Acid \n Volcanic \n Rocks","Basic \n Volcanic \n Rocks",
"Intermediate \n Volcanic \n Rocks","Water \n Bodies"]
#,"17","18","19","20"]
if export:
#df_1 = df_1["m_chi"]
#df_1 = df_1.rename(columns={'m_chi' : '1000'},inplace=True)
count_lf_3 = counter(lf_3)
#print(lf_3)
lf_3.rename(columns={column:'Metamorphics count:'+count_lf_3},inplace=True)
# print(lf_3)
lf_3=lf_3['Metamorphics count:'+count_lf_3]
count_lf_5 = counter(lf_5)
lf_5.rename(columns={column:'Acid Plutonic count:'+count_lf_5},inplace=True)
lf_5 = lf_5['Acid Plutonic count:'+count_lf_5]
count_lf_6 = counter(lf_6)
lf_6.rename(columns={column:'Basic Plutonic count:'+count_lf_6},inplace=True)
lf_6 = lf_6['Basic Plutonic count:'+count_lf_6]
count_lf_14 = counter(lf_14)
lf_14.rename(columns={column:'Basin Volcanic count:'+count_lf_14},inplace=True)
lf_14 = lf_14['Basin Volcanic count:'+count_lf_14]
count_lf_9 = counter(lf_9)
lf_9.rename(columns={column:'carb_sed count:'+count_lf_9},inplace=True)
lf_9=lf_9['carb_sed count:'+count_lf_9]
count_lf_10 = counter(lf_10)
lf_10.rename(columns={column:'mix_sed count:'+count_lf_10},inplace=True)
# mixed_sed = lf_10[['mix_sed','secondary_burned_data']]
# count_lf_10 = counter(lf_10)
# mixed_sed.to_csv(target+'mixed_sed.csv',mode='w',header=True,index=False)
lf_10 = lf_10['mix_sed count:'+count_lf_10]
count_lf_11 = counter(lf_11)
lf_11.rename(columns={column:'sili_sed count:'+count_lf_11},inplace=True)
lf_11 = lf_11['sili_sed count:'+count_lf_11]
count_lf_12 = counter(lf_12)
lf_12.rename(columns={column:'uncon_sed count:'+count_lf_12},inplace=True)
lf_12 = lf_12['uncon_sed count:'+count_lf_12]
lf_12.reset_index(drop=True, inplace=True)
lf_9.reset_index(drop=True, inplace=True)
lf_10.reset_index(drop=True, inplace=True)
lf_11.reset_index(drop=True, inplace=True)
lf_3.reset_index(drop=True, inplace=True)
lf_5.reset_index(drop=True, inplace=True)
lf_6.reset_index(drop=True, inplace=True)
lf_14.reset_index(drop=True, inplace=True)
#df_5.reset_index(drop=True, inplace=True)
#df_6.reset_index(drop=True, inplace=True)
#df_7.reset_index(drop=True, inplace=True)
export_df = pd.concat([lf_12,lf_9,lf_10,lf_11,lf_3,lf_5,lf_6,lf_14],axis=1)
#export_df = export_df.join(df_3)
#export_df = export_df.join(df_4)
#export_df = export_df.join(df_5)
#export_df = export_df.join(df_6)
#export_df = export_df.join(df_7)
export_df.to_csv(target+column+"lithology_bins_MChiSegmented_pandas.csv", mode="w",header=True,index=False)
#ks stat testing
#convert to list (array)
#df_1 = df_1["m_chi"].tolist()
#df_2 = df_2["m_chi"].tolist()
#df_3 = df_3["m_chi"].tolist()
#df_4 = df_4["m_chi"].tolist()
#df_5 = df_5["m_chi"].tolist()
#df_6 = df_6["m_chi"].tolist()
#df_7 = df_7["m_chi"].tolist()
#df_8 = df_8["m_chi"].tolist()
#df_9 = df_9["m_chi"].tolist()
#df_10 = df_10["m_chi"].tolist()
#df_11 = df_11["m_chi"].tolist()
#df_12 = df_12["m_chi"].tolist()
#df_13 = df_13["m_chi"].tolist()
#df_14 = df_14["m_chi"].tolist()
#df = df["m_chi"].tolist()
#print stats.ks_2samp(df_1,df)
#print stats.ks_2samp(df_2,df)
#print stats.ks_2samp(df_3,df)
#print stats.ks_2samp(df_4,df)
#print stats.ks_2samp(df_5,df)
#print stats.ks_2samp(df_6,df)
#
#print "one sided ks follows"
#print stats.kstest(df_3,'norm',alternative='two-sided')
#print stats.ks_2samp(df_7,df)
#print stats.ks_2samp(df_8,df)
#print stats.ks_2samp(df_9,df)
#print stats.ks_2samp(df_10,df)
#print stats.ks_2samp(df_12,df)
#print stats.ks_2samp(df_13,df)
#print stats.ks_2samp(df_14,df_14)
# ok now we are ready to plot:
# Create a figure
fig = plt.figure(1, figsize=(20, 10))
# Create an axes
ax = fig.add_subplot(111)
#df_x = df["secondary_burned_data"]
#df_y = df["m_chi"]
# Create the boxplot
#if not export:
# bp = ax.boxplot(data_to_plot, labels = names)
#bp = ax.scatter(df_x,df_y,marker='.')
#plt.title("Precipitation bins \n no chi precipitation", fontsize = 24)
#plt.title("Precipitation bins \n chi precipitation", fontsize = 24)
plt.title("Lithology bins \n no chi precipitation", fontsize = 24)
#plt.title("Lithology bins \n chi precipitation", fontsize = 24)
if lithology and not export:
fig.savefig(target+'lithology.png', bbox_inches='tight')
#if precipitation and not export:
#fig.savefig(target+'precipiation-annual.png', bbox_inches='tight')