Merge pull request #86 from sagitiminsky/change_radius_to_link_instead_of_gaugue

added files for links which are nearby gauges and made changed filter

Author: sagitiminsky

CellEnMon/config.py

@@ -33,10 +33,10 @@ def add_days_to_date(date, delta_days=0):
 
 #09062011_13082011 dutch
 #01012013_01022013 israel
-smoothing_dme=4
+smoothing_dme=1
 smoothing_ims="xx" # not used yet
 
-RADIUS=15
+RADIUS=50
 
 export_type="dutch"
 date = {

CellEnMon/data/cellenmon_dataset.py

@@ -18,6 +18,8 @@
 import numpy as np
 from math import radians, cos, sin, asin, sqrt
 import torch.nn.functional as F
+from datetime import datetime
+import config
 
 # from data.image_folder import make_dataset
 # from PIL import Image
@@ -169,7 +171,7 @@ def __getitem__(self, index):
 
             if time_stamp_A_start_time[:13] in [x[:13] for x in data_dict_B['data']]: # x[:13] is: dd-mm-yyyy hh
                 for l in data_dict_B['data'].keys():
-                    if l.startswith(time_stamp_A_start_time[:13]):
+                    if l.startswith(time_stamp_A_start_time[:13]) and config.RADOIS >= dist:
                         slice_start_B=list(data_dict_B['data'].keys()).index(l)
                         break
 

CellEnMon/data/exporter.py

@@ -307,13 +307,13 @@ def load_dme(self, is_train=False):
                     df = pd.read_csv(f"{config.dme_root_files}/raw/{link_file_name}")
                     if 'PowerTLTMmax[dBm]' in df and 'PowerTLTMmin[dBm]' in df and 'PowerRLTMmax[dBm]' in df and 'PowerRLTMmax[dBm]' in df:
 
-                        PowerTLTMmax = self.smoothing(np.array(df[~df["PowerTLTMmax[dBm]"].isnull()]["PowerTLTMmax[dBm]"].astype(float)),n=smoothing_n)
+                        PowerTLTMmax = np.array(df[~df["PowerTLTMmax[dBm]"].isnull()]["PowerTLTMmax[dBm]"].astype(float))
 
-                        PowerTLTMmin = self.smoothing(np.array(df[~df["PowerTLTMmin[dBm]"].isnull()]["PowerTLTMmin[dBm]"].astype(float)), n=smoothing_n)
+                        PowerTLTMmin = np.array(df[~df["PowerTLTMmin[dBm]"].isnull()]["PowerTLTMmin[dBm]"].astype(float))
 
-                        PowerRLTMmax = self.smoothing(np.array(df[~df["PowerRLTMmax[dBm]"].isnull()]["PowerRLTMmax[dBm]"].astype(float)), n=smoothing_n)
+                        PowerRLTMmax = np.array(df[~df["PowerRLTMmax[dBm]"].isnull()]["PowerRLTMmax[dBm]"].astype(float))
 
-                        PowerRLTMmin = self.smoothing(np.array(df[~df["PowerRLTMmin[dBm]"].isnull()]["PowerRLTMmin[dBm]"].astype(float)), n=smoothing_n)
+                        PowerRLTMmin = np.array(df[~df["PowerRLTMmin[dBm]"].isnull()]["PowerRLTMmin[dBm]"].astype(float))