generate image per measurement

opencellid_parsing/003c.convert_data_into_xy_center_at_00.py

@@ -44,8 +44,10 @@ code_to_country = pickle.load(open(code_to_country_fp, 'rb'))
 ############################
 
 pickle_fp_with_groundtruth = '{}/with_groundtruth_converted.pickle'.format(folderpath)
+tmp_fp_with_groundtruth = '{}/with_groundtruth_converted.boundary'.format(folderpath)
 
 all_data = []
+boundaries = []
 for filepath in glob.glob("{}/with_groundtruth/*/*/*/*/*.csv".format(folderpath)):
     __, __, radio, mcc, mnc, area, cell_and_loc = list(filter(None, filepath.rstrip('.csv').split('/')))
     cell, cell_lon, cell_lat = cell_and_loc.split('_')
@@ -54,6 +56,12 @@ for filepath in glob.glob("{}/with_groundtruth/*/*/*/*/*.csv".format(folderpath)
     cell_lat = float(cell_lat)
     cell_lat_rad = cell_lat * np.pi / 180
     data = []
+    min_x = float('inf')
+    min_y = float('inf')
+    min_sig = float('inf')
+    max_x = float('-inf')
+    max_y = float('-inf')
+    max_sig = float('-inf')
     with open(filepath, 'r') as f:
         lines = f.readlines()
     for line in lines:
@@ -64,6 +72,12 @@ for filepath in glob.glob("{}/with_groundtruth/*/*/*/*/*.csv".format(folderpath)
         lat = float(tmp[1])
         sig = float(tmp[2])
         x, y = convert_gps_into_xy((lat, lon), (cell_lat_rad, cell_lon_rad))
+        min_x = min(min_x, x)
+        min_y = min(min_y, y)
+        min_sig = min(min_sig, sig)
+        max_x = max(max_x, x)
+        max_y = max(max_y, y)
+        max_sig = max(max_sig, sig)
         data.append([lon, lat, sig, x, y])
     all_data.append({
         'radio': radio,
@@ -76,7 +90,13 @@ for filepath in glob.glob("{}/with_groundtruth/*/*/*/*/*.csv".format(folderpath)
         'latitude': cell_lat,
         'x': 0.0,
         'y': 0.0,
+        'boundary': (min_x, min_y, max_x, max_y),
         'measurements': np.array(data)  # lon, lat, sig, x, y
     })
+    boundaries.append((min_x, max_x, min_y, max_y, min_sig, max_sig))
 
 pickle.dump(all_data, open(pickle_fp_with_groundtruth, 'wb'))
+with open(tmp_fp_with_groundtruth, 'w') as f:
+    f.write("#min_x,max_x,min_y,max_y,min_sig,max_sig\n")
+    for boundary in boundaries:
+        f.write("{:.4f},{:.4f},{:.4f},{:.4f},{:.0f},{:.0f}\n".format(boundary[0], boundary[1], boundary[2], boundary[3], boundary[4], boundary[5]))

opencellid_parsing/003d_optional.generate_image.py

@@ -0,0 +1,48 @@
+#!/usr/bin/python
+
+import os
+import sys
+import glob
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def blocking_display_signal_map(
+    data_vector: np.ndarray,
+    fp: str,
+    vmin: float = -80.0,
+    vmax: float = -40.0
+):
+    '''
+    '''
+    plt.scatter(data_vector[:, 3], data_vector[:, 4], s=50, c=data_vector[:, 2])
+    plt.colorbar()
+    # plt.show()
+    plt.draw()
+    plt.savefig("{}.png".format(fp), dpi=50)
+    plt.pause(0.1)
+    q = input("press Enter to continue... type q to quit: ")
+    if q == 'q':
+        sys.exit()
+    plt.close()
+
+
+if len(sys.argv) < 3:
+    print('Usage: {} pickle_fp'.format(sys.argv[0]))
+    sys.exit(0)
+
+pickle_fp = sys.argv[1]
+
+############################
+# load
+############################
+all_data = pickle.load(open(pickle_fp, 'rb'))
+
+for data in all_data:
+    filename = (
+        "{}_{}_{}_{}_{}_{}.png"
+        .format(data['country_code'], data['radio'], data['mcc'], data['mnc'], data['area'], data['cell'])
+    )
+    blocking_display_signal_map(data['measurements'], filename)
+