Started script to collect GIS imformation for dataset.

lp_data/incident_locations.csv

+incident_index,IPP_lat,IPP_lon,find_lat,find_lon
+1,31.39708335,-111.20643,31.406333,-111.19683
+2,31.42903334,-110.29328,31.429517,-110.30412
+3,31.50571667,-110.67622,31.51,-110.65333
+4,31.72261664,-110.18783,31.741767,-110.19178
+5,31.83521665,-110.3567,31.862267,-110.39655
+6,31.90240002,-109.27847,31.8395,-109.27667
+7,31.92245,-109.96732,31.921483,-110.03358
+8,32.01236667,-109.31572,31.99935,-109.3078
+9,32.33333333,-110.85283,32.3715,-110.86467
+10,32.33583333,-110.91017,32.3665,-110.88017
+11,32.33699999,-110.91667,32.359833,-110.89767
+12,32.40916665,-110.70983,32.400833,-110.69233
+13,32.41976668,-110.74733,32.3655,-110.77583
+14,32.43543332,-110.78933,32.428833,-110.79433
+15,32.82141666,-111.20212,32.824667,-111.22717
+16,33.37055,-111.11522,33.375283,-111.08918
+17,33.38884999,-111.36565,33.4012,-111.37517
+18,33.39680001,-111.34805,33.4215,-111.3605
+19,33.39705,-111.34787,33.409333,-111.31717
+20,33.39733334,-111.348,33.441667,-111.36767
+21,33.39750001,-111.34783,33.415667,-111.365
+22,33.47801666,-111.43767,33.415667,-111.36478
+23,33.60398331,-112.51512,33.590333,-112.52283
+24,33.70541668,-111.33805,33.67915,-111.31695
+25,34.0927,-111.42465,34.068767,-111.43592
+26,34.09278333,-111.42098,34.102333,-111.49033
+27,34.55,-111.63333,34.55,-111.61667
+28,34.55976664,-111.65395,34.540067,-111.70348
+29,34.6,-112.55,34.633333,-112.63333
+30,34.63041668,-112.5553,34.627233,-112.54577
+31,34.82166665,-111.80667,34.818333,-111.79833
+32,34.82386665,-111.77513,34.831667,-111.743
+33,34.86666667,-111.88333,34.866667,-111.8
+34,34.88683332,-111.784,34.902667,-111.78683
+35,34.89333331,-111.86333,34.906667,-111.87603
+36,34.90286668,-111.81313,34.891833,-111.80482
+37,34.91666667,-111.8,34.966667,-111.86667
+38,34.92530003,-111.7341,34.897333,-111.74117
+39,34.97868334,-111.89643,34.946833,-111.88967
+40,35.1938,-114.05703,35.208717,-114.13195
+41,35.24375,-111.59967,35.232517,-111.6015
+42,35.33068333,-111.71108,35.334233,-111.6975
+43,36.23878333,-112.6892,36.246433,-112.70008
+44,41.54819,-80.33056,41.59006,-80.32647
+45,42.0097,-74.42595,42.03544,-74.35565
+46,42.02893,-74.33659,42.02902,-74.35191
+47,42.17965,-74.21362,42.18345,-74.19585
+48,42.31735,-76.47791,42.30271,-76.48976
+49,42.34432,-77.47638,42.36077,-77.48593
+50,42.74271,-73.45475,42.75163,-73.46259
+51,43.06902,-74.48481,43.05475,-74.4839
+52,43.42473,-73.73209,43.42878,-73.73981
+53,43.42498,-74.41496,43.41592,-74.4142
+54,43.42736,-74.4481,43.44347,-74.45004
+55,43.4332,-74.41433,43.45684,-74.41519
+56,43.4449,-74.4086,43.43407,-74.40747
+57,43.51063,-74.57393,43.53014,-74.57169
+58,43.65649,-76.00019,43.65566,-76.0115
+59,43.73413,-74.25577,43.73701,-74.28305
+60,43.8756,-74.43076,43.91132,-74.37437
+61,43.95385,-75.15748,43.99774,-75.16148
+62,44.16065,-73.85545,44.1547,-73.85942
+63,44.19013,-74.81336,44.18772,-74.79719
+64,44.28656,-74.61429,44.28923,-74.60208
+65,48.1103,-121.4917,48.142,-121.4739

lpm_maps.py

+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.io import loadmat
+import pandas as pd
+
+from arcgis_terrain import get_terrain_map, lat_lon2meters
+
+#Bryson Howell (blhowell@vt.edu), 7/10/24
+#Script made to create 3D Gridworld environments for RL experiments
+
+
+#Formats data into a csv that can be loaded as a Pandas dataframe.
+#Finds incident keys for LPM fit data
+def incident_list():
+
+    listname = './lp_data/incident_locations.csv'
+    biglistname = './lp_data/DD.DDD WGS Coordinates_cleaned.xlsx - DD Format.csv'
+
+    #load data
+    incidents = pd.read_csv(listname)
+    biglist = pd.read_csv(biglistname,dtype={'LKP Coord. (N/S)': np.float64, 'LKP Coord. (E/W)': np.float64,
+        'Find Coord (N/S)': np.float64, 'Find Coord (E/W)': np.float64},nrows=116)
+    #print(incidents.columns)
+    #print(biglist.columns)
+
+    #Look for incident key for each item in the LPM data
+    count = 0
+    for index, row in incidents.iterrows():
+        for index2, row2 in biglist.iterrows():
+            #Check difference between coordinates
+            tol = 0.0001
+            if(abs(row['IPP_lat'] - row2['LKP Coord. (N/S)']) <= tol):
+                if(abs(row['IPP_lon'] - row2['LKP Coord. (E/W)']) <= tol):
+                    if(abs(row['find_lat'] - row2['Find Coord (N/S)']) <= tol):
+                        if(abs(row['find_lon'] - row2['Find Coord (E/W)']) <= tol):
+                            print("Found Incident: %s" % row2['Key#'])
+                            if(row2['Subject Category'] != 'Hiker'):
+                                print("!!Mismatch Subject = %s" % row2['Subject Category'])
+                            count += 1
+    print('Found %d/%d incidents' % (count,incidents.shape[0]))
+
+    #Make dataframe / csv file
+    #new_df = pd.DataFrame(columns=incidents.columns)
+    #new_df.insert(0,'key')
+    #print(new_df.columns)
+
+#From the set of LPM coordinates, download and save map/linear features for areas as necessary
+def collect_terrain():
+
+
+    #Collection of map centers, in meters. Use to check if we need to download a new area
+    centers = []
+
+
+    listname = './lp_data/incident_locations.csv'
+    samples = 1
+    incidents = pd.read_csv(listname,nrows=samples)
+
+    #Loop through search incidents
+    for index, row in incidents.iterrows():
+        ipp_point = [row['IPP_lat'],row['IPP_lon']]
+        find_point = [row['find_lat'],row['find_lon']]
+        #Convert to meters
+        ipp_xy = lat_lon2meters(ipp_point[0],ipp_point[1])
+        find_xy = lat_lon2meters(find_point[0],find_point[1])
+        #See if IPP has at least 1500 cells on either side.
+        #First find nearest center point
+        
+
+    return
+
+    #Load elevation
+    res = 10 #Resolution of GIS collection
+    size = 6000     #Maximum size of map. Is this is latlon, grid cells, ?
+    #heading is direction of trajectories from north (default 0 degrees?)
+    [e,e_interp,x,y,data,lat_lon] = get_terrain_map(ipp_point, sample_dist = res, extent = size, show_plot = True)
+
+
+    #Load Linear Features. Probably need to convert some Matlab code...
+
+
+
+
+    return
+
+
+#Collect GIS maps and layers for SAR initial positions
+def main():
+
+
+    extent = 3000   #size in meters of map area
+
+    #incident_list()
+    collect_terrain()
+
+    return
+
+
+if __name__ == "__main__":
+
+    main()