Cleaned up terrain collection code

feature_set.py

@@ -82,7 +82,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
         query_cnt = 0
         while type(q)==list and query_cnt <= 30: # have to do this because arcgis is sketchy as hell and doesnt always come back
             try:
-                print("querying {} layer...".format(name_list[i]))
+                print("     querying {} layer...".format(name_list[i]))
                 query_starttime = time.time()
 
                 #Want to query where the geom filter aligns
@@ -99,13 +99,13 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
                 gis = GIS(url = 'https://virginiatech.maps.arcgis.com')
                 lyr = FeatureLayer(url=url, gis=gis)
 
-        print("query time {}".format(query_endtime - query_starttime))
+        #print("query time {}".format(query_endtime - query_starttime))
 
         if query_cnt > 30 and not q:
             print("{} layer failed too many times, leaving empty".format(name_list[i]))
             continue
-        print("{} layer sucessfully queried".format(name_list[i]))
-        print("%s layer has %d points" %(name_list[i],len(q.features)))
+        print("     {} layer sucessfully queried".format(name_list[i]))
+        print("     %s layer has %d points" %(name_list[i],len(q.features)))
         # re-build into list of x-y values
         # feat_points = []
         query_dict = q.to_dict()
@@ -200,7 +200,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
                 y_pts_inac = test_pts[mask,1]
 
                 inac_endtime = time.time()
-                print("{} inac took {}".format(j,inac_endtime - inac_starttime))
+                #print("{} inac took {}".format(j,inac_endtime - inac_starttime))
 
                 pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
 
@@ -275,7 +275,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
 
     # save terrain as csv file (this method is pretty slow, but can compensate with interp)
     if(get_elev):
-        print("Getting terrain map, this will probably take a while....")
+        print("     Getting terrain map, this will probably take a while....")
         [e,e_interp,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
                                              sample_dist = sample_dist,
                                              extent = extent,

lp_data/indexed_incidents.csv

 incident_index,area,IPP_lat,IPP_lon,find_lat,find_lon
-1,0,31.39708335,-111.20643,31.406333,-111.19683
-2,1,31.42903334,-110.29328,31.429517,-110.30412
-3,2,31.50571667,-110.67622,31.51,-110.65333
-4,3,31.72261664,-110.18783,31.741767,-110.19178
-5,4,31.83521665,-110.3567,31.862267,-110.39655
-6,5,31.90240002,-109.27847,31.8395,-109.27667
-7,6,31.92245,-109.96732,31.921483,-110.03358
-8,7,32.01236667,-109.31572,31.99935,-109.3078
-9,8,32.33333333,-110.85283,32.3715,-110.86467
-10,8,32.33583333,-110.91017,32.3665,-110.88017
-11,8,32.33699999,-110.91667,32.359833,-110.89767
-12,9,32.40916665,-110.70983,32.400833,-110.69233
-13,9,32.41976668,-110.74733,32.3655,-110.77583
-14,9,32.43543332,-110.78933,32.428833,-110.79433
-15,10,32.82141666,-111.20212,32.824667,-111.22717
-16,11,33.37055,-111.11522,33.375283,-111.08918
-17,12,33.38884999,-111.36565,33.4012,-111.37517
-18,12,33.39680001,-111.34805,33.4215,-111.3605
-19,12,33.39705,-111.34787,33.409333,-111.31717
-20,12,33.39733334,-111.348,33.441667,-111.36767
-21,12,33.39750001,-111.34783,33.415667,-111.365
-22,13,33.47801666,-111.43767,33.415667,-111.36478
-23,14,33.60398331,-112.51512,33.590333,-112.52283
-24,15,33.70541668,-111.33805,33.67915,-111.31695
-25,16,34.0927,-111.42465,34.068767,-111.43592
-26,16,34.09278333,-111.42098,34.102333,-111.49033
-27,17,34.55,-111.63333,34.55,-111.61667
-28,17,34.55976664,-111.65395,34.540067,-111.70348
-29,18,34.6,-112.55,34.633333,-112.63333
-30,18,34.63041668,-112.5553,34.627233,-112.54577
-31,19,34.82166665,-111.80667,34.818333,-111.79833
-32,19,34.82386665,-111.77513,34.831667,-111.743
-33,19,34.86666667,-111.88333,34.866667,-111.8
-34,19,34.88683332,-111.784,34.902667,-111.78683
-35,19,34.89333331,-111.86333,34.906667,-111.87603
-36,20,34.90286668,-111.81313,34.891833,-111.80482
-37,20,34.91666667,-111.8,34.966667,-111.86667
-38,20,34.92530003,-111.7341,34.897333,-111.74117
-39,21,34.97868334,-111.89643,34.946833,-111.88967
-40,22,35.1938,-114.05703,35.208717,-114.13195
-41,23,35.24375,-111.59967,35.232517,-111.6015
-42,24,35.33068333,-111.71108,35.334233,-111.6975
-43,25,36.23878333,-112.6892,36.246433,-112.70008
-44,26,41.54819,-80.33056,41.59006,-80.32647
-45,27,42.0097,-74.42595,42.03544,-74.35565
-46,27,42.02893,-74.33659,42.02902,-74.35191
-47,28,42.17965,-74.21362,42.18345,-74.19585
-48,29,42.31735,-76.47791,42.30271,-76.48976
-49,30,42.34432,-77.47638,42.36077,-77.48593
-50,31,42.74271,-73.45475,42.75163,-73.46259
-51,32,43.06902,-74.48481,43.05475,-74.4839
-52,33,43.42473,-73.73209,43.42878,-73.73981
-53,34,43.42498,-74.41496,43.41592,-74.4142
-54,34,43.42736,-74.4481,43.44347,-74.45004
-55,34,43.4332,-74.41433,43.45684,-74.41519
-56,34,43.4449,-74.4086,43.43407,-74.40747
-57,35,43.51063,-74.57393,43.53014,-74.57169
-58,36,43.65649,-76.00019,43.65566,-76.0115
-59,37,43.73413,-74.25577,43.73701,-74.28305
-60,38,43.8756,-74.43076,43.91132,-74.37437
-61,39,43.95385,-75.15748,43.99774,-75.16148
-62,40,44.16065,-73.85545,44.1547,-73.85942
-63,41,44.19013,-74.81336,44.18772,-74.79719
-64,42,44.28656,-74.61429,44.28923,-74.60208
-65,43,48.1103,-121.4917,48.142,-121.4739
+0,0,31.39708335,-111.20643,31.406333,-111.19683
+1,1,31.42903334,-110.29328,31.429517,-110.30412
+2,2,31.50571667,-110.67622,31.51,-110.65333
+3,3,31.72261664,-110.18783,31.741767,-110.19178
+4,4,31.83521665,-110.3567,31.862267,-110.39655
+5,5,31.90240002,-109.27847,31.8395,-109.27667
+6,6,31.92245,-109.96732,31.921483,-110.03358
+7,7,32.01236667,-109.31572,31.99935,-109.3078
+8,8,32.33333333,-110.85283,32.3715,-110.86467
+9,8,32.33583333,-110.91017,32.3665,-110.88017
+10,8,32.33699999,-110.91667,32.359833,-110.89767
+11,9,32.40916665,-110.70983,32.400833,-110.69233
+12,9,32.41976668,-110.74733,32.3655,-110.77583
+13,9,32.43543332,-110.78933,32.428833,-110.79433
+14,10,32.82141666,-111.20212,32.824667,-111.22717
+15,11,33.37055,-111.11522,33.375283,-111.08918
+16,12,33.38884999,-111.36565,33.4012,-111.37517
+17,12,33.39680001,-111.34805,33.4215,-111.3605
+18,12,33.39705,-111.34787,33.409333,-111.31717
+19,12,33.39733334,-111.348,33.441667,-111.36767
+20,12,33.39750001,-111.34783,33.415667,-111.365
+21,13,33.47801666,-111.43767,33.415667,-111.36478
+22,14,33.60398331,-112.51512,33.590333,-112.52283
+23,15,33.70541668,-111.33805,33.67915,-111.31695
+24,16,34.0927,-111.42465,34.068767,-111.43592
+25,16,34.09278333,-111.42098,34.102333,-111.49033
+26,17,34.55,-111.63333,34.55,-111.61667
+27,17,34.55976664,-111.65395,34.540067,-111.70348
+28,18,34.6,-112.55,34.633333,-112.63333
+29,18,34.63041668,-112.5553,34.627233,-112.54577
+30,19,34.82166665,-111.80667,34.818333,-111.79833
+31,19,34.82386665,-111.77513,34.831667,-111.743
+32,19,34.86666667,-111.88333,34.866667,-111.8
+33,19,34.88683332,-111.784,34.902667,-111.78683
+34,19,34.89333331,-111.86333,34.906667,-111.87603
+35,20,34.90286668,-111.81313,34.891833,-111.80482
+36,20,34.91666667,-111.8,34.966667,-111.86667
+37,20,34.92530003,-111.7341,34.897333,-111.74117
+38,21,34.97868334,-111.89643,34.946833,-111.88967
+39,22,35.1938,-114.05703,35.208717,-114.13195
+40,23,35.24375,-111.59967,35.232517,-111.6015
+41,24,35.33068333,-111.71108,35.334233,-111.6975
+42,25,36.23878333,-112.6892,36.246433,-112.70008
+43,26,41.54819,-80.33056,41.59006,-80.32647
+44,27,42.0097,-74.42595,42.03544,-74.35565
+45,27,42.02893,-74.33659,42.02902,-74.35191
+46,28,42.17965,-74.21362,42.18345,-74.19585
+47,29,42.31735,-76.47791,42.30271,-76.48976
+48,30,42.34432,-77.47638,42.36077,-77.48593
+49,31,42.74271,-73.45475,42.75163,-73.46259
+50,32,43.06902,-74.48481,43.05475,-74.4839
+51,33,43.42473,-73.73209,43.42878,-73.73981
+52,34,43.42498,-74.41496,43.41592,-74.4142
+53,34,43.42736,-74.4481,43.44347,-74.45004
+54,34,43.4332,-74.41433,43.45684,-74.41519
+55,34,43.4449,-74.4086,43.43407,-74.40747
+56,35,43.51063,-74.57393,43.53014,-74.57169
+57,36,43.65649,-76.00019,43.65566,-76.0115
+58,37,43.73413,-74.25577,43.73701,-74.28305
+59,38,43.8756,-74.43076,43.91132,-74.37437
+60,39,43.95385,-75.15748,43.99774,-75.16148
+61,40,44.16065,-73.85545,44.1547,-73.85942
+62,41,44.19013,-74.81336,44.18772,-74.79719
+63,42,44.28656,-74.61429,44.28923,-74.60208
+64,43,48.1103,-121.4917,48.142,-121.4739

lpm_hikersim.py

 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
-import os
+import os, sys, inspect
 from os import path, getcwd
 import pandas as pd
 
-#Runs replicates of lost person model on data sets collected by lpm_maps.py
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.insert(0, parentdir)
+
+from arcgis_terrain import get_terrain_map, lat_lon2meters
+from LostPersonModel.main_hiker import run_replicate   #change this import to where you have the LPM repo
+
+
+#Runs replicates of lost person model on data sets collected by lpm_maps.py, producing a heatmap
 #Bryson Howell, 7/24/24
 
 
 
 def main(exp_name='test', n_envs=1, n_iter=10):
 
+    test_extent = 20000
+    download_extent = 40000
+
+
+    #Pull incident locations
+    listname = './lp_data/indexed_incidents.csv'
+    incidents = pd.read_csv(listname)
+    ipp_latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
+    find_latlons = incidents[["find_lat","find_lon"]].to_numpy(dtype=np.float32())
+    areas = incidents['area'].to_numpy(dtype=np.float32())
+
+    start = 0
+    n_envs = np.size(np.unique(areas))
+    for i in range(start, start+n_envs):
+        #Pull incident locations for environment
+        i_area = np.argwhere(areas==i)
+        i_area = np.transpose(i_area)[0]
+        s = 'Area {0} - indices are {1}'
+        lst = [i,i_area]
+        print(s.format(*lst))
+
+
+        #test - print start / end locations on grid
+        grid = np.zeros((3000,3000),dtype=np.int8())
+
+        for incident in range(0,len(i_area)):
+            #Dealing with large areas. Find the IPP point and resize around it
+            if(len(i_area) > 0):
+                for subzone in range(0,len(i_area)):
+                    #First lat/lon in area is at the center
+                    if(subzone == 0):
+                        sub_center = ipp_latlons[i_area[incident]]
+                        center_xy = lat_lon2meters(sub_center[0],sub_center[1])
+                    #Determine position of other IPPs from the first
+                    else:
+                        cur_ipp = ipp_latlons[i_area[incident+subzone]]
+                        ipp_xy = lat_lon2meters(cur_ipp[0],cur_ipp[1])
+                        print(cur_ipp)
+
+            #Convert
+        #Locate find position from IPP
+        #Convert both to meters
+        #Use extent / cell count to determine which cell we're in:
+
+
+    return
+
+
+    #Set up which environments to run through.
     start = 7
+    n_envs = 1
+    if(n_envs == -1):
+        n_envs = np.size(np.unique(areas))
+
+    #Iterate through environment datasets
     for i in range(start, start+n_envs):
         dir = './map_layers/' + exp_name + '_' + str(i) + '/'
         #Load datasets
@@ -49,11 +111,21 @@ def main(exp_name='test', n_envs=1, n_iter=10):
         map_data = (bw_inac, bw_lf, elev)
 
         #Pull incident locations for environment
-        listname = './lp_data/indexed_incidents.csv'
-        incidents = pd.read_csv(listname)
-        latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
-        areas = incidents['area'].to_numpy(dtype=np.float32())
+        i_area = np.argwhere(areas==i)
+        i_area = np.transpose(i_area)[0]
+        for incident in range(0,len(i_area)):
+            #s = 'Environment {0} - start {1} find {2}'
+            #s_lst = [i,]
+            print()
+            #For large downloads, re-size and center on IPP
+            #if(len(i_area) > 1)):
+
+
+
+        #for j in range
+
 
+        #we know initial point is at 0,0 but where is the final point...?
 
 
     return