Ready to collect all terrains

arcgis_terrain.py

@@ -139,8 +139,8 @@ def get_terrain_map(lat_lon = [0,0], sample_dist = 10, extent = 100, heading = 0
 
     if verbosity:
         print("total calls: {}".format(np.square(cc)))
-    print("For extent of %d, we're making %d calls with window %d" % (extent,cc,sample_extents))
-
+    print("     For extent of %d, we're making %d calls with window %d" % (extent,cc,sample_extents))
+    
     # set max values
     elv_layer.extent['xmin'] = lhc_pt[0]
     elv_layer.extent['xmax'] = lhc_pt[0] + extent
@@ -154,7 +154,7 @@ def get_terrain_map(lat_lon = [0,0], sample_dist = 10, extent = 100, heading = 0
     e = np.empty([0,sc*cc])
     data = []
     for j in range(cc): # outter loop for y values
-        print('Iteration %d/%d' % (j,cc))
+        print('          Iteration %d/%d' % (j,cc))
         # lists of values for a single row, reset at every y iteration
         x_row = np.empty([sc,0])
         y_row = np.empty([sc,0])

feature_set.py

@@ -56,14 +56,14 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
     #print("Making grid of size %d" % scaled_extent)
     viz_cnt = 0
     viz_cnt_inac = 0
-    viz_map = np.zeros([scaled_extent,scaled_extent,len(name_list)])
-    viz_map_inac = np.zeros([scaled_extent,scaled_extent,len(inac_layers)])
+    viz_map = np.zeros([scaled_extent,scaled_extent,len(name_list)],dtype=np.uint8)
+    viz_map_inac = np.zeros([scaled_extent,scaled_extent,len(inac_layers)],dtype=np.uint8)
 
     for i,url in enumerate(url_list):
 
         # binary map, will use feature coords to populate (one per layer)
-        bin_map = np.zeros([scaled_extent,scaled_extent])
-        inac_bin_map = np.zeros([scaled_extent,scaled_extent])
+        bin_map = np.zeros([scaled_extent,scaled_extent],dtype=np.uint8)
+        inac_bin_map = np.zeros([scaled_extent,scaled_extent],dtype=np.uint8)
 
         #Array of rings and a spatial reference
         #Docs - https://developers.arcgis.com/python/api-reference/arcgis.geometry.html
@@ -295,10 +295,10 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
     plot_name = ''
     if plot_data:
         if(get_elev):
-            t = 'Elevation ' + case_name
+            t = 'Area ' + file_id + ' - Elevation'
             plt.title(t)
             plt.imshow(e_interp)
-            plot_name = plot_dir + 'elevation_' + case_name
+            plot_name = plot_dir + file_id + '_elevation'
             plt.savefig(plot_name)
             plt.close()
             #plt.show()
@@ -313,18 +313,18 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
             # flip y values
             # col_idx = viz_map.shape[0] - col_idx
             # plt_list.append(go.Scatter(x=row_idx, y=col_idx, mode='markers', name=name_list[i]))
-            t = name_list[i] + " " + case_name
+            t = 'Area ' + file_id + ' - ' + name_list[i]
             plt.title(t)
             plt.imshow(viz_map[:,:,i])
-            plot_name = plot_dir + name_list[i] + '_features'
+            plot_name = plot_dir + file_id + '_' + name_list[i]
             plt.savefig(plot_name)
             plt.close()
 
         for i in range(viz_map_inac.shape[-1]):
-            t = "Inacessible " + inac_layers[i] + " " + case_name
+            t = 'Area ' + file_id + ' - ' + "Inacessible " + inac_layers[i]
             plt.title(t)
             plt.imshow(viz_map_inac[:,:,i])
-            plot_name = plot_dir + 'inac_' + inac_layers[i]
+            plot_name = plot_dir + file_id + '_inac_' + inac_layers[i]
             plt.savefig(plot_name)
             plt.close()
 

lpm_hikersim.py

@@ -18,7 +18,7 @@ from LostPersonModel.main_hiker import run_replicate   #change this import to wh
 
 
 
-def main(exp_name='test', n_envs=1, n_iter=10):
+def main(exp_name='test', start_idx=0, n_envs=1, n_iter=10):
 
     test_extent = 20000
     download_extent = 40000
@@ -31,16 +31,22 @@ def main(exp_name='test', n_envs=1, n_iter=10):
     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):
+    #Limit what environments we run
+    if(n_envs < 0):
+        n_envs = np.size(np.unique(areas))
+    end_idx = start_idx + n_envs
+    if(end_idx > np.size(np.unique(areas))):
+        end_idx = np.size(np.unique(areas))
+
+    #Calculate cells from extent
+    scale_factor = 3/20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
+    extent = np.ceil(scale_factor*download_extent).astype(np.int32)
+
+
+    for i in range(start_idx, end_idx):
         #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())
@@ -58,12 +64,35 @@ def main(exp_name='test', n_envs=1, n_iter=10):
                         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:
 
+        #Actually maybe not, main_hiker takes start / find as meters positions
+        #Position Matrix
+        #Then grid is basically recreated, we have position matrix x[:] y[:] behavior[:] starts empty
+            #First index is ? second is timestep, with 2 added to end (why)
+            #I think I need to fix these. They're 1D arrays.
+
+        #Hiker Movement
+            #First x,y meters position is set to 0,0
+            #second is chosen randomly, from -1, 0, 1 so we randomly move to an adjacent cell
+            #Behavior is not initialized so you start by backtracking, to the initial position...
+
+
+        #p_behavior - probability file of hiker actions. looks like 6 bins. We use fitted profile.
+        #T - length of simulation. walking speed * simulation hours (fitted to 100 hours, 850 m/hr?)
+        #LL - map limits (3000,3000,1)
+        #alpha is smoothing
+
+        #Result is list of x, y coordinates of the hiker as well as behavior selected.
+        #I should probably rewrite the whole thing...
+        #But what I can do is walk back through the steps, adding to a 'heatmap' by doing +1 on cells that are visited
+        #Then, we normalize it all to 0-1
+
+        run_replicate(ipp, find, map, T, p_behavior, alpha, LL)
+
+
 
     return
 
@@ -137,7 +166,8 @@ def main(exp_name='test', n_envs=1, n_iter=10):
 
 if __name__ == "__main__":
 
-    n_env = 1           #Number of environments to test
-    n_iter = 10
+    start = 0       #Environment to start at (0 is first, 8 is first large map)
+    n = 1           #Number of environments to test
+    mc = 10         #Monte Carlo iterations
 
-    main()
+    main(exp_name='hiker',start_idx=start,n_envs=n,n_iter=mc)

lpm_maps.py

@@ -313,7 +313,7 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1,format_ipps=Fal
 
     #Decide if we want to save pics, and get the elev map (takes a while)
     do_plot = True
-    do_elev = False
+    do_elev = True
 
     #Loop through unique areas to download
     for i in range(start_idx, end_idx):
@@ -326,67 +326,25 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1,format_ipps=Fal
         #Now download area centered on first index of area
         print("Collecting GIS data %d/%d" % ((i-start_idx)+1,(end_idx-start_idx)))
         #Download local map
-        if(len(i_area) > 0):
+        if(len(i_area) == 1):
             size = extent_test
         else:
             size = extent_download
         ipp = ipp_latlons[i_area[0]]
+
         s = '     Getting Area {0} with IPP(s) {1} - center is {2}'
         lst = [i,i_area,ipp]
         print(s.format(*lst))
         folder_i = folder + '/' + folder + "_" + str(i)
+
+        #Call feature_set to make GIS calls
         grab_features(ipp, size, sample_dist = res,
             case_name = folder_i, save_files = False, save_to_folder = True,
             file_id = str(i), plot_data=do_plot, get_elev=do_elev)
         print("     Saved terrain in %s" % folder_i)
-    return
-
-
-    #Limit what we download
-    if(max_down < 0):
-        max_down = len(ipp_list)
-
-
-    latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
-
-
-    #Now, if there's only one key in an area we can avoid downloading a lot.
-    #Need to ouput data in a way that indicates if an IPP is a big area or not...
-    count = 0
-    do_plot = True
-    do_elev = True
-    for i in range(start_down,len(ipp_lat_list)):
-        print("Collecting GIS data %d/%d" % (i,len(ipp_lat_list)))
-        #Download local map
-        if(keys.count(i) < count_thres):
-            size = extent_test
-        else:
-            size = extent_download
-        if(count < max_down):
-            count = count + 1
-            print('Getting initial point - ')
-            print(ipp_lat_list[i])
-            folder_i = folder + "_" + str(i)
-            grab_features(ipp_lat_list[i], size, sample_dist = res, case_name = folder_i, save_files = False, save_to_folder = True, file_id = str(i), plot_data=do_plot, get_elev=do_elev)
-
 
     return
 
-
-    #Load GIS Features for new area
-
-
-    #grab_features()
-
-
-
-    #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)
-
-
 #Collect GIS maps and layers for SAR initial positions
 def main():
 
@@ -397,9 +355,8 @@ def main():
     #gis_test()
     #group_IPP(listname) #Format incident data correctly
 
-    #Friday, 7/26 - make sure we get window size of 625 with larger environment.
-    #Also, try reducing size of downloads...
-    collect_terrain(folder_name='test',start_idx=0,max_collect=1)
+    #This is ready to run now. Can we get heatmaps at the same time?
+    collect_terrain(folder_name='trust',start_idx=0,max_collect=-1)
     #investigate()