added ability to sort shuffled observations before plotting

observations.py

@@ -7,6 +7,8 @@ import os
 import glob
 import pandas as pd
 
+is_sorted = lambda a: np.all(a[:-1] <= a[1:])
+
 def decode_observations(observation_files):
 
     # Read set of CSV files into a pandas dataframe.
@@ -98,19 +100,30 @@ if __name__ == '__main__':
     makeplots = True
     # Make plots
     if makeplots:
+
+        # If observations are shuffled, sort them again
+        ntimes = ocoords.shape[0]
+        for k in range(ntimes):
+            if is_sorted(ocoords[k,:]):
+                pass
+            else:
+                indices = np.argsort(ocoords[k,:])
+                ocoords[k,:] = ocoords[k,indices]
+                observations[k,:] = observations[k,indices]
+
         fig, ax1 = p.subplots(1,1,constrained_layout=True)
         fig.set_size_inches(4,3)
 
         tint = 300
         t = np.linspace(0,tint*(ocoords.shape[0]-1),ocoords.shape[0])[:,None]+np.zeros(ocoords.shape)
         t = t/3600.
-        c = ax1.scatter(t,ocoords,s=5,c=observations,vmin=290,vmax=296)
-        #c = ax1.pcolormesh(t,ocoords,observations,vmin=290,vmax=296)
-        #ax1.contour(t,ocoords,observations,
-        #        np.linspace(290,290+0.003*2000,13),
-        #        colors='white',
-        #        linestyles='--',
-        #        linewidths=0.75)
+        #c = ax1.scatter(t,ocoords,s=5,c=observations,vmin=290,vmax=296)
+        c = ax1.pcolormesh(t,ocoords,observations,vmin=290,vmax=296)
+        ax1.contour(t,ocoords,observations,
+                np.linspace(290,290+0.003*2000,13),
+                colors='white',
+                linestyles='--',
+                linewidths=0.75)
         ax1.set_ylim([0,2000])
         ax1.set_ylabel(r'Height (m)')
         ax1.set_xlabel(r'Time (s)')