towards displaying concentration uncertainty

conc_uncertainty/ice.grids

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+/ncrc/home1/Robert.Grumbine/scratch/Verification_data/ice5min.new

conc_uncertainty/overlay_concentrations.py

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+
+
+"""
+Given N > 1 concentration grids, find average and variance. Then 
+HSV output of reslt, with H = H(average), S = S(variance) s.t.
+high saturation = low variance. V = fixed/reserved for a player to be
+named later
+
+
+"""
+
+import sys
+import os
+import datetime
+
+from math import *
+import numpy as np
+import numpy.ma as ma
+
+import netCDF4 as nc
+import pygrib
+
+import matplotlib
+import matplotlib.pyplot as plt
+#from matplotlib  import colormaps
+#from matplotlib  import colors
+
+#---------------------------------------------------
+# Read in some concentrations, find mean and sqrt(variance)
+base='/ncrc/home1/Robert.Grumbine/scratch/Verification_data/ice5min.new/'
+fn="ice5min.201903"
+
+fname = base + fn
+grbs = pygrib.open(fname)
+grbindex = pygrib.index(fname)
+#debug: print(grbindex, flush=True)
+#debug: print(grbindex.keys, flush=True)
+
+lats, lons = grbs[1].latlons()
+nlon = lats.shape[1]
+nlat = lats.shape[0]
+print("nx, ny ",nlon, nlat)
+
+sumx = np.zeros((nlat, nlon))
+sumx2 = np.zeros((nlat, nlon))
+ndays=30
+for i in range(1,ndays+1):
+  print(i, grbs[i].values.max() )
+  sumx += grbs[i].values
+  sumx2 += grbs[i].values*grbs[i].values
+
+sumx /= ndays
+sumx2 /= ndays
+print("sumx ",sumx.max(), sumx.min(), "sumx2 ",sumx2.max(), sumx2.min() )
+
+var = sumx2 - sumx*sumx
+var = np.max(var, 0)
+print("var ",var.max(), var.min() )
+sd = np.sqrt(var)
+print("sd ",sd.max(), sd.min() )
+
+
+
+
+
+#---------------------------------------------------
+#create a colormap
+#  then assign bounds
+#  then run norm to normalize vs. values of the bounds
+#  finally, plot figure and color bar
+
+colors = [
+  "#0000ff",
+  "#0000bb",
+  "#000088",
+  "#000044" ]
+my_cmap = matplotlib.colors.ListedColormap(colors, name="my_cmap")
+bounds = [1./255., 0.15, 0.40, 0.80, 1.0]
+norm = matplotlib.colors.BoundaryNorm(bounds, my_cmap.N)
+
+# Demo of the basic color bar:
+fig,ax = plt.subplots(figsize=(8,6))
+
+#fig.colorbar(
+#    matplotlib.cm.ScalarMappable(cmap=my_cmap, norm=norm),
+#    cax=ax, orientation='horizontal',
+#    extend='both',
+#    spacing='proportional'
+#  )
+
+plt.pcolormesh(sumx, cmap = my_cmap)
+fig.savefig("sumx.png")
+plt.close()
+
+#fig,ax = plt.subplots(figsize=(8,6))
+#plt.pcolormesh(sumx2, cmap = my_cmap)
+#fig.savefig("sumx2.png")
+#plt.close()
+
+
+#colorsys.rgb_to_hsv(r, g, b)