updated read_regotrack.py to work with allocatable array outputs

examples/global-lunar-bombardment/read_regotrack.py

@@ -0,0 +1,78 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import ctem
+import os
+
+#path = '/Users/owner/Desktop/yh/ctem_20220808'
+#path = '/Users/owner/Desktop/raytest/off'
+#path = '/Users/owner/Desktop/melt_distribution/'
+path = os.getcwd()
+gridsize = 2000
+pix = 2000
+pixkm = pix / 1000
+
+# rego = ctem.util.read_linked_list_binary(path + '/rego_000001.dat', gridsize)
+# melt = ctem.util.read_linked_list_binary(path + '/melt_000001.dat', gridsize)
+# #age = ctem.util.read_linked_list_binary(path + '/age_000001.dat', gridsize, kind='SP')
+# stack = ctem.util.read_unformatted_binary(path + '/stack_000001.dat', gridsize, kind='I4B')
+
+rego = ctem.util.read_linked_list_binary(path + '/surface_rego.dat', gridsize)
+melt = ctem.util.read_linked_list_binary(path + '/surface_melt.dat', gridsize)
+#age = ctem.util.read_linked_list_binary(path + '/surface_age.dat', gridsize, kind='SP')
+stack = ctem.util.read_unformatted_binary(path + '/surface_stacknum.dat', gridsize, kind='I4B')
+
+regodepth = np.zeros((gridsize,gridsize))
+for i in np.arange(gridsize):
+    for j in np.arange(gridsize):
+        regodepth[i,j] = np.sum(rego[i,j][2:]) / 1000
+
+meltdepth = np.zeros((gridsize,gridsize))
+for i in np.arange(gridsize):
+    for j in np.arange(gridsize):
+        meltdepth[i,j] = np.sum(melt[i,j][2:]) / stack[i,j]
+
+#agearr = np.reshape(age[255,300], (60,196), order='F')
+# agearr2 = np.reshape(age[140,150], (60,86), order='F')
+# agearr3 = np.reshape(age[200,90], (60,7), order='F')
+
+dpi = 72
+x = np.arange(0,1000,200)
+
+fig = plt.figure(figsize=(gridsize/dpi, gridsize/dpi), dpi=dpi)
+ax = plt.axes([0,0,1,1])
+rd = ax.imshow(regodepth, interpolation='nearest')#, vmin=0.0, vmax=10.0)
+cbar = fig.colorbar(rd, label = 'Regolith Depth (km)')#, shrink=0.8)
+ax.invert_yaxis()
+ax.set_xlabel('x (km)')
+ax.set_ylabel('y (km)')
+xt = ax.get_xticks().tolist()
+xt[1] = pixkm*x[1]
+xt[2] = pixkm*x[2]
+xt[3] = pixkm*x[3]
+xt[4] = pixkm*x[4]
+yt = ax.get_yticks().tolist()
+yt[1] = pixkm*x[1]
+yt[2] = pixkm*x[2]
+yt[3] = pixkm*x[3]
+yt[4] = pixkm*x[4]
+ax.set_xticklabels(xt)
+ax.set_yticklabels(yt)
+
+fig2 = plt.figure(figsize=(gridsize/dpi, gridsize/dpi), dpi=dpi)
+ax2 = plt.axes([0,0,1,1])
+md = ax2.imshow(meltdepth, interpolation='nearest')#, vmin=0.0, vmax=0.5)
+cbar = fig2.colorbar(md, label = 'Melt Fraction')#, shrink=0.8)
+ax2.invert_yaxis()
+
+#xax = np.arange(60)
+#f = plt.figure()
+#a = f.add_subplot(111)
+#a.bar(xax, (agearr[:,0]/stack[255,300]))
+# f, a = plt.subplots(2,2)
+# a[0,0].bar(xax, agearr[:,73])#[0:60])
+# a[0,0].set_xlabel('Age bin')
+# a[0,0].set_ylabel('Amount of melt')
+# a[0,1].bar(xax, agearr2[:,8])#[0:60])
+# a[1,0].bar(xax, agearr2[:,9])#[0:60])
+# a[1,1].bar(xax, agearr3[:,0])#[0:60])
+plt.show()