initial upload

test/old_tests/update_input_file/data_post_process.py

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+# This script shows how to recompose the output data for further analysis
+# As the result data is collectively written out by each process, the data is composed on subgrid basis.
+# the data needs to be reindexed and reassembled to a global grid.
+
+import numpy as np
+import sys
+
+sys.path.append("../../utils/")
+from tomoatt_data_retrieval import get_data_from_h5
+
+
+#
+# plot calculated travel time field
+
+# number of grids
+nx = 50
+ny = 50
+nz = 10
+
+# number of subgrid
+ndiv_x = 2
+ndiv_y = 2
+ndiv_z = 1
+
+# read data
+_src_id = 0
+_inv_id = 0
+
+# filling 0 for digit
+inv_id = str(_inv_id).zfill(4)
+src_id = str(_src_id).zfill(4)
+
+# file name
+#fname_data = "OUTPUT_FILES/T_res_inv_{}_src_{}.dat".format(inv_id, src_id)
+fname_data = "OUTPUT_FILES/out_data_sim_{}.h5".format(_src_id)
+# path for grid data
+fname_grid = "OUTPUT_FILES/out_data_grid.h5"
+# name dataset to be read
+dataset_name = "/Data/fun_inv_{}".format(inv_id)
+
+data, r, t, p = get_data_from_h5(fname_data, fname_grid, dataset_name, nz, ny, nx, ndiv_z, ndiv_y, ndiv_x, verbose=True)
+
+# plot
+import matplotlib.pyplot as plt
+
+fig, axs = plt.subplots(3, 1, figsize=(15, 5))
+
+#axs[0].imshow(data[5,:,:], cmap='jet', interpolation='bilinear')
+ir_slice=5
+axs[0].pcolormesh(p[ir_slice,0,:], t[ir_slice,:,0], data[ir_slice,:,:], shading='auto')
+axs[0].set_title('lon-lat')
+axs[0].set_xlabel('longitude')
+axs[0].set_ylabel('latitude')
+axs[0].set_aspect(1)
+
+it_slice=15
+axs[1].pcolormesh(p[0,it_slice,:], r[:,it_slice,0], data[:,it_slice,:], shading='auto')
+axs[1].set_title('r-lon')
+axs[1].set_xlabel('longitude')
+axs[1].set_ylabel('latitude')
+axs[1].set_aspect(10)
+
+ip_slice=15
+axs[2].pcolormesh(t[0,:,ip_slice], r[:,0,ip_slice], data[:,:,ip_slice], shading='auto')
+axs[2].set_title('r-lat')
+axs[2].set_xlabel('longitude')
+axs[2].set_ylabel('latitude')
+axs[2].set_aspect(10)
+
+plt.tight_layout()
+plt.show()