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