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https://gitlab.kitware.com/vtk/vtk-m
synced 2024-09-08 13:23:51 +00:00
Update split data 3D to also support export to HDF5
This commit is contained in:
oruebel
Gunther H. Weber
parent
e9fcd7ca78
commit
8d1d713dce
@ -6,16 +6,24 @@ import sys
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# For readBOV
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from functools import reduce
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import operator
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try:
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import h5py
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USE_HDF = True
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except:
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USE_HDF = False
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# Read a 3D text file from disk into a NumPy array
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# ... Plain text
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def read_file(fn):
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"""
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Read a 3D plain text file from disk into a NumPy array
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"""
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data = np.fromfile(fn, dtype=float, sep=" ")
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data = data[3:].reshape((int(data[2]),int(data[0]),int(data[1])))
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return data
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# ... VisItBOV
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def readBOV(filename):
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"""
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Read data from a VisIt BOV file
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"""
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with open(filename, 'r') as f:
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header = dict([(lambda x: (x[0].strip().lower(), x[1].strip()))(l.strip().split(':')) for l in f.readlines()])
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if 'data_endian' in header:
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@ -32,11 +40,22 @@ def readBOV(filename):
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return (header['variable'], header['centering'].lower(), np.fromfile(dataname, dtype, count).reshape(tuple(reversed(shape))))
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return None
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# Save a block from a 3D NumPy array to disk
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# Python order is slice, row, col
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# TXT file order is row, col, slice
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# offset and size are in file order
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def save_piece(fn, array, offset, n_blocks, block_index, size):
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"""
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Save a block from a 3D NumPy array to disk.
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Python order is slice, row, col
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TXT file order is row, col, slice
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offset and size are in file order
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Args:
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fn (str): filename
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array (np.array) : Array with the full data
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offset (tuple) : Tuple of int offsets
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n_blocks (tuple) : Tuple of ints with the number of blocks per dimension
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block_index (tuple) : Tuple of ints with index of the block
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size (tuple) : Tuple of ints with the size of the block in each dimension
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"""
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with open(fn, 'w') as f:
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perm = [1, 2, 0]
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f.write('#GLOBAL_EXTENTS ' + ' '.join(map(str, [array.shape[i] for i in perm])) + '\n')
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@ -51,51 +70,79 @@ def save_piece(fn, array, offset, n_blocks, block_index, size):
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np.savetxt(f, array[s, offset[0]:offset[0]+size[0],offset[1]:offset[1]+size[1]], fmt='%.16g')
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f.write('\n')
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# Compute split points for splitting into n blocks
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def split_points(shape, nblocks):
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"""
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Compute split points for splitting into n blocks:
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Args:
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shape (int): Length of the axis
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nblocks (int): Number of blocks to split the axis into
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Return:
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List of split points along the axis
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"""
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dx = float(shape-1) / nblocks
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return [ math.floor(i*dx) for i in range(nblocks)] + [ shape - 1 ]
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if len(sys.argv) < 2:
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print("Error: Usage split_data_3d.py <filename> <outfilepattern> [<n_blocks_per_axis>|<n_blocks_x> <n_blocks_y> <n_blocks_z>]", file=sys.stderr)
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sys.exit(1)
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def save_hdf(filename, data, **kwargs):
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"""
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Save the data to HDF5.
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The axes of the data will be transposed and reorded to match the order of save_piece function.
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# Parse parameters
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in_filename = sys.argv[1]
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Args:
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filename (str) : Name fo the HDF5 file
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data (np.array): 3D array with the data
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kwargs (dict) : Dict with keyword arguments for the h5py create_dataset function
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"""
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f = h5py.File(filename, 'w')
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f.create_dataset(name='data', data=np.swapaxes(np.transpose(data), 0, 1), **kwargs)
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name, ext = os.path.splitext(in_filename)
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#out_filename_pattern = name + '_split_%d.txt'
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out_filename_pattern = sys.argv[2]
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if __name__ == '__main__':
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n_blocks = (2, 2, 2)
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if len(sys.argv) > 3:
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if len(sys.argv) >= 6:
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n_blocks = (int(sys.argv[3]), int(sys.argv[4]), int(sys.argv[5]))
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if len(sys.argv) < 2:
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print("Error: Usage split_data_3d.py <filename> <outfilepattern> [<n_blocks_per_axis>|<n_blocks_x> <n_blocks_y> <n_blocks_z>]", file=sys.stderr)
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sys.exit(1)
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# Parse parameters
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in_filename = sys.argv[1]
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name, ext = os.path.splitext(in_filename)
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#out_filename_pattern = name + '_split_%d.txt'
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out_filename_pattern = sys.argv[2]
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n_blocks = (2, 2, 2)
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if len(sys.argv) > 3:
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if len(sys.argv) >= 6:
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n_blocks = (int(sys.argv[3]), int(sys.argv[4]), int(sys.argv[5]))
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else:
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n_blocks = (int(sys.argv[3]), int(sys.argv[3]), int(sys.argv[3]))
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# Read data
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if ext == '.bov':
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data = readBOV(in_filename)[2]
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else:
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n_blocks = (int(sys.argv[3]), int(sys.argv[3]), int(sys.argv[3]))
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data = read_file(in_filename)
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# Read data
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if ext == '.bov':
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data = readBOV(in_filename)[2]
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else:
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data = read_file(in_filename)
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# export to hdf5 as well
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if USE_HDF:
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save_hdf((out_filename_pattern % 0).replace('.txt', '.h5'), data)
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# Python order is slice, row, col
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# Compute split points
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split_points_s = split_points(data.shape[0], n_blocks[2])
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split_points_r = split_points(data.shape[1], n_blocks[0])
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split_points_c = split_points(data.shape[2], n_blocks[1])
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# Python order is slice, row, col
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# Compute split points
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split_points_s = split_points(data.shape[0], n_blocks[2])
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split_points_r = split_points(data.shape[1], n_blocks[0])
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split_points_c = split_points(data.shape[2], n_blocks[1])
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# Create the file that records the slice values
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slice_filename = name + '_slices.txt'
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# Create the file that records the slice values
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slice_filename = name + '_slices.txt'
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# Save blocks
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block_no = 0
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for block_index_s, (s_start, s_stop) in enumerate(zip(split_points_s, split_points_s[1:])):
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for block_index_r, (r_start, r_stop) in enumerate(zip(split_points_r, split_points_r[1:])):
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for block_index_c, (c_start, c_stop) in enumerate(zip(split_points_c, split_points_c[1:])):
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n_s = s_stop - s_start + 1
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n_r = r_stop - r_start + 1
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n_c = c_stop - c_start + 1
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save_piece(out_filename_pattern % block_no, data, (r_start, c_start, s_start), n_blocks, (block_index_r, block_index_c, block_index_s), (n_r, n_c, n_s))
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block_no += 1
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# Save blocks
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block_no = 0
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for block_index_s, (s_start, s_stop) in enumerate(zip(split_points_s, split_points_s[1:])):
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for block_index_r, (r_start, r_stop) in enumerate(zip(split_points_r, split_points_r[1:])):
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for block_index_c, (c_start, c_stop) in enumerate(zip(split_points_c, split_points_c[1:])):
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n_s = s_stop - s_start + 1
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n_r = r_stop - r_start + 1
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n_c = c_stop - c_start + 1
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save_piece(out_filename_pattern % block_no, data, (r_start, c_start, s_start), n_blocks, (block_index_r, block_index_c, block_index_s), (n_r, n_c, n_s))
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block_no += 1
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