MATLAB工具箱-npy-matlab工具箱1.rar

"""Roundtrip testing of npy-matlab. Write data in NPY format to be read and rewritten using npy-matlab. Then re-read the matlab written data again and check if it is still the same. """ import os import os.path as op from subprocess import call import numpy as np import skimage.data # For numpy data types see: # https://docs.scipy.org/doc/numpy-1.15.0/user/basics.types.html SUPPORTED_DTYPES = ['uint8', 'uint16', 'uint32', 'uint64', 'int8', 'int16', 'int32', 'int64', 'float32', 'float64'] # Directory of this file test_dir = op.dirname(op.realpath(__file__)) def _save_testing_data(): """Write testing data to /tests/data. Returns ------- all_saved_data : list of tuples all_saved_data[i][0] is the path to written data all_saved_data[i][1] is actual data """ # Make a data directory data_dir = op.join(test_dir, 'data') if not op.exists(data_dir): op.mkdir(data_dir) # Test 1D data n = 10000 t = np.linspace(-10., 10., n) sine = (1 + np.sin(t)) * 64 # Test 3D data chelsea = skimage.data.chelsea() # Save all test data in NPY format all_saved_data = list() for dtype in SUPPORTED_DTYPES: sine_tr = sine.astype(dtype) fpath = op.join(data_dir, 'sine_{}.npy'.format(dtype)) np.save(fpath, sine_tr) all_saved_data.append((fpath, sine_tr)) chelsea_tr = chelsea.astype(dtype) fpath = op.join(data_dir, 'chelsea_{}.npy'.format(dtype)) np.save(fpath, chelsea_tr) all_saved_data.append((fpath, chelsea_tr)) return all_saved_data def test_roundtrip(): """Test roundtrip.""" # Write testing data from Python all_saved_data = _save_testing_data() # General command structure for calling octave # note, there is no closing `"` sign. Must be added later. main_dir = os.sep.join(test_dir.split(os.sep)[:-1]) func_dir = op.join(main_dir, 'npy-matlab') cmd_octave = 'octave --no-gui --eval "' cmd_octave += "addpath('{}');".format(func_dir) # Now for each data type and testing data: # read with octave # re-write with octave # re-read with numpy and assert it is the same for i in range(len(all_saved_data)): # Get the original data fpath = all_saved_data[i][0] original_data = all_saved_data[i][1] # Read into octave and write back from octave new_fpath = op.join(op.split(fpath)[0], 'matlab_' + op.split(fpath)[1]) cmd_read = "new_data=readNPY('{}');".format(fpath) cmd_write = "writeNPY(new_data, '{}');".format(new_fpath) cmd_complete = cmd_octave + cmd_read + cmd_write + '"' print(cmd_complete) call(cmd_complete, shell=True) # Read back with numpy and assert it's the same new_data = np.load(new_fpath) np.testing.assert_array_equal(original_data, new_data.squeeze())