1340 lines
44 KiB
Python
1340 lines
44 KiB
Python
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''' Nose test generators
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Need function load / save / roundtrip tests
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'''
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import os
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from collections import OrderedDict
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from os.path import join as pjoin, dirname
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from glob import glob
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from io import BytesIO
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import re
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from tempfile import mkdtemp
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import warnings
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import shutil
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import gzip
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from numpy.testing import (assert_array_equal, assert_array_almost_equal,
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assert_equal, assert_, assert_warns, assert_allclose)
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import pytest
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from pytest import raises as assert_raises
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import numpy as np
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from numpy import array
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import scipy.sparse as SP
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import scipy.io
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from scipy.io.matlab import MatlabOpaque, MatlabFunction, MatlabObject
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import scipy.io.matlab._byteordercodes as boc
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from scipy.io.matlab._miobase import (
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matdims, MatWriteError, MatReadError, matfile_version)
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from scipy.io.matlab._mio import mat_reader_factory, loadmat, savemat, whosmat
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from scipy.io.matlab._mio5 import (
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MatFile5Writer, MatFile5Reader, varmats_from_mat, to_writeable,
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EmptyStructMarker)
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import scipy.io.matlab._mio5_params as mio5p
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from scipy._lib._util import VisibleDeprecationWarning
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test_data_path = pjoin(dirname(__file__), 'data')
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def mlarr(*args, **kwargs):
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"""Convenience function to return matlab-compatible 2-D array."""
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arr = np.array(*args, **kwargs)
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arr.shape = matdims(arr)
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return arr
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# Define cases to test
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theta = np.pi/4*np.arange(9,dtype=float).reshape(1,9)
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case_table4 = [
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{'name': 'double',
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'classes': {'testdouble': 'double'},
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'expected': {'testdouble': theta}
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}]
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case_table4.append(
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{'name': 'string',
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'classes': {'teststring': 'char'},
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'expected': {'teststring':
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array(['"Do nine men interpret?" "Nine men," I nod.'])}
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})
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case_table4.append(
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{'name': 'complex',
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'classes': {'testcomplex': 'double'},
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'expected': {'testcomplex': np.cos(theta) + 1j*np.sin(theta)}
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})
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A = np.zeros((3,5))
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A[0] = list(range(1,6))
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A[:,0] = list(range(1,4))
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case_table4.append(
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{'name': 'matrix',
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'classes': {'testmatrix': 'double'},
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'expected': {'testmatrix': A},
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})
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case_table4.append(
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{'name': 'sparse',
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'classes': {'testsparse': 'sparse'},
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'expected': {'testsparse': SP.coo_matrix(A)},
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})
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B = A.astype(complex)
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B[0,0] += 1j
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case_table4.append(
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{'name': 'sparsecomplex',
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'classes': {'testsparsecomplex': 'sparse'},
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'expected': {'testsparsecomplex': SP.coo_matrix(B)},
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})
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case_table4.append(
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{'name': 'multi',
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'classes': {'theta': 'double', 'a': 'double'},
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'expected': {'theta': theta, 'a': A},
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})
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case_table4.append(
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{'name': 'minus',
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'classes': {'testminus': 'double'},
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'expected': {'testminus': mlarr(-1)},
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})
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case_table4.append(
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{'name': 'onechar',
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'classes': {'testonechar': 'char'},
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'expected': {'testonechar': array(['r'])},
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})
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# Cell arrays stored as object arrays
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CA = mlarr(( # tuple for object array creation
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[],
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mlarr([1]),
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mlarr([[1,2]]),
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mlarr([[1,2,3]])), dtype=object).reshape(1,-1)
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CA[0,0] = array(
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['This cell contains this string and 3 arrays of increasing length'])
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case_table5 = [
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{'name': 'cell',
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'classes': {'testcell': 'cell'},
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'expected': {'testcell': CA}}]
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CAE = mlarr(( # tuple for object array creation
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mlarr(1),
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mlarr(2),
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mlarr([]),
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mlarr([]),
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mlarr(3)), dtype=object).reshape(1,-1)
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objarr = np.empty((1,1),dtype=object)
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objarr[0,0] = mlarr(1)
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case_table5.append(
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{'name': 'scalarcell',
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'classes': {'testscalarcell': 'cell'},
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'expected': {'testscalarcell': objarr}
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})
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case_table5.append(
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{'name': 'emptycell',
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'classes': {'testemptycell': 'cell'},
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'expected': {'testemptycell': CAE}})
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case_table5.append(
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{'name': 'stringarray',
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'classes': {'teststringarray': 'char'},
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'expected': {'teststringarray': array(
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['one ', 'two ', 'three'])},
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})
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case_table5.append(
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{'name': '3dmatrix',
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'classes': {'test3dmatrix': 'double'},
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'expected': {
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'test3dmatrix': np.transpose(np.reshape(list(range(1,25)), (4,3,2)))}
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})
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st_sub_arr = array([np.sqrt(2),np.exp(1),np.pi]).reshape(1,3)
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dtype = [(n, object) for n in ['stringfield', 'doublefield', 'complexfield']]
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st1 = np.zeros((1,1), dtype)
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st1['stringfield'][0,0] = array(['Rats live on no evil star.'])
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st1['doublefield'][0,0] = st_sub_arr
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st1['complexfield'][0,0] = st_sub_arr * (1 + 1j)
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case_table5.append(
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{'name': 'struct',
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'classes': {'teststruct': 'struct'},
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'expected': {'teststruct': st1}
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})
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CN = np.zeros((1,2), dtype=object)
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CN[0,0] = mlarr(1)
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CN[0,1] = np.zeros((1,3), dtype=object)
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CN[0,1][0,0] = mlarr(2, dtype=np.uint8)
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CN[0,1][0,1] = mlarr([[3]], dtype=np.uint8)
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CN[0,1][0,2] = np.zeros((1,2), dtype=object)
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CN[0,1][0,2][0,0] = mlarr(4, dtype=np.uint8)
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CN[0,1][0,2][0,1] = mlarr(5, dtype=np.uint8)
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case_table5.append(
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{'name': 'cellnest',
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'classes': {'testcellnest': 'cell'},
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'expected': {'testcellnest': CN},
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})
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st2 = np.empty((1,1), dtype=[(n, object) for n in ['one', 'two']])
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st2[0,0]['one'] = mlarr(1)
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st2[0,0]['two'] = np.empty((1,1), dtype=[('three', object)])
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st2[0,0]['two'][0,0]['three'] = array(['number 3'])
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case_table5.append(
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{'name': 'structnest',
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'classes': {'teststructnest': 'struct'},
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'expected': {'teststructnest': st2}
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})
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a = np.empty((1,2), dtype=[(n, object) for n in ['one', 'two']])
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a[0,0]['one'] = mlarr(1)
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a[0,0]['two'] = mlarr(2)
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a[0,1]['one'] = array(['number 1'])
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a[0,1]['two'] = array(['number 2'])
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case_table5.append(
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{'name': 'structarr',
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'classes': {'teststructarr': 'struct'},
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'expected': {'teststructarr': a}
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})
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ODT = np.dtype([(n, object) for n in
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['expr', 'inputExpr', 'args',
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'isEmpty', 'numArgs', 'version']])
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MO = MatlabObject(np.zeros((1,1), dtype=ODT), 'inline')
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m0 = MO[0,0]
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m0['expr'] = array(['x'])
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m0['inputExpr'] = array([' x = INLINE_INPUTS_{1};'])
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m0['args'] = array(['x'])
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m0['isEmpty'] = mlarr(0)
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m0['numArgs'] = mlarr(1)
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m0['version'] = mlarr(1)
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case_table5.append(
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{'name': 'object',
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'classes': {'testobject': 'object'},
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'expected': {'testobject': MO}
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})
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fp_u_str = open(pjoin(test_data_path, 'japanese_utf8.txt'), 'rb')
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u_str = fp_u_str.read().decode('utf-8')
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fp_u_str.close()
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case_table5.append(
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{'name': 'unicode',
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'classes': {'testunicode': 'char'},
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'expected': {'testunicode': array([u_str])}
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})
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case_table5.append(
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{'name': 'sparse',
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'classes': {'testsparse': 'sparse'},
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'expected': {'testsparse': SP.coo_matrix(A)},
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})
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case_table5.append(
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{'name': 'sparsecomplex',
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'classes': {'testsparsecomplex': 'sparse'},
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'expected': {'testsparsecomplex': SP.coo_matrix(B)},
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})
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case_table5.append(
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{'name': 'bool',
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'classes': {'testbools': 'logical'},
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'expected': {'testbools':
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array([[True], [False]])},
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})
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case_table5_rt = case_table5[:]
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# Inline functions can't be concatenated in matlab, so RT only
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case_table5_rt.append(
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{'name': 'objectarray',
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'classes': {'testobjectarray': 'object'},
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'expected': {'testobjectarray': np.repeat(MO, 2).reshape(1,2)}})
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def types_compatible(var1, var2):
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"""Check if types are same or compatible.
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0-D numpy scalars are compatible with bare python scalars.
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"""
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type1 = type(var1)
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type2 = type(var2)
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if type1 is type2:
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return True
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if type1 is np.ndarray and var1.shape == ():
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return type(var1.item()) is type2
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if type2 is np.ndarray and var2.shape == ():
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return type(var2.item()) is type1
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return False
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def _check_level(label, expected, actual):
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""" Check one level of a potentially nested array """
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if SP.issparse(expected): # allow different types of sparse matrices
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assert_(SP.issparse(actual))
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assert_array_almost_equal(actual.toarray(),
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expected.toarray(),
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err_msg=label,
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decimal=5)
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return
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# Check types are as expected
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assert_(types_compatible(expected, actual),
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f"Expected type {type(expected)}, got {type(actual)} at {label}")
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# A field in a record array may not be an ndarray
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# A scalar from a record array will be type np.void
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if not isinstance(expected,
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(np.void, np.ndarray, MatlabObject)):
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assert_equal(expected, actual)
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return
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# This is an ndarray-like thing
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assert_(expected.shape == actual.shape,
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msg=f'Expected shape {expected.shape}, got {actual.shape} at {label}')
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ex_dtype = expected.dtype
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if ex_dtype.hasobject: # array of objects
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if isinstance(expected, MatlabObject):
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assert_equal(expected.classname, actual.classname)
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for i, ev in enumerate(expected):
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level_label = "%s, [%d], " % (label, i)
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_check_level(level_label, ev, actual[i])
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return
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if ex_dtype.fields: # probably recarray
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for fn in ex_dtype.fields:
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level_label = f"{label}, field {fn}, "
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_check_level(level_label,
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expected[fn], actual[fn])
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return
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if ex_dtype.type in (str, # string or bool
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np.str_,
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np.bool_):
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assert_equal(actual, expected, err_msg=label)
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return
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# Something numeric
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assert_array_almost_equal(actual, expected, err_msg=label, decimal=5)
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def _load_check_case(name, files, case):
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for file_name in files:
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matdict = loadmat(file_name, struct_as_record=True)
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label = f"test {name}; file {file_name}"
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for k, expected in case.items():
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k_label = f"{label}, variable {k}"
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assert_(k in matdict, "Missing key at %s" % k_label)
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_check_level(k_label, expected, matdict[k])
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def _whos_check_case(name, files, case, classes):
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for file_name in files:
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label = f"test {name}; file {file_name}"
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whos = whosmat(file_name)
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expected_whos = [
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(k, expected.shape, classes[k]) for k, expected in case.items()]
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whos.sort()
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expected_whos.sort()
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assert_equal(whos, expected_whos,
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f"{label}: {whos!r} != {expected_whos!r}"
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)
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# Round trip tests
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def _rt_check_case(name, expected, format):
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mat_stream = BytesIO()
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savemat(mat_stream, expected, format=format)
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mat_stream.seek(0)
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_load_check_case(name, [mat_stream], expected)
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# generator for tests
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def _cases(version, filt='test%(name)s_*.mat'):
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if version == '4':
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cases = case_table4
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elif version == '5':
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cases = case_table5
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else:
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assert version == '5_rt'
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cases = case_table5_rt
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for case in cases:
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name = case['name']
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expected = case['expected']
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if filt is None:
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files = None
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else:
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use_filt = pjoin(test_data_path, filt % dict(name=name))
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files = glob(use_filt)
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assert len(files) > 0, \
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f"No files for test {name} using filter {filt}"
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classes = case['classes']
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yield name, files, expected, classes
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|
||
|
|
@pytest.mark.parametrize('version', ('4', '5'))
|
||
|
|
def test_load(version):
|
||
|
|
for case in _cases(version):
|
||
|
|
_load_check_case(*case[:3])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('version', ('4', '5'))
|
||
|
|
def test_whos(version):
|
||
|
|
for case in _cases(version):
|
||
|
|
_whos_check_case(*case)
|
||
|
|
|
||
|
|
|
||
|
|
# generator for round trip tests
|
||
|
|
@pytest.mark.parametrize('version, fmts', [
|
||
|
|
('4', ['4', '5']),
|
||
|
|
('5_rt', ['5']),
|
||
|
|
])
|
||
|
|
def test_round_trip(version, fmts):
|
||
|
|
for case in _cases(version, filt=None):
|
||
|
|
for fmt in fmts:
|
||
|
|
_rt_check_case(case[0], case[2], fmt)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gzip_simple():
|
||
|
|
xdense = np.zeros((20,20))
|
||
|
|
xdense[2,3] = 2.3
|
||
|
|
xdense[4,5] = 4.5
|
||
|
|
x = SP.csc_matrix(xdense)
|
||
|
|
|
||
|
|
name = 'gzip_test'
|
||
|
|
expected = {'x':x}
|
||
|
|
format = '4'
|
||
|
|
|
||
|
|
tmpdir = mkdtemp()
|
||
|
|
try:
|
||
|
|
fname = pjoin(tmpdir,name)
|
||
|
|
mat_stream = gzip.open(fname, mode='wb')
|
||
|
|
savemat(mat_stream, expected, format=format)
|
||
|
|
mat_stream.close()
|
||
|
|
|
||
|
|
mat_stream = gzip.open(fname, mode='rb')
|
||
|
|
actual = loadmat(mat_stream, struct_as_record=True)
|
||
|
|
mat_stream.close()
|
||
|
|
finally:
|
||
|
|
shutil.rmtree(tmpdir)
|
||
|
|
|
||
|
|
assert_array_almost_equal(actual['x'].toarray(),
|
||
|
|
expected['x'].toarray(),
|
||
|
|
err_msg=repr(actual))
|
||
|
|
|
||
|
|
|
||
|
|
def test_multiple_open():
|
||
|
|
# Ticket #1039, on Windows: check that files are not left open
|
||
|
|
tmpdir = mkdtemp()
|
||
|
|
try:
|
||
|
|
x = dict(x=np.zeros((2, 2)))
|
||
|
|
|
||
|
|
fname = pjoin(tmpdir, "a.mat")
|
||
|
|
|
||
|
|
# Check that file is not left open
|
||
|
|
savemat(fname, x)
|
||
|
|
os.unlink(fname)
|
||
|
|
savemat(fname, x)
|
||
|
|
loadmat(fname)
|
||
|
|
os.unlink(fname)
|
||
|
|
|
||
|
|
# Check that stream is left open
|
||
|
|
f = open(fname, 'wb')
|
||
|
|
savemat(f, x)
|
||
|
|
f.seek(0)
|
||
|
|
f.close()
|
||
|
|
|
||
|
|
f = open(fname, 'rb')
|
||
|
|
loadmat(f)
|
||
|
|
f.seek(0)
|
||
|
|
f.close()
|
||
|
|
finally:
|
||
|
|
shutil.rmtree(tmpdir)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mat73():
|
||
|
|
# Check any hdf5 files raise an error
|
||
|
|
filenames = glob(
|
||
|
|
pjoin(test_data_path, 'testhdf5*.mat'))
|
||
|
|
assert_(len(filenames) > 0)
|
||
|
|
for filename in filenames:
|
||
|
|
fp = open(filename, 'rb')
|
||
|
|
assert_raises(NotImplementedError,
|
||
|
|
loadmat,
|
||
|
|
fp,
|
||
|
|
struct_as_record=True)
|
||
|
|
fp.close()
|
||
|
|
|
||
|
|
|
||
|
|
def test_warnings():
|
||
|
|
# This test is an echo of the previous behavior, which was to raise a
|
||
|
|
# warning if the user triggered a search for mat files on the Python system
|
||
|
|
# path. We can remove the test in the next version after upcoming (0.13).
|
||
|
|
fname = pjoin(test_data_path, 'testdouble_7.1_GLNX86.mat')
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter('error')
|
||
|
|
# This should not generate a warning
|
||
|
|
loadmat(fname, struct_as_record=True)
|
||
|
|
# This neither
|
||
|
|
loadmat(fname, struct_as_record=False)
|
||
|
|
|
||
|
|
|
||
|
|
def test_regression_653():
|
||
|
|
# Saving a dictionary with only invalid keys used to raise an error. Now we
|
||
|
|
# save this as an empty struct in matlab space.
|
||
|
|
sio = BytesIO()
|
||
|
|
savemat(sio, {'d':{1:2}}, format='5')
|
||
|
|
back = loadmat(sio)['d']
|
||
|
|
# Check we got an empty struct equivalent
|
||
|
|
assert_equal(back.shape, (1,1))
|
||
|
|
assert_equal(back.dtype, np.dtype(object))
|
||
|
|
assert_(back[0,0] is None)
|
||
|
|
|
||
|
|
|
||
|
|
def test_structname_len():
|
||
|
|
# Test limit for length of field names in structs
|
||
|
|
lim = 31
|
||
|
|
fldname = 'a' * lim
|
||
|
|
st1 = np.zeros((1,1), dtype=[(fldname, object)])
|
||
|
|
savemat(BytesIO(), {'longstruct': st1}, format='5')
|
||
|
|
fldname = 'a' * (lim+1)
|
||
|
|
st1 = np.zeros((1,1), dtype=[(fldname, object)])
|
||
|
|
assert_raises(ValueError, savemat, BytesIO(),
|
||
|
|
{'longstruct': st1}, format='5')
|
||
|
|
|
||
|
|
|
||
|
|
def test_4_and_long_field_names_incompatible():
|
||
|
|
# Long field names option not supported in 4
|
||
|
|
my_struct = np.zeros((1,1),dtype=[('my_fieldname',object)])
|
||
|
|
assert_raises(ValueError, savemat, BytesIO(),
|
||
|
|
{'my_struct':my_struct}, format='4', long_field_names=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_long_field_names():
|
||
|
|
# Test limit for length of field names in structs
|
||
|
|
lim = 63
|
||
|
|
fldname = 'a' * lim
|
||
|
|
st1 = np.zeros((1,1), dtype=[(fldname, object)])
|
||
|
|
savemat(BytesIO(), {'longstruct': st1}, format='5',long_field_names=True)
|
||
|
|
fldname = 'a' * (lim+1)
|
||
|
|
st1 = np.zeros((1,1), dtype=[(fldname, object)])
|
||
|
|
assert_raises(ValueError, savemat, BytesIO(),
|
||
|
|
{'longstruct': st1}, format='5',long_field_names=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_long_field_names_in_struct():
|
||
|
|
# Regression test - long_field_names was erased if you passed a struct
|
||
|
|
# within a struct
|
||
|
|
lim = 63
|
||
|
|
fldname = 'a' * lim
|
||
|
|
cell = np.ndarray((1,2),dtype=object)
|
||
|
|
st1 = np.zeros((1,1), dtype=[(fldname, object)])
|
||
|
|
cell[0,0] = st1
|
||
|
|
cell[0,1] = st1
|
||
|
|
savemat(BytesIO(), {'longstruct': cell}, format='5',long_field_names=True)
|
||
|
|
#
|
||
|
|
# Check to make sure it fails with long field names off
|
||
|
|
#
|
||
|
|
assert_raises(ValueError, savemat, BytesIO(),
|
||
|
|
{'longstruct': cell}, format='5', long_field_names=False)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cell_with_one_thing_in_it():
|
||
|
|
# Regression test - make a cell array that's 1 x 2 and put two
|
||
|
|
# strings in it. It works. Make a cell array that's 1 x 1 and put
|
||
|
|
# a string in it. It should work but, in the old days, it didn't.
|
||
|
|
cells = np.ndarray((1,2),dtype=object)
|
||
|
|
cells[0,0] = 'Hello'
|
||
|
|
cells[0,1] = 'World'
|
||
|
|
savemat(BytesIO(), {'x': cells}, format='5')
|
||
|
|
|
||
|
|
cells = np.ndarray((1,1),dtype=object)
|
||
|
|
cells[0,0] = 'Hello, world'
|
||
|
|
savemat(BytesIO(), {'x': cells}, format='5')
|
||
|
|
|
||
|
|
|
||
|
|
def test_writer_properties():
|
||
|
|
# Tests getting, setting of properties of matrix writer
|
||
|
|
mfw = MatFile5Writer(BytesIO())
|
||
|
|
assert_equal(mfw.global_vars, [])
|
||
|
|
mfw.global_vars = ['avar']
|
||
|
|
assert_equal(mfw.global_vars, ['avar'])
|
||
|
|
assert_equal(mfw.unicode_strings, False)
|
||
|
|
mfw.unicode_strings = True
|
||
|
|
assert_equal(mfw.unicode_strings, True)
|
||
|
|
assert_equal(mfw.long_field_names, False)
|
||
|
|
mfw.long_field_names = True
|
||
|
|
assert_equal(mfw.long_field_names, True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_use_small_element():
|
||
|
|
# Test whether we're using small data element or not
|
||
|
|
sio = BytesIO()
|
||
|
|
wtr = MatFile5Writer(sio)
|
||
|
|
# First check size for no sde for name
|
||
|
|
arr = np.zeros(10)
|
||
|
|
wtr.put_variables({'aaaaa': arr})
|
||
|
|
w_sz = len(sio.getvalue())
|
||
|
|
# Check small name results in largish difference in size
|
||
|
|
sio.truncate(0)
|
||
|
|
sio.seek(0)
|
||
|
|
wtr.put_variables({'aaaa': arr})
|
||
|
|
assert_(w_sz - len(sio.getvalue()) > 4)
|
||
|
|
# Whereas increasing name size makes less difference
|
||
|
|
sio.truncate(0)
|
||
|
|
sio.seek(0)
|
||
|
|
wtr.put_variables({'aaaaaa': arr})
|
||
|
|
assert_(len(sio.getvalue()) - w_sz < 4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_save_dict():
|
||
|
|
# Test that both dict and OrderedDict can be saved (as recarray),
|
||
|
|
# loaded as matstruct, and preserve order
|
||
|
|
ab_exp = np.array([[(1, 2)]], dtype=[('a', object), ('b', object)])
|
||
|
|
for dict_type in (dict, OrderedDict):
|
||
|
|
# Initialize with tuples to keep order
|
||
|
|
d = dict_type([('a', 1), ('b', 2)])
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'dict': d})
|
||
|
|
stream.seek(0)
|
||
|
|
vals = loadmat(stream)['dict']
|
||
|
|
assert_equal(vals.dtype.names, ('a', 'b'))
|
||
|
|
assert_array_equal(vals, ab_exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_1d_shape():
|
||
|
|
# New 5 behavior is 1D -> row vector
|
||
|
|
arr = np.arange(5)
|
||
|
|
for format in ('4', '5'):
|
||
|
|
# Column is the default
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'oned': arr}, format=format)
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_equal(vals['oned'].shape, (1, 5))
|
||
|
|
# can be explicitly 'column' for oned_as
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'oned':arr},
|
||
|
|
format=format,
|
||
|
|
oned_as='column')
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_equal(vals['oned'].shape, (5,1))
|
||
|
|
# but different from 'row'
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'oned':arr},
|
||
|
|
format=format,
|
||
|
|
oned_as='row')
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_equal(vals['oned'].shape, (1,5))
|
||
|
|
|
||
|
|
|
||
|
|
def test_compression():
|
||
|
|
arr = np.zeros(100).reshape((5,20))
|
||
|
|
arr[2,10] = 1
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'arr':arr})
|
||
|
|
raw_len = len(stream.getvalue())
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_array_equal(vals['arr'], arr)
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'arr':arr}, do_compression=True)
|
||
|
|
compressed_len = len(stream.getvalue())
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_array_equal(vals['arr'], arr)
|
||
|
|
assert_(raw_len > compressed_len)
|
||
|
|
# Concatenate, test later
|
||
|
|
arr2 = arr.copy()
|
||
|
|
arr2[0,0] = 1
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'arr':arr, 'arr2':arr2}, do_compression=False)
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_array_equal(vals['arr2'], arr2)
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'arr':arr, 'arr2':arr2}, do_compression=True)
|
||
|
|
vals = loadmat(stream)
|
||
|
|
assert_array_equal(vals['arr2'], arr2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_single_object():
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'A':np.array(1, dtype=object)})
|
||
|
|
|
||
|
|
|
||
|
|
def test_skip_variable():
|
||
|
|
# Test skipping over the first of two variables in a MAT file
|
||
|
|
# using mat_reader_factory and put_variables to read them in.
|
||
|
|
#
|
||
|
|
# This is a regression test of a problem that's caused by
|
||
|
|
# using the compressed file reader seek instead of the raw file
|
||
|
|
# I/O seek when skipping over a compressed chunk.
|
||
|
|
#
|
||
|
|
# The problem arises when the chunk is large: this file has
|
||
|
|
# a 256x256 array of random (uncompressible) doubles.
|
||
|
|
#
|
||
|
|
filename = pjoin(test_data_path,'test_skip_variable.mat')
|
||
|
|
#
|
||
|
|
# Prove that it loads with loadmat
|
||
|
|
#
|
||
|
|
d = loadmat(filename, struct_as_record=True)
|
||
|
|
assert_('first' in d)
|
||
|
|
assert_('second' in d)
|
||
|
|
#
|
||
|
|
# Make the factory
|
||
|
|
#
|
||
|
|
factory, file_opened = mat_reader_factory(filename, struct_as_record=True)
|
||
|
|
#
|
||
|
|
# This is where the factory breaks with an error in MatMatrixGetter.to_next
|
||
|
|
#
|
||
|
|
d = factory.get_variables('second')
|
||
|
|
assert_('second' in d)
|
||
|
|
factory.mat_stream.close()
|
||
|
|
|
||
|
|
|
||
|
|
def test_empty_struct():
|
||
|
|
# ticket 885
|
||
|
|
filename = pjoin(test_data_path,'test_empty_struct.mat')
|
||
|
|
# before ticket fix, this would crash with ValueError, empty data
|
||
|
|
# type
|
||
|
|
d = loadmat(filename, struct_as_record=True)
|
||
|
|
a = d['a']
|
||
|
|
assert_equal(a.shape, (1,1))
|
||
|
|
assert_equal(a.dtype, np.dtype(object))
|
||
|
|
assert_(a[0,0] is None)
|
||
|
|
stream = BytesIO()
|
||
|
|
arr = np.array((), dtype='U')
|
||
|
|
# before ticket fix, this used to give data type not understood
|
||
|
|
savemat(stream, {'arr':arr})
|
||
|
|
d = loadmat(stream)
|
||
|
|
a2 = d['arr']
|
||
|
|
assert_array_equal(a2, arr)
|
||
|
|
|
||
|
|
|
||
|
|
def test_save_empty_dict():
|
||
|
|
# saving empty dict also gives empty struct
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'arr': {}})
|
||
|
|
d = loadmat(stream)
|
||
|
|
a = d['arr']
|
||
|
|
assert_equal(a.shape, (1,1))
|
||
|
|
assert_equal(a.dtype, np.dtype(object))
|
||
|
|
assert_(a[0,0] is None)
|
||
|
|
|
||
|
|
|
||
|
|
def assert_any_equal(output, alternatives):
|
||
|
|
""" Assert `output` is equal to at least one element in `alternatives`
|
||
|
|
"""
|
||
|
|
one_equal = False
|
||
|
|
for expected in alternatives:
|
||
|
|
if np.all(output == expected):
|
||
|
|
one_equal = True
|
||
|
|
break
|
||
|
|
assert_(one_equal)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_writeable():
|
||
|
|
# Test to_writeable function
|
||
|
|
res = to_writeable(np.array([1])) # pass through ndarrays
|
||
|
|
assert_equal(res.shape, (1,))
|
||
|
|
assert_array_equal(res, 1)
|
||
|
|
# Dict fields can be written in any order
|
||
|
|
expected1 = np.array([(1, 2)], dtype=[('a', '|O8'), ('b', '|O8')])
|
||
|
|
expected2 = np.array([(2, 1)], dtype=[('b', '|O8'), ('a', '|O8')])
|
||
|
|
alternatives = (expected1, expected2)
|
||
|
|
assert_any_equal(to_writeable({'a':1,'b':2}), alternatives)
|
||
|
|
# Fields with underscores discarded
|
||
|
|
assert_any_equal(to_writeable({'a':1,'b':2, '_c':3}), alternatives)
|
||
|
|
# Not-string fields discarded
|
||
|
|
assert_any_equal(to_writeable({'a':1,'b':2, 100:3}), alternatives)
|
||
|
|
# String fields that are valid Python identifiers discarded
|
||
|
|
assert_any_equal(to_writeable({'a':1,'b':2, '99':3}), alternatives)
|
||
|
|
# Object with field names is equivalent
|
||
|
|
|
||
|
|
class klass:
|
||
|
|
pass
|
||
|
|
|
||
|
|
c = klass
|
||
|
|
c.a = 1
|
||
|
|
c.b = 2
|
||
|
|
assert_any_equal(to_writeable(c), alternatives)
|
||
|
|
# empty list and tuple go to empty array
|
||
|
|
res = to_writeable([])
|
||
|
|
assert_equal(res.shape, (0,))
|
||
|
|
assert_equal(res.dtype.type, np.float64)
|
||
|
|
res = to_writeable(())
|
||
|
|
assert_equal(res.shape, (0,))
|
||
|
|
assert_equal(res.dtype.type, np.float64)
|
||
|
|
# None -> None
|
||
|
|
assert_(to_writeable(None) is None)
|
||
|
|
# String to strings
|
||
|
|
assert_equal(to_writeable('a string').dtype.type, np.str_)
|
||
|
|
# Scalars to numpy to NumPy scalars
|
||
|
|
res = to_writeable(1)
|
||
|
|
assert_equal(res.shape, ())
|
||
|
|
assert_equal(res.dtype.type, np.array(1).dtype.type)
|
||
|
|
assert_array_equal(res, 1)
|
||
|
|
# Empty dict returns EmptyStructMarker
|
||
|
|
assert_(to_writeable({}) is EmptyStructMarker)
|
||
|
|
# Object does not have (even empty) __dict__
|
||
|
|
assert_(to_writeable(object()) is None)
|
||
|
|
# Custom object does have empty __dict__, returns EmptyStructMarker
|
||
|
|
|
||
|
|
class C:
|
||
|
|
pass
|
||
|
|
|
||
|
|
assert_(to_writeable(c()) is EmptyStructMarker)
|
||
|
|
# dict keys with legal characters are convertible
|
||
|
|
res = to_writeable({'a': 1})['a']
|
||
|
|
assert_equal(res.shape, (1,))
|
||
|
|
assert_equal(res.dtype.type, np.object_)
|
||
|
|
# Only fields with illegal characters, falls back to EmptyStruct
|
||
|
|
assert_(to_writeable({'1':1}) is EmptyStructMarker)
|
||
|
|
assert_(to_writeable({'_a':1}) is EmptyStructMarker)
|
||
|
|
# Unless there are valid fields, in which case structured array
|
||
|
|
assert_equal(to_writeable({'1':1, 'f': 2}),
|
||
|
|
np.array([(2,)], dtype=[('f', '|O8')]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_recarray():
|
||
|
|
# check roundtrip of structured array
|
||
|
|
dt = [('f1', 'f8'),
|
||
|
|
('f2', 'S10')]
|
||
|
|
arr = np.zeros((2,), dtype=dt)
|
||
|
|
arr[0]['f1'] = 0.5
|
||
|
|
arr[0]['f2'] = 'python'
|
||
|
|
arr[1]['f1'] = 99
|
||
|
|
arr[1]['f2'] = 'not perl'
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'arr': arr})
|
||
|
|
d = loadmat(stream, struct_as_record=False)
|
||
|
|
a20 = d['arr'][0,0]
|
||
|
|
assert_equal(a20.f1, 0.5)
|
||
|
|
assert_equal(a20.f2, 'python')
|
||
|
|
d = loadmat(stream, struct_as_record=True)
|
||
|
|
a20 = d['arr'][0,0]
|
||
|
|
assert_equal(a20['f1'], 0.5)
|
||
|
|
assert_equal(a20['f2'], 'python')
|
||
|
|
# structs always come back as object types
|
||
|
|
assert_equal(a20.dtype, np.dtype([('f1', 'O'),
|
||
|
|
('f2', 'O')]))
|
||
|
|
a21 = d['arr'].flat[1]
|
||
|
|
assert_equal(a21['f1'], 99)
|
||
|
|
assert_equal(a21['f2'], 'not perl')
|
||
|
|
|
||
|
|
|
||
|
|
def test_save_object():
|
||
|
|
class C:
|
||
|
|
pass
|
||
|
|
c = C()
|
||
|
|
c.field1 = 1
|
||
|
|
c.field2 = 'a string'
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'c': c})
|
||
|
|
d = loadmat(stream, struct_as_record=False)
|
||
|
|
c2 = d['c'][0,0]
|
||
|
|
assert_equal(c2.field1, 1)
|
||
|
|
assert_equal(c2.field2, 'a string')
|
||
|
|
d = loadmat(stream, struct_as_record=True)
|
||
|
|
c2 = d['c'][0,0]
|
||
|
|
assert_equal(c2['field1'], 1)
|
||
|
|
assert_equal(c2['field2'], 'a string')
|
||
|
|
|
||
|
|
|
||
|
|
def test_read_opts():
|
||
|
|
# tests if read is seeing option sets, at initialization and after
|
||
|
|
# initialization
|
||
|
|
arr = np.arange(6).reshape(1,6)
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'a': arr})
|
||
|
|
rdr = MatFile5Reader(stream)
|
||
|
|
back_dict = rdr.get_variables()
|
||
|
|
rarr = back_dict['a']
|
||
|
|
assert_array_equal(rarr, arr)
|
||
|
|
rdr = MatFile5Reader(stream, squeeze_me=True)
|
||
|
|
assert_array_equal(rdr.get_variables()['a'], arr.reshape((6,)))
|
||
|
|
rdr.squeeze_me = False
|
||
|
|
assert_array_equal(rarr, arr)
|
||
|
|
rdr = MatFile5Reader(stream, byte_order=boc.native_code)
|
||
|
|
assert_array_equal(rdr.get_variables()['a'], arr)
|
||
|
|
# inverted byte code leads to error on read because of swapped
|
||
|
|
# header etc.
|
||
|
|
rdr = MatFile5Reader(stream, byte_order=boc.swapped_code)
|
||
|
|
assert_raises(Exception, rdr.get_variables)
|
||
|
|
rdr.byte_order = boc.native_code
|
||
|
|
assert_array_equal(rdr.get_variables()['a'], arr)
|
||
|
|
arr = np.array(['a string'])
|
||
|
|
stream.truncate(0)
|
||
|
|
stream.seek(0)
|
||
|
|
savemat(stream, {'a': arr})
|
||
|
|
rdr = MatFile5Reader(stream)
|
||
|
|
assert_array_equal(rdr.get_variables()['a'], arr)
|
||
|
|
rdr = MatFile5Reader(stream, chars_as_strings=False)
|
||
|
|
carr = np.atleast_2d(np.array(list(arr.item()), dtype='U1'))
|
||
|
|
assert_array_equal(rdr.get_variables()['a'], carr)
|
||
|
|
rdr.chars_as_strings = True
|
||
|
|
assert_array_equal(rdr.get_variables()['a'], arr)
|
||
|
|
|
||
|
|
|
||
|
|
def test_empty_string():
|
||
|
|
# make sure reading empty string does not raise error
|
||
|
|
estring_fname = pjoin(test_data_path, 'single_empty_string.mat')
|
||
|
|
fp = open(estring_fname, 'rb')
|
||
|
|
rdr = MatFile5Reader(fp)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
fp.close()
|
||
|
|
assert_array_equal(d['a'], np.array([], dtype='U1'))
|
||
|
|
# Empty string round trip. Matlab cannot distinguish
|
||
|
|
# between a string array that is empty, and a string array
|
||
|
|
# containing a single empty string, because it stores strings as
|
||
|
|
# arrays of char. There is no way of having an array of char that
|
||
|
|
# is not empty, but contains an empty string.
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'a': np.array([''])})
|
||
|
|
rdr = MatFile5Reader(stream)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
assert_array_equal(d['a'], np.array([], dtype='U1'))
|
||
|
|
stream.truncate(0)
|
||
|
|
stream.seek(0)
|
||
|
|
savemat(stream, {'a': np.array([], dtype='U1')})
|
||
|
|
rdr = MatFile5Reader(stream)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
assert_array_equal(d['a'], np.array([], dtype='U1'))
|
||
|
|
stream.close()
|
||
|
|
|
||
|
|
|
||
|
|
def test_corrupted_data():
|
||
|
|
import zlib
|
||
|
|
for exc, fname in [(ValueError, 'corrupted_zlib_data.mat'),
|
||
|
|
(zlib.error, 'corrupted_zlib_checksum.mat')]:
|
||
|
|
with open(pjoin(test_data_path, fname), 'rb') as fp:
|
||
|
|
rdr = MatFile5Reader(fp)
|
||
|
|
assert_raises(exc, rdr.get_variables)
|
||
|
|
|
||
|
|
|
||
|
|
def test_corrupted_data_check_can_be_disabled():
|
||
|
|
with open(pjoin(test_data_path, 'corrupted_zlib_data.mat'), 'rb') as fp:
|
||
|
|
rdr = MatFile5Reader(fp, verify_compressed_data_integrity=False)
|
||
|
|
rdr.get_variables()
|
||
|
|
|
||
|
|
|
||
|
|
def test_read_both_endian():
|
||
|
|
# make sure big- and little- endian data is read correctly
|
||
|
|
for fname in ('big_endian.mat', 'little_endian.mat'):
|
||
|
|
fp = open(pjoin(test_data_path, fname), 'rb')
|
||
|
|
rdr = MatFile5Reader(fp)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
fp.close()
|
||
|
|
assert_array_equal(d['strings'],
|
||
|
|
np.array([['hello'],
|
||
|
|
['world']], dtype=object))
|
||
|
|
assert_array_equal(d['floats'],
|
||
|
|
np.array([[2., 3.],
|
||
|
|
[3., 4.]], dtype=np.float32))
|
||
|
|
|
||
|
|
|
||
|
|
def test_write_opposite_endian():
|
||
|
|
# We don't support writing opposite endian .mat files, but we need to behave
|
||
|
|
# correctly if the user supplies an other-endian NumPy array to write out.
|
||
|
|
float_arr = np.array([[2., 3.],
|
||
|
|
[3., 4.]])
|
||
|
|
int_arr = np.arange(6).reshape((2, 3))
|
||
|
|
uni_arr = np.array(['hello', 'world'], dtype='U')
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {
|
||
|
|
'floats': float_arr.byteswap().view(float_arr.dtype.newbyteorder()),
|
||
|
|
'ints': int_arr.byteswap().view(int_arr.dtype.newbyteorder()),
|
||
|
|
'uni_arr': uni_arr.byteswap().view(uni_arr.dtype.newbyteorder()),
|
||
|
|
})
|
||
|
|
rdr = MatFile5Reader(stream)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
assert_array_equal(d['floats'], float_arr)
|
||
|
|
assert_array_equal(d['ints'], int_arr)
|
||
|
|
assert_array_equal(d['uni_arr'], uni_arr)
|
||
|
|
stream.close()
|
||
|
|
|
||
|
|
|
||
|
|
def test_logical_array():
|
||
|
|
# The roundtrip test doesn't verify that we load the data up with the
|
||
|
|
# correct (bool) dtype
|
||
|
|
with open(pjoin(test_data_path, 'testbool_8_WIN64.mat'), 'rb') as fobj:
|
||
|
|
rdr = MatFile5Reader(fobj, mat_dtype=True)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
x = np.array([[True], [False]], dtype=np.bool_)
|
||
|
|
assert_array_equal(d['testbools'], x)
|
||
|
|
assert_equal(d['testbools'].dtype, x.dtype)
|
||
|
|
|
||
|
|
|
||
|
|
def test_logical_out_type():
|
||
|
|
# Confirm that bool type written as uint8, uint8 class
|
||
|
|
# See gh-4022
|
||
|
|
stream = BytesIO()
|
||
|
|
barr = np.array([False, True, False])
|
||
|
|
savemat(stream, {'barray': barr})
|
||
|
|
stream.seek(0)
|
||
|
|
reader = MatFile5Reader(stream)
|
||
|
|
reader.initialize_read()
|
||
|
|
reader.read_file_header()
|
||
|
|
hdr, _ = reader.read_var_header()
|
||
|
|
assert_equal(hdr.mclass, mio5p.mxUINT8_CLASS)
|
||
|
|
assert_equal(hdr.is_logical, True)
|
||
|
|
var = reader.read_var_array(hdr, False)
|
||
|
|
assert_equal(var.dtype.type, np.uint8)
|
||
|
|
|
||
|
|
|
||
|
|
def test_roundtrip_zero_dimensions():
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'d':np.empty((10, 0))})
|
||
|
|
d = loadmat(stream)
|
||
|
|
assert d['d'].shape == (10, 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mat4_3d():
|
||
|
|
# test behavior when writing 3-D arrays to matlab 4 files
|
||
|
|
stream = BytesIO()
|
||
|
|
arr = np.arange(24).reshape((2,3,4))
|
||
|
|
assert_raises(ValueError, savemat, stream, {'a': arr}, True, '4')
|
||
|
|
|
||
|
|
|
||
|
|
def test_func_read():
|
||
|
|
func_eg = pjoin(test_data_path, 'testfunc_7.4_GLNX86.mat')
|
||
|
|
fp = open(func_eg, 'rb')
|
||
|
|
rdr = MatFile5Reader(fp)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
fp.close()
|
||
|
|
assert isinstance(d['testfunc'], MatlabFunction)
|
||
|
|
stream = BytesIO()
|
||
|
|
wtr = MatFile5Writer(stream)
|
||
|
|
assert_raises(MatWriteError, wtr.put_variables, d)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mat_dtype():
|
||
|
|
double_eg = pjoin(test_data_path, 'testmatrix_6.1_SOL2.mat')
|
||
|
|
fp = open(double_eg, 'rb')
|
||
|
|
rdr = MatFile5Reader(fp, mat_dtype=False)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
fp.close()
|
||
|
|
assert_equal(d['testmatrix'].dtype.kind, 'u')
|
||
|
|
|
||
|
|
fp = open(double_eg, 'rb')
|
||
|
|
rdr = MatFile5Reader(fp, mat_dtype=True)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
fp.close()
|
||
|
|
assert_equal(d['testmatrix'].dtype.kind, 'f')
|
||
|
|
|
||
|
|
|
||
|
|
def test_sparse_in_struct():
|
||
|
|
# reproduces bug found by DC where Cython code was insisting on
|
||
|
|
# ndarray return type, but getting sparse matrix
|
||
|
|
st = {'sparsefield': SP.coo_matrix(np.eye(4))}
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'a':st})
|
||
|
|
d = loadmat(stream, struct_as_record=True)
|
||
|
|
assert_array_equal(d['a'][0, 0]['sparsefield'].toarray(), np.eye(4))
|
||
|
|
|
||
|
|
|
||
|
|
def test_mat_struct_squeeze():
|
||
|
|
stream = BytesIO()
|
||
|
|
in_d = {'st':{'one':1, 'two':2}}
|
||
|
|
savemat(stream, in_d)
|
||
|
|
# no error without squeeze
|
||
|
|
loadmat(stream, struct_as_record=False)
|
||
|
|
# previous error was with squeeze, with mat_struct
|
||
|
|
loadmat(stream, struct_as_record=False, squeeze_me=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_scalar_squeeze():
|
||
|
|
stream = BytesIO()
|
||
|
|
in_d = {'scalar': [[0.1]], 'string': 'my name', 'st':{'one':1, 'two':2}}
|
||
|
|
savemat(stream, in_d)
|
||
|
|
out_d = loadmat(stream, squeeze_me=True)
|
||
|
|
assert_(isinstance(out_d['scalar'], float))
|
||
|
|
assert_(isinstance(out_d['string'], str))
|
||
|
|
assert_(isinstance(out_d['st'], np.ndarray))
|
||
|
|
|
||
|
|
|
||
|
|
def test_str_round():
|
||
|
|
# from report by Angus McMorland on mailing list 3 May 2010
|
||
|
|
stream = BytesIO()
|
||
|
|
in_arr = np.array(['Hello', 'Foob'])
|
||
|
|
out_arr = np.array(['Hello', 'Foob '])
|
||
|
|
savemat(stream, dict(a=in_arr))
|
||
|
|
res = loadmat(stream)
|
||
|
|
# resulted in ['HloolFoa', 'elWrdobr']
|
||
|
|
assert_array_equal(res['a'], out_arr)
|
||
|
|
stream.truncate(0)
|
||
|
|
stream.seek(0)
|
||
|
|
# Make Fortran ordered version of string
|
||
|
|
in_str = in_arr.tobytes(order='F')
|
||
|
|
in_from_str = np.ndarray(shape=a.shape,
|
||
|
|
dtype=in_arr.dtype,
|
||
|
|
order='F',
|
||
|
|
buffer=in_str)
|
||
|
|
savemat(stream, dict(a=in_from_str))
|
||
|
|
assert_array_equal(res['a'], out_arr)
|
||
|
|
# unicode save did lead to buffer too small error
|
||
|
|
stream.truncate(0)
|
||
|
|
stream.seek(0)
|
||
|
|
in_arr_u = in_arr.astype('U')
|
||
|
|
out_arr_u = out_arr.astype('U')
|
||
|
|
savemat(stream, {'a': in_arr_u})
|
||
|
|
res = loadmat(stream)
|
||
|
|
assert_array_equal(res['a'], out_arr_u)
|
||
|
|
|
||
|
|
|
||
|
|
def test_fieldnames():
|
||
|
|
# Check that field names are as expected
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, {'a': {'a':1, 'b':2}})
|
||
|
|
res = loadmat(stream)
|
||
|
|
field_names = res['a'].dtype.names
|
||
|
|
assert_equal(set(field_names), {'a', 'b'})
|
||
|
|
|
||
|
|
|
||
|
|
def test_loadmat_varnames():
|
||
|
|
# Test that we can get just one variable from a mat file using loadmat
|
||
|
|
mat5_sys_names = ['__globals__',
|
||
|
|
'__header__',
|
||
|
|
'__version__']
|
||
|
|
for eg_file, sys_v_names in (
|
||
|
|
(pjoin(test_data_path, 'testmulti_4.2c_SOL2.mat'), []), (pjoin(
|
||
|
|
test_data_path, 'testmulti_7.4_GLNX86.mat'), mat5_sys_names)):
|
||
|
|
vars = loadmat(eg_file)
|
||
|
|
assert_equal(set(vars.keys()), set(['a', 'theta'] + sys_v_names))
|
||
|
|
vars = loadmat(eg_file, variable_names='a')
|
||
|
|
assert_equal(set(vars.keys()), set(['a'] + sys_v_names))
|
||
|
|
vars = loadmat(eg_file, variable_names=['a'])
|
||
|
|
assert_equal(set(vars.keys()), set(['a'] + sys_v_names))
|
||
|
|
vars = loadmat(eg_file, variable_names=['theta'])
|
||
|
|
assert_equal(set(vars.keys()), set(['theta'] + sys_v_names))
|
||
|
|
vars = loadmat(eg_file, variable_names=('theta',))
|
||
|
|
assert_equal(set(vars.keys()), set(['theta'] + sys_v_names))
|
||
|
|
vars = loadmat(eg_file, variable_names=[])
|
||
|
|
assert_equal(set(vars.keys()), set(sys_v_names))
|
||
|
|
vnames = ['theta']
|
||
|
|
vars = loadmat(eg_file, variable_names=vnames)
|
||
|
|
assert_equal(vnames, ['theta'])
|
||
|
|
|
||
|
|
|
||
|
|
def test_round_types():
|
||
|
|
# Check that saving, loading preserves dtype in most cases
|
||
|
|
arr = np.arange(10)
|
||
|
|
stream = BytesIO()
|
||
|
|
for dts in ('f8','f4','i8','i4','i2','i1',
|
||
|
|
'u8','u4','u2','u1','c16','c8'):
|
||
|
|
stream.truncate(0)
|
||
|
|
stream.seek(0) # needed for BytesIO in Python 3
|
||
|
|
savemat(stream, {'arr': arr.astype(dts)})
|
||
|
|
vars = loadmat(stream)
|
||
|
|
assert_equal(np.dtype(dts), vars['arr'].dtype)
|
||
|
|
|
||
|
|
|
||
|
|
def test_varmats_from_mat():
|
||
|
|
# Make a mat file with several variables, write it, read it back
|
||
|
|
names_vars = (('arr', mlarr(np.arange(10))),
|
||
|
|
('mystr', mlarr('a string')),
|
||
|
|
('mynum', mlarr(10)))
|
||
|
|
|
||
|
|
# Dict like thing to give variables in defined order
|
||
|
|
class C:
|
||
|
|
def items(self):
|
||
|
|
return names_vars
|
||
|
|
stream = BytesIO()
|
||
|
|
savemat(stream, C())
|
||
|
|
varmats = varmats_from_mat(stream)
|
||
|
|
assert_equal(len(varmats), 3)
|
||
|
|
for i in range(3):
|
||
|
|
name, var_stream = varmats[i]
|
||
|
|
exp_name, exp_res = names_vars[i]
|
||
|
|
assert_equal(name, exp_name)
|
||
|
|
res = loadmat(var_stream)
|
||
|
|
assert_array_equal(res[name], exp_res)
|
||
|
|
|
||
|
|
|
||
|
|
def test_one_by_zero():
|
||
|
|
# Test 1x0 chars get read correctly
|
||
|
|
func_eg = pjoin(test_data_path, 'one_by_zero_char.mat')
|
||
|
|
fp = open(func_eg, 'rb')
|
||
|
|
rdr = MatFile5Reader(fp)
|
||
|
|
d = rdr.get_variables()
|
||
|
|
fp.close()
|
||
|
|
assert_equal(d['var'].shape, (0,))
|
||
|
|
|
||
|
|
|
||
|
|
def test_load_mat4_le():
|
||
|
|
# We were getting byte order wrong when reading little-endian floa64 dense
|
||
|
|
# matrices on big-endian platforms
|
||
|
|
mat4_fname = pjoin(test_data_path, 'test_mat4_le_floats.mat')
|
||
|
|
vars = loadmat(mat4_fname)
|
||
|
|
assert_array_equal(vars['a'], [[0.1, 1.2]])
|
||
|
|
|
||
|
|
|
||
|
|
def test_unicode_mat4():
|
||
|
|
# Mat4 should save unicode as latin1
|
||
|
|
bio = BytesIO()
|
||
|
|
var = {'second_cat': 'Schrödinger'}
|
||
|
|
savemat(bio, var, format='4')
|
||
|
|
var_back = loadmat(bio)
|
||
|
|
assert_equal(var_back['second_cat'], var['second_cat'])
|
||
|
|
|
||
|
|
|
||
|
|
def test_logical_sparse():
|
||
|
|
# Test we can read logical sparse stored in mat file as bytes.
|
||
|
|
# See https://github.com/scipy/scipy/issues/3539.
|
||
|
|
# In some files saved by MATLAB, the sparse data elements (Real Part
|
||
|
|
# Subelement in MATLAB speak) are stored with apparent type double
|
||
|
|
# (miDOUBLE) but are in fact single bytes.
|
||
|
|
filename = pjoin(test_data_path,'logical_sparse.mat')
|
||
|
|
# Before fix, this would crash with:
|
||
|
|
# ValueError: indices and data should have the same size
|
||
|
|
d = loadmat(filename, struct_as_record=True)
|
||
|
|
log_sp = d['sp_log_5_4']
|
||
|
|
assert_(isinstance(log_sp, SP.csc_matrix))
|
||
|
|
assert_equal(log_sp.dtype.type, np.bool_)
|
||
|
|
assert_array_equal(log_sp.toarray(),
|
||
|
|
[[True, True, True, False],
|
||
|
|
[False, False, True, False],
|
||
|
|
[False, False, True, False],
|
||
|
|
[False, False, False, False],
|
||
|
|
[False, False, False, False]])
|
||
|
|
|
||
|
|
|
||
|
|
def test_empty_sparse():
|
||
|
|
# Can we read empty sparse matrices?
|
||
|
|
sio = BytesIO()
|
||
|
|
import scipy.sparse
|
||
|
|
empty_sparse = scipy.sparse.csr_matrix([[0,0],[0,0]])
|
||
|
|
savemat(sio, dict(x=empty_sparse))
|
||
|
|
sio.seek(0)
|
||
|
|
res = loadmat(sio)
|
||
|
|
assert_array_equal(res['x'].shape, empty_sparse.shape)
|
||
|
|
assert_array_equal(res['x'].toarray(), 0)
|
||
|
|
# Do empty sparse matrices get written with max nnz 1?
|
||
|
|
# See https://github.com/scipy/scipy/issues/4208
|
||
|
|
sio.seek(0)
|
||
|
|
reader = MatFile5Reader(sio)
|
||
|
|
reader.initialize_read()
|
||
|
|
reader.read_file_header()
|
||
|
|
hdr, _ = reader.read_var_header()
|
||
|
|
assert_equal(hdr.nzmax, 1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_empty_mat_error():
|
||
|
|
# Test we get a specific warning for an empty mat file
|
||
|
|
sio = BytesIO()
|
||
|
|
assert_raises(MatReadError, loadmat, sio)
|
||
|
|
|
||
|
|
|
||
|
|
def test_miuint32_compromise():
|
||
|
|
# Reader should accept miUINT32 for miINT32, but check signs
|
||
|
|
# mat file with miUINT32 for miINT32, but OK values
|
||
|
|
filename = pjoin(test_data_path, 'miuint32_for_miint32.mat')
|
||
|
|
res = loadmat(filename)
|
||
|
|
assert_equal(res['an_array'], np.arange(10)[None, :])
|
||
|
|
# mat file with miUINT32 for miINT32, with negative value
|
||
|
|
filename = pjoin(test_data_path, 'bad_miuint32.mat')
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
loadmat(filename)
|
||
|
|
|
||
|
|
|
||
|
|
def test_miutf8_for_miint8_compromise():
|
||
|
|
# Check reader accepts ascii as miUTF8 for array names
|
||
|
|
filename = pjoin(test_data_path, 'miutf8_array_name.mat')
|
||
|
|
res = loadmat(filename)
|
||
|
|
assert_equal(res['array_name'], [[1]])
|
||
|
|
# mat file with non-ascii utf8 name raises error
|
||
|
|
filename = pjoin(test_data_path, 'bad_miutf8_array_name.mat')
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
loadmat(filename)
|
||
|
|
|
||
|
|
|
||
|
|
def test_bad_utf8():
|
||
|
|
# Check that reader reads bad UTF with 'replace' option
|
||
|
|
filename = pjoin(test_data_path,'broken_utf8.mat')
|
||
|
|
res = loadmat(filename)
|
||
|
|
assert_equal(res['bad_string'],
|
||
|
|
b'\x80 am broken'.decode('utf8', 'replace'))
|
||
|
|
|
||
|
|
|
||
|
|
def test_save_unicode_field(tmpdir):
|
||
|
|
filename = os.path.join(str(tmpdir), 'test.mat')
|
||
|
|
test_dict = {'a':{'b':1,'c':'test_str'}}
|
||
|
|
savemat(filename, test_dict)
|
||
|
|
|
||
|
|
|
||
|
|
def test_save_custom_array_type(tmpdir):
|
||
|
|
class CustomArray:
|
||
|
|
def __array__(self, dtype=None, copy=None):
|
||
|
|
return np.arange(6.0).reshape(2, 3)
|
||
|
|
a = CustomArray()
|
||
|
|
filename = os.path.join(str(tmpdir), 'test.mat')
|
||
|
|
savemat(filename, {'a': a})
|
||
|
|
out = loadmat(filename)
|
||
|
|
assert_array_equal(out['a'], np.array(a))
|
||
|
|
|
||
|
|
|
||
|
|
def test_filenotfound():
|
||
|
|
# Check the correct error is thrown
|
||
|
|
assert_raises(OSError, loadmat, "NotExistentFile00.mat")
|
||
|
|
assert_raises(OSError, loadmat, "NotExistentFile00")
|
||
|
|
|
||
|
|
|
||
|
|
def test_simplify_cells():
|
||
|
|
# Test output when simplify_cells=True
|
||
|
|
filename = pjoin(test_data_path, 'testsimplecell.mat')
|
||
|
|
res1 = loadmat(filename, simplify_cells=True)
|
||
|
|
res2 = loadmat(filename, simplify_cells=False)
|
||
|
|
assert_(isinstance(res1["s"], dict))
|
||
|
|
assert_(isinstance(res2["s"], np.ndarray))
|
||
|
|
assert_array_equal(res1["s"]["mycell"], np.array(["a", "b", "c"]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('version, filt, regex', [
|
||
|
|
(0, '_4*_*', None),
|
||
|
|
(1, '_5*_*', None),
|
||
|
|
(1, '_6*_*', None),
|
||
|
|
(1, '_7*_*', '^((?!hdf5).)*$'), # not containing hdf5
|
||
|
|
(2, '_7*_*', '.*hdf5.*'),
|
||
|
|
(1, '8*_*', None),
|
||
|
|
])
|
||
|
|
def test_matfile_version(version, filt, regex):
|
||
|
|
use_filt = pjoin(test_data_path, 'test*%s.mat' % filt)
|
||
|
|
files = glob(use_filt)
|
||
|
|
if regex is not None:
|
||
|
|
files = [file for file in files if re.match(regex, file) is not None]
|
||
|
|
assert len(files) > 0, \
|
||
|
|
f"No files for version {version} using filter {filt}"
|
||
|
|
for file in files:
|
||
|
|
got_version = matfile_version(file)
|
||
|
|
assert got_version[0] == version
|
||
|
|
|
||
|
|
|
||
|
|
def test_opaque():
|
||
|
|
"""Test that we can read a MatlabOpaque object."""
|
||
|
|
data = loadmat(pjoin(test_data_path, 'parabola.mat'))
|
||
|
|
assert isinstance(data['parabola'], MatlabFunction)
|
||
|
|
assert isinstance(data['parabola'].item()[3].item()[3], MatlabOpaque)
|
||
|
|
|
||
|
|
|
||
|
|
def test_opaque_simplify():
|
||
|
|
"""Test that we can read a MatlabOpaque object when simplify_cells=True."""
|
||
|
|
data = loadmat(pjoin(test_data_path, 'parabola.mat'), simplify_cells=True)
|
||
|
|
assert isinstance(data['parabola'], MatlabFunction)
|
||
|
|
|
||
|
|
|
||
|
|
def test_deprecation():
|
||
|
|
"""Test that access to previous attributes still works."""
|
||
|
|
# This should be accessible immediately from scipy.io import
|
||
|
|
with assert_warns(DeprecationWarning):
|
||
|
|
scipy.io.matlab.mio5_params.MatlabOpaque
|
||
|
|
|
||
|
|
# These should be importable but warn as well
|
||
|
|
with assert_warns(DeprecationWarning):
|
||
|
|
from scipy.io.matlab.miobase import MatReadError # noqa: F401
|
||
|
|
|
||
|
|
|
||
|
|
def test_gh_17992(tmp_path):
|
||
|
|
rng = np.random.default_rng(12345)
|
||
|
|
outfile = tmp_path / "lists.mat"
|
||
|
|
array_one = rng.random((5,3))
|
||
|
|
array_two = rng.random((6,3))
|
||
|
|
list_of_arrays = [array_one, array_two]
|
||
|
|
# warning suppression only needed for NumPy < 1.24.0
|
||
|
|
with np.testing.suppress_warnings() as sup:
|
||
|
|
sup.filter(VisibleDeprecationWarning)
|
||
|
|
savemat(outfile,
|
||
|
|
{'data': list_of_arrays},
|
||
|
|
long_field_names=True,
|
||
|
|
do_compression=True)
|
||
|
|
# round trip check
|
||
|
|
new_dict = {}
|
||
|
|
loadmat(outfile,
|
||
|
|
new_dict)
|
||
|
|
assert_allclose(new_dict["data"][0][0], array_one)
|
||
|
|
assert_allclose(new_dict["data"][0][1], array_two)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gh_19659(tmp_path):
|
||
|
|
d = {
|
||
|
|
"char_array": np.array([list("char"), list("char")], dtype="U1"),
|
||
|
|
"string_array": np.array(["string", "string"]),
|
||
|
|
}
|
||
|
|
outfile = tmp_path / "tmp.mat"
|
||
|
|
# should not error:
|
||
|
|
savemat(outfile, d, format="4")
|