# -*- coding: latin-1 -*- ''' Nose test generators Need function load / save / roundtrip tests ''' from __future__ import division, print_function, absolute_import import os from collections import OrderedDict from os.path import join as pjoin, dirname from glob import glob from io import BytesIO from tempfile import mkdtemp from scipy._lib.six import u, text_type, string_types import warnings import shutil import gzip from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_equal, assert_) from pytest import raises as assert_raises from scipy._lib._numpy_compat import suppress_warnings import numpy as np from numpy import array import scipy.sparse as SP import scipy.io.matlab.byteordercodes as boc from scipy.io.matlab.miobase import matdims, MatWriteError, MatReadError from scipy.io.matlab.mio import (mat_reader_factory, loadmat, savemat, whosmat) from scipy.io.matlab.mio5 import (MatlabObject, MatFile5Writer, MatFile5Reader, MatlabFunction, varmats_from_mat, to_writeable, EmptyStructMarker) from scipy.io.matlab import mio5_params as mio5p test_data_path = pjoin(dirname(__file__), 'data') def mlarr(*args, **kwargs): """Convenience function to return matlab-compatible 2D array.""" arr = np.array(*args, **kwargs) arr.shape = matdims(arr) return arr # Define cases to test theta = np.pi/4*np.arange(9,dtype=float).reshape(1,9) case_table4 = [ {'name': 'double', 'classes': {'testdouble': 'double'}, 'expected': {'testdouble': theta} }] case_table4.append( {'name': 'string', 'classes': {'teststring': 'char'}, 'expected': {'teststring': array([u('"Do nine men interpret?" "Nine men," I nod.')])} }) case_table4.append( {'name': 'complex', 'classes': {'testcomplex': 'double'}, 'expected': {'testcomplex': np.cos(theta) + 1j*np.sin(theta)} }) A = np.zeros((3,5)) A[0] = list(range(1,6)) A[:,0] = list(range(1,4)) case_table4.append( {'name': 'matrix', 'classes': {'testmatrix': 'double'}, 'expected': {'testmatrix': A}, }) case_table4.append( {'name': 'sparse', 'classes': {'testsparse': 'sparse'}, 'expected': {'testsparse': SP.coo_matrix(A)}, }) B = A.astype(complex) B[0,0] += 1j case_table4.append( {'name': 'sparsecomplex', 'classes': {'testsparsecomplex': 'sparse'}, 'expected': {'testsparsecomplex': SP.coo_matrix(B)}, }) case_table4.append( {'name': 'multi', 'classes': {'theta': 'double', 'a': 'double'}, 'expected': {'theta': theta, 'a': A}, }) case_table4.append( {'name': 'minus', 'classes': {'testminus': 'double'}, 'expected': {'testminus': mlarr(-1)}, }) case_table4.append( {'name': 'onechar', 'classes': {'testonechar': 'char'}, 'expected': {'testonechar': array([u('r')])}, }) # Cell arrays stored as object arrays CA = mlarr(( # tuple for object array creation [], mlarr([1]), mlarr([[1,2]]), mlarr([[1,2,3]])), dtype=object).reshape(1,-1) CA[0,0] = array( [u('This cell contains this string and 3 arrays of increasing length')]) case_table5 = [ {'name': 'cell', 'classes': {'testcell': 'cell'}, 'expected': {'testcell': CA}}] CAE = mlarr(( # tuple for object array creation mlarr(1), mlarr(2), mlarr([]), mlarr([]), mlarr(3)), dtype=object).reshape(1,-1) objarr = np.empty((1,1),dtype=object) objarr[0,0] = mlarr(1) case_table5.append( {'name': 'scalarcell', 'classes': {'testscalarcell': 'cell'}, 'expected': {'testscalarcell': objarr} }) case_table5.append( {'name': 'emptycell', 'classes': {'testemptycell': 'cell'}, 'expected': {'testemptycell': CAE}}) case_table5.append( {'name': 'stringarray', 'classes': {'teststringarray': 'char'}, 'expected': {'teststringarray': array( [u('one '), u('two '), u('three')])}, }) case_table5.append( {'name': '3dmatrix', 'classes': {'test3dmatrix': 'double'}, 'expected': { 'test3dmatrix': np.transpose(np.reshape(list(range(1,25)), (4,3,2)))} }) st_sub_arr = array([np.sqrt(2),np.exp(1),np.pi]).reshape(1,3) dtype = [(n, object) for n in ['stringfield', 'doublefield', 'complexfield']] st1 = np.zeros((1,1), dtype) st1['stringfield'][0,0] = array([u('Rats live on no evil star.')]) st1['doublefield'][0,0] = st_sub_arr st1['complexfield'][0,0] = st_sub_arr * (1 + 1j) case_table5.append( {'name': 'struct', 'classes': {'teststruct': 'struct'}, 'expected': {'teststruct': st1} }) CN = np.zeros((1,2), dtype=object) CN[0,0] = mlarr(1) CN[0,1] = np.zeros((1,3), dtype=object) CN[0,1][0,0] = mlarr(2, dtype=np.uint8) CN[0,1][0,1] = mlarr([[3]], dtype=np.uint8) CN[0,1][0,2] = np.zeros((1,2), dtype=object) CN[0,1][0,2][0,0] = mlarr(4, dtype=np.uint8) CN[0,1][0,2][0,1] = mlarr(5, dtype=np.uint8) case_table5.append( {'name': 'cellnest', 'classes': {'testcellnest': 'cell'}, 'expected': {'testcellnest': CN}, }) st2 = np.empty((1,1), dtype=[(n, object) for n in ['one', 'two']]) st2[0,0]['one'] = mlarr(1) st2[0,0]['two'] = np.empty((1,1), dtype=[('three', object)]) st2[0,0]['two'][0,0]['three'] = array([u('number 3')]) case_table5.append( {'name': 'structnest', 'classes': {'teststructnest': 'struct'}, 'expected': {'teststructnest': st2} }) a = np.empty((1,2), dtype=[(n, object) for n in ['one', 'two']]) a[0,0]['one'] = mlarr(1) a[0,0]['two'] = mlarr(2) a[0,1]['one'] = array([u('number 1')]) a[0,1]['two'] = array([u('number 2')]) case_table5.append( {'name': 'structarr', 'classes': {'teststructarr': 'struct'}, 'expected': {'teststructarr': a} }) ODT = np.dtype([(n, object) for n in ['expr', 'inputExpr', 'args', 'isEmpty', 'numArgs', 'version']]) MO = MatlabObject(np.zeros((1,1), dtype=ODT), 'inline') m0 = MO[0,0] m0['expr'] = array([u('x')]) m0['inputExpr'] = array([u(' x = INLINE_INPUTS_{1};')]) m0['args'] = array([u('x')]) m0['isEmpty'] = mlarr(0) m0['numArgs'] = mlarr(1) m0['version'] = mlarr(1) case_table5.append( {'name': 'object', 'classes': {'testobject': 'object'}, 'expected': {'testobject': MO} }) fp_u_str = open(pjoin(test_data_path, 'japanese_utf8.txt'), 'rb') u_str = fp_u_str.read().decode('utf-8') fp_u_str.close() case_table5.append( {'name': 'unicode', 'classes': {'testunicode': 'char'}, 'expected': {'testunicode': array([u_str])} }) case_table5.append( {'name': 'sparse', 'classes': {'testsparse': 'sparse'}, 'expected': {'testsparse': SP.coo_matrix(A)}, }) case_table5.append( {'name': 'sparsecomplex', 'classes': {'testsparsecomplex': 'sparse'}, 'expected': {'testsparsecomplex': SP.coo_matrix(B)}, }) case_table5.append( {'name': 'bool', 'classes': {'testbools': 'logical'}, 'expected': {'testbools': array([[True], [False]])}, }) case_table5_rt = case_table5[:] # Inline functions can't be concatenated in matlab, so RT only case_table5_rt.append( {'name': 'objectarray', 'classes': {'testobjectarray': 'object'}, 'expected': {'testobjectarray': np.repeat(MO, 2).reshape(1,2)}}) def types_compatible(var1, var2): """Check if types are same or compatible. 0-D numpy scalars are compatible with bare python scalars. """ type1 = type(var1) type2 = type(var2) if type1 is type2: return True if type1 is np.ndarray and var1.shape == (): return type(var1.item()) is type2 if type2 is np.ndarray and var2.shape == (): return type(var2.item()) is type1 return False def _check_level(label, expected, actual): """ Check one level of a potentially nested array """ if SP.issparse(expected): # allow different types of sparse matrices assert_(SP.issparse(actual)) assert_array_almost_equal(actual.todense(), expected.todense(), err_msg=label, decimal=5) return # Check types are as expected assert_(types_compatible(expected, actual), "Expected type %s, got %s at %s" % (type(expected), type(actual), label)) # A field in a record array may not be an ndarray # A scalar from a record array will be type np.void if not isinstance(expected, (np.void, np.ndarray, MatlabObject)): assert_equal(expected, actual) return # This is an ndarray-like thing assert_(expected.shape == actual.shape, msg='Expected shape %s, got %s at %s' % (expected.shape, actual.shape, label)) ex_dtype = expected.dtype if ex_dtype.hasobject: # array of objects if isinstance(expected, MatlabObject): assert_equal(expected.classname, actual.classname) for i, ev in enumerate(expected): level_label = "%s, [%d], " % (label, i) _check_level(level_label, ev, actual[i]) return if ex_dtype.fields: # probably recarray for fn in ex_dtype.fields: level_label = "%s, field %s, " % (label, fn) _check_level(level_label, expected[fn], actual[fn]) return if ex_dtype.type in (text_type, # string or bool np.unicode_, np.bool_): assert_equal(actual, expected, err_msg=label) return # Something numeric assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) def _load_check_case(name, files, case): for file_name in files: matdict = loadmat(file_name, struct_as_record=True) label = "test %s; file %s" % (name, file_name) for k, expected in case.items(): k_label = "%s, variable %s" % (label, k) assert_(k in matdict, "Missing key at %s" % k_label) _check_level(k_label, expected, matdict[k]) def _whos_check_case(name, files, case, classes): for file_name in files: label = "test %s; file %s" % (name, file_name) whos = whosmat(file_name) expected_whos = [] for k, expected in case.items(): expected_whos.append((k, expected.shape, classes[k])) whos.sort() expected_whos.sort() assert_equal(whos, expected_whos, "%s: %r != %r" % (label, whos, expected_whos) ) # Round trip tests def _rt_check_case(name, expected, format): mat_stream = BytesIO() savemat(mat_stream, expected, format=format) mat_stream.seek(0) _load_check_case(name, [mat_stream], expected) # generator for load tests def test_load(): for case in case_table4 + case_table5: name = case['name'] expected = case['expected'] filt = pjoin(test_data_path, 'test%s_*.mat' % name) files = glob(filt) assert_(len(files) > 0, "No files for test %s using filter %s" % (name, filt)) _load_check_case(name, files, expected) # generator for whos tests def test_whos(): for case in case_table4 + case_table5: name = case['name'] expected = case['expected'] classes = case['classes'] filt = pjoin(test_data_path, 'test%s_*.mat' % name) files = glob(filt) assert_(len(files) > 0, "No files for test %s using filter %s" % (name, filt)) _whos_check_case(name, files, expected, classes) # generator for round trip tests def test_round_trip(): for case in case_table4 + case_table5_rt: case_table4_names = [case['name'] for case in case_table4] name = case['name'] + '_round_trip' expected = case['expected'] for format in (['4', '5'] if case['name'] in case_table4_names else ['5']): _rt_check_case(name, expected, format) 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'].todense(), expected['x'].todense(), 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 mres = loadmat(fname, struct_as_record=True) # This neither mres = 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 dict can be saved (as recarray), loaded as matstruct dict_types = ((dict, False), (OrderedDict, True),) ab_exp = np.array([[(1, 2)]], dtype=[('a', object), ('b', object)]) ba_exp = np.array([[(2, 1)]], dtype=[('b', object), ('a', object)]) for dict_type, is_ordered in dict_types: # Initialize with tuples to keep order for OrderedDict d = dict_type([('a', 1), ('b', 2)]) stream = BytesIO() savemat(stream, {'dict': d}) stream.seek(0) vals = loadmat(stream)['dict'] assert_equal(set(vals.dtype.names), set(['a', 'b'])) if is_ordered: # Input was ordered, output in ab order assert_array_equal(vals, ab_exp) else: # Not ordered input, either order output if vals.dtype.names[0] == 'a': assert_array_equal(vals, ab_exp) else: assert_array_equal(vals, ba_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(object): 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(object): 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(object): 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 distiguish # 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().newbyteorder(), 'ints': int_arr.byteswap().newbyteorder(), 'uni_arr': uni_arr.byteswap().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_mat4_3d(): # test behavior when writing 3D 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'].todense(), 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 out_d = loadmat(stream, struct_as_record=False) # previous error was with squeeze, with mat_struct out_d = 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'], string_types)) 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.tostring(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), set(('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(object): 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': u('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'].todense(), 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 suppress_warnings() as sup: sup.filter(message="unclosed file") # Py3k ResourceWarning 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 suppress_warnings() as sup: sup.filter(message="unclosed file") # Py3k ResourceWarning 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 = {u'a':{u'b':1,u'c':'test_str'}} savemat(filename, test_dict) def test_filenotfound(): # Check the correct error is thrown assert_raises(IOError, loadmat, "NotExistentFile00.mat") assert_raises(IOError, loadmat, "NotExistentFile00")