#!/usr/bin/env python
''' Nose test generators

Need function load / save / roundtrip tests

'''
import sys
import os
from os.path import join as pjoin, dirname
from glob import glob
if sys.version_info[0] >= 3:
    from io import BytesIO
else:
    from io import StringIO as BytesIO
from tempfile import mkdtemp
# functools is only available in Python >=2.5
try:
    from functools import partial
except ImportError:
    from scipy.io.arff.myfunctools import partial

import warnings
import shutil
import gzip

from numpy.testing import \
     assert_array_equal, \
     assert_array_almost_equal, \
     assert_equal, \
     assert_raises, run_module_suite
from numpy.testing.utils import WarningManager

from nose.tools import assert_true

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, MatFileReader, \
    MatWriteError
from scipy.io.matlab.mio import find_mat_file, mat_reader_factory, \
    loadmat, savemat
from scipy.io.matlab.mio5 import MatlabObject, MatFile5Writer, \
      MatFile5Reader, MatlabFunction, varmats_from_mat

# Use future defaults to silence unwanted test warnings
savemat_future = partial(savemat, oned_as='row')
class MatFile5Reader_future(MatFile5Reader):
    def __init__(self, *args, **kwargs):
        sar = kwargs.get('struct_as_record')
        if sar is None:
            kwargs['struct_as_record'] = True
        super(MatFile5Reader_future, self).__init__(*args, **kwargs)


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',
     'expected': {'testdouble': theta}
     }]
case_table4.append(
    {'name': 'string',
     'expected': {'teststring':
                  array(['"Do nine men interpret?" "Nine men," I nod.'])},
     })
case_table4.append(
    {'name': 'complex',
     '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',
     'expected': {'testmatrix': A},
     })
case_table4.append(
    {'name': 'sparse',
     'expected': {'testsparse': SP.coo_matrix(A)},
     })
B = A.astype(complex)
B[0,0] += 1j
case_table4.append(
    {'name': 'sparsecomplex',
     'expected': {'testsparsecomplex': SP.coo_matrix(B)},
     })
case_table4.append(
    {'name': 'multi',
     'expected': {'theta': theta,
                  'a': A},
     })
case_table4.append(
    {'name': 'minus',
     'expected': {'testminus': mlarr(-1)},
     })
case_table4.append(
    {'name': 'onechar',
     'expected': {'testonechar': array(['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(
    ['This cell contains this string and 3 arrays of increasing length'])
case_table5 = [
    {'name': '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',
     'expected': {'testscalarcell': objarr}
     })
case_table5.append(
    {'name': 'emptycell',
     'expected': {'testemptycell': CAE}})
case_table5.append(
    {'name': 'stringarray',
     'expected': {'teststringarray': array(
    ['one  ', 'two  ', 'three'])},
     })
case_table5.append(
    {'name': '3dmatrix',
     '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(['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',
     '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',
     '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(['number 3'])
case_table5.append(
    {'name': 'structnest',
     '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(['number 1'])
a[0,1]['two'] = array(['number 2'])
case_table5.append(
    {'name': 'structarr',
     '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(['x'])
m0['inputExpr'] = array([' x = INLINE_INPUTS_{1};'])
m0['args'] = array(['x'])
m0['isEmpty'] = mlarr(0)
m0['numArgs'] = mlarr(1)
m0['version'] = mlarr(1)
case_table5.append(
    {'name': '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',
    'expected': {'testunicode': array([u_str])}
    })
case_table5.append(
    {'name': 'sparse',
     'expected': {'testsparse': SP.coo_matrix(A)},
     })
case_table5.append(
    {'name': 'sparsecomplex',
     'expected': {'testsparsecomplex': SP.coo_matrix(B)},
     })

case_table5_rt = case_table5[:]
# Inline functions can't be concatenated in matlab, so RT only
case_table5_rt.append(
    {'name': 'objectarray',
     'expected': {'testobjectarray': np.repeat(MO, 2).reshape(1,2)}})


def types_compatible(var1, var2):
    ''' Check if types are same or compatible

    0d 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_true(SP.issparse(actual))
        assert_array_almost_equal(actual.todense(),
                                  expected.todense(),
                                  err_msg = label,
                                  decimal = 5)
        return
    # Check types are as expected
    assert_true(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_true(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 (np.str, # string
                         np.unicode_):
        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 list(case.items()):
            k_label = "%s, variable %s" % (label, k)
            assert_true(k in matdict, "Missing key at %s" % k_label)
            _check_level(k_label, expected, matdict[k])


# Round trip tests
def _rt_check_case(name, expected, format):
    mat_stream = BytesIO()
    savemat_future(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_true(len(files) > 0,
                    "No files for test %s using filter %s" % (name, filt))
        yield _load_check_case, name, files, expected


# generator for round trip tests
def test_round_trip():
    for case in case_table4 + case_table5_rt:
        name = case['name'] + '_round_trip'
        expected = case['expected']
        format = case in case_table4 and '4' or '5'
        yield _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_future(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, oned_as='row')
        os.unlink(fname)
        savemat(fname, x, oned_as='row')
        loadmat(fname)
        os.unlink(fname)

        # Check that stream is left open
        f = open(fname, 'wb')
        savemat(f, x, oned_as='column')
        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_true(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():
    fname = pjoin(test_data_path, 'testdouble_7.1_GLNX86.mat')
    warn_ctx = WarningManager()
    warn_ctx.__enter__()
    try:
        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)
        # This should - because of deprecated system path search
        assert_raises(DeprecationWarning, find_mat_file, fname)
    finally:
        warn_ctx.__exit__()


def test_regression_653():
    """Regression test for #653."""
    assert_raises(TypeError, savemat_future, BytesIO(), {'d':{1:2}}, format='5')


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)])
    mat_stream = BytesIO()
    savemat_future(BytesIO(), {'longstruct': st1}, format='5')
    fldname = 'a' * (lim+1)
    st1 = np.zeros((1,1), dtype=[(fldname, object)])
    assert_raises(ValueError, savemat_future, 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_future, 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)])
    mat_stream = BytesIO()
    savemat_future(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_future, 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
    mat_stream = BytesIO()
    savemat_future(BytesIO(), {'longstruct': cell}, format='5',long_field_names=True)
    #
    # Check to make sure it fails with long field names off
    #
    assert_raises(ValueError, savemat_future, 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'
    mat_stream = BytesIO()
    savemat_future(BytesIO(), {'x': cells}, format='5')

    cells = np.ndarray((1,1),dtype=object)
    cells[0,0]='Hello, world'
    mat_stream = BytesIO()
    savemat_future(BytesIO(), {'x': cells}, format='5')


def test_writer_properties():
    # Tests getting, setting of properties of matrix writer
    mfw = MatFile5Writer(BytesIO(), oned_as='row')
    yield assert_equal, mfw.global_vars, []
    mfw.global_vars = ['avar']
    yield assert_equal, mfw.global_vars, ['avar']
    yield assert_equal, mfw.unicode_strings, False
    mfw.unicode_strings = True
    yield assert_equal, mfw.unicode_strings, True
    yield assert_equal, mfw.long_field_names, False
    mfw.long_field_names = True
    yield 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, oned_as='column')
    # 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})
    yield assert_true, w_sz - len(sio.getvalue()) > 4
    # Whereas increasing name size makes less difference
    sio.truncate(0)
    sio.seek(0)
    wtr.put_variables({'aaaaaa': arr})
    yield assert_true, len(sio.getvalue()) - w_sz < 4


def test_save_dict():
    # Test that dict can be saved (as recarray), loaded as matstruct
    dict_types = (dict,)
    try:
        from collections import OrderedDict
    except ImportError:
        pass
    else:
        dict_types += (OrderedDict,)
    exp_arr = np.zeros((1, 1), dtype = [('a', object), ('b', object)])
    exp_arr['a'] = 1
    exp_arr['b'] = 2
    for dict_type in dict_types:
        d = dict_type(a=1, b=2)
        stream = BytesIO()
        savemat_future(stream, {'dict': d})
        stream.seek(0)
        vals = loadmat(stream)
        assert_array_equal(vals['dict'], exp_arr)


def test_1d_shape():
    # Current 5 behavior is 1D -> column vector
    arr = np.arange(5)
    stream = BytesIO()
    warn_ctx = WarningManager()
    warn_ctx.__enter__()
    try:
        # silence warnings for tests
        warnings.simplefilter('ignore')
        savemat(stream, {'oned':arr}, format='5')
        vals = loadmat(stream)
        assert_equal(vals['oned'].shape, (5,1))
        # Current 4 behavior is 1D -> row vector
        stream = BytesIO()
        savemat(stream, {'oned':arr}, format='4')
        vals = loadmat(stream)
        assert_equal(vals['oned'].shape, (1, 5))
        for format in ('4', '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))
    finally:
        warn_ctx.__exit__()


def test_compression():
    arr = np.zeros(100).reshape((5,20))
    arr[2,10] = 1
    stream = BytesIO()
    savemat_future(stream, {'arr':arr})
    raw_len = len(stream.getvalue())
    vals = loadmat(stream)
    yield assert_array_equal, vals['arr'], arr
    stream = BytesIO()
    savemat_future(stream, {'arr':arr}, do_compression=True)
    compressed_len = len(stream.getvalue())
    vals = loadmat(stream)
    yield assert_array_equal, vals['arr'], arr
    yield assert_true, raw_len>compressed_len
    # Concatenate, test later
    arr2 = arr.copy()
    arr2[0,0] = 1
    stream = BytesIO()
    savemat_future(stream, {'arr':arr, 'arr2':arr2}, do_compression=False)
    vals = loadmat(stream)
    yield assert_array_equal, vals['arr2'], arr2
    stream = BytesIO()
    savemat_future(stream, {'arr':arr, 'arr2':arr2}, do_compression=True)
    vals = loadmat(stream)
    yield assert_array_equal, vals['arr2'], arr2


def test_single_object():
    stream = BytesIO()
    savemat_future(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)
    yield assert_true, 'first' in d
    yield assert_true, 'second' in d
    #
    # Make the factory
    #
    factory = 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')
    yield assert_true, '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(np.object))
    assert_true(a[0,0] is None)
    stream = BytesIO()
    arr = np.array((), dtype='U')
    # before ticket fix, this used to give data type not understood
    savemat_future(stream, {'arr':arr})
    d = loadmat(stream)
    a2 = d['arr']
    assert_array_equal(a2, arr)


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_future(stream, {'arr': arr})
    d = loadmat(stream, struct_as_record=False)
    a20 = d['arr'][0,0]
    yield assert_equal, a20.f1, 0.5
    yield assert_equal, a20.f2, 'python'
    d = loadmat(stream, struct_as_record=True)
    a20 = d['arr'][0,0]
    yield assert_equal, a20['f1'], 0.5
    yield assert_equal, a20['f2'], 'python'
    # structs always come back as object types
    yield assert_equal, a20.dtype, np.dtype([('f1', 'O'),
                                             ('f2', 'O')])
    a21 = d['arr'].flat[1]
    yield assert_equal, a21['f1'], 99
    yield 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_future(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_future(stream, {'a': arr})
    rdr = MatFile5Reader_future(stream)
    back_dict = rdr.get_variables()
    rarr = back_dict['a']
    assert_array_equal(rarr, arr)
    rdr = MatFile5Reader_future(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_future(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_future(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_future(stream, {'a': arr})
    rdr = MatFile5Reader_future(stream)
    assert_array_equal(rdr.get_variables()['a'], arr)
    rdr = MatFile5Reader_future(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_future(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_future(stream, {'a': np.array([''])})
    rdr = MatFile5Reader_future(stream)
    d = rdr.get_variables()
    assert_array_equal(d['a'], np.array([], dtype='U1'))
    stream.truncate(0)
    stream.seek(0)
    savemat_future(stream, {'a': np.array([], dtype='U1')})
    rdr = MatFile5Reader_future(stream)
    d = rdr.get_variables()
    assert_array_equal(d['a'], np.array([], dtype='U1'))
    stream.close()


def test_mat4_3d():
    # test behavior when writing 3D arrays to matlab 4 files
    stream = BytesIO()
    arr = np.arange(24).reshape((2,3,4))

    warn_ctx = WarningManager()
    warn_ctx.__enter__()
    try:
        warnings.simplefilter('error')
        assert_raises(DeprecationWarning, savemat_future,
                      stream, {'a': arr}, True, '4')
        # For now, we save a 3D array as 2D
        warnings.simplefilter('ignore')
        savemat_future(stream, {'a': arr}, format='4')
    finally:
        warn_ctx.__exit__()

    d = loadmat(stream)
    assert_array_equal(d['a'], arr.reshape((6,4)))


def test_func_read():
    func_eg = pjoin(test_data_path, 'testfunc_7.4_GLNX86.mat')
    fp = open(func_eg, 'rb')
    rdr = MatFile5Reader_future(fp)
    d = rdr.get_variables()
    fp.close()
    yield assert_true, isinstance(d['testfunc'], MatlabFunction)
    stream = BytesIO()
    wtr = MatFile5Writer(stream, oned_as='row')
    yield 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_future(fp, mat_dtype=False)
    d = rdr.get_variables()
    fp.close()
    yield assert_equal, d['testmatrix'].dtype.kind, 'u'

    fp = open(double_eg, 'rb')
    rdr = MatFile5Reader_future(fp, mat_dtype=True)
    d = rdr.get_variables()
    fp.close()
    yield 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_future(stream, {'a':st})
    d = loadmat(stream, struct_as_record=True)
    yield 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_future(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_future(stream, in_d)
    out_d = loadmat(stream, squeeze_me=True)
    assert_true(isinstance(out_d['scalar'], float))
    assert_true(isinstance(out_d['string'], str))
    assert_true(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_future(stream, dict(a=in_arr))
    res = loadmat(stream)
    # resulted in [u'HloolFoa', u'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_future(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_future(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_future(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
    eg_file = pjoin(test_data_path, 'testmulti_7.4_GLNX86.mat')
    sys_v_names = ['__globals__',
                   '__header__',
                   '__version__']
    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=['theta'])
    assert_equal(set(vars.keys()), set(['theta'] + sys_v_names))


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_future(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_future(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_future(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]])


if __name__ == "__main__":
    run_module_suite()
