Python numpy.str_() Examples

The following are 30 code examples of numpy.str_(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_sequence.py    From kipoiseq with MIT License 6 votes vote down vote up
def test_fasta_based_dataset(intervals_file, fasta_file):
    # just test the functionality
    dl = StringSeqIntervalDl(intervals_file, fasta_file)
    ret_val = dl[0]
    assert isinstance(ret_val["inputs"], np.ndarray)
    assert ret_val["inputs"].shape == ()
    # # test with set wrong seqlen:
    # dl = StringSeqIntervalDl(intervals_file, fasta_file, required_seq_len=3)
    # with pytest.raises(Exception):
    #     dl[0]

    dl = StringSeqIntervalDl(intervals_file, fasta_file, label_dtype="str")
    ret_val = dl[0]
    assert isinstance(ret_val['targets'][0], np.str_)
    dl = StringSeqIntervalDl(intervals_file, fasta_file, label_dtype="int")
    ret_val = dl[0]
    assert isinstance(ret_val['targets'][0], np.int_)
    dl = StringSeqIntervalDl(intervals_file, fasta_file, label_dtype="bool")
    ret_val = dl[0]
    assert isinstance(ret_val['targets'][0], np.bool_)
    vals = dl.load_all()
    assert vals['inputs'][0] == 'GT' 
Example #2
Source File: test_uvflag.py    From pyuvdata with BSD 2-Clause "Simplified" License 6 votes vote down vote up
def test_collapse_pol_flag():
    uvf = UVFlag(test_f_file)
    uvf.to_flag()
    assert uvf.weights_array is None
    uvf2 = uvf.copy()
    uvf2.polarization_array[0] = -4
    uvf.__add__(uvf2, inplace=True, axis="pol")  # Concatenate to form multi-pol object
    uvf2 = uvf.copy()
    uvf2.collapse_pol()
    assert len(uvf2.polarization_array) == 1
    assert uvf2.polarization_array[0] == np.str_(
        ",".join(map(str, uvf.polarization_array))
    )
    assert uvf2.mode == "metric"
    assert hasattr(uvf2, "metric_array")
    assert hasattr(uvf2, "flag_array")
    assert uvf2.flag_array is None 
Example #3
Source File: test_uvflag.py    From pyuvdata with BSD 2-Clause "Simplified" License 6 votes vote down vote up
def test_to_waterfall_bl_multi_pol():
    uvf = UVFlag(test_f_file)
    uvf.weights_array = np.ones_like(uvf.weights_array)
    uvf2 = uvf.copy()
    uvf2.polarization_array[0] = -4
    uvf.__add__(uvf2, inplace=True, axis="pol")  # Concatenate to form multi-pol object
    uvf2 = uvf.copy()  # Keep a copy to run with keep_pol=False
    uvf.to_waterfall()
    assert uvf.type == "waterfall"
    assert uvf.metric_array.shape == (
        len(uvf.time_array),
        len(uvf.freq_array),
        len(uvf.polarization_array),
    )
    assert uvf.weights_array.shape == uvf.metric_array.shape
    assert len(uvf.polarization_array) == 2
    # Repeat with keep_pol=False
    uvf2.to_waterfall(keep_pol=False)
    assert uvf2.type == "waterfall"
    assert uvf2.metric_array.shape == (len(uvf2.time_array), len(uvf.freq_array), 1)
    assert uvf2.weights_array.shape == uvf2.metric_array.shape
    assert len(uvf2.polarization_array) == 1
    assert uvf2.polarization_array[0] == np.str_(
        ",".join(map(str, uvf.polarization_array))
    ) 
Example #4
Source File: pyanitools.py    From deepchem with MIT License 6 votes vote down vote up
def store_data(self, store_loc, **kwargs):
    """Put arrays to store
        """
    #print(store_loc)
    g = self.store.create_group(store_loc)
    for k, v, in kwargs.items():
      #print(type(v[0]))

      #print(k)
      if type(v) == list:
        if len(v) != 0:
          if type(v[0]) is np.str_ or type(v[0]) is str:
            v = [a.encode('utf8') for a in v]

      g.create_dataset(
          k, data=v, compression=self.clib, compression_opts=self.clev) 
Example #5
Source File: test_uvflag.py    From pyuvdata with BSD 2-Clause "Simplified" License 6 votes vote down vote up
def test_collapse_pol_or():
    uvf = UVFlag(test_f_file)
    uvf.to_flag()
    assert uvf.weights_array is None
    uvf2 = uvf.copy()
    uvf2.polarization_array[0] = -4
    uvf.__add__(uvf2, inplace=True, axis="pol")  # Concatenate to form multi-pol object
    uvf2 = uvf.copy()
    uvf2.collapse_pol(method="or")
    assert len(uvf2.polarization_array) == 1
    assert uvf2.polarization_array[0] == np.str_(
        ",".join(map(str, uvf.polarization_array))
    )
    assert uvf2.mode == "flag"
    assert hasattr(uvf2, "flag_array")
    assert hasattr(uvf2, "metric_array")
    assert uvf2.metric_array is None 
Example #6
Source File: test_utils.py    From argos with GNU General Public License v3.0 5 votes vote down vote up
def setUp(self):
        pass
        self.b_lit = b'bytes literal'
        self.s_lit = 'literal literal'
        self.u_lit = u'unicode literal'

        self.np_b_lit = np.bytes_('numpy bytes literal')
        self.np_s_lit = np.str_('numpy unicode literal')
        self.np_u_lit = np.unicode_('numpy unicode literal') 
Example #7
Source File: components_test.py    From batchflow with Apache License 2.0 5 votes vote down vote up
def fest_getitem_getattr(self, source, indices_type):
        index, full, a12_68, a25_48, a32_42, a35_40, a37_39, a38 = source(indices_type)

        assert (full[index[38]].labels == 138).all()
        assert (a12_68[index[38]].labels == 138).all()
        assert (a25_48[index[38]].labels == 138).all()
        assert (a32_42[index[38]].labels == 138).all()
        assert (a35_40[index[38]].labels == 138).all()
        assert (a37_39[index[38]].labels == 138).all()

        if index.dtype.type is np.str_:
            assert (a38[index[38]].labels == 138).all()
        else:
            with pytest.raises(TypeError):
                assert (a38[index[38]].labels == 138).all() 
Example #8
Source File: test_arrayprint.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_0d_arrays(self):
        unicode = type(u'')

        assert_equal(unicode(np.array(u'café', '<U4')), u'café')

        if sys.version_info[0] >= 3:
            assert_equal(repr(np.array('café', '<U4')),
                         "array('café', dtype='<U4')")
        else:
            assert_equal(repr(np.array(u'café', '<U4')),
                         "array(u'caf\\xe9', dtype='<U4')")
        assert_equal(str(np.array('test', np.str_)), 'test')

        a = np.zeros(1, dtype=[('a', '<i4', (3,))])
        assert_equal(str(a[0]), '([0, 0, 0],)')

        assert_equal(repr(np.datetime64('2005-02-25')[...]),
                     "array('2005-02-25', dtype='datetime64[D]')")

        assert_equal(repr(np.timedelta64('10', 'Y')[...]),
                     "array(10, dtype='timedelta64[Y]')")

        # repr of 0d arrays is affected by printoptions
        x = np.array(1)
        np.set_printoptions(formatter={'all':lambda x: "test"})
        assert_equal(repr(x), "array(test)")
        # str is unaffected
        assert_equal(str(x), "1")

        # check `style` arg raises
        assert_warns(DeprecationWarning, np.array2string,
                                         np.array(1.), style=repr)
        # but not in legacy mode
        np.array2string(np.array(1.), style=repr, legacy='1.13')
        # gh-10934 style was broken in legacy mode, check it works
        np.array2string(np.array(1.), legacy='1.13') 
Example #9
Source File: __init__.py    From LearningApacheSpark with MIT License 5 votes vote down vote up
def _can_convert_to_string(value):
        vtype = type(value)
        return isinstance(value, basestring) or vtype in [np.unicode_, np.string_, np.str_] 
Example #10
Source File: tests.py    From LearningApacheSpark with MIT License 5 votes vote down vote up
def test_string(self):
        lr = LogisticRegression()
        for col in ['features', u'features', np.str_('features')]:
            lr.setFeaturesCol(col)
            self.assertEqual(lr.getFeaturesCol(), 'features')
        self.assertRaises(TypeError, lambda: LogisticRegression(featuresCol=2.3)) 
Example #11
Source File: Preprocessing.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up
def get_data(lst,preproc):
   data = []
   result = []
   for path in lst:
       f = dicom.read_file(path)
       img = preproc(f.pixel_array.astype(float) / np.max(f.pixel_array))
       dst_path = path.rsplit(".", 1)[0] + ".64x64.jpg"
       scipy.misc.imsave(dst_path, img)
       result.append(dst_path)
       data.append(img)
   data = np.array(data, dtype=np.uint8)
   data = data.reshape(data.size)
   data = np.array(data, dtype=np.str_)
   data = data.reshape(data.size)
   return [data,result] 
Example #12
Source File: testing.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def rands_array(nchars, size, dtype='O'):
    """Generate an array of byte strings."""
    retval = (np.random.choice(RANDS_CHARS, size=nchars * np.prod(size))
              .view((np.str_, nchars)).reshape(size))
    if dtype is None:
        return retval
    else:
        return retval.astype(dtype) 
Example #13
Source File: test_inference.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_is_scalar_numpy_array_scalars(self):
        assert is_scalar(np.int64(1))
        assert is_scalar(np.float64(1.))
        assert is_scalar(np.int32(1))
        assert is_scalar(np.object_('foobar'))
        assert is_scalar(np.str_('foobar'))
        assert is_scalar(np.unicode_(u('foobar')))
        assert is_scalar(np.bytes_(b'foobar'))
        assert is_scalar(np.datetime64('2014-01-01'))
        assert is_scalar(np.timedelta64(1, 'h')) 
Example #14
Source File: test_constructors.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_constructor_empty_with_string_dtype(self):
        # GH 9428
        expected = DataFrame(index=[0, 1], columns=[0, 1], dtype=object)

        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=str)
        tm.assert_frame_equal(df, expected)
        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.str_)
        tm.assert_frame_equal(df, expected)
        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.unicode_)
        tm.assert_frame_equal(df, expected)
        df = DataFrame(index=[0, 1], columns=[0, 1], dtype='U5')
        tm.assert_frame_equal(df, expected) 
Example #15
Source File: test_scalarmath.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_scalar_comparison_to_none(self):
        # Scalars should just return False and not give a warnings.
        # The comparisons are flagged by pep8, ignore that.
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', FutureWarning)
            assert_(not np.float32(1) == None)
            assert_(not np.str_('test') == None)
            # This is dubious (see below):
            assert_(not np.datetime64('NaT') == None)

            assert_(np.float32(1) != None)
            assert_(np.str_('test') != None)
            # This is dubious (see below):
            assert_(np.datetime64('NaT') != None)
        assert_(len(w) == 0)

        # For documentation purposes, this is why the datetime is dubious.
        # At the time of deprecation this was no behaviour change, but
        # it has to be considered when the deprecations are done.
        assert_(np.equal(np.datetime64('NaT'), None))


#class TestRepr(object):
#    def test_repr(self):
#        for t in types:
#            val = t(1197346475.0137341)
#            val_repr = repr(val)
#            val2 = eval(val_repr)
#            assert_equal( val, val2 ) 
Example #16
Source File: test_regression.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_object_array_to_fixed_string(self):
        # Ticket #1235.
        a = np.array(['abcdefgh', 'ijklmnop'], dtype=np.object_)
        b = np.array(a, dtype=(np.str_, 8))
        assert_equal(a, b)
        c = np.array(a, dtype=(np.str_, 5))
        assert_equal(c, np.array(['abcde', 'ijklm']))
        d = np.array(a, dtype=(np.str_, 12))
        assert_equal(a, d)
        e = np.empty((2, ), dtype=(np.str_, 8))
        e[:] = a[:]
        assert_equal(a, e) 
Example #17
Source File: test_arrayprint.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up
def test_0d_arrays(self):
        unicode = type(u'')

        assert_equal(unicode(np.array(u'café', '<U4')), u'café')

        if sys.version_info[0] >= 3:
            assert_equal(repr(np.array('café', '<U4')),
                         "array('café', dtype='<U4')")
        else:
            assert_equal(repr(np.array(u'café', '<U4')),
                         "array(u'caf\\xe9', dtype='<U4')")
        assert_equal(str(np.array('test', np.str_)), 'test')

        a = np.zeros(1, dtype=[('a', '<i4', (3,))])
        assert_equal(str(a[0]), '([0, 0, 0],)')

        assert_equal(repr(np.datetime64('2005-02-25')[...]),
                     "array('2005-02-25', dtype='datetime64[D]')")

        assert_equal(repr(np.timedelta64('10', 'Y')[...]),
                     "array(10, dtype='timedelta64[Y]')")

        # repr of 0d arrays is affected by printoptions
        x = np.array(1)
        np.set_printoptions(formatter={'all':lambda x: "test"})
        assert_equal(repr(x), "array(test)")
        # str is unaffected
        assert_equal(str(x), "1")

        # check `style` arg raises
        assert_warns(DeprecationWarning, np.array2string,
                                         np.array(1.), style=repr)
        # but not in legacy mode
        np.array2string(np.array(1.), style=repr, legacy='1.13')
        # gh-10934 style was broken in legacy mode, check it works
        np.array2string(np.array(1.), legacy='1.13') 
Example #18
Source File: test_gp.py    From flare with MIT License 5 votes vote down vote up
def dumpcompare(obj1, obj2):
    '''this source code comes from
    http://stackoverflow.com/questions/15785719/how-to-print-a-dictionary-line-by-line-in-python'''

    assert isinstance(obj1, type(
        obj2)), "the two objects are of different types"

    if isinstance(obj1, dict):

        assert len(obj1.keys()) == len(
            obj2.keys()), f"key1 {list(obj1.keys())}, \n key2 {list(obj2.keys())}"

        for k1, k2 in zip(sorted(obj1.keys()), sorted(obj2.keys())):

            assert k1 == k2, f"key {k1} is not the same as {k2}"
            if (k1 != "name"):
                assert dumpcompare(obj1[k1], obj2[k2]
                                   ), f"value {k1} is not the same as {k2}"

    elif isinstance(obj1, (list, tuple)):

        assert len(obj1) == len(obj2)
        for k1, k2 in zip(obj1, obj2):
            assert dumpcompare(k1, k2), f"list elements are different"

    elif isinstance(obj1, np.ndarray):

        assert obj1.shape == obj2.shape

        if (not isinstance(obj1[0], np.str_)):
            assert np.equal(obj1, obj2).all(), "ndarray is not all the same"
        else:
            for xx, yy in zip(obj1, obj2):
                assert dumpcompare(xx, yy)
    else:
        assert obj1 == obj2

    return True 
Example #19
Source File: unischema.py    From petastorm with Apache License 2.0 5 votes vote down vote up
def _numpy_to_spark_mapping():
    """Returns a mapping from numpy to pyspark.sql type. Caches the mapping dictionary inorder to avoid instantiation
    of multiple objects in each call."""

    # Refer to the attribute of the function we use to cache the map using a name in the variable instead of a 'dot'
    # notation to avoid copy/paste/typo mistakes
    cache_attr_name = 'cached_numpy_to_pyspark_types_map'
    if not hasattr(_numpy_to_spark_mapping, cache_attr_name):
        import pyspark.sql.types as T

        setattr(_numpy_to_spark_mapping, cache_attr_name,
                {
                    np.int8: T.ByteType(),
                    np.uint8: T.ShortType(),
                    np.int16: T.ShortType(),
                    np.uint16: T.IntegerType(),
                    np.int32: T.IntegerType(),
                    np.int64: T.LongType(),
                    np.float32: T.FloatType(),
                    np.float64: T.DoubleType(),
                    np.string_: T.StringType(),
                    np.str_: T.StringType(),
                    np.unicode_: T.StringType(),
                    np.bool_: T.BooleanType(),
                })

    return getattr(_numpy_to_spark_mapping, cache_attr_name)


# TODO: Changing fields in this class or the UnischemaField will break reading due to the schema being pickled next to
# the dataset on disk 
Example #20
Source File: astropy_py3compat.py    From kgsgo-dataset-preprocessor with Mozilla Public License 2.0 5 votes vote down vote up
def encode_ascii(s):
        if isinstance(s, str):
            return s.encode('ascii')
        elif isinstance(s, numpy.ndarray) and \
                issubclass(s.dtype.type, numpy.str_):
            ns = numpy.char.encode(s, 'ascii').view(type(s))
            if ns.dtype.itemsize != s.dtype.itemsize / 4:
                ns = ns.astype((numpy.bytes_, s.dtype.itemsize / 4))
            return ns
        return s 
Example #21
Source File: vasprun.py    From pyiron with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def clean_key(a, remove_char=" "):
    """
    Replaces blanck spaces from a string for a dictionary key with "_"

    Args:
        a (str): String to be cleaned
        remove_char (str): Character to be replaced

    Returns:
        str: The clean string
    """
    if isinstance(a, (str, np.str, np.str_)):
        return a.replace(remove_char, "_")
    else:
        return a 
Example #22
Source File: vasprun.py    From pyiron with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def clean_character(a, remove_char=" "):
    """
    Args:
        a (str): String to be cleaned
        remove_char (str): Character to be replaced

    Returns:
        str: The clean string
    """
    if isinstance(a, (str, np.str, np.str_)):
        return a.replace(remove_char, "")
    else:
        return a 
Example #23
Source File: sparse_list.py    From pyiron with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def __getitem__(self, item):
        new_dict = {}
        if isinstance(item, int):
            for key, value in self._lists.items():
                if value[item] is not None:
                    new_dict[key] = value[item]
            return SparseArrayElement(**new_dict)
        elif isinstance(item, (str, np.str, np.str_)):
            return self._lists[item]

        elif isinstance(item, (list, np.ndarray)):
            # print("key(__getitem__) len, type, item[0]: ", len(item), type(item), item[0])
            if len(item) == len(self):
                if isinstance(item[0], (np.bool_, bool)):
                    item = np.arange(len(item))[item]
        for key, value in self._lists.items():
            # print ('key: ', key, type(value))
            if isinstance(item, slice):
                new_dict[key] = value[item]
            else:
                if isinstance(value, (list, tuple)):
                    new_dict[key] = [value[i] for i in item]
                else:
                    if len(value) > 0:
                        try:
                            new_dict[key] = value[item]
                        except IndexError:
                            print("Index error:: ", key, item, value)
                    # else:
                    #     new_dict[key] = []
        # print ("new_dict: ", new_dict, self.__class__)
        return self.__class__(**new_dict) 
Example #24
Source File: test_uvflag.py    From pyuvdata with BSD 2-Clause "Simplified" License 5 votes vote down vote up
def test_collapse_pol(test_outfile):
    uvf = UVFlag(test_f_file)
    uvf.weights_array = np.ones_like(uvf.weights_array)
    uvf2 = uvf.copy()
    uvf2.polarization_array[0] = -4
    uvf.__add__(uvf2, inplace=True, axis="pol")  # Concatenate to form multi-pol object
    uvf2 = uvf.copy()
    uvf2.collapse_pol()
    assert len(uvf2.polarization_array) == 1
    assert uvf2.polarization_array[0] == np.str_(
        ",".join(map(str, uvf.polarization_array))
    )
    assert uvf2.mode == "metric"
    assert hasattr(uvf2, "metric_array")
    assert hasattr(uvf2, "flag_array")
    assert uvf2.flag_array is None

    # test check passes just to be sure
    assert uvf2.check()

    # test writing it out and reading in to make sure polarization_array has
    # correct type
    uvf2.write(test_outfile, clobber=True)
    with h5py.File(test_outfile, "r") as h5:
        assert h5["Header/polarization_array"].dtype.type is np.string_
    uvf = UVFlag(test_outfile)
    assert uvf._polarization_array.expected_type == str
    assert uvf._polarization_array.acceptable_vals is None
    assert uvf == uvf2
    os.remove(test_outfile) 
Example #25
Source File: aipy_extracts.py    From pyuvdata with BSD 2-Clause "Simplified" License 5 votes vote down vote up
def __init__(self, filename, status="old", corrmode="r"):
        """
        Initialize from a miriad file.

        Parameters
        ----------
        filename : str
            filename to initialize from
        status : str
            options are: 'old', 'new', 'append'
        corrmode : str
            options are 'r' (float32 data storage) or 'j' (int16 with shared exponent)
        """
        assert status in ["old", "new", "append"]
        assert corrmode in ["r", "j"]
        # when reading mutliple files we may get a numpy array of file names
        # numpy casts arrays as np.str_ and cython does not like this
        _miriad.UV.__init__(self, str(filename), status, corrmode)

        self.status = status
        self.nchan = _miriad.MAXCHAN

        if status == "old":
            self.vartable = self._gen_vartable()
            self.read()
            self.rewind()  # Update variables for the user
            try:
                self.nchan = self["nchan"]
            except KeyError:
                pass
        else:
            self.vartable = {"corr": corrmode} 
Example #26
Source File: _encoders.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def _check_X(self, X):
        """
        Perform custom check_array:
        - convert list of strings to object dtype
        - check for missing values for object dtype data (check_array does
          not do that)
        - return list of features (arrays): this list of features is
          constructed feature by feature to preserve the data types
          of pandas DataFrame columns, as otherwise information is lost
          and cannot be used, eg for the `categories_` attribute.

        """
        if not (hasattr(X, 'iloc') and getattr(X, 'ndim', 0) == 2):
            # if not a dataframe, do normal check_array validation
            X_temp = check_array(X, dtype=None)
            if (not hasattr(X, 'dtype')
                    and np.issubdtype(X_temp.dtype, np.str_)):
                X = check_array(X, dtype=np.object)
            else:
                X = X_temp
            needs_validation = False
        else:
            # pandas dataframe, do validation later column by column, in order
            # to keep the dtype information to be used in the encoder.
            needs_validation = True

        n_samples, n_features = X.shape
        X_columns = []

        for i in range(n_features):
            Xi = self._get_feature(X, feature_idx=i)
            Xi = check_array(Xi, ensure_2d=False, dtype=None,
                             force_all_finite=needs_validation)
            X_columns.append(Xi)

        return X_columns, n_samples, n_features 
Example #27
Source File: test_scalarmath.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_scalar_comparison_to_none(self):
        # Scalars should just return False and not give a warnings.
        # The comparisons are flagged by pep8, ignore that.
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', FutureWarning)
            assert_(not np.float32(1) == None)
            assert_(not np.str_('test') == None)
            # This is dubious (see below):
            assert_(not np.datetime64('NaT') == None)

            assert_(np.float32(1) != None)
            assert_(np.str_('test') != None)
            # This is dubious (see below):
            assert_(np.datetime64('NaT') != None)
        assert_(len(w) == 0)

        # For documentation purposes, this is why the datetime is dubious.
        # At the time of deprecation this was no behaviour change, but
        # it has to be considered when the deprecations are done.
        assert_(np.equal(np.datetime64('NaT'), None))


#class TestRepr(object):
#    def test_repr(self):
#        for t in types:
#            val = t(1197346475.0137341)
#            val_repr = repr(val)
#            val2 = eval(val_repr)
#            assert_equal( val, val2 ) 
Example #28
Source File: Preprocessing.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up
def get_data(lst,preproc):
   data = []
   result = []
   for path in lst:
       f = dicom.read_file(path)
       img = preproc(f.pixel_array.astype(float) / np.max(f.pixel_array))
       dst_path = path.rsplit(".", 1)[0] + ".64x64.jpg"
       scipy.misc.imsave(dst_path, img)
       result.append(dst_path)
       data.append(img)
   data = np.array(data, dtype=np.uint8)
   data = data.reshape(data.size)
   data = np.array(data, dtype=np.str_)
   data = data.reshape(data.size)
   return [data,result] 
Example #29
Source File: test_arrayprint.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_0d_arrays(self):
        unicode = type(u'')

        assert_equal(unicode(np.array(u'café', '<U4')), u'café')

        if sys.version_info[0] >= 3:
            assert_equal(repr(np.array('café', '<U4')),
                         "array('café', dtype='<U4')")
        else:
            assert_equal(repr(np.array(u'café', '<U4')),
                         "array(u'caf\\xe9', dtype='<U4')")
        assert_equal(str(np.array('test', np.str_)), 'test')

        a = np.zeros(1, dtype=[('a', '<i4', (3,))])
        assert_equal(str(a[0]), '([0, 0, 0],)')

        assert_equal(repr(np.datetime64('2005-02-25')[...]),
                     "array('2005-02-25', dtype='datetime64[D]')")

        assert_equal(repr(np.timedelta64('10', 'Y')[...]),
                     "array(10, dtype='timedelta64[Y]')")

        # repr of 0d arrays is affected by printoptions
        x = np.array(1)
        np.set_printoptions(formatter={'all':lambda x: "test"})
        assert_equal(repr(x), "array(test)")
        # str is unaffected
        assert_equal(str(x), "1")

        # check `style` arg raises
        assert_warns(DeprecationWarning, np.array2string,
                                         np.array(1.), style=repr)
        # but not in legacy mode
        np.array2string(np.array(1.), style=repr, legacy='1.13')
        # gh-10934 style was broken in legacy mode, check it works
        np.array2string(np.array(1.), legacy='1.13') 
Example #30
Source File: test_draw.py    From synthpop with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def index():
    return np.array(['v', 'w', 'x', 'y', 'z'], dtype=np.str_)