Python numpy.integer() 使用实例

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Example 1

def pack_samples(self, samples, dtype=None):
        """Pack samples into one integer per sample

        Store one sample in a single integer instead of a list of
        integers with length `len(self.nsoutdims)`. Example:

        >>> p = pauli_mpp(nr_sites=2, local_dim=2)
        >>> p.outdims
        (6, 6)
        >>> p.pack_samples(np.array([[0, 1], [1, 0], [1, 2], [5, 5]]))
        array([ 1,  6,  8, 35])

        """
        assert samples.ndim == 2
        assert samples.shape[1] == len(self.nsoutdims)
        samples = np.ravel_multi_index(samples.T, self.nsoutdims)
        if dtype not in (True, False, None) and issubclass(dtype, np.integer):
            info = np.iinfo(dtype)
            assert samples.min() >= info.min
            assert samples.max() <= info.max
            samples = samples.astype(dtype)
        return samples 

Example 2

def __init__(self, config, model_dir, ob_shape_list):
    self.model_dir = model_dir

    self.cnn_format = config.cnn_format
    self.memory_size = config.memory_size
    self.actions = np.empty(self.memory_size, dtype = np.uint8)
    self.rewards = np.empty(self.memory_size, dtype = np.integer)
    # print(self.memory_size, config.screen_height, config.screen_width)
    # self.screens = np.empty((self.memory_size, config.screen_height, config.screen_width), dtype = np.float16)
    self.screens = np.empty([self.memory_size] + ob_shape_list, dtype = np.float16)
    self.terminals = np.empty(self.memory_size, dtype = np.bool)
    self.history_length = config.history_length
    # self.dims = (config.screen_height, config.screen_width)
    self.dims = tuple(ob_shape_list)
    self.batch_size = config.batch_size
    self.count = 0
    self.current = 0

    # pre-allocate prestates and poststates for minibatch
    self.prestates = np.empty((self.batch_size, self.history_length) + self.dims, dtype = np.float16)
    self.poststates = np.empty((self.batch_size, self.history_length) + self.dims, dtype = np.float16)
    # self.prestates = np.empty((self.batch_size, self.history_length, self.dims), dtype = np.float16)
    # self.poststates = np.empty((self.batch_size, self.history_length, self.dims), dtype = np.float16) 

Example 3

def test_auto_dtype_largeint(self):
        # Regression test for numpy/numpy#5635 whereby large integers could
        # cause OverflowErrors.

        # Test the automatic definition of the output dtype
        #
        # 2**66 = 73786976294838206464 => should convert to float
        # 2**34 = 17179869184 => should convert to int64
        # 2**10 = 1024 => should convert to int (int32 on 32-bit systems,
        #                 int64 on 64-bit systems)

        data = TextIO('73786976294838206464 17179869184 1024')

        test = np.ndfromtxt(data, dtype=None)

        assert_equal(test.dtype.names, ['f0', 'f1', 'f2'])

        assert_(test.dtype['f0'] == np.float)
        assert_(test.dtype['f1'] == np.int64)
        assert_(test.dtype['f2'] == np.integer)

        assert_allclose(test['f0'], 73786976294838206464.)
        assert_equal(test['f1'], 17179869184)
        assert_equal(test['f2'], 1024) 

Example 4

def test_with_incorrect_minlength(self):
        x = np.array([], dtype=int)
        assert_raises_regex(TypeError, "an integer is required",
                            lambda: np.bincount(x, minlength="foobar"))
        assert_raises_regex(ValueError, "must be positive",
                            lambda: np.bincount(x, minlength=-1))
        assert_raises_regex(ValueError, "must be positive",
                            lambda: np.bincount(x, minlength=0))

        x = np.arange(5)
        assert_raises_regex(TypeError, "an integer is required",
                            lambda: np.bincount(x, minlength="foobar"))
        assert_raises_regex(ValueError, "minlength must be positive",
                            lambda: np.bincount(x, minlength=-1))
        assert_raises_regex(ValueError, "minlength must be positive",
                            lambda: np.bincount(x, minlength=0)) 

Example 5

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        self.assertTrue(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        self.assertTrue(not allclose(a, b))
        b[0] = np.inf
        self.assertTrue(allclose(a, b))
        # Test allclose w/ masked
        a = masked_array(a)
        a[-1] = masked
        self.assertTrue(allclose(a, b, masked_equal=True))
        self.assertTrue(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        self.assertTrue(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        self.assertTrue(allclose(a, a)) 

Example 6

def _parse_fields(point):
    output = []
    for k, v in point['fields'].items():
        k = escape(k, key_escape)
        # noinspection PyUnresolvedReferences
        if isinstance(v, bool):
            output.append('{k}={v}'.format(k=k, v=str(v).upper()))
        elif isinstance(v, (int, np.integer)):
            output.append('{k}={v}i'.format(k=k, v=v))
        elif isinstance(v, str):
            output.append('{k}="{v}"'.format(k=k, v=v.translate(str_escape)))
        elif v is None or np.isnan(v):
            continue
        else:
            # Floats and other numerical formats go here.
            # TODO: Add unit test
            output.append('{k}={v}'.format(k=k, v=v))
    return ','.join(output) 

Example 7

def get_subvolume(self, bounds):
        if bounds.start is None or bounds.stop is None:
            image_subvol = self.image_data
            label_subvol = self.label_data
        else:
            image_subvol = self.image_data[
                    bounds.start[0]:bounds.stop[0],
                    bounds.start[1]:bounds.stop[1],
                    bounds.start[2]:bounds.stop[2]]
            label_subvol = None

        if np.issubdtype(image_subvol.dtype, np.integer):
            raise ValueError('Sparse volume access does not support image data coercion.')

        seed = bounds.seed
        if seed is None:
            seed = np.array(image_subvol.shape, dtype=np.int64) // 2

        return Subvolume(image_subvol, label_subvol, seed, bounds.label_id) 

Example 8

def __init__(self, X_train, y_train, X_test, y_test, categorical=True):
        self._x_train = X_train
        self._x_test = X_test

        # are the targets to be made one hot vectors
        if categorical:
            self._y_train = np_utils.to_categorical(y_train)
            self._y_test = np_utils.to_categorical(y_test)
            self._output_size = self._y_train.shape[1]

        # handle sparse output classification
        elif issubclass(y_train.dtype.type, np.integer):
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.max() + 1  # assume 0 based indexes

        # not classification, just copy them
        else:
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.shape[1] 

Example 9

def __init__(self, X_train, y_train, X_test, y_test, categorical=True):
        self._x_train = X_train
        self._x_test = X_test

        # are the targets to be made one hot vectors
        if categorical:
            self._y_train = np_utils.to_categorical(y_train)
            self._y_test = np_utils.to_categorical(y_test)
            self._output_size = self._y_train.shape[1]

        # handle sparse output classification
        elif issubclass(y_train.dtype.type, np.integer):
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.max() + 1  # assume 0 based indexes

        # not classification, just copy them
        else:
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.shape[1] 

Example 10

def __init__(self, X_train, y_train, X_test, y_test, categorical=True):
        self._x_train = X_train
        self._x_test = X_test

        # are the targets to be made one hot vectors
        if categorical:
            self._y_train = np_utils.to_categorical(y_train)
            self._y_test = np_utils.to_categorical(y_test)
            self._output_size = self._y_train.shape[1]

        # handle sparse output classification
        elif issubclass(y_train.dtype.type, np.integer):
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.max() + 1  # assume 0 based indexes

        # not classification, just copy them
        else:
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.shape[1] 

Example 11

def __init__(self, X_train, y_train, X_test, y_test, categorical=True):
        self._x_train = X_train
        self._x_test = X_test

        # are the targets to be made one hot vectors
        if categorical:
            self._y_train = np_utils.to_categorical(y_train)
            self._y_test = np_utils.to_categorical(y_test)
            self._output_size = self._y_train.shape[1]

        # handle sparse output classification
        elif issubclass(y_train.dtype.type, np.integer):
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.max() + 1  # assume 0 based indexes

        # not classification, just copy them
        else:
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.shape[1] 

Example 12

def test_auto_dtype_largeint(self):
        # Regression test for numpy/numpy#5635 whereby large integers could
        # cause OverflowErrors.

        # Test the automatic definition of the output dtype
        #
        # 2**66 = 73786976294838206464 => should convert to float
        # 2**34 = 17179869184 => should convert to int64
        # 2**10 = 1024 => should convert to int (int32 on 32-bit systems,
        #                 int64 on 64-bit systems)

        data = TextIO('73786976294838206464 17179869184 1024')

        test = np.ndfromtxt(data, dtype=None)

        assert_equal(test.dtype.names, ['f0', 'f1', 'f2'])

        assert_(test.dtype['f0'] == np.float)
        assert_(test.dtype['f1'] == np.int64)
        assert_(test.dtype['f2'] == np.integer)

        assert_allclose(test['f0'], 73786976294838206464.)
        assert_equal(test['f1'], 17179869184)
        assert_equal(test['f2'], 1024) 

Example 13

def test_with_incorrect_minlength(self):
        x = np.array([], dtype=int)
        assert_raises_regex(TypeError, "an integer is required",
                            lambda: np.bincount(x, minlength="foobar"))
        assert_raises_regex(ValueError, "must be positive",
                            lambda: np.bincount(x, minlength=-1))
        assert_raises_regex(ValueError, "must be positive",
                            lambda: np.bincount(x, minlength=0))

        x = np.arange(5)
        assert_raises_regex(TypeError, "an integer is required",
                            lambda: np.bincount(x, minlength="foobar"))
        assert_raises_regex(ValueError, "minlength must be positive",
                            lambda: np.bincount(x, minlength=-1))
        assert_raises_regex(ValueError, "minlength must be positive",
                            lambda: np.bincount(x, minlength=0)) 

Example 14

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        self.assertTrue(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        self.assertTrue(not allclose(a, b))
        b[0] = np.inf
        self.assertTrue(allclose(a, b))
        # Test allclose w/ masked
        a = masked_array(a)
        a[-1] = masked
        self.assertTrue(allclose(a, b, masked_equal=True))
        self.assertTrue(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        self.assertTrue(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        self.assertTrue(allclose(a, a)) 

Example 15

def _changeArrayDType(img, dtype, **kwargs):
    if dtype == 'noUint':
        return toNoUintArray(img)
    if issubclass(np.dtype(dtype).type, np.integer):
        return toUIntArray(img, dtype, **kwargs)
    return img.astype(dtype)


# def bitDepth(path, img=None):
#     '''
#     there are no python filetypes between 8bit and 16 bit
#     so, to find out whether an image is 12 or 14 bit resolved
#     we need to check actual file size and image shape
#     '''
#     if img is None:
#         img = imread(img)
#     size = os.path.getsize(path)*8
#     print (size, img.size,8888888,img.shape,  size/img.size)
#     kh
#     return size/img.size 

Example 16

def __init__(self, config, model_dir):
    self.model_dir = model_dir

    self.cnn_format = config.cnn_format
    self.memory_size = config.memory_size
    self.actions = np.empty(self.memory_size, dtype = np.uint8)
    self.rewards = np.empty(self.memory_size, dtype = np.integer)
    self.screens = np.empty((self.memory_size, config.screen_height, config.screen_width), dtype = np.float16)
    self.terminals = np.empty(self.memory_size, dtype = np.bool)
    self.history_length = config.history_length
    self.dims = (config.screen_height, config.screen_width)
    self.batch_size = config.batch_size
    self.count = 0
    self.current = 0

    # pre-allocate prestates and poststates for minibatch
    self.prestates = np.empty((self.batch_size, self.history_length) + self.dims, dtype = np.float16)
    self.poststates = np.empty((self.batch_size, self.history_length) + self.dims, dtype = np.float16) 

Example 17

def _get_block(self, values, key_remainder=None):
        item_block = None
        for i, v in enumerate(values):
            # Lists in the aggregate key index in tandem;
            # so, index into those lists (the first list is `values`)
            v_key_remainder = key_remainder
            if isinstance(values, tuple) or isinstance(values, list):
                if key_remainder is not None:
                    broadcasted_key_remainder = ()
                    for k in key_remainder:
                        if hasattr(k, '__len__') and len(k)==np.size(k):
                            broadcasted_key_remainder += (k[i],)
                        else:
                            broadcasted_key_remainder += (k,)
                    v_key_remainder = broadcasted_key_remainder
            
            # Make a single read at an integer index of axis 0
            elem = self._get_element(v, v_key_remainder)
            if item_block is None:
                item_block = np.zeros((len(values),)+elem.shape,
                                      self.dtype)
            item_block[i] = elem
        return item_block 

Example 18

def fcn(self, data_in):
        """
        If return list, [0] goes to original, [1] goes to affected
        """
        inst_nrb_merge = _MergeNRBs(nrb_left=self.nrb_left, 
                                        nrb_right=self.nrb_right,
                                        pix=self.parameters['pix_switchpt'],
                                        left_side_scale=self.parameters['scale_left'])
        
        if self.fullRange:
            pix = _np.arange(self.wn.size, dtype=_np.integer)
            
        else:
            list_rng_pix = _find_nearest(self.wn, self.rng)[1]
            pix = _np.arange(list_rng_pix[0],list_rng_pix[1]+1,
                             dtype=_np.integer)
            
        nrb_merged = inst_nrb_merge.calculate()
        kkd = _np.zeros(data_in.shape)

        # Note: kk_widget.fcn return imag part
        kkd[..., pix] = self.kk_widget.fcn([nrb_merged[pix], data_in[..., pix]])
        
        return [_np.vstack((self.nrb_left, self.nrb_right, nrb_merged)),
                kkd] 

Example 19

def __init__(self, path, size ,historySize, dims , batchSize):

        self.size = size
        self.dims = dims
        # preallocate memory
        self.actions = np.empty(self.size, dtype=np.uint8)
        self.rewards = np.empty(self.size, dtype=np.integer)
        self.screens = np.empty((self.size, self.dims[0], self.dims[1] ), dtype=np.uint8)
        self.terminals = np.empty(self.size, dtype=np.bool)



        self.history_length = historySize
        self.batch_size = batchSize

        self.buffer = np.zeros((self.batch_size, self.history_length) + self.dims, dtype=np.uint8)

        self.count = 0
        self.current = 0

        # pre-allocate prestates and poststates for minibatch
        self.prestates = np.empty([self.batch_size, self.history_length] + self.dims, dtype=np.uint8)
        self.poststates = np.empty([self.batch_size, self.history_length] + self.dims, dtype=np.uint8) 

Example 20

def _prepare_mask(mask, label, erode=True):
    fgmask = mask.copy()

    if np.issubdtype(fgmask.dtype, np.integer):
        if isinstance(label, string_types):
            label = FSL_FAST_LABELS[label]

        fgmask[fgmask != label] = 0
        fgmask[fgmask == label] = 1
    else:
        fgmask[fgmask > .95] = 1.
        fgmask[fgmask < 1.] = 0

    if erode:
        # Create a structural element to be used in an opening operation.
        struc = nd.generate_binary_structure(3, 2)
        # Perform an opening operation on the background data.
        fgmask = nd.binary_opening(fgmask, structure=struc).astype(np.uint8)

    return fgmask 

Example 21

def check_random_state(seed):
    """Turn seed into a np.random.RandomState instance.

    If seed is None, return the RandomState singleton used by np.random.
    If seed is an int, return a new RandomState instance seeded with seed.
    If seed is already a RandomState instance, return it.
    Otherwise raise ValueError.
    """
    if seed is None or seed is np.random:
        return np.random.mtrand._rand
    if isinstance(seed, (int, np.integer)):
        return np.random.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return seed
    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                     ' instance' % seed) 

Example 22

def check_random_state(seed):
    """Turn seed into a np.random.RandomState instance

    If seed is None, return the RandomState singleton used by np.random.
    If seed is an int, return a new RandomState instance seeded with seed.
    If seed is already a RandomState instance, return it.
    Otherwise raise ValueError.
    """
    if seed is None or seed is np.random:
        return np.random.mtrand._rand
    if isinstance(seed, (numbers.Integral, np.integer)):
        return np.random.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return seed
    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                     ' instance' % seed) 

Example 23

def _can_reindex(self, indexer):
        """
        *this is an internal non-public method*

        Check if we are allowing reindexing with this particular indexer

        Parameters
        ----------
        indexer : an integer indexer

        Raises
        ------
        ValueError if its a duplicate axis
        """

        # trying to reindex on an axis with duplicates
        if not self.is_unique and len(indexer):
            raise ValueError("cannot reindex from a duplicate axis") 

Example 24

def unique1d(values):
    """
    Hash table-based unique
    """
    if np.issubdtype(values.dtype, np.floating):
        table = _hash.Float64HashTable(len(values))
        uniques = np.array(table.unique(_ensure_float64(values)),
                           dtype=np.float64)
    elif np.issubdtype(values.dtype, np.datetime64):
        table = _hash.Int64HashTable(len(values))
        uniques = table.unique(_ensure_int64(values))
        uniques = uniques.view('M8[ns]')
    elif np.issubdtype(values.dtype, np.timedelta64):
        table = _hash.Int64HashTable(len(values))
        uniques = table.unique(_ensure_int64(values))
        uniques = uniques.view('m8[ns]')
    elif np.issubdtype(values.dtype, np.integer):
        table = _hash.Int64HashTable(len(values))
        uniques = table.unique(_ensure_int64(values))
    else:
        table = _hash.PyObjectHashTable(len(values))
        uniques = table.unique(_ensure_object(values))
    return uniques 

Example 25

def shift(self, periods, axis=0, mgr=None):
        """ shift the block by periods """
        N = len(self.values.T)
        indexer = np.zeros(N, dtype=int)
        if periods > 0:
            indexer[periods:] = np.arange(N - periods)
        else:
            indexer[:periods] = np.arange(-periods, N)
        new_values = self.values.to_dense().take(indexer)
        # convert integer to float if necessary. need to do a lot more than
        # that, handle boolean etc also
        new_values, fill_value = com._maybe_upcast(new_values)
        if periods > 0:
            new_values[:periods] = fill_value
        else:
            new_values[periods:] = fill_value
        return [self.make_block_same_class(new_values,
                                           placement=self.mgr_locs)] 

Example 26

def to_sparse(self, fill_value=None, kind='block'):
        """
        Convert to SparseDataFrame

        Parameters
        ----------
        fill_value : float, default NaN
        kind : {'block', 'integer'}

        Returns
        -------
        y : SparseDataFrame
        """
        from pandas.core.sparse import SparseDataFrame
        return SparseDataFrame(self._series, index=self.index,
                               columns=self.columns, default_kind=kind,
                               default_fill_value=fill_value) 

Example 27

def __getitem__(self, key):

        # shortcut if we are an actual column
        is_mi_columns = isinstance(self.columns, MultiIndex)
        try:
            if key in self.columns and not is_mi_columns:
                return self._getitem_column(key)
        except:
            pass

        # see if we can slice the rows
        indexer = convert_to_index_sliceable(self, key)
        if indexer is not None:
            return self._getitem_slice(indexer)

        if isinstance(key, (Series, np.ndarray, Index, list)):
            # either boolean or fancy integer index
            return self._getitem_array(key)
        elif isinstance(key, DataFrame):
            return self._getitem_frame(key)
        elif is_mi_columns:
            return self._getitem_multilevel(key)
        else:
            return self._getitem_column(key) 

Example 28

def test_grouper_multilevel_freq(self):

        # GH 7885
        # with level and freq specified in a pd.Grouper
        from datetime import date, timedelta
        d0 = date.today() - timedelta(days=14)
        dates = date_range(d0, date.today())
        date_index = pd.MultiIndex.from_product(
            [dates, dates], names=['foo', 'bar'])
        df = pd.DataFrame(np.random.randint(0, 100, 225), index=date_index)

        # Check string level
        expected = df.reset_index().groupby([pd.Grouper(
            key='foo', freq='W'), pd.Grouper(key='bar', freq='W')]).sum()
        # reset index changes columns dtype to object
        expected.columns = pd.Index([0], dtype='int64')

        result = df.groupby([pd.Grouper(level='foo', freq='W'), pd.Grouper(
            level='bar', freq='W')]).sum()
        assert_frame_equal(result, expected)

        # Check integer level
        result = df.groupby([pd.Grouper(level=0, freq='W'), pd.Grouper(
            level=1, freq='W')]).sum()
        assert_frame_equal(result, expected) 

Example 29

def test_floats(self):
        arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'floating')

        arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'],
                       dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'mixed-integer')

        arr = np.array([1, 2, 3, 4, 5], dtype='f4')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'floating')

        arr = np.array([1, 2, 3, 4, 5], dtype='f8')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'floating') 

Example 30

def test_fancy_setitem_int_labels(self):
        # integer index defers to label-based indexing

        df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))

        tmp = df.copy()
        exp = df.copy()
        tmp.ix[[0, 2, 4]] = 5
        exp.values[:3] = 5
        assert_frame_equal(tmp, exp)

        tmp = df.copy()
        exp = df.copy()
        tmp.ix[6] = 5
        exp.values[3] = 5
        assert_frame_equal(tmp, exp)

        tmp = df.copy()
        exp = df.copy()
        tmp.ix[:, 2] = 5

        # tmp correctly sets the dtype
        # so match the exp way
        exp[2] = 5
        assert_frame_equal(tmp, exp) 

Example 31

def test_default_type_conversion(self):
        df = sql.read_sql_table("types_test_data", self.conn)

        self.assertTrue(issubclass(df.FloatCol.dtype.type, np.floating),
                        "FloatCol loaded with incorrect type")
        self.assertTrue(issubclass(df.IntCol.dtype.type, np.integer),
                        "IntCol loaded with incorrect type")
        self.assertTrue(issubclass(df.BoolCol.dtype.type, np.bool_),
                        "BoolCol loaded with incorrect type")

        # Int column with NA values stays as float
        self.assertTrue(issubclass(df.IntColWithNull.dtype.type, np.floating),
                        "IntColWithNull loaded with incorrect type")
        # Bool column with NA values becomes object
        self.assertTrue(issubclass(df.BoolColWithNull.dtype.type, np.object),
                        "BoolColWithNull loaded with incorrect type") 

Example 32

def test_default_type_conversion(self):
        df = sql.read_sql_table("types_test_data", self.conn)

        self.assertTrue(issubclass(df.FloatCol.dtype.type, np.floating),
                        "FloatCol loaded with incorrect type")
        self.assertTrue(issubclass(df.IntCol.dtype.type, np.integer),
                        "IntCol loaded with incorrect type")
        # sqlite has no boolean type, so integer type is returned
        self.assertTrue(issubclass(df.BoolCol.dtype.type, np.integer),
                        "BoolCol loaded with incorrect type")

        # Int column with NA values stays as float
        self.assertTrue(issubclass(df.IntColWithNull.dtype.type, np.floating),
                        "IntColWithNull loaded with incorrect type")
        # Non-native Bool column with NA values stays as float
        self.assertTrue(issubclass(df.BoolColWithNull.dtype.type, np.floating),
                        "BoolColWithNull loaded with incorrect type") 

Example 33

def test_default_type_conversion(self):
        df = sql.read_sql_table("types_test_data", self.conn)

        self.assertTrue(issubclass(df.FloatCol.dtype.type, np.floating),
                        "FloatCol loaded with incorrect type")
        self.assertTrue(issubclass(df.IntCol.dtype.type, np.integer),
                        "IntCol loaded with incorrect type")
        # MySQL has no real BOOL type (it's an alias for TINYINT)
        self.assertTrue(issubclass(df.BoolCol.dtype.type, np.integer),
                        "BoolCol loaded with incorrect type")

        # Int column with NA values stays as float
        self.assertTrue(issubclass(df.IntColWithNull.dtype.type, np.floating),
                        "IntColWithNull loaded with incorrect type")
        # Bool column with NA = int column with NA values => becomes float
        self.assertTrue(issubclass(df.BoolColWithNull.dtype.type, np.floating),
                        "BoolColWithNull loaded with incorrect type") 

Example 34

def __init__(self, f, colspecs, delimiter, comment):
        self.f = f
        self.buffer = None
        self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t '
        self.comment = comment
        if colspecs == 'infer':
            self.colspecs = self.detect_colspecs()
        else:
            self.colspecs = colspecs

        if not isinstance(self.colspecs, (tuple, list)):
            raise TypeError("column specifications must be a list or tuple, "
                            "input was a %r" % type(colspecs).__name__)

        for colspec in self.colspecs:

            if not (isinstance(colspec, (tuple, list)) and
                    len(colspec) == 2 and
                    isinstance(colspec[0], (int, np.integer, type(None))) and
                    isinstance(colspec[1], (int, np.integer, type(None)))):
                raise TypeError('Each column specification must be '
                                '2 element tuple or list of integers') 

Example 35

def set_atom_categorical(self, block, items, info=None, values=None):
        # currently only supports a 1-D categorical
        # in a 1-D block

        values = block.values
        codes = values.codes
        self.kind = 'integer'
        self.dtype = codes.dtype.name
        if values.ndim > 1:
            raise NotImplementedError("only support 1-d categoricals")
        if len(items) > 1:
            raise NotImplementedError("only support single block categoricals")

        # write the codes; must be in a block shape
        self.ordered = values.ordered
        self.typ = self.get_atom_data(block, kind=codes.dtype.name)
        self.set_data(_block_shape(codes))

        # write the categories
        self.meta = 'category'
        self.set_metadata(block.values.categories)

        # update the info
        self.update_info(info) 

Example 36

def _handle_date_column(col, format=None):
    if isinstance(format, dict):
        return to_datetime(col, errors='ignore', **format)
    else:
        if format in ['D', 's', 'ms', 'us', 'ns']:
            return to_datetime(col, errors='coerce', unit=format, utc=True)
        elif (issubclass(col.dtype.type, np.floating) or
              issubclass(col.dtype.type, np.integer)):
            # parse dates as timestamp
            format = 's' if format is None else format
            return to_datetime(col, errors='coerce', unit=format, utc=True)
        elif com.is_datetime64tz_dtype(col):
            # coerce to UTC timezone
            # GH11216
            return (to_datetime(col, errors='coerce')
                    .astype('datetime64[ns, UTC]'))
        else:
            return to_datetime(col, errors='coerce', format=format, utc=True) 

Example 37

def _get_dtype(self, sqltype):
        from sqlalchemy.types import (Integer, Float, Boolean, DateTime,
                                      Date, TIMESTAMP)

        if isinstance(sqltype, Float):
            return float
        elif isinstance(sqltype, Integer):
            # TODO: Refine integer size.
            return np.dtype('int64')
        elif isinstance(sqltype, TIMESTAMP):
            # we have a timezone capable type
            if not sqltype.timezone:
                return datetime
            return DatetimeTZDtype
        elif isinstance(sqltype, DateTime):
            # Caution: np.datetime64 is also a subclass of np.number.
            return datetime
        elif isinstance(sqltype, Date):
            return date
        elif isinstance(sqltype, Boolean):
            return bool
        return object 

Example 38

def _sql_type_name(self, col):
        dtype = self.dtype or {}
        if col.name in dtype:
            return dtype[col.name]

        col_type = self._get_notnull_col_dtype(col)
        if col_type == 'timedelta64':
            warnings.warn("the 'timedelta' type is not supported, and will be "
                          "written as integer values (ns frequency) to the "
                          "database.", UserWarning, stacklevel=8)
            col_type = "integer"

        elif col_type == "datetime64":
            col_type = "datetime"

        elif col_type == "empty":
            col_type = "string"

        elif col_type == "complex":
            raise ValueError('Complex datatypes not supported')

        if col_type not in _SQL_TYPES:
            col_type = "string"

        return _SQL_TYPES[col_type][self.pd_sql.flavor] 

Example 39

def __getitem__(self, key):
        """

        """
        try:
            return self._get_val_at(self.index.get_loc(key))

        except KeyError:
            if isinstance(key, (int, np.integer)):
                return self._get_val_at(key)
            raise Exception('Requested index not in this series!')

        except TypeError:
            # Could not hash item, must be array-like?
            pass

        # is there a case where this would NOT be an ndarray?
        # need to find an example, I took out the case for now

        key = _values_from_object(key)
        dataSlice = self.values[key]
        new_index = Index(self.index.view(ndarray)[key])
        return self._constructor(dataSlice, index=new_index).__finalize__(self) 

Example 40

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        self.assertTrue(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        self.assertTrue(not allclose(a, b))
        b[0] = np.inf
        self.assertTrue(allclose(a, b))
        # Test all close w/ masked
        a = masked_array(a)
        a[-1] = masked
        self.assertTrue(allclose(a, b, masked_equal=True))
        self.assertTrue(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        self.assertTrue(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        self.assertTrue(allclose(a, a)) 

Example 41

def check_random_state(seed):
    """Turn seed into a np.random.RandomState instance
    Parameters
    ----------
    seed : None | int | instance of RandomState
        If seed is None, return the RandomState singleton used by np.random.
        If seed is an int, return a new RandomState instance seeded with seed.
        If seed is already a RandomState instance, return it.
        Otherwise raise ValueError.
    """
    if seed is None or seed is np.random:
        return np.random.mtrand._rand
    if isinstance(seed, (numbers.Integral, np.integer)):
        return np.random.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return seed
    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                     ' instance' % seed) 

Example 42

def guessCfgType( value ):
    # For guessing the data type (bool, integer, float, or string only) from ConfigParser
    if value.lower() == 'true':
        return True
    if value.lower() == 'false':
        return False
    try:
        value = np.int( value )
        return value
    except: 
        pass
    try: 
        value = np.float32( value )
        return value
    except: 
        pass 
    return value 

Example 43

def check_window_length(window_length):
    """
    Ensure the window length provided to a transform is valid.
    """
    if window_length is None:
        raise InvalidWindowLength("window_length must be provided")
    if not isinstance(window_length, Integral):
        raise InvalidWindowLength(
            "window_length must be an integer-like number")
    if window_length == 0:
        raise InvalidWindowLength("window_length must be non-zero")
    if window_length < 0:
        raise InvalidWindowLength("window_length must be positive") 

Example 44

def _extract_field_names(self, event):
        # extract field names from sids (price, volume etc), make sure
        # every sid has the same fields.
        sid_keys = []
        for sid in itervalues(event.data):
            keys = set([name for name, value in sid.items()
                        if isinstance(value,
                                      (int,
                                       float,
                                       numpy.integer,
                                       numpy.float,
                                       numpy.long))
                        ])
            sid_keys.append(keys)

        # with CUSTOM data events, there may be different fields
        # per sid. So the allowable keys are the union of all events.
        union = set.union(*sid_keys)
        unwanted_fields = {
            'portfolio',
            'sid',
            'dt',
            'type',
            'source_id',
            '_initial_len',
        }
        return union - unwanted_fields 

Example 45

def RATWriteArray(rat, array, field, start=0):
    """
    Pure Python implementation of writing a chunk of the RAT
    from a numpy array. Type of array is coerced to one of the types
    (int, double, string) supported. Called from RasterAttributeTable.WriteArray
    """
    if array is None:
        raise ValueError("Expected array of dim 1")

    # if not the array type convert it to handle lists etc
    if not isinstance(array, numpy.ndarray):
        array = numpy.array(array)

    if array.ndim != 1:
        raise ValueError("Expected array of dim 1")

    if (start + array.size) > rat.GetRowCount():
        raise ValueError("Array too big to fit into RAT from start position")

    if numpy.issubdtype(array.dtype, numpy.integer):
        # is some type of integer - coerce to standard int
        # TODO: must check this is fine on all platforms
        # confusingly numpy.int 64 bit even if native type 32 bit
        array = array.astype(numpy.int32)
    elif numpy.issubdtype(array.dtype, numpy.floating):
        # is some type of floating point - coerce to double
        array = array.astype(numpy.double)
    elif numpy.issubdtype(array.dtype, numpy.character):
        # cast away any kind of Unicode etc
        array = array.astype(numpy.character)
    else:
        raise ValueError("Array not of a supported type (integer, double or string)")

    return RATValuesIONumPyWrite(rat, field, start, array) 

Example 46

def default(self, obj):
    if isinstance(obj, np.integer):
      return int(obj)
    elif isinstance(obj, np.ndarray):
      return obj.tolist()
    elif isinstance(obj, np.floating):
      return float(obj)
    else:
      return super(MyEncoder, self).default(obj) 

Example 47

def writeHDF5Meta(self, root, name, data, **dsOpts):
        if isinstance(data, np.ndarray):
            dsOpts['maxshape'] = (None,) + data.shape[1:]
            root.create_dataset(name, data=data, **dsOpts)
        elif isinstance(data, list) or isinstance(data, tuple):
            gr = root.create_group(name)
            if isinstance(data, list):
                gr.attrs['_metaType_'] = 'list'
            else:
                gr.attrs['_metaType_'] = 'tuple'
            #n = int(np.log10(len(data))) + 1
            for i in range(len(data)):
                self.writeHDF5Meta(gr, str(i), data[i], **dsOpts)
        elif isinstance(data, dict):
            gr = root.create_group(name)
            gr.attrs['_metaType_'] = 'dict'
            for k, v in data.items():
                self.writeHDF5Meta(gr, k, v, **dsOpts)
        elif isinstance(data, int) or isinstance(data, float) or isinstance(data, np.integer) or isinstance(data, np.floating):
            root.attrs[name] = data
        else:
            try:   ## strings, bools, None are stored as repr() strings
                root.attrs[name] = repr(data)
            except:
                print("Can not store meta data of type '%s' in HDF5. (key is '%s')" % (str(type(data)), str(name)))
                raise 

Example 48

def writeHDF5Meta(self, root, name, data, **dsOpts):
        if isinstance(data, np.ndarray):
            dsOpts['maxshape'] = (None,) + data.shape[1:]
            root.create_dataset(name, data=data, **dsOpts)
        elif isinstance(data, list) or isinstance(data, tuple):
            gr = root.create_group(name)
            if isinstance(data, list):
                gr.attrs['_metaType_'] = 'list'
            else:
                gr.attrs['_metaType_'] = 'tuple'
            #n = int(np.log10(len(data))) + 1
            for i in range(len(data)):
                self.writeHDF5Meta(gr, str(i), data[i], **dsOpts)
        elif isinstance(data, dict):
            gr = root.create_group(name)
            gr.attrs['_metaType_'] = 'dict'
            for k, v in data.items():
                self.writeHDF5Meta(gr, k, v, **dsOpts)
        elif isinstance(data, int) or isinstance(data, float) or isinstance(data, np.integer) or isinstance(data, np.floating):
            root.attrs[name] = data
        else:
            try:   ## strings, bools, None are stored as repr() strings
                root.attrs[name] = repr(data)
            except:
                print("Can not store meta data of type '%s' in HDF5. (key is '%s')" % (str(type(data)), str(name)))
                raise 

Example 49

def repeat(self, nr_sites):
        """Construct a longer MP-POVM by repetition

        The resulting POVM will have length `nr_sites`. If `nr_sites`
        is not an integer multiple of `len(self)`, `self` must
        factorize (have leg  dimension one) at the position where it
        will be cut. For example, consider the tensor product MP-POVM
        of Pauli X and Pauli Y. Calling `repeat(nr_sites=5)` will
        construct the tensor product POVM XYXYX:

        >>> import mpnum as mp
        >>> import mpnum.povm as mpp
        >>> x, y = (mpp.MPPovm.from_local_povm(lp(3), 1) for lp in
        ...         (mpp.x_povm, mpp.y_povm))
        >>> xy = mp.chain([x, y])
        >>> xyxyx = mp.chain([x, y, x, y, x])
        >>> mp.norm(xyxyx - xy.repeat(5)) <= 1e-10
        True

        """
        n_repeat, n_last = nr_sites // len(self), nr_sites % len(self)
        if n_last > 0:
            assert self.ranks[n_last - 1] == 1, \
                "Partial repetition requires factorizing MP-POVM"
        return mp.chain([self] * n_repeat
                        + ([MPPovm(self.lt[:n_last])] if n_last > 0 else [])) 

Example 50

def est_pmf(self, samples, normalize=True, eps=1e-10):
        """Estimate probability mass function from samples

        :param np.ndarray samples: `(n_samples, len(self.nsoutdims))`
            array of samples
        :param bool normalize: True: Return normalized probability
            estimates (default). False: Return integer outcome counts.
        :returns: Estimated probabilities as ndarray `est_pmf` with
            shape `self.nsoutdims`

        `n_samples * est_pmf[i1, ..., ik]` provides the number of
        occurences of outcome `(i1, ..., ik)` in `samples`.

        """
        n_samples = samples.shape[0]
        n_out = np.prod(self.nsoutdims)
        if samples.ndim > 1:
            samples = self.pack_samples(samples)
        counts = np.bincount(samples, minlength=n_out)
        assert counts.shape == (n_out,)
        counts = counts.reshape(self.nsoutdims)
        assert counts.sum() == n_samples
        if normalize:
            return counts / n_samples
        else:
            return counts 
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