本文提供了Python中numpy.integer的使用示例，包括在数据处理、数组操作和类型转换等场景的应用。通过多个代码片段展示了如何在不同情况下正确使用numpy.integer。
The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
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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