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 do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 2
def do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 3
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 4
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 5
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 6
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 7
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 8
def inner(a, b):
"""
Returns the inner product of a and b for arrays of floating point types.
Like the generic NumPy equivalent the product sum is over the last dimension
of a and b.
Notes
-----
The first argument is not conjugated.
"""
fa = filled(a, 0)
fb = filled(b, 0)
if len(fa.shape) == 0:
fa.shape = (1,)
if len(fb.shape) == 0:
fb.shape = (1,)
return np.inner(fa, fb).view(MaskedArray) Example 9
def do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 10
def default(self, obj): if isinstance(obj, np.integer): return int(obj) elif isinstance(obj, np.floating): return float(obj) elif isinstance(obj, np.ndarray): return obj.tolist() elif isinstance(obj, WhitespaceNLP.Doc): return repr(obj) elif isinstance(obj, AsianNLP.Doc): return repr(obj) elif 'spacy' in sys.modules: import spacy if isinstance(obj, spacy.tokens.doc.Doc): return repr(obj) else: return super(MyEncoder, self).default(obj)
Example 11
def do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 12
def array_serializer(data, **kwargs):
"""Convert the array data to a serialized binary format"""
if isinstance(data.flatten()[0], np.floating):
use_dtype = '<f4'
nan_mask = ~np.isnan(data)
assert np.allclose(
data.astype(use_dtype)[nan_mask], data[nan_mask]), \
'Converting the type should not screw things up.'
elif isinstance(data.flatten()[0], np.integer):
use_dtype = '<i4'
assert (data.astype(use_dtype) == data).all(), \
'Converting the type should not screw things up.'
else:
raise TypeError('Must be a float or an int: {}'.format(data.dtype))
data_file = NamedTemporaryFile('rb+', suffix='.dat')
data.astype(use_dtype).tofile(data_file.file)
data_file.seek(0)
return FileProp(data_file, use_dtype) Example 13
def do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 14
def do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 15
def upcast_float16_ufunc(fn):
"""Decorator that enforces computation is not done in float16 by NumPy.
Some ufuncs in NumPy will compute float values on int8 and uint8
in half-precision (float16), which is not enough, and not compatible
with the C code.
:param fn: numpy ufunc
:returns: function similar to fn.__call__, computing the same
value with a minimum floating-point precision of float32
"""
def ret(*args, **kwargs):
out_dtype = numpy.find_common_type(
[a.dtype for a in args], [numpy.float16])
if out_dtype == 'float16':
# Force everything to float32
sig = 'f' * fn.nin + '->' + 'f' * fn.nout
kwargs.update(sig=sig)
return fn(*args, **kwargs)
return ret Example 16
def upcast_int8_nfunc(fn):
"""Decorator that upcasts input of dtype int8 to float32.
This is so that floating-point computation is not carried using
half-precision (float16), as some NumPy functions do.
:param fn: function computing a floating-point value from inputs
:returns: function similar to fn, but upcasting its uint8 and int8
inputs before carrying out the computation.
"""
def ret(*args, **kwargs):
args = list(args)
for i, a in enumerate(args):
if getattr(a, 'dtype', None) in ('int8', 'uint8'):
args[i] = a.astype('float32')
return fn(*args, **kwargs)
return ret Example 17
def _get_inplace_dtype(obj1, obj2):
"""
Returns the dtype of obj1,
Raise error if
1) obj1 is real and obj2 is complex
2) obj1 is integer and obj2 is floating
Parameters
----------
obj1 : numpy.ndarray like array
obj2 : numpy.ndarray like array
Returns
-------
out : np.dtype
"""
if isrealobj(obj1):
if iscomplexobj(obj2):
raise TypeError("Cannot cast complex dtype to real dtype")
if issubclass(obj1.dtype.type, np.integer):
if issubclass(obj2.dtype.type, (np.floating, np.complexfloating)):
raise TypeError("Cannot cast floating to integer")
return obj1.dtype Example 18
def _get_common_dtype_with_scalar(scalar, obj1):
"""
return the common dtype between a native scalar (int, float, complex)
and the dtype of an ndarray like array.
Parameters
----------
scalar : { int, float, complex }
obj1 : numpy.ndarray like array.
"""
if issubclass(type(scalar), (int, float, np.integer, np.floating)):
return obj1.dtype
elif issubclass(type(scalar), (complex, np.complexfloating)):
if isrealobj(obj1):
return floattocomplex(obj1.dtype)
else:
return obj1.dtype
else:
raise TypeError("scalar type is not supported") Example 19
def default(self, val):
if isinstance(val, (datetime)):
return str(val)
elif isinstance(val, np.integer):
return int(val)
elif isinstance(val, np.floating):
return float(val)
elif isinstance(val, np.bool_):
return bool(val)
elif isinstance(val, np.ndarray):
return val.tolist()
elif is_proxy(val) or isinstance(val, Artifact):
return repr(val)
elif callable(val):
try:
return utils.fn_info(val)
except:
pass
else:
try:
return super(Encoder, self).default(val)
except Exception as e:
print("Could not serialize type: {}".format(type(val)))
return str(type(val)) Example 20
def do(self, a, b):
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if len(np.asarray(b).shape) == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) Example 21
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 22
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 23
def format(self):
if callable(self._format):
return self._format(self)
if isinstance(self.value, (float, np.floating)):
if self._format is None:
return self._defaultFormat % self.value
else:
return self._format % self.value
else:
return asUnicode(self.value) Example 24
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 25
def format(self):
if callable(self._format):
return self._format(self)
if isinstance(self.value, (float, np.floating)):
if self._format is None:
return self._defaultFormat % self.value
else:
return self._format % self.value
else:
return asUnicode(self.value) Example 26
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 27
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows = int(np.sqrt(n_samples))
while n_samples % rows != 0:
rows -= 1
nh, nw = rows, n_samples/rows
if X.ndim == 2:
X = np.reshape(X, (X.shape[0], int(np.sqrt(X.shape[1])), int(np.sqrt(X.shape[1]))))
if X.ndim == 4:
# BCHW -> BHWC
X = X.transpose(0,2,3,1)
h, w = X[0].shape[:2]
img = np.zeros((h*nh, w*nw, 3))
elif X.ndim == 3:
h, w = X[0].shape[:2]
img = np.zeros((h*nh, w*nw))
for n, x in enumerate(X):
j = n/nw
i = n%nw
img[j*h:j*h+h, i*w:i*w+w] = x
imsave(save_path, img) Example 28
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows = int(np.sqrt(n_samples))
while n_samples % rows != 0:
rows -= 1
nh, nw = rows, n_samples//rows
if X.ndim == 2:
X = np.reshape(X, (X.shape[0], int(np.sqrt(X.shape[1])), int(np.sqrt(X.shape[1]))))
if X.ndim == 4:
# BCHW -> BHWC
X = X.transpose(0,2,3,1)
h, w = X[0].shape[:2]
img = np.zeros((h*nh, w*nw, 3))
elif X.ndim == 3:
h, w = X[0].shape[:2]
img = np.zeros((h*nh, w*nw))
for n, x in enumerate(X):
j = n//nw
i = n%nw
img[j*h:j*h+h, i*w:i*w+w] = x
imsave(save_path, img) Example 29
def _getconv(dtype):
""" Find the correct dtype converter. Adapted from matplotlib """
def floatconv(x):
x.lower()
if b'0x' in x:
return float.fromhex(asstr(x))
return float(x)
typ = dtype.type
if issubclass(typ, np.bool_):
return lambda x: bool(int(x))
if issubclass(typ, np.uint64):
return np.uint64
if issubclass(typ, np.int64):
return np.int64
if issubclass(typ, np.integer):
return lambda x: int(float(x))
elif issubclass(typ, np.longdouble):
return np.longdouble
elif issubclass(typ, np.floating):
return floatconv
elif issubclass(typ, np.complex):
return lambda x: complex(asstr(x))
elif issubclass(typ, np.bytes_):
return bytes
else:
return str Example 30
def masked_equal(x, value, copy=True):
"""
Mask an array where equal to a given value.
This function is a shortcut to ``masked_where``, with
`condition` = (x == value). For floating point arrays,
consider using ``masked_values(x, value)``.
See Also
--------
masked_where : Mask where a condition is met.
masked_values : Mask using floating point equality.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.arange(4)
>>> a
array([0, 1, 2, 3])
>>> ma.masked_equal(a, 2)
masked_array(data = [0 1 -- 3],
mask = [False False True False],
fill_value=999999)
"""
output = masked_where(equal(x, value), x, copy=copy)
output.fill_value = value
return output Example 31
def sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None):
"Function version of the eponymous method."
a = narray(a, copy=True, subok=True)
if axis is None:
a = a.flatten()
axis = 0
if fill_value is None:
if endwith:
# nan > inf
if np.issubdtype(a.dtype, np.floating):
filler = np.nan
else:
filler = minimum_fill_value(a)
else:
filler = maximum_fill_value(a)
else:
filler = fill_value
sindx = filled(a, filler).argsort(axis=axis, kind=kind, order=order)
# save meshgrid memory for 1d arrays
if a.ndim == 1:
indx = sindx
else:
indx = np.meshgrid(*[np.arange(x) for x in a.shape], sparse=True,
indexing='ij')
indx[axis] = sindx
return a[indx] Example 32
def test_vector_return_type(self):
a = np.array([1, 0, 1])
exact_types = np.typecodes['AllInteger']
inexact_types = np.typecodes['AllFloat']
all_types = exact_types + inexact_types
for each_inexact_types in all_types:
at = a.astype(each_inexact_types)
an = norm(at, -np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 0.0)
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
an = norm(at, -1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 0.0)
an = norm(at, 0)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2)
an = norm(at, 1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
an = norm(at, 2)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0**(1.0/2.0))
an = norm(at, 4)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0**(1.0/4.0))
an = norm(at, np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 1.0) Example 33
def float_samples_to_int16(y):
"""Convert floating-point numpy array of audio samples to int16."""
if not issubclass(y.dtype.type, np.floating):
raise ValueError('input samples not floating-point')
return (y * np.iinfo(np.int16).max).astype(np.int16) Example 34
def samples_to_wav_data(samples, sample_rate): """Converts floating point samples to wav data.""" wav_io = six.BytesIO() scipy.io.wavfile.write(wav_io, sample_rate, float_samples_to_int16(samples)) return wav_io.getvalue()
Example 35
def __contains__(self, value, memo=None):
if self.dtype is None:
raise TypeError("cannot determine if {0} is in {1}: no dtype specified".format(repr(value), self))
if self.dims is None:
raise TypeError("cannot determine if {0} is in {1}: no dims specified".format(repr(value), self))
if value is None:
return self.nullable
def recurse(value, dims):
if dims == ():
if issubclass(self.dtype.type, (numpy.bool_, numpy.bool)):
return value is True or value is False
elif issubclass(self.dtype.type, numpy.integer):
iinfo = numpy.iinfo(self.dtype.type)
return isinstance(value, numbers.Integral) and iinfo.min <= value <= iinfo.max
elif issubclass(self.dtype.type, numpy.floating):
return isinstance(value, numbers.Real)
elif issubclass(self.dtype.type, numpy.complex):
return isinstance(value, numbers.Complex)
else:
raise TypeError("unexpected dtype: {0}".format(self.dtype))
else:
try:
iter(value)
len(value)
except TypeError:
return False
else:
return len(value) == dims[0] and all(recurse(x, dims[1:]) for x in value)
return recurse(value, self.dims) Example 36
def _getconv(dtype):
""" Find the correct dtype converter. Adapted from matplotlib """
def floatconv(x):
x.lower()
if b'0x' in x:
return float.fromhex(asstr(x))
return float(x)
typ = dtype.type
if issubclass(typ, np.bool_):
return lambda x: bool(int(x))
if issubclass(typ, np.uint64):
return np.uint64
if issubclass(typ, np.int64):
return np.int64
if issubclass(typ, np.integer):
return lambda x: int(float(x))
elif issubclass(typ, np.longdouble):
return np.longdouble
elif issubclass(typ, np.floating):
return floatconv
elif issubclass(typ, np.complex):
return lambda x: complex(asstr(x))
elif issubclass(typ, np.bytes_):
return bytes
else:
return str Example 37
def masked_equal(x, value, copy=True):
"""
Mask an array where equal to a given value.
This function is a shortcut to ``masked_where``, with
`condition` = (x == value). For floating point arrays,
consider using ``masked_values(x, value)``.
See Also
--------
masked_where : Mask where a condition is met.
masked_values : Mask using floating point equality.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.arange(4)
>>> a
array([0, 1, 2, 3])
>>> ma.masked_equal(a, 2)
masked_array(data = [0 1 -- 3],
mask = [False False True False],
fill_value=999999)
"""
output = masked_where(equal(x, value), x, copy=copy)
output.fill_value = value
return output Example 38
def sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None):
"Function version of the eponymous method."
a = narray(a, copy=True, subok=True)
if axis is None:
a = a.flatten()
axis = 0
if fill_value is None:
if endwith:
# nan > inf
if np.issubdtype(a.dtype, np.floating):
filler = np.nan
else:
filler = minimum_fill_value(a)
else:
filler = maximum_fill_value(a)
else:
filler = fill_value
sindx = filled(a, filler).argsort(axis=axis, kind=kind, order=order)
# save meshgrid memory for 1d arrays
if a.ndim == 1:
indx = sindx
else:
indx = np.meshgrid(*[np.arange(x) for x in a.shape], sparse=True,
indexing='ij')
indx[axis] = sindx
return a[indx] Example 39
def test_vector_return_type(self):
a = np.array([1, 0, 1])
exact_types = np.typecodes['AllInteger']
inexact_types = np.typecodes['AllFloat']
all_types = exact_types + inexact_types
for each_inexact_types in all_types:
at = a.astype(each_inexact_types)
an = norm(at, -np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 0.0)
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
an = norm(at, -1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 0.0)
an = norm(at, 0)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2)
an = norm(at, 1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
an = norm(at, 2)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0**(1.0/2.0))
an = norm(at, 4)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0**(1.0/4.0))
an = norm(at, np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 1.0) Example 40
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(JSONEncoder, self).default(obj) Example 41
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
elif isinstance(obj, np.bool_):
return bool(obj)
elif isinstance(obj, set):
return list(obj)
else:
return super(MyEncoder, self).default(obj) Example 42
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(MsgEncoder, self).default(obj)
#Normalized versions with summary stats to be sent to the cloud Example 43
def sanitize_dataframe(df):
"""Sanitize a DataFrame to prepare it for serialization.
* Make a copy
* Raise ValueError if it has a hierarchical index.
* Convert categoricals to strings.
* Convert np.int dtypes to Python int objects
* Convert floats to objects and replace NaNs by None.
* Convert DateTime dtypes into appropriate string representations
"""
import pandas as pd
import numpy as np
df = df.copy()
if isinstance(df.index, pd.core.index.MultiIndex):
raise ValueError('Hierarchical indices not supported')
if isinstance(df.columns, pd.core.index.MultiIndex):
raise ValueError('Hierarchical indices not supported')
for col_name, dtype in df.dtypes.iteritems():
if str(dtype) == 'category':
# XXXX: work around bug in to_json for categorical types
# https://github.com/pydata/pandas/issues/10778
df[col_name] = df[col_name].astype(str)
elif np.issubdtype(dtype, np.integer):
# convert integers to objects; np.int is not JSON serializable
df[col_name] = df[col_name].astype(object)
elif np.issubdtype(dtype, np.floating):
# For floats, convert nan->None: np.float is not JSON serializable
col = df[col_name].astype(object)
df[col_name] = col.where(col.notnull(), None)
elif str(dtype).startswith('datetime'):
# Convert datetimes to strings
# astype(str) will choose the appropriate resolution
df[col_name] = df[col_name].astype(str).replace('NaT', '')
return df Example 44
def default(self, obj):
if isinstance(obj, numpy.integer):
return int(obj)
elif isinstance(obj, numpy.floating):
return float(obj)
elif isinstance(obj, numpy.ndarray):
return obj.tolist()
else:
return super(MyEncoder, self).default(obj) Example 45
def filternsaval(context, value, key):
''' Parse plateifu or mangaid into better form '''
if type(value) == list:
newvalue = ', '.join([str(np.round(v, 4)) for v in value])
elif isinstance(value, (float, np.floating)):
newvalue = np.round(value, 4)
else:
newvalue = value
return newvalue Example 46
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows = int(np.sqrt(n_samples))
while n_samples % rows != 0:
rows -= 1
nh, nw = rows, n_samples//rows
if X.ndim == 2:
X = np.reshape(X, (X.shape[0], int(np.sqrt(X.shape[1])), int(np.sqrt(X.shape[1]))))
if X.ndim == 4:
# BCHW -> BHWC
X = X.transpose(0,2,3,1)
h, w = X[0].shape[:2]
img = np.zeros((h*nh, w*nw, 3))
elif X.ndim == 3:
h, w = X[0].shape[:2]
img = np.zeros((h*nh, w*nw))
for n, x in enumerate(X):
#j = n/nw
#i = n%nw
j, i = divmod(n, nw)
img[j*h:j*h+h, i*w:i*w+w] = x
imsave(save_path, img) Example 47
def is_floating(self):
return self.inferred_type in ['floating', 'mixed-integer-float'] Example 48
def is_numeric(self):
return self.inferred_type in ['integer', 'floating'] Example 49
def _maybe_cast_indexer(self, key):
"""
If we have a float key and are not a floating index
then try to cast to an int if equivalent
"""
if is_float(key) and not self.is_floating():
try:
ckey = int(key)
if ckey == key:
key = ckey
except (ValueError, TypeError):
pass
return key Example 50
def _can_hold_element(self, element):
if is_list_like(element):
element = np.array(element)
tipo = element.dtype.type
return (issubclass(tipo, (np.floating, np.integer)) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))
return (isinstance(element, (float, int, np.float_, np.int_)) and
not isinstance(element, (bool, np.bool_, datetime, timedelta,
np.datetime64, np.timedelta64))) 