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 nan_to_num(array, fill_value = 0.0, copy = True):
"""
Replace NaNs with another fill value.
Parameters
----------
array : array_like
Input data.
fill_value : float, optional
NaNs will be replaced by ``fill_value``. Default is 0.0, in keeping
with ``numpy.nan_to_num``.
copy : bool, optional
Whether to create a copy of `array` (True) or to replace values
in-place (False). The in-place operation only occurs if
casting to an array does not require a copy.
Returns
-------
out : ndarray
Array without NaNs. If ``array`` was not of floating or complearray type,
``array`` is returned unchanged.
Notes
-----
Contrary to ``numpy.nan_to_num``, this functions does not handle
infinite values.
See Also
--------
numpy.nan_to_num : replace NaNs and Infs with zeroes.
"""
array = np.array(array, subok = True, copy = copy)
dtype = array.dtype.type
# Non-inexact types do not have NaNs
if not np.issubdtype(dtype, np.inexact):
return array
iscomplex = np.issubdtype(dtype, np.complexfloating)
dest = (array.real, array.imag) if iscomplex else (array,)
for d in dest:
np.copyto(d, fill_value, where = np.isnan(d))
return array Example 2
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask Example 3
def _divide_by_count(a, b, out=None):
"""
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then then a is used instead so that the
division is in place. Note that this is only called with `a` an inexact
type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
"""
with np.errstate(invalid='ignore'):
if isinstance(a, np.ndarray):
if out is None:
return np.divide(a, b, out=a, casting='unsafe')
else:
return np.divide(a, b, out=out, casting='unsafe')
else:
if out is None:
return a.dtype.type(a / b)
else:
# This is questionable, but currently a numpy scalar can
# be output to a zero dimensional array.
return np.divide(a, b, out=out, casting='unsafe') Example 4
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask Example 5
def _divide_by_count(a, b, out=None):
"""
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then then a is used instead so that the
division is in place. Note that this is only called with `a` an inexact
type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
"""
with np.errstate(invalid='ignore'):
if isinstance(a, np.ndarray):
if out is None:
return np.divide(a, b, out=a, casting='unsafe')
else:
return np.divide(a, b, out=out, casting='unsafe')
else:
if out is None:
return a.dtype.type(a / b)
else:
# This is questionable, but currently a numpy scalar can
# be output to a zero dimensional array.
return np.divide(a, b, out=out, casting='unsafe') Example 6
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask Example 7
def _divide_by_count(a, b, out=None):
"""
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then then a is used instead so that the
division is in place. Note that this is only called with `a` an inexact
type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
"""
with np.errstate(invalid='ignore'):
if isinstance(a, np.ndarray):
if out is None:
return np.divide(a, b, out=a, casting='unsafe')
else:
return np.divide(a, b, out=out, casting='unsafe')
else:
if out is None:
return a.dtype.type(a / b)
else:
# This is questionable, but currently a numpy scalar can
# be output to a zero dimensional array.
return np.divide(a, b, out=out, casting='unsafe') Example 8
def minimum_dtype(x, dtype=np.bool_):
"""returns the "most basic" dtype which represents `x` properly, which
provides at least the same value range as the specified dtype."""
def check_type(x, dtype):
try:
converted = dtype.type(x)
except (ValueError, OverflowError):
return False
# False if some overflow has happened
return converted == x or math.isnan(x)
def type_loop(x, dtype, dtype_dict, default=None):
while True:
try:
dtype = np.dtype(dtype_dict[dtype.name])
if check_type(x, dtype):
return np.dtype(dtype)
except KeyError:
if default is not None:
return np.dtype(default)
raise ValueError("Can not determine dtype of %r" % x)
dtype = np.dtype(dtype)
if check_type(x, dtype):
return dtype
if np.issubdtype(dtype, np.inexact):
return type_loop(x, dtype, _next_float_dtype)
else:
return type_loop(x, dtype, _next_int_dtype, default=np.float32) Example 9
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask Example 10
def _divide_by_count(a, b, out=None):
"""
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then then a is used instead so that the
division is in place. Note that this is only called with `a` an inexact
type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
"""
with np.errstate(invalid='ignore'):
if isinstance(a, np.ndarray):
if out is None:
return np.divide(a, b, out=a, casting='unsafe')
else:
return np.divide(a, b, out=out, casting='unsafe')
else:
if out is None:
return a.dtype.type(a / b)
else:
# This is questionable, but currently a numpy scalar can
# be output to a zero dimensional array.
return np.divide(a, b, out=out, casting='unsafe') Example 11
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask Example 12
def _divide_by_count(a, b, out=None):
"""
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then then a is used instead so that the
division is in place. Note that this is only called with `a` an inexact
type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
"""
with np.errstate(invalid='ignore', divide='ignore'):
if isinstance(a, np.ndarray):
if out is None:
return np.divide(a, b, out=a, casting='unsafe')
else:
return np.divide(a, b, out=out, casting='unsafe')
else:
if out is None:
return a.dtype.type(a / b)
else:
# This is questionable, but currently a numpy scalar can
# be output to a zero dimensional array.
return np.divide(a, b, out=out, casting='unsafe') Example 13
def _median(a, axis=None, out=None, overwrite_input=False):
# can't be reasonably be implemented in terms of percentile as we have to
# call mean to not break astropy
a = np.asanyarray(a)
# Set the partition indexes
if axis is None:
sz = a.size
else:
sz = a.shape[axis]
if sz % 2 == 0:
szh = sz // 2
kth = [szh - 1, szh]
else:
kth = [(sz - 1) // 2]
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
kth.append(-1)
if overwrite_input:
if axis is None:
part = a.ravel()
part.partition(kth)
else:
a.partition(kth, axis=axis)
part = a
else:
part = partition(a, kth, axis=axis)
if part.shape == ():
# make 0-D arrays work
return part.item()
if axis is None:
axis = 0
indexer = [slice(None)] * part.ndim
index = part.shape[axis] // 2
if part.shape[axis] % 2 == 1:
# index with slice to allow mean (below) to work
indexer[axis] = slice(index, index+1)
else:
indexer[axis] = slice(index-1, index+1)
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact) and sz > 0:
# warn and return nans like mean would
rout = mean(part[indexer], axis=axis, out=out)
return np.lib.utils._median_nancheck(part, rout, axis, out)
else:
# if there are no nans
# Use mean in odd and even case to coerce data type
# and check, use out array.
return mean(part[indexer], axis=axis, out=out) Example 14
def object_to_json(o, level=0, indent=4, space=" ", newline="\n"):
ret = ""
if isinstance(o, dict):
ret += "{" + newline
comma = ""
for k,v in o.iteritems():
ret += comma
comma = ",\n"
ret += space * indent * level
ret += '"' + str(k) + '":' + space
ret += object_to_json(v, level + 1)
ret += newline + space * indent * (level - 1) + "}"
elif isinstance(o, basestring):
ret += '"' + o + '"'
elif isinstance(o, list):
ret += "[" + ", ".join([object_to_json(e, level+1) for e in o]) + "]"
elif isinstance(o, bool):
ret += "true" if o else "false"
elif isinstance(o, (int, long)):
ret += str(o)
elif isinstance(o, datetime):
ret += o.strftime('%Y-%m-%d %H:%M:%S.%f')
elif isinstance(o, bson.ObjectId):
ret += 'ObjectId(%s)' % o
elif isinstance(o, float):
ret += '%.7g' % o
elif isinstance(o, numpy.ndarray) and numpy.issubdtype(o.dtype, numpy.integer):
ret += "[" + ','.join(map(str, o.flatten().tolist())) + "]"
elif isinstance(o, numpy.ndarray) and numpy.issubdtype(o.dtype, numpy.inexact):
ret += "[" + ','.join(map(lambda x: '%.7g' % x, o.flatten().tolist())) + "]"
elif isinstance(o, bson.timestamp.Timestamp):
ret += o.as_datetime().strftime('%Y-%m-%d %H:%M:%S.%f')
elif o is None:
ret += 'null'
else:
raise TypeError("Unknown type '%s' for json serialization" % str(type(o)))
return ret Example 15
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask Example 16
def _divide_by_count(a, b, out=None):
"""
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then then a is used instead so that the
division is in place. Note that this is only called with `a` an inexact
type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
"""
with np.errstate(invalid='ignore'):
if isinstance(a, np.ndarray):
if out is None:
return np.divide(a, b, out=a, casting='unsafe')
else:
return np.divide(a, b, out=out, casting='unsafe')
else:
if out is None:
return a.dtype.type(a / b)
else:
# This is questionable, but currently a numpy scalar can
# be output to a zero dimensional array.
return np.divide(a, b, out=out, casting='unsafe') Example 17
def to_json(o, level=0):
INDENT = 2
SPACE = " "
NEWLINE = "\n"
ret = ""
if isinstance(o, dict):
ret += "{" + NEWLINE
comma = ""
for k in sorted(o.keys()):
v = o[k]
ret += comma
comma = ",\n"
ret += SPACE * INDENT * (level+1)
ret += '"' + str(k) + '":' + SPACE
ret += to_json(v, level + 1)
ret += NEWLINE + SPACE * INDENT * level + "}"
elif isinstance(o, str):
ret += '"' + o + '"'
elif isinstance(o, list) or isinstance(o, tuple):
ret += "[" + ",".join([to_json(e, level+1) for e in o]) + "]"
elif isinstance(o, bool):
ret += "true" if o else "false"
elif isinstance(o, int):
ret += str(o)
elif isinstance(o, float):
ret += '%.7g' % o
elif isinstance(o, np.ndarray) and np.issubdtype(o.dtype, np.integer):
ret += "[" + ','.join(map(str, o.flatten().tolist())) + "]"
elif isinstance(o, np.ndarray) and np.issubdtype(o.dtype, np.inexact):
ret += "[" + \
','.join(map(lambda x: '%.7g' % x, o.flatten().tolist())) + "]"
elif o is None:
ret += 'null'
elif hasattr(o, '__class__'):
ret += '"' + o.__class__.__name__ + '"'
else:
raise TypeError(
"Unknown type '%s' for json serialization" % str(type(o)))
return ret
# format object and its properties into printable dict Example 18
def check_dtype(dtype, func_str, a, n):
if np.isscalar(a) or not a.shape:
if func_str not in ("sum", "prod", "len"):
raise ValueError("scalar inputs are supported only for 'sum', "
"'prod' and 'len'")
a_dtype = np.dtype(type(a))
else:
a_dtype = a.dtype
if dtype is not None:
# dtype set by the user
# Careful here: np.bool != np.bool_ !
if np.issubdtype(dtype, np.bool_) and \
not('all' in func_str or 'any' in func_str):
raise TypeError("function %s requires a more complex datatype "
"than bool" % func_str)
if not np.issubdtype(dtype, np.integer) and func_str in ('len', 'nanlen'):
raise TypeError("function %s requires an integer datatype" % func_str)
# TODO: Maybe have some more checks here
return np.dtype(dtype)
else:
try:
return np.dtype(_forced_types[func_str])
except KeyError:
if func_str in _forced_float_types:
if np.issubdtype(a_dtype, np.floating):
return a_dtype
else:
return np.dtype(np.float64)
else:
if func_str == 'sum':
# Try to guess the minimally required int size
if np.issubdtype(a_dtype, np.int64):
# It's not getting bigger anymore
# TODO: strictly speaking it might need float
return np.dtype(np.int64)
elif np.issubdtype(a_dtype, np.integer):
maxval = np.iinfo(a_dtype).max * n
return minimum_dtype(maxval, a_dtype)
elif np.issubdtype(a_dtype, np.bool_):
return minimum_dtype(n, a_dtype)
else:
# floating, inexact, whatever
return a_dtype
elif func_str in _forced_same_type:
return a_dtype
else:
if isinstance(a_dtype, np.integer):
return np.dtype(np.int64)
else:
return a_dtype Example 19
def _median(a, axis=None, out=None, overwrite_input=False):
if overwrite_input:
if axis is None:
asorted = a.ravel()
asorted.sort()
else:
a.sort(axis=axis)
asorted = a
else:
asorted = sort(a, axis=axis)
if axis is None:
axis = 0
elif axis < 0:
axis += asorted.ndim
if asorted.ndim == 1:
idx, odd = divmod(count(asorted), 2)
return asorted[idx + odd - 1 : idx + 1].mean(out=out)
counts = count(asorted, axis=axis)
h = counts // 2
# create indexing mesh grid for all but reduced axis
axes_grid = [np.arange(x) for i, x in enumerate(asorted.shape)
if i != axis]
ind = np.meshgrid(*axes_grid, sparse=True, indexing='ij')
# insert indices of low and high median
ind.insert(axis, np.maximum(0, h - 1))
low = asorted[tuple(ind)]
ind[axis] = h
high = asorted[tuple(ind)]
# duplicate high if odd number of elements so mean does nothing
odd = counts % 2 == 1
if asorted.ndim > 1:
np.copyto(low, high, where=odd)
elif odd:
low = high
if np.issubdtype(asorted.dtype, np.inexact):
# avoid inf / x = masked
s = np.ma.sum([low, high], axis=0, out=out)
np.true_divide(s.data, 2., casting='unsafe', out=s.data)
else:
s = np.ma.mean([low, high], axis=0, out=out)
return s 