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""" Utility functions for sparse matrix module 

""" 

 

from __future__ import division, print_function, absolute_import 

 

import operator 

import warnings 

import numpy as np 

 

__all__ = ['upcast', 'getdtype', 'isscalarlike', 'isintlike', 

'isshape', 'issequence', 'isdense', 'ismatrix', 'get_sum_dtype'] 

 

supported_dtypes = ['bool', 'int8', 'uint8', 'short', 'ushort', 'intc', 

'uintc', 'longlong', 'ulonglong', 'single', 'double', 

'longdouble', 'csingle', 'cdouble', 'clongdouble'] 

supported_dtypes = [np.typeDict[x] for x in supported_dtypes] 

 

_upcast_memo = {} 

 

 

def upcast(*args): 

"""Returns the nearest supported sparse dtype for the 

combination of one or more types. 

 

upcast(t0, t1, ..., tn) -> T where T is a supported dtype 

 

Examples 

-------- 

 

>>> upcast('int32') 

<type 'numpy.int32'> 

>>> upcast('bool') 

<type 'numpy.bool_'> 

>>> upcast('int32','float32') 

<type 'numpy.float64'> 

>>> upcast('bool',complex,float) 

<type 'numpy.complex128'> 

 

""" 

 

t = _upcast_memo.get(hash(args)) 

if t is not None: 

return t 

 

upcast = np.find_common_type(args, []) 

 

for t in supported_dtypes: 

if np.can_cast(upcast, t): 

_upcast_memo[hash(args)] = t 

return t 

 

raise TypeError('no supported conversion for types: %r' % (args,)) 

 

 

def upcast_char(*args): 

"""Same as `upcast` but taking dtype.char as input (faster).""" 

t = _upcast_memo.get(args) 

if t is not None: 

return t 

t = upcast(*map(np.dtype, args)) 

_upcast_memo[args] = t 

return t 

 

 

def upcast_scalar(dtype, scalar): 

"""Determine data type for binary operation between an array of 

type `dtype` and a scalar. 

""" 

return (np.array([0], dtype=dtype) * scalar).dtype 

 

 

def downcast_intp_index(arr): 

""" 

Down-cast index array to np.intp dtype if it is of a larger dtype. 

 

Raise an error if the array contains a value that is too large for 

intp. 

""" 

if arr.dtype.itemsize > np.dtype(np.intp).itemsize: 

if arr.size == 0: 

return arr.astype(np.intp) 

maxval = arr.max() 

minval = arr.min() 

if maxval > np.iinfo(np.intp).max or minval < np.iinfo(np.intp).min: 

raise ValueError("Cannot deal with arrays with indices larger " 

"than the machine maximum address size " 

"(e.g. 64-bit indices on 32-bit machine).") 

return arr.astype(np.intp) 

return arr 

 

 

def to_native(A): 

return np.asarray(A, dtype=A.dtype.newbyteorder('native')) 

 

 

def getdtype(dtype, a=None, default=None): 

"""Function used to simplify argument processing. If 'dtype' is not 

specified (is None), returns a.dtype; otherwise returns a np.dtype 

object created from the specified dtype argument. If 'dtype' and 'a' 

are both None, construct a data type out of the 'default' parameter. 

Furthermore, 'dtype' must be in 'allowed' set. 

""" 

# TODO is this really what we want? 

if dtype is None: 

try: 

newdtype = a.dtype 

except AttributeError: 

if default is not None: 

newdtype = np.dtype(default) 

else: 

raise TypeError("could not interpret data type") 

else: 

newdtype = np.dtype(dtype) 

if newdtype == np.object_: 

warnings.warn("object dtype is not supported by sparse matrices") 

 

return newdtype 

 

 

def get_index_dtype(arrays=(), maxval=None, check_contents=False): 

""" 

Based on input (integer) arrays `a`, determine a suitable index data 

type that can hold the data in the arrays. 

 

Parameters 

---------- 

arrays : tuple of array_like 

Input arrays whose types/contents to check 

maxval : float, optional 

Maximum value needed 

check_contents : bool, optional 

Whether to check the values in the arrays and not just their types. 

Default: False (check only the types) 

 

Returns 

------- 

dtype : dtype 

Suitable index data type (int32 or int64) 

 

""" 

 

int32min = np.iinfo(np.int32).min 

int32max = np.iinfo(np.int32).max 

 

dtype = np.intc 

if maxval is not None: 

if maxval > int32max: 

dtype = np.int64 

 

if isinstance(arrays, np.ndarray): 

arrays = (arrays,) 

 

for arr in arrays: 

arr = np.asarray(arr) 

if not np.can_cast(arr.dtype, np.int32): 

if check_contents: 

if arr.size == 0: 

# a bigger type not needed 

continue 

elif np.issubdtype(arr.dtype, np.integer): 

maxval = arr.max() 

minval = arr.min() 

if minval >= int32min and maxval <= int32max: 

# a bigger type not needed 

continue 

 

dtype = np.int64 

break 

 

return dtype 

 

 

def get_sum_dtype(dtype): 

"""Mimic numpy's casting for np.sum""" 

if dtype.kind == 'u' and np.can_cast(dtype, np.uint): 

return np.uint 

if np.can_cast(dtype, np.int_): 

return np.int_ 

return dtype 

 

 

def isscalarlike(x): 

"""Is x either a scalar, an array scalar, or a 0-dim array?""" 

return np.isscalar(x) or (isdense(x) and x.ndim == 0) 

 

 

def isintlike(x): 

"""Is x appropriate as an index into a sparse matrix? Returns True 

if it can be cast safely to a machine int. 

""" 

# Fast-path check to eliminate non-scalar values. operator.index would 

# catch this case too, but the exception catching is slow. 

if np.ndim(x) != 0: 

return False 

try: 

operator.index(x) 

except (TypeError, ValueError): 

try: 

loose_int = bool(int(x) == x) 

except (TypeError, ValueError): 

return False 

if loose_int: 

warnings.warn("Inexact indices into sparse matrices are deprecated", 

DeprecationWarning) 

return loose_int 

return True 

 

 

def isshape(x, nonneg=False): 

"""Is x a valid 2-tuple of dimensions? 

 

If nonneg, also checks that the dimensions are non-negative. 

""" 

try: 

# Assume it's a tuple of matrix dimensions (M, N) 

(M, N) = x 

except Exception: 

return False 

else: 

if isintlike(M) and isintlike(N): 

if np.ndim(M) == 0 and np.ndim(N) == 0: 

if not nonneg or (M >= 0 and N >= 0): 

return True 

return False 

 

 

def issequence(t): 

return ((isinstance(t, (list, tuple)) and 

(len(t) == 0 or np.isscalar(t[0]))) or 

(isinstance(t, np.ndarray) and (t.ndim == 1))) 

 

 

def ismatrix(t): 

return ((isinstance(t, (list, tuple)) and 

len(t) > 0 and issequence(t[0])) or 

(isinstance(t, np.ndarray) and t.ndim == 2)) 

 

 

def isdense(x): 

return isinstance(x, np.ndarray) 

 

 

def validateaxis(axis): 

if axis is not None: 

axis_type = type(axis) 

 

# In NumPy, you can pass in tuples for 'axis', but they are 

# not very useful for sparse matrices given their limited 

# dimensions, so let's make it explicit that they are not 

# allowed to be passed in 

if axis_type == tuple: 

raise TypeError(("Tuples are not accepted for the 'axis' " 

"parameter. Please pass in one of the " 

"following: {-2, -1, 0, 1, None}.")) 

 

# If not a tuple, check that the provided axis is actually 

# an integer and raise a TypeError similar to NumPy's 

if not np.issubdtype(np.dtype(axis_type), np.integer): 

raise TypeError("axis must be an integer, not {name}" 

.format(name=axis_type.__name__)) 

 

if not (-2 <= axis <= 1): 

raise ValueError("axis out of range") 

 

 

def check_shape(args, current_shape=None): 

"""Imitate numpy.matrix handling of shape arguments""" 

if len(args) == 0: 

raise TypeError("function missing 1 required positional argument: " 

"'shape'") 

elif len(args) == 1: 

try: 

shape_iter = iter(args[0]) 

except TypeError: 

new_shape = (operator.index(args[0]), ) 

else: 

new_shape = tuple(operator.index(arg) for arg in shape_iter) 

else: 

new_shape = tuple(operator.index(arg) for arg in args) 

 

if current_shape is None: 

if len(new_shape) != 2: 

raise ValueError('shape must be a 2-tuple of positive integers') 

elif new_shape[0] < 0 or new_shape[1] < 0: 

raise ValueError("'shape' elements cannot be negative") 

 

else: 

# Check the current size only if needed 

current_size = np.prod(current_shape, dtype=int) 

 

# Check for negatives 

negative_indexes = [i for i, x in enumerate(new_shape) if x < 0] 

if len(negative_indexes) == 0: 

new_size = np.prod(new_shape, dtype=int) 

if new_size != current_size: 

raise ValueError('cannot reshape array of size {} into shape {}' 

.format(current_size, new_shape)) 

elif len(negative_indexes) == 1: 

skip = negative_indexes[0] 

specified = np.prod(new_shape[0:skip] + new_shape[skip+1:]) 

unspecified, remainder = divmod(current_size, specified) 

if remainder != 0: 

err_shape = tuple('newshape' if x < 0 else x for x in new_shape) 

raise ValueError('cannot reshape array of size {} into shape {}' 

''.format(current_size, err_shape)) 

new_shape = new_shape[0:skip] + (unspecified,) + new_shape[skip+1:] 

else: 

raise ValueError('can only specify one unknown dimension') 

 

# Add and remove ones like numpy.matrix.reshape 

if len(new_shape) != 2: 

new_shape = tuple(arg for arg in new_shape if arg != 1) 

 

if len(new_shape) == 0: 

new_shape = (1, 1) 

elif len(new_shape) == 1: 

new_shape = (1, new_shape[0]) 

 

if len(new_shape) > 2: 

raise ValueError('shape too large to be a matrix') 

 

return new_shape 

 

 

def check_reshape_kwargs(kwargs): 

"""Unpack keyword arguments for reshape function. 

 

This is useful because keyword arguments after star arguments are not 

allowed in Python 2, but star keyword arguments are. This function unpacks 

'order' and 'copy' from the star keyword arguments (with defaults) and 

throws an error for any remaining. 

""" 

 

order = kwargs.pop('order', 'C') 

copy = kwargs.pop('copy', False) 

if kwargs: # Some unused kwargs remain 

raise TypeError('reshape() got unexpected keywords arguments: {}' 

.format(', '.join(kwargs.keys()))) 

return order, copy 

 

 

############################################################################### 

# Wrappers for NumPy types that are deprecated 

 

def matrix(*args, **kwargs): 

with warnings.catch_warnings(record=True): 

warnings.filterwarnings( 

'ignore', '.*the matrix subclass is not the recommended way.*') 

return np.matrix(*args, **kwargs) 

 

 

def asmatrix(*args, **kwargs): 

with warnings.catch_warnings(record=True): 

warnings.filterwarnings( 

'ignore', '.*the matrix subclass is not the recommended way.*') 

return np.asmatrix(*args, **kwargs) 

 

 

def bmat(*args, **kwargs): 

with warnings.catch_warnings(record=True): 

warnings.filterwarnings( 

'ignore', '.*the matrix subclass is not the recommended way.*') 

return np.bmat(*args, **kwargs)