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""" Utility functions for sparse matrix module """
'isshape', 'issequence', 'isdense', 'ismatrix', 'get_sum_dtype']
'uintc', 'longlong', 'ulonglong', 'single', 'double', 'longdouble', 'csingle', 'cdouble', 'clongdouble']
"""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,))
"""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
"""Determine data type for binary operation between an array of type `dtype` and a scalar. """ return (np.array([0], dtype=dtype) * scalar).dtype
""" 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
return np.asarray(A, dtype=A.dtype.newbyteorder('native'))
"""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
""" 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
"""Mimic numpy's casting for np.sum""" if np.issubdtype(dtype, np.float_): return np.float_ 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
"""Is x either a scalar, an array scalar, or a 0-dim array?""" return np.isscalar(x) or (isdense(x) and x.ndim == 0)
"""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
"""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: 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
return ((isinstance(t, (list, tuple)) and (len(t) == 0 or np.isscalar(t[0]))) or (isinstance(t, np.ndarray) and (t.ndim == 1)))
return ((isinstance(t, (list, tuple)) and len(t) > 0 and issequence(t[0])) or (isinstance(t, np.ndarray) and t.ndim == 2))
return isinstance(x, np.ndarray)
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")
"""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(new_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
"""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
with warnings.catch_warnings(record=True): warnings.filterwarnings( 'ignore', '.*the matrix subclass is not the recommended way.*') return np.matrix(*args, **kwargs)
with warnings.catch_warnings(record=True): warnings.filterwarnings( 'ignore', '.*the matrix subclass is not the recommended way.*') return np.asmatrix(*args, **kwargs)
with warnings.catch_warnings(record=True): warnings.filterwarnings( 'ignore', '.*the matrix subclass is not the recommended way.*') return np.bmat(*args, **kwargs)
""" This class simply exists to hold the methods necessary for fancy indexing. """ """ Given a slice object, use numpy arange to change it to a 1D array. """ start, stop, step = j.indices(shape) return np.arange(start, stop, step)
""" Parse index. Always return a tuple of the form (row, col). Where row/col is a integer, slice, or array of integers. """ # First, check if indexing with single boolean matrix. from .base import spmatrix # This feels dirty but... if (isinstance(index, (spmatrix, np.ndarray)) and (index.ndim == 2) and index.dtype.kind == 'b'): return index.nonzero()
# Parse any ellipses. index = self._check_ellipsis(index)
# Next, parse the tuple or object if isinstance(index, tuple): if len(index) == 2: row, col = index elif len(index) == 1: row, col = index[0], slice(None) else: raise IndexError('invalid number of indices') else: row, col = index, slice(None)
# Next, check for validity, or transform the index as needed. row, col = self._check_boolean(row, col) return row, col
"""Process indices with Ellipsis. Returns modified index.""" if index is Ellipsis: return (slice(None), slice(None)) elif isinstance(index, tuple): # Find first ellipsis for j, v in enumerate(index): if v is Ellipsis: first_ellipsis = j break else: first_ellipsis = None
# Expand the first one if first_ellipsis is not None: # Shortcuts if len(index) == 1: return (slice(None), slice(None)) elif len(index) == 2: if first_ellipsis == 0: if index[1] is Ellipsis: return (slice(None), slice(None)) else: return (slice(None), index[1]) else: return (index[0], slice(None))
# General case tail = () for v in index[first_ellipsis+1:]: if v is not Ellipsis: tail = tail + (v,) nd = first_ellipsis + len(tail) nslice = max(0, 2 - nd) return index[:first_ellipsis] + (slice(None),)*nslice + tail
return index
from .base import isspmatrix # ew... # Supporting sparse boolean indexing with both row and col does # not work because spmatrix.ndim is always 2. if isspmatrix(row) or isspmatrix(col): raise IndexError( "Indexing with sparse matrices is not supported " "except boolean indexing where matrix and index " "are equal shapes.") if isinstance(row, np.ndarray) and row.dtype.kind == 'b': row = self._boolean_index_to_array(row) if isinstance(col, np.ndarray) and col.dtype.kind == 'b': col = self._boolean_index_to_array(col) return row, col
if i.ndim > 1: raise IndexError('invalid index shape') return i.nonzero()[0]
i, j = self._check_boolean(i, j)
i_slice = isinstance(i, slice) if i_slice: i = self._slicetoarange(i, self.shape[0])[:, None] else: i = np.atleast_1d(i)
if isinstance(j, slice): j = self._slicetoarange(j, self.shape[1])[None, :] if i.ndim == 1: i = i[:, None] elif not i_slice: raise IndexError('index returns 3-dim structure') elif isscalarlike(j): # row vector special case j = np.atleast_1d(j) if i.ndim == 1: i, j = np.broadcast_arrays(i, j) i = i[:, None] j = j[:, None] return i, j else: j = np.atleast_1d(j) if i_slice and j.ndim > 1: raise IndexError('index returns 3-dim structure')
i, j = np.broadcast_arrays(i, j)
if i.ndim == 1: # return column vectors for 1-D indexing i = i[None, :] j = j[None, :] elif i.ndim > 2: raise IndexError("Index dimension must be <= 2")
return i, j |