1702 lines
61 KiB
Python
1702 lines
61 KiB
Python
"""Array printing function
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$Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
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"""
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__all__ = ["array2string", "array_str", "array_repr", "set_string_function",
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"set_printoptions", "get_printoptions", "printoptions",
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"format_float_positional", "format_float_scientific"]
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__docformat__ = 'restructuredtext'
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#
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# Written by Konrad Hinsen <hinsenk@ere.umontreal.ca>
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# last revision: 1996-3-13
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# modified by Jim Hugunin 1997-3-3 for repr's and str's (and other details)
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# and by Perry Greenfield 2000-4-1 for numarray
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# and by Travis Oliphant 2005-8-22 for numpy
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# Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
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# scalars but for different purposes. scalartypes.c.src has str/reprs for when
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# the scalar is printed on its own, while arrayprint.py has strs for when
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# scalars are printed inside an ndarray. Only the latter strs are currently
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# user-customizable.
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import functools
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import numbers
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import sys
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try:
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from _thread import get_ident
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except ImportError:
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from _dummy_thread import get_ident
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import numpy as np
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from . import numerictypes as _nt
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from .umath import absolute, isinf, isfinite, isnat
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from . import multiarray
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from .multiarray import (array, dragon4_positional, dragon4_scientific,
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datetime_as_string, datetime_data, ndarray,
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set_legacy_print_mode)
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from .fromnumeric import any
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from .numeric import concatenate, asarray, errstate
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from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
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flexible)
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from .overrides import array_function_dispatch, set_module
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import operator
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import warnings
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import contextlib
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_format_options = {
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'edgeitems': 3, # repr N leading and trailing items of each dimension
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'threshold': 1000, # total items > triggers array summarization
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'floatmode': 'maxprec',
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'precision': 8, # precision of floating point representations
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'suppress': False, # suppress printing small floating values in exp format
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'linewidth': 75,
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'nanstr': 'nan',
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'infstr': 'inf',
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'sign': '-',
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'formatter': None,
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# Internally stored as an int to simplify comparisons; converted from/to
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# str/False on the way in/out.
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'legacy': sys.maxsize}
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def _make_options_dict(precision=None, threshold=None, edgeitems=None,
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linewidth=None, suppress=None, nanstr=None, infstr=None,
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sign=None, formatter=None, floatmode=None, legacy=None):
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"""
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Make a dictionary out of the non-None arguments, plus conversion of
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*legacy* and sanity checks.
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"""
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options = {k: v for k, v in locals().items() if v is not None}
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if suppress is not None:
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options['suppress'] = bool(suppress)
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modes = ['fixed', 'unique', 'maxprec', 'maxprec_equal']
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if floatmode not in modes + [None]:
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raise ValueError("floatmode option must be one of " +
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", ".join('"{}"'.format(m) for m in modes))
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if sign not in [None, '-', '+', ' ']:
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raise ValueError("sign option must be one of ' ', '+', or '-'")
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if legacy == False:
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options['legacy'] = sys.maxsize
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elif legacy == '1.13':
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options['legacy'] = 113
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elif legacy == '1.21':
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options['legacy'] = 121
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elif legacy is None:
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pass # OK, do nothing.
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else:
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warnings.warn(
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"legacy printing option can currently only be '1.13', '1.21', or "
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"`False`", stacklevel=3)
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if threshold is not None:
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# forbid the bad threshold arg suggested by stack overflow, gh-12351
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if not isinstance(threshold, numbers.Number):
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raise TypeError("threshold must be numeric")
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if np.isnan(threshold):
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raise ValueError("threshold must be non-NAN, try "
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"sys.maxsize for untruncated representation")
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if precision is not None:
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# forbid the bad precision arg as suggested by issue #18254
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try:
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options['precision'] = operator.index(precision)
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except TypeError as e:
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raise TypeError('precision must be an integer') from e
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return options
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@set_module('numpy')
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def set_printoptions(precision=None, threshold=None, edgeitems=None,
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linewidth=None, suppress=None, nanstr=None, infstr=None,
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formatter=None, sign=None, floatmode=None, *, legacy=None):
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"""
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Set printing options.
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These options determine the way floating point numbers, arrays and
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other NumPy objects are displayed.
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Parameters
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----------
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precision : int or None, optional
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Number of digits of precision for floating point output (default 8).
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May be None if `floatmode` is not `fixed`, to print as many digits as
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necessary to uniquely specify the value.
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threshold : int, optional
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Total number of array elements which trigger summarization
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rather than full repr (default 1000).
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To always use the full repr without summarization, pass `sys.maxsize`.
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edgeitems : int, optional
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Number of array items in summary at beginning and end of
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each dimension (default 3).
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linewidth : int, optional
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The number of characters per line for the purpose of inserting
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line breaks (default 75).
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suppress : bool, optional
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If True, always print floating point numbers using fixed point
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notation, in which case numbers equal to zero in the current precision
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will print as zero. If False, then scientific notation is used when
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absolute value of the smallest number is < 1e-4 or the ratio of the
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maximum absolute value to the minimum is > 1e3. The default is False.
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nanstr : str, optional
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String representation of floating point not-a-number (default nan).
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infstr : str, optional
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String representation of floating point infinity (default inf).
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sign : string, either '-', '+', or ' ', optional
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Controls printing of the sign of floating-point types. If '+', always
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print the sign of positive values. If ' ', always prints a space
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(whitespace character) in the sign position of positive values. If
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'-', omit the sign character of positive values. (default '-')
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formatter : dict of callables, optional
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If not None, the keys should indicate the type(s) that the respective
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formatting function applies to. Callables should return a string.
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Types that are not specified (by their corresponding keys) are handled
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by the default formatters. Individual types for which a formatter
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can be set are:
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- 'bool'
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- 'int'
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- 'timedelta' : a `numpy.timedelta64`
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- 'datetime' : a `numpy.datetime64`
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- 'float'
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- 'longfloat' : 128-bit floats
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- 'complexfloat'
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- 'longcomplexfloat' : composed of two 128-bit floats
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- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
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- 'object' : `np.object_` arrays
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Other keys that can be used to set a group of types at once are:
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- 'all' : sets all types
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- 'int_kind' : sets 'int'
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- 'float_kind' : sets 'float' and 'longfloat'
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- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
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- 'str_kind' : sets 'numpystr'
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floatmode : str, optional
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Controls the interpretation of the `precision` option for
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floating-point types. Can take the following values
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(default maxprec_equal):
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* 'fixed': Always print exactly `precision` fractional digits,
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even if this would print more or fewer digits than
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necessary to specify the value uniquely.
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* 'unique': Print the minimum number of fractional digits necessary
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to represent each value uniquely. Different elements may
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have a different number of digits. The value of the
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`precision` option is ignored.
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* 'maxprec': Print at most `precision` fractional digits, but if
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an element can be uniquely represented with fewer digits
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only print it with that many.
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* 'maxprec_equal': Print at most `precision` fractional digits,
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but if every element in the array can be uniquely
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represented with an equal number of fewer digits, use that
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many digits for all elements.
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legacy : string or `False`, optional
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If set to the string `'1.13'` enables 1.13 legacy printing mode. This
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approximates numpy 1.13 print output by including a space in the sign
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position of floats and different behavior for 0d arrays. This also
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enables 1.21 legacy printing mode (described below).
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If set to the string `'1.21'` enables 1.21 legacy printing mode. This
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approximates numpy 1.21 print output of complex structured dtypes
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by not inserting spaces after commas that separate fields and after
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colons.
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If set to `False`, disables legacy mode.
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Unrecognized strings will be ignored with a warning for forward
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compatibility.
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.. versionadded:: 1.14.0
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.. versionchanged:: 1.22.0
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See Also
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--------
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get_printoptions, printoptions, set_string_function, array2string
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Notes
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-----
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`formatter` is always reset with a call to `set_printoptions`.
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Use `printoptions` as a context manager to set the values temporarily.
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Examples
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--------
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Floating point precision can be set:
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>>> np.set_printoptions(precision=4)
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>>> np.array([1.123456789])
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[1.1235]
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Long arrays can be summarised:
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>>> np.set_printoptions(threshold=5)
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>>> np.arange(10)
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array([0, 1, 2, ..., 7, 8, 9])
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Small results can be suppressed:
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>>> eps = np.finfo(float).eps
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>>> x = np.arange(4.)
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>>> x**2 - (x + eps)**2
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array([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
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>>> np.set_printoptions(suppress=True)
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>>> x**2 - (x + eps)**2
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array([-0., -0., 0., 0.])
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A custom formatter can be used to display array elements as desired:
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>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
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>>> x = np.arange(3)
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>>> x
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array([int: 0, int: -1, int: -2])
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>>> np.set_printoptions() # formatter gets reset
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>>> x
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array([0, 1, 2])
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To put back the default options, you can use:
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>>> np.set_printoptions(edgeitems=3, infstr='inf',
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... linewidth=75, nanstr='nan', precision=8,
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... suppress=False, threshold=1000, formatter=None)
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Also to temporarily override options, use `printoptions` as a context manager:
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>>> with np.printoptions(precision=2, suppress=True, threshold=5):
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... np.linspace(0, 10, 10)
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array([ 0. , 1.11, 2.22, ..., 7.78, 8.89, 10. ])
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"""
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opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
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suppress, nanstr, infstr, sign, formatter,
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floatmode, legacy)
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# formatter is always reset
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opt['formatter'] = formatter
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_format_options.update(opt)
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# set the C variable for legacy mode
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if _format_options['legacy'] == 113:
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set_legacy_print_mode(113)
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# reset the sign option in legacy mode to avoid confusion
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_format_options['sign'] = '-'
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elif _format_options['legacy'] == 121:
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set_legacy_print_mode(121)
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elif _format_options['legacy'] == sys.maxsize:
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set_legacy_print_mode(0)
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@set_module('numpy')
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def get_printoptions():
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"""
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Return the current print options.
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Returns
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-------
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print_opts : dict
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Dictionary of current print options with keys
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- precision : int
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- threshold : int
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- edgeitems : int
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- linewidth : int
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- suppress : bool
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- nanstr : str
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- infstr : str
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- formatter : dict of callables
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- sign : str
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For a full description of these options, see `set_printoptions`.
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See Also
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--------
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set_printoptions, printoptions, set_string_function
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"""
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opts = _format_options.copy()
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opts['legacy'] = {
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113: '1.13', 121: '1.21', sys.maxsize: False,
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}[opts['legacy']]
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return opts
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def _get_legacy_print_mode():
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"""Return the legacy print mode as an int."""
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return _format_options['legacy']
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@set_module('numpy')
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@contextlib.contextmanager
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def printoptions(*args, **kwargs):
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"""Context manager for setting print options.
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Set print options for the scope of the `with` block, and restore the old
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options at the end. See `set_printoptions` for the full description of
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available options.
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Examples
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--------
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>>> from numpy.testing import assert_equal
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>>> with np.printoptions(precision=2):
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... np.array([2.0]) / 3
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array([0.67])
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The `as`-clause of the `with`-statement gives the current print options:
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>>> with np.printoptions(precision=2) as opts:
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... assert_equal(opts, np.get_printoptions())
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See Also
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--------
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set_printoptions, get_printoptions
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"""
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opts = np.get_printoptions()
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try:
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np.set_printoptions(*args, **kwargs)
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yield np.get_printoptions()
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finally:
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np.set_printoptions(**opts)
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def _leading_trailing(a, edgeitems, index=()):
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"""
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Keep only the N-D corners (leading and trailing edges) of an array.
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Should be passed a base-class ndarray, since it makes no guarantees about
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preserving subclasses.
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"""
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axis = len(index)
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if axis == a.ndim:
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return a[index]
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if a.shape[axis] > 2*edgeitems:
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return concatenate((
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_leading_trailing(a, edgeitems, index + np.index_exp[ :edgeitems]),
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_leading_trailing(a, edgeitems, index + np.index_exp[-edgeitems:])
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), axis=axis)
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else:
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return _leading_trailing(a, edgeitems, index + np.index_exp[:])
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def _object_format(o):
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""" Object arrays containing lists should be printed unambiguously """
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if type(o) is list:
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fmt = 'list({!r})'
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else:
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fmt = '{!r}'
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return fmt.format(o)
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def repr_format(x):
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return repr(x)
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def str_format(x):
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return str(x)
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def _get_formatdict(data, *, precision, floatmode, suppress, sign, legacy,
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formatter, **kwargs):
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# note: extra arguments in kwargs are ignored
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# wrapped in lambdas to avoid taking a code path with the wrong type of data
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formatdict = {
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'bool': lambda: BoolFormat(data),
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'int': lambda: IntegerFormat(data),
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'float': lambda: FloatingFormat(
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data, precision, floatmode, suppress, sign, legacy=legacy),
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'longfloat': lambda: FloatingFormat(
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data, precision, floatmode, suppress, sign, legacy=legacy),
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'complexfloat': lambda: ComplexFloatingFormat(
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data, precision, floatmode, suppress, sign, legacy=legacy),
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'longcomplexfloat': lambda: ComplexFloatingFormat(
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data, precision, floatmode, suppress, sign, legacy=legacy),
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'datetime': lambda: DatetimeFormat(data, legacy=legacy),
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'timedelta': lambda: TimedeltaFormat(data),
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'object': lambda: _object_format,
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'void': lambda: str_format,
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'numpystr': lambda: repr_format}
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# we need to wrap values in `formatter` in a lambda, so that the interface
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# is the same as the above values.
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def indirect(x):
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return lambda: x
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if formatter is not None:
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fkeys = [k for k in formatter.keys() if formatter[k] is not None]
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if 'all' in fkeys:
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for key in formatdict.keys():
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formatdict[key] = indirect(formatter['all'])
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if 'int_kind' in fkeys:
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for key in ['int']:
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formatdict[key] = indirect(formatter['int_kind'])
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if 'float_kind' in fkeys:
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for key in ['float', 'longfloat']:
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formatdict[key] = indirect(formatter['float_kind'])
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if 'complex_kind' in fkeys:
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for key in ['complexfloat', 'longcomplexfloat']:
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formatdict[key] = indirect(formatter['complex_kind'])
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if 'str_kind' in fkeys:
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formatdict['numpystr'] = indirect(formatter['str_kind'])
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for key in formatdict.keys():
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if key in fkeys:
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formatdict[key] = indirect(formatter[key])
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return formatdict
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|
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def _get_format_function(data, **options):
|
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"""
|
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find the right formatting function for the dtype_
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"""
|
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dtype_ = data.dtype
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dtypeobj = dtype_.type
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formatdict = _get_formatdict(data, **options)
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if dtypeobj is None:
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return formatdict["numpystr"]()
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elif issubclass(dtypeobj, _nt.bool_):
|
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return formatdict['bool']()
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elif issubclass(dtypeobj, _nt.integer):
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if issubclass(dtypeobj, _nt.timedelta64):
|
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return formatdict['timedelta']()
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else:
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return formatdict['int']()
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elif issubclass(dtypeobj, _nt.floating):
|
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if issubclass(dtypeobj, _nt.longfloat):
|
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return formatdict['longfloat']()
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else:
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return formatdict['float']()
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elif issubclass(dtypeobj, _nt.complexfloating):
|
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if issubclass(dtypeobj, _nt.clongfloat):
|
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return formatdict['longcomplexfloat']()
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else:
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return formatdict['complexfloat']()
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elif issubclass(dtypeobj, (_nt.unicode_, _nt.string_)):
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return formatdict['numpystr']()
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elif issubclass(dtypeobj, _nt.datetime64):
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return formatdict['datetime']()
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elif issubclass(dtypeobj, _nt.object_):
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return formatdict['object']()
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elif issubclass(dtypeobj, _nt.void):
|
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if dtype_.names is not None:
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return StructuredVoidFormat.from_data(data, **options)
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else:
|
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return formatdict['void']()
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else:
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return formatdict['numpystr']()
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|
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|
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def _recursive_guard(fillvalue='...'):
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"""
|
|
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
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Decorates a function such that if it calls itself with the same first
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argument, it returns `fillvalue` instead of recursing.
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|
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Largely copied from reprlib.recursive_repr
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|
"""
|
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|
|
def decorating_function(f):
|
|
repr_running = set()
|
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|
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@functools.wraps(f)
|
|
def wrapper(self, *args, **kwargs):
|
|
key = id(self), get_ident()
|
|
if key in repr_running:
|
|
return fillvalue
|
|
repr_running.add(key)
|
|
try:
|
|
return f(self, *args, **kwargs)
|
|
finally:
|
|
repr_running.discard(key)
|
|
|
|
return wrapper
|
|
|
|
return decorating_function
|
|
|
|
|
|
# gracefully handle recursive calls, when object arrays contain themselves
|
|
@_recursive_guard()
|
|
def _array2string(a, options, separator=' ', prefix=""):
|
|
# The formatter __init__s in _get_format_function cannot deal with
|
|
# subclasses yet, and we also need to avoid recursion issues in
|
|
# _formatArray with subclasses which return 0d arrays in place of scalars
|
|
data = asarray(a)
|
|
if a.shape == ():
|
|
a = data
|
|
|
|
if a.size > options['threshold']:
|
|
summary_insert = "..."
|
|
data = _leading_trailing(data, options['edgeitems'])
|
|
else:
|
|
summary_insert = ""
|
|
|
|
# find the right formatting function for the array
|
|
format_function = _get_format_function(data, **options)
|
|
|
|
# skip over "["
|
|
next_line_prefix = " "
|
|
# skip over array(
|
|
next_line_prefix += " "*len(prefix)
|
|
|
|
lst = _formatArray(a, format_function, options['linewidth'],
|
|
next_line_prefix, separator, options['edgeitems'],
|
|
summary_insert, options['legacy'])
|
|
return lst
|
|
|
|
|
|
def _array2string_dispatcher(
|
|
a, max_line_width=None, precision=None,
|
|
suppress_small=None, separator=None, prefix=None,
|
|
style=None, formatter=None, threshold=None,
|
|
edgeitems=None, sign=None, floatmode=None, suffix=None,
|
|
*, legacy=None):
|
|
return (a,)
|
|
|
|
|
|
@array_function_dispatch(_array2string_dispatcher, module='numpy')
|
|
def array2string(a, max_line_width=None, precision=None,
|
|
suppress_small=None, separator=' ', prefix="",
|
|
style=np._NoValue, formatter=None, threshold=None,
|
|
edgeitems=None, sign=None, floatmode=None, suffix="",
|
|
*, legacy=None):
|
|
"""
|
|
Return a string representation of an array.
|
|
|
|
Parameters
|
|
----------
|
|
a : ndarray
|
|
Input array.
|
|
max_line_width : int, optional
|
|
Inserts newlines if text is longer than `max_line_width`.
|
|
Defaults to ``numpy.get_printoptions()['linewidth']``.
|
|
precision : int or None, optional
|
|
Floating point precision.
|
|
Defaults to ``numpy.get_printoptions()['precision']``.
|
|
suppress_small : bool, optional
|
|
Represent numbers "very close" to zero as zero; default is False.
|
|
Very close is defined by precision: if the precision is 8, e.g.,
|
|
numbers smaller (in absolute value) than 5e-9 are represented as
|
|
zero.
|
|
Defaults to ``numpy.get_printoptions()['suppress']``.
|
|
separator : str, optional
|
|
Inserted between elements.
|
|
prefix : str, optional
|
|
suffix : str, optional
|
|
The length of the prefix and suffix strings are used to respectively
|
|
align and wrap the output. An array is typically printed as::
|
|
|
|
prefix + array2string(a) + suffix
|
|
|
|
The output is left-padded by the length of the prefix string, and
|
|
wrapping is forced at the column ``max_line_width - len(suffix)``.
|
|
It should be noted that the content of prefix and suffix strings are
|
|
not included in the output.
|
|
style : _NoValue, optional
|
|
Has no effect, do not use.
|
|
|
|
.. deprecated:: 1.14.0
|
|
formatter : dict of callables, optional
|
|
If not None, the keys should indicate the type(s) that the respective
|
|
formatting function applies to. Callables should return a string.
|
|
Types that are not specified (by their corresponding keys) are handled
|
|
by the default formatters. Individual types for which a formatter
|
|
can be set are:
|
|
|
|
- 'bool'
|
|
- 'int'
|
|
- 'timedelta' : a `numpy.timedelta64`
|
|
- 'datetime' : a `numpy.datetime64`
|
|
- 'float'
|
|
- 'longfloat' : 128-bit floats
|
|
- 'complexfloat'
|
|
- 'longcomplexfloat' : composed of two 128-bit floats
|
|
- 'void' : type `numpy.void`
|
|
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
|
|
|
|
Other keys that can be used to set a group of types at once are:
|
|
|
|
- 'all' : sets all types
|
|
- 'int_kind' : sets 'int'
|
|
- 'float_kind' : sets 'float' and 'longfloat'
|
|
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
|
|
- 'str_kind' : sets 'numpystr'
|
|
threshold : int, optional
|
|
Total number of array elements which trigger summarization
|
|
rather than full repr.
|
|
Defaults to ``numpy.get_printoptions()['threshold']``.
|
|
edgeitems : int, optional
|
|
Number of array items in summary at beginning and end of
|
|
each dimension.
|
|
Defaults to ``numpy.get_printoptions()['edgeitems']``.
|
|
sign : string, either '-', '+', or ' ', optional
|
|
Controls printing of the sign of floating-point types. If '+', always
|
|
print the sign of positive values. If ' ', always prints a space
|
|
(whitespace character) in the sign position of positive values. If
|
|
'-', omit the sign character of positive values.
|
|
Defaults to ``numpy.get_printoptions()['sign']``.
|
|
floatmode : str, optional
|
|
Controls the interpretation of the `precision` option for
|
|
floating-point types.
|
|
Defaults to ``numpy.get_printoptions()['floatmode']``.
|
|
Can take the following values:
|
|
|
|
- 'fixed': Always print exactly `precision` fractional digits,
|
|
even if this would print more or fewer digits than
|
|
necessary to specify the value uniquely.
|
|
- 'unique': Print the minimum number of fractional digits necessary
|
|
to represent each value uniquely. Different elements may
|
|
have a different number of digits. The value of the
|
|
`precision` option is ignored.
|
|
- 'maxprec': Print at most `precision` fractional digits, but if
|
|
an element can be uniquely represented with fewer digits
|
|
only print it with that many.
|
|
- 'maxprec_equal': Print at most `precision` fractional digits,
|
|
but if every element in the array can be uniquely
|
|
represented with an equal number of fewer digits, use that
|
|
many digits for all elements.
|
|
legacy : string or `False`, optional
|
|
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
|
|
approximates numpy 1.13 print output by including a space in the sign
|
|
position of floats and different behavior for 0d arrays. If set to
|
|
`False`, disables legacy mode. Unrecognized strings will be ignored
|
|
with a warning for forward compatibility.
|
|
|
|
.. versionadded:: 1.14.0
|
|
|
|
Returns
|
|
-------
|
|
array_str : str
|
|
String representation of the array.
|
|
|
|
Raises
|
|
------
|
|
TypeError
|
|
if a callable in `formatter` does not return a string.
|
|
|
|
See Also
|
|
--------
|
|
array_str, array_repr, set_printoptions, get_printoptions
|
|
|
|
Notes
|
|
-----
|
|
If a formatter is specified for a certain type, the `precision` keyword is
|
|
ignored for that type.
|
|
|
|
This is a very flexible function; `array_repr` and `array_str` are using
|
|
`array2string` internally so keywords with the same name should work
|
|
identically in all three functions.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1e-16,1,2,3])
|
|
>>> np.array2string(x, precision=2, separator=',',
|
|
... suppress_small=True)
|
|
'[0.,1.,2.,3.]'
|
|
|
|
>>> x = np.arange(3.)
|
|
>>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
|
|
'[0.00 1.00 2.00]'
|
|
|
|
>>> x = np.arange(3)
|
|
>>> np.array2string(x, formatter={'int':lambda x: hex(x)})
|
|
'[0x0 0x1 0x2]'
|
|
|
|
"""
|
|
|
|
overrides = _make_options_dict(precision, threshold, edgeitems,
|
|
max_line_width, suppress_small, None, None,
|
|
sign, formatter, floatmode, legacy)
|
|
options = _format_options.copy()
|
|
options.update(overrides)
|
|
|
|
if options['legacy'] <= 113:
|
|
if style is np._NoValue:
|
|
style = repr
|
|
|
|
if a.shape == () and a.dtype.names is None:
|
|
return style(a.item())
|
|
elif style is not np._NoValue:
|
|
# Deprecation 11-9-2017 v1.14
|
|
warnings.warn("'style' argument is deprecated and no longer functional"
|
|
" except in 1.13 'legacy' mode",
|
|
DeprecationWarning, stacklevel=3)
|
|
|
|
if options['legacy'] > 113:
|
|
options['linewidth'] -= len(suffix)
|
|
|
|
# treat as a null array if any of shape elements == 0
|
|
if a.size == 0:
|
|
return "[]"
|
|
|
|
return _array2string(a, options, separator, prefix)
|
|
|
|
|
|
def _extendLine(s, line, word, line_width, next_line_prefix, legacy):
|
|
needs_wrap = len(line) + len(word) > line_width
|
|
if legacy > 113:
|
|
# don't wrap lines if it won't help
|
|
if len(line) <= len(next_line_prefix):
|
|
needs_wrap = False
|
|
|
|
if needs_wrap:
|
|
s += line.rstrip() + "\n"
|
|
line = next_line_prefix
|
|
line += word
|
|
return s, line
|
|
|
|
|
|
def _extendLine_pretty(s, line, word, line_width, next_line_prefix, legacy):
|
|
"""
|
|
Extends line with nicely formatted (possibly multi-line) string ``word``.
|
|
"""
|
|
words = word.splitlines()
|
|
if len(words) == 1 or legacy <= 113:
|
|
return _extendLine(s, line, word, line_width, next_line_prefix, legacy)
|
|
|
|
max_word_length = max(len(word) for word in words)
|
|
if (len(line) + max_word_length > line_width and
|
|
len(line) > len(next_line_prefix)):
|
|
s += line.rstrip() + '\n'
|
|
line = next_line_prefix + words[0]
|
|
indent = next_line_prefix
|
|
else:
|
|
indent = len(line)*' '
|
|
line += words[0]
|
|
|
|
for word in words[1::]:
|
|
s += line.rstrip() + '\n'
|
|
line = indent + word
|
|
|
|
suffix_length = max_word_length - len(words[-1])
|
|
line += suffix_length*' '
|
|
|
|
return s, line
|
|
|
|
def _formatArray(a, format_function, line_width, next_line_prefix,
|
|
separator, edge_items, summary_insert, legacy):
|
|
"""formatArray is designed for two modes of operation:
|
|
|
|
1. Full output
|
|
|
|
2. Summarized output
|
|
|
|
"""
|
|
def recurser(index, hanging_indent, curr_width):
|
|
"""
|
|
By using this local function, we don't need to recurse with all the
|
|
arguments. Since this function is not created recursively, the cost is
|
|
not significant
|
|
"""
|
|
axis = len(index)
|
|
axes_left = a.ndim - axis
|
|
|
|
if axes_left == 0:
|
|
return format_function(a[index])
|
|
|
|
# when recursing, add a space to align with the [ added, and reduce the
|
|
# length of the line by 1
|
|
next_hanging_indent = hanging_indent + ' '
|
|
if legacy <= 113:
|
|
next_width = curr_width
|
|
else:
|
|
next_width = curr_width - len(']')
|
|
|
|
a_len = a.shape[axis]
|
|
show_summary = summary_insert and 2*edge_items < a_len
|
|
if show_summary:
|
|
leading_items = edge_items
|
|
trailing_items = edge_items
|
|
else:
|
|
leading_items = 0
|
|
trailing_items = a_len
|
|
|
|
# stringify the array with the hanging indent on the first line too
|
|
s = ''
|
|
|
|
# last axis (rows) - wrap elements if they would not fit on one line
|
|
if axes_left == 1:
|
|
# the length up until the beginning of the separator / bracket
|
|
if legacy <= 113:
|
|
elem_width = curr_width - len(separator.rstrip())
|
|
else:
|
|
elem_width = curr_width - max(len(separator.rstrip()), len(']'))
|
|
|
|
line = hanging_indent
|
|
for i in range(leading_items):
|
|
word = recurser(index + (i,), next_hanging_indent, next_width)
|
|
s, line = _extendLine_pretty(
|
|
s, line, word, elem_width, hanging_indent, legacy)
|
|
line += separator
|
|
|
|
if show_summary:
|
|
s, line = _extendLine(
|
|
s, line, summary_insert, elem_width, hanging_indent, legacy)
|
|
if legacy <= 113:
|
|
line += ", "
|
|
else:
|
|
line += separator
|
|
|
|
for i in range(trailing_items, 1, -1):
|
|
word = recurser(index + (-i,), next_hanging_indent, next_width)
|
|
s, line = _extendLine_pretty(
|
|
s, line, word, elem_width, hanging_indent, legacy)
|
|
line += separator
|
|
|
|
if legacy <= 113:
|
|
# width of the separator is not considered on 1.13
|
|
elem_width = curr_width
|
|
word = recurser(index + (-1,), next_hanging_indent, next_width)
|
|
s, line = _extendLine_pretty(
|
|
s, line, word, elem_width, hanging_indent, legacy)
|
|
|
|
s += line
|
|
|
|
# other axes - insert newlines between rows
|
|
else:
|
|
s = ''
|
|
line_sep = separator.rstrip() + '\n'*(axes_left - 1)
|
|
|
|
for i in range(leading_items):
|
|
nested = recurser(index + (i,), next_hanging_indent, next_width)
|
|
s += hanging_indent + nested + line_sep
|
|
|
|
if show_summary:
|
|
if legacy <= 113:
|
|
# trailing space, fixed nbr of newlines, and fixed separator
|
|
s += hanging_indent + summary_insert + ", \n"
|
|
else:
|
|
s += hanging_indent + summary_insert + line_sep
|
|
|
|
for i in range(trailing_items, 1, -1):
|
|
nested = recurser(index + (-i,), next_hanging_indent,
|
|
next_width)
|
|
s += hanging_indent + nested + line_sep
|
|
|
|
nested = recurser(index + (-1,), next_hanging_indent, next_width)
|
|
s += hanging_indent + nested
|
|
|
|
# remove the hanging indent, and wrap in []
|
|
s = '[' + s[len(hanging_indent):] + ']'
|
|
return s
|
|
|
|
try:
|
|
# invoke the recursive part with an initial index and prefix
|
|
return recurser(index=(),
|
|
hanging_indent=next_line_prefix,
|
|
curr_width=line_width)
|
|
finally:
|
|
# recursive closures have a cyclic reference to themselves, which
|
|
# requires gc to collect (gh-10620). To avoid this problem, for
|
|
# performance and PyPy friendliness, we break the cycle:
|
|
recurser = None
|
|
|
|
def _none_or_positive_arg(x, name):
|
|
if x is None:
|
|
return -1
|
|
if x < 0:
|
|
raise ValueError("{} must be >= 0".format(name))
|
|
return x
|
|
|
|
class FloatingFormat:
|
|
""" Formatter for subtypes of np.floating """
|
|
def __init__(self, data, precision, floatmode, suppress_small, sign=False,
|
|
*, legacy=None):
|
|
# for backcompatibility, accept bools
|
|
if isinstance(sign, bool):
|
|
sign = '+' if sign else '-'
|
|
|
|
self._legacy = legacy
|
|
if self._legacy <= 113:
|
|
# when not 0d, legacy does not support '-'
|
|
if data.shape != () and sign == '-':
|
|
sign = ' '
|
|
|
|
self.floatmode = floatmode
|
|
if floatmode == 'unique':
|
|
self.precision = None
|
|
else:
|
|
self.precision = precision
|
|
|
|
self.precision = _none_or_positive_arg(self.precision, 'precision')
|
|
|
|
self.suppress_small = suppress_small
|
|
self.sign = sign
|
|
self.exp_format = False
|
|
self.large_exponent = False
|
|
|
|
self.fillFormat(data)
|
|
|
|
def fillFormat(self, data):
|
|
# only the finite values are used to compute the number of digits
|
|
finite_vals = data[isfinite(data)]
|
|
|
|
# choose exponential mode based on the non-zero finite values:
|
|
abs_non_zero = absolute(finite_vals[finite_vals != 0])
|
|
if len(abs_non_zero) != 0:
|
|
max_val = np.max(abs_non_zero)
|
|
min_val = np.min(abs_non_zero)
|
|
with errstate(over='ignore'): # division can overflow
|
|
if max_val >= 1.e8 or (not self.suppress_small and
|
|
(min_val < 0.0001 or max_val/min_val > 1000.)):
|
|
self.exp_format = True
|
|
|
|
# do a first pass of printing all the numbers, to determine sizes
|
|
if len(finite_vals) == 0:
|
|
self.pad_left = 0
|
|
self.pad_right = 0
|
|
self.trim = '.'
|
|
self.exp_size = -1
|
|
self.unique = True
|
|
self.min_digits = None
|
|
elif self.exp_format:
|
|
trim, unique = '.', True
|
|
if self.floatmode == 'fixed' or self._legacy <= 113:
|
|
trim, unique = 'k', False
|
|
strs = (dragon4_scientific(x, precision=self.precision,
|
|
unique=unique, trim=trim, sign=self.sign == '+')
|
|
for x in finite_vals)
|
|
frac_strs, _, exp_strs = zip(*(s.partition('e') for s in strs))
|
|
int_part, frac_part = zip(*(s.split('.') for s in frac_strs))
|
|
self.exp_size = max(len(s) for s in exp_strs) - 1
|
|
|
|
self.trim = 'k'
|
|
self.precision = max(len(s) for s in frac_part)
|
|
self.min_digits = self.precision
|
|
self.unique = unique
|
|
|
|
# for back-compat with np 1.13, use 2 spaces & sign and full prec
|
|
if self._legacy <= 113:
|
|
self.pad_left = 3
|
|
else:
|
|
# this should be only 1 or 2. Can be calculated from sign.
|
|
self.pad_left = max(len(s) for s in int_part)
|
|
# pad_right is only needed for nan length calculation
|
|
self.pad_right = self.exp_size + 2 + self.precision
|
|
else:
|
|
trim, unique = '.', True
|
|
if self.floatmode == 'fixed':
|
|
trim, unique = 'k', False
|
|
strs = (dragon4_positional(x, precision=self.precision,
|
|
fractional=True,
|
|
unique=unique, trim=trim,
|
|
sign=self.sign == '+')
|
|
for x in finite_vals)
|
|
int_part, frac_part = zip(*(s.split('.') for s in strs))
|
|
if self._legacy <= 113:
|
|
self.pad_left = 1 + max(len(s.lstrip('-+')) for s in int_part)
|
|
else:
|
|
self.pad_left = max(len(s) for s in int_part)
|
|
self.pad_right = max(len(s) for s in frac_part)
|
|
self.exp_size = -1
|
|
self.unique = unique
|
|
|
|
if self.floatmode in ['fixed', 'maxprec_equal']:
|
|
self.precision = self.min_digits = self.pad_right
|
|
self.trim = 'k'
|
|
else:
|
|
self.trim = '.'
|
|
self.min_digits = 0
|
|
|
|
if self._legacy > 113:
|
|
# account for sign = ' ' by adding one to pad_left
|
|
if self.sign == ' ' and not any(np.signbit(finite_vals)):
|
|
self.pad_left += 1
|
|
|
|
# if there are non-finite values, may need to increase pad_left
|
|
if data.size != finite_vals.size:
|
|
neginf = self.sign != '-' or any(data[isinf(data)] < 0)
|
|
nanlen = len(_format_options['nanstr'])
|
|
inflen = len(_format_options['infstr']) + neginf
|
|
offset = self.pad_right + 1 # +1 for decimal pt
|
|
self.pad_left = max(self.pad_left, nanlen - offset, inflen - offset)
|
|
|
|
def __call__(self, x):
|
|
if not np.isfinite(x):
|
|
with errstate(invalid='ignore'):
|
|
if np.isnan(x):
|
|
sign = '+' if self.sign == '+' else ''
|
|
ret = sign + _format_options['nanstr']
|
|
else: # isinf
|
|
sign = '-' if x < 0 else '+' if self.sign == '+' else ''
|
|
ret = sign + _format_options['infstr']
|
|
return ' '*(self.pad_left + self.pad_right + 1 - len(ret)) + ret
|
|
|
|
if self.exp_format:
|
|
return dragon4_scientific(x,
|
|
precision=self.precision,
|
|
min_digits=self.min_digits,
|
|
unique=self.unique,
|
|
trim=self.trim,
|
|
sign=self.sign == '+',
|
|
pad_left=self.pad_left,
|
|
exp_digits=self.exp_size)
|
|
else:
|
|
return dragon4_positional(x,
|
|
precision=self.precision,
|
|
min_digits=self.min_digits,
|
|
unique=self.unique,
|
|
fractional=True,
|
|
trim=self.trim,
|
|
sign=self.sign == '+',
|
|
pad_left=self.pad_left,
|
|
pad_right=self.pad_right)
|
|
|
|
|
|
@set_module('numpy')
|
|
def format_float_scientific(x, precision=None, unique=True, trim='k',
|
|
sign=False, pad_left=None, exp_digits=None,
|
|
min_digits=None):
|
|
"""
|
|
Format a floating-point scalar as a decimal string in scientific notation.
|
|
|
|
Provides control over rounding, trimming and padding. Uses and assumes
|
|
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
|
|
|
|
Parameters
|
|
----------
|
|
x : python float or numpy floating scalar
|
|
Value to format.
|
|
precision : non-negative integer or None, optional
|
|
Maximum number of digits to print. May be None if `unique` is
|
|
`True`, but must be an integer if unique is `False`.
|
|
unique : boolean, optional
|
|
If `True`, use a digit-generation strategy which gives the shortest
|
|
representation which uniquely identifies the floating-point number from
|
|
other values of the same type, by judicious rounding. If `precision`
|
|
is given fewer digits than necessary can be printed. If `min_digits`
|
|
is given more can be printed, in which cases the last digit is rounded
|
|
with unbiased rounding.
|
|
If `False`, digits are generated as if printing an infinite-precision
|
|
value and stopping after `precision` digits, rounding the remaining
|
|
value with unbiased rounding
|
|
trim : one of 'k', '.', '0', '-', optional
|
|
Controls post-processing trimming of trailing digits, as follows:
|
|
|
|
* 'k' : keep trailing zeros, keep decimal point (no trimming)
|
|
* '.' : trim all trailing zeros, leave decimal point
|
|
* '0' : trim all but the zero before the decimal point. Insert the
|
|
zero if it is missing.
|
|
* '-' : trim trailing zeros and any trailing decimal point
|
|
sign : boolean, optional
|
|
Whether to show the sign for positive values.
|
|
pad_left : non-negative integer, optional
|
|
Pad the left side of the string with whitespace until at least that
|
|
many characters are to the left of the decimal point.
|
|
exp_digits : non-negative integer, optional
|
|
Pad the exponent with zeros until it contains at least this many digits.
|
|
If omitted, the exponent will be at least 2 digits.
|
|
min_digits : non-negative integer or None, optional
|
|
Minimum number of digits to print. This only has an effect for
|
|
`unique=True`. In that case more digits than necessary to uniquely
|
|
identify the value may be printed and rounded unbiased.
|
|
|
|
-- versionadded:: 1.21.0
|
|
|
|
Returns
|
|
-------
|
|
rep : string
|
|
The string representation of the floating point value
|
|
|
|
See Also
|
|
--------
|
|
format_float_positional
|
|
|
|
Examples
|
|
--------
|
|
>>> np.format_float_scientific(np.float32(np.pi))
|
|
'3.1415927e+00'
|
|
>>> s = np.float32(1.23e24)
|
|
>>> np.format_float_scientific(s, unique=False, precision=15)
|
|
'1.230000071797338e+24'
|
|
>>> np.format_float_scientific(s, exp_digits=4)
|
|
'1.23e+0024'
|
|
"""
|
|
precision = _none_or_positive_arg(precision, 'precision')
|
|
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
|
|
exp_digits = _none_or_positive_arg(exp_digits, 'exp_digits')
|
|
min_digits = _none_or_positive_arg(min_digits, 'min_digits')
|
|
if min_digits > 0 and precision > 0 and min_digits > precision:
|
|
raise ValueError("min_digits must be less than or equal to precision")
|
|
return dragon4_scientific(x, precision=precision, unique=unique,
|
|
trim=trim, sign=sign, pad_left=pad_left,
|
|
exp_digits=exp_digits, min_digits=min_digits)
|
|
|
|
|
|
@set_module('numpy')
|
|
def format_float_positional(x, precision=None, unique=True,
|
|
fractional=True, trim='k', sign=False,
|
|
pad_left=None, pad_right=None, min_digits=None):
|
|
"""
|
|
Format a floating-point scalar as a decimal string in positional notation.
|
|
|
|
Provides control over rounding, trimming and padding. Uses and assumes
|
|
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
|
|
|
|
Parameters
|
|
----------
|
|
x : python float or numpy floating scalar
|
|
Value to format.
|
|
precision : non-negative integer or None, optional
|
|
Maximum number of digits to print. May be None if `unique` is
|
|
`True`, but must be an integer if unique is `False`.
|
|
unique : boolean, optional
|
|
If `True`, use a digit-generation strategy which gives the shortest
|
|
representation which uniquely identifies the floating-point number from
|
|
other values of the same type, by judicious rounding. If `precision`
|
|
is given fewer digits than necessary can be printed, or if `min_digits`
|
|
is given more can be printed, in which cases the last digit is rounded
|
|
with unbiased rounding.
|
|
If `False`, digits are generated as if printing an infinite-precision
|
|
value and stopping after `precision` digits, rounding the remaining
|
|
value with unbiased rounding
|
|
fractional : boolean, optional
|
|
If `True`, the cutoffs of `precision` and `min_digits` refer to the
|
|
total number of digits after the decimal point, including leading
|
|
zeros.
|
|
If `False`, `precision` and `min_digits` refer to the total number of
|
|
significant digits, before or after the decimal point, ignoring leading
|
|
zeros.
|
|
trim : one of 'k', '.', '0', '-', optional
|
|
Controls post-processing trimming of trailing digits, as follows:
|
|
|
|
* 'k' : keep trailing zeros, keep decimal point (no trimming)
|
|
* '.' : trim all trailing zeros, leave decimal point
|
|
* '0' : trim all but the zero before the decimal point. Insert the
|
|
zero if it is missing.
|
|
* '-' : trim trailing zeros and any trailing decimal point
|
|
sign : boolean, optional
|
|
Whether to show the sign for positive values.
|
|
pad_left : non-negative integer, optional
|
|
Pad the left side of the string with whitespace until at least that
|
|
many characters are to the left of the decimal point.
|
|
pad_right : non-negative integer, optional
|
|
Pad the right side of the string with whitespace until at least that
|
|
many characters are to the right of the decimal point.
|
|
min_digits : non-negative integer or None, optional
|
|
Minimum number of digits to print. Only has an effect if `unique=True`
|
|
in which case additional digits past those necessary to uniquely
|
|
identify the value may be printed, rounding the last additional digit.
|
|
|
|
-- versionadded:: 1.21.0
|
|
|
|
Returns
|
|
-------
|
|
rep : string
|
|
The string representation of the floating point value
|
|
|
|
See Also
|
|
--------
|
|
format_float_scientific
|
|
|
|
Examples
|
|
--------
|
|
>>> np.format_float_positional(np.float32(np.pi))
|
|
'3.1415927'
|
|
>>> np.format_float_positional(np.float16(np.pi))
|
|
'3.14'
|
|
>>> np.format_float_positional(np.float16(0.3))
|
|
'0.3'
|
|
>>> np.format_float_positional(np.float16(0.3), unique=False, precision=10)
|
|
'0.3000488281'
|
|
"""
|
|
precision = _none_or_positive_arg(precision, 'precision')
|
|
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
|
|
pad_right = _none_or_positive_arg(pad_right, 'pad_right')
|
|
min_digits = _none_or_positive_arg(min_digits, 'min_digits')
|
|
if not fractional and precision == 0:
|
|
raise ValueError("precision must be greater than 0 if "
|
|
"fractional=False")
|
|
if min_digits > 0 and precision > 0 and min_digits > precision:
|
|
raise ValueError("min_digits must be less than or equal to precision")
|
|
return dragon4_positional(x, precision=precision, unique=unique,
|
|
fractional=fractional, trim=trim,
|
|
sign=sign, pad_left=pad_left,
|
|
pad_right=pad_right, min_digits=min_digits)
|
|
|
|
|
|
class IntegerFormat:
|
|
def __init__(self, data):
|
|
if data.size > 0:
|
|
max_str_len = max(len(str(np.max(data))),
|
|
len(str(np.min(data))))
|
|
else:
|
|
max_str_len = 0
|
|
self.format = '%{}d'.format(max_str_len)
|
|
|
|
def __call__(self, x):
|
|
return self.format % x
|
|
|
|
|
|
class BoolFormat:
|
|
def __init__(self, data, **kwargs):
|
|
# add an extra space so " True" and "False" have the same length and
|
|
# array elements align nicely when printed, except in 0d arrays
|
|
self.truestr = ' True' if data.shape != () else 'True'
|
|
|
|
def __call__(self, x):
|
|
return self.truestr if x else "False"
|
|
|
|
|
|
class ComplexFloatingFormat:
|
|
""" Formatter for subtypes of np.complexfloating """
|
|
def __init__(self, x, precision, floatmode, suppress_small,
|
|
sign=False, *, legacy=None):
|
|
# for backcompatibility, accept bools
|
|
if isinstance(sign, bool):
|
|
sign = '+' if sign else '-'
|
|
|
|
floatmode_real = floatmode_imag = floatmode
|
|
if legacy <= 113:
|
|
floatmode_real = 'maxprec_equal'
|
|
floatmode_imag = 'maxprec'
|
|
|
|
self.real_format = FloatingFormat(
|
|
x.real, precision, floatmode_real, suppress_small,
|
|
sign=sign, legacy=legacy
|
|
)
|
|
self.imag_format = FloatingFormat(
|
|
x.imag, precision, floatmode_imag, suppress_small,
|
|
sign='+', legacy=legacy
|
|
)
|
|
|
|
def __call__(self, x):
|
|
r = self.real_format(x.real)
|
|
i = self.imag_format(x.imag)
|
|
|
|
# add the 'j' before the terminal whitespace in i
|
|
sp = len(i.rstrip())
|
|
i = i[:sp] + 'j' + i[sp:]
|
|
|
|
return r + i
|
|
|
|
|
|
class _TimelikeFormat:
|
|
def __init__(self, data):
|
|
non_nat = data[~isnat(data)]
|
|
if len(non_nat) > 0:
|
|
# Max str length of non-NaT elements
|
|
max_str_len = max(len(self._format_non_nat(np.max(non_nat))),
|
|
len(self._format_non_nat(np.min(non_nat))))
|
|
else:
|
|
max_str_len = 0
|
|
if len(non_nat) < data.size:
|
|
# data contains a NaT
|
|
max_str_len = max(max_str_len, 5)
|
|
self._format = '%{}s'.format(max_str_len)
|
|
self._nat = "'NaT'".rjust(max_str_len)
|
|
|
|
def _format_non_nat(self, x):
|
|
# override in subclass
|
|
raise NotImplementedError
|
|
|
|
def __call__(self, x):
|
|
if isnat(x):
|
|
return self._nat
|
|
else:
|
|
return self._format % self._format_non_nat(x)
|
|
|
|
|
|
class DatetimeFormat(_TimelikeFormat):
|
|
def __init__(self, x, unit=None, timezone=None, casting='same_kind',
|
|
legacy=False):
|
|
# Get the unit from the dtype
|
|
if unit is None:
|
|
if x.dtype.kind == 'M':
|
|
unit = datetime_data(x.dtype)[0]
|
|
else:
|
|
unit = 's'
|
|
|
|
if timezone is None:
|
|
timezone = 'naive'
|
|
self.timezone = timezone
|
|
self.unit = unit
|
|
self.casting = casting
|
|
self.legacy = legacy
|
|
|
|
# must be called after the above are configured
|
|
super().__init__(x)
|
|
|
|
def __call__(self, x):
|
|
if self.legacy <= 113:
|
|
return self._format_non_nat(x)
|
|
return super().__call__(x)
|
|
|
|
def _format_non_nat(self, x):
|
|
return "'%s'" % datetime_as_string(x,
|
|
unit=self.unit,
|
|
timezone=self.timezone,
|
|
casting=self.casting)
|
|
|
|
|
|
class TimedeltaFormat(_TimelikeFormat):
|
|
def _format_non_nat(self, x):
|
|
return str(x.astype('i8'))
|
|
|
|
|
|
class SubArrayFormat:
|
|
def __init__(self, format_function):
|
|
self.format_function = format_function
|
|
|
|
def __call__(self, arr):
|
|
if arr.ndim <= 1:
|
|
return "[" + ", ".join(self.format_function(a) for a in arr) + "]"
|
|
return "[" + ", ".join(self.__call__(a) for a in arr) + "]"
|
|
|
|
|
|
class StructuredVoidFormat:
|
|
"""
|
|
Formatter for structured np.void objects.
|
|
|
|
This does not work on structured alias types like np.dtype(('i4', 'i2,i2')),
|
|
as alias scalars lose their field information, and the implementation
|
|
relies upon np.void.__getitem__.
|
|
"""
|
|
def __init__(self, format_functions):
|
|
self.format_functions = format_functions
|
|
|
|
@classmethod
|
|
def from_data(cls, data, **options):
|
|
"""
|
|
This is a second way to initialize StructuredVoidFormat, using the raw data
|
|
as input. Added to avoid changing the signature of __init__.
|
|
"""
|
|
format_functions = []
|
|
for field_name in data.dtype.names:
|
|
format_function = _get_format_function(data[field_name], **options)
|
|
if data.dtype[field_name].shape != ():
|
|
format_function = SubArrayFormat(format_function)
|
|
format_functions.append(format_function)
|
|
return cls(format_functions)
|
|
|
|
def __call__(self, x):
|
|
str_fields = [
|
|
format_function(field)
|
|
for field, format_function in zip(x, self.format_functions)
|
|
]
|
|
if len(str_fields) == 1:
|
|
return "({},)".format(str_fields[0])
|
|
else:
|
|
return "({})".format(", ".join(str_fields))
|
|
|
|
|
|
def _void_scalar_repr(x):
|
|
"""
|
|
Implements the repr for structured-void scalars. It is called from the
|
|
scalartypes.c.src code, and is placed here because it uses the elementwise
|
|
formatters defined above.
|
|
"""
|
|
return StructuredVoidFormat.from_data(array(x), **_format_options)(x)
|
|
|
|
|
|
_typelessdata = [int_, float_, complex_, bool_]
|
|
|
|
|
|
def dtype_is_implied(dtype):
|
|
"""
|
|
Determine if the given dtype is implied by the representation of its values.
|
|
|
|
Parameters
|
|
----------
|
|
dtype : dtype
|
|
Data type
|
|
|
|
Returns
|
|
-------
|
|
implied : bool
|
|
True if the dtype is implied by the representation of its values.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.core.arrayprint.dtype_is_implied(int)
|
|
True
|
|
>>> np.array([1, 2, 3], int)
|
|
array([1, 2, 3])
|
|
>>> np.core.arrayprint.dtype_is_implied(np.int8)
|
|
False
|
|
>>> np.array([1, 2, 3], np.int8)
|
|
array([1, 2, 3], dtype=int8)
|
|
"""
|
|
dtype = np.dtype(dtype)
|
|
if _format_options['legacy'] <= 113 and dtype.type == bool_:
|
|
return False
|
|
|
|
# not just void types can be structured, and names are not part of the repr
|
|
if dtype.names is not None:
|
|
return False
|
|
|
|
return dtype.type in _typelessdata
|
|
|
|
|
|
def dtype_short_repr(dtype):
|
|
"""
|
|
Convert a dtype to a short form which evaluates to the same dtype.
|
|
|
|
The intent is roughly that the following holds
|
|
|
|
>>> from numpy import *
|
|
>>> dt = np.int64([1, 2]).dtype
|
|
>>> assert eval(dtype_short_repr(dt)) == dt
|
|
"""
|
|
if type(dtype).__repr__ != np.dtype.__repr__:
|
|
# TODO: Custom repr for user DTypes, logic should likely move.
|
|
return repr(dtype)
|
|
if dtype.names is not None:
|
|
# structured dtypes give a list or tuple repr
|
|
return str(dtype)
|
|
elif issubclass(dtype.type, flexible):
|
|
# handle these separately so they don't give garbage like str256
|
|
return "'%s'" % str(dtype)
|
|
|
|
typename = dtype.name
|
|
# quote typenames which can't be represented as python variable names
|
|
if typename and not (typename[0].isalpha() and typename.isalnum()):
|
|
typename = repr(typename)
|
|
|
|
return typename
|
|
|
|
|
|
def _array_repr_implementation(
|
|
arr, max_line_width=None, precision=None, suppress_small=None,
|
|
array2string=array2string):
|
|
"""Internal version of array_repr() that allows overriding array2string."""
|
|
if max_line_width is None:
|
|
max_line_width = _format_options['linewidth']
|
|
|
|
if type(arr) is not ndarray:
|
|
class_name = type(arr).__name__
|
|
else:
|
|
class_name = "array"
|
|
|
|
skipdtype = dtype_is_implied(arr.dtype) and arr.size > 0
|
|
|
|
prefix = class_name + "("
|
|
suffix = ")" if skipdtype else ","
|
|
|
|
if (_format_options['legacy'] <= 113 and
|
|
arr.shape == () and not arr.dtype.names):
|
|
lst = repr(arr.item())
|
|
elif arr.size > 0 or arr.shape == (0,):
|
|
lst = array2string(arr, max_line_width, precision, suppress_small,
|
|
', ', prefix, suffix=suffix)
|
|
else: # show zero-length shape unless it is (0,)
|
|
lst = "[], shape=%s" % (repr(arr.shape),)
|
|
|
|
arr_str = prefix + lst + suffix
|
|
|
|
if skipdtype:
|
|
return arr_str
|
|
|
|
dtype_str = "dtype={})".format(dtype_short_repr(arr.dtype))
|
|
|
|
# compute whether we should put dtype on a new line: Do so if adding the
|
|
# dtype would extend the last line past max_line_width.
|
|
# Note: This line gives the correct result even when rfind returns -1.
|
|
last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1)
|
|
spacer = " "
|
|
if _format_options['legacy'] <= 113:
|
|
if issubclass(arr.dtype.type, flexible):
|
|
spacer = '\n' + ' '*len(class_name + "(")
|
|
elif last_line_len + len(dtype_str) + 1 > max_line_width:
|
|
spacer = '\n' + ' '*len(class_name + "(")
|
|
|
|
return arr_str + spacer + dtype_str
|
|
|
|
|
|
def _array_repr_dispatcher(
|
|
arr, max_line_width=None, precision=None, suppress_small=None):
|
|
return (arr,)
|
|
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@array_function_dispatch(_array_repr_dispatcher, module='numpy')
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def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
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"""
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Return the string representation of an array.
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Parameters
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----------
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arr : ndarray
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Input array.
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max_line_width : int, optional
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Inserts newlines if text is longer than `max_line_width`.
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Defaults to ``numpy.get_printoptions()['linewidth']``.
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precision : int, optional
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Floating point precision.
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Defaults to ``numpy.get_printoptions()['precision']``.
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suppress_small : bool, optional
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Represent numbers "very close" to zero as zero; default is False.
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Very close is defined by precision: if the precision is 8, e.g.,
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numbers smaller (in absolute value) than 5e-9 are represented as
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zero.
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Defaults to ``numpy.get_printoptions()['suppress']``.
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Returns
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-------
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string : str
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The string representation of an array.
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See Also
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--------
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array_str, array2string, set_printoptions
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Examples
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--------
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>>> np.array_repr(np.array([1,2]))
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'array([1, 2])'
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>>> np.array_repr(np.ma.array([0.]))
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'MaskedArray([0.])'
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>>> np.array_repr(np.array([], np.int32))
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'array([], dtype=int32)'
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>>> x = np.array([1e-6, 4e-7, 2, 3])
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>>> np.array_repr(x, precision=6, suppress_small=True)
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'array([0.000001, 0. , 2. , 3. ])'
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"""
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return _array_repr_implementation(
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arr, max_line_width, precision, suppress_small)
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@_recursive_guard()
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def _guarded_repr_or_str(v):
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if isinstance(v, bytes):
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return repr(v)
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return str(v)
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def _array_str_implementation(
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a, max_line_width=None, precision=None, suppress_small=None,
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array2string=array2string):
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"""Internal version of array_str() that allows overriding array2string."""
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if (_format_options['legacy'] <= 113 and
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a.shape == () and not a.dtype.names):
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return str(a.item())
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# the str of 0d arrays is a special case: It should appear like a scalar,
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# so floats are not truncated by `precision`, and strings are not wrapped
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# in quotes. So we return the str of the scalar value.
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if a.shape == ():
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# obtain a scalar and call str on it, avoiding problems for subclasses
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# for which indexing with () returns a 0d instead of a scalar by using
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# ndarray's getindex. Also guard against recursive 0d object arrays.
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return _guarded_repr_or_str(np.ndarray.__getitem__(a, ()))
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return array2string(a, max_line_width, precision, suppress_small, ' ', "")
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def _array_str_dispatcher(
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a, max_line_width=None, precision=None, suppress_small=None):
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return (a,)
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@array_function_dispatch(_array_str_dispatcher, module='numpy')
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def array_str(a, max_line_width=None, precision=None, suppress_small=None):
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"""
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Return a string representation of the data in an array.
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The data in the array is returned as a single string. This function is
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similar to `array_repr`, the difference being that `array_repr` also
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returns information on the kind of array and its data type.
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Parameters
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----------
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a : ndarray
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Input array.
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max_line_width : int, optional
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Inserts newlines if text is longer than `max_line_width`.
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Defaults to ``numpy.get_printoptions()['linewidth']``.
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precision : int, optional
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|
Floating point precision.
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|
Defaults to ``numpy.get_printoptions()['precision']``.
|
|
suppress_small : bool, optional
|
|
Represent numbers "very close" to zero as zero; default is False.
|
|
Very close is defined by precision: if the precision is 8, e.g.,
|
|
numbers smaller (in absolute value) than 5e-9 are represented as
|
|
zero.
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|
Defaults to ``numpy.get_printoptions()['suppress']``.
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See Also
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--------
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array2string, array_repr, set_printoptions
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Examples
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--------
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>>> np.array_str(np.arange(3))
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'[0 1 2]'
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"""
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return _array_str_implementation(
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a, max_line_width, precision, suppress_small)
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# needed if __array_function__ is disabled
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_array2string_impl = getattr(array2string, '__wrapped__', array2string)
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_default_array_str = functools.partial(_array_str_implementation,
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array2string=_array2string_impl)
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_default_array_repr = functools.partial(_array_repr_implementation,
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array2string=_array2string_impl)
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def set_string_function(f, repr=True):
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"""
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Set a Python function to be used when pretty printing arrays.
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Parameters
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----------
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f : function or None
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|
Function to be used to pretty print arrays. The function should expect
|
|
a single array argument and return a string of the representation of
|
|
the array. If None, the function is reset to the default NumPy function
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to print arrays.
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repr : bool, optional
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If True (default), the function for pretty printing (``__repr__``)
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is set, if False the function that returns the default string
|
|
representation (``__str__``) is set.
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See Also
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|
--------
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|
set_printoptions, get_printoptions
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|
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Examples
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--------
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>>> def pprint(arr):
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... return 'HA! - What are you going to do now?'
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...
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>>> np.set_string_function(pprint)
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>>> a = np.arange(10)
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>>> a
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HA! - What are you going to do now?
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>>> _ = a
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>>> # [0 1 2 3 4 5 6 7 8 9]
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We can reset the function to the default:
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>>> np.set_string_function(None)
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>>> a
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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`repr` affects either pretty printing or normal string representation.
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|
Note that ``__repr__`` is still affected by setting ``__str__``
|
|
because the width of each array element in the returned string becomes
|
|
equal to the length of the result of ``__str__()``.
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>>> x = np.arange(4)
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>>> np.set_string_function(lambda x:'random', repr=False)
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>>> x.__str__()
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'random'
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>>> x.__repr__()
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'array([0, 1, 2, 3])'
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"""
|
|
if f is None:
|
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if repr:
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|
return multiarray.set_string_function(_default_array_repr, 1)
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|
else:
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|
return multiarray.set_string_function(_default_array_str, 0)
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|
else:
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|
return multiarray.set_string_function(f, repr)
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