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"""Array printing function
$Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
"""
__all__ = ["array2string", "array_str", "array_repr",
"set_printoptions", "get_printoptions", "printoptions",
"format_float_positional", "format_float_scientific"]
__docformat__ = 'restructuredtext'
#
# Written by Konrad Hinsen <hinsenk@ere.umontreal.ca>
# last revision: 1996-3-13
# modified by Jim Hugunin 1997-3-3 for repr's and str's (and other details)
# and by Perry Greenfield 2000-4-1 for numarray
# and by Travis Oliphant 2005-8-22 for numpy
# Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
# scalars but for different purposes. scalartypes.c.src has str/reprs for when
# the scalar is printed on its own, while arrayprint.py has strs for when
# scalars are printed inside an ndarray. Only the latter strs are currently
# user-customizable.
import functools
import numbers
import sys
try:
from _thread import get_ident
except ImportError:
from _dummy_thread import get_ident
import contextlib
import operator
import warnings
import numpy as np
from . import numerictypes as _nt
from .fromnumeric import any
from .multiarray import (
array,
datetime_as_string,
datetime_data,
dragon4_positional,
dragon4_scientific,
ndarray,
)
from .numeric import asarray, concatenate, errstate
from .numerictypes import complex128, flexible, float64, int_
from .overrides import array_function_dispatch, set_module
from .printoptions import format_options
from .umath import absolute, isfinite, isinf, isnat
def _make_options_dict(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
sign=None, formatter=None, floatmode=None, legacy=None,
override_repr=None):
"""
Make a dictionary out of the non-None arguments, plus conversion of
*legacy* and sanity checks.
"""
options = {k: v for k, v in list(locals().items()) if v is not None}
if suppress is not None:
options['suppress'] = bool(suppress)
modes = ['fixed', 'unique', 'maxprec', 'maxprec_equal']
if floatmode not in modes + [None]:
raise ValueError("floatmode option must be one of " +
", ".join(f'"{m}"' for m in modes))
if sign not in [None, '-', '+', ' ']:
raise ValueError("sign option must be one of ' ', '+', or '-'")
if legacy is False:
options['legacy'] = sys.maxsize
elif legacy == False:
warnings.warn(
f"Passing `legacy={legacy!r}` is deprecated.",
FutureWarning, stacklevel=3
)
options['legacy'] = sys.maxsize
elif legacy == '1.13':
options['legacy'] = 113
elif legacy == '1.21':
options['legacy'] = 121
elif legacy == '1.25':
options['legacy'] = 125
elif legacy == '2.1':
options['legacy'] = 201
elif legacy == '2.2':
options['legacy'] = 202
elif legacy is None:
pass # OK, do nothing.
else:
warnings.warn(
"legacy printing option can currently only be '1.13', '1.21', "
"'1.25', '2.1', '2.2' or `False`", stacklevel=3)
if threshold is not None:
# forbid the bad threshold arg suggested by stack overflow, gh-12351
if not isinstance(threshold, numbers.Number):
raise TypeError("threshold must be numeric")
if np.isnan(threshold):
raise ValueError("threshold must be non-NAN, try "
"sys.maxsize for untruncated representation")
if precision is not None:
# forbid the bad precision arg as suggested by issue #18254
try:
options['precision'] = operator.index(precision)
except TypeError as e:
raise TypeError('precision must be an integer') from e
return options
@set_module('numpy')
def set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None,
infstr=None, formatter=None, sign=None, floatmode=None,
*, legacy=None, override_repr=None):
"""
Set printing options.
These options determine the way floating point numbers, arrays and
other NumPy objects are displayed.
Parameters
----------
precision : int or None, optional
Number of digits of precision for floating point output (default 8).
May be None if `floatmode` is not `fixed`, to print as many digits as
necessary to uniquely specify the value.
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr (default 1000).
To always use the full repr without summarization, pass `sys.maxsize`.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension (default 3).
linewidth : int, optional
The number of characters per line for the purpose of inserting
line breaks (default 75).
suppress : bool, optional
If True, always print floating point numbers using fixed point
notation, in which case numbers equal to zero in the current precision
will print as zero. If False, then scientific notation is used when
absolute value of the smallest number is < 1e-4 or the ratio of the
maximum absolute value to the minimum is > 1e3. The default is False.
nanstr : str, optional
String representation of floating point not-a-number (default nan).
infstr : str, optional
String representation of floating point infinity (default inf).
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. (default '-')
.. versionchanged:: 2.0
The sign parameter can now be an integer type, previously
types were floating-point types.
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
- 'numpystr' : types `numpy.bytes_` and `numpy.str_`
- 'object' : `np.object_` arrays
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'
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. Can take the following values
(default maxprec_equal):
* '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. This also
enables 1.21 legacy printing mode (described below).
If set to the string ``'1.21'`` enables 1.21 legacy printing mode. This
approximates numpy 1.21 print output of complex structured dtypes
by not inserting spaces after commas that separate fields and after
colons.
If set to ``'1.25'`` approximates printing of 1.25 which mainly means
that numeric scalars are printed without their type information, e.g.
as ``3.0`` rather than ``np.float64(3.0)``.
If set to ``'2.1'``, shape information is not given when arrays are
summarized (i.e., multiple elements replaced with ``...``).
If set to ``'2.2'``, the transition to use scientific notation for
printing ``np.float16`` and ``np.float32`` types may happen later or
not at all for larger values.
If set to `False`, disables legacy mode.
Unrecognized strings will be ignored with a warning for forward
compatibility.
.. versionchanged:: 2.2
override_repr: callable, optional
If set a passed function will be used for generating arrays' repr.
Other options will be ignored.
See Also
--------
get_printoptions, printoptions, array2string
Notes
-----
* ``formatter`` is always reset with a call to `set_printoptions`.
* Use `printoptions` as a context manager to set the values temporarily.
* These print options apply only to NumPy ndarrays, not to scalars.
**Concurrency note:** see :ref:`text_formatting_options`
Examples
--------
Floating point precision can be set:
>>> import numpy as np
>>> np.set_printoptions(precision=4)
>>> np.array([1.123456789])
[1.1235]
Long arrays can be summarised:
>>> np.set_printoptions(threshold=5)
>>> np.arange(10)
array([0, 1, 2, ..., 7, 8, 9], shape=(10,))
Small results can be suppressed:
>>> eps = np.finfo(float).eps
>>> x = np.arange(4.)
>>> x**2 - (x + eps)**2
array([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
>>> np.set_printoptions(suppress=True)
>>> x**2 - (x + eps)**2
array([-0., -0., 0., 0.])
A custom formatter can be used to display array elements as desired:
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(3)
>>> x
array([int: 0, int: -1, int: -2])
>>> np.set_printoptions() # formatter gets reset
>>> x
array([0, 1, 2])
To put back the default options, you can use:
>>> np.set_printoptions(edgeitems=3, infstr='inf',
... linewidth=75, nanstr='nan', precision=8,
... suppress=False, threshold=1000, formatter=None)
Also to temporarily override options, use `printoptions`
as a context manager:
>>> with np.printoptions(precision=2, suppress=True, threshold=5):
... np.linspace(0, 10, 10)
array([ 0. , 1.11, 2.22, ..., 7.78, 8.89, 10. ], shape=(10,))
"""
_set_printoptions(precision, threshold, edgeitems, linewidth, suppress,
nanstr, infstr, formatter, sign, floatmode,
legacy=legacy, override_repr=override_repr)
def _set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None,
infstr=None, formatter=None, sign=None, floatmode=None,
*, legacy=None, override_repr=None):
new_opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
suppress, nanstr, infstr, sign, formatter,
floatmode, legacy)
# formatter and override_repr are always reset
new_opt['formatter'] = formatter
new_opt['override_repr'] = override_repr
updated_opt = format_options.get() | new_opt
updated_opt.update(new_opt)
if updated_opt['legacy'] == 113:
updated_opt['sign'] = '-'
return format_options.set(updated_opt)
@set_module('numpy')
def get_printoptions():
"""
Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
- sign : str
- formatter : dict of callables
- floatmode : str
- legacy : str or False
For a full description of these options, see `set_printoptions`.
Notes
-----
These print options apply only to NumPy ndarrays, not to scalars.
**Concurrency note:** see :ref:`text_formatting_options`
See Also
--------
set_printoptions, printoptions
Examples
--------
>>> import numpy as np
>>> np.get_printoptions()
{'edgeitems': 3, 'threshold': 1000, ..., 'override_repr': None}
>>> np.get_printoptions()['linewidth']
75
>>> np.set_printoptions(linewidth=100)
>>> np.get_printoptions()['linewidth']
100
"""
opts = format_options.get().copy()
opts['legacy'] = {
113: '1.13', 121: '1.21', 125: '1.25', 201: '2.1',
202: '2.2', sys.maxsize: False,
}[opts['legacy']]
return opts
def _get_legacy_print_mode():
"""Return the legacy print mode as an int."""
return format_options.get()['legacy']
@set_module('numpy')
@contextlib.contextmanager
def printoptions(*args, **kwargs):
"""Context manager for setting print options.
Set print options for the scope of the `with` block, and restore the old
options at the end. See `set_printoptions` for the full description of
available options.
Examples
--------
>>> import numpy as np
>>> from numpy.testing import assert_equal
>>> with np.printoptions(precision=2):
... np.array([2.0]) / 3
array([0.67])
The `as`-clause of the `with`-statement gives the current print options:
>>> with np.printoptions(precision=2) as opts:
... assert_equal(opts, np.get_printoptions())
See Also
--------
set_printoptions, get_printoptions
Notes
-----
These print options apply only to NumPy ndarrays, not to scalars.
**Concurrency note:** see :ref:`text_formatting_options`
"""
token = _set_printoptions(*args, **kwargs)
try:
yield get_printoptions()
finally:
format_options.reset(token)
def _leading_trailing(a, edgeitems, index=()):
"""
Keep only the N-D corners (leading and trailing edges) of an array.
Should be passed a base-class ndarray, since it makes no guarantees about
preserving subclasses.
"""
axis = len(index)
if axis == a.ndim:
return a[index]
if a.shape[axis] > 2 * edgeitems:
return concatenate((
_leading_trailing(a, edgeitems, index + np.index_exp[:edgeitems]),
_leading_trailing(a, edgeitems, index + np.index_exp[-edgeitems:])
), axis=axis)
else:
return _leading_trailing(a, edgeitems, index + np.index_exp[:])
def _object_format(o):
""" Object arrays containing lists should be printed unambiguously """
if type(o) is list:
fmt = 'list({!r})'
else:
fmt = '{!r}'
return fmt.format(o)
def repr_format(x):
if isinstance(x, (np.str_, np.bytes_)):
return repr(x.item())
return repr(x)
def str_format(x):
if isinstance(x, (np.str_, np.bytes_)):
return str(x.item())
return str(x)
def _get_formatdict(data, *, precision, floatmode, suppress, sign, legacy,
formatter, **kwargs):
# note: extra arguments in kwargs are ignored
# wrapped in lambdas to avoid taking a code path
# with the wrong type of data
formatdict = {
'bool': lambda: BoolFormat(data),
'int': lambda: IntegerFormat(data, sign),
'float': lambda: FloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'longfloat': lambda: FloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'complexfloat': lambda: ComplexFloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'longcomplexfloat': lambda: ComplexFloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'datetime': lambda: DatetimeFormat(data, legacy=legacy),
'timedelta': lambda: TimedeltaFormat(data),
'object': lambda: _object_format,
'void': lambda: str_format,
'numpystr': lambda: repr_format}
# we need to wrap values in `formatter` in a lambda, so that the interface
# is the same as the above values.
def indirect(x):
return lambda: x
if formatter is not None:
fkeys = [k for k in formatter.keys() if formatter[k] is not None]
if 'all' in fkeys:
for key in formatdict.keys():
formatdict[key] = indirect(formatter['all'])
if 'int_kind' in fkeys:
for key in ['int']:
formatdict[key] = indirect(formatter['int_kind'])
if 'float_kind' in fkeys:
for key in ['float', 'longfloat']:
formatdict[key] = indirect(formatter['float_kind'])
if 'complex_kind' in fkeys:
for key in ['complexfloat', 'longcomplexfloat']:
formatdict[key] = indirect(formatter['complex_kind'])
if 'str_kind' in fkeys:
formatdict['numpystr'] = indirect(formatter['str_kind'])
for key in formatdict.keys():
if key in fkeys:
formatdict[key] = indirect(formatter[key])
return formatdict
def _get_format_function(data, **options):
"""
find the right formatting function for the dtype_
"""
dtype_ = data.dtype
dtypeobj = dtype_.type
formatdict = _get_formatdict(data, **options)
if dtypeobj is None:
return formatdict["numpystr"]()
elif (getattr(dtypeobj, "__module__", None) != "numpy"
and not issubclass(dtypeobj, str)):
# Use `str()` as a default format for non-NumPy dtypes. This should be
# improved. We use `str` assuming that `repr` is likely to duplicate
# information that is contained in the dtype.
# (Do this early, because e.g. quaddtype subclasses floating.)
return formatdict['void']()
elif issubclass(dtypeobj, _nt.bool):
return formatdict['bool']()
elif issubclass(dtypeobj, _nt.integer):
if issubclass(dtypeobj, _nt.timedelta64):
return formatdict['timedelta']()
else:
return formatdict['int']()
elif issubclass(dtypeobj, _nt.floating):
if issubclass(dtypeobj, _nt.longdouble):
return formatdict['longfloat']()
else:
return formatdict['float']()
elif issubclass(dtypeobj, _nt.complexfloating):
if issubclass(dtypeobj, _nt.clongdouble):
return formatdict['longcomplexfloat']()
else:
return formatdict['complexfloat']()
elif issubclass(dtypeobj, (_nt.str_, _nt.bytes_)):
return formatdict['numpystr']()
elif issubclass(dtypeobj, _nt.datetime64):
return formatdict['datetime']()
elif issubclass(dtypeobj, _nt.object_):
return formatdict['object']()
elif issubclass(dtypeobj, _nt.void):
if dtype_.names is not None:
return StructuredVoidFormat.from_data(data, **options)
else:
return formatdict['void']()
else:
return formatdict['numpystr']()
def _recursive_guard(fillvalue='...'):
"""
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
Decorates a function such that if it calls itself with the same first
argument, it returns `fillvalue` instead of recursing.
Largely copied from reprlib.recursive_repr
"""
def decorating_function(f):
repr_running = set()
@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,
*, 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="",
*, 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.
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.bytes_` and `numpy.str_`
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']``.
.. versionchanged:: 2.0
The sign parameter can now be an integer type, previously
types were floating-point types.
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.
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
--------
>>> import numpy as np
>>> 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.get().copy()
options.update(overrides)
if options['legacy'] <= 113:
if a.shape == () and a.dtype.names is None:
return repr(a.item())
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, we break the cycle:
recurser = None
def _none_or_positive_arg(x, name):
if x is None:
return -1
if x < 0:
raise ValueError(f"{name} must be >= 0")
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