"""Array printing function
$Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
"""
from __future__ import division, absolute_import, print_function
__all__ = ["array2string", "array_str", "array_repr", "set_string_function",
"set_printoptions", "get_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 sys
import functools
if sys.version_info[0] >= 3:
try:
from _thread import get_ident
except ImportError:
from _dummy_thread import get_ident
else:
try:
from thread import get_ident
except ImportError:
from dummy_thread import get_ident
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, not_equal, isnan, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, dtype, ndarray,
set_legacy_print_mode)
from .fromnumeric import ravel, any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
import warnings
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers array summarization
'floatmode': 'maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing small floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
'legacy': False}
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):
""" make a dictionary out of the non-None arguments, plus sanity checks """
options = {k: v for k, v in 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('"{}"'.format(m) for m in modes))
if sign not in [None, '-', '+', ' ']:
raise ValueError("sign option must be one of ' ', '+', or '-'")
if legacy not in [None, False, '1.13']:
warnings.warn("legacy printing option can currently only be '1.13' or "
"`False`", stacklevel=3)
return options
def set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
formatter=None, sign=None, floatmode=None, **kwarg):
"""
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).
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 '-')
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.string_` and `numpy.unicode_`
- 'object' : `np.object_` arrays
- 'str' : all other strings
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 'str' and 'numpystr'
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. 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
See Also
--------
get_printoptions, set_string_function, array2string
Notes
-----
`formatter` is always reset with a call to `set_printoptions`.
Examples
--------
Floating point precision can be set:
>>> np.set_printoptions(precision=4)
>>> print(np.array([1.123456789]))
[ 1.1235]
Long arrays can be summarised:
>>> np.set_printoptions(threshold=5)
>>> print(np.arange(10))
[0 1 2 ..., 7 8 9]
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)
"""
legacy = kwarg.pop('legacy', None)
if kwarg:
msg = "set_printoptions() got unexpected keyword argument '{}'"
raise TypeError(msg.format(kwarg.popitem()[0]))
opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
suppress, nanstr, infstr, sign, formatter,
floatmode, legacy)
# formatter is always reset
opt['formatter'] = formatter
_format_options.update(opt)
# set the C variable for legacy mode
if _format_options['legacy'] == '1.13':
set_legacy_print_mode(113)
# reset the sign option in legacy mode to avoid confusion
_format_options['sign'] = '-'
elif _format_options['legacy'] is False:
set_legacy_print_mode(0)
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
- formatter : dict of callables
- sign : str
For a full description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, set_string_function
"""
return _format_options.copy()
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):
return repr(x)
def str_format(x):
return str(x)
def _get_formatdict(data, **opt):
prec, fmode = opt['precision'], opt['floatmode']
supp, sign = opt['suppress'], opt['sign']
legacy = opt['legacy']
# wrapped in lambdas to avoid taking a code path with the wrong type of data
formatdict = {
'bool': lambda: BoolFormat(data),
'int': lambda: IntegerFormat(data),
'float': lambda:
FloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'longfloat': lambda:
FloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'complexfloat': lambda:
ComplexFloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'longcomplexfloat': lambda:
ComplexFloatingFormat(data, prec, fmode, supp, 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,
'str': lambda: str}
# 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
formatter = opt['formatter']
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:
for key in ['numpystr', 'str']:
formatdict[key] = 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 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.longfloat):
return formatdict['longfloat']()
else:
return formatdict['float']()
elif issubclass(dtypeobj, _nt.complexfloating):
if issubclass(dtypeobj, _nt.clongfloat):
return formatdict['longcomplexfloat']()
else:
return formatdict['complexfloat']()
elif issubclass(dtypeobj, (_nt.unicode_, _nt.string_)):
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(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="",
**kwarg):
"""
Return a string representation of an array.
Parameters
----------
a : array_like
Input array.
max_line_width : int, optional
The maximum number of columns the string should span. Newline
characters splits the string appropriately after array elements.
precision : int or None, optional
Floating point precision. Default is the current printing
precision (usually 8), which can be altered using `set_printoptions`.
suppress_small : bool, optional
Represent very small numbers as zero. A number is "very small" if it
is smaller than the current printing precision.
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)``.
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_`
- 'str' : all other strings
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 'str' and 'numpystr'
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension.
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.
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. 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])
>>> print(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)})
'[0x0L 0x1L 0x2L]'
"""
legacy = kwarg.pop('legacy', None)
if kwarg:
msg = "array2string() got unexpected keyword argument '{}'"
raise TypeError(msg.format(kwarg.popitem()[0]))
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'] == '1.13':
if style is np._NoValue:
style = repr
if a.shape == () and not a.dtype.names:
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'] != '1.13':
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 != '1.13':
s# 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 _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 == '1.13':
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 == '1.13':
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(
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 == '1.13':
line += ", "
else:
line += separator
for i in range(trailing_items, 1, -1):
word = recurser(index + (-i,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if legacy == '1.13':
# width of the seperator is not considered on 1.13
elem_width = curr_width
word = recurser(index + (-1,), next_hanging_indent, next_width)
s, line = _extendLine(
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 == '1.13':
# 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(object):
""" Formatter for subtypes of np.floating """
def __init__(self, data, precision, floatmode, suppress_small, sign=False,
**kwarg):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
self._legacy = kwarg.get('legacy', False)
if self._legacy == '1.13':
# 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
elif self.exp_format:
trim, unique = '.', True
if self.floatmode == 'fixed' or self._legacy == '1.13':
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)
# for back-compat with np 1.13, use 2 spaces & sign and full prec
if self._legacy == '1.13':
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
self.unique = False
else:
# first pass printing to determine sizes
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 == '1.13':
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
if self.floatmode in ['fixed', 'maxprec_equal']:
self.precision = self.pad_right
self.unique = False
self.trim = 'k'
else:
self.unique = True
self.trim = '.'
if self._legacy != '1.13':
# 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,
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,
unique=self.unique,
fractional=True,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
pad_right=self.pad_right)
# for back-compatibility, we keep the classes for each float type too
class FloatFormat(FloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn("FloatFormat has been replaced by FloatingFormat",
DeprecationWarning, stacklevel=2)
super(FloatFormat, self).__init__(*args, **kwargs)
class LongFloatFormat(FloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn("LongFloatFormat has been replaced by FloatingFormat",
DeprecationWarning, stacklevel=2)
super(LongFloatFormat, self).__init__(*args, **kwargs)
def format_float_scientific(x, precision=None, unique=True, trim='k',
sign=False, pad_left=None, exp_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`
was omitted, print all necessary digits, otherwise digit generation is
cut off after `precision` digits and the remaining value is rounded.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value.
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.
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')
return dragon4_scientific(x, precision=precision, unique=unique,
trim=trim, sign=sign, pad_left=pad_left,
exp_digits=exp_digits)
def format_float_positional(x, precision=None, unique=True,
fractional=True, trim='k', sign=False,
pad_left=None, pad_right=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`
was omitted, print out all necessary digits, otherwise digit generation
is cut off after `precision` digits and the remaining value is rounded.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value.
fractional : boolean, optional
If `True`, the cutoff of `precision` digits refers to the total number
of digits after the decimal point, including leading zeros.
If `False`, `precision` refers 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.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
Examples
--------
>>> np.format_float_scientific(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')
return dragon4_positional(x, precision=precision, unique=unique,
fractional=fractional, trim=trim,
sign=sign, pad_left=pad_left,
pad_right=pad_right)
class IntegerFormat(object):
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(object):
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(object):
""" Formatter for subtypes of np.complexfloating """
def __init__(self, x, precision, floatmode, suppress_small,
sign=False, **kwarg):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
floatmode_real = floatmode_imag = floatmode
if kwarg.get('legacy', False) == '1.13':
floatmode_real = 'maxprec_equal'
floatmode_imag = 'maxprec'
self.real_format = FloatingFormat(x.real, precision, floatmode_real,
suppress_small, sign=sign, **kwarg)
self.imag_format = FloatingFormat(x.imag, precision, floatmode_imag,
suppress_small, sign='+', **kwarg)
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
# for back-compatibility, we keep the classes for each complex type too
class ComplexFormat(ComplexFloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn(
"ComplexFormat has been replaced by ComplexFloatingFormat",
DeprecationWarning, stacklevel=2)
super(ComplexFormat, self).__init__(*args, **kwargs)
class LongComplexFormat(ComplexFloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn(
"LongComplexFormat has been replaced by ComplexFloatingFormat",
DeprecationWarning, stacklevel=2)
super(LongComplexFormat, self).__init__(*args, **kwargs)
class _TimelikeFormat(object):
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(DatetimeFormat, self).__init__(x)
def __call__(self, x):
if self.legacy == '1.13':
return self._format_non_nat(x)
return super(DatetimeFormat, self).__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(object):
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(object):
"""
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))
# for backwards compatibility
class StructureFormat(StructuredVoidFormat):
def __init__(self, *args, **kwargs):
# NumPy 1.14, 2018-02-14
warnings.warn(
"StructureFormat has been replaced by StructuredVoidFormat",
DeprecationWarning, stacklevel=2)
super(StructureFormat, self).__init__(*args, **kwargs)
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_]
if issubclass(intc, int):
_typelessdata.append(intc)
if issubclass(longlong, int):
_typelessdata.append(longlong)
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=np.int8)
"""
dtype = np.dtype(dtype)
if _format_options['legacy'] == '1.13' 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 *
>>> assert eval(dtype_short_repr(dt)) == dt
"""
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(arr, max_line_width=None, precision=None, suppress_small=None):
"""
Return the string representation of an array.
Parameters
----------
arr : ndarray
Input array.
max_line_width : int, optional
The maximum number of columns the string should span. Newline
characters split the string appropriately after array elements.
precision : int, optional
Floating point precision. Default is the current printing precision
(usually 8), which can be altered using `set_printoptions`.
suppress_small : bool, optional
Represent very small numbers as zero, default is False. Very small
is defined by `precision`, if the precision is 8 then
numbers smaller than 5e-9 are represented as zero.
Returns
-------
string : str
The string representation of an array.
See Also
--------
array_str, array2string, set_printoptions
Examples
--------
>>> np.array_repr(np.array([1,2]))
'array([1, 2])'
>>> np.array_repr(np.ma.array([0.]))
'MaskedArray([ 0.])'
>>> np.array_repr(np.array([], np.int32))
'array([], dtype=int32)'
>>> x = np.array([1e-6, 4e-7, 2, 3])
>>> np.array_repr(x, precision=6, suppress_small=True)
'array([ 0.000001, 0. , 2. , 3. ])'
"""
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'] == '1.13' 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'] == '1.13':
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
_guarded_str = _recursive_guard()(str)
def array_str(a, max_line_width=None, precision=None, suppress_small=None):
"""
Return a string representation of the data in an array.
The data in the array is returned as a single string. This function is
similar to `array_repr`, the difference being that `array_repr` also
returns information on the kind of array and its data type.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`. The
default is, indirectly, 75.
precision : int, optional
Floating point precision. Default is the current printing precision
(usually 8), which can be altered using `set_printoptions`.
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.
See Also
--------
array2string, array_repr, set_printoptions
Examples
--------
>>> np.array_str(np.arange(3))
'[0 1 2]'
"""
if (_format_options['legacy'] == '1.13' and
a.shape == () and not a.dtype.names):
return str(a.item())
# the str of 0d arrays is a special case: It should appear like a scalar,
# so floats are not truncated by `precision`, and strings are not wrapped
# in quotes. So we return the str of the scalar value.
if a.shape == ():
# obtain a scalar and call str on it, avoiding problems for subclasses
# for which indexing with () returns a 0d instead of a scalar by using
# ndarray's getindex. Also guard against recursive 0d object arrays.
return _guarded_str(np.ndarray.__getitem__(a, ()))
return array2string(a, max_line_width, precision, suppress_small, ' ', "")
def set_string_function(f, repr=True):
"""
Set a Python function to be used when pretty printing arrays.
Parameters
----------
f : function or None
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
to print arrays.
repr : bool, optional
If True (default), the function for pretty printing (``__repr__``)
is set, if False the function that returns the default string
representation (``__str__``) is set.
See Also
--------
set_printoptions, get_printoptions
Examples
--------
>>> def pprint(arr):
... return 'HA! - What are you going to do now?'
...
>>> np.set_string_function(pprint)
>>> a = np.arange(10)
>>> a
HA! - What are you going to do now?
>>> print(a)
[0 1 2 3 4 5 6 7 8 9]
We can reset the function to the default:
>>> np.set_string_function(None)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
`repr` affects either pretty printing or normal string representation.
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__()``.
>>> x = np.arange(4)
>>> np.set_string_function(lambda x:'random', repr=False)
>>> x.__str__()
'random'
>>> x.__repr__()
'array([ 0, 1, 2, 3])'
"""
if f is None:
if repr:
return multiarray.set_string_function(array_repr, 1)
else:
return multiarray.set_string_function(array_str, 0)
else:
return multiarray.set_string_function(f, repr)
set_string_function(array_str, 0)
set_string_function(array_repr, 1)