"""Array printing function $Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $ """ __all__ = ["array2string", "array_str", "array_repr", "set_string_function", "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 numpy as np from . import numerictypes as _nt from .umath import absolute, isinf, isfinite, isnat from . import multiarray from .multiarray import (array, dragon4_positional, dragon4_scientific, datetime_as_string, datetime_data, ndarray, set_legacy_print_mode) from .fromnumeric import any from .numeric import concatenate, asarray, errstate from .numerictypes import (longlong, intc, int_, float_, complex_, bool_, flexible) from .overrides import array_function_dispatch, set_module import operator import warnings import contextlib _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, # Internally stored as an int to simplify comparisons; converted from/to # str/False on the way in/out. 'legacy': sys.maxsize} 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 conversion of *legacy* and 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 == False: options['legacy'] = sys.maxsize elif legacy == '1.13': options['legacy'] = 113 elif legacy == '1.21': options['legacy'] = 121 elif legacy is None: pass # OK, do nothing. else: warnings.warn( "legacy printing option can currently only be '1.13', '1.21', 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): """ 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 '-') 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 `False`, disables legacy mode. Unrecognized strings will be ignored with a warning for forward compatibility. .. versionadded:: 1.14.0 .. versionchanged:: 1.22.0 See Also -------- get_printoptions, printoptions, set_string_function, array2string Notes ----- `formatter` is always reset with a call to `set_printoptions`. Use `printoptions` as a context manager to set the values temporarily. Examples -------- Floating point precision can be set: >>> 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]) 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. ]) """ 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'] == 113: set_legacy_print_mode(113) # reset the sign option in legacy mode to avoid confusion _format_options['sign'] = '-' elif _format_options['legacy'] == 121: set_legacy_print_mode(121) elif _format_options['legacy'] == sys.maxsize: set_legacy_print_mode(0) @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 - formatter : dict of callables - sign : str For a full description of these options, see `set_printoptions`. See Also -------- set_printoptions, printoptions, set_string_function """ opts = _format_options.copy() opts['legacy'] = { 113: '1.13', 121: '1.21', sys.maxsize: False, }[opts['legacy']] return opts def _get_legacy_print_mode(): """Return the legacy print mode as an int.""" return _format_options['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 -------- >>> 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 """ opts = np.get_printoptions() try: np.set_printoptions(*args, **kwargs) yield np.get_printoptions() finally: np.set_printoptions(**opts) 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, *, 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), '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 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.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, 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.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']``. 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=2) 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, **options): self.format_function = format_function self.threshold = options['threshold'] self.edge_items = options['edgeitems'] def __call__(self, a): self.summary_insert = "..." if a.size > self.threshold else "" return self.format_array(a) def format_array(self, a): if np.ndim(a) == 0: return self.format_function(a) if self.summary_insert and a.shape[0] > 2*self.edge_items: formatted = ( [self.format_array(a_) for a_ in a[:self.edge_items]] + [self.summary_insert] + [self.format_array(a_) for a_ in a[-self.edge_items:]] ) else: formatted = [self.format_array(a_) for a_ in a] return "[" + ", ".join(formatted) + "]" 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, **options) 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 # should care about endianness *unless size is 1* (e.g., int8, bool) if not dtype.isnative: 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 if not dtype.isnative: # deal with cases like dtype('<u2') that are identical to an # established dtype (in this case uint16) # except that they have a different endianness. return "'%s'" % str(dtype) # 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,) @array_function_dispatch(_array_repr_dispatcher, module='numpy') 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 Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int, 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']``. 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. ])' """ return _array_repr_implementation( arr, max_line_width, precision, suppress_small) @_recursive_guard() def _guarded_repr_or_str(v): if isinstance(v, bytes): return repr(v) return str(v) def _array_str_implementation( a, max_line_width=None, precision=None, suppress_small=None, array2string=array2string): """Internal version of array_str() that allows overriding array2string.""" if (_format_options['legacy'] <= 113 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_repr_or_str(np.ndarray.__getitem__(a, ())) return array2string(a, max_line_width, precision, suppress_small, ' ', "") def _array_str_dispatcher( a, max_line_width=None, precision=None, suppress_small=None): return (a,) @array_function_dispatch(_array_str_dispatcher, module='numpy') 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`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int, 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']``. See Also -------- array2string, array_repr, set_printoptions Examples -------- >>> np.array_str(np.arange(3)) '[0 1 2]' """ return _array_str_implementation( a, max_line_width, precision, suppress_small) # needed if __array_function__ is disabled _array2string_impl = getattr(array2string, '__wrapped__', array2string) _default_array_str = functools.partial(_array_str_implementation, array2string=_array2string_impl) _default_array_repr = functools.partial(_array_repr_implementation, array2string=_array2string_impl) 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? >>> _ = 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(_default_array_repr, 1) else: return multiarray.set_string_function(_default_array_str, 0) else: return multiarray.set_string_function(f, repr)