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A collection of utility functions and classes. Originally, many
(but not all) were from the Python Cookbook -- hence the name cbook.
This module is safe to import from anywhere within matplotlib;
it imports matplotlib only at runtime.
from __future__ import (absolute_import, division, print_function,
import six
from six.moves import xrange, zip
from itertools import repeat
import datetime
import errno
from functools import reduce
import glob
import gzip
import io
import locale
import os
import re
import sys
import threading
import time
import traceback
import types
import warnings
from weakref import ref, WeakKeyDictionary
import numpy as np
import as ma
class MatplotlibDeprecationWarning(UserWarning):
A class for issuing deprecation warnings for Matplotlib users.
In light of the fact that Python builtin DeprecationWarnings are ignored
by default as of Python 2.7 (see link below), this class was put in to
allow for the signaling of deprecation, but via UserWarnings which are not
ignored by default.
mplDeprecation = MatplotlibDeprecationWarning
def _generate_deprecation_message(since, message='', name='',
alternative='', pending=False,
if not message:
altmessage = ''
if pending:
message = (
'The %(func)s %(obj_type)s will be deprecated in a '
'future version.')
message = (
'The %(func)s %(obj_type)s was deprecated in version '
if alternative:
altmessage = ' Use %s instead.' % alternative
message = ((message % {
'func': name,
'name': name,
'alternative': alternative,
'obj_type': obj_type,
'since': since}) +
return message
def warn_deprecated(
since, message='', name='', alternative='', pending=False,
Used to display deprecation warning in a standard way.
since : str
The release at which this API became deprecated.
message : str, optional
Override the default deprecation message. The format
specifier `%(func)s` may be used for the name of the function,
and `%(alternative)s` may be used in the deprecation message
to insert the name of an alternative to the deprecated
function. `%(obj_type)` may be used to insert a friendly name
for the type of object being deprecated.
name : str, optional
The name of the deprecated function; if not provided the name
is automatically determined from the passed in function,
though this is useful in the case of renamed functions, where
the new function is just assigned to the name of the
deprecated function. For example::
def new_function():
oldFunction = new_function
alternative : str, optional
An alternative function that the user may use in place of the
deprecated function. The deprecation warning will tell the user about
this alternative if provided.
pending : bool, optional
If True, uses a PendingDeprecationWarning instead of a
obj_type : str, optional
The object type being deprecated.
Basic example::
# To warn of the deprecation of "matplotlib.name_of_module"
warn_deprecated('1.4.0', name='matplotlib.name_of_module',
message = _generate_deprecation_message(
since, message, name, alternative, pending, obj_type)
warnings.warn(message, mplDeprecation, stacklevel=1)
def deprecated(since, message='', name='', alternative='', pending=False,
Decorator to mark a function as deprecated.
since : str
The release at which this API became deprecated. This is
message : str, optional
Override the default deprecation message. The format
specifier `%(func)s` may be used for the name of the function,
and `%(alternative)s` may be used in the deprecation message
to insert the name of an alternative to the deprecated
function. `%(obj_type)` may be used to insert a friendly name
for the type of object being deprecated.
name : str, optional
The name of the deprecated function; if not provided the name
is automatically determined from the passed in function,
though this is useful in the case of renamed functions, where
the new function is just assigned to the name of the
deprecated function. For example::
def new_function():
oldFunction = new_function
alternative : str, optional
An alternative function that the user may use in place of the
deprecated function. The deprecation warning will tell the user about
this alternative if provided.
pending : bool, optional
If True, uses a PendingDeprecationWarning instead of a
Basic example::
def the_function_to_deprecate():
def deprecate(func, message=message, name=name, alternative=alternative,
import functools
import textwrap
if isinstance(func, classmethod):
func = func.__func__
except AttributeError:
# classmethods in Python2.6 and below lack the __func__
# attribute so we need to hack around to get it
method = func.__get__(None, object)
if hasattr(method, '__func__'):
func = method.__func__
elif hasattr(method, 'im_func'):
func = method.im_func
# Nothing we can do really... just return the original
# classmethod
return func
is_classmethod = True
is_classmethod = False
if not name:
name = func.__name__
message = _generate_deprecation_message(
since, message, name, alternative, pending, obj_type)
def deprecated_func(*args, **kwargs):
warnings.warn(message, mplDeprecation, stacklevel=2)
return func(*args, **kwargs)
old_doc = deprecated_func.__doc__
if not old_doc:
old_doc = ''
old_doc = textwrap.dedent(old_doc).strip('\n')
message = message.strip()
new_doc = (('\n.. deprecated:: %(since)s'
'\n %(message)s\n\n' %
{'since': since, 'message': message}) + old_doc)
if not old_doc:
# This is to prevent a spurious 'unexected unindent' warning from
# docutils when the original docstring was blank.
new_doc += r'\ '
deprecated_func.__doc__ = new_doc
if is_classmethod:
deprecated_func = classmethod(deprecated_func)
return deprecated_func
return deprecate
# On some systems, locale.getpreferredencoding returns None,
# which can break unicode; and the sage project reports that
# some systems have incorrect locale specifications, e.g.,
# an encoding instead of a valid locale name. Another
# pathological case that has been reported is an empty string.
# On some systems, getpreferredencoding sets the locale, which has
# side effects. Passing False eliminates those side effects.
def unicode_safe(s):
import matplotlib
if isinstance(s, bytes):
preferredencoding = locale.getpreferredencoding(
if not preferredencoding:
preferredencoding = None
except (ValueError, ImportError, AttributeError):
preferredencoding = None
if preferredencoding is None:
return six.text_type(s)
return six.text_type(s, preferredencoding)
return s
class converter(object):
Base class for handling string -> python type with support for
missing values
def __init__(self, missing='Null', missingval=None):
self.missing = missing
self.missingval = missingval
def __call__(self, s):
if s == self.missing:
return self.missingval
return s
def is_missing(self, s):
return not s.strip() or s == self.missing
class tostr(converter):
'convert to string or None'
def __init__(self, missing='Null', missingval=''):
converter.__init__(self, missing=missing, missingval=missingval)
class todatetime(converter):
'convert to a datetime or None'
def __init__(self, fmt='%Y-%m-%d', missing='Null', missingval=None):
'use a :func:`time.strptime` format string for conversion'
converter.__init__(self, missing, missingval)
self.fmt = fmt
def __call__(self, s):
if self.is_missing(s):
return self.missingval
tup = time.strptime(s, self.fmt)
return datetime.datetime(*tup[:6])
class todate(converter):
'convert to a date or None'
def __init__(self, fmt='%Y-%m-%d', missing='Null', missingval=None):
'use a :func:`time.strptime` format string for conversion'
converter.__init__(self, missing, missingval)
self.fmt = fmt
def __call__(self, s):
if self.is_missing(s):
return self.missingval
tup = time.strptime(s, self.fmt)
class tofloat(converter):
'convert to a float or None'
def __init__(self, missing='Null', missingval=None):
converter.__init__(self, missing)
self.missingval = missingval
def __call__(self, s):
if self.is_missing(s):
return self.missingval
return float(s)
class toint(converter):
'convert to an int or None'
def __init__(self, missing='Null', missingval=None):
converter.__init__(self, missing)
def __call__(self, s):
if self.is_missing(s):
return self.missingval
return int(s)
class _BoundMethodProxy(object):
Our own proxy object which enables weak references to bound and unbound
methods and arbitrary callables. Pulls information about the function,
class, and instance out of a bound method. Stores a weak reference to the
instance to support garbage collection.
@organization: IBM Corporation
@copyright: Copyright (c) 2005, 2006 IBM Corporation
@license: The BSD License
Minor bugfixes by Michael Droettboom
def __init__(self, cb):
self._hash = hash(cb)
self._destroy_callbacks = []
if six.PY3:
self.inst = ref(cb.__self__, self._destroy)
self.inst = ref(cb.im_self, self._destroy)
except TypeError:
self.inst = None
if six.PY3:
self.func = cb.__func__
self.klass = cb.__self__.__class__
self.func = cb.im_func
self.klass = cb.im_class
except AttributeError:
self.inst = None
self.func = cb
self.klass = None
def add_destroy_callback(self, callback):
def _destroy(self, wk):
for callback in self._destroy_callbacks:
except ReferenceError:
def __getstate__(self):
d = self.__dict__.copy()
# de-weak reference inst
inst = d['inst']
if inst is not None:
d['inst'] = inst()
return d
def __setstate__(self, statedict):
self.__dict__ = statedict
inst = statedict['inst']
# turn inst back into a weakref
if inst is not None:
self.inst = ref(inst)
def __call__(self, *args, **kwargs):
Proxy for a call to the weak referenced object. Take
arbitrary params to pass to the callable.
Raises `ReferenceError`: When the weak reference refers to
a dead object
if self.inst is not None and self.inst() is None:
raise ReferenceError
elif self.inst is not None:
# build a new instance method with a strong reference to the
# instance
mtd = types.MethodType(self.func, self.inst())
# not a bound method, just return the func
mtd = self.func
# invoke the callable and return the result
return mtd(*args, **kwargs)
def __eq__(self, other):
Compare the held function and instance with that held by
another proxy.
if self.inst is None:
return self.func == other.func and other.inst is None
return self.func == other.func and self.inst() == other.inst()
except Exception:
return False
def __ne__(self, other):
Inverse of __eq__.
return not self.__eq__(other)
def __hash__(self):
return self._hash
class CallbackRegistry(object):
Handle registering and disconnecting for a set of signals and
>>> def oneat(x):
... print('eat', x)
>>> def ondrink(x):
... print('drink', x)
>>> from matplotlib.cbook import CallbackRegistry
>>> callbacks = CallbackRegistry()
>>> id_eat = callbacks.connect('eat', oneat)
>>> id_drink = callbacks.connect('drink', ondrink)
>>> callbacks.process('drink', 123)
drink 123
>>> callbacks.process('eat', 456)
eat 456
>>> callbacks.process('be merry', 456) # nothing will be called
>>> callbacks.disconnect(id_eat)
>>> callbacks.process('eat', 456) # nothing will be called
In practice, one should always disconnect all callbacks when they
are no longer needed to avoid dangling references (and thus memory
leaks). However, real code in matplotlib rarely does so, and due
to its design, it is rather difficult to place this kind of code.
To get around this, and prevent this class of memory leaks, we
instead store weak references to bound methods only, so when the
destination object needs to die, the CallbackRegistry won't keep
it alive. The Python stdlib weakref module can not create weak
references to bound methods directly, so we need to create a proxy
object to handle weak references to bound methods (or regular free
functions). This technique was shared by Peter Parente on his
`"Mindtrove" blog
def __init__(self):
self.callbacks = dict()
self._cid = 0
self._func_cid_map = {}
def __getstate__(self):
# We cannot currently pickle the callables in the registry, so
# return an empty dictionary.
return {}
def __setstate__(self, state):
# re-initialise an empty callback registry
def connect(self, s, func):
register *func* to be called when a signal *s* is generated
func will be called
self._func_cid_map.setdefault(s, WeakKeyDictionary())
# Note proxy not needed in python 3.
# TODO rewrite this when support for python2.x gets dropped.
proxy = _BoundMethodProxy(func)
if proxy in self._func_cid_map[s]:
return self._func_cid_map[s][proxy]
self._cid += 1
cid = self._cid
self._func_cid_map[s][proxy] = cid
self.callbacks.setdefault(s, dict())
self.callbacks[s][cid] = proxy
return cid
def _remove_proxy(self, proxy):
for signal, proxies in list(six.iteritems(self._func_cid_map)):
del self.callbacks[signal][proxies[proxy]]
except KeyError:
if len(self.callbacks[signal]) == 0:
del self.callbacks[signal]
del self._func_cid_map[signal]
def disconnect(self, cid):
disconnect the callback registered with callback id *cid*
for eventname, callbackd in list(six.iteritems(self.callbacks)):
del callbackd[cid]
except KeyError:
for signal, functions in list(
for function, value in list(six.iteritems(functions)):
if value == cid:
del functions[function]
def process(self, s, *args, **kwargs):
process signal *s*. All of the functions registered to receive
callbacks on *s* will be called with *\*args* and *\*\*kwargs*
if s in self.callbacks:
for cid, proxy in list(six.iteritems(self.callbacks[s])):
proxy(*args, **kwargs)
except ReferenceError:
class Scheduler(threading.Thread):
Base class for timeout and idle scheduling
idlelock = threading.Lock()
id = 0
def __init__(self):
threading.Thread.__init__(self) =
self._stopped = False += 1
self._stopevent = threading.Event()
def stop(self):
if self._stopped:
self._stopped = True
class Timeout(Scheduler):
Schedule recurring events with a wait time in seconds
def __init__(self, wait, func):
self.wait = wait
self.func = func
def run(self):
while not self._stopevent.isSet():
b = self.func(self)
if not b:
class Idle(Scheduler):
Schedule callbacks when scheduler is idle
# the prototype impl is a bit of a poor man's idle handler. It
# just implements a short wait time. But it will provide a
# placeholder for a proper impl ater
waittime = 0.05
def __init__(self, func):
self.func = func
def run(self):
while not self._stopevent.isSet():
b = self.func(self)
if not b:
class silent_list(list):
override repr when returning a list of matplotlib artists to
prevent long, meaningless output. This is meant to be used for a
homogeneous list of a given type
def __init__(self, type, seq=None):
self.type = type
if seq is not None:
def __repr__(self):
return '<a list of %d %s objects>' % (len(self), self.type)
def __str__(self):
return repr(self)
def __getstate__(self):
# store a dictionary of this SilentList's state
return {'type': self.type, 'seq': self[:]}
def __setstate__(self, state):
self.type = state['type']
class IgnoredKeywordWarning(UserWarning):
A class for issuing warnings about keyword arguments that will be ignored
by matplotlib
def local_over_kwdict(local_var, kwargs, *keys):
Enforces the priority of a local variable over potentially conflicting
argument(s) from a kwargs dict. The following possible output values are
considered in order of priority:
local_var > kwargs[keys[0]] > ... > kwargs[keys[-1]]
The first of these whose value is not None will be returned. If all are
None then None will be returned. Each key in keys will be removed from the
kwargs dict in place.
local_var: any object
The local variable (highest priority)
kwargs: dict
Dictionary of keyword arguments; modified in place
keys: str(s)
Name(s) of keyword arguments to process, in descending order of
out: any object
Either local_var or one of kwargs[key] for key in keys
For each key in keys that is removed from kwargs but not used as
the output value
out = local_var
for key in keys:
kwarg_val = kwargs.pop(key, None)
if kwarg_val is not None:
if out is None:
out = kwarg_val
warnings.warn('"%s" keyword argument will be ignored' % key,
return out
def strip_math(s):
'remove latex formatting from mathtext'
remove = (r'\mathdefault', r'\rm', r'\cal', r'\tt', r'\it', '\\', '{', '}')
s = s[1:-1]
for r in remove:
s = s.replace(r, '')
return s
class Bunch(object):
Often we want to just collect a bunch of stuff together, naming each
item of the bunch; a dictionary's OK for that, but a small do- nothing
class is even handier, and prettier to use. Whenever you want to
group a few variables::
>>> point = Bunch(datum=2, squared=4, coord=12)
>>> point.datum
By: Alex Martelli
def __init__(self, **kwds):
def __repr__(self):
keys = six.iterkeys(self.__dict__)
return 'Bunch(%s)' % ', '.join(['%s=%s' % (k, self.__dict__[k])
for k
in keys])
def unique(x):
'Return a list of unique elements of *x*'
return list(six.iterkeys(dict([(val, 1) for val in x])))
def iterable(obj):
'return true if *obj* is iterable'
except TypeError:
return False
return True
def is_string_like(obj):
'Return True if *obj* looks like a string'
if isinstance(obj, six.string_types):
return True
# numpy strings are subclass of str, ma strings are not
if ma.isMaskedArray(obj):
if obj.ndim == 0 and obj.dtype.kind in 'SU':
return True
return False
obj + ''
return False
return True
def is_sequence_of_strings(obj):
Returns true if *obj* is iterable and contains strings
if not iterable(obj):
return False
if is_string_like(obj):
return False
for o in obj:
if not is_string_like(o):
return False
return True
def is_writable_file_like(obj):
'return true if *obj* looks like a file object with a *write* method'
return hasattr(obj, 'write') and six.callable(obj.write)
def file_requires_unicode(x):
Returns `True` if the given writable file-like object requires Unicode
to be written to it.
except TypeError:
return True
return False
def is_scalar(obj):
'return true if *obj* is not string like and is not iterable'
return not is_string_like(obj) and not iterable(obj)
def is_numlike(obj):
'return true if *obj* looks like a number'
obj + 1
return False
return True
def to_filehandle(fname, flag='rU', return_opened=False):
*fname* can be a filename or a file handle. Support for gzipped
files is automatic, if the filename ends in .gz. *flag* is a
read/write flag for :func:`file`
if is_string_like(fname):
if fname.endswith('.gz'):
# get rid of 'U' in flag for gzipped files.
flag = flag.replace('U', '')
fh =, flag)
elif fname.endswith('.bz2'):
# get rid of 'U' in flag for bz2 files
flag = flag.replace('U', '')
import bz2
fh = bz2.BZ2File(fname, flag)
fh = open(fname, flag)
opened = True
elif hasattr(fname, 'seek'):
fh = fname
opened = False
raise ValueError('fname must be a string or file handle')
if return_opened:
return fh, opened
return fh
def is_scalar_or_string(val):
"""Return whether the given object is a scalar or string like."""
return is_string_like(val) or not iterable(val)
def _string_to_bool(s):
if not is_string_like(s):
return s
if s == 'on':
return True
if s == 'off':
return False
raise ValueError("string argument must be either 'on' or 'off'")
def get_sample_data(fname, asfileobj=True):
Return a sample data file. *fname* is a path relative to the
`mpl-data/sample_data` directory. If *asfileobj* is `True`
return a file object, otherwise just a file path.
Set the rc parameter to the directory where we should
look, if sample_data files are stored in a location different than
default (which is 'mpl-data/sample_data` at the same level of 'matplotlib`
Python module files).
If the filename ends in .gz, the file is implicitly ungzipped.
import matplotlib
if matplotlib.rcParams['']:
root = matplotlib.rcParams['']
root = os.path.join(os.path.dirname(__file__),
"mpl-data", "sample_data")
path = os.path.join(root, fname)
if asfileobj:
if (os.path.splitext(fname)[-1].lower() in
('.csv', '.xrc', '.txt')):
mode = 'r'
mode = 'rb'
base, ext = os.path.splitext(fname)
if ext == '.gz':
return, mode)
return open(path, mode)
return path
def flatten(seq, scalarp=is_scalar_or_string):
Returns a generator of flattened nested containers
For example:
>>> from matplotlib.cbook import flatten
>>> l = (('John', ['Hunter']), (1, 23), [[([42, (5, 23)], )]])
>>> print(list(flatten(l)))
['John', 'Hunter', 1, 23, 42, 5, 23]
By: Composite of Holger Krekel and Luther Blissett
and Recipe 1.12 in cookbook
for item in seq:
if scalarp(item):
yield item
for subitem in flatten(item, scalarp):
yield subitem
class Sorter(object):
Sort by attribute or item
Example usage::
sort = Sorter()
list = [(1, 2), (4, 8), (0, 3)]
dict = [{'a': 3, 'b': 4}, {'a': 5, 'b': 2}, {'a': 0, 'b': 0},
{'a': 9, 'b': 9}]
sort(list) # default sort
sort(list, 1) # sort by index 1
sort(dict, 'a') # sort a list of dicts by key 'a'
def _helper(self, data, aux, inplace):
result = [data[i] for junk, i in aux]
if inplace:
data[:] = result
return result
def byItem(self, data, itemindex=None, inplace=1):
if itemindex is None:
if inplace:
result = data
result = data[:]
return result
aux = [(data[i][itemindex], i) for i in range(len(data))]
return self._helper(data, aux, inplace)
def byAttribute(self, data, attributename, inplace=1):
aux = [(getattr(data[i], attributename), i) for i in range(len(data))]
return self._helper(data, aux, inplace)
# a couple of handy synonyms
sort = byItem
__call__ = byItem
class Xlator(dict):
All-in-one multiple-string-substitution class
Example usage::
text = "Larry Wall is the creator of Perl"
adict = {
"Larry Wall" : "Guido van Rossum",
"creator" : "Benevolent Dictator for Life",
"Perl" : "Python",
print multiple_replace(adict, text)
xlat = Xlator(adict)
print xlat.xlat(text)
def _make_regex(self):
""" Build re object based on the keys of the current dictionary """
return re.compile("|".join(map(re.escape, list(six.iterkeys(self)))))
def __call__(self, match):
""" Handler invoked for each regex *match* """
return self[]
def xlat(self, text):
""" Translate *text*, returns the modified text. """
return self._make_regex().sub(self, text)
def soundex(name, len=4):
""" soundex module conforming to Odell-Russell algorithm """
# digits holds the soundex values for the alphabet
soundex_digits = '01230120022455012623010202'
sndx = ''
fc = ''
# Translate letters in name to soundex digits
for c in name.upper():
if c.isalpha():
if not fc:
fc = c # Remember first letter
d = soundex_digits[ord(c) - ord('A')]
# Duplicate consecutive soundex digits are skipped
if not sndx or (d != sndx[-1]):
sndx += d
# Replace first digit with first letter
sndx = fc + sndx[1:]
# Remove all 0s from the soundex code
sndx = sndx.replace('0', '')
# Return soundex code truncated or 0-padded to len characters
return (sndx + (len * '0'))[:len]
class Null(object):
""" Null objects always and reliably "do nothing." """
def __init__(self, *args, **kwargs):
def __call__(self, *args, **kwargs):
return self
def __str__(self):
return "Null()"
def __repr__(self):
return "Null()"
if six.PY3:
def __bool__(self):
return 0
def __nonzero__(self):
return 0
def __getattr__(self, name):
return self
def __setattr__(self, name, value):
return self
def __delattr__(self, name):
return self
def mkdirs(newdir, mode=0o777):
make directory *newdir* recursively, and set *mode*. Equivalent to ::
> mkdir -p NEWDIR
if not os.path.exists(newdir):
parts = os.path.split(newdir)
for i in range(1, len(parts) + 1):
thispart = os.path.join(*parts[:i])
if not os.path.exists(thispart):
os.makedirs(thispart, mode)
except OSError as err:
# Reraise the error unless it's about an already existing directory
if err.errno != errno.EEXIST or not os.path.isdir(newdir):
class GetRealpathAndStat(object):
def __init__(self):
self._cache = {}
def __call__(self, path):
result = self._cache.get(path)
if result is None:
realpath = os.path.realpath(path)
if sys.platform == 'win32':
stat_key = realpath
stat = os.stat(realpath)
stat_key = (stat.st_ino, stat.st_dev)
result = realpath, stat_key
self._cache[path] = result
return result
get_realpath_and_stat = GetRealpathAndStat()
def dict_delall(d, keys):
'delete all of the *keys* from the :class:`dict` *d*'
for key in keys:
del d[key]
except KeyError:
class RingBuffer(object):
""" class that implements a not-yet-full buffer """
def __init__(self, size_max):
self.max = size_max = []
class __Full:
""" class that implements a full buffer """
def append(self, x):
""" Append an element overwriting the oldest one. """[self.cur] = x
self.cur = (self.cur + 1) % self.max
def get(self):
""" return list of elements in correct order """
return[self.cur:] +[:self.cur]
def append(self, x):
"""append an element at the end of the buffer"""
if len( == self.max:
self.cur = 0
# Permanently change self's class from non-full to full
self.__class__ = __Full
def get(self):
""" Return a list of elements from the oldest to the newest. """
def __get_item__(self, i):
return[i % len(]
def get_split_ind(seq, N):
*seq* is a list of words. Return the index into seq such that::
len(' '.join(seq[:ind])<=N
sLen = 0
# todo: use Alex's xrange pattern from the cbook for efficiency
for (word, ind) in zip(seq, xrange(len(seq))):
sLen += len(word) + 1 # +1 to account for the len(' ')
if sLen >= N:
return ind
return len(seq)
def wrap(prefix, text, cols):
'wrap *text* with *prefix* at length *cols*'
pad = ' ' * len(prefix.expandtabs())
available = cols - len(pad)
seq = text.split(' ')
Nseq = len(seq)
ind = 0
lines = []
while ind < Nseq:
lastInd = ind
ind += get_split_ind(seq[ind:], available)
# add the prefix to the first line, pad with spaces otherwise
ret = prefix + ' '.join(lines[0]) + '\n'
for line in lines[1:]:
ret += pad + ' '.join(line) + '\n'
return ret
# A regular expression used to determine the amount of space to
# remove. It looks for the first sequence of spaces immediately
# following the first newline, or at the beginning of the string.
_find_dedent_regex = re.compile("(?:(?:\n\r?)|^)( *)\S")
# A cache to hold the regexs that actually remove the indent.
_dedent_regex = {}
def dedent(s):
Remove excess indentation from docstring *s*.
Discards any leading blank lines, then removes up to n whitespace
characters from each line, where n is the number of leading
whitespace characters in the first line. It differs from
textwrap.dedent in its deletion of leading blank lines and its use
of the first non-blank line to determine the indentation.
It is also faster in most cases.
# This implementation has a somewhat obtuse use of regular
# expressions. However, this function accounted for almost 30% of
# matplotlib startup time, so it is worthy of optimization at all
# costs.
if not s: # includes case of s is None
return ''
match = _find_dedent_regex.match(s)
if match is None:
return s
# This is the number of spaces to remove from the left-hand side.
nshift = match.end(1) - match.start(1)
if nshift == 0:
return s
# Get a regex that will remove *up to* nshift spaces from the
# beginning of each line. If it isn't in the cache, generate it.
unindent = _dedent_regex.get(nshift, None)
if unindent is None:
unindent = re.compile("\n\r? {0,%d}" % nshift)
_dedent_regex[nshift] = unindent
result = unindent.sub("\n", s).strip()
return result
def listFiles(root, patterns='*', recurse=1, return_folders=0):
Recursively list files
from Parmar and Martelli in the Python Cookbook
import os.path
import fnmatch
# Expand patterns from semicolon-separated string to list
pattern_list = patterns.split(';')
results = []
for dirname, dirs, files in os.walk(root):
# Append to results all relevant files (and perhaps folders)
for name in files:
fullname = os.path.normpath(os.path.join(dirname, name))
if return_folders or os.path.isfile(fullname):
for pattern in pattern_list:
if fnmatch.fnmatch(name, pattern):
# Block recursion if recursion was disallowed
if not recurse:
return results
def get_recursive_filelist(args):
Recurse all the files and dirs in *args* ignoring symbolic links
and return the files as a list of strings
files = []
for arg in args:
if os.path.isfile(arg):
if os.path.isdir(arg):
newfiles = listFiles(arg, recurse=1, return_folders=1)
return [f for f in files if not os.path.islink(f)]
def pieces(seq, num=2):
"Break up the *seq* into *num* tuples"
start = 0
while 1:
item = seq[start:start + num]
if not len(item):
yield item
start += num
def exception_to_str(s=None):
if six.PY3:
sh = io.StringIO()
sh = io.BytesIO()
if s is not None:
print(s, file=sh)
return sh.getvalue()
def allequal(seq):
Return *True* if all elements of *seq* compare equal. If *seq* is
0 or 1 length, return *True*
if len(seq) < 2:
return True
val = seq[0]
for i in xrange(1, len(seq)):
thisval = seq[i]
if thisval != val:
return False
return True
def alltrue(seq):
Return *True* if all elements of *seq* evaluate to *True*. If
*seq* is empty, return *False*.
if not len(seq):
return False
for val in seq:
if not val:
return False
return True
def onetrue(seq):
Return *True* if one element of *seq* is *True*. It *seq* is
empty, return *False*.
if not len(seq):
return False
for val in seq:
if val:
return True
return False
def allpairs(x):
return all possible pairs in sequence *x*
Condensed by Alex Martelli from this thread_ on c.l.python
.. _thread:*python*&hl=en&lr=&ie=UTF-8&
return [(s, f) for i, f in enumerate(x) for s in x[i + 1:]]
class maxdict(dict):
A dictionary with a maximum size; this doesn't override all the
relevant methods to contrain size, just setitem, so use with
def __init__(self, maxsize):
self.maxsize = maxsize
self._killkeys = []
def __setitem__(self, k, v):
if k not in self:
if len(self) >= self.maxsize:
del self[self._killkeys[0]]
del self._killkeys[0]
dict.__setitem__(self, k, v)
class Stack(object):
Implement a stack where elements can be pushed on and you can move
back and forth. But no pop. Should mimic home / back / forward
in a browser
def __init__(self, default=None):
self._default = default
def __call__(self):
'return the current element, or None'
if not len(self._elements):
return self._default
return self._elements[self._pos]
def __len__(self):
return self._elements.__len__()
def __getitem__(self, ind):
return self._elements.__getitem__(ind)
def forward(self):
'move the position forward and return the current element'
N = len(self._elements)
if self._pos < N - 1:
self._pos += 1
return self()
def back(self):
'move the position back and return the current element'
if self._pos > 0:
self._pos -= 1
return self()
def push(self, o):
push object onto stack at current position - all elements
occurring later than the current position are discarded
self._elements = self._elements[:self._pos + 1]
self._pos = len(self._elements) - 1
return self()
def home(self):
'push the first element onto the top of the stack'
if not len(self._elements):
return self()
def empty(self):
return len(self._elements) == 0
def clear(self):
'empty the stack'
self._pos = -1
self._elements = []
def bubble(self, o):
raise *o* to the top of the stack and return *o*. *o* must be
in the stack
if o not in self._elements:
raise ValueError('Unknown element o')
old = self._elements[:]
bubbles = []
for thiso in old:
if thiso == o:
for thiso in bubbles:
return o
def remove(self, o):
'remove element *o* from the stack'
if o not in self._elements:
raise ValueError('Unknown element o')
old = self._elements[:]
for thiso in old:
if thiso == o:
def popall(seq):
'empty a list'
for i in xrange(len(seq)):
def finddir(o, match, case=False):
return all attributes of *o* which match string in match. if case
is True require an exact case match.
if case:
names = [(name, name) for name in dir(o) if is_string_like(name)]
names = [(name.lower(), name) for name in dir(o)
if is_string_like(name)]
match = match.lower()
return [orig for name, orig in names if name.find(match) >= 0]
def reverse_dict(d):
'reverse the dictionary -- may lose data if values are not unique!'
return dict([(v, k) for k, v in six.iteritems(d)])
def restrict_dict(d, keys):
Return a dictionary that contains those keys that appear in both
d and keys, with values from d.
return dict([(k, v) for (k, v) in six.iteritems(d) if k in keys])
def report_memory(i=0): # argument may go away
'return the memory consumed by process'
from matplotlib.compat.subprocess import Popen, PIPE
pid = os.getpid()
if sys.platform == 'sunos5':
a2 = Popen('ps -p %d -o osz' % pid, shell=True,
except OSError:
raise NotImplementedError(
"report_memory works on Sun OS only if "
"the 'ps' program is found")
mem = int(a2[-1].strip())
elif sys.platform.startswith('linux'):
a2 = Popen('ps -p %d -o rss,sz' % pid, shell=True,
except OSError:
raise NotImplementedError(
"report_memory works on Linux only if "
"the 'ps' program is found")
mem = int(a2[1].split()[1])
elif sys.platform.startswith('darwin'):
a2 = Popen('ps -p %d -o rss,vsz' % pid, shell=True,
except OSError:
raise NotImplementedError(
"report_memory works on Mac OS only if "
"the 'ps' program is found")
mem = int(a2[1].split()[0])
elif sys.platform.startswith('win'):
a2 = Popen(["tasklist", "/nh", "/fi", "pid eq %d" % pid],
except OSError:
raise NotImplementedError(
"report_memory works on Windows only if "
"the 'tasklist' program is found")
mem = int(a2.strip().split()[-2].replace(',', ''))
raise NotImplementedError(
"We don't have a memory monitor for %s" % sys.platform)
return mem
_safezip_msg = 'In safezip, len(args[0])=%d but len(args[%d])=%d'
def safezip(*args):
'make sure *args* are equal len before zipping'
Nx = len(args[0])
for i, arg in enumerate(args[1:]):
if len(arg) != Nx:
raise ValueError(_safezip_msg % (Nx, i + 1, len(arg)))
return list(zip(*args))
def issubclass_safe(x, klass):
'return issubclass(x, klass) and return False on a TypeError'
return issubclass(x, klass)
except TypeError:
return False
def safe_masked_invalid(x):
x = np.asanyarray(x)
xm =, copy=False)
except TypeError:
return x
return xm
class MemoryMonitor(object):
def __init__(self, nmax=20000):
self._nmax = nmax
self._mem = np.zeros((self._nmax,), np.int32)
def clear(self):
self._n = 0
self._overflow = False
def __call__(self):
mem = report_memory()
if self._n < self._nmax:
self._mem[self._n] = mem
self._n += 1
self._overflow = True
return mem
def report(self, segments=4):
n = self._n
segments = min(n, segments)
dn = int(n / segments)
ii = list(xrange(0, n, dn))
ii[-1] = n - 1
print('memory report: i, mem, dmem, dmem/nloops')
print(0, self._mem[0])
for i in range(1, len(ii)):
di = ii[i] - ii[i - 1]
if di == 0:
dm = self._mem[ii[i]] - self._mem[ii[i - 1]]
print('%5d %5d %3d %8.3f' % (ii[i], self._mem[ii[i]],
dm, dm / float(di)))
if self._overflow:
print("Warning: array size was too small for the number of calls.")
def xy(self, i0=0, isub=1):
x = np.arange(i0, self._n, isub)
return x, self._mem[i0:self._n:isub]
def plot(self, i0=0, isub=1, fig=None):
if fig is None:
from .pylab import figure
fig = figure()
ax = fig.add_subplot(111)
ax.plot(*self.xy(i0, isub))
def print_cycles(objects, outstream=sys.stdout, show_progress=False):
A list of objects to find cycles in. It is often useful to
pass in gc.garbage to find the cycles that are preventing some
objects from being garbage collected.
The stream for output.
If True, print the number of objects reached as they are found.
import gc
from types import FrameType
def print_path(path):
for i, step in enumerate(path):
# next "wraps around"
next = path[(i + 1) % len(path)]
outstream.write(" %s -- " % str(type(step)))
if isinstance(step, dict):
for key, val in six.iteritems(step):
if val is next:
outstream.write("[%s]" % repr(key))
if key is next:
outstream.write("[key] = %s" % repr(val))
elif isinstance(step, list):
outstream.write("[%d]" % step.index(next))
elif isinstance(step, tuple):
outstream.write("( tuple )")
outstream.write(" ->\n")
def recurse(obj, start, all, current_path):
if show_progress:
outstream.write("%d\r" % len(all))
all[id(obj)] = None
referents = gc.get_referents(obj)
for referent in referents:
# If we've found our way back to the start, this is
# a cycle, so print it out
if referent is start:
# Don't go back through the original list of objects, or
# through temporary references to the object, since those
# are just an artifact of the cycle detector itself.
elif referent is objects or isinstance(referent, FrameType):
# We haven't seen this object before, so recurse
elif id(referent) not in all:
recurse(referent, start, all, current_path + [obj])
for obj in objects:
outstream.write("Examining: %r\n" % (obj,))
recurse(obj, obj, {}, [])
class Grouper(object):
This class provides a lightweight way to group arbitrary objects
together into disjoint sets when a full-blown graph data structure
would be overkill.
Objects can be joined using :meth:`join`, tested for connectedness
using :meth:`joined`, and all disjoint sets can be retreived by
using the object as an iterator.
The objects being joined must be hashable and weak-referenceable.
For example:
>>> from matplotlib.cbook import Grouper
>>> class Foo(object):
... def __init__(self, s):
... self.s = s
... def __repr__(self):
... return self.s
>>> a, b, c, d, e, f = [Foo(x) for x in 'abcdef']
>>> grp = Grouper()
>>> grp.join(a, b)
>>> grp.join(b, c)
>>> grp.join(d, e)
>>> sorted(map(tuple, grp))
[(a, b, c), (d, e)]
>>> grp.joined(a, b)
>>> grp.joined(a, c)
>>> grp.joined(a, d)
def __init__(self, init=[]):
mapping = self._mapping = {}
for x in init:
mapping[ref(x)] = [ref(x)]
def __contains__(self, item):
return ref(item) in self._mapping
def clean(self):
Clean dead weak references from the dictionary
mapping = self._mapping
to_drop = [key for key in mapping if key() is None]
for key in to_drop:
val = mapping.pop(key)
def join(self, a, *args):
Join given arguments into the same set. Accepts one or more
mapping = self._mapping
set_a = mapping.setdefault(ref(a), [ref(a)])
for arg in args:
set_b = mapping.get(ref(arg))
if set_b is None:
mapping[ref(arg)] = set_a
elif set_b is not set_a:
if len(set_b) > len(set_a):
set_a, set_b = set_b, set_a
for elem in set_b:
mapping[elem] = set_a
def joined(self, a, b):
Returns True if *a* and *b* are members of the same set.
mapping = self._mapping
return mapping[ref(a)] is mapping[ref(b)]
except KeyError:
return False
def __iter__(self):
Iterate over each of the disjoint sets as a list.
The iterator is invalid if interleaved with calls to join().
class Token:
token = Token()
# Mark each group as we come across if by appending a token,
# and don't yield it twice
for group in six.itervalues(self._mapping):
if not group[-1] is token:
yield [x() for x in group]
# Cleanup the tokens
for group in six.itervalues(self._mapping):
if group[-1] is token:
del group[-1]
def get_siblings(self, a):
Returns all of the items joined with *a*, including itself.
siblings = self._mapping.get(ref(a), [ref(a)])
return [x() for x in siblings]
def simple_linear_interpolation(a, steps):
if steps == 1:
return a
steps = int(np.floor(steps))
new_length = ((len(a) - 1) * steps) + 1
new_shape = list(a.shape)
new_shape[0] = new_length
result = np.zeros(new_shape, a.dtype)
result[0] = a[0]
a0 = a[0:-1]
a1 = a[1:]
delta = ((a1 - a0) / steps)
for i in range(1, steps):
result[i::steps] = delta * i + a0
result[steps::steps] = a1
return result
def recursive_remove(path):
if os.path.isdir(path):
for fname in (glob.glob(os.path.join(path, '*')) +
glob.glob(os.path.join(path, '.*'))):
if os.path.isdir(fname):
def delete_masked_points(*args):
Find all masked and/or non-finite points in a set of arguments,
and return the arguments with only the unmasked points remaining.
Arguments can be in any of 5 categories:
1) 1-D masked arrays
2) 1-D ndarrays
3) ndarrays with more than one dimension
4) other non-string iterables
5) anything else
The first argument must be in one of the first four categories;
any argument with a length differing from that of the first
argument (and hence anything in category 5) then will be
passed through unchanged.
Masks are obtained from all arguments of the correct length
in categories 1, 2, and 4; a point is bad if masked in a masked
array or if it is a nan or inf. No attempt is made to
extract a mask from categories 2, 3, and 4 if :meth:`np.isfinite`
does not yield a Boolean array.
All input arguments that are not passed unchanged are returned
as ndarrays after removing the points or rows corresponding to
masks in any of the arguments.
A vastly simpler version of this function was originally
written as a helper for Axes.scatter().
if not len(args):
return ()
if (is_string_like(args[0]) or not iterable(args[0])):
raise ValueError("First argument must be a sequence")
nrecs = len(args[0])
margs = []
seqlist = [False] * len(args)
for i, x in enumerate(args):
if (not is_string_like(x)) and iterable(x) and len(x) == nrecs:
seqlist[i] = True
if ma.isMA(x):
if x.ndim > 1:
raise ValueError("Masked arrays must be 1-D")
x = np.asarray(x)
masks = [] # list of masks that are True where good
for i, x in enumerate(margs):
if seqlist[i]:
if x.ndim > 1:
continue # Don't try to get nan locations unless 1-D.
if ma.isMA(x):
masks.append(~ma.getmaskarray(x)) # invert the mask
xd =
xd = x
mask = np.isfinite(xd)
if isinstance(mask, np.ndarray):
except: # Fixme: put in tuple of possible exceptions?
if len(masks):
mask = reduce(np.logical_and, masks)
igood = mask.nonzero()[0]
if len(igood) < nrecs:
for i, x in enumerate(margs):
if seqlist[i]:
margs[i] = x.take(igood, axis=0)
for i, x in enumerate(margs):
if seqlist[i] and ma.isMA(x):
margs[i] = x.filled()
return margs
def boxplot_stats(X, whis=1.5, bootstrap=None, labels=None):
Returns list of dictionaries of staticists to be use to draw a series of
box and whisker plots. See the `Returns` section below to the required
keys of the dictionary. Users can skip this function and pass a user-
defined set of dictionaries to the new `axes.bxp` method instead of
relying on MPL to do the calcs.
X : array-like
Data that will be represented in the boxplots. Should have 2 or fewer
whis : float, string, or sequence (default = 1.5)
As a float, determines the reach of the whiskers past the first and
third quartiles (e.g., Q3 + whis*IQR, QR = interquartile range, Q3-Q1).
Beyond the whiskers, data are considered outliers and are plotted as
individual points. Set this to an unreasonably high value to force the
whiskers to show the min and max data. Alternatively, set this to an
ascending sequence of percentile (e.g., [5, 95]) to set the whiskers
at specific percentiles of the data. Finally, can `whis` be the
string 'range' to force the whiskers to the min and max of the data.
In the edge case that the 25th and 75th percentiles are equivalent,
`whis` will be automatically set to 'range'
bootstrap : int or None (default)
Number of times the confidence intervals around the median should
be bootstrapped (percentile method).
labels : sequence
Labels for each dataset. Length must be compatible with dimensions
of `X`
bxpstats : list of dict
A list of dictionaries containing the results for each column
of data. Keys of each dictionary are the following:
======== ===================================
Key Value Description
======== ===================================
label tick label for the boxplot
mean arithemetic mean value
med 50th percentile
q1 first quartile (25th percentile)
q3 third quartile (75th percentile)
cilo lower notch around the median
cihi upper notch around the median
whislo end of the lower whisker
whishi end of the upper whisker
fliers outliers
======== ===================================
Non-bootstrapping approach to confidence interval uses Gaussian-based
asymptotic approximation:
.. math::
\mathrm{med} \pm 1.57 \\times \\frac{\mathrm{iqr}}{\sqrt{N}}
General approach from:
McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of
Boxplots", The American Statistician, 32:12-16.
def _bootstrap_median(data, N=5000):
# determine 95% confidence intervals of the median
M = len(data)
percentiles = [2.5, 97.5]
ii = np.random.randint(M, size=(N, M))
bsData = x[ii]
estimate = np.median(bsData, axis=1, overwrite_input=True)
CI = np.percentile(estimate, percentiles)
return CI
def _compute_conf_interval(data, med, iqr, bootstrap):
if bootstrap is not None:
# Do a bootstrap estimate of notch locations.
# get conf. intervals around median
CI = _bootstrap_median(data, N=bootstrap)
notch_min = CI[0]
notch_max = CI[1]
N = len(data)
notch_min = med - 1.57 * iqr / np.sqrt(N)
notch_max = med + 1.57 * iqr / np.sqrt(N)
return notch_min, notch_max
# output is a list of dicts
bxpstats = []
# convert X to a list of lists
X = _reshape_2D(X)
ncols = len(X)
if labels is None:
labels = repeat(None)
elif len(labels) != ncols:
raise ValueError("Dimensions of labels and X must be compatible")
input_whis = whis
for ii, (x, label) in enumerate(zip(X, labels), start=0):
# empty dict
stats = {}
if label is not None:
stats['label'] = label
# restore whis to the input values in case it got changed in the loop
whis = input_whis
# note tricksyness, append up here and then mutate below
# if empty, bail
if len(x) == 0:
stats['fliers'] = np.array([])
stats['mean'] = np.nan
stats['med'] = np.nan
stats['q1'] = np.nan
stats['q3'] = np.nan
stats['cilo'] = np.nan
stats['cihi'] = np.nan
stats['whislo'] = np.nan
stats['whishi'] = np.nan
stats['med'] = np.nan
# up-convert to an array, just to be safe
x = np.asarray(x)
# arithmetic mean
stats['mean'] = np.mean(x)
# medians and quartiles
q1, med, q3 = np.percentile(x, [25, 50, 75])
# interquartile range
stats['iqr'] = q3 - q1
if stats['iqr'] == 0:
whis = 'range'
# conf. interval around median
stats['cilo'], stats['cihi'] = _compute_conf_interval(
x, med, stats['iqr'], bootstrap
# lowest/highest non-outliers
if np.isscalar(whis):
if np.isreal(whis):
loval = q1 - whis * stats['iqr']
hival = q3 + whis * stats['iqr']
elif whis in ['range', 'limit', 'limits', 'min/max']:
loval = np.min(x)
hival = np.max(x)
whismsg = ('whis must be a float, valid string, or '
'list of percentiles')
raise ValueError(whismsg)
loval = np.percentile(x, whis[0])
hival = np.percentile(x, whis[1])
# get high extreme
wiskhi = np.compress(x <= hival, x)
if len(wiskhi) == 0 or np.max(wiskhi) < q3:
stats['whishi'] = q3
stats['whishi'] = np.max(wiskhi)
# get low extreme
wisklo = np.compress(x >= loval, x)
if len(wisklo) == 0 or np.min(wisklo) > q1:
stats['whislo'] = q1
stats['whislo'] = np.min(wisklo)
# compute a single array of outliers
stats['fliers'] = np.hstack([
np.compress(x < stats['whislo'], x),
np.compress(x > stats['whishi'], x)
# add in the remaining stats
stats['q1'], stats['med'], stats['q3'] = q1, med, q3
return bxpstats
# FIXME I don't think this is used anywhere
def unmasked_index_ranges(mask, compressed=True):
Find index ranges where *mask* is *False*.
*mask* will be flattened if it is not already 1-D.
Returns Nx2 :class:`numpy.ndarray` with each row the start and stop
indices for slices of the compressed :class:`numpy.ndarray`
corresponding to each of *N* uninterrupted runs of unmasked
values. If optional argument *compressed* is *False*, it returns
the start and stop indices into the original :class:`numpy.ndarray`,
not the compressed :class:`numpy.ndarray`. Returns *None* if there
are no unmasked values.
y = ma.array(np.arange(5), mask = [0,0,1,0,0])
ii = unmasked_index_ranges(ma.getmaskarray(y))
# returns array [[0,2,] [2,4,]]
# returns array [3,4,]
ii = unmasked_index_ranges(ma.getmaskarray(y), compressed=False)
# returns array [[0, 2], [3, 5]]
# returns array [3,4,]
Prior to the transforms refactoring, this was used to support
masked arrays in Line2D.
mask = mask.reshape(mask.size)
m = np.concatenate(((1,), mask, (1,)))
indices = np.arange(len(mask) + 1)
mdif = m[1:] - m[:-1]
i0 = np.compress(mdif == -1, indices)
i1 = np.compress(mdif == 1, indices)
assert len(i0) == len(i1)
if len(i1) == 0:
return None # Maybe this should be np.zeros((0,2), dtype=int)
if not compressed:
return np.concatenate((i0[:, np.newaxis], i1[:, np.newaxis]), axis=1)
seglengths = i1 - i0
breakpoints = np.cumsum(seglengths)
ic0 = np.concatenate(((0,), breakpoints[:-1]))
ic1 = breakpoints
return np.concatenate((ic0[:, np.newaxis], ic1[:, np.newaxis]), axis=1)
# a dict to cross-map linestyle arguments
_linestyles = [('-', 'solid'),
('--', 'dashed'),
('-.', 'dashdot'),
(':', 'dotted')]
ls_mapper = dict(_linestyles)
ls_mapper.update([(ls[1], ls[0]) for ls in _linestyles])
def align_iterators(func, *iterables):
This generator takes a bunch of iterables that are ordered by func
It sends out ordered tuples::
(func(row), [rows from all iterators matching func(row)])
It is used by :func:`matplotlib.mlab.recs_join` to join record arrays
class myiter:
def __init__(self, it): = it
self.key = self.value = None
def iternext(self):
self.value = next(
self.key = func(self.value)
except StopIteration:
self.value = self.key = None
def __call__(self, key):
retval = None
if key == self.key:
retval = self.value
elif self.key and key > self.key:
raise ValueError("Iterator has been left behind")
return retval
# This can be made more efficient by not computing the minimum key for each
# iteration
iters = [myiter(it) for it in iterables]
minvals = minkey = True
while 1:
minvals = ([_f for _f in [it.key for it in iters] if _f])
if minvals:
minkey = min(minvals)
yield (minkey, [it(minkey) for it in iters])
def is_math_text(s):
# Did we find an even number of non-escaped dollar signs?
# If so, treat is as math text.
s = six.text_type(s)
except UnicodeDecodeError:
raise ValueError(
"matplotlib display text must have all code points < 128 or use "
"Unicode strings")
dollar_count = s.count(r'$') - s.count(r'\$')
even_dollars = (dollar_count > 0 and dollar_count % 2 == 0)
return even_dollars
def _reshape_2D(X):
Converts a non-empty list or an ndarray of two or fewer dimensions
into a list of iterable objects so that in
for v in _reshape_2D(X):
v is iterable and can be used to instantiate a 1D array.
if hasattr(X, 'shape'):
# one item
if len(X.shape) == 1:
if hasattr(X[0], 'shape'):
X = list(X)
X = [X, ]
# several items
elif len(X.shape) == 2:
nrows, ncols = X.shape
if nrows == 1:
X = [X]
elif ncols == 1:
X = [X.ravel()]
X = [X[:, i] for i in xrange(ncols)]
raise ValueError("input `X` must have 2 or fewer dimensions")
if not hasattr(X[0], '__len__'):
X = [X]
X = [np.ravel(x) for x in X]
return X
def violin_stats(X, method, points=100):
Returns a list of dictionaries of data which can be used to draw a series
of violin plots. See the `Returns` section below to view the required keys
of the dictionary. Users can skip this function and pass a user-defined set
of dictionaries to the `axes.vplot` method instead of using MPL to do the
X : array-like
Sample data that will be used to produce the gaussian kernel density
estimates. Must have 2 or fewer dimensions.
method : callable
The method used to calculate the kernel density estimate for each
column of data. When called via `method(v, coords)`, it should
return a vector of the values of the KDE evaluated at the values
specified in coords.
points : scalar, default = 100
Defines the number of points to evaluate each of the gaussian kernel
density estimates at.
A list of dictionaries containing the results for each column of data.
The dictionaries contain at least the following:
- coords: A list of scalars containing the coordinates this particular
kernel density estimate was evaluated at.
- vals: A list of scalars containing the values of the kernel density
estimate at each of the coordinates given in `coords`.
- mean: The mean value for this column of data.
- median: The median value for this column of data.
- min: The minimum value for this column of data.
- max: The maximum value for this column of data.
# List of dictionaries describing each of the violins.
vpstats = []
# Want X to be a list of data sequences
X = _reshape_2D(X)
for x in X:
# Dictionary of results for this distribution
stats = {}
# Calculate basic stats for the distribution
min_val = np.min(x)
max_val = np.max(x)
# Evaluate the kernel density estimate
coords = np.linspace(min_val, max_val, points)
stats['vals'] = method(x, coords)
stats['coords'] = coords
# Store additional statistics for this distribution
stats['mean'] = np.mean(x)
stats['median'] = np.median(x)
stats['min'] = min_val
stats['max'] = max_val
# Append to output
return vpstats
class _NestedClassGetter(object):
# recipe from
When called with the containing class as the first argument,
and the name of the nested class as the second argument,
returns an instance of the nested class.
def __call__(self, containing_class, class_name):
nested_class = getattr(containing_class, class_name)
# make an instance of a simple object (this one will do), for which we
# can change the __class__ later on.
nested_instance = _NestedClassGetter()
# set the class of the instance, the __init__ will never be called on
# the class but the original state will be set later on by pickle.
nested_instance.__class__ = nested_class
return nested_instance
class _InstanceMethodPickler(object):
Pickle cannot handle instancemethod saving. _InstanceMethodPickler
provides a solution to this.
def __init__(self, instancemethod):
"""Takes an instancemethod as its only argument."""
if six.PY3:
self.parent_obj = instancemethod.__self__
self.instancemethod_name = instancemethod.__func__.__name__
self.parent_obj = instancemethod.im_self
self.instancemethod_name = instancemethod.im_func.__name__
def get_instancemethod(self):
return getattr(self.parent_obj, self.instancemethod_name)
# Numpy > 1.6.x deprecates putmask in favor of the new copyto.
# So long as we support versions 1.6.x and less, we need the
# following local version of putmask. We choose to make a
# local version of putmask rather than of copyto because the
# latter includes more functionality than the former. Therefore
# it is easy to make a local version that gives full putmask
# behavior, but duplicating the full copyto behavior would be
# more difficult.
except AttributeError:
_putmask = np.putmask
def _putmask(a, mask, values):
return np.copyto(a, values, where=mask)
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