OwlCyberSecurity - MANAGER
Edit File: __init__.cpython-38.pyc
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This module is safe to import from anywhere within matplotlib; it imports matplotlib only at runtime. � N)�Path)� WeakMethod� )� deprecated�warn_deprecated�_rename_parameter�_delete_parameter�_make_keyword_only�(_suppress_matplotlib_deprecation_warning� MatplotlibDeprecationWarning�mplDeprecationz3.0c C sh t | t�rdz t�tjd ��� }|s(d }W n ttt fk rH d }Y nX |d krZt | �S t | |�S | S )Nzaxes.formatter.use_locale) � isinstance�bytes�locale�getpreferredencoding� matplotlib�rcParams�strip� ValueError� ImportError�AttributeError�str)�sZpreferredencoding� r �;/usr/lib/python3/dist-packages/matplotlib/cbook/__init__.py� unicode_safe) s � r c C s t �� d S �N)� traceback� print_exc)�excr r r �_exception_printerC s r c @ s0 e Zd ZdZdd� Zdd� Zdd� Zdd � Zd S ) � _StrongRefzU Wrapper similar to a weakref, but keeping a strong reference to the object. c C s || _ d S r ��_obj)�self�objr r r �__init__L s z_StrongRef.__init__c C s | j S r r" �r$ r r r �__call__O s z_StrongRef.__call__c C s t |t�o| j|jkS r )r r! r# )r$ �otherr r r �__eq__R s z_StrongRef.__eq__c C s t | j�S r )�hashr# r' r r r �__hash__U s z_StrongRef.__hash__N)�__name__� __module__� __qualname__�__doc__r& r( r* r, r r r r r! G s r! c @ sL e Zd ZdZefdd�Zdd� Zdd� Zdd � Zd d � Z d d � Z dd� Z dS )�CallbackRegistrya� Handle registering and disconnecting for a set of signals and callbacks: >>> 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. Parameters ---------- exception_handler : callable, optional If provided must have signature :: def handler(exc: Exception) -> None: If not None this function will be called with any `Exception` subclass raised by the callbacks in `CallbackRegistry.process`. The handler may either consume the exception or re-raise. The callable must be pickle-able. The default handler is :: def h(exc): traceback.print_exc() c C s || _ i | _t�� | _i | _d S r )�exception_handler� callbacks� itertools�count�_cid_gen� _func_cid_map)r$ r2 r r r r& � s zCallbackRegistry.__init__c C s d| j iS )Nr2 )r2 r' r r r � __getstate__� s z CallbackRegistry.__getstate__c C s | j f |� d S r )r& �r$ �stater r r � __setstate__� s z CallbackRegistry.__setstate__c C s� | j �|i � zt|| j�}W n tk r: t|�}Y nX || j | krX| j | | S t| j�}|| j | |< | j�|i � || j| |< |S )zCRegister *func* to be called when signal *s* is generated. ) r7 � setdefaultr � _remove_proxy� TypeErrorr! �nextr6 r3 )r$ r �func�proxy�cidr r r �connect� s zCallbackRegistry.connectc C sh t | j�� �D ]T\}}z| j| || = W n tk r> Y nX t| j| �dkr| j|= | j|= qd S �Nr )�listr7 �itemsr3 �KeyError�len)r$ rA �signalZproxiesr r r r= � s z CallbackRegistry._remove_proxyc C s� t | j�� �D ]n\}}z ||= W n tk r8 Y qY qX t | j�� �D ],\}}t |�� �D ]\}}||kr\||= q\qH dS qdS )zCDisconnect the callback registered with callback id *cid*. N)rE r3 rF rG r7 )r$ rB Z eventnameZ callbackdrI Z functionsZfunction�valuer r r � disconnect� s zCallbackRegistry.disconnectc O s| t | j�|i ��� �D ]`\}}|� }|dk rz|||� W q tk rt } z | jdk rb| �|� n� W 5 d}~X Y qX qdS )z� Process signal *s*. All of the functions registered to receive callbacks on *s* will be called with ``*args`` and ``**kwargs``. N)rE r3 �getrF � Exceptionr2 )r$ r �args�kwargsrB �refr@ r r r r �process� s zCallbackRegistry.processN) r- r. r/ r0 r r& r8 r; rC r= rK rQ r r r r r1 Y s 5 r1 c @ s6 e Zd ZdZd dd�Zdd� ZeZdd� Zd d � ZdS ) � silent_listz� 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 Nc C s || _ |d k r| �|� d S r ��type�extend)r$ rT �seqr r r r& � s zsilent_list.__init__c C s dt | �| jf S )Nz<a list of %d %s objects>)rH rT r' r r r �__repr__� s zsilent_list.__repr__c C s | j | d d � d�S )N)rT rV )rT r' r r r r8 � s zsilent_list.__getstate__c C s |d | _ | �|d � d S )NrT rV rS r9 r r r r; � s zsilent_list.__setstate__)N) r- r. r/ r0 r& rW �__str__r8 r; r r r r rR � s rR c @ s e Zd ZdZdS )�IgnoredKeywordWarningze A class for issuing warnings about keyword arguments that will be ignored by matplotlib N�r- r. r/ r0 r r r r rY � s rY c G sB | }|D ]4}|� |d�}|dk r|dkr.|}qtd| t� q|S )a 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. Parameters ---------- 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 priority Returns ------- out : any object Either local_var or one of kwargs[key] for key in keys Raises ------ IgnoredKeywordWarning For each key in keys that is removed from kwargs but not used as the output value Nz%"%s" keyword argument will be ignored)�pop�_warn_externalrY )Z local_varrO �keys�out�keyZ kwarg_valr r r �local_over_kwdict s $ �r` c C sV t | �dkrR| d | d kr(dkrRn n&| dd� } dD ]\}}| �||�} q<| S )zi Remove latex formatting from mathtext. Only handles fully math and fully non-math strings. � r ����$r ) )z\times�x)z \mathdefault� )z\rmre )z\calre )z\ttre )z\itre )�\re )�{re )�}re )rH �replace)r ZtexZplainr r r � strip_math3 s , rj ztypes.SimpleNamespace)Z alternativec @ s e Zd ZdZdS )�BunchaH Often we want to just collect a bunch of stuff together, naming each item of the bunch; a dictionary's OK for that, but a small do- nothing class is even handier, and prettier to use. Whenever you want to group a few variables:: >>> point = Bunch(datum=2, squared=4, coord=12) >>> point.datum NrZ r r r r rk J s rk �3.1z np.iterablec C s( z t | � W n tk r" Y dS X dS )z return true if *obj* is iterableFT)�iterr> �r% r r r �iterableX s ro z)isinstance(..., collections.abc.Hashable)c C s( z t | � W n tk r" Y dS X dS )z#Returns true if *obj* can be hashedFT)r+ r> rn r r r � is_hashableb s rp c C s t t| dd��S )zDReturn whether *obj* looks like a file object with a *write* method.�writeN)�callable�getattrrn r r r �is_writable_file_likel s rt c C s. z| � d� W n tk r$ Y dS X dS dS )zf Return whether the given writable file-like object requires Unicode to be written to it. � TFN)rq r> �rd r r r �file_requires_unicodeq s rw z isinstance(..., numbers.Number)c C s t | tjtjf�S )z(return true if *obj* looks like a number)r �numbers�Number�npZnumberrn r r r � is_numlike~ s r{ �rFc C s� t | tj�rt�| �} t | t�r�| �d�rD|�dd�}t�| |�}n:| �d�rpddl }|�dd�}|� | |�}nt| ||d�}d}n t | d �r�| }d }nt d ��|r�||fS |S ) aa Convert a path to an open file handle or pass-through a file-like object. Consider using `open_file_cm` instead, as it allows one to properly close newly created file objects more easily. Parameters ---------- fname : str or PathLike or file-like object If `str` or `os.PathLike`, the file is opened using the flags specified by *flag* and *encoding*. If a file-like object, it is passed through. flag : str, default 'r' Passed as the *mode* argument to `open` when *fname* is `str` or `os.PathLike`; ignored if *fname* is file-like. return_opened : bool, default False If True, return both the file object and a boolean indicating whether this was a new file (that the caller needs to close). If False, return only the new file. encoding : str or None, default None Passed as the *mode* argument to `open` when *fname* is `str` or `os.PathLike`; ignored if *fname* is file-like. Returns ------- fh : file-like opened : bool *opened* is only returned if *return_opened* is True. �.gz�Ure z.bz2r N)�encodingT�seekFz'fname must be a PathLike or file handle) r �os�PathLike�fspathr �endswithri �gzip�open�bz2ZBZ2File�hasattrr )�fname�flagZ return_openedr �fhr� �openedr r r � to_filehandle� s& r� c c s8 t | |d|�\}}|r.|� |V W 5 Q R X n|V dS )z;Pass through file objects and context-manage `.PathLike`\s.TN)r� )Z path_or_file�moder r� r� r r r � open_file_cm� s r� c C s t | t�pt�| � S )z;Return whether the given object is a scalar or string like.)r r rz ro )�valr r r �is_scalar_or_string� s r� Tc C s� t �tjd�rtjd }ntj�t�� d�}tj�|| �}|r�tj�| �d � � dkr\d}nd}tj�| �\}}|dkr�t � ||�S t ||�S n|S dS ) a� 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 examples.directory 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. zexamples.directoryZ sample_datarb )z.csvz.xrcz.txtr| �rbr} N) �dict� __getitem__r r r� �path�joinZ_get_data_path�splitext�lowerr� r� )r� Z asfileobj�rootr� r� �baseZextr r r �get_sample_data� s r� c c s6 | D ],}||�s|dkr |V qt ||�E dH qdS )a� Return 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 From: https://code.activestate.com/recipes/121294/ and Recipe 1.12 in cookbook N)�flatten)rV Zscalarp�itemr r r r� � s r� � c C s t j| |dd� dS )z� make directory *newdir* recursively, and set *mode*. Equivalent to :: > mkdir -p NEWDIR > chmod MODE NEWDIR T)r� �exist_okN)r� �makedirs)Znewdirr� r r r �mkdirs s r� c @ s e Zd Zdd� Zdd� ZdS )�GetRealpathAndStatc C s i | _ d S r )�_cacher' r r r r& s zGetRealpathAndStat.__init__c C s\ | j �|�}|d krXtj�|�}tjdkr0|}nt�|�}|j|j f}||f}|| j |< |S )N�win32) r� rL r� r� �realpath�sys�platform�stat�st_ino�st_dev)r$ r� �resultr� �stat_keyr� r r r r( s zGetRealpathAndStat.__call__N)r- r. r/ r& r( r r r r r� s r� c C s* t j�| �}t �|�}|j|jf}||fS r )r� r� r� r� r� r� )r� r� r� r� r r r �get_realpath_and_stat% s r� z(?:(?:\n\r?)|^)( *)\Szinspect.cleandocc C s| | sdS t �| �}|dkr | S |�d�|�d� }|dkr>| S t�|d�}|dkrht�d| �}|t|< |�d| �� � }|S )a� 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. re Nr r z ? {0,%d}� ) �_find_dedent_regex�match�end�start� _dedent_regexrL �re�compile�subr )r r� ZnshiftZunindentr� r r r �dedent5 s r� �*c C s� ddl }ddl}|�d�}g }|�| �D ]f\}} } | D ]N} |j�|j�|| ��} |s`|j�| �r6|D ] } |�| | �rd|�| � q6qdq6|s( q�q(|S )zU Recursively list files from Parmar and Martelli in the Python Cookbook r N�;) �os.path�fnmatch�split�walkr� �normpathr� �isfile�append)r� Zpatterns�recurseZreturn_foldersr� r� Z pattern_listZresults�dirname�dirs�files�name�fullname�patternr r r � listFiles^ s r� c @ s e Zd ZdZdd� Zdd� ZdS )�maxdictz� A dictionary with a maximum size; this doesn't override all the relevant methods to constrain the size, just setitem, so use with caution c C s t �| � || _g | _d S r )r� r& �maxsize� _killkeys)r$ r� r r r r& � s zmaxdict.__init__c C sH || kr6t | �| jkr*| | jd = | jd= | j�|� t�| ||� d S rD )rH r� r� r� r� � __setitem__)r$ �k�vr r r r� � s zmaxdict.__setitem__N)r- r. r/ r0 r& r� r r r r r� { s r� c @ sr e Zd ZdZddd�Zdd� Zdd� Zd d � Zd d � Zd d� Z dd� Z dd� Z dd� Z dd� Z dd� Zdd� ZdS ) �Stackzb Stack of elements with a movable cursor. Mimics home/back/forward in a web browser. Nc C s | � � || _d S r )�clear�_default)r$ �defaultr r r r& � s zStack.__init__c C s t | j�s| jS | j| j S dS )z$Return the current element, or None.N)rH � _elementsr� �_posr' r r r r( � s zStack.__call__c C s t | j�S r �rH r� r' r r r �__len__� s z Stack.__len__c C s | j | S r )r� )r$ Zindr r r r� � s zStack.__getitem__c C s" t | jd t| j�d �| _| � S )z9Move the position forward and return the current element.r )�minr� rH r� r' r r r �forward� s z Stack.forwardc C s | j dkr| j d8 _ | � S )z6Move the position back and return the current element.r r )r� r' r r r �back� s z Stack.backc C s2 | j d| jd � |g | _ t| j �d | _| � S )zs Push *o* to the stack at current position. Discard all later elements. *o* is returned. Nr )r� r� rH )r$ �or r r �push� s z Stack.pushc C s$ t | j�sdS | �| jd � | � S )zk Push the first element onto the top of the stack. The first element is returned. Nr )rH r� r� r' r r r �home� s z Stack.homec C s t | j�dkS )z"Return whether the stack is empty.r r� r' r r r �empty� s z Stack.emptyc C s d| _ g | _dS )zEmpty the stack.rb N)r� r� r' r r r r� � s z Stack.clearc C sl || j krtd��| j dd� }| �� g }|D ]"}||krH|�|� q0| �|� q0|D ]}| �|� qX|S )zq Raise *o* to the top of the stack. *o* must be present in the stack. *o* is returned. �Unknown element oN)r� r r� r� r� )r$ r� �oldZbubbles�thiso�_r r r �bubble� s z Stack.bubblec C sH || j krtd��| j dd� }| �� |D ]}||kr,| �|� q,dS )zRemove *o* from the stack.r� N)r� r r� r� )r$ r� r� r� r r r �remove� s z Stack.remove)N)r- r. r/ r0 r& r( r� r� r� r� r� r� r� r� r� r� r r r r r� � s r� c C s dd� }t �� }tjdkrD|ddd| ddgd �}t|d �� �}n�tjd kr||ddd| dd gd �}t|d �� d �}n�tjdkr�|ddd| ddgd�}t|d �� d �}nLtjdkr�|dddd| gd�}t|�� �� d �dd��}ntd tj ��|S ) z*Return the memory consumed by the process.c S s< z t �| �W S t jk r6 td|| d f ��Y nX d S )Nz;report_memory works on %s only if the '%s' program is foundr )� subprocessZ check_outputZCalledProcessError�NotImplementedError)�commandZos_namer r r �call� s ��zreport_memory.<locals>.callZsunos5Zpsz-pz%dz-oZoszzSun OSrb Zlinuxzrss,szZLinuxr �darwinzrss,vszzMac OSr r� Ztasklistz/nhz/fiz pid eq %dZWindows����,re z%We don't have a memory monitor for %s) r� �getpidr� r� �intr r� ri r� )�ir� �pid�linesZmemr r r � report_memory� s$ �r� z0In safezip, len(args[0])=%d but len(args[%d])=%dc G sX t | d �}t| dd� �D ].\}}t |�|kr tt||d t |�f ��q tt| � �S )z-make sure *args* are equal len before zippingr r N)rH � enumerater � _safezip_msgrE �zip)rN ZNxr� �argr r r �safezip s r� c C sb t j| d|d�} | jjs(| �|��d�} z t jj| dd�}|�� W n t k r\ | Y S X |S )NT)Zsubok�copy�SF)r� ) rz �array�dtypeZisnativeZbyteswapZ newbyteorder�maZmasked_invalidZ shrink_maskr> )rd r� Zxmr r r �safe_masked_invalid s r� c sX ddl � �fdd��� �����fdd���D ]$}��d|�d�� �||i g � q.dS ) aY *objects* A list of objects to find cycles in. It is often useful to pass in gc.garbage to find the cycles that are preventing some objects from being garbage collected. *outstream* The stream for output. *show_progress* If True, print the number of objects reached as they are found. r Nc s� t | �D ]�\}}| |d t| � }� �dt|� � t|t�r�|�� D ]@\}}||krl� �d�|�� q�||krH� �d�|�� q�qHnDt|t�r�� �d|� |� � n$t|t �r �d� n� �t |�� � �d� q� �d� d S ) Nr z %s -- z[{!r}]z [key] = {!r}z[%d]z ( tuple )z -> r� ) r� rH rq rT r r� rF �formatrE �index�tuple�repr)r� r� �stepr? r_ r� )� outstreamr r � print_path: s$ z print_cycles.<locals>.print_pathc s� �r�� dt|� � d |t| �< � �| �}|D ]N}||krF�|� q0|�ks0t|tj�r^q0q0t|�|kr0�||||| g � q0d S )Nz%d )rq rH �idZ get_referentsr �types� FrameType)r% r� �allZ current_pathZ referentsZreferent��gc�objectsr r r� � show_progressr r r� Q s z print_cycles.<locals>.recursez Examining: r� )r rq )r r r r% r r r � print_cycles+ s r c @ sR e Zd ZdZddd�Zdd� Zdd� Zd d � Zd d � Zd d� Z dd� Z dd� Z dS )�Groupera 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 retrieved 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) True >>> grp.joined(a, c) True >>> grp.joined(a, d) False r c C s dd� |D �| _ d S )Nc S s i | ]}t �|�t �|�g�qS r )�weakrefrP ��.0rd r r r � <dictcomp>� s z$Grouper.__init__.<locals>.<dictcomp>)�_mapping)r$ Zinitr r r r& � s zGrouper.__init__c C s t �|�| jkS r )r rP r )r$ r� r r r � __contains__� s zGrouper.__contains__c C s6 | j }dd� |D �}|D ]}|�|�}|�|� qdS )z/Clean dead weak references from the dictionary.c S s g | ]}|� d kr|�qS r r )r r_ r r r � <listcomp>� s z!Grouper.clean.<locals>.<listcomp>N)r r[ r� )r$ �mappingZto_dropr_ r� r r r �clean� s z Grouper.cleanc G s� | j }|�t�|�t�|�g�}|D ]\}|�t�|�t�|�g�}||k r$t|�t|�krd|| }}|�|� |D ] }|||< qrq$| �� dS )zY Join given arguments into the same set. Accepts one or more arguments. N)r r< r rP rL rH rU r )r$ �arN r �set_ar� Zset_b�elemr r r r� � s z Grouper.joinc C s0 | � � | j�t�|�t� �| j�t�|��kS )z7Return whether *a* and *b* are members of the same set.)r r rL r rP �object)r$ r �br r r �joined� s �zGrouper.joinedc C s4 | � � | j�t�|�d �}|r0|�t�|�� d S r )r r r[ r rP r� )r$ r r r r r r� � s zGrouper.removec c s> | � � dd� | j�� D �}|�� D ]}dd� |D �V q$dS )z� Iterate over each of the disjoint sets as a list. The iterator is invalid if interleaved with calls to join(). c S s i | ]}t |�|�qS r )r )r �groupr r r r � s z$Grouper.__iter__.<locals>.<dictcomp>c S s g | ] }|� �qS r r r r r r r � s z$Grouper.__iter__.<locals>.<listcomp>N)r r �values)r$ Z unique_groupsr r r r �__iter__� s zGrouper.__iter__c C s2 | � � | j�t�|�t�|�g�}dd� |D �S )z:Return all of the items joined with *a*, including itself.c S s g | ] }|� �qS r r r r r r r � s z(Grouper.get_siblings.<locals>.<listcomp>)r r rL r rP )r$ r Zsiblingsr r r � get_siblings� s zGrouper.get_siblingsN)r ) r- r. r/ r0 r& r r r� r r� r r r r r r r m s # r c st | � t| �df�}t�t| ��| �t�t| �d | d �� t�� �fdd�|jD ��� t� �f| jdd� �S )av Resample an array with ``steps - 1`` points between original point pairs. Parameters ---------- a : array, shape (n, ...) steps : int Returns ------- array, shape ``((n - 1) * steps + 1, ...)`` Along each column of *a*, ``(steps - 1)`` points are introduced between each original values; the values are linearly interpolated. rb r c s g | ]}t �� �|��qS r )rz Zinterp)r �fp�rd Zxpr r r � s z/simple_linear_interpolation.<locals>.<listcomp>N)�reshaperH rz �arangeZ column_stack�T�shape)r �stepsZfpsr r! r �simple_linear_interpolation� s �r' c G s� t | �s dS t| d �r td��t | d �}g }dgt | � }t| �D ]f\}}t|t�s�t�|�r�t |�|kr�d||< t|tjj �r�|j dkr�td��n t� |�}|� |� qFg }t|�D ]�\}}|| r�|j dkr�q�t|tjj ��r|� tj� |� � |j}n|}z&t�|�}t|tj��r*|� |� W q� tk �rB Y q�X q�t |��r�tj�|�}|�� d } t | �|k �r�t|�D ] \}}|| �r~|| ||< �q~t|�D ]0\}}|| �r�t|tjj ��r�|�� ||< �q�|S )an 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(). r r �!First argument must be a sequenceFTr �Masked arrays must be 1-D)rH r� r r� r r rz ro r� � MaskedArray�ndim�asarrayr� � getmaskarray�dataZisfinite�ndarrayrM Z logical_and�reduce�nonzero�filled) rN �nrecs�margs�seqlistr� rd �masksZxd�maskZigoodr r r �delete_masked_points� sR r8 c G s( t | �s dS t| d �r td��t | d �}g }dgt | � }g }t| �D ]�\}}t|�sft |�|krr|�|� qJt|tjj�r�|j dkr�td��t� |�}|j dkr�t |�}d||< tj� |�r�|�tj� |�� |�|� qJt |��r$tj�|�}t|�D ]$\}}|| r�tjj||d�||< q�|S ) a{ Find all masked and/or non-finite points in a set of arguments, and return the arguments as masked arrays with a common mask. 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 and 4 if :meth:`np.isfinite` does not yield a Boolean array. Category 3 is included to support RGB or RGBA ndarrays, which are assumed to have only valid values and which are passed through unchanged. All input arguments that are not passed unchanged are returned as masked arrays if any masked points are found, otherwise as ndarrays. r r r( Fr r) T)r7 )rH r� r r� r� r rz r� r* r+ � asanyarrayr� Z is_maskedr- Z logical_orr0 r� )rN r3 r4 r5 r6 r� rd r7 r r r �_combine_masks3 s4 r: � �?c s� d dd�� � fdd�}g }t | d�} t| �}|dkr@t�d�}nt|�|krTtd��|}tt| |��D �]f\} \} } i } | dk r�| | d < |}|�| � t| �d k�rt� g �| d < tj | d < tj | d < tj | d< tj | d< tj | d< tj | d< tj | d< tj | d< tj | d < qft� | �} t� | �| d < t� | dddg�\} }}|| | d< | d d k�rb|�rbd}|| || d |�\| d< | d< t�|��r�t�|��r�| || d }||| d }n(|dk�r�t�| �}t�| �}ntd��n t� | |d �}t� | |d �}| | |k }t|�d k�s.t�|�|k �r8|| d< nt�|�| d< | | |k }t|�d k�spt�|�| k�rz| | d< nt�|�| d< t�| | | d k | | | d k g�| d < | || | d< | d < | d< qf|S ) aG Returns list of dictionaries of statistics used to draw a series of box and whisker plots. The `Returns` section enumerates 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 calculations. Parameters ---------- X : array-like Data that will be represented in the boxplots. Should have 2 or fewer dimensions. whis : float, string, or sequence (default = 1.5) As a float, determines the reach of the whiskers beyond the first and third quartiles. In other words, where IQR is the interquartile range (`Q3-Q1`), the upper whisker will extend to last datum less than `Q3 + whis*IQR`. Similarly, the lower whisker will extend to the first datum greater than `Q1 - whis*IQR`. Beyond the whiskers, data are considered outliers and are plotted as individual points. This can be set to an ascending sequence of percentiles (e.g., [5, 95]) to set the whiskers at specific percentiles of the data. Finally, `whis` can be the string ``'range'`` to force the whiskers to the minimum and maximum of the data. In the edge case that the 25th and 75th percentiles are equivalent, `whis` can be automatically set to ``'range'`` via the `autorange` option. bootstrap : int, optional Number of times the confidence intervals around the median should be bootstrapped (percentile method). labels : array-like, optional Labels for each dataset. Length must be compatible with dimensions of `X`. autorange : bool, optional (False) When `True` and the data are distributed such that the 25th and 75th percentiles are equal, ``whis`` is set to ``'range'`` such that the whisker ends are at the minimum and maximum of the data. Returns ------- 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 arithmetic 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 ======== =================================== Notes ----- 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. � c S sL t | �}ddg}tjj|||fd�}| | }tj|ddd�}t�||�}|S )Ng @g `X@)�sizer T)�axisZoverwrite_input)rH rz ZrandomZrandint�median� percentile)r. �N�MZ percentilesZbs_indexZbsDataZestimate�CIr r r �_bootstrap_median� s z(boxplot_stats.<locals>._bootstrap_medianc sb |d k r&� | |d�}|d }|d }n4t | �}|d| t�|� }|d| t�|� }||fS )N)rA r r g ��Q� �?)rH rz Zsqrt)r. �med�iqr� bootstraprC Z notch_minZ notch_maxrA �rD r r �_compute_conf_interval� s z-boxplot_stats.<locals>._compute_conf_interval�XNz-Dimensions of labels and X must be compatible�labelr Zfliers�meanrE �q1�q3ZciloZcihiZwhisloZwhishi� �2 �K rF �range)rR �limitZlimitszmin/maxz:whis must be a float, valid string, or list of percentilesr )r< )� _reshape_2DrH r4 �repeatr r� r� r� rz r� �nanr, rL r@ ZisscalarZisrealr� �maxZhstack)rJ ZwhisrG �labelsZ autorangerI ZbxpstatsZncolsZ input_whisZiird rK �statsrM rE rN ZlovalZhivalZwiskhiZwisklor rH r � boxplot_statso s� O � � rZ ZsolidZdashedZdashdotZdotted)�-z--z-.�:c C s i | ]\}}||�qS r r )r r� r� r r r r @ s r c C s� t j| td�} | jsg S t �| dd� | dd� k�\}|d7 }|�� }| d rZdg| }| d rp|�t| �� tt |ddd� |ddd� ��S )zq Return a list of (ind0, ind1) such that mask[ind0:ind1].all() is True and we cover all such regions �r� Nrb r r ra ) rz r, �boolr= r1 �tolistr� rH rE r� )r7 �idxr r r �contiguous_regionsC s ra c C s4 t | �} | �d�| �d� }|dko.|d dk}|S )Nrc z\$r ra )r r5 )r Z dollar_countZ even_dollarsr r r � is_math_text] s rb c C s0 t | d�r tj�| t��tj�S t�| t�S dS )zt Convert a sequence to a float array; if input was a masked array, masked values are converted to nans. r7 N)r� rz r� r, �floatr2 rV rv r r r �_to_unmasked_float_arrayf s rd c C sv t | d�rt| j�dk r"t�| �S z,| dd�df j}|dk rJt�| �W S | W S ttfk rp t�| � Y S X dS )zy Converts a sequence of less than 1 dimension, to an array of 1 dimension; leaves everything else untouched. r% r Nra )r� rH r% rz � atleast_1dr+ � IndexErrorr> )rd r+ r r r � _check_1dq s rg c C s� t �t| t j�r| jnt �| ��} t| �dkr4g gS | jdkrVt �| d �dkrV| gS | jdkrndd� | D �S td� |���dS )a� Use Fortran ordering to convert ndarrays and lists of iterables to lists of 1D arrays. Lists of iterables are converted by applying `np.asarray` to each of their elements. 1D ndarrays are returned in a singleton list containing them. 2D ndarrays are converted to the list of their *columns*. *name* is used to generate the error message for invalid inputs. r r )r ra c S s g | ]}t �|d ��qS )rb )rz r" r r r r r � s z _reshape_2D.<locals>.<listcomp>z"{} must have 2 or fewer dimensionsN) rz re r r/ r$ r, rH r+ r r� )rJ r� r r r rT � s " rT �d c C s� g }t | d�} | D ]v}i }t�|�}t�|�}t�|||�}|||�|d< ||d< t�|�|d< t�|�|d< ||d< ||d< |�|� q|S )a7 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 calculations. Parameters ---------- 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. Returns ------- 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. rJ Zvals�coordsrL r? r� rW )rT rz r� rW ZlinspacerL r? r� ) rJ �methodZpointsZvpstatsrd rY Zmin_valZmax_valri r r r � violin_stats� s ) rk c G s� t �dt|� tdt| � d d�f�}| |dddd�f< |dddd�f |dddd�f< ||dd�ddd�f< |dd�ddd�f |dd�ddd�f< |S )a- Convert continuous line to pre-steps. Given a set of ``N`` points, convert to ``2N - 1`` points, which when connected linearly give a step function which changes values at the beginning of the intervals. Parameters ---------- x : array The x location of the steps. May be empty. y1, ..., yp : array y arrays to be turned into steps; all must be the same length as ``x``. Returns ------- out : array The x and y values converted to steps in the same order as the input; can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is length ``N``, each of these arrays will be length ``2N + 1``. For ``N=0``, the length will be 0. Examples -------- >> x_s, y1_s, y2_s = pts_to_prestep(x, y1, y2) r ra r Nr� �rz �zerosrH rW �rd rN r& r r r �pts_to_prestep� s ( (ro c G s� t �dt|� tdt| � d d�f�}| |dddd�f< |dddd�f |dddd�f< ||dd�ddd�f< |dd�ddd�f |dd�ddd�f< |S )a( Convert continuous line to post-steps. Given a set of ``N`` points convert to ``2N + 1`` points, which when connected linearly give a step function which changes values at the end of the intervals. Parameters ---------- x : array The x location of the steps. May be empty. y1, ..., yp : array y arrays to be turned into steps; all must be the same length as ``x``. Returns ------- out : array The x and y values converted to steps in the same order as the input; can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is length ``N``, each of these arrays will be length ``2N + 1``. For ``N=0``, the length will be 0. Examples -------- >> x_s, y1_s, y2_s = pts_to_poststep(x, y1, y2) r ra r Nr� rl rn r r r �pts_to_poststep s ( (rp c G s� t �dt|� dt| � f�}t �| �} | dd� | dd� d |dddd�f< |dddd�f< | dd� |ddd�f< | dd� |ddd�f< ||dd�ddd�f< |dd�ddd�f |dd�ddd�f< |S )a� Convert continuous line to mid-steps. Given a set of ``N`` points convert to ``2N`` points which when connected linearly give a step function which changes values at the middle of the intervals. Parameters ---------- x : array The x location of the steps. May be empty. y1, ..., yp : array y arrays to be turned into steps; all must be the same length as ``x``. Returns ------- out : array The x and y values converted to steps in the same order as the input; can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is length ``N``, each of these arrays will be length ``2N``. Examples -------- >> x_s, y1_s, y2_s = pts_to_midstep(x, y1, y2) r ra Nrb r )rz rm rH r9 rn r r r �pts_to_midstep0 s <(rq c C s | |fS r r )rd �yr r r �<lambda>V ru rs )r� r& z steps-prez steps-postz steps-midc C sJ z| j j| jfW S tk rD t| �} tj| jd td�| f Y S X dS )a� A helper function to get the index of an input to plot against if x values are not explicitly given. Tries to get `y.index` (works if this is a pd.Series), if that fails, return np.arange(y.shape[0]). This will be extended in the future to deal with more types of labeled data. Parameters ---------- y : scalar or array-like The proposed y-value Returns ------- x, y : ndarray The x and y values to plot. r r] N)r� r r rg rz r# r% rc )rr r r r �index_of] s rt c C sB t | tjj�r6z | d W S tk r, Y nX td��tt| ��S )Nr z/matplotlib does not support generators as input)r � collections�abc�Iteratorr> � RuntimeErrorr? rm rn r r r �safe_first_elementy s ry c C s t | tjj�rt| �S | S )z Converts dictview object to list)r ru rv � MappingViewrE )r. r r r �sanitize_sequence� s �r{ r c s� |dkrt � }t | �} t � �|�� D ]�\}}g }g }|D ]8} z |�| �| �� |�| � W q8 tk rn Y q8X q8||kr�z |�| �|�� |�|� W n tk r� Y nX |r$|d �|< t|�dkr$tdd|�d|�d|d �d�d � q$��| � �fd d �|D �} | �r"td j | d ����fdd �|D �} | �rJtdj | d ���|dk �r�||�� � fdd ��D �} | �r�tdj | ||d����S )aR Helper function to normalize kwarg inputs The order they are resolved are: 1. aliasing 2. required 3. forbidden 4. allowed This order means that only the canonical names need appear in `allowed`, `forbidden`, `required` Parameters ---------- alias_mapping, dict, optional A mapping between a canonical name to a list of aliases, in order of precedence from lowest to highest. If the canonical value is not in the list it is assumed to have the highest priority. required : iterable, optional A tuple of fields that must be in kwargs. forbidden : iterable, optional A list of keys which may not be in kwargs allowed : tuple, optional A tuple of allowed fields. If this not None, then raise if `kw` contains any keys not in the union of `required` and `allowed`. To allow only the required fields pass in ``()`` for `allowed` Raises ------ TypeError To match what python raises if invalid args/kwargs are passed to a callable. Nrb r rl z Saw kwargs z which are all aliases for z. Kept value from zU. Passing multiple aliases for the same property will raise a TypeError %(removal)s.)�messagec s g | ]}|� kr|�qS r r �r r� ��retr r r � s z$normalize_kwargs.<locals>.<listcomp>z,The required keys {keys!r} are not in kwargs)r] c s g | ]}|� kr|�qS r r r} r~ r r r � s z)The forbidden keys {keys!r} are in kwargsc s g | ]}|� kr|�qS r r r} )� allowed_setr r r � s zXkwargs contains {keys!r} which are not in the required {req!r} or allowed {allow!r} keys)r] ZreqZallow) r� rF r� r[ rG rH r �updater> r� ) �kwZ alias_mappingZrequiredZ forbiddenZallowedZ canonicalZ alias_listZtmp�seenr Z fail_keysr )r� r r �normalize_kwargs� sb , � �� ��r� c C s& z| j W S tk r | Y S X d S r )r� r )rr Z default_namer r r � get_label� s r� z�LOCKERROR: matplotlib is trying to acquire the lock {!r} and has failed. This maybe due to any other process holding this lock. If you are sure no other matplotlib process is running try removing these folders and trying again. c @ s< e Zd ZdZdZG dd� de�Zdd� Zdd� Zd d � Z d S ) �Lockeda< Context manager to handle locks. Based on code from conda. (c) 2012-2013 Continuum Analytics, Inc. / https://www.continuum.io/ All Rights Reserved conda is distributed under the terms of the BSD 3-clause license. Consult LICENSE_CONDA or https://opensource.org/licenses/BSD-3-Clause. z.matplotlib_lockc @ s e Zd ZdS )zLocked.TimeoutErrorN)r- r. r/ r r r r � TimeoutError s r� c C sT || _ dtt�� � | _tj �| j | j| j �| _tj �| j | jd �| _d| _ d S )Nr[ z-*T) r� r r� r� r� r� �LOCKFN� lock_pathr� r� )r$ r� r r r r& s zLocked.__init__c C s� d}d}|rDt � | j�}|rZ|d �| j�sZt�|� |d8 }qqZqt�| j�}| �|��|s�zt � | j � W q� t k r� Y q�X nd| _ d S )NrP 皙�����?r r F)�globr� r� r� �time�sleep�_lockstrr� r� r� r� r� �OSErrorr� )r$ �retries� sleeptimer� Zerr_strr r r � __enter__& s zLocked.__enter__c C s@ | j r<| j| jfD ](}zt�|� W q tk r8 Y qX qd S r )r� r� r� r� �rmdirr� )r$ �exc_type� exc_valuer r� r r r �__exit__< s zLocked.__exit__N) r- r. r/ r0 r� rx r� r& r� r� r r r r r� s r� c c s� t | �} | �| jd �}d}d}t|�D ]N}z*|�d�� W 5 Q R � W q�W 5 Q R X W q( tk rt t�|� Y q(X q(td� |���z dV W 5 |� � X dS )af Context manager for locking a path. Usage:: with _lock_path(path): ... Another thread or process that attempts to lock the same path will wait until this context manager is exited. The lock is implemented by creating a temporary file in the parent directory, so that directory must exist and be writable. z.matplotlib-lockrP r� Zxbz�Lock error: Matplotlib failed to acquire the following lock file: {} This maybe due to another process holding this lock file. If you are sure no other Matplotlib process is running, remove this file and try again.N) r Z with_namer� rR r� �FileExistsErrorr� r� r� r� �unlink)r� r� r� r� r� r r r � _lock_pathE s � r� Zzorder)r_ c C s |t | ��S )z�Get the topmost artist of a list. In case of a tie, return the *last* of the tied artists, as it will be drawn on top of the others. `max` returns the first maximum in case of ties, so we need to iterate over the list in reverse order. )�reversed)ZartistsZ _cached_maxr r r �_topmost_artistl s r� c C s t | t�o| |kS )z�Return whether *obj* is a string equal to string *s*. This helper solely exists to handle the case where *obj* is a numpy array, because in such cases, a naive ``obj == s`` would yield an array, which cannot be used in a boolean context. )r r �r% r r r r � _str_equalx s r� c C s t | t�o| �� |kS )a Return whether *obj* is a string equal, when lowercased, to string *s*. This helper solely exists to handle the case where *obj* is a numpy array, because in such cases, a naive ``obj == s`` would yield an array, which cannot be used in a boolean context. )r r r� r� r r r �_str_lower_equal� s r� c s� � dkrt �t| �S � fdd�}| �� D ]|\}}d}dD ]X}|| t� �kr8d}|D ]:}||| �}|| |_d�|| �|_t� || |� qTq8|s(t d�|���q(t � d �r�t d ��| � _ � S ) aR Class decorator for defining property aliases. Use as :: @cbook._define_aliases({"property": ["alias", ...], ...}) class C: ... For each property, if the corresponding ``get_property`` is defined in the class so far, an alias named ``get_alias`` will be defined; the same will be done for setters. If neither the getter nor the setter exists, an exception will be raised. The alias map is stored as the ``_alias_map`` attribute on the class and can be used by `~.normalize_kwargs` (which assumes that higher priority aliases come last). Nc s t �t�� ��� fdd��}|S )Nc s t | � �||�S r �rs )r$ rN rO �r� r r rj � s z3_define_aliases.<locals>.make_alias.<locals>.method)� functools�wrapsrs )r� rj ��clsr� r � make_alias� s z#_define_aliases.<locals>.make_aliasF)Zget_Zset_TzAlias for `{}`.z)Neither getter nor setter exists for {!r}� _alias_mapz$Parent class already defines aliases) r� �partial�_define_aliasesrF �varsr- r� r0 �setattrr r� r� r� ) Zalias_dr� r� Zprop�aliases�exists�prefix�aliasrj r r� r r� � s* � r� c C sP t jdd� }t jddd� }t �| d|f | |df | d|f | |df f�S )a] Get the elements on the perimeter of ``arr``, Parameters ---------- arr : ndarray, shape (M, N) The input array Returns ------- perimeter : ndarray, shape (2*(M - 1) + 2*(N - 1),) The elements on the perimeter of the array:: [arr[0,0] ... arr[0,-1] ... arr[-1, -1] ... arr[-1,0] ...] Examples -------- >>> i, j = np.ogrid[:3,:4] >>> a = i*10 + j >>> a array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]]) >>> _array_perimeter(a) array([ 0, 1, 2, 3, 13, 23, 22, 21, 20, 10]) r rb )rz Zs_Z concatenate)Zarrr� Zbackwardr r r �_array_perimeter� s �r� c + sx t � �� �fdd�|D �}z(|�� D ]\}}t� ||� q$dV W 5 |D ](\}}|�krdt� |� qHt� ||� qHX dS )zMTemporarily set some attributes; restore original state at context exit. c s g | ]}|t � |��f�qS r r� )r �attr�r% �sentinelr r r � s z _setattr_cm.<locals>.<listcomp>N)r �delattrr� rF )r% rO Zorigsr� Zorigr� r r� r � _setattr_cm� s r� c C sV t �� }t�d�D ]0}|dkr" qDt�d|j�dd��s< qD|j}qt � | ||� dS )a6 `warnings.warn` wrapper that sets *stacklevel* to "outside Matplotlib". The original emitter of the warning can be obtained by patching this function back to `warnings.warn`, i.e. ``cbook._warn_external = warnings.warn`` (or ``functools.partial(warnings.warn, stacklevel=2)``, etc.). r Nz"\A(matplotlib|mpl_toolkits)(\Z|\.)r- re ) r� � _getframer4 r5 r� r� � f_globalsrL �f_back�warnings�warn)r| �category�frame� stacklevelr r r r\ � s �r\ c @ s< e Zd Zdd� Zdd� Zdd� Zdd� Zd d � Zd d � Zd S )� _OrderedSetc C s t �� | _d S r )ru � OrderedDict�_odr' r r r r& s z_OrderedSet.__init__c C s || j kS r )r� �r$ r_ r r r r s z_OrderedSet.__contains__c C s t | j�S r )rm r� r' r r r r s z_OrderedSet.__iter__c C s t | j�S r )rH r� r' r r r r� s z_OrderedSet.__len__c C s | j �|d � d | j |< d S r �r� r[ r� r r r �add s z_OrderedSet.addc C s | j �|d � d S r r� r� r r r �discard s z_OrderedSet.discardN) r- r. r/ r& r r r� r� r� r r r r r� s r� c C s� t j| tjdkr ddddgn ddddgdd�}|ddd �f }|d }|dk}t �|d �D ].}|| �t�d || d || ||< qZ|S ) zS Convert a premultiplied ARGB32 buffer to an unmultiplied RGBA8888 buffer. �littlera r r � �r> .Nrb ).rb � )rz �taker� � byteorderZrollaxisZastyper� )ZbufZrgbaZrgbZalphar7 Zchannelr r r �._premultiplied_argb32_to_unmultiplied_rgba8888# s � ��r� c C s� t jdkrDtj| ddddgdd�}|ddd �f }|dd d�f }n8tj| ddddgdd�}|ddd�f }|ddd�f }|�� d kr�tj||d |d d � |S ) zS Convert an unmultiplied RGBA8888 buffer to a premultiplied ARGB32 buffer. r� ra r r r� r� .Nrb r� Zunsafe)r^ Zcasting)r� r� rz r� r� Zmultiply)Zrgba8888Zargb32Zrgb24Zalpha8r r r �._unmultiplied_rgba8888_to_premultiplied_argb325 s r� c K s� |� dt| �� tj| ftjtjd�|��}|jr`tdt| �� d|j�d�� d|j �d�� ���|� d|j� |� d|j � |jS ) aj Run *command*, returning its stdout output if it succeeds. If it fails (exits with nonzero return code), raise an exception whose text includes the failed command and captured stdout and stderr output. Regardless of the return code, the command is logged at DEBUG level on *logger*. In case of success, the output is likewise logged. z%s)�stdout�stderrzThe command z, failed and generated the following output: zutf-8z and the following error: z stdout: %sz stderr: %s) �debugr r� �run�PIPE� returncoderx r� �decoder� )r� ZloggerrO �procr r r �_check_and_log_subprocessI s � ��(�r� c K s2 | � � D ]$\}}t|tj�rtd|�d���qdS )zl If any value in *kwargs* is a `np.matrix`, raise a TypeError with the key name in its message. z Argument z cannot be a np.matrixN)rF r rz Zmatrixr> )rO r� r� r r r �_check_not_matrixc s r� c K s< |� � D ].\}}|| krtd�||d�tt| �����qdS )z� For each *key, value* pair in *kwargs*, check that *value* is in *values*; if not, raise an appropriate ValueError. 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