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mZ ddlmZmZ ddlmZmZ ddlmZ ddlZdd	lmZmZmZ dd
lmZmZmZ ddlmZm Z m!Z!m"Z"m#Z#m$Z$ ddl%m&Z& dodd„Z'dpdd„Z(dd„ Z)dd„ Z*dd„ Z+dqdd„Z,dd„ Z-dd „ Z.d!d"„ Z/d#d$„ Z0e&drd%d&„ƒZ1edsd'd(„ƒZ2d)d*„ Z3d+d,„ Z4ed-d.„ ƒZ5d/d0„ Z6d1d2„ Z7d3d4„ Z8dtd7d8„Z9d9d:„ Z:dud;d<„Z;dvd>d?„Z<dwd@dA„Z=dxdBdC„Z>G dDdE„ dEej?ƒZ@dFdG„ ZAdydHdI„ZBdzdJdK„ZCdLdM„ ZDd{dNdO„ZEd|ddQœdRdS„ZFG dTdU„ dUeGƒZHdVdW„ ZIdXdY„ ZJdZd[„ ZKd\d]„ ZLd^d_„ ZMd`da„ ZNdbdc„ ZOddde„ ZPG dfdg„ dgeGƒZQdhdi„ ZRe$�rŽe#ddj�dkdl„ ƒZSneRZSeTƒ ZUeTƒ ZVdmdn„ ZWdS )}zSome utility functions.é    N)Úcontextmanager)ÚdatetimeÚ	timedeltaÚtimezone)ÚBytesIOÚStringIO)ÚceilÚsqrt)ÚPathé   )ÚloggerÚwarnÚverbose)Úcheck_random_stateÚ_ensure_intÚ_validate_typeé   )Ú_infer_dimension_Úsvd_flipÚstable_cumsumÚ	_safe_svdÚjitÚ	has_numba)Úfill_docFc                 c   s–   t |ƒ}t| ƒ}|| }d }}t|d ƒD ]>}||7 }|rTt ||¡| ||… fn
| ||… V  ||7 }q,|rˆt ||¡| |d… fn| | V  dS )z?Split list in n (approx) equal pieces, possibly giving indices.r   r   N)ÚintÚlenÚrangeÚnpÚarange)ÚvÚnÚidxZtotÚszÚstartÚstopÚi© r&   úQ/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/utils/numerics.pyÚ
split_list   s    *
r(   c                    sL   t |ƒ}tj| ||d�}t t | j| ¡|¡}‡ fdd„|D ƒ}t||ƒS )z0Do what numpy.array_split does, but add indices.©Úaxisc                 3   s.   | ]&}t  |d  ˆ  |d d ˆ  ¡V  qdS )r   éÿÿÿÿr   N)r   r   )Ú.0Úsp©Ún_per_splitr&   r'   Ú	<genexpr>0   s   ÿz"array_split_idx.<locals>.<genexpr>)r   r   Zarray_splitr   ÚshapeÚzip)ZaryZindices_or_sectionsr*   r/   Z	ary_splitZ	idx_splitr&   r.   r'   Úarray_split_idx*   s    
ÿr3   c                    s    ‡ ‡fdd„t dtˆ ƒˆƒD ƒS )z¬Generate chunks from a sequence.

    Parameters
    ----------
    sequence : iterable
        Any iterable object
    size : int
        The chunksize to be returned
    c                 3   s   | ]}ˆ ||ˆ … V  qd S ©Nr&   )r,   Úp©ÚsequenceÚsizer&   r'   r0   ?   s     z create_chunks.<locals>.<genexpr>r   )r   r   r6   r&   r6   r'   Úcreate_chunks5   s    
r9   c                 C   s&   | j t | ¡rdndd�}t ||¡S )zÊCompute norm of an array.

    Parameters
    ----------
    X : array
        Data whose norm must be found.

    Returns
    -------
    value : float
        Sum of squares of the input array X.
    ÚFÚC)Úorder)Zravelr   Z	isfortranÚdot)ÚXZX_flatr&   r&   r'   Úsum_squaredB   s    r?   c                 C   s(   t  t j| d dd�¡}d||dk< |S )z(Compute scaling based on estimated norm.r   r   r)   ç      ð?r   )r   r	   Úsum)ÚdataZnormsr&   r&   r'   Ú_compute_row_normsS   s    rC   ÚfullçVçž¯Ò<c                 C   s~  ddl m} |dk	r*|dkr*tt |¡ƒ}| jdk sH| jd | jd krPtdƒ‚t 	| |  
¡  dd¡¡sptdƒ‚| jdkrŽ| jd | jd ks’t‚| jd }d	d
lm} || dd�\}}}	|dkrÆdn||ddd…f  }
|||
ƒ}|tj|dd� }|�r*|| |dtjtjf t |¡  dd�\}}}	|dk�r8dn||ddd…f  }
|||
ƒ}||k  ¡ �rŒ|d	k�rŒ|dk�rŒ|dk�rŒtdƒ n4t|tƒ�rÀ||k�rÀtd|| jd	 | jd f ƒ‚t |jd ¡ d| jd  d ¡}|dk�rø| }}n|dk�r|}|}n| }}|t |¡dtjf k }||d	kM }t |j¡}d||  ||< t ||dtjdd…f  |	¡}|||fS )a   Compute a regularized pseudoinverse of Hermitian matrices.

    Regularization is performed by adding a constant value to each diagonal
    element of the matrix before inversion. This is known as "diagonal
    loading". The loading factor is computed as ``reg * np.trace(x) / len(x)``.

    The pseudo-inverse is computed through SVD decomposition and inverting the
    singular values. When the matrix is rank deficient, some singular values
    will be close to zero and will not be used during the inversion. The number
    of singular values to use can either be manually specified or automatically
    estimated.

    Parameters
    ----------
    x : ndarray, shape (..., n, n)
        Square, Hermitian matrices to invert.
    reg : float
        Regularization parameter. Defaults to 0.
    rank : int | None | 'full'
        This controls the effective rank of the covariance matrix when
        computing the inverse. The rank can be set explicitly by specifying an
        integer value. If ``None``, the rank will be automatically estimated.
        Since applying regularization will always make the covariance matrix
        full rank, the rank is estimated before regularization in this case. If
        'full', the rank will be estimated after regularization and hence
        will mean using the full rank, unless ``reg=0`` is used.
        Defaults to 'full'.
    rcond : float | 'auto'
        Cutoff for detecting small singular values when attempting to estimate
        the rank of the matrix (``rank='auto'``). Singular values smaller than
        the cutoff are set to zero. When set to 'auto', a cutoff based on
        floating point precision will be used. Defaults to 1e-15.

    Returns
    -------
    x_inv : ndarray, shape (..., n, n)
        The inverted matrix.
    loading_factor : float
        Value added to the diagonal of the matrix during regularization.
    rank : int
        If ``rank`` was set to an integer value, this value is returned,
        else the estimated rank of the matrix, before regularization, is
        returned.
    r   )Ú_estimate_rank_from_sNrD   éþÿÿÿr+   zInput matrix must be square.z*Input matrix must be Hermitian (symmetric)r   )ÚsvdT)Z	hermitianÚauto.r   r)   rE   zBCovariance matrix is rank-deficient and no regularization is done.zXInvalid value for the rank parameter (%d) given the shape of the input matrix (%d x %d).©r   )r+   r@   )ÚrankrF   r   ÚoperatorÚindexÚndimr1   Ú
ValueErrorr   ÚallcloseZconjZswapaxesÚAssertionErrorZ	mne.fixesrH   ÚmeanÚnewaxisÚeyeÚanyr   Ú
isinstancer   ZreshapeÚasarrayÚzerosÚmatmul)ÚxÚregrK   ZrcondrF   r    rH   ÚUÚsZVhZtolZrank_beforeZloading_factorZ
rank_afterÚmaskÚcmpÚretZs_invZx_invr&   r&   r'   Ú	_reg_pinvZ   sX    -"
 
þ"
ÿÿ
þ$


 ra   c                 C   s*   t jt  | ¡t  | t¡t  | t¡f }|S )z/Generate event structure from number of epochs.)r   Zc_r   rX   r   Zones)Zn_epochsÚeventsr&   r&   r'   Ú_gen_eventsÅ   s    
ÿrc   c              	   C   sN  ddl m} ddlm} t | ¡}||ƒ}	tt||d  ƒƒ}
|dk	rZtt|
t|ƒ ƒƒ}
d}d}g }t	d| j
d |
ƒD ]¦}||
 }| dd…||…f }|j
d || k r° �q |||d |	|||d	 d
�rþ||j
d  }||dd…||…f< ||j
d 7 }qxt d||f ¡ | ||f¡ qx|dd…d|…f } |  ¡ �sFtdƒ‚| |fS )z2Reject data segments using peak-to-peak amplitude.r   )Ú_is_good)Úchannel_indices_by_typeÚsfreqNr   r   Zch_namesÚbads)Z
ignore_chszArtifact detected in [%d, %d]zKNo clean segment found. Please consider updating your rejection thresholds.)Úepochsrd   Zio.pickre   r   Z
empty_liker   r   Úfloatr   r1   r   ÚinfoÚappendrU   ÚRuntimeError)rB   ZrejectZflatZdecimrj   Ztsteprd   re   Z
data_cleanZidx_by_typeÚstepZ
this_startZ	this_stopZ	drop_indsÚfirstÚlastZdata_bufferr&   r&   r'   Ú_reject_data_segmentsÌ   s8    
 ÿ
rp   c                 C   sX   ddl m} ddlm} ddlm} ddlm} t| ||||fdƒ | j	sR|  
¡  | jS )zBGet data view from MNE object instance like Raw, Epochs or Evoked.r   )ÚBaseRaw)Ú
BaseEpochs©ÚEvoked)Ú_BaseTFRÚInstance)Zio.baserq   rh   rr   Ú rt   Útime_frequency.tfrru   r   ZpreloadZ	load_dataÚ_data)Úinstrq   rr   rt   ru   r&   r&   r'   Ú_get_inst_dataî   s    r{   c                 C   s²   t | ƒdkst |ƒdkr tdƒ‚t | t¡}t |t¡}|| d¡8 }|| d¡8 }|jddd�}|jddd�dd…|j|jkr„dntf }t 	|j
|¡tt |ƒd ƒ ||  S )z;Compute pearson correlations between a vector and a matrix.r   zx or y has zero lengthr   )ZddofN)r   rO   r   Úarrayri   rR   Zstdr1   ÚEllipsisr=   ÚT)rZ   Úyr>   ÚYZx_sdZy_sdr&   r&   r'   Úcompute_corrû   s    *r�   c                 C   s"   t |ƒ}|j| d�}t |¡}|S )aä  Emulate the randperm matlab function.

    It returns a vector containing a random permutation of the
    integers between 0 and n_samples-1. It returns the same random numbers
    than randperm matlab function whenever the random_state is the same
    as the matlab's random seed.

    This function is useful for comparing against matlab scripts
    which use the randperm function.

    Note: the randperm(n_samples) matlab function generates a random
    sequence between 1 and n_samples, whereas
    random_permutation(n_samples, random_state) function generates
    a random sequence between 0 and n_samples-1, that is:
    randperm(n_samples) = random_permutation(n_samples, random_state) - 1

    Parameters
    ----------
    n_samples : int
        End point of the sequence to be permuted (excluded, i.e., the end point
        is equal to n_samples-1)
    %(random_state)s

    Returns
    -------
    randperm : ndarray, int
        Randomly permuted sequence between 0 and n-1.
    )r8   )r   Úuniformr   Úargsort)Ú	n_samplesZrandom_stateÚrngr!   Zrandpermr&   r&   r'   Úrandom_permutation
  s    
r†   c                    s�   t | ||ƒ}t|tƒrlt d|f ¡ t|ƒ‰ ‡ fdd„| ¡ D ƒ}|D ] \}}| |dd…f  |9  < qHn t d¡ | |dd…tjf 9 } dS )z+Scale data type-dependently for estimation.z    Scaling using mapping %s.c                    s$   g | ]\}}|ˆ krˆ | |f‘qS r&   r&   ©r,   Úkr   ©Z
picks_dictr&   r'   Ú
<listcomp>7  s    ÿz(_apply_scaling_array.<locals>.<listcomp>Nz!    Scaling using computed norms.)Ú_check_scaling_inputsrV   Údictr   ÚdebugÚitemsr   rS   )rB   Ú
picks_listÚscalingsr   r!   Zscalingr&   r‰   r'   Ú_apply_scaling_array0  s    

r‘   c                 C   s6   t | tƒrdd„ |  ¡ D ƒ} nt | tjƒr2d|  } | S )Nc                 S   s   i | ]\}}|d | “qS )r@   r&   r‡   r&   r&   r'   Ú
<dictcomp>B  s      z$_invert_scalings.<locals>.<dictcomp>r@   )rV   rŒ   rŽ   r   Úndarray)r�   r&   r&   r'   Ú_invert_scalings@  s
    
r”   c                 C   s    t t| ||ƒƒ}t| ||dd�S )NF)r   )r”   r‹   r‘   ©rB   r�   r�   r&   r&   r'   Ú_undo_scaling_arrayH  s    ÿr–   c                 c   s.   t | ||d� z
dV  W 5 t| ||d� X dS )zScale, use, unscale array.)r�   r�   N)r‘   r–   r•   r&   r&   r'   Ú_scaled_arrayN  s    
r—   c                 C   s   t | ||ƒ}d}t|tƒrœt| ƒ}tt|Ž ƒd }t| ƒtdd„ |D ƒƒksPt‚ttt	 
|¡ƒƒttt| ƒƒƒksvt‚t	 |¡}|D ]\}}|| ||< q„n<t|t	jƒrÆt|ƒt| ƒkrÀtdƒ‚|}n|dkrÐntdƒ‚|dk	�rt	 |dk¡dksøt‚| |ddd…f |dd…df  9 } dS )	z&Scale resulting data after estimation.Nr   c                 s   s   | ]}t |ƒV  qd S r4   )r   ©r,   rˆ   r&   r&   r'   r0   _  s     z%_apply_scaling_cov.<locals>.<genexpr>z2Scaling factors and data are of incompatible shapezArff...ç        r   )r‹   rV   rŒ   r   Úlistr2   rA   rQ   Úsortedr   Úconcatenater   rX   r“   rO   rl   )rB   r�   r�   ÚscalesZ
n_channelsZcovindsZch_tr!   r&   r&   r'   Ú_apply_scaling_covX  s(    
&

rž   c                 C   s   t t| ||ƒƒ}t| ||ƒS r4   )r”   r‹   rž   r•   r&   r&   r'   Ú_undo_scaling_covr  s    ÿrŸ   c                 C   sx   t dddd�}d}t|tƒr2|dkr2dt| ƒ }nBt|t ƒrL| |¡ |}n(t|tjƒr^|}n|dkrhntd| ƒ‚|S )	zAux function.g  4&õkCg  @åœ0¢Bg    €„.A)ÚmagZgradZeegNZnormr@   z)No way! That's not a rescaling option: %s)rŒ   rV   ÚstrrC   Úupdater   r“   ÚNotImplementedError)rB   r�   r�   Zrescale_dict_Z	scalings_r&   r&   r'   r‹   x  s    

ÿr‹   é   Úmd5c              	   C   s\   |dkrt  ¡ }n|dkr"t  ¡ }t| dƒ�"}| |¡}|s>qJ| |¡ q.W 5 Q R X | ¡ S )zöCalculate the hash for a file.

    Parameters
    ----------
    fname : str
        Filename.
    block_size : int
        Block size to use when reading.

    Returns
    -------
    hash_ : str
        The hexadecimal digest of the hash.
    r¥   Úsha1Úrb)Úhashlibr¥   r¦   ÚopenÚreadr¢   Ú	hexdigest)ÚfnameÚ
block_sizeZ	hash_typeZhasherZfidrB   r&   r&   r'   ÚhashfuncŒ  s    

r®   c                 C   sR   |   d¡st‚| dd… }tj |¡rBt| ƒt|ƒkrBt | ¡ nt | |¡ dS )zReplace a file based on MD5sum.z.newNéüÿÿÿ)	ÚendswithrQ   ÚosÚpathÚisfiler®   ÚremoveÚshutilÚmove)r¬   Z	fname_oldr&   r&   r'   Ú_replace_md5¨  s
    r·   c                    s2   |dkrˆ }‡ fdd„t | |ˆ  d |ƒD ƒ}|S )aÌ  Generate slices of time indexes.

    Parameters
    ----------
    start : int
        Index where first slice should start.
    stop : int
        Index where last slice should maximally end.
    length : int
        Number of time sample included in a given slice.
    step: int | None
        Number of time samples separating two slices.
        If step = None, step = length.

    Returns
    -------
    slices : list
        List of slice objects.
    Nc                    s   g | ]}t ||ˆ  d ƒ‘qS rJ   )Úslice)r,   Út©Úlengthr&   r'   rŠ   Ì  s     z!create_slices.<locals>.<listcomp>r   )r   )r#   r$   rm   r»   Zslicesr&   rº   r'   Úcreate_slices³  s    
ÿr¼   Tc           
      C   s"  |}|}|dkrt j n|}|dkr*t jn|}t  |¡s@| d }t  |¡sV| d }d}|dk	r¬t|ƒ}tt|| ƒƒ| d|  }tt|| ƒƒ| }||r dnd| 7 }n|s´t‚|rÐ||krÐtd||f ƒ‚| |k}|| |kM }|�r| ¡ �s|rüdnd	}	td
|||	| d | d f ƒ‚|S )zSafely find sample boundaries.Nr   r+   Tç      à?g      à¿z1tmin (%s) must be less than or equal to tmax (%s)rw   zwhen include_tmax=False zVNo samples remain when using tmin=%s and tmax=%s %s(original time bounds are [%s, %s]))	r   ÚinfÚisfiniteri   r   ÚroundrQ   rO   rU   )
ÚtimesZtminZtmaxrf   Úraise_errorZinclude_tmaxZ	orig_tminZ	orig_tmaxr^   Úextrar&   r&   r'   Ú
_time_maskÑ  s6    

ÿþrÄ   c                 C   sþ   |}|}|dkrt j n|}|dkr*t jn|}t  |¡s@| d }t  |¡sR| d }|dkrbtdƒ‚t|ƒ}tt|| ƒƒ| d|  }tt|| ƒƒ| d|  }|r¾||kr¾td||f ƒ‚| |k}|| |kM }|rú| ¡ sútd||| d | d f ƒ‚|S )z!Safely find frequency boundaries.Nr   r+   zsfreq can not be Noner½   z1fmin (%s) must be less than or equal to fmax (%s)z]No frequencies remain when using fmin=%s and fmax=%s (original frequency bounds are [%s, %s]))r   r¾   r¿   rO   ri   r   rÀ   rU   )Zfreqsrf   ZfminZfmaxrÂ   Z	orig_fminZ	orig_fmaxr^   r&   r&   r'   Ú
_freq_maskò  s0    

ÿþrÅ   c                 C   s&  ddl m} ddlm} ddlm} | s2tdƒ‚nt| ƒdkrFtdƒ t	| d ƒ}t
| d ||fd	ƒ | D ]}t
||d	d
ƒ qjdd„ | D ƒ} t| d |ƒrº|r¬dd„ | D ƒ} ddl m} nddlm} |rôtdd„ | D ƒƒ}	|	rô| D ]}| |	¡ qä|| dd� || dd�}
t| ƒ|
_d|
j |
_|
S )a’  Make grand average of a list of Evoked or AverageTFR data.

    For :class:`mne.Evoked` data, the function interpolates bad channels based
    on the ``interpolate_bads`` parameter. If ``interpolate_bads`` is True,
    the grand average file will contain good channels and the bad channels
    interpolated from the good MEG/EEG channels.
    For :class:`mne.time_frequency.AverageTFR` data, the function takes the
    subset of channels not marked as bad in any of the instances.

    The ``grand_average.nave`` attribute will be equal to the number
    of evoked datasets used to calculate the grand average.

    .. note:: A grand average evoked should not be used for source
              localization.

    Parameters
    ----------
    all_inst : list of Evoked or AverageTFR
        The evoked datasets.
    interpolate_bads : bool
        If True, bad MEG and EEG channels are interpolated. Ignored for
        AverageTFR.
    drop_bads : bool
        If True, drop all bad channels marked as bad in any data set.
        If neither interpolate_bads nor drop_bads is True, in the output file,
        every channel marked as bad in at least one of the input files will be
        marked as bad, but no interpolation or dropping will be performed.

    Returns
    -------
    grand_average : Evoked | AverageTFR
        The grand average data. Same type as input.

    Notes
    -----
    .. versionadded:: 0.11.0
    r   rs   )Ú
AverageTFR)Úequalize_channelsz3Please pass a list of Evoked or AverageTFR objects.r   z8Only a single dataset was passed to mne.grand_average().r   zAll elementszof the same typec                 S   s   g | ]}|  ¡ ‘qS r&   ©Úcopy©r,   rz   r&   r&   r'   rŠ   D  s     z!grand_average.<locals>.<listcomp>c                 S   s*   g | ]"}t |jd  ƒdkr"| ¡ n|‘qS )rg   r   )r   rj   Úinterpolate_badsrÊ   r&   r&   r'   rŠ   I  s   ÿ)Úcombine_evoked)Úcombine_tfrc                 S   s    h | ]}|j d  D ]}|’qqS )rg   )rj   )r,   rz   Úbr&   r&   r'   Ú	<setcomp>P  s       z grand_average.<locals>.<setcomp>FrÈ   Úequal)ÚweightszGrand average (n = %d))Zevokedrt   Ztime_frequencyrÆ   Zchannels.channelsrÇ   rO   r   r   Útyper   rV   rÌ   rx   rÍ   rš   Zdrop_channelsZnaveÚcomment)Zall_instrË   Z	drop_badsrt   rÆ   rÇ   Z	inst_typerz   Úcombinerg   Úgrand_averager&   r&   r'   rÕ     s:    '
ÿ
rÕ   c                   @   s   e Zd Zdd„ Zdd„ ZdS )Ú_HashableNdarrayc                 C   s   t | ƒS r4   )Úobject_hash©Úselfr&   r&   r'   Ú__hash__`  s    z_HashableNdarray.__hash__c                 C   s   t S r4   )r£   )rÙ   Úotherr&   r&   r'   Ú__eq__c  s    z_HashableNdarray.__eq__N)Ú__name__Ú
__module__Ú__qualname__rÚ   rÜ   r&   r&   r&   r'   rÖ   _  s   rÖ   c                 C   s
   |   t¡S r4   )ÚviewrÖ   )rZ   r&   r&   r'   Ú_hashable_ndarrayg  s    rá   c                 C   s  ddl m} |dkrt ¡ }t| dƒrTt| ƒ}|D ]}t||ƒ t| | |ƒ q2�n¢t| tƒrl| 	| ¡ �nŠt| t
tttdƒfƒr²| 	t
t| ƒƒ d¡¡ | 	t
| ƒ d¡¡ �nDt| tjtjtjfƒ�rt | ¡} | 	t
| jƒ d¡¡ | 	t
| jƒ d¡¡ | 	|  ¡ ¡ næt| tƒ�r*tt| ƒƒ nÌ| | ¡�r¦| 	t
t| ƒƒ d¡¡ t| |j|jfƒ�sttdt| ƒ› �ƒ‚| 	| j ¡ ¡ | 	| j ¡ ¡ | 	| j ¡ ¡ nPt| dƒ�râ| 	t
t| ƒƒ d¡¡ | D ]}t||ƒ �qÎntdt| ƒ| f ƒ‚t|  ¡ d	ƒS )
a   Hash a reasonable python object.

    Parameters
    ----------
    x : object
        Object to hash. Can be anything comprised of nested versions of:
        {dict, list, tuple, ndarray, str, bytes, float, int, None}.
    h : hashlib HASH object | None
        Optional, object to add the hash to. None creates an MD5 hash.

    Returns
    -------
    digest : int
        The digest resulting from the hash.
    r   ©ÚsparseNÚkeyszutf-8zUnsupported sparse type Ú__len__úunsupported type: %s (%s)é   )!Úscipyrã   r¨   r¥   ÚhasattrÚ
_sort_keysr×   rV   Úbytesr¢   r¡   ri   r   rÒ   Úencoder   r“   ÚnumberZbool_rW   r1   ÚdtypeÚtobytesr   Ú_dt_to_stampÚissparseZ
csr_matrixZ
csc_matrixrl   rB   ÚindicesÚindptrr«   )rZ   Úhrã   rä   ÚkeyÚxxr&   r&   r'   r×   k  sB    



r×   c                    s–  ddl m} ˆ dkrtƒ ‰ t| ƒ}|ˆ kr.dS t| ttttt	dƒt
fƒrVt | ¡}�n4t| tjƒr–t t g ¡¡}| jdksŠt| jƒˆ kr”|| j7 }nôt| tjƒrª| j}nàt| tƒrît | ¡}|  ¡ D ]$\}}|t|ˆ ƒ7 }|t|ˆ ƒ7 }qÆnœt| ttfƒ�r t | ¡t‡ fdd„| D ƒƒ }njt| tƒ�r<tt| ƒˆ ƒ}nN| | ¡�sT| | ¡�rvtdd„ | | j| j| jfD ƒƒ}ntdt	| ƒ| f ƒ‚|ˆ |< |S )a“  Estimate the size of a reasonable python object.

    Parameters
    ----------
    x : object
        Object to approximate the size of.
        Can be anything comprised of nested versions of:
        {dict, list, tuple, ndarray, str, bytes, float, int, None}.
    memo : dict | None
        The memodict.

    Returns
    -------
    size : int
        The estimated size in bytes of the object.
    r   râ   Nc                 3   s   | ]}t |ˆ ƒV  qd S r4   )Úobject_size©r,   rö   ©Úmemor&   r'   r0   Ë  s     zobject_size.<locals>.<genexpr>c                 s   s   | ]}t  |¡V  qd S r4   )ÚsysÚ	getsizeofrø   r&   r&   r'   r0   Ï  s   ÿræ   ) rè   rã   rŒ   ÚidrV   rë   r¡   r   ri   rÒ   r
   rû   rü   r   r“   r|   ÚbaseÚnbytesÚgenericrŽ   r÷   rš   ÚtuplerA   r   rð   Zisspmatrix_cscZisspmatrix_csrrB   rò   ró   rl   )rZ   rú   rã   Zid_r8   rõ   Úvaluer&   rù   r'   r÷   ¢  s<    

"ÿ
r÷   c                    s6   t |  ¡ ƒ‰ t dd„ ˆ D ƒ¡}‡ fdd„|D ƒ‰ ˆ S )zSort and return keys of dict.c                 S   s   g | ]}t |ƒ‘qS r&   )r¡   r˜   r&   r&   r'   rŠ   Ú  s     z_sort_keys.<locals>.<listcomp>c                    s   g | ]}ˆ | ‘qS r&   r&   )r,   Úii©rä   r&   r'   rŠ   Û  s     )rš   rä   r   rƒ   )rZ   r!   r&   r  r'   rê   ×  s    rê   c                 C   s@   z$|rt jj}nt jj}|| |ƒ W n tk
r:   Y dS X dS )NFT)r   ZtestingZassert_allcloseZassert_array_equalrQ   )ÚarÎ   rP   Úfuncr&   r&   r'   Ú_array_equal_nanß  s    
r  rw   ©rP   c             	   C   s2  ddl m} d}t| ƒt|ƒkr^ttfD ]}t| |ƒr(t||ƒr( q^q(|dt| ƒt|ƒf  S t| tƒrðt| ƒ}t|ƒ}t|ƒt|ƒ }	t	|	ƒr ||d|	  7 }|D ]F}
|
|krÂ||d|
  7 }q¤|t
| |
 ||
 |dt|
ƒ  |d�7 }q¤�n>t| ttfƒ�rnt	| ƒt	|ƒk�r0||d	t	| ƒt	|ƒf  7 }n:tt| |ƒƒD ]*\}\}}|t
|||d|  |d
�7 }�q>�nÀt| tƒ�r t| ||ƒ�s.||d| |f  7 }�nŽt| ttttjfƒ�rØ| |k�r.||d| |f  7 }�nV| dk�r |dk	�r.||d|  7 }�n.t| tjƒ�r,t| ||ƒ�s.||d 7 }�nt| ttfƒ�r\|  ¡ | ¡ k�r.||d 7 }nÒt| tƒ�rˆ| |  ¡ dk�r.||d 7 }n¦| | ¡�rì|j| jk�r¼||d| j|jf  7 }n.| | }| ¡  |jdk�r.||d|j  7 }nBt| dƒ�r|t
|   ¡ |  ¡ ||d
�7 }nt!|dt| ƒ| f  ƒ‚|S )aä  Compute all differences between two python variables.

    Parameters
    ----------
    a : object
        Currently supported: dict, list, tuple, ndarray, int, str, bytes,
        float, StringIO, BytesIO.
    b : object
        Must be same type as ``a``.
    pre : str
        String to prepend to each line.
    allclose : bool
        If True (default False), use assert_allclose.

    Returns
    -------
    diffs : str
        A string representation of the differences.
    r   râ   rw   z type mismatch (%s, %s)
z left missing keys %s
z right missing key %s
z[%s])ÚprerP   z length mismatch (%s, %s)
r  z value mismatch (%s, %s)
Nz! left is None, right is not (%s)
z array mismatch
z StringIO mismatch
z datetime mismatch
z/ sparse matrix a and b shape mismatch(%s vs %s)z, sparse matrix a and b differ on %s elementsÚ__getstate__z: unsupported type %s (%s))"rè   rã   rÒ   r   ri   rV   rŒ   rê   Úsetr   Úobject_diffÚreprrš   r  Ú	enumerater2   r  r¡   rë   r   r   r“   r   r   Úgetvaluer   Útotal_secondsZ
isspmatrixr1   Zeliminate_zerosZnnzré   r
  rl   )r  rÎ   r	  rP   rã   ÚoutÚsubZk1sZk2sÚm1rõ   r  Zxx1Zxx2Úcr&   r&   r'   r  ë  s‚    
þ  
 ÿ



ÿ
ÿ
ÿ
r  c                   @   s,   e Zd ZdZd
dd„Zddd„Zdd	„ ZdS )Ú_PCAz#Principal component analysis (PCA).NFc                 C   s   || _ || _d S r4   )Ún_componentsÚwhiten)rÙ   r  r  r&   r&   r'   Ú__init__G  s    z_PCA.__init__c                 C   sb   |  ¡ }|  |¡\}}}|d d …d | j…f }| jrL|t|jd d ƒ9 }n||d | j… 9 }|S )Nr   r   )rÉ   Ú_fitÚn_components_r  r	   r1   )rÙ   r>   r   r\   ÚSÚ_r&   r&   r'   Úfit_transformK  s    z_PCA.fit_transformc                 C   sÔ  | j d krt|jƒ}n| j }|j\}}|dkr@||k r¢tdƒ‚nbd|  krZt||ƒkstn td|t||ƒf ƒ‚n.|dkr¢t|tjtjfƒs¢td|t	|ƒf ƒ‚tj
|dd�| _|| j8 }t|dd	�\}}}t||ƒ\}}|}|d
 |d  }	|	 ¡ }
|	|
 }| ¡ }|dk�r t|	||ƒ}n4d|  k �r8dk �rTn nt|ƒ}t ||¡d }|t||ƒk �rx|	|d …  
¡ | _nd| _|| | _| _|d |… | _|| _|	d |… | _|d |… | _|d |… | _|||fS )NZmlez?n_components='mle' is only supported if n_samples >= n_featuresr   zZn_components=%r must be between 0 and min(n_samples, n_features)=%r with svd_solver='full'r   zSn_components=%r must be of type int when greater than or equal to 1, was of type=%rr)   F)Zfull_matricesr   r@   r™   )r  Úminr1   rO   rV   ÚnumbersÚIntegralr   ÚintegerrÒ   rR   Zmean_r   r   rA   rÉ   r   r   ÚsearchsortedZnoise_variance_Z
n_samples_Zn_features_Úcomponents_r  Úexplained_variance_Úexplained_variance_ratio_Úsingular_values_)rÙ   r>   r  r„   Z
n_featuresr\   r  ÚVr#  r$  Z	total_varr%  r&  Zratio_cumsumr&   r&   r'   r  Y  sT    


ý
ý


ÿ
ÿz	_PCA._fit)NF)N)rÝ   rÞ   rß   Ú__doc__r  r  r  r&   r&   r&   r'   r  A  s   

r  c                 C   s¨   | j tkr| jdkst‚|  t¡} t | ¡}t |dk¡d d }| d rZt 	dg|g¡}t |dk ¡d d }| d rŒt 	|t
| ƒgg¡}t
|ƒt
|ƒks t‚||fS )z6Group boolean mask into contiguous onset:offset pairs.r   r   r+   )rî   ÚboolrN   rQ   Zastyper   r   ÚdiffÚwhererœ   r   )r^   r*  ZonsetsÚoffsetsr&   r&   r'   Ú_mask_to_onsets_offsets›  s    

r-  c              
   C   s4   d}t dddddddtjd�}t| | d�}|| S )a¨  Convert Julian integer to a datetime object.

    Parameters
    ----------
    jd : int
        Julian date - number of days since julian day 0
        Julian day number 0 assigned to the day starting at
        noon on January 1, 4713 BC, proleptic Julian calendar
        November 24, 4714 BC, in the proleptic Gregorian calendar

    Returns
    -------
    jd_date : datetime
        Datetime representation of jd

    éŒ=% é²  r   é   r   ©Útzinfo)Údays)r   r   Úutcr   )ÚjdÚjd_t0Údatetime_t0Údtr&   r&   r'   Ú_julian_to_dtª  s    r9  c              
   C   s0   d}t dddddddtjd�}| | }||j S )a¥  Convert datetime object to a Julian integer.

    Parameters
    ----------
    jd_date : datetime

    Returns
    -------
    jd : float
        Julian date corresponding to jd_date
        - number of days since julian day 0
        Julian day number 0 assigned to the day starting at
        noon on January 1, 4713 BC, proleptic Julian calendar
        November 24, 4714 BC, in the proleptic Gregorian calendar

    r.  r/  r   r0  r   r1  )r   r   r4  r3  )Zjd_dater6  r7  r8  r&   r&   r'   Ú_dt_to_julianÄ  s    r:  c                 C   s    t tt| ||dddtjd�ƒƒS )a  Convert calendar date (year, month, day) to a Julian integer.

    Parameters
    ----------
    year : int
        Year as an integer.
    month : int
        Month as an integer.
    day : int
        Day as an integer.

    Returns
    -------
    jd: int
        Julian date.
    r0  r   r1  )r   r:  r   r   r4  )ÚyearÚmonthÚdayr&   r&   r'   Ú_cal_to_julianÞ  s    ÿr>  c                 C   s   t | ƒ}|j|j|jfS )a$  Convert calendar date (year, month, day) to a Julian integer.

    Parameters
    ----------
    jd: int, float
        Julian date.

    Returns
    -------
    year : int
        Year as an integer.
    month : int
        Month as an integer.
    day : int
        Day as an integer.

    )r9  r;  r<  r=  )r5  Ztmp_dater&   r&   r'   Ú_julian_to_caló  s    r?  c                 C   s2   t | tƒr | jd ks | jtjk	r.td| f ƒ‚d S )Nz'Date must be datetime object in UTC: %r)rV   r   r2  r   r4  rO   )r8  r&   r&   r'   Ú	_check_dt	  s    
ÿr@  c                 C   s   t | ƒ t|  ¡ d ƒ| jfS )z)Convert a datetime object to a timestamp.r   )r@  r   Ú	timestampÚmicrosecond)Zinp_dater&   r&   r'   rð     s    rð   c                 C   sH   dd„ | D ƒ}t |ƒdkr$| d¡ tjdtjd�t|d |d d� S )z=Convert timestamp to datetime object in Windows-friendly way.c                 S   s   g | ]}t |ƒ‘qS r&   )r   )r,   r]   r&   r&   r'   rŠ     s     z _stamp_to_dt.<locals>.<listcomp>r   r   )Útz)ÚsecondsÚmicroseconds)r   rk   r   Úfromtimestampr   r4  r   )Z	utc_stampZstampr&   r&   r'   Ú_stamp_to_dt  s    
ÿrG  c                   @   s0   e Zd ZdZdd„ Zdd„ Zdd„ Zdd	„ Zd
S )Ú_ReuseCyclezÕCycle over a variable, preferring to reuse earlier indices.

    Requires the values in ``x`` to be hashable and unique. This holds
    nicely for matplotlib's color cycle, which gives HTML hex color strings.
    c                 C   s*   t ƒ | _tƒ | _t|ƒdks t‚|| _d S ©Nr   )rš   rò   rŒ   Úpoppedr   rQ   rZ   )rÙ   rZ   r&   r&   r'   r  &  s    z_ReuseCycle.__init__c                 c   s   |   ¡ V  q d S r4   )Ú__next__rØ   r&   r&   r'   Ú__iter__,  s    z_ReuseCycle.__iter__c                 C   sX   t | jƒs&ttt | jƒƒƒ| _tƒ | _| j d¡}| j| }|| jksJt‚|| j|< |S rI  )	r   rò   rš   r   rZ   rŒ   rJ  ÚpoprQ   )rÙ   r!   Úvalr&   r&   r'   rK  0  s    


z_ReuseCycle.__next__c                 C   sT   z| j  |¡}W n" tk
r2   td|f ƒ Y nX t | j|¡}| j ||¡ d S )NzCould not find value: %s)rJ  rM  ÚKeyErrorr   r   r"  rò   Úinsert)rÙ   rN  r!   Úlocr&   r&   r'   Úrestore:  s    z_ReuseCycle.restoreN)rÝ   rÞ   rß   r(  r  rL  rK  rR  r&   r&   r&   r'   rH    s
   
rH  c                 C   s   t j| t jd�}|| }|S )N)rî   )r   r   Úfloat64)r    ÚdrZ   r&   r&   r'   Ú_arange_div_fallbackD  s    rU  )Zfastmathc                 C   s,   t  | t j¡}t| ƒD ]}|| ||< q|S r4   )r   ÚemptyrS  r   )r    rT  r  r%   r&   r&   r'   Ú_arange_divK  s    rW  c                    s   ‡ fdd„}|S )Nc                    s2   t ˆ ƒ‰tƒ  ‰tˆ< ˆtˆ< ‡ ‡‡fdd„}|S )Nc                     sZ   t | ƒ}|ˆkrˆ |¡}nˆ | Ž }|ˆ|< tˆƒtˆ krVˆD ]}ˆ |¡  q,q@q,|S r4   )r×   rM  r   Ú_LRU_CACHE_MAXSIZES)ÚargsÚhash_Zthis_valrõ   ©ÚfunZfun_hashZ
this_cacher&   r'   Ú	cache_fun_  s    
z1_custom_lru_cache.<locals>.dec.<locals>.cache_fun)ÚhashrŒ   Ú_LRU_CACHESrX  )r\  r]  ©Úmaxsizer[  r'   ÚdecZ  s
    z_custom_lru_cache.<locals>.decr&   )ra  rb  r&   r`  r'   Ú_custom_lru_cacheY  s    rc  )F)r   r   )r   rD   rE   )N)N)r¤   r¥   )Nr   )NNNTT)NNT)TT)N)N)F)rw   )Xr(  r¨   r  rL   r±   rµ   rû   Ú
contextlibr   r   r   r   Úior   r   Úmathr   r	   Úpathlibr
   Únumpyr   Z_loggingr   r   r   Úcheckr   r   r   Úfixesr   r   r   r   r   r   Údocsr   r(   r3   r9   r?   rC   ra   rc   rp   r{   r�   r†   r‘   r”   r–   r—   rž   rŸ   r‹   r®   r·   r¼   rÄ   rÅ   rÕ   r“   rÖ   rá   r×   r÷   rê   r  r  Úobjectr  r-  r9  r:  r>  r?  r@  rð   rG  rH  rU  rW  rŒ   r_  rX  rc  r&   r&   r&   r'   Ú<module>   sŒ    


k"%
	

  ÿ
!

Q
7
5
VZ
%