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Editing: extras.cpython-311.pyc
� ���c�� � �< � d Z g d�ZddlZddlZddlmZ ddlmZmZm Z m Z mZmZm Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZ ddlZddlmZm Z ddl!m"Z" dd l#m$Z$ dd l%m&Z& ddl'm(Z( d� Z)dNd �Z*e+fd�Z,d� Z- G d� d� � Z. G d� de.� � Z/ G d� de.� � Z0 G d� de.� � Z1 G d� de.� � Z2 e2d� � Z3 e2d� � Z4 e2d� � Z5 e0d� � xZ6Z7 e0d� � Z8 e0d� � Z9 e0d � � Z: e0d!� � Z; e/d"� � Z< e/d#� � Z=d$� Z>d%� Z?ej? j e?_ d&� Z@e@j �Kej@ j dej@ j �A d'� � � �B � � d(z e@_ dOejC d*�d+�ZDdPd,�ZEdOd-�ZFdNd.�ZGdNd/�ZHd0� ZId1� ZJejC fd2�ZKejC fd3�ZLdQd4�ZMdRd5�ZNdSd6�ZOdSd7�ZPdRd8�ZQdRd9�ZRd:� ZSdSd;�ZTdTd=�ZUdUd>�ZVdd<ejC d<ejC fd?�ZW G d@� dAe(� � ZX G dB� dCeX� � ZY eY� � ZZdVdD�Z[dE� Z\dNdF�Z]dG� Z^dNdH�Z_dI� Z`dJ� ZadK� ZbdNdL�Zc ejd ejc j ecj � � ec_ dWdM�Ze ejd eje j eej � � ee_ dS )Xz� Masked arrays add-ons. A collection of utilities for `numpy.ma`. :author: Pierre Gerard-Marchant :contact: pierregm_at_uga_dot_edu :version: $Id: extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $ ).�apply_along_axis�apply_over_axes� atleast_1d� atleast_2d� atleast_3d�average�clump_masked�clump_unmasked�column_stack� compress_cols�compress_nd�compress_rowcols� compress_rows�count_masked�corrcoef�cov�diagflat�dot�dstack�ediff1d�flatnotmasked_contiguous�flatnotmasked_edges�hsplit�hstack�isin�in1d�intersect1d� mask_cols�mask_rowcols� mask_rows� masked_all�masked_all_like�median�mr_�ndenumerate�notmasked_contiguous�notmasked_edges�polyfit� row_stack� setdiff1d�setxor1d�stack�unique�union1d�vander�vstack� N� )�core)�MaskedArray�MAError�add�array�asarray�concatenate�filled�count�getmask�getmaskarray�make_mask_descr�masked�masked_array�mask_or�nomask�ones�sort�zeros�getdata�get_masked_subclassr r )�ndarrayr6 )�normalize_axis_index)�normalize_axis_tuple)�_ureduce)�AxisConcatenatorc �F � t | t t t f� � S )z6 Is seq a sequence (ndarray, list or tuple)? )� isinstancerG �tuple�list)�seqs �1/usr/lib/python3/dist-packages/numpy/ma/extras.py� issequencerR * s � � �c�G�U�D�1�2�2�2� c �J � t | � � }|� |� � S )a� Count the number of masked elements along the given axis. Parameters ---------- arr : array_like An array with (possibly) masked elements. axis : int, optional Axis along which to count. If None (default), a flattened version of the array is used. Returns ------- count : int, ndarray The total number of masked elements (axis=None) or the number of masked elements along each slice of the given axis. See Also -------- MaskedArray.count : Count non-masked elements. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(9).reshape((3,3)) >>> a = ma.array(a) >>> a[1, 0] = ma.masked >>> a[1, 2] = ma.masked >>> a[2, 1] = ma.masked >>> a masked_array( data=[[0, 1, 2], [--, 4, --], [6, --, 8]], mask=[[False, False, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> ma.count_masked(a) 3 When the `axis` keyword is used an array is returned. >>> ma.count_masked(a, axis=0) array([1, 1, 1]) >>> ma.count_masked(a, axis=1) array([0, 2, 1]) )r<