i((ddlZddZGddZy)Nc|ddlm}tj|jtj s t d|j}|jd}|"tj||j}n+|j|k7rtd|d|jd |j}d |_|j|jd }|j|jd }||||||S#t$r'td |jd |jdwxYw)aMap values from input array from input_vals to output_vals. Parameters ---------- input_arr : array of int, shape (M[, ...]) The input label image. input_vals : array of int, shape (K,) The values to map from. output_vals : array, shape (K,) The values to map to. out : array, same shape as `input_arr` The output array. Will be created if not provided. It should have the same dtype as `output_vals`. Returns ------- out : array, same shape as `input_arr` The array of mapped values. Notes ----- If `input_arr` contains values that aren't covered by `input_vals`, they are set to 0. Examples -------- >>> import numpy as np >>> import skimage as ski >>> ski.util.map_array( ... input_arr=np.array([[0, 2, 2, 0], [3, 4, 5, 0]]), ... input_vals=np.array([1, 2, 3, 4, 6]), ... output_vals=np.array([6, 7, 8, 9, 10]), ... ) array([[0, 7, 7, 0], [8, 9, 0, 0]]) ) _map_arrayz7The dtype of an array to be remapped should be integer.dtypezbIf out array is provided, it should have the same shape as the input array. Input array has shape z", provided output array has shape .)rz`If out array is provided, it should be either contiguous or 1-dimensional. Got array with shape z and strides Fcopy)_remaprnp issubdtyperinteger TypeErrorshapereshapeempty ValueErrorviewAttributeErrorstridesastype) input_arr input_vals output_valsoutr orig_shapeout_views |/var/www/ITT-ServiceAPI-Services/ITT-Document-Scan/mrzscanner/mrzenv/lib/python3.12/site-packages/skimage/util/_map_array.py map_arrayr s9J# =="** 5QRRJ!!"%I {hhz):):; j  66@\B&&)ii[ 3   88:""9??"?J$$SYYU$;Ky(J < J   669ii[A{{m1 &   s -D 0D;cReZdZdZdZdZd dZedZdZ dZ d Z d Z d Z y) ArrayMapuClass designed to mimic mapping by NumPy array indexing. This class is designed to replicate the use of NumPy arrays for mapping values with indexing: >>> values = np.array([0.25, 0.5, 1.0]) >>> indices = np.array([[0, 0, 1], [2, 2, 1]]) >>> values[indices] array([[0.25, 0.25, 0.5 ], [1. , 1. , 0.5 ]]) The issue with this indexing is that you need a very large ``values`` array if the values in the ``indices`` array are large. >>> values = np.array([0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0]) >>> indices = np.array([[0, 0, 10], [0, 10, 10]]) >>> values[indices] array([[0.25, 0.25, 1. ], [0.25, 1. , 1. ]]) Using this class, the approach is similar, but there is no need to create a large values array: >>> in_indices = np.array([0, 10]) >>> out_values = np.array([0.25, 1.0]) >>> values = ArrayMap(in_indices, out_values) >>> values ArrayMap(array([ 0, 10]), array([0.25, 1. ])) >>> print(values) ArrayMap: 0 → 0.25 10 → 1.0 >>> indices = np.array([[0, 0, 10], [0, 10, 10]]) >>> values[indices] array([[0.25, 0.25, 1. ], [0.25, 1. , 1. ]]) Parameters ---------- in_values : array of int, shape (K,) The source values from which to map. out_values : array, shape (K,) The destination values from which to map. c<||_||_d|_d|_y)N) in_values out_values_max_str_lines_array)selfr%r&s r__init__zArrayMap.__init__zs"$ cFtj|jdzS)zrcsEP{6{6r+