import numpy as np
from cythonnestednumpy import HashSort
img1 = np.full([900,1800,3],255,dtype=np.uint8)
img2 = np.full([900,1800,3],255,dtype=np.uint8)
img2[...,0]=0
img3 = np.full([900,1800,3],255,dtype=np.uint8)
img3[...,2]=1
a=np.concatenate([img1,img2,img3])
cyne=HashSort(a,unordered=True)
# 1st column: absolut index (using a.flatten() or a.ravel())
# 2nd - n column: dimension (the more dimensions your array has, the more columns will show up)
# 3rd column: The index in cyne.iterray
# 4th column: 1 is for the first item (unique) found. 0 means that there has been found the same value before.
# 5th column: How many matches
# 6th column: Hashcode
cyne.generate_hash_array(last_dim=None)
resultdata=cyne.sort_by_absolut_index(ascending=True)
# Out[3]:
# array([[ 0, 0, 1, 1, 2497830064280488930],
# [ 30, 1, 0, 2, 2497830064280488930],
# [ 60, 2, 0, 3, 2497830064280488930],
# [ 90, 3, 0, 4, 2497830064280488930],
# ...
# [ 780, 26, 0, 7, -5024405870974420794],
# [ 810, 27, 0, 8, -5024405870974420794],
# [ 840, 28, 0, 9, -5024405870974420794],
# [ 870, 29, 0, 10, -5024405870974420794]], dtype=int64)
cyne.sort_by_absolut_index(ascending=False)
# Out[4]:
# array([[ 870, 29, 0, 10, -5024405870974420794],
# [ 840, 28, 0, 9, -5024405870974420794],
# [ 810, 27, 0, 8, -5024405870974420794],
# [ 780, 26, 0, 7, -5024405870974420794],
# [ 750, 25, 0, 6, -5024405870974420794],
# [ 720, 24, 0, 5, -5024405870974420794],
# ...
# [ 240, 8, 0, 9, 2497830064280488930],
# [ 210, 7, 0, 8, 2497830064280488930],
# [ 180, 6, 0, 7, 2497830064280488930],
# [ 150, 5, 0, 6, 2497830064280488930],
# [ 120, 4, 0, 5, 2497830064280488930],
# [ 90, 3, 0, 4, 2497830064280488930],
# [ 60, 2, 0, 3, 2497830064280488930],
# [ 30, 1, 0, 2, 2497830064280488930],
# [ 0, 0, 1, 1, 2497830064280488930]], dtype=int64)
cyne.get_unique_dims_data()
# Out[3]:
# [array([255, 255, 255], dtype=uint8),
# array([255, 255, 255], dtype=uint8),
# array([255, 255, 255], dtype=uint8)]
cyne.get_unique_dims_data(start_dim=2,end_dim=-1)
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255]],
#
# [[255, 255, 255],
# [255, 255, 255],
# ...
# [ 0, 255, 255]],
#
# [[ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255],
# [ 0, 255, 255]],
cyne.get_all_values(start_dim=0,end_dim=-1)
# ...,
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255]],
# ...,
# [[255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [255, 255, 1]],
# ...,
#allva=cyne.get_unique_dims_values()
cyne.group_equal_values()
# [255, 255, 1]],
# [[255, 255, 1],
# [255, 255, 1],
# [255, 255, 1],
# [ 0, 255, 255],
# [ 0, 255, 255],
# ...
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# [255, 255, 255]],
groupedvalues=cyne.group_equal_values()
byqty=cyne.sort_by_quantity(ascending=False)
cyne.sort_by_hash(ascending=False)
# Out[3]:
# array([[ 270, 9, 0, 10, 2497830064280488930],
# [ 240, 8, 0, 9, 2497830064280488930],
# [ 210, 7, 0, 8, 2497830064280488930],
# [ 180, 6, 0, 7, 2497830064280488930],
# ...
# [ 750, 25, 0, 6, -5024405870974420794],
# [ 720, 24, 0, 5, -5024405870974420794],
# [ 690, 23, 0, 4, -5024405870974420794],
# [ 660, 22, 0, 3, -5024405870974420794],
# [ 630, 21, 0, 2, -5024405870974420794],
# [ 600, 20, 1, 1, -5024405870974420794]], dtype=int64)
class HashSort(builtins.object)
| HashSort(a, unordered=True)
|
| The HashSort class is designed to perform sorting and grouping operations on multi-dimensional NumPy arrays
| using a hash-based algorithm. It utilizes the xxhash https://xxhash.com/ function (Cython! Not Python!) for efficient hash computation.
|
| Parameters:
| - a (numpy.ndarray): The input multi-dimensional NumPy array.
| - unordered (bool): If True, will create the index array with multi processing
|
| Methods:
| - generate_hash_array(last_dim=None): Generates a hash array based on the provided array and optional last_dim.
| - sort_by_absolut_index(ascending=True): Sorts the hash array by absolute index in ascending or descending order.
| - get_unique_dims_data(start_dim=0, end_dim=-1): Returns unique dimensions data based on hash array.
| - get_all_values(start_dim=0, end_dim=-1): Returns all values based on hash array and specified dimensions.
| - group_equal_values(start_dim=0, end_dim=-1): Groups equal values based on hash array and specified dimensions.
| - sort_by_hash(ascending=False): Sorts the hash array by hash values in ascending or descending order.
| - sort_by_quantity(ascending=False): Sorts the hash array by quantity values in ascending or descending order.
|
| Methods defined here:
|
| __init__(self, a, unordered=True)
| Initializes a new instance of the HashSort class.
|
| Parameters:
| - a (numpy.ndarray): The input multi-dimensional NumPy array.
| - unordered (bool): If True, will create the index array with multi processing
|
| generate_hash_array(self, last_dim=None)
| Generates a hash array based on the provided array and optional last_dim.
|
| Parameters:
| - last_dim (int, optional): The last dimension to consider. If None, uses the last dimension of the array.
|
| Returns:
| - HashSort: The current HashSort instance.
|
| get_all_values(self, start_dim=0, end_dim=-1)
| Returns all values based on hash array and specified dimensions.
|
| Parameters:
| - start_dim (int, optional): The starting dimension index to consider.
| - end_dim (int, optional): The ending dimension index to consider.
|
| Returns:
| - numpy.ndarray: All values based on the specified dimensions.
|
| get_shape_array(self, last_dim)
| Returns the shape array based on the provided last dimension.
|
| Parameters:
| - last_dim (int): The last dimension to consider.
|
| Returns:
| - Tuple: A tuple containing the shape array and the product of array shape elements from last_dim onwards.
|
| get_unique_dims_data(self, start_dim=0, end_dim=-1)
| Returns unique dimensions data based on hash array.
|
| Parameters:
| - start_dim (int, optional): The starting dimension index to consider.
| - end_dim (int, optional): The ending dimension index to consider.
|
| Returns:
| - List[numpy.ndarray]: A list containing unique dimensions data.
|
| group_equal_values(self, start_dim=0, end_dim=-1)
| Groups equal values based on hash array and specified dimensions.
|
| Parameters:
| - start_dim (int, optional): The starting dimension index to consider.
| - end_dim (int, optional): The ending dimension index to consider.
|
| Returns:
| - numpy.ndarray: Grouped values based on the specified dimensions.
|
| sort_by_absolut_index(self, ascending=True)
| Sorts the hash array by absolute index (np.flatten()/np.ravel() in ascending or descending order.
|
| Parameters:
| - ascending (bool, optional): If True, sorts in ascending order; otherwise, sorts in descending order.
|
| Returns:
| - numpy.ndarray: The sorted hash array.
|
| sort_by_hash(self, ascending=False)
| Sorts the hash array by hash values in ascending or descending order.
|
| Parameters:
| - ascending (bool, optional): If True, sorts in ascending order; otherwise, sorts in descending order.
|
| Returns:
| - numpy.ndarray: The sorted hash array.
|
| sort_by_quantity(self, ascending=False)
| Sorts the hash array by quantity values in ascending or descending order.
|
| Parameters:
| - ascending (bool, optional): If True, sorts in ascending order; otherwise, sorts in descending order.
|
| Returns:
| - numpy.ndarray: The sorted hash array.