https://github.com/hansalemaos/cythonflatiter
Locates values in NumPy Arrays with Cython
https://github.com/hansalemaos/cythonflatiter
cython isin iter numpy
Last synced: 17 days ago
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Locates values in NumPy Arrays with Cython
- Host: GitHub
- URL: https://github.com/hansalemaos/cythonflatiter
- Owner: hansalemaos
- License: mit
- Created: 2023-12-09T00:30:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-09T00:30:27.000Z (over 1 year ago)
- Last Synced: 2025-04-01T13:04:08.027Z (25 days ago)
- Topics: cython, isin, iter, numpy
- Language: Python
- Homepage: https://pypi.org/project/cythonflatiter
- Size: 24.4 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
- License: LICENSE
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README
# Locates values in NumPy Arrays with Cython
## pip install cythonflatiter
### Tested against Windows / Python 3.11 / Anaconda
## Cython (and a C/C++ compiler) must be installed
FlatIterArray is a utility class for efficiently searching multi-dimensional array data.
```python
| __init__(self, a, dtype=, unordered=True)
| Initializes a FlatIterArray instance.
|
| Parameters:
| - a (numpy.ndarray): The input array.
| - dtype (numpy.dtype, optional): The data type of the index array that will be created. Defaults to np.int64. (It's better not to change that, because it corresponds to cython.Py_ssize_t)
| - unordered (bool, optional): Flag indicating whether to use unordered iterations. Defaults to True. (index array will be created using multiprocessing)
|
| get_flat_pointer_array_from_orig_data(self)
| Returns a flat pointer array from the original data.
| If you change data here, it changes also in the original array
|
| Returns:
| - numpy.ndarray: Flat pointer array.
|
| search_multiple_values_in_array(self, values)
| Searches for multiple values in the array and returns indices and values.
|
| Parameters:
| - values (list or numpy.ndarray): List of values to search for.
|
| Returns:
| - tuple: Array of indices and array of found values.
|
| search_single_value_in_array(self, value)
| Searches for a single value in the array and returns indices.
|
| Parameters:
| - value: The value to search for.
|
| Returns:
| - numpy.ndarray: Array of indices.
|
| sequence_is_in_dimension(self, seq, last_dim)
| Checks if a sequence is in a dimension
|
| Parameters:
| - seq (list or numpy.ndarray): List of values representing the sequence.
| - last_dim: The dimension to search in.
|
| Returns:
| - numpy.ndarray: Array of indices.
|
| update_iterarray(self, dtype=, unordered=True)
| Updates the iterray attribute with new parameters.
|
| Parameters:
| - dtype (numpy.dtype, optional): The data type of the index array that will be created. Defaults to np.int64.
| - unordered (bool, optional): Flag indicating whether to use unordered iterations. Defaults to True. (index array will be created using multiprocessing)
|
| value_is_in_dimension(self, value, last_dim)
| Checks if a value is in a dimension
|
| Parameters:
| - value: The value to search for.
| - last_dim: The dimension to search in.
|
| Returns:
| - numpy.ndarray: Array of indices.
import numpy as np
import cv2
from cythonflatiter import FlatIterArray
data = cv2.imread(r"C:\Users\hansc\Desktop\2023-08-29xx16_07_30-Window.png")
f = FlatIterArray(data, dtype=np.int64, unordered=True)
results255inarray = f.search_single_value_in_array(255)
# results255inarray
# Out[6]:
# array([[195330, 34, 0, 0],
# [201075, 35, 0, 0],
# [206820, 36, 0, 0],
# ...,
# [488324, 84, 1914, 2],
# [494069, 85, 1914, 2],
# [499814, 86, 1914, 2]], dtype=int64)
# print(data[84,1914,2])
# print(data[34,0,0])
# 255
# 255
indices, found_values = f.search_multiple_values_in_array(values=[255, 11, 0])
# indices,found_values
# Out[12]:
# (array([[195330, 34, 0, 0],
# [201075, 35, 0, 0],
# [206820, 36, 0, 0],
# ...,
# [488324, 84, 1914, 2],
# [494069, 85, 1914, 2],
# [499814, 86, 1914, 2]], dtype=int64),
# array([255, 255, 255, ..., 255, 255, 255], dtype=uint8))
concat_values = np.hstack([indices, found_values.reshape((-1, 1))])
# print(concat_values)
# [[195330 34 0 0 255]
# [201075 35 0 0 255]
# [206820 36 0 0 255]
# ...
# [488324 84 1914 2 255]
# [494069 85 1914 2 255]
# [499814 86 1914 2 255]]
# print(data[85,1914,2])
# print(data[36,0,0])
lastdimwith255 = f.value_is_in_dimension(255, 3)
# lastdimwith255
# Out[31]:
# array([[195330, 34, 0],
# [201075, 35, 0],
# [206820, 36, 0],
# ...,
# [488322, 84, 1914],
# [494067, 85, 1914],
# [499812, 86, 1914]], dtype=int64)
# print(data[86,1914])
# print(data[34,0])
# [255 255 255]
# [255 255 255]
penultimatedimwith255255 = f.sequence_is_in_dimension([255, 255, 255], 2)
# penultimatedimwith255255
# Out[38]:
# array([[ 0, 0],
# [ 5745, 1],
# [ 11490, 2],
# ...,
# [5538180, 964],
# [5543925, 965],
# [5549670, 966]], dtype=int64)
# data[964]
# Out[40]:
# array([[255, 255, 255],
# [255, 255, 255],
# [255, 255, 255],
# ...,
# [ 43, 43, 43],
# [ 43, 43, 43],
# [ 43, 43, 43]], dtype=uint8)
ultimatedimwith255255255 = f.sequence_is_in_dimension([255, 255, 255], 3)
# ultimatedimwith255255255
# Out[42]:
# array([[195330, 34, 0],
# [201075, 35, 0],
# [206820, 36, 0],
# ...,
# [488322, 84, 1914],
# [494067, 85, 1914],
# [499812, 86, 1914]], dtype=int64)
# [750 433 0]
# [751 433 0]
# [752 433 0]
# [753 433 0]
# [754 433 0]
# [755 433 0]
# [756 433 0]
# [757 433 0]
# [758 433 0]
```