hesseflux.argsort¶
argsort : argmax, argmin and argsort for array_like and Python iterables.
This module was written by Matthias Cuntz while at Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany, and continued while at Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Nancy, France.
Copyright (c) 2014-2020 Matthias Cuntz - mc (at) macu (dot) de Released under the MIT License; see LICENSE file for details.
- Written Dec 2014 by Matthias Cuntz (mc (at) macu (dot) de)
- Added argmin, argmax, Jul 2019, Matthias Cuntz
- Using numpy docstring format, extending examples from numpy docstrings, May 2020, Matthias Cuntz
The following functions are provided
argmax(a, *args, **kwargs) |
Wrapper for numpy.argmax, numpy.ma.argmax, and using max for Python iterables. |
argmin(a, *args, **kwargs) |
Wrapper for numpy.argmin, numpy.ma.argmin, and using min for Python iterables. |
argsort(a, *args, **kwargs) |
Wrapper for numpy.argsort, numpy.ma.argsort, and using sorted for Python iterables. |
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argmax(a, *args, **kwargs)[source]¶ Wrapper for numpy.argmax, numpy.ma.argmax, and using max for Python iterables.
Passes all keywords directly to the individual routines, i.e.
numpy.argmax(a, axis=None, out=None)
numpy.ma.argmax(self, axis=None, fill_value=None, out=None)
No keyword will be passed to max routine for Python iterables.
Parameters: - a (array_like) – input array, masked array, or Python iterable
- *args (optional) – all arguments of numpy.argmax or numpy.ma.argmax
- **kwargs (optional) – all keyword arguments of numpy.argmax or numpy.ma.argmax
Returns: index_array – Array of indices of the largest element in input array a. It has the same shape as a.shape with the dimension along axis removed. a[np.unravel_index(argmax(a), a.shape)] is the maximum value of a.
Return type: ndarray, int
Examples
>>> import numpy as np
# One-dimensional array >>> a = np.array([0,4,6,2,1,5,3,5]) >>> ii = argmax(a) >>> print(ii) 2 >>> print(a[ii]) 6
# One-dimensional masked array >>> a = np.ma.array([0,4,6,2,1,5,3,5], mask=[0,0,1,1,0,0,0,0]) >>> ii = argmax(a) >>> print(ii) 5 >>> print(a[ii]) 5 >>> ii = argmax(a, fill_value=6) >>> print(ii) 2
# List >>> a = [0,4,6,2,1,5,3,5] >>> ii = argmax(a) >>> print(ii) 2 >>> print(a[ii]) 6
>>> # from numpy.argmax docstring >>> a = np.arange(6).reshape(2,3) + 10 >>> a array([[10, 11, 12], [13, 14, 15]]) >>> np.argmax(a) 5 >>> np.argmax(a, axis=0) array([1, 1, 1]) >>> np.argmax(a, axis=1) array([2, 2])
# Indexes of the maximal elements of a N-dimensional array: >>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape) >>> ind (1, 2) >>> a[ind] 15
>>> b = np.arange(6) >>> b[1] = 5 >>> b array([0, 5, 2, 3, 4, 5]) >>> np.argmax(b) # Only the first occurrence is returned. 1
Notes
argmax for iterables was taken from https://stackoverflow.com/questions/16945518/finding-the-index-of-the-value-which-is-the-min-or-max-in-python
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argmin(a, *args, **kwargs)[source]¶ Wrapper for numpy.argmin, numpy.ma.argmin, and using min for Python iterables.
- Passes all keywords directly to the individual routines, i.e.
numpy.argmin(a, axis=None, out=None)
numpy.ma.argmin(self, axis=None, fill_value=None, out=None)
No keyword will be passed to min routine for Python iterables.
Parameters: - a (array_like) – input array, masked array, or Python iterable
- *args (optional) – all arguments of numpy.argmin or numpy.ma.argmin
- **kwargs (optional) – all keyword arguments of numpy.argmin or numpy.ma.argmin
Returns: index_array – Array of indices of the largest element in input array a. It has the same shape as a.shape with the dimension along axis removed. a[np.unravel_index(argmin(a), a.shape)] is the minimum value of a.
Return type: ndarray, int
Examples
>>> import numpy as np
# One-dimensional array >>> a = np.array([0,4,6,2,1,5,3,5]) >>> ii = argmin(a) >>> print(ii) 0 >>> print(a[ii]) 0
# One-dimensional masked array >>> a = np.ma.array([0,4,6,2,1,5,3,5], mask=[1,0,1,1,0,0,0,0]) >>> ii = argmin(a) >>> print(ii) 4 >>> print(a[ii]) 1 >>> ii = argmin(a, fill_value=1) >>> print(ii) 0
# List >>> a = [0,4,6,2,1,5,3,5] >>> ii = argmin(a) >>> print(ii) 0 >>> print(a[ii]) 0
>>> # from numpy.argmin docstring >>> a = np.arange(6).reshape(2,3) + 10 >>> a array([[10, 11, 12], [13, 14, 15]]) >>> np.argmin(a) 0 >>> np.argmin(a, axis=0) array([0, 0, 0]) >>> np.argmin(a, axis=1) array([0, 0])
# Indices of the minimum elements of a N-dimensional array: >>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape) >>> ind (0, 0) >>> a[ind] 10
>>> b = np.arange(6) + 10 >>> b[4] = 10 >>> b array([10, 11, 12, 13, 10, 15]) >>> np.argmin(b) # Only the first occurrence is returned. 0
Notes
argmin for iterables was taken from https://stackoverflow.com/questions/16945518/finding-the-index-of-the-value-which-is-the-min-or-max-in-python
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argsort(a, *args, **kwargs)[source]¶ Wrapper for numpy.argsort, numpy.ma.argsort, and using sorted for Python iterables.
- Passes all keywords directly to the individual routines, i.e.
- numpy.argsort(a, axis=-1, kind=’quicksort’, order=None) numpy.ma.argsort(a, axis=None, kind=’quicksort’, order=None, fill_value=None) sorted(iterable[, cmp[, key[, reverse]]])
Only key cannot be given for Python iterables because the input array is used as key in the sorted function.
Parameters: - a (array_like) – input array, masked array, or Python iterable
- *args (optional) – all arguments of numpy.argsort, numpy.ma.argsort, and sorted (except key argument)
- **kwargs (optional) – all keyword arguments of numpy.argsort, numpy.ma.argsort, and sorted (except key argument)
Returns: index_array – Array of indices that sort a along the specified axis. If a is one-dimensional,
a[index_array]yields a sorted a.Return type: ndarray, int
Examples
>>> import numpy as np
# 1D array >>> a = np.array([0,4,6,2,1,5,3,5]) >>> ii = argsort(a) >>> print(a[ii]) [0 1 2 3 4 5 5 6]
>>> ii = argsort(a, kind='quicksort') >>> print(a[ii]) [0 1 2 3 4 5 5 6]
# 1D masked array >>> a = np.ma.array([0,4,6,2,1,5,3,5], mask=[0,0,1,1,0,0,0,0]) >>> ii = argsort(a) >>> print(a[ii]) [0 1 3 4 5 5 – –]
>>> ii = argsort(a, fill_value=1) >>> print(a[ii]) [0 -- -- 1 3 4 5 5]
# list >>> a = [0,4,6,2,1,5,3,5] >>> ii = argsort(a) >>> b = [ a[i] for i in ii ] >>> print(b) [0, 1, 2, 3, 4, 5, 5, 6]
>>> a = [0,4,6,2,1,5,3,5] >>> ii = argsort(a, reverse=True) >>> b = [ a[i] for i in ii ] >>> print(b) [6, 5, 5, 4, 3, 2, 1, 0]
# from numpy.argsort docstring # One-dimensional array: >>> x = np.array([3, 1, 2]) >>> np.argsort(x) array([1, 2, 0])
>>> # Two-dimensional array: >>> x = np.array([[0, 3], [2, 2]]) >>> x array([[0, 3], [2, 2]]) >>> ind = np.argsort(x, axis=0) # sorts along first axis (down) >>> ind array([[0, 1], [1, 0]]) >>> np.take_along_axis(x, ind, axis=0) # same as np.sort(x, axis=0) array([[0, 2], [2, 3]]) >>> ind = np.argsort(x, axis=1) # sorts along last axis (across) >>> ind array([[0, 1], [0, 1]]) >>> np.take_along_axis(x, ind, axis=1) # same as np.sort(x, axis=1) array([[0, 3], [2, 2]])
# Indices of the sorted elements of a N-dimensional array: >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape) >>> ind (array([0, 1, 1, 0]), array([0, 0, 1, 1])) >>> x[ind] # same as np.sort(x, axis=None) array([0, 2, 2, 3])
>>> # Sorting with keys: >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')]) >>> x array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')]) >>> np.argsort(x, order=('x','y')) array([1, 0]) >>> np.argsort(x, order=('y','x')) array([0, 1])