add documentation for delete, extend test script, handle negative scalars
This commit is contained in:
Zoltán Vörös
parent
6eda5ec53c
commit
923cb823a3
9 changed files with 243 additions and 42 deletions
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@ -206,7 +206,15 @@ static mp_obj_t transform_delete(size_t n_args, const mp_obj_t *pos_args, mp_map
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size_t *index_array = m_new(size_t, index_len);
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if(mp_obj_is_int(indices)) {
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*index_array++ = (size_t)mp_obj_get_int(indices);
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ssize_t value = (ssize_t)mp_obj_get_int(indices);
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if(value < 0) {
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value += axis_len;
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}
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if((value < 0) || (value > (ssize_t)axis_len)) {
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mp_raise_ValueError(translate("index is out of bounds"));
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} else {
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*index_array++ = (size_t)value;
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}
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} else {
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mp_obj_iter_buf_t iter_buf;
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mp_obj_t item, iterable = mp_getiter(indices, &iter_buf);
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@ -33,7 +33,7 @@
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#include "user/user.h"
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#include "utils/utils.h"
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#define ULAB_VERSION 4.0.0
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#define ULAB_VERSION 4.1.0
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#define xstr(s) str(s)
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#define str(s) #s
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@ -27,7 +27,7 @@ copyright = '2019-2022, Zoltán Vörös and contributors'
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author = 'Zoltán Vörös'
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# The full version, including alpha/beta/rc tags
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release = '4.0.0'
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release = '4.1.0'
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# -- General configuration ---------------------------------------------------
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@ -15,32 +15,33 @@ the firmware was compiled with complex support.
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7. `numpy.compress\* <#compress>`__
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8. `numpy.conjugate\* <#conjugate>`__
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9. `numpy.convolve\* <#convolve>`__
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10. `numpy.diff <#diff>`__
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11. `numpy.dot <#dot>`__
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12. `numpy.equal <#equal>`__
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13. `numpy.flip\* <#flip>`__
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14. `numpy.imag\* <#imag>`__
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15. `numpy.interp <#interp>`__
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16. `numpy.isfinite <#isfinite>`__
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17. `numpy.isinf <#isinf>`__
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18. `numpy.max <#max>`__
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19. `numpy.maximum <#maximum>`__
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20. `numpy.mean <#mean>`__
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21. `numpy.median <#median>`__
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22. `numpy.min <#min>`__
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23. `numpy.minimum <#minimum>`__
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24. `numpy.not_equal <#equal>`__
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25. `numpy.polyfit <#polyfit>`__
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26. `numpy.polyval <#polyval>`__
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27. `numpy.real\* <#real>`__
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28. `numpy.roll <#roll>`__
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29. `numpy.sort <#sort>`__
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30. `numpy.sort_complex\* <#sort_complex>`__
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31. `numpy.std <#std>`__
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32. `numpy.sum <#sum>`__
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33. `numpy.trace <#trace>`__
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34. `numpy.trapz <#trapz>`__
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35. `numpy.where <#where>`__
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10. `numpy.delete <#delete>`__
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11. `numpy.diff <#diff>`__
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12. `numpy.dot <#dot>`__
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13. `numpy.equal <#equal>`__
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14. `numpy.flip\* <#flip>`__
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15. `numpy.imag\* <#imag>`__
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16. `numpy.interp <#interp>`__
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17. `numpy.isfinite <#isfinite>`__
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18. `numpy.isinf <#isinf>`__
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19. `numpy.max <#max>`__
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20. `numpy.maximum <#maximum>`__
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21. `numpy.mean <#mean>`__
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22. `numpy.median <#median>`__
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23. `numpy.min <#min>`__
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24. `numpy.minimum <#minimum>`__
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25. `numpy.not_equal <#equal>`__
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26. `numpy.polyfit <#polyfit>`__
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27. `numpy.polyval <#polyval>`__
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28. `numpy.real\* <#real>`__
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29. `numpy.roll <#roll>`__
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30. `numpy.sort <#sort>`__
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31. `numpy.sort_complex\* <#sort_complex>`__
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32. `numpy.std <#std>`__
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33. `numpy.sum <#sum>`__
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34. `numpy.trace <#trace>`__
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35. `numpy.trapz <#trapz>`__
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36. `numpy.where <#where>`__
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all
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---
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@ -412,6 +413,67 @@ accept complex arrays.
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delete
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------
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``numpy``:
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https://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html
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The function returns a new array with sub-arrays along an axis deleted.
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It takes two positional arguments, the array, and the indices, which
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will be removed, as well as the ``axis`` keyword argument with a default
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value of ``None``. If the ``axis`` is ``None``, the will be flattened
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first.
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The second positional argument can be a scalar, or any ``micropython``
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iterable. Since ``range`` can also be passed in place of the indices,
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slicing can be emulated. If the indices are negative, the elements are
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counted from the end of the axis.
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Note that the function creates a copy of the indices first, because it
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is not guaranteed that the indices are ordered. Keep this in mind, when
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working with large arrays.
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.. code::
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# code to be run in micropython
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from ulab import numpy as np
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a = np.array(range(25), dtype=np.uint8).reshape((5,5))
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print('a:\n', a)
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print('\naxis = 0\n', np.delete(a, 2, axis=0))
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print('\naxis = 1\n', np.delete(a, -2, axis=1))
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print('\naxis = None\n', np.delete(a, [0, 1, 2, 22]))
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.. parsed-literal::
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a:
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array([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[10, 11, 12, 13, 14],
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[15, 16, 17, 18, 19],
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[20, 21, 22, 23, 24]], dtype=uint8)
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axis = 0
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array([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[15, 16, 17, 18, 19],
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[20, 21, 22, 23, 24]], dtype=uint8)
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axis = 1
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array([[0, 1, 2, 4],
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[5, 6, 7, 9],
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[10, 11, 12, 14],
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[15, 16, 17, 19],
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[20, 21, 22, 24]], dtype=uint8)
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axis = None
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array([3, 4, 5, ..., 21, 23, 24], dtype=uint8)
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diff
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----
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@ -34,8 +34,8 @@
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-07T19:45:28.079350Z",
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"start_time": "2022-01-07T19:45:28.073911Z"
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"end_time": "2022-01-12T16:41:02.299473Z",
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"start_time": "2022-01-12T16:41:02.282389Z"
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}
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},
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"outputs": [],
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@ -52,8 +52,8 @@
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-07T19:45:28.654136Z",
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"start_time": "2022-01-07T19:45:28.634610Z"
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"end_time": "2022-01-12T16:41:02.475299Z",
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"start_time": "2022-01-12T16:41:02.401569Z"
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}
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},
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"outputs": [],
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@ -241,6 +241,7 @@
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"1. [numpy.compress*](#compress)\n",
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"1. [numpy.conjugate*](#conjugate)\n",
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"1. [numpy.convolve*](#convolve)\n",
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"1. [numpy.delete](#delete)\n",
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"1. [numpy.diff](#diff)\n",
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"1. [numpy.dot](#dot)\n",
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"1. [numpy.equal](#equal)\n",
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@ -738,6 +739,74 @@
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"print(np.convolve(x, y))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## delete\n",
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"\n",
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"`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html\n",
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"\n",
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"The function returns a new array with sub-arrays along an axis deleted. It takes two positional arguments, the array, and the indices, which will be removed, as well as the `axis` keyword argument with a default value of `None`. If the `axis` is `None`, the will be flattened first. \n",
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"\n",
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"The second positional argument can be a scalar, or any `micropython` iterable. Since `range` can also be passed in place of the indices, slicing can be emulated. If the indices are negative, the elements are counted from the end of the axis.\n",
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"\n",
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"Note that the function creates a copy of the indices first, because it is not guaranteed that the indices are ordered. Keep this in mind, when working with large arrays."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-12T17:03:29.099233Z",
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"start_time": "2022-01-12T17:03:29.084117Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"a:\n",
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" array([[0, 1, 2, 3, 4],\n",
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" [5, 6, 7, 8, 9],\n",
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" [10, 11, 12, 13, 14],\n",
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" [15, 16, 17, 18, 19],\n",
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" [20, 21, 22, 23, 24]], dtype=uint8)\n",
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"\n",
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"axis = 0\n",
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" array([[0, 1, 2, 3, 4],\n",
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" [5, 6, 7, 8, 9],\n",
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" [15, 16, 17, 18, 19],\n",
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" [20, 21, 22, 23, 24]], dtype=uint8)\n",
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"\n",
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"axis = 1\n",
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" array([[0, 1, 2, 4],\n",
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" [5, 6, 7, 9],\n",
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" [10, 11, 12, 14],\n",
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" [15, 16, 17, 19],\n",
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" [20, 21, 22, 24]], dtype=uint8)\n",
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"\n",
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"axis = None\n",
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" array([3, 4, 5, ..., 21, 23, 24], dtype=uint8)\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"%%micropython -unix 1\n",
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"\n",
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"from ulab import numpy as np\n",
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"\n",
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"a = np.array(range(25), dtype=np.uint8).reshape((5,5))\n",
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"print('a:\\n', a)\n",
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"print('\\naxis = 0\\n', np.delete(a, 2, axis=0))\n",
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"print('\\naxis = 1\\n', np.delete(a, -2, axis=1))\n",
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"print('\\naxis = None\\n', np.delete(a, [0, 1, 2, 22]))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -1,3 +1,9 @@
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Wed, 12 Jan 2022
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version 4.1.0
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add numpy.delete
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Sat, 8 Jan 2022
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version 4.0.0
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@ -17,8 +17,8 @@
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-07T18:24:12.745063Z",
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"start_time": "2022-01-07T18:24:12.733067Z"
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"end_time": "2022-01-12T17:00:33.582729Z",
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"start_time": "2022-01-12T17:00:33.566591Z"
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}
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},
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"outputs": [
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@ -61,7 +61,7 @@
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"author = 'Zoltán Vörös'\n",
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"\n",
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"# The full version, including alpha/beta/rc tags\n",
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"release = '4.0.0'\n",
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"release = '4.1.0'\n",
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"\n",
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"\n",
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"# -- General configuration ---------------------------------------------------\n",
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@ -215,11 +215,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-07T18:24:27.671415Z",
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"start_time": "2022-01-07T18:24:24.933205Z"
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"end_time": "2022-01-12T17:03:49.038101Z",
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"start_time": "2022-01-12T17:03:48.886617Z"
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}
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},
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"outputs": [],
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@ -256,11 +256,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-07T19:52:29.910335Z",
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"start_time": "2022-01-07T19:52:28.432391Z"
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"end_time": "2022-01-12T17:03:52.084601Z",
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"start_time": "2022-01-12T17:03:50.354118Z"
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}
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},
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"outputs": [],
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@ -18,3 +18,9 @@ for dtype in dtypes:
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print(np.delete(a, 2, axis=0))
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print(np.delete(a, 2, axis=1))
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print(np.delete(a, 2))
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for dtype in dtypes:
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a = np.array(range(25), dtype=dtype).reshape((5,5))
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print(np.delete(a, -3, axis=0))
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print(np.delete(a, -3, axis=1))
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print(np.delete(a, -3))
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@ -93,3 +93,53 @@ array([[0.0, 1.0, 3.0, 4.0],
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[15.0, 16.0, 18.0, 19.0],
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[20.0, 21.0, 23.0, 24.0]], dtype=float64)
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array([0.0, 1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0], dtype=float64)
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array([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[15, 16, 17, 18, 19],
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[20, 21, 22, 23, 24]], dtype=uint8)
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array([[0, 1, 3, 4],
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[5, 6, 8, 9],
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[10, 11, 13, 14],
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[15, 16, 18, 19],
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[20, 21, 23, 24]], dtype=uint8)
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24], dtype=uint8)
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array([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[15, 16, 17, 18, 19],
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[20, 21, 22, 23, 24]], dtype=int8)
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array([[0, 1, 3, 4],
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[5, 6, 8, 9],
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[10, 11, 13, 14],
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[15, 16, 18, 19],
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[20, 21, 23, 24]], dtype=int8)
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24], dtype=int8)
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array([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[15, 16, 17, 18, 19],
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[20, 21, 22, 23, 24]], dtype=uint16)
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array([[0, 1, 3, 4],
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[5, 6, 8, 9],
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[10, 11, 13, 14],
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[15, 16, 18, 19],
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[20, 21, 23, 24]], dtype=uint16)
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24], dtype=uint16)
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array([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[15, 16, 17, 18, 19],
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[20, 21, 22, 23, 24]], dtype=int16)
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array([[0, 1, 3, 4],
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[5, 6, 8, 9],
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[10, 11, 13, 14],
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[15, 16, 18, 19],
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[20, 21, 23, 24]], dtype=int16)
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24], dtype=int16)
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array([[0.0, 1.0, 2.0, 3.0, 4.0],
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[5.0, 6.0, 7.0, 8.0, 9.0],
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[15.0, 16.0, 17.0, 18.0, 19.0],
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[20.0, 21.0, 22.0, 23.0, 24.0]], dtype=float64)
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array([[0.0, 1.0, 3.0, 4.0],
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[5.0, 6.0, 8.0, 9.0],
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[10.0, 11.0, 13.0, 14.0],
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[15.0, 16.0, 18.0, 19.0],
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[20.0, 21.0, 23.0, 24.0]], dtype=float64)
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array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 23.0, 24.0], dtype=float64)
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Reference in a new issue