add asarray, size documentation
This commit is contained in:
Zoltán Vörös
parent
1fc2f18358
commit
d40672d946
4 changed files with 231 additions and 45 deletions
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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 = '4.1.0'
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release = '4.2.0'
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# -- General configuration ---------------------------------------------------
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@ -11,37 +11,39 @@ the firmware was compiled with complex support.
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3. `numpy.argmax <#argmax>`__
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4. `numpy.argmin <#argmin>`__
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5. `numpy.argsort <#argsort>`__
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6. `numpy.clip <#clip>`__
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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.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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6. `numpy.asarray <#asarray>`__
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7. `numpy.clip <#clip>`__
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8. `numpy.compress\* <#compress>`__
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9. `numpy.conjugate\* <#conjugate>`__
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10. `numpy.convolve\* <#convolve>`__
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11. `numpy.delete <#delete>`__
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12. `numpy.diff <#diff>`__
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13. `numpy.dot <#dot>`__
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14. `numpy.equal <#equal>`__
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15. `numpy.flip\* <#flip>`__
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16. `numpy.imag\* <#imag>`__
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17. `numpy.interp <#interp>`__
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18. `numpy.isfinite <#isfinite>`__
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19. `numpy.isinf <#isinf>`__
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20. `numpy.max <#max>`__
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21. `numpy.maximum <#maximum>`__
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22. `numpy.mean <#mean>`__
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23. `numpy.median <#median>`__
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24. `numpy.min <#min>`__
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25. `numpy.minimum <#minimum>`__
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26. `numpy.not_equal <#equal>`__
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27. `numpy.polyfit <#polyfit>`__
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28. `numpy.polyval <#polyval>`__
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29. `numpy.real\* <#real>`__
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30. `numpy.roll <#roll>`__
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31. `numpy.size <#size>`__
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32. `numpy.sort <#sort>`__
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33. `numpy.sort_complex\* <#sort_complex>`__
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34. `numpy.std <#std>`__
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35. `numpy.sum <#sum>`__
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36. `numpy.trace <#trace>`__
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37. `numpy.trapz <#trapz>`__
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38. `numpy.where <#where>`__
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all
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---
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@ -269,6 +271,53 @@ example:
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asarray
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-------
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``numpy``:
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https://docs.scipy.org/doc/numpy/reference/generated/numpy.asarray.html
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The function takes a single positional argument, and an optional keyword
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argument, ``dtype``, with a default value of ``None``.
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If the positional argument is an ``ndarray``, and its ``dtypes`` is
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identical to the value of the keyword argument, or if the keyword
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argument is ``None``, then the positional argument is simply returned.
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If the original ``dtype``, and the value of the keyword argument are
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different, then a copy is returned, with appropriate ``dtype``
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conversion.
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If the positional argument is an iterable, then the function is simply
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an alias for ``array``.
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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(9), dtype=np.uint8)
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b = np.asarray(a)
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c = np.asarray(a, dtype=np.int8)
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print('a:{}'.format(a))
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print('b:{}'.format(b))
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print('a == b: {}'.format(a is b))
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print('\nc:{}'.format(c))
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print('a == c: {}'.format(a is c))
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.. parsed-literal::
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a:array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)
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b:array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)
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a == b: True
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c:array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int8)
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a == c: False
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clip
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----
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@ -1381,6 +1430,39 @@ Vertical rolls require two internal copies of single columns.
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size
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----
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The function takes a single positional argument, and an optional keyword
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argument, ``axis``, with a default value of ``None``, and returns the
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size of an array along that axis. If ``axis`` is ``None``, the total
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length of the array (the product of the elements of its shape) is
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returned.
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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.ones((2, 3))
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print(a)
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print('size(a, axis=0): ', np.size(a, axis=0))
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print('size(a, axis=1): ', np.size(a, axis=1))
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print('size(a, axis=None): ', np.size(a, axis=None))
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.. parsed-literal::
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array([[1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0]], dtype=float64)
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size(a, axis=0): 2
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size(a, axis=1): 3
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size(a, axis=None): 6
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sort
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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": "2022-01-12T16:41:02.299473Z",
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"start_time": "2022-01-12T16:41:02.282389Z"
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"end_time": "2022-01-14T19:55:15.200755Z",
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"start_time": "2022-01-14T19:55:15.193656Z"
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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": "2022-01-12T16:41:02.475299Z",
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"start_time": "2022-01-12T16:41:02.401569Z"
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"end_time": "2022-01-14T19:55:17.871864Z",
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"start_time": "2022-01-14T19:55:17.858935Z"
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}
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},
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"outputs": [],
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@ -237,6 +237,7 @@
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"1. [numpy.argmax](#argmax)\n",
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"1. [numpy.argmin](#argmin)\n",
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"1. [numpy.argsort](#argsort)\n",
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"1. [numpy.asarray](#asarray)\n",
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"1. [numpy.clip](#clip)\n",
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"1. [numpy.compress*](#compress)\n",
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"1. [numpy.conjugate*](#conjugate)\n",
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@ -261,6 +262,7 @@
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"1. [numpy.polyval](#polyval)\n",
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"1. [numpy.real*](#real)\n",
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"1. [numpy.roll](#roll)\n",
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"1. [numpy.size](#size)\n",
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"1. [numpy.sort](#sort)\n",
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"1. [numpy.sort_complex*](#sort_complex)\n",
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"1. [numpy.std](#std)\n",
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@ -543,6 +545,62 @@
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"print('\\nthe original array:\\n', a)"
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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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"## asarray\n",
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"\n",
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"`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.asarray.html\n",
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"\n",
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"The function takes a single positional argument, and an optional keyword argument, `dtype`, with a default value of `None`. \n",
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"\n",
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"If the positional argument is an `ndarray`, and its `dtypes` is identical to the value of the keyword argument, or if the keyword argument is `None`, then the positional argument is simply returned. If the original `dtype`, and the value of the keyword argument are different, then a copy is returned, with appropriate `dtype` conversion. \n",
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"\n",
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"If the positional argument is an iterable, then the function is simply an alias for `array`."
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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": 10,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-14T20:05:22.017031Z",
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"start_time": "2022-01-14T20:05:22.002463Z"
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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:array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)\n",
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"b:array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)\n",
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"a == b: True\n",
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"\n",
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"c:array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int8)\n",
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"a == c: False\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(9), dtype=np.uint8)\n",
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"b = np.asarray(a)\n",
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"c = np.asarray(a, dtype=np.int8)\n",
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"print('a:{}'.format(a))\n",
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"print('b:{}'.format(b))\n",
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"print('a == b: {}'.format(a is b))\n",
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"\n",
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"print('\\nc:{}'.format(c))\n",
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"print('a == c: {}'.format(a is c))"
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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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@ -1941,6 +1999,52 @@
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"print(\"\\na rolled with None:\\n\", a)"
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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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"## size\n",
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"\n",
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"The function takes a single positional argument, and an optional keyword argument, `axis`, with a default value of `None`, and returns the size of an array along that axis. If `axis` is `None`, the total length of the array (the product of the elements of its shape) is returned."
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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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-14T19:58:44.044501Z",
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"start_time": "2022-01-14T19:58:44.034585Z"
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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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"array([[1.0, 1.0, 1.0],\n",
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" [1.0, 1.0, 1.0]], dtype=float64)\n",
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"size(a, axis=0): 2\n",
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"size(a, axis=1): 3\n",
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"size(a, axis=None): 6\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.ones((2, 3))\n",
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"\n",
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"print(a)\n",
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"print('size(a, axis=0): ', np.size(a, axis=0))\n",
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"print('size(a, axis=1): ', np.size(a, axis=1))\n",
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"print('size(a, axis=None): ', np.size(a, axis=None))"
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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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@ -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": "2022-01-12T17:00:33.582729Z",
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"start_time": "2022-01-12T17:00:33.566591Z"
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"end_time": "2022-01-14T19:54:52.171096Z",
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"start_time": "2022-01-14T19:54:52.162815Z"
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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 = '4.1.0'\n",
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"release = '4.2.0'\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": 1,
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-12T17:03:49.038101Z",
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"start_time": "2022-01-12T17:03:48.886617Z"
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"end_time": "2022-01-14T20:05:37.425494Z",
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"start_time": "2022-01-14T20:05:35.620545Z"
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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": 2,
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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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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"end_time": "2022-01-14T20:06:04.832792Z",
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"start_time": "2022-01-14T20:06:00.259738Z"
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}
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},
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"outputs": [],
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