added back misc

Python/Misc/_alu_tests.txt

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+|       x        |       y        |zx|nx|zy|ny|f|no|      out       |zr|ng|
+|0000000000000000|1111111111111111| 1| 0| 1| 0|1| 0|0000000000000000| 1| 0|
+|0000000000000000|1111111111111111| 1| 1| 1| 1|1| 1|0000000000000001| 0| 0|
+|0000000000000000|1111111111111111| 1| 1| 1| 0|1| 0|1111111111111111| 0| 1|
+|0000000000000000|1111111111111111| 0| 0| 1| 1|0| 0|0000000000000000| 1| 0|
+|0000000000000000|1111111111111111| 1| 1| 0| 0|0| 0|1111111111111111| 0| 1|
+|0000000000000000|1111111111111111| 0| 0| 1| 1|0| 1|1111111111111111| 0| 1|
+|0000000000000000|1111111111111111| 1| 1| 0| 0|0| 1|0000000000000000| 1| 0|
+|0000000000000000|1111111111111111| 0| 0| 1| 1|1| 1|0000000000000000| 1| 0|
+|0000000000000000|1111111111111111| 1| 1| 0| 0|1| 1|0000000000000001| 0| 0|
+|0000000000000000|1111111111111111| 0| 1| 1| 1|1| 1|0000000000000001| 0| 0|
+|0000000000000000|1111111111111111| 1| 1| 0| 1|1| 1|0000000000000000| 1| 0|
+|0000000000000000|1111111111111111| 0| 0| 1| 1|1| 0|1111111111111111| 0| 1|
+|0000000000000000|1111111111111111| 1| 1| 0| 0|1| 0|1111111111111110| 0| 1|
+|0000000000000000|1111111111111111| 0| 0| 0| 0|1| 0|1111111111111111| 0| 1|
+|0000000000000000|1111111111111111| 0| 1| 0| 0|1| 1|0000000000000001| 0| 0|
+|0000000000000000|1111111111111111| 0| 0| 0| 1|1| 1|1111111111111111| 0| 1|
+|0000000000000000|1111111111111111| 0| 0| 0| 0|0| 0|0000000000000000| 1| 0|
+|0000000000000000|1111111111111111| 0| 1| 0| 1|0| 1|1111111111111111| 0| 1|
+|0000000000010001|0000000000000011| 1| 0| 1| 0|1| 0|0000000000000000| 1| 0|
+|0000000000010001|0000000000000011| 1| 1| 1| 1|1| 1|0000000000000001| 0| 0|
+|0000000000010001|0000000000000011| 1| 1| 1| 0|1| 0|1111111111111111| 0| 1|
+|0000000000010001|0000000000000011| 0| 0| 1| 1|0| 0|0000000000010001| 0| 0|
+|0000000000010001|0000000000000011| 1| 1| 0| 0|0| 0|0000000000000011| 0| 0|
+|0000000000010001|0000000000000011| 0| 0| 1| 1|0| 1|1111111111101110| 0| 1|
+|0000000000010001|0000000000000011| 1| 1| 0| 0|0| 1|1111111111111100| 0| 1|
+|0000000000010001|0000000000000011| 0| 0| 1| 1|1| 1|1111111111101111| 0| 1|
+|0000000000010001|0000000000000011| 1| 1| 0| 0|1| 1|1111111111111101| 0| 1|
+|0000000000010001|0000000000000011| 0| 1| 1| 1|1| 1|0000000000010010| 0| 0|
+|0000000000010001|0000000000000011| 1| 1| 0| 1|1| 1|0000000000000100| 0| 0|
+|0000000000010001|0000000000000011| 0| 0| 1| 1|1| 0|0000000000010000| 0| 0|
+|0000000000010001|0000000000000011| 1| 1| 0| 0|1| 0|0000000000000010| 0| 0|
+|0000000000010001|0000000000000011| 0| 0| 0| 0|1| 0|0000000000010100| 0| 0|
+|0000000000010001|0000000000000011| 0| 1| 0| 0|1| 1|0000000000001110| 0| 0|
+|0000000000010001|0000000000000011| 0| 0| 0| 1|1| 1|1111111111110010| 0| 1|
+|0000000000010001|0000000000000011| 0| 0| 0| 0|0| 0|0000000000000001| 0| 0|
+|0000000000010001|0000000000000011| 0| 1| 0| 1|0| 1|0000000000010011| 0| 0|

Python/Misc/_numpy-primer.py

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+import numpy as np
+
+## basics
+
+a = [60, 28, 49, 81]
+b = [55, 634, 704, 2020]
+
+# create numpy array
+A = np.array(a, dtype='i')
+# print(A)
+# [60 28 49 81]
+
+AB = np.array([a, b])
+# print(AB)
+# [[  60   28   49   81]
+#  [  55  634  704 2020]]
+
+print(AB.ndim)
+# 2
+print(AB.shape)
+# (2, 4)
+
+arr_zero = np.zeros(4)
+# print(arr_zero)
+# [0. 0. 0. 0.]
+
+arr_ones = np.ones((2, 3))
+# print(arr_ones)
+# [[ 1.  1.  1.]
+#  [ 1.  1.  1.]]
+
+arr_range = np.arange(0, 10)
+# print(arr_range)
+# [0 1 2 3 4 5 6 7 8 9]
+
+arr_linspace = np.linspace(0, 1, 5)
+# print(arr_linspace)
+# [0.   0.25 0.5  0.75 1.  ]
+
+arr_log = np.logspace(0, 1, 5)
+# print(arr_log)
+# [ 1.          1.77827941  3.16227766  5.62341325 10.        ]
+
+# slicing
+print(A[1:3])
+# [28 49]
+print(AB[0:2, 2:4])
+# [[  49   81]
+#  [ 704 2020]]
+
+# reverse order
+print(A[::-1])
+# [81 49 28 60]
+
+# operations
+print(arr_range.mean())
+# 4.5
+print(arr_range.sum())
+# 45
+print(AB.min())
+# 28
+print(AB.max())
+# 2020
+print(np.sin(AB))
+# [[-0.30481062  0.27090579 -0.95375265 -0.62988799]
+#  [-0.99975517 -0.56605794  0.27947339  0.04406199]]
+print(np.multiply(a, b))
+# [  3300  17752  34496 163620]
+print(np.sqrt(b))
+# [ 7.41619849 25.17935662 26.53299832 44.94441011]
+
+# min value in each column
+print(np.minimum(a, b))
+# [55 28 49 81]
+
+# sum of axis (columns)
+print(np.sum(AB, axis=1))
+# [ 218 3413]
+
+## indexing
+
+# create array with 8 integers
+A = 2 * np.arange(8)**2 + 1
+# print(A)
+# [ 1  3  9 19 33 51 73 99]
+
+# create array with 16 integers
+B = np.arange(16)
+B.shape = (4, 4)
+# print(B)
+# [[ 0  1  2  3]
+#  [ 4  5  6  7]
+#  [ 8  9 10 11]
+#  [12 13 14 15]]
+
+print(A[[3, -1, 1]])
+# [19 99  3]
+print(B[:, [2, -1, 0]])
+# [[ 2  3  0]
+#  [ 6  7  4]
+#  [10 11  8]
+#  [14 15 12]]
+
+# elements from A at indices given by fibonacci sequence
+fibseq = np.array([0, 1, 1, 2, 3])
+# print(A[fibseq])
+# [ 1  3  3  9 19]
+
+# diagonal
+i = np.arange(4)
+print(B[i, i])
+# [ 0  5 10 15]
+
+# diagonal of part
+print(B[i[:3], i[:3] + 1])
+# [ 1  6 11]
+
+## masking
+
+# create array with 16 integers
+C = np.arange(16)
+C.shape = (4, 4)
+# print(C)
+# [[ 0  1  2  3]
+#  [ 4  5  6  7]
+#  [ 8  9 10 11]
+#  [12 13 14 15]]
+
+# create mask with True
+mask = np.ones(4, dtype=bool)
+# print(mask)
+# [ True  True  True  True]
+
+# diagonal
+print(C[mask, mask])
+# [ 0  5 10 15]
+
+# conditional mask
+conditional = (C >= 9)
+# print(C[conditional])
+# [ 9 10 11 12 13 14 15]
+
+# get all values smaller than 9
+print(C[C < 9])
+# [0 1 2 3 4 5 6 7 8]
+
+# get all values smaller than 5 or larger than 10
+print(C[(C < 5) | (C > 10)])
+# [ 0  1  2  3  4 11 12 13 14 15]
+
+# where
+print(C[np.where(C > 10)])
+# [11 12 13 14 15]
+
+# get all columns with a value less than 3
+print(C[:, np.where(C < 3)[1]])
+# [[ 0  1  2]
+#  [ 4  5  6]
+#  [ 8  9 10]
+#  [12 13 14]]
+
+## operations
+
+A = np.arange(6)
+print(A)
+# [0 1 2 3 4 5]
+print(A + A)
+# [ 0  2  4  6  8 10]
+print(A - 1)
+# [-1  0  1  2  3  4]
+print(A * 6)
+# [ 0  6 12 18 24 30]
+
+# gradient
+X = np.array([2, 4, 5])
+Y = np.array([6, 2, 8])
+print(np.gradient(X))
+# [ 2.   1.5  1. ] 		=> (4 - 2) / 1, (5 - 2) / 2, (5 - 4)/ 1
+print(np.gradient(Y))
+# [-4.  1.  6.] 		=> (2 - 6) / 1, (8 - 6) / 2, (8 - 2)/ 1
+
+# numerical derivative
+print(np.gradient(Y) / np.gradient(X))
+# [-2.          0.66666667  6.        ]
+
+B = np.arange(4)
+B.shape = (2, 2)
+# print(B)
+# [[0 1]
+#  [2 3]]
+
+v = np.array([[2],[4]])
+# print(v)
+# [[2]
+#  [4]]
+
+# scalar (multiplication)
+print(B * v)
+# [[ 0  2]
+#  [ 8 12]]
+
+# dot product
+print(np.dot(B, v))
+# [[ 4]
+#  [16]]
+
+# operations on higher dimensions
+M = np.arange(12)
+M.shape = (4, 3)
+# print(M)
+# [[ 0  1  2]
+#  [ 3  4  5]
+#  [ 6  7  8]
+#  [ 9 10 11]]
+
+v = np.array([16, 17, 18])
+
+print(M + v)
+# [[16 18 20]
+#  [19 21 23]
+#  [22 24 26]
+#  [25 27 29]]

Python/Misc/_pandas-series.py

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+import pandas as pd
+
+# creating series (one-dimensional array)
+simple_series = pd.Series([42, 55, 73], dtype='f8')
+# 0     42.0
+# 1     55.0
+# 2     73.0
+# dtype: float64
+
+# creating series with specified index
+index_series = pd.Series([42, 55, 73], index=["electron", "proton", "neutron"], dtype='f8')
+# electron      42.0
+# proton        55.0
+# neutron       73.0
+# dtype: float64
+
+# accessing value in series
+print(index_series["electron"])
+# 42.0
+
+# accessing multiple values in series (range)
+print(index_series["electron":"neutron"])
+# electron      42.0
+# proton        55.0
+# neutron       73.0
+# dtype: float64
+
+# accessing multiple values in series using indexing
+print(index_series[1:])
+# proton        55.0
+# neutron       73.0
+# dtype: float64
+
+# creating series from dictionary
+dict_series = pd.Series({ "electron": 6, "neutron": 28, "proton": 496, "neutrino": 8128 })
+# electron      6
+# neutrino      8128
+# neutron       28
+# proton        496
+# dtype: int64
+
+# combining two series into one column
+print(index_series + dict_series)
+# electron      48.0
+# neutrino      NaN
+# neutron       101.0
+# proton        551.0
+# dtype: float64
+
+# combining two series as dataframe
+combined_series = pd.DataFrame({ "A": index_series, "B": dict_series })
+#               A       B
+# electron      42.0    6
+# neutrino      NaN     8128
+# neutron       73.0    28
+# proton        55.0    496
+
+# accessing columns as series
+print(combined_series["A"])
+# electron      42.0
+# neutrino      NaN
+# neutron       73.0
+# proton        55.0
+# Name: A, dtype: float64
+
+# add new row with index
+append_series = combined_series._append(pd.DataFrame({ "A": [-8128] }, index=["antineutrino"]))
+#               A       B
+# electron      42.0    6.0
+# neutrino      NaN     8128.0
+# neutron       73.0    28.0
+# proton        55.0    496.0
+# antineutrino  -8128.0 NaN
+
+# drop row
+append_series = append_series.drop("neutron")
+#               A       B
+# electron      42.0    6.0
+# neutrino      NaN     8128.0
+# proton        55.0    496.0
+# antineutrino  -8128.0 NaN
+
+# transpose
+print(combined_series.T)
+#       electron    neutrino    neutron     proton
+# A     42.0        NaN         73.0        55.0
+# B     6.0         8128.0      28.0        496.0
+
+# masking
+print(combined_series > 120)
+#               A       B
+# electron      False   False
+# neutrino      False   True
+# neutron       False   False
+# proton        False   True
+
+# add masking as column (e.g. larger than 120)
+combined_series["large"] = (combined_series["A"] > 120) | (combined_series["B"] > 120)
+#               A       B       large
+# electron      42.0    6       False
+# neutrino      NaN     8128    True
+# neutron       73.0    28      False
+# proton        55.0    496     True
+
+# delete a column
+del combined_series["large"]
+#               A       B
+# electron      42.0    6
+# neutrino      NaN     8128
+# neutron       73.0    28
+# proton        55.0    496

Python/Misc/_square.c

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+#include <stdio.h>
+
+int square(int num) {
+    return num * num;
+}