# Python and Numpy#

Python is easy to use and popular among scientists and engineers for its simplicity. It is suitable for almost every task, and the versatility makes is one of the best tool as the programming interface for numerical applications.

## Organize Python Code#

To help the discussions for Python code organization, we make a simple categorization:

• One file containing Python code as a script.

• One Python file is a “module”.

• One directory containing Python files (satisfying some rules) is a “package”.

• “Module” is usually used in a loose way to refer to things that may be imported by Python import statement. Then a “module” can mean (the strictly defined) module or a package.

### How a Python Script Works#

A script is a text file that the program loader sends to an engine (usually interpreter) to execute with the content. We usually write scripts for automating repetitive work, and they should be short for quick implementation. Assume that we have a simple task: count the number of lines in a file. For the simple example script, see: A Python script that counts lines in a file (step0.py).

Running a script requires the executable permission to be set. Otherwise, the script file cannot be executed in the shell as a command:

$ls -al step0.py # executable bit is not set -rw-r--r-- 1 yungyuc staff 574 Apr 7 22:18 step0.py$ ./step0.py pstake.py # can't run without permission
-bash: ./step0.py: Permission denied

$chmod a+x step0.py # set the executable bit$ ls -al step0.py      # executable bit is set
-rwxr-xr-x  1 yungyuc  staff  574 Apr  7 22:18 step0.py*

$./step0.py pstake.py # properly it runs 811 lines in pstake.py  If we do not need to run the script like an executable, it can be run by explicitly calling Python: $ chmod u-x step0.py

811 lines


### Make a Module#

A Python module is pretty much a Python source file. A Python script is also a file containing Python code. They differ in the way the code is invoked. A script is used as a command. A module is imported as a library.

Can a script be used as a module? It depends on how it is written. For example, the script step0.py is not suitable for being imported as a module:

$python3 -c 'import step0' missing file name  The import statement runs the Python source code and puts the results in the module namespace. However, the code in step0.py simply does the work without leaving anything useful in the module to be imported. We should modify the script to be make it suitable for a module. See: A Python module file (step1.py). Because a module is not supposed to be run as a command, the file no longer has a shebang. In addition to that, we made two changes. First, the module file factors out the line-counting code from the script example: if os.path.exists(fname): with open(fname) as fobj: lines = fobj.readlines() sys.stdout.write('{} lines in {}\n'.format(len(lines), fname)) else: sys.stdout.write('{} not found\n'.format(fname))  into a function: def count_line(fname): if os.path.exists(fname): with open(fname) as fobj: lines = fobj.readlines() sys.stdout.write('{} lines in {}\n'.format(len(lines), fname)) else: sys.stdout.write('{} not found\n'.format(fname))  Second, the rest of the code is moved into an if test: # This tests whether the code is evaluated as a script. if __name__ == '__main__': if len(sys.argv) < 2: sys.stdout.write('missing file name\n') elif len(sys.argv) > 2: sys.stdout.write('only one argument is allowed\n') else: count_line(sys.argv[1])  Note __main__ is the name of the scope in which the top-level code execute in Python. After the change, step1 acts like a module. When it is imported, nothing happens in the calling site: $ python3 -c 'import step1'


To run the code defined in the step1 module, the function count_line() should be explicitly called:

$python3 -c 'import step1 ; step1.count_line("pstake.py")' 811 lines in pstake.py  While modifying the file, we keep the capability to run the file as a script: $ python3 step0.py pstake.py
811 lines in pstake.py
$python3 step1.py pstake.py 811 lines in pstake.py  #### Move Everything inside Function# We can improve the module by factoring out all code to functions. See: Move all code to functions (step2.py). It moves the argument-processing code to the function main(): def main(): if len(sys.argv) < 2: sys.stdout.write('missing file name\n') elif len(sys.argv) > 2: sys.stdout.write('only one argument is allowed\n') else: count_line(sys.argv[1]) # This tests whether the file is evaluated as a script. if __name__ == '__main__': main()  step1.py and step2.py have the same behavior, except step2.py has a main() function, which allows it to behave like a script: $ # only import the module
$python3 -c 'import step2'$ # import and then run the new main function
$python3 -c 'import step2 ; step2.main()' pstake.py 811 lines in pstake.py  #### Run Module as Script# If there is a module, Python allows to run the module as a script without knowing where the module file is. The functionality is supported with the -m argument. $ python3 -m step1 pstake.py
811 lines in pstake.py


### Make a Package#

When the code grows to a point that a single file is not enough to house everything, we should split the code into multiple files and put them in a directory. Python package provides a framework to organize source-code files in a directory and allows them to be imported like a module. Now we will turn the line-counting example code into a package. Make a directory with the following layout:

Python recognizes a directory containing an __init__.py file as a package. The __init__.py file usually just takes functions (or classes) from the internal modules via relative import:

from ._core import count_line
from ._core import main


Code that does the real work is put in _core.py:

def count_line(fname):
if os.path.exists(fname):
with open(fname) as fobj:
sys.stdout.write('{} lines in {}\n'.format(len(lines), fname))
else:

def main():
if len(sys.argv) < 2:
sys.stdout.write('missing file name\n')
elif len(sys.argv) > 2:
sys.stdout.write('only one argument is allowed\n')
else:
count_line(sys.argv[1])


For simple and short code like the example we are showing, it doesn’t matter how to organize the code. But if there are 10,000 lines of Python, the directory structure becomes handy. The import statements in the __init__.py serves as documentation for where to find the implementation. The real code in implementation files (_core.py) may be organized differently, but users don’t need to know the detail.

While having the code implemented in _core.py, the package step3 allows users to run the code directly from its top-level namespace:

Call count_line()#
$# This works just like a module.$ python3 -c 'import step3 ; step3.count_line("pstake.py")'
811 lines in pstake.py

Call main()#
$# This also works just like a module.$ python3 -c 'import step3 ; step3.main()' pstake.py
811 lines in pstake.py


Since the package is a directory, and no file in the directory contains the complete code to do all the work, we cannot run the package as a script:

$python3 step3/__init__.py pstake.py Traceback (most recent call last): File "/Users/yungyuc/work/web/ynote/nsd/02numpy/code/step3/__init__.py", line 12, in <module> from ._core import count_line ImportError: attempted relative import with no known parent package  But the -m option still works, because we have added __main__.py: $ python3 -m step3 pstake.py
811 lines in pstake.py


### A Real Useful Script#

Here is a real-world example: A useful example script (pstake.py). It converts pstricks commands (see: Example PSTricks TeX file (cce.tex)) to an image file.

$rm -f cce.png$ ./pstake.py cce.tex cce.png > /dev/null 2>&1


## Numpy for Array-Centric Code#

Arrays offer the highest performance when dealing with homogeneous data. In Python, the numpy library provides everything we need for arrays.

Arrays use contiguous memory. Python provides a different set of sequential data: Sequence Types — list, tuple, range, which do not require contiguity.

Make a list (one of Python sequence types) of integers#
>>> lst = [1, 1, 2, 3, 5]
>>> print('A list:', lst)
A list: [1, 1, 2, 3, 5]


When using Python, it is conventional to alias it as np:

Import numpy and alias it to np#
>>> import numpy as np


There are many ways to create a numpy array. But in pure Python code, the most common approach is to convert from a sequence:

Make an array from a sequence#
>>> array = np.array(lst)
>>> print('An array:', np.array(array))
An array: [1 1 2 3 5]


Note

It should be obvious that the created array uses a copy of the input sequence:

>>> array[2] = 8
>>> print(array)  # The array changes.
[1 1 8 3 5]
>>> print(lst)  # The input sequence is not changed.
[1, 1, 2, 3, 5]


### Basic Meta-Data#

A numpy array object contains two data: the contiguous data buffer for the array elements, and the meta-data describing the buffer. Here is a list of some frequently used meta-data.

#### shape#

numpy.ndarray.shape returns a tuple for array dimensions:

>>> array = np.array([0, 1, 2, 3, 4, 5])
>>> print("shape:", array.shape)
shape: (6,)
>>> array = np.array([[0, 1, 2], [3, 4, 5]])
>>> print("shape:", array.shape)
shape: (2, 3)


#### size#

numpy.ndarray.size returns the number of elements in an array:

>>> array = np.array([0, 1, 2, 3])
>>> print("size:", array.size)
size: 4
>>> array = np.array([[0, 1, 2], [3, 4, 5]])
>>> print("size:", array.size)
size: 6


#### dtype#

numpy.ndarray.dtype returns the data type of the array’s elements.

>>> array = np.array([[0, 1, 2], [3, 4, 5]])
>>> print("dtype:", array.dtype)
dtype: int64


When creating an ndarray, the factory function numpy.array() uses the input sequence to determine the appropriate dtype for the constructed array:

All-integer input results in an integer array#
>>> array1 = np.array([1, 1, 2, 3, 5])
>>> print("only int:", array1, type(array1), array1.dtype)
only int: [1 1 2 3 5] <class 'numpy.ndarray'> int64

All-real input results in a real array#
>>> array2 = np.array([1.0, 1.0, 2.0, 3.0, 5.0])
>>> print("only real:", array2, type(array2), array2.dtype)
only real: [1. 1. 2. 3. 5.] <class 'numpy.ndarray'> float64

Input mixed with integer and real number result in a real array#
>>> array3 = np.array([1, 1, 2, 3, 5.0])
>>> print("int and real:", array3, type(array3), array3.dtype)
int and real: [1. 1. 2. 3. 5.] <class 'numpy.ndarray'> float64


This is where a Python list (or sequence) differs from an array. A list does not know the type of the data it contains, but the array does. The type information allows numpy to process the array data using pre-compiled C code.

The following table lists commonly used dtypes:

Commonly used dtypes#

dtype (object)

dtype string name

C++ type name

numpy.bool_

"bool"

bool

numpy.int8

"int8"

int8_t

numpy.int16

"int16"

int16_t

numpy.int32

"int32"

int32_t

numpy.int64

"int64"

int64_t

numpy.uint8

"uint8"

uint8_t

numpy.uint16

"uint16"

uint16_t

numpy.uint32

"uint32"

uint32_t

numpy.uint64

"uint64"

uint64_t

numpy.float32

"float32"

float

numpy.float64

"float64"

double

When specifying dtype in a numpy function, the string names are oftentimes more convenient than the object, like the example code in Construction.

For the full list of dtypes, see Data types in the numpy document

#### itemsize#

numpy.ndarray.itemsize is the length of one array element in bytes.

>>> array = np.array([[0, 1, 2], [3, 4, 5]])
>>> print("itemsize:", array.itemsize)
itemsize: 8


#### nbytes#

numpy.ndarray.nbytes is the total bytes consumed by the elements of the array:

>>> array = np.array([[0, 1, 2], [3, 4, 5]])
>>> print("nbytes:", array.nbytes)
nbytes: 48


### Construction#

There are several ways to construct numpy arrays.

Note

See Array creation routines for more complete information.

#### empty()#

numpy.empty() allocates memory but does not initialize the elements. After the empty array object is created, the value garbage:

>>> empty_array = np.empty(4)
>>> print(empty_array)
[0.0e+000 4.9e-324 9.9e-324 1.5e-323]


numpy.ndarray.fill() can be used to fill the value of the array object:

>>> empty_array.fill(7)
>>> print(empty_array)
[7. 7. 7. 7.]


We can also fill the array using the ellipsis (...):

>>> empty_array[...] = 11
>>> print(empty_array)
[11. 11. 11. 11.]


For one-dimensional arrays, [:] can also be used to assign value of the full array:

>>> empty_array[:] = 13
>>> print(empty_array)
[13. 13. 13. 13.]


If the keyword argument dtype is not specified, the array is assumed to be double-precision floating point:

>>> print(np.empty(4).dtype)
float64


For readability (not everyone knows or remembers the default data type), it is a good practice to always supply the dtype argument:

>>> print(np.empty(4, dtype='int32').dtype)
int32
>>> print(np.empty(4, dtype='float64').dtype)
float64


#### zeros()#

numpy.zeros() creates an array object and initialize the elements to 0:

>>> zeroed_array = np.zeros(4)
>>> print(zeroed_array)
[0. 0. 0. 0.]


#### ones()#

numpy.ones() creates an array object and initialize the elements to 1:

>>> unity_array = np.ones(4)
>>> print(unity_array)
[1. 1. 1. 1.]


#### fulls()#

numpy.full() is a shorthand for numpy.empty() and then numpy.ndarray.fill():

>>> empty_array = np.empty(4)
>>> empty_array.fill(7)
>>> print(empty_array)
[7. 7. 7. 7.]
>>> filled_real_array = np.full(4, 7.0)
>>> print(filled_real_array)
[7. 7. 7. 7.]


But numpy.full() uses the initial value to determine the data type of the constructed array:

>>> filled_int_array = np.full(4, 7)
>>> print(filled_int_array, filled_int_array.dtype)
[7 7 7 7] int64
>>> filled_real_array = np.full(4, 7.0)
>>> print(filled_real_array, filled_real_array.dtype)
[7. 7. 7. 7.] float64


Although it may be convenient in interactive mode, it is suggested to explicitly add the dtype argument to improve readability:

>>> filled_int_array = np.full(4, 7, dtype='int64')
>>> print(filled_int_array, filled_int_array.dtype)
[7 7 7 7] int64
>>> filled_real_array = np.full(4, 7, dtype='float64')
>>> print(filled_real_array, filled_real_array.dtype)
[7. 7. 7. 7.] float64


#### arange()#

numpy.arange() returns an array with the given step size:

>>> ranged_array = np.arange(4)
>>> print("Build an array with range:", ranged_array)
Build an array with range: [0 1 2 3]
>>> ranged_real_array = np.arange(4.0)
>>> print("Build with real range:", ranged_real_array)
Build with real range: [0. 1. 2. 3.]


#### linspace()#

numpy.linspace() returns an array whose elements are evenly placed in a closed interval:

>>> linear_array = np.linspace(11, 13, num=6)
>>> print("6 evenly placed elements:", linear_array)
6 evenly placed elements: [11.  11.4 11.8 12.2 12.6 13. ]


### Multi-dimensional arrays#

Multi-dimensional arrays are the building-block of matrices and linear algebra and much more useful than one-dimensional arrays. We need to understand the memory layout needs to be understood before using them. There are two ways to order the elements in the multi-dimensional arrays:

1. Row-majoring, or the “C”-style in the numpy terminology. In the row-major arrays, the trailing index changes the fastest while looping in the memory sequentially.

2. Column-majoring, or the “F”-style in the numpy terminology. It is also the layout used by Fortran. In the column-major arrays, the leading index changes the fastest while loop in the memory sequentially.

ndarray by default uses row-majoring:

$\begin{split}A = \left(\begin{array}{ccc} a_{00} & a_{01} & a_{02} \\ a_{10} & a_{11} & a_{12} \end{array}\right) = \left(\begin{array}{ccc} 0 & 1 & 2 \\ 3 & 4 & 5 \end{array}\right)\end{split}$
>>> original_array = np.arange(6)
>>> print("original 1D array:", original_array)
original 1D array: [0 1 2 3 4 5]

>>> print("reshaped 2D array:\n%s" % original_array.reshape((2,3)))
reshaped 2D array:
[[0 1 2]
[3 4 5]]


We can reshape an array with column-majoring (“F”-style):

>>> print("reshaped 2D array:\n%s" % original_array.reshape((2,3), order='f'))
reshaped 2D array:
[[0 2 4]
[1 3 5]]


#### Stacking#

In addition to manipulating the shape, multi-dimensional arrays can also be created by stacking one-dimensional arrays:

>>> ranged_array = np.arange(10)
>>> print("A 1D array:", ranged_array)
A 1D array: [0 1 2 3 4 5 6 7 8 9]
>>> hstack_array = np.hstack([ranged_array, ranged_array])
>>> print("Horizontally stacked array:", hstack_array)
Horizontally stacked array: [0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]
>>> vstack_array = np.vstack([ranged_array, ranged_array+100])
>>> print("Vertically stacked array:", vstack_array)
Vertically stacked array: [[  0   1   2   3   4   5   6   7   8   9]
[100 101 102 103 104 105 106 107 108 109]]


#### Higher Dimension#

Example for 3D arrays:

>>> original_array = np.arange(24)
>>> print("original 1D array:\n%s" % original_array)
original 1D array:
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
>>> reshaped_array = original_array.reshape((2,3,4))
>>> print("reshaped 3D array:\n%s" % reshaped_array)
reshaped 3D array:
[[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]]

[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]


Numpy supports operations to be done along any axes of multi-dimensional arrays. For example, to sum the above array of shape (2, 3, 4) along axis 0

$a_{jk} = \sum_{i=0}^1a_{ijk} ,\; j=0, 1, 2; \; k=0, 1, 2, 3$

the resulting array has shape (3, 4):

>>> summed = reshaped_array.sum(axis=0)
>>> print("Summation along axis 0:\n%s" % summed)
Summation along axis 0:
[[12 14 16 18]
[20 22 24 26]
[28 30 32 34]]


For summing along axis 1

$a_{ik} = \sum_{j=0}^2a_{ijk} ,\; i=0, 1; \; k=0, 1, 2, 3$

the resulting array has shape (2, 4):

>>> summed = reshaped_array.sum(axis=1)
>>> print("Summation along axis 1:\n%s" % summed)
Summation along axis 1:
[[12 15 18 21]
[48 51 54 57]]


### Sub-Array Extraction#

numpy.ndarray supports creation of sub-arrays through the bracket operator ([] or __getitem__()).

#### Slicing#

Array slicing allows to create sub-arrays sharing the buffer of the original one by using the slice object. Here we demonstrate how it works. First, create the original array:

>>> array = np.arange(10)
>>> print("This is the original array:", array)
This is the original array: [0 1 2 3 4 5 6 7 8 9]


Calling __getitem__() to create a sub-array of the first half of the original array:

>>> sub_array = array[:5]
>>> print("This is the sub-array:", sub_array)
This is the sub-array: [0 1 2 3 4]


Try to write to the sub-array:

>>> sub_array[:] = np.arange(4, -1, -1)
>>> print("The sub-array is changed:", sub_array)
The sub-array is changed: [4 3 2 1 0]


After that, we see the content of the original array is changed because both arrays share the same memory buffer:

>>> print("And the original array is changed too (!):", array)
And the original array is changed too (!): [4 3 2 1 0 5 6 7 8 9]


We may also avoid sharing the buffer by copying after slicing:

>>> sub_array = array[:5].copy()


For convenience, in real code we usually write like the above one-liner, but it might not be the most clear way to show what really happened. The two things it does can be expanded:

>>> # Get a slice from the original array.
>>> sub_array = array[:5]
>>> # Copy all elements of the sub-array to a new one.
>>> sub_array = sub_array.copy()


Aided by copy(), the new array uses a separate buffer. Writing to the new array doesn’t change the original array:

>>> sub_array[:] = np.arange(4, -1, -1)
>>> print("The sub-array is changed, again:", sub_array)
The sub-array is changed, again: [4 3 2 1 0]
>>> print("But original array remains the same:", array)
But original array remains the same: [0 1 2 3 4 5 6 7 8 9]


Slicing works for multi-dimensional arrays as well. This example take a one-dimensional sub-array from a three-dimensional array:

>>> array = np.arange(24).reshape((2,3,4))
>>> print("orignal:\n%s" % array)
orignal:
[[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]]

[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]
>>> print(array[:,1,3].shape)
(2,)


Set the values in the sliced array and see the original array changes:

>>> array[:,1,3] = np.arange(300,302)
>>> print("find 300, 301:\n%s" % array)
find 300, 301:
[[[  0   1   2   3]
[  4   5   6 300]
[  8   9  10  11]]

[[ 12  13  14  15]
[ 16  17  18 301]
[ 20  21  22  23]]]


The same can be done to a two-dimensional sliced array:

>>> array[:,0,:] = np.arange(200,208).reshape((2,4))
>>> print(array[:,0,:].shape)
(2, 4)
>>> print("find the number [200,208):\n%s" % array)
find the number [200,208):
[[[200 201 202 203]
[  4   5   6   7]
[  8   9  10  11]]

[[204 205 206 207]
[ 16  17  18  19]
[ 20  21  22  23]]]


#### Integer Indexing#

The second approach to create sub-arrays is to use an index array, which should contain integers. Let’s say we have an index array named slct (shorthand for “selector”):

>>> slct = np.array([1, 3])

>>> array = np.arange(100, 106)
>>> print("select by indice 1, 3:", array[slct])
select by indice 1, 3: [101 103]


The statement array[slct] works like a loop:

>>> new_array = np.empty(slct.shape, dtype=array.dtype)
>>> for it, idx in enumerate(slct):
...   new_array[it] = array[idx]
...
>>> print("select by looping:", new_array)
select by looping: [101 103]


Note

Looping in Python gives horrible performance. Later it will be discussed in Array Code in C++. Be alarming to the slow Python loops in your high-performance code.

Sub-arrays created by the indexing array may be of any size and possibly longer than the source array:

>>> slct = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5])
>>> print("new array is bigger than the old one:", array[slct])
new array is bigger than the old one: [100 100 101 101 102 102 103 103 104 104 105 105]


The selected new array must use a different buffer than the original one. Value written to the sub-array has nothing to do with the original array:

>>> array = np.arange(100, 106)
>>> new_array = array[slct]
>>> new_array = -10
>>> print("original array remains unchanged:", array)
original array remains unchanged: [100 101 102 103 104 105]


But don’t be confused with the following code, which changes the original arrays by calling __setitem__():

>>> array = np.arange(100, 106)
>>> array[slct] = -10
>>> print("directly change the original array:", array)
directly change the original array: [100 -10 102 -10 104 105]


Note

array[key] invokes __getitem__(), while array[key] = value invokes __setitem__().

Index arrays work with multi-dimensional arrays. If the index array is one-dimensional and the original array is two-dimensional, the resulting array will be two-dimensional:

>>> array2 = np.arange(100, 106).reshape((2, 3))
>>> slct = np.array([1])
>>> print(array2[slct].shape)
(1, 3)
>>> print("select by indice 1:", array2[slct])
select by indice 1: [[103 104 105]]


To get one-dimensional output, create a two-dimensional array and feed it to __getitem__() in different axis:

>>> slct = np.array([[0,0], [0,1], [1,2]])
>>> print("select by indice (0,0), (0,1), (1,2):", array2[slct[:,0], slct[:,1]],
...       "using", slct)
select by indice (0,0), (0,1), (1,2): [100 101 105] using [[0 0]
[0 1]
[1 2]]


#### Boolean Selection#

The third approach to create sub-arrays is to use a Boolean array (an ndarray of dtype bool). A Boolean array for selection is similar to index arrays.

>>> ranged_array = np.arange(10)
>>> less_than_5 = ranged_array < 5
>>> print("The mask for less than 5:", less_than_5)
The mask for less than 5: [ True  True  True  True  True False False False False False]
>>> print("The values that are less than 5", ranged_array[less_than_5])
The values that are less than 5 [0 1 2 3 4]


Unlike index array, Boolean array cannot make arrays larger than the original one. The most we can get is to select all elements from the original:

>>> all_on_mask = np.ones(10, dtype='bool')
All on mask: [ True  True  True  True  True  True  True  True  True  True]
(10,)
Everything: [0 1 2 3 4 5 6 7 8 9]


If the mask is all-false, the resulting array will have zero length:

>>> all_off_mask = np.zeros(10, dtype='bool')
All off mask: [False False False False False False False False False False]
(0,)
Nothing: []


In the same way as index array, Boolean array always copies the original array:

>>> new_array = ranged_array[all_on_mask]
>>> new_array[:] = -1
>>> print(ranged_array)
[0 1 2 3 4 5 6 7 8 9]


__setitem__() may also take a Boolean array to set the original array: .. code-block:: pycon

>>> ranged_array[all_on_mask] = -1
>>> print(ranged_array)
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1]


Array arithmetic usually requires the participating arrays to have the same shape:

>>> a = np.arange(2)
>>> print("a =", a)
a = [0 1]
>>> b = np.arange(10,12):
>>> print("b =", b)
b = [10 11]
>>> print("a+b =", a+b) # good: same shape
a+b = [10 12]


When two arrays have different shapes, the operation fails:

>>> c = np.arange(3); print("c =", c)
c = [0 1 2]
>>> print(a+c) # bad: different shape
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: operands could not be broadcast together with shapes (2,) (3,)


But broadcasting may make arrays of different shapes participate in one operation. For example, set up two arrays of shapes 2x1 and 1x3, respectively:

>>> a = np.arange(5,7).reshape((2,1))
>>> print("a:\n%s, shape=%s" % (a, a.shape))
a:
[[5]
[6]], shape=(2, 1)
>>> b = np.arange(10,13).reshape((1,3))
>>> print("b:\n%s, shape=%s" % (b, b.shape))
b:
[[10 11 12]], shape=(1, 3)


Now the two array of different shapes may add:

>>> r = a+b
>>> print("a+b:\n%s, shape=%s" % (r, r.shape))
a+b:
[[15 16 17]
[16 17 18]], shape=(2, 3)


and multiply:

>>> r = a*b
>>> print("a*b:\n%s, shape=%s" % (r, r.shape))
a*b:
[[50 55 60]
[60 66 72]], shape=(2, 3)


1. All input arrays with number of dimension smaller than the input array of largest number of dimension, have 1’s prepended to their shapes.

2. The size in each dimension of the output shape is the maximum of all the input sizes in that dimension.

3. An input can be used in the calculation if its size in a particular dimension either matches the output size in that dimension, or has value exactly 1.

4. If an input has a dimension size of 1 in its shape, the first data entry in that dimension will be used for all calculations along that dimension.

Note

Broadcasting is a powerful tool. It allows to write complex array calculation. The down side is that the code may usually be too complex to understand. Oftentimes element-wise code is much more maintainable than broadcasting code.

## Python Tools for Numerical Analysis#

There are two equally important activities for software development. One is to write code. We will need to learn some basic concepts to write meaningful code.

The other is to use code written by other people. Especially in the early stage of development, we want to quickly see the results. We may just use the results of other software. We may directly incorporate the foreign (usually, also called “third-party”) software, if the situation allows. Otherwise, we can replace the quick prototype in a later phase.

In this lecture, I will introduce 3 useful tools for numerical analysis that you may use throughout the course and your future work.

### Drawing Using Matplotlib#

Matplotlib is a library for 2D plotting. It can be used standalone or integrated with Jupyter notebook.

The recipe of (blindly) using matplotlib:

1. Visit the gallery: https://matplotlib.org/gallery/index.html. Pick the category of the plot you want to make.

2. Copy the example code and run.

3. Modify the example to what you want.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm

x = np.linspace(0, 3 * np.pi, 500)
y = np.sin(x)
dydx = np.cos(0.5 * (x[:-1] + x[1:]))  # first derivative

# Create a set of line segments so that we can color them individually
# This creates the points as a N x 1 x 2 array so that we can stack points
# together easily to get the segments. The segments array for line collection
# needs to be (numlines) x (points per line) x 2 (for x and y)
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)

fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)

# Create a continuous norm to map from data points to colors
norm = plt.Normalize(dydx.min(), dydx.max())
lc = LineCollection(segments, cmap='viridis', norm=norm)
# Set the values used for colormapping
lc.set_array(dydx)
lc.set_linewidth(2)
fig.colorbar(line, ax=axs[0])

# Use a boundary norm instead
cmap = ListedColormap(['r', 'g', 'b'])
norm = BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(dydx)
lc.set_linewidth(2)
fig.colorbar(line, ax=axs[1])

axs[0].set_xlim(x.min(), x.max())
axs[0].set_ylim(-1.1, 1.1)
plt.show()


### Linear Algebra with Numpy#

Numpy provides wrappers for BLAS and LAPACK and can readily be used for solving linear systems. For example, consider the system:

$\begin{split}3x_1 + x_2 + 5x_3 &= 9 \\ x_1 + 2x_2 + x_3 &= 8 \\ 4x_1 + 3x_2 + x_3 &= 2\end{split}$
>>> a = np.array([[3,1,5], [1,2,1], [4,3,1]])
>>> b = np.array([9,8,2])
>>> x = np.linalg.solve(a, b)
>>> print(x)
[-3.4  4.2  3. ]
>>> print(np.dot(a, x))
[9. 8. 2.]


### Package Managers#

To write code we need a runtime environment that has the dependency software installed. Although manually building all the dependencies from source is sometimes unavoidable, it’s too time-consuming to do it always.

Usually we will use a package manager to help. A package manager provides recipes for building package from source, and also pre-built binary packages. It defines the dependencies between the packages. For example, for scipy to work, numpy needs to be installed beforehand. A package manager should allow automatic installation of numpy when you request scipy.

In the numerical analysis world, conda is one of the most versatile package manager that we will use. There are two major sources of packages:

In addition to conda, pip is another popular choice. pip is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes.

## Exercises#

1. List all primitive types supported by numpy.ndarray on x86-64.

2. Port “step0.py” to use bash.

3. Modify the script “step0.py” so that it reads the environment variable named “PYTHON_BIN” that specifies the location of the Python executable for the script. Hint: play a trick (or tricks) using bash, and note it’s possible to write no-op command in bash.

## References#

1

Broadcasting arrays in Numpy by Eli Bendersky