|
1 | | -## Installation of Scipy |
2 | | -### Install using the command: |
3 | | -#### C:\Users\Your Name>pip install scipy |
4 | | - You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder. |
5 | | -### Importing SciPy |
6 | | -#### from scipy import constants |
7 | | -## Key Features of SciPy |
8 | | -### 1. Numerical Integration |
9 | | -#### It helps in computing definite or indefinite integrals of functions |
10 | | -``` |
11 | | -from scipy import integrate |
| 1 | +# List of sections |
12 | 2 |
|
13 | | -#Define the function to integrate |
14 | | -def f(x): |
15 | | - return x**2 |
16 | | -
|
17 | | -#Compute definite integral of f from 0 to 1 |
18 | | -result, error = integrate.quad(f, 0, 1) |
19 | | -print(result) |
20 | | -``` |
21 | | -#### Output |
22 | | -``` |
23 | | -0.33333333333333337 |
24 | | -``` |
25 | | -### 2. Optimization |
26 | | -#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation |
27 | | -``` |
28 | | -from scipy.optimize import minimize |
29 | | -
|
30 | | -# Define an objective function to minimize |
31 | | -def objective(x): |
32 | | - return x**2 + 10*np.sin(x) |
33 | | -
|
34 | | -# Minimize the objective function starting from x=0 |
35 | | -result = minimize(objective, x0=0) |
36 | | -print(result.x) |
37 | | -``` |
38 | | -#### Output |
39 | | -``` |
40 | | -array([-1.30644012]) |
41 | | -``` |
42 | | -### 3. Linear Algebra |
43 | | -#### Solving Linear computations |
44 | | -``` |
45 | | -from scipy import linalg |
46 | | -import numpy as np |
47 | | -
|
48 | | -# Define a square matrix |
49 | | -A = np.array([[1, 2], [3, 4]]) |
50 | | -
|
51 | | -# Define a vector |
52 | | -b = np.array([5, 6]) |
53 | | -
|
54 | | -# Solve Ax = b for x |
55 | | -x = linalg.solve(A, b) |
56 | | -print(x) |
57 | | -``` |
58 | | -#### Output |
59 | | -``` |
60 | | -array([-4. , 4.5]) |
61 | | -``` |
62 | | -### 4. Statistics |
63 | | -#### Performing statistics functions, like here we'll be distributing the data |
64 | | -``` |
65 | | -from scipy import stats |
66 | | -import numpy as np |
67 | | -
|
68 | | -# Generate random data from a normal distribution |
69 | | -data = stats.norm.rvs(loc=0, scale=1, size=1000) |
70 | | -
|
71 | | -# Fit a normal distribution to the data |
72 | | -mean, std = stats.norm.fit(data) |
73 | | -``` |
74 | | -### 5. Signal Processing |
75 | | -#### To process spectral signals, like EEG or MEG |
76 | | -``` |
77 | | -from scipy import signal |
78 | | -import numpy as np |
79 | | -
|
80 | | -# Create a signal (e.g., sine wave) |
81 | | -t = np.linspace(0, 1, 1000) |
82 | | -signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) |
83 | | -
|
84 | | -# Apply a low-pass Butterworth filter |
85 | | -b, a = signal.butter(4, 0.1, 'low') |
86 | | -filtered_signal = signal.filtfilt(b, a, signal) |
87 | | -``` |
88 | | -The various filters applied that are applied here, are a part of signal analysis at a deeper level. |
89 | | -### 6. Sparse Matrix |
90 | | -#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created |
91 | | -#### There are two types of Sparse Matrices: |
92 | | -##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing |
93 | | -##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing |
94 | | -#### In CSC format |
95 | | -``` |
96 | | -from scipy import sparse |
97 | | -import numpy as np |
98 | | -
|
99 | | -data = np.array([[0, 0], [0, 1], [2, 0]]) |
100 | | -
|
101 | | -row_indices = np.array([1, 2, 1]) |
102 | | -col_indices = np.array([1, 0, 2]) |
103 | | -values = np.array([1, 2, 1]) |
104 | | -
|
105 | | -sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) |
106 | | -``` |
107 | | -#### In CSR format |
108 | | -``` |
109 | | -from scipy import sparse |
110 | | -import numpy as np |
111 | | -
|
112 | | -data = np.array([[0, 0], [0, 1], [2, 0]]) |
113 | | -sparse_matrix = sparse.csr_matrix(data) |
114 | | -``` |
115 | | -### 7. Image Processing |
116 | | -#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. |
117 | | -``` |
118 | | -from scipy import ndimage |
119 | | -import matplotlib.pyplot as plt |
120 | | -
|
121 | | -image = plt.imread('path/to/image.jpg') |
122 | | -plt.imshow(image) |
123 | | -plt.show() |
124 | | -
|
125 | | -# Apply Gaussian blur to the image |
126 | | -blurred_image = ndimage.gaussian_filter(image, sigma=1) |
127 | | -plt.imshow(blurred_image) |
128 | | -plt.show() |
129 | | -``` |
130 | | -#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. |
| 3 | +- [Installation of Scipy and its key uses](pandas_series_vs_numpy_ndarray.md) |
131 | 4 |
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