From 0a63d2e17d69b8d8d3032bf36c51a183dc0e2450 Mon Sep 17 00:00:00 2001
From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com>
Date: Fri, 17 May 2024 23:59:25 +0530
Subject: [PATCH]  Installation_of_Scipy_&_its_key_uses.md

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+## Installation of Scipy
+### Install using the command:
+#### C:\Users\Your Name>pip install scipy
+ You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder.
+### Importing SciPy
+#### from scipy import constants
+## Key Features of SciPy
+### 1. Numerical Integration
+#### It helps in computing definite or indefinite integrals of functions
+```
+from scipy import integrate
+
+#Define the function to integrate
+def f(x):
+    return x**2
+
+#Compute definite integral of f from 0 to 1
+result, error = integrate.quad(f, 0, 1)
+print(result)
+```
+#### Output
+```
+0.33333333333333337
+```
+### 2. Optimization
+#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation
+```
+from scipy.optimize import minimize
+
+# Define an objective function to minimize
+def objective(x):
+    return x**2 + 10*np.sin(x)
+
+# Minimize the objective function starting from x=0
+result = minimize(objective, x0=0)
+print(result.x)
+```
+#### Output
+```
+array([-1.30644012])
+```
+### 3. Linear Algebra
+#### Solving Linear computations
+```
+from scipy import linalg
+import numpy as np
+
+# Define a square matrix
+A = np.array([[1, 2], [3, 4]])
+
+# Define a vector
+b = np.array([5, 6])
+
+# Solve Ax = b for x
+x = linalg.solve(A, b)
+print(x)
+```
+#### Output
+```
+array([-4. ,  4.5])
+```
+### 4. Statistics
+#### Performing statistics functions, like here we'll be distributing the data
+```
+from scipy import stats
+import numpy as np
+
+# Generate random data from a normal distribution
+data = stats.norm.rvs(loc=0, scale=1, size=1000)
+
+# Fit a normal distribution to the data
+mean, std = stats.norm.fit(data)
+```
+### 5. Signal Processing
+#### To process spectral signals, like EEG or MEG
+```
+from scipy import signal
+import numpy as np
+
+# Create a signal (e.g., sine wave)
+t = np.linspace(0, 1, 1000)
+signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000)
+
+# Apply a low-pass Butterworth filter
+b, a = signal.butter(4, 0.1, 'low')
+filtered_signal = signal.filtfilt(b, a, signal)
+```
+The various filters applied that are applied here, are a part of signal analysis at a deeper level.
+### 6. Sparse Matrix
+#### 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
+#### There are two types of Sparse Matrices:
+##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing
+##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing
+#### In CSC format
+```
+from scipy import sparse
+import numpy as np
+
+data = np.array([[0, 0], [0, 1], [2, 0]])
+
+row_indices = np.array([1, 2, 1])  
+col_indices = np.array([1, 0, 2])  
+values = np.array([1, 2, 1])       
+
+sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices)))
+```
+#### In CSR format
+```
+from scipy import sparse
+import numpy as np
+
+data = np.array([[0, 0], [0, 1], [2, 0]])
+sparse_matrix = sparse.csr_matrix(data)
+```
+### 7. Image Processing
+#### 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.
+```
+from scipy import ndimage
+import matplotlib.pyplot as plt
+
+image = plt.imread('path/to/image.jpg')
+plt.imshow(image)
+plt.show()
+
+# Apply Gaussian blur to the image
+blurred_image = ndimage.gaussian_filter(image, sigma=1)
+plt.imshow(blurred_image)
+plt.show()
+```
+#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image.