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  1. app.py +61 -0
  2. requirements.txt +4 -0
app.py ADDED
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+ from matplotlib import pyplot as plt
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+ import numpy as np
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+ import streamlit as st
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+ import pandas as pd
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+
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+ np.set_printoptions(precision=3)
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+
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+ st.title("Eigen Values and Eigen Vectors")
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+ st.write(
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+ "This app shows the effect of linear transformation with respect to eigen values and eigen vectors"
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+ )
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+
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+ def getSquareY(x):
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+ if x==-1 or x == 1:
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+ return 0
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+ else:
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+ return 1
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+
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+ getSquareYVectorised = np.vectorize(getSquareY)
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+
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+ def getCircle(x):
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+ return np.sqrt(1-np.square(x))
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+
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+ with st.sidebar:
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+ data = st.selectbox('Select type of dataset', ['Square', 'Circle'])
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+ st.write("---")
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+ st.text("Enter transformation matrix elements")
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+ a_00 = st.slider(label = '$A_{0,0}$', min_value = -5, max_value=5, value=1)
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+ a_01 = st.slider(label = '$A_{0,1}$', min_value = -5, max_value=5, value=0)
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+ a_10 = st.slider(label = '$A_{1,0}$', min_value = -5, max_value=5, value=0)
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+ a_11 = st.slider(label = '$A_{1,1}$', min_value = -5, max_value=5, value=1)
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+
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+ def transform(x,y):
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+ return a_00 * x + a_01 * y, a_10 * x + a_11 * y
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+
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+
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+ x = np.linspace(-1,1,1000)
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+ y = getSquareYVectorised(x) if data == 'Square' else getCircle(x)
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+
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+ x_dash_up, y_dash_up = transform(x,y)
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+ x_dash_down, y_dash_down = transform(x,-y)
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+
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+ t = np.array([[a_00,a_01], [a_10, a_11]], dtype=np.float64)
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+
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+ try:
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+ evl, evec = np.linalg.eig(t)
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+ fig, ax = plt.subplots()
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+ ax.plot(x_dash_up,y_dash_up,'r')
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+ ax.plot(x_dash_down,y_dash_down, 'g')
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+ ax.quiver(0,0,evec[0][0],evec[0][1],scale=1,scale_units ='xy',angles='xy', facecolor='yellow', label='$\lambda_0$')
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+ ax.quiver(0,0,evec[1][0],evec[1][1],scale=1,scale_units ='xy',angles='xy', facecolor='blue',label='$\lambda_1$')
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+ ax.set_xlim(-5,5)
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+ ax.set_ylim(-5,5)
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+ ax.set_aspect('equal', adjustable='box')
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+ fig.legend()
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+ st.pyplot(fig)
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+
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+ df = pd.DataFrame({'Eigen Values': evl, 'Eigen Vectors': [str(evec[0]), str(evec[1])]})
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+ st.table(df)
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+ except:
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+ st.write("Given matrix has eigen vectors in complex space")
requirements.txt ADDED
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+ matplotlib
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+ numpy
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+ pandas
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+ streamlit