Plotting

import numpy as np import matplotlib.pyplot as plt

y = np.arange(0, 11) ** 3 plt.plot(y, 'red') plt.xlim([0, 10])

mean = [69, 0] cov = [[15, 8], [8, 15]] np.random.seed(5) x, y = np.random.multivariate_normal(mean, cov, 2000).T y += 180 plt.scatter(x, y, color='magenta') plt.xlabel('Height (in)') plt.ylabel('Weight (lbs)') plt.title('Men\'s Height vs Weigh')

x = np.arange(0, 28651, 5730) r = np.log(0.5) t = 5730 y = np.exp((r / t) * x) plt.plot(x, y) plt.xlabel('Time (years)') plt.ylabel('Fraction Remaining') plt.title('Exponential Decay of C-14') plt.yscale('log') plt.xlim([0,28650])

x = np.arange(0, 21000, 1000) r = np.log(0.5) t1 = 5730 t2 = 1600 y1 = np.exp((r / t1) * x) y2 = np.exp((r / t2) * x) plt.plot(x, y1, 'r--', x, y2, 'green') plt.xlabel('Time (years)') plt.ylabel('Fraction Remaining') plt.title('Exponential Decay of Radioactive Elements') plt.xlim([0, 20000]) plt.ylim([0, 1]) plt.legend(['C-14', 'Ra-226'])

np.random.seed(5) student_grades = np.random.normal(68, 15, 50) plt.hist(student_grades, bins=10, edgecolor = "black") plt.xlabel('Grades') plt.ylabel('Number of Students') plt.title('Project A') plt.xlim([0, 100]) plt.ylim([0, 30])

y0 = np.arange(0, 11) ** 3 mean = [69, 0] cov = [[15, 8], [8, 15]] np.random.seed(5) x1, y1 = np.random.multivariate_normal(mean, cov, 2000).T y1 += 180 x2 = np.arange(0, 28651, 5730) r2 = np.log(0.5) t2 = 5730 y2 = np.exp((r2 / t2) * x2) x3 = np.arange(0, 21000, 1000) r3 = np.log(0.5) t31 = 5730 t32 = 1600 y31 = np.exp((r3 / t31) * x3) y32 = np.exp((r3 / t32) * x3) np.random.seed(5) student_grades = np.random.normal(68, 15, 50) fig = plt.figure() ax1 = plt.subplot2grid((3, 2), (0, 0)) ax2 = plt.subplot2grid((3, 2), (0, 1)) ax3 = plt.subplot2grid((3, 2), (1, 0)) ax4 = plt.subplot2grid((3, 2), (1, 1)) ax5 = plt.subplot2grid((3, 2), (2, 0), colspan= 2) # Titles plt.suptitle("All in One") ax2.set_title('Men\'s Height vs Weigh', fontsize='x-small') ax3.set_title('Exponential Decay of C-14', fontsize='x-small') ax4.set_title('Exponential Decay of Radioactive Elements', fontsize='x-small') ax5.set_title('Project A', fontsize='x-small') # Graphs ax1.plot(y0, 'red') ax2.scatter(x1, y1, color='magenta') ax3.plot(x2, y2) ax4.plot(x3, y31, 'r--', x3, y32, 'green') ax5.hist(student_grades, bins=10, edgecolor='black') # Scale ax3.set_yscale('log') # Limits ax1.set_xlim([0, 10]) ax3.set_xlim([0, 28650]) ax4.set_xlim([0, 20000]) ax4.set_ylim([0, 1]) ax5.set_xlim([0, 100]) ax5.set_ylim([0, 30]) # Labels ax2.set_xlabel('Height (in)', fontsize='x-small') ax2.set_ylabel('Weight (lbs)', fontsize='x-small') ax3.set_xlabel('Time (years)', fontsize='x-small') ax3.set_ylabel('Fraction Remaining', fontsize='x-small') ax4.set_ylabel('Fraction Remaining', fontsize='x-small') ax4.set_xlabel('Time (years)', fontsize='x-small') ax5.set_xlabel('Grades', fontsize='x-small') ax5.set_ylabel('Number of Students', fontsize='x-small') # Legend ax4.legend(['C-14', 'Ra-226'], fontsize='x-small') fig.tight_layout()

np.random.seed(5) fruit = np.random.randint(0, 20, (4,3)) columns = ['Farrah', 'Fred', 'Felicia'] apples = fruit[0] bananas = fruit[1] oranges = fruit[2] peaches = fruit[3] width = 0.5 fig, ax = plt.subplots() ax.bar(columns, apples, width, label='apples', color='red') ax.bar(columns, bananas, width, bottom=fruit[0], label='bananas', color='yellow') ax.bar(columns, oranges, width, bottom=fruit[1] + fruit[0], label='oranges', color='#ff8000') ax.bar(columns, peaches, width, bottom=fruit[2] + fruit[1] + fruit[0], label='peaches', color='#ffe5b4') ax.set_ylabel('Quantity of Fruit') ax.set_ylim([0, 80]) ax.set_title('Number of Fruit per Person') ax.legend()

np.random.seed(5) x = np.random.randn(2000) * 10 y = np.random.randn(2000) * 10 z = np.random.rand(2000) + 40 - np.sqrt(np.square(x) + np.square(y)) plt.scatter(x, y, c=z) plt.xlabel('x coordinate (m)') plt.ylabel('y coordinate (m)') plt.title('Mountain Elevation') colorBar = plt.colorbar() colorBar.set_label('elevation (m)')

from mpl_toolkits.mplot3d import Axes3D

lib = np.load("pca.npz") data = lib["data"] labels = lib["labels"] data_means = np.mean(data, axis=0) norm_data = data - data_means _, _, Vh = np.linalg.svd(norm_data) pca_data = np.matmul(norm_data, Vh[:3].T) fig = plt.figure(figsize=(8, 6)) ax = Axes3D(fig) cmap = plt.get_cmap(u'plasma') ax.scatter(pca_data[:,0], pca_data[:, 1], pca_data[:,2], c=labels, cmap=plt.get_cmap('plasma')) ax.set_title('PCA of Iris Dataset', fontsize='20')

plt.get_cmap('plasma')