Source Code of Python Project Py-LM-DivPlot:

Download this Source Code at python  file: Py_LM_DivPlot.py  - (2.35 KB Python file)   download

Continue   Py-LM-DivPlot Python program, 2D Surface plotting used data from .CSV file   import pandas as pd import matplotlib.pyplot as plt import csv   # reading CSV file df = pd.read_csv('winequalityN1.csv', delimiter=',') # converting column data to list q = df['quality'].tolist() a = df['alcohol'].tolist() # printing list data print('Quality, 24 values:', q) print("") print("") print('Alcohol, 24 values:', a)   #the Visualizing data through bar plot. plt.figure(1) plt.xlabel('quality') plt.ylabel('alcohol') plt.title('Qualities of Alcohol, 24 Values - Bar Plot') plt.bar(df['quality'], df['alcohol'], color = 'g') plt.show()   #the Visualizing data through line solid plot plt.figure(2) plt.xlabel('quality') plt.ylabel('alcohol') plt.title('Quality and Alcohol Report, 24 values - Line Solid Plot', fontsize = 12) plt.plot(q, a, color = 'g', linestyle = 'solid', label = "Quality-Alcohol Data") plt.xticks(rotation = 25), #plt.legend() plt.show() #the Visualizing data through line(dashed)-Marker plot plt.figure(3) plt.xlabel('quality') plt.ylabel('alcohol') plt.title('Quality and Alcohol Report, 24 values - Line(Dashed)-Marker Plot', fontsize = 12) plt.plot(q, a, color = 'g', marker = 'x', linestyle = 'dashed', label = "Quality-Alcohol Data") plt.xticks(rotation = 25) plt.show() #the Visualizing data through line(solid)-Grid plot plt.figure(4) plt.xlabel('quality') plt.ylabel('alcohol') plt.title('Quality and Alcohol Report, 24 values - Line(Solid)-Grid Plot', fontsize = 12) plt.plot(q, a, color = 'g', linestyle = 'solid', label = "Quality-Alcohol Data") plt.xticks(rotation = 25) plt.grid() plt.show() #the Visualizing data through Scatter-Grid plot plt.figure(5) plt.scatter(q, a, color = 'g',s = 100) plt.xlabel('quality') plt.ylabel('alcohol') plt.title('Quality & Alcohol Report, 24 values - Scatter-Grid Plot', fontsize = 12) #plt.Scatter(q, a, color = 'g',s = 100) plt.xticks(rotation = 25) plt.grid() plt.show() #the Visualizing data through pie plot plt.figure(6) plt.pie(a,labels = q,autopct = '%.2f%%') plt.title('Quality & Alcohol Marks, 24 values - Pie Plot', fontsize = 12) plt.show()   #the Visualizing data through Histogram plot import numpy as np x = np.random.normal(170, 10, 250) plt.figure(7) plt.title('Histogram Plot, Random values - Hitogram Plot', fontsize = 12) plt.hist(x) plt.show()

Output of Python Project Py-LM-DivPlot:

24 values of inequalityN1.csv file of Columns (Quality and Alcohol)

the Visualizing data of .cvs file of Columns (Quality and Alcohol), 24 values - bar plot

the Visualizing data of .cvs file of Columns (Quality and Alcohol), 24 values - Line Solid plot

the Visualizing data of .cvs file of Columns (Quality and Alcohol), 24 values - Line(Solid)-Grid plot

the Visualizing data of .cvs file of Columns (Quality and Alcohol), 24 values - Line(Dashed)-Marker plot

the Visualizing data of .cvs file of Columns (Quality and Alcohol), 24 values - Scatter plot

the Visualizing data of .cvs file of Columns (Quality and Alcohol), 24 values - pie plot

Random values -  Hitogram plot

Source Code of Python Project Py-LM-3d:

Download this Source Code at python  file: Py_LM_3d.py  - (5.98 KB Python file)   download

Continue   Py-LM-3d Python program, 3D Surface plotting   # 3D Surface plotting in Python using Matplotlib #1- import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np #1.1 x_lst = np.linspace(-5, 5, 4) y_lst = np.linspace(-5, 5, 4) x_grid, y_grid = np.meshgrid(x_lst, y_lst) z_grid = x_grid + y_grid # values at face corners (at the grid points) c_grid = np.abs(x_grid + y_grid) # same size like x_val, y_val, z_val print('1- Values at Face Corners, at the Grid Points' ) print("c_grid = ...", c_grid) print('') # color map and normalization c_min, c_max = np.min(c_grid), np.max(c_grid) # could also be min and max of c_faces norm = mpl.colors.Normalize(vmin=c_min, vmax=c_max) cmap = mpl.cm.viridis fig = plt.figure(1) plt.title('plot with grid values') ax = fig.add_subplot(111, projection='3d') surf = ax.plot_surface(x_grid, y_grid, z_grid, rstride=1, cstride=1, # no downsampling facecolors = cmap(norm(c_grid)), shade=False) cbar = plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, pad=0.2) ax.dist = 8 plt.show()     #1.2 # averaged corner values for each face c_faces = np.lib.stride_tricks.sliding_window_view(c_grid, (2,2)) c_faces = np.mean(c_faces, axis=(2, 3)) # size reduces by 1 print('2- Averaged Corner Values for Each Face' ) print("c_faces ...", c_faces) print('') # color map and normalization c_grid ##c_min, c_max = np.min(c_faces), np.max(c_faces) # could also be min and max of c_faces norm = mpl.colors.Normalize(vmin=c_min, vmax=c_max) cmap = mpl.cm.viridis ###-stand fig = plt.figure(2) plt.title("Plot with averaged values") ax = fig.add_subplot(111, projection='3d') surf = ax.plot_surface(x_grid, y_grid, z_grid, rstride=1, cstride=1, # no downsampling facecolors = cmap(norm(c_faces)), shade=False) cbar = plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, pad=0.2) ax.dist = 8 plt.show()     #3- from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter #import matplotlib.pyplot as plt #import numpy as np fig = plt.figure(3) ax = fig.add_subplot(111, projection='3d') X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) my_col = cm.jet(Z/np.amax(Z)) print('3- 3D Plot Projections' ) print("R...", R) print('') surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors = my_col, linewidth=0, antialiased=False) ax.set_zlim(-1.01, 1.01) ax.set_title('3D Plot Projections') plt.show()   #4- # 3D surface having 2D contour plot projections # Import libraries from mpl_toolkits import mplot3d #import numpy as np #import matplotlib.pyplot as plt # Creating dataset x = np.outer(np.linspace(-3, 3, 32), np.ones(32)) y = x.copy().T # transpose z = (np.sin(x **2) + np.cos(y **2) ) # Creating figure fig = plt.figure(4) #figsize =(14, 9)) ax = plt.axes(projection ='3d') # Creating color map my_cmap = plt.get_cmap('hot') # Creating plot surf = ax.plot_surface(x, y, z, rstride = 8, cstride = 8, alpha = 0.8, cmap = my_cmap) cset = ax.contourf(x, y, z, zdir ='z', offset = np.min(z), cmap = my_cmap) cset = ax.contourf(x, y, z, zdir ='x', offset =-5, cmap = my_cmap) cset = ax.contourf(x, y, z, zdir ='y', offset = 5, cmap = my_cmap) fig.colorbar(surf, ax = ax, shrink = 0.5, aspect = 5) print('4- 3D surface having 2D contour plot projections' ) print(surf) print('') # Adding labels ax.set_xlabel('X-axis') ax.set_xlim(-5, 5) ax.set_ylabel('Y-axis') ax.set_ylim(-5, 5) ax.set_zlabel('Z-axis') ax.set_zlim(np.min(z), np.max(z)) ax.set_title('3D surface having 2D contour plot projections') # show plot plt.show()   #5- # Surface plot # Import libraries from mpl_toolkits import mplot3d #import numpy as np #import matplotlib.pyplot as plt # Creating dataset x = np.outer(np.linspace(-3, 3, 32), np.ones(32)) y = x.copy().T # transpose z = (np.sin(x **2) + np.cos(y **2) ) # Creating figure fig = plt.figure(5) #figsize =(14, 9)) ax = plt.axes(projection ='3d') # Creating color map my_cmap = plt.get_cmap('hot') # Creating plot surf = ax.plot_surface(x, y, z, cmap = my_cmap, edgecolor ='none') print('5- Surface plot' ) print(surf) print('') fig.colorbar(surf, ax = ax, shrink = 0.5, aspect = 5) ax.set_title('Surface plot') # show plot plt.show()   #6 #- 3D surface ... # Import libraries from mpl_toolkits import mplot3d ##import numpy as np #import matplotlib.pyplot as plt   # Creating dataset x = np.outer(np.linspace(-3, 3, 32), np.ones(32)) y = x.copy().T # transpose z = (np.sin(x **2) + np.cos(y **2) ) # Creating figure fig = plt.figure(6) ax = plt.axes(projection ='3d')   # Creating plot ax.plot_surface(x, y, z) cpl= ax.plot_surface(x, y, z) print('6- 3D surface ...' ) print(cpl) print('') ax.set_title('3D surface ...') # show plot plt.show() ------------------------------------------------------------------------------------------- #7- from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter #import matplotlib.pyplot as plt #import numpy as np fig = plt.figure(7) ax = fig.add_subplot(111, projection='3d') X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) my_col = cm.jet(np.random.rand(Z.shape[0],Z.shape[1])) surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors = my_col, linewidth=0, antialiased=False) ax.set_zlim(-1.01, 1.01) print('7- linear colormap ...' ) print('R = ...', R) print('') ax.set_title('linear colormap') plt.show()

Output of Python Project Py-LM-3d:

Print list of values ...

Plot with grid values

Plot with averaged values

3D Plot Projections

3D surface having 2D contour plot projections

Surface plot

3D surface ...

Source Code of Python Project Py-LM-3d2dAdam:

Download this Source Code at python  file: Py_LM_3d2dAdam.py  - (1.87 KB Python file)   download

Continue   Py-LM-3d2dAdam Python program, to implement the gradient descent optimization algorithm with Adam   # creates a three-dimensional surface plot of the objective function -- x**2.0 + y**2.0 # 3d plot of the test function from numpy import arange from numpy import meshgrid from matplotlib import pyplot # objective function def objective(x, y): return x**2.0 + y**2.0 # define range for input r_min, r_max = -1.0, 1.0 # sample input range uniformly at 0.1 increments xaxis = arange(r_min, r_max, 0.1) yaxis = arange(r_min, r_max, 0.1) # create a mesh from the axis x, y = meshgrid(xaxis, yaxis) # compute targets results = objective(x, y) print('1- 3D surface plot of the objective function -- x**2.0 + y**2.0' ) print("results = ...", results) print('') # create a surface plot with the jet color scheme figure = pyplot.figure(1) #axis = figure.gca(projection='3d') axis= figure.add_subplot(111, projection='3d') axis.plot_surface(x, y, results, cmap='jet') axis.set_title('3D surface plot') # show the plot pyplot.show()   # create a two-dimensional plot of the objective function -- x**2.0 + y**2.0, # contour plot of the test function from numpy import asarray #from numpy import arange #from numpy import meshgrid #from matplotlib import pyplot # objective function def objective(x, y): return x**2.0 + y**2.0 # define range for input bounds = asarray([[-1.0, 1.0], [-1.0, 1.0]]) # sample input range uniformly at 0.1 increments xaxis = arange(bounds[0,0], bounds[0,1], 0.1) yaxis = arange(bounds[1,0], bounds[1,1], 0.1) # create a mesh from the axis x, y = meshgrid(xaxis, yaxis) # compute targets results = objective(x, y) print('2- 2D surface plot of objective function -- x**2.0 + y**2.0' ) print("results = ...", results) print('') # create a filled contour plot with 50 levels and jet color scheme figure = pyplot.figure(2) pyplot.contourf(x, y, results, levels=50, cmap='jet') pyplot.title('2D surface plot') # show the plot pyplot.show()

Output of Python Project Py-LM-3d2dAdam:

the Visualizing data through bar plot

3D surface plot of the objective function -- x**2.0 + y**2.0

2D plot of the objective function -- x**2.0 + y**2.0