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Articles by Rishikesh Kumar Rishi
Page 24 of 102
Draw a curve connecting two points instead of a straight line in matplotlib
To draw a curve connecting two points instead of a straight line in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Define a draw_curve() method to make a curve with a mathematical expression.Plot point1 and point2 data points.Plot x and y data points returned from the draw_curve() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True def draw_curve(p1, p2): a = (p2[1] - p1[1]) / (np.cosh(p2[0]) - np.cosh(p1[0])) b ...
Read MoreMatplotlib – Date manipulation to show the year tick every 12 months
To make matplotlib date manipulation so that the year tick shows up every 12 months, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create d, y, s, years, months, monthsFmt and yearsFmt using Pandas, Numpy and matplotlib dates.Use "%B" in DateFormatter to show full month names.Ue "%Y" in DateFormatter to show years.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Plot "dts" and "s" data points using plot() method.Set minor or major axes locator and formatter. Set minor_locator as months ...
Read MorePlot data from a .txt file using matplotlib
To plot data from .txt file using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize empty lists for bar_names and bar_heights.Open a sample .txt file in read "r" mode and append to bar's name and height list.Make a bar plot.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True bar_names = [] bar_heights = [] for line in open("test_data.txt", "r"): bar_name, bar_height = line.split() bar_names.append(bar_name) bar_heights.append(bar_height) plt.bar(bar_names, bar_heights) plt.show()"test_data.txt" contains the following data −Javed ...
Read MoreHow to plot 2d FEM results using matplotlib?
The Finite Element Method (FEM) is used in a variety of tasks such as modeling of different material types, testing complex geometries, visualizing the local effects acting on a small area of a design. It basically breaks a large spatial domain into simple parts called "finite elements". The simple equations that model these finite elements are then collected into a larger system of equations to model the entire domain.To plot 2d FEM results using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create nodes, elements and node values data ...
Read MoreLegend with vertical line in matplotlib
To add a legend with vertical line in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Plot the vertical line with red color.The line can have both a solid linestyle connecting all the vertices, and a marker at each vertex.Place a legend on the plot with vertical line.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib import lines plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() color = 'red' ax.plot([0, 0], [0, 3], ...
Read MoreHow to exponentially scale the Y axis with matplotlib?
To exponentially scale the Y-axis with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Inintialize a variable dt for steps.Create x and y data points using numpy.Plot the x and y data points using numpy.Set the exponential scale for the Y-axis, using plt.yscale('symlog').To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True dt = 0.01 x = np.arange(-50.0, 50.0, dt) y = np.arange(0, 100.0, dt) plt.plot(x, y) plt.yscale('symlog') plt.show()OutputIt will produce the following ...
Read MoreHow to draw the largest polygon from a set of points in matplotlib?
To draw the largest polygon from a set of points in matplotlib, we can take the following steps −Import "Polygon" from matplotlib.patches.Set the figure size and adjust the padding between and around the subplots.Create a list of data points for the largest polygon.Get the polygon instance.Create a figure and a set of subplots.Add a polygon instance patch.Set the x and y scale limit.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Polygon plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True y = np.array([[1, 1], [0.5, 1.5], [2, 1], [1, 2], [2, ...
Read MoreHow to change the face color of a plot using Matplotlib?
To change the face color of a plot using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots.Plot the x and y data points using plot() method with color=yellow and linewidth=7.Set the facecolor of the axes, using set_facecolor().To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Create x and y data points x ...
Read MorePlotting a masked surface plot using Python, Numpy and Matplotlib
To plot a masked surface plot using Python, Numpy and Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Return the coordinate matrices from coordinate vectors, pi and theta.Create x, y and z with masked data points.Create a surface plot with x, y, and z data points.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = ...
Read MoreHow to set the border color of the dots in matplotlib's scatterplots?
To set the border color of the dots in matplotlib scatterplots, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable "N" to store the number of sample data.Create x and y data points using numpy.Plot the x and y data points using scatter() method. To set the border color of the dots, use the edgecolors parameter in the scatter() method. Here, we have used "red" as the border color of the dots by using edgecolors='red'.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot ...
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