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Articles by Rishikesh Kumar Rishi
Page 25 of 102
Updating the X-axis values using Matplotlib animation
To update the X-axis values using Matplotlib animation, 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.Create x and y data points using numpy.Plot x and y data points using plot method on axis (ax).Make an animation by repeatedly calling a function animate that sets the X-axis value as per the frame.To display the figure, use show() method.Exampleimport matplotlib.pylab as plt import matplotlib.animation as animation import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x ...
Read MoreHow to apply a mask on the matrix in Matplotlib imshow?
To apply a mask on the matrix in matplotlib imshow(), we can use np.ma.masked_where() method with lower and upper limit.StepsInitialize two variables, l and u, to mask the input matrix.Create random data of 5×5 dimension.Mask the input matrix, lower of l value, and above of u.Create a figure and a set of subplots with nrows=1 and ncols=Display the data as an image, i.e., on a 2D regular raster, at axes 0 andSet the title of the axes, 0 andTo display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ...
Read MoreHow to show the Logarithmic plot of a cumulative distribution function in Matplotlib?
To show the Logarithmic plot of a cumulative distribution function in Matplotlib, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create data, X2 and F2 using numpy.Plot X2 and F2 using plot() method.Make x and y scale logarithmic.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 100 data = np.random.randn(N) X2 = np.sort(data) F2 = np.array(range(N))/float(N) plt.plot(X2, F2) plt.xscale('log') plt.yscale('log') plt.show()Output
Read MoreHow to visualize scalar 2D data with Matplotlib?
To visualize scalar 2D data with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for data samples.Create x and y data points using numpy.Get coordinate matrices from coordinate vectors.Get z data points using numpy.Create a pseudocolor plot with a non-regular rectangular grid.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True n = 256 x = np.linspace(-3., 3., n) y = np.linspace(-3., 3., n) X, Y = np.meshgrid(x, ...
Read MoreHow to use pyplot.arrow or patches.Arrow in matplotlib?
To use pyplot.arrow or patches.Arrow() in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize four variables, x_tail, y_tail, x_head and y_head.Create a figure and a set of subplots.Get a fancy arrow instance.Add an artist (step 4) using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches as mpatches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x_tail = 0.1 y_tail = 0.1 x_head = 0.9 y_head = 0.9 fig, ax = plt.subplots() arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head), mutation_scale=100, color='green') ...
Read MoreHow to add black border to matplotlib 2.0 'ax' object In Python 3?
To add black border to matplotlib 2.0 'ax' object in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set axes edgecolor to black.Set axes linewidth to 2.50.Initialize a variable, N, to get the number of sample data.Create x and y data points using numpy.Plot x and y data points using plot() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rcParams["axes.edgecolor"] = "black" plt.rcParams["axes.linewidth"] = 2.50 N = 10 x = np.random.randint(low=0, high=N, size=N) y ...
Read MoreHow to plot a 3D patch collection in matplotlib?
To plot a 3D patch collection in 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.Get the current axes and set projection as 3d.Iterate ["x", "y", "z"] list, and set the circle patch using pathpatch_2d_to_3d() method to convert a PathPatch to a PathPatch3D object.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.patches import Circle import mpl_toolkits.mplot3d.art3d as art3d plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.gca(projection='3d') for i in ["x", ...
Read MoreHow to fill the area under a curve in a Seaborn distribution plot?
To fill the area under a curve in a Seaborn distribution plot, we can use distplot() and fill_between() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create a list of data points.Plot a univariate distribution of observations.To fill the area under the curve, use fill_between() method.Set or retrieve autoscaling margins, x=0 and y=0.To display the figure, use show() method.Exampleimport seaborn as sns import scipy.stats as stats import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = [2.0, 7.5, 9.0, 8.5] ax = sns.distplot(x, fit_kws={"color": "red"}, kde=False, fit=stats.gamma, hist=None, label="label 1") l1 = ...
Read MoreHow to adjust 'tick frequency' in Matplotlib for string X-axis?
To adjust tick frequency for X-axis, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data points.Create x and y data points using numpy.Plot x and y data points using plot() method.Initialize a variable freq_x to adjust the frequency of the xticks.Use xticks() method to set the xticks.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 10 x = np.random.randint(low=0, high=N, size=N) y = np.random.randint(low=0, high=N, ...
Read MoreSaving scatterplot animations with matplotlib
To save scatterplot animations with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize four variables, steps, nodes, positions and solutions.Append positions and solutions values in the list.Create a figure and a set of subplots.Initialize a variable for marker size.Configure the grid lines.Make an animation by repeatedly calling a function *animate*, to clear the axis, add new axis sublot, and plot scatter points on the axis.Save the animated scatter plot as a .gif file.Exampleimport matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np plt.rcParams["figure.figsize"] = [7.50, ...
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