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Matplotlib Articles
Page 61 of 91
Plotting a probability density function by sample with Matplotlib
To plot a probability density function by sample, we can use numpy for x and y data points.StepsCreate x and p data points using numpy.Plot x and p data points using plot() method.Scale X-axis in a range.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(-100, 100) p = np.exp(-x ** 2) plt.plot(x, p) plt.xlim(-20, 20) plt.show()Output
Read MoreHow to plot a 2D matrix in Python with colorbar Matplotlib?
To plot a 2D matrix in Python with colorbar, we can use numpy to create a 2D array matrix and use that matrix in the imshow() method.StepsCreate data2D using numpy.Use imshow() method to display data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance *mappable* using colorbar() method and imshow() scalar mappable image.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data2D = np.random.random((50, 50)) im = plt.imshow(data2D, cmap="copper_r") plt.colorbar(im) plt.show()Output
Read MoreRotating axis text for each subplot in Matplotlib
To rotate axis text for each subplot, we can use text with rotation in the argument.StepsCreate a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.Adjust the subplot layout parameters using subplots_adjust() method.Add a centered title to the figure using suptitle() method.Set the title of the axis.Set the x and y label of the plot.Create the axis with some co-ordinate points.Add text to the figure with some arguments like fontsize, fontweight and add rotation.Plot a single point and annotate that point with some text and arrowhead.To display the ...
Read MoreLayering a contourf plot and surface_plot in Matplotlib
To layer a contourf plot and surface_plot in matplotlib, we can take the following Steps −Initialize the variables, delta, xrange, yrange, x and y using numpy.Create a new figure or activate an existing figure using figure() method.Get the current axis where projection='3d'.Create a 3d countour plot with x and y data points.Plot the surface with x and y data points.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True delta = 0.025 xrange = np.arange(-5.0, 20.0, delta) yrange = np.arange(-5.0, 20.0, delta) x, y = np.meshgrid(xrange, yrange) ...
Read MorePlotting points on the surface of a sphere in Python's Matplotlib
To plot points on the surface of a sphere in Python, we can use plot_surface() method.StepsCreate a new figure or activate an existing figure using figure() method.Add a set of subplots using add_subplot() method with 3d projection.Initialize a variable, r.Get the theta value for spherical points and x, y, and z data points using numpy.Plot the surface using plot_surface() 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 fig = plt.figure() ax = fig.add_subplot(projection='3d') r = 0.05 u, v = np.mgrid[0:2 * np.pi:30j, 0:np.pi:20j] x = np.cos(u) * ...
Read MoreCreating a 3D plot in Matplotlib from a 3D numpy array
To create a 3D plot from a 3D numpy array, we can create a 3D array using numpy and extract the x, y, and z points.Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.Create a random data of size=(3, 3, 3).Extract x, y, and z data from the 3D array.Plot 3D scattered points on the created axisTo display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax ...
Read MoreHow to use matplotlib.animate to animate a contour plot in Python?
To animate a contour plot in matplotlib in Python, we can take the following steps−Create a random data of shape 10☓10 dimension.Create a figure and a set of subplots using subplots() method.Makes an animation by repeatedly calling a function *func* using FuncAnimation() class.To update the contour value in a function, we can define a method animate that can be used in FuncAnimation() class.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(800).reshape(10, 10, 8) fig, ax = plt.subplots() def animate(i): ax.clear() ax.contourf(data[:, ...
Read MoreHow to position and align a Matplotlib figure legend?
To position and align a matplotlib figure legend, we can take the following steps−Plot line1 and line2 using plot() method.Place a legend on the figure. Use bbox_to_anchor to set the position and make horizontal alignment of the legend elements.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 line1, = plt.plot([1, 5, 1, 7], linewidth=0.7) line2, = plt.plot([5, 1, 7, 1], linewidth=2.0) plt.legend([line1, line2], ["line1", "line2"], bbox_to_anchor=(0.45, 1.0), ncol=2) plt.show()Output
Read MoreHow can I convert numbers to a color scale in Matplotlib?
To convert numbers to a color scale in matplotlib, we can take the following steps.StepsCreate x, y and c data points using numpy.Convert the data points to Pandas dataframe.Create a new figure or activate an existing figure using subplots() method.Get the hot colormap.To linearly normalize the data, we can use Normalize() class.Plot the scatter points with x and y data points and linearly normalized colormap.Set the xticks for x data points.To make the colorbar, create a scalar mappable object.Use colorbar() method to make the colorbar.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, colors import numpy as ...
Read MoreExporting an svg file from a Matplotlib figure
To export an SVG file from a matplotlib figure, 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 random x and y data points using numpy.Plot x and y data points using plot() method.Save the .svg format file using savefig() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.random.rand(10) y = np.random.rand(10) ax.plot(x, y, ls='dotted', linewidth=2, color='red') plt.savefig("myimg.svg")OutputWhen we execute this code, it will create an SVG file called "myimg.svg" and ...
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