Let's take an example to see how imshow() handles the alpha channel with an M×N×4 input.StepsSet the figure size and adjust the padding between and around the subplots.Return a new array of given shape and type, filled with 1's.Handle the alpha channel.Display the data as an image, i.e., on a 2D regular raster.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 d = np.ones((100, 100, 4), dtype=np.uint8)*255 d[:, :, 1] = np.linspace(0, 255, num=100) plt.imshow(d) plt.show()OutputRead More

To set a Matplotlib rectangle edge to outside of specified width, 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.Initialize a variable line_width to set the rectangle outside of specified width. Use the variables xy, w and h for rectangle's center, width and height.Get a rectangle instance, with xy anchor points and its height and width.Get the offset transformbox instance.Add an artist patch, r (Step 5).Get the container for an OffsetBox ... Read More

To add a cursor to a curve in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create t and s data points using numpy.Create a figure and a set of subplots.Get the cursor class instance, to update the cursor points on the plot.In mouse_event, get the x and y data of the current position of the mouse.Get the x and y data points' indices.Set the x and y positions.Set the text position and redraw agg buffer and mouse event.Plot t and s data points using plot() method.Set some axis ... Read More

To create animated GIF files out of D3.js animation, 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 axes to the current figure and make it the current axes.Plot a line with empty lists.To initialize the line, pass empty lists.To animate the sine curve, update the sine curve values and return the line instance.Get a movie writer instance using PillowWriter() class.Save the .gif file using PillowWriter.Exampleimport numpy as np from matplotlib import pyplot as plt from matplotlib import animation plt.rcParams["figure.figsize"] ... Read More

To convert matplotlib figure to PIL image object, 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.Plot a list using plot() method.Initialize the in-memory buffer.Save the buffered image.Use PIL image to get the image object.Show the current image.Close the in-memory I/O buffer.Exampleimport io from PIL import Image import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.figure() plt.plot([1, 2]) img_buf = io.BytesIO() plt.savefig(img_buf, format='png') im = Image.open(img_buf) im.show(title="My Image") img_buf.close()OutputRead More

To draw node colormap in matplotlib/netwokx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Return the cycle graph $C_n$ of cyclically connected nodes.Position the nodes on a circle.Draw the graph G with Matplotlib.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.cycle_graph(24) pos = nx.circular_layout(G) nx.draw(G, pos, node_color=range(24), node_size=800, cmap='copper') plt.show()Output

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 More

To set two matplotlib imshow() plots to have the same colormap scale, we can take the followingStepsSet the figure size and adjust the padding between and around the subplots.Create d1 and d2 matrices using Numpy.Get the resultant matrix to get the maximum and minmum value.Use amin and amax methods for minimum and maximum values.Create a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement, with nrows=1, ncols=2 at index 1Using imshow() method with vmin and vmax, define the data range that the colormap covers.Repeat steps 6 and 7 with dataTo display ... Read More

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 More

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()OutputRead More