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To show mouse release event coordinates with 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 a line in the range of 10.Bind the function *onclick* to the event *button_release_event*.Print event and its x and y data.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams['backend'] = 'TkAgg' plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True def onclick(event): print(event.button, event.xdata, event.ydata) fig, ax = plt.subplots() ax.plot(range(10)) fig.canvas.mpl_connect('button_release_event', onclick) plt.show()OutputMouseButton.LEFT 4.961566107601828 1.6644009000562534 MouseButton.LEFT 6.782345894140708 3.7026907931745727 MouseButton.LEFT 2.98552602918754 7.177807987999249Read More
To make longer subplot tick marks in matplotlib, we can use tick_params() method for minor and major ticks length and width.StepsAdd a subplot to the current figure using subplot() method.Plot a range(2) values for x and y data points.Turn the minor ticks of the colorbar ON without extruding into the "extend regions".Use tick_params for changing the appearance of ticks and tick labels.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 ax1 = plt.subplot() ax1.plot(range(2), range(2), linewidth=2) ax1.minorticks_on() ax1.tick_params('both', length=20, width=2, which='major') ax1.tick_params('both', length=10, width=1, which='minor') plt.show()OutputRead More
To make bold font weight LaTeX axes label in matplotlib, we can take the following steps−Create x and y data points using numpy.Using subplot() method, add a subplot to the current figure.Set x and y ticks with data points x and y using set_xticks and set_yticks methods, respectively.Plot x and y using plot() method with color=red.To set bold font weight, we can use LaTeX representation.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, font_manager as fm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rcParams["font.fantasy"] = "Comic Sans MS" x = np.array([1, 2, 3, ... Read More
To make bold font weight LaTeX axes label in matplotlib, we can take the following steps−Create data points for x.Create data points for y, i.e., y=sin(x).Plot the curve x and y with LaTex representation.To activate the label, use legend() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, font_manager as fm fprop = fm.FontProperties(fname='/usr/share/fonts/truetype/malayalam/Karumbi.ttf') plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 1000) y = np.sin(x) plt.plot(x, y, label=r'$\sin (x)$', c="red", lw=2) plt.title(label=r'$\sin (x)$', fontproperties=fprop) plt.show()OutputRead More
Matplotlib does not support the functionality to plot implicit equations, however, you can try a code like the one we have shown here.StepsCreate xrange and yrange data points using numpy.Return coordinate matrices from coordinate vectors using meshgrid() method.Create an equation from x and y.Create a 3D contour using contour() method with x, y and the equation.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 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) equation = np.sin(x) - ... Read More
To set the aspect ratio of a 3D plot in matplotlib, we can take the following steps−Using figure() method, create a new figure or activate an existing figure.Get the current axes, creating one if necessary, with projection='3d'.Create data points, R, Y and z, using numpy.Create a surface plot using R, Y and z.Set the aspect ratio using set_aspect('auto').Save the figure using savefig() method.Examplefrom matplotlib import pyplot as plt from matplotlib import cm import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.gca(projection='3d') R, Y = np.meshgrid(np.arange(0, 100, 1), np.arange(0, 60, 1)) z = ... Read More
To plot a Cartesian coordinate system in matplotlib, we can take the following Steps −Initialize a variable (N) with a value.Create random data points for x and y.Plot the points using scatter method with x and y data points.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 N = 50 x = np.random.rand(N) y = np.random.rand(N) plt.scatter(x, y) plt.show()Output
To plot multiple X or Y axis, we can use twinx() or twiny() methods, we can take the following Steps −Using subplots() method, create a figure and a set of subplots.Plot [1, 2, 3, 4, 5] data points on the left Y-axis scales.Using twinx() method, create a twin of Axes with a shared X-axis but independent Y-axis, ax2.Plot [11, 12, 31, 41, 15] data points on the right Y-axis scale, with blue color.Using tight_layout() method, adjust the padding between and around the subplots.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ... Read More
To get empty tick labels before showing a plot in matplotlib, we can take the following Steps −Create a list of data points.Add a subplot to the current figure using subplot() method.Set ticks and ticklabels using set_xticks() method and set_xticklabels() method.To get the empty tick labels, use get_xticklabels(which='minor').To display the method, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 2, 3, 4] ax1 = plt.subplot() ax1.set_xticks(x) ax1.set_xticklabels(["one", "two", "three", "four"]) print("Empty tick labels: ", ax1.get_xticklabels(which='minor')) plt.show()OutputRead More
Gaussian filtering an image with NaN values makes all the values of a matrix NaN, which produces an NaN valued matrix.StepsCreate a figure and a set of subplots.Create a matrix with NaN value in that matrix.Display the data as an image, i.e., on a 2D regular raster, data.Apply Gaussian filter on the data.Display the data as an image, i.e., on a 2D regular raster, gaussian_filter_data.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt from scipy.ndimage import gaussian_filter plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, axes = plt.subplots(2) data = np.array([[1., 1.2, 0.89, ... Read More
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