Use the plot method of matplotlib and set the legend with different sets of colors.StepsSet the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.Plot the lines using plt.plot() method with [9, 5], [2, 5] and [4, 7, 8] array.Initialize two variables; location = 0 for the best location and border_drawn_flag = True (True, if border to be drawn for legend. False, if border is not drawn).Use plt.legend() method for the legend and set the location and border_drawn_flag accordingly to get the perfect legend in the diagram.Show the figure using plt.show() method.Exampleimport matplotlib.pyplot as plt plt.ylabel("Y-axis ") ... Read More

Using the savefig method of the pyplot package, we can save the figure remotely by specifying the location of the figure.StepsTo use a different backend, set it using matplotlib.use('Agg') method.Plot the lines using plot() method.Using savefig() method, we can save the image remotely, just putting the directory.To show the figure, use plt.show().Exampleimport matplotlib matplotlib.use('Agg') from matplotlib import pyplot as plt plt.plot([1, 2, 3]) plt.savefig("remotely_fig.png")Output

To plot multiple lines in a diagram, we can use the cycler that could help to set a new color from the given list of colors. (Here, ‘r’ => ‘red’, ‘g’ => ‘green’, ‘y’ => ‘yellow’, ‘b’ => ‘blue’).StepsUse a cycler to set the color for the group of lines. The color list consists of ‘r’ for red, ‘g’ for green, ‘b’ for blue, and ‘y’ for yellow.The cycler class helps to create a new Cycler object from a single positional argument, a pair of positional arguments, or the combination of keyword arguments.Plot the number of lines with different colors.Use ... Read More

In this program, we will up sample an image. Up sampling is increasing the spatial resolution while keeping the 2D representation of an image. It is typically used for zooming in on a small region of an image. We will use the pyrup() function in the openCV library to complete this task.Original ImageAlgorithmStep 1: Read the image. Step 2: Pass the image as a parameter to the pyrup() function. Step 3: Display the output.Example Codeimport cv2 image = cv2.imread('testimage.jpg') print("Size of image before pyrUp: ", image.shape) image = cv2.pyrUp(image) print("Size of image after pyrUp: ", image.shape) cv2.imshow('UpSample', image)OutputSize ... Read More

In this program, we will perform the Blackhat operation on an image using OpenCV. BlackHat transform is used to enhance dark objects of interest in a bright background. We will use the morphologyEx(image, cv2.MORPH_BLACKHAT, kernel) function.Original ImageAlgorithmStep 1: Import cv2. Step 2: Read the image. Step 3: Define the kernel size. Step 4: Pass the image and kernel to the cv2.morphologyex() function. Step 5: Display the output.Example Codeimport cv2 image = cv2.imread('image_test.jpg') filter_size = (5,5) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, filter_size) image = cv2.morphologyEx(image, cv2.MORPH_BLACKHAT, kernel) cv2.imshow('BlackHat', image)Output

In this program, we will perform the TopHat operation on images. TopHat operation is a morphological operation that is used to extract small elements and details from given images. TopHat is used to enhance bright objects in a dark background. We will use the morphologyEx(image, cv2.MORPH_TOPHAT, kernel) functionOriginal ImageAlgorithmStep 1: Import cv2. Step 2: Read the image. Step 3: Define the kernel size. Step 4: Pass the image and kernel to the cv2.morphologyex() function. Step 5: Display the output.Example Codeimport cv2 image = cv2.imread('tophat.jpg') filter_size = (5, 5) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, filter_size) image = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel) cv2.imshow('TopHat', image)OutputExplanationAs ... Read More

The following program code shows how you can plot a newline in matplotlib label with Tex.StepsSetup X-axis and Y-axis labels for the diagram with to plot a newline in the labels.Set the current .rcParams for axes facecolor; the group is axed.Use a cycler to set the color for the group of lines. The color list consists of ‘r’ for red, ‘g’ for green, ‘b’ for blue, and ‘y’ for yellow.The cycler class helps to create a new Cycler object from a single positional argument, a pair of positional arguments, or the combination of keyword arguments.Plot the number of lines ... Read More

Using the FuncAnimation method, we can create a film. We will create a user-defined method, update, to keep on changing the position of particles and at the end, the method would return the scatter instance.StepsGet the particles initial position, velocity, force, and size.Create a new figure, or activate an existing figure with figsize = (7, 7).Add an axes to the current figure and make it the current axes, with xlim and ylim.Plot the scatter for initial position of the particles.Makes an animation by repeatedly calling a function *func*. We can pass a user-defined method that helps to change the position ... Read More

First, we can create a list of colors, and then, we can use the colors while plotting a line in a loop.StepsReturn evenly spaced numbers over a specified interval, store in x.Update x for four lines and get another variable for evenly_spaced_interval.Make a list of colors.Iterate color and set color for all the lines.To show the figure, use plt.show() method.Examplefrom matplotlib import pyplot as plt, cm import numpy as np x = np.linspace(0, 10, 100) lines = [x, x+10, x+5, x+11] evenly_spaced_interval = np.linspace(0, 1, len(lines)) colors = [cm.rainbow(x) for x in evenly_spaced_interval] for i, color in enumerate(colors): ... Read More

Using style='plain' in the ticklabel_format() method, we can restrict the value being changed into exponential form.StepsPass two lists to draw a line using plot() method.Use ticklabel_format() method with style='plain'. If a parameter is not set, the corresponding property of the formatter is left unchanged. Style='plain' turns off scientific notation.To show the figure, use plt.show() method.Examplefrom matplotlib import pyplot as plt plt.plot([1, 2, 3, 4, 5], [11, 12, 13, 14, 15]) plt.ticklabel_format(style='plain')    # to prevent scientific notation. plt.show()Output