Programming Articles - Page 1252 of 3366

To differentiate antialiasing through curves, we can take the following Steps −Add a subplot to the current figure, using the subplot() method, where nrows=1, ncols=2 and index=1.Plot the curve using the plot() method, where antialiased flag is false and color is red.Place the legend at the upper-left corner using the legend() method.Add a subplot to the current figure, using the subplot() method, where nrows=1, ncols=2 and index=2.Plot the curve using the plot() method, where antialiased flag is true and color is green.Place the legend at the upper-right corner using the legend() method.To display the figure, use the show() method.Exampleimport numpy as np from matplotlib import pyplot as plt ... Read More

To get the legend as a separate picture, we can take the following steps −Create x and y points using numpy.Using the figure() method, create a new figure, or activate an existing figure for Line plot and Legend plot figures.Add an '~.axes.Axes' to the figure as part of a subplot arrangement, using the add_subplot() method at nrow=1, ncols=1 and at index=1.Create line1 and line2 using x, y and y1 points.Place the legend for line1 and line2, set ordered labels, put at center location.Save the figure only with legend using the savefig() method.Exampleimport numpy as np from matplotlib import pyplot as plt x = np.linspace(1, 100, ... Read More

To plot logarithmic Y-axis bins in Python, we can take the following steps −Create x and y points using numpy.Set the Y-axis scale using the yscale() method.Plot the x and y points, using the plot() method with linestyle="dashdot" and label="y=log(x)".To activate the label of the line, use the legend() method.To display the figure, use the 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 x = np.linspace(1, 100, 1000) y = np.log(x) plt.yscale('log') plt.plot(x, y, c="red", lw=3, linestyle="dashdot", label="y=log(x)") plt.legend() plt.show()OutputRead More

To hide axes (X and Y) and gridlines, we can take the following steps −Create x and y points using numpy.Plot a horizontal line (y=0) for X-Axis, using the plot() method with linestyle, labels.Plot x and y points using the plot() method with linestyle, labels.To hide the grid, use plt.grid(False).To hide the axes, use plt.axis('off')To activate the labels' legend, use the legend() method.To display the figure, use the 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 x = np.linspace(-10, 10, 50) y = np.sin(x) plt.axhline(y=0, c="green", linestyle="dashdot", label="y=0") plt.plot(x, y, c="red", lw=5, linestyle="dashdot", label="y=sin(x)") plt.grid(False) plt.axis('off') ... Read More

To plot matplotlib contour, we can take the following steps −Create data points for x, y and h using numpy.Using the countourf() method, create a colored 3D (alike) plot.Using the set_over() method, set the color for high out-of-range values when "norm.clip = False".Using the set_under() method, set the color for low out-of-range values when "norm.clip = False".Using the changed() method, call this whenever the mappable is changed to notify all the callbackSM listeners to the "changed" signal.Use the show() method to display the figure.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = ... Read More

Let's take an example to see how to display a 3D plot of a 3D array isosurface in matplotlib −Exampleimport numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(-5, 5, 0.25) y = np.arange(-5, 5, 0.25) x, y = np.meshgrid(x, y) h = x ** 2 + y ** 2 fig = plt.figure() ax = Axes3D(fig) ax.plot_surface(x, y, h, rstride=1, cstride=1, cmap=plt.cm.rainbow, linewidth=0, antialiased=False) plt.show()Output

To save the plot to a numpy array in RGB format, we can take the following steps −Create r, g and b random array using numpy.Zip r, g and b (grom step 1) to make an rgb tuple list.Convert rgb into a numpy array to plot it.Plot the numpy array that is in rgb format.Save the figure at the current location.To display the figure, use the 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 r = np.random.rand(100) g = np.random.rand(100) b = np.random.rand(100) rgb = zip(r, g, b) arr = np.array([item for item in rgb]) plt.plot(arr) plt.savefig("myplot.png") ... Read More

Let's see the steps to complete the program.Initialise the array.Initialise max_sum array of size n.Find the max sum for every index and store it in max_sum array.Compute the sum of all the elements and store it in a variable sum.Write a loop that iterates from i = k to n.Add a[i] - a[i - k] to the sum.Update the result with max of result, sum.Update the result with max of result, sum + max_sum[i - k].ExampleLet's see the code. Live Demo#include using namespace std; int getMaxSum(int a[], int n, int k) {    int maxSum[n];    maxSum[0] = a[0];    int ... Read More

Let's see the steps to complete the program.Initialise the array.Make all zeroes in the array to -1.Have a map an empty map to store the previous indexes.Initialise sum to 0, max length to 0 and ending index to -1.Write a loop that iterates till n.Add current element to sum.If the sum is equal to 0.Update the max length with i + 1.And ending index to i.If the sum is present in previous sums map and i - previousIndexes[sum] is greater than max length.Update the max length and ending index.Else add the sum to the previous indexes map.Print the starting index ... Read More

In this tutorial, we are going to write a program that finds the largest subarray have sum greater than k.Let's see the steps to solve the problem.Initialise the array.Iterate over the array and store sum at each index in a vector along with the index.Sort the above sums based on sum and index.Initialise an array to store the indexes.Write a loop that iterates till n.Update the values with min index of above indexes array and previous sums array index.Initialise sum to 0.Write a loop that iterates till n.Add current element to sum.If the sum is greater than k.The maximum subarray ... Read More