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To set the networkx edge labels offset, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a graph with edges, name, or graph attributes.Add multiple nodes.Position the nodes using Fruchterman-Reingold force-directed algorithm.Draw the graph G with Matplotlib.Draw edge labels.To display the figure, use show() method.Exampleimport matplotlib.pylab as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.DiGraph() G.add_nodes_from([1, 2, 3, 4]) G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)]) pos = nx.spring_layout(G) for u, v, d in G.edges(data=True): d['weight'] ... Read More
To plot contourf and log scale in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create x, y, X, Y, Z1, Z2 and z data points using numpy.Create a figure and a set of subplots.Plot contours using contourf() method.Create a colorbar for a scalar mappable instance.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np from numpy import ma from matplotlib import ticker, cm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 100 x ... Read More
To set horizontal and vertical, major and minor grid lines of a plot, we can use grid() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Make horizontal grid lines for major ticks.Locate minor locator on the axes.Use grid() method to make minor grid lines.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.ticker import MultipleLocator plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() ax.yaxis.grid(which="major", color='r', linestyle='-', linewidth=2) ml = MultipleLocator(0.10) ax.xaxis.set_minor_locator(ml) ax.xaxis.grid(which="minor", color='k', linestyle='-.', linewidth=0.7) plt.show()OutputRead More
To plot contour with hatching, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using numpy.Flat the x and y data points.Create a figure and a set of subplots.Plot a contour with different hatches.Create a colorbar for a scalar mappable instance.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-3, 5, 150).reshape(1, -1) y = np.linspace(-3, 5, 120).reshape(-1, 1) z = np.cos(x) + np.sin(y) x, y = ... Read More
To move a tick label without moving corresponding tick in Matplotlib, we can use axvline() method and can annotate it accordingly.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, delta.Create x and y data points using numpy.Plot delta using axvline() methodAnnotate that line using annotate() method.Plot x and y data points using plot() method.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 = 2.0 x = np.linspace(-10, 10, 100) y = np.sinc(x - delta) plt.axvline(delta, ls="--", ... Read More
To axes axis label object in Matplotlib, we can use ax.xaxis.get_label().get_text() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize a variable, N, for number samples.Create random data points using numpy.Plot x data points using plot() method.Set X-axis label using set_xlabel() method.To get the xlabel, use get_label() method and get_text() method.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 fig, ax = plt.subplots() N = 100 x = np.random.rand(N) ax.plot(x) ax.set_xlabel("X-axis") x_lab = ax.xaxis.get_label() print("Label is: ... Read More
To adjust one subplot's height in absolute way in Matplotlib, 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.For absolute height of subplot, use Axes() classAdd an axes to the figure.Plot the data points on the axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as pl pl.rcParams["figure.figsize"] = [7.50, 4.50] pl.rcParams["figure.autolayout"] = True figure = pl.figure() axes = pl.Axes(figure, [.4, .6, .25, .25]) figure.add_axes(axes) pl.plot([1, 2, 3, 4], [1, 2, 3, 4]) axes = pl.Axes(figure, [.4, ... Read More
Construct deterministic finite automata that accepts at most 3 a’s over an alphabet ∑={a,b}.At most 3 a’s means,The string contains 0 to max 3 a’s and any number of b’s.L= {Є,a,aa,aaa,ab,abb,bab,bbabaa, bbabaabbb,…..}Construct DFALet’s construct DFA step by step −Step 1Valid inputs − aaa, a, aa,ε .Step 2Valid inputs − b, ba, baa, baaa, bb, bba, bbba,…Step 3Valid input − bab, abba, abbbaa, babba,…Step 4Valid inputs − babab, aabb, aaba, bbbaaba, …Step 5Valid inputs − aaabbb, aaabab, baaaba, …Step 6InValid inputs − aaaa, aaabab, baaaba,
To calculate the curl of a vector field in Python and plot in with Matplotlib, we can use quiver() method and calculate the corresponding data.StepsSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Add a 3D axes to the figure as part of a subplot arrangement.Create x, y and z data points using numpy meshgrid.Create u, v and w data curl vector positions.Use quiver() method to get vectors.Turn off the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] ... Read More
CKY means Cocke-Kasami-Younger. It is one of the earliest recognition and parsing algorithms. The standard version of CKY can only recognize languages defined by context-free grammars in Chomsky Normal Form (CNF).It is also possible to extend the CKY algorithm to handle some grammars which are not in CNF (Hard to understand).Based on a “dynamic programming” approach −Build solutions compositionally from sub-solutionsIt uses the grammar directly.AlgorithmBegin for ( i = 1 to n do ) Vi1 { A | A → a is a production where i th symbol of x is a } for ( j = ... Read More