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A Turing machine (TM) can be formally described as seven tuples −(Q, X, ∑, δ, q0, B, F)Where, Q is a finite set of states.X is the tape alphabet.∑ is the input alphabet.δ is a transition function:δ:QxX->QxXx{left shift, right shift}.q0 is the initial state.B is the blank symbol.F is the final state.Input − n a natural numberOutput − n + 2Let’s represent natural numbers in unary form (e.g. 3 = 111, 5 = 11111) and 0 will be represented by the empty symbol.AlgorithmMove the tape head to the left of the first 1 (if it exists).Change that empty cell to ... Read More
To plot a phase spectrum, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Get random seed value.Initialize dt for sampling interval and find sampling frequency.Create random data points for t.To generate noise, get nse, r, cnse and s using numpy.Create a figure and a set of subplots using subplots() method.Set the title of the plot.Plot the phase spectrum.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 np.random.seed(0) dt = 0.01 # sampling interval Fs = 1 ... Read More
To generate more colors on a pie chart in Matplotlib, we can generate n number of colors and dataStepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for number of data samples.Create random data points using numpy.Create a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Create a pie chart using pie() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import random import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True n = 40 color = ["#" + ... Read More
To plot vertical histogram in Python and Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of data points.Plot a histogram with vertical orientation.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 2, 3, 1, 2, 3, 4, 1, 3, 4, 5] plt.hist(x, orientation="vertical") plt.show()Output
To update a Matplotlib figure as the data file changes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize variables m and n, to get a set of subplots.Create a list of colors, to plot color dynamically.Plot dynamic data points using plot() method with random data points.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import random plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True m = 2 n = 4 fix, axes = plt.subplots(nrows=m, ncols=n) hexadecimal_alphabets = '0123456789ABCDEF' color = ["#" ... Read More
A Turing machine (TM) can be formally described as seven tuples −(Q, X, ∑, δ, q0, B, F)Where, Q is a finite set of states.X is the tape alphabet.∑ is the input alphabet.δ is a transition function: 𝛿:QxX->QxXx{left shift, right shift}.q0 is the initial state.B is the blank symbol.F is the final state.A Turing machine T recognises a string x (over ∑) if and only when T starts in the initial position and x is written on the tape, T halts in a final state.T is said to recognize a language A, if x is recognised by T and if ... Read More
To save Matplotlib 3d roatating plots, 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.Axes' to the figure as part of a subplot arrangement.Return a tuple X, Y, Z with a test data set.Plot a 3D wireframe.Rotate the axis with an angle.Redraw the current figure.Run the GUI event loop for some seconds.To display the figure, use show() method.Examplefrom mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111, ... Read More
To draw inline labels in Matplotlib, we can use labelLines() method. −StepsSet the figure size and adjust the padding between and around the subplots.Create random data points x using numpy and a list of data points, A.Iterate the list of A, and plot X and a (iterated item) with label.Label all the lines with their respective legends, for lines drawn.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt from labellines import labelLines plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True X = np.linspace(0, 1, 500) A = [1, 2, 5, 10, 20] ... Read More
To plot magintude spectrum, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Get random seed value.Initialize dt for sampling interval and find the sampling frequency.Create random data points for t.To generate noise, get nse, r, cnse and s using numpyCreate a figure and a set of subplots using subplots() method.Set the title of the plot.Plot the longitudinal magnitude spectrum.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 np.random.seed(0) dt = 0.01 # sampling interval Fs ... Read More
To plot magintude spectrum, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Get random seed value.Initialize dt for sampling interval and find the sampling frequency.Create random data points for t.To generate noise, get nse, r, cnse and s using numpyCreate a figure and a set of subplots, using subplots() method.Set the title of the plot.Plot the magnitude spectrum.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 np.random.seed(0) dt = 0.01 # sampling interval Fs = ... Read More