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To get the Windows Performance counter using the Powershell, we can use the Get-Counter cmdlet.There are various performance counters available to measure the performance of the windows operating system. Get-Counter cmdlet is used to retrieve the performance of the local or the remote systems with the specific counter name.When you just run a Get-Counter command, it shows the main basic counters like Nic, Processor, disks, etc on the local system. as shown below.ExamplePS C:\> Get-Counter Timestamp CounterSamples --------- -------------- 4/7/2021 7:41:42 PM ... Read More
To plot the lines first and points last, we can take the following Steps −Create xpoints, y1points and y2points using numpy, to draw lines.Plot the curves using the plot() method with x, y1 and y2 points.Draw the scatter points using the scatter 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 xpoints = np.linspace(1, 1.5, 10) y1points = np.log(xpoints) y2points = np.exp(xpoints) plt.plot(xpoints, y1points) plt.plot(xpoints, y2points) for i in xpoints: plt.scatter(i, np.random.randint(10)) plt.show()OutputRead More
To create a stacked lines graph with Python, we can take the following Steps −Create x, y, y1 and y2 points using numpy.Plot the lines using numpy with the above data (Step 1) and labels mentioned.Fill the color between curve y=e^x and y=0, using the fill_between() method.Fill the color between curve y=2x and y=0, using the fill_between() method.Fill the color between curve y=log(x) and y=0, using fill_between() method.Place the curve text using the legend() method.To display the figure, use the 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(1, 5, 100) y = x * 2 y1 = np.log(x) ... Read More
It seems difficult to change the projection of an existing axis, but we can take the following steps to create different type projections −Using subplot() method, add a subplot to the current figure, with nrows=1, ncols=3 and current index=1.Add a title to the current axis.Using subplot() method, add a subplot to the current figure, with nrows=1, ncols=3 and current index=2, projection=hammer.Add a title to current axis, hammer.Using subplot() method, add a subplot to the current figure, with nrows=1, ncols=3 and current index=3, projection=polar.Add a title to current axis, polar.To display the figure, use the show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = ... Read More
Whenever plots are saved in .eps/.ps, then the transparency of the plots get lost.To compare them, we can take the following Steps −Create x_data and y_data using numpy.Plot x_data and y_data (Step 1), using the plot() method, with less aplha value, to make it more transparent.Use the grid() method to prove the transparency of the line.Save the created plot in .eps format.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_data = np.linspace(1, 10, 100) y_data = np.sin(x_data) plt.plot(x_data, y_data, c='green', marker='o', alpha=.35, ms=10, lw=1) plt.grid() plt.savefig("lost_transparency_img.eps") plt.show()OutputThe PostScript backend ... Read More
To plot a gradient color line in matplotlib, we can take the following steps −Create x, y and c data points, using numpy.Create scatter points over the axes (closely so as to get a line), using the scatter() method with c and marker='_'.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, 1, 1000) y = np.exp(x) c = np.tan(x) plt.scatter(x, y, c=c, marker='_') plt.show()Output
To put some superscript in Python, we can take the following steps −Create points for a and f using numpy.Plot f = ma curve using the plot() method, with label f=ma.Add title for the plot with superscript, i.e., kgms-2.Add xlabel for the plot with superscript, i.e., ms-2.Add ylabel for the plot with superscript, i.e., kg.To place the legend, use 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 a = np.linspace(1, 10, 100) m = 20 f = m*a plt.plot(a, f, c="red", lw=5, label="f=ma") plt.title("Force $\mathregular{kgms^{-2}}$") plt.xlabel("Acceleration $\mathregular{ms^{-2}}$") plt.ylabel("Acceleration $\mathregular{kg}$") ... Read More
To rotate tick labels for Seaborn barplot, we can take the following steps −Make a dataframe using Pandas.Plot the bar using Seaborn's barplot() method.Rotate the xticks label by 45 angle.To display the figure, use the show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [np.random.randint(10) for i in range(10)], "YAxis": [i for i in range(10)]}) bar_plot = sns.barplot(x='X-Axis', y='Y-Axis', data=df) plt.xticks(rotation=45) plt.show()Output
To plot interactive matplotlib’s imshow window, we can take the following steps −Using the subplots() method, create a figure and a set of subplots.Create an array to plot an image, using numpy.Display the image using the imshow() method.To make a slider axis, create an axes and a slider, with facecolor=yellow.To update the image, while changing the slider, we can write a user-defined method, i.e., update(). Using the draw_idle() method, request a widget redraw once the control returns to the GUI event loop.To display the figure, use the show() method.Exampleimport numpy as np from matplotlib import pyplot as plt from matplotlib.widgets import Slider ... Read More
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