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We can run the Collection using Newman through a shared link. To run Collection using Newman through share link we should follow the below steps −Step 1 − Click on the arrow to the right of the Collection name. Then click on the Share button.Step 2 − The SHARE COLLECTION1 window opens up. Click on Get public link. Then copy the link which is highlighted in the below image.Step 3 − Open terminal and execute the command −newman run So, here the command shall be −newman run https://www.getpostman.com/collections/ea0e981dbfa12ec5d657Please note − This link is unique to a user.If we want to ... Read More
We can write Assertions in Postman with Chai Assertion Library. Assertions are added in tests to verify if the actual and expected results are similar. In case, they are dissimilar, an Assertion error gets thrown along with the cause of the error.Boolean value of either true or false is returned by an Assertion. In Postman, Assertion is handled by Chai Assertion Library which is developed in JavaScript. It is provided in the Postman application by default.Assertions in Postman are added under the Tests tab. The documentation of the Chai library details are present in the link − https://www.chaijs.com/.Let us create ... Read More
We can get the Response in different formats in Mock Server. A Mock Server is created to avoid sending requests on the real time or production data. The steps to create a Response in different format in Mock Server are listed below −Step 1 − Click on New from the top of the Postman application. Then click on the Mock Server link.Step 2 − Choose the option GET from the Method field. Add /user/home in the Request Path, 200 in Response Code and the text – This is Postman Tutori in Tutorialspoint in the Response Body field.Step 3 − Provide ... Read More
We can import Collections in Postman. To perform the this task, follow the below steps −Step 1 − Click on the Import menu in the Postman application.Step 2 − Import pop-up shall open with the options to import from a File, Folder, Link, Raw text and Code Repository.Step 3 − We can either import by clicking on the Upload Files button or by drag and drop option. From the Code repository tab, we can import from GitHub.
We can add test verifications to the Response obtained from a request with the help of scripts. These scripts are incorporated in the Tests tab. The tests only get executed provided the request is successful.A test developed under the Tests tab is written in JavaScript. Once a request is sent, a Response is obtained along with the test results in the Test Results tab. Tests which have passed are marked in green and the failed ones are marked in red.Add the below JavaScript verifications within the Tests tab −tests["Status Code should be 200"] = responseCode.code === 200 tests["Response time lesser ... Read More
To apply a mask on the matrix in matplotlib imshow(), we can use np.ma.masked_where() method with lower and upper limit.StepsInitialize two variables, l and u, to mask the input matrix.Create random data of 5×5 dimension.Mask the input matrix, lower of l value, and above of u.Create a figure and a set of subplots with nrows=1 and ncols=Display the data as an image, i.e., on a 2D regular raster, at axes 0 andSet the title of the axes, 0 andTo 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 ... Read More
There are different types of Asserts in Postman. We can add Assertions on different sections of the Response received. Some of the Assert types are listed below −Assert on Response Codepm.test["Status Code is 200"], function(){ pm.response.to.have.status(200) })The above assertion passes if the Response code is 200.pm.test["Status is OK"], function(){ pm.response.to.have.property('status', ' OK') })The above assertion is applied on the Response property – status with value as OK.Assert on Response timepm.test("Response time below 600 milliseconds", function () { pm.expect(pm.response.responseTime).to.be.below(600) })The above assertion passes if the Response time is below 600ms.Assert on Response Formatpm.test("Response is of JSON type ", ... Read More
To show the Logarithmic plot of a cumulative distribution function in Matplotlib, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create data, X2 and F2 using numpy.Plot X2 and F2 using plot() method.Make x and y scale logarithmic.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 N = 100 data = np.random.randn(N) X2 = np.sort(data) F2 = np.array(range(N))/float(N) plt.plot(X2, F2) plt.xscale('log') plt.yscale('log') plt.show()OutputRead More
To visualize scalar 2D data with 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 data samples.Create x and y data points using numpy.Get coordinate matrices from coordinate vectors.Get z data points using numpy.Create a pseudocolor plot with a non-regular rectangular grid.To display the figure, use 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 n = 256 x = np.linspace(-3., 3., n) y = np.linspace(-3., 3., n) X, Y = np.meshgrid(x, ... Read More
To use pyplot.arrow or patches.Arrow() in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize four variables, x_tail, y_tail, x_head and y_head.Create a figure and a set of subplots.Get a fancy arrow instance.Add an artist (step 4) using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches as mpatches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x_tail = 0.1 y_tail = 0.1 x_head = 0.9 y_head = 0.9 fig, ax = plt.subplots() arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head), mutation_scale=100, color='green') ... Read More