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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to give Matplolib imshow plot colorbars a label?
To give matplotlib imshow() plot colorbars a label, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create 5×5 data points using Numpy.Use imshow() method to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, im.Set colorbar label using set_label() method.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 data = np.random.rand(5, 5) im = plt.imshow(data, cmap="copper") cbar = plt.colorbar(im) cbar.set_label("Colorbar") plt.show()Output
Read MoreHow to set a title above each marker which represents a same label in Matplotlib?
To set a title above each marker which represents the same label in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using Numpy.Create four curves, c1, c2, c3 and c4 using plot() method.Place a legend on the figure, such that the same label marker would come together.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, legend_handler plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) c1, = plt.plot(x, np.sin(x), ls='dashed', label='y=sin(x)') c2, ...
Read MoreHow to change the color and add grid lines to a Python Matplotlib surface plot?
To change the color and add grid lines to a Python surface plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and h data points using numpy.Create a new figure or activate an existing figure.Get 3D axes object, with figure (from Step 3).Create a surface plot, with orange color, edgecolors and linewidth.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, ...
Read MoreHow to find the matplotlib style name?
To find the matplotlib style name, we can take the following steps −import matplotlib.pyplot as pltprint(plt.style.library)Exampleimport matplotlib.pyplot as plt print(plt.style.library)Output{'bmh': RcParams({'axes.edgecolor': '#bcbcbc', 'axes.facecolor': '#eeeeee', 'axes.grid': True, 'axes.labelsize': 'large', 'axes.prop_cycle': cycler('color', ['#348ABD', '#A60628', '#7A68A6', '#467821', '#D55E00', '#CC79A7', '#56B4E9', '#009E73', '#F0E442', '#0072B2']), 'axes.titlesize': 'x-large', 'grid.color': '#b2b2b2', 'grid.linestyle': '--', 'grid.linewidth': 0.5, 'legend.fancybox': True, 'lines.linewidth': 2.0, 'mathtext.fontset': 'cm', 'patch.antialiased': True, 'patch.edgecolor': '#eeeeee', 'patch.facecolor': 'blue', 'patch.linewidth': 0.5, 'text.hinting_factor': 8, 'xtick.direction': 'in', 'ytick.direction': 'in'}), 'classic': RcParams({'_internal.classic_mode': True, 'agg.path.chunksize': 0, ...
Read MoreHow to get an interactive plot of a pyplot when using PyCharm?
To get an interactive plot of a pyplot when using PyCharm, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set the background style.Plot the data on the axes.To display the figure, use show() method.Exampleimport matplotlib as mpl import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True mpl.use('Qt5Agg') plt.plot(range(10)) plt.show()Output
Read MoreHow to increase the spacing between subplots in Matplotlib with subplot2grid?
To increase the spacing between subplots with subplot2grid, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Add a grid layout to place subplots within a figure.Update the subplot parameters of the grid.Add a subplot to the current figure.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ax = plt.GridSpec(2, 2) ax.update(wspace=0.5, hspace=0.5) ax1 = plt.subplot(ax[0, :]) ax2 = plt.subplot(ax[1, 0]) ax3 = plt.subplot(ax[1, 1]) plt.show()Output
Read MoreHow to get multiple overlapping plots with independent scaling in Matplotlib?
To get multiple overlapping plots with independent scaling in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Plot a list of data points using plot() method on a seperate Y-axis and overlapping X-axis.Create a twin Axes sharing the X-axis.Plot a list of data points using plot() method on a seperate Y-axis and overlapping X-axis.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 fig, ax1 = plt.subplots() ax1.plot([1, 2, 3, 4, 5], color='red') ...
Read MoreREST Client Testing using Restito Tool
RESTREST (Representational State Transfer) is a modern technique of enabling communication between two software systems. One such system is known as REST Client; the other is known as REST Server. It is an architectural technique based on a stateless communications protocol, such as HTTP. It organizes or structures data in XML, YAML, and other machine-readable formats. However, JSON is mostly used. REST is based on objectoriented programming model.REST is data-driven, unlike SOAP which is function-driven. REST is also referred to as RESTful APIs or RESTful web services. The description format of REST services does not follow a standard. REST service ...
Read MoreHow to display a sequence of images using Matplotlib?
To display a sequence of images using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of images that have to be drawn.Turn off the axes.Iterate the images and redraw over the axes.Take a pause after each draw.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True images = ['opera.jpg', 'mountain.jpg', '9.jpg'] plt.axis('off') img = None for f in images: im = plt.imread(f) if img is None: img = plt.imshow(im) plt.pause(0.5) else: ...
Read MoreHow to draw a filled arc in Matplotlib?
To draw a filled arc in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize two variables, r, yoff.Create x and y data points using Numpy.Fill the area between x and y plots.Set the axis aspect and draw the figure canvas.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 fg, ax = plt.subplots(1, 1) r = 2. yoff = -1 x = np.arange(-1., 1.05, 0.05) y ...
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