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Programming Articles
Page 2213 of 2547
Matplotlib figure to image as a numpy array
We can use the following steps to convert a figure into a numpy array −Read a figure from a directory; convert it into numpy array.Use imshow() method to display the image.Use show() method to display it.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True im = plt.imread("bird.jpg") print("Numpy array of the image is: ", im) im = plt.imshow(im) plt.show()OutputWhen we execute the code, it will show "bird.jpg" in a plot and show its numpy array on the console.Numpy array of the image is: [[[162 162 170] [162 162 170] [160 163 170] ... [ 97 98 92] [ 98 100 95] [ 94 96 91]] [[159 159 167] [159 159 167] [157 160 167] ... [ 94 95 89] [ 95 97 92] [ 92 94 89]] [[157 158 163] [157 158 163] [154 157 164] ... [ 93 94 89] [ 95 95 93] [ 95 95 93]] ... [[163 163 165] [163 163 165] [164 164 164] ... [187 165 151] [158 131 112] [133 105 84]] [[163 163 165] [163 163 165] [163 163 163] ... [160 134 117] [143 112 92] [127 96 75]] [[164 164 166] [163 163 165] [163 163 163] ... [145 116 98] [129 98 78] [124 92 71]]]
Read MoreAuto adjust font size in Seaborn heatmap using Matplotlib
To adjust font size in Seaborn, we can take followig steps−Create a dictionary with some mathematical expressionsCreate a dataframe using Pandas data frame.Create a heatmap using heatmap() method.To adjust the font size in Seaborn heatmap, change the fontsize value.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True d = { 'y=1/x': [1 / i for i in range(1, 10)], 'y=x': [i for i in range(1, 10)], 'y=x^2': [i * i for i in range(1, 10)], ...
Read MoreHow do I show logarithmically spaced grid lines at all ticks on a log-log plot using Matplotlib?
To show logarithmically spaced grid lines at all ticks on a log-log plot using matplotlib, we can take the following steps−Create data points for x and y using numpy.Using loglog method, make a plot with log scaling on both the X and Y axis.Use grid() method, lay out a grid in the current line style. Supply the list of x an y positions.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(0, 10, 1) y = np.exp(x) plt.loglog(x, y, c='r') plt.grid(True, which="both", axis='x') plt.show()Output
Read MoreSet Max value for color bar on Seaborn heatmap using Matplotlib
To set a value for color bar on Seaborn heatmap, we can take following Steps−Create random data using numpy.Use heatmap() method to plot rectangular data as a color-encoded matrix.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) ax = sns.heatmap(data, vmax=1) plt.show()Output
Read MorePutting arrowheads on vectors in Matplotlib's 3D plot
To draw arrow heads vectors in 3D matplotlb's plot, we can take the following steps −Create a 2D array, where x, y, z, u, v and w are the coordinates of the arrow locations and direction components of arrow vectors.Using figure() method, create a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement, using add_subplot() method.Plot a 3D field of arrows, using quiver() method.Using ylim, xlim, zlim, limit the range of the axes.Set the title of the plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = ...
Read MoreRemove the X-axis ticks while keeping the grids (Matplotlib)
To remove the X-ticks while keeping the grids, we can take the following steps−Use gca() method to get the current axes, creating one if necessary.Plot the x and np.sin(x) using plot() method with linewidth=5, label y=sin(x).Remove yticks and xticks by passing empty array in the argument of set_xticklabels and set_yticklabels methods respectively.Configure grid lines by putting flag as True.Place the legend for the plot label in the argument.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 2*np.pi, 100) ax = plt.gca() ax.plot(x, np.sin(x), c='r', lw=5, ...
Read MorePlotting a transparent histogram with non-transparent edge in Matplotlib
To plot a transparent histogram with non-transparent edge, we can take the following steps−Create a set of random data points (y).Initialize the number of bins to be drawn.To plot the histogram, we can use hist() method with edge color and facecolor tuplesTo display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True y = np.random.rand(100) nbins = 5 plt.hist(y, bins=nbins, edgecolor=(1, 0, 0, 1), lw=5, facecolor=(.09, .12, .65, .87), rwidth=0.8) plt.show()Output
Read MoreHow to shade the regions between the curves in Matplotlib?
To shade the regions between curves, we can use the fill_between() method.StepsInitialize the variable n. Initiliize x and y data points using numpy.Create a figure and a set of subplots, fig and ax.Plot the curve using plot method.Use fill_between() method, fill the area between the two curves.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True n = 256 X = np.linspace(-np.pi, np.pi, n, endpoint=True) Y = np.sin(2 * X) fig, ax = plt.subplots() ax.plot(X, Y, color='blue', alpha=1.0) ax.fill_between(X, 0, Y, color='blue', alpha=.2) plt.show()Output
Read MoreHow to center an annotation horizontally over a point in Matplotlib?
To center an annotation horizontally over a point, we can take the following steps−Create points for x and y using numpy.Create labels using xpoints.Use scatter() method to scatter the points.Iterate labels, xpoints and ypoints and annotate the plot with label, x and y with different properties, make horizontal alignment ha=center.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True xpoints = np.linspace(1, 10, 10) ypoints = np.random.rand(10) labels = ["%.2f" % i for i in xpoints] plt.scatter(xpoints, ypoints, c=xpoints) for label, x, y in zip(labels, xpoints, ypoints): ...
Read MoreExtract Matplotlib colormap in hex-format
To extract matplotlib colormap in hex-format, we can take the following steps −Get the rainbow color map.Iterate in the range of rainbow colormap length.Using rgb2hex method, convert rgba tuple to a hexadecimal representation of a color.Examplefrom matplotlib import cm import matplotlib cmap = cm.rainbow for i in range(cmap.N): rgba = cmap(i) print("Hexadecimal representation of rgba:{} is {}".format(rgba, matplotlib.colors.rgb2hex(rgba)))Output............... ........................ .................................... Hexadecimal representation of rgba:(1.0, 0.3954512068705424, 0.2018824091570102, 1.0) is #ff6533 Hexadecimal representation of rgba:(1.0, 0.38410574917192575, 0.1958454670071669, 1.0) is #ff6232 Hexadecimal representation of rgba:(1.0, 0.37270199199091436, 0.18980109344182594, 1.0) is #ff5f30 .........................................................
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