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After enabling the pick event property of artists in Matplotlib, the task is to use the pick event to enable and disable the line plots for a given axis in a set of plots.In order to pick a specific line plot, we use Legend.We will use a Binary classification plot to create the ROC Curve. ROC curve or Receiver Operating Characteristics curve is used for diagnostics, weather prediction and other applications. It contains True Negative Rate (TPR) and False Positive Rate (FPR). Using ROC, we will create multiple plots of the curve.Let us Import the libraries first. Here ‘nbAgg’ is used ... Read More
To specify the details of a plot, we use annotations. To create annotations in Matplotlib Plots, we can use the ‘annotate’ method.Exampleimport matplotlib.pyplot as plt import numpy as np #Let us create a plot and use annotation at the point (5,3), x = np.arange(0,4*np.pi,0.1) plt.plot(np.sin(x), 'b-*') a = plt.annotate("(3,0)", xy=(3, 0), xycoords='data', xytext=(4.0,0.5), textcoords='data', arrowprops=dict(arrowstyle="->", color="green", lw=5, connectionstyle=("arc3,rad=0."))) plt.setp(a, size=25) #Display the plot plt.show()Output
When we plot a figure in Matplotlib, it creates four spines around the figure, top, left, bottom and right. Spines are nothing but a box surrounded with the pictorial representation of the grid which displays some ticks and tickable axes on left(y) and bottom(x).Let us see how to customize the spines in a given figure. We will create six figures to see and customize the spines for it.First import the required libraries for the workbook.import numpy as np import matplotlib.pyplot as pltLet us draw graph for sines, theta = np.linspace(0, 2*np.pi, 128) y = np.sin(theta) fig = plt.figure(figsize=(8, 6))Define the ... Read More
To customize the color and colormaps of a plot, we can use the colormap property from the color library. There are two types of colormap we can create: (a) discrete colormap and (b) continuous colormap.We will first see how to create a discrete colormap followed by continuous colormap.In the example, we will use ‘iris’ dataset to create three plots such that the first plot uses the default colormap and the other two uses RGB map to create a mixed colored plot. However, we can create as many color maps as we have clusters.Exampleimport matplotlib.pyplot as plt import pandas as pd ... Read More
Let us consider that we have some data in which we have to deal with the actual time. To plot the time-zones on the map, we can use the ‘cartopy’ or ‘metPy’ package in Python. However, we can install ‘cartopy’ package in the Anaconda environment using the commands, conda install -c conda-forge cartopyOrconda install -c conda-forge metpyNow, let’s see how to plot time zones in a map using Matplotlib.Exampleimport numpy as np import cartopy.crs as ccrs import matplotlib.animation as animation import matplotlib.pyplot as plt #Defining the plot size and axes plt.figure(figsize=(10, 9)) ax = plt.axes(projection=ccrs.PlateCarree()) #Apply the color ... Read More
Instead of giving a single shortest path, Yen’s k-shortest path algorithm gives k shortest paths so that we can get the second shortest path and the third shortest path and so on.Let us consider a scenario that we have to travel from place A to place B and there are multiple routes available between place A and place B, but we have to find the shortest path and neglect all the paths that are less considered in terms of its time complexity in order to reach the destination.Let us understand with an example-Consider the given example as the bridge which ... Read More
An Image contains a 2-D matrix RGB data points which can be defined by the dots point per inch [ DPI ] of the image. The resolution of the image is important because a hi-resolution image will have much more clarity.We have a method ‘plt.savefig()’ in Matplotlib which determines the size of the image in terms of its pixels. Ideally it is having an ‘dpi’ parameter.Let’s see how we can manage the resolution of a graph in Matplotlib.Exampleimport matplotlib.pyplot as plt import numpy as np #Prepare the data for histogram np.random.seed(1961) nd = np.random.normal(13, 5, 1000) #Define the ... Read More
In order to provide path effects like shadow effect in a plot or a graph, we can use the path_effect attribute.For example, let’s see how we can use the path_effect attribute in Matplotlib add a shadow effect to a sigmoid function.import matplotlib.pyplot as plt import numpy as np from matplotlib.patheffects import PathPatchEffect, SimpleLineShadow, NormalNow let us define the size of the figure and plot the sigmoid function, plt.style.use('seaborn-deep') plt.subplots(figsize=(10, 10))Let us define the datapoints for the plot, x = np.linspace(-10, 10, 50) y = 1+ np.exp(-x))Let us define the shadow property in the plot, plt.plot(x, y, linewidth=8, color='blue', path_effects= [SimpleLineShadow(), ... Read More
Matplotlib has a default color cycle for all the graphs and plots, however, in order to draw plots with multiple color cycles, we can use the cycler property of Matplotlib. It is used to plot repetitive patterns for the axis.First, we will use the Object Oriented APIs such as pyplot to plot the specific visualization.from cycler import cycler import numpy as np from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure from IPython.core.display import displayIn this example, we will create two objects which will repeat the cycle after every four objects. Thus, after creating two objects, the last two ... Read More
Let us assume that we want to animate a nature of function which is exponentially decaying like y = a(b)^x where b = growth factor and a = initial value.An exponentially decay function would look like this, However, for now, we want to animate and plot the exponentially decaying tan function.First import the libraries, import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimationDefine the axes, fig, a = plt.subplots()Plotting a blank figure with axes, xdata, ydata = [], [] line, = ax.plot(xdata, ydata)Set the limit of the grids, ax.set_xlim(0, 10) ax.set_ylim(-3.0, 3.0) ax.grid() Define the function to ... Read More