Category: matplotlib

Matplotlib – Anaconda Distribution ”; Previous Next Matplotlib library is a widely-used plotting library in Python and it is commonly included in the Anaconda distribution. What is Anaconda Distribution? Anaconda is a distribution of Python and other open-source packages that aims to simplify the process of setting up a Python environment for data science, machine learning and scientific computing. Anaconda distribution is available for installation at https://www.anaconda.com/download/. For installation on Windows, 32 and 64 bit binaries are available − Anaconda3-5.1.0-Windows-x86.exe Anaconda3-5.1.0-Windows-x86_64.exe Installation is a fairly straightforward wizard based process. You can choose between adding Anaconda in PATH variable and registering Anaconda as your default Python. For installation on Linux, download installers for 32 bit and 64 bit installers from the downloads page − Anaconda3-5.1.0-Linux-x86.sh Anaconda3-5.1.0-Linux-x86_64.sh Now, run the following command from the Linux terminal − Syntax $ bash Anaconda3-5.0.1-Linux-x86_64.sh Canopy and ActiveState are the most sought after choices for Windows, macOS and common Linux platforms. The Windows users can find an option in WinPython. Here is how Matplotlib is typically associated with the Anaconda distribution: Matplotlib in Anaconda Pre-Installed Matplotlib library is often included in the default installation of the Anaconda distribution. When we install Anaconda Matplotlib is available along with many other essential libraries for data visualization. Integration with Jupyter Anaconda comes with Jupyter Notebook which is a popular interactive computing environment. Matplotlib seamlessly integrates with Jupyter and makes our work easy to create and visualize plots within Jupyter Notebooks. Anaconda Navigator Anaconda Navigator is a graphical user interface that comes with the Anaconda distribution. This allows users to manage environments, install packages and launch applications. It provides an easy way to access and launch Jupyter Notebooks for Matplotlib plotting. Conda Package Manager Anaconda uses the ”conda” package manager which simplifies the installation, updating and managing of Python packages. We can use ”conda” to install or update Matplotlib within our Anaconda environment. Verifying Matplotlib Installation To verify whether Matplotlib is installed in our Anaconda environment we can use the following steps. Open the Anaconda Navigator. Navigate to the “Envinornments” tab. Look for “Matplotlib” in the list of installed packages. If it”s there Matplotlib is installed. Alternatively we can open Anaconda Prompt or a terminal and run the below mentioned code Syntax conda list matplotlib This command will list the installed version of Matplotlib in our current Anaconda environment. Output Installing or Updating Matplotlib in Anaconda If Matplotlib is not installed or we want to update it then we can use the following commands in the Anaconda Prompt or terminal. To install Matplotlib Syntax conda install matplotlib As the matplotlib is already installed in the system it’s shown the message the All requested packages already installed. To update Matplotlib Syntax conda update matplotlib The above mentioned commands will manage the installation or update of Matplotlib and its dependencies in our Anaconda environment. Finally we can say Matplotlib is a fundamental part of the Anaconda distribution making it convenient for users to create high-quality visualizations in their Python environments for data analysis and scientific computing. Print Page Previous Next Advertisements ”;

Matplotlib – Box Plots ”; Previous Next A box plot represents the distribution of a dataset in a graph. It displays the summary statistics of a dataset, including the minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. The box represents the interquartile range (IQR) between the first and third quartiles, while whiskers extend from the box to the minimum and maximum values. Outliers, if present, may be displayed as individual points beyond the whiskers. Imagine you have the exam scores of students from three classes. A box plot is a way to show how these scores are spread out − Minimum and Maximum − The smallest and largest scores are shown as the ends of the plot. Quartiles (Q1, Q2, Q3) − The scores are split into four parts. The middle score is the median (Q2). The scores below the median are the first quartile (Q1), and those above are the third quartile (Q3). It helps you see where most of the scores lie. Interquartile Range (IQR) − The range between Q1 and Q3 is called the interquartile range. Box − The box in the middle represents the interquartile range. So, it is showing you where half of the scores are. Whiskers − Lines (whiskers) extend from the box to the smallest and largest scores, helping you see how spread out the scores are. Outliers − If there are any scores way above or below the rest, they might be shown as dots beyond the whiskers. These are like the standout scores. Box Plot in Matplotlib We can create a box plot in Matplotlib using the boxplot() function. This function allows us to customize the appearance of the box plot, such as changing the whisker length, adding notches, and specifying the display of outliers. The boxplot() Function The boxplot() function in Matplotlib takes one or more datasets as input and generates a box plot for each dataset. Following is the syntax of boxplot() function in Matplotlib − Syntax plt.boxplot(x, notch=None, patch_artist=None, widths=None, labels=None, …) Where, x is the dataset or a list of datasets for which the box plot is to be created. If notch (optional) is True, it creates a vertical box plot; if False, creates a horizontal box plot. If patch_artist (optional) is True, it fills the box with color. widths (optional) represents the width of the boxes. labels (optional) sets labels for each dataset, useful when plotting multiple box plots. These are just a few parameters; there are more optionals parameters available for customization. Horizontal Box Plot with Notches We can create a horizontal box plot with notches to display the distribution of a dataset in a horizontal orientation. It includes notches around the median lines, providing a visual estimate of the uncertainty around the median values. Example In the following example, we are creating a horizontal box plot with notches around the medians for three data sets, where each box represents a set of values along the y-axis categories − import matplotlib.pyplot as plt # Data data = [[1, 2, 3, 4, 5], [3, 6, 8, 10, 12], [5, 10, 15, 20, 25]] # Creating a horizontal box plot with notches plt.boxplot(data, vert=False, notch=True) plt.title(”Horizontal Box Plot with Notches”) plt.xlabel(”Values”) plt.ylabel(”Categories”) plt.show() Output After executing the above code, we get the following output − Box Plot with Custom Colors We can create a box plot with custom colors, graphically representing the data with different colors to fill the boxes. Each box represents the distribution of values within a category, and by adding a custom color, we introduce a stylistic touch that makes it easier to differentiate between categories. Example In here, we are enhancing the box plot by filling the boxes with a custom color i.e. skyblue − import matplotlib.pyplot as plt data = [[1, 2, 3, 4, 5], [3, 6, 8, 10, 12], [5, 10, 15, 20, 25]] # Creating a box plot with custom colors plt.boxplot(data, patch_artist=True, boxprops=dict(facecolor=”skyblue”)) plt.title(”Box Plot with Custom Colors”) plt.xlabel(”Categories”) plt.ylabel(”Values”) plt.show() Output Following is the output of the above code − Grouped Box Plot We can create a grouped box plot to compare the distributions of multiple groups side by side. Each group has its own set of boxes, where each box represents the distribution of values within that group. Example Now, we are creating a grouped box plot to compare the exam scores of students from three different classes (A, B, and C). Each box represents the distribution of scores within a class, allowing us to easily observe and compare the central tendencies, spreads, and potential outliers across the three classes − import matplotlib.pyplot as plt import numpy as np class_A_scores = [75, 80, 85, 90, 95] class_B_scores = [70, 75, 80, 85, 90] class_C_scores = [65, 70, 75, 80, 85] # Creating a grouped box plot plt.boxplot([class_A_scores, class_B_scores, class_C_scores], labels=[”Class A”, ”Class B”, ”Class C”]) plt.title(”Exam Scores by Class”) plt.xlabel(”Classes”) plt.ylabel(”Scores”) plt.show() Output On executing the above code we will get the following output − Box Plot with Outliers A box plot with outliers is a graphical representation of data that includes additional information about extreme values in the dataset. In a standard box plot, we represent outliers, data points significantly different from the majority, as individual points beyond the “whiskers” that extend from the box. This plot helps in identifying exceptional values that may have a significant impact on the overall distribution of the data. Example In the example below, we are creating a box plot that provides a visual representation of the sales distribution for each product, and the outliers highlight

Matplotlib – Jupyter Notebook ”; Previous Next Jupyter is a loose acronym meaning Julia, Python, and R. These programming languages were the first target languages of the Jupyter application, but nowadays, the notebook technology also supports many other languages. In 2001, Fernando Pérez started developing Ipython. IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python. Matplotlib in Jupyter Notebook provides an interactive environment for creating visualizations right alongside our code. Let”s go through the steps to start using Matplotlib in a Jupyter Notebook. Matplotlib library in Jupyter Notebook provides a convenient way to visualize data interactively by allowing for an exploratory and explanatory workflow when working on data analysis, machine learning or any other Python-based project. Consider the following features provided by IPython − Interactive shells (terminal and Qt-based). A browser-based notebook with support for code, text, mathematical expressions, inline plots and other media. Support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into one”s own projects. In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter. IPython will continue to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython will move under the Jupyter name. Jupyter added support for Julia, R, Haskell and Ruby. Starting Jupyter Notebook The below are the steps to be done by one by one to work in the Jupyter Notebook. Launch Jupyter Notebook Open Anaconda Navigator. Launch Jupyter Notebook from the Navigator or in the terminal/Anaconda Prompt type jupyter notebook and hit Enter. Create or Open a Notebook Once Jupyter Notebook opens in our web browser then navigate to the directory where we want to work. After click on “New” and choose a Python notebook which is often referred to as an “Untitled” notebook. Import Matplotlib In a Jupyter Notebook cell import Matplotlib library by using the lines of code. import matplotlib.pyplot as plt %matplotlib inline %matplotlib inline is a magic command that tells Jupyter Notebook to display Matplotlib plots inline within the notebook. Create Plots We can now use Matplotlib functions to create our plots. For example let’s create a line plot by using the numpy data. Example import numpy as np import matplotlib.pyplot as plt # Generating sample data x = np.linspace(0, 20, 200) y = np.sin(x) # Plotting the data plt.figure(figsize=(8, 4)) plt.plot(x, y, label=”sin(x)”) plt.title(”Sine Wave”) plt.xlabel(”x”) plt.ylabel(”sin(x)”) plt.legend() plt.grid(True) plt.show() Output Interact with Plots Once the plot is generated then it will be displayed directly in the notebook below the cell. We can interact with the plot i.e. panning, zooming can be done if we used %matplotlib notebook instead of %matplotlib inline at the import stage. Multiple Plots We can create multiple plots by creating new cells and running more Matplotlib commands. Markdown Cells We can add explanatory text in Markdown cells above or between code cells to describe our plots or analysis. Saving Plots We can use plt.savefig(”filename.png”) to save a plot as an image file within our Jupyter environment. Closing Jupyter Notebook Once we have finished working in the notebook we can shut it down from the Jupyter Notebook interface or close the terminal/Anaconda Prompt where Jupyter Notebook was launched. Hide Matplotlib descriptions in Jupyter notebook To hide matplotlib descriptions of an instance while calling plot() method, we can take the following steps Open Ipython instance. import numpy as np from matplotlib, import pyplot as plt Create points for x, i.e., np.linspace(1, 10, 1000) Now, plot the line using plot() method. To hide the instance, use plt.plot(x); i.e., (with semi-colon) Or, use _ = plt.plot(x) Example In this example we are hiding the description code. import numpy as np from matplotlib import pyplot as plt x = np.linspace(1, 10, 1000) plt.plot(x) plt.show() Output [<matplotlib.lines.Line2D at 0x1f6d31d9130>] Print Page Previous Next Advertisements ”;

Discuss Matplotlib ”; Previous Next Matplotlib is one of the most popular Python packages used for data visualization. It is a cross-platform library for making 2D plots from data in arrays. It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt, WxPythonotTkinter. It can be used in Python and IPython shells, Jupyter notebook and web application servers also. Print Page Previous Next Advertisements ”;

Matplotlib – Quiver Plot ”; Previous Next A quiver plot displays the velocity vectors as arrows with components (u,v) at the points (x,y). quiver(x,y,u,v) The above command plots vectors as arrows at the coordinates specified in each corresponding pair of elements in x and y. Parameters The following table lists down the different parameters for the Quiver plot − x 1D or 2D array, sequence. The x coordinates of the arrow locations y 1D or 2D array, sequence. The y coordinates of the arrow locations u 1D or 2D array, sequence. The x components of the arrow vectors v 1D or 2D array, sequence. The y components of the arrow vectors c 1D or 2D array, sequence. The arrow colors The following code draws a simple quiver plot − import matplotlib.pyplot as plt import numpy as np x,y = np.meshgrid(np.arange(-2, 2, .2), np.arange(-2, 2, .25)) z = x*np.exp(-x**2 – y**2) v, u = np.gradient(z, .2, .2) fig, ax = plt.subplots() q = ax.quiver(x,y,u,v) plt.show() Print Page Previous Next Advertisements ”;

Matplotlib – PyLab module ”; Previous Next PyLab is a procedural interface to the Matplotlib object-oriented plotting library. Matplotlib is the whole package; matplotlib.pyplot is a module in Matplotlib; and PyLab is a module that gets installed alongside Matplotlib. PyLab is a convenience module that bulk imports matplotlib.pyplot (for plotting) and NumPy (for Mathematics and working with arrays) in a single name space. Although many examples use PyLab, it is no longer recommended. Basic Plotting Plotting curves is done with the plot command. It takes a pair of same-length arrays (or sequences) − from numpy import * from pylab import * x = linspace(-3, 3, 30) y = x**2 plot(x, y) show() The above line of code generates the following output − To plot symbols rather than lines, provide an additional string argument. symbols – , –, -., , . , , , o , ^ , v , , s , + , x , D , d , 1 , 2 , 3 , 4 , h , H , p , | , _ colors b, g, r, c, m, y, k, w Now, consider executing the following code − from pylab import * x = linspace(-3, 3, 30) y = x**2 plot(x, y, ”r.”) show() It plots the red dots as shown below − Plots can be overlaid. Just use the multiple plot commands. Use clf() to clear the plot. from pylab import * plot(x, sin(x)) plot(x, cos(x), ”r-”) plot(x, -sin(x), ”g–”) show() The above line of code generates the following output − Print Page Previous Next Advertisements ”;

Matplotlib – 3D Wireframe Plots ”; Previous Next A 3D wireframe plot is a way of representing data in three dimensions using lines to represent the shape of an object. A wireframe plot connects the data points of an object to create a mesh-like structure to show the shape of the object. Imagine we have a cube and instead of drawing the solid faces of the cube, we only show the lines outlining its edges and corners. The outline we get is the 3D wireframe plot − 3D Wireframe Plot in Matplotlib In Matplotlib, a 3D wireframe plot is a type of visualization where data is represented by a network of lines forming the edges of a three-dimensional surface. We can create a 3D wireframe plot in Matplotlib using the plot_wireframe() function in the ”mpl_toolkits.mplot3d” module. This function accepts the X, Y, and Z coordinates of a 3D object and connects these coordinates with lines to create a 3D outline of the object. Let’s start by drawing a basic 3D wireframe plot. Basic 3D Wireframe Plot A basic 3D wireframe plot in Matplotlib displays the surface of a 3D object as a mesh of lines, allowing you to visualize the shape and structure of the surface. The wireframe plot is formed by joining a series of points on the sphere”s surface with straight lines running along the x, y, and z axes. To create a wireframe plot, you can define arrays for the x, y, and z coordinates of the surface points you want to visualize. Then, you can pass these arrays to the plot_wireframe() function to generate the wireframe plot. Example In the following example, we are creating a basic 3D wireframe plot of a spherical surface. First, we generate the X, Y, and Z points of the sphere by varying them with the angles ”theta” and ”phi”. Then, we use the plot_wireframe() function to create lines that connect the data points of the sphere. In the resultant plot, we get a 3D wireframe plot of a spherical surface − import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Generating data for a spherical surface theta = np.linspace(0, 2*np.pi, 100) phi = np.linspace(0, np.pi, 100) theta, phi = np.meshgrid(theta, phi) r = 1 x = r * np.sin(phi) * np.cos(theta) y = r * np.sin(phi) * np.sin(theta) z = r * np.cos(phi) # Creating a 3D plot fig = plt.figure() ax = fig.add_subplot(111, projection=”3d”) # Plotting the spherical wireframe ax.plot_wireframe(x, y, z, color=”blue”) # Adding labels and title ax.set_xlabel(”X”) ax.set_ylabel(”Y”) ax.set_zlabel(”Z”) ax.set_title(”Basic 3D Wireframe Plot”) # Displaying the plot plt.show() Output Following is the output of the above code − Toroidal 3D Wireframe Plot In Matplotlib, a toroidal 3D wireframe plot represents the surface of a torus using lines in three-dimensional space. A torus is a doughnut-shaped object with a hole in the middle. The wireframe plot connects the lines on surface of the torus to create its outline. Example In here, we are generating a toroidal 3D wireframe plot. We start by creating the surface of the torus by varying the X and Y coordinates with angles ”theta” and ”phi” and with major radius ”R” and minor radius ”r”, while the Z coordinate varies with ”r” and ”phi”. Then, we use the plot_wireframe() function to connect the coordinates with lines creating a resultant plot which represents a 3D wireframe plot of a torus − import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Generating data for a toroidal surface theta = np.linspace(0, 2*np.pi, 100) phi = np.linspace(0, 2*np.pi, 100) theta, phi = np.meshgrid(theta, phi) R = 2 r = 1 x = (R + r * np.cos(phi)) * np.cos(theta) y = (R + r * np.cos(phi)) * np.sin(theta) z = r * np.sin(phi) # Creating a 3D plot fig = plt.figure() ax = fig.add_subplot(111, projection=”3d”) # Plotting the toroidal wireframe ax.plot_wireframe(x, y, z, color=”green”) # Adding labels and title ax.set_xlabel(”X”) ax.set_ylabel(”Y”) ax.set_zlabel(”Z”) ax.set_title(”Toroidal 3D Wireframe Plot”) # Displaying the plot plt.show() Output On executing the above code we will get the following output − Paraboloid 3D Wireframe Plot A paraboloid 3D wireframe plot in Matplotlib displays the outline of a paraboloid using lines on a three-dimensional graph. A paraboloid is a three-dimensional parabola that resembles a bowl. The 3D wireframe plot connects the data points to create a mesh-like structure of the paraboloid. Example The following example creates a 3D wireframe plot of a paraboloid in a 3D space. We create the paraboloid by evenly spacing the X, Y, and Z on a 3D graph. Then, we connect the coordinates with lines using the plot_wireframe() function to create a 3D wireframe plot − import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Generating data for a paraboloid surface x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) Z = X**2 + Y**2 # Creating a 3D plot fig = plt.figure() ax = fig.add_subplot(111, projection=”3d”) # Plotting the paraboloid wireframe ax.plot_wireframe(X, Y, Z, color=”purple”) # Adding labels and title ax.set_xlabel(”X”) ax.set_ylabel(”Y”) ax.set_zlabel(”Z”) ax.set_title(”Paraboloid 3D Wireframe Plot”) # Displaying the plot plt.show() Output After executing the above code, we get the following output − Cylindrical 3D Wireframe Plot In Matplotlib, a cylindrical wireframe plot is a visualization of the geometry of a cylinder in a three-dimensional space. A cylinder is a three-dimensional shape with a circular cross-section that extends along its length. The data points on the surface of the cylinder are connected with lines to create a 3D wireframe plot. Example Now, we are generating a 3D wireframe plot for a

Matplotlib – Basemap ”; Previous Next What is Basemap? The Matplotlib Basemap toolkit is an extension to Matplotlib that provides functionality for creating maps and visualizations involving geographical data. It allows users to plot data on various map projections, draw coastlines, countries and other map features . These are used to handle geographic coordinates seamlessly within Matplotlib. The below are the key features of Matplotlib Basemap. Map Projections Basemap supports various map projections by allowing users to visualize data in different coordinate systems like cylindrical, conic or azimuthal projections. Examples are Mercator, Lambert Conformal Conic and Orthographic projections. Plotting Geographic Data Basemap allows the plotting of geographical data such as points, lines, or polygons over maps. Users can overlay datasets onto maps and visualize geographic relationships. Map Elements Basemap provides functions to add map elements like coastlines, countries, states, rivers and political boundaries by enhancing the visual context of the maps. Coordinate Transformation It facilitates seamless conversion between different coordinate systems such as latitude and longitude to map projection coordinates and vice versa. Installing Basemap If we want to work with Basemap of matplotlib library we have to install it in our working environment. The below is the code. Example pip install basemap Note − When we are working with Jupyter notebook then we have to install basemap through the anaconda command terminal. Output Basic Workflow with Basemap The below is the basic workflow of the Basemap of the Matplotlib library. Let’s see each of them in detail for better understanding. Create a Basemap Instance Instantiate a Basemap object by specifying the map projection, bounding coordinates and other parameters. To create a Basemap instance using the Basemap toolkit in Matplotlib we can define the map projection and specify the desired map boundaries. Example In this example we are generating a basic map with coastlines and country boundaries using the specified Mercator projection and the provided bounding coordinates. We can further customize the map by adding features, plotting data points or using different projections and resolutions based on our requirements. from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt # Create a Basemap instance with a specific projection and bounding coordinates map = Basemap( projection=”merc”, llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution=”c”) # Draw coastlines and countries map.drawcoastlines() map.drawcountries() # Show the map plt.show() Output Plot Data on the Map In this we use the Basemap methods to draw map features, plot data points or visualize geographical datasets. Example In this example we are generating random latitude and longitude points and then uses the map() function to project these coordinates onto the Basemap instance. The scatter() method is then used to plot these projected points on the map as red markers. from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # Create a Basemap instance with a specific projection and bounding coordinates map = Basemap( projection=”merc”, llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution=”c”) # Draw coastlines and countries map.drawcoastlines() map.drawcountries() # Generate random data (longitude, latitude) for plotting num_points = 100 lons = np.random.uniform(low=-180.0, high=180.0, size=num_points) lats = np.random.uniform(low=-80.0, high=80.0, size=num_points) # Plot the data points on the map x, y = map(lons, lats) # Project the latitudes and longitudes to map coordinates map.scatter(x, y, marker=”o”, color=”red”, zorder=10) # Plotting the data points # Show the map with plotted data plt.title(”Data Points on Map”) plt.show() Output Display the Map We can use Matplotlib library to show() function to display the final map. Example Here”s an example demonstrating the usage of Basemap to create a map and plot data points. from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # Creating a Basemap instance with a specific projection and bounding coordinates map = Basemap( projection=”merc”, llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution=”c”) # Drawing coastlines, countries, and states map.drawcoastlines() map.drawcountries() map.drawstates() # Generating random data for plotting num_points = 100 lons = np.random.uniform(low=-180.0, high=180.0, size=num_points) lats = np.random.uniform(low=-80.0, high=80.0, size=num_points) data_values = np.random.rand(num_points) * 100 # Random values for data # Plotting data points on the map x, y = map(lons, lats) # Projecting latitudes and longitudes to map coordinates map.scatter(x, y, c=data_values, cmap=”viridis”, marker=”o”, alpha=0.7) # Adding a colorbar to represent the data values plt.colorbar(label=”Data Values”) # Display the map with plotted data plt.title(”Basemap Example with Data Points”) plt.show() Output Applications of Matplotlib Basemap Geospatial Analysis − Analyzing and visualizing geographical data such as climate patterns, population distributions or seismic activities. Cartography − Creating custom maps for publications, presentations or research purposes. Data Visualization − Integrating geographical data with other datasets for exploratory data analysis. Note Matplotlib Basemap is being phased out in favor of newer geospatial libraries like Cartopy which offer more extensive functionality and better integration with Matplotlib. Cartopy builds on the concepts of Basemap but provides a more modern, flexible and actively developed interface for handling geospatial data within Matplotlib. Print Page Previous Next Advertisements ”;

Matplotlib – PostScript ”; Previous Next PostScript is a page description language and a dynamically typed, stack-based programming language often abbreviated as PS. Created by Adobe Systems in the early 1980s, its primary purpose is to describe the layout and graphics of printed pages. It is widely used in electronic publishing and desktop publishing applications. A PostScript file can be printed or displayed on different devices without losing quality. This is the key advantage when generating plots for different purposes. PostScript in Matplotlib In the context of Matplotlib, PostScript serves as a backend rendering engine (used for displaying figures on the screen or writing to files), enabling users to generate publication-quality plots and graphics. When you choose the PostScript backend, Matplotlib generates PostScript code to describe the layout and appearance of the plot. The PostScript code generated by Matplotlib includes instructions for drawing lines, shapes, text, and other graphical elements on a page. These instructions are written in the PostScript programming language. The Matplotlib PostScript Backend (matplotlib.backends.backend_ps) can produce both .ps and .eps files. Create a PostScript file Let”s explore how to create a simple plot and save it as a PostScript file using Matplotlib. Example 1 This example demonstrates how to create a simple plot with wrapped text and saves it as a PostScript file(.ps). import matplotlib import matplotlib.pyplot as plt import textwrap from pylab import * # Generate a string containing printable characters (ASCII 32 to 126) text_to_wrap = “”.join(c for c in map(chr, range(32, 127)) if c.isprintable()) # Wrap the string to fit within the figure wrapped_text = “n”.join(textwrap.wrap(text_to_wrap)) # Add the wrapped text to the figure figtext(0, 0.5, wrapped_text) # Save the figure to a PostScript file named “test.ps” savefig(“test.ps”) print(”Successfully created the PostScript (PS) file…”) Output If you visit the folder where the Output is saved you can observe resultant PostScript file named test.ps. Successfully created the PostScript (PS) file… Example 2 Here is another example that demonstrates how to use the PostScript backend to generate a plot and save it to an encapsulated PostScript (EPS) file. import numpy as np from matplotlib import pyplot as plt # Generate data x_data = np.linspace(1, 10, 100) y_data = np.sin(x_data) # Create the plot plt.plot(x_data, y_data, c=”green”, marker=”o”) plt.grid() # Save the figure to a PostScript file named “example.eps” plt.savefig(“example.eps”) print(”Successfully created the encapsulated PostScript (EPS) file…”) Output If you visit the folder where the Output is saved you can observe resultant PostScript file named example.eps. Successfully created the encapsulated PostScript (EPS) file… Customizing PostScript Output Adjusting the PostScript output settings in Matplotlib allows you to enhance the visual quality of Encapsulated PostScript (EPS) files. By default, Matplotlib uses a distillation process when creating EPS files. This distillation step removes specific PostScript operators that LaTeX considers illegal in an EPS file. One effective workaround involves modifying the resolution parameter to achieve better visual results. The rcParams[“ps.distiller.res”] parameter controls the resolution of the EPS files, with the default value set to 6000. Increasing this value can result in larger files but may significantly improve visual quality and maintain reasonable scalability. Example 1 This example demonstrates how adjusting resolution parameter can enhance the visual quality of EPS files. import numpy as np import matplotlib.pyplot as plt # Set the resolution for EPS files plt.rcParams[“ps.distiller.res”] = 12000 # Set the figure size and enable autolayout plt.rcParams[“figure.figsize”] = [7, 3.50] plt.rcParams[“figure.autolayout”] = True # Generate data x_data = np.linspace(1, 10, 100) y_data = np.sin(x_data) # Plotting plt.plot(x_data, y_data, label=”Sine Wave”, color=”green”) # Save the figure as an EPS file plt.savefig(”Output customized file.eps”, format=”eps”, bbox_inches=”tight”) print(”Successfully created the output customized PostScript (EPS) file…”) Output If you visit the folder where the Output is saved you can observe resultant PostScript file named Output customized file.eps. Successfully created the output customized PostScript (EPS) file… Example 2 Here is an example that demonstrates how the transparency is preserved when saving the plot as an .eps file by setting the transparent=True parameter in the savefig() function. import numpy as np import matplotlib.pyplot as plt # Adjust figure size and autolayout plt.rcParams[“figure.figsize”] = [7.50, 3.50] plt.rcParams[“figure.autolayout”] = True # Generate data x_data = np.linspace(1, 10, 100) y_data = np.sin(x_data) # Plot data with transparency plt.plot(x_data, y_data, c=”green”, marker=”o”, alpha=.35, ms=10, lw=1) plt.grid() # Save plot as .eps by preserving the transparency plt.savefig(“lost_transparency_img.eps”, transparent=True) # Display plot plt.show() Output On executing the above code you will get the following output − Whenever plots are saved in .eps/.ps, then the transparency of the plots get lost. You can observe the difference in th eabove image. Print Page Previous Next Advertisements ”;

Matplotlib – Ribbon Box ”; Previous Next In general, a “ribbon box” is a graphical representation used to visualize data distributions across different categories or groups. The term “ribbon box” refers to a specific type of plot that displays these distributions in a visually appealing and informative manner. It is particularly useful when you have multiple categories or groups and want to compare the distributions of a variable across these categories. In a ribbon box plot − Each category or group is typically represented along the x-axis. The y-axis often represents the range or distribution of a numeric variable. Each “ribbon” corresponds to the distribution of the numeric variable within a particular category or group. The ribbon can be shaded or colored to indicate the density or intensity of the distribution within each category. This allows for easy comparison of distributions across different categories. Here, we created a simple ribbon box plot with three categories (Category 1, Category 2, Category 3) along the x-axis and their corresponding distribution of values along the y-axis. We shade the ribbon boxes to indicate the intensity of the distribution within each category. Ribbon Box in Matplotlib In Matplotlib, a “ribbon box” is a visual representation used to display the distribution of a numeric variable across different categories or groups. While matplotlib does not have a specific function to create ribbon box plots, you can use other techniques available, such as − Use matplotlib”s plot() function to plot the central line for each category or group. This line represents the central tendency of the data within each category, such as the mean or median. Using the fill_between function to fill the area between two curves, where one curve represents the upper boundary of the ribbon and the other curve represents the lower boundary. Customize the appearance of the plot as needed by adding labels, titles, legends, gridlines, etc. Ribbon Box Plot with Confidence Interval In matplotlib, a simple ribbon box plot with confidence interval is a graphical representation used to display the central tendency of a dataset along with the uncertainty around that central value. It is like plotting the average of something (like daily temperatures) and shading an area around it to show how much the actual values might vary due to uncertainty. Example In the following example, we are creating a ribbon box plot showing the central tendency and confidence interval (uncertainity) around a sine wave, using matplotlib”s plot() and fill_between() functions, respectively − import matplotlib.pyplot as plt import numpy as np # Generating data x = np.linspace(0, 10, 100) y_mean = np.sin(x) # Standard deviation y_std = 0.1 # Plotting the central line plt.plot(x, y_mean, color=”blue”, label=”Mean”) # Plotting the shaded area representing the uncertainty (confidence interval) plt.fill_between(x, y_mean – y_std, y_mean + y_std, color=”blue”, alpha=0.2, label=”Uncertainty”) plt.xlabel(”X”) plt.ylabel(”Y”) plt.title(”Simple Ribbon Box Plot with Confidence Interval”) plt.legend() plt.grid(True) plt.show() Output Following is the output of the above code − Multiple Ribbon Box Plots In Matplotlib, multiple ribbon box plots are a way to compare the distributions of multiple datasets using ribbon box plots within the same plot. Each ribbon box plot represents the spread and central tendency of a different dataset, allowing for easy comparison between them. Example In here, we are generating multiple ribbon box plots with different colors to represent two sine and cosine waves along with their uncertainty bands using matplotlib − import matplotlib.pyplot as plt import numpy as np # Generating data x = np.linspace(0, 10, 100) y_means = [np.sin(x), np.cos(x)] # Standard deviations y_stds = [0.1, 0.15] colors = [”blue”, ”green”] # Plotting multiple ribbon box plots with different colors for y_mean, y_std, color in zip(y_means, y_stds, colors): plt.plot(x, y_mean, color=color, label=”Mean”, alpha=0.7) plt.fill_between(x, y_mean – y_std, y_mean + y_std, color=color, alpha=0.2) plt.xlabel(”X”) plt.ylabel(”Y”) plt.title(”Multiple Ribbon Box Plots with Different Colors”) plt.legend() plt.grid(True) plt.show() Output On executing the above code we will get the following output − Stacked Ribbon Box Plot In Matplotlib, stacked ribbon box plots are a type of graphical representation used to compare the distributions of multiple datasets while also showing the combined distribution of all the datasets. In a stacked ribbon box plot, each dataset is represented by its own ribbon box plot, just like in multiple ribbon box plots. However, instead of displaying the box plots side by side, they are stacked vertically on top of each other. This stacking allows for a direct comparison of the distributions of each dataset while also showing how they contribute to the overall distribution when combined. Example Now, we are plotting a stacked ribbon box plot, stacking the distributions of a sine and cosine wave to compare their variations across the x-axis using matplotlib − import matplotlib.pyplot as plt import numpy as np # Generating example data x = np.linspace(0, 10, 100) y1 = np.sin(x) y2 = np.cos(x) # Plotting stacked ribbon box plot plt.plot(x, y1, color=”blue”, label=”Dataset 1”) plt.fill_between(x, y1, color=”blue”, alpha=0.2) plt.plot(x, y2, color=”green”, label=”Dataset 2”) plt.fill_between(x, y2, color=”green”, alpha=0.2) plt.xlabel(”X”) plt.ylabel(”Y”) plt.title(”Stacked Ribbon Box Plot”) plt.legend() plt.grid(True) plt.show() Output After executing the above code, we get the following output − Horizontal Ribbon Box Plot A horizontal ribbon box plot in Matplotlib is a graphical representation that shows the distribution of a dataset along a horizontal axis using ribbon-shaped boxes. In a horizontal ribbon box plot, the dataset”s values are grouped into categories or bins, and for each category, a ribbon-shaped box is drawn horizontally. The length of each box represents the range of values within that category, while the position along the horizontal axis indicates the category itself. Example In the following example,