Category: matplotlib

Matplotlib – Symlog ”; Previous Next What is Symlog? symlog is a scale in Matplotlib that combines both linear and logarithmic scaling by providing a way to plot data that includes both positive and negative values while accommodating a wide range of magnitudes. Characteristics of Symlog Scale The below are the characteristics of Symlog scale. Let’s go through one by one in detail. Linear Near Zero In the context of the symlog scale in Matplotlib library linear near zero refers to the behavior of the scale around the zero point. The symlog scale combines linear and logarithmic scaling particularly in the vicinity of zero to allow for a more nuanced representation of data. Linear Behavior Near Zero Close to Zero − For values close to zero i.e. within a defined range around zero the symlog scale behaves linearly and similar to a linear scale. Linear Threshold (linthresh) − The linthresh parameter defines the range around zero where the scale behaves linearly. Values within this threshold are represented linearly. Linear Region − Within the specified threshold around zero the symlog scale behaves like a typical linear scale preserving the direct relationship between data values and their representation on the plot. Example In this example we are setting the linthresh parameter as 0.1 to range around zero (-0.1 to 0.1 in this case) behaves linearly. Values within this range are represented linearly on the plot by allowing for a more focused and precise visualization of data close to zero while accommodating larger values away from zero logarithmically. import matplotlib.pyplot as plt import numpy as np # Sample data with positive and negative values x = np.linspace(-10, 10, 100) y = np.sinh(x) # Hyperbolic sine function for demonstration # Creating a plot with symlog scale on y-axis plt.plot(x, y) plt.yscale(”symlog”, linthresh=0.1) # Set y-axis to symlog scale with a linear threshold of 0.1 plt.xlabel(”X-axis”) plt.ylabel(”Y-axis (Symlog Scale)”) plt.title(”Plot with Symlog Scale (Linear Near Zero)”) plt.show() Output The use cases of Linear near zero Focus on Small Changes near Zero − Allows better representation of small changes or variations near zero without losing information about larger values. Handling Data with Zero-Centered Patterns − Useful for datasets where patterns or changes are centered around zero. Symmetric logarithmic scaling Symmetric logarithmic scaling is often referred to as symlog scaling in Matplotlib which is a scaling method that combines both linear and logarithmic scales to represent data symmetrically around zero. This scale is particularly useful when dealing with datasets that contain values spanning positive and negative ranges and require a nuanced representation across a wide range of values. Characteristics of Symlog Scaling Symmetric Behavior − Symlog scaling maintains symmetry around zero,accommodating both positive and negative values. Linear Region Near Zero − Close to zero within a defined range around zero the scale behaves linearly by preserving the direct proportionality of values and their representation on the plot. Logarithmic Scaling Away from Zero − As values move away from zero i.e. both positive and negative the scale transitions into a logarithmic scale allowing representation of larger absolute values logarithmically. Linear Threshold (linthresh) − The linthresh parameter defines the range around zero where the scale behaves linearly. Example In this example the symlog scale with a linthresh of 0.1 specifies that values within the range of -0.1 to 0.1 (around zero) are represented linearly on the x-axis. import matplotlib.pyplot as plt import numpy as np # Sample data with positive and negative values x = np.linspace(-10, 10, 100) y = np.sinh(x) # Hyperbolic sine function for demonstration # Creating a plot with symlog scale on y-axis plt.plot(x, y) plt.xscale(”symlog”, linthresh=0.1) # Set x-axis to symlog scale with a linear threshold of 0.1 plt.xlabel(”X-axis”) plt.ylabel(”Y-axis (Symlog Scale)”) plt.title(”Plot with Symlog Scale”) plt.show() Output Use Cases Handling Data with Both Positive and Negative Values − Useful for datasets spanning positive and negative ranges. Focused Visualization − This allows to focus on small changes near zero while accommodating larger values away from zero logarithmically. Logarithmic away from zero Logarithmic away from zero refers to the behavior of a logarithmic scale in a plot particularly in the context of the symlog scale in Matplotlib. The below are the characteristics of the Logarithmic away from zero. Near Zero − Close to zero within a specified range around zero which is defined by the linthresh parameter. The scale behaves linearly by preserving a direct relationship between the plotted values and their representation on the plot. Away from Zero − As the values move further away from zero both positively and negatively the scale transitions into a logarithmic scaling behavior. Logarithmic Behavior − The scale represents larger absolute values logarithmically using powers of a base value i.e. usually 10 or e to visually compress the range of these larger values. Compression of Values − Larger absolute values away from zero are compressed on the plot due to the logarithmic scaling making it easier to visualize a wide range of values efficiently. Example In this example we are setting the linthresh parameters as 0.2 and 0.1 for xscale and yscale repectively. import matplotlib.pyplot as plt import numpy as np # Sample data with positive and negative values x = np.linspace(-10, 10, 100) y = np.sinh(x) # Hyperbolic sine function for demonstration # Creating a plot with symlog scale on y-axis plt.plot(x, y) plt.xscale(”symlog”, linthresh=0.2) # Set x-axis to symlog scale with a linear threshold of 0.2 plt.yscale(”symlog”, linthresh=0.1) # Set x-axis to symlog scale with a linear threshold of 0.1 plt.xlabel(”X-axis”) plt.ylabel(”Y-axis (Symlog Scale)”) plt.title(”Plot with Symlog Scale”) plt.show() Output Print Page Previous Next Advertisements ”;

Matplotlib – Subplot2grid() Function ”; Previous Next This function gives more flexibility in creating an axes object at a specific location of the grid. It also allows the axes object to be spanned across multiple rows or columns. Plt.subplot2grid(shape, location, rowspan, colspan) In the following example, a 3X3 grid of the figure object is filled with axes objects of varying sizes in row and column spans, each showing a different plot. import matplotlib.pyplot as plt a1 = plt.subplot2grid((3,3),(0,0),colspan = 2) a2 = plt.subplot2grid((3,3),(0,2), rowspan = 3) a3 = plt.subplot2grid((3,3),(1,0),rowspan = 2, colspan = 2) import numpy as np x = np.arange(1,10) a2.plot(x, x*x) a2.set_title(”square”) a1.plot(x, np.exp(x)) a1.set_title(”exp”) a3.plot(x, np.log(x)) a3.set_title(”log”) plt.tight_layout() plt.show() Upon execution of the above line code, the following output is generated − Print Page Previous Next Advertisements ”;

Matplotlib – Markers ”; Previous Next In Matplotlib markers are used to highlight individual data points on a plot. The marker parameter in the plot() function is used to specify the marker style. The following is the syntax for using markers in Matplotlib. Syntax The following is the syntax and parameters of using the markers in matplotlib library. plt.plot(x, y, marker=”marker_style”) Where, x and y − Arrays or sequences of values representing the data points to be plotted. marker − Specifies the marker style to be used. It can be a string or one of the following marker styles: Sr.No. Marker & Definition 1 . Point marker 2 , Pixel marker 3 o Circle marker 4 v Triangle down marker 5 ^ Triangle up marker 6 Triangle left marker 7 > Triangle right marker 8 1 Downward-pointing triangle marker 9 2 Upward-pointing triangle marker 10 3 Left-pointing triangle marker 11 4 Right-pointing triangle marker 12 s Square marker 13 p Pentagon marker 14 * Star marker 15 h Hexagon marker (1) 16 H Hexagon marker (2) 17 + Plus marker 18 x Cross marker 19 D Diamond marker 20 d Thin diamond marker 21 − Horizontal line marker Scatterplot with pentagonal marker Here in this example we are creating the scatterplot with the pentagonal marker by using the scatter() function of the pyplot module. Example import matplotlib.pyplot as plt # Data x = [22,1,7,2,21,11,14,5] y = [24,2,12,5,5,5,9,12] plt.scatter(x,y, marker = ”p”) # Customize the plot (optional) plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(” Scatter Plot with pentagonal marker”) # Display the plot plt.show() Output Line plot with triangular marker In this example we are creating a line plot with the triangle marker by providing marker values as ”v” to the plot() function of the pyplot module. Example import matplotlib.pyplot as plt # Data x = [22,1,7,2,21,11,14,5] y = [24,2,12,5,5,5,9,12] plt.plot(x,y, marker = ”v”) # Customize the plot (optional) plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(” Line Plot with triangular marker”) # Display the plot plt.show() Output Print Page Previous Next Advertisements ”;

Matplotlib – Pyplot API ”; Previous Next A new untitled notebook with the .ipynbextension (stands for the IPython notebook) is displayed in the new tab of the browser. matplotlib.pyplot is a collection of command style functions that make Matplotlib work like MATLAB. Each Pyplot function makes some change to a figure. For example, a function creates a figure, a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. Types of Plots Sr.No Function & Description 1 Bar Make a bar plot. 2 Barh Make a horizontal bar plot. 3 Boxplot Make a box and whisker plot. 4 Hist Plot a histogram. 5 hist2d Make a 2D histogram plot. 6 Pie Plot a pie chart. 7 Plot Plot lines and/or markers to the Axes. 8 Polar Make a polar plot.. 9 Scatter Make a scatter plot of x vs y. 10 Stackplot Draws a stacked area plot. 11 Stem Create a stem plot. 12 Step Make a step plot. 13 Quiver Plot a 2-D field of arrows. Image Functions Sr.No Function & Description 1 Imread Read an image from a file into an array. 2 Imsave Save an array as in image file. 3 Imshow Display an image on the axes. Axis Functions Sr.No Function & Description 1 Axes Add axes to the figure. 2 Text Add text to the axes. 3 Title Set a title of the current axes. 4 Xlabel Set the x axis label of the current axis. 5 Xlim Get or set the x limits of the current axes. 6 Xscale . 7 Xticks Get or set the x-limits of the current tick locations and labels. 8 Ylabel Set the y axis label of the current axis. 9 Ylim Get or set the y-limits of the current axes. 10 Yscale Set the scaling of the y-axis. 11 Yticks Get or set the y-limits of the current tick locations and labels. Figure Functions Sr.No Function & Description 1 Figtext Add text to figure. 2 Figure Creates a new figure. 3 Show Display a figure. 4 Savefig Save the current figure. 5 Close Close a figure window. Print Page Previous Next Advertisements ”;

Matplotlib – Environment Setup ”; Previous Next Matplotlib llibrary is highly compatible with various operating systems and Python environments. Setting up Matplotlib is relatively straightforward and its versatility makes it a valuable tool for visualizing data in Python. It involves ensuring that it is installed and configuring its behavior within our Python environment. The below is the step-by-step guide to set the environment of the matplotlib library. Installation Matplotlib is often included in Python distributions like Anaconda. However if it”s not installed we can do so using pip. The following is the command to install the matplotlib library. pip install matplotlib Checking the Installation If we want to verify whether the installation is done or not then open a Python interpreter or a Jupyter Notebook and import Matplotlib library pyplot module by using the below code line. import matplotlib.pyplot as plt If no errors occur then the installation is successful otherwise there is a trouble in installation. Backend Selection Matplotlib has different “backends” responsible for rendering the plots. These backends can display figures in different environments e.g. in a Jupyter Notebook a separate window etc. Interactive Backends (Great for Jupyter Notebook) For enabling interactive plotting within Jupyter Notebook, we use the magic command %matplotlib. The below is the code line to be executed. %matplotlib inline # or %matplotlib notebook The %matplotlib inline command displays static images of our plot in the notebook while %matplotlib notebook allows interactive plots such as panning and zooming. Non-Interactive Backend (when not using Jupyter) When not working within a Jupyter environment then Matplotlib can use non-interactive backends. It automatically selects a suitable backend for our system. We can set a backend explicitly. import matplotlib matplotlib.use(”Agg”) # Backend selection, ”Agg” for non-interactive backend Configuration and Style Matplotlib allows customization of default settings and styles. We can create a configuration file named `matplotlibrc` to customize the behavior. Locating the Configuration File To find where our Matplotlib configuration file is located we can run the below code using the Python editor. import matplotlib matplotlib.matplotlib_fname() Creating/Editing Configuration We can modify this file directly or create a new one to adjust various settings such as default figure size, line styles, fonts etc. Testing the Setup To ensure everything is set up correctly we can create a simple plot using Matplotlib. Example import matplotlib.pyplot as plt x = [i2 for i in range(2,30)] y = [i3 for i in range(2,30)] plt.plot(x, y) plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(”Test Plot”) plt.show() Running this code should display a simple plot with a line chart in the selected environment. Output Incase Python 2.7 or 3.4 versions are not installed for all users, the Microsoft Visual C++ 2008 (64 bit or 32 bit forPython 2.7) or Microsoft Visual C++ 2010 (64 bit or 32 bit for Python 3.4) redistributable packages need to be installed. If you are using Python 2.7 on a Mac, execute the following command − xcode-select -install Upon execution of the above command, the subprocess32 – a dependency, may be compiled. On extremely old versions of Linux and Python 2.7, you may need to install the master version of subprocess32. Matplotlib requires a large number of dependencies − Python (>= 2.7 or >= 3.4) NumPy setuptools dateutil pyparsing libpng pytz FreeType cycler six Optionally, you can also install a number of packages to enable better user interface toolkits. tk PyQt4 PyQt5 pygtk wxpython pycairo Tornado For better support of animation output format and image file formats, LaTeX, etc., you can install the following − _mpeg/avconv ImageMagick Pillow (>=2.0) LaTeX and GhostScript (for rendering text with LaTeX). LaTeX and GhostScript (for rendering text with LaTeX). Print Page Previous Next Advertisements ”;

Matplotlib – Choosing Colormaps ”; Previous Next Colormap also known as a color table or a palette, is a range of colors that represents a continuous range of values. Allowing you to represent information effectively through color variations. See the below image referencing a few built-in colormaps in matplotlib − Matplotlib offers a variety of built-in (available in matplotlib.colormaps module) and third-party colormaps for various applications. Choosing Colormaps in Matplotlib Choosing an appropriate colormap involves finding a suitable representation in 3D colorspace for your dataset. The factors for selecting an appropriate colormap for any given data set include − Nature of Data − Whether representing form or metric data Knowledge of the Dataset − Understanding the dataset”s characteristics. Intuitive Color Scheme − Considering if there”s an intuitive color scheme for the parameter being plotted. Field Standards − Considering if there is a standard in the field the audience may be expecting. A perceptually uniform colormap is recommended for most of the applications, ensuring equal steps in data are perceived as equal steps in the color space. This improves human brain interpretation, particularly when changes in lightness are more perceptible than changes in hue. Categories of Colormaps Colormaps are categorized based on their function − Sequential − Incremental changes in lightness and saturation, often using a single hue. Suitable for representing ordered information. Diverging − Changes in lightness and saturation of two colors, meeting at an unsaturated color. Ideal for data with a critical middle value, like topography or data deviating around zero. Cyclic − Changes in the lightness of two colors, meeting at the middle and beginning/end at an unsaturated color. Suitable for values that wrap around at endpoints, such as phase angle or time of day. Qualitative − Miscellaneous colors with no specific order. Used for representing information without ordering or relationships. Sequential Colormaps Sequential colormaps show a monotonically increasing in lightness values as they increase through the colormap. This characteristic ensures a smooth transition in the perception of color changes, making them suitable for representing ordered information. However, it”s essential to note that the perceptual range may vary among colormaps, depending on the range of values they span. Let”s explore Sequential colormaps by visualizing their gradients and understanding how their lightness values evolve. Example The following example provides a visual representation of the gradients for different Sequential colormaps available in Matplotlib. import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl # List of Sequential colormaps to visualize cmap_list = [”Greys”, ”Purples”, ”Blues”, ”Greens”, ”Oranges”, ”Reds”, ”YlOrBr”, ”YlOrRd”, ”OrRd”, ”PuRd”, ”RdPu”, ”BuPu”, ”GnBu”, ”PuBu”, ”YlGnBu”, ”PuBuGn”, ”BuGn”, ”YlGn”] # Plot the color gradients gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient, gradient)) # Create figure and adjust figure height to the number of colormaps nrows = len(cmap_list) figh = 0.35 + 0.15 + (nrows + (nrows – 1) * 0.1) * 0.22 fig, axs = plt.subplots(nrows=nrows + 1, figsize=(7, figh)) fig.subplots_adjust(top=1 – 0.35 / figh, bottom=0.15 / figh, left=0.2, right=0.99) axs[0].set_title(”Sequential colormaps”, fontsize=14) for ax, name in zip(axs, cmap_list): ax.imshow(gradient, aspect=”auto”, cmap=mpl.colormaps[name]) ax.text(-0.1, 0.5, name, va=”center”, ha=”right”, fontsize=10, transform=ax.transAxes) # Turn off all ticks & spines, not just the ones with colormaps. for ax in axs: ax.set_axis_off() # Show the plot plt.show() Output On executing the above code we will get the following output − Diverging Colormaps Diverging colormaps are characterized by monotonically increasing and then decreasing lightness values, with the maximum lightness close to a neutral midpoint. Example The following example provides a visual representation of the gradients for different Diverging colormaps available in Matplotlib. import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl # List of Diverging colormaps to visualize cmap_list = [”PiYG”, ”PRGn”, ”BrBG”, ”PuOr”, ”RdGy”, ”RdBu”, ”RdYlBu”, ”RdYlGn”, ”Spectral”, ”coolwarm”, ”bwr”, ”seismic”] # Plot the color gradients gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient, gradient)) # Create figure and adjust figure height to the number of colormaps nrows = len(cmap_list) figh = 0.35 + 0.15 + (nrows + (nrows – 1) * 0.1) * 0.22 fig, axs = plt.subplots(nrows=nrows + 1, figsize=(7, figh)) fig.subplots_adjust(top=1 – 0.35 / figh, bottom=0.15 / figh, left=0.2, right=0.99) axs[0].set_title(”Diverging colormaps”, fontsize=14) for ax, name in zip(axs, cmap_list): ax.imshow(gradient, aspect=”auto”, cmap=mpl.colormaps[name]) ax.text(-0.1, 0.5, name, va=”center”, ha=”left”, fontsize=10, transform=ax.transAxes) # Turn off all ticks & spines, not just the ones with colormaps. for ax in axs: ax.set_axis_off() # Show the plot plt.show() Output On executing the above code we will get the following output − Cyclic Colormaps Cyclic colormaps present a unique design where the colormap starts and ends on the same color, meeting at a symmetric center point. The progression of lightness values should change monotonically from the start to the middle and inversely from the middle to the end. Example In the following example, you can explore and visualize various Cyclic colormaps available in Matplotlib. import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl # List of Cyclic colormaps to visualize cmap_list = [”twilight”, ”twilight_shifted”, ”hsv”] # Plot the color gradients gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient, gradient)) # Create figure and adjust figure height to the number of colormaps nrows = len(cmap_list) figh = 0.35 + 0.15 + (nrows + (nrows – 1) * 0.1) * 0.22 fig, axs = plt.subplots(nrows=nrows + 1, figsize=(7, figh)) fig.subplots_adjust(top=1 – 0.35 / figh, bottom=0.15 / figh, left=0.2, right=0.99) axs[0].set_title(”Cyclic colormaps”, fontsize=14) for ax, name in zip(axs, cmap_list): ax.imshow(gradient, aspect=”auto”, cmap=mpl.colormaps[name]) ax.text(-0.1, 0.5, name, va=”center”, ha=”left”, fontsize=10, transform=ax.transAxes) # Turn off all ticks & spines, not just the ones with colormaps. for ax in axs: ax.set_axis_off() # Show the

Matplotlib – Styles ”; Previous Next What is Style in Matplotlib? In Matplotlib library styles are configurations that allow us to change the visual appearance of our plots easily. They act as predefined sets of aesthetic choices by altering aspects such as colors, line styles, fonts, gridlines and more. These styles help in quickly customizing the look and feel of our plots without manually adjusting individual elements each time. We can experiment with different styles to find the one that best suits our data or visual preferences. Styles provide a quick and efficient way to enhance the visual presentation of our plots in Matplotlib library. Built-in Styles Matplotlib comes with a variety of built-in styles that offer different color schemes, line styles, font sizes and other visual properties. Examples include ggplot, seaborn, classic, dark_background and more. Changing Styles Use plt.style.use(”style_name”) to apply a specific style to our plots. Key Aspects of Matplotlib Styles Predefined Styles − Matplotlib library comes with various built-in styles that offer different aesthetics for our plots. Ease of Use − By applying a style we can instantly change the overall appearance of our plot to match different themes or visual preferences. Consistency − Styles ensure consistency across multiple plots or figures within the same style setting. Using Styles There are several steps involved in using the available styles in matlplotlib library. Let’s see them one by one. Setting a Style For setting the required style we have to use plt.style.use(”style_name”) to set a specific style before creating our plots. For example if we want to set the ggplot style we have to use the below code. import matplotlib.pyplot as plt plt.style.use(”ggplot”) # Setting the ”ggplot” style Available Styles We can view the list of available styles using plt.style.available. Example import matplotlib.pyplot as plt print(plt.style.available) # Prints available styles Output [”Solarize_Light2”, ”_classic_test_patch”, ”_mpl-gallery”, ”_mpl-gallery-nogrid”, ”bmh”, ”classic”, ”dark_background”, ”fast”, ”fivethirtyeight”, ”ggplot”, ”grayscale”, ”seaborn”, ”seaborn-bright”, ”seaborn-colorblind”, ”seaborn-dark”, ”seaborn-dark-palette”, ”seaborn-darkgrid”, ”seaborn-deep”, ”seaborn-muted”, ”seaborn-notebook”, ”seaborn-paper”, ”seaborn-pastel”, ”seaborn-poster”, ”seaborn-talk”, ”seaborn-ticks”, ”seaborn-white”, ”seaborn-whitegrid”, ”tableau-colorblind10”] Applying Custom Styles We can create custom style files with specific configurations and then use plt.style.use(”path_to_custom_style_file”) to apply them. Applying the seaborn-darkgrid style In this example the style ”seaborn-darkgrid” is applying to the plot altering its appearance. Example import matplotlib.pyplot as plt # Using a specific style plt.style.use(”seaborn-darkgrid”) # Creating a sample plot plt.plot([1, 2, 3, 4], [10, 15, 25, 30]) plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(”Sample Plot”) plt.show() Output Applying ggplot style In this example we are using the ggplot style for our plot. Example import matplotlib.pyplot as plt # Using a specific style plt.style.use(”seaborn-white”) # Creating a sample plot plt.plot([1, 2, 3, 4], [10, 15, 25, 30]) plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(”Sample Plot”) plt.show() Output Print Page Previous Next Advertisements ”;

Matplotlib – Enabling LaTex Rendering in Annotations ”; Previous Next What is LaTex Rendering? LaTeX rendering refers to the process of converting LaTeX markup language which contains typesetting instructions and commands, into formatted output. This output is typically high-quality documents, mathematical formulas, scientific papers or technical reports with precise and consistent typography. LaTeX rendering is widely used in academia, scientific research, technical documentation and publishing due to its robust typesetting capabilities and its ability to produce professional-looking documents. Key Aspects of LaTeX Rendering Typesetting − LaTeX is renowned for its superior typesetting capabilities ensuring professional-grade document formatting for academic and technical content. Mathematical Formulas − LaTeX is extensively used for its exceptional support in typesetting complex mathematical equations by making it a preferred choice for academic and scientific publications. Markup Language − LaTeX uses a markup language in which, where users write documents with plain text and include commands to specify formatting, structure and content. Compilation − The LaTeX source code needs to be compiled using a LaTeX compiler such as pdflatex, xelatex, lualatex. During compilation the compiler interprets the LaTeX commands and generates the final output in various formats like PDF, DVI or PostScript. Customization − LaTeX allows users to create custom styles, templates and packages by enabling precise control over document formatting and layout. Benefits of LaTeX Rendering Quality and Consistency − LaTeX ensures high-quality and consistent document formatting across various platforms and devices. Mathematical Typesetting − It excels in handling complex mathematical notation and making it indispensable for scientific and mathematical content. Cross-Platform Compatibility − LaTeX documents can be easily compiled and viewed on different operating systems. Version Control − Plain text-based source files facilitate version control systems by making collaboration and document history management easier. Enabling Latex Rendering To enable LaTeX rendering for creating documents, equations or annotations we typically need the following. LaTeX Installation Install a LaTeX distribution like TeX Live, MiKTeX or MacTeX which includes the necessary LaTeX compiler and packages. Text Editor Choose a text editor or an integrated development environment (IDE) that supports LaTeX such as TeXstudio, TeXworks, Overleaf or editors like Sublime Text, VS Code or Atom with LaTeX plugins/extensions. Write LaTeX Code Create a .tex file and write LaTeX code using the appropriate commands and syntax to structure our document which include equations or format text. Compilation Use the LaTeX compiler to compile the .tex file into the desired output format such as PDF, DVI, PS. Run the appropriate command in the terminal or use the integrated features of our chosen editor/IDE. For example in a terminal we might run the following code. Example pdflatex your_file.tex Or within an editor/IDE there”s often a Build or Compile button to initiate the compilation process. LaTeX Rendering in Matplotlib for Annotations For Matplotlib annotations using LaTeX for text formatting within plots we have to ensure the below. Matplotlib Support − Matplotlib supports LaTeX for annotations by using LaTeX syntax within plt.annotate() or similar functions. LaTeX Installation − Ensure we have a working LaTeX installation on our system that Matplotlib can access for rendering LaTeX text within annotations. Correct Syntax − Use the correct LaTeX syntax r”$…$” within Matplotlib functions for annotations to render the desired LaTeX-formatted text. By following the above mentioned steps we can enable LaTeX rendering for various purposes such as document creation, mathematical notation or annotations in visualization libraries like Matplotlib. Enabling LaTex Rendering In this example we are going to use the LaTex rendering in the annotations of the plot. Example import matplotlib.pyplot as plt # Sample data x = [1, 2, 3, 4] y = [2, 5, 7, 10] plt.plot(x, y, ”o-”, label=”Data”) # Annotating a point with LaTeX-rendered text plt.annotate(r”$sum_{i=1}^{4} y_i$”, # LaTeX expression within the annotation xy=(x[2], y[2]), # Coordinates of the annotation point xytext=(2.5, 6), # Text position arrowprops=dict(facecolor=”black”, arrowstyle=”->”), fontsize=12, color=”green”) # Labeling axes and title plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(”Plot with LaTeX rendering in Annotation”) plt.legend() plt.show() Output On executing the above code you will get the following output − Example Here this is another example of using the LaTex rendering in annotations of a plot. import matplotlib.pyplot as plt # Generating some data points x = [1, 2, 3, 4] y = [2, 5, 7, 10] plt.plot(x, y, ”o-”, label=”Data”) # Annotating a point with LaTeX rendering plt.annotate(r”textbf{Max Value}”, xy=(x[y.index(max(y))], max(y)), xytext=(2.5, 8), arrowprops=dict(facecolor=”black”, shrink=0.05), fontsize=12, color=”white”, bbox=dict(boxstyle=”round,pad=0.3”, edgecolor=”red”, facecolor=”green”)) plt.xlabel(”X-axis”) plt.ylabel(”Y-axis”) plt.title(”Example Plot with LaTeX Annotation”) plt.legend() plt.show() Output On executing the above code you will get the following output − Change the axis tick font in a Matplotlib plot when rendering using LaTex Here this is the example to change the axis tick font in matplotlib when rendering using LaTeX Example import numpy as np import matplotlib.pyplot as plt plt.rcParams[“figure.figsize”] = [7.00, 3.50] plt.rcParams[“figure.autolayout”] = True x = np.array([1, 2, 3, 4]) y = np.exp(x) ax1 = plt.subplot() ax1.set_xticks(x) ax1.set_yticks(y) ax1.plot(x, y, c=”red”) ax1.set_xticklabels([r”$bf{one}$”, r”$bf{two}$”, r”$bf{three}$”, r”$bf{four}$”], rotation=45) ax1.set_yticklabels([r”$bf{:.2f}$”.format(y[0]), r”$bf{:.2f}$”.format(y[1]), r”$bf{:.2f}$”.format(y[2]), r”$bf{:.2f}$”.format(y[3])], rotation=45) plt.tight_layout() plt.show() Output On executing the above code you will get the following output − Print Page Previous Next Advertisements ”;

Matplotlib – Artists ”; Previous Next Understanding Artists in Matplotlib In Matplotlib, almost everything you see on a plot is an instance of an Artist, which are objects that represent various components of a plot. Whether it”s a line representing data, a text label, or even the tick marks on an axis, everything in a Matplotlib plot is an Artist object. Before exploring the Artist hierarchy, let’s observe the below image to understand the basic components of a figure in matplotlib − In the figure, each element, such as lines and points that represent data, along with minor ticks and points associated text labels, can be identified as individual Artist objects. Artist Hierarchy In matplotlib”s hierarchy, Artists can be broadly categorized into two types − Primitive Artists − These are the basic building blocks of a plot. Examples include Line2D, Rectangle, Text, AxesImage, and more. These are the standard graphical objects that we want to paint onto our canvas. Composite Artists − These are higher-level structures that contain other Artists. Examples include Figure, Axis, and Axes. A Figure is like a canvas that holds everything, while Axes represents a specific plot within the figure. Creating and Accessing the Artists Artists are typically created through plotting methods on an Axes object. These methods not only facilitate the creation of visual elements but also return specific artist instances corresponding to the plotted elements. Below are some common plotting methods and the corresponding artists they generate − annotate − Generates an Annotation artist for text annotations. bar − Creates a Rectangle artist for bar charts. errorbar − Produces Line2D and Rectangle artists for error bar plots. fill − Generates a Polygon artist for shared areas. hist − Creates Rectangle artists for histograms. imshow − Generates an AxesImage artist for image data. legend − Produces a Legend artist for Axes legends. plot − Creates Line2D artists for XY plots. Returns a list when multiple datasets are plotted. scatter − Generates a PolyCollection artist for scatter charts. text − Produces a Text artist for displaying text on the plot. Example Let”s see an example for creating and accessing the artists using the plot() method. import matplotlib.pyplot as plt import matplotlib.artist as martist import numpy as np # Create a subplot fig, ax = plt.subplots() # Generate random data x, y = np.random.rand(2, 100) # Use the plot method to create a Line2D artist lines = ax.plot(x, y, ”-”, label=”example”) # Accessing the Line2D artists created by the plot method print(”Line2D artists created by the plot method:”, lines) # Retrieve all Line2D artists associated with the Axes lines_on_axes = ax.get_lines() # Display the retrieved Line2D artists print(”Line2D artists obtained using get_lines():”, lines_on_axes) # Accessing the first (and in this case, the only) Line2D artist print(”Accessing the first Line2D artist”, ax.get_lines()[0]) Output On executing the above code we will get the following output − Line2D artists created by the plot method: [<matplotlib.lines.Line2D object at 0x000001DB3666A620>] Line2D artists obtained using get_lines(): Accessing the first Line2D artist Line2D(example) Modifying the Artists Properties Artists have various properties that define their appearance and behavior. These properties include color, linestyle, linewidth, and markers. Modifying these properties enables us to control the visual aspects of the plot. Example The following example demonstrates how to create a simple plot and then modify the properties of the associated Line2D artist. import matplotlib.pyplot as plt import numpy as np # Creating a simple plot fig, ax = plt.subplots(figsize=(7, 4)) x = np.linspace(0, 2 * np.pi, 100) y = np.sin(x) # Use the plot method to create a Line2D artist lines = ax.plot(x, y) # Displaying initial properties of the Line2D artist print(”Initial properties of the Line2D artist:”) print(”Color:”, ax.get_lines()[0].get_color()) print(”Line Style:”, ax.get_lines()[0].get_linestyle()) print(”Marker Size:”, ax.get_lines()[0].get_markersize()) # Modifying properties lines[0].set(color=”green”, linewidth=2) lines[0].set_linestyle(”:”) lines[0].set_marker(”s”) lines[0].set_markersize(10) # Accessing the modified properties of the Line2D artist print(”nModified properties of the Line2D artist:”) print(”Color:”, ax.get_lines()[0].get_color()) print(”Line Style:”, ax.get_lines()[0].get_linestyle()) print(”Marker Size:”, ax.get_lines()[0].get_markersize()) Output On executing the above code we will get the following output − Initial properties of the Line2D artist: Color: #1f77b4 Line Style: - Marker Size: 6.0 Modified properties of the Line2D artist: Color: green Line Style: : Marker Size: 10.0 Manipulating Artist Data Beyond styling properties, artists may also contain data. For example, the Line2D object has a data property that can be modified using the set_data() method. Example Here is an example that manipulate artist data. import matplotlib.pyplot as plt import numpy as np # Creating a simple plot fig, ax = plt.subplots(figsize=(7, 4)) x = np.linspace(0, 2 * np.pi, 100) # Initial sinusoidal curve y = np.sin(x) # Use the plot method to create a Line2D artist a sinusoidal curve lines = ax.plot(x, y) # Modifying the artist data with the cosine lines[0].set_data([x, np.cos(x)]) # Displaying the plot plt.show() Output On executing the above code we will get the following output − Manual Addition of Artists While many artists have helper methods, there are scenarios where manual addition is necessary. For instance, adding patches like circles or rectangles directly to an Axes object can be possible by using the add_artist() method. Example In this example, we manually add circle patches to the axes. import matplotlib.pyplot as plt import numpy as np import matplotlib.patches as mpatches # Creating a simple plot fig, ax = plt.subplots(figsize=(7, 4)) x = np.linspace(0, 2 * np.pi, 100) # Initial sinusoidal curve y = np.sin(x) # Use the plot method to create a Line2D artist for a sinusoidal curve

Matplotlib – Create Logo ”; Previous Next Creating a logo generally means designing a unique symbol or image that represents a brand, company, product, or organization. It involves combining visual elements like shapes, colors, and text to convey the identity and message of the entity it represents. Create Logo in Matplotlib You can create a logo in Matplotlib using the library”s plotting functionalities to design a visual symbol or image that represents a brand, company, product, or organization. Similar to other plotting tasks, creating a logo in Matplotlib requires combining various graphical elements such as shapes, colors, text, and possibly images to craft a unique and recognizable emblem. Simple Geometric Logo A simple geometric logo in Matplotlib refers to creating a basic graphical representation using geometric shapes like squares, circles, triangles, or lines. You can use functions like plt.plot(), plt.scatter(), plt.fill(), or plt.text() to draw various geometric shapes and add text to your plot. By combining these elements and arranging them creatively, you can design a simple logo for your project, brand, or visualization. Example In the following example, we create a simple circular logo using Matplotlib. We use the Circle() function to draw a blue-colored circle and add it to the plot using the add_artist() function. We then turn off the axes, and set the aspect ratio to ensure the circle appears round − import matplotlib.pyplot as plt # Creating a circle circle = plt.Circle((0.5, 0.5), 0.4, color=”blue”) # Creating the plot fig, ax = plt.subplots() ax.add_artist(circle) # Customizing the plot ax.set_aspect(”equal”) ax.axis(”off”) # Displaying the logo plt.show() Output Following is the output of the above code − Creating a Text Logo A text logo in Matplotlib involves creating a logo or visual representation using text elements. Instead of relying on geometric shapes or images, a text logo uses characters and symbols to convey a brand or identity. In Matplotlib, you can use the plt.text() function to add text to your plot. This function allows you to specify the position, text content, font size, color, and other properties of the text you want to display. Example In here, we create a text-based logo using Matplotlib. We use the text() function to place the word “Logo” at the center of the plot with “red” color and customized font size − import matplotlib.pyplot as plt # Creating text text = plt.text(0.5, 0.5, ”Logo”, fontsize=50, color=”red”, ha=”center”, va=”center”) # Customizing the plot plt.axis(”off”) # Displaying the logo plt.show() Output On executing the above code we will get the following output − Creating a Complex Shape Logo A complex shape logo in Matplotlib refers to creating a logo using intricate or detailed shapes, patterns, and designs. Unlike simple geometric logos, which rely on basic shapes like squares, circles, or triangles, complex shape logos often involve more elaborate and intricate elements. In Matplotlib, you can combine various plotting functions, such as plt.plot(), plt.fill(), plt.scatter(), and plt.polygon(), to create complex shapes and patterns. By carefully arranging these elements and applying different colors, styles, and transformations, you can design intricate logos that convey a unique brand identity or message. Complex shape logos can include a wide range of elements, such as curves, curves, arcs, polygons, and other geometric shapes. They may also consist of text, images, or other visual elements. Example Now, we create a logo with a complex shape by combining an ellipse, a polygon, and a star. We create each shape the Ellipse and Polygon classes, respectively. We then use the add_artist() function to add each shape to the plot − import matplotlib.pyplot as plt from matplotlib.patches import Ellipse, Polygon # Creating an ellipse ellipse = Ellipse((0.5, 0.5), width=0.7, height=0.4, angle=45, color=”orange”) # Creating a polygon vertices = [[0.2, 0.4], [0.8, 0.4], [0.6, 0.8], [0.4, 0.8]] polygon = Polygon(vertices, closed=True, color=”blue”) # Creating a star star_vertices = [[0.3, 0.6], [0.4, 0.9], [0.5, 0.6], [0.6, 0.9], [0.7, 0.6], [0.5, 0.4], [0.3, 0.6]] star = Polygon(star_vertices, closed=True, color=”green”) # Creating the plot fig, ax = plt.subplots() ax.add_artist(ellipse) ax.add_patch(polygon) ax.add_patch(star) # Customizing the plot ax.set_aspect(”equal”) ax.axis(”off”) # Displaying the logo plt.show() Output After executing the above code, we get the following output − Creating an Image Logo An image logo in Matplotlib refers to using an image or picture as a logo in a plot. Instead of creating shapes, lines, or text to form a logo, you can insert an existing image file, such as a PNG, JPEG, or GIF, into your Matplotlib plot. To add an image logo to a Matplotlib plot, you can use the plt.imshow() function, which displays an image on the plot. You will first need to load the image file and then pass the image data to plt.imshow() to display it within your plot. Example In the following example, we create a logo using an external image file with Matplotlib. We load the image using the imread() function and then display it using the imshow() function − import matplotlib.pyplot as plt import matplotlib.image as mpimg # Loading image img = mpimg.imread(”tutorialspoint_logo.png”) # Displaying image plt.imshow(img) plt.axis(”off”) # Displaying the logo plt.show() Output On executing the above code we will get the following output − Print Page Previous Next Advertisements ”;