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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to automatically annotate the maximum value in a Pyplot?
To annotate the maximum value in a Pyplot, you can automatically find the peak point and add a text annotation with an arrow pointing to it. This is useful for highlighting important data points in your visualizations. Steps to Annotate Maximum Value Set the figure size and adjust the padding between and around the subplots Create a new figure or activate an existing figure Make a list of x and y data points Plot x and y data points using matplotlib Find the maximum in Y array and position corresponding to that max element in the array ...
Read MoreHow to add third level of ticks in Python Matplotlib?
Adding a third level of ticks in Matplotlib allows you to create more granular visual references on your plots. This is achieved by creating twin axes and using different tick locators with varying tick lengths. Understanding the Approach The key concept involves using twiny() to create a twin axis sharing the y-axis, then configuring different tick levels with FixedLocator and tick_params(). Complete Example import matplotlib.pyplot as plt import numpy as np import matplotlib.ticker # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data t = np.arange(0.0, ...
Read MoreHow can I plot hysteresis threshold in Matplotlib?
Hysteresis thresholding is an edge detection technique that uses two thresholds to identify strong and weak edges, connecting weak edges to strong ones. In Matplotlib, we can visualize this process using scikit-image filters and display the results. What is Hysteresis Thresholding? Hysteresis thresholding works with two threshold values: High threshold: Identifies strong edges (definitely edges) Low threshold: Identifies potential weak edges Weak edges connected to strong edges are kept, isolated weak edges are removed Complete Example Here's how to plot hysteresis threshold results using the Sobel edge detector ? import matplotlib.pyplot ...
Read MoreHow to make semilogx and semilogy plots in Matplotlib?
To make semilogx and semilogy plots in Matplotlib, you can use logarithmic scaling on one axis while keeping the other axis linear. This is useful for visualizing data with exponential relationships or wide value ranges. Basic Steps Set the figure size and adjust the padding between and around the subplots Create a new figure or activate an existing figure Scatter and plot x and y data points Make a plot with log scaling on the X axis using semilogx() Make a plot with log scaling on the Y axis using semilogy() To display the figure, use show() ...
Read MoreProgrammatically Stop Interaction for specific Figure in Jupyter notebook
To programmatically stop interaction for specific figures in Jupyter notebook, we can use plt.ioff() to turn off interactive mode. This prevents figures from automatically displaying when created or modified. Understanding Interactive Mode By default, matplotlib in Jupyter notebooks runs in interactive mode. When interactive mode is on, plots are displayed immediately. Using plt.ioff() turns off this behavior, giving you control over when figures are displayed. Step-by-Step Process Follow these steps to control figure interaction ? Enable matplotlib backend with %matplotlib auto Import matplotlib and configure figure settings Create your plot normally Use plt.ioff() to ...
Read MoreHow to animate 3D plot_surface in Matplotlib?
To animate 3D plot_surface in Matplotlib, we can create dynamic surface plots that change over time. This technique is useful for visualizing time-dependent data or mathematical functions that evolve. Key Components The animation requires several key components ? Initialize variables for number of mesh grids (N), frequency per second (fps), and frame numbers (frn) Create x, y and z arrays for the surface mesh Make a function to generate z-array values for each frame Define an update function that removes the previous plot and creates a new surface Use FuncAnimation to orchestrate the animation ...
Read MoreHow to combine several matplotlib axes subplots into one figure?
To combine several matplotlib axes subplots into one figure, we can use subplots() method with nrows parameter to create multiple subplot arrangements in a single figure. Steps Set the figure size and adjust the padding between and around the subplots Create x, y1 and y2 data points using numpy Create a figure and a set of subplots using subplots() method Plot x, y1 and y2 data points using plot() method To display the figure, use show() method Example − Vertical Subplots Here's how to create two subplots arranged vertically ? import numpy ...
Read MoreHow to get XKCD font working in Matplotlib?
To get XKCD font working in Matplotlib, we can use plt.xkcd() to turn on sketch-style drawing mode. This creates hand-drawn style plots similar to the popular XKCD webcomic. Steps Set the figure size and adjust the padding between and around the subplots Create x and y data points using numpy Use plt.xkcd() to turn on sketch-style drawing mode Create a new figure or activate an existing figure Add an axis to the figure as part of a subplot arrangement Plot x and y data points using plot() method Place text and title on the plot To display ...
Read MoreHow to make colorbar orientation horizontal in Python using Matplotlib?
In Matplotlib, colorbars are displayed vertically by default. To create a horizontal colorbar, use the orientation="horizontal" parameter in the colorbar() method. Basic Syntax plt.colorbar(mappable, orientation="horizontal") Example Let's create a scatter plot with a horizontal colorbar ? import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Generate random data points x, y, z = np.random.rand(3, 50) # Create figure and subplot f, ax = plt.subplots() # Create scatter plot with color mapping points = ax.scatter(x, y, c=z, s=50, ...
Read MoreHow to plot an array in Python using Matplotlib?
To plot an array in Python, we use Matplotlib, a powerful plotting library. This tutorial shows how to create line plots from NumPy arrays with proper formatting and styling. Basic Array Plotting Here's how to plot a simple array using Matplotlib ? import numpy as np import matplotlib.pyplot as plt # Create arrays x = np.array([1, 2, 3, 4, 5]) y = np.array([2, 4, 1, 5, 3]) # Create the plot plt.figure(figsize=(8, 5)) plt.plot(x, y, color="red", marker='o') plt.title("Basic Array Plot") plt.xlabel("X values") plt.ylabel("Y values") plt.grid(True, alpha=0.3) plt.show() Single Array Plotting When ...
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