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Articles on Trending Technologies
Technical articles with clear explanations and examples
Saving scatterplot animations with matplotlib
To save scatterplot animations with matplotlib, we need to create an animation loop that generates multiple frames and exports them as a video file or GIF. This technique is useful for visualizing data that changes over time or showing algorithmic processes. Basic Setup First, let's set up the required imports and create sample data for our animation ? import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Animation parameters steps = 50 nodes = 100 positions = ...
Read MoreHow to plot a line (polygonal chain) with matplotlib with minimal smoothing?
To plot a line (polygonal chain) with matplotlib with minimal smoothing, you can use scipy's interpolation methods. The pchip() function provides monotonic cubic interpolation that preserves the shape of your data while adding smooth curves between points. Steps to Create a Smooth Line Plot Set the figure size and adjust the padding between and around the subplots Initialize a variable N to get the number of data points Create x and y data points using numpy Get 1-D monotonic cubic interpolation using pchip() method Plot the interpolated line and original data points Display the figure using show() ...
Read MoreCheck if points are inside ellipse faster than contains_point method (Matplotlib)
When checking if multiple points are inside an ellipse, the mathematical approach is significantly faster than using Matplotlib's contains_point() method. This technique uses the ellipse equation with coordinate transformation to handle rotated ellipses efficiently. Mathematical Approach for Point-in-Ellipse Testing The key is to transform coordinates and apply the ellipse equation directly rather than using the slower contains_point() method ? import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np # Set up the figure plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots(1) ax.set_aspect('equal') # Generate random test points ...
Read MoreChanging the color of a single X-axis tick label in Matplotlib
To change the color of a single X-axis tick label in matplotlib, you can use the tick_params() method to change all tick labels, or target specific labels using get_xticklabels() for individual customization. Method 1: Changing All X-axis Tick Labels Use tick_params() to change the color of all X-axis tick labels at once − 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 # Create figure and subplot fig = plt.figure() ax = fig.add_subplot(111) # Create data x = np.linspace(-10, 10, 100) y = ...
Read MoreHow to appropriately plot the losses values acquired by (loss_curve_) from MLPClassifier? (Matplotlib)
The MLPClassifier from scikit-learn provides a loss_curve_ attribute that tracks training loss at each iteration. Plotting these values helps visualize training convergence across different hyperparameters and datasets. Understanding MLPClassifier Loss Curves The loss_curve_ attribute stores the loss function value after each iteration during training. By plotting these values, we can compare how different solvers and learning rates affect convergence behavior. Complete Example Here's how to plot loss curves for different MLPClassifier configurations across multiple datasets ‒ import warnings import matplotlib.pyplot as plt from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import MinMaxScaler from sklearn import datasets ...
Read MoreHow to use Font Awesome symbol as marker in matplotlib?
Font Awesome symbols can be used as custom markers in matplotlib plots by using Unicode characters with the text() function. This approach allows you to create visually appealing plots with unique symbolic markers. Setup and Prerequisites First, configure the figure settings and import required libraries ? import numpy as np import matplotlib.pyplot as plt # Set figure size and enable automatic layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True Using Unicode Symbols as Markers Define a list of Unicode symbols and plot them using the text() function ? import numpy ...
Read MoreHow to save a Librosa spectrogram plot as a specific sized image?
Librosa is a Python package that helps to analyze audio and music files. This package also helps to create music information retrieval systems. In this article, we will see how to save a Librosa spectrogram plot as an image of specific size. Understanding Spectrogram Parameters Before creating the spectrogram, we need to understand the key parameters that control the output image dimensions ? hl (hop_length) − Number of samples per time-step in spectrogram hi (height) − Height of the output image (number of mel bins) wi (width) − Width of the output image (time frames) ...
Read MoreHow to plot an image with non-linear Y-axis with Matplotlib using imshow?
To plot an image with a non-linear Y-axis using Matplotlib's imshow() method, you need to customize the Y-axis tick positions while displaying your 2D data. This technique is useful when you want specific spacing or values on your Y-axis that don't follow a linear pattern. Step-by-Step Approach The process involves the following steps: Set the figure size and adjust the padding between and around the subplots Add a subplot to the current figure Set non-linear Y-axis ticks using custom positions Create or prepare your 2D data array Display the data as an image using imshow() Display ...
Read MoreHow to create a matplotlib colormap that treats one value specially?
To create a matplotlib colormap that treats one value specially, we can use set_under(), set_over(), or set_bad() methods to assign special colors for out-of-range or invalid values. Basic Approach Using set_under() The set_under() method assigns a special color to values below the colormap range ? import matplotlib.pyplot as plt import numpy as np # Create sample data data = np.random.randn(5, 5) eps = np.spacing(0.0) # Get colormap and set special color for low values cmap = plt.get_cmap('rainbow') cmap.set_under('red') # Create plot fig, ax = plt.subplots(figsize=(8, 6)) im = ax.imshow(data, interpolation='nearest', vmin=eps, cmap=cmap) fig.colorbar(im, ...
Read MoreHow to show a figure that has been closed in Matplotlib?
When you close a figure in Matplotlib, it's removed from memory and cannot be displayed again using the standard plt.show(). However, you can restore a closed figure by creating a new canvas manager and transferring the figure data. Understanding the Problem Once plt.close() is called on a figure, the canvas connection is broken. To display it again, we need to create a new canvas and reassign the figure to it. Example: Restoring a Closed Figure Here's how to show a figure that has been closed ? import numpy as np import matplotlib.pyplot as plt ...
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