TarickWake

Xarray Peak Detection and Visualization 📈

TL;DR

Enhance your data analysis with custom Xarray accessors for detecting and visualizing both positive and negative peaks in 1D signals. This Python code snippet makes it easy to identify and annotate peaks directly on your plots. doc xarray

Features

• Find Peaks: Utilize the find_peaks accessor to detect peaks in your DataArray.
• Plot Peaks: With the plot_peaks accessor, visualize positive and negative peaks on your plots, including annotations for peak indices.

Output

TarickWake

Reading .mat Files Using pymatreader 📊

To efficiently read MATLAB .mat files in Python, pymatreader simplifies the process by returning data as a nested dictionary, akin to scipy.io.loadmat style.

Usage Example 🚀

import pymatreader

# Specify the path to your .mat file

TarickWake

📸 TIFF loading FAST !

This Python script is designed to load images from a specified directory using a ThreadPoolExecutor for concurrent execution. It reads all .tif files from the "tif" directory and processes them in parallel, utilizing multiple threads to speed up the image loading process.

✅Important Note: The order of the images is preserved throughout this process. The executor.map method guarantees that the results are returned in the same order as the input paths, ensuring that the correspondence between the file paths and the loaded images is maintained.

🛠️ Dependencies

• 🖥️ os (built-in): For interacting with the operating system to list files in a directory.
• 📷 cv2 (OpenCV, to be installed): For reading image files.
• 📁 pathlib (built-in): For manipulating filesystem paths in a platform-independent way.
• 🌐 concurrent.futures.ThreadPoolExecutor (built-in): For managing a pool of threads to execute calls asynchronously.

TarickWake

"""
Author:  Clement TIFFON
Date: 14-06-2023
Description: This is a simple GUI for the OpenAI Text-to-Speech API. This is an example of a Streamlit app that uses the OpenAI API to convert text to speech. The app allows users to enter text in a chat input field, which is then converted to speech using the OpenAI API. The app displays the chat history and the corresponding audio files. The app uses the OpenAI Python client library to interact with the OpenAI API. The app also uses the Streamlit chat input and audio components to create a chat interface for the user. The app is a simple example of how to use the OpenAI API with Streamlit to create a text-to-speech application.

to run the app, you need to install the following libraries:
- streamlit
- openai
- numpy

TarickWake

import codecs
import nbformat
import re

def grab_value_from_notebook(file_path, pattern):
    """
    This function opens a Jupyter notebook file and searches for a specific pattern in the outputs of the code cells.
    If the pattern is found, it returns the first group of the match as a float.
    """
    

TarickWake

import matplotlib as mpl

def draw_loglog_slope(fig, ax, origin, width_inches, slope, inverted=False, color=None, polygon_kwargs=None, label=True, labelcolor=None, label_kwargs=None, zorder=None):
    """
    This function draws slopes or "convergence triangles" into loglog plots.

    @param fig: The figure
    @param ax: The axes object to draw to
    @param origin: The 2D origin (usually lower-left corner) coordinate of the triangle
    @param width_inches: The width in inches of the triangle