Command-line Tools

Python and data science snippets on the command line

A tool to get Python and data science snippets at Data Science Simplified on the command line. You can read my article to learn how I created this tool. Installation python3 -m pip install –user python-snippet Note: The –user is important. It ensures you install it in your directory and not in the global system. python3 -m ensures that you install python_snippet using Python 3. Make sure your Python version is >= 3.6.2. python3 –version Usage Search for posts based […]

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Types

Dynamically typed Object Oriented Programming Language

PySkiylia A Dynamically typed, Object Oriented Program Language, written in Python. Skiylia ![](https://raw.githubusercontent.com/Skiylia-Lang/skiylia-lang.github.com/7a2533a517895c08b8aa52c32396c292a0563d49/Skiylia_Logo_text.svg =60%/x) Skiylia is dynamically typed, object oriented, and most importantly interpreted. While it may share many similarities with C derivatives, its heritage is definitely Pythonic. The main directory housing the PySkiylia interpreter is here. Within that directory is a separate document listing the most important syntax for Skiylia. Sample code ///This section contains a small snippet of Skiylia code that calculates the factorial of a number/// def […]

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Analysis

An open-source toolkit for entropic time series analysis

EntropyHub Information and uncertainty can be regarded as two sides of the same coin: the more uncertainty there is, the more information we gain by removing that uncertainty. In the context of information and probability theory, Entropy quantifies that uncertainty. The concept of entropy has its origins in classical physics under the second law of thermodynamics, a law considered to underpin our fundamental understanding of time in physics. Attempting to analyse the analog world around us requires that we measure […]

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Validation

An option valuation webapp in Python

Option Valuation with Python An option valuation webapp in Python. Models Installation Clone this repository : git clone https://github.com/romaincaraes/option-valuation-python cd option-valuation-python You may want to use a virtual environment to keep a clean Python installation : python3 -m venv venv/ source venv/bin/activate Install the required dependencies : pip3 install -r requirements.txt Usage To use the Option Valuation web app, run the option.py script in option-valuation/ folder : cd option-valuation streamlit run option.py A new tab should open in your browser […]

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Automation

A lib version of the sports master planning code

sports_maps A lib version of the sports master planning code. Aimed a producing sport overviews for certain cities. Demoes of reports for Sedan and the Kremlin Bicetre. Repository Ownership Practice: Sport Sector: Asset management Original Author(s): Luc Jonveaux Contact Details for Current Repository Owner(s): [email protected] Data Running the Code Relatively straightforward import gpdSport data = gpdSport.dataSets() DATA = data.CreateDF(“data/) data.CreateVille(8409); #produces the an analysis for SEDAN fig = data.CreateMap(ZOOM=12) # creates a map, with a basemap zoom at 12 data.createReport(); […]

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HTTP

human-friendly CLI HTTP client for the API era

HTTPie HTTPie (pronounced aitch-tee-tee-pie) is a command-line HTTP client. Its goal is to make CLI interaction with web services as human-friendly as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers. The http & https commands allow for creating and sending arbitrary HTTP requests. They use simple and natural syntax and provide formatted and colorized output. GitHub https://github.com/httpie/httpie    

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Computer Vision

Python based SDK for multi human pose estimation through RGB webcam

PoseCamera PoseCamera is python based SDK for multi human pose estimation through RGB webcam. Install install posecamera package through pip pip install posecamera If you are having issues with the installation on Windows OS then check this page Usage See Google colab notebook https://colab.research.google.com/drive/18uoYeKmliOFV8dTdOrXocClCA7nTwRcX?usp=sharing draw pose keypoints on image import posecamera import cv2 det = posecamera.pose_tracker.PoseTracker() image = cv2.imread(“example.jpg”) pose = det(image) for name, (y, x, score) in pose.keypoints.items(): cv2.circle(image, (int(x), int(y)), 4, (255, 0, 0), -1) cv2.imshow(“PoseCamera”, image) cv2.waitKey(0) […]

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