PyGeoHydro A part of HyRiver software stack for accessing hydrology data through web services Features PyGeoHydro is a part of HyRiver software stack that is designed to aid in watershed analysis through web services. This package provides access to some of the public web services that offer geospatial hydrology data. It has three main modules: pygeohydro, plot, and helpers. The pygeohydro module can pull data from the following web services: NWIS for daily mean streamflow observations, NID for accessing the […]

Weather-Forecasting Python scripts were created to visualize the weather for over 500 cities across the world at varying distances from the equator. To understand weather patterns for forecasting, a series of scatter plots were created. The scatter plots depicted the relationship between Temperature versus Latitude, Humidity versus Latitude, Cloudiness versus Latitiude, and Wind Speed versus Latitude. One of the relationship is shown below: Linear regressions for each relationship were created separating them in Northern and Southern Hemispheres. More than 500 […]

maup maup is the geospatial toolkit for redistricting data. The package streamlines the basic workflows that arise when working with blocks, precincts, and districts, such as The project’s priorities are to be efficient by using spatial indices wheneverpossible and to integrate well with the existing ecosystem aroundpandas, geopandas andshapely. The package is distributedunder the MIT License. Installation We recommend installing maup from conda-forgeusing conda: conda install -c conda-forge maup You can get conda by installingMiniconda, a free Pythondistribution made especially […]

pyserde Yet another serialization library on top of dataclasses. TL;DR Put additional @serialize and @deserialize decorator in your ordinary dataclass. @deserialize @serialize @dataclass class Foo: i: int s: str f: float b: bool Now you can convert an object to JSON, >>> to_json(Foo(i=10, s=’foo’, f=100.0, b=True)) {“i”: 10, “s”: “foo”, “f”: 100.0, “b”: true} Converted back from JSON to the object quite easily! >>> from_json(Foo, ‘{“i”: 10, “s”: “foo”, “f”: 100.0, “b”: true}’) Foo(i=10, s=’foo’, f=100.0, b=True) pyserde supports other […]

BaseballRatings This project uses historical baseball games data to calculate an Elo-like rating for MLB teams based on regular season match ups. The Elo rating system was originally developed for ranking chess players but also can Wikipedia has more technical details on the rating system: https://en.m.wikipedia.org/wiki/Elo_rating_system A similar analysis was done by 538: https://projects.fivethirtyeight.com/complete-history-of-mlb/ This repo consists of three primary pieces of code: Parser.py imports and cleans the game-level data. It performs basic calculations, such as determining game winners. Elo.py […]

PyProcar PyProcar is a robust, open-source Python library used for pre- and post-processing of the electronic structure data coming from DFT calculations. PyProcar provides a set of functions that manage data obtained from the PROCAR format. Basically, the PROCAR format is a projection of the Kohn-Sham states over atomic orbitals. That projection is performed to every k-point in the considered mesh, every energy band and every atom. PyProcar is capable of performing a multitude of tasks including plotting plain and […]

ipyregulartable An ipywidgets wrapper of regular-table for Jupyter. Examples Two Billion Rows Notebook Click Events Notebook Edit Events Notebook Styling Notebook Pandas Data Model For interactive/streaming sorting/pivoting/aggregation, take a look at Perspective, Streaming pivot visualization via WebAssembly, which also leverages regular-table. Notebook Series DataFrame DataFrame – Row Pivots DataFrame – Column Pivots DataFrame – Pivot Table Installation You can install using pip: pip install ipyregulartable Or if you use jupyterlab: pip install ipyregulartable jupyter labextension install @jupyter-widgets/jupyterlab-manager    

Visual Automata Visual Automata is a Python 3 library built as a wrapper for the Automata library to add more visualization features. Prerequisites pip install automata-libpip install pandaspip install graphvizpip install colormathpip install jupyterlab Installing pip install visual-automata Finite Automaton (FA) VisualDFA Importing Import needed classes. from automata.fa.dfa import DFA from visual_automata.fa.dfa import VisualDFA Instantiating DFAs Define an visual_automata DFA that can accept any string ending with 00 or 11. dfa = VisualDFA( states={“q0”, “q1”, “q2”, “q3”, “q4”}, input_symbols={“0”, “1”}, […]