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Hi 👋 My name is Mostafa Farrag

Software Engineer / Hydroinformatics Researcher

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gitPython linux

git mysql Fast API Firebase Flask Django Premiere Pro git git

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Mostafa Farrag's Projects

cookiecutter icon cookiecutter

A command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, VueJS projects.

cuda-samples icon cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

deap icon deap

Distributed Evolutionary Algorithms in Python

eo-learn icon eo-learn

Earth observation processing framework for machine learning in Python

filling_missing_data icon filling_missing_data

Filling periods of missing rainfall data using nearest neighboring station and neural network

flopy icon flopy

A Python package to create, run, and post-process MODFLOW-based models.

forpy icon forpy

Some functions and subroutines used in general modelling

fwdet icon fwdet

Calculate floodwater depth based on an inundation polygon (e.g. from remote sensing) and a DEM. See: http://csdms.colorado.edu/wiki/Model:FwDET

geemap icon geemap

A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets

geoplot icon geoplot

High-level geospatial data visualization library for Python.

geostatspy icon geostatspy

GeostatsPy Python package for spatial data analytics and geostatistics. Mostly a reimplementation of GSLIB, Geostatistical Library (Deutsch and Journel, 1992) in Python. Geostatistics in a Python package. I hope this resources is helpful, Prof. Michael Pyrcz

glofrim icon glofrim

Globally Applicable Framework for Integrated Hydrological-Hydrodynamic Modelling (GLOFRIM)

hydraulic-and-hydrologic-modelling-tool icon hydraulic-and-hydrologic-modelling-tool

HHMT is an integrated package of hydraulic and hydrologic analysis tools, in which the user interacts with the system through the use of a graphical user interface (GUI). The system is capable of performing steady and unsteady flow water surface profile calculation and several hydraulic computation

kaggle-titanic icon kaggle-titanic

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

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