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Gregor Jäger (gjaeger)

gjaeger

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Location:Mainz, Germany

Home Page:www.f-sim.de

Twitter:@FSim_de

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Gregor Jäger's starred repositories

pyerrors

Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd

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movingpandas

Movement trajectory classes and functions built on top of GeoPandas

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pyaf

PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.

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fdsreader

Python reader for FDS data

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How-to-Check-if-Time-Series-Data-is-Stationary-with-Python

How to Check if Time Series Data is Stationary with Python

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word_cloud

A little word cloud generator in Python

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prt5parser

prt5-file parser for NIST's Fire Dynamics Simulator (FDS) http://www.chraibi.de/prt5parser/

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MachineLearningDataSets

Datasets for my machine learning video tutorials.

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data-science-from-scratch

code for Data Science From Scratch book

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MF_landing_data_analysis

Primers and examples to analyze the pedestrian tracking dataset collected at Eindhoven University of Technology (see https://doi.org/10.1103/PhysRevE.95.032316 )

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fdsgeogen

Tool for automated FDS input file generation

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windrose

A Python Matplotlib, Numpy library to manage wind data, draw windrose (also known as a polar rose plot), draw probability density function and fit Weibull distribution

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smv

Smokeview

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PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

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fire-risk

Numerical Models for Developing Community Scale Risk Model

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pandas-videos

Jupyter notebook and datasets from the pandas video series

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Apollo-11

Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.

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fds

Fire Dynamics Simulator

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TikZ-StructuralAnalysis

TikZ Library for Structural Analysis

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pystra

Python Structural Reliability Analysis

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JuPedSim

Simulating pedestrians with state of the art models.

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Mutual-Information

In probability theory and information theory, the mutual information of two random variables is a quantity that measures the mutual dependence of the two random variables. This script performs MI over Mutual Information over discrete random variables

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