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Notebooks for "Python for Signal Processing" book
Easy, safe, clean Python 2/3 compatibility
A python module to read and write geoprobe format volumes, horizons, and faults
Introduction to Python workshop in the Department of Geology and Geophysics at the University of Hawaii at Manoa
2-D FFT and primitive filter example
Examples of neat Python and Matlab plotting and other tasks & hacks. Runing matlab code from python
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
slides + IPython notebooks for the Stony Brook University "Python for Scientific Computing" class.
Introduction to python use in geosciences.
A short course covering Python basics, with an emphasis on geoscience applications.
Useful functions, tutorials, and other Python-related things.please see /not_so_obvious_python_stuff.ipynb
Python Data Science Handbook: full text in Jupyter Notebooks
Content of the my blog - Python For Geosciences
A collection of Python demos for geostatistical methods.
A python code for running VELEST (1D velocity calculation using travel time inversion)
PyWavelets - Discrete Wavelet Transform in Python
Repository for the Introduction to Quantitative Geology website
Jupyter notebooks contributed by QuantEcon developers, users and the community
Quick-Data-Science-Experiments
This tool is to develop an easy-to-use tool package called Landslide Susceptibility Mapping Tool Pack (LSM Tool Pack) for producing landslide susceptibility maps based on integrating R with ArcMap Software. The proposed tool contains 5 main modules namely: (1) Data Preparation (DP), (2) Feature (Factor) Selection (FS), (3) Logistic Regression (LR), (4) Random Forest (RF), (5) Performance Evaluation (PE) and (6) Create Raster Stack (Multi-Bands).
Collection of scientific computing examples in R
NSERC-USRA Funded Research Project on RBF Interpolation
Random codes that I have generated.
Python tools for random-data analysis.
R Package for Producing and Visualizing Hillshaded Maps from Elevation Matrices, in 2D and 3D
A Python 3 based program for seismic refraction data processing.
2017 workshop content for http://wenfo.org/wald/advanced-remote-sensing
Reproduced papers from the Reproducibility Zoo
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.