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cme211-notes

Notes for CME 211

cme213_project_neuralnetwork

final project for cme213 at stanford spring 2016. starter code written by prof eric darve and course TAs. neural network.

dgs-for-cores

The Digital Grain Size Project: Automated Estimates of Grain Size from Images of Sediment. GUI designed for sediment images where vertical variation of grain size is important, such as cores :camera:

dgs-for-cores1d

1D implementation of DGS-for-Cores

dgs-non-gui

The Digital Grain Size Project: Automated Estimates of Grain Size from Images of Sediment. A non-GUI version of DGS, useful for batch processing images within several folders. Operates from matlab command line :camera:

dgs-python

Digital Grain Size algorithm in Python (command line, no graphical user interface). Includes both serial and parallel versions of the code.

enelx_l2rpn

Code for L2RPN competition by Enel X team

esri-deadly-heatwaves

Deadly Heatwave: Where and when they will occur!

ff_plot_w

gs_tools

tools to work with grain size data

numa_plotting_tools

python tools to plot output from tsunami inundation simulations in numa

point_count_core

point_count_core.py: Software to make regular manual measurements of grains in an image of sediment

probabilistic_energy_forecast

Probabilistic forecasting of energy time-series data

pydelft

Python tools for working with Delft3d FLOW.

pydgs

Wavelet-based digital grain size analysis

readcsvdata

class to hold generic csv data. creates an attribute for every row or column.

save-the-ping-pong-balls

Experiments with sensors on raspberry pi to monitor fridge temperature.

swe_xz

solve the 2D-V non-hydrostatic shallow water equations

tsudb

Program to read in, process, analyze, and plot data from a Tsunami Deposit Database

tsusedmod

Inverse model for calculating tsunami tsunami hydrodynamic flow parameters using characteristics of tsunami sand deposits. Model published by Dr. Bruce Jaffe and Dr. Guy Gelfenbaum of the US Geological Survey.

understanding_the_amazon

231n final project: kaggle challenge to label satellite image chips with atmospheric conditions and various classes of land cover/land use.