chanjeunlam Goto Github PK

school

ANPA, Python SummerSchool 2019

scienceplots

Matplotlib styles for scientific plotting

scientific-python-lectures

Lectures on scientific computing with python, as IPython notebooks.

scientific-visualization-book

An open access book on scientific visualization using python and matplotlib

scikit-downscale

Statistical climate downscaling in Python

scipy-lecture-notes

Tutorial material on the scientific Python ecosystem

scipy2013

SciPy 2013 Data Processing Tutorial

scipy2021-talk

Materials for Scipy 2021

scripts_paper_arc3o_part1

These are the scripts for the paper: Burgard, C., Notz, D., Pedersen, L. T., and Tonboe, R. T.: The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output, The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, 2020.

scripts_paper_arc3o_part2

These are the scripts for the paper: Burgard, C., Notz, D., Pedersen, L. T., and Tonboe, R. T.: The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation, The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, 2020.

scripts_paper_drivers_ao_warming_cmip5

These are the scripts used in the GRL paper "Drivers of Arctic Ocean warming in CMIP5 models": https://doi.org/10.1002/2016GL072342

scs_eddy

scs_kuroshio

Numerical simulation of the Kuroshio intrusion into the South China Sea by a passive tracer

scycle

SCycle simulates sequences of earthquake cycles on the timescale of hundreds to thousands of years, with a focus on the interaction between short-term and long-term deformation.

sdedit

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

sea_mountain

seaborn

Statistical data visualization in Python

seagrid

Orthogonal curvilinear grid creator for Matlab

seaiceqbo

Assessing the influence of the QBO in Arctic sea ice perturbation experiments

seamls2019

Materials from the South East Asia Machine Learning School 2019 event

seasonal

Robustly estimate trend and periodicity in a timeseries.

sedinet

Deep learning framework for optical granulometry (estimation of sedimentological variables from sediment imagery)

self-learning-computer-science

the resources I use to learn computer science in my spare time

self-supervised-depth-completion

ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"

seminar

Notes for the weekly seminar on deep learning for climate modeling

serazin2019_scale-dependent

Paper repository: "Scale-dependent analysis of in situ observations in the mesoscale to submesoscale range around New Caledonia"

shamecom

收集校招污点公司或组织，帮助学弟学妹避雷。互联网不曾遗忘！

shared_notebooks

Shared notebooks and data explorations.

sharppy

Sounding/Hodograph Analysis and Research Program in Python

shell-tutorial

Shell 简易教程