brews

name: risingverse-py27
channels:
  - conda-forge
  - defaults
dependencies:
  - click
  - emcee
  - gspread=3.1.0
  - numpy>=1.14
  - netCDF4

brews

#! /usr/bin/env python

# 2018-07-18
# S. B. Malevich <malevich@email.arizona.edu>

# Python script to parse the NOAA servers for fire history data. 
# We grab latitude, longitude and data file URLs along with some other meta 
# data and write it all to a tab-delimited file. Should run on default libs for
# Python 3.x (no py2).

brews

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

brews

percents_example = [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.038767 0.210932 0.388233 0.5434 0.670989 0.763819 0.827314 0.875141 0.9251 0.998257 1.110161 1.271364 1.481631 1.737837 2.033275 2.357837 2.697437 3.030084 3.338291 3.600196 3.810219 3.964794 4.079458 4.171464 4.267533 4.384367 4.533731 4.702413 4.864823 4.971393 4.968025 4.803981 4.447635 3.904993 3.215972 2.45426 1.708694 1.050809 0.56383 0.190557 0.040983 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];
part_sizes = [0.039811 0.045709 0.052481 0.060256 0.069183 0.079433 0.091201 0.104713 0.120226 0.138038 0.158489 0.18197 0.20893 0.239883 0.275423 0.316228 0.363078 0.416869 0.47863 0.549541 0.630957 0.724436 0.831764 0.954993 1.096478 1.258925 1.44544 1.659587 1.905461 2.187762 2.511886 2.884031 3.311311 3.801894 4.365158 5.011872 5.754399 6.606934 7.585776 8.709636 10 11.481536 13.182567 15.135612 17.378008 19.952623 22.908677 26.30268 30.199517 34.673685 39.810717 45.708819 52.480746 60.255959 69.183097 79.432823 91.201084 104.712

brews

import requests
import json


def fetchmeta(doi, fmt='dict', **kwargs):
    """Fetch metadata for a given DOI.

    Parameters
    ----------
    doi : str

brews

#! /usr/bin/env bash
# 2018-05-23
# S. B. Malevich <malevich@email.arizona.edu>
#
# Script to (hopefully) create a conda environment and install the `dada2` R 
# package (https://benjjneb.github.io/dada2/) on the Ocelote HPC. Run with:
#
# bash dada2_install.sh
#
# This may take some time. I'm assuming you have `miniconda` already installed 

brews

#! /usr/bin/env bash

# Modified from David Mertz's original script.

NOW=$(date "+%Y-%m-%d")
mkdir -p $HOME/tmp/envs-$NOW
ENVS=$(conda env list | grep '^\w' | cut -d' ' -f1)
for env in $ENVS; do
    source activate $env
    conda env export > $HOME/tmp/envs-$NOW/$env.yml

brews

#! /usr/bin/env bash

# 2018-01-12
# Make a netCDF file that's diff between TS and TOS (TS - TOS).

cp data/regrid/tos_sfc_Odec_CCSM3_TraCE21ka_TSREGRID.nc data/combined_tmp.nc
ncks -A data/ts_sfc_Adec_CCSM3_TraCE21ka.nc data/combined_tmp.nc
ncap2 -s 'tdif=(ts-tos)' data/combined_tmp.nc data/combined.nc

# Cleanup

brews

# 2017-07-14
# S. Brewster Malevich <malevich@email.arizona.edu>

# How to pull hyperlinks from an html directory, read with read_fhx() in a
# somewhat error-resistant way.

# There are a couple ways to do this. Python or `wget` would be far 
# better options, but let's do this with R.

# Another thing, this pesters the poor NCDC server every time you run this. 

brews

import scipy
import numpy as np

def matern(u, phi, kappa):
    """Computes Matérn correlation function for given distances and parameters

    Roughly based on the matern function from Ribeiro and Diggle's 'geoR' 
    package for R.

    Parameters: