benoit-cty

'''
Script to archive Slack messages from a channel list.
You have to create a Slack Bot and invite him to private channels.
View https://github.com/docmarionum1/slack-archive-bot for how to configure your account.
Then provide the bot token to this script with the list of channels.
'''

TOKEN='xoxb-xxxxx-xxxxxx-xxxxxxxxxxx'

channels = {

adamlwgriffiths

Synology Gitea setup

Install Docker

Package Centre > Docker > Install

Create folders

    /docker/gitea/postgresql
 /docker/gitea/gitea

obrl-soil

# Source this file at the top of an R or Rmd file using rgrass7 together
# with GRASS installed via OSGeo4W (https://trac.osgeo.org/osgeo4w/) on a
# Windows machine.

# e.g. source(file.path(getwd(), 'rgrass7-setup-win-osgeo4w.R'))

# You can alternatively add the contents to a project-specific .Rprofile.

# Its better to set these variables in an R session rather than in System,
# especially if you have other GIS software like PostGIS installed, which has

robbibt

import xarray as xr
import geopandas as gpd
import rasterio

# Open your shapefile and xarray object
ds = raster_mask
gdf = vector_mask

# Select shapefile feature you want to analyse
# and reproject to same CRS as xarray

sdondley

Super Guide to the split-window tmux Subcommand (and Beyond)

Guide overview

tmux, like other great software, is deceptive. On the one hand, it's fairly easy to get set up and start using right away. On the other hand, it's difficult to take advantage of tmux's adanced features without spending some quality alone time with the manual. But the problem with manuals is that they aren't geared toward beginners. They are geared toward helping seasoned developers and computer enthusiasts quickly obtain the

tysonwepprich

# Start with all Lepidoptera museum records from
# Boyle, J., H. Dalgleish, and J. Puzey. 2019a. 
# Data from: Monarch butterfly and milkweed declines substantially predate 
# the use of genetically modified crops. Dryad Digital Repository.
# https://datadryad.org/resource/doi:10.5061/dryad.sk37gd2

# these data were downloaded, cleaned, and saved with code provided in Dryad: 
# PART 1.1 Filtering museum data.R
lep <- readRDS("lep_records.rds")

ScottWales

#!/usr/bin/env python
from __future__ import print_function

import xarray
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import matplotlib.animation as anim
import cartopy

filenames = ['/g/data3/w97/dc8106/AMZ_def_EXPs/121GPsc_E0/AMZDEF.daily_tasmax.1978_2011_121GPsc_E0_SAmerica.nc']

thomasp85

library(ggplot2)
library(gganimate)
library(sf)
earth <- sf::st_as_sf(rnaturalearth::countries110)
views <- data.frame(rbind(
  st_bbox(earth[earth$name == 'Denmark',]),
  st_bbox(earth[earth$name == 'Australia',])
))
p <- ggplot() + 
  geom_sf(data = earth, fill = 'white') + 

noamross

# TODO (maybe)
# - Vectorize on URLs
# - Allow for downloading whole directory contents if path is a directory
#     -  Make a recursive = TRUE argument for this case
# - Error messages/input checking

#' Gets a file from a github repo, using the Data API blob endpoint
#'
#' This avoids the 1MB limit of the content API and uses [gh::gh] to deal with
#' authorization and such.  See https://developer.github.com/v3/git/blobs/

edzer

require("sf")
require("dplyr")
require("hexbin")

# Linux libertine font "sf", converted to path with Inkscape,
# added points between existing points 2 times, then turned all segments into straight lines.
# Saved as SVG with absolute coordinates (Preferences > SVG Output > Path Data).
# Loaded coords from SVG source code, remove letters from start and end, and replace " " with ","
coords_f <- c(218.1169,163.46992,215.56952,177.96334,213.51976,189.84421,211.82546,200.33884,210.34442,210.67351,208.24728,226.35176,205.51032,243.54066,201.92029,259.27223,197.26391,270.57846,195.45112,272.90665,193.28288,274.70167,190.97247,275.85687,188.73314,276.26564,187.03291,276.03164,185.79476,275.38887,184.84097,274.42619,183.99382,273.23248,182.45947,271.13533,180.24976,269.10927,177.54243,267.58084,174.51519,266.97658,171.25987,267.58973,169.08867,269.18036,167.87718,271.37526,167.501,273.8012,168.44294,277.0032,171.48203,279.79643,176.93817,281.77214,185.13126,282.52154,191.01986,281.80176,196.83737,279.60686,202.29944,