Tommy Lees tommylees112

`R` Spatial Analysis Notes

sp - defines the set of base classes for spatial data in R. Most useful for creating, converting, merging, transforming (e.g. projection), and plotting (see spplot) Spatial* objects.
rgdal - wrapper

	conda create -n w2w python=3.8 --yes
	conda activate w2w
	conda install -c conda-forge pytorch xarray=0.16 pytorch torchvision cudatoolkit --yes
	conda install dask --yes
	conda install -c conda-forge rasterio scikit-image --yes
	conda install -c conda-forge seaborn=0.11 --yes
	conda install -c anaconda networkx --yes
	conda install -c conda-forge zarr gcsfs netcdf4 numba tqdm jupyterlab tensorboard ipython pip ruamel.yaml descartes statsmodels scikit-learn black mypy --yes
	conda install -c conda-forge geopandas --yes
	conda install -c conda-forge cdsapi --yes

	"""
	Mask xarray dataset using a shapefile [closed]
	https://stackoverflow.com/a/64587946/9940782
	"""
	from typing import Tuple
	import geopandas as gpd
	import xarray as xr
	import rasterio as rio
	from pathlib import Path
	import numpy as np

	from tika import parser
	import os


	def extract_text_from_pdfs_recursively(dir):
	for root, dirs, files in os.walk(dir):
	for file in files:
	path_to_pdf = os.path.join(root, file)
	[stem, ext] = os.path.splitext(path_to_pdf)
	if ext == '.pdf':

	library(rvest)
	library(tidyverse)
	library(tidygraph)
	library(ggraph)

	wc_squads <- html("https://en.wikipedia.org/wiki/2018_FIFA_World_Cup_squads")

	tables <- wc_squads %>%
	html_table()

	/*
	Fit a GEV distribution in stan
	Code is largely based on previous code by
	Cameron Bracken: http://bechtel.colorado.edu/~bracken/tutorials/stan/
	and Yenan Wu: http://discourse.mc-stan.org/t/troubles-in-rejecting-initial-value/1827
	*/
	functions{
	real gev_lpdf(vector y, real mu, real sigma, real xi) {
	vector[rows(y)] z;
	vector[rows(y)] lp;

	"""
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