sdtaylor

# Various tests with Common Alert Protocal (CAP) feeds at https://severeweather.wmo.int/v2/

from datetime import datetime, timedelta
import requests
import re
#import xmltodict

import feedparser
from capparselib.parsers import CAPParser

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# Built-in modules
import os
import datetime
import itertools
from glob import glob
from aenum import MultiValueEnum

# Basics of Python data handling and visualization
import numpy as np

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library(tidyverse)


timing_results = tribble(
  ~treatment, ~object,    ~mouse_id, ~time, 
  'vehicle',  'familiar', '1',    15,
  'vehicle',  'novel', '1',    55,
  
  'vehicle',  'familiar', '2',    19,
  'vehicle',  'novel', '2',    60,

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def processSpecies(speciesName):
	#do stuff for this species
	return(result)

speciesList=['a','b','c','d']

#############################################
#how stuff is typically done
results=[]
for species in speciesList:

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conda create --name geo python=3.8
conda activate geo

# with a spyder install outside the geo environment, but
# pointing to the geo environment bin/python, the following
# must be installed in the geo env to get the spyder ipython console working
pip install spyder-kernels~=2.0.0

# primary geospatial things
pip install xarray rasterio geopandas numpy pandas scipy netcdf4 dask

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library(tidyverse)

identify_continuous_non_zero_series = function(df, min_sequence_size =5){
  # adds a new column called 'continuous_series' to the df data.frame
  # identifying where 'value' column is >0 for at least min_sequence_size
  
  # filter to only continous chunks > 0, assign each an ID, 
  # and flag each chunk >= min_sequence_size.
  # the cumsum() trick is from https://stackoverflow.com/a/42734207/6615512
  temp_df = df %>%

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R packages with some pre-made shapefiles

https://github.com/hrbrmstr/albersusa
specifically:

• https://github.com/hrbrmstr/albersusa/raw/master/inst/extdata/composite_us_states.geojson.gz
• https://github.com/hrbrmstr/albersusa/raw/master/inst/extdata/composite_us_counties.geojson.gz

https://github.com/pdil/usmap

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library(tidyverse)

#------------------------------------
# Have x-axis labels offset slightly for clarity using guide_axis(n.dodge = 2),
# but also have different length tickmarks to make  it look nicer. 
# See https://twitter.com/ecologyofgavin/status/1344102509585997824
# Derived from https://stackoverflow.com/a/51312611/6615512
# Requires ggplot 3.0.0 or greater
#------------------------------------

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library(tidyverse)

# Create a "date bin" data.frame with columns c('start','end')
# start_date, and last_date are dates
# time_bin_size should be string like '1 year','6 month', etc. see ?seq.Date for more examples
bin_dates = function(first_date, last_date, time_bin_size='1 year'){
  if(!(lubridate::is.Date(first_date) & lubridate::is.Date(last_date))) stop('first_date and last_date must be dates')
  if(first_date>=last_date) stop('first_date must come before last date')
  
  full_range = seq.Date(first_date,last_date, by=time_bin_size)

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library(tidyverse)
library(sf)

# where lon, lat are columns for longitude, latitude, respectively.
# Any other columns are retained in the sf data.frame.

read_csv('site_list.csv') %>%
  st_as_sf(coords = c('lon','lat'), crs=4326)