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gdbassett / salt_webapp.py
Created August 29, 2024 01:58
#!/usr/bin/python
## start with salt_webapp.py in a separate line on the python configuration page int he DIGI web UI
### Import the server
from SimpleHTTPServer import SimpleHTTPRequestHandler
from BaseHTTPServer import HTTPServer, BaseHTTPRequestHandler
from urlparse import urlparse, parse_qs
@gdbassett
gdbassett / freshwater_logs.py
Created August 24, 2024 17:40
#!/usr/bin/python
## logs in /userfs/WEB/logging/python.log
## start with freshwater_logs.py /userfs/WEB/logging/python.log in a separate line on the python configuration page int he DIGI web UI
# imports
import sys
try:
import os.path
except ImportError:
@gdbassett
gdbassett / dia_gdb_v2.py
Last active August 24, 2024 17:53
#!/usr/bin/python
############################################################################
# #
# Copyright (c)2008, 2009, Digi International (Digi). All Rights Reserved. #
# #
# Permission to use, copy, modify, and distribute this software and its #
# documentation, without fee and without a signed licensing agreement, is #
# hereby granted, provided that the software is used on Digi products only #
# and that the software contain this copyright notice, and the following #
@gdbassett
gdbassett / json_to_jsonld.py
Last active February 11, 2022 19:58
Convert json to json-ld. I couldn't find any examples in python so produced one. This is very basic as I'm still learning json-ld. It does not use bnodes.
from urllib.parse import quote, unquote
import logging
from uuid import uuid4
from collections import defaultdict
def flatten(l):
for el in l:
if type(el) in [list, tuple]:
yield from flatten(el)
else:
@gdbassett
gdbassett / Quad waffle chart
Created February 12, 2021 15:54
library(tidyverse)
options(repr.plot.width=16, repr.plot.height=16)
tibble(axis_1 = c(0, 0, 1, 1), axis_2 = c(0, 1, 0, 1),
count = c(72164, 303490, 129222, 594717)) %>%
mutate(scale = 581) %>%
mutate(scaled_count = count/scale) %>%
mutate(data = purrr::map(scaled_count, ~ tibble(x=rep(1:32, 32), y=rep(1:32, each=32)) %>% head(round(.x)))) %>%
unnest(data) %>%
@gdbassett
gdbassett / Catmull-Rom Splines in R
Last active November 5, 2020 22:37
# based on https://github.com/vz-risk/VCDB/commits/master
tj <- function(ti, Pi, Pj, alpha) {
#xi, yi = Pi
#xj, yj = Pj
xi <- Pi[1]
yi <- Pi[2]
xj <- Pj[1]
yj <- Pj[2]
return( ( ( (xj-xi)^2 + (yj-yi)^2 )^0.5 )^alpha + ti)
@gdbassett
gdbassett / covid_pop.R
Created April 6, 2020 20:44
Proportion of population having died from covid19 over time by country
# https://population.un.org/wpp/Download/Standard/Population/
pop <- readr::read_csv("~/Documents/Data/covid19/population.csv") %>%
select(`Region, subregion, country or area *`, `Country code`, `2020`) %>%
rename(name = `Region, subregion, country or area *`, alpha_3 = `Country code`, population=`2020`) %>%
mutate(population = as.integer(gsub(" ", "", population))) %>%
mutate(population = population * 1000) %>%
glimpse()
# From https://github.com/CSSEGISandData/COVID-19
covid <- readr::read_csv("~/Documents/Development/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv") %>%
@gdbassett
gdbassett / tidy_tuesday_simpsons.ipynb
Created August 27, 2019 18:01
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@gdbassett
gdbassett / test_data.csv
Last active July 10, 2019 14:36
enum x n freq shape1 shape2 x2 y2
Web Applications 581 2013 0.2886 581.5 1432.5 0 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.005 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.01 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.015 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.02 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.025 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.03 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.035 1
Web Applications 581 2013 0.2886 581.5 1432.5 0.04 1
@gdbassett
gdbassett / vega_cdf_bar_data.csv
Last active March 14, 2019 01:19
We can make this file beautiful and searchable if this error is corrected: It looks like row 8 should actually have 15 columns, instead of 12 in line 7.
enum,x,n,freq,method,x1,n1,shape1,shape2,mean,lower,upper,sig,y2,x2
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.4971023064317177,0.050000011597819416,1,0
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.4971023064317177,0.050000011597819416,1,0.005
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.4971023064317177,0.050000011597819416,1,0.01
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.4971023064317177,0.050000011597819416,1,0.015
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.4971023064317177,0.050000011597819416,1,0.02
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.4971023064317177,0.050000011597819416,1,0.025
Hacking,1055,2215,0.4763,bayes,1055,2215,1055.5,1160.5,0.4763086642599278,0.45552701193652323,0.49710230643171