adamhsparks

# get DPIRD CVT zones from the Public Services Slip WA as an {sf} object in R
# data are CC 4.0 and available from: <https://data.wa.gov.au/slip>
wfs_wa <-
  "https://public-services.slip.wa.gov.au/public/services/SLIP_Public_Services/Boundaries_WFS/MapServer/WFSServer"

url <- httr2::url_parse(wfs_wa)
url$query <- list(service = "wfs",
                  #version = "2.0.0", # facultative
                  request = "GetCapabilities")

adamhsparks

# Fetch historical weather data with Python using various packages

# use plotnine for graphing
from plotnine import *
import pandas as pd
import pathlib


# pynasapower
# Fetches NASA POWER data

adamhsparks

library("nasapower")
library("purrr")
library("dplyr")

df <- data.frame(
  stringsAsFactors = FALSE,
  lon = c(151.81, 112.5, 115.5),
  lat = c(-27.48, -55.5, -50.5),
  date = c("3/3/2023", "5/12/2023", "1/3/2024")
)

adamhsparks

- Project '~/tmp/OceanOmics-classifier-comparison' loaded. [renv 1.0.3]
> targets::tar_make()
> dispatched target all_results_files
o completed target all_results_files [0 seconds]
> dispatched branch all_results_ecfccd47
o completed branch all_results_ecfccd47 [0 seconds]
> dispatched branch all_results_eccc5a34
o completed branch all_results_eccc5a34 [0 seconds]
> dispatched branch all_results_05574669
o completed branch all_results_05574669 [0 seconds]

adamhsparks

# Get all DPIRD boundary data sets available from the Public Services Slip WA and save data for use in an R package.

# HT to Thierry Onkelinx, Hans Van Calster, Floris Vanderhaeghe for their post,
# <https://inbo.github.io/tutorials/tutorials/spatial_wfs_services/>, but I
# modified this to work to save .Rds files for use in an R package, not just
# saving to disk and chose to use {httr2} in place of {httr}.

# NOTE: This URL is only for public boundaries,
<https://public-services.slip.wa.gov.au/public/services/SLIP_Public_Services/Boundaries_WFS/MapServer/WFSServer>,
there are others, see <https://catalogue.data.wa.gov.au/dataset> for other orgs and types of data

adamhsparks

# Find stations in both the DPIRD and SILO weather station networks given a
#  vector of locations or capture messages when no stations are found

library("tidygeocoder")
library("data.table")
library("weatherOz")
library("purrr")

# Step 1: Create a vector of named locations to geocode

adamhsparks

StatQuantileBin <- ggplot2::ggproto("StatQuantileBin", ggplot2::StatBin, 
   default_aes = ggplot2::aes(x = ggplot2::after_stat(density), y = ggplot2::after_stat(density), weight = 1),                                   
   compute_group = function(data, scales, 
                            binwidth = NULL, bins = 30, breaks = NULL, trim = 0,
                            closed = c("right", "left"), pad = FALSE,
                            flipped_aes = FALSE,
                            # The following arguments are not used, but must
                            # be listed so parameters are computed correctly
                            origin = NULL, right = NULL, drop = NULL,

adamhsparks

# Download any data file from a private GitHub repo
# regardless of how large it is.
# Returns a string that will then need to be parsed
# by read_csv or the like to turn it into a data frame.

# Dependencies
require(tidyverse)
require(httr) 
require(rlist)
require(jsonlite)

adamhsparks

# Entering data
week <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
YLI <- c(3, 4, 5, 2, 6, 5, 4, 3, 5, 6, 2, 4)
YLS <- c(10, 11, 11, 12, 10, 11, 11, 10, 12, 13, 10, 11)
rainfall <- c(1, 10, 8, 6, 65, 30, 2, 10, 8, 6, 65, 30)

# Creating Data Frame
perf <- data.frame(week, YLI, YLS, rainfall)

# Plotting Charts and adding a secondary axis

adamhsparks

function audpc(evaluation, dates)
    n = length(evaluation) - 1
    disvec = Base.zeros(n)
    datevec = Base.zeros(n)
    out = 0.0

    for i in 1:n
        disvec[i] = (evaluation[i] + evaluation[i + 1]) / 2
        datevec[i] = dates[i + 1] - dates[i]
        out = sum(disvec .* datevec)