matteodefelice

import marimo

__generated_with = "0.16.5"
app = marimo.App(width="medium")


@app.cell
def _(mo):
    mo.md(
        r"""

matteodefelice

library(tidyverse)
# ERA5
e5 = read_csv('reanalysis-era5-single-levels-timeseries-sfc9vc2xo5f.csv') |>
  mutate(
    t2m = t2m - 273.15
  ) |>
  group_by(
    valid_time = floor_date(valid_time, unit = "days")
  ) |>
  summarise(

matteodefelice

# %% [markdown]
# https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by#overview

# %%
# required to locate and authenticate with the stac collection
import planetary_computer
import pystac_client

# required to load a zarr array using xarray
import xarray as xr

matteodefelice

# %%
import xarray as xr
import numpy as np
import geopandas as gpd
import regionmask
# %%
gd = xr.open_dataset("C:/Users/matte/data/tasmax_day_ACCESS-CM2_historical_r1i1p1f1_gn_1999.nc")
# gd.coords['lon'] = (gd.coords['lon'] + 180) % 360 - 180
# gd = gd.sortby(gd.lon)
# %%

matteodefelice

library(tidyverse)
library(countrycode)
library(scico)
library(patchwork)
library(ggtext)


mapdata <- map_data("world") |>
  mutate(iso3_code = countrycode(region, "country.name", "iso3c"))

matteodefelice

# %%
import xarray as xr
import pandas as pd
# %%
df = (
    xr.open_mfdataset("C:/Users/matte/data/CPC/preci*")
    .resample(time = "MS").sum()
    .load()
)
# %%

matteodefelice

library(lubridate)
library(tidyverse)
library(zoo)
read_station <- function(filename) {
  read_csv(filename,
    col_types = cols(DATE = col_datetime(format = "%Y%m%d")),
    skip = 20
  )
}
station_files <- c(

matteodefelice

color_palette = c(
  'Biomass' = '#7daf4bff',
  'Coal' = '#af4b91b2',
  'Gas' = '#d7642dff',
  'Hydro-power' = '#00a0e1ff',
  'Nuclear' = '#466eb4ff',
  'Oil' =  '#af4b91ff',
  'Other' = '#b9c33799',
  'Solar' = '#e6a532ff',

matteodefelice

# Before you start install all the referenced packages below
# including the development release of ecmwfr you will also
# need the rnaturalearthdata in addition to the normal package
# To run the code copy and paste segments as this is still
# rough around the edges and requires user interaction to
# set your CDS API key.

# load libraries
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("khufkens/ecmwfr")

matteodefelice

library(tidyverse)

df <- read_csv("ItalianCovidData/Daily Covis19 Italian Data Cumulative") %>%
  select(1:5) %>%
  gather(type, value, -Date, -Region) %>%
  group_by(Date, Region)

total <- df %>%
  dplyr::filter(Date == max(df$Date)) %>%
  group_by(