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

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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(

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

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# 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")

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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(

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library(tidyverse)
library(eurostat)
library(lubridate)

n_veih <- get_eurostat("tran_r_vehst") %>% # https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=tran_r_vehst&lang=en
  dplyr::filter(
    str_length(geo) == 2,
    unit == "P_THAB", # NR: Number, P_THAB: Per thousand inhabitants
    vehicle == "CAR" # Passenger cars
  )

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import xarray as xr
import numpy as np
import pandas as pd
from pyproj import Proj, transform
import os.path

# Data for projections
# CHECK WITH https://epsg.io/transform#s_srs=4326&t_srs=3035&x=17.6830006&y=48.4970017
inProj = Proj(init='epsg:4326')
outProj = Proj(init='epsg:3035')

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library(ncdf4)

# files are 229x161x8760
FILE_1 = nc_open('/emhires-data/data-warehouse/gridded/ERA5/era5-hourly-100m_v_component_of_wind-2018.europe.nc')
FILE_2 = nc_open('/emhires-data/data-warehouse/gridded/ERA5/era5-hourly-10m_v_component_of_wind-2018.europe.nc')

lat = ncvar_get(FILE_1, 'latitude')
lon = ncvar_get(FILE_1, 'longitude')

data_1 = ncvar_get(FILE_1, 'v100') # 2.5 GB in memory

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import cdsapi
import os

c = cdsapi.Client()

VARS = ['100m_u_component_of_wind','100m_v_component_of_wind','10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature','surface_solar_radiation_downward_clear_sky','surface_solar_radiation_downwards', '2m_dewpoint_temperature','mean_sea_level_pressure']

YEARS = [x for x in map(str, range(1979, 2019))]

for V in VARS:

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