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# -*- coding: UTF-8 -*-
# Python
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"""
Functions to call ECMWF Reanalysis with CDS-api
- ERA5-land daily request
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- request a list of daily variables dedicated to the calculus of ET0
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and the generation of MODSPA daily forcing files
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heavily modified from @rivallandv's original file
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@author: auclairj
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"""
import os # for path exploration and file management
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from typing import List, Tuple # to declare variables
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import numpy as np # for math on arrays
import xarray as xr # to manage nc files

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import rioxarray # to manage georeferenced images
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from datetime import datetime # to manage dates
from p_tqdm import p_map # for multiprocessing with progress bars
from dateutil.rrule import rrule, MONTHLY
from fnmatch import fnmatch # for file name matching
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import pandas as pd # to manage dataframes
import rasterio as rio # to manage geotiff images
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import geopandas as gpd # to manage shapefile crs projections
from rasterio.mask import mask # to mask images
from shapely.geometry import box # to extract parcel statistics

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from rasterio.enums import Resampling # reprojection algorithms
import netCDF4 as nc # to write netcdf4 files
from tqdm import tqdm # to follow progress
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from multiprocessing import Pool # to parallelize reprojection
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from psutil import virtual_memory # to check available ram

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from psutil import cpu_count # to get number of physical cores available
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from modspa_pixel.config.config import config # to import config file
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from modspa_pixel.source.modspa_samir import calculate_time_slices_to_load # to optimise I/O operations
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import re # for string comparison
import warnings # to suppress pandas warning
# CDS API external library
# source: https://pypi.org/project/cdsapi/
import cdsapi # to download cds data
import requests # to request data
# FAO ET0 calculator external library
# Notes
# source: https://github.com/Evapotranspiration/ETo
# documentation: https://eto.readthedocs.io/en/latest/
import eto # to calculate ET0
def era5_enclosing_shp_aera(area: List[float], pas: float) -> Tuple[float, float, float, float]:
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"""
Find the four coordinates including the boxbound scene
to agree with gridsize resolution
system projection: WGS84 lat/lon degree
Arguments
=========
1. area: ``List[float]``
bounding box of the demanded area
list of floats: [lat north, lon west, lat south, lon east] in degree WGS84
2. pas: ``float``
gridsize
Returns
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1. era5_area: ``Tuple[float, float, float, float]``
coordinates list corresponding to N,W,S,E corners of the grid in decimal degree
.. note::
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gdal coordinates reference upper left corner of pixel, ERA5 coordinates refere to center of grid. To resolve this difference an offset of pas/2 is applied
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"""
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lat_max, lon_min, lat_min, lon_max = area
# North
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era5_lat_max = round((lat_max//pas+2)*pas, 2)
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# West
era5_lon_min = round((lon_min//pas)*pas, 2)
# South
era5_lat_min = round((lat_min//pas)*pas, 2)
# Est
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era5_lon_max = round((lon_max//pas+2)*pas, 2)
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era5_area = era5_lat_max, era5_lon_min, era5_lat_min, era5_lon_max
return era5_area # [N,W,S,E]
def call_era5land_daily(args: Tuple[str, str, str, str, List[int], str]) -> None:
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"""
Query of one month of daily ERA5-land data of a selected variable
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according to a selected statistic
Documentation:
`cds_climate <https://datastore.copernicus-climate.eu/documents/app-c3s-daily-era5-statistics/C3S_Application-Documentation_ERA5-daily-statistics-v2.pdf>`_
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Arguments
=========
(packed in args: ``tuple``)
1. year: ``str``
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year at YYYY format.
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month at MM format.
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user-selectable variable
cf. Appendix A Table 3 for list of input variables availables.
4. statistic: ``str``
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daily statistic choosed, 3 possibility
daily_mean or daily_minimum or daily_maximum.
5. area: ``List[int]``
bounding box of the demanded area
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area = [lat_max, lon_min, lat_min, lon_max]
6. output_path: ``str``
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path for output file.
Returns
=======
``None``
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"""
year, month, variable, statistic, area, output_path = args
# set name of output file for each month (statistic, variable, year, month)
output_filename = \
output_path+os.sep +\
"ERA5-land_"+year+"_"+month+"_"+variable+"_"+statistic+".nc"
if os.path.isfile(output_filename):
print(output_filename, ' already exist')
else:
try:
c = cdsapi.Client(timeout=300)
result = c.service("tool.toolbox.orchestrator.workflow",
params={
"realm": "c3s",
"project": "app-c3s-daily-era5-statistics",
"version": "master",
"kwargs": {
"dataset": "reanalysis-era5-land",
"product_type": "reanalysis",
"variable": variable,
"statistic": statistic,
"year": year,
"month": month,
"time_zone": "UTC+00:0",
"frequency": "1-hourly",
"grid": "0.1/0.1",
"area": {"lat": [area[2], area[0]],
"lon": [area[1], area[3]]}
},
"workflow_name": "application"
})
location = result[0]['location']
res = requests.get(location, stream=True)
print("Writing data to " + output_filename)
with open(output_filename, 'wb') as fh:
for r in res.iter_content(chunk_size=1024):
fh.write(r)
fh.close()
except:
print('!! request', variable, ' failed !! -> year', year, 'month', month)
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return None
def call_era5land_daily_for_MODSPA(start_date: str, end_date: str, area: List[float], output_path: str, processes: int = 9) -> None:
"""
request ERA5-land daily variables needed for ET0 calculus and MODSPA forcing
`reanalysis_era5 <https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview>`_
Information on requested variables
----------------------------------
called land surface variables :
* **2m_temperature**
* **surface_solar_radiation_downward**
* **total_precipitation**
* **10m_u_component_of_wind**
* **10m_v_component_of_wind**
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Arguments
=========
1. start_date: ``str``
start date in YYYY-MM-DD format
2. end_date: ``str``
end date in YYYY-MM-DD format
3. area: ``List[float]``
bounding box of the demanded area
area = [lat_max, lon_min, lat_min, lon_max]
4. output_path: ``str``
output file name, ``.nc`` extension
5. processes: ``int`` ``default = 9``
number of logical processors on which to run the download command.
can be higher than your actual number of processor cores,
download operations have a low CPU demand.
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Returns
=======
``None``
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"""
# list of first day of each month date into period
strt_dt = datetime.strptime(start_date, '%Y-%m-%d').replace(day=1)
end_dt = datetime.strptime(end_date, '%Y-%m-%d').replace(day=1)
periods = [dt for dt in rrule(
freq=MONTHLY, dtstart=strt_dt, until=end_dt, bymonthday=1)]
dico = {
'2m_temperature': ['daily_minimum', 'daily_maximum'],
'10m_u_component_of_wind': ['daily_mean'],
'10m_v_component_of_wind': ['daily_mean'],
'total_precipitation': ['daily_maximum'],
'surface_solar_radiation_downwards': ['daily_maximum']
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}
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args = []
# loop on variable to upload
for variable in dico.keys():
# loop on statistic associated to variable to upload
for statistic in dico[variable]:
# loop on year and month
for dt in periods:
year = str(dt.year)
month = '0'+str(dt.month)
month = month[-2:]
# Requete ERA5-land
args.append((year, month, variable, statistic, area, output_path))
# Start pool
p_map(call_era5land_daily, args, **{"num_cpus": processes})
return None
def filename_to_datetime(filename: str) -> datetime.date:
"""
filename_to_datetime returns a ``datetime.date`` object for the date of the given file name.
Arguments
=========
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name or path of the product
Returns
=======
1. date: ``datetime.date``
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datetime.date object, date of the product
"""
# Search for a date pattern (yyyy_mm_dd) in the product name or path
match = re.search('\d{4}_\d{2}', filename)
format = '%Y_%m'
datetime_object = datetime.strptime(match[0], format)
return datetime_object.date()
def concat_monthly_nc_file(list_era5land_monthly_files: List[str], list_variables: List[str], output_path: str) -> List[str]:
"""
Concatenate monthly netcdf datasets into a single file for each given variable.
Arguments
=========
1. list_era5land_monthly_files: ``List[str]``
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list of daily files per month
2. list_variables: ``List[str]``
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names of the required variables as written in the filename
3. output_path: ``List[str]``
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path to which save the aggregated files
Returns
=======
1. list_era5land_files: ``List[str]``
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the list of paths to the aggregated files
"""
if not os.path.exists(output_path): os.mkdir(output_path)
list_era5land_monthly_files.sort()
list_era5land_files = []
# concatenate all dates into a single file for each variable
for variable in list_variables:
curr_var_list = []
dates = []
for file in list_era5land_monthly_files:
# find specific variable
if fnmatch(file, '*' + variable + '*'):
curr_var_list.append(file)
dates.append(filename_to_datetime(file))
curr_datasets = []
for file in curr_var_list:
# open all months for the given variable
curr_datasets.append(xr.open_dataset(file))
# Create file name
try:
concatenated_file = output_path + os.sep + 'era5-land_' + dates[0].strftime('%m-%Y') + '_' + dates[-1].strftime('%m-%Y') + '_' + variable + '.nc'
except:
print(variable)
# Concatenate monthly datasets
concatenated_dataset = xr.concat(curr_datasets, dim = 'time')
# Save datasets
concatenated_dataset.to_netcdf(path = concatenated_file, mode = 'w',)
# Add filename to output list
list_era5land_files.append(concatenated_file)
return list_era5land_files
def uz_to_u2(u_z: List[float], h: float) -> List[float]:
"""
The wind speed measured at heights other than 2 m can be adjusted according
to the follow equation
Arguments
----------
u_z : TYPE float array
measured wind speed z m above the ground surface, ms- 1.
h : TYPE float
height of the measurement above the ground surface, m.
Returns
-------
u2 : TYPE float array
average daily wind speed in meters per second (ms- 1 ) measured at 2 m above the ground.
"""
u2 = u_z*4.87/(np.log(67.8*h - 5.42))
return u2
def ea_calc(T: float) -> float:
"""
comments
Actual vapour pressure (ea) derived from dewpoint temperature '
Arguments
----------
T : Temperature in degree celsius.
Returns
-------
e_a :the actual Vapour pressure in Kpa
"""
e_a = 0.6108*np.exp(17.27*T/(T+237.15))
return e_a
def load_variable(file_name: str) -> xr.Dataset:
"""
Loads an ERA5 meteorological variable into a xarray
dataset according to the modspa architecture.
Arguments
=========
1. file_name: ``str``
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netcdf file to load
Returns
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1. variable: ``xr.Dataset``
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output xarray dataset
"""
# Rename temperature variables according to the statistic (max or min)
if fnmatch(file_name, '*era5-land*2m_temperature_daily_maximum*'): # maximum temperature
variable = xr.open_dataset(file_name).rename({'t2m': 't2m_max'}).drop_vars('realization') # netcdfs from ERA5 carry an unecessary 'realization' coordinate, so it is dropped
elif fnmatch(file_name, '*era5-land*2m_temperature_daily_minimum*'): # minimum temperature
variable = xr.open_dataset(file_name).rename({'t2m': 't2m_min'}).drop_vars('realization')
elif fnmatch(file_name, '*era5-land*2m_dewpoint_temperature_daily_maximum*'): # maximum dewpoint temperature
variable = xr.open_dataset(file_name).rename({'d2m': 'd2m_max'}).drop_vars('realization')
elif fnmatch(file_name, '*era5-land*2m_dewpoint_temperature_daily_minimum*'): # minimum temperature
variable = xr.open_dataset(file_name).rename({'d2m': 'd2m_min'}).drop_vars('realization')
# Other variables can be loaded without modification
else:

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try:
variable = xr.open_dataset(file_name).drop_vars('realization')
except:
variable = xr.open_dataset(file_name)
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return variable
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def combine_weather2netcdf(rain_file: str, ET0_tile: str, ndvi_path: str, save_path: str, available_ram: int) -> None:
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Convert the Rain and ET0 geotiffs into a single weather netcdf dataset.
Arguments
=========
1. rain_file: ``str``
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path to Rain tif
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path to ET0 tif
3. ndvi_path: ``str``
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path to ndvi cube
4. save_path: ``str``
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save path of weather netcdf dataset
5. available_ram: ``int``
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available ram in GiB for conversion
Returns
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``None``
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"""
# Open tif files
rain_tif = rio.open(rain_file)
ET0_tif = rio.open(ET0_tile)
# Open ndvi netcdf to get structure
ndvi = xr.open_dataset(ndvi_path)
dates = ndvi.time
# Get empty dimensions
dimensions = ndvi.drop_sel(time = ndvi.time).dims # create dataset with a time dimension of length 0
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# Create empty dataset with same structure
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weather = ndvi.drop_vars(['NDVI']).copy(deep = True)
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# Set dataset attributes
weather.attrs['name'] = 'ModSpa Pixel weather dataset'
weather.attrs['description'] = 'Weather variables (Rain and ET0) for the ModSpa SAMIR (FAO-56) model at the pixel scale. Variables are scaled to be stored as integers.'
weather.attrs['scaling'] = "{'Rain': 1000, 'ET0': 1000}"
weather['Rain'] = (dimensions, np.zeros(tuple(dimensions[d] for d in list(dimensions)), dtype = np.uint16))
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weather['Rain'].attrs['units'] = 'mm'
weather['Rain'].attrs['standard_name'] = 'total_precipitation'
weather['Rain'].attrs['description'] = 'Accumulated daily precipitation in mm'
weather['Rain'].attrs['scale factor'] = '1000'
weather['ET0'] = (dimensions, np.zeros(tuple(dimensions[d] for d in list(dimensions)), dtype = np.uint16))
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weather['ET0'].attrs['units'] = 'mm'
weather['ET0'].attrs['standard_name'] = 'evapotranspiration'
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weather['ET0'].attrs['description'] = 'Accumulated daily reference evapotranspiration in mm'
weather['ET0'].attrs['scale factor'] = '1000'
# Create encoding dictionnary
for variable in list(weather.keys()):
# Write encoding dict
encoding_dict = {}
encod = {}
encod['dtype'] = 'u2'
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encod['_FillValue'] = 0
# TODO: figure out optimal file chunk size
file_chunksize = (1, dimensions['y'], dimensions['x'])
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encod['chunksizes'] = file_chunksize
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# TODO: check if compression affects reading speed
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encod['zlib'] = True
encod['complevel'] = 1
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encoding_dict[variable] = encod
# Save empty output
print('\nWriting empty weather dataset')
weather.to_netcdf(save_path, encoding = encoding_dict, unlimited_dims = 'time')
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weather.close()
# Get geotiff dimensions (time, x, y)
dims = (rain_tif.count, rain_tif.height, rain_tif.width)
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# Determine the memory requirement of operation
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nb_bytes = 2 # int16
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nb_vars = 1 # one variable written at a time
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memory_requirement = ((dims[0] * dims[1] * dims[2]) * nb_vars * nb_bytes) / (1024**3) # in GiB
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# Get the number of time bands that can be loaded at once
time_slice, remainder, already_written = calculate_time_slices_to_load(dims[2], dims[1], dims[0], nb_vars, 0, 0, 0, nb_bytes, available_ram)
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print('\nApproximate memory requirement of conversion:', round(memory_requirement, 3), 'GiB\nAvailable memory:', available_ram, 'GiB\n\nLoading blocks of', time_slice, 'time bands.\n')
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# Open empty dataset
weather = nc.Dataset(save_path, mode = 'r+')
# Create progress bar
progress_bar = tqdm(total = dims[0], desc='Writing weather data', unit=' bands')
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for i in range(dims[0]):
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if time_slice == dims[0] and not already_written: # if whole dataset fits in memory and it has not already been loaded
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weather.variables['Rain'][:,:,:] = rain_tif.read()
weather.variables['ET0'][:,:,:] = ET0_tif.read()
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already_written = True
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elif i % time_slice == 0 and not already_written: # load a time slice every time i is divisible by the size of the time slice
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if i + time_slice <= dims[0]: # if the time slice does not gow over the dataset size
weather.variables['Rain'][i: i + time_slice, :, :] = rain_tif.read(tuple(k+1 for k in range(i, i + time_slice)))
weather.variables['ET0'][i: i + time_slice, :, :] = ET0_tif.read(tuple(k+1 for k in range(i, i + time_slice)))
else: # load the remainder when the time slice would go over the dataset size
weather.variables['Rain'][i: i + remainder, :, :] = rain_tif.read(tuple(k+1 for k in range(i, i + remainder)))
weather.variables['ET0'][i: i + remainder, :, :] = ET0_tif.read(tuple(k+1 for k in range(i, i + remainder)))
progress_bar.update()
# Write dates in weather dataset
weather.variables['time'].units = f'days since {np.datetime_as_string(dates[0], unit = "D")} 00:00:00' # set correct unit
weather.variables['time'][:] = np.arange(0, len(dates)) # save dates as integers representing the number of days since the first day
weather.sync() # flush data to disk
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progress_bar.close()
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rain_tif.close()
ET0_tif.close()
weather.close()

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def calculate_ET0_pixel(pixel_dataset: xr.Dataset, lat: float, lon: float, h: float = 10, safran: bool = False) -> np.ndarray:
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"""
Calculate ET0 over the year for a single pixel of the ERA5 weather dataset.
Arguments
=========
1. pixel_dataset: ``xr.Dataset``
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extracted dataset that contains all information for a single pixel
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latitudinal coordinate of that pixel
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longitudinal coordinate of that pixel
4. h: ``float`` ``default = 10``
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height of ERA5 wind measurement in meters

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5. safran: ``bool`` ``default = False``
boolean to adapt to a custom SAFRAN weather dataset
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Returns
=======
1. ET0_values: ``np.ndarray``
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numpy array containing the ET0 values for each day
"""

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# TODO: adapt for safran
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# Conversion of xarray dataset to dataframe for ET0 calculation
# Conversion of temparature
ET0 = pixel_dataset.t2m_min.to_dataframe().rename(columns = {'t2m_min' : 'T_min'}) - 273.15 # conversion of temperatures from K to °C
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ET0['T_max'] = pixel_dataset.t2m_max.to_dataframe()['t2m_max'].values - 273.15 # conversion of temperatures from K to °C
ET0['R_s'] = pixel_dataset.ssrd.to_dataframe()['ssrd'].values / 1e6 # to convert downward total radiation from J/m² to MJ/m²

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if safran:
# Add relative humidity
ET0['RH_max'] = pixel_dataset.RH_max.to_dataframe()['RH_max'].values
ET0['RH_min'] = pixel_dataset.RH_max.to_dataframe()['RH_min'].values
# Add wind
ET0['U_z'] = pixel_dataset.U_z.to_dataframe()['U_z'].values
else:
# Conversion of eastward and northward wind values to scalar wind
ET0['U_z'] = np.sqrt(pixel_dataset.u10.to_dataframe()['u10'].values**2 + pixel_dataset.v10.to_dataframe()['v10'].values**2)
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# Start ET0 calculation
eto_calc = eto.ETo()
warnings.filterwarnings('ignore') # remove pandas warning
# ET0 calculation for given pixel (lat, lon) values
eto_calc.param_est(ET0,
freq = 'D', # daily frequence
# Elevation of the met station above mean sea level (m) (only needed if P is not in df).
z_msl = 0.,
lat = lat,
lon = lon,
TZ_lon = None,
z_u = h) # h: height of raw wind speed measurement
# Retrieve ET0 values
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ET0_values = eto_calc.eto_fao(max_ETo = 15, min_ETo = 0, interp = True, maxgap = 10).values # ETo_FAO_mm
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return ET0_values
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def convert_interleave_mode(args: Tuple[str, str, bool]) -> None:
"""
Convert Geotiff files obtained from OTB to Band interleave mode for faster band reading.
Arguments
=========
(packed in args: ``tuple``)
1. input_image: ``str``
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input image to convert
2. output_image: ``str``
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output image to save
3. remove: ``bool`` ``default = True``
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weather to remove input image
Returns
=======
``None``
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"""
input_image, output_image, remove = args
# Open the input file in read mode
with rio.open(input_image, "r") as src:
# Open the output file in write mode
with rio.open(output_image, 'w', driver = src.driver, height = src.height, width = src.width, count = src.count, dtype = src.dtypes[0], crs = src.crs, transform = src.transform, interleave = 'BAND',) as dst:
# Loop over the blocks or windows of the input file
for _, window in src.block_windows(1):
# Write the data to the output file
dst.write(src.read(window = window), window = window)
# Remove unecessary image
if remove:
os.remove(input_image)
return None

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def era5Land_daily_to_yearly_pixel(list_era5land_files: List[str], output_file: str, raw_S2_image_ref: str, ndvi_path: str, start_date: str, end_date: str, h: float = 10, max_ram: int = 8, use_OTB: bool = False, weather_overwrite: bool = False, safran: bool = False) -> str:
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"""
Calculate ET0 values from the ERA5 netcdf weather variables.
Output netcdf contains the ET0 and precipitation values for

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each day in the selected time period and reprojected on the
same grid as the NDVI values.
Arguments
=========
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1. list_era5land_files: ``List[str]``
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list of netcdf files containing the necessary variables
2. output_file: ``str``
3. raw_S2_image_ref: ``str``
raw Sentinel 2 image at right resolution for reprojection
4. ndvi_path: ``str``
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path to ndvi dataset, used for attributes and coordinates
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5. start_date: ``str``
beginning of the time window to download (format: ``YYYY-MM-DD``)
6. end_date: ``str``
end of the time window to download (format: ``YYYY-MM-DD``)
7. h: ``float`` ``default = 10``
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height of ERA5 wind measurements in meters
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8. max_ram: ``int`` ``default = 8``
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max ram (in GiB) for reprojection and conversion. Two
subprocesses are spawned for OTB, each receiviving
half of requested memory.

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9. use_OTB: ``bool`` ``default = False``
boolean to choose to use OTB or not, tests will be added later
10. weather_overwrite: ``bool`` ``default = False``
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boolean to choose to overwrite weather netCDF

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11. safran: ``bool`` ``default = False``

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boolean to adapt to a custom SAFRAN weather dataset
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Returns
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1. output_file_final: ``str``
path to ``netCDF4`` file containing precipitation and ET0 data
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"""
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# Test if file exists
if os.path.exists(output_file + '.nc') and not weather_overwrite:
return output_file + '.nc'
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# Test if memory requirement is not loo large
if np.ceil(virtual_memory().available / (1024**3)) < max_ram:
print('\nRequested', max_ram, 'GiB of memory when available memory is approximately', round(virtual_memory().available / (1024**3), 1), 'GiB.\n\nExiting script.\n')
return None
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# Load all monthly files into a single xarray dataset that contains all dates (daily frequency)
raw_weather_ds = None
for file in list_era5land_files:
if not raw_weather_ds:
raw_weather_ds = load_variable(file)
else:
temp = load_variable(file)
raw_weather_ds = xr.merge([temp, raw_weather_ds])
del temp
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# Clip extra dates
raw_weather_ds = raw_weather_ds.sel({'time': slice(start_date, end_date)}).sortby(variables = 'time')
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# Create ET0 variable (that will be saved) and set attributes

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raw_weather_ds = raw_weather_ds.assign(ET0 = (raw_weather_ds.dims, np.zeros(tuple(raw_weather_ds.dims[d] for d in list(raw_weather_ds.dims)), dtype = np.float32)))
if safran:
# Loop on y and x coordinates to calculate ET0 per "pixel"
# Fast enough for small datasets (low resolution)
for y in raw_weather_ds.coords['y'].values:
for x in raw_weather_ds.coords['x'].values:
# Select whole time period for given (lat, lon) values
select_ds = raw_weather_ds.sel({'y' : era5Land_daily_to_yearly_parcel(), 'x' : x}).drop_vars(['y', 'x'])
# TODO: adapt for SAFRAN
# Calculate ET0 values for given pixel
ET0_values = calculate_ET0_pixel(select_ds, y, x, h)
# Write ET0 values in xarray Dataset
raw_weather_ds['ET0'].loc[{'y' : y, 'x' : x}] = ET0_values
# Get necessary data for final dataset
final_weather_ds = raw_weather_ds.drop_vars(names = ['ssrd', 't2m_max', 't2m_min', 'RH_max', 'RH_min', 'U_z']) # remove unwanted variables
else:
# Loop on lattitude and longitude coordinates to calculate ET0 per "pixel"
# Fast enough for small datasets (low resolution)
for lat in raw_weather_ds.coords['lat'].values:
for lon in raw_weather_ds.coords['lon'].values:
# Select whole time period for given (lat, lon) values
select_ds = raw_weather_ds.sel({'lat' : lat, 'lon' : lon}).drop_vars(['lat', 'lon'])
# Calculate ET0 values for given pixel
ET0_values = calculate_ET0_pixel(select_ds, lat, lon, h)
# Write ET0 values in xarray Dataset
raw_weather_ds['ET0'].loc[{'lat' : lat, 'lon' : lon}] = ET0_values
# Get necessary data for final dataset
final_weather_ds = raw_weather_ds.drop_vars(names = ['ssrd', 'v10', 'u10', 't2m_max', 't2m_min']) # remove unwanted variables
# Scale data and rewrite netcdf attributes
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final_weather_ds['tp'] = final_weather_ds['tp'] * 1000 # conversion from m to mm
# Change datatype to reduce memory usage
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final_weather_ds['tp'] = (final_weather_ds['tp'] * 1000).astype('u2').chunk(chunks = {"time": 1})
final_weather_ds['ET0'] = (final_weather_ds['ET0'] * 1000).astype('u2').chunk(chunks = {"time": 1})

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# TODO: fix for safran

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final_weather_ds.rio.write_crs('EPSG:4326', inplace = True)
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# Set variable attributes
final_weather_ds['ET0'].attrs['units'] = 'mm'
final_weather_ds['ET0'].attrs['standard_name'] = 'Potential evapotranspiration'
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final_weather_ds['ET0'].attrs['comment'] = 'Potential evapotranspiration accumulated over the day, calculated with the FAO-56 method (scale factor = 1000)'
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final_weather_ds['tp'].attrs['units'] = 'mm'
final_weather_ds['tp'].attrs['standard_name'] = 'Precipitation'
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final_weather_ds['tp'].attrs['comment'] = 'Volume of total daily precipitation expressed as water height in milimeters (scale factor = 1000)'
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# TODO: find how to test OTB installation from python
if use_OTB:
# Save dataset to geotiff, still in wgs84 (lat, lon) coordinates
output_file_rain = output_file + '_rain.tif'
output_file_ET0 = output_file + '_ET0.tif'
final_weather_ds.tp.rio.to_raster(output_file_rain, dtype = 'uint16')
final_weather_ds.ET0.rio.to_raster(output_file_ET0, dtype = 'uint16')
# Reprojected image paths
output_file_rain_reproj = output_file + '_rain_reproj.tif'
output_file_ET0_reproj = output_file + '_ET0_reproj.tif'
# Converted image paths
output_file_final = output_file + '.nc'
# otbcli_SuperImpose commands
OTB_command_reproj1 = 'otbcli_Superimpose -inr ' + raw_S2_image_ref + ' -inm ' + output_file_rain + ' -out ' + output_file_rain_reproj + ' uint16 -interpolator linear -ram ' + str(int(max_ram * 1024/2))
OTB_command_reproj2 = 'otbcli_Superimpose -inr ' + raw_S2_image_ref + ' -inm ' + output_file_ET0 + ' -out ' + output_file_ET0_reproj + ' uint16 -interpolator linear -ram ' + str(int(max_ram * 1024/2))
commands_reproj = [OTB_command_reproj1, OTB_command_reproj2]
with Pool(2) as p:
p.map(os.system, commands_reproj)
# Combine to netCDF file
combine_weather2netcdf(output_file_rain_reproj, output_file_ET0_reproj, ndvi_path, output_file_final, available_ram = max_ram)
# remove old files and rename outputs
os.remove(output_file_rain)
os.remove(output_file_ET0)
os.remove(output_file_rain_reproj)
os.remove(output_file_ET0_reproj)
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else:
# Set dataset attributes
final_weather_ds.attrs['name'] = 'ModSpa Pixel weather dataset'
final_weather_ds.attrs['description'] = 'Weather variables (Rain and ET0) for the ModSpa SAMIR (FAO-56) model at the pixel scale. Variables are scaled to be stored as integers.'
final_weather_ds.attrs['scaling'] = "{'Rain': 1000, 'ET0': 1000}"
# Set file names
output_file_final = output_file + '.nc'
# Open reference image
ref = rioxarray.open_rasterio(raw_S2_image_ref)

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# Get metadata
target_crs = ref.rio.crs
spatial_ref = ref.spatial_ref.load()
# Define ressources
mem_limit = min([int(np.ceil(len(ref.x) * len(ref.y) * len(final_weather_ds.time) * len(final_weather_ds.data_vars) * np.dtype(np.float32).itemsize / (1024 ** 2)) * 1.1), 0.8 * virtual_memory().available / (1024**2), max_ram * 1024])
nb_threads = min([cpu_count(logical = True), len(os.sched_getaffinity(0))])
# Reproject
final_weather_ds = final_weather_ds.rio.reproject(target_crs, transform = ref.rio.transform(), shape = (ref.rio.height, ref.rio.width), resampling = Resampling.bilinear, num_threads = nb_threads, warp_mem_limit = mem_limit)
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# Rename
final_weather_ds = final_weather_ds.rename({'tp': 'Rain'})
# Create encoding dictionnary
for variable in list(final_weather_ds.keys()):
# Write encoding dict
encod = {}
encod['dtype'] = 'u2'
if '_FillValue' in final_weather_ds[variable].attrs:
del final_weather_ds[variable].attrs['_FillValue']
encod['_FillValue'] = 0
# TODO: figure out optimal file chunk size
file_chunksize = (1, final_weather_ds.dims['y'], final_weather_ds.dims['x'])
encod['chunksizes'] = file_chunksize
# TODO: check if compression affects reading speed
encod['zlib'] = True
encod['complevel'] = 1
final_weather_ds[variable].encoding.update(encod)

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# Rewrite georeferencing
final_weather_ds.rio.write_crs(target_crs, inplace = True)

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final_weather_ds.rio.write_transform(inplace = True)

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final_weather_ds['spatial_ref'] = spatial_ref
final_weather_ds.attrs['crs'] = final_weather_ds.rio.crs.to_string()

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final_weather_ds = final_weather_ds.set_coords('spatial_ref')
# Save empty output
print('\nReprojecting weather dataset')
final_weather_ds.to_netcdf(output_file_final)
final_weather_ds.close()
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return output_file_final
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def era5Land_daily_to_yearly_parcel(list_era5land_files: List[str], output_file: str, start_date: str, end_date: str, h: float = 10) -> str:
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"""
Calculate ET0 values from the ERA5 netcdf weather variables.
Output netcdf contains the ET0 and precipitation values for
each day in the selected time period.
Arguments
=========
1. list_era5land_files: ``List[str]``
list of netcdf files containing the necessary variables
2. output_file: ``str``
output file name without extension
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3. start_date: ``str``
beginning of the time window to download (format: ``YYYY-MM-DD``)
4. end_date: ``str``
end of the time window to download (format: ``YYYY-MM-DD``)
5. h: ``float`` ``default = 10``
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height of ERA5 wind measurements in meters
Returns
=======
1. output_file_rain: ``str``
path to ``Geotiff`` file containing precipitation data
2. output_file_ET0: ``str``
path to ``Geotiff`` file containing ET0 data
"""
# Load all monthly files into a single xarray dataset that contains all dates (daily frequency)
raw_weather_ds = None
for file in list_era5land_files:
if not raw_weather_ds:
raw_weather_ds = load_variable(file)
else:
temp = load_variable(file)
raw_weather_ds = xr.merge([temp, raw_weather_ds])
del temp
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# Clip extra dates
raw_weather_ds = raw_weather_ds.sel({'time': slice(start_date, end_date)})
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# Create ET0 variable (that will be saved) and set attributes
raw_weather_ds = raw_weather_ds.assign(ET0 = (raw_weather_ds.dims, np.zeros(tuple(raw_weather_ds.dims[d] for d in list(raw_weather_ds.dims)), dtype = 'float64')))
# Loop on lattitude and longitude coordinates to calculate ET0 per "pixel"
for lat in raw_weather_ds.coords['lat'].values:
for lon in raw_weather_ds.coords['lon'].values:
# Select whole time period for given (lat, lon) values
select_ds = raw_weather_ds.sel({'lat' : lat, 'lon' : lon}).drop_vars(['lat', 'lon'])
# Calculate ET0 values for given pixel
ET0_values = calculate_ET0_pixel(select_ds, lat, lon, h)
# Write ET0 values in xarray Dataset
raw_weather_ds['ET0'].loc[{'lat' : lat, 'lon' : lon}] = ET0_values
# Get necessary data for final dataset and rewrite netcdf attributes

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final_weather_ds = raw_weather_ds.drop_vars(names = ['ssrd', 'v10', 'u10', 't2m_max', 't2m_min']) # remove unwanted variables
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final_weather_ds['tp'] = final_weather_ds['tp'] * 1000 # conversion from m to mm
# Change datatype to reduce memory usage
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final_weather_ds['tp'] = (final_weather_ds['tp'] * 1000).astype('u2').chunk(chunks = {"time": 1})
final_weather_ds['ET0'] = (final_weather_ds['ET0'] * 1000).astype('u2').chunk(chunks = {"time": 1})
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# Write projection
final_weather_ds = final_weather_ds.rio.write_crs('EPSG:4326')
# Set variable attributes
final_weather_ds['ET0'].attrs['units'] = 'mm'
final_weather_ds['ET0'].attrs['standard_name'] = 'Potential evapotranspiration'
final_weather_ds['ET0'].attrs['comment'] = 'Potential evapotranspiration accumulated over the day, calculated with the FAO-56 method (scale factor = 1000)'
final_weather_ds['tp'].attrs['units'] = 'mm'
final_weather_ds['tp'].attrs['standard_name'] = 'Precipitation'
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final_weather_ds['tp'].attrs['comment'] = 'Volume of total daily precipitation expressed as water height in milimeters (scale factor = 1000)'
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# Save dataset to geotiff, still in wgs84 (lat, lon) coordinates
output_file_rain = output_file + '_rain.tif'
output_file_ET0 = output_file + '_ET0.tif'
final_weather_ds.tp.rio.to_raster(output_file_rain, dtype = 'uint16')
final_weather_ds.ET0.rio.to_raster(output_file_ET0, dtype = 'uint16')
return output_file_rain, output_file_ET0
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def extract_rasterstats(args: tuple) -> List[float]:
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"""
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Generate a dataframe for a given raster and a geopandas shapefile object.
It iterates over the features of the shapefile geometry (polygons). This
information is stored in a list.
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It returns a list that contains the raster values, a feature ``id``
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and the date for the image and every polygon in the shapefile geometry.
It also has identification data relative to the shapefile: landcover (``LC``),
land cover identifier (``id``) This list is returned to be later agregated
in a ``DataFrame``.
This function is used to allow multiprocessing for weather extraction.
Arguments (packed in args: ``tuple``)
=====================================
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1. raster_path: ``str``
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path to multiband Geotiff
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2. shapefile: ``str``
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path to shapefile
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3. config_file: ``str``
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path to config file
Returns
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1. raster_stats: ``List[float]``
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list containing weather values and feature information for every
polygon in the shapefile
"""
# Open arguments packed in args
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raster_path, shapefile, config_file = args
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# Open config file
config_params = config(config_file)
# Create dataframe where zonal statistics will be stored
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raster_stats = []
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# Get dates
dates = pd.to_datetime(pd.date_range(start = config_params.start_date, end = config_params.end_date, freq = 'D')).values
# Open ndvi image and shapefile geometry