# -*- coding: UTF-8 -*-
# Python
"""
Functions to call ECMWF Reanalysis with CDS-api
- ERA5-land daily request
- request a list of daily variables dedicated to the calculus of ET0
and the generation of MODSPA daily forcing files
heavily modified from @rivallandv's original file
@author: auclairj
"""
import os # for path exploration and file management
import numpy as np # for math on arrays
import xarray as xr # to manage nc files
import rioxarray # to manage georeferenced images
from datetime import datetime # to manage dates
from fnmatch import fnmatch # for file name matching
import pandas as pd # to manage dataframes
import rasterio as rio # to manage geotiff images
import geopandas as gpd # to manage shapefile crs projections
from rasterio.mask import mask # to mask images
from rasterio.enums import Resampling # reprojection algorithms
import netCDF4 as nc # to write netcdf4 files
from tqdm import tqdm # to follow progress
from multiprocessing import Pool # to parallelize reprojection
from psutil import virtual_memory # to check available ram
from psutil import cpu_count # to get number of physical cores available
from modspa_pixel.config.config import config # to import config file
from modspa_pixel.source.modspa_samir import calculate_time_slices_to_load # to optimise I/O operations
import warnings # to suppress pandas warning
# CDS API external library
# source: https://pypi.org/project/cdsapi/
import cdsapi # to download cds 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(bbox: list[float], pas: float) -> tuple[float, float, float, float]:
"""
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
=======
1. era5_area: ``tuple[float, float, float, float]``
coordinates list corresponding to N,W,S,E corners of the grid in decimal degree
.. note::
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
"""
lat_max, lon_min, lat_min, lon_max = bbox[3], bbox[0], bbox[1], bbox[2]
# North
era5_lat_max = round((lat_max // pas + 1) * pas, 2)
# West
era5_lon_min = round((lon_min // pas) * pas, 2)
# South
era5_lat_min = round((lat_min // pas) * pas, 2)
# Est
era5_lon_max = round((lon_max // pas + 1) * pas, 2)
era5_area = [era5_lat_max, era5_lon_min, era5_lat_min, era5_lon_max]
return era5_area # [N,W,S,E]
def split_dates_by_year(start_date: str, end_date: str) -> list[tuple[str, str]] | list:
"""
Given a start and end date, returns tuples of start and end dates IN THE SAME YEAR.
Arguments
=========
1. start_date: ``str``
start date in YYYY-MM-DD format
2. end_date: ``str``
end date in YYYY-MM-DD format
Returns
=======
1. dates: ``list[tuple[str, str]] | list``
output tuples of start and end dates
"""
start = datetime.strptime(start_date, '%Y-%m-%d')
end = datetime.strptime(end_date, '%Y-%m-%d')
if start.year == end.year:
return [(start_date, end_date)]
dates = []
current_start = start
while current_start.year <= end.year:
if current_start.year == end.year:
current_end = end
else:
current_end = datetime(current_start.year, 12, 31)
dates.append((current_start.strftime('%Y-%m-%d'), current_end.strftime('%Y-%m-%d')))
current_start = datetime(current_start.year + 1, 1, 1)
return dates
def call_era5landhourly(args: tuple) -> None:
"""
Download weather data for the given variable. Arguments are packed in a tuple for multiprocessing.
Arguments
=========
1. variable: ``str``
name of ER5-Land weather variable
2. output_path: ``str``
output path to download netcdf file
3. start_date: ``str``
start date in YYYY-MM-DD format
(start and end date must be in the same
year to reduce data to download)
4. end_date: ``str``
end date in YYYY-MM-DD format
(start and end date must be in the same
year to reduce data to download)
5. bbox: ``list[float, float, float, float]``
bounding box of area to download data
6. gridsize: ``float`` ``default = 0.1``
gridsize of data to download
Returns
=======
1. output_filename: ``str``
output file name
"""
variable, output_path, start_date, end_date, bbox, gridsize = args
# full path name of the output file
output_filename = os.path.join(output_path, 'ERA5-land_' + variable + '_' + start_date + '_' + end_date + '.nc')
# Get time periods for download
start_date = datetime.strptime(start_date, '%Y-%m-%d')
end_date = datetime.strptime(end_date, '%Y-%m-%d')
# Generate time inputs
months = []
current = start_date
while current <= end_date:
month_str = current.strftime('%m')
if month_str not in months:
months.append(month_str)
# Move to the next month
if current.month == 12:
current = current.replace(year=current.year + 1, month=1)
else:
current = current.replace(month=current.month + 1)
# Generate the list of days
days = [f'{day:02}' for day in range(1, 32)]
# Generate time
time = [f'{hour:02}:00' for hour in range(0, 24)]
# Get modified bbox
area = era5_enclosing_shp_aera(bbox, gridsize)
# Check if file already exists
if os.path.isfile(output_filename):
print('\n', output_filename, 'already exist !\n')
else:
# cds api request
client = cdsapi.Client(timeout = 300)
try:
client.retrieve('reanalysis-era5-single-levels',
request = {
'product_type': ['reanalysis'],
'variable': [variable],
'year': [start_date.strftime(format = '%Y')],
'month': months,
'day': days,
'time': time,
'data_format': 'netcdf',
'download_format': 'unarchived',
'area': area,
'grid': [gridsize, gridsize],
},
target = output_filename)
print('\n', output_filename, ' downloaded !\n')
except Exception as e:
print('\nRequest failed, error message:\n\n', e, '\n')
return output_filename
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.
"""
return u_z * 4.87/(np.log(67.8 * h - 5.42))
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
"""
return 0.6108 * np.exp(17.27 * T / (T + 237.15))
def combine_weather2netcdf(rain_file: str, ET0_tile: str, ndvi_path: str, save_path: str, available_ram: int) -> None:
"""
Convert the Rain and ET0 geotiffs into a single weather netcdf dataset.
Arguments
=========
1. rain_file: ``str``
path to Rain tif
2. ET0_tile: ``str``
path to ET0 tif
3. ndvi_path: ``str``
path to ndvi cube
4. save_path: ``str``
save path of weather netcdf dataset
5. available_ram: ``int``
available ram in GiB for conversion
Returns
=======
``None``
"""
# 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).sizes # create dataset with a time dimension of length 0
# Create empty dataset with same structure
weather = ndvi.drop_vars(['NDVI']).copy(deep = True)
weather = weather.drop_sel(time = weather.time)
# 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}"
# Set variable attributes
weather['Rain'] = (dimensions, np.zeros(tuple(dimensions[d] for d in list(dimensions)), dtype = np.uint16))
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))
weather['ET0'].attrs['units'] = 'mm'
weather['ET0'].attrs['standard_name'] = 'evapotranspiration'
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'
encod['_FillValue'] = 0
# TODO: figure out optimal file chunk size
file_chunksize = (1, dimensions['y'], dimensions['x'])
encod['chunksizes'] = file_chunksize
# TODO: check if compression affects reading speed
encod['zlib'] = True
encod['complevel'] = 1
encoding_dict[variable] = encod
# Save empty output
print('\nWriting empty weather dataset')
weather.to_netcdf(save_path, encoding = encoding_dict, unlimited_dims = 'time')
weather.close()
# Get geotiff dimensions (time, x, y)
dims = (rain_tif.count, rain_tif.height, rain_tif.width)
# Determine the memory requirement of operation
nb_bytes = 2 # int16
nb_vars = 1 # one variable written at a time
memory_requirement = ((dims[0] * dims[1] * dims[2]) * nb_vars * nb_bytes) / (1024**3) # in GiB
# 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)
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')
# 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')
# Data variables
for i in range(dims[0]):
if time_slice == dims[0] and not already_written: # if whole dataset fits in memory and it has not already been loaded
weather.variables['Rain'][:,:,:] = rain_tif.read()
weather.variables['ET0'][:,:,:] = ET0_tif.read()
already_written = True
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
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
# Close progress bar
progress_bar.close()
# Close datasets
rain_tif.close()
ET0_tif.close()
weather.close()
return None
def calculate_ET0_pixel(pixel_dataset: xr.Dataset, lat: float, lon: float, h: float = 10, safran: bool = False) -> np.ndarray:
"""
Calculate ET0 over the year for a single pixel of the ERA5 weather dataset.
Arguments
=========
1. pixel_dataset: ``xr.Dataset``
extracted dataset that contains all information for a single pixel
2. lat: ``float``
latitudinal coordinate of that pixel
3. lon: ``float``
longitudinal coordinate of that pixel
4. h: ``float`` ``default = 10``
height of ERA5 wind measurement in meters
5. safran: ``bool`` ``default = False``
boolean to adapt to a custom SAFRAN weather dataset
Returns
=======
1. ET0_values: ``np.ndarray``
numpy array containing the ET0 values for each day
"""
# TODO: adapt for safran
# Conversion of xarray dataset to dataframe for ET0 calculation
# Conversion of temparature
ET0 = pixel_dataset.t2m.resample(time = '1D').min().to_dataframe().rename(columns = {'t2m' : 'T_min'}) - 273.15 # conversion of temperatures from K to °C
ET0['T_max'] = pixel_dataset.t2m.resample(time = '1D').max().to_dataframe()['t2m'].values - 273.15 # conversion of temperatures from K to °C
ET0['R_s'] = pixel_dataset.ssrd.resample(time = '1D').sum().to_dataframe()['ssrd'].values / 1e6 # to convert downward total radiation from J/m² to MJ/m²
# Calculate relative humidity
pixel_dataset['ea'] = ea_calc(pixel_dataset.t2m - 273.15)
pixel_dataset['es'] = ea_calc(pixel_dataset.d2m - 273.15)
pixel_dataset['rh'] = np.clip(100.*(pixel_dataset.es / pixel_dataset.ea), a_min = 0, a_max = 100)
ET0['RH_max'] = pixel_dataset.rh.resample(time = '1D').max().to_dataframe()['rh'].values
ET0['RH_min'] = pixel_dataset.rh.resample(time = '1D').min().to_dataframe()['rh'].values
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.resample(time = '1D').mean().to_dataframe()['u10'].values**2 + pixel_dataset.v10.resample(time = '1D').mean().to_dataframe()['v10'].values**2)
# 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
ET0_values = eto_calc.eto_fao(max_ETo = 15, min_ETo = 0, interp = True, maxgap = 10).values # ETo_FAO_mm
return ET0_values
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``
input image to convert
2. output_image: ``str``
output image to save
3. remove: ``bool`` ``default = True``
weather to remove input image
Returns
=======
``None``
"""
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
def era5Land_daily_to_yearly_pixel(weather_files: list[str], variables: 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:
"""
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 and reprojected on the
same grid as the NDVI values.
Arguments
=========
1. weather_file: ``str``
path to netCDF raw weather files
2. variables: ``list[str]``
list of variables downloaded from era5
3. output_file: ``str``
output file name without extension
4. raw_S2_image_ref: ``str``
raw Sentinel 2 image at right resolution for reprojection
5. ndvi_path: ``str``
path to ndvi dataset, used for attributes and coordinates
6. start_date: ``str``
beginning of the time window to download (format: ``YYYY-MM-DD``)
7. end_date: ``str``
end of the time window to download (format: ``YYYY-MM-DD``)
8. h: ``float`` ``default = 10``
height of ERA5 wind measurements in meters
9. max_ram: ``int`` ``default = 8``
max ram (in GiB) for reprojection and conversion. Two
subprocesses are spawned for OTB, each receiviving
half of requested memory.
10. use_OTB: ``bool`` ``default = False``
boolean to choose to use OTB or not, tests will be added later
11. weather_overwrite: ``bool`` ``default = False``
boolean to choose to overwrite weather netCDF
12. safran: ``bool`` ``default = False``
boolean to adapt to a custom SAFRAN weather dataset
Returns
=======
1. output_file_final: ``str``
path to ``netCDF4`` file containing precipitation and ET0 data
"""
# Test if file exists
if os.path.exists(output_file + '.nc') and not weather_overwrite:
return output_file + '.nc'
# 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
# Load all weather files in a single dataset
raw_weather_ds = xr.Dataset()
for var in variables:
temp = []
for file in weather_files:
if fnmatch(file, '*' + var + '*'):
temp.append(file)
raw_weather_ds = xr.merge([raw_weather_ds, xr.open_mfdataset(temp).drop_vars(['number', 'expver']).rename({'valid_time': 'time', 'latitude': 'lat', 'longitude': 'lon'})])
# Clip extra dates
raw_weather_ds = raw_weather_ds.sel({'time': slice(start_date, end_date)}).sortby(variables = 'time')
resampled_weather_ds = raw_weather_ds.resample(time = '1D').sum()
# Create ET0 variable (that will be saved) and set attributes
resampled_weather_ds = resampled_weather_ds.assign(ET0 = (resampled_weather_ds.sizes, np.zeros(tuple(resampled_weather_ds.sizes[d] for d in list(resampled_weather_ds.sizes)), 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' : y, 'x' : x}).drop_vars(['y', 'x'])
# TODO: adapt for new ERA5 data
# Calculate ET0 values for given pixel
ET0_values = calculate_ET0_pixel(select_ds, y, x, h)
# Write ET0 values in xarray Dataset
resampled_weather_ds['ET0'].loc[{'y' : y, 'x' : x}] = ET0_values
# Get necessary data for final dataset
final_weather_ds = resampled_weather_ds.drop_vars(names = ['ssrd', 't2m', 'd2m', '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
resampled_weather_ds['ET0'].loc[{'lat' : lat, 'lon' : lon}] = ET0_values
# Get necessary data for final dataset
final_weather_ds = resampled_weather_ds.drop_vars(names = ['ssrd', 'v10', 'u10', 't2m', 'd2m']) # remove unwanted variables
# Scale data and rewrite netcdf attributes
final_weather_ds['tp'] = final_weather_ds['tp'] * 1000 # conversion from m to mm
# Change datatype to reduce memory usage
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})
# TODO: fix for safran
# Write projection
final_weather_ds.rio.write_crs('EPSG:4326', inplace = True)
# 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'
final_weather_ds['tp'].attrs['comment'] = 'Volume of total daily precipitation expressed as water height in milimeters (scale factor = 1000)'
# 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)
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)
# 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)
# 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.sizes['y'], final_weather_ds.sizes['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)
# Rewrite georeferencing
final_weather_ds.rio.write_crs(target_crs, inplace = True)
final_weather_ds.rio.write_transform(inplace = True)
final_weather_ds['spatial_ref'] = spatial_ref
final_weather_ds.attrs['crs'] = final_weather_ds.rio.crs.to_string()
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()
return output_file_final
def era5Land_daily_to_yearly_parcel(weather_files: list[str], variables: list[str], output_file: str, start_date: str, end_date: str, h: float = 10) -> str:
"""
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. weather_file: ``list[str]``
path to netCDF raw weather files
2. variables: ``list[str]``
list of variables downloaded from era5
3. output_file: ``str``
output file name without extension
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``
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 weather files in a single dataset
raw_weather_ds = xr.Dataset()
for var in variables:
temp = []
for file in weather_files:
if fnmatch(file, '*' + var + '*'):
temp.append(file)
raw_weather_ds = xr.merge([raw_weather_ds, xr.open_mfdataset(temp).drop_vars(['number', 'expver']).rename({'valid_time': 'time', 'latitude': 'lat', 'longitude': 'lon'})])
# Clip extra dates
raw_weather_ds = raw_weather_ds.sel({'time': slice(start_date, end_date)})
# Create ET0 variable (that will be saved) and set attributes
raw_weather_ds = raw_weather_ds.assign(ET0 = (raw_weather_ds.sizes, np.zeros(tuple(raw_weather_ds.sizes[d] for d in list(raw_weather_ds.sizes)), 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
final_weather_ds = raw_weather_ds.drop_vars(names = ['ssrd', 'v10', 'u10', 't2m_max', 't2m_min']) # remove unwanted variables
final_weather_ds['tp'] = final_weather_ds['tp'] * 1000 # conversion from m to mm
# Change datatype to reduce memory usage
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})
# 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'
final_weather_ds['tp'].attrs['comment'] = 'Volume of total daily precipitation expressed as water height in milimeters (scale factor = 1000)'
# 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
def extract_rasterstats(args: tuple) -> list[float]:
"""
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.
It returns a list that contains the raster values, a feature ``id``
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``)
=====================================
1. raster_path: ``str``
path to multiband Geotiff
2. shapefile: ``str``
path to shapefile
3. config_file: ``str``
path to config file
Returns
=======
1. raster_stats: ``list[float]``
list containing weather values and feature information for every
polygon in the shapefile
"""
# Open arguments packed in args
raster_path, shapefile, config_file = args
# Open config file
config_params = config(config_file)
# Create dataframe where zonal statistics will be stored
raster_stats = []
# 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
raster_dataset = rio.open(raster_path, mode = 'r')
# Get input raster spatial reference and epsg code to reproject shapefile in the same spatial reference
target_epsg = raster_dataset.crs
# Open shapefile with geopandas and reproject its geometry
shapefile = gpd.read_file(shapefile)
shapefile['geometry'] = shapefile['geometry'].to_crs(target_epsg)
# Get no data value
nodata = raster_dataset.nodata
# Get the number of bands
nbands = raster_dataset.count
# Create progress bar
progress_bar = tqdm(total = len(shapefile.index), desc='Extracting zonal statistics', unit=' polygons')
# Loop on the individual polygons in the shapefile geometry
for index, row in shapefile.iterrows():
# Get the feature geometry as a shapely object
geom = row.geometry
# id number of the current parcel geometry
id = index + 1
# Get land cover
LC = row.LC
# Crop the raster using the bounding box
try:
masked_raster, _ = mask(raster_dataset, [geom], crop = True)
except:
print('\nShapefile bounds are not contained in weather dataset bounds.\n\nExiting script.')
return None
# Mask the raster using the geometry
masked_raster, _ = mask(raster_dataset, [geom], crop = True)
# Replace the nodata values with nan
masked_raster = masked_raster.astype(np.float32)
masked_raster[masked_raster == nodata] = np.nan
# Calculate the zonal statistics
mean = np.nanmean(masked_raster, axis = (1,2))
# Add statistics to output list
raster_stats.extend([[dates[i], id, mean[i], LC] for i in range(nbands)])
# Update progress bar
progress_bar.update(1)
# Close dataset and progress bar
raster_dataset.close()
progress_bar.close()
return raster_stats
def extract_weather_dataframe(rain_path: str, ET0_path: str, shapefile: str, config_file: str, save_path: str) -> None:
"""
Extract a weather dataframe for each variable (Rain, ET0) and merge them in one
dataframe. This dataframe is saved as ``csv`` file.
Arguments
=========
1. rain_path: ``str``
path to rain Geotiff file
2. ET0_path: ``str``
path to ET0 Geotiff file
3. shapefile: ``str``
path to shapefile
4. config_file: ``str``
path to config file
5. save_path: ``str``
save path for weather dataframe
Returns
=======
``None``
"""
# Generate arguments for multiprocessing
args = [(rain_path, shapefile, config_file), (ET0_path, shapefile, config_file)]
print('\nStarting weather data extraction on two cores..\n')
# Extract weather values for both weather varialbes
with Pool(2) as p:
results = p.map(extract_rasterstats, args)
# Collect results in a single dataframe
weather_dataframe = pd.DataFrame(results[0], columns = ['date', 'id', 'Rain', 'LC'])
weather_dataframe['ET0'] = pd.DataFrame(results[1], columns = ['date', 'id', 'ET0', 'LC'])['ET0']
# Reorder columns
weather_dataframe = weather_dataframe.reindex(columns = ['date', 'id', 'Rain', 'ET0', 'LC'])
# Format datatypes
weather_dataframe['Rain'] = np.round(weather_dataframe['Rain']).astype(int)
weather_dataframe['ET0'] = np.round(weather_dataframe['ET0']).astype(int)
# Change date type
weather_dataframe['date'] = pd.to_datetime(weather_dataframe['date'])
# Save dataframe to csv
weather_dataframe.to_csv(save_path, index = False)
return None