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simulate_S2_spectra_soil.py
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simulate_S2_spectra_soil.py
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"""
Generate PROSAIL RTM simulations with soil spectra and a corresponding look up table.
@author Selene Ledain
"""
import logging
import os
from pathlib import Path
from typing import Any, Dict, List, Optional
from datetime import datetime
from rtm_inv.core.lookup_table import LookupTable, generate_lut, simulate_from_lut
import pickle
import numpy as np
import pandas as pd
import yaml
def get_logger():
"""
Returns a logger object with stream and file handler
"""
CURRENT_TIME: str = datetime.now().strftime("%Y%m%d-%H%M%S")
LOGGER_NAME: str = "CropCovEO"
LOG_FORMAT: str = "%(asctime)s %(name)-12s %(levelname)-8s %(message)s"
LOG_DIR: str = str(Path.home()) # ..versionadd:: 0.2.1
LOG_FILE: str = os.path.join(LOG_DIR, f"{CURRENT_TIME}_{LOGGER_NAME}.log")
LOGGING_LEVEL: int = logging.INFO
# create file handler which logs even debug messages
logger = logging.getLogger(LOGGER_NAME)
logger.setLevel(LOGGING_LEVEL)
fh: logging.FileHandler = logging.FileHandler(LOG_FILE)
fh.setLevel(LOGGING_LEVEL)
# create console handler with a higher log level
ch: logging.StreamHandler = logging.StreamHandler()
ch.setLevel(LOGGING_LEVEL)
# create formatter and add it to the handlers
formatter: logging.Formatter = logging.Formatter(LOG_FORMAT)
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
return logger
def generate_spectra(
output_dir: Path,
lut_params: Path,
lut_config: Dict[str, Any],
rtm_config: Dict[str, Any],
traits: List[str]
) -> None:
logger = get_logger()
# run PROSAIL forward runs for the different parametrizations available
logger.info('Starting PROSAIL runs')
sensor_suffix = ''
if rtm_config['sensor'] == 'Sentinel2A':
sensor_suffix = '_S2A'
elif rtm_config['sensor'] == 'Sentinel2B':
sensor_suffix = '_S2B'
pheno_phases = \
lut_params.name.split('.csv')[0] + sensor_suffix
# generate lookup-table
trait_str = '-'.join(traits)
fpath_lut = output_dir.joinpath(
f'{pheno_phases}_{trait_str}_lut_no-constraints.pkl')
print(fpath_lut)
# if LUT exists, continue, else generate it
if not fpath_lut.exists():
lut_inp = lut_config.copy()
lut_inp['lut_params'] = lut_params
lut = generate_lut(**lut_inp)
lut = simulate_from_lut(lut, **rtm_config)
# special case CCC (Canopy Chlorophyll Content) ->
# this is not a direct RTM output
if 'ccc' in traits:
lut['ccc'] = lut['lai'] * lut['cab']
# convert to g m-2 as this is the more common unit
# ug -> g: factor 1e-6; cm2 -> m2: factor 1e-4
lut['ccc'] *= 1e-2
lut.dropna(inplace=True)
# save LUT to file
with open(fpath_lut, 'wb') as f:
pickle.dump(lut, f, protocol=3)
else:
pass
logger.info('Finished PROSAIL runs')
def generate_spectra_soil(
output_dir: Path,
lut_params: Path,
lut_config: Dict[str, Any],
rtm_config: Dict[str, Any],
traits: List[str],
soil_df: pd.DataFrame
) -> None:
logger = get_logger()
# run PROSAIL forward runs for the different parametrizations available
logger.info('Starting PROSAIL runs')
logger.info(rtm_config['sensor'])
sensor_suffix = ''
if rtm_config['sensor'] == 'Sentinel2A':
sensor_suffix = '_S2A'
elif rtm_config['sensor'] == 'Sentinel2B':
sensor_suffix = '_S2B'
pheno_phases = \
lut_params.name.split('.csv')[0] + sensor_suffix
# generate lookup-table
trait_str = '-'.join(traits)
fpath_lut = output_dir.joinpath(
f'{pheno_phases}_{trait_str}_lut_no-constraints.pkl') #
# if LUT exists, continue, else generate it
if not fpath_lut.exists():
# Generate LUT
lut_inp = lut_config.copy()
lut_inp['lut_params'] = lut_params
lut = generate_lut(**lut_inp)
# Simulate with RTM
rtm_inp = rtm_config.copy()
# Create LUT subgroups of size lut_size/len(soil_df).
# Pass each subgroup with a soil spectra and simulate.
# Concatenate all simulations
sub_luts = get_random_subgroups(lut._samples, len(soil_df))
lut_allsoils = []
for i, (original_idx, sub_lut) in enumerate(sub_luts):
logger.info(f'Simulating with soil spectra {i+1} of {len(soil_df)}')
rtm_inp['rsoil0'] = None
rtm_inp['soil_spectrum1'] = soil_df.iloc[i].values
rtm_inp['soil_spectrum2'] = np.zeros_like(soil_df.iloc[i].values)
sub_lut = dataframe_to_lookup_table(sub_lut, lut)
lut_soilspectra = simulate_from_lut(sub_lut, **rtm_config)
lut_soilspectra.index = original_idx # keep original order
lut_allsoils.append(lut_soilspectra)
lut = pd.concat(lut_allsoils).sort_index()
# special case CCC (Canopy Chlorophyll Content) ->
# this is not a direct RTM output
if 'ccc' in traits:
lut['ccc'] = lut['lai'] * lut['cab']
# convert to g m-2 as this is the more common unit
# ug -> g: factor 1e-6; cm2 -> m2: factor 1e-4
lut['ccc'] *= 1e-2
# prepare LUT for model training
# lut = lut[band_selection + traits].copy()
lut.dropna(inplace=True)
# save LUT to file
with open(fpath_lut, 'wb+') as f:
pickle.dump(lut, f)
else:
pass
logger.info('Finished PROSAIL runs')
def get_random_subgroups(df, n):
# Step 1: Shuffle the indices of the DataFrame
indices = np.arange(len(df))
np.random.shuffle(indices)
# Step 2: Split the shuffled indices into `n` subgroups of roughly equal size
subgroups = np.array_split(indices, n)
# Step 3: Create subgroups of the DataFrame using these indices
subgroups_dfs = [(indices, df.iloc[indices]) for indices in subgroups]
return subgroups_dfs
def dataframe_to_lookup_table(df: pd.DataFrame, original_lut: LookupTable) -> LookupTable:
"""
Converts a DataFrame to a LookupTable object.
:param df: DataFrame to convert
:param original_lut: Original LookupTable object to copy parameters from
:return: LookupTable object
"""
lut = LookupTable(original_lut._params_df)
lut.samples = df.reset_index(drop=True).copy()
return lut
def load_config(config_path: str) -> Dict:
'''
Load configuration file
:param config_path: path to yaml file
:returns: dictionary of parameters
'''
with open(config_path, "r") as config_file:
config = yaml.safe_load(config_file)
return config
if __name__ == '__main__':
cwd = Path(__file__).parent.absolute()
import os
os.chdir(cwd)
# Load configuration file
config_path = 'config.yaml'
config = load_config(config_path)
########################
# EXTRACT PARAMETERS AND PATHS FOR RUNNING
# Store results/simulations
out_dir = Path(config['out_dir'])
out_dir.mkdir(exist_ok=True)
# Prepare other params
lut_params = Path(config['LUT'].pop('lut_params')) # TO DO
fpath_srf = Path(config['RTM']['fpath_srf'])
lut_config = config['LUT']
rtm_config = config['RTM']
soil_path = Path(config['soil_path']) if config['soil_path'] is not None else None
traits = config['traits']
########################
# RUN PROSAIL IN FORWARD MODE
if soil_path is None:
# Call RTM and generate LUT
try:
generate_spectra(
output_dir=out_dir,
lut_params=lut_params,
lut_config=lut_config,
rtm_config=rtm_config,
traits=traits
)
except Exception as e:
print(f'Error: {e}')
pass
if soil_path is not None:
# Loop over soil spectra
soil_df = pd.read_pickle(soil_path)
try:
generate_spectra_soil(
output_dir=out_dir,
lut_params=lut_params,
lut_config=lut_config,
rtm_config=rtm_config,
traits=traits,
soil_df=soil_df
)
except Exception as e:
print(f'Error: {e}')
pass