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"""Create reference files."""
import logging
import os
import numpy as np
import xarray as xr
from rich.logging import RichHandler
from ruamel.yaml import YAML
import mvcm.spectral_tests as tst
_LOG_FORMAT = "%(message)s"
logging.basicConfig(level="NOTSET", datefmt="[%X]", format=_LOG_FORMAT, handlers=[RichHandler()])
logger = logging.getLogger(__name__)
_scene_list = [
"Land_Day",
"Land_Day_Coast",
"Land_Day_Desert",
"Land_Day_Desert_Coast",
"Ocean_Day",
"Ocean_Night",
"Polar_Day_Ocean",
"Polar_Night_Ocean",
"Polar_Day_Land",
"Polar_Day_Coast",
"Polar_Day_Desert",
"Polar_Day_Desert_Coast",
"Polar_Day_Snow",
"Land_Night",
"Polar_Night_Land",
"Polar_Night_Snow",
"Day_Snow",
"Night_Snow",
]
_threshold_file = "/home/pveglio/mvcm/tests/fixtures/thresholds.mvcm.snpp.v0.0.1.yaml"
_test_path = "/ships19/hercules/pveglio/mvcm_git_tests"
def main() -> None:
"""Create reference file."""
with open(_threshold_file) as f:
text = f.read()
thresholds = YAML(typ="safe").load(text)
output_dict: dict = {}
for scene_name in _scene_list:
logger.info(f"Running tests for {scene_name} \n")
if os.path.isfile(f"{_test_path}/ref_confidence_{scene_name}.nc"):
logger.info(f"Skipping {scene_name}, file already exists")
continue
if not os.path.isfile(f"{_test_path}/test_scene_{scene_name}.nc"):
logger.info(f"Skipping {scene_name}, test file not present in {_test_path}")
continue
viirs_data = xr.open_dataset(f"{_test_path}/test_scene_{scene_name}.nc")
bits = {
"test": np.zeros(viirs_data.M15.shape, dtype=np.int8),
"qa": np.zeros(viirs_data.M15.shape, dtype=np.int8),
}
if np.all(viirs_data[scene_name].values == 0):
logger.info("Skipping, no pixels in scene.")
continue
my_scene = tst.CloudTests(data=viirs_data, scene_name=scene_name, thresholds=thresholds) # type: ignore
# 11um Test
logger.info(f"Running 11um test for {scene_name}")
confidence, bits = my_scene.test_11um("M15", np.ones(viirs_data.M15.shape), bits)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "11um"
)
# surface_temperature_test() is not performed right now
# SST Test
logger.info(f"Running SST test for {scene_name} \n")
confidence, bits = my_scene.sst_test("M15", "M16", np.ones(viirs_data.M15.shape), bits)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "sst"
)
# 8.6 - 11 um Test
logger.info(f"Running 8.6 - 11 um test for {scene_name} \n")
confidence, bits = my_scene.bt_diff_86_11um("M14-M15", np.ones(viirs_data.M15.shape), bits)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "86_11um_diff"
)
# 11 - 12um Test
logger.info(f"Running 11 - 12um test for {scene_name} \n")
confidence, bits = my_scene.test_11_12um_diff(
"M15-M16", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "11_12um_diff"
)
# 11um Variability Test
logger.info(f"Running 11um variability test for {scene_name} \n")
confidence, bits = my_scene.variability_11um_test(
"M15", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape,
confidence,
bits,
output_dict,
"variability_11um",
)
# 11 - 4um Test Ocean
logger.info(f"Running 11 - 4um test for {scene_name} \n")
confidence, bits = my_scene.bt_difference_11_4um_test_ocean(
"M15-M12", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "11_4um_ocean"
)
# 11 - 4um Test Land
# confidence, bits = my_scene.bt_difference_11_4um_test_land(
# "M15-M12", np.ones(viirs_data.M15.shape), bits
# )
# bits, output_dict = update_and_clean(
# viirs_data.M15.shape, confidence, bits, output_dict, "11_4um_land"
# )
# 11 - 4um Oceanic Stratus Test
logger.info(f"Running 11 - 4um Oceanic Stratus test for {scene_name} \n")
confidence, bits = my_scene.oceanic_stratus_11_4um_test(
"M15-M12", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape,
confidence,
bits,
output_dict,
"oceanic_stratus_11_4um",
)
# NIR Reflectance Test
logger.info(f"Running nir reflectance test for {scene_name} \n")
confidence, bits = my_scene.nir_reflectance_test("M07", np.ones(viirs_data.M15.shape), bits)
bits, output_dict = update_and_clean(
viirs_data.M15.shape,
confidence,
bits,
output_dict,
"nir_reflectance",
)
# Vis/NIR Ratio test
logger.info(f"Running vis/nir ratio test for {scene_name} \n")
confidence, bits = my_scene.vis_nir_ratio_test(
"M07-M05ratio", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "vis_nir_ratio"
)
# 1.6 - 2.1um Test
logger.info(f"Running 1.6 - 2.1um test for {scene_name} \n")
confidence, bits = my_scene.test_16_21um_reflectance(
"M10", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape,
confidence,
bits,
output_dict,
"16_21um_reflectance",
)
# Visible Reflectance Test
logger.info(f"Running visible reflectance test for {scene_name} \n")
confidence, bits = my_scene.visible_reflectance_test(
"M05", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "vis_refl"
)
# GEMI Test
logger.info(f"Running GEMI test for {scene_name} \n")
confidence, bits = my_scene.gemi_test("GEMI", np.ones(viirs_data.M15.shape), bits)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "gemi"
)
# 1.38 High Clouds Test
logger.info(f"Running 1.38um high clouds test for {scene_name} \n")
confidence, bits = my_scene.test_1_38um_high_clouds(
"M09", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "high_clouds"
)
# 4 -12um Thin Cirrus Test
logger.info(f"Running 4-12um thin cirrus test for {scene_name} \n")
confidence, bits = my_scene.thin_cirrus_4_12um_BTD_test(
"M13-M16", np.ones(viirs_data.M15.shape), bits
)
bits, output_dict = update_and_clean(
viirs_data.M15.shape, confidence, bits, output_dict, "thin_cirrus"
)
logger.info(f"Saving reference file for scene {scene_name} \n")
output = xr.Dataset.from_dict(output_dict)
output.to_netcdf(f"{_test_path}/ref_confidence_{scene_name}.nc")
def update_and_clean(
shape: tuple, confidence: np.ndarray, bits: dict, output_dict: dict, test_name: str
) -> tuple[dict, dict]:
"""Update and clean output dictionary."""
output_dict[f"confidence_{test_name}"] = {"dims": ("x", "y"), "data": confidence}
output_dict[f"qa_bit_{test_name}"] = {"dims": ("x", "y"), "data": bits["qa"]}
output_dict[f"test_bit_{test_name}"] = {"dims": ("x", "y"), "data": bits["test"]}
bits = {
"test": np.zeros(shape, dtype=np.int8),
"qa": np.zeros(shape, dtype=np.int8),
}
return bits, output_dict
if __name__ == "__main__":
main()