making FLiESANN main entry-point, using NASADEM package for elevation

FLiESANN/FLiESANN.py

@@ -4,6 +4,6 @@
 from .determine_ctype import determine_ctype
 from .prepare_FLiES_ANN_inputs import prepare_FLiES_ANN_inputs
 from .run_FLiES_ANN_inference import run_FLiES_ANN_inference
-from .process_FLiES_ANN import process_FLiES_ANN
+from .process_FLiES_ANN import FLiESANN
 from .generate_FLiES_inputs_table import generate_FLiES_inputs_table
 from .process_FLiES_table import process_FLiES_table

FLiESANN/process_FLiES_ANN.py

@@ -8,13 +8,14 @@
 from solar_apparent_time import solar_day_of_year_for_area, solar_hour_of_day_for_area
 from sun_angles import calculate_SZA_from_DOY_and_hour
 from koppengeiger import load_koppen_geiger
+from NASADEM import NASADEM, NASADEMConnection
 
 from .constants import *
 from .determine_atype import determine_atype
 from .determine_ctype import determine_ctype
 from .run_FLiES_ANN_inference import run_FLiES_ANN_inference
 
-def process_FLiES_ANN(
+def FLiESANN(
         albedo: Union[Raster, np.ndarray],
         COT: Union[Raster, np.ndarray] = None,
         AOT: Union[Raster, np.ndarray] = None,
@@ -28,6 +29,7 @@ def process_FLiES_ANN(
         day_of_year: Union[Raster, np.ndarray] = None,
         hour_of_day: Union[Raster, np.ndarray] = None,
         GEOS5FP_connection: GEOS5FP = None,
+        NASADEM_connection: NASADEMConnection = NASADEM,
         resampling: str = "cubic",
         ANN_model=None,
         model_filename=DEFAULT_MODEL_FILENAME,
@@ -141,6 +143,9 @@ def process_FLiES_ANN(
             resampling=resampling
         )
 
+    if elevation_km is None and geometry is not None:
+        elevation_km = NASADEM.elevation_km(geometry=geometry)
+
     # Preprocess COT and determine aerosol/cloud types
     COT = np.clip(COT, 0, None)  # Ensure COT is non-negative
     COT = rt.where(COT < 0.001, 0, COT)  # Set very small COT values to 0
@@ -224,4 +229,6 @@ def process_FLiES_ANN(
         for key in results.keys():
             results[key] = rt.Raster(results[key], geometry=geometry)
 
-    return results
+    return results
+
+FLiESANN = FLiESANN

FLiESANN/process_FLiES_table.py

@@ -1,9 +1,9 @@
 import pandas as pd
 
-from .process_FLiES_ANN import process_FLiES_ANN
+from .process_FLiES_ANN import FLiESANN
 
 def process_FLiES_table(FLiES_inputs_df: pd.DataFrame) -> pd.DataFrame:
-    FLiES_results = process_FLiES_ANN(
+    FLiES_results = FLiESANN(
         day_of_year=FLiES_inputs_df.doy,
         albedo=FLiES_inputs_df.albedo,
         COT=FLiES_inputs_df.COT,

FLiESANN/version.txt

@@ -1 +1 @@
-1.3.0
+1.4.0

Processing FLiES with a raster and default parameters.ipynb

@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running FLiES for an ECOSTRESS Scene\n",
+    "\n",
+    "This is an example of running the artificial neural network emulator of the Forest Light Environmental Simulator (FLiES) corresponding to an ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) scene."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os.path import join\n",
+    "from datetime import datetime, date, time\n",
+    "from dateutil import parser\n",
+    "import rasters as rt\n",
+    "from geos5fp import GEOS5FP\n",
+    "from koppengeiger import load_koppen_geiger\n",
+    "from solar_apparent_time import UTC_to_solar\n",
+    "import sun_angles\n",
+    "from FLiESANN import process_FLiES_ANN\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "import logging\n",
+    "logging.disable(logging.CRITICAL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example ECOSTRESS albedo scene."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "albedo_filename = \"ECOv002_L2T_STARS_11SPS_20240728_0712_01_albedo.tif\"\n",
+    "albedo_cmap = LinearSegmentedColormap.from_list(name=\"albedo\", colors=[\"black\", \"white\"])\n",
+    "albedo = rt.Raster.open(albedo_filename, cmap=albedo_cmap)\n",
+    "albedo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's get the acquisition time of the scene."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_UTC = parser.parse(albedo_filename.split(\"_\")[6])\n",
+    "longitude = albedo.geometry.centroid_latlon.x\n",
+    "latitude = albedo.geometry.centroid_latlon.y\n",
+    "time_solar = UTC_to_solar(time_UTC, longitude)\n",
+    "doy_solar = time_solar.timetuple().tm_yday\n",
+    "hour_of_day_solar = time_solar.hour + time_solar.minute / 60 + time_solar.second / 3600\n",
+    "print(f\"{time_UTC:%Y-%m-%d %H:%M:%S} UTC\")\n",
+    "print(f\"{time_solar:%Y-%m-%d %H:%M:%S} solar apparent time at longitude {longitude}\")\n",
+    "print(f\"day of year {doy_solar} at longitude {longitude}\")\n",
+    "print(f\"hour of day {hour_of_day_solar} at longitude {longitude}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geometry = albedo.geometry\n",
+    "geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FLiES_results = process_FLiES_ANN(\n",
+    "    geometry=geometry,\n",
+    "    time_UTC=time_UTC,\n",
+    "    albedo=albedo\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rg = FLiES_results[\"Rg\"]\n",
+    "Rg.cmap = \"bwr\"\n",
+    "Rg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "FLiESANN",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

processing_FLiES_with_raster_and_default_parameters.py

@@ -0,0 +1,64 @@
+# %% [markdown]
+# # Running FLiES for an ECOSTRESS Scene
+# 
+# This is an example of running the artificial neural network emulator of the Forest Light Environmental Simulator (FLiES) corresponding to an ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) scene.
+
+# %%
+from os.path import join
+from datetime import datetime, date, time
+from dateutil import parser
+import rasters as rt
+from geos5fp import GEOS5FP
+from koppengeiger import load_koppen_geiger
+from solar_apparent_time import UTC_to_solar
+import sun_angles
+from FLiESANN import FLiESANN
+from matplotlib.colors import LinearSegmentedColormap
+import logging
+logging.disable(logging.CRITICAL)
+
+# %% [markdown]
+# Here's an example ECOSTRESS albedo scene.
+
+# %%
+albedo_filename = "ECOv002_L2T_STARS_11SPS_20240728_0712_01_albedo.tif"
+albedo_cmap = LinearSegmentedColormap.from_list(name="albedo", colors=["black", "white"])
+albedo = rt.Raster.open(albedo_filename, cmap=albedo_cmap)
+albedo
+
+# %% [markdown]
+# Let's get the acquisition time of the scene.
+
+# %%
+time_UTC = parser.parse(albedo_filename.split("_")[6])
+longitude = albedo.geometry.centroid_latlon.x
+latitude = albedo.geometry.centroid_latlon.y
+time_solar = UTC_to_solar(time_UTC, longitude)
+doy_solar = time_solar.timetuple().tm_yday
+hour_of_day_solar = time_solar.hour + time_solar.minute / 60 + time_solar.second / 3600
+print(f"{time_UTC:%Y-%m-%d %H:%M:%S} UTC")
+print(f"{time_solar:%Y-%m-%d %H:%M:%S} solar apparent time at longitude {longitude}")
+print(f"day of year {doy_solar} at longitude {longitude}")
+print(f"hour of day {hour_of_day_solar} at longitude {longitude}")
+
+
+# %%
+geometry = albedo.geometry
+geometry
+
+# %%
+FLiES_results = FLiESANN(
+    geometry=geometry,
+    time_UTC=time_UTC,
+    albedo=albedo
+)
+
+# %%
+Rg = FLiES_results["Rg"]
+Rg.cmap = "bwr"
+Rg
+
+# %%
+
+
+

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools>=60", "setuptools-scm>=8.0", "wheel"]
 
 [project]
 name = "FLiESANN"
-version = "1.3.0"
+version = "1.4.0"
 description = "Forest Light Environmental Simulator (FLiES) Radiative Transfer Model Artificial Neural Network (ANN) Implementation in Python"
 readme = "README.md"
 authors = [
@@ -17,6 +17,8 @@ dependencies = [
     "geos5fp",
     "keras==2.15",
     "koppengeiger",
+    "MCD12C1-2019-v006",
+    "NASADEM>=1.1.1",
     "netCDF4",
     "numpy<2",
     "pandas",
@@ -32,7 +34,6 @@ requires-python = ">=3.10"
 [project.optional-dependencies]
 dev = [
     "build",
-    "MCD12C1-2019-v006",
     "pytest>=6.0",
     "pytest-cov",
     "jupyter",