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Merge pull request #18 from gregory-halverson-jpl/main
making `FLiESANN` main entry-point, using `NASADEM` package for elevation
2 parents 78a1894 + 3113d1a commit 69297ec

10 files changed

+219
-8673
lines changed

‎ECOv002_L2T_LSTE_34366_004_11SPS_20240728T204025_0712_01_LST.tif

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‎ECOv002_L2T_LSTE_34366_004_11SPS_20240728T204025_0712_01_height.tif

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‎FLiESANN/FLiESANN.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,6 @@
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from .determine_ctype import determine_ctype
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from .prepare_FLiES_ANN_inputs import prepare_FLiES_ANN_inputs
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from .run_FLiES_ANN_inference import run_FLiES_ANN_inference
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from .process_FLiES_ANN import process_FLiES_ANN
7+
from .process_FLiES_ANN import FLiESANN
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from .generate_FLiES_inputs_table import generate_FLiES_inputs_table
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from .process_FLiES_table import process_FLiES_table

‎FLiESANN/process_FLiES_ANN.py

Lines changed: 9 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -8,13 +8,14 @@
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from solar_apparent_time import solar_day_of_year_for_area, solar_hour_of_day_for_area
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from sun_angles import calculate_SZA_from_DOY_and_hour
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from koppengeiger import load_koppen_geiger
11+
from NASADEM import NASADEM, NASADEMConnection
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from .constants import *
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from .determine_atype import determine_atype
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from .determine_ctype import determine_ctype
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from .run_FLiES_ANN_inference import run_FLiES_ANN_inference
1617

17-
def process_FLiES_ANN(
18+
def FLiESANN(
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albedo: Union[Raster, np.ndarray],
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COT: Union[Raster, np.ndarray] = None,
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AOT: Union[Raster, np.ndarray] = None,
@@ -28,6 +29,7 @@ def process_FLiES_ANN(
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day_of_year: Union[Raster, np.ndarray] = None,
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hour_of_day: Union[Raster, np.ndarray] = None,
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GEOS5FP_connection: GEOS5FP = None,
32+
NASADEM_connection: NASADEMConnection = NASADEM,
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resampling: str = "cubic",
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ANN_model=None,
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model_filename=DEFAULT_MODEL_FILENAME,
@@ -141,6 +143,9 @@ def process_FLiES_ANN(
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resampling=resampling
142144
)
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146+
if elevation_km is None and geometry is not None:
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elevation_km = NASADEM.elevation_km(geometry=geometry)
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144149
# Preprocess COT and determine aerosol/cloud types
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COT = np.clip(COT, 0, None) # Ensure COT is non-negative
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COT = rt.where(COT < 0.001, 0, COT) # Set very small COT values to 0
@@ -224,4 +229,6 @@ def process_FLiES_ANN(
224229
for key in results.keys():
225230
results[key] = rt.Raster(results[key], geometry=geometry)
226231

227-
return results
232+
return results
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234+
FLiESANN = FLiESANN

‎FLiESANN/process_FLiES_table.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,9 +1,9 @@
11
import pandas as pd
22

3-
from .process_FLiES_ANN import process_FLiES_ANN
3+
from .process_FLiES_ANN import FLiESANN
44

55
def process_FLiES_table(FLiES_inputs_df: pd.DataFrame) -> pd.DataFrame:
6-
FLiES_results = process_FLiES_ANN(
6+
FLiES_results = FLiESANN(
77
day_of_year=FLiES_inputs_df.doy,
88
albedo=FLiES_inputs_df.albedo,
99
COT=FLiES_inputs_df.COT,

‎FLiESANN/version.txt

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
1-
1.3.0
1+
1.4.0

‎Processing FLiES with a raster and default parameters.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
6+
"source": [
7+
"# Running FLiES for an ECOSTRESS Scene\n",
8+
"\n",
9+
"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."
10+
]
11+
},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
17+
"source": [
18+
"from os.path import join\n",
19+
"from datetime import datetime, date, time\n",
20+
"from dateutil import parser\n",
21+
"import rasters as rt\n",
22+
"from geos5fp import GEOS5FP\n",
23+
"from koppengeiger import load_koppen_geiger\n",
24+
"from solar_apparent_time import UTC_to_solar\n",
25+
"import sun_angles\n",
26+
"from FLiESANN import process_FLiES_ANN\n",
27+
"from matplotlib.colors import LinearSegmentedColormap\n",
28+
"import logging\n",
29+
"logging.disable(logging.CRITICAL)"
30+
]
31+
},
32+
{
33+
"cell_type": "markdown",
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"metadata": {},
35+
"source": [
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"Here's an example ECOSTRESS albedo scene."
37+
]
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},
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{
40+
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
45+
"albedo_filename = \"ECOv002_L2T_STARS_11SPS_20240728_0712_01_albedo.tif\"\n",
46+
"albedo_cmap = LinearSegmentedColormap.from_list(name=\"albedo\", colors=[\"black\", \"white\"])\n",
47+
"albedo = rt.Raster.open(albedo_filename, cmap=albedo_cmap)\n",
48+
"albedo"
49+
]
50+
},
51+
{
52+
"cell_type": "markdown",
53+
"metadata": {},
54+
"source": [
55+
"Let's get the acquisition time of the scene."
56+
]
57+
},
58+
{
59+
"cell_type": "code",
60+
"execution_count": null,
61+
"metadata": {},
62+
"outputs": [],
63+
"source": [
64+
"time_UTC = parser.parse(albedo_filename.split(\"_\")[6])\n",
65+
"longitude = albedo.geometry.centroid_latlon.x\n",
66+
"latitude = albedo.geometry.centroid_latlon.y\n",
67+
"time_solar = UTC_to_solar(time_UTC, longitude)\n",
68+
"doy_solar = time_solar.timetuple().tm_yday\n",
69+
"hour_of_day_solar = time_solar.hour + time_solar.minute / 60 + time_solar.second / 3600\n",
70+
"print(f\"{time_UTC:%Y-%m-%d %H:%M:%S} UTC\")\n",
71+
"print(f\"{time_solar:%Y-%m-%d %H:%M:%S} solar apparent time at longitude {longitude}\")\n",
72+
"print(f\"day of year {doy_solar} at longitude {longitude}\")\n",
73+
"print(f\"hour of day {hour_of_day_solar} at longitude {longitude}\")\n"
74+
]
75+
},
76+
{
77+
"cell_type": "code",
78+
"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
82+
"geometry = albedo.geometry\n",
83+
"geometry"
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]
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},
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{
87+
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
90+
"outputs": [],
91+
"source": [
92+
"FLiES_results = process_FLiES_ANN(\n",
93+
" geometry=geometry,\n",
94+
" time_UTC=time_UTC,\n",
95+
" albedo=albedo\n",
96+
")"
97+
]
98+
},
99+
{
100+
"cell_type": "code",
101+
"execution_count": null,
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"metadata": {},
103+
"outputs": [],
104+
"source": [
105+
"Rg = FLiES_results[\"Rg\"]\n",
106+
"Rg.cmap = \"bwr\"\n",
107+
"Rg"
108+
]
109+
},
110+
{
111+
"cell_type": "code",
112+
"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
119+
"kernelspec": {
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"display_name": "FLiESANN",
121+
"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Processing FLiES with a raster.ipynb

Lines changed: 0 additions & 8665 deletions
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‎processing_FLiES_with_raster_and_default_parameters.py

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# %% [markdown]
2+
# # Running FLiES for an ECOSTRESS Scene
3+
#
4+
# 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.
5+
6+
# %%
7+
from os.path import join
8+
from datetime import datetime, date, time
9+
from dateutil import parser
10+
import rasters as rt
11+
from geos5fp import GEOS5FP
12+
from koppengeiger import load_koppen_geiger
13+
from solar_apparent_time import UTC_to_solar
14+
import sun_angles
15+
from FLiESANN import FLiESANN
16+
from matplotlib.colors import LinearSegmentedColormap
17+
import logging
18+
logging.disable(logging.CRITICAL)
19+
20+
# %% [markdown]
21+
# Here's an example ECOSTRESS albedo scene.
22+
23+
# %%
24+
albedo_filename = "ECOv002_L2T_STARS_11SPS_20240728_0712_01_albedo.tif"
25+
albedo_cmap = LinearSegmentedColormap.from_list(name="albedo", colors=["black", "white"])
26+
albedo = rt.Raster.open(albedo_filename, cmap=albedo_cmap)
27+
albedo
28+
29+
# %% [markdown]
30+
# Let's get the acquisition time of the scene.
31+
32+
# %%
33+
time_UTC = parser.parse(albedo_filename.split("_")[6])
34+
longitude = albedo.geometry.centroid_latlon.x
35+
latitude = albedo.geometry.centroid_latlon.y
36+
time_solar = UTC_to_solar(time_UTC, longitude)
37+
doy_solar = time_solar.timetuple().tm_yday
38+
hour_of_day_solar = time_solar.hour + time_solar.minute / 60 + time_solar.second / 3600
39+
print(f"{time_UTC:%Y-%m-%d %H:%M:%S} UTC")
40+
print(f"{time_solar:%Y-%m-%d %H:%M:%S} solar apparent time at longitude {longitude}")
41+
print(f"day of year {doy_solar} at longitude {longitude}")
42+
print(f"hour of day {hour_of_day_solar} at longitude {longitude}")
43+
44+
45+
# %%
46+
geometry = albedo.geometry
47+
geometry
48+
49+
# %%
50+
FLiES_results = FLiESANN(
51+
geometry=geometry,
52+
time_UTC=time_UTC,
53+
albedo=albedo
54+
)
55+
56+
# %%
57+
Rg = FLiES_results["Rg"]
58+
Rg.cmap = "bwr"
59+
Rg
60+
61+
# %%
62+
63+
64+

‎pyproject.toml

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@@ -3,7 +3,7 @@ requires = ["setuptools>=60", "setuptools-scm>=8.0", "wheel"]
33

44
[project]
55
name = "FLiESANN"
6-
version = "1.3.0"
6+
version = "1.4.0"
77
description = "Forest Light Environmental Simulator (FLiES) Radiative Transfer Model Artificial Neural Network (ANN) Implementation in Python"
88
readme = "README.md"
99
authors = [
@@ -17,6 +17,8 @@ dependencies = [
1717
"geos5fp",
1818
"keras==2.15",
1919
"koppengeiger",
20+
"MCD12C1-2019-v006",
21+
"NASADEM>=1.1.1",
2022
"netCDF4",
2123
"numpy<2",
2224
"pandas",
@@ -32,7 +34,6 @@ requires-python = ">=3.10"
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[project.optional-dependencies]
3335
dev = [
3436
"build",
35-
"MCD12C1-2019-v006",
3637
"pytest>=6.0",
3738
"pytest-cov",
3839
"jupyter",

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