mhpi · leoglonz · Apr 6, 2025 · Mar 4, 2025 · Mar 5, 2025 · Mar 5, 2025
diff --git a/.gitignore b/.gitignore
@@ -171,4 +171,12 @@ cython_debug/
 .pypirc
 
 # Ignore model save files (too large)
-*.pt
+*.pt
+
+# Scratch / development / old / and other temporary files
+*scratch*
+*_dev*
+*_old*
+temp/
+tmp/
+*.tmp
diff --git a/bmi_config_files/bmi_config.yaml b/bmi_config_files/bmi_config.yaml
@@ -1,5 +1,5 @@
 ### Full-catchment configuration ###
-train_cfg_file: ../trained_models/merit_conus_40yr/config.yaml
+train_cfg_file: models/hydrofabric_15yr/config.yaml
 initial_state: zero  # zero: set initial states of the model to zero
 verbose: 1  # 0: no output, 1: print output
 

diff --git a/...onfig_files/bmi_config_cat_88306_5yr.yaml → ...onfig_files/bmi_config_cat-88306_5yr.yaml b/...onfig_files/bmi_config_cat_88306_5yr.yaml → ...onfig_files/bmi_config_cat-88306_5yr.yaml
@@ -3,37 +3,39 @@ catchment_id: 'cat-88306'
 catchment_name: 'JRB-88306'
 
 # Static catchment attributes
+aridity: 1.041
+meanP: 991.3
+ETPOT_Hargr: 1031.0
+NDVI: 0.594
 FW: 0.004622
+meanslope: 10.55
+SoilGrids1km_sand: 38.65
+SoilGrids1km_clay: 23.31
+SoilGrids1km_silt: 38.06
+glaciers: 0.0
 HWSD_clay: 25.0
+HWSD_gravel: 15.0
 HWSD_sand: 33.0
-T_clay: 20.0
-uparea: 3.254
-T_gravel: 10.0
+HWSD_silt: 42.0
 meanelevation: 335.1
-meanP: 991.3
-HWSD_gravel: 15.0
-seasonality_P: 0.1035
-T_sand: 41.0
-SoilGrids1km_silt: 38.06
+meanTa: 9.845
 permafrost: 0.0
-snowfall_fraction: 0.1049
-SoilGrids1km_sand: 38.65
-Porosity: 0.01
-T_silt: 39.0
-glaciers: 0.0
-HWSD_silt: 42.0
-meanslope: 10.55
 permeability: -15.05
+seasonality_P: 0.1035
 seasonality_PET: 0.5703
-ETPOT_Hargr: 1031.0
-meanTa: 9.845
-SoilGrids1km_clay: 23.31
 snow_fraction: 0.02967
-aridity: 1.041
-NDVI: 0.594
+snowfall_fraction: 0.1049
+T_clay: 20.0
+T_gravel: 10.0
+T_sand: 41.0
+T_silt: 39.0
+Porosity: 0.01
+uparea: 3.254
 
-train_cfg_file: ../trained_models/merit_conus_40yr/config.yaml
+config_model: models/hydrofabric_15yr/config.yaml
+stepwise: False  # True: stepwise inference, False: Single forward simulation on all data in one go
 initial_state: zero  # zero: set initial states of the model to zero
+dtype: np.float32
 verbose: 1  # 0: no output, 1: print output
 
 # Simulation window

diff --git a/environment.yaml → envs/environment.yaml b/environment.yaml → envs/environment.yaml
diff --git a/example/bmi_demo.ipynb b/example/bmi_demo.ipynb
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Forward $\\delta$ HBV 2.0UH with BMI\n",
+    "\n",
+    "\n",
+    "Notes:\n",
+    "- This example uses a subset of AORC forcings that can be downloaded from S3 here.\n",
+    "\n",
+    "- An environment can be set up with either pip or conda using `./envs/requirements.txt` or `./envs/ngen_env.yaml`, respectively.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')\n",
+    "     \n",
+    "# import numpy as np\n",
+    "\n",
+    "from dHBV_2_0.bmi import DeltaModelBmi\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dHbv2Bmi' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 28\u001b[0m\n\u001b[0;32m     24\u001b[0m attr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(attr_path)\n\u001b[0;32m     25\u001b[0m \u001b[38;5;66;03m# obs = np.load(obs_path)\u001b[39;00m\n\u001b[0;32m     26\u001b[0m \n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Create an instance of the dHBV 2.0 through BMI\u001b[39;00m\n\u001b[1;32m---> 28\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mdHbv2Bmi\u001b[49m()\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m### BMI initialization ###\u001b[39;00m\n\u001b[0;32m     31\u001b[0m model\u001b[38;5;241m.\u001b[39minitialize(bmi_cfg_file\u001b[38;5;241m=\u001b[39mPath(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC:/Users/LeoLo/Desktop/noaa_owp/dHBV_2_0/bmi_config_files/bmi_config_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbasin_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.yaml\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'dHbv2Bmi' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "\n",
+    "# os.chdir(os.path.expanduser('../dHBV_2_0/'))\n",
+    "\n",
+    "\n",
+    "### Select a basin from the sample data ###\n",
+    "basin_id = \"cat-88306\"\n",
+    "### ----------------------------------- ###\n",
+    "\n",
+    "\n",
+    "# Load the USGS data\n",
+    "# REPLACE THIS PATH WITH YOUR LOCAL FILE PATH:\n",
+    "forc_path = f'C:/Users/LeoLo/Desktop/noaa_owp/dHBV_2_0/data/aorc/juniata_river_basin/forcings_5yr_{basin_id}.npy'\n",
+    "attr_path = f'C:/Users/LeoLo/Desktop/noaa_owp/dHBV_2_0/data/aorc/juniata_river_basin/attributes_5yr_{basin_id}.npy'\n",
+    "# obs_path = f'/Users/LeoLo/Desktop/noaa_owp/dHBV_2_0/data/aorc/juniata_river_basin/obs_5yr_{basin_id}.npy'\n",
+    "\n",
+    "forc = np.load(forc_path)\n",
+    "attr = np.load(attr_path)\n",
+    "# obs = np.load(obs_path)\n",
+    "\n",
+    "# Create an instance of the dHBV 2.0 through BMI\n",
+    "model = dHbv2Bmi()\n",
+    "\n",
+    "### BMI initialization ###\n",
+    "model.initialize(bmi_cfg_file=Path(f'C:/Users/LeoLo/Desktop/noaa_owp/dHBV_2_0/bmi_config_files/bmi_config_{basin_id}.yaml'))\n",
+    "\n",
+    "streamflow_pred = np.zeros(forc.shape[0])\n",
+    "\n",
+    "for i in range(0, forc.shape[0]):\n",
+    "    # Extract forcing/attribute data for the current time step\n",
+    "    prcp = forc[i, :0, 0]\n",
+    "    temp = forc[i, :0, 1]\n",
+    "    pet = forc[i, :0, 2]\n",
+    "\n",
+    "    # # Check if any of the inputs are NaN\n",
+    "    # if np.isnan([prcp, temp, pet]).any():\n",
+    "    #     if model.verbose > 0:\n",
+    "    #         print(f\"Skipping timestep {i} due to NaN values in inputs.\")\n",
+    "    #     continue\n",
+    "\n",
+    "    model.set_value('atmosphere_water__liquid_equivalent_precipitation_rate', prcp)\n",
+    "    model.set_value('land_surface_air__temperature', temp)\n",
+    "    model.set_value('land_surface_water__potential_evaporation_volume_flux', pet)\n",
+    "\n",
+    "    ### BMI update ###\n",
+    "    model.update()\n",
+    "\n",
+    "    # Retrieve and scale the runoff output\n",
+    "    dest_array = np.zeros(1)\n",
+    "    model.get_value('land_surface_water__runoff_volume_flux', dest_array)\n",
+    "    \n",
+    "    # streamflow_pred[i] = dest_array[0] * 1000  # Convert to mm/hr\n",
+    "    streamflow_pred[i] = dest_array[0]\n",
+    "\n",
+    "\n",
+    "# Calculate NSE for the model predictions\n",
+    "obs = obs.dropna()\n",
+    "sim = streamflow_pred.dropna()\n",
+    "\n",
+    "denom = ((obs - obs.mean()) ** 2).sum()\n",
+    "num = ((sim - obs) ** 2).sum()\n",
+    "nse = 1 - num / denom\n",
+    "print(f\"NSE: {nse:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dMG\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "initialize_config() missing 1 required positional argument: 'config'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdMG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitialize_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mTypeError\u001b[0m: initialize_config() missing 1 required positional argument: 'config'"
+     ]
+    }
+   ],
+   "source": [
+    "dMG.utils.initialize_config()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}