Merge pull request #2 from noaa-oar-arl/zm

Tweak readme; pass linting/formatting

.pre-commit-config.yaml

@@ -28,9 +28,10 @@ repos:
     rev: "7.1.1"
     hooks:
       - id: flake8
-        args: [--max-line-length=88]
+        args: [--max-line-length=88, "--ignore=E203,W503"]
 
   - repo: https://github.com/codespell-project/codespell
     rev: v2.3.0
     hooks:
       - id: codespell
+        args: [--ignore-words-list=readin]

README.md

@@ -2,17 +2,17 @@
 
 Repository for a fire spread forecast model, generating hourly fire radiative power (FRP) prediction for air quality forecasting applications.
 
-## Required python libraries
+## Required Python libraries
 
-- Numpy
-- pandas
-- SciPy
+- Keras
+- Matplotlib (for output plotting only)
 - MetPy
-- datetime
 - netCDF4
-- Matplotlib (for output plotting only)
+- NumPy
+- pandas
+- SciPy
 - Tensorflow
-- Keras
+- xarray
 - XGBoost
 
 ## Components
@@ -73,7 +73,7 @@ Trained machine learning models are available in `model/fira_models.zip`
 1. Filename format: `[input name].[start time].f[simuluation time].nc`
    `input name` and `start time` can be specified in namelist.
 
-2. Input netcdf file components:
+2. Input netCDF file components:
 
 | **Variable Name**   | **Variable Description**                                                           |
 | ------------------- | ---------------------------------------------------------------------------------- |
@@ -110,7 +110,7 @@ Trained machine learning models are available in `model/fira_models.zip`
 1. Filename format: `[output name].[start time].f[forecast hour].nc`
    `output name` and `start time` can be specified in namelist.
 
-2. Output netcdf file components:
+2. Output netCDF file components:
 
 | **Variable Name**   | **Variable Description**                                               |
 | ------------------- | ---------------------------------------------------------------------- |

src/fire_inputgen.py

@@ -645,6 +645,7 @@ def main_driver(initial_hour, forecast_hour, f_input, f_output, lat_lim, lon_lim
     lw[lw == 0] = np.nan
 
     # fire frame
+    INPUTFRAME = LATFRAME = LONFRAME = None
     for j in np.arange(1, num + 1, 1):
         total = total + 1
         index = np.argwhere(lw == j)
@@ -680,7 +681,7 @@ def main_driver(initial_hour, forecast_hour, f_input, f_output, lat_lim, lon_lim
             loc[0] - fsize : loc[0] + fsize + 1, loc[1] - fsize : loc[1] + fsize + 1
         ]
 
-        if "INPUTFRAME" in locals():
+        if INPUTFRAME is not None:
             INPUTFRAME = np.append(INPUTFRAME, np.expand_dims(X_fire, axis=0), axis=0)
             LATFRAME = np.append(LATFRAME, np.expand_dims(X_lat, axis=0), axis=0)
             LONFRAME = np.append(LONFRAME, np.expand_dims(X_lon, axis=0), axis=0)