Fixed Streamflow outputs that cause a NaN (#12)

* added lower bound to streamflow predictions

README.md

@@ -1,13 +1,12 @@
 # Marquette
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 [![Python](https://img.shields.io/pypi/pyversions/msmhelper)]()
-
 ![image](https://github.com/taddyb/marquette/assets/16233925/4a7b8745-7576-4150-bf4a-9469d44a9425)
 
 A river mapping library to take .shp files and create River Network CSVs
 
-## Quick Start
-1. clone
-2. set up env from `environment.yml`
-3. put your data files into the `data/` folder (if it's not there, then create it), then reference them from your config
+## Quick Start:
+1. `conda create -n marquette python=3.11`
+2. `conda activate marquette`
+3. `pip install -r requirements.txt`
 

marquette/merit/_streamflow_conversion_functions.py

@@ -124,6 +124,7 @@ def calculate_merit_flow(cfg: DictConfig) -> None:
     streamflow_predictions_root = zarr.open(
         Path(cfg.create_streamflow.predictions), mode="r"
     )
+    log.info("Reading Zarr Store")
     runoff = streamflow_predictions_root.Runoff[:]
 
     log.info("Creating areas areas_array")
@@ -139,8 +140,9 @@ def calculate_merit_flow(cfg: DictConfig) -> None:
 
     log.info("Converting runoff data")
     streamflow_m3_s_data = runoff * areas_array
-    mask = np.any(np.isnan(runoff), axis=0)
-    streamflow_m3_s_data[:, mask] = 0.0
+    streamflow_m3_s_data = np.nan_to_num(
+        streamflow_m3_s_data, nan=1e-6, posinf=1e-6, neginf=1e-6
+    )
 
     date_range = pd.date_range(
         start=cfg.create_streamflow.start_date,