Fixing PEP8 compliance

pages/pages/1_⚡_Alaskan_Tundra_Lightning.py

@@ -1,4 +1,7 @@
+import folium
 import streamlit as st
+from folium.plugins import Draw
+from streamlit_folium import st_folium
 
 st.set_page_config(
     page_title="streamlit-folium documentation: Draw Support",
@@ -9,21 +12,10 @@ st.set_page_config(
 """
 # streamlit-folium: Draw Support
 
-Folium supports some of the [most popular leaflet plugins](https://python-visualization.github.io/folium/plugins.html). In this example,
-we can add the [`Draw`](https://python-visualization.github.io/folium/plugins.html#folium.plugins.Draw) plugin to our map, which allows for drawing geometric shapes on the map.
-
-When a shape is drawn on the map, the coordinates that represent that shape are passed back as a geojson feature via
-the `all_drawings` and `last_active_drawing` data fields.
-
 Draw something below to see the return value back to Streamlit!
 """
 
 with st.echo(code_location="below"):
-    import folium
-    import streamlit as st
-    from folium.plugins import Draw
-
-    from streamlit_folium import st_folium
 
     m = folium.Map(location=[39.949610, -75.150282], zoom_start=5)
     Draw(export=True).add_to(m)

pages/pages/2_🌍_Alaska_WRF.py

@@ -1,4 +1,7 @@
+import folium
 import streamlit as st
+from folium.plugins import Draw
+from streamlit_folium import st_folium
 
 st.set_page_config(
     page_title="streamlit-folium documentation: Draw Support",
@@ -9,21 +12,10 @@ st.set_page_config(
 """
 # streamlit-folium: Draw Support
 
-Folium supports some of the [most popular leaflet plugins](https://python-visualization.github.io/folium/plugins.html). In this example,
-we can add the [`Draw`](https://python-visualization.github.io/folium/plugins.html#folium.plugins.Draw) plugin to our map, which allows for drawing geometric shapes on the map.
-
-When a shape is drawn on the map, the coordinates that represent that shape are passed back as a geojson feature via
-the `all_drawings` and `last_active_drawing` data fields.
-
 Draw something below to see the return value back to Streamlit!
 """
 
 with st.echo(code_location="below"):
-    import folium
-    import streamlit as st
-    from folium.plugins import Draw
-
-    from streamlit_folium import st_folium
 
     m = folium.Map(location=[39.949610, -75.150282], zoom_start=5)
     Draw(export=True).add_to(m)

pages/pages/3_x_geemap.py

@@ -0,0 +1,428 @@
+import ee
+import geemap.foliumap as geemap
+import geemap.colormaps as cm
+import geopandas as gpd
+import streamlit as st
+
+
+@st.cache
+def uploaded_file_to_gdf(data):
+    import tempfile
+    import os
+    import uuid
+
+    _, file_extension = os.path.splitext(data.name)
+    file_id = str(uuid.uuid4())
+    file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
+
+    with open(file_path, "wb") as file:
+        file.write(data.getbuffer())
+
+    if file_path.lower().endswith(".kml"):
+        gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
+        gdf = gpd.read_file(file_path, driver="KML")
+    else:
+        gdf = gpd.read_file(file_path)
+
+    return gdf
+
+
+def app():
+
+    st.title("Global Surface Water Datasets")
+
+    with st.expander("How to use this app"):
+
+        markdown = """
+        This interactive app allows you to explore and compare different datasets of Global Surface Water Extent (GSWE). How to use this web app?    
+        - **Step 1:** Select a basemap from the dropdown menu on the right. The default basemap is `HYBRID`, a Google Satellite basemap with labels.   
+        - **Step 2:** Select a region of interest (ROI) from the country dropdown menu or upload an ROI. The default ROI is the entire globe. 
+        - **Step 3:** Select surface water datasets from the dropdown menu. You can select multiple datasets to display on the map.
+        """
+        st.markdown(markdown)
+
+    col1, col2 = st.columns([3, 1])
+
+    Map = geemap.Map(Draw_export=False, locate_control=True, plugin_LatLngPopup=True)
+
+    roi = ee.FeatureCollection("users/giswqs/public/countries")
+    # countries = roi.aggregate_array("name").getInfo()
+    # countries.sort()
+    countries = ["United States of America"]
+
+    lc_basemaps = [
+        "ESA Global Land Cover 2020",
+        "ESRI Global Land Cover 2020",
+        "JRC Global Surface Water",
+        "USDA NASS Cropland 2020",
+        "US NLCD 2019",
+    ]
+
+    google_basemaps = ["OpenStreetMap"] + [
+        "Google " + b for b in list(geemap.basemaps.keys())[1:5]
+    ]
+    basemaps = google_basemaps + lc_basemaps
+    with col2:
+
+        latitude = st.number_input("Map center latitude", -90.0, 90.0, 40.0, step=0.5)
+        longitude = st.number_input(
+            "Map center longitude", -180.0, 180.0, -100.0, step=0.5
+        )
+        zoom = st.slider("Map zoom level", 1, 22, 4)
+
+        select = st.checkbox("Select a country")
+        if select:
+            country = st.selectbox(
+                "Select a country from dropdown list",
+                countries,
+                index=countries.index("United States of America"),
+            )
+            st.session_state["ROI"] = roi.filter(ee.Filter.eq("name", country))
+        else:
+
+            with st.expander("Click here to upload an ROI", False):
+                upload = st.file_uploader(
+                    "Upload a GeoJSON, KML or Shapefile (as a zif file) to use as an ROI. 😇👇",
+                    type=["geojson", "kml", "zip"],
+                )
+
+                if upload:
+                    gdf = uploaded_file_to_gdf(upload)
+                    st.session_state["ROI"] = geemap.gdf_to_ee(gdf, geodesic=False)
+                    # Map.add_gdf(gdf, "ROI")
+                else:
+                    st.session_state["ROI"] = roi
+
+        basemap = st.selectbox(
+            "Select a basemap",
+            basemaps,
+            index=basemaps.index("Google HYBRID"),
+        )
+        if basemap in google_basemaps:
+            Map.add_basemap(basemap.replace("Google ", ""))
+        elif basemap in lc_basemaps:
+
+            if basemap == "ESA Global Land Cover 2020":
+                dataset = ee.ImageCollection("ESA/WorldCover/v100").first()
+                if st.session_state["ROI"] is not None:
+                    dataset = dataset.clipToCollection(st.session_state["ROI"])
+
+                Map.addLayer(dataset, {}, "ESA Landcover")
+                Map.add_legend(title="ESA Landcover", builtin_legend="ESA_WorldCover")
+            elif basemap == "ESRI Global Land Cover 2020":
+
+                esri_lulc10 = ee.ImageCollection(
+                    "projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m"
+                )
+                legend_dict = {
+                    "names": [
+                        "Water",
+                        "Trees",
+                        "Grass",
+                        "Flooded Vegetation",
+                        "Crops",
+                        "Scrub/Shrub",
+                        "Built Area",
+                        "Bare Ground",
+                        "Snow/Ice",
+                        "Clouds",
+                    ],
+                    "colors": [
+                        "#1A5BAB",
+                        "#358221",
+                        "#A7D282",
+                        "#87D19E",
+                        "#FFDB5C",
+                        "#EECFA8",
+                        "#ED022A",
+                        "#EDE9E4",
+                        "#F2FAFF",
+                        "#C8C8C8",
+                    ],
+                }
+
+                vis_params = {"min": 1, "max": 10, "palette": legend_dict["colors"]}
+                esri_lulc10 = esri_lulc10.mosaic()
+
+                if st.session_state["ROI"] is not None:
+                    esri_lulc10 = esri_lulc10.clipToCollection(st.session_state["ROI"])
+                Map.addLayer(esri_lulc10, vis_params, "ESRI Global Land Cover")
+                Map.add_legend(title="ESRI Landcover", builtin_legend="ESRI_LandCover")
+
+            elif basemap == "US NLCD 2019":
+                nlcd = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select(
+                    "landcover"
+                )
+                if st.session_state["ROI"] is not None:
+                    nlcd = nlcd.clipToCollection(st.session_state["ROI"])
+                Map.addLayer(nlcd, {}, "US NLCD 2019")
+                Map.add_legend(title="NLCD Land Cover", builtin_legend="NLCD")
+
+            elif basemap == "USDA NASS Cropland 2020":
+                cropland = (
+                    ee.ImageCollection("USDA/NASS/CDL")
+                    .filterDate("2010-01-01", "2020-01-01")
+                    .first()
+                    .select("cropland")
+                )
+
+                if st.session_state["ROI"] is not None:
+                    cropland = cropland.clipToCollection(st.session_state["ROI"])
+
+                Map.addLayer(cropland, {}, "USDA NASS Cropland 2020")
+
+            # elif "HydroSHEDS" in datasets:
+            #     hydrolakes = ee.FeatureCollection(
+            #         "projects/sat-io/open-datasets/HydroLakes/lake_poly_v10"
+            #     )
+            #     if st.session_state["ROI"] is not None:
+            #         hydrolakes = hydrolakes.filterBounds(st.session_state["ROI"])
+            #     Map.addLayer(hydrolakes, {"color": "#00008B"}, "HydroSHEDS - HydroLAKES")
+
+    # roi = ee.FeatureCollection("users/giswqs/MRB/NWI_HU8_Boundary_Simplify")
+    style = {
+        "color": "000000ff",
+        "width": 1,
+        "lineType": "solid",
+        "fillColor": "00000000",
+    }
+
+    # select_holder = col2.empty()
+    with col2:
+        datasets = st.multiselect(
+            "Select surface water datasets",
+            [
+                "ESA Land Use",
+                "JRC Max Water Extent",
+                "OpenStreetMap",
+                "HydroLakes",
+                "LAGOS",
+                "US NED Depressions",
+                "Global River Width",
+            ],
+        )
+
+    # styles = {
+    #     "ESA Land Use": {
+    #         "color": "dca0dcff",
+    #         "width": width,
+    #         "fillColor": "e4bbe4ff",
+    #     },
+    #     "JRC Max Water Extent": {
+    #         "color": "ffc2cbff",
+    #         "width": width,
+    #         "fillColor": "fdd1d8ff",
+    #     },
+    #     "OpenStreetMap": {
+    #         "color": "bf03bfff",
+    #         "width": width,
+    #         "fillColor": "ebb2ebff",
+    #     },
+    #     "HydroLakes": {
+    #         "color": "4e0583ff",
+    #         "width": width,
+    #         "fillColor": "a47fbfff",
+    #     },
+    #     "LAGOS": {
+    #         "color": "8f228fff",
+    #         "width": width,
+    #         "fillColor": "cb99cbff",
+    #     },
+    #     "US NED Depressions": {
+    #         "color": "8d32e2ff",
+    #         "width": width,
+    #         "fillColor": "c394efff",
+    #     },
+    # }
+
+    width = 1
+    styles = {
+        "ESA Land Use": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "dca0dcff",
+        },
+        "JRC Max Water Extent": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "ffc2cbff",
+        },
+        "OpenStreetMap": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "bf03bfff",
+        },
+        "HydroLakes": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "4e0583ff",
+        },
+        "LAGOS": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "8f228fff",
+        },
+        "US NED Depressions": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "8d32e2ff",
+        },
+        "Global River Width": {
+            "color": "000000ff",
+            "width": width,
+            "fillColor": "0000ffff",
+        },
+    }
+
+    if "ESA Land Use" in datasets:
+        dataset = ee.FeatureCollection("users/giswqs/MRB/ESA_entireUS")
+        Map.addLayer(dataset.style(**styles["ESA Land Use"]), {}, "ESA Land Use")
+
+    if "JRC Max Water Extent" in datasets:
+        dataset = ee.FeatureCollection("users/giswqs/MRB/JRC_entireUS")
+        Map.addLayer(
+            dataset.style(**styles["JRC Max Water Extent"]), {}, "JRC Max Water Extent"
+        )
+
+    if "OpenStreetMap" in datasets:
+        dataset = ee.FeatureCollection("users/giswqs/MRB/OSM_entireUS")
+        Map.addLayer(dataset.style(**styles["OpenStreetMap"]), {}, "OpenStreetMap")
+
+    if "HydroLakes" in datasets:
+        dataset = ee.FeatureCollection("users/giswqs/MRB/HL_entireUS")
+        Map.addLayer(dataset.style(**styles["HydroLakes"]), {}, "HydroLakes")
+
+    if "LAGOS" in datasets:
+        dataset = ee.FeatureCollection("users/giswqs/MRB/LAGOS_entireUS")
+        Map.addLayer(dataset.style(**styles["LAGOS"]), {}, "LAGOS")
+
+    if "US NED Depressions" in datasets:
+        depressions = ee.FeatureCollection("users/giswqs/MRB/US_depressions")
+        Map.addLayer(
+            depressions.style(**styles["US NED Depressions"]), {}, "US NED Depressions"
+        )
+
+    if datasets:
+        legend_datasets = datasets[:]
+        # if "Global River Width" in legend_datasets:
+        #     legend_datasets.remove("Global River Width")
+        legend_dict = {}
+        for dataset in legend_datasets:
+            legend_dict[dataset] = styles[dataset]["fillColor"][:6]
+
+        if len(legend_datasets) > 0:
+
+            Map.add_legend(title="Surface Water", legend_dict=legend_dict)
+
+    # if "JRC Global Surface Water" in datasets:
+    #     jrc = ee.Image("JRC/GSW1_3/GlobalSurfaceWater")
+    #     vis = {
+    #         "bands": ["occurrence"],
+    #         "min": 0.0,
+    #         "max": 100.0,
+    #         "palette": cm.palettes.coolwarm_r,
+    #     }
+    #     Map.addLayer(jrc, vis, "JRC Global Surface Water")
+    #     Map.add_colorbar(vis, label="Surface water occurrence (%)")
+
+    if "Global River Width" in datasets:
+        water_mask = ee.ImageCollection(
+            "projects/sat-io/open-datasets/GRWL/water_mask_v01_01"
+        ).median()
+
+        grwl_summary = ee.FeatureCollection(
+            "projects/sat-io/open-datasets/GRWL/grwl_SummaryStats_v01_01"
+        )
+        grwl_water_vector = ee.FeatureCollection(
+            "projects/sat-io/open-datasets/GRWL/water_vector_v01_01"
+        )
+
+        if st.session_state["ROI"] is not None:
+            water_mask = water_mask.clipToCollection(st.session_state["ROI"])
+            grwl_summary = grwl_summary.filterBounds(st.session_state["ROI"])
+
+        Map.addLayer(water_mask, {"palette": "blue"}, "GRWL RIver Mask")
+        Map.addLayer(
+            grwl_water_vector.style(**{"fillColor": "00000000", "color": "FF5500"}),
+            {},
+            "GRWL Centerline",
+            False,
+        )
+        Map.addLayer(
+            grwl_summary.style(**{"fillColor": "00000000", "color": "EE5500"}),
+            {},
+            "GRWL Centerline Simplified",
+        )
+
+    show = False
+    if select and country is not None:
+        name = country
+        style["color"] = "#000000"
+        style["width"] = 2
+        show = True
+    elif upload:
+        name = "ROI"
+        style["color"] = "#FFFF00"
+        style["width"] = 2
+        show = True
+    else:
+        name = "World"
+
+    Map.addLayer(st.session_state["ROI"].style(**style), {}, name, show)
+    Map.centerObject(st.session_state["ROI"])
+
+    with col2:
+        wbds = [
+            "NHD-HUC2",
+            "NHD-HUC4",
+            "NHD-HUC6",
+            "NHD-HUC8",
+            "NHD-HUC10",
+        ]
+        wbd = st.multiselect("Select watershed boundaries", wbds)
+
+        if "NHD-HUC2" in wbd:
+            huc2 = ee.FeatureCollection("USGS/WBD/2017/HUC02")
+            Map.addLayer(huc2.style(**{"fillColor": "00000000"}), {}, "NHD-HUC2")
+
+        if "NHD-HUC4" in wbd:
+            huc4 = ee.FeatureCollection("USGS/WBD/2017/HUC04")
+            Map.addLayer(huc4.style(**{"fillColor": "00000000"}), {}, "NHD-HUC4")
+
+        if "NHD-HUC6" in wbd:
+            huc6 = ee.FeatureCollection("USGS/WBD/2017/HUC06")
+            Map.addLayer(huc6.style(**{"fillColor": "00000000"}), {}, "NHD-HUC6")
+
+        if "NHD-HUC8" in wbd:
+            huc8 = ee.FeatureCollection("USGS/WBD/2017/HUC08")
+            Map.addLayer(huc8.style(**{"fillColor": "00000000"}), {}, "NHD-HUC8")
+
+        if "NHD-HUC10" in wbd:
+            huc10 = ee.FeatureCollection("USGS/WBD/2017/HUC10")
+            Map.addLayer(huc10.style(**{"fillColor": "00000000"}), {}, "NHD-HUC10")
+
+    with col1:
+        Map.set_center(longitude, latitude, zoom)
+        Map.to_streamlit(height=680)
+
+    with col2:
+        with st.expander("Data Sources"):
+
+            desc = """
+                - [ESA Global Land Cover](https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100?hl=en)
+                - [ESRI Global Land Cover](https://samapriya.github.io/awesome-gee-community-datasets/projects/esrilc2020/)
+                - [Global River Width Dataset](https://samapriya.github.io/awesome-gee-community-datasets/projects/grwl/)
+                - [JRC Global Surface Water](https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_3_GlobalSurfaceWater)
+                - [HydroSHEDS - HydroLAKES](https://samapriya.github.io/awesome-gee-community-datasets/projects/hydrolakes/)
+                - [OSM Global Surface Water](https://samapriya.github.io/awesome-gee-community-datasets/projects/osm_water/)
+                - [US NLCD](https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_NLCD)
+                - [US NED Depressions (10m)](https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m)
+                - [USDA NASS Cropland](https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL)
+                - [NHD Waterboday](https://samapriya.github.io/awesome-gee-community-datasets/projects/nhd)
+                - [NHD-HUC2](https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC02)
+                - [NHD-HUC4](https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC04)
+                - [NHD-HUC6](https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC06)
+                - [NHD-HUC8](https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC08)
+                - [NHD-HUC10](https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC10)
+            """
+            st.markdown(desc)

wildfire_occurrence/model/analysis/wrf_analysis.py

@@ -468,7 +468,8 @@ if __name__ == "__main__":
         output_dir = 'output'  # os.path.dirname(os.path.abspath(filename))
 
         # TODO: make this for loop parallel later
-        for t_idx, delta_time in enumerate(wrf_analysis.wrf_dataset.Times.values):
+        for t_idx, delta_time in enumerate(
+                wrf_analysis.wrf_dataset.Times.values):
 
             logging.info(f'Processing t_idx: {t_idx}, timestamp: {delta_time}')
             output_filename = os.path.join(

wildfire_occurrence/model/data_download/ncep_fnl.py

@@ -234,9 +234,9 @@ class NCEP_FNL(object):
 if __name__ == "__main__":
 
     dates = [
-        #'2003-06-23',
-        #'2005-06-11',
-        #'2023-06-04'
+        # '2003-06-23',
+        # '2005-06-11',
+        # '2023-06-04'
         '2023-06-23'
     ]
 

wildfire_occurrence/model/pipelines/wrf_pipeline.py

@@ -5,9 +5,9 @@ import logging
 import datetime
 from glob import glob
 from pathlib import Path
-from itertools import repeat
+# from itertools import repeat
 from omegaconf import OmegaConf
-from multiprocessing import Pool, cpu_count
+from multiprocessing import cpu_count  # , Pool
 from jinja2 import Environment, PackageLoader, select_autoescape
 
 from wildfire_occurrence.model.config import Config
@@ -61,7 +61,8 @@ class WRFPipeline(object):
         self.local_wps_path = os.path.join(self.simulation_dir, 'WPS')
         self.local_wrf_path = os.path.join(self.simulation_dir, 'em_real')
         self.local_wrf_output = os.path.join(self.simulation_dir, 'output')
-        self.local_wrf_output_vars = os.path.join(self.simulation_dir, 'variables')
+        self.local_wrf_output_vars = os.path.join(
+            self.simulation_dir, 'variables')
 
         # Setup configuration filenames
         self.wps_conf_filename = os.path.join(self.conf_dir, 'namelist.wps')
@@ -134,11 +135,10 @@ class WRFPipeline(object):
                 f'{self.conf.container_path} ' + \
                 f'mpirun -np {cpu_count()} --oversubscribe ./geogrid.exe'
         else:
-            geodrid_cmd = 'bash /panfs/ccds02/nobackup/projects/ilab/projects/LobodaTFO/operations/2015-07-23_2015-08-02/WPS/run_geogrid.sh'
-            #'mpirun -np 40 --host gpu016 --oversubscribe singularity exec -B /explore/nobackup/projects/ilab,' + \
-            #'$NOBACKUP,/lscratch,/panfs/ccds02/nobackup/projects/ilab ' + \
-            #f'{self.conf.container_path} ' + \
-            #'bash /panfs/ccds02/nobackup/projects/ilab/projects/LobodaTFO/operations/2015-07-23_2015-08-02/WPS/run_geogrid.sh'
+            geodrid_cmd = 'mpirun -np 40 --host gpu016 --oversubscribe' + \
+                'singularity exec -B /explore/nobackup/projects/ilab,' + \
+                '$NOBACKUP,/lscratch,/panfs/ccds02/nobackup/projects/ilab ' + \
+                f'{self.conf.container_path} ./geogrid.exe'
 
         # run geogrid command
         os.system(geodrid_cmd)
@@ -186,7 +186,8 @@ class WRFPipeline(object):
                 f'{self.conf.container_path} ./ungrib.exe'
         else:
             ungrib_cmd = \
-                'srun --mpi=pmix -N 1 -n 1 singularity exec -B /explore/nobackup/projects/ilab,' + \
+                'srun --mpi=pmix -N 1 -n 1 singularity exec -B ' + \
+                '/explore/nobackup/projects/ilab,' + \
                 '$NOBACKUP,/panfs/ccds02/nobackup/projects/ilab ' + \
                 f'{self.conf.container_path} ' + \
                 './ungrib.exe'
@@ -220,7 +221,8 @@ class WRFPipeline(object):
                 f'mpirun -np {cpu_count()} --oversubscribe ./metgrid.exe'
         else:
             metgrid_cmd = \
-                f'srun --mpi=pmix -N 1 -n {cpu_count()} singularity exec -B /explore/nobackup/projects/ilab,' + \
+                f'srun --mpi=pmix -N 1 -n {cpu_count()} singularity ' + \
+                'exec -B /explore/nobackup/projects/ilab,' + \
                 '$NOBACKUP,/panfs/ccds02/nobackup/projects/ilab ' + \
                 f'{self.conf.container_path} ' + \
                 './metgrid.exe'
@@ -267,7 +269,8 @@ class WRFPipeline(object):
                 f'mpirun -np {cpu_count()} --oversubscribe ./real.exe'
         else:
             real_cmd = \
-                'srun --mpi=pmix -N 2 -n 80 singularity exec -B /explore/nobackup/projects/ilab,' + \
+                'srun --mpi=pmix -N 2 -n 80 singularity ' + \
+                'exec -B /explore/nobackup/projects/ilab,' + \
                 '$NOBACKUP,/panfs/ccds02/nobackup/projects/ilab ' + \
                 f'{self.conf.container_path} ' + \
                 './real.exe'
@@ -301,7 +304,8 @@ class WRFPipeline(object):
                 f'mpirun -np {cpu_count()} --oversubscribe ./wrf.exe'
         else:
             wrf_cmd = \
-                'srun --mpi=pmix -N 2 -n 80 singularity exec -B /explore/nobackup/projects/ilab,' + \
+                'srun --mpi=pmix -N 2 -n 80 singularity ' + \
+                'exec -B /explore/nobackup/projects/ilab,' + \
                 '$NOBACKUP,/panfs/ccds02/nobackup/projects/ilab ' + \
                 f'{self.conf.container_path} ' + \
                 './wrf.exe'