diff --git a/README.md b/README.md
index 0d61671..3170bb3 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,7 @@ The Fortran git repositories for each BMI can be found at https://github.com/nhm
3. bmi-prms6-groundwater
4. bmi-prms6-streamflow
-The python pymt gir repositories can be found here:
+The python pymt git repositories can be found here:
https://github.com/pymt-lab
1. pymt_prms_surface
@@ -47,7 +47,7 @@ Follow these steps:
1. [Create an account](https://csdms.rc.colorado.edu/hub/signup) on the CSDMS JupyterHub, providing a username and password--they can be whatever you like
1. [Request authorization](https://github.com/csdms/help-desk/issues/new?assignees=mdpiper&labels=jupyterhub&template=new-csdms-jupyterhub-account.md&title=CSDMS+JupyterHub+account) for your new account through the CSDMS Help Desk--if you don't already have a GitHub account, you'll be asked to make one
-1. Once approved, [run Jupyter Notebooks](https://csdms.rc.colorado.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fnhm-usgs%2Fbmi-prms-demo&urlpath=tree%2Fbmi-prms-demo%2Fnotebooks&branch=master)
+1. Once approved, [run Jupyter Notebooks](https://workbench-csdms.colorado.edu/)
Disclaimer
----------
diff --git a/notebooks/03_Surf_Soil_GW_SF_Validation.ipynb b/notebooks/03_Surf_Soil_GW_SF_Validation.ipynb
index 0bfa347..ef792c1 100644
--- a/notebooks/03_Surf_Soil_GW_SF_Validation.ipynb
+++ b/notebooks/03_Surf_Soil_GW_SF_Validation.ipynb
@@ -36,15 +36,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[1mmodels: Avulsion, Plume, Sedflux3D, Subside, PRMSSurface, PRMSStreamflow, PRMSSoil, PRMSGroundwater, FrostNumber, Ku, Hydrotrend, Cem, Waves\u001b[0m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
@@ -260,17 +252,17 @@
"outputs": [],
"source": [
"# CSDMS JupyterHub set path to HRU and streamsegment shapefiles\n",
- "hru_shp = '/data/prms/GIS/nhru_10U.shp'\n",
- "hru_strmseg = '/data/prms/GIS/nsegment_10U.shp'\n",
+ "# hru_shp = '/data/prms/GIS/nhru_10U.shp'\n",
+ "# hru_strmseg = '/data/prms/GIS/nsegment_10U.shp'\n",
"# set path to Gridmet weights file for mapping Gridmet gridded data to HRU\n",
- "weight_file = '/data/prms/weights.csv'\n",
+ "# weight_file = '/data/prms/weights.csv'\n",
"\n",
"# If using notebook not in CSDMS JupyterHub. See README for instruction on where to \n",
"# get the data and uncomment out the following lines\n",
- "hru_shp = './GIS/nhru_10U.shp'\n",
- "hru_strmseg = './GIS/nsegment_10U.shp'\n",
+ "hru_shp = '/opt/data/GIS/nhru_10U.shp'\n",
+ "hru_strmseg = '/opt/data/GIS/nsegment_10U.shp'\n",
"# set path to Gridmet weights file for mapping Gridmet gridded data to HRU\n",
- "weight_file = './GIS/weights.csv'\n",
+ "weight_file = '/opt/data/GIS/weights.csv'\n",
"\n",
"gdf_ps = helper.get_gdf(hru_shp, msurf)"
]
@@ -751,18 +743,18 @@
" seg_gwflow (time, nsegment) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" seg_sroff (time, nsegment) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" seg_ssflow (time, nsegment) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
- " seg_inflow (time, nsegment) float64 0.0 1.626e-260 0.0 ... 0.0 0.0\n",
+ " seg_inflow (time, nsegment) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" seg_outflow (time, nsegment) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
- " seg_upstream_inflow (time, nsegment) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0
"
],
"text/plain": [
"\n",
@@ -1974,9 +1960,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "PRMS",
"language": "python",
- "name": "python3"
+ "name": "pyprms"
},
"language_info": {
"codemirror_mode": {
diff --git a/notebooks/04_Run_Coupled_PRMS.ipynb b/notebooks/04_Run_Coupled_PRMS.ipynb
index 428478c..20f4869 100644
--- a/notebooks/04_Run_Coupled_PRMS.ipynb
+++ b/notebooks/04_Run_Coupled_PRMS.ipynb
@@ -26,15 +26,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[33;01mâž¡ models: Avulsion, Plume, Sedflux3D, Subside, Rafem, PRMSSurface, PRMSStreamflow, PRMSSoil, PRMSGroundwater, FrostNumber, Ku, Hydrotrend, GIPL, ECSimpleSnow, Cem, Waves\u001b[39;49;00m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
@@ -56,18 +48,18 @@
"metadata": {},
"outputs": [],
"source": [
- "# CSDMS JupyterHub set path to HRU and streamsegment shapefiles\n",
- "hru_shp = '/data/prms/GIS/nhru_10U.shp'\n",
- "hru_strmseg = '/data/prms/GIS/nsegment_10U.shp'\n",
- "# set path to Gridmet weights file for mapping Gridmet gridded data to HRU\n",
- "weight_file = '/data/prms/weights.csv'\n",
+ "# # If using locally set path HRU and streamsegment shapefiles from data download in README\n",
+ "# hru_shp = '../GIS/nhru_10U.shp'\n",
+ "# hru_strmseg = '../GIS/nsegment_10U.shp'\n",
+ "# # set path to Gridmet weights file for mapping Gridmet gridded data to HRU\n",
+ "# weight_file = '../GIS/weights.csv'\n",
"\n",
- "# If using notebook not in CSDMS JupyterHub. See README for instruction on where to \n",
+ "# If using notebook in CSDMS JupyterHub. See README for instruction on where to \n",
"# get the data and uncomment out the following lines\n",
- "# hru_shp = './GIS/nhru_10U.shp'\n",
- "# hru_strmseg = './GIS/nsegment_10U.shp'\n",
- "# # set path to Gridmet weights file for mapping Gridmet gridded data to HRU\n",
- "# weight_file = './GIS/weights.csv'"
+ "hru_shp = '/opt/data/GIS/nhru_10U.shp'\n",
+ "hru_strmseg = '/opt/data/GIS/nsegment_10U.shp'\n",
+ "# set path to Gridmet weights file for mapping Gridmet gridded data to HRU\n",
+ "weight_file = '/opt/data/GIS/weights.csv'"
]
},
{
@@ -177,7 +169,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
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