From 8a876f16feaf53d4764ab92eeac00e804eafee2f Mon Sep 17 00:00:00 2001 From: aleaf Date: Thu, 21 Sep 2023 16:13:46 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20=20@=20db036?= =?UTF-8?q?0491bf85dae72673f513f78c8906089c601=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- latest/.buildinfo | 2 +- latest/10min.html | 6 +- ...tebooks_Pleasant_lake_lgr_example_24_1.png | Bin 30740 -> 30740 bytes ...tebooks_Pleasant_lake_lgr_example_34_0.png | Bin 111883 -> 111873 bytes latest/_modules/index.html | 6 +- latest/_modules/mfsetup/discretization.html | 6 +- latest/_modules/mfsetup/fileio.html | 6 +- latest/_modules/mfsetup/grid.html | 6 +- latest/_modules/mfsetup/interpolate.html | 6 +- latest/_modules/mfsetup/mf6model.html | 8 +- latest/_modules/mfsetup/mfmodel.html | 24 +- latest/_modules/mfsetup/mfnwtmodel.html | 6 +- latest/_modules/mfsetup/tdis.html | 16 +- latest/_modules/mfsetup/tmr.html | 6 +- .../Pleasant_lake_lgr_example.ipynb.txt | 188 +++--- latest/_static/documentation_options.js | 2 +- latest/api/index.html | 6 +- latest/api/mfsetup.discretization.html | 6 +- latest/api/mfsetup.fileio.html | 6 +- latest/api/mfsetup.grid.html | 14 +- latest/api/mfsetup.interpolate.html | 6 +- latest/api/mfsetup.mf6model.html | 6 +- latest/api/mfsetup.mfmodel.html | 6 +- latest/api/mfsetup.mfnwtmodel.html | 6 +- latest/api/mfsetup.tdis.html | 12 +- latest/api/mfsetup.tmr.html | 6 +- latest/concepts/index.html | 6 +- latest/concepts/perimeter-bcs.html | 6 +- latest/config-file-defaults.html | 548 +++++++++--------- latest/config-file-gallery.html | 63 +- latest/config-file.html | 6 +- latest/contributing.html | 6 +- latest/examples.html | 6 +- latest/genindex.html | 6 +- latest/index.html | 10 +- latest/input/basic-stress.html | 10 +- latest/input/dis.html | 6 +- latest/input/ic.html | 6 +- latest/input/index.html | 6 +- latest/input/lak.html | 6 +- latest/input/obs.html | 6 +- latest/input/oc.html | 6 +- latest/input/props.html | 6 +- latest/input/sfr.html | 6 +- latest/installation.html | 6 +- .../notebooks/Pleasant_lake_lgr_example.html | 30 +- .../notebooks/Pleasant_lake_lgr_example.ipynb | 188 +++--- latest/objects.inv | Bin 9972 -> 9973 bytes latest/philosophy.html | 10 +- latest/py-modindex.html | 6 +- latest/references.html | 6 +- latest/release-history.html | 6 +- latest/search.html | 6 +- latest/searchindex.js | 2 +- latest/structure.html | 6 +- latest/troubleshooting.html | 6 +- 56 files changed, 680 insertions(+), 669 deletions(-) diff --git a/latest/.buildinfo b/latest/.buildinfo index c3f4729d..cf9d2b1a 100644 --- a/latest/.buildinfo +++ b/latest/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 7120a413fddcd01edc1a4040adba86c4 +config: a2fae29f9adf14a325f19777fee33d24 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/latest/10min.html b/latest/10min.html index f2a371e4..4ce231a0 100644 --- a/latest/10min.html +++ b/latest/10min.html @@ -4,7 +4,7 @@ - 10 Minutes to Modflow-setup — modflow-setup 0.3.0.post11+g3a6ec42 documentation + 10 Minutes to Modflow-setup — modflow-setup 0.3.0.post15+gdb03604 documentation @@ -41,7 +41,7 @@ modflow-setup
- 0.3.0.post11+g3a6ec42 + 0.3.0.post15+gdb03604
@@ -443,7 +443,7 @@

5) Make a minimum working configuration file and model build script

© Copyright 2019-2023, Modflow-setup developers |. - Last updated on Aug 22, 2023. + Last updated on Sep 21, 2023.

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- 0.3.0.post11+g3a6ec42 + 0.3.0.post15+gdb03604
@@ -119,7 +119,7 @@

All modules for which code is available

© Copyright 2019-2023, Modflow-setup developers |. - Last updated on Aug 22, 2023. + Last updated on Sep 21, 2023.

diff --git a/latest/_modules/mfsetup/discretization.html b/latest/_modules/mfsetup/discretization.html index ec2d1412..63583743 100644 --- a/latest/_modules/mfsetup/discretization.html +++ b/latest/_modules/mfsetup/discretization.html @@ -3,7 +3,7 @@ - mfsetup.discretization — modflow-setup 0.3.0.post11+g3a6ec42 documentation + mfsetup.discretization — modflow-setup 0.3.0.post15+gdb03604 documentation @@ -38,7 +38,7 @@ modflow-setup
- 0.3.0.post11+g3a6ec42 + 0.3.0.post15+gdb03604
@@ -847,7 +847,7 @@

Source code for mfsetup.discretization

 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/fileio.html b/latest/_modules/mfsetup/fileio.html
index 0381ef4a..72424962 100644
--- a/latest/_modules/mfsetup/fileio.html
+++ b/latest/_modules/mfsetup/fileio.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.fileio — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.fileio — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -1329,7 +1329,7 @@ 
Source code for mfsetup.fileio
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/grid.html b/latest/_modules/mfsetup/grid.html
index 55dd413a..24f4965f 100644
--- a/latest/_modules/mfsetup/grid.html
+++ b/latest/_modules/mfsetup/grid.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.grid — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.grid — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -1261,7 +1261,7 @@ 
Source code for mfsetup.grid
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/interpolate.html b/latest/_modules/mfsetup/interpolate.html
index cf0c537c..64d4db6c 100644
--- a/latest/_modules/mfsetup/interpolate.html
+++ b/latest/_modules/mfsetup/interpolate.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.interpolate — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.interpolate — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -517,7 +517,7 @@ 
Source code for mfsetup.interpolate
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/mf6model.html b/latest/_modules/mfsetup/mf6model.html
index 8c56202c..38d49c77 100644
--- a/latest/_modules/mfsetup/mf6model.html
+++ b/latest/_modules/mfsetup/mf6model.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.mf6model — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.mf6model — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -850,7 +850,7 @@ 
Source code for mfsetup.mf6model
             # including lake package lakes and non lake, non well BCs
             # (high-K lakes are excluded, since we may want head obs at those locations,
             #  to serve as pseudo lake stage observations)
-            iobs_domain = ~((self.isbc == 1) | np.any(self.isbc > 2))
+            iobs_domain = ~((self.isbc == 1) | np.any(self.isbc > 2, axis=0))
 
         # munge the observation data
         df = setup_head_observations(self,
@@ -1112,7 +1112,7 @@ 
Source code for mfsetup.mf6model
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/mfmodel.html b/latest/_modules/mfsetup/mfmodel.html
index 223934f3..566bc01f 100644
--- a/latest/_modules/mfsetup/mfmodel.html
+++ b/latest/_modules/mfsetup/mfmodel.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.mfmodel — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.mfmodel — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -676,8 +676,8 @@ 
Source code for mfsetup.mfmodel
                             bbox = self.bbox
                         elif self.parent.modelgrid is not None:
                             bbox = self.parent.modelgrid.bbox
-                            model_proj_str = self.parent.modelgrid.proj_str
-                            assert model_proj_str is not None
+                            model_crs = self.parent.modelgrid.crs
+                            assert model_crs is not None
 
                         if features_crs != self.modelgrid.crs:
                             bbox_filter = project(bbox, self.modelgrid.crs, features_crs).bounds
@@ -703,10 +703,14 @@ 
Source code for mfsetup.mfmodel
                     return
             else:
                 df = self._features[f]
-            if id_column is not None and include_ids is not None:
+            if id_column is not None:
                 id_column = id_column.lower()
+                # convert any floating point dtypes to integer
+                if df[id_column].dtype == float:
+                    df[id_column] = df[id_column].astype(int)
                 df.index = df[id_column]
-                df = df.loc[include_ids]
+            if include_ids is not None:
+                df = df.loc[include_ids].copy()
             dfs_list.append(df)
         df = pd.concat(dfs_list)
         if len(df) == 0:
@@ -1045,10 +1049,12 @@ 
Source code for mfsetup.mfmodel
             if 'lak' in self.package_list:
                 lakes_shapefile = self.cfg['lak'].get('source_data', {}).get('lakes_shapefile', {}).copy()
                 if lakes_shapefile:
-                    lakesdata = self.load_features(**lakes_shapefile)  # caches loaded features
+                    kwargs = get_input_arguments(lakes_shapefile, self.load_features)
+                    lakesdata = self.load_features(**kwargs)  # caches loaded features
                     lakes_shapefile['lakesdata'] = lakesdata
                     lakes_shapefile.pop('filename')
-                    lakarr2d = make_lakarr2d(self.modelgrid, **lakes_shapefile)
+                    kwargs = get_input_arguments(lakes_shapefile, make_lakarr2d)
+                    lakarr2d = make_lakarr2d(self.modelgrid, **kwargs)
             self._lakarr_2d = lakarr2d
             self._set_isbc2d()
 
@@ -1906,7 +1912,7 @@ 
Source code for mfsetup.mfmodel
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/mfnwtmodel.html b/latest/_modules/mfsetup/mfnwtmodel.html
index 1d08f32d..8f68ae49 100644
--- a/latest/_modules/mfsetup/mfnwtmodel.html
+++ b/latest/_modules/mfsetup/mfnwtmodel.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.mfnwtmodel — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.mfnwtmodel — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -1002,7 +1002,7 @@ 
Source code for mfsetup.mfnwtmodel
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/tdis.html b/latest/_modules/mfsetup/tdis.html
index 8f8eef1e..e80b20af 100644
--- a/latest/_modules/mfsetup/tdis.html
+++ b/latest/_modules/mfsetup/tdis.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.tdis — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.tdis — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -205,7 +205,8 @@ 
Source code for mfsetup.tdis
             data = defaults.copy()
             data.update(v)
             if is_valid_perioddata(data):
-                data = get_input_arguments(data, setup_perioddata_group)
+                data = get_input_arguments(data, setup_perioddata_group,
+                                           errors='raise')
                 perioddata_groups.append(data)
             else:
                 print_item(k, data)
@@ -810,7 +811,12 @@ 
Source code for mfsetup.tdis
     aggregated.reset_index(inplace=True)
 
     # add datetime back in
-    aggregated['start_datetime'] = pd.Timestamp(start) if start is not None else start_datetime
+    aggregated['start_datetime'] = start if start is not None else start_datetime
+    # enforce consistent datetime dtypes
+    # (otherwise pd.concat of multiple outputs from this function may fail)
+    for col in 'start_datetime', 'end_datetime':
+        if col in aggregated.columns:
+            aggregated[col] = aggregated[col].astype('datetime64[ns]')
 
     # drop original datetime column, which doesn't reflect dates for period averages
     drop_cols = [datetime_column]
@@ -936,7 +942,7 @@ 
Source code for mfsetup.tdis
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_modules/mfsetup/tmr.html b/latest/_modules/mfsetup/tmr.html
index 0309fc01..c496d303 100644
--- a/latest/_modules/mfsetup/tmr.html
+++ b/latest/_modules/mfsetup/tmr.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.tmr — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.tmr — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -1096,7 +1096,7 @@ 
Source code for mfsetup.tmr
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/_sources/notebooks/Pleasant_lake_lgr_example.ipynb.txt b/latest/_sources/notebooks/Pleasant_lake_lgr_example.ipynb.txt
index f7bdeabc..f11fdcfc 100644
--- a/latest/_sources/notebooks/Pleasant_lake_lgr_example.ipynb.txt
+++ b/latest/_sources/notebooks/Pleasant_lake_lgr_example.ipynb.txt
@@ -40,10 +40,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:05.662908Z",
-     "iopub.status.busy": "2023-08-22T13:45:05.662607Z",
-     "iopub.status.idle": "2023-08-22T13:45:07.039235Z",
-     "shell.execute_reply": "2023-08-22T13:45:07.038512Z"
+     "iopub.execute_input": "2023-09-21T16:07:53.385465Z",
+     "iopub.status.busy": "2023-09-21T16:07:53.385051Z",
+     "iopub.status.idle": "2023-09-21T16:07:55.162996Z",
+     "shell.execute_reply": "2023-09-21T16:07:55.162035Z"
     }
    },
    "outputs": [],
@@ -81,10 +81,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:07.042890Z",
-     "iopub.status.busy": "2023-08-22T13:45:07.042482Z",
-     "iopub.status.idle": "2023-08-22T13:45:07.982371Z",
-     "shell.execute_reply": "2023-08-22T13:45:07.981788Z"
+     "iopub.execute_input": "2023-09-21T16:07:55.167492Z",
+     "iopub.status.busy": "2023-09-21T16:07:55.166701Z",
+     "iopub.status.idle": "2023-09-21T16:07:56.320323Z",
+     "shell.execute_reply": "2023-09-21T16:07:56.319579Z"
     }
    },
    "outputs": [],
@@ -106,10 +106,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:07.985861Z",
-     "iopub.status.busy": "2023-08-22T13:45:07.985441Z",
-     "iopub.status.idle": "2023-08-22T13:45:07.992064Z",
-     "shell.execute_reply": "2023-08-22T13:45:07.991610Z"
+     "iopub.execute_input": "2023-09-21T16:07:56.324249Z",
+     "iopub.status.busy": "2023-09-21T16:07:56.323520Z",
+     "iopub.status.idle": "2023-09-21T16:07:56.332648Z",
+     "shell.execute_reply": "2023-09-21T16:07:56.332138Z"
     }
    },
    "outputs": [
@@ -139,10 +139,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:08.029391Z",
-     "iopub.status.busy": "2023-08-22T13:45:08.029011Z",
-     "iopub.status.idle": "2023-08-22T13:45:08.035853Z",
-     "shell.execute_reply": "2023-08-22T13:45:08.035305Z"
+     "iopub.execute_input": "2023-09-21T16:07:56.375962Z",
+     "iopub.status.busy": "2023-09-21T16:07:56.375190Z",
+     "iopub.status.idle": "2023-09-21T16:07:56.381868Z",
+     "shell.execute_reply": "2023-09-21T16:07:56.381260Z"
     }
    },
    "outputs": [
@@ -180,10 +180,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:08.038727Z",
-     "iopub.status.busy": "2023-08-22T13:45:08.038280Z",
-     "iopub.status.idle": "2023-08-22T13:45:08.043722Z",
-     "shell.execute_reply": "2023-08-22T13:45:08.043132Z"
+     "iopub.execute_input": "2023-09-21T16:07:56.385045Z",
+     "iopub.status.busy": "2023-09-21T16:07:56.384443Z",
+     "iopub.status.idle": "2023-09-21T16:07:56.388768Z",
+     "shell.execute_reply": "2023-09-21T16:07:56.388248Z"
     }
    },
    "outputs": [
@@ -215,10 +215,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:08.046516Z",
-     "iopub.status.busy": "2023-08-22T13:45:08.046088Z",
-     "iopub.status.idle": "2023-08-22T13:45:08.917611Z",
-     "shell.execute_reply": "2023-08-22T13:45:08.917124Z"
+     "iopub.execute_input": "2023-09-21T16:07:56.391710Z",
+     "iopub.status.busy": "2023-09-21T16:07:56.391117Z",
+     "iopub.status.idle": "2023-09-21T16:07:57.507471Z",
+     "shell.execute_reply": "2023-09-21T16:07:57.506650Z"
     }
    },
    "outputs": [
@@ -227,11 +227,11 @@
      "output_type": "stream",
      "text": [
       "creating shapely Polygons of grid cells...\n",
-      "finished in 0.01s\n",
+      "finished in 0.02s\n",
       "\n",
       "writing postproc/shps/plsnt_lgr_parent_grid.shp... Done\n",
       "creating shapely Polygons of grid cells...\n",
-      "finished in 0.11s\n",
+      "finished in 0.13s\n",
       "\n",
       "writing postproc/shps/plsnt_lgr_inset_grid.shp... Done\n"
      ]
@@ -254,10 +254,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:08.920228Z",
-     "iopub.status.busy": "2023-08-22T13:45:08.919920Z",
-     "iopub.status.idle": "2023-08-22T13:45:08.923362Z",
-     "shell.execute_reply": "2023-08-22T13:45:08.922561Z"
+     "iopub.execute_input": "2023-09-21T16:07:57.510775Z",
+     "iopub.status.busy": "2023-09-21T16:07:57.510380Z",
+     "iopub.status.idle": "2023-09-21T16:07:57.513999Z",
+     "shell.execute_reply": "2023-09-21T16:07:57.513358Z"
     }
    },
    "outputs": [],
@@ -277,10 +277,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:08.925824Z",
-     "iopub.status.busy": "2023-08-22T13:45:08.925444Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.198092Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.197546Z"
+     "iopub.execute_input": "2023-09-21T16:07:57.517571Z",
+     "iopub.status.busy": "2023-09-21T16:07:57.517014Z",
+     "iopub.status.idle": "2023-09-21T16:08:07.670361Z",
+     "shell.execute_reply": "2023-09-21T16:08:07.669595Z"
     }
    },
    "outputs": [],
@@ -301,17 +301,17 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.200945Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.200662Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.209997Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.209509Z"
+     "iopub.execute_input": "2023-09-21T16:08:07.674034Z",
+     "iopub.status.busy": "2023-09-21T16:08:07.673526Z",
+     "iopub.status.idle": "2023-09-21T16:08:07.684263Z",
+     "shell.execute_reply": "2023-09-21T16:08:07.683747Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Pleasant Lake test case version 0.1.post11+g3a6ec42\n",
+       "Pleasant Lake test case version 0.1.post15+gdb03604\n",
        "Parent model: /home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/pleasant\n",
        "5 layer(s), 25 row(s), 25 column(s)\n",
        "delr: [200.00...200.00] meters\n",
@@ -351,10 +351,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.212211Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.211933Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.215481Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.215077Z"
+     "iopub.execute_input": "2023-09-21T16:08:07.687438Z",
+     "iopub.status.busy": "2023-09-21T16:08:07.686896Z",
+     "iopub.status.idle": "2023-09-21T16:08:07.692082Z",
+     "shell.execute_reply": "2023-09-21T16:08:07.691468Z"
     }
    },
    "outputs": [
@@ -385,10 +385,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.217652Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.217381Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.221549Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.221150Z"
+     "iopub.execute_input": "2023-09-21T16:08:07.694955Z",
+     "iopub.status.busy": "2023-09-21T16:08:07.694448Z",
+     "iopub.status.idle": "2023-09-21T16:08:07.700718Z",
+     "shell.execute_reply": "2023-09-21T16:08:07.700117Z"
     }
    },
    "outputs": [
@@ -445,17 +445,17 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.223714Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.223270Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.233013Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.232532Z"
+     "iopub.execute_input": "2023-09-21T16:08:07.703775Z",
+     "iopub.status.busy": "2023-09-21T16:08:07.703182Z",
+     "iopub.status.idle": "2023-09-21T16:08:07.715336Z",
+     "shell.execute_reply": "2023-09-21T16:08:07.714769Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.3.0.post11+g3a6ec42\n",
+       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.3.0.post15+gdb03604\n",
        " Parent model: ./plsnt_lgr_parent\n",
        " 5 layer(s), 70 row(s), 80 column(s)\n",
        " delr: [40.00...40.00] meters\n",
@@ -495,17 +495,17 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.236118Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.235162Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.472118Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.471547Z"
+     "iopub.execute_input": "2023-09-21T16:08:07.718247Z",
+     "iopub.status.busy": "2023-09-21T16:08:07.717824Z",
+     "iopub.status.idle": "2023-09-21T16:08:08.002829Z",
+     "shell.execute_reply": "2023-09-21T16:08:08.002183Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 13,
@@ -514,7 +514,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -564,10 +564,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.475586Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.474663Z",
-     "iopub.status.idle": "2023-08-22T13:45:17.880171Z",
-     "shell.execute_reply": "2023-08-22T13:45:17.879618Z"
+     "iopub.execute_input": "2023-09-21T16:08:08.006909Z",
+     "iopub.status.busy": "2023-09-21T16:08:08.006469Z",
+     "iopub.status.idle": "2023-09-21T16:08:08.535929Z",
+     "shell.execute_reply": "2023-09-21T16:08:08.535192Z"
     }
    },
    "outputs": [
@@ -696,10 +696,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:17.883004Z",
-     "iopub.status.busy": "2023-08-22T13:45:17.882616Z",
-     "iopub.status.idle": "2023-08-22T13:45:21.349848Z",
-     "shell.execute_reply": "2023-08-22T13:45:21.349411Z"
+     "iopub.execute_input": "2023-09-21T16:08:08.539550Z",
+     "iopub.status.busy": "2023-09-21T16:08:08.538992Z",
+     "iopub.status.idle": "2023-09-21T16:08:12.660716Z",
+     "shell.execute_reply": "2023-09-21T16:08:12.660127Z"
     }
    },
    "outputs": [
@@ -729,7 +729,7 @@
       "use of the software.\n",
       "\n",
       " \n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/08/22 13:45:17\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/21 16:08:08\n",
       " \n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -748,8 +748,8 @@
       "    Solving:  Stress period:    12    Time step:     1\n",
       "    Solving:  Stress period:    13    Time step:     1\n",
       " \n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/08/22 13:45:21\n",
-      " Elapsed run time:  3.457 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/21 16:08:12\n",
+      " Elapsed run time:  4.106 Seconds\n",
       " \n",
       " Normal termination of simulation.\n"
      ]
@@ -781,10 +781,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:21.352450Z",
-     "iopub.status.busy": "2023-08-22T13:45:21.351919Z",
-     "iopub.status.idle": "2023-08-22T13:45:21.366360Z",
-     "shell.execute_reply": "2023-08-22T13:45:21.365837Z"
+     "iopub.execute_input": "2023-09-21T16:08:12.664399Z",
+     "iopub.status.busy": "2023-09-21T16:08:12.663619Z",
+     "iopub.status.idle": "2023-09-21T16:08:12.682021Z",
+     "shell.execute_reply": "2023-09-21T16:08:12.681011Z"
     }
    },
    "outputs": [],
@@ -821,10 +821,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:21.368884Z",
-     "iopub.status.busy": "2023-08-22T13:45:21.368466Z",
-     "iopub.status.idle": "2023-08-22T13:45:21.466355Z",
-     "shell.execute_reply": "2023-08-22T13:45:21.465791Z"
+     "iopub.execute_input": "2023-09-21T16:08:12.686477Z",
+     "iopub.status.busy": "2023-09-21T16:08:12.686211Z",
+     "iopub.status.idle": "2023-09-21T16:08:12.828991Z",
+     "shell.execute_reply": "2023-09-21T16:08:12.828151Z"
     }
    },
    "outputs": [],
@@ -866,16 +866,16 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:21.469341Z",
-     "iopub.status.busy": "2023-08-22T13:45:21.468981Z",
-     "iopub.status.idle": "2023-08-22T13:45:22.771892Z",
-     "shell.execute_reply": "2023-08-22T13:45:22.771400Z"
+     "iopub.execute_input": "2023-09-21T16:08:12.832939Z",
+     "iopub.status.busy": "2023-09-21T16:08:12.832608Z",
+     "iopub.status.idle": "2023-09-21T16:08:14.540920Z",
+     "shell.execute_reply": "2023-09-21T16:08:14.540300Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -978,10 +978,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:22.774176Z",
-     "iopub.status.busy": "2023-08-22T13:45:22.773987Z",
-     "iopub.status.idle": "2023-08-22T13:45:57.065647Z",
-     "shell.execute_reply": "2023-08-22T13:45:57.065082Z"
+     "iopub.execute_input": "2023-09-21T16:08:14.544308Z",
+     "iopub.status.busy": "2023-09-21T16:08:14.543741Z",
+     "iopub.status.idle": "2023-09-21T16:08:55.872178Z",
+     "shell.execute_reply": "2023-09-21T16:08:55.871554Z"
     }
    },
    "outputs": [
@@ -1104,7 +1104,7 @@
       "skipped, not implemented yet\n",
       "\n",
       "obs_2 package...\n",
-      "skipped, not implemented yet\n",
+      "writing postproc/plsnt_lgr_parent/shps/obs2_stress_period_data.shp... Done\n",
       "creating postproc/plsnt_lgr_inset/pdfs...\n",
       "creating postproc/plsnt_lgr_inset/shps...\n",
       "creating postproc/plsnt_lgr_inset/rasters...\n",
@@ -1206,7 +1206,7 @@
       "skipped, not implemented yet\n",
       "\n",
       "obs_1 package...\n",
-      "skipped, not implemented yet\n"
+      "writing postproc/plsnt_lgr_inset/shps/obs1_stress_period_data.shp... Done\n"
      ]
     }
    ],
@@ -1227,10 +1227,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-08-22T13:45:57.068727Z",
-     "iopub.status.busy": "2023-08-22T13:45:57.068334Z",
-     "iopub.status.idle": "2023-08-22T13:45:58.499712Z",
-     "shell.execute_reply": "2023-08-22T13:45:58.499245Z"
+     "iopub.execute_input": "2023-09-21T16:08:55.876414Z",
+     "iopub.status.busy": "2023-09-21T16:08:55.875073Z",
+     "iopub.status.idle": "2023-09-21T16:08:57.604737Z",
+     "shell.execute_reply": "2023-09-21T16:08:57.604000Z"
     }
    },
    "outputs": [
@@ -1241,9 +1241,7 @@
       "summarizing plsnt_lgr_parent input...\n",
       "skipped, not implemented yet\n",
       "skipped, not implemented yet\n",
-      "skipped, not implemented yet\n",
       "summarizing plsnt_lgr_inset input...\n",
-      "skipped, not implemented yet\n",
       "skipped, not implemented yet\n"
      ]
     }
@@ -1270,7 +1268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/latest/_static/documentation_options.js b/latest/_static/documentation_options.js
index 433f6ec2..e770d34f 100644
--- a/latest/_static/documentation_options.js
+++ b/latest/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.3.0.post11+g3a6ec42',
+    VERSION: '0.3.0.post15+gdb03604',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/latest/api/index.html b/latest/api/index.html
index 65f2b4af..974b3c0b 100644
--- a/latest/api/index.html
+++ b/latest/api/index.html
@@ -4,7 +4,7 @@
   
 
   
-  Code Reference — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Code Reference — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/api/mfsetup.discretization.html b/latest/api/mfsetup.discretization.html
index a2989c7a..116a46cb 100644
--- a/latest/api/mfsetup.discretization.html
+++ b/latest/api/mfsetup.discretization.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.discretization module — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.discretization module — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -579,7 +579,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.fileio.html b/latest/api/mfsetup.fileio.html
index 9c7dd3ae..8249b1cc 100644
--- a/latest/api/mfsetup.fileio.html
+++ b/latest/api/mfsetup.fileio.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.fileio module — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.fileio module — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -368,7 +368,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.grid.html b/latest/api/mfsetup.grid.html
index 0928746f..37169fd1 100644
--- a/latest/api/mfsetup.grid.html
+++ b/latest/api/mfsetup.grid.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.grid module — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.grid module — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -177,7 +177,7 @@
 
wktstr, optional
Well-known text string describing model CRS.

 

 
crsobj, optional
A Python int, dict, str, or pyproj.crs.CRS instance
-passed to pyproj.crs.CRS.from_user_input()
+passed to pyproj.crs.CRS.from_user_input()
 Can be any of:

 

 
    
@@ -190,7 +190,7 @@
 
  • An EPSG integer code [i.e. 4326]
    • 
 
  • A tuple of (“auth_name”: “auth_code”) [i.e (‘epsg’, ‘4326’)]
    • 
 
  • An object with a to_wkt method.
    • 
-
  • A pyproj.crs.CRS class
    • 
+
  • A pyproj.crs.CRS class
    • 
 

 

 

@@ -615,7 +615,7 @@
 
 
gridgrid.StructuredGrid instance

 
crsobj
A Python int, dict, str, or pyproj.crs.CRS instance
-passed to pyproj.crs.CRS.from_user_input()
+passed to pyproj.crs.CRS.from_user_input()
 Can be any of:

 

 
    
@@ -628,7 +628,7 @@
 
  • An EPSG integer code [i.e. 4326]
    • 
 
  • A tuple of (“auth_name”: “auth_code”) [i.e (‘epsg’, ‘4326’)]
    • 
 
  • An object with a to_wkt method.
    • 
-
  • A pyproj.crs.CRS class
    • 
+
  • A pyproj.crs.CRS class
    • 
 

 

 

@@ -668,7 +668,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.interpolate.html b/latest/api/mfsetup.interpolate.html
index e705dd58..5e562dcc 100644
--- a/latest/api/mfsetup.interpolate.html
+++ b/latest/api/mfsetup.interpolate.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.interpolate module — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.interpolate module — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -340,7 +340,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.mf6model.html b/latest/api/mfsetup.mf6model.html
index 0c38fbd5..4b442bfe 100644
--- a/latest/api/mfsetup.mf6model.html
+++ b/latest/api/mfsetup.mf6model.html
@@ -4,7 +4,7 @@
   
 
   
-  MF6model class — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  MF6model class — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -334,7 +334,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.mfmodel.html b/latest/api/mfsetup.mfmodel.html
index e6fb6b6f..eda57c5c 100644
--- a/latest/api/mfsetup.mfmodel.html
+++ b/latest/api/mfsetup.mfmodel.html
@@ -4,7 +4,7 @@
   
 
   
-  MFsetupMixin class — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  MFsetupMixin class — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -423,7 +423,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.mfnwtmodel.html b/latest/api/mfsetup.mfnwtmodel.html
index 2d2a240a..e1d5bd71 100644
--- a/latest/api/mfsetup.mfnwtmodel.html
+++ b/latest/api/mfsetup.mfnwtmodel.html
@@ -4,7 +4,7 @@
   
 
   
-  MFnwtModel class — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  MFnwtModel class — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -235,7 +235,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.tdis.html b/latest/api/mfsetup.tdis.html
index ee1cae1e..cda55d62 100644
--- a/latest/api/mfsetup.tdis.html
+++ b/latest/api/mfsetup.tdis.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.tdis module — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.tdis module — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -345,14 +345,14 @@
 
Sets up time discretization for a model; outputs a DataFrame with
 stress period dates/times and properties. Stress periods can be established
 by explicitly specifying perlen as a list of period lengths in
-model units. Or, stress periods can be generated via pandas.date_range(),
+model units. Or, stress periods can be generated via pandas.date_range(),
 using three of the start_date_time, end_date_time, nper, and freq arguments.

 

 
Parameters

 

-
start_date_timestr or datetime-like
Left bound for generating stress period dates. See pandas.date_range().

+
start_date_timestr or datetime-like
Left bound for generating stress period dates. See pandas.date_range().

 

-
end_date_timestr or datetime-like, optional
Right bound for generating stress period dates. See pandas.date_range().

+
end_date_timestr or datetime-like, optional
Right bound for generating stress period dates. See pandas.date_range().

 

 
nperint, optional
Number of stress periods. Only used if perlen is None, or in combination with freq
 if an end_date_time isn’t specified.

@@ -473,7 +473,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/api/mfsetup.tmr.html b/latest/api/mfsetup.tmr.html
index 868837d4..30dd8f73 100644
--- a/latest/api/mfsetup.tmr.html
+++ b/latest/api/mfsetup.tmr.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.tmr module — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  mfsetup.tmr module — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -215,7 +215,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/concepts/index.html b/latest/concepts/index.html
index 9eb2b765..8202c364 100644
--- a/latest/concepts/index.html
+++ b/latest/concepts/index.html
@@ -4,7 +4,7 @@
   
 
   
-  Modflow-setup concepts and methods — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Modflow-setup concepts and methods — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/concepts/perimeter-bcs.html b/latest/concepts/perimeter-bcs.html
index 8ccdc7d6..ceab9e77 100644
--- a/latest/concepts/perimeter-bcs.html
+++ b/latest/concepts/perimeter-bcs.html
@@ -4,7 +4,7 @@
   
 
   
-  Specifying perimeter boundary conditions from another model — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Specifying perimeter boundary conditions from another model — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/config-file-defaults.html b/latest/config-file-defaults.html
index 16d3ed37..e9ab2cb0 100644
--- a/latest/config-file-defaults.html
+++ b/latest/config-file-defaults.html
@@ -4,7 +4,7 @@
   
 
   
-  Configuration defaults — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Configuration defaults — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -256,146 +256,147 @@ 
MODFLOW-6 configuration defaults142    littoral_zone_buffer_width: 20
 143  output_files:
 144    lookup_file: '{}_lak_lookup.csv'  # output file that maps lake ids to source polygon feature ids
-145    connections_lookup_file: '{}_lak_connections_lookup.csv'  # output file that maps lake/gw connections to zones
-146
-147mvr:
-148  options:
-149    print_flows: True
-150
-151chd:
-152  options:
-153    print_input: False
-154    print_flows: False
-155    save_flows: True
-156    boundnames: True
-157  source_data:
-158    shapefile:
-159      all_touched: True
-160    head:
-161      stat: 'min'
-162  mfsetup_options:
-163    external_files: True  # option to write stress_period_data to external files
-164    external_filename_fmt: "chd_{:03d}.dat"
-165
-166drn:
-167  options:
-168    print_input: False
-169    print_flows: False
-170    save_flows: True
-171    boundnames: True
-172  source_data:
-173    shapefile:
-174      all_touched: True
-175    elev:
-176      stat: 'min'
-177    cond:
-178      stat: 'mean'
-179  mfsetup_options:
-180    external_files: True  # option to write stress_period_data to external files
-181    external_filename_fmt: "drn_{:03d}.dat"
-182
-183ghb:
-184  options:
-185    print_input: False
-186    print_flows: False
-187    save_flows: True
-188    boundnames: True
-189  source_data:
-190    shapefile:
-191      all_touched: True
-192    bhead:
-193      stat: 'min'
-194    cond:
-195      stat: 'mean'
-196  mfsetup_options:
-197    external_files: True  # option to write stress_period_data to external files
-198    external_filename_fmt: "ghb_{:03d}.dat"
-199
-200riv:
-201  options:
-202    print_input: True
-203    print_flows: True
-204    save_flows: True
-205    boundnames: True
-206  source_data:
-207    shapefile:
-208      all_touched: True
-209    stage:
-210      stat: 'min'
-211    cond:
-212      stat: 'mean'
-213  output_files:
-214    rivdata_file: '{}_rivdata.csv' # table with auxillary information on river reaches (routing, source hydrography IDs, etc.)
-215  mfsetup_options:
-216    default_rbot_thickness: 1.
-217    external_files: True  # option to write stress_period_data to external files
-218    external_filename_fmt: "riv_{:03d}.dat"
-219
-220wel:
-221  options:
-222    print_input: True
-223    print_flows: True
-224    save_flows: True
-225    boundnames: True
-226  output_files:
-227    lookup_file: '{}_wel_lookup.csv' # output file that maps wel package data to site numbers
-228    dropped_wells_file: '{}_dropped_wells.csv' # output file that records wells that were dropped during model setup
-229  mfsetup_options:
-230    minimum_layer_thickness: 2.
-231    external_files: True  # option to write stress_period_data to external files
-232    external_filename_fmt: "wel_{:03d}.dat"
-233
+145    lak_polygons_shapefile: '{}_lak_polygons.shp'
+146    connections_lookup_file: '{}_lak_connections_lookup.csv'  # output file that maps lake/gw connections to zones
+147    connections_shapefile: '{}_lak_cells.shp'
+148mvr:
+149  options:
+150    print_flows: True
+151
+152chd:
+153  options:
+154    print_input: False
+155    print_flows: False
+156    save_flows: True
+157    boundnames: True
+158  source_data:
+159    shapefile:
+160      all_touched: True
+161    head:
+162      stat: 'min'
+163  mfsetup_options:
+164    external_files: True  # option to write stress_period_data to external files
+165    external_filename_fmt: "chd_{:03d}.dat"
+166
+167drn:
+168  options:
+169    print_input: False
+170    print_flows: False
+171    save_flows: True
+172    boundnames: True
+173  source_data:
+174    shapefile:
+175      all_touched: True
+176    elev:
+177      stat: 'min'
+178    cond:
+179      stat: 'mean'
+180  mfsetup_options:
+181    external_files: True  # option to write stress_period_data to external files
+182    external_filename_fmt: "drn_{:03d}.dat"
+183
+184ghb:
+185  options:
+186    print_input: False
+187    print_flows: False
+188    save_flows: True
+189    boundnames: True
+190  source_data:
+191    shapefile:
+192      all_touched: True
+193    bhead:
+194      stat: 'min'
+195    cond:
+196      stat: 'mean'
+197  mfsetup_options:
+198    external_files: True  # option to write stress_period_data to external files
+199    external_filename_fmt: "ghb_{:03d}.dat"
+200
+201riv:
+202  options:
+203    print_input: True
+204    print_flows: True
+205    save_flows: True
+206    boundnames: True
+207  source_data:
+208    shapefile:
+209      all_touched: True
+210    stage:
+211      stat: 'min'
+212    cond:
+213      stat: 'mean'
+214  output_files:
+215    rivdata_file: '{}_rivdata.csv' # table with auxillary information on river reaches (routing, source hydrography IDs, etc.)
+216  mfsetup_options:
+217    default_rbot_thickness: 1.
+218    external_files: True  # option to write stress_period_data to external files
+219    external_filename_fmt: "riv_{:03d}.dat"
+220
+221wel:
+222  options:
+223    print_input: True
+224    print_flows: True
+225    save_flows: True
+226    boundnames: True
+227  output_files:
+228    lookup_file: '{}_wel_lookup.csv' # output file that maps wel package data to site numbers
+229    dropped_wells_file: '{}_dropped_wells.csv' # output file that records wells that were dropped during model setup
+230  mfsetup_options:
+231    minimum_layer_thickness: 2.
+232    external_files: True  # option to write stress_period_data to external files
+233    external_filename_fmt: "wel_{:03d}.dat"
 234
 235
-236oc:
-237  head_fileout_fmt: '{}.hds'
-238  budget_fileout_fmt: '{}.cbc'
-239  # example of using MODFLOW 6-style text input
-240  period_options: {0: ['save head last',
-241                       'save budget last']
-242  }
-243
-244obs:
-245  options:
-246    digits: 10
-247    print_input: True
-248  source_data:
-249    column_mappings:
-250      hydlbl: ['obsprefix', 'obsnme', 'common_name']
-251    x_location_col: 'x' # x coordinates in wtm
-252    y_location_col: 'y' # y coordinates in wtm
-253  mfsetup_options:
-254    allow_obs_in_bc_cells: False
-255    obsname_character_limit: 40  # modflow 6 limit
-256    filename_fmt: '{}.head.obs'  # only head obs supported at this point
-257
-258ims:
-259  options:
-260    print_option: 'all'
-261    csv_outer_output: 'solver_outer_out.csv'
-262  nonlinear:
-263    outer_dvclose: 1.e-1
-264    outer_maximum: 200
-265    under_relaxation:  'dbd'
-266    under_relaxation_theta: 0.7
-267    under_relaxation_kappa: 0.1
-268    under_relaxation_gamma: 0.0
-269    under_relaxation_momentum: 0.0
-270    backtracking_number: 0
-271  linear:
-272    inner_maximum: 100
-273    inner_dvclose: 1.e-2
-274    rcloserecord: [0.0001, 'relative_rclose']
-275    linear_acceleration: 'bicgstab'
-276    relaxation_factor: 0.0  # either ILU(0) or ILUT preconditioning
-277    preconditioner_levels: 7
-278    preconditioner_drop_tolerance: 0.001
-279    number_orthogonalizations: 0
-280    scaling_method: None
-281    reordering_method: None
-282
-283mfsetup_options:
-284  keep_original_arrays: False
+236
+237oc:
+238  head_fileout_fmt: '{}.hds'
+239  budget_fileout_fmt: '{}.cbc'
+240  # example of using MODFLOW 6-style text input
+241  period_options: {0: ['save head last',
+242                       'save budget last']
+243  }
+244
+245obs:
+246  options:
+247    digits: 10
+248    print_input: True
+249  source_data:
+250    column_mappings:
+251      hydlbl: ['obsprefix', 'obsnme', 'common_name']
+252    x_location_col: 'x' # x coordinates in wtm
+253    y_location_col: 'y' # y coordinates in wtm
+254  mfsetup_options:
+255    allow_obs_in_bc_cells: False
+256    obsname_character_limit: 40  # modflow 6 limit
+257    filename_fmt: '{}.head.obs'  # only head obs supported at this point
+258
+259ims:
+260  options:
+261    print_option: 'all'
+262    csv_outer_output: 'solver_outer_out.csv'
+263  nonlinear:
+264    outer_dvclose: 1.e-1
+265    outer_maximum: 200
+266    under_relaxation:  'dbd'
+267    under_relaxation_theta: 0.7
+268    under_relaxation_kappa: 0.1
+269    under_relaxation_gamma: 0.0
+270    under_relaxation_momentum: 0.0
+271    backtracking_number: 0
+272  linear:
+273    inner_maximum: 100
+274    inner_dvclose: 1.e-2
+275    rcloserecord: [0.0001, 'relative_rclose']
+276    linear_acceleration: 'bicgstab'
+277    relaxation_factor: 0.0  # either ILU(0) or ILUT preconditioning
+278    preconditioner_levels: 7
+279    preconditioner_drop_tolerance: 0.001
+280    number_orthogonalizations: 0
+281    scaling_method: None
+282    reordering_method: None
+283
+284mfsetup_options:
+285  keep_original_arrays: False
 

 

 
@@ -495,137 +496,140 @@ 
MODFLOW-NWT configuration defaults 92  source_data:
  93    littoral_zone_buffer_width: 20
  94  output_files:
- 95    lookup_file: 'lak_lookup.csv'
- 96
- 97sfr:
- 98
- 99chd:
-100  options:
-101  source_data:
-102    shapefile:
-103      all_touched: True
-104    shead:
-105      stat: 'min'
-106    ehead:
-107      stat: 'min'
-108  mfsetup_options:
-109    external_files: True  # option to write stress_period_data to external files
-110    external_filename_fmt: "chd_{:03d}.dat"
-111
-112drn:
-113  options:
-114  source_data:
-115    shapefile:
-116      all_touched: True
-117    elev:
-118      stat: 'min'
-119    cond:
-120      stat: 'mean'
-121  mfsetup_options:
-122    external_files: True  # option to write stress_period_data to external files
-123    external_filename_fmt: "drn_{:03d}.dat"
-124
-125ghb:
-126  options:
-127  source_data:
-128    shapefile:
-129      all_touched: True
-130    bhead:
-131      stat: 'min'
-132    cond:
-133      stat: 'mean'
-134  mfsetup_options:
-135    external_files: False  # option to write stress_period_data to external files
-136    external_filename_fmt: "ghb_{:03d}.dat"
-137
-138riv:
-139  source_data:
-140    shapefile:
-141      all_touched: True
-142    stage:
-143      stat: 'min'
-144    cond:
-145      stat: 'mean'
-146  output_files:
-147    rivdata_file: '{}_rivdata.csv' # table with auxillary information on river reaches (routing, source hydrography IDs, etc.)
-148  mfsetup_options:
-149    default_rbot_thickness: 1.
-150    external_files: True  # option to write stress_period_data to external files
-151    external_filename_fmt: "riv_{:03d}.dat"
-152
-153wel:
-154  options: ['SPECIFY', '0.01']
-155  output_files:
-156    lookup_file: '{}_wel_lookup.csv' # output file that maps wel package data to site numbers
-157    dropped_wells_file: '{}_dropped_wells.csv' # output file that records wells that were dropped during model setup
-158  mfsetup_options:
-159    minimum_layer_thickness: 2.
-160    external_files: True  # option to write stress_period_data to external files
-161    external_filename_fmt: "wel_{:03d}.dat"
-162
-163mnw:
-164  defaults: {losstype: 'skin',
-165             pumploc: -1,
-166             rw: 0.1, # well radius, m
-167             rskin: 0.1524, # radius to outer limit of skin
-168             kskin: 50 # m/day
-169  }
-170
-171oc:
-172  head_fileout_fmt: '{}.hds'
-173  budget_fileout_fmt: '{}.cbc'
-174  period_options: {0: ['save head last',
-175                       'save budget last']
-176  }
-177
-178hyd:
-179  hydnoh: -999
-180  source_data:
-181    column_mappings:
-182      hydlbl: ['obsprefix', 'obsnme', 'common_name']
-183    x_location_col: 'x' # x coordinates in wtm
-184    y_location_col: 'y' # y coordinates in wtm
-185  mfsetup_options:
-186    allow_obs_in_bc_cells: False
-187    obsname_character_limit: 14  # hydmod limit
-188
-189gag:
-190  starting_unit_number: 250
-191  lak_outtype: 1 # see gage package documentation for Data Set 2a outtype
-192  sfr_outtype: 0 # see gage package documentation for Data Set 2b outtype
-193  column_mappings:
-194    x_location_col: ['x_utm', 'X']
-195    y_location_col: ['y_utm', 'Y']
-196
-197nwt:
-198  headtol: 1.e-6
-199  fluxtol: 6000
-200  maxiterout: 1000
-201  thickfact: 0.00001
-202  linmeth: 2
-203  iprnwt: 1
-204  ibotav: 1
-205  options: 'SPECIFIED'
-206  Continue: True
-207  dbdtheta: 0.9
-208  dbdkappa: 0.00001
-209  dbdgamma: 0
-210  momfact: 0
-211  backflag: 0
-212  iacl: 2
-213  norder: 0
-214  level: 7
-215  north: 0
-216  iredsys: 0
-217  rrctols: 0
-218  idroptol: 1
-219  epsrn: 0.01
-220  hclosexmd: 1.e-5
-221  mxiterxmd: 85
-222  use_existing_file:
-223
-224mfsetup_options:
-225  keep_original_arrays: False
+ 95    lookup_file: '{}_lak_lookup.csv'  # output file that maps lake ids to source polygon feature ids
+ 96    lak_polygons_shapefile: '{}_lak_polygons.shp'
+ 97    connections_lookup_file: '{}_lak_connections_lookup.csv'  # output file that maps lake/gw connections to zones
+ 98    connections_shapefile: '{}_lak_cells.shp'
+ 99
+100sfr:
+101
+102chd:
+103  options:
+104  source_data:
+105    shapefile:
+106      all_touched: True
+107    shead:
+108      stat: 'min'
+109    ehead:
+110      stat: 'min'
+111  mfsetup_options:
+112    external_files: True  # option to write stress_period_data to external files
+113    external_filename_fmt: "chd_{:03d}.dat"
+114
+115drn:
+116  options:
+117  source_data:
+118    shapefile:
+119      all_touched: True
+120    elev:
+121      stat: 'min'
+122    cond:
+123      stat: 'mean'
+124  mfsetup_options:
+125    external_files: True  # option to write stress_period_data to external files
+126    external_filename_fmt: "drn_{:03d}.dat"
+127
+128ghb:
+129  options:
+130  source_data:
+131    shapefile:
+132      all_touched: True
+133    bhead:
+134      stat: 'min'
+135    cond:
+136      stat: 'mean'
+137  mfsetup_options:
+138    external_files: False  # option to write stress_period_data to external files
+139    external_filename_fmt: "ghb_{:03d}.dat"
+140
+141riv:
+142  source_data:
+143    shapefile:
+144      all_touched: True
+145    stage:
+146      stat: 'min'
+147    cond:
+148      stat: 'mean'
+149  output_files:
+150    rivdata_file: '{}_rivdata.csv' # table with auxillary information on river reaches (routing, source hydrography IDs, etc.)
+151  mfsetup_options:
+152    default_rbot_thickness: 1.
+153    external_files: True  # option to write stress_period_data to external files
+154    external_filename_fmt: "riv_{:03d}.dat"
+155
+156wel:
+157  options: ['SPECIFY', '0.01']
+158  output_files:
+159    lookup_file: '{}_wel_lookup.csv' # output file that maps wel package data to site numbers
+160    dropped_wells_file: '{}_dropped_wells.csv' # output file that records wells that were dropped during model setup
+161  mfsetup_options:
+162    minimum_layer_thickness: 2.
+163    external_files: True  # option to write stress_period_data to external files
+164    external_filename_fmt: "wel_{:03d}.dat"
+165
+166mnw:
+167  defaults: {losstype: 'skin',
+168             pumploc: -1,
+169             rw: 0.1, # well radius, m
+170             rskin: 0.1524, # radius to outer limit of skin
+171             kskin: 50 # m/day
+172  }
+173
+174oc:
+175  head_fileout_fmt: '{}.hds'
+176  budget_fileout_fmt: '{}.cbc'
+177  period_options: {0: ['save head last',
+178                       'save budget last']
+179  }
+180
+181hyd:
+182  hydnoh: -999
+183  source_data:
+184    column_mappings:
+185      hydlbl: ['obsprefix', 'obsnme', 'common_name']
+186    x_location_col: 'x' # x coordinates in wtm
+187    y_location_col: 'y' # y coordinates in wtm
+188  mfsetup_options:
+189    allow_obs_in_bc_cells: False
+190    obsname_character_limit: 14  # hydmod limit
+191
+192gag:
+193  starting_unit_number: 250
+194  lak_outtype: 1 # see gage package documentation for Data Set 2a outtype
+195  sfr_outtype: 0 # see gage package documentation for Data Set 2b outtype
+196  column_mappings:
+197    x_location_col: ['x_utm', 'X']
+198    y_location_col: ['y_utm', 'Y']
+199
+200nwt:
+201  headtol: 1.e-6
+202  fluxtol: 6000
+203  maxiterout: 1000
+204  thickfact: 0.00001
+205  linmeth: 2
+206  iprnwt: 1
+207  ibotav: 1
+208  options: 'SPECIFIED'
+209  Continue: True
+210  dbdtheta: 0.9
+211  dbdkappa: 0.00001
+212  dbdgamma: 0
+213  momfact: 0
+214  backflag: 0
+215  iacl: 2
+216  norder: 0
+217  level: 7
+218  north: 0
+219  iredsys: 0
+220  rrctols: 0
+221  idroptol: 1
+222  epsrn: 0.01
+223  hclosexmd: 1.e-5
+224  mxiterxmd: 85
+225  use_existing_file:
+226
+227mfsetup_options:
+228  keep_original_arrays: False
 

 

 
@@ -643,7 +647,7 @@ 
MODFLOW-NWT configuration defaults
     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/config-file-gallery.html b/latest/config-file-gallery.html
index 36234965..60870193 100644
--- a/latest/config-file-gallery.html
+++ b/latest/config-file-gallery.html
@@ -4,7 +4,7 @@
   
 
   
-  Configuration File Gallery — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Configuration File Gallery — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -1501,34 +1501,35 @@ 
Plainfield Lakes MODFLOW-NWT test case167      filename: 'plainfieldlakes/source_data/all_lakes.shp'
 168      id_column: 'HYDROID'
 169      include_ids: [600054357, 600054355, 600054434, 600054319] # list of WDNR HYDROIDs
-170    bathymetry_raster:
-171      filename: 'plainfieldlakes/source_data/pfl_bathymetry.tif'
-172      length_units: 'meters'
-173    stage_area_volume_file:
-174      filename: 'plainfieldlakes/source_data/all_lakes_stage_area_volume.csv' #'../../bcs/lakes/area_stage_vol_Long.csv'
-175      length_units: 'meters'
-176      id_column: 'hydroid'
-177      column_mappings:
-178        volume_m3: 'volume'
-179  output_files:
-180    lookup_file: 'lak_lookup.csv'
-181
-182nwt:
-183  headtol: 1.e-6 # read remaining parameters from default cfg
-184
-185hyd:
-186  source_data:
-187    filenames: ['plainfieldlakes/source_data/nwis_heads_info_file.csv',
-188                'plainfieldlakes/source_data/lake_sites.csv',
-189                'plainfieldlakes/source_data/wdnr_gw_sites.csv',
-190                'plainfieldlakes/source_data/uwsp_heads.csv'
-191                ]
-192    x_location_col: 'x_utm' # x coordinates in wtm
-193    y_location_col: 'y_utm' # y coordinates in wtm
-194
-195chd:
-196  perimeter_boundary:
-197    parent_head_file: 'plainfieldlakes/pfl.hds'
+170      name_column: 'Name'
+171    bathymetry_raster:
+172      filename: 'plainfieldlakes/source_data/pfl_bathymetry.tif'
+173      length_units: 'meters'
+174    stage_area_volume_file:
+175      filename: 'plainfieldlakes/source_data/all_lakes_stage_area_volume.csv' #'../../bcs/lakes/area_stage_vol_Long.csv'
+176      length_units: 'meters'
+177      id_column: 'hydroid'
+178      column_mappings:
+179        volume_m3: 'volume'
+180  output_files:
+181    lookup_file: 'lak_lookup.csv'
+182
+183nwt:
+184  headtol: 1.e-6 # read remaining parameters from default cfg
+185
+186hyd:
+187  source_data:
+188    filenames: ['plainfieldlakes/source_data/nwis_heads_info_file.csv',
+189                'plainfieldlakes/source_data/lake_sites.csv',
+190                'plainfieldlakes/source_data/wdnr_gw_sites.csv',
+191                'plainfieldlakes/source_data/uwsp_heads.csv'
+192                ]
+193    x_location_col: 'x_utm' # x coordinates in wtm
+194    y_location_col: 'y_utm' # y coordinates in wtm
+195
+196chd:
+197  perimeter_boundary:
+198    parent_head_file: 'plainfieldlakes/pfl.hds'
 

 

 
@@ -1546,7 +1547,7 @@ 
Plainfield Lakes MODFLOW-NWT test case
     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/config-file.html b/latest/config-file.html
index 56ac240b..6d015b7f 100644
--- a/latest/config-file.html
+++ b/latest/config-file.html
@@ -4,7 +4,7 @@
   
 
   
-  The configuration file — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  The configuration file — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/contributing.html b/latest/contributing.html
index b7b9eb16..dfe2edf4 100644
--- a/latest/contributing.html
+++ b/latest/contributing.html
@@ -4,7 +4,7 @@
   
 
   
-  Contributing to modflow-setup — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Contributing to modflow-setup — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/examples.html b/latest/examples.html
index 8945002d..a2f23277 100644
--- a/latest/examples.html
+++ b/latest/examples.html
@@ -4,7 +4,7 @@
   
 
   
-  Examples — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Examples — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/genindex.html b/latest/genindex.html
index d9bed468..5b3ed617 100644
--- a/latest/genindex.html
+++ b/latest/genindex.html
@@ -3,7 +3,7 @@
 
   
   
-  Index — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Index — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -38,7 +38,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -716,7 +716,7 @@ 
Y

 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/index.html b/latest/index.html
index 3ccb2696..8c9781c3 100644
--- a/latest/index.html
+++ b/latest/index.html
@@ -4,7 +4,7 @@
   
 
   
-  modflow-setup 0.3.0.post11+g3a6ec42 — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  modflow-setup 0.3.0.post15+gdb03604 — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -40,7 +40,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -92,7 +92,7 @@
           

   

 
diff --git a/latest/py-modindex.html b/latest/py-modindex.html
index a840d2fd..d0cedb86 100644
--- a/latest/py-modindex.html
+++ b/latest/py-modindex.html
@@ -3,7 +3,7 @@
 
   
   
-  Python Module Index — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Python Module Index — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -175,7 +175,7 @@ 
Python Module Index

 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/references.html b/latest/references.html
index b0b426c2..3959bd0e 100644
--- a/latest/references.html
+++ b/latest/references.html
@@ -4,7 +4,7 @@
   
 
   
-  References — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  References — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -40,7 +40,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/release-history.html b/latest/release-history.html
index afcf5d34..2989b26a 100644
--- a/latest/release-history.html
+++ b/latest/release-history.html
@@ -4,7 +4,7 @@
   
 
   
-  Release History — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Release History — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/search.html b/latest/search.html
index 105181f8..7fe76f77 100644
--- a/latest/search.html
+++ b/latest/search.html
@@ -3,7 +3,7 @@
 
   
   
-  Search — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Search — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 

   
@@ -123,7 +123,7 @@
 
   

     
© Copyright 2019-2023, Modflow-setup developers |.
-      Last updated on Aug 22, 2023.
+      Last updated on Sep 21, 2023.
       

   

 
diff --git a/latest/searchindex.js b/latest/searchindex.js
index aa809499..9a038b52 100644
--- a/latest/searchindex.js
+++ b/latest/searchindex.js
@@ -1 +1 @@
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"Supporting modules": [[1, "supporting-modules"]], "mfsetup.discretization module": [[2, "module-mfsetup.discretization"]], "mfsetup.fileio module": [[3, "module-mfsetup.fileio"]], "mfsetup.grid module": [[4, "module-mfsetup.grid"]], "mfsetup.interpolate module": [[5, "module-mfsetup.interpolate"]], "MF6model class": [[6, "module-mfsetup.mf6model"]], "MFsetupMixin class": [[7, "module-mfsetup.mfmodel"]], "MFnwtModel class": [[8, "module-mfsetup.mfnwtmodel"]], "mfsetup.tdis module": [[9, "module-mfsetup.tdis"]], "mfsetup.tmr module": [[10, "module-mfsetup.tmr"]], "Modflow-setup concepts and methods": [[11, "modflow-setup-concepts-and-methods"]], "Specifying perimeter boundary conditions from another model": [[12, "specifying-perimeter-boundary-conditions-from-another-model"]], "Features and Limitations": [[12, "features-and-limitations"]], "Input": [[12, "input"]], "Time discretization": [[12, "time-discretization"]], "Specifying the locations of perimeter boundary cells": [[12, "specifying-the-locations-of-perimeter-boundary-cells"]], "The configuration file": [[13, "the-configuration-file"]], "The YAML format": [[13, "the-yaml-format"]], "Configuration file structure": [[13, "configuration-file-structure"]], "Package blocks": [[13, "package-blocks"]], "Sub-blocks": [[13, "sub-blocks"]], "Directly specifying MODFLOW input": [[13, "directly-specifying-modflow-input"]], "Source_data sub-blocks": [[13, "source-data-sub-blocks"]], "Some additional notes on YAML": [[13, "some-additional-notes-on-yaml"]], "Configuration defaults": [[14, "configuration-defaults"]], "MODFLOW-6 configuration defaults": [[14, "modflow-6-configuration-defaults"]], "MODFLOW-NWT configuration defaults": [[14, "modflow-nwt-configuration-defaults"]], "Configuration File Gallery": [[15, "configuration-file-gallery"]], "Shellmound test case": [[15, "shellmound-test-case"]], "Shellmound TMR inset test case": [[15, "shellmound-tmr-inset-test-case"]], "Pleasant Lake test case": [[15, "pleasant-lake-test-case"]], "LGR parent model configuration": [[15, "lgr-parent-model-configuration"]], "pleasant_lgr_inset.yml": [[15, "pleasant-lgr-inset-yml"]], "Pleasant Lake MODFLOW-NWT test case": [[15, "pleasant-lake-modflow-nwt-test-case"]], "Plainfield Lakes MODFLOW-NWT test case": [[15, "plainfield-lakes-modflow-nwt-test-case"]], "Contributing to modflow-setup": [[16, "contributing-to-modflow-setup"]], "Getting started": [[16, "getting-started"]], "Bug reports and enhancement requests": [[16, "bug-reports-and-enhancement-requests"]], "Code contributions": [[16, "code-contributions"]], "Seven Steps for Contributing": [[16, "seven-steps-for-contributing"]], "1) Forking the modflow-setup repository using Git": [[16, "forking-the-modflow-setup-repository-using-git"]], "Getting started with Git": [[16, "getting-started-with-git"]], "Forking": [[16, "forking"]], "Creating a branch": [[16, "creating-a-branch"]], "2 & 3) Creating a development environment with the required dependencies": [[16, "creating-a-development-environment-with-the-required-dependencies"]], "4) Installing the modflow-setup source code": [[16, "installing-the-modflow-setup-source-code"]], "5) Making changes and writing tests": [[16, "making-changes-and-writing-tests"]], "Writing tests": [[16, "writing-tests"]], "Running the test suite": [[16, "running-the-test-suite"]], "6) Updating the Documentation": [[16, "updating-the-documentation"]], "7) Submitting a Pull Request": [[16, "submitting-a-pull-request"]], "Style Guide & Linting": [[16, "style-guide-linting"]], "Examples": [[17, "examples"]], "Example problems": [[17, null]], "modflow-setup 0.3.0.post11+g3a6ec42": [[18, "modflow-setup-version"]], "Getting Started": [[18, null]], "User Guide": [[18, null]], "Reference": [[18, null]], "Bibliography": [[18, null]], "Specifying boundary conditions with the \u2018basic\u2019 MODFLOW stress packages": [[19, "specifying-boundary-conditions-with-the-basic-modflow-stress-packages"]], "List-based basic stress packages": [[19, "list-based-basic-stress-packages"]], "Constant Head (CHD) Package": [[19, "constant-head-chd-package"]], "Drain DRN Package": [[19, "drain-drn-package"]], "General Head Boundary (GHB) Package": [[19, "general-head-boundary-ghb-package"]], "River (RIV) package": [[19, "river-riv-package"]], "Well (WEL) Package": [[19, "well-wel-package"]], "Grid-based basic stress packages": [[19, "grid-based-basic-stress-packages"]], "Recharge (RCH) Package": [[19, "recharge-rch-package"]], "Direct input": [[19, "direct-input"]], "Grid-independent input": [[19, "grid-independent-input"]], "The Discretization Package": [[20, "the-discretization-package"]], "Specifying Initial Conditions": [[21, "specifying-initial-conditions"]], "Input instructions by package": [[22, "input-instructions-by-package"]], "The Lake (LAK) Package": [[23, "the-lake-lak-package"]], "Head observations": [[24, "head-observations"]], "MODFLOW Output Control": [[25, "modflow-output-control"]], "Stress period input format": [[25, "stress-period-input-format"]], "Output filenames and other arguments": [[25, "output-filenames-and-other-arguments"]], "Alternative stress period input formats": [[25, "alternative-stress-period-input-formats"]], "Specifying Aquifer Properties": [[26, "specifying-aquifer-properties"]], "The Streamflow Routing (SFR) Package": [[27, "the-streamflow-routing-sfr-package"]], "Installation": [[28, "installation"]], "Installing python dependencies with Conda": [[28, "installing-python-dependencies-with-conda"]], "Download and install a python distribution and Conda-like package installer": [[28, "download-and-install-a-python-distribution-and-conda-like-package-installer"]], "Download an environment file": [[28, "download-an-environment-file"]], "Creating a Conda environment using Mamba": [[28, "creating-a-conda-environment-using-mamba"]], "Keeping the Conda environment up to date": [[28, "keeping-the-conda-environment-up-to-date"]], "Installing Modflow-setup": [[28, "installing-modflow-setup"]], "Installing and updating Modflow-setup from PyPI": [[28, "installing-and-updating-modflow-setup-from-pypi"]], "Installing the latest develop version of Modflow-setup": [[28, "installing-the-latest-develop-version-of-modflow-setup"]], "Installing the Modflow-setup source code in-place": [[28, "installing-the-modflow-setup-source-code-in-place"]], "Installing the IPython kernel to use Modflow-setup in Jupyter Notebooks": [[28, "installing-the-ipython-kernel-to-use-modflow-setup-in-jupyter-notebooks"]], "Best practices": [[28, "best-practices"]], "Considerations for USGS Users": [[28, "id4"]], "Installing the DOI SSL certificate for use with pip": [[28, "id5"]], "Installing the DOI SSL certificate for use with conda": [[28, "installing-the-doi-ssl-certificate-for-use-with-conda"]], "Troubleshooting issues with the USGS network": [[28, "troubleshooting-issues-with-the-usgs-network"]], "SSL-related error messages when using conda": [[28, "ssl-related-error-messages-when-using-conda"]], "SSL-related error messages when using pip": [[28, "ssl-related-error-messages-when-using-pip"]], "If you are on the USGS network, using Windows, and you get this error message:": [[28, "if-you-are-on-the-usgs-network-using-windows-and-you-get-this-error-message"]], "Pleasant Lake Example": [[29, "Pleasant-Lake-Example"]], "Model details": [[29, "Model-details"]], "Just make a model grid": [[29, "Just-make-a-model-grid"]], "Working directory gottcha": [[29, "Working-directory-gottcha"]], "Write shapefiles of the inset and parent modelgrids": [[29, "Write-shapefiles-of-the-inset-and-parent-modelgrids"]], "Change the working directory back to the notebook location": [[29, "Change-the-working-directory-back-to-the-notebook-location"]], "Build the whole model": [[29, "Build-the-whole-model"]], "Plot the inset and parent model grids with Lake Package connections by layer": [[29, "Plot-the-inset-and-parent-model-grids-with-Lake-Package-connections-by-layer"]], "write the MODFLOW input files": [[29, "write-the-MODFLOW-input-files"]], "Run the model": [[29, "Run-the-model"]], "Plot the head results": [[29, "Plot-the-head-results"]], "First combine the parent and inset model head results": [[29, "First-combine-the-parent-and-inset-model-head-results"]], "Make the plot": [[29, "Make-the-plot"]], "Use Modflow-export to export the modflow input to PDFs, rasters and shapefiles": [[29, "Use-Modflow-export-to-export-the-modflow-input-to-PDFs,-rasters-and-shapefiles"]], "Modflow-export can also create a summary table of the model inputs": [[29, "Modflow-export-can-also-create-a-summary-table-of-the-model-inputs"]], "Philosophy": [[30, "philosophy"]], "Motivation": [[30, "motivation"]], "What modflow-setup does": [[30, "what-modflow-setup-does"]], "What modflow-setup doesn\u2019t do": [[30, "what-modflow-setup-doesnt-do"]], "References": [[31, "references"]], "Release History": [[32, "release-history"]], "Version 0.3.1 (2023-08-17)": [[32, "version-0-3-1-2023-08-17"]], "Version 0.3.0 (2023-07-25)": [[32, "version-0-3-0-2023-07-25"]], "Version 0.2.0 (2023-02-06)": [[32, "version-0-2-0-2023-02-06"]], "Version 0.1.0 Initial release (2022-09-30)": [[32, "version-0-1-0-initial-release-2022-09-30"]], "Basic program structure and usage": [[33, "basic-program-structure-and-usage"]], "Setting up a full model": [[33, "setting-up-a-full-model"]], "Loading a model": [[33, "loading-a-model"]], "Setting up individual packages": [[33, "setting-up-individual-packages"]], "Troubleshooting": [[34, "troubleshooting"]], "My script doesn\u2019t run": [[34, "my-script-doesn-t-run"]], "Updating your python environment": [[34, "updating-your-python-environment"]], "Updating Modflow-setup": [[34, "updating-modflow-setup"]]}, "indexentries": {"modflowgwfdis (class in mfsetup.discretization)": [[2, "mfsetup.discretization.ModflowGwfdis"]], "adjust_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.adjust_layers"]], "cellids_to_kij() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.cellids_to_kij"]], "create_vertical_pass_through_cells() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.create_vertical_pass_through_cells"]], "deactivate_idomain_above() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.deactivate_idomain_above"]], "fill_cells_vertically() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.fill_cells_vertically"]], "fill_empty_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.fill_empty_layers"]], "find_remove_isolated_cells() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.find_remove_isolated_cells"]], "fix_model_layer_conflicts() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.fix_model_layer_conflicts"]], "get_highest_active_layer() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.get_highest_active_layer"]], "get_layer() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.get_layer"]], "get_layer_thicknesses() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.get_layer_thicknesses"]], "make_ibound() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_ibound"]], "make_idomain() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_idomain"]], "make_irch() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_irch"]], "make_lgr_idomain() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_lgr_idomain"]], "mfsetup.discretization": [[2, "module-mfsetup.discretization"]], "module": [[2, "module-mfsetup.discretization"], [3, "module-mfsetup.fileio"], [4, "module-mfsetup.grid"], [5, "module-mfsetup.interpolate"], [6, "module-mfsetup.mf6model"], [7, "module-mfsetup.mfmodel"], [8, "module-mfsetup.mfnwtmodel"], [9, "module-mfsetup.tdis"], [10, "module-mfsetup.tmr"]], "populate_values() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.populate_values"]], "thickness (mfsetup.discretization.modflowgwfdis property)": [[2, "mfsetup.discretization.ModflowGwfdis.thickness"]], "verify_minimum_layer_thickness() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.verify_minimum_layer_thickness"]], "voxels_to_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.voxels_to_layers"]], "weighted_average_between_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.weighted_average_between_layers"]], "add_version_to_fileheader() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.add_version_to_fileheader"]], "append_csv() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.append_csv"]], "check_source_files() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.check_source_files"]], "dump() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.dump"]], "dump_json() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.dump_json"]], "dump_yml() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.dump_yml"]], "exe_exists() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.exe_exists"]], "flopy_mf2005_load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.flopy_mf2005_load"]], "flopy_mf6model_load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.flopy_mf6model_load"]], "flopy_mfsimulation_load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.flopy_mfsimulation_load"]], "load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load"]], "load_array() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_array"]], "load_cfg() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_cfg"]], "load_json() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_json"]], "load_modelgrid() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_modelgrid"]], "load_yml() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_yml"]], "mfsetup.fileio": [[3, "module-mfsetup.fileio"]], "read_ggofile() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.read_ggofile"]], "read_lak_ggo() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.read_lak_ggo"]], "read_mf6_block() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.read_mf6_block"]], "remove_file_header() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.remove_file_header"]], "save_array() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.save_array"]], "set_cfg_paths_to_absolute() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.set_cfg_paths_to_absolute"]], "setup_external_filepaths() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.setup_external_filepaths"]], "which() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.which"]], "mfsetupgrid (class in mfsetup.grid)": [[4, "mfsetup.grid.MFsetupGrid"]], "bbox (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.bbox"]], "botm (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.botm"]], "bounds (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.bounds"]], "crs (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.crs"]], "dataframe (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.dataframe"]], "get_cellface_midpoint() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_cellface_midpoint"]], "get_crs() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_crs"]], "get_crs_length_units() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_crs_length_units"]], "get_dataframe() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.get_dataframe"]], "get_grid_bounding_box() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_grid_bounding_box"]], "get_ij() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_ij"]], "get_intercell_connections() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_intercell_connections"]], "get_intercell_connections() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.get_intercell_connections"]], "get_kij_from_node3d() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_kij_from_node3d"]], "get_nearest_point_on_grid() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_nearest_point_on_grid"]], "get_point_on_national_hydrogeologic_grid() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_point_on_national_hydrogeologic_grid"]], "get_transform() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_transform"]], "get_vertices() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.get_vertices"]], "intercell_connections (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.intercell_connections"]], "length_multiplier (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.length_multiplier"]], "length_units (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.length_units"]], "mfsetup.grid": [[4, "module-mfsetup.grid"]], "polygons (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.polygons"]], "proj_str (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.proj_str"]], "rasterize() (in module mfsetup.grid)": [[4, "mfsetup.grid.rasterize"]], "rotation (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.rotation"]], "setup_structured_grid() (in module mfsetup.grid)": [[4, "mfsetup.grid.setup_structured_grid"]], "size (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.size"]], "top (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.top"]], "transform (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.transform"]], "vertices (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.vertices"]], "wkt (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.wkt"]], "write_bbox_shapefile() (in module mfsetup.grid)": [[4, "mfsetup.grid.write_bbox_shapefile"]], "write_bbox_shapefile() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.write_bbox_shapefile"]], "write_shapefile() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.write_shapefile"]], "xul (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.xul"]], "yul (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.yul"]], "interpolator (class in mfsetup.interpolate)": [[5, "mfsetup.interpolate.Interpolator"]], "get_source_dest_model_xys() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.get_source_dest_model_xys"]], "interp_weights (mfsetup.interpolate.interpolator property)": [[5, "mfsetup.interpolate.Interpolator.interp_weights"]], "interp_weights() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.interp_weights"]], "interpolate() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.interpolate"]], "interpolate() (mfsetup.interpolate.interpolator method)": [[5, "mfsetup.interpolate.Interpolator.interpolate"]], "mfsetup.interpolate": [[5, "module-mfsetup.interpolate"]], "regrid() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.regrid"]], "regrid3d() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.regrid3d"]], "source_values_mask (mfsetup.interpolate.interpolator property)": [[5, "mfsetup.interpolate.Interpolator.source_values_mask"]], "mf6model (class in mfsetup.mf6model)": [[6, "mfsetup.mf6model.MF6model"]], "get_flopy_external_file_input() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_flopy_external_file_input"]], "get_package_list() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_package_list"]], "get_raster_statistics_for_cells() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_raster_statistics_for_cells"]], "get_raster_values_at_cell_centers() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_raster_values_at_cell_centers"]], "idomain (mfsetup.mf6model.mf6model property)": [[6, 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file and model build script": [[0, "make-a-minimum-working-configuration-file-and-model-build-script"]], "Code Reference": [[1, "code-reference"]], "Model classes": [[1, "model-classes"]], "Supporting modules": [[1, "supporting-modules"]], "mfsetup.discretization module": [[2, "module-mfsetup.discretization"]], "mfsetup.fileio module": [[3, "module-mfsetup.fileio"]], "mfsetup.grid module": [[4, "module-mfsetup.grid"]], "mfsetup.interpolate module": [[5, "module-mfsetup.interpolate"]], "MF6model class": [[6, "module-mfsetup.mf6model"]], "MFsetupMixin class": [[7, "module-mfsetup.mfmodel"]], "MFnwtModel class": [[8, "module-mfsetup.mfnwtmodel"]], "mfsetup.tdis module": [[9, "module-mfsetup.tdis"]], "mfsetup.tmr module": [[10, "module-mfsetup.tmr"]], "Modflow-setup concepts and methods": [[11, "modflow-setup-concepts-and-methods"]], "Specifying perimeter boundary conditions from another model": [[12, "specifying-perimeter-boundary-conditions-from-another-model"]], "Features and Limitations": [[12, "features-and-limitations"]], "Input": [[12, "input"]], "Time discretization": [[12, "time-discretization"]], "Specifying the locations of perimeter boundary cells": [[12, "specifying-the-locations-of-perimeter-boundary-cells"]], "The configuration file": [[13, "the-configuration-file"]], "The YAML format": [[13, "the-yaml-format"]], "Configuration file structure": [[13, "configuration-file-structure"]], "Package blocks": [[13, "package-blocks"]], "Sub-blocks": [[13, "sub-blocks"]], "Directly specifying MODFLOW input": [[13, "directly-specifying-modflow-input"]], "Source_data sub-blocks": [[13, "source-data-sub-blocks"]], "Some additional notes on YAML": [[13, "some-additional-notes-on-yaml"]], "Configuration defaults": [[14, "configuration-defaults"]], "MODFLOW-6 configuration defaults": [[14, "modflow-6-configuration-defaults"]], "MODFLOW-NWT configuration defaults": [[14, "modflow-nwt-configuration-defaults"]], "Configuration File Gallery": [[15, "configuration-file-gallery"]], "Shellmound test case": [[15, "shellmound-test-case"]], "Shellmound TMR inset test case": [[15, "shellmound-tmr-inset-test-case"]], "Pleasant Lake test case": [[15, "pleasant-lake-test-case"]], "LGR parent model configuration": [[15, "lgr-parent-model-configuration"]], "pleasant_lgr_inset.yml": [[15, "pleasant-lgr-inset-yml"]], "Pleasant Lake MODFLOW-NWT test case": [[15, "pleasant-lake-modflow-nwt-test-case"]], "Plainfield Lakes MODFLOW-NWT test case": [[15, "plainfield-lakes-modflow-nwt-test-case"]], "Contributing to modflow-setup": [[16, "contributing-to-modflow-setup"]], "Getting started": [[16, "getting-started"]], "Bug reports and enhancement requests": [[16, "bug-reports-and-enhancement-requests"]], "Code contributions": [[16, "code-contributions"]], "Seven Steps for Contributing": [[16, "seven-steps-for-contributing"]], "1) Forking the modflow-setup repository using Git": [[16, "forking-the-modflow-setup-repository-using-git"]], "Getting started with Git": [[16, "getting-started-with-git"]], "Forking": [[16, "forking"]], "Creating a branch": [[16, "creating-a-branch"]], "2 & 3) Creating a development environment with the required dependencies": [[16, "creating-a-development-environment-with-the-required-dependencies"]], "4) Installing the modflow-setup source code": [[16, "installing-the-modflow-setup-source-code"]], "5) Making changes and writing tests": [[16, "making-changes-and-writing-tests"]], "Writing tests": [[16, "writing-tests"]], "Running the test suite": [[16, "running-the-test-suite"]], "6) Updating the Documentation": [[16, "updating-the-documentation"]], "7) Submitting a Pull Request": [[16, "submitting-a-pull-request"]], "Style Guide & Linting": [[16, "style-guide-linting"]], "Examples": [[17, "examples"]], "Example problems": [[17, null]], "modflow-setup 0.3.0.post15+gdb03604": [[18, "modflow-setup-version"]], "Getting Started": [[18, null]], "User Guide": [[18, null]], "Reference": [[18, null]], "Bibliography": [[18, null]], "Specifying boundary conditions with the \u2018basic\u2019 MODFLOW stress packages": [[19, "specifying-boundary-conditions-with-the-basic-modflow-stress-packages"]], "List-based basic stress packages": [[19, "list-based-basic-stress-packages"]], "Constant Head (CHD) Package": [[19, "constant-head-chd-package"]], "Drain DRN Package": [[19, "drain-drn-package"]], "General Head Boundary (GHB) Package": [[19, "general-head-boundary-ghb-package"]], "River (RIV) package": [[19, "river-riv-package"]], "Well (WEL) Package": [[19, "well-wel-package"]], "Grid-based basic stress packages": [[19, "grid-based-basic-stress-packages"]], "Recharge (RCH) Package": [[19, "recharge-rch-package"]], "Direct input": [[19, "direct-input"]], "Grid-independent input": [[19, "grid-independent-input"]], "The Discretization Package": [[20, "the-discretization-package"]], "Specifying Initial Conditions": [[21, "specifying-initial-conditions"]], "Input instructions by package": [[22, "input-instructions-by-package"]], "The Lake (LAK) Package": [[23, "the-lake-lak-package"]], "Head observations": [[24, "head-observations"]], "MODFLOW Output Control": [[25, "modflow-output-control"]], "Stress period input format": [[25, "stress-period-input-format"]], "Output filenames and other arguments": [[25, "output-filenames-and-other-arguments"]], "Alternative stress period input formats": [[25, "alternative-stress-period-input-formats"]], "Specifying Aquifer Properties": [[26, "specifying-aquifer-properties"]], "The Streamflow Routing (SFR) Package": [[27, "the-streamflow-routing-sfr-package"]], "Installation": [[28, "installation"]], "Installing python dependencies with Conda": [[28, "installing-python-dependencies-with-conda"]], "Download and install a python distribution and Conda-like package installer": [[28, "download-and-install-a-python-distribution-and-conda-like-package-installer"]], "Download an environment file": [[28, "download-an-environment-file"]], "Creating a Conda environment using Mamba": [[28, "creating-a-conda-environment-using-mamba"]], "Keeping the Conda environment up to date": [[28, "keeping-the-conda-environment-up-to-date"]], "Installing Modflow-setup": [[28, "installing-modflow-setup"]], "Installing and updating Modflow-setup from PyPI": [[28, "installing-and-updating-modflow-setup-from-pypi"]], "Installing the latest develop version of Modflow-setup": [[28, "installing-the-latest-develop-version-of-modflow-setup"]], "Installing the Modflow-setup source code in-place": [[28, "installing-the-modflow-setup-source-code-in-place"]], "Installing the IPython kernel to use Modflow-setup in Jupyter Notebooks": [[28, "installing-the-ipython-kernel-to-use-modflow-setup-in-jupyter-notebooks"]], "Best practices": [[28, "best-practices"]], "Considerations for USGS Users": [[28, "id4"]], "Installing the DOI SSL certificate for use with pip": [[28, "id5"]], "Installing the DOI SSL certificate for use with conda": [[28, "installing-the-doi-ssl-certificate-for-use-with-conda"]], "Troubleshooting issues with the USGS network": [[28, "troubleshooting-issues-with-the-usgs-network"]], "SSL-related error messages when using conda": [[28, "ssl-related-error-messages-when-using-conda"]], "SSL-related error messages when using pip": [[28, "ssl-related-error-messages-when-using-pip"]], "If you are on the USGS network, using Windows, and you get this error message:": [[28, "if-you-are-on-the-usgs-network-using-windows-and-you-get-this-error-message"]], "Pleasant Lake Example": [[29, "Pleasant-Lake-Example"]], "Model details": [[29, "Model-details"]], "Just make a model grid": [[29, "Just-make-a-model-grid"]], "Working directory gottcha": [[29, "Working-directory-gottcha"]], "Write shapefiles of the inset and parent modelgrids": [[29, "Write-shapefiles-of-the-inset-and-parent-modelgrids"]], "Change the working directory back to the notebook location": [[29, 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[[30, "motivation"]], "What modflow-setup does": [[30, "what-modflow-setup-does"]], "What modflow-setup doesn\u2019t do": [[30, "what-modflow-setup-doesnt-do"]], "References": [[31, "references"]], "Release History": [[32, "release-history"]], "Version 0.3.1 (2023-08-17)": [[32, "version-0-3-1-2023-08-17"]], "Version 0.3.0 (2023-07-25)": [[32, "version-0-3-0-2023-07-25"]], "Version 0.2.0 (2023-02-06)": [[32, "version-0-2-0-2023-02-06"]], "Version 0.1.0 Initial release (2022-09-30)": [[32, "version-0-1-0-initial-release-2022-09-30"]], "Basic program structure and usage": [[33, "basic-program-structure-and-usage"]], "Setting up a full model": [[33, "setting-up-a-full-model"]], "Loading a model": [[33, "loading-a-model"]], "Setting up individual packages": [[33, "setting-up-individual-packages"]], "Troubleshooting": [[34, "troubleshooting"]], "My script doesn\u2019t run": [[34, "my-script-doesn-t-run"]], "Updating your python environment": [[34, "updating-your-python-environment"]], "Updating Modflow-setup": [[34, "updating-modflow-setup"]]}, "indexentries": {"modflowgwfdis (class in mfsetup.discretization)": [[2, "mfsetup.discretization.ModflowGwfdis"]], "adjust_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.adjust_layers"]], "cellids_to_kij() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.cellids_to_kij"]], "create_vertical_pass_through_cells() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.create_vertical_pass_through_cells"]], "deactivate_idomain_above() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.deactivate_idomain_above"]], "fill_cells_vertically() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.fill_cells_vertically"]], "fill_empty_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.fill_empty_layers"]], "find_remove_isolated_cells() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.find_remove_isolated_cells"]], "fix_model_layer_conflicts() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.fix_model_layer_conflicts"]], "get_highest_active_layer() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.get_highest_active_layer"]], "get_layer() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.get_layer"]], "get_layer_thicknesses() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.get_layer_thicknesses"]], "make_ibound() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_ibound"]], "make_idomain() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_idomain"]], "make_irch() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_irch"]], "make_lgr_idomain() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.make_lgr_idomain"]], "mfsetup.discretization": [[2, "module-mfsetup.discretization"]], "module": [[2, "module-mfsetup.discretization"], [3, "module-mfsetup.fileio"], [4, "module-mfsetup.grid"], [5, "module-mfsetup.interpolate"], [6, "module-mfsetup.mf6model"], [7, "module-mfsetup.mfmodel"], [8, "module-mfsetup.mfnwtmodel"], [9, "module-mfsetup.tdis"], [10, "module-mfsetup.tmr"]], "populate_values() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.populate_values"]], "thickness (mfsetup.discretization.modflowgwfdis property)": [[2, "mfsetup.discretization.ModflowGwfdis.thickness"]], "verify_minimum_layer_thickness() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.verify_minimum_layer_thickness"]], "voxels_to_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.voxels_to_layers"]], "weighted_average_between_layers() (in module mfsetup.discretization)": [[2, "mfsetup.discretization.weighted_average_between_layers"]], "add_version_to_fileheader() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.add_version_to_fileheader"]], "append_csv() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.append_csv"]], "check_source_files() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.check_source_files"]], "dump() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.dump"]], "dump_json() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.dump_json"]], "dump_yml() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.dump_yml"]], "exe_exists() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.exe_exists"]], "flopy_mf2005_load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.flopy_mf2005_load"]], "flopy_mf6model_load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.flopy_mf6model_load"]], "flopy_mfsimulation_load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.flopy_mfsimulation_load"]], "load() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load"]], "load_array() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_array"]], "load_cfg() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_cfg"]], "load_json() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_json"]], "load_modelgrid() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_modelgrid"]], "load_yml() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.load_yml"]], "mfsetup.fileio": [[3, "module-mfsetup.fileio"]], "read_ggofile() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.read_ggofile"]], "read_lak_ggo() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.read_lak_ggo"]], "read_mf6_block() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.read_mf6_block"]], "remove_file_header() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.remove_file_header"]], "save_array() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.save_array"]], "set_cfg_paths_to_absolute() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.set_cfg_paths_to_absolute"]], "setup_external_filepaths() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.setup_external_filepaths"]], "which() (in module mfsetup.fileio)": [[3, "mfsetup.fileio.which"]], "mfsetupgrid (class in mfsetup.grid)": [[4, "mfsetup.grid.MFsetupGrid"]], "bbox (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.bbox"]], "botm (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.botm"]], "bounds (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.bounds"]], "crs (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.crs"]], "dataframe (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.dataframe"]], "get_cellface_midpoint() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_cellface_midpoint"]], "get_crs() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_crs"]], "get_crs_length_units() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_crs_length_units"]], "get_dataframe() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.get_dataframe"]], "get_grid_bounding_box() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_grid_bounding_box"]], "get_ij() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_ij"]], "get_intercell_connections() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_intercell_connections"]], "get_intercell_connections() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.get_intercell_connections"]], "get_kij_from_node3d() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_kij_from_node3d"]], "get_nearest_point_on_grid() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_nearest_point_on_grid"]], "get_point_on_national_hydrogeologic_grid() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_point_on_national_hydrogeologic_grid"]], "get_transform() (in module mfsetup.grid)": [[4, "mfsetup.grid.get_transform"]], "get_vertices() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.get_vertices"]], "intercell_connections (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.intercell_connections"]], "length_multiplier (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.length_multiplier"]], "length_units (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.length_units"]], "mfsetup.grid": [[4, "module-mfsetup.grid"]], "polygons (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.polygons"]], "proj_str (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.proj_str"]], "rasterize() (in module mfsetup.grid)": [[4, "mfsetup.grid.rasterize"]], "rotation (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.rotation"]], "setup_structured_grid() (in module mfsetup.grid)": [[4, "mfsetup.grid.setup_structured_grid"]], "size (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.size"]], "top (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.top"]], "transform (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.transform"]], "vertices (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.vertices"]], "wkt (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.wkt"]], "write_bbox_shapefile() (in module mfsetup.grid)": [[4, "mfsetup.grid.write_bbox_shapefile"]], "write_bbox_shapefile() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.write_bbox_shapefile"]], "write_shapefile() (mfsetup.grid.mfsetupgrid method)": [[4, "mfsetup.grid.MFsetupGrid.write_shapefile"]], "xul (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.xul"]], "yul (mfsetup.grid.mfsetupgrid property)": [[4, "mfsetup.grid.MFsetupGrid.yul"]], "interpolator (class in mfsetup.interpolate)": [[5, "mfsetup.interpolate.Interpolator"]], "get_source_dest_model_xys() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.get_source_dest_model_xys"]], "interp_weights (mfsetup.interpolate.interpolator property)": [[5, "mfsetup.interpolate.Interpolator.interp_weights"]], "interp_weights() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.interp_weights"]], "interpolate() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.interpolate"]], "interpolate() (mfsetup.interpolate.interpolator method)": [[5, "mfsetup.interpolate.Interpolator.interpolate"]], "mfsetup.interpolate": [[5, "module-mfsetup.interpolate"]], "regrid() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.regrid"]], "regrid3d() (in module mfsetup.interpolate)": [[5, "mfsetup.interpolate.regrid3d"]], "source_values_mask (mfsetup.interpolate.interpolator property)": [[5, "mfsetup.interpolate.Interpolator.source_values_mask"]], "mf6model (class in mfsetup.mf6model)": [[6, "mfsetup.mf6model.MF6model"]], "get_flopy_external_file_input() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_flopy_external_file_input"]], "get_package_list() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_package_list"]], "get_raster_statistics_for_cells() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_raster_statistics_for_cells"]], "get_raster_values_at_cell_centers() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.get_raster_values_at_cell_centers"]], "idomain (mfsetup.mf6model.mf6model property)": [[6, "mfsetup.mf6model.MF6model.idomain"]], "load_from_config() (mfsetup.mf6model.mf6model class method)": [[6, "mfsetup.mf6model.MF6model.load_from_config"]], "mfsetup.mf6model": [[6, "module-mfsetup.mf6model"]], "perioddata (mfsetup.mf6model.mf6model property)": [[6, "mfsetup.mf6model.MF6model.perioddata"]], "setup_chd() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_chd"]], "setup_drn() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_drn"]], "setup_ghb() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_ghb"]], "setup_ic() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_ic"]], "setup_ims() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_ims"]], "setup_lak() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_lak"]], "setup_npf() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_npf"]], "setup_obs() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_obs"]], "setup_oc() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_oc"]], "setup_rch() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_rch"]], "setup_riv() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_riv"]], "setup_simulation_mover() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_simulation_mover"]], "setup_sto() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_sto"]], "setup_tdis() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_tdis"]], "setup_wel() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.setup_wel"]], "write_input() (mfsetup.mf6model.mf6model method)": [[6, "mfsetup.mf6model.MF6model.write_input"]], "mfsetupmixin (class in mfsetup.mfmodel)": [[7, "mfsetup.mfmodel.MFsetupMixin"]], "get_boundary_cells() (mfsetup.mfmodel.mfsetupmixin method)": [[7, "mfsetup.mfmodel.MFsetupMixin.get_boundary_cells"]], "high_k_lake_recharge (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.high_k_lake_recharge"]], "interp_weights (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.interp_weights"]], "isbc (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.isbc"]], "lakarr (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.lakarr"]], "lake_bathymetry (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.lake_bathymetry"]], "load_cfg() (mfsetup.mfmodel.mfsetupmixin class method)": [[7, "mfsetup.mfmodel.MFsetupMixin.load_cfg"]], "load_features() (mfsetup.mfmodel.mfsetupmixin method)": [[7, "mfsetup.mfmodel.MFsetupMixin.load_features"]], "load_grid() (mfsetup.mfmodel.mfsetupmixin method)": [[7, "mfsetup.mfmodel.MFsetupMixin.load_grid"]], "mfsetup.mfmodel": [[7, "module-mfsetup.mfmodel"]], "model_version (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.model_version"]], "package_list (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.package_list"]], "parent_layers (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.parent_layers"]], "parent_mask (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.parent_mask"]], "parent_stress_periods (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.parent_stress_periods"]], "perimeter_bc_type (mfsetup.mfmodel.mfsetupmixin property)": [[7, "mfsetup.mfmodel.MFsetupMixin.perimeter_bc_type"]], "regrid_from_parent() (mfsetup.mfmodel.mfsetupmixin method)": [[7, "mfsetup.mfmodel.MFsetupMixin.regrid_from_parent"]], "setup_external_filepaths() (mfsetup.mfmodel.mfsetupmixin method)": [[7, "mfsetup.mfmodel.MFsetupMixin.setup_external_filepaths"]], "setup_from_cfg() (mfsetup.mfmodel.mfsetupmixin class method)": [[7, "mfsetup.mfmodel.MFsetupMixin.setup_from_cfg"]], "setup_from_yaml() (mfsetup.mfmodel.mfsetupmixin class method)": [[7, "mfsetup.mfmodel.MFsetupMixin.setup_from_yaml"]], "setup_grid() (mfsetup.mfmodel.mfsetupmixin method)": [[7, "mfsetup.mfmodel.MFsetupMixin.setup_grid"]], "source_path (mfsetup.mfmodel.mfsetupmixin attribute)": [[7, "mfsetup.mfmodel.MFsetupMixin.source_path"]], "mfnwtmodel (class in mfsetup.mfnwtmodel)": [[8, "mfsetup.mfnwtmodel.MFnwtModel"]], "ibound (mfsetup.mfnwtmodel.mfnwtmodel property)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.ibound"]], "ipakcb (mfsetup.mfnwtmodel.mfnwtmodel property)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.ipakcb"]], "load() (mfsetup.mfnwtmodel.mfnwtmodel class method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.load"]], "mfsetup.mfnwtmodel": [[8, "module-mfsetup.mfnwtmodel"]], "perioddata (mfsetup.mfnwtmodel.mfnwtmodel property)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.perioddata"]], "setup_chd() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_chd"]], "setup_drn() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_drn"]], "setup_ghb() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_ghb"]], "setup_hyd() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_hyd"]], "setup_riv() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_riv"]], "setup_tdis() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_tdis"]], "setup_upw() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_upw"]], "setup_wel() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.setup_wel"]], "write_input() (mfsetup.mfnwtmodel.mfnwtmodel method)": [[8, "mfsetup.mfnwtmodel.MFnwtModel.write_input"]], "add_date_comments_to_tdis() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.add_date_comments_to_tdis"]], "aggregate_dataframe_to_stress_period() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.aggregate_dataframe_to_stress_period"]], "aggregate_xarray_to_stress_period() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.aggregate_xarray_to_stress_period"]], "concat_periodata_groups() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.concat_periodata_groups"]], "convert_freq_to_period_start() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.convert_freq_to_period_start"]], "get_parent_stress_periods() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.get_parent_stress_periods"]], "mfsetup.tdis": [[9, "module-mfsetup.tdis"]], "parse_perioddata_groups() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.parse_perioddata_groups"]], "setup_perioddata() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.setup_perioddata"]], "setup_perioddata_group() (in module mfsetup.tdis)": [[9, "mfsetup.tdis.setup_perioddata_group"]], "get_qx_qy_qz() (in module mfsetup.tmr)": [[10, "mfsetup.tmr.get_qx_qy_qz"]], "mfsetup.tmr": [[10, "module-mfsetup.tmr"]]}})
\ No newline at end of file
diff --git a/latest/structure.html b/latest/structure.html
index d7f4576d..ca3b5d95 100644
--- a/latest/structure.html
+++ b/latest/structure.html
@@ -4,7 +4,7 @@
   
 
   
-  Basic program structure and usage — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Basic program structure and usage — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604
               

 
 
diff --git a/latest/troubleshooting.html b/latest/troubleshooting.html
index 7210a45e..fdfd7a3e 100644
--- a/latest/troubleshooting.html
+++ b/latest/troubleshooting.html
@@ -4,7 +4,7 @@
   
 
   
-  Troubleshooting — modflow-setup 0.3.0.post11+g3a6ec42 documentation
+  Troubleshooting — modflow-setup 0.3.0.post15+gdb03604 documentation
       
       
       
@@ -41,7 +41,7 @@
             modflow-setup
           
               

-                0.3.0.post11+g3a6ec42
+                0.3.0.post15+gdb03604