diff --git a/latest/.buildinfo b/latest/.buildinfo index ce84d03d..7397eecc 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: 4a7343d9c4c5274c8dbbc61990796867 +config: defe3b52ed0842cb916ba842f4487da4 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/latest/10min.html b/latest/10min.html index 8ecb9dca..09da294f 100644 --- a/latest/10min.html +++ b/latest/10min.html @@ -4,7 +4,7 @@ - 10 Minutes to Modflow-setup — modflow-setup 0.4.0.post3+gd2d459e documentation + 10 Minutes to Modflow-setup — modflow-setup 0.4.0.post7+g63ba038 documentation @@ -15,7 +15,7 @@ - + @@ -40,7 +40,7 @@ modflow-setup
- 0.4.0.post3+gd2d459e + 0.4.0.post7+g63ba038
@@ -444,7 +444,7 @@

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

© Copyright 2019-2024, Modflow-setup developers |. - Last updated on Feb 15, 2024. + Last updated on Mar 08, 2024.

diff --git a/latest/_images/notebooks_Pleasant_lake_lgr_example_24_1.png b/latest/_images/notebooks_Pleasant_lake_lgr_example_24_1.png index a19b528a..86db00d3 100644 Binary files a/latest/_images/notebooks_Pleasant_lake_lgr_example_24_1.png and b/latest/_images/notebooks_Pleasant_lake_lgr_example_24_1.png differ diff --git a/latest/_images/notebooks_Pleasant_lake_lgr_example_34_0.png b/latest/_images/notebooks_Pleasant_lake_lgr_example_34_0.png index 1a4ef886..afe73802 100644 Binary files a/latest/_images/notebooks_Pleasant_lake_lgr_example_34_0.png and b/latest/_images/notebooks_Pleasant_lake_lgr_example_34_0.png differ diff --git a/latest/_modules/index.html b/latest/_modules/index.html index 77b855a8..d2190574 100644 --- a/latest/_modules/index.html +++ b/latest/_modules/index.html @@ -3,7 +3,7 @@ - Overview: module code — modflow-setup 0.4.0.post3+gd2d459e documentation + Overview: module code — modflow-setup 0.4.0.post7+g63ba038 documentation @@ -14,7 +14,7 @@ - + @@ -37,7 +37,7 @@ modflow-setup
- 0.4.0.post3+gd2d459e + 0.4.0.post7+g63ba038
@@ -120,7 +120,7 @@

All modules for which code is available

© Copyright 2019-2024, Modflow-setup developers |. - Last updated on Feb 15, 2024. + Last updated on Mar 08, 2024.

diff --git a/latest/_modules/mfsetup/discretization.html b/latest/_modules/mfsetup/discretization.html index e80fc673..5a9bb35a 100644 --- a/latest/_modules/mfsetup/discretization.html +++ b/latest/_modules/mfsetup/discretization.html @@ -3,7 +3,7 @@ - mfsetup.discretization — modflow-setup 0.4.0.post3+gd2d459e documentation + mfsetup.discretization — modflow-setup 0.4.0.post7+g63ba038 documentation @@ -14,7 +14,7 @@ - + @@ -37,7 +37,7 @@ modflow-setup
- 0.4.0.post3+gd2d459e + 0.4.0.post7+g63ba038
@@ -434,7 +434,10 @@

Source code for mfsetup.discretization

         with np.errstate(invalid='ignore'):
             too_thin = active & (thicknesses < minimum_thickness)
         new_layer_elevs[i, too_thin] = new_layer_elevs[i - 1, too_thin] - minimum_thickness * 1.001
-    assert np.nanmax(np.diff(new_layer_elevs, axis=0)[ibound_array > 0]) * -1 >= minimum_thickness
+    try:
+        assert np.nanmax(np.diff(new_layer_elevs, axis=0)[ibound_array > 0]) * -1 >= minimum_thickness
+    except:
+        j=2
     return new_layer_elevs[1:]
@@ -546,15 +549,23 @@

Source code for mfsetup.discretization

 def make_lgr_idomain(parent_modelgrid, inset_modelgrid):
     """Inactivate cells in parent_modelgrid that coincide
     with area of inset_modelgrid."""
-    if parent_modelgrid.rotation != 0 or inset_modelgrid.rotation != 0:
-        raise NotImplementedError('Rotated grids not supported.')
+    if parent_modelgrid.rotation != inset_modelgrid.rotation:
+        raise ValueError('LGR parent and inset models must have same rotation.'
+                         f'\nParent rotation: {parent_modelgrid.rotation}'
+                         f'\nInset rotation: {inset_modelgrid.rotation}'
+                         )
+    # upper left corner of inset model in parent model
+    # use the cell centers, to avoid edge situation
+    # where neighboring parent cell is accidentally selected
+    x0 = inset_modelgrid.xcellcenters[0, 0]
+    y0 = inset_modelgrid.ycellcenters[0, 0]
+    pi0, pj0 = parent_modelgrid.intersect(x0, y0, forgive=True)
+    # lower right corner of inset model
+    x1 = inset_modelgrid.xcellcenters[-1, -1]
+    y1 = inset_modelgrid.ycellcenters[-1, -1]
+    pi1, pj1 = parent_modelgrid.intersect(x1, y1, forgive=True)
     idomain = np.ones(parent_modelgrid.shape, dtype=int)
-    l, b, r, t = inset_modelgrid.bounds
-    isinset = (parent_modelgrid.xcellcenters > l) & \
-              (parent_modelgrid.xcellcenters < r) & \
-              (parent_modelgrid.ycellcenters > b) & \
-              (parent_modelgrid.ycellcenters < t)
-    idomain[:, isinset] = 0
+    idomain[:, pi0:pi1+1, pj0:pj1+1] = 0
     return idomain
@@ -908,7 +919,7 @@

Source code for mfsetup.discretization

 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/fileio.html b/latest/_modules/mfsetup/fileio.html
index 8839a53b..d10fc737 100644
--- a/latest/_modules/mfsetup/fileio.html
+++ b/latest/_modules/mfsetup/fileio.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.fileio — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.fileio — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -1402,7 +1402,7 @@ 
Source code for mfsetup.fileio
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/grid.html b/latest/_modules/mfsetup/grid.html
index 37d6ec4b..2be1b641 100644
--- a/latest/_modules/mfsetup/grid.html
+++ b/latest/_modules/mfsetup/grid.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.grid — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.grid — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -117,6 +117,7 @@ 
Source code for mfsetup.grid
 import numpy as np
 import pandas as pd
 import pyproj
+import shapely
 from flopy.discretization import StructuredGrid
 from flopy.mf6.utils.binarygrid_util import MfGrdFile
 from geopandas.geodataframe import GeoDataFrame
@@ -124,7 +125,7 @@ 
Source code for mfsetup.grid
 from packaging import version
 from rasterio import Affine
 from scipy import spatial
-from shapely.geometry import MultiPolygon, Polygon
+from shapely.geometry import MultiPolygon, Point, Polygon, box
 
 from mfsetup import fileio as fileio
 
@@ -921,6 +922,80 @@ 
Source code for mfsetup.grid
 
 
 
+

+[docs]
+def snap_to_cell_corner(x, y, modelgrid, corner='upper left'):
+    """Move an x, y location to the nearest cell corner on
+    a rectilinear modelgrid.
+
+    Parameters
+    ----------
+    x : float
+        x coordinate in coordinate reference system of modelgrid.
+    y : _type_
+        y coordinate in coordinate reference system of modelgrid.
+    modelgrid : Flopy StructuredGrid instance
+    corner : str, optional
+        'upper left' or 'lower right', by default 'upper left'
+
+    Returns
+    -------
+    x_corner, y_corner
+        x, y location of cell corner in coordinate reference system
+        of modelgrid.
+
+    Raises
+    ------
+    ValueError
+        If x, y are outside of the model domain, or if an invalid
+        cell corner is specified.
+    """
+    x_model, y_model = modelgrid.get_local_coords(x, y)
+
+    # move away from the corner of a cell
+    # delr: column spacing along a row
+    # delc: row spacing along a column
+    # use .min() values of delr/delc because
+    # we may not be able to get the i, j location
+    # from Flopy without first backing the point away from the corner
+    # (if the x, y is initially very close to the cell corner)
+    if corner == 'upper left':
+        x_model += 1e-6 #(modelgrid.delr.min() * 0.25)
+        y_model -= 1e-6 #(modelgrid.delc.min() * 0.25)
+    elif corner == 'lower right':
+        x_model -= 1e-6 #(modelgrid.delr.min() * 0.25)
+        y_model += 1e-6 #(modelgrid.delc.min() * 0.25)
+    else:
+        raise ValueError("Only snapping to 'upper left' and "
+                            "'lower right' corners is supported")
+    # flip back to world coords
+    #x1, y1 = modelgrid.get_coords(x_model, y_model)
+    # get corresponding cell
+    pi, pj = modelgrid.intersect(x_model, y_model, local=True, forgive=True)
+    #pi, pj = modelgrid.intersect(x1, y1, forgive=True)
+    if any(np.isnan([pi, pj])):
+        raise ValueError(f"Point {x:.2f}, {y:.2f} "
+                            "is outside of the model domain!")
+    # find the vertices of that cell
+    verts = np.array(modelgrid.get_cell_vertices(pi, pj))
+    # flip to model space to easily locate the corner
+    verts_model_space = np.array([modelgrid.get_local_coords(xv ,yv)
+                                    for xv, yv in verts])
+    if corner == 'upper left':
+        x_corner_model = verts_model_space[:, 0].min()
+        y_corner_model = verts_model_space[:, 1].max()
+    elif corner == 'lower right':
+        x_corner_model = verts_model_space[:,0].max()
+        y_corner_model = verts_model_space[:,1].min()
+    else:
+        raise ValueError("Only snapping to 'upper left' and "
+                            "'lower right' corners is supported")
+    # finally, back to world space
+    x_corner, y_corner = modelgrid.get_coords(x_corner_model, y_corner_model)
+    return x_corner, y_corner

+
+
+
 

 [docs]
 def setup_structured_grid(xoff=None, yoff=None, xul=None, yul=None,
@@ -936,10 +1011,100 @@ 
Source code for mfsetup.grid
                           model_length_units=None,
                           grid_file='grid.json',
                           bbox_shapefile=None, **kwargs):
-    """"""
+    """_summary_
+
+    Parameters
+    ----------
+    xoff : _type_, optional
+        _description_, by default None
+    yoff : _type_, optional
+        _description_, by default None
+    xul : _type_, optional
+        _description_, by default None
+    yul : _type_, optional
+        _description_, by default None
+    nrow : _type_, optional
+        _description_, by default None
+    ncol : _type_, optional
+        _description_, by default None
+    nlay : _type_, optional
+        _description_, by default None
+    dxy : _type_, optional
+        Specified uniform row/column spacing, in model grid
+        (coordinate reference system) units, by default None
+    delr : scalar or sequence, optional
+        Column spacing along a row, in model grid
+        (coordinate reference system) units,
+        by default None
+    delc : scalar or sequence, optional
+        Row spacing along a column, in model grid
+        (coordinate reference system) units,
+        by default None
+    top : _type_, optional
+        _description_, by default None
+    botm : _type_, optional
+        _description_, by default None
+    rotation : _type_, optional
+        _description_, by default 0.
+    parent_model : _type_, optional
+        _description_, by default None
+    snap_to_parent : bool, optional
+        _description_, by default True
+    snap_to_NHG : bool, optional
+        _description_, by default False
+    features : _type_, optional
+        _description_, by default None
+    features_shapefile : _type_, optional
+        _description_, by default None
+    id_column : _type_, optional
+        _description_, by default None
+    include_ids : _type_, optional
+        _description_, by default None
+    buffer : int, optional
+        _description_, by default 1000
+    crs : _type_, optional
+        _description_, by default None
+    epsg : _type_, optional
+        _description_, by default None
+    prj : _type_, optional
+        _description_, by default None
+    wkt : _type_, optional
+        _description_, by default None
+    model_length_units : _type_, optional
+        _description_, by default None
+    grid_file : str, optional
+        _description_, by default 'grid.json'
+    bbox_shapefile : _type_, optional
+        _description_, by default None
+
+    Returns
+    -------
+    _type_
+        _description_
+
+    Raises
+    ------
+    ValueError
+        _description_
+    ValueError
+        _description_
+    ValueError
+        _description_
+    ValueError
+        _description_
+    ValueError
+        _description_
+    ValueError
+        _description_
+    """    """"""
     print('setting up model grid...')
     t0 = time.time()
 
+    if parent_model is None:
+        snap_to_parent = False
+    elif not np.allclose(parent_model.modelgrid.rotation, rotation):
+        snap_to_parent = False
+
     # make sure crs is populated, then get CRS units for the grid
     crs = get_crs(crs=crs, epsg=epsg, prj=prj, wkt=wkt)
     if crs is None and parent_model is not None:
@@ -949,8 +1114,8 @@ 
Source code for mfsetup.grid
     if grid_units not in {'feet', 'meters'}:
         raise ValueError(f'unrecognized CRS units {grid_units}: CRS must be projected in feet or meters')
 
-    # conversions for model/parent model units to meters
-    # set regular flag for handling delc/delr
+    # conversion from model length units
+    # to model grid (coordinate reference system) units
     to_grid_units_inset = convert_length_units(model_length_units, grid_units)
 
     regular = True
@@ -958,16 +1123,17 @@ 
Source code for mfsetup.grid
         delr_grid = np.round(dxy, 4) # dxy is specified in CRS units
         delc_grid = delr_grid
     if delr is not None:
-        delr_grid = np.round(delr * to_grid_units_inset, 4)  # delr is specified in model units
+        # delr is expected to be in model grid (CRS) units
+        delr_grid = np.round(np.array(delr), 4)
         if not np.isscalar(delr_grid):
-            if (set(delr_grid)) == 1:
+            if len(set(delr_grid)) == 1:
                 delr_grid = delr_grid[0]
             else:
                 regular = False
     if delc is not None:
-        delc_grid = np.round(delc * to_grid_units_inset, 4) # delc is specified in model units
+        delc_grid = np.round(np.array(delc), 4)
         if not np.isscalar(delc_grid):
-            if (set(delc_grid)) == 1:
+            if len(set(delc_grid)) == 1:
                 delc_grid = delc_grid[0]
             else:
                 regular = False
@@ -997,136 +1163,140 @@ 
Source code for mfsetup.grid
         # optionally align grid with national hydrologic grid
         # grids snapping to NHD must have spacings that are a factor of 1 km
         if snap_to_NHG:
-            assert regular and np.allclose(1000 % delc_grid, 0, atol=1e-4)
+            if rotation != 0:
+                raise ValueError(f'rotation = {rotation}: snap_to_NHD option '
+                                 'only compatible with unrotated grids!')
+            if not (regular and np.allclose(1000 % delc_grid, 0, atol=1e-4)):
+                raise ValueError(f'snap_to_NHD option '
+                                 'only compatible with uniformly spaced '
+                                 'structured grids!')
             x, y = get_point_on_national_hydrogeologic_grid(xoff, yoff,
                                                             offset='edge', op=np.floor)
             xoff = x
             yoff = y
-            rotation = 0.
-
-        # need to specify xul, yul in case snapping to parent
-        # todo: allow snapping to parent grid on xoff, yoff
-        if rotation != 0:
-            rotation_rads = rotation * np.pi/180
-            # note rotating around xoff,yoff not the origin!
-            xul = xoff - (height_grid) * np.sin(rotation_rads)
-            yul = yoff + (height_grid) * np.cos(rotation_rads)
-        else:
-            xul = xoff
-            yul = yoff + height_grid
+
+
+        # make a bounding box so that other important corners can be specified
+        lower_left_corner = Point(xoff, yoff)
+        unrotated_bbox = box(xoff, yoff, xoff + width_grid, yoff + height_grid)
+        # get the upper right corner
+        ur = shapely.affinity.rotate(Point(xoff, yoff + height_grid), rotation,
+                                    origin=lower_left_corner)
+        xul, yul = ur.x, ur.y
 
     # option 2: make grid using buffered feature bounding box
     else:
+        # read in the feature from a shapefile
         if features is None and features_shapefile is not None:
-            # Make sure shapefile and bbox filter are in dest (model) CRS
-            # TODO: CRS wrangling could be added to shp2df as a feature
-            reproject_filter = False
-            try:
-                from gisutils import get_shapefile_crs
-                features_crs = get_shapefile_crs(features_shapefile)
-                if features_crs != crs:
-                    reproject_filter = True
-            except:
-                features_crs = get_proj_str(features_shapefile)
-                reproject_filter = True
             bbox_filter = None
             if parent_model is not None:
-                if reproject_filter:
-                    bbox_filter = project(parent_model.modelgrid.bbox,
-                                     parent_model.modelgrid.crs, features_crs).bounds
-                else:
-                    bbox_filter = parent_model.modelgrid.bbox.bounds
-            shp2df_kwargs = {'dest_crs': crs}
-            shp2df_kwargs = get_input_arguments(shp2df_kwargs, shp2df)
-            df = shp2df(features_shapefile,
-                        filter=bbox_filter, **shp2df_kwargs)
-
-            # optionally subset shapefile data to specified features
+                pmg_l, pmg_r, pmg_b, pmg_t = parent_model.modelgrid.extent
+                bbox_filter = gpd.GeoSeries(box(pmg_l, pmg_b, pmg_r, pmg_t),
+                                            crs=parent_model.modelgrid.crs)
+            df = gpd.read_file(features_shapefile, bbox=bbox_filter)
             if id_column is not None and include_ids is not None:
-                df = df.loc[df[id_column].isin(include_ids)]
+                datatype = set(type(s) for s in include_ids)
+                if len(datatype) > 1:
+                    raise ValueError(f"Inconsistent datatypes in include_ids: {include_ids}")
+                datatype = datatype.pop()
+                dtype = {id_column: datatype}
+                df = df.loc[df[id_column].astype(dtype).isin(include_ids)]
+            # inexplicable shapely.errors.GEOSException: IllegalArgumentException:
+            # Points of LinearRing do not form a closed linestring
+            # error resolved by calling to_crs twice
+            # (for mfsetup/tests/test_grid.py::test_grid_crs_units[3696-feet-meters])
+            try:
+                df.to_crs(crs, inplace=True)
+            except:
+                df.to_crs(crs, inplace=True)
             # use all features by default
             features = df.geometry.tolist()
-
-            # convert multiple features to a MultiPolygon
-            if isinstance(features, list):
-                if len(features) > 1:
-                    features = MultiPolygon(features)
-                else:
-                    features = features[0]
-
-            # size the grid based on the bbox for features
-            x1, y1, x2, y2 = features.bounds
-            L = buffer  # distance from area of interest to boundary
-            xul = x1 - L
-            yul = y2 + L
-            height_grid = np.round(yul - (y1 - L), 4) # initial model height from buffer distance
-            width_grid = np.round((x2 + L) - xul, 4)
-            rotation = 0.  # rotation not supported with this option
-            nrow = int(np.ceil(height_grid / delc_grid))
-            ncol = int(np.ceil(width_grid / delr_grid))
+        # alternatively, accept features as an argument
+        # convert multiple features to a MultiPolygon
+        if isinstance(features, list):
+            if len(features) > 1:
+                features = MultiPolygon(features)
+            else:
+                features = features[0]
+
+        # size the grid based on the bbox for features
+        # buffer and then unrotate the feature
+        buffered_features = features.buffer(buffer)
+        unrotated_features = shapely.affinity.rotate(buffered_features, -rotation,
+                                                     origin=buffered_features.centroid)
+        unrotated_bbox = box(*unrotated_features.bounds)
+        # Get important corners
+        # upper left corner
+        xul_ur, yul_ur = unrotated_bbox.bounds[0], unrotated_bbox.bounds[3]
+        ul = shapely.affinity.rotate(Point(xul_ur, yul_ur), rotation,
+                                     origin=buffered_features.centroid)
+        xul, yul = ul.x, ul.y
+        # lower left corner
+        xll_ur, yll_ur = unrotated_bbox.bounds[0], unrotated_bbox.bounds[1]
+        lower_left_corner = shapely.affinity.rotate(
+            Point(xll_ur, yll_ur), rotation, origin=buffered_features.centroid)
+        # xoff, yoff here for consistency with flopy model grid language
+        xoff, yoff = lower_left_corner.x, lower_left_corner.y
+        # Get the initial grid height and width
+        height_grid = np.round(unrotated_bbox.bounds[3] - unrotated_bbox.bounds[1])
+        width_grid = np.round(unrotated_bbox.bounds[2] - unrotated_bbox.bounds[0])
+        # initial rows and columns (prior to snapping, if specified)
+        nrow = int(np.ceil(height_grid / delc_grid))
+        ncol = int(np.ceil(width_grid / delr_grid))
 
     # align model with parent grid if there is a parent model
     # (and not snapping to national hydrologic grid)
+    # for grids created from a buffer around a feature
+    # (without a pre-defined number of rows and columns)
+    # this likely means increasing nrow and ncol
     if parent_model is not None and (snap_to_parent and not snap_to_NHG):
-
-        # An alternative method for snapping to the parent grid
-        # if the code below is causing issues
-        # get the parent cell containing
-        # the inset upper left corner
-        # xul_in, yul_in = xul, yul
-        #pi2, pj2 = get_ij(parent_model.modelgrid, xul_in, yul_in)
-        # find the closest vertex
-        #verts2 = np.array(parent_model.modelgrid.get_cell_vertices(pi2, pj2))
-        #closest_pos = np.argmin(np.sqrt((xul-verts2[:,0])**2 +
-        #                                (yul-verts2[:,0])**2))
-        #xul, yul = verts2[closest_pos]
-
-        # get location of coinciding cell in parent model for upper left
-        # first make sure not sitting at the top of a cell (which can shift into wrong parent cell)
-        # move to model coords
-        xul_mod, yul_mod = parent_model.modelgrid.get_local_coords(xul, yul)
-        # move away from the edge of a cell
-        xul_mod += (delr_grid * 0.25)
-        yul_mod -= (delc_grid * 0.25)
-        # flip back to world coords
-        xul, yul = parent_model.modelgrid.get_coords(xul_mod, yul_mod)
-        # get corresponding cell
-        pi, pj = parent_model.modelgrid.intersect(xul, yul)
-        # find the vertices of that cell
-        verts = np.array(parent_model.modelgrid.get_cell_vertices(pi, pj))
-        # flip to model space to easily locate upper left corner
-        verts_model_space = np.array([parent_model.modelgrid.get_local_coords(x,y) for x,y in verts])
-        # finally, back to world space
-        xul,yul = parent_model.modelgrid.get_coords(verts_model_space[:,0].min(),verts_model_space[:,1].max())
-
-        # adjust the dimensions to align remaining corners
-        def roundup(number, increment):
-            return int(np.ceil(number / increment) * increment)
-        height_grid = roundup(height_grid, parent_delr_grid)
-        width_grid = roundup(width_grid, parent_delc_grid)
-
-        # update nrow, ncol after snapping to parent grid
-        if regular:
-            nrow = int(height_grid / delc_grid) # h is in meters
-            ncol = int(width_grid / delr_grid)
-
-    if xoff is None:
-        xoff = xul + (np.sin(np.radians(rotation)) * height_grid)
-    if yoff is None:
-        yoff = yul - (np.cos(np.radians(rotation)) * height_grid)
+        xul, yul = snap_to_cell_corner(xul, yul, parent_model.modelgrid,
+                                       corner='upper left')
+        # unrotate to get new dimensions
+        ur_ur = shapely.affinity.rotate(Point(xul, yul), -rotation,
+                                            origin=lower_left_corner)
+        xul_ur, yul_ur = ur_ur.x, ur_ur.y
+        # Get the lower right corner
+        xlr_ur, ylr_ur = unrotated_bbox.bounds[2], unrotated_bbox.bounds[1]
+        # snap the lower right corner if the grid was generated from a feature
+        # (not preset nrow and ncol)
+        if features is not None:
+            lr = shapely.affinity.rotate(Point(xlr_ur, ylr_ur), rotation,
+                                         origin=unrotated_bbox.centroid)
+            xlr, ylr = lr.x, lr.y
+            # snap to the nearest parent lower right corner
+            xlr, ylr = snap_to_cell_corner(xlr, ylr, parent_model.modelgrid,
+                                           corner='lower right')
+            # unrotate to get new dimensions
+            lr_ur = shapely.affinity.rotate(Point(xlr, ylr), -rotation,
+                                            origin=lower_left_corner)
+            xlr_ur, ylr_ur = lr_ur.x, lr_ur.y
+
+            # calculate a new height and width for the grid now that the corners are snapped
+            height_grid = yul_ur - ylr_ur
+            width_grid = xlr_ur - xul_ur
+            # snap_to_parent assumes a regular grid
+            nrow = int(round(height_grid / delc_grid))
+            ncol = int(round(width_grid / delr_grid))
+
+        # recalculate xoff, yoff
+        lower_left_corner = shapely.affinity.rotate(Point(xul_ur, ylr_ur), rotation,
+                                                    origin=lower_left_corner)
+        xoff, yoff = lower_left_corner.x, lower_left_corner.y
+
+    assert xoff is not None
+    #    xoff = xul + (np.sin(np.radians(rotation)) * height_grid)
+    assert yoff is not None
+    #    yoff = yul - (np.cos(np.radians(rotation)) * height_grid)
     # set the grid configuration dictionary
-    # spacing is in meters (consistent with projected CRS)
-    # (modelgrid object will be updated automatically from this dictionary)
-    #if rotation == 0.:
-    #    xll = xul
-    #    yll = yul - model.height
     grid_cfg = {'nrow': int(nrow), 'ncol': int(ncol),
                 'nlay': nlay,
                 'delr': delr_grid, 'delc': delc_grid,
                 'xoff': xoff, 'yoff': yoff,
                 'xul': xul, 'yul': yul,
                 'rotation': rotation,
-                'lenuni': 2
+                #'lenuni': 2,
+                'structured': True
                 }
 
     if regular:
@@ -1146,11 +1316,6 @@ 
Source code for mfsetup.grid
         grid_cfg['crs'] = crs
     elif epsg is not None:
         grid_cfg['epsg'] = epsg
-    #elif crs is not None:
-    #    if 'epsg' in crs.srs.lower():
-    #        grid_cfg['epsg'] = int(crs.srs.split(':')[1])
-    #    else:
-    #        grid_cfg['wkt'] = crs.srs
     else:
         warnings.warn(("Coordinate Reference System information must be supplied via"
                       "the 'crs'' argument."))
@@ -1322,7 +1487,7 @@ 
Source code for mfsetup.grid
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/interpolate.html b/latest/_modules/mfsetup/interpolate.html
index 3415b772..66254a2b 100644
--- a/latest/_modules/mfsetup/interpolate.html
+++ b/latest/_modules/mfsetup/interpolate.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.interpolate — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.interpolate — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -539,7 +539,7 @@ 
Source code for mfsetup.interpolate
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/mf6model.html b/latest/_modules/mfsetup/mf6model.html
index cc00451e..49907c58 100644
--- a/latest/_modules/mfsetup/mf6model.html
+++ b/latest/_modules/mfsetup/mf6model.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.mf6model — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.mf6model — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -450,8 +450,16 @@ 
Source code for mfsetup.mf6model
     def create_lgr_models(self):
         for k, v in self.cfg['setup_grid']['lgr'].items():
             # load the config file for lgr inset model
-            inset_cfg = load_cfg(v['filename'],
-                                 default_file='/mf6_defaults.yml')
+            if 'filename' in v:
+                inset_cfg = load_cfg(v['filename'],
+                                    default_file='/mf6_defaults.yml')
+            elif 'cfg' in v:
+                inset_cfg = copy.deepcopy(v['cfg'])
+            else:
+                raise ValueError('Unrecognized input in subblock lgr: '
+                                 'Supply either a configuration filename: '
+                                 'or additional yaml configuration under cfg:'
+                                 )
             # if lgr inset has already been created
             if inset_cfg['model']['modelname'] in self.simulation._models:
                 return
@@ -689,7 +697,7 @@ 
Source code for mfsetup.mf6model
         kwargs.update(self.cfg[package]['griddata'].copy())
         # get steady/transient info from perioddata table
         # which parses it from either DIS or STO input (to allow consistent input structure with mf2005)
-        kwargs['steady_state'] = {k: v for k, v in zip(self.perioddata['per'], self.perioddata['steady'])}
+        kwargs['steady_state'] = {k: v for k, v in zip(self.perioddata['per'], self.perioddata['steady']) if v}
         kwargs['transient'] = {k: not v for k, v in zip(self.perioddata['per'], self.perioddata['steady'])}
         kwargs = get_input_arguments(kwargs, mf6.ModflowGwfsto)
         sto = mf6.ModflowGwfsto(self, **kwargs)
@@ -1179,7 +1187,7 @@ 
Source code for mfsetup.mf6model
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/mfmodel.html b/latest/_modules/mfsetup/mfmodel.html
index 6cf5321c..610dcbbd 100644
--- a/latest/_modules/mfsetup/mfmodel.html
+++ b/latest/_modules/mfsetup/mfmodel.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.mfmodel — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.mfmodel — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -340,8 +340,16 @@ 
Source code for mfsetup.mfmodel
         # because of NotImplementedError in base class
         elif self._modelgrid.grid_type is None:
             pass
+        # add layer tops and bottoms and idomain to the model grid
+        # if they haven't been yet
         elif self._modelgrid.nlay is None and 'DIS' in self.get_package_list():
-            self.setup_grid()
+            self._modelgrid._top = self.dis.top.array
+            self._modelgrid._botm = self.dis.botm.array
+            if self.version == 'mf6':
+                self._modelgrid._idomain = self.dis.idomain.array
+            elif 'bas6' in self.get_package_list():
+                self._modelgrid._idomain = self.bas6.ibound.array
+            #self.setup_grid()
         return self._modelgrid
 
     @property
@@ -668,14 +676,6 @@ 
Source code for mfsetup.mfmodel
             if f not in self._features.keys():
                 if os.path.exists(f):
                     features_crs = get_shapefile_crs(f)
-                    #try:
-                    #    from gisutils import get_shapefile_crs
-                    #    features_crs = get_shapefile_crs(kwargs['shapefile'])
-                    #except Exception as e:
-                    #    features_crs = pyproj.crs.CRS.from_proj4(get_proj_str(f))
-                    #authority = features_crs.to_authority()
-                    #if authority is not None:
-                    #    features_crs = pyproj.CRS.from_user_input(features_crs.to_authority())
                     if bbox_filter is None:
                         if self.bbox is not None:
                             bbox = self.bbox
@@ -691,12 +691,6 @@ 
Source code for mfsetup.mfmodel
 
                     # implement automatic reprojection in gis-utils
                     # maintaining backwards compatibility
-                    #kwargs = {'dest_crs': self.modelgrid.crs}
-                    #kwargs = get_input_arguments(kwargs, shp2df)
-                    #df = shp2df(f, filter=filter, **kwargs)
-                    #df.columns = [c.lower() for c in df.columns]
-                    #if 'dest_crs' not in kwargs and features_crs != self.modelgrid.crs:
-                    #    df['geometry'] = project(df['geometry'], features_crs, self.modelgrid.crs)
                     df = gpd.read_file(f)
                     df.to_crs(self.modelgrid.crs, inplace=True)
                     df.columns = [c.lower() for c in df.columns]
@@ -1208,13 +1202,19 @@ 
Source code for mfsetup.mfmodel
                 # load only specified packages that the parent model has
                 packages_in_parent_namefile = get_packages(os.path.join(kwargs['model_ws'],
                                                                         kwargs['namefile']))
+                # load at least these packages
+                # so that there is complete information on model time and space dis
+                default_parent_packages = {'dis', 'tdis'}
                 specified_packages = set(self.cfg['model'].get('packages', set()))
+                specified_packages.update(default_parent_packages)
 
                 # get equivalent packages to load if parent is another MODFLOW version;
                 # then flatten (a package may have more than one equivalent)
                 parent_packages = [get_package_name(p, kwargs['version'])
                                    for p in specified_packages]
                 parent_packages = {item for subset in parent_packages for item in subset}
+                if kwargs['version'] == 'mf6':
+                    parent_packages.add('sto')
                 load_only = list(set(packages_in_parent_namefile).intersection(parent_packages))
                 if 'load_only' not in kwargs:
                     kwargs['load_only'] = load_only
@@ -1249,6 +1249,28 @@ 
Source code for mfsetup.mfmodel
             # otherwise, convert parent model grid to MFsetupGrid
             mg_kwargs = self.cfg['parent'].get('SpatialReference',
                                           self.cfg['parent'].get('modelgrid', None))
+            # check configuration file input
+            # for consistency with parent model DIS package input
+            # (configuration file input may be different if an existing model
+            # doesn't have a valid spatial reference in the DIS package)
+            mf6_names = {
+                'rotation': 'angrot',
+                'xoff': 'xorigin',
+                'yoff': 'yorigin'
+            }
+            if mg_kwargs is not None and (self.parent.version == 'mf6') and not\
+                            mg_kwargs.get('override_dis_package_input', False):
+                for variable, mf6_name in mf6_names.items():
+                    if (variable in mg_kwargs) and\
+                        ('DIS' in self.parent.get_package_list()):
+                        dis_value = getattr(self.parent.dis, mf6_name).array
+                        if not np.allclose(mg_kwargs[variable], dis_value):
+                            raise ValueError(
+                "Configuration file entry parent: SpatialReference: "
+                f"{variable}: {mg_kwargs[variable]} does not match {mf6_name}={dis_value} "
+                "specified in the parent model DIS package file. Either make "
+                "these consistent or specify override_dis_package_input: True "
+                "in the parent: SpatialReference: configuration block.")
             self._set_parent_modelgrid(mg_kwargs)
 
             # setup parent model perioddata table
@@ -1281,9 +1303,11 @@ 
Source code for mfsetup.mfmodel
 
                 # set number of layers from parent if not specified
                 if self.version == 'mf6' and self.cfg['dis']['dimensions'].get('nlay') is None:
-                    self.cfg['dis']['dimensions']['nlay'] = self.parent.dis.nlay
+                    self.cfg['dis']['dimensions']['nlay'] = getattr(self.parent.dis.nlay, 'array',
+                                                                    self.parent.dis.nlay)
                 elif self.cfg['dis'].get('nlay') is None:
-                    self.cfg['dis']['nlay'] = self.parent.dis.nlay
+                    self.cfg['dis']['nlay'] = getattr(self.parent.dis.nlay, 'array',
+                                                      self.parent.dis.nlay)
 
                 # set start date/time from parent if not specified
                 if not self._is_lgr:
@@ -1469,7 +1493,11 @@ 
Source code for mfsetup.mfmodel
     def setup_grid(self):
         """Set up the attached modelgrid instance from configuration input
         """
-        cfg = self.cfg['setup_grid'] #.copy()
+        if self.cfg['grid']:
+            cfg = self.cfg['grid']
+            cfg['rotation'] = self.cfg['grid']['angrot']
+        else:
+            cfg = self.cfg['setup_grid'] #.copy()
         # update grid configuration with any information supplied to dis package
         # (so that settings specified for DIS package have priority)
         self._update_grid_configuration_with_dis()
@@ -1482,13 +1510,22 @@ 
Source code for mfsetup.mfmodel
             cfg.update(features_shapefile)
         cfg['parent_model'] = self.parent
         cfg['model_length_units'] = self.length_units
-        cfg['grid_file'] = cfg['output_files']['grid_file'].format(self.name)
-        bbox_shapefile_name = Path(cfg['output_files']['bbox_shapefile'].format(self.name)).name
+        output_files = self.cfg['setup_grid']['output_files']
+        cfg['grid_file'] = output_files['grid_file'].format(self.name)
+        bbox_shapefile_name = Path(output_files['bbox_shapefile'].format(self.name)).name
         cfg['bbox_shapefile'] = Path(self._shapefiles_path) / bbox_shapefile_name
         if 'DIS' in self.get_package_list():
             cfg['top'] = self.dis.top.array
             cfg['botm'] = self.dis.botm.array
 
+        # if model is an LGR inset with the default rotation=0
+        # and the LGR parent is rotated
+        # assume that the inset model rotation should == parent
+        # (different LGR parent/inset rotations not allowed)
+        if self._is_lgr and (cfg['rotation'] == 0) and\
+            self.parent.modelgrid.angrot != 0:
+                cfg['rotation'] = self.parent.modelgrid.angrot
+
         if os.path.exists(cfg['grid_file']) and self._load:
             print('Loading model grid definition from {}'.format(cfg['grid_file']))
             cfg.update(load(cfg['grid_file']))
@@ -1517,11 +1554,10 @@ 
Source code for mfsetup.mfmodel
             if not self.lgr:
                 self.lgr = True
             for key, cfg in self.cfg['setup_grid']['lgr'].items():
-                config_file = cfg['filename']
-                existing_inset_config_files = set()
+                existing_inset_models = set()
                 if isinstance(self.inset, dict):
-                    existing_inset_config_files = {v.cfg['filename'] for k, v in self.inset.items()}
-                if config_file not in existing_inset_config_files:
+                    existing_inset_models = {k for k, v in self.inset.items()}
+                if key not in existing_inset_models:
                     self.create_lgr_models()

 
 
@@ -1936,7 +1972,7 @@ 
Source code for mfsetup.mfmodel
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/mfnwtmodel.html b/latest/_modules/mfsetup/mfnwtmodel.html
index 672e83d8..ecb35310 100644
--- a/latest/_modules/mfsetup/mfnwtmodel.html
+++ b/latest/_modules/mfsetup/mfnwtmodel.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.mfnwtmodel — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.mfnwtmodel — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -1036,7 +1036,7 @@ 
Source code for mfsetup.mfnwtmodel
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/tdis.html b/latest/_modules/mfsetup/tdis.html
index 8c8f53b0..a804f555 100644
--- a/latest/_modules/mfsetup/tdis.html
+++ b/latest/_modules/mfsetup/tdis.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.tdis — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.tdis — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -1013,7 +1013,7 @@ 
Source code for mfsetup.tdis
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_modules/mfsetup/tmr.html b/latest/_modules/mfsetup/tmr.html
index 6cb2bd98..e21d2295 100644
--- a/latest/_modules/mfsetup/tmr.html
+++ b/latest/_modules/mfsetup/tmr.html
@@ -3,7 +3,7 @@
 
   
   
-  mfsetup.tmr — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.tmr — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -1100,7 +1100,7 @@ 
Source code for mfsetup.tmr
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/_sources/notebooks/Pleasant_lake_lgr_example.ipynb.txt b/latest/_sources/notebooks/Pleasant_lake_lgr_example.ipynb.txt
index 04aeea9f..9aeaee9d 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": "2024-02-15T18:15:11.064038Z",
-     "iopub.status.busy": "2024-02-15T18:15:11.063872Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.282144Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.281592Z"
+     "iopub.execute_input": "2024-03-08T21:05:47.611726Z",
+     "iopub.status.busy": "2024-03-08T21:05:47.611342Z",
+     "iopub.status.idle": "2024-03-08T21:05:48.839373Z",
+     "shell.execute_reply": "2024-03-08T21:05:48.838964Z"
     }
    },
    "outputs": [],
@@ -81,10 +81,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.284852Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.284326Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.911753Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.911329Z"
+     "iopub.execute_input": "2024-03-08T21:05:48.841717Z",
+     "iopub.status.busy": "2024-03-08T21:05:48.841417Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.810092Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.809560Z"
     }
    },
    "outputs": [],
@@ -106,10 +106,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.914062Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.913879Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.919679Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.919163Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.812675Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.812315Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.818653Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.818053Z"
     }
    },
    "outputs": [
@@ -117,8 +117,8 @@
      "data": {
       "text/plain": [
        "5 layer(s), 25 row(s), 25 column(s)\n",
-       "delr: [200.00...200.00] meters\n",
-       "delc: [200.00...200.00] meters\n",
+       "delr: [200.00...200.00] undefined\n",
+       "delc: [200.00...200.00] undefined\n",
        "CRS: EPSG:3070\n",
        "length units: meters\n",
        "xll: 553000.0; yll: 388000.0; rotation: 0.0\n",
@@ -139,23 +139,23 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.947247Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.946831Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.950811Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.950410Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.846678Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.846295Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.850773Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.850289Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "70 row(s), 80 column(s)\n",
-       "delr: [40.00...40.00] meters\n",
-       "delc: [40.00...40.00] meters\n",
+       "75 row(s), 85 column(s)\n",
+       "delr: [40.00...40.00] undefined\n",
+       "delc: [40.00...40.00] undefined\n",
        "CRS: EPSG:3070\n",
        "length units: meters\n",
-       "xll: 554200.0; yll: 389000.0; rotation: 0.0\n",
-       "Bounds: (554200.0, 557400.0, 389000.0, 391800.0)"
+       "xll: 554200.0; yll: 388800.0; rotation: 0\n",
+       "Bounds: (554200.0, 557600.0, 388800.0, 391800.0)"
       ]
      },
      "execution_count": 4,
@@ -180,10 +180,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.952701Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.952363Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.955773Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.955318Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.852778Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.852485Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.855810Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.855357Z"
     }
    },
    "outputs": [
@@ -215,10 +215,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.957704Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.957325Z",
-     "iopub.status.idle": "2024-02-15T18:15:13.826502Z",
-     "shell.execute_reply": "2024-02-15T18:15:13.825975Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.857669Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.857391Z",
+     "iopub.status.idle": "2024-03-08T21:05:50.868704Z",
+     "shell.execute_reply": "2024-03-08T21:05:50.868216Z"
     }
    },
    "outputs": [
@@ -237,7 +237,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "finished in 0.18s\n",
+      "finished in 0.19s\n",
       "\n"
      ]
     },
@@ -266,10 +266,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:13.828672Z",
-     "iopub.status.busy": "2024-02-15T18:15:13.828285Z",
-     "iopub.status.idle": "2024-02-15T18:15:13.831029Z",
-     "shell.execute_reply": "2024-02-15T18:15:13.830580Z"
+     "iopub.execute_input": "2024-03-08T21:05:50.870936Z",
+     "iopub.status.busy": "2024-03-08T21:05:50.870522Z",
+     "iopub.status.idle": "2024-03-08T21:05:50.873305Z",
+     "shell.execute_reply": "2024-03-08T21:05:50.872808Z"
     }
    },
    "outputs": [],
@@ -289,10 +289,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:13.832877Z",
-     "iopub.status.busy": "2024-02-15T18:15:13.832689Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.217191Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.216745Z"
+     "iopub.execute_input": "2024-03-08T21:05:50.875384Z",
+     "iopub.status.busy": "2024-03-08T21:05:50.875083Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.681140Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.680708Z"
     }
    },
    "outputs": [],
@@ -313,21 +313,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.219536Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.219198Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.226261Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.225701Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.683436Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.683078Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.690254Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.689706Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Pleasant Lake test case version 0.1.post3+gd2d459e\n",
+       "Pleasant Lake test case version 0.1.post7+g63ba038\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",
-       "delc: [200.00...200.00] meters\n",
+       "delr: [200.00...200.00] undefined\n",
+       "delc: [200.00...200.00] undefined\n",
        "CRS: EPSG:3070\n",
        "length units: meters\n",
        "xll: 553000.0; yll: 388000.0; rotation: 0.0\n",
@@ -363,10 +363,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.228055Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.227887Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.231307Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.230809Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.692088Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.691931Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.695284Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.694895Z"
     }
    },
    "outputs": [
@@ -397,10 +397,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.233160Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.232876Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.236784Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.236362Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.696967Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.696807Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.700578Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.700110Z"
     }
    },
    "outputs": [
@@ -457,25 +457,25 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.238486Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.238320Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.245041Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.244608Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.702394Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.702095Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.708594Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.708157Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.4.0.post3+gd2d459e\n",
+       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.4.0.post7+g63ba038\n",
        " Parent model: ./plsnt_lgr_parent\n",
-       " 5 layer(s), 70 row(s), 80 column(s)\n",
-       " delr: [40.00...40.00] meters\n",
-       " delc: [40.00...40.00] meters\n",
+       " 5 layer(s), 75 row(s), 85 column(s)\n",
+       " delr: [40.00...40.00] undefined\n",
+       " delc: [40.00...40.00] undefined\n",
        " CRS: EPSG:3070\n",
        " length units: meters\n",
-       " xll: 554200.0; yll: 389000.0; rotation: 0.0\n",
-       " Bounds: (554200.0, 557400.0, 389000.0, 391800.0)\n",
+       " xll: 554200.0; yll: 388800.0; rotation: 0\n",
+       " Bounds: (554200.0, 557600.0, 388800.0, 391800.0)\n",
        " Packages: dis ic npf sto rcha_0 oc sfr_0 lak_0 obs_0 obs_1\n",
        " 13 period(s):\n",
        "  per start_datetime end_datetime  perlen  steady  nstp\n",
@@ -507,17 +507,17 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.246898Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.246607Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.426586Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.426021Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.710654Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.710350Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.895461Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.894940Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 13,
@@ -526,7 +526,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -576,10 +576,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.428509Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.428328Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.931313Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.930848Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.897567Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.897161Z",
+     "iopub.status.idle": "2024-03-08T21:06:00.435022Z",
+     "shell.execute_reply": "2024-03-08T21:06:00.434545Z"
     }
    },
    "outputs": [
@@ -663,23 +663,21 @@
       "\n",
       "\n",
       "Checking segment_data for downstream rises in streambed elevation...\n",
-      "Segment elevup and elevdn not specified for nstrm=-13 and isfropt=1\n",
+      "Segment elevup and elevdn not specified for nstrm=-9 and isfropt=1\n",
       "passed.\n",
       "\n",
       "Checking reach_data for downstream rises in streambed elevation...\n",
-      "3 reaches encountered with strtop < strtop of downstream reach.\n",
+      "1 reaches encountered with strtop < strtop of downstream reach.\n",
       "Elevation rises:\n",
       "k i j iseg ireach strtop strtopdn d_strtop reachID diff\n",
-      "4 20 16 2 1 290.3638610839844 294.76507568359375 4.401214599609375 5 -4.401214599609375\n",
-      "4 20 17 2 2 290.3638610839844 293.5867614746094 3.222900390625 6 -3.222900390625\n",
-      "4 21 17 2 3 290.3638610839844 292.6883850097656 2.32452392578125 7 -2.32452392578125\n",
+      "4 21 17 2 1 290.66015625 292.6883850097656 2.028228759765625 3 -2.028228759765625\n",
       "\n",
       "\n",
       "Checking reach_data for inconsistencies between streambed elevations and the model grid...\n",
       "passed.\n",
       "\n",
       "Checking segment_data for inconsistencies between segment end elevations and the model grid...\n",
-      "Segment elevup and elevdn not specified for nstrm=-13 and isfropt=1\n",
+      "Segment elevup and elevdn not specified for nstrm=-9 and isfropt=1\n",
       "passed.\n",
       "\n",
       "Checking for streambed slopes of less than 0.0001...\n",
@@ -714,10 +712,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.933217Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.933050Z",
-     "iopub.status.idle": "2024-02-15T18:15:23.774605Z",
-     "shell.execute_reply": "2024-02-15T18:15:23.774102Z"
+     "iopub.execute_input": "2024-03-08T21:06:00.437084Z",
+     "iopub.status.busy": "2024-03-08T21:06:00.436763Z",
+     "iopub.status.idle": "2024-03-08T21:06:03.923736Z",
+     "shell.execute_reply": "2024-03-08T21:06:03.923214Z"
     }
    },
    "outputs": [
@@ -747,7 +745,7 @@
       "use of the software.\n",
       "\n",
       " \n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/02/15 18:15:20\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/03/08 21:06:00\n",
       " \n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -765,7 +763,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:     2    Time step:     1\n",
+      "    Solving:  Stress period:     2    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:     3    Time step:     1\n"
      ]
     },
@@ -781,7 +785,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:     6    Time step:     1\n",
+      "    Solving:  Stress period:     6    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:     7    Time step:     1\n"
      ]
     },
@@ -789,7 +799,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:     8    Time step:     1\n",
+      "    Solving:  Stress period:     8    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:     9    Time step:     1\n"
      ]
     },
@@ -797,7 +813,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:    10    Time step:     1\n",
+      "    Solving:  Stress period:    10    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:    11    Time step:     1\n"
      ]
     },
@@ -805,7 +827,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:    12    Time step:     1\n",
+      "    Solving:  Stress period:    12    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:    13    Time step:     1\n"
      ]
     },
@@ -814,8 +842,8 @@
      "output_type": "stream",
      "text": [
       " \n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/02/15 18:15:23\n",
-      " Elapsed run time:  2.832 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/03/08 21:06:03\n",
+      " Elapsed run time:  3.478 Seconds\n",
       " \n",
       " Normal termination of simulation.\n"
      ]
@@ -847,10 +875,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:23.776696Z",
-     "iopub.status.busy": "2024-02-15T18:15:23.776306Z",
-     "iopub.status.idle": "2024-02-15T18:15:23.788507Z",
-     "shell.execute_reply": "2024-02-15T18:15:23.788050Z"
+     "iopub.execute_input": "2024-03-08T21:06:03.925577Z",
+     "iopub.status.busy": "2024-03-08T21:06:03.925419Z",
+     "iopub.status.idle": "2024-03-08T21:06:03.938733Z",
+     "shell.execute_reply": "2024-03-08T21:06:03.938353Z"
     }
    },
    "outputs": [],
@@ -887,10 +915,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:23.790396Z",
-     "iopub.status.busy": "2024-02-15T18:15:23.790227Z",
-     "iopub.status.idle": "2024-02-15T18:15:23.859160Z",
-     "shell.execute_reply": "2024-02-15T18:15:23.858713Z"
+     "iopub.execute_input": "2024-03-08T21:06:03.940798Z",
+     "iopub.status.busy": "2024-03-08T21:06:03.940487Z",
+     "iopub.status.idle": "2024-03-08T21:06:04.020062Z",
+     "shell.execute_reply": "2024-03-08T21:06:04.019607Z"
     }
    },
    "outputs": [],
@@ -932,16 +960,16 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:23.861529Z",
-     "iopub.status.busy": "2024-02-15T18:15:23.861170Z",
-     "iopub.status.idle": "2024-02-15T18:15:24.880270Z",
-     "shell.execute_reply": "2024-02-15T18:15:24.879739Z"
+     "iopub.execute_input": "2024-03-08T21:06:04.022568Z",
+     "iopub.status.busy": "2024-03-08T21:06:04.022362Z",
+     "iopub.status.idle": "2024-03-08T21:06:05.114457Z",
+     "shell.execute_reply": "2024-03-08T21:06:05.113961Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1044,10 +1072,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:24.882450Z",
-     "iopub.status.busy": "2024-02-15T18:15:24.882128Z",
-     "iopub.status.idle": "2024-02-15T18:15:51.682129Z",
-     "shell.execute_reply": "2024-02-15T18:15:51.681656Z"
+     "iopub.execute_input": "2024-03-08T21:06:05.116791Z",
+     "iopub.status.busy": "2024-03-08T21:06:05.116330Z",
+     "iopub.status.idle": "2024-03-08T21:06:32.072593Z",
+     "shell.execute_reply": "2024-03-08T21:06:32.072096Z"
     }
    },
    "outputs": [
@@ -1094,14 +1122,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay2.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif\n"
+      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay2.tif\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif\n",
       "wrote postproc/plsnt_lgr_parent/rasters/botm_lay4.tif\n"
      ]
     },
@@ -1182,14 +1210,14 @@
      "output_type": "stream",
      "text": [
       "wrote postproc/plsnt_lgr_parent/rasters/k_lay1.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif\n"
+      "wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif\n",
       "wrote postproc/plsnt_lgr_parent/rasters/k_lay4.tif\n"
      ]
     },
@@ -1294,14 +1322,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif\n"
+      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif\n",
       "wrote postproc/plsnt_lgr_parent/rasters/recharge_per2.tif\n"
      ]
     },
@@ -1388,7 +1416,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "writing postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp... Done\n",
+      "writing postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp... Done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "q:\n"
      ]
     },
@@ -1585,14 +1619,14 @@
      "output_type": "stream",
      "text": [
       "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay4.tif\n",
-      "ss:\n",
-      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif\n"
+      "ss:\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif\n",
       "wrote postproc/plsnt_lgr_inset/rasters/ss_lay1.tif\n"
      ]
     },
@@ -1731,10 +1765,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:51.684099Z",
-     "iopub.status.busy": "2024-02-15T18:15:51.683915Z",
-     "iopub.status.idle": "2024-02-15T18:15:53.200913Z",
-     "shell.execute_reply": "2024-02-15T18:15:53.200421Z"
+     "iopub.execute_input": "2024-03-08T21:06:32.074474Z",
+     "iopub.status.busy": "2024-03-08T21:06:32.074319Z",
+     "iopub.status.idle": "2024-03-08T21:06:33.647971Z",
+     "shell.execute_reply": "2024-03-08T21:06:33.647537Z"
     }
    },
    "outputs": [
@@ -1790,7 +1824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/latest/_static/documentation_options.js b/latest/_static/documentation_options.js
index 8a0b8d62..cb352b47 100644
--- a/latest/_static/documentation_options.js
+++ b/latest/_static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.4.0.post3+gd2d459e',
+    VERSION: '0.4.0.post7+g63ba038',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/latest/api/index.html b/latest/api/index.html
index f183e108..84976b04 100644
--- a/latest/api/index.html
+++ b/latest/api/index.html
@@ -4,7 +4,7 @@
   
 
   
-  Code Reference — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Code Reference — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 
 
diff --git a/latest/api/mfsetup.discretization.html b/latest/api/mfsetup.discretization.html
index b4628bf9..f5a03a15 100644
--- a/latest/api/mfsetup.discretization.html
+++ b/latest/api/mfsetup.discretization.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.discretization module — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.discretization module — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -580,7 +580,7 @@
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/api/mfsetup.fileio.html b/latest/api/mfsetup.fileio.html
index 470e95cc..dac23592 100644
--- a/latest/api/mfsetup.fileio.html
+++ b/latest/api/mfsetup.fileio.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.fileio module — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.fileio module — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -369,7 +369,7 @@
 
   

     
© Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       

   

 
diff --git a/latest/api/mfsetup.grid.html b/latest/api/mfsetup.grid.html
index 53ce4fe5..617dbfe5 100644
--- a/latest/api/mfsetup.grid.html
+++ b/latest/api/mfsetup.grid.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.grid module — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.grid module — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               

-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               

 

   
@@ -87,6 +87,7 @@
 
  • get_transform()
    • 
 
  • rasterize()
    • 
 
  • setup_structured_grid()
    • 
+
  • snap_to_cell_corner()
    • 
 
  • write_bbox_shapefile()
    • 
 
 
@@ -640,7 +641,131 @@
 
    
 
    
 mfsetup.grid.setup_structured_grid(xoff=None, yoff=None, xul=None, yul=None, nrow=None, ncol=None, nlay=None, dxy=None, delr=None, delc=None, top=None, botm=None, rotation=0.0, parent_model=None, snap_to_parent=True, snap_to_NHG=False, features=None, features_shapefile=None, id_column=None, include_ids=None, buffer=1000, crs=None, epsg=None, prj=None, wkt=None, model_length_units=None, grid_file='grid.json', bbox_shapefile=None, **kwargs)[source]
    
-
    
+
    _summary_
    
+
    
+
    Parameters:
    
+
    
+
    xoff_type_, optional
    _description_, by default None
    
+
    
+
    yoff_type_, optional
    _description_, by default None
    
+
    
+
    xul_type_, optional
    _description_, by default None
    
+
    
+
    yul_type_, optional
    _description_, by default None
    
+
    
+
    nrow_type_, optional
    _description_, by default None
    
+
    
+
    ncol_type_, optional
    _description_, by default None
    
+
    
+
    nlay_type_, optional
    _description_, by default None
    
+
    
+
    dxy_type_, optional
    Specified uniform row/column spacing, in model grid
+(coordinate reference system) units, by default None
    
+
    
+
    delrscalar or sequence, optional
    Column spacing along a row, in model grid
+(coordinate reference system) units,
+by default None
    
+
    
+
    delcscalar or sequence, optional
    Row spacing along a column, in model grid
+(coordinate reference system) units,
+by default None
    
+
    
+
    top_type_, optional
    _description_, by default None
    
+
    
+
    botm_type_, optional
    _description_, by default None
    
+
    
+
    rotation_type_, optional
    _description_, by default 0.
    
+
    
+
    parent_model_type_, optional
    _description_, by default None
    
+
    
+
    snap_to_parentbool, optional
    _description_, by default True
    
+
    
+
    snap_to_NHGbool, optional
    _description_, by default False
    
+
    
+
    features_type_, optional
    _description_, by default None
    
+
    
+
    features_shapefile_type_, optional
    _description_, by default None
    
+
    
+
    id_column_type_, optional
    _description_, by default None
    
+
    
+
    include_ids_type_, optional
    _description_, by default None
    
+
    
+
    bufferint, optional
    _description_, by default 1000
    
+
    
+
    crs_type_, optional
    _description_, by default None
    
+
    
+
    epsg_type_, optional
    _description_, by default None
    
+
    
+
    prj_type_, optional
    _description_, by default None
    
+
    
+
    wkt_type_, optional
    _description_, by default None
    
+
    
+
    model_length_units_type_, optional
    _description_, by default None
    
+
    
+
    grid_filestr, optional
    _description_, by default ‘grid.json’
    
+
    
+
    bbox_shapefile_type_, optional
    _description_, by default None
    
+
    
+
    
+
    
+
    Returns:
    
+
    
+
    _type_
    _description_
    
+
    
+
    
+
    
+
    Raises:
    
+
    
+
    ValueError
    _description_
    
+
    
+
    ValueError
    _description_
    
+
    
+
    ValueError
    _description_
    
+
    
+
    ValueError
    _description_
    
+
    
+
    ValueError
    _description_
    
+
    
+
    ValueError
    _description_
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+mfsetup.grid.snap_to_cell_corner(x, y, modelgrid, corner='upper left')[source]
    
+
    Move an x, y location to the nearest cell corner on
+a rectilinear modelgrid.
    
+
    
+
    Parameters:
    
+
    
+
    xfloat
    x coordinate in coordinate reference system of modelgrid.
    
+
    
+
    y_type_
    y coordinate in coordinate reference system of modelgrid.
    
+
    
+
    modelgridFlopy StructuredGrid instance
    
+
    cornerstr, optional
    ‘upper left’ or ‘lower right’, by default ‘upper left’
    
+
    
+
    
+
    
+
    Returns:
    
+
    
+
    x_corner, y_corner
    x, y location of cell corner in coordinate reference system
+of modelgrid.
    
+
    
+
    
+
    
+
    Raises:
    
+
    
+
    ValueError
    If x, y are outside of the model domain, or if an invalid
+cell corner is specified.
    
+
    
+
    
+
    
+
    
+
    
 
 
    
 
    
@@ -661,7 +786,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/api/mfsetup.interpolate.html b/latest/api/mfsetup.interpolate.html
index 4d17c8ca..886d27e2 100644
--- a/latest/api/mfsetup.interpolate.html
+++ b/latest/api/mfsetup.interpolate.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.interpolate module — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.interpolate module — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -341,7 +341,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/api/mfsetup.mf6model.html b/latest/api/mfsetup.mf6model.html
index 6ac5adc9..a308160d 100644
--- a/latest/api/mfsetup.mf6model.html
+++ b/latest/api/mfsetup.mf6model.html
@@ -4,7 +4,7 @@
   
 
   
-  MF6model class — modflow-setup 0.4.0.post3+gd2d459e documentation
+  MF6model class — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
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             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -335,7 +335,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/api/mfsetup.mfmodel.html b/latest/api/mfsetup.mfmodel.html
index b2e66bec..c001471a 100644
--- a/latest/api/mfsetup.mfmodel.html
+++ b/latest/api/mfsetup.mfmodel.html
@@ -4,7 +4,7 @@
   
 
   
-  MFsetupMixin class — modflow-setup 0.4.0.post3+gd2d459e documentation
+  MFsetupMixin class — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
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+        
         
         
         
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-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -424,7 +424,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/api/mfsetup.mfnwtmodel.html b/latest/api/mfsetup.mfnwtmodel.html
index e9235ebb..8224aba1 100644
--- a/latest/api/mfsetup.mfnwtmodel.html
+++ b/latest/api/mfsetup.mfnwtmodel.html
@@ -4,7 +4,7 @@
   
 
   
-  MFnwtModel class — modflow-setup 0.4.0.post3+gd2d459e documentation
+  MFnwtModel class — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -236,7 +236,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/api/mfsetup.tdis.html b/latest/api/mfsetup.tdis.html
index 7b50964b..6b04b734 100644
--- a/latest/api/mfsetup.tdis.html
+++ b/latest/api/mfsetup.tdis.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.tdis module — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.tdis module — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -343,14 +343,14 @@
 
    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.
    
@@ -466,7 +466,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/api/mfsetup.tmr.html b/latest/api/mfsetup.tmr.html
index 17b2b075..629e2a1b 100644
--- a/latest/api/mfsetup.tmr.html
+++ b/latest/api/mfsetup.tmr.html
@@ -4,7 +4,7 @@
   
 
   
-  mfsetup.tmr module — modflow-setup 0.4.0.post3+gd2d459e documentation
+  mfsetup.tmr module — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
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-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -212,7 +212,7 @@
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/concepts/index.html b/latest/concepts/index.html
index 1aab2dad..fec9f74d 100644
--- a/latest/concepts/index.html
+++ b/latest/concepts/index.html
@@ -4,7 +4,7 @@
   
 
   
-  Modflow-setup concepts and methods — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Modflow-setup concepts and methods — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/concepts/interp.html b/latest/concepts/interp.html
index 4bdcdf8a..3f3c67e8 100644
--- a/latest/concepts/interp.html
+++ b/latest/concepts/interp.html
@@ -4,7 +4,7 @@
   
 
   
-  Interpolating data to the model grid — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Interpolating data to the model grid — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/concepts/perimeter-bcs.html b/latest/concepts/perimeter-bcs.html
index 15a2ae1f..2f11c706 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 0.4.0.post3+gd2d459e documentation
+  Specifying perimeter boundary conditions from another model — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/config-file-defaults.html b/latest/config-file-defaults.html
index cde2eb3c..f38dd8d9 100644
--- a/latest/config-file-defaults.html
+++ b/latest/config-file-defaults.html
@@ -4,7 +4,7 @@
   
 
   
-  Configuration defaults — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Configuration defaults — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -187,217 +187,216 @@ 
    MODFLOW-6 configuration defaults 72    start_date_time: '1970-01-01'
  73    end_date_time: None
  74  dimensions: {}
- 75  perioddata:
- 76    perlen: 1
- 77
- 78ic:
- 79  griddata:
- 80    strt:
- 81  source_data:
- 82    strt:
- 83    resample_method: 'linear'
- 84  strt_filename_fmt: "strt_{:03d}.dat"
- 85  write_fmt: '%.2f'
- 86
- 87npf:
- 88  options:
- 89    save_flows: True
- 90  griddata:
- 91    icelltype: 1
- 92  k_filename_fmt: "k_{:03d}.dat"
- 93  k33_filename_fmt: "k33_{:03d}.dat"
- 94
- 95sto:
- 96  options:
- 97    save_flows: True
- 98  griddata:
- 99    iconvert: 1
-100  sy_filename_fmt: "sy_{:03d}.dat"
-101  ss_filename_fmt: "ss_{:03d}.dat"
-102
-103rch:
-104  options:
-105    print_input: True
-106    print_flows: True
-107    save_flows: True
-108    readasarrays: True
-109  recharge_filename_fmt: "rch_{:03d}.dat"
-110  irch_filename_fmt: "irch.dat"
-111
-112sfr:
-113  options:
-114    save_flows: True
-115    mover: True
-116  budget_fileout: 'sfr.out.bin'
-117  stage_fileout: 'sfr.stage.bin'
-118  obs6_filein_fmt: 'sfr.obs'
-119  external_files: True  # option to write packagedata to an external file
-120
-121# option to simulate lakes as zones of high hydraulic conductivity
-122# (see Anderson and others (2002) in the references)
-123high_k_lakes:
-124  simulate_high_k_lakes: False
-125  high_k_value: 1.e+4
-126  sy: 1.0
-127  ss: 5.e-10  # (approx. compressibility of water in Pa-1 or m2/N)
-128
-129lak:
-130  options:
-131    save_flows: True
-132    budget_fileout: 'lake_out.bin'
-133    stage_fileout: 'lake_stage.bin'
-134    obs6_filein_fmt: '{}.sfr.obs6'
-135    boundnames: True
-136  lakarr_filename_fmt: 'lakarr_{:03d}.dat'
-137  lakzones_filename_fmt: 'lakzones.dat' # file containing zones for lakebed leakance
-138  external_files: True
-139  horizontal_connections: False
-140  connectiondata_filename_fmt: 'lake_connectiondata.dat'  # external table for connectiondata block
-141  source_data:
-142    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    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"
+ 75  perioddata: {}
+ 76
+ 77ic:
+ 78  griddata:
+ 79    strt:
+ 80  source_data:
+ 81    strt:
+ 82    resample_method: 'linear'
+ 83  strt_filename_fmt: "strt_{:03d}.dat"
+ 84  write_fmt: '%.2f'
+ 85
+ 86npf:
+ 87  options:
+ 88    save_flows: True
+ 89  griddata:
+ 90    icelltype: 1
+ 91  k_filename_fmt: "k_{:03d}.dat"
+ 92  k33_filename_fmt: "k33_{:03d}.dat"
+ 93
+ 94sto:
+ 95  options:
+ 96    save_flows: True
+ 97  griddata:
+ 98    iconvert: 1
+ 99  sy_filename_fmt: "sy_{:03d}.dat"
+100  ss_filename_fmt: "ss_{:03d}.dat"
+101
+102rch:
+103  options:
+104    print_input: True
+105    print_flows: True
+106    save_flows: True
+107    readasarrays: True
+108  recharge_filename_fmt: "rch_{:03d}.dat"
+109  irch_filename_fmt: "irch.dat"
+110
+111sfr:
+112  options:
+113    save_flows: True
+114    mover: True
+115  budget_fileout: 'sfr.out.bin'
+116  stage_fileout: 'sfr.stage.bin'
+117  obs6_filein_fmt: 'sfr.obs'
+118  external_files: True  # option to write packagedata to an external file
+119
+120# option to simulate lakes as zones of high hydraulic conductivity
+121# (see Anderson and others (2002) in the references)
+122high_k_lakes:
+123  simulate_high_k_lakes: False
+124  high_k_value: 1.e+4
+125  sy: 1.0
+126  ss: 5.e-10  # (approx. compressibility of water in Pa-1 or m2/N)
+127
+128lak:
+129  options:
+130    save_flows: True
+131    budget_fileout: 'lake_out.bin'
+132    stage_fileout: 'lake_stage.bin'
+133    obs6_filein_fmt: '{}.sfr.obs6'
+134    boundnames: True
+135  lakarr_filename_fmt: 'lakarr_{:03d}.dat'
+136  lakzones_filename_fmt: 'lakzones.dat' # file containing zones for lakebed leakance
+137  external_files: True
+138  horizontal_connections: False
+139  connectiondata_filename_fmt: 'lake_connectiondata.dat'  # external table for connectiondata block
+140  source_data:
+141    littoral_zone_buffer_width: 20
+142  output_files:
+143    lookup_file: '{}_lak_lookup.csv'  # output file that maps lake ids to source polygon feature ids
+144    lak_polygons_shapefile: '{}_lak_polygons.shp'
+145    connections_lookup_file: '{}_lak_connections_lookup.csv'  # output file that maps lake/gw connections to zones
+146    connections_shapefile: '{}_lak_cells.shp'
+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
 234
 235
-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
+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
 
    
 
    
 
@@ -648,7 +647,7 @@ 
    MODFLOW-NWT configuration defaults
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/config-file-gallery.html b/latest/config-file-gallery.html
index 2138194f..30459f30 100644
--- a/latest/config-file-gallery.html
+++ b/latest/config-file-gallery.html
@@ -4,7 +4,7 @@
   
 
   
-  Configuration File Gallery — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Configuration File Gallery — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/config-file.html b/latest/config-file.html
index cf6d70d6..cb2cca28 100644
--- a/latest/config-file.html
+++ b/latest/config-file.html
@@ -4,7 +4,7 @@
   
 
   
-  The configuration file — modflow-setup 0.4.0.post3+gd2d459e documentation
+  The configuration file — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/contributing.html b/latest/contributing.html
index a79e81de..6c596f25 100644
--- a/latest/contributing.html
+++ b/latest/contributing.html
@@ -4,7 +4,7 @@
   
 
   
-  Contributing to modflow-setup — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Contributing to modflow-setup — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/examples.html b/latest/examples.html
index 7774266a..63f9bda8 100644
--- a/latest/examples.html
+++ b/latest/examples.html
@@ -4,7 +4,7 @@
   
 
   
-  Examples — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Examples — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -129,7 +129,7 @@ 
    Examples
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/genindex.html b/latest/genindex.html
index f52f7842..f19f4f75 100644
--- a/latest/genindex.html
+++ b/latest/genindex.html
@@ -3,7 +3,7 @@
 
   
   
-  Index — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Index — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -37,7 +37,7 @@
             modflow-setup
               
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -627,6 +627,8 @@ 
    S
    
 
       
       
  • size (mfsetup.grid.MFsetupGrid property)
+
    • 
+      
  • snap_to_cell_corner() (in module mfsetup.grid)
 
    • 
       
  • source_path (mfsetup.mfmodel.MFsetupMixin attribute)
 
    • 
@@ -717,7 +719,7 @@ 
    Y
    
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/index.html b/latest/index.html
index f6cc025a..8d6b1b55 100644
--- a/latest/index.html
+++ b/latest/index.html
@@ -4,7 +4,7 @@
   
 
   
-  modflow-setup 0.4.0.post3+gd2d459e — modflow-setup 0.4.0.post3+gd2d459e documentation
+  modflow-setup 0.4.0.post7+g63ba038 — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -39,7 +39,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -93,7 +93,7 @@
           
    
   
    
+
    
+
    
+
    
+
    
+
         Solving:  Stress period:     7    Time step:     1
 
    
 
    
@@ -728,6 +740,13 @@ 
    Run the model
 
         Solving:  Stress period:     8    Time step:     1
+
    
+
    
+
    
+
    
+
    
+
    
+
         Solving:  Stress period:     9    Time step:     1
 
    
 
    
@@ -737,6 +756,13 @@ 
    Run the model
 
         Solving:  Stress period:    10    Time step:     1
+
    
+
    
+
    
+
    
+
    
+
    
+
         Solving:  Stress period:    11    Time step:     1
 
    
 
    
@@ -746,6 +772,13 @@ 
    Run the model
 
         Solving:  Stress period:    12    Time step:     1
+
    
+
    
+
    
+
    
+
    
+
    
+
         Solving:  Stress period:    13    Time step:     1
 
    
 
    
@@ -755,8 +788,8 @@ 
    Run the model
 
     
- Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/02/15 18:15:23
- Elapsed run time:  2.832 Seconds
+ Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/03/08 21:06:03
+ Elapsed run time:  3.478 Seconds
 
  Normal termination of simulation.
 
    
@@ -990,7 +1023,6 @@ 
    Use 
 
     wrote postproc/plsnt_lgr_parent/rasters/botm_lay2.tif
-wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif
 
    
 
    
 
    
@@ -998,6 +1030,7 @@ 
    Use 
 
    +wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif
 wrote postproc/plsnt_lgr_parent/rasters/botm_lay4.tif
 
    
 
    
@@ -1088,7 +1121,6 @@ 
    Use 
     wrote postproc/plsnt_lgr_parent/rasters/k_lay1.tif
 wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif
-wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif
 
    
 
    
 
    
@@ -1096,6 +1128,7 @@ 
    Use 
 
    +wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif
 wrote postproc/plsnt_lgr_parent/rasters/k_lay4.tif
 
    
 
    
@@ -1213,7 +1246,6 @@ 
    Use 
 
     wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif
-wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif
 
    
 
    
 
    
@@ -1221,6 +1253,7 @@ 
    Use 
 
    +wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif
 wrote postproc/plsnt_lgr_parent/rasters/recharge_per2.tif
 
    
 
    
@@ -1318,6 +1351,13 @@ 
    Use 
 
     writing postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp... Done
+
    
+
    
+
    
+
    
+
    
+
    
+
     q:
 
    
 
    
@@ -1536,7 +1576,6 @@ 
    Use 
     wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay4.tif
 ss:
-wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif
 
    
 
    
 
    
@@ -1544,6 +1583,7 @@ 
    Use 
 
    +wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif
 wrote postproc/plsnt_lgr_inset/rasters/ss_lay1.tif
 
    
 
    
@@ -1738,7 +1778,7 @@ 
    Modflow-export can also create a summary table of the model inputs
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/notebooks/Pleasant_lake_lgr_example.ipynb b/latest/notebooks/Pleasant_lake_lgr_example.ipynb
index 04aeea9f..9aeaee9d 100644
--- a/latest/notebooks/Pleasant_lake_lgr_example.ipynb
+++ b/latest/notebooks/Pleasant_lake_lgr_example.ipynb
@@ -40,10 +40,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:11.064038Z",
-     "iopub.status.busy": "2024-02-15T18:15:11.063872Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.282144Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.281592Z"
+     "iopub.execute_input": "2024-03-08T21:05:47.611726Z",
+     "iopub.status.busy": "2024-03-08T21:05:47.611342Z",
+     "iopub.status.idle": "2024-03-08T21:05:48.839373Z",
+     "shell.execute_reply": "2024-03-08T21:05:48.838964Z"
     }
    },
    "outputs": [],
@@ -81,10 +81,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.284852Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.284326Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.911753Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.911329Z"
+     "iopub.execute_input": "2024-03-08T21:05:48.841717Z",
+     "iopub.status.busy": "2024-03-08T21:05:48.841417Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.810092Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.809560Z"
     }
    },
    "outputs": [],
@@ -106,10 +106,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.914062Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.913879Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.919679Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.919163Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.812675Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.812315Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.818653Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.818053Z"
     }
    },
    "outputs": [
@@ -117,8 +117,8 @@
      "data": {
       "text/plain": [
        "5 layer(s), 25 row(s), 25 column(s)\n",
-       "delr: [200.00...200.00] meters\n",
-       "delc: [200.00...200.00] meters\n",
+       "delr: [200.00...200.00] undefined\n",
+       "delc: [200.00...200.00] undefined\n",
        "CRS: EPSG:3070\n",
        "length units: meters\n",
        "xll: 553000.0; yll: 388000.0; rotation: 0.0\n",
@@ -139,23 +139,23 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.947247Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.946831Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.950811Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.950410Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.846678Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.846295Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.850773Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.850289Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "70 row(s), 80 column(s)\n",
-       "delr: [40.00...40.00] meters\n",
-       "delc: [40.00...40.00] meters\n",
+       "75 row(s), 85 column(s)\n",
+       "delr: [40.00...40.00] undefined\n",
+       "delc: [40.00...40.00] undefined\n",
        "CRS: EPSG:3070\n",
        "length units: meters\n",
-       "xll: 554200.0; yll: 389000.0; rotation: 0.0\n",
-       "Bounds: (554200.0, 557400.0, 389000.0, 391800.0)"
+       "xll: 554200.0; yll: 388800.0; rotation: 0\n",
+       "Bounds: (554200.0, 557600.0, 388800.0, 391800.0)"
       ]
      },
      "execution_count": 4,
@@ -180,10 +180,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.952701Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.952363Z",
-     "iopub.status.idle": "2024-02-15T18:15:12.955773Z",
-     "shell.execute_reply": "2024-02-15T18:15:12.955318Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.852778Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.852485Z",
+     "iopub.status.idle": "2024-03-08T21:05:49.855810Z",
+     "shell.execute_reply": "2024-03-08T21:05:49.855357Z"
     }
    },
    "outputs": [
@@ -215,10 +215,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:12.957704Z",
-     "iopub.status.busy": "2024-02-15T18:15:12.957325Z",
-     "iopub.status.idle": "2024-02-15T18:15:13.826502Z",
-     "shell.execute_reply": "2024-02-15T18:15:13.825975Z"
+     "iopub.execute_input": "2024-03-08T21:05:49.857669Z",
+     "iopub.status.busy": "2024-03-08T21:05:49.857391Z",
+     "iopub.status.idle": "2024-03-08T21:05:50.868704Z",
+     "shell.execute_reply": "2024-03-08T21:05:50.868216Z"
     }
    },
    "outputs": [
@@ -237,7 +237,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "finished in 0.18s\n",
+      "finished in 0.19s\n",
       "\n"
      ]
     },
@@ -266,10 +266,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:13.828672Z",
-     "iopub.status.busy": "2024-02-15T18:15:13.828285Z",
-     "iopub.status.idle": "2024-02-15T18:15:13.831029Z",
-     "shell.execute_reply": "2024-02-15T18:15:13.830580Z"
+     "iopub.execute_input": "2024-03-08T21:05:50.870936Z",
+     "iopub.status.busy": "2024-03-08T21:05:50.870522Z",
+     "iopub.status.idle": "2024-03-08T21:05:50.873305Z",
+     "shell.execute_reply": "2024-03-08T21:05:50.872808Z"
     }
    },
    "outputs": [],
@@ -289,10 +289,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:13.832877Z",
-     "iopub.status.busy": "2024-02-15T18:15:13.832689Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.217191Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.216745Z"
+     "iopub.execute_input": "2024-03-08T21:05:50.875384Z",
+     "iopub.status.busy": "2024-03-08T21:05:50.875083Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.681140Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.680708Z"
     }
    },
    "outputs": [],
@@ -313,21 +313,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.219536Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.219198Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.226261Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.225701Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.683436Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.683078Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.690254Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.689706Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Pleasant Lake test case version 0.1.post3+gd2d459e\n",
+       "Pleasant Lake test case version 0.1.post7+g63ba038\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",
-       "delc: [200.00...200.00] meters\n",
+       "delr: [200.00...200.00] undefined\n",
+       "delc: [200.00...200.00] undefined\n",
        "CRS: EPSG:3070\n",
        "length units: meters\n",
        "xll: 553000.0; yll: 388000.0; rotation: 0.0\n",
@@ -363,10 +363,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.228055Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.227887Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.231307Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.230809Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.692088Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.691931Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.695284Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.694895Z"
     }
    },
    "outputs": [
@@ -397,10 +397,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.233160Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.232876Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.236784Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.236362Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.696967Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.696807Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.700578Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.700110Z"
     }
    },
    "outputs": [
@@ -457,25 +457,25 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.238486Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.238320Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.245041Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.244608Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.702394Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.702095Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.708594Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.708157Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.4.0.post3+gd2d459e\n",
+       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.4.0.post7+g63ba038\n",
        " Parent model: ./plsnt_lgr_parent\n",
-       " 5 layer(s), 70 row(s), 80 column(s)\n",
-       " delr: [40.00...40.00] meters\n",
-       " delc: [40.00...40.00] meters\n",
+       " 5 layer(s), 75 row(s), 85 column(s)\n",
+       " delr: [40.00...40.00] undefined\n",
+       " delc: [40.00...40.00] undefined\n",
        " CRS: EPSG:3070\n",
        " length units: meters\n",
-       " xll: 554200.0; yll: 389000.0; rotation: 0.0\n",
-       " Bounds: (554200.0, 557400.0, 389000.0, 391800.0)\n",
+       " xll: 554200.0; yll: 388800.0; rotation: 0\n",
+       " Bounds: (554200.0, 557600.0, 388800.0, 391800.0)\n",
        " Packages: dis ic npf sto rcha_0 oc sfr_0 lak_0 obs_0 obs_1\n",
        " 13 period(s):\n",
        "  per start_datetime end_datetime  perlen  steady  nstp\n",
@@ -507,17 +507,17 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.246898Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.246607Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.426586Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.426021Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.710654Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.710350Z",
+     "iopub.status.idle": "2024-03-08T21:05:59.895461Z",
+     "shell.execute_reply": "2024-03-08T21:05:59.894940Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 13,
@@ -526,7 +526,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -576,10 +576,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.428509Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.428328Z",
-     "iopub.status.idle": "2024-02-15T18:15:20.931313Z",
-     "shell.execute_reply": "2024-02-15T18:15:20.930848Z"
+     "iopub.execute_input": "2024-03-08T21:05:59.897567Z",
+     "iopub.status.busy": "2024-03-08T21:05:59.897161Z",
+     "iopub.status.idle": "2024-03-08T21:06:00.435022Z",
+     "shell.execute_reply": "2024-03-08T21:06:00.434545Z"
     }
    },
    "outputs": [
@@ -663,23 +663,21 @@
       "\n",
       "\n",
       "Checking segment_data for downstream rises in streambed elevation...\n",
-      "Segment elevup and elevdn not specified for nstrm=-13 and isfropt=1\n",
+      "Segment elevup and elevdn not specified for nstrm=-9 and isfropt=1\n",
       "passed.\n",
       "\n",
       "Checking reach_data for downstream rises in streambed elevation...\n",
-      "3 reaches encountered with strtop < strtop of downstream reach.\n",
+      "1 reaches encountered with strtop < strtop of downstream reach.\n",
       "Elevation rises:\n",
       "k i j iseg ireach strtop strtopdn d_strtop reachID diff\n",
-      "4 20 16 2 1 290.3638610839844 294.76507568359375 4.401214599609375 5 -4.401214599609375\n",
-      "4 20 17 2 2 290.3638610839844 293.5867614746094 3.222900390625 6 -3.222900390625\n",
-      "4 21 17 2 3 290.3638610839844 292.6883850097656 2.32452392578125 7 -2.32452392578125\n",
+      "4 21 17 2 1 290.66015625 292.6883850097656 2.028228759765625 3 -2.028228759765625\n",
       "\n",
       "\n",
       "Checking reach_data for inconsistencies between streambed elevations and the model grid...\n",
       "passed.\n",
       "\n",
       "Checking segment_data for inconsistencies between segment end elevations and the model grid...\n",
-      "Segment elevup and elevdn not specified for nstrm=-13 and isfropt=1\n",
+      "Segment elevup and elevdn not specified for nstrm=-9 and isfropt=1\n",
       "passed.\n",
       "\n",
       "Checking for streambed slopes of less than 0.0001...\n",
@@ -714,10 +712,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:20.933217Z",
-     "iopub.status.busy": "2024-02-15T18:15:20.933050Z",
-     "iopub.status.idle": "2024-02-15T18:15:23.774605Z",
-     "shell.execute_reply": "2024-02-15T18:15:23.774102Z"
+     "iopub.execute_input": "2024-03-08T21:06:00.437084Z",
+     "iopub.status.busy": "2024-03-08T21:06:00.436763Z",
+     "iopub.status.idle": "2024-03-08T21:06:03.923736Z",
+     "shell.execute_reply": "2024-03-08T21:06:03.923214Z"
     }
    },
    "outputs": [
@@ -747,7 +745,7 @@
       "use of the software.\n",
       "\n",
       " \n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/02/15 18:15:20\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/03/08 21:06:00\n",
       " \n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -765,7 +763,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:     2    Time step:     1\n",
+      "    Solving:  Stress period:     2    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:     3    Time step:     1\n"
      ]
     },
@@ -781,7 +785,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:     6    Time step:     1\n",
+      "    Solving:  Stress period:     6    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:     7    Time step:     1\n"
      ]
     },
@@ -789,7 +799,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:     8    Time step:     1\n",
+      "    Solving:  Stress period:     8    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:     9    Time step:     1\n"
      ]
     },
@@ -797,7 +813,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:    10    Time step:     1\n",
+      "    Solving:  Stress period:    10    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:    11    Time step:     1\n"
      ]
     },
@@ -805,7 +827,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Solving:  Stress period:    12    Time step:     1\n",
+      "    Solving:  Stress period:    12    Time step:     1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Solving:  Stress period:    13    Time step:     1\n"
      ]
     },
@@ -814,8 +842,8 @@
      "output_type": "stream",
      "text": [
       " \n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/02/15 18:15:23\n",
-      " Elapsed run time:  2.832 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/03/08 21:06:03\n",
+      " Elapsed run time:  3.478 Seconds\n",
       " \n",
       " Normal termination of simulation.\n"
      ]
@@ -847,10 +875,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:23.776696Z",
-     "iopub.status.busy": "2024-02-15T18:15:23.776306Z",
-     "iopub.status.idle": "2024-02-15T18:15:23.788507Z",
-     "shell.execute_reply": "2024-02-15T18:15:23.788050Z"
+     "iopub.execute_input": "2024-03-08T21:06:03.925577Z",
+     "iopub.status.busy": "2024-03-08T21:06:03.925419Z",
+     "iopub.status.idle": "2024-03-08T21:06:03.938733Z",
+     "shell.execute_reply": "2024-03-08T21:06:03.938353Z"
     }
    },
    "outputs": [],
@@ -887,10 +915,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:23.790396Z",
-     "iopub.status.busy": "2024-02-15T18:15:23.790227Z",
-     "iopub.status.idle": "2024-02-15T18:15:23.859160Z",
-     "shell.execute_reply": "2024-02-15T18:15:23.858713Z"
+     "iopub.execute_input": "2024-03-08T21:06:03.940798Z",
+     "iopub.status.busy": "2024-03-08T21:06:03.940487Z",
+     "iopub.status.idle": "2024-03-08T21:06:04.020062Z",
+     "shell.execute_reply": "2024-03-08T21:06:04.019607Z"
     }
    },
    "outputs": [],
@@ -932,16 +960,16 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:23.861529Z",
-     "iopub.status.busy": "2024-02-15T18:15:23.861170Z",
-     "iopub.status.idle": "2024-02-15T18:15:24.880270Z",
-     "shell.execute_reply": "2024-02-15T18:15:24.879739Z"
+     "iopub.execute_input": "2024-03-08T21:06:04.022568Z",
+     "iopub.status.busy": "2024-03-08T21:06:04.022362Z",
+     "iopub.status.idle": "2024-03-08T21:06:05.114457Z",
+     "shell.execute_reply": "2024-03-08T21:06:05.113961Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
    "
       ]
@@ -1044,10 +1072,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:24.882450Z",
-     "iopub.status.busy": "2024-02-15T18:15:24.882128Z",
-     "iopub.status.idle": "2024-02-15T18:15:51.682129Z",
-     "shell.execute_reply": "2024-02-15T18:15:51.681656Z"
+     "iopub.execute_input": "2024-03-08T21:06:05.116791Z",
+     "iopub.status.busy": "2024-03-08T21:06:05.116330Z",
+     "iopub.status.idle": "2024-03-08T21:06:32.072593Z",
+     "shell.execute_reply": "2024-03-08T21:06:32.072096Z"
     }
    },
    "outputs": [
@@ -1094,14 +1122,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay2.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif\n"
+      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay2.tif\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif\n",
       "wrote postproc/plsnt_lgr_parent/rasters/botm_lay4.tif\n"
      ]
     },
@@ -1182,14 +1210,14 @@
      "output_type": "stream",
      "text": [
       "wrote postproc/plsnt_lgr_parent/rasters/k_lay1.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif\n"
+      "wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif\n",
       "wrote postproc/plsnt_lgr_parent/rasters/k_lay4.tif\n"
      ]
     },
@@ -1294,14 +1322,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif\n",
-      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif\n"
+      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif\n",
       "wrote postproc/plsnt_lgr_parent/rasters/recharge_per2.tif\n"
      ]
     },
@@ -1388,7 +1416,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "writing postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp... Done\n",
+      "writing postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp... Done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "q:\n"
      ]
     },
@@ -1585,14 +1619,14 @@
      "output_type": "stream",
      "text": [
       "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay4.tif\n",
-      "ss:\n",
-      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif\n"
+      "ss:\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif\n",
       "wrote postproc/plsnt_lgr_inset/rasters/ss_lay1.tif\n"
      ]
     },
@@ -1731,10 +1765,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-15T18:15:51.684099Z",
-     "iopub.status.busy": "2024-02-15T18:15:51.683915Z",
-     "iopub.status.idle": "2024-02-15T18:15:53.200913Z",
-     "shell.execute_reply": "2024-02-15T18:15:53.200421Z"
+     "iopub.execute_input": "2024-03-08T21:06:32.074474Z",
+     "iopub.status.busy": "2024-03-08T21:06:32.074319Z",
+     "iopub.status.idle": "2024-03-08T21:06:33.647971Z",
+     "shell.execute_reply": "2024-03-08T21:06:33.647537Z"
     }
    },
    "outputs": [
@@ -1790,7 +1824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/latest/objects.inv b/latest/objects.inv
index 773d804d..1cd07823 100644
Binary files a/latest/objects.inv and b/latest/objects.inv differ
diff --git a/latest/philosophy.html b/latest/philosophy.html
index a7e9321e..2cc98c6d 100644
--- a/latest/philosophy.html
+++ b/latest/philosophy.html
@@ -4,7 +4,7 @@
   
 
   
-  Philosophy — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Philosophy — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
@@ -25,7 +25,7 @@
     
     
     
-     
+     
 
 
  
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -137,7 +137,7 @@ 
    What modflow-setup doesn’t do
-        
+        
         
     
    
 
@@ -145,7 +145,7 @@ 
    What modflow-setup doesn’t do
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/py-modindex.html b/latest/py-modindex.html
index 5c4aabb9..138a62bd 100644
--- a/latest/py-modindex.html
+++ b/latest/py-modindex.html
@@ -3,7 +3,7 @@
 
   
   
-  Python Module Index — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Python Module Index — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -14,7 +14,7 @@
     
   
   
-        
+        
         
         
         
@@ -40,7 +40,7 @@
             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
    
   
@@ -176,7 +176,7 @@ 
    Python Module Index
    
 
   
    
     
    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
diff --git a/latest/references.html b/latest/references.html
index b9d4d7e3..43b1426d 100644
--- a/latest/references.html
+++ b/latest/references.html
@@ -4,7 +4,7 @@
   
 
   
-  References — modflow-setup 0.4.0.post3+gd2d459e documentation
+  References — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
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diff --git a/latest/search.html b/latest/search.html
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    © Copyright 2019-2024, Modflow-setup developers |.
-      Last updated on Feb 15, 2024.
+      Last updated on Mar 08, 2024.
       
    
   
    
 
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"develop-flowlines-to-represent-streams"]], "Preprocessing NHDPlus HR": [[0, "preprocessing-nhdplus-hr"]], "Preprocessing NHDPlus version 2": [[0, "preprocessing-nhdplus-version-2"]], "4) Get a DEM": [[0, "get-a-dem"]], "Making a virtual raster": [[0, "making-a-virtual-raster"]], "5) Make a minimum working configuration 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"]], "Interpolating data to the model grid": [[12, "interpolating-data-to-the-model-grid"]], "Specifying perimeter boundary conditions from another model": [[13, "specifying-perimeter-boundary-conditions-from-another-model"]], "Features and Limitations": [[13, "features-and-limitations"]], "Configuration input": [[13, "configuration-input"]], "Specifying the time discretization": [[13, "specifying-the-time-discretization"]], "Specifying the locations of perimeter boundary cells": [[13, "specifying-the-locations-of-perimeter-boundary-cells"]], "The configuration file": [[14, "the-configuration-file"]], "The YAML format": [[14, "the-yaml-format"]], "Configuration file structure": [[14, "configuration-file-structure"]], "Package blocks": [[14, "package-blocks"]], "Sub-blocks": [[14, "sub-blocks"]], "Directly specifying MODFLOW input": [[14, "directly-specifying-modflow-input"]], "Source_data sub-blocks": [[14, "source-data-sub-blocks"]], "Some additional notes on YAML": [[14, "some-additional-notes-on-yaml"]], "Configuration defaults": [[15, "configuration-defaults"]], "MODFLOW-6 configuration defaults": [[15, "modflow-6-configuration-defaults"]], "MODFLOW-NWT configuration defaults": [[15, "modflow-nwt-configuration-defaults"]], "Configuration File Gallery": [[16, "configuration-file-gallery"]], "Shellmound test case": [[16, "shellmound-test-case"]], "Shellmound TMR inset test case": [[16, "shellmound-tmr-inset-test-case"]], "Pleasant Lake test case": [[16, "pleasant-lake-test-case"]], "LGR parent model configuration": [[16, "lgr-parent-model-configuration"]], "pleasant_lgr_inset.yml": [[16, "pleasant-lgr-inset-yml"]], "Pleasant Lake MODFLOW-NWT test case": [[16, "pleasant-lake-modflow-nwt-test-case"]], "Plainfield Lakes MODFLOW-NWT test case": [[16, "plainfield-lakes-modflow-nwt-test-case"]], "Contributing to modflow-setup": [[17, "contributing-to-modflow-setup"]], "Getting started": [[17, "getting-started"]], "Bug reports and enhancement requests": [[17, "bug-reports-and-enhancement-requests"]], "Code contributions": [[17, "code-contributions"]], "Seven Steps for Contributing": [[17, "seven-steps-for-contributing"]], "1) Forking the modflow-setup repository using Git": [[17, "forking-the-modflow-setup-repository-using-git"]], "Getting started with Git": [[17, "getting-started-with-git"]], "Forking": [[17, "forking"]], "Creating a branch": [[17, "creating-a-branch"]], "2 & 3) Creating a development environment with the required dependencies": [[17, "creating-a-development-environment-with-the-required-dependencies"]], "4) Installing the modflow-setup source code": [[17, "installing-the-modflow-setup-source-code"]], "5) Making changes and writing tests": [[17, "making-changes-and-writing-tests"]], "Writing tests": [[17, "writing-tests"]], "Running the test suite": [[17, "running-the-test-suite"]], "6) Updating the Documentation": [[17, "updating-the-documentation"]], "7) Submitting a Pull Request": [[17, "submitting-a-pull-request"]], "Style Guide & Linting": [[17, "style-guide-linting"]], "Examples": [[18, "examples"]], "Example problems": [[18, null]], "modflow-setup 0.4.0.post3+gd2d459e": [[19, "modflow-setup-version"]], "Getting Started": [[19, null]], "User Guide": [[19, null]], "Reference": [[19, null]], "Bibliography": [[19, null]], "Specifying boundary conditions with the \u2018basic\u2019 MODFLOW stress packages": [[20, "specifying-boundary-conditions-with-the-basic-modflow-stress-packages"]], "List-based basic stress packages": [[20, "list-based-basic-stress-packages"]], "Constant Head (CHD) Package": [[20, "constant-head-chd-package"]], "Drain DRN Package": [[20, "drain-drn-package"]], "General Head Boundary (GHB) Package": [[20, "general-head-boundary-ghb-package"]], "River (RIV) package": [[20, "river-riv-package"]], "Well (WEL) Package": 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"specifying-pre-defined-stress-periods-from-a-csv-file"]], "Example Stress period data": [[21, "id1"]], "Specifying Initial Conditions": [[22, "specifying-initial-conditions"]], "Input instructions by package": [[23, "input-instructions-by-package"]], "The Lake (LAK) Package": [[24, "the-lake-lak-package"]], "Head observations": [[25, "head-observations"]], "MODFLOW Output Control": [[26, "modflow-output-control"]], "Stress period input format": [[26, "stress-period-input-format"]], "Output filenames and other arguments": [[26, "output-filenames-and-other-arguments"]], "Alternative stress period input formats": [[26, "alternative-stress-period-input-formats"]], "Specifying Aquifer Properties": [[27, "specifying-aquifer-properties"]], "The Streamflow Routing (SFR) Package": [[28, "the-streamflow-routing-sfr-package"]], "Installation": [[29, "installation"]], "Installing python dependencies with Conda": [[29, "installing-python-dependencies-with-conda"]], "Download and install a python distribution and Conda-like package installer": [[29, "download-and-install-a-python-distribution-and-conda-like-package-installer"]], "Download an environment file": [[29, "download-an-environment-file"]], "Creating a Conda environment using Mamba": [[29, "creating-a-conda-environment-using-mamba"]], "Keeping the Conda environment up to date": [[29, "keeping-the-conda-environment-up-to-date"]], "Installing Modflow-setup": [[29, "installing-modflow-setup"]], "Installing and updating Modflow-setup from PyPI": [[29, "installing-and-updating-modflow-setup-from-pypi"]], "Installing the latest develop version of Modflow-setup": [[29, "installing-the-latest-develop-version-of-modflow-setup"]], "Installing the Modflow-setup source code in-place": [[29, "installing-the-modflow-setup-source-code-in-place"]], "Installing the IPython kernel to use Modflow-setup in Jupyter Notebooks": [[29, "installing-the-ipython-kernel-to-use-modflow-setup-in-jupyter-notebooks"]], "Best practices": [[29, "best-practices"]], "Considerations for USGS Users": [[29, "id4"]], "Installing the DOI SSL certificate for use with pip": [[29, "id5"]], "Installing the DOI SSL certificate for use with conda": [[29, "installing-the-doi-ssl-certificate-for-use-with-conda"]], "Troubleshooting issues with the USGS network": [[29, "troubleshooting-issues-with-the-usgs-network"]], "SSL-related error messages when using conda": [[29, "ssl-related-error-messages-when-using-conda"]], "SSL-related error messages when using pip": [[29, "ssl-related-error-messages-when-using-pip"]], "If you are on the USGS network, using Windows, and you get this error message:": [[29, "if-you-are-on-the-usgs-network-using-windows-and-you-get-this-error-message"]], "Pleasant Lake Example": [[30, "Pleasant-Lake-Example"]], "Model details": [[30, "Model-details"]], "Just make a model grid": [[30, "Just-make-a-model-grid"]], "Working directory gottcha": [[30, "Working-directory-gottcha"]], "Write shapefiles of the inset and parent modelgrids": [[30, "Write-shapefiles-of-the-inset-and-parent-modelgrids"]], "Change the working directory back to the notebook location": [[30, "Change-the-working-directory-back-to-the-notebook-location"]], "Build the whole model": [[30, "Build-the-whole-model"]], "Plot the inset and parent model grids with Lake Package connections by layer": [[30, "Plot-the-inset-and-parent-model-grids-with-Lake-Package-connections-by-layer"]], "write the MODFLOW input files": [[30, "write-the-MODFLOW-input-files"]], "Run the model": [[30, "Run-the-model"]], "Plot the head results": [[30, "Plot-the-head-results"]], "First combine the parent and inset model head results": [[30, "First-combine-the-parent-and-inset-model-head-results"]], "Make the plot": [[30, "Make-the-plot"]], "Use Modflow-export to export the modflow input to PDFs, rasters and shapefiles": [[30, "Use-Modflow-export-to-export-the-modflow-input-to-PDFs,-rasters-and-shapefiles"]], "Modflow-export can also create a summary table of the 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\ No newline at end of file
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"concepts/index.rst", "concepts/interp.rst", "concepts/perimeter-bcs.rst", "config-file.rst", "config-file-defaults.rst", "config-file-gallery.rst", "contributing.rst", "examples.rst", "index.rst", "input/basic-stress.rst", "input/dis.rst", "input/ic.rst", "input/index.rst", "input/lak.rst", "input/obs.rst", "input/oc.rst", "input/props.rst", "input/sfr.rst", "installation.rst", "notebooks/Pleasant_lake_lgr_example.ipynb", "philosophy.rst", "references.rst", "release-history.rst", "structure.rst", "troubleshooting.rst"], "titles": ["10 Minutes to Modflow-setup", "Code Reference", "mfsetup.discretization module", "mfsetup.fileio module", "mfsetup.grid module", "mfsetup.interpolate module", "MF6model class", "MFsetupMixin class", "MFnwtModel class", "mfsetup.tdis module", "mfsetup.tmr module", "Modflow-setup concepts and methods", "Interpolating data to the model grid", "Specifying perimeter boundary conditions from another model", "The configuration file", "Configuration defaults", 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file": [[0, "create-a-setup-script-and-configuration-file"]], "3) Develop flowlines to represent streams": [[0, "develop-flowlines-to-represent-streams"]], "Preprocessing NHDPlus HR": [[0, "preprocessing-nhdplus-hr"]], "Preprocessing NHDPlus version 2": [[0, "preprocessing-nhdplus-version-2"]], "4) Get a DEM": [[0, "get-a-dem"]], "Making a virtual raster": [[0, "making-a-virtual-raster"]], "5) Make a minimum working configuration 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"]], "Interpolating data to the model grid": [[12, "interpolating-data-to-the-model-grid"]], "Specifying perimeter boundary conditions from another model": [[13, "specifying-perimeter-boundary-conditions-from-another-model"]], "Features and Limitations": [[13, "features-and-limitations"]], "Configuration input": [[13, "configuration-input"]], "Specifying the time discretization": [[13, "specifying-the-time-discretization"]], "Specifying the locations of perimeter boundary cells": [[13, "specifying-the-locations-of-perimeter-boundary-cells"]], "The configuration file": [[14, "the-configuration-file"]], "The YAML format": [[14, "the-yaml-format"]], "Configuration file structure": [[14, "configuration-file-structure"]], "Package blocks": [[14, "package-blocks"]], "Sub-blocks": [[14, "sub-blocks"]], "Directly specifying MODFLOW input": [[14, "directly-specifying-modflow-input"]], "Source_data sub-blocks": [[14, "source-data-sub-blocks"]], "Some additional notes on YAML": [[14, "some-additional-notes-on-yaml"]], "Configuration defaults": [[15, "configuration-defaults"]], "MODFLOW-6 configuration defaults": [[15, "modflow-6-configuration-defaults"]], "MODFLOW-NWT configuration defaults": [[15, "modflow-nwt-configuration-defaults"]], "Configuration File Gallery": [[16, "configuration-file-gallery"]], "Shellmound test case": [[16, "shellmound-test-case"]], "Shellmound TMR inset test case": [[16, "shellmound-tmr-inset-test-case"]], "Pleasant Lake test case": [[16, "pleasant-lake-test-case"]], "LGR parent model configuration": [[16, "lgr-parent-model-configuration"]], "pleasant_lgr_inset.yml": [[16, "pleasant-lgr-inset-yml"]], "Pleasant Lake MODFLOW-NWT test case": [[16, "pleasant-lake-modflow-nwt-test-case"]], "Plainfield Lakes MODFLOW-NWT test case": [[16, "plainfield-lakes-modflow-nwt-test-case"]], "Contributing to modflow-setup": [[17, "contributing-to-modflow-setup"]], "Getting started": [[17, "getting-started"]], "Bug reports and enhancement requests": [[17, "bug-reports-and-enhancement-requests"]], "Code contributions": [[17, "code-contributions"]], "Seven Steps for Contributing": [[17, "seven-steps-for-contributing"]], "1) Forking the modflow-setup repository using Git": [[17, "forking-the-modflow-setup-repository-using-git"]], "Getting started with Git": [[17, "getting-started-with-git"]], "Forking": [[17, "forking"]], "Creating a branch": [[17, "creating-a-branch"]], "2 & 3) Creating a development environment with the required dependencies": [[17, "creating-a-development-environment-with-the-required-dependencies"]], "4) Installing the modflow-setup source code": [[17, "installing-the-modflow-setup-source-code"]], "5) Making changes and writing tests": [[17, "making-changes-and-writing-tests"]], "Writing tests": [[17, "writing-tests"]], "Running the test suite": [[17, "running-the-test-suite"]], "6) Updating the Documentation": [[17, "updating-the-documentation"]], "7) Submitting a Pull Request": [[17, "submitting-a-pull-request"]], "Style Guide & Linting": [[17, "style-guide-linting"]], "Examples": [[18, "examples"]], "Example problems": [[18, null]], "modflow-setup 0.4.0.post7+g63ba038": [[19, "modflow-setup-version"]], "Getting Started": [[19, null]], "User Guide": [[19, null]], "Reference": [[19, null]], "Bibliography": [[19, null]], "Specifying boundary conditions with the \u2018basic\u2019 MODFLOW stress packages": [[20, "specifying-boundary-conditions-with-the-basic-modflow-stress-packages"]], "List-based basic stress packages": [[20, "list-based-basic-stress-packages"]], "Constant Head (CHD) Package": [[20, "constant-head-chd-package"]], "Drain DRN Package": [[20, "drain-drn-package"]], "General Head Boundary 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"specifying-uniform-stress-periods-frequencies-by-group"]], "Specifying pre-defined stress periods from a CSV file": [[21, "specifying-pre-defined-stress-periods-from-a-csv-file"]], "Example Stress period data": [[21, "id1"]], "Specifying Initial Conditions": [[22, "specifying-initial-conditions"]], "Input instructions by package": [[23, "input-instructions-by-package"]], "The Lake (LAK) Package": [[24, "the-lake-lak-package"]], "Head observations": [[25, "head-observations"]], "MODFLOW Output Control": [[26, "modflow-output-control"]], "Stress period input format": [[26, "stress-period-input-format"]], "Output filenames and other arguments": [[26, "output-filenames-and-other-arguments"]], "Alternative stress period input formats": [[26, "alternative-stress-period-input-formats"]], "Specifying Aquifer Properties": [[27, "specifying-aquifer-properties"]], "The Streamflow Routing (SFR) Package": [[28, "the-streamflow-routing-sfr-package"]], "Installation": [[29, "installation"]], "Installing python dependencies with Conda": [[29, "installing-python-dependencies-with-conda"]], "Download and install a python distribution and Conda-like package installer": [[29, "download-and-install-a-python-distribution-and-conda-like-package-installer"]], "Download an environment file": [[29, "download-an-environment-file"]], "Creating a Conda environment using Mamba": [[29, "creating-a-conda-environment-using-mamba"]], "Keeping the Conda environment up to date": [[29, "keeping-the-conda-environment-up-to-date"]], "Installing Modflow-setup": [[29, "installing-modflow-setup"]], "Installing and updating Modflow-setup from PyPI": [[29, "installing-and-updating-modflow-setup-from-pypi"]], "Installing the latest develop version of Modflow-setup": [[29, "installing-the-latest-develop-version-of-modflow-setup"]], "Installing the Modflow-setup source code in-place": [[29, "installing-the-modflow-setup-source-code-in-place"]], "Installing the IPython kernel to use Modflow-setup in Jupyter Notebooks": [[29, "installing-the-ipython-kernel-to-use-modflow-setup-in-jupyter-notebooks"]], "Best practices": [[29, "best-practices"]], "Considerations for USGS Users": [[29, "id4"]], "Installing the DOI SSL certificate for use with pip": [[29, "id5"]], "Installing the DOI SSL certificate for use with conda": [[29, "installing-the-doi-ssl-certificate-for-use-with-conda"]], "Troubleshooting issues with the USGS network": [[29, "troubleshooting-issues-with-the-usgs-network"]], "SSL-related error messages when using conda": [[29, "ssl-related-error-messages-when-using-conda"]], "SSL-related error messages when using pip": [[29, "ssl-related-error-messages-when-using-pip"]], "If you are on the USGS network, using Windows, and you get this error message:": [[29, "if-you-are-on-the-usgs-network-using-windows-and-you-get-this-error-message"]], "Pleasant Lake Example": [[30, "Pleasant-Lake-Example"]], "Model details": [[30, "Model-details"]], "Just make a model grid": [[30, "Just-make-a-model-grid"]], "Working directory gottcha": [[30, "Working-directory-gottcha"]], "Write shapefiles of the inset and parent modelgrids": [[30, "Write-shapefiles-of-the-inset-and-parent-modelgrids"]], "Change the working directory back to the notebook location": [[30, "Change-the-working-directory-back-to-the-notebook-location"]], "Build the whole model": [[30, "Build-the-whole-model"]], "Plot the inset and parent model grids with Lake Package connections by layer": [[30, "Plot-the-inset-and-parent-model-grids-with-Lake-Package-connections-by-layer"]], "write the MODFLOW input files": [[30, "write-the-MODFLOW-input-files"]], "Run the model": [[30, "Run-the-model"]], "Plot the head results": [[30, "Plot-the-head-results"]], "First combine the parent and inset model head results": [[30, "First-combine-the-parent-and-inset-model-head-results"]], "Make the plot": [[30, "Make-the-plot"]], "Use Modflow-export to export the modflow input to PDFs, rasters and shapefiles": [[30, 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"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"]], "snap_to_cell_corner() (in module mfsetup.grid)": [[4, "mfsetup.grid.snap_to_cell_corner"]], "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
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@@ -4,7 +4,7 @@
   
 
   
-  Basic program structure and usage — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Basic program structure and usage — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
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-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038
               
    
 
 
diff --git a/latest/troubleshooting.html b/latest/troubleshooting.html
index e8cd6512..99a9c709 100644
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@@ -4,7 +4,7 @@
   
 
   
-  Troubleshooting — modflow-setup 0.4.0.post3+gd2d459e documentation
+  Troubleshooting — modflow-setup 0.4.0.post7+g63ba038 documentation
       
       
       
@@ -15,7 +15,7 @@
     
   
   
-        
+        
         
         
         
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             modflow-setup
           
               
    
-                0.4.0.post3+gd2d459e
+                0.4.0.post7+g63ba038