From e89975b99968add23f82b12f0f52dbb8121f396f Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 8 Feb 2023 08:53:43 -0600
Subject: [PATCH 01/28] feat(discretization.Grid): move coordinate reference
 system handling to a pyproj.CRS instance attached to the grid classes as a
 .crs attribute

---
 autotest/test_grid.py                    | 196 ++++++++++++++++++-----
 flopy/discretization/grid.py             |  69 +++++---
 flopy/discretization/structuredgrid.py   |  37 ++++-
 flopy/discretization/unstructuredgrid.py |  33 +++-
 flopy/discretization/vertexgrid.py       |  29 ++++
 flopy/utils/crs.py                       | 118 ++++++++++++++
 6 files changed, 406 insertions(+), 76 deletions(-)
 create mode 100644 flopy/utils/crs.py

diff --git a/autotest/test_grid.py b/autotest/test_grid.py
index 2698e05c4f..3c26937341 100644
--- a/autotest/test_grid.py
+++ b/autotest/test_grid.py
@@ -9,16 +9,38 @@
 from flaky import flaky
 from matplotlib import pyplot as plt
 from modflow_devtools.markers import requires_exe, requires_pkg
+from packaging import version
 from pytest_cases import parametrize_with_cases
 
+import flopy
 from flopy.discretization import StructuredGrid, UnstructuredGrid, VertexGrid
 from flopy.mf6 import MFSimulation
 from flopy.modflow import Modflow, ModflowDis
+from flopy.utils.crs import get_authority_crs
 from flopy.utils.cvfdutil import gridlist_to_disv_gridprops, to_cvfd
 from flopy.utils.triangle import Triangle
 from flopy.utils.voronoi import VoronoiGrid
 
 
+@pytest.fixture
+def minimal_unstructured_grid_info():
+    d = {
+        # pass in simple 2 cell minimal grid to make grid valid
+        "vertices": [
+            [0, 0.0, 1.0],
+            [1, 1.0, 1.0],
+            [2, 2.0, 1.0],
+            [3, 0.0, 0.0],
+            [4, 1.0, 0.0],
+            [5, 2.0, 0.0],
+        ],
+        "iverts": [[0, 1, 4, 3], [1, 2, 5, 4]],
+        "xcenters": [0.5, 1.5],
+        "ycenters": [0.5, 0.5],
+    }
+    return d
+
+
 def test_rotation():
     m = Modflow(rotation=20.0)
     dis = ModflowDis(
@@ -556,6 +578,124 @@ def test_unstructured_from_gridspec(example_data_path):
         assert min(grid.botm) == min([xyz[2] for xyz in expected_verts])
 
 
+@requires_pkg("pyproj")
+@pytest.mark.parametrize(
+    "crs,expected_srs",
+    (
+        (None, None),
+        (26916, "EPSG:26916"),
+        ("epsg:5070", "EPSG:5070"),
+        (
+            "+proj=tmerc +lat_0=0 +lon_0=-90 +k=0.9996 +x_0=520000 +y_0=-4480000 +datum=NAD83 +units=m +no_defs ",
+            "EPSG:3070",
+        ),
+        pytest.param(4269, None, marks=pytest.mark.xfail),
+    ),
+)
+def test_grid_crs(
+    minimal_unstructured_grid_info, crs, expected_srs, function_tmpdir
+):
+    import pyproj
+
+    d = minimal_unstructured_grid_info
+    delr = np.ones(10)
+    delc = np.ones(10)
+    sg = StructuredGrid(delr=delr, delc=delc, crs=crs)
+    if crs is not None:
+        assert isinstance(sg.crs, pyproj.CRS)
+        assert sg.crs.srs == expected_srs
+
+    usg = UnstructuredGrid(**d, crs=crs)
+    assert getattr(sg.crs, "srs", None) == expected_srs
+
+    vg = VertexGrid(vertices=d["vertices"], crs=crs)
+    assert getattr(sg.crs, "srs", None) == expected_srs
+
+    # test input of pyproj.CRS object
+    if crs == 26916:
+        sg2 = StructuredGrid(delr=delr, delc=delc, crs=sg.crs)
+
+        if crs is not None:
+            assert isinstance(sg2.crs, pyproj.CRS)
+        assert getattr(sg2.crs, "srs", None) == expected_srs
+
+        # test input of projection file
+        prjfile = function_tmpdir / "grid_crs.prj"
+        with open(prjfile, "w") as dest:
+            dest.write(sg.crs.to_wkt())
+
+        sg3 = StructuredGrid(delr=delr, delc=delc, prjfile=prjfile)
+        if crs is not None:
+            assert isinstance(sg3.crs, pyproj.CRS)
+        assert getattr(sg3.crs, "srs", None) == expected_srs
+
+
+@requires_pkg("pyproj")
+@pytest.mark.parametrize(
+    "crs,expected_srs",
+    (
+        (None, None),
+        (26916, "EPSG:26916"),
+        ("epsg:5070", "EPSG:5070"),
+        (
+            "+proj=tmerc +lat_0=0 +lon_0=-90 +k=0.9996 +x_0=520000 +y_0=-4480000 +datum=NAD83 +units=m +no_defs ",
+            "EPSG:3070",
+        ),
+        pytest.param(4269, None, marks=pytest.mark.xfail),
+    ),
+)
+def test_grid_set_crs(crs, expected_srs, function_tmpdir):
+    import pyproj
+
+    delr = np.ones(10)
+    delc = np.ones(10)
+    sg = StructuredGrid(delr=delr, delc=delc)
+    sg.crs = crs
+    if crs is not None:
+        assert isinstance(sg.crs, pyproj.CRS)
+    assert getattr(sg.crs, "srs", None) == expected_srs
+
+    # test input of projection file
+    if crs is not None:
+        prjfile = function_tmpdir / "grid_crs.prj"
+        with open(prjfile, "w") as dest:
+            dest.write(sg.crs.to_wkt())
+        sg = StructuredGrid(delr=delr, delc=delc)
+        sg.prjfile = prjfile
+        if crs is not None:
+            assert isinstance(sg.crs, pyproj.CRS)
+        assert getattr(sg.crs, "srs", None) == expected_srs
+        assert sg.prjfile == prjfile
+
+    # test setting another crs
+    sg.crs = 26915
+    assert sg.crs == get_authority_crs(26915)
+
+    if version.parse(flopy.__version__) < version.parse("3.3.7"):
+        pyproj_crs = get_authority_crs(crs)
+        sg = StructuredGrid(delr=delr, delc=delc)
+        sg.epsg = pyproj_crs.to_epsg()
+        if crs is not None:
+            assert isinstance(sg.crs, pyproj.CRS)
+        assert getattr(sg.crs, "srs", None) == expected_srs
+        assert sg.epsg == pyproj_crs.to_epsg()
+
+        sg = StructuredGrid(delr=delr, delc=delc)
+        sg.proj4 = pyproj_crs.to_proj4()
+        if crs is not None:
+            assert isinstance(sg.crs, pyproj.CRS)
+        assert getattr(sg.crs, "srs", None) == expected_srs
+        assert sg.proj4 == pyproj_crs.to_proj4()
+
+        if crs is not None:
+            sg = StructuredGrid(delr=delr, delc=delc)
+            sg.prj = prjfile
+            if crs is not None:
+                assert isinstance(sg.crs, pyproj.CRS)
+            assert getattr(sg.crs, "srs", None) == expected_srs
+            assert sg.prj == prjfile
+
+
 def test_epsgs():
     import flopy.export.shapefile_utils as shp
 
@@ -682,32 +822,20 @@ def test_unstructured_grid_dimensions():
     assert not g.grid_varies_by_layer
 
 
-def test_unstructured_minimal_grid_ctor():
+def test_unstructured_minimal_grid_ctor(minimal_unstructured_grid_info):
     # pass in simple 2 cell minimal grid to make grid valid
-    vertices = [
-        [0, 0.0, 1.0],
-        [1, 1.0, 1.0],
-        [2, 2.0, 1.0],
-        [3, 0.0, 0.0],
-        [4, 1.0, 0.0],
-        [5, 2.0, 0.0],
-    ]
-    iverts = [[0, 1, 4, 3], [1, 2, 5, 4]]
-    xcenters = [0.5, 1.5]
-    ycenters = [0.5, 0.5]
-    g = UnstructuredGrid(
-        vertices=vertices, iverts=iverts, xcenters=xcenters, ycenters=ycenters
-    )
+    d = minimal_unstructured_grid_info
+    g = UnstructuredGrid(**d)
     assert np.allclose(g.ncpl, np.array([2], dtype=int))
     assert g.nlay == 1
     assert g.nnodes == 2
     assert g.is_valid
     assert not g.is_complete
     assert not g.grid_varies_by_layer
-    assert g._vertices == vertices
-    assert g._iverts == iverts
-    assert g._xc == xcenters
-    assert g._yc == ycenters
+    assert g._vertices == d["vertices"]
+    assert g._iverts == d["iverts"]
+    assert g._xc == d["xcenters"]
+    assert g._yc == d["ycenters"]
     grid_lines = [
         [(0.0, 0), (0.0, 1.0)],
         [(0.0, 1), (1.0, 1.0)],
@@ -728,33 +856,17 @@ def test_unstructured_minimal_grid_ctor():
     assert zv is None
 
 
-def test_unstructured_complete_grid_ctor():
+def test_unstructured_complete_grid_ctor(minimal_unstructured_grid_info):
     # pass in simple 2 cell complete grid to make grid valid, and put each
     # cell in a different layer
-    vertices = [
-        [0, 0.0, 1.0],
-        [1, 1.0, 1.0],
-        [2, 2.0, 1.0],
-        [3, 0.0, 0.0],
-        [4, 1.0, 0.0],
-        [5, 2.0, 0.0],
-    ]
-    iverts = [[0, 1, 4, 3], [1, 2, 5, 4]]
-    xcenters = [0.5, 1.5]
-    ycenters = [0.5, 0.5]
+    d = minimal_unstructured_grid_info
     ncpl = [1, 1]
     top = [1, 0]
     top = np.array(top)
     botm = [0, -1]
     botm = np.array(botm)
     g = UnstructuredGrid(
-        vertices=vertices,
-        iverts=iverts,
-        xcenters=xcenters,
-        ycenters=ycenters,
-        ncpl=ncpl,
-        top=top,
-        botm=botm,
+        ncpl=ncpl, top=top, botm=botm, **minimal_unstructured_grid_info
     )
     assert np.allclose(g.ncpl, np.array([1, 1], dtype=int))
     assert g.nlay == 2
@@ -762,10 +874,10 @@ def test_unstructured_complete_grid_ctor():
     assert g.is_valid
     assert not g.is_complete
     assert g.grid_varies_by_layer
-    assert g._vertices == vertices
-    assert g._iverts == iverts
-    assert g._xc == xcenters
-    assert g._yc == ycenters
+    assert g._vertices == d["vertices"]
+    assert g._iverts == d["iverts"]
+    assert g._xc == d["xcenters"]
+    assert g._yc == d["ycenters"]
     grid_lines = {
         0: [
             [(0.0, 0.0), (0.0, 1.0)],
diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index e64adb7e76..a6452c961e 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -5,6 +5,7 @@
 
 from ..plot.plotutil import UnstructuredPlotUtilities
 from ..utils import geometry
+from ..utils.crs import get_crs
 from ..utils.gridutil import get_lni
 
 
@@ -42,12 +43,15 @@ class Grid:
         ibound/idomain value for each cell
     lenuni : ndarray(int)
         model length units
-    espg : str, int
-        optional espg projection code
-    proj4 : str
-        optional proj4 projection string code
-    prj : str
-        optional projection file name path
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
     xoff : float
         x coordinate of the origin point (lower left corner of model grid)
         in the spatial reference coordinate system
@@ -67,10 +71,8 @@ class Grid:
         bottom elevations of all cells
     idomain : ndarray(int)
         ibound/idomain value for each cell
-    proj4 : proj4 SpatialReference
-        spatial reference locates the grid in a coordinate system
-    epsg : epsg SpatialReference
-        spatial reference locates the grid in a coordinate system
+    crs : pyproj.CRS
+        Coordinate reference system (CRS) for the model grid
     lenuni : int
         modflow lenuni parameter
     xoffset : float
@@ -144,9 +146,11 @@ def __init__(
         botm=None,
         idomain=None,
         lenuni=None,
+        crs=None,
         epsg=None,
         proj4=None,
         prj=None,
+        prjfile=None,
         xoff=0.0,
         yoff=0.0,
         angrot=0.0,
@@ -170,9 +174,13 @@ def __init__(
         self._lenuni = lenuni
 
         self._units = lenunits[self._lenuni]
+        self._crs = get_crs(
+            prjfile=prjfile, prj=prj, epsg=epsg, proj4=proj4, crs=crs
+        )
         self._epsg = epsg
         self._proj4 = proj4
         self._prj = prj
+        self._prjfile = prjfile
         self._xoff = xoff
         self._yoff = yoff
         if angrot is None:
@@ -244,32 +252,31 @@ def angrot(self):
     def angrot_radians(self):
         return self._angrot * np.pi / 180.0
 
+    @property
+    def crs(self):
+        return self._crs
+
+    @crs.setter
+    def crs(self, crs):
+        self._crs = get_crs(crs=crs)
+
     @property
     def epsg(self):
-        return self._epsg
+        return self._crs.to_epsg()
 
     @epsg.setter
     def epsg(self, epsg):
-        self._epsg = epsg
+        self._crs = get_crs(epsg=epsg)
+        self._epsg = self._crs.to_epsg()
 
     @property
     def proj4(self):
-        proj4 = None
-        if self._proj4 is not None:
-            if "epsg" in self._proj4.lower():
-                proj4 = self._proj4
-                # set the epsg if proj4 specifies it
-                tmp = [i for i in self._proj4.split() if "epsg" in i.lower()]
-                self._epsg = int(tmp[0].split(":")[1])
-            else:
-                proj4 = self._proj4
-        elif self.epsg is not None:
-            proj4 = f"epsg:{self.epsg}"
-        return proj4
+        return self._crs.to_proj4()
 
     @proj4.setter
     def proj4(self, proj4):
-        self._proj4 = proj4
+        self._crs = get_crs(proj4=proj4)
+        self._proj4 = self._crs.to_proj4()
 
     @property
     def prj(self):
@@ -277,7 +284,17 @@ def prj(self):
 
     @prj.setter
     def prj(self, prj):
-        self._proj4 = prj
+        self._crs = get_crs(prj=prj)
+        self._prj = prj
+
+    @property
+    def prjfile(self):
+        return self._prjfile
+
+    @prjfile.setter
+    def prjfile(self, prjfile):
+        self._crs = get_crs(prjfile=prjfile)
+        self._prjfile = prjfile
 
     @property
     def top(self):
diff --git a/flopy/discretization/structuredgrid.py b/flopy/discretization/structuredgrid.py
index dd2c750dc9..ef70e5d9fa 100644
--- a/flopy/discretization/structuredgrid.py
+++ b/flopy/discretization/structuredgrid.py
@@ -85,10 +85,35 @@ class for a structured model grid
 
     Parameters
     ----------
-    delc
-        delc array
-    delr
-        delr array
+    delr : ndarray(float)
+        column spacing along a row.
+    delc : ndarray(float)
+        row spacing along a column.
+    top : ndarray(float)
+        top elevations of cells in topmost layer
+    botm : ndarray(float)
+        bottom elevations of all cells
+    idomain : ndarray(int)
+        ibound/idomain value for each cell
+    lenuni : ndarray(int)
+        model length units
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
+    xoff : float
+        x coordinate of the origin point (lower left corner of model grid)
+        in the spatial reference coordinate system
+    yoff : float
+        y coordinate of the origin point (lower left corner of model grid)
+        in the spatial reference coordinate system
+    angrot : float
+        rotation angle of model grid, as it is rotated around the origin point
 
     Properties
     ----------
@@ -120,9 +145,11 @@ def __init__(
         botm=None,
         idomain=None,
         lenuni=None,
+        crs=None,
         epsg=None,
         proj4=None,
         prj=None,
+        prjfile=None,
         xoff=0.0,
         yoff=0.0,
         angrot=0.0,
@@ -137,9 +164,11 @@ def __init__(
             botm,
             idomain,
             lenuni,
+            crs,
             epsg,
             proj4,
             prj,
+            prjfile,
             xoff,
             yoff,
             angrot,
diff --git a/flopy/discretization/unstructuredgrid.py b/flopy/discretization/unstructuredgrid.py
index 38ff1efea4..7868da4c46 100644
--- a/flopy/discretization/unstructuredgrid.py
+++ b/flopy/discretization/unstructuredgrid.py
@@ -32,6 +32,14 @@ class UnstructuredGrid(Grid):
         list of y center coordinates for all cells in the grid if the grid
         varies by layer or for all cells in a layer if the same grid is used
         for all layers
+    top : list or ndarray
+        top elevations for all cells in the grid.
+    botm : list or ndarray
+        bottom elevations for all cells in the grid.
+    idomain : ndarray(int)
+        ibound/idomain value for each cell
+    lenuni : ndarray(int)
+        model length units
     ncpl : ndarray
         one dimensional array of size nlay with the number of cells in each
         layer.  This can also be passed in as a tuple or list as long as it
@@ -43,11 +51,24 @@ class UnstructuredGrid(Grid):
         If the model grid defined in verts and iverts applies for all model
         layers, then the length of iverts can be equal to ncpl[0] and there
         is no need to repeat all of the vertex information for cells in layers
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
         beneath the top layer.
-    top : list or ndarray
-        top elevations for all cells in the grid.
-    botm : list or ndarray
-        bottom elevations for all cells in the grid.
+    xoff : float
+        x coordinate of the origin point (lower left corner of model grid)
+        in the spatial reference coordinate system
+    yoff : float
+        y coordinate of the origin point (lower left corner of model grid)
+        in the spatial reference coordinate system
+    angrot : float
+        rotation angle of model grid, as it is rotated around the origin point
     iac : list or ndarray
         optional number of connections per node array
     ja : list or ndarray
@@ -95,9 +116,11 @@ def __init__(
         idomain=None,
         lenuni=None,
         ncpl=None,
+        crs=None,
         epsg=None,
         proj4=None,
         prj=None,
+        prjfile=None,
         xoff=0.0,
         yoff=0.0,
         angrot=0.0,
@@ -110,9 +133,11 @@ def __init__(
             botm,
             idomain,
             lenuni,
+            crs,
             epsg,
             proj4,
             prj,
+            prjfile,
             xoff,
             yoff,
             angrot,
diff --git a/flopy/discretization/vertexgrid.py b/flopy/discretization/vertexgrid.py
index a2e8abc337..0161f5eac3 100644
--- a/flopy/discretization/vertexgrid.py
+++ b/flopy/discretization/vertexgrid.py
@@ -19,6 +19,31 @@ class for a vertex model grid
         list of vertices that make up the grid
     cell2d
         list of cells and their vertices
+    top : list or ndarray
+        top elevations for all cells in the grid.
+    botm : list or ndarray
+        bottom elevations for all cells in the grid.
+    idomain : ndarray(int)
+        ibound/idomain value for each cell
+    lenuni : ndarray(int)
+        model length units
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
+    xoff : float
+        x coordinate of the origin point (lower left corner of model grid)
+        in the spatial reference coordinate system
+    yoff : float
+        y coordinate of the origin point (lower left corner of model grid)
+        in the spatial reference coordinate system
+    angrot : float
+        rotation angle of model grid, as it is rotated around the origin point
 
     Properties
     ----------
@@ -42,9 +67,11 @@ def __init__(
         botm=None,
         idomain=None,
         lenuni=None,
+        crs=None,
         epsg=None,
         proj4=None,
         prj=None,
+        prjfile=None,
         xoff=0.0,
         yoff=0.0,
         angrot=0.0,
@@ -58,9 +85,11 @@ def __init__(
             botm,
             idomain,
             lenuni,
+            crs,
             epsg,
             proj4,
             prj,
+            prjfile,
             xoff,
             yoff,
             angrot,
diff --git a/flopy/utils/crs.py b/flopy/utils/crs.py
new file mode 100644
index 0000000000..19a6dbb301
--- /dev/null
+++ b/flopy/utils/crs.py
@@ -0,0 +1,118 @@
+"""Utilities related to coordinate reference system handling.
+"""
+import warnings
+from pathlib import Path
+
+from ..utils import import_optional_dependency
+
+
+def get_authority_crs(crs):
+    """Try to get the authority representation for a
+    coordinate reference system (CRS), for more robust
+    comparison with other CRS objects.
+
+    Parameters
+    ----------
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+
+    Returns
+    -------
+    authority_crs : pyproj.CRS instance
+        CRS instance initiallized with the name
+        and authority code (e.g. epsg: 5070) produced by
+        :meth:`pyproj.crs.CRS.to_authority`
+
+    Notes
+    -----
+    :meth:`pyproj.crs.CRS.to_authority` will return None if a matching
+    authority name and code can't be found. In this case,
+    the input crs instance will be returned.
+
+    References
+    ----------
+    http://pyproj4.github.io/pyproj/stable/api/crs/crs.html
+
+    """
+    pyproj = import_optional_dependency("pyproj")
+    if crs is not None:
+        crs = pyproj.crs.CRS.from_user_input(crs)
+        authority = crs.to_authority()
+        if authority is not None:
+            return pyproj.CRS.from_user_input(authority)
+        return crs
+
+
+def get_shapefile_crs(shapefile):
+    """Get the coordinate reference system for a shapefile.
+
+    Parameters
+    ----------
+    shapefile : str or pathlike
+        Path to a shapefile or an associated
+        projection (.prj) file.
+
+    Returns
+    -------
+    crs : pyproj.CRS instance
+
+    """
+    pyproj = import_optional_dependency("pyproj")
+    shapefile = Path(shapefile)
+    prjfile = shapefile.with_suffix(".prj")
+    if prjfile.exists():
+        with open(prjfile) as src:
+            wkt = src.read()
+            crs = pyproj.crs.CRS.from_wkt(wkt)
+            return get_authority_crs(crs)
+
+
+def get_crs(prjfile=None, prj=None, epsg=None, proj4=None, crs=None):
+    """Helper function to produce a pyproj.CRS object from
+    various input. Longer-term, this would just handle the ``crs``
+    and ``prjfile`` arguments, but in the near term, we need to
+    warn users about deprecating epsg and proj_str."""
+    if crs is not None:
+        crs = get_authority_crs(crs)
+    if prj is not None:
+        warnings.warn(
+            "the prj argument will be deprecated and will be removed in version "
+            "3.3.7. Use prjfile instead.",
+            PendingDeprecationWarning,
+        )
+        prjfile = prj
+    if epsg is not None:
+        warnings.warn(
+            "the epsg argument will be deprecated and will be removed in version "
+            "3.3.7. Use crs instead.",
+            PendingDeprecationWarning,
+        )
+        if crs is None:
+            crs = get_authority_crs(epsg)
+    elif prjfile is not None:
+        prjfile_crs = get_shapefile_crs(prjfile)
+        if (crs is not None) and (crs != prjfile_crs):
+            raise ValueError(
+                "Different coordinate reference systems "
+                f"in crs argument and supplied projection file: {prjfile}\n"
+                f"\nuser supplied crs: {crs}  !=\ncrs from projection file: {prjfile_crs}"
+            )
+        else:
+            crs = prjfile_crs
+    elif proj4 is not None:
+        warnings.warn(
+            "the proj4 argument will be deprecated and will be removed in version "
+            "3.3.7. Use crs instead.",
+            PendingDeprecationWarning,
+        )
+        if crs is None:
+            crs = get_authority_crs(proj4)
+    if crs is not None and not crs.is_projected:
+        raise ValueError(
+            f"Only projected coordinate reference systems are supported.\n{crs}"
+        )
+    return crs

From 6d4007cff2a1b1e3df5e7699ec002588e5c44396 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 8 Feb 2023 08:53:43 -0600
Subject: [PATCH 02/28] feat(discretization.Grid): move coordinate reference
 system handling to a pyproj.CRS instance attached to the grid classes as a
 .crs attribute

---
 autotest/test_grid.py        | 35 +----------------------------------
 flopy/discretization/grid.py |  3 +++
 flopy/mbase.py               |  8 ++++----
 flopy/mf6/mfmodel.py         | 32 +++++++++++++-------------------
 flopy/modflow/mf.py          |  9 +++------
 flopy/modflow/mfdis.py       | 25 +++++++++++++++++--------
 flopy/mt3d/mt.py             |  3 +--
 flopy/seawat/swt.py          |  3 +--
 flopy/utils/crs.py           |  6 +++---
 9 files changed, 46 insertions(+), 78 deletions(-)

diff --git a/autotest/test_grid.py b/autotest/test_grid.py
index 3c26937341..798929f389 100644
--- a/autotest/test_grid.py
+++ b/autotest/test_grid.py
@@ -641,6 +641,7 @@ def test_grid_crs(
             "+proj=tmerc +lat_0=0 +lon_0=-90 +k=0.9996 +x_0=520000 +y_0=-4480000 +datum=NAD83 +units=m +no_defs ",
             "EPSG:3070",
         ),
+        ("ESRI:102733", "ESRI:102733"),
         pytest.param(4269, None, marks=pytest.mark.xfail),
     ),
 )
@@ -696,40 +697,6 @@ def test_grid_set_crs(crs, expected_srs, function_tmpdir):
             assert sg.prj == prjfile
 
 
-def test_epsgs():
-    import flopy.export.shapefile_utils as shp
-
-    # test setting a geographic (lat/lon) coordinate reference
-    # (also tests shapefile_utils.CRS parsing of geographic crs info)
-    delr = np.ones(10)
-    delc = np.ones(10)
-    mg = StructuredGrid(delr=delr, delc=delc)
-
-    mg.epsg = 102733
-    assert mg.epsg == 102733, f"mg.epsg is not 102733 ({mg.epsg})"
-
-    t_value = mg.__repr__()
-    if not "proj4_str:epsg:102733" in t_value:
-        raise AssertionError(
-            f"proj4_str:epsg:102733 not in mg.__repr__(): ({t_value})"
-        )
-
-    mg.epsg = 4326  # WGS 84
-    crs = shp.CRS(epsg=4326)
-    if crs.grid_mapping_attribs is not None:
-        assert crs.crs["proj"] == "longlat"
-        t_value = crs.grid_mapping_attribs["grid_mapping_name"]
-        assert (
-            t_value == "latitude_longitude"
-        ), f"grid_mapping_name is not latitude_longitude: {t_value}"
-
-    t_value = mg.__repr__()
-    if not "proj4_str:epsg:4326" in t_value:
-        raise AssertionError(
-            f"proj4_str:epsg:4326 not in sr.__repr__(): ({t_value})"
-        )
-
-
 def test_tocvfd1():
     vertdict = {}
     vertdict[0] = [(0, 0), (100, 0), (100, 100), (0, 100), (0, 0)]
diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index a6452c961e..f447645863 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -813,6 +813,8 @@ def set_coord_info(
         xoff=None,
         yoff=None,
         angrot=None,
+        crs=None,
+        prjfile=None,
         epsg=None,
         proj4=None,
         merge_coord_info=True,
@@ -839,6 +841,7 @@ def set_coord_info(
         self._xoff = xoff
         self._yoff = yoff
         self._angrot = angrot
+        self._crs = get_crs(prjfile=prjfile, epsg=epsg, proj4=proj4, crs=crs)
         self._epsg = epsg
         self._proj4 = proj4
         self._require_cache_updates()
diff --git a/flopy/mbase.py b/flopy/mbase.py
index 9c88dbd463..ad82da304a 100644
--- a/flopy/mbase.py
+++ b/flopy/mbase.py
@@ -129,7 +129,7 @@ def __init__(self):
     def update_modelgrid(self):
         if self._modelgrid is not None:
             self._modelgrid = Grid(
-                proj4=self._modelgrid.proj4,
+                crs=self._modelgrid.crs,
                 xoff=self._modelgrid.xoffset,
                 yoff=self._modelgrid.yoffset,
                 angrot=self._modelgrid.angrot,
@@ -354,7 +354,7 @@ class BaseModel(ModelInterface):
         the lower-left corner of the grid, ``xul``/``yul`` for the
         x- and y-coordinates of the upper-left corner of the grid
         (deprecated), ``rotation`` for the grid rotation (default 0.0),
-        ``proj4_str`` for a PROJ string, and ``start_datetime`` for
+        ``crs`` for the coordinate reference system, and ``start_datetime`` for
         model start date (default "1-1-1970").
 
     """
@@ -407,12 +407,12 @@ def __init__(
         self._yul = kwargs.pop("yul", None)
 
         self._rotation = kwargs.pop("rotation", 0.0)
-        self._proj4_str = kwargs.pop("proj4_str", None)
+        self._crs = kwargs.pop("crs", None)
         self._start_datetime = kwargs.pop("start_datetime", "1-1-1970")
 
         # build model discretization objects
         self._modelgrid = Grid(
-            proj4=self._proj4_str,
+            crs=self._crs,
             xoff=xll,
             yoff=yll,
             angrot=self._rotation,
diff --git a/flopy/mf6/mfmodel.py b/flopy/mf6/mfmodel.py
index 6a9581fd16..da02f90f4b 100644
--- a/flopy/mf6/mfmodel.py
+++ b/flopy/mf6/mfmodel.py
@@ -122,11 +122,9 @@ def __init__(
         self._xul = kwargs.pop("xul", None)
         self._yul = kwargs.pop("yul", None)
         rotation = kwargs.pop("rotation", 0.0)
-        proj4 = kwargs.pop("proj4_str", None)
+        crs = kwargs.pop("crs", None)
         # build model grid object
-        self._modelgrid = Grid(
-            proj4=proj4, xoff=xll, yoff=yll, angrot=rotation
-        )
+        self._modelgrid = Grid(crs=crs, xoff=xll, yoff=yll, angrot=rotation)
 
         self.start_datetime = None
         # check for extraneous kwargs
@@ -348,8 +346,7 @@ def modelgrid(self):
                         botm=None,
                         idomain=None,
                         lenuni=None,
-                        proj4=self._modelgrid.proj4,
-                        epsg=self._modelgrid.epsg,
+                        crs=self._modelgrid.crs,
                         xoff=self._modelgrid.xoffset,
                         yoff=self._modelgrid.yoffset,
                         angrot=self._modelgrid.angrot,
@@ -362,8 +359,7 @@ def modelgrid(self):
                     botm=dis.botm.array,
                     idomain=dis.idomain.array,
                     lenuni=dis.length_units.array,
-                    proj4=self._modelgrid.proj4,
-                    epsg=self._modelgrid.epsg,
+                    crs=self._modelgrid.crs,
                     xoff=self._modelgrid.xoffset,
                     yoff=self._modelgrid.yoffset,
                     angrot=self._modelgrid.angrot,
@@ -383,8 +379,7 @@ def modelgrid(self):
                         botm=None,
                         idomain=None,
                         lenuni=None,
-                        proj4=self._modelgrid.proj4,
-                        epsg=self._modelgrid.epsg,
+                        crs=self._modelgrid.crs,
                         xoff=self._modelgrid.xoffset,
                         yoff=self._modelgrid.yoffset,
                         angrot=self._modelgrid.angrot,
@@ -397,8 +392,7 @@ def modelgrid(self):
                     botm=dis.botm.array,
                     idomain=dis.idomain.array,
                     lenuni=dis.length_units.array,
-                    proj4=self._modelgrid.proj4,
-                    epsg=self._modelgrid.epsg,
+                    crs=self._modelgrid.crs,
                     xoff=self._modelgrid.xoffset,
                     yoff=self._modelgrid.yoffset,
                     angrot=self._modelgrid.angrot,
@@ -458,8 +452,7 @@ def modelgrid(self):
                 idomain=idomain,
                 lenuni=dis.length_units.array,
                 ncpl=ncpl,
-                proj4=self._modelgrid.proj4,
-                epsg=self._modelgrid.epsg,
+                crs=self._modelgrid.crs,
                 xoff=self._modelgrid.xoffset,
                 yoff=self._modelgrid.yoffset,
                 angrot=self._modelgrid.angrot,
@@ -481,8 +474,7 @@ def modelgrid(self):
                         botm=None,
                         idomain=None,
                         lenuni=None,
-                        proj4=self._modelgrid.proj4,
-                        epsg=self._modelgrid.epsg,
+                        crs=self._modelgrid.crs,
                         xoff=self._modelgrid.xoffset,
                         yoff=self._modelgrid.yoffset,
                         angrot=self._modelgrid.angrot,
@@ -495,8 +487,7 @@ def modelgrid(self):
                     botm=dis.botm.array,
                     idomain=dis.idomain.array,
                     lenuni=dis.length_units.array,
-                    proj4=self._modelgrid.proj4,
-                    epsg=self._modelgrid.epsg,
+                    crs=self._modelgrid.crs,
                     xoff=self._modelgrid.xoffset,
                     yoff=self._modelgrid.yoffset,
                     angrot=self._modelgrid.angrot,
@@ -532,7 +523,10 @@ def modelgrid(self):
         if angrot is None:
             angrot = self._modelgrid.angrot
         self._modelgrid.set_coord_info(
-            xorig, yorig, angrot, self._modelgrid.epsg, self._modelgrid.proj4
+            xorig,
+            yorig,
+            angrot,
+            self._modelgrid.crs,
         )
         self._mg_resync = not self._modelgrid.is_complete
         return self._modelgrid
diff --git a/flopy/modflow/mf.py b/flopy/modflow/mf.py
index c61baf1aba..c7531c8a9d 100644
--- a/flopy/modflow/mf.py
+++ b/flopy/modflow/mf.py
@@ -292,8 +292,7 @@ def modelgrid(self):
                 botm=self.disu.bot.array,
                 idomain=ibound,
                 lenuni=self.disu.lenuni,
-                proj4=self._modelgrid.proj4,
-                epsg=self._modelgrid.epsg,
+                crs=self._modelgrid.crs,
                 xoff=self._modelgrid.xoffset,
                 yoff=self._modelgrid.yoffset,
                 angrot=self._modelgrid.angrot,
@@ -313,8 +312,7 @@ def modelgrid(self):
                 self.dis.botm.array,
                 ibound,
                 self.dis.lenuni,
-                proj4=self._modelgrid.proj4,
-                epsg=self._modelgrid.epsg,
+                crs=self._modelgrid.crs,
                 xoff=self._modelgrid.xoffset,
                 yoff=self._modelgrid.yoffset,
                 angrot=self._modelgrid.angrot,
@@ -339,8 +337,7 @@ def modelgrid(self):
             xoff,
             yoff,
             self._modelgrid.angrot,
-            self._modelgrid.epsg,
-            self._modelgrid.proj4,
+            self._modelgrid.crs,
         )
         self._mg_resync = not self._modelgrid.is_complete
         return self._modelgrid
diff --git a/flopy/modflow/mfdis.py b/flopy/modflow/mfdis.py
index 88d361ddd8..e4ae2a2d4e 100644
--- a/flopy/modflow/mfdis.py
+++ b/flopy/modflow/mfdis.py
@@ -13,6 +13,7 @@
 
 from ..pakbase import Package
 from ..utils import Util2d, Util3d
+from ..utils.crs import get_crs
 from ..utils.flopy_io import line_parse
 from ..utils.reference import TemporalReference
 
@@ -87,10 +88,15 @@ class ModflowDis(Package):
     rotation : float
         counter-clockwise rotation (in degrees) of the grid about the lower-
         left corner. default is 0.0
-    proj4_str : str
-        PROJ4 string that defines the projected coordinate system
-        (e.g. '+proj=utm +zone=14 +datum=WGS84 +units=m +no_defs ').
-        Can be an EPSG code (e.g. 'EPSG:32614'). Default is None.
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
     start_datetime : str
         starting datetime of the simulation. default is '1/1/1970'
 
@@ -142,6 +148,8 @@ def __init__(
         yul=None,
         rotation=None,
         proj4_str=None,
+        crs=None,
+        prjfile=None,
         start_datetime=None,
     ):
         # set default unit number of one is not specified
@@ -240,8 +248,9 @@ def __init__(
             yul = model._yul
         if rotation is None:
             rotation = model._rotation
-        if proj4_str is None:
-            proj4_str = model._proj4_str
+        crs = get_crs(prjfile=prjfile, proj4=proj4_str, crs=crs)
+        if crs is None:
+            crs = model._crs
         if start_datetime is None:
             start_datetime = model._start_datetime
 
@@ -255,7 +264,7 @@ def __init__(
             xll = mg._xul_to_xll(xul)
         if yul is not None:
             yll = mg._yul_to_yll(yul)
-        mg.set_coord_info(xoff=xll, yoff=yll, angrot=rotation, proj4=proj4_str)
+        mg.set_coord_info(xoff=xll, yoff=yll, angrot=rotation, crs=crs)
 
         self.tr = TemporalReference(
             itmuni=self.itmuni, start_datetime=start_datetime
@@ -927,7 +936,7 @@ def load(cls, f, model, ext_unit_dict=None, check=True):
             xul=xul,
             yul=yul,
             rotation=rotation,
-            proj4_str=proj4_str,
+            crs=proj4_str,
             start_datetime=start_datetime,
             unitnumber=unitnumber,
             filenames=filenames,
diff --git a/flopy/mt3d/mt.py b/flopy/mt3d/mt.py
index d9868d9c14..e3ded4ee81 100644
--- a/flopy/mt3d/mt.py
+++ b/flopy/mt3d/mt.py
@@ -280,8 +280,7 @@ def modelgrid(self):
             top=top,
             botm=botm,
             idomain=ibound,
-            proj4=self._modelgrid.proj4,
-            epsg=self._modelgrid.epsg,
+            crs=self._modelgrid.crs,
             xoff=self._modelgrid.xoffset,
             yoff=self._modelgrid.yoffset,
             angrot=self._modelgrid.angrot,
diff --git a/flopy/seawat/swt.py b/flopy/seawat/swt.py
index f895906a4f..05a6468464 100644
--- a/flopy/seawat/swt.py
+++ b/flopy/seawat/swt.py
@@ -202,8 +202,7 @@ def modelgrid(self):
             self.dis.botm.array,
             idomain=ibound,
             lenuni=self.dis.lenuni,
-            proj4=self._modelgrid.proj4,
-            epsg=self._modelgrid.epsg,
+            crs=self._modelgrid.crs,
             xoff=self._modelgrid.xoffset,
             yoff=self._modelgrid.yoffset,
             angrot=self._modelgrid.angrot,
diff --git a/flopy/utils/crs.py b/flopy/utils/crs.py
index 19a6dbb301..e0cb120905 100644
--- a/flopy/utils/crs.py
+++ b/flopy/utils/crs.py
@@ -81,14 +81,14 @@ def get_crs(prjfile=None, prj=None, epsg=None, proj4=None, crs=None):
     if prj is not None:
         warnings.warn(
             "the prj argument will be deprecated and will be removed in version "
-            "3.3.7. Use prjfile instead.",
+            "3.4. Use prjfile instead.",
             PendingDeprecationWarning,
         )
         prjfile = prj
     if epsg is not None:
         warnings.warn(
             "the epsg argument will be deprecated and will be removed in version "
-            "3.3.7. Use crs instead.",
+            "3.4. Use crs instead.",
             PendingDeprecationWarning,
         )
         if crs is None:
@@ -106,7 +106,7 @@ def get_crs(prjfile=None, prj=None, epsg=None, proj4=None, crs=None):
     elif proj4 is not None:
         warnings.warn(
             "the proj4 argument will be deprecated and will be removed in version "
-            "3.3.7. Use crs instead.",
+            "3.4. Use crs instead.",
             PendingDeprecationWarning,
         )
         if crs is None:

From 7f03748f711617dcff89ec32e6b6f6fb79826b1c Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 21 Feb 2023 15:34:40 -0600
Subject: [PATCH 03/28] refactor(export): replace epsg and proj4 input
 arguments with general crs argument and prjfile argument

---
 autotest/test_export.py         |  36 +++++-----
 flopy/discretization/grid.py    |   6 +-
 flopy/export/shapefile_utils.py | 119 ++++++++++++++++----------------
 flopy/export/utils.py           | 114 +++++++++++++-----------------
 flopy/utils/crs.py              |   7 +-
 5 files changed, 133 insertions(+), 149 deletions(-)

diff --git a/autotest/test_export.py b/autotest/test_export.py
index e81549af8e..8e678d7e54 100644
--- a/autotest/test_export.py
+++ b/autotest/test_export.py
@@ -536,7 +536,9 @@ def test_export_netcdf(function_tmpdir, namfile):
 def test_export_array2(function_tmpdir):
     nrow = 7
     ncol = 11
-    epsg = 4111
+    # epsg = 4111  # this may not be a valid EPSG code
+    # (raises an error with pyproj)
+    crs = 3070
 
     # no epsg code
     modelgrid = StructuredGrid(
@@ -549,7 +551,7 @@ def test_export_array2(function_tmpdir):
 
     # with modelgrid epsg code
     modelgrid = StructuredGrid(
-        delr=np.ones(ncol) * 1.1, delc=np.ones(nrow) * 1.1, epsg=epsg
+        delr=np.ones(ncol) * 1.1, delc=np.ones(nrow) * 1.1, crs=crs
     )
     filename = os.path.join(function_tmpdir, "myarray2.shp")
     a = np.arange(nrow * ncol).reshape((nrow, ncol))
@@ -562,7 +564,7 @@ def test_export_array2(function_tmpdir):
     )
     filename = os.path.join(function_tmpdir, "myarray3.shp")
     a = np.arange(nrow * ncol).reshape((nrow, ncol))
-    export_array(modelgrid, filename, a, epsg=epsg)
+    export_array(modelgrid, filename, a, crs=crs)
     assert os.path.isfile(filename), "did not create array shapefile"
 
 
@@ -570,7 +572,9 @@ def test_export_array2(function_tmpdir):
 def test_export_array_contours(function_tmpdir):
     nrow = 7
     ncol = 11
-    epsg = 4111
+    # epsg = 4111  # this may not be a valid EPSG code
+    # (raises an error with pyproj)
+    crs = 3070
 
     # no epsg code
     modelgrid = StructuredGrid(
@@ -581,22 +585,24 @@ def test_export_array_contours(function_tmpdir):
     export_array_contours(modelgrid, filename, a)
     assert os.path.isfile(filename), "did not create contour shapefile"
 
-    # with modelgrid epsg code
+    # with modelgrid coordinate reference
     modelgrid = StructuredGrid(
-        delr=np.ones(ncol) * 1.1, delc=np.ones(nrow) * 1.1, epsg=epsg
+        delr=np.ones(ncol) * 1.1,
+        delc=np.ones(nrow) * 1.1,
+        crs=crs,
     )
     filename = function_tmpdir / "myarraycontours2.shp"
     a = np.arange(nrow * ncol).reshape((nrow, ncol))
     export_array_contours(modelgrid, filename, a)
     assert os.path.isfile(filename), "did not create contour shapefile"
 
-    # with passing in epsg code
+    # with passing in coordinate reference
     modelgrid = StructuredGrid(
         delr=np.ones(ncol) * 1.1, delc=np.ones(nrow) * 1.1
     )
     filename = function_tmpdir / "myarraycontours3.shp"
     a = np.arange(nrow * ncol).reshape((nrow, ncol))
-    export_array_contours(modelgrid, filename, a, epsg=epsg)
+    export_array_contours(modelgrid, filename, a, crs=crs)
     assert os.path.isfile(filename), "did not create contour shapefile"
 
 
@@ -889,7 +895,6 @@ def test_polygon_from_ij(function_tmpdir):
 
 @flaky
 @requires_pkg("netCDF4", "pyproj")
-@requires_spatial_reference
 def test_polygon_from_ij_with_epsg(function_tmpdir):
     ws = function_tmpdir
     m = Modflow("toy_model", model_ws=ws)
@@ -917,7 +922,7 @@ def test_polygon_from_ij_with_epsg(function_tmpdir):
         xoff=mg._xul_to_xll(600000.0, -45.0),
         yoff=mg._yul_to_yll(5170000, -45.0),
         angrot=-45.0,
-        proj4="EPSG:26715",
+        crs="EPSG:26715",
     )
 
     recarray = np.array(
@@ -943,16 +948,7 @@ def test_polygon_from_ij_with_epsg(function_tmpdir):
     ]
 
     fpth = os.path.join(ws, "test.shp")
-    recarray2shp(recarray, geoms, fpth, epsg=26715)
-
-    # tries to connect to https://spatialreference.org,
-    # might fail with CERTIFICATE_VERIFY_FAILED (on Mac,
-    # run Python Install Certificates) but intermittent
-    # 502s are also possible and possibly unavoidable)
-    ep = EpsgReference()
-    prj = ep.to_dict()
-
-    assert 26715 in prj
+    recarray2shp(recarray, geoms, fpth, crs=26715)
 
     fpth = os.path.join(ws, "test.prj")
     fpth2 = os.path.join(ws, "26715.prj")
diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index f447645863..3b83e50fbf 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -262,7 +262,8 @@ def crs(self, crs):
 
     @property
     def epsg(self):
-        return self._crs.to_epsg()
+        if self._crs is not None:
+            return self._crs.to_epsg()
 
     @epsg.setter
     def epsg(self, epsg):
@@ -271,7 +272,8 @@ def epsg(self, epsg):
 
     @property
     def proj4(self):
-        return self._crs.to_proj4()
+        if self._crs is not None:
+            return self._crs.to_proj4()
 
     @proj4.setter
     def proj4(self, proj4):
diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py
index 68bc182d05..044d1ce80c 100644
--- a/flopy/export/shapefile_utils.py
+++ b/flopy/export/shapefile_utils.py
@@ -16,6 +16,7 @@
 
 from ..datbase import DataInterface, DataType
 from ..utils import Util3d, flopy_io, import_optional_dependency
+from ..utils.crs import get_crs
 
 # web address of spatial reference dot org
 srefhttp = "https://spatialreference.org"
@@ -54,7 +55,8 @@ def write_gridlines_shapefile(filename: Union[str, os.PathLike], mg):
         wr.record(i)
 
     wr.close()
-    write_prj(filename, mg=mg)
+    write_prj(filename, modelgrid=mg)
+    return
 
 
 def write_grid_shapefile(
@@ -62,6 +64,8 @@ def write_grid_shapefile(
     mg,
     array_dict,
     nan_val=np.nan,
+    crs=None,
+    prjfile=None,
     epsg=None,
     prj: Optional[Union[str, os.PathLike]] = None,
     verbose=False,
@@ -79,12 +83,15 @@ def write_grid_shapefile(
         dictionary of model input arrays
     nan_val : float
         value to fill nans
-    epsg : str, int
-        epsg code
-    prj : str or PathLike, optional, default None
-        projection file path
-    verbose : bool, default False
-        whether to print verbose output
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
 
     Returns
     -------
@@ -207,7 +214,8 @@ def write_grid_shapefile(
         print(f"wrote {flopy_io.relpath_safe(path)}")
 
     # write the projection file
-    write_prj(path, mg, epsg, prj, verbose=verbose)
+    write_prj(path, mg, crs=crs, epsg=epsg, prj=prj, prjfile=prjfile)
+    return
 
 
 def model_attributes_to_shapefile(
@@ -240,10 +248,15 @@ def model_attributes_to_shapefile(
         modelgrid : fp.modflow.Grid object
             if modelgrid is supplied, user supplied modelgrid is used in lieu
             of the modelgrid attached to the modflow model object
-        epsg : int
-            epsg projection information
-        prj : str or PathLike
-            prj file path
+        crs : pyproj.CRS, optional if `prj` is specified
+            Coordinate reference system (CRS) for the model grid
+            (must be projected; geographic CRS are not supported).
+            The value can be anything accepted by
+            :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+            such as an authority string (eg "EPSG:26916") or a WKT string.
+        prjfile : str or pathlike, optional if `crs` is specified
+            ESRI-style projection file with well-known text defining the CRS
+            for the model grid (must be projected; geographic CRS are not supported).
 
     Returns
     -------
@@ -387,10 +400,10 @@ def model_attributes_to_shapefile(
                                 array_dict[name] = arr
 
     # write data arrays to a shapefile
-    write_grid_shapefile(path, grid, array_dict, verbose=verbose)
-    epsg = kwargs.get("epsg", None)
-    prj = kwargs.get("prj", None)
-    write_prj(path, grid, epsg, prj, verbose=verbose)
+    write_grid_shapefile(path, grid, array_dict)
+    crs = kwargs.get("crs", None)
+    prjfile = kwargs.get("prjfile", None)
+    write_prj(path, grid, crs=crs, prjfile=prjfile)
 
 
 def shape_attr_name(name, length=6, keep_layer=False):
@@ -533,6 +546,8 @@ def recarray2shp(
     geoms,
     shpname: Union[str, os.PathLike] = "recarray.shp",
     mg=None,
+    crs=None,
+    prjfile=None,
     epsg=None,
     prj: Optional[Union[str, os.PathLike]] = None,
     verbose=False,
@@ -556,21 +571,19 @@ def recarray2shp(
         recarray.
     shpname : str or PathLike, default "recarray.shp"
         Path for the output shapefile
-    epsg : int
-        EPSG code. See https://www.epsg-registry.org/ or spatialreference.org
-    prj : str or PathLike, optional, default None
-        Existing projection file to be used with new shapefile.
-    verbose : bool
-        Whether to print verbose output
+    crs : pyproj.CRS, optional if `prj` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    prjfile : str or pathlike, optional if `crs` is specified
+        ESRI-style projection file with well-known text defining the CRS
+        for the model grid (must be projected; geographic CRS are not supported).
 
     Notes
     -----
     Uses pyshp.
-    epsg code requires an internet connection the first time to get the
-    projection file text from spatialreference.org, but then stashes the text
-    in the file epsgref.json (located in the user's data directory) for
-    subsequent use. See flopy.reference for more details.
-
     """
     from ..utils.geospatial_utils import GeoSpatialCollection
 
@@ -624,52 +637,38 @@ def recarray2shp(
             w.record(*r)
 
     w.close()
-    write_prj(shpname, mg, epsg, prj)
-    if verbose:
-        print(f"wrote {flopy_io.relpath_safe(shpname)}")
+    write_prj(shpname, mg, crs=crs, epsg=epsg, prj=prj, prjfile=prjfile)
+    print(f"wrote {flopy_io.relpath_printstr(os.getcwd(), shpname)}")
+    return
 
 
 def write_prj(
-    shpname: Union[str, os.PathLike],
-    mg=None,
+    shpname,
+    modelgrid=None,
+    crs=None,
     epsg=None,
-    prj: Optional[Union[str, os.PathLike]] = None,
+    prj=None,
+    prjfile=None,
     wkt_string=None,
-    verbose=False,
 ):
     # projection file name
-    prjname = Path(shpname).with_suffix(".prj")
-
-    # figure which CRS option to use
-    # prioritize args over grid reference
-    # no proj4 option because it is too difficult
-    # to create prjfile from proj4 string without OGR
-    prjtxt = wkt_string
-    if epsg is not None:
-        prjtxt = CRS.getprj(epsg)
-    # copy a supplied prj file
-    elif prj is not None:
-        if prjname.exists():
-            if verbose:
-                print(
-                    f".prj file {flopy_io.relpath_safe(prjname)} already exists"
-                )
-        else:
-            shutil.copy(str(prj), str(prjname))
+    output_projection_file = Path(shpname).with_suffix(".prj")
 
-    elif mg is not None:
-        if mg.epsg is not None:
-            prjtxt = CRS.getprj(mg.epsg)
+    crs = get_crs(
+        prjfile=prjfile, prj=prj, epsg=epsg, crs=crs, wkt_string=wkt_string
+    )
+    if crs is None and modelgrid is not None:
+        crs = modelgrid.crs
+    if crs is not None:
+        with open(output_projection_file, "w") as dest:
+            dest.write(crs.to_wkt())
 
     else:
         print(
             "No CRS information for writing a .prj file.\n"
-            "Supply an epsg code or .prj file path to the "
-            "model spatial reference or .export() method."
-            "(writing .prj files from proj4 strings not supported)"
+            "Supply an valid coordinate system reference to the attached modelgrid object "
+            "or .export() method."
         )
-    if prjtxt is not None:
-        prjname.write_text(prjtxt)
 
 
 class CRS:
diff --git a/flopy/export/utils.py b/flopy/export/utils.py
index 27670be8fa..9891a801fc 100644
--- a/flopy/export/utils.py
+++ b/flopy/export/utils.py
@@ -15,6 +15,7 @@
     flopy_io,
     import_optional_dependency,
 )
+from ..utils.crs import get_crs
 from . import NetCdf, netcdf, shapefile_utils, vtk
 from .longnames import NC_LONG_NAMES
 from .unitsformat import NC_UNITS_FORMAT
@@ -593,11 +594,16 @@ def model_export(
         modelgrid: flopy.discretization.Grid
             user supplied modelgrid object which will supercede the built
             in modelgrid object
-        epsg : int
-            epsg projection code
-        prj : str
-            prj file name
-        if fmt is set to 'vtk', parameters of vtk.export_model
+        crs : pyproj.CRS, optional if `prj` is specified
+            Coordinate reference system (CRS) for the model grid
+            (must be projected; geographic CRS are not supported).
+            The value can be anything accepted by
+            :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+            such as an authority string (eg "EPSG:26916") or a WKT string.
+        prjfile : str or pathlike, optional if `crs` is specified
+            ESRI-style projection file with well-known text defining the CRS
+            for the model grid (must be projected; geographic CRS are not supported).
+            if fmt is set to 'vtk', parameters of vtk.export_model
 
     """
     assert isinstance(ml, ModelInterface)
@@ -680,10 +686,15 @@ def package_export(
         modelgrid: flopy.discretization.Grid
             user supplied modelgrid object which will supercede the built
             in modelgrid object
-        epsg : int
-            epsg projection code
-        prj : str
-            prj file name
+        crs : pyproj.CRS, optional if `prj` is specified
+            Coordinate reference system (CRS) for the model grid
+            (must be projected; geographic CRS are not supported).
+            The value can be anything accepted by
+            :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+            such as an authority string (eg "EPSG:26916") or a WKT string.
+        prjfile : str or pathlike, optional if `crs` is specified
+            ESRI-style projection file with well-known text defining the CRS
+            for the model grid (must be projected; geographic CRS are not supported).
         if fmt is set to 'vtk', parameters of vtk.export_package
 
     Returns
@@ -874,6 +885,15 @@ def mflist_export(f: Union[str, os.PathLike, NetCdf], mfl, **kwargs):
     **kwargs : keyword arguments
         modelgrid : flopy.discretization.Grid
             model grid instance which will supercede the flopy.model.modelgrid
+        crs : pyproj.CRS, optional if `prj` is specified
+            Coordinate reference system (CRS) for the model grid
+            (must be projected; geographic CRS are not supported).
+            The value can be anything accepted by
+            :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+            such as an authority string (eg "EPSG:26916") or a WKT string.
+        prjfile : str or pathlike, optional if `crs` is specified
+            ESRI-style projection file with well-known text defining the CRS
+            for the model grid (must be projected; geographic CRS are not supported).
 
     """
     if not isinstance(mfl, (DataListInterface, DataInterface)):
@@ -938,11 +958,16 @@ def mflist_export(f: Union[str, os.PathLike, NetCdf], mfl, **kwargs):
                     ]
                 )
                 ra = df.to_records(index=False)
-            epsg = kwargs.get("epsg", None)
-            prj = kwargs.get("prj", None)
+            crs = kwargs.get("crs", None)
+            prjfile = kwargs.get("prjfile", None)
             polys = np.array([Polygon(v) for v in verts])
             recarray2shp(
-                ra, geoms=polys, shpname=f, mg=modelgrid, epsg=epsg, prj=prj
+                ra,
+                geoms=polys,
+                shpname=f,
+                mg=modelgrid,
+                crs=crs,
+                prjfile=prjfile,
             )
 
     elif isinstance(f, NetCdf) or isinstance(f, dict):
@@ -1521,7 +1546,7 @@ def export_array(
     kwargs:
         keyword arguments to np.savetxt (ascii)
         rasterio.open (GeoTIFF)
-        or flopy.export.shapefile_utils.write_grid_shapefile2
+        or flopy.export.shapefile_utils.write_grid_shapefile
 
     Notes
     -----
@@ -1645,17 +1670,13 @@ def export_array(
     elif filename.lower().endswith(".shp"):
         from ..export.shapefile_utils import write_grid_shapefile
 
-        epsg = kwargs.get("epsg", None)
-        prj = kwargs.get("prj", None)
-        if epsg is None and prj is None:
-            epsg = modelgrid.epsg
+        crs = get_crs(**kwargs)
         write_grid_shapefile(
             filename,
             modelgrid,
             array_dict={fieldname: a},
             nan_val=nodata,
-            epsg=epsg,
-            prj=prj,
+            crs=crs,
         )
 
 
@@ -1663,9 +1684,6 @@ def export_contours(
     filename: Union[str, os.PathLike],
     contours,
     fieldname="level",
-    epsg=None,
-    prj: Optional[Union[str, os.PathLike]] = None,
-    verbose=False,
     **kwargs,
 ):
     """
@@ -1679,12 +1697,6 @@ def export_contours(
         (object returned by matplotlib.pyplot.contour)
     fieldname : str
         gis attribute table field name
-    epsg : int
-        EPSG code. See https://www.epsg-registry.org/ or spatialreference.org
-    prj : str or PathLike, optional, default None
-        Existing projection file to be used with new shapefile.
-    verbose : bool, optional, default False
-        whether to show verbose output
     **kwargs : key-word arguments to flopy.export.shapefile_utils.recarray2shp
 
     Returns
@@ -1710,20 +1722,11 @@ def export_contours(
     # convert the dictionary to a recarray
     ra = np.array(level, dtype=[(fieldname, float)]).view(np.recarray)
 
-    recarray2shp(
-        ra, geoms, filename, epsg=epsg, prj=prj, verbose=verbose, **kwargs
-    )
+    recarray2shp(ra, geoms, filename, **kwargs)
+    return
 
 
-def export_contourf(
-    filename: Union[str, os.PathLike],
-    contours,
-    fieldname="level",
-    epsg=None,
-    prj: Optional[Union[str, os.PathLike]] = None,
-    verbose=False,
-    **kwargs,
-):
+def export_contourf(filename, contours, fieldname="level", **kwargs):
     """
     Write matplotlib filled contours to shapefile.
 
@@ -1737,12 +1740,6 @@ def export_contourf(
         Name of shapefile attribute field to contain the contour level.  The
         fieldname column in the attribute table will contain the lower end of
         the range represented by the polygon.  Default is 'level'.
-    epsg : int
-        EPSG code. See https://www.epsg-registry.org/ or spatialreference.org
-    prj : str or PathLike, optional, default None
-        Existing projection file to be used with new shapefile.
-    verbose : bool, optional, default False
-        whether to show verbose output
 
     **kwargs : keyword arguments to flopy.export.shapefile_utils.recarray2shp
 
@@ -1809,9 +1806,8 @@ def export_contourf(
     # Create recarray
     ra = np.array(level, dtype=[(fieldname, float)]).view(np.recarray)
 
-    recarray2shp(
-        ra, geoms, filename, epsg=epsg, prj=prj, verbose=verbose, **kwargs
-    )
+    recarray2shp(ra, geoms, filename, **kwargs)
+    return
 
 
 def export_array_contours(
@@ -1822,9 +1818,6 @@ def export_array_contours(
     interval=None,
     levels=None,
     maxlevels=1000,
-    epsg=None,
-    prj: Optional[Union[str, os.PathLike]] = None,
-    verbose=False,
     **kwargs,
 ):
     """
@@ -1846,22 +1839,11 @@ def export_array_contours(
         list of contour levels
     maxlevels : int
         maximum number of contour levels
-    epsg : int
-        EPSG code. See https://www.epsg-registry.org/ or spatialreference.org
-    prj : str or PathLike, optional, default None
-        Existing projection file to be used with new shapefile.
-    verbose : bool, optional, default False
-        whether to show verbose output
     **kwargs : keyword arguments to flopy.export.shapefile_utils.recarray2shp
 
     """
     import matplotlib.pyplot as plt
 
-    if epsg is None:
-        epsg = modelgrid.epsg
-    if prj is None:
-        prj = modelgrid.proj4
-
     if interval is not None:
         imin = np.nanmin(a)
         imax = np.nanmax(a)
@@ -1871,9 +1853,9 @@ def export_array_contours(
         levels = np.arange(imin, imax, interval)
     ax = plt.subplots()[-1]
     ctr = contour_array(modelgrid, ax, a, levels=levels)
-    export_contours(
-        filename, ctr, fieldname, epsg, prj, verbose=verbose, **kwargs
-    )
+
+    kwargs["modelgrid"] = modelgrid
+    export_contours(filename, ctr, fieldname, **kwargs)
     plt.close()
 
 
diff --git a/flopy/utils/crs.py b/flopy/utils/crs.py
index e0cb120905..6f24940cf3 100644
--- a/flopy/utils/crs.py
+++ b/flopy/utils/crs.py
@@ -71,7 +71,9 @@ def get_shapefile_crs(shapefile):
             return get_authority_crs(crs)
 
 
-def get_crs(prjfile=None, prj=None, epsg=None, proj4=None, crs=None):
+def get_crs(
+    prjfile=None, prj=None, epsg=None, proj4=None, crs=None, wkt_string=None
+):
     """Helper function to produce a pyproj.CRS object from
     various input. Longer-term, this would just handle the ``crs``
     and ``prjfile`` arguments, but in the near term, we need to
@@ -111,6 +113,9 @@ def get_crs(prjfile=None, prj=None, epsg=None, proj4=None, crs=None):
         )
         if crs is None:
             crs = get_authority_crs(proj4)
+    elif wkt_string is not None:
+        if crs is None:
+            crs = get_authority_crs(wkt_string)
     if crs is not None and not crs.is_projected:
         raise ValueError(
             f"Only projected coordinate reference systems are supported.\n{crs}"

From 9b93832de174829d2f3c019739cb683a6b44c79f Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 22 Feb 2023 10:06:23 -0600
Subject: [PATCH 04/28] refactor(utils/mfreadnam.py): write crs 'srs' attribute
 string to namefile header under 'crs' key; read 'proj4' key as crs input

---
 flopy/utils/mfreadnam.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/flopy/utils/mfreadnam.py b/flopy/utils/mfreadnam.py
index 27c34963b2..be900cde26 100644
--- a/flopy/utils/mfreadnam.py
+++ b/flopy/utils/mfreadnam.py
@@ -212,6 +212,7 @@ def attribs_from_namfile_header(namefile):
         "xul": None,
         "yul": None,
         "rotation": 0.0,
+        "crs": None,
         "proj4_str": None,
     }
     if namefile is None:
@@ -259,7 +260,15 @@ def attribs_from_namfile_header(namefile):
                 proj4 = ":".join(item.split(":")[1:]).strip()
                 if proj4.lower() == "none":
                     proj4 = None
-                defaults["proj4_str"] = proj4
+                defaults["crs"] = proj4
+            except:
+                print(f"   could not parse proj4_str in {namefile}")
+        elif "crs" in item.lower():
+            try:
+                crs = ":".join(item.split(":")[1:]).strip()
+                if crs.lower() == "none":
+                    proj4 = None
+                defaults["crs"] = crs
             except:
                 print(f"   could not parse proj4_str in {namefile}")
         elif "start" in item.lower():

From 8a59dd4f28fbc2c369f8db69ddcd8a742fbaf5fa Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 22 Feb 2023 10:09:06 -0600
Subject: [PATCH 05/28] refactor(mt3d/mt.py; seawat/swt.py): pass modelgrid.crs
 attribute to self._modelgrid.set_coord_info() instead of epsg or proj4

---
 flopy/mt3d/mt.py    | 11 ++++-------
 flopy/seawat/swt.py |  3 +--
 2 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/flopy/mt3d/mt.py b/flopy/mt3d/mt.py
index e3ded4ee81..c3d82cdda4 100644
--- a/flopy/mt3d/mt.py
+++ b/flopy/mt3d/mt.py
@@ -312,12 +312,9 @@ def modelgrid(self):
                     yoff = self._modelgrid._yul_to_yll(self.mf._yul)
                 else:
                     yoff = 0.0
-        proj4 = self._modelgrid.proj4
-        if proj4 is None:
-            proj4 = self.mf._modelgrid.proj4
-        epsg = self._modelgrid.epsg
-        if epsg is None:
-            epsg = self.mf._modelgrid.epsg
+        crs = self._modelgrid.crs
+        if crs is None:
+            crs = self.mf._modelgrid.crs
         angrot = self._modelgrid.angrot
         if angrot is None or angrot == 0.0:  # angrot normally defaulted to 0.0
             if self.mf._modelgrid.angrot is not None:
@@ -325,7 +322,7 @@ def modelgrid(self):
             else:
                 angrot = 0.0
 
-        self._modelgrid.set_coord_info(xoff, yoff, angrot, epsg, proj4)
+        self._modelgrid.set_coord_info(xoff, yoff, angrot, crs=crs)
         self._mg_resync = not self._modelgrid.is_complete
         return self._modelgrid
 
diff --git a/flopy/seawat/swt.py b/flopy/seawat/swt.py
index 05a6468464..689ff5af0f 100644
--- a/flopy/seawat/swt.py
+++ b/flopy/seawat/swt.py
@@ -226,8 +226,7 @@ def modelgrid(self):
             xoff,
             yoff,
             self._modelgrid.angrot,
-            self._modelgrid.epsg,
-            self._modelgrid.proj4,
+            self._modelgrid.crs,
         )
         self._mg_resync = not self._modelgrid.is_complete
         return self._modelgrid

From 93d2be1514f69d1e0bb75beae8a867b1807eb289 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 22 Feb 2023 10:19:52 -0600
Subject: [PATCH 06/28] refactor(discretization/grid.py): write crs to
 Grid.__repr__ instead of proj4 (needed for a792e7377b8c);
 fix(Grid.read_usgs_model_reference_file): to handle quoting in
 usgs.model.reference

---
 flopy/discretization/grid.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index 3b83e50fbf..5d2a41737f 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -210,8 +210,8 @@ def __repr__(self):
                 f"yll:{self.yoffset!s}",
                 f"rotation:{self.angrot!s}",
             ]
-        if self.proj4 is not None:
-            items.append(f"proj4_str:{self.proj4}")
+        if self.crs is not None:
+            items.append(f"crs:{self.crs.srs}")
         if self.units is not None:
             items.append(f"units:{self.units}")
         if self.lenuni is not None:
@@ -939,7 +939,7 @@ def read_usgs_model_reference_file(self, reffile="usgs.model.reference"):
                         if line.strip()[0] != "#":
                             info = line.strip().split("#")[0].split()
                             if len(info) > 1:
-                                data = " ".join(info[1:])
+                                data = " ".join(info[1:]).strip("'").strip('"')
                                 if info[0] == "xll":
                                     self._xoff = float(data)
                                 elif info[0] == "yll":

From 96fb6b08e6a38edbe0143a1bd26298201da2d8e2 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 22 Feb 2023 10:23:29 -0600
Subject: [PATCH 07/28] test(test_modflow.py): refactor to use crs attribute
 and to use valid coordinate references

---
 autotest/test_modflow.py           | 22 +++++++++++-----------
 examples/data/usgs.model.reference |  4 ++--
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/autotest/test_modflow.py b/autotest/test_modflow.py
index d0bfec78bc..837e5118ec 100644
--- a/autotest/test_modflow.py
+++ b/autotest/test_modflow.py
@@ -110,7 +110,7 @@ def test_mt_modelgrid(function_tmpdir):
     ml = Modflow(
         modelname="test",
         xll=500.0,
-        proj4_str="epsg:2193",
+        crs="epsg:2193",
         rotation=12.5,
         start_datetime="1/1/2016",
     )
@@ -131,7 +131,7 @@ def test_mt_modelgrid(function_tmpdir):
 
     assert mt.modelgrid.xoffset == ml.modelgrid.xoffset
     assert mt.modelgrid.yoffset == ml.modelgrid.yoffset
-    assert mt.modelgrid.epsg == ml.modelgrid.epsg
+    assert mt.modelgrid.crs == ml.modelgrid.crs
     assert mt.modelgrid.angrot == ml.modelgrid.angrot
     assert np.array_equal(mt.modelgrid.idomain, ml.modelgrid.idomain)
 
@@ -160,7 +160,7 @@ def test_mt_modelgrid(function_tmpdir):
     assert (
         swt.modelgrid.yoffset == mt.modelgrid.yoffset == ml.modelgrid.yoffset
     )
-    assert mt.modelgrid.epsg == ml.modelgrid.epsg == swt.modelgrid.epsg
+    assert mt.modelgrid.crs == ml.modelgrid.crs == swt.modelgrid.crs
     assert mt.modelgrid.angrot == ml.modelgrid.angrot == swt.modelgrid.angrot
     assert np.array_equal(mt.modelgrid.idomain, ml.modelgrid.idomain)
     assert np.array_equal(swt.modelgrid.idomain, ml.modelgrid.idomain)
@@ -194,7 +194,7 @@ def test_mt_modelgrid(function_tmpdir):
     assert (
         mt.modelgrid.yoffset == ml.modelgrid.yoffset == swt.modelgrid.yoffset
     )
-    assert mt.modelgrid.epsg == ml.modelgrid.epsg == swt.modelgrid.epsg
+    assert mt.modelgrid.crs == ml.modelgrid.crs == swt.modelgrid.crs
     assert mt.modelgrid.angrot == ml.modelgrid.angrot == swt.modelgrid.angrot
     assert np.array_equal(mt.modelgrid.idomain, ml.modelgrid.idomain)
     assert np.array_equal(swt.modelgrid.idomain, ml.modelgrid.idomain)
@@ -251,7 +251,7 @@ def test_sr(function_tmpdir):
         model_ws=ws,
         xll=12345,
         yll=12345,
-        proj4_str="test test test",
+        crs=26916,
     )
     ModflowDis(m, 10, 10, 10)
     m.write_input()
@@ -261,7 +261,7 @@ def test_sr(function_tmpdir):
         raise AssertionError()
     if extents[3] != 12355:
         raise AssertionError()
-    if mm.modelgrid.proj4 != "test test test":
+    if mm.modelgrid.crs.srs != "EPSG:26916":
         raise AssertionError()
 
     mm.dis.top = 5000
@@ -511,7 +511,7 @@ def test_read_usgs_model_reference(function_tmpdir, model_reference_path):
     )
     m.write_input()
 
-    # test reading of SR information from usgs.model.reference
+    # test reading of proj4 string from usgs.model.reference
     m2 = Modflow.load("junk.nam", model_ws=ws)
     from flopy.discretization import StructuredGrid
 
@@ -527,21 +527,21 @@ def test_read_usgs_model_reference(function_tmpdir, model_reference_path):
     assert m2.modelgrid.xoffset == mg.xoffset
     assert m2.modelgrid.yoffset == mg.yoffset
     assert m2.modelgrid.angrot == mg.angrot
-    assert m2.modelgrid.epsg == mg.epsg
+    assert m2.modelgrid.crs == mg.crs
 
-    # test reading non-default units from usgs.model.reference
+    # test reading epsg code from usgs.model.reference
     shutil.copy(mrf_path, f"{mrf_path}_copy")
     with open(f"{mrf_path}_copy") as src:
         with open(mrf_path, "w") as dst:
             for line in src:
                 if "epsg" in line:
-                    line = line.replace("102733", "4326")
+                    line = "epsg 26916\n"
                 dst.write(line)
 
     m2 = Modflow.load("junk.nam", model_ws=ws)
     m2.modelgrid.read_usgs_model_reference_file(mrf_path)
 
-    assert m2.modelgrid.epsg == 4326
+    assert m2.modelgrid.crs.to_epsg() == 26916
     # have to delete this, otherwise it will mess up other tests
     to_del = glob.glob(f"{mrf_path}*")
     for f in to_del:
diff --git a/examples/data/usgs.model.reference b/examples/data/usgs.model.reference
index 9a89b9ead9..ccce54a281 100644
--- a/examples/data/usgs.model.reference
+++ b/examples/data/usgs.model.reference
@@ -6,5 +6,5 @@ time_units days        # model time units (days, years, etc.)
 start_date 1/1/1900    # state date of the model
 start_time 00:00:00    # start time of the model
 model modflow-2000     # MODFLOW model type (MODFLOW-NWT, etc.)
-epsg 102733            # epsg code 
-# proj4 'proj4 string' # or proj4 string 
+#            # epsg code
+proj4 '+proj=lcc +lat_0=31.8333333333333 +lon_0=-81 +lat_1=32.5 +lat_2=34.8333333333333 +x_0=609600 +y_0=0 +datum=NAD83 +units=us-ft +no_defs +type=crs' # or proj4 string

From 0c88ca2d1c7e8426e8f573963a50971745c83ade Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 22 Feb 2023 10:24:38 -0600
Subject: [PATCH 08/28] fix(export/shapefile_utils.py): write well-known text
 as utf-8 to avoid issues with encoding the degree symbol to ascii; fix the
 same issue in test_grid.py. Also replace epsg code 4326 in example name files
 with epsgs for projected CRS (geographic CRS no longer allowed)

---
 autotest/test_grid.py                                  | 10 ++++++----
 .../mt3d_test/mfnwt_mt3dusgs/sft_crnkNic/CrnkNic.nam   |  2 +-
 examples/data/options/sagehen/sagehen.nam              |  2 +-
 flopy/export/shapefile_utils.py                        |  5 +++--
 flopy/utils/crs.py                                     |  2 +-
 5 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/autotest/test_grid.py b/autotest/test_grid.py
index 798929f389..322b2d8f74 100644
--- a/autotest/test_grid.py
+++ b/autotest/test_grid.py
@@ -621,8 +621,9 @@ def test_grid_crs(
 
         # test input of projection file
         prjfile = function_tmpdir / "grid_crs.prj"
-        with open(prjfile, "w") as dest:
-            dest.write(sg.crs.to_wkt())
+        with open(prjfile, "w", encoding="utf-8") as dest:
+            write_text = sg.crs.to_wkt()
+            dest.write(write_text)
 
         sg3 = StructuredGrid(delr=delr, delc=delc, prjfile=prjfile)
         if crs is not None:
@@ -659,8 +660,9 @@ def test_grid_set_crs(crs, expected_srs, function_tmpdir):
     # test input of projection file
     if crs is not None:
         prjfile = function_tmpdir / "grid_crs.prj"
-        with open(prjfile, "w") as dest:
-            dest.write(sg.crs.to_wkt())
+        with open(prjfile, "w", encoding="utf-8") as dest:
+            write_text = sg.crs.to_wkt()
+            dest.write(write_text)
         sg = StructuredGrid(delr=delr, delc=delc)
         sg.prjfile = prjfile
         if crs is not None:
diff --git a/examples/data/mt3d_test/mfnwt_mt3dusgs/sft_crnkNic/CrnkNic.nam b/examples/data/mt3d_test/mfnwt_mt3dusgs/sft_crnkNic/CrnkNic.nam
index 562046da6b..8bfb715143 100644
--- a/examples/data/mt3d_test/mfnwt_mt3dusgs/sft_crnkNic/CrnkNic.nam
+++ b/examples/data/mt3d_test/mfnwt_mt3dusgs/sft_crnkNic/CrnkNic.nam
@@ -1,5 +1,5 @@
 # Name file for MODFLOW-NWT, generated by Flopy version 3.2.6.
-#xul:0; yul:15; rotation:0; proj4_str:EPSG:4326; units:meters; lenuni:2; length_multiplier:1.0 ;start_datetime:1-1-1970
+#xul:0; yul:15; rotation:0; proj4_str:EPSG:26916; units:meters; lenuni:2; length_multiplier:1.0 ;start_datetime:1-1-1970
 LIST               2  CrnkNic.list
 OC                14  CrnkNic.oc
 NWT               32  CrnkNic.nwt
diff --git a/examples/data/options/sagehen/sagehen.nam b/examples/data/options/sagehen/sagehen.nam
index 0a3856af21..9a701c231c 100644
--- a/examples/data/options/sagehen/sagehen.nam
+++ b/examples/data/options/sagehen/sagehen.nam
@@ -1,5 +1,5 @@
 #Name file for GSFLOW MODFLOW-NWT model, generated by GSFloPy
-#xul:0; yul:6570; rotation:0; proj4_str:EPSG:4326; units:meters; lenuni:2; length_multiplier:1.0 ;start_datetime:1/1/1970
+#xul:0; yul:6570; rotation:0; proj4_str:EPSG:26910; units:meters; lenuni:2; length_multiplier:1.0 ;start_datetime:1/1/1970
 LIST           26   sagehen.list
 BAS6           8    sagehen.bas
 OC             9    sagehen.oc
diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py
index 044d1ce80c..b3931a558f 100644
--- a/flopy/export/shapefile_utils.py
+++ b/flopy/export/shapefile_utils.py
@@ -660,8 +660,9 @@ def write_prj(
     if crs is None and modelgrid is not None:
         crs = modelgrid.crs
     if crs is not None:
-        with open(output_projection_file, "w") as dest:
-            dest.write(crs.to_wkt())
+        with open(output_projection_file, "w", encoding="utf-8") as dest:
+            write_text = crs.to_wkt()
+            dest.write(write_text)
 
     else:
         print(
diff --git a/flopy/utils/crs.py b/flopy/utils/crs.py
index 6f24940cf3..9ceff54a5b 100644
--- a/flopy/utils/crs.py
+++ b/flopy/utils/crs.py
@@ -65,7 +65,7 @@ def get_shapefile_crs(shapefile):
     shapefile = Path(shapefile)
     prjfile = shapefile.with_suffix(".prj")
     if prjfile.exists():
-        with open(prjfile) as src:
+        with open(prjfile, encoding="utf-8") as src:
             wkt = src.read()
             crs = pyproj.crs.CRS.from_wkt(wkt)
             return get_authority_crs(crs)

From f9160d8981a9d2a5929e9b29f72a7c2a77294131 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 28 Feb 2023 21:20:08 -0600
Subject: [PATCH 09/28] fix(discretization/grid.py): crs wasn't getting set
 properly

---
 flopy/discretization/grid.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index 5d2a41737f..388cd77df4 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -821,6 +821,7 @@ def set_coord_info(
         proj4=None,
         merge_coord_info=True,
     ):
+        new_crs = get_crs(prjfile=prjfile, epsg=epsg, proj4=proj4, crs=crs)
         if merge_coord_info:
             if xoff is None:
                 xoff = self._xoff
@@ -828,10 +829,8 @@ def set_coord_info(
                 yoff = self._yoff
             if angrot is None:
                 angrot = self._angrot
-            if epsg is None:
-                epsg = self._epsg
-            if proj4 is None:
-                proj4 = self._proj4
+            if new_crs is None:
+                new_crs = self._crs
 
         if xoff is None:
             xoff = 0.0
@@ -843,9 +842,8 @@ def set_coord_info(
         self._xoff = xoff
         self._yoff = yoff
         self._angrot = angrot
-        self._crs = get_crs(prjfile=prjfile, epsg=epsg, proj4=proj4, crs=crs)
-        self._epsg = epsg
-        self._proj4 = proj4
+        self._prjfile = prjfile
+        self.crs = new_crs
         self._require_cache_updates()
 
     def load_coord_info(self, namefile=None, reffile="usgs.model.reference"):

From 23e5b4778d24ccfa248b02de4618b3f4829af1d5 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 28 Feb 2023 21:25:56 -0600
Subject: [PATCH 10/28] refactor(export/netcdf.py): * refactor to use
 pyproj.CRS instead of epsg and proj4 to store CRS information; * use
 pyproj.CRS.to_cf() to get Climate and Forecast (CF) grid mapping input for
 the NetCDF file * drop support for pyproj versions < 2.2.0 * move contents of
 _initialize_attributes to __init__() method

---
 flopy/export/netcdf.py | 134 ++++++++++++++++++++++++-----------------
 1 file changed, 78 insertions(+), 56 deletions(-)

diff --git a/flopy/export/netcdf.py b/flopy/export/netcdf.py
index 056f77bdfb..4d8ab5a65d 100644
--- a/flopy/export/netcdf.py
+++ b/flopy/export/netcdf.py
@@ -9,8 +9,10 @@
 from typing import Optional, Union
 
 import numpy as np
+from packaging import version
 
 from ..utils import import_optional_dependency
+from ..utils.crs import get_authority_crs
 from .longnames import NC_LONG_NAMES
 from .metadata import acdd
 
@@ -202,14 +204,13 @@ def __init__(
         self.start_datetime = dt.strftime("%Y-%m-%dT%H:%M:%SZ")
         self.logger.log(f"start datetime:{self.start_datetime}")
 
-        proj4_str = self.model_grid.proj4
-        if proj4_str is None:
-            proj4_str = "epsg:4326"
+        crs = get_authority_crs(self.model_grid.crs)
+        if crs is None:
             self.logger.warn(
                 "model has no coordinate reference system specified. "
-                f"Using default proj4 string: {proj4_str}"
             )
-        self.proj4_str = proj4_str
+        self.model_crs = crs
+        self.transformer = None
         self.grid_units = self.model_grid.units
         self.z_positive = z_positive
         if self.grid_units is None:
@@ -220,10 +221,49 @@ def __init__(
 
         self.time_units = self.model_time.time_units
 
-        # this gives us confidence that every NetCdf instance
-        # has the same attributes
         self.log("initializing attributes")
-        self._initialize_attributes()
+        self.nc_crs_str = "epsg:4326"
+        self.nc_crs_longname = "https://www.opengis.net/def/crs/EPSG/0/4326"
+        self.nc_semi_major = float(6378137.0)
+        self.nc_inverse_flat = float(298.257223563)
+
+        self.global_attributes = {}
+        self.global_attributes["namefile"] = self.model.namefile
+        self.global_attributes["model_ws"] = self.model.model_ws
+        self.global_attributes["exe_name"] = self.model.exe_name
+        self.global_attributes["modflow_version"] = self.model.version
+
+        self.global_attributes["create_hostname"] = socket.gethostname()
+        self.global_attributes["create_platform"] = platform.system()
+        self.global_attributes["create_directory"] = os.getcwd()
+
+        htol, rtol = -999, -999
+        try:
+            htol, rtol = self.model.solver_tols()
+        except Exception as e:
+            self.logger.warn(f"unable to get solver tolerances:{e!s}")
+        self.global_attributes["solver_head_tolerance"] = htol
+        self.global_attributes["solver_flux_tolerance"] = rtol
+        spatial_attribs = {
+            "xll": self.model_grid.xoffset,
+            "yll": self.model_grid.yoffset,
+            "rotation": self.model_grid.angrot,
+            "crs": self.model_grid.crs,
+        }
+        for n, v in spatial_attribs.items():
+            self.global_attributes["flopy_sr_" + n] = v
+        self.global_attributes[
+            "start_datetime"
+        ] = self.model_time.start_datetime
+
+        self.fillvalue = FILLVALUE
+
+        # initialize attributes
+        self.grid_crs = None
+        self.zs = self.model_grid.xyzcellcenters[2].copy()
+        self.ys = None
+        self.xs = None
+        self.nc = None
         self.log("initializing attributes")
 
         self.time_values_arg = time_values
@@ -384,6 +424,10 @@ def copy(self, output_filename):
         new_net.nc.sync()
         return new_net
 
+    @property
+    def nc_crs(self):
+        return get_authority_crs(self.nc_crs_str)
+
     @classmethod
     def zeros_like(
         cls, other, output_filename=None, verbose=None, logger=None
@@ -635,7 +679,7 @@ def _initialize_attributes(self):
             "nc" not in self.__dict__.keys()
         ), "NetCdf._initialize_attributes() error: nc attribute already set"
 
-        self.nc_epsg_str = "epsg:4326"
+        self.nc_crs_str = "epsg:4326"
         self.nc_crs_longname = "https://www.opengis.net/def/crs/EPSG/0/4326"
         self.nc_semi_major = float(6378137.0)
         self.nc_inverse_flat = float(298.257223563)
@@ -661,7 +705,7 @@ def _initialize_attributes(self):
             "xll": self.model_grid.xoffset,
             "yll": self.model_grid.yoffset,
             "rotation": self.model_grid.angrot,
-            "proj4_str": self.model_grid.proj4,
+            "crs": self.model_grid.crs,
         }
         for n, v in spatial_attribs.items():
             self.global_attributes["flopy_sr_" + n] = v
@@ -686,20 +730,15 @@ def initialize_geometry(self):
         from ..utils.parse_version import Version
 
         # Check if using newer pyproj version conventions
-        pyproj220 = Version(pyproj.__version__) >= Version("2.2.0")
-
-        proj4_str = self.proj4_str
-        print(f"initialize_geometry::proj4_str = {proj4_str}")
+        if version.parse(pyproj.__version__) < version.parse("2.2"):
+            raise ValueError(
+                "The FloPy NetCDF module requires pyproj >= 2.2.0."
+            )
 
-        self.log(f"building grid crs using proj4 string: {proj4_str}")
-        if pyproj220:
-            self.grid_crs = pyproj.CRS(proj4_str)
-        else:
-            if proj4_str.lower().startswith("epsg:"):
-                proj4_str = "+init=" + proj4_str
-            self.grid_crs = pyproj.Proj(proj4_str, preserve_units=True)
+        print(f"initialize_geometry::")
 
-        print(f"initialize_geometry::self.grid_crs = {self.grid_crs}")
+        self.log(f"model crs: {self.model_crs}")
+        print(f"model crs: {self.model_crs}")
 
         vmin, vmax = self.model_grid.botm.min(), self.model_grid.top.max()
         if self.z_positive == "down":
@@ -711,40 +750,27 @@ def initialize_geometry(self):
         xs = self.model_grid.xyzcellcenters[0].copy()
 
         # Transform to a known CRS
-        if pyproj220:
-            nc_crs = pyproj.CRS(self.nc_epsg_str)
+        if self.model_crs is not None and self.nc_crs is not None:
             self.transformer = pyproj.Transformer.from_crs(
-                self.grid_crs, nc_crs, always_xy=True
+                self.model_crs, self.nc_crs, always_xy=True
             )
-        else:
-            nc_epsg_str = self.nc_epsg_str
-            if nc_epsg_str.lower().startswith("epsg:"):
-                nc_epsg_str = "+init=" + nc_epsg_str
-            nc_crs = pyproj.Proj(nc_epsg_str)
-            self.transformer = None
 
-        print(f"initialize_geometry::nc_crs = {nc_crs}")
+        print(f"initialize_geometry::nc_crs = {self.nc_crs}")
 
-        if pyproj220:
+        if self.transformer is not None:
             print(f"transforming coordinates using = {self.transformer}")
 
-        self.log("projecting grid cell center arrays")
-        if pyproj220:
+            self.log("projecting grid cell center arrays")
             self.xs, self.ys = self.transformer.transform(xs, ys)
-        else:
-            self.xs, self.ys = pyproj.transform(self.grid_crs, nc_crs, xs, ys)
 
-        # get transformed bounds and record to check against ScienceBase later
-        xmin, xmax, ymin, ymax = self.model_grid.extent
-        bbox = np.array(
-            [[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]]
-        )
-        if pyproj220:
+            # get transformed bounds and record to check against ScienceBase later
+            xmin, xmax, ymin, ymax = self.model_grid.extent
+            bbox = np.array(
+                [[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]]
+            )
             x, y = self.transformer.transform(*bbox.transpose())
-        else:
-            x, y = pyproj.transform(self.grid_crs, nc_crs, *bbox.transpose())
-        self.bounds = x.min(), y.min(), x.max(), y.max()
-        self.vbounds = vmin, vmax
+            self.bounds = x.min(), y.min(), x.max(), y.max()
+            self.vbounds = vmin, vmax
 
     def initialize_file(self, time_values=None):
         """
@@ -759,13 +785,12 @@ def initialize_file(self, time_values=None):
                 self.model.dis.perlen and self.start_datetime
 
         """
-        from ..export.shapefile_utils import CRS
         from ..version import __version__ as version
 
         if self.nc is not None:
             raise Exception("nc file already initialized")
 
-        if self.grid_crs is None:
+        if self.model_crs is None:
             self.log("initializing geometry")
             self.initialize_geometry()
             self.log("initializing geometry")
@@ -817,7 +842,7 @@ def initialize_file(self, time_values=None):
         # Metadata variables
         crs = self.nc.createVariable("crs", "i4")
         crs.long_name = self.nc_crs_longname
-        crs.epsg_code = self.nc_epsg_str
+        crs.crs_wkt = self.nc_crs.to_wkt()
         crs.semi_major_axis = self.nc_semi_major
         crs.inverse_flattening = self.nc_inverse_flat
         self.log("setting CRS info")
@@ -919,12 +944,9 @@ def initialize_file(self, time_values=None):
         )
         y[:] = self.model_grid.xyzcellcenters[1]
 
-        # grid mapping variable
-        crs = CRS(
-            prj=self.model_grid.prj,
-            epsg=self.model_grid.epsg,
-        )
-        attribs = crs.grid_mapping_attribs
+        # convert pyproj.CRS object to a
+        # Climate and Forecast (CF) Grid Mapping Version 1.8 dict.
+        attribs = self.nc_crs.to_cf()
         if attribs is not None:
             self.log("creating grid mapping variable")
             self.create_variable(

From 502b4ba0538a22f4c745cee826f00b461005e637 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 28 Feb 2023 21:32:07 -0600
Subject: [PATCH 11/28] refactor(export/shapefile_utils.py): remove CRS and
 EpsgReference classes; superceded by pyproj.CRS

---
 flopy/export/shapefile_utils.py | 381 --------------------------------
 1 file changed, 381 deletions(-)

diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py
index b3931a558f..b055caef70 100644
--- a/flopy/export/shapefile_utils.py
+++ b/flopy/export/shapefile_utils.py
@@ -670,384 +670,3 @@ def write_prj(
             "Supply an valid coordinate system reference to the attached modelgrid object "
             "or .export() method."
         )
-
-
-class CRS:
-    """
-    Container to parse and store coordinate reference system parameters,
-    and translate between different formats.
-    """
-
-    def __init__(self, prj=None, esri_wkt=None, epsg=None):
-        self.wktstr = None
-        if prj is not None:
-            with open(prj) as prj_input:
-                self.wktstr = prj_input.read()
-        elif esri_wkt is not None:
-            self.wktstr = esri_wkt
-        elif epsg is not None:
-            wktstr = CRS.getprj(epsg)
-            if wktstr is not None:
-                self.wktstr = wktstr
-        if self.wktstr is not None:
-            self.parse_wkt()
-
-    @property
-    def crs(self):
-        """
-        Dict mapping crs attributes to proj4 parameters
-        """
-        proj = None
-        if self.projcs is not None:
-            # projection
-            if "mercator" in self.projcs.lower():
-                if (
-                    "transvers" in self.projcs.lower()
-                    or "tm" in self.projcs.lower()
-                ):
-                    proj = "tmerc"
-                else:
-                    proj = "merc"
-            elif (
-                "utm" in self.projcs.lower() and "zone" in self.projcs.lower()
-            ):
-                proj = "utm"
-            elif "stateplane" in self.projcs.lower():
-                proj = "lcc"
-            elif "lambert" and "conformal" and "conic" in self.projcs.lower():
-                proj = "lcc"
-            elif "albers" in self.projcs.lower():
-                proj = "aea"
-        elif self.projcs is None and self.geogcs is not None:
-            proj = "longlat"
-
-        # datum
-        datum = None
-        if (
-            "NAD" in self.datum.lower()
-            or "north" in self.datum.lower()
-            and "america" in self.datum.lower()
-        ):
-            datum = "nad"
-            if "83" in self.datum.lower():
-                datum += "83"
-            elif "27" in self.datum.lower():
-                datum += "27"
-        elif "84" in self.datum.lower():
-            datum = "wgs84"
-
-        # ellipse
-        ellps = None
-        if "1866" in self.spheroid_name:
-            ellps = "clrk66"
-        elif "grs" in self.spheroid_name.lower():
-            ellps = "grs80"
-        elif "wgs" in self.spheroid_name.lower():
-            ellps = "wgs84"
-
-        return {
-            "proj": proj,
-            "datum": datum,
-            "ellps": ellps,
-            "a": self.semi_major_axis,
-            "rf": self.inverse_flattening,
-            "lat_0": self.latitude_of_origin,
-            "lat_1": self.standard_parallel_1,
-            "lat_2": self.standard_parallel_2,
-            "lon_0": self.central_meridian,
-            "k_0": self.scale_factor,
-            "x_0": self.false_easting,
-            "y_0": self.false_northing,
-            "units": self.projcs_unit,
-            "zone": self.utm_zone,
-        }
-
-    @property
-    def grid_mapping_attribs(self):
-        """
-        Map parameters for CF Grid Mappings
-        https://cfconventions.org/cf-conventions/cf-conventions.html#appendix-grid-mappings,
-        Appendix F: Grid Mappings
-
-        """
-        if self.wktstr is not None:
-            sp = [
-                p
-                for p in [
-                    self.standard_parallel_1,
-                    self.standard_parallel_2,
-                ]
-                if p is not None
-            ]
-            sp = sp if len(sp) > 0 else None
-            proj = self.crs["proj"]
-            names = {
-                "aea": "albers_conical_equal_area",
-                "aeqd": "azimuthal_equidistant",
-                "laea": "lambert_azimuthal_equal_area",
-                "longlat": "latitude_longitude",
-                "lcc": "lambert_conformal_conic",
-                "merc": "mercator",
-                "tmerc": "transverse_mercator",
-                "utm": "transverse_mercator",
-            }
-            attribs = {
-                "grid_mapping_name": names[proj],
-                "semi_major_axis": self.crs["a"],
-                "inverse_flattening": self.crs["rf"],
-                "standard_parallel": sp,
-                "longitude_of_central_meridian": self.crs["lon_0"],
-                "latitude_of_projection_origin": self.crs["lat_0"],
-                "scale_factor_at_projection_origin": self.crs["k_0"],
-                "false_easting": self.crs["x_0"],
-                "false_northing": self.crs["y_0"],
-            }
-            return {k: v for k, v in attribs.items() if v is not None}
-
-    @property
-    def proj4(self):
-        """
-        Not implemented yet
-        """
-        return None
-
-    def parse_wkt(self):
-        self.projcs = self._gettxt('PROJCS["', '"')
-        self.utm_zone = None
-        if self.projcs is not None and "utm" in self.projcs.lower():
-            self.utm_zone = self.projcs[-3:].lower().strip("n").strip("s")
-        self.geogcs = self._gettxt('GEOGCS["', '"')
-        self.datum = self._gettxt('DATUM["', '"')
-        tmp = self._getgcsparam("SPHEROID")
-        self.spheroid_name = tmp.pop(0)
-        self.semi_major_axis = tmp.pop(0)
-        self.inverse_flattening = tmp.pop(0)
-        self.primem = self._getgcsparam("PRIMEM")
-        self.gcs_unit = self._getgcsparam("UNIT")
-        self.projection = self._gettxt('PROJECTION["', '"')
-        self.latitude_of_origin = self._getvalue("latitude_of_origin")
-        self.central_meridian = self._getvalue("central_meridian")
-        self.standard_parallel_1 = self._getvalue("standard_parallel_1")
-        self.standard_parallel_2 = self._getvalue("standard_parallel_2")
-        self.scale_factor = self._getvalue("scale_factor")
-        self.false_easting = self._getvalue("false_easting")
-        self.false_northing = self._getvalue("false_northing")
-        self.projcs_unit = self._getprojcs_unit()
-
-    def _gettxt(self, s1, s2):
-        s = self.wktstr.lower()
-        strt = s.find(s1.lower())
-        if strt >= 0:  # -1 indicates not found
-            strt += len(s1)
-            end = s[strt:].find(s2.lower()) + strt
-            return self.wktstr[strt:end]
-
-    def _getvalue(self, k):
-        s = self.wktstr.lower()
-        strt = s.find(k.lower())
-        if strt >= 0:
-            strt += len(k)
-            end = s[strt:].find("]") + strt
-            try:
-                return float(self.wktstr[strt:end].split(",")[1])
-            except (
-                IndexError,
-                TypeError,
-                ValueError,
-                AttributeError,
-            ):
-                pass
-
-    def _getgcsparam(self, txt):
-        nvalues = 3 if txt.lower() == "spheroid" else 2
-        tmp = self._gettxt(f'{txt}["', "]")
-        if tmp is not None:
-            tmp = tmp.replace('"', "").split(",")
-            name = tmp[0:1]
-            values = list(map(float, tmp[1:nvalues]))
-            return name + values
-        else:
-            return [None] * nvalues
-
-    def _getprojcs_unit(self):
-        if self.projcs is not None:
-            tmp = self.wktstr.lower().split('unit["')[-1]
-            uname, ufactor = tmp.strip().strip("]").split('",')[0:2]
-            ufactor = float(ufactor.split("]")[0].split()[0].split(",")[0])
-            return uname, ufactor
-        return None, None
-
-    @staticmethod
-    def getprj(epsg, addlocalreference=True, text="esriwkt"):
-        """
-        Gets projection file (.prj) text for given epsg code from
-        spatialreference.org
-        See: https://www.epsg-registry.org/
-
-        Parameters
-        ----------
-        epsg : int
-            epsg code for coordinate system
-        addlocalreference : boolean
-            adds the projection file text associated with epsg to a local
-            database, epsgref.json, located in the user's data directory.
-
-        Returns
-        -------
-        str
-            text for a projection (*.prj) file.
-
-        """
-        epsgfile = EpsgReference()
-        wktstr = epsgfile.get(epsg)
-        if wktstr is None:
-            wktstr = CRS.get_spatialreference(epsg, text=text)
-        if addlocalreference and wktstr is not None:
-            epsgfile.add(epsg, wktstr)
-        return wktstr
-
-    @staticmethod
-    def get_spatialreference(epsg, text="esriwkt"):
-        """
-        Gets text for given epsg code and text format from spatialreference.org
-        Fetches the reference text using the url:
-            https://spatialreference.org/ref/epsg/<epsg code>/<text>/
-        See: https://www.epsg-registry.org/
-
-        Parameters
-        ----------
-        epsg : int
-            epsg code for coordinate system
-        text : str
-            string added to url
-
-        Returns
-        -------
-        str
-
-        """
-        from ..utils.flopy_io import get_url_text
-
-        epsg_categories = (
-            "epsg",
-            "esri",
-        )
-        urls = []
-        for cat in epsg_categories:
-            url = f"{srefhttp}/ref/{cat}/{epsg}/{text}/"
-            urls.append(url)
-            result = get_url_text(url)
-            if result is not None:
-                break
-        if result is not None:
-            return result.replace("\n", "")
-        elif result is None and text != "epsg":
-            error_msg = (
-                f"No internet connection or epsg code {epsg} not found at:\n"
-            )
-            for idx, url in enumerate(urls):
-                error_msg += f"  {idx + 1:>2d}: {url}\n"
-            print(error_msg)
-        # epsg code not listed on spatialreference.org
-        # may still work with pyproj
-        elif text == "epsg":
-            return f"epsg:{epsg}"
-
-    @staticmethod
-    def getproj4(epsg):
-        """
-        Gets projection file (.prj) text for given epsg code from
-        spatialreference.org. See: https://www.epsg-registry.org/
-
-        Parameters
-        ----------
-        epsg : int
-            epsg code for coordinate system
-
-        Returns
-        -------
-        str
-            text for a projection (*.prj) file.
-        """
-        return CRS.get_spatialreference(epsg, text="proj4")
-
-
-class EpsgReference:
-    r"""
-    Sets up a local database of text representations of coordinate reference
-    systems, keyed by EPSG code.
-
-    The database is epsgref.json, located in either "%LOCALAPPDATA%\flopy"
-    for Windows users, or $HOME/.local/share/flopy for others.
-    """
-
-    def __init__(self):
-        if sys.platform.startswith("win"):
-            flopy_appdata = Path(os.path.expandvars(r"%LOCALAPPDATA%\flopy"))
-        else:
-            flopy_appdata = Path.home() / ".local" / "share" / "flopy"
-        if not flopy_appdata.exists():
-            flopy_appdata.mkdir(parents=True, exist_ok=True)
-        dbname = "epsgref.json"
-        self.location = flopy_appdata / dbname
-
-    def to_dict(self):
-        """
-        returns dict with EPSG code integer key, and WKT CRS text
-        """
-        data = {}
-        if self.location.exists():
-            loaded_data = json.loads(self.location.read_text())
-            # convert JSON key from str to EPSG integer
-            for key, value in loaded_data.items():
-                try:
-                    data[int(key)] = value
-                except ValueError:
-                    data[key] = value
-        return data
-
-    def _write(self, data):
-        with self.location.open("w") as f:
-            json.dump(data, f, indent=0)
-            f.write("\n")
-
-    def reset(self, verbose=True):
-        if self.location.exists():
-            if verbose:
-                print(f"Resetting {flopy_io.relpath_safe(self.location)}")
-            self.location.unlink()
-        elif verbose:
-            print(
-                f"{flopy_io.relpath_safe(self.location)} does not exist, no reset required"
-            )
-
-    def add(self, epsg, prj):
-        """
-        add an epsg code to epsgref.json
-        """
-        data = self.to_dict()
-        data[epsg] = prj
-        self._write(data)
-
-    def get(self, epsg):
-        """
-        returns prj from a epsg code, otherwise None if not found
-        """
-        data = self.to_dict()
-        return data.get(epsg)
-
-    def remove(self, epsg):
-        """
-        removes an epsg entry from epsgref.json
-        """
-        data = self.to_dict()
-        if epsg in data:
-            del data[epsg]
-            self._write(data)
-
-    @staticmethod
-    def show():
-        ep = EpsgReference()
-        prj = ep.to_dict()
-        for k, v in prj.items():
-            print(f"{k}:\n{v}\n")

From c0d6e11fe26649dc4452bb15eff4a61cf5292fdd Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 28 Feb 2023 21:33:37 -0600
Subject: [PATCH 12/28] test: * remove tests related to CRS and EpsgReference
 classes in shapefile_utils (deprecated) * refactor export tests to use crs
 instead of epsg/proj4 * remove test_export::test_write_grid_shapefile; better
 one in test_shapefile_utils.py * add additional testing for
 Grid.set_coord_info()

---
 autotest/test_epsgreference.py |  37 ---------
 autotest/test_export.py        | 137 ++++++++-------------------------
 autotest/test_grid.py          |  57 ++++++++++++--
 3 files changed, 80 insertions(+), 151 deletions(-)
 delete mode 100644 autotest/test_epsgreference.py

diff --git a/autotest/test_epsgreference.py b/autotest/test_epsgreference.py
deleted file mode 100644
index 715c3b17ca..0000000000
--- a/autotest/test_epsgreference.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from flaky import flaky
-
-from flopy.export.shapefile_utils import CRS, EpsgReference
-
-
-@flaky
-def test_epsgreference():
-    ep = EpsgReference()
-    ep.reset()
-    ep.show()
-
-    prjtxt = CRS.getprj(32614)  # WGS 84 / UTM zone 14N
-    if prjtxt is None:
-        print("unable to retrieve CRS prj txt")
-        return
-    assert isinstance(prjtxt, str), type(prjtxt)
-    prj = ep.to_dict()
-    assert 32614 in prj
-    ep.show()
-
-    ep.add(9999, "junk")
-    prj = ep.to_dict()
-    assert 9999 in prj
-    assert ep.get(9999) == "junk"
-    ep.show()
-
-    ep.remove(9999)
-    prj = ep.to_dict()
-    assert 9999 not in prj
-    ep.show()
-
-    assert ep.get(9999) is None
-
-    ep.reset()
-    prj = ep.to_dict()
-    assert len(prj) == 0
-    ep.show()
diff --git a/autotest/test_export.py b/autotest/test_export.py
index 8e678d7e54..a182acf1d0 100644
--- a/autotest/test_export.py
+++ b/autotest/test_export.py
@@ -13,18 +13,13 @@
     excludes_platform,
     requires_exe,
     requires_pkg,
-    requires_spatial_reference,
 )
 from modflow_devtools.misc import has_pkg
 
 import flopy
 from flopy.discretization import StructuredGrid, UnstructuredGrid
 from flopy.export import NetCdf
-from flopy.export.shapefile_utils import (
-    EpsgReference,
-    recarray2shp,
-    shp2recarray,
-)
+from flopy.export.shapefile_utils import recarray2shp, shp2recarray
 from flopy.export.utils import (
     export_array,
     export_array_contours,
@@ -53,6 +48,7 @@
     import_optional_dependency,
 )
 from flopy.utils import postprocessing as pp
+from flopy.utils.crs import get_authority_crs
 from flopy.utils.geometry import Polygon
 
 
@@ -105,6 +101,7 @@ def test_freyberg_export(function_tmpdir, example_data_path):
         load_only=["DIS", "BAS6", "NWT", "OC", "RCH", "WEL", "DRN", "UPW"],
     )
     # test export without instantiating an sr
+    m.modelgrid.crs = None
     shape = function_tmpdir / f"{name}_drn_sparse.shp"
     m.drn.stress_period_data.export(shape, sparse=True)
     for suffix in [".dbf", ".shp", ".shx"]:
@@ -114,7 +111,7 @@ def test_freyberg_export(function_tmpdir, example_data_path):
     assert not shape.with_suffix(".prj").exists()
 
     m.modelgrid = StructuredGrid(
-        delc=m.dis.delc.array, delr=m.dis.delr.array, epsg=3070
+        delc=m.dis.delc.array, delr=m.dis.delr.array, crs=3070
     )
     # test export with an sr, regardless of whether or not wkt was found
     m.drn.stress_period_data.export(shape, sparse=True)
@@ -124,19 +121,16 @@ def test_freyberg_export(function_tmpdir, example_data_path):
         part.unlink()
 
     m.modelgrid = StructuredGrid(
-        delc=m.dis.delc.array, delr=m.dis.delr.array, epsg=3070
+        delc=m.dis.delc.array, delr=m.dis.delr.array, crs=3070
     )
     # verify that attributes have same sr as parent
-    assert m.drn.stress_period_data.mg.epsg == m.modelgrid.epsg
-    assert m.drn.stress_period_data.mg.proj4 == m.modelgrid.proj4
+    assert m.drn.stress_period_data.mg.crs == m.modelgrid.crs
     assert m.drn.stress_period_data.mg.xoffset == m.modelgrid.xoffset
     assert m.drn.stress_period_data.mg.yoffset == m.modelgrid.yoffset
     assert m.drn.stress_period_data.mg.angrot == m.modelgrid.angrot
 
     # get wkt text was fetched from spatialreference.org
-    wkt = flopy.export.shapefile_utils.CRS.get_spatialreference(
-        m.modelgrid.epsg
-    )
+    wkt = m.modelgrid.crs.to_wkt()
 
     # if wkt text was fetched from spatialreference.org
     if wkt is not None:
@@ -162,12 +156,18 @@ def test_freyberg_export(function_tmpdir, example_data_path):
                 assert part.read_text() == wkt
 
 
+# for now, test with and without a coordinate reference system
+@pytest.mark.parametrize("crs", (None, 26916))
 @requires_pkg("netCDF4", "pyproj")
-def test_export_output(function_tmpdir, example_data_path):
-    ml = Modflow.load("freyberg.nam", model_ws=example_data_path / "freyberg")
+def test_export_output(crs, function_tmpdir, example_data_path):
+    ml = Modflow.load(
+        "freyberg.nam", model_ws=str(example_data_path / "freyberg")
+    )
+    ml.modelgrid.crs = crs
     hds_pth = os.path.join(ml.model_ws, "freyberg.githds")
     hds = flopy.utils.HeadFile(hds_pth)
 
+    function_tmpdir = Path(".")
     out_pth = os.path.join(function_tmpdir, "freyberg.out.nc")
     nc = flopy.export.utils.output_helper(
         out_pth, ml, {"freyberg.githds": hds}
@@ -181,6 +181,17 @@ def test_export_output(function_tmpdir, example_data_path):
     # close the netcdf file
     nc.nc.close()
 
+    # verify that the CRS was written correctly
+    import netCDF4
+    import pyproj
+
+    ds = netCDF4.Dataset(out_pth)
+    read_crs = pyproj.CRS.from_cf(ds["latitude_longitude"].__dict__)
+    # currently, NetCDF files are only written
+    # in the 4326 coordinate reference system
+    # (lat/lon WGS 84)
+    assert read_crs == get_authority_crs(4326)
+
 
 @requires_pkg("shapefile")
 def test_write_gridlines_shapefile(function_tmpdir):
@@ -194,7 +205,7 @@ def test_write_gridlines_shapefile(function_tmpdir):
         # cell spacing along model rows
         delc=np.ones(10) * 1.1,
         # cell spacing along model columns
-        epsg=26715,
+        crs=26715,
     )
     outshp = function_tmpdir / "gridlines.shp"
     write_gridlines_shapefile(outshp, sg)
@@ -207,56 +218,6 @@ def test_write_gridlines_shapefile(function_tmpdir):
         assert len(sf) == 22
 
 
-@flaky
-@requires_pkg("shapefile", "shapely")
-def test_write_grid_shapefile(function_tmpdir):
-    from shapefile import Reader
-
-    from flopy.discretization import StructuredGrid
-    from flopy.export.shapefile_utils import write_grid_shapefile
-
-    sg = StructuredGrid(
-        delr=np.ones(10) * 1.1,
-        # cell spacing along model rows
-        delc=np.ones(10) * 1.1,
-        # cell spacing along model columns
-        epsg=26715,
-    )
-    outshp = function_tmpdir / "junk.shp"
-    write_grid_shapefile(outshp, sg, array_dict={})
-
-    for suffix in [".dbf", ".prj", ".shp", ".shx"]:
-        assert outshp.with_suffix(suffix).exists()
-
-    # test that vertices aren't getting altered by writing shapefile
-    # check that pyshp reads integers
-    # this only check that row/column were recorded as "N"
-    # not how they will be cast by python or numpy
-    sfobj = Reader(str(outshp))
-    for f in sfobj.fields:
-        if f[0] == "row" or f[0] == "column":
-            assert f[1] == "N"
-    recs = list(sfobj.records())
-    for r in recs[0]:
-        assert isinstance(r, int)
-    sfobj.close()
-
-    # check that row and column appear as integers in recarray
-    ra = shp2recarray(outshp)
-    assert np.issubdtype(ra.dtype["row"], np.integer)
-    assert np.issubdtype(ra.dtype["column"], np.integer)
-
-    try:  # check that fiona reads integers
-        import fiona
-
-        with fiona.open(outshp) as src:
-            meta = src.meta
-            assert "int" in meta["schema"]["properties"]["row"]
-            assert "int" in meta["schema"]["properties"]["column"]
-    except ImportError:
-        pass
-
-
 @requires_pkg("shapefile")
 def test_export_shapefile_polygon_closed(function_tmpdir):
     from shapefile import Reader
@@ -270,9 +231,7 @@ def test_export_shapefile_polygon_closed(function_tmpdir):
     nrow = int((yur - yll) / spacing)
     print(nrow, ncol)
 
-    m = flopy.modflow.Modflow(
-        "test.nam", proj4_str="EPSG:32614", xll=xll, yll=yll
-    )
+    m = flopy.modflow.Modflow("test.nam", crs="EPSG:32614", xll=xll, yll=yll)
 
     flopy.modflow.ModflowDis(
         m, delr=spacing, delc=spacing, nrow=nrow, ncol=ncol
@@ -381,41 +340,6 @@ def test_netcdf_classmethods(function_tmpdir, example_data_path):
     new_f.nc.close()
 
 
-def test_wkt_parse(example_shapefiles):
-    """Test parsing of Coordinate Reference System parameters
-    from well-known-text in .prj files."""
-
-    from flopy.export.shapefile_utils import CRS
-
-    geocs_params = [
-        "wktstr",
-        "geogcs",
-        "datum",
-        "spheroid_name",
-        "semi_major_axis",
-        "inverse_flattening",
-        "primem",
-        "gcs_unit",
-    ]
-
-    for prj in example_shapefiles:
-        with open(prj) as src:
-            wkttxt = src.read()
-            wkttxt = wkttxt.replace("'", '"')
-        if len(wkttxt) > 0 and "projcs" in wkttxt.lower():
-            crsobj = CRS(esri_wkt=wkttxt)
-            assert isinstance(crsobj.crs, dict)
-            for k in geocs_params:
-                assert crsobj.__dict__[k] is not None
-            projcs_params = [
-                k for k in crsobj.__dict__ if k not in geocs_params
-            ]
-            if crsobj.projcs is not None:
-                for k in projcs_params:
-                    if k in wkttxt.lower():
-                        assert crsobj.__dict__[k] is not None
-
-
 @requires_pkg("shapefile")
 def test_shapefile_ibound(function_tmpdir, example_data_path):
     from shapefile import Reader
@@ -481,8 +405,7 @@ def test_shapefile_export_modelgrid_override(function_tmpdir, namfile):
         grid.botm,
         grid.idomain,
         grid.lenuni,
-        grid.epsg,
-        grid.proj4,
+        grid.crs,
         xoff=grid.xoffset,
         yoff=grid.yoffset,
         angrot=grid.angrot,
@@ -864,7 +787,7 @@ def test_polygon_from_ij(function_tmpdir):
         xoff=mg._xul_to_xll(600000.0, -45.0),
         yoff=mg._yul_to_yll(5170000, -45.0),
         angrot=-45.0,
-        proj4="EPSG:26715",
+        crs="EPSG:26715",
     )
 
     recarray = np.array(
diff --git a/autotest/test_grid.py b/autotest/test_grid.py
index 322b2d8f74..fa8011a5c7 100644
--- a/autotest/test_grid.py
+++ b/autotest/test_grid.py
@@ -41,6 +41,25 @@ def minimal_unstructured_grid_info():
     return d
 
 
+@pytest.fixture
+def minimal_vertex_grid_info(minimal_unstructured_grid_info):
+    usg_info = minimal_unstructured_grid_info
+    d = {}
+    d["vertices"] = minimal_unstructured_grid_info["vertices"]
+    cell2d = []
+    for n in range(len(usg_info["iverts"])):
+        cell2d_n = [
+            n,
+            usg_info["xcenters"][n],
+            usg_info["ycenters"][n],
+        ] + usg_info["iverts"][n]
+        cell2d.append(cell2d_n)
+    d["cell2d"] = cell2d
+    d["ncpl"] = len(cell2d)
+    d["nlay"] = 1
+    return d
+
+
 def test_rotation():
     m = Modflow(rotation=20.0)
     dis = ModflowDis(
@@ -657,6 +676,20 @@ def test_grid_set_crs(crs, expected_srs, function_tmpdir):
         assert isinstance(sg.crs, pyproj.CRS)
     assert getattr(sg.crs, "srs", None) == expected_srs
 
+    # test setting the crs via set_coord_info
+    sg.set_coord_info()
+    if crs is not None:
+        assert isinstance(sg.crs, pyproj.CRS)
+    assert getattr(sg.crs, "srs", None) == expected_srs
+    sg.set_coord_info(crs=crs)
+    if crs is not None:
+        assert isinstance(sg.crs, pyproj.CRS)
+    assert getattr(sg.crs, "srs", None) == expected_srs
+    sg.set_coord_info(crs=26915, merge_coord_info=False)
+    assert getattr(sg.crs, "srs", None) == "EPSG:26915"
+    # reset back to test case crs
+    sg = StructuredGrid(delr=delr, delc=delc, crs=crs)
+
     # test input of projection file
     if crs is not None:
         prjfile = function_tmpdir / "grid_crs.prj"
@@ -669,19 +702,29 @@ def test_grid_set_crs(crs, expected_srs, function_tmpdir):
             assert isinstance(sg.crs, pyproj.CRS)
         assert getattr(sg.crs, "srs", None) == expected_srs
         assert sg.prjfile == prjfile
+        sg.set_coord_info(prjfile=prjfile)
+        if crs is not None:
+            assert isinstance(sg.crs, pyproj.CRS)
+            assert getattr(sg.crs, "srs", None) == expected_srs
+        assert sg.prjfile == prjfile
 
     # test setting another crs
     sg.crs = 26915
     assert sg.crs == get_authority_crs(26915)
 
-    if version.parse(flopy.__version__) < version.parse("3.3.7"):
+    if (
+        version.parse(flopy.__version__) < version.parse("3.4")
+        and crs is not None
+    ):
         pyproj_crs = get_authority_crs(crs)
-        sg = StructuredGrid(delr=delr, delc=delc)
-        sg.epsg = pyproj_crs.to_epsg()
-        if crs is not None:
-            assert isinstance(sg.crs, pyproj.CRS)
-        assert getattr(sg.crs, "srs", None) == expected_srs
-        assert sg.epsg == pyproj_crs.to_epsg()
+        epsg = pyproj_crs.to_epsg()
+        if epsg is not None:
+            sg = StructuredGrid(delr=delr, delc=delc, crs=crs)
+            sg.epsg = epsg
+            if crs is not None:
+                assert isinstance(sg.crs, pyproj.CRS)
+            assert getattr(sg.crs, "srs", None) == expected_srs
+            assert sg.epsg == epsg
 
         sg = StructuredGrid(delr=delr, delc=delc)
         sg.proj4 = pyproj_crs.to_proj4()

From c83e4144fa0204215337e2eb38984abdfa2dd808 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 28 Feb 2023 22:02:01 -0600
Subject: [PATCH 13/28] fix(export/shapefile_utils.py): use relpath_safe for
 printing written filepaths

---
 flopy/export/shapefile_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py
index b055caef70..98f02e7b5d 100644
--- a/flopy/export/shapefile_utils.py
+++ b/flopy/export/shapefile_utils.py
@@ -638,7 +638,7 @@ def recarray2shp(
 
     w.close()
     write_prj(shpname, mg, crs=crs, epsg=epsg, prj=prj, prjfile=prjfile)
-    print(f"wrote {flopy_io.relpath_printstr(os.getcwd(), shpname)}")
+    print(f"wrote {flopy_io.relpath_safe(os.getcwd(), shpname)}")
     return
 
 

From 9158f5e48f678b9d9c1f85d0d12c5a521c7df809 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 09:01:17 -0600
Subject: [PATCH 14/28] fix(discretization/grid.py): assign epsg and proj4 to
 crs attribute when reading usgs.model.reference

---
 autotest/test_modflow.py     | 2 ++
 flopy/discretization/grid.py | 4 ++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/autotest/test_modflow.py b/autotest/test_modflow.py
index 837e5118ec..514fbb3d61 100644
--- a/autotest/test_modflow.py
+++ b/autotest/test_modflow.py
@@ -536,6 +536,8 @@ def test_read_usgs_model_reference(function_tmpdir, model_reference_path):
             for line in src:
                 if "epsg" in line:
                     line = "epsg 26916\n"
+                if "proj4" in line:
+                    line = "# proj4\n"
                 dst.write(line)
 
     m2 = Modflow.load("junk.nam", model_ws=ws)
diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index 388cd77df4..5de68b1af4 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -949,9 +949,9 @@ def read_usgs_model_reference_file(self, reffile="usgs.model.reference"):
                                 elif info[0] == "rotation":
                                     self._angrot = float(data)
                                 elif info[0] == "epsg":
-                                    self._epsg = int(data)
+                                    self.crs = int(data)
                                 elif info[0] == "proj4":
-                                    self._proj4 = data
+                                    self.crs = data
                                 elif info[0] == "start_date":
                                     start_datetime = data
 

From c5ec3bd6eb4cdbf60a9cd17ab75c35b7fb2a98e7 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 21 Feb 2023 15:34:40 -0600
Subject: [PATCH 15/28] refactor(export): replace epsg and proj4 input
 arguments with general crs argument and prjfile argument

---
 flopy/export/shapefile_utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py
index 98f02e7b5d..bc894ba153 100644
--- a/flopy/export/shapefile_utils.py
+++ b/flopy/export/shapefile_utils.py
@@ -663,7 +663,6 @@ def write_prj(
         with open(output_projection_file, "w", encoding="utf-8") as dest:
             write_text = crs.to_wkt()
             dest.write(write_text)
-
     else:
         print(
             "No CRS information for writing a .prj file.\n"

From 32c2a01b907e81ef8417d0532831f91c7c7fb958 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 09:21:45 -0600
Subject: [PATCH 16/28] fix(discretization/grid.py): pass crs to
 write_shapefile()

---
 flopy/discretization/grid.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index 5de68b1af4..2139403eb7 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -1025,10 +1025,14 @@ def write_shapefile(self, filename="grid.shp", epsg=None, prj=None):
         """
         from ..export.shapefile_utils import write_grid_shapefile
 
-        if epsg is None and prj is None:
-            epsg = self.epsg
         write_grid_shapefile(
-            filename, self, array_dict={}, nan_val=-1.0e9, epsg=epsg, prj=prj
+            filename,
+            self,
+            array_dict={},
+            nan_val=-1.0e9,
+            crs=self.crs,
+            epsg=epsg,
+            prj=prj,
         )
         return
 

From 058af6444e23150d5452d6a36a02cacb8df204ff Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 09:22:16 -0600
Subject: [PATCH 17/28] test: add autotest/test_shapefile_utils.py

---
 autotest/test_shapefile_utils.py | 47 ++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 autotest/test_shapefile_utils.py

diff --git a/autotest/test_shapefile_utils.py b/autotest/test_shapefile_utils.py
new file mode 100644
index 0000000000..4f227b3c71
--- /dev/null
+++ b/autotest/test_shapefile_utils.py
@@ -0,0 +1,47 @@
+"""Test functions in flopy/export/shapefile_utils.py
+"""
+import numpy as np
+from modflow_devtools.markers import requires_pkg
+
+from flopy.discretization import StructuredGrid, UnstructuredGrid, VertexGrid
+from flopy.export.shapefile_utils import shp2recarray
+from flopy.utils.crs import get_shapefile_crs
+
+from .test_grid import minimal_unstructured_grid_info, minimal_vertex_grid_info
+
+
+@requires_pkg("pyproj")
+def test_write_grid_shapefile(
+    minimal_unstructured_grid_info, minimal_vertex_grid_info, function_tmpdir
+):
+    import pyproj
+
+    d = minimal_unstructured_grid_info
+    delr = np.ones(10)
+    delc = np.ones(10)
+    crs = 26916
+    shapefilename = function_tmpdir / "grid.shp"
+    sg = StructuredGrid(delr=delr, delc=delc, nlay=1, crs=crs)
+    sg.write_shapefile(shapefilename)
+    data = shp2recarray(shapefilename)
+    # check that row and column appear as integers in recarray
+    assert np.issubdtype(data.dtype["row"], np.integer)
+    assert np.issubdtype(data.dtype["column"], np.integer)
+    assert len(data) == sg.nnodes
+    written_crs = get_shapefile_crs(shapefilename)
+    assert written_crs.to_epsg() == crs
+
+    usg = UnstructuredGrid(**d, crs=crs)
+    usg.write_shapefile(shapefilename)
+    data = shp2recarray(shapefilename)
+    assert len(data) == usg.nnodes
+    written_crs = get_shapefile_crs(shapefilename)
+    assert written_crs.to_epsg() == crs
+
+    d = minimal_vertex_grid_info
+    vg = VertexGrid(**d, crs=crs)
+    vg.write_shapefile(shapefilename)
+    data = shp2recarray(shapefilename)
+    assert len(data) == vg.nnodes
+    written_crs = get_shapefile_crs(shapefilename)
+    assert written_crs.to_epsg() == crs

From 935cdd86d97f364a206ebe84b597f693fd2512eb Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 09:25:16 -0600
Subject: [PATCH 18/28] docs(conf.py): add intersphinx_mapping

---
 .docs/conf.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.docs/conf.py b/.docs/conf.py
index 8cec71a8d7..2f9b38f78c 100644
--- a/.docs/conf.py
+++ b/.docs/conf.py
@@ -249,3 +249,13 @@
 
 # Output file base name for HTML help builder.
 htmlhelp_basename = "flopydoc"
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3/", None),
+    "numpy": ("https://docs.scipy.org/doc/numpy/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable", None),
+    "matplotlib": ("https://matplotlib.org", None),
+    "pyproj": ("https://pyproj4.github.io/pyproj/stable/", None),
+}

From e12d66454c5c6f8ab1d32dc31952d137a4c1a9ab Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 09:32:03 -0600
Subject: [PATCH 19/28] docs(examples/Notebooks): refactor notebooks to use crs
 argument for coordinate reference input

---
 .../flopy3_demo_of_modelgrid_classes.ipynb    | 180 +++++++++-----
 examples/Notebooks/flopy3_export.ipynb        | 232 +++++++-----------
 2 files changed, 206 insertions(+), 206 deletions(-)

diff --git a/examples/Notebooks/flopy3_demo_of_modelgrid_classes.ipynb b/examples/Notebooks/flopy3_demo_of_modelgrid_classes.ipynb
index af53bd473f..823197860c 100644
--- a/examples/Notebooks/flopy3_demo_of_modelgrid_classes.ipynb
+++ b/examples/Notebooks/flopy3_demo_of_modelgrid_classes.ipynb
@@ -45,6 +45,7 @@
    "source": [
     "import sys\n",
     "import os\n",
+    "from pathlib import Path\n",
     "import shutil\n",
     "from tempfile import TemporaryDirectory\n",
     "import numpy as np\n",
@@ -98,18 +99,30 @@
      "shell.execute_reply": "2023-04-06T17:02:01.684223Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/var/folders/4x/bmhyjcdn3mgfdvkk_jgz6bsrlnfk3t/T/tmpz_pr__cn')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# set paths to each of our model types for this example notebook\n",
-    "spth = os.path.join(\"..\", \"data\", \"freyberg_multilayer_transient\")\n",
-    "spth6 = os.path.join(\"..\", \"data\", \"mf6-freyberg\")\n",
-    "vpth = os.path.join(\"..\", \"data\")\n",
-    "upth = os.path.join(\"..\", \"data\")\n",
-    "u_data_ws = os.path.join(\"..\", \"data\", \"unstructured\")\n",
+    "spth = \"../data/freyberg_multilayer_transient\"\n",
+    "spth6 = \"../data/mf6-freyberg\"\n",
+    "vpth = \"../data\"\n",
+    "upth = \"../data\"\n",
+    "u_data_ws = \"../data/unstructured\"\n",
     "\n",
     "# temporary workspace\n",
     "temp_dir = TemporaryDirectory()\n",
-    "gridgen_ws = temp_dir.name"
+    "gridgen_ws = Path(temp_dir.name)\n",
+    "gridgen_ws"
    ]
   },
   {
@@ -169,8 +182,7 @@
     "`xll` : geographic location of lower left model corner x-coordinate  \n",
     "`yll` : geographic location of lower left model corner y-coordinate  \n",
     "`rotation` : modelgrid rotation in degrees  \n",
-    "`epsg` : epsg code of modelgrid coordinate system  \n",
-    "`proj4_str` : proj4 projection information  \n",
+    "`crs` : returns the coordinate reference system that the model grid is referenced to\n",
     "\n",
     "can be automatcally read in and applied to the modelgrid. FloPy will also write this information out when the user saves their model to file. \n",
     "\n",
@@ -193,7 +205,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "xll:622241.1904510253; yll:3343617.741737109; rotation:15.0; proj4_str:+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs; units:meters; lenuni:2\n"
+      "xll:622241.1904510253; yll:3343617.741737109; rotation:15.0; crs:EPSG:32614; units:meters; lenuni:2\n"
      ]
     }
    ],
@@ -264,8 +276,7 @@
     "   - `xoffset` : returns the x-coordinate for the modelgrid's lower left corner  \n",
     "   - `yoffset` : returns the y-coordinate for the modelgrid's lower left corner  \n",
     "   - `angrot` : returns the rotation of the modelgrid in degrees  \n",
-    "   - `epsg` : returns the modelgrid epsg code  \n",
-    "   - `proj4` : returns the modelgrid proj4_str information  "
+    "   - `crs` : returns the coordinate reference system that the model grid is referenced to\n"
    ]
   },
   {
@@ -287,8 +298,7 @@
       "xoff: 622241.1904510253\n",
       "yoff: 3343617.741737109\n",
       "angrot: 15.0\n",
-      "epsg: None\n",
-      "proj4: +proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs\n"
+      "crs: EPSG:32614\n"
      ]
     }
    ],
@@ -296,12 +306,11 @@
     "xoff = modelgrid.xoffset\n",
     "yoff = modelgrid.yoffset\n",
     "angrot = modelgrid.angrot\n",
-    "epsg = modelgrid.epsg\n",
-    "proj4 = modelgrid.proj4\n",
+    "crs = modelgrid.crs\n",
     "\n",
     "print(\n",
-    "    \"xoff: {}\\nyoff: {}\\nangrot: {}\\nepsg: {}\\nproj4: {}\".format(\n",
-    "        xoff, yoff, angrot, epsg, proj4\n",
+    "    \"xoff: {}\\nyoff: {}\\nangrot: {}\\ncrs: {}\".format(\n",
+    "        xoff, yoff, angrot, crs\n",
     "    )\n",
     ")"
    ]
@@ -333,7 +342,7 @@
      "text": [
       "Before: xll:0.0; yll:0.0; rotation:0.0; units:meters; lenuni:2\n",
       "\n",
-      "After: xll:622241.1904510253; yll:3343617.741737109; rotation:15.0; proj4_str:+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs; units:meters; lenuni:2\n"
+      "After: xll:622241.1904510253; yll:3343617.741737109; rotation:15.0; crs:EPSG:32614; units:meters; lenuni:2\n"
      ]
     }
    ],
@@ -343,7 +352,7 @@
     "\n",
     "# set reference infromation\n",
     "modelgrid1.set_coord_info(\n",
-    "    xoff=xoff, yoff=yoff, angrot=angrot, epsg=epsg, proj4=proj4\n",
+    "    xoff=xoff, yoff=yoff, angrot=angrot, crs=crs\n",
     ")\n",
     "\n",
     "print(\"After: {}\".format(modelgrid1))"
@@ -372,7 +381,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "xll:100; yll:1000; rotation:55; proj4_str:+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs; units:meters; lenuni:2\n"
+      "xll:100; yll:1000; rotation:55; crs:EPSG:32614; units:meters; lenuni:2\n"
      ]
     }
    ],
@@ -489,9 +498,11 @@
     "   - `botm` : Array of layer Botm elevations\n",
     "   - `idomain` : An ibound or idomain array that specifies active and inactive cells\n",
     "   - `lenuni` : Model length unit integer\n",
-    "   - `epsg` : epsg code of model coordinate system\n",
-    "   - `proj4` : proj4 str describining model coordinate system\n",
-    "   - `prj` : path to \".prj\" projection file that describes the model coordinate system\n",
+    "   - `crs` : Coordinate reference system (CRS) for the model grid\n",
+    "        (must be projected; geographic CRS are not supported). The value can be anything accepted by\n",
+    "        [pyproj.CRS.from_user_input()](https://pyproj4.github.io/pyproj/stable/api/crs/crs.html#pyproj.crs.CRS.from_user_input)\n",
+    "        such as an authority string (eg \"EPSG:26916\") or a well-known text (WKT) string.\n",
+    "   - `prjfile` : Alternatively, supply a path to an ESRI-style projection (``*.prj``) file containing WKT that describes the model coordinate system.\n",
     "   - `xoff` : x-coordinate of the lower-left corner of the modelgrid\n",
     "   - `yoff` : y-coordinate of the lower-left corner of the modelgrid\n",
     "   - `angrot` : model grid rotation\n",
@@ -519,7 +530,7 @@
     {
      "data": {
       "text/plain": [
-       "xll:3579; yll:10000; rotation:0; units:undefined; lenuni:0"
+       "xll:3579; yll:10000; rotation:0; crs:EPSG:26916; units:undefined; lenuni:0"
       ]
      },
      "execution_count": 12,
@@ -565,6 +576,7 @@
     "    xoff=xll,\n",
     "    yoff=yll,\n",
     "    angrot=rotation,\n",
+    "    crs=26916\n",
     ")\n",
     "modelgrid"
    ]
@@ -760,9 +772,11 @@
     "   - `botm` : Array of layer Botm elevations\n",
     "   - `idomain` : An ibound or idomain array that specifies active and inactive cells\n",
     "   - `lenuni` : Model length unit integer\n",
-    "   - `epsg` : epsg code of model coordinate system\n",
-    "   - `proj4` : proj4 str describining model coordinate system\n",
-    "   - `prj` : path to \".prj\" projection file that describes the model coordinate system\n",
+    "   - `crs` : Coordinate reference system (CRS) for the model grid\n",
+    "        (must be projected; geographic CRS are not supported). The value can be anything accepted by\n",
+    "        [pyproj.CRS.from_user_input()](https://pyproj4.github.io/pyproj/stable/api/crs/crs.html#pyproj.crs.CRS.from_user_input)\n",
+    "        such as an authority string (eg \"EPSG:26916\") or a well-known text (WKT) string.\n",
+    "   - `prjfile` : Alternatively, supply a path to an ESRI-style projection (``*.prj``) file containing WKT that describes the model coordinate system.\n",
     "   - `xoff` : x-coordinate of the lower-left corner of the modelgrid\n",
     "   - `yoff` : y-coordinate of the lower-left corner of the modelgrid\n",
     "   - `angrot` : model grid rotation\n",
@@ -972,9 +986,11 @@
     "   - `idomain` : An ibound or idomain array that specifies active and inactive cells\n",
     "   - `lenuni` : Model length unit integer\n",
     "   - `ncpl` : one dimensional array of number of cells per model layer\n",
-    "   - `epsg` : epsg code of model coordinate system\n",
-    "   - `proj4` : proj4 str describining model coordinate system\n",
-    "   - `prj` : path to \".prj\" projection file that describes the model coordinate system\n",
+    "   - `crs` : Coordinate reference system (CRS) for the model grid\n",
+    "        (must be projected; geographic CRS are not supported). The value can be anything accepted by\n",
+    "        [pyproj.CRS.from_user_input()](https://pyproj4.github.io/pyproj/stable/api/crs/crs.html#pyproj.crs.CRS.from_user_input)\n",
+    "        such as an authority string (eg \"EPSG:26916\") or a well-known text (WKT) string.\n",
+    "   - `prjfile` : Alternatively, supply a path to an ESRI-style projection (``*.prj``) file containing WKT that describes the model coordinate system.\n",
     "   - `xoff` : x-coordinate of the lower-left corner of the modelgrid\n",
     "   - `yoff` : y-coordinate of the lower-left corner of the modelgrid\n",
     "   - `angrot` : model grid rotation   \n",
@@ -1161,7 +1177,7 @@
     "    xoff=622241.1904510253,\n",
     "    yoff=3343617.741737109,\n",
     "    angrot=15.0,\n",
-    "    proj4=\"+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs\",\n",
+    "    crs=\"epsg:32614\",\n",
     ")"
    ]
   },
@@ -1237,9 +1253,7 @@
     "   - `yoffset` : returns the current yoffset of the modelgrid\n",
     "   - `angrot` : returns the angle of rotation of the modelgrid in degrees\n",
     "   - `angrot_radians` : returns the angle of rotation of the modelgrid in radians\n",
-    "   - `epsg` : returns the modelgrid epsg code if it is set\n",
-    "   - `proj4` : returns the modelgrid proj4 string if it is set\n",
-    "   - `prj` : returns the path to the modelgrid projection file if it is set"
+    "   - `crs` : returns a [pyproj.CRS](https://pyproj4.github.io/pyproj/stable/api/crs/crs.html) object instance describing the coordinate reference system for the model grid."
    ]
   },
   {
@@ -1262,7 +1276,7 @@
       "\n",
       "rotation (deg): 15.0, (rad): 0.2618\n",
       "\n",
-      "proj4_str: +proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs\n"
+      "crs: EPSG:32614\n"
      ]
     }
    ],
@@ -1276,7 +1290,7 @@
     "        modelgrid.angrot, modelgrid.angrot_radians\n",
     "    )\n",
     ")\n",
-    "print(\"proj4_str: {}\".format(modelgrid.proj4))"
+    "print(\"crs: {}\".format(modelgrid.crs))"
    ]
   },
   {
@@ -1465,8 +1479,11 @@
     "   - `xoff` : lower-left corner of modelgrid x-coordinate location\n",
     "   - `yoff` : lower-left corner of modelgrid y-coordinate location\n",
     "   - `angrot` : rotation of model grid in degrees\n",
-    "   - `epsg` : epsg code for model grid projection\n",
-    "   - `proj4` : proj4 string describing the model grid projection\n",
+    "   - `crs` : Coordinate reference system (CRS) for the model grid\n",
+    "        (must be projected; geographic CRS are not supported). The value can be anything accepted by\n",
+    "        [pyproj.CRS.from_user_input()](https://pyproj4.github.io/pyproj/stable/api/crs/crs.html#pyproj.crs.CRS.from_user_input)\n",
+    "        such as an authority string (eg \"EPSG:26916\") or a well-known text (WKT) string.\n",
+    "   - `prjfile` : Alternatively, supply a path to an ESRI-style projection (``*.prj``) file containing WKT that describes the model coordinate system.\n",
     "   - `merge_coord_info` : boolean flag to either merge changes with the existing coordinate info or clear existing coordinate info before applying changes."
    ]
   },
@@ -1486,7 +1503,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "xll:50000; yll:3343617.741737109; rotation:15.0; proj4_str:+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs; units:meters; lenuni:2\n",
+      "xll:50000; yll:3343617.741737109; rotation:15.0; crs:EPSG:32614; units:meters; lenuni:2\n",
       "xll:0.0; yll:0.0; rotation:45; units:meters; lenuni:2\n"
      ]
     },
@@ -1678,6 +1695,7 @@
     "    xoff=622241.1904510253,\n",
     "    yoff=3343617.741737109,\n",
     "    angrot=15.0,\n",
+    "    crs=32614\n",
     ")\n",
     "\n",
     "# generate some synthetic pumping data\n",
@@ -1739,9 +1757,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING: MFFileMgt's set_sim_path has been deprecated.  Please use MFSimulation's set_sim_path in the future.\n",
+      "FloPy is using the following executable to run the model: ../../../../../../Users/aleaf/Documents/software/modflow_exes/mf6\n",
       "                                   MODFLOW 6\n",
       "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
-      "                        VERSION 6.4.1 Release 12/09/2022\n",
+      "                            VERSION 6.3.0 03/08/2022\n",
       "\n",
       "   MODFLOW 6 compiled Dec 10 2022 05:57:01 with Intel(R) Fortran Intel(R) 64\n",
       "   Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n",
@@ -1787,14 +1807,9 @@
    ],
    "source": [
     "# write inputs and run the mf6 simulation with the new wells we added above\n",
-    "sim.set_sim_path(gridgen_ws)\n",
+    "sim.simulation_data.mfpath.set_sim_path(gridgen_ws)\n",
     "sim.write_simulation()\n",
-    "success, buff = sim.run_simulation(silent=True, report=True)\n",
-    "if success:\n",
-    "    for line in buff:\n",
-    "        print(line)\n",
-    "else:\n",
-    "    raise ValueError(\"Failed to run.\")\n",
+    "sim.run_simulation(silent=False)\n",
     "\n",
     "# load the binary head file from the model\n",
     "ml = sim.freyberg\n",
@@ -1834,16 +1849,45 @@
    "source": [
     "#### `write_shapefile()` \n",
     "\n",
-    "Method to write a shapefile of the grid with just the cellid attributes. Input parameters include:\n",
-    "\n",
-    "   - `filename` : shapefile name\n",
-    "   - `epsg` : optional epsg code of the coordinate system projection\n",
-    "   - `prj` : optional, input projection file to be used to define the coordinate system projection"
+    "Method to write a shapefile of the grid with just the cellid attributes. If the model grid is already registered to a coordinate reference system, only a name for the shapefile is needed as input. Alternatively, ``crs`` and ``prjfile`` arguments can be supplied (same as for the model grid), and a projection (``*.prj``) file for that reference will be written. If no coordinate reference is supplied, the shapefile will be written without a projection file."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Derived Projected CRS: EPSG:32614>\n",
+       "Name: WGS 84 / UTM zone 14N\n",
+       "Axis Info [cartesian]:\n",
+       "- E[east]: Easting (metre)\n",
+       "- N[north]: Northing (metre)\n",
+       "Area of Use:\n",
+       "- name: Between 102°W and 96°W, northern hemisphere between equator and 84°N, onshore and offshore. Canada - Manitoba; Nunavut; Saskatchewan. Mexico. United States (USA).\n",
+       "- bounds: (-102.0, 0.0, -96.0, 84.0)\n",
+       "Coordinate Operation:\n",
+       "- name: UTM zone 14N\n",
+       "- method: Transverse Mercator\n",
+       "Datum: World Geodetic System 1984 ensemble\n",
+       "- Ellipsoid: WGS 84\n",
+       "- Prime Meridian: Greenwich"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ml.modelgrid.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-06T17:02:08.731255Z",
@@ -1855,15 +1899,33 @@
    "outputs": [],
    "source": [
     "# write a shapefile\n",
-    "shp_name = os.path.join(gridgen_ws, \"freyberg-6_grid.shp\")\n",
-    "epsg = \"32614\"\n",
+    "shp_name = gridgen_ws / \"freyberg-6_grid.shp\"\n",
     "\n",
-    "ml.modelgrid.write_shapefile(shp_name, epsg)"
+    "ml.modelgrid.write_shapefile(shp_name, #crs=32614\n",
+    "                            )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROJCRS[\"WGS 84 / UTM zone 14N\",BASEGEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],ID[\"EPSG\",4326]],CONVERSION[\"UTM zone 14N\",METHOD[\"Transverse Mercator\",ID[\"EPSG\",9807]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",-99,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"Scale factor at natural origin\",0.9996,SCALEUNIT[\"unity\",1],ID[\"EPSG\",8805]],PARAMETER[\"False easting\",500000,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1]],USAGE[SCOPE[\"Engineering survey, topographic mapping.\"],AREA[\"Between 102°W and 96°W, northern hemisphere between equator and 84°N, onshore and offshore. Canada - Manitoba; Nunavut; Saskatchewan. Mexico. United States (USA).\"],BBOX[0,-102,84,-96]],ID[\"EPSG\",32614]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(shp_name.with_suffix(\".prj\")) as src:\n",
+    "    print(src.read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-06T17:02:09.174947Z",
@@ -1884,9 +1946,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "flopy",
    "language": "python",
-   "name": "python3"
+   "name": "flopy"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/Notebooks/flopy3_export.ipynb b/examples/Notebooks/flopy3_export.ipynb
index a271847f50..f5cb715241 100644
--- a/examples/Notebooks/flopy3_export.ipynb
+++ b/examples/Notebooks/flopy3_export.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:38.245244Z",
-     "iopub.status.busy": "2023-02-22T02:46:38.244803Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.114024Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.112951Z"
+     "iopub.execute_input": "2022-12-15T13:09:52.596232Z",
+     "iopub.status.busy": "2022-12-15T13:09:52.595883Z",
+     "iopub.status.idle": "2022-12-15T13:09:53.936555Z",
+     "shell.execute_reply": "2022-12-15T13:09:53.931847Z"
     }
    },
    "outputs": [
@@ -25,7 +25,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]\n",
+      "3.11.0 | packaged by conda-forge | (main, Jan 15 2023, 05:44:48) [Clang 14.0.6 ]\n",
       "flopy version: 3.3.7\n"
      ]
     }
@@ -60,10 +60,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.162021Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.161288Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.811531Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.810851Z"
+     "iopub.execute_input": "2022-12-15T13:09:53.993644Z",
+     "iopub.status.busy": "2022-12-15T13:09:53.992762Z",
+     "iopub.status.idle": "2022-12-15T13:09:54.960099Z",
+     "shell.execute_reply": "2022-12-15T13:09:54.959132Z"
     }
    },
    "outputs": [],
@@ -85,17 +85,17 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.815407Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.815111Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.822437Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.821733Z"
+     "iopub.execute_input": "2022-12-15T13:09:54.964270Z",
+     "iopub.status.busy": "2022-12-15T13:09:54.963842Z",
+     "iopub.status.idle": "2022-12-15T13:09:54.982100Z",
+     "shell.execute_reply": "2022-12-15T13:09:54.981041Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "xll:622241.1904510253; yll:3343617.741737109; rotation:15.0; proj4_str:+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs; units:meters; lenuni:2"
+       "xll:622241.1904510253; yll:3343617.741737109; rotation:15.0; crs:EPSG:32614; units:meters; lenuni:2"
       ]
      },
      "execution_count": 3,
@@ -112,10 +112,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.825291Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.824903Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.829878Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.829221Z"
+     "iopub.execute_input": "2022-12-15T13:09:54.987642Z",
+     "iopub.status.busy": "2022-12-15T13:09:54.985964Z",
+     "iopub.status.idle": "2022-12-15T13:09:54.993928Z",
+     "shell.execute_reply": "2022-12-15T13:09:54.992930Z"
     }
    },
    "outputs": [
@@ -146,17 +146,16 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.832641Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.832370Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.836346Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.835575Z"
+     "iopub.execute_input": "2022-12-15T13:09:54.998159Z",
+     "iopub.status.busy": "2022-12-15T13:09:54.997632Z",
+     "iopub.status.idle": "2022-12-15T13:09:55.002333Z",
+     "shell.execute_reply": "2022-12-15T13:09:55.001413Z"
     }
    },
    "outputs": [],
    "source": [
-    "proj4_str = \"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\"\n",
     "ml.modelgrid.set_coord_info(\n",
-    "    xoff=123456.7, yoff=765432.1, angrot=15.0, proj4=proj4_str\n",
+    "    xoff=123456.7, yoff=765432.1, angrot=15.0, crs=32614  # EPSG code for WGS84 UTM 14N\n",
     ")\n",
     "ml.dis.start_datetime = \"7/4/1776\""
    ]
@@ -166,10 +165,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.839138Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.838845Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.843504Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.842834Z"
+     "iopub.execute_input": "2022-12-15T13:09:55.006173Z",
+     "iopub.status.busy": "2022-12-15T13:09:55.005499Z",
+     "iopub.status.idle": "2022-12-15T13:09:55.011129Z",
+     "shell.execute_reply": "2022-12-15T13:09:55.010348Z"
     }
    },
    "outputs": [
@@ -202,10 +201,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.846436Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.846044Z",
-     "iopub.status.idle": "2023-02-22T02:46:39.849860Z",
-     "shell.execute_reply": "2023-02-22T02:46:39.849112Z"
+     "iopub.execute_input": "2022-12-15T13:09:55.016256Z",
+     "iopub.status.busy": "2022-12-15T13:09:55.015644Z",
+     "iopub.status.idle": "2022-12-15T13:09:55.019730Z",
+     "shell.execute_reply": "2022-12-15T13:09:55.018974Z"
     }
    },
    "outputs": [],
@@ -220,37 +219,17 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:39.853831Z",
-     "iopub.status.busy": "2023-02-22T02:46:39.853492Z",
-     "iopub.status.idle": "2023-02-22T02:46:44.137011Z",
-     "shell.execute_reply": "2023-02-22T02:46:44.136432Z"
+     "iopub.execute_input": "2022-12-15T13:09:55.023257Z",
+     "iopub.status.busy": "2022-12-15T13:09:55.022809Z",
+     "iopub.status.idle": "2022-12-15T13:10:00.978846Z",
+     "shell.execute_reply": "2022-12-15T13:10:00.977156Z"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<flopy.export.netcdf.NetCdf at 0x7f59802e4dc0>"
+       "<flopy.export.netcdf.NetCdf at 0x116637350>"
       ]
      },
      "execution_count": 8,
@@ -278,10 +257,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:44.140914Z",
-     "iopub.status.busy": "2023-02-22T02:46:44.140387Z",
-     "iopub.status.idle": "2023-02-22T02:46:44.223091Z",
-     "shell.execute_reply": "2023-02-22T02:46:44.222357Z"
+     "iopub.execute_input": "2022-12-15T13:10:00.983997Z",
+     "iopub.status.busy": "2022-12-15T13:10:00.983118Z",
+     "iopub.status.idle": "2022-12-15T13:10:01.104035Z",
+     "shell.execute_reply": "2022-12-15T13:10:01.102874Z"
     }
    },
    "outputs": [
@@ -289,11 +268,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n",
-      "wrote ../../../../../../tmp/tmpd5shx34h/top.shp\n"
+      "wrote ../../../../../../../var/folders/4x/bmhyjcdn3mgfdvkk_jgz6bsrlnfk3t/T/tmpx73wc2e1/top.shp\n"
      ]
     }
    ],
@@ -319,13 +294,21 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:44.227303Z",
-     "iopub.status.busy": "2023-02-22T02:46:44.226660Z",
-     "iopub.status.idle": "2023-02-22T02:46:47.856872Z",
-     "shell.execute_reply": "2023-02-22T02:46:47.856082Z"
+     "iopub.execute_input": "2022-12-15T13:10:01.108323Z",
+     "iopub.status.busy": "2022-12-15T13:10:01.108016Z",
+     "iopub.status.idle": "2022-12-15T13:10:06.688317Z",
+     "shell.execute_reply": "2022-12-15T13:10:06.687258Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrote ../../../../../../../Users/aleaf/Documents/GitHub/flopy/examples/Notebooks\n"
+     ]
+    }
+   ],
    "source": [
     "ml.drn.stress_period_data.export(os.path.join(pth, \"drn.shp\"), sparse=True)"
    ]
@@ -342,10 +325,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:47.861204Z",
-     "iopub.status.busy": "2023-02-22T02:46:47.860873Z",
-     "iopub.status.idle": "2023-02-22T02:46:47.965601Z",
-     "shell.execute_reply": "2023-02-22T02:46:47.964768Z"
+     "iopub.execute_input": "2022-12-15T13:10:06.692767Z",
+     "iopub.status.busy": "2022-12-15T13:10:06.692195Z",
+     "iopub.status.idle": "2022-12-15T13:10:06.811913Z",
+     "shell.execute_reply": "2022-12-15T13:10:06.810780Z"
     }
    },
    "outputs": [
@@ -353,11 +336,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n",
-      "wrote ../../../../../../tmp/tmpd5shx34h/hk.shp\n"
+      "wrote ../../../../../../../var/folders/4x/bmhyjcdn3mgfdvkk_jgz6bsrlnfk3t/T/tmpx73wc2e1/hk.shp\n"
      ]
     }
    ],
@@ -379,27 +358,17 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:47.970059Z",
-     "iopub.status.busy": "2023-02-22T02:46:47.969701Z",
-     "iopub.status.idle": "2023-02-22T02:46:48.037508Z",
-     "shell.execute_reply": "2023-02-22T02:46:48.036908Z"
+     "iopub.execute_input": "2022-12-15T13:10:06.816869Z",
+     "iopub.status.busy": "2022-12-15T13:10:06.816525Z",
+     "iopub.status.idle": "2022-12-15T13:10:06.912220Z",
+     "shell.execute_reply": "2022-12-15T13:10:06.911075Z"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<flopy.export.netcdf.NetCdf at 0x7f5936d359f0>"
+       "<flopy.export.netcdf.NetCdf at 0x1165a9210>"
       ]
      },
      "execution_count": 12,
@@ -429,27 +398,17 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:48.040906Z",
-     "iopub.status.busy": "2023-02-22T02:46:48.040578Z",
-     "iopub.status.idle": "2023-02-22T02:46:48.554001Z",
-     "shell.execute_reply": "2023-02-22T02:46:48.553348Z"
+     "iopub.execute_input": "2022-12-15T13:10:06.917722Z",
+     "iopub.status.busy": "2022-12-15T13:10:06.917295Z",
+     "iopub.status.idle": "2022-12-15T13:10:07.652862Z",
+     "shell.execute_reply": "2022-12-15T13:10:07.651784Z"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<flopy.export.netcdf.NetCdf at 0x7f5936d35390>"
+       "<flopy.export.netcdf.NetCdf at 0x116601210>"
       ]
      },
      "execution_count": 13,
@@ -475,24 +434,13 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:48.557877Z",
-     "iopub.status.busy": "2023-02-22T02:46:48.557328Z",
-     "iopub.status.idle": "2023-02-22T02:46:52.509240Z",
-     "shell.execute_reply": "2023-02-22T02:46:52.508538Z"
+     "iopub.execute_input": "2022-12-15T13:10:07.657768Z",
+     "iopub.status.busy": "2022-12-15T13:10:07.657079Z",
+     "iopub.status.idle": "2022-12-15T13:10:12.908166Z",
+     "shell.execute_reply": "2022-12-15T13:10:12.907156Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "fnc = ml.export(os.path.join(pth, \"model.nc\"))"
    ]
@@ -513,23 +461,13 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:52.513054Z",
-     "iopub.status.busy": "2023-02-22T02:46:52.512752Z",
-     "iopub.status.idle": "2023-02-22T02:46:56.913471Z",
-     "shell.execute_reply": "2023-02-22T02:46:56.912827Z"
+     "iopub.execute_input": "2022-12-15T13:10:12.912097Z",
+     "iopub.status.busy": "2022-12-15T13:10:12.911660Z",
+     "iopub.status.idle": "2022-12-15T13:10:19.297611Z",
+     "shell.execute_reply": "2022-12-15T13:10:19.296458Z"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initialize_geometry::proj4_str = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\n",
-      "initialize_geometry::self.grid_crs = +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +type=crs\n",
-      "initialize_geometry::nc_crs = epsg:4326\n",
-      "transforming coordinates using = proj=noop ellps=GRS80\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -560,10 +498,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-22T02:46:56.916658Z",
-     "iopub.status.busy": "2023-02-22T02:46:56.916322Z",
-     "iopub.status.idle": "2023-02-22T02:46:56.920261Z",
-     "shell.execute_reply": "2023-02-22T02:46:56.919691Z"
+     "iopub.execute_input": "2022-12-15T13:10:19.302256Z",
+     "iopub.status.busy": "2022-12-15T13:10:19.301850Z",
+     "iopub.status.idle": "2022-12-15T13:10:19.307843Z",
+     "shell.execute_reply": "2022-12-15T13:10:19.306763Z"
     }
    },
    "outputs": [],
@@ -579,9 +517,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "flopy",
    "language": "python",
-   "name": "python3"
+   "name": "flopy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -593,7 +531,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,

From 945f3b192fc795ff23a220abe8d06a460f655950 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 10:53:01 -0600
Subject: [PATCH 20/28] refactor(export/netcdf.py): * remove
 _initialize_attributes() method * call initialize_geometry() in workflow
 regardless if model has a crs or not (and set bounds and vbounds attributes
 in both cases)

---
 flopy/export/netcdf.py | 64 +++++-------------------------------------
 1 file changed, 7 insertions(+), 57 deletions(-)

diff --git a/flopy/export/netcdf.py b/flopy/export/netcdf.py
index 4d8ab5a65d..1b514ca2c7 100644
--- a/flopy/export/netcdf.py
+++ b/flopy/export/netcdf.py
@@ -671,57 +671,6 @@ def write(self):
         self.nc.close()
         self.log("writing nc file")
 
-    def _initialize_attributes(self):
-        """private method to initial the attributes
-        of the NetCdf instance
-        """
-        assert (
-            "nc" not in self.__dict__.keys()
-        ), "NetCdf._initialize_attributes() error: nc attribute already set"
-
-        self.nc_crs_str = "epsg:4326"
-        self.nc_crs_longname = "https://www.opengis.net/def/crs/EPSG/0/4326"
-        self.nc_semi_major = float(6378137.0)
-        self.nc_inverse_flat = float(298.257223563)
-
-        self.global_attributes = {}
-        self.global_attributes["namefile"] = self.model.namefile
-        self.global_attributes["model_ws"] = self.model.model_ws
-        self.global_attributes["exe_name"] = self.model.exe_name
-        self.global_attributes["modflow_version"] = self.model.version
-
-        self.global_attributes["create_hostname"] = socket.gethostname()
-        self.global_attributes["create_platform"] = platform.system()
-        self.global_attributes["create_directory"] = os.getcwd()
-
-        htol, rtol = -999, -999
-        try:
-            htol, rtol = self.model.solver_tols()
-        except Exception as e:
-            self.logger.warn(f"unable to get solver tolerances:{e!s}")
-        self.global_attributes["solver_head_tolerance"] = htol
-        self.global_attributes["solver_flux_tolerance"] = rtol
-        spatial_attribs = {
-            "xll": self.model_grid.xoffset,
-            "yll": self.model_grid.yoffset,
-            "rotation": self.model_grid.angrot,
-            "crs": self.model_grid.crs,
-        }
-        for n, v in spatial_attribs.items():
-            self.global_attributes["flopy_sr_" + n] = v
-        self.global_attributes[
-            "start_datetime"
-        ] = self.model_time.start_datetime
-
-        self.fillvalue = FILLVALUE
-
-        # initialize attributes
-        self.grid_crs = None
-        self.zs = None
-        self.ys = None
-        self.xs = None
-        self.nc = None
-
     def initialize_geometry(self):
         """initialize the geometric information
         needed for the netcdf file
@@ -757,6 +706,7 @@ def initialize_geometry(self):
 
         print(f"initialize_geometry::nc_crs = {self.nc_crs}")
 
+        xmin, xmax, ymin, ymax = self.model_grid.extent
         if self.transformer is not None:
             print(f"transforming coordinates using = {self.transformer}")
 
@@ -764,13 +714,14 @@ def initialize_geometry(self):
             self.xs, self.ys = self.transformer.transform(xs, ys)
 
             # get transformed bounds and record to check against ScienceBase later
-            xmin, xmax, ymin, ymax = self.model_grid.extent
             bbox = np.array(
                 [[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]]
             )
             x, y = self.transformer.transform(*bbox.transpose())
             self.bounds = x.min(), y.min(), x.max(), y.max()
-            self.vbounds = vmin, vmax
+        else:
+            self.bounds = xmin, ymin, xmax, ymax
+        self.vbounds = vmin, vmax
 
     def initialize_file(self, time_values=None):
         """
@@ -790,10 +741,9 @@ def initialize_file(self, time_values=None):
         if self.nc is not None:
             raise Exception("nc file already initialized")
 
-        if self.model_crs is None:
-            self.log("initializing geometry")
-            self.initialize_geometry()
-            self.log("initializing geometry")
+        self.log("initializing geometry")
+        self.initialize_geometry()
+        self.log("initializing geometry")
 
         netCDF4 = import_optional_dependency("netCDF4")
 

From a0de3a3bc1128f44c0cc2a2bd845c10fefd92656 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 11:49:25 -0600
Subject: [PATCH 21/28] test(test_export.py::test_export_output): name output
 file based on test case to hopefully avoid file conflicts with parallel test
 runs

---
 autotest/test_export.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/autotest/test_export.py b/autotest/test_export.py
index a182acf1d0..11ea7550e5 100644
--- a/autotest/test_export.py
+++ b/autotest/test_export.py
@@ -168,7 +168,7 @@ def test_export_output(crs, function_tmpdir, example_data_path):
     hds = flopy.utils.HeadFile(hds_pth)
 
     function_tmpdir = Path(".")
-    out_pth = os.path.join(function_tmpdir, "freyberg.out.nc")
+    out_pth = function_tmpdir / f"freyberg_{crs}.out.nc"
     nc = flopy.export.utils.output_helper(
         out_pth, ml, {"freyberg.githds": hds}
     )

From 1074af8a4ab79691f9dd310b35b59326f2c4c70d Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 11:50:53 -0600
Subject: [PATCH 22/28] feat(export): support pathlike export file paths

---
 autotest/test_export.py | 21 ++++++++---
 flopy/export/netcdf.py  |  9 ++---
 flopy/export/utils.py   | 82 ++++++++++++++++++++---------------------
 3 files changed, 59 insertions(+), 53 deletions(-)

diff --git a/autotest/test_export.py b/autotest/test_export.py
index 11ea7550e5..0a495b1a9f 100644
--- a/autotest/test_export.py
+++ b/autotest/test_export.py
@@ -58,14 +58,23 @@ def namfiles() -> List[Path]:
 
 
 @requires_pkg("shapefile")
-def test_output_helper_shapefile_export(function_tmpdir, example_data_path):
-    ws = example_data_path / "freyberg_multilayer_transient"
-    ml = Modflow.load("freyberg.nam", model_ws=ws)
-    head = HeadFile(ws / "freyberg.hds")
-    cbc = CellBudgetFile(ws / "freyberg.cbc")
+@pytest.mark.parametrize("pathlike", (True, False))
+def test_output_helper_shapefile_export(
+    pathlike, function_tmpdir, example_data_path
+):
+    ml = Modflow.load(
+        "freyberg.nam",
+        model_ws=str(example_data_path / "freyberg_multilayer_transient"),
+    )
+    head = HeadFile(os.path.join(ml.model_ws, "freyberg.hds"))
+    cbc = CellBudgetFile(os.path.join(ml.model_ws, "freyberg.cbc"))
 
+    if pathlike:
+        outpath = function_tmpdir / "test-pathlike.shp"
+    else:
+        outpath = os.path.join(function_tmpdir, "test.shp")
     flopy.export.utils.output_helper(
-        function_tmpdir / "test.shp",
+        outpath,
         ml,
         {"HDS": head, "cbc": cbc},
         mflay=1,
diff --git a/flopy/export/netcdf.py b/flopy/export/netcdf.py
index 1b514ca2c7..4310f92501 100644
--- a/flopy/export/netcdf.py
+++ b/flopy/export/netcdf.py
@@ -160,11 +160,8 @@ def __init__(
         forgive=False,
         **kwargs,
     ):
-        if isinstance(output_filename, os.PathLike):
-            output_filename = str(
-                Path(output_filename).expanduser().absolute()
-            )
-        assert output_filename.lower().endswith(".nc")
+        output_filename = Path(output_filename)
+        assert output_filename.suffix == ".nc"
         if verbose is None:
             verbose = model.verbose
         if logger is not None:
@@ -174,7 +171,7 @@ def __init__(
         self.var_attr_dict = {}
         self.log = self.logger.log
         if os.path.exists(output_filename):
-            self.logger.warn("removing existing nc file: " + output_filename)
+            self.logger.warn(f"removing existing nc file: {output_filename}")
             os.remove(output_filename)
         self.output_filename = output_filename
 
diff --git a/flopy/export/utils.py b/flopy/export/utils.py
index 9891a801fc..605c2c9a56 100644
--- a/flopy/export/utils.py
+++ b/flopy/export/utils.py
@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 from typing import Optional, Union
 
 import numpy as np
@@ -401,10 +402,8 @@ def output_helper(
         elif verbose:
             print(msg)
     times = [t for t in common_times[::stride]]
-
-    if isinstance(f, os.PathLike):
-        f = str(f)
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(
             f, ml, time_values=times, logger=logger, forgive=forgive, **kwargs
         )
@@ -513,7 +512,9 @@ def output_helper(
                 mask_array3d=mask_array3d,
             )
 
-    elif isinstance(f, str) and f.endswith(".shp"):
+    elif (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         attrib_dict = {}
         for _, out_obj in oudic.items():
             if (
@@ -611,13 +612,13 @@ def model_export(
     if package_names is None:
         package_names = [pak.name[0] for pak in ml.packagelist]
 
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(f, ml, **kwargs)
 
-    if isinstance(f, str) and f.lower().endswith(".shp"):
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         shapefile_utils.model_attributes_to_shapefile(
             f, ml, package_names=package_names, **kwargs
         )
@@ -704,13 +705,13 @@ def package_export(
     """
     assert isinstance(pak, PackageInterface)
 
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(f, pak.parent, **kwargs)
 
-    if isinstance(f, str) and f.lower().endswith(".shp"):
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         shapefile_utils.model_attributes_to_shapefile(
             f, pak.parent, package_names=pak.name, verbose=verbose, **kwargs
         )
@@ -817,10 +818,8 @@ def generic_array_export(
             flopy model object
 
     """
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         assert "model" in kwargs.keys(), (
             "creating a new netCDF using generic_array_helper requires a "
             "'model' kwarg"
@@ -907,13 +906,13 @@ def mflist_export(f: Union[str, os.PathLike, NetCdf], mfl, **kwargs):
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(f, mfl.model, **kwargs)
 
-    if isinstance(f, str) and f.lower().endswith(".shp"):
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         sparse = kwargs.get("sparse", False)
         kper = kwargs.get("kper", 0)
         squeeze = kwargs.get("squeeze", True)
@@ -1063,13 +1062,13 @@ def transient2d_export(f: Union[str, os.PathLike], t2d, fmt=None, **kwargs):
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(f, t2d.model, **kwargs)
 
-    if isinstance(f, str) and f.lower().endswith(".shp"):
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         array_dict = {}
         for kper in range(t2d.model.modeltime.nper):
             u2d = t2d[kper]
@@ -1219,13 +1218,13 @@ def array3d_export(f: Union[str, os.PathLike], u3d, fmt=None, **kwargs):
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(f, u3d.model, **kwargs)
 
-    if isinstance(f, str) and f.lower().endswith(".shp"):
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         array_dict = {}
         for ilay in range(modelgrid.nlay):
             u2d = u3d[ilay]
@@ -1396,21 +1395,22 @@ def array2d_export(
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if isinstance(f, os.PathLike):
-        f = str(f)
-
-    if isinstance(f, str) and f.lower().endswith(".nc"):
+    if (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".nc":
         f = NetCdf(f, u2d.model, **kwargs)
 
-    if isinstance(f, str) and f.lower().endswith(".shp"):
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".shp":
         name = shapefile_utils.shape_attr_name(u2d.name, keep_layer=True)
         shapefile_utils.write_grid_shapefile(
             f, modelgrid, {name: u2d.array}, verbose=verbose
         )
         return
 
-    elif isinstance(f, str) and f.lower().endswith(".asc"):
-        export_array(modelgrid, f, u2d.array, verbose=verbose, **kwargs)
+    elif (isinstance(f, str) or isinstance(f, Path)) and 
+        Path(f).suffix.lower() == ".asc":
+        export_array(modelgrid, f, u2d.array, **kwargs)
         return
 
     elif isinstance(f, NetCdf) or isinstance(f, dict):

From bbc416d1bf60a0f2998216ceb120a5f49c8a4a5c Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 1 Mar 2023 12:39:56 -0600
Subject: [PATCH 23/28] docs: updated flopy3_shapefile_export and
 flopy3_shapefile_features to use crs arg and to reflect deprecation of
 EpsgReference class

---
 .../Notebooks/flopy3_shapefile_export.ipynb   |  12 +-
 .../Notebooks/flopy3_shapefile_features.ipynb | 281 +++---------------
 2 files changed, 51 insertions(+), 242 deletions(-)

diff --git a/examples/Notebooks/flopy3_shapefile_export.ipynb b/examples/Notebooks/flopy3_shapefile_export.ipynb
index 141b188238..6e519bdff5 100644
--- a/examples/Notebooks/flopy3_shapefile_export.ipynb
+++ b/examples/Notebooks/flopy3_shapefile_export.ipynb
@@ -136,7 +136,7 @@
    "outputs": [],
    "source": [
     "grid = m.modelgrid\n",
-    "grid.set_coord_info(xoff=273170, yoff=5088657, epsg=26916)"
+    "grid.set_coord_info(xoff=273170, yoff=5088657, crs=26916)"
    ]
   },
   {
@@ -711,7 +711,7 @@
    "outputs": [],
    "source": [
     "fname = \"{}/bcs.shp\".format(outdir)\n",
-    "recarray2shp(spd.to_records(), geoms=polygons, shpname=fname, epsg=grid.epsg)"
+    "recarray2shp(spd.to_records(), geoms=polygons, shpname=fname, crs=grid.crs)"
    ]
   },
   {
@@ -884,7 +884,7 @@
     "geoms = [Point(x, y) for x, y in zip(welldata.x_utm, welldata.y_utm)]\n",
     "\n",
     "fname = \"{}/wel_data.shp\".format(outdir)\n",
-    "recarray2shp(welldata.to_records(), geoms=geoms, shpname=fname, epsg=grid.epsg)"
+    "recarray2shp(welldata.to_records(), geoms=geoms, shpname=fname, crs=grid.crs)"
    ]
   },
   {
@@ -960,7 +960,7 @@
     "    rivdata.to_records(index=False),\n",
     "    geoms=lines,\n",
     "    shpname=lines_shapefile,\n",
-    "    epsg=grid.epsg,\n",
+    "    crs=grid.crs,\n",
     ")"
    ]
   },
@@ -1240,7 +1240,7 @@
     "    linesdata.drop(\"geometry\", axis=1).to_records(),\n",
     "    geoms=linesdata.geometry.values,\n",
     "    shpname=lines_shapefile,\n",
-    "    epsg=grid.epsg,\n",
+    "    crs=grid.crs,\n",
     ")"
    ]
   },
@@ -1425,7 +1425,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/Notebooks/flopy3_shapefile_features.ipynb b/examples/Notebooks/flopy3_shapefile_features.ipynb
index 245716407e..31cccb7884 100644
--- a/examples/Notebooks/flopy3_shapefile_features.ipynb
+++ b/examples/Notebooks/flopy3_shapefile_features.ipynb
@@ -9,8 +9,6 @@
     "* `recarray2shp` convience function for writing a numpy record array to a shapefile\n",
     "* `shp2recarray` convience function for quickly reading a shapefile into a numpy recarray\n",
     "* `utils.geometry` classes for writing shapefiles of model input/output. For example, quickly writing a shapefile of model cells with errors identified by the checker\n",
-    "* demonstration of how the `EpsgReference` class works for retrieving projection file information (WKT text) from spatialreference.org, and caching that information locally for when an internet connection isn't available\n",
-    "* how to reset `EpsgReference` if it becomes corrupted\n",
     "* examples of how the `Point` and `LineString` classes can be used to quickly plot pathlines and endpoints from MODPATH (these are also used by the `PathlineFile` and `EndpointFile` classes to write shapefiles of this output)"
    ]
   },
@@ -30,8 +28,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]\n",
-      "numpy version: 1.24.1\n",
+      "3.11.0 | packaged by conda-forge | (main, Jan 15 2023, 05:44:48) [Clang 14.0.6 ]\n",
+      "numpy version: 1.24.2\n",
       "matplotlib version: 3.6.3\n",
       "flopy version: 3.3.7\n"
      ]
@@ -125,7 +123,7 @@
    "outputs": [],
    "source": [
     "grid = m.modelgrid\n",
-    "grid.set_coord_info(xoff=600000, yoff=5170000, proj4=\"EPSG:26715\", angrot=45)"
+    "grid.set_coord_info(xoff=600000, yoff=5170000, crs=\"EPSG:26715\", angrot=45)"
    ]
   },
   {
@@ -309,7 +307,7 @@
     },
     {
      "data": {
-      "image/png": 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SjRs3xsSJE5GdnS12LCKDkq/SU758eUgkkrdukZGRudtER0ejYcOGMDMzg6WlJerWrYs3b97krk9ISEBoaCgsLS1hZWWFXr16IS0trfCeEZEeEgQBCxcuhJ+fP15kGf05nVXNX+xYlE8yM2vYdfgCqoBumD49CvUbNMTTp0/FjkVkMPJVes6fP49nz57l3g4dOgQA6NChA4A/C0/Tpk3RpEkTnDt3DufPn8egQYMglf7/bkJDQ/Hrr7/i0KFD2L17N37++Wf07du3EJ8SkX5JSEhA608+wbBhw2Di1hz2Xb+EwspR7FhUQBKJFCq/TlB3icL5qzdRy9kF+/fvFzsWkUGQCIIgFPTOw4YNw+7du3Hnzh1IJBL4+PigcePGmDp16ju3v3nzJpycnHD+/Hl4enoCAPbv34/mzZvjjz/+QMmSH/a5hJSUFKhUKiQnJ8PS0rKg8Ym03i+//IL2HToiLiEJVk2HwbSKt9iRqBDlvE5Gwt75eH3vAsaMGYNp06ZBLpeLHYuoyIj9/l3gz/RkZmZi3bp16NmzJyQSCeLi4nD27Fmo1Wr4+fnBwcEB9erVw6lTp3LvEx0dDSsrq9zCAwBBQUGQSqX/+i3FGRkZSElJyXMj0mcajQZz5sxBQGAgEmAOdcRCFh49JDNVwa7dJFjV745Zs+cgsG49PH78WOxYRHqrwKVn+/btSEpKQvfu3QEA9+/fBwB8/vnn6NOnD/bv34/atWujUaNGuHPnDgAgNjYWarU6z+PI5XLY2Nj862mcM2bMgEqlyr2VKVOmoLGJtN6LFy/QomVLjB49GmYerWHfeQbklur/viPpJIlECpV3e6i7zMTlG3fh7OKK3bt3ix2LSC8VuPSsXLkSzZo1y52S+us7Zvr164cePXrA3d0d8+fPR7Vq1fDdd999VMhx48YhOTk598b/CZG+OnXqFJxd3XD4xGmo20+Gdf0ekMg43WEIjEvXgH3EQmTbV0XLli0xcuRIZGZmih2LSK8UqPQ8fPgQhw8fRu/evXOXlShRAgDg5OSUZ9saNWrg0aNHAABHR8e3vocmOzsbCQkJcHR8/wczlUolLC0t89yI9IlGo8GMGTNQr359JMutoQ5fCJNKXmLHomImM7GAbZsJsG7YGwsWLoJ/QCB+//13sWMR6Y0ClZ5Vq1ZBrVYjJCQkd1n58uVRsmRJ3Lp1K8+2t2/fRrly5QAAvr6+SEpKwsWLF3PXHz16FBqNBt7e/LwCGaa4uDgEBzfF+M8+g3md9rDvNB1ySzuxY5FIJBIJLL0+gbrrl7h29xFcXN2wfft2sWMR6YV8lx6NRoNVq1YhIiIiz1kGEokEo0ePxqJFi7B161bcvXsXEydOxG+//YZevXoB+POoT9OmTdGnTx+cO3cOp0+fxqBBg9C5c+cPPnOLSJ8cP34czi6u+PmXC1B3+ALWdcMgkcrEjkVaQFmyGtThC6ApURNt2rTBkCFDkJGRIXYsIp2W7w8LHD58GI8ePULPnj3fWjds2DCkp6dj+PDhSEhIgKurKw4dOoRKlSrlbrN+/XoMGjQIjRo1glQqRbt27bBo0aKPexZEOiYnJwfTp0/H51OmwKSMM+zbj4Tc3EbsWKRlpMbmsG09DkaXdmPx0mU4eeo0tm7ZnOc1lYg+3Eddp0csYp/nT/QxYmNj0blLV5w4cRwqvy5Q+XXi0R36Txmxd5G4axbkWalYtXJl7kVhiXSJ2O/f/O4tomJ0+PBh1HJ2QfTFK3DoNB1WAV1ZeOiDKB0rQx2+ACjtjo4dO2LAgAFIT08XOxaRTmHpISoG2dnZmDBhApo0aYI3FqWhjlgE43IuYsciHSNVmsK25WjYBEdi+bffwauON27fvi12LCKdwdJDVMSePHmC+g0aIipqBlSBYbDrMAUyMyuxY5GOkkgksHBrBoewObj7LAHutWtjw4YNYsci0gksPURFaP/+/XB2ccX5azeh7hIFlW9HSCT8s6OPZ6SuCPuw+ZCUr4PQ0FD07t0br1+/FjsWkVbjqy9REcjKysKYMWPQrFkzZFhXgDp8IYzL1BI7FukZqZEJbENGwLbZEHy/dh08verg5s2bYsci0losPUSF7PHjxwisWw+z58yFVf0esGs3CTJTldixSE9JJBKYuzSButs8PIhPRW0PT6xevVrsWERaiaWHqBDt2rULzi6uiPntHhy6zoTKux2ns6hYGNmXg323eZBX9kP37t0REdEdr169EjsWkVbhqzFRIcjMzMSIESPQqlUrZNtXhX34QihL1RA7FhkYqZExbJsPg23ICKzf9APca3vg+vXrYsci0hosPUQf6cGDB/DzD8DCRV/BumEf2LaZAJmJhdixyICZ12oIh/D5eJycCQ9PL3z77bfQwevQEhU6lh6ij7Bt2za4uLnh+r0/oA6dBUuv1pBIJGLHIoLCtgzsQ+fAqHo99OnTB11DQ5Gamip2LCJRsfQQFUBGRgYGDx6Mtm3bQijhDHX4fChLVBU7FlEeUoUStk0Hw67laGz9aTvcansgJiZG7FhEomHpIcqne/fuwcfXD0uWfQObxv1h23ospMbmYsciei8zp3pQhy/A0zQN6nj7YOnSpZzuIoPE0kOUD5s3b4armxtuPoyFOnQ2LGq34HQW6QSFTSmoQ2fDuFYQBg4ciI6dOiE5OVnsWETFiqWH6AOkp6ejf//+6NSpE1CmNtThC6B0rCx2LKJ8kciNYNN4AOxaj8X2XXvh4uaOixcvih2LqNiw9BD9h9u3b8OzjjdWrFwFm+BBsG05GlKlqdixiArMrHoA1BELEZ8ph6+vH7766itOd5FBYOkh+hfr16+Hm3tt3HuWCIewubBwa8rpLNILCitH2HeZBWPXZhgyZAjatG2LxMREsWMRFSmWHqJ3eP36NXr16oVu3bpBWqEO7MPmwUhdQexYRIVKIlfAplEf2LedgL0HDsPF1Q1nz54VOxZRkWHpIfqHGzduoLanF1avWw/bZkNhGzICUiMTsWMRFRnTKj5QRyzCS40p/AMCMHfuXE53kV5i6SH6m++//x4enl54+OIV1N3mwdylMaezyCDIVWrYd5kJs9qtMGrUKLRo0RIvX74UOxZRoWLpIQKQlpaGsPBw9OjRA/LKfrDvNhdG9uXEjkVUrCQyOawb9IR9+8k4dOIUnF3dcPr0abFjERUalh4yeNeuXUNtD09s3LwVtiEjYNt8GKRGxmLHIhKNaSUvqMMXIkmqQt169TBz5kxoNBqxYxF9NJYeMliCIODbb7+Fp1cdPE7OhEPYPJjXaih2LCKtILe0g33nKJh7tcW4cePQrHlzxMfHix2L6KOw9JBBSk1NRdeuXdGnTx8Y1agP+9A5UNiWETsWkVaRSGWwrhcBdYcpOHb6LJxdXPHzzz+LHYuowFh6yODExMTA1b02tm7fCbuWo2EbPAhShVLsWERay6SiB9ThC5GqtEeDBg0wbdo05OTkiB2LKN9YeshgCIKAJUuWoI63D2JfA+qw+TBzqid2LCKdILewhV3HqbDw6YSJkyYhqHETxMbGih2LKF9YesggJCcno32HDoiMjIRxrSDYd50FhU0psWMR6RSJVAarwFCoO03DmQuX4eziiiNHjogdi+iDsfSQ3rtw4QJc3Nyxc+8B2H0yDjaNB0AiNxI7FpHOMinnCnXEIrw2L4nGjRtj0qRJnO4incDSQ3pLEAQsXLgQvr5+iM9UQB2+AGbV/MWORaQXZGbWsGs/BZYBoZg2bTrqN2iIp0+fih2L6F+x9JBeSkxMxCdt2mDYsGEwcWsO+65fQmHlKHYsIr0ikcpg5dcZ6s7Tce7KDTi7uOLAgQNixyJ6L5Ye0jtnz56Fs6sb9h08Avu2E2DTqA8kMoXYsYj0lnFZZ6gjFiJdVQ5NmzbF2LFjkZ2dLXYsorew9JDe0Gg0mDt3LvwDApCgMYU6YhFMq/iIHYvIIMhMVbBrPxlW9bpj1uw5CKxbD48fPxY7FlEeLD2kF16+fIkWLVti1KhRMPNoDfsuMyFXqcWORWRQJBIpVD7toe4yE5dv3oWziyv27NkjdiyiXCw9pPNOnz6NWi6uOHziNNTtJ8O6fg9IZHKxYxEZLOPSNWAfvgDZ9lXRokULjBo1CllZWWLHImLpId2l0Wgwc+ZM1K1XD8kyK6jDF8KkkpfYsYgIgMzEErZtJsC6QS/MX7AAfv4B+P3338WORQaOpYd0UlxcHIKbNsO48eNhXqcd7DtHQW5pJ3YsIvobiUQCyzptoO46C9fuPISLqxu2b98udiwyYCw9pHNOnDgBZxdXnIg+B3WHKbCuGw6JVCZ2LCJ6D2XJarCPWAiNoxPatGmDoUOHIiMjQ+xYZIBYekhn5OTk4IsvvkCDhg2RZqyGOmIRTCrUFjsWEX0AmbE5bD8ZD+ugfvh6yVL4+Prh/v37YsciA8PSQzohNjYWQY2bYPLnn8PStxPsOk6F3NxG7FhElA8SiQSWHi2hDp2Nmw+fwcXNDVu3bhU7FhkQlh7SekeOHIGziyvOXIiButM0WAWEcjqLSIcpHStDHb4AKO2GDh06YODAgUhPTxc7FhkAlh7SWtnZ2Zg4cSIaN26M1+YloY5YCJNyrmLHIqJCIFWawbblp7BpMhDfrFiJOt4+uHPnjtixSM+x9JBWevr0Keo3aIjp06OgCugGuw5fQGZmLXYsIipEEokEFu7N4dBtDm4/eQE3d3ds3LhR7Fikx1h6SOvs378ftZxdcP7qTai7REHl1wkSCX9VifSVkUNFqMPmQ1LOE127dkWfPn3w5s0bsWORHsrXO0n58uUhkUjeukVGRubZThAENGvWDBKJ5K1rMjx69AghISEwNTWFWq3G6NGj+cV0BODP6ayxY8eiWbNmyLCuAHXEQhiXqSV2LCIqBlKlKWxbjIJN0yFYtWYtPDy98Ntvv4kdi/RMvq7Vf/78eeTk5OT+fP36dTRu3BgdOnTIs92CBQsgkUjeun9OTg5CQkLg6OiIM2fO4NmzZwgPD4dCoUBUVFQBnwLpg8ePH6Njp844e/YsrOp3h2Wdtjy6Q2RgJBIJLFybQFmyKh7smgX32h74ZtlShIeHix2N9ES+3lXs7e3h6OiYe9u9ezcqVaqEevXq5W4TExODuXPn4rvvvnvr/gcPHsSNGzewbt06uLm5oVmzZpg6dSoWL16MzMzMj382pJN2794NZxdXXL5xF+ouM6Hybs/CQ2TAjOzLw77bPMgr+yEiIgLdu/fAq1evxI5FeqDA7yyZmZlYt24devbsmXtU5/Xr1+jatSsWL14MR0fHt+4THR0NZ2dnODg45C4LDg5GSkoKfv311/fuKyMjAykpKXlupPsyMzMxYsQItGzZEtn2VWEfsRDGpWuIHYuItIDUyBi2zYfBNmQ41m3cBHcPT1y/fl3sWKTjClx6tm/fjqSkJHTv3j132fDhw+Hn54fWrVu/8z6xsbF5Cg+A3J9jY2Pfu68ZM2ZApVLl3sqUKVPQ2KQlfv/9d/gHBGLhoq9g3bA3bNtMgMzEQuxYRKRlzGs1gkPYfDxOTIenVx2sXLkSgiCIHYt0VIFLz8qVK9GsWTOULFkSALBz504cPXoUCxYsKKxsucaNG4fk5OTc2+PHjwt9H1R8tm/fDhdXN1y7+wjqrl/C0uuTd34GjIgIABR2ZWDfbS4U1eqid+/eCO3WDampqWLHIh1UoNLz8OFDHD58GL17985ddvToUdy7dw9WVlaQy+WQy//8jHS7du1Qv359AICjoyOeP3+e57H++vld02F/USqVsLS0zHMj3ZORkYEhQ4agTZs20JSoCXX4AihLVhM7FhHpAKlCCdumg2HXchS2/LgNbrU9cOXKFbFjkY4pUOlZtWoV1Go1QkJCcpeNHTsWV69eRUxMTO4NAObPn49Vq1YBAHx9fXHt2jXExcXl3u/QoUOwtLSEk5PTRzwN0nb37t2Dj68fFi9dBuugfrBtPQ5SY3OxYxGRjjFzqg91+AI8TcuBVx1vLFu2jNNd9MHydco6AGg0GqxatQoRERG5R3MA5J7R9U9ly5ZFhQoVAABNmjSBk5MTwsLCMGvWLMTGxmLChAmIjIyEUqn8iKdB2mzLli3o0asXshUWUIfOhtKxstiRiEiHKWxKQR06BwlHV2LAgAE4cvQoVn77LWcB6D/l+0jP4cOH8ejRI/Ts2TPfO5PJZNi9ezdkMhl8fX3RrVs3hIeH44svvsj3Y5H2S09Px4ABA9CxY0egtPuf01ksPERUCCRyI9g2GQC71mOxfddeuLi54+LFi2LHIi0nEXTwuGBKSgpUKhWSk5PZ7LXU7du30a59B9y4+RusGvWBuWtTfliZiIpEVuIzJO6ahewXDzF37hwMGjSIrzdaSuz3b14Bjgrdhg0b4F67Nu4+S4BD2BxYuDXjCxARFRmFdQnYd50FY5emf54s0bYtEhMTxY5FWoilhwrN69ev/zydNDQUkvJ1YB82H0bqimLHIiIDIJErYBPUF/ZtPsPeA4fh4uaOc+fOiR2LtAxLDxWKmzdvwtOrDr5fuw62zYbANmQEpEYmYsciIgNjWtUX6oiFeJljAj9/f8ybN49nd1Eulh76aKtXr0ZtD088iE+Futs8mLs04XQWEYlGrnKAfZcZMHNviZEjR6Jly1ZISEgQOxZpAZYeKrBXr14hIqI7unfvDnllP9h3mwcj+3JixyIigkSmgHXDXrBvNwkHj/2MWi6uOHPmjNixSGQsPVQg169fh3ttD6zf9ANsQ0bAtvkwSI2MxY5FRJSHaeU6UEcsRLLUEoF16+LLL7+ERqMROxaJhKWH8kUQBHz77bfw8PTC4+RMOITPh3mthmLHIiJ6L7mlPew6RcHcqy3Gjh2L5iEhiI+PFzsWiYClhz5YamoquoaGok+fPjCqXg/2oXOgsOU33hOR9pPI5LCuFwF1hyk4ejIazi6u+Pnnn8WORcWMpYc+SExMDNxqe2DrT9th13I0bJsOhlTBrw4hIt1iUtED6ohFSFXaoUGDBpg2bRpycnLEjkXFhKWH/pUgCFi6dCnqePvgaZoG6vAFMHOqJ3YsIqICk1vYwq7jNFj4dMLESZPQuEkwnj9/LnYsKgYsPfReycnJ6NipEwYOHAjjWkFQh86GwqaU2LGIiD6aRCqDVWAo1B2n4vT5S6jl7IKjR4+KHYuKGEsPvdOFCxfg4uaO7bv2wq71WNg0HgCJ3EjsWEREhcqkvBvU4Yvw2qwkgoKCMGnSJE536TGWHspDEAQsWrQIvr5+iM+UQx2xEGbVA8SORURUZGTm1rDrMAWW/l0xbdp0NGjYCE+fPhU7FhUBlh7KlZiYiDZt22Lo0KEwcWsO+y6zoLByFDsWEVGRk0hlsPLvAnXn6Tgb8yucXVxx8OBBsWNRIWPpIQDA2bNn4eLqhr0HDsO+7QTYNOoDiVwhdiwiomJlXNYZ6oiFSFeVQ3BwMMaNG4fs7GyxY1EhYekxcIIgYO7cufAPCMBLjSnUEYtgWsVH7FhERKKRmapg134yrOpF4MtZs1G3Xn388ccfYseiQsDSY8BevnyJFi1aYtSoUTCr3Qr2XWZCrlKLHYuISHQSiRQqnw5Qd5mBS7/ehrOLK/bu3St2LPpILD0G6vTp03B2dcOhE6dg334yrBv0hEQmFzsWEZFWMS7tBPuIhciyrYyQkBCMHj0aWVlZYseiAmLpMTAajQYzZ85E3Xr1kCRVQR2+EKaVvMSORUSktWQmlrBtOxHWDXpi3vz58A8IxMOHD8WORQXA0mNA4uPj0ax5c4wbNw7mXm1h3zkKcks7sWMREWk9iUQCyzptoe7yJa7e+R0urm7YsWOH2LEon1h6DMSJEyfg7OKKY6fPQt1hCqzrRUAilYkdi4hIpyhLVYd9+ELkONTAJ598gqFDhyIzM1PsWPSBWHr0XE5ODqZOnYqGDRsiVWkPdfhCmFT0EDsWEZHOkhmbw/aT8bBu1BdfL14CH18/3L9/X+xY9AFYevRYbGwsgho3waTJk2Hh0wl2HadCbmErdiwiIp0nkUhg6dkK6tDZuPH7U7i4uWHr1q1ix6L/wNKjp44cOQJnF1ecuXAZ6k7TYBUYyuksIqJCpixRBerwBUApV3To0AEDBw5Eenq62LHoPVh69ExOTg4mTZqExo0b47V5SagjFsGknKvYsYiI9JZUaQbbVmNg02QgvlmxEt4+vrhz547YsegdWHr0yNOnT1G/QUNMmzYdlgGhsGs/BTIza7FjERHpPYlEAgv35nDoNge3/4iHm7s7Nm3aJHYs+geWHj1x4MABOLu44tyVG1B3ng4rv86cziIiKmZGDhVhHzYfknKe6NKlC/r27Ys3b96IHYv+h6VHx2VnZ2Ps2LFo2rQp0lXloI5YCOOyzmLHIiIyWFKlKWxbjIJN08H47vs18PCqg99++03sWASWHp32+PFjBNath1mz58CqXnfYtZ8MmalK7FhERAZPIpHAwjUY6rC5eBCXjNoeHli7dq3YsQweS4+O2rNnD5xdXHH55l2ou8yEyqc9JBL+cxIRaRMj+/Kw7zYfsoq+CA8PR/fuPfDq1SuxYxksvkvqmKysLIwcORItWrRAtn1V2IcvgHHpGmLHIiKi95AaGcM2ZDhsmw/Huo2b4OHphV9//VXsWAaJpUeH/P777/DzD8CChQth3aAXbNtMgMzEUuxYRET0AcydG0EdNg8PE97Aw9ML3333HQRBEDuWQWHp0RHbt2+Hi6sbrt15CHXXWbCs0wYSiUTsWERElA9GdmVh320OFNUC0atXL4R264a0tDSxYxkMlh4tl5GRgaFDh6JNmzbQODrBPmIhlCWriR2LiIgKSKowhm3TIbBrOQpbftwOV/fauHLlitixDAJLjxa7f/8+fHz98PWSpbAO6gfbT8ZDZmwudiwiIioEZk71oQ6fj6dpOfCq441vvvmG011FjKVHS23duhUubm64+fAZ1KGzYenRktNZRER6RmFTCurQOVDWbIT+/fujU+fOSElJETuW3mLp0TLp6ekYOHAgOnToAJR2gzp8AZSOlcWORURERUQiN4Jtk4GwazUG23buhoubOy5duiR2LL3E0qNF7ty5gzrePvhmxUrYNBkI25afQqo0EzsWEREVA7MagVCHL0Rcugw+Pr74+uuvOd1VyFh6tMTGjRvh5u6O209ewKHbHFi4N+d0FhGRgVFYl4B911kwdgnG4MGD0bZdOyQlJYkdS2+w9IjszZs36NOnD7p27QpJOU+ow+bDyKGi2LGIiEgkErkCNkH9YN9mPPbsPwQXVzecO3dO7Fh6gaVHRDdv3oSHpxdWrVkLm6ZDYNtiFKRKU7FjERGRFjCt6gd1xEK8yDGGv38A5s+fz+muj5Sv0lO+fHlIJJK3bpGRkUhISMDgwYNRrVo1mJiYoGzZshgyZAiSk5PzPMajR48QEhICU1NTqNVqjB49GtnZ2YX6pHTBmjVrUNvDEw/iU6HuNhcWrk04nUVERHnIVQ6w7zITpu4tMGLECLRs1QoJCQlix9JZ8vxsfP78eeTk5OT+fP36dTRu3BgdOnTA06dP8fTpU8yZMwdOTk54+PAh+vfvj6dPn2Lr1q0AgJycHISEhMDR0RFnzpzBs2fPEB4eDoVCgaioqMJ9Zlrq1atXiIwchNWrv4e5cxCsg/pDamQsdiwiItJSEpkC1g17QVm2Fg7uWwhnF1ds2fwD/Pz8xI6mcyTCRxwrGzZsGHbv3o07d+688yjFli1b0K1bN7x69QpyuRz79u1DixYt8PTpUzg4OAAAli1bhjFjxiA+Ph5GRkYftN+UlBSoVCokJyfD0lJ3vnvq+vXraNu+A+4/+B1WjQfAvFYjsSMREZEOyU6JR8Ku2ch4dgszoqIwatQoSKW680kVsd+/CzxSmZmZWLduHXr27PneaZm/npRc/ucBpejoaDg7O+cWHgAIDg5GSkrKv37jbEZGBlJSUvLcdIkgCFi5ciU8vergcWI6HMLms/AQEVG+yS3tYd85CuZebTBmzBg0DwlBfHy82LF0RoFLz/bt25GUlITu3bu/c/2LFy8wdepU9O3bN3dZbGxsnsIDIPfn2NjY9+5rxowZUKlUubcyZcoUNHaxS01NRWi3bujduzcU1erCvttcKOx0Jz8REWkXiUwO63rdoe4wBUdPRsPZxRUnT54UO5ZOKHDpWblyJZo1a4aSJUu+tS4lJQUhISFwcnLC559//jH5AADjxo1DcnJy7u3x48cf/ZjF4cqVK3Cr7YEtP26DXctRsG06GFKFUuxYRESkB0wqekAdsRCpRraoX78+pk+fDo1GI3YsrVag0vPw4UMcPnwYvXv3fmtdamoqmjZtCgsLC2zbtg0KhSJ3naOjI54/f55n+79+dnR0fO/+lEolLC0t89y0mSAIWLZsGbzqeONpWg7U4Qtg5lRf7FhERKRn5BZ2sOs0HRbeHTBh4kQ0aRL81vss/b8ClZ5Vq1ZBrVYjJCQkz/KUlBQ0adIERkZG2LlzJ4yN856V5Ovri2vXriEuLi532aFDh2BpaQknJ6eCRNE6KSkp6NipEwYMGABlzSCoQ+dAYVNK7FhERKSnJFIZrOqGQd1xKk6euwhnF1ccPXpU7FhaKd+lR6PRYNWqVYiIiMj9gDLw/4Xn1atXWLlyJVJSUhAbG4vY2Njc09ybNGkCJycnhIWF4cqVKzhw4AAmTJiAyMhIKJW6P+1z8eJFuLi5Y/uuvbBrPRa2TQZAIv+wM9KIiIg+hkl5N6jDF+GVqSOCgoIwefLkPJeZoQKcsn7w4EEEBwfj1q1bqFq1au7y48ePo0GDBu+8z4MHD1C+fHkAf06NDRgwAMePH4eZmRkiIiIwc+bMPAXqv4h9yts/CYKAr7/+GiNHjoLcrhysW34KhXUJsWMREZEBEjQ5SI7ejJTTGxEQGIgfNm1EiRLa8Z4k9vv3R12nRyxiD9rfJSYmokfPntixfTssPFrBun4PSOSK/74jERFREUp/dBWJe+bCXCHBxg3r0aRJE7Ejif7+rTtXNNJC586dg4ubO/YeOAz7Np/BJqgvCw8REWkF47IuUIcvRLqqLJo2bYrx48cb5Nc+/R1LTwEIgoB58+bBz98fL3NMoI5YCNOqvmLHIiIiykNmZgW79p9DVTccM7+chXr1G+CPP/4QO5ZoWHryKSEhAS1btsLIkSNh5t4S9l1mQK5y+O87EhERiUAikULl0wHqLlG4eP0WnF1csXfvXrFjiYKlJx/OnDmDWi6uOHjsZ9i3mwTrhr0gkXE6i4iItJ9x6ZqwD1+ATNtKCAkJwejRo5GVlSV2rGLF0vMBNBoNvvzySwTWrYskqeWf01mV64gdi4iIKF9kpirYtZ0Iq/o9MW/+fAQE1sWjR4/EjlVsWHr+Q3x8PJqHhGDs2LEw92oL+05RkFvaix2LiIioQCQSKVTebaHu8iWu3H4AZxdX7Ny5U+xYxYKl51/8/PPPf17Z8mQ01B2mwLpeBCSyD7+eEBERkbZSlqoO+/CFyHGojtatW2PYsGHIzMwUO1aRYul5h5ycHEybNg0NGjRAqtIO6ohFMKnoIXYsIiKiQiUzNoftJ5/BulEffPX1Yvj6+ePBgwdixyoyLD3/8Pz5czRuEoyJkybBwqcT7DpOg9zCVuxYRERERUIikcDSszXUobPw6/0ncHFzw48//ih2rCLB0vM3R48eRS1nF5w+fwnqjlNhFRgKiVQmdiwiIqIipyxRFeqIBRBKOqN9+/aIjIxEenq62LEKFUsP/pzOmjRpEoKCgvDarCTU4YtgUt5N7FhERETFSqo0g22rsbBpMhDfLP8W3j6+uHv3rtixCo3Bl56nT5+iQcNGmDZtOiz9u8KuwxTIzK3FjkVERCQKiUQCC/fmUHebg9t/xMPN3R2bNm0SO1ahMOjSc/DgQTi7uOJszK9Qd54OK/8unM4iIiICYORQEfZh84GyHujSpQv69u2LN2/eiB3roxhk6cnOzsa4ceMQHByMdFU5qCMWwriss9ixiIiItIpUaQrbFqNgEzwI332/Bl51vHHr1i2xYxWYwZWeP/74A3Xr1ceXs2bDql4E7NpPhsxUJXYsIiIirSSRSGDh1hTqsLm49zwJ7rU9sG7dOrFjFYhBlZ69e/fC2cUVF3+9DXWXGVD5dIBEYlBDQEREVCBG9uVh320eZBW9ERYWhh49euDVq1dix8oXg3jHz8rKwujRoxESEoIs28p/TmeVdhI7FhERkU6RGpnANmQEbJsPw9oNm+Dh6YVff/1V7FgfTO9Lz8OHD+EfEIh58+fDukFP2LadCJmJpdixiIiIdJa5cxDUYfPwMOE1PDy9sGrVKgiCIHas/6TXpWfHjh1wdnHF1du/Q93lS1jWaQuJRCJ2LCIiIp1nZFcW9t3mQlE1ED179kS3sDCkpaWJHetf6WXpyczMxNChQ/HJJ59A4+gE+4iFUJaqLnYsIiIivSJVGMO22RDYthiJH7b+BLfaHrh69arYsd5L70rP/fv34ePrh68XL4F1o76w/WQ8ZMbmYsciIiLSW+Y1G8AhbD6epGTD06sOli9frpXTXXpVerZu3QoXNzfc+P0p1KGzYenZitNZRERExUBhWxr2obOhdGqIfv36oXOXLkhJSRE7Vh56UXrS09MxcOBAdOjQASjlCnX4AihLVBE7FhERkUGRKpSwDY6EXasx+GnHLri618bly5fFjpVL50vPnTt3UMfHF9+sWAmbJgNh22oMpEozsWMREREZLLMagVCHL8DzNxJ4e/tg8eLFWjHdpdOlZ+vWrXBzd8ftP+Lh0G0OLNybczqLiIhICyisS8K+62wYuwRj0KBBaNe+PZKSkkTNJBG0oXrlU0pKClSqP786wsypHmyaREKqNBU5FREREb3L61tnkHhgERzsbPDk8SMkJyfD0rL4r5knL/Y9FiLroH6wqN2CR3eIiIi0mGk1P0gUSjzZMlnUHDo9vTVt9EAYyXX6KRAREem9tOtH8HLHDFSsVFnUHDrdGLr5lMePA/xQ1oZTW0RERNpGk5mOl3sX4OWe+ejauSNOnfxZ1Dw6XXoAwKW0FXYPCUBzZ0exoxAREdH/ZMY/RPy6kci+ewbff/891qxeDTMzcc+u1vnSAwCWxgos7lobUz+pxekuIiIiEQmCgNQrBxG3bgTK25vj0sULiIiIEDsWAD0pPQAgkUgQ5lMO2wb6oYIdr9NDRERU3DQZr/Fyz1wk7F+E7mHdcPH8OdSoUUPsWLn0pvT8pWZJFXYNDkBrt5JiRyEiIjIYmXH3Eb9uBITfz2P9+vX49ttvYWqqXZ+51bvSAwDmSjkWdHLDzLbOUHK6i4iIqMgIgoDUy3vxfO0oVC5hg8uXLqFr165ix3onvW0EEokEneuUxY5B/qhkz+kuIiKiwqbJeI2Xu2Yh4eAS9O3dE+fPnUXVqlXFjvVeelt6/lLd0RK7BgegXe3SYkchIiLSGxmxdxG3ZhjwRwx++OEHLF26FMbGxmLH+ld6X3oAwNRIjrkdXTGngytMFDKx4xAREeksQRCQcnEX4taPRo1yjrhy+TI6duwodqwPYhCl5y/tPUpj5yB/VHUwFzsKERGRztGkp+HljhlIPPwNIgf0xy/RZ1CpUiWxY30wgyo9AFDFwQI7IgPQ2auM2FGIiIh0RsbTW4hbMwzSZ79i27ZtWLRoEZRKpdix8sXgSg8AmBjJMLOdCxZ2doOZEae7iIiI3kcQBKSc24bnGz6Fc+WyuHolBp988onYsQrEIEvPX1q7lcKuwQGoUaL4v96eiIhI2+W8ScWLbVOReGwlhg8ditOnTqJ8+fJixyqwfJWe8uXLQyKRvHWLjIwEAKSnpyMyMhK2trYwNzdHu3bt8Pz58zyP8ejRI4SEhMDU1BRqtRqjR49GdnZ24T2jfKpob45tA/0Q6l1WtAxERETaJv2Pm4hfPRSK+DvYtWsX5s6dCyMjI7FjfZR8lZ7z58/j2bNnubdDhw4BADp06AAAGD58OHbt2oUtW7bgxIkTePr0Kdq2bZt7/5ycHISEhCAzMxNnzpzB6tWr8f3332PSpEmF+JTyz1ghw/Q2zvi6qzvMlXJRsxAREYlJEDRIPrsVcRvHwt2pMq5dvYIWLVqIHatQSARBEAp652HDhmH37t24c+cOUlJSYG9vjw0bNqB9+/YAgN9++w01atRAdHQ0fHx8sG/fPrRo0QJPnz6Fg4MDAGDZsmUYM2YM4uPjP7hBpqSkQKVSITk5GZaWhTs19fDlKwzacBnXniQX6uMSERFpu5zXyUjYOx+v713AmDFjMHXqVCgUikJ7/KJ8//4QBf5MT2ZmJtatW4eePXtCIpHg4sWLyMrKQlBQUO421atXR9myZREdHQ0AiI6OhrOzc27hAYDg4GCkpKTg119/fe++MjIykJKSkudWVMrZmmHrAF909ytfZPsgIiLSNumPryNu9VAoEx9g3759mDlzZqEWHm1Q4NKzfft2JCUloXv37gCA2NhYGBkZwcrKKs92Dg4OiI2Nzd3m74Xnr/V/rXufGTNmQKVS5d7KlCna082Vchk+b1UTy7p5wNKY011ERKS/BE0Oks5sQtzG8fByqYFrV6+gadOmYscqEgUuPStXrkSzZs1QsmTRf5v5uHHjkJycnHt7/Phxke8TAJrWcsSeIYFwLWNVLPsjIiIqTjmvEvFi62SknFqPzz4bj+PHjqJUqVJixyoyBTqM8fDhQxw+fBg//fRT7jJHR0dkZmYiKSkpz9Ge58+fw9HRMXebc+fO5Xmsv87u+mubd1EqlaJdAKmMjSm29PPF7AO/YcXJB6JkICIiKmxvHl5B0p65sDSWY8fBg3k+nqKvCnSkZ9WqVVCr1QgJCcld5uHhAYVCgSNHjuQuu3XrFh49egRfX18AgK+vL65du4a4uLjcbQ4dOgRLS0s4OTkV9DkUOSO5FJ+FOOHbcE9YmerX/CYRERkWQZODpJPrEffDBPh5uuHa1SsGUXiAApQejUaDVatWISIiAnL5/x8oUqlU6NWrF0aMGIFjx47h4sWL6NGjB3x9feHj4wMAaNKkCZycnBAWFoYrV67gwIEDmDBhAiIjI3XiUtZBTg7YMyQQHuWsxY5CRESUb9mpL/Fi80Sk/PIDvpgyBYcPHfzXmRZ9k+/prcOHD+PRo0fo2bPnW+vmz58PqVSKdu3aISMjA8HBwViyZEnueplMht27d2PAgAHw9fWFmZkZIiIi8MUXX3zcsyhGpaxMsKmvD+YevI1lJ+6JHYeIiOiDvHlwCYl758HazBh7jx5FvXr1xI5U7D7qOj1iEfs8/78cvxWHEZuvIOFVpmgZiIiI/o2gyUHSqfVI+WULgoIaY/26tVCr1aJkEfv926C/e+tj1a+mxt4hgahTwUbsKERERG/JTnmB+E3jkXbuR8yIisKB/ftEKzzagKXnIzmqjLGhtzcGN6wMiUTsNERERH96c+884tYMhSonCSeOH8fYsWMhlRr2275hP/tCIpdJMbJJNazt6Q07c+3/QDYREekvIScbice+Q9zWKQiq54/rV68gICBA7FhagaWnEAVUscPeoQHwq2QrdhQiIjJA2clxiN84Fq8u7cScOXOwe9cu2NryPekvLD2FTG1hjLW9vDE8qCqknO4iIqJi8vrOL4hbMxQ20tc4dfIkRo4cafDTWf/E0SgCMqkEQ4OqYH1vH6gtON1FRERFR8jJQsLh5Yj/aRqaNW6Ia1dicq+PR3mx9BQh30q22Ds0EIFV7MSOQkREeigrKRbxGz7Fmyv7sHDhQmzftg3W1ryA7vuw9BQxO3MlVveog9HB1SDjfBcRERWSV7dOI271UNgbZSM6+gyGDBkCCU8j/lcsPcVAKpUgskFlbOrrA0dLY7HjEBGRDhOyM5FwaClebJ+BViFNcTXmMjw9PcWOpRNYeoqRV3kb7B0aiIbVDffCUEREVHBZCU8Qv+FTpF8/jCVLlmDrli1QqVRix9IZLD3FzMbMCN+Ge2J88+qQc7qLiIg+0KsbJxC3djgcTYFzZ3/BgAEDOJ2VTyw9IpBKJehbtxI29/dFKSsTseMQEZEW02Rl4OX+r/Fi12y0+6QVrly+BDc3N7Fj6SSWHhHVLmuNPUMC0NjJQewoRESkhbJePkb8+lHI/O04VqxYgY0bNsDCwkLsWDqLpUdkVqZGWB7mgUktnKCQ8TAlERH9Ke36UTxfOwKlVUa4cP4cevfuzemsj8TSowUkEgl6BlTA1v5+KGPD6S4iIkOmyUzHy70L8HLPPHTp2B6XL16As7Oz2LH0AkuPFnEtY4U9QwLR3NlR7ChERCSCzPiHiF83Etl3z2DVqlVYu2YNzM3NxY6lN1h6tIylsQKLu9bG1NY1YSTjPw8RkSEQBAFpVw8hbu0IlLMzw8UL59G9e3exY+kdvqtqIYlEgjDf8vhpoB/K25qKHYeIiIqQJvMNXu6Zh5f7FqJ7eDdcunAeTk5OYsfSSyw9WqxWKRV2DwlEK9eSYkchIqIikBn3APFrh0P4/RzWr1+Pb7/9Fqam/M9uUWHp0XLmSjkWdnbDjLbOUMr5z0VEpA8EQUBqzD48XzsSlUrY4PKlS+jatavYsfQe30V1gEQiQZc6ZbE90h8V7c3EjkNERB9Bk/EaL3fNRsKBxejTqwcunDuLqlWrih3LILD06JAaJSyxa1AA2rqXEjsKEREVQEbsXcSvHQ48voQffvgBy5Ytg7Exv4i6uLD06BgzpRzzOrlhdnsXGCv4z0dEpAsEQUDKxV2IWz8a1cqocSUmBh07dhQ7lsHhu6aO6uBZBrsGBaCqA6/fQESkzTTpaXi5YyYSD3+Dgf374ewv0ahUqZLYsQwSS48Oq+JggR2RAejkWUbsKERE9A4Zz24jbs1wSJ5dw48//oivvvoKSqVS7FgGi6VHx5kYyfBlexcs6OQGUyOZ2HGIiAj/m846vwNx6z9FrUqlcTUmBm3bthU7lsFj6dETn7iXwq7BAajuyG/fJSISU86bVLzcNg2JR1dg6JDBOHP6FCpUqCB2LAJLj16pZG+O7ZH+CPUuK3YUIiKDlPHkJuLXDIUs7hZ27tyJefPmwcjISOxY9D8sPXrGWCHD9DbO+KqLO8yVcrHjEBEZBEHQIPnsVjzfMBZu1Svh2tUraNmypdix6B9YevRUS9eS2D04ALVKWYodhYhIr+W8TsaLH79A0vHvMXrUSJz8+QTKluURd23E0qPHytuZ4ccBfujuV17sKEREein98XXErRkKZeID7Nu3D19++SUUCoXYseg9WHr0nFIuw+etamJZt9qwMOZ0FxFRYRAEDZLP/IC4jePh5VwD165eQdOmTcWORf+BpcdANK1VAnuHBMK1tErsKEREOi3nVSJebJmM5FPrMH78OBw/dhSlSvHrgXQBS48BKWNjii39/dArgKdOEhEVRPrDq4hbPRQmqX/g4MGDmDZtGuRyHkXXFSw9BsZILsXEFk5YEe4JlQnnnYmIPoSgyUHSqQ2I+2ECfD1cce3qFQQFBYkdi/KJpcdANXZywN6hgahd1krsKEREWi07LQEvNk9CSvQmfP75ZBw5fAglSpQQOxYVAEuPAStlZYIf+vmiX72KYkchItJKbx5cRvzqoTBPf46jR45g0qRJkMn4lT+6iqXHwClkUoxrVgOrenjBxoxXDSUiAv6czkr8eS3itkxCXR9PXLt6BfXr1xc7Fn0klh4CADSopsbeIYGoU95G7ChERKLKTnmBFz98hrRzWxE1fToOHNgPtVotdiwqBCw9lMtRZYwNfbwxuGFlSCRipyEiKn5v7l1A3JqhsMxKwInjxzFu3DhIpXyr1Bf8l6Q85DIpRjaphjU968DOnNNdRGQYhJxsJB5fhbitnyOonj+uXb2CgIAAsWNRIct36Xny5Am6desGW1tbmJiYwNnZGRcuXMhdn5aWhkGDBqF06dIwMTGBk5MTli1blucx0tPTERkZCVtbW5ibm6Ndu3Z4/vz5xz8bKjSBVeyxd0ggfCvaih2FiKhIZafEIX7TOLy6uAOzZ8/G7l27YGdnJ3YsKgL5Kj2JiYnw9/eHQqHAvn37cOPGDcydOxfW1ta524wYMQL79+/HunXrcPPmTQwbNgyDBg3Czp07c7cZPnw4du3ahS1btuDEiRN4+vQp2rZtW3jPigqF2tIY63p7Y1hQFU53EZFeen3nLOJWD4UN0nDq5EmMGjWK01l6TCIIgvChG48dOxanT5/GyZMn37tNrVq10KlTJ0ycODF3mYeHB5o1a4Zp06YhOTkZ9vb22LBhA9q3bw8A+O2331CjRg1ER0fDx8fnP3OkpKRApVIhOTkZlpb8FvHicObeCwzdFIP41AyxoxARfTQhJwuJx79H6oUdaNmqFb5ftQo2NjyRo6iJ/f6drzq7c+dOeHp6okOHDlCr1XB3d8eKFSvybOPn54edO3fiyZMnEAQBx44dw+3bt9GkSRMAwMWLF5GVlZXnSpbVq1dH2bJlER0d/c79ZmRkICUlJc+NipdfJTvsGxqIwCo85EtEui0rKRbxG8bgTcxeLFiwADu2b2fhMRD5Kj3379/H0qVLUaVKFRw4cAADBgzAkCFDsHr16txtvvrqKzg5OaF06dIwMjJC06ZNsXjxYtStWxcAEBsbCyMjI1hZWeV5bAcHB8TGxr5zvzNmzIBKpcq9lSlTJp9PkwqDnbkSq3vUwejgapByuouIdNDrW2cQt2YY7BSZOHPmNIYOHQoJ5+8NRr6+JU2j0cDT0xNRUVEAAHd3d1y/fh3Lli1DREQEgD9Lzy+//IKdO3eiXLly+PnnnxEZGYmSJUsW+HtKxo0bhxEjRuT+nJKSwuIjEqlUgsgGleFV3gZDNl5GbEq62JGIiP6TkJ2FxGMrkXppN9q0bYvvVq586z/fpP/yVXpKlCgBJyenPMtq1KiBH3/8EQDw5s0bjB8/Htu2bUNISAgAwMXFBTExMZgzZw6CgoLg6OiIzMxMJCUl5fmFe/78ORwdHd+5X6VSCaVSmZ+oVMTqVLDB3qGBGLk5BsduxYsdh4jovbISnyJx1yxkv3iExYsXY8CAATy6Y6DyNb3l7++PW7du5Vl2+/ZtlCtXDgCQlZWFrKystz75LpPJoNFoAPz5oWaFQoEjR47krr916xYePXoEX1/fAj0JEoeNmRFWRnhhXLPqkHO+i4i00KubPyNuzTA4mAg4e/YXDBw4kIXHgOXrSM/w4cPh5+eHqKgodOzYEefOncPy5cuxfPlyAIClpSXq1auH0aNHw8TEBOXKlcOJEyewZs0azJs3DwCgUqnQq1cvjBgxAjY2NrC0tMTgwYPh6+v7QWdukXaRSiXoV68SPP833fUk6Y3YkYiIoMnKQOLRFUiL2Y+OnTphxfLlPNuX8nfKOgDs3r0b48aNw507d1ChQgWMGDECffr0yV0fGxuLcePG4eDBg0hISEC5cuXQt29fDB8+PLddp6enY+TIkdi4cSMyMjIQHByMJUuWvHd665/EPuWN3i3pdSZGbbmKwzd5oUkiEk/Wyz+QsGsWNElP8fVXi9C7d28e3dESYr9/57v0aAOxB43eTxAEfHf6d8zcdxNZOTr3q0VEOi7t12NIOrQE5cuWwU9bt8DFxUXsSPQ3Yr9/87KTVKgkEgl6BVTA1v5+KGNjInYcIjIQmqx0vNy3EC93z0XnDu0Qc+kiCw+9haWHioRrGSvsHhyIZrU+bMqSiKigMl88Qvy6kci6fQqrVq3CurVrYW5uLnYs0kIsPVRkVCYKLAmtjS9a14SRjL9qRFS4BEFA2tVDiFszHOVszXDp4gV0795d7FikxfhOREVKIpEg3Lc8fhroh3K2pmLHISI9ocl8g4Q98/By30KEd+uKSxfOv3UdOaJ/YumhYlGrlAq7BweghUsJsaMQkY7LjHuA+HUjkPPgHNauXYvvvvsOpqb8TxX9N5YeKjYWxgp81cUdUW2coZTzV4+I8kcQBKTG7EfculGo5GCFy5cuolu3bmLHIh3Cdx4qVhKJBF29y2J7pD8q2puJHYeIdIQm4zVe7pqNhANfo1ePCJw/dxbVqlUTOxbpGJYeEkWNEpbYNSgAbd1LiR2FiLRc5vN7iFs7HHh8CZs2bcI333wDExNeEoPyj6WHRGOmlGNuR1fMau8CYwV/FYkoL0EQkHppD56vG4XqZdSIuXwZnTp1EjsW6TC+05CoJBIJOnqWwc5BAaii5nU1iOhPmoxXeLlzJhIOLcXA/v1w9pdoVK5cWexYpONYekgrVHWwwM5BAejoWVrsKEQksoxntxG3ehgkT6/hxx9/xFdffQWlUil2LNIDLD2kNUyMZJjV3hXzOrrC1EgmdhwiKmaCICDlwg7Erf8UNSuWwtWYGLRt21bsWKRHWHpI67StXRo7BwWguqOF2FGIqJjkvEnFy+3TkXhkBYYMHoToM6dRoUIFsWORnmHpIa1UWW2O7ZH+6OpdVuwoRFTEMp78hvg1QyF7/ht27NiB+fPnw8jISOxYpIdYekhrGStkiGrjjEVd3GGulIsdh4gKmSBokHz2R8RtHAPXahVx7eoVtGrVSuxYpMdYekjrtXItiV2DA1CzpKXYUYiokOS8TsaLn6Yi6fgqjBwxAqdO/oyyZXlkl4oWSw/phAp2ZvhxgB8ifMuJHYWIPlL6H78ibs1QGL28hz179mDWrFlQKBRixyIDwNJDOsNYIcOU1rWwNLQ2LIw53UWkawRBg+TozYjbOB6etarj2tUraN68udixyICw9JDOaeZcAnsGB8K1tErsKET0gXJeJeHF1s+RfHItxo0dgxPHj6F0aV6Xi4oXSw/ppLK2ptjS3w89/XlKK5G2S390FXFrhsIk5TEOHDiA6dOnQy7n0Voqfiw9pLOM5FJMaumEFeGeUJnw8wBE2kbQ5CDp9EbEbZoAH3dnXLt6BY0bNxY7Fhkwlh7SeY2dHLBnSADcy1qJHYWI/icnLREvtkxCyukNmDx5Eo4eOYwSJUqIHYsMHEsP6YXS1qbY3M8X/epVFDsKkcF783sM4lYPgdnrWBw5cgSTJ0+GTMavliHxsfSQ3lDIpBjXrAZWdfeCtSmnu4iKm6DJQdLPaxG3eSICvT1w/dpVNGjQQOxYRLlYekjvNKiuxt6hgfAqby12FCKDkZ36Ai9++AypZ7dg+rRpOHjwANRqtdixiPJg6SG9VEJlgo19fBDZoBIkErHTEOm3N/cvIm71UFhkJeD48eMYP348pFK+vZD24W8l6S25TIrRwdWxukcd2JrxywuJCpuQk43E498jbstkNAz0xfWrVxAYGCh2LKL3YukhvVe3qj32DQ2ET0UbsaMQ6Y3slDjEbxqPtAvbMGvWLOzdswd2dnZixyL6Vyw9ZBDUlsZY39sHQxtV4XQX0Ud6ffcs4lYPgzVScerkSYwePZrTWaQT+FtKBkMmlWB446pY38sb9hZKseMQ6RwhJwsJR79F/I9TEdyoHq5diYGvr6/YsYg+GEsPGRy/ynbYOyQQgVV4KJ7oQ2UlxSJ+41i8ubwH8+fPx84dO2Bjwylj0i0sPWSQ7C2UWN2jDkY1qQopp7uI/tXr22cQt2YY7OQZOHPmNIYNGwYJ54lJB7H0kMGSSiUY1LAKNvbxgYMlp7uI/knIzkLC4W8Qvy0KLZs1wdWYy/Dy8hI7FlGBsfSQwfOuaIu9QwJRv5q92FGItEZW4jPEb/gU6VcP4Ouvv8aPW7fCyspK7FhEH4WlhwiArbkS30V4YWyz6pBxvosM3KubJxG3ZijUxjn45ZdoREZGcjqL9AJLD9H/SKUS9K9XCZv7+aCkyljsOETFTsjOxMsDi/Fi55do06oFrsZcRu3atcWORVRoWHqI/sGjnA32Dg1EUA1+bxAZjqyEJ4hfPwoZN47im2++wQ+bNsHS0lLsWESFiqWH6B2sTI2wItwTE0JqQCHjYX3Sb2m/HsPzNcNQ0kKOC+fPoW/fvpzOIr3E0kP0HhKJBL0DK2JLfz+UtjYROw5RodNkpePlvkV4uXsuOrVvi5hLF+Hi4iJ2LKIiw9JD9B/cylhhz5BANK3pKHYUokKT+eIR4teNRNbtk/juu++wbu1amJubix2LqEix9BB9AJWJAku71caUVjVhJOOfDem2tGuHEbd2BMrZmOLihfPo0aMHp7PIIPDVm+gDSSQSRPiVx08D/VDO1lTsOET5psl8g5d75uHl3gUI69oZFy+cR82aNcWORVRs8l16njx5gm7dusHW1hYmJiZwdnbGhQsX8mxz8+ZNtGrVCiqVCmZmZvDy8sKjR49y16enpyMyMhK2trYwNzdHu3bt8Pz5849/NkTFoFYpFXYPDkALlxJiRyH6YJnxvyN+3Qjk3D+LNWvWYNWqVTAzMxM7FlGxylfpSUxMhL+/PxQKBfbt24cbN25g7ty5sLa2zt3m3r17CAgIQPXq1XH8+HFcvXoVEydOhLHx/1/3ZPjw4di1axe2bNmCEydO4OnTp2jbtm3hPSuiImZhrMBXXdwxvU0tGMl5wJS0lyAISL1yAHFrR6KiWoVLFy8gLCxM7FhEopAIgiB86MZjx47F6dOncfLkyfdu07lzZygUCqxdu/ad65OTk2Fvb48NGzagffv2AIDffvsNNWrUQHR0NHx8fP4zR0pKClQqFZKTk3kdCRLdjacpGLThEu6/eCV2FKI8NBmvkXBwMV7dOIG+fftiwYIFMDHhmYgkHrHfv/P1X9SdO3fC09MTHTp0gFqthru7O1asWJG7XqPRYM+ePahatSqCg4OhVqvh7e2N7du3525z8eJFZGVlISgoKHdZ9erVUbZsWURHR79zvxkZGUhJSclzI9IWTiUtsWtwANq4lxI7ClGuzOf3Eb92OIRHF7Fx40Z88803LDxk8PJVeu7fv4+lS5eiSpUqOHDgAAYMGIAhQ4Zg9erVAIC4uDikpaVh5syZaNq0KQ4ePIg2bdqgbdu2OHHiBAAgNjYWRkZGb31xnYODA2JjY9+53xkzZkClUuXeypQpU4CnSlR0zJRyzOvoilntXGCs4HQXiUcQBKRe3ovn60ahaml7xFy6hM6dO4sdi0gryPOzsUajgaenJ6KiogAA7u7uuH79OpYtW4aIiAhoNBoAQOvWrTF8+HAAgJubG86cOYNly5ahXr16BQo5btw4jBgxIvfnlJQUFh/SOhKJBB29ysCtrBUGrr+Eu3FpYkciA6PJeIWE/V/h1W+nMHDgQMydOzfP5ymJDF2+/ktaokQJODk55VlWo0aN3DOz7OzsIJfL/3UbR0dHZGZmIikpKc82z58/h6Pjuy/+plQqYWlpmedGpK2qOlhg5yB/dPAoLXYUMiAZz+4gbs0wSJ5cxdatW7F48WIWHqJ/yFfp8ff3x61bt/Isu337NsqVKwcAMDIygpeX179u4+HhAYVCgSNHjuSuv3XrFh49egRfX98CPQkibWNqJMfsDq6Y19EVJgqZ2HFIjwmCgJQLOxG3fjRqViiFKzGX0a5dO7FjEWmlfE1vDR8+HH5+foiKikLHjh1x7tw5LF++HMuXL8/dZvTo0ejUqRPq1q2LBg0aYP/+/di1axeOHz8OAFCpVOjVqxdGjBgBGxsbWFpaYvDgwfD19f2gM7eIdEnb2qXhUtoKgzZcwm+xqWLHIT2Tk56GxH0L8ep2NIYOHYpZs2bByMhI7FhEWitfp6wDwO7duzFu3DjcuXMHFSpUwIgRI9CnT58823z33XeYMWMG/vjjD1SrVg1TpkxB69atc9enp6dj5MiR2LhxIzIyMhAcHIwlS5a8d3rrn8Q+5Y0ov9KzcjBl1w1sPPfovzcm+gAZT28hcfcsGOWkY+2a1XleY4m0ldjv3/kuPdpA7EEjKqidV55i3I9X8SozR+wopKMEQYPU89uR/PNqeHh4YsvmH3I/PkCk7cR+/+a5tUTFqJVrSeweEginEizrlH85b1Lw4qepSDz2HUaOGIHTp06y8BDlA0sPUTGrYGeGnwb6IdyXb1b04dL/uIH41UNh9PIe9uzZg1mzZkGhUIgdi0insPQQicBYIcMXrWthSWhtWCjzdT4BGRhB0CD5ly2I2zgOtWtWxbWrV9C8eXOxYxHpJJYeIhE1dy6BPUMC4VJaJXYU0kI5r5LwYusUJP+8BmPHfIqfTxxH6dK8/hNRQbH0EImsrK0ptvT3RU//CmJHIS2S/uga4tYMhXHyQ+zfvx9RUVGQy3lUkOhjsPQQaQGlXIZJLZ2wPMwDlsZ8YzNkgiYHSac3Im7TZ/B2q4Xr166iSZMmYsci0gssPURapElNR+wdGgj3slZiRyER5KQl4sWWyUg5vQETJ07AsaNHUKJECbFjEekNlh4iLVPa2hSb+/mib92KYkehYvTm9xjErRkCs9fPcPjwYUyZMgUyGb/ChKgwsfQQaSGFTIrxzWvgu+6esDblacn6TNDkIOnkOsRtnogAr9q4dvUKGjZsKHYsIr3E0kOkxRpWd8DeoYHwKm8tdhQqAtmpL/Fi8wSk/rIZ06ZOxaFDB+Hg4CB2LCK9xdJDpOVKqEywsY8PBtavJHYUKkRv7l9E3OohsMh4gWPHjuGzzz6DVMqXZKKixL8wIh0gl0nxadPqWN2zDmzN+C3aukzQ5CDxxPeI2zIZDQN9ce3qFdStW1fsWEQGgaWHSIfUq2qPvUMD4VPRRuwoVADZKfF4sWkc0s5vw5dffom9e/bA3t5e7FhEBoOlh0jHOFgaY31vHwxpVAUSidhp6EO9vnsOcauHwkqTgpM//4xPP/2U01lExYx/cUQ6SCaVYETjqljfyxt25kqx49C/EHKykHh0JeJ//AJNGtTFtatX4OfnJ3YsIoPE0kOkw/wq22Hf0EAEVLYTOwq9Q3byc8RvHIfXl3dj3rx52LVrJ2xsODVJJBaWHiIdZ2+hxOqedTCycVVIOd2lNV7fjkbc6qGwlb3B6dOnMHz4cEg4H0kkKpYeIj0gk0owuFEVbOjjAwdLTneJScjOQsLhbxC/bTpCmjbGtSsxqFOnjtixiAgsPUR6xaeiLfYOCUS9qjwjSAxZic8Qv+FTpF89gK+++go//fgjrKysxI5FRP/D0kOkZ2zNlVjV3QtjmlaHjPNdxebVb6cQt2YY1MY5iI4+g0GDBnE6i0jLsPQQ6SGpVIIB9Sthcz8flFQZix1HrwnZmXh5cAle7JiJT1o2x9WYy/Dw8BA7FhG9A0sPkR7zKGeDPUMCEVRDLXYUvZSV8ATx60cj49cjWLZsGTb/8AMsLS3FjkVE78HSQ6TnrM2MsCLcExNCakDO6a5C8+rGCcStGYYS5lKcP3cW/fr143QWkZZj6SEyABKJBL0DK2JLf1+UsjIRO45O02Sl4+X+RXixazY6tm+LK5cvwdXVVexYRPQBWHqIDIh7WWvsHRKI4JoOYkfRSVkvHiN+3Shk3TqJlStXYt3atTA3Nxc7FhF9IJYeIgOjMlVgWTcPfN7SCUYyvgR8qLRrR/B87XCUtTbGxQvn0bNnT05nEekYvuIRGSCJRILu/hXw4wA/lLUxFTuOVtNkpuPFnvl4uXc+wrp2xqWLF1CzZk2xYxFRAbD0EBkw59Iq7B4SgBCXEmJH0UqZ8b8jft0I5NyLxpo1a7Bq1SqYmZmJHYuICoilh8jAWRor8HUXd0z7pBaM5HxJAABBEJB65SDi1o5EBbUlLl+6iLCwMLFjEdFH4iscEUEikaCbTzlsG+iHCnaGfSRDk/EaL/fMRcL+RegREYaL58+hevXqYsciokLA0kNEuWqWVGHX4AC0dispdhRRZMbdR/y6ERB+P48NGzZgxYoVMDHhKf5E+oKlh4jyMFfKsaCTG75s5wxjhWG8RAiCgNTLe/F87ShUKWmLmMuX0aVLF7FjEVEhM4xXNCLKF4lEgk5eZbEjMgCV1fp9HRpNxiu83DULCQeXoF+fXjh39hdUqVJF7FhEVARYeojovao5WmDnIH+09ygtdpQikRF7F3FrhgN/xGDLli1YsmQJjI35Ba1E+oqlh4j+lamRHHM6uGJuB1eYKGRixykUgiAg5eIuxK0fDafyJXA1Jgbt27cXOxYRFTGWHiL6IO08SmPXYH9Uc7AQO8pHyUlPw8vtUUg8/A0GRw5E9JnTqFixotixiKgYsPQQ0QerrLbAjkH+6FKnjNhRCiTj6S3ErxkGaewNbN++HQsWLIBSqRQ7FhEVE5YeIsoXY4UMM9q6YGFnN5gZ6cZ0lyAISDm3DXEbPoVLlXK4dvUKWrduLXYsIipmLD1EVCCt3Uph1+AA1ChhKXaUf5XzJgUvf5qKxGMrMXzYMJw+dRLlypUTOxYRiYClh4gKrKK9ObYN9EOYj3aWiPQ/biJ+zTAoXt7F7t27MWfOHCgUCrFjEZFIWHqI6KMYK2SY+kktLO5aGxZKudhxAACCoEHyL1sRt3Es3GtUxrWrVxASEiJ2LCISWb5Lz5MnT9CtWzfY2trCxMQEzs7OuHDhwju37d+/PyQSCRYsWJBneUJCAkJDQ2FpaQkrKyv06tULaWlpBXoCRKQdQlxKYPeQADiXUomaI+d1Ml5snYKkE99jzKejcfLnEyhdWj+vM0RE+ZOv0pOYmAh/f38oFArs27cPN27cwNy5c2Ftbf3Wttu2bcMvv/yCkiXf/g6f0NBQ/Prrrzh06BB2796Nn3/+GX379i34syAirVDO1gxbB/iih395Ufaf/vg64lYPhXHyQ+zfvx8zZsyAXK4dR5+ISHz5ejX48ssvUaZMGaxatSp3WYUKFd7a7smTJxg8eDAOHDjw1iHlmzdvYv/+/Th//jw8PT0BAF999RWaN2+OOXPmvLMkEZHuUMplmNyyJnwq2mL0litISc8u8n0Kmhwk/7IFKac2ICAwEJs2buBrCRG9JV9Henbu3AlPT0906NABarUa7u7uWLFiRZ5tNBoNwsLCMHr0aNSsWfOtx4iOjoaVlVVu4QGAoKAgSKVSnD179p37zcjIQEpKSp4bEWm34JqO2DMkEG5lrIp0PzmvEvFi62SknFqPCRM+w7GjR1h4iOid8lV67t+/j6VLl6JKlSo4cOAABgwYgCFDhmD16tW523z55ZeQy+UYMmTIOx8jNjYWarU6zzK5XA4bGxvExsa+8z4zZsyASqXKvZUpo5sXRiMyNGVsTLG5ny/6BL59RLgwvHl4BXGrh8A07SkOHz6ML774AjKZblw7iIiKX76mtzQaDTw9PREVFQUAcHd3x/Xr17Fs2TJERETg4sWLWLhwIS5dugSJRFJoIceNG4cRI0bk/pySksLiQ6QjjORSfBbiBJ+Kthi55QqSXmd99GMKmhwkn96E5OhNaNCgITZuWA8HB4dCSEtE+ixfR3pKlCgBJyenPMtq1KiBR48eAQBOnjyJuLg4lC1bFnK5HHK5HA8fPsTIkSNRvnx5AICjoyPi4uLyPEZ2djYSEhLg6Oj4zv0qlUpYWlrmuRGRbmlUwwF7hwTCs9zbJz7kR3bqS7zYPBGpv/yAqV98gUMHD7DwENEHydeRHn9/f9y6dSvPstu3b+de3TQsLAxBQUF51gcHByMsLAw9evQAAPj6+iIpKQkXL16Eh4cHAODo0aPQaDTw9vYu8BMhIu1X0soEG/v6YP6h21hy/F6+7//m/kUk7ZsPa3MT7D16FPXq1SuClESkr/JVeoYPHw4/Pz9ERUWhY8eOOHfuHJYvX47ly5cDAGxtbWFra5vnPgqFAo6OjqhWrRqAP48MNW3aFH369MGyZcuQlZWFQYMGoXPnzvzwIZEBUMik+LRpdXhXtMXwH2KQ8CrzP+8jaHKQdHIdUn7ZgibBwVi3di3s7e2LIS0R6ZN8TW95eXlh27Zt2LhxI2rVqoWpU6diwYIFCA0NzddO169fj+rVq6NRo0Zo3rw5AgICcosTERmGelXtsW9oILwr2PzrdtkpLxC/aTzSzv+EmTNnYt/evSw8RFQgEkEQBLFD5FdKSgpUKhWSk5P5+R4iHZedo8GiI3fw1bG7+Oer0et755G0bwHsVObYsvkH+Pv7ixOSiAqF2O/f/O4tIhKVXCbFiCbVsLanN+zMlQAAIScbice+Q/zWKWhcLwDXrl5h4SGij6aT12f/6+AUL1JIpD9cHIzwQw8XjN54HvvmjUZm3H1Mnz4dkZGRkEgk/Hsn0gN//R2LNcmkk9Nb9+/fR6VKlcSOQURERAXw+PFjUb4IWCeP9NjY/PnBx0ePHkGlEvcbnbXJXxdtfPz4MT/r9A8cm3fjuLwbx+XdOC7vxnF5t3eNiyAISE1NFe1sbZ0sPVLpnx9FUqlU/AV7B17A8f04Nu/GcXk3jsu7cVzejePybv8cFzEPVvCDzERERGQQWHqIiIjIIOhk6VEqlZg8eTKUSqXYUbQKx+X9ODbvxnF5N47Lu3Fc3o3j8m7aOC46efYWERERUX7p5JEeIiIiovxi6SEiIiKDwNJDREREBoGlh4iIiAxCkZWeJ0+eoFu3brC1tYWJiQmcnZ1x4cKF3PWCIGDSpEkoUaIETExMEBQUhDt37uR5jISEBISGhsLS0hJWVlbo1asX0tLS8mxz9epVBAYGwtjYGGXKlMGsWbPeyrJlyxZUr14dxsbGcHZ2xt69e/Os/5AshUVbxmXFihUIDAyEtbU1rK2tERQUhHPnzuXZxhDH5e82bdoEiUSCTz75JM9yQx2XpKQkREZGokSJElAqlahatepbf0uLFy9G+fLlYWxsDG9v77d+pwqLNo3LggULUK1aNZiYmKBMmTIYPnw40tPT82xTXOMCFM/YpKeno3v37nB2doZcLn/rb+Qvx48fR+3ataFUKlG5cmV8//33b22jT78zHzIuP/30Exo3bgx7e3tYWlrC19cXBw4ceGs7QxuXvzt9+jTkcjnc3NzeWvfR4yIUgYSEBKFcuXJC9+7dhbNnzwr3798XDhw4INy9ezd3m5kzZwoqlUrYvn27cOXKFaFVq1ZChQoVhDdv3uRu07RpU8HV1VX45ZdfhJMnTwqVK1cWunTpkrs+OTlZcHBwEEJDQ4Xr168LGzduFExMTIRvvvkmd5vTp08LMplMmDVrlnDjxg1hwoQJgkKhEK5du5avLPo2Ll27dhUWL14sXL58Wbh586bQvXt3QaVSCX/88YdBj8tfHjx4IJQqVUoIDAwUWrdunWedIY5LRkaG4OnpKTRv3lw4deqU8ODBA+H48eNCTExM7jabNm0SjIyMhO+++0749ddfhT59+ghWVlbC8+fP9XZc1q9fLyiVSmH9+vXCgwcPhAMHDgglSpQQhg8fXuzjUpxjk5aWJvTv319Yvny5EBwc/NbfiCAIwv379wVTU1NhxIgRwo0bN4SvvvpKkMlkwv79+4t9bLRpXIYOHSp8+eWXwrlz54Tbt28L48aNExQKhXDp0iWDHpe/JCYmChUrVhSaNGkiuLq65llXGONSJKVnzJgxQkBAwHvXazQawdHRUZg9e3busqSkJEGpVAobN24UBEEQbty4IQAQzp8/n7vNvn37BIlEIjx58kQQBEFYsmSJYG1tLWRkZOTZd7Vq1XJ/7tixoxASEpJn/97e3kK/fv0+OEth0aZx+afs7GzBwsJCWL169QdnKSzaNi7Z2dmCn5+f8O233woRERF5/kANdVyWLl0qVKxYUcjMzHxvnjp16giRkZG5P+fk5AglS5YUZsyYkY9n/d+0aVwiIyOFhg0b5tn/iBEjBH9//9yfi2tc/spXHGPzd//8G/nLp59+KtSsWTPPsk6dOgnBwcG5P+vb78zfvW9c3sXJyUmYMmVK7s+GPC6dOnUSJkyYIEyePPmt0lMY41Ik01s7d+6Ep6cnOnToALVaDXd3d6xYsSJ3/YMHDxAbG4ugoKDcZSqVCt7e3oiOjgYAREdHw8rKCp6enrnbBAUFQSqV4uzZs7nb1K1bF0ZGRrnbBAcH49atW0hMTMzd5u/7+Wubv/bzIVkKizaNyz+9fv0aWVlZuV/masjj8sUXX0CtVqNXr15vZTXUcdm5cyd8fX0RGRkJBwcH1KpVC1FRUcjJyQEAZGZm4uLFi3mySKVSBAUF6fW4+Pn54eLFi7mH2O/fv4+9e/eiefPmxT4uQPGNzYf4r9deffydKQiNRoPU1NTc115DHpdVq1bh/v37mDx58lvrCmtciqT03L9/H0uXLkWVKlVw4MABDBgwAEOGDMHq1asBALGxsQAABweHPPdzcHDIXRcbGwu1Wp1nvVwuh42NTZ5t3vUYf9/H+7b5+/r/ylJYtGlc/mnMmDEoWbJk7i+UoY7LqVOnsHLlyjx/+H9nqONy//59bN26FTk5Odi7dy8mTpyIuXPnYtq0aQCAFy9eICcnx+DGpWvXrvjiiy8QEBAAhUKBSpUqoX79+hg/fjyA4h0XoPjG5kO8b/xSUlLw5s0bvfydKYg5c+YgLS0NHTt2BKCff0sf4s6dOxg7dizWrVsHufzt70IvrHEpkm9Z12g08PT0RFRUFADA3d0d169fx7JlyxAREVEUu9QJ2jouM2fOxKZNm3D8+HEYGxsX+/61ZVxSU1MRFhaGFStWwM7Ortj2+z7aMi5/ZVGr1Vi+fDlkMhk8PDzw5MkTzJ49+53/KyvqLNoyLsePH0dUVBSWLFkCb29v3L17F0OHDsXUqVMxceLEYs0CaNfYaBNtHZcNGzZgypQp2LFjx1vFoThoy7jk5OSga9eumDJlCqpWrVqk+yqSIz0lSpSAk5NTnmU1atTAo0ePAACOjo4AgOfPn+fZ5vnz57nrHB0dERcXl2d9dnY2EhIS8mzzrsf4+z7et83f1/9XlsKiTePylzlz5mDmzJk4ePAgXFxccpcb4rjcu3cPv//+O1q2bAm5XA65XI41a9Zg586dkMvluHfvnkGOy19ZqlatCplMlidLbGwsMjMzYWdnB5lMZnDjMnHiRISFhaF3795wdnZGmzZtEBUVhRkzZkCj0RTruADFNzYf4n3jZ2lpCRMTE738ncmPTZs2oXfv3ti8eXOeKRtDHJfU1FRcuHABgwYNyn3t/eKLL3DlyhXI5XIcPXq00MalSEqPv78/bt26lWfZ7du3Ua5cOQBAhQoV4OjoiCNHjuSuT0lJwdmzZ+Hr6wsA8PX1RVJSEi5evJi7zdGjR6HRaODt7Z27zc8//4ysrKzcbQ4dOoRq1arB2to6d5u/7+evbf7az4dkKSzaNC4AMGvWLEydOhX79+/PMx/7oVkKi7aMS/Xq1XHt2jXExMTk3lq1aoUGDRogJiYGZcqUMchx+SvL3bt3odFo8mQpUaIEjIyMYGRkBA8PjzxZNBoNjhw5otfj8vr1a0ileV9G/yqGgiAU67gAxTc2H+K/Xnv18XfmQ23cuBE9evTAxo0bERISkmedIY6LpaXlW6+9/fv3R7Vq1RATEwNvb+/CG5cP/shzPpw7d06Qy+XC9OnThTt37gjr168XTE1NhXXr1uVuM3PmTMHKykrYsWOHcPXqVaF169bvPA3O3d1dOHv2rHDq1CmhSpUqeU6DS0pKEhwcHISwsDDh+vXrwqZNmwRTU9O3TlmXy+XCnDlzhJs3bwqTJ09+5ynr/5VF38Zl5syZgpGRkbB161bh2bNnubfU1FSDHpd/eteZBoY4Lo8ePRIsLCyEQYMGCbdu3RJ2794tqNVqYdq0abnbbNq0SVAqlcL3338v3LhxQ+jbt69gZWUlxMbG6u24TJ48WbCwsBA2btwo3L9/Xzh48KBQqVIloWPHjsU+LsU5NoIgCL/++qtw+fJloWXLlkL9+vWFy5cvC5cvX85d/9cp66NHjxZu3rwpLF68+J2nrOvT78yHjMv69esFuVwuLF68OM9rb1JSkkGPyz+96+ytwhiXIik9giAIu3btEmrVqiUolUqhevXqwvLly/Os12g0wsSJEwUHBwdBqVQKjRo1Em7dupVnm5cvXwpdunQRzM3NBUtLS6FHjx553pQFQRCuXLkiBAQECEqlUihVqpQwc+bMt7Js3rxZqFq1qmBkZCTUrFlT2LNnT76zFBZtGZdy5coJAN66TZ48OV9ZCou2jMs/vav0GOq4nDlzRvD29haUSqVQsWJFYfr06UJ2dnaebb766iuhbNmygpGRkVCnTh3hl19+KaSRyEtbxiUrK0v4/PPPhUqVKgnGxsZCmTJlhIEDBwqJiYl5tiuucRGE4hub972G/N2xY8cENzc3wcjISKhYsaKwatWqt/Lq2+/Mf41LvXr13rk+IiIiz+MY2rj807tKjyB8/LhIBEEQPvy4EBEREZFu4ndvERERkUFg6SEiIiKDwNJDREREBoGlh4iIiAwCSw8REREZBJYeIiIiMggsPURERGQQWHqIiIjIILD0EBERkUFg6SEiIiKDwNJDREREBoGlh4iIiAzC/wFFrMDRfVMWuQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -327,8 +325,8 @@
    "metadata": {},
    "source": [
     "### write the shapefile  \n",
-    "* the projection (.prj) file can be written using an epsg code  \n",
-    "* or copied from an existing .prj file"
+    "* if a coordinate reference is supplied via the ``crs`` argument, a projection (.prj) file will be written \n",
+    "* alternatively, an existing ESRI-style projection file can be specified with ``prjfile``"
    ]
   },
   {
@@ -342,12 +340,20 @@
      "shell.execute_reply": "2023-02-22T02:44:55.012542Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrote ../../../../../../../Users/aleaf/Documents/GitHub/flopy/examples/Notebooks\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "\n",
     "recarray2shp(\n",
-    "    chk.summary_array, geoms, os.path.join(workspace, \"test.shp\"), epsg=26715\n",
+    "    chk.summary_array, geoms, os.path.join(workspace, \"test.shp\"), crs=26715\n",
     ")\n",
     "shape_path = os.path.join(workspace, \"test.prj\")\n",
     "\n",
@@ -374,7 +380,15 @@
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrote ../../../../../../../Users/aleaf/Documents/GitHub/flopy/examples/Notebooks\n"
+     ]
+    }
+   ],
    "source": [
     "if not skip:\n",
     "    shutil.copy(shape_path, os.path.join(workspace, \"26715.prj\"))\n",
@@ -382,7 +396,7 @@
     "        chk.summary_array,\n",
     "        geoms,\n",
     "        os.path.join(workspace, \"test.shp\"),\n",
-    "        prj=os.path.join(workspace, \"26715.prj\"),\n",
+    "        prjfile=os.path.join(workspace, \"26715.prj\"),\n",
     "    )"
    ]
   },
@@ -432,7 +446,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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tx8Cmy0zoGJqKjkXvwMD+Qyh8V0JW2QsDBgzAyJEjkZGRIToWkVYpVOmpUqUKZDLZa9vYsWPzx/z222/o0qUL5HI5zM3N0aBBAzx8+DD//qysLIwfPx62trYwNTVFly5d8Pjx4+J7RUTl1I4dO+Dm4YHbjxNgP2gZzD06cDmrjNExNIFNp6mwbjcBmwKD4OlVF7///rvoWERao1ClJywsDM+ePcvfTp06BQDo3bs3AODu3bto3LgxPv74Y5w7dw7R0dGYM2cOjIyM8vfh7++Pffv2YceOHbh48SLS09PRqVMn5OXlFePLIio/MjIy8Omnn6J///6QfeAJO9+VMLD/UHQsKiKZTAZzt7ZQDFqO+wkv4FHHE4GBgaJjEWkFmSRJUlEf7O/vj8OHD+POnTuQyWTo168f9PX1ERQU9MbxKpUKdnZ2CAoKQt++fQEAT58+hZOTE44ePQofH593et60tDTI5XKoVCpYWPCL16j8unXrFnr26o3fb92GvNWnMHNty6M75Yg6OxMpIT8iPTYEfn5D8MMP38PUlMuVVH6J/vwu8jk92dnZCA4OxrBhwyCTyaBWq3HkyBHUqFEDPj4+UCgUqF+/foGvYw8PD0dOTg7atm2bf5ujoyOcnZ0RGhr61ufKyspCWlpagY2ovAsODoZHnTq4+zwVCt/lMHfzYeEpZ3QMjGDTwR82HSchePsO1PGqixs3boiORVRuFbn07N+/H6mpqRgyZAgAQKlUIj09HYsXL0a7du1w8uRJdO/eHT169MD58+cBAPHx8TAwMICVlVWBfdnb2yM+Pv6tz7Vo0SLI5fL8zcnJqaixiTTeq1evMHToUPj6+kL3wwawG7QCBnZVRMeiEmTm3Ar2vivxMDkDnl51sXHjRrzHQXgieosil56NGzeiffv2cHR0BACo1WoAQNeuXTFp0iS4u7tj5syZ6NSpE3788cd/3ZckSf/6L9hZs2ZBpVLlb48ePSpqbCKNduPGDdTx9ELQth3/dwRgMnQMjEXHolKgb+sEu0HLoV+zKUaMGIGBgwbhxYsXomMRlStFKj0PHjxASEgIRowYkX+bra0t9PT0ULt27QJja9WqlX/1loODA7Kzs5GSklJgjFKphL29/Vufz9DQEBYWFgU2ovJEkiRs2rQJnl518SD5FRS+K2Dm0lp0LCplOvqGsGk3Hradp2LXnn1wr+OJ6Oho0bGIyo0ilZ5NmzZBoVCgY8eO+bcZGBigbt26uHXrVoGxt2/fRuXKlQEAnp6e0NfXz7/qCwCePXuGuLg4NGrUqChRiMq89PR0+A4ejGHDhkG/RhPYDVoOA9sPRMcigUxrN4di8Co8Tc9D3Xr18eOPP3K5i6gY6BX2AWq1Gps2bYKfnx/09Ao+fNq0aejbty+aNm2KFi1a4Pjx4zh06BDOnTsHAJDL5Rg+fDimTJkCGxsbWFtbY+rUqXBxcUHr1vxXLWmfmJgY9OjVG38+fASbTlNg9kkL0ZFIQ+hbV4Ri4DIkn9mIzz77DKfPnMHGDRt4pJvoPRT6SE9ISAgePnyIYcOGvXZf9+7d8eOPP2LJkiVwcXHBhg0bsGfPHjRu3Dh/zMqVK9GtWzf06dMH3t7eMDExwaFDh6Crq/t+r4SoDJEkCevWrYNX3Xp48iIX9oNXsfDQa2R6BrBp+xlsu87E/kNH4erugfDwcNGxiMqs9/qeHlFEX+dP9D7S0tIw8tNP8cvOnTBzbw+rliOgo28oOhZpuJyUZ0g5tAS5iQ+wfPkyjBs3jl9hQGWO6M9v/vYWUSmKjIyEm0cd7D1wCLZdZsDGZywLD70TfasKsBuwBEau7TBhwgT06NkTqampomMRlSksPUSlQJIk/PDDD6hfvwGeZ8igGBwA01pNRMeiMkampw/r1p/CrvtsHDl+Ci5u7rh27ZroWERlBksPUQlLTU1Fz169MG7cOBi5+sBuwFLoW1UQHYvKMJMaDaHwC0BSnjEaeXtjxYoVvLqL6B2w9BCVoLCwMLi6e+DQsZOw6/Y5rFuPgkxPX3QsKgf05Paw678Iph6dMWXKFHTu3AXJycmiYxFpNJYeohIgSRJWrVqFRo28kZhrCMXgVTCpye+iouIl09WHVcvhsOv5JU6e/RXOrm7/+juGRNqOpYeomCUnJ6PL//0ci7FHR9j1Xwx9SwfRsagcM/moHhR+AVDpWKBJ06b49ttv838aiIj+P5YeomJ0+fJluLi548Tpc7DrOQfWLUdApsvlLCp5ehZ2sO27EGZ1e2DmzJno0LEjEhISRMci0igsPUTFQK1WY+nSpWjStClSYA6FXwBMPqovOhZpGZmuHqya+UHRex7OXLgMF1c3/Prrr6JjEWkMlh6i95SYmIiOnTph+vTpMPXsBrt+C6FnoRAdi7SY8YeeUPitxgtDW7Ro0QLffPMN8vLyRMciEo7fyEz0Hi5cuIDeffshOe0lrDpMhvGHnqIjEeWT1HlQXdoO1eWdaNGiJbZv2wp7e3vRsUiLif785pEeoiJQq9VYuHAhmrdogRf61lD4BbDwkMaR6ejCsskgKPp8jUthEXBxdcOZM2dExyIShqWHqJCUSiXatvXB7C++gHm9XrDtuwB65raiYxG9lXEVdygGr8ZLkwpo3bo1vvzySy53kVZi6SEqhLNnz8LZxRUXroZD0edrWDb1hUxHV3Qsov+ka2YF297zYOE9AN98swAtWrbC06dPRcciKlUsPUTvIC8vD/PmzUOr1q3x0sQBdn4BMK7iLjoWUaHIdHRh6d0fin4LcDXqBlxc3XDy5EnRsYhKDUsP0X949uwZWrVug3nz5sOiUX/Y9p4PPTNr0bGIiszoAxco/AKQKa8MHx8fzJo1C7m5uaJjEZU4lh6if3Hq1Cm4uLrhckQMFP2+gaV3fy5nUbmgayKHba+5sGzmh2+XLEXTZs3x+PFj0bGIShRLD9Eb5ObmYvbs2fDx8UGGhRMUgwNg9IGr6FhExUom04G8QW8o+i9CxI3bcHF1w9GjR0XHIioxLD1E//D48WM0a94CixZ/C3kTX9j2+gq6ppaiYxGVGKNKtWHnF4Acm4/QsWNHTJs2DTk5OaJjERU7lh6ivzl27BhcXN1wPe53KPovhLxhH8hk/N+Eyj9dYwvY9JgDqxbDsGLlSng3boIHDx6IjkVUrPhuTgQgJycH06dPR4cOHZBtXe2v5axKn4iORVSqZDIZLOr1gKL/t4i5/Sdc3dxx4MAB0bGIig1LD2m9hw8fonGTpli+YgUsmw+Dbc850DWRi45FJIxhxY9h5xeAPPta6NatGyZOnIjs7GzRsYjeG0sPabWDBw/CxdUN0bfuQdH/W8jr9+ByFhEAXSMz2HT7HFatPsX3P6xBg4aNcO/ePdGxiN4L391JK2VnZ2PSpEno2rUr8uw/ht3gABhW/Fh0LCKNIpPJYOHVBYqBS3Hzz6dwdXfH7t27RcciKjKWHtI69+/fRyPvxlj93fewajUSNt1mQ9fYXHQsIo1lWKE6FINXARXd0Lt3b4wZMwaZmZmiYxEVGksPaZW9e/fC1d0dcXcfQzFwCSy8ukImk4mORaTxdAxNYdNlBqzbjsFP6zeifoOGuHPnjuhYRIXC0kNaISsrC+PGjUPPnj0hVXCBYvBKGFaoIToWUZkik8lg7tEB9oOW4fbjBLh7eGDHjh2iYxG9M5YeKvf++OMP1G/QEGt/WgfrNp/BputM6BiZiY5FVGYZ2H8IO9+VkFX2Qv/+/fHpp58iIyNDdCyi/8TSQ+Xazp074e7hgd8fKWE/aBnM63TkchZRMdAxNIFNp6mwbjceP28OhGfdevj9999FxyL6Vyw9VC5lZGRg1KhR6NevH+BUBwrflTCwryY6FlG5IpPJYO7mA4XvctxXqlDH0xNBQUGiYxG9FUsPlTu3bt1C3Xr1sXHTFlj7jINN52nQMTQRHYuo3DKwqwK7QSuh+2FDDB48GEOHDsXLly9FxyJ6DUsPlSvBwcHwqOOJu/GpUAxaBnP3dlzOIioFOgZGsOk4CTYdJiFo2w54etXFjRs3RMciKoClh8qFV69eYdiwYfD19YVu1Xqw810BA0VV0bGItI6ZSysofFfgQXIGPL3q4ueff4YkSaJjEQFg6aFy4ObNm6jjVReBwdtg08EfNp2mQMfAWHQsIq1lYPsB7AYtg37NJhg+fDgGDhqE9PR00bGIWHqobNu8eTPqeHrhQdJLKAavhJlLa9GRiAiAjr4RbNpNgG3nqdi1Zz/cPOogOjpadCzSciw9VCalp6fD9/9OmNSv0Rh2g5bDwPYD0bGI6B9MazeHYvBKPE3PQ9169fHTTz9xuYuEYemhMic2NhYenl7Y/stu2HSaApv2E6GjbyQ6FhG9hb51RSgGLoPhJ60wevRo9O3XD2lpaaJjkRZi6aEyQ5IkrF+/Hl516+GxKgf2vith9kkL0bGI6B3I9Axg03YMbLvMwL6Dh+Hq7oGIiAjRsUjLsPRQmZCWloZ+//d19wa1msNu4FLo21QSHYuICsm0VhMoBgdAmamLBg0a4vvvv+dyF5Ualh7SeJGRkXDzqIO9Bw7Btst02PiMg46+oehYRFRE+lYVYDdgCYxcfTB+/Hj06NkTqampomORFmDpIY0lSRLWrFmD+vUb4HmGDIrBq2Baq6noWERUDGR6+rBuPQp23T/HkeOn4OrmjmvXromOReUcSw9pJJVKhV69e2Ps2LEwcvWB3YCl0LdyFB2LiIqZSY1GUPgFIDHPCN7ejbFy5Uoud1GJYekhjRMWFgYXN3ccPHoCdt0+h3XrUZDp6YuORUQlRE9uD7v+i2Hi0QmTJ09Gly5dkZycLDoWlUOFKj1VqlSBTCZ7bRs7duxrY0eNGgWZTIZVq1YVuD0rKwvjx4+Hra0tTE1N0aVLFzx+/Pi9XgSVD5IkISAgAI0aeSMxxwCKwatgUrOR6FhEVApkuvqwajkcdj3n4MSZ83BxdUNoaKjoWFTOFKr0hIWF4dmzZ/nbqVOnAAC9e/cuMG7//v24evUqHB1fX47w9/fHvn37sGPHDly8eBHp6eno1KkT8vLy3uNlUFmXnJyMrt26wd/fH8buHWA34FvoWzqIjkVEpczko/pQ+AUgRWaBJk2bYsmSJVCr1aJjUTlRqNJjZ2cHBweH/O3w4cOoVq0amjVrlj/myZMnGDduHLZu3Qp9/YJLEiqVChs3bsTy5cvRunVreHh4IDg4GLGxsQgJCSmeV0RlzpUrV+Dq5o7jIWdh12MOrFuNhEyXy1lE2krPwg52/RbCrG53zJgxAx06dkRCQoLoWFQOFPmcnuzsbAQHB2PYsGGQyWQAALVaDV9fX0ybNg2ffPLJa48JDw9HTk4O2rZtm3+bo6MjnJ2d//UwZlZWFtLS0gpsVPap1WosW7YMjZs0QTLMoPALgEn1+qJjEZEGkOnqwarZECh6z8OZC5fh4uqGCxcuiI5FZVyRS8/+/fuRmpqKIUOG5N/27bffQk9PDxMmTHjjY+Lj42FgYAArK6sCt9vb2yM+Pv6tz7Vo0SLI5fL8zcnJqaixSUMkJiaiU+fOmDZtGkw9u8Ku3yLoWShExyIiDWP8oScUfgF4YWCD5s2bY8GCBVzuoiIrcunZuHEj2rdvn3/eTnh4OAICArB58+b8Iz/vSpKkf33MrFmzoFKp8rdHjx4VNTZpgIsXL8LZ1Q0h5y9B0WsurJoPhUxXT3QsItJQeua2sO27AOb1e+OLOXPQtq0Pnj9/LjoWlUFFKj0PHjxASEgIRowYkX/bhQsXoFQq8cEHH0BPTw96enp48OABpkyZgipVqgAAHBwckJ2djZSUlAL7UyqVsLe3f+vzGRoawsLCosBGZY9arcaiRYvQrHlzpOlbQzE4AMbV6oqORURlgExHF5ZNfaHo8zUuXAuHi6sbzpw5IzoWlTFFKj2bNm2CQqFAx44d82/z9fVFTEwMoqKi8jdHR0dMmzYNJ06cAAB4enpCX18//6ovAHj27Bni4uLQqBEvTS7PlEolfHza4fPZs2FWrxfs+i6AnoWt6FhEVMYYV3GHYvBqvDRxQOvWrTF37lxe/UvvrNBrCmq1Gps2bYKfnx/09P7/w21sbGBjY1NgrL6+PhwcHFCzZk0AgFwux/DhwzFlyhTY2NjA2toaU6dOhYuLC1q3bv2eL4U01blz59C3X3+kvsyCovd8GFf1EB2JiMowXTMr2PaeD9XlX/D119/g3PlfsWP7NlSoUEF0NNJwhT7SExISgocPH2LYsGFFesKVK1eiW7du6NOnD7y9vWFiYoJDhw5BV1e3SPsjzZWXl4f58+ejZatWSDeyh51fAAsPERULmY4uLL37Q9HvG1yJjIWziytOnjwpOhZpOJlUBn/kJC0tDXK5HCqViuf3aKj4+Hj06z8A58+fg7xRf8gb9YVMh8WWiIpf3stUJB9dgYz7kZg5cybmz59fYCWCNIfoz2/+9hYVu5CQEDi7uOJyeDTs+y6AZeMBLDxEVGJ0TS1h2+sryJsOxuJvl6BZ8xb8eSN6I5YeKja5ubn44osv0LZtW2SYV4LCbzWMKruKjkVEWkAm04G8QW8o+i9EeNwtuLi64ejRo6JjkYZh6aFi8eTJEzRv0RILFy6CvIkvbHvPg66ppehYRKRljCp9ArvBq5BtUw0dO3bEtGnTkJOTIzoWaQiWHnpvx48fh4urG8Jif4Oi/0LIG/aBTMa/WkQkhq6JHLY95sCy+TCsWLkSjZs0xcOHD0XHIg3ATyYqspycHMyYMQPt27dHllVVKAYHwMjJWXQsIqK/lrvq94Ci/7eIvn0fLq5uOHjwoOhYJBhLDxXJw4cP0aRpMyxdthyWzYfCtueX0DWRi45FRFSAYcWPYTc4AHn2H6Nr167w9/dHdna26FgkCEsPFdqhQ4fg6uaOqN/vwn7AYsjr9+RyFhFpLF0jM9h0mw2rViPx3fc/oGEjb9y/f190LBKAn1T0zrKzszF58mR06dIFuXY1YDc4AIYVa4mORUT0n2QyGSy8ukIxcAlu3HsCV3d37NmzR3QsKmUsPfRO7t+/j0bejRGw+jtYtRwJm+5fQNfYXHQsIqJCMaxQAwq/VZAcXdCrVy+MHTsWmZmZomNRKWHpof+0b98+uLq7I+7uYygGLoFF3a6QyWSiYxERFYmOoSlsusyEddsx+HHdBtRv0BB//PGH6FhUClh66K2ysrIwfvx49OjRA1IFFygGr4RhhRqiYxERvTeZTAZzjw6wH7QMtx8nwM3DAzt27BAdi0oYSw+90d27d9GgYSOs+fEnWLcZDZuuM6FjZCY6FhFRsTKw/xB2vish+8AT/fv3x6effoqMjAzRsaiEsPTQa3755Re4ubvjtwfxUAxcCvM6nbicRUTllo6hCWw6TYW1zzj8vDkQdevVx61bt0THohLA0kP5MjIyMHr0aPTt2xdwqgPF4FUwdPhIdCwiohInk8lg7t4OCt/luPs8FR51PBEcHCw6FhUzlh4CANy6dQt169XH+o2bYO0zDjadp0HH0ER0LCKiUmVgVwV2g1ZA98P68PX1xdChQ/Hq1SvRsaiYsPQQtm7dCo86nrgbnwp73+Uwd2/H5Swi0lo6Bsaw6TgZNh38EbRtB+p4euHGjRuiY1ExYOnRYq9evcLw4cMxaNAg6FStBzvfFTBQVBUdi4hII5i5tIbCdwUeJL+Cp1ddbNq0CZIkiY5F74GlR0vdvHkTdbzqYkvwVti0nwibjpOhY2AsOhYRkUYxsP0AdoOWQ79GEwwbNgyDfH2Rnp4uOhYVEUuPFtq8eTM8veriQeJLKAatgJlrGy5nERG9hY6+EWzaT4BNpynYuXsv3Ot4IiYmRnQsKgKWHi2Snp4O38GDMXToUOhV94ad73IY2FUWHYuIqEww+6QF7H1X4klaLrzq1sO6deu43FXGsPRoidjYWNTx9ML2X3b/dYJe+4nQ0TcSHYuIqEzRt6kEu4FLYVi7JUaNGoV+/fsjLS1NdCx6Ryw95ZwkSdiwYQO86tbDI1U27H1XwMy5pehYRERllo6+IWx8xsK2ywzsPXAIbh51EBkZKToWvQOWnnLsxYsX6D9gAEaOHAmDWs1hN3AZ9G2cRMciIioXTGs1gWLwKjzPkKF+/Qb44YcfuNyl4Vh6yqnIyEi4untgz/6DsO08DTY+46Cjbyg6FhFRuaJv5Qi7AUth5OqDcePGoWevXkhNTRUdi96CpaeckSQJa9asQf0GDfE8QwaF70qY1m4mOhYRUbkl09OHdetRsOv2OQ4dOwlXdw+EhYWJjkVvwNJTjqhUKvTq3Rtjx46FkXNr2A1YAn3riqJjERFpBZOajaAYvAqJOQZo1Mgbq1at4nKXhmHpKSeuX78OV3cPHDx6ArbdZsG6zWeQ6RmIjkVEpFX0LR1gN+BbGHt0xKRJk9Cla1ckJyeLjkX/h6WnjJMkCQEBAWjYsBESsvWhGLwKpjW9RcciItJaMl19WLccAbuec3Di9Dm4uLnj8uXLomMRWHrKtJSUFHTr3h3+/v4wdu8AuwHfQt/SQXQsIiICYPJRfSj8ApACMzRp2hRLly6FWq0WHUursfSUUVevXoWLmzuOnToDux5zYN1qJGS6+qJjERHR3+hZKGDXbxFMPbti+vTp6NipExITE0XH0losPWWMWq3G8uXL4d24MZLVJlAMDoBJ9fqiYxER0VvIdPVg1XwoFL2+wulfQ+Hs6oYLFy6IjqWVWHrKkKSkJHTq3BlTp06FqWdX2PVfDD25QnQsIiJ6B8bVvKDwC0CavjWat2iBhQsXcrmrlLH0lBGXLl2Cs6sbQs5fgqLXXFg1HwqZrp7oWEREVAh65raw67sA5vV6YfYXX6BtWx8olUrRsbQGS4+GU6vVWLRoEZo2awaVriUUgwNgXK2u6FhERFREMh1dWDb1haL3fFy4Gg5nF1ecPXtWdCytwNKjwZRKJXzatcfns2fDrF5P2PVbCD0LW9GxiIioGBhX9YCdXwBeGjugVevWmDdvHvLy8kTHKtdkUhn8usi0tDTI5XKoVCpYWFiIjlMizp8/jz59+yHlZSasOkyGcdU6oiMREVEJkNR5UF3+BWmXtqNps2bYvm0rKlSoIDpWiRD9+c0jPRomLy8P8+fPR4uWLZFupIDCbzULDxFROSbT0YWld38o+n2Dy+HRcHF1w6lTp0THKpdYejRIfHw8Wrdpi7lffQWLhn1h2+dr6JlZi45FRESlwOgDVyj8ViPDwgk+Pj6YPXs2cnNzRccqV1h6NMTp06fh4uqG0OtRUPT9BpaNB0Kmoys6FhERlSJdU0vY9voK8ia+WLRoMZo1b4HHjx+LjlVusPQIlpubizlz5qBNmzZ4ZeYIhV8AjCu7iY5FRESCyGQ6kDfsA0X/hbge9ztcXN1w7Ngx0bHKBZYegZ4+fYrmLVpiwYKFkDceBNve86FraiU6FhERaQAjJ2coBgcg27oaOnTogOnTpyMnJ0d0rDKtUKWnSpUqkMlkr21jx45FTk4OZsyYARcXF5iamsLR0RGDBw/G06dPC+wjKysL48ePh62tLUxNTdGlSxetPHR3/PhxOLu4IizmNyj6L4S8UV/IZOygRET0/+mayGHbcw4smw/D8hUr0LhJUzx8+FB0rDKrUJ+yYWFhePbsWf72v7PLe/fujVevXiEiIgJz5sxBREQE9u7di9u3b6NLly4F9uHv7499+/Zhx44duHjxItLT09GpUyet+W6CnJwczJw5E+3bt0eWVVUo/AJg5OQsOhYREWkomUwH8vo9oOi/GNG37sHF1Q0HDx4UHatMeq/v6fH398fhw4dx584dyGSy1+4PCwtDvXr18ODBA3zwwQdQqVSws7NDUFAQ+vbtC+CvJR4nJyccPXoUPj4+7/S8oq/zL6pHjx6hd5++uHbtGuRNfWFRrweP7hAR0TvLy3iBlGOr8PLOVfj7++Pbb7+FgYGB6FjvTPTnd5E/cbOzsxEcHIxhw4a9sfAAgEqlgkwmg6WlJQAgPDwcOTk5aNu2bf4YR0dHODs7IzQ09K3PlZWVhbS0tAJbWXP48GG4uLoh6re7UPRfDHn9Xiw8RERUKLrG5rDp/gWsWo3E6u++RyPvxrh//77oWGVGkT919+/fj9TUVAwZMuSN92dmZmLmzJkYMGBAfpuLj4+HgYEBrKwKnqxrb2+P+Pj4tz7XokWLIJfL8zcnJ6eixi512dnZmDx5Mjp37owcu+qw8wuAUaVaomMREVEZJZPJYOHVFYqBSxB39zFc3d2xd+9e0bHKhCKXno0bN6J9+/ZwdHR87b6cnBz069cParUaa9as+c99SZL01qNFADBr1iyoVKr87dGjR0WNXar+/PNPeDdugoDV38Gq5QjYdp8DXWNz0bGIiKgcMKxQA4rBKyFVcEHPnj0xbtw4ZGVliY6l0YpUeh48eICQkBCMGDHitftycnLQp08f3L9/H6dOnSqwZufg4IDs7GykpKQUeIxSqYS9vf1bn8/Q0BAWFhYFNk23f/9+uLq5I/aPh1AM+BYWdbv9a7EjIiIqLB0jM9h0nQnrNqOx9qd1qN+gIf744w/RsTRWkUrPpk2boFAo0LFjxwK3/6/w3LlzByEhIbCxsSlwv6enJ/T19Qv8psizZ88QFxeHRo0aFSWKxsnKysKECRPQvXt3qCt8AsXgVTB0rCk6FhERlVMymQzmdTrBftAy3HqkhLuHB3bu3Ck6lkbSK+wD1Go1Nm3aBD8/P+jp/f+H5+bmolevXoiIiMDhw4eRl5eXf56OtbU1DAwMIJfLMXz4cEyZMgU2NjawtrbG1KlT4eLigtatWxffqxLk7t276NW7D2JiY2HVehTM63Ti0R0iIioVBvbVYOe7Esknvke/fv1w5swZrFq1CsbGxqKjaYxCX7J+8uRJ+Pj44NatW6hRo0b+7X/++SeqVq36xsecPXsWzZs3B/DXCc7Tpk3Dtm3bkJGRgVatWmHNmjWFOjlZ9CVvb7Jr1y4MHT4cufrmsOo8HYYOH4mOREREWkiSJKRHn4DqzHp8XLMG9uzehZo1NWPFQfTn93t9T48ooift7zIzMzFp0iT8+OOPMK3VFNY+46BjaCI0ExERUbbyPlIOLYHsVTLW/fQjBg0aJDqS8M9vflHMe7h9+zbq1quPdRt+hrXPWNh0nsbCQ0REGsFAURV2viugW7UefH19MWzYMLx69Up0LKFYeopo27Zt8KhTB388S4a97zKYu7fn+TtERKRRdAyMYd1xMmzaT0Rg8DbU8aqLmzdvio4lDEtPIb169QojRozAwIEDIatSD3a+K2Gg+FB0LCIiojeSyWQwc20DxeCVeJD4EnU8vbBp0yaUwbNb3htLTyH89ttv8KxbD5uDgmHTfgJsOk6GjgHPiiciIs1nYPsB7HyXQ79GYwwbNgy+gwcjPT1ddKxSxdLzjrZs2YI6nl74M+EFFINWwMy1LZeziIioTNHRN4JN+4mw6TQFO3btgYenF2JiYkTHKjUsPf/h5cuX8PMbgiFDhkDvo0awG7QCBnaVRcciIiIqMrNPWsDedwUeq3L+uiBn3TqtWO5i6fkXcXFx8Kjjia07dsKm42TYdPCHjoGR6FhERETvTd/GCXYDl8KgVnOMGjUK/fr3R1pamuhYJYql5w0kScKGDRvg6VUXj1TZsB+8EmbOLUXHIiIiKlY6+oaw8RkH287TsPfAIbh51EFkZKToWCWGpecfXrx4gQEDB2LkyJEw+Lg57AYug77Nu39bNBERUVljWrsZFINX4XmGDPXrN8CaNWvK5XIXS8/fREVFwb2OJ3bv3Q/bztNg024cdPQNRcciIiIqcfpWjrAbsARGLm0xduxY9OrdGyqVSnSsYsXSg7+Ws9auXYt69RvgaboaisGrYFq7mehYREREpUqmZwDrNqNh220WDh49ARc3d4SFhYmOVWy0vvSoVCr07tMHY8aMgZFzaygGLoW+dUXRsYiIiIQxrekNxeBVSMwxQKNG3li1alW5WO7S6tJz/fp1uLp74MDhY7DtOhPWbT6DTM9AdCwiIiLh9C0dYDfgWxi7d8CkSZPQtVs3JCcni471XrSy9EiShNWrV6Nhw0ZIyNaDwi8Aph83Fh2LiIhIo8h09WHdaiTseszB8ZCzcHVzx5UrV0THKjKtKz0pKSno3qMHJk6cCGP3DrDrvwT6lg6iYxEREWksk+r1oRgcgGSYoXGTJli6dCnUarXoWIWmVaXn6tWrcHVzx9ETIbDr8QWsW42ETE9fdCwiIiKNpydXwK7fIph6dsX06dPRqXNnJCYmio5VKFpReiRJwvLly+HduDGS1CZQ+K2GSfUGomMRERGVKTJdPVg1HwpFr7kIOX8JLq5uuHjxouhY76zcl56kpCR06tQZU6dOhWmdLrDrvxh6coXoWERERGWWcbW6UAwOgErPCs2aN8eiRYvKxHJXuS49ly5dgoubO06dvwi7XnNh1WIYZLp6omMRERGVeXoWtrDrtxBm9Xrh89mz4ePTDkqlUnSsf1UuS49arcbixYvRtFkzpOrIoRgcAJNqdUXHIiIiKldkOrqwauoLRe/5OH8lDC6ubjh37pzoWG9V7kpPQkIC2nfogFmzZsGsbg/Y9VsIPQtb0bGIiIjKLeOqHlD4rUa6kT1atmqFefPmIS8vT3Ss18ikMvgVi2lpaZDL5VCpVLCwsMi//fz58+jbrz+S0zNg1X4SjD/0FJiSiIhIu0jqPKhCd0IVuh3NmjXHju3b4ODw/78W5m2f36WlXBzpycvLw9dff42WLVvihaEdFIMDWHiIiIhKmUxHF5aNB8C+7wJcDo+Gs4srQkJCRMfKV+ZLT3x8PFq3aYsv586FeYO+sO3zNfTMbUTHIiIi0lpGlV2h8AtAhnkltG3bFrNnz0Zubq7oWGW79Jw7dw4urm4IvR4JRd9vYNlkIGQ6uqJjERERaT1dUyvY9p4HeRNfLFq0GM1btMTTp0+FZirT5/QAgElVd1h3nAJdUyvBqYiIiOhNMh/FIeXIMpjo5CE1JYXn9BSVbY+5LDxEREQazMjJGTY95iJVlSY0R5kuPTIdHSTsnI3ctATRUYiIiOgtMu6FI/GXL2BnK/YrZMp06Tl54gSspDQot0zEqz+uiY5DREREfyOp85ByfjOUu+aiReMGuHI5VGieMl166tWrh9iYaPi0aoaEPfORfGYDpLwc0bGIiIi0Xm5aAhJ2zEJ62D4sXrwYx44ehS2P9Lwfa2trHDxwACtWrEBG5BEkbJ+JXNVz0bGIiIi01qs/rkG5ZSIs1Wm48OuvmDFjBnR0xFcO8QmKgUwmw6RJk3Dp0kXY6mb+tdx1W+whNCIiIm0j5eUg+cwGJOyZj7YtmiI2OgqNGjUSHStfuSg9/1OvXj3EREehY7s2SNi3EMkhP0HK5XIXERFRSctVPUfC9ll4FXkYK1aswKFDB2Fjo1lfFlyuSg8AWFpaYu+ePfjuu++QGXMCCdumIyflmehYRERE5dar25eh3DIRNroZCL10CZMmTYJMJhMd6zXlrvQAfy13jRs3DleuXIbCKA/KQH+8/P2i6FhERETlipSbg+SQn5CwbwE6+LRGbHQU6tWrJzrWW5XL0vM/derUQUxUJLp36YjEA4uRdHINpNxs0bGIiIjKvJyUZ0jYNh2ZMSfw3XffYd/evbC0tBQd61+V69IDABYWFti5Ywd+/PFHZN04jYStU5GT/ER0LCIiojLr5e8XoQz0h8IoD5cvh2LcuHEauZz1T+W+9AB/LXeNGjUKYdeuwtFMF8rASXh585zoWERERGWKlJuNpJNrkHhgMbp17oDoyAh4enqKjvXOtKL0/I+bmxuiIiPQp1d3JB5ahqRjq6HOyRQdi4iISOPlJD+BcutUZN04jbVr1+KXnTvzf/y7rNCq0gMAZmZmCA4KwsaNG5Fz+wISgqciJ/GR6FhEREQa6+XNc1AG+sPRTBdh165i9OjRZWI565+0rvQAfy13DRs2DOHXw1DZ2hjPgyYhPfa06FhEREQaRZ2TiaRjq5F4aBl69+yOqIhwuLm5iY5VZIUqPVWqVIFMJnttGzt2LABAkiR89dVXcHR0hLGxMZo3b44bN24U2EdWVhbGjx8PW1tbmJqaokuXLnj8+HHxvaJC+OSTTxB+PQy+A/oh6ehKJB5ZAXU2l7uIiIhyEh/9tRpy+wI2bNiArcHBMDc3Fx3rvRSq9ISFheHZs2f526lTpwAAvXv3BgAsWbIEK1aswPfff4+wsDA4ODigTZs2ePHiRf4+/P39sW/fPuzYsQMXL15Eeno6OnXqhLy8vGJ8We/O1NQUmzZtQmBgINT3riAheBKyE/4UkoWIiEgTpMeexvOgSXCyMsL1sGsYPnx4mVzO+ieZJElSUR/s7++Pw4cP486dOwAAR0dH+Pv7Y8aMGQD+Oqpjb2+Pb7/9FqNGjYJKpYKdnR2CgoLQt29fAMDTp0/h5OSEo0ePwsfH552eNy0tDXK5HCqVChYWFkWN/5rff/8dPXv1xq3bdyBv9SnMXNuWi//IRERE70KdnYnkU2vxMu40/PyG4IcfvoepqWmx7b+kPr/fVZHP6cnOzkZwcDCGDRsGmUyG+/fvIz4+Hm3bts0fY2hoiGbNmiE09K8f/wwPD0dOTk6BMY6OjnB2ds4f8yZZWVlIS0srsJWEjz/+GNfDrmHYkMFIPv4dkg4vgzrrVYk8FxERkSbJTvgTCUGTkHf3MrZs2YLNmzcVa+HRBEUuPfv370dqaiqGDBkCAIiPjwcA2NvbFxhnb2+ff198fDwMDAxgZWX11jFvsmjRIsjl8vzNycmpqLH/k7GxMdatW4ft27dDenAdCUGTkP38Xok9HxERkUiSJOFF9Akog6agqsICkRHhGDx4sOhYJaLIpWfjxo1o3749HB0dC9z+z+UgSZL+c4nov8bMmjULKpUqf3v0qOQvMe/Xrx+iIiNRvaItngdPxYvIo3iPlUAiIiKNo856haTDy5B8/DsM9fNF+PUwfPzxx6JjlZgilZ4HDx4gJCQEI0aMyL/NwcEBAF47YqNUKvOP/jg4OCA7OxspKSlvHfMmhoaGsLCwKLCVhurVq+Pa1SsY/ekIJJ9cg6SD30Kd9bJUnpuIiKgkZT+/h4TgyZAeXMe2bduwfv16GBsbi45VoopUejZt2gSFQoGOHTvm31a1alU4ODjkX9EF/HXez/nz59GoUSMAgKenJ/T19QuMefbsGeLi4vLHaBojIyP88MMP2LVrF/AkGspAf2Q9uyM6FhERUZFIkoQXkUfxPHgqqjvaIDIiAv379xcdq1QUuvSo1Wps2rQJfn5+0NPTy79dJpPB398fCxcuxL59+xAXF4chQ4bAxMQEAwYMAADI5XIMHz4cU6ZMwenTpxEZGYlBgwbBxcUFrVu3Lr5XVQJ69eqFmKgo1K7iCOXWaUi7fpDLXUREVKaos14i6dASJJ9cg1Ejh+Pa1SuoUaOG6FilRu+/hxQUEhKChw8fYtiwYa/dN336dGRkZGDMmDFISUlB/fr1cfLkyQJfZrRy5Uro6emhT58+yMjIQKtWrbB582bo6uq+3yspBR9++CGuXA7F9OnTERAQgOxHsbBqPxG6RmaioxEREf2rrPg/kHLoW+jlpOOXX37J/449bfJe39Mjiujr/AHgwIED8B3sh2xdI1h1mg5Dx5pCchAREf0bSZLwIuIwVOd+hpurK3bv+gUffvihkCyiP7+18re3ikPXrl0RGxMN1+pVoNw2HWnX9nK5i4iINEpeZjqS9i9ESshPGDfmM1wOvSSs8GgClp73ULlyZVy6eAGTJ01CytmfkbT3a+RllMwXJxIRERVG1tNbSNgyETrxN7Fv3z4EBATA0NBQdCyhWHrek76+PpYuXYrDhw9DP+kPJGyZiMzHN0XHIiIiLSVJEtKu7cXzbdPhUr0yYmOi0a1bN9GxNAJLTzHp2LEjYmOiUeeTGlBunwXVlV2QJLXoWEREpEXyMtKQtPdrpJz9GZP9/RF66SIqV64sOpbGYOkpRpUqVcKv589h5ozpUP0aiMTd85D3MlV0LCIi0gKZj28iIdAfeol3cPjwYSxbtgz6+vqiY2kUlp5ipqenh4ULF+L48eMwUj2AMnAiMh/Gio5FRETllCSpobqyC8rts+BR6yPExkQX+PJg+v9YekpI27ZtERsTjfruzlDumI3US9shqfNExyIionIk75UKibvnIfX8FsyYPg0Xfj1foj/KXdax9JQgR0dHnD1zGnPmfIG0S9uQuGsu8tJT/vuBRERE/yHzURyUWybCSPUAx48fx6JFiwr8UgK9jqWnhOnq6mLevHkICQmB6atnUAZOQMafUaJjERFRGSWp85AaugPK7Z+jnlttxMZEw8fHR3SsMoGlp5S0bNkSsTHRaFy3DpS/zEHqhWAudxERUaHkpacgcddcpF3cii++mI2zZ07D0dFRdKwyg6WnFNnb2+PkyRP45uuv8eLKL0j85QvkvkgSHYuIiMqAjD+joAycAJOXT3Hq1CnMnz+fy1mFxNJTynR1dTF79mycPXsW5lmJUG6ZgIx74aJjERGRhpLUeUi9sBXKX+agkZcHYmOi0apVK9GxyiSWHkGaNm2K2JhotGjcAMpdc5FyfjOXu4iIqIDcF0lI2PkFXlzZia/nz0fIqZNwcHAQHavMYukRyM7ODseOHsXixYuRHrYPiTtmITctQXQsIiLSABn3wqEMnAiL7EScOXMGX3zxBXR1dUXHKtNYegTT0dHBjBkzcOHXX2GpToNyy0S8+uOa6FhERCSIpM5DyvnNUO6aixbe9REbE41mzZqJjlUusPRoiEaNGiEmOgptWzRFwp75SDmzEVJejuhYRERUinLTEpCwYxbSw/Zh8eLFOHb0KOzs7ETHKjdYejSIjY0NDh06iBUrVuBl5CEkbJ+FXNVz0bGIiKgUvLobBuWWiZDnqfDr+fOYMWMGdHT4MV2cOJsaRiaTYdKkSQi9dAk2uhl/LXfdviw6FhERlRApLxcpZ39Gwu55aNO8CeJiouHt7S06VrnE0qOh6tWrh9joKHTwaY2EfQuQHPITpFwudxERlSe5KiUSts/Ey4iDWL58OQ4fPgQbGxvRscotlh4NZmlpiX179+K7775DZswJJGybjpyUZ6JjERFRMXh1+zKUWybAWucVLl28iMmTJ0Mmk4mOVa6x9Gg4mUyGcePG4fLlUCiM8qAM9MfL3y+KjkVEREUk5eYgOWQdEvYtQPu2rRAbHYX69euLjqUVWHrKCE9PT0RHRqBb5w5IPLAYSSfXQMrNFh2LiIgKISc1HgnbpiMz5jhWr16N/fv2wcrKSnQsrcHSU4bI5XL8snMn1q5di6wbp6HcOhU5yU9ExyIionfw8veLUG6ZCDvDXFy+HIrx48dzOauUsfSUMTKZDKNHj0bYtatwNNP9a7nr5jnRsYiI6C2k3GwknVyLxAOL0bVTe8RERcLT01N0LK3E0lNGubm5ISoiHL17dkfioWVIOr4a6pxM0bGIiOhvcpKfQLl1GrJuhGDt2rXY9csvkMvlomNpLZaeMszc3Bxbg4OxceNG5Ny6gITgqchJfCQ6FhERAXh58zyUgf6oYCrDtatXMHr0aC5nCcbSU8bJZDIMGzYM18Ou4QMrIzwPmoT02NOiYxERaS11ThaSjn+HxENL0atHN0RHRsDd3V10LAJLT7nh7OyMiPDrGNS/H5KOrkTikZVQZ3O5i4ioNOUkPUJC8BRk/34eGzZswLatW2Fubi46Fv0flp5yxNTUFJs3b8KWLVuQd/cyEoInIzvhT9GxiIi0QnrcaTwPnAQnS0OEXw/D8OHDuZylYVh6yqHBgwcjMiIcVe3MoQyaghfRJyFJkuhYRETlkjo7E0lHVyHpyEoM6NcHkRHhcHZ2Fh2L3oClp5z6+OOPEX49DEP9fJF8fDWSjiyHOuuV6FhEROVKdsIDJARPQe4fodi8eTMCt2yBqamp6Fj0Fiw95ZixsTHWr1+Pbdu2Qfoz7K/lruf3RMciIirzJEnCi+iTUAZPRhU7M4RfD4Ofn5/oWPQfWHq0QP/+/REZEYHqjjZ4HjwVLyKPcrmLiKiI1FmvkHRkOZKPr8YQ30EID7uG2rVri45F74ClR0vUqFED165ewaiRw5F8cg2SDi2BOuul6FhERGVKtvIeEoInQ/ozDFu3bsWGDRtgYmIiOha9I5YeLWJkZIQ1a9Zg165dwOMoKAP9kRX/h+hYREQaT5IkvIg8iudBU/FRBWtERkRgwIABomNRIbH0aKFevXohJioKtas4Qrl1GtLCD3G5i4joLdRZr5B0aAmST67BpyOGIezaVdSoUUN0LCoClh4t9eGHH+Jy6CWMG/MZUkJ+QtL+hcjLTBcdi4hIo2TF/wFloD/wOAo7d+7E2rVrYWRkJDoWFRFLjxYzNDREQEAA9u3bB534m0gI9EfW01uiYxERCSdJEtLCD0G5dRpqVXZAdGQk+vTpIzoWvSeWHkK3bt0QGxMNl48+gHLbdKRd28flLiLSWurMdCQdWISUkJ8w9rPRuHI5FNWqVRMdi4oBSw8BACpXrozQSxcxyd8fKWc3Imnv18jLSBMdi4ioVGU9vQVloD90nt3A3r17sXr1ahgaGoqORcWEpYfy6evrY9myZTh8+DD0Eu8gIdAfmY9vio5FRFTiJElC2rV9eL5tOpyrOSEmOgrdu3cXHYuKWaFLz5MnTzBo0CDY2NjAxMQE7u7uCA8Pz78/PT0d48aNQ6VKlWBsbIxatWph7dq1BfaRlZWF8ePHw9bWFqampujSpQseP378/q+GikXHjh0RGxMNj1ofQbl9FlRXdkGS1KJjERGViLyMF0ja9w1Szm7EpIkTEXrpIqpUqSI6FpWAQpWelJQUeHt7Q19fH8eOHcPNmzexfPlyWFpa5o+ZNGkSjh8/juDgYPz222+YNGkSxo8fjwMHDuSP8ff3x759+7Bjxw5cvHgR6enp6NSpE/Ly8orthdH7cXJywoVfz2PG9GlIPb8FibvnIe+VSnQsIqJilfn4NyRsmQi9hNs4ePAgli9fDgMDA9GxqITIpEKcsTpz5kxcunQJFy5ceOsYZ2dn9O3bF3PmzMm/zdPTEx06dMDXX38NlUoFOzs7BAUFoW/fvgCAp0+fwsnJCUePHoWPj89/5khLS4NcLodKpYKFhcW7xqciOnHiBAYMHIT0HMCq01QYOfHXg4mobJMkNdKu7oXqQhDq16+PX3bugJOTk+hY5Z7oz+9CHek5ePAgvLy80Lt3bygUCnh4eGD9+vUFxjRu3BgHDx7EkydPIEkSzp49i9u3b+eXmfDwcOTk5KBt27b5j3F0dISzszNCQ0Pf+LxZWVlIS0srsFHp8fHxQWxMNOq7fwLl9s+RGroDkppH5YiobMp7pULinvlIPb8Z06dNxa/nz7HwaIlClZ579+5h7dq1qF69Ok6cOIHRo0djwoQJCAwMzB+zevVq1K5dG5UqVYKBgQHatWuHNWvWoHHjxgCA+Ph4GBgYwMrKqsC+7e3tER8f/8bnXbRoEeRyef7Gv5ylz9HREWfPnMYXX8xG2sWtSNw1F3kvU0THIiIqlMxHcVBumQjDlPs4duwYFi9eDH19fdGxqJQUqvSo1WrUqVMHCxcuhIeHB0aNGoWRI0cWOFF59erVuHLlCg4ePIjw8HAsX74cY8aMQUhIyL/uW5IkyGSyN943a9YsqFSq/O3Ro0eFiU3FRFdXF/Pnz8epU6dg8vIplFsmIOPPKNGxiIj+k6TOQ2roDii3f466rrUQGxONdu3aiY5FpaxQpadChQqoXbt2gdtq1aqFhw8fAgAyMjLw+eefY8WKFejcuTNcXV0xbtw49O3bF8uWLQMAODg4IDs7GykpBY8SKJVK2Nvbv/F5DQ0NYWFhUWAjcVq1aoXYmGg08vKA8pc5SL2wlctdRKSx8l6mIHH3XKRd3IrZsz/HubNnULFiRdGxSIBClR5vb2/culXwZwpu376NypUrAwBycnKQk5MDHZ2Cu9XV1YVa/dclz56entDX18epU6fy73/27Bni4uLQqFGjIr0IKn0ODg4IOXUSX8+fjxdXdiLxlznIfZEkOhYRUQEZD6Kh3DIRJulPcfLkSXz99dfQ09MTHYsEKVTpmTRpEq5cuYKFCxfijz/+wLZt27Bu3TqMHTsWAGBhYYFmzZph2rRpOHfuHO7fv4/NmzcjMDAw/0ue5HI5hg8fjilTpuD06dOIjIzEoEGD4OLigtatWxf/K6QSo6uriy+++AJnzpyBeVYCEgInIuNe+H8/kIiohEnqPKRe2Arlzi/Q0NMNsTHR/Iyhwl2yDgCHDx/GrFmzcOfOHVStWhWTJ0/GyJEj8++Pj4/HrFmzcPLkSSQnJ6Ny5cr49NNPMWnSpPxzdjIzMzFt2jRs27YNGRkZaNWqFdasWfPOJyiLvuSNXpeQkIBBvr44eeIELBr0gmUTX8h0dEXHIiItlPsiCSlHliPjURzmffUVPv/8c+jq8v1IE4j+/C506dEEoieN3kytVmPp0qX4fPZsGDrWhHWnadCzsBMdi4i0SMb9CKQcXQErUyPs3LEdzZs3Fx2J/kb05zd/e4uKjY6ODmbMmIFfz5+HZZ4Kyi0T8epumOhYRKQFJHUeUs5vgfKXL9GsYT3ExkSz8NBrWHqo2Hl7eyM2JhptmjdBwu55SDn7M6S8XNGxiKicyk1LRMKOz5EetheLFi3CiePHoFAoRMciDcTSQyXCxsYGhw8fwvLly/Ey4iASts9ErkopOhYRlTMZd8OgDJwIeV4qzp87h5kzZ752BTHR//BvBpUYmUyGyZMn49LFi7DReQXllgl4dfuy6FhEVA5IeblIOfszlLvnoXUzb8TFROd/8z/R27D0UImrX78+YqKj0L5tKyTsW4DkkHWQ8nJExyKiMipXpUTC9pl4GXEQy5Ytw+FDh2BjYyM6FpUBLD1UKqysrLB/3z6sXr0amTHHkbBtOnJS3/xba0REb/PqzhUoAyfCWucVLl64gClTpnA5i94Z/6ZQqZHJZBg/fjwuXw6FnUEulFsm4uXvF0XHIqIyQMrLQXLIOiTs/Qbt27REbHQUGjRoIDoWlTEsPVTqPD09ERMVia6d2iPxwGIknVwLKTdbdCwi0lA5qfFI2DYdGdHHEBAQgP379sHKykp0LCqDWHpICLlcjl2//IK1a9ci60YIErZOQ07yE9GxiEjDvLx1CcotE2FnkIvLl0MxYcKE/G/3Jyoslh4SRiaTYfTo0bh29QoqmMqgDPTHy5vnRcciIg0g5WYj+dRaJO5fhC4d2yEmKhJeXl6iY1EZx9JDwrm7uyMqMgK9enRD4qGlSDr+HdQ5WaJjEZEgOclPkLBtOjLjQrBmzRrs3rULcrlcdCwqB1h6SCOYm5tj29at2LBhA7J/P4+E4CnISXokOhYRlbKXN89DGegPBxPg2tUr+Oyzz7icRcWGpYc0hkwmw/DhwxF+PQxOVkZ4HjgJ6XGnRcciolKgzslC0vHvkXhoKXp274royAi4u7uLjkXlDEsPaRxnZ2dEhl/HwH59kXRkJZKOroI6O1N0LCIqITlJj5CwdSqyfz+H9evXY/u2bTA3Nxcdi8ohlh7SSKamptiyZTM2b96M3D9CkRA8BdkJD0THIqJilh53Bs+DJqOS3ADXw65hxIgRXM6iEsPSQxrNz88PEeHXUdXODMrgyXgRfRKSJImORUTvSZ2diaSjq5B0ZAX69+mFyPDrcHFxER2LyjmWHtJ4tWrVwvWwaxjiOwjJx1cj6chyqLNeiY5FREWUnfAACcFTkPtHKDZt2oSgwECYmZmJjkVagKWHygQTExNs2LABW7duhfRnGBKCJyNbeU90LCIqBEmSkB5zEsrgyahsa4rw62EYMmSI6FikRVh6qEwZMGAAIiMi8FEFazwPmooXkUe53EVUBqizM5B0ZAWSjq2G36CBiLgehtq1a4uORVqGpYfKnBo1aiDs2lV8OmIYkk+uQdKhJVzuItJg2cr7SAiaBPX9awgODsbGjRthYmIiOhZpIZYeKpOMjIywdu1a7Ny5E3gcBWWgP7Li/xAdi4j+RpIkvIg6hudBU1CtgjWiIiMwcOBA0bFIi7H0UJnWp08fREdGolZlByi3TkNa+CEudxFpAHXWKyQdWorkEz9g5PChCLt6BTVq1BAdi7QcSw+VedWqVcOVy6EY+9lopIT8hKQDi6DOTBcdi0hrZcX/AWWgP/AoAjt37sSPP/4IY2Nj0bGIWHqofDA0NMTq1auxb98+6Dy78ddy19NbomMRaRVJkpAWfgjKrdPw8Qf2iI6KQp8+fUTHIsrH0kPlSrdu3RATHQWXjz7A823TkXZtH5e7iEqBOjMdSQcWISXkJ4wZPQpXr1xGtWrVRMciKoClh8qdKlWq4NLFC5g0cSJSzm5E0r5vkJfxQnQsonIr6+ktKAP9IXsWhz179uC7776DoaGh6FhEr2HpoXLJwMAAy5cvx6FDh6CXcBsJWyYi8/FvomMRlSuSJCEtbD+U22bAuZoTYqKi0KNHD9GxiN6KpYfKtU6dOiE2JhoetT+CcvtMqK7shiSpRcciKvPyMl4gad83SDmzARMnjEfopYuoWrWq6FhE/4qlh8o9Jycn/Hr+HKZPm4rU85uRuGc+8l6pRMciKrOynvyGhC0Toau8hYMHD2LFihUwMDAQHYvoP7H0kFbQ19fH4sWLcezYMRim3Idyy0RkPooTHYuoTJEkNVRXd+P5tplwr1UNsTHR6Ny5s+hYRO+MpYe0Srt27RAXG4N6brWh3P45UkN3QFLniY5FpPHyXqmQuGc+Us9txrSpU3Dh1/P44IMPRMciKhSWHtI6jo6OOHvmNGbP/hxpF7cicfdc5L1MER2LSGNlPoqDMnAiDFPu4+jRo/j222+hr68vOhZRobH0kFbS09PD119/jZMnT8Ik/SmUWyYi40G06FhEGkWS1FCF7oRy++fwcv4YsTHRaN++vehYREXG0kNarXXr1oiNiUYjL3cod36B1AtbudxFBCDvZQoSd82F6mIwPv98Fs6fO4uKFSuKjkX0Xlh6SOs5ODgg5NRJzJ83Dy+u7ETiL3OQ+yJJdCwiYTIeREO5ZSKMXzzGyZMn8c0330BPT090LKL3xtJDBEBXVxdz5szBmTNnYJaphDJwIjLuR4iORVSqJHUeUi9uhXLnF2jo6YbYmGi0bt1adCyiYsPSQ/Q3zZo1Q2xMNJo3qgflL18i5fwWLneRVshNT0biL18i7fJOzPvqK5wOOYUKFSqIjkVUrFh6iP5BoVDg+LFjWLRoEdLD9iJhx+fITUsUHYuoxGTcj0TClokwy3yOM6dP48svv4Surq7oWETFjqWH6A10dHQwc+ZMnD93DvK81L+Wu+6GiY5FVKwkdR5Sfg2CcteXaNrA66+jnM2bi45FVGJYeoj+RePGjREXE402zRpDuXseUs7+DCkvV3QsoveWm5aIxJ2zkX5tNxYuWIATJ45DoVCIjkVUolh6iP6DjY0NDh8+hGXLluFlxEEkbJ+JXJVSdCyiIsu4GwZl4ERY5CTj/LlzmDVrFnR0+HFA5R//lhO9A5lMhilTpuDihQuw1nkFZeBEvLpzRXQsokKR8nKRcvZnKHfPQ6umjRAbE43GjRuLjkVUagpdep48eYJBgwbBxsYGJiYmcHd3R3h4eIExv/32G7p06QK5XA5zc3M0aNAADx8+zL8/KysL48ePh62tLUxNTdGlSxc8fvz4/V8NUQlr0KABYqOj0L5NSyTs/QbJIesg5eWIjkX0n3LTlEjYMQsvIw5i6dKlOHL4MGxtbUXHIipVhSo9KSkp8Pb2hr6+Po4dO4abN29i+fLlsLS0zB9z9+5dNG7cGB9//DHOnTuH6OhozJkzB0ZGRvlj/P39sW/fPuzYsQMXL15Eeno6OnXqhLw8XhpMms/Kygr79+1DQEAAMqKPIWHbdOSkxouORfRWr+5chXLLRFgjHRd+/RVTp07lchZpJZkkSdK7Dp45cyYuXbqECxcuvHVMv379oK+vj6CgoDfer1KpYGdnh6CgIPTt2xcA8PTpUzg5OeHo0aPw8fH5zxxpaWmQy+VQqVSwsLB41/hExe769evo2bsPnsYrYekzHqYfc6mANIeUl4OUc5vx4voBdO7SBZs3bYK1tbXoWKTFRH9+F6rqHzx4EF5eXujduzcUCgU8PDywfv36/PvVajWOHDmCGjVqwMfHBwqFAvXr18f+/fvzx4SHhyMnJwdt27bNv83R0RHOzs4IDQ194/NmZWUhLS2twEakCby8vBATFYkuHdsh8cBiJJ1cCyk3W3QsIuSkxiNh2wxkRB3FqlWrcGD/fhYe0nqFKj337t3D2rVrUb16dZw4cQKjR4/GhAkTEBgYCABQKpVIT0/H4sWL0a5dO5w8eRLdu3dHjx49cP78eQBAfHw8DAwMYGVlVWDf9vb2iI9/8xLBokWLIJfL8zcnJ6eivFaiEiGXy7F71y6sWbMGWTdC/lruSn4iOhZpsVe3QqEM9IetfjZCQy9h4sSJkMlkomMRCVeo5S0DAwN4eXkVOCIzYcIEhIWF4fLly3j69CkqVqyI/v37Y9u2bfljunTpAlNTU2zfvh3btm3D0KFDkZWVVWDfbdq0QbVq1fDjjz++9rxZWVkFxqelpcHJyYnLW6RxoqKi0KNXbzx6/ASWbcfBtHYz0ZFIi0i52Ug5+zNeRBxG9x498PPGjQXOuSQSrUwtb1WoUAG1a9cucFutWrXyr8yytbWFnp7ev45xcHBAdnY2UlJSCoxRKpWwt7d/4/MaGhrCwsKiwEakidzd3REdGYGe3bsi8dBSJB3/HuqcrP9+INF7ykl5ioRt05EZexI//PAD9uzezcJD9A+FKj3e3t64detWgdtu376NypUrA/jrSFDdunX/dYynpyf09fVx6tSp/PufPXuGuLg4NGrUqEgvgkiTmJubY/u2bVi/fj2yfz+HhK1TkZP0SHQsKsde/vYrlIH+sDeWcPXqFYwZM4bLWURvoFeYwZMmTUKjRo2wcOFC9OnTB9euXcO6deuwbt26/DHTpk1D37590bRpU7Ro0QLHjx/HoUOHcO7cOQB/nf8wfPhwTJkyBTY2NrC2tsbUqVPh4uKC1q1bF+uLIxJFJpNhxIgRqF+/Pnr26o37QZMhb/0ZzJxbio5G5Yg6JwspZ9YjPeo4+vTti/Xr1vFIONG/KNQ5PQBw+PBhzJo1C3fu3EHVqlUxefJkjBw5ssCYn3/+GYsWLcLjx49Rs2ZNzJs3D127ds2/PzMzE9OmTcO2bduQkZGBVq1aYc2aNe98grLoNUGiwnj58iU+GzMGQYGBMHNpDavWo6FjYPTfDyT6FzlJj5F8aAnUqU/x/XerMWLECB7dIY0n+vO70KVHE4ieNKKi2LJlC0Z/NgYws4NV5+kwsKssOhKVUek3ziL11BpU+cAJe3fvgqurq+hIRO9E9Oc3v5KTqJT4+fkh/HoYKtuaQhk8GekxJ1EG/81BAqlzMpF0dBWSDi9Hv949ERURzsJDVAgsPUSlqHbt2oi4HoYhvoOQdGw1ko4shzo7Q3QsKgOyEx4gIWgKcu5cwqZNmxAcFAQzMzPRsYjKFJYeolJmYmKCDRs2YOvWrZD+DENC0CRkK++JjkUaSpIkpMecgjJoMirbmiIi/DqGDBkiOhZRmcTSQyTIgAEDEBkRgY8qWON50FS8iDrG5S4qQJ2dgeQjK5B0LACDBw1AxPWw174HjYjeHUsPkUA1atRA2LWrGDl8KJJP/ICkQ0uhznolOhZpgGzlfSQETUbe/WsICgrCzz//DBMTE9GxiMo0lh4iwYyMjPDjjz9i586dwKMIKAP9kRX/h+hYJIgkSXgRdRzK4Kmo5mCJyIhwDBo0SHQsonKBpYdIQ/Tp0wfRUVGoVdkByq3TkBZ+iMtdWkad9QpJh5Yi+cT3GD7UD2HXrqJmzZqiYxGVGyw9RBqkWrVquHI5FGNGj0JKyE9IOrAI6sx00bGoFGQ/vwtl0CTgUQR27NiBn376CcbGxqJjEZUrLD1EGsbQ0BDfffcd9u7dC51nN/5a7np6678fSGWSJEl4EXEYz4On4mMnBaIiI9G3b1/RsYjKJZYeIg3VvXt3xERHwbmaE5TbZiAtbD+Xu8oZdWY6kg4uRvKpH/HZqE9x9cplfPTRR6JjEZVbLD1EGqxKlSoIvXQR/hMnIOXMBiTu+xp5GS9Ex6JikPXsNpSBkyB7Govdu3fj+++/h6GhoehYROUaSw+RhjMwMMDy5ctx8OBB6CfcQcKWich8/JvoWFREkiQhLewAlFun45MPKyImKgo9e/YUHYtIK7D0EJURnTt3Rkx0FNxrVYNy+0yoru6GJKlFx6JCyMt4gaR93yDlzHpMGD8Ol0MvoWrVqqJjEWkNlh6iMuSDDz7AhV/PY9rUKUg9txmJe+Yj75VKdCx6B1lPfkNC4EToKm/hwIEDWLlyJQwMDETHItIqLD1EZYy+vj6+/fZbHDt2DIYp96HcMhGZj+JEx6K3kCQ1VFf3QLl9JtxqfojYmGh06dJFdCwircTSQ1RGtWvXDrEx0ajrWgvK7Z9DFbqTy10aJu+VCol7vkbquU2YMnkyLl74FR988IHoWERai6WHqAyrWLEizp09g88/nwXVxWAk7pqLvJcpomMRgMxHcVAGToRB8l0cOXIES5Ysgb6+vuhYRFqNpYeojNPT08M333yDkydPwiT9CZRbJiLjQbToWFpLktRQXf4Fyh2z4eX8MWJjotGhQwfRsYgILD1E5Ubr1q0RGxONhp5uUO78AqkXt0JS54mOpVXyXqYicfdXUF0IwqyZM3D+3FlUqlRJdCwi+j8sPUTliIODA06HnMK8r75C2uWdSPxlDnLTk0XH0gqZD2Kg3DIBxmmPcOLECSxYsAB6enqiYxHR37D0EJUzurq6+PLLL3Hm9GmYZSqh3DIBGfcjRMcqtyR1HlIvboNy5xdoUMcVsTHRaNOmjehYRPQGLD1E5VTz5s0RGxONZg3qQrlrLlJ+DeRyVzHLTU9G4q4vkRa6HXPnfokzp0NQoUIF0bGI6C1YeojKMYVCgRMnjmPhggVIv7YHiTtnIzctUXSsciHjfiQStkyE6at4nD59GnPnzoWurq7oWET0L1h6iMo5HR0dzJo1C+fPnYNFTjKUgRORcTdMdKwyS1LnIeXXICh3fYkm9T0RFxuDFi1aiI5FRO+ApYdISzRu3BixMdFo3cwbyt3zkHL2Z0h5uaJjlSm5LxKRsHM20q/uwoJvvsHJkyegUChExyKid8TSQ6RFbG1tcfjQISxduhQvIw4iYftM5KqUomOVCRl3r0O5ZSIscpJx7tw5fP7559DR4VsoUVnC/2OJtIyOjg6mTp2KixcuwFrnFZSBE/HqzhXRsTSWlJeLlHOboNz9FVo2aYi4mGg0adJEdCwiKgKWHiIt1aBBA8RGR6Fd6xZI2PsNkk+vh5SXIzqWRslNUyJhx+dIv74fS5YswdEjR2Brays6FhEVEUsPkRazsrLCgf37sWrVKmREHUXCthnISY0XHUsjvPrjKpRbJsIKL3DxwgVMmzaNy1lEZRz/DybScjKZDBMnTkRo6CXY6mdDGeiPl7cuiY4ljJSXg+QzG5Cw52v4tGqO2OgoNGzYUHQsIioGLD1EBACoW7cuYqOj0KWDDxL3L0LyqbWQcrNFxypVOanxSNg+ExmRR7By5UocPHAA1tbWomMRUTFh6SGifHK5HLt37cIPP/yAzNhTSNg2HTkpT0XHKhWvboVCGegPW91MhIZegr+/P2QymehYRFSMWHqIqACZTIYxY8bg6tUrsDeW/lruunledKwSI+XmIPnUj0jYvxCd2rVBTHQU6tatKzoWEZUAlh4ieiMPDw/EREWiZ7cuSDy0FEnHv4c6J0t0rGKVk/IUCdumITP2JL7//nvs3bMHlpaWomMRUQlh6SGitzI3N8f2bduwbt06ZP9+DglbpyIn6ZHoWMXi5W8XoAz0h8JIjStXLmPs2LFcziIq51h6iOhfyWQyjBw5EtfDrqGS3ADPgyYjPe6M6FhFps7JQtKJ75F48Ft079IJMVGRqFOnjuhYRFQKWHqI6J24uLggMvw6+vfphaQjK5B0dBXU2ZmiYxVKTtJjJGydhqybZ/HTTz9h544dsLCwEB2LiEoJSw8RvTMzMzMEBQZi06ZNyP0jFAnBU5Cd8EB0rHeSfuMsngdNQkULPVwPu4ZPP/2Uy1lEWoalh4gKbciQIQi/HobKtqZQBk1GeswpSJIkOtYbqXMykXRsNZIOL0ffXj0QFREOV1dX0bGISACWHiIqktq1ayPiehj8fAci6VgAko6sgDo7Q3SsArITHyIheApybl/Azz//jOCgIJiZmYmORUSCsPQQUZGZmJhg48aNCA4Ohvr+NSQETUa28r7oWACA9NgQKIMmo7K1CcKvh2Ho0KFcziLScoUuPU+ePMGgQYNgY2MDExMTuLu7Izw8/I1jR40aBZlMhlWrVhW4PSsrC+PHj4etrS1MTU3RpUsXPH78uEgvgIjEGzhwICIjwlHNwRLPg6bgRdRxYctd6uyM/BOtfQf0Q/j1MHzyySdCshCRZilU6UlJSYG3tzf09fVx7Ngx3Lx5E8uXL3/jl3nt378fV69ehaOj42v3+fv7Y9++fdixYwcuXryI9PR0dOrUCXl5eUV+IUQkVs2aNRF27SpGDh+K5BPfI+nQUqizXpVqhuyEP5EQPBl5964i8P9OuDY1NS3VDESkuWRSIf45NnPmTFy6dAkXLlz413FPnjxB/fr1ceLECXTs2BH+/v7w9/cHAKhUKtjZ2SEoKAh9+/YFADx9+hROTk44evQofHx8/jNHWloa5HI5VCoVLzcl0kA7d+7E8BEjkGcoh2WnaTB0+KhEn0+SJKRHn4DqzHrUrFEde3bvwscff1yiz0lEhSf687tQR3oOHjwILy8v9O7dGwqFAh4eHli/fn2BMWq1Gr6+vpg2bdobDymHh4cjJycHbdu2zb/N0dERzs7OCA0NfePzZmVlIS0trcBGRJqrb9++iIqMRE0nBZRbpyEt/FCJLXeps14h6dBSJJ/4HsOH+uF62DUWHiJ6o0KVnnv37mHt2rWoXr06Tpw4gdGjR2PChAkIDAzMH/Ptt99CT08PEyZMeOM+4uPjYWBgACsrqwK329vbIz4+/o2PWbRoEeRyef7m5ORUmNhEJMBHH32Eq1cuY8zoUUgJ+QlJBxZDnZlerM+R/fwulEGTgEcR2L59O3766ScYGxsX63MQUflRqNKjVqtRp04dLFy4EB4eHhg1ahRGjhyJtWvXAvjrKE5AQAA2b95c6KskJEl662NmzZoFlUqVvz16VD5++4eovDM0NMR3332HPXv2QPYsFsrASch6dvu99ytJEl5EHMHz4KmoWckOkRER6NevXzEkJqLyrFClp0KFCqhdu3aB22rVqoWHDx8CAC5cuAClUokPPvgAenp60NPTw4MHDzBlyhRUqVIFAODg4IDs7GykpKQU2I9SqYS9vf0bn9fQ0BAWFhYFNiIqO3r06IGYqCh88mFFKLdOR1rYgSIvd6mzXiLp4GIkn1qL0Z+OxNUrl1G9evViTkxE5VGhSo+3tzdu3bpV4Lbbt2+jcuXKAABfX1/ExMQgKioqf3N0dMS0adNw4sQJAICnpyf09fVx6tSp/H08e/YMcXFxaNSo0fu+HiLSUFWrVsXl0EuYOGE8Us6sR9K+b5CX8aJQ+8h6dgfKLf6QPYnF7t278cMPP8DIyKiEEhNReaNXmMGTJk1Co0aNsHDhQvTp0wfXrl3DunXrsG7dOgCAjY0NbGxsCjxGX18fDg4OqFmzJgBALpdj+PDhmDJlCmxsbGBtbY2pU6fCxcUFrVu3LqaXRUSayMDAACtWrECLFi0wyHcwEgInwqrTNBhWrPWvj5MkCS/CD0J1bhPc3d2x65ed+PDDD0spNRGVF4U60lO3bl3s27cP27dvh7OzM77++musWrUKAwcOLNSTrly5Et26dUOfPn3g7e0NExMTHDp0CLq6uoXaDxGVTZ07d0ZsTDTcan4I5faZUF3dA0lSv3FsXmY6kvYvRMrp9Rg/biwuh15i4SGiIinU9/RoCtHX+RNR8cjJycHs2bOxdOlSmFSrC+sO/tA1keffn/Xkd6QcWQqDvEwEBW5B165dBaYlovcl+vObv71FRMLo6+tjyZIlOHr0KAxT7kEZOBGZj+IgSWqoru6FcvsMuFavgtiYaBYeInpvZfJIj0qlgqWlJR49esQjPUTlxNOnT+E3ZCiuXb0KA/uqyH5+D+PHj8fcuXOhr68vOh4RFYO0tDQ4OTkhNTUVcrn8vx9QzMpk6bl37x6qVasmOgYREREVwaNHj1CpUqVSf95CXb2lKaytrQEADx8+FNIUNdX/GjSPgL2Oc/NmnJc347y8GeflzTgvb/ameZEkCS9evHjjj5GXhjJZenR0/joVSS6X8y/YG/ALHN+Oc/NmnJc347y8GeflzTgvb/bPeRF5sIInMhMREZFWYOkhIiIirVAmS4+hoSHmzp0LQ0ND0VE0Cufl7Tg3b8Z5eTPOy5txXt6M8/JmmjgvZfLqLSIiIqLCKpNHeoiIiIgKi6WHiIiItAJLDxEREWkFlh4iIiLSCiVWep48eYJBgwbBxsYGJiYmcHd3R3h4eP79kiThq6++gqOjI4yNjdG8eXPcuHGjwD6ysrIwfvx42NrawtTUFF26dMHjx48LjElJSYGvry/kcjnkcjl8fX2RmppaYMzDhw/RuXNnmJqawtbWFhMmTEB2dnaBMbGxsWjWrBmMjY1RsWJFzJ8/HyVxjremzEt0dDT69+8PJycnGBsbo1atWggICHgtr7bNy98lJSWhUqVKkMlkr43R1nnZvHkzXF1dYWRkBAcHB4wbN67A/do4L2FhYWjVqhUsLS1hZWWFtm3bIioqqsCY8jYvCxYsQKNGjWBiYgJLS8s3ZtGk911Ac+ZGW9973+XvzP+U6HuvVAKSk5OlypUrS0OGDJGuXr0q3b9/XwoJCZH++OOP/DGLFy+WzM3NpT179kixsbFS3759pQoVKkhpaWn5Y0aPHi1VrFhROnXqlBQRESG1aNFCcnNzk3Jzc/PHtGvXTnJ2dpZCQ0Ol0NBQydnZWerUqVP+/bm5uZKzs7PUokULKSIiQjp16pTk6OgojRs3Ln+MSqWS7O3tpX79+kmxsbHSnj17JHNzc2nZsmXldl42btwojR8/Xjp37px09+5dKSgoSDI2Npa+++47rZ6Xv+vatavUvn17CYCUkpKi9fOyfPlyydHRUdq6dav0xx9/SHFxcdLBgwe1el7S0tIkKysraciQIdLvv/8uxcXFST179pQUCoWUnZ1dbuflyy+/lFasWCFNnjxZksvlr2XRpPddTZsbbX3v/a95+buSfO8tkdIzY8YMqXHjxm+9X61WSw4ODtLixYvzb8vMzJTkcrn0448/SpIkSampqZK+vr60Y8eO/DFPnjyRdHR0pOPHj0uSJEk3b96UAEhXrlzJH3P58mUJgPT7779LkiRJR48elXR0dKQnT57kj9m+fbtkaGgoqVQqSZIkac2aNZJcLpcyMzPzxyxatEhydHSU1Gr1+0xFAZo0L28yZswYqUWLFvl/1uZ5WbNmjdSsWTPp9OnTr/2Pp43zkpycLBkbG0shISFvzaON8xIWFiYBkB4+fJg/JiYmRgKQ/8FR3ubl7zZt2vTGDzBNet+VJM2amzcp7++9f/df81LS770lsrx18OBBeHl5oXfv3lAoFPDw8MD69evz779//z7i4+PRtm3b/NsMDQ3RrFkzhIaGAgDCw8ORk5NTYIyjoyOcnZ3zx1y+fBlyuRz169fPH9OgQQPI5fICY5ydnQv8uJmPjw+ysrLyD+FdvnwZzZo1K/AFSj4+Pnj69Cn+/PPPcjkvb6JSqfJ/zPV/+9HGebl58ybmz5+PwMDA/N95+zttnJdTp05BrVbjyZMnqFWrFipVqoQ+ffrg0aNHWj0vNWvWhK2tLTZu3Ijs7GxkZGRg48aN+OSTT1C5cuVyOS/vQpPedwHNmps3Ke/vve+qNN57S6T03Lt3D2vXrkX16tVx4sQJjB49GhMmTEBgYCAAID4+HgBgb29f4HH29vb598XHx8PAwABWVlb/OkahULz2/AqFosCYfz6PlZUVDAwM/nXM//78vzHFQZPm5Z8uX76MX375BaNGjcq/TRvnJSsrC/3798fSpUvxwQcfvDGvNs7LvXv3oFarsXDhQqxatQq7d+9GcnIy2rRpk3+ehjbOi7m5Oc6dO4fg4GAYGxvDzMwMJ06cwNGjR6Gnp5e/n/I0L+9Ck953Ac2am3/Shvfed1Fa770l8ivrarUaXl5eWLhwIQDAw8MDN27cwNq1azF48OD8cTKZrMDjJEl67bZ/+ueYN40vjjHS/50Y9V95CkPT5uV/bty4ga5du+LLL79EmzZtCtynbfMya9Ys1KpVC4MGDfrX/WrbvKjVauTk5GD16tX5/6Lbvn07HBwccPbsWfj4+Lw1y9v2X1SaNC8ZGRkYNmwYvL29sX37duTl5WHZsmXo0KEDwsLCYGxs/NYsb9t/UZXmvLwLTXnfBTRvbv5Hm957/0tpvfeWyJGeChUqoHbt2gVuq1WrFh4+fAgAcHBwAPB6M1MqlfmtzcHBAdnZ2UhJSfnXMc+fP3/t+RMSEgqM+efzpKSkICcn51/HKJVKAK833PehSfPyPzdv3kTLli0xcuRIfPHFFwXu08Z5OXPmDHbt2gU9PT3o6emhVatWAABbW1vMnTs3fz/aNi8VKlQAgAJ57OzsYGtrWyCPts3Ltm3b8Oeff2LTpk2oW7cuGjRogG3btuH+/fs4cOBA/n7K07y8C0163wU0a27+R5vee99Fab33lkjp8fb2xq1btwrcdvv27fw17qpVq8LBwQGnTp3Kvz87Oxvnz59Ho0aNAACenp7Q19cvMObZs2eIi4vLH9OwYUOoVCpcu3Ytf8zVq1ehUqkKjImLi8OzZ8/yx5w8eRKGhobw9PTMH/Prr78WuJzy5MmTcHR0RJUqVYpjSgBo1rwAf/0ro0WLFvDz88OCBQtey6uN87Jnzx5ER0cjKioKUVFR2LBhAwDgwoULGDt2bP5+tG1evL29AaBAnuTkZCQmJubn0cZ5efXqFXR0dAr8K/N/f1ar1fn7KU/z8i406X0X0Ky5AbTvvfddlNp77zud7lxI165dk/T09KQFCxZId+7ckbZu3SqZmJhIwcHB+WMWL14syeVyae/evVJsbKzUv3//N14GV6lSJSkkJESKiIiQWrZs+cZLSl1dXaXLly9Lly9fllxcXN54yXqrVq2kiIgIKSQkRKpUqVKBSydTU1Mle3t7qX///lJsbKy0d+9eycLCotgvD9SkeYmLi5Ps7OykgQMHSs+ePcvflEqlVs/LP509e/a1Kwi0dV66du0qffLJJ9KlS5ek2NhYqVOnTlLt2rXzL83Wxnn57bffJENDQ+mzzz6Tbt68KcXFxUmDBg2S5HK59PTp03I7Lw8ePJAiIyOlefPmSWZmZlJkZKQUGRkpvXjxQpIkzXrf1bS50db33v+al38qqffeEik9kiRJhw4dkpydnSVDQ0Pp448/ltatW1fgfrVaLc2dO1dycHCQDA0NpaZNm0qxsbEFxmRkZEjjxo2TrK2tJWNjY6lTp04FLg2VJElKSkqSBg4cKJmbm0vm5ubSwIEDC0ySJP012R07dpSMjY0la2trady4cQUueZOkvy4zbdKkiWRoaCg5ODhIX331VbFfNilJmjMvc+fOlQC8tlWuXLnAfrRtXv7pTf/jSZJ2zotKpZKGDRsmWVpaStbW1lL37t1f2482zsvJkyclb29vSS6XS1ZWVlLLli2ly5cvFxhT3ubFz8/vje8fZ8+ezR+jSe+7kqQ5c6Ot773v8nfm70rqvVcmSSX09ZdEREREGoS/vUVERERagaWHiIiItAJLDxEREWkFlh4iIiLSCiw9REREpBVYeoiIiEgrsPQQERGRVmDpISIiIq3A0kNERERagaWHiIiItAJLDxEREWkFlh4iIiLSCv8P54SSMfjCQucAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -446,211 +460,6 @@
     "    ra.geometry[0].plot()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### How the epsg feature works  \n",
-    "* requires an internet connection the first time to get the prj text from [spatialreference.org](https://spatialreference.org/) using ```requests```  \n",
-    "* if it doesn't exist, ```epsgref.json``` is created in the user's data directory\n",
-    "* the prj text is then stashed in this JSON file hashed by the EPSG numeric code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-22T02:44:55.165963Z",
-     "iopub.status.busy": "2023-02-22T02:44:55.165184Z",
-     "iopub.status.idle": "2023-02-22T02:44:55.170376Z",
-     "shell.execute_reply": "2023-02-22T02:44:55.169477Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from flopy.export.shapefile_utils import EpsgReference\n",
-    "\n",
-    "if not skip:\n",
-    "    ep = EpsgReference()\n",
-    "    prj = ep.to_dict()\n",
-    "    prj"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-22T02:44:55.174251Z",
-     "iopub.status.busy": "2023-02-22T02:44:55.173639Z",
-     "iopub.status.idle": "2023-02-22T02:44:55.177454Z",
-     "shell.execute_reply": "2023-02-22T02:44:55.176739Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from flopy.export.shapefile_utils import CRS, EpsgReference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-22T02:44:55.181001Z",
-     "iopub.status.busy": "2023-02-22T02:44:55.180582Z",
-     "iopub.status.idle": "2023-02-22T02:44:55.186573Z",
-     "shell.execute_reply": "2023-02-22T02:44:55.185794Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "26715:\n",
-      "PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
-      "\n",
-      "4326:\n",
-      "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]\n",
-      "\n",
-      "26916:\n",
-      "PROJCS[\"NAD_1983_UTM_Zone_16N\",GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-87],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "if not skip:\n",
-    "    CRS.getprj(4326)\n",
-    "\n",
-    "    prj = ep.to_dict()\n",
-    "    for k, v in prj.items():\n",
-    "        print(\"{}:\\n{}\\n\".format(k, v))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### working with the ```flopy.export.shapefile_utils.EpsgReference``` handler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-22T02:44:55.190359Z",
-     "iopub.status.busy": "2023-02-22T02:44:55.189761Z",
-     "iopub.status.idle": "2023-02-22T02:44:55.195162Z",
-     "shell.execute_reply": "2023-02-22T02:44:55.194322Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "26715:\n",
-      "PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
-      "\n",
-      "4326:\n",
-      "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]\n",
-      "\n",
-      "26916:\n",
-      "PROJCS[\"NAD_1983_UTM_Zone_16N\",GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-87],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
-      "\n",
-      "9999:\n",
-      "junk\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ep = EpsgReference()\n",
-    "\n",
-    "if not skip:\n",
-    "    ep.add(9999, \"junk\")\n",
-    "    EpsgReference.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### remove an entry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-22T02:44:55.199492Z",
-     "iopub.status.busy": "2023-02-22T02:44:55.199011Z",
-     "iopub.status.idle": "2023-02-22T02:44:55.203793Z",
-     "shell.execute_reply": "2023-02-22T02:44:55.202996Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "26715:\n",
-      "PROJCS[\"NAD_1927_UTM_Zone_15N\",GEOGCS[\"GCS_North_American_1927\",DATUM[\"D_North_American_1927\",SPHEROID[\"Clarke_1866\",6378206.4,294.9786982138982]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
-      "\n",
-      "4326:\n",
-      "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]\n",
-      "\n",
-      "26916:\n",
-      "PROJCS[\"NAD_1983_UTM_Zone_16N\",GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-87],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "if not skip:\n",
-    "    ep.remove(9999)\n",
-    "    EpsgReference.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### start over with a new file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-22T02:44:55.207813Z",
-     "iopub.status.busy": "2023-02-22T02:44:55.207246Z",
-     "iopub.status.idle": "2023-02-22T02:44:55.212291Z",
-     "shell.execute_reply": "2023-02-22T02:44:55.211450Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Resetting ../../../../.local/share/flopy/epsgref.json\n"
-     ]
-    }
-   ],
-   "source": [
-    "if not skip:\n",
-    "    ep.reset()\n",
-    "    prj = ep.to_dict()\n",
-    "    prj"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -664,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.216552Z",
@@ -681,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.233607Z",
@@ -709,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.247030Z",
@@ -722,10 +531,10 @@
     {
      "data": {
       "text/plain": [
-       "<flopy.utils.geometry.LineString at 0x7f8c444ba4d0>"
+       "<flopy.utils.geometry.LineString at 0x1a3d7e350>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -736,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.256945Z",
@@ -752,13 +561,13 @@
        "<AxesSubplot: >"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -773,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.420046Z",
@@ -785,7 +594,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -811,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.561764Z",
@@ -828,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.574408Z",
@@ -853,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.581030Z",
@@ -865,7 +674,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -884,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-22T02:44:55.929290Z",
@@ -909,9 +718,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "flopy",
    "language": "python",
-   "name": "python3"
+   "name": "flopy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -923,7 +732,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,

From ecaf2e2c15ecb0174b772339b15ba63b57b42a64 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Mon, 20 Mar 2023 17:08:34 -0500
Subject: [PATCH 24/28] fix(flopy/export/utils.py): add line continuations;
 missing verbose arg

---
 flopy/export/utils.py | 52 ++++++++++++++++++++++++++-----------------
 1 file changed, 32 insertions(+), 20 deletions(-)

diff --git a/flopy/export/utils.py b/flopy/export/utils.py
index 605c2c9a56..899f4e0b67 100644
--- a/flopy/export/utils.py
+++ b/flopy/export/utils.py
@@ -402,8 +402,9 @@ def output_helper(
         elif verbose:
             print(msg)
     times = [t for t in common_times[::stride]]
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(
             f, ml, time_values=times, logger=logger, forgive=forgive, **kwargs
         )
@@ -612,8 +613,9 @@ def model_export(
     if package_names is None:
         package_names = [pak.name[0] for pak in ml.packagelist]
 
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(f, ml, **kwargs)
 
     if (isinstance(f, str) or isinstance(f, Path)) and Path(
@@ -705,8 +707,9 @@ def package_export(
     """
     assert isinstance(pak, PackageInterface)
 
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(f, pak.parent, **kwargs)
 
     if (isinstance(f, str) or isinstance(f, Path)) and Path(
@@ -818,8 +821,9 @@ def generic_array_export(
             flopy model object
 
     """
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         assert "model" in kwargs.keys(), (
             "creating a new netCDF using generic_array_helper requires a "
             "'model' kwarg"
@@ -906,8 +910,9 @@ def mflist_export(f: Union[str, os.PathLike, NetCdf], mfl, **kwargs):
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(f, mfl.model, **kwargs)
 
     if (isinstance(f, str) or isinstance(f, Path)) and Path(
@@ -1062,8 +1067,9 @@ def transient2d_export(f: Union[str, os.PathLike], t2d, fmt=None, **kwargs):
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(f, t2d.model, **kwargs)
 
     if (isinstance(f, str) or isinstance(f, Path)) and Path(
@@ -1218,8 +1224,9 @@ def array3d_export(f: Union[str, os.PathLike], u3d, fmt=None, **kwargs):
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(f, u3d.model, **kwargs)
 
     if (isinstance(f, str) or isinstance(f, Path)) and Path(
@@ -1395,8 +1402,9 @@ def array2d_export(
     if "modelgrid" in kwargs:
         modelgrid = kwargs.pop("modelgrid")
 
-    if (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".nc":
+    if (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".nc":
         f = NetCdf(f, u2d.model, **kwargs)
 
     if (isinstance(f, str) or isinstance(f, Path)) and Path(
@@ -1408,8 +1416,9 @@ def array2d_export(
         )
         return
 
-    elif (isinstance(f, str) or isinstance(f, Path)) and 
-        Path(f).suffix.lower() == ".asc":
+    elif (isinstance(f, str) or isinstance(f, Path)) and Path(
+        f
+    ).suffix.lower() == ".asc":
         export_array(modelgrid, f, u2d.array, **kwargs)
         return
 
@@ -1726,7 +1735,9 @@ def export_contours(
     return
 
 
-def export_contourf(filename, contours, fieldname="level", **kwargs):
+def export_contourf(
+    filename, contours, fieldname="level", verbose=False, **kwargs
+):
     """
     Write matplotlib filled contours to shapefile.
 
@@ -1740,7 +1751,8 @@ def export_contourf(filename, contours, fieldname="level", **kwargs):
         Name of shapefile attribute field to contain the contour level.  The
         fieldname column in the attribute table will contain the lower end of
         the range represented by the polygon.  Default is 'level'.
-
+    verbose : bool, optional, default False
+        whether to show verbose output
     **kwargs : keyword arguments to flopy.export.shapefile_utils.recarray2shp
 
     Returns

From 9845290a516370e1ed57cb75ad06657d16bdf728 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Tue, 28 Mar 2023 10:48:39 -0500
Subject: [PATCH 25/28] docs(discretization): replace ndarray(int/float) with
 'int/float or ndarray' (similar to Scipy)

---
 flopy/discretization/grid.py             | 14 +++++++-------
 flopy/discretization/structuredgrid.py   | 12 ++++++------
 flopy/discretization/unstructuredgrid.py |  4 ++--
 flopy/discretization/vertexgrid.py       |  4 ++--
 4 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index 2139403eb7..6c1cef9ca2 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -35,13 +35,13 @@ class Grid:
     ----------
     grid_type : enumeration
         type of model grid ('structured', 'vertex', 'unstructured')
-    top : ndarray(float)
+    top : float or ndarray
         top elevations of cells in topmost layer
-    botm : ndarray(float)
+    botm : float or ndarray
         bottom elevations of all cells
-    idomain : ndarray(int)
+    idomain : int or ndarray
         ibound/idomain value for each cell
-    lenuni : ndarray(int)
+    lenuni : int or ndarray
         model length units
     crs : pyproj.CRS, optional if `prj` is specified
         Coordinate reference system (CRS) for the model grid
@@ -65,11 +65,11 @@ class Grid:
     ----------
     grid_type : enumeration
         type of model grid ('structured', 'vertex', 'unstructured')
-    top : ndarray(float)
+    top : float or ndarray
         top elevations of cells in topmost layer
-    botm : ndarray(float)
+    botm : float or ndarray
         bottom elevations of all cells
-    idomain : ndarray(int)
+    idomain : int or ndarray
         ibound/idomain value for each cell
     crs : pyproj.CRS
         Coordinate reference system (CRS) for the model grid
diff --git a/flopy/discretization/structuredgrid.py b/flopy/discretization/structuredgrid.py
index ef70e5d9fa..475239e975 100644
--- a/flopy/discretization/structuredgrid.py
+++ b/flopy/discretization/structuredgrid.py
@@ -85,17 +85,17 @@ class for a structured model grid
 
     Parameters
     ----------
-    delr : ndarray(float)
+    delr : float or ndarray
         column spacing along a row.
-    delc : ndarray(float)
+    delc : float or ndarray
         row spacing along a column.
-    top : ndarray(float)
+    top : float or ndarray
         top elevations of cells in topmost layer
-    botm : ndarray(float)
+    botm : float or ndarray
         bottom elevations of all cells
-    idomain : ndarray(int)
+    idomain : int or ndarray
         ibound/idomain value for each cell
-    lenuni : ndarray(int)
+    lenuni : int or ndarray
         model length units
     crs : pyproj.CRS, optional if `prj` is specified
         Coordinate reference system (CRS) for the model grid
diff --git a/flopy/discretization/unstructuredgrid.py b/flopy/discretization/unstructuredgrid.py
index 7868da4c46..4c233ec31f 100644
--- a/flopy/discretization/unstructuredgrid.py
+++ b/flopy/discretization/unstructuredgrid.py
@@ -36,9 +36,9 @@ class UnstructuredGrid(Grid):
         top elevations for all cells in the grid.
     botm : list or ndarray
         bottom elevations for all cells in the grid.
-    idomain : ndarray(int)
+    idomain : int or ndarray
         ibound/idomain value for each cell
-    lenuni : ndarray(int)
+    lenuni : int or ndarray
         model length units
     ncpl : ndarray
         one dimensional array of size nlay with the number of cells in each
diff --git a/flopy/discretization/vertexgrid.py b/flopy/discretization/vertexgrid.py
index 0161f5eac3..e0c49618db 100644
--- a/flopy/discretization/vertexgrid.py
+++ b/flopy/discretization/vertexgrid.py
@@ -23,9 +23,9 @@ class for a vertex model grid
         top elevations for all cells in the grid.
     botm : list or ndarray
         bottom elevations for all cells in the grid.
-    idomain : ndarray(int)
+    idomain : int or ndarray
         ibound/idomain value for each cell
-    lenuni : ndarray(int)
+    lenuni : int or ndarray
         model length units
     crs : pyproj.CRS, optional if `prj` is specified
         Coordinate reference system (CRS) for the model grid

From a625abdd111d0a6d2908c4bd89204a621491fae8 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 19 Apr 2023 14:46:52 -0500
Subject: [PATCH 26/28] test(test_export): change invalid 4111 epsg code to
 4431

---
 autotest/test_export.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/autotest/test_export.py b/autotest/test_export.py
index 0a495b1a9f..9faa91182a 100644
--- a/autotest/test_export.py
+++ b/autotest/test_export.py
@@ -468,9 +468,7 @@ def test_export_netcdf(function_tmpdir, namfile):
 def test_export_array2(function_tmpdir):
     nrow = 7
     ncol = 11
-    # epsg = 4111  # this may not be a valid EPSG code
-    # (raises an error with pyproj)
-    crs = 3070
+    crs = 4431
 
     # no epsg code
     modelgrid = StructuredGrid(
@@ -504,9 +502,7 @@ def test_export_array2(function_tmpdir):
 def test_export_array_contours(function_tmpdir):
     nrow = 7
     ncol = 11
-    # epsg = 4111  # this may not be a valid EPSG code
-    # (raises an error with pyproj)
-    crs = 3070
+    crs = 4431
 
     # no epsg code
     modelgrid = StructuredGrid(

From 5f718643fe544ccf9a76e0ae66115096267ec145 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 19 Apr 2023 14:48:31 -0500
Subject: [PATCH 27/28] docs: reference prjfile arg instead of prj

---
 flopy/discretization/grid.py             | 2 +-
 flopy/discretization/structuredgrid.py   | 2 +-
 flopy/discretization/unstructuredgrid.py | 2 +-
 flopy/discretization/vertexgrid.py       | 2 +-
 flopy/export/shapefile_utils.py          | 6 +++---
 flopy/export/utils.py                    | 6 +++---
 flopy/modflow/mfdis.py                   | 2 +-
 7 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/flopy/discretization/grid.py b/flopy/discretization/grid.py
index 6c1cef9ca2..f857545b31 100644
--- a/flopy/discretization/grid.py
+++ b/flopy/discretization/grid.py
@@ -43,7 +43,7 @@ class Grid:
         ibound/idomain value for each cell
     lenuni : int or ndarray
         model length units
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
diff --git a/flopy/discretization/structuredgrid.py b/flopy/discretization/structuredgrid.py
index 475239e975..84d39d430a 100644
--- a/flopy/discretization/structuredgrid.py
+++ b/flopy/discretization/structuredgrid.py
@@ -97,7 +97,7 @@ class for a structured model grid
         ibound/idomain value for each cell
     lenuni : int or ndarray
         model length units
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
diff --git a/flopy/discretization/unstructuredgrid.py b/flopy/discretization/unstructuredgrid.py
index 4c233ec31f..64dddfab22 100644
--- a/flopy/discretization/unstructuredgrid.py
+++ b/flopy/discretization/unstructuredgrid.py
@@ -51,7 +51,7 @@ class UnstructuredGrid(Grid):
         If the model grid defined in verts and iverts applies for all model
         layers, then the length of iverts can be equal to ncpl[0] and there
         is no need to repeat all of the vertex information for cells in layers
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
diff --git a/flopy/discretization/vertexgrid.py b/flopy/discretization/vertexgrid.py
index e0c49618db..340439d3b2 100644
--- a/flopy/discretization/vertexgrid.py
+++ b/flopy/discretization/vertexgrid.py
@@ -27,7 +27,7 @@ class for a vertex model grid
         ibound/idomain value for each cell
     lenuni : int or ndarray
         model length units
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py
index bc894ba153..54351a99af 100644
--- a/flopy/export/shapefile_utils.py
+++ b/flopy/export/shapefile_utils.py
@@ -83,7 +83,7 @@ def write_grid_shapefile(
         dictionary of model input arrays
     nan_val : float
         value to fill nans
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
@@ -248,7 +248,7 @@ def model_attributes_to_shapefile(
         modelgrid : fp.modflow.Grid object
             if modelgrid is supplied, user supplied modelgrid is used in lieu
             of the modelgrid attached to the modflow model object
-        crs : pyproj.CRS, optional if `prj` is specified
+        crs : pyproj.CRS, optional if `prjfile` is specified
             Coordinate reference system (CRS) for the model grid
             (must be projected; geographic CRS are not supported).
             The value can be anything accepted by
@@ -571,7 +571,7 @@ def recarray2shp(
         recarray.
     shpname : str or PathLike, default "recarray.shp"
         Path for the output shapefile
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
diff --git a/flopy/export/utils.py b/flopy/export/utils.py
index 899f4e0b67..3b94cb10a9 100644
--- a/flopy/export/utils.py
+++ b/flopy/export/utils.py
@@ -596,7 +596,7 @@ def model_export(
         modelgrid: flopy.discretization.Grid
             user supplied modelgrid object which will supercede the built
             in modelgrid object
-        crs : pyproj.CRS, optional if `prj` is specified
+        crs : pyproj.CRS, optional if `prjfile` is specified
             Coordinate reference system (CRS) for the model grid
             (must be projected; geographic CRS are not supported).
             The value can be anything accepted by
@@ -689,7 +689,7 @@ def package_export(
         modelgrid: flopy.discretization.Grid
             user supplied modelgrid object which will supercede the built
             in modelgrid object
-        crs : pyproj.CRS, optional if `prj` is specified
+        crs : pyproj.CRS, optional if `prjfile` is specified
             Coordinate reference system (CRS) for the model grid
             (must be projected; geographic CRS are not supported).
             The value can be anything accepted by
@@ -888,7 +888,7 @@ def mflist_export(f: Union[str, os.PathLike, NetCdf], mfl, **kwargs):
     **kwargs : keyword arguments
         modelgrid : flopy.discretization.Grid
             model grid instance which will supercede the flopy.model.modelgrid
-        crs : pyproj.CRS, optional if `prj` is specified
+        crs : pyproj.CRS, optional if `prjfile` is specified
             Coordinate reference system (CRS) for the model grid
             (must be projected; geographic CRS are not supported).
             The value can be anything accepted by
diff --git a/flopy/modflow/mfdis.py b/flopy/modflow/mfdis.py
index e4ae2a2d4e..ce26bf709f 100644
--- a/flopy/modflow/mfdis.py
+++ b/flopy/modflow/mfdis.py
@@ -88,7 +88,7 @@ class ModflowDis(Package):
     rotation : float
         counter-clockwise rotation (in degrees) of the grid about the lower-
         left corner. default is 0.0
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS, optional if `prjfile` is specified
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by

From dadaa005bfcf7402abc0986f8041a0e4134bc901 Mon Sep 17 00:00:00 2001
From: "Leaf, Andrew T" <aleaf@usgs.gov>
Date: Wed, 19 Apr 2023 14:50:54 -0500
Subject: [PATCH 28/28] refactor(flopy/utils/crs.py): move deprecated args to
 get_crs() to **kwargs, and add deprecated directives

---
 flopy/utils/crs.py | 54 +++++++++++++++++++++++++++++++++++-----------
 1 file changed, 41 insertions(+), 13 deletions(-)

diff --git a/flopy/utils/crs.py b/flopy/utils/crs.py
index 9ceff54a5b..a41dcaa141 100644
--- a/flopy/utils/crs.py
+++ b/flopy/utils/crs.py
@@ -13,7 +13,7 @@ def get_authority_crs(crs):
 
     Parameters
     ----------
-    crs : pyproj.CRS, optional if `prj` is specified
+    crs : pyproj.CRS
         Coordinate reference system (CRS) for the model grid
         (must be projected; geographic CRS are not supported).
         The value can be anything accepted by
@@ -71,30 +71,58 @@ def get_shapefile_crs(shapefile):
             return get_authority_crs(crs)
 
 
-def get_crs(
-    prjfile=None, prj=None, epsg=None, proj4=None, crs=None, wkt_string=None
-):
+def get_crs(prjfile=None, crs=None, **kwargs):
     """Helper function to produce a pyproj.CRS object from
     various input. Longer-term, this would just handle the ``crs``
     and ``prjfile`` arguments, but in the near term, we need to
-    warn users about deprecating epsg and proj_str."""
+    warn users about deprecating the ``prj``, ``epsg``, ``proj4``
+    and ``wkt_string`` inputs.
+
+    Parameters
+    ----------
+    prjfile : str or pathlike, optional
+        _description_, by default None
+    prj : str or pathlike, optional
+        .. deprecated:: 3.4
+        use ``prjfile`` instead.
+    epsg : int, optional
+        .. deprecated:: 3.4
+        use ``crs`` instead.
+    proj4 : str, optional
+        .. deprecated:: 3.4
+        use ``crs`` instead.
+    crs : pyproj.CRS, optional if `prjfile` is specified
+        Coordinate reference system (CRS) for the model grid
+        (must be projected; geographic CRS are not supported).
+        The value can be anything accepted by
+        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
+        such as an authority string (eg "EPSG:26916") or a WKT string.
+    wkt_string : str, optional
+        .. deprecated:: 3.4
+        use ``crs`` instead.
+
+    Returns
+    -------
+    crs : pyproj.CRS instance
+
+    """
     if crs is not None:
         crs = get_authority_crs(crs)
-    if prj is not None:
+    if kwargs.get("prj") is not None:
         warnings.warn(
             "the prj argument will be deprecated and will be removed in version "
             "3.4. Use prjfile instead.",
             PendingDeprecationWarning,
         )
-        prjfile = prj
-    if epsg is not None:
+        prjfile = kwargs.get("prj")
+    if kwargs.get("epsg") is not None:
         warnings.warn(
             "the epsg argument will be deprecated and will be removed in version "
             "3.4. Use crs instead.",
             PendingDeprecationWarning,
         )
         if crs is None:
-            crs = get_authority_crs(epsg)
+            crs = get_authority_crs(kwargs.get("epsg"))
     elif prjfile is not None:
         prjfile_crs = get_shapefile_crs(prjfile)
         if (crs is not None) and (crs != prjfile_crs):
@@ -105,17 +133,17 @@ def get_crs(
             )
         else:
             crs = prjfile_crs
-    elif proj4 is not None:
+    elif kwargs.get("proj4") is not None:
         warnings.warn(
             "the proj4 argument will be deprecated and will be removed in version "
             "3.4. Use crs instead.",
             PendingDeprecationWarning,
         )
         if crs is None:
-            crs = get_authority_crs(proj4)
-    elif wkt_string is not None:
+            crs = get_authority_crs(kwargs.get("proj4"))
+    elif kwargs.get("wkt_string") is not None:
         if crs is None:
-            crs = get_authority_crs(wkt_string)
+            crs = get_authority_crs(kwargs.get("wkt_string"))
     if crs is not None and not crs.is_projected:
         raise ValueError(
             f"Only projected coordinate reference systems are supported.\n{crs}"