diff --git a/README.md b/README.md
index 8ca834a75..278782305 100644
--- a/README.md
+++ b/README.md
@@ -56,7 +56,8 @@ https://python-hydro.github.io/pyro2
   - We require `numpy`, `numba`, `matplotlib`, and `h5py` for running pyro
     and `setuptools_scm` for the install.
 
-  - There are several ways to install pyro.  The simplest way is via PyPI/pip:
+  - There are several ways to install pyro.  The simplest way is via
+    PyPI/pip:
 
     ```
     pip install pyro-hydro
@@ -85,7 +86,8 @@ https://python-hydro.github.io/pyro2
       print matplotlib.pyplot.get_backend()
       ```
 
- - If you want to run the unit tests, you need to have `pytest` installed.
+ - If you want to run the unit tests, you need to have `pytest`
+   installed.
 
  - Finally, you can run a quick test of the advection solver:
 
@@ -105,8 +107,8 @@ on the grid are described in a jupyter notebook:
 https://github.com/python-hydro/pyro2/blob/main/pyro/mesh/mesh-examples.ipynb
 
 
-Many of the methods here rely on multigrid.  The basic multigrid solver is
-demonstrated in the juputer notebook:
+Many of the methods here rely on multigrid.  The basic multigrid
+solver is demonstrated in the juputer notebook:
 
 https://github.com/python-hydro/pyro2/blob/main/pyro/multigrid/multigrid-constant-coefficients.ipynb
 
@@ -123,7 +125,8 @@ pyro provides the following solvers (all in 2-d):
   - `advection_fv4`: a fourth-order accurate finite-volume advection
     solver that uses RK4 time integration.
 
-  - `advection_nonuniform`: a solver for advection with a non-uniform velocity field.
+  - `advection_nonuniform`: a solver for advection with a non-uniform
+    velocity field.
 
   - `advection_rk`: a second-order unsplit solver for linear advection
     that uses Runge-Kutta integration instead of characteristic
@@ -132,27 +135,28 @@ pyro provides the following solvers (all in 2-d):
   - `advection_weno`: a method-of-lines WENO solver for linear
     advection.
 
-  - `burgers`: a second-order unsplit solver for invsicid Burgers' equation.
+  - `burgers`: a second-order unsplit solver for invsicid Burgers'
+    equation.
 
-  - `viscous_burgers`: a second-order unsplit solver for viscous Burgers' equation
-    with constant-coefficient diffusion. It uses Crank-Nicolson time-discretized
-    solver for solving diffusion.
+  - `viscous_burgers`: a second-order unsplit solver for viscous
+    Burgers' equation with constant-coefficient diffusion. It uses
+    Crank-Nicolson time-discretized solver for solving diffusion.
 
   - `compressible`: a second-order unsplit solver for the Euler
     equations of compressible hydrodynamics.  This uses characteristic
     tracing and corner coupling for the prediction of the interface
     states and a 2-shock or HLLC approximate Riemann solver.
 
-  - `compressible_fv4`: a fourth-order accurate finite-volume compressible
-     hydro solver that uses RK4 time integration.  This is built from the
-     method of McCourquodale and Colella (2011).
+  - `compressible_fv4`: a fourth-order accurate finite-volume
+    compressible hydro solver that uses RK4 time integration.  This
+    is built from the method of McCourquodale and Colella (2011).
 
   - `compressible_rk`: a second-order unsplit solver for Euler
-     equations that uses Runge-Kutta integration instead of
-     characteristic tracing.
+    equations that uses Runge-Kutta integration instead of
+    characteristic tracing.
 
-  - `compressible_sdc`: a fourth-order compressible solver,
-     using spectral-deferred correction (SDC) for the time integration.
+  - `compressible_sdc`: a fourth-order compressible solver, using
+    spectral-deferred correction (SDC) for the time integration.
 
   - `diffusion`: a Crank-Nicolson time-discretized solver for the
     constant-coefficient diffusion equation.
@@ -188,29 +192,30 @@ of the simulation specifying the analysis routines that can be used
 with their data.
 
   - `pyro/util/compare.py`: this takes two simulation output files as
-    input and compares zone-by-zone for exact agreement. This is used as
-    part of the regression testing.
+    input and compares zone-by-zone for exact agreement. This is used
+    as part of the regression testing.
 
       usage: `./compare.py file1 file2`
 
-  - `pyro/plot.py`: this takes the an output file as input and plots the
-    data using the solver's dovis method.
+  - `pyro/plot.py`: this takes the an output file as input and plots
+    the data using the solver's dovis method.
 
       usage: `./plot.py file`
 
   - `pyro/analysis/`
 
-      * `dam_compare.py`: this takes an output file from the
-        shallow water dam break problem and plots a slice through the domain
-        together with the analytic solution (calculated in the script).
+      * `dam_compare.py`: this takes an output file from the shallow
+        water dam break problem and plots a slice through the domain
+        together with the analytic solution (calculated in the
+        script).
 
          usage: `./dam_compare.py file`
 
-      * `gauss_diffusion_compare.py`: this is for the diffusion solver's
-        Gaussian diffusion problem. It takes a sequence of output
-        files as arguments, computes the angle-average, and the plots
-        the resulting points over the analytic solution for comparison
-        with the exact result.
+      * `gauss_diffusion_compare.py`: this is for the diffusion
+        solver's Gaussian diffusion problem. It takes a sequence of
+        output files as arguments, computes the angle-average, and the
+        plots the resulting points over the analytic solution for
+        comparison with the exact result.
 
           usage: `./gauss_diffusion_compare.py file*`
 
@@ -233,9 +238,9 @@ with their data.
 
           usage: `./sedov_compare.py file`
 
-      * `smooth_error.py`: this takes an output file from the advection
-        solver's smooth problem and compares to the analytic solution,
-        outputting the L2 norm of the error.
+      * `smooth_error.py`: this takes an output file from the
+        advection solver's smooth problem and compares to the analytic
+        solution, outputting the L2 norm of the error.
 
           usage: `./smooth_error.py file`
 
@@ -248,8 +253,8 @@ with their data.
 
 ## Understanding the algorithms
 
-  There is a set of notes that describe the background and details of the
-  algorithms that pyro implements:
+  There is a set of notes that describe the background and details of
+  the algorithms that pyro implements:
 
   http://open-astrophysics-bookshelf.github.io/numerical_exercises/
 
@@ -260,10 +265,10 @@ with their data.
 
 ## Regression and unit testing
 
-  The `pyro/test.py` script will run several of the problems (as well as
-  some stand-alone multigrid tests) and compare the solution to stored
-  benchmarks (in each solver's `tests/` subdirectory).  The return value
-  of the script is the number of tests that failed.
+  The `pyro/test.py` script will run several of the problems (as well
+  as some stand-alone multigrid tests) and compare the solution to
+  stored benchmarks (in each solver's `tests/` subdirectory).  The
+  return value of the script is the number of tests that failed.
 
   Unit tests are controlled by pytest and can be run simply via
 
@@ -273,8 +278,8 @@ with their data.
 
 ## Acknowledgements
 
-  If you use pyro in a class or workshop, please e-mail us to let us know
-  (we'd like to start listing these on the website).
+  If you use pyro in a class or workshop, please e-mail us to let us
+  know (we'd like to start listing these on the website).
 
   If pyro was used for a publication, please cite the article found in
   the `CITATION` file.
diff --git a/docs/source/refs.bib b/docs/source/refs.bib
index 4f2b53b59..8c6f4cb12 100644
--- a/docs/source/refs.bib
+++ b/docs/source/refs.bib
@@ -32,7 +32,6 @@ @article{ZALESAK1979335
 year = "1979",
 issn = "0021-9991",
 doi = "https://doi.org/10.1016/0021-9991(79)90051-2",
-url = "http://www.sciencedirect.com/science/article/pii/0021999179900512",
 author = "Steven T Zalesak",
 abstract = "The theory of flux-corrected transport (FCT) developed by Boris and Book [J. Comput. Phys. 11 (1973) 38; 18 (1975) 248; 20 (1976) 397] is placed in a simple, generalized format, and a new algorithm for implementing the critical flux limiting stage' in multidimensions without resort to time splitting is presented. The new flux limiting algorithm allows the use of FCT techniques in multidimensional fluid problems for which time splitting would produce unacceptable numerical results, such as those involving incompressible or nearly incompressible flow fields. The “clipping” problem associated with the original one dimensional flux limiter is also eliminated or alleviated. Test results and applications to a two dimensional fluid plasma problem are presented."
 }
diff --git a/pyro/compressible/unsplit_fluxes.py b/pyro/compressible/unsplit_fluxes.py
index 7ad6f55ca..bed82da57 100644
--- a/pyro/compressible/unsplit_fluxes.py
+++ b/pyro/compressible/unsplit_fluxes.py
@@ -287,6 +287,7 @@ def unsplit_fluxes(my_data, my_aux, rp, ivars, solid, tc, dt):
 
     riemann = rp.get_param("compressible.riemann")
 
+    riemannFunc = None
     if riemann == "HLLC":
         riemannFunc = ifc.riemann_hllc
     elif riemann == "CGF":
diff --git a/pyro/compressible_rk/fluxes.py b/pyro/compressible_rk/fluxes.py
index 5a25f4b09..15d2146b1 100644
--- a/pyro/compressible_rk/fluxes.py
+++ b/pyro/compressible_rk/fluxes.py
@@ -166,6 +166,7 @@ def fluxes(my_data, rp, ivars, solid, tc):
 
     riemann = rp.get_param("compressible.riemann")
 
+    riemannFunc = None
     if riemann == "HLLC":
         riemannFunc = interface.riemann_hllc
     elif riemann == "CGF":
diff --git a/pyro/compressible_sr/unsplit_fluxes.py b/pyro/compressible_sr/unsplit_fluxes.py
index 186785e46..0cb1f7a55 100644
--- a/pyro/compressible_sr/unsplit_fluxes.py
+++ b/pyro/compressible_sr/unsplit_fluxes.py
@@ -270,6 +270,7 @@ def unsplit_fluxes(my_data, my_aux, rp, ivars, solid, tc, dt):
 
     riemann = rp.get_param("compressible.riemann")
 
+    riemannFunc = None
     if riemann == "HLLC":
         riemannFunc = ifc.riemann_hllc
     elif riemann == "CGF":
diff --git a/pyro/incompressible/simulation.py b/pyro/incompressible/simulation.py
index 7f8445b99..7ab3ad8c0 100644
--- a/pyro/incompressible/simulation.py
+++ b/pyro/incompressible/simulation.py
@@ -37,6 +37,7 @@ def initialize(self, other_bc=False, aux_vars=()):
 
         # phi -- used for the projections. Has neumann BC's if v is dirichlet
         # Assuming BC's are either all periodic or all dirichlet
+        phi_bc = None
         if bc.xlb == "periodic":
             phi_bc = bc
         elif bc.xlb == "dirichlet":
diff --git a/pyro/lm_atm/simulation.py b/pyro/lm_atm/simulation.py
index 4ea9302c1..1b732db75 100644
--- a/pyro/lm_atm/simulation.py
+++ b/pyro/lm_atm/simulation.py
@@ -75,6 +75,7 @@ def initialize(self):
                    self.rp.get_param("mesh.xrboundary"),
                    self.rp.get_param("mesh.ylboundary"),
                    self.rp.get_param("mesh.yrboundary")]:
+            bctype = None
             if bc == "periodic":
                 bctype = "periodic"
             elif bc in ["reflect", "slipwall"]:
diff --git a/pyro/mesh/array_indexer.py b/pyro/mesh/array_indexer.py
index c5e1d4354..eca661974 100644
--- a/pyro/mesh/array_indexer.py
+++ b/pyro/mesh/array_indexer.py
@@ -507,6 +507,7 @@ def pretty_print(self, n=0, fmt=None, show_ghost=True):
 
         # print j descending, so it looks like a grid (y increasing
         # with height)
+        ilo = ihi = jlo = jhi = -1
         if show_ghost:
             if self.idir == 1:
                 ilo = 0
@@ -534,6 +535,8 @@ def pretty_print(self, n=0, fmt=None, show_ghost=True):
         for j in reversed(range(jlo, jhi+1)):
             for i in range(ilo, ihi+1):
 
+                gc = None
+
                 if self.idir == 1:
                     if (j < self.g.jlo or j > self.g.jhi or
                         i < self.g.ilo or i > self.g.ihi+1):
diff --git a/pyro/mesh/mesh-examples.ipynb b/pyro/mesh/mesh-examples.ipynb
index 5c9bc381d..a678b8555 100644
--- a/pyro/mesh/mesh-examples.ipynb
+++ b/pyro/mesh/mesh-examples.ipynb
@@ -94,39 +94,39 @@
       "\n",
       "class Grid2d(builtins.object)\n",
       " |  Grid2d(nx, ny, ng=1, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0)\n",
-      " |  \n",
+      " |\n",
       " |  the 2-d grid class.  The grid object will contain the coordinate\n",
       " |  information (at various centerings).\n",
-      " |  \n",
+      " |\n",
       " |  A basic (1-d) representation of the layout is::\n",
-      " |  \n",
+      " |\n",
       " |     |     |      |     X     |     |      |     |     X     |      |     |\n",
       " |     +--*--+- // -+--*--X--*--+--*--+- // -+--*--+--*--X--*--+- // -+--*--+\n",
       " |        0          ng-1    ng   ng+1         ... ng+nx-1 ng+nx      2ng+nx-1\n",
-      " |  \n",
+      " |\n",
       " |                          ilo                      ihi\n",
-      " |  \n",
+      " |\n",
       " |     |<- ng guardcells->|<---- nx interior zones ----->|<- ng guardcells->|\n",
-      " |  \n",
+      " |\n",
       " |  The '*' marks the data locations.\n",
-      " |  \n",
+      " |\n",
       " |  Methods defined here:\n",
-      " |  \n",
+      " |\n",
       " |  __eq__(self, other)\n",
       " |      are two grids equivalent?\n",
-      " |  \n",
+      " |\n",
       " |  __init__(self, nx, ny, ng=1, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0)\n",
       " |      Create a Grid2d object.\n",
-      " |      \n",
+      " |\n",
       " |      The only data that we require is the number of points that\n",
       " |      make up the mesh in each direction.  Optionally we take the\n",
       " |      extrema of the domain (default is [0,1]x[0,1]) and number of\n",
       " |      ghost cells (default is 1).\n",
-      " |      \n",
+      " |\n",
       " |      Note that the Grid2d object only defines the discretization,\n",
       " |      it does not know about the boundary conditions, as these can\n",
       " |      vary depending on the variable.\n",
-      " |      \n",
+      " |\n",
       " |      Parameters\n",
       " |      ----------\n",
       " |      nx : int\n",
@@ -143,34 +143,34 @@
       " |          Physical coordinate at the lower y boundary\n",
       " |      ymax : float, optional\n",
       " |          Physical coordinate at the upper y boundary\n",
-      " |  \n",
+      " |\n",
       " |  __str__(self)\n",
       " |      print out some basic information about the grid object\n",
-      " |  \n",
+      " |\n",
       " |  coarse_like(self, N)\n",
       " |      return a new grid object coarsened by a factor n, but with\n",
       " |      all the other properties the same\n",
-      " |  \n",
+      " |\n",
       " |  fine_like(self, N)\n",
       " |      return a new grid object finer by a factor n, but with\n",
       " |      all the other properties the same\n",
-      " |  \n",
+      " |\n",
       " |  scratch_array(self, nvar=1)\n",
       " |      return a standard numpy array dimensioned to have the size\n",
       " |      and number of ghostcells as the parent grid\n",
-      " |  \n",
+      " |\n",
       " |  ----------------------------------------------------------------------\n",
       " |  Data descriptors defined here:\n",
-      " |  \n",
+      " |\n",
       " |  __dict__\n",
-      " |      dictionary for instance variables (if defined)\n",
-      " |  \n",
+      " |      dictionary for instance variables\n",
+      " |\n",
       " |  __weakref__\n",
-      " |      list of weak references to the object (if defined)\n",
-      " |  \n",
+      " |      list of weak references to the object\n",
+      " |\n",
       " |  ----------------------------------------------------------------------\n",
       " |  Data and other attributes defined here:\n",
-      " |  \n",
+      " |\n",
       " |  __hash__ = None\n",
       "\n"
      ]
@@ -384,7 +384,7 @@
     {
      "data": {
       "text/plain": [
-       "0.5749769043407793"
+       "np.float64(0.5749769043407793)"
       ]
      },
      "execution_count": 10,
@@ -742,9 +742,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 960x720 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -865,20 +865,6 @@
    "source": [
     "f.pretty_print(fmt=\"%6.2g\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -898,7 +884,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/pyro/mesh/patch.py b/pyro/mesh/patch.py
index a430fbef7..ca24015bf 100644
--- a/pyro/mesh/patch.py
+++ b/pyro/mesh/patch.py
@@ -703,11 +703,12 @@ def create(self):
         if self.idir == 1:
             _tmp = np.zeros((self.grid.qx+1, self.grid.qy, self.nvar),
                             dtype=self.dtype)
+            self.data = ArrayIndexerFC(_tmp, idir=self.idir, grid=self.grid)
+
         elif self.idir == 2:
             _tmp = np.zeros((self.grid.qx, self.grid.qy+1, self.nvar),
                             dtype=self.dtype)
-
-        self.data = ArrayIndexerFC(_tmp, idir=self.idir, grid=self.grid)
+            self.data = ArrayIndexerFC(_tmp, idir=self.idir, grid=self.grid)
 
         self.initialized = 1
 
diff --git a/pyro/swe/unsplit_fluxes.py b/pyro/swe/unsplit_fluxes.py
index 2847dcde8..70b062d39 100644
--- a/pyro/swe/unsplit_fluxes.py
+++ b/pyro/swe/unsplit_fluxes.py
@@ -253,6 +253,7 @@ def unsplit_fluxes(my_data, rp, ivars, solid, tc, dt):
 
     riemann = rp.get_param("swe.riemann")
 
+    riemannFunc = None
     if riemann == "HLLC":
         riemannFunc = ifc.riemann_hllc
     elif riemann == "Roe":