From 0b69f4d7563f7fb88eaa0a22c22006ce8e19b749 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 17:18:50 -0400
Subject: [PATCH 001/120] FEAT: Update binary search `search_altitudes`

- Make it into a general function (not just altitude)
- Handles ooblate planets now!
- Optional argument to not search ghost cells
---
 include/grid.h                    |  3 ---
 src/solver_grid_interpolation.cpp | 27 +++++++++++++++------------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index fed807e9..ce777925 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -531,9 +531,6 @@ class Grid
     bool in_grid;
   };
 
-  // Return the index of the last element that has altitude smaller than or euqal to the input
-  uint64_t search_altitude(const precision_t alt_in) const;
-
   // Calculate the range of a spherical grid
   void get_sphere_grid_range(struct sphere_range &sr) const;
   // Calculate the range of a cubesphere grid
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 39bb2dc3..a98f9b50 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -72,22 +72,23 @@ int64_t get_cube_surface_number(const arma_vec &point_in) {
 // Helper variables / function ends. The following are all member functions of Grid class
 
 // --------------------------------------------------------------------------
-// Return the index of the last element that has altitude smaller than or euqal to the input
+// Return the index of the last element that has a value smaller than or equal to the input
+// - Optional argument (nGCs=0) since we cannot see grid info.
 // --------------------------------------------------------------------------
 
-uint64_t Grid::search_altitude(const precision_t alt_in) const {
+uint64_t binary_search_array(precision_t val_in, arma_vec ref_arr, int64_t nGCs = 0) {
   // Copy from std::upper_bound. Can't directly use it
   // mainly because geoAlt_scgc(0, 0, *) can't be formed as an iterator
   uint64_t first, last, len;
   first = nGCs;
-  last = nAlts - nGCs;
+  last = ref_arr.size();
   len = last - first;
 
   while (len > 0) {
     uint64_t half = len >> 1;
     uint64_t mid = first + half;
 
-    if (geoAlt_scgc(0, 0, mid) > alt_in)
+    if (ref_arr(mid) > val_in)
       len = half;
 
     else {
@@ -227,10 +228,12 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
 
   // The altitude may not be linearly spaced, so use binary search to find
   // the first element smaller than or equal to the altitude of the give point
-  // Implemented in search_altitude
-  coef.iAlt = search_altitude(alt_in);
-  coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-              / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+  // Implemented in binary_search_array
+  // - since Alt can be latitude-dependent, this needs to be done last.
+  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
+                  coef.iCol, coef.iAlt));
 
   // Put the coefficient into the vector
   coef.in_grid = true;
@@ -302,10 +305,10 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   coef.iCol = static_cast<uint64_t>(col_frac_index);
   coef.rCol = col_frac_index - coef.iCol;
   coef.iCol += nGCs - 1;
-  // Use binary search to find the index for altitude
-  coef.iAlt = search_altitude(alt_in);
-  coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-              / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+  // Use binary search to find the index for altitude (handles oblate planets)
+  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
 
   // Put the coefficient into the vector
   coef.in_grid = true;

From 235e527ec61d5f0a4f8aa18f95313e66846d3fdf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 19:20:39 -0400
Subject: [PATCH 002/120] BUG: Complete the change from MagLat to MagInvLat.

MagLat is not defined on the geo grid (yet)

> But invLat is made for both the geo grid & mag grid. And included in 'ions'/'bfield' outputs
---
 include/grid.h    | 3 +--
 src/fill_grid.cpp | 4 ++--
 src/output.cpp    | 4 ++++
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index ce777925..f6a660ec 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -69,8 +69,7 @@ class Grid
   // These define the magnetic grid:
   // Armidillo Cube Versions:
   arma_cube magLon_scgc, magX_scgc;
-  // The magnetic latitude and altitude need to be defined better. This should be the angle between
-  // magnetic equator and the point, but sometimes it is invariant latitude.
+  // The magnetic latitude is the angle between the magnetic equator and the point.
   arma_cube magLat_scgc, magY_scgc;
   // This is often just the altitude....
   arma_cube magAlt_scgc, magZ_scgc;
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 2e0b2418..183017cb 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -125,7 +125,7 @@ void Grid::calc_mlt() {
   // calculated and converted to an hour.
 
   for (int iZ = 0; iZ < nZ; iZ++) {
-    dlat_north = 1.0 - (cPI / 2.0 - magLat_scgc.slice(iZ)) / cPI;
+    dlat_north = 1.0 - (cPI / 2.0 - magInvLat_scgc.slice(iZ)) / cPI;
     x_blend = dlat_north * mag_pole_north_gse[0](0, 0, iZ) +
               (1.0 - dlat_north) * mag_pole_south_gse[0](0, 0, iZ);
     y_blend = dlat_north * mag_pole_north_gse[1](0, 0, iZ) +
@@ -171,7 +171,7 @@ void Grid::fill_grid_bfield(Planets planet) {
           bfield_info = get_bfield(lon, lat, alt, DoDebug,
                                    planet);
           // This is Invariant Latitude
-          magLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
+          // (regular magnetic latitude is not defined yet for the geo grid)
           magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
           magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
 
diff --git a/src/output.cpp b/src/output.cpp
index 2a16a8c0..61115d97 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -260,6 +260,10 @@ bool output(const Neutrals &neutrals,
                                                     "Magnetic Longitude",
                                                     "degrees",
                                                     grid.magLon_scgc * cRtoD);
+        AllOutputContainers[iOutput].store_variable("invLat",
+                                                      "Magnetic Invariant Latitude",
+                                                      "degrees",
+                                                      grid.magInvLat_scgc * cRtoD);
         AllOutputContainers[iOutput].store_variable("mlt",
                                                     "Magnetic Local Time",
                                                     "hours",

From c578a436ab2f0e573de3a4ab15463396b60b4583 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 19:53:25 -0400
Subject: [PATCH 003/120] BUG: do not fill dipole grid's mag coords in
 fill_grid_bfield

ideally this is refactored, but whatever. The way this was being done kept messing with the order of points.
---
 src/fill_grid.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 183017cb..5dd2f98e 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -172,8 +172,12 @@ void Grid::fill_grid_bfield(Planets planet) {
                                    planet);
           // This is Invariant Latitude
           // (regular magnetic latitude is not defined yet for the geo grid)
+          // - magLat is not used by the geo grid, so not defined
           magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
-          magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
+
+          // init_mag grid already initialized magLon
+          if (iGridShape_ != iDipole_)
+            magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
 
           bfield_mag_scgc(iLon, iLat, iAlt) = 0.0;
 

From 464998fbbcbdccf7e49c78add316aa8df93b3cc8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 22:05:25 -0400
Subject: [PATCH 004/120] FEAT: Add methods to do dipole interpolation

---
 include/grid.h                    | 32 +++++++++++++
 src/solver_grid_interpolation.cpp | 80 +++++++++++++++++++++++++++++++
 2 files changed, 112 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index f6a660ec..874dcb94 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -433,6 +433,20 @@ class Grid
   bool set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                const std::vector<precision_t> &Lats,
                                const std::vector<precision_t> &Alts);
+
+  /**
+   * \brief Set the interpolation coefficients for the dipole grid
+   * \param Lons The longitude of points
+   * \param Lats The latitude of points
+   * \param Alts The altitude of points
+   * \pre Lons, Lats and Alts have the same size
+   * \return true if the function succeeds, false if the instance is not a
+   *         mag grid or the size of Lons, Lats and Alts are not the same.
+   */
+  bool set_dipole_interpolation_coefs(const std::vector<precision_t> &Lons,
+                                      const std::vector<precision_t> &Lats,
+                                      const std::vector<precision_t> &Alts);
+  
   /**
    * \brief Create a map of geographic locations to data and do the interpolation
    * \param data The value at the positions of geoLon, geoLat, and geoAlt
@@ -511,6 +525,17 @@ class Grid
     bool col_min_exclusive;
     bool col_max_exclusive;
   };
+  // The struct representing the range of a dipole grid (in magnetic coordinates)
+  struct dipole_range{
+    precision_t lon_min;
+    precision_t lon_max;
+    precision_t dLon;
+    precision_t lat_min;
+    precision_t lat_max;
+    precision_t dLat;
+    precision_t alt_min;
+    precision_t alt_max;
+  };
 
   // The index and coefficient used for interpolation
   // Each point is processed by the function set_interpolation_coefs and stored
@@ -534,6 +559,8 @@ class Grid
   void get_sphere_grid_range(struct sphere_range &sr) const;
   // Calculate the range of a cubesphere grid
   void get_cubesphere_grid_range(struct cubesphere_range &cr) const;
+  // Calculate the range of a dipole grid
+  void get_dipole_grid_range(struct dipole_range &dr) const;
 
   // Helper function for set_interpolation_coefs
   void set_interp_coef_sphere(const sphere_range &sr,
@@ -544,6 +571,11 @@ class Grid
                                   const precision_t lon_in,
                                   const precision_t lat_in,
                                   const precision_t alt_in);
+  // (note these are magnetic coordinates)
+  void set_interp_coef_dipole(const dipole_range &dr,
+                              const precision_t lon_in,
+                              const precision_t lat_in,
+                              const precision_t alt_in);
 
   // Processed interpolation coefficients
   std::vector<struct interp_coef_t> interp_coefs;
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index a98f9b50..a974de13 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -180,6 +180,28 @@ void Grid::get_cubesphere_grid_range(struct cubesphere_range &cr) const {
   }
 }
 
+// --------------------------------------------------------------------------
+// Get the range of a Dipole grid
+// --------------------------------------------------------------------------
+
+void Grid::get_dipole_grid_range(struct dipole_range &dr) const {
+  // Retrieve the range and delta of longitude, latitude and altitude
+  // ** Note these are magnetic coordinates. Not labeled for consistency **
+  dr.lon_min = magLon_Corner(nGCs, nGCs, nGCs);
+  dr.lon_max = magLon_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+
+  dr.lat_min = magLat_Corner(nGCs, nGCs, nGCs);
+  dr.lat_max = magLat_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+
+  // magAlt and geoAlt are the same, doesn't matter which we use:
+  dr.alt_min = geoAlt_scgc(nGCs, nGCs, nGCs);
+  dr.alt_max = geoAlt_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+
+  // MagLon steps are uniform:
+  dr.dLon = magLon_Corner(1, 0, 0) - magLon_Corner(0, 0, 0);
+}
+
+
 // --------------------------------------------------------------------------
 // Set interpolation coefficients helper function for spherical grid
 // Almost the copy of interp_sphere_linear_helper
@@ -315,6 +337,64 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   interp_coefs.push_back(coef);
 }
 
+void Grid::set_interp_coef_dipole(const dipole_range &dr,
+                                  const precision_t lon_in,
+                                  const precision_t lat_in,
+                                  const precision_t alt_in) {
+
+  // The structure which will be put into the interp_coefs. Initialize in_grid to be false
+  struct interp_coef_t coef;
+  coef.in_grid = false;
+
+  // Determine whether the point is inside this grid
+  // Treat north pole specially because latitude is inclusive for both -cPI/2 and cPI/2
+  if (lon_in < dr.lon_min || lon_in >= dr.lon_max || lat_in < dr.lat_min
+      || lat_in > dr.lat_max || (lat_in == dr.lat_max && dr.lat_max != cPI / 2)
+      || alt_in < dr.alt_min || alt_in > dr.alt_max) {
+    interp_coefs.push_back(coef);
+    return;
+  }
+
+  // ASSUMPTION: LONGITUDE IS LINEARLY SPACED, nGCs >= 1
+  // For the cell containing it, directly calculate its x index
+  // Find y & z indices using binary search
+
+  // The number of dLon between the innermost ghost cell and the given point
+  coef.rRow = (lon_in - dr.lon_min) / dr.dLon + 0.5;
+  // Take the integer part
+  coef.iRow = static_cast<uint64_t>(coef.rRow);
+  // Calculate the fractional part, which is the ratio for Longitude
+  coef.rRow -= coef.iRow;
+  // The actual x-axis index of the bottom-left of the cube used for interpolation
+  coef.iRow += nGCs - 1;
+
+
+  // Different from the sphere, latitude & altitude are not evenly spaced.
+  // Use binary search for both.
+
+  // Lat needs to be done a little different because it could be increasing or
+  // decreasing (depending on the hemisphere we're in). Take the absolute value!
+  coef.iCol = binary_search_array(abs(lat_in),
+                                  abs(magInvLat_scgc.tube(coef.iRow, coef.iCol)), nGCs);
+
+  // need alt index to find lat coef
+  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+                                  nGCs);
+
+  // then we can do the ratios:
+  coef.rCol = (lat_in - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (magLat_scgc(coef.iRow, coef.iCol + 1, coef.iAlt)
+                 - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt));
+
+  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1)
+                 - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
+
+  // Put the coefficient into the vector
+  coef.in_grid = true;
+  interp_coefs.push_back(coef);
+}
+
 // --------------------------------------------------------------------------
 // Set the interpolation coefficients
 // --------------------------------------------------------------------------

From 955bcf93ab5986b8de67e2bf90a3353c3a2676ba Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 14 Mar 2025 12:34:51 -0400
Subject: [PATCH 005/120] FEAT: Inputs for new dipole grid

---
 share/run/UA/inputs/defaults.json |  2 ++
 share/run/aether.json             |  8 +++-----
 src/init_mag_grid.cpp             | 21 ++++++---------------
 src/inputs.cpp                    | 27 +++++++++++++--------------
 4 files changed, 24 insertions(+), 34 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 861e4f81..e5ab817c 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -83,6 +83,8 @@
         "Shape" : "dipole",
         "nLatsPerBlock" : 18,
 		"LonRange" : [0.0, 360.0],
+		"LatRange" : [85.0, 360.0],
+		"AltRange" : [0.0, 360.0],
         "nLonsPerBlock" : 22,
         "nAlts" : 50,
         "MinAlt" : 80.0,
diff --git a/share/run/aether.json b/share/run/aether.json
index 76bef500..ea5387e2 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -26,15 +26,13 @@
         "IsUniformAlt" : false},
     
     "ionGrid": {
-        "dAltStretch": 0.6,
-        "LatStretch": 1,
         "Shape": "dipole",
         "nLonsPerBlock": 36,
         "nLatsPerBlock" : 18,
         "nAlts":36,
-        "LatMax":88,
-        "MinAlt":100.0,
-        "MinApex":150.0
+        "LatRange":[10, 80],
+        "AltRange":[80.0, 1000],
+		"LonRange" : [0.0, 360.0]
         },
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index d9b56927..1cb64f1a 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -634,33 +634,24 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   int64_t nGCs = get_nGCs();
 
   // Get inputs:
+
+  precision_t max_lat = grid_input.lat_min;
+  precision_t max_lat = grid_input.lat_max;
+
   precision_t min_alt = grid_input.alt_min * cKMtoM;
-  precision_t LatStretch = grid_input.LatStretch;
-  precision_t Gamma = grid_input.FieldLineStretch;
-  precision_t min_apex = grid_input.min_apex * cKMtoM;
-  precision_t max_lat = grid_input.max_blat;
+  precision_t max_alt = grid_input.alt_max * cKMtoM;
 
   // Normalize inputs to planet radius... (update when earth is oblate)
   precision_t planetRadius = planet.get_radius(0.0);
   // Altitude to begin modeling, normalized to planet radius
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
-  precision_t min_apex_re = (min_apex + planetRadius) / planetRadius;
-
-  if (LatStretch != 1) {
-    report.error("LatStretch values =/= 1 are not yet supported!");
-    DidWork = false;
-  }
+  precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;
   }
 
-  if (min_alt >= min_apex) {
-    report.error("min_apex must be more than min_alt");
-    DidWork = false;
-  }
-
   // Get some coordinates and sizes in normalized coordinates:
   arma_vec lower_left_norm = quadtree_ion.get_vect("LL"); // origin
   arma_vec size_right_norm = quadtree_ion.get_vect("SR"); // lon_lims
diff --git a/src/inputs.cpp b/src/inputs.cpp
index f8351ca9..c35d8637 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -452,22 +452,22 @@ Inputs::grid_input_struct Inputs::get_grid_inputs(std::string gridtype) {
   grid_specs.lon_min = min_max[0] * cDtoR;
   grid_specs.lon_max = min_max[1] * cDtoR;
 
-  grid_specs.alt_min = check_settings_pt(gridtype, "MinAlt");
+  min_max = get_setting_intarr(gridtype, "LatRange");
+  grid_specs.lat_min = min_max[0] * cDtoR;
+  grid_specs.lat_max = min_max[1] * cDtoR;
+
   // The rest of the settings are different for mag/geo grids,
   // First take the magnetic options, then "else" should be (cube-)sphere
-
+  // - This checks if "dipole" is in shape, to account for the # of root nodes.
   if (grid_specs.shape.find("dipole") != std::string::npos) {
-    // Latitude range (base of field line) is specified with max lat & min apex.
-    grid_specs.max_blat = check_settings_pt(gridtype, "LatMax") * cDtoR;
-    grid_specs.min_apex = check_settings_pt(gridtype, "MinApex");
-    // stretch the baselatitudes (not yet implemented)
-    grid_specs.LatStretch = check_settings_pt(gridtype, "LatStretch");
-    // controls the spacing of points along field line, <<1 for more pts at low alts
-    grid_specs.FieldLineStretch = check_settings_pt(gridtype, "dAltStretch");
+    // Invariant latitude range (of real corners) is specified with max/min.
+    // max alt of open field lines, and min alt, is set in AltRange
+    min_max = get_setting_intarr(gridtype, "AltRange");
+    grid_specs.alt_min = min_max[0];
+    grid_specs.alt_max = min_max[1];
+
   } else {
-    min_max = get_setting_intarr(gridtype, "LatRange");
-    grid_specs.lat_min = min_max[0] * cDtoR;
-    grid_specs.lat_max = min_max[1] * cDtoR;
+    grid_specs.alt_min = check_settings_pt(gridtype, "MinAlt");
     grid_specs.alt_file = check_settings_str(gridtype, "AltFile");
     grid_specs.IsUniformAlt = get_setting_bool(gridtype, "IsUniformAlt");
 
@@ -551,9 +551,8 @@ precision_t Inputs::get_dt_output(int iOutput) {
 
   if (iOutput < nOutputs)
     value = settings.at("Outputs").at("dt").at(iOutput);
-  else{
+  else
     report.error("Output Error; more output types than dt's provided.");
-  }
 
   return value;
 }

From c5688465468ac75e1e5a9792ae4160a899c128cf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 14 Mar 2025 14:10:12 -0400
Subject: [PATCH 006/120] FEAT: Quadtree for new dipole - 4 & 6 root nodes

---
 include/dipole.h                  | 126 +++++++++---------------------
 include/quadtree.h                |   2 +
 share/run/UA/inputs/defaults.json |   2 +-
 share/run/aether.json             |   2 +-
 src/quadtree.cpp                  |  38 ++++-----
 5 files changed, 58 insertions(+), 112 deletions(-)

diff --git a/include/dipole.h b/include/dipole.h
index c8aed455..9ad2f8a4 100644
--- a/include/dipole.h
+++ b/include/dipole.h
@@ -8,119 +8,71 @@
 #include <memory>
 
 /*************************************************
- * \brief A namespace with all (1-root) dipole grid logic.
+ * \brief A namespace with all (4-root) dipole grid logic.
  *************************************************/
-namespace Dipole {
-
-  /// The normalized origins of each face of the cube (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0}
-  };
-
-  /// Normalized right steps in cube
-  static const arma_mat RIGHTS = {
-				  {2.0, 0.0, 0.0}
-  };
-
-  /// Normalized up steps in cube
-  static const arma_mat UPS = {
-			       {0.0, 1.0, 0.0}
-  };
+namespace Dipole4 {
 
+/// The normalized origins of each node (i.e. corner)
+static const arma_mat ORIGINS = {
+  { 0.0,  -0.5, 0.0},
+  { 0.0, -0.25, 0.0},
+  { 0.0,   0.0, 0.0},
+  { 0.0,  0.25, 0.0}
 };
 
-/*************************************************
- * \brief A namespace with all (2-root) dipole grid logic.
- *************************************************/
-namespace Dipole2 {
-
-  /// The normalized origins of each face of the cube (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0},
-           { 0.0,  0.0, 0.0}
-  };
-
-  /// Normalized right steps in cube
-  static const arma_mat RIGHTS = {
-				  {2.0, 0.0, 0.0},
-				  {2.0, 0.0, 0.0}
-  };
-
-  /// Normalized up steps in cube
-  static const arma_mat UPS = {
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0}
-  };
-
+/// Normalized right steps in node
+static const arma_mat RIGHTS = {
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0}
 };
 
-/*************************************************
- * \brief A namespace with all (4-root) dipole grid logic.
- *************************************************/
-namespace Dipole4 {
-
-  /// The normalized origins of each node (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0},
-				   { 1.0, -0.5, 0.0},
-				   { 1.0,  0.0, 0.0},
-           { 0.0,  0.0, 0.0}
-  };
-
-  /// Normalized right steps in node
-  static const arma_mat RIGHTS = {
-				  {1.0, 0.0, 0.0},
-				  {1.0, 0.0, 0.0},
-				  {1.0, 0.0, 0.0},
-				  {1.0, 0.0, 0.0}
-  };
-
-  /// Normalized up steps in node
-  static const arma_mat UPS = {
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0}
-  };
+/// Normalized up steps in node
+static const arma_mat UPS = {
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0}
+};
 
 };
 
 
 /*************************************************
  * \brief A namespace with all (6-root) dipole grid logic.
- * This is the same as the Sphere6
  *************************************************/
 
 namespace Dipole6 {
 
 /// The normalized origins of each face of the cube (i.e. corner)
 static const arma_mat ORIGINS = {
-    {    0.0, -0.5, 0.0},
-    {2.0/3.0, -0.5, 0.0},
-    {4.0/3.0, -0.5, 0.0},
-    {    0.0, 0.0, 0.0},
-    {2.0/3.0, 0.0, 0.0},
-    {4.0/3.0, 0.0, 0.0}
+  {    0.0, -0.5, 0.0},
+  {    0.0, -1.0 / 3.0, 0.0},
+  {    0.0, -1.0 / 6.0, 0.0},
+  {    0.0, 0.0, 0.0},
+  {    0.0, 1.0 / 6.0, 0.0},
+  {    0.0, 1.0 / 3.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat RIGHTS = {
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0}
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat UPS = {
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0}
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0}
 };
 
 }
diff --git a/include/quadtree.h b/include/quadtree.h
index e63ec1d5..18b584c7 100644
--- a/include/quadtree.h
+++ b/include/quadtree.h
@@ -181,6 +181,8 @@ class Quadtree {
   bool IsSphere = false;
   /// Defines whether the quadtree is a cubesphere or not:
   bool IsCubeSphere = false;
+  /// Defines whether the quadtree is a dipole or not:
+  bool IsDipole = false;
   
 };
 
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index e5ab817c..09ade2c6 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -80,7 +80,7 @@
         "AltFile" : ""},
 
     "ionGrid" : {
-        "Shape" : "dipole",
+        "Shape" : "dipole4",
         "nLatsPerBlock" : 18,
 		"LonRange" : [0.0, 360.0],
 		"LatRange" : [85.0, 360.0],
diff --git a/share/run/aether.json b/share/run/aether.json
index ea5387e2..96f1f08e 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -26,7 +26,7 @@
         "IsUniformAlt" : false},
     
     "ionGrid": {
-        "Shape": "dipole",
+        "Shape": "dipole4",
         "nLonsPerBlock": 36,
         "nLatsPerBlock" : 18,
         "nAlts":36,
diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index 6e5f6cf6..0e2aae65 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -8,8 +8,8 @@
 //   - Cubesphere, this has 6 root nodes (2 polar, 4 equatorial)
 //   - Sphere, this has 1 root node (whole grid)
 //   - Sphere6, this is a spherical grid, but has 6 root nodes (2 lats, 3 lons)
-//   - Dipole, which may be the same as Sphere
 //   - Dipole4, which has 4 root nodes (4 lats, 1 lon)
+//   - Dipole6, which has 4 root nodes (6 lats, 1 lon)
 
 #include "aether.h"
 
@@ -22,11 +22,8 @@ Quadtree::Quadtree(std::string shape) {
   if (shape == "sphere")
     nRootNodes = 1;
 
-  if (shape == "dipole")
-    nRootNodes = 1;
-
-  if (shape == "dipole2")
-    nRootNodes = 2;
+  if (shape == "dipole4")
+    nRootNodes = 4;
 
   if (shape == "dipole6")
     nRootNodes = 6;
@@ -66,25 +63,19 @@ void Quadtree::build(std::string gridtype) {
     IsSphere = true;
   }
 
-  if (grid_input.shape == "dipole") {
-    origins = Dipole::ORIGINS;
-    rights = Dipole::RIGHTS;
-    ups = Dipole::UPS;
-    IsSphere = true;
-  }
-
-  if (grid_input.shape == "dipole2") {
-    origins = Dipole2::ORIGINS;
-    rights = Dipole2::RIGHTS;
-    ups = Dipole2::UPS;
-    IsSphere = true;
+  if (grid_input.shape == "dipole4") {
+    origins = Dipole4::ORIGINS;
+    rights = Dipole4::RIGHTS;
+    ups = Dipole4::UPS;
+    IsDipole = true;
   }
 
   if (grid_input.shape == "dipole6") {
     origins = Dipole6::ORIGINS;
     rights = Dipole6::RIGHTS;
     ups = Dipole6::UPS;
-    IsSphere = true;
+    IsDipole = true;
+
   }
 
   arma_vec o(3), r(3), u(3);
@@ -112,10 +103,11 @@ void Quadtree::build(std::string gridtype) {
   // restrict the domain.  This will only work for the spherical
   // grid so far:
 
-  if (grid_input.lon_min > 0.0 ||
-      grid_input.lon_max < 2.0 * cPI ||
-      grid_input.lat_min > -cPI / 2.0 ||
-      grid_input.lat_max < cPI / 2.0) {
+  if ((grid_input.lon_min > 0.0 ||
+       grid_input.lon_max < 2.0 * cPI ||
+       grid_input.lat_min > -cPI / 2.0 ||
+       grid_input.lat_max < cPI / 2.0)
+      && (IsSphere)) {
     // We are dealing with less than the whole Earth...
     origins(0) = grid_input.lon_min / cPI;
     origins(1) = grid_input.lat_min / cPI;

From 3027485ee1d33948ea82fe581d6a1615945ef4fc Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 17 Mar 2025 16:07:17 -0400
Subject: [PATCH 007/120] FEAT/DOC: Update Python script to match new dipole
 grid

- Does not have final pole ghost cells yet...
---
 edu/examples/Dipole/dipole.py | 479 +++++++++++++++++-----------------
 1 file changed, 235 insertions(+), 244 deletions(-)

diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index f8bae674..a50a3093 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -6,122 +6,153 @@
 # Set to None or '' to not save, just show (pan/zoom capabilities).
 fig_save_path = None
 
+# Number of lats/alts (without ghost cells)
+nLatsPerBlock_in = 12 
+nAltsPerBlock_in = 12 
 
-nf = 100  # number of field lines
-nz = 200  # number of grid cells on each field line
-alt_min = 90  # in km, min altitude
+# in degrees, where to begin & end grid between (90,0)
+max_blat = 85
+min_blat = 12
 
-# in degrees, start/end latitudes of field lines. between (90,0)
-max_blat = 89.9
-min_blat = 2.25
+# In km (above surface)
+min_alt = 80
+max_alt = 800
 
-gams = 0.1
-# point distribution along field lines.
-# higher puts more pts at high altitudes.
+# Number of "blocks" to simulate - i.e. # of processors in Aether run (>4 & even)
+nBlocks = 6
 
-baselat_spacing_factor = 6  # exponential factor for spacing baselats (uses cos() too)
+nGCs = 2
 
 # consts:
-Re = 6371  # in km
-
-
-# leave empty for "auto" (aaron b choose), or put in custom limits here.
-# [left, right, bottom, top]
-limits_left_plot = None  # default is whole image
-limits_right_plot = None  # default is [-0.1, 2.0, -0.3, 1.75]
-
-
-# ------------------------------------------------------------------------
-# Main code is here:
-# ------------------------------------------------------------------------
-
-####        Useful Functions:          ####
-
-
-def calc_baselats(blat_min, blat_max, n_f, factor):
-    """Lay down base latitudes
-
-    Args:
-        blat_min (float): min latitude to start (positive), in degrees.
-        blat_max (float): max latitude in degrees, positiv & less than 90.0
-        nf (int): Number of field lines. Must be even!
-        factor (float): Factor to use to space latitudes.
-
-    Returns:
-        numpy array: starting latitudes
-
-    Notes: follows very similar approach to doi:10.1029/2000JA000035
-    - Not exactly exponential, but exponential and a cosine!
-    - Spaced according to B-field strength.
-
-    """
-    # Space out base latitudes:
-
-    baselats = np.linspace(
-        np.cos(np.deg2rad(blat_min ** (1 / factor))),
-        np.cos(np.deg2rad(blat_max ** (1 / factor))),
-        num=n_f,
-    )
-
-    # baselats are in southern hemisphere
-    baselats = -1 * np.flip(np.rad2deg(np.arccos(baselats)) ** factor)
-
-    return baselats
-
-
-def calc_q(alt, theta, re=None, is_alt=False, isnt_re=False):
-    """Calculate q (distance along field line) for a point
-
-    Args:
-        alt (float, array-like): altitude (kn or Re)
-        theta (float or array-like): magnetic latitude (in degrees).
-            0 at mag equator (measured from North Pole).
-        re (float): radius of earth in km. only used if alt is altitude and/or in km
-        is_alt (bool): altitude is altitude? False means it's radius. Default is False
-        isnt_re (bool): altitude is in km? False means altitude has units [Re]. Default is False
-
-    Returns:
-        (array or float): q-value for the given (alt, theta) point.
-
-    Notes:
-    - make sure theta has units degrees and is measured from north pole
-    - See <doi:10.1029/2000JA000035> for more information.
-    - is_alt & isnt_re are from debugging & aren't used anymore.
-
-    """
-
-    if is_alt:  # convert altitude to radius
-        alt = alt + re
-    if isnt_re:  # convert km to re
-        alt = alt / re
-
-    return np.cos(np.deg2rad(90 - theta)) / (alt**2)
-
-
-def calc_p(alt, theta, re=None, is_alt=False, isnt_re=False):
-    """Calculate p-value (l-shell) for a given altitude & latitude
-
-    Args:
-        alt (float): altitude (in km or Re), (above surface or from origin)
-        theta (float): latitude, in degrees
-        re (float): earth radius in km. optional.
-        is_alt (bool): is alt altitude? if it's radius, set to False (default).
-        isnt_re (bool): altitude is in km? False (default) means altitude has units [Re].
-
-    Returns:
-        (array or float): p-value. Same as l-Shell, in Re.
-
-    Notes:
-    - make sure theta has units degrees and is measured from north pole
-    - See <doi:10.1029/2000JA000035> for more information.
-    - is_alt & isnt_re are from debugging & aren't used anymore.
-
-    """
-    if is_alt:
-        alt = alt + re
-    if isnt_re:
-        alt = alt / re
-    return alt / (np.sin(np.deg2rad(90 - theta)) ** 2)
+Re_KM = 6371  # in km
+cPI = np.pi
+
+
+########                  ~~~~~~~~~~~~~~~~~~~~                  ########
+# OUTLINE:
+# - constants & inputs (above)
+# - def main (the function to create the grid)
+# - def the ploting function
+# - def all conversions
+# - call main:
+#   - make the grid as Aether would
+#   - call the plotting script
+
+def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock, nAltsPerBlock):
+
+    pcenters = np.zeros([len(origins), nLatsPerBlock, nAltsPerBlock])
+    qcenters = np.zeros([len(origins), nLatsPerBlock, nAltsPerBlock])
+
+    # Loop through QT's origins
+    for n, origin in enumerate(origins):
+        close_this_block = False
+        isSouth = False
+        # If we're in south hemisphere, flip everything. WIll be undone later.
+        if origin < -0.01:
+            isSouth = True
+            origin  = -1*origin - extent
+
+        lat0 = (2*(lat_max - lat_min))*origin    
+        dlat = extent * ((lat_max - lat_min)*2) / (nLatsPerBlock - nGCs)
+        
+        # Put latitudes down evenly (centers & corners)
+        # - This forms the invariant latitudes which field lines must pass thru
+        lat1d = []
+        lat1d_co = []   
+        pcenters1d = np.empty(nLatsPerBlock)
+        qs = np.empty((nLatsPerBlock, nAltsPerBlock))
+        pcenters2d = np.empty((nLatsPerBlock, nAltsPerBlock))
+        
+
+        for i in range(nLatsPerBlock):
+            lat1d.append(lat0 + (i - nGCs + 0.5) * dlat + lat_min)
+            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+            # Easier to save later if we get this:
+            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+
+        for i in range(nLatsPerBlock+1):
+            lat1d_co.append(lat0 + (i - nGCs) * dlat + lat_min)
+
+        pcorners = alt_minRE / np.sin(np.pi/2 - np.deg2rad(lat1d_co)) **2
+
+        ## Determine if field lines should close. There are two conditions:
+        # - If the lowest l-shell in this block < altMin
+        if np.min(pcorners) < alt_maxRE: 
+            close_this_block = True
+        # - Or if we are touching the qeuator
+        if origin < 0.01: # NH equator
+            close_this_block + True
+        if np.abs(extent + origin) < 0.01: # SH equator
+            close_this_block = True
+
+        ## Setting up the q-values...
+
+        # The idea here is that we wither want the field line to close (wrap over equator)
+        # or to have its boundaries entirely within min/max alt. 
+        # We do not want field lines ending before max_alt, and vice-versa
+        # Q_max is obtained from the minimum altitude point on the highest latitude field line
+        q_max_center = rp2q(alt_minRE, pcenters1d[-1])
+        if close_this_block:
+            q_min_center = 0 # if the block is closed, q_min = 0. This is the equator!
+        else:
+            # If open, q_min is the highest altitude point on the lowest latitude field line
+            q_min_center = rp2q(alt_maxRE, pcenters1d[0])
+
+        
+        delQ = (q_max_center - q_min_center) / (nAltsPerBlock - nGCs*2) 
+        for iAlt in range(nAltsPerBlock):
+            qs[:, iAlt] = ((q_min_center + (iAlt - nGCs + 0.5) * delQ))
+        
+        # If we were in South hemisphere, multiply by -1
+        # (and reverse so the points are in the same order as NH)
+        if isSouth:
+            qs = -1*np.flip(qs)
+        qcenters[n,:] = qs
+        pcenters[n,:] = pcenters2d
+
+    return qcenters, pcenters
+
+
+####        Useful Functions for conversions:          ####
+
+def rt2q(r, t):
+    return np.cos(cPI/2 - t)/r**2
+
+def rt2p(r, t):
+    return r/(np.sin(cPI/2 - t)**2)
+
+def rt2qp(r, t):
+    q = rt2q(r, t)
+    p = rt2p(r, t)
+    return q, p
+
+def rt2xy(r, t):
+    x = r*np.cos(t)
+    y = r*np.sin(t)
+    return x, y
+
+
+def rq2t(r,q):
+    return np.arcsin(q * r**2)
+def rp2t(r, p):
+    return np.arccos(np.sqrt(r/p))
+
+def rp2q(r, p):
+    return np.sqrt((1-r/p)/r**4)
+
+def qp2xy(q, p):
+    r_ = qp_solve(q, p)
+    t_ = rq2t(r_, q)
+    return rt2xy(r_, t_)
+
+def tp2r(t, p):
+    return p * (np.cos(t)**2)
+
+def alt2r(alt, re):
+    return (alt + re)/re
+
+def r2alt(r, re):
+    return r*re - re
 
 
 def qp_solve(q, p):
@@ -145,149 +176,109 @@ def qp_solve(q, p):
     return new_r
 
 
-####        The main stuff:          ####
-
-
-def calc_exp_grid(nf0, nz0, altminre, baselats, pvals, gamma):
-    """Exponential Grid laydown, keeps grid parallel & perpendicular to B
-
-    Args:
-        nf0 (int): number of field lines
-        nz0 (int): number of points along each field line. MUST BE EVEN!
-        altminre (float): min altitude (in Re from center of earth) to trace from
-        baselats (list/array): latitudes to start at (from calc_baselats)
-        pvals (flaot): p-values (dipole coords, so it's L-shells) for all field lines
-        gamma (float): factor used to space points along field line.
-            Lower values increase point density @ low altitudes
-
-    Returns:
-        [np.array, np.array]: (nf, nz) dimensionsl arrays of:
-            - Latitudes (in deg)
-            - Radii (in re) of grid
-
-    ** This can be vectorized & shortened A LOT.
-    Left in this state for readability and in case things need to be changed.
-
+def make_plot(qs, ps, alt_min_RE, Re_km=6371,
+              abs_bot = False # Take the absolute value of latitude on bottom plot?
+              ):
+
+    rs = qp_solve(qs, ps)
+    ts = rq2t(rs, qs)
+    
+    fig = plt.figure(figsize=(8,11))
+    
+    gs = plt.GridSpec(8,9)
+    
+    ax0 = fig.add_subplot(gs[:5,:3])
+    for x,y in zip(*qp2xy(qs, ps)):
+        ax0.scatter(x,y, s=5)
+    
+    xlim, ylim = ax0.get_xlim(), ax0.get_ylim()
+    
+    circle1 = plt.Circle((0, 0), 1, color='k', alpha = .7)
+    ax0.add_patch(circle1)
+    
+    ax0.set_ylim(ylim)
+    ax0.set_xlim(xlim)
+    ax0.set_aspect(1)
+    ax0.set_title('in Re:')
+    
+    
+    ax1 = fig.add_subplot(gs[:2,4:])
+    counts, _, _ = ax1.hist(rs.flatten(), bins=60)
+    ax1.vlines(alt_min_RE, 0, max(counts)*1.1, linestyle = '--', alpha=.7, color='k')
+    ax1.set_title(f"{np.sum(rs < alt_min_RE) / np.prod(rs.shape)*100:.2f}% of points below min_alt\n"
+                 f"{np.sum(rs < 1) / np.prod(rs.shape)*100:.2f}% of points below 0 Re")
+    ax1.set_xlabel('Each cell altitude in Re')
+    ax1.set_ylabel('bin count')
+    
+    
+    ax1p2 = fig.add_subplot(gs[2,4:])
+    alt_min_KM = r2alt(alt_min_RE, Re_km)
+    counts, bins, _ = ax1p2.hist(r2alt(rs, Re_km).flatten(), bins=200)
+    ax1p2.vlines(alt_min_KM, 0, max(counts)*1.1, linestyle = '--', alpha=.7, color='k')
+    ax1p2.set_xlim(-100, 1000)
+    ax1p2.set_xlabel('altitude in km')
+
+    another_hist_ax = fig.add_subplot(gs[3:5, 4:])
+    another_hist_ax.hist(np.rad2deg(ts.flatten()), bins=90)
+    another_hist_ax.set_xlabel('Magnetic Latitude (deg)')
+    # another_hist_ax.set_xlabel(
+
+    
+    ax2 = fig.add_subplot(gs[5:,:])
+    for x,y in zip(np.rad2deg(ts), r2alt(rs, Re_km)):
+        if abs_bot:
+            ax2.scatter(np.abs(x),y)
+        else:
+            ax2.scatter(x,y)
+            
+    ax2.hlines(100, 0 if abs_bot else -90, 90, color='k', alpha=.8)
+    ax2.set_ylim(0,1500)
+    ax2.set_xlabel('Magnetic Latitude (deg)')
+    ax2.set_ylabel('Altitude (km)')
+    
+    
+    
+    plt.tight_layout()
+
+    return fig
+
+
+def generate_sym_quadtree(nBlocks):
     """
+    Makes the latitude portion of the quadtree
+    input: nBlocks
+    outputs:
+        origins (normed y-coordinate of lower-left)
+        extent (size_up_norm)
+    """
+    origins = np.linspace(-0.5, 0.5, num=nBlocks, endpoint=False)
+    extent = 1/nBlocks
 
-    lats_2d = []
-    rs_2d = []
-
-    nzh = int(nz0 / 2)
-
-    for f_iter in range(nf0):
-        # q in sh & nh
-        q_S = calc_q(altminre, baselats[f_iter], is_alt=False, isnt_re=False)
-        q_N = calc_q(altminre, -baselats[f_iter], is_alt=False, isnt_re=False)
-
-        # linear spacing - made it really readable, could be done "cleaner"
-        delqp = (q_N - q_S) / nz0
-        qp0 = []
-        for i in range(nz0):
-            qp0.append(q_S + i * delqp)
-
-        # exp grid laydown, (same for all calls here, speed not an issue though)
-        delqp = altminre * delqp
-        f00s = []
-        for i in range(nz0):
-            f00s.append(gamma + (1 - gamma) * np.exp(-(((i - nzh) / (nz0 / 10)) ** 2)))
-
-        # spacing according to sinh function
-        ft = []
-        for i in range(nz0):
-            fb0 = (1 - f00s[i]) / np.exp(-q_S / delqp - 1)
-            fa = f00s[i] - fb0
-            ft.append(fa + fb0 * np.exp(-(qp0[i] - q_S) / delqp))
-
-        # q values, from south -> equator
-        qpnew = []
-        for i in range(nzh):
-            delq = qp0[i] - q_S
-            qpnew.append(q_S + ft[i] * delq)
-
-        # qpnew is from south-equator. extend it to north pole,
-        # so *-1 & reverse order so it is ascending
-        qpnew.extend(np.flip(np.array(qpnew)) * -1)
-
-        ilats = []
-        irs = []
-
-        for i in range(nz0):
-            # use qpsolve to get r from (q,p)
-            irs.append(qp_solve(qpnew[i], pvals[f_iter]))
-            # Use dipole equations to get lat from q and r
-            # q = cos(theta)/r**2
-            ilats.append(np.rad2deg(np.arcsin(qpnew[i] * irs[-1] ** 2)))
-
-        # Put into 2-D; lists easier & then return numpy
-        lats_2d.append(ilats)
-        rs_2d.append(irs)
-
-    return np.array(lats_2d), np.array(rs_2d)
-
-
-####        Actual computation:          ####
-
-
-AltMinRe = (alt_min + Re) / Re  # alt min in Re
-
-# baselats
-baselats = calc_baselats(min_blat, max_blat, nf, baselat_spacing_factor)
-
-# l-shells
-pvals = calc_p(AltMinRe, baselats)
-
-# take those, make lats & radii
-lats, rs = calc_exp_grid(nf, nz, AltMinRe, baselats, pvals, gams)
-
-
-print("making plot")
-
-# change variables in case anyone wants to make different plots
-xs = lats[:, :]
-ys = rs[:, :]
+    return origins, extent
 
 
-fig, ax = plt.subplots(1, 2, figsize=(7, 7))
 
-# scatter points, same color is same field line
-for x, y in zip(xs, ys):
-    ax[0].scatter(y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)))
-    ax[1].scatter(y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)))
+# ------------------------------------------------------------------------
+# Main code is here:
+# ------------------------------------------------------------------------
+if __name__ == "__main__":
 
-# change variables again, overwrite previous xs, ys
-# Take every 4th field line so it's more clear to see things
-xs = lats[:, :-8:4]
-ys = rs[:, :-8:4]
-# black dashed lines, same nz value at different nf's
-for x, y in zip(xs.T, ys.T):
-    ax[0].plot(
-        y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)), linestyle="--", color="k"
-    )
-    ax[1].plot(
-        y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)), linestyle="--", color="k"
-    )
+    alt_maxRE = alt2r(max_alt, Re_KM)
+    alt_minRE = alt2r(min_alt, Re_KM)
+    
+    nLatsPerBlock = nLatsPerBlock_in + nGCs*2
+    nAltsPerBlock = nAltsPerBlock_in + nGCs*2
 
-# make square-ish
-ax[0].set_aspect(1)
-ax[1].set_aspect(1)
+    origins, extent = generate_sym_quadtree(nBlocks)
 
-# custom limits?
-if limits_left_plot:
-    ax[1].set_xlim(limits_left_plot[0], limits_left_plot[1])
-    ax[1].set_ylim(limits_left_plot[2], limits_left_plot[3])
+    qs, ps = main(alt_minRE, alt_maxRE, min_blat, max_blat, origins, extent, nLatsPerBlock, nAltsPerBlock)
 
+    # if we want r&theta now:
+    # rs = qp_solve(qs, ps)
+    # ts = rq2t(rs, qs)
+    # Otherwise the plotting function does it:
 
-if limits_right_plot:
-    ax[1].set_xlim(limits_right_plot[0], limits_right_plot[1])
-    ax[1].set_ylim(limits_right_plot[2], limits_right_plot[3])
-else:
-    ax[1].set_xlim(-0.1, 2)
-    ax[1].set_ylim(-0.3, 1.75)
+    fig = make_plot(qs, ps, alt_minRE, Re_KM, abs_bot=False)
 
-# save or show:
-if fig_save_path:
-    plt.savefig(fig_save_path)
-else:
     plt.show()
-plt.close("all")
+

From defd757f4d0ded903fa9fc0e0ea8b5ef50e1c2e2 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 17 Mar 2025 16:53:05 -0400
Subject: [PATCH 008/120] FEAT: Put pole-most points into python script

---
 edu/examples/Dipole/dipole.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index a50a3093..fd6d8fee 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -3,8 +3,7 @@
 
 ####        Set inputs          ####
 
-# Set to None or '' to not save, just show (pan/zoom capabilities).
-fig_save_path = None
+# To save plot, change last line of the code to save it, otherwise it is just 'show'n
 
 # Number of lats/alts (without ghost cells)
 nLatsPerBlock_in = 12 
@@ -32,11 +31,11 @@
 # OUTLINE:
 # - constants & inputs (above)
 # - def main (the function to create the grid)
-# - def the ploting function
 # - def all conversions
-# - call main:
+# - def the ploting function
+# - Run script:
 #   - make the grid as Aether would
-#   - call the plotting script
+#   - call the plotting function
 
 def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock, nAltsPerBlock):
 
@@ -62,17 +61,23 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
         pcenters1d = np.empty(nLatsPerBlock)
         qs = np.empty((nLatsPerBlock, nAltsPerBlock))
         pcenters2d = np.empty((nLatsPerBlock, nAltsPerBlock))
-        
 
         for i in range(nLatsPerBlock):
             lat1d.append(lat0 + (i - nGCs + 0.5) * dlat + lat_min)
-            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+
+        # IF touching pole, put last ghost cell at 89.9 degrees & the 2nd to last 1/2 way there.
+        if origin + extent > 0.49:
+            lat1d[-1] = 89.9
+            lat1d[-2] = (lat1d[-1] + lat1d[-2]) /2
+
+        for i in range(nLatsPerBlock):
+            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
             # Easier to save later if we get this:
-            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
 
-        for i in range(nLatsPerBlock+1):
+        # Corners (only used here to determins if we need to close >1 block per hemisphere)
+        for i in range(nLatsPerBlock+1): 
             lat1d_co.append(lat0 + (i - nGCs) * dlat + lat_min)
-
         pcorners = alt_minRE / np.sin(np.pi/2 - np.deg2rad(lat1d_co)) **2
 
         ## Determine if field lines should close. There are two conditions:
@@ -276,6 +281,7 @@ def generate_sym_quadtree(nBlocks):
     # if we want r&theta now:
     # rs = qp_solve(qs, ps)
     # ts = rq2t(rs, qs)
+
     # Otherwise the plotting function does it:
 
     fig = make_plot(qs, ps, alt_minRE, Re_KM, abs_bot=False)

From ff16298288922b641afafb223e2d8e157a146a64 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 18 Mar 2025 12:24:19 -0400
Subject: [PATCH 009/120] STY: more comments, formatting

---
 edu/examples/Dipole/dipole.py | 44 +++++++++++++++++++----------------
 1 file changed, 24 insertions(+), 20 deletions(-)

diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index fd6d8fee..71bd85f8 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -3,13 +3,14 @@
 
 ####        Set inputs          ####
 
-# To save plot, change last line of the code to save it, otherwise it is just 'show'n
+# To save plot, change last line of the code, otherwise it is just shown
 
 # Number of lats/alts (without ghost cells)
 nLatsPerBlock_in = 12 
 nAltsPerBlock_in = 12 
 
 # in degrees, where to begin & end grid between (90,0)
+# Grid is mirrored across N/S hemisphere
 max_blat = 85
 min_blat = 12
 
@@ -17,7 +18,7 @@
 min_alt = 80
 max_alt = 800
 
-# Number of "blocks" to simulate - i.e. # of processors in Aether run (>4 & even)
+# Number of "blocks" to simulate - i.e. # of processors in Aether run (must be >4 & even)
 nBlocks = 6
 
 nGCs = 2
@@ -71,20 +72,20 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             lat1d[-2] = (lat1d[-1] + lat1d[-2]) /2
 
         for i in range(nLatsPerBlock):
-            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
+            pcenters1d[i] = alt_minRE / (np.sin(cPI/2 - np.deg2rad(lat1d[i]))**2)
             # Easier to save later if we get this:
-            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
+            pcenters2d[i, :] = alt_minRE / (np.sin(cPI/2 - np.deg2rad(lat1d[i]))**2)
 
-        # Corners (only used here to determins if we need to close >1 block per hemisphere)
+        # Corners (only used here to determine if we need to close > 1 block per hemisphere)
         for i in range(nLatsPerBlock+1): 
             lat1d_co.append(lat0 + (i - nGCs) * dlat + lat_min)
-        pcorners = alt_minRE / np.sin(np.pi/2 - np.deg2rad(lat1d_co)) **2
+        pcorners = alt_minRE / np.sin(cPI/2 - np.deg2rad(lat1d_co)) **2
 
         ## Determine if field lines should close. There are two conditions:
         # - If the lowest l-shell in this block < altMin
         if np.min(pcorners) < alt_maxRE: 
             close_this_block = True
-        # - Or if we are touching the qeuator
+        # - Or if we are touching the equator
         if origin < 0.01: # NH equator
             close_this_block + True
         if np.abs(extent + origin) < 0.01: # SH equator
@@ -92,10 +93,11 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
 
         ## Setting up the q-values...
 
-        # The idea here is that we wither want the field line to close (wrap over equator)
+        # The idea here is that we either want the field line to close (wrap over equator)
         # or to have its boundaries entirely within min/max alt. 
-        # We do not want field lines ending before max_alt, and vice-versa
-        # Q_max is obtained from the minimum altitude point on the highest latitude field line
+        # We do not want field lines ending before max_alt, and vice-versa.
+        # By definition, q=0 at the equator and +/- infinity at the N/S poles, so:
+        # - Q_max is obtained from the minimum altitude point on the highest latitude field line
         q_max_center = rp2q(alt_minRE, pcenters1d[-1])
         if close_this_block:
             q_min_center = 0 # if the block is closed, q_min = 0. This is the equator!
@@ -109,7 +111,7 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             qs[:, iAlt] = ((q_min_center + (iAlt - nGCs + 0.5) * delQ))
         
         # If we were in South hemisphere, multiply by -1
-        # (and reverse so the points are in the same order as NH)
+        # (and reverse so the points are ascending in q, same order as NH)
         if isSouth:
             qs = -1*np.flip(qs)
         qcenters[n,:] = qs
@@ -119,6 +121,14 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
 
 
 ####        Useful Functions for conversions:          ####
+####        - Not all used... 
+####        - Format: in2out, in as few letters as necessary
+####          example: cart to geo "xy2rt": (x,y) --> (r, theta)
+
+## NOTE: theta for the dipole coordinate system is defined as co-latitude, not latitude
+## Thus, we do (cPI-theta) for rt2(q/p).
+## Then things are kept as-is, until conversion back to spherical when 
+## colatitude is again considered
 
 def rt2q(r, t):
     return np.cos(cPI/2 - t)/r**2
@@ -205,8 +215,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
     ax0.set_xlim(xlim)
     ax0.set_aspect(1)
     ax0.set_title('in Re:')
-    
-    
+
     ax1 = fig.add_subplot(gs[:2,4:])
     counts, _, _ = ax1.hist(rs.flatten(), bins=60)
     ax1.vlines(alt_min_RE, 0, max(counts)*1.1, linestyle = '--', alpha=.7, color='k')
@@ -214,8 +223,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
                  f"{np.sum(rs < 1) / np.prod(rs.shape)*100:.2f}% of points below 0 Re")
     ax1.set_xlabel('Each cell altitude in Re')
     ax1.set_ylabel('bin count')
-    
-    
+
     ax1p2 = fig.add_subplot(gs[2,4:])
     alt_min_KM = r2alt(alt_min_RE, Re_km)
     counts, bins, _ = ax1p2.hist(r2alt(rs, Re_km).flatten(), bins=200)
@@ -226,9 +234,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
     another_hist_ax = fig.add_subplot(gs[3:5, 4:])
     another_hist_ax.hist(np.rad2deg(ts.flatten()), bins=90)
     another_hist_ax.set_xlabel('Magnetic Latitude (deg)')
-    # another_hist_ax.set_xlabel(
 
-    
     ax2 = fig.add_subplot(gs[5:,:])
     for x,y in zip(np.rad2deg(ts), r2alt(rs, Re_km)):
         if abs_bot:
@@ -240,9 +246,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
     ax2.set_ylim(0,1500)
     ax2.set_xlabel('Magnetic Latitude (deg)')
     ax2.set_ylabel('Altitude (km)')
-    
-    
-    
+
     plt.tight_layout()
 
     return fig

From 3f8465fdb6399a7f30a3abf9fe5a95f80aba3df8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 18 Mar 2025 20:06:09 -0400
Subject: [PATCH 010/120] FEAT: New dipole grid is in place. Kinda.

Here's what I know:
- it doesn't run
- nan's in density and velocity, i think
- maybe in chapman integrals?
- k-coord is not right (should be alt above equator, traced along dipole -maybe)
- altitude is always negative???

But the rest is good! LOL
---
 src/init_mag_grid.cpp | 699 ++++++++++++------------------------------
 1 file changed, 192 insertions(+), 507 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 1cb64f1a..8a4df97a 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -10,6 +10,7 @@
 // or with approach from (Swisdak, 2006), who solved it analytically:
 //  https://arxiv.org/pdf/physics/0606044
 //
+// Overloaded for single conversions & arma_cubes
 // ----------------------------------------------------------------------
 
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
@@ -31,7 +32,7 @@ std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
   // now that r is determined we can solve for theta
   // theta = asin(sqrt(r/p));
   theta = acos(q * pow(r, 2.0));
-  // Then make sure its the correct sign & direction
+  // Then make sure its the correct sign & direction (not colatitude)
   theta = cPI / 2 - theta;
 
   return {r, theta};
@@ -57,367 +58,6 @@ std::pair<arma_cube, arma_cube> qp_to_r_theta(arma_cube q, arma_cube p) {
   return {r, theta};
 }
 
-// ----------------------------------------------------------------------
-// The general idea here is to make the physical cells within the
-// upper and lower limits.  The cell EDGES will be these limits, so
-// that the cell CENTERS (which this function calculates) will be
-// 1/2 dlat away from these locations.
-// The two limits coming in are the lowest northern latitude field line
-// and the highest northern latitude field line (i.e., they are both
-// positive values and are over half the domain.)
-// If the block is touching the equator boundaries or the polar
-// boundaries, then these ghost cells extend beyond these boundaries and
-// the EDGES go to [-88, -1, 1, or 88] degrees latitude, depending on
-// the boundary. This function deals with CENTERS, though, so the
-// centers are selected so the the edges will be correct when calculated
-// down stream.
-// If we are running on 1 processor only, then this is all thrown out
-// the window and code puts the centers are the +/- upper_lim. It first
-// builds the southern hemisphere with nLats/2 points, then mirrors them.
-// ----------------------------------------------------------------------
-
-arma_vec Grid::baselat_spacing(precision_t extent,
-                               precision_t origin,
-                               precision_t upper_lim,
-                               precision_t lower_lim,
-                               precision_t spacing_factor) {
-  std::string function = "Grid::baselat_spacing";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  if (report.test_verbose(3))
-    std::cout << "inputs : " << iProc << " " <<  extent << " " << origin << " "
-              << lower_lim * cRtoD << " " << lower_lim * cRtoD << "\n";
-
-  // intermediate latitude values
-  precision_t lat_low, lat_high, lat_low0, lat_high0;
-  // intermediate calculation values
-  precision_t dlat, bb, aa, ang0, angq, nLats_here, extent_here;
-  precision_t dlat0, dlatLower, dLatUpper;
-
-  // Now we can allocate the return array,
-  arma_vec Lats(nLats);
-
-  // Noting the special case of 1 root node & 1 processor...
-  bool DO_FLIPBACK = false;
-
-  int64_t iStart, iEnd;
-
-  if (extent > 0.5) {
-    // This is when running on 1 processor:
-    DO_FLIPBACK = true;
-    nLats_here = nLats / 2;
-    extent_here = 0.5;
-    iStart = 0;
-    iEnd = nLats_here;
-  } else {
-    // Span only physical cells with extent:
-    nLats_here = nLats - 2 * nGCs;
-    extent_here = extent;
-    // Want to fill in only physical cells, then do ghostcells later:
-    iStart = nGCs;
-    iEnd = nLats - nGCs;
-  }
-
-  // get the upper & lower latitude bounds for our division of the quadree
-  if (origin < 0) {
-    // negative origin == Southern hemisphere: lat_high <=> lat_low
-    lat_low, lat_high = -upper_lim, -lower_lim;
-    lat_low0 = lat_low;
-    lat_low = -lower_lim + (upper_lim - lower_lim) * (origin / 0.5);
-    lat_high = lat_low + (upper_lim - lower_lim) * (extent_here / 0.5);
-  } else {
-    // Northern hemisphere:
-    lat_low, lat_high = lower_lim, upper_lim;
-    lat_low0 = lower_lim;
-    lat_low = lower_lim + (upper_lim - lower_lim) * (origin / 0.5);
-    lat_high = lat_low + (upper_lim - lower_lim) * (extent_here / 0.5);
-  }
-
-  if (report.test_verbose(3))
-    std::cout << "lat_low, lat_high : "
-              << lat_low*cRtoD << " " << lat_high*cRtoD << " " << lower_lim << " " <<
-              upper_lim << "\n";
-
-  // normalized spacing in latitude
-  // NOTE: spacing factor != 1 will not work yet. but framework is here...
-  bb = (lat_high - lat_low) / (pow(lat_high, spacing_factor) - pow(lat_low,
-                               spacing_factor));
-  aa = lat_high - bb * pow(lat_high, spacing_factor);
-  dlat = (lat_high - lat_low) / (nLats_here);
-  // Save dlat so that we can use it in ghostcells if they are interior:
-  dlat0 = dlat;
-
-  if (!HasYdim) {
-    // edge case for 1-D (or no latitudinal extent, really)
-    // In 1-D, the base latitudes will be 1/2 way between LatMax & minApex,
-    // dlat is adjustable if it doesn't suit your needs.
-    DO_FLIPBACK = false;
-    dlat = 1.0 * cDtoR;
-    nLats_here = nLats + 1;
-  }
-
-  // Fill in physical cell centers:
-  for (int64_t j = iStart; j < iEnd; j++) {
-    ang0 = lat_low + (float(j - iStart) + 0.5) * dlat;
-    angq = aa + bb * pow(ang0, spacing_factor);
-    Lats[j] = angq;
-  }
-
-  if (DO_FLIPBACK) {
-    // In the flipback case (single processor, global sim), we want baselats
-    // to be strictly increasing, same as geo grid!
-    // remember : nLats_here = nLats / 2
-    for (int64_t j = 0; j < nLats_here; j++)
-      // mirror south to north:
-      Lats[j + nLats_here] = -1 * Lats[nLats_here - j - 1];
-  } else {
-    // Here we are filling ghostcells, first the lower GCs, then the upper GCs.
-    // If they are interior GCs, use the default dlat.  If they are exterior GCs
-    // (i.e., poleward of max lat or equatorward of min lat), then adjust the dlat
-    // to force the last cell edges to be at [-89.9, -1, 1, 89.9] depending on cells.
-    // Do the lower ghostcells:
-    // If the GCs are interior, leave dlat alone.
-    dlat = dlat0;
-
-    // South polar region:
-    if (fabs( fabs(lat_low) - fabs(upper_lim)) < 0.001) {
-      if (report.test_verbose(2))
-        std::cout << "Near south pole!\n";
-
-      dlat = (89.9 * cDtoR + lat_low) / nGCs;
-    }
-
-    // North equatorial region:
-    if (fabs( fabs(lat_low) - fabs(lower_lim)) < 0.001) {
-      if (report.test_verbose(2))
-        std::cout << "Near northern equator!\n";
-
-      dlat = (lat_low - 1.0 * cDtoR) / nGCs;
-    }
-
-    // The user may not want to go all the way to the pole or the equator.
-    // if we are very close to the pole or equator, then the calculated dlat
-    // will be small so we don't hit either.  If we are far enough away from
-    // either, we can just leave dlat alone.
-    if (dlat > dlat0)
-      dlat = dlat0;
-
-    // Fill in GCs:
-    for (int64_t j = 0; j < iStart; j++) {
-      ang0 = lat_low + (float(j - iStart) + 0.5) * dlat;
-      angq = aa + bb * pow(ang0, spacing_factor);
-      Lats[j] = angq;
-    }
-
-    // Do the upper ghostcells:
-    // If the GCs are interior, leave dlat alone.
-    dlat = dlat0;
-
-    // North polar region:
-    if (lat_high == upper_lim) {
-      if (report.test_verbose(2))
-        std::cout << "Near north pole!\n";
-
-      dlat = (89.9 * cDtoR - lat_high) / nGCs;
-    }
-
-    // South equatorial region:
-    if (fabs( fabs(lat_high) - fabs(lower_lim)) < 0.001) {
-      if (report.test_verbose(2))
-        std::cout << "Near southern equator!\n";
-
-      dlat = -(1.0 * cDtoR + lat_high) / nGCs;
-    }
-
-    // The user may not want to go all the way to the pole or the equator.
-    // if we are very close to the pole or equator, then the calculated dlat
-    // will be small so we don't hit either.  If we are far enough away from
-    // either, we can just leave dlat alone.
-    if (dlat > dlat0)
-      dlat = dlat0;
-
-    // Fill in the GCs:
-    for (int64_t j = iEnd; j < nLats; j++) {
-      ang0 = lat_high + (float(j - iEnd) + 0.5) * dlat;
-      angq = aa + bb * pow(ang0, spacing_factor);
-      Lats[j] = angq;
-    }
-  }
-
-  if (report.test_verbose(3))
-    std::cout << "Lats from baselat_spacing :\n" << Lats * cRtoD << "\n";
-
-  report.exit(function);
-  return Lats;
-}
-
-// // Gravity vectors in the dipole basis
-// void calc_dipole_gravity(Planets planet){
-
-// // rhat = -(2*cos/(del)) qhat + (sin/(del)) phat
-
-
-// }
-
-
-// === SPACING ALONG FIELD LINE === //
-// Coordinates along the field line to begin modeling
-// - Created in dipole (p,q) coordinates, stored as magnetic coords
-// - North & south hemisphere base-latitudes, shouldn't be *too* hard to support offset
-//   dipole and/or oblate Earth.
-// isCorner is a bool, if false then the p's and q's are stored for later (p,q cell centers).
-// Field line filling only needs to be redone for the "down" edges, left is the same p,q
-// and then for "lower", we just shift the p,q after
-
-void Grid::fill_field_lines(arma_vec baseLatsLoc,
-                            precision_t min_altRe, precision_t Gamma,
-                            Planets planet,
-                            bool isCorner = false) {
-
-  std::string function = "Grid::fill_field_lines";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  precision_t q_Start, delqp;
-
-  // allocate & calculate some things outside of the main loop
-  // - mostly just factors to make the code easier to read
-  precision_t qp0, fb0, ft, delq, qp2, fa, fb, term0, term1, term2, term3;
-  // exp_q_dist is the fraction of total q-distance to step for each pt along field line
-  arma_vec exp_q_dist(nAlts);
-
-  // corners/edges have one more lat dimension...
-  int64_t nLatLoc = baseLatsLoc.n_elem;
-
-  // temp holding of results from q,p -> r,theta conversion:
-  std:: pair<precision_t, precision_t> r_theta;
-  report.print(3, " calculating lshells!");
-
-  // Find L-Shell for each baseLat
-  // using L=R/sin2(theta), where theta is from north pole
-  arma_vec Lshells(nLatLoc);
-
-  for (int64_t iLat = 0; iLat < nLatLoc; iLat++)
-    Lshells(iLat) = (min_altRe) / pow(sin(cPI / 2 - baseLatsLoc(iLat)), 2.0);
-
-  report.print(3, "lshells calculated!");
-
-  if (!isCorner) {
-    for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-      for (int64_t iLat = 0; iLat < nLatLoc; iLat ++) {
-        for (int64_t iAlt = 0; iAlt < nAlts; iAlt ++) {
-          magP_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-          j_center_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-        }
-      }
-    }
-  } else {
-    for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-      for (int64_t iLat = 0; iLat < nLatLoc; iLat ++) {
-        for (int64_t iAlt = 0; iAlt < nAlts; iAlt ++) {
-          magP_Down(iLon, iLat, iAlt) = Lshells(iLat);
-          j_edge_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-          j_corner_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-        }
-      }
-    }
-  }
-
-  report.print(3, "dipole p-values stored for later.");
-
-  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++)
-    exp_q_dist(iAlt) = Gamma + (1 - Gamma) * exp(-pow(((iAlt - nAlts) /
-                                                       (nAlts / 5.0)), 2.0));
-
-  report.print(3, "expQ");
-
-  // This is wrong (same lat everywhere), but get_radius doesnt support oblate earth yet.
-  precision_t planetRadius = planet.get_radius(0.0);
-
-  // mag alts and lats:
-  arma_mat bAlts(nLatLoc, nAlts), bLats(nLatLoc, nAlts);
-
-  if (report.test_verbose(3))
-    std::cout << "Setting min alt (actually r in Re) : "
-              << min_altRe << " "
-              << planetRadius << " "
-              << (min_altRe - 1.0) * planetRadius / 1000.0 << "\n";
-
-  for (int iLat = 0; iLat < nLatLoc; iLat++) {
-    q_Start = -cos(cPI / 2 + baseLatsLoc(iLat)) / pow(min_altRe, 2.0);
-
-    // calculate const stride in dipole coords, same as sami2/3 (huba & joyce 2000)
-    // Note this is not the:
-    // ==  >>   sinh(gamma*qi)/sinh(gamma*q_S)  <<  ==
-    // but a different formula where the spacing is more easily controlled.
-    // Doesn't have any lat/lon dependence so won't work for offset dipoles
-    delqp = (-q_Start) / (nAlts + 1);
-    delqp = min_altRe * delqp;
-
-    for (int iAlt = 0; iAlt < nAlts; iAlt++) {
-      qp0 = q_Start + iAlt * (delqp);
-      fb0 = (1 - exp_q_dist(iAlt)) / exp(-q_Start / delqp - 1);
-      ft = exp_q_dist(iAlt) - fb0 + fb0 * exp(-(qp0 - q_Start) / delqp);
-      delq = qp0 - q_Start;
-
-      // Q value at this point:
-      qp2 = q_Start + ft * delq;
-
-      if (isCorner) {
-        // save the q for the "down" case:
-        for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-          magQ_Down(iLon, iLat, iAlt) = qp2;
-
-          if (iLat < nLats)
-            k_edge_scgc(iLon, iLat, iAlt) = qp2;
-
-          k_corner_scgc(iLon, iLat, iAlt) = qp2;
-        }
-      } else {
-        for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-          magQ_scgc(iLon, iLat, iAlt) = qp2;
-          k_center_scgc(iLon, iLat, iAlt) = qp2;
-        }
-
-        r_theta = qp_to_r_theta(qp2, Lshells(iLat));
-        bAlts(iLat, iAlt) = r_theta.first;
-        bLats(iLat, iAlt) = r_theta.second;
-      }
-    }
-  }
-
-  report.print(3, "QP-rtheta done!");
-
-  if (isCorner) { // we don't need the rest, yet
-    report.exit(function);
-    return;
-  }
-
-  arma_vec rNorm1d(nAlts), lat1dAlong(nAlts);
-
-  // rad_unit_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
-
-  for (int64_t iLat = 0; iLat < nLatLoc; iLat++) {
-    for (int64_t iLon = 0; iLon < nLons; iLon++) {
-      // Not currently used. Dipole isn't offset. Leaving just in case.
-      // Lon = magPhi_scgc(iLon, iLat, 1);
-
-      for (int64_t iAlt = 0; iAlt < nAlts; iAlt++) {
-        lat1dAlong(iAlt) = bLats(iLat, iAlt);
-        rNorm1d(iAlt) = bAlts(iLat, iAlt);
-      }
-
-      // Lay things down in the same order as the geo grid.
-      //centers only
-      magAlt_scgc.tube(iLon, iLat) = rNorm1d * planetRadius;
-      magLat_scgc.tube(iLon, iLat) = lat1dAlong;
-    }
-  }
-
-  report.exit(function);
-  return;
-}
 
 ////////////////////////////////////////////
 // convert cell coordinates to geographic //
@@ -455,96 +95,6 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   return llr;
 }
 
-// Use magP and magQ to make alt edges:
-// This does the heavy lifting for the edges & corners of the dipole grid.
-// These will be 1/2 way btwn each q point, which is pretty close to evenly spaced.
-// They will not, however, line up from one field line to the next.
-// It's not going to be *too* hard to get the corners to line up, but it messes with the
-// orthogonality too much for me to figure out right now.
-void Grid::dipole_alt_edges(Planets planet, precision_t min_altRe) {
-
-  std::string function = "Grid::dipole_alt_edges";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  // P-coordinates will be the same along alt coord, we saved p-vals when we made them
-  // in the fill field line function.
-  precision_t pTmp;
-
-  for (int64_t iLon = 0; iLon < nLons; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++) {
-      pTmp = magP_Down(iLon, iLat, 0);
-
-      for (int64_t iAlt = 0; iAlt < nAlts; iAlt ++)
-        magP_Corner(iLon, iLat, iAlt) = pTmp;
-    }
-  }
-
-  // Here are some shortcuts that exploit the symmetry.
-  // This is done by each coord so cases like offset dipoles or oblate planets are easier later
-
-  // first, use the fact that p is the same along each field line (alt)
-  for (int64_t iLon = 0; iLon < nLons + 1; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++)
-      magP_Corner(iLon, iLat, nAlts) = magP_Corner(iLon, iLat, nAlts - 1);
-  }
-
-  // And final step, use the longitude symmetry.
-  // It's fine, until the dipole is offset. then the entire fill_field_lines needs to be redone.
-  for (int64_t iAlt = 0; iAlt < nAlts + 1; iAlt++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++)
-      magP_Corner(nLons, iLat, iAlt) = magP_Corner(nLons - 1, iLat, iAlt);
-  }
-
-  // For q-coord we'll avg q_down (from different baseLat) above and below the point...
-  // May need to change the dipole spacing func's to get this working exactly though.
-  // With how the field line pts are currently put in, this ends up being quite a hassle.
-  // Not to mention, there would be a corner at q=0 (so r=A_LOT).
-  // Top and bottom-most corners take the same q-step as the previous cell.
-  precision_t qTmp;
-
-  for (int64_t iLon = 0; iLon < nLons; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++) {
-      for (int64_t iAlt = 1; iAlt < nAlts; iAlt ++)
-        magQ_Corner(iLon, iLat, iAlt) = (magQ_Down(iLon, iLat,
-                                                   iAlt - 1) + magQ_Down(iLon, iLat, iAlt)) / 2;
-
-      magQ_Corner(iLon, iLat, 0) = (2 * magQ_Corner(iLon, iLat, 1) - magQ_Corner(iLon,
-                                    iLat, 2));
-    }
-  }
-
-  // for last (alt) corner, take the same step as the prev corner to the highest center.
-  // this will force the highest corner to be above the last center
-  for (int64_t iLon = 0; iLon < nLons; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats; iLat++) {
-      qTmp = 2 * magQ_Corner(iLon, iLat, nAlts - 1) - magQ_Corner(iLon, iLat,
-                                                                  nAlts - 2);
-      magQ_Corner(iLon, iLat, nAlts) = qTmp;
-    }
-  }
-
-  // last lon corner, copy previous. It's the same!
-  for (int64_t iAlt = 0; iAlt < nAlts + 1; iAlt ++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++)
-      magQ_Corner(nLons, iLat, iAlt) = magQ_Corner(nLons - 1, iLat, iAlt);
-  }
-
-  // Now we have (p,q) coords corners, convert to lon/lat/alt and we r off to the races
-  std::pair <arma_cube, arma_cube> rtheta;
-  precision_t planetRadius;
-  rtheta = qp_to_r_theta(magQ_Corner, magP_Corner);
-  magLat_Corner = rtheta.second;
-
-  // Change if the dipole is offset and/or planet is oblate:
-  planetRadius =  planet.get_radius(magLat_scgc.at(1));
-  magAlt_Corner = rtheta.first * planetRadius;
-
-  report.exit(function);
-  return;
-}
-
-
 // -----------------------------------------------------------------------
 // Convert XyzDipole to XyzGeo
 //
@@ -618,15 +168,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsGeoGrid = false;
   IsMagGrid = true;
   IsCubeSphereGrid = false;
+  IsDipole = true;
 
-  report.print(0, "Creating inter-node connections Grid");
+  // report.print(0, "Creating inter-node connections Grid");
 
   //if (!Is0D & !Is1Dz)
   //  create_sphere_connection(quadtree_ion);
 
   report.print(0, "Creating Dipole Grid");
 
-  report.print(3, "Getting mgrid_inputs inputs in dipole grid");
+  report.print(3, "Getting grid inputs for dipole grid");
 
   Inputs::grid_input_struct grid_input = input.get_grid_inputs("ionGrid");
 
@@ -635,7 +186,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   // Get inputs:
 
-  precision_t max_lat = grid_input.lat_min;
+  precision_t min_lat = grid_input.lat_min;
   precision_t max_lat = grid_input.lat_max;
 
   precision_t min_alt = grid_input.alt_min * cKMtoM;
@@ -647,6 +198,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
   precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
+  magLat_scgc.zeros();
+  magInvLat_scgc.zeros();
+  magLon_scgc.zeros();
+  magAlt_scgc.zeros();
+  magLon_Corner.zeros();
+  magLat_Corner.zeros();
+  magAlt_Corner.zeros();
+
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;
@@ -655,8 +214,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Get some coordinates and sizes in normalized coordinates:
   arma_vec lower_left_norm = quadtree_ion.get_vect("LL"); // origin
   arma_vec size_right_norm = quadtree_ion.get_vect("SR"); // lon_lims
-  arma_vec size_up_norm = quadtree_ion.get_vect("SU");    //[1] = lat_lims
-  report.print(3, "Initializing (dipole) longitudes");
+  arma_vec size_up_norm = quadtree_ion.get_vect("SU");    // lat_extent
+  report.print(3, "Got all settings. Initializing longitudes.");
 
   precision_t dlon = size_right_norm(0) * cPI / (nLons - 2 * nGCs);
   precision_t lon0 = lower_left_norm(0) * cPI;
@@ -672,7 +231,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Dimension iterators
   int64_t iLon, iLat, iAlt;
 
-  // Longitudes (symmetric, for now):
+  /////////////////
+  // Longitudes: //
+  /////////////////
+
   // - Make a 1d vector
   // - copy it into the 3d cube
   for (iLon = 0; iLon < nLons; iLon++) {
@@ -680,7 +242,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     lon1dLeft(iLon) = lon0 + (iLon - nGCs) * dlon; // corners
   }
 
-
   lon1dLeft(nLons) = lon0 + (nLons - nGCs) * dlon;
 
   for (iLat = 0; iLat < nLats; iLat++) {
@@ -691,78 +252,201 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       // left edges
       magLon_Left.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_edge_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-    }
-  }
 
-  for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
-    for (iLat = 0; iLat < nLats + 1; iLat++) {
-      // Corners
       magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
     }
   }
 
+  if (magLon_scgc.has_nan())
+    report.error("NAN IN MAGLON");
+
+  // for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
+  //   for (iLat = 0; iLat < nLats + 1; iLat++) {
+  //     // Corners
+  //     magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
+  //     i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
+  //   }
+  // }
+
   report.print(3, "Done initializing longitudes, moving to latitude");
 
   ////////////////
   // Latitudes: //
   ////////////////
 
-  // min_lat calculated from min_apex
-  precision_t min_lat = acos(sqrt(1 / min_apex_re));
+  // Invariant latitude is evenly spaced across each block.
+  // Latitude limits are adjusted here, not in quadtree
+
+  // - From the quadtree, we see the origin & extent of this block
+  // - That is normalized, without any influence from settings
+  // - Scale it with the latitude limits provided by the user
+  // - Put invariant latitudes down, linearly, between this range.
+
+  // This has to be done differently in the north & south hemisphere.
+  // So note if we are in the southern hemisphere and invert it afterwards.
+
+  bool isSouth = false;
+  precision_t lat_origin = lower_left_norm(1);
 
-  // latitude of field line base:
-  // todo: needs support for variable stretching. it's like, halfway there.
+  if (lat_origin < -0.01) { // handles some imprecision
+    isSouth = true;
+    lat_origin = -1.0 * lat_origin - size_up_norm(1);
+  }
 
-  if (report.test_verbose(2))
-    std::cout << "computing baselats : " << max_lat* cRtoD  << " " << min_lat* cRtoD
-              << "\n";
+  precision_t lat0 = 2.0 * (max_lat - min_lat) * lat_origin;
+  precision_t dlat = 2.0 * size_up_norm(1) * (max_lat -  min_lat) /
+                     (nLats - nGCs);
 
-  arma_vec baseLats = baselat_spacing(size_up_norm(1), lower_left_norm(1),
-                                      max_lat, min_lat, 1.0);
+  arma_vec lat1d(nLats);
+  arma_vec lat1dDown(nLats + 1);
 
-  if (report.test_verbose(2))
-    std::cout << "baselats : " << baseLats * cRtoD << "\n";
+  for (iLat = 0; iLat < nLats; iLat++) {
+    lat1d(iLat) = lat0 + (iLat - nGCs + 0.5) * dlat + min_lat; // centers
+    lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners
+  }
 
-  // downward sides (latitude shifted by 1/2 step):
-  // TODO: This only works for linear latitude spacing, which is all that's supported right now.
-  // When the exponential spacing (or something else) is fixed, this needs updating.
-  precision_t dlat;
-  dlat = baseLats(1) - baseLats(0);
+  lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat; // last corner
 
-  // put one cell halfway btwn each base latitude, leave 1st and last cell for now...
-  for (int64_t iLat = 1; iLat < nLats; iLat ++)
-    baseLats_down(iLat) = (baseLats(iLat - 1) + baseLats(iLat)) / 2.0;
+  // At the pole:
+  // - put last ghost cell's corner at 89.9 degrees latitude
+  // - put 2nd to last corner 1/2 way between 89.9 and the last real corner
+  // - evenly space the ghost cells between these.
 
-  // Put in 1st and last cell. Done this way so it's easier to put in supercell or something else
-  baseLats_down(0) = baseLats(0) * 1.5 - baseLats(1) * 0.5;
-  baseLats_down(nLats) = baseLats(nLats - 1) * 1.5 - baseLats(nLats - 2) * 0.5;
+  if (lat_origin + size_up_norm(1) > 0.49) { // touching the pole
+    lat1dDown(nLats) = 89.9 * 180.0 / cPI;
+    lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;
+    lat1d(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 1)) / 2.0;
+    lat1d(nLats - 2) = (lat1dDown(nLats - 1) + lat1dDown(nLats - 2)) / 2.0;
+    // lat1d(-1) = (lat1dDown(-1) + lat1dDown(-2)) / 2.0;
+  }
 
-  if (report.test_verbose(2))
-    std::cout << "baselats_down : " << baseLats_down * cRtoD << "\n";
+  // l-shells of centers (nLats)
+  arma_vec Pcenters = min_alt_re / pow(sin(cPI / 2 - lat1d), 2);
 
-  report.print(3, "baselats done!");
+  // l-shells of corners
+  arma_vec Pcorners = min_alt_re / pow(sin(cPI / 2 - lat1dDown), 2);
 
-  // latitude & altitude of points on field lines (2D)
-  // Cell centers
-  fill_field_lines(baseLats, min_alt_re, Gamma, planet);
-  // Corners (final bool argument) tells function to place stuff in the corner.
-  // This is only down for the "down" edges, where the base latitudes are different.
-  fill_field_lines(baseLats_down, min_alt_re, Gamma, planet, true);
 
-  // The baseLats are the Invariant Latitudes of the grid, so we can just fill in all of the
-  // points with these values
-  for (iAlt = 0; iAlt < nAlts; iAlt++)
-    for (iLat = 0; iLat < nLats; iLat++)
-      for (iLon = 0; iLon < nLons; iLon++)
-        magInvLat_scgc(iLon, iLat, iAlt) = baseLats(iLat);
+  if (isSouth){ // so the values are increasing:
+    lat1d = -1*reverse(lat1d);
+  }
 
-  report.print(4, "Field-aligned Edges");
-  dipole_alt_edges(planet, min_alt_re);
 
-  report.print(3,
-               "Done generating symmetric latitude & altitude spacing in dipole.");
+  for (iLon = 0; iLon < nLons; iLon++) {
+    for (iAlt = 0; iAlt < nAlts; iAlt++) {
+      magInvLat_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = lat1d;
+      magP_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = Pcenters;
+      magP_Down.subcube(iLon, 0, iAlt, iLon, nLats, iAlt) = Pcorners;
+    }
+  }
+
+  report.print(3, "Done initializing invariant latitudes");
+
+  ////////////////
+  // Altitudes: //
+  ////////////////
+
+  // - Trace each field line from q_min to q_max. Identical for all field lines within this block.
+  // - Obtain the minimum "altitude" (q) from the highest latitude
+  // field line on each block
+  // - Obtain the maximum "altitude" from the lowest latitude *open* field line.
+  // (closed field lines are treated differently)
+  //  - In other words, since we are tracing from q_min to q_max, use the highest field
+  //    line to get q_min and the lowest for q_max. This forces all field lines to
+  //    start & end within the bounds.
+  // - Evenly space all points' "altitude" linear across these two values
+  // - Altitude here refers to the dipole q-coordinate - cos(magLat)/r^2
+  // - Blocks touching a pole or the equator are treated differently
+
+  // Field lines close if:
+  // - touching the (magnetic) equator
+  // - minimum Lshell in this block is < max_alt (the q-value would be undefined)
+
+  precision_t q_min = 0; // q=0 at equator (for closed blocks)
+  bool close_this_block = false;
+
+  if ((Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
+      || (abs(lat_origin) < 0.01)) // equator, with some imprecision
+    close_this_block = true;
+
+  std::cout << "closing block?: " << close_this_block << "\n";
+
+  if (!close_this_block)
+    // invLats are still all in North Hemisphere & increasing.
+    // Use minimum p & alt to solve for q
+    // q = sqrt((1-r/p)/r^4)
+    // TODO: use nGCs or 0 to index Pcorners??
+    q_min = pow(((1 - min_alt_re / Pcorners(nGCs)) / pow(min_alt_re, 4.0)), 0.5);
+
+  // Trace each field line up to q_max, obtained from the lowest field line in the block
+  precision_t q_max = pow(((1 - max_alt_re / Pcorners(nLats)) / pow(max_alt_re,
+                           4.0)), 0.5);
+
+  precision_t delQ = (q_max - q_min) / (nAlts - nGCs * 2.0);
+
+  arma_vec magQ1d(nAlts);
+  arma_vec magQ_corner_1d(nAlts + 1);
+
+  for (iAlt = 0; iAlt < nAlts; iAlt ++) {
+    magQ1d(iAlt) = q_min + (iAlt - nGCs + 0.5) * delQ;
+    magQ_corner_1d(iAlt) = q_min + (iAlt - nGCs) * delQ;
+  }
+
+  magQ_corner_1d(nAlts) = q_min - nGCs * delQ;
+
+  if (isSouth) {
+    magQ1d = -1.0 * reverse(magQ1d);
+    magQ_corner_1d = -1.0 * reverse(magQ_corner_1d);
+  }
+
+  report.print(3, "Made q's");
+  ////////////////////////////
+  // That is the grid made. //
+  ////////////////////////////
+
+
+  std::pair<precision_t, precision_t> rtheta, rtheta_corner;
+  precision_t altitude;
+  arma_vec tmp(nLons), tmp2(nLons+1);
+
+  // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
+  for (iLat = 0; iLat < nLats; iLat ++) {
+    for (iAlt = 0; iAlt < nAlts; iAlt++) {
+
+      // this is for cell centers:
+      rtheta = qp_to_r_theta(magQ1d(iAlt), Pcenters(iLat));
+      tmp.zeros();
+      tmp += rtheta.first;
+      magAlt_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+      k_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+      tmp.zeros();
+      tmp += rtheta.second;
+      magLat_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+      j_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+
+      // this is for cell corners:
+      rtheta_corner = qp_to_r_theta(magQ_corner_1d(iAlt), Pcorners(iLat));
+      tmp2.zeros();
+      tmp2 += rtheta_corner.first;
+      magAlt_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
+      tmp2.zeros();
+      tmp2 += rtheta_corner.second;
+      magLat_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
+    }
+  }
 
+  if (magLat_scgc.has_nan())
+    report.error("NAN IN MAGLAT");
+    
+  if (magLat_Corner.has_nan() || magLat_Corner.has_inf())
+  report.error("NAN IN MAGLAT_C");
+  
+  if (magAlt_Corner.has_nan())
+    report.error("NAN IN MAGALT_CORNER");
+
+  report.print(4,"got my mlats");
+  
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);
 
@@ -797,7 +481,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   arma_cube bm = sqrt(br % br + bt % bt);
   // Latitudinal direction of radial:
   arma_cube s = sign(magLat_scgc);
-  // s.elem(find(s == 0)).ones();
+  s.elem(find(s == 0)).ones();
 
   rad_unit_vcgc[1] = bt / bm % s;
   rad_unit_vcgc[2] = -br / bm;
@@ -824,7 +508,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
 
   // put back into altitude. we've been carrying around radius:
-  magAlt_scgc = magAlt_scgc - planetRadius;
+  // magAlt_scgc = magAlt_scgc - planetRadius;
+  // this breaks things more???
 
   report.exit(function);
   return DidWork;

From d64be6bd6755dd44fe852ca4914d98d5efa49521 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 18 Mar 2025 22:17:31 -0400
Subject: [PATCH 011/120] BUG: Quadtree was dividing dipole into (nonexistent)
 fourths. Everything died

---
 include/sphere.h | 31 +++++++++++++++++++++++++++++++
 src/quadtree.cpp | 10 ++++++++++
 2 files changed, 41 insertions(+)

diff --git a/include/sphere.h b/include/sphere.h
index 4f3902a0..32de5fdf 100644
--- a/include/sphere.h
+++ b/include/sphere.h
@@ -29,6 +29,37 @@ namespace Sphere {
 
 };
 
+/*************************************************
+ * \brief A namespace with all (4-root) sphere grid logic.
+ *************************************************/
+namespace Sphere4 {
+
+  /// The normalized origins of each node (i.e. corner)
+  static const arma_mat ORIGINS = {
+    { 0.0,  -0.5, 0.0},
+    { 0.0, -0.25, 0.0},
+    { 0.0,   0.0, 0.0},
+    { 0.0,  0.25, 0.0}
+  };
+  
+  /// Normalized right steps in node
+  static const arma_mat RIGHTS = {
+    {2.0, 0.0, 0.0},
+    {2.0, 0.0, 0.0},
+    {2.0, 0.0, 0.0},
+    {2.0, 0.0, 0.0}
+  };
+  
+  /// Normalized up steps in node
+  static const arma_mat UPS = {
+    {0.0, 0.25, 0.0},
+    {0.0, 0.25, 0.0},
+    {0.0, 0.25, 0.0},
+    {0.0, 0.25, 0.0}
+  };
+  
+  };
+
 /*************************************************
  * \brief A namespace with all (6-root) sphere grid logic.
  *************************************************/
diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index 0e2aae65..ff5d45a2 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -22,6 +22,9 @@ Quadtree::Quadtree(std::string shape) {
   if (shape == "sphere")
     nRootNodes = 1;
 
+    if (shape == "sphere4")
+    nRootNodes = 4;
+
   if (shape == "dipole4")
     nRootNodes = 4;
 
@@ -63,6 +66,13 @@ void Quadtree::build(std::string gridtype) {
     IsSphere = true;
   }
 
+  if (grid_input.shape == "sphere4") {
+    origins = Sphere4::ORIGINS;
+    rights = Sphere4::RIGHTS;
+    ups = Sphere4::UPS;
+    IsSphere = true;
+  }
+
   if (grid_input.shape == "dipole4") {
     origins = Dipole4::ORIGINS;
     rights = Dipole4::RIGHTS;

From 553e6e6a136ec92e878db09f3626471cbe62146a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 12:43:14 -0400
Subject: [PATCH 012/120] FEAT: Orthogonal dipole grid is working

- clean up comments, debugging, etc.
- fix issue with quadtree values being abs()
---
 src/init_mag_grid.cpp | 51 ++++++++++---------------------------------
 1 file changed, 12 insertions(+), 39 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8a4df97a..1652f089 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -198,14 +198,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
   precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
-  magLat_scgc.zeros();
-  magInvLat_scgc.zeros();
-  magLon_scgc.zeros();
-  magAlt_scgc.zeros();
-  magLon_Corner.zeros();
-  magLat_Corner.zeros();
-  magAlt_Corner.zeros();
-
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;
@@ -261,14 +253,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   if (magLon_scgc.has_nan())
     report.error("NAN IN MAGLON");
 
-  // for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
-  //   for (iLat = 0; iLat < nLats + 1; iLat++) {
-  //     // Corners
-  //     magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-  //     i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-  //   }
-  // }
-
   report.print(3, "Done initializing longitudes, moving to latitude");
 
   ////////////////
@@ -313,24 +297,24 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - put 2nd to last corner 1/2 way between 89.9 and the last real corner
   // - evenly space the ghost cells between these.
 
-  if (lat_origin + size_up_norm(1) > 0.49) { // touching the pole
-    lat1dDown(nLats) = 89.9 * 180.0 / cPI;
+  // Check if we're touching the pole, need to look at original quadtree values
+  if ((abs(lower_left_norm(1) + size_up_norm(1)) > 0.49) // north pole
+      || (lower_left_norm(1) < -0.49)) { // south pole
+    lat1dDown(nLats) = 89.9 * cDtoR;
     lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;
     lat1d(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 1)) / 2.0;
     lat1d(nLats - 2) = (lat1dDown(nLats - 1) + lat1dDown(nLats - 2)) / 2.0;
-    // lat1d(-1) = (lat1dDown(-1) + lat1dDown(-2)) / 2.0;
   }
 
-  // l-shells of centers (nLats)
+  // l-shells of centers
   arma_vec Pcenters = min_alt_re / pow(sin(cPI / 2 - lat1d), 2);
 
   // l-shells of corners
   arma_vec Pcorners = min_alt_re / pow(sin(cPI / 2 - lat1dDown), 2);
 
 
-  if (isSouth){ // so the values are increasing:
-    lat1d = -1*reverse(lat1d);
-  }
+  if (isSouth)  // so the values are increasing:
+    lat1d = -1 * reverse(lat1d);
 
 
   for (iLon = 0; iLon < nLons; iLon++) {
@@ -367,11 +351,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   bool close_this_block = false;
 
   if ((Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
-      || (abs(lat_origin) < 0.01)) // equator, with some imprecision
+      || (abs(lat_origin - size_up_norm(1)) < 0.01)) // equator, with some imprecision
     close_this_block = true;
 
-  std::cout << "closing block?: " << close_this_block << "\n";
-
   if (!close_this_block)
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
@@ -400,7 +382,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     magQ_corner_1d = -1.0 * reverse(magQ_corner_1d);
   }
 
-  report.print(3, "Made q's");
   ////////////////////////////
   // That is the grid made. //
   ////////////////////////////
@@ -408,7 +389,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   std::pair<precision_t, precision_t> rtheta, rtheta_corner;
   precision_t altitude;
-  arma_vec tmp(nLons), tmp2(nLons+1);
+  arma_vec tmp(nLons), tmp2(nLons + 1);
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
   for (iLat = 0; iLat < nLats; iLat ++) {
@@ -436,17 +417,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     }
   }
 
-  if (magLat_scgc.has_nan())
-    report.error("NAN IN MAGLAT");
-    
-  if (magLat_Corner.has_nan() || magLat_Corner.has_inf())
-  report.error("NAN IN MAGLAT_C");
-  
-  if (magAlt_Corner.has_nan())
-    report.error("NAN IN MAGALT_CORNER");
-
-  report.print(4,"got my mlats");
-  
+  // magAlt is in units of planet radius
+  magAlt_scgc *= planetRadius;
+
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);
 

From 4f453673a79c42ffed721b11a027dbb2a59fc172 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 12:43:51 -0400
Subject: [PATCH 013/120] FEAT: Show which cubes are not all_finite in stdout

---
 src/tools.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tools.cpp b/src/tools.cpp
index dc2f621f..d041bf49 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -654,7 +654,7 @@ bool all_finite(arma_cube cube, std::string name) {
       "," + std::to_string(loc[1]) +
       "," + std::to_string(loc[2]) + ")";
     int size = locations.size();
-    std::cout << "all_finite : " << cube(loc[0], loc[1], loc[2]) << "\n";
+    std::cout << "all_finite ("<<name<<"): " << cube(loc[0], loc[1], loc[2]) << "\n";
     std::string error_message =
       std::to_string(size) +
       " Nonfinite values exist in " + name +

From 704cdc731bbe853d5d69eb5abbd7bb6dccaa2df9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 17:31:27 -0400
Subject: [PATCH 014/120] DOC: Start updating dipole writeup. Committing to see
 if github likes latex in md

---
 doc/internals/grid.md | 50 +++++++++++++++++++++++++++++++++++++------
 1 file changed, 44 insertions(+), 6 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index ad91f315..8f1b1d38 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -114,12 +114,50 @@ bottom of the field-line. Each fieldline starts at the lowest modeled altitude
 and curves towards the equator. In the northern hemisphere, this means that the
 fieldlines curve south, while in the southern hemisphere they curve north.
 
-The latitudinal spacing is determined by the `LatStretch` factor in the settings.
-The base latitudes are then scaled in such a way that **higher** `LatStretch` leads to
-more points near the equator, 1.0 is roughly linear, and then values less than 1.0 will
-distribute more points near the poles. The exact spacing is calculated where the
-difference between successive values is proportional to:
-`cos(lat_max)^(1/LatStretch)`. Using an even number of latitudes is required.
+The dipole grid requires >4 root nodes which ensures the coordinates are 
+mutually orthogonal. The available shapes are `dipole4` and `dipole6`, for 
+compatibility with the neutral grid being a sphere or cubesphere. In both cases,
+each node the entire longitude range and given a portion of the latitude range.
+So in the case of `dipole4`, the four nodes are each given 1/4 of the available
+latitudes and all of the longitudes.
+
+The dipole grid is evenly spaced in **invariant latitude** (where the field line
+passes the minumum altitude) and **q** (the dipole coordinate
+specifying how far along the field line a point lies). Q is dimensionless and defined 
+to be -infinity at the south pole, +infinity at the north pole, and 0 at the
+magnetic equator. The equations for p (L-shell) and q are taken as the following,
+where r is the distance from the origin and $\theta$ is *colatitude*:
+
+$$
+p = \frac{r}{\sin^2\theta}
+
+\newline
+\newline
+
+q = \frac{\cos{\theta}}{r^2}
+$$
+
+
+
+these are the steps taken:
+
+1. Receive latitude range of this block from the quadtree. This will look 
+something like `lower_left_norm=(0.0, -0.5, 0.0)` and `size_up_norm=(0, 0.25, 0)`
+for the node nearest the south pole in dipole4. From this, determine if we are
+in the southern hemisphere. If we are, everything will be done as if it was the north
+hemisphere and then reversed & negated at the end.
+2. Store the latitude (j) component of `lower_left_norm` as `lat_origin`. If this
+node is in the southern hemisphere, store the top of the node's extent as lat_origin.
+3. Scale this node's portion of the quadtree to be limited by the user-provided
+`lat_range`. These for the invariant latitudes, which are evenly spaced between 
+the latitude range provided and dictate where each field line passes through the minimum
+altitude provided.
+4. Determine if this node will have closed or open field lines. There are two conditions:
+   - If the node is touching the equator
+   - If the lowest L-shell is below the maximum altitude. This is rare, but prevents unexpected behavior.
+5. If the field line closes, 
+
+
 
 Along the `k` dimension, field lines terminate after a specified number of points.
 When using the Dipole grid option, there is not an option to set the maximum altitude.

From 07c42c27face840cc0df251cb2a48f33fdb3822e Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 17:33:44 -0400
Subject: [PATCH 015/120] doc: testing github markdown again

---
 doc/internals/grid.md | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 8f1b1d38..aab1b5cd 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -128,14 +128,9 @@ to be -infinity at the south pole, +infinity at the north pole, and 0 at the
 magnetic equator. The equations for p (L-shell) and q are taken as the following,
 where r is the distance from the origin and $\theta$ is *colatitude*:
 
-$$
-p = \frac{r}{\sin^2\theta}
+$p = \frac{r}{\sin^2\theta}$
 
-\newline
-\newline
-
-q = \frac{\cos{\theta}}{r^2}
-$$
+$q = \frac{\cos{\theta}}{r^2}$
 
 
 

From 79ec550b59e6294195ef2e14e34d019f22744340 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 18:53:30 -0400
Subject: [PATCH 016/120] BUGS: Fix mismatch max/min & clear up bool logic in
 init_mag_grid q-limits

---
 src/init_mag_grid.cpp | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 1652f089..8e8cb260 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -347,22 +347,25 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - touching the (magnetic) equator
   // - minimum Lshell in this block is < max_alt (the q-value would be undefined)
 
-  precision_t q_min = 0; // q=0 at equator (for closed blocks)
+  precision_t q_min;
   bool close_this_block = false;
 
-  if ((Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
-      || (abs(lat_origin - size_up_norm(1)) < 0.01)) // equator, with some imprecision
+  if (Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
+    close_this_block = true;
+  
+  if(lat_origin < 0.01) // equator, with some imprecision
     close_this_block = true;
 
-  if (!close_this_block)
+  if (close_this_block)
+    q_min = 0; // q=0 at equator (for closed blocks)
+  else
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-    // TODO: use nGCs or 0 to index Pcorners??
-    q_min = pow(((1 - min_alt_re / Pcorners(nGCs)) / pow(min_alt_re, 4.0)), 0.5);
+  q_min = pow(((1 - max_alt_re / Pcorners(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - max_alt_re / Pcorners(nLats)) / pow(max_alt_re,
+  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats)) / pow(min_alt_re,
                            4.0)), 0.5);
 
   precision_t delQ = (q_max - q_min) / (nAlts - nGCs * 2.0);

From dd9d4e199ea8878d7f4ad92d5eeddfcf7f3f0f5f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 18:58:51 -0400
Subject: [PATCH 017/120] DOC: explain how the dipole grid is made.

---
 doc/internals/grid.md | 79 ++++++++++++++++++++++++++++---------------
 1 file changed, 51 insertions(+), 28 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index aab1b5cd..00a8dca6 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -18,19 +18,21 @@ again, 16 processors are needed, etc. However, in the altitude/radial direction,
 the number of points that are specified in the aether.json is unchanged, as it
 does not rely on the number of processors used.
 
-- [Grid Types Explained](#grid-types-explained)
-- [Grid Shapes Explained](#grid-shapes-explained)
-  - [TL;DR](#tldr)
-  - [The Sphere Grid](#the-sphere-grid)
-  - [The Cubesphere Grid](#the-cubesphere-grid)
-  - [The Dipole Grid](#the-dipole-grid)
-  - [Root Nodes](#root-nodes)
-    - [Sphere](#sphere)
-    - [Cubesphere](#cubesphere)
-    - [Specifying Root Nodes](#specifying-root-nodes)
-- [Specifying the Grid](#specifying-the-grid)
-  - [Horizontal Resolution](#horizontal-resolution)
-  - [Vertical Resolution](#vertical-resolution)
+- [Grids in Aether](#grids-in-aether)
+  - [Grid Types Explained](#grid-types-explained)
+  - [Grid Shapes Explained](#grid-shapes-explained)
+    - [TL;DR](#tldr)
+    - [The Sphere Grid](#the-sphere-grid)
+    - [The Cubesphere Grid](#the-cubesphere-grid)
+    - [The Dipole Grid](#the-dipole-grid)
+      - [Inputs:](#inputs)
+    - [Root Nodes](#root-nodes)
+      - [Sphere](#sphere)
+      - [Cubesphere](#cubesphere)
+      - [Specifying Root Nodes](#specifying-root-nodes)
+  - [Specifying the Grid](#specifying-the-grid)
+    - [Horizontal Resolution](#horizontal-resolution)
+    - [Vertical Resolution](#vertical-resolution)
 
 ## Grid Types Explained
 
@@ -110,7 +112,9 @@ scale-height.
 
 The dipole grid is aligned with the magnetic field. The `k` dimension is along
 the fieldline, `i` is magnetic longitude, and `j` is roughly latitude for the
-bottom of the field-line. Each fieldline starts at the lowest modeled altitude
+bottom of the field-line. 
+
+Each fieldline starts at the lowest modeled altitude
 and curves towards the equator. In the northern hemisphere, this means that the
 fieldlines curve south, while in the southern hemisphere they curve north.
 
@@ -133,8 +137,7 @@ $p = \frac{r}{\sin^2\theta}$
 $q = \frac{\cos{\theta}}{r^2}$
 
 
-
-these are the steps taken:
+Here is how the dipole grid is generated:
 
 1. Receive latitude range of this block from the quadtree. This will look 
 something like `lower_left_norm=(0.0, -0.5, 0.0)` and `size_up_norm=(0, 0.25, 0)`
@@ -147,24 +150,44 @@ node is in the southern hemisphere, store the top of the node's extent as lat_or
 `lat_range`. These for the invariant latitudes, which are evenly spaced between 
 the latitude range provided and dictate where each field line passes through the minimum
 altitude provided.
+   - At the poles, put the last corner at $89.9^\circ$ magnetic latitude. Add
+another corner 1/2 way between this point and the last "real" corner, and put
+cell centers between these corners.
 4. Determine if this node will have closed or open field lines. There are two conditions:
    - If the node is touching the equator
    - If the lowest L-shell is below the maximum altitude. This is rare, but prevents unexpected behavior.
-5. If the field line closes, 
+5. Determine the limits & values of the q-coordinate for all points along all field
+lines on this node. The q-values on each node are identical, and the p-value is
+constant along each field line (by definition). To solve for q, use the p-values
+from step 3 and the altitude, as described below. Use $q=\sqrt{(1-r/p)/r^4}$.
+   - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
+magnetic equator and two ghost cell centers across the equator for message passing.
+   - If the field line does not close, $q_{min}$ is calculated from the highest
+altitude point on the lowest latitude field line. This is the point farthest
+from the planet on the most equatorward field line (and since q=0 at the
+equator, it has the lowest allowed q-value).
+   - The maximum q-value is solved for identically in open & closed blocks with
+the lower altitude limit and the highest latitude field line. The point closest
+to the planet on the highest latitude field line has the highest allowed q-value
+(q=$\pm$infinity at the poles).
+6. We now have p (step 3) and q (step 5) for all points on the grid. From this
+we solve for `(r, \theta)`, and any other coordinates we need.
+
+See [the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
+experiment with the available options.
 
+#### Inputs:
 
+- ***Shape***: either `dipole4` or `dipole6`. Cannot (yet) be run on a single core.
+- ***nLonsPerBlock***: number of magnetic longitudes
+- ***nLatsPerBlock***: number of field lines (invariant latitudes)
+- ***nAlts***: Number of points along each field line. A number of these will 
+be discarded for being at too low of altitude.
+- ***AltRange***: [`min_alt`, `max_alt`] - the altitude (in km) range to bound 
+cells by. 
+- ***LatRange***: [`min_lat`, `max_lat`] - the limits on invariant latitudes 
+(in degrees). Sets the limits on the latitudes where field lines cross `min_alt`.
 
-Along the `k` dimension, field lines terminate after a specified number of points.
-When using the Dipole grid option, there is not an option to set the maximum altitude.
-Rather, points are laid down from the pole towards the equator, stopping when the field
-line reaches the equator. Ghost cells are then used to pass information across the
-equator. The spacing of points along each field line is the same as
-[(Huba, Joyce & Fedder, 2000)](https://doi.org/10.1029/2000JA000035).
-Using an even number of points along the `k` dimension (`nAlts`) means that no points
-will lie on the magnetic equator and thus the field lines from the high latitude
-regions will not reach beyond the plasmasphere. See
-[the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
-experiment with the available options.
 
 ### Root Nodes
 

From c00e905335bf1731cb8685b85c217bf6df38af38 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 09:59:57 -0400
Subject: [PATCH 018/120] sty/doc: Change grid.md formatting to make it
 prettier?

---
 doc/internals/grid.md | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 00a8dca6..9a530d58 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -128,14 +128,19 @@ latitudes and all of the longitudes.
 The dipole grid is evenly spaced in **invariant latitude** (where the field line
 passes the minumum altitude) and **q** (the dipole coordinate
 specifying how far along the field line a point lies). Q is dimensionless and defined 
-to be -infinity at the south pole, +infinity at the north pole, and 0 at the
-magnetic equator. The equations for p (L-shell) and q are taken as the following,
+to be $-\infty$ at the south pole, $+\infty$ at the north pole, and 0 at the
+magnetic equator. The equations for p (L-shell) and q are the following,
 where r is the distance from the origin and $\theta$ is *colatitude*:
 
-$p = \frac{r}{\sin^2\theta}$
+```math
+p = \frac{r}{\sin^2\theta}
 
-$q = \frac{\cos{\theta}}{r^2}$
+```
+
+```math
+q = \frac{\cos{\theta}}{r^2}
 
+```
 
 Here is how the dipole grid is generated:
 
@@ -159,7 +164,7 @@ cell centers between these corners.
 5. Determine the limits & values of the q-coordinate for all points along all field
 lines on this node. The q-values on each node are identical, and the p-value is
 constant along each field line (by definition). To solve for q, use the p-values
-from step 3 and the altitude, as described below. Use $q=\sqrt{(1-r/p)/r^4}$.
+from step 3 and the altitude, as described below. Use $q=\sqrt{(1-\frac{r}{p})/r^4}$.
    - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
 magnetic equator and two ghost cell centers across the equator for message passing.
    - If the field line does not close, $q_{min}$ is calculated from the highest
@@ -170,28 +175,28 @@ equator, it has the lowest allowed q-value).
 the lower altitude limit and the highest latitude field line. The point closest
 to the planet on the highest latitude field line has the highest allowed q-value
 (q=$\pm$infinity at the poles).
-6. We now have p (step 3) and q (step 5) for all points on the grid. From this
-we solve for `(r, \theta)`, and any other coordinates we need.
+6. We now have `p` (step 3) and `q` (step 5) for all points on the grid. From this
+we solve for $(r, \theta)$, and any other coordinates we need.
 
 See [the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
 experiment with the available options.
 
 #### Inputs:
 
-- ***Shape***: either `dipole4` or `dipole6`. Cannot (yet) be run on a single core.
+- ***Shape***: either `dipole4` or `dipole6`. Cannot be run on a single core.
 - ***nLonsPerBlock***: number of magnetic longitudes
 - ***nLatsPerBlock***: number of field lines (invariant latitudes)
 - ***nAlts***: Number of points along each field line. A number of these will 
 be discarded for being at too low of altitude.
-- ***AltRange***: [`min_alt`, `max_alt`] - the altitude (in km) range to bound 
+- ***AltRange***: (`min_alt`, `max_alt`) - the altitude (in km) range to bound 
 cells by. 
-- ***LatRange***: [`min_lat`, `max_lat`] - the limits on invariant latitudes 
+- ***LatRange***: (`min_lat`, `max_lat`) - the limits on invariant latitudes 
 (in degrees). Sets the limits on the latitudes where field lines cross `min_alt`.
 
 
 ### Root Nodes
 
->This document uses the words "block" and "node" somewhat
+> This document uses the words "block" and "node" somewhat
 interchangably. Technically, a "block" is single (`i, j, k`) grid, while a
 "node" can be multiple "blocks" that make up a section of the globe.
 

From bc0a2763d4960b9fdc7d477dd03ba8100612d9be Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 10:08:49 -0400
Subject: [PATCH 019/120] sty: Does github let me number equations?

---
 doc/internals/grid.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 9a530d58..9fba4e49 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -134,12 +134,12 @@ where r is the distance from the origin and $\theta$ is *colatitude*:
 
 ```math
 p = \frac{r}{\sin^2\theta}
-
+\tag{1}
 ```
 
 ```math
 q = \frac{\cos{\theta}}{r^2}
-
+\tag{2}
 ```
 
 Here is how the dipole grid is generated:
@@ -155,16 +155,17 @@ node is in the southern hemisphere, store the top of the node's extent as lat_or
 `lat_range`. These for the invariant latitudes, which are evenly spaced between 
 the latitude range provided and dictate where each field line passes through the minimum
 altitude provided.
-   - At the poles, put the last corner at $89.9^\circ$ magnetic latitude. Add
+   - At the poles, put the last corner at $89.9^\circ$ magnetic latitude, or
+$0.1^\circ$ and $179.9^\circ$ magnetic ***co***latitude. Add
 another corner 1/2 way between this point and the last "real" corner, and put
 cell centers between these corners.
 4. Determine if this node will have closed or open field lines. There are two conditions:
    - If the node is touching the equator
    - If the lowest L-shell is below the maximum altitude. This is rare, but prevents unexpected behavior.
-5. Determine the limits & values of the q-coordinate for all points along all field
+5. Determine the limits, then values, of the q-coordinate for all points along each field
 lines on this node. The q-values on each node are identical, and the p-value is
 constant along each field line (by definition). To solve for q, use the p-values
-from step 3 and the altitude, as described below. Use $q=\sqrt{(1-\frac{r}{p})/r^4}$.
+from step 3 and the altitude, as described below and Equation 3.
    - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
 magnetic equator and two ghost cell centers across the equator for message passing.
    - If the field line does not close, $q_{min}$ is calculated from the highest

From 0d1d3518ee8f8794ccedb920541e68b2d9024ba0 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 10:15:36 -0400
Subject: [PATCH 020/120] STY: it does not. revert previous commit, fix eq
 format, change some wording. done w this file for now

---
 doc/internals/grid.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 9fba4e49..92da6fdb 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -134,12 +134,10 @@ where r is the distance from the origin and $\theta$ is *colatitude*:
 
 ```math
 p = \frac{r}{\sin^2\theta}
-\tag{1}
 ```
 
 ```math
 q = \frac{\cos{\theta}}{r^2}
-\tag{2}
 ```
 
 Here is how the dipole grid is generated:
@@ -165,7 +163,7 @@ cell centers between these corners.
 5. Determine the limits, then values, of the q-coordinate for all points along each field
 lines on this node. The q-values on each node are identical, and the p-value is
 constant along each field line (by definition). To solve for q, use the p-values
-from step 3 and the altitude, as described below and Equation 3.
+from step 3 and the altitude, as described below and $q=\sqrt{(1-r/p)/r^4}$.
    - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
 magnetic equator and two ghost cell centers across the equator for message passing.
    - If the field line does not close, $q_{min}$ is calculated from the highest
@@ -179,8 +177,8 @@ to the planet on the highest latitude field line has the highest allowed q-value
 6. We now have `p` (step 3) and `q` (step 5) for all points on the grid. From this
 we solve for $(r, \theta)$, and any other coordinates we need.
 
-See [the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
-experiment with the available options.
+See [edu/examples/Dipole](../../edu/examples/Dipole) for more detailed information
+and to experiment with the available options in a Python script.
 
 #### Inputs:
 

From 5b405a8fdc4b81fdae25c972ca6910a6631029a1 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 11:15:27 -0400
Subject: [PATCH 021/120] DOC: Decide what the dipole (i,j,k) coords are!

Need to finish implementing
---
 doc/internals/coordinates.md | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/doc/internals/coordinates.md b/doc/internals/coordinates.md
index 418e0412..515857b2 100644
--- a/doc/internals/coordinates.md
+++ b/doc/internals/coordinates.md
@@ -20,15 +20,16 @@
 
 ## Dipole Coordinates
 - Longitude (radians) - radians east of the meridian that contains the north magnetic pole and north rotation axis
-- P (?) - Identifies the field line, related to L-shell
-- Q (meters?) - The distance along the field line from some reference point, related to magnetic latitude.
+- P (meters) - Identifies the field line. This is the same as L-shell and is constant along wach field line.
+- Q (dimensionless) - parameterizes the distance along the field line, related to magnetic latitude & radius. This varies along the field line, but the values are idential for all field lines within each node. q=0 at the equator, and approaches positive (negative) infinity as theta points towards the north (south) pole.
 
 ## More Dipole Coordinates
 - L-shell (Planetary Radii) - The distance from the planet's center at which the magnetic field encounters the dipole's equatorial plane
-- Magnetic Latitude (radians) - angle between the dipole's equatorial plane and the point.
-- Invariant Latitude (degrees) - angle between the dipole's equatorial plane and the point at which the field-line passes through a reference radius of the planet.  This is constant along the field-line and is related to the L-Shell.
+- Magnetic Latitude (radians) - angle between the dipole's equatorial plane and a point.
+- Invariant Latitude (radians) - angle between the dipole's equatorial plane and the point at which the field-line passes through a reference radius of the planet ([specified in the inputs](../internals/grid.md#inputs)).  This is constant along the field-line and is related to the L-Shell.
 - Magnetic Local Time (hours) - Angle between the sun, the north magnetic pole, and the point. Explicitly, this is done in PSE XY coordinates, ignoring the Z coorinate.
 
+> The dipole `(i,j,k)` coordinates are (magnetic longitude, magnetic latitude, radius).
 
 # Coordinates in Aether
 
@@ -46,12 +47,15 @@ Because Aether considers gravity to be a function of radius and explicitly inclu
 
 ## i, j, k Coordinates
 
-As described in the grid.md file, Aether uses a logical '(i, j, k)' 3D grid structure.  Therefore, we refer to the 'primary' coordinates as the ijk coordinate system.  What this means is that the i-coordinate is in the i-direction, the j-coordinate is in the j-direction, and the k-coordinate is in the k-direction.  
+As described in the grid.md file, Aether uses a logical `(i, j, k)` 3D grid structure.  Therefore, we refer to the 'primary' coordinates as the ijk coordinate system.  What this means is that the i-coordinate is in the i-direction, the j-coordinate is in the j-direction, and the k-coordinate is in the k-direction.  
 
 For the (perfectly) spherical grid, the i-coordinate is longitude, the j-coordinate is latitude, and the k-coordinate is radius.
 
 For the Cubedsphere grid, the i-coordinate is RIGHT, the j-coodinate is UP, and the k-coordinate is radius.  Each face of the cubedsphere has the same coordinate system, but only with reference to that face.  This means that if each face is looked at independently, the lower left corner is at (about) i = -45, j = -45 deg, while the upper right corner is at i = +45, j = +45 deg. Radius is treated the same as in a spherical grid.
 
+For the dipole coordinate system, the i-coordinate is magnetic longitude, the j-coordinate is magnetic latitude, and the k-coordinate is radius. The dipole is orthogonal to a dipolar magnetic field.
+
+
 Should the official coordinates be in the native coordinates (which could be different for each system), or should the coordinates be in meters, such that when gradients are taken, they are in /m?
 
 Maybe we could have:
@@ -69,6 +73,6 @@ Locations:
 All locations should be described in the following coordinates:
 - i, j, k
 - lon, lat, radius (+alt)
-- magnetic lon, invariant lat?
+- magnetic lon, invariant lat (only the dipole magnetic grid then has magnetic latitude)
 
 

From 42701eefc85893c79cef906b67d0ad8e0053f4d9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 11:22:57 -0400
Subject: [PATCH 022/120] MAINT: Clean up unused/outdated bits. clear up
 comments around magnetic coordinates

---
 include/bfield.h        |  4 ----
 include/grid.h          | 19 ++++---------------
 include/init_mag_grid.h |  7 -------
 src/dipole.cpp          | 24 ------------------------
 src/init_mag_grid.cpp   | 21 ---------------------
 5 files changed, 4 insertions(+), 71 deletions(-)

diff --git a/include/bfield.h b/include/bfield.h
index 91a09ff3..df64fac8 100644
--- a/include/bfield.h
+++ b/include/bfield.h
@@ -10,10 +10,6 @@ struct bfield_info_type {
   precision_t lat;
 };
 
-precision_t get_lshell(precision_t lat, precision_t rNorm);
-arma_vec get_lat_from_r_and_lshell(arma_vec r, precision_t lshell);
-precision_t get_lat_from_r_and_lshell(precision_t r, precision_t lshell);
-
 arma_vec get_magnetic_pole(int IsNorth,
 		       Planets planet);
 
diff --git a/include/grid.h b/include/grid.h
index 874dcb94..3e32b600 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -66,16 +66,16 @@ class Grid
   arma_cube g11_upper_Down, g12_upper_Down, g21_upper_Down, g22_upper_Down;
   arma_cube sqrt_g_Down;
 
-  // These define the magnetic grid:
-  // Armidillo Cube Versions:
-  arma_cube magLon_scgc, magX_scgc;
+  // These define the magnetic grid (only defined for a dipole grid):
   // The magnetic latitude is the angle between the magnetic equator and the point.
   arma_cube magLat_scgc, magY_scgc;
-  // This is often just the altitude....
+  // This is the same as radius.
   arma_cube magAlt_scgc, magZ_scgc;
+  // These exist for all grid types:
   // Invariant latitude is the magnetic latitude that the field line hits at the lowest altitude.
   // This is basically the L-shell, but models want it expressed as latitude and not L-shell.
   arma_cube magInvLat_scgc;
+  arma_cube magLon_scgc, magX_scgc;
   // This is the angle from the sun, to the magnetic pole to the point.
   arma_cube magLocalTime_scgc;
 
@@ -98,17 +98,6 @@ class Grid
   arma_cube magAlt_Down;
   arma_cube magAlt_Below;
   arma_cube magAlt_Corner;
-
-  //For easier interpolation:
-  arma_vec baseLats_down;
-
-  // these need to be stored in (p,q) coords for a bit, its messy:
-  arma_cube magP_Down;
-  arma_cube magP_Below;
-  arma_cube magQ_Down;
-  arma_cube magQ_Below;
-  arma_cube magP_Corner;
-  arma_cube magQ_Corner;
   
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
diff --git a/include/init_mag_grid.h b/include/init_mag_grid.h
index a9493aaa..b1af1f4c 100644
--- a/include/init_mag_grid.h
+++ b/include/init_mag_grid.h
@@ -17,13 +17,6 @@ bool init_dipole_grid(Grid &mGrid, Planets planet);
 // return (r,theta)
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q, precision_t p);
 
-// Take limits & specs of field line, fill it with points.
-// std:: tuple <arma_mat, arma_mat, arma_vec> Grid::fill_field_lines (arma_vec lShells, 
-//                                                 int64_t nAlts,
-//                                                 int64_t nLats,
-//                                                 precision_t Gamma);
-
-
 // convert mag to geographic
 std::vector <arma_cube> mag_to_geo(arma_cube magLon, 
                                    arma_cube magLat,
diff --git a/src/dipole.cpp b/src/dipole.cpp
index d97c66f3..2887e547 100644
--- a/src/dipole.cpp
+++ b/src/dipole.cpp
@@ -6,30 +6,6 @@
 
 #include "aether.h"
 
-// -----------------------------------------------------------------------------
-// get the l-shell given latitude (in radians) and normalized radius
-// -----------------------------------------------------------------------------
-
-precision_t get_lshell(precision_t lat, precision_t rNorm) {
-  precision_t cosLat = cos(lat);
-  precision_t lshell = rNorm / (cosLat * cosLat);
-  return lshell;
-}
-
-precision_t get_lat_from_r_and_lshell(precision_t r, precision_t lshell) {
-  precision_t cosLat = sqrt(r / lshell);
-
-  if (cosLat < -1.0)
-    cosLat = -1.0;
-
-  if (cosLat > 1.0)
-    cosLat = 1.0;
-
-  precision_t lat = acos(cosLat);
-  return lat;
-}
-
-
 
 // -----------------------------------------------------------------------------
 // Calculate a tilted offset dipole field given the planetary
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8e8cb260..977b0b27 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -38,27 +38,6 @@ std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
   return {r, theta};
 }
 
-std::pair<arma_cube, arma_cube> qp_to_r_theta(arma_cube q, arma_cube p) {
-  // return quanties
-  arma_cube r, theta;
-  // Intermediate quantities:
-  arma_cube term0, term1, term2, term3;
-
-  term0 = 256.0 / 27.0 * (q % q) % (p % p % p % p);
-  term1 = pow((1.0 + sqrt(1.0 + term0)), 2.0 / 3.0);
-  term2 = pow(term0, 1.0 / 3.0);
-  term3 = 0.5 * pow(((term1 % term1 + term1 % term2 + term2 % term2) / term1),
-                    3.0 / 2.0);
-
-  r = p % (4.0 * term3) / ((1.0 + term3) % (1.0 + sqrt(2.0 * term3 - 1.0)));
-
-  // now that r is determined we can solve for theta
-  theta = asin(q % (r % r));
-
-  return {r, theta};
-}
-
-
 ////////////////////////////////////////////
 // convert cell coordinates to geographic //
 ////////////////////////////////////////////

From 66e5763f5e3428648869d412539b42adf271f084 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 11:23:50 -0400
Subject: [PATCH 023/120] Do not re-define magnetic coords for dipole grid in
 fill_grid

- The dipole bfield still needs work...
---
 src/fill_grid.cpp | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 5dd2f98e..330a6992 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -170,15 +170,14 @@ void Grid::fill_grid_bfield(Planets planet) {
 
           bfield_info = get_bfield(lon, lat, alt, DoDebug,
                                    planet);
-          // This is Invariant Latitude
-          // (regular magnetic latitude is not defined yet for the geo grid)
-          // - magLat is not used by the geo grid, so not defined
-          magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
-
-          // init_mag grid already initialized magLon
-          if (iGridShape_ != iDipole_)
+          // Magnetic coordinates:
+          // init_mag grid already initializes magLon & magInvLat
+          // #TODO: make sure the bfield is correct for the dipole 
+          // - maybe Dot product the B_vec with (ijk)_vec?
+          if (iGridShape_ != iDipole_){
+            magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
-
+          }
           bfield_mag_scgc(iLon, iLat, iAlt) = 0.0;
 
           for (iDim = 0; iDim < 3; iDim++) {

From 9251259ad923c6f571e28c13e08c3b1bf2898fad Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 19:03:18 -0400
Subject: [PATCH 024/120] BUG: Rewrite init_mag_grid again. Fix order of
 points, add in (i,j,k), corners, edges, etc.

- All appears OK except some of the corners. IDK why. Need to commit this and move on. Will revisit & document
---
 src/init_mag_grid.cpp | 181 +++++++++++++++++++++++++++++++-----------
 1 file changed, 133 insertions(+), 48 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 977b0b27..6a6fb987 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -10,7 +10,6 @@
 // or with approach from (Swisdak, 2006), who solved it analytically:
 //  https://arxiv.org/pdf/physics/0606044
 //
-// Overloaded for single conversions & arma_cubes
 // ----------------------------------------------------------------------
 
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
@@ -223,7 +222,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       // left edges
       magLon_Left.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_edge_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-
+      // corners
       magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
     }
@@ -266,7 +265,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   for (iLat = 0; iLat < nLats; iLat++) {
     lat1d(iLat) = lat0 + (iLat - nGCs + 0.5) * dlat + min_lat; // centers
-    lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners
+    lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners & edges
   }
 
   lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat; // last corner
@@ -291,19 +290,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // l-shells of corners
   arma_vec Pcorners = min_alt_re / pow(sin(cPI / 2 - lat1dDown), 2);
 
-
-  if (isSouth)  // so the values are increasing:
-    lat1d = -1 * reverse(lat1d);
-
-
-  for (iLon = 0; iLon < nLons; iLon++) {
-    for (iAlt = 0; iAlt < nAlts; iAlt++) {
-      magInvLat_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = lat1d;
-      magP_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = Pcenters;
-      magP_Down.subcube(iLon, 0, iAlt, iLon, nLats, iAlt) = Pcorners;
-    }
-  }
-
   report.print(3, "Done initializing invariant latitudes");
 
   ////////////////
@@ -331,8 +317,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   if (Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
     close_this_block = true;
-  
-  if(lat_origin < 0.01) // equator, with some imprecision
+
+  if (lat_origin < 0.01) // equator, with some imprecision
     close_this_block = true;
 
   if (close_this_block)
@@ -341,12 +327,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-  q_min = pow(((1 - max_alt_re / Pcorners(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
+    q_min = pow(((1 - max_alt_re / Pcenters(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats)) / pow(min_alt_re,
+  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats-nGCs)) / pow(min_alt_re,
                            4.0)), 0.5);
 
+  // Counter-intuitive, but the maximum value of q is actually where we start
+  // (lowest altitude), since q=0 at equator.
   precision_t delQ = (q_max - q_min) / (nAlts - nGCs * 2.0);
 
   arma_vec magQ1d(nAlts);
@@ -359,48 +347,145 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   magQ_corner_1d(nAlts) = q_min - nGCs * delQ;
 
-  if (isSouth) {
-    magQ1d = -1.0 * reverse(magQ1d);
-    magQ_corner_1d = -1.0 * reverse(magQ_corner_1d);
-  }
+  report.print(3,
+               "Done generating points for magnetic grid. Plugging everything in");
 
   ////////////////////////////
   // That is the grid made. //
   ////////////////////////////
+  // Now to store everything....
+  // It's all done at the end to make things more simple earlier, but that makes this part messier.
 
-
-  std::pair<precision_t, precision_t> rtheta, rtheta_corner;
-  precision_t altitude;
-  arma_vec tmp(nLons), tmp2(nLons + 1);
+  // temp holding names:
+  std::pair<precision_t, precision_t> rtheta, rtheta_edge;
+  precision_t radius, radius_edge, theta, theta_edge, invLat, invLat_edge,
+              pcenter, pedge, qcenter, qedge;
+  // we need to turn single floats into vectors/cubes:
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
+  // Currently the grid is symmetric in longitude.
+  // Interte through centers & edges first, then do corners afterwards
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
+      // We have to reverse & negate things; want latitudes from south->north
+      // and altitude low->high. Altitude is in the correct direction, so change how we 
+      // access values in the latitude dimension.
+
+      if (isSouth) {
+        qcenter = magQ1d(iAlt);
+        pcenter = Pcenters(nLats - iLat - 1);
+
+        qedge = magQ_corner_1d(iAlt);
+        pedge = Pcorners(nLats - iLat - 1);
+
+        invLat = lat1d(nLats - iLat - 1) * -1;
+        invLat_edge = lat1dDown(nLats - iLat - 1) * -1;
+
+        rtheta = qp_to_r_theta(qcenter, pcenter);
+        rtheta_edge = qp_to_r_theta(qcenter, pcenter);
 
-      // this is for cell centers:
-      rtheta = qp_to_r_theta(magQ1d(iAlt), Pcenters(iLat));
-      tmp.zeros();
-      tmp += rtheta.first;
-      magAlt_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-      k_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-      tmp.zeros();
-      tmp += rtheta.second;
-      magLat_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-      j_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-
-      // this is for cell corners:
-      rtheta_corner = qp_to_r_theta(magQ_corner_1d(iAlt), Pcorners(iLat));
-      tmp2.zeros();
-      tmp2 += rtheta_corner.first;
-      magAlt_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
-      tmp2.zeros();
-      tmp2 += rtheta_corner.second;
-      magLat_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
+        // Flip hemisphere of latitude & q (cannot be done before qp_to_rtheta)
+        radius = rtheta.first;
+        theta = rtheta.second * -1.0;
+        qcenter *= -1.0;
+
+        radius_edge = rtheta_edge.first;
+        theta_edge = rtheta.second * -1.0;
+        qedge *= 1.0;
+      } else {
+        qcenter = magQ1d(iAlt);
+        pcenter = Pcenters(iLat);
+
+        qedge = magQ_corner_1d(iAlt);
+        pedge = Pcorners(iLat);
+
+        invLat = lat1d(iLat);
+        invLat_edge = lat1dDown(iLat);
+
+        rtheta = qp_to_r_theta(qcenter, pcenter);
+        rtheta_edge = qp_to_r_theta(qedge, pedge);
+
+        radius = rtheta.first;
+        theta = rtheta.second;
+
+        radius_edge = rtheta_edge.first;
+        theta_edge = rtheta_edge.second;
+      }
+
+      for (iLon = 0; iLon < nLons; iLon ++){
+        magLat_scgc(iLon, iLat, iAlt) = theta;
+        j_center_scgc(iLon, iLat, iAlt) = theta;
+
+        magLat_Down(iLon, iLat, iAlt) = theta_edge;
+        j_edge_scgc(iLon, iLat, iAlt) = theta_edge;
+
+        magAlt_scgc(iLon, iLat, iAlt) = radius;
+        k_center_scgc(iLon, iLat, iAlt) = radius;
+
+        magAlt_Below(iLon, iLat, iAlt) = radius_edge;
+        k_edge_scgc(iLon, iLat, iAlt) = radius_edge;
+
+        // extra coordinates
+        magP_scgc(iLon, iLat, iAlt) = pcenter;
+        magQ_scgc(iLon, iLat, iAlt) = qcenter;
+        magInvLat_scgc(iLon, iLat, iAlt) = invLat;
+      }
+    }
+  }
+
+  report.print(3, "Centers are in");
+
+  precision_t radius_corner, theta_corner, invLat_corner,
+              pcorner, qcorner;
+
+  for (iLat = 0; iLat < nLats + 1; iLat ++) {
+    for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
+
+      // Same process as the centers & edges (above)
+      if (isSouth) {
+        qcorner = magQ_corner_1d(iAlt);
+        pcorner = Pcorners(nLats - iLat);
+        invLat_corner = lat1dDown(nLats - iLat) * -1;
+        rtheta = qp_to_r_theta(qcorner, pcorner);
+
+        radius_corner = rtheta.first;
+        theta_corner = rtheta.second * -1;
+        qcorner *= -1;
+      } else {
+        qcorner = magQ_corner_1d(iAlt);
+        pcorner = Pcorners(iLat);
+        invLat_corner = lat1dDown(iLat);
+        rtheta = qp_to_r_theta(qcorner, pcorner);
+        radius_corner = rtheta.first;
+        theta_corner = rtheta.second;
+      }
+      for (iLon = 0; iLon < nLons + 1; iLon ++){
+        magLat_Corner(iLon, iLat, iAlt) = theta_corner;
+        j_corner_scgc(iLon, iLat, iAlt) = theta_corner;
+
+        magAlt_Corner(iLon, iLat, iAlt) = radius_corner;
+        k_corner_scgc(iLon, iLat, iAlt) = radius_corner;
+
+        magP_Corner(iLon, iLat, iAlt) = pcorner;
+        magQ_Corner(iLon, iLat, iAlt) = qcorner;
+        magInvLat_Corner(iLon, iLat, iAlt) = invLat_corner;
+      }
     }
   }
 
-  // magAlt is in units of planet radius
+  report.print(3, "Corners done too");
+
+  // all distances, so far, are in units of planet radii, turn into meters.
+  // Except for Q, leave that dimensionless.
   magAlt_scgc *= planetRadius;
+  k_center_scgc *= planetRadius;
+  magAlt_Below *= planetRadius;
+  k_edge_scgc *= planetRadius;
+  magP_scgc *= planetRadius;
+  magAlt_Corner *= planetRadius;
+  k_corner_scgc *= planetRadius;
+  magP_Corner *= planetRadius;
+
 
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);

From 112ce907ce42b5865ba54f0a560142a0d3c3f67d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 19:10:33 -0400
Subject: [PATCH 025/120] STY: astyle, and make sure grids are addressed
 consistently

snuck in a few variables from previous commit too
---
 include/grid.h        |  4 ++++
 src/fill_grid.cpp     |  2 +-
 src/grid.cpp          |  9 ++-------
 src/init_mag_grid.cpp | 12 +++++++-----
 4 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index 3e32b600..40129d70 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -99,6 +99,10 @@ class Grid
   arma_cube magAlt_Below;
   arma_cube magAlt_Corner;
   
+  arma_cube magP_Corner;
+  arma_cube magQ_Corner;
+  arma_cube magInvLat_Corner;
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 330a6992..deb2b181 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -174,7 +174,7 @@ void Grid::fill_grid_bfield(Planets planet) {
           // init_mag grid already initializes magLon & magInvLat
           // #TODO: make sure the bfield is correct for the dipole 
           // - maybe Dot product the B_vec with (ijk)_vec?
-          if (iGridShape_ != iDipole_){
+          if (IsGeoGrid){
             magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
           }
diff --git a/src/grid.cpp b/src/grid.cpp
index 9ed462c3..4e5c9934 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -65,7 +65,7 @@ Grid::Grid(std::string gridtype) {
   if (grid_input.nZ == 1)
     HasZdim = false;
 
-  if (mklower(grid_input.shape) == "sphere")
+  if (mklower(grid_input.shape).find("sphere") != std::string::npos)
     iGridShape_ = iSphere_;
 
   if (mklower(grid_input.shape) == "cubesphere")
@@ -179,14 +179,9 @@ Grid::Grid(std::string gridtype) {
   magLat_Corner.set_size(nX + 1, nY + 1, nZ + 1);
   magAlt_Corner.set_size(nX + 1, nY + 1, nZ + 1);
 
-  magP_Down.set_size(nX, nY + 1, nZ);
-  magP_Below.set_size(nX, nY, nZ + 1);
-  magQ_Down.set_size(nX, nY + 1, nZ);
-  magQ_Below.set_size(nX, nY, nZ + 1);
   magP_Corner.set_size(nX + 1, nY + 1, nZ + 1);
   magQ_Corner.set_size(nX + 1, nY + 1, nZ + 1);
-
-  baseLats_down.set_size(nY + 1);
+  magInvLat_Corner.set_size(nX + 1, nY + 1, nZ + 1);
 
   radius_scgc.set_size(nX, nY, nZ);
   radius2_scgc.set_size(nX, nY, nZ);
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 6a6fb987..569ad0f8 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -330,8 +330,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     q_min = pow(((1 - max_alt_re / Pcenters(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats-nGCs)) / pow(min_alt_re,
-                           4.0)), 0.5);
+  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats - nGCs)) / pow(
+                             min_alt_re,
+                             4.0)), 0.5);
 
   // Counter-intuitive, but the maximum value of q is actually where we start
   // (lowest altitude), since q=0 at equator.
@@ -368,7 +369,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       // We have to reverse & negate things; want latitudes from south->north
-      // and altitude low->high. Altitude is in the correct direction, so change how we 
+      // and altitude low->high. Altitude is in the correct direction, so change how we
       // access values in the latitude dimension.
 
       if (isSouth) {
@@ -412,7 +413,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
         theta_edge = rtheta_edge.second;
       }
 
-      for (iLon = 0; iLon < nLons; iLon ++){
+      for (iLon = 0; iLon < nLons; iLon ++) {
         magLat_scgc(iLon, iLat, iAlt) = theta;
         j_center_scgc(iLon, iLat, iAlt) = theta;
 
@@ -459,7 +460,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
         radius_corner = rtheta.first;
         theta_corner = rtheta.second;
       }
-      for (iLon = 0; iLon < nLons + 1; iLon ++){
+
+      for (iLon = 0; iLon < nLons + 1; iLon ++) {
         magLat_Corner(iLon, iLat, iAlt) = theta_corner;
         j_corner_scgc(iLon, iLat, iAlt) = theta_corner;
 

From 1ae6be7e9bbc5f07b0b8143d2cd490c10ce4db20 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 21 Mar 2025 17:27:44 -0400
Subject: [PATCH 026/120] BUG/FEAT: Change & correct how dipole grid spacing is
 done

---
 src/grid_spacing.cpp | 229 ++++++++-----------------------------------
 1 file changed, 41 insertions(+), 188 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index ea3e4d74..09a4926f 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -23,6 +23,24 @@ void Grid::calc_grid_spacing(Planets planet) {
   report.print(3, "ending calc_grid_spacing");
 }
 
+void Grid::calc_dipole_grid_spacing(Planets planet) {
+
+  report.print(3, "starting calc_grid_spacing");
+
+  calc_i_grid_spacing();
+  calc_j_grid_spacing();
+  calc_k_grid_spacing();
+
+  report.print(3, "ending calc_grid_spacing");
+}
+
+
+// This is the del value from (Swisdak, 2006) & others. Used in Dipole distance calc's.
+// Note the cos->sin, since magLat is latitude, not colatitude.
+inline arma_cube delTc(arma_cube theta) {
+  return (sqrt(3 * sin(theta) % sin(theta) + 1));
+}
+
 // ---------------------------------------
 // Grid spacing for altitude:
 // ---------------------------------------
@@ -130,6 +148,17 @@ void Grid::calc_k_grid_spacing() {
   dr_edge.slice(iZ) =
     radius_scgc.slice(iZ) - radius_scgc.slice(iZ - 1);
 
+  // For the sphere & cubesphere, k is in meters:
+  if (iGridShape_ == iSphere_ || iGridShape_  == iCubesphere_){
+    dk_center_m_scgc = dk_center_scgc;
+    dk_edge_m = dk_edge;
+  }
+  // This needs to be turned into a distance for the dipole:
+  if (iGridShape_ == iDipole_){
+    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
+    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTc(magLat_scgc);
+  }
+
   // For a stretched grid, calculate some useful quantities:
   // lower is defined for the current cell, which
   // means that upper(iZ) is lower(iZ+1)
@@ -145,10 +174,6 @@ void Grid::calc_k_grid_spacing() {
   dk_ratio_sq = dk_ratio % dk_ratio;
   dk_one_minus_r2 = 1.0 - dk_ratio_sq;
 
-  // k is in meters:
-  dk_edge_m = dk_edge;
-  dk_center_m_scgc = dk_center_scgc;
-
   report.print(4, "ending calc_k_grid_spacing");
   return;
 }
@@ -220,17 +245,19 @@ void Grid::calc_i_grid_spacing() {
   di_center_m_scgc = di_center_scgc % radius_scgc;
   di_edge_m = di_edge % radius_scgc;
 
-  // If the shape is a sphere, then the first coordinate is longitude.  The physical
-  // distance needs to be changed by the cos of the latitude, which is the j coordinate.
+  // If the shape is a sphere or dipole, then the first coordinate is longitude.  
+  // The physical distance needs to be changed by the cos of the latitude, 
+  // which is the j coordinate in the sphere (different for dipole).
   if (iGridShape_ == iSphere_) {
     di_center_m_scgc = di_center_m_scgc % abs(cos(j_center_scgc));
     // edge is in-line with the j center
     di_edge_m = di_edge_m % abs(cos(j_center_scgc));
   }
-
-  // Need a similar thing for the dipole grid here!
+  // Dipole will use cos(geoLat):
   if (iGridShape_ == iDipole_) {
-    // do something here!
+    di_center_m_scgc = di_center_m_scgc % abs(cos(geoLat_scgc));
+    // edge is in-line with the j center
+    di_edge_m = di_edge_m % abs(cos(geoLat_scgc));
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -316,7 +343,11 @@ void Grid::calc_j_grid_spacing() {
     dj_edge_m = dj_edge % radius_scgc;
   }
 
-  // Need to do something for the dipole grid?
+  // Dipole will have different scaling...
+  if (iGridShape_ == iSphere_ || iGridShape_ == iCubesphere_) {
+    dj_center_m_scgc = dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+    dj_edge_m = dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+  }
 
   // For a stretched grid, calculate some useful quantities:
   // egde is defined for the current cell, which
@@ -335,181 +366,3 @@ void Grid::calc_j_grid_spacing() {
 
   report.print(4, "ending calc_j_grid_spacing");
 }
-
-// -----------------------------------------------------------------------------
-//  Calaculate Grid Spacing for Dipole Grid
-// -----------------------------------------------------------------------------
-
-void Grid::calc_dipole_grid_spacing(Planets planet) {
-
-  int64_t iLon, iLat, iAlt;
-
-  report.print(3, "starting calc_dipole_grid_spacing");
-
-  // This is close, but may need to be adjusted later.
-  // These quantities are obtained from integrating the scale factor (h)
-  // The along-field-line distance (alt) should be right, but the lat distance
-  // is the shortest distance from a point to the adjacent field line, not the adjacent cell.
-
-  report.print(3, "starting alt");
-  calc_alt_dipole_grid_spacing();
-  report.print(3, "starting lat");
-  calc_lat_dipole_grid_spacing();
-  report.print(3, "starting long");
-  calc_long_dipole_grid_spacing();
-
-  calc_i_grid_spacing();
-
-  std::vector<arma_cube> lon_lat_radius;
-  lon_lat_radius.push_back(geoLon_scgc);
-  lon_lat_radius.push_back(geoLat_scgc);
-  lon_lat_radius.push_back(radius_scgc);
-  std::vector<arma_cube> xyz;
-
-  xyz = transform_llr_to_xyz_3d(lon_lat_radius);
-  geoX_scgc = xyz[0];
-  geoY_scgc = xyz[0];
-  geoZ_scgc = xyz[0];
-
-  report.print(3, "ending calc_dipole_grid_spacing");
-}
-
-// for sanity (only marginally helpful):
-inline arma_mat delTm(arma_mat theta) {
-  return (sqrt(3 * cos(theta) % cos(theta) + 1));
-}
-inline arma_cube delTc(arma_cube theta) {
-  return (sqrt(3 * cos(theta) % cos(theta) + 1));
-}
-
-// -----------------------------------------------------------------------------
-// Grid spacing for altitude:
-//   - Dipole grid needs to be handled differently!
-// -----------------------------------------------------------------------------
-
-void Grid::calc_alt_dipole_grid_spacing() {
-
-  int64_t iAlt;
-  precision_t planetRadius;
-
-  for (iAlt = 1; iAlt < nAlts - 1; iAlt++) {
-
-    dalt_center_scgc.slice(iAlt) =
-      abs(magAlt_scgc.slice(iAlt + 1) % sin(magLat_scgc.slice(iAlt + 1))
-          % (1 / delTm(magLat_scgc.slice(iAlt + 1)))
-          - magAlt_scgc.slice(iAlt - 1) % sin(magLat_scgc.slice(iAlt - 1))
-          % (1 / delTm(magLat_scgc.slice(iAlt - 1)))) * 2;
-    dk_center_scgc.slice(iAlt) = dalt_center_scgc.slice(iAlt);
-
-    dalt_lower_scgc.slice(iAlt) =
-      abs(magAlt_scgc.slice(iAlt) % sin(magLat_scgc.slice(iAlt))
-          % (1 / delTm(magLat_scgc.slice(iAlt)))
-          - magAlt_scgc.slice(iAlt - 1) % sin(magLat_scgc.slice(iAlt - 1))
-          % (1 / delTm(magLat_scgc.slice(iAlt - 1)))) * 2;
-    dk_edge.slice(iAlt) = dalt_lower_scgc.slice(iAlt);
-
-    dr_edge.slice(iAlt) =
-      radius_scgc.slice(iAlt) - radius_scgc.slice(iAlt - 1);
-  }
-
-  dalt_center_scgc.slice(0) = dalt_center_scgc.slice(1);
-  dalt_center_scgc.slice(nAlts - 1) = dalt_center_scgc.slice(nAlts - 2);
-  dk_center_scgc.slice(0) = dalt_center_scgc.slice(0);
-  dk_center_scgc.slice(nAlts - 1) = dalt_center_scgc.slice(nAlts - 2);
-
-  dalt_lower_scgc.slice(0) = dalt_lower_scgc.slice(1);
-  dr_edge.slice(0) = dr_edge.slice(1);
-  dk_edge.slice(0) = dalt_lower_scgc.slice(1);
-  iAlt = nAlts - 1;
-  dalt_lower_scgc.slice(iAlt) =
-    magAlt_scgc.slice(iAlt) - magAlt_scgc.slice(iAlt - 1);
-  dk_edge.slice(iAlt) = dalt_lower_scgc.slice(iAlt);
-  dr_edge.slice(iAlt) =
-    radius_scgc.slice(iAlt) - radius_scgc.slice(iAlt - 1);
-
-  // For a stretched grid, calculate some useful quantities:
-  // lower is defined for the current cell, which
-  // means that upper(iAlt) is lower(iAlt+1)
-  // ratio = upper / lower
-  for (iAlt = 0; iAlt < nAlts - 1; iAlt++) {
-    dalt_ratio_scgc.slice(iAlt) =
-      dalt_lower_scgc.slice(iAlt + 1) / dalt_lower_scgc.slice(iAlt);
-    dk_ratio.slice(iAlt) =
-      dk_edge.slice(iAlt + 1) / dk_edge.slice(iAlt);
-  }
-
-  iAlt = nAlts - 1;
-  dalt_ratio_scgc.slice(iAlt) = dalt_ratio_scgc.slice(iAlt - 1);
-  dk_ratio.slice(iAlt) = dk_ratio.slice(iAlt - 1);
-
-  // Need the square of the ratio:
-  dalt_ratio_sq_scgc = dalt_ratio_scgc % dalt_ratio_scgc;
-  dk_ratio_sq = dk_ratio % dk_ratio;
-  dk_one_minus_r2 = 1.0 - dk_ratio_sq;
-
-  // k is in meters:
-  dk_edge_m = dk_edge;
-  dk_center_m_scgc = dk_center_scgc;
-}
-
-// ---------------------------------------
-// Grid spacing for latitude:
-// Again, different for the dipole...
-//  - uhoh, might not be right. not actually perpendicular to q-p, but no way around that, i think.
-// ---------------------------------------
-
-void Grid::calc_lat_dipole_grid_spacing() {
-
-  int64_t iLat;
-
-  for (iLat = 1; iLat < nLats - 1; iLat++) {
-    dlat_center_scgc.col(iLat) =
-      abs(magAlt_scgc.col(iLat + 1) % sin(magLat_scgc.col(iLat + 1))
-          % (1 / delTc(magLat_scgc.col(iLat + 1)))
-          - magAlt_scgc.col(iLat - 1) % sin(magLat_scgc.col(iLat - 1))
-          % (1 / delTc(magLat_scgc.col(iLat - 1)))) * 2;
-  }
-
-  // Bottom (one sided):
-  iLat = 0;
-  dlat_center_scgc.col(iLat) =
-    geoLat_scgc.col(iLat + 1) - geoLat_scgc.col(iLat);
-  // Top (one sided):
-  iLat = nLats - 1;
-  dlat_center_scgc.col(iLat) =
-    geoLat_scgc.col(iLat) - geoLat_scgc.col(iLat - 1);
-
-  // Make this into a distance:
-  dlat_center_dist_scgc = dlat_center_scgc % radius_scgc;
-  dj_center_scgc = dlat_center_scgc;
-  dj_center_m_scgc = dlat_center_dist_scgc;
-}
-
-// ---------------------------------------
-// Grid spacing for longitude:
-// ---------------------------------------
-
-void Grid::calc_long_dipole_grid_spacing() {
-
-  int64_t iLon;
-
-  for (iLon = 1; iLon < nLons - 1; iLon++)
-    dlon_center_scgc.row(iLon) =
-      (magLon_scgc.row(iLon + 1) - magLon_scgc.row(iLon - 1)) / 2.0;
-
-  // this might be fine for the dipole, if it works for the geo grid...
-
-  // Bottom (one sided):
-  iLon = 0;
-  dlon_center_scgc.row(iLon) =
-    magLon_scgc.row(iLon + 1) - magLon_scgc.row(iLon);
-  // Top (one sided):
-  iLon = nLons - 1;
-  dlon_center_scgc.row(iLon) =
-    magLon_scgc.row(iLon) - magLon_scgc.row(iLon - 1);
-
-  // Make this into a distance:
-  dlon_center_dist_scgc =
-    // dlon_center_scgc % radius_scgc % abs(cos(geoLat_scgc));
-    dlon_center_scgc % magAlt_scgc % cos(magLat_scgc);
-}

From c6ddab2532434b51fe4ee13cb5d7460b5bd7eef1 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sat, 22 Mar 2025 13:47:18 -0400
Subject: [PATCH 027/120] FEAT: Add generic k-direction gradient

---
 src/solver_gradients.cpp | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 6843eaa3..5434b7fb 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -321,6 +321,44 @@ arma_cube calc_gradient_lat(arma_cube value, Grid grid) {
   return calc_gradient2o_j(value, grid);
 }
 
+// --------------------------------------------------------------------------
+// Calculate the 2nd order gradient in the native k direction
+//   - these formulas assume that the grid is uniform.
+// --------------------------------------------------------------------------
+
+arma_cube calc_gradient2o_k(arma_cube value, Grid grid) {
+
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
+  int64_t iZ;
+
+  arma_cube gradient(nX, nY, nZ);
+  gradient.zeros();
+
+  if (grid.get_HasZdim()) {
+    // Interior:
+    for (iZ = 1; iZ < nZ - 1; iZ++)
+      gradient.slice(iZ) =
+                (value.slice(iZ + 1) - value.slice(iZ - 1)) /
+                (2 * grid.dk_center_m_scgc.slice(iZ));
+
+    // Lower (one sided):
+    iZ = 0;
+    gradient.slice(iZ) =
+              (value.slice(iZ + 1) - value.slice(iZ)) /
+              grid.dk_center_m_scgc.slice(iZ);
+
+    // Upper (one sided):
+    iZ = nZ - 1;
+    gradient.slice(iZ) =
+              (value.slice(iZ) - value.slice(iZ - 1)) /
+              grid.dk_center_m_scgc.slice(iZ);
+  }
+
+  return gradient;
+}
+
 // --------------------------------------------------------------------------
 // Calculate the gradient in the altitudinal direction
 // --------------------------------------------------------------------------

From 59d32e822691ae7b2a80c04a09e31c7f4ffdffca Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sat, 22 Mar 2025 14:00:04 -0400
Subject: [PATCH 028/120] FEAT: Dipole gradient. Might be redundant...

- The gradient in the dipole grid is, I think, the same as the spherical grid. Except the k-direction
- We can do away with this separate method and just put an if statement around that in calc_gradient_vector?
---
 include/solvers.h        |  1 +
 src/solver_gradients.cpp | 26 ++++++++++++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/include/solvers.h b/include/solvers.h
index 02e805e7..ca59515c 100644
--- a/include/solvers.h
+++ b/include/solvers.h
@@ -87,6 +87,7 @@ arma_cube calc_gradient_lat(arma_cube value, Grid grid);
 arma_cube calc_gradient_alt(arma_cube value, Grid grid);
 std::vector<arma_cube> calc_gradient_vector(arma_cube value_scgc, Grid grid);
 std::vector<arma_cube> calc_gradient_cubesphere(arma_cube value, Grid grid);
+std::vector<arma_cube> calc_gradient_dipole(arma_cube value, Grid grid);
 arma_cube calc_gradient_alt_4th(arma_cube value, Grid grid);
 arma_mat project_onesided_alt_3rd(arma_cube value, Grid grid, int64_t iAlt);
 
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 5434b7fb..49867725 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -20,6 +20,8 @@ std::vector<arma_cube> calc_gradient_vector(arma_cube value_scgc, Grid grid) {
 
   if (grid.iGridShape_ == grid.iCubesphere_)
     gradient_vcgc = calc_gradient_cubesphere(value_scgc, grid);
+  else if (grid.iGridShape_ == grid.iDipole_)
+    gradient_vcgc = calc_gradient_dipole(value_scgc, grid);
   else {
 
     report.print(4, "Going into calc_gradient_lon");
@@ -453,6 +455,30 @@ arma_mat project_onesided_alt_3rd(arma_cube value, Grid grid, int64_t iAlt) {
   return valueOut;
 }
 
+// --------------------------------------------------------------------------
+// Calculate the gradient on the dipole grid
+// - This is identical to the spherical grid, except the k/alt direction.
+// --------------------------------------------------------------------------
+std::vector<arma_cube> calc_gradient_dipole(arma_cube value_scgc, Grid grid) {
+
+  std::vector<arma_cube> gradient_vcgc;
+
+  report.print(3, "Calculating dipole griadient");
+
+  report.print(4, "Going into calc_gradient_lon");
+  gradient_vcgc.push_back(calc_gradient2o_i(value_scgc, grid));
+
+
+  report.print(4, "Going into calc_gradient_lat");
+  gradient_vcgc.push_back(calc_gradient2o_j(value_scgc, grid));
+
+
+  report.print(4, "Going into calc_gradient_alt");
+  gradient_vcgc.push_back(calc_gradient2o_k(value_scgc, grid));
+
+  return gradient_vcgc;
+}
+
 // --------------------------------------------------------------------------
 // Calculate the gradient in cubesphere spatial discretization
 // --------------------------------------------------------------------------

From f61f2a5c4ac0856812d6fa273d78955c667fcf68 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 10:27:42 -0400
Subject: [PATCH 029/120] MAINT: Remove some unused func's and variables from
 mag grid things

---
 include/grid.h        | 24 -------------------
 src/grid.cpp          |  1 -
 src/init_mag_grid.cpp | 54 -------------------------------------------
 3 files changed, 79 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index 40129d70..e0cd3f77 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -83,7 +83,6 @@ class Grid
   // Phi => Longitude
   // P   => L-shell
   // Q   => Distance along field line
-  arma_cube magPhi_scgc;
   arma_cube magP_scgc;
   arma_cube magQ_scgc;
 
@@ -317,32 +316,9 @@ class Grid
   void calc_cent_acc(Planets planet);
 
   // Make mag-field grid:
-  void convert_dipole_geo_xyz(Planets planet, precision_t XyzDipole[3],
-                              precision_t XyzGeo[3]);
-
   bool init_dipole_grid(Quadtree quadtree_ion, Planets planet);
   // Support functions:
   void calc_dipole_grid_spacing(Planets planet);
-  void calc_alt_dipole_grid_spacing();
-  void calc_lat_dipole_grid_spacing();
-  void calc_long_dipole_grid_spacing();
-  void fill_field_lines(arma_vec baseLats, precision_t min_altRe,
-                        precision_t Gamma, Planets planet,
-                        bool isCorner);
-  void dipole_alt_edges(Planets planet, precision_t min_altRe);
-  // get the latitude spacing given the quadtree start & size, and the latitude limits
-  // extent: quadtree up
-  // origin: quadtree origin
-  // upper_lim: upper latitude limit (input)
-  // lower_lim: lower latitude limit (from min_apex)
-  // nLats: number of latitudes (nY)
-  // spacing_factor: (not supported yet), so always 1.0. Will adjust baselat spacing, eventually.
-  arma_vec baselat_spacing(precision_t extent,
-                          precision_t origin,
-                          precision_t upper_lim,
-                          precision_t lower_lim,
-                          // int16_t nLats,
-                          precision_t spacing_factor);
 
   // Update ghost cells with values from other processors
   void exchange(arma_cube &data, const bool pole_inverse);
diff --git a/src/grid.cpp b/src/grid.cpp
index 4e5c9934..3e99f8cc 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -157,7 +157,6 @@ Grid::Grid(std::string gridtype) {
   magAlt_scgc.set_size(nX, nY, nZ);
   magInvLat_scgc.set_size(nX, nY, nZ);
 
-  magPhi_scgc.set_size(nX, nY, nZ);
   magP_scgc.set_size(nX, nY, nZ);
   magQ_scgc.set_size(nX, nY, nZ);
 
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 569ad0f8..bfe4776b 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -73,60 +73,6 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   return llr;
 }
 
-// -----------------------------------------------------------------------
-// Convert XyzDipole to XyzGeo
-//
-// -----------------------------------------------------------------------
-
-void Grid::convert_dipole_geo_xyz(Planets planet, precision_t XyzDipole[3],
-                                  precision_t XyzGeo[3]) {
-
-  std::string function = "Grid::convert_dipole_geo_xyz";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  precision_t XyzRemoveShift[3];
-  precision_t XyzRemoveTilt[3];
-  precision_t XyzRemoveRot[3];
-
-  // get planetary parameters
-  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
-  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
-  precision_t radius = planet.get_radius(0.0);
-
-
-  // get the dipole shift, but normalize it to equatorial radius
-  precision_t dipole_center[3];
-  std::vector<precision_t> temp_dipole_center = planet.get_dipole_center();
-
-  if ((temp_dipole_center[0] != 0) or (temp_dipole_center[1] != 0) or
-      (temp_dipole_center[2] != 0)) {
-    report.print(0,
-                 "Dipole center != 0, but that is not supported yet. Setting to 0!");
-    temp_dipole_center = {0, 0, 0};
-
-  }
-
-  transform_float_vector_to_array(temp_dipole_center, dipole_center);
-
-  dipole_center[0] = dipole_center[0] / radius;
-  dipole_center[1] = dipole_center[1] / radius;
-  dipole_center[2] = dipole_center[2] / radius;
-
-  // Remove Tilt
-  transform_rot_y(XyzDipole, magnetic_pole_tilt, XyzRemoveTilt);
-
-  // Remove Rot
-  transform_rot_z(XyzRemoveTilt, magnetic_pole_rotation, XyzRemoveRot);
-
-  // Remove Shift
-  vector_add(XyzRemoveRot, dipole_center, XyzGeo);
-
-  report.exit(function);
-  return;
-
-}
-
 // ----------------------------------------------------------------------
 // Initialize the dipole grid.
 // - inputs (min_apex, min_alt, LatStretch, FieldLineStretch, max_lat_dipole)

From b06b008b1a7b3d413f7a4526a55539cd252b3d54 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 10:28:51 -0400
Subject: [PATCH 030/120] FEAT: Add bones for converting offset dipole to geo
 (grid)

---
 src/init_mag_grid.cpp | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index bfe4776b..83720da4 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -55,16 +55,22 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
 
   precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
   precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
+  std::vector<precision_t> dipole_center = planet.get_dipole_center();
 
   // Reverse our dipole rotations:
   xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
   xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
 
-  // offset dipole (not yet implemented):
-  // std::vector<precision_t> dipole_center = planet.get_dipole_center();
-  // xyz_geo[0] = xyzRot2[0] + dipole_center[0];
-  // xyz_geo[1] = xyzRot2[1] + dipole_center[1];
-  // xyz_geo[2] = xyzRot2[2] + dipole_center[2];
+  // offset dipole (not fully suported yet, so will be zero. 
+  if ((dipole_center[0] != 0) || (dipole_center[1] != 0) ||
+    (dipole_center[2] != 0)) {
+  report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
+  dipole_center = {0, 0, 0};
+  }
+
+  xyz_geo[0] = xyzRot2[0] + dipole_center[0];
+  xyz_geo[1] = xyzRot2[1] + dipole_center[1];
+  xyz_geo[2] = xyzRot2[2] + dipole_center[2];
 
   // transform back to lon, lat, radius:
   llr = transform_xyz_to_llr_3d(xyzRot2);

From 699ce3be4c4f493de693c2e0a4a9b3d875a30bcc Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 11:11:17 -0400
Subject: [PATCH 031/120] FEAT/STY: Overload get_radius to accept floats &
 cubes

Two calls were replaced. Do we want to switch everything to cubes from floats?
---
 include/planets.h     |  3 ++-
 src/fill_grid.cpp     |  8 +-------
 src/init_mag_grid.cpp |  2 +-
 src/planets.cpp       | 13 +++++++++++++
 4 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/include/planets.h b/include/planets.h
index a512daac..f6ba44e9 100644
--- a/include/planets.h
+++ b/include/planets.h
@@ -61,11 +61,12 @@ class Planets {
   /**********************************************************************
      \brief Returns radius of the planet, which can be a function of latitude
 
-     currently, this ignores the latitude, but should be implemented.
+     Overloaded for floats & cubes, much easier to call this on a cube.
 
      \param latitude the latitude to get the radius at.
    **/
   precision_t get_radius(precision_t latitude);
+  arma_cube get_radius(arma_cube latitude);
 
   /**********************************************************************
      \brief Returns the longitude offset to convert from longitude to local time
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index deb2b181..0fb4c2bb 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -215,16 +215,10 @@ void Grid::fill_grid_radius(Planets planet) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  int64_t iLon, iLat, iAlt;
-
   // This generalizes things so that radius could be a function of all
   // three dimensions.  The Cubesphere has different latitudes in the first
   // and second dimensions.
-  for (iLon = 0; iLon < nLons; iLon++)
-    for (iLat = 0; iLat < nLats; iLat++)
-      for (iAlt = 0; iAlt < nAlts; iAlt++)
-        radius_scgc(iLon, iLat, iAlt) =
-          planet.get_radius(geoLat_scgc(iLon, iLat, iAlt));
+  radius_scgc = planet.get_radius(geoLat_scgc);
 
   radius_scgc = radius_scgc + geoAlt_scgc;
   radius2_scgc = radius_scgc % radius_scgc;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 83720da4..b20118be 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -446,7 +446,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   geoLon_scgc = llr[0];
   geoLat_scgc = llr[1];
-  geoAlt_scgc = llr[2] - planetRadius;
+  geoAlt_scgc = llr[2] - planet.get_radius(geoLat_scgc);
   report.print(4,
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
diff --git a/src/planets.cpp b/src/planets.cpp
index 0528cfd7..1216d787 100644
--- a/src/planets.cpp
+++ b/src/planets.cpp
@@ -66,6 +66,7 @@ precision_t Planets::get_cos_dec(Times time) {
 
 // -----------------------------------------------------------------------------
 // Get the radius of the planet as a function of latitude (meters)
+// - Overloaded to support passing cubes, avoids loops in the calling function.
 // -----------------------------------------------------------------------------
 
 precision_t Planets::get_radius(precision_t latitude) {
@@ -75,6 +76,18 @@ precision_t Planets::get_radius(precision_t latitude) {
     return planet.radius;
 }
 
+arma_cube Planets::get_radius(arma_cube latitude) {
+  if (input.get_do_lat_dependent_radius())
+    return planet.polar_radius + (planet.delta_radius * cos(latitude));
+  else{
+    arma_cube radius(latitude.n_rows,
+                    latitude.n_cols,
+                    latitude.n_slices);
+    radius = planet.radius;
+    return radius;
+  }
+}
+
 // -----------------------------------------------------------------------------
 // Get the rotation of the dipole (in radians, rotation in longitude)
 // -----------------------------------------------------------------------------

From 77698d43f0a59fe51fc2961cbc6c14d3cde4b14c Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 11:27:26 -0400
Subject: [PATCH 032/120] BUG: Fix dipole grid's radius calculations

---
 src/init_mag_grid.cpp | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index b20118be..123fc177 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -123,6 +123,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t max_alt = grid_input.alt_max * cKMtoM;
 
   // Normalize inputs to planet radius... (update when earth is oblate)
+  // Here we are using the equatorial radius.
   precision_t planetRadius = planet.get_radius(0.0);
   // Altitude to begin modeling, normalized to planet radius
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
@@ -458,8 +459,13 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   report.print(4,
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
-  // Calculate the radius, of planet
-  fill_grid_radius(planet);
+  // Fill grid radius, radius2, radius2i
+  // fill_grid_radius uses radius of the planet & geo_alt
+  // That would be redundant here since we already know the radius (magAlt)
+  // This is NOT yet offset: to offset do magAlt + dipole_cnter_m
+  radius_scgc = magAlt_scgc;
+  radius2_scgc = radius_scgc % radius_scgc;
+  radius2i_scgc = 1.0 / radius2_scgc;
 
   // Figure out what direction is radial:
   rad_unit_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
@@ -470,15 +476,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  arma_cube br = 2 * sin(abs(magLat_scgc));
-  arma_cube bt = cos(magLat_scgc);
-  arma_cube bm = sqrt(br % br + bt % bt);
-  // Latitudinal direction of radial:
-  arma_cube s = sign(magLat_scgc);
-  s.elem(find(s == 0)).ones();
-
-  rad_unit_vcgc[1] = bt / bm % s;
-  rad_unit_vcgc[2] = -br / bm;
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[2] = -2* sin(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;

From f0b231c7dfb9376baca29079fc76a942430ce2fa Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 25 Mar 2025 16:27:30 -0400
Subject: [PATCH 033/120] Revert "FEAT/STY: Overload get_radius to accept
 floats & cubes"

This reverts commit 699ce3be4c4f493de693c2e0a4a9b3d875a30bcc.
---
 include/planets.h     |  3 +--
 src/fill_grid.cpp     |  8 +++++++-
 src/init_mag_grid.cpp |  2 +-
 src/planets.cpp       | 13 -------------
 4 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/include/planets.h b/include/planets.h
index f6ba44e9..a512daac 100644
--- a/include/planets.h
+++ b/include/planets.h
@@ -61,12 +61,11 @@ class Planets {
   /**********************************************************************
      \brief Returns radius of the planet, which can be a function of latitude
 
-     Overloaded for floats & cubes, much easier to call this on a cube.
+     currently, this ignores the latitude, but should be implemented.
 
      \param latitude the latitude to get the radius at.
    **/
   precision_t get_radius(precision_t latitude);
-  arma_cube get_radius(arma_cube latitude);
 
   /**********************************************************************
      \brief Returns the longitude offset to convert from longitude to local time
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 0fb4c2bb..deb2b181 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -215,10 +215,16 @@ void Grid::fill_grid_radius(Planets planet) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
+  int64_t iLon, iLat, iAlt;
+
   // This generalizes things so that radius could be a function of all
   // three dimensions.  The Cubesphere has different latitudes in the first
   // and second dimensions.
-  radius_scgc = planet.get_radius(geoLat_scgc);
+  for (iLon = 0; iLon < nLons; iLon++)
+    for (iLat = 0; iLat < nLats; iLat++)
+      for (iAlt = 0; iAlt < nAlts; iAlt++)
+        radius_scgc(iLon, iLat, iAlt) =
+          planet.get_radius(geoLat_scgc(iLon, iLat, iAlt));
 
   radius_scgc = radius_scgc + geoAlt_scgc;
   radius2_scgc = radius_scgc % radius_scgc;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 123fc177..e10f3b80 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -447,7 +447,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   geoLon_scgc = llr[0];
   geoLat_scgc = llr[1];
-  geoAlt_scgc = llr[2] - planet.get_radius(geoLat_scgc);
+  geoAlt_scgc = llr[2] - planetRadius;
   report.print(4,
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
diff --git a/src/planets.cpp b/src/planets.cpp
index 1216d787..0528cfd7 100644
--- a/src/planets.cpp
+++ b/src/planets.cpp
@@ -66,7 +66,6 @@ precision_t Planets::get_cos_dec(Times time) {
 
 // -----------------------------------------------------------------------------
 // Get the radius of the planet as a function of latitude (meters)
-// - Overloaded to support passing cubes, avoids loops in the calling function.
 // -----------------------------------------------------------------------------
 
 precision_t Planets::get_radius(precision_t latitude) {
@@ -76,18 +75,6 @@ precision_t Planets::get_radius(precision_t latitude) {
     return planet.radius;
 }
 
-arma_cube Planets::get_radius(arma_cube latitude) {
-  if (input.get_do_lat_dependent_radius())
-    return planet.polar_radius + (planet.delta_radius * cos(latitude));
-  else{
-    arma_cube radius(latitude.n_rows,
-                    latitude.n_cols,
-                    latitude.n_slices);
-    radius = planet.radius;
-    return radius;
-  }
-}
-
 // -----------------------------------------------------------------------------
 // Get the rotation of the dipole (in radians, rotation in longitude)
 // -----------------------------------------------------------------------------

From 34f7eb1077209383feecd48c669eaae1c4aadb45 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 25 Mar 2025 16:40:28 -0400
Subject: [PATCH 034/120] BUGS: Fix type issues in mag_to_geo & clarify
 reporting in solver_gradients. The code is stable.

---
 src/init_mag_grid.cpp    | 12 ++++++------
 src/solver_gradients.cpp |  6 +++---
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index e10f3b80..67be3a25 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -62,15 +62,15 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
 
   // offset dipole (not fully suported yet, so will be zero. 
-  if ((dipole_center[0] != 0) || (dipole_center[1] != 0) ||
-    (dipole_center[2] != 0)) {
+  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
+      (dipole_center[2] != 0.0)) {
   report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-  dipole_center = {0, 0, 0};
+  dipole_center = {0.0, 0.0, 0.0};
   }
 
-  xyz_geo[0] = xyzRot2[0] + dipole_center[0];
-  xyz_geo[1] = xyzRot2[1] + dipole_center[1];
-  xyz_geo[2] = xyzRot2[2] + dipole_center[2];
+  xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);
+  xyz_geo.push_back(xyzRot2[1] + dipole_center[1]);
+  xyz_geo.push_back(xyzRot2[2] + dipole_center[2]);
 
   // transform back to lon, lat, radius:
   llr = transform_xyz_to_llr_3d(xyzRot2);
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 49867725..464239cd 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -465,15 +465,15 @@ std::vector<arma_cube> calc_gradient_dipole(arma_cube value_scgc, Grid grid) {
 
   report.print(3, "Calculating dipole griadient");
 
-  report.print(4, "Going into calc_gradient_lon");
+  report.print(4, "Going into calc_gradient_i (dipole)");
   gradient_vcgc.push_back(calc_gradient2o_i(value_scgc, grid));
 
 
-  report.print(4, "Going into calc_gradient_lat");
+  report.print(4, "Going into calc_gradient_j (dipole)");
   gradient_vcgc.push_back(calc_gradient2o_j(value_scgc, grid));
 
 
-  report.print(4, "Going into calc_gradient_alt");
+  report.print(4, "Going into calc_gradient_K (DIPOLE)");
   gradient_vcgc.push_back(calc_gradient2o_k(value_scgc, grid));
 
   return gradient_vcgc;

From dd595f5579b633d40f936fb13f94b2b7a2673952 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 31 Mar 2025 14:33:01 -0400
Subject: [PATCH 035/120] BUG: Fix order of pts in mag grid (k-direction) &
 start changing to dipole p,q for i,j from lon,lat

---
 src/init_mag_grid.cpp | 56 ++++++++++++++++++++++++-------------------
 1 file changed, 32 insertions(+), 24 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 67be3a25..506c0310 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -314,6 +314,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   std::pair<precision_t, precision_t> rtheta, rtheta_edge;
   precision_t radius, radius_edge, theta, theta_edge, invLat, invLat_edge,
               pcenter, pedge, qcenter, qedge;
+  int64_t iAlt2;
   // we need to turn single floats into vectors/cubes:
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
@@ -322,8 +323,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       // We have to reverse & negate things; want latitudes from south->north
-      // and altitude low->high. Altitude is in the correct direction, so change how we
-      // access values in the latitude dimension.
+      // and altitude low->high. 
+      // - Altitude is in the reverse direction in both hemispheres.
+      // - Latitude is reversed in southern hemisphere.
+      iAlt2 = nAlts - iAlt - 1;
 
       if (isSouth) {
         qcenter = magQ1d(iAlt);
@@ -367,22 +370,22 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       }
 
       for (iLon = 0; iLon < nLons; iLon ++) {
-        magLat_scgc(iLon, iLat, iAlt) = theta;
-        j_center_scgc(iLon, iLat, iAlt) = theta;
+        magLat_scgc(iLon, iLat, iAlt2) = theta;
+        magLat_Down(iLon, iLat, iAlt2) = theta_edge;
 
-        magLat_Down(iLon, iLat, iAlt) = theta_edge;
-        j_edge_scgc(iLon, iLat, iAlt) = theta_edge;
+        magAlt_scgc(iLon, iLat, iAlt2) = radius;
+        magAlt_Below(iLon, iLat, iAlt2) = radius_edge;
 
-        magAlt_scgc(iLon, iLat, iAlt) = radius;
-        k_center_scgc(iLon, iLat, iAlt) = radius;
+        magInvLat_scgc(iLon, iLat, iAlt2) = invLat;
 
-        magAlt_Below(iLon, iLat, iAlt) = radius_edge;
-        k_edge_scgc(iLon, iLat, iAlt) = radius_edge;
+        magP_scgc(iLon, iLat, iAlt2) = pcenter;
+        j_center_scgc(iLon, iLat, iAlt2) = pcenter;
+        j_edge_scgc(iLon, iLat, iAlt2) = pedge;
+
+        magQ_scgc(iLon, iLat, iAlt2) = qcenter;
+        k_center_scgc(iLon, iLat, iAlt2) = qcenter;
+        k_edge_scgc(iLon, iLat, iAlt2) = qedge;
 
-        // extra coordinates
-        magP_scgc(iLon, iLat, iAlt) = pcenter;
-        magQ_scgc(iLon, iLat, iAlt) = qcenter;
-        magInvLat_scgc(iLon, iLat, iAlt) = invLat;
       }
     }
   }
@@ -394,6 +397,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   for (iLat = 0; iLat < nLats + 1; iLat ++) {
     for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
+      iAlt2 = nAlts - iAlt;
 
       // Same process as the centers & edges (above)
       if (isSouth) {
@@ -415,15 +419,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       }
 
       for (iLon = 0; iLon < nLons + 1; iLon ++) {
-        magLat_Corner(iLon, iLat, iAlt) = theta_corner;
-        j_corner_scgc(iLon, iLat, iAlt) = theta_corner;
+        magLat_Corner(iLon, iLat, iAlt2) = theta_corner;
+        magAlt_Corner(iLon, iLat, iAlt2) = radius_corner;
+
+        magInvLat_Corner(iLon, iLat, iAlt2) = invLat_corner;
 
-        magAlt_Corner(iLon, iLat, iAlt) = radius_corner;
-        k_corner_scgc(iLon, iLat, iAlt) = radius_corner;
+        magP_Corner(iLon, iLat, iAlt2) = pcorner;
+        j_corner_scgc(iLon, iLat, iAlt2) = pcorner;
 
-        magP_Corner(iLon, iLat, iAlt) = pcorner;
-        magQ_Corner(iLon, iLat, iAlt) = qcorner;
-        magInvLat_Corner(iLon, iLat, iAlt) = invLat_corner;
+        magQ_Corner(iLon, iLat, iAlt2) = qcorner;
+        k_corner_scgc(iLon, iLat, iAlt2) = qcorner;
       }
     }
   }
@@ -433,15 +438,18 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // all distances, so far, are in units of planet radii, turn into meters.
   // Except for Q, leave that dimensionless.
   magAlt_scgc *= planetRadius;
-  k_center_scgc *= planetRadius;
   magAlt_Below *= planetRadius;
-  k_edge_scgc *= planetRadius;
   magP_scgc *= planetRadius;
   magAlt_Corner *= planetRadius;
-  k_corner_scgc *= planetRadius;
   magP_Corner *= planetRadius;
+  magQ_Corner *= planetRadius;
+  magQ_scgc *= planetRadius;
 
+  k_center_scgc *= planetRadius;
+  k_edge_scgc *= planetRadius;
+  k_corner_scgc *= planetRadius;
 
+  // Convert to geographic, rotating and (maybe) shifting the dipole grid.
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);
 

From dc4045afa41103bde3bc24fdc755cd2aa665aeb3 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 14:29:43 -0400
Subject: [PATCH 036/120] sty: clean up error reporting around offset dipole

---
 src/init_mag_grid.cpp | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 506c0310..a390631d 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -61,11 +61,14 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
   xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
 
-  // offset dipole (not fully suported yet, so will be zero. 
+  // offset dipole (not fully suported yet, so will be zero)
   if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
       (dipole_center[2] != 0.0)) {
-  report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-  dipole_center = {0.0, 0.0, 0.0};
+    
+    if (iProc == 0) // only one error
+      report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
+    
+    dipole_center = {0.0, 0.0, 0.0};
   }
 
   xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);

From 2df83cb7ff8ce88b1e055ea92f4a812d70976ab6 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 16:26:10 -0400
Subject: [PATCH 037/120] BUG/STY: Fix some missing points in dipole grid,
 formatting

---
 src/init_mag_grid.cpp | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index a390631d..bc94f593 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -64,10 +64,10 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   // offset dipole (not fully suported yet, so will be zero)
   if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
       (dipole_center[2] != 0.0)) {
-    
+
     if (iProc == 0) // only one error
       report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-    
+
     dipole_center = {0.0, 0.0, 0.0};
   }
 
@@ -178,15 +178,17 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       // left edges
       magLon_Left.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_edge_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
+    }
+  }
+
+  for (iLat = 0; iLat < nLats + 1; iLat ++) {
+    for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
       // corners
       magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
     }
   }
 
-  if (magLon_scgc.has_nan())
-    report.error("NAN IN MAGLON");
-
   report.print(3, "Done initializing longitudes, moving to latitude");
 
   ////////////////
@@ -224,7 +226,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners & edges
   }
 
-  lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat; // last corner
+  lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat + min_lat; // last corner
 
   // At the pole:
   // - put last ghost cell's corner at 89.9 degrees latitude
@@ -302,7 +304,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     magQ_corner_1d(iAlt) = q_min + (iAlt - nGCs) * delQ;
   }
 
-  magQ_corner_1d(nAlts) = q_min - nGCs * delQ;
+  magQ_corner_1d(nAlts) = q_min + (nAlts - nGCs) * delQ;
 
   report.print(3,
                "Done generating points for magnetic grid. Plugging everything in");
@@ -318,7 +320,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t radius, radius_edge, theta, theta_edge, invLat, invLat_edge,
               pcenter, pedge, qcenter, qedge;
   int64_t iAlt2;
-  // we need to turn single floats into vectors/cubes:
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
   // Currently the grid is symmetric in longitude.
@@ -326,7 +327,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       // We have to reverse & negate things; want latitudes from south->north
-      // and altitude low->high. 
+      // and altitude low->high.
       // - Altitude is in the reverse direction in both hemispheres.
       // - Latitude is reversed in southern hemisphere.
       iAlt2 = nAlts - iAlt - 1;
@@ -406,12 +407,12 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       if (isSouth) {
         qcorner = magQ_corner_1d(iAlt);
         pcorner = Pcorners(nLats - iLat);
-        invLat_corner = lat1dDown(nLats - iLat) * -1;
+        invLat_corner = lat1dDown(nLats - iLat) * -1.0;
         rtheta = qp_to_r_theta(qcorner, pcorner);
 
         radius_corner = rtheta.first;
-        theta_corner = rtheta.second * -1;
-        qcorner *= -1;
+        theta_corner = rtheta.second * -1.0;
+        qcorner *= -1.0;
       } else {
         qcorner = magQ_corner_1d(iAlt);
         pcorner = Pcorners(iLat);
@@ -487,8 +488,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
-  rad_unit_vcgc[2] = -2* sin(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;

From fd005d50fffcf191bd974cf42b288a5574f62e7e Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 17:42:41 -0400
Subject: [PATCH 038/120] FEAT: Check for non-physical cells on geo & mag grids

---
 include/grid.h        | 4 ++++
 src/init_geo_grid.cpp | 4 ++++
 src/init_mag_grid.cpp | 5 +++++
 3 files changed, 13 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index e0cd3f77..23de18ab 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -102,6 +102,10 @@ class Grid
   arma_cube magQ_Corner;
   arma_cube magInvLat_Corner;
 
+  // Dipole grid has cells below the surface of earth. These variables hold masks
+  // to either access those cells or ignore them (with .elem()).
+  arma::uvec isTooLowCell, isPhysicalCell;
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 78b75acb..7d3d611b 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -150,6 +150,10 @@ void Grid::create_altitudes(Planets planet) {
     }
   }
 
+  // All cells on the geographic grid *should* be ok
+  isTooLowCell = find(geoAlt_scgc < 0.0);
+  isPhysicalCell = find(geoAlt_scgc > 0.0);
+
   report.exit(function);
   return;
 }
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index bc94f593..a4596c37 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -505,6 +505,11 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   calc_dipole_grid_spacing(planet);
 
+
+  // Generate mask for physicsl cells
+  isTooLowCell = find(magAlt_scgc < min_alt_re);
+  isPhysicalCell = find(magAlt_scgc > min_alt_re);
+
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From 88fc85a99c94c94336e889a2c044f5f806283262 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 19:29:27 -0400
Subject: [PATCH 039/120] BUG: Catch errors making any kind of mag grid

---
 src/main/main.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 9d0a0a3f..eba6d868 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -92,8 +92,6 @@ int main() {
 
     if (mGrid.iGridShape_ == mGrid.iDipole_) {
       didWork = mGrid.init_dipole_grid(quadtree_ion, planet);
-      if (!didWork)
-        throw std::string("init_dipole_grid failed!");
     } else {
       std::cout << "Making Spherical Magnetic Grid\n";
       mGrid.set_IsDipole(false);
@@ -101,6 +99,10 @@ int main() {
       mGrid.set_IsGeoGrid(false);
     }
 
+    if (!didWork)
+    throw std::string("Initializing magneitic grid failed!");
+
+
     didWork = grid_match(gGrid, mGrid, quadtree, quadtree_ion);
 
     // Initialize Neutrals on geographic grid:

From 471f5f582151237ce0301d1247ed0613007e8d67 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 4 Apr 2025 18:09:24 -0400
Subject: [PATCH 040/120] BUG: Fix radius vs alt units & geo/mag lat on dipole
 grid

---
 src/grid_spacing.cpp  | 16 ++++++++--------
 src/init_mag_grid.cpp | 16 ++++++++--------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 09a4926f..56142840 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -155,8 +155,8 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
-    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTc(magLat_scgc);
+    dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
+    dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTc(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -253,11 +253,11 @@ void Grid::calc_i_grid_spacing() {
     // edge is in-line with the j center
     di_edge_m = di_edge_m % abs(cos(j_center_scgc));
   }
-  // Dipole will use cos(geoLat):
+  // Dipole will use cos(magLat)
   if (iGridShape_ == iDipole_) {
-    di_center_m_scgc = di_center_m_scgc % abs(cos(geoLat_scgc));
+    di_center_m_scgc = di_center_m_scgc % abs(cos(magLat_scgc));
     // edge is in-line with the j center
-    di_edge_m = di_edge_m % abs(cos(geoLat_scgc));
+    di_edge_m = di_edge_m % abs(cos(magLat_scgc));
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -344,9 +344,9 @@ void Grid::calc_j_grid_spacing() {
   }
 
   // Dipole will have different scaling...
-  if (iGridShape_ == iSphere_ || iGridShape_ == iCubesphere_) {
-    dj_center_m_scgc = dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
-    dj_edge_m = dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+  if (iGridShape_ == iDipole_) {
+    dj_center_m_scgc = magAlt_scgc % dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+    dj_edge_m = magAlt_scgc % dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index a4596c37..5b6d0579 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -441,10 +441,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   // all distances, so far, are in units of planet radii, turn into meters.
   // Except for Q, leave that dimensionless.
-  magAlt_scgc *= planetRadius;
-  magAlt_Below *= planetRadius;
+  magAlt_scgc;
+  magAlt_Below;
+  magAlt_Corner;
   magP_scgc *= planetRadius;
-  magAlt_Corner *= planetRadius;
   magP_Corner *= planetRadius;
   magQ_Corner *= planetRadius;
   magQ_scgc *= planetRadius;
@@ -454,7 +454,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   k_corner_scgc *= planetRadius;
 
   // Convert to geographic, rotating and (maybe) shifting the dipole grid.
-  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
+  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc * planetRadius,
                                            planet);
 
   geoLon_scgc = llr[0];
@@ -464,7 +464,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
   std::vector <arma_cube> llr_corner = mag_to_geo(magLon_Corner, magLat_Corner,
-                                                  magAlt_Corner, planet);
+                                                  magAlt_Corner * planetRadius, planet);
   geoLon_Corner = llr_corner[0];
   geoLat_Corner = llr_corner[1];
   geoAlt_Corner = llr_corner[2] - planetRadius;
@@ -475,7 +475,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // fill_grid_radius uses radius of the planet & geo_alt
   // That would be redundant here since we already know the radius (magAlt)
   // This is NOT yet offset: to offset do magAlt + dipole_cnter_m
-  radius_scgc = magAlt_scgc;
+  radius_scgc = magAlt_scgc * planetRadius;
   radius2_scgc = radius_scgc % radius_scgc;
   radius2i_scgc = 1.0 / radius2_scgc;
 
@@ -507,8 +507,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
 
   // Generate mask for physicsl cells
-  isTooLowCell = find(magAlt_scgc < min_alt_re);
-  isPhysicalCell = find(magAlt_scgc > min_alt_re);
+  isTooLowCell = find(geoAlt_scgc < 0.0);
+  isPhysicalCell = find(geoAlt_scgc > 0.0);
 
   report.print(4, "Done altitude spacing for the dipole grid.");
 

From 31040728cbda0da5462c50a3af7f5124ac086ec9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 15:49:02 -0400
Subject: [PATCH 041/120] STY: Minor changes to dipole things

---
 include/grid.h        | 2 +-
 src/init_mag_grid.cpp | 5 +----
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index 23de18ab..122a8af1 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -331,6 +331,7 @@ class Grid
 
   bool IsLatLonGrid;
   bool IsCubeSphereGrid;
+  bool IsDipole;
   bool DoesTouchNorthPole;
   bool DoesTouchSouthPole;
   /// The processor to the East/Right/X+:
@@ -435,7 +436,6 @@ class Grid
   bool HasBField;
   bool IsExperimental;
   bool IsMagGrid;
-  bool IsDipole = false;
   std::string gridType;
 
   int64_t nX, nLons;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 5b6d0579..3050565f 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -65,9 +65,6 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
       (dipole_center[2] != 0.0)) {
 
-    if (iProc == 0) // only one error
-      report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-
     dipole_center = {0.0, 0.0, 0.0};
   }
 
@@ -507,7 +504,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
 
   // Generate mask for physicsl cells
-  isTooLowCell = find(geoAlt_scgc < 0.0);
+  isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
 
   report.print(4, "Done altitude spacing for the dipole grid.");

From 70796dad7ae27d670491f4071f3c9c30772efaf4 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 15:51:51 -0400
Subject: [PATCH 042/120] BUG: Fix dipole gradients!

---
 include/tools.h      |  3 +++
 src/dipole.cpp       |  6 ++++++
 src/grid_spacing.cpp | 15 ++++-----------
 3 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/include/tools.h b/include/tools.h
index 20b1948a..5c83ed34 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -293,5 +293,8 @@ std::vector<int> indef_vector(arma_cube cube);
 // --------------------------------------------------------------------------
 
 arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in);
+// Used for dipole gradients & distances. 
+// Probably needs to be moved.
+arma_cube delTheta(arma_cube magLat);
 
 #endif  // INCLUDE_TOOLS_H_
diff --git a/src/dipole.cpp b/src/dipole.cpp
index 2887e547..553da188 100644
--- a/src/dipole.cpp
+++ b/src/dipole.cpp
@@ -114,3 +114,9 @@ bfield_info_type get_dipole(precision_t lon,
 
   return bfield_info;
 }
+
+// This is the del value from (Swisdak, 2006) & others. Used in Dipole distance calc's.
+// Note the cos->sin, since magLat is latitude, not colatitude.
+arma_cube delTheta(arma_cube magLat) {
+  return (sqrt(3 * sin(magLat) % sin(magLat) + 1));
+}
\ No newline at end of file
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 56142840..04ff6e12 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -34,13 +34,6 @@ void Grid::calc_dipole_grid_spacing(Planets planet) {
   report.print(3, "ending calc_grid_spacing");
 }
 
-
-// This is the del value from (Swisdak, 2006) & others. Used in Dipole distance calc's.
-// Note the cos->sin, since magLat is latitude, not colatitude.
-inline arma_cube delTc(arma_cube theta) {
-  return (sqrt(3 * sin(theta) % sin(theta) + 1));
-}
-
 // ---------------------------------------
 // Grid spacing for altitude:
 // ---------------------------------------
@@ -155,8 +148,8 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
-    dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTc(magLat_scgc);
+    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
+    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -345,8 +338,8 @@ void Grid::calc_j_grid_spacing() {
 
   // Dipole will have different scaling...
   if (iGridShape_ == iDipole_) {
-    dj_center_m_scgc = magAlt_scgc % dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
-    dj_edge_m = magAlt_scgc % dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
+    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:

From 4abac089ba706055e5121754f4a32fc6139055e5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 16:57:45 -0400
Subject: [PATCH 043/120] STY: Use scale height func in chapman integrals

---
 src/calc_neutral_derived.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/calc_neutral_derived.cpp b/src/calc_neutral_derived.cpp
index 541dbb77..1b1ea783 100644
--- a/src/calc_neutral_derived.cpp
+++ b/src/calc_neutral_derived.cpp
@@ -572,9 +572,7 @@ void Neutrals::calc_chapman(Grid grid) {
 
   for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
-    species[iSpecies].scale_height_scgc =
-      cKB * temperature_scgc /
-      (species[iSpecies].mass * grid.gravity_mag_scgc);
+    calc_scale_height(grid);
 
     xp3d = grid.radius_scgc / species[iSpecies].scale_height_scgc;
     y3d = sqrt(0.5 * xp3d) % abs(grid.cos_sza_scgc);

From fd13016585c3885a79698e19d45611ecc06f15c2 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 17:20:27 -0400
Subject: [PATCH 044/120] FEAT: Add cube with boolean "UseThisCell" for dipole
 grid. default=true

---
 include/grid.h        | 1 +
 src/grid.cpp          | 3 +++
 src/init_mag_grid.cpp | 1 +
 3 files changed, 5 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index 122a8af1..f421acd5 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -105,6 +105,7 @@ class Grid
   // Dipole grid has cells below the surface of earth. These variables hold masks
   // to either access those cells or ignore them (with .elem()).
   arma::uvec isTooLowCell, isPhysicalCell;
+  arma_cube UseThisCell; // (bool values whether altitude is valid)
 
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
diff --git a/src/grid.cpp b/src/grid.cpp
index 3e99f8cc..89a4315b 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -270,6 +270,9 @@ Grid::Grid(std::string gridtype) {
   HasBField = 0;
   IsExperimental = false;
 
+  UseThisCell.set_size(nX, nY, nZ);
+  UseThisCell.fill(true);
+
   cent_acc_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
   for (int i = 0; i < 3; i++)
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 3050565f..778a952e 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -506,6 +506,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Generate mask for physicsl cells
   isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
+  UseThisCell.elem(isTooLowCell).fill(false);
 
   report.print(4, "Done altitude spacing for the dipole grid.");
 

From 6b5ba696d9ce9ee18549a778f2de01a36521fa1c Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 19:25:17 -0400
Subject: [PATCH 045/120] SPEED: speed up calc_efield a tiny bit

---
 src/calc_ion_drift.cpp | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index ab1c6acb..ef3160cf 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -10,10 +10,7 @@
 void Ions::calc_efield(Grid grid) {
 
   // efield = - grad(potential)
-  efield_vcgc = calc_gradient_vector(potential_scgc, grid);
-
-  for (int64_t iComp = 0; iComp < 3; iComp++)
-    efield_vcgc[iComp] = -efield_vcgc[iComp];
+  efield_vcgc = calc_gradient_vector(-1.0 * potential_scgc, grid);
 
   // Remove component along b-field (should be zero, anyways!)
   arma_cube edotb = dot_product(efield_vcgc, grid.bfield_unit_vcgc);
@@ -28,12 +25,16 @@ void Ions::calc_efield(Grid grid) {
 // --------------------------------------------------------------------------
 
 void Ions::calc_exb_drift(Grid grid) {
+  std::string function = "Ions::calc_exb";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
   arma_cube bmag2 =
     (grid.bfield_mag_scgc) % (grid.bfield_mag_scgc);
   exb_vcgc = cross_product(efield_vcgc, grid.bfield_vcgc);
 
   for (int64_t iComp = 0; iComp < 3; iComp++)
     exb_vcgc[iComp] = exb_vcgc[iComp] / bmag2;
+  report.exit(function);
 }
 
 // --------------------------------------------------------------------------
@@ -42,6 +43,10 @@ void Ions::calc_exb_drift(Grid grid) {
 
 std::vector<arma_cube> Ions::calc_ion_electron_pressure_gradient(int64_t iIon,
     Grid grid) {
+
+      std::string function = "Ions::elec_ion_pressure_gradient";
+      static int iFunction = -1;
+      report.enter(function, iFunction);
   std::vector<arma_cube> pressure_gradient_vcgc;
   arma_cube total_pressure_scgc;
 
@@ -57,6 +62,7 @@ std::vector<arma_cube> Ions::calc_ion_electron_pressure_gradient(int64_t iIon,
     cKB;
 
   pressure_gradient_vcgc = calc_gradient_vector(total_pressure_scgc, grid);
+  report.exit(function);
 
   return pressure_gradient_vcgc;
 }

From 218c7a56ffe96a981bfb8f036645e8ca22bf3636 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 19:28:53 -0400
Subject: [PATCH 046/120] STY: remove std namespace from init_mag_grid

---
 src/init_mag_grid.cpp | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 778a952e..4cfdfd08 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -87,10 +87,9 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
 // ----------------------------------------------------------------------
 bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
-  using namespace std;
   bool DidWork = true;
 
-  string function = "Grid::init_dipole_grid";
+  std::string function = "Grid::init_dipole_grid";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
@@ -514,11 +513,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   fill_grid_bfield(planet);
   report.print(4, "Done filling dipole grid with b-field!");
 
-
-  // put back into altitude. we've been carrying around radius:
-  // magAlt_scgc = magAlt_scgc - planetRadius;
-  // this breaks things more???
-
   report.exit(function);
   return DidWork;
 }

From a4724ffd31266920ca9a93751abaffb3fb0ae365 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 19:35:40 -0400
Subject: [PATCH 047/120] STY/Bugs: Get chapman integrals working on dipole,
 clean up code a bit.

---
 src/calc_neutral_derived.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/calc_neutral_derived.cpp b/src/calc_neutral_derived.cpp
index 1b1ea783..d6802d93 100644
--- a/src/calc_neutral_derived.cpp
+++ b/src/calc_neutral_derived.cpp
@@ -583,8 +583,8 @@ void Neutrals::calc_chapman(Grid grid) {
       species[iSpecies].density_scgc.slice(iAlt) %
       species[iSpecies].scale_height_scgc.slice(iAlt);
 
-    species[iSpecies].rho_alt_int_scgc.slice(iAlt) = integral3d.slice(
-                                                       iAlt) * species[iSpecies].mass;
+    species[iSpecies].rho_alt_int_scgc.slice(iAlt) = integral3d.slice(iAlt)
+                                                     * species[iSpecies].mass;
 
     for (iAlt = nAlts - 2; iAlt >= 0; iAlt--) {
       // dr is used here instead of dalt, since we only want the radial integration, while
@@ -619,14 +619,16 @@ void Neutrals::calc_chapman(Grid grid) {
         integral1d = integral3d.tube(iLon, iLat);
         log_int1d = log_int3d.tube(iLon, iLat);
         xp1d = xp3d.tube(iLon, iLat);
-        y1d = y3d.tube(iLon, iLat);
+        // y1d = y3d.tube(iLon, iLat);
         erfcy1d = erfcy3d.tube(iLon, iLat);
         radius1d = grid.radius_scgc.tube(iLon, iLat);
-        H1d = species[iSpecies].scale_height_scgc.tube(iLon, iLat);
+        // H1d = species[iSpecies].scale_height_scgc.tube(iLon, iLat);
 
         for (iAlt = nGCs; iAlt < nAlts; iAlt++) {
+          if (!grid.UseThisCell(iLon, iLat, iAlt))
+            continue; // masks off cells below surface of earth, not the best implementation.
           // This is on the dayside:
-          if (sza1d(iAlt) < cPI / 2 || sza1d(iAlt) > 3 * cPI / 2) {
+          else if (sza1d(iAlt) < cPI / 2 || sza1d(iAlt) > 3 * cPI / 2) {
             species[iSpecies].chapman_scgc(iLon, iLat, iAlt) =
               integral1d(iAlt) * sqrt(0.5 * cPI * xp1d(iAlt)) * erfcy1d(iAlt);
           } else {

From f97eb6f01a1e8785ea2f557c43bf521f8910f1db Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 13:09:58 -0400
Subject: [PATCH 048/120] DOC: add explainer on ninja builds, correct some info
 on installing & running

---
 doc/installation/dependencies.md | 17 +++++++-----
 doc/usage/running_aether.md      | 46 ++++++++++++++++++++++++++++----
 2 files changed, 51 insertions(+), 12 deletions(-)

diff --git a/doc/installation/dependencies.md b/doc/installation/dependencies.md
index c02dcd5a..c0d41a6f 100644
--- a/doc/installation/dependencies.md
+++ b/doc/installation/dependencies.md
@@ -21,7 +21,7 @@ If a path is printed, `cmake` is installed. To check the version, run `cmake
 The layout of this page is as follows:
 
 - [Installing Dependencies](#installing-dependencies)
-  - [Install gcc](#install-gcc)
+  - [Install C++ Compiler](#install-gcc)
   - [Install cmake](#install-cmake)
   - [Install JSON libraries](#install-json-libraries)
   - [Install Armadillo (and boost)](#install-armadillo-and-boost)
@@ -29,20 +29,23 @@ The layout of this page is as follows:
 
 ## Install gcc
 
-This comes installed by default on Ubuntu. On MacOS this can be installed, for
+On MacOS this can be installed, for
 example, using:
 
 ```bash
 sudo port install gcc11
 ```
 
-> As development began, gcc11 was the latest version; there are newer versions
-> of `gcc` available now (latest version is gcc14), which have not yet been
-> validated.
+On Ubuntu, `gcc` (the C-compiler) is pre-installed, but the C++ compiler is not. This 
+can be installed with (subsituting your machine's package manager command):
+
+``bash
+sudo apt install g++
+```
 
 ## Install cmake
 
-Aether uses [CMake](https://cmake.org/) instead of `make`. If you don't have it
+Aether uses [CMake](https://cmake.org/) instead of GNU make. If you don't have it
 installed, you need it.
 
 For MacOS, this can be installed with:
@@ -57,7 +60,7 @@ For Ubuntu/Debian Linux:
 sudo apt install cmake
 ```
 
-This can be done on RedHat using yum also.
+This can be done on RedHat using `yum` also.
 
 ## Install JSON libraries
 
diff --git a/doc/usage/running_aether.md b/doc/usage/running_aether.md
index af7a9260..2811afa6 100644
--- a/doc/usage/running_aether.md
+++ b/doc/usage/running_aether.md
@@ -18,17 +18,18 @@ cp -R share/run ./run.first_run
 This creates the directory where you will do your run.  In that directory is a
 link to the aether executable and an input file called aether.json.
 
-You can then run the executable from the directory you created.
+You can then run the executable from the directory you created. This uses four cores,
+which is the minimum for the dipole grid.
 
 ```bash
 cd run.first_run
-./aether
+mpirun -np 4 ./aether
 ```
 
 You should see something like:
 
 ```bash
-run.first_run% ./aether
+run.first_run% mpirun -np 4 ./aether
 > Need to NOT adjust F10.7, but that isn't included yet!!!
 > Writing file : 3DALL_20110320_000000
 > Writing file : 3DBFI_20110320_000000
@@ -59,6 +60,16 @@ The successful end of this will show a timing summary, similar to:
     timing_total (s) : 2.94398
 ```
 
+### Running in 1D
+
+If you want to quickly run Aether to test if things are working, there are a few changes
+that need to be made to run in one dimension. 
+
+1. Change the input file's, `aether.json`, value for the neutral and ion grids **both**
+to `"sphere"`. No number!
+2. Run the code with `./aether`. This will not use MPI, however armadillo may use
+multiple OpenMP processes for math, so be careful on cluster login nodes.
+
 ## Output Files
 
 Aether outputs to a subdirectory called UA/output. At this time, all processors
@@ -127,11 +138,36 @@ model.  This file is in UA/inputs/defaults.json.
 This is a json file that sets all of the defaults within Aether.  This file
 should never be modified!
 
-### For Developers
+## For Developers
 
-Within Aether, the inputs.cpp file has a large handful of of get_ routines to
+Within Aether, the inputs.cpp file has a large handful of of `get_` routines to
 get the values of the settings that the user has set.
 
+To speedup builds, it can be faster to use Ninja instead of GNU make. Ninja 
+automatically parallelizes to fit your machine, can re-run cmake for small changes, and
+has other small differences from GNU make. To use ninja for builds:
+
+1. Ensure it is installed. This can be done with conda or your system's package manager
+(note on Ubuntu it is called "ninja-build)
+2. Clear the GNU make build pecs from `build`. This is most easily done by removing the
+`CMakeCache.txt` file, but you can remove the entire contents of the build directory.
+3. Tell cmake to generate build scripts for Ninja. From `Aether/build/`, run:
+`cmake -GNinja [any options] ../`.
+4. Build with `ninja`. This will use as many cores asz your system has.
+
+The dfevelopment process can be further sped up since Ninja can change directories
+before compiling. This is useful, for example, to not need to cd out of run when testing
+changes. From `Aether/run/`, you can compile and run the code with the one-liner:
+
+```bash
+ninja -C ../build && mpirun -np 4 ./aether
+```
+
+The `-C` flag specifies which directory to move to before building. Obviously, change it
+if yours is different. When changing header files, Ninja often catches the change and
+will re-run cmake automatically. If not, you will need to remove `CMakeCache.txt` and
+re-run cmake (again using the `-GNinja` flag).
+
 ## aether.json file
 
 The file aether.json is read in AFTER the defaults file and these settings

From 90c576951bd9a52dbd3ebd603dafe4d960060a42 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 15:49:44 -0400
Subject: [PATCH 049/120] DOC: Improve dipole docs. add .ipynb with plots, etc.

---
 .gitignore                                    |   1 +
 doc/internals/coordinates.md                  |   6 +-
 edu/examples/Dipole/MoreDipoleInfo.ipynb      | 334 ++++++++++++++++++
 edu/examples/Dipole/dipole.py                 |   8 +-
 .../Dipole/plots/ghost-cells-dipole.png       | Bin 0 -> 101955 bytes
 .../Dipole/plots/order-of-cells-dipole.png    | Bin 0 -> 109324 bytes
 .../Dipole/plots/q-p-dipole-global-plot.png   | Bin 0 -> 71055 bytes
 7 files changed, 343 insertions(+), 6 deletions(-)
 create mode 100644 edu/examples/Dipole/MoreDipoleInfo.ipynb
 create mode 100644 edu/examples/Dipole/plots/ghost-cells-dipole.png
 create mode 100644 edu/examples/Dipole/plots/order-of-cells-dipole.png
 create mode 100644 edu/examples/Dipole/plots/q-p-dipole-global-plot.png

diff --git a/.gitignore b/.gitignore
index 12bc3ad8..6c8fc705 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ doc/generated
 
 # Python extras
 __pycache__
+.ipynb_checkpoints
 
 # autogenerated files
 tags
diff --git a/doc/internals/coordinates.md b/doc/internals/coordinates.md
index 515857b2..d33d1984 100644
--- a/doc/internals/coordinates.md
+++ b/doc/internals/coordinates.md
@@ -21,7 +21,7 @@
 ## Dipole Coordinates
 - Longitude (radians) - radians east of the meridian that contains the north magnetic pole and north rotation axis
 - P (meters) - Identifies the field line. This is the same as L-shell and is constant along wach field line.
-- Q (dimensionless) - parameterizes the distance along the field line, related to magnetic latitude & radius. This varies along the field line, but the values are idential for all field lines within each node. q=0 at the equator, and approaches positive (negative) infinity as theta points towards the north (south) pole.
+- Q (dimensionless) - parameterizes the distance along the field line, related to magnetic latitude & radius. This varies along the field line, but the values are idential for all field lines within each node. q=0 at the equator, and approaches positive (negative) infinity as theta points towards the north (south) pole. Thus, q values will be negative in the southern hemisphere and the change in q "upwards" will be negative in the northern hemisphere. See [../../edu/examples/Dipole](../../edu/examples/Dipole) for more information.
 
 ## More Dipole Coordinates
 - L-shell (Planetary Radii) - The distance from the planet's center at which the magnetic field encounters the dipole's equatorial plane
@@ -29,7 +29,7 @@
 - Invariant Latitude (radians) - angle between the dipole's equatorial plane and the point at which the field-line passes through a reference radius of the planet ([specified in the inputs](../internals/grid.md#inputs)).  This is constant along the field-line and is related to the L-Shell.
 - Magnetic Local Time (hours) - Angle between the sun, the north magnetic pole, and the point. Explicitly, this is done in PSE XY coordinates, ignoring the Z coorinate.
 
-> The dipole `(i,j,k)` coordinates are (magnetic longitude, magnetic latitude, radius).
+> The dipole `(i,j,k)` coordinates are (magnetic longitude, p, q).
 
 # Coordinates in Aether
 
@@ -53,7 +53,7 @@ For the (perfectly) spherical grid, the i-coordinate is longitude, the j-coordin
 
 For the Cubedsphere grid, the i-coordinate is RIGHT, the j-coodinate is UP, and the k-coordinate is radius.  Each face of the cubedsphere has the same coordinate system, but only with reference to that face.  This means that if each face is looked at independently, the lower left corner is at (about) i = -45, j = -45 deg, while the upper right corner is at i = +45, j = +45 deg. Radius is treated the same as in a spherical grid.
 
-For the dipole coordinate system, the i-coordinate is magnetic longitude, the j-coordinate is magnetic latitude, and the k-coordinate is radius. The dipole is orthogonal to a dipolar magnetic field.
+For the dipole coordinate system, the i-coordinate is magnetic longitude, the j-coordinate is L-shell, and the k-coordinate is Q: a dimensionless parameter, normalized to the planet radius, representing diatance along a magnetic field line. The dipole is orthogonal to a dipolar magnetic field.
 
 
 Should the official coordinates be in the native coordinates (which could be different for each system), or should the coordinates be in meters, such that when gradients are taken, they are in /m?
diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
new file mode 100644
index 00000000..7ff0ff7a
--- /dev/null
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d3792d6f-1f9b-4364-a1a8-f5d37dabdf41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dipole # use the coordinate transforms from dipole.py\n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "\n",
+    "save_figs = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "175f59e3-7801-4b34-ac69-1a91231b9786",
+   "metadata": {},
+   "source": [
+    "# Dipole Information continued\n",
+    "\n",
+    "This document has more information on the dipole coordinate system. Some things are impossible to convey in just words...\n",
+    "\n",
+    "\n",
+    "First, a primer on the dipole coordinates and some plots of how they look in 2D space:\n",
+    "\n",
+    "## Dipole (p,q) coordinates\n",
+    "\n",
+    "The magnetic coordinates in Aether are orthogonal to a dipolar magnetic field. The `p` coordinate is the same as L-shell, and `q` paramaterizes the displacement along the magnetic field line. In terms of radius and ***co***latitude, `p` and `q` are calculated as\n",
+    "\n",
+    "$$\n",
+    "p = \\frac{r}{\\sin^2{\\theta}}\n",
+    "\\tag{1}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "q = \\frac{\\cos{\\theta}}{r^2}\n",
+    "\\tag{2}\n",
+    "$$\n",
+    "\n",
+    "Here is a quick plot of `p` and `q` in 2D space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "54ce013a-a7a3-4e5a-8fde-c15276486ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make r, theta\n",
+    "rs = np.linspace(.95, 2.5, num=100)\n",
+    "thetas = np.deg2rad(np.linspace(-88,  88, num=100))\n",
+    "# turn that into x,y for plotting\n",
+    "xs, ys = dipole.rt2xy(*np.meshgrid(rs, thetas.copy()))\n",
+    "qs, ps = dipole.rt2qp(*np.meshgrid(rs, thetas))\n",
+    "\n",
+    "# then the plotting:\n",
+    "fig, axs = plt.subplots(2,2,figsize=(6,5), sharey='row', height_ratios=[.05, 1])\n",
+    "\n",
+    "qc = axs[1,0].pcolor(xs, ys, qs, vmin = -0.8, vmax=0.8, cmap='RdBu_r')\n",
+    "fig.colorbar(qc, cax=axs[0,0], orientation='horizontal', label ='Q-values (dimensionless)')\n",
+    "\n",
+    "pc = axs[1,1].pcolor(xs, ys, ps, vmin = 1, vmax=8, cmap='rainbow')\n",
+    "fig.colorbar(pc, cax=axs[0,1], orientation='horizontal', label ='P-values (Planet Radii)')\n",
+    "\n",
+    "circle0 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=-2)\n",
+    "axs[1, 0].add_patch(circle0)\n",
+    "axs[1, 0].set_xlim(-0.1, 2.6)\n",
+    "axs[1, 0].set_ylim(-2.6, 2.6)\n",
+    "axs[1, 0].set_aspect(1);\n",
+    "\n",
+    "circle1 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=-2)\n",
+    "axs[1, 1].add_patch(circle1)\n",
+    "axs[1, 1].set_xlim(-0.1, 2.6)\n",
+    "axs[1, 1].set_ylim(-2.6, 2.6)\n",
+    "axs[1, 1].set_aspect(1);\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "if save_figs:\n",
+    "    fig.savefig('plots/q-p-dipole-global-plot.png')\n",
+    "\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29767ef6-0901-48fc-962c-ea57c44c2506",
+   "metadata": {},
+   "source": [
+    "In Aether, both p and q are stored as native coordinate for the dipole grid, normalized to the planet radius in meters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc6301bc-92ed-432c-86f0-857ddaacecce",
+   "metadata": {},
+   "source": [
+    "## Aether's p,q Values\n",
+    "\n",
+    "First, let's get some dipole coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1a190f87-6708-43af-94c7-8cdbf6a2cacf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use four blocks:\n",
+    "origins, extent = dipole.generate_sym_quadtree(4)\n",
+    "\n",
+    "# call dipole.main, which returns q & p\n",
+    "# These values are a bit unreasonable, but it makes the plots more clear.\n",
+    "qs, ps = dipole.main(alt_minRE=1.01, alt_maxRE=1.3,\n",
+    "                     lat_min=15, lat_max=80,\n",
+    "                     origins=origins, extent=extent, \n",
+    "                     nLatsPerBlock=14, nAltsPerBlock=18)\n",
+    "\n",
+    "# get cartesian & spherical coords:\n",
+    "rs = dipole.qp_solve(qs, ps) #radius\n",
+    "ts = np.rad2deg(dipole.rq2t(rs, qs)) #theta (magLat)\n",
+    "\n",
+    "xs, ys = dipole.qp2xy(qs, ps)\n",
+    "\n",
+    "# also want the altitude:\n",
+    "alts = dipole.r2alt(rs, 6371) #(in km)\n",
+    "\n",
+    "n_y = 14\n",
+    "n_z = 18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a8b44e8-896d-488b-baab-a37abbf00da2",
+   "metadata": {},
+   "source": [
+    "### Ghost Cells\n",
+    "\n",
+    "The dipole grid has 2 ghost cells in each block (or on each node), similar to the spherical grid.\n",
+    "\n",
+    "In the longitudinal direction, these behave as expected.\n",
+    "\n",
+    "The latitudinal ghost cells take one of two forms:\n",
+    "- If the node is touching the pole, the final field line is traced from 89$^\\circ$ magnetic latitude. This ghost cell will form a supercell.\n",
+    "- Othwerwise, nothing special happens and the final two field lines take the same step in invariant latitude as the rest. This means that boundaries between nodes will overlap. So the invariant latitudes of interior field lines will be identical, but the cell locations will not (since the q-values are different).\n",
+    "\n",
+    "The altitudinal ghost cells also take two forms:\n",
+    "- If the node is touching the equator, the last physical cell lies in the same hemisphere as the rest of the cells on that node. The two ghost cells in the altitudinal direction continue to take the same step in q as the previous, thus wrap over the magnetic equator. These ghost cells will line up with the ghost cells from the equator-most node on the opposite hemisphere.\n",
+    "- Othwerwise, the same step in q is taken and there is nothing special.\n",
+    "\n",
+    "\n",
+    "An image for clarity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7ae05677-85d0-48a6-b878-daabb15be856",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=(8,6))\n",
+    "\n",
+    "for n in range(4):\n",
+    "    axs.scatter(ts[n], alts[n], s=12)\n",
+    "\n",
+    "# Doing this manually (not looping) since each node is different:\n",
+    "# (interior) latitude ghost cells:\n",
+    "axs.scatter(0,-1000, marker='x', color='k', s=105, label='latitude ghost cells') # only to get label\n",
+    "axs.scatter(ts[0, -2:, :], alts[0, -2:, :], marker='x', alpha = 0.7, s=105, color='blue')\n",
+    "axs.scatter(ts[1,  :2, :], alts[1, :2, :], marker='x', alpha = 0.7, s=105, color='orange')\n",
+    "axs.scatter(ts[2, -2:, :], alts[2, -2:, :], marker='x', alpha = 0.7, s=105, color='green')\n",
+    "axs.scatter(ts[3,  :2, :], alts[3,  :2, :], marker='x', alpha = 0.7, s=105, color='red')\n",
+    "\n",
+    "axs.scatter(0,-1000, marker='+', color='k', s=105, label='altitude ghost cells')\n",
+    "axs.scatter(ts[1, :, -2:], alts[1, :, -2:], marker='+', alpha = 0.7, s=125, color='orange')\n",
+    "axs.scatter(ts[2, :, -2:], alts[2, :, -2:], marker='+', alpha = 0.7, s=125, color='green')\n",
+    "\n",
+    "axs.scatter(0,-1000, marker='D', color='k', s=105, label='pole ghost cells')\n",
+    "axs.scatter(ts[0, :2, :], alts[0, :2, :], marker='D', alpha = 0.7, s=100, color='blue')\n",
+    "axs.scatter(ts[3, -2:, :], alts[3, -2:, :], marker='D', alpha = 0.7, s=100, color='red')\n",
+    "\n",
+    "axs.legend()\n",
+    "\n",
+    "axs.set_ylim(-100, 6000)\n",
+    "\n",
+    "fig.suptitle(\"Latitude & Altitude Ghost Cells\")\n",
+    "axs.set_xlabel(\"Magnetic Latitude (deg)\")\n",
+    "axs.set_ylabel(\"Altitude (km)\")\n",
+    "\n",
+    "if save_figs:\n",
+    "    fig.savefig(\"plots/ghost-cells-dipole.png\")\n",
+    "\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b944a0-ac97-4063-b89a-41d4e78da2da",
+   "metadata": {},
+   "source": [
+    "(The equator-most ghost cells are not shown)\n",
+    "\n",
+    "\n",
+    "### Order of points\n",
+    "\n",
+    "The order of points in the dipole grid is the same as the spherical grid. Most simply, the field lines are arranged in the order of increasing invariant latitude & from lowest to highest altitude.\n",
+    "\n",
+    "Where this gets complicated is that the actual values are not necessarily increasing. For example, the altitude of the ghost cells at the ends of field lines touching the equator is not increasing anymore, since the field lines begin curving back towards the center of the planet. Further, since q=0 at the equator the k-coordinate of all points in the southern hemisphere (grid cells with negative values of magnetic latitude) will have negative k-coordinates. \n",
+    "\n",
+    "To help illustrate this, here is a plot where the color of each point is its index along the j or k axis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "42aa573f-d79a-400c-950d-b6259b4683d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1,2, figsize=(10,6))\n",
+    "\n",
+    "# The weird indexing and adding/subtracting just makes the plot look better\n",
+    "# Ignore some ghost cells, make colorbar clip extrema, etc.\n",
+    "\n",
+    "for i in range(4):\n",
+    "    for j in range(n_y):\n",
+    "        ax0 = axs[0].scatter(xs[i, j, :-2], ys[i, j, :-2], s=12, \n",
+    "                             c=range(n_z-2), vmin=0, vmax=n_z-3,\n",
+    "                             cmap='plasma') # distinct colormap\n",
+    "\n",
+    "    for j in range(2, n_z-2):\n",
+    "        ax1 = axs[1].scatter(xs[i, :, j], ys[i, :, j], s=12, \n",
+    "                             c=range(n_y), vmin=1, vmax=n_y-2, \n",
+    "                             zorder=4-i) # reverse order points are drawn in\n",
+    "\n",
+    "circle0 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=0)\n",
+    "circle1 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=0)\n",
+    "axs[0].add_patch(circle0)\n",
+    "axs[1].add_patch(circle1)\n",
+    "axs[0].set_aspect(1);\n",
+    "axs[1].set_aspect(1);\n",
+    "fig.colorbar(ax0, ax=axs[0], orientation='horizontal', label='altitude (k) index of each cell')\n",
+    "fig.colorbar(ax1, ax=axs[1], orientation='horizontal', label='latitude (j) index of each cell')\n",
+    "\n",
+    "fig.suptitle(\"Indices of dipole grid cells\")\n",
+    "\n",
+    "if save_figs:\n",
+    "    fig.savefig(\"plots/order-of-cells-dipole.png\")\n",
+    "    \n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "025f5263-732e-41f2-9a5d-fae07190afe3",
+   "metadata": {},
+   "source": [
+    "Since altitude is negative in the southern hemisphere and the k-coordinate is decreasing in the northern hemisphere, the sign of the d_k distances may not be intuitive. \n",
+    "\n",
+    "If there are any other questions, contact Aaron B and this file can be updated with more plots :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2ca9881-8114-4b63-86ca-68b1575ea59d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:mypy]",
+   "language": "python",
+   "name": "conda-env-mypy-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index 71bd85f8..676a0b3e 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -111,10 +111,12 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             qs[:, iAlt] = ((q_min_center + (iAlt - nGCs + 0.5) * delQ))
         
         # If we were in South hemisphere, multiply by -1
-        # (and reverse so the points are ascending in q, same order as NH)
+        # And put data in the same order as we get back from Aether
         if isSouth:
-            qs = -1*np.flip(qs)
-        qcenters[n,:] = qs
+            qs = -1.0*qs
+            pcenters2d = np.flip(pcenters2d, axis=0)
+
+        qcenters[n,:] = np.flip(qs, axis=1)
         pcenters[n,:] = pcenters2d
 
     return qcenters, pcenters
diff --git a/edu/examples/Dipole/plots/ghost-cells-dipole.png b/edu/examples/Dipole/plots/ghost-cells-dipole.png
new file mode 100644
index 0000000000000000000000000000000000000000..c77bd3e36baf1e2f3a77c954be150b2f0534e1cc
GIT binary patch
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HcmV?d00001


From ed941f810ee28ad089c7e15982dc9adc422888bf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 16:17:29 -0400
Subject: [PATCH 050/120] BUG: Fix units in dipole dk_*_meters

---
 src/grid_spacing.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 04ff6e12..f1a46625 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -148,8 +148,8 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
-    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTheta(magLat_scgc);
+    dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
+    dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:

From 1f475791e8c99aad46a8f04154bafd310cb109ac Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 18:59:23 -0400
Subject: [PATCH 051/120] STY: Limits outputs from grid_match to high verbosity

---
 src/grid_match.cpp | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 25da6188..3a3d5775 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -3,8 +3,8 @@
 
 #include "aether.h"
 
-bool grid_match(Grid gGrid, 
-                Grid mGrid, 
+bool grid_match(Grid gGrid,
+                Grid mGrid,
                 Quadtree gQuadtree,
                 Quadtree mQuadtree) {
 
@@ -33,14 +33,16 @@ bool grid_match(Grid gGrid,
           norms = sphere_to_cube(lon, lat);
           iNode = gQuadtree.find_point(norms);
         }
-        std::cout << "lon, lat, node: " << lon*cRtoD << " "
-        << lat*cRtoD << " "
-        << norms(0) << " "
-        << norms(1) << " "
-        << norms(2) << " "
-        << iNode << "\n";
+        if (report.test_verbose(6))
+          std::cout << "lon, lat, node: " << lon*cRtoD << " "
+                    << lat*cRtoD << " "
+                    << norms(0) << " "
+                    << norms(1) << " "
+                    << norms(2) << " "
+                    << iNode << "\n";
       }
     }
   }
+
   return true;
 }

From f3e7f81f542767cd7be6a2da83dad0d09f0a462a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 21:16:28 -0400
Subject: [PATCH 052/120] BUG: Fix init_mag_grid gravity unit vector

---
 src/init_mag_grid.cpp | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 4cfdfd08..2612db20 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -437,9 +437,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   // all distances, so far, are in units of planet radii, turn into meters.
   // Except for Q, leave that dimensionless.
-  magAlt_scgc;
-  magAlt_Below;
-  magAlt_Corner;
   magP_scgc *= planetRadius;
   magP_Corner *= planetRadius;
   magQ_Corner *= planetRadius;
@@ -484,8 +481,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
-  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;
@@ -506,7 +503,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
   UseThisCell.elem(isTooLowCell).fill(false);
-
+  
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From aae064fb9300259554a399044da53e3e6fd506f5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 21:18:26 -0400
Subject: [PATCH 053/120] BUG: Ensure dk is negative for the dipole grid.

Dipole grid's dk's may not be decreasing
---
 src/grid_spacing.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index f1a46625..1a2fe168 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -148,6 +148,11 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
+    // the dk's may not be negative (like the sphere). make sure they are
+    // this gets rid of SO many errors...
+    dk_center_scgc = -1.0 * abs(dk_center_scgc);
+    dk_edge = -1.0 * abs(dk_edge);
+    dr_edge = -1.0 * abs(dr_edge);
     dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
     dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }

From 2274a46abd7c48c8d4e8047185b51d060f034c94 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 21:24:29 -0400
Subject: [PATCH 054/120] BUG: Remove need to do altitude gradient. use k only.

change all d_alt_lower - dk_edge_m
and d_alt_denter - dk_center_m
---
 src/calc_dt.cpp                     |  2 +-
 src/calc_ion_temperature.cpp        |  4 ++--
 src/grid_spacing.cpp                |  2 +-
 src/ions_bcs.cpp                    |  2 +-
 src/neutrals_bcs.cpp                |  4 ++--
 src/neutrals_energy.cpp             |  2 +-
 src/neutrals_momentum_viscosity.cpp |  2 +-
 src/solver_gradients.cpp            |  5 ++---
 src/solver_vertical_rusanov.cpp     | 24 ++++++++++++------------
 9 files changed, 23 insertions(+), 24 deletions(-)

diff --git a/src/calc_dt.cpp b/src/calc_dt.cpp
index dc51d5b4..f949532f 100644
--- a/src/calc_dt.cpp
+++ b/src/calc_dt.cpp
@@ -141,7 +141,7 @@ precision_t calc_dt_vertical(Grid grid, std::vector<arma_cube> cMax_vcgc) {
   precision_t dt;
 
   if (grid.get_nZ(false) > 1) {
-    arma_cube dtz = grid.dalt_center_scgc / cMax_vcgc[2];
+    arma_cube dtz = grid.dk_center_m_scgc / cMax_vcgc[2];
     dt = dtz.min();
   } else
     dt = 1e32;
diff --git a/src/calc_ion_temperature.cpp b/src/calc_ion_temperature.cpp
index 8db03280..0989c136 100644
--- a/src/calc_ion_temperature.cpp
+++ b/src/calc_ion_temperature.cpp
@@ -84,7 +84,7 @@ void Ions::calc_ion_temperature(Neutrals neutrals, Grid grid,
         lambda1d(1) = lambda1d(2);
         lambda1d(0) = lambda1d(2);
         front1d  = 3.0 / 2.0 * cKB * density_scgc.tube(iLon, iLat);
-        dalt1d   = grid.dalt_lower_scgc.tube(iLon, iLat);
+        dalt1d   = grid.dk_edge_m.tube(iLon, iLat);
         sources1d = (heating_neutral_friction_scgc.tube(iLon, iLat) +
                      heating_neutral_heat_transfer_scgc.tube(iLon, iLat));
         sources1d = sources1d / front1d;
@@ -122,7 +122,7 @@ void Ions::calc_ion_temperature(Neutrals neutrals, Grid grid,
           lambda1d(1) = lambda1d(2);
           lambda1d(0) = lambda1d(2);
           front1d  = 3.0 / 2.0 * cKB * species[iIon].density_scgc.tube(iLon, iLat);
-          dalt1d   = grid.dalt_lower_scgc.tube(iLon, iLat);
+          dalt1d   = grid.dk_edge_m.tube(iLon, iLat);
           sources1d = (species[iIon].heating_neutral_friction_scgc.tube(iLon, iLat) +
                        species[iIon].heating_neutral_heat_transfer_scgc.tube(iLon, iLat));
           sources1d = sources1d / front1d;
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 1a2fe168..943fd3cd 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -12,7 +12,7 @@ void Grid::calc_grid_spacing(Planets planet) {
 
   report.print(3, "starting calc_grid_spacing");
 
-  calc_alt_grid_spacing();
+  // calc_alt_grid_spacing();
   calc_lat_grid_spacing();
   calc_long_grid_spacing();
 
diff --git a/src/ions_bcs.cpp b/src/ions_bcs.cpp
index 29d4cc89..c17757a7 100644
--- a/src/ions_bcs.cpp
+++ b/src/ions_bcs.cpp
@@ -91,7 +91,7 @@ bool Ions::set_upper_bcs(Grid grid) {
         species[iSpecies].temperature_scgc.slice(iAlt) /
         species[iSpecies].temperature_scgc.slice(iAlt - 1) %
         species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dalt_lower_scgc.slice(iAlt) / h);
+        exp(-grid.dk_edge_m.slice(iAlt) / h);
       species[iSpecies].velocity_vcgc[2].slice(iAlt).zeros();
     }
   }
diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index e9308e23..c4cab8fb 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -93,7 +93,7 @@ bool Neutrals::set_upper_bcs(Grid grid) {
       h = species[iSpecies].scale_height_scgc.slice(iAlt);
       species[iSpecies].density_scgc.slice(iAlt) =
         species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dalt_lower_scgc.slice(iAlt) / h);
+        exp(-grid.dk_edge_m.slice(iAlt) / h);
     }
   }
 
@@ -216,7 +216,7 @@ bool Neutrals::set_lower_bcs(Grid grid,
         temperature_scgc.slice(iAlt + 1) /
         temperature_scgc.slice(iAlt) %
         species[iSpecies].density_scgc.slice(iAlt + 1) %
-        exp(grid.dalt_lower_scgc.slice(iAlt) / sh_ave);
+        exp(grid.dk_edge_m.slice(iAlt) / sh_ave);
     }
 
     for (iAlt = nGCs - 1; iAlt >= 0; iAlt--) {
diff --git a/src/neutrals_energy.cpp b/src/neutrals_energy.cpp
index 893ab86e..7e812dd6 100644
--- a/src/neutrals_energy.cpp
+++ b/src/neutrals_energy.cpp
@@ -60,7 +60,7 @@ void Neutrals::update_temperature(Grid grid, Times time) {
         //temp1d = temp1d + dt * sources1d;
         //sources1d.zeros();
 
-        dalt1d = grid.dalt_lower_scgc.tube(iLon, iLat);
+        dalt1d = grid.dk_edge_m.tube(iLon, iLat);
         conduction1d.zeros();
 
         conduction1d = solver_conduction(temp1d,
diff --git a/src/neutrals_momentum_viscosity.cpp b/src/neutrals_momentum_viscosity.cpp
index 4efaaaee..2baa68f3 100644
--- a/src/neutrals_momentum_viscosity.cpp
+++ b/src/neutrals_momentum_viscosity.cpp
@@ -64,7 +64,7 @@ void Neutrals::update_horizontal_velocity(Grid grid, Times time) {
           lambda1d = lambda3d.tube(iLon, iLat);
           rhor21d = rhor23d.tube(iLon, iLat);
           sources1d.zeros();
-          dalt1d = grid.dalt_lower_scgc.tube(iLon, iLat);
+          dalt1d = grid.dk_edge_m.tube(iLon, iLat);
           visc1d.zeros();
 
           visc1d = solver_conduction(vel1d,
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 464239cd..6773f30e 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -448,10 +448,9 @@ arma_mat project_onesided_alt_3rd(arma_cube value, Grid grid, int64_t iAlt) {
       grid.MeshCoef1s3rdp5.slice(iAlt) % value.slice(iAlt + 5);
   */
   gradient = (value.slice(iAlt + 2) - value.slice(iAlt + 1)) /
-             grid.dalt_lower_scgc.slice(iAlt + 2);
+             grid.dk_edge_m.slice(iAlt + 2);
 
-  valueOut = value.slice(iAlt + 1) - gradient % grid.dalt_lower_scgc.slice(
-               iAlt + 1);
+  valueOut = value.slice(iAlt + 1) - gradient % grid.dk_edge_m.slice(iAlt + 1);
   return valueOut;
 }
 
diff --git a/src/solver_vertical_rusanov.cpp b/src/solver_vertical_rusanov.cpp
index 8027cd94..50e376d8 100644
--- a/src/solver_vertical_rusanov.cpp
+++ b/src/solver_vertical_rusanov.cpp
@@ -36,12 +36,12 @@ void calc_facevalues_alts_rusanov(Grid &grid,
 
   // Only do calculation on physical cells
   for (iZ = nGCs; iZ < nZs - nGCs; iZ++) {
-    ida = 2.0 / grid.dalt_lower_scgc.slice(iZ + 1);
+    ida = 2.0 / grid.dk_edge_m.slice(iZ + 1);
     dVarUp = ida %
              (factor1 * (inVar.slice(iZ + 1) - inVar.slice(iZ)) -
               factor2 * (inVar.slice(iZ + 2) - inVar.slice(iZ - 1)));
 
-    ida = 2.0 / grid.dalt_lower_scgc.slice(iZ);
+    ida = 2.0 / grid.dk_edge_m.slice(iZ);
     dVarDown = ida %
                (factor1 * (inVar.slice(iZ) - inVar.slice(iZ - 1)) -
                 factor2 * (inVar.slice(iZ + 1) - inVar.slice(iZ - 2)));
@@ -55,9 +55,9 @@ void calc_facevalues_alts_rusanov(Grid &grid,
 
   // Ghostcell closest to the bottom physical cell:
   iZ = nGCs - 1;
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ + 1);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ + 1);
   dVarUp = ida % (inVar.slice(iZ + 1) - inVar.slice(iZ));
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ);
   dVarDown = ida % (inVar.slice(iZ) - inVar.slice(iZ - 1));
 
   for (iX = nGCs; iX < nXs - nGCs; iX++)
@@ -67,9 +67,9 @@ void calc_facevalues_alts_rusanov(Grid &grid,
 
   // Ghostcell closest to the top physical cell:
   iZ = nZs - nGCs;
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ + 1);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ + 1);
   dVarUp = ida % (inVar.slice(iZ + 1) - inVar.slice(iZ));
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ);
   dVarDown = ida % (inVar.slice(iZ) - inVar.slice(iZ - 1));
 
   for (iX = nGCs; iX < nXs - nGCs; iX++)
@@ -80,10 +80,10 @@ void calc_facevalues_alts_rusanov(Grid &grid,
   for (iZ = nGCs; iZ < nZs - nGCs + 1; iZ++) {
     outLeft.slice(iZ) =
       inVar.slice(iZ - 1) +
-      0.5 * dVarLimited.slice(iZ - 1) % grid.dalt_lower_scgc.slice(iZ);
+      0.5 * dVarLimited.slice(iZ - 1) % grid.dk_edge_m.slice(iZ);
     outRight.slice(iZ) =
       inVar.slice(iZ) -
-      0.5 * dVarLimited.slice(iZ) % grid.dalt_lower_scgc.slice(iZ);
+      0.5 * dVarLimited.slice(iZ) % grid.dk_edge_m.slice(iZ);
   }
 
   /*
@@ -94,7 +94,7 @@ void calc_facevalues_alts_rusanov(Grid &grid,
         << inVar(7,19,19) << " "
         << inVar(7,19,20) << " "
         << dVarLimited(7,19,18) << " "
-        << grid.dalt_lower_scgc(7,19,17) << " "
+        << grid.dk_edge_m(7,19,17) << " "
         << outRight(7, 19, 17) << " "
         << outRight(7, 19, 18) << " "
         << outLeft(7, 19, 17) << " "
@@ -142,7 +142,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
     outGrad.slice(iZ) = 0.5 *
                         (varLeft.slice(iZ + 1) + varRight.slice(iZ + 1) -
                          varLeft.slice(iZ) - varRight.slice(iZ)) /
-                        grid.dalt_center_scgc.slice(iZ);
+                        grid.dk_center_m_scgc.slice(iZ);
 
   /*
   if (iProc == 11)
@@ -151,7 +151,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
         << varLeft(7, 19, 18) << " "
         << varRight(7, 19, 17) << " "
         << varRight(7, 19, 18) << " "
-        << grid.dalt_center_scgc(7, 19, 17) << " "
+        << grid.dk_edge_m(7, 19, 17) << " "
         << outGrad(7, 19, 17) << "\n";
   */
   for (iZ = nGCs; iZ < nZs - nGCs + 1; iZ++) {
@@ -177,7 +177,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
   for (iZ = nGCs; iZ < nZs - nGCs; iZ++)
     outDiff.slice(iZ) =
       (diffFlux.slice(iZ + 1) - diffFlux.slice(iZ)) /
-      grid.dalt_center_scgc.slice(iZ);
+      grid.dk_center_m_scgc.slice(iZ);
 
   report.exit(function);
   return;

From 224a5d44e2da3ef5882641b32ff716a2ce7a1d30 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 14:47:31 -0400
Subject: [PATCH 055/120] BUG: um, dk needs to be POSITIVE for dipole.
 nan_checks pass now

---
 src/grid_spacing.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 943fd3cd..1e88cc1c 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -148,11 +148,11 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    // the dk's may not be negative (like the sphere). make sure they are
+    // the dk's may be negative (not allowed). make sure they are positive
     // this gets rid of SO many errors...
-    dk_center_scgc = -1.0 * abs(dk_center_scgc);
-    dk_edge = -1.0 * abs(dk_edge);
-    dr_edge = -1.0 * abs(dr_edge);
+    dk_center_scgc = abs(dk_center_scgc);
+    dk_edge = abs(dk_edge);
+    dr_edge =  abs(dr_edge);
     dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
     dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }

From 7bb99ed767ff76a887b1d4f0db1dd164f27c2edf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 14:50:23 -0400
Subject: [PATCH 056/120] DOC: Update dipole ipynb w prev commit change

---
 edu/examples/Dipole/MoreDipoleInfo.ipynb | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
index 7ff0ff7a..1e8b9ca1 100644
--- a/edu/examples/Dipole/MoreDipoleInfo.ipynb
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d3792d6f-1f9b-4364-a1a8-f5d37dabdf41",
    "metadata": {},
    "outputs": [],
@@ -12,7 +12,7 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import LinearSegmentedColormap\n",
     "\n",
-    "save_figs = True"
+    "save_figs = False"
    ]
   },
   {
@@ -237,6 +237,8 @@
     "\n",
     "Where this gets complicated is that the actual values are not necessarily increasing. For example, the altitude of the ghost cells at the ends of field lines touching the equator is not increasing anymore, since the field lines begin curving back towards the center of the planet. Further, since q=0 at the equator the k-coordinate of all points in the southern hemisphere (grid cells with negative values of magnetic latitude) will have negative k-coordinates. \n",
     "\n",
+    "The vertical solver does not like negative dk values, so all dk are made positive.\n",
+    "\n",
     "To help illustrate this, here is a plot where the color of each point is its index along the j or k axis:"
    ]
   },
@@ -296,7 +298,7 @@
    "id": "025f5263-732e-41f2-9a5d-fae07190afe3",
    "metadata": {},
    "source": [
-    "Since altitude is negative in the southern hemisphere and the k-coordinate is decreasing in the northern hemisphere, the sign of the d_k distances may not be intuitive. \n",
+    "Since altitude is negative in the southern hemisphere and the k-coordinate is decreasing in the northern hemisphere, the actual sign of the d_k distances may not be intuitive. Internally, we take the absolute value of dk.\n",
     "\n",
     "If there are any other questions, contact Aaron B and this file can be updated with more plots :)"
    ]

From d88cd134f8e06df84e19ccf4043b06dbde7df4ca Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 14:52:59 -0400
Subject: [PATCH 057/120] FEAT: Remove need to do lat/lon gradients, instead we
 can use j&i

---
 src/calc_dt.cpp      | 4 ++--
 src/grid_spacing.cpp | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/calc_dt.cpp b/src/calc_dt.cpp
index f949532f..beb0de18 100644
--- a/src/calc_dt.cpp
+++ b/src/calc_dt.cpp
@@ -41,11 +41,11 @@ precision_t calc_dt_sphere(Grid grid, std::vector<arma_cube> cMax_vcgc) {
   arma_cube dtCube;
 
   // Longitudinal Direction:
-  dtCube = grid.dlon_center_dist_scgc / cMax_vcgc[0];
+  dtCube = grid.di_center_m_scgc / cMax_vcgc[0];
   dta(0) = dtCube.min();
 
   // Latitudinal Direction:
-  dtCube = grid.dlat_center_dist_scgc / cMax_vcgc[1];
+  dtCube = grid.dj_center_m_scgc / cMax_vcgc[1];
   dta(1) = dtCube.min();
 
   // Vertical Direction:
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 1e88cc1c..d7fd245f 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -13,8 +13,8 @@ void Grid::calc_grid_spacing(Planets planet) {
   report.print(3, "starting calc_grid_spacing");
 
   // calc_alt_grid_spacing();
-  calc_lat_grid_spacing();
-  calc_long_grid_spacing();
+  // calc_lat_grid_spacing();
+  // calc_long_grid_spacing();
 
   calc_i_grid_spacing();
   calc_j_grid_spacing();

From 0a3e91faba2b004c1b631805614cc27f9b70e564 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 16:57:52 -0400
Subject: [PATCH 058/120] STY: Alter some verbosity when running

---
 src/exchange_messages.cpp | 14 +++++++-------
 src/main/main.cpp         |  3 ++-
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index bd5964ab..befbdb7f 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -966,7 +966,7 @@ bool exchange_one_var(Grid &grid,
   precision_t oneSign = 1.0;
 
   for (int iDir = 0; iDir < 4; iDir++) {
-    if (report.test_verbose(2))
+    if (report.test_verbose(4))
       std::cout << "packing one var : " << iDir << " " << iProc
                 << " " << grid.interchangesOneVar[iDir].iProc_to
                 << " " << grid.interchangesOneVar[iDir].iTag << "\n";
@@ -980,20 +980,20 @@ bool exchange_one_var(Grid &grid,
     // Current PE is the sender, so check if receiver exists:
     if (grid.interchangesOneVar[iDir].iProc_to > -1) {
       iP = 0;
-      report.print(2, "Packing Border");
+      report.print(4, "Packing Border");
       DidWork = pack_border(var_scgc,
                             grid.interchangesOneVar[iDir].buffer,
                             &iP,
                             nG,
                             iDir);
-      report.print(2, "Done Packing Border");
+      report.print(4, "Done Packing Border");
     }
   }
 
   // Send all faces asynchronously:
   for (int iDir = 0; iDir < 4; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
-      report.print(2, "Sending one face");
+      report.print(4, "Sending one face");
       DidWork = grid.send_one_var_one_face(iDir);
     }
   }
@@ -1001,7 +1001,7 @@ bool exchange_one_var(Grid &grid,
   // Receive all faces asynchronously:
   for (int iDir = 0; iDir < 4; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
-      report.print(2, "Receiving one face");
+      report.print(4, "Receiving one face");
       DidWork = grid.receive_one_var_one_face(iDir);
     }
   }
@@ -1016,7 +1016,7 @@ bool exchange_one_var(Grid &grid,
   for (int iDir = 0; iDir < 4; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       iP = 0;
-      report.print(2, "Unpacking Border");
+      report.print(4, "Unpacking Border");
       DidWork = unpack_border(var_to_pass,
                               grid.interchangesOneVar[iDir].rbuffer,
                               &iP,
@@ -1025,7 +1025,7 @@ bool exchange_one_var(Grid &grid,
                               grid.interchangesOneVar[iDir].DoReverseX,
                               grid.interchangesOneVar[iDir].DoReverseY,
                               grid.interchangesOneVar[iDir].XbecomesY);
-      report.print(2, "Done Unpacking Border");
+      report.print(4, "Done Unpacking Border");
     }
   }
 
diff --git a/src/main/main.cpp b/src/main/main.cpp
index eba6d868..64d25548 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -256,6 +256,8 @@ int main() {
 
     } // End of outer time loop - done with run!
 
+    report.report_errors();
+
     report.exit(function);
     report.times();
 
@@ -268,7 +270,6 @@ int main() {
     }
   }
 
-
   // End parallel tasks:
   iErr = MPI_Finalize();
   return iErr;

From 03e521ea340b5ca55283dcb1f1cebd71d7ec432d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 17:59:06 -0400
Subject: [PATCH 059/120] BUG: Gravity & b-field on dipole, fixed

---
 src/fill_grid.cpp     | 30 +++++++++++++++++++++---------
 src/init_mag_grid.cpp | 18 ++++++++++++------
 2 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index deb2b181..11018b76 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -170,21 +170,18 @@ void Grid::fill_grid_bfield(Planets planet) {
 
           bfield_info = get_bfield(lon, lat, alt, DoDebug,
                                    planet);
+
           // Magnetic coordinates:
           // init_mag grid already initializes magLon & magInvLat
-          // #TODO: make sure the bfield is correct for the dipole 
-          // - maybe Dot product the B_vec with (ijk)_vec?
-          if (IsGeoGrid){
+          if (IsGeoGrid) {
             magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
           }
-          bfield_mag_scgc(iLon, iLat, iAlt) = 0.0;
 
           for (iDim = 0; iDim < 3; iDim++) {
             bfield_vcgc[iDim](iLon, iLat, iAlt) = bfield_info.b[iDim] * cNTtoT;
-            bfield_mag_scgc(iLon, iLat, iAlt) =
-              bfield_mag_scgc(iLon, iLat, iAlt) +
-              bfield_vcgc[iDim](iLon, iLat, iAlt) * bfield_vcgc[iDim](iLon, iLat, iAlt);
+            bfield_mag_scgc(iLon, iLat, iAlt) += pow(bfield_vcgc[iDim](iLon, iLat, iAlt),
+                                                     2);
           }
 
           bfield_mag_scgc(iLon, iLat, iAlt) =
@@ -193,8 +190,23 @@ void Grid::fill_grid_bfield(Planets planet) {
       }
     }
 
-    for (iDim = 0; iDim < 3; iDim++)
-      bfield_unit_vcgc[iDim] = bfield_vcgc[iDim] / (bfield_mag_scgc + 1e-32);
+    // Now we modify the dipole's magnetic field to account for any imprecision.
+    // Take the bfield_mag and put it into the third component (b-hat = k-hat)
+    if (IsDipole) {
+      bfield_vcgc[2] = bfield_mag_scgc;
+      bfield_vcgc[1].zeros();
+      bfield_vcgc[0].zeros();
+
+      bfield_unit_vcgc[0].zeros();
+      bfield_unit_vcgc[1].zeros();
+      bfield_unit_vcgc[2].ones();
+
+      bfield_unit_vcgc[2] % sign(magInvLat_scgc * -1.0);
+
+      // slight complication -
+    } else
+      for (iDim = 0; iDim < 3; iDim++)
+        bfield_unit_vcgc[iDim] = bfield_vcgc[iDim] / (bfield_mag_scgc + 1e-32);
 
     int IsNorth = 1, IsSouth = 0;
     mag_pole_north_ll = get_magnetic_pole(IsNorth, planet);
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 2612db20..c910dbd6 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -447,7 +447,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   k_corner_scgc *= planetRadius;
 
   // Convert to geographic, rotating and (maybe) shifting the dipole grid.
-  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc * planetRadius,
+  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc,
+                                           magAlt_scgc * planetRadius,
                                            planet);
 
   geoLon_scgc = llr[0];
@@ -473,6 +474,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   radius2i_scgc = 1.0 / radius2_scgc;
 
   // Figure out what direction is radial:
+  // This is all in the dipole's i,j,k coordinate system...
   rad_unit_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
   gravity_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
@@ -482,11 +484,12 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   }
 
   rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)),
+                                                 0.5);
 
   precision_t mu = planet.get_mu();
-  gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;
-  gravity_vcgc[2] = mu * rad_unit_vcgc[2] % radius2i_scgc;
+  gravity_vcgc[1] = - mu * rad_unit_vcgc[1] % radius2i_scgc;
+  gravity_vcgc[2] = - mu * rad_unit_vcgc[2] % radius2i_scgc;
   gravity_potential_scgc.set_size(nX, nY, nAlts);
   gravity_potential_scgc.zeros();
   gravity_mag_scgc = sqrt(
@@ -499,11 +502,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   calc_dipole_grid_spacing(planet);
 
 
-  // Generate mask for physicsl cells
+  //////////////////////////////////////
+  // Generate mask for physicsl cells //
+  //////////////////////////////////////
+
   isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
   UseThisCell.elem(isTooLowCell).fill(false);
-  
+
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From 18c674012df1472dc90a7efac9eedaae4c85d387 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 10 Apr 2025 11:58:14 -0400
Subject: [PATCH 060/120] FEAT: Create function to read armadillo cubes into
 Python. Contains instructions :)

is debugged too, btw. LMK if you find issues though.
---
 srcPython/read_armadillo.py | 127 ++++++++++++++++++++++++++++++++++++
 1 file changed, 127 insertions(+)
 create mode 100644 srcPython/read_armadillo.py

diff --git a/srcPython/read_armadillo.py b/srcPython/read_armadillo.py
new file mode 100644
index 00000000..03d45e81
--- /dev/null
+++ b/srcPython/read_armadillo.py
@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+""" 
+Routines to read Armadillo objects
+==================================
+
+
+This is just a couple of functions which will read Armadillo exports into Python.
+Directly exporting Armadillo cubes can be very useful during development and/or
+debugging, but is not suitable for production runs.
+
+To export an Armadillo cube from Aether:
+(substitute the cube name & file name. This does support multiple processors)
+
+grid.geoLon_scgc.save("geoLon_" + tostr(iProc, 3) + ".txt", arma_ascii));
+
+Notes:
+- tostr() is defined in src/tools.cpp of Aether & zero-pads an int to return a str.
+- This will rewrite the existing file each time it is called.
+- Output is to the same directory the executable is called from.
+- This uses the arma_ascii format, which is way less efficient than HDF5 or binary. 
+  I have found this format is the easiest to work with, but your mileage may vary.
+- The several python armadillo implementations look abandoned and/or did not work for me.
+- See the armadillo documentation for more information on saving cubes or other data types:
+  https://arma.sourceforge.net/docs.html#save_load_mat
+
+"""
+
+import numpy as np
+from glob import glob
+import os, errno
+
+def check_file_inputs(files):
+    """ Make sorted list of files (that exist) from a str or list
+
+    Inputs
+    ------
+        files (str or list) Can be list of files, single file, directory, or a pattern to glob
+
+    Returns
+    -------
+        list: sorted list of files that so indeed exist
+    
+    """
+    print(type(files))
+    if isinstance(files, str): # Probably need to glob
+        print(type(files))
+        if "*" in files: # Definitely need to glob
+            files2read = np.sort(glob(files))
+        elif os.path.isfile(files):
+            files2read = [files] # Single file needs to be made into list
+        elif os.path.isdir(files):
+            # We were given a directory. Read all .txt files without log in name
+            files_ = np.sort(glob(os.path.join(files, "*.txt")))
+            files2read = [f for f in files_ if "log" not in f]
+        else: # pretty error message from stack overflow
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), *files)
+        
+        if len(files2read) == 0:
+            raise ValueError(
+                f"Could not find any armadillo cubes from '{files}'."
+                " Check path or provide files.\n")
+
+        return files2read
+
+    # Sort list & check if all files exist. error if not.
+    try: # attempt to handle anything listlike (np arrays, dict keys, etc.)
+        files2read = [f for f in np.sort(files) if os.path.isfile(f)]
+        if len(files2read) != len(files):
+            bad_files = [f for f in files if f not in files2read]
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), *bad_files)
+
+    except: # Not expected types
+        raise TypeError("Need list or str. Could not handle type: " + type(files))
+    
+    return files2read
+
+
+def cube2np(files2read):
+    """ Read armadillo cubes from .txt files, automatically globs and/or input.
+        return np array of shape (nFiles, n_x, n_y, n_x)
+
+    Inputs
+    ------
+        files (str or list-like): either path to files or list of files. If it's a str,
+            the pattern is globbed & sorted, or the directory's .txt files are sorted.
+            If it's list-like, the list is sorted.
+
+    Outputs
+    -------
+        np.array of shape (nFiles, n_x, n_y, n_z) & dtype float. If we are only reading
+            one file, return shape is just (n_x, n_y, n_z)
+
+    Usage
+    -----
+
+    lons = cube2np("../run/geolon_*.txt")
+    lons = cube2np(np.sort(glob.glob("../run/geolon_*.txt")))
+
+    """
+
+    # Sanitize input
+    files2read = check_file_inputs(files2read)
+
+    out = [] # output holder
+    for thisf in files2read:
+        with open(thisf, 'r') as f:
+            _ = f.readline() # first line is a header, not needed
+            shape = f.readline().strip() # next line holds the shape of the cube
+            shape = shape.split(' ')
+            if len(shape) != 3:
+                raise ValueError(
+                    f"File ({thisf}) does not appear to be an armadillo cube.\n"
+                    f"Found shape: {shape}")
+            shape = np.array(shape, dtype=int) # convert shape to np array of int's
+            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
+            for i in range(int(shape[0])): # n_x
+                for j in range(int(shape[2])): # n_z
+                    # each line is n_y long. Convert it to a python list & retain it
+                    l = f.readline().strip().replace('  ',',').split(',')
+                    ls[i,:, j] = l # n_y
+        out.append(ls) # speed not a huge issue, work with lists
+
+    # remove 0th dimension if we only are reading one file
+    if len(files2read) == 1:
+        out = out[0] 
+
+    return np.array(out)
\ No newline at end of file

From 58e2f21625eaade63549712c714b312a2b5c61a4 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 14 Apr 2025 12:35:52 -0400
Subject: [PATCH 061/120] FEAT: Better handling of alt gc's. And add mat of
 first ghost cell.

---
 include/grid.h        | 10 +++++++---
 src/grid.cpp          |  3 +++
 src/init_geo_grid.cpp | 16 ++++++++++++++--
 src/init_mag_grid.cpp | 15 +++++++++++++--
 4 files changed, 37 insertions(+), 7 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index f421acd5..f4ef10bb 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -102,10 +102,14 @@ class Grid
   arma_cube magQ_Corner;
   arma_cube magInvLat_Corner;
 
-  // Dipole grid has cells below the surface of earth. These variables hold masks
-  // to either access those cells or ignore them (with .elem()).
+  // Masks to either access the non-physical (ghost) cells, or ignore them - use with
+  // .elem()). Together they *should* hold the indices of all cells.
   arma::uvec isTooLowCell, isPhysicalCell;
-  arma_cube UseThisCell; // (bool values whether altitude is valid)
+  // (bool values whether altitude is valid)
+  arma_cube UseThisCell;
+  // Matrices whose elements denote the altitude index of the interiormost ghost cell
+  // in the k-up and k-down direction (altitude for geo grids, q for dipole).
+  arma_mat first_lower_gc, first_upper_gc;
 
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
diff --git a/src/grid.cpp b/src/grid.cpp
index 89a4315b..e435fba7 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -12,6 +12,7 @@
 Grid::Grid(std::string gridtype) {
 
   // At this point, we only need 2 ghostcells.  Hardcode this:
+  // This is also (kinda?) set in sizes.h for the geo & mag grid independently
   nGCs = 2;
 
   Inputs::grid_input_struct grid_input = input.get_grid_inputs(gridtype);
@@ -272,6 +273,8 @@ Grid::Grid(std::string gridtype) {
 
   UseThisCell.set_size(nX, nY, nZ);
   UseThisCell.fill(true);
+  first_lower_gc.set_size(nX, nY);
+  first_upper_gc.set_size(nX, nY);
 
   cent_acc_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 7d3d611b..95138d5b 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -151,8 +151,20 @@ void Grid::create_altitudes(Planets planet) {
   }
 
   // All cells on the geographic grid *should* be ok
-  isTooLowCell = find(geoAlt_scgc < 0.0);
-  isPhysicalCell = find(geoAlt_scgc > 0.0);
+  isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);
+  isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
+  // get the ghost cell indices on each lat/lon point. 
+  // may be redundant can fill lower with nGCs-1, but this is here for now
+  arma::uvec theGCs;
+  for (iLon=0; iLon<nLons; iLon++){
+    for (iLat = 0; iLat<nLats; iLat++){
+      // find *last* cell below alt_min
+      theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
+      // Get the last element if the col-vec
+      first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
+    }
+  }
+  first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.exit(function);
   return;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index c910dbd6..5bb3d267 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -506,10 +506,21 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Generate mask for physicsl cells //
   //////////////////////////////////////
 
-  isTooLowCell = find(geoAlt_scgc <= 0.0);
-  isPhysicalCell = find(geoAlt_scgc > 0.0);
+  isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);
+  isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
   UseThisCell.elem(isTooLowCell).fill(false);
 
+  arma::uvec theGCs;
+  for (iLon=0; iLon<nLons; iLon++){
+    for (iLat = 0; iLat<nLats; iLat++){
+      // find *last* cell below alt_min
+      theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
+      // Get the last element if the col-vec
+      first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
+    }
+  }
+  first_upper_gc.fill(nAlts - nGCs * 2 - 1);
+
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From e0db896334d230b5184c0e19a46632ce590fd7ca Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 14 Apr 2025 18:22:05 -0400
Subject: [PATCH 062/120] FEAT: Add func to read armadillo mats

---
 srcPython/read_armadillo.py | 64 +++++++++++++++++++++++++++++++++----
 1 file changed, 58 insertions(+), 6 deletions(-)

diff --git a/srcPython/read_armadillo.py b/srcPython/read_armadillo.py
index 03d45e81..4c75c0b8 100644
--- a/srcPython/read_armadillo.py
+++ b/srcPython/read_armadillo.py
@@ -41,9 +41,8 @@ def check_file_inputs(files):
         list: sorted list of files that so indeed exist
     
     """
-    print(type(files))
+    
     if isinstance(files, str): # Probably need to glob
-        print(type(files))
         if "*" in files: # Definitely need to glob
             files2read = np.sort(glob(files))
         elif os.path.isfile(files):
@@ -76,8 +75,8 @@ def check_file_inputs(files):
 
 
 def cube2np(files2read):
-    """ Read armadillo cubes from .txt files, automatically globs and/or input.
-        return np array of shape (nFiles, n_x, n_y, n_x)
+    """ Read armadillo cubes from .txt files, automatically globs input.
+        return np array of shape (nFiles, n_x, n_y, n_z)
 
     Inputs
     ------
@@ -118,10 +117,63 @@ def cube2np(files2read):
                     # each line is n_y long. Convert it to a python list & retain it
                     l = f.readline().strip().replace('  ',',').split(',')
                     ls[i,:, j] = l # n_y
-        out.append(ls) # speed not a huge issue, work with lists
+        out.append(ls.T) # speed not a huge issue, work with lists
+
+    # remove 0th dimension if we only are reading one file
+    if len(files2read) == 1:
+        out = out[0] 
+
+    return np.array(out)
+
+
+
+
+def mat2np(files2read):
+    """ Read armadillo matrices from .txt files, automatically globs input.
+        return np array of shape (nFiles, n_x, n_y)
+
+    Inputs
+    ------
+        files (str or list-like): either path to files or list of files. If it's a str,
+            the pattern is globbed & sorted, or the directory's .txt files are sorted.
+            If it's list-like, the list is sorted.
+
+    Outputs
+    -------
+        np.array of shape (nFiles, n_x, n_y) & dtype float. If we are only reading
+            one file, return shape is just (n_x, n_y)
+
+    Usage
+    -----
+
+    lons = mat2np("../run/geolon_*.txt")
+    lons = mat2np(np.sort(glob.glob("../run/geolon_*.txt")))
+
+    """
+
+    # Sanitize input
+    files2read = check_file_inputs(files2read)
+
+    out = [] # output holder
+    for thisf in files2read:
+        with open(thisf, 'r') as f:
+            _ = f.readline() # first line is a header, not needed
+            shape = f.readline().strip() # next line holds the shape of the cube
+            shape = shape.split(' ')
+            if len(shape) != 2:
+                raise ValueError(
+                    f"File ({thisf}) does not appear to be an armadillo matrix.\n"
+                    f"Found shape: {shape}")
+            shape = np.array(shape, dtype=int) # convert shape to np array of int's
+            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
+            for i in range(int(shape[0])): # n_x
+                # each line is n_y long. Convert it to a python list & retain it
+                l = f.readline().strip().replace('  ',',').split(',')
+                ls[i,:] = l # n_y
+        out.append(ls.T) # speed not a huge issue, work with lists
 
     # remove 0th dimension if we only are reading one file
     if len(files2read) == 1:
         out = out[0] 
 
-    return np.array(out)
\ No newline at end of file
+    return np.array(out)

From 6bb4d6d60ada41ea57c08d740afcee410d2bb2f6 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 15 Apr 2025 13:38:26 -0400
Subject: [PATCH 063/120] bug??: Make sure the altitude limits & ghost cells on
 the dipole grid are correct

---
 src/init_mag_grid.cpp | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 5bb3d267..dcf1a288 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -121,7 +121,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt = grid_input.alt_min * cKMtoM;
   precision_t max_alt = grid_input.alt_max * cKMtoM;
 
-  // Normalize inputs to planet radius... (update when earth is oblate)
+  // Normalize inputs to planet radius... (update one day to support oblate Planet)
   // Here we are using the equatorial radius.
   precision_t planetRadius = planet.get_radius(0.0);
   // Altitude to begin modeling, normalized to planet radius
@@ -281,10 +281,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-    q_min = pow(((1 - max_alt_re / Pcenters(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
+    q_min = pow(((1 - max_alt_re / Pcorners(0)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats - nGCs)) / pow(
+  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats -1)) / pow(
                              min_alt_re,
                              4.0)), 0.5);
 
@@ -302,8 +302,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   magQ_corner_1d(nAlts) = q_min + (nAlts - nGCs) * delQ;
 
-  report.print(3,
-               "Done generating points for magnetic grid. Plugging everything in");
+  report.print(3, "Done generating points for magnetic grid. Plugging everything in");
 
   ////////////////////////////
   // That is the grid made. //
@@ -510,15 +509,20 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
   UseThisCell.elem(isTooLowCell).fill(false);
 
-  arma::uvec theGCs;
   for (iLon=0; iLon<nLons; iLon++){
     for (iLat = 0; iLat<nLats; iLat++){
       // find *last* cell below alt_min
-      theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
-      // Get the last element if the col-vec
-      first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
+      first_lower_gc(iLon, iLat) = find(geoAlt_scgc.tube(iLon, iLat) 
+                                        < grid_input.alt_min * cKMtoM).max();
     }
   }
+  if (first_lower_gc.min() < nGCs-1 || first_lower_gc.max() > nAlts-nGCs-1){
+    report.error("Invalid magnetic grid!! Either:");
+    report.error(" - Lowest latitude field line is entirely below min_alt");
+    report.error(" - Highest altitude field line is above min_alt");
+    report.error("This should not happen. Something is terribly wrong. Goodbye.");
+    return false;
+  }
   first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.print(4, "Done altitude spacing for the dipole grid.");

From 94fcdea310342a830fd30ac5937d7a8b43d443dc Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 15 Apr 2025 18:20:17 -0400
Subject: [PATCH 064/120] FEAT: Get vertical neutral (lower) bc's working with
 new dipole grid

---
 src/advance.cpp      |   4 +-
 src/neutrals_bcs.cpp | 157 +++++++++++++++++++++++--------------------
 src/neutrals_ics.cpp |  22 +++---
 3 files changed, 98 insertions(+), 85 deletions(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index 023a73d5..c474636e 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -106,8 +106,8 @@ bool advance(Planets &planet,
   if (didWork)
     didWork = ions.set_bcs(gGrid, time, indices);
 
-  //if (didWork)
-  //  didWork = neutralsMag.set_bcs(mGrid, time, indices);
+  if (didWork)
+    didWork = neutralsMag.set_bcs(mGrid, time, indices);
 
   didWork = neutralsMag.check_for_nonfinites("Ion Grid: set bcs");
 
diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index c4cab8fb..8f91fb7c 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -92,8 +92,8 @@ bool Neutrals::set_upper_bcs(Grid grid) {
 
       h = species[iSpecies].scale_height_scgc.slice(iAlt);
       species[iSpecies].density_scgc.slice(iAlt) =
-        species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dk_edge_m.slice(iAlt) / h);
+                                      species[iSpecies].density_scgc.slice(iAlt - 1) %
+                                      exp(-grid.dk_edge_m.slice(iAlt) / h);
     }
   }
 
@@ -118,6 +118,8 @@ bool Neutrals::set_lower_bcs(Grid grid,
   json bcs = input.get_boundary_condition_types();
   int64_t nGCs = grid.get_nGCs();
   int64_t iSpecies, iAlt, iDir;
+  int64_t nLats = grid.get_nLats();
+  int64_t nLons = grid.get_nLons();
 
   std::string bcsType = mklower(bcs["type"]);
 
@@ -125,7 +127,10 @@ bool Neutrals::set_lower_bcs(Grid grid,
   // MSIS BCs - only works if FORTRAN is enabled!
   //-----------------------------------------------
 
-  if (bcsType == "msis") {
+  // ALB changes to lower BCs only really work now for dipole grid. Don't use msis
+  // if we are handed a dipole grid.
+
+  if (bcsType == "msis" && !grid.IsDipole) {
 
     report.print(2, "Using MSIS for Boundary Conditions");
 
@@ -168,78 +173,84 @@ bool Neutrals::set_lower_bcs(Grid grid,
           std::cout << "  Found in MSIS!\n";
 
         species[iSpecies].density_scgc.slice(0) =
-          msis.get_mat(species[iSpecies].cName);
+                                        msis.get_mat(species[iSpecies].cName);
       } else {
         if (report.test_verbose(3))
           std::cout << "  NOT Found in MSIS - setting constant\n";
 
         species[iSpecies].density_scgc.slice(0).
-        fill(species[iSpecies].lower_bc_density);
+                                      fill(species[iSpecies].lower_bc_density);
       }
 
     }
 
   } // type == Msis
 
+  precision_t sh_ave;
+
   //-----------------------------------------------
-  // Planet BCs - set to fixed constant values.
+  // Fill the lower+ ghost cells
   //-----------------------------------------------
+  // - Planet BCs are in here too, can be refactored out
+  // - Dipole grid must use planet BCs, for now.
+  // - This kind-of assumes nGCs=2, so may need to be updated.
+  // - If the first_lower_gc is at iAlt = 1, this may cause issues.
+  // - The equator-most (j-hat) grid cell will be entirely below min_alt!
+  for (int iLon = nGCs; iLon < nLons - nGCs; iLon++) {
+    for (int iLat = nGCs; iLat < nLats - nGCs; iLat++) {
+      for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
-  if (bcsType == "planet") {
-
-    report.print(2, "setting lower bcs to planet");
-
-    // Set the lower boundary condition in the last ghost cell:
-    for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-      species[iSpecies].density_scgc.slice(nGCs - 1).
-      fill(species[iSpecies].lower_bc_density);
-    }
-
-    temperature_scgc.slice(nGCs - 1).fill(initial_temperatures[0]);
-    didWork = true;
-  }
-
-  // fill the second+ grid cells with the bottom temperature:
-  for (iAlt = nGCs - 2; iAlt >= 0; iAlt--)
-    temperature_scgc.slice(iAlt) = temperature_scgc.slice(iAlt + 1);
-
-  arma_mat sh_ave;
-
-  // fill the lower ghost cells with a hydrostatic solution:
-  for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-    for (iAlt = nGCs - 2; iAlt >= 0; iAlt--) {
-      sh_ave =
-        (species[iSpecies].scale_height_scgc.slice(iAlt) +
-         species[iSpecies].scale_height_scgc.slice(iAlt + 1)) / 2;
-
-      species[iSpecies].density_scgc.slice(iAlt) =
-        temperature_scgc.slice(iAlt + 1) /
-        temperature_scgc.slice(iAlt) %
-        species[iSpecies].density_scgc.slice(iAlt + 1) %
-        exp(grid.dk_edge_m.slice(iAlt) / sh_ave);
-    }
-
-    for (iAlt = nGCs - 1; iAlt >= 0; iAlt--) {
-      //std::cout << "before project : " << iAlt << " " << iSpecies << " "
-      //  << species[iSpecies].velocity_vcgc[2](10,10,2) << "\n";
-      species[iSpecies].velocity_vcgc[2].slice(iAlt) =
-        species[iSpecies].velocity_vcgc[2].slice(iAlt + 1);
-      //project_onesided_alt_3rd(species[iSpecies].velocity_vcgc[2], grid, iAlt);
+        // k-index of 1st lower ghost cell is not constant on the dipole grid.
+        // On the latlon grid with nGCS=2, this will be 1
+        iAlt = grid.first_lower_gc(iLon, iLat);
+
+        //-----------------------------------------------
+        // Planet BCs - set to fixed constant values.
+        //-----------------------------------------------
+        if (bcsType == "planet" || grid.IsDipole) {
+
+          // Fill all lower ghost cells density with lower boundary condition:
+          species[iSpecies].density_scgc.subcube(iLon, iLat, 0,
+                                                 iLon, iLat, iAlt - 1).fill(
+                                                   species[iSpecies].lower_bc_density);
+          // only fill 1st GC with lower temperature
+          temperature_scgc(iLon, iLat, iAlt) = initial_temperatures[0];
+        }  // planet bc type
+
+        // Set all lower ghost cells to bottom temperature:
+        temperature_scgc.subcube(iLon, iLat, 0, iLon, iLat, iAlt - 1).fill(
+                          temperature_scgc(iLon, iLat, iAlt));
+
+
+        // 1st ghost cell density is filled with a hydrostatic solution.
+        sh_ave = (species[iSpecies].scale_height_scgc(iLon, iLat, iAlt)
+                  + species[iSpecies].scale_height_scgc(iLon, iLat, iAlt + 1)) / 2;
+
+        species[iSpecies].density_scgc(iLon, iLat, iAlt) =
+          temperature_scgc(iLon, iLat, iAlt + 1)
+          / temperature_scgc(iLon, iLat, iAlt)
+          * species[iSpecies].density_scgc(iLon, iLat, iAlt + 1)
+          * exp(-grid.dk_edge_m(iLon, iLat, iAlt) / sh_ave);
+
+        // Vertical velocities: (In GITM this projected down with mesh coeffs)
+        // Take lowest physical cell's vertical velocity and project it down nGCs cells.
+        // All "GCs" lower than that have 0 vertical velocity since they're nonphysical.
+        species[iSpecies].velocity_vcgc[2].subcube(
+          iLon, iLat, iAlt - 1, size(1, 1, nGCs)).fill(
+            species[iSpecies].velocity_vcgc[2](iLon, iLat, iAlt + 1));
+        //project_onesided_alt_3rd(species[iSpecies].velocity_vcgc[2], grid, iAlt);
+
+        if (iAlt > nGCs - 1) { // Fill all lower GCs w/ zero vertical velocity
+          species[iSpecies].velocity_vcgc[2].subcube(iLon, iLat, 0,
+                                                     size(1, 1, iAlt - 1)).zeros();
+
+        }
+      }
     }
   }
 
-  // Force vertical velocities to be zero in the ghost cells:
-  for (iDir = 0; iDir < 2; iDir++) {
-    for (iAlt = 0; iAlt < nGCs; iAlt++) {
-      for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-        // species velocity:
-        species[iSpecies].velocity_vcgc[iDir].slice(iAlt).zeros();
-      }
+  didWork = true;
 
-      // bulk velocity:
-      //velocity_vcgc[iDir].slice(iAlt).zeros();
-    }
-  }
 
   calc_bulk_velocity();
 
@@ -281,8 +292,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = 0; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX - 1, iY) -
-          temperature_scgc.tube(iX - 2, iY);
+                          2 * temperature_scgc.tube(iX - 1, iY) -
+                          temperature_scgc.tube(iX - 2, iY);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -291,8 +302,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX - 1, iY) -
-            species[iSpecies].density_scgc.tube(iX - 2, iY);
+                                          2 * species[iSpecies].density_scgc.tube(iX - 1, iY) -
+                                          species[iSpecies].density_scgc.tube(iX - 2, iY);
       }
     }
   }
@@ -303,8 +314,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = 0; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX + 1, iY) -
-          temperature_scgc.tube(iX + 2, iY);
+                          2 * temperature_scgc.tube(iX + 1, iY) -
+                          temperature_scgc.tube(iX + 2, iY);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -313,8 +324,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX + 1, iY) -
-            species[iSpecies].density_scgc.tube(iX + 2, iY);
+                                          2 * species[iSpecies].density_scgc.tube(iX + 1, iY) -
+                                          species[iSpecies].density_scgc.tube(iX + 2, iY);
       }
     }
   }
@@ -325,8 +336,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = nX - nGCs; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX, iY - 1) -
-          temperature_scgc.tube(iX, iY - 2);
+                          2 * temperature_scgc.tube(iX, iY - 1) -
+                          temperature_scgc.tube(iX, iY - 2);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -335,8 +346,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX, iY - 1) -
-            species[iSpecies].density_scgc.tube(iX, iY - 2);
+                                          2 * species[iSpecies].density_scgc.tube(iX, iY - 1) -
+                                          species[iSpecies].density_scgc.tube(iX, iY - 2);
       }
     }
   }
@@ -347,8 +358,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = nGCs - 1; iY >= 0; iY--) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX, iY + 1) -
-          temperature_scgc.tube(iX, iY + 2);
+                          2 * temperature_scgc.tube(iX, iY + 1) -
+                          temperature_scgc.tube(iX, iY + 2);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -357,8 +368,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX, iY + 1) -
-            species[iSpecies].density_scgc.tube(iX, iY + 2);
+                                          2 * species[iSpecies].density_scgc.tube(iX, iY + 1) -
+                                          species[iSpecies].density_scgc.tube(iX, iY + 2);
       }
     }
   }
diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 225eeb19..88bc01da 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -99,7 +99,7 @@ bool Neutrals::initial_conditions(Grid grid,
               std::cout << "  NOT Found in MSIS - setting constant\n";
 
             species[iSpecies].density_scgc.slice(0).
-            fill(species[iSpecies].lower_bc_density);
+                                          fill(species[iSpecies].lower_bc_density);
             fill_with_hydrostatic(iSpecies, 1, nAlts, grid);
           }
 
@@ -129,8 +129,6 @@ bool Neutrals::initial_conditions(Grid grid,
       arma_vec alt1d(nAlts);
       arma_vec temp1d(nAlts);
 
-      arma_mat H2d(nLons, nLats);
-
       if (nInitial_temps > 0) {
         for (iLon = 0; iLon < nLons; iLon++) {
           for (iLat = 0; iLat < nLats; iLat++) {
@@ -155,7 +153,7 @@ bool Neutrals::initial_conditions(Grid grid,
                     iA++;
 
                   iA--;
-                  // alt will be between iA and iA+1:
+                  // alt will be between iA and iA+1
                   r = (alt - initial_altitudes[iA]) /
                       (initial_altitudes[iA + 1] - initial_altitudes[iA]);
                   temp1d[iAlt] =
@@ -169,14 +167,18 @@ bool Neutrals::initial_conditions(Grid grid,
           }
         }
       } else
-        temp1d = 200.0;
+        temperature_scgc.fill(200.0);
 
       // Make the initial condition in the lower ghost cells to be consistent
-      // with the actual lowwer BC:
-
-      for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-        species[iSpecies].density_scgc.slice(0).
-        fill(species[iSpecies].lower_bc_density);
+      // with the actual lower BC:
+      for (iLon = 0; iLon < nLons; iLon ++) {
+        for (iLat = 0; iLat < nLats; iLat++) {
+          for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+            species[iSpecies].density_scgc.subcube(
+              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)).fill(
+                species[iSpecies].lower_bc_density);
+          }
+        }
       }
 
       report.print(2, "Calculating scale height");

From 392dc09afaa3b61a01daa8250c7c0c5d6ad7a024 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 15 Apr 2025 19:26:33 -0400
Subject: [PATCH 065/120] BUG: ammend prev commit: IC's and BCs were missing
 some important cells.

---
 src/neutrals_bcs.cpp | 4 ++--
 src/neutrals_ics.cpp | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 8f91fb7c..985594c6 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -196,8 +196,8 @@ bool Neutrals::set_lower_bcs(Grid grid,
   // - This kind-of assumes nGCs=2, so may need to be updated.
   // - If the first_lower_gc is at iAlt = 1, this may cause issues.
   // - The equator-most (j-hat) grid cell will be entirely below min_alt!
-  for (int iLon = nGCs; iLon < nLons - nGCs; iLon++) {
-    for (int iLat = nGCs; iLat < nLats - nGCs; iLat++) {
+  for (int iLon = 0; iLon < nLons; iLon++) {
+    for (int iLat = 0; iLat < nLats; iLat++) {
       for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
         // k-index of 1st lower ghost cell is not constant on the dipole grid.
diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 88bc01da..a1430d96 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -175,7 +175,7 @@ bool Neutrals::initial_conditions(Grid grid,
         for (iLat = 0; iLat < nLats; iLat++) {
           for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
             species[iSpecies].density_scgc.subcube(
-              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)).fill(
+              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)+1).fill(
                 species[iSpecies].lower_bc_density);
           }
         }

From 89d766c8571d5f352488ecc762bc1654e36fd6ad Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 17 Apr 2025 10:27:03 -0400
Subject: [PATCH 066/120] DOC: Some plots of the dipole corners & their
 connections

---
 edu/examples/Dipole/MoreDipoleInfo.ipynb      |  79 +++++++++++++++---
 .../plots/highlatitude_corner_connections.png | Bin 0 -> 625233 bytes
 .../plots/midlatitude_corner_connections.png  | Bin 0 -> 353691 bytes
 3 files changed, 66 insertions(+), 13 deletions(-)
 create mode 100644 edu/examples/Dipole/plots/highlatitude_corner_connections.png
 create mode 100644 edu/examples/Dipole/plots/midlatitude_corner_connections.png

diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
index 1e8b9ca1..6d93769d 100644
--- a/edu/examples/Dipole/MoreDipoleInfo.ipynb
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d3792d6f-1f9b-4364-a1a8-f5d37dabdf41",
    "metadata": {},
    "outputs": [],
@@ -112,7 +112,7 @@
    "id": "cc6301bc-92ed-432c-86f0-857ddaacecce",
    "metadata": {},
    "source": [
-    "## Aether's p,q Values\n",
+    "# Aether's p,q Values on the Dipole Grid\n",
     "\n",
     "First, let's get some dipole coordinates:"
    ]
@@ -152,7 +152,7 @@
    "id": "7a8b44e8-896d-488b-baab-a37abbf00da2",
    "metadata": {},
    "source": [
-    "### Ghost Cells\n",
+    "## Ghost Cells\n",
     "\n",
     "The dipole grid has 2 ghost cells in each block (or on each node), similar to the spherical grid.\n",
     "\n",
@@ -223,15 +223,76 @@
     "plt.show();"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "97273c1d-6c47-4509-b639-f8f53a5c2de0",
+   "metadata": {},
+   "source": [
+    "## Corners and cell centers\n",
+    "\n",
+    "The corners and centers look a little confusing. They are not automatically generated by the python script, so these plots were made using actual model outputs. \n",
+    "\n",
+    "The parameters used in this run are:\n",
+    "\n",
+    "```json\n",
+    "\n",
+    "    \"ionGrid\": {\n",
+    "        \"Shape\": \"dipole4\",\n",
+    "        \"nLonsPerBlock\": 14,\n",
+    "        \"nLatsPerBlock\" : 20,\n",
+    "        \"nAlts\":30,\n",
+    "        \"AltRange\":[80,1500],\n",
+    "        \"LatRange\":[7, 87]\n",
+    "        },\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Some notes:\n",
+    "- Cell centers are the larger dots, and the corners are the smaller dots.\n",
+    "- In each panel, dashed lines are drawn between the $\\hat{j}$ and/or $\\hat{k}$ corners for clarity.\n",
+    "- Horizontal, solid, black lines are drawn at 0 and 80 km (min_alt)\n",
+    "- This will be mirrored in the southern hemisphere\n",
+    "\n",
+    "First, a look at the midlatitudes:\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "94817fae-2328-4a62-986d-bf5f4c049ff5.png": {
+     "image/png": 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vsBIlCx5j9GvYkFJqgFlKsQqCUFCl1MS+lGJdrVZfK9nrjIdZJUtbVRmMh5tE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tvtOJ3O9cVQQItxmdSBz2OWoCp3ajJgXqQMH2f4+Q+jTL2Iz+crfu+wEsmLYrEYg4ODnD9/HpvN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ksINxNB15JgCAPiUtmhKP/9xlH533WSY6PSTf2Y6+hsOlw+F8fOHyMcCyNKIhExgiiJhGNh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\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C4/FA0zRSB9bMzAxUVcXQ0BDJBvPBiQdx7T3XkEwm8b6ffB9Zoe1augXWWz/BarXWVdCy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aZAb29vzVbg5eB2uzEwMNBwWW+hUEChUADHcVucZ2azWXOGlkUTvGi7TXbg0NN9kIFaG3Rd+DGE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aUl+Hw+EgfWlStXAFR1uowu8svLywiFQggGg4bLDaLRKC5fuoTlzA/xvuF/AqdzbMG2/dVq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\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paQnDw8PgOA52ux3btm1TfS9EUcTo6KiiFpNWzpDJZDAxMYHp6WnF7zpfvGh+fh4TExOK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\nTkSuQmCC\n"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "945adde8-905b-4009-aee0-db0963977a75",
+   "metadata": {},
+   "source": [
+    "![midlatitude_corner_connections.png](attachment:94817fae-2328-4a62-986d-bf5f4c049ff5.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "f2d75913-95bf-43e3-8611-47a2a8eb4c28.png": {
+     "image/png": 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PErkMGJhYE5pXN2u6/lQbaMliyDmEvUf3omuiC9W51Whe3QwX50Ltv8zciTe1iVchM2sG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g8ZGRmYM2cOdYC+3+/HyMgIlSg1PDyMxsZGWK1WzJ8/nzg+MzOTWqBgcTNlZWVh6dKlTAKg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\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YwLFzIFqSXTejDj3X9WABFuDQikNUHIjnefh8Pni9Xqr3kN0TPp8PoihqzoFMJhNMJhPV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\n0XIgh8MBt9uN7OzsEeefscJ/TjgBy8BJDwCjdzF3/PX6M9ejs2c3Ltl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dBkMD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zY4qcyaMkJjY2OJt40WvF4vBgcHqTlNQ0MDBgcHqYuaWK1WNDQ0mMpMoc1aJ/MPpedYJKLO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\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Vfw/FYjH09/fDbrdj6dKlOf+mJGC1t7cjkUhIxF3LvDAWi+PNN4HTTpuLDGqNTyQSkgkv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\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sNpvqu1kikUAsFgPDMHIwhIxlGEaXAz3xhKWI5+dyOQwMDMjbIjCZTLKnot6aPDTE5Anv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4tEilFkKhEERRNGSunkql5CwjpSCvVW744IPNAAT8/Oc1uOoqp9yaWkusuPLKiPxSOBVM5Xs+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spKKisrM3DE7NmzMwTdi4uLWbFihW7aXUNDg+oELCoqUkXa9WzKlClUVVWRTCZ5/MXHjTGQ\ny4VUK3HicSearhMFBQVMmzZNvWZGJqrkig30EgOGwufzUVxcrM5JTqeTRYsW5bSTJAm/35+zyder\nsmmEgWbMyNTWNdP1zH4WtFUUs/sVmHffvn3s37+fadOmGUZglZWV0dhYzp135nP88QIDSSi6VCBk\nEJxOSKcrWL8+xs03F6lRVXrk0V13RZg2rYDa2toMgXA9XPPee2H8fj9z587NwDZ6bffvD5GXl8e0\nadPUir9GGKi0NMnu3TJQy89/PpvLLjPW1kql4L//24uSb+3jiiuKueIKMwzkZvr06UiSxLvvvgsY\nYyCn05lz70KhkDoXZBMAZu88jFezTCQSzNQwL1Zr9OBgHVOmjGtfVVRUGO5lxkX7ze2cc/ycc45C\nlq9da93eTKogmUzS1dUFKHNEtrC8mdnBkQrOSaMsJGmUokn2xeGtrm97+8T29rIML78s84UvjJCf\nn6fOs1OnTjUl4t9v+2fAPx86AmvNyxtJ+pSgHPF8JWS4+M8/ZM8lZ1FXnTspNHe8wqyfHwtj6Q96\nVQrBQshuqJlZG2cpeoL5sOG1DQCs/eRaRuIjzCidwbmLzmWwfdBWKVc9UwW9UyjJnx7N51l2OLz2\n9717n2lk0P3b788Bju+39YR7uO/d+2gJttBQ0sCaZWtISkooyaZTN3HJk5cYhpjbNasJw05anais\nY3WcP3b9kdnVs0nvNBbNnVs315IIdLvdtib6/Px8XU9xtvn9/oxF0ey4ZgSj1qZNm8aUKVNseSYW\nLFhALBazRWAJUGbUNhwOE4lEqKqqYmQkj0ceKaWvr5D584XnT0SkKB7Pl1/WAjsZJbRPAW+plMxH\nPnKQ3/7WT0lJCRdfrLxnelFVo6MR+vuxFVIrUlzsEBZCkDi7Gqoe0EskEhx9dJw333SwbFkhX/2q\n4lXZvDnXYzp+vkIUVUkffPNNuOAC44UsmUyq4zfbOGTb0NAQsixbesz0TIRWl5eXZ8x1DQ3G5Fw6\nrdwbUKrujYyMIMuyrdS5ydhkondCoZBaicjoPT3UqKBIpJfbbx/iuuscKGml9sVR7V5fu7Z5s8zZ\nZ/eyYcNBPv95F4sWLVI3vH9P8kqbJqBXlr219R83EssIA1357p3s+ciaDJDvdruZNWsWbd1/4TO/\nOE8JtqjWx0DZlba0diB4gMUPLFaq8iXgS49/CYrhL5f8hVfbXlWjo77Q8AVGB0bVzb4gKZPJJKFQ\nyNSLq+Ict3IMokCRPv7RjrGgoIBQKEQoFLJcIx0OB2Vl4w5GKwy0tW0rV6y4wtb85XA4WLFihW1s\nVldXp/u5HgZy+pxQZA8DOZ1Oy+ugxUB6VbdFFcFs05ItHo/HNHJDpIm+0vEKMytmGgrySw6J+VPn\nW25O7a4jxcXFtlLjy8rKbG2ICwsLTaMGtbZgwQLS6bQlxvT5fCxevJhEImH5vDidTmbMmGHoVJUk\nScUMis5VEQ8/7GB4OMDs2VoMFABmAAF+/3tBOiZRZAfSCPkBSYpx3XV9HH98lAsvLEEUVNcjj3p7\nw8Tj1rhGVErUCpkbmdDAA3sYaGBghM98Bnbt8rNggaJ/VV9vFDEGiURk7LxdCI1RKww0PDyMJEn4\nfL4JVVIWFZBLS0sntGcTkVtAzrtsZ42WJImDBw+STqctCd9Dsclglf7+ftLpNIFAwHCeOhQMpIjD\nt3LZZXGUZ9o7IXF4q+srEqjsZhU8/3yab32rg1tv7WflyiK1qmA28Z9KpVSHwKE6XUEhJ8vLy3X7\nmgz+CYfDpFIpAoHAhLIwDsU+dARWQl92gWQC7tn6Da5Z+aBaMUdYddlCZX4Ojn1Qp/lc249JhUA1\nlS3Lvn3MtzM8YXXF+qBEWFtbG7FYjLq6OiJEcoDKAyc9wPn3nq9EP5bre9qsTOgumIWnSpJES7DF\n0CvmxMmBoQm62w/Rtu7dyqpHVuWE8m9ZvQX5JuXiX7ziA8wvOUR7ZNcjnLXlLH5+ys/xOD056RYO\nHHicHtYsW/N3HOXhNbveBCvwqzUzQCrLsuodf/llP+ee6yOZLM0AIQ88AONeRi8qMwzAfJRtoHMM\n4IxyzDHdtLa6MjxtesCpr8+fEQ5vZLIsU1JSQjgctkV2idQCq35BKf8LCokmFsTqamOP6dVXh7nt\ntjTK0hDgvvsU4Ga2kDmdQfUYE1lYBQCbaPRVMplUI7eyAc6aNco9NQKnCrE4ql4X4b2djPX39xMM\nBqmtrc25b5P1XhUWFjJv3jwSiYTu83+oXrFoNEpHR8eYOPxUNm0KcMkl9vUU7FxfO6aE8SeBZiDM\njTfCjTcWsGePxLx51qkN77f9PVJRD5cZYqCogoHWnfd4zobloyuOU4IOwiiSeT6gMBMDmRFY1YHq\ncYm9BAoRVpybxgao+Er0U1ZWRm9vLwMDAxQVFbF3714cDgezZs2if7Q/AwPd94X7WLNpDQwDEvzu\nrN9Z4h8Ryah9R9PptKF3u6GhQS0uYYWB2sPtE0qdOFTH4v9hoH9+s5O6BQqJZSXLIPozW0NHR0dV\ncuXZZz2sWlVDMlmjg4GSKC+xFlf4gMUIKlyZ50f4xCd66e8fzlg/9TBQPF5MJBIhLy9PN01WmCzL\nlJeXq5F/IipJz2KxGLIsq1GByWTSFCuKtd7v95NMJqmu9hjiny1bFMLrootSKFH3DksM1NiYJBLp\nVXGcmSWTSWRZxuPxIMuyGrmud/+EDq2ejEcwGFSLHxQWFqptPR4Pa9Y4Tdfoc85JcvBgD8lkkvz8\nfFMyN5lMqimEeveutbUVh8NBTU0NDocjo60ZVjnhBON+a2tr8Xq9KhktxuB2u3G73bb6FW31TKku\nOQSk+fGP67j6amv8U1tbS1VVFW632xIDrVrlIJWynucVDDSK4vUJsHZtLWvX5tHYKDNrVu7vBwcH\naW9vp6ysLCdiczJmNr9MBv90dnYSDoeZOXPmpDVlJ2ofOgLLybjXUWuuNDT3tqvhs9kaET//6EVc\ntvt/VB2l7CqFWnM4HLpesP857X+46IGLxvuYBLkUjUaJRCJs27ONi569KAeoXL7wcgBuOe4W1r29\nTtfT1tSkaFNNnz5d9yXu7u6mq6uLyspKQ72tt99+G3e3olGgJ/WfDqfx9HhoaWmhoaFBt4/du3cT\nj8eZNWuW7mY7kUiwa9cuPB6Pbmg2KFEaHR0dJNwJVj26SjeU/4xNZ/D7Vb9n/vT5hhvSrq4uRkZG\nqKqqMny5ent7CYVClJeXGy5EwWCQYDBIUVGRIWiIx+P09PSolb2yTU2FCAEOuOx3l4ELvE4vKTml\n3m+P08NvTv0NzlEnESKm5EY4HEaWZfx+vylAikajGekARmYGIP6ZLBaLIUkSwaCLc8/16YKQ88+H\nX/zCyaWXliGmxPXr4bbbxCLp1E0LtDKRjmFlDofDMlxca0uWLGF0dNRWKmYgEKCmpiZnoTIKt3/q\nKYXI27ChmBtvHNfKMlvITj3VXPtBz0TEBUws7RDGo68KCgpyzsuMnNuyRUmpPHCgh9dfh5NOKp20\nJ0tUyYvH4xQWFmY8D4cavWPkrT7UftPpNM3NzciyzMqVJdxwgzI3TURTys71tWOFhVGgEWXT5ASm\nARW8T8FwE7bDnSr5QZohBkopGEhoBWVjoI1HncUNv3tYyazIg3uPuTqnUrPWsjHQfV+4jzU/W6OQ\nYDF47IzHbOGf8vJyent7GR4eJpVKqdEVW/du5ezHzs7AQE6HE5yw7rR13LLjFlKyvvh2Z2enWvBG\nD6g3NTURCoUMwXYkEmHv3r3kDeYZRgalB9N4ej309/frRgpIksQ777yDy+ViyZIlumSZ2JQUFxcb\n4qgDBw4QiURwFjhVofMcDHTPGTyz6hkWNCygsrJSd+1uamoinU4zbdo01bGTn5+fETHS0tKCJEnU\n1dWp60s0GmVkZASfz0dpaSldXV0kEgkqKipy1sFYLEZfXx/RaBS/369Wec225qFmZv1glpL14ILL\ntl0G6GOg+066D0fUwa7WXWrKpR4RGAwG8Xg8+P1+mpqaSCQSzJ49O2OOlySJ0dFRVf8KoLm5mWg0\nmiFALIoHZV/HlpYWQqEQ06ZNs1zv2traGBoaspUG1dnZSV9fH9XV1TkO9mwT+L2iosISN+zcuZM/\n/OEPeDweliw51nD9OP982Lgxyg03dKB482cZYqA774zg80VpaekkLy9PN1VS2LRp0wiFQrz33nvk\n5eUZ4nyfz0dDQwPRaJS3334bj8djKPmRn5/P8uXLGRkZUd+v5cuXG45BEGPt7e309/dz5JFHmkou\n3HVXBNjP179eyw9/OI/f/95pioE2bnyHj350n61iMDt37iSdTrN48WKi0ahKUOmt+bt37yaZTLJg\nwYKcqC6hkSki0Pfu3Us8HmfevHlUVxeYrtHB4H4eeOA9Pv/5GhoaGkxJ9aamJiKRCLNmzcp53uPx\nuDqOiooKOjo6CIVCY9GAZaZY5Q9/aMPtDlJfX6/7bmgzTjo6OhgcHBybt6pM+/3Tnw7icPQzZcoU\n3fTHaDRKe3s7n/kMvPmmG1luobOz1jDaVZiWELPCQEuX1pHhpTHsc4BxDOQB5gKF2MzafF/tnwX/\n/PPvULPMSAEolYSSvHJCoRBbX1/Pqhf+K0MjQlR6vXTOMfyCV3WrFAoA98z+Z7jwmQtzyKVL51wK\nwG3H38Y3/vcbk0plkySJgegAFz53IUlPMgeo/OyvP+O5859j9pTZrD01N9lYlmVVUNmInDIrD639\n/uS5J3NP5z05YvQOHHgcHlMxelA2TMLTOZnvYbwc8yO7HzEO5U8meWz7Y9w45UbDfqLRKOFw2HSB\nCYfDBINB08iWSCTCwMCAqddrdHSUvr4+AoEAVVVVOUD/jIVnjB0QxTU+xkPs/upuHtv9WIZorhyW\naW1tpbS01DSlT+g8WbHfe/fuRZIkFi1aZEqA7NixA0mSWLhwoWm7xsZGYrEY06dPN/VEh8NhOjs7\nKSwstFws+vv7icfjlJaWWoZjDw8PI8syBQUFuqBdRF89+2w+iUQKWU6jeBUVEyDkj3/0AzNUXavl\ny/UBzuholP5+Y72tD8IcDoft45uljOp5TL/4xSouuKAMSZL4xjfgyivNF7LmZvjb3wpZujQ5ISJK\neB4DgYAtL7PWtKBJz8zAaTKZ5OGHB1m3DkpKqtX0h4na4OAg8Xgct9udQ1JOxnsVi8V09U4OtV+t\ntba2Eo/H8Xq9hptlO2Z2fe2Y4pRo4fbbJa67Lg+YjZ3KRXbscImuH+5UyQ/SDDFQDEqKyolGo/oY\naABwwBXLPs3dfa/Q1zfIwMCAbvr6tn3bWL1ldS655IErPnkFd793N/19/bbG6/f7aWhooLi4WMUC\nA9EBznr6LHXNFxhIlmW8Ti+fnvlprj3pWl0HQTKZJBgMmq5bVhhIiL6ftuA07mq9SzcyyO1wc/Kc\nk4lEIiqRrV0DRXEKbcpYJBKho6NDTd0UFez0tCuFrk5bWxu1tbU81WJS7CWR5N6X7uXSoy/F7/fr\nkkYixQOUKIT+/n7q6uoy1pKRkRGSyWTG5k+s3UI3bHh4mEgkonuMZDJJb28vwWCQkpISHA4HxcXF\n+hhIyNA4Ufd7ehgo1B2itbWVYDBIaWkpqVQqJ+JGkiSampoAWLZsGdFoVNWG0hJYsViMPXv24Ha7\nWbZsGaA4UkX1PW27Xbt2qal8woSovrbtu+++i8fjYc6cORnjkmU5o0KhIGkrKip010qBh0HBcy6X\nS7fYktBoE20HBgbweDwUFBTkPM9ut1tNzdu2LZ9EIo4su9Bu/cT68Ze/BIA81q1Lc8stxhiotzdC\nT08+06dPt5S2gMxKfYerrdPpVMlGq7ZFRUUEAgF1XyQE+PXwD8CVV87miCP6cLlc3HqrxNVXO00x\nUEcHlJWVsHhxnqVjU1uJ0CoC3ahSXzKZVKPKxPUX1020NVujv//9MLfemiQ/38nRR5vfv+x+tSZ0\nqoqLi9XCCaKtFVZ5/HHFGaftNxwOk5+fbxqlZ6fflSv1x6t14JWUlOD1eunt7Z1wASE4dAykENbd\nfOc78J3vFKJgoEJbGMhqvHYxUCgUYnR0lEAgkPPc/rPgnw8dgeV2KOui9hY7AHcKlteezoGWtzhn\n+3+RkDM1IpCV3x4x42TkL/9Bt++SkhIicoQ1m9eQdOaSSz9/8+c8d/5zLGhYwA0n3ZDz+1gsxoED\nB/B6vcwy2DnJssxT+57SrWAoI5NMJ3l6/9NcM+0aw98LsyKojL4XwGvvwF62nLUlJ2Td4/Tw36f8\nN2X5ZabRPtoy1HqmLe1rZKLNwchBY5FP2UVnqNNWP2ahxnbGY1UaOruNUcj/tz75Lb73yPeUHziV\naL2ZpTNzNMU6hjoA82qGYK+6oBYom/WXSqXUe2d13FgsRjwet0wNFASinaiuwcFBQiFFpNOKqOnu\n7iYcDjNjxgxdIBCJRNXKgi7XAKlUB4podYPaxuWCgoLxRVFEpOzdu5dTTpGZPn26Oo5du5SKPFZA\nJR6PZ5RCN7NIJEJ+fv7fTawx27RjtlrIQiE477xaNm+uRYPzLa2oqIja2lpbUWRaC4fD6vNmRpjp\ngVMlZLsPZeYPsGZNgDVrsFV9T2si+ko5TnXOfZuM96q1tZVoNEpDQ4PheR2KV2x4eJihoSEcDgcz\nZ860ncZiZEbg344NDQ2NeZ6LUUhj14TSGI3scIqOHq5Uyb+HGWKguIKBWjveYdVLOhho7LefXnEe\n3/r4b+jq6qK1tRWfz6dWVCsvL6cv0sfqLatzIoFkWcbj9HD8ouNZdeQqXQfa4OAgPT09lJSUZESa\niI2YWDuf2vcUKTllioGOXHikrp6oHr4ZHR1VN/rV1dXq+mY05+7YsYMDBw4wUjzCI6se0cVAP/rc\njyjLLyMcDhOLxUin0zkEFmTiH4fDkbEOmmEkp9OJy+UinU6TTCbpCHeYYqD+WP+YxlE853tBpgAZ\nEdjZlQj1cFJ2WzMMJD4Tae5mGOj6j1/PD/b9QA0Z3HqePgYaSAzgcDjUtUKPwBJjEgLyQldNSzRp\nz0GLk8S117YV1zB7/RZ/i7biGHrRWtn9hsNhRkZGdB19oq3A3mJjrYdpxDMr2ra2tiLLMkuWLMnB\na16vl1gszt69bhIJPy5XO6nUMFAPjDuAXC7w+1Pcf38fJSXJsegWiffee4/TTvMza9YsnE4n6XSa\n9va4OieY4TMR6WZFSkmSpOqdTobsEum+Zo5w7XuurSBpZD6fD1FV3goDxWIevvOdGmbPnscYJ2pr\nzNXV1bjdbkMCy4g80otA1yO7stfo5mZBZCjE2U03lXPTTU5T/GNEoiUSCZWAE/O4tq0VVunoyOw3\nlUrR2NiI0+lk7ty5GbhwIv2aCaiLiHnhwBMEnB0bGRlhZGSEgoICNRJtshgonU6rDtyKiplAIXfe\nmc9Xv/rBYqChoSH6+vp0JTD+WfDPP8aO6TDarz99PV7HuMa5E/A64Ef/djnFhVN57r2fk5R15apI\nyfDHfb817Luuro5Xgq+QcpkDK7OIo2g0alixBsbE9cIHcTn1NxguFLLGipwCawLLaBMjSRIvNL/A\nVc9eRSwVo/WaVjYct4FLj7iUDcdtoO3aNo6tP9a0D7AmsKy+17aZXjrdOJQ/nWZK4RRbBNbhJKes\n2gQTQTXkX5IlklISSZZIpBPc+uqtANx07E3gxDBaTwAuMyJJlLi2amdU7nmy7WRZzik1bWRGJaEP\nta1VtcJHH41y1VUwOppPOi0qCmb2m0rJTJ2aGXEpyzKRSCSjyp0AWmAdgdXe3s67776rgg0jS6fT\n7Nmzh3feeScHbGebLMvs3LlTTfOwslAoxPDwsG61KLu2Zo2yYOlNaZIktDNg9WqlTXOz4gHauFGJ\n3tq4Ufk72/Ly8qirq5uw/pWoPFRTUzNhwq+qSgZEVEimdsdEbGhoSC3xrBcBMlHv1eDgoJo2ZUaM\nHopXrLi4mOnTpzNt2jRb6a/vpzU0NDB16lSuuGIWsuzi4osVkLRy5eT71KZXSpIC4CRpPLVA7xk0\nM5Em4PUqVbc8HuVfr3diqZJ/DzPCQN896gKKC6ey9c3b9TGQpGCgF977BXV1dZSWliLLspqO5XQ6\naWho4JXgK4aRQCkpxSsHX2HBggW6EcOJRIJoNKpLssD4hqYr3IXLYY6B2tvb2b59uzonq6ehQ2CJ\ntH4RvWnlxPN4PPy18698detXDTHQp+o/BYy/s9m4Tg/fiHVSROaINka4xOfzkU6nicfjNJQ1mGKg\nupI6NeJG73thRgRWKpVSr792PALrCIxhl8CSJImh+JAhBvrRGz8CB1z5b1dC2hoDaQmsbBPPkziv\nbKIpu50Wr+i1NcI1WqJJnKc4bjbuN2qrh2u0ZFc8Hlc1avVwlbZfoQdlJOzsdDp5++0k99yTYnTU\nTzotntFsDJRi6lTl3ov7G41GSaVSjI6OZkQQinNwu92G2EKWZfbu3cs777yj9meEWUKhELt372bP\nnj05pFS2RSIRdu3axcGDB3NIKT3Trq1GZIyeaYk0Kwz09NNK2/PPlywxkDYCq6CggPr6ekPsqm2r\ntdLSUmpqajLkSYzaak3BOTHGK2uXaz7XN6Nr1t3drVbyFPOftq210Hlmv52dnSoJnP3MT6RIT3a/\nWqutraWysjLHgWfneQiHw/T09KiRb2Y2NDREU1OTWuUx21wuF7Nnz2bGjBlceeVsZLmaK68sssRA\nVvqJhxMD/bPgn8NCYL366qv8x3/8h1oG+Yknnsj4XpZlvvOd71BXV0d+fj6f/vSn2blzZ0abeDzO\n1772NTWv/tRTT6Wjo2PCYznhqBtpveI9Niw9mUunL2bD0pNpu3IHn17+VQC6Qj3owiInuMohETB/\nQIWop565vC76pX7DCUlMLmYPoizL1BXUkca4Kt2UwimTjq6C8QVVr03zUDN5/5nHuhfXgQNWb1lN\nzQ9rWLlgJXedfBfXf+J6qgJVlh5MIRQPh0ZgCVBx9tKz8Tg9OMi8dg4cuHFz8tyTbfXzQZJcT+x7\nwhDoSymJqz92NauXrka+SWblAv2Zyw6Ble19NDI7UVpgDN6M2jmdTkvBdbuklKi0AcaklLBEIqGm\noGb329ysAI5rr/UAHrZs8SPLYuEe71fxKEQ56qid7NixQ/18dHRUBYbiOkSjUVWA0yoyTYAmq3MQ\n7bxer2W0ViQSIRaLMTw8bIu86e7uprGx0ZJEE9bc3MzevXvVMYH5QqbEbgTRxnC8+aZS4WftWvjF\nL5R/6+th2zZbQ7A0t9tNVVWVpU6InuXnS2zaVIoC3pUop8mkrQnPXVVVle6cYwR49bxX6XRaXeeE\neKmRTaRfPbOryXa4LZlMcvDgQfVvJX2j+rBUyhVmJ71yoibSBDZsgEsvVf5ta/vHKSFtZEYY6OOL\nlNDSg+EufQyUr2CgkE/xrDc0NOD3+1XvuFivTTFQnoKBjNLwzQrhjIyMsGfPHnp7e5lSPMWYrJEU\nDJROp0mlUjmbCj0MJDRmYrFYRiSyEQb6t//5N+78652QssZAom87BJaWaBBjyW6jNZ/Ppzqozl9+\nvi4GIg1uh5sT5pxgGIGlPY62inI2gaVtI0y0FdFGeiSX9vycTqdK0G3Zu8UUA52//HxOnHciIzeO\n6GIgSZLUcQmnkcAHWsvGSVYElnaenQiBld3WDiklIqVEWz1MkE1KGfWZ3dbMgdfcDA0NTjZvVs7h\nt791IcvifmdjoH6OOGI/fX196rkJvWCtw0N85vf7VckNPRMRiQ6HQx2bdk+gNYE3/H6/JSk1MjLC\n6OiomnJv1lZEp+3du5fR0VFLkkeWZd57772MuU6JlDLDQLGx/2SES8AMA5ml5GWbEXnk8/mYMmVK\nRqS2HXIuEFDOQ6kuXQi4LfGPXr+JREJ1BGhxmLatFVZZuXK8bSQSUfubPn16ztowkX7PGFNn0bsO\nTqeT6dOn6xJuVjYRrBKPxwkGg+q7AsozrsXheXl5E3beCjMa7+HGQP8M+OewEFiRSIRly5Zx5513\n6n5/2223cfvtt3PnnXfyv//7v9TU1HD88cerIr4A11xzDY8//jgPPfQQf/rTnwiHw5xyyikTjh54\n4vX1VFcs5vrTt3HXF9/j+tO3UVU+LhxYW1itTw05QHLCtBJ9vRhQJr7pRcaRQFK+xPKFyw3zws3A\nm7B0Ok1VQRVuh1uXrLHSnrJDYJm1yaim6Mj6PGucYE1OmbWxSxi93v46NUU1bFm9Ba/Li9PhxOP0\n4HQ48bq8/PC4H1KWX2bYT3b4vFWbQ43AEmCqM9JpDPTH0h6tiB87BJYeKDNrd7gIrPcjqkrr0bQi\naQR4y8vLy62KpT6uM4ClKKRFDLcbHA5/Bgj51a+ilJVljs0KvFmdg9A9sUtgWZWZBtT50k71QUmS\n1L7tVMmSZZnh4WHC4XDOdddbyNrb4cEHR4AmYA9ARrUeMw9QR0eHql32QZrL5aKkZBqwiE2blOcl\nkbAXMSYsGAwSi8VwuVyGBSMm4r3q6uoimUzi8/motggFm4xXrKenx/YaOpHrYNcikQi7d++mq6uL\nnsPRoYGJ1AI9OxTRUZEmcNddyr//KJ5HMzPCQGLdryusNcVA00sUJX2n08msWbPweDwqkZBOp80x\nUKGCgbRkg4jagHEMpDe3p9NpQqEQwWCQqoAxBnI7FIeV0GCyQ2C53W51fg+Hw6ZR6NWBalSGL531\nedZ4QVkjRAUuLbliRE6JcYh1wmgcom0qleLPLX+mtqhWHwM5vdx2/G3UltbicDh0I7CycYsegWWE\nbdxuNw6HQxV+F+M1i14TBFZHpMMYA0ku+kb7cLvdhtHHWuecWQRWtnPOiMAySyHUzpMCh1hFYAms\npIdrtGNIJBJqVJUeTrNLSmW3FZhEr62ynASAWhSRsejYuHw4na6M9eOuu0apqHCp9w7Go620eEeL\ni9rb22lvb9clhMRvtdWPQZ880mIgKwJLtBUYyCzlMBKJIEkSbrc7gxwzIrCi0SiJRIJwOKy+A6Kt\nEQbatKkPaEGpRiBZYqCBAQepVIq2traMeVHPJptOaWYORx5Qz/r1UwFZTVmzEzEmrKenR9Wd1eJW\nLSFkhVUqK8fbtre3A4oWmB4Onmy/oLwjRjpXentoKww0Gcw6MDDAvn37aG1tzXAOgzK/iRT0Q7X3\nAwP9o+Ofw6KBdeKJJ3LiiSfqfifLMnfccQff+ta3WDkWH/frX/+a6upqHnzwQb785S8zPDzMpk2b\nuP/++znuuOMAeOCBB5g2bRovvPACJ5xwgu2xfOm1e/jS2/+fvfOOk6Qq1/+3Ovd0T46bZmdzJpnx\nqgiCCsj1wrKkZSUIInAlSFgULt5rIAcVvXq5+1MXVNwFVFAkiYqyihdkCbvLhpmdnKd7ejqH6vr9\nUXNqqrsr9OwOmPb5fPgA3WeqT1dX1XnO877v836H9gt/w8K5xxTOJZ+nKTgb14Tq+198KboccMq7\nrjI99uuvv06uP4cz60RxKdNu91uOgPXs/me58bkb+fy/fp77/nJfie/C5rM2c9icw0zLQKYjYDmd\nTsNuij8+9cecdf9Zmrxp1E3RrgxxOuWBVmN+ufuXXP2rq6lsruS8951H15VdPPDaAwUmn727exFt\nda0+B8wFLD3RsTIRnI7ItaBhAfI+kyhyTo0iW4lOiqJMSzArV8Aqd5yd2FTuOD2xtxtrR97KHRsI\nwGOPwSmnaKO55x44/HA3L7/stjVmNyJvTU1NVFdX2xIK/d/aRW7eKgFLkDe3212WwCg2daKLUzGM\n6v0jkXEA7rijimuvLa9j4aWXJhgaGmJkZITDDjtsWl5MPT09+P1+6urqDtgvTDX4VP/7ggvUDKz5\n88v3TaqqqqK1tVXLzjNDOSafqVRKSzOfN29eWVG+6ZiHDg8P09vby+joKCtXrrQ8/kz6RykKPPUU\nvOtdIbq7u8jn8/j9/ml1qZwu/l5MR98OfPqp7/DpP32HfZ99jkXzPjz1hqLwavvPOOW4q7nvud9r\nHlh6uBxw2nummsN4PB7NnFqWZV555RXyA3lcksu4uYuOA01MTLBv3z48Hg+rVq3SRBAw5kA1NTU4\nHA7+2PNHvrnnm1x/5vXc+6d7SzjQ99Z/jzWta/D7/UQiEaLRaIGvjRkHqqysJJVKaePFGCMO9D+f\n/B8u/vbFqoA16c9UzIH05X9er5dUKkUymdTWajN+4/P5mJiYKBCwzHiJ2+3mTz1/4r6/3Mfy9y3n\njMPOKOFApy0+jVBPSPtOVhlY4nP0G3TRcdgsyCdJEm63m0wmo61tVnzE4XBoXGlB/QLLssdZ1bM0\nvyoj6LmNmShVPE7/HWa6hNAsA6ucUj8oL6vqQDKwjNbsQAAeesjFmWeCeiEnuOceOProCp5/vnD9\nGBlJEIm4tZLVfD5vGMRbsGBBSfZzsVE+FApYxaJUsaAlxgoO5HA4yOfzJTxLUZQSvmQ2FqaEbRHA\nsxOEIpGINj6ZTJLNZgvGGnGgiYkI4ODaawPccYdiy4Eef9zB8cerWWROp5Ply5cbzgVKM4Ty+Txd\nXV3U1dWVNFAoN5vo1FNh3z6J8XH4939XaGy0XvtXrSo97qxZs3A4HCWB0eI5WHGV7m517NjYmOZp\nOtemBXE5x+3rK1xXurq6CIfDJJNJ5s+fb3lcq/PwzneWn4GlrnPwm9/k+chHehkeVlUwo6ZU4XCY\nnp4e2yZdoN7jzc3NpnujvzYHamlpIZvNvq0Nrt5yE/f9+/czODjICSecoL3m9Xr50Ic+xLZt2/jM\nZz7Dyy+/TDabLRgze/ZsVq9ezbZt26YlYAnRpbluZcHLDQ0N9CWf5MtdP+X6d72He9tfnOrAg0rc\n/vv4f2fxgneZHvrp9qe58bkbufqUq/nWK98qIVYPr3uYpoC5RGkVfewId7DoG5PG7rPhrv+7C4CN\n79/IRHpCE2usjg/lCViVlZW43W6e7XyW9b9YX2KuefkRl4MP7vj4HVz74rWG3gQi6mgmhkiSRFVV\nleU83G43gUDAcKHWzkcIcML5vzif858+n/bPtReYfObzeUa8IxoJM4Isy7jdbkujR5ExU5w+r4cg\nLZIk2ZbqbevZxvqj1/PVP3/VsIORW1Ez6coRwgSBtPo8sM+YmukSQivyZjTO7XbbChbTEbCsoo+g\nLkTAZGfBBNkszJtXwYc/XDhu167SzCoj8gb23xUKyZsVFEXRPsdOwBKp1lCegFVM3uygJ2/lQFEU\n3ve+CC+9BEuXVnPNNfYdC/fvh7GxENu2wYknVk9LvEqn05rYU1VVZSvCFmNsbEwznhXQewaoprXq\n6yJa2tVV6g/hcDjKLsOzMvlUFHjwwW6OOEKhtrbGsKPXgRxXIJFIaKWJjY2NluLVgZwHK2zdCmec\n0c+ttw7wkY+o/lsLFiw4aON4K/y9mI6+LUgAOXjtpUH27XyKxsZGmpqaeLVvM3f2P8LysQYePv4m\n1j7z5UIO5FY50ILWdxQcTjxfc7kcz3Y8yxee/QLXrzMWl/QcKBgMaiVtooOclY3C/vH9HL3laHX+\nQbjthdsAaw7kdru1LC/xXDTjQMFgkJGREaLRKPX19ciyzC/3/pIzHjmjhANdsvIS8MHnP/B57uq5\nq4QDiU5WsizjdDrx+/2agCWeoW63m8rKypL1SZ+BJQQDo/W9I9zBojsXqUmuEpz50zM586dnlnCg\nWCxG3BvH5XIRj8e1bnbFPi/6VvCi7F90eRZ+RmZcQwhY+rXcDEKofLH3Rc775Hnc/Lubjbs45l18\nZPFHNHHUCHphyu12F4igehQH59xut5Y5ZjVOjPV4PNr5UhTFlCuJgJD4e6sgnggG+Xw+W1HK5XIR\nCATweDy2HMjlcmldlwUnMOdLPqCRa65xc+edKgdqba0oWD9UY3bV07G2trbkt9bzIn1JoMiYssuq\ngqlOe8W/h96WQZzruro6LVtND31GlZhDXV1dybUuIDiQeC7U1tYamu0Xj6+qqsLn85HNZi2v82Qy\nyQc+kOapp6pYurSN//xPN9dcY82BhodrePnlPv7lX9y2JWQ1NTX4fD7tvITDYUKhkGEH0Orqarxe\nr2WwcnBwkNraWqqrq/F4PPj9ftu1/5VXqmhqchVcAy6Xy7CzdVVVFU6ns2CsGVeprKxElvM8+mgf\nH/qQ6odqdq4rKyuRJKmAu5kdNxgM0tzcrD3rReMao47VYmwgELA9D//3f+r/l5uB9fTTMl/8Yhe3\n3lrFRz6iin5G3denU5po1C1Qj+lyoJm0cACmxWFnCm+5gKXv1qRHc3MzXV1d2hiPx1PSfam5uVn7\n+2Kk0+mCSJM+jfzxE24iUDFFcjp6f8uir38YJgA/3LbvRQA2LnkvE+kYC2rms/adX2JsWCIUCjF3\n7tyCqFylp5Lbtt0Gk1O5+493g7uUWMWH47z88svMmzfPsLzEKvpYnJ4ucOMHbyyJ/FkhGAxy1FFH\nWd5os2fPZig2xPofrS/pJJSRM9y3/T4GvzpIc7CZaz5mvFMyeoDp4fV6WbJkieUYK08W7XzUmbw+\nCYfDwapVq7CC1+vlsMMOsxzj8/k48sgjLbNr3G43Rx11lK3Z9puON/ncS5+jeXEzD697mLVb1pYQ\n/Z9c8BOOnn+0pRDmcrlYunSp7efV1tbi8XhsxZX6+nr8fr+tQl5dXY3b7bYVkTweD4FAwHacLMtl\nzQ/Kz+oCLKOPUJhtc9xxapZV8Vz1ZRF6rwa7Y1uh3KwqPSGz+77TGQsHLmCVuwjF43FNNBbfs5wI\n0EMPhbjuOqisrMOkEashhH/AgYhX+Xyenp4eZFlm2bJl2nzL8QzQkyS7TkfTwZYtChddFOT22xN8\n7nPWkcfporhdtFmpo8B0z4MZ1C6PedSSijAbNwI0s2/fHJzOmSVLxRClBWvXFkZQ3e6/LdPRmYQp\nB/LCN46+iGzWycjICG+8+Tuu+d0tkAYC8OnffBdq4cW1m3h+z6PsH+9iQc18Pr7iOlLxANFolIaG\nhlIO9LvboBtIov63q5QDDXUM8Ze//IXly5dTUVFBY2OjVj4qTOHBggNVoApYKVRrPYc1B6qqqmJs\nbIxIJKJtVGfNmmVYjqv3wVq2bBmjyVHOuPcMQw70nZ3fYdt12/DIHjau3ViyAZIkqaCTtN/v1yL9\nAjU1NYZZh0IAcbvdplkB2vnwoFaAOVHT5aRSDhQMBlk92QJ2YGBAE3r0qKurK9kwt7a2FhiFNzQ0\n0NDQYMiBhDeN1+ultbXVkictXryY/4v9Hzf8/AYWdS8y5UA//syPOXbesQQCAVMeVF1dzZIlS7QN\nrJn34ezZs7WW8GDMLRVFYe7cuVonMoHa2tqCvUc+n6epqYlMJlOyqa6srCzgm36/XzsvxaioqGDF\nihWAusexWrt9Ph/Lly9HURS2b9+uHdsIbrebZcuWkcvlePXVVy3HnnGGhzPOUBMAzjtvB6lUKafR\n85+FCxcWBNasvDnb2to00VMPfRaZ+D0WmKR/GHEls3vCKAN93rx5hmP15ZWCA1ll+ORyOU0MFPzX\nDsL/a+HChbS1tQH2HCiTqeGGG6q59VaJ97yn1njgJIrXbeETZSTG2AXVYrEYfX19DAwMcPjhh2vi\n4H//t/Xa/8tf1nPNNfWTr1nzH6NnjBlqa2t58slKrrlG4a67Elx1lfkCbfYcNUJ1dTXV1dUkEgk6\nOjoA9Xc3En6qqqq0a+OOO6zPw8MPS7pqDnOoHCgLdAEeNm6sAdpob7f+rWfCTuOfkQO95QKWQPGF\nX85mwGrMLbfcwn/+538avpfJFdaTNtetVAMRoiXPJG785M81oSuRSDA2vAugpO1vLj8pIBRdY8XE\nqkPpsPw+oIoSIlpQnLq++ZOb2fCzKZnUqHQvmUySz+fxer2mC4skSbbndvOrm03NNbP5LA+89kBJ\nO+O3EwFPgMfOfIxTHpp6ahidj5mGXWmSVTZUQRadG8545AwAXrzwRZ7vfr6g7NEuk07MpZxsG7/f\nX1bGUrkLTEtLi+0YUDcK5ZhpV1VVsWbNmrIe0kuWLCGTyZSVrbFo0SKSyWRZIlNtbS0ul6tEoDEy\na5dlmerqajKZjEZ0Y7EYw8PDVFVVGRIIAX1av10G1ltVPpjL5TTyVs54fbS1XMFLkLfq6mrtWWMV\nAXK54Npro6jF207OP7+a88/HsoWzgKIomoBlde7NEAqFkGW5JAPLriWz3jMgEonQ09NzQJ0T9VAJ\nDqgGg7O57roWrrvOupX1dKAosHlzF2vWpPF6PRqxtsJ0zoMVVM0ggWrsL6G2aq+nzMfJQWM65ZX/\nCDDlQLUwZ0ENx645gaGhITq7grANlf9UAHHABasWnMy7J43dQfU26Y2rWXumHCiNyoMmgLpSDjSo\nDBbwtqamJoaGhojH40SjUa3RiCkHWreZDd/doKaEpeHxC0rXfOHT5/f7NQGr2AfLaB33eDx4PB7y\n+TyZTMaWAz0z+AwXH3FxWV07GxoaqKurKytIU1lZyZo1a2zHBTwBHjt7ehxoOs0tzDaFRueueI01\n40nFHGjdw+uAA+dALperrDWpsrLSdq2TJMlWzAe1RM9MGCmGXdmPQEtLCy0tLWV4FEkcccQRJSKb\n2TxXrFhBKpUqq6S+paWFeDxe8lvqvT2bm5s1E/7iYNHIyAjxeJz6+noqKytxOBxatp8eQgiy2qMI\nvFUcKBqNoihKQbacFcTzo6KioizxCqY4kP4+MuNAoK6vP/6x2iBj48ZKNm50l73up9Np7VwdCP8Y\nGRnR/lZ/rUxn7e/v7ycejzNnzpyD6mQ8xYFcwAI+//k8n/+8NGMcKJeT2by5g3e+U81uL+eetzsP\n3d3qf9vdvyoHGkeLFrEMqDDNYJ9OQFQ087BqmjUdDtTY2Khl7s0ERPav3+8v+x46WLzlApbYDA8O\nDhYsrsPDw1qUrKWlhUwmQzgcLoiEDA8Pc/TRRxse94YbbuDqq6/W/n9iYoJ58+YRuSFSsuAFKpp4\n7OM3csozX9FeK87SSiaTdHd3kyLF+TvOL4nKFUAyJhJ2Hlf6SE8xQZQVGUmRYAy+dtzX+ML2LxiW\n7vX29jIxMcGCBQsOeCOVz+e1TkJG38+Bg/3hA3S9PUAY+VBk82oN2KZTNnHhYxeatln+W4FZFt2q\nplW8e+673+bZ/O2h3Id1uRk2Pp+vrGwkKIy26GFkzO52uwui66ASrXA4DFiLKJIk0dbWVuCFYoaa\nmhrT6G0xRNp8OeRN3wGxnIVEZF+Jkp9yoBewBKwiQA8+CKefHp4cWYO+Q8Qdd6gEoq1NXWyLF/to\nNKqJmgeSpixKD4ujlNPxDBgYGCCdTpNIJA5KwColMg6T1w8M//u/w1x8cZhbb5W4/PKFZf2eM+Wd\noPrOBTnllIWoKSOV0+7yODSkZoRZXQ9WKKe88h8F5XCgmpoali1bxmPBGznlV1+BKJCEbx11Cb09\nYdrapgjs+Pg43d3djMZHOf3500s5kAIEgQiQgp984ie2HMjlclFfX8/IyAhDQ0MsXrxYy4Qw5EAZ\nCZJwxVFX8PX2rxuu+Xv27EFRFNasWaO1cS9XeF+xYgVOpxNFUWw50JA8VHYwZyYI+yEO9I+PcjiQ\nUVdls3EVFRVlZ4nX19cbNpjScyC9kFR8T42PjzMxMUEgEKCyshKn06l1gtbD5/MZejoa7Y9aWloM\n7998Pl8SiPf5fKTT6YI5KoqiCeb6scVm72KsOG6x4FdsoWA1FlR7D32QMJfLTXbXdRhyIJeLScP0\nMSCHyoHU9cpszRPeXpIkadlXZhno+Xy+wAZFj1wup3HXxsbGgrFtbU7LtX/+/DzZrOqJNjw8rIko\nRrCagx7qei5P/uNAyBBm67wQSe26rIux9923l6uuinPnnRVccYV5hqv+uG1tLsvzsHx5PStXVtny\nqUAAHnigmXPPbUQN4FXMGAcKh8N0dnZSVVVlWd1ULgeazv6pHPT29hKLxVi0aNFb6neqx1suYC1Y\nsICWlhaeeeYZjjzySECN+P/ud7/jtttUj4N3vOMduN1unnnmGdatUyM2AwMDvPHGG9x+++2Gx/V6\nvdNSDrOymmq/6b3nceGfvl+SpSXLMvF4nKe7njaMygl8ZOFHeDb2rCGRKMekHVSicvpWA4Iogzvr\n5p3170S52fjz7TyuJiYmGBsbIxAImCrP27dvxzU4adppcBh5QsY95Kavr8+wVFBRFP7yl7/gdDpZ\ns2aN4U09NDTEwMAA9fX1ptGsN998k2w2y5u5Nzn3l+eW+FD8+JM/5rVTX8Pj8Ziej/Hxcfr7+6mq\nqjJNER4ZGSEUClFXV2eaajsyMkIkEqG+vr6klFUgFAoRiUSoqamhtrbWkHBuOWUL6zavU1P/K8yF\nzr6+Pjwej6U/TSQSIZvNEgwGTR80iqIQCoXweDwEg0HTY2WzWdLpNF6v15JsizbZdoS83Gv9bx2B\nQIBZs2bZPkvK9bVyOBymXUiLMZ0FxKrcthg1NTWsWrXKtvRUwOv1UlNTU5Y4BmoJTjqd1nzu9DCL\nADU2Knz96+NccQWIuuCbboKVK+2NwwV5OxDz9lgsRjKZNPxdyvUMiEajxONxJEmy7RRoB683x3e+\ns59LLpmFqgYwbYJjhKnyPdUwdOPGuWzcGCgrqnmw/lHCCNrj8Uz6ztVM+s4J0l4eZtJI/p8B0+FA\nWTkNLtj08fO48DffJ51JEovF2LVrF3PmzKGxsZF0Oq1yoAELDuSCDyz/AL8f+T39vf0oRxZmyRut\nC83Nzdr6mkwmVe8VMw6UBZfTxfvnv5+b199cshaLDSugbWiKjZCHh4e1TJHi55PL5SIWi7F79258\nIZ+5wfiojHvYzfj4uCEZj0aj7N27l0AgwLJlywyP0d7eTiwWo7W11ZBT5HI5duzYgdPppMPVwbqH\n15VwoO+d8D1eP+11KioqSN9gnJXT19fHxMQEzc3NmgG1w+EoWKu6urpIp9PMnj1bEwAymYyWFVdb\nW0t3dze5XI5Zs2aVZHSn02nC4TBDQ0PU1NTQ2NhIRUWFIQf6/rHf57zN54EXaDSvJAiHwzidTi0L\n2qjMbGRkBIfDQXV1NYqi0N3dTT6fL9jACXN5r9eriTmyLNPe3o4sy1oZXyqV0qoXio3E9+zZQy6X\nY+XKlZoYYbZZ3rlzp1aObhds27lzJ9lslhUrVpQ1NpPJsHz5cltesGvXLq0U1k7A+sUvfkEkEuG4\n444zFGRFdnplZSW7du0ikUiwdOnSEj5QHOzr7OwkHA7T1NRUMNbj8ZTsPXbv3m24uS3uZAeqQB2N\nRlm4cGHBfdPa2loy93379jExMUFbW1vB+j5nzhxqa2sLfuf29nYikQjz588vCUIGg0EymYwWINu/\nfz/hcJjW1lZD3iUEr0AgQH9/P6FQSLOOMeNAv/99irVr96Omr87l8cfhuefM17w1a3oYHR1l1qxZ\nthnovb29jIyMGHotjY6OoigKgUCAiooKenp6GB4epqWlhQ0b5liu/Sec0M9rrw1pIqFVM5aBgQEG\nBwdpamqyzGCU5Qluv30n112nAHOAVksONDIyQl9fHw0NDZYl1yoH6gZeBqq55poPc801LlMONDo6\nSm9vL3V1dWzYsMDyPJx/vgu/39r32OVyTWYGNwAn8L//C5/+dHkcqBwj+fe9z/44/2yYEQErFoux\nb98+7f/379/P9u3bqauro7W1lSuvvJKvfe1rLFmyhCVLlvC1r32NiooKzj77bECN4l944YV8/vOf\np76+nrq6Oq655hrWrFmjdSU8WJz6gdtRPqCKYRd89Hsl7+fzeZR8np0Df8TR5CBPaVTOJbmo8dWQ\nubq0Nh7K39Sbpa6jQE7J8cS+Jzj+X443/FsrE1RQF+lQKISiKKY+XIqicNLSk/hO33eMOwmhGoxb\nGZ6Lf5ttKI1Si4uRyWQYjAyy/pn1ZJ3ZEh+KM7ecyePHP86sGvO0+EwmQzKZtFzwk0mVrFulKcfj\ncSKRiOWYWCxGKBTC6/Xyh+E/lESPb/rNTXxm+WcgAbceeysb/7LRUOjMZDIMDQ3ZprQL0t/a2mr6\n/bLZLJ2dnUiSxFFHHWV6rEgkQldXl616Pzo6Sl9fH/X19ZblRyIaUFtba+pvAOr19sYbb+D1elm0\naJFlBCMUCmkbBrssl1AoRDabpbq6uqzOhqI7RjEpNSq/1JcOChhlav0tYzqRFbPsNKtjr1q1Suuk\nUwyjCFAmk0VRXECe//3fSj79abjlFnWBtjIOl2VZy/Y6kPJBkTovSHrxPMvxDBgYGNA+/2AzLQYG\nBibLFXJs2rRi2iKPGVRdzQGsQI3yNulet//bA/VOyOfztLe3a5upU0/1FHR5LBczbSR/CIUo5j+Z\nTIb9+/cTjUb53e9+R1NTExUVFSj5PK/2/A6H34QDOVzMap7F9tO3k8vlGBoqzFIy4kBer5fa2loi\nkQiJRAK/32/OgQDZJfNy6mVOrz295D19CYcZ94hGo4yPj5uu5YJD/euKf+VbXd8yNhjHxUlLTiKb\nzRIOhwkGgwX3vizLJeUkwouroaGBqqoqcrmcaRBhcHCQ3t5exsbGcAadrPvNOkMvrvMePY8fvPcH\nVDgqcDqdhhu4VCpFIpFAlmXC4TDd3d3U1NQUZBHH43HNfkL/WmdnJ4FAQPt9MpmMoUifzWbp6+uj\nt7eXuXPnUltba5hBd9NvbuLcWefCGFzzsWu4c+BOQw6USCQYGBjQDM4lSTLkEX19fciyzKpVq3C5\nXNpaoC9TjUajdHZ2UllZydKlSwH12hAlZ8Lke3BwkLGxMebMmVNwzTocDs1MPJfL0d3dbSp0AFrW\nUWdnp1ZSZcbjcrkckUiE1157jcbGRssNuCzLDAwM4PF4mD9/vmWwTGTE1NTUMG/ePMvMFNFRcGJi\ngqamppL7RvgGgRoAD4fDNDc3F4hS6XSaXC6nZX2BGlTTm4xbwa4D4EyPlSSp5PxZHbc4QCiuLbM5\nNDQ04Pf7yefzhEKhkrFGHCiZzAAeLrsswLe+5WB4GC691HzN27ZNnW80GiWbzVpmoJt1IVQUReNA\n4vuJ86Aoiu3a39go0dcnEwqFaGxstCxRLrcToiq4R4Act902m+uvn57IYwb1sVUBtE3+O6B73Xq+\nB8OB0uk0e/bswefzsWjRIk47zaFxoAsvtP5O+nXS3lDf+ljThVgT/H7/QZWE/jUxIwLWSy+9xId1\nrb1EWvunPvUpvv/973PdddeRTCa59NJLCYfDvOc97+Hpp58ueEDec889uFwu1q1bRzKZ5LjjjuP7\n3//+W9q5qBh/fvNHvDyyD8kk0UH2ySyevdiUONkJWKFQiOHhYZ557RkcGBBEBZySk/5ov+kc7TKw\n7N4XxGv32G62rttaEvVzO9x868RvUec3z3bQf4adyGX1+8myzC/3/JIsJj4UuSxP7H2Ci997sekx\nzFo/G40pp+NfOWPC6bBh9DgjZ/jOn7/DU+ufYkXbCq7/xPWWx7GLyBW3hj7QMfpx5XYgtDteOp02\n7BRjNE4QPrt7WZTpiTkaRXebg+pqNDo6SjQaxel04vP5bMcODw/bRoZAvSZff/11XC4Xq1evxul0\nap2awF7AGhkZ0dL6rYTsWCxGOp2msrKyrN9ORHf+VjDd9GOPx8MVV6zksstyuFwSoRBs3GhvHJ7L\n5bQU/emKh/rUebMNhp1nQDQa46mnohx9tFR2OZEZ0uk0IyMjHHssRCJzqKqanshjBbV8D045xQWo\n1/10MrsOxD8qn8+zb98+LYvDSPgtFzNlJP/XhKLAU0/BRz+qRm7/luHxeFi6dCn79u0jnU7T3d3N\n4OAgO3p+yR9ju5BKkx1AAtkrs2TuEubOnUtnZycDAwPU1dVpv7vZJmPu3Lm0trYyMDDAT//yU3aH\ndxuX701yoN6JXsPj6DeJ+uerLMvEYjGqq6ttOVBXVxd79uxh1D/K1tO3logwboebu064izp/nSag\nFGd4CH6j/wyxfvl8Pqqqqmw5kMhk/e3Qb829uHJZftvzWz6x8BMFhv16iM/Re4sVjzXiSeI3E2ub\nFZcSvEh4JYbSIVMO9IPtP+C+E+9j5aKV3H7R7YbroOBAonujoiglHEEfBPV4PBrfFEKTmJMRB5Ik\nCafTiSzL5HI5rRtm8TgBp9NJLpdDlmXbLsziuIlEgnw+b8lrRJmd/jtbjY1Go4yMjGjVBGa8xuFw\nMDo6itfrZfbs2bhcLtOxbrebUCjEm2++SVNTk2V5TyQSUa1UJkVRIQjqOz6L37Ourg6n01lwvYis\nvuLu4kbiUSgU0nxe9edQjNUHwEX1QDHebmFMD7HpFxzD7rjr11fxoQ+tpL+/nxtuyPOjH1mveY8+\nKvFv/6YeV9hHmD3T9KKUHhMTExp/FNlsxUKT1do/MOAgFArz4osyp53mM61OMTquEUKhEIlEgg99\nyMnJJ9fQ3Kxw3XUWJ63M44LKdTZvltiwwYco0bTiQMXPJavzkEgkiEQieL3eguC6EK8ymYzmCTcd\nnl5RUcHs2bPx+Xz87/9aXw9bt0qcdFLZh7aFyKgVZbwHC0WBbdtmxsesXMyIgHXMMcdYXlySJPGl\nL32JL33pS6ZjfD4f3/zmN/nmN785E1OaFjp6f8uiez8MHYC3xKsdUKNynnoPV514lemCZSdgpdNp\nfvbaz3hmzzNINQZjFJAVmTnV5h3+9OnzRihH4Hq241lu+PUNbFmyha4ru3jgtQcKzDVjQzEtvdsI\n5YpTVmNEjXl/rF8lD5RGKp2Kk75oX1ni1NsxRhCQR3Y/Yk44s6rodtgS866H5Yhl+nFWm8LpCE7T\nGWcndAkiazdOGJpPp6ug3+83je4+vO5hTl56MolEgm0921i+fLnt2Hg8zraebZw1/yxgihS2j7Yz\n2zebC959AXNrVcIoiJq+jl+85vP5LK95sREURqxWAtbo6ChjY2Om7XX16OzsJBaLsXDhQtvacpHF\nVl9fX5ZfVCwWK2hh/VZD3F/lmoeKbqYH0qVFGOG6XC5L8cvKM+D//b8BrrwSvvWtet7xjgMTZwT6\n+/tRFGXaGW92SCaTk1mGKrE6kPI9mJ5/VLF4tWTJkrKMeM0wU0byf01s3QpnnAFbtqiR1L91SJLE\nkiVLqKqq4tGff5drn7hZ9chaYsKB3BKeBg+fO+Fz1AfqGR0dJR6PE4vFNFJvxoHEuvPo649y5c+v\n5PSjTzcu35vkQK01raTTaSKRSEGZvT4DXb+xef3117VyMTsOlMlk2Na1jfv23mfKgQb2DZDL5QgE\nAkxMTJBIJAwFLP16IDJ5xTpmxYF8Ph+yLJPJZBiID5h6cTkVJ0PJoQIBphiCuzidzhJRqniMkYCV\nzWaRZVk7b0a8xO12oyiKNvahnQ+ZcqBcPscfev7AYcsOI5vNGnIOMT+xpsqyrGWZFI9xOp3ab+ly\nuchms2Sz2RIBq3gNc7lcmoDl9Xotg3gul2qpIawWzMaJsZlMhkQigdfrteQ24ncrp3uwCJbl83l8\nPp8lr5mfm8/2we2a/YHVWK/Ly47BHSxevFhbBwUH2je0j7mVc7nw3Rcyu3q29t30AiEYWygYCU0i\nGy4YDBaU1hqN7evrI5PJaM+g4rHielQUhZ07d+JwOFi5cmXBvIyEpt7eXmRZpqmpqSC73kyUikQi\nJZ0wzQQhI0xnrCRJuFwu1YOv03rN6+lRn2/BYJB58+bZ7rGh9LuJe6q+vl6bp5EgZLb2K4rCr34V\n4hvfqKS1dRaTzU4t52A2T0VR6O9XkzOamprIZrNlnzM7iMB3LqeOvfVWhY0bp5/ZZXYeEokE/f39\nVFdXa2tdOp1m9+7dZLNZfD4fS5cuxe12E4vFGBkZwe/32wY99RUgdtdDV1fpfP+W8OSTcNVVUFkJ\n55339nzm29aF8G8ZzXUr1WzDFYWvS0i4HC4tKvfwuoctO6cEAgGtQ0BxNORD8z/Ee+55D8SACkw9\ntlySi39d/q+mn2FXQmhF3jrCHSy6axGMANJUh5j2z7WzsHZKNo3IEdNjgHH00WyM2YZfEKrZwdnk\nI3lDxiznZeZUznlbxSk7fyiA3livJeHsi/ZZHqecrCnhK2M3brqZVeWOsyNb5Y4TQpcYZ5UpJUSp\n2rm1ptHdtVvWsu+yfTy550lu+PUNeOZ4uOKpK0zHdl3Zxc/f+Dkbn9lI3bw6qsaqNKLnSDqQwzJf\neeIrPHrZo5owBoWZVuX6X4lxFRUVtlGYcrvv5PN54vG41lHHDuPj44TDYfx+f1kCVldXF6lUisWL\nF5c1PhQKEQ6HaWhoKNtQXXiK6M9JOcbhhRkt009pCQQCrF692jbybQTVTyGJ6lchcdllLVx2WXld\nE42QSCS0UgMjX8EDNS9XFIX9+/eTTCZ5//uzKIr6RzOV2WUEvXjldDpZvHjxQYlXMHNG8m8X8vk8\n6XSaVCrFnj1p3vveJoSh5KSN54x1VXqr0dzczJnrLuDS39+srsNeIAX4rDmQKIcSzyVFUTSvIofD\nUfCsr/RUctu222AUyMLWN7YKC7hCKCoH+rcV/8auXbuQZbnAXNoogCdJEsFgkEgkwsTEhC0Heuf/\neycMAJXmHKgv3weoBs1CwNJDfMaBClii/CidTjO3Zi7yiIkXlywzt3aulsmTz+dLvpee3wiuIIQb\nIeKI86bnQG63W8to0gdujM6b/rVcLkdPrMeSA42lxjSxyYi/6PmWXmgyGqP/e3FM/VgzPuVyubTS\nN0VRLHmXOC/Cj8uq27TT6SSfz5NKpWx96IQgpM9YNuNA2WxWE6XCGfMs/7Vb1nLNgmu484U7qW6u\nZlVsleXYz9R+hge2P0BjayOneU4rELscEQdyXOYrz3yFRz79CDWeGtLpNLIsF/AdI16UzWaJxWIF\n94YZrykWjzKZDJlMRrt3rcbG43Htui/+TYxEqbGxMXK5XInnpZHQlMvl2LdvH5Ikcdhhh2nXgVUJ\nYbFAZlduKL6v2+0umK/dmjd/vlqK9pvfKJx7rjUHMhOPRHdU/evlzBcEBwqhmq27ueCCWi64wHxd\nsxOwRkZGSKfTuN1umpqa6OvrKxhrxoHsjpvJZOjs7ERRFD784TpeeglqahSuNy6AKZnvgcBMvBLv\nhUIhqqqqppW1b389HPB03xLIskwqleLNN1O8851T19L551N2h/GDxSEBi8kuhR8t7FK4+UNXMeSZ\nXRCVa6ywNlG26q7jknSnWtL/5xRBdDlc3Hb8bTQEzL1eplNCWLxQnrbytCmhSPfnxZ1jjMiZHjOR\ngSVIVUtVC65+l6UXl9XnvJ0ZWJlMhm0922irazM3f82potvBlivqo49W338mSwhFe3G7cVB+BpZe\nwLKKEn50wUd5au9T3PDrG3gx96JpdDcjZ5h3+zwIAS645IlLDD9XjG25rQXUJnSsf3x9wb2XT6vC\nadaZ1cSuWCzGtp5trJ29Vhsny7Khp0IxyhW69FFeu7GiTMHtdtsKWIqiaO2gy8nwyWQymv9IuQKE\naGRQUVFRtoA1MDDA6OhogVdIOcbhP/jBBOef72PLFs9BZbQciG+VKh75gaVAAnVXf+A+TH196oa4\nrq6uJBvsYMzLBwYGSCaTuFyug+qOWC7y+Tx79+4lFovhdDpZsmTJjKSfH6yR/IFgOiV/wlcplUqR\nSqUKMlzUx3kVqu/GFP6ePLtqa+by2Gdv5JQtX4EkEIb/PfXfCQdbCziQPoBX/DySJInFixcDpRwo\nl58UHCZQs7wCQBA8Dg85JTfFk5wqB2oKNqEoCmNjYwV+VmYBvKqqKkMBy5ADiSVep5foOZDIDoep\njbjwSBKfa5WBlU6ntY5cxWMEPB4PsiyzfXA7p3zoFL7Z/k1jLy7JxSkrTsGdm8o2Kj7veu4iNvmi\n7F0IPuJ8FPNGj8ejmfeD9bNSiF1/7Pkj8+fMN+ZAeTXw2FLVUvDZxdDzFrfbTTqdLhlrxG0EP9ML\nWGbZ5WKs3gLArAW9GCvEGK/Xa7rBFaKULMu4XC5Lzqjv1GfHgXbu3smdL9xJZWMlL3tfNuVAaTnN\nV3/7VQBuev4mbmq/CQnJdOw3tn0DgLtfvJu7//NuPA6PdmzBgTIOVex65H2PkEqleLn3ZU70n6gd\nS9wP+mf9+Pg4PT09BINBzS/VjAOJe0AcR98pufiaLBalhJeZUZOZ4rHJZFILmJXjgSXM2P1+v2EG\nVrHIoygKo6OjyLKsCWTlZGDt27ePbDarzUlRFNs1b906ia1bo/zXf9Xg91tn9VrNofj8lluSp65f\njcAsoAFBnsvxlCqG8HcDDH20rDjQe99rPd/Ozk7y+bzWEGBsbGxamV3THasGrfYYilf6seVAZOE6\nHA42bPBaXg9nnikxeSvMGBQFnntOYf16ew40NjZGLBbTSt/F81qN1zj4a/CffzgBKx6PH5BvVjQW\nhQx8+93ncOmff4gzk+ez7/qs9r4sy7zwwgsAHH744aYC0lBsiLU/XKsZVwqfqwwZHDkH+WxeJU4Z\nuP8T9zMcH9bI1SfaPkFkQO3UIxaCYlRVVWmROyOTdNF165c7fsnlv7+8YKG88akbufKoK7n3l/eq\nQlYGtp6+FbIQz059XjQaJZVKkUwmDRf7aDSqGTibzXNiYkIj+0Zj4vE4T+x8gpt/fzNXnXIV3/q/\nb5X4UHzj2G/gx691mTGCmIuehBmNURRF820qRi6X04hLJpMxJF2yLPOLN37Bzb+9ma+f93VcOZeh\nOakz4+TYecdaznl8fFwzFrc6f8lkEq/Xazqm3GPl83lN2LAaJ35zkfZuVrIgIm+gnjur+Y2NjfHb\nfb/luOBxhvdFmjSnPnAq2UQWwoATfvjKD02P55AcyAkZsupYLFKEC8a6Uf+tR2LyNQUyqQybXtwE\nI/DFp7+It8bLOVXnMBQb4se7f0zneCeto62cc/g5WsZYMYaHh0kmk0iSZHlOwuGwZp4oBD4zDA0N\nlXUdAFo5j2gTbzd+ZGSEZDJJIBCwnQeo19HQ0JDWpdLu+AJ9fX2aobH4m2AQHnwQzjmn1DTzrrug\nuVkBdgI51q1bCgR4/fXyM3Gi0ahlV85ysGULrFvnQE0VibN1q/p6mV9bQywW05o2VFdXF5y3oSHV\noFPoIYIvp9Nw2mmwa5c5EUgkEuzfvx9FUZg/f74W1X4roCjw7LNwzDE5zVRWmESXex1Ywep6ePBB\n1cdiBj6mAI88Ap/6FHz/+zInnpjWiJn4d1tbmyYWjIyMaOUPAk6nc9Kg3MuDDyZZv15BGMfORGfJ\nA8VBcSA/3LzkE/zn9sdxpPN89v1THCgSifCHV/5ARUVFSee9RCJBKpWirq7OlAMBKu/IAROw9cKt\nHDXrKB564yGNAx3beCy5iRypVIpgMEgymaSvr0/zXslms9p/66870c0ulUppfkY/e/1nfPrJT5dw\noEvfeSnf3v9t9dmfgK1nFXIgWZa1LCrBG4RZs7geBO8o5jciO0g8t4ESwVPguY7nuPN3d1I7u5YH\nT36Qcx49p4QD3fbB2/DjR5bUOYVCoYLAgfBiArRNRS6X08YqiqJxQo/HU3KvirFjY2O2a1c6neaF\n/S/w3X3f5ZsXf9OYA2XBmXPywdYPkk6nGR8fN+SQkUiEXC6n8a1kMsn4+HiBCCW4TSAQ0OakHyuC\nZ5FIRCtv1M9dNPiZmJjQ/s7n8xl+v3Q6TTKZZGRkRMuWsToPokO06F5uhmQySTQaZcfYDtpWtply\noE/84BPQD+Tg5uduht2qF5xJoYaaIZkD8kBmkuuYBFXJoibRTI7NCNKkoHKgyf/OpDI8vvNxRnpG\neLTvUVb9yyrWrl7LUGyIR3ofYX9oP22hNs4+7Gyag81aAEx06ZVlWcsyhsL7U3DLWCxGPB7XuFIw\nGCw5f8Vjxb1UX19vOjYejxeMraqqKsmaFKX24rigNlMQ463mKxCNRonFYprYJYywjcYKiIwcYYAv\nxjY0xC050Jo1IWAfMMa6dWqGrxkHSiQSBXMQmftGop+YrzhnVrj//iQXXeRFJdvWHEjMweg5Mzg4\nSDQaxev14vf7teeNx+OhoyNuyYFeeCFOKpU0vCdHRkYYHh7G4XCwYMEC7VlXDj8Vv53L5bIdm0gk\nSCSSvPiii9NOmyAej+N2u5k7d24J7xLHtdofC4hmWMFgkCVLllhyoNraHPl8YFrc2+47PfFEkptv\nTiDLET7+8VQBB8rlcgXrvOh2q4fL5SIY9PGtb3m57DI/4AH8bxv/kZS/1YLKaWJiYqLsjIBDOIRD\nOIRDOIRDOISZxKZNChdeqIpjp55qPVZwlkgkMiOeaIc40CEcwiEcwiEcwiH89aCUxX/g4DnQP1wG\n1iEcwiEcwiEcwiEcwtuNCy54a/3HDuEQDuEQDuEQDuEQ/hbxdqZE/cMJWP39/TPa4Ukgm83yxhtv\nAHDkkUeqpUWv/5iuSBfzq+dz1pqzeOi5h/jiM1+EetRMumJMwGWrL+Pq46+m+QAKRBVFIZlM4nA4\nLP1wrnryKr63/XtTvhM6uBwuzj/ifO752D3T/vwDhdG5ern/Zc1AFdRSxo8v+fjbNicr/L3N9+2G\noig82/EshzUdxopvrzAspXQ73GTz5ZlnS5LEmavO5Mev/5j7TryPy391OZ9/3+f55p+/WVJS8cNT\nf8hHF31UM1B1Op38au+vDMsvfnjqDzlm7jFaKvV4dpwV35qcbxIYRy0tbACP08M9H7mHy7ZeppZz\nC0uoKJBGLe+umJrvVz78FeZWzeVTP/sUmz+5mffXv1/rUrLQwrkwn8/z6quvArB69WpLz5FYLMbe\nvXtxuVysWbPG8hzm83lee+01FEVh5cqVtt5kExMTtLe3l3VsUH/zN954g1wux+LFiw1T043w+uuv\nk8vlWLBggW0HRQFZlrnvvjf44hfz3HPPMq66qoKrr4b77itNrf7hD+Hjutswn8+zY8eOaX+mHkND\nQ/T39+Pz+VixYqqzxxNPwPr19nMQUBRFM7Q2w733ql4PRou+JMFXvgJXXFH4ejabZceOHSiKQltb\nm2Fr66EhWLHCuAuPx2NdmqhHLKbQ0rIfiACtqIubeny7FPGZmsPBIp1OMzIywtjYGA0NDcyZM4cn\nnhBm63HAx9atTsPf8O8RM8mBQqEQ3d3dzJo1C4/HU1DuoF8nZ3lncZh0GM+8/Azf3ftd9VIxwih8\nesWnWbdqHcFgkNWrV1veH6FQiK6uLrxeLytXrtTKzvSG5QKiHMPr9XJ/9/2WHOjsxWdzxZorqK2t\n1Xz5zJBIJEin0wQCAVuvSdFGvbh0uZhTNAebuejxi7T3/9b4xCEOZI2Z5kAC9338Pi5/4nJ+eNoP\n8Tg9przmhIUnaCV4gCUH+peWfyGZTNLY2MhwfHiKA0VQSwgDQJXKgW49+laufvRq1f5RcKCxyX9X\nou1rJEnihqNuwBvzcvMfb2bzBZs53Hs4ExMTBV6XRhDcw+PxsGrVKsvzMTg4yMDAADU1NSyw8Q+I\nRqPs27evbE4zMDDA4OBgWccGtfTuzTffxOFwsGbNGttGPaCuPTt37kSSJFavXm3pl1b8XTZt2seN\nNzr51rfWcNllUtkcKJFIsHv3biRJYtWqVQfkAdre3s7ExAQNDQ3MmzdPe326HEiWZdty9gPhQOJZ\n73A4WL58uSHfnSn+EYnkmDPn/4BuYCWwRju+EQcaHx9n//79BAIBqquXWs7hxRfHicXUsUuXLrWc\nh6Iomm1QudeRQCwWY3h4mEgkwsKFC6muruanP01y7rkJVG+zGrZudRwUB0omkwXNQ94O/MMJWIFA\nYEZMZYuRzWa1jhPP9T5XYlB64ws3wiDqhthDiYAlIeFp9vAf6//DtJPhxMQEIyMjBINBQ4Erm81q\nD6ajjjrKdK5LWpaQd5t09kNm6aylb8k5MoKRaeWX//Rlrn7f1eCBTads4sLHLsThLTVd/Gvg722+\nfw1s2bGFM356BuvXrCfnyqnl8UWQJZnzDz+f723/nvaay+FCzsslRrUep4d7T7mXH531IwAu+5fL\nALj2mGtL2puLe0cvoKw9Yi0fWPwB07ENDWpThDrqeGT9I6zdspZMOoPT60T2y3j8Hh5e97BK6hqm\nfmOn5ESemPSVEPc1qjfFF//wRfV/PLDhiQ2Qh9c+/RoL6xbaXhfvfe97icfjtuU+brebJUuWAPZm\n7+l0mvr6enK5XFlm3qJTYUNDQ1nXcSwW04zkm5uby/KW0v/NrFmzyiJ8oPqmffSjXv71X32sWtXI\nWWepHViyWZXkCP/ebFYlU11dU0QkFArhdrsJBALMnj172h5YwjPG7/fT1tamnZuhITj33PLmIDA8\nPMzg4CBz5841/U0+/Wn48peNjTs9HvV9o5/nsMMOY3x8XGscUoxHHpmaYzFyOXj0UeN20cUIh3u5\n++4MV19dAdQBAR5/HGz2/DM6hwNFNBplaGhIM+r1er0oikIgEEBw6k2bAlx4ITgcfz3PqpnGTHKg\neDyOz+cjHA5TV1eH3++noqLCkAMpYwr0orJKA51HQsIzz8NXL/oqw/uHNc8m/b0xNDQ06Q+jdjn1\n+XyMjIwgSZJmON7d3U1lZWUJ4ff5fHi9XqqqqliSseZAa5asseRQehzsuTTiFA7J8TfLJw5xIHu8\nFRyo+6pumgJNGv8BLHmNXqS240ACwWBwigNNTHKgQBEHmqP7jXFMdZr0oe0YnZKTr/35a6pvqeBA\nMmy/cDtzZs0xFXrF87eurq7A1Nxs7KxZs7Rnjt3YXC5HbW2t5Vh9UEns6WbNmmU4vjgANTExoXV3\nLg7gmQWrotEofr+fqqoqje/ZNeIC1dvpYx/zc/bZdcyfX8FppzksOVBnp0JTk6KZzPv9furq6gwD\nePomFUbikvDUFRxICGCjo05LDtTZqdDYWHjcjo4O0uk08+fPL2heo5/Dpz/ttORAF16o4PWqjZTE\ncQOBgNZoQb9+CJFHkiQeecRpyT8eeUThqqusBaF8Pk9v717uvLOSa65ZhSpgWXMgce4qKip45JGA\n5RwefzzLiSf68fv9ts/S8fFx2tvbCQQCLF++3HIsqOciFAoxPDys+cH5/X7tHqyoUD9v0yZmhAP1\n9vYSi8XweDyGQdW3Av9wAtZbBfGAerr9ab60+0tTXTwM2giLTmdWLaiNkEqlGB8fN910mXXg0aO3\ntxdf3Icz50RxKqWd/RJuDs8fTldXl9YGWw9Zltm+fTtOp5PDDz/c8LO6uroIh8PMnj3bMNIiMgTG\nM+Oc/rRxe9+7nrqL35z2G1bMXoFys3HO4a5duwBYuHChocKeSqXo6OjA5/OZZr2Ew2GGh4eprq42\nbWk6OjrK+Pg4OU/OtB3xXc/exYvnvMjSuUu54GbjGpFIJEIkEqGystL0BhbZAD6fTxNWjCCMCWtq\nakwfrslkkkQioT0szRCNRpEkCb/fbxkNicfjWlcgKG33/KH5H+I9m96jGoI64MHXHzQ9llNy0jeh\ndl4TZOjao6/l7j/eXRIlNLsvmoPNXHN0eTvccseevPRkuq7s4nsvfY+9A3tZ2LiQi953kfb54loc\nS4xx/bPXTxm/6/iYoVmqAxY2LyTgsV8BXC5XWV41Ho+n7ExNr9fL8uXLy+qoAmonmGAwaJtRICAW\nPZ/PV7YoND4+DkB1dXXZ4hWgGcEKYrJ58xRpKpyT+voDD0wJIcPDasvJhoaGAzJwD4VCZLNZ3G53\nATGazhxgqutOLpczbLYh0NysdtpZu7Y0qvnww+YkqbKy0jILrrNTPZZRp2ynE/bvN/1TDcPDwwwN\nDU0SsDY2bQpy4YXG0cS3ag4HglAopJnzClRXV9PU1KRt+k49deq3PFTyZ46mpiZSqRQjIyN0dnbi\ndrv53cDvuOGVG0o5UCUq95FRM1z95hxIrpfp7+/XhDGBeDzO+Pi4dm07nU6WLl1KRUUFDodDe74Z\nPU+cTif19fV0dXWpHAgnCgYcaNzNEcoRjI2NaZ3E9BgfH6ejo4PKykotgFCMnTt3ks1mWbJkieHa\nGw6H6e7uJu1Mc/rPSjmFoii4x90szC5k/Mpxw4y5dDrN3r178Xg8LF26VOteGwgEtGfb2NgYw8PD\n1NTUaN29hAifyWSor6+nr6+PeDxOc3NzybqTz+e1bIaamhoclQ5TDnTnE3fyxCeeYHFwsSlnGx5W\nhcmqqiqtI2Qxz5yYmCAWixEMBqmqqiKTyWhdWkUmjCzLDA8P4/V6C66PdDpNT08PDoeDhQsXMjEx\ngSzLhtlxqVSK7u5uHA4HDQ0NeDweLQhdjGQyyf79+8nlcqxatUrjSYYc6NvvUbsgu+05UNdwFwzB\nV4//Kl987YuWHKhCqeD111/H4/Fo5slmvCYWi9HR0YHH49E2s2Zj+/v7+cMf/kAwGOTEE0/UONCm\nP29i3+A+ls1dxvnvOJ+mQBPRaJRX/vUVfD4ft3/kdq7/1fXqQSQKdouyIqsNdEZRG/Q2AE5YPGux\nIaeYmJhg3759+P1+VqxYgdfrNc0Sj0QitLe3U1FRwfLlyy25bSQSYd++fdqmvrq62pQDFY9dvHix\nqW9g8VhQeZvX6y0RhYzGCoTDYQBtP1COCJHP5xkfHycajWrZn48/vtySf9x/f4QTT2zH5/NphuKN\njY2m58FqDoJD1dTUkEql2LFjB4FAoOw5iOPG43Ht+xffc8VzsOJAXm+EV18tna/R99Mft7NzuSX/\n2L3b+Lh6dHZ2Tjas8gLL2bTJZ8uBqqurOeKII5AkyZYDdXUdeJMhMyiKwuDgICMjIwUdaOvq6mhq\natK65f4jcKBDAlaZECnDX3juC0izSlvWFmPzJzczFB+yjIYYfQaYC1TlKPcPvfQQ1/7qWq46+Sq+\n/eq3SxbK7578Xer8daafITZb+XzecozVpkxs2n6+8+emrYCzuSw/2/EzlrUuMzmKSir0rauLITrL\nWCGdThOLxSxLquLxOJFIhJ/1/sx8vqksP/rzj9jYsNH0OLFYjJGREQBTASuVSjE0NERFRYWlgNXX\n10c+n6eystJUwIpEIvT19VFXV2eZAt3T00MymWTx4sWmwomiKOzevRtFUVizZg1P7X+qJArrkibn\nMTT5R02YPkFkReYIzxHceeqdzJ8/XxP9rnjPFSVRwgqlgl27dlFZWWmaUSIgWrnW1tbalsmIbpqV\nlZUFJbfNwWY2HlP4O4quLRUVFTidTjYcvoEbn76RjJIpIG8iWvrdk7/LeT87TxOrHz/r8bLEq7ca\n5Yo2TqdzWuV1lZWV0xLIQN38ut1uSwJaDFmWtQ6XYuNSrhCSSCSIx+NIkmR5b1lhaGhIm7v+XE5X\njFGFn5ytUA1w8slqBtcDD6jHWbAANmwoFa9EZ5tyRMe2NpUEGkGW7bs5ihbpAOeeO4drr1V/i+kQ\nnYOdw4FCdAFyOBzU19fT1NRkWXJ/CNaYN2+e1lFu6x+38o2ubyA1GnAgCbX0KAXEYPNZ5hyooaGB\nioqKkme4kUAlyqTAPognyzI/+b+fcMOvb+D606/n3j/dW8KBvv6xr1Prq8XhcJDP55FluaDMJp/P\nawFLgXg8TjQapbKykkAgoHVVNZpHX18fnZ2dyLLME4NPmHKKXDbHpmc2cXH6Yt7xjneUPCdzuVxB\n5+TXX38dRVFYvXq1xmfS6TSJRKIker9nzx5A3UwlEgmi0aihWAfqJq29vZ22tjae6njKnAMlsmx+\nbjOXHn8pixYtMuSh4XBY41vDw8NIkkRra2vBeZqYmGBoaIjm5mbt9xdd2gSPSafT9Pf3lwQSQOU9\n4rNFWcz8+fMNn7PRyb7zIgvzyCOPNL12xsbG6O3tRZIk1qxZY5iJ5pJcalBrGM1+wAy5fI4V+RV8\n5pjPsHj+Yr7wb18AjDmQElPYu3cvoVDItqRVCJLxeNw2MDQ2NqZ1c9Rf483BZr5w7BcKxmYyGSYm\nJhgeHsbv97PhyA3c+Isb1a6FuiVHcKC7PnEXl/+/y7X3rDiQJEkFWTdWEEK11R5DPxYoOK7Z7yte\nF2PdbrfpPVE8FlTBpLGxsYQDGY0VaG1tJRwOa1zLaL7FEB0dvV4vHo8HRVFs+Ud3t3pckVnv9/sL\nnpt6iDkYcblsNsvYmFoz2tLSQm4yfaicOQgxRhy3t7cXgPr6ek00ERDnTIy14kCRyNTYaDRKIBAw\nve71x7XjH/PnF86hGH19fYTDYSRJ4uKLF/H5z6scwo4D6TPF7Odgfazi45Y7bnx8XAvENjU10dDQ\nULKPFN0ofT7ftDj63xIOCVhloCPcwaJ7FqmeOV7MxSsF3jn7nbyUe4mAJ8A1hxdGQ/r6+kgmkzQ3\nNxtGz62ii0bv66NDlZ5Kbtt2m1rGCNzzp3vAAxvfv5GJ9IS2UGbCGYaGhkw/oxyRTCwsZtk84v2B\n+ABOyWmYpeZUnPRF+yxTN8X3NRsjHq5W9bZCgbaqAxfH6Yv3HfB89Z9ltcEU0RGrMbIsFyyydsey\n8zsqZ1wmk9HEwnAmbBiFzSpZnHknMpNPZKd5Srzb4ebjbR8nk8kUXEtGUcKRkRESiURZtfqRSIRw\nOIzP57MVsMbGxhgbG2P27NladNoM8XicPXv24PF4WLNmDc3BZr534vc47/vnkXPncDkLswgGBwZh\nEO4+7W6ufu5q+nv7mWiZsJyToihadKi5udnyHhPt2SsrK20FC3Gv2HkNzASmk9U0nQwyAafTyWGH\nHaa1XIbyhRDR4re2tvaAfB8mJiY04aM4ujcdMSabzWpC2Jw5c8o6Z83N1uV0iqKwf/9+0uk0ixYt\nsr32N2xQfSWM0vLdbvV9M8TjcfZPKnINDQ2mmat2OJg5lItEIsHw8DANDQ0aYW9qasLj8dDQ0PC2\n3BP/6JAkCWrhI7d/RPUErDDhQArgh9V1q3nD+wYV7oqSZ/2+ffsAaGtrMwym2AlUxfykhAP99jbo\nAXJw2x9uA6mUA411j5FIJAiHw+zfv5+ampqCDG4jfjM6Osro6CgtLS0EAgFLDpRKpYjFYkiSRF/M\nmlOMxEdwOBykUilDAQum+I3X69XanItnoxEHkiQJt9utZWxZ8SSHw4HL5SKXy5HNZumN9ZrO1yW5\nGE2M4nK5yGazhnxCcCCxYRWlXfrncTEnE/MSooXT6TTlSWJsPp8nn89b8ikxVvhlut1u0zXX5XKR\nyWSQZRmfz8dQbMiUAzlwkGfq/JhyIMXNx5d8nPGB8YLPMuJA+4f3k0wmbTN2Qb0W4/G4Jr5aob+/\nn5GRETKZjHbezTA2Nsabb77Jjh07mDdvHh8IfoBvH/9tPrv1s+Q8uZJMyj079kASbjjuBm7ZcQuD\nA4Mk5yVLxAqYul/j8Tjt7e1UV1ebBnb0JXuRSIRgMGj6HBdj9fzVDOWIR+WMLf4MK0GouJy7WLgx\nQmVlJWvWrGF0dJSBgQHy+XzZYowoWTTLvrKbw/DwMIqiEAwGCQQCGqcqRxBqa5s6biQSIRaL4XA4\nmD17dllzMONAYmwmk2Hfvn243W6WLl1qeM/rfxs7/nH22RKTRQIlGBkZYXBQ3UzPnz8fr9fL0NAQ\nLpfLVPQ0gt0cPvUpL05nS9mVEFD6u0UiEUZGRliwYIF2n8yePRtZlqmtrTW9J0SmelNT0yEB6x8Z\nzYFmtcbd5rqVJIn3z3s/L6x/wfCCFFE8sxugXPImSVJJdEgzKxXX9uQhbvzgjQVRke6xbsBefLIi\n/uUKWHNr5iKPGT/xZFlmTuUc02MI0iVJkinpEGOs5jodkautrg15n/V8rTbG5Yhl5YwRpMzlclmK\nHOWIYXpCZDUulUqxrWcbH178YTa/utk0Citn1GP91/H/xX/s+A/TlPgta7dQGVdFWrvsh1QqVdY4\nQMu4MyJJxdDXfRe/LkoYxINbjNU/yP9l1r/wi7N/wfNjzxN2hwuyCPY59vHSRS8xb9481i5fq0WC\nrYSFRCJBJBIhHo/bCmrhcJj+/n7b7DpQs2U6Ozupq6ujra3NciyokTF9WYUdUqkUbrf7bRMDirPD\nyhFCFAW2b2/hgx+sxuE4sLRsIToZCR/TEWME6QwEAgdkIl8MRYEf/WiAZcuSuN2ussjGgZYmAlrp\nT1VVFa2tZm7c9jiYORhBUeCpp+CEExQmJiKaXxKoa6MQsLxe7wE1STkEc8yumg0tqDxIQSsRLIbk\nlDjhXSfw+rmvGx5HZMLoSbh+42kWxBsbG9OyGsX7phwojsp/soCnlAON5NUsab/fr2UM6WEUxNOv\nE/pMEqP12e/3k8/nyeVytNa0Iveac4rZtbNxOp3aGlj8PkzxG72AJWDGb7xeL9lslkwmY8uB3G63\nNt8FdQuMS+TzIOdlWqpbyhKwPB5PgTCm5zvFvMXhcOB0OpFlmWw2WyBgFfMkp9OpZfOIDDX9sYrH\nijm5XC7TjBQxNpPJ8OrQqxztPNqSAymy+tq/H/3vfDP0Ta79F2MOtPnEzTRkG4i741pGnxm3F9m1\nPp/Ptrohl8vhdDpxu92WY2VZJpPJ4Ha78Xg8BWPD4TCyLFNVVaWdu0QioTVHEL/j+1rexy/O/gUv\nJl5kMDdYwIFeDL/IA598gMWLF3Pue84lkUiQSqUMuZn4LWKxmPbfdgLWyMgI+/bto7W11VSQ0YTs\noSG2b9/O3LlzbcfKskx7ezvBYJDGxkbDe7hYlBJWHUa/n1UGltlx7cZ6PB6qqqoYGBhAURRb/nHO\nORJjY9DVNY+jj24lGDSvBjATsPL5vFZBItZP/XkoRxAKh9XjiJJgEUwqdw5m81UU+NnPejnppBYC\nAY8tb1UUxZZ/NDbC+LjxHIT9xezZs6mvr2diYoLe3l78fr+tgCWqbDweD7NmzbKcQ2urD5hjew6m\nvhf8/vewdGmesbFRrWQb1DVSZG+WY1Hyj4BDAlYZCHgCPHbmY5zy0CmmYyQkXD4X645cZ5tBZbaI\n2WVgiYdeKBUy9FVQD6JNyDClVyxiB5OBZWUAqP+MU1eeyn3779PmKSAh4cLFSUtPOighrRxxqlwB\na1vPNs55zzl87aWvGc9XUec7U+LUwWZpTfdYVtFHgK2vbeVzv/oc3/jkN+jMd5pHYRUXn1z5Sc46\n4ixuWnsTYJwSH5SC7Nq1C5fLZduRQpB3O1Eqn89rD2u7sYqiaMct3vCPj48zMDBAfX29JvgIAUsf\nLUskEtRX1HP1qqtLyhji8bg2Xng22Zkwio2SFZEWEKUPYuzQkOrD1NmpZgNt2DBlGj4xMYGiKGV1\n/hAkJZ/Pl2202NnZSSKRYOHChWULMl1dXQSDQWpra8v2vzIj+OUIIVu2wBlnwJYtfk4/vayPK0Fr\nayvDw8OG4ke5YkwqlWJ0dBTAthy2XDz4YIINGwa59Va4+OLWsju8lFuaWAzRca6mpuaAfMRmYg5G\n+MlPZM46a5Q77xzmmGPU55okSWV1kjuEg0PAE+Cx9Y9xyv9OciCjWIMELp+LM448w/AY+o2CuK6G\nhoYYGhqira2Nqqoq0yCeLMskk0ni8TiBQICx5BinP2zCgdyoPlwZePxTpRxIfEYwGESSJE3oEeuo\nEb/RC1j6zacRPxECVjqdZt3h67hr510lnIK8mtH00aUfxel0FohSAkYZWEBZApb4LnYZWDBVsrWt\ncxvnn3o+X/r9l0zne9zi43A4HBqv0KM4c9ztdmsClh5GPMnlcmkdrMCa2wgBLZVKaZ9ntkkWmVUe\nj8cyA93hcPDbjt9y5wt3svDwhXSmLTgQLo5ZeAwnrziZuz58F26325ADEYfu7m7tc2VZNvwNBFdx\nOBza9zDr3iYCeD6fj1wup4mpRmusfqwQCMXYkZERotEobW1t2oY8Ho9rPk9ibDKZpL6ino3v3Vjw\ne6VSKRRFIZ1Ok0wmtXmbcSAxv2g0SnV1tSUHEtej4FiiWsWIA9XUTB3XLiCnzwLL5XJEo1HTdUMv\nSsmyrHUfNOrqZ5SBlclktACkPqhpJ9zoOZBe7LLnHxI/+QnccINCfX2VJQcyE9wcDgeLFy8mFApp\nAoh+vuXMIRxW+bXw2zXL3p6ugPWzn4X46lfj+P0OrrjCvO5uOqWJ0ag5t1m8eLHWtXi6yGQympH+\nrFmzZowDSZLEk09muemmQSKRNMceO7VHbmhomJFg6d8bDglYZUK0wxWG1C6Hi7ySLzRi/OzDHL30\naNNjTCfDygjipnx8z+OG0SHxvx9Z+BGejT9r2NrXTqCaTgaW1TEUReHN0JtsPX1riY+A2+HmrhPu\nos5fZ5uBdbDiVDljntj9BBuf2Uj9/HoeXvcwa7esLZnvrcfdSp2/7m3NwLITsKyij8VjBIkyNWZX\nA+N87pnPQbUq2hlBzqqZaMWeUsUp8ULUmU5WVTmZWqJUzq5ETD+2+PwYZVsZvSZIYjEpE5sCYYxv\nJH4ZoVjAMhOl8vm8NrayspLHH4fTTy8kDTfdpJKGk0+eErvsysrEHPL5PG63u6wstmw2WyDWlYN4\nPM7o6CihUGha3UgGBgaIRCLMmjWrZDE2IwGxmBoBhBzgYt1kl/f2djDp62AKr9db0DK6GHZERFHg\npz+NsGSJQk2NNUkvBx0dsGiRAnQCChs31rJxY+20vptdaaJAcXBlOmnyMzUHM0ydhzeB1OSxXLzw\nQgPvelfTAZWLHsL0kc1nIWjBgXxuHt7wMO9e+m4URSEcDpNMJpkzR40w6zcr+tKfbDbL6OgoVVVV\npkG8mpoaenp6NH+lx3Y9Zs6B3PDe2e/lT5k/WXIg8QwU3nl68aB4DiL7IpfLaeuVw+Ew5Gp+v59c\nLsefu/7MBz7wAUNO4XK4uO3422ipadFKCItR3DLdSMAya6suxupFHrP7xOFw8Oe+P3PfzvtY/M7F\nxvNlcr4V6obUqBxNvOZ0OnE4HLjdbq2jmR5G/MbtdmtdKfXHMuI2ojxSrEtWmepC7JJlWTuWKQd6\nU/2bS391KVRZc6CmBjWzRJRHGnGg7rFujSOAuYAlyt/038NOwAoGg1qWiCzLht9f8BJ9NkYmk8Hn\n85XwnWw2q2Wq6TOyqqurSzLoQF3jFUVhbGyMfD7PEUccoXk2GUHMT2QwWnGgujoHyWRS86bz+Xym\nHOihhxw0N2dIp9NIkmQrjAHaM6S6utp0r6UXj/QBQqN7yCirKhwOMzY2RiaTKeBldhlYe/fuxeFw\nMHfu3LLFmFgM1K+tAIotB7ISj4LBYME5nI4gFI+rmVLPPTfBiSeq4o3Z/q7cwJi69qdRTedcXH/9\nXK6/3mv73fQox54BKBCCi31Upyu4lTuHfD5PNpvVuuyaQT0PGaAd8HDddVWAjz//uYkjj6yfVpMk\nq3n+veGQgFUmTl1xqtZ55YIjL2AoNmTbsrYYB5uB5fV6mTVrFmO7x4yjQ4paj1/jq0G+1nhRsxOw\nZsoD69mOZ7lh2w1suWgLXVd2lZyrgX1qhy4zYcmMmOlxsAJWR7iDRfcu0nzDzvnZOeCAFy98kee7\nn9fme/aqsxnYN2D5WSKlHd6eDKxcLmcZfRTQi1ympqSAsLUS7aDdDnfJBkFCwp13c9LSk2x9t8Tn\n2olSomygnLHlZmqBefmg/j1B3mRZNszWMivH0/+9aDQgIpdW0AtYVqLUBz8Y54UXFD74QTeRiI/T\nT59K2xa8J5NRI2Fvvpnit7/NcPTRUlnZWqKEp9wUYzE+EAiULRSIzjPT7T4oCJ8ZSTAiAaqmFgP2\noNZ4z9fGvhWwIkNbt8LZZzezeXOQ008/+HJL9TsMo9ZruYBW3eszi66uLjKZDIsWLSq7VNTqOptJ\nqMeUUGvYBoFmoI7DD3dwSLt6+1DMgQajg/zglR/QHe0u4UDpdFrzUauvr8fn8xlmYNXX1zM8PKx2\nAdatacUcyePxaH4yfr+foeyQKQdyep1UeavYfuZ2Dl9xeMn30HOcQCCgCVhCbDfKwHI4HPh8PpLJ\npBYwsOJpf+j+A3e+cCeLjlrEZz78mRIOdPrS0xntGtV4jpGAVWyRcCAZWELkcTgchvPtCHew7L5l\n0AsEYd3D6u63mAOd0noK0aGotoZZCVhinRD/1o8VWUP6943GluNtJcQcq3VXCFi5XA6v12vNgcSl\nNHnpmXEgV97FMQuOsS3hK+YrYg5m40QHOSsfLL2FQjQa1RoqGa3NerFLZGCJsktZlpEkSeNcgtME\ng0HtOhEZOUbQZ2sJ4dEq6OFwOLTPBZU7mXGgn/zEgdeb4NVX4T3vqWBoCFMOdMYZEg88kGD7doWj\njqqY6h5psDbV1095a9XX11sG/PRZVUIoNMtwMcpoKu4+WDzWiONkMhnt2TJ//nzteHY+USoHGgH2\noq6NU2Ot5jsdMaZcr6pnn4Wbb57LvHnzOOooc45Z7hzU79CHKs5VAI261w/8uMVjZVlm9+7dVFVV\nGWbOG+3Z7fhPOXMQ3rt+v5+VK1eajlOPG0DltwqwGKhm5Uo4AO3qLUFDQwPV1dVl7c9mCocErAOE\nWctaK9gJWK2trQXKezF8Ph+zZs3CEXBM+T0UfIDa/W1O5RxTciU26Gaih8vloqqqytJnRZiYmopC\ndy+CBOCbIkTtn2tnYe2UZB7yhnA6nZabJZ/PZ5sSbFeiJkmSZmpajOZAs0paxOmePGWrmlbx7rnv\n1sYlk0kGGMDlcll2RBSfd7Cm8mb+D2ZjyvHJmshNmJqSuhwujQjjUktPAcNMtLs+ombO2Yk1gpSV\nO87j8dhunKfjfyXGFl/HItKof0+MFb4ddhCbgoqKirKzqpLJFM8/n+P973cQjVZYilL/9V9Rrr8e\n7ruvkkQCy9bF1147wcMPw733BnnHOxy22VrClLOcbC2Y8gKYTk29GXmzgmj77nA4pvVZgQDcf/8o\nF12kRh8BHn9cfb1cgaWvr49UKsWsWbMOyMxSjY5N/f+GDQE2bDiwLDA93O4M99zTz1VXgeqT4NK+\n20xiYGCAsbExJEkikUgYNhgpht11NhMIhdQ1orq6msceg1NOqQfqAOktOQ+HUD4SiQRj3WP8a9O/\nsvyDpS3IfT4fNTU1jI+PMzg4SFtbm6GAVVFRQUVFBYlEglAoxMqVK01LiWtqaojFYvzf8P+xdPZS\n5DcNNvkKyE6ZlqoWLVuqeL2oqqrSMlwCgQAjIyPaMx3UNauysrJk7RIBi2QySWVlpeF61RHuYNE3\nFkEH4IJLHruES56/hPbPtRfwxWQySaIigcPh0Ay5izNdnE6nZj4u5gWFApbgUMVz0WdgWWUoNQea\nVf4jgT6ZrZgDjYyMECWqCR5GJYTlCFjiv4u5lFh3hcBhJ2A5HI6CddsMojQRIJKNmDenwYnskNUA\nngUHcikubjvuNhodjbZrqOA2VVVVuN1uS/8rUO8Z/Xytxvr9fgKBgGW3cH0Qr6GhQRNE9AE48bf6\n1yorK20N4kUHxPr6eqLRKIqiaBnaxuuuKqDu2RPkqKMCDA9Lphxo3ToHl1wi8Y1vVLJ0aZDxcSsO\n5OR//gd+/etKFi2qZMUK87VpyxYHS5ZU4Ha7URTFkmcIHiJJkhbEMxOwhGen3mxcPE+K/0aMNbof\nRdVCZWWlVn5bU1Njy4sDAbjnnjBXXVWBuj6q5yAWg29/u5T/uFwuamtrC467d+9efD4fLS0tJc+g\nujrzShkBlQO5Jj/fyQUX1HLBBeYcyOVyUVdXZ8u3c7kIt98e57rrqoHl2nczW/vFccsJtooOpy6X\ni46ODq2JQnNzs+nfa1VQFvznmGOmn9lkJHbl83kGBwdpaGggEPDw2GN+Tjnl/YgN698aB5rJrP1y\ncUjAmiHIsswbb7yhteA1WlTsMqz07xWnOW84fAPNwWa27tzKpu2bjDufOCXcs9xc82/mwppI5zdD\nVVWV7cK8ZMkS7b+L53naytNUobjoxmoOFO4cly8vJb161NTU2Nb0zp8/n/k2fUhXr15tOtcNh29Q\nfT0eOkWLvhn5hvn9fo466ijLBd3j8XDEEUdMCUEmWLVqFZlMxlLYmTNnjm09v9frZcmSJbZmkKKF\n7f+8+j/mxux5GQJwx7F3cO0L15KRM5y64lTDzDl5QtZar1rB5/MVmKSbQXT9sRO6oLTDkRWsDNzF\n/PRp7VAodlllB+pL6vQZSlaL2uBgjM99Du69t4JMRjIlZOk0XH+9Kopdfrkqirlcxq2L83l4+GE1\nYnfllVVceSV4PFNkr1gY2707zXPPpTj6aEm7x61Ennw+r0UEy62vTyQSByRECfI2Hc8sUK+f8XFV\nMPvWtxq47DL1+5YrsMiyzMjICLIsH5DfAYjzlULd/biLXj9wuFwuAoEmIM6mTQ1ceKH63WYKigJb\nt46xcGE/kqQGUMoRr6wi4mvXqiUGB/Pds9ksXV1dRCIR3G43q1atIptVCfSmTdKMnwcrvF1ZZn9v\ncLvdZDIZ8vk8o6OjNDQ0MDExwf79+wkEAixevJiWlhbGx8cJhUIFnaiKuVF9fb0qiE2a0Gr+WEXr\n9RnLz1Azu399A/99wX/jdrhLvZp84Jnt4aLjLwKmunLpsUinNovAgyiJkiSJ5uZmQx+8iooKxsbG\nUBSFpUuXaq/r5zm7cvJ71qJyoMllrZj/+P1+VqxYAcD+/fs13x89Zs+eXXDevF4vTU1NWjabJEmm\nUXu/3691z9Lf00Yc6NGLHuXUH56qZV8bcaDGxkbq6+stbQuE34/gJOI86tdRn8/H6tWrS7jU3Llq\n1ob47ZcsWaKVuxWjra0NSZLIZDKa8bgZFi1aRENDA6lUiu+9+T1rDjQX7jjuDq590ZwDnbXyLJSY\nmm1v1TBFURQqKytJpVIsXbrUUgAQ5UMiQG0FPQfS83Cjz9cH8Y4//njtPWGwbWahsGDBArLZrCm3\n1B97+fLlWmdROw7U2dnCl7/sZs6caiYmzEWpTAa+8Y1GIM/FF9cA5hxIUSR+/esGoIbPfraaz37W\nnAOtW+fgL39pors7wZIlFdp1Y/yMd7J48WKi0Sjj4+OTa7GxWuB0OgueKSKAJ4QoPVwuV8FYPcbG\nxgA0v1WrsXpEo1Gy2Twwn/vvP4yLLoIXXij1qpriP+6CrquxWIyJiQmi0WiJZ5XH47FtIgRiTcyg\nZgg5il4vRbnHVYXaZqCVTZvm2K79Xq+3rOOKsW1tC9i8uYtVqyZwOtWMQyPxSr9m2fGfnTvV/z/Q\nckNQ16POzk6tG/mSJUtQb32JTZuYUQ5kNc+/df5zSMCaIehLyOxKBO1qT43SnG987kYy2YwqtDgg\nx+RnIZW0t51bOzPmwXYwmudNv7mJmz54E19+/stT4wwI0dsNs7le/b6rAdj0SdXXw8gzA+wzqwDb\njDKYMje1gsdj32XD6XSWlUVTUVHB7wd+T1/KvJ23y+Hiog9dxDUfu4ZrPjYlfhpmGZb5M86aNcuW\njIEqmK5ataqsY7a1tVl6FOmxaNEiEolECQE2EqtEpE1/zvfs2UMmk2Hx4sUlvgrV1dVa9H54eIRt\n2+DjHw8YLmrpNHziE6DWaDq58kprUUoNg4tsAHXjYa6bKqh97afGWmVrXXNNRJet5SwrWyufz+P1\nestOCz6Q8kHhlwOUmOWX83kf/nCeN97wsWpVkEsvVRfd+fPLE1jGxsY0EfVAO7cEAvDtb3dx6aVx\nYAFQOyPRMYfDwUUXzeEidS/OBRcc3PGKsXlzlPPO6+LWW2H9+payBbzNm62vswceOHDPq9HRUXp7\ne7USl6amJhwOB6eeOvV5M30ezPB2ZJn9vcLtdjN79mx6e3vp6+ujpqZG62QnxIlAIEBlZSXRaJSh\noSHNMLmY/9TV1dHb20sikSCRSKjlRQYdBq979joYAGT47COfhQB4HB5ySm4qS9ipcqDFVYvp7+83\nLHXTw+v10tDQgN/vt+wSB6q4HgwGC56FhiVpDhe52qlAlh3/KXfDJUlS2eufy+UquZ8tOZBvytvM\njAM5HA7bdUDPkYz4kiRJhsGq4rXC5/OZBsnEb1QOTwKVY/xx5I90R7tNOZDb5eaiY8rkQDW2H4kk\nSWX/rmaCqRHWrFmjeVXZff6aNWtIJBIl59uIA7W0tBAMBrXqi1wux+7duzXBUf/75PN5mpqaSKVS\nTExEeeWVPHPnYppZPsWB1HKHSy6x40CJybEuRLcIcw6UQPW/dCDIqdXa9IUvRPj5z+E731HLr+ye\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msyqv6DG4SPJqSVpjRaNmeF28Zgpu4nQ6tedGOp3WNn5CaBNjBMSzPp1O23IgIVxks1nm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\n"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c93cd416-bf6e-4998-8cb4-a63d5b5fdaf2",
+   "metadata": {},
+   "source": [
+    "And then a look at the high latitude region. Note the limits on the axis are slightly different. \n",
+    "\n",
+    "![highlatitude_corner_connections.png](attachment:f2d75913-95bf-43e3-8611-47a2a8eb4c28.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "34b944a0-ac97-4063-b89a-41d4e78da2da",
    "metadata": {},
    "source": [
-    "(The equator-most ghost cells are not shown)\n",
     "\n",
     "\n",
-    "### Order of points\n",
+    "## Order of points\n",
     "\n",
     "The order of points in the dipole grid is the same as the spherical grid. Most simply, the field lines are arranged in the order of increasing invariant latitude & from lowest to highest altitude.\n",
     "\n",
@@ -302,14 +363,6 @@
     "\n",
     "If there are any other questions, contact Aaron B and this file can be updated with more plots :)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2ca9881-8114-4b63-86ca-68b1575ea59d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/edu/examples/Dipole/plots/highlatitude_corner_connections.png b/edu/examples/Dipole/plots/highlatitude_corner_connections.png
new file mode 100644
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z@UiVwUandREmXZtn-vUCeifuO5mUSGp%&bb9M`^IBxVpSXl@b8`8VMt>;N()(D{BH
zcwZ@1z{OHQbb6QI-yGok%H>yh@ds4~CyK;E8aJ4M$JW&gV($Yp1;o$?ywg`j!DsYR
z(RA9S+Uvs`x;7*}WI<Ztr@EC)K0~DWq0>+_A!`&C%PXE=yZ$RR5Pibz(?t&Y9^{}$
zQ}l@wLU~Hu8na=Wbr@I`KuioG$9jib2_B^R2U}HX@-hh@(BFT|dBFqAJ!tf9*W4dX
zIH2|8^QA%9<PB0<q)&))c|t*RZQ7qC=OtP~+=G>Aj)^*umkYBAJHJ(Mi%p+ocXxNR
zO4@lU^sl)|!T8@Rw7d?xrTy9}iB<lkt4j*9FrLs4#cRu#;s)3XWZo4Q--D*<*WKL(
z7bkn9#EyG86qgs<Yb;W|qvz|$7T814_AmF%f?p{Ogo|i6Z8hk>d)Gln*j%^Fl(>sS
z(8jr*JGh_S9<Y|qDrxmF(TFt&?3a--FaOqJc;pEKR#4MFPx(RQl#kokVW2PQ2-4;U
z&24y1jh_66hxp51%!<#-)jR#)uA=I~4E}lPmDm5-4L!Nt^51^yp8-nN9fVo)4|f&4
z1E^I0yp|<mU;StJ^KZA-?AZT&FxUSd5603CjOtHQXpsAff<I>rjP>*MF5mhW#&7=&

literal 0
HcmV?d00001


From 0dc3e6c12b3111961b6fe807830d5c8a0c33ac93 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 17 Apr 2025 10:27:43 -0400
Subject: [PATCH 067/120] BUG/STY: Astyle! And fixing init_mag_grid to work
 better for hi-res

---
 src/init_mag_grid.cpp | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index dcf1a288..6983f761 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -281,10 +281,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-    q_min = pow(((1 - max_alt_re / Pcorners(0)) / pow(max_alt_re, 4.0)), 0.5);
+    q_min = pow(((1 - max_alt_re / Pcenters(0)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats -1)) / pow(
+  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats - 1)) / pow(
                              min_alt_re,
                              4.0)), 0.5);
 
@@ -302,7 +302,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   magQ_corner_1d(nAlts) = q_min + (nAlts - nGCs) * delQ;
 
-  report.print(3, "Done generating points for magnetic grid. Plugging everything in");
+  report.print(3,
+               "Done generating points for magnetic grid. Plugging everything in");
 
   ////////////////////////////
   // That is the grid made. //
@@ -509,20 +510,22 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
   UseThisCell.elem(isTooLowCell).fill(false);
 
-  for (iLon=0; iLon<nLons; iLon++){
-    for (iLat = 0; iLat<nLats; iLat++){
+  for (iLon = 0; iLon < nLons; iLon++) {
+    for (iLat = 0; iLat < nLats; iLat++) {
       // find *last* cell below alt_min
-      first_lower_gc(iLon, iLat) = find(geoAlt_scgc.tube(iLon, iLat) 
-                                        < grid_input.alt_min * cKMtoM).max();
+      first_lower_gc(iLon, iLat) = find(geoAlt_scgc.tube(iLon, iLat) < min_alt).max();
     }
   }
-  if (first_lower_gc.min() < nGCs-1 || first_lower_gc.max() > nAlts-nGCs-1){
+
+  if (first_lower_gc.min() < nGCs - 1 ||
+      first_lower_gc.max() > nAlts - nGCs - 1) {
     report.error("Invalid magnetic grid!! Either:");
     report.error(" - Lowest latitude field line is entirely below min_alt");
     report.error(" - Highest altitude field line is above min_alt");
     report.error("This should not happen. Something is terribly wrong. Goodbye.");
     return false;
   }
+
   first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.print(4, "Done altitude spacing for the dipole grid.");

From 862b40a7ad363eaed937ea7794679ec46d38b2c9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 17 Apr 2025 12:57:39 -0400
Subject: [PATCH 068/120] BUG: Use radial distance for BC's, not k-distance

---
 src/neutrals_bcs.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 985594c6..7d93ef56 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -230,7 +230,7 @@ bool Neutrals::set_lower_bcs(Grid grid,
           temperature_scgc(iLon, iLat, iAlt + 1)
           / temperature_scgc(iLon, iLat, iAlt)
           * species[iSpecies].density_scgc(iLon, iLat, iAlt + 1)
-          * exp(-grid.dk_edge_m(iLon, iLat, iAlt) / sh_ave);
+          * exp(-grid.dr_edge(iLon, iLat, iAlt) / sh_ave);
 
         // Vertical velocities: (In GITM this projected down with mesh coeffs)
         // Take lowest physical cell's vertical velocity and project it down nGCs cells.

From bf53dfe5389e03c86a8187205722ddb414c9f517 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:38:50 -0400
Subject: [PATCH 069/120] BUG: something is wrong with gravity!

---
 src/init_mag_grid.cpp | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 6983f761..3816f867 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -492,10 +492,13 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   gravity_vcgc[2] = - mu * rad_unit_vcgc[2] % radius2i_scgc;
   gravity_potential_scgc.set_size(nX, nY, nAlts);
   gravity_potential_scgc.zeros();
-  gravity_mag_scgc = sqrt(
-                       gravity_vcgc[0] % gravity_vcgc[0] +
-                       gravity_vcgc[1] % gravity_vcgc[1] +
-                       gravity_vcgc[2] % gravity_vcgc[2]);
+
+  gravity_mag_scgc = mu / pow(radius_scgc, 2);
+  
+  //gravity_mag_scgc = sqrt(
+  //                     gravity_vcgc[0] % gravity_vcgc[0] +
+  //                     gravity_vcgc[1] % gravity_vcgc[1] +
+  //                     gravity_vcgc[2] % gravity_vcgc[2]);
 
   report.print(4, "Done gravity calculations for the dipole grid.");
 

From 6d25699b43e25c32ae6e1b30d3a34da701ffcb33 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:39:08 -0400
Subject: [PATCH 070/120] BUG: need to start at the first physical cell

---
 src/neutrals.cpp | 58 ++++++++++++++++++++++++++++++++----------------
 1 file changed, 39 insertions(+), 19 deletions(-)

diff --git a/src/neutrals.cpp b/src/neutrals.cpp
index 471cbb67..823b9828 100644
--- a/src/neutrals.cpp
+++ b/src/neutrals.cpp
@@ -255,17 +255,27 @@ void Neutrals::fill_with_hydrostatic(int64_t iStart,
 
   int64_t iNeutral, iSpecies;
 
+  int64_t iX, iY, iZ;
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t iFirst;
+
   for (iNeutral = 0; iNeutral < nSpeciesAdvect; iNeutral++) {
     iSpecies = species_to_advect[iNeutral];
-
-    // Integrate with hydrostatic equilibrium up:
-    for (int iAlt = iStart; iAlt < iEnd; iAlt++) {
-      species[iSpecies].density_scgc.slice(iAlt) =
-        temperature_scgc.slice(iAlt - 1) /
-        temperature_scgc.slice(iAlt) %
-        species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dr_edge.slice(iAlt) /
-            species[iSpecies].scale_height_scgc.slice(iAlt));
+    for (iX = nGCs; iX < nX - nGCs; iX++) {
+      for (iY = nGCs; iY < nY - nGCs; iY++) {
+        iFirst = grid.first_lower_gc(iX, iY) + iStart;
+        // Integrate with hydrostatic equilibrium up:
+        for (int iAlt = iFirst; iAlt < iEnd; iAlt++) {
+          species[iSpecies].density_scgc(iX, iY, iAlt) =
+            temperature_scgc(iX, iY, iAlt - 1) /
+            temperature_scgc(iX, iY, iAlt) *
+            species[iSpecies].density_scgc(iX, iY, iAlt - 1) *
+            exp(-grid.dr_edge(iX, iY, iAlt) /
+                species[iSpecies].scale_height_scgc(iX, iY, iAlt));
+        }
+      }
     }
   }
 
@@ -283,14 +293,25 @@ void Neutrals::fill_with_hydrostatic(int64_t iSpecies,
                                      int64_t iEnd,
                                      Grid grid) {
 
-  // Integrate with hydrostatic equilibrium up:
-  for (int iAlt = iStart; iAlt < iEnd; iAlt++) {
-    species[iSpecies].density_scgc.slice(iAlt) =
-      temperature_scgc.slice(iAlt - 1) /
-      temperature_scgc.slice(iAlt) %
-      species[iSpecies].density_scgc.slice(iAlt - 1) %
-      exp(-grid.dr_edge.slice(iAlt) /
-          species[iSpecies].scale_height_scgc.slice(iAlt));
+  int64_t iX, iY, iZ;
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t iFirst;
+
+  for (iX = nGCs; iX < nX - nGCs; iX++) {
+    for (iY = nGCs; iY < nY - nGCs; iY++) {
+      iFirst = grid.first_lower_gc(iX, iY) + iStart;
+      // Integrate with hydrostatic equilibrium up:I
+      for (int iAlt = iFirst; iAlt < iEnd; iAlt++) {
+        species[iSpecies].density_scgc(iX, iY, iAlt) =
+          temperature_scgc(iX, iY, iAlt - 1) /
+          temperature_scgc(iX, iY, iAlt) *
+          species[iSpecies].density_scgc(iX, iY, iAlt - 1) *
+          exp(-grid.dr_edge(iX, iY, iAlt) /
+              species[iSpecies].scale_height_scgc(iX, iY, iAlt));
+      }
+    }
   }
 
   calc_mass_density();
@@ -466,8 +487,7 @@ bool Neutrals::restart_file(std::string dir, std::string cGridtype,
         else
           RestartContainer.store_variable(cName,
                                           velocity_unit,
-                                          species[iSpecies].
-                                          velocity_vcgc[iDir]);
+                                          species[iSpecies].velocity_vcgc[iDir]);
       }
     }
 

From f80320ee736f536e21e217d791b8e5ba1f73f0f5 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:39:19 -0400
Subject: [PATCH 071/120] BUG: first GC + nGCs - 1 = 2, I think

---
 src/neutrals_ics.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index a1430d96..8da58901 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -99,7 +99,7 @@ bool Neutrals::initial_conditions(Grid grid,
               std::cout << "  NOT Found in MSIS - setting constant\n";
 
             species[iSpecies].density_scgc.slice(0).
-                                          fill(species[iSpecies].lower_bc_density);
+            fill(species[iSpecies].lower_bc_density);
             fill_with_hydrostatic(iSpecies, 1, nAlts, grid);
           }
 
@@ -174,9 +174,11 @@ bool Neutrals::initial_conditions(Grid grid,
       for (iLon = 0; iLon < nLons; iLon ++) {
         for (iLat = 0; iLat < nLats; iLat++) {
           for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+
             species[iSpecies].density_scgc.subcube(
-              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)+1).fill(
+              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat) + 1).fill(
                 species[iSpecies].lower_bc_density);
+
           }
         }
       }
@@ -188,7 +190,7 @@ bool Neutrals::initial_conditions(Grid grid,
       report.print(2, "Filling with hydrostatic");
 
       for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
-        fill_with_hydrostatic(iSpecies, nGCs, nAlts, grid);
+       fill_with_hydrostatic(iSpecies, nGCs - 1, nAlts, grid);
     } // type = planet
   }
 

From f4c319f008ca60d2e3fc3d81eab3506130061a93 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:39:30 -0400
Subject: [PATCH 072/120] FEAT: output magnitude of gravity

---
 src/output.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/output.cpp b/src/output.cpp
index 61115d97..c9fa6dca 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -89,7 +89,8 @@ bool output(const Neutrals &neutrals,
 
     // make sure the output dt is set correctly. Otherwise these errors aren't caught correctly.
     precision_t dt_output = input.get_dt_output(iOutput);
-    if (dt_output == 0.0){
+
+    if (dt_output == 0.0) {
       report.exit(function);
       return false;
     }
@@ -243,6 +244,9 @@ bool output(const Neutrals &neutrals,
         AllOutputContainers[iOutput].store_variable("Gvertical",
                                                     "m/s^2",
                                                     grid.gravity_vcgc[2]);
+        AllOutputContainers[iOutput].store_variable("Gmag",
+                                                    "m/s^2",
+                                                    grid.gravity_mag_scgc);
         AllOutputContainers[iOutput].store_variable("Gpotential",
                                                     "m^2/s^2",
                                                     grid.gravity_potential_scgc);
@@ -261,9 +265,9 @@ bool output(const Neutrals &neutrals,
                                                     "degrees",
                                                     grid.magLon_scgc * cRtoD);
         AllOutputContainers[iOutput].store_variable("invLat",
-                                                      "Magnetic Invariant Latitude",
-                                                      "degrees",
-                                                      grid.magInvLat_scgc * cRtoD);
+                                                    "Magnetic Invariant Latitude",
+                                                    "degrees",
+                                                    grid.magInvLat_scgc * cRtoD);
         AllOutputContainers[iOutput].store_variable("mlt",
                                                     "Magnetic Local Time",
                                                     "hours",

From 9ea7544d89f0259d80b1ebeeba864f4f83076f2b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 08:06:36 -0400
Subject: [PATCH 073/120] BUG: need sphere4 to go along with dipole4

---
 share/run/aether.json | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index 96f1f08e..56439570 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -8,17 +8,12 @@
         "iFunctionVerbose" : {
             "Grid::create_altitudes": 0},	
         "dt" : 10.0,
-        "check_for_nans" : false
-    },
+        "check_for_nans" : false},
 
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 
-    "GeoBlockSize" : {
-        "nLons" : 18,
-        "nLats" : 18,
-        "nAlts" : 50},
-
     "neuGrid" : {
+        "Shape": "sphere4",
 	"nLonsPerBlock" : 24,
 	"nLatsPerBlock" : 22,
 	"nAlts" : 40,
@@ -28,12 +23,11 @@
     "ionGrid": {
         "Shape": "dipole4",
         "nLonsPerBlock": 36,
-        "nLatsPerBlock" : 18,
-        "nAlts":36,
-        "LatRange":[10, 80],
-        "AltRange":[80.0, 1000],
-		"LonRange" : [0.0, 360.0]
-        },
+        "nLatsPerBlock": 18,
+        "nAlts": 50,
+        "LatRange": [10, 80],
+        "AltRange": [80.0, 1000],
+	"LonRange": [0.0, 360.0] },
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
 

From ae3ce01c9e1aba839afc495d04436ca3bc75325f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 08:42:51 -0400
Subject: [PATCH 074/120] BUG: to capture e-region, need 100 points

---
 share/run/aether.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index 56439570..d10243a7 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -24,7 +24,7 @@
         "Shape": "dipole4",
         "nLonsPerBlock": 36,
         "nLatsPerBlock": 18,
-        "nAlts": 50,
+        "nAlts": 100,
         "LatRange": [10, 80],
         "AltRange": [80.0, 1000],
 	"LonRange": [0.0, 360.0] },

From 3bcd3fa5ad93634e08928416d248f1f5c71669dd Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:40:04 -0400
Subject: [PATCH 075/120] STY: Astyle, yo

---
 src/init_mag_grid.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 3816f867..7deebd46 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -145,7 +145,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   arma_vec lon1dLeft(nLons + 1);
 
-  // if we are not doing anything in the lon direction, then set dlon to
+  // If we are not doing anything in the lon direction, then set dlon to
   // something reasonable:
   if (!HasXdim)
     dlon = 1.0 * cDtoR;
@@ -158,7 +158,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   /////////////////
 
   // - Make a 1d vector
-  // - copy it into the 3d cube
+  // - Copy it into the 3d cube
   for (iLon = 0; iLon < nLons; iLon++) {
     lon1d(iLon) = lon0 + (iLon - nGCs + 0.5) * dlon;
     lon1dLeft(iLon) = lon0 + (iLon - nGCs) * dlon; // corners
@@ -504,9 +504,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   calc_dipole_grid_spacing(planet);
 
-
   //////////////////////////////////////
-  // Generate mask for physicsl cells //
+  // Generate mask for physical cells //
   //////////////////////////////////////
 
   isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);

From e12e25e9486f46dfc23d26418ca76d9b738116b5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:41:01 -0400
Subject: [PATCH 076/120] FEAT: Make ortho dipole4 the default ionGrid in
 settings files

---
 share/run/UA/inputs/defaults.json | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 09ade2c6..f3c5b6e0 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -79,19 +79,14 @@
 	"IsUniformAlt" : true,
         "AltFile" : ""},
 
-    "ionGrid" : {
-        "Shape" : "dipole4",
-        "nLatsPerBlock" : 18,
-		"LonRange" : [0.0, 360.0],
-		"LatRange" : [85.0, 360.0],
-		"AltRange" : [0.0, 360.0],
-        "nLonsPerBlock" : 22,
-        "nAlts" : 50,
-        "MinAlt" : 80.0,
-        "MinApex" : 120.0,
-        "LatMax":88.0,
-        "LatStretch":1.0,
-        "dAltStretch" : 0.6},
+	"ionGrid": {
+		"Shape": "dipole4",
+		"nLonsPerBlock": 14,
+		"nLatsPerBlock" : 20,
+		"nAlts":30,
+		"AltRange":[80,1500],
+		"LatRange":[7, 87]
+		},
 
     "Oblate" : {
 	"isOblate" : false,

From 7b8f0de78f78b02ef8387a3d243e5eb5549ff9cb Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:44:50 -0400
Subject: [PATCH 077/120] STY: Formatting of share/run/UA/inputs/defaults.json

---
 share/run/UA/inputs/defaults.json | 126 +++++++++++++++---------------
 1 file changed, 64 insertions(+), 62 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index f3c5b6e0..4573571a 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -1,38 +1,40 @@
 
 {
     "Debug" : {
-	"iVerbose" : 0,
-	"doInheritVerbose" : false,
-	"dt" : 60.0,
-	"TimingPercent" : 1.0,
-	"iTimingDepth" : 5,
-	"iProc" : 0,
-        "iFunctionVerbose" : {
-            "Grid::create_altitudes": 0},	
-	"check_for_nans" : false,
-	"nan_test" : {
+		"iVerbose" : 0,
+		"doInheritVerbose" : false,
+		"dt" : 60.0,
+		"TimingPercent" : 1.0,
+		"iTimingDepth" : 5,
+		"iProc" : 0,
+		"iFunctionVerbose" : {
+			"Grid::create_altitudes": 0},	
+		"check_for_nans" : false,
+		"nan_test" : {
             "insert" : false,
-            "variable" : "temperature_scgc"} },
+            "variable" : "temperature_scgc"} 
+		},
 
     "InitialConditions" : {
-	"type" : "Planet"},    
+		"type" : "Planet"},    
 
     "BoundaryConditions" : {
-	"type" : "Planet"},
+		"type" : "Planet"},
 
     "Advection" : {
-	"Neutrals" : {
-	    "Vertical" : "rusanov",
-	    "Horizontal" : "default",
-	    "useBulkWinds" : true,
-	    "useImplicitFriction" : true},
-	"Ions" : {
-	    "Along" : "rusanov",
-	    "Across" : "default"} },
+		"Neutrals" : {
+			"Vertical" : "rusanov",
+			"Horizontal" : "default",
+			"useBulkWinds" : true,
+			"useImplicitFriction" : true},
+		"Ions" : {
+			"Along" : "rusanov",
+			"Across" : "default"}
+		},
     
     "Student" : {
-	"name" : "",
-	"is" : false },
+		"name" : "",
+		"is" : false },
 
     "Planet" : {
         "name" : "earth",
@@ -41,42 +43,42 @@
     "BField" : "dipole",
 
     "Electrodynamics" : {
-	"Potential" : "weimer",
-	"DiffuseAurora" : "fta",
-	"Dir" : "UA/inputs/ext/ie/",
-	"File" : ""},
+		"Potential" : "weimer",
+		"DiffuseAurora" : "fta",
+		"Dir" : "UA/inputs/ext/ie/",
+		"File" : ""},
 
     "Euv" : {
-	"doUse" : true,
-	"Model" : "euvac",
-	"File" : "UA/inputs/euv.csv",
-	"IncludePhotoElectrons" : true,
-	"HeatingEfficiency" : 0.05,
-	"dt" : 60.0},
+		"doUse" : true,
+		"Model" : "euvac",
+		"File" : "UA/inputs/euv.csv",
+		"IncludePhotoElectrons" : true,
+		"HeatingEfficiency" : 0.05,
+		"dt" : 60.0},
 
     "DoCalcBulkIonTemp" : false,
     
     "Eddy" : {
-	"Coefficient" : 50.0,
-	"BottomPressure" : 0.01,
-	"TopPressure" : 0.005,
-	"UseInEnergy": true,
-	"UseInMomentum": true},
+		"Coefficient" : 50.0,
+		"BottomPressure" : 0.01,
+		"TopPressure" : 0.005,
+		"UseInEnergy": true,
+		"UseInMomentum": true},
 
     "StartTime" : [2011, 3, 20, 0, 0, 0],
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 
     "neuGrid" : {
         "Shape" : "sphere",
-	"LatRange" : [-90.0, 90.0],
+		"LatRange" : [-90.0, 90.0],
         "nLatsPerBlock" : 18,
-	"LonRange" : [0.0, 360.0],
+		"LonRange" : [0.0, 360.0],
         "nLonsPerBlock" : 20,
         "nAlts" : 40,
-	"MinAlt" : 100.0,
-	"dAltkm" : 5.0,
+		"MinAlt" : 100.0,
+		"dAltkm" : 5.0,
         "dAltScale" : 0.25,
-	"IsUniformAlt" : true,
+		"IsUniformAlt" : true,
         "AltFile" : ""},
 
 	"ionGrid": {
@@ -85,23 +87,23 @@
 		"nLatsPerBlock" : 20,
 		"nAlts":30,
 		"AltRange":[80,1500],
-		"LatRange":[7, 87]
-		},
+		"LatRange":[7, 87]},
 
     "Oblate" : {
-	"isOblate" : false,
+		"isOblate" : false,
         "isJ2" : false},
 
     "Ensembles" : {
         "nMembers" : 1},
 
     "Sources" : {
-	"Grid" : {
-		"Coriolis" : true,
-		"Cent_acc": true },
-	"Neutrals" : {
-	    "NO_cool" : false,
-	    "O_cool": false } },
+		"Grid" : {
+			"Coriolis" : true,
+			"Cent_acc": true },
+		"Neutrals" : {
+		    "NO_cool" : false,
+	    	"O_cool": false } 
+		},
     
     "Seed" : 0,
 
@@ -115,18 +117,18 @@
     "OmniwebFile" : ["UA/inputs/omni_20110319.txt"],
     
     "Logfile" : {
-	"name" : ["UA/output/log_geo.txt", "UA/output/log_mag.txt"],
-	"append" : false,
-	"dt" : 10.0,
-	"species" : ["O2", "O2+"]},
+		"name" : ["UA/output/log_geo.txt", "UA/output/log_mag.txt"],
+		"append" : false,
+		"dt" : 10.0,
+		"species" : ["O2", "O2+"]},
 
     "Outputs" : {
-	"type" : ["states", "grid"],
-	"dt" : [900, -1]},
+		"type" : ["states", "grid"],
+		"dt" : [900, -1]},
 
     "Restart" : {
-	"do" : false,
-	"OutDir" : "UA/restartOut",
-	"InDir" : "UA/restartIn",
-	"dt" : 3600.0}
+		"do" : false,
+		"OutDir" : "UA/restartOut",
+		"InDir" : "UA/restartIn",
+		"dt" : 3600.0}
 }

From 6d5a5cc4588d36da5c8e04ae501b2e186e284589 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:46:33 -0400
Subject: [PATCH 078/120] FEAT: Make sphere4 the default neutral shape. This
 will require mpi!

---
 share/run/UA/inputs/defaults.json | 2 +-
 share/run/aether.json             | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 4573571a..557a06bf 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -69,7 +69,7 @@
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 
     "neuGrid" : {
-        "Shape" : "sphere",
+        "Shape" : "sphere4",
 		"LatRange" : [-90.0, 90.0],
         "nLatsPerBlock" : 18,
 		"LonRange" : [0.0, 360.0],
diff --git a/share/run/aether.json b/share/run/aether.json
index d10243a7..65ac4f7e 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -14,9 +14,9 @@
 
     "neuGrid" : {
         "Shape": "sphere4",
-	"nLonsPerBlock" : 24,
-	"nLatsPerBlock" : 22,
-	"nAlts" : 40,
+        "nLonsPerBlock" : 24,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 40,
         "dAltScale" : 0.25,
         "IsUniformAlt" : false},
     
@@ -35,6 +35,7 @@
         "Potential" : "Weimer05",
         "DiffuseAurora" : "fta",
         "File": "UA/inputs/b20110320n_omni.bin"},
+        
     "Outputs" : {
         "type" : ["states", "grid"],
         "dt" : [900, -1] },

From 99cb393d6c1dedf131d22c883f0d36dbfc282b1a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 21 Apr 2025 13:26:40 -0400
Subject: [PATCH 079/120] FEAT: Framework for adding tests in at runtime

---
 CMakeLists.txt                    | 3 ++-
 include/aether.h                  | 3 +++
 include/inputs.h                  | 6 ++++++
 share/run/UA/inputs/defaults.json | 5 ++++-
 src/inputs.cpp                    | 8 ++++++++
 src/main/main.cpp                 | 3 +++
 6 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a479a1fe..b37b2c65 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,6 +18,7 @@ endif()
 
 # Directory variables
 file(GLOB SRC_FILES ${PROJECT_SOURCE_DIR}/src/*.cpp)
+file(GLOB TEST_FILES ${PROJECT_SOURCE_DIR}/srcTest/*.cpp)
 set(MAIN_DIR ${PROJECT_SOURCE_DIR}/src/main)
 set(TESTS_DIR ${PROJECT_SOURCE_DIR}/tests)
 set(OUT_DIR ${PROJECT_SOURCE_DIR}/src/output)
@@ -34,7 +35,7 @@ elseif(TEST_EXCHANGE)
 elseif(TEST_GRADIENT)
   add_executable(aether ${SRC_FILES} ${MSIS_FILES} ${MAIN_DIR}/main_test_gradient.cpp)
 else()
-  add_executable(aether ${SRC_FILES} ${MSIS_FILES} ${IE_FILES} ${MAIN_DIR}/main.cpp)
+  add_executable(aether ${SRC_FILES} ${TEST_FILES} ${MSIS_FILES} ${IE_FILES} ${MAIN_DIR}/main.cpp)
 endif()
 
 if(USE_DOUBLE_PRECISION)
diff --git a/include/aether.h b/include/aether.h
index 28c6a4b5..ec51bdc2 100644
--- a/include/aether.h
+++ b/include/aether.h
@@ -143,4 +143,7 @@ using json = nlohmann::json;
 // not commented
 #include "external_msis.h"
 
+// To hold all the test functions
+#include "test.h"
+
 #endif  // INCLUDE_AETHER_H_
diff --git a/include/inputs.h b/include/inputs.h
index 4abc9167..db266aac 100644
--- a/include/inputs.h
+++ b/include/inputs.h
@@ -562,6 +562,12 @@ class Inputs {
      \param 
    **/
   std::vector<precision_t> get_satellite_dts();
+
+    /**********************************************************************
+     \brief returns settings["
+     \param 
+   **/
+  json get_tests();
   
   // General get_setting functions with error checks:
   
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 557a06bf..f86b7c47 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -130,5 +130,8 @@
 		"do" : false,
 		"OutDir" : "UA/restartOut",
 		"InDir" : "UA/restartIn",
-		"dt" : 3600.0}
+		"dt" : 3600.0},
+
+	"DoTests": {
+				"exit_on_fail": true}
 }
diff --git a/src/inputs.cpp b/src/inputs.cpp
index c35d8637..921afd41 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -1163,6 +1163,14 @@ bool Inputs::get_advection_neutrals_implicitfriction() {
   return get_setting_bool("Advection", "Neutrals", "useImplicitFriction");
 }
 
+// -----------------------------------------------------------------------
+// See what tests are requested
+// -----------------------------------------------------------------------
+
+json Inputs::get_tests() {
+  return get_setting_json("DoTests");
+}
+
 // --------------------------------------------------------------------------
 // check to see if class is ok
 // --------------------------------------------------------------------------
diff --git a/src/main/main.cpp b/src/main/main.cpp
index 64d25548..d620f28c 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -31,6 +31,9 @@ int main() {
       report.print(-1, "Hello " +
                    input.get_student_name() + " - welcome to Aether!");
 
+    // Find out what tests we are running:
+    json tests = input.get_tests();
+
     // For now, the number of processors and blocks are set by the 
     // neutral grid shape, since this could be sphere (1 root) or
     // cubesphere (6 root)

From 79a4c851aa8942d1d1205d3ae5bb6dc1b0aa209e Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 21 Apr 2025 13:27:51 -0400
Subject: [PATCH 080/120] FEAT: Create (semi-functional) gradient test

---
 include/test.h                    |  17 ++
 share/run/UA/inputs/defaults.json |   2 +-
 src/main/main.cpp                 |   4 +
 srcTest/test_gradient.cpp         | 260 ++++++++++++++++++++++++++++++
 4 files changed, 282 insertions(+), 1 deletion(-)
 create mode 100644 include/test.h
 create mode 100644 srcTest/test_gradient.cpp

diff --git a/include/test.h b/include/test.h
new file mode 100644
index 00000000..ea74ab86
--- /dev/null
+++ b/include/test.h
@@ -0,0 +1,17 @@
+// Copyright 2020, the Aether Development Team (see doc/dev_team.md for members)
+// Full license can be found in License.md
+
+#ifndef INCLUDE_TEST_H_
+#define INCLUDE_TEST_H_
+
+#include "aether.h"
+
+
+// Gradient tests
+// Cubesphere is not done nor tested
+bool test_gradient(Planets planet, Quadtree quadtree, json test_config, Grid gGrid, Grid mGrid);
+bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid);
+bool test_gradient_ijk(Planets planet, Grid grid);
+
+
+#endif
\ No newline at end of file
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index f86b7c47..1b4138ab 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -132,6 +132,6 @@
 		"InDir" : "UA/restartIn",
 		"dt" : 3600.0},
 
-	"DoTests": {
+	"DoTests": {"test_gradient": false,
 				"exit_on_fail": true}
 }
diff --git a/src/main/main.cpp b/src/main/main.cpp
index d620f28c..0ec17726 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -116,6 +116,10 @@ int main() {
     Ions ions(gGrid, planet);
     Ions ionsMag(mGrid, planet);
 
+    if (tests["test_gradient"]) {
+      test_gradient(planet, quadtree, tests, gGrid, mGrid);
+      }
+
     // -----------------------------------------------------------------
     // This is a unit test for checking for nans and infinities.
     // Is simply adds nans and infinities in a few places, then
diff --git a/srcTest/test_gradient.cpp b/srcTest/test_gradient.cpp
new file mode 100644
index 00000000..6270e35a
--- /dev/null
+++ b/srcTest/test_gradient.cpp
@@ -0,0 +1,260 @@
+
+// Copyright 2020, the Aether Development Team (see doc/dev_team.md for members)
+// Full license can be found in License.md
+
+#include <iostream>
+
+#include "aether.h"
+
+bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
+                   Grid gGrid, Grid mGrid) {
+  std::string function = "test_gradient";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  bool didWork;
+
+  report.print(2, "Testing neutral grid");
+
+  if (gGrid.IsCubeSphereGrid)
+    didWork = test_gradient_cubesphere(planet, quadtree, gGrid);
+
+  if (gGrid.IsDipole || gGrid.IsLatLonGrid)
+    didWork = test_gradient_ijk(planet, gGrid);
+
+  MPI_Barrier(aether_comm);
+
+  if (!didWork && test_config["exit_on_fail"])
+    throw std::string("Gradient test failed - neutral grid");
+
+  report.print(2, "Testing ion grid");
+
+  if (mGrid.IsCubeSphereGrid) // it's technically possible...
+    didWork = test_gradient_cubesphere(planet, quadtree, mGrid);
+
+  if (mGrid.IsDipole || mGrid.IsLatLonGrid)
+    didWork = test_gradient_ijk(planet, mGrid);
+
+  if (!didWork && test_config["exit_on_fail"])
+    throw std::string("Gradient test failed - ion grid");
+
+
+
+  report.exit(function);
+
+  return didWork;
+}
+
+bool test_gradient_ijk(Planets planet, Grid grid) {
+
+  std::string function = "test_gradient_dipole";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  int64_t nIs = grid.get_nX();
+  int64_t nJs = grid.get_nY();
+  int64_t nKs = grid.get_nZ();
+  int64_t nGCs = grid.get_nGCs();
+
+  int64_t nX, nY, nZ;
+  precision_t tol = 1e-3;
+  bool didWork = true;
+
+  arma_cube predicted_gradient, true_gradient;
+  arma::uvec err_points;
+
+  arma_cube gradient_error;
+  gradient_error.set_size(nIs, nJs, nKs);
+  gradient_error.zeros();
+
+  int64_t nCellsTot = nX * nY * nZ;
+  int64_t nCellsNGCs = (nX - 2 * nGCs) * (nY - 2 * nGCs) * (nZ - 2 * nGCs);
+
+  report.print(2, "Beginning i-gradient");
+
+  /////////////////////////////////////////////////////////////
+  // Test the gradient in i-direction, d/dx(sin x) = cos(x)  //
+  /////////////////////////////////////////////////////////////
+
+  predicted_gradient = calc_gradient2o_i(sin(grid.i_center_scgc), grid);
+  true_gradient = cos(grid.i_center_scgc) / (grid.di_center_m_scgc);
+
+  gradient_error = abs(predicted_gradient - true_gradient) / abs(true_gradient);
+  err_points = find(abs(gradient_error.subcube(nGCs, nGCs,
+                                               nGCs, // don't look at ghost cells
+                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
+                    > tol);
+
+  didWork = all_finite(predicted_gradient, "Gradient_4o_i");
+
+  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
+            << ") => Points in i-gradient, above tol: " <<
+            100.0 * err_points.n_elem / predicted_gradient.n_elem
+            << "% (" << err_points.n_elem << ", " <<
+            predicted_gradient.n_elem << ")\n";
+
+  if (err_points.n_elem > true_gradient.n_elem * tol)
+    didWork = false;
+
+  report.print(2, "Beginning j-gradient");
+
+  /////////////////////////////////////////////////////////////
+  // Test the gradient in j-direction, d/dx(cos x) = -sin(x) //
+  /////////////////////////////////////////////////////////////
+
+  predicted_gradient = calc_gradient2o_j(cos(grid.j_center_scgc), grid);
+  true_gradient = -1.0 * sin(grid.j_center_scgc) / grid.dj_center_m_scgc;
+
+  gradient_error = predicted_gradient - true_gradient;
+  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
+                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
+                    > tol);
+
+  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_j");
+
+  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
+            << ") => Points in j-gradient, above tol: " <<
+            100.0 * err_points.n_elem / predicted_gradient.n_elem
+            << "% (" << err_points.n_elem << ", " <<
+            predicted_gradient.n_elem << ")\n";
+
+  if (err_points.n_elem > true_gradient.n_elem * tol)
+    didWork = false;
+
+
+  report.print(2, "Beginning k-gradient");
+
+  //////////////////////////////////////////////////////
+  // Test the gradient in k-direction, d/dx(x^2) = 2x //
+  //////////////////////////////////////////////////////
+
+  predicted_gradient = calc_gradient2o_k(grid.radius2_scgc, grid);
+  true_gradient = 2.0 * grid.radius_scgc / grid.dk_center_m_scgc;
+
+  gradient_error = (predicted_gradient - true_gradient);
+  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
+                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
+                    > tol);
+
+  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_k");
+
+  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
+            << ") => Points in k-gradient, above tol: " <<
+            100.0 * err_points.n_elem / predicted_gradient.n_elem
+            << "% (" << err_points.n_elem << ", " <<
+            predicted_gradient.n_elem << ")\n";
+
+  if (err_points.n_elem > true_gradient.n_elem * tol)
+    didWork = false;
+
+  report.exit(function);
+
+  return didWork;
+}
+
+
+// This is non-functional. 
+// Taken from src/main/main_test_gradient.cpp with enough edits to compile.
+bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid) {
+
+
+  std::string function = "test_gradient_cubesphere";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Set tolerance limit
+  precision_t tol = 1e-5;
+
+  // Print current side number
+  std::string side_num = std::to_string(quadtree.iSide + 1);
+  std::cout << "Initiating Test 1 for Side Number (1-based index): " << side_num
+            << std::endl;
+
+  /**
+    * Extract some test data generated by Aether Model
+    */
+
+  // Cell center coordinates
+  arma_mat aether_lon_cc = grid.geoLon_scgc.slice(0);
+  arma_mat aether_lat_cc = grid.geoLat_scgc.slice(0);
+
+  int64_t nXs = grid.get_nY();
+  int64_t nYs = grid.get_nX();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+
+  // Test scalar field and gradients
+  arma_cube scgc(nXs, nYs, nAlts);
+  arma_cube grad_lon_analytical(nXs, nYs, nAlts);
+  arma_cube grad_lat_analytical(nXs, nYs, nAlts);
+
+  // Radius Information
+  precision_t planet_R = planet.get_radius(0);
+  // radius of planet + altitude
+  // just pick alt at (0,0) loction
+  arma_vec R_Alts = grid.geoAlt_scgc.tube(0, 0) + planet_R;
+
+  for (int iAlt = 0; iAlt < nAlts; iAlt++) {
+    arma_mat curr_scalar(nXs, nYs, arma::fill::zeros); // setup zero mat
+    arma_mat curr_grad_lon(nXs, nYs);
+    arma_mat curr_grad_lat(nXs, nYs);
+    precision_t A = 1;
+    precision_t B = 1;
+
+    for (int j = 0; j < nYs; j++) {
+      for (int i = 0; i < nXs; i++) {
+        precision_t curr_lat = aether_lat_cc(i, j);
+        precision_t curr_lon = aether_lon_cc(i, j);
+
+        curr_scalar(i, j) = std::sin(curr_lat);
+        curr_grad_lon(i, j) = 0.;
+        curr_grad_lat(i, j) = std::cos(
+                                curr_lat); // Assume R=1, we will scale the numerical result
+      }
+    }
+
+    scgc.slice(iAlt) = curr_scalar;
+    grad_lon_analytical.slice(iAlt) = curr_grad_lon;
+    grad_lat_analytical.slice(iAlt) = curr_grad_lat;
+  }
+
+  std::vector<arma_cube> test_res = calc_gradient_cubesphere(scgc, grid);
+
+  // Perform Tests
+  for (int iAlt = 0; iAlt < nAlts; iAlt++) {
+    arma_mat curr_grad_lon = grad_lon_analytical.slice(iAlt);
+    arma_mat curr_grad_lat = grad_lat_analytical.slice(iAlt);
+    arma_mat curr_numgrad_lon = test_res[0].slice(iAlt);
+    arma_mat curr_numgrad_lat = test_res[1].slice(iAlt);
+
+
+    // Evaluate actual cells only
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        if (std::abs(curr_grad_lat(i, j) - curr_numgrad_lat(i,
+                                                            j) * R_Alts(iAlt)) > 1e-4) { // For float precision
+          std::cout << "Found Incorrect latitudinal gradient for face " + side_num +
+                    ", test f = sin(lat)" << std::endl;
+          std::cout << std::abs(curr_grad_lat(i, j) - curr_numgrad_lat(i,
+                                j)* R_Alts(iAlt)) << std::endl;
+          std::cout << iAlt << std::endl;
+          goto endloop1;
+        }
+
+        if (std::abs(curr_grad_lon(i, j) - curr_numgrad_lon(i,
+                                                            j) * R_Alts(iAlt)) > 1e-4) { // For float precision
+          std::cout << "Found Incorrect longitudinal gradient for face " + side_num +
+                    ", test f = sin(lat)" << std::endl;
+          goto endloop1;
+        }
+      }
+    }
+  }
+
+endloop1:
+
+  report.exit(function);
+  report.times();
+
+  return false;
+}
\ No newline at end of file

From 042302ff7fa4cc237258e88faa2827c592d0d283 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 21 Apr 2025 14:20:04 -0400
Subject: [PATCH 081/120] DOC: Fix MacOS install instructions to use g++ over
 clang

---
 doc/installation/dependencies.md | 20 ++++++++++++--------
 doc/installation/installation.md |  6 +++---
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/doc/installation/dependencies.md b/doc/installation/dependencies.md
index c0d41a6f..eabdf3a1 100644
--- a/doc/installation/dependencies.md
+++ b/doc/installation/dependencies.md
@@ -21,25 +21,29 @@ If a path is printed, `cmake` is installed. To check the version, run `cmake
 The layout of this page is as follows:
 
 - [Installing Dependencies](#installing-dependencies)
-  - [Install C++ Compiler](#install-gcc)
+  - [Install Compiler](#install-compiler)
   - [Install cmake](#install-cmake)
   - [Install JSON libraries](#install-json-libraries)
   - [Install Armadillo (and boost)](#install-armadillo-and-boost)
   - [Install NetCDF (optional)](#install-netcdf-optional)
 
-## Install gcc
+## Install Compiler
 
-On MacOS this can be installed, for
-example, using:
+On MacOS this can be installed, for example, using:
 
 ```bash
-sudo port install gcc11
+sudo port install g++
 ```
 
+> NOTE: On Macos, cmake will default to using clang instead of gcc.
+You need to set an environmental variable to tell cmake to use g++ instead. 
+Do this with `export CXX=/opt/local/bin/g++`,
+and replace the path if it is placed somewhere different.
+
 On Ubuntu, `gcc` (the C-compiler) is pre-installed, but the C++ compiler is not. This 
 can be installed with (subsituting your machine's package manager command):
 
-``bash
+```bash
 sudo apt install g++
 ```
 
@@ -103,7 +107,7 @@ sudo port install lapack
 sudo port install OpenBLAS
 sudo port install boost
 sudo port install armadillo
-sudo port install openmpi-bin libopenmpi-dev
+sudo port install openmpi
  ```
 
 ## Install NetCDF (optional)
@@ -122,7 +126,7 @@ sudo port install netcdf-cxx4
 If you want the gcc version of netcdf, then:
 
 ```bash
-sudo port install netcdf-cxx4 +gcc10
+sudo port install netcdf-cxx4
 ```
 
 On Ubuntu, gcc is the default compiler, it seems like you can probably just do:
diff --git a/doc/installation/installation.md b/doc/installation/installation.md
index bec10985..850d79fb 100644
--- a/doc/installation/installation.md
+++ b/doc/installation/installation.md
@@ -97,10 +97,10 @@ Here `FLAG` is a flag name and `VALUE` is the desired value (note the `-D`).  A
 more complete discussion of the available compilation flags can be found on the
 [Compilation Options](build_opts.md) page.
 
-If your default compiler isn't a GCC compiler, you will likely need to specify
-the desired GCC compiler at this step using:
+> If your default compiler isn't a GCC compiler, you will likely need to specify
+the desired GCC compiler at this step using the environmental variable `$CXX`, or:
 
-```bash
+> ```bash
 cmake -DCMAKE_CXX_COMPILER=<gcc or mpi executable with full path>
 ```
 

From 3f3fa44a752da0227101afdb2586f2de8ee85e29 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 10:12:58 -0400
Subject: [PATCH 082/120] FEAT: Add a globbed run* to .gitignore - supports any
 name of rundir's

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 6c8fc705..7269e4bc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,7 +9,7 @@
 
 # Run and build directories
 run/
-run.test/
+run*/
 build/
 
 # Doxygen generated files

From 5ef672ecaf998afe551e4a0b6f2507bfec05d980 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 11:30:25 -0400
Subject: [PATCH 083/120] FEAT: Post processor improvements! Including:

- NetCDF/hdf5 python packages are optional. matplotlib is still mandatory though
- Look in pwd & pwd/UA/output (lets you postproc from run/)
- Add arg to set path to output files
---
 srcPython/postAether.py | 43 +++++++++++++++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 6 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index fcdea2e1..2a08fffb 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -8,8 +8,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import matplotlib.cm as cm
-from netCDF4 import Dataset
-from h5py import File
 import argparse
 import os
 import json
@@ -36,6 +34,9 @@ def parse_args():
     parser.add_argument('-oned', \
                         help='strip 1d files of ghostcells and store in one file', \
                         action="store_true")
+    parser.add_argument('-dir', default=None, type=str,
+                        help="Directory to find Aether files in. Will look in current"
+                        " directory & $PWD/UA/output/")
 
     args = parser.parse_args()
 
@@ -544,6 +545,13 @@ def get_base_files():
             IsFound, item = if_unique(ensembleFiles, fileInfo['ensembleFile'])
             if (IsFound):
                 filesInfo[i]['ensembleMembers'] = ensembleCounter[item]
+                
+    if len(filesInfo) == 0:
+        try:
+            os.chdir("UA/output")
+            get_base_files()
+        except:
+            print("No input files found!!")
     
     return filesInfo
 
@@ -948,18 +956,33 @@ def write_and_plot_data(dataToWrite,
 # main code
 #----------------------------------------------------------------------------
 
-if __name__ == '__main__':  # main code block
+def main(args):
 
-    args = parse_args()
     isVerbose = args.v
+    
+    if args.dir:
+        if isVerbose:
+            print("changing directory to: ", args.dir)
+        os.chdir(args.dir)
 
     filesInfo = get_base_files()
+    
+    if len(filesInfo) == 0:
+        return
 
     iVar = 3
     iAlt = args.alt
 
     output_netcdf = False if args.hdf5 else True
-
+    
+    if filesInfo[0]['isNetCDF']:
+        try:
+            from netCDF4 import Dataset
+            from h5py import File
+        except InputError:
+            raise InputError(
+              "Attempting to postprocess NetCDF files, but NetCDF is not installed for Python")    
+    
     for iFile, fileInfo in enumerate(filesInfo):
         coreFile = fileInfo['coreFile']
         isNetCDF = fileInfo['isNetCDF']
@@ -1004,4 +1027,12 @@ def write_and_plot_data(dataToWrite,
                     if (isVerbose):
                         print('    ', command)
                     os.system(command)
-    
+
+# call main:
+if __name__ == '__main__':  
+
+    args = parse_args()
+
+    # This allows code to cleanly exit on error
+    main(args)
+    
\ No newline at end of file

From 30fa3dd3423967b51fd3387acf17256548fcfff8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 11:59:18 -0400
Subject: [PATCH 084/120] BUG: Whoops prev commit on postprocessor was missing
 two pieces

---
 srcPython/postAether.py | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index 2a08fffb..f4e0455c 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -12,6 +12,11 @@
 import os
 import json
 from struct import unpack
+try:
+    from netCDF4 import Dataset
+    from h5py import File
+except InputError:
+    print("NetCDF and/or h5py not found")
 
 # ----------------------------------------------------------------------
 # Function to parse input arguments
@@ -549,7 +554,7 @@ def get_base_files():
     if len(filesInfo) == 0:
         try:
             os.chdir("UA/output")
-            get_base_files()
+            filesInfo = get_base_files()
         except:
             print("No input files found!!")
     
@@ -974,15 +979,7 @@ def main(args):
     iAlt = args.alt
 
     output_netcdf = False if args.hdf5 else True
-    
-    if filesInfo[0]['isNetCDF']:
-        try:
-            from netCDF4 import Dataset
-            from h5py import File
-        except InputError:
-            raise InputError(
-              "Attempting to postprocess NetCDF files, but NetCDF is not installed for Python")    
-    
+
     for iFile, fileInfo in enumerate(filesInfo):
         coreFile = fileInfo['coreFile']
         isNetCDF = fileInfo['isNetCDF']

From febb59b4b7d28341205ca729486eb1804805321f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 12:36:40 -0400
Subject: [PATCH 085/120] BUG: Fix radial unit vector typos in init_mag_grid

- Sign was wrong
- should have been sin^2, not just sin in denominator
---
 src/init_mag_grid.cpp | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 7deebd46..4b324266 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -483,9 +483,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)),
-                                                 0.5);
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+                                                % sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = 2.0 * sin(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+                                                      % sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = - mu * rad_unit_vcgc[1] % radius2i_scgc;
@@ -494,11 +495,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   gravity_potential_scgc.zeros();
 
   gravity_mag_scgc = mu / pow(radius_scgc, 2);
-  
-  //gravity_mag_scgc = sqrt(
-  //                     gravity_vcgc[0] % gravity_vcgc[0] +
-  //                     gravity_vcgc[1] % gravity_vcgc[1] +
-  //                     gravity_vcgc[2] % gravity_vcgc[2]);
 
   report.print(4, "Done gravity calculations for the dipole grid.");
 

From fa4c0bb71125a86b93950645e655f2b57f850546 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 07:49:54 -0400
Subject: [PATCH 086/120] DOC: added a couple of comments

---
 src/calc_ion_drift.cpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index ef3160cf..6f1c0cad 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -34,6 +34,7 @@ void Ions::calc_exb_drift(Grid grid) {
 
   for (int64_t iComp = 0; iComp < 3; iComp++)
     exb_vcgc[iComp] = exb_vcgc[iComp] / bmag2;
+
   report.exit(function);
 }
 
@@ -44,9 +45,9 @@ void Ions::calc_exb_drift(Grid grid) {
 std::vector<arma_cube> Ions::calc_ion_electron_pressure_gradient(int64_t iIon,
     Grid grid) {
 
-      std::string function = "Ions::elec_ion_pressure_gradient";
-      static int iFunction = -1;
-      report.enter(function, iFunction);
+  std::string function = "Ions::elec_ion_pressure_gradient";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
   std::vector<arma_cube> pressure_gradient_vcgc;
   arma_cube total_pressure_scgc;
 
@@ -201,6 +202,9 @@ void Ions::calc_ion_drift(Neutrals neutrals,
           species[iIon].par_velocity_vcgc[iComp] =
             (species[iIon].par_velocity_vcgc[iComp] + a_par[iComp] * dt) /
             (1 + nuin_sum * dt);
+
+          // These need to change, since they are dependent on the
+          // grid. Closed, dipole fieldlines should NOT do this!!!
           species[iIon].par_velocity_vcgc[iComp].slice(nZ - 1).zeros();
           species[iIon].par_velocity_vcgc[iComp].slice(nZ - 2).zeros();
           species[iIon].par_velocity_vcgc[iComp].slice(nZ - 3) =
@@ -238,6 +242,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   }  // for iIon
 
+  // This is the mass weighted total bulk velocity:
   for (int64_t iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp] = velocity_vcgc[iComp] / sum_rho;
 

From b699a5ea65561e34c265b0e366173bdac793ebf5 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 07:54:04 -0400
Subject: [PATCH 087/120] BUG: change sign of b-field in north

---
 src/fill_grid.cpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 11018b76..faaff08c 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -193,15 +193,13 @@ void Grid::fill_grid_bfield(Planets planet) {
     // Now we modify the dipole's magnetic field to account for any imprecision.
     // Take the bfield_mag and put it into the third component (b-hat = k-hat)
     if (IsDipole) {
-      bfield_vcgc[2] = bfield_mag_scgc;
+      bfield_vcgc[2] = bfield_mag_scgc % sign(magInvLat_scgc * -1.0);
       bfield_vcgc[1].zeros();
       bfield_vcgc[0].zeros();
 
       bfield_unit_vcgc[0].zeros();
       bfield_unit_vcgc[1].zeros();
-      bfield_unit_vcgc[2].ones();
-
-      bfield_unit_vcgc[2] % sign(magInvLat_scgc * -1.0);
+      bfield_unit_vcgc[2] = 1.0 * sign(magInvLat_scgc * -1.0);
 
       // slight complication -
     } else

From 5a5204e4724a4c9be4c8aeb3cefd3ad5afb5a016 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 07:54:49 -0400
Subject: [PATCH 088/120] BUG: still issues with gravity direction

---
 src/init_mag_grid.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 4b324266..478b418c 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -483,14 +483,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+  // Gravity should be negative in the k direction, since the grid switches directions.
+  // j direction should switch signs when crossing the equator (+ in south, - in north)  
+  rad_unit_vcgc[1] = sign(magLat_scgc) % cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
                                                 % sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = 2.0 * sin(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+  rad_unit_vcgc[2] = - 2.0 * abs(sin(magLat_scgc)) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
                                                       % sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
-  gravity_vcgc[1] = - mu * rad_unit_vcgc[1] % radius2i_scgc;
-  gravity_vcgc[2] = - mu * rad_unit_vcgc[2] % radius2i_scgc;
+  gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;
+  gravity_vcgc[2] = mu * rad_unit_vcgc[2] % radius2i_scgc;
   gravity_potential_scgc.set_size(nX, nY, nAlts);
   gravity_potential_scgc.zeros();
 

From 7f3274369bf443cffd5e861d149d5a52faf9830c Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:48:16 -0400
Subject: [PATCH 089/120] FEAT: output grid spacing

---
 src/output.cpp | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/src/output.cpp b/src/output.cpp
index c9fa6dca..fc1e7767 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -28,6 +28,9 @@ std::string get_filename_from_type(std::string type_output) {
 
   if (type_output == "bfield")
     filename = "3DBF";
+    
+  if (type_output == "delta")
+    filename = "3DDE";
 
   if (type_output == "moment")
     filename = "3DMO";
@@ -255,6 +258,22 @@ bool output(const Neutrals &neutrals,
                                                     grid.radius_scgc);
       }
 
+      if (type_output == "delta") {
+        AllOutputContainers[iOutput].store_variable("dim",
+                                                    "di Center m",
+                                                    "m",
+                                                    grid.di_center_m_scgc);
+        AllOutputContainers[iOutput].store_variable("djm",
+                                                    "dj Center m",
+                                                    "m",
+                                                    grid.dj_center_m_scgc);
+        AllOutputContainers[iOutput].store_variable("dkm",
+                                                    "dk Center m",
+                                                    "m",
+                                                    grid.dk_center_m_scgc);
+
+      }
+
       if (type_output == "bfield" || type_output == "ions") {
         AllOutputContainers[iOutput].store_variable("mlat",
                                                     "Magnetic Latitude",

From cdc8bacbcb937e36895834510c593d5b5c2774b3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:49:17 -0400
Subject: [PATCH 090/120] BUG: spacing should have times -1 in south

---
 src/grid_spacing.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index d7fd245f..ad6d5b45 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -343,8 +343,8 @@ void Grid::calc_j_grid_spacing() {
 
   // Dipole will have different scaling...
   if (iGridShape_ == iDipole_) {
-    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
-    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
+    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc) % sign(magLat_scgc);
+    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc) % sign(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:

From 0bb93cdd85dae976a6ef09e468e11df6c5995661 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:49:52 -0400
Subject: [PATCH 091/120] STY: removed a random comment

---
 src/fill_grid.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index faaff08c..e98e0603 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -200,8 +200,6 @@ void Grid::fill_grid_bfield(Planets planet) {
       bfield_unit_vcgc[0].zeros();
       bfield_unit_vcgc[1].zeros();
       bfield_unit_vcgc[2] = 1.0 * sign(magInvLat_scgc * -1.0);
-
-      // slight complication -
     } else
       for (iDim = 0; iDim < 3; iDim++)
         bfield_unit_vcgc[iDim] = bfield_vcgc[iDim] / (bfield_mag_scgc + 1e-32);

From 551525371442699c355832e66954725db449e8ce Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:50:50 -0400
Subject: [PATCH 092/120] STY: iDim -> iComp + remove int64_t everywhere

---
 src/calc_ion_drift.cpp | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index 6f1c0cad..ec64aec6 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -101,7 +101,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
   std::vector<arma_cube> total_acc = make_cube_vector(nX, nY, nZ, 3);
   std::vector<arma_cube> efield_acc = make_cube_vector(nX, nY, nZ, 3);
 
-  int64_t iIon, iNeutral, iDim;
+  int64_t iIon, iNeutral, iComp;
 
   std::vector<arma_cube> grad_Pi_plus_Pe;
   arma_cube rho, nuin, nuin_sum, Nie, sum_rho;
@@ -115,7 +115,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   fill_electrons();
 
-  for (int64_t iComp = 0; iComp < 3; iComp++)
+  for (iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp].zeros();
 
   std::vector<arma_cube> a_par = make_cube_vector(nX, nY, nZ, 3);
@@ -124,7 +124,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   for (iIon = 0; iIon < nSpecies; iIon++) {
 
-    for (int64_t iComp = 0; iComp < 3; iComp++)
+    for (iComp = 0; iComp < 3; iComp++)
       species[iIon].perp_velocity_vcgc[iComp].zeros();
 
     if (species[iIon].DoAdvect) {
@@ -139,16 +139,16 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
       // This is assuming that the 3rd dim is radial.
       // Want actual gravity for 3rd dim
-      for (iDim = 0; iDim < 3; iDim ++) {
-        gravity_vcgc[iDim] = grid.gravity_vcgc[iDim];
-        grad_Pi_plus_Pe[iDim] = grad_Pi_plus_Pe[iDim] / rho;
-        efield_acc[iDim] = Nie % efield_vcgc[iDim] / rho;
+      for (iComp = 0; iComp < 3; iComp ++) {
+        gravity_vcgc[iComp] = grid.gravity_vcgc[iComp];
+        grad_Pi_plus_Pe[iComp] = grad_Pi_plus_Pe[iComp] / rho;
+        efield_acc[iComp] = Nie % efield_vcgc[iComp] / rho;
       }
 
       // Neutral Wind Forcing:
       report.print(5, "neutral winds");
 
-      for (int64_t iComp = 0; iComp < 3; iComp++)
+      for (iComp = 0; iComp < 3; iComp++)
         wind_acc[iComp].zeros();
 
       nuin_sum.zeros();
@@ -157,14 +157,14 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         nuin = species[iIon].nu_ion_neutral_vcgc[iNeutral];
         nuin_sum = nuin_sum + species[iIon].nu_ion_neutral_vcgc[iNeutral];
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           wind_acc[iComp] = wind_acc[iComp] +
                             nuin % neutrals.velocity_vcgc[iComp];
         }
       }
 
       // Total Forcing (sum everything - this is A_s):
-      for (int64_t iComp = 0; iComp < 3; iComp++) {
+      for (iComp = 0; iComp < 3; iComp++) {
         total_acc[iComp] =
           - grad_Pi_plus_Pe[iComp]
           + gravity_vcgc[iComp]
@@ -176,7 +176,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         // With a Planetary Magnetic field
         arma_cube a_dot_b = dot_product(total_acc, grid.bfield_unit_vcgc);
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           a_par[iComp] = a_dot_b % grid.bfield_unit_vcgc[iComp];
           a_perp[iComp] = total_acc[iComp] - a_par[iComp];
         }
@@ -190,7 +190,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
           Nie % Nie % grid.bfield_mag_scgc % grid.bfield_mag_scgc;
         bottom.clamp(1e-32, 1e32);
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           // I redefined A to be an acceleration instead of a force, which
           // then changes the definition of top
           top = rho % nuin % a_perp[iComp] + Nie % a_x_b[iComp];
@@ -214,7 +214,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         }
       } else {
         // No Planetary Magnetic field
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           a_par[iComp] = total_acc[iComp];
           // Steady state:
           //species[iIon].par_velocity_vcgc[iComp] =
@@ -230,9 +230,9 @@ void Ions::calc_ion_drift(Neutrals neutrals,
       // Calculate the mass-weighted average total velocity
       sum_rho = sum_rho + rho;
 
-      for (int64_t iComp = 0; iComp < 3; iComp++) {
+      for (iComp = 0; iComp < 3; iComp++) {
         species[iIon].velocity_vcgc[iComp] =
-          //species[iIon].perp_velocity_vcgc[iComp] +
+          species[iIon].perp_velocity_vcgc[iComp] +
           species[iIon].par_velocity_vcgc[iComp];
         velocity_vcgc[iComp] = velocity_vcgc[iComp] +
                                rho % (species[iIon].velocity_vcgc[iComp]);
@@ -243,7 +243,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
   }  // for iIon
 
   // This is the mass weighted total bulk velocity:
-  for (int64_t iComp = 0; iComp < 3; iComp++)
+  for (iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp] = velocity_vcgc[iComp] / sum_rho;
 
   report.exit(function);

From 3866b608549ca0624b765cc2b828cefe09887d1c Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 25 Apr 2025 12:54:49 -0400
Subject: [PATCH 093/120] FEAT: Add isClosed to all grids & doesTouch(S/N) pole
 to dipole

---
 include/grid.h        |  3 +++
 src/grid.cpp          |  5 +++++
 src/init_mag_grid.cpp | 12 ++++++++----
 3 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index f4ef10bb..c11eb4e6 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -111,6 +111,9 @@ class Grid
   // in the k-up and k-down direction (altitude for geo grids, q for dipole).
   arma_mat first_lower_gc, first_upper_gc;
 
+  // Whether to close field lines on dipole grid (Always false for geo grids)
+  bool IsClosed;
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
diff --git a/src/grid.cpp b/src/grid.cpp
index e435fba7..168a0999 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -271,6 +271,11 @@ Grid::Grid(std::string gridtype) {
   HasBField = 0;
   IsExperimental = false;
 
+  // Spatial info defaults
+  IsClosed = false;
+  DoesTouchNorthPole = false;
+  DoesTouchSouthPole = false;
+
   UseThisCell.set_size(nX, nY, nZ);
   UseThisCell.fill(true);
   first_lower_gc.set_size(nX, nY);
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 478b418c..c47ecb2f 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -236,6 +236,11 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;
     lat1d(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 1)) / 2.0;
     lat1d(nLats - 2) = (lat1dDown(nLats - 1) + lat1dDown(nLats - 2)) / 2.0;
+
+    if (sign(lower_left_norm(1)) > 0)
+      DoesTouchNorthPole = true;
+    else
+      DoesTouchSouthPole = true;
   }
 
   // l-shells of centers
@@ -267,15 +272,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - minimum Lshell in this block is < max_alt (the q-value would be undefined)
 
   precision_t q_min;
-  bool close_this_block = false;
 
   if (Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
-    close_this_block = true;
+    IsClosed = true;
 
   if (lat_origin < 0.01) // equator, with some imprecision
-    close_this_block = true;
+    IsClosed = true;
 
-  if (close_this_block)
+  if (IsClosed)
     q_min = 0; // q=0 at equator (for closed blocks)
   else
     // invLats are still all in North Hemisphere & increasing.

From db2d8fbbcfb66216f7ad6c312226ccf57223253a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 27 Apr 2025 14:48:00 -0400
Subject: [PATCH 094/120] DOC: Update docs & default input files to be
 consistent & correct

---
 README.md                         | 25 +++++----
 doc/README.md                     |  1 +
 doc/internals/grid.md             | 85 +++++++++++++++----------------
 share/run/UA/inputs/defaults.json | 15 +++---
 share/run/aether.json             |  2 +-
 5 files changed, 67 insertions(+), 61 deletions(-)

diff --git a/README.md b/README.md
index d6fd6ebe..ab60df80 100644
--- a/README.md
+++ b/README.md
@@ -19,14 +19,14 @@ developed.
 
 ## Contents
 
-- [Aether](#aether)
-  - [Contents](#contents)
-  - [Quick Start](#quick-start)
-    - [Dependencies](#dependencies)
-    - [Getting the Code](#getting-the-code)
-    - [Compiling \& Running](#compiling--running)
-  - [Code Manual](#code-manual)
-  - [Further Documentation](#further-documentation)
+- [Contents](#contents)
+- [Quick Start](#quick-start)
+  - [Dependencies](#dependencies)
+  - [Getting the Code](#getting-the-code)
+  - [Compiling \& Running](#compiling--running)
+  - [Inputs](#inputs)
+- [Code Manual](#code-manual)
+- [Further Documentation](#further-documentation)
 
 ## Quick Start
 
@@ -141,9 +141,16 @@ structure like this:
 cd ..
 cp -R share/run ./run.test
 cd run.test
-./aether
+mpirun -np 4 ./aether
 ```
 
+This will run the default configuration of Aether, which requires four MPI processors.
+The default grids are `sphere4` and `dipole4` for the neutrals and ions (respectively),
+which both require four "root nodes", or MPI workers. To read more about root nodes and
+the available grids, please see [this page in the documentation](doc/internals/grid.md).
+
+### Inputs
+
 There are essentially two input files that specify the settings in the code.
 When you are in a run directory, they are:
 
diff --git a/doc/README.md b/doc/README.md
index d1601e3d..e51ddd53 100644
--- a/doc/README.md
+++ b/doc/README.md
@@ -32,6 +32,7 @@ Contents within `doc`:
   - [Ensembles](internals/ensembles.md)
   - [Indices](internals/indices.md)
   - [The Grid](internals/grid.md)
+  - [Coordinate Systems](internals/coordinates.md)
 - [Doxyfile](Doxyfile)
 - [README (this page)](README.md)
 - [Citations](citations.md)
diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 92da6fdb..4be0269f 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -18,21 +18,21 @@ again, 16 processors are needed, etc. However, in the altitude/radial direction,
 the number of points that are specified in the aether.json is unchanged, as it
 does not rely on the number of processors used.
 
-- [Grids in Aether](#grids-in-aether)
-  - [Grid Types Explained](#grid-types-explained)
-  - [Grid Shapes Explained](#grid-shapes-explained)
-    - [TL;DR](#tldr)
-    - [The Sphere Grid](#the-sphere-grid)
-    - [The Cubesphere Grid](#the-cubesphere-grid)
-    - [The Dipole Grid](#the-dipole-grid)
-      - [Inputs:](#inputs)
-    - [Root Nodes](#root-nodes)
-      - [Sphere](#sphere)
-      - [Cubesphere](#cubesphere)
-      - [Specifying Root Nodes](#specifying-root-nodes)
-  - [Specifying the Grid](#specifying-the-grid)
-    - [Horizontal Resolution](#horizontal-resolution)
-    - [Vertical Resolution](#vertical-resolution)
+- [Grid Types Explained](#grid-types-explained)
+- [Grid Shapes Explained](#grid-shapes-explained)
+  - [TL;DR](#tldr)
+  - [The Sphere Grid](#the-sphere-grid)
+  - [The Cubesphere Grid](#the-cubesphere-grid)
+  - [The Dipole Grid](#the-dipole-grid)
+    - [Inputs:](#inputs)
+  - [Root Nodes](#root-nodes)
+    - [Sphere](#sphere)
+    - [Cubesphere](#cubesphere)
+    - [Dipole](#dipole)
+    - [Specifying Root Nodes](#specifying-root-nodes)
+- [Specifying the Grid](#specifying-the-grid)
+  - [Horizontal Resolution](#horizontal-resolution)
+  - [Vertical Resolution](#vertical-resolution)
 
 ## Grid Types Explained
 
@@ -77,8 +77,8 @@ system can simulate a sub-region of the Earth if desired.
 
 The user needs to specify the shape of the grid, which specifies the grid shape
 and the number of root nodes. Shapes include: `sphere` (1 root node), `sphere6`
-(6 root nodes), `cubesphere` (6 root nodes), `dipole` (1 root node), `dipole4`
-(4 root nodes), and `dipole6` (6 root nodes).
+(6 root nodes), `cubesphere` (6 root nodes),`dipole4` (4 root nodes), and
+`dipole6` (6 root nodes).
 
 ### The Sphere Grid
 
@@ -118,13 +118,6 @@ Each fieldline starts at the lowest modeled altitude
 and curves towards the equator. In the northern hemisphere, this means that the
 fieldlines curve south, while in the southern hemisphere they curve north.
 
-The dipole grid requires >4 root nodes which ensures the coordinates are 
-mutually orthogonal. The available shapes are `dipole4` and `dipole6`, for 
-compatibility with the neutral grid being a sphere or cubesphere. In both cases,
-each node the entire longitude range and given a portion of the latitude range.
-So in the case of `dipole4`, the four nodes are each given 1/4 of the available
-latitudes and all of the longitudes.
-
 The dipole grid is evenly spaced in **invariant latitude** (where the field line
 passes the minumum altitude) and **q** (the dipole coordinate
 specifying how far along the field line a point lies). Q is dimensionless and defined 
@@ -236,6 +229,20 @@ left-right direction and the up-down direction. For a cubesphere grid, the
 number of processors that can be used to specify the grid are then: 6, 24 (6
 \* 4), 96 (6 \* 4^2), 384 (6 \* 4^3), etc.
 
+#### Dipole
+
+The dipole grid requires >4 root nodes to ensure the coordinates are 
+mutually orthogonal. The available shapes are `dipole4` and `dipole6`, for 
+compatibility with the neutral grid being a sphere or cubesphere. In both cases,
+each root node covers the entire longitude range and given a portion of the latitude 
+range. So in the case of `dipole4`, the four nodes each cover 1/4 of the available
+latitudes and all of the longitudes. The available latitudes are scaled to the latitude
+limits specified in the input file, so the divisions will not be at $\pm45^\circ$ and
+$0^\circ$ latitude, rather will be offset to evenly divide the entire range across the
+blocks. Dividing the root nodes works identically to the spherical grid, for example
+`dipole4` can be used with 16 MPI tasks and each root node is divided into four blocks,
+forming a 2x2 grid.
+
 #### Specifying Root Nodes
 
 The root nodes indicate the span of the grid that they cover. This is done in a
@@ -365,27 +372,17 @@ this is the number of points along the dipole flux tube.
 ```
 
 ```json
-  "ionGrid" : {
-    "Shape" : "dipole",
-    "LatRange" : [-90.0, 90.0],
-    "nLatsPerBlock" : 18,
-    "LonRange" : [0.0, 360.0],
-    "nLonsPerBlock" : 36,
-    "nAlts" : 200,
-        "MinAlt" : 80.0,
-        "MinApex" : 120.0,
-        "MaxAlt" : 5000.0},
+    "ionGrid": {
+        "Shape": "dipole4",
+        "nLonsPerBlock": 36,
+        "nLatsPerBlock": 18,
+        "nAlts": 100,
+        "LatRange": [10, 80],
+        "AltRange": [80.0, 1000],
+	      "LonRange": [0.0, 360.0]},
 ```
 
 The dipole grid has both open field-lines and closed field-lines. The closed
 field-lines are near the equator, while the open field-lines are near the poles.
-The variable `MaxAlt` sets where this differentiation occurs - if the apex
-height of the field-line is above this altitude, then it is open. All
-field-lines in Aether start at the `MinAlt` and rise along a dipolar shape until
-they either encounter the equatorial plane or `MaxAlt`. In the south, these
-field-lines tilt towards the north (from `MinAlt` to `MaxAlt`) and in the north,
-the field-lines tilt towards the south (from `MinAlt` to `MaxAlt`).
-
-- The spacing is uniform in longitude.
-- The spacing along the field-line has non-uniform spacing.
-- The spacing in latitude is non-uniform.
+The variable `MaxAlt` sets where the differentiation occurs - if the apex
+height of all field-lines on this block are above this altitude, then it is open. 
\ No newline at end of file
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 1b4138ab..91afec57 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -81,13 +81,14 @@
 		"IsUniformAlt" : true,
         "AltFile" : ""},
 
-	"ionGrid": {
-		"Shape": "dipole4",
-		"nLonsPerBlock": 14,
-		"nLatsPerBlock" : 20,
-		"nAlts":30,
-		"AltRange":[80,1500],
-		"LatRange":[7, 87]},
+    "ionGrid": {
+        "Shape": "dipole4",
+        "nLonsPerBlock": 36,
+        "nLatsPerBlock": 18,
+        "nAlts": 100,
+        "LatRange": [10, 80],
+        "AltRange": [80.0, 1000],
+        "LonRange": [0.0, 360.0]},
 
     "Oblate" : {
 		"isOblate" : false,
diff --git a/share/run/aether.json b/share/run/aether.json
index 65ac4f7e..65780a75 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -27,7 +27,7 @@
         "nAlts": 100,
         "LatRange": [10, 80],
         "AltRange": [80.0, 1000],
-	"LonRange": [0.0, 360.0] },
+        "LonRange": [0.0, 360.0]},
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
 

From beba721299f1d758433f6d9b80d8a590de48401a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 27 Apr 2025 16:46:08 -0400
Subject: [PATCH 095/120] DOC: Add section on OpenMP & toc to running_aether.md

I did some digging. Parallelization is weird.
---
 doc/usage/running_aether.md | 43 +++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/doc/usage/running_aether.md b/doc/usage/running_aether.md
index 2811afa6..7702d8e9 100644
--- a/doc/usage/running_aether.md
+++ b/doc/usage/running_aether.md
@@ -4,6 +4,22 @@ This document assumes you have already downloaded and built the Aether model. If
 not, you should return to [one](../../README.md) of
 [these](../installation/installation.md) pages before continuing.
 
+- [The first run](#the-first-run)
+  - [Running in 1D](#running-in-1d)
+  - [Using OpenMP](#using-openmp)
+- [Output Files](#output-files)
+  - [Blocks](#blocks)
+  - [Ensembles](#ensembles)
+  - [Post processing](#post-processing)
+- [Input Files](#input-files)
+- [defaults.json file](#defaultsjson-file)
+- [For Developers](#for-developers)
+- [aether.json file](#aetherjson-file)
+- [planet.in file](#planetin-file)
+- [orbits.csv file](#orbitscsv-file)
+- [chemistry file](#chemistry-file)
+
+
 ## The first run
 
 Once you have compiled you can run Aether. To remember which runs you're doing,
@@ -70,6 +86,33 @@ to `"sphere"`. No number!
 2. Run the code with `./aether`. This will not use MPI, however armadillo may use
 multiple OpenMP processes for math, so be careful on cluster login nodes.
 
+### Using OpenMP
+
+> This section is mostly a placeholder. Everything is correct, but has little
+> effect on Aether's speed. This is only really a concern on laptops with low core counts
+> or shared systems.
+
+Armadillo contains several optimizations which utilize OpenMP for parallelization beyond
+the block decomposition on the entire sphere. Thus, runs on 4 MPI processors can benefit
+from devoting additional processors to OpenMP parallelization.
+
+The number of OpenMP tasks Armadillo is able to utilize can be set before compiling or
+at runtime. To change this *before* compiling, change the value of`ARMA_OPENMP_THREADS`
+in the [Armadillo config.hpp file](../../share/include/armadillo_bits/config.hpp#173)
+from 8. This will require re-compiling & possibly re-running `cmake`. The more flexible 
+option is to use a variable at runtime:
+
+The easier way to set the number of OpenMP threads is to use the variable 
+`OMP_NUM_THREADS`. This can be set before running the executable with 
+`export OMP_NUM_THREADS=2`, or at runtime with:
+
+```bash
+OMP_NUM_THREADS=2 mpirun -np 4 ./aether
+```
+At this stage in development, there is not much speedup available from OpenMP. For
+example, the change in runtime from the default value of 8 (from Armadillo) and 1
+(disabling OpenMP) in a 10-minute run is about one minute, or about 10%.
+
 ## Output Files
 
 Aether outputs to a subdirectory called UA/output. At this time, all processors

From b6eccccae5b2ddef8a7f89fb8221723199cf8dc9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 27 Apr 2025 16:46:44 -0400
Subject: [PATCH 096/120] BUG: Make check_for_nans TRUE be default. there are
 some gremlins

---
 share/run/aether.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index 65780a75..cc3e330d 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -8,7 +8,7 @@
         "iFunctionVerbose" : {
             "Grid::create_altitudes": 0},	
         "dt" : 10.0,
-        "check_for_nans" : false},
+        "check_for_nans" : true},
 
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 

From fc39fa0dc3636adfa02630f1310acf30ac94032f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:04:36 -0400
Subject: [PATCH 097/120] FEAT: added ability to message pass

---
 include/grid.h | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index c11eb4e6..ebee7cc9 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -267,6 +267,7 @@ class Grid
 
   void set_IsDipole(bool value);
   bool get_IsDipole();
+  bool get_IsClosed();
 
   int64_t get_nPointsInGrid();
 
@@ -327,6 +328,8 @@ class Grid
   void report_grid_boundaries();
   void calc_cent_acc(Planets planet);
 
+  void create_dipole_connection(Quadtree quadtree);
+
   // Make mag-field grid:
   bool init_dipole_grid(Quadtree quadtree_ion, Planets planet);
   // Support functions:
@@ -350,17 +353,26 @@ class Grid
   int iProcYp;
   /// The processor to the South/Down/Y-:
   int iProcYm;
+  // This is special, since message passing in the z direction will only be
+  // between closed magnetic field lines, so we don't need a +/- (p/m):
+  int iProcZ;
 
+  bool isExchangeInitialized = false;
+  
   arma_vec edge_Xp;
   arma_vec edge_Yp;
   arma_vec edge_Xm;
   arma_vec edge_Ym;
+  // again, z will only be in one
+  arma_vec edge_Z;
 
   int64_t iRoot;
   int64_t iRootXp;
   int64_t iRootXm;
   int64_t iRootYp;
   int64_t iRootYm;
+  // again, z will only be in one
+  int64_t iRootZ;
 
   struct messages_struct
   {

From 989984c83121127defce1704bb9c0039f89d6ec1 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:05:50 -0400
Subject: [PATCH 098/120] FEAT: message passing on multiple grids + iDir = 4

---
 src/exchange_messages.cpp | 127 +++++++++++++++++++++++++++++++-------
 1 file changed, 103 insertions(+), 24 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index befbdb7f..8e0a41be 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -105,6 +105,7 @@ bool Ions::exchange_old(Grid &grid) {
 //     1 - top
 //     2 - left
 //     3 - bottom
+//     4 - vertical (k direction) for closed field lines
 //
 // cells (assume gc = 2):
 // 0  1 | 2 3 4 ... n-gc*2 n-gc-1 | n-gc n-1
@@ -124,6 +125,7 @@ bool pack_border(const arma_cube &value,
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
+  int64_t iZstart, iZend;
 
   // ----------------------------
   // left / right message passing
@@ -159,8 +161,22 @@ bool pack_border(const arma_cube &value,
     }
   }
 
+  // ----------------------------
+  // k-dir (only top)
+  if (iDir == 4) {
+    iXstart = nG;
+    iXend = nX - nG;
+    iYstart = nG;
+    iYend = nY - nG;
+    iZstart = nZ - nG;
+    iZend = nZ;
+  } else {
+    iZstart = nG;
+    iZend = nZ - nG;
+  }
+
   try {
-    for (int64_t iZ = nG; iZ < nZ - nG; iZ++) {
+    for (int64_t iZ = iZstart; iZ < iZend; iZ++) {
       for (int64_t iY = iYstart; iY < iYend; iY++) {
         for (int64_t iX = iXstart; iX < iXend; iX++) {
           packed[*iCounter] = value(iX, iY, iZ);
@@ -186,6 +202,7 @@ bool pack_border(const arma_cube &value,
 //     1 - top
 //     2 - left
 //     3 - bottom
+//     4 - k-dir top
 //   DoReverseX and DoReverseY are because packing always happens from
 //     lower left to upper right, while face we are unpacking too may
 //     have a different (left - right and up - down) geometry
@@ -210,7 +227,8 @@ bool unpack_border(arma_cube &value,
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
-  int64_t xInc = 1, yInc = 1;
+  int64_t iZstart, iZend;
+  int64_t xInc = 1, yInc = 1, zInc = 1;
 
   int64_t iXOff = 0;
   int64_t nCx = nX - 2 * nG;
@@ -261,10 +279,26 @@ bool unpack_border(arma_cube &value,
     }
   }
 
+  if (iDir == 4) {
+    iXstart = nG;
+    iXend = nX - nG;
+    iYstart = nG;
+    iYend = nY - nG;
+    // need to reverse direction, since packing is from the bottom up,
+    // which means unpacking should be from the top down
+    iZend = nZ - nG;
+    iZstart = nZ;
+    zInc = -1;
+  }  else {
+    iZstart = nG;
+    iZend = nZ - nG;
+  }
+
   try {
     int64_t iXp, iYp;
 
-    for (int64_t iZ = nG; iZ < nZ - nG; iZ++) {
+    for (int64_t iZ = iZstart; iZ < iZend; iZ += zInc) {
+
       if (XbecomesY) {
         for (int64_t iX = iXstart; iX < iXend; iX += xInc) {
           iXp = iX;
@@ -311,7 +345,6 @@ bool unpack_border(arma_cube &value,
   } catch (...) {
     DidWork = false;
   }
-
   return DidWork;
 }
 
@@ -440,6 +473,12 @@ bool Grid::send_one_var_one_face(int64_t iFace) {
 
   bool DidWork = true;
 
+  if (report.test_verbose(4))
+    std::cout << "in send_one_var_one_face : " << iFace << " from: " <<
+    iProc << " to: " <<
+    interchangesOneVar[iFace].iProc_to << " tag: " <<
+    interchangesOneVar[iFace].iTag << "\n";
+
   MPI_Isend(interchangesOneVar[iFace].buffer,
             interchangesOneVar[iFace].iSizeTotal,
             MPI_BYTE,
@@ -480,6 +519,13 @@ bool Grid::receive_one_var_one_face(int64_t iFace) {
 
   bool DidWork = true;
 
+  if (report.test_verbose(4))
+    std::cout << "in receive_one_var_one_face : " << iFace << " from: " <<
+    iProc << " to: " << 
+    interchangesOneVar[iFace].iProc_to << " tag: " <<
+    interchangesOneVar[iFace].iTag << "\n";
+
+
   MPI_Recv(interchangesOneVar[iFace].rbuffer,
            interchangesOneVar[iFace].iSizeTotal,
            MPI_BYTE,
@@ -508,6 +554,7 @@ Grid::messages_struct Grid::make_new_interconnection(int64_t iDir,
 
   int64_t nPtsX = nGCs * (nY - nGCs * 2) * (nZ - nGCs * 2);
   int64_t nPtsY = nGCs * (nX - nGCs * 2) * (nZ - nGCs * 2);
+  int64_t nPtsZ = nGCs * (nX - nGCs * 2) * (nY - nGCs * 2);
 
   new_inter.iFace = iDir;
   new_inter.DoReverseX = DoReverseX;
@@ -515,16 +562,24 @@ Grid::messages_struct Grid::make_new_interconnection(int64_t iDir,
   new_inter.IsPole = IsPole;
   new_inter.XbecomesY = XbecomesY;
 
+  // Along i axis (left or right):
   if (iDir == 0 || iDir == 2) {
     new_inter.iSizeTotal = nVars * nPtsX * sizeof(precision_t);
     new_inter.index.set_size(nGCs, nY);
     new_inter.ratio.set_size(nGCs, nY);
-  } else {
+  }
+
+  // Along j axis (up or down):
+  if (iDir == 1 || iDir == 3) {
     new_inter.iSizeTotal = nVars * nPtsY * sizeof(precision_t);
     new_inter.index.set_size(nGCs, nX);
     new_inter.ratio.set_size(nGCs, nX);
   }
 
+  // Along K axis (up only for now):
+  if (iDir == 4)
+    new_inter.iSizeTotal = nVars * nPtsZ * sizeof(precision_t);
+
   new_inter.buffer = static_cast<precision_t*>(malloc(new_inter.iSizeTotal));
   new_inter.rbuffer = static_cast<precision_t*>(malloc(new_inter.iSizeTotal));
 
@@ -538,7 +593,6 @@ Grid::messages_struct Grid::make_new_interconnection(int64_t iDir,
   return new_inter;
 }
 
-
 /*
 // -----------------------------------------------------------------------------
 // Exchange messages for the NEUTRALS:
@@ -772,6 +826,15 @@ bool Neutrals::exchange_really_old(Grid &grid) {
 
 // -----------------------------------------------------------------------------
 // Initialize interfaces between horizontal sides on a grid
+// Directions:
+//  0 = + i (right)
+//  1 = + j (up)
+//  2 = - i (left)
+//  3 = - j (down)
+//  4 = + k (vertical) - only along closed dipole field lines
+// For the cubesphere grid:
+//  iRoot = 4 is the south polar region
+//  iRoot = 5 is the north polar region
 // -----------------------------------------------------------------------------
 
 bool exchange_sides_init(Grid &grid, int64_t nVarsToPass) {
@@ -911,6 +974,24 @@ bool exchange_sides_init(Grid &grid, int64_t nVarsToPass) {
                                   ReverseY,
                                   XbecomesY));
 
+  if (grid.get_IsDipole() && grid.get_IsClosed()) {
+    // This operates in the N/S (j or Y) direction:
+    ReverseX = false;
+    ReverseY = false;
+    IsPole = false;
+    XbecomesY = false;
+    grid.interchangesOneVar.push_back(
+      grid.make_new_interconnection(4,
+                                    nVarsToPass,
+                                    grid.iProcZ,
+                                    grid.edge_Z,
+                                    IsPole,
+                                    ReverseX,
+                                    ReverseY,
+                                    XbecomesY));
+
+  }
+
   report.exit(function);
   return DidWork;
 }
@@ -942,30 +1023,31 @@ bool exchange_one_var(Grid &grid,
 
   bool DidWork = true;
 
-  int iTag, iDir;
+  int iTag, iDir, nDir;
   int iSpecies;
 
-  static int64_t iX, nX = grid.get_nX();
-  static int64_t iY, nY = grid.get_nY();
-  static int64_t iZ, nZ = grid.get_nZ();
+  static int64_t nX = grid.get_nX();
+  static int64_t nY = grid.get_nY();
+  static int64_t nZ = grid.get_nZ();
   static int64_t nG = grid.get_nGCs();
-  static int64_t nPtsX = nG * (nY - nG * 2) * (nZ - nG * 2);
-  static int64_t nPtsY = nG * (nX - nG * 2) * (nZ - nG * 2);
-  static bool IsFirstTime = true;
   static arma_cube var_scgc;
 
   static int64_t nVarsToPass = 1;
 
-  if (IsFirstTime) {
+  if (!grid.isExchangeInitialized) {
     DidWork = exchange_sides_init(grid, nVarsToPass);
-    var_scgc.set_size(nX, nY, nX);
-    IsFirstTime = false;
+    grid.isExchangeInitialized = true;
   }
+  var_scgc.set_size(nX, nY, nZ);
 
   int64_t iP;
   precision_t oneSign = 1.0;
 
-  for (int iDir = 0; iDir < 4; iDir++) {
+  nDir = 4;
+  if (grid.get_IsDipole() && grid.get_IsClosed())
+    nDir++;
+
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (report.test_verbose(4))
       std::cout << "packing one var : " << iDir << " " << iProc
                 << " " << grid.interchangesOneVar[iDir].iProc_to
@@ -991,7 +1073,7 @@ bool exchange_one_var(Grid &grid,
   }
 
   // Send all faces asynchronously:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       report.print(4, "Sending one face");
       DidWork = grid.send_one_var_one_face(iDir);
@@ -999,7 +1081,7 @@ bool exchange_one_var(Grid &grid,
   }
 
   // Receive all faces asynchronously:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       report.print(4, "Receiving one face");
       DidWork = grid.receive_one_var_one_face(iDir);
@@ -1007,13 +1089,13 @@ bool exchange_one_var(Grid &grid,
   }
 
   // Wait for messages to get there:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0)
       MPI_Wait(&grid.interchangesOneVar[iDir].requests, MPI_STATUS_IGNORE);
   }
 
   // Unpack all faces:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       iP = 0;
       report.print(4, "Unpacking Border");
@@ -1029,9 +1111,6 @@ bool exchange_one_var(Grid &grid,
     }
   }
 
-  // Wait for all processors to be done.
-  MPI_Barrier(aether_comm);
-
   // If this is a cubesphere grid, interpolate ghostcells to their proper location
   //if (grid.IsCubeSphereGrid & grid.gcInterpolationSet) {
   //  report.print(3, "Interpolating Ghostcells to Proper Location");

From 1c2ef28a463b1e3336f2a16d61b1dd82bdcbaa64 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:06:30 -0400
Subject: [PATCH 099/120] FEAT: gotta know if dipole and closed!

---
 src/grid.cpp | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/src/grid.cpp b/src/grid.cpp
index 168a0999..93270b37 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -520,6 +520,23 @@ void Grid::set_IsDipole(bool value) {
   IsDipole = value;
 }
 
+// --------------------------------------------------------------------------
+// Get whether the grid is a dipole grid
+// --------------------------------------------------------------------------
+
+bool Grid::get_IsDipole() {
+  return IsDipole;
+}
+
+// --------------------------------------------------------------------------
+// Get whether the dipole grid is closed (true) or open (false)
+// --------------------------------------------------------------------------
+
+bool Grid::get_IsClosed() {
+  return IsClosed;
+}
+
+
 // --------------------------------------------------------------------------
 // Get total number of grid points
 // --------------------------------------------------------------------------

From 9c9ac140e84313cd38e2af3c7c8570e9a204e0fe Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:07:08 -0400
Subject: [PATCH 100/120] FEAT: dipole quadtree is like sphere quadtree

---
 src/quadtree.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index ff5d45a2..35ebb2e5 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -514,19 +514,18 @@ int64_t Quadtree::find_point(arma_vec point) {
 
   if (IsSphere)
     wrap_point = wrap_point_sphere(point);
-
   if (IsCubeSphere)
     wrap_point = wrap_point_cubesphere(point);
+  if (IsDipole)
+    wrap_point = wrap_point_sphere(point);
 
   int64_t iNode = -1;
 
   for (int64_t iRoot = 0; iRoot < nRootNodes; iRoot++) {
     iNode = find_point(wrap_point, root_nodes[iRoot]);
-
     if (iNode > -1)
       break;
   }
-
   return iNode;
 }
 
@@ -542,9 +541,10 @@ int64_t Quadtree::find_root(arma_vec point) {
 
   if (IsSphere)
     wrap_point = wrap_point_sphere(point);
-
   if (IsCubeSphere)
     wrap_point = wrap_point_cubesphere(point);
+  if (IsDipole)
+    wrap_point = wrap_point_cubesphere(point);
 
   int64_t iNode = -1, iRoot;
 

From 1f0622b3ad0782fdf2f47f1329cd5b22ce1d7de1 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:07:58 -0400
Subject: [PATCH 101/120] FEAT: connectivity for dipole grid

---
 src/init_mag_grid.cpp | 128 ++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 124 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index c47ecb2f..6add43e5 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -5,6 +5,127 @@
 
 #include "aether.h"
 
+// ----------------------------------------------------------------------
+// Create connectivity between the nodes for message passing for dipole
+//  (this looks a lot like sphere, since they are very related)
+// ----------------------------------------------------------------------
+
+void Grid::create_dipole_connection(Quadtree quadtree) {
+
+  std::string function = "Grid::create_dipole_connection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  IsLatLonGrid = true;
+
+  // Get some coordinates and sizes in normalized coordinates:
+  arma_vec lower_left_norm = quadtree.get_vect("LL");
+  arma_vec middle_norm = quadtree.get_vect("MID");
+  arma_vec size_right_norm = quadtree.get_vect("SR");
+  arma_vec size_up_norm = quadtree.get_vect("SU");
+
+  // Move to the next block in 4 directions:
+  arma_vec down_norm = middle_norm - 0.51 * size_up_norm;
+  arma_vec up_norm = middle_norm + 0.51 * size_up_norm;
+  arma_vec left_norm = middle_norm - 0.51 * size_right_norm;
+  arma_vec right_norm = middle_norm + 0.51 * size_right_norm;
+
+  // The first component could wrap around:
+  right_norm(0) = fmod(right_norm(0), quadtree.limit_high(0));
+  left_norm(0) = fmod((left_norm(0) + quadtree.limit_high(0)),
+                      quadtree.limit_high(0));
+
+  // These should be the exact edge of the face.
+  // The from and to processors should get these in the same place,
+  // so they can be used to match which processor to send / receive info
+  edge_Xp = middle_norm + size_right_norm / 2.0;
+  // wrap in longitude:
+  edge_Xp(0) = fmod(edge_Xp(0), quadtree.limit_high(0));
+  edge_Xm = middle_norm - size_right_norm / 2.0;
+  edge_Yp = middle_norm + size_up_norm / 2.0;
+  edge_Ym = middle_norm - size_up_norm / 2.0;
+  // by default, edge_Z isn't even an edge, since most processors should 
+  // not exchange messages in the Z direction.
+  edge_Z = middle_norm;
+
+  iProcYm = quadtree.find_point(down_norm) + iMember * nGrids;
+  iProcYp = quadtree.find_point(up_norm) + iMember * nGrids;
+  iProcXm = quadtree.find_point(left_norm) + iMember * nGrids;
+  iProcXp = quadtree.find_point(right_norm) + iMember * nGrids;
+  iProcZ = iProc;
+
+  iRoot = quadtree.find_root(middle_norm);
+  iRootYm = quadtree.find_root(down_norm);
+  iRootYp = quadtree.find_root(up_norm);
+  iRootXm = quadtree.find_root(left_norm);
+  iRootXp = quadtree.find_root(right_norm);
+  iRootZ = iRoot;
+
+  // If we are a closed field-line, then we want to exchange messages
+  // along the Z direction, which turns out to be the same processor
+  // as the Y direction, so just take that one:
+  IsClosed = false;
+  if ((middle_norm(1) < 0) && (up_norm(1) > 0)) {
+    // We are in the south and need to pass to the north:
+    iRootZ = iRootYp;
+    iProcZ = iProcYp;
+    edge_Z = edge_Yp;
+    // To make the point unique, we need to alter the edge location
+    // otherwise the message passing will get confused. Since the
+    // points are "higher" than the other edges, let's just add some
+    // to the 3rd dimension:
+    edge_Z(2) = 5.0;
+    IsClosed = true;
+  }
+  if ((middle_norm(1) > 0) && (down_norm(1) < 0)) {
+    // We are in the north and need to pass to the south:
+    iRootZ = iRootYm;
+    iProcZ = iProcYm;
+    edge_Z = edge_Ym;
+    // See note above...
+    edge_Z(2) = 5.0;
+    IsClosed = true;
+  }
+
+  // Check if touching South Pole:
+  if (lower_left_norm(1) == quadtree.limit_low(1)) {
+    DoesTouchSouthPole = true;
+
+    // edges need to be adjusted to deal with longitudes, since the
+    // pole will 180deg different for the from and to processors
+    if (edge_Ym(0) < 1.0)
+      edge_Ym(0) += 0.5;
+    else
+      edge_Ym(0) -= 0.5;
+  }
+
+  // Check if touching North Pole:
+  if (lower_left_norm(1) + size_up_norm(1) == quadtree.limit_high(1)) {
+    DoesTouchNorthPole = true;
+
+    // edge need to be adjusted to deal with longitudes, since the
+    // pole will 180deg different for the from and to processors
+    if (edge_Yp(0) < 1.0)
+      edge_Yp(0) += 0.5;
+    else
+      edge_Yp(0) -= 0.5;
+  }
+
+  if (report.test_verbose(2))
+    std::cout << "connectivity : "
+              << "  iProc : " << iProc << "\n"
+              << "  isnorth : " << DoesTouchNorthPole << "\n"
+              << "  issouth : " << DoesTouchSouthPole << "\n"
+              << "  iProcYm : " << iProcYm << "\n"
+              << "  iProcYp : " << iProcYp << "\n"
+              << "  iProcXm : " << iProcXm << "\n"
+              << "  iProcXp : " << iProcXp << "\n";
+
+  report.exit(function);
+  return;
+}
+
+
 // ----------------------------------------------------------------------
 // Routine to convert p and q to r and theta. Can be solved iteratively,
 // or with approach from (Swisdak, 2006), who solved it analytically:
@@ -99,10 +220,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsCubeSphereGrid = false;
   IsDipole = true;
 
-  // report.print(0, "Creating inter-node connections Grid");
-
-  //if (!Is0D & !Is1Dz)
-  //  create_sphere_connection(quadtree_ion);
+  report.print(0, "Creating inter-node dipole connections");
+  if (!Is0D & !Is1Dz)
+    create_dipole_connection(quadtree_ion);
 
   report.print(0, "Creating Dipole Grid");
 

From 12ba82c8f08778739bb1d96965737b06c0bc0f2d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:08:31 -0400
Subject: [PATCH 102/120] FEAT: exchange messages for ion grid

---
 src/advance.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/advance.cpp b/src/advance.cpp
index c474636e..b85395ae 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -127,6 +127,7 @@ bool advance(Planets &planet,
   if (gGrid.get_HasXdim() || gGrid.get_HasYdim()) {
     neutrals.exchange_old(gGrid);
     ions.exchange_old(gGrid);
+    ionsMag.exchange_old(mGrid);
     advect(gGrid, time, neutrals);
   }
 

From 0bb1649e7e157fce367a70b45c29d829a7a2ae8f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 30 Apr 2025 11:16:30 -0400
Subject: [PATCH 103/120] BUG: row/col switched in solver_gradients 4o_j

---
 src/solver_gradients.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 6773f30e..ef1c06c7 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -264,7 +264,7 @@ arma_cube calc_gradient4o_j(arma_cube value, Grid grid) {
     iY = 0;
     gradient.col(iY) =
       (value.col(iY + 1) - value.col(iY)) /
-      grid.dj_center_m_scgc.row(iY);
+      grid.dj_center_m_scgc.col(iY);
 
     // Upper (one sided):
     iY = nY - 1;

From 4961430013ba9fd7bfb5b1d44e95c5f9908eb8b2 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 8 May 2025 09:42:05 -0400
Subject: [PATCH 104/120] BUG: init_mag_grid wasn't correctly identifying if
 its touching the pole

---
 src/init_mag_grid.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 6add43e5..8de44039 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -350,7 +350,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - evenly space the ghost cells between these.
 
   // Check if we're touching the pole, need to look at original quadtree values
-  if ((abs(lower_left_norm(1) + size_up_norm(1)) > 0.49) // north pole
+  if ((lower_left_norm(1) + size_up_norm(1) > 0.49) // north pole
       || (lower_left_norm(1) < -0.49)) { // south pole
     lat1dDown(nLats) = 89.9 * cDtoR;
     lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;

From c740361928b267312c102ca99e302bf9c3cc8c2f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 8 May 2025 09:43:05 -0400
Subject: [PATCH 105/120] STY: astyle, yo

---
 src/init_mag_grid.cpp | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8de44039..f5c3f584 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -44,7 +44,7 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
   edge_Xm = middle_norm - size_right_norm / 2.0;
   edge_Yp = middle_norm + size_up_norm / 2.0;
   edge_Ym = middle_norm - size_up_norm / 2.0;
-  // by default, edge_Z isn't even an edge, since most processors should 
+  // by default, edge_Z isn't even an edge, since most processors should
   // not exchange messages in the Z direction.
   edge_Z = middle_norm;
 
@@ -65,6 +65,7 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
   // along the Z direction, which turns out to be the same processor
   // as the Y direction, so just take that one:
   IsClosed = false;
+
   if ((middle_norm(1) < 0) && (up_norm(1) > 0)) {
     // We are in the south and need to pass to the north:
     iRootZ = iRootYp;
@@ -77,6 +78,7 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
     edge_Z(2) = 5.0;
     IsClosed = true;
   }
+
   if ((middle_norm(1) > 0) && (down_norm(1) < 0)) {
     // We are in the north and need to pass to the south:
     iRootZ = iRootYm;
@@ -221,6 +223,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsDipole = true;
 
   report.print(0, "Creating inter-node dipole connections");
+
   if (!Is0D & !Is1Dz)
     create_dipole_connection(quadtree_ion);
 
@@ -608,11 +611,11 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   }
 
   // Gravity should be negative in the k direction, since the grid switches directions.
-  // j direction should switch signs when crossing the equator (+ in south, - in north)  
-  rad_unit_vcgc[1] = sign(magLat_scgc) % cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
-                                                % sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = - 2.0 * abs(sin(magLat_scgc)) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
-                                                      % sin(magLat_scgc)), 0.5);
+  // j direction should switch signs when crossing the equator (+ in south, - in north)
+  rad_unit_vcgc[1] = sign(magLat_scgc) % cos(magLat_scgc)
+                     / pow(abs(1.0 + 3.0 * sin(magLat_scgc) % sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = - 2.0 * abs(sin(magLat_scgc))
+                     / pow(abs(1.0 + 3.0 * sin( magLat_scgc) % sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;

From eb9e29b5cbce1de3e49a96623d6b3e80cf5a082d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 8 May 2025 13:49:22 -0400
Subject: [PATCH 106/120] BUG: Dipole missing factor of 2 in number of GCs

---
 edu/examples/Dipole/MoreDipoleInfo.ipynb | 12 ++++++------
 edu/examples/Dipole/dipole.py            |  2 +-
 src/init_mag_grid.cpp                    |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
index 6d93769d..547388a1 100644
--- a/edu/examples/Dipole/MoreDipoleInfo.ipynb
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -130,7 +130,7 @@
     "# call dipole.main, which returns q & p\n",
     "# These values are a bit unreasonable, but it makes the plots more clear.\n",
     "qs, ps = dipole.main(alt_minRE=1.01, alt_maxRE=1.3,\n",
-    "                     lat_min=15, lat_max=80,\n",
+    "                     lat_min=15, lat_max=84,\n",
     "                     origins=origins, extent=extent, \n",
     "                     nLatsPerBlock=14, nAltsPerBlock=18)\n",
     "\n",
@@ -178,7 +178,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -311,7 +311,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 4 Axes>"
       ]
@@ -367,9 +367,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:mypy]",
+   "display_name": "Python [conda env:interactive]",
    "language": "python",
-   "name": "conda-env-mypy-py"
+   "name": "conda-env-interactive-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -381,7 +381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index 676a0b3e..f188a3ea 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -53,7 +53,7 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             origin  = -1*origin - extent
 
         lat0 = (2*(lat_max - lat_min))*origin    
-        dlat = extent * ((lat_max - lat_min)*2) / (nLatsPerBlock - nGCs)
+        dlat = extent * (2*(lat_max - lat_min)) / (nLatsPerBlock - nGCs*2)
         
         # Put latitudes down evenly (centers & corners)
         # - This forms the invariant latitudes which field lines must pass thru
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index f5c3f584..1bdfd0c2 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -335,7 +335,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   precision_t lat0 = 2.0 * (max_lat - min_lat) * lat_origin;
   precision_t dlat = 2.0 * size_up_norm(1) * (max_lat -  min_lat) /
-                     (nLats - nGCs);
+                     (nLats - nGCs * 2);
 
   arma_vec lat1d(nLats);
   arma_vec lat1dDown(nLats + 1);

From d8c29af5ff73cd3ce328b717d7fdee3421e0b7f6 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:00:48 -0400
Subject: [PATCH 107/120] BUG: need a communicator for members of same ensemble

---
 include/parallel.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/parallel.h b/include/parallel.h
index 6da886c5..11372be3 100644
--- a/include/parallel.h
+++ b/include/parallel.h
@@ -30,6 +30,7 @@ extern std::string cGrid;
 
 /// communicator for all of aether
 extern MPI_Comm aether_comm;
+extern MPI_Comm aether_member_comm;
 
 /**********************************************************************
   \brief initialize mpi and figure out ensembles and grid blocks

From fe71babaf326c466d4eaeea6fc749d7f93bec290 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:02:54 -0400
Subject: [PATCH 108/120] BUG: static causes issues for second grid

---
 src/exchange_messages.cpp | 138 +++++++++++++++++++++++++++++---------
 1 file changed, 107 insertions(+), 31 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index 8e0a41be..895bb854 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -36,6 +36,58 @@ bool Neutrals::exchange_old(Grid &grid) {
   return DidWork;
 }
 
+void average_value_at_pole(Grid &grid, arma_cube &value, arma_cube &velocity,
+                           int64_t iLast, int64_t iPole,
+                           bool doesTouchPole) {
+  // Now let's deal with the poles:
+  // north pole first:
+  int64_t iX, nX = grid.get_nX();
+  int64_t iZ, nZ = grid.get_nZ();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t iY, iInc = 1;
+
+  if (iPole < iLast)
+    iInc = -1;
+
+  double weight, sumValue, sumWeight, totalValue, totalWeight, poleValue;
+
+  for (iZ = nGCs; iZ < nZ - nGCs; iZ++) {
+
+    sumValue = 0.0;
+    sumWeight = 0.0;
+
+    if (doesTouchPole) {
+      for (iX = nGCs; iX < nX - nGCs; iX++) {
+        // determine the weight based on the northward velocity:
+        weight = iInc * velocity(iX, iLast, iZ) / 1000.0 + 1.0;
+
+        if (weight < 0.01)
+          weight = 0.01;
+
+        sumValue = sumValue + value(iX, iLast, iZ) * weight;
+        sumWeight = sumWeight + weight;
+      }
+    }
+
+    MPI_Allreduce(&sumValue, &totalValue, 1, MPI_DOUBLE, MPI_SUM,
+                  aether_comm);
+    MPI_Allreduce(&sumWeight, &totalWeight, 1, MPI_DOUBLE, MPI_SUM,
+                  aether_comm);
+    poleValue = totalValue / totalWeight;
+
+    //if (iZ == 10)
+    //std::cout << "pole value : " << poleValue << " " << totalValue << " " << totalWeight << " "
+    //<< iProc << " " << doesTouchPole << "\n";
+
+    if (doesTouchPole) {
+      for (iX = nGCs; iX < nX - nGCs; iX++)
+        for (iY = iLast + iInc; iY == iPole; iY += iInc)
+          value(iX, iY, iZ) = poleValue;
+    }
+  }
+
+}
+
 // -----------------------------------------------------------------------------
 // This is the main exchange messages for the neutrals.
 //   We are exchanging densities, temperatures, and velocities
@@ -53,7 +105,7 @@ bool Ions::exchange_old(Grid &grid) {
   for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
     DidWork = exchange_one_var(grid, species[iSpecies].density_scgc, false);
 
-  DidWork = exchange_one_var(grid, temperature_scgc, false);
+  //DidWork = exchange_one_var(grid, temperature_scgc, false);
   DidWork = exchange_one_var(grid, electron_temperature_scgc, false);
 
   // velocity components:
@@ -64,6 +116,25 @@ bool Ions::exchange_old(Grid &grid) {
   // don't reverse vertical across the pole:
   DidWork = exchange_one_var(grid, velocity_vcgc[2], false);
 
+  int64_t iPole = grid.get_nY() - 1;
+  int64_t iLast = iPole - nGCs;
+
+  average_value_at_pole(grid, temperature_scgc, velocity_vcgc[1],
+                        iLast, iPole, grid.DoesTouchNorthPole);
+
+  for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
+    average_value_at_pole(grid, species[iSpecies].density_scgc, velocity_vcgc[1],
+                          iLast, iPole, grid.DoesTouchNorthPole);
+
+  iPole = 0;
+  iLast = nGCs;
+  average_value_at_pole(grid, temperature_scgc, velocity_vcgc[1],
+                        iLast, iPole, grid.DoesTouchSouthPole);
+
+  for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
+    average_value_at_pole(grid, species[iSpecies].density_scgc, velocity_vcgc[1],
+                          iLast, iPole, grid.DoesTouchSouthPole);
+
   report.exit(function);
   return DidWork;
 }
@@ -119,9 +190,9 @@ bool pack_border(const arma_cube &value,
                  int iDir) {
 
   bool DidWork = true;
-  static int64_t nX = value.n_rows;
-  static int64_t nY = value.n_cols;
-  static int64_t nZ = value.n_slices;
+  int64_t nX = value.n_rows;
+  int64_t nY = value.n_cols;
+  int64_t nZ = value.n_slices;
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
@@ -221,9 +292,9 @@ bool unpack_border(arma_cube &value,
                    bool XbecomesY) {
 
   bool DidWork = true;
-  static int64_t nX = value.n_rows;
-  static int64_t nY = value.n_cols;
-  static int64_t nZ = value.n_slices;
+  int64_t nX = value.n_rows;
+  int64_t nY = value.n_cols;
+  int64_t nZ = value.n_slices;
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
@@ -345,6 +416,7 @@ bool unpack_border(arma_cube &value,
   } catch (...) {
     DidWork = false;
   }
+
   return DidWork;
 }
 
@@ -394,7 +466,7 @@ bool pack_one_var_on_one_face(arma_cube var_scgc,
                               int iDirToPass,
                               Grid &grid) {
 
-  static int nG = grid.get_nGCs();
+  int nG = grid.get_nGCs();
   int iDir = grid.interchangesOneVar[iDirToPass].iFace;
   int iReceiver = grid.interchangesOneVar[iDirToPass].iProc_to;
   precision_t *buffer = grid.interchangesOneVar[iDirToPass].buffer;
@@ -475,9 +547,9 @@ bool Grid::send_one_var_one_face(int64_t iFace) {
 
   if (report.test_verbose(4))
     std::cout << "in send_one_var_one_face : " << iFace << " from: " <<
-    iProc << " to: " <<
-    interchangesOneVar[iFace].iProc_to << " tag: " <<
-    interchangesOneVar[iFace].iTag << "\n";
+              iProc << " to: " <<
+              interchangesOneVar[iFace].iProc_to << " tag: " <<
+              interchangesOneVar[iFace].iTag << "\n";
 
   MPI_Isend(interchangesOneVar[iFace].buffer,
             interchangesOneVar[iFace].iSizeTotal,
@@ -521,9 +593,9 @@ bool Grid::receive_one_var_one_face(int64_t iFace) {
 
   if (report.test_verbose(4))
     std::cout << "in receive_one_var_one_face : " << iFace << " from: " <<
-    iProc << " to: " << 
-    interchangesOneVar[iFace].iProc_to << " tag: " <<
-    interchangesOneVar[iFace].iTag << "\n";
+              iProc << " to: " <<
+              interchangesOneVar[iFace].iProc_to << " tag: " <<
+              interchangesOneVar[iFace].iTag << "\n";
 
 
   MPI_Recv(interchangesOneVar[iFace].rbuffer,
@@ -1026,24 +1098,28 @@ bool exchange_one_var(Grid &grid,
   int iTag, iDir, nDir;
   int iSpecies;
 
-  static int64_t nX = grid.get_nX();
-  static int64_t nY = grid.get_nY();
-  static int64_t nZ = grid.get_nZ();
-  static int64_t nG = grid.get_nGCs();
-  static arma_cube var_scgc;
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
+  int64_t nG = grid.get_nGCs();
+  arma_cube var_scgc;
 
-  static int64_t nVarsToPass = 1;
+  int64_t nVarsToPass = 1;
 
   if (!grid.isExchangeInitialized) {
     DidWork = exchange_sides_init(grid, nVarsToPass);
     grid.isExchangeInitialized = true;
+    std::cout << "initializing : " << grid.get_gridtype() << " " << nZ << " " <<
+              iProc << "\n";
   }
+
   var_scgc.set_size(nX, nY, nZ);
 
   int64_t iP;
   precision_t oneSign = 1.0;
 
   nDir = 4;
+
   if (grid.get_IsDipole() && grid.get_IsClosed())
     nDir++;
 
@@ -1139,10 +1215,10 @@ bool test_ghostcell_interpolation(Grid &grid) {
 
   bool didWork = true;
 
-  static int64_t iX, nX = grid.get_nX();
-  static int64_t iY, nY = grid.get_nY();
-  static int64_t iZ, nZ = grid.get_nZ();
-  static int64_t nG = grid.get_nGCs();
+  int64_t iX, nX = grid.get_nX();
+  int64_t iY, nY = grid.get_nY();
+  int64_t iZ, nZ = grid.get_nZ();
+  int64_t nG = grid.get_nGCs();
   int64_t iStart, iEnd, jStart, jEnd, iDir;
 
   // Check the latitudes and longitudes to make sure that they map to
@@ -1252,9 +1328,9 @@ arma_cube interpolate_ghostcells(arma_cube varIn, Grid &grid) {
   bool didWork = true;
 
   int64_t iDir;
-  static int64_t iX, ix_, nX = grid.get_nX();
-  static int64_t iY, iy_, nY = grid.get_nY();
-  static int64_t iG, nG = grid.get_nGCs();
+  int64_t iX, ix_, nX = grid.get_nX();
+  int64_t iY, iy_, nY = grid.get_nY();
+  int64_t iG, nG = grid.get_nGCs();
   precision_t r_;
   arma_cube varOut = varIn;
 
@@ -1322,10 +1398,10 @@ bool find_ghostcell_interpolation_coefs(Grid &grid) {
 
   bool didWork = true;
 
-  static int64_t iX, nX = grid.get_nX();
-  static int64_t iY, nY = grid.get_nY();
-  static int64_t iZ, nZ = grid.get_nZ();
-  static int64_t nG = grid.get_nGCs();
+  int64_t iX, nX = grid.get_nX();
+  int64_t iY, nY = grid.get_nY();
+  int64_t iZ, nZ = grid.get_nZ();
+  int64_t nG = grid.get_nGCs();
 
   // Test to see if the longitudes are the same as the original
   arma_cube yOther = grid.refy_angle * cRtoD;

From 165582890d29ed1a3fc8961876b3333d4033496b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:03:55 -0400
Subject: [PATCH 109/120] FEAT: new communicator for ensemble

---
 src/init_parallel.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/init_parallel.cpp b/src/init_parallel.cpp
index c940148a..e89751d2 100644
--- a/src/init_parallel.cpp
+++ b/src/init_parallel.cpp
@@ -22,6 +22,7 @@ std::string cMember;
 std::string cGrid;
 
 MPI_Comm aether_comm;
+MPI_Comm aether_member_comm;
 
 bool init_parallel(Quadtree &quadtree, Quadtree &quadtree_ion) {
 
@@ -71,6 +72,11 @@ bool init_parallel(Quadtree &quadtree, Quadtree &quadtree_ion) {
     iMember = iProc / nGrids;
     iGrid = iProc % nGrids;
 
+    // Need a communicator for each ensemble member, this allows
+    // communication between all blocks in one ensemble member without
+    // the others getting the messages:
+    MPI_Comm_split(aether_comm, iMember, iGrid, &aether_member_comm);
+
     if (report.test_verbose(2))
       std::cout << "iProc : " << iProc
                 << "; iMember : " << iMember

From 5fe11f9af853b870389ae468dd67712107423d38 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:05:16 -0400
Subject: [PATCH 110/120] FEAT: sync mean across ensemble member

---
 src/tools.cpp | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/src/tools.cpp b/src/tools.cpp
index d041bf49..c62b3172 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -238,6 +238,23 @@ precision_t sync_mean_across_all_procs(precision_t value) {
   return global_value;
 }
 
+// ----------------------------------------------------------------------------
+// Calculate the average value across all processors
+//   - this is the same as sync_mean_across_all_procs, but is limited to
+//     processors in a given member
+// ----------------------------------------------------------------------------
+
+precision_t sync_mean_across_member(precision_t value) {
+  precision_t global_value;
+  double vSend, vReceive;
+  double nSend, nReceive;
+  vSend = value;
+  nSend = 1.0;
+  MPI_Allreduce(&vSend, &vReceive, 1, MPI_DOUBLE, MPI_SUM, aether_member_comm);
+  MPI_Allreduce(&nSend, &nReceive, 1, MPI_DOUBLE, MPI_SUM, aether_member_comm);
+  global_value = vReceive / nReceive;
+  return global_value;
+}
 // ----------------------------------------------------------------------------
 // Generate a vector of normally distributed random doubles
 // ----------------------------------------------------------------------------

From ab076ec80aa56574bc1a1a0ed4c7796f65640170 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 22 May 2025 20:46:10 -0400
Subject: [PATCH 111/120] BUG: Fix read_armadillo.py, indexing was
 weird/inconsistent

---
 srcPython/read_armadillo.py | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/srcPython/read_armadillo.py b/srcPython/read_armadillo.py
index 4c75c0b8..9f9427ed 100644
--- a/srcPython/read_armadillo.py
+++ b/srcPython/read_armadillo.py
@@ -111,13 +111,10 @@ def cube2np(files2read):
                     f"File ({thisf}) does not appear to be an armadillo cube.\n"
                     f"Found shape: {shape}")
             shape = np.array(shape, dtype=int) # convert shape to np array of int's
-            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
-            for i in range(int(shape[0])): # n_x
-                for j in range(int(shape[2])): # n_z
-                    # each line is n_y long. Convert it to a python list & retain it
-                    l = f.readline().strip().replace('  ',',').split(',')
-                    ls[i,:, j] = l # n_y
-        out.append(ls.T) # speed not a huge issue, work with lists
+        # Read in cube, transform shape, use same indexing as arma does
+        one_cube = np.loadtxt(thisf, skiprows=2, ).reshape(shape)
+        
+        out.append(one_cube) # speed not a huge issue, work with lists
 
     # remove 0th dimension if we only are reading one file
     if len(files2read) == 1:
@@ -165,12 +162,10 @@ def mat2np(files2read):
                     f"File ({thisf}) does not appear to be an armadillo matrix.\n"
                     f"Found shape: {shape}")
             shape = np.array(shape, dtype=int) # convert shape to np array of int's
-            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
-            for i in range(int(shape[0])): # n_x
-                # each line is n_y long. Convert it to a python list & retain it
-                l = f.readline().strip().replace('  ',',').split(',')
-                ls[i,:] = l # n_y
-        out.append(ls.T) # speed not a huge issue, work with lists
+        
+        one_mat = np.loadtxt(thisf, skiprows=2).reshape(shape)
+
+        out.append(ls) # speed not a huge issue, work with lists
 
     # remove 0th dimension if we only are reading one file
     if len(files2read) == 1:

From a22076165d6f9d159236a87720c2bc1bda3f2630 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 11:55:28 -0400
Subject: [PATCH 112/120] I hate warnings

---
 ext/IE/mh86.f | 126 +++++++++++++++++++++++++++-----------------------
 1 file changed, 69 insertions(+), 57 deletions(-)

diff --git a/ext/IE/mh86.f b/ext/IE/mh86.f
index 35785c53..87548c6a 100644
--- a/ext/IE/mh86.f
+++ b/ext/IE/mh86.f
@@ -105,14 +105,12 @@ SUBROUTINE MHEMODL (RMLAT,RMLT,HP,BY,BZ,MODL,ET,EP,EPOT)
 
       TIME = RMLTLON/15.
       TIME = RMLT
-   20 IF (TIME .GT. TMX) THEN
-	TIME = TIME - 24.
-	GO TO 20
-      ENDIF
-   30 IF (TIME .LT. TMN) THEN
-	TIME = TIME + 24.
-	GO TO 30
-      ENDIF
+      do while (time > tmx)
+        time = time - 24.0
+      enddo
+      do while (time < tmn)
+        time = time + 24.0
+      enddo
 
       H = SIGN (1.,RMLAT)
       RMLA = MIN(MAX(ABS(RMLAT),RMLAMN(MODL)), RMLAMX(MODL))
@@ -255,8 +253,9 @@ SUBROUTINE MHINIT (MODL,IUN,IPR,ISTAT)
       ENDIF
 
       READ (IUN,*) NLABS
-      DO 10 I=1,NLABS
-   10 READ (IUN,'(A)',END=998,ERR=999) LABELS
+      DO I=1,NLABS
+        READ (IUN,'(A)',END=998,ERR=999) LABELS
+      enddo
 
       READ (IUN,*,END=998,ERR=999) KFIT
       IF (KFIT .NE. 2) THEN
@@ -297,8 +296,9 @@ SUBROUTINE MHINIT (MODL,IUN,IPR,ISTAT)
 
       IF (MODL .EQ. 2) THEN
 C          Reverse the sign of BETA for MHS to conform to MHI convention
-	DO 20 I=1,NBETA
-   20   BETA(I,NDX,MODL) = -BETA(I,NDX,MODL)
+	  DO I=1,NBETA
+          BETA(I,NDX,MODL) = -BETA(I,NDX,MODL)
+        enddo
       ENDIF
       ENDDO
 C  30 IF (IPR .EQ. 1) WRITE (6,'(''MHINIT: Read ''A,'' pars: I MODL BY B
@@ -392,10 +392,11 @@ SUBROUTINE GETFIT (NDX,MODL,TIME,RMLA,DFIT,ESFIT,EEFIT,ISTAT)
       DFIT = 0.0
       DDXFIT = 0.
       DDYFIT = 0.
-      DO 30 I=1,NBETA
-      DFIT   = DFIT   + BETA(I,NDX,MODL)*F(1,1,I)
-      DDXFIT = DDXFIT + BETA(I,NDX,MODL)*F(2,1,I)/Y
-   30 DDYFIT = DDYFIT + BETA(I,NDX,MODL)*F(1,2,I)
+      DO I=1,NBETA
+        DFIT   = DFIT   + BETA(I,NDX,MODL)*F(1,1,I)
+        DDXFIT = DDXFIT + BETA(I,NDX,MODL)*F(2,1,I)/Y
+        DDYFIT = DDYFIT + BETA(I,NDX,MODL)*F(1,2,I)
+      enddo 
       DDYFIT = -DDYFIT
       DFIT   = -DFIT/1000.
       ESFIT  = -DDYFIT
@@ -422,10 +423,11 @@ SUBROUTINE BASPRC (TX,TY,NX,NY,KX,KY,X,Y,F)
 
       NDERIV = MIN0 (3,KX-1,KY-1)
       N = NX*NY
-      DO 10 I=1,N
-      DO 10 J=1,9
-   10 F(J,I) = 0.
-
+      DO I=1,N
+        DO J=1,9
+          F(J,I) = 0.
+        enddo
+      enddo
       XP = MOD (X,TX(NX+1))
       CALL INTERV (TX, NX+KX, XP, ILEFTX, MFLAG)
       YP = Y
@@ -449,24 +451,25 @@ SUBROUTINE BASPRC (TX,TY,NX,NY,KX,KY,X,Y,F)
       CALL BSPLVD (TY, KY, YP, ILEFTY, A, VALY, NDERIV)
       NPX = NX - (KX-1)
 
-      DO 20 MX=1,KX
-      IX = LFTMKX + MX
-      IF (IX .GE. NX-(KX-2)) IX = IX-NX+(KX-1)
-
-      DO 30 MY=1,KY
-      IY = LFTMKY + MY - 1
-      IF (IY .GT. 0) THEN
-	DO 40 JX=1,3
-	LX = MX + KX*(JX-1)
-	DO 50 JY=1,3
-	J = JX + (JY-1)*3
-	LY = MY + KY*(JY-1)
-	I = IX + (IY-1)*NPX
-   50   F(J,I) = VALX(LX)*VALY(LY)
-   40   CONTINUE
-      ENDIF
-   30 CONTINUE
-   20 CONTINUE
+      DO MX=1,KX
+        IX = LFTMKX + MX
+        IF (IX .GE. NX-(KX-2)) IX = IX-NX+(KX-1)
+
+        DO MY=1,KY
+          IY = LFTMKY + MY - 1
+          IF (IY .GT. 0) THEN
+	      DO JX=1,3
+	        LX = MX + KX*(JX-1)
+	        DO JY=1,3
+	          J = JX + (JY-1)*3
+	          LY = MY + KY*(JY-1)
+	          I = IX + (IY-1)*NPX
+                F(J,I) = VALX(LX)*VALY(LY)
+              enddo
+            enddo
+          ENDIF
+        enddo
+      enddo
 
       RETURN
       END
@@ -631,14 +634,15 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C     ORDER  BEFORE  BSPLVB  IS CALLED TO PUT VALUES FOR THE NEXT
 C     HIGHER ORDER ON TOP OF IT.
       IDERIV = MHIGH
-      DO 15 M=2,MHIGH
+      DO M=2,MHIGH
          JP1MID = 1
-         DO 11 J=IDERIV,K
+         DO J=IDERIV,K
             DBIATX(J,IDERIV) = DBIATX(JP1MID,1)
-   11       JP1MID = JP1MID + 1
+            JP1MID = JP1MID + 1
+         enddo
          IDERIV = IDERIV - 1
          CALL BSPLVB(T,KP1-IDERIV,2,X,LEFT,DBIATX)
-   15    CONTINUE
+      enddo
 C
 C     AT THIS POINT,  B(LEFT-K+I, K+1-J)(X) IS IN  DBIATX(I,J) FOR
 C     I=J,...,K AND J=1,...,MHIGH ('=' NDERIV). IN PARTICULAR, THE
@@ -647,15 +651,17 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C     RATE THEIR B-REPR. BY DIFFERENCING, THEN EVALUATE AT  X.
 C
       JLOW = 1
-      DO 20 I=1,K
-         DO 19 J=JLOW,K
-   19       A(J,I) = 0.
+      DO I=1,K
+         DO J=JLOW,K
+           A(J,I) = 0.
+         enddo
          JLOW = I
-   20    A(I,I) = 1.
+         A(I,I) = 1.
+      enddo
 C     AT THIS POINT, A(.,J) CONTAINS THE B-COEFFS FOR THE J-TH OF THE
 C     K  B-SPLINES OF INTEREST HERE.
 C
-      DO 40 M=2,MHIGH
+      DO M=2,MHIGH
          KP1MM = KP1 - M
          FKP1MM = FLOAT(KP1MM)
          IL = LEFT
@@ -665,14 +671,16 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C        B-SPLINES FROM THOSE FOR PRECEDING DERIVATIVE BY DIFFERENCING
 C        AND STORE AGAIN IN  A(.,J) . THE FACT THAT  A(I,J) = 0  FOR
 C        I .LT. J  IS USED.
-         DO 25 LDUMMY=1,KP1MM
+         DO LDUMMY=1,KP1MM
             FACTOR = FKP1MM/(T(IL+KP1MM) - T(IL))
 C           THE ASSUMPTION THAT T(LEFT).LT.T(LEFT+1) MAKES DENOMINATOR
 C           IN  FACTOR  NONZERO.
-            DO 24 J=1,I
-   24          A(I,J) = (A(I,J) - A(I-1,J))*FACTOR
+            DO J=1,I
+              A(I,J) = (A(I,J) - A(I-1,J))*FACTOR
+            enddo
             IL = IL - 1
-   25       I = I - 1
+            I = I - 1
+         enddo
 C
 C        FOR I=1,...,K, COMBINE B-COEFFS A(.,I) WITH B-SPLINE VALUES
 C        STORED IN DBIATX(.,M) TO GET VALUE OF  (M-1)ST  DERIVATIVE OF
@@ -681,12 +689,15 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C        OF ORDER M THERE IS SAFE SINCE THE REMAINING B-SPLINE DERIVAT-
 C        IVES OF THE SAME ORDER DO NOT USE THIS VALUE DUE TO THE FACT
 C        THAT  A(J,I) = 0  FOR J .LT. I .
-   30    DO 40 I=1,K
+   30    DO I=1,K
             SUM = 0.
             JLOW = MAX0(I,M)
-            DO 35 J=JLOW,K
-   35          SUM = A(J,I)*DBIATX(J,M) + SUM
-   40       DBIATX(I,M) = SUM
+            DO J=JLOW,K
+              SUM = A(J,I)*DBIATX(J,M) + SUM
+            enddo
+            DBIATX(I,M) = SUM
+         enddo
+       enddo
    99                                   RETURN
       END
 
@@ -772,10 +783,11 @@ SUBROUTINE BSPLVB ( T, JHIGH, INDEX, X, LEFT, BIATX )
          DELTAR(J) = T(LEFT+J) - X
          DELTAL(J) = X - T(LEFT+1-J)
          SAVED = 0.
-         DO 26 I=1,J
+         DO I=1,J
             TERM = BIATX(I)/(DELTAR(I) + DELTAL(JP1-I))
             BIATX(I) = SAVED + DELTAR(I)*TERM
-   26       SAVED = DELTAL(JP1-I)*TERM
+            SAVED = DELTAL(JP1-I)*TERM
+         enddo
          BIATX(JP1) = SAVED
          J = JP1
          IF (J .LT. JHIGH)              GO TO 20

From e1172caec844fc71f82ebdf416ad8d8d2a7ca515 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 14:57:45 -0400
Subject: [PATCH 113/120] BUG: Typo in input/importError in postaether

---
 srcPython/postAether.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index f4e0455c..b3523cab 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -15,7 +15,7 @@
 try:
     from netCDF4 import Dataset
     from h5py import File
-except InputError:
+except ImportError:
     print("NetCDF and/or h5py not found")
 
 # ----------------------------------------------------------------------

From c039efd0d31cd2b4f3de2915f84e1cd5196ac37d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 16:21:25 -0400
Subject: [PATCH 114/120] FEAT: Dipole interpolation coefficients

---
 include/grid.h                    |   4 +-
 src/solver_grid_interpolation.cpp | 128 +++++++++++++++++++++++++-----
 2 files changed, 111 insertions(+), 21 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index ebee7cc9..cde1a92c 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -426,7 +426,9 @@ class Grid
    */
   bool set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                const std::vector<precision_t> &Lats,
-                               const std::vector<precision_t> &Alts);
+                               const std::vector<precision_t> &Alts,
+                               bool areLocsGeo=true,
+                               bool areLocsIJK=true);
 
   /**
    * \brief Set the interpolation coefficients for the dipole grid
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index a974de13..2c6ea23c 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -399,39 +399,127 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
 // Set the interpolation coefficients
 // --------------------------------------------------------------------------
 
-bool Grid::set_interpolation_coefs(const std::vector<precision_t> &Lons,
-                                   const std::vector<precision_t> &Lats,
-                                   const std::vector<precision_t> &Alts) {
-  // If this is not a geo grid, return false
-  if (!IsGeoGrid)
-    return false;
+bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
+                                   const std::vector<precision_t> &j_coords,
+                                   const std::vector<precision_t> &k_coords,
+                                   bool areLocsGeo,// geo or mag?
+                                   bool areLocsIJK // Are locs in 'native' coords?
+                                  ) {
+  /*
+  Inputs:
+    i_coord: longitude, either geo or mag (depends if areLocsGeo)
+    j_coord:
+      - Latitude if geographic
+      - Invariant latitude if magnetic (areLocsGeo = false)
+      - L-shell / dipole 'p': if magnetic and (areLocsIJK = false)
+    k_coord:
+      - Altitude/radius if NOT areLocsIJK
+      - distance along field line, or dipole 'q' if areLocsIJK
+  */
+
+  std::string function = "Grid::set_interpolation_coefs";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
 
   // If the size of Lons, Lats and Alts are not the same, return false
-  if (Lons.size() != Lats.size() || Lats.size() != Alts.size())
+  if (i_coords.size() != j_coords.size() || j_coords.size() != k_coords.size()) {
+    report.error("Length of i,j,k vectors do not match!");
     return false;
+  }
 
   // Clear the previous interpolation coefficients
   interp_coefs.clear();
 
-  // Handle according to whether it is cubesphere or not
-  if (IsCubeSphereGrid) {
-    // Calculate the range of the grid
-    struct cubesphere_range cr;
-    get_cubesphere_grid_range(cr);
+  if (IsGeoGrid) {
+    // Handle according to whether it is cubesphere or not
+    if (IsCubeSphereGrid) {
+      // Calculate the range of the grid
+      struct cubesphere_range cr;
+      get_cubesphere_grid_range(cr);
+
+      // Calculate the index and coefficients for each point
+      for (size_t i = 0; i < i_coords.size(); ++i)
+        set_interp_coef_cubesphere(cr, i_coords[i], j_coords[i], k_coords[i]);
+    } else if (IsLatLonGrid) {
+      // Calculate the range of the grid
+      struct sphere_range sr;
+      get_sphere_grid_range(sr);
+
+      // Calculate the index and coefficients for each point
+      for (size_t i = 0; i < i_coords.size(); ++i)
+        set_interp_coef_sphere(sr, i_coords[i], j_coords[i], k_coords[i]);
+    }
+  }
+
+  else { // IsDipole
 
-    // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < Lons.size(); ++i)
-      set_interp_coef_cubesphere(cr, Lons[i], Lats[i], Alts[i]);
-  } else {
     // Calculate the range of the grid
-    struct sphere_range sr;
-    get_sphere_grid_range(sr);
+    struct dipole_range dr;
+    get_dipole_grid_range(dr);
+
+    Planets planet;
+
+    // make holders for dipole coordinates.
+    int64_t iLoc, nPts = i_coords.size();
+    std::vector<precision_t> mlon(nPts), p_coord(nPts), q_coord(nPts), dipijk(3);
+
+    // these are the magnetic coordinates. A temporary step!
+    // this is a vector of cubes with shape (nPts, 1, 1) - avoids having to overload things
+    std::vector<arma_cube> magCoords;
+
+    if (areLocsGeo) {
+      arma_cube cubeCoord;
+      cubeCoord = vec2cube(i_coords);
+      magCoords.push_back(cubeCoord);
+      cubeCoord = vec2cube(j_coords);
+      magCoords.push_back(cubeCoord);
+      cubeCoord = vec2cube(k_coords);
+      magCoords.push_back(cubeCoord);
+
+      magCoords = geo_to_mag(magCoords[0], magCoords[1], magCoords[2], planet);
+      // for (iLoc = 0; iLoc < nPts; iLoc++) {
+      //   magCoords = geo_to_mag(i_coords[iLoc], j_coords[iLoc], k_coords[iLoc], planet);
+      //   mlon[iLoc] = magCoords[0];
+      //   p_coord[iLoc] = magCoords[1];
+      //   q_coord[iLoc] = magCoords[2];
+    }
+
+    else {
+      magCoords = {vec2cube(i_coords),vec2cube(j_coords), vec2cube(k_coords)};
+    }
+
+    // std::vector<precision_t> dipcoords = geo_to_mag(i_coords[0], j_coords[0], k_coords[0], planet);
+    std::vector<precision_t> dipCoords;
+    if (!areLocsIJK) {
+      std::vector<precision_t> planet_radii(nPts);
+      for (iLoc = 0; iLoc < nPts; iLoc++) {
+        // Convert from mag->dipole coordinates.
+        if (areLocsGeo){ // we were given the geo-latitude
+          planet_radii[iLoc] = planet.get_radius(j_coords[iLoc]);
+        } else{
+          // equatorial radius :(
+          planet_radii[iLoc] = planet.get_radius(0.0);
+        }
+        dipCoords = mag_to_ijk(i_coords[iLoc], j_coords[iLoc], k_coords[iLoc], planet_radii[iLoc]);
+        mlon[iLoc] = dipCoords[0];
+        p_coord[iLoc] = dipCoords[1];
+        q_coord[iLoc] = dipCoords[2];
+      }
+    } else {
+      // just save the values
+      for (iLoc = 0; iLoc < nPts; iLoc++) {
+        mlon[iLoc] = i_coords[iLoc];
+        p_coord[iLoc] = j_coords[iLoc];
+        q_coord[iLoc] = k_coords[iLoc];
+      }
+    }
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < Lons.size(); ++i)
-      set_interp_coef_sphere(sr, Lons[i], Lats[i], Alts[i]);
+    for (size_t i = 0; i < i_coords.size(); ++i)
+      set_interp_coef_dipole(dr, mlon[i], p_coord[i], q_coord[i]);
   }
 
+  report.exit(function);
   return true;
 }
 

From fea307eddf87877b573fd9c87899cba58abed2c1 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 17:04:28 -0400
Subject: [PATCH 115/120] FEAT: Conversions btwn mag/geo (moved one out of
 init_mag_grid)

---
 include/tools.h       |  28 ++++++++++
 src/init_mag_grid.cpp |  42 ---------------
 src/tools.cpp         | 121 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 149 insertions(+), 42 deletions(-)

diff --git a/include/tools.h b/include/tools.h
index 5c83ed34..fd899c48 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -297,4 +297,32 @@ arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in);
 // Probably needs to be moved.
 arma_cube delTheta(arma_cube magLat);
 
+////////////////////////////////////////////
+// convert cell coordinates to geographic //
+////////////////////////////////////////////
+std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
+                                   arma_cube magAlt,
+                                   Planets planet);
+
+////////////////////////////////////////////
+// convert mag coordinates to dipole ijk  //
+////////////////////////////////////////////
+std::vector<precision_t> mag_to_ijk(precision_t mlon,
+                                    precision_t mlat, 
+                                    precision_t radius,
+                                    precision_t planet_radius);
+
+
+////////////////////////////////////////////
+// convert cell coordinates to geographic //
+////////////////////////////////////////////
+std::vector<arma_cube> geo_to_mag(arma_cube glon,
+                                    arma_cube glat, 
+                                    arma_cube radius,
+																	Planets &planet) ;
+
+
+arma_cube vec2cube(std::vector<precision_t> ivec);
+
+
 #endif  // INCLUDE_TOOLS_H_
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 1bdfd0c2..8b8e0836 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -160,48 +160,6 @@ std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
   return {r, theta};
 }
 
-////////////////////////////////////////////
-// convert cell coordinates to geographic //
-////////////////////////////////////////////
-std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
-                                   arma_cube magAlt,
-                                   Planets planet) {
-  std::string function = "Grid::mag_to_geo";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  std::vector<arma_cube> llr, xyz_mag, xyz_geo, xyzRot1, xyzRot2;
-  llr.push_back(magLon);
-  llr.push_back(magLat);
-  llr.push_back(magAlt);
-  xyz_mag = transform_llr_to_xyz_3d(llr);
-
-  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
-  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
-  std::vector<precision_t> dipole_center = planet.get_dipole_center();
-
-  // Reverse our dipole rotations:
-  xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
-  xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
-
-  // offset dipole (not fully suported yet, so will be zero)
-  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
-      (dipole_center[2] != 0.0)) {
-
-    dipole_center = {0.0, 0.0, 0.0};
-  }
-
-  xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);
-  xyz_geo.push_back(xyzRot2[1] + dipole_center[1]);
-  xyz_geo.push_back(xyzRot2[2] + dipole_center[2]);
-
-  // transform back to lon, lat, radius:
-  llr = transform_xyz_to_llr_3d(xyzRot2);
-
-  report.exit(function);
-  return llr;
-}
-
 // ----------------------------------------------------------------------
 // Initialize the dipole grid.
 // - inputs (min_apex, min_alt, LatStretch, FieldLineStretch, max_lat_dipole)
diff --git a/src/tools.cpp b/src/tools.cpp
index c62b3172..4ced3b73 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -856,3 +856,124 @@ arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in) {
 
   return ans;
 }
+
+////////////////////////////////////////////
+// convert cell coordinates to geographic //
+////////////////////////////////////////////
+std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
+                                   arma_cube magAlt,
+                                   Planets planet) {
+  std::string function = "Grid::mag_to_geo";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  std::vector<arma_cube> llr, xyz_mag, xyz_geo, xyzRot1, xyzRot2;
+  llr.push_back(magLon);
+  llr.push_back(magLat);
+  llr.push_back(magAlt);
+  xyz_mag = transform_llr_to_xyz_3d(llr);
+
+  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
+  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
+  std::vector<precision_t> dipole_center = planet.get_dipole_center();
+
+  // Reverse our dipole rotations:
+  xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
+  xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
+
+  // offset dipole (not fully suported yet, so will be zero)
+  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
+      (dipole_center[2] != 0.0)) {
+
+    dipole_center = {0.0, 0.0, 0.0};
+  }
+
+  xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);
+  xyz_geo.push_back(xyzRot2[1] + dipole_center[1]);
+  xyz_geo.push_back(xyzRot2[2] + dipole_center[2]);
+
+  // transform back to lon, lat, radius:
+  llr = transform_xyz_to_llr_3d(xyzRot2);
+
+  report.exit(function);
+  return llr;
+}
+
+////////////////////////////////////////////
+//  convert cell coordinates to magnetic  //
+////////////////////////////////////////////
+
+std::vector<arma_cube> geo_to_mag(arma_cube glon,
+                                    arma_cube glat, 
+                                    arma_cube radius,
+                                    Planets &planet) {
+
+    std::string function = "Grid::geo_to_gmag";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  std::vector<arma_cube> llr, xyz_mag, xyz_geo, xyzRot1, xyzRot2;
+  llr.push_back(glon);
+  llr.push_back(glat);
+  llr.push_back(radius);
+  xyz_mag = transform_llr_to_xyz_3d(llr);
+
+  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
+  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
+  std::vector<precision_t> dipole_center = planet.get_dipole_center();
+
+  // Reverse our dipole rotations:
+  xyzRot1 = rotate_around_z_3d(xyz_mag, -magnetic_pole_tilt);
+  xyzRot2 = rotate_around_y_3d(xyzRot1, -magnetic_pole_rotation);
+
+  // offset dipole (not fully suported yet, so will be zero)
+  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
+      (dipole_center[2] != 0.0)) {
+
+    dipole_center = {0.0, 0.0, 0.0};
+  }
+
+  xyz_geo.push_back(xyzRot2[0] - dipole_center[0]);
+  xyz_geo.push_back(xyzRot2[1] - dipole_center[1]);
+  xyz_geo.push_back(xyzRot2[2] - dipole_center[2]);
+
+  // transform back to lon, lat, radius:
+  llr = transform_xyz_to_llr_3d(xyzRot2);
+
+  report.exit(function);
+  return llr;
+}
+
+
+std::vector<precision_t> mag_to_ijk(precision_t mlon,
+                                    precision_t mLat, 
+                                    precision_t radius,
+                                    precision_t planet_radius) {
+
+  precision_t i_lon, j_p, k_q;
+  
+  // precision_t planet_radius = planet.get_radius();
+
+  i_lon = mlon;
+  j_p = radius / planet_radius / pow(cos(mLat), 2);
+  k_q = sin(mLat) / pow(radius / planet_radius, 2.);
+
+  return {i_lon, j_p, k_q};
+}
+
+// -----------------------------------------------------------------------
+// Transform a flat vector into a 1D cube (avoids having to overload everything)
+// - this is overloaded for one vec/cube
+// -----------------------------------------------------------------------
+
+arma_cube vec2cube(std::vector<precision_t> ivec) {
+  arma_cube outvec;
+  int sizei = ivec.size();
+  arma_cube I;
+
+  I.set_size(sizei, 1, 1);
+  for (int i =0; i<sizei; i++){
+    I[i] = ivec[i];
+  }
+  return I;
+}
\ No newline at end of file

From d310131a8455b77a8ffdae5c4e934de0da747d6f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 17:23:25 -0400
Subject: [PATCH 116/120] add thermoelectric heating=false to defaults.json

---
 share/run/UA/inputs/defaults.json | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 78086e2c..fd7bfc84 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -112,7 +112,8 @@
 		  "IncludeIonizationHeating": true,
 		  "IncludeElectronIonCollisionalHeating":true,
 		  "IncludeElectronNeutralElasticCollisionalHeating":true,
-		  "IncludeElectronNeutralInelasticCollisionalHeating":true}},
+		  "IncludeElectronNeutralInelasticCollisionalHeating":true,
+			"IncludeThermoelectricHeating": false}},
     
     "Seed" : 0,
 

From 6d5ae64b5a33f2ad3b3c8a70a13985cf09da8384 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 17:25:11 -0400
Subject: [PATCH 117/120] sty: rename binary > bisect search

---
 src/solver_grid_interpolation.cpp | 38 +++++++++++++++++--------------
 1 file changed, 21 insertions(+), 17 deletions(-)

diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 2c6ea23c..409ffd3d 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -76,7 +76,8 @@ int64_t get_cube_surface_number(const arma_vec &point_in) {
 // - Optional argument (nGCs=0) since we cannot see grid info.
 // --------------------------------------------------------------------------
 
-uint64_t binary_search_array(precision_t val_in, arma_vec ref_arr, int64_t nGCs = 0) {
+uint64_t bisect_search_array(precision_t val_in, arma_vec ref_arr,
+                             int64_t nGCs = 0) {
   // Copy from std::upper_bound. Can't directly use it
   // mainly because geoAlt_scgc(0, 0, *) can't be formed as an iterator
   uint64_t first, last, len;
@@ -186,16 +187,16 @@ void Grid::get_cubesphere_grid_range(struct cubesphere_range &cr) const {
 
 void Grid::get_dipole_grid_range(struct dipole_range &dr) const {
   // Retrieve the range and delta of longitude, latitude and altitude
-  // ** Note these are magnetic coordinates. Not labeled for consistency **
-  dr.lon_min = magLon_Corner(nGCs, nGCs, nGCs);
-  dr.lon_max = magLon_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+  // ** Note the max/min are magnetic coordinates.  **
+  dr.lon_min = i_corner_scgc(nGCs, nGCs, nGCs);
+  dr.lon_max = i_corner_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
 
-  dr.lat_min = magLat_Corner(nGCs, nGCs, nGCs);
-  dr.lat_max = magLat_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+  dr.lat_min = j_corner_scgc(nGCs, nGCs, nGCs);
+  dr.lat_max = j_corner_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
 
   // magAlt and geoAlt are the same, doesn't matter which we use:
-  dr.alt_min = geoAlt_scgc(nGCs, nGCs, nGCs);
-  dr.alt_max = geoAlt_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+  dr.alt_min = k_corner_scgc(nGCs, nGCs, nGCs);
+  dr.alt_max = k_corner_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
 
   // MagLon steps are uniform:
   dr.dLon = magLon_Corner(1, 0, 0) - magLon_Corner(0, 0, 0);
@@ -250,9 +251,10 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
 
   // The altitude may not be linearly spaced, so use binary search to find
   // the first element smaller than or equal to the altitude of the give point
-  // Implemented in binary_search_array
+  // Implemented in bisect_search_array
   // - since Alt can be latitude-dependent, this needs to be done last.
-  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.iAlt = bisect_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+                                  nGCs);
   coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
               / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
                   coef.iCol, coef.iAlt));
@@ -328,9 +330,11 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   coef.rCol = col_frac_index - coef.iCol;
   coef.iCol += nGCs - 1;
   // Use binary search to find the index for altitude (handles oblate planets)
-  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.iAlt = bisect_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+                                  nGCs);
   coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
-              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
+                  coef.iCol, coef.iAlt));
 
   // Put the coefficient into the vector
   coef.in_grid = true;
@@ -357,7 +361,7 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
 
   // ASSUMPTION: LONGITUDE IS LINEARLY SPACED, nGCs >= 1
   // For the cell containing it, directly calculate its x index
-  // Find y & z indices using binary search
+  // Find y & z indices using a bisecting search
 
   // The number of dLon between the innermost ghost cell and the given point
   coef.rRow = (lon_in - dr.lon_min) / dr.dLon + 0.5;
@@ -370,15 +374,15 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
 
 
   // Different from the sphere, latitude & altitude are not evenly spaced.
-  // Use binary search for both.
+  // Use the bisect search function for both.
 
   // Lat needs to be done a little different because it could be increasing or
   // decreasing (depending on the hemisphere we're in). Take the absolute value!
-  coef.iCol = binary_search_array(abs(lat_in),
-                                  abs(magInvLat_scgc.tube(coef.iRow, coef.iCol)), nGCs);
+  coef.iCol = bisect_search_array(abs(lat_in),
+                                  abs(j_center_scgc.tube(coef.iRow, coef.iCol)), nGCs);
 
   // need alt index to find lat coef
-  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+  coef.iAlt = bisect_search_array(alt_in, k_center_scgc.tube(coef.iRow, coef.iCol),
                                   nGCs);
 
   // then we can do the ratios:

From cfd09cf0323265bb18fdfc188a4a9701fa7c8e17 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 18:41:04 -0400
Subject: [PATCH 118/120] test: Some stuff for the gradient test on the dipole
 grid. it doesnt work.

Hopefully this code can either help someone (me) firgure it out again later, or be deleted.

To revert this commit we need to remove the debug flag from the call to test_gradient_ijk. NBD.
---
 include/test.h            |   2 +-
 src/main/main.cpp         |   9 +-
 src/solver_gradients.cpp  |   7 +-
 srcTest/test_gradient.cpp | 284 +++++++++++++++++++++++++-------------
 4 files changed, 196 insertions(+), 106 deletions(-)

diff --git a/include/test.h b/include/test.h
index ea74ab86..d6f9b287 100644
--- a/include/test.h
+++ b/include/test.h
@@ -11,7 +11,7 @@
 // Cubesphere is not done nor tested
 bool test_gradient(Planets planet, Quadtree quadtree, json test_config, Grid gGrid, Grid mGrid);
 bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid);
-bool test_gradient_ijk(Planets planet, Grid grid);
+bool test_gradient_ijk(Planets planet, Grid grid, bool debug);
 
 
 #endif
\ No newline at end of file
diff --git a/src/main/main.cpp b/src/main/main.cpp
index 0ec17726..f5e6957a 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -12,7 +12,7 @@ int main() {
   
   int iErr = 0;
   std::string sError;
-  bool didWork = true;
+  bool didWork = true, testsPassing=true;
 
   Times time;
 
@@ -117,8 +117,11 @@ int main() {
     Ions ionsMag(mGrid, planet);
 
     if (tests["test_gradient"]) {
-      test_gradient(planet, quadtree, tests, gGrid, mGrid);
-      }
+      testsPassing = test_gradient(planet, quadtree, tests, gGrid, mGrid);
+    }
+
+    if (!testsPassing && tests["exit_on_fail"])
+      throw std::string("Cannot continue!!");
 
     // -----------------------------------------------------------------
     // This is a unit test for checking for nans and infinities.
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index ef1c06c7..761b17e5 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -367,10 +367,9 @@ arma_cube calc_gradient2o_k(arma_cube value, Grid grid) {
 
 arma_cube calc_gradient_alt(arma_cube value, Grid grid) {
 
-  int64_t nX = grid.get_nLons();
-  int64_t nY = grid.get_nLats();
-  int64_t nZ = grid.get_nAlts();
-  int64_t nGCs = grid.get_nGCs();
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
   int64_t iK;
 
   arma_cube gradient(nX, nY, nZ);
diff --git a/srcTest/test_gradient.cpp b/srcTest/test_gradient.cpp
index 6270e35a..cc0e546f 100644
--- a/srcTest/test_gradient.cpp
+++ b/srcTest/test_gradient.cpp
@@ -6,23 +6,94 @@
 
 #include "aether.h"
 
+
+// Modularize the test function so it's easy to change
+std::vector<arma_cube> test_func(Grid grid, Planets planet, bool debug) {
+
+  // one element for each coord; compatibility w/ doing individual functions
+  // out_vals has 6 elements:
+  // - first 3 are the function & last 3 are expected gradient
+  std::vector<arma_cube> out_vals;
+  arma_cube one_elem, i_coords, j_coords, k_coords;
+
+  if (grid.IsLatLonGrid) {
+    i_coords = grid.geoLon_scgc;
+    j_coords = grid.geoLat_scgc;
+    k_coords = grid.radius_scgc;
+
+    // use the func cos(i) * sin(j) * r^2
+    one_elem = cos(i_coords) % sin(j_coords) % k_coords % k_coords;
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+
+    // The true gradient values:
+    out_vals.push_back(-600.0 * sin(i_coords) % tan(j_coords) % k_coords);
+    out_vals.push_back(cos(i_coords) % cos(j_coords) % k_coords);
+    out_vals.push_back(2.0 * cos(i_coords) % sin(j_coords) % k_coords);
+  }
+
+  if (grid.IsDipole) {
+    std::cout<<"I AMN DIPOLE\n";
+    precision_t planetRadius = planet.get_radius(0.0);
+    i_coords = grid.magLon_scgc;
+    j_coords = grid.magP_scgc;
+    k_coords = grid.magQ_scgc;
+
+    // use the func cos(i) * sin(j) * r^2
+    // one_elem = cos(grid.magLon_scgc) % sin(grid.magLat_scgc) % grid.radius_scgc % grid.radius_scgc;
+    one_elem = i_coords % i_coords + j_coords % j_coords + k_coords % k_coords;
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+
+    arma_cube delT = pow(1 + 3.0 * cos(cPI/2. - grid.magLat_scgc) % cos(cPI/2. - grid.magLat_scgc),
+                         0.5);
+
+    // arma_cube r = grid.radius_scgc / planetRadius;
+    // // The true gradient values:
+    // out_vals.push_back(-k_coords % k_coords % sin(i_coords) % j_coords % j_coords
+    //                    / (r % pow(cos(grid.magLat_scgc), 2.0))); // mayB sin
+    // out_vals.push_back(2.0 * delT % j_coords % j_coords % cos(i_coords) % j_coords
+    //                    / pow(cos(grid.magLat_scgc), 3.0)); // mayb sin???
+    // out_vals.push_back(2.0 * delT % k_coords % cos(i_coords) % j_coords % j_coords
+    //                    / pow(r, 3.0));
+
+    out_vals.push_back(2.0 * i_coords
+                       / (grid.radius_scgc % cos(grid.magLat_scgc))); // mayB sin
+    out_vals.push_back(2.0 * j_coords
+                       % delT / pow(cos(grid.magLat_scgc), 3.0)); // mayb sin???
+    out_vals.push_back(2.0 * k_coords
+                       % delT / pow(grid.radius_scgc, 3.0));
+
+  }
+
+  if (debug) {
+    std::string numproc = tostr(iProc, 2);
+    std::string gridshape = "gridshape-" + tostr(grid.iGridShape_, 2);
+    i_coords.save(gridshape + "_proc-" + numproc + "_i_center.txt", arma_ascii);
+    j_coords.save(gridshape + "_proc-" + numproc + "_j_center.txt", arma_ascii);
+    k_coords.save(gridshape + "_proc-" + numproc + "_k_center.txt", arma_ascii);
+  }
+
+  return out_vals;
+}
+
 bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
                    Grid gGrid, Grid mGrid) {
   std::string function = "test_gradient";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  bool didWork;
+  bool didWork = true;
+  bool debug = test_config["dump_debug_cubes"];
 
   report.print(2, "Testing neutral grid");
 
-  if (gGrid.IsCubeSphereGrid)
-    didWork = test_gradient_cubesphere(planet, quadtree, gGrid);
-
   if (gGrid.IsDipole || gGrid.IsLatLonGrid)
-    didWork = test_gradient_ijk(planet, gGrid);
-
-  MPI_Barrier(aether_comm);
+    didWork = didWork && test_gradient_ijk(planet, gGrid, debug);
+  else
+    report.error("Cubesphere gradient test not built yet sorry");
 
   if (!didWork && test_config["exit_on_fail"])
     throw std::string("Gradient test failed - neutral grid");
@@ -30,122 +101,139 @@ bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
   report.print(2, "Testing ion grid");
 
   if (mGrid.IsCubeSphereGrid) // it's technically possible...
-    didWork = test_gradient_cubesphere(planet, quadtree, mGrid);
+    didWork = didWork && test_gradient_cubesphere(planet, quadtree, mGrid);
 
   if (mGrid.IsDipole || mGrid.IsLatLonGrid)
-    didWork = test_gradient_ijk(planet, mGrid);
-
-  if (!didWork && test_config["exit_on_fail"])
-    throw std::string("Gradient test failed - ion grid");
-
+    didWork = didWork && test_gradient_ijk(planet, mGrid, debug);
 
+  // if (!didWork && test_config["exit_on_fail"])
+  //   throw std::string("Gradient test failed - ion grid");
 
   report.exit(function);
 
   return didWork;
 }
 
-bool test_gradient_ijk(Planets planet, Grid grid) {
+void send_message(std::string Message, int nGood, int nBad) {
+  std::string newMessage;
+  newMessage = "iProc: " + tostr(iProc, 2) + " " + Message;
+  printf("%s has FAILED! (%i/%i); or (%f) perc\n", newMessage.data(), nBad, nGood,
+         100.*nBad / nGood);
+  return;
+}
+
 
-  std::string function = "test_gradient_dipole";
+bool test_gradient_ijk(Planets planet, Grid grid, bool debug) {
+
+  std::string function = "test_gradient_ijk";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  int64_t nIs = grid.get_nX();
-  int64_t nJs = grid.get_nY();
-  int64_t nKs = grid.get_nZ();
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
   int64_t nGCs = grid.get_nGCs();
 
-  int64_t nX, nY, nZ;
-  precision_t tol = 1e-3;
-  bool didWork = true;
-
-  arma_cube predicted_gradient, true_gradient;
-  arma::uvec err_points;
+  // numbers of grid points without ghost cells:
+  int64_t nI, nJ, nK;
+  nI = nX - 2 * nGCs;
+  nJ = nY - 2 * nGCs;
+  nK = nZ - 2 * nGCs;
 
-  arma_cube gradient_error;
-  gradient_error.set_size(nIs, nJs, nKs);
-  gradient_error.zeros();
+  bool didWork = true;
 
   int64_t nCellsTot = nX * nY * nZ;
-  int64_t nCellsNGCs = (nX - 2 * nGCs) * (nY - 2 * nGCs) * (nZ - 2 * nGCs);
-
-  report.print(2, "Beginning i-gradient");
-
-  /////////////////////////////////////////////////////////////
-  // Test the gradient in i-direction, d/dx(sin x) = cos(x)  //
-  /////////////////////////////////////////////////////////////
-
-  predicted_gradient = calc_gradient2o_i(sin(grid.i_center_scgc), grid);
-  true_gradient = cos(grid.i_center_scgc) / (grid.di_center_m_scgc);
-
-  gradient_error = abs(predicted_gradient - true_gradient) / abs(true_gradient);
-  err_points = find(abs(gradient_error.subcube(nGCs, nGCs,
-                                               nGCs, // don't look at ghost cells
-                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
-                    > tol);
-
-  didWork = all_finite(predicted_gradient, "Gradient_4o_i");
-
-  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
-            << ") => Points in i-gradient, above tol: " <<
-            100.0 * err_points.n_elem / predicted_gradient.n_elem
-            << "% (" << err_points.n_elem << ", " <<
-            predicted_gradient.n_elem << ")\n";
+  int64_t nCellsNGCs = nI * nJ * nK;
+
+  report.print(2, "Beginning gradient test");
+
+  std::vector<arma_cube> tmp, func_values, true_gradient, predicted_gradient;
+  tmp = test_func(grid, planet, debug);
+
+  func_values.push_back(tmp[0]);
+  func_values.push_back(tmp[1]);
+  func_values.push_back(tmp[2]);
+  true_gradient.push_back(tmp[3]);
+  true_gradient.push_back(tmp[4]);
+  true_gradient.push_back(tmp[5]);
+
+  if (grid.IsDipole) {
+    predicted_gradient.push_back(calc_gradient2o_i(func_values[0], grid));
+    predicted_gradient.push_back(calc_gradient2o_j(func_values[1], grid));
+    predicted_gradient.push_back(calc_gradient2o_k(func_values[2], grid));
+  } else {
+    predicted_gradient.push_back(calc_gradient2o_i(func_values[0], grid));
+    predicted_gradient.push_back(calc_gradient2o_j(func_values[1], grid));
+    predicted_gradient.push_back(calc_gradient2o_k(func_values[2], grid));
+  }
 
-  if (err_points.n_elem > true_gradient.n_elem * tol)
+  arma::uvec bad_is, bad_js, bad_ks;
+
+  // Look for values > 5% different from expected
+  bad_is = find(abs(
+                  (predicted_gradient[0].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)
+                   - true_gradient[0].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2))
+                  / true_gradient[0].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)) > 0.25);
+  bad_js = find(abs(
+                  (predicted_gradient[1].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)
+                   - true_gradient[1].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2))
+                  / true_gradient[1].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)) > 0.25);
+  bad_ks = find(abs(
+                  (predicted_gradient[2].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)
+                   - true_gradient[2].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2))
+                  / true_gradient[2].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)) > 0.25);
+
+  // ghost cells are hard; if more than 1% of *real* cells are out of spec, the test fails.
+  if (bad_is.n_elem > 0.1 * nCellsNGCs) {
+    send_message("grad_i:", nCellsNGCs, bad_is.n_elem);
     didWork = false;
+  }
 
-  report.print(2, "Beginning j-gradient");
-
-  /////////////////////////////////////////////////////////////
-  // Test the gradient in j-direction, d/dx(cos x) = -sin(x) //
-  /////////////////////////////////////////////////////////////
-
-  predicted_gradient = calc_gradient2o_j(cos(grid.j_center_scgc), grid);
-  true_gradient = -1.0 * sin(grid.j_center_scgc) / grid.dj_center_m_scgc;
-
-  gradient_error = predicted_gradient - true_gradient;
-  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
-                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
-                    > tol);
-
-  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_j");
-
-  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
-            << ") => Points in j-gradient, above tol: " <<
-            100.0 * err_points.n_elem / predicted_gradient.n_elem
-            << "% (" << err_points.n_elem << ", " <<
-            predicted_gradient.n_elem << ")\n";
-
-  if (err_points.n_elem > true_gradient.n_elem * tol)
+  if (bad_js.n_elem > 0.1 * nCellsNGCs) {
+    send_message("grad_j:", nCellsNGCs, bad_js.n_elem);
     didWork = false;
+  }
 
+  if (bad_ks.n_elem > 0.1 * nCellsNGCs) {
+    send_message("grad_k:", nCellsNGCs, bad_ks.n_elem);
+    didWork = false;
+  }
 
-  report.print(2, "Beginning k-gradient");
-
-  //////////////////////////////////////////////////////
-  // Test the gradient in k-direction, d/dx(x^2) = 2x //
-  //////////////////////////////////////////////////////
-
-  predicted_gradient = calc_gradient2o_k(grid.radius2_scgc, grid);
-  true_gradient = 2.0 * grid.radius_scgc / grid.dk_center_m_scgc;
-
-  gradient_error = (predicted_gradient - true_gradient);
-  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
-                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
-                    > tol);
-
-  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_k");
+  // Output if requested:
+  std::string numproc = tostr(iProc, 2);
+
+  if (debug) {
+    std::string numproc = tostr(iProc, 2);
+    std::string gridshape = "gridshape-" + tostr(grid.iGridShape_, 2) + "_iproc-";
+    grid.di_center_m_scgc.save(gridshape + numproc + "_di_center_m.txt",
+                               arma_ascii);
+    grid.dj_center_m_scgc.save(gridshape + numproc + "_dj_center_m.txt",
+                               arma_ascii);
+    grid.dk_center_m_scgc.save(gridshape + numproc + "_dk_center_m.txt",
+                               arma_ascii);
+
+    func_values[0].save(gridshape + numproc + "_testfunc.txt", arma_ascii);
+    true_gradient[0].save(gridshape + numproc + "_actual_grad_i.txt",
+                          arma_ascii);
+    true_gradient[1].save(gridshape + numproc + "_actual_grad_j.txt",
+                          arma_ascii);
+    true_gradient[2].save(gridshape + numproc + "_actual_grad_k.txt",
+                          arma_ascii);
+
+    predicted_gradient[0].save(gridshape + numproc + "_i-predicted-grad.txt",
+                               arma_ascii);
+    predicted_gradient[1].save(gridshape + numproc + "_j-predicted-grad.txt",
+                               arma_ascii);
+    predicted_gradient[2].save(gridshape + numproc + "_k-predicted-grad.txt",
+                               arma_ascii);
+  }
 
-  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
-            << ") => Points in k-gradient, above tol: " <<
-            100.0 * err_points.n_elem / predicted_gradient.n_elem
-            << "% (" << err_points.n_elem << ", " <<
-            predicted_gradient.n_elem << ")\n";
+  // For completeness, check for non-finites
+  didWork = didWork && all_finite(true_gradient, "TRUE GRADIENT");
+  didWork = didWork && all_finite(func_values, "FUNCTION");
+  didWork = didWork && all_finite(predicted_gradient, "AETHER'S GRADIENT");
 
-  if (err_points.n_elem > true_gradient.n_elem * tol)
-    didWork = false;
+  report.report_errors();
 
   report.exit(function);
 
@@ -153,7 +241,7 @@ bool test_gradient_ijk(Planets planet, Grid grid) {
 }
 
 
-// This is non-functional. 
+// This is non-functional.
 // Taken from src/main/main_test_gradient.cpp with enough edits to compile.
 bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid) {
 

From 61ed7792715a27cc269aff2fba9c8556744fd6b4 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 18:42:12 -0400
Subject: [PATCH 119/120] STY: ASTYLE, YO

---
 include/grid.h           | 30 +++++++++--------------
 include/tools.h          | 53 +++++++++++++++++++++-------------------
 src/output.cpp           |  2 +-
 src/solver_gradients.cpp |  2 +-
 4 files changed, 42 insertions(+), 45 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index cde1a92c..9bb98b27 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -11,10 +11,9 @@
 // Grid class
 // ----------------------------------------------------------------------------
 
-class Grid
-{
+class Grid {
 
-public:
+ public:
   const int iSphere_ = 1;
   const int iCubesphere_ = 2;
   const int iDipole_ = 3;
@@ -97,7 +96,7 @@ class Grid
   arma_cube magAlt_Down;
   arma_cube magAlt_Below;
   arma_cube magAlt_Corner;
-  
+
   arma_cube magP_Corner;
   arma_cube magQ_Corner;
   arma_cube magInvLat_Corner;
@@ -358,7 +357,7 @@ class Grid
   int iProcZ;
 
   bool isExchangeInitialized = false;
-  
+
   arma_vec edge_Xp;
   arma_vec edge_Yp;
   arma_vec edge_Xm;
@@ -374,8 +373,7 @@ class Grid
   // again, z will only be in one
   int64_t iRootZ;
 
-  struct messages_struct
-  {
+  struct messages_struct {
     int64_t iFace;
     int64_t iProc_to;
     int64_t iSizeTotal;
@@ -442,7 +440,7 @@ class Grid
   bool set_dipole_interpolation_coefs(const std::vector<precision_t> &Lons,
                                       const std::vector<precision_t> &Lats,
                                       const std::vector<precision_t> &Alts);
-  
+
   /**
    * \brief Create a map of geographic locations to data and do the interpolation
    * \param data The value at the positions of geoLon, geoLat, and geoAlt
@@ -453,7 +451,7 @@ class Grid
    */
   std::vector<precision_t> get_interpolation_values(const arma_cube &data) const;
 
-private:
+ private:
   bool IsGeoGrid;
   bool HasBField;
   bool IsExperimental;
@@ -481,8 +479,7 @@ class Grid
 
   // interpolation members
   // The struct representing the range of a spherical grid
-  struct sphere_range
-  {
+  struct sphere_range {
     precision_t lon_min;
     precision_t lon_max;
     precision_t dLon;
@@ -493,8 +490,7 @@ class Grid
     precision_t alt_max;
   };
   // The struct representing the range of a cubesphere grid
-  struct cubesphere_range
-  {
+  struct cubesphere_range {
     // The minimum value and delta change of row and col
     // We don't use row_max and col_max because they are not promised to be
     // greater than min, for example the right norm of suface 2 expands along
@@ -521,7 +517,7 @@ class Grid
     bool col_max_exclusive;
   };
   // The struct representing the range of a dipole grid (in magnetic coordinates)
-  struct dipole_range{
+  struct dipole_range {
     precision_t lon_min;
     precision_t lon_max;
     precision_t dLon;
@@ -536,8 +532,7 @@ class Grid
   // Each point is processed by the function set_interpolation_coefs and stored
   // in the form of this structure.
   // If the point is out of the grid, in_grid = false and all other members are undefined
-  struct interp_coef_t
-  {
+  struct interp_coef_t {
     // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
     uint64_t iRow;
     uint64_t iCol;
@@ -578,8 +573,7 @@ class Grid
   // Initialize connections between processors
   void init_connection();
   // Used for message exchange
-  struct idx2d_t
-  {
+  struct idx2d_t {
     // Index of row and column
     int64_t ilon;
     int64_t ilat;
diff --git a/include/tools.h b/include/tools.h
index fd899c48..e8200476 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -8,11 +8,11 @@
 // Structure for a 2x2 matrix for a cubesphere:
 // ----------------------------------------------------------------------------
 
-struct mat_2x2{
-    arma_mat A11; 
-    arma_mat A12; 
-    arma_mat A21; 
-    arma_mat A22;
+struct mat_2x2 {
+  arma_mat A11;
+  arma_mat A12;
+  arma_mat A21;
+  arma_mat A22;
 };
 
 // -----------------------------------------------------------------------------
@@ -23,9 +23,9 @@ struct mat_2x2{
 // -----------------------------------------------------------------------------
 
 bool find_interpolation_coefficients(arma_vec inX,
-				     arma_vec outX,
-				     arma_vec &outIndex,
-				     arma_vec &outRatio);
+                                     arma_vec outX,
+                                     arma_vec &outIndex,
+                                     arma_vec &outRatio);
 
 // -----------------------------------------------------------------------------
 // This takes the index and ratio determined in the above function and
@@ -33,8 +33,8 @@ bool find_interpolation_coefficients(arma_vec inX,
 // -----------------------------------------------------------------------------
 
 arma_vec interpolate1d(arma_vec inY,
-		       arma_vec &index,
-		       arma_vec &ratio);
+                       arma_vec &index,
+                       arma_vec &ratio);
 
 // -----------------------------------------------------------------------------
 // Set all of the ghost cells to a constant value that is fed in.
@@ -42,8 +42,8 @@ arma_vec interpolate1d(arma_vec inY,
 // -----------------------------------------------------------------------------
 
 void set_gcs_to_value(arma_cube &var_scgc,
-		      precision_t value,
-		      int64_t nGCs);
+                      precision_t value,
+                      int64_t nGCs);
 
 // ----------------------------------------------------------------------------
 // Fix corners in an arma cube
@@ -101,39 +101,39 @@ precision_t sync_mean_across_all_procs(precision_t value);
 // ----------------------------------------------------------------------
 
 std::vector<double> get_normal_random_vect(double mean,
-					   double std,
-					   int64_t nValues,
-					   int seed);
+                                           double std,
+                                           int64_t nValues,
+                                           int seed);
 
 // ----------------------------------------------------------------------
 // Generate a vector of uniformly distributed random unsigned ints
 // ----------------------------------------------------------------------
 
 std::vector<unsigned int> get_random_unsigned_vect(int64_t nValues,
-						   int seed);
+                                                   int seed);
 
 // ----------------------------------------------------------------------
 // Make a vector of arma cubes:
 // ----------------------------------------------------------------------
 
 std::vector<arma_cube> make_cube_vector(int64_t nLons,
-					int64_t nLats,
-					int64_t nAlts,
-					int64_t nComps);
+                                        int64_t nLats,
+                                        int64_t nAlts,
+                                        int64_t nComps);
 
 // ----------------------------------------------------------------------
 // Take the dot product between two armadilo cubes
 // ----------------------------------------------------------------------
 
 arma_cube dot_product(std::vector<arma_cube> vec1,
-		      std::vector<arma_cube> vec2);
+                      std::vector<arma_cube> vec2);
 
 // ----------------------------------------------------------------------
 // Take the cross product between two arma cubes
 // ----------------------------------------------------------------------
 
 std::vector<arma_cube> cross_product(std::vector<arma_cube> vec1,
-				     std::vector<arma_cube> vec2);
+                                     std::vector<arma_cube> vec2);
 
 // ----------------------------------------------------------------------
 // Convert an armadillo vector to a c++ vector
@@ -220,7 +220,8 @@ bool is_approx_equal(arma_vec &vec1, arma_vec &vec2, precision_t tol);
 //-------------------------------------------------------------
 // Overload col vector function with row vec
 //-------------------------------------------------------------
-bool is_approx_equal(Row<precision_t> &vec1, Row<precision_t> &vec2, precision_t tol);
+bool is_approx_equal(Row<precision_t> &vec1, Row<precision_t> &vec2,
+                     precision_t tol);
 
 //-------------------------------------------------------------
 // Checks whether a vector is constant (all values the same)
@@ -234,7 +235,8 @@ bool is_approx_constant(arma_vec &vec, precision_t tol);
 // u and v are spherical velocities
 // u1 and u2 are contravariant velocities
 // --------------------------------------------------------------------------
-void sphvect2ref(arma_mat& u, arma_mat& v, arma_mat& u1, arma_mat& u2, mat_2x2 &A_inv_mat);
+void sphvect2ref(arma_mat& u, arma_mat& v, arma_mat& u1, arma_mat& u2,
+                 mat_2x2 &A_inv_mat);
 
 // --------------------------------------------------------------------------
 // Convert spherical vector (velocities) to reference (contravariant) vector
@@ -242,7 +244,8 @@ void sphvect2ref(arma_mat& u, arma_mat& v, arma_mat& u1, arma_mat& u2, mat_2x2 &
 // u and v are spherical velocities
 // u1 and u2 are contravariant velocities
 // --------------------------------------------------------------------------
-void refvect2sph(arma_mat &u1, arma_mat &u2, arma_mat &u, arma_mat &v, mat_2x2 &A_mat);
+void refvect2sph(arma_mat &u1, arma_mat &u2, arma_mat &u, arma_mat &v,
+                 mat_2x2 &A_mat);
 
 //-----------------------------------------------------------------------
 // Checks if armacube(s) has all finite values, if not, adds them to
@@ -293,7 +296,7 @@ std::vector<int> indef_vector(arma_cube cube);
 // --------------------------------------------------------------------------
 
 arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in);
-// Used for dipole gradients & distances. 
+// Used for dipole gradients & distances.
 // Probably needs to be moved.
 arma_cube delTheta(arma_cube magLat);
 
diff --git a/src/output.cpp b/src/output.cpp
index fc1e7767..205701a7 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -28,7 +28,7 @@ std::string get_filename_from_type(std::string type_output) {
 
   if (type_output == "bfield")
     filename = "3DBF";
-    
+
   if (type_output == "delta")
     filename = "3DDE";
 
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 761b17e5..b67617fb 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -73,7 +73,7 @@ arma_cube calc_gradient2o_i(arma_cube value, Grid grid) {
     for (iX = 1; iX < nX - 1; iX++)
       gradient.row(iX) =
         (value.row(iX + 1) - value.row(iX - 1)) /
-        (2 * grid.di_center_m_scgc.row(iX));
+        (2.0 * grid.di_center_m_scgc.row(iX));
 
     // Lower (one sided):
     iX = 0;

From 85f9c8722f106e872c57cd4c8e5f4b9fe0ef6e90 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 18:42:26 -0400
Subject: [PATCH 120/120] add radius to bfield output

---
 src/output.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/output.cpp b/src/output.cpp
index 205701a7..f4adf0d9 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -287,6 +287,9 @@ bool output(const Neutrals &neutrals,
                                                     "Magnetic Invariant Latitude",
                                                     "degrees",
                                                     grid.magInvLat_scgc * cRtoD);
+        AllOutputContainers[iOutput].store_variable("radius",
+                                                    "m",
+                                                    grid.radius_scgc);
         AllOutputContainers[iOutput].store_variable("mlt",
                                                     "Magnetic Local Time",
                                                     "hours",