From 27a9dc291938672785e46a13939825bc7dc3cb94 Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Tue, 5 Nov 2024 11:31:14 -0800
Subject: [PATCH 1/9] attempting to implement cs_tallies

---
 examples/fixed_source/cooper_combo/input.py  |   4 +-
 examples/fixed_source/kobayashi3-TD/input.py |   7 +-
 mcdc/card.py                                 |  13 ++
 mcdc/global_.py                              |   1 +
 mcdc/kernel.py                               | 148 ++++++++++++++++---
 mcdc/main.py                                 | 102 ++++++++++++-
 mcdc/tally.py                                |  81 ++++++++++
 mcdc/type_.py                                |  71 +++++++++
 8 files changed, 403 insertions(+), 24 deletions(-)

diff --git a/examples/fixed_source/cooper_combo/input.py b/examples/fixed_source/cooper_combo/input.py
index 952a6083..5e3e4059 100644
--- a/examples/fixed_source/cooper_combo/input.py
+++ b/examples/fixed_source/cooper_combo/input.py
@@ -56,8 +56,8 @@
 
 mcdc.tally.mesh_tally(
     scores=["flux"],
-    x=np.linspace(0.0, 4.0, 40),
-    y=np.linspace(0.0, 4.0, 40),
+    x=np.linspace(0.0, 4.0, 41),
+    y=np.linspace(0.0, 4.0, 41),
 )
 
 # Setting
diff --git a/examples/fixed_source/kobayashi3-TD/input.py b/examples/fixed_source/kobayashi3-TD/input.py
index 4bdea735..7308f928 100644
--- a/examples/fixed_source/kobayashi3-TD/input.py
+++ b/examples/fixed_source/kobayashi3-TD/input.py
@@ -61,14 +61,19 @@
     x=np.linspace(0.0, 60.0, 31),
     y=np.linspace(0.0, 100.0, 51),
     t=np.linspace(0.0, 200.0, 21),
+    # g=np.array([-0.5, 3.5, 6.5]) # fast (0, 1, 2, 3) and thermal (4, 5, 6) groups
 )
 
 mcdc.tally.cell_tally(source_cell, scores=["flux"])
 mcdc.tally.cell_tally(void_cell, scores=["flux"])
 mcdc.tally.cell_tally(shield_cell, scores=["flux"])
 
+
+mcdc.tally.cs_tally(N_cs_bins=[10], size=[1], scores=["flux"])
+
+
 # Setting
-mcdc.setting(N_particle=80)
+mcdc.setting(N_particle=50)
 
 # Run
 mcdc.run()
diff --git a/mcdc/card.py b/mcdc/card.py
index 0b300c16..141679c2 100644
--- a/mcdc/card.py
+++ b/mcdc/card.py
@@ -394,3 +394,16 @@ def __init__(self, cell_ID):
         # Set card data
         self.cell_ID = cell_ID
         self.N_bin = 1
+
+
+class CSTallyCard(TallyCard):
+    def __init__(self):
+        TallyCard.__init__(self, "CS tally")
+
+        # Set card data
+        self.x = np.array([-INF, INF])
+        self.y = np.array([-INF, INF])
+        self.z = np.array([-INF, INF])
+        self.N_bin = 1
+        self.N_cs_bins = 1
+        self.size = 1
diff --git a/mcdc/global_.py b/mcdc/global_.py
index 3e3d8f58..ef28b0c3 100644
--- a/mcdc/global_.py
+++ b/mcdc/global_.py
@@ -36,6 +36,7 @@ def reset(self):
         self.mesh_tallies = []
         self.surface_tallies = []
         self.cell_tallies = []
+        self.cs_tallies = []
 
         self.setting = {
             "tag": "Setting",
diff --git a/mcdc/kernel.py b/mcdc/kernel.py
index f67f0466..aad7a2ac 100644
--- a/mcdc/kernel.py
+++ b/mcdc/kernel.py
@@ -1977,22 +1977,6 @@ def score_cell_tally(P_arr, distance, tally, data, mcdc):
     cell_idx = stride["tally"]
     score = 0
 
-    # # Particle 4D direction
-    # ux = P["ux"]
-    # uy = P["uy"]
-    # uz = P["uz"]
-    # ut = 1.0 / physics.get_speed(P_arr, mcdc)
-
-    # # Particle initial and final coordinate
-    # x = P["x"]
-    # y = P["y"]
-    # z = P["z"]
-    # t = P["t"]
-    # x_final = x + ux * distance
-    # y_final = y + uy * distance
-    # z_final = z + uz * distance
-    # t_final = t + ut * distance
-
     # Score
     flux = distance * P["w"]
     for i in range(tally["N_score"]):
@@ -2006,10 +1990,136 @@ def score_cell_tally(P_arr, distance, tally, data, mcdc):
             SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
             score = flux * SigmaF
 
-        # print(f'cell_idx = {cell_idx}, score = {score}')
         tally_bin[TALLY_SCORE, cell_idx] += score
 
 
+@njit
+def score_cs_tally(P_arr, distance, tally, data, mcdc):
+    P = P_arr[0]
+    tally_bin = data[TALLY]
+    material = mcdc["materials"][P["material_ID"]]
+    N_cs_bins = tally["filter"]["N_cs_bins"]
+    cs_bin_size = tally["filter"]["cs_bin_size"]
+    cs_centers = tally["filter"]["cs_centers"]
+    stride = tally["stride"]
+
+    # Particle 4D direction
+    ux = P["ux"]
+    uy = P["uy"]
+    uz = P["uz"]
+    ut = 1.0 / physics.get_speed(P_arr, mcdc)
+
+    # Particle initial and final coordinate
+    x = P["x"]
+    y = P["y"]
+    z = P["z"]
+    t = P["t"]
+    x_final = x + ux * distance
+    y_final = y + uy * distance
+    z_final = z + uz * distance
+    t_final = t + ut * distance
+
+    # Check each coarse bin
+    for bin_idx in range(N_cs_bins):
+        center = cs_centers[bin_idx]
+
+        # Bin edges
+        x_min, x_max = center[0] - cs_bin_size / 2, center[0] + cs_bin_size / 2
+        y_min, y_max = center[1] - cs_bin_size / 2, center[1] + cs_bin_size / 2
+        z_min, z_max = center[2] - cs_bin_size / 2, center[2] + cs_bin_size / 2
+
+        # Check for intersection with coarse bin
+        # If particle starts or ends inside the bin, it should score into the bin
+
+        # TODO: handle case where particle crosses a corner, but doesn't start or end in the bin
+
+        if (
+            (x_min <= x <= x_max or x_min <= x_final <= x_max)
+            and (y_min <= y <= y_max or y_min <= y_final <= y_max)
+            and (z_min <= z <= z_max or z_min <= z_final <= z_max)
+        ):
+
+            distance_in_bin = min(
+                distance,
+                calculate_distance_in_coarse_bin(
+                    P_arr, distance, center, cs_bin_size, mcdc
+                ),
+            )
+
+            # Calculate flux and other scores
+            flux = distance_in_bin * P["w"]
+            for i in range(tally["N_score"]):
+                score_type = tally["scores"][i]
+                if score_type == SCORE_FLUX:
+                    score = flux
+                elif score_type == SCORE_DENSITY:
+                    score = flux / physics.get_speed(P_arr, mcdc)
+                elif score_type == SCORE_TOTAL:
+                    SigmaT = get_MacroXS(XS_TOTAL, material, P_arr, mcdc)
+                    score = flux * SigmaT
+                elif score_type == SCORE_FISSION:
+                    SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
+                    score = flux * SigmaF
+                adapt.global_add(
+                    tally_bin, (TALLY_SCORE, bin_idx + i), score
+                )  # TODO: fix this score indexing, this is wrong
+
+
+@njit
+def calculate_distance_in_coarse_bin(P_arr, distance, center, cs_bin_size, mcdc):
+    def intersect_plane(p0, p1, min_val, max_val, axis):
+        if p1[axis] != p0[axis]:  # Avoid division by zero if line is parallel to plane
+            t_min = (min_val - p0[axis]) / (p1[axis] - p0[axis])
+            t_max = (max_val - p0[axis]) / (p1[axis] - p0[axis])
+            return [t_min, t_max] if t_min <= t_max else [t_max, t_min]
+        return [float("-inf"), float("inf")]  # No intersection if parallel
+
+    P = P_arr[0]
+    # Particle 4D direction
+    ux = P["ux"]
+    uy = P["uy"]
+    uz = P["uz"]
+    ut = 1.0 / physics.get_speed(P_arr, mcdc)
+
+    # Particle initial and final coordinate
+    x = P["x"]
+    y = P["y"]
+    z = P["z"]
+    t = P["t"]
+    x_final = x + ux * distance
+    y_final = y + uy * distance
+    z_final = z + uz * distance
+    t_final = t + ut * distance
+
+    start = np.array([x, y, z])
+    end = np.array([x_final, y_final, z_final])
+
+    # Bin edges
+    x_min, x_max = center[0] - cs_bin_size / 2, center[0] + cs_bin_size / 2
+    y_min, y_max = center[1] - cs_bin_size / 2, center[1] + cs_bin_size / 2
+    z_min, z_max = center[2] - cs_bin_size / 2, center[2] + cs_bin_size / 2
+
+    # Calculate intersection parameters for each axis
+    tx_min, tx_max = intersect_plane(start, end, x_min, x_max, 0)
+    ty_min, ty_max = intersect_plane(start, end, y_min, y_max, 1)
+    tz_min, tz_max = intersect_plane(start, end, z_min, z_max, 2)
+
+    # Determine the entry and exit parameters
+    t_entry = max(tx_min, ty_min, tz_min)
+    t_exit = min(tx_max, ty_max, tz_max)
+
+    # Check if there's a valid intersection
+    if t_entry > t_exit or t_exit < 0 or t_entry > 1:
+        return 0.0
+
+    # Calculate entry and exit points in 3D space
+    entry_point = start + t_entry * (end - start)
+    exit_point = start + t_exit * (end - start)
+
+    # Return the distance between entry and exit points
+    return np.linalg.norm(exit_point - entry_point)
+
+
 @njit
 def tally_reduce(data, mcdc):
     tally_bin = data[TALLY]
@@ -2403,6 +2513,10 @@ def move_to_event(P_arr, data, mcdc):
         for tally in mcdc["mesh_tallies"]:
             score_mesh_tally(P_arr, distance, tally, data, mcdc)
 
+        for tally in mcdc["cs_tallies"]:
+            # print(f'kernel.py cs_tally = {tally}')
+            score_cs_tally(P_arr, distance, tally, data, mcdc)
+
         cell = mcdc["cells"][P["cell_ID"]]
         for tally in mcdc["cell_tallies"]:
             if tally["filter"]["cell_ID"] == P["cell_ID"] and cell["N_tally"] != 0:
diff --git a/mcdc/main.py b/mcdc/main.py
index 1381ab68..09dbb508 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -217,6 +217,11 @@ def copy_field(dst, src, name):
     else:
         data = getattr(src, name)
 
+    # print(f'dst, src, name = {dst, src, name}')
+    # print(f'dst = {dst.dtype.names}')
+    # print(f'name = {name}')
+    # print(f'dst[name] = {dst[name]}')
+
     if isinstance(dst[name], np.ndarray):
         if isinstance(data, np.ndarray) and dst[name].shape != data.shape:
             for dim in data.shape:
@@ -463,6 +468,7 @@ def prepare():
     type_.make_type_mesh_tally(input_deck)
     type_.make_type_surface_tally(input_deck)
     type_.make_type_cell_tally(input_deck)
+    type_.make_type_cs_tally(input_deck)
     type_.make_type_setting(input_deck)
     type_.make_type_uq(input_deck)
     type_.make_type_domain_decomp(input_deck)
@@ -706,6 +712,7 @@ def prepare():
     N_mesh_tally = len(input_deck.mesh_tallies)
     N_surface_tally = len(input_deck.surface_tallies)
     N_cell_tally = len(input_deck.cell_tallies)
+    N_cs_tally = len(input_deck.cs_tallies)
     tally_size = 0
 
     # Mesh tallies
@@ -895,22 +902,109 @@ def prepare():
         # Filter strides
         stride = N_score
         if Nt > 1:
-            mcdc["mesh_tallies"][i]["stride"]["t"] = stride
+            mcdc["cell_tallies"][i]["stride"]["t"] = stride
             stride *= Nt
         if Ng > 1:
-            mcdc["mesh_tallies"][i]["stride"]["g"] = stride
+            mcdc["cell_tallies"][i]["stride"]["g"] = stride
             stride *= Ng
         if N_azi > 1:
-            mcdc["mesh_tallies"][i]["stride"]["azi"] = stride
+            mcdc["cell_tallies"][i]["stride"]["azi"] = stride
             stride *= N_azi
         if Nmu > 1:
-            mcdc["mesh_tallies"][i]["stride"]["mu"] = stride
+            mcdc["cell_tallies"][i]["stride"]["mu"] = stride
             stride *= Nmu
 
         # Set tally stride and accumulate total tally size
         mcdc["cell_tallies"][i]["stride"]["tally"] = tally_size
         tally_size += mcdc["cell_tallies"][i]["N_bin"]
 
+    # CS tallies
+    for i in range(N_cs_tally):
+        # Direct assignment
+        copy_field(mcdc["cs_tallies"][i], input_deck.cs_tallies[i], "N_bin")
+
+        # Filters (variables with possible different sizes)
+        if not input_deck.technique["domain_decomposition"]:
+            for name in ["x", "y", "z", "t", "mu", "azi", "g"]:
+                N = len(getattr(input_deck.cs_tallies[i], name))
+                mcdc["cs_tallies"][i]["filter"][name][:N] = getattr(
+                    input_deck.cs_tallies[i], name
+                )
+
+        # else:  # decomposed mesh filters
+        #     mxn, mxp, myn, myp, mzn, mzp = dd_mesh_bounds(i)
+
+        #     # Filters
+        #     new_x = input_deck.mesh_tallies[i].x[mxn:mxp]
+        #     new_y = input_deck.mesh_tallies[i].y[myn:myp]
+        #     new_z = input_deck.mesh_tallies[i].z[mzn:mzp]
+        #     mcdc["mesh_tallies"][i]["filter"]["x"] = new_x
+        #     mcdc["mesh_tallies"][i]["filter"]["y"] = new_y
+        #     mcdc["mesh_tallies"][i]["filter"]["z"] = new_z
+        #     for name in ["t", "mu", "azi", "g"]:
+        #         N = len(getattr(input_deck.mesh_tallies[i], name))
+        #         mcdc["mesh_tallies"][i]["filter"][name][:N] = getattr(
+        #             input_deck.mesh_tallies[i], name
+        #         )
+
+        # Set tally scores
+        N_score = len(input_deck.cs_tallies[i].scores)
+        mcdc["cs_tallies"][i]["N_score"] = N_score
+        for j in range(N_score):
+            score_name = input_deck.cs_tallies[i].scores[j]
+            score_type = None
+            if score_name == "flux":
+                score_type = SCORE_FLUX
+            elif score_name == "density":
+                score_type = SCORE_DENSITY
+            elif score_name == "total":
+                score_type = SCORE_TOTAL
+            elif score_name == "fission":
+                score_type = SCORE_FISSION
+            elif score_name == "net-current":
+                score_type = SCORE_NET_CURRENT
+            mcdc["cs_tallies"][i]["scores"][j] = score_type
+
+        # Filter grid sizes
+        Nmu = len(input_deck.cs_tallies[i].mu) - 1
+        N_azi = len(input_deck.cs_tallies[i].azi) - 1
+        Ng = len(input_deck.cs_tallies[i].g) - 1
+        Nx = len(input_deck.cs_tallies[i].x) - 1
+        Ny = len(input_deck.cs_tallies[i].y) - 1
+        Nz = len(input_deck.cs_tallies[i].z) - 1
+        Nt = len(input_deck.cs_tallies[i].t) - 1
+
+        # Update N_bin
+        mcdc["cs_tallies"][i]["N_bin"] *= N_score
+
+        # Filter strides
+        stride = N_score
+        if Nz > 1:
+            mcdc["cs_tallies"][i]["stride"]["z"] = stride
+            stride *= Nz
+        if Ny > 1:
+            mcdc["cs_tallies"][i]["stride"]["y"] = stride
+            stride *= Ny
+        if Nx > 1:
+            mcdc["cs_tallies"][i]["stride"]["x"] = stride
+            stride *= Nx
+        if Nt > 1:
+            mcdc["cs_tallies"][i]["stride"]["t"] = stride
+            stride *= Nt
+        if Ng > 1:
+            mcdc["cs_tallies"][i]["stride"]["g"] = stride
+            stride *= Ng
+        if N_azi > 1:
+            mcdc["cs_tallies"][i]["stride"]["azi"] = stride
+            stride *= N_azi
+        if Nmu > 1:
+            mcdc["cs_tallies"][i]["stride"]["mu"] = stride
+            stride *= Nmu
+
+        # Set tally stride and accumulate total tally size
+        mcdc["cs_tallies"][i]["stride"]["tally"] = tally_size
+        tally_size += mcdc["cs_tallies"][i]["N_bin"]
+
     # Set tally data
     if not input_deck.technique["uq"]:
         tally = np.zeros((3, tally_size), dtype=type_.float64)
diff --git a/mcdc/tally.py b/mcdc/tally.py
index 79933808..db75e917 100644
--- a/mcdc/tally.py
+++ b/mcdc/tally.py
@@ -7,6 +7,7 @@
     MeshTallyCard,
     SurfaceTallyCard,
     CellTallyCard,
+    CSTallyCard,
 )
 from mcdc.constant import (
     INF,
@@ -159,6 +160,21 @@ def surface_tally(
 
 
 def cell_tally(cell, scores=["flux"]):
+    """
+    Create a tally card to collect MC solutions.
+
+    Parameters
+    ----------
+    cell : CellCard
+        Cell to which the tally is attached to
+    scores : list of str {"flux", "net-current"}
+        List of physical quantities to be scored.
+
+    Returns
+    -------
+    CellTallyCard
+        The tally card.
+    """
 
     # Make tally card
     card = CellTallyCard(cell.ID)
@@ -187,3 +203,68 @@ def cell_tally(cell, scores=["flux"]):
     global_.input_deck.cell_tallies.append(card)
 
     return card
+
+
+def cs_tally(
+    N_cs_bins=10,
+    size=1,
+    x=np.array([-INF, INF]),
+    y=np.array([-INF, INF]),
+    z=np.array([-INF, INF]),
+    t=np.array([-INF, INF]),
+    mu=np.array([-1.0, 1.0]),
+    azi=np.array([-PI, PI]),
+    g=np.array([-INF, INF]),
+    E=np.array([0.0, INF]),
+    scores=["flux"],
+):
+    # Make tally card
+    card = CSTallyCard()
+
+    # Set ID
+    card.ID = len(global_.input_deck.cs_tallies)
+    card.N_cs_bins = N_cs_bins
+    card.size = size
+
+    # Set mesh
+    card.x = x
+    card.y = y
+    card.z = z
+
+    # Set other filters
+    card.t = t
+    card.mu = mu
+    card.azi = azi
+
+    # Set energy group grid
+    if type(g) == type("string") and g == "all":
+        G = global_.input_deck.materials[0].G
+        card.g = np.linspace(0, G, G + 1) - 0.5
+    else:
+        card.g = g
+    if global_.input_deck.setting["mode_CE"]:
+        card.g = E
+
+    # Calculate total number bins
+    Nx = len(card.x) - 1
+    Ny = len(card.y) - 1
+    Nz = len(card.z) - 1
+    Nt = len(card.t) - 1
+    Nmu = len(card.mu) - 1
+    N_azi = len(card.azi) - 1
+    Ng = len(card.g) - 1
+    card.N_bin = Nx * Ny * Nz * Nt * Nmu * N_azi * Ng
+
+    # Scores
+    for s in scores:
+        score_checked = check_support(
+            "score type",
+            s,
+            ["flux", "total", "fission", "density"],
+        )
+        card.scores.append(score_checked)
+
+    # Add to deck
+    global_.input_deck.cs_tallies.append(card)
+
+    return card
diff --git a/mcdc/type_.py b/mcdc/type_.py
index 20606f23..7fd7bbeb 100644
--- a/mcdc/type_.py
+++ b/mcdc/type_.py
@@ -45,6 +45,7 @@
 mesh_tally = None
 surface_tally = None
 cell_tally = None
+cs_tally = None
 technique = None
 
 global_ = None
@@ -894,6 +895,74 @@ def make_type_cell_tally(input_deck):
     cell_tally = into_dtype(struct)
 
 
+def make_type_cs_tally(input_deck):
+    global cs_tally
+    struct = []
+
+    # Maximum numbers of mesh and filter grids and scores
+    Nmax_cs_bins = 2
+    Nmax_x = 2
+    Nmax_y = 2
+    Nmax_z = 2
+    Nmax_t = 2
+    Nmax_mu = 2
+    Nmax_azi = 2
+    Nmax_g = 2
+    Nmax_score = 1
+    for card in input_deck.cs_tallies:
+        Nmax_cs_bins = max(Nmax_cs_bins, len(card.N_cs_bins))
+        Nmax_x = max(Nmax_x, len(card.x))
+        Nmax_y = max(Nmax_y, len(card.y))
+        Nmax_z = max(Nmax_z, len(card.z))
+        Nmax_t = max(Nmax_t, len(card.t))
+        Nmax_mu = max(Nmax_mu, len(card.mu))
+        Nmax_azi = max(Nmax_azi, len(card.azi))
+        Nmax_g = max(Nmax_g, len(card.g))
+        Nmax_score = max(Nmax_score, len(card.scores))
+
+    # # reduce tally sizes for subdomains
+    # if input_deck.technique["domain_decomposition"]:
+    #     Nmax_x, Nmax_y, Nmax_z = dd_meshtally(input_deck)
+
+    # Set the filter
+    filter_ = [
+        ("N_cs_bins", int64),
+        ("cs_bin_size", float64),
+        ("cs_centers", float64, (Nmax_cs_bins,)),
+        ("x", float64, (Nmax_x,)),
+        ("y", float64, (Nmax_y,)),
+        ("z", float64, (Nmax_z,)),
+        ("t", float64, (Nmax_t,)),
+        ("mu", float64, (Nmax_mu,)),
+        ("azi", float64, (Nmax_azi,)),
+        ("g", float64, (Nmax_g,)),
+    ]
+    struct += [("filter", filter_)]
+
+    # Tally strides
+    stride = [
+        ("tally", int64),
+        ("sensitivity", int64),
+        ("mu", int64),
+        ("azi", int64),
+        ("g", int64),
+        ("t", int64),
+        ("x", int64),
+        ("y", int64),
+        ("z", int64),
+    ]
+    struct += [("stride", stride)]
+
+    # Total number of bins
+    struct += [("N_bin", int64)]
+
+    # Scores
+    struct += [("N_score", int64), ("scores", int64, (Nmax_score,))]
+
+    # Make tally structure
+    cs_tally = into_dtype(struct)
+
+
 # ==============================================================================
 # Setting
 # ==============================================================================
@@ -1318,6 +1387,7 @@ def make_type_global(input_deck):
     N_mesh_tally = len(input_deck.mesh_tallies)
     N_surface_tally = len(input_deck.surface_tallies)
     N_cell_tally = len(input_deck.cell_tallies)
+    N_cs_tally = len(input_deck.cs_tallies)
 
     # Cell data sizes
     N_cell_surface = 0
@@ -1389,6 +1459,7 @@ def make_type_global(input_deck):
             ("mesh_tallies", mesh_tally, (N_mesh_tally,)),
             ("surface_tallies", surface_tally, (N_surface_tally,)),
             ("cell_tallies", cell_tally, (N_cell_tally,)),
+            ("cs_tallies", cs_tally, (N_cs_tally,)),
             ("setting", setting),
             ("technique", technique),
             ("domain_decomp", domain_decomp),

From efb2501be37598fd26d608afdf86f4c506559573 Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Mon, 16 Dec 2024 13:14:52 -0700
Subject: [PATCH 2/9] Compressed sensing tallies, still some issues

---
 examples/fixed_source/cooper_combo/input.py   |   2 +-
 examples/fixed_source/cooper_combo/process.py |   2 +
 examples/fixed_source/kobayashi3-TD/input.py  |  12 +-
 examples/fixed_source/sphere_in_cube/input.py |  18 +-
 mcdc/card.py                                  |   2 +-
 mcdc/kernel.py                                | 164 ++++----
 mcdc/main.py                                  | 351 ++++++++++++++----
 mcdc/tally.py                                 |   8 +-
 mcdc/type_.py                                 |  23 +-
 9 files changed, 421 insertions(+), 161 deletions(-)

diff --git a/examples/fixed_source/cooper_combo/input.py b/examples/fixed_source/cooper_combo/input.py
index 5e3e4059..8daf6352 100644
--- a/examples/fixed_source/cooper_combo/input.py
+++ b/examples/fixed_source/cooper_combo/input.py
@@ -61,7 +61,7 @@
 )
 
 # Setting
-mcdc.setting(N_particle=50)
+mcdc.setting(N_particle=1e5)
 mcdc.implicit_capture()
 
 # Run
diff --git a/examples/fixed_source/cooper_combo/process.py b/examples/fixed_source/cooper_combo/process.py
index 8ab5fad0..8103e3e8 100644
--- a/examples/fixed_source/cooper_combo/process.py
+++ b/examples/fixed_source/cooper_combo/process.py
@@ -13,6 +13,8 @@
     phi = tally["flux/mean"][:]
     phi_sd = tally["flux/sdev"][:]
 
+    np.save("flux_results.npy", phi)
+
 
 # Plot result
 X, Y = np.meshgrid(x_mid, x_mid)
diff --git a/examples/fixed_source/kobayashi3-TD/input.py b/examples/fixed_source/kobayashi3-TD/input.py
index 7308f928..116f4312 100644
--- a/examples/fixed_source/kobayashi3-TD/input.py
+++ b/examples/fixed_source/kobayashi3-TD/input.py
@@ -60,7 +60,7 @@
     scores=["flux"],
     x=np.linspace(0.0, 60.0, 31),
     y=np.linspace(0.0, 100.0, 51),
-    t=np.linspace(0.0, 200.0, 21),
+    # t=np.linspace(0.0, 200.0, 21),
     # g=np.array([-0.5, 3.5, 6.5]) # fast (0, 1, 2, 3) and thermal (4, 5, 6) groups
 )
 
@@ -69,11 +69,17 @@
 mcdc.tally.cell_tally(shield_cell, scores=["flux"])
 
 
-mcdc.tally.cs_tally(N_cs_bins=[10], size=[1], scores=["flux"])
+mcdc.tally.cs_tally(
+    N_cs_bins=[150],
+    cs_bin_size=[8],
+    x=np.linspace(0.0, 60.0, 31),
+    y=np.linspace(0.0, 100.0, 51),
+    scores=["flux"],
+)
 
 
 # Setting
-mcdc.setting(N_particle=50)
+mcdc.setting(N_particle=1e5)
 
 # Run
 mcdc.run()
diff --git a/examples/fixed_source/sphere_in_cube/input.py b/examples/fixed_source/sphere_in_cube/input.py
index 61157306..215838f0 100644
--- a/examples/fixed_source/sphere_in_cube/input.py
+++ b/examples/fixed_source/sphere_in_cube/input.py
@@ -40,17 +40,25 @@
 # =============================================================================
 mcdc.tally.mesh_tally(
     scores=["fission"],
-    x=np.linspace(0.0, 4.0, 2),
-    y=np.linspace(0.0, 4.0, 2),
-    z=np.linspace(0.0, 4.0, 2),
+    x=np.linspace(0.0, 4.0, 41),
+    y=np.linspace(0.0, 4.0, 41),
+    # z=np.linspace(0.0, 4.0, 41),
     # t=np.linspace(0.0, 200.0, 2),
 )
 
-
 mcdc.tally.cell_tally(sphere_cell, scores=["fission"])
 
+mcdc.tally.cs_tally(
+    N_cs_bins=[1601],
+    cs_bin_size=[1],
+    x=np.linspace(0.0, 4.0, 41),
+    y=np.linspace(0.0, 4.0, 41),
+    scores=["fission"],
+)
+
+
 # Setting
-mcdc.setting(N_particle=100)
+mcdc.setting(N_particle=1e5)
 mcdc.implicit_capture()
 
 # Run
diff --git a/mcdc/card.py b/mcdc/card.py
index 141679c2..880fe050 100644
--- a/mcdc/card.py
+++ b/mcdc/card.py
@@ -406,4 +406,4 @@ def __init__(self):
         self.z = np.array([-INF, INF])
         self.N_bin = 1
         self.N_cs_bins = 1
-        self.size = 1
+        self.cs_bin_size = 1
diff --git a/mcdc/kernel.py b/mcdc/kernel.py
index e145264e..3819673e 100644
--- a/mcdc/kernel.py
+++ b/mcdc/kernel.py
@@ -20,6 +20,8 @@
 from mcdc.constant import *
 from mcdc.print_ import print_error, print_msg
 
+from shapely.geometry import LineString, box
+
 # =============================================================================
 # Domain Decomposition
 # =============================================================================
@@ -1990,18 +1992,27 @@ def score_cell_tally(P_arr, distance, tally, data, mcdc):
             SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
             score = flux * SigmaF
 
-        tally_bin[TALLY_SCORE, cell_idx] += score
+        tally_bin[TALLY_SCORE, cell_idx + i] += score
 
 
 @njit
 def score_cs_tally(P_arr, distance, tally, data, mcdc):
+    # Each time that this function is called, EVERY cs bin needs to be checked to see if the particle is in it.
+    # The particle needs to score into all the bins that it is within
     P = P_arr[0]
     tally_bin = data[TALLY]
     material = mcdc["materials"][P["material_ID"]]
     N_cs_bins = tally["filter"]["N_cs_bins"]
-    cs_bin_size = tally["filter"]["cs_bin_size"]
+
+    # rescaling the size to be in terms of problem units, not mesh cells/pixels
+    Nx = len(tally["filter"]["x"]) - 1
+    cs_bin_size = (
+        tally["filter"]["cs_bin_size"] / Nx
+    )  # TODO: might cause problems in nonysmmetric problems
+
     cs_centers = tally["filter"]["cs_centers"]
     stride = tally["stride"]
+    bin_idx = stride["tally"]
 
     # Particle 4D direction
     ux = P["ux"]
@@ -2020,61 +2031,46 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
     t_final = t + ut * distance
 
     # Check each coarse bin
-    for bin_idx in range(N_cs_bins):
-        center = cs_centers[bin_idx]
-
-        # Bin edges
-        x_min, x_max = center[0] - cs_bin_size / 2, center[0] + cs_bin_size / 2
-        y_min, y_max = center[1] - cs_bin_size / 2, center[1] + cs_bin_size / 2
-        z_min, z_max = center[2] - cs_bin_size / 2, center[2] + cs_bin_size / 2
-
-        # Check for intersection with coarse bin
-        # If particle starts or ends inside the bin, it should score into the bin
-
-        # TODO: handle case where particle crosses a corner, but doesn't start or end in the bin
+    for j in range(N_cs_bins):
+        center = (cs_centers[0][j], cs_centers[1][j])
+
+        # Last bin covers the whole problem
+        if j == N_cs_bins - 1:
+            cs_bin_size = INF
+
+        distance_in_bin = min(
+            distance,
+            calculate_distance_in_coarse_bin(
+                P_arr, distance, center, cs_bin_size, mcdc
+            ),
+        )
 
-        if (
-            (x_min <= x <= x_max or x_min <= x_final <= x_max)
-            and (y_min <= y <= y_max or y_min <= y_final <= y_max)
-            and (z_min <= z <= z_max or z_min <= z_final <= z_max)
-        ):
+        # Calculate flux and other scores
+        flux = distance_in_bin * P["w"]
+        for i in range(tally["N_score"]):
+            score_type = tally["scores"][i]
+            if score_type == SCORE_FLUX:
+                score = flux
+            elif score_type == SCORE_DENSITY:
+                score = flux / physics.get_speed(P_arr, mcdc)
+            elif score_type == SCORE_TOTAL:
+                SigmaT = get_MacroXS(XS_TOTAL, material, P_arr, mcdc)
+                score = flux * SigmaT
+            elif score_type == SCORE_FISSION:
+                SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
+                score = flux * SigmaF
 
-            distance_in_bin = min(
-                distance,
-                calculate_distance_in_coarse_bin(
-                    P_arr, distance, center, cs_bin_size, mcdc
-                ),
-            )
+            tally_bin[TALLY_SCORE, bin_idx + j * tally["N_score"] + i] += score
 
-            # Calculate flux and other scores
-            flux = distance_in_bin * P["w"]
-            for i in range(tally["N_score"]):
-                score_type = tally["scores"][i]
-                if score_type == SCORE_FLUX:
-                    score = flux
-                elif score_type == SCORE_DENSITY:
-                    score = flux / physics.get_speed(P_arr, mcdc)
-                elif score_type == SCORE_TOTAL:
-                    SigmaT = get_MacroXS(XS_TOTAL, material, P_arr, mcdc)
-                    score = flux * SigmaT
-                elif score_type == SCORE_FISSION:
-                    SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
-                    score = flux * SigmaF
-                adapt.global_add(
-                    tally_bin, (TALLY_SCORE, bin_idx + i), score
-                )  # TODO: fix this score indexing, this is wrong
+            # adapt.global_add(
+            #     tally_bin, (TALLY_SCORE, bin_idx + i), score
+            # )  # TODO: fix this score indexing, this is wrong
 
 
 @njit
 def calculate_distance_in_coarse_bin(P_arr, distance, center, cs_bin_size, mcdc):
-    def intersect_plane(p0, p1, min_val, max_val, axis):
-        if p1[axis] != p0[axis]:  # Avoid division by zero if line is parallel to plane
-            t_min = (min_val - p0[axis]) / (p1[axis] - p0[axis])
-            t_max = (max_val - p0[axis]) / (p1[axis] - p0[axis])
-            return [t_min, t_max] if t_min <= t_max else [t_max, t_min]
-        return [float("-inf"), float("inf")]  # No intersection if parallel
-
     P = P_arr[0]
+
     # Particle 4D direction
     ux = P["ux"]
     uy = P["uy"]
@@ -2091,33 +2087,51 @@ def intersect_plane(p0, p1, min_val, max_val, axis):
     z_final = z + uz * distance
     t_final = t + ut * distance
 
-    start = np.array([x, y, z])
-    end = np.array([x_final, y_final, z_final])
+    start = np.array([x, y])
+    end = np.array([x_final, y_final])
 
-    # Bin edges
+    # Edges of the coarse bin
     x_min, x_max = center[0] - cs_bin_size / 2, center[0] + cs_bin_size / 2
     y_min, y_max = center[1] - cs_bin_size / 2, center[1] + cs_bin_size / 2
-    z_min, z_max = center[2] - cs_bin_size / 2, center[2] + cs_bin_size / 2
 
-    # Calculate intersection parameters for each axis
-    tx_min, tx_max = intersect_plane(start, end, x_min, x_max, 0)
-    ty_min, ty_max = intersect_plane(start, end, y_min, y_max, 1)
-    tz_min, tz_max = intersect_plane(start, end, z_min, z_max, 2)
-
-    # Determine the entry and exit parameters
-    t_entry = max(tx_min, ty_min, tz_min)
-    t_exit = min(tx_max, ty_max, tz_max)
-
-    # Check if there's a valid intersection
-    if t_entry > t_exit or t_exit < 0 or t_entry > 1:
+    def clip_segment_to_box(start, end, x_min, x_max, y_min, y_max):
+        t0, t1 = 0.0, 1.0
+
+        dx = end[0] - start[0]
+        dy = end[1] - start[1]
+
+        def clip(p, q):
+            nonlocal t0, t1
+            if p < 0:
+                t = q / p
+                if t > t1:
+                    return False
+                if t > t0:
+                    t0 = t
+            elif p > 0:
+                t = q / p
+                if t < t0:
+                    return False
+                if t < t1:
+                    t1 = t
+            elif q < 0:
+                return False
+            return True
+
+        if clip(-dx, start[0] - x_min):
+            if clip(dx, x_max - start[0]):
+                if clip(-dy, start[1] - y_min):
+                    if clip(dy, y_max - start[1]):
+                        if t1 < 1:
+                            end = start + t1 * np.array([dx, dy])
+                        if t0 > 0:
+                            start = start + t0 * np.array([dx, dy])
+                        return np.linalg.norm(end - start)
         return 0.0
 
-    # Calculate entry and exit points in 3D space
-    entry_point = start + t_entry * (end - start)
-    exit_point = start + t_exit * (end - start)
+    distance_inside = clip_segment_to_box(start, end, x_min, x_max, y_min, y_max)
 
-    # Return the distance between entry and exit points
-    return np.linalg.norm(exit_point - entry_point)
+    return distance_inside
 
 
 @njit
@@ -2140,20 +2154,12 @@ def tally_reduce(data, mcdc):
 
 @njit
 def tally_accumulate(data, mcdc):
-
-    # print(f'fixed_source_data = {data[TALLY][TALLY_SCORE][-1]}')
-
     tally_bin = data[TALLY]
-    # print(data[0][0, 30000], data[0][0, 30001])
     N_bin = tally_bin.shape[1]
 
-    # print(f'N_bin = {N_bin}')
-
     for i in range(N_bin):
         # Accumulate score and square of score into sum and sum_sq
         score = tally_bin[TALLY_SCORE, i]
-        # if (score != 0 and i > 29999):
-        #     print(f'score = {score}, i = {i}')
         tally_bin[TALLY_SUM, i] += score
         tally_bin[TALLY_SUM_SQ, i] += score * score
 
@@ -2520,6 +2526,10 @@ def move_to_event(P_arr, data, mcdc):
             ID = cell["tally_IDs"][i]
             tally = mcdc["cell_tallies"][ID]
             score_cell_tally(P_arr, distance, tally, data, mcdc)
+
+        # CS tallies
+        for tally in mcdc["cs_tallies"]:
+            score_cs_tally(P_arr, distance, tally, data, mcdc)
     if mcdc["setting"]["mode_eigenvalue"]:
         eigenvalue_tally(P_arr, distance, mcdc)
 
diff --git a/mcdc/main.py b/mcdc/main.py
index 298da724..6207c86a 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -2,8 +2,13 @@
 import importlib.metadata
 import matplotlib.pyplot as plt
 import numba as nb
+from scipy.stats.qmc import Halton
+import cvxpy as cp
 
 from matplotlib import colors as mpl_colors
+from matplotlib.patches import Rectangle
+from shapely.geometry import Polygon, box
+import scipy.fft as spfft
 
 # Parse command-line arguments
 #   TODO: Will be inside run() once Python/Numba adapter is integrated
@@ -190,6 +195,11 @@ def run():
         loop_fixed_source(data_arr, mcdc_arr)
     mcdc["runtime_simulation"] = MPI.Wtime() - simulation_start
 
+    # Compressed sensing reconstruction - after the sim runs and gets results
+    N_cs_bins = mcdc["cs_tallies"]["filter"]["N_cs_bins"][0]
+    if N_cs_bins != 0:
+        cs_reconstruct(data, mcdc)
+
     # Output: generate hdf5 output files
     output_start = MPI.Wtime()
     generate_hdf5(data, mcdc)
@@ -203,6 +213,176 @@ def run():
     closeout(mcdc)
 
 
+def calculate_cs_overlap(corners, grid):
+    quadrilateral = Polygon(corners)
+
+    # Get the dimensions of the grid
+    grid_height, grid_width = grid.shape
+
+    # Create an array to hold the overlap values
+    overlap = np.zeros_like(grid, dtype=float)
+
+    # Loop over each grid cell
+    for i in range(grid_height):
+        for j in range(grid_width):
+            # Define the current grid cell as a shapely box
+            cell = box(j - 0.5, i - 0.5, j + 0.5, i + 0.5)
+
+            # Calculate the intersection area between the cell and the quadrilateral
+            intersection = cell.intersection(quadrilateral).area
+
+            # Normalize by the cell area (which is 1 in this case)
+            overlap[i, j] = intersection
+
+    overlap_1d = overlap.flatten()
+
+    return overlap_1d
+
+
+def calculate_cs_A(data, mcdc):
+    x_grid = mcdc["mesh_tallies"]["filter"]["x"][0]
+    y_grid = mcdc["mesh_tallies"]["filter"]["y"][0]
+    Nx = len(x_grid) - 1
+    Ny = len(y_grid) - 1
+
+    N_cs_bins = mcdc["cs_tallies"]["filter"]["N_cs_bins"][0]
+    bin_size = mcdc["cs_tallies"]["filter"]["cs_bin_size"][0]
+
+    S = [[] for _ in range(N_cs_bins)]
+
+    [x_centers, y_centers] = mcdc["cs_tallies"]["filter"]["cs_centers"][0]
+
+    # Calculate the overlap grid for each bin, and flatten into a row of S
+    for ibin in range(N_cs_bins):
+        bin_x_min = x_centers[ibin] - bin_size / 2
+        bin_x_max = x_centers[ibin] + bin_size / 2
+        bin_y_min = y_centers[ibin] - bin_size / 2
+        bin_y_max = y_centers[ibin] + bin_size / 2
+
+        overlap = np.zeros((len(y_grid) - 1, len(x_grid) - 1))
+
+        for i in range(len(y_grid) - 1):
+            for j in range(len(x_grid) - 1):
+                cell_x_min = x_grid[j]
+                cell_x_max = x_grid[j + 1]
+                cell_y_min = y_grid[i]
+                cell_y_max = y_grid[i + 1]
+
+                # Calculate overlap in x and y directions
+                overlap_x = max(
+                    0, min(bin_x_max, cell_x_max) - max(bin_x_min, cell_x_min)
+                )
+                overlap_y = max(
+                    0, min(bin_y_max, cell_y_max) - max(bin_y_min, cell_y_min)
+                )
+
+                # Calculate fractional overlap
+                cell_area = (cell_x_max - cell_x_min) * (cell_y_max - cell_y_min)
+                overlap[i, j] = (overlap_x * overlap_y) / cell_area
+
+        S[ibin] = overlap.flatten()
+
+    print(f"N_cs_bin = {N_cs_bins}")
+    S[-1] = np.ones(Nx * Ny)
+    S = np.array(S)
+    S_summed = np.sum(S, axis=0).reshape(Ny, Nx)
+    plt.imshow(S_summed)
+    plt.colorbar()
+    plt.title("S_Summed")
+    plt.show()
+
+    print(S.shape[1])
+    assert S.shape[1] == Nx * Ny, "Size of S must match Nx * Ny."
+    assert (
+        S.shape[1] == mcdc["cs_tallies"]["N_bin"][0]
+    ), "Size of S must match number of cells in desired mesh tally"
+    np.save("sphere_S.npy", S)
+    # TODO: can this be done in a different way? idk
+    # Construct the DCT matrix T
+    idct_basis_x = spfft.idct(np.identity(Nx), axis=0)
+    idct_basis_y = spfft.idct(np.identity(Ny), axis=0)
+
+    T_inv = np.kron(idct_basis_y, idct_basis_x)
+    A = S @ T_inv
+    return A, T_inv
+
+
+def calculate_cs_sparse_solution(data, mcdc, A, b):
+    print(f"A = {A}")
+    print(f"b = {b}")
+    N_fine_cells = mcdc["cs_tallies"]["N_bin"][0]
+    print(f"N_fine_cells = {N_fine_cells}")
+
+    # setting up the problem with CVXPY
+    vx = cp.Variable(N_fine_cells)
+
+    # Basis pursuit denoising
+    l = 0
+    objective = cp.Minimize(0.5 * cp.norm(A @ vx - b, 2) + l * cp.norm(vx, 1))
+    prob = cp.Problem(objective)
+    result = prob.solve(verbose=False)
+
+    # # Basis pursuit
+    # objective = cp.Minimize(cp.norm(vx, 1))
+    # constraints = [A @ vx == b]
+    # prob = cp.Problem(objective, constraints)
+    # result = prob.solve(verbose=True)
+    # print(f'vx.value = {vx.value}')
+
+    # formatting the sparse solution
+    sparse_solution = np.array(vx.value).squeeze()
+
+    return sparse_solution
+
+
+def cs_reconstruct(data, mcdc):
+    tally_bin = data[TALLY]
+
+    print(tally_bin.shape)
+
+    tally = mcdc["cs_tallies"][0]
+    stride = tally["stride"]
+    bin_idx = stride["tally"]
+    N_cs_bins = tally["filter"]["N_cs_bins"]
+    Nx = len(mcdc["mesh_tallies"]["filter"]["x"][0]) - 1
+    Ny = len(mcdc["mesh_tallies"]["filter"]["y"][0]) - 1
+
+    b = tally_bin[TALLY_SUM, bin_idx : bin_idx + N_cs_bins]
+
+    print(f"measurements b = {b}")
+
+    A, T_inv = calculate_cs_A(data, mcdc)
+    x = calculate_cs_sparse_solution(data, mcdc, A, b)
+
+    np.save("sparse_solution.npy", x)
+    print(f"sparse solution shape = {x.shape}")
+    print(x)
+
+    recon = T_inv @ x
+    print(f"recon.shape = {recon.shape}")
+    print(f"recon = {recon}")
+
+    recon_reshaped = recon.reshape(Ny, Nx)
+
+    plt.imshow(recon_reshaped)
+    plt.title("Ny, Nx")
+    plt.colorbar()
+    plt.show()
+
+    return recon
+    # reconstruction
+
+    # # Find the sparse solution, and then find the reconstruction
+    # sparse_solution[res] = finding_sparse_solution(A[res], fluxes[res].size, b, bpdn_lambda)
+    # reconstruction[res] = np.reshape(un_matrix[res] @ sparse_solution[res], fluxes[res].shape)
+    # rescaled_recons[res] = downsample_to_resolution(reconstruction[res], input_flux.shape)
+
+    # if return_centers:
+    #     return reconstruction, input_flux, rescaled_recons, bin_centers[0]
+    # else:
+    #     return reconstruction, input_flux, rescaled_recons
+
+
 # =============================================================================
 # utilities for handling discrepancies between input and program types
 # =============================================================================
@@ -217,11 +397,6 @@ def copy_field(dst, src, name):
     else:
         data = getattr(src, name)
 
-    # print(f'dst, src, name = {dst, src, name}')
-    # print(f'dst = {dst.dtype.names}')
-    # print(f'name = {name}')
-    # print(f'dst[name] = {dst[name]}')
-
     if isinstance(dst[name], np.ndarray):
         if isinstance(data, np.ndarray) and dst[name].shape != data.shape:
             for dim in data.shape:
@@ -406,6 +581,45 @@ def dd_mesh_bounds(idx):
     return mesh_xn, mesh_xp, mesh_yn, mesh_yp, mesh_zn, mesh_zp
 
 
+def generate_cs_centers(mcdc, N_dim=2, seed=123456789):
+    N_cs_bins = int(mcdc["cs_tallies"]["filter"]["N_cs_bins"])
+    x_lims = (
+        mcdc["cs_tallies"]["filter"]["x"][0][-1],
+        mcdc["cs_tallies"]["filter"]["x"][0][0],
+    )
+    y_lims = (
+        mcdc["cs_tallies"]["filter"]["y"][0][-1],
+        mcdc["cs_tallies"]["filter"]["y"][0][0],
+    )
+
+    # Generate Halton sequence according to the seed
+    halton_seq = Halton(d=N_dim, seed=seed)
+    points = halton_seq.random(n=N_cs_bins)
+
+    # Extract x and y coordinates as tuples separately, scaled to the problem dimensions
+    x_coords = tuple(points[:, 0] * (x_lims[1] - x_lims[0]) + x_lims[0])
+    y_coords = tuple(points[:, 1] * (y_lims[1] - y_lims[0]) + y_lims[0])
+
+    # # Generate the centers of the cells
+    # x_centers = np.linspace(0.05, 3.95, 40)
+    # y_centers = np.linspace(0.05, 3.95, 40)
+
+    # # Create the meshgrid for all cell centers
+    # X, Y = np.meshgrid(x_centers, y_centers)
+
+    # # Flatten the arrays to get the list of coordinates
+    # x_coords = X.flatten()
+    # y_coords = Y.flatten()
+
+    # x_coords_list = x_coords.tolist()
+    # y_coords_list = y_coords.tolist()
+
+    # x_coords_list.append(2.0)
+    # y_coords_list.append(2.0)
+
+    return (x_coords, y_coords)
+
+
 def prepare():
     """
     Preparing the MC transport simulation:
@@ -928,6 +1142,13 @@ def prepare():
         # Direct assignment
         copy_field(mcdc["cs_tallies"][i], input_deck.cs_tallies[i], "N_bin")
 
+        mcdc["cs_tallies"][i]["filter"]["N_cs_bins"] = input_deck.cs_tallies[
+            i
+        ].N_cs_bins[0]
+        mcdc["cs_tallies"][i]["filter"]["cs_bin_size"] = input_deck.cs_tallies[
+            i
+        ].cs_bin_size[0]
+
         # Filters (variables with possible different sizes)
         if not input_deck.technique["domain_decomposition"]:
             for name in ["x", "y", "z", "t", "mu", "azi", "g"]:
@@ -936,21 +1157,7 @@ def prepare():
                     input_deck.cs_tallies[i], name
                 )
 
-        # else:  # decomposed mesh filters
-        #     mxn, mxp, myn, myp, mzn, mzp = dd_mesh_bounds(i)
-
-        #     # Filters
-        #     new_x = input_deck.mesh_tallies[i].x[mxn:mxp]
-        #     new_y = input_deck.mesh_tallies[i].y[myn:myp]
-        #     new_z = input_deck.mesh_tallies[i].z[mzn:mzp]
-        #     mcdc["mesh_tallies"][i]["filter"]["x"] = new_x
-        #     mcdc["mesh_tallies"][i]["filter"]["y"] = new_y
-        #     mcdc["mesh_tallies"][i]["filter"]["z"] = new_z
-        #     for name in ["t", "mu", "azi", "g"]:
-        #         N = len(getattr(input_deck.mesh_tallies[i], name))
-        #         mcdc["mesh_tallies"][i]["filter"][name][:N] = getattr(
-        #             input_deck.mesh_tallies[i], name
-        #         )
+        mcdc["cs_tallies"][i]["filter"]["cs_centers"] = generate_cs_centers(mcdc)
 
         # Set tally scores
         N_score = len(input_deck.cs_tallies[i].scores)
@@ -970,45 +1177,12 @@ def prepare():
                 score_type = SCORE_NET_CURRENT
             mcdc["cs_tallies"][i]["scores"][j] = score_type
 
-        # Filter grid sizes
-        Nmu = len(input_deck.cs_tallies[i].mu) - 1
-        N_azi = len(input_deck.cs_tallies[i].azi) - 1
-        Ng = len(input_deck.cs_tallies[i].g) - 1
-        Nx = len(input_deck.cs_tallies[i].x) - 1
-        Ny = len(input_deck.cs_tallies[i].y) - 1
-        Nz = len(input_deck.cs_tallies[i].z) - 1
-        Nt = len(input_deck.cs_tallies[i].t) - 1
-
         # Update N_bin
         mcdc["cs_tallies"][i]["N_bin"] *= N_score
 
-        # Filter strides
-        stride = N_score
-        if Nz > 1:
-            mcdc["cs_tallies"][i]["stride"]["z"] = stride
-            stride *= Nz
-        if Ny > 1:
-            mcdc["cs_tallies"][i]["stride"]["y"] = stride
-            stride *= Ny
-        if Nx > 1:
-            mcdc["cs_tallies"][i]["stride"]["x"] = stride
-            stride *= Nx
-        if Nt > 1:
-            mcdc["cs_tallies"][i]["stride"]["t"] = stride
-            stride *= Nt
-        if Ng > 1:
-            mcdc["cs_tallies"][i]["stride"]["g"] = stride
-            stride *= Ng
-        if N_azi > 1:
-            mcdc["cs_tallies"][i]["stride"]["azi"] = stride
-            stride *= N_azi
-        if Nmu > 1:
-            mcdc["cs_tallies"][i]["stride"]["mu"] = stride
-            stride *= Nmu
-
         # Set tally stride and accumulate total tally size
         mcdc["cs_tallies"][i]["stride"]["tally"] = tally_size
-        tally_size += mcdc["cs_tallies"][i]["N_bin"]
+        tally_size += mcdc["cs_tallies"][i]["filter"]["N_cs_bins"]
 
     # Set tally data
     if not input_deck.technique["uq"]:
@@ -1404,7 +1578,10 @@ def prepare():
 
 
 def cardlist_to_h5group(dictlist, input_group, name):
-    main_group = input_group.create_group(name + "s")
+    if name[-1] != "s":
+        main_group = input_group.create_group(name + "s")
+    else:
+        main_group = input_group.create_group(name)
     for item in dictlist:
         group = main_group.create_group(name + "_%i" % getattr(item, "ID"))
         card_to_h5group(item, group)
@@ -1430,7 +1607,10 @@ def card_to_h5group(card, group):
 
 
 def dictlist_to_h5group(dictlist, input_group, name):
-    main_group = input_group.create_group(name + "s")
+    if name[-1] != "s":
+        main_group = input_group.create_group(name + "s")
+    else:
+        main_group = input_group.create_group(name)
     for item in dictlist:
         group = main_group.create_group(name + "_%i" % item["ID"])
         dict_to_h5group(item, group)
@@ -1525,11 +1705,12 @@ def generate_hdf5(data, mcdc):
                     input_deck.mesh_tallies, input_group, "mesh_tallies"
                 )
                 cardlist_to_h5group(
-                    input_deck.surface_tallies, input_group, "surface_tally"
+                    input_deck.surface_tallies, input_group, "surface_tallies"
                 )
                 cardlist_to_h5group(
                     input_deck.cell_tallies, input_group, "cell_tallies"
                 )
+                cardlist_to_h5group(input_deck.cs_tallies, input_group, "cs_tallies")
                 dict_to_h5group(input_deck.setting, input_group.create_group("setting"))
                 dict_to_h5group(
                     input_deck.technique, input_group.create_group("technique")
@@ -1670,34 +1851,72 @@ def generate_hdf5(data, mcdc):
                 N_bin = tally["N_bin"]
                 start = tally["stride"]["tally"]
                 tally_bin = data[TALLY][:, start : start + N_bin]
+                tally_bin = tally_bin.reshape(shape)
+
+                # Roll tally so that score is in the front
+                tally_bin = np.rollaxis(tally_bin, 1, 0)
+
+                # Iterate over scores
+                for i in range(N_score):
+                    score_type = tally["scores"][i]
+                    score_tally_bin = np.squeeze(tally_bin[i])
+                    if score_type == SCORE_FLUX:
+                        score_name = "flux"
+                    elif score_type == SCORE_NET_CURRENT:
+                        score_name = "net-current"
+                    elif score_type == SCORE_FISSION:
+                        score_name = "fission"
+                    group_name = "tallies/cell_tally_%i/%s/" % (ID, score_name)
 
-                # print(f'data = {data[TALLY][2]}')
-                # if data[TALLY][1].all() == data[TALLY][2].all:
-                #     print('hell yeah')
-                # print(f'data keys = {data[TALLY].dtype.names}')
+                    mean = score_tally_bin[TALLY_SUM]
+                    sdev = score_tally_bin[TALLY_SUM_SQ]
 
-                # print(f'tally_bin = {tally_bin}')
+                    f.create_dataset(group_name + "mean", data=mean)
+                    f.create_dataset(group_name + "sdev", data=sdev)
+                    if mcdc["technique"]["uq"]:
+                        mc_var = score_tally_bin[TALLY_UQ_BATCH_VAR]
+                        tot_var = score_tally_bin[TALLY_UQ_BATCH]
+                        uq_var = tot_var - mc_var
+                        f.create_dataset(group_name + "uq_var", data=uq_var)
+
+            # CS tallies
+            for ID, tally in enumerate(mcdc["cs_tallies"]):
+                if mcdc["technique"]["iQMC"]:
+                    break
+                N_cs_bins = tally["filter"]["N_cs_bins"]
 
+                # Shape
+                N_score = tally["N_score"]
+
+                if not mcdc["technique"]["uq"]:
+                    shape = (3, N_cs_bins, N_score)
+                else:
+                    shape = (5, N_cs_bins, N_score)
+
+                # Reshape tally
+                start = tally["stride"]["tally"]
+                tally_bin = data[TALLY][:, start : start + N_cs_bins]
                 tally_bin = tally_bin.reshape(shape)
 
                 # Roll tally so that score is in the front
-                tally_bin = np.rollaxis(tally_bin, 1, 0)
+                tally_bin = np.rollaxis(tally_bin, 2, 0)
 
                 # Iterate over scores
                 for i in range(N_score):
                     score_type = tally["scores"][i]
                     score_tally_bin = np.squeeze(tally_bin[i])
-                    # print(f'score_tally_bin = {score_tally_bin}')
                     if score_type == SCORE_FLUX:
                         score_name = "flux"
                     elif score_type == SCORE_NET_CURRENT:
                         score_name = "net-current"
                     elif score_type == SCORE_FISSION:
                         score_name = "fission"
-                    group_name = "tallies/cell_tally_%i/%s/" % (ID, score_name)
+                    group_name = "tallies/cs_tally_%i/%s/" % (ID, score_name)
+
+                    center_points = tally["filter"]["cs_centers"]
+                    f.create_dataset(group_name + "center_points", data=center_points)
 
                     mean = score_tally_bin[TALLY_SUM]
-                    # print(f'ID = {ID}, score_name = {score_name}, mean = {mean}')
                     sdev = score_tally_bin[TALLY_SUM_SQ]
 
                     f.create_dataset(group_name + "mean", data=mean)
diff --git a/mcdc/tally.py b/mcdc/tally.py
index db75e917..d035b135 100644
--- a/mcdc/tally.py
+++ b/mcdc/tally.py
@@ -207,7 +207,7 @@ def cell_tally(cell, scores=["flux"]):
 
 def cs_tally(
     N_cs_bins=10,
-    size=1,
+    cs_bin_size=1,
     x=np.array([-INF, INF]),
     y=np.array([-INF, INF]),
     z=np.array([-INF, INF]),
@@ -223,14 +223,16 @@ def cs_tally(
 
     # Set ID
     card.ID = len(global_.input_deck.cs_tallies)
-    card.N_cs_bins = N_cs_bins
-    card.size = size
 
     # Set mesh
     card.x = x
     card.y = y
     card.z = z
 
+    # Set bin properties
+    card.N_cs_bins = N_cs_bins
+    card.cs_bin_size = [cs_bin_size[0] / (len(x) - 1) * (x[-1] - x[0])]
+
     # Set other filters
     card.t = t
     card.mu = mu
diff --git a/mcdc/type_.py b/mcdc/type_.py
index 7fd7bbeb..e9a031a4 100644
--- a/mcdc/type_.py
+++ b/mcdc/type_.py
@@ -900,7 +900,6 @@ def make_type_cs_tally(input_deck):
     struct = []
 
     # Maximum numbers of mesh and filter grids and scores
-    Nmax_cs_bins = 2
     Nmax_x = 2
     Nmax_y = 2
     Nmax_z = 2
@@ -909,8 +908,8 @@ def make_type_cs_tally(input_deck):
     Nmax_azi = 2
     Nmax_g = 2
     Nmax_score = 1
+    N_cs_centers = 1
     for card in input_deck.cs_tallies:
-        Nmax_cs_bins = max(Nmax_cs_bins, len(card.N_cs_bins))
         Nmax_x = max(Nmax_x, len(card.x))
         Nmax_y = max(Nmax_y, len(card.y))
         Nmax_z = max(Nmax_z, len(card.z))
@@ -919,6 +918,7 @@ def make_type_cs_tally(input_deck):
         Nmax_azi = max(Nmax_azi, len(card.azi))
         Nmax_g = max(Nmax_g, len(card.g))
         Nmax_score = max(Nmax_score, len(card.scores))
+        N_cs_centers = card.N_cs_bins[0]
 
     # # reduce tally sizes for subdomains
     # if input_deck.technique["domain_decomposition"]:
@@ -926,9 +926,16 @@ def make_type_cs_tally(input_deck):
 
     # Set the filter
     filter_ = [
-        ("N_cs_bins", int64),
+        ("N_cs_bins", int),
         ("cs_bin_size", float64),
-        ("cs_centers", float64, (Nmax_cs_bins,)),
+        (
+            "cs_centers",
+            float64,
+            (
+                2,
+                N_cs_centers,
+            ),
+        ),
         ("x", float64, (Nmax_x,)),
         ("y", float64, (Nmax_y,)),
         ("z", float64, (Nmax_z,)),
@@ -937,6 +944,7 @@ def make_type_cs_tally(input_deck):
         ("azi", float64, (Nmax_azi,)),
         ("g", float64, (Nmax_g,)),
     ]
+
     struct += [("filter", filter_)]
 
     # Tally strides
@@ -950,12 +958,17 @@ def make_type_cs_tally(input_deck):
         ("x", int64),
         ("y", int64),
         ("z", int64),
+        # ("N_cs_bins", int64),   # TODO: get rid of this line?
     ]
     struct += [("stride", stride)]
 
-    # Total number of bins
+    # Total number of bins (will be used for the reconstruction)
+    # TODO: Might be able to get rid of this (just get N_bin from the mesh)
     struct += [("N_bin", int64)]
 
+    # Number of compressed sensing bins
+    # struct += [("N_cs_bins", int64)]
+
     # Scores
     struct += [("N_score", int64), ("scores", int64, (Nmax_score,))]
 

From af53369189b8b10b828b56e23774f599cbc8fa8f Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Wed, 18 Dec 2024 14:32:53 -0700
Subject: [PATCH 3/9] cs kinda maybe works, but not in numba yet

---
 .../sphere_in_cube/center_points.npy          | Bin 3328 -> 25744 bytes
 examples/fixed_source/sphere_in_cube/input.py |   9 +-
 .../fixed_source/sphere_in_cube/process.py    |  48 +++++++-
 .../sphere_in_cube/sparse_solution.npy        | Bin 12928 -> 12928 bytes
 .../fixed_source/sphere_in_cube/sphere_S.npy  | Bin 2560128 -> 20492928 bytes
 mcdc/card.py                                  |   2 +-
 mcdc/kernel.py                                |  33 +++---
 mcdc/main.py                                  | 108 +++++++++---------
 mcdc/tally.py                                 |  12 +-
 mcdc/type_.py                                 |   2 +-
 10 files changed, 132 insertions(+), 82 deletions(-)

diff --git a/examples/fixed_source/sphere_in_cube/center_points.npy b/examples/fixed_source/sphere_in_cube/center_points.npy
index 674a38b94e8344798bd49d8a95eb2f036e9244cc..0fef328c9483617d3fb39271e95cdb0ab4b66783 100644
GIT binary patch
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diff --git a/examples/fixed_source/sphere_in_cube/input.py b/examples/fixed_source/sphere_in_cube/input.py
index f002e7ad..d53a4e0d 100644
--- a/examples/fixed_source/sphere_in_cube/input.py
+++ b/examples/fixed_source/sphere_in_cube/input.py
@@ -7,7 +7,7 @@
 # Homogeneous pure-fission sphere inside a pure-scattering cube
 
 # Set materials
-pure_f = mcdc.material(fission=np.array([1.0]), nu_p=np.array([1.2]))
+pure_f = mcdc.material(fission=np.array([1.0]), nu_p=np.array([1.1]))
 pure_s = mcdc.material(scatter=np.array([[1.0]]))
 
 # Set surfaces
@@ -49,16 +49,15 @@
 mcdc.tally.cell_tally(sphere_cell, scores=["fission"])
 
 mcdc.tally.cs_tally(
-    N_cs_bins=[200],
-    cs_bin_size=[3],
+    N_cs_bins=[1601],
+    cs_bin_size=np.array([1.0, 1.0]),
     x=np.linspace(0.0, 4.0, 41),
     y=np.linspace(0.0, 4.0, 41),
     scores=["fission"],
 )
 
-
 # Setting
-mcdc.setting(N_particle=1e5)
+mcdc.setting(N_particle=1e3)
 mcdc.implicit_capture()
 
 # Run
diff --git a/examples/fixed_source/sphere_in_cube/process.py b/examples/fixed_source/sphere_in_cube/process.py
index a0ed55ca..fdf3e1cd 100644
--- a/examples/fixed_source/sphere_in_cube/process.py
+++ b/examples/fixed_source/sphere_in_cube/process.py
@@ -1,6 +1,8 @@
 import h5py
 import numpy as np
 import matplotlib.pyplot as plt
+import scipy.fft as spfft
+import cvxpy as cp
 
 # Load results
 with h5py.File("output.h5", "r") as f:
@@ -8,10 +10,50 @@
     center_points = np.load("center_points.npy")
     cs_results = f["tallies"]["cs_tally_0"]["fission"]["mean"][:]
     cs_alphas = cs_results[:-1] / np.max(cs_results[:-1])
-
     # plt.scatter(center_points[0][:-1], center_points[1][:-1], alpha=cs_alphas)
     # plt.show()
 
     mesh_results = f["tallies"]["mesh_tally_0"]["fission"]["mean"][:]
-    # plt.imshow(mesh_results)
-    # plt.show()
+    plt.imshow(cs_results[:-1].reshape(mesh_results.shape))
+    plt.title("cs")
+    plt.show()
+    plt.imshow(mesh_results)
+    plt.title("mesh")
+    plt.show()
+
+    print(f"cs_results = {cs_results}")
+    print(f"sh_results = {mesh_results.flatten()}")
+
+
+Nx = 40
+Ny = 40
+S = np.load("sphere_S.npy")
+mesh_b = S @ mesh_results.flatten()
+cs_b = cs_results
+
+
+# b = mesh_b
+
+# print(f'shape of mesh b = {mesh_b.shape}')
+# print(f'shape of cs b = {cs_b.shape}')
+
+# idct_basis_x = spfft.idct(np.identity(Nx), axis=0)
+# idct_basis_y = spfft.idct(np.identity(Ny), axis=0)
+
+# T_inv = np.kron(idct_basis_y, idct_basis_x)
+# A = S @ T_inv
+# N_fine_cells = 1600
+
+# vx = cp.Variable(N_fine_cells)
+# l = 0
+# objective = cp.Minimize(0.5 * cp.norm(A @ vx - b, 2) + l * cp.norm(vx, 1))
+# prob = cp.Problem(objective)
+# result = prob.solve(verbose=False)
+# sparse_solution = np.array(vx.value).squeeze()
+
+# recon = T_inv @ sparse_solution
+# recon_reshaped = recon.reshape(Ny, Nx)
+
+# plt.imshow(recon_reshaped)
+# plt.title('reconstruction')
+# plt.show()
diff --git a/examples/fixed_source/sphere_in_cube/sparse_solution.npy b/examples/fixed_source/sphere_in_cube/sparse_solution.npy
index 5052ca3446b31dbe2496c55ad7170e62fa9a6d5f..a8121470298d80f848725caaa2760909bd1990a3 100644
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diff --git a/mcdc/card.py b/mcdc/card.py
index 2f047a82..91af4a9a 100644
--- a/mcdc/card.py
+++ b/mcdc/card.py
@@ -426,4 +426,4 @@ def __init__(self):
         self.z = np.array([-INF, INF])
         self.N_bin = 1
         self.N_cs_bins = 1
-        self.cs_bin_size = 1
+        self.cs_bin_size = np.array([1.0, 1.0])
diff --git a/mcdc/kernel.py b/mcdc/kernel.py
index f3ffcdb5..83530bf6 100644
--- a/mcdc/kernel.py
+++ b/mcdc/kernel.py
@@ -2009,7 +2009,8 @@ def score_cell_tally(P_arr, distance, tally, data, mcdc):
             SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
             score = flux * SigmaF
 
-        tally_bin[TALLY_SCORE, cell_idx + i] += score
+        adapt.global_add(tally_bin, (TALLY_SCORE, cell_idx + i), round(score))
+        # tally_bin[TALLY_SCORE, cell_idx + i] += score
 
 
 @njit
@@ -2021,13 +2022,9 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
     material = mcdc["materials"][P["material_ID"]]
     N_cs_bins = tally["filter"]["N_cs_bins"]
 
-    # rescaling the size to be in terms of problem units, not mesh cells/pixels
-    Nx = len(tally["filter"]["x"]) - 1
-    cs_bin_size = (
-        tally["filter"]["cs_bin_size"] / Nx
-    )  # TODO: might cause problems in nonysmmetric problems
-
+    cs_bin_size = tally["filter"]["cs_bin_size"]
     cs_centers = tally["filter"]["cs_centers"]
+
     stride = tally["stride"]
     bin_idx = stride["tally"]
 
@@ -2053,9 +2050,9 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
 
         # Last bin covers the whole problem
         if j == N_cs_bins - 1:
-            cs_bin_size = INF
+            cs_bin_size = np.array([INF, INF])
 
-        distance_in_bin = min(
+        distance_in_bin = np.minimum(
             distance,
             calculate_distance_in_coarse_bin(
                 P_arr, distance, center, cs_bin_size, mcdc
@@ -2077,11 +2074,12 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
                 SigmaF = get_MacroXS(XS_FISSION, material, P_arr, mcdc)
                 score = flux * SigmaF
 
-            tally_bin[TALLY_SCORE, bin_idx + j * tally["N_score"] + i] += score
-
-            # adapt.global_add(
-            #     tally_bin, (TALLY_SCORE, bin_idx + i), score
-            # )  # TODO: fix this score indexing, this is wrong
+            adapt.global_add(
+                tally_bin,
+                (TALLY_SCORE, bin_idx + j * tally["N_score"] + i),
+                round(score),
+            )
+            # tally_bin[TALLY_SCORE, bin_idx + j * tally["N_score"] + i] += score
 
 
 @njit
@@ -2092,7 +2090,6 @@ def calculate_distance_in_coarse_bin(P_arr, distance, center, cs_bin_size, mcdc)
     ux = P["ux"]
     uy = P["uy"]
     uz = P["uz"]
-    ut = 1.0 / physics.get_speed(P_arr, mcdc)
 
     # Particle initial and final coordinate
     x = P["x"]
@@ -2102,14 +2099,13 @@ def calculate_distance_in_coarse_bin(P_arr, distance, center, cs_bin_size, mcdc)
     x_final = x + ux * distance
     y_final = y + uy * distance
     z_final = z + uz * distance
-    t_final = t + ut * distance
 
     start = np.array([x, y])
     end = np.array([x_final, y_final])
 
     # Edges of the coarse bin
-    x_min, x_max = center[0] - cs_bin_size / 2, center[0] + cs_bin_size / 2
-    y_min, y_max = center[1] - cs_bin_size / 2, center[1] + cs_bin_size / 2
+    x_min, x_max = center[0] - cs_bin_size[0] / 2, center[0] + cs_bin_size[0] / 2
+    y_min, y_max = center[1] - cs_bin_size[1] / 2, center[1] + cs_bin_size[1] / 2
 
     def clip_segment_to_box(start, end, x_min, x_max, y_min, y_max):
         t0, t1 = 0.0, 1.0
@@ -2549,6 +2545,7 @@ def move_to_event(P_arr, data, mcdc):
         # CS tallies
         for tally in mcdc["cs_tallies"]:
             score_cs_tally(P_arr, distance, tally, data, mcdc)
+
     if mcdc["setting"]["mode_eigenvalue"]:
         eigenvalue_tally(P_arr, distance, mcdc)
 
diff --git a/mcdc/main.py b/mcdc/main.py
index 4e421818..8882a396 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -144,18 +144,24 @@ def calculate_cs_A(data, mcdc):
     Ny = len(y_grid) - 1
 
     N_cs_bins = mcdc["cs_tallies"]["filter"]["N_cs_bins"][0]
-    bin_size = mcdc["cs_tallies"]["filter"]["cs_bin_size"][0]
+    cs_bin_size = mcdc["cs_tallies"]["filter"]["cs_bin_size"][0]
 
     S = [[] for _ in range(N_cs_bins)]
 
     [x_centers, y_centers] = mcdc["cs_tallies"]["filter"]["cs_centers"][0]
+    x_centers[-1] = (x_grid[-1] + x_grid[0]) / 2
+    y_centers[-1] = (y_grid[-1] + y_grid[0]) / 2
+    np.save("center_points.npy", mcdc["cs_tallies"]["filter"]["cs_centers"][0])
 
     # Calculate the overlap grid for each bin, and flatten into a row of S
     for ibin in range(N_cs_bins):
-        bin_x_min = x_centers[ibin] - bin_size / 2
-        bin_x_max = x_centers[ibin] + bin_size / 2
-        bin_y_min = y_centers[ibin] - bin_size / 2
-        bin_y_max = y_centers[ibin] + bin_size / 2
+        if ibin == N_cs_bins - 1:
+            cs_bin_size = np.array([Nx, Ny])
+
+        bin_x_min = x_centers[ibin] - cs_bin_size[0] / 2
+        bin_x_max = x_centers[ibin] + cs_bin_size[0] / 2
+        bin_y_min = y_centers[ibin] - cs_bin_size[1] / 2
+        bin_y_max = y_centers[ibin] + cs_bin_size[1] / 2
 
         overlap = np.zeros((len(y_grid) - 1, len(x_grid) - 1))
 
@@ -167,11 +173,15 @@ def calculate_cs_A(data, mcdc):
                 cell_y_max = y_grid[i + 1]
 
                 # Calculate overlap in x and y directions
-                overlap_x = max(
-                    0, min(bin_x_max, cell_x_max) - max(bin_x_min, cell_x_min)
+                overlap_x = np.maximum(
+                    0,
+                    np.minimum(bin_x_max, cell_x_max)
+                    - np.maximum(bin_x_min, cell_x_min),
                 )
-                overlap_y = max(
-                    0, min(bin_y_max, cell_y_max) - max(bin_y_min, cell_y_min)
+                overlap_y = np.maximum(
+                    0,
+                    np.minimum(bin_y_max, cell_y_max)
+                    - np.maximum(bin_y_min, cell_y_min),
                 )
 
                 # Calculate fractional overlap
@@ -179,22 +189,21 @@ def calculate_cs_A(data, mcdc):
                 overlap[i, j] = (overlap_x * overlap_y) / cell_area
 
         S[ibin] = overlap.flatten()
-
-    print(f"N_cs_bin = {N_cs_bins}")
-    S[-1] = np.ones(Nx * Ny)
     S = np.array(S)
+
     S_summed = np.sum(S, axis=0).reshape(Ny, Nx)
     plt.imshow(S_summed)
     plt.colorbar()
     plt.title("S_Summed")
     plt.show()
 
-    print(S.shape[1])
+    assert np.allclose(S[-1], np.ones(Nx * Ny)), "Last row of S must be all ones"
     assert S.shape[1] == Nx * Ny, "Size of S must match Nx * Ny."
     assert (
         S.shape[1] == mcdc["cs_tallies"]["N_bin"][0]
     ), "Size of S must match number of cells in desired mesh tally"
     np.save("sphere_S.npy", S)
+
     # TODO: can this be done in a different way? idk
     # Construct the DCT matrix T
     idct_basis_x = spfft.idct(np.identity(Nx), axis=0)
@@ -206,10 +215,7 @@ def calculate_cs_A(data, mcdc):
 
 
 def calculate_cs_sparse_solution(data, mcdc, A, b):
-    print(f"A = {A}")
-    print(f"b = {b}")
     N_fine_cells = mcdc["cs_tallies"]["N_bin"][0]
-    print(f"N_fine_cells = {N_fine_cells}")
 
     # setting up the problem with CVXPY
     vx = cp.Variable(N_fine_cells)
@@ -247,23 +253,23 @@ def cs_reconstruct(data, mcdc):
 
     b = tally_bin[TALLY_SUM, bin_idx : bin_idx + N_cs_bins]
 
-    print(f"measurements b = {b}")
+    # print(f"measurements b = {b}")
 
     A, T_inv = calculate_cs_A(data, mcdc)
     x = calculate_cs_sparse_solution(data, mcdc, A, b)
 
     np.save("sparse_solution.npy", x)
-    print(f"sparse solution shape = {x.shape}")
-    print(x)
+    # print(f"sparse solution shape = {x.shape}")
+    # print(x)
 
     recon = T_inv @ x
-    print(f"recon.shape = {recon.shape}")
-    print(f"recon = {recon}")
+    # print(f"recon.shape = {recon.shape}")
+    # print(f"recon = {recon}")
 
     recon_reshaped = recon.reshape(Ny, Nx)
 
     plt.imshow(recon_reshaped)
-    plt.title("Ny, Nx")
+    plt.title("Reconstruction")
     plt.colorbar()
     plt.show()
 
@@ -479,43 +485,43 @@ def dd_mesh_bounds(idx):
     return mesh_xn, mesh_xp, mesh_yn, mesh_yp, mesh_zn, mesh_zp
 
 
-def generate_cs_centers(mcdc, N_dim=2, seed=123456789):
-    N_cs_bins = int(mcdc["cs_tallies"]["filter"]["N_cs_bins"])
-    x_lims = (
-        mcdc["cs_tallies"]["filter"]["x"][0][-1],
-        mcdc["cs_tallies"]["filter"]["x"][0][0],
-    )
-    y_lims = (
-        mcdc["cs_tallies"]["filter"]["y"][0][-1],
-        mcdc["cs_tallies"]["filter"]["y"][0][0],
-    )
+def generate_cs_centers(mcdc, N_dim=3, seed=123456789):
+    # N_cs_bins = int(mcdc["cs_tallies"]["filter"]["N_cs_bins"])
+    # x_lims = (
+    #     mcdc["cs_tallies"]["filter"]["x"][0][-1],
+    #     mcdc["cs_tallies"]["filter"]["x"][0][0],
+    # )
+    # y_lims = (
+    #     mcdc["cs_tallies"]["filter"]["y"][0][-1],
+    #     mcdc["cs_tallies"]["filter"]["y"][0][0],
+    # )
 
-    # Generate Halton sequence according to the seed
-    halton_seq = Halton(d=N_dim, seed=seed)
-    points = halton_seq.random(n=N_cs_bins)
+    # # Generate Halton sequence according to the seed
+    # halton_seq = Halton(d=N_dim, seed=seed)
+    # points = halton_seq.random(n=N_cs_bins)
 
-    # Extract x and y coordinates as tuples separately, scaled to the problem dimensions
-    x_coords = tuple(points[:, 0] * (x_lims[1] - x_lims[0]) + x_lims[0])
-    y_coords = tuple(points[:, 1] * (y_lims[1] - y_lims[0]) + y_lims[0])
+    # # Extract x and y coordinates as tuples separately, scaled to the problem dimensions
+    # x_coords = tuple(points[:, 0] * (x_lims[1] - x_lims[0]) + x_lims[0])
+    # y_coords = tuple(points[:, 1] * (y_lims[1] - y_lims[0]) + y_lims[0])
 
-    # # Generate the centers of the cells
-    # x_centers = np.linspace(0.05, 3.95, 40)
-    # y_centers = np.linspace(0.05, 3.95, 40)
+    # Generate the centers of the cells
+    x_centers = np.linspace(0.05, 3.95, 40)
+    y_centers = np.linspace(0.05, 3.95, 40)
 
-    # # Create the meshgrid for all cell centers
-    # X, Y = np.meshgrid(x_centers, y_centers)
+    # Create the meshgrid for all cell centers
+    X, Y = np.meshgrid(x_centers, y_centers)
 
-    # # Flatten the arrays to get the list of coordinates
-    # x_coords = X.flatten()
-    # y_coords = Y.flatten()
+    # Flatten the arrays to get the list of coordinates
+    x_coords = X.flatten()
+    y_coords = Y.flatten()
 
-    # x_coords_list = x_coords.tolist()
-    # y_coords_list = y_coords.tolist()
+    x_coords_list = x_coords.tolist()
+    y_coords_list = y_coords.tolist()
 
-    # x_coords_list.append(2.0)
-    # y_coords_list.append(2.0)
+    x_coords_list.append(2.0)
+    y_coords_list.append(2.0)
 
-    return (x_coords, y_coords)
+    return (x_coords_list, y_coords_list)
 
 
 def prepare():
diff --git a/mcdc/tally.py b/mcdc/tally.py
index 22f8712e..cd61a77d 100644
--- a/mcdc/tally.py
+++ b/mcdc/tally.py
@@ -209,7 +209,7 @@ def cell_tally(cell, scores=["flux"]):
 
 def cs_tally(
     N_cs_bins=10,
-    cs_bin_size=1,
+    cs_bin_size=([1.0, 1.0]),
     x=np.array([-INF, INF]),
     y=np.array([-INF, INF]),
     z=np.array([-INF, INF]),
@@ -231,9 +231,15 @@ def cs_tally(
     card.y = y
     card.z = z
 
-    # Set bin properties
+    # Set bin properties, convert bin size to problem units
     card.N_cs_bins = N_cs_bins
-    card.cs_bin_size = [cs_bin_size[0] / (len(x) - 1) * (x[-1] - x[0])]
+    print(f"tally.py, {card.cs_bin_size[0]}")
+    print(type(card.cs_bin_size), card.cs_bin_size.shape)
+    card.cs_bin_size[0] = cs_bin_size[0] / (len(x) - 1) * (x[-1] - x[0])
+    card.cs_bin_size[1] = cs_bin_size[1] / (len(y) - 1) * (y[-1] - y[0])
+
+    print(f"tally.py, {card.cs_bin_size[0]}")
+    print(type(card.cs_bin_size), card.cs_bin_size.shape)
 
     # Set other filters
     card.t = t
diff --git a/mcdc/type_.py b/mcdc/type_.py
index 85f9e010..af421d3d 100644
--- a/mcdc/type_.py
+++ b/mcdc/type_.py
@@ -945,7 +945,7 @@ def make_type_cs_tally(input_deck):
     # Set the filter
     filter_ = [
         ("N_cs_bins", int),
-        ("cs_bin_size", float64),
+        ("cs_bin_size", float64, (2,)),
         (
             "cs_centers",
             float64,

From e0d4e875e82fdef10e08bf63176d88b5d511d208 Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Thu, 19 Dec 2024 10:38:31 -0700
Subject: [PATCH 4/9] compressed sensing smoother integration, tested on sphere

---
 .../sphere_in_cube/center_points.npy          | Bin 25744 -> 0 bytes
 examples/fixed_source/sphere_in_cube/input.py |   4 +-
 .../fixed_source/sphere_in_cube/process.py    |  88 +++++++--------
 .../sphere_in_cube/sparse_solution.npy        | Bin 12928 -> 0 bytes
 .../fixed_source/sphere_in_cube/sphere_S.npy  | Bin 20492928 -> 0 bytes
 mcdc/main.py                                  | 101 +++++-------------
 mcdc/tally.py                                 |   5 -
 mcdc/type_.py                                 |   2 +
 8 files changed, 77 insertions(+), 123 deletions(-)
 delete mode 100644 examples/fixed_source/sphere_in_cube/center_points.npy
 delete mode 100644 examples/fixed_source/sphere_in_cube/sparse_solution.npy
 delete mode 100644 examples/fixed_source/sphere_in_cube/sphere_S.npy

diff --git a/examples/fixed_source/sphere_in_cube/center_points.npy b/examples/fixed_source/sphere_in_cube/center_points.npy
deleted file mode 100644
index 0fef328c9483617d3fb39271e95cdb0ab4b66783..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/examples/fixed_source/sphere_in_cube/input.py b/examples/fixed_source/sphere_in_cube/input.py
index d53a4e0d..3a858582 100644
--- a/examples/fixed_source/sphere_in_cube/input.py
+++ b/examples/fixed_source/sphere_in_cube/input.py
@@ -49,8 +49,8 @@
 mcdc.tally.cell_tally(sphere_cell, scores=["fission"])
 
 mcdc.tally.cs_tally(
-    N_cs_bins=[1601],
-    cs_bin_size=np.array([1.0, 1.0]),
+    N_cs_bins=[150],
+    cs_bin_size=np.array([3.0, 3.0]),
     x=np.linspace(0.0, 4.0, 41),
     y=np.linspace(0.0, 4.0, 41),
     scores=["fission"],
diff --git a/examples/fixed_source/sphere_in_cube/process.py b/examples/fixed_source/sphere_in_cube/process.py
index fdf3e1cd..d7896a54 100644
--- a/examples/fixed_source/sphere_in_cube/process.py
+++ b/examples/fixed_source/sphere_in_cube/process.py
@@ -4,56 +4,58 @@
 import scipy.fft as spfft
 import cvxpy as cp
 
-# Load results
-with h5py.File("output.h5", "r") as f:
-    # trying to compare cs results and mesh results
-    center_points = np.load("center_points.npy")
-    cs_results = f["tallies"]["cs_tally_0"]["fission"]["mean"][:]
-    cs_alphas = cs_results[:-1] / np.max(cs_results[:-1])
-    # plt.scatter(center_points[0][:-1], center_points[1][:-1], alpha=cs_alphas)
-    # plt.show()
+# Note: there are some lines in main.py with np.save(...) that I added
+# for ease of post-processing, like getting the center points used and
+# the sampling matrix S. None are required for the input file and this
+# script to run, but may be useful for debugging purposes
 
-    mesh_results = f["tallies"]["mesh_tally_0"]["fission"]["mean"][:]
-    plt.imshow(cs_results[:-1].reshape(mesh_results.shape))
-    plt.title("cs")
-    plt.show()
-    plt.imshow(mesh_results)
-    plt.title("mesh")
+with h5py.File("output.h5", "r") as f:
+    S = f["tallies"]["cs_tally_0"]["S"][:]
+    recon = f["tallies"]["cs_tally_0"]["fission"]["reconstruction"]
+    plt.imshow(recon)
+    plt.title("Reconstruction, $\lambda$ = 0.5")  # assuming l in main.py remains at 0.5
+    plt.colorbar()
     plt.show()
 
-    print(f"cs_results = {cs_results}")
-    print(f"sh_results = {mesh_results.flatten()}")
+    cs_results = f["tallies"]["cs_tally_0"]["fission"]["mean"][:]
 
+    # mesh_results = f["tallies"]["mesh_tally_0"]["fission"]["mean"][:]
+    # plt.imshow(mesh_results)
+    # plt.title("mesh results")
+    # plt.show()
 
 Nx = 40
 Ny = 40
-S = np.load("sphere_S.npy")
-mesh_b = S @ mesh_results.flatten()
-cs_b = cs_results
-
+N_fine_cells = Nx * Ny
 
+# Can use this for post-processing
+# mesh_b = S @ mesh_results.flatten()
 # b = mesh_b
 
-# print(f'shape of mesh b = {mesh_b.shape}')
-# print(f'shape of cs b = {cs_b.shape}')
-
-# idct_basis_x = spfft.idct(np.identity(Nx), axis=0)
-# idct_basis_y = spfft.idct(np.identity(Ny), axis=0)
-
-# T_inv = np.kron(idct_basis_y, idct_basis_x)
-# A = S @ T_inv
-# N_fine_cells = 1600
-
-# vx = cp.Variable(N_fine_cells)
-# l = 0
-# objective = cp.Minimize(0.5 * cp.norm(A @ vx - b, 2) + l * cp.norm(vx, 1))
-# prob = cp.Problem(objective)
-# result = prob.solve(verbose=False)
-# sparse_solution = np.array(vx.value).squeeze()
-
-# recon = T_inv @ sparse_solution
-# recon_reshaped = recon.reshape(Ny, Nx)
-
-# plt.imshow(recon_reshaped)
-# plt.title('reconstruction')
-# plt.show()
+# Use this for analyzing the in-situ results
+cs_b = cs_results
+b = cs_b
+
+# Constructing T and A
+idct_basis_x = spfft.idct(np.identity(Nx), axis=0)
+idct_basis_y = spfft.idct(np.identity(Ny), axis=0)
+
+T_inv = np.kron(idct_basis_y, idct_basis_x)
+A = S @ T_inv
+
+# Basis pursuit denoising solver - change l to get different results
+vx = cp.Variable(N_fine_cells)
+l = 10
+objective = cp.Minimize(0.5 * cp.norm(A @ vx - b, 2) + l * cp.norm(vx, 1))
+prob = cp.Problem(objective)
+result = prob.solve(verbose=False)
+sparse_solution = np.array(vx.value).squeeze()
+
+# Obtaining the reconstruction
+recon = T_inv @ sparse_solution
+recon_reshaped = recon.reshape(Ny, Nx)
+
+plt.imshow(recon_reshaped)
+plt.title(f"Reconstruction, $\lambda$ = {l}")
+plt.colorbar()
+plt.show()
diff --git a/examples/fixed_source/sphere_in_cube/sparse_solution.npy b/examples/fixed_source/sphere_in_cube/sparse_solution.npy
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diff --git a/mcdc/main.py b/mcdc/main.py
index 8882a396..bdc5a274 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -93,7 +93,7 @@ def run():
         loop_fixed_source(data_arr, mcdc_arr)
     mcdc["runtime_simulation"] = MPI.Wtime() - simulation_start
 
-    # Compressed sensing reconstruction - after the sim runs and gets results
+    # Compressed sensing reconstruction
     N_cs_bins = mcdc["cs_tallies"]["filter"]["N_cs_bins"][0]
     if N_cs_bins != 0:
         cs_reconstruct(data, mcdc)
@@ -151,7 +151,6 @@ def calculate_cs_A(data, mcdc):
     [x_centers, y_centers] = mcdc["cs_tallies"]["filter"]["cs_centers"][0]
     x_centers[-1] = (x_grid[-1] + x_grid[0]) / 2
     y_centers[-1] = (y_grid[-1] + y_grid[0]) / 2
-    np.save("center_points.npy", mcdc["cs_tallies"]["filter"]["cs_centers"][0])
 
     # Calculate the overlap grid for each bin, and flatten into a row of S
     for ibin in range(N_cs_bins):
@@ -190,19 +189,13 @@ def calculate_cs_A(data, mcdc):
 
         S[ibin] = overlap.flatten()
     S = np.array(S)
-
-    S_summed = np.sum(S, axis=0).reshape(Ny, Nx)
-    plt.imshow(S_summed)
-    plt.colorbar()
-    plt.title("S_Summed")
-    plt.show()
+    mcdc["cs_tallies"]["filter"]["cs_S"] = S
 
     assert np.allclose(S[-1], np.ones(Nx * Ny)), "Last row of S must be all ones"
     assert S.shape[1] == Nx * Ny, "Size of S must match Nx * Ny."
     assert (
         S.shape[1] == mcdc["cs_tallies"]["N_bin"][0]
     ), "Size of S must match number of cells in desired mesh tally"
-    np.save("sphere_S.npy", S)
 
     # TODO: can this be done in a different way? idk
     # Construct the DCT matrix T
@@ -221,7 +214,7 @@ def calculate_cs_sparse_solution(data, mcdc, A, b):
     vx = cp.Variable(N_fine_cells)
 
     # Basis pursuit denoising
-    l = 0
+    l = 0.5
     objective = cp.Minimize(0.5 * cp.norm(A @ vx - b, 2) + l * cp.norm(vx, 1))
     prob = cp.Problem(objective)
     result = prob.solve(verbose=False)
@@ -241,9 +234,6 @@ def calculate_cs_sparse_solution(data, mcdc, A, b):
 
 def cs_reconstruct(data, mcdc):
     tally_bin = data[TALLY]
-
-    print(tally_bin.shape)
-
     tally = mcdc["cs_tallies"][0]
     stride = tally["stride"]
     bin_idx = stride["tally"]
@@ -253,38 +243,13 @@ def cs_reconstruct(data, mcdc):
 
     b = tally_bin[TALLY_SUM, bin_idx : bin_idx + N_cs_bins]
 
-    # print(f"measurements b = {b}")
-
     A, T_inv = calculate_cs_A(data, mcdc)
     x = calculate_cs_sparse_solution(data, mcdc, A, b)
 
-    np.save("sparse_solution.npy", x)
-    # print(f"sparse solution shape = {x.shape}")
-    # print(x)
-
     recon = T_inv @ x
-    # print(f"recon.shape = {recon.shape}")
-    # print(f"recon = {recon}")
-
     recon_reshaped = recon.reshape(Ny, Nx)
 
-    plt.imshow(recon_reshaped)
-    plt.title("Reconstruction")
-    plt.colorbar()
-    plt.show()
-
-    return recon
-    # reconstruction
-
-    # # Find the sparse solution, and then find the reconstruction
-    # sparse_solution[res] = finding_sparse_solution(A[res], fluxes[res].size, b, bpdn_lambda)
-    # reconstruction[res] = np.reshape(un_matrix[res] @ sparse_solution[res], fluxes[res].shape)
-    # rescaled_recons[res] = downsample_to_resolution(reconstruction[res], input_flux.shape)
-
-    # if return_centers:
-    #     return reconstruction, input_flux, rescaled_recons, bin_centers[0]
-    # else:
-    #     return reconstruction, input_flux, rescaled_recons
+    tally["filter"]["cs_reconstruction"] = recon_reshaped
 
 
 # =============================================================================
@@ -486,42 +451,25 @@ def dd_mesh_bounds(idx):
 
 
 def generate_cs_centers(mcdc, N_dim=3, seed=123456789):
-    # N_cs_bins = int(mcdc["cs_tallies"]["filter"]["N_cs_bins"])
-    # x_lims = (
-    #     mcdc["cs_tallies"]["filter"]["x"][0][-1],
-    #     mcdc["cs_tallies"]["filter"]["x"][0][0],
-    # )
-    # y_lims = (
-    #     mcdc["cs_tallies"]["filter"]["y"][0][-1],
-    #     mcdc["cs_tallies"]["filter"]["y"][0][0],
-    # )
-
-    # # Generate Halton sequence according to the seed
-    # halton_seq = Halton(d=N_dim, seed=seed)
-    # points = halton_seq.random(n=N_cs_bins)
-
-    # # Extract x and y coordinates as tuples separately, scaled to the problem dimensions
-    # x_coords = tuple(points[:, 0] * (x_lims[1] - x_lims[0]) + x_lims[0])
-    # y_coords = tuple(points[:, 1] * (y_lims[1] - y_lims[0]) + y_lims[0])
-
-    # Generate the centers of the cells
-    x_centers = np.linspace(0.05, 3.95, 40)
-    y_centers = np.linspace(0.05, 3.95, 40)
-
-    # Create the meshgrid for all cell centers
-    X, Y = np.meshgrid(x_centers, y_centers)
-
-    # Flatten the arrays to get the list of coordinates
-    x_coords = X.flatten()
-    y_coords = Y.flatten()
+    N_cs_bins = int(mcdc["cs_tallies"]["filter"]["N_cs_bins"])
+    x_lims = (
+        mcdc["cs_tallies"]["filter"]["x"][0][-1],
+        mcdc["cs_tallies"]["filter"]["x"][0][0],
+    )
+    y_lims = (
+        mcdc["cs_tallies"]["filter"]["y"][0][-1],
+        mcdc["cs_tallies"]["filter"]["y"][0][0],
+    )
 
-    x_coords_list = x_coords.tolist()
-    y_coords_list = y_coords.tolist()
+    # Generate Halton sequence according to the seed
+    halton_seq = Halton(d=N_dim, seed=seed)
+    points = halton_seq.random(n=N_cs_bins)
 
-    x_coords_list.append(2.0)
-    y_coords_list.append(2.0)
+    # Extract x and y coordinates as tuples separately, scaled to the problem dimensions
+    x_coords = tuple(points[:, 0] * (x_lims[1] - x_lims[0]) + x_lims[0])
+    y_coords = tuple(points[:, 1] * (y_lims[1] - y_lims[0]) + y_lims[0])
 
-    return (x_coords_list, y_coords_list)
+    return (x_coords, y_coords)
 
 
 def prepare():
@@ -1948,7 +1896,14 @@ def generate_hdf5(data, mcdc):
                     group_name = "tallies/cs_tally_%i/%s/" % (ID, score_name)
 
                     center_points = tally["filter"]["cs_centers"]
-                    f.create_dataset(group_name + "center_points", data=center_points)
+                    S = tally["filter"]["cs_S"]
+                    reconstruction = tally["filter"]["cs_reconstruction"]
+
+                    f.create_dataset(
+                        "tallies/cs_tally_%i/center_points" % (ID), data=center_points
+                    )
+                    f.create_dataset("tallies/cs_tally_%i/S" % (ID), data=S)
+                    f.create_dataset(group_name + "reconstruction", data=reconstruction)
 
                     mean = score_tally_bin[TALLY_SUM]
                     sdev = score_tally_bin[TALLY_SUM_SQ]
diff --git a/mcdc/tally.py b/mcdc/tally.py
index cd61a77d..e1ff98e2 100644
--- a/mcdc/tally.py
+++ b/mcdc/tally.py
@@ -233,14 +233,9 @@ def cs_tally(
 
     # Set bin properties, convert bin size to problem units
     card.N_cs_bins = N_cs_bins
-    print(f"tally.py, {card.cs_bin_size[0]}")
-    print(type(card.cs_bin_size), card.cs_bin_size.shape)
     card.cs_bin_size[0] = cs_bin_size[0] / (len(x) - 1) * (x[-1] - x[0])
     card.cs_bin_size[1] = cs_bin_size[1] / (len(y) - 1) * (y[-1] - y[0])
 
-    print(f"tally.py, {card.cs_bin_size[0]}")
-    print(type(card.cs_bin_size), card.cs_bin_size.shape)
-
     # Set other filters
     card.t = t
     card.mu = mu
diff --git a/mcdc/type_.py b/mcdc/type_.py
index af421d3d..be513907 100644
--- a/mcdc/type_.py
+++ b/mcdc/type_.py
@@ -954,6 +954,8 @@ def make_type_cs_tally(input_deck):
                 N_cs_centers,
             ),
         ),
+        ("cs_S", float64, (N_cs_centers, (Nmax_x - 1) * (Nmax_y - 1))),
+        ("cs_reconstruction", float64, ((Nmax_x - 1), (Nmax_y - 1))),
         ("x", float64, (Nmax_x,)),
         ("y", float64, (Nmax_y,)),
         ("z", float64, (Nmax_z,)),

From 819f31bb723674989eb26ee8eb2d1057285bcb04 Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Thu, 19 Dec 2024 11:08:04 -0700
Subject: [PATCH 5/9] removing unnecessary imports

---
 .../fixed_source/sphere_in_cube/process.py    |  9 +++--
 mcdc/kernel.py                                |  2 -
 mcdc/main.py                                  | 37 +------------------
 3 files changed, 7 insertions(+), 41 deletions(-)

diff --git a/examples/fixed_source/sphere_in_cube/process.py b/examples/fixed_source/sphere_in_cube/process.py
index d7896a54..c77451d0 100644
--- a/examples/fixed_source/sphere_in_cube/process.py
+++ b/examples/fixed_source/sphere_in_cube/process.py
@@ -19,10 +19,11 @@
 
     cs_results = f["tallies"]["cs_tally_0"]["fission"]["mean"][:]
 
-    # mesh_results = f["tallies"]["mesh_tally_0"]["fission"]["mean"][:]
-    # plt.imshow(mesh_results)
-    # plt.title("mesh results")
-    # plt.show()
+    mesh_results = f["tallies"]["mesh_tally_0"]["fission"]["mean"][:]
+    plt.imshow(mesh_results)
+    plt.title("mesh results")
+    plt.colorbar()
+    plt.show()
 
 Nx = 40
 Ny = 40
diff --git a/mcdc/kernel.py b/mcdc/kernel.py
index 83530bf6..c93c9310 100644
--- a/mcdc/kernel.py
+++ b/mcdc/kernel.py
@@ -32,8 +32,6 @@ def round(float_val):
     # return np.uint64(int_val).view(np.float64)
 
 
-from shapely.geometry import LineString, box
-
 # =============================================================================
 # Domain Decomposition
 # =============================================================================
diff --git a/mcdc/main.py b/mcdc/main.py
index bdc5a274..42204839 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -3,17 +3,10 @@
 import mcdc.config as config
 import matplotlib.pyplot as plt
 import numba as nb
-from scipy.stats.qmc import Halton
-import cvxpy as cp
-
-import matplotlib.pyplot as plt
 from matplotlib import colors as mpl_colors
-from matplotlib.patches import Rectangle
-from shapely.geometry import Polygon, box
 import scipy.fft as spfft
-
-import numba as nb
-
+from scipy.stats.qmc import Halton
+import cvxpy as cp
 
 from mcdc.card import UniverseCard
 from mcdc.print_ import (
@@ -111,32 +104,6 @@ def run():
     closeout(mcdc)
 
 
-def calculate_cs_overlap(corners, grid):
-    quadrilateral = Polygon(corners)
-
-    # Get the dimensions of the grid
-    grid_height, grid_width = grid.shape
-
-    # Create an array to hold the overlap values
-    overlap = np.zeros_like(grid, dtype=float)
-
-    # Loop over each grid cell
-    for i in range(grid_height):
-        for j in range(grid_width):
-            # Define the current grid cell as a shapely box
-            cell = box(j - 0.5, i - 0.5, j + 0.5, i + 0.5)
-
-            # Calculate the intersection area between the cell and the quadrilateral
-            intersection = cell.intersection(quadrilateral).area
-
-            # Normalize by the cell area (which is 1 in this case)
-            overlap[i, j] = intersection
-
-    overlap_1d = overlap.flatten()
-
-    return overlap_1d
-
-
 def calculate_cs_A(data, mcdc):
     x_grid = mcdc["mesh_tallies"]["filter"]["x"][0]
     y_grid = mcdc["mesh_tallies"]["filter"]["y"][0]

From 56781cb2d9dbd5b043a0b71c161f5f7b2e136447 Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Fri, 20 Dec 2024 11:33:51 -0700
Subject: [PATCH 6/9] fixed bug with sizing of last bin

---
 examples/fixed_source/kobayashi3-TD/input.py | 4 ++--
 mcdc/main.py                                 | 8 +++++++-
 mcdc/type_.py                                | 2 +-
 3 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/examples/fixed_source/kobayashi3-TD/input.py b/examples/fixed_source/kobayashi3-TD/input.py
index 116f4312..98bef5e1 100644
--- a/examples/fixed_source/kobayashi3-TD/input.py
+++ b/examples/fixed_source/kobayashi3-TD/input.py
@@ -71,7 +71,7 @@
 
 mcdc.tally.cs_tally(
     N_cs_bins=[150],
-    cs_bin_size=[8],
+    cs_bin_size=[8.0, 8.0],
     x=np.linspace(0.0, 60.0, 31),
     y=np.linspace(0.0, 100.0, 51),
     scores=["flux"],
@@ -79,7 +79,7 @@
 
 
 # Setting
-mcdc.setting(N_particle=1e5)
+mcdc.setting(N_particle=1e2)
 
 # Run
 mcdc.run()
diff --git a/mcdc/main.py b/mcdc/main.py
index 42204839..3b6eee67 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -122,13 +122,16 @@ def calculate_cs_A(data, mcdc):
     # Calculate the overlap grid for each bin, and flatten into a row of S
     for ibin in range(N_cs_bins):
         if ibin == N_cs_bins - 1:
-            cs_bin_size = np.array([Nx, Ny])
+            cs_bin_size = np.array([x_grid[-1] + x_grid[0], y_grid[-1] + y_grid[0]])
 
         bin_x_min = x_centers[ibin] - cs_bin_size[0] / 2
         bin_x_max = x_centers[ibin] + cs_bin_size[0] / 2
         bin_y_min = y_centers[ibin] - cs_bin_size[1] / 2
         bin_y_max = y_centers[ibin] + cs_bin_size[1] / 2
 
+        print(f"center = {x_centers[ibin], y_centers[ibin]}")
+        print(f"size = {cs_bin_size}")
+
         overlap = np.zeros((len(y_grid) - 1, len(x_grid) - 1))
 
         for i in range(len(y_grid) - 1):
@@ -155,6 +158,9 @@ def calculate_cs_A(data, mcdc):
                 overlap[i, j] = (overlap_x * overlap_y) / cell_area
 
         S[ibin] = overlap.flatten()
+
+    # for i in range(len(S[-1])):
+    #     print(S[-1][i])
     S = np.array(S)
     mcdc["cs_tallies"]["filter"]["cs_S"] = S
 
diff --git a/mcdc/type_.py b/mcdc/type_.py
index be513907..cc81f4ee 100644
--- a/mcdc/type_.py
+++ b/mcdc/type_.py
@@ -955,7 +955,7 @@ def make_type_cs_tally(input_deck):
             ),
         ),
         ("cs_S", float64, (N_cs_centers, (Nmax_x - 1) * (Nmax_y - 1))),
-        ("cs_reconstruction", float64, ((Nmax_x - 1), (Nmax_y - 1))),
+        ("cs_reconstruction", float64, ((Nmax_y - 1), (Nmax_x - 1))),
         ("x", float64, (Nmax_x,)),
         ("y", float64, (Nmax_y,)),
         ("z", float64, (Nmax_z,)),

From d337a0d8de1cee2d009e6a88f91bd20a756c4cb6 Mon Sep 17 00:00:00 2001
From: Ilham Variansyah <variansi@oregonstate.edu>
Date: Sat, 21 Dec 2024 06:29:30 +0700
Subject: [PATCH 7/9] add cvxpy to Python dependencies

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 85c16f5f..5afecf3c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,6 +59,7 @@ dependencies = [
     "black",
     "sympy",
     "pre-commit",
+    "cvxpy"
 ]
 [project.optional-dependencies]
     docs = ["sphinx==7.2.6", "furo", "sphinx_toolbox"]

From 267a2e98723bef5de2c977782511db2e5eb31016 Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Fri, 27 Dec 2024 13:25:07 -0700
Subject: [PATCH 8/9] fixed cs numba issue, stopped dynamically changing
 cs_bin_size

---
 examples/fixed_source/cooper_combo/input.py   |   2 +-
 .../fixed_source/kobayashi3-TD/process.py     |  21 +--
 mcdc/kernel.py                                | 168 +++++++++++-------
 mcdc/main.py                                  |   7 +-
 4 files changed, 110 insertions(+), 88 deletions(-)

diff --git a/examples/fixed_source/cooper_combo/input.py b/examples/fixed_source/cooper_combo/input.py
index 8daf6352..c84c4f4e 100644
--- a/examples/fixed_source/cooper_combo/input.py
+++ b/examples/fixed_source/cooper_combo/input.py
@@ -61,7 +61,7 @@
 )
 
 # Setting
-mcdc.setting(N_particle=1e5)
+mcdc.setting(N_particle=1e2)
 mcdc.implicit_capture()
 
 # Run
diff --git a/examples/fixed_source/kobayashi3-TD/process.py b/examples/fixed_source/kobayashi3-TD/process.py
index 6e37fc1c..211b2d89 100644
--- a/examples/fixed_source/kobayashi3-TD/process.py
+++ b/examples/fixed_source/kobayashi3-TD/process.py
@@ -11,6 +11,10 @@
 
 # Results
 with h5py.File("output.h5", "r") as f:
+    cs_recon = f["tallies/cs_tally_0/flux/reconstruction"][:]
+    plt.imshow(cs_recon)
+    plt.show()
+
     tallies = f["tallies/mesh_tally_0"]
     flux = tallies["flux"]
     grid = tallies["grid"]
@@ -30,20 +34,3 @@
         print(
             f'cell {i+1} mean = {flux_score["mean"][()]}, sdev = {flux_score["sdev"][()]}'
         )
-
-fig, ax = plt.subplots()
-cax = ax.pcolormesh(X, Y, phi[0], vmin=phi[0].min(), vmax=phi[0].max())
-text = ax.text(0.02, 1.02, "", transform=ax.transAxes)
-ax.set_aspect("equal", "box")
-ax.set_xlabel("$y$ [cm]")
-ax.set_ylabel("$x$ [cm]")
-
-
-def animate(i):
-    cax.set_array(phi[i])
-    cax.set_clim(phi[i].min(), phi[i].max())
-    text.set_text(r"$t \in [%.1f,%.1f]$ s" % (t[i], t[i + 1]))
-
-
-anim = animation.FuncAnimation(fig, animate, interval=10, frames=len(t) - 1)
-plt.show()
diff --git a/mcdc/kernel.py b/mcdc/kernel.py
index c93c9310..f564ecb8 100644
--- a/mcdc/kernel.py
+++ b/mcdc/kernel.py
@@ -2044,18 +2044,22 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
 
     # Check each coarse bin
     for j in range(N_cs_bins):
-        center = (cs_centers[0][j], cs_centers[1][j])
+        center = np.array([cs_centers[0][j], cs_centers[1][j]])
+        start = np.array([x, y])
+        end = np.array([x_final, y_final])
+
+        distance_inside = calculate_distance_in_coarse_bin(
+            start, end, distance, center, cs_bin_size
+        )
 
         # Last bin covers the whole problem
         if j == N_cs_bins - 1:
-            cs_bin_size = np.array([INF, INF])
+            cs_bin_size_full_problem = np.array([INF, INF], dtype=np.float64)
+            distance_inside = calculate_distance_in_coarse_bin(
+                start, end, distance, center, cs_bin_size_full_problem
+            )
 
-        distance_in_bin = np.minimum(
-            distance,
-            calculate_distance_in_coarse_bin(
-                P_arr, distance, center, cs_bin_size, mcdc
-            ),
-        )
+        distance_in_bin = np.minimum(distance, distance_inside)  # this line is good
 
         # Calculate flux and other scores
         flux = distance_in_bin * P["w"]
@@ -2080,69 +2084,105 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
             # tally_bin[TALLY_SCORE, bin_idx + j * tally["N_score"] + i] += score
 
 
-@njit
-def calculate_distance_in_coarse_bin(P_arr, distance, center, cs_bin_size, mcdc):
-    P = P_arr[0]
+# @njit
+# def cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max):
+#     # Uses Liang-Barsky alog
+#     t0, t1 = 0.0, 1.0
+
+#     dx = end[0] - start[0]
+#     dy = end[1] - start[1]
+
+#     def clip(p, q):
+#         nonlocal t0, t1
+#         if p < 0:
+#             t = q / p
+#             if t > t1:
+#                 return False
+#             if t > t0:
+#                 t0 = t
+#         elif p > 0:
+#             t = q / p
+#             if t < t0:
+#                 return False
+#             if t < t1:
+#                 t1 = t
+#         elif q < 0:
+#             return False
+#         return True
+
+#     if clip(-dx, start[0] - x_min):
+#         if clip(dx, x_max - start[0]):
+#             if clip(-dy, start[1] - y_min):
+#                 if clip(dy, y_max - start[1]):
+#                     if t1 < 1:
+#                         end = start + t1 * np.array([dx, dy])
+#                     if t0 > 0:
+#                         start = start + t0 * np.array([dx, dy])
+#                     return np.linalg.norm(end - start)
+#     return 0.0
+
+
+@njit(debug=True)
+def cs_clip(p, q, t0, t1):
+    if p < 0:
+        t = q / p
+        if t > t1:
+            return False, t0, t1
+        if t > t0:
+            t0 = t
+    elif p > 0:
+        t = q / p
+        if t < t0:
+            return False, t0, t1
+        if t < t1:
+            t1 = t
+    elif q < 0:
+        return False, t0, t1
+    return True, t0, t1
+
+
+@njit(debug=True)
+def cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max):
+    # Uses Liang-Barsky algorithm for finding tracklength in box
+    t0, t1 = 0.0, 1.0
+    dx = end[0] - start[0]
+    dy = end[1] - start[1]
+
+    # Perform clipping for each boundary
+    result, t0, t1 = cs_clip(-dx, start[0] - x_min, t0, t1)
+    if not result:
+        return 0.0
+    result, t0, t1 = cs_clip(dx, x_max - start[0], t0, t1)
+    if not result:
+        return 0.0
+    result, t0, t1 = cs_clip(-dy, start[1] - y_min, t0, t1)
+    if not result:
+        return 0.0
+    result, t0, t1 = cs_clip(dy, y_max - start[1], t0, t1)
+    if not result:
+        return 0.0
 
-    # Particle 4D direction
-    ux = P["ux"]
-    uy = P["uy"]
-    uz = P["uz"]
+    # Update start and end points based on clipping results
+    if t1 < 1:
+        end = start + t1 * np.array([dx, dy])
+    if t0 > 0:
+        start = start + t0 * np.array([dx, dy])
 
-    # Particle initial and final coordinate
-    x = P["x"]
-    y = P["y"]
-    z = P["z"]
-    t = P["t"]
-    x_final = x + ux * distance
-    y_final = y + uy * distance
-    z_final = z + uz * distance
+    return np.linalg.norm(end - start)
 
-    start = np.array([x, y])
-    end = np.array([x_final, y_final])
 
+@njit
+def calculate_distance_in_coarse_bin(start, end, distance, center, cs_bin_size):
     # Edges of the coarse bin
-    x_min, x_max = center[0] - cs_bin_size[0] / 2, center[0] + cs_bin_size[0] / 2
-    y_min, y_max = center[1] - cs_bin_size[1] / 2, center[1] + cs_bin_size[1] / 2
-
-    def clip_segment_to_box(start, end, x_min, x_max, y_min, y_max):
-        t0, t1 = 0.0, 1.0
-
-        dx = end[0] - start[0]
-        dy = end[1] - start[1]
-
-        def clip(p, q):
-            nonlocal t0, t1
-            if p < 0:
-                t = q / p
-                if t > t1:
-                    return False
-                if t > t0:
-                    t0 = t
-            elif p > 0:
-                t = q / p
-                if t < t0:
-                    return False
-                if t < t1:
-                    t1 = t
-            elif q < 0:
-                return False
-            return True
-
-        if clip(-dx, start[0] - x_min):
-            if clip(dx, x_max - start[0]):
-                if clip(-dy, start[1] - y_min):
-                    if clip(dy, y_max - start[1]):
-                        if t1 < 1:
-                            end = start + t1 * np.array([dx, dy])
-                        if t0 > 0:
-                            start = start + t0 * np.array([dx, dy])
-                        return np.linalg.norm(end - start)
-        return 0.0
+    x_min = center[0] - cs_bin_size[0] / 2
+    x_max = center[0] + cs_bin_size[0] / 2
+    y_min = center[1] - cs_bin_size[1] / 2
+    y_max = center[1] + cs_bin_size[1] / 2
 
-    distance_inside = clip_segment_to_box(start, end, x_min, x_max, y_min, y_max)
+    # distance_inside = cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max)
+    distance_inside = 0.1234
 
-    return distance_inside
+    return np.float64(distance_inside)
 
 
 @njit
diff --git a/mcdc/main.py b/mcdc/main.py
index 3b6eee67..f2778de3 100644
--- a/mcdc/main.py
+++ b/mcdc/main.py
@@ -122,6 +122,7 @@ def calculate_cs_A(data, mcdc):
     # Calculate the overlap grid for each bin, and flatten into a row of S
     for ibin in range(N_cs_bins):
         if ibin == N_cs_bins - 1:
+            # could just change to -INF, INF
             cs_bin_size = np.array([x_grid[-1] + x_grid[0], y_grid[-1] + y_grid[0]])
 
         bin_x_min = x_centers[ibin] - cs_bin_size[0] / 2
@@ -129,9 +130,6 @@ def calculate_cs_A(data, mcdc):
         bin_y_min = y_centers[ibin] - cs_bin_size[1] / 2
         bin_y_max = y_centers[ibin] + cs_bin_size[1] / 2
 
-        print(f"center = {x_centers[ibin], y_centers[ibin]}")
-        print(f"size = {cs_bin_size}")
-
         overlap = np.zeros((len(y_grid) - 1, len(x_grid) - 1))
 
         for i in range(len(y_grid) - 1):
@@ -158,9 +156,6 @@ def calculate_cs_A(data, mcdc):
                 overlap[i, j] = (overlap_x * overlap_y) / cell_area
 
         S[ibin] = overlap.flatten()
-
-    # for i in range(len(S[-1])):
-    #     print(S[-1][i])
     S = np.array(S)
     mcdc["cs_tallies"]["filter"]["cs_S"] = S
 

From d84cecaab5454b1bd4ba77786a7831b378359e3e Mon Sep 17 00:00:00 2001
From: Ethan Lame <ethan0824@gmail.com>
Date: Fri, 27 Dec 2024 13:35:04 -0700
Subject: [PATCH 9/9] comment cleanup

---
 mcdc/kernel.py | 52 ++++++--------------------------------------------
 1 file changed, 6 insertions(+), 46 deletions(-)

diff --git a/mcdc/kernel.py b/mcdc/kernel.py
index f564ecb8..7d9b5ff9 100644
--- a/mcdc/kernel.py
+++ b/mcdc/kernel.py
@@ -2081,48 +2081,9 @@ def score_cs_tally(P_arr, distance, tally, data, mcdc):
                 (TALLY_SCORE, bin_idx + j * tally["N_score"] + i),
                 round(score),
             )
-            # tally_bin[TALLY_SCORE, bin_idx + j * tally["N_score"] + i] += score
-
-
-# @njit
-# def cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max):
-#     # Uses Liang-Barsky alog
-#     t0, t1 = 0.0, 1.0
-
-#     dx = end[0] - start[0]
-#     dy = end[1] - start[1]
-
-#     def clip(p, q):
-#         nonlocal t0, t1
-#         if p < 0:
-#             t = q / p
-#             if t > t1:
-#                 return False
-#             if t > t0:
-#                 t0 = t
-#         elif p > 0:
-#             t = q / p
-#             if t < t0:
-#                 return False
-#             if t < t1:
-#                 t1 = t
-#         elif q < 0:
-#             return False
-#         return True
-
-#     if clip(-dx, start[0] - x_min):
-#         if clip(dx, x_max - start[0]):
-#             if clip(-dy, start[1] - y_min):
-#                 if clip(dy, y_max - start[1]):
-#                     if t1 < 1:
-#                         end = start + t1 * np.array([dx, dy])
-#                     if t0 > 0:
-#                         start = start + t0 * np.array([dx, dy])
-#                     return np.linalg.norm(end - start)
-#     return 0.0
-
-
-@njit(debug=True)
+
+
+@njit
 def cs_clip(p, q, t0, t1):
     if p < 0:
         t = q / p
@@ -2141,7 +2102,7 @@ def cs_clip(p, q, t0, t1):
     return True, t0, t1
 
 
-@njit(debug=True)
+@njit
 def cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max):
     # Uses Liang-Barsky algorithm for finding tracklength in box
     t0, t1 = 0.0, 1.0
@@ -2179,10 +2140,9 @@ def calculate_distance_in_coarse_bin(start, end, distance, center, cs_bin_size):
     y_min = center[1] - cs_bin_size[1] / 2
     y_max = center[1] + cs_bin_size[1] / 2
 
-    # distance_inside = cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max)
-    distance_inside = 0.1234
+    distance_inside = cs_tracklength_in_box(start, end, x_min, x_max, y_min, y_max)
 
-    return np.float64(distance_inside)
+    return distance_inside
 
 
 @njit