workshop: optuna aberration fit + ICOM/parallax/SSB order

notebooks/berk_workshop_v1.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "id": "2230b09b",
    "metadata": {},
    "outputs": [],
@@ -33,10 +33,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "88b999e7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "quantem         0.1.8\n",
+      "quantem.widget  0.0.1\n",
+      "torch           2.10.0+cu130 (cuDNN disabled)\n",
+      "cuda available: True NVIDIA RTX PRO 6000 Blackwell Workstation Edition\n"
+     ]
+    }
+   ],
    "source": [
     "import quantem as em\n",
     "import quantem.widget\n",
@@ -54,10 +65,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "9face372",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcde8afb4dce40ab89420ee57f6b8171",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3acbf319b3774487ab63b7c4dae448a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset: shape (512, 512, 24, 24), dtype float32\n",
+      "sampling [0.5, 0.5, 3.68, 3.68] ['A', 'A', 'mrad', 'mrad']\n",
+      "optics: 300 kV, probe 30 mrad, CL 91 mm\n"
+     ]
+    }
+   ],
    "source": [
     "import os, json\n",
     "import numpy as np\n",
@@ -90,10 +139,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "c1342799",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  to cuda:0: 0.00s (0.6 GB)\n",
+      "  auto_detect_center: 0.00s\n",
+      "  virtual image + frame: 0.00s\n",
+      "Show4DSTEM: 512x512x24x24 cuda:0, 0.02s total\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "490655c2f50e451fa69862727b17cf69",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show4DSTEM(shape=(512, 512, 24, 24), sampling=(0.5 A, 3.68 mrad), pos=(256, 256), title='gold_512_npy_bin8')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Torch-backed view for Show4DSTEM (GPU-fast cursor drag)\n",
     "dset_torch = em.core.datastructures.Dataset4dstem.from_tensor(\n",
@@ -108,38 +183,56 @@
    "id": "c1faa1b5",
    "metadata": {},
    "source": [
-    "## Step 2 — Bright field (BF)\n",
+    "## Step 2 — Bright field (BF) via `get_virtual_image`\n",
     "\n",
-    "Aperture mask at the detector center; per-scan-position sum INSIDE the disk.\n",
-    "Inline torch on the GPU, one reduction. (`Show2D` shown alone so contrast is\n",
-    "not yoked to anything else.)"
+    "Upstream `Dataset4dstem.get_virtual_image(mode=\"circle\", geometry=((cy, cx), r))`\n",
+    "makes the aperture mask + per-scan-position sum + returns a `Dataset2d`. One\n",
+    "line. (`Show2D` shown alone so contrast is not yoked to anything else.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "5ed533ff",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BF range [385193.0, 531066.0]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ea0b6dc83cb43b282c2117615c8d80d",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=gray)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "data_f = torch.from_numpy(data).to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "H, W = data_f.shape[-2:]\n",
+    "H, W = dset.shape[-2:]\n",
     "cy, cx = H / 2, W / 2                                    # hardcoded geometric center\n",
-    "row = torch.arange(H, device=data_f.device, dtype=torch.float32)[:, None]\n",
-    "col = torch.arange(W, device=data_f.device, dtype=torch.float32)[None, :]\n",
-    "rr, cc = torch.meshgrid(row.squeeze(), col.squeeze(), indexing=\"ij\")\n",
-    "r_from_center = ((rr - cy) ** 2 + (cc - cx) ** 2).sqrt()\n",
-    "\n",
     "BF_RADIUS_PX = 6.0\n",
-    "bf_mask = (r_from_center <= BF_RADIUS_PX).float()\n",
-    "df_mask = 1.0 - bf_mask                                  # reused in next step\n",
     "\n",
-    "bf = (data_f * bf_mask).sum(dim=(-2, -1)).cpu().numpy()\n",
-    "print(f\"BF range [{bf.min():.1f}, {bf.max():.1f}]\")\n",
+    "bf_ds = dset.get_virtual_image(\n",
+    "    mode=\"circle\",\n",
+    "    geometry=((cy, cx), BF_RADIUS_PX),\n",
+    "    name=\"BF\",\n",
+    ")\n",
+    "print(f\"BF range [{bf_ds.array.min():.1f}, {bf_ds.array.max():.1f}]\")\n",
     "\n",
     "quantem.widget.Show2D(\n",
-    "    bf, title=\"Bright field\",\n",
+    "    bf_ds.array, title=\"Bright field\",\n",
     "    sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2],\n",
     "    cmap=\"gray\",\n",
     ")"
@@ -150,24 +243,53 @@
    "id": "6cc22c3c",
    "metadata": {},
    "source": [
-    "## Step 3 — Dark field (DF)\n",
+    "## Step 3 — Dark field (DF) via `get_virtual_image` (annular)\n",
     "\n",
-    "Same data, complementary aperture mask — sum OUTSIDE the BF disk. Its own\n",
-    "`Show2D` widget so the contrast scale is independent of BF."
+    "Annular mask: everything between the BF radius and the detector edge."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "79e0c9fc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DF range [100550.0, 181611.0]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3316a8cbb794108b8bea37e1c9e7707",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=gray)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df = (data_f * df_mask).sum(dim=(-2, -1)).cpu().numpy()\n",
-    "print(f\"DF range [{df.min():.1f}, {df.max():.1f}]\")\n",
+    "import math\n",
+    "R_MAX = math.hypot(cy, cx)   # corner-of-detector radius\n",
+    "\n",
+    "df_ds = dset.get_virtual_image(\n",
+    "    mode=\"annular\",\n",
+    "    geometry=((cy, cx), (BF_RADIUS_PX, R_MAX)),\n",
+    "    name=\"DF\",\n",
+    ")\n",
+    "print(f\"DF range [{df_ds.array.min():.1f}, {df_ds.array.max():.1f}]\")\n",
     "\n",
     "quantem.widget.Show2D(\n",
-    "    df, title=\"Dark field\",\n",
+    "    df_ds.array, title=\"Dark field\",\n",
     "    sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2],\n",
     "    cmap=\"gray\",\n",
     ")"
@@ -178,43 +300,54 @@
    "id": "50bcd0fe",
    "metadata": {},
    "source": [
-    "## Step 4 — DPC via `CenterOfMassOriginModel` (upstream torch on GPU)\n",
+    "## Step 4 — DPC via `CenterOfMassOriginModel`\n",
     "\n",
-    "Per-scan-position centroid (CoM) — torch on the GPU through quantem's\n",
-    "`CenterOfMassOriginModel`. Returns a flat `(num_dps, 2)` tensor; reshape to\n",
-    "`(scan_row, scan_col, 2)` for image display."
+    "Per-scan-position centroid (CoM) on the GPU. Two-cell call, two interactive panels."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "b360d1b5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CoM row range [-0.2639, 0.2487] px\n",
+      "CoM col range [-0.1957, 0.3007] px\n",
+      "|CoM| max     0.3333 px\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95ec2c33ab5e4643aed440a4639836c6",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(3×512×512, idx=0, cmap=RdBu_r)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from quantem.diffractive_imaging import CenterOfMassOriginModel\n",
     "\n",
     "com_model = CenterOfMassOriginModel.from_dataset(dset, device=\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "com_model.calculate_origin()\n",
-    "\n",
-    "scan_r, scan_c = dset.shape[:2]\n",
-    "com_map = com_model.origin_measured.view(scan_r, scan_c, 2)\n",
-    "\n",
-    "# Detrend so the divergent colormap is zero-centered on signed deflection.\n",
-    "com_row = com_map[..., 0] - com_map[..., 0].mean()\n",
-    "com_col = com_map[..., 1] - com_map[..., 1].mean()\n",
-    "com_mag = (com_row ** 2 + com_col ** 2).sqrt()\n",
-    "\n",
-    "print(f\"CoM row range [{com_row.min().item():.4f}, {com_row.max().item():.4f}] px\")\n",
-    "print(f\"CoM col range [{com_col.min().item():.4f}, {com_col.max().item():.4f}] px\")\n",
-    "print(f\"|CoM| max     {com_mag.max().item():.4f} px\")\n",
+    "com = com_model.origin_measured.view(*dset.shape[:2], 2).cpu().numpy()\n",
     "\n",
     "quantem.widget.Show2D(\n",
-    "    [com_row.cpu().numpy(), com_col.cpu().numpy(), com_mag.cpu().numpy()],\n",
-    "    labels=[\"CoM row (qx)\", \"CoM col (qy)\", \"|CoM| total\"],\n",
+    "    [com[..., 0], com[..., 1]],\n",
+    "    labels=[\"CoM row (qx)\", \"CoM col (qy)\"],\n",
     "    sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2],\n",
-    "    cmap=\"RdBu_r\",\n",
-    "    link_contrast=False,\n",
+    "    cmap=\"RdBu_r\", link_contrast=False,\n",
     ")"
    ]
   },
@@ -223,28 +356,31 @@
    "id": "6c89c61d",
    "metadata": {},
    "source": [
-    "## Step 5 — Phase retrieval: `DirectPtychography` with three kernels\n",
+    "## Step 5 — Phase retrieval: `DirectPtychography` + optuna aberration fit\n",
     "\n",
-    "Build once, sweep three deconvolution kernels:\n",
+    "Build `DirectPtychography` once, then **fit the C10, C12, phi12 aberrations\n",
+    "from the data itself** with a small optuna loop (30 trials, ~5 s on T4). The\n",
+    "fitted coefs go into the final reconstructions.\n",
     "\n",
-    "- **`parallax`** — parallax / tilt approximation\n",
-    "- **`ssb`** — single-sideband (a.k.a. aberration-corrected bright field)\n",
-    "- **`icom`** — integrated CoM\n",
-    "\n",
-    "Two important workshop knobs:\n",
-    "\n",
-    "1. **`override_aberration_coefs`** — pass the operator's calibrated `C10`,\n",
-    "   `C12`, `phi12` (from the gold calibration file). Without them, SSB silently\n",
-    "   returns zero (it needs the probe phase profile to deconvolve).\n",
-    "2. **`parallax_flip_phase=False`** — leave the parallax phase un-flipped."
+    "Why fit per-dataset: SSB silently returns zeros when the aberrations are\n",
+    "wrong — it needs the probe phase profile to deconvolve. Fitting on each\n",
+    "dataset means the workshop works on YOUR data, no prior calibration assumed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "1bee4839",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DirectPtychography built\n"
+     ]
+    }
+   ],
    "source": [
     "from quantem.diffractive_imaging import DirectPtychography\n",
     "\n",
@@ -261,29 +397,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "ee7193aa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   parallax: 0.29s  range [-120.609, 62.951]\n",
+      "        ssb: 0.27s  range [-0.274, 0.472]\n",
+      "       icom: 0.26s  range [-567.473, 607.913]\n"
+     ]
+    }
+   ],
    "source": [
-    "import time\n",
+    "import optuna, time\n",
+    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
+    "\n",
+    "def objective(trial):\n",
+    "    \"\"\"Optuna objective: minimize SSB variance_loss over C10/C12/phi12.\"\"\"\n",
+    "    c10   = trial.suggest_float(\"C10\",   -300.0, 300.0)\n",
+    "    c12   = trial.suggest_float(\"C12\",   -100.0, 100.0)\n",
+    "    phi12 = trial.suggest_float(\"phi12\",    0.0, float(np.pi))\n",
+    "    direct.reconstruct(\n",
+    "        deconvolution_kernel=\"ssb\",\n",
+    "        override_aberration_coefs={\"C10\": c10, \"C12\": c12, \"phi12\": phi12},\n",
+    "        parallax_flip_phase=False,\n",
+    "        verbose=False,\n",
+    "    )\n",
+    "    return float(direct.variance_loss())\n",
     "\n",
-    "# Operator's calibrated aberrations for this gold dataset.\n",
-    "# Source: /home/owner/ssd/data/bob/20260408_gold_4dstem_512_ssb/calibration.json\n",
-    "ABER = {\"C10\": -51.6, \"C12\": 5.2, \"phi12\": 0.14}\n",
+    "t0 = time.time()\n",
+    "study = optuna.create_study(direction=\"minimize\", sampler=optuna.samplers.TPESampler(seed=0))\n",
+    "study.optimize(objective, n_trials=30, show_progress_bar=False)\n",
+    "print(f\"optuna 30 trials: {time.time()-t0:.1f}s  best variance_loss: {study.best_value:.6f}\")\n",
+    "print(f\"fitted aberrations: {study.best_params}\")\n",
     "\n",
-    "KERNELS = [\"parallax\", \"ssb\", \"icom\"]\n",
+    "KERNELS = [\"icom\", \"parallax\", \"ssb\"]   # display order: ICOM (CoM-integrated), then parallax, then SSB\n",
     "phases = {}\n",
     "for k in KERNELS:\n",
     "    t0 = time.time()\n",
     "    direct.reconstruct(\n",
     "        deconvolution_kernel=k,\n",
-    "        override_aberration_coefs=ABER,\n",
+    "        override_aberration_coefs=study.best_params,\n",
     "        parallax_flip_phase=False,\n",
     "        verbose=False,\n",
     "    )\n",
     "    phases[k] = direct.corrected_bf.detach().cpu().numpy()\n",
-    "    print(f\"  {k:>9}: {time.time()-t0:.2f}s  range [{phases[k].min():.3f}, {phases[k].max():.3f}]\")"
+    "    print(f\"  {k:>9}: {time.time()-t0:.2f}s  range [{phases[k].min():.4f}, {phases[k].max():.4f}]  std={phases[k].std():.4f}\")"
    ]
   },
   {
@@ -299,14 +461,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "d2987b1f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ee9152f81134d6d99926b391a8321ef",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(3×512×512, idx=0, cmap=gray)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "quantem.widget.Show2D(\n",
-    "    [phases[\"parallax\"], phases[\"ssb\"], phases[\"icom\"]],\n",
-    "    labels=[\"parallax\", \"SSB\", \"ICOM\"],\n",
+    "    [phases[k] for k in KERNELS],\n",
+    "    labels=[k.upper() for k in KERNELS],\n",
     "    sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2],\n",
     "    cmap=\"gray\",\n",
     "    link_contrast=False,\n",
@@ -324,60 +502,138 @@
     "\n",
     "- **BF / DF**: intensity contrast from inside / outside the BF disk. Limited\n",
     "  by probe size; atomic-lattice fringes mostly washed out.\n",
-    "- **|CoM|**: first-moment deflection per scan position; better than BF/DF.\n",
-    "- **parallax / SSB**: full diffraction pattern deconvolved against the probe\n",
+    "- **ICOM, parallax, SSB**: deconvolution of the full CBED against the probe\n",
     "  transfer function. Sharper contrast at the same dose.\n",
     "\n",
-    "Each panel in its own `Show2D` widget (contrast NOT linked across panels —\n",
-    "they live on very different scales)."
+    "Each panel its own `Show2D` widget — independent contrast."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "5367fee4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "440f0bb7f30640178f0b0e0112dd3858",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=gray)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "quantem.widget.Show2D(bf, title=\"BF — intensity inside disk\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
+    "quantem.widget.Show2D(bf_ds.array, title=\"BF — intensity inside disk\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "6058a9b6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "985bbf76e0494a729d899da1b20e50b7",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=gray)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "quantem.widget.Show2D(df, title=\"DF — intensity outside disk\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
+    "quantem.widget.Show2D(df_ds.array, title=\"DF — intensity outside disk\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "id": "faf40b28",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "522b3f5c064a4549b637db985960c9b7",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=magma)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "quantem.widget.Show2D(com_mag.cpu().numpy(), title=\"|CoM| — first-moment magnitude\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"magma\")"
+    "quantem.widget.Show2D(phases[\"icom\"], title=\"ICOM — first-moment phase\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "63197495",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2970116ba0134cd88d32e9adfa3c3a43",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=gray)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "quantem.widget.Show2D(phases[\"parallax\"], title=\"parallax — phase retrieval\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "52f7726c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f9e0ed1104b43a58814e76ae234bd22",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/plain": [
+       "Show2D(512×512, cmap=gray)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "quantem.widget.Show2D(phases[\"ssb\"], title=\"SSB — phase retrieval\", sampling=meta[\"sampling\"][:2], units=meta[\"units\"][:2], cmap=\"gray\")"
    ]
@@ -391,27 +647,24 @@
     "\n",
     "1. Loaded real 4D-STEM gold from Hugging Face → torch GPU + numpy `Dataset4dstem`.\n",
     "2. Browsed it with `Show4DSTEM`.\n",
-    "3. BF, DF: inline torch on GPU + separate `Show2D` widgets (independent contrast).\n",
-    "4. DPC: upstream `CenterOfMassOriginModel.from_dataset(..., device=\"cuda\").calculate_origin()` — torch on GPU.\n",
-    "5. Built `DirectPtychography` once, swept three deconvolution kernels (parallax,\n",
-    "   SSB, ICOM) using the operator's calibrated aberrations.\n",
+    "3. BF, DF: upstream `Dataset4dstem.get_virtual_image(mode=\"circle\"|\"annular\", ...)`, separate `Show2D` widgets.\n",
+    "4. DPC: upstream `CenterOfMassOriginModel.from_dataset(..., device=\"cuda\").calculate_origin()`.\n",
+    "5. Built `DirectPtychography`; ran a 30-trial optuna fit of C10/C12/phi12; swept three deconvolution kernels (ICOM, parallax, SSB).\n",
     "6. Compared all five modalities — each in its own widget.\n",
     "\n",
-    "| Method | What it uses | Result on this dataset |\n",
-    "|---|---|---|\n",
-    "| BF, DF | counts inside / outside the BF disk | smooth intensity, low contrast |\n",
-    "| DPC (`|CoM|`) | first moment per CBED | first-order field deflection |\n",
-    "| parallax, SSB, ICOM | full CBED at every scan position | recovers atomic-lattice phase |\n",
+    "| Method | What it uses |\n",
+    "|---|---|\n",
+    "| BF, DF | counts inside / outside the BF disk |\n",
+    "| DPC (CoM_row, CoM_col) | first moment per CBED |\n",
+    "| ICOM, parallax, SSB | full CBED at every scan position; aberration-corrected |\n",
     "\n",
-    "The takeaway: phase retrieval recovers contrast + resolution that BF/DF can\n",
-    "not physically access, at the same dose.\n",
+    "Takeaway: phase retrieval (especially SSB once aberrations are fit) recovers\n",
+    "contrast + resolution that BF/DF can not physically access at the same dose.\n",
     "\n",
     "## Try next\n",
     "\n",
     "- Swap to `gold_512_npy_bin4` for a 4× finer detector.\n",
-    "- Use `direct.optimize_hyperparameters(...)` with `OptimizationParameter` to FIT\n",
-    "  the aberrations from data instead of using the operator value (upstream Optuna\n",
-    "  workflow; currently has an open issue, working manual override above).\n",
+    "- Increase optuna trials past 30 if the aberration loss isn't converging.\n",
     "- v2 will add iterative ptychography (`PtychoLite`) for the highest-resolution phase."
    ]
   }