Clean up redundant function

diffpose/deepfluoro.py

@@ -325,7 +325,7 @@ def get_random_offset(batch_size: int, device) -> RigidTransform:
         "se3_exp_map",
     )
 
-# %% ../notebooks/api/00_deepfluoro.ipynb 33
+# %% ../notebooks/api/00_deepfluoro.ipynb 32
 from torchvision.transforms import Compose, Lambda, Normalize, Resize
 
 

diffpose/ljubljana.py

@@ -131,7 +131,7 @@ def get_random_offset(view, batch_size: int, device) -> RigidTransform:
         t1 = torch.distributions.Normal(75, 30).sample((batch_size,))
         t2 = torch.distributions.Normal(-80, 30).sample((batch_size,))
         t3 = torch.distributions.Normal(-5, 30).sample((batch_size,))
-        r1 = torch.distributions.Normal(0.0, 0.1).sample((batch_size,))
+        r1 = torch.distributions.Normal(0, 0.1).sample((batch_size,))
         r2 = torch.distributions.Normal(0, 0.05).sample((batch_size,))
         r3 = torch.distributions.Normal(1.55, 0.05).sample((batch_size,))
     else:

notebooks/api/00_deepfluoro.ipynb

@@ -732,41 +732,6 @@
     "    )"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e14ac65e-3c57-4c67-be3a-1d7184f669b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@beartype\n",
-    "def get_random_offset(view, batch_size: int, device) -> RigidTransform:\n",
-    "    if view == \"ap\":\n",
-    "        t1 = torch.distributions.Normal(-6, 20).sample((batch_size,))\n",
-    "        t2 = torch.distributions.Normal(175, 30).sample((batch_size,))\n",
-    "        t3 = torch.distributions.Normal(-5, 15).sample((batch_size,))\n",
-    "        r1 = torch.distributions.Normal(0, 0.1).sample((batch_size,))\n",
-    "        r2 = torch.distributions.Normal(0, 0.1).sample((batch_size,))\n",
-    "        r3 = torch.distributions.Normal(-0.15, 0.25).sample((batch_size,))\n",
-    "    elif view == \"lat\":\n",
-    "        t1 = torch.distributions.Normal(75, 15).sample((batch_size,))\n",
-    "        t2 = torch.distributions.Normal(-80, 20).sample((batch_size,))\n",
-    "        t3 = torch.distributions.Normal(-5, 10).sample((batch_size,))\n",
-    "        r1 = torch.distributions.Normal(0.0, 0.1).sample((batch_size,))\n",
-    "        r2 = torch.distributions.Normal(0, 0.05).sample((batch_size,))\n",
-    "        r3 = torch.distributions.Normal(1.55, 0.05).sample((batch_size,))\n",
-    "    else:\n",
-    "        raise ValueError(f\"view must be 'ap' or 'lat', not '{view}'\")\n",
-    "\n",
-    "    logmap = torch.stack([r1, r2, r3, t1, t2, t3], dim=1).to(device)\n",
-    "    T = convert(\n",
-    "        [logmap[..., :3], logmap[..., 3:]],\n",
-    "        \"se3_log_map\",\n",
-    "        \"se3_exp_map\",\n",
-    "    )\n",
-    "    return T"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "76efedd3-103c-43dd-944b-fe0c06e2c87b",

notebooks/api/01_ljubljana.ipynb

@@ -202,7 +202,7 @@
     "        t1 = torch.distributions.Normal(75, 30).sample((batch_size,))\n",
     "        t2 = torch.distributions.Normal(-80, 30).sample((batch_size,))\n",
     "        t3 = torch.distributions.Normal(-5, 30).sample((batch_size,))\n",
-    "        r1 = torch.distributions.Normal(0.0, 0.1).sample((batch_size,))\n",
+    "        r1 = torch.distributions.Normal(0, 0.1).sample((batch_size,))\n",
     "        r2 = torch.distributions.Normal(0, 0.05).sample((batch_size,))\n",
     "        r3 = torch.distributions.Normal(1.55, 0.05).sample((batch_size,))\n",
     "    else:\n",