Cleaned up notebook

notebooks/pretrain_mae_tensorflow.ipynb

@@ -48,95 +48,6 @@
     "json_file.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(177481, 9)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aligndata.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class FrameGenerator:\n",
-    "  def __init__(self, zarr_file, aligndata, normalisations, n_frames, training = False, inst = None):\n",
-    "    \"\"\" Returns a set of frames with their associated label. \n",
-    "\n",
-    "      Args:\n",
-    "        path: Video file paths.\n",
-    "        n_frames: Number of frames. \n",
-    "        training: Boolean to determine if training dataset is being created.\n",
-    "    \"\"\"\n",
-    "    self.inst = inst\n",
-    "    self.zarr_file = zarr_file\n",
-    "    self.aligndata = aligndata\n",
-    "    self.normalizations = normalisations\n",
-    "    self.n_frames = n_frames\n",
-    "    self.training = training\n",
-    "    self.idx = 0\n",
-    "    # self.class_names = sorted(set(p.name for p in self.path.iterdir() if p.is_dir()))\n",
-    "    # self.class_ids_for_name = dict((name, idx) for idx, name in enumerate(self.class_names))\n",
-    "\n",
-    "    def get_aia_frames(self, idx):\n",
-    "        \"\"\"Get AIA image for a given index.\n",
-    "        Returns a numpy array of shape (num_wavelengths, num_frames, height, width).\n",
-    "        \"\"\"\n",
-    "        aia_image_dict = {}\n",
-    "        for wavelength in self.wavelengths:\n",
-    "            aia_image_dict[wavelength] = []\n",
-    "            for frame in range(self.num_frames):\n",
-    "                idx_row_element = self.aligndata.iloc[idx + frame]\n",
-    "                idx_wavelength = idx_row_element[f\"idx_{wavelength}\"]\n",
-    "                year = str(idx_row_element.name.year)\n",
-    "                img = self.zarr_file[year][wavelength][idx_wavelength, :, :]\n",
-    "\n",
-    "                if self.mask is not None:\n",
-    "                    img = img * self.mask\n",
-    "\n",
-    "                aia_image_dict[wavelength].append(img)\n",
-    "\n",
-    "                if self.normalizations:\n",
-    "                    aia_image_dict[wavelength][-1] -= self.normalizations[\"AIA\"][\n",
-    "                        wavelength\n",
-    "                    ][\"mean\"]\n",
-    "                    aia_image_dict[wavelength][-1] /= self.normalizations[\"AIA\"][\n",
-    "                        wavelength\n",
-    "                    ][\"std\"]\n",
-    "\n",
-    "        aia_image = np.array(list(aia_image_dict.values()))\n",
-    "\n",
-    "        return aia_image\n",
-    "\n",
-    "    def __call__(self):\n",
-    "        # video_paths, classes = self.get_files_and_class_names()\n",
-    "\n",
-    "        # pairs = list(zip(video_paths, classes))\n",
-    "\n",
-    "        # if self.training:\n",
-    "        #   random.shuffle(pairs)\n",
-    "\n",
-    "        for idx in range(self.aligndata.shape[0]):\n",
-    "        #   video_frames = frames_from_video_file(path, self.n_frames) \n",
-    "        #   label = self.class_ids_for_name[name] # Encode labels   \n",
-    "            yield self.get_aia_frames(idx)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -221,27 +132,6 @@
     "aiadl = AIALoader(aia_data, aligndata, normalisations, 1, 2)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'tuple' object has no attribute 'shape'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m/home/walsh/repos/SDO-FM/notebooks/pretrain_mae_tensorflow.ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bsdofm-workbench-n1-16cpu-60ram-t4x2.us-central1-a.sdo-fm-2024/home/walsh/repos/SDO-FM/notebooks/pretrain_mae_tensorflow.ipynb#X10sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m aiadl\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(\u001b[39m0\u001b[39;49m)\u001b[39m.\u001b[39;49mshape\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'shape'"
-     ]
-    }
-   ],
-   "source": [
-    "aiadl.__getitem__(0).shape"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 22,
@@ -288,7 +178,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   53/88740 [..............................] - ETA: 42:27:25 - loss: 2.0440"
+      "  118/88740 [..............................] - ETA: 42:51:56 - loss: 2.0085"
      ]
     }
    ],