put back effective_nights

hera_notebook_templates/notebooks/lststack.ipynb

@@ -1009,7 +1009,7 @@
     "        )\n",
     "        outfile = outdir / cal_fname\n",
     "        \n",
-    "        #np.savez(outfile, **calibration_parameters)\n",
+    "        np.savez(outfile, **calibration_parameters)\n",
     "        \n",
     "    # Recompute auto and cross averages after calibration - needed for STD files \n",
     "    cross_lstavg = [\n",
@@ -1214,9 +1214,8 @@
     "                    for _weighted_diff_dpss in weighted_diff_dpss\n",
     "                ], axis=0)\n",
     "                \n",
-    "                \n",
-    "                #effective_nights = np.sum(np.mean(mask[:, band], axis=1))\n",
-    "                effective_nights = np.sum(is_unflagged_night)\n",
+    "                # Calculate effective nights\n",
+    "                effective_nights = np.sum(np.mean(mask[:, band], axis=1))\n",
     "                \n",
     "                # TODO: Sample covariance is overestimated and has a fudge factor, this is probably close to correct\n",
     "                # but not exactly right. We should revisit this.\n",
@@ -1280,6 +1279,24 @@
     "    return lstavg, all_models"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbcbe32b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for iap, antpair in enumerate(stack.antpairs): \n",
+    "        # Get the baseline vector and length\n",
+    "        bl_vec = (antpos[antpair[1]] - antpos[antpair[0]])[:]\n",
+    "        bl_len = np.linalg.norm(bl_vec) / constants.c\n",
+    "        filter_centers, filter_half_widths = vis_clean.gen_filter_properties(\n",
+    "            ax='freq',\n",
+    "            bl_len=max(bl_len, 7.0 / constants.c),\n",
+    "            **filter_properties,\n",
+    "        )"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "af3b55b2",