filter based on delay resolution

HERA-Team · Mar 4, 2025 · 0dc84f8 · 0dc84f8
1 parent 663a717
commit 0dc84f8
Showing 1 changed file with 5 additions and 5 deletions.
diff --git a/notebooks/single_baseline_2D_filtered_SNRs.ipynb b/notebooks/single_baseline_2D_filtered_SNRs.ipynb
@@ -80,13 +80,13 @@
     "FM_HIGH_FREQ = float(os.environ.get(\"FM_HIGH_FREQ\", 108.0)) # in MHz\n",
     "\n",
     "FILTER_DELAY = float(os.environ.get(\"FILTER_DELAY\", 750)) # in ns\n",
-    "POST_FILTER_1D_DELAY = float(os.environ.get(\"POST_FILTER_1D_DELAY\", 1.0)) # in ns\n",
+    "POST_FILTER_DELAY_RES_FACTOR = float(os.environ.get(\"POST_FILTER_DELAY_RES_FACTOR\", 1.0)) # in ns\n",
     "MIN_FRATE_HALF_WIDTH = float(os.environ.get(\"MIN_FRATE_HALF_WIDTH\", 2.0)) # in mHz\n",
     "EIGENVAL_CUTOFF = float(os.environ.get(\"EIGENVAL_CUTOFF\", 1e-12))\n",
     "\n",
     "for setting in ['RED_AVG_FILE', 'CORNER_TURN_MAP_YAML', 'SNR_SUFFIX']:\n",
     "    print(f'{setting} = \"{eval(setting)}\"')\n",
-    "for setting in ['FM_LOW_FREQ', 'FM_HIGH_FREQ', 'FILTER_DELAY', 'POST_FILTER_1D_DELAY', 'MIN_FRATE_HALF_WIDTH', 'EIGENVAL_CUTOFF']:\n",
+    "for setting in ['FM_LOW_FREQ', 'FM_HIGH_FREQ', 'FILTER_DELAY', 'POST_FILTER_DELAY_RES_FACTOR', 'MIN_FRATE_HALF_WIDTH', 'EIGENVAL_CUTOFF']:\n",
     "    print(f'{setting} = {eval(setting)}')"
    ]
   },
@@ -312,11 +312,12 @@
     "            filtered_SNR[bl][tslice, band] /= SNR_correction\n",
     "\n",
     "            # filter out very low delay modes in 1D         \n",
+    "            filt_dly_1d = ((band.stop - band.start) * np.median(np.diff(data.freqs)))**-1 * POST_FILTER_DELAY_RES_FACTOR\n",
     "            d_mdl_1D, _, _ = fourier_filter(data.freqs[band], \n",
     "                                            filtered_SNR[bl][tslice, band], \n",
     "                                            wgts=wgts[tslice, band], \n",
     "                                            filter_centers=[0], \n",
-    "                                            filter_half_widths=[POST_FILTER_1D_DELAY / 1e9],\n",
+    "                                            filter_half_widths=[filt_dly_1d],\n",
     "                                            mode='dpss_solve', \n",
     "                                            eigenval_cutoff=[EIGENVAL_CUTOFF],\n",
     "                                            suppression_factors=[EIGENVAL_CUTOFF], \n",
@@ -326,8 +327,7 @@
     "    \n",
     "            # calculate and apply another correction factor based on the leverage to flatten out the SNR\n",
     "            correction_factors = np.full_like(wgts_1D[tslice, band], np.nan)     \n",
-    "            X = dpss_operator(data.freqs[band], [0], filter_half_widths=[POST_FILTER_1D_DELAY / 1e9], \n",
-    "                              eigenval_cutoff=[EIGENVAL_CUTOFF])[0]\n",
+    "            X = dpss_operator(data.freqs[band], [0], filter_half_widths=[filt_dly_1d], eigenval_cutoff=[EIGENVAL_CUTOFF])[0]\n",
     "            for tind in range(wgts_1D[tslice, band].shape[0]):\n",
     "                W = wgts_1D[tind, band]\n",
     "                if not np.all(W == 0):\n",