diff --git a/hera_notebook_templates/notebooks/lststack.ipynb b/hera_notebook_templates/notebooks/lststack.ipynb
index 827a2c1..b18e0b9 100644
--- a/hera_notebook_templates/notebooks/lststack.ipynb
+++ b/hera_notebook_templates/notebooks/lststack.ipynb
@@ -24,7 +24,7 @@
    "id": "543efff3",
    "metadata": {},
    "source": [
-    "**by Steven Murray, Tyler Cox and Josh Dillon**, last updated 4th Jun, 2024."
+    "**by Steven Murray, Tyler Cox and Josh Dillon**, last updated 31st July, 2024."
    ]
   },
   {
@@ -144,7 +144,7 @@
     "do_extra_flagging: bool = False\n",
     "do_lstcal: bool = True\n",
     "\n",
-    "outdir: str = \".\"\n",
+    "outdir: str = Path(\"~/lststack-outputs\").expanduser()\n",
     "bl_chunk_size: int = 0\n",
     "rephase: bool = True\n",
     "fname_format: str = '{inpaint_mode}/zen.{kind}.{lst:7.5f}.{blchunk}.sum.uvh5'\n",
@@ -673,10 +673,10 @@
    "source": [
     "all_ee = lambda bl: bl[2] == 'ee'\n",
     "all_nn = lambda bl: bl[2] == 'nn'\n",
-    "short_bls = lambda bl: getbllen(bl[0], bl[1])<=60.0\n",
-    "long_bls = lambda bl: getbllen(bl[0], bl[1])>60.0\n",
-    "intersector_bls = lambda bl: sectors[bl[0]] != sectors[bl[1]]\n",
-    "intrasector_bls = lambda bl: sectors[bl[0]] == sectors[bl[1]]\n",
+    "short_bls = lambda bl: getbllen(bl[0], bl[1])<=60.0 and len(set(bl[2]))==1\n",
+    "long_bls = lambda bl: getbllen(bl[0], bl[1])>60.0 and len(set(bl[2]))==1\n",
+    "intersector_bls = lambda bl: sectors[bl[0]] != sectors[bl[1]] and len(set(bl[2]))==1\n",
+    "intrasector_bls = lambda bl: sectors[bl[0]] == sectors[bl[1]] and len(set(bl[2]))==1\n",
     "\n",
     "subsets = {\n",
     "    'all': lambda bl: True,\n",
@@ -924,7 +924,7 @@
     "    \n",
     "    fig, ax = plt.subplots(\n",
     "        len(stackconf.autopairs)*len(stackconf.pols), len(auto_stacks), \n",
-    "        sharex=True, sharey=True, squeeze=False, constrained_layout=True,\n",
+    "        sharex=True, squeeze=False, constrained_layout=True,\n",
     "        figsize=(12, 6)\n",
     "    )\n",
     "\n",
@@ -933,13 +933,12 @@
     "            for p, pol in enumerate(stackconf.pols):\n",
     "                axx = ax[j*len(stackconf.pols) + p, i]\n",
     "                \n",
-    "                for k, t in enumerate(stack.time_array[::stack.Nbls]):\n",
+    "                for k, t in enumerate(stack.times):\n",
     "                    flg = stack.get_flags(autopair + (pol,))[k]\n",
     "                    d = stack.get_data(autopair+(pol,))[k]\n",
-    "                    \n",
     "                    axx.plot(\n",
     "                        stack.freq_array / 1e6,\n",
-    "                        np.where(flg, np.nan, d.real),\n",
+    "                        np.where(flg, np.nan, np.abs(d)),\n",
     "                        label=f\"{int(t)}\" if not p else None,\n",
     "                        **styles[int(t)]\n",
     "                    )\n",
@@ -949,7 +948,7 @@
     "                # plot the mean\n",
     "                axx.plot(\n",
     "                    stack.freq_array / 1e6,\n",
-    "                    np.where(avg['flags'][j, :, p], np.nan, avg['data'][j, :, p].real),\n",
+    "                    np.where(avg['flags'][j, :, p], np.nan, np.abs(avg['data'][j, :, p])),\n",
     "                    label='LSTBIN',\n",
     "                    color='k', lw=2\n",
     "                )\n",
@@ -1132,6 +1131,72 @@
     "    original_data_mean = [lstavg['data'].copy() for lstavg in cross_lstavg]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13f99665-3f4b-4e13-b306-6836d64f89b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from copy import deepcopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d877cd3b-0a15-46da-bbd8-f89103abf1ce",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# FILLED_CACHE = deepcopy(_INPAINT_CACHE_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0fead3f-be97-4ae1-9bfc-d0315f323a98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from hera_cal.lst_stack import LSTStack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96cbe153-186e-42ad-82c6-4b82d63da2c3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# smallstack = LSTStack(stack.select(bls=stack.antpairs[:30], inplace=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a9e0899-6786-40ea-a44e-51b693ed6b94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %load_ext line_profiler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "293728f0-2894-4404-8bf9-4739a0b0441b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# %lprun -f avg.average_and_inpaint_simultaneously_single_bl avg.average_and_inpaint_simultaneously(smallstack, auto_stacks[0], inpaint_bands = inpaint_bands, return_models = make_plots, cache = {}, filter_properties = {\"min_dly\": inpaint_mindelay,  \"horizon\": inpaint_horizon, \"standoff\": inpaint_standoff},eigenval_cutoff=[inpaint_eigencutoff], max_gap_factor=inpaint_max_gap_factor,max_convolved_flag_frac=inpaint_max_convolved_flag_frac,use_unbiased_estimator=inpaint_use_unbiased_estimator,sample_cov_fraction=inpaint_sample_cov_fraction)\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1353,7 +1418,6 @@
     "    lstbin.io.format_outfile_name,\n",
     "    lst=stackconf.lst_grid_edges[0],\n",
     "    pols=stackconf.pols,\n",
-    "    #fname_format=fname_format,\n",
     "    inpaint_mode=True,\n",
     "    lst_branch_cut=stackconf.properties[\"lst_branch_cut\"],\n",
     ")"
@@ -1539,9 +1603,10 @@
     "    dndzsq = zdist_pred.pdf(xc)\n",
     "\n",
     "    for i, zsq in enumerate(zsquare):\n",
-    "        ax[0].hist(zsq.metrics.flatten(), bins=x, label=f'LST Bin {i}', density=True, histtype='step')\n",
+    "        for j, pol in enumerate(zsq.pols):\n",
+    "            ax[0].hist(zsq.metrics[..., j].flatten(), bins=x, label=f\"LST Bin {i}. Pol '{pol}'\", density=True, histtype='step', lw=3 if pol[0]==pol[1] else 1, ls=['-', ':'][i], color=f'C{j}', alpha=0.7)\n",
     "\n",
-    "    ax[0].plot(xc, dndzsq, label='Predicted')\n",
+    "    ax[0].plot(xc, dndzsq, color='k', ls ='--', label='Predicted')\n",
     "    ax[0].set_xscale('log')\n",
     "    ax[0].set_yscale('log')\n",
     "    ax[0].set_ylim(1e-12, 1e4)\n",
@@ -1553,13 +1618,15 @@
     "    x = np.logspace(-5, np.log10(max(np.nanmax(zsq.metrics) for zsq in zsquare)), 100)\n",
     "\n",
     "    for zsq in zsquare:\n",
-    "        zsq = zsq.metrics[np.isfinite(zsq.metrics)]\n",
-    "        size = zsq.size\n",
-    "    \n",
-    "        cdf_data = [np.sum(zsq < c)/size for c in x]\n",
-    "        ax[1].plot(x, cdf_data)\n",
+    "        for j, pol in enumerate(zsq.pols):\n",
+    "            zsqm = zsq.metrics[..., j]\n",
+    "            zsqm = zsqm[np.isfinite(zsqm)]\n",
+    "            size = zsqm.size\n",
+    "\n",
+    "            cdf_data = [np.sum(zsqm < c)/size for c in x]\n",
+    "            ax[1].plot(x, cdf_data, lw=3 if pol[0]==pol[1] else 1, ls=['-', ':'][i], color=f'C{j}', alpha=0.7)\n",
     "    \n",
-    "    ax[1].plot(x, zdist_pred.cdf(x))\n",
+    "    ax[1].plot(x, zdist_pred.cdf(x), color='k', ls='--')\n",
     "    ax[1].set_xlabel(r\"$Z^2$\")\n",
     "    ax[1].set_title(\"CDF of $Z^2$\")\n",
     "    ax[1].set_xscale('log')"
@@ -1625,10 +1692,10 @@
     "    inpaint_flags = stack.inpainted()\n",
     "    for iap, (a, b) in enumerate(zuv.antpairs):\n",
     "        for ipol, pol in enumerate(zuv.pols):\n",
-    "            pol = {'ee': 0, 'nn': 1}[pol]\n",
-    "\n",
     "\n",
     "            for night, zsqn in enumerate(zuv.metrics[:, iap, :, ipol]):\n",
+    "                key = (lstidx, a, b, ipol, jdint)\n",
+    "\n",
     "                jdint = zuv.nights[night]\n",
     "\n",
     "                # Get contiguous regions of bad (|Z| > 3) data\n",
@@ -1637,7 +1704,7 @@
     "                    ranges = consecutive(badfreqs)\n",
     "\n",
     "                    for rng in ranges:\n",
-    "                        allbad[(lstidx, a, b, pol, jdint, rng[0], rng[1])] = zsqn[rng[0]:rng[1]]\n",
+    "                        allbad[key+(rng[0], rng[1])] = zsqn[rng[0]:rng[1]]\n",
     "\n",
     "                if stackconf.inpaint_files is not None:\n",
     "                    badfreqs = np.nonzero(inpaint_flags[night, iap, :, ipol])\n",
@@ -1645,7 +1712,7 @@
     "                        ranges = consecutive(badfreqs)\n",
     "\n",
     "                        for rng in ranges:\n",
-    "                            allbad[(lstidx, a, b, pol, jdint, rng[0], rng[1])] = zsqn[rng[0]:rng[1]]\n",
+    "                            allbad[key+(rng[0], rng[1])] = zsqn[rng[0]:rng[1]]\n",
     "    \n",
     "                else:\n",
     "                    # Get contiguous regions of inpainted data (anything that is flagged outside FM)\n",
@@ -1656,40 +1723,45 @@
     "                            ranges = consecutive(badfreqs)\n",
     "\n",
     "                            for rng in ranges:\n",
-    "                                inpainted_regions[(lstidx, a, b, pol, jdint, rng[0], rng[1])] = zsqn[band][rng[0]:rng[1]]\n",
+    "                                inpainted_regions[key+(rng[0], rng[1])] = zsqn[band][rng[0]:rng[1]]\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "76f321de-8fd9-41b5-a2fb-92d5ff4d94db",
+   "id": "61998a93-638a-4f4b-9e54-161ecc543691",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "chunk_lengths = [b - a for _, _, _, _, _, a, b in allbad.keys()]\n",
-    "if len(chunk_lengths) > 0:\n",
-    "    print(\"Biggest Frequency Chunk With |Z|>3: \", np.max(chunk_lengths))"
+    "if save_metric_data:\n",
+    "    # Write out the \"bad\" data\n",
+    "    fname = out_fname.format(kind=\"HIGHZ\")\n",
+    "\n",
+    "    with h5py.File(outdir / fname, 'w') as fl:\n",
+    "        fl['indices'] = np.array(list(allbad.keys()))  # integer array\n",
+    "        fl['zsq'] = np.concatenate(tuple(allbad.values()))\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "61998a93-638a-4f4b-9e54-161ecc543691",
+   "id": "76f321de-8fd9-41b5-a2fb-92d5ff4d94db",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "if save_metric_data:\n",
-    "    # Write out the \"bad\" data\n",
-    "    fname = out_fname.format(kind=\"HIGHZ\")\n",
+    "chunk_lengths_all = [\n",
+    "    [b - a for _, _, _, polidx, _, a, b in allbad.keys() if polidx==i]\n",
+    "    for i in range(len(cross_stacks[0].pols))\n",
+    "]\n",
     "\n",
-    "    with h5py.File(outdir / fname, 'w') as fl:\n",
-    "        fl['indices'] = np.array(list(allbad.keys()))  # integer array\n",
-    "        fl['zsq'] = np.concatenate(tuple(allbad.values()))\n"
+    "for i, chunk_lengths in enumerate(chunk_lengths_all):\n",
+    "    if len(chunk_lengths) > 0:\n",
+    "        print(f\"Biggest Frequency Chunk With |Z|>3 for Pol {cross_stacks[0].pols[i]}: \", np.max(chunk_lengths))\n"
    ]
   },
   {
@@ -1717,13 +1789,16 @@
    },
    "outputs": [],
    "source": [
-    "if make_plots and len(chunk_lengths) > 0:\n",
-    "    plt.hist(chunk_lengths, bins=np.arange(np.min(chunk_lengths), np.max(chunk_lengths)+1))\n",
-    "    plt.yscale('log')\n",
-    "    plt.xlabel(\"Channel-Chunk Length with |Z|>3\")\n",
-    "    plt.ylabel(\"Number of Occurences\");\n",
-    "elif make_plots:\n",
-    "    print(\"No |Z| > 3 data found\")"
+    "if make_plots:\n",
+    "    for i, chunk_lengths in enumerate(chunk_lengths_all):\n",
+    "        if len(chunk_lengths) > 0:\n",
+    "            plt.hist(chunk_lengths, bins=np.arange(np.min(chunk_lengths), np.max(chunk_lengths)+1), label=cross_stacks[0].pols[i], histtype='step')\n",
+    "            plt.yscale('log')\n",
+    "            plt.xlabel(\"Channel-Chunk Length with |Z|>3\")\n",
+    "            plt.ylabel(\"Number of Occurences\");\n",
+    "        elif make_plots:\n",
+    "            print(f\"No |Z| > 3 data found for pol {cross_stacks[0].pols[i]}\")\n",
+    "    plt.legend()"
    ]
   },
   {
@@ -1795,7 +1870,7 @@
     "                bplot = ax.boxplot(\n",
     "                    allz, positions = [i-0.3 + 0.05*n], \n",
     "                    showfliers=False, whis=(0,100), showmeans=True,\n",
-    "                    labels=[f\"chs {band[0]}-{band[1]}\" if (n==len(data_jd_ints)//2 and j==(len(subsets)-1)) else \"\"], \n",
+    "                    tick_labels=[f\"chs {band[0]}-{band[1]}\" if (n==len(data_jd_ints)//2 and j==(len(subsets)-1)) else \"\"], \n",
     "                )\n",
     "                bplot['boxes'][0].set_color(styles[night]['color'])\n",
     "                bplot['boxes'][0].set_linestyle(styles[night]['ls'])\n",
@@ -2047,7 +2122,7 @@
     "def make_baseline_zsq_plot(zscores, stack):\n",
     "    # TODO: need a better cmap to easily see what's \"good\" and \"bad\"\n",
     "    \n",
-    "    fig, axx = plt.subplots(len(bands_considered)-3, 2, sharey=True, figsize=(24, 5*(len(bands_considered)-3)), layout='constrained')\n",
+    "    fig, axx = plt.subplots(len(bands_considered)-3, len(stackconf.pols), sharey=True, figsize=(24, 5*(len(bands_considered)-3)), layout='constrained')\n",
     "    \n",
     "    cmap = mpl.colors.ListedColormap([\"C0\", f\"C1\", f\"C3\"])\n",
     "    for i, band in enumerate(bands_considered):\n",
@@ -2061,17 +2136,12 @@
     "        uvws = stack.uvw_array[:stack.Nbls][:, :2]\n",
     "        uvws[uvws[:, 1] < 0] *= -1\n",
     "\n",
-    "        ax[0].scatter(uvws[:, 0], uvws[:, 1], c=mean_zsq[:, 0], norm=mpl.colors.LogNorm( vmin=1, vmax=1000), marker='H', s=60, cmap=cmap)\n",
-    "        ax[0].set_title(stackconf.pols[0])\n",
-    "        ax[0].set_aspect(\"equal\", 'datalim')\n",
-    "        ax[0].set_xlim(-200, 200)\n",
-    "\n",
-    "        cbar = ax[1].scatter(uvws[:, 0], uvws[:, 1], c=mean_zsq[:, 1], norm=mpl.colors.LogNorm( vmin=1, vmax=1000), marker='H', s=60, cmap=cmap)\n",
-    "        ax[1].set_title(stackconf.pols[1])\n",
-    "        ax[1].set_aspect(\"equal\", 'datalim')\n",
-    "        ax[1].set_xlim(-200, 200)\n",
-    "        ax[0].grid(True)\n",
-    "        ax[1].grid(True)\n",
+    "        for ipol, pol in enumerate(stackconf.pols):\n",
+    "            cbar = ax[ipol].scatter(uvws[:, 0], uvws[:, 1], c=mean_zsq[:, ipol], norm=mpl.colors.LogNorm( vmin=1, vmax=1000), marker='H', s=60, cmap=cmap)\n",
+    "            ax[ipol].set_title(pol)\n",
+    "            ax[ipol].set_aspect(\"equal\", 'datalim')\n",
+    "            ax[ipol].set_xlim(-200, 200)\n",
+    "            ax[ipol].grid(True)\n",
     "        \n",
     "        ax[0].set_ylabel(str(band))\n",
     "        \n",
@@ -2426,21 +2496,28 @@
    },
    "outputs": [],
    "source": [
-    "def get_worst_mean_over_each_band(zscores, n=1):\n",
+    "def get_worst_mean_over_each_band(zscores, n=1, autopols_only=True):\n",
     "    bad_fellas = {}\n",
     "\n",
     "    nights0 = [data_jd_ints.index(jd) for jd in zscores[0].time_array[::zscores[0].Nbls].astype(int)]\n",
     "    nights1 = [data_jd_ints.index(jd) for jd in zscores[1].time_array[::zscores[1].Nbls].astype(int)]\n",
+    "    \n",
+    "    if autopols_only:\n",
+    "        polidx = [i for i, p in enumerate(stackconf.pols) if len(set(p))==1]\n",
+    "    else:\n",
+    "        polidx = np.arange(len(stackconf.pols))\n",
     "        \n",
-    "    newmeans = {band: np.ones((len(zscores), len(data_jd_ints), len(stackconf.antpairs), len(stackconf.pols)))*np.nan for band in metrics['band_reduced_mean']}\n",
+    "    npols = len(polidx)\n",
+    "    \n",
+    "    newmeans = {band: np.ones((len(zscores), len(data_jd_ints), len(stackconf.antpairs), npols))*np.nan for band in metrics['band_reduced_mean']}\n",
     "    \n",
     "    for band, zsqs in metrics['band_reduced_mean'].items():\n",
     "        # zsqs is length(lstbins), where each is an array of shape (nights, antpairs, pols)\n",
     "        # however, the number of nights for each lstbin could be different, so make them the same here....\n",
-    "        newmeans[band][0, nights0] = zsqs[0]\n",
-    "        newmeans[band][1, nights1] = zsqs[1]\n",
+    "        newmeans[band][0, nights0] = zsqs[0][..., polidx]\n",
+    "        newmeans[band][1, nights1] = zsqs[1][..., polidx]\n",
     "        \n",
-    "    lst_night_bl_pols = [(lst, jd, bl + (pol,)) for lst in range(len(zscores)) for jd in data_jd_ints for bl in stackconf.antpairs for pol in stackconf.pols]\n",
+    "    lst_night_bl_pols = [(lst, jd, bl + (pol,)) for lst in range(len(zscores)) for jd in data_jd_ints for bl in stackconf.antpairs for j, pol in enumerate(stackconf.pols) if j in polidx]\n",
     "    \n",
     "    for band, zsq in newmeans.items():\n",
     "        zsq = np.where(np.isnan(zsq.flatten()), -1, zsq.flatten())\n",
@@ -2469,7 +2546,7 @@
    },
    "outputs": [],
    "source": [
-    "def get_worst_mean_for_continuously_bad_stuff(zscores, n=1):\n",
+    "def get_worst_mean_for_continuously_bad_stuff(zscores, n=1, autopols_only=True):\n",
     "    \n",
     "    bad_fellas = {}\n",
     "    \n",
@@ -2484,12 +2561,17 @@
     "            if ch[0] <= s < ch[1]:\n",
     "                sized[ch][k] = v\n",
     "\n",
+    "    if autopols_only:\n",
+    "        polidx = set([i for i, p in enumerate(stackconf.pols) if len(set(p))==1])\n",
+    "    else:\n",
+    "        polidx = set(range(len(stackconf.pols)))\n",
+    "        \n",
     "    for chsize, thesebads in sized.items():\n",
     "        if not thesebads:\n",
     "            continue\n",
     "            \n",
-    "        keys = list(thesebads.keys())\n",
-    "        meanz = np.array([np.nanmean(v) for v in thesebads.values()])\n",
+    "        keys = [k for k in thesebads.keys() if k[3] in polidx]\n",
+    "        meanz = np.array([np.nanmean(v) for k, v in thesebads.items() if k[3] in polidx])\n",
     "        \n",
     "        nn = min(n, len(meanz))\n",
     "        worst_idx = np.argpartition(meanz, -nn)[-nn:]\n",
@@ -2517,25 +2599,32 @@
    },
    "outputs": [],
    "source": [
-    "def get_worst_continuous_bad_zscore(zscores, n=1):\n",
+    "def get_worst_continuous_bad_zscore(zscores, n=1, autopols_only=True):\n",
     "    bad_fellas = {}\n",
     "    nights0 = [data_jd_ints.index(jd) for jd in zscores[0].time_array[::zscores[0].Nbls].astype(int)]\n",
     "    nights1 = [data_jd_ints.index(jd) for jd in zscores[1].time_array[::zscores[1].Nbls].astype(int)]\n",
     "    \n",
     "    smallbands = [b for b in bands_considered if b[1] - b[0] <= 200]\n",
     "    \n",
+    "    if autopols_only:\n",
+    "        polidx = [i for i, p in enumerate(stackconf.pols) if len(set(p))==1]\n",
+    "    else:\n",
+    "        polidx = np.arange(len(stackconf.pols))\n",
+    "        \n",
+    "    npols = len(polidx)\n",
+    "\n",
     "    newmeans = np.ones(\n",
-    "        (len(smallbands), len(zscores), len(data_jd_ints), len(stackconf.antpairs), len(stackconf.pols))\n",
+    "        (len(smallbands), len(zscores), len(data_jd_ints), len(stackconf.antpairs), npols)\n",
     "    )*np.nan\n",
     "    \n",
     "    for i, band in enumerate(smallbands):\n",
     "        zsqs = metrics['band_reduced_mean'][band]\n",
     "        # zsqs is length(lstbins), where each is an array of shape (nights, antpairs, pols)\n",
     "        # however, the number of nights for each lstbin could be different, so make them the same here....\n",
-    "        newmeans[i, 0, nights0] = zsqs[0]\n",
-    "        newmeans[i, 1, nights1] = zsqs[1]\n",
+    "        newmeans[i, 0, nights0] = zsqs[0][..., polidx]\n",
+    "        newmeans[i, 1, nights1] = zsqs[1][..., polidx]\n",
     "\n",
-    "    lst_night_bl_pols = [(lst, jd, bl + (pol,)) for lst in range(len(zscores)) for jd in data_jd_ints for bl in stackconf.antpairs for pol in stackconf.pols]\n",
+    "    lst_night_bl_pols = [(lst, jd, bl + (pol,)) for lst in range(len(zscores)) for jd in data_jd_ints for bl in stackconf.antpairs for j, pol in enumerate(stackconf.pols) if j in polidx]\n",
     "\n",
     "    zsq = np.nanmin(newmeans, axis=0)\n",
     "    \n",
@@ -2566,12 +2655,17 @@
    },
    "outputs": [],
    "source": [
-    "def get_bad_inpaints(zscores, n=1):\n",
+    "def get_bad_inpaints(zscores, n=1, autopols_only=True):\n",
     "    \n",
     "    bad_fellas = {}\n",
     "    \n",
     "    nights = [zsq.nights.tolist() for zsq in zscores]\n",
     "\n",
+    "    if autopols_only:\n",
+    "        polidx = set([i for i, p in enumerate(stackconf.pols) if len(set(p))==1])\n",
+    "    else:\n",
+    "        polidx = set(range(len(stackconf.pols)))\n",
+    "\n",
     "    chsizes = [(2, 5), (5, 10), (10, 20)]    \n",
     "    sized = {ch: {} for ch in chsizes}\n",
     "    for k, v in inpainted_regions.items():\n",
@@ -2586,11 +2680,11 @@
     "    for chsize, bads in sized.items():\n",
     "        \n",
     "        \n",
-    "        keys = list(bads.keys())\n",
+    "        keys = [k for k in bads.keys() if k[3] in polidx]\n",
     "        \n",
     "        meanz = np.array([\n",
     "            np.nanmean(zscores[lst].metrics[nights[lst].index(jdint), zscores[lst].antpairs.index((a,b)), low:high, pol]) \n",
-    "            for (lst, a, b, pol, jdint, low, high) in bads.keys()\n",
+    "            for (lst, a, b, pol, jdint, low, high) in bads.keys() if pol in polidx\n",
     "        ])\n",
     "        \n",
     "        nn = min(n, len(meanz))\n",
@@ -2759,9 +2853,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "h6c",
    "language": "python",
-   "name": "python3"
+   "name": "h6c"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2773,7 +2867,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.6"
   },
   "toc": {
    "base_numbering": 1,