diff --git a/legacy/check_noise_satp3_v_toast.ipynb b/legacy/check_noise_satp3_v_toast.ipynb
new file mode 100644
index 0000000..9824e7a
--- /dev/null
+++ b/legacy/check_noise_satp3_v_toast.ipynb
@@ -0,0 +1,476 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compare noise levels: TOAST vs SO-SAT V3 vs early SATP3 data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pymaster as nmt\n",
+    "import sys\n",
+    "sys.path.append(\"/global/homes/k/kwolz/bbdev/SOOPERCOOL\")\n",
+    "import soopercool.utils as ut\n",
+    "import soopercool.ps_utils as pu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_noise_map(nside, id_sim, noise_dir):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    sim_str = str(id_sim).zfill(4)\n",
+    "    map = hp.read_map(noise_dir.replace(\"[id_sim]\", sim_str), field=range(3))\n",
+    "    return hp.ud_grade(map, nside_out=nside)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_workspace(nmt_bin, nside, mask_dir):\n",
+    "    \"\"\"\n",
+    "    Compute the NaMaster workspace to compute decoupled pseudo-C_ells.\n",
+    "    \"\"\"\n",
+    "    w = nmt.NmtWorkspace()\n",
+    "    mask = hp.ud_grade(hp.read_map(mask_dir), nside_out=nside)\n",
+    "    f = nmt.NmtField(mask, None, spin=2, purify_b=True)\n",
+    "    w.compute_coupling_matrix(f, f, nmt_bin)\n",
+    "    \n",
+    "    return w, mask\n",
+    "\n",
+    "\n",
+    "def read_nmt_binning(path_to_binning):\n",
+    "        \"\"\"\n",
+    "        Read the binning file and return the corresponding NmtBin object.\n",
+    "        \"\"\"\n",
+    "        import pymaster as nmt\n",
+    "        binning = np.load(path_to_binning)\n",
+    "        return nmt.NmtBin.from_edges(binning[\"bin_low\"],\n",
+    "                                     binning[\"bin_high\"] + 1)\n",
+    "\n",
+    "\n",
+    "def nmt_bin_from_edges(bin_edges, nside):\n",
+    "    \"\"\"\n",
+    "    Computes a NaMaster NmtBin object given an input array of bin edges.\n",
+    "    \"\"\"\n",
+    "    bin_edges = np.array(bin_edges)\n",
+    "    bin_edges = bin_edges[bin_edges < 3*nside]\n",
+    "    bin_edges = np.concatenate((bin_edges, [3*nside]))\n",
+    "    return nmt.NmtBin.from_edges(bin_edges[:-1], bin_edges[1:])\n",
+    "\n",
+    "\n",
+    "def get_decoupled_ps_namaster(map1, map2, mask, nmt_bin, wsp):\n",
+    "    \"\"\"\n",
+    "    Compute decoupled pseudo C_ells from a map pair given a NaMaster workspace.\n",
+    "    \"\"\"\n",
+    "    f1 = nmt.NmtField(mask, map1[1:], purify_b=True)\n",
+    "    f2 = nmt.NmtField(mask, map2[1:], purify_b=True)\n",
+    "    pcl = nmt.compute_coupled_cell(f1, f2)\n",
+    "    cl_dict = {\n",
+    "        f: wsp.decouple_cell(pcl)[f_idx]\n",
+    "        for f_idx, f in enumerate([\"EE\", \"EB\", \"BE\", \"BB\"])\n",
+    "    }\n",
+    "    cl_dict[\"l\"] = nmt_bin.get_effective_ells()\n",
+    "\n",
+    "    return cl_dict\n",
+    "\n",
+    "\n",
+    "def get_decoupled_ps(map1, map2, mask, nmt_bin, coupling_inv):\n",
+    "    \"\"\"\n",
+    "    Compute decoupled pseudo C_ells from a map pair given a NaMaster workspace.\n",
+    "    \"\"\"\n",
+    "    nbin = nmt_bin.get_n_bands()\n",
+    "    assert nbin == coupling_inv.shape[-1], f\"nmt_bin has {nbin} ell-bands, while coupling_inv has {coupling_inv.shape[-1]}.\"\n",
+    "    f1 = nmt.NmtField(mask, map1[1:], purify_b=True)\n",
+    "    f2 = nmt.NmtField(mask, map2[1:], purify_b=True)\n",
+    "    pclb = nmt_bin.bin_cell(nmt.compute_coupled_cell(f1, f2))\n",
+    "    stacked_pclb = np.zeros((9, nbin))\n",
+    "    for i in range(4):\n",
+    "        stacked_pclb[5+i, :] += pclb[i]\n",
+    "    coupling_inv = coupling_inv.reshape((nbin*9, nbin*9))\n",
+    "    stacked_pclb = stacked_pclb.reshape(nbin*9)\n",
+    "    print(\"matmul\", coupling_inv.shape, stacked_pclb.shape)\n",
+    "    decoupled_pclb_vec = coupling_inv @ stacked_pclb\n",
+    "    decoupled_pclb_vec = decoupled_pclb_vec.reshape(9, nbin)[5:]\n",
+    "\n",
+    "    cl_dict = {\n",
+    "        f: decoupled_pclb_vec[f_idx]\n",
+    "        for f_idx, f in enumerate([\"EE\", \"EB\", \"BE\", \"BB\"])\n",
+    "    }\n",
+    "    cl_dict[\"l\"] = nmt_bin.get_effective_ells()\n",
+    "\n",
+    "    return cl_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "globals = {\n",
+    "    \"Nsims\": 10,\n",
+    "    \"nside\": 512,\n",
+    "    # \"bin_edges\": [\n",
+    "    #     2, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160,\n",
+    "    #     170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300\n",
+    "    # ],\n",
+    "    \"binning\": \"/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/output_purify_noiseless/pre_processing/binning.npz\",\n",
+    "    \"noise_dir\": \"/global/cfs/projectdirs/sobs/awg_bb/bbmaster_paper/Noise_forpaper/Atm_10m-reso/[id_sim]/filterbin_coadd-full_map.fits\",\n",
+    "    \"nhits_dir\": \"/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/output_purify_noiseless/masks/nhits_map.fits\", \n",
+    "    \"mask_dir\": \"/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/output_purify_noiseless/masks/analysis_mask.fits\", \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nhits = hp.read_map(globals[\"nhits_dir\"])\n",
+    "hp.mollview(nhits, title=\"Nhits\")\n",
+    "\n",
+    "fsky = sum(nhits)/max(nhits)/len(nhits)\n",
+    "noise_dir = globals[\"noise_dir\"]\n",
+    "#bin_edges = globals[\"bin_edges\"]\n",
+    "binning_file = globals[\"binning\"]\n",
+    "Nsims = globals[\"Nsims\"]\n",
+    "nside = globals[\"nside\"]\n",
+    "mask_dir = globals[\"mask_dir\"]\n",
+    "npix = hp.nside2npix(nside)\n",
+    "#nmt_bin = nmt_bin_from_edges(bin_edges, nside)\n",
+    "nmt_bin = read_nmt_binning(binning_file)\n",
+    "lb = nmt_bin.get_effective_ells()\n",
+    "wsp, mask = compute_workspace(nmt_bin, nside, mask_dir)\n",
+    "hp.mollview(mask, title=\"analysis mask\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "noise_sims = np.zeros((Nsims, 3, npix))\n",
+    "\n",
+    "for id_sim in range(Nsims):\n",
+    "    print(id_sim)\n",
+    "    noise_sims[id_sim] += load_noise_map(nside, id_sim, noise_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coupling_inv (9, 154, 9, 154)\n",
+      "bpwin (9, 154, 9, 1536)\n"
+     ]
+    }
+   ],
+   "source": [
+    "couplings_file = \"/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/output_purify_noiseless/couplings/couplings_nonexnone_filtered.npz\"\n",
+    "coupling_inv = np.load(couplings_file)[\"inv_coupling\"]\n",
+    "print(\"coupling_inv\", coupling_inv.shape)\n",
+    "print(\"bpwin\", np.load(couplings_file)[\"bp_win\"].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "matmul (1386, 1386) (1386,)\n",
+      "(154,)\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "nl_toast_bb = []\n",
+    "nl_toast_ee = []\n",
+    "\n",
+    "for id_sim in range(Nsims):\n",
+    "    print(id_sim)\n",
+    "    # nl = get_decoupled_ps_namaster(\n",
+    "    #     noise_sims[id_sim], noise_sims[id_sim], mask, nmt_bin, wsp\n",
+    "    # )\n",
+    "    nl = get_decoupled_ps(noise_sims[id_sim], noise_sims[id_sim], mask,\n",
+    "                          nmt_bin, coupling_inv)\n",
+    "    print(nl[\"BB\"].shape)\n",
+    "    #nl = pu.decouple_pseudo_cls(coupled_pseudo_cells, coupling_inv)\n",
+    "    nl_toast_bb += [nl[\"BB\"]]\n",
+    "    nl_toast_ee += [nl[\"EE\"]]\n",
+    "nl_toast_bb_mean = np.mean(np.array(nl_toast_bb), axis=0)\n",
+    "nl_toast_bb_std = np.std(np.array(nl_toast_bb), axis=0)\n",
+    "nl_toast_ee_mean = np.mean(np.array(nl_toast_ee), axis=0)\n",
+    "nl_toast_ee_std = np.std(np.array(nl_toast_ee), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/k/kwolz/bbdev/SOOPERCOOL/soopercool/SO_Noise_Calculator_Public_v3_1_2.py:228: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  tube_count * N_tels /\n",
+      "/global/homes/k/kwolz/bbdev/SOOPERCOOL/soopercool/SO_Noise_Calculator_Public_v3_1_2.py:228: RuntimeWarning: divide by zero encountered in double_scalars\n",
+      "  tube_count * N_tels /\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/global/homes/k/kwolz/bbdev/SOOPERCOOL\")\n",
+    "import soopercool.utils as ut\n",
+    "\n",
+    "survey_years = 1.\n",
+    "N_instr = 2\n",
+    "\n",
+    "nl_adrien_filtered = ut.get_noise_spectrum_adrien(\n",
+    "    np.arange(3*nside), N_yr=survey_years, N_instr=2, fsky=fsky, filtered=True\n",
+    ")\n",
+    "nl_adrien_unfiltered = ut.get_noise_spectrum_adrien(\n",
+    "    np.arange(3*nside), N_yr=survey_years, N_instr=2, fsky=fsky, filtered=False\n",
+    ")\n",
+    "nl_goal_opt = ut.get_noise_spectrum(\n",
+    "    np.arange(3*nside), fsky_eff=fsky, has_oof=True, N_tubes=[0., N_instr, 1.],\n",
+    "    survey_years=1., freq_ghz=93\n",
+    ")\n",
+    "nl_baseline_pess = ut.get_noise_spectrum(\n",
+    "    np.arange(3*nside), fsky_eff=fsky, has_oof=True, N_tubes=[0., N_instr, 1.],\n",
+    "    survey_years=1., freq_ghz=93, sensitivity=\"baseline\", oof_mode=\"pessimistic\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hp.mollview(mask*1.e6*noise_sims[0, 1], min=-0.5, max=0.5, title=\"mask * TOAST noise\")\n",
+    "plt.show()\n",
+    "plt.clf()\n",
+    "nl_goal_opt_arr = [nl_goal_opt[k] for k in [\"TT\", \"EE\", \"BB\", \"TE\"]]\n",
+    "hp.mollview(mask*hp.synfast(nl_goal_opt_arr, nside=nside)[1],\n",
+    "            title=\"mask * goal-opt Gaussian\", min=-0.5, max=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f431d6532b0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "msk = np.logical_and(lb <= 600, lb >= 20)\n",
+    "ls = np.arange(3*nside)\n",
+    "mnl = np.logical_and(ls <= 600, ls >= 20)\n",
+    "\n",
+    "plt.errorbar(lb[msk], 1.e12*nl_toast_bb_mean[msk], 1.e12*nl_toast_bb_std[msk],\n",
+    "             c=\"k\", ls=\"\", marker=\".\", label=f\"{Nsims} TOAST sims, filtered & TF-corrected\")\n",
+    "plt.plot(ls[mnl], nl_adrien_filtered[\"BB\"][mnl], \"r-\", label=\"SATP3 filtered & TF-corrected (Adrien)\")\n",
+    "plt.plot(ls[mnl], nl_adrien_unfiltered[\"BB\"][mnl], c=\"darkorange\", ls=\"-\",\n",
+    "         label=\"SATP3 unfiltered (Adrien)\")\n",
+    "plt.plot(ls[mnl], nl_goal_opt[\"BB\"][mnl], c=\"navy\", ls=\":\", label=\"V3 model goal-opt\")\n",
+    "plt.plot(ls[mnl], nl_baseline_pess[\"BB\"][mnl], c=\"lightgreen\", ls=\":\",\n",
+    "         label=\"V3 model baseline-pess\")\n",
+    "plt.yscale(\"log\")\n",
+    "#plt.xscale(\"log\")\n",
+    "plt.xlabel(r\"$\\ell$\", fontsize=16)\n",
+    "plt.ylabel(r\"$N_\\ell$\", fontsize=16)\n",
+    "plt.xlim((20, 200))\n",
+    "plt.title(\"Noise estimates for 2 SATs at 93 GHz after 1 year\", fontsize=12)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "boolean index did not match indexed array along dimension 0; dimension is 154 but corresponding boolean dimension is 1536",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ratio_toast_blpe \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1.e12\u001b[39m\u001b[38;5;241m*\u001b[39mnl_toast_bb_mean[mnl][ib] \u001b[38;5;241m/\u001b[39m nl_baseline_pess[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBB\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;28mint\u001b[39m(lb[msk][ib])]\n\u001b[1;32m      2\u001b[0m                    \u001b[38;5;28;01mfor\u001b[39;00m ib \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(lb[msk]))]\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39maxhline(\u001b[38;5;241m0\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39maxhline(\u001b[38;5;241m1\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[32], line 1\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ratio_toast_blpe \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1.e12\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[43mnl_toast_bb_mean\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmnl\u001b[49m\u001b[43m]\u001b[49m[ib] \u001b[38;5;241m/\u001b[39m nl_baseline_pess[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBB\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;28mint\u001b[39m(lb[msk][ib])]\n\u001b[1;32m      2\u001b[0m                    \u001b[38;5;28;01mfor\u001b[39;00m ib \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(lb[msk]))]\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39maxhline(\u001b[38;5;241m0\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39maxhline(\u001b[38;5;241m1\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 154 but corresponding boolean dimension is 1536"
+     ]
+    }
+   ],
+   "source": [
+    "ratio_toast_blpe = [1.e12*nl_toast_bb_mean[mnl][ib] / nl_baseline_pess[\"BB\"][int(lb[msk][ib])]\n",
+    "                   for ib in range(len(lb[msk]))]\n",
+    "plt.axhline(0, color=\"k\")\n",
+    "plt.axhline(1, color=\"k\")\n",
+    "#mask = lb < 300\n",
+    "plt.plot(lb[msk], ratio_toast_blpe, \"b-\", label=\"Ratio TOAST / V3 noise\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/legacy/check_transfer_sims.ipynb b/legacy/check_transfer_sims.ipynb
new file mode 100644
index 0000000..354e2e0
--- /dev/null
+++ b/legacy/check_transfer_sims.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Check Carlos' transfer function simulations at NERSC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pymaster as nmt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import healpy as hp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "globals = {\n",
+    "    \"Nsims\": 10,\n",
+    "    \"nside\": 512,\n",
+    "    \"cosmo\": {\n",
+    "        \"cosmomc_theta\": 0.0104085,\n",
+    "        \"As\": 2.1e-9,\n",
+    "        \"ombh2\": 0.02237,\n",
+    "        \"omch2\": 0.1200,\n",
+    "        \"ns\": 0.9649,\n",
+    "        \"Alens\": 1.0,\n",
+    "        \"tau\": 0.0544,\n",
+    "        \"r\": 0.00,\n",
+    "    },\n",
+    "    \"power_law\": {\n",
+    "        \"amp\": 1.0,\n",
+    "        \"delta_ell\": 10,\n",
+    "        \"power_law_index\": 2.,\n",
+    "    },\n",
+    "    \"bin_edges\": [2, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,\n",
+    "                  150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260,\n",
+    "                  270, 280, 290, 300],\n",
+    "    \"mask\": \"/global/homes/k/kwolz/bbdev/SOOPERCOOL/outputs_toast/masks/analysis_mask.fits\",\n",
+    "    \"sims\": {\n",
+    "        \"tf\": \"/global/cfs/projectdirs/sobs/awg_bb/sims/master-pipeline/[id_sim]/PLsim_20240323_nside512_[id_sim]_pureB_pwf_beam.fits\",\n",
+    "        \"tf_filtered\": \"/global/cfs/projectdirs/sobs/awg_bb/bbmaster_paper/TF_for_paper/pureB_pwf_beam/[id_sim]/filterbin_coadd-full_map.fits\",\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_theory_cl(nside, params):\n",
+    "    \"\"\"\n",
+    "    Specs: TT, EE, BB, TE (anafast compatible)\n",
+    "    \"\"\"\n",
+    "    import camb\n",
+    "    params = camb.set_params(**params)\n",
+    "    results = camb.get_results(params)\n",
+    "    powers = results.get_cmb_power_spectra(params, CMB_unit='muK', raw_cl=True)\n",
+    "    return np.array(powers[\"total\"][:, :4][:3*nside]).T.astype(np.float32)\n",
+    "\n",
+    "\n",
+    "def beam_gaussian(ll, fwhm_amin):\n",
+    "    \"\"\"\n",
+    "    Returns the SHT of a Gaussian beam.\n",
+    "    Args:\n",
+    "        l (float or array): multipoles.\n",
+    "        fwhm_amin (float): full-widht half-max in arcmins.\n",
+    "    Returns:\n",
+    "        float or array: beam sampled at `l`.\n",
+    "    \"\"\"\n",
+    "    sigma_rad = np.radians(fwhm_amin / 2.355 / 60)\n",
+    "    return np.exp(-0.5 * ll * (ll + 1) * sigma_rad**2).astype(np.float32)\n",
+    "\n",
+    "\n",
+    "def beam_hpix(ll, nside):\n",
+    "    \"\"\"\n",
+    "    Returns the SHT of the beam associated with a HEALPix\n",
+    "    pixel size.\n",
+    "    Args:\n",
+    "        l (float or array): multipoles.\n",
+    "        nside (int): HEALPix resolution parameter.\n",
+    "    Returns:\n",
+    "        float or array: beam sampled at `l`.\n",
+    "    \"\"\"\n",
+    "    fwhm_hp_amin = 60 * 41.7 / nside\n",
+    "    return beam_gaussian(ll, fwhm_hp_amin)\n",
+    "\n",
+    "\n",
+    "def get_power_law_cl(nside, params, nside_pixwin=None, smooth_arcmin=None):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    pl_ps = np.zeros((4, 3*nside))\n",
+    "    if nside_pixwin is not None:\n",
+    "        pixwin = beam_hpix(np.arange(3*nside), nside_pixwin)**2.\n",
+    "    else:\n",
+    "        pixwin = 1.\n",
+    "    if smooth_arcmin is not None:\n",
+    "        beam = beam_gaussian(np.arange(3*nside), smooth_arcmin)**2.\n",
+    "    else:\n",
+    "        beam = 1.\n",
+    "    for i, spec in enumerate([\"TT\", \"EE\", \"BB\", \"TE\"]):\n",
+    "        if isinstance(params[\"amp\"], dict):\n",
+    "            A = params[\"amp\"][spec]\n",
+    "        else:\n",
+    "            A = params[\"amp\"]\n",
+    "        # A is power spectrum amplitude at pivot ell == 1 - delta_ell\n",
+    "        pl_ps[i] = A / (np.arange(3*nside) + params[\"delta_ell\"]) ** params[\"power_law_index\"]  # noqa\n",
+    "\n",
+    "    return pl_ps * pixwin * beam\n",
+    "\n",
+    "\n",
+    "def load_sim(sims_dir, id_sim, nside):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    fname = sims_dir.replace(\"[id_sim]\", str(id_sim).zfill(4))\n",
+    "    return hp.ud_grade(hp.read_map(fname, field=range(3)), nside_out=nside)\n",
+    "\n",
+    "\n",
+    "def get_nmt_bin(bin_edges, nside):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    bin_edges = np.array(bin_edges)\n",
+    "    bin_edges = bin_edges[bin_edges < 3*nside]\n",
+    "    bin_edges = np.concatenate((bin_edges, [3*nside]))\n",
+    "    return nmt.NmtBin.from_edges(bin_edges[:-1], bin_edges[1:])\n",
+    "\n",
+    "\n",
+    "def compute_workspace(nmt_bin, mask):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    f = nmt.NmtField(mask, None, spin=2, n_iter=0)\n",
+    "    wsp = nmt.NmtWorkspace()\n",
+    "    wsp.compute_coupling_matrix(f, f, nmt_bin)\n",
+    "    return wsp\n",
+    "\n",
+    "\n",
+    "def compute_cl(mask, map, wsp):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    f = nmt.NmtField(mask, [map[1], map[2]])\n",
+    "    pcl = nmt.compute_coupled_cell(f, f)\n",
+    "    cl = wsp.decouple_cell(pcl)\n",
+    "    return cl\n",
+    "\n",
+    "\n",
+    "def bin_theory(cl, wsp):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    assert np.array(cl).ndim == 2\n",
+    "    return wsp.decouple_cell(wsp.couple_cell(cl))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nsims = globals[\"Nsims\"]\n",
+    "cosmo = globals[\"cosmo\"]\n",
+    "power_law = globals[\"power_law\"]\n",
+    "nside = globals[\"nside\"]\n",
+    "bin_edges = globals[\"bin_edges\"]\n",
+    "\n",
+    "theory_cl = get_theory_cl(nside, cosmo)\n",
+    "power_law_cl = get_power_law_cl(nside, power_law, 512, 30.)\n",
+    "nmt_bin = get_nmt_bin(bin_edges, nside)\n",
+    "lb = nmt_bin.get_effective_ells()\n",
+    "mask = hp.ud_grade(hp.read_map(globals[\"mask\"]), nside_out=nside)\n",
+    "wsp = compute_workspace(nmt_bin, mask)\n",
+    "theory_clb = bin_theory(theory_cl, wsp)\n",
+    "power_law_clb = bin_theory(power_law_cl, wsp)\n",
+    "clb = []\n",
+    "clb_filt = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for id_sim in range(Nsims):\n",
+    "    print(id_sim)\n",
+    "    map = load_sim(globals[\"sims\"][\"tf\"], id_sim, nside)\n",
+    "    map_filtered = load_sim(globals[\"sims\"][\"tf_filtered\"], id_sim, nside)\n",
+    "    clb.append(compute_cl(mask, map, wsp))\n",
+    "    clb_filt.append(compute_cl(mask, map_filtered, wsp))\n",
+    "\n",
+    "clb_mean, clb_std = (np.mean(np.array(clb), axis=0),\n",
+    "                   np.std(np.array(clb), axis=0))\n",
+    "clb_filt_mean, clb_filt_std = (np.mean(np.array(clb_filt), axis=0),\n",
+    "                             np.std(np.array(clb_filt), axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "msk = np.logical_and(lb >= 30, lb <= 300)\n",
+    "x = lb[msk]\n",
+    "y = clb_mean[3][msk]\n",
+    "yerr = clb_std[3][msk]\n",
+    "yf = clb_filt_mean[3][msk]\n",
+    "yferr = clb_filt_std[3][msk]\n",
+    "yth = power_law_clb[2][msk]\n",
+    "\n",
+    "plt.errorbar(x*0.97, y, yerr,\n",
+    "             color=\"navy\", label=\"Power law sims\")\n",
+    "plt.errorbar(x*1.02, yf, yferr,\n",
+    "             color=\"darkorange\", label=\"Power law sims, filtered\")\n",
+    "plt.plot(x, yth, \"k--\", label=\"Theory, beamed\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xscale(\"log\")\n",
+    "plt.ylabel(r\"$C_\\ell^{BB}$\")\n",
+    "plt.xlabel(r\"$\\ell$\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "plt.clf()\n",
+    "\n",
+    "plt.plot(x, clb_filt_mean[3][msk] / yth, color=\"navy\", label=\"BB transfer function\")\n",
+    "plt.legend()\n",
+    "plt.ylim((0, 1))\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(x, clb_filt_mean[0][msk] / yth, color=\"navy\", label=\"EE transfer function\")\n",
+    "plt.legend()\n",
+    "plt.ylim((0, 1))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.io import fits\n",
+    "import healpy as hp\n",
+    "theory_fname = \"/global/cfs/cdirs/sobs/users/krach/BBSims/CMB_r0_20201207/reference_spectra/Cls_Planck2018_r0.fits\"\n",
+    "cl_cmb = hp.read_cl(theory_fname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4, 4001)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cl_cmb.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pipeline/coadder.py b/legacy/coadder.py
similarity index 100%
rename from pipeline/coadder.py
rename to legacy/coadder.py
diff --git a/pipeline/covfefe.py b/legacy/covfefe.py
similarity index 100%
rename from pipeline/covfefe.py
rename to legacy/covfefe.py
diff --git a/pipeline/filterer.py b/legacy/filterer.py
similarity index 100%
rename from pipeline/filterer.py
rename to legacy/filterer.py
diff --git a/legacy/generate_wmap_cov_sims.py b/legacy/generate_wmap_cov_sims.py
new file mode 100644
index 0000000..fae87a3
--- /dev/null
+++ b/legacy/generate_wmap_cov_sims.py
@@ -0,0 +1,220 @@
+import numpy as np
+import pymaster as nmt
+import sacc
+import healpy as hp
+import os
+from itertools import combinations_with_replacement as cwr
+import soopercool.mpi_utils as mpi_utils
+
+""" This script loads WMAP noise bundele sims, coadd them, and store them to
+compute a noise-only covariance for consistency checks. """
+
+def load_wmap_noise(nside, freq, id_sim, id_bundle):
+    """
+    Load WMAP noise maps from NERSC at nside 256.
+    """
+    assert nside == 256
+    to_muk = 1.e3
+    str_bundle = str(id_bundle + 1)
+    str_freq = str(int(freq)).zfill(3)
+    bands_dict = {'023': 'K1', '033': 'Ka1'}
+    fname_in = f"/global/cfs/cdirs/sobs/users/cranucci/wmap/nside256_coords_eq/noise/{id_sim:04d}"
+    fname_in += f"/noise_maps_mK_band{bands_dict[str_freq]}_yr{str_bundle}.fits"
+    return to_muk * hp.read_map(fname_in, field=range(3))
+
+
+def coadd_bundles(bundles_list):
+    """
+    Coadd a list of map bundles into a single map.
+    """
+    coadd = np.zeros_like(bundles_list[0])
+    for bundle in bundles_list:
+        coadd += bundle
+
+    return coadd
+
+
+def compute_workspace(nmt_bin, nside, mask_dir):
+    """
+    Compute the NaMaster workspace to compute decoupled pseudo-C_ells.
+    """
+    w = nmt.NmtWorkspace()
+    mask = hp.ud_grade(hp.read_map(mask_dir), nside_out=nside)
+    f = nmt.NmtField(mask, None, spin=2, purify_b=True)
+    w.compute_coupling_matrix(f, f, nmt_bin)
+    
+    return w, mask
+
+
+def nmt_bin_from_edges(bin_edges, nside):
+    """
+    Computes a NaMaster NmtBin object given an input array of bin edges.
+    """
+    bin_edges = np.array(bin_edges)
+    bin_edges = bin_edges[bin_edges < 3*nside]
+    bin_edges = np.concatenate((bin_edges, [3*nside]))
+    return nmt.NmtBin.from_edges(bin_edges[:-1], bin_edges[1:])
+
+
+def get_decoupled_ps(map1, map2, mask, nmt_bin, wsp):
+    """
+    Compute decoupled pseudo C_ells from a map pair given a NaMaster workspace.
+    """
+    f1 = nmt.NmtField(mask, map1[1:], purify_b=True)
+    f2 = nmt.NmtField(mask, map2[1:], purify_b=True)
+    pcl = nmt.compute_coupled_cell(f1, f2)
+    cl_dict = {
+        f: wsp.decouple_cell(pcl)[f_idx]
+        for f_idx, f in enumerate(["EE", "EB", "BE", "BB"])
+    }
+    cl_dict["l"] = nmt_bin.get_effective_ells()
+
+    return cl_dict
+
+
+def generate_sim(nside, id_sim, Nbundles, freqs_list, sims_dir):
+    """
+    Load, coadd, and store WMAP noise maps to disk.
+    """
+    for f in freqs_list:
+        f_str = str(f).zfill(3)
+        bundles_list = [
+            load_wmap_noise(nside, f, id_sim, id_bundle)
+            for id_bundle in range(Nbundles)
+        ]
+        coadd = coadd_bundles(bundles_list)
+        dirname = f"{sims_dir}/{str(id_sim).zfill(4)}"
+        os.makedirs(dirname, exist_ok=True)
+        fname = f"{dirname}/wmap_f{f_str}_noise_coadded.fits"
+        print(fname)
+        hp.write_map(fname, coadd, overwrite=True)
+
+
+def compute_cells(mapset_list, sims_dir, id_sim, sim_range, cl_dir, mask,
+                  nmt_bin, wsp):
+    """
+    Load simulated maps from disk and compute cross-frequency C_ells.
+    """
+    os.makedirs(cl_dir, exist_ok=True)
+    cross_ps_names = cwr(mapset_list, 2)
+    #id_range = [s for s in sim_range if s != id_sim]
+
+    for ms1, ms2 in cross_ps_names:
+        #id_sim2 = np.random.choice(id_range)
+        id_sim2 = id_sim
+        map1, map2 = (
+            hp.read_map(f"{sims_dir}/{str(id_sim).zfill(4)}/{ms}_noise_coadded.fits", field=(0,1,2))  # noqa
+            for id_sim, ms in zip([id_sim, id_sim2], [ms1, ms2])
+        )
+        cl_dict = get_decoupled_ps(map1, map2, mask, nmt_bin, wsp)
+        f = f"{cl_dir}/decoupled_cross_pcls_nobeam_{ms1}_{ms2}_{id_sim:04d}.npz"
+        np.savez(f, **cl_dict)
+
+
+def compute_covariance(Nsims, mapset_list, cl_dir, cov_dir):
+    """
+    """
+    field_pairs = [f"{m1}{m2}" for m1 in "EB" for m2 in "EB"]
+    cross_ps_names = list(cwr(mapset_list, 2))
+    #print(cross_ps_names)
+    cov_names = []
+    for i, (ms1, ms2) in enumerate(cross_ps_names):
+        for j, (ms3, ms4) in enumerate(cross_ps_names):
+            if i > j:
+                continue
+            cov_names.append((ms1, ms2, ms3, ms4))
+    
+    cl_dict = {}
+    for ms1, ms2 in cross_ps_names:
+        cl_list = []
+        for iii in range(Nsims):
+            cells_dict = np.load(
+                f"{cl_dir}/decoupled_cross_pcls_nobeam_{ms1}_{ms2}_{iii:04d}.npz", # noqa
+            )
+            #print(ms1, ms2, iii, len(cells_dict["EE"]))
+            cl_vec = np.concatenate(
+                [
+                    cells_dict[field_pair] for field_pair in field_pairs
+                ]
+            )
+            cl_list.append(cl_vec)
+        cl_dict[ms1, ms2] = np.array(cl_list)
+
+    n_bins = cl_dict[list(cross_ps_names)[i]].shape[-1]//len(field_pairs)
+    
+    full_cov_dict = {}
+
+    for id_mapset in range(len(cov_names)):
+        ms1, ms2, ms3, ms4 = cov_names[id_mapset]
+
+        cl12 = cl_dict[ms1, ms2]
+        cl34 = cl_dict[ms3, ms4]
+
+        cl12_mean = np.mean(cl12, axis=0)
+        cl34_mean = np.mean(cl34, axis=0)
+
+        cov = np.mean(
+            np.einsum("ij,ik->ijk", cl12-cl12_mean, cl34-cl34_mean),
+            axis=0
+        )
+        full_cov_dict[ms1, ms2, ms3, ms4] = cov
+
+        cov_dict = {}
+        for i, field_pair_1 in enumerate(field_pairs):
+            for j, field_pair_2 in enumerate(field_pairs):
+
+                cov_block = cov[i*n_bins:(i+1)*n_bins, j*n_bins:(j+1)*n_bins]
+                cov_dict[field_pair_1 + field_pair_2] = cov_block
+
+        os.makedirs(cov_dir, exist_ok=True)
+        fname = f"{cov_dir}/mc_cov_{ms1}_{ms2}_{ms3}_{ms4}.npz"
+        print(fname)
+        np.savez(fname, **cov_dict)
+
+
+def main():
+    Nsims = 100
+    Nbundles = 9
+    freqs_list = [23, 33]
+    mapset_list = [f"wmap_f{str(f).zfill(3)}" for f in freqs_list]
+    nside = 256
+    mask_dir = "/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/outputs_wmap/masks/analysis_mask.fits"
+    cl_dir = "/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/outputs_wmap_noise/cells_sims"
+    cov_dir = "/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/outputs_wmap_noise/covariances"
+    pre_dir = "/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/outputs_wmap_noise/pre_processing"
+    sims_dir = "/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/outputs_wmap_noise/sims"
+    bin_edges = [2, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160,
+                170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300]
+
+    os.makedirs(pre_dir, exist_ok=True)
+    nmt_bin = nmt_bin_from_edges(bin_edges, nside)
+    print(nmt_bin.get_effective_ells())
+
+
+    wsp, mask = compute_workspace(nmt_bin, nside, mask_dir)
+
+    # Initialize MPI
+    use_mpi4py = True
+    mpi_utils.init(use_mpi4py)
+
+    print("-------------------------------------------------------------------")
+    print("           Generating sims                                         ")
+    print("-------------------------------------------------------------------")
+    for id_sim in mpi_utils.taskrange(2*Nsims - 1):
+        generate_sim(nside, id_sim, Nbundles, freqs_list, sims_dir)
+
+    print("-------------------------------------------------------------------")
+    print("           Computing C_ells                                        ")
+    print("-------------------------------------------------------------------")
+    for id_sim in mpi_utils.taskrange(2*Nsims - 1):
+        compute_cells(mapset_list, sims_dir, id_sim, range(Nsims), cl_dir, mask,
+                      nmt_bin, wsp)
+    
+    print("-------------------------------------------------------------------")
+    print("           Computing covariances                                   ")
+    print("-------------------------------------------------------------------")
+    compute_covariance(Nsims, mapset_list, cl_dir, cov_dir)
+
+
+main()
+
diff --git a/pipeline/mask_handler.py b/legacy/mask_handler.py
similarity index 100%
rename from pipeline/mask_handler.py
rename to legacy/mask_handler.py
diff --git a/pipeline/mcmer.py b/legacy/mcmer.py
similarity index 100%
rename from pipeline/mcmer.py
rename to legacy/mcmer.py
diff --git a/pipeline/mocker.py b/legacy/mocker.py
similarity index 100%
rename from pipeline/mocker.py
rename to legacy/mocker.py
diff --git a/pipeline/pcler.py b/legacy/pcler.py
similarity index 100%
rename from pipeline/pcler.py
rename to legacy/pcler.py
diff --git a/pipeline/pre_processer.py b/legacy/pre_processer.py
similarity index 100%
rename from pipeline/pre_processer.py
rename to legacy/pre_processer.py
diff --git a/legacy/read_soopersims_covariance_sims.ipynb b/legacy/read_soopersims_covariance_sims.ipynb
new file mode 100644
index 0000000..41ce41d
--- /dev/null
+++ b/legacy/read_soopersims_covariance_sims.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Read SOOPERSIMS covaiance simulations, beam & map them, and store to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "globals = {\n",
+    "    \"sims_dir\": \"/pscratch/sd/c/cranucci/BB/simulations/sims_cov\",\n",
+    "    #\"sims_dir\": \"/pscratch/sd/k/kwolz/bbdev/SOOPERSIMS/output_cov_wmap_planck/sims\",\n",
+    "    #\"nside\": 256,\n",
+    "    \"nside\": 512,\n",
+    "    \"Nsims\": 2,\n",
+    "    \"freqs\": [23, 93, 145, 353],\n",
+    "    #\"freqs\": [23, 30, 33, 93, 145, 217, 353],\n",
+    "    \"beam_arcmin\": {\n",
+    "        23: 52.8, 33: 39.6, 30: 32.34, 93: 30, 100: 9.66, 143: 7.27,\n",
+    "        145: 17, 217: 5.01, 353: 4.86\n",
+    "    }, \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def beam_gaussian(ll, fwhm_amin):\n",
+    "    \"\"\"\n",
+    "    Returns the SHT of a Gaussian beam.\n",
+    "    Args:\n",
+    "        l (float or array): multipoles.\n",
+    "        fwhm_amin (float): full-widht half-max in arcmins.\n",
+    "    Returns:\n",
+    "        float or array: beam sampled at `l`.\n",
+    "    \"\"\"\n",
+    "    sigma_rad = np.radians(fwhm_amin / 2.355 / 60)\n",
+    "    return np.exp(-0.5 * ll * (ll + 1) * sigma_rad**2)\n",
+    "\n",
+    "\n",
+    "def get_signal_sim(id_sim, freq_ghz, nside, beam_window, sims_dir):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    nside = int(nside)\n",
+    "    id_str = str(id_sim).zfill(4)\n",
+    "    freq_str = str(int(freq_ghz)).zfill(3) + \"GHz\"\n",
+    "    lmax_str = \"lmax\" + str(int(3*nside - 1))\n",
+    "    alm_dir = f\"{sims_dir}/{id_str}/alm_{freq_str}_{lmax_str}_{id_str}.fits\"\n",
+    "    alm_smooth = hp.smoothalm(hp.read_alm(alm_dir, hdu=(1,2,3)), \n",
+    "                              beam_window=beam_window)\n",
+    "\n",
+    "    return hp.alm2map(alm_smooth, nside)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/pscratch/sd/c/cranucci/BB/simulations/sims_cov/0375/alm_145GHz_lmax767_0375.fits'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[49], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m beam_arcmin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mglobals\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeam_arcmin\u001b[39m\u001b[38;5;124m\"\u001b[39m][freq_ghz]\n\u001b[1;32m      5\u001b[0m beam_window \u001b[38;5;241m=\u001b[39m beam_gaussian(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m*\u001b[39mnside), beam_arcmin)\n\u001b[0;32m----> 6\u001b[0m maps \u001b[38;5;241m=\u001b[39m \u001b[43mget_signal_sim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mid_sim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfreq_ghz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnside\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mbeam_window\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mglobals\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msims_dir\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(maps\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m      9\u001b[0m hp\u001b[38;5;241m.\u001b[39mmollview(maps[\u001b[38;5;241m1\u001b[39m])\n",
+      "Cell \u001b[0;32mIn[48], line 22\u001b[0m, in \u001b[0;36mget_signal_sim\u001b[0;34m(id_sim, freq_ghz, nside, beam_window, sims_dir)\u001b[0m\n\u001b[1;32m     20\u001b[0m lmax_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlmax\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mint\u001b[39m(\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m*\u001b[39mnside \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     21\u001b[0m alm_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msims_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mid_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/alm_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfreq_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlmax_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mid_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.fits\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 22\u001b[0m alm_smooth \u001b[38;5;241m=\u001b[39m hp\u001b[38;5;241m.\u001b[39msmoothalm(\u001b[43mhp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_alm\u001b[49m\u001b[43m(\u001b[49m\u001b[43malm_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhdu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m, \n\u001b[1;32m     23\u001b[0m                           beam_window\u001b[38;5;241m=\u001b[39mbeam_window)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hp\u001b[38;5;241m.\u001b[39malm2map(alm_smooth, nside)\n",
+      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/healpy/fitsfunc.py:621\u001b[0m, in \u001b[0;36mread_alm\u001b[0;34m(filename, hdu, return_mmax)\u001b[0m\n\u001b[1;32m    619\u001b[0m opened_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    620\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, allowed_paths):\n\u001b[0;32m--> 621\u001b[0m     filename \u001b[38;5;241m=\u001b[39m \u001b[43mpf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m     opened_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    624\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m unit \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39matleast_1d(hdu):\n",
+      "File \u001b[0;32m/pscratch/sd/s/susannaz/conda_envs/master_env/lib/python3.9/site-packages/astropy/io/fits/hdu/hdulist.py:213\u001b[0m, in \u001b[0;36mfitsopen\u001b[0;34m(name, mode, memmap, save_backup, cache, lazy_load_hdus, ignore_missing_simple, use_fsspec, fsspec_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m name:\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmpty filename: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mHDUList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromfile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    217\u001b[0m \u001b[43m    \u001b[49m\u001b[43msave_backup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    220\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_missing_simple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    221\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_fsspec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_fsspec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    222\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfsspec_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfsspec_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/pscratch/sd/s/susannaz/conda_envs/master_env/lib/python3.9/site-packages/astropy/io/fits/hdu/hdulist.py:476\u001b[0m, in \u001b[0;36mHDUList.fromfile\u001b[0;34m(cls, fileobj, mode, memmap, save_backup, cache, lazy_load_hdus, ignore_missing_simple, **kwargs)\u001b[0m\n\u001b[1;32m    457\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfromfile\u001b[39m(\n\u001b[1;32m    459\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    467\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    468\u001b[0m ):\n\u001b[1;32m    469\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    470\u001b[0m \u001b[38;5;124;03m    Creates an `HDUList` instance from a file-like object.\u001b[39;00m\n\u001b[1;32m    471\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[38;5;124;03m    documentation for details of the parameters accepted by this method).\u001b[39;00m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 476\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_readfrom\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    477\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    478\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    479\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    480\u001b[0m \u001b[43m        \u001b[49m\u001b[43msave_backup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_backup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    481\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_missing_simple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_missing_simple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/pscratch/sd/s/susannaz/conda_envs/master_env/lib/python3.9/site-packages/astropy/io/fits/hdu/hdulist.py:1146\u001b[0m, in \u001b[0;36mHDUList._readfrom\u001b[0;34m(cls, fileobj, data, mode, memmap, cache, lazy_load_hdus, ignore_missing_simple, use_fsspec, fsspec_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m   1143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fileobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1144\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fileobj, _File):\n\u001b[1;32m   1145\u001b[0m         \u001b[38;5;66;03m# instantiate a FITS file object (ffo)\u001b[39;00m\n\u001b[0;32m-> 1146\u001b[0m         fileobj \u001b[38;5;241m=\u001b[39m \u001b[43m_File\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1147\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1148\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1149\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1150\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1151\u001b[0m \u001b[43m            \u001b[49m\u001b[43muse_fsspec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_fsspec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1152\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfsspec_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfsspec_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1153\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1154\u001b[0m     \u001b[38;5;66;03m# The Astropy mode is determined by the _File initializer if the\u001b[39;00m\n\u001b[1;32m   1155\u001b[0m     \u001b[38;5;66;03m# supplied mode was None\u001b[39;00m\n\u001b[1;32m   1156\u001b[0m     mode \u001b[38;5;241m=\u001b[39m fileobj\u001b[38;5;241m.\u001b[39mmode\n",
+      "File \u001b[0;32m/pscratch/sd/s/susannaz/conda_envs/master_env/lib/python3.9/site-packages/astropy/io/fits/file.py:217\u001b[0m, in \u001b[0;36m_File.__init__\u001b[0;34m(self, fileobj, mode, memmap, overwrite, cache, use_fsspec, fsspec_kwargs)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open_fileobj(fileobj, mode, overwrite)\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fileobj, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open_filename\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open_filelike(fileobj, mode, overwrite)\n",
+      "File \u001b[0;32m/pscratch/sd/s/susannaz/conda_envs/master_env/lib/python3.9/site-packages/astropy/io/fits/file.py:626\u001b[0m, in \u001b[0;36m_File._open_filename\u001b[0;34m(self, filename, mode, overwrite)\u001b[0m\n\u001b[1;32m    623\u001b[0m ext \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplitext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    625\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_read_compressed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, magic, mode, ext\u001b[38;5;241m=\u001b[39mext):\n\u001b[0;32m--> 626\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIO_FITS_MODES\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose_on_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    629\u001b[0m \u001b[38;5;66;03m# Make certain we're back at the beginning of the file\u001b[39;00m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;66;03m# BZ2File does not support seek when the file is open for writing, but\u001b[39;00m\n\u001b[1;32m    631\u001b[0m \u001b[38;5;66;03m# when opening a file for write, bz2.BZ2File always truncates anyway.\u001b[39;00m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/pscratch/sd/c/cranucci/BB/simulations/sims_cov/0375/alm_145GHz_lmax767_0375.fits'"
+     ]
+    }
+   ],
+   "source": [
+    "nside = globals[\"nside\"]\n",
+    "id_sim = 375\n",
+    "freq_ghz = 145\n",
+    "beam_arcmin = globals[\"beam_arcmin\"][freq_ghz]\n",
+    "beam_window = beam_gaussian(np.arange(3*nside), beam_arcmin)\n",
+    "maps = get_signal_sim(id_sim, freq_ghz, nside,\n",
+    "                      beam_window, globals[\"sims_dir\"])\n",
+    "print(maps.shape)\n",
+    "hp.mollview(maps[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f7ef811ca00>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l = np.arange(3*globals[\"nside\"])\n",
+    "cl2dl = l*(l+1)/2./np.pi\n",
+    "cl = hp.anafast(maps)\n",
+    "plt.plot(cl2dl*cl[0], label=\"TT\")\n",
+    "plt.plot(cl2dl*cl[1], label=\"EE\")\n",
+    "plt.plot(cl2dl*cl[2], label=\"BB\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "master_env",
+   "language": "python",
+   "name": "master_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/legacy/run_planck_for_bbpower.sh b/legacy/run_planck_for_bbpower.sh
new file mode 100644
index 0000000..59ba483
--- /dev/null
+++ b/legacy/run_planck_for_bbpower.sh
@@ -0,0 +1,90 @@
+#!/usr/bin/env bash
+
+paramfile='../paramfiles/paramfile_planck.yaml'
+
+echo "Running pipeline with paramfile: ${paramfile}"
+
+#OpenMP settings:
+export OMP_NUM_THREADS=1
+export OMP_PLACES=threads
+export OMP_PROC_BIND=spread
+
+# Run serially
+echo "Pre-processing real data"
+echo "-----------------------------"
+python pre_processer.py --globals ${paramfile} --verbose
+python mask_handler.py --globals ${paramfile} --plots --verbose
+python pre_processer_ext.py --globals ${paramfile} --planck --data
+# python pre_processer_ext.py --globals ${paramfile} --planck --noise
+# python pre_processer_ext.py --globals ${paramfile} --planck --sims
+
+# echo "Running filterer for data"
+# echo "-------------------------"
+# python filterer.py --globals ${paramfile} --data
+
+echo "Running mcm..."
+echo "--------------"
+python mcmer.py --globals ${paramfile} --plot
+
+
+# # run in parallel with salloc -N 1 -C cpu -q interactive -t 00:30:00
+# echo "Generating transfer sims"  # 1m20 for 30 sims
+# echo "-----------------------------"
+# srun -n 30 -c 8 python pre_processer.py --globals ${paramfile} --verbose --sims
+
+# echo "Running filterer for transfer"  # 1m20 for 30 sims
+# echo "-----------------------------"
+# srun -n 30 -c 8 --cpu_bind=cores python filterer.py --globals ${paramfile} --transfer
+
+# echo "Running cl estimation for tf estimation"  # 1m50 for 30 sims
+# echo "---------------------------------------"
+# srun -n 30 -c 8 --cpu_bind=cores python pcler.py --globals ${paramfile} --tf_est --verbose
+
+# echo "Running transfer estimation"
+# echo "---------------------------"
+# python transfer.py --globals ${paramfile}
+
+# echo "Running cl estimation for validation"
+# echo "------------------------------------"
+# srun -n 30 -c 8 --cpu_bind=cores python pcler.py --globals ${paramfile} --tf_val --verbose
+
+# echo "Transfer validation"
+# echo "---------------------"
+# python transfer_validator.py --globals ${paramfile}
+
+
+# # run in parallel with salloc -N 1 -C cpu -q interactive -t 01:00:00
+# echo "Running pcler on data"
+# echo "---------------------"
+# python pcler.py --globals ${paramfile} --data
+
+# echo "Generating sims"
+# echo "-------------------------"
+# #srun -n 25 -c 10 --cpu_bind=cores python mocker.py --globals ${paramfile} --sims
+# srun -n 25 -c 10 --cpu_bin=cores python generate_simulations.py --globals ${paramfile}
+
+# echo "Running filterer for sims"  # 2m50 for 100 sims / 10 splits
+# echo "-------------------------"
+# srun -n 25 -c 10 --cpu_bind=cores python filterer.py --globals ${paramfile} --sims --plots
+
+# echo "Running pcler on sims"  # 5m  for 100 sims / 21 splits
+# echo "---------------------"
+# srun -n 25 -c 10 --cpu_bind=cores python pcler.py --globals ${paramfile} --sims --verbose
+
+# echo "Running coadder on data"
+# echo "---------------------"
+# python coadder.py --globals ${paramfile} --data 
+
+# echo "Running coadder on sims"  # 0m37s  for 100 sims / 21 splits
+# echo "---------------------"
+# srun -n 25 -c 10 --cpu_bind=cores python coadder.py --globals ${paramfile} --sims
+
+# echo "Running covariance estimation"
+# echo "-----------------------------"
+# python covfefe.py --globals ${paramfile}
+
+# echo "Create sacc files for sims and data"
+# echo "-----------------------------------"
+# python saccer.py --globals ${paramfile} --data
+# #  2m  for 100 sims / 21 splits
+# srun -n 25 -c 10 --cpu_bind=cores python saccer.py --globals ${paramfile} --sims
\ No newline at end of file
diff --git a/legacy/run_wmap_for_bbpower.sh b/legacy/run_wmap_for_bbpower.sh
new file mode 100644
index 0000000..3f166ea
--- /dev/null
+++ b/legacy/run_wmap_for_bbpower.sh
@@ -0,0 +1,88 @@
+#!/usr/bin/env bash
+
+paramfile='../paramfiles/paramfile_wmap.yaml'
+
+echo "Running pipeline with paramfile: ${paramfile}"
+
+#OpenMP settings:
+export OMP_NUM_THREADS=1
+export OMP_PLACES=threads
+export OMP_PROC_BIND=spread
+
+# Run serially
+# echo "Pre-processing real data"
+# echo "-----------------------------"
+# python pre_processer.py --globals ${paramfile} --verbose
+# python mask_handler.py --globals ${paramfile} --plots --verbose
+# python pre_processer_ext.py --globals ${paramfile} --wmap --data
+# python pre_processer_ext.py --globals ${paramfile} --wmap --noise
+
+# echo "Running filterer for data"
+# echo "-------------------------"
+# python filterer.py --globals ${paramfile} --data
+
+# echo "Running mcm..."
+# echo "--------------"
+# python mcmer.py --globals ${paramfile} --plot
+
+
+# run in parallel with salloc -N 1 -C cpu -q interactive -t 00:30:00
+echo "Generating transfer sims"  # 1m20 for 30 sims
+echo "-----------------------------"
+srun -n 30 -c 8 python pre_processer.py --globals ${paramfile} --verbose --sims
+
+echo "Running filterer for transfer"  # 1m20 for 30 sims
+echo "-----------------------------"
+srun -n 30 -c 8 --cpu_bind=cores python filterer.py --globals ${paramfile} --transfer
+
+echo "Generating sims"
+echo "-------------------------"
+srun -n 25 -c 10 --cpu_bind=cores python mocker.py --globals ${paramfile} --sims
+
+echo "Running filterer for sims"  # 2m50 for 100 sims / 10 splits
+echo "-------------------------"
+srun -n 25 -c 10 --cpu_bind=cores python filterer.py --globals ${paramfile} --sims
+
+echo "Running cl estimation for tf estimation"  # 1m50 for 30 sims
+echo "---------------------------------------"
+srun -n 30 -c 8 --cpu_bind=cores python pcler.py --globals ${paramfile} --tf_est --verbose
+
+echo "Running transfer estimation"
+echo "---------------------------"
+python transfer.py --globals ${paramfile}
+
+echo "Running cl estimation for validation"
+echo "------------------------------------"
+srun -n 30 -c 8 --cpu_bind=cores python pcler.py --globals ${paramfile} --tf_val --verbose
+
+echo "Transfer validation"
+echo "---------------------"
+python transfer_validator.py --globals ${paramfile}
+
+
+# run in parallel with salloc -N 1 -C cpu -q interactive -t 01:00:00
+echo "Running pcler on data"
+echo "---------------------"
+python pcler.py --globals ${paramfile} --data 
+
+echo "Running pcler on sims"  # 5m  for 100 sims / 21 splits
+echo "---------------------"
+srun -n 25 -c 10 --cpu_bind=cores python pcler.py --globals ${paramfile} --sims --verbose
+
+echo "Running coadder on data"
+echo "---------------------"
+python coadder.py --globals ${paramfile} --data 
+
+echo "Running coadder on sims"  # 0m37s  for 100 sims / 21 splits
+echo "---------------------"
+srun -n 25 -c 10 --cpu_bind=cores python coadder.py --globals ${paramfile} --sims
+
+echo "Running covariance estimation"
+echo "-----------------------------"
+python covfefe.py --globals ${paramfile}
+
+echo "Create sacc files for sims and data"
+echo "-----------------------------------"
+python saccer.py --globals ${paramfile} --data
+#  2m  for 100 sims / 21 splits
+srun -n 25 -c 10 --cpu_bind=cores python saccer.py --globals ${paramfile} --sims
\ No newline at end of file
diff --git a/pipeline/saccer.py b/legacy/saccer.py
similarity index 100%
rename from pipeline/saccer.py
rename to legacy/saccer.py
diff --git a/pipeline/transfer.py b/legacy/transfer.py
similarity index 100%
rename from pipeline/transfer.py
rename to legacy/transfer.py
diff --git a/legacy/utils_wmap_planck.py b/legacy/utils_wmap_planck.py
new file mode 100644
index 0000000..73a84e3
--- /dev/null
+++ b/legacy/utils_wmap_planck.py
@@ -0,0 +1,862 @@
+import numpy as np
+import os
+import healpy as hp
+import matplotlib.pyplot as plt
+from matplotlib import cm
+import camb
+from pathlib import Path
+
+"""
+Some collection of extended utilities (Kevin, 12 June 2024) that were use for 
+WMAP and Planck runs, as well as noise level comparisons.
+"""
+
+
+def get_theory_cls(cosmo_params, lmax, lmin=0):
+    """
+    """
+    params = camb.set_params(**cosmo_params)
+    results = camb.get_results(params)
+    powers = results.get_cmb_power_spectra(params, CMB_unit='muK', raw_cl=True)
+    lth = np.arange(lmin, lmax+1)
+
+    cl_th = {
+        "TT": powers["total"][:, 0][lmin:lmax+1],
+        "EE": powers["total"][:, 1][lmin:lmax+1],
+        "TE": powers["total"][:, 3][lmin:lmax+1],
+        "BB": powers["total"][:, 2][lmin:lmax+1]
+    }
+    for spec in ["EB", "TB"]:
+        cl_th[spec] = np.zeros_like(lth, dtype=np.float32)
+
+    return lth, cl_th
+
+
+def generate_noise_map_white(nside, noise_rms_muKarcmin, ncomp=3):
+    """
+    """
+    size = 12 * nside**2
+
+    pixel_area_deg = hp.nside2pixarea(nside, degrees=True)
+    pixel_area_arcmin = 60**2 * pixel_area_deg
+
+    noise_rms_muK_T = noise_rms_muKarcmin / np.sqrt(pixel_area_arcmin)
+
+    out_map = np.zeros((ncomp, size))
+    out_map[0, :] = np.random.randn(size) * noise_rms_muK_T
+
+    if ncomp == 3:
+        noise_rms_muK_P = np.sqrt(2) * noise_rms_muK_T
+        out_map[1, :] = np.random.randn(size) * noise_rms_muK_P
+        out_map[2, :] = np.random.randn(size) * noise_rms_muK_P
+        return out_map
+    return out_map
+
+
+def get_noise_cls(noise_kwargs, lmax, lmin=0, fsky=0.1,
+                  is_beam_deconvolved=False):
+    """
+    Load polarization noise from SO SAT noise model.
+    Assume polarization noise is half of that.
+    """
+    import soopercool.SO_Noise_Calculator_Public_v3_1_2 as noise_calc
+    oof_dict = {"pessimistic": 0, "optimistic": 1}
+    oof_mode = noise_kwargs["one_over_f_mode"]
+    oof_mode = oof_dict[oof_mode]
+
+    sensitivity_mode = noise_kwargs["sensitivity_mode"]
+
+    noise_model = noise_calc.SOSatV3point1(
+        sensitivity_mode=sensitivity_mode,
+        N_tubes=[1., 1., 1.],
+        one_over_f_mode=oof_mode,
+        survey_years=noise_kwargs["survey_years"]
+    )
+    lth, _, nlth_P = noise_model.get_noise_curves(
+        fsky,
+        lmax + 1,
+        delta_ell=1,
+        deconv_beam=is_beam_deconvolved
+    )
+    lth = np.concatenate(([0, 1], lth))[lmin:]
+    nlth_P = np.array(
+        [np.concatenate(([0, 0], nl))[lmin:] for nl in nlth_P]
+    )
+
+    # Attention: at the moment, the noise model's frequencies must match
+    # soopercool's frequency tags.
+    freq_tags = [int(f) for f in noise_model.get_bands()]
+    nl_all_frequencies = {}
+    for i_f, freq_tag in enumerate(freq_tags):
+        nl_th_dict = {pq: nlth_P[i_f]
+                      for pq in ["EE", "EB", "BE", "BB"]}
+        nl_th_dict["TT"] = 0.5*nlth_P[i_f]
+        nl_th_dict["TE"] = 0.*nlth_P[i_f]
+        nl_th_dict["TB"] = 0.*nlth_P[i_f]
+        nl_all_frequencies[freq_tag] = nl_th_dict
+
+    return lth, nl_all_frequencies
+
+
+def generate_noise_map(nl_T, nl_P, hitmap, n_splits, is_anisotropic=True):
+    """
+    """
+    # healpix ordering ["TT", "EE", "BB", "TE"]
+    noise_mat = np.array([nl_T, nl_P, nl_P, np.zeros_like(nl_P)])
+    # Normalize the noise
+    noise_mat *= n_splits
+
+    noise_map = hp.synfast(noise_mat, hp.get_nside(hitmap), pol=True, new=True)
+
+    if is_anisotropic:
+        # Weight with hitmap
+        noise_map[:, hitmap != 0] /= np.sqrt(hitmap[hitmap != 0] / np.max(hitmap)) # noqa
+
+    return noise_map
+
+
+def random_src_mask(mask, nsrcs, mask_radius_arcmin):
+    """
+    pspy.so_map
+    """
+    ps_mask = mask.copy()
+    src_ids = np.random.choice(np.where(mask == 1)[0], nsrcs)
+    for src_id in src_ids:
+        vec = hp.pix2vec(hp.get_nside(mask), src_id)
+        disc = hp.query_disc(hp.get_nside(mask), vec,
+                             np.deg2rad(mask_radius_arcmin / 60))
+        ps_mask[disc] = 0
+    return ps_mask
+
+
+def get_beam_windows_SAT(meta, plot=False):
+    """
+    Compute and save dictionary with beam window functions for each map set.
+    """
+    import soopercool.SO_Noise_Calculator_Public_v3_1_2 as noise_calc
+    oof_dict = {"pessimistic": 0, "optimistic": 1}
+
+    noise_model = noise_calc.SOSatV3point1(
+        survey_years=meta.noise["survey_years"],
+        sensitivity_mode=meta.noise["sensitivity_mode"],
+        one_over_f_mode=oof_dict[meta.noise["one_over_f_mode"]]
+    )
+
+    lth = np.arange(3*meta.nside)
+    beam_arcmin = {int(freq_band): beam_arcmin
+                   for freq_band, beam_arcmin in zip(noise_model.get_bands(),
+                                                     noise_model.get_beams())}
+    beams_dict = {}
+    for map_set in meta.map_sets_list:
+        if "SAT" not in meta.exp_tag_from_map_set(map_set):
+            continue
+        freq_tag = meta.freq_tag_from_map_set(map_set)
+        beams_dict[map_set] = beam_gaussian(lth, beam_arcmin[freq_tag])
+        file_root = meta.file_root_from_map_set(map_set)
+
+        if not os.path.exists(file_root):
+            np.savetxt(f"{meta.beam_directory}/beam_{file_root}.dat",
+                       np.transpose([lth, beams_dict[map_set]]))
+        if plot:
+            plt.plot(lth, beams_dict[map_set], label=map_set)
+    if plot:
+        plt.yscale("log")
+        plt.legend()
+        plt.savefig(f"{meta.beam_directory}/beams.png")
+
+
+def get_beam_exp(ll, experiment, freq_ghz):
+    """
+    Reads the beam for a given experiment ("wmap", "planck", "sat")
+    """
+    if "sat" in str(experiment).lower():
+        fwhm = {27: 91., 39: 63., 93: 30., 145: 17., 225: 11., 280: 9.}
+        if int(freq_ghz) not in fwhm:
+            raise ValueError(f"{freq_ghz} GHz is not a SAT channel.")
+        return beam_gaussian(ll, fwhm[int(freq_ghz)])
+    elif "wmap" in str(experiment).lower():
+        bands = {23: "K1", 33: "Ka1"}
+        if int(freq_ghz) not in bands:
+            raise ValueError(f"{freq_ghz} GHz is not a WMAP channel.")
+        fdir = "/global/cfs/cdirs/cmb/data/wmap9/dr5/ancillary/beams/"
+        fdir += f"wmap_ampl_bl_{bands[int(freq_ghz)]}_9yr_v5p1.txt"
+        
+        if int(freq_ghz) not in bands:
+            raise ValueError(f"{freq_ghz} GHz is not a Planck channel.")
+    elif "planck" in str(experiment).lower():
+        fdir = "/pscratch/sd/k/kwolz/bbdev/SOOPERCOOL/data_planck/beams/"
+        fdir += f"beam_pol_planck_f{str(freq_ghz).zfill(3)}.dat"
+    else:
+        raise ValueError(f"Your experiment {experiment} has yet to be built!")
+
+    l, b = np.loadtxt(fdir, unpack=True, usecols=(0, 1))
+    lmax_file = int(l[-1])
+    bl = np.full_like(ll, b[-1], dtype=np.float32)
+    if lmax_file < ll[-1]:
+        bl[ll <= lmax_file] = b[ll[ll <= lmax_file]]
+    else:
+        bl = b[ll]
+    return bl
+
+
+def beam_gaussian(ll, fwhm_amin):
+    """
+    Returns the SHT of a Gaussian beam.
+    Args:
+        l (float or array): multipoles.
+        fwhm_amin (float): full-widht half-max in arcmins.
+    Returns:
+        float or array: beam sampled at `l`.
+    """
+    sigma_rad = np.radians(fwhm_amin / 2.355 / 60)
+    return np.exp(-0.5 * ll * (ll + 1) * sigma_rad**2).astype(np.float32)
+
+
+def beam_hpix(ll, nside):
+    """
+    Returns the SHT of the beam associated with a HEALPix
+    pixel size.
+    Args:
+        l (float or array): multipoles.
+        nside (int): HEALPix resolution parameter.
+    Returns:
+        float or array: beam sampled at `l`.
+    """
+    fwhm_hp_amin = 60 * 41.7 / nside
+    return beam_gaussian(ll, fwhm_hp_amin)
+
+
+def create_binning(nside, delta_ell):
+    """
+    """
+    bin_low = np.arange(0, 3*nside, delta_ell)
+    bin_high = bin_low + delta_ell - 1
+    bin_high[-1] = 3*nside - 1
+    bin_center = (bin_low + bin_high) / 2
+
+    return bin_low, bin_high, bin_center
+
+
+def power_law_cl(ell, amp, delta_ell, power_law_index,
+                 nside_pixwin=None, smooth_arcmin=None):
+    """
+    """
+    if nside_pixwin is not None:
+        pixwin = beam_hpix(ell, nside_pixwin)**2.
+    else:
+        pixwin = 1.
+    if smooth_arcmin is not None:
+        beam = beam_gaussian(ell, smooth_arcmin)**2.
+    else:
+        beam = 1.
+
+    pl_ps = {}
+    for spec in ["TT", "TE", "TB", "EE", "EB", "BB"]:
+        if isinstance(amp, dict):
+            A = amp[spec]
+        else:
+            A = amp
+        # A is power spectrum amplitude at pivot ell == 1 - delta_ell
+        pl_ps[spec] = A / (ell + delta_ell) ** power_law_index
+        pl_ps[spec] *= pixwin * beam
+
+    return pl_ps
+
+
+def m_filter_map(map, map_file, mask, m_cut):
+    """
+    Applies the m-cut mock filter to a given map with a given sky mask.
+
+    Parameters
+    ----------
+    map : array-like
+        Healpix TQU map to be filtered.
+    map_file : str
+        File path of the unfiltered map.
+    mask : array-like
+        Healpix map storing the sky mask.
+    m_cut : int
+        Maximum nonzero m-degree of the multipole expansion. All higher
+        degrees are set to zero.
+    """
+    map_file_filtered = map_file.replace('.fits', '_filtered.fits')
+    # if os.path.isfile(map_file_filtered):
+    #     print(f"  Filtered map exists at {map_file_filtered}. Skip.")
+    #     return
+    print(f"  Filtering map at {map_file}")
+    map_masked = map * mask
+    nside = hp.get_nside(map)
+    lmax = 3 * nside - 1
+
+    alms = hp.map2alm(map_masked, lmax=lmax)
+
+    n_modes_to_filter = (m_cut + 1) * (lmax + 1) - ((m_cut + 1) * m_cut) // 2
+    alms[:, :n_modes_to_filter] = 0.
+
+    filtered_map = hp.alm2map(alms, nside=nside, lmax=lmax)
+
+    hp.write_map(map_file.replace('.fits', '_filtered.fits'),
+                 filtered_map, overwrite=True,
+                 dtype=np.float32)
+
+
+def toast_filter_map(map, map_file, mask,
+                     template, config, schedule,
+                     nside, instrument, band,
+                     sbatch_job_name, sbatch_dir,
+                     nhits_map_only=False, sim_noise=False):
+    """
+    Create sbatch scripts for each simulation, based on given template file.
+
+    Parameters
+    ----------
+    map : array-like (unused)
+        This is an unused argument included for compatibility with other
+        filters. TOAST won't read the map itself.
+    map_file : str
+        File path of the unfiltered map.
+    mask : array-like (unused)
+        This is an unused argument included for compatibility with other
+        filters. TOAST won't read the mask itself.
+    template : str
+        Path to sbatch template file in Jinja2.
+    config : str
+        Path to TOAST toml config file.
+    schedule : str
+        Path to TOAST schedule file.
+    nside : int
+        Healpix Nside parameter of the filtered map.
+    instrument : str
+        Name of the instrument simulated by TOAST.
+    band : str
+        Name of the frequency band simulated by TOAST.
+    sbatch_job_name : str
+        Sbatch job name
+    sbatch_dir : str
+        Sbatch output directory.
+    nhits_map_only : bool
+        If True, only get a hits map from TOAST schedule file.
+    sim_noise : bool
+        If True, simulate noise with TOAST.
+    """
+    from jinja2 import Environment, FileSystemLoader
+    from pathlib import Path
+
+    del map, mask  # delete unused arguments
+
+    # Path(...).resovle() will return absolute path.
+    map_file = Path(map_file).resolve()
+    if nhits_map_only:
+        map_dir = map_file.parent
+        map_dir.mkdir(parents=True, exist_ok=True)
+    template_file = Path(template).resolve()
+    template_dir = template_file.parent
+    template_name = template_file.name
+    config_file = Path(config).resolve()
+    schedule_file = Path(schedule).resolve()
+    sbatch_dir = Path(sbatch_dir).resolve()
+    sbatch_outdir = sbatch_dir/sbatch_job_name
+    sbatch_outdir.mkdir(parents=True, exist_ok=True)
+    sbatch_file = sbatch_dir/(sbatch_job_name + '.sh')
+    sbatch_log = sbatch_dir/(sbatch_job_name + '.log')
+
+    jinja2_env = Environment(
+        loader=FileSystemLoader(template_dir),
+        trim_blocks=True,
+        lstrip_blocks=True)
+    jinja2_temp = jinja2_env.get_template(template_name)
+
+    with open(sbatch_file, mode='w') as f:
+        f.write(jinja2_temp.render(
+            sbatch_job_name=sbatch_job_name,
+            sbatch_log=sbatch_log,
+            outdir=str(sbatch_outdir),
+            nside=nside,
+            band=band,
+            telescope=instrument,
+            config=str(config_file),
+            schedule=str(schedule_file),
+            map_file=str(map_file),
+            nhits_map_only=nhits_map_only,
+            sim_noise=sim_noise,
+            ))
+    os.chmod(sbatch_file, 0o755)
+    return sbatch_file
+
+
+def get_split_pairs_from_coadd_ps_name(map_set1, map_set2,
+                                       all_splits_ps_names,
+                                       cross_splits_ps_names,
+                                       auto_splits_ps_names):
+    """
+    """
+    split_pairs_list = {
+        "auto": [],
+        "cross": []
+    }
+    for split_ms1, split_ms2 in all_splits_ps_names:
+        if (not (split_ms1.startswith(map_set1) and
+                 split_ms2.startswith(map_set2))):
+            continue
+
+        if (split_ms1, split_ms2) in cross_splits_ps_names:
+            split_pairs_list["cross"].append((split_ms1, split_ms2))
+        elif (split_ms1, split_ms2) in auto_splits_ps_names:
+            split_pairs_list["auto"].append((split_ms1, split_ms2))
+
+    return split_pairs_list
+
+
+def plot_map(map, fname, vrange_T=300, vrange_P=10, title=None, TQU=True):
+    fields = "TQU" if TQU else "QU"
+    for i, m in enumerate(fields):
+        vrange = vrange_T if m == "T" else vrange_P
+        plt.figure(figsize=(16, 9))
+        hp.mollview(map[i], title=f"{title}_{m}", unit=r'$\mu$K$_{\rm CMB}$',
+                    cmap=cm.coolwarm, min=-vrange, max=vrange)
+        hp.graticule()
+        plt.savefig(f"{fname}_{m}.png", bbox_inches="tight")
+
+
+def beam_alms(alms, bl):
+    """
+    """
+    if bl is not None:
+        for i, alm in enumerate(alms):
+            alms[i] = hp.almxfl(alm, bl)
+
+    return alms
+
+
+def generate_map_from_alms(alms, nside, pureE=False, pureB=False,
+                           pureT=False, bl=None):
+    """
+    """
+    alms = beam_alms(alms, bl)
+    Tlm, Elm, Blm = alms
+    if pureE:
+        alms = [Tlm*0., Elm, Blm*0.]
+    elif pureB:
+        alms = [Tlm*0., Elm*0., Blm]
+    elif pureT:
+        alms = [Tlm, Elm*0., Blm*0.]
+
+    return hp.alm2map(alms, nside, lmax=3*nside - 1)
+
+
+def bin_validation_power_spectra(cls_dict, nmt_binning,
+                                 bandpower_window_function):
+    """
+    Bin multipoles of transfer function validation power spectra into
+    binned bandpowers.
+    """
+    nl = nmt_binning.lmax + 1
+    cls_binned_dict = {}
+
+    for spin_comb in ["spin0xspin0", "spin0xspin2", "spin2xspin2"]:
+        bpw_mat = bandpower_window_function[f"bp_win_{spin_comb}"]
+
+        for val_type in ["tf_val", "cosmo"]:
+            if spin_comb == "spin0xspin0":
+                cls_vec = np.array([cls_dict[val_type]["TT"][:nl]])
+                cls_vec = cls_vec.reshape(1, nl)
+            elif spin_comb == "spin0xspin2":
+                cls_vec = np.array([cls_dict[val_type]["TE"][:nl],
+                                    cls_dict[val_type]["TB"][:nl]])
+            elif spin_comb == "spin2xspin2":
+                cls_vec = np.array([cls_dict[val_type]["EE"][:nl],
+                                    cls_dict[val_type]["EB"][:nl],
+                                    cls_dict[val_type]["EB"][:nl],
+                                    cls_dict[val_type]["BB"][:nl]])
+
+            cls_vec_binned = np.einsum("ijkl,kl", bpw_mat, cls_vec)
+
+            if spin_comb == "spin0xspin0":
+                cls_binned_dict[val_type, "TT"] = cls_vec_binned[0]
+            elif spin_comb == "spin0xspin2":
+                cls_binned_dict[val_type, "TE"] = cls_vec_binned[0]
+                cls_binned_dict[val_type, "TB"] = cls_vec_binned[1]
+            elif spin_comb == "spin2xspin2":
+                cls_binned_dict[val_type, "EE"] = cls_vec_binned[0]
+                cls_binned_dict[val_type, "EB"] = cls_vec_binned[1]
+                cls_binned_dict[val_type, "BE"] = cls_vec_binned[2]
+                cls_binned_dict[val_type, "BB"] = cls_vec_binned[3]
+
+    return cls_binned_dict
+
+
+def plot_transfer_function(lb, tf_dict, lmin, lmax, field_pairs, file_name):
+    """
+    Plot the transfer function given an input dictionary.
+    """
+    plt.figure(figsize=(25, 25))
+    grid = plt.GridSpec(9, 9, hspace=0.3, wspace=0.3)
+
+    for id1, f1 in enumerate(field_pairs):
+        for id2, f2 in enumerate(field_pairs):
+            ax = plt.subplot(grid[id1, id2])
+
+            ax.set_title(f"{f1} $\\rightarrow$ {f2}", fontsize=14)
+
+            ax.errorbar(
+                lb, tf_dict[f"{f1}_to_{f2}"], tf_dict[f"{f1}_to_{f2}_std"],
+                marker=".", markerfacecolor="white",
+                color="navy")
+
+            if id1 == 8:
+                ax.set_xlabel(r"$\ell$", fontsize=14)
+            else:
+                ax.set_xticks([])
+
+            if f1 == f2:
+                ax.axhline(1., color="k", ls="--")
+            else:
+                ax.axhline(0, color="k", ls="--")
+                ax.ticklabel_format(axis="y", style="scientific",
+                                    scilimits=(0, 0), useMathText=True)
+
+            ax.set_xlim(lmin, lmax)
+            if id1 == id2:
+                ax.set_ylim(0, 1)
+            else:
+                ax.set_ylim(-0.01, 0.01)
+
+    plt.savefig(file_name, bbox_inches="tight")
+
+
+def plot_transfer_validation(meta, map_set_1, map_set_2,
+                             cls_theory, cls_theory_binned,
+                             cls_mean_dict, cls_std_dict):
+    """
+    Plot the transfer function validation power spectra and save to disk.
+    """
+    nmt_binning = meta.read_nmt_binning()
+    lb = nmt_binning.get_effective_ells()
+
+    for val_type in ["tf_val", "cosmo"]:
+        plt.figure(figsize=(16, 16))
+        grid = plt.GridSpec(9, 3, hspace=0.3, wspace=0.3)
+
+        for id1, id2 in [(i, j) for i in range(3) for j in range(3)]:
+            f1, f2 = "TEB"[id1], "TEB"[id2]
+            spec = f2 + f1 if id1 > id2 else f1 + f2
+
+            main = plt.subplot(grid[3*id1:3*(id1+1)-1, id2])
+            sub = plt.subplot(grid[3*(id1+1)-1, id2])
+
+            # Plot theory
+            ell = cls_theory[val_type]["l"]
+            rescaling = 1 if val_type == "tf_val" \
+                else ell * (ell + 1) / (2*np.pi)
+            main.plot(ell, rescaling*cls_theory[val_type][spec], color="k")
+
+            offset = 0.5
+            rescaling = 1 if val_type == "tf_val" else lb*(lb + 1) / (2*np.pi)
+
+            # Plot filtered & unfiltered (decoupled)
+            if not meta.validate_beam:
+                main.errorbar(
+                    lb - offset, rescaling*cls_mean_dict[val_type,
+                                                         "unfiltered",
+                                                         spec],
+                    rescaling*cls_std_dict[val_type, "unfiltered", spec],
+                    color="navy", marker=".", markerfacecolor="white",
+                    label=r"Unfiltered decoupled $C_\ell$", ls="None"
+                )
+            main.errorbar(
+                lb + offset, rescaling*cls_mean_dict[val_type,
+                                                     "filtered",
+                                                     spec],
+                rescaling*cls_std_dict[val_type, "filtered", spec],
+                color="darkorange", marker=".", markerfacecolor="white",
+                label=r"Filtered decoupled $C_\ell$", ls="None"
+            )
+            
+
+            if f1 == f2:
+                main.set_yscale("log")
+
+            # Plot residuals
+            sub.axhspan(-2, 2, color="gray", alpha=0.2)
+            sub.axhspan(-1, 1, color="gray", alpha=0.7)
+            sub.axhline(0, color="k")
+
+            if not meta.validate_beam:
+                residual_unfiltered = (
+                    (cls_mean_dict[val_type, "unfiltered", spec]
+                     - cls_theory_binned[val_type, spec])
+                    / cls_std_dict[val_type, "unfiltered", spec]
+                )
+                sub.plot(
+                    lb - offset,
+                    residual_unfiltered * np.sqrt(meta.tf_est_num_sims),
+                    color="navy", marker=".", markerfacecolor="white",
+                    ls="None"
+                )
+            residual_filtered = (
+                (cls_mean_dict[val_type, "filtered", spec]
+                 - cls_theory_binned[val_type, spec])
+                / cls_std_dict[val_type, "filtered", spec]
+            )
+            sub.plot(lb + offset,
+                     residual_filtered * np.sqrt(meta.tf_est_num_sims),
+                     color="darkorange", marker=".",
+                     markerfacecolor="white", ls="None")
+
+            # Multipole range
+            main.set_xlim(2, meta.lmax)
+            sub.set_xlim(*main.get_xlim())
+            main.set_ylim(1e-5, 1e-2)
+
+            # Suplot y range
+            sub.set_ylim((-5., 5.))
+
+            # Cosmetix
+            main.set_title(f1+f2, fontsize=14)
+            if spec == "TT":
+                main.legend(fontsize=13)
+            main.set_xticklabels([])
+            if id1 != 2:
+                sub.set_xticklabels([])
+            else:
+                sub.set_xlabel(r"$\ell$", fontsize=13)
+
+            if id2 == 0:
+                if isinstance(rescaling, float):
+                    main.set_ylabel(r"$C_\ell$", fontsize=13)
+                else:
+                    main.set_ylabel(r"$\ell(\ell+1)C_\ell/2\pi$",
+                                    fontsize=13)
+                sub.set_ylabel(r"$\Delta C_\ell / (\sigma/\sqrt{N_\mathrm{sims}})$",  # noqa
+                               fontsize=13)
+
+        plot_dir = meta.plot_dir_from_output_dir(meta.coupling_directory)
+        plot_suffix = (f"__{map_set_1}_{map_set_2}" if meta.validate_beam
+                       else "")
+        plt.savefig(f"{plot_dir}/decoupled_{val_type}{plot_suffix}.pdf",
+                    bbox_inches="tight")
+
+
+def get_binary_mask_from_nhits(nhits_map, nside, zero_threshold=1e-3):
+    """
+    Make binary mask by smoothing, normalizing and thresholding nhits map.
+    """
+    nhits_smoothed = hp.smoothing(
+        hp.ud_grade(nhits_map, nside, power=-2, dtype=np.float64),
+        fwhm=np.pi/180)
+    nhits_smoothed[nhits_smoothed < 0] = 0
+    nhits_smoothed /= np.amax(nhits_smoothed)
+    binary_mask = np.zeros_like(nhits_smoothed)
+    binary_mask[nhits_smoothed > zero_threshold] = 1
+
+    return binary_mask
+
+
+def get_apodized_mask_from_nhits(nhits_map, nside,
+                                 galactic_mask=None,
+                                 point_source_mask=None,
+                                 zero_threshold=1e-3,
+                                 apod_radius=10.,
+                                 apod_radius_point_source=4.,
+                                 apod_type="C1"):
+    """
+    Produce an appropriately apodized mask from an nhits map as used in
+    the BB pipeline paper (https://arxiv.org/abs/2302.04276).
+
+    Procedure:
+    * Make binary mask by smoothing, normalizing and thresholding nhits map
+    * (optional) multiply binary mask by galactic mask
+    * Apodize (binary * galactic)
+    * (optional) multiply (binary * galactic) with point source mask
+    * (optional) apodize (binary * galactic * point source)
+    * Multiply everything by (smoothed) nhits map
+    """
+    import pymaster as nmt
+
+    # Smooth and normalize hits map
+    nhits_map = hp.smoothing(
+        hp.ud_grade(nhits_map, nside, power=-2, dtype=np.float64),
+        fwhm=np.pi/180)
+    nhits_map /= np.amax(nhits_map)
+
+    # Get binary mask
+    binary_mask = get_binary_mask_from_nhits(nhits_map, nside, zero_threshold)
+
+    # Multiply by Galactic mask
+    if galactic_mask is not None:
+        binary_mask *= hp.ud_grade(galactic_mask, nside)
+
+    # Apodize the binary mask
+    binary_mask = nmt.mask_apodization(binary_mask, apod_radius,
+                                       apotype=apod_type)
+
+    # Multiply with point source mask
+    if point_source_mask is not None:
+        binary_mask *= hp.ud_grade(point_source_mask, nside)
+        binary_mask = nmt.mask_apodization(binary_mask,
+                                           apod_radius_point_source,
+                                           apotype=apod_type)
+
+    return nhits_map * binary_mask
+
+
+def get_spin_derivatives(map):
+    """
+    First and second spin derivatives of a given spin-0 map.
+    """
+    nside = hp.npix2nside(np.shape(map)[-1])
+    ell = np.arange(3*nside)
+    alpha1i = np.sqrt(ell*(ell + 1.))
+    alpha2i = np.sqrt((ell - 1.)*ell*(ell + 1.)*(ell + 2.))
+    first = hp.alm2map(hp.almxfl(hp.map2alm(map), alpha1i), nside=nside)
+    second = hp.alm2map(hp.almxfl(hp.map2alm(map), alpha2i), nside=nside)
+    cmap = cm.YlOrRd
+    cmap.set_under("w")
+
+    return first, second
+
+
+def read_map_from_alm(id_sim, freq_ghz, nside, beam_window, sims_dir):
+    """
+    """
+    nside = int(nside)
+    id_str = str(id_sim).zfill(4)
+    freq_str = str(int(freq_ghz)).zfill(3) + "GHz"
+    lmax_str = "lmax" + str(int(3*nside - 1))
+    alm_dir = f"{sims_dir}/{id_str}/alm_{freq_str}_{lmax_str}_{id_str}.fits"
+    alm_smooth = hp.smoothalm(hp.read_alm(alm_dir, hdu=(1,2,3)), 
+                              beam_window=beam_window)
+
+    return hp.alm2map(alm_smooth, nside)
+
+
+def load_lensing_cl(nside, beam_arcmin=30.):
+    """
+    """
+    import healpy as hp
+    cls_theory = {}
+    theory_fname = "/global/cfs/cdirs/sobs/users/krach/BBSims/CMB_r0_20201207/reference_spectra/Cls_Planck2018_r0.fits"
+    cl_cmb = hp.read_cl(theory_fname)
+    crosses = ["TT", "EE", "BB", "TE"]
+    beam_smooth = beam_gaussian(np.arange(3*nside), beam_arcmin)
+    beam_pixwin = beam_hpix(np.arange(3*nside), 512)
+
+    for i, cf in enumerate(crosses):
+        cls_theory[cf] = (
+            cl_cmb[i, :3*nside] * beam_pixwin**2 * beam_smooth**2
+        )
+    for cf in ["ET", "TB", "BT", "BE", "EB"]:
+        cls_theory[cf] = np.zeros(3*nside)
+    return cls_theory
+
+
+def get_noise_spectrum_adrien(ll, N_yr=1, N_instr=2, fsky=0.058, filtered=True):
+    """
+    """
+    assert ll[0] < 2, "Input multipoles must start at either 0 or 1."
+    nls_theory = {}
+    if filtered:
+        Nwhite_muKsq, ell_knee, alpha = (8.67e-4, 110, -3.5)
+    else:
+        Nwhite_muKsq, ell_knee, alpha = (7.05e-4, 90, -2.0)
+    N_instr = 2
+    N_yr = 5
+    eff = 0.85*0.2
+    sky_ratio = fsky / 0.04
+    N_hrs = 80
+    A = N_hrs*sky_ratio / (N_instr*N_yr*365*24*eff)
+    msk = ll > 1.
+    ll = ll[msk]
+    for spec in ["EE", "EB", "BB"]:
+        nls_theory[spec] = np.array(
+            [0., 0.] + list(A* Nwhite_muKsq*(1. + (ll/ell_knee)**alpha))
+        )
+    for spec in ["TT", "TE", "TB"]:
+        nls_theory[spec] = np.zeros(len(ll) + 2)
+    return nls_theory
+
+
+def get_noise_spectrum(ll, fsky_eff=0.058, has_oof=True, N_tubes=[0.,2.,1.],
+                       survey_years=1., freq_ghz=93, sensitivity="goal",
+                       oof_mode="optimistic"):
+    """
+    """
+    import soopercool.SO_Noise_Calculator_Public_v3_1_2 as noise_calc
+    assert ll[0] < 2, "Input multipoles must start at either 0 or 1."
+    
+    oof_dict = {"pessimistic": 0, "optimistic": 1}
+
+    f_idx = {"27": 0, "39":1, "93":2, "145":3, "225": 4, "280": 5}
+    f_str = str(int(freq_ghz))
+
+    nls_theory = {}
+    noise_model = noise_calc.SOSatV3point1(
+        sensitivity_mode=sensitivity,
+        N_tubes=N_tubes,
+        one_over_f_mode=oof_dict[oof_mode],
+        survey_years=survey_years
+    )
+    lth, _, nlth_P = noise_model.get_noise_curves(
+        fsky_eff,
+        ll[-1] + 1,
+        delta_ell=1,
+        deconv_beam=False
+    )
+    if not has_oof:
+        nlth_P = np.array([len(nl)*[nl[-1]] for nl in nlth_P])
+    lth = np.concatenate(([0, 1], lth))[ll[0]:]
+    nlth_P = np.array(
+        [np.concatenate(([0, 0], nl))[ll[0]:] for nl in nlth_P]
+    )
+    for spec in ["EE", "EB", "BB"]:
+        nls_theory[spec] = nlth_P[f_idx[f_str]]
+    for spec in ["TT", "TE", "TB"]:
+        nls_theory[spec] = np.zeros_like(lth)
+    return nls_theory
+
+
+def make_noise_sim(nls_theory_dict, id_sim, id_bundle, nbundle, nside, noise_sims_dir,
+                   overwrite=True):
+    """
+    """
+    id_str = str(nbundle*id_sim + id_bundle).zfill(4)
+    np.random.seed(4000 + nbundle*id_sim + id_bundle)
+    Nell = [nls_theory_dict[spec]
+            for spec in ["TT", "TE", "TB", "EE", "EB", "BB"]]
+    maps = hp.synfast(Nell, nside)
+    fname = noise_sims_dir.replace("[id_sim]", id_str)
+
+    Path("/".join(fname.split("/")[:-1])).mkdir(parents=False, exist_ok=True)
+    if overwrite:
+        hp.write_map(fname, maps, overwrite=True, dtype=np.float32)
+    elif not os.path.isfile(fname):
+        hp.write_map(fname, maps, dtype=np.float32)
+
+
+def read_gaussian_noise_sim(id_sim, id_bundle, nbundle, nside, noise_sims_dir):
+    """
+    """
+    id_str = str(nbundle*id_sim + id_bundle).zfill(4)
+    fname = noise_sims_dir.replace("[id_sim]", id_str)
+
+    return np.sqrt(nbundle) * hp.ud_grade(hp.read_map(fname, field=range(3)),
+                                          nside_out=nside)
+
+
+def read_planck_noise_sim(id_sim, id_bundle, nside, noise_sims_dir):
+    """
+    """
+    sim_str = str(id_sim).zfill(4)
+    bundle_str = str(int(id_bundle))
+    fname = noise_sims_dir.replace("[id_sim]", sim_str).replace("[id_bundle]", bundle_str)
+
+    return hp.ud_grade(hp.read_map(fname, field=range(3)), nside_out=nside)
+
+
+def read_signal_sim(id_sim, nside, signal_sims_dir):
+    """
+    """
+    id_str = str(id_sim).zfill(4)
+    fname = signal_sims_dir.replace("[id_sim]", id_str)
+
+    return 1.e6*hp.ud_grade(hp.read_map(fname, field=range(3)), nside_out=nside)
diff --git a/paramfiles/paramfile_refactored.yaml b/paramfiles/paramfile_refactored.yaml
new file mode 100644
index 0000000..1d9d728
--- /dev/null
+++ b/paramfiles/paramfile_refactored.yaml
@@ -0,0 +1,166 @@
+output_directory: &out_dir outputs
+
+map_sets:
+  SATp3_f090:
+    map_dir: "~/Documents/development/soopercool_refactoring/SOOPERCOOL/pipeline/outputs_refactor_high_dec/bundled_maps" #"/home/laposta/Documents/development/test_bundling/bundling/coadded_cmb_maps_4splits_hp_nside256"
+    beam_dir: "/home/laposta/Documents/development/cloned_repo/SOOPERCOOL/data_256/beams"
+    map_template: "coadd_f090_bundle{id_bundle}_{map|hits}.fits"
+    beam_file: "beam_cmb_sat1_f093.dat"
+    n_bundles: 4          # Number of bundles
+    freq_tag: 90         # Freq. tag (e.g. useful to coadd freqs)
+    exp_tag: "SATp3"      # Experiment tag (useful to get noise-bias free cross-split spectra)
+    filtering_tag: "mcut30_90"
+
+  SATp3_f150:
+    map_dir: "~/Documents/development/soopercool_refactoring/SOOPERCOOL/pipeline/outputs_refactor_high_dec/bundled_maps" #"/home/laposta/Documents/development/test_bundling/bundling/coadded_cmb_maps_4splits_hp_nside256"
+    beam_dir: "/home/laposta/Documents/development/cloned_repo/SOOPERCOOL/data_256/beams"
+    map_template: "coadd_f150_bundle{id_bundle}_{map|hits}.fits"
+    beam_file: "beam_cmb_sat1_f145.dat"
+    n_bundles: 4          # Number of bundles
+    freq_tag: 150        # Freq. tag (e.g. useful to coadd freqs)
+    exp_tag: "SATp3"      # Experiment tag (useful to get noise-bias free cross-split spectra)
+    filtering_tag: "mcut30_150"
+
+####################
+# Masking metadata #
+####################
+masks:
+  analysis_mask: !path [*out_dir, masks/analysis_mask.fits]
+
+  # Path to products (binary)
+  galactic_mask: "/home/laposta/Documents/development/forked_repo/BBMASTER/pipeline/outputs_old/masks/planck_galactic_mask_gal070.fits"
+
+  point_source_catalog: null
+  point_source_mask: null # "/path/to/point_source_mask.fits"
+
+  external_mask: null
+
+  apod_radius: 10.0
+  apod_radius_point_source: 4.0
+  apod_type: "C1"
+
+####################################
+# Metadata related to the analysis #
+####################################
+## General parameters
+general_pars:
+  nside: 256
+  lmin: 30
+  lmax: 600
+  binning_file: !path [*out_dir, binning/binning_nside256_deltal21.npz]
+  pure_B: True
+  # Where the beam window is lower than beam_floor, set it to beam_floor
+  beam_floor: 1.e-5
+
+## Filtering related parameters
+filtering:
+
+  slurm: False # Run TOAST filtering locally or with SLURM scheduller
+  # `slurm_autosubmit` only works if `slurm` is True.
+  # `slurm_autosubmit` set to True to auto-submit generated sbatch scripts.
+  # Set to False would generate scripts but not submitted, give you a chance to check generated scripts.
+  slurm_autosubmit: False
+  scripts_dir: "../sbatch"                             # directory of filtering scripts
+
+  ## Define filtering tags
+  tags_settings:
+
+    nofilterr: null
+
+    mcut30_90:
+      # Filtering parameters
+      filtering_type: "m_filterer"
+      m_cut: 30
+
+    mcut30_150:
+      # Filtering parameters
+      filtering_type: "m_filterer"
+      m_cut: 30
+
+    mcut15:
+      # Filtering parameters
+      filtering_type: "m_filterer"
+      m_cut: 15
+
+    toast_SAT1_f090:
+      # Filtering parameters
+      filtering_type: "toast"
+      template: "../paramfiles/run_toast.sh.j2"            # TOAST template script.
+      config: "../paramfiles/defaults.toml"                # TOAST toml config file
+      schedule: "../outputs/schedules/schedule_sat.txt"    # TOAST schedule file
+      tf_instrument: "SAT1"                                # Instrument used for transfer function calculation
+      tf_band: "SAT_f090"                                  # Band used for transfer function calculation
+
+transfer_settings:
+  transfer_directory: !path [*out_dir, transfer_functions]
+
+  # For estimation
+  ## Number of sims for tf estimation and validation
+  tf_est_num_sims: 10
+
+  ## Parameters of the PL sims used for TF estimation
+  power_law_pars_tf_est:
+    amp: 1.0
+    delta_ell: 10
+    power_law_index: 2.
+
+  ## Optional beams applied on PL sims
+  # If true, beams will be applied only on the validation simulations. By default (false)
+  # beam are applied to both the estimation and validation sims,
+  # to account for potential effect of the beam on the TF (e.g. commutativity)
+  do_not_beam_est_sims: False
+  beams_list: ["SATp3_f090", "SATp3_f150"]
+
+  ## Path to the sims for TF estimation
+  unfiltered_map_dir:
+    mcut30_90: !path [*out_dir, tf_est_sims]
+    mcut30_150: !path [*out_dir, tf_est_sims]
+  unfiltered_map_template:
+    mcut30_90: "{pure_type}_power_law_tf_est_{id_sim:04d}_SATp3_f090.fits"
+    mcut30_150: "{pure_type}_power_law_tf_est_{id_sim:04d}_SATp3_f150.fits"
+  filtered_map_dir:
+    mcut30_90: !path [*out_dir, filtered_tf_est_sims]
+    mcut30_150: !path [*out_dir, filtered_tf_est_sims]
+  filtered_map_template:
+    mcut30_90: "{pure_type}_power_law_tf_est_{id_sim:04d}_SATp3_f090_filtered.fits"
+    mcut30_150: "{pure_type}_power_law_tf_est_{id_sim:04d}_SATp3_f150_filtered.fits"
+
+  # For validation
+  ## Number of sims for tf estimation and validation
+  tf_val_num_sims: 10
+
+  ## Parameters of the PL sims used for TF validation
+  power_law_pars_tf_val:
+    amp:
+      TT: 10.
+      EE: 1.
+      BB: 0.05
+      TE: 2.5
+      TB: 0.
+      EB: 0.
+    delta_ell: 10
+    power_law_index: 0.5
+
+covariance:
+  cov_num_sims: 10
+
+  noise_sims_dir: !path [*out_dir, noise_sims]
+  noise_sims_template: "homogeneous_noise_{map_set}_bundle{id_bundle}_{id_sim:04d}.fits"
+
+  signal_sims_dir: !path [*out_dir, filtered_cmb_sims]
+  signal_sims_template: "cmb_{map_set}_{id_sim:04d}_filtered.fits"
+
+  fiducial_cmb: !path [*out_dir, cmb_sims/cl_theory.npz]
+
+  # If needed, cosmological parameters
+  cosmo:
+    cosmomc_theta: 0.0104085
+    As: 2.1e-9
+    ombh2: 0.02237
+    omch2: 0.1200
+    ns: 0.9649
+    Alens: 1.0
+    tau: 0.0544
+    r: 0.01
+
+
diff --git a/pipeline/bundling/bundle_atomic_maps.py b/pipeline/bundling/bundle_atomic_maps.py
new file mode 100644
index 0000000..e23a8e1
--- /dev/null
+++ b/pipeline/bundling/bundle_atomic_maps.py
@@ -0,0 +1,324 @@
+import argparse
+from soopercool import BBmeta
+
+from pixell import enmap, enplot
+import numpy as np
+import glob
+import random
+
+
+def get_submap_center_radec(imap):
+    y, x = imap.shape[-2], imap.shape[-1]
+    dec, ra = np.rad2deg(enmap.pix2sky(imap.shape, imap.wcs, [y//2, x//2]))
+    return ra, dec
+
+
+def get_submap_corners_radec(imap):
+    y, x = imap.shape[-2], imap.shape[-1]
+    corners = [[0, 0], [0, x], [y, 0], [y, x]]
+    radec_corners = []
+    for corner in corners:
+        dec, ra = np.rad2deg(enmap.pix2sky(imap.shape, imap.wcs, corner))
+        radec_corners.append([ra, dec])
+    return np.array(radec_corners)
+
+
+def build_map_list(map_dir, freq_tag, type="wmap", wafer="all"):
+    """
+    Build list of atomic maps from a given directory
+
+    Parameters
+    ----------
+    map_dir : str
+        The directory where the maps are stored
+    freq_tag : str
+        The frequency tag of the maps (e.g. "f090", "f150")
+    type : str
+        The type of the maps (e.g. "wmap", "weights")
+    wafer : str
+        The wafer number of the maps, "all" for all wafers
+        either "0", ...
+    """
+    if wafer == "all":
+        wafer = "[0-9]"
+    regex = f"{map_dir}/**/*ws{wafer}_{freq_tag}*{type}.fits"
+    out = glob.glob(regex, recursive=True)
+    out.sort()
+    return out
+
+
+def group_list(list, n_groups, seed=1234):
+    """
+    """
+    list = np.asarray(list)
+    ids_groups = np.arange(n_groups)
+    n = len(list) // n_groups
+    ids_list = np.zeros(len(list), dtype=int)
+    for id_group in ids_groups:
+        if id_group == ids_groups[-1]:
+            ids_list[id_group*n:] = id_group
+        else:
+            ids_list[id_group*n:(id_group+1)*n] = id_group
+    random.seed(seed)
+    random.shuffle(ids_list)
+    return {
+        id_group: list[ids_list == id_group] for id_group in ids_groups
+    }, ids_list
+
+
+def coadd_maps(map_list, res=10):
+    """
+    """
+    template_geom = enmap.band_geometry((np.deg2rad(-75), np.deg2rad(25)),
+                                        res=np.deg2rad(10/60))
+    car = enmap.zeros((3, *template_geom[0]), template_geom[1])
+    wcar = enmap.zeros((3, *template_geom[0]), template_geom[1])
+    hits = enmap.zeros(*template_geom)
+
+    shape, wcs = car.geometry
+    for f in map_list:
+        m = enmap.read_map(f)
+        car = enmap.insert(car, m, op=np.ndarray.__iadd__)
+        w = enmap.read_map(f.replace("wmap", "weights"))
+        w = np.moveaxis(w.diagonal(), -1, 0)
+        wcar = enmap.insert(wcar, w, op=np.ndarray.__iadd__)
+        h = enmap.read_map(f.replace("wmap", "hits"))[0]
+        hits = enmap.insert(hits, h, op=np.ndarray.__iadd__)
+    wcar[wcar == 0] = np.inf
+    return car / wcar, wcar, hits
+
+
+def get_CAR_template(ncomp, res, dec_cut=None):
+    """
+    """
+    if dec_cut is None:
+        shape, wcs = enmap.fullsky_geometry(res=np.deg2rad(res/60))
+    else:
+        shape, wcs = enmap.band_geometry(np.deg2rad(dec_cut),
+                                         res=np.deg2rad(res/60))
+    shape = (ncomp, *shape)
+    return enmap.zeros(shape, wcs)
+
+
+def build_mask_from_boxes(template, box_edges):
+    """
+    """
+    mask = template.copy()
+    for ra0, ra1, dec0, dec1 in box_edges:
+        box = np.array([[dec0, ra0], [dec1, ra1]])
+        box = np.deg2rad(box)
+        sub = enmap.submap(template, box=box)
+        sub[:] = 1.
+        mask = enmap.insert(mask, sub, op=np.ndarray.__iadd__)
+    mask[mask != 0] = 1
+    return mask
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+    maps_dir = f"{out_dir}/bundled_maps"
+    BBmeta.make_dir(maps_dir)
+
+    plot_dir = f"{out_dir}/plots/bundled_maps"
+    BBmeta.make_dir(plot_dir)
+
+    freq_tags = args.ftags.split(",")
+    n_bundles = args.n_bundles
+
+    fname_list = {}
+    for ftag in freq_tags:
+        fname_list[ftag] = build_map_list(
+            args.map_dir, ftag, type="wmap",
+            wafer="all")
+        print(f"Found maps for {ftag}: {len(fname_list[ftag])}")
+
+    # Per-wafer
+    for id_waf in range(7):
+        for ftag in freq_tags:
+            fname_list[ftag, id_waf] = build_map_list(args.map_dir, ftag,
+                                                      type="wmap",
+                                                      wafer=str(id_waf))
+            print(f"Found maps for {ftag} in wafer {id_waf}: \
+                  {len(fname_list[ftag, id_waf])}")
+
+    template_CAR = get_CAR_template(ncomp=1, res=10, dec_cut=[-75, 25])
+
+    # Build a mask
+    box_edges = [
+        [-108, -180, -30, 9],
+        [180, 144, -30, 9]
+    ]
+    restricted_region = build_mask_from_boxes(template_CAR, box_edges)
+
+    # Filter map_lists
+    keep = {(ftag, id_waf): [] for ftag in freq_tags for id_waf in range(7)}
+    for ftag in freq_tags:
+        keep[ftag] = []
+        for id_waf in range(7):
+            for fname in fname_list[ftag, id_waf]:
+                m = enmap.read_map(fname)
+
+                binary = enmap.insert(template_CAR.copy(), m[0])
+                binary[binary != 0] = 1
+                overlap = np.sum(binary * restricted_region) / np.sum(binary)
+
+                if overlap >= 0.5:
+                    # print(f"Found map !")
+                    keep[ftag, id_waf].append(fname)
+                    keep[ftag].append(fname)
+
+    # Check that the number of maps is the same
+    for ftag in freq_tags:
+        for ftag2 in freq_tags:
+            if ftag == ftag2:
+                continue
+            keep[ftag] = [
+                f for f in keep[ftag]
+                if f.replace(ftag, ftag2) in keep[ftag2]
+            ]
+
+        for id_waf in range(7):
+            keep[ftag, id_waf] = [
+                f for f in keep[ftag, id_waf]
+                if f in keep[ftag]]
+
+    for ftag in freq_tags:
+        print(F"Found {len(keep[ftag])} maps for {ftag} (after RA/DEC cuts)")
+        for id_waf in range(7):
+            print(f"Found {len(keep[ftag, id_waf])} maps for {ftag} \
+                   in wafer {id_waf} (after RA/DEC cuts)")
+
+    # Group the maps
+    to_coadd, ids = {}, {}
+    for ftag in freq_tags:
+        for id_waf in range(7):
+            sublist, ids_list = group_list(keep[ftag, id_waf], n_bundles)
+            to_coadd[ftag, id_waf] = sublist
+            ids[ftag, id_waf] = ids_list
+
+    # Full coadd list
+    to_coadd_full = {}
+    for ftag in freq_tags:
+        for id_bundle in range(n_bundles):
+            to_coadd_full[ftag, id_bundle] = np.concatenate(
+                [to_coadd[ftag, id_waf][id_bundle]
+                 for id_waf in range(7)])
+
+    coadded_maps = {}
+    coadded_weights = {}
+    coadded_hits = {}
+    unit_conv = 1e6 * 13.2  # pW to uK
+    for ftag in freq_tags:
+        for id_bundle in range(n_bundles):
+            meta.timer.start("coadding")
+            for id_waf in range(7):
+                signal, weight, hits = coadd_maps(to_coadd[ftag, id_waf][id_bundle])  # noqa
+                coadded_maps[ftag, id_waf, id_bundle] = signal * unit_conv
+                coadded_weights[ftag, id_waf, id_bundle] = weight
+                coadded_hits[ftag, id_waf, id_bundle] = hits
+
+            signal, weight, hits = coadd_maps(to_coadd_full[ftag, id_bundle])
+            coadded_maps[ftag, id_bundle] = signal * unit_conv
+            coadded_weights[ftag, id_bundle] = weight
+            coadded_hits[ftag, id_bundle] = hits
+
+            meta.timer.stop("coadding", f"Coadded {ftag} bundle {id_bundle}")
+
+    for ftag in freq_tags:
+        for id_bundle in range(n_bundles):
+            plots = enplot.get_plots(
+                coadded_maps[ftag, id_bundle],
+                range=[100000, 100, 100], colorbar=1, ticks=5
+            )
+            for field, plot in zip("TQU", plots):
+                enplot.write(
+                    f"{plot_dir}/coadd_{ftag}_bundle{id_bundle}_allwaf_{field}.png",  # noqa
+                    plot)
+            # Plot hits
+            plot_hit = enplot.get_plots(coadded_hits[ftag, id_bundle],
+                                        range=[100000], colorbar=1, ticks=5)[0]
+            enplot.write(
+                f"{plot_dir}/coadd_{ftag}_bundle{id_bundle}_allwaf_hits.png",
+                plot_hit
+            )
+            file_name = f"{maps_dir}/TQU_CAR_coadd_{ftag}_bundle{id_bundle}_allwaf.fits"  # noqa
+            enmap.write_map(file_name, coadded_maps[ftag, id_bundle])
+            enmap.write_map(file_name.replace("TQU", "weights"),
+                            coadded_weights[ftag, id_bundle])
+            enmap.write_map(file_name.replace("TQU", "hits"),
+                            coadded_hits[ftag, id_bundle])
+
+            with open(f"{maps_dir}/atomic_map_list_{ftag}_bundle{id_bundle}.txt", "w") as f:  # noqa
+                for fname in to_coadd_full[ftag, id_bundle]:
+                    f.write(f"{fname.replace(args.map_dir+'/', '')}\n")
+
+            for id_waf in range(7):
+                plots = enplot.get_plots(
+                    coadded_maps[ftag, id_waf, id_bundle],
+                    range=[100000, 100, 100], colorbar=1, ticks=5
+                )
+                for field, plot in zip("TQU", plots):
+                    enplot.write(f"{plot_dir}/coadd_{ftag}_bundle{id_bundle}_waf{id_waf}_{field}.png", plot)  # noqa
+                # Plot hits
+                plot_hit = enplot.get_plots(
+                    coadded_hits[ftag, id_waf, id_bundle],
+                    range=[100000], colorbar=1, ticks=5)[0]
+                enplot.write(f"{plot_dir}/coadd_{ftag}_bundle{id_bundle}_waf{id_waf}_hits.png", plot_hit)  # noqa
+                file_name = f"{maps_dir}/TQU_CAR_coadd_{ftag}_bundle{id_bundle}_waf{id_waf}.fits"  # noqa
+                enmap.write_map(file_name, coadded_maps[ftag, id_bundle])
+                enmap.write_map(
+                    file_name.replace("TQU", "weights"),
+                    coadded_weights[ftag, id_waf, id_bundle]
+                )
+                enmap.write_map(file_name.replace("TQU", "hits"),
+                                coadded_hits[ftag, id_waf, id_bundle])
+
+                with open(f"{maps_dir}/atomic_map_list_{ftag}_waf{id_waf}_bundle{id_bundle}.txt", "w") as f:  # noqa
+                    for fname in to_coadd[ftag, id_waf][id_bundle]:
+                        f.write(f"{fname.replace(args.map_dir+'/', '')}\n")
+
+    # HEALPIX reprojection
+    from pixell import reproject
+    import healpy as hp
+    for ftag in freq_tags:
+        for id_bundle in range(n_bundles):
+            TQU_hp = reproject.map2healpix(coadded_maps[ftag, id_bundle],
+                                           nside=256)
+            hits_hp = reproject.map2healpix(
+                coadded_hits[ftag, id_bundle], nside=256, extensive=True,
+                method="spline"
+            )
+            hp.write_map(f"{maps_dir}/coadd_{ftag}_bundle{id_bundle}_map.fits",
+                         TQU_hp, overwrite=True, dtype=np.float32)
+            hp.write_map(
+                f"{maps_dir}/coadd_{ftag}_bundle{id_bundle}_hits.fits",
+                hits_hp, overwrite=True, dtype=np.float32
+            )
+
+            for id_waf in range(7):
+                TQU_hp = reproject.map2healpix(
+                    coadded_maps[ftag, id_waf, id_bundle], nside=256
+                )
+                hits_hp = reproject.map2healpix(
+                    coadded_hits[ftag, id_waf, id_bundle], nside=256,
+                    extensive=True, method="spline"
+                )
+                hp.write_map(f"{maps_dir}/coadd_{ftag}_wafer{id_waf}_bundle{id_bundle}_map.fits", TQU_hp, overwrite=True, dtype=np.float32)  # noqa
+                hp.write_map(f"{maps_dir}/coadd_{ftag}_wafer{id_waf}_bundle{id_bundle}_hits.fits", hits_hp, overwrite=True, dtype=np.float32)  # noqa
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--n_bundles", type=int, help="Number of bundles.")
+    parser.add_argument("--ftags",
+                        help="Frequency tags separated by commas ','")
+    parser.add_argument("--map_dir", help="Path to the atomic root dir.")
+    parser.add_argument("--seed", type=int,
+                        help="Seed for the random number generator.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/coadd_pseudo_cells.py b/pipeline/coadd_pseudo_cells.py
new file mode 100644
index 0000000..2433419
--- /dev/null
+++ b/pipeline/coadd_pseudo_cells.py
@@ -0,0 +1,152 @@
+from soopercool import BBmeta
+from itertools import product
+import numpy as np
+import argparse
+import pymaster as nmt
+
+
+def main(args):
+    """
+    This script is used to coadd the cross-split power spectra
+    (e.g. SAT1_f093__0 x SAT_f093__1) into cross-map-set power
+    spectra (e.g. SAT1_f093 x SAT_f093). It will produce both
+    cross and auto map-set power spectra from which we derive
+    the noise power spectra.
+    """
+    meta = BBmeta(args.globals)
+    do_plots = not args.no_plots
+
+    out_dir = meta.output_directory
+    cells_dir = f"{out_dir}/cells"
+
+    plot_dir = f"{out_dir}/plots/cells"
+    BBmeta.make_dir(plot_dir)
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    lb = nmt_bins.get_effective_ells()
+    field_pairs = [m1+m2 for m1, m2 in product("TEB", repeat=2)]
+
+    ps_names = {
+        "cross": meta.get_ps_names_list(type="cross", coadd=False),
+        "auto": meta.get_ps_names_list(type="auto", coadd=False)
+    }
+
+    cross_split_list = meta.get_ps_names_list(type="all", coadd=False)
+    cross_map_set_list = meta.get_ps_names_list(type="all", coadd=True)
+
+    # Load split C_ells
+
+    # Initialize output dictionary
+    cells_coadd = {
+        "cross": {
+            (ms1, ms2): {
+                fp: [] for fp in field_pairs
+            } for ms1, ms2 in cross_map_set_list
+        },
+        "auto": {
+            (ms1, ms2): {
+                fp: [] for fp in field_pairs
+            } for ms1, ms2 in cross_map_set_list
+        }
+    }
+
+    # Loop over all map set pairs
+    for map_name1, map_name2 in cross_split_list:
+
+        map_set1, _ = map_name1.split("__")
+        map_set2, _ = map_name2.split("__")
+
+        cells_dict = np.load(
+            f"{cells_dir}/decoupled_pcls_{map_name1}_x_{map_name2}.npz"  # noqa
+        )
+
+        if (map_name1, map_name2) in ps_names["cross"]:
+            type = "cross"
+        elif (map_name1, map_name2) in ps_names["auto"]:
+            type = "auto"
+
+        for field_pair in field_pairs:
+
+            cells_coadd[type][map_set1, map_set2][field_pair] += [cells_dict[field_pair]] # noqa
+
+    # Average the cross-split power spectra
+    cells_coadd["noise"] = {}
+    for map_set1, map_set2 in cross_map_set_list:
+        cells_coadd["noise"][(map_set1, map_set2)] = {}
+        for field_pair in field_pairs:
+            for type in ["cross", "auto"]:
+                cells_coadd[type][map_set1, map_set2][field_pair] = \
+                    np.mean(
+                        cells_coadd[type][map_set1, map_set2][field_pair],
+                        axis=0
+                    )
+
+            cells_coadd["noise"][(map_set1, map_set2)][field_pair] = \
+                cells_coadd["auto"][map_set1, map_set2][field_pair] - \
+                cells_coadd["cross"][map_set1, map_set2][field_pair]
+
+        for type in ["cross", "auto", "noise"]:
+            cells_to_save = {
+                fp: cells_coadd[type][map_set1, map_set2][fp]
+                for fp in field_pairs
+            }
+            np.savez(
+                f"{cells_dir}/decoupled_{type}_pcls_{map_set1}_x_{map_set2}.npz",  # noqa
+                lb=lb,
+                **cells_to_save
+            )
+
+    if do_plots:
+
+        import matplotlib.pyplot as plt
+
+        for type in ["cross", "auto", "noise"]:
+            for fp in field_pairs:
+
+                plt.figure(figsize=(10, 8))
+                plt.xlabel(r"$\ell$", fontsize=15)
+                plt.ylabel(r"$C_\ell^\mathrm{%s} \; [\mu K_\mathrm{CMB}^2]$" % fp, # noqa
+                           fontsize=15)
+                for map_set1, map_set2 in cross_map_set_list:
+
+                    plt.plot(lb, cells_coadd[type][(map_set1, map_set2)][fp],
+                             label=f"{map_set1} x {map_set2}",
+                             marker="o", lw=0.7,
+                             markerfacecolor="white")
+
+                plt.legend(fontsize=14)
+                plt.title(type, fontsize=15)
+
+                plt.xlim(0, 2*meta.nside)
+
+                if fp == fp[::-1]:
+                    plt.yscale("log")
+                    if fp == "TT":
+                        plt.ylim(1e0, 1e9)
+                    elif fp in ["EE", "BB"]:
+                        plt.ylim(1e-6, 1e3)
+
+                else:
+                    if fp in ["EB", "BE"]:
+                        plt.ylim(-0.01, 0.01)
+                    else:
+                        plt.ylim(-4, 4)
+
+                plt.savefig(
+                    f"{plot_dir}/{type}_pcls_{fp}.png",
+                    bbox_inches="tight"
+                )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pseudo-Cl calculator")
+    parser.add_argument("--globals", type=str, help="Path to the yaml file")
+    parser.add_argument("--no-plots", action="store_true",
+                        help="Do not make plots")
+    mode = parser.add_mutually_exclusive_group()
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/pipeline/coadd_sims_pseudo_cells.py b/pipeline/coadd_sims_pseudo_cells.py
new file mode 100644
index 0000000..e16e795
--- /dev/null
+++ b/pipeline/coadd_sims_pseudo_cells.py
@@ -0,0 +1,108 @@
+from soopercool import BBmeta
+from itertools import product
+import numpy as np
+import argparse
+import pymaster as nmt
+
+
+def main(args):
+    """
+    This script is used to coadd the cross-split power spectra
+    (e.g. SAT1_f093__0 x SAT_f093__1) into cross-map-set power
+    spectra (e.g. SAT1_f093 x SAT_f093). It will produce both
+    cross and auto map-set power spectra from which we derive
+    the noise power spectra.
+    """
+    meta = BBmeta(args.globals)
+    # do_plots = not args.no_plots
+
+    out_dir = meta.output_directory
+    cells_dir = f"{out_dir}/cells_sims"
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    lb = nmt_bins.get_effective_ells()
+    field_pairs = [m1+m2 for m1, m2 in product("TEB", repeat=2)]
+
+    ps_names = {
+        "cross": meta.get_ps_names_list(type="cross", coadd=False),
+        "auto": meta.get_ps_names_list(type="auto", coadd=False)
+    }
+
+    cross_split_list = meta.get_ps_names_list(type="all", coadd=False)
+    cross_map_set_list = meta.get_ps_names_list(type="all", coadd=True)
+
+    # Load split C_ells
+    for id_sim in range(meta.covariance["cov_num_sims"]):
+        # Initialize output dictionary
+        cells_coadd = {
+            "cross": {
+                (ms1, ms2): {
+                    fp: [] for fp in field_pairs
+                } for ms1, ms2 in cross_map_set_list
+            },
+            "auto": {
+                (ms1, ms2): {
+                    fp: [] for fp in field_pairs
+                } for ms1, ms2 in cross_map_set_list
+            }
+        }
+
+        # Loop over all map set pairs
+        for map_name1, map_name2 in cross_split_list:
+
+            map_set1, _ = map_name1.split("__")
+            map_set2, _ = map_name2.split("__")
+
+            cells_dict = np.load(
+                f"{cells_dir}/decoupled_pcls_{map_name1}_x_{map_name2}_{id_sim:04d}.npz"  # noqa
+            )
+
+            if (map_name1, map_name2) in ps_names["cross"]:
+                type = "cross"
+            elif (map_name1, map_name2) in ps_names["auto"]:
+                type = "auto"
+
+            for field_pair in field_pairs:
+
+                cells_coadd[type][map_set1, map_set2][field_pair] += [cells_dict[field_pair]] # noqa
+
+        # Average the cross-split power spectra
+        cells_coadd["noise"] = {}
+        for map_set1, map_set2 in cross_map_set_list:
+            cells_coadd["noise"][(map_set1, map_set2)] = {}
+            for field_pair in field_pairs:
+                for type in ["cross", "auto"]:
+                    cells_coadd[type][map_set1, map_set2][field_pair] = \
+                        np.mean(
+                            cells_coadd[type][map_set1, map_set2][field_pair],
+                            axis=0
+                        )
+
+                cells_coadd["noise"][(map_set1, map_set2)][field_pair] = \
+                    cells_coadd["auto"][map_set1, map_set2][field_pair] - \
+                    cells_coadd["cross"][map_set1, map_set2][field_pair]
+
+            for type in ["cross", "auto", "noise"]:
+                cells_to_save = {
+                    fp: cells_coadd[type][map_set1, map_set2][fp]
+                    for fp in field_pairs
+                }
+                np.savez(
+                    f"{cells_dir}/decoupled_{type}_pcls_{map_set1}_x_{map_set2}_{id_sim:04d}.npz",  # noqa
+                    lb=lb,
+                    **cells_to_save
+                )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pseudo-Cl calculator")
+    parser.add_argument("--globals", type=str, help="Path to the yaml file")
+    parser.add_argument("--no-plots", action="store_true",
+                        help="Do not make plots")
+    mode = parser.add_mutually_exclusive_group()
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/pipeline/compute_covariance_from_sims.py b/pipeline/compute_covariance_from_sims.py
new file mode 100644
index 0000000..55d1bfc
--- /dev/null
+++ b/pipeline/compute_covariance_from_sims.py
@@ -0,0 +1,135 @@
+import argparse
+from soopercool import BBmeta
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+import pymaster as nmt
+
+
+def main(args):
+    """
+    This function handles the covariance matrix computation.
+    We can use a set of simulations to build a numerical estimate
+    of the covariance matrices.
+    TODO: implement an analytical estimate of the covariances
+    """
+    meta = BBmeta(args.globals)
+    do_plots = not args.no_plots
+
+    out_dir = meta.output_directory
+    cov_dir = f"{out_dir}/covariances"
+    meta.make_dir(cov_dir)
+
+    if do_plots:
+        plot_dir = f"{out_dir}/plots/covariances"
+        meta.make_dir(plot_dir)
+
+    cl_dir = f"{out_dir}/cells_sims"
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    n_bins = nmt_bins.get_n_bands()
+
+    cov_settings = meta.covariance
+
+    Nsims = cov_settings["cov_num_sims"]
+
+    field_pairs = [f"{m1}{m2}" for m1 in "TEB" for m2 in "TEB"]
+
+    cross_ps_names = meta.get_ps_names_list(type="all", coadd=True)
+
+    # Build the covariance matrix elements to compute
+    cov_names = []
+    for i, (ms1, ms2) in enumerate(cross_ps_names):
+        for j, (ms3, ms4) in enumerate(cross_ps_names):
+            if i > j:
+                continue
+            cov_names.append((ms1, ms2, ms3, ms4))
+
+    # Load the simulations
+    cl_dict = {}
+    for ms1, ms2 in cross_ps_names:
+        cl_dict[ms1, ms2] = []
+        for iii in range(Nsims):
+            cells_dict = np.load(
+                f"{cl_dir}/decoupled_cross_pcls_{ms1}_x_{ms2}_{iii:04d}.npz", # noqa
+            )
+            cl_vec = np.concatenate(
+                [
+                    cells_dict[field_pair] for field_pair in field_pairs
+                ]
+            )
+            cl_dict[ms1, ms2].append(cl_vec)
+
+        cl_dict[ms1, ms2] = np.array(cl_dict[ms1, ms2])
+
+    full_cov_dict = {}
+    for ms1, ms2, ms3, ms4 in cov_names:
+
+        cl12 = cl_dict[ms1, ms2]
+        cl34 = cl_dict[ms3, ms4]
+
+        cl12_mean = np.mean(cl12, axis=0)
+        cl34_mean = np.mean(cl34, axis=0)
+        cov = np.mean(
+            np.einsum("ij,ik->ijk", cl12-cl12_mean, cl34-cl34_mean),
+            axis=0
+        )
+        full_cov_dict[ms1, ms2, ms3, ms4] = cov
+
+        cov_dict = {}
+        for i, field_pair_1 in enumerate(field_pairs):
+            for j, field_pair_2 in enumerate(field_pairs):
+
+                cov_block = cov[i*n_bins:(i+1)*n_bins, j*n_bins:(j+1)*n_bins]
+                cov_dict[field_pair_1 + field_pair_2] = cov_block
+
+        np.savez(
+            f"{cov_dir}/mc_cov_{ms1}_x_{ms2}_{ms3}_x_{ms4}.npz",
+            **cov_dict
+        )
+
+    if do_plots:
+        n_fields = len(field_pairs)
+        n_spec = len(cross_ps_names)
+
+        full_size = n_spec*n_fields*n_bins
+        full_cov = np.zeros((full_size, full_size))
+
+        for i, (ms1, ms2) in enumerate(cross_ps_names):
+            for j, (ms3, ms4) in enumerate(cross_ps_names):
+                if i > j:
+                    continue
+
+                full_cov[
+                    i*n_fields*n_bins:(i+1)*n_fields*n_bins,
+                    j*n_fields*n_bins:(j+1)*n_fields*n_bins
+                ] = full_cov_dict[ms1, ms2, ms3, ms4]
+
+        # Symmetrize
+        full_cov = np.triu(full_cov)
+        full_cov += full_cov.T - np.diag(full_cov.diagonal())
+        covdiag = full_cov.diagonal()
+        full_corr = full_cov / np.outer(np.sqrt(covdiag), np.sqrt(covdiag))
+
+        plt.figure(figsize=(8, 8))
+        im = plt.imshow(full_corr, vmin=-1, vmax=1, cmap="RdBu_r")
+        divider = make_axes_locatable(plt.gca())
+        cax = divider.append_axes("right", size=0.3, pad=0.1)
+        plt.colorbar(im, cax=cax)
+        plt.savefig(f"{plot_dir}/full_corr.png", dpi=300,
+                    bbox_inches="tight")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Covariance computation from simulations"
+    )
+    parser.add_argument("--globals", type=str, help="Path to the yaml file")
+    parser.add_argument("--no-plots", action="store_true",
+                        help="Do not make plots")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/pipeline/compute_pseudo_cells.py b/pipeline/compute_pseudo_cells.py
new file mode 100644
index 0000000..c12486b
--- /dev/null
+++ b/pipeline/compute_pseudo_cells.py
@@ -0,0 +1,90 @@
+from soopercool import BBmeta
+from soopercool import ps_utils as pu
+from soopercool import map_utils as mu
+import argparse
+import numpy as np
+import pymaster as nmt
+import re
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+    # do_plots = not args.no_plots
+    # verbose = args.verbose
+
+    out_dir = meta.output_directory
+    cells_dir = f"{out_dir}/cells"
+    couplings_dir = f"{out_dir}/couplings"
+
+    BBmeta.make_dir(cells_dir)
+
+    mask = mu.read_map(meta.masks["analysis_mask"], ncomp=1)
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    n_bins = nmt_bins.get_n_bands()
+
+    # Create namaster fields
+    fields = {}
+    for map_name in meta.maps_list:
+        map_set, id_bundle = map_name.split("__")
+
+        # Load maps
+        map_dir = meta.map_dir_from_map_set(map_set)
+        map_template = meta.map_template_from_map_set(map_set)
+
+        map_file = map_template.replace(
+            "{id_bundle}",
+            str(id_bundle)
+        )
+        type_options = [f for f in re.findall(r"\{.*?\}", map_template) if "|" in f][0] # noqa
+        # Select the hitmap
+        option = type_options.replace("{", "").replace("}", "").split("|")[0]
+
+        map_file = map_file.replace(
+                type_options,
+                option
+            )
+
+        m = mu.read_map(f"{map_dir}/{map_file}", ncomp=3)
+        field_spin0 = nmt.NmtField(mask, m[:1])
+        field_spin2 = nmt.NmtField(mask, m[1:], purify_b=meta.pure_B)
+
+        fields[map_set, id_bundle] = {
+            "spin0": field_spin0,
+            "spin2": field_spin2
+        }
+
+    inv_couplings_beamed = {}
+
+    for ms1, ms2 in meta.get_ps_names_list(type="all", coadd=True):
+        inv_couplings_beamed[ms1, ms2] = np.load(f"{couplings_dir}/couplings_{ms1}_{ms2}.npz")["inv_coupling"].reshape([n_bins*9, n_bins*9]) # noqa
+
+    for map_name1, map_name2 in meta.get_ps_names_list(type="all",
+                                                       coadd=False):
+        map_set1, id_split1 = map_name1.split("__")
+        map_set2, id_split2 = map_name2.split("__")
+        pcls = pu.get_coupled_pseudo_cls(
+                fields[map_set1, id_split1],
+                fields[map_set2, id_split2],
+                nmt_bins
+                )
+
+        decoupled_pcls = pu.decouple_pseudo_cls(
+                pcls, inv_couplings_beamed[map_set1, map_set2]
+                )
+
+        np.savez(f"{cells_dir}/decoupled_pcls_{map_name1}_x_{map_name2}.npz",  # noqa
+                 **decoupled_pcls, lb=nmt_bins.get_effective_ells())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--no-plots", action="store_false", help="Do not make plots.") # noqa
+    parser.add_argument("--verbose", action="store_true", help="Verbose mode.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/compute_sims_pseudo_cells.py b/pipeline/compute_sims_pseudo_cells.py
new file mode 100644
index 0000000..514ee7e
--- /dev/null
+++ b/pipeline/compute_sims_pseudo_cells.py
@@ -0,0 +1,77 @@
+from soopercool import BBmeta
+from soopercool import ps_utils as pu
+from soopercool import map_utils as mu
+import argparse
+import numpy as np
+import pymaster as nmt
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+    # do_plots = not args.no_plots
+    # verbose = args.verbose
+
+    out_dir = meta.output_directory
+    cells_dir = f"{out_dir}/cells_sims"
+    couplings_dir = f"{out_dir}/couplings"
+    sims_dir = f"{out_dir}/cov_sims"
+
+    BBmeta.make_dir(cells_dir)
+
+    mask = mu.read_map(meta.masks["analysis_mask"], ncomp=1)
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    n_bins = nmt_bins.get_n_bands()
+
+    inv_couplings_beamed = {}
+
+    for ms1, ms2 in meta.get_ps_names_list(type="all", coadd=True):
+        inv_couplings_beamed[ms1, ms2] = np.load(f"{couplings_dir}/couplings_{ms1}_{ms2}.npz")["inv_coupling"].reshape([n_bins*9, n_bins*9]) # noqa
+
+    for id_sim in range(meta.covariance["cov_num_sims"]):
+        base_dir = f"{sims_dir}/{id_sim:04d}"
+
+        # Create namaster fields
+        fields = {}
+        for map_name in meta.maps_list:
+            map_set, id_bundle = map_name.split("__")
+            map_fname = f"{base_dir}/cov_sims_{map_set}_bundle{id_bundle}.fits"
+
+            m = mu.read_map(map_fname, ncomp=3)
+            field_spin0 = nmt.NmtField(mask, m[:1])
+            field_spin2 = nmt.NmtField(mask, m[1:], purify_b=meta.pure_B)
+
+            fields[map_set, id_bundle] = {
+                "spin0": field_spin0,
+                "spin2": field_spin2
+            }
+
+        for map_name1, map_name2 in meta.get_ps_names_list(type="all",
+                                                           coadd=False):
+            map_set1, id_split1 = map_name1.split("__")
+            map_set2, id_split2 = map_name2.split("__")
+            pcls = pu.get_coupled_pseudo_cls(
+                    fields[map_set1, id_split1],
+                    fields[map_set2, id_split2],
+                    nmt_bins
+                    )
+
+            decoupled_pcls = pu.decouple_pseudo_cls(
+                    pcls, inv_couplings_beamed[map_set1, map_set2]
+                    )
+
+            np.savez(f"{cells_dir}/decoupled_pcls_{map_name1}_x_{map_name2}_{id_sim:04d}.npz",  # noqa
+                     **decoupled_pcls, lb=nmt_bins.get_effective_ells())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--no-plots", action="store_false", help="Do not make plots.") # noqa
+    parser.add_argument("--verbose", action="store_true", help="Verbose mode.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/create_sacc_file.py b/pipeline/create_sacc_file.py
new file mode 100644
index 0000000..bb56876
--- /dev/null
+++ b/pipeline/create_sacc_file.py
@@ -0,0 +1,159 @@
+import argparse
+from soopercool import BBmeta
+import sacc
+from itertools import product
+import numpy as np
+import pymaster as nmt
+
+
+def multi_eye(size, k_list):
+    """
+    """
+    return np.sum([np.eye(size, k=k) for k in k_list], axis=0)
+
+
+def thin_covariance(cov, n_bins, n_fields, order=None):
+    """
+    """
+    if order is None:
+        return cov
+    else:
+        k_list = list(range(-order, order+1))
+        eye = multi_eye(size=n_bins, k_list=k_list)
+        eye = np.tile(eye, (n_fields, n_fields))
+
+        return eye * cov
+
+
+def main(args):
+    """
+    This script will compile outputs of `coadder.py`
+    and `covfefe.py` into a single `sacc` file for
+    the data and a `sacc` file for each simulation.
+    """
+
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+    sacc_dir = f"{out_dir}/saccs"
+    BBmeta.make_dir(sacc_dir)
+
+    cov_dir = f"{out_dir}/covariances"
+
+    binning = np.load(meta.binning_file)
+    nmt_binning = nmt.NmtBin.from_edges(binning["bin_low"],
+                                        binning["bin_high"] + 1)
+    lb = nmt_binning.get_effective_ells()
+
+    field_pairs = [m1+m2 for m1, m2 in product("TEB", repeat=2)]
+
+    if args.data:
+        cl_dir = f"{out_dir}/cells"
+        Nsims = 1
+    elif args.sims:
+        cl_dir = f"{out_dir}/cells_sims"
+        Nsims = meta.covariance["cov_num_sims"]
+
+    data_types = {"T": "0", "E": "e", "B": "b"}
+    map_sets = meta.map_sets_list
+    ps_names = meta.get_ps_names_list(type="all", coadd=True)
+
+    covs = {}
+    for i, (ms1, ms2) in enumerate(ps_names):
+        for j, (ms3, ms4) in enumerate(ps_names):
+
+            if i > j:
+                continue
+            cov_dict = np.load(
+                f"{cov_dir}/mc_cov_{ms1}_x_{ms2}_{ms3}_x_{ms4}.npz"
+            )
+
+            cov_size = len(field_pairs)*len(lb)
+            cov = np.zeros((cov_size, cov_size))
+            for ifp1, fp1 in enumerate(field_pairs):
+                for ifp2, fp2 in enumerate(field_pairs):
+                    cov[ifp1*len(lb):(ifp1+1)*len(lb),
+                        ifp2*len(lb):(ifp2+1)*len(lb)] = cov_dict[fp1+fp2]
+
+            covs[ms1, ms2, ms3, ms4] = thin_covariance(
+                cov, len(lb), len(field_pairs), order=3
+            )
+
+    full_cov_size = len(ps_names)*len(lb)*len(field_pairs)
+    full_cov = np.zeros((full_cov_size, full_cov_size))
+
+    for i, (ms1, ms2) in enumerate(ps_names):
+        for j, (ms3, ms4) in enumerate(ps_names):
+            if i > j:
+                continue
+
+            full_cov[
+                i*len(field_pairs)*len(lb):(i+1)*len(field_pairs)*len(lb),
+                j*len(field_pairs)*len(lb):(j+1)*len(field_pairs)*len(lb)
+            ] = covs[ms1, ms2, ms3, ms4]
+
+    # Symmetrize
+    full_cov = np.triu(full_cov)
+    full_cov += full_cov.T - np.diag(full_cov.diagonal())
+
+    for id_sim in range(Nsims):
+
+        sim_label = f"_{id_sim:04d}" if Nsims > 1 else ""
+
+        s = sacc.Sacc()
+
+        for ms in map_sets:
+            for spin, qty in zip(
+                [0, 2],
+                ["cmb_temperature", "cmb_polarization"]
+            ):
+
+                s.add_tracer(**{
+                    "tracer_type": "NuMap",
+                    "name": f"{ms}_s{spin}",
+                    "quantity": qty,
+                    "spin": spin,
+                    "nu": [meta.freq_tag_from_map_set(ms)],
+                    "ell": lb,
+                    "beam": np.ones_like(lb),  # TODO,
+                    "bandpass": [1.]  # TODO
+                })
+
+        for i, (ms1, ms2) in enumerate(ps_names):
+
+            cl_file = f"{cl_dir}/decoupled_cross_pcls_{ms1}_x_{ms2}{sim_label}.npz" # noqa
+            cells = np.load(cl_file)
+
+            for fp in field_pairs:
+
+                f1, f2 = fp
+                spin1 = 0 if f1 == "T" else 2
+                spin2 = 0 if f2 == "T" else 2
+                s.add_ell_cl(**{
+                    "data_type": f"cl_{data_types[f1]}{data_types[f2]}",
+                    "tracer1": f"{ms1}_s{spin1}",
+                    "tracer2": f"{ms2}_s{spin2}",
+                    "ell": lb,
+                    "x": cells[fp],
+                    "window": np.ones_like(lb)  # TODO
+                })
+
+        s.add_covariance(full_cov)
+
+        s.save_fits(
+            f"{sacc_dir}/cl_and_cov_sacc{sim_label}.fits",
+            overwrite=True
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Sacc compilation of power spectra and covariances."
+    )
+
+    parser.add_argument("--globals", type=str, help="Path to the yaml file")
+    mode = parser.add_mutually_exclusive_group()
+    mode.add_argument("--sims", action="store_true")
+    mode.add_argument("--data", action="store_true")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/filtering/filter_TQU_map.py b/pipeline/filtering/filter_TQU_map.py
new file mode 100644
index 0000000..3bce5ff
--- /dev/null
+++ b/pipeline/filtering/filter_TQU_map.py
@@ -0,0 +1,40 @@
+import argparse
+from soopercool import BBmeta
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    filtered_sims_dir = f"{meta.output_directory}/{args.out_dir}"
+    BBmeta.make_dir(filtered_sims_dir)
+
+    mask_file = meta.masks["analysis_mask"]
+
+    id_min, id_max = args.sim_ids.split(",")
+    id_min = int(id_min)
+    id_max = int(id_max)
+
+    filter_function = meta.get_filter_function(args.filter_tag)
+
+    for sim_id in range(id_min, id_max + 1):
+        map_name = f"{args.map_dir}/{args.map_template.format(sim_id=sim_id)}"
+
+        filter_function(
+            map_name,
+            mask_file,
+            filtered_sims_dir
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--map-dir", help="Path to the map file.")
+    parser.add_argument("--map-template", help="Template for the map file.")
+    parser.add_argument("--sim-ids", help="Bundle ID.")
+    parser.add_argument("--out-dir", help="Name of the output directory")
+    parser.add_argument("--filter-tag", help="Filtering tag.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/filtering/filter_sotodlib.py b/pipeline/filtering/filter_sotodlib.py
new file mode 100644
index 0000000..aaa6c9d
--- /dev/null
+++ b/pipeline/filtering/filter_sotodlib.py
@@ -0,0 +1,110 @@
+import argparse
+from soopercool import BBmeta
+from soopercool import map_utils as mu
+import sotodlib_utils as su
+import re
+import numpy as np
+from pixell import enmap
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+
+    bundle_id = re.findall(r"bundle[0-9]{1}", args.atomics_list)[0]
+    freq_tag = re.findall(r"f[0-9]{3}", args.atomics_list)[0]
+
+    fsims_dir = f"{out_dir}/sotodlib_filtered/{freq_tag}_bundle{bundle_id}"
+    BBmeta.make_dir(fsims_dir)
+
+    # First read the provided atomic maps list
+    with open(args.atomics_list, "r") as f:
+        atomics = f.read().splitlines()
+
+    id_min, id_max = args.sim_ids.split(",")
+    id_min = int(id_min)
+    id_max = int(id_max)
+
+    # Pre-load atomic ids
+    atomic_ids = []
+    for atom in atomics:
+        obs_id = re.findall(r"[0-9]{10}", atom)[0]
+        wafer = re.findall(r"ws[0-9]{1}", atom)[0]
+        atomic_ids.append((obs_id, wafer))
+
+    # Pre-load the access manager for filtering
+    aman = {}
+    for obs_id, wafer in atomic_ids:
+        db_obs_id = su.get_obs_id_from_map_id(args.context, obs_id)
+        aman[(wafer, obs_id)] = su.get_aman(
+            args.context,
+            db_obs_id,
+            wafer, freq_tag,
+            thinning_factor=1.0,
+            seed=1234
+        )
+
+    # Loop over the simulations
+    for sim_id in range(id_min, id_max + 1):
+        map_fname = args.map_template.format(sim_id=sim_id)
+        map_file = f"{args.map_dir}/{map_fname}"
+        print(map_file)
+        sim = mu.read_map(map_file, ncomp=3)
+        _, wcs = sim.geometry
+
+        template = su.get_CAR_template(3, 10)
+        template_w = su.get_CAR_template(3, 10)
+
+        for obs_id, wafer in atomic_ids:
+
+            fsim_wmap, fsim_w = su.filter_sim(
+                aman[wafer, obs_id],
+                sim, wcs, return_nofilter=False
+            )
+            fsim_w = np.moveaxis(fsim_w.diagonal(), -1, 0)
+            template = enmap.insert(template, fsim_wmap,
+                                    op=np.ndarray.__iadd__)
+            template_w = enmap.insert(template_w, fsim_w,
+                                      op=np.ndarray.__iadd__)
+
+        template_w[template_w == 0] = np.inf
+        fsim = template / template_w
+
+        out_fname = args.map_template.format(sim_id=sim_id).replace(".fits", "_filtered.fits")  # noqa
+        out_file = f"{fsims_dir}/{out_fname}"
+        enmap.write_map(out_file, fsim, dtype=np.float32, overwrite=True)
+
+
+b = """
+    for i in Nsims:
+        sim = ...
+        for a in atomics:
+            P=read_pointing
+            t = map2tod(sim, P)
+            tf = filter(t)
+            m1 = tod2map(tf, P)
+            m2 = tod2map(t, P)
+
+        sim1 = bundling(m1)
+        sim2 = bundling(m2)
+"""
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--atomic-maps-dir",
+                        help="Path to the atomic maps root dir.",
+                        required=False)
+    parser.add_argument("--atomics-list", help="List of atomic maps",
+                        required=False)
+    parser.add_argument("--context", help="Context file", required=False)
+    parser.add_argument("--map-dir", help="Map to filter", required=False)
+    parser.add_argument("--map-template", help="Map template", required=False)
+    parser.add_argument("--sim-ids", help="Sim id", required=False)
+
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/filtering/mcut_filter.sh b/pipeline/filtering/mcut_filter.sh
new file mode 100755
index 0000000..88d59ae
--- /dev/null
+++ b/pipeline/filtering/mcut_filter.sh
@@ -0,0 +1,11 @@
+paramfile="../paramfiles/paramfile_refactored.yaml"
+map_dir="outputs_refactor/tf_est_sims"
+map_template="pureB_power_law_tf_est_{sim_id:04d}_SATp3_f150.fits"
+map_template="cmb_maps_sat1_f090_bundle0_{sim_id:04d}.fits"
+sim_ids="0,99"
+out_dir="mcut30_filtered_tf_est_sims"
+filter_tag="mcut30"
+
+python filtering/filter_TQU_map.py --globals ${paramfile} --map-dir ${map_dir} \
+                                   --map-template ${map_template} --sim-ids ${sim_ids} \
+                                   --out-dir ${out_dir} --filter-tag ${filter_tag}
\ No newline at end of file
diff --git a/pipeline/filtering/sotodlib_filter.sh b/pipeline/filtering/sotodlib_filter.sh
new file mode 100755
index 0000000..af76c93
--- /dev/null
+++ b/pipeline/filtering/sotodlib_filter.sh
@@ -0,0 +1,17 @@
+paramfile="../paramfiles/paramfile_refactored.yaml"
+atomic_maps_dir="d"
+atomics_list="outputs_refactor_high_dec/bundled_maps/atomic_map_list_f090_bundle0.txt"
+context="d"
+
+map_dir="outputs_refactor/tf_est_sims"
+map_template="pureB_power_law_tf_est_{sim_id:04d}_SATp3_f150.fits"
+sim_ids="0,9"
+
+python filtering/filter_sotodlib.py \
+    --globals ${paramfile} \
+    --atomic-maps-dir ${atomic_maps_dir} \
+    --atomics-list ${atomics_list} \
+    --context ${context} \
+    --map-dir ${map_dir} \
+    --map-template ${map_template} \
+    --sim-ids ${sim_ids} \
\ No newline at end of file
diff --git a/pipeline/filtering/sotodlib_utils.py b/pipeline/filtering/sotodlib_utils.py
new file mode 100644
index 0000000..e4bccd4
--- /dev/null
+++ b/pipeline/filtering/sotodlib_utils.py
@@ -0,0 +1,480 @@
+from sotodlib.core import Context
+from sotodlib.core import metadata
+from sotodlib.tod_ops import (flags, fft_ops, filters,
+                              detrend_tod, apodize, sub_polyf)
+from sotodlib.hwp import hwp
+import sotodlib.coords.demod as demod_mm
+
+from pixell import enmap, enplot, reproject, utils
+import so3g
+import pandas as pd
+import sqlite3 as sq
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+import sys
+sys.path.append("/global/homes/k/kwolz/bbdev/SOOPERCOOL/soopercool")
+from utils import power_law_cl
+
+
+def model_func(x, sigma, fk, alpha):
+    """
+    """
+    return sigma**2 * (1 + (x/fk)**alpha)
+
+
+def log_fit_func(x, sigma, fk, alpha):
+    """
+    """
+    return np.log(model_func(x, sigma, fk, alpha))
+
+
+def generate_gaussian_map(ps_dict, map_shape, wcs, seed=1234):
+    """
+    ps_dict has elements "TT", "TE", "TB", "EE", "EB", "BB"
+    """
+    cov_ind = {
+        "TT": (0,0), "TE": (0,1), "TB": (0,2),
+        "EE": (1,1), "EB": (1,2), "BB": (2,2)
+    }
+    ells = np.arange(0, len(ps_dict["EE"]), 1)
+
+    # Gaussian power spectrum covariance
+    cov = np.zeros([3, 3, len(ps_dict["EE"])])
+    for field_pair, ps in ps_dict.items():
+        cov[cov_ind[field_pair]][2:] = 2*ps[2:]/(2*ells[2:] + 1)
+    cov_upper = cov
+    for il in range(len(ps_dict["EE"])):
+        cov_upper = cov[:, :, il]
+        cov[:, :, il] = cov_upper + cov_upper.T - np.diag(cov_upper.diagonal())
+    
+    # plt.clf()
+    # plt.plot(ells, cov[2,2,:], label="BB")
+    # plt.plot(ells, cov[1,1,:], label="EE")
+    # plt.plot(ells, cov[1,2,:], label="EB")
+    # plt.yscale("log")
+    # plt.xscale("log")
+    # plt.legend()
+    # plt.show()
+
+    return enmap.rand_map((3, map_shape[0], map_shape[1]),
+                           wcs, cov, spin=[0, 2], seed=seed)
+
+
+def get_aman(context, obs_id, wafer, freq, thinning_factor=1.0, seed=1234):
+    ctx = Context(context)
+    if (thinning_factor > 1) or (thinning_factor <= 0):
+        raise ValueError(f"Thinning: {thinning_factor} is not between 0 and 1")
+    print(f"Doing {obs_id} / {wafer} / {freq}")
+    # Get metadata for this observation
+    meta = ctx.get_meta(obs_id)
+    # Apply detector cuts
+    print("All", meta.dets.count)
+    # Select wafer and frequency (remember atomic maps are saved per wafer and per frequency)
+    meta.restrict('dets', meta.det_info.wafer_slot == wafer)
+    print(wafer, meta.dets.count)
+    meta.restrict('dets', meta.det_info.wafer.bandpass == freq)
+    print(freq, meta.dets.count)
+    # Remove detectors with rubbish coordinates
+    meta.restrict('dets', meta.dets.vals[~np.isnan(meta.focal_plane.xi)])
+    print("xi", meta.dets.count)
+    meta.restrict('dets', meta.dets.vals[~np.isnan(meta.focal_plane.gamma)])
+    print("gamma", meta.dets.count)
+    if thinning_factor < 1:
+        np.random.seed(seed)
+        keep = np.random.rand(meta.dets.count) <= thinning_factor
+        meta.restrict('dets', meta.dets.vals[keep])
+        print("thinning", meta.dets.count)    
+    print("Getting pointing information")
+    aman = ctx.get_obs(meta, no_signal=True)
+    # aman now contains the tod pointing information (with no signal)
+    return aman
+
+def get_obs_id_from_map_id(context, map_id):
+    ctx = Context(context)
+    # Query all observations
+    obslist = ctx.obsdb.query()
+    # Extract their ctimes
+    ctimes_tod = np.array([int(ob['obs_id'].split('_')[1]) for ob in obslist])
+    # Find the time-closest observation for each atomic map and get its obs_id
+    #obs_ids = {}
+    #for t in map_ids:
+    ix = np.argmin(np.fabs(int(map_id)-ctimes_tod))
+    t_tod = ctimes_tod[ix]
+    tdiff = np.fabs(int(map_id)-t_tod)
+    assert tdiff <= 5
+    #obs_ids[map_ids] = obslist['obs_id'][ix]
+    return obslist['obs_id'][ix]
+
+def filter_sim(aman, input_map, wcs, return_nofilter=False):
+    """
+    """
+    # Observe map into TOD
+    print("Observing into TOD")
+    dsT_sim, demodQ_sim, demodU_sim = demod_mm.from_map(
+        aman, input_map, wrap=True
+    )
+
+    if return_nofilter:
+        # Map it back before filtering
+        print("Mapping back")
+        res_nofilt = demod_mm.make_map(aman, wcs_kernel=wcs)
+
+    #Process the TOD (here's where filtering happens)
+    print("Calibrating TOD")
+    aman = calibrate_obs_tomoki(aman)
+
+    # Map it again
+    print("Mapping back")
+    res_filt = demod_mm.make_map(aman, wcs_kernel=wcs)
+
+    if return_nofilter:
+        return (res_filt['weighted_map'], res_filt['weight'],
+                res_nofilt['weighted_map'], res_nofilt['weight'])
+
+    return res_filt['weighted_map'], res_filt['weight']
+
+
+def get_CAR_template(ncomp, res, dec_cut=[-75, 25]):
+    """
+    """
+    if dec_cut is None:
+        shape, wcs = enmap.fullsky_geometry(res=np.deg2rad(res/60))
+    else:
+        shape, wcs = enmap.band_geometry(np.deg2rad(dec_cut), res=np.deg2rad(res/60))
+    shape = (ncomp, *shape)
+    return enmap.zeros(shape, wcs)
+
+
+def get_CAR_template(ncomp, res, dec_cut=None):
+    """
+    """
+    if dec_cut is None:
+        shape, wcs = enmap.fullsky_geometry(res=np.deg2rad(res/60), proj="car")
+    else:
+        shape, wcs = enmap.band_geometry(np.deg2rad(dec_cut), res=np.deg2rad(res/60))
+    shape = (ncomp, *shape)
+    return enmap.zeros(shape, wcs)
+
+
+def calibrate_obs_tomoki(obs, dtype_tod=np.float32, site='so_sat1',
+                         det_left_right=False, det_in_out=False,
+                         det_upper_lower=False):
+    """
+    """
+    from scipy.optimize import curve_fit
+    from scipy.stats import kurtosis, skew
+
+    obs.wrap("weather", np.full(1, "toco"))
+    obs.wrap("site",    np.full(1, site))
+    obs.flags.wrap(
+        'glitch_flags',
+        so3g.proj.RangesMatrix.zeros(obs.shape[:2]),
+        [(0, 'dets'), (1, 'samps')]
+    )
+    # Restrict non optical detectors, which have nans in their
+    # focal plane coordinates and will crash the mapmaking operation.
+    obs.restrict('dets', obs.dets.vals[obs.det_info.wafer.type == 'OPTC'])
+    obs.restrict(
+        'dets',
+        obs.dets.vals[(0.2<obs.det_cal.r_frac)&(obs.det_cal.r_frac<0.8)]
+    )
+    
+    if obs.signal is not None:
+        if det_left_right or det_in_out or det_upper_lower:
+            # we add a metadata manager for the detector flags
+            obs.wrap('det_flags', metadata.for_tod(obs))
+            if det_left_right or det_in_out:
+                xi = obs.focal_plane.xi
+                # sort xi 
+                xi_median = np.median(xi)    
+            if det_upper_lower or det_in_out:
+                eta = obs.focal_plane.eta
+                # sort eta
+                eta_median = np.median(eta)
+            if det_left_right:
+                mask = xi <= xi_median
+                obs.det_flags.wrap_dets('det_left', np.logical_not(mask))
+                mask = xi > xi_median
+                obs.det_flags.wrap_dets('det_right', np.logical_not(mask))
+            if det_upper_lower:
+                mask = eta <= eta_median
+                obs.det_flags.wrap_dets('det_lower', np.logical_not(mask))
+                mask = eta > eta_median
+                obs.det_flags.wrap_dets('det_upper', np.logical_not(mask))
+            if det_in_out:
+                # the bounding box is the center of the detset
+                xi_center = np.min(xi) + 0.5 * (np.max(xi) - np.min(xi))
+                eta_center = np.min(eta) + 0.5 * (np.max(eta) - np.min(eta))
+                radii = np.sqrt((xi_center-xi)**2 + (eta_center-eta)**2)
+                radius_median = np.median(radii)
+                mask = radii <= radius_median
+                obs.det_flags.wrap_dets('det_in', np.logical_not(mask))
+                mask = radii > radius_median
+                obs.det_flags.wrap_dets('det_out', np.logical_not(mask))
+        
+        nperseg = 200*1000
+        
+        obs.focal_plane.gamma = np.arctan(np.tan(obs.focal_plane.gamma))
+        flags.get_turnaround_flags(obs, t_buffer=0.1, truncate=True)
+        obs.signal = np.multiply(obs.signal.T, obs.det_cal.phase_to_pW).T
+        freq, Pxx = fft_ops.calc_psd(obs, nperseg=nperseg, merge=True)
+        #print('First psd, number of nusamps', obs.nusamps.count)
+        #print('First psd, shape of P',Pxx.shape)
+        wn = fft_ops.calc_wn(obs)
+        obs.wrap('wn', wn, [(0, 'dets')])
+
+        # satp3
+        bgs = obs.det_cal.bg
+        bgs90 = [0,1,4,5,8,9]
+        bgs150 = [2,3,6,7,10,11]
+        m90 = np.array([bg in bgs90 for bg in bgs])
+        m150 = np.array([bg in bgs150 for bg in bgs])
+        #obs = obs.restrict('dets', obs.dets.vals[m90]) # SA TODO
+        if obs.hwp_solution.primary_encoder == 1:
+            if np.all(obs.det_cal.bg.all() in bgs90):
+                # 90 GHz
+                print('90')
+                obs.hwp_angle = np.mod(
+                    -1*np.unwrap(obs.hwp_angle) + np.deg2rad(-1.66-2.29+90),
+                    2*np.pi
+                )
+            elif np.all(obs.det_cal.bg.all() in bgs150):
+                # 150 GHz
+                print('150')
+                obs.hwp_angle = np.mod(
+                    -1*np.unwrap(obs.hwp_angle) + np.deg2rad(-1.66-1.99+90),
+                    2*np.pi
+                )
+        elif obs.hwp_solution.primary_encoder == 2:
+            print('2')
+            if np.all(obs.det_cal.bg.all() in bgs90):
+                # 90 GHz
+                obs.hwp_angle = np.mod(
+                    -1*np.unwrap(obs.hwp_angle) + np.deg2rad(-1.66-2.29-90),
+                    2*np.pi
+                )
+            elif np.all(obs.det_cal.bg.all() in bgs150):
+                # 150 GHz
+                obs.hwp_angle = np.mod(
+                    -1*np.unwrap(obs.hwp_angle) + np.deg2rad(-1.66-1.99-90),
+                    2*np.pi
+                )
+
+        obs.restrict('dets', obs.dets.vals[(20<obs.wn*1e6)&(obs.wn*1e6<40)])
+        print(f'dets: {obs.dets.count}')
+        
+        # peak to peak restrict
+        obs.restrict('dets', obs.dets.vals[np.ptp(obs.signal, axis=1) < 0.5])
+        print(f'dets: {obs.dets.count}')
+        
+        if obs.dets.count<=1: return obs
+        
+        hwp.get_hwpss(obs)
+        hwp.subtract_hwpss(obs)
+        obs.move('signal', None)
+        obs.move('hwpss_remove', 'signal')
+        freq, Pxx = fft_ops.calc_psd(obs, nperseg=nperseg, merge=False)
+        #print('Second psd, number of nusamps', obs.nusamps.count)
+        #print('Second psd, shape of P',Pxx.shape)
+        obs.Pxx = Pxx
+        
+        detrend_tod(obs, method='median')
+        apodize.apodize_cosine(obs, apodize_samps=2000)
+        
+        # the demodulation will happen here
+        speed = (np.sum(np.abs(np.diff(np.unwrap(obs.hwp_angle)))) /
+                (obs.timestamps[-1] - obs.timestamps[0])) / (2 * np.pi)
+        bpf_center = 4 * speed
+        bpf_width = speed * 2. * 0.9
+        bpf_cfg = {'type': 'sine2',
+                   'center': bpf_center,
+                   'width': bpf_width,
+                   'trans_width': 0.1}
+
+        lpf_cutoff = speed * 0.9
+        lpf_cfg = {'type': 'sine2',
+                   'cutoff': lpf_cutoff,
+                   'trans_width': 0.1}
+        hwp.demod_tod(obs, bpf_cfg=bpf_cfg, lpf_cfg=lpf_cfg)
+        
+        obs.restrict(
+            'samps',
+            (obs.samps.offset+2000, obs.samps.offset + obs.samps.count-2000)
+        )
+        obs.move('signal', None)
+        detrend_tod(obs, signal_name='dsT', method='linear')
+        detrend_tod(obs, signal_name='demodQ', method='linear')
+        detrend_tod(obs, signal_name='demodU', method='linear')
+        freq, Pxx_demodQ = fft_ops.calc_psd(obs, signal=obs.demodQ,
+                                            nperseg=nperseg, merge=False)
+        freq, Pxx_demodU = fft_ops.calc_psd(obs, signal=obs.demodU,
+                                            nperseg=nperseg, merge=False)
+        obs.wrap('Pxx_demodQ', Pxx_demodQ, [(0, 'dets'), (1, 'nusamps')])
+        obs.wrap('Pxx_demodU', Pxx_demodU, [(0, 'dets'), (1, 'nusamps')])
+        
+        mask = np.ones_like(obs.dsT, dtype='bool')
+    
+        lamQ, lamU = [], []
+        AQ, AU = [], []
+
+        for di, det in enumerate(obs.dets.vals[:]):
+            x = obs.dsT[di][mask[di]]
+            y1 = obs.demodQ[di][mask[di]]
+            y2 = obs.demodU[di][mask[di]]
+
+            z1 = np.polyfit(x, y1, 1)
+            z2 = np.polyfit(x, y2, 1)
+            _lamQ, _AQ = z1[0], z1[1]
+            _lamU, _AU = z2[0], z2[1]
+
+            lamQ.append(_lamQ)
+            lamU.append(_lamU)
+            AQ.append(_AQ)
+            AU.append(_AU)
+
+        lamQ, lamU = np.array(lamQ), np.array(lamU)
+        obs.wrap('lamQ', lamQ, [(0, 'dets')])
+        obs.wrap('lamU', lamU, [(0, 'dets')])
+
+        AQ, AU = np.array(AQ), np.array(AU)
+        obs.wrap('AQ', AQ, [(0, 'dets')])
+        obs.wrap('AU', AU, [(0, 'dets')])
+
+        obs.demodQ -= (obs.dsT * obs.lamQ[:, np.newaxis]
+                       + obs.AQ[:, np.newaxis])
+        obs.demodU -= (obs.dsT * obs.lamU[:, np.newaxis]
+                       + obs.AU[:, np.newaxis])
+
+        freq, Pxx_demodQ_new = fft_ops.calc_psd(obs, signal=obs.demodQ,
+                                                nperseg=nperseg, merge=False)
+        freq, Pxx_demodU_new = fft_ops.calc_psd(obs, signal=obs.demodU,
+                                                nperseg=nperseg, merge=False)
+        obs.Pxx_demodQ = Pxx_demodQ_new
+        obs.Pxx_demodU = Pxx_demodU_new
+        
+        mask_valid_freqs = (1e-4<obs.freqs) & (obs.freqs < 1.9)
+        x = obs.freqs[mask_valid_freqs]
+        obs.wrap_new('sigma', ('dets', ))
+        obs.wrap_new('fk', ('dets', ))
+        obs.wrap_new('alpha', ('dets', ))
+
+        for di, det in enumerate(obs.dets.vals):
+            y = obs.Pxx_demodQ[di, mask_valid_freqs]
+            popt, pcov = curve_fit(
+                log_fit_func, x, np.log(y),
+                p0=(np.sqrt(np.median(y[x>0.2])), 0.01, -2.), maxfev=100000
+            )
+            obs.sigma[di] = popt[0]
+            obs.fk[di] = popt[1]
+            obs.alpha[di] = popt[2]
+        
+        kurt_threshold=0.5
+        skew_threshold=0.5
+        
+        valid_scan = np.logical_and(
+            np.logical_or(obs.flags["left_scan"].mask(),
+                          obs.flags["right_scan"].mask()),
+                          ~obs.flags["turnarounds"].mask())
+
+        subscan_indices_l = sub_polyf._get_subscan_range_index(
+            obs.flags["left_scan"].mask()
+        )
+        subscan_indices_r = sub_polyf._get_subscan_range_index(
+            obs.flags["right_scan"].mask()
+        )
+        subscan_indices = np.vstack([subscan_indices_l, subscan_indices_r])
+        subscan_indices= subscan_indices[np.argsort(subscan_indices[:, 0])]
+
+        subscan_Qstds = np.zeros([obs.dets.count, len(subscan_indices)])
+        subscan_Ustds = np.zeros([obs.dets.count, len(subscan_indices)])
+        subscan_Qkurt = np.zeros([obs.dets.count, len(subscan_indices)])
+        subscan_Ukurt = np.zeros([obs.dets.count, len(subscan_indices)])
+        subscan_Qskew = np.zeros([obs.dets.count, len(subscan_indices)])
+        subscan_Uskew = np.zeros([obs.dets.count, len(subscan_indices)])
+
+        for subscan_i, subscan in enumerate(subscan_indices):
+            _Qsig= obs.demodQ[:,subscan[0]:subscan[1]+1]
+            _Usig= obs.demodU[:,subscan[0]:subscan[1]+1]
+
+            _Qmean = np.mean(_Qsig, axis=1)[:,np.newaxis]
+            _Umean = np.mean(_Usig, axis=1)[:,np.newaxis]
+
+            _Qstd = np.std(_Qsig, axis=1)
+            _Ustd = np.std(_Usig, axis=1)
+
+            _Qkurt = kurtosis(_Qsig, axis=1)
+            _Ukurt = kurtosis(_Usig, axis=1)
+
+            _Qskew = skew(_Qsig, axis=1)
+            _Uskew = skew(_Usig, axis=1)
+
+            obs.demodQ[:,subscan[0]:subscan[1]+1] -= _Qmean
+            obs.demodU[:,subscan[0]:subscan[1]+1] -= _Umean
+
+            subscan_Qstds[:, subscan_i] = _Qstd
+            subscan_Ustds[:, subscan_i] = _Ustd
+            subscan_Qkurt[:, subscan_i] = _Qkurt
+            subscan_Ukurt[:, subscan_i] = _Ukurt
+            subscan_Qskew[:, subscan_i] = _Qskew
+            subscan_Uskew[:, subscan_i] = _Uskew
+
+        badsubscan_indicator = ((np.abs(subscan_Qkurt) > kurt_threshold)
+                                | (np.abs(subscan_Ukurt) > kurt_threshold)
+                                | (np.abs(subscan_Qskew) > skew_threshold)
+                                | (np.abs(subscan_Uskew) > skew_threshold))
+        badsubscan_flags = np.zeros([obs.dets.count, obs.samps.count],
+                                    dtype='bool')
+
+        for subscan_i, subscan in enumerate(subscan_indices):
+            flag_values = badsubscan_indicator[:, subscan_i, np.newaxis]
+            badsubscan_flags[:, subscan[0]:subscan[1]+1] = flag_values
+        badsubscan_flags = so3g.proj.RangesMatrix.from_mask(badsubscan_flags)
+
+        obs.flags.wrap('bad_subscan', badsubscan_flags)
+        
+        filt = filters.counter_1_over_f(np.median(obs.fk),
+                                        -2*np.median(obs.alpha))
+        obs.demodQ = filters.fourier_filter(obs, filt, signal_name='demodQ')
+        obs.demodU = filters.fourier_filter(obs, filt, signal_name='demodU')
+
+        freq, Pxx_demodQ = fft_ops.calc_psd(obs, signal=obs.demodQ,
+                                            nperseg=nperseg, merge=False)
+        freq, Pxx_demodU = fft_ops.calc_psd(obs, signal=obs.demodU,
+                                            nperseg=nperseg, merge=False)
+
+        obs.Pxx_demodQ = Pxx_demodQ
+        obs.Pxx_demodU = Pxx_demodU
+        
+        wn = fft_ops.calc_wn(obs, obs.Pxx_demodQ, low_f=0.1, high_f=1.)
+        obs.wrap('inv_var', wn**(-2), [(0, 'dets')])
+        if True:
+            lo, hi = np.percentile(obs.inv_var, [3, 97])
+            obs.restrict(
+                'dets',
+                obs.dets.vals[(lo < obs.inv_var) & (obs.inv_var < hi)]
+            )
+        if obs.dets.count<=1:
+            return obs
+    
+        glitches_T = flags.get_glitch_flags(
+            obs, signal_name='dsT', merge=True, name='glitches_T'
+        )
+        glitches_Q = flags.get_glitch_flags(
+            obs, signal_name='demodQ', merge=True, name='glitches_Q'
+        )
+        glitches_U = flags.get_glitch_flags(
+            obs, signal_name='demodU', merge=True, name='glitches_U'
+        )
+        obs.flags.reduce(
+            flags=['glitches_T', 'glitches_Q', 'glitches_U'],
+            method='union', wrap=True, new_flag='glitches', remove_reduced=True
+        )
+        obs.flags.move('glitch_flags', None)
+        obs.flags.reduce(
+            flags=['turnarounds', 'bad_subscan', 'glitches'],
+            method='union', wrap=True, new_flag='glitch_flags',
+            remove_reduced=True
+        )
+    return obs
diff --git a/pipeline/generate_simulations.py b/pipeline/generate_simulations.py
new file mode 100644
index 0000000..a12b026
--- /dev/null
+++ b/pipeline/generate_simulations.py
@@ -0,0 +1,69 @@
+import argparse
+from soopercool import BBmeta
+import healpy as hp
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    do_plots = not args.no_plots
+
+    out_dir = meta.output_directory
+    masks_dir = f"{out_dir}/masks"
+
+    sims_dir = f"{out_dir}/cov_sims"
+    BBmeta.make_dir(sims_dir)
+
+    noise_map_dir = meta.covariance["noise_sims_dir"]
+    signal_map_dir = meta.covariance["signal_sims_dir"]
+
+    noise_map_template = meta.covariance["noise_sims_template"]
+    signal_map_template = meta.covariance["signal_sims_template"]
+
+    binary = hp.read_map(f"{masks_dir}/binary_mask.fits")
+
+    for id_sim in range(meta.covariance["cov_num_sims"]):
+
+        base_dir = f"{sims_dir}/{id_sim:04d}"
+        BBmeta.make_dir(base_dir)
+        for ms in meta.map_sets_list:
+
+            fname = signal_map_template.format(id_sim=id_sim, map_set=ms)
+            cmb = hp.read_map(f"{signal_map_dir}/{fname}", field=[0, 1, 2])
+            for id_bundle in range(meta.n_bundles_from_map_set(ms)):
+
+                fname = noise_map_template.format(id_sim=id_sim, map_set=ms,
+                                                  id_bundle=id_bundle)
+                noise = hp.read_map(
+                    f"{noise_map_dir}/{fname}", field=[0, 1, 2]
+                )
+
+                split_map = cmb + noise
+
+                map_name = f"cov_sims_{ms}_bundle{id_bundle}.fits"
+                hp.write_map(f"{base_dir}/{map_name}", split_map*binary,
+                             overwrite=True,
+                             dtype=np.float32)
+
+                if do_plots:
+                    for i, f in enumerate("TQU"):
+                        hp.mollview(split_map[i]*binary,
+                                    cmap="RdYlBu_r",
+                                    title=f"{ms} - {id_sim} - {f}",
+                                    min=-300 if f == "T" else -100,
+                                    max=300 if f == "T" else 100)
+                        plt.savefig(f"{base_dir}/cov_sims_{ms}_bundle{id_bundle}_{f}.png") # noqa
+                        plt.clf()
+                        plt.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--no-plots", action="store_true", help="Do not plot the maps.") # noqa
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/get_analysis_mask.py b/pipeline/get_analysis_mask.py
new file mode 100644
index 0000000..7621a7f
--- /dev/null
+++ b/pipeline/get_analysis_mask.py
@@ -0,0 +1,194 @@
+import argparse
+import re
+from soopercool import BBmeta
+from soopercool import map_utils as mu
+from soopercool import utils as su
+import numpy as np
+import healpy as hp
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+    do_plots = not args.no_plots
+    verbose = args.verbose
+
+    out_dir = meta.output_directory
+
+    masks_dir = f"{out_dir}/masks"
+    plot_dir = f"{out_dir}/plots/masks"
+    BBmeta.make_dir(masks_dir)
+    if do_plots:
+        BBmeta.make_dir(plot_dir)
+
+    masks_settings = meta.masks
+
+    # First loop over the (map_set, id_bundles)
+    # pairs to define a common binary mask
+    hit_maps = []
+    for map_set in meta.map_sets_list:
+        n_bundles = meta.n_bundles_from_map_set(map_set)
+        for id_bundle in range(n_bundles):
+
+            map_dir = meta.map_dir_from_map_set(map_set)
+            map_template = meta.map_template_from_map_set(map_set)
+
+            map_file = map_template.replace(
+                "{id_bundle}",
+                str(id_bundle)
+            )
+            type_options = [
+                f for f in re.findall(r"\{.*?\}", map_template)
+                if "|" in f
+            ][0]
+            # Select the hitmap
+            option = type_options.replace("{", "")
+            option = option.replace("}", "").split("|")[1]
+
+            map_file = map_file.replace(
+                type_options,
+                option
+            )
+
+            print(f"Reading hitmap for {map_set} - bundle {id_bundle}")
+            if verbose:
+                print(f"    file_name: {map_dir}/{map_file}")
+
+            hits = mu.read_map(f"{map_dir}/{map_file}", ncomp=1)
+            hit_maps.append(hits)
+
+    # Create binary and normalized hitmap
+    binary = np.ones_like(hit_maps[0])
+    sum_hits = np.zeros_like(hit_maps[0])
+    for hit_map in hit_maps:
+        binary[hit_map == 0] = 0
+        sum_hits += hit_map
+    sum_hits[binary == 0] = 0
+
+    # Normalize and smooth hitmaps
+    sum_hits = hp.smoothing(sum_hits, fwhm=np.deg2rad(1.))
+    sum_hits /= np.amax(sum_hits)
+
+    # Save products
+    mu.write_map(f"{masks_dir}/binary_mask.fits",
+                 binary, dtype=np.int32)
+    mu.write_map(f"{masks_dir}/normalized_hits.fits",
+                 sum_hits, dtype=np.float32)
+
+    if do_plots:
+        mu.plot_map(binary,
+                    title="Binary mask",
+                    file_name=f"{plot_dir}/binary_mask")
+
+        mu.plot_map(sum_hits,
+                    title="Normalized hitcount",
+                    file_name=f"{plot_dir}/normalized_hits")
+
+    analysis_mask = binary.copy()
+
+    if masks_settings["galactic_mask"] is not None:
+        print("Reading galactic mask ...")
+        if verbose:
+            print(f"    file_name: {masks_settings['galactic_mask']}")
+        gal_mask = mu.read_map(masks_settings["galactic_mask"], ncomp=1)
+        if do_plots:
+            mu.plot_map(gal_mask,
+                        title="Galactic mask",
+                        file_name=f"{plot_dir}/galactic_mask")
+        analysis_mask *= gal_mask
+
+    if masks_settings["external_mask"] is not None:
+        print("Reading external mask ...")
+        if verbose:
+            print(f"    file_name: {masks_settings['external_mask']}")
+        ext_mask = mu.read_map(masks_settings["external_mask"], ncomp=1)
+        if do_plots:
+            mu.plot_map(
+                ext_mask,
+                title="External mask",
+                file_name=f"{plot_dir}/external_mask")
+        analysis_mask *= ext_mask
+
+    import pymaster as nmt
+    analysis_mask = nmt.mask_apodization(
+        analysis_mask,
+        masks_settings["apod_radius"],
+        apotype=masks_settings["apod_type"]
+    )
+
+    if masks_settings["point_source_mask"] is not None:
+        print("Reading point source mask ...")
+        if verbose:
+            print(f"    file_name: {masks_settings['point_source_mask']}")
+        ps_mask = mu.read_map(masks_settings["point_source_mask"], ncomp=1)
+        ps_mask = nmt.mask_apodization(
+            ps_mask,
+            masks_settings["apod_radius_point_source"],
+            apotype=masks_settings["apod_type"]
+        )
+        if do_plots:
+            mu.plot_map(
+                ps_mask,
+                title="Point source mask",
+                file_name=f"{plot_dir}/point_source_mask")
+
+        analysis_mask *= ps_mask
+
+    # Weight with hitmap
+    analysis_mask *= sum_hits
+    mu.write_map(f"{masks_dir}/analysis_mask.fits",
+                 analysis_mask, dtype=np.float32)
+
+    if do_plots:
+        mu.plot_map(analysis_mask, title="Analysis mask",
+                    file_name=f"{plot_dir}/analysis_mask")
+
+    # Compute and plot spin derivatives
+    first, second = su.get_spin_derivatives(analysis_mask)
+
+    if do_plots:
+        mu.plot_map(first, title="First spin derivative",
+                    file_name=f"{plot_dir}/first_spin_derivative")
+        mu.plot_map(second, title="Second spin derivative",
+                    file_name=f"{plot_dir}/second_spin_derivative")
+
+        import matplotlib.pyplot as plt
+        plt.figure()
+        plt.plot(first)
+        plt.show()
+        plt.figure()
+        plt.plot(second)
+        plt.show()
+
+    if args.verbose:
+        print("---------------------------------------------------------")
+        print("Using custom mask. "
+              "Its spin derivatives have global min and max of:")
+        print("first:     ", np.amin(first), np.amax(first),
+              "\nsecond:    ", np.amin(second), np.amax(second))
+        print("---------------------------------------------------------")
+
+    print("\nSUMMARY")
+    print("-------")
+    print(f"Wrote analysis mask to {masks_dir}/analysis_mask.fits")
+    print("Parameters")
+    print(f"    Galactic mask: {masks_settings['galactic_mask']}")
+    print(f"    External mask: {masks_settings['external_mask']}")
+    print(f"    Point source mask: {masks_settings['point_source_mask']}")
+    print(f"    Apodization type: {masks_settings['apod_type']}")
+    print(f"    Apodization radius: {masks_settings['apod_radius']}")
+    print(f"    Apodization radius point source: {masks_settings['apod_radius_point_source']}") # noqa
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Get analysis mask")
+    parser.add_argument("--globals", help="Path to the paramfile")
+    parser.add_argument("--verbose", help="Verbose mode",
+                        action="store_true")
+    parser.add_argument("--no-plots", help="Plot the results",
+                        action="store_true")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/pipeline/get_full_couplings.py b/pipeline/get_full_couplings.py
new file mode 100644
index 0000000..eed9404
--- /dev/null
+++ b/pipeline/get_full_couplings.py
@@ -0,0 +1,60 @@
+import argparse
+from soopercool import BBmeta
+import numpy as np
+import pymaster as nmt
+from soopercool import coupling_utils as cu
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+    couplings_dir = f"{out_dir}/couplings"
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+
+    tf_settings = meta.transfer_settings
+
+    ps_names = meta.get_ps_names_list("all", coadd=True)
+    filtering_pairs = meta.get_independent_filtering_pairs()
+
+    tf_dir = tf_settings["transfer_directory"]
+
+    tf_dict = {}
+    for ftag1, ftag2 in filtering_pairs:
+        tf = np.load(f"{tf_dir}/transfer_function_{ftag1}_x_{ftag2}.npz")
+        tf_dict[ftag1, ftag2] = tf["full_tf"]
+
+    mcms_dict = cu.load_mcms(couplings_dir,
+                             ps_names=ps_names, full_mcm=True)
+
+    # First for the data (i.e. including beams, tf, and mcm)
+    ps_names_and_ftags = {
+        (ms1, ms2): (meta.filtering_tag_from_map_set(ms1),
+                     meta.filtering_tag_from_map_set(ms2))
+        for ms1, ms2 in ps_names
+    }
+
+    couplings = cu.get_couplings_dict(
+        mcms_dict, nmt_bins,
+        transfer_dict=tf_dict,
+        ps_names_and_ftags=ps_names_and_ftags
+    )
+
+    for ms1, ms2 in ps_names:
+        np.savez(
+            f"{couplings_dir}/couplings_{ms1}_{ms2}.npz",
+            **couplings[ms1, ms2]
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--unity", action="store_true")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/get_mode_coupling.py b/pipeline/get_mode_coupling.py
new file mode 100644
index 0000000..b6ae7c8
--- /dev/null
+++ b/pipeline/get_mode_coupling.py
@@ -0,0 +1,107 @@
+import argparse
+from soopercool import BBmeta
+from soopercool import map_utils as mu
+import pymaster as nmt
+import numpy as np
+
+
+def main(args):
+    """
+    ...
+    """
+    meta = BBmeta(args.globals)
+    do_plots = not args.no_plots
+    # verbose = args.verbose
+
+    out_dir = meta.output_directory
+
+    mcm_dir = f"{out_dir}/couplings"
+    BBmeta.make_dir(mcm_dir)
+    plot_dir = f"{out_dir}/plots/couplings"
+    if do_plots:
+        BBmeta.make_dir(plot_dir)
+
+    nl = 3 * meta.nside
+    nspec = 7
+
+    mask = mu.read_map(meta.masks["analysis_mask"], ncomp=1)
+
+    field_spin0 = nmt.NmtField(mask, None, spin=0)
+    field_spin2 = nmt.NmtField(mask, None, spin=2, purify_b=meta.pure_B)
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    n_bins = nmt_bins.get_n_bands()
+
+    binner = np.array([nmt_bins.bin_cell(np.array([cl]))[0]
+                       for cl in np.eye(nl)]).T
+
+    # Alright, compute and reshape coupling matrix.
+    print("Computing MCM")
+    w = nmt.NmtWorkspace()
+    w.compute_coupling_matrix(field_spin0, field_spin2, nmt_bins, is_teb=True)
+
+    mcm = np.transpose(w.get_coupling_matrix().reshape([nl, nspec, nl, nspec]),
+                       axes=[1, 0, 3, 2])
+    mcm_binned = np.einsum('ij,kjlm->kilm', binner, mcm)
+
+    # Load beams to correct the mode coupling matrix
+    beams = {}
+    for map_set in meta.map_sets_list:
+        beam_dir = meta.beam_dir_from_map_set(map_set)
+        beam_file = meta.beam_file_from_map_set(map_set)
+
+        l, bl = np.loadtxt(f"{beam_dir}/{beam_file}", unpack=True)
+        beams[map_set] = bl[:nl]
+
+    # Beam-correct the mode coupling matrix
+    beamed_mcm = {}
+    for map_set1, map_set2 in meta.get_ps_names_list("all", coadd=True):
+        beamed_mcm[map_set1, map_set2] = mcm * \
+            np.outer(beams[map_set1],
+                     beams[map_set2])[np.newaxis, :, np.newaxis, :]
+
+    # Save files
+    # Starting with the un-beamed non-purified MCM
+    np.savez(
+        f"{mcm_dir}/mcm.npz",
+        binner=binner,
+        spin0xspin0=mcm[0, :, 0, :].reshape([1, nl, 1, nl]),
+        spin0xspin2=mcm[1:3, :, 1:3, :],
+        spin2xspin2=mcm[3:, :, 3:, :],
+        spin0xspin0_binned=mcm_binned[0, :, 0, :].reshape([1, n_bins, 1, nl]),
+        spin0xspin2_binned=mcm_binned[1:3, :, 1:3, :],
+        spin2xspin2_binned=mcm_binned[3:, :, 3:, :]
+    )
+
+    # Then the beamed MCM
+    for map_set1, map_set2 in meta.get_ps_names_list("all", coadd=True):
+        m = beamed_mcm[map_set1, map_set2]
+        mcm_binned = np.einsum('ij,kjlm->kilm', binner, m)
+        np.savez(
+            f"{mcm_dir}/mcm_{map_set1}_{map_set2}.npz",
+            binner=binner,
+            spin0xspin0=m[0, :, 0, :].reshape([1, nl, 1, nl]),
+            spin0xspin2=m[1:3, :, 1:3, :],
+            spin2xspin2=m[3:, :, 3:, :],
+            spin0xspin0_binned=mcm_binned[0, :, 0, :].reshape([1, n_bins,
+                                                               1, nl]),
+            spin0xspin2_binned=mcm_binned[1:3, :, 1:3, :],
+            spin2xspin2_binned=mcm_binned[3:, :, 3:, :]
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Compute mask mode coupling matrices"
+    )
+    parser.add_argument("--globals", help="Path to the paramfile")
+    parser.add_argument("--verbose", help="Verbose mode",
+                        action="store_true")
+    parser.add_argument("--no-plots", help="Plot the results",
+                        action="store_true")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/pipeline/misc/get_binning.py b/pipeline/misc/get_binning.py
new file mode 100644
index 0000000..be6c233
--- /dev/null
+++ b/pipeline/misc/get_binning.py
@@ -0,0 +1,40 @@
+from soopercool import BBmeta
+import numpy as np
+from soopercool.utils import create_binning
+import argparse
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+    binning_dir = f"{out_dir}/binning"
+    BBmeta.make_dir(binning_dir)
+
+    bin_low, bin_high, bin_center = create_binning(meta.nside,
+                                                   args.deltal)
+    print(bin_low, bin_high, bin_center)
+    file_name = f"binning_nside{meta.nside}_deltal{args.deltal}.npz"
+
+    bin_low2, bin_high2, bin_center2 = create_binning(meta.nside,
+                                                      args.deltal,
+                                                      end_first_bin=30)
+    print(bin_low2, bin_high2, bin_center2)
+
+    np.savez(
+        f"{binning_dir}/{file_name}",
+        bin_low=bin_low2,
+        bin_high=bin_high2,
+        bin_center=bin_center2
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    parser.add_argument("--deltal", type=int,
+                        help="Delta ell for the binning.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/misc/get_noise_ps_for_sims.py b/pipeline/misc/get_noise_ps_for_sims.py
new file mode 100644
index 0000000..e211d3b
--- /dev/null
+++ b/pipeline/misc/get_noise_ps_for_sims.py
@@ -0,0 +1,105 @@
+import argparse
+from soopercool import BBmeta
+import pymaster as nmt
+import numpy as np
+from itertools import product
+from scipy.interpolate import interp1d
+import matplotlib.pyplot as plt
+
+
+def smooth_array(arr, kernel_size):
+    ker = np.ones(kernel_size) / kernel_size
+    return np.convolve(arr, ker, mode='same')
+
+
+def interp_array(x, y):
+    """
+    """
+    return interp1d(x, y, fill_value='extrapolate')
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+    do_plots = not args.no_plots
+    # verbose = args.verbose
+
+    out_dir = meta.output_directory
+
+    cells_dir = f"{out_dir}/cells"
+    noise_dir = f"{out_dir}/noise_interp"
+    BBmeta.make_dir(noise_dir)
+    plot_dir = f"{out_dir}/plots/noise_interpolation"
+    if do_plots:
+        BBmeta.make_dir(plot_dir)
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+    lb = nmt_bins.get_effective_ells()
+
+    nl = 3 * meta.nside
+    ell = np.arange(nl)
+    field_pairs = [m1+m2 for m1, m2 in product("TEB", repeat=2)]
+
+    # Load beams to correct the mode coupling matrix
+    beams = {}
+    for map_set in meta.map_sets_list:
+        beam_dir = meta.beam_dir_from_map_set(map_set)
+        beam_file = meta.beam_file_from_map_set(map_set)
+
+        l, bl = np.loadtxt(f"{beam_dir}/{beam_file}", unpack=True)
+        beams[map_set] = bl[:nl]
+
+    cross_map_set_list = meta.get_ps_names_list(type="all", coadd=True)
+
+    for map_set1, map_set2 in cross_map_set_list:
+
+        noise_dict = np.load(f"{cells_dir}/decoupled_noise_pcls_{map_set1}_x_{map_set2}.npz") # noqa
+        plt.figure()
+        plt.plot(lb, noise_dict["EE"])
+        plt.title(f"{map_set1} x {map_set2} - EE")
+        plt.yscale("log")
+        plt.show()
+
+        nb1, nb2 = (meta.n_bundles_from_map_set(map_set1),
+                    meta.n_bundles_from_map_set(map_set2))
+        interp_noise = {}
+        for field_pair in field_pairs:
+            nb = noise_dict[field_pair]
+            n_int = interp_array(lb, nb)
+            nl = n_int(ell) * beams[map_set1] * beams[map_set2]
+
+            interp_noise[field_pair] = nl * np.sqrt(nb1) * np.sqrt(nb2)
+
+            np.savez(f"{noise_dir}/nl_{map_set1}_x_{map_set2}.npz",
+                     ell=ell, **interp_noise)
+
+            if do_plots:
+                plt.figure(figsize=(10, 8))
+                plt.xlabel(r"$\ell$", fontsize=15)
+                plt.ylabel(r"$N_\ell^\mathrm{%s}$" % field_pair, fontsize=15)
+                plt.plot(lb, nb, label="Original")
+                plt.plot(ell, nl, ls="--",
+                         label="Interpolated + Beam deconvolved")
+                plt.legend()
+                plt.xlabel(r"$\ell$")
+                plt.ylabel(r"$C_{\ell}$")
+                plt.title(f"{map_set1} x {map_set2} - {field_pair}")
+                plt.yscale("log")
+                plt.xlim(0, 2 * meta.nside)
+                plt.savefig(f"{plot_dir}/noise_interp_{map_set1}_x_{map_set2}_{field_pair}.png") # noqa
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Compute mask mode coupling matrices"
+    )
+    parser.add_argument("--globals", help="Path to the paramfile")
+    parser.add_argument("--no-plots", help="Plot the results",
+                        action="store_true")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/pipeline/sacc_plotter.py b/pipeline/sacc_plotter.py
index 8de49f2..52b0400 100644
--- a/pipeline/sacc_plotter.py
+++ b/pipeline/sacc_plotter.py
@@ -4,61 +4,42 @@
 from itertools import product
 import sacc
 import numpy as np
-from soopercool import ps_utils
+import pymaster as nmt
 
 
 def load_bpwins(coupling_file):
     """
     """
     bp_win = np.load(coupling_file)
-    bpw_mat = {}
-    for spin_pair in ["spin0xspin0", "spin0xspin2", "spin2xspin2"]:
-        bpw_mat[spin_pair] = bp_win[f"bp_win_{spin_pair}"]
-
-    return bpw_mat
+    return bp_win["bp_win"]
 
 
 def binned_theory_from_unbinned(clth, bpw_mat):
     """
     """
-    clth_binned_dict = {}
-    for spin_pair, modes in zip(
-        ["spin0xspin0", "spin0xspin2", "spin2xspin2"],
-        [["TT"], ["TE", "TB"], ["EE", "EB", "BE", "BB"]]
-    ):
-
-        clth_vec = np.concatenate(
-            [clth[mode] for mode in modes]
-        ).reshape(len(modes), -1)
-        clth_binned = np.einsum("ijkl,kl", bpw_mat[spin_pair], clth_vec)
-        clth_binned_dict[spin_pair] = clth_binned
-
-    clth_binned = ps_utils.field_pairs_from_spins(clth_binned_dict)
-
-    to_update = []
-    for k, v in clth_binned.items():
-        if not (k[::-1] in clth_binned):
-            to_update.append((k[::-1], v))
+    modes = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"]
+    clth_vec = np.concatenate(
+        [clth[mode] for mode in modes]
+    ).reshape(len(modes), -1)
+    clth_binned = np.einsum("ijkl,kl->ij", bpw_mat, clth_vec)
+    print(clth_binned.shape)
 
-    for k, v in to_update:
-        clth_binned[k] = v
+    cl_out = {}
+    for i, m in enumerate(modes):
+        cl_out[m] = clth_binned[i]
+    return cl_out
 
-    return clth_binned
 
-
-def multipole_min_from_tf(tf_file, snr_cut=3.):
+def multipole_min_from_tf(tf_file, field_pairs, snr_cut=3.):
     """
     """
     tf = np.load(tf_file)
     idx_bad_tf = {}
-    for spin_pair in ["spin0xspin0", "spin0xspin2", "spin2xspin2"]:
-        tf_mean = tf[f"tf_{spin_pair}"][0, 0]
-        tf_std = tf[f"tf_std_{spin_pair}"][0, 0]
-        snr = tf_mean / tf_std
-        idx = np.where(snr < 3.)[0]
-        idx_bad_tf[spin_pair] = idx.max()
-
-    idx_bad_tf["spin2xspin0"] = idx_bad_tf["spin0xspin2"]
+    for fp in field_pairs:
+        name = f"{fp}_to_{fp}"
+        snr = tf[name] / tf[f"{name}_std"]
+        idx = np.where(snr < snr_cut)[0]
+        idx_bad_tf[fp] = idx.max()
 
     return idx_bad_tf
 
@@ -115,17 +96,24 @@ def plot_spectrum(lb, cb, cb_err, title, ylabel, xlim,
         plt.show()
 
 
-def sacc_plotter(args):
+def main(args):
     """
     This script will read the spectra and covariance
     stored in the `sacc` files and plot the power
     spectra.
     """
     meta = BBmeta(args.globals)
-    sacc_dir = meta.sacc_directory
-    coupling_dir = meta.coupling_directory
 
-    nmt_binning = meta.read_nmt_binning()
+    out_dir = meta.output_directory
+    sacc_dir = f"{out_dir}/saccs"
+    coupling_dir = f"{out_dir}/couplings"
+
+    plot_dir = f"{out_dir}/plots/sacc_spectra"
+    BBmeta.make_dir(plot_dir)
+
+    binning = np.load(meta.binning_file)
+    nmt_binning = nmt.NmtBin.from_edges(binning["bin_low"],
+                                        binning["bin_high"] + 1)
     lb = nmt_binning.get_effective_ells()
 
     field_pairs = [m1+m2 for m1, m2 in product("TEB", repeat=2)]
@@ -134,9 +122,12 @@ def sacc_plotter(args):
     spins = {"T": 0, "E": 2, "B": 2}
     types = {"T": "0", "E": "e", "B": "b"}
 
-    Nsims = meta.num_sims if args.sims else 1
+    if args.data:
+        Nsims = 1
+    elif args.sims:
+        Nsims = meta.covariance["cov_num_sims"]
 
-    psth = meta.load_fiducial_cl(cl_type="cosmo")
+    psth = np.load(meta.covariance["fiducial_cmb"])
     ps_th = {}
     for field_pair in field_pairs:
         if field_pair in psth:
@@ -148,26 +139,22 @@ def sacc_plotter(args):
     idx_bad_tf = {}
     bpw_mats = {}
 
+    transfer_dir = meta.transfer_settings["transfer_directory"]
     for ftag1, ftag2 in meta.get_independent_filtering_pairs():
         idx = multipole_min_from_tf(
-            f"{coupling_dir}/transfer_function_{ftag1}x{ftag2}.npz",
+            f"{transfer_dir}/transfer_function_{ftag1}_x_{ftag2}.npz",
+            field_pairs=field_pairs,
             snr_cut=3
         )
         idx_bad_tf[ftag1, ftag2] = idx
 
-        bpw_file = f"couplings_{ftag1}x{ftag2}_unfiltered.npz"
-        bpw_mats[ftag1, ftag2] = load_bpwins(f"{coupling_dir}/{bpw_file}")
-
-    # Bandpower window functions
-    fields_to_spin = {
-        "T": "spin0",
-        "E": "spin2",
-        "B": "spin2"
-    }
+    for ms1, ms2 in ps_names:
+        bpw_file = f"couplings_{ms1}_{ms2}.npz"
+        bpw_mats[ms1, ms2] = load_bpwins(f"{coupling_dir}/{bpw_file}")
 
     clth_binned = {
-        (ftag1, ftag2): binned_theory_from_unbinned(ps_th, bpw_mat)
-        for (ftag1, ftag2), bpw_mat in bpw_mats.items()
+        (ms1, ms2): binned_theory_from_unbinned(ps_th, bpw_mat)
+        for (ms1, ms2), bpw_mat in bpw_mats.items()
     }
 
     plot_data = {
@@ -192,14 +179,12 @@ def sacc_plotter(args):
 
             for fp in field_pairs:
                 mask_th = (psth["l"] <= 2 * meta.nside - 1)
-                x_th, y_th = psth["l"][mask_th], psth[fp][mask_th]
+                x_th, y_th = psth["l"][mask_th], ps_th[fp][mask_th]
 
-                s1, s2 = fields_to_spin[fp[0]], fields_to_spin[fp[1]]
-
-                idx_bad = idx_bad_tf[ftag1, ftag2][f"{s1}x{s2}"]
+                idx_bad = idx_bad_tf[ftag1, ftag2][fp]
                 mask = ((np.arange(len(lb)) > idx_bad) &
                         (lb <= 2 * meta.nside - 1))
-                th_binned = clth_binned[ftag1, ftag2][fp][mask]
+                th_binned = clth_binned[ms1, ms2][fp][mask]
 
                 plot_data[ms1, ms2, fp]["x_th"] = x_th
                 plot_data[ms1, ms2, fp]["y_th"] = y_th
@@ -216,7 +201,6 @@ def sacc_plotter(args):
 
             for fp in field_pairs:
                 f1, f2 = fp
-                s1, s2 = fields_to_spin[f1], fields_to_spin[f2]
 
                 ell, cl, cov = s.get_ell_cl(
                     f"cl_{types[f1]}{types[f2]}",
@@ -224,7 +208,7 @@ def sacc_plotter(args):
                     f"{ms2}_s{spins[f2]}",
                     return_cov=True)
 
-                idx_bad = idx_bad_tf[ftag1, ftag2][f"{s1}x{s2}"]
+                idx_bad = idx_bad_tf[ftag1, ftag2][fp]
                 mask = (
                     (np.arange(len(ell)) > idx_bad) &
                     (ell <= 2 * meta.nside - 1))
@@ -237,11 +221,6 @@ def sacc_plotter(args):
                 plot_data[ms1, ms2, fp]["title"] = f"{ms1} x {ms2} - {fp}"
                 plot_data[ms1, ms2, fp]["ylabel"] = fp
 
-    plot_dir = meta.plot_dir_from_output_dir(
-            meta.cell_sims_directory_rel if args.sims
-            else meta.cell_data_directory_rel
-    )
-
     for ms1, ms2 in ps_names:
         for fp in field_pairs:
 
@@ -275,4 +254,4 @@ def sacc_plotter(args):
     mode.add_argument("--sims", action="store_true")
     mode.add_argument("--data", action="store_true")
     args = parser.parse_args()
-    sacc_plotter(args)
+    main(args)
diff --git a/pipeline/simulations/generate_mock_cmb_sky.py b/pipeline/simulations/generate_mock_cmb_sky.py
new file mode 100644
index 0000000..40139b2
--- /dev/null
+++ b/pipeline/simulations/generate_mock_cmb_sky.py
@@ -0,0 +1,55 @@
+import argparse
+from soopercool import BBmeta
+import healpy as hp
+from soopercool import utils
+import numpy as np
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+
+    sims_dir = f"{out_dir}/cmb_sims"
+    BBmeta.make_dir(sims_dir)
+
+    lmax_sim = 3*meta.nside - 1
+
+    # Create the CMB fiducial cl
+    lth, clth = utils.get_theory_cls(
+        meta.covariance["cosmo"],
+        lmax=lmax_sim  # ensure cl accuracy up to lmax
+    )
+    np.savez(f"{sims_dir}/cl_theory.npz",
+             l=lth, **clth)
+
+    beams = {
+        ms: meta.read_beam(ms)[1]
+        for ms in meta.map_sets_list
+    }
+
+    hp_ordering = ["TT", "TE", "TB", "EE", "EB", "BB"]
+
+    for id_sim in range(meta.covariance["cov_num_sims"]):
+        alms = hp.synalm([clth[fp] for fp in hp_ordering])
+        for ms in meta.map_sets_list:
+
+            alms_beamed = [hp.almxfl(alm, beams[ms]) for alm in alms]
+
+            map = hp.alm2map(alms_beamed, nside=meta.nside)
+
+            import matplotlib.pyplot as plt
+            hp.mollview(map[1], title=f"{ms} - {id_sim}")
+            plt.show()
+
+            hp.write_map(f"{sims_dir}/cmb_{ms}_{id_sim:04d}.fits", map,
+                         overwrite=True, dtype=np.float32)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/simulations/generate_noise_from_data.py b/pipeline/simulations/generate_noise_from_data.py
new file mode 100644
index 0000000..5250fd1
--- /dev/null
+++ b/pipeline/simulations/generate_noise_from_data.py
@@ -0,0 +1,83 @@
+import argparse
+from soopercool import BBmeta
+import numpy as np
+import healpy as hp
+
+
+def build_noise_ps_matrix(nl_dict, map_sets_list):
+    """
+    """
+    ms_and_fields = [(ms, f) for ms in map_sets_list for f in "TEB"]
+    cls = []
+    for i, (ms1, f1) in enumerate(ms_and_fields):
+        for j, (ms2, f2) in enumerate(ms_and_fields):
+            if j < i:
+                continue
+            cls.append(nl_dict[ms1, ms2][f1+f2])
+    return cls
+
+
+def generate_noise_alms_from_cls(cls, map_sets_list):
+    """
+    """
+    noise_alms = hp.synalm(cls)
+
+    final_alms = {}
+    for i, ms in enumerate(map_sets_list):
+        alms = noise_alms[i*3:(i+1)*3]
+        final_alms[ms] = alms
+
+    return final_alms
+
+
+def generate_noise_maps_from_alms(final_alms, map_sets_list, nside):
+    """
+    """
+    noise_maps = {
+        ms: hp.alm2map(final_alms[ms], nside=nside)
+        for ms in map_sets_list
+    }
+    return noise_maps
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+    nl_dir = f"{out_dir}/noise_interp"
+
+    noise_sims_dir = f"{out_dir}/noise_sims"
+    BBmeta.make_dir(noise_sims_dir)
+
+    # Load noise power spectra
+    nl_dict = {}
+    for ms1, ms2 in meta.get_ps_names_list(type="all", coadd=True):
+        nl_file = f"{nl_dir}/nl_{ms1}_x_{ms2}.npz"
+        nl = np.load(nl_file)
+        nl_dict[ms1, ms2] = {k: nl[k] for k in nl.keys()}
+
+    cls = build_noise_ps_matrix(nl_dict, meta.map_sets_list)
+
+    for id_sim in range(meta.covariance["cov_num_sims"]):
+        for id_bundle in range(4):
+            noise_alms = generate_noise_alms_from_cls(cls, meta.map_sets_list)
+            noise_maps = generate_noise_maps_from_alms(
+                noise_alms,
+                meta.map_sets_list,
+                meta.nside
+            )
+
+            for ms in meta.map_sets_list:
+                fname = f"homogeneous_noise_{ms}_bundle{id_bundle}_{id_sim:04d}.fits" # noqa
+                hp.write_map(f"{noise_sims_dir}/{fname}", noise_maps[ms],
+                             overwrite=True,
+                             dtype=np.float32)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/simulations/generate_tf_estimation_sims.py b/pipeline/simulations/generate_tf_estimation_sims.py
new file mode 100644
index 0000000..53965cc
--- /dev/null
+++ b/pipeline/simulations/generate_tf_estimation_sims.py
@@ -0,0 +1,127 @@
+import argparse
+from soopercool import BBmeta, utils
+import numpy as np
+import healpy as hp
+import matplotlib.pyplot as plt
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+    # verbose = args.verbose
+    do_plots = not args.no_plots
+
+    out_dir = meta.output_directory
+    sim_dir = f"{out_dir}/tf_est_sims"
+    BBmeta.make_dir(sim_dir)
+
+    plot_dir = f"{out_dir}/plots/tf_est_sims"
+    if do_plots:
+        BBmeta.make_dir(plot_dir)
+
+    lmax_sim = 3 * meta.nside - 1
+    lth = np.arange(lmax_sim + 1)
+
+    tf_settings = meta.transfer_settings
+    cl_power_law_tf_est = utils.power_law_cl(
+        lth, **tf_settings["power_law_pars_tf_est"]
+    )
+    np.savez(f"{sim_dir}/cl_power_law_tf_est.npz",
+             ell=lth, **cl_power_law_tf_est)
+
+    Nsims = tf_settings["tf_est_num_sims"]
+
+    hp_ordering = ["TT", "TE", "TB", "EE", "EB", "BB"]
+
+    if tf_settings["beams_list"] is not None:
+        beams = {}
+        for beam_label in tf_settings["beams_list"]:
+            _, bl = meta.read_beam(beam_label)
+            beams[beam_label] = bl
+    else:
+        beams = {None: None}
+
+    for id_sim in range(Nsims):
+
+        almsTEB = hp.synalm(
+            [cl_power_law_tf_est[k] for k in hp_ordering],
+            lmax=lmax_sim
+        )
+
+        for beam_label, bl in beams.items():
+
+            suffix = ""
+            if tf_settings["do_not_beam_est_sims"]:
+                bl = None
+
+            elif beam_label is not None:
+                suffix = f"_{beam_label}"
+
+            sims = {
+                f"pure{f}": utils.generate_map_from_alms(
+                    almsTEB * select[:, None],
+                    meta.nside,
+                    bl=bl
+                )
+                for f, select in zip("TEB", np.eye(3))
+            }
+
+            for f in "TEB":
+                fname = f"pure{f}_power_law_tf_est_{id_sim:04d}{suffix}.fits"
+                hp.write_map(
+                    f"{sim_dir}/{fname}",
+                    sims[f"pure{f}"],
+                    overwrite=True,
+                    dtype=np.float32
+                )
+
+    if do_plots:
+        ps_hp_order = ["TT", "EE", "BB", "TE", "EB", "TB"]
+        for beam_label in beams:
+            suffix = "" if beam_label is None else f"_{beam_label}"
+            for f in "TEB":
+
+                cls_dict = {fp: [] for fp in hp_ordering}
+
+                for id_sim in range(Nsims):
+                    fname = f"pure{f}_power_law_tf_est_{id_sim:04d}{suffix}"
+                    alms = hp.map2alm(
+                        hp.read_map(
+                            f"{sim_dir}/{fname}.fits",
+                            field=[0, 1, 2]
+                        )
+                    )
+                    cls = hp.alm2cl(alms)
+
+                    for i, fp in enumerate(ps_hp_order):
+                        cls_dict[fp] += [cls[i]]
+
+                for fp in ps_hp_order:
+                    cls_dict[fp] = np.mean(cls_dict[fp], axis=0)
+
+                plt.figure(figsize=(10, 8))
+                plt.xlabel(r"$\ell$", fontsize=15)
+                plt.ylabel(r"$C_\ell$", fontsize=15)
+                for fp in ps_hp_order:
+                    plt.plot(cls_dict[fp], label=fp, lw=1.7)
+                plt.yscale("symlog", linthresh=1e-6)
+                plt.xlim(0, lmax_sim)
+                plt.legend()
+                plt.title(f"Power law pure{f} simulation")
+                plt.savefig(f"{plot_dir}/power_law_pure{f}{suffix}.png",
+                            bbox_inches="tight")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate pureT/E/B simulations \
+                     for transfer function estimation")
+    parser.add_argument("--globals", type=str,
+                        help="Path to the yaml with global parameters")
+    parser.add_argument("--no-plots", action="store_true",
+                        help="Pass to generate plots")
+    parser.add_argument("--verbose", action="store_true")
+
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/transfer/compute_pseudo_cells_tf_estimation.py b/pipeline/transfer/compute_pseudo_cells_tf_estimation.py
new file mode 100644
index 0000000..745883e
--- /dev/null
+++ b/pipeline/transfer/compute_pseudo_cells_tf_estimation.py
@@ -0,0 +1,105 @@
+import argparse
+from soopercool import BBmeta
+import healpy as hp
+import pymaster as nmt
+import numpy as np
+from soopercool import ps_utils
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+    out_dir = meta.output_directory
+
+    pcls_tf_est_dir = f"{out_dir}/cells_tf_est"
+    BBmeta.make_dir(pcls_tf_est_dir)
+
+    binning = np.load(meta.binning_file)
+    nmt_bins = nmt.NmtBin.from_edges(binning["bin_low"],
+                                     binning["bin_high"] + 1)
+
+    mask_file = meta.masks["analysis_mask"]
+    mask = hp.read_map(mask_file)
+
+    filtering_tags = meta.get_filtering_tags()
+    filtering_tag_pairs = meta.get_independent_filtering_pairs()
+
+    if None in filtering_tags and len(filtering_tags) < 1:
+        raise ValueError("There must be at least one filter \
+                          applied to the data to be able to \
+                          compute a transfer function for it")
+
+    tf_settings = meta.transfer_settings
+
+    for id_sim in range(tf_settings["tf_est_num_sims"]):
+
+        fields = {
+            ftag: {
+                "filtered": {},
+                "unfiltered": {}
+            } for ftag in filtering_tags
+        }
+
+        for ftag in filtering_tags:
+            for pure_type in ["pureT", "pureE", "pureB"]:
+
+                unfiltered_map_dir = tf_settings["unfiltered_map_dir"][ftag]
+                unfiltered_map_tmpl = tf_settings["unfiltered_map_template"][ftag] # noqa
+
+                unfiltered_map_file = unfiltered_map_tmpl.format(
+                    id_sim=id_sim, pure_type=pure_type
+                )
+                unfiltered_map_file = f"{unfiltered_map_dir}/{unfiltered_map_file}" # noqa
+
+                filtered_map_dir = tf_settings["filtered_map_dir"][ftag]
+                filtered_map_tmpl = tf_settings["filtered_map_template"][ftag]
+                filtered_map_file = filtered_map_tmpl.format(
+                    id_sim=id_sim, pure_type=pure_type
+                )
+                filtered_map_file = f"{filtered_map_dir}/{filtered_map_file}"
+
+                map = hp.read_map(unfiltered_map_file,
+                                  field=[0, 1, 2])
+                map_filtered = hp.read_map(filtered_map_file,
+                                           field=[0, 1, 2])
+
+                field = {
+                    "spin0": nmt.NmtField(mask, map[:1]),
+                    "spin2": nmt.NmtField(mask, map[1:],
+                                          purify_b=meta.pure_B)
+                }
+
+                field_filtered = {
+                    "spin0": nmt.NmtField(mask, map_filtered[:1]),
+                    "spin2": nmt.NmtField(mask, map_filtered[1:],
+                                          purify_b=meta.pure_B)
+                }
+
+                fields[ftag]["unfiltered"][pure_type] = field
+                fields[ftag]["filtered"][pure_type] = field_filtered
+
+        for ftag1, ftag2 in filtering_tag_pairs:
+            if ftag1 is None and ftag2 is None:
+                continue
+
+            pcls_mat_filtered = ps_utils.get_pcls_mat_transfer(
+                fields[ftag1]["filtered"],
+                nmt_bins, fields2=fields[ftag2]["filtered"]
+            )
+            pcls_mat_unfiltered = ps_utils.get_pcls_mat_transfer(
+                fields[ftag1]["unfiltered"],
+                nmt_bins, fields2=fields[ftag2]["unfiltered"]
+            )
+
+            np.savez(f"{pcls_tf_est_dir}/pcls_mat_tf_est_{ftag1}_x_{ftag2}_filtered_{id_sim:04d}.npz", # noqa
+                     pcls_mat=pcls_mat_filtered)
+            np.savez(f"{pcls_tf_est_dir}/pcls_mat_tf_est_{ftag1}_x_{ftag2}_unfiltered_{id_sim:04d}.npz", # noqa
+                     pcls_mat=pcls_mat_unfiltered)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/transfer/compute_pseudo_cells_tf_validation.py b/pipeline/transfer/compute_pseudo_cells_tf_validation.py
new file mode 100644
index 0000000..e69de29
diff --git a/pipeline/transfer/compute_transfer_function.py b/pipeline/transfer/compute_transfer_function.py
new file mode 100644
index 0000000..1b7a634
--- /dev/null
+++ b/pipeline/transfer/compute_transfer_function.py
@@ -0,0 +1,61 @@
+import argparse
+from soopercool import BBmeta
+from soopercool import coupling_utils as cu
+import numpy as np
+
+
+def main(args):
+    """
+    """
+    meta = BBmeta(args.globals)
+
+    out_dir = meta.output_directory
+    tf_dir = f"{out_dir}/transfer_functions"
+    BBmeta.make_dir(tf_dir)
+
+    cells_dir = f"{out_dir}/cells_tf_est"
+
+    tf_settings = meta.transfer_settings
+
+    filtering_pairs = meta.get_independent_filtering_pairs()
+
+    pcls_mat_dict = cu.read_pcls_matrices(
+        cells_dir, filtering_pairs,
+        tf_settings["tf_est_num_sims"]
+    )
+
+    # Average the pseudo-cl matrices
+    pcls_mat_filtered_mean = cu.average_pcls_matrices(
+        pcls_mat_dict,
+        filtering_pairs,
+        filtered=True
+    )
+    pcls_mat_unfiltered_mean = cu.average_pcls_matrices(
+        pcls_mat_dict,
+        filtering_pairs,
+        filtered=False
+    )
+
+    # Compute and save the transfer functions
+    trans = cu.get_transfer_dict(
+        pcls_mat_filtered_mean,
+        pcls_mat_unfiltered_mean,
+        pcls_mat_dict,
+        filtering_pairs
+    )
+
+    full_tf = {}
+    for ftag1, ftag2 in filtering_pairs:
+        tf = trans[ftag1, ftag2]
+        np.savez(
+            f"{tf_dir}/transfer_function_{ftag1}_x_{ftag2}.npz",
+            **tf
+        )
+        full_tf[ftag1, ftag2] = tf["full_tf"]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--globals", help="Path to the global parameter file.")
+    args = parser.parse_args()
+    main(args)
diff --git a/pipeline/transfer/validate_transfer_function.py b/pipeline/transfer/validate_transfer_function.py
new file mode 100644
index 0000000..e69de29
diff --git a/soopercool/coupling_utils.py b/soopercool/coupling_utils.py
index 42c86bb..9e26b8e 100644
--- a/soopercool/coupling_utils.py
+++ b/soopercool/coupling_utils.py
@@ -44,8 +44,7 @@ def get_transfer_with_error(mean_pcls_mat_filt,
 def get_transfer_dict(mean_pcls_mat_filt_dict,
                       mean_pcls_mat_unfilt_dict,
                       pcls_mat_dict,
-                      filtering_pairs,
-                      spin_pairs):
+                      filtering_pairs):
     """
     """
     tf_dict = {(ftag1, ftag2): {} for ftag1, ftag2 in filtering_pairs}
@@ -72,7 +71,7 @@ def get_transfer_dict(mean_pcls_mat_filt_dict,
     return tf_dict
 
 
-def read_pcls_matrices(pcls_mat_dir, filtering_pairs, spin_pairs, Nsims):
+def read_pcls_matrices(pcls_mat_dir, filtering_pairs, Nsims):
     """
     """
     pcls_mat_dict = {
@@ -87,7 +86,7 @@ def read_pcls_matrices(pcls_mat_dir, filtering_pairs, spin_pairs, Nsims):
     for id_sim in range(Nsims):
         for label in ["filtered", "unfiltered"]:
             for ftag1, ftag2 in filtering_pairs:
-                suffix = f"{ftag1}x{ftag2}_{label}_{id_sim:04d}"
+                suffix = f"{ftag1}_x_{ftag2}_{label}_{id_sim:04d}"
                 pcls_mat = np.load(
                     f"{pcls_mat_dir}/pcls_mat_tf_est_{suffix}.npz")
                 pcls_mat_dict[ftag1, ftag2][label] += [pcls_mat["pcls_mat"]]
@@ -95,7 +94,7 @@ def read_pcls_matrices(pcls_mat_dir, filtering_pairs, spin_pairs, Nsims):
     return pcls_mat_dict
 
 
-def load_mcms(coupling_dir, spin_pairs, ps_names=None, full_mcm=False):
+def load_mcms(coupling_dir, ps_names=None, full_mcm=False):
     """
     """
     file_root = "mcm"
@@ -135,7 +134,7 @@ def read_mcm(mcm_file, binned=False, full_mcm=False):
 
 
 def average_pcls_matrices(pcls_mat_dict, filtering_pairs,
-                          spin_pairs, filtered):
+                          filtered):
     """
     """
     label = "filtered" if filtered else "unfiltered"
@@ -179,7 +178,7 @@ def compute_couplings(mcm, nmt_binning, transfer=None):
     return bpw_windows, inv_coupling
 
 
-def get_couplings_dict(mcm_dict, nmt_binning, spin_pairs,
+def get_couplings_dict(mcm_dict, nmt_binning,
                        transfer_dict=None,
                        ps_names_and_ftags=None,
                        filtering_pairs=None):
diff --git a/soopercool/map_utils.py b/soopercool/map_utils.py
new file mode 100644
index 0000000..5d65756
--- /dev/null
+++ b/soopercool/map_utils.py
@@ -0,0 +1,52 @@
+import healpy as hp
+import matplotlib.pyplot as plt
+
+
+def read_map(map_file, ncomp):
+    """
+    """
+    return hp.read_map(map_file, field=[i for i in range(ncomp)])
+
+
+def write_map(map_file, map, dtype=None):
+    """
+    """
+    hp.write_map(map_file, map, overwrite=True, dtype=dtype)
+
+
+def plot_map(map, title=None, file_name=None, lims=None):
+    """
+    """
+    ncomp = map.shape[0] if len(map.shape) == 2 else 1
+    cmap = "YlOrRd" if ncomp == 1 else "RdYlBu_r"
+    kwargs = {"title": title, "cmap": cmap}
+
+    if lims is None:
+        range_args = [{} for i in range(ncomp)]
+
+    if ncomp == 1:
+        if lims is not None:
+            range_args = [{
+                "min": lims[0],
+                "max": lims[1]
+            }]
+        to_plot = [map]
+
+    elif ncomp == 3:
+        if lims is not None:
+            range_args = [
+                {
+                    "min": lims[i][0],
+                    "max": lims[i][1]
+                } for i in lims(3)
+            ]
+    for i in range(ncomp):
+        hp.mollview(to_plot[i], **kwargs, **range_args[i])
+
+        if file_name:
+            if ncomp == 1:
+                plt.savefig(f"{file_name}.png", bbox_inches="tight")
+            else:
+                plt.savefig(f"{file_name}_{'TQU'[i]}.png", bbox_inches="tight")
+        else:
+            plt.show()
diff --git a/soopercool/metadata_manager.py b/soopercool/metadata_manager.py
index e746686..87c8946 100644
--- a/soopercool/metadata_manager.py
+++ b/soopercool/metadata_manager.py
@@ -23,31 +23,20 @@ def __init__(self, fname_config):
         """
 
         # Load the configuration file
-        with open(fname_config) as f:
-            self.config = yaml.load(f, Loader=yaml.FullLoader)
+        self.config = self._yaml_loader(fname_config)
 
         # Set the high-level parameters as attributes
         for key in self.config:
             setattr(self, key, self.config[key])
 
-        # Set all the `_directory` attributes
-        self._set_directory_attributes()
-
         # Set the general attributes (nside, lmax, etc...)
         self._set_general_attributes()
 
-        # Copy the configuration file to output directory
-        with open(f"{self.output_dirs['root']}/config.yaml", "w") as f:
-            yaml.dump(self.config, f)
-
         # Basic sanity checks
         if self.lmax > 3*self.nside-1:
             raise ValueError("lmax should be lower or equal "
                              f"to 3*nside-1 = {3*self.nside-1}")
 
-        # Path to binning
-        self.path_to_binning = f"{self.pre_process_directory}/{self.binning_file}"  # noqa
-
         # Initialize method to parse map_sets metadata
         map_sets_attributes = list(self.map_sets[
             next(iter(self.map_sets))].keys())
@@ -58,41 +47,19 @@ def __init__(self, fname_config):
         self.map_sets_list = self._get_map_sets_list()
         self.maps_list = self._get_map_list()
 
-        # Determine if input hit counts map exists
-        self.use_input_nhits = (self.masks["input_nhits_path"] is not None)
-
-        # Initialize masks file_names
-        for mask_type in ["binary_mask", "galactic_mask", "point_source_mask",
-                          "analysis_mask", "nhits_map"]:
-            setattr(
-                self,
-                f"{mask_type}_name",
-                getattr(self, f"_get_{mask_type}_name")()
-            )
-
-        # Simulation
-        self._init_simulation_params()
-
-        # Filtering
-        self._init_filtering_params()
-
-        # Tf estimation
-        self.tf_est_sims_dir = f"{self.pre_process_directory}/tf_est_sims"
-        self.tf_val_sims_dir = f"{self.pre_process_directory}/tf_val_sims"
-        self.cosmo_sims_dir = f"{self.pre_process_directory}/cosmo_sims"
-
-        # Fiducial cls
-        self.cosmo_cls_file = f"{self.pre_process_directory}/cosmo_cls.npz"
-        self.tf_est_cls_file = f"{self.pre_process_directory}/tf_est_cls.npz"
-        self.tf_val_cls_file = f"{self.pre_process_directory}/tf_val_cls.npz"
-        self.noise_cls_file = {
-            map_set: f"{self.pre_process_directory}/noise_cls_{map_set}.npz"
-            for map_set in self.map_sets_list
-        }
-
         # Initialize a timer
         self.timer = Timer()
 
+    def _yaml_loader(self, config):
+        """
+        Custom yaml loader to load the configuration file.
+        """
+        def path_constructor(loader, node):
+            return "/".join(loader.construct_sequence(node))
+        yaml.SafeLoader.add_constructor("!path", path_constructor)
+        with open(config, "r") as f:
+            return yaml.load(f, Loader=yaml.SafeLoader)
+
     def _set_directory_attributes(self):
         """
         Set the directory attributes that are listed
@@ -136,7 +103,7 @@ def _get_map_list(self):
         """
         out_list = [
             f"{map_set}__{id_split}" for map_set in self.map_sets_list
-                for id_split in range(self.n_splits_from_map_set(map_set))  # noqa
+                for id_split in range(self.n_bundles_from_map_set(map_set))  # noqa
         ]
         return out_list
 
@@ -272,8 +239,9 @@ def get_effective_ells(self):
     def read_beam(self, map_set):
         """
         """
-        file_root = self.file_root_from_map_set(map_set)
-        beam_file = f"{self.beam_directory}/beam_{file_root}.dat"
+        beam_dir = self.beam_dir_from_map_set(map_set)
+        beam_file = self.beam_file_from_map_set(map_set)
+        beam_file = f"{beam_dir}/{beam_file}"
         l, bl = np.loadtxt(beam_file, unpack=True)
         if self.beam_floor is not None:
             bl[bl < self.beam_floor] = self.beam_floor
@@ -408,7 +376,7 @@ def get_map_filename(self, map_set, id_split, id_sim=None):
     def get_filter_function(self, filter_tag):
         from soopercool.utils import m_filter_map, toast_filter_map
 
-        tag_settings = self.tags_settings[filter_tag]
+        tag_settings = self.filtering["tags_settings"][filter_tag]
         filtering_type = tag_settings["filtering_type"]
 
         if filtering_type == "m_filterer":
@@ -428,13 +396,13 @@ def get_filter_function(self, filter_tag):
             filter_function = toast_filter_map
         else:
             raise NotImplementedError(
-                f"Filterer type {self.filtering_type} "
+                f"Filterer type {tag_settings['filtering_type']} "
                 "not implemented"
             )
 
-        def filter_operation(map, map_file, mask, extra_kwargs={}):
+        def filter_operation(map_file, mask_file, out_dir, extra_kwargs={}):
             return filter_function(
-                map, map_file, mask, **kwargs, **extra_kwargs)
+                map_file, mask_file, out_dir, **kwargs, **extra_kwargs)
 
         return filter_operation
 
@@ -646,8 +614,8 @@ def get_n_split_pairs_from_map_sets(self, map_set_1, map_set_2,
             noise-biased spectra, while "cross" returns all unique
             noise-biased spectra. "all" is the union of both.
         """
-        n_splits_1 = self.n_splits_from_map_set(map_set_1)
-        n_splits_2 = self.n_splits_from_map_set(map_set_2)
+        n_splits_1 = self.n_bundles_from_map_set(map_set_1)
+        n_splits_2 = self.n_bundles_from_map_set(map_set_2)
         exp_tag_1 = self.exp_tag_from_map_set(map_set_1)
         exp_tag_2 = self.exp_tag_from_map_set(map_set_2)
         if type == "cross":
@@ -712,6 +680,18 @@ def get_inverse_couplings(self, beamed=False):
                     inv_couplings[filter_flag] = c
         return inv_couplings
 
+    @classmethod
+    def make_dir(cls, dir):
+        """
+        Make a directory if it does not exist.
+
+        Parameters
+        ----------
+        dir : str
+            Path to the directory.
+        """
+        os.makedirs(dir, exist_ok=True)
+
 
 class Timer:
     """
diff --git a/soopercool/mpi_utils.py b/soopercool/mpi_utils.py
index 71df7a4..c2487a0 100755
--- a/soopercool/mpi_utils.py
+++ b/soopercool/mpi_utils.py
@@ -5,10 +5,10 @@
 
 # set the default value
 _initialized = False
-_switch      = False
-rank    = 0
-size    = 1
-comm    = None
+_switch = False
+rank = 0
+size = 1
+comm = None
 
 
 def print_rnk0(text, rank):
@@ -27,24 +27,26 @@ def init(switch=False):
         _initialized = True
     else:
         print("MPI is already intialized")
-        return exit_code 
+        return exit_code
 
     if not switch:
-        print("WARNING: MPI is turned off by default. Use mpi.init(switch=True) to initialize MPI")
+        print("WARNING: MPI is turned off by default. "
+              "Use mpi.init(switch=True) to initialize MPI")
         print("MPI is turned off")
         return exit_code
     else:
         _switch = True
 
-    try:        
-        from  mpi4py import MPI
-        comm    = MPI.COMM_WORLD
-        rank    = comm.Get_rank()
-        size    = comm.Get_size()
-        print("MPI: rank %d is initalized" %rank)
+    try:
+        from mpi4py import MPI
+        comm = MPI.COMM_WORLD
+        rank = comm.Get_rank()
+        size = comm.Get_size()
+        print("MPI: rank %d is initalized" % rank)
 
     except ImportError as exc:
-        sys.stderr.write("IMPORT ERROR: " + __file__ + " (" + str(exc) + "). Could not load mpi4py. MPI will not be used.\n")
+        sys.stderr.write("IMPORT ERROR: " + __file__ + " (" + str(exc) + "). "
+                         "Could not load mpi4py. MPI will not be used.\n")
 
 
 def is_initialized():
@@ -62,10 +64,12 @@ def taskrange(imax, imin=0, shift=0):
     """
     global rank, size
 
-    if not isinstance(imin, int) or not isinstance(imax, int) or not isinstance(shift, int):
+    if (not isinstance(imin, int) or not isinstance(imax, int)
+            or not isinstance(shift, int)):
         raise TypeError("imin, imax and shift must be integers")
     elif not is_initialized():
-        print("MPI is not yet properly initialized. Are you sure this is what you want to do?")
+        print("MPI is not yet properly initialized. "
+              "Are you sure this is what you want to do?")
 
     if not is_mpion():
         return np.arange(imin, imax + 1)
@@ -73,14 +77,14 @@ def taskrange(imax, imin=0, shift=0):
     ntask = math.ceil((imax - imin + 1)/size)*size
 
     subrange = None
-    if ntask <= 0 :
+    if ntask <= 0:
         print_rnk0("number of task can't be zero", rank)
-        subrange = np.arange(0, 0) # return zero range
+        subrange = np.arange(0, 0)  # return zero range
     else:
         if ntask != imax - imin + 1:
             print_rnk0(f"WARNING: setting ntask={ntask}", rank)
         perrank = ntask // size
         print_rnk0(f"Running {ntask} simulations on {size} nodes", rank)
         subrange = np.arange(rank*perrank, (rank + 1)*perrank)
-    
+
     return subrange
diff --git a/soopercool/utils.py b/soopercool/utils.py
index e209bee..1bc38b0 100644
--- a/soopercool/utils.py
+++ b/soopercool/utils.py
@@ -186,11 +186,17 @@ def beam_hpix(ll, nside):
     return beam_gaussian(ll, fwhm_hp_amin)
 
 
-def create_binning(nside, delta_ell):
+def create_binning(nside, delta_ell, end_first_bin=None):
     """
     """
-    bin_low = np.arange(0, 3*nside, delta_ell)
-    bin_high = bin_low + delta_ell - 1
+    if end_first_bin is not None:
+        bin_low = np.arange(end_first_bin, 3*nside, delta_ell)
+        bin_high = bin_low + delta_ell - 1
+        bin_low = np.concatenate(([0], bin_low))
+        bin_high = np.concatenate(([end_first_bin-1], bin_high))
+    else:
+        bin_low = np.arange(0, 3*nside, delta_ell)
+        bin_high = bin_low + delta_ell - 1
     bin_high[-1] = 3*nside - 1
     bin_center = (bin_low + bin_high) / 2
 
@@ -212,7 +218,46 @@ def power_law_cl(ell, amp, delta_ell, power_law_index):
     return pl_ps
 
 
-def m_filter_map(map, map_file, mask, m_cut):
+def m_filter_map(map_file, mask_file, out_dir, m_cut):
+    """
+    Applies the m-cut mock filter to a given map with a given sky mask.
+
+    Parameters
+    ----------
+    map : array-like
+        Healpix TQU map to be filtered.
+    map_file : str
+        File path of the unfiltered map.
+    mask : array-like
+        Healpix map storing the sky mask.
+    m_cut : int
+        Maximum nonzero m-degree of the multipole expansion. All higher
+        degrees are set to zero.
+    """
+    map = hp.read_map(map_file, field=(0, 1, 2))
+    mask = hp.read_map(mask_file)
+    mask[mask != 0] = 1.
+
+    map_masked = map * mask
+    nside = hp.get_nside(map)
+    lmax = 3 * nside - 1
+
+    alms = hp.map2alm(map_masked, lmax=lmax)
+
+    n_modes_to_filter = (m_cut + 1) * (lmax + 1) - ((m_cut + 1) * m_cut) // 2
+    alms[:, :n_modes_to_filter] = 0.
+
+    filtered_map = hp.alm2map(alms, nside=nside, lmax=lmax)
+
+    fname = os.path.basename(map_file)
+    fname_out = fname.replace(".fits", "_filtered.fits")
+
+    hp.write_map(f"{out_dir}/{fname_out}",
+                 filtered_map, overwrite=True,
+                 dtype=np.float32)
+
+
+def m_filter_map_old(map, map_file, mask, m_cut):
     """
     Applies the m-cut mock filter to a given map with a given sky mask.