Figure2B-native-anchor-stackups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97f9fef6-8b4c-43ff-a807-8ff097f5c7f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the number of threads for many common libraries\n",
    "from os import environ\n",
    "N_THREADS = '1'\n",
    "environ['OMP_NUM_THREADS'] = N_THREADS\n",
    "environ['OPENBLAS_NUM_THREADS'] = N_THREADS\n",
    "environ['MKL_NUM_THREADS'] = N_THREADS\n",
    "environ['VECLIB_MAXIMUM_THREADS'] = N_THREADS\n",
    "environ['NUMEXPR_NUM_THREADS'] = N_THREADS\n",
    "\n",
    "# https://superfastpython.com/numpy-number-blas-threads/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7d52a96-52d5-4b0a-b50e-d3b5ae0f7649",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# from saddle import saddleplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d77a148-ef01-4d12-938b-7e27bbe92e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.print_figure_kwargs={'bbox_inches':None}\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from itertools import chain\n",
    "\n",
    "# Hi-C utilities imports:\n",
    "import cooler\n",
    "import bioframe\n",
    "import cooltools\n",
    "from cooltools.lib.numutils import fill_diag\n",
    "\n",
    "# Visualization imports:\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.ticker import EngFormatter\n",
    "\n",
    "from itertools import cycle\n",
    "\n",
    "# from ipywidgets import interact, fixed\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "\n",
    "# enable editable text ...\n",
    "mpl.rcParams[\"pdf.fonttype\"]=42\n",
    "mpl.rcParams[\"svg.fonttype\"]=\"none\"\n",
    "mpl.rcParams['axes.linewidth'] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1518684-b18d-427f-a8ac-4dc9196da45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install --upgrade --no-cache --no-deps --ignore-install cooler\n",
    "# ls /home/dekkerlab/dots-test\n",
    "# import higlass as hg\n",
    "# import jscatter\n",
    "import scipy\n",
    "import logging\n",
    "import multiprocess as mp\n",
    "# import mpire for nested multi-processing\n",
    "from mpire import WorkerPool\n",
    "# bbi for stackups ...\n",
    "import bbi\n",
    "from data_catalog import bws, bws_vlim, telo_dict\n",
    "from helper_func import (\n",
    "    get_stack,\n",
    "    show_stacks,\n",
    "    plot_stackups_lite,\n",
    "    plot_stackups_sets,\n",
    "    plot_stackups_sets_new,\n",
    "    to_bigbed3,\n",
    ")\n",
    "\n",
    "\n",
    "from tqdm import tqdm\n",
    "from tqdm.notebook import trange, tqdm\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df5ece7e-4708-4044-9b69-e445bd3af6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bws_vlim = {\n",
    "    \"mM.atac\": dict(vmin=0.2,vmax=1.),\n",
    "    # \"mT.atac\": dict(vmin=0.005,vmax=0.3),\n",
    "    \"mG.atac\": dict(vmin=0.2,vmax=1.),\n",
    "    # \"pM.atac\": dict(vmin=0.005,vmax=0.3),\n",
    "    # # \"pT.atac\": dict(vmin=0.005,vmax=0.3),\n",
    "    \"pG.atac\": dict(vmin=0.2,vmax=1.),\n",
    "    \"H3K36me3\": dict(vmin=0.03,vmax=0.08),\n",
    "    \"H3K4me1\": dict(vmin=0.04,vmax=0.1),\n",
    "    \"MED1\": dict(vmin=0.04,vmax=0.15),\n",
    "    \"H3K27me3\": dict(vmin=0.2,vmax=1),\n",
    "    \"H3K4me3\": dict(vmin=1,vmax=14),\n",
    "    \"H3K27ac\": dict(vmin=1,vmax=14),\n",
    "    \"ctcf\": dict(vmin=2,vmax=5),\n",
    "    # some rna-seq - Async rangap control and depletion ...\n",
    "    \"rg0r1fwd\": dict(vmin=0.5,vmax=12),\n",
    "    \"rg0r1rev\": dict(vmin=0.5,vmax=12),\n",
    "    \"rg8r1fwd\": dict(vmin=0.5,vmax=12),\n",
    "    \"rg8r1rev\": dict(vmin=0.5,vmax=12),\n",
    "}\n",
    "\n",
    "\n",
    "bws_vlim[\"mM.atac\"] = dict(vmin=0.1,vmax=1.)\n",
    "bws_vlim[\"mG.atac\"] = dict(vmin=0.15,vmax=1.)\n",
    "bws_vlim[\"pG.atac\"] = dict(vmin=0.1,vmax=1.)\n",
    "bws_vlim[\"H3K27me3\"] = dict(vmin=0.15,vmax=0.85)\n",
    "bws_vlim[\"H3K9me3\"] = dict(vmin=0.15,vmax=1)\n",
    "bws_vlim[\"H3K4me3\"] = dict(vmin=0.5,vmax=14)\n",
    "bws_vlim[\"H3K27ac\"] = dict(vmin=0.9,vmax=18)\n",
    "bws_vlim[\"ctcf\"] = dict(vmin=1.8,vmax=6)\n",
    "bws_vlim[\"evm\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "bws_vlim[\"evp\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "bws_vlim[\"dots\"] = dict(vmin=0,vmax=0.5)\n",
    "# # # some rna-seq - Async rangap control and depletion ...\n",
    "bws_vlim[\"rna\"] = dict(vmin=0.5,vmax=40)\n",
    "bws_vlim[\"rg0r1fwd\"] = dict(vmin=0.5,vmax=35)\n",
    "bws_vlim[\"rg0r1rev\"] = dict(vmin=0.5,vmax=35)\n",
    "bws_vlim[\"rg8r1fwd\"] = dict(vmin=0.5,vmax=35)\n",
    "bws_vlim[\"rg8r1rev\"] = dict(vmin=0.5,vmax=35)\n",
    "\n",
    "\n",
    "from palettable.scientific import sequential\n",
    "lajolla_r = sequential.LaJolla_10_r.get_mpl_colormap()\n",
    "bilbao_r = sequential.Bilbao_20_r.get_mpl_colormap()\n",
    "batlow_r = sequential.Batlow_20_r.get_mpl_colormap()\n",
    "devon_r = sequential.Devon_20_r.get_mpl_colormap()\n",
    "davos_r = sequential.Davos_20_r.get_mpl_colormap()\n",
    "# fall_cmap = fall\n",
    "lajolla = sequential.LaJolla_10.get_mpl_colormap()\n",
    "bilbao = sequential.Bilbao_20.get_mpl_colormap()\n",
    "batlow = sequential.Batlow_20.get_mpl_colormap()\n",
    "devon = sequential.Devon_20.get_mpl_colormap()\n",
    "davos = sequential.Davos_20.get_mpl_colormap()\n",
    "\n",
    "imola_r = sequential.Imola_20_r.get_mpl_colormap()\n",
    "imola = sequential.Imola_20.get_mpl_colormap()\n",
    "\n",
    "nuuk_r = sequential.Nuuk_20_r.get_mpl_colormap()\n",
    "nuuk = sequential.Nuuk_20.get_mpl_colormap()\n",
    "\n",
    "hawaii_r = sequential.Hawaii_20_r.get_mpl_colormap()\n",
    "hawaii = sequential.Hawaii_20.get_mpl_colormap()\n",
    "\n",
    "oleron_r = sequential.Oleron_20_r.get_mpl_colormap()\n",
    "oleron = sequential.Oleron_20.get_mpl_colormap()\n",
    "\n",
    "oslo_r = sequential.Oslo_20_r.get_mpl_colormap()\n",
    "oslo = sequential.Oslo_20.get_mpl_colormap()\n",
    "\n",
    "bamako_r = sequential.Bamako_20_r.get_mpl_colormap()\n",
    "bamako = sequential.Bamako_20.get_mpl_colormap()\n",
    "\n",
    "grayc_r = sequential.GrayC_20_r.get_mpl_colormap()\n",
    "grayc = sequential.GrayC_20.get_mpl_colormap()\n",
    "\n",
    "lapaz_r = sequential.LaPaz_20_r.get_mpl_colormap()\n",
    "lapaz = sequential.LaPaz_20.get_mpl_colormap()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba3fb11-3c0e-4213-91a4-62d32303f187",
   "metadata": {},
   "source": [
    "### arms here just in case ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "913bc019-e027-4cd1-8d2c-1b480839fb05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define genomic view that will be used to call dots and pre-compute expected\n",
    "\n",
    "# Use bioframe to fetch the genomic features from the UCSC.\n",
    "hg38_chromsizes = bioframe.fetch_chromsizes('hg38')\n",
    "hg38_cens = bioframe.fetch_centromeres('hg38')\n",
    "hg38_arms_full = bioframe.make_chromarms(hg38_chromsizes, hg38_cens)\n",
    "# autosomal only ...\n",
    "included_arms = hg38_arms_full[\"name\"].to_list()[:44]\n",
    "hg38_arms = hg38_arms_full[hg38_arms_full[\"name\"].isin(included_arms)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2badbdfe-bbf4-406c-b743-7427624ce954",
   "metadata": {},
   "source": [
    "# Read pre-called Intrinsic Domains (IDs) ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "611ded10-8329-46e5-95f7-d8318203646b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids_fnames = {\n",
    "    \"mega_2X_enrichment\": \"native_comps_10kb/fourth_mega.bedpe\",\n",
    "    \"5hr_2X_enrichment_old\": \"native_comps_10kb/second_bulk.bedpe\",\n",
    "    \"5hr_2X_enrichment\": \"native_comps_10kb/5hr_2X_second.bedpe\",\n",
    "    \"mega_3X_enrichment\": \"native_comps_10kb/fifth_mega3x.bedpe\",\n",
    "    \"cyto_2x_enrichment\": \"native_comps_10kb/third_mCyto.bedpe\",\n",
    "}\n",
    "\n",
    "# let's load them all into a dictionary ...\n",
    "ids_dict = {}\n",
    "for id_name, fname in ids_fnames.items():\n",
    "    ids_dict[id_name] = pd.read_csv(fname, sep=\"\\t\")\n",
    "    # ...\n",
    "    print(f\"loaded {len(ids_dict[id_name]):5d} ID interactions {id_name:>20} in BEDPE format ...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e61b886-642a-4ab9-9660-bdab319b5c72",
   "metadata": {},
   "source": [
    "# Read pre-called Intrinsic Domain anchors - ID anchors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f169671-ae27-4fa1-b62e-11ced9238e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_anchor_fnames = {\n",
    "    \"mega_2X_enrichment\": \"ID_anchors/mega_2X_enrichment.fourth_mega.max_size.bed\",\n",
    "    \"5hr_2X_enrichment_old\": \"ID_anchors/5hr_2X_enrichment.second_bulk.max_size.bed\",\n",
    "    \"5hr_2X_enrichment\": \"ID_anchors/5hr_2X_enrichment.pixel_derived.bed\",\n",
    "    \"5hr_2X_enrichment_nosing\": \"ID_anchors/5hr_2X_enrichment.pixel_derived.no_singletons.bed\",\n",
    "    \"5hr_2X_enrichment_signal\": \"ID_anchors/5hr_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"10hr_2X_enrichment_signal\": \"ID_anchors/10hrs_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"N93p5_2X_enrichment_signal\": \"ID_anchors/N93p5_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"pCyto_2X_enrichment_signal\": \"ID_anchors/pCyto_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"mCyto_2X_enrichment_signal\": \"ID_anchors/mCyto_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"mega_3X_enrichment\": \"ID_anchors/mega_3X_enrichment.fifth_mega3x.max_size.bed\",\n",
    "    \"MEGA_2X_enrichment\": \"ID_anchors/MEGAp5_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"MEGAN93_2X_enrichment\": \"ID_anchors/MEGAN93p5_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"MEGAminus_2X_enrichment\": \"ID_anchors/MEGA_minus_ctrl_2X_enrichment.pixel_derived.signal_peaks.bed\",\n",
    "    \"cyto_2x_enrichment\": \"ID_anchors/cyto_2x_enrichment.third_mCyto.max_size.bed\",\n",
    "}\n",
    "\n",
    "\n",
    "id_anchors_dict = {}\n",
    "for id_name, fname in id_anchor_fnames.items():\n",
    "    id_anchors_dict[id_name] = pd.read_csv(fname, sep=\"\\t\")\n",
    "    # ...\n",
    "    print(f\"loaded {len(id_anchors_dict[id_name]):5d} ID anchors {id_name:>20} in BED format ...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b820cf19-aff1-43a4-b4f4-08790b38bfc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-18T16:02:31.706805Z",
     "iopub.status.busy": "2023-09-18T16:02:31.706321Z",
     "iopub.status.idle": "2023-09-18T16:02:31.709187Z",
     "shell.execute_reply": "2023-09-18T16:02:31.708731Z",
     "shell.execute_reply.started": "2023-09-18T16:02:31.706787Z"
    }
   },
   "source": [
    "### To stackups now ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a67b20-afff-4d01-80c1-59cfa5dbfa47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create of the stackup, the flanks are +- 50 Kb, number of bins is 100 :\n",
    "_flank = 100_000 # Length of flank to one side from the boundary, in basepairs\n",
    "_nbins = 100   # Number of bins to split the region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1baacd0e-7525-46c2-97f7-63e7a053d44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! ls ev_bigwig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6906c095-019c-4a7b-95e4-d0495dcb66b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # bws\n",
    "bws[\"evm\"] = \"ev_bigwig/m5hR1R2.10kb.bw\"\n",
    "bws[\"evp\"] = \"ev_bigwig/p5hR1R2.10kb.bw\"\n",
    "bws[\"idcov\"] = \"pix_clust_cov.bw\"\n",
    "\n",
    "\n",
    "bws[\"nevm\"] = \"ev_bigwig/N93m5.10kb.bw\"\n",
    "bws[\"nevp\"] = \"ev_bigwig/N93p5.10kb.bw\"\n",
    "\n",
    "# # plug insulation there jus for fun ...\n",
    "# bws[\"evm\"] = \"ranGAP1-0-G1s-R1R2.hg38.mapq_30.1000.b10000.insul.w50000.bw\"\n",
    "# bws[\"evp\"] = \"ranGAP1-7-G1s-R1R2.hg38.mapq_30.1000.b10000.insul.w50000.bw\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae6ad9b9-c34c-416e-9ab9-8c524c000147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# _chosen_stackup_name = \"mega_2X_enrichment\"\n",
    "_chosen_stackup_name = \"5hr_2X_enrichment_signal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0f9c964-dc4d-4224-a0b8-83cefe69c86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in bw_kyes_to_use:\n",
    "    print(k, bws[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49480fd-f973-46b8-af25-ca260fc39f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! ls /abyss/sergpolly/data_ranger/bigwigs/rnaseq/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76b17d75-a585-4712-a91f-ab5fa77d221c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "bw_kyes_to_use = [\n",
    "    # 'mM.atac',\n",
    "    'mG.atac',\n",
    "    # 'pG.atac',\n",
    "    'H3K4me3',\n",
    "    'H3K27ac',\n",
    "    'H3K27me3',\n",
    "    # 'ctcf',\n",
    "    \"evm\",\n",
    "    \"evp\",\n",
    "    \"nevm\",\n",
    "    \"nevp\",\n",
    "    # \"ids\",\n",
    "    # \"dots\",\n",
    "    \"rg0r1fwd\",\n",
    "    \"rg0r1rev\",\n",
    "]\n",
    "\n",
    "\n",
    "def _job(packed_data, bw_sample):\n",
    "    # unpack shared data\n",
    "    flank, nbins, track_dict, bed_df = packed_data\n",
    "    bw_track = track_dict[bw_sample]\n",
    "    stack_kwargs = dict(kind=\"mid\", flank=flank, nbins=nbins)\n",
    "    from helper_func import get_stack\n",
    "    return (\n",
    "        bw_sample,\n",
    "        get_stack(bw_track, bed_df, **stack_kwargs),\n",
    "    )\n",
    "\n",
    "_shared = (\n",
    "    _flank,\n",
    "    _nbins,\n",
    "    bws,\n",
    "    id_anchors_dict[_chosen_stackup_name],\n",
    ")\n",
    "\n",
    "with WorkerPool(\n",
    "    n_jobs=len(bws),\n",
    "    shared_objects=_shared,\n",
    "    start_method=\"forkserver\",  # little faster than spawn, fork is the fastest\n",
    "    use_dill=True,\n",
    ") as wpool:\n",
    "    results = wpool.map(_job, bw_kyes_to_use, progress_bar=True)\n",
    "\n",
    "# sort out the results ...\n",
    "stackups_anchor = {sample: _pstack for sample, _pstack in results}\n",
    "\n",
    "# Combine RNA + and - into a single stack ...\n",
    "## Let's combine RNA-seq in a special way: fwd + rev[::-1]\n",
    "# ...\n",
    "# redefine RNA-seq track by combining fwd and rev tracks in a special way:\n",
    "_rna_fwd = stackups_anchor.pop(\"rg0r1fwd\")\n",
    "_rna_rev = stackups_anchor.pop(\"rg0r1rev\")\n",
    "#\n",
    "stackups_anchor[\"rna\"] = _rna_fwd + _rna_rev[:,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e48330-b428-4bf4-8c2f-bed0609b50af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for k in bw_kyes_to_use:\n",
    "#     print(k, bws[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96c4c85d-2da4-4234-804e-de4ee1e0fc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bws_vlim\n",
    "# num_stackup_groups, stackup_samples, stackup_group_heights, stack_width = _get_hms_nested_shape(hms_dict)\n",
    "# stackup_group_heights\n",
    "# _get_hms_nested_shape?\n",
    "#  _get_hms_nested_shape({\"hui_\": _stacks_dict})\n",
    "# keys_to_display = [ 'mG.atac', 'H3K4me3', 'H3K27ac', 'H3K27me3', \"rna\", \"evm\", \"evp\"]\n",
    "# bws_vlim[\"mG.atac\"] = dict(vmin=0.1,vmax=1)\n",
    "# bws_vlim[\"H3K4me3\"] = dict(vmin=0.1,vmax=11)\n",
    "# bws_vlim[\"H3K27ac\"] = dict(vmin=0.1,vmax=10)\n",
    "# bws_vlim[\"H3K27me3\"] = dict(vmin=0.1,vmax=0.9)\n",
    "# bws_vlim[\"rna\"] = dict(vmin=0.1,vmax=25)\n",
    "# bws_vlim[\"evm\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "# bws_vlim[\"evp\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "keys_to_display = [ 'mG.atac', 'H3K4me3', 'H3K27ac', 'H3K27me3', \"rna\", \"evm\", \"evp\"]\n",
    "\n",
    "bws_vlim[\"mM.atac\"] = dict(vmin=0.1,vmax=1.)\n",
    "bws_vlim[\"mG.atac\"] = dict(vmin=0.15,vmax=1.)\n",
    "bws_vlim[\"pG.atac\"] = dict(vmin=0.1,vmax=1.)\n",
    "bws_vlim[\"H3K27me3\"] = dict(vmin=0.15,vmax=0.85)\n",
    "bws_vlim[\"H3K9me3\"] = dict(vmin=0.15,vmax=1)\n",
    "bws_vlim[\"H3K4me3\"] = dict(vmin=0.5,vmax=14)\n",
    "bws_vlim[\"H3K27ac\"] = dict(vmin=0.9,vmax=18)\n",
    "bws_vlim[\"ctcf\"] = dict(vmin=1.8,vmax=6)\n",
    "bws_vlim[\"evm\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "bws_vlim[\"evp\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "bws_vlim[\"nevm\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "bws_vlim[\"nevp\"] = dict(vmin=-1.3,vmax=1.3)\n",
    "bws_vlim[\"dots\"] = dict(vmin=0,vmax=0.5)\n",
    "# # # some rna-seq - Async rangap control and depletion ...\n",
    "bws_vlim[\"rna\"] = dict(vmin=0.5,vmax=40)\n",
    "bws_vlim[\"rg0r1fwd\"] = dict(vmin=0.5,vmax=35)\n",
    "bws_vlim[\"rg0r1rev\"] = dict(vmin=0.5,vmax=35)\n",
    "bws_vlim[\"rg8r1fwd\"] = dict(vmin=0.5,vmax=35)\n",
    "bws_vlim[\"rg8r1rev\"] = dict(vmin=0.5,vmax=35)\n",
    "\n",
    "bws_vlim[\"rna\"] = dict(vmin=2,vmax=30)\n",
    "bws_vlim[\"H3K4me3\"] = dict(vmin=1,vmax=12)\n",
    "bws_vlim[\"H3K27ac\"] = dict(vmin=1.5,vmax=12)\n",
    "bws_vlim[\"mG.atac\"] = dict(vmin=0.2,vmax=0.7)\n",
    "bws_vlim[\"H3K27me3\"] = dict(vmin=0.2,vmax=0.9)\n",
    "\n",
    "# num_extra_plots,\n",
    "# hms_dict_dict,\n",
    "# ...\n",
    "scales = {k: \"linear\" for k in keys_to_display}\n",
    "vlims = {k: (bws_vlim[k][\"vmin\"], bws_vlim[k][\"vmax\"]) for k in keys_to_display}\n",
    "titles = {k: k for k in keys_to_display}\n",
    "cmaps = {k: davos_r for k in keys_to_display}\n",
    "# cmaps = {k: \"Blues\" for k in keys_to_display}\n",
    "binsizes = {k: ((2*_flank) // _nbins) for k in keys_to_display}\n",
    "# modify EV-related things ...\n",
    "for _evk in [\"evm\",\"evp\"]:\n",
    "    scales[_evk] = \"linear\"\n",
    "    cmaps[_evk] = \"RdBu_r\"\n",
    "\n",
    "cmaps[\"dots\"] = davos_r\n",
    "\n",
    "_df = id_anchors_dict[_chosen_stackup_name]\n",
    "_valency_groups = _df.groupby(pd.cut(_df[\"valency\"],[0,np.inf]), observed=True)\n",
    "hms_dict = {}\n",
    "for name, g in _valency_groups:\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    hms_dict[name] = {k: stackups_anchor[k][g.index[_idx]] for k in keys_to_display}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11f1707c-c108-4037-b729-7931ee1bb5f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d20f35d0-1285-42db-ad64-f670094b5952",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d586bdce-c4ae-4023-b897-24ba153a75e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0642e937-fde0-4ff2-bab9-b049ee94144a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1745cff8-3fdb-4a0c-b096-0263e3c853d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9422ef7-3091-4983-841e-f289816360f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "axx = plot_stackups_sets_new(\n",
    "    1,\n",
    "    hms_dict,  # heatmaps dict, that controls the order, the amount etc etc ...\n",
    "    scales,\n",
    "    vlims,\n",
    "    titles,\n",
    "    cmaps,\n",
    "    binsizes,\n",
    "    fillmissing=True,\n",
    "    len_per_thousand=0.73,\n",
    "    width_per_stack=0.33,\n",
    "    profile_height=0.33,\n",
    "    cbar_height = 0.07,\n",
    "    spacing_v=.03,  # fixed distance between axes (vertically)\n",
    "    spacing_h=.03,  # fixed distance between axes (horizontally)\n",
    "    fig_fontsize=6,\n",
    "    # fig_fname=\"Fig2C_stackup.svg\",\n",
    "    # fig_dpi=300,\n",
    "    # **imshow_kwargs,\n",
    ")\n",
    "\n",
    "ax, = axx\n",
    "for ii, (name, g) in enumerate(_valency_groups):\n",
    "    _size_id = 0\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27me3\"][g.index].mean(axis=1).argsort()\n",
    "    ax.fill_betweenx(\n",
    "        np.arange(len(g)),\n",
    "        g[\"size\"].iloc[_idx],\n",
    "        0,\n",
    "        # marker=\"\",\n",
    "        # alpha=0.8,\n",
    "        linewidth=1,\n",
    "        edgecolor=\"gray\",\n",
    "        facecolor=\"lightgrey\",\n",
    "        interpolate=True,\n",
    "    )\n",
    "    # ax.set_xlim([0,250_000])\n",
    "    ax.set_xlim([250_000, 0])\n",
    "    ax.set_ylim(len(g), 0)\n",
    "    # ax.set_xscale(\"log\")\n",
    "    ax.set_yticks([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticks([])\n",
    "    ax.set_xticklabels([])\n",
    "\n",
    "# fig.savefig(\"Fig2C_stackup.svg\", dpi=300)\n",
    "plt.savefig(\"Fig2C_stackup.svg\", format=\"svg\", dpi=300)\n",
    "# # fig.savefig(\"Fig2C_stackup.svg\", dpi=300)\n",
    "# fig.savefig(\"Fig2C_stackup.svg\", format=\"svg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fea3429-7572-4a02-90af-3f2bb04fd1b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "296ea0d5-9a19-4cd8-a619-d9d31042f238",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "efab47d1-7d50-49d9-aebe-f6830f998261",
   "metadata": {},
   "source": [
    "# Extended figure EV stackup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27645c87-a582-4ef5-b632-703257128c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_to_display = [ \"nevm\", \"nevp\"]\n",
    "\n",
    "_df = id_anchors_dict[_chosen_stackup_name]\n",
    "_valency_groups = _df.groupby(pd.cut(_df[\"valency\"],[0,np.inf]), observed=True)\n",
    "hms_dict = {}\n",
    "for name, g in _valency_groups:\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    hms_dict[name] = {k: stackups_anchor[k][g.index[_idx]] for k in keys_to_display}\n",
    "\n",
    "\n",
    "# num_extra_plots,\n",
    "# hms_dict_dict,\n",
    "# ...\n",
    "scales = {k: \"linear\" for k in keys_to_display}\n",
    "vlims = {k: (bws_vlim[k][\"vmin\"], bws_vlim[k][\"vmax\"]) for k in keys_to_display}\n",
    "titles = {k: k for k in keys_to_display}\n",
    "cmaps = {k: davos_r for k in keys_to_display}\n",
    "# cmaps = {k: \"Blues\" for k in keys_to_display}\n",
    "binsizes = {k: ((2*_flank) // _nbins) for k in keys_to_display}\n",
    "# modify EV-related things ...\n",
    "for _evk in [\"nevm\",\"nevp\"]:\n",
    "    scales[_evk] = \"linear\"\n",
    "    cmaps[_evk] = \"RdBu_r\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d56b7b7-0936-41e6-b27a-db36756b6f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "axx = plot_stackups_sets_new(\n",
    "    1,\n",
    "    hms_dict,  # heatmaps dict, that controls the order, the amount etc etc ...\n",
    "    scales,\n",
    "    vlims,\n",
    "    titles,\n",
    "    cmaps,\n",
    "    binsizes,\n",
    "    fillmissing=True,\n",
    "    len_per_thousand=0.73,\n",
    "    width_per_stack=0.33,\n",
    "    profile_height=0.33,\n",
    "    cbar_height = 0.07,\n",
    "    spacing_v=.03,  # fixed distance between axes (vertically)\n",
    "    spacing_h=.03,  # fixed distance between axes (horizontally)\n",
    "    fig_fontsize=6,\n",
    "    # fig_fname=\"Fig2C_stackup.svg\",\n",
    "    # fig_dpi=300,\n",
    "    # **imshow_kwargs,\n",
    ")\n",
    "\n",
    "ax, = axx\n",
    "for ii, (name, g) in enumerate(_valency_groups):\n",
    "    _size_id = 0\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27me3\"][g.index].mean(axis=1).argsort()\n",
    "    ax.fill_betweenx(\n",
    "        np.arange(len(g)),\n",
    "        g[\"size\"].iloc[_idx],\n",
    "        0,\n",
    "        # marker=\"\",\n",
    "        # alpha=0.8,\n",
    "        linewidth=1,\n",
    "        edgecolor=\"gray\",\n",
    "        facecolor=\"lightgrey\",\n",
    "        interpolate=True,\n",
    "    )\n",
    "    # ax.set_xlim([0,250_000])\n",
    "    ax.set_xlim([250_000, 0])\n",
    "    ax.set_ylim(len(g), 0)\n",
    "    # ax.set_xscale(\"log\")\n",
    "    ax.set_yticks([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticks([])\n",
    "    ax.set_xticklabels([])\n",
    "\n",
    "# fig.savefig(\"Fig2C_stackup.svg\", dpi=300)\n",
    "# plt.savefig(\"Fig2C_stackup.svg\", format=\"svg\", dpi=300)\n",
    "# # fig.savefig(\"Fig2C_stackup.svg\", dpi=300)\n",
    "# fig.savefig(\"Fig2C_stackup.svg\", format=\"svg\", dpi=300)\n",
    "\n",
    "# fig.savefig(\"Fig2C_stackup.svg\", dpi=300)\n",
    "plt.savefig(\"FigE2B_stackup.svg\", format=\"svg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f18c9da9-49d3-4932-883a-fe61e775cb65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7dd8c6-cb6a-460c-aea1-934c2e295e21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f689dab8-6c0a-4c4f-b2f1-bd1ecc6d54d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f7b0d6-9392-430b-adde-d01de0b9e018",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48738588-5587-40bc-822a-371df418f659",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "716b1b07-1ac5-4ab8-b969-3e7c0013a4f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeae43ea-e0c3-4dc6-832f-1bb29af56eb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d937601-d692-4e4f-b506-f0384d1b02f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da2c59ee-97f7-4f14-8bb3-76d6fed6e930",
   "metadata": {},
   "source": [
    "# legacy plotting below ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f202bd13-856f-4297-bb95-fef64aef6ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "_idx_order = id_anchors_dict[_chosen_stackup_name][\"size\"].argsort()\n",
    "# _idx_order = ov[\"size\"].argsort()\n",
    "# _idx_order = nov[\"size\"].argsort()\n",
    "# _idx_order = stackups_anchor[\"H3K27ac\"].sum(axis=1).argsort()\n",
    "\n",
    "# kkk = ['mG.atac', 'H3K27me3', 'H3K4me3', 'H3K27ac', 'H3K36me3', 'H3K4me1', 'MED1', 'ctcf']\n",
    "kkk = ['mM.atac', 'mG.atac', 'pG.atac', 'H3K27me3', 'H3K4me3', 'H3K27ac', 'ctcf', \"evm\", \"evp\"]\n",
    "\n",
    "# hms_dict = {k: stackups_anchor[k][order_by_len_idx] for k in kkk}\n",
    "hms_dict = {k: stackups_anchor[k][_idx_order] for k in kkk}\n",
    "scales = {k: \"log\" for k in kkk}\n",
    "vlims = {k: (bws_vlim[k][\"vmin\"], bws_vlim[k][\"vmax\"]) for k in kkk}\n",
    "titles = {k: k for k in kkk}\n",
    "cmaps = {k: davos_r for k in kkk}\n",
    "# cmaps[\"H3K4me3\"] = \"Reds\"\n",
    "binsizes = {k: ((2*_flank) // _nbins) for k in kkk}\n",
    "\n",
    "# modify EV-related things ...\n",
    "for _evk in [\"evm\",\"evp\"]:\n",
    "    scales[_evk] = \"linear\"\n",
    "    cmaps[_evk] = \"RdBu_r\"\n",
    "\n",
    "# kkk.append(\"idcov\")\n",
    "# _kkk = \"idcov\"\n",
    "# hms_dict[_kkk] = stackups_anchor[_kkk][_idx_order]\n",
    "# scales[_kkk] = \"linear\"\n",
    "# vlims[_kkk] = (1,25)\n",
    "# titles[_kkk] = _kkk\n",
    "# cmaps[_kkk] = davos_r\n",
    "# binsizes[_kkk] = ((2*_flank) // _nbins)\n",
    "\n",
    "plot_stackups_lite(\n",
    "    None,\n",
    "    hms_dict,  # heatmaps dict, that controls the order, the amount etc etc ...\n",
    "    scales,\n",
    "    vlims,\n",
    "    titles,\n",
    "    cmaps,\n",
    "    binsizes,\n",
    "    fillmissing=False,\n",
    "    len_per_thousand=4.9,\n",
    "    width_per_stack=3.2,\n",
    "    extra_height=3.,  # height that goes toward the profile and colorbar\n",
    "    interpolation=\"gaussian\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c627f4a-8514-4ae0-bafe-d4d9445a2c66",
   "metadata": {},
   "source": [
    "### exploring - testing valency related stuff ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "898fa659-f475-4cef-a9f2-35b4f20eacb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_to_display = [ 'H3K27me3', 'mG.atac', 'H3K4me3', 'H3K27ac'] + [\"evm\",\"evp\"]\n",
    "\n",
    "_df = id_anchors_dict[_chosen_stackup_name]\n",
    "_valency_groups = _df.groupby(pd.cut(_df[\"valency\"],[0,1,np.inf]), observed=True)\n",
    "hms_dict = {}\n",
    "for name, g in _valency_groups:\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27ac\"][g.index].mean(axis=1).argsort()\n",
    "    hms_dict[name] = {k: stackups_anchor[k][g.index[_idx]] for k in keys_to_display}\n",
    "\n",
    "# ...\n",
    "scales = {k: \"log\" for k in keys_to_display}\n",
    "vlims = {k: (bws_vlim[k][\"vmin\"], bws_vlim[k][\"vmax\"]) for k in keys_to_display}\n",
    "titles = {k: k for k in keys_to_display}\n",
    "cmaps = {k: davos_r for k in keys_to_display}\n",
    "binsizes = {k: ((2*_flank) // _nbins) for k in keys_to_display}\n",
    "\n",
    "for _evk in [\"evm\",\"evp\"]:\n",
    "    scales[_evk] = \"linear\"\n",
    "    cmaps[_evk] = \"RdBu_r\"\n",
    "\n",
    "\n",
    "_xfsize = 12\n",
    "axx = plot_stackups_sets(\n",
    "    2,\n",
    "    hms_dict,\n",
    "    scales,\n",
    "    vlims,\n",
    "    titles,\n",
    "    cmaps,\n",
    "    binsizes,\n",
    "    fillmissing=False,\n",
    "    len_per_thousand=4.9,\n",
    "    width_per_stack=3.,\n",
    "    extra_height=3.,  # height that goes toward the profile and colorbar\n",
    "    delta_h=.3,  # fixed distance between axes (vertically)\n",
    "    delta_w=.5,  # fixed distance between axes (horizontal\n",
    "    fig_fontsize=_xfsize,\n",
    "    interpolation=\"gaussian\",\n",
    ")\n",
    "\n",
    "##################################################################\n",
    "for ii, (name, g) in enumerate(_valency_groups):\n",
    "\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27ac\"][g.index].mean(axis=1).argsort()\n",
    "\n",
    "    axx[0][ii].scatter(\n",
    "        x = g[\"valency\"].iloc[_idx],\n",
    "        y = np.arange(len(g)),\n",
    "        marker=\"s\",\n",
    "        alpha=0.8,\n",
    "    )\n",
    "    axx[0][ii].set_ylim(len(g), 0)\n",
    "    axx[0][ii].set_xlim(0,150)\n",
    "    axx[1][ii].scatter(\n",
    "        x = g[\"size\"].iloc[_idx],\n",
    "        y = np.arange(len(g)),\n",
    "        marker=\"s\",\n",
    "        alpha=0.8,\n",
    "    )\n",
    "    axx[1][ii].set_xlim([0,250_000])\n",
    "    axx[1][ii].set_ylim(len(g), 0)\n",
    "    # axx[1][ii].set_xscale(\"log\")\n",
    "\n",
    "    axx[0][ii].set_yticks([])\n",
    "    axx[0][ii].set_yticklabels([])\n",
    "    axx[1][ii].set_yticks([])\n",
    "    axx[1][ii].set_yticklabels([])\n",
    "\n",
    "    if ii == 2:\n",
    "        # axx[0][ii].set_xticks([0,1])\n",
    "        # axx[0][ii].set_xticklabels([0,1],fontsize=_xfsize)\n",
    "        for _tidx, tick in enumerate(axx[0][ii].xaxis.get_majorticklabels()):\n",
    "            if _tidx == 0:\n",
    "                tick.set_horizontalalignment(\"left\")\n",
    "            else:\n",
    "                tick.set_horizontalalignment(\"right\")\n",
    "        axx[0][ii].tick_params(axis=\"x\", length=6)\n",
    "    else:\n",
    "        axx[0][ii].set_xticks([])\n",
    "        axx[0][ii].set_xticklabels([])\n",
    "\n",
    "    if ii == 2:\n",
    "        # axx[1][ii].set_xticks([_tpmin,_tpmax])\n",
    "        # # axx[1][ii].set_xticklabels([f\"{_tpmin:.1f}\",f\"{_tpmax:.1f}\"],fontsize=_xfsize)\n",
    "        for _tidx, tick in enumerate(axx[1][ii].xaxis.get_majorticklabels()):\n",
    "            if _tidx == 0:\n",
    "                tick.set_horizontalalignment(\"left\")\n",
    "            else:\n",
    "                tick.set_horizontalalignment(\"right\")\n",
    "        axx[1][ii].tick_params(axis=\"x\", length=6)\n",
    "    else:\n",
    "        axx[1][ii].set_xticks([])\n",
    "        axx[1][ii].set_xticklabels([])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e769d53-31eb-4474-ac29-b4aded5f457e",
   "metadata": {},
   "source": [
    "## Old Fig 2B ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae02962f-3ff8-4fd2-bc49-4cc87471c93f",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_to_display = [ 'mG.atac', 'H3K4me3', 'H3K27ac', 'H3K27me3', \"evm\", \"evp\"]\n",
    "\n",
    "_df = id_anchors_dict[_chosen_stackup_name]\n",
    "_valency_groups = _df.groupby(pd.cut(_df[\"valency\"],[0,np.inf]), observed=True)\n",
    "hms_dict = {}\n",
    "for name, g in _valency_groups:\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27me3\"][g.index].mean(axis=1).argsort()\n",
    "    hms_dict[name] = {k: stackups_anchor[k][g.index[_idx]] for k in keys_to_display}\n",
    "\n",
    "# ...\n",
    "scales = {k: \"log\" for k in keys_to_display}\n",
    "vlims = {k: (bws_vlim[k][\"vmin\"], bws_vlim[k][\"vmax\"]) for k in keys_to_display}\n",
    "titles = {k: k for k in keys_to_display}\n",
    "cmaps = {k: davos_r for k in keys_to_display}\n",
    "binsizes = {k: ((2*_flank) // _nbins) for k in keys_to_display}\n",
    "\n",
    "# modify EV-related things ...\n",
    "for _evk in [\"evm\",\"evp\"]:\n",
    "    scales[_evk] = \"linear\"\n",
    "    cmaps[_evk] = \"RdBu_r\"\n",
    "\n",
    "\n",
    "_xfsize = 12\n",
    "axx = plot_stackups_sets(\n",
    "    2,\n",
    "    hms_dict,\n",
    "    scales,\n",
    "    vlims,\n",
    "    titles,\n",
    "    cmaps,\n",
    "    binsizes,\n",
    "    fillmissing=False,\n",
    "    len_per_thousand=4.9,\n",
    "    width_per_stack=3.,\n",
    "    extra_height=3.,  # height that goes toward the profile and colorbar\n",
    "    delta_h=.3,  # fixed distance between axes (vertically)\n",
    "    delta_w=.5,  # fixed distance between axes (horizontal\n",
    "    fig_fontsize=_xfsize,\n",
    "    interpolation=\"gaussian\",\n",
    ")\n",
    "\n",
    "##################################################################\n",
    "for ii, (name, g) in enumerate(_valency_groups):\n",
    "\n",
    "    _valency_id = 1\n",
    "    _size_id = 0\n",
    "\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27me3\"][g.index].mean(axis=1).argsort()\n",
    "\n",
    "    axx[_valency_id][ii].scatter(\n",
    "        x = g[\"valency\"].iloc[_idx],\n",
    "        y = np.arange(len(g)),\n",
    "        marker=\"s\",\n",
    "        alpha=0.8,\n",
    "    )\n",
    "    axx[_valency_id][ii].set_ylim(len(g), 0)\n",
    "    axx[_valency_id][ii].set_xlim(0,150)\n",
    "    axx[_size_id][ii].fill_betweenx(\n",
    "        np.arange(len(g)),\n",
    "        g[\"size\"].iloc[_idx],\n",
    "        0,\n",
    "        # marker=\"\",\n",
    "        # alpha=0.8,\n",
    "        linewidth=5,\n",
    "        edgecolor=\"gray\",\n",
    "        facecolor=\"lightgrey\",\n",
    "        interpolate=True,\n",
    "    )\n",
    "    axx[_size_id][ii].set_xlim([0,250_000])\n",
    "    axx[_size_id][ii].set_ylim(len(g), 0)\n",
    "    # axx[_size_id][ii].set_xscale(\"log\")\n",
    "\n",
    "    axx[_valency_id][ii].set_yticks([])\n",
    "    axx[_valency_id][ii].set_yticklabels([])\n",
    "    axx[_size_id][ii].set_yticks([])\n",
    "    axx[_size_id][ii].set_yticklabels([])\n",
    "\n",
    "    if ii == 2:\n",
    "        # axx[_valency_id][ii].set_xticks([0,1])\n",
    "        # axx[_valency_id][ii].set_xticklabels([0,1],fontsize=_xfsize)\n",
    "        for _tidx, tick in enumerate(axx[_valency_id][ii].xaxis.get_majorticklabels()):\n",
    "            if _tidx == 0:\n",
    "                tick.set_horizontalalignment(\"left\")\n",
    "            else:\n",
    "                tick.set_horizontalalignment(\"right\")\n",
    "        axx[_valency_id][ii].tick_params(axis=\"x\", length=6)\n",
    "    else:\n",
    "        axx[_valency_id][ii].set_xticks([])\n",
    "        axx[_valency_id][ii].set_xticklabels([])\n",
    "\n",
    "    if ii == 2:\n",
    "        # axx[_size_id][ii].set_xticks([_tpmin,_tpmax])\n",
    "        # # axx[_size_id][ii].set_xticklabels([f\"{_tpmin:.1f}\",f\"{_tpmax:.1f}\"],fontsize=_xfsize)\n",
    "        for _tidx, tick in enumerate(axx[_size_id][ii].xaxis.get_majorticklabels()):\n",
    "            if _tidx == 0:\n",
    "                tick.set_horizontalalignment(\"left\")\n",
    "            else:\n",
    "                tick.set_horizontalalignment(\"right\")\n",
    "        axx[_size_id][ii].tick_params(axis=\"x\", length=6)\n",
    "    else:\n",
    "        axx[_size_id][ii].set_xticks([])\n",
    "        axx[_size_id][ii].set_xticklabels([])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe19efe0-60f8-4257-b818-848261394728",
   "metadata": {},
   "source": [
    "# newer Fig2B with genes ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aeb06be-0da2-4b30-ae03-29ca6efa5e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_to_display = [ 'mG.atac', 'H3K4me3', 'H3K27ac', 'H3K27me3', \"rna\", \"evm\", \"evp\"]\n",
    "\n",
    "_df = id_anchors_dict[_chosen_stackup_name]\n",
    "_valency_groups = _df.groupby(pd.cut(_df[\"valency\"],[0,np.inf]), observed=True)\n",
    "hms_dict = {}\n",
    "for name, g in _valency_groups:\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    hms_dict[name] = {k: stackups_anchor[k][g.index[_idx]] for k in keys_to_display}\n",
    "\n",
    "# ...\n",
    "scales = {k: \"log\" for k in keys_to_display}\n",
    "vlims = {k: (bws_vlim[k][\"vmin\"], bws_vlim[k][\"vmax\"]) for k in keys_to_display}\n",
    "titles = {k: k for k in keys_to_display}\n",
    "cmaps = {k: davos_r for k in keys_to_display}\n",
    "binsizes = {k: ((2*_flank) // _nbins) for k in keys_to_display}\n",
    "\n",
    "# modify EV-related things ...\n",
    "for _evk in [\"evm\",\"evp\"]:\n",
    "    scales[_evk] = \"linear\"\n",
    "    cmaps[_evk] = \"RdBu_r\"\n",
    "\n",
    "\n",
    "_xfsize = 12\n",
    "axx = plot_stackups_sets(\n",
    "    1,\n",
    "    hms_dict,\n",
    "    scales,\n",
    "    vlims,\n",
    "    titles,\n",
    "    cmaps,\n",
    "    binsizes,\n",
    "    fillmissing=False,\n",
    "    len_per_thousand=4.9,\n",
    "    width_per_stack=3.,\n",
    "    extra_height=3.,  # height that goes toward the profile and colorbar\n",
    "    delta_h=.3,  # fixed distance between axes (vertically)\n",
    "    delta_w=.5,  # fixed distance between axes (horizontal\n",
    "    fig_fontsize=_xfsize,\n",
    "    interpolation=\"gaussian\",\n",
    ")\n",
    "\n",
    "##################################################################\n",
    "for ii, (name, g) in enumerate(_valency_groups):\n",
    "\n",
    "    _size_id = 0\n",
    "\n",
    "    _idx = g[\"size\"].argsort()\n",
    "    # _idx = stackups_anchor[\"H3K27me3\"][g.index].mean(axis=1).argsort()\n",
    "\n",
    "    axx[_size_id][ii].fill_betweenx(\n",
    "        np.arange(len(g)),\n",
    "        g[\"size\"].iloc[_idx],\n",
    "        0,\n",
    "        # marker=\"\",\n",
    "        # alpha=0.8,\n",
    "        linewidth=5,\n",
    "        edgecolor=\"gray\",\n",
    "        facecolor=\"lightgrey\",\n",
    "        interpolate=True,\n",
    "    )\n",
    "    axx[_size_id][ii].set_xlim([0,250_000])\n",
    "    axx[_size_id][ii].set_ylim(len(g), 0)\n",
    "    # axx[_size_id][ii].set_xscale(\"log\")\n",
    "\n",
    "    axx[_size_id][ii].set_yticks([])\n",
    "    axx[_size_id][ii].set_yticklabels([])\n",
    "\n",
    "    if ii == 2:\n",
    "        # axx[_size_id][ii].set_xticks([_tpmin,_tpmax])\n",
    "        # # axx[_size_id][ii].set_xticklabels([f\"{_tpmin:.1f}\",f\"{_tpmax:.1f}\"],fontsize=_xfsize)\n",
    "        for _tidx, tick in enumerate(axx[_size_id][ii].xaxis.get_majorticklabels()):\n",
    "            if _tidx == 0:\n",
    "                tick.set_horizontalalignment(\"left\")\n",
    "            else:\n",
    "                tick.set_horizontalalignment(\"right\")\n",
    "        axx[_size_id][ii].tick_params(axis=\"x\", length=6)\n",
    "    else:\n",
    "        axx[_size_id][ii].set_xticks([])\n",
    "        axx[_size_id][ii].set_xticklabels([])\n",
    "\n",
    "plt.savefig(\"FigE2B_stackup_Nup93.svg\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcc45f01-cdc4-445d-b81f-53c86f81efd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4940b88a-3f77-438f-a688-4e2e00863a7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d379c8e3-b428-4d11-8cf0-3080acd448e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe76e8f4-2530-49ff-8ece-1fdd8fc7aba9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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