topinsulators.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "if not os.path.exists('typeIII.pickle'):\n",
    "  !curl --remote-name -H 'Accept: application/vnd.github.v3.raw' --location 'https://api.github.com/repos/mayhd3/DIPC/contents/typeIII.pickle'\n",
    "if not os.path.exists('qeinput.pickle'):\n",
    "  !curl --remote-name -H 'Accept: application/vnd.github.v3.raw' --location 'https://api.github.com/repos/mayhd3/DIPC/contents/qeinput.pickle'\n",
    "if os.path.exists('c2db.db'):\n",
    "  try:\n",
    "    import ase.db\n",
    "    import ase.io\n",
    "  except ImportError:\n",
    "    !{sys.executable} -m pip install ase asr gpaw\n",
    "    import ase.db\n",
    "    import ase.io"
   ]
  },
  {
   "source": [
    "import pickle\n",
    "from itertools import product, compress\n",
    "from math import hypot\n",
    "from enum import IntEnum\n",
    "from io import StringIO\n",
    "import pandas as pd\n",
    "\n",
    "print(\"Collecting type III heterojunctions...\")\n",
    "\n",
    "# Check that a is in ascending order\n",
    "def ascending(a): return all(\n",
    "    a[i]<a[i+1] for i in range(len(a)-1)\n",
    ")\n",
    "\n",
    "def candidate(left, right):\n",
    "    def calculation(attr, suffix): # Apply attr to each combination of band energies with given suffix\n",
    "        return (\n",
    "            attr(row,band)\n",
    "                for row in (left,right)\n",
    "                for band in ('vbm'+suffix,'cbm'+suffix)\n",
    "        )\n",
    "\n",
    "    def wrtevac(gaps): # Align band gaps on evac=0\n",
    "        return (\n",
    "            next(gaps) - left.evac,\n",
    "            next(gaps) - left.evac,\n",
    "            next(gaps) - right.evac,\n",
    "            next(gaps) - right.evac\n",
    "        )\n",
    "\n",
    "    def latpbc(row):\n",
    "        return compress(\n",
    "            row.data['results-asr.structureinfo.json']['kwargs']['data']['spglib_dataset']['std_lattice'],\n",
    "            row.data['structure.json']['1']['pbc']\n",
    "        )\n",
    "\n",
    "    def latfit(l, r):\n",
    "        mina, maxa = min(l[0][0],r[0][0]), max(l[0][0],r[0][0])\n",
    "        moda = maxa % mina\n",
    "        if mina < moda+moda:\n",
    "            moda = mina - moda\n",
    "        low, far = (\n",
    "            min(l, r, key=lambda l: l[1][1]),\n",
    "            max(l, r, key=lambda l: l[1][1])\n",
    "        )\n",
    "        quoy, mody = divmod(far[1][1],low[1][1])\n",
    "        if low[1][1] < mody+mody:\n",
    "            mody = low[1][1] - mody\n",
    "            quoy = quoy + 1\n",
    "        modx = (far[1][0] - quoy*low[1][0]) % low[0][0]\n",
    "        if low[0][0] < modx+modx:\n",
    "            modx = low[0][0] - modx\n",
    "        return moda + hypot(modx,mody)\n",
    "\n",
    "    functionals = ( # Compare bands calculated with the same functional, prefer G0W0 > HSE > PBE\n",
    "        (prefix,wrtevac(calculation(getattr,suffix)))\n",
    "        for prefix,suffix in (('GW','_gw'),('HSE','_hse'),('PBE',''))\n",
    "        if all(calculation(hasattr,suffix))\n",
    "    )\n",
    "\n",
    "    broken = list( # Gaps that are broken and rising\n",
    "        (prefix,structure)\n",
    "        for prefix,structure in functionals\n",
    "        if ascending(structure)\n",
    "    )\n",
    "\n",
    "    badtmd = all(( # Materials are TMDs in different phases\n",
    "        hasattr(left,'class'),\n",
    "        hasattr(right,'class'),\n",
    "        getattr(left,'class','').startswith('TMDC'),\n",
    "        getattr(right,'class','').startswith('TMDC'),\n",
    "        getattr(left,'class','') != getattr(right,'class','')\n",
    "    ))\n",
    "\n",
    "    if all((\n",
    "        left.thermodynamic_stability_level == 3, # Only accept thermodynamically stable materials\n",
    "        right.thermodynamic_stability_level == 3, \n",
    "        left.is_magnetic == 0, # Do not accept magnetic materials\n",
    "        right.is_magnetic == 0,\n",
    "        broken, # At least one functional predicts a type III heterojunction band structure\n",
    "        left.spgnum == right.spgnum, # Materials must have the same space group\n",
    "        left.crystal_type == right.crystal_type, # Materials must have the same crystal structure\n",
    "        not 'C' in left.crystal_type, # Only accept A or AB type crystal structures\n",
    "        not badtmd # TMDs must have the same phase\n",
    "    )):\n",
    "        prefixes,structures = zip(*broken) # Separate titles from band structures \n",
    "        depths = (bands[3] - bands[0] for bands in structures) # ΔE = emax - emin\n",
    "        aratio = left.cell_area/right.cell_area # R = Al / Ar\n",
    "        (lkps,rkps) = ( # Extract asr k-points\n",
    "            (\n",
    "            row.data['results-asr.hse.json']['kwargs']['data']['kvbm'],\n",
    "            row.data['results-asr.hse.json']['kwargs']['data']['kcbm']\n",
    "            )\n",
    "                for row in (left, right)\n",
    "        )\n",
    "        return (\n",
    "            left.formula,\n",
    "            left.uid,\n",
    "            right.formula,\n",
    "            right.uid,\n",
    "            prefixes,\n",
    "            structures,\n",
    "            left.spacegroup,\n",
    "            left.crystal_type,\n",
    "            tuple(depths),\n",
    "            aratio,\n",
    "            lkps,\n",
    "            rkps,\n",
    "            latfit(tuple(latpbc(left)),tuple(latpbc(right)))\n",
    "        )\n",
    "\n",
    "Headers = IntEnum('Headers',( # Column titles\n",
    "        'Left',\n",
    "        'Left_uid',\n",
    "        'Right',\n",
    "        'Right_uid',\n",
    "        'Calc',\n",
    "        'Bands_vbm_cbm',\n",
    "        'Group',\n",
    "        'Type',\n",
    "        'ΔE',\n",
    "        'R',\n",
    "        'Left_kvbm_kcbm',\n",
    "        'Right_kvbm_kcbm',\n",
    "        'Fit'\n",
    "    ),\n",
    "    start=0\n",
    ")\n",
    "\n",
    "try:\n",
    "    with open('typeIII.pickle','rb') as pk:\n",
    "        typeIII = pickle.load(pk)\n",
    "        print(\"Loaded from pickle.\")\n",
    "except (OSError, IOError):\n",
    "    db = ase.db.connect('c2db.db') # Connect to the database\n",
    "    rows = list(db.select(','.join(( # Find rows that have all of the parameters used to choose candidates\n",
    "        'evac',\n",
    "        'thermodynamic_stability_level',\n",
    "        'is_magnetic',\n",
    "        'spgnum',\n",
    "        'crystal_type',\n",
    "        'has_asr_structureinfo',\n",
    "        'has_asr_hse'\n",
    "    ))))\n",
    "    typeIII = sorted( # Find pairs of rows that could be type III heterojunctions\n",
    "        filter(\n",
    "            lambda row: row != None, # Remove pairs that were not candidates\n",
    "            [candidate(l,r) for l in rows for r in rows] # Take every pair in the selection\n",
    "        ),\n",
    "        key = lambda pair: pair[Headers.Fit] # Sort by how well the lattices fit\n",
    "    )\n",
    "    print(\"Loaded from ase db.\")\n",
    "    with open('typeIII.pickle','wb') as pk: # Cache results to make plot changes load faster\n",
    "        pickle.dump(typeIII, pk)\n",
    "        print(\"Dumped to pickle.\")\n",
    "\n",
    "def redirect(images,format): # Capture espresso inputs from stdout in a dict indexed by uid\n",
    "    old = sys.stdout\n",
    "    result = StringIO()\n",
    "    sys.stdout = result\n",
    "    ase.io.write('-',images,format)\n",
    "    sys.stdout = old\n",
    "    return result.getvalue()\n",
    "\n",
    "print(\"Generating espresso inputs...\")\n",
    "\n",
    "try:\n",
    "    with open('qeinput.pickle','rb') as pk:\n",
    "        qeinput = pickle.load(pk)\n",
    "        print(\"Loaded from pickle.\")\n",
    "except (OSError, IOError):\n",
    "    uids = pd.unique([uid for row in typeIII for uid in (row[Headers.Left_uid],row[Headers.Right_uid])])\n",
    "    db = ase.db.connect('c2db.db')\n",
    "    qeinput = {uid:redirect(db.get(\"uid=\"+uid).toatoms(),'espresso-in') for uid in uids}\n",
    "    with open('qeinput.pickle','wb') as pk:\n",
    "        pickle.dump(qeinput, pk)\n",
    "        print(\"Dumped to pickle.\")"
   ],
   "cell_type": "code",
   "metadata": {
    "tags": []
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting type III heterojunctions...\nLoaded from pickle.\nGenerating espresso inputs...\nLoaded from pickle.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Screening 1016 type III heterojunctions with 254 materials from 14 space groups:\nLeft               Left_uid  Right              Right_uid  Calc    Bands_vbm_cbm            Group    Type          ΔE    R  Left_kvbm_kcbm           Right_kvbm_kcbm            Fit\n------  -------------------  -------  -------------------  ------  -----------------------  -------  ----------  ----  ---  -----------------------  -----------------------  -----\nMgBr2    MgBr2-bee1987fb4e6  HfI2       HfI2-05a69240794c  PBE     (-6.9,-3.4) (-3.1,-2.5)  P-6m2    AB2-187-bi   4.5  1    (+0.0,+0.0) (+0.0,+0.0)  (+0.3,+0.3) (+0.3,-0.1)    0\nHg2Se2  Hg2Se2-f2919d1605f0  In2I2     I2In2-9ab97b2e26fc  HSE     (-6.1,-5.8) (-5.7,-4.4)  P-1      AB-2-i       1.7  1    (+0.0,+0.0) (+0.0,+0.0)  (+0.1,+0.1) (+0.3,+0.3)    0\nHgCl2    HgCl2-ce3ed4728e8f  SnF2       SnF2-d6c3ab6de6b8  PBE     (-7.7,-5.2) (-5.2,-3.6)  P-4m2    AB2-115-dg   4.1  1    (+0.0,+0.0) (+0.0,+0.0)  (+0.5,+0.5) (+0.0,+0.0)    0\nNiSe2    NiSe2-c1ffa88ee5ca  TiSe2     TiSe2-320be81440f3  HSE     (-5.2,-4.9) (-4.7,-4.6)  P-3m1    AB2-164-bd   0.6  1    (+0.0,+0.0) (+0.2,+0.0)  (+0.0,+0.0) (+0.5,+0.5)    0\nAg2F2    Ag2F2-0556adc9bc7b  Bi2In2   Bi2In2-9bc00588f556  HSE     (-6.6,-4.2) (-4.1,-4.0)  P4/nmm   AB-129-bc    2.7  1    (+0.5,+0.5) (-0.0,-0.0)  (-0.0,-0.0) (-0.2,+0.1)    0\nMo2Se4  Mo2Se4-82a84bbe4a89  W2Se4     W2Se4-800574ca4834  PBE     (-5.1,-5.1) (-5.0,-4.9)  P2_1/m   AB2-11-e     0.2  1    (+0.1,-0.0) (+0.1,-0.0)  (+0.0,-0.0) (+0.1,-0.0)    0\nTl2Cl6  Tl2Cl6-428082203077  Co2I6     Co2I6-04fd9e7d4375  PBE     (-8.1,-5.7) (-5.1,-4.6)  P-31m    AB3-162-dk   3.5  1    (-0.0,-0.0) (-0.0,-0.0)  (-0.0,-0.0) (+0.3,+0.3)    0.1\nHgS        HgS-5256ed7d716e  PbTe       PbTe-3bc08d486d65  HSE     (-6.0,-5.3) (-5.0,-3.5)  P3m1     AB-156-bc    2.5  1    (+0.0,+0.0) (+0.0,+0.0)  (+0.0,+0.0) (+0.0,+0.0)    0.1\nBi2Se3  Bi2Se3-709937cca934  Sb2Te3   Sb2Te3-3e3e0acdba55  HSE     (-6.3,-5.6) (-5.2,-4.5)  P1       A2B3-1-a     1.9  0.9  (+0.0,+0.2) (+0.0,+0.0)  (+0.2,+0.0) (+0.0,+0.0)    0.3\nTl2Br6  Tl2Br6-ef8df28dd878  In2I6     In2I6-d90f9a6ab379  PBE     (-7.1,-6.0) (-5.9,-5.2)  P-62m    AB3-189-ci   1.9  0.9  (-0.0,-0.0) (-0.0,-0.0)  (-0.0,-0.0) (-0.0,-0.0)    0.6\nCu2O2    Cu2O2-7ee25fce4d7e  Au2O2     Au2O2-c99b8d2aa925  HSE     (-7.1,-6.0) (-5.9,-5.0)  P2/m     AB-10-fgm    2    0.8  (+0.5,+0.5) (+0.3,+0.5)  (+0.5,+0.5) (+0.3,+0.5)    0.8\nTl2S2    S2Tl2-fb0b06c4bab6  Tl2Te2   Te2Tl2-44ffc9d405b8  HSE     (-6.8,-5.7) (-5.5,-4.9)  C2       AB-5-c       1.9  0.8  (+0.2,-0.1) (+0.0,+0.0)  (+0.1,+0.0) (+0.0,+0.0)    1.3\nCu2S2    Cu2S2-ed5f6e977271  Au2Te2   Au2Te2-d57228736661  HSE     (-6.8,-5.5) (-5.2,-4.1)  Pm       AB-6-ab      2.7  0.7  (+0.0,+0.5) (+0.2,-0.0)  (+0.0,+0.5) (+0.2,-0.0)    1.9\nTl2Cl6  Tl2Cl6-a7ad562f5ed6  Bi2I6     Bi2I6-01a2d5e89adc  PBE     (-8.0,-6.3) (-6.2,-5.1)  P321     AB3-150-dg   2.9  0.8  (-0.0,-0.0) (-0.0,-0.0)  (-0.0,-0.0) (-0.0,-0.0)    2\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "spgs = pd.unique([row[Headers.Group] for row in typeIII]) # Find which space groups we found pairs from\n",
    "print(\"Screening \" + str(len(typeIII)) + \" type III heterojunctions with \" + str(len(qeinput)) + \" materials from \"+ str(len(spgs)) + \" space groups:\")\n",
    "\n",
    "best = [ # The best pair from each spacegroup\n",
    "    next(row for row in typeIII if row[Headers.Group] == spg)\n",
    "    for spg in spgs\n",
    "]\n",
    "\n",
    "def pretty(row, calcs, figs=3):\n",
    "    fmt = \"{:\"+\".\"+str(figs)+\"f}\"\n",
    "    plus = \"{:\"+\"+\"+\".\"+str(figs)+\"f}\"\n",
    "    points = \"(\"+plus+\",\"+plus+\") (\"+plus+\",\"+plus+\")\"\n",
    "    calc = next(calc for calc in calcs if calc in row[Headers.Calc])\n",
    "    return (\n",
    "        row[i][row[Headers.Calc].index(calc)]\n",
    "            if i == Headers.Calc else\n",
    "        points.format(*row[i][row[Headers.Calc].index(calc)])\n",
    "            if i == Headers.Bands_vbm_cbm else\n",
    "        fmt.format(row[i][row[Headers.Calc].index(calc)])\n",
    "            if i == Headers.ΔE else\n",
    "        points.format(row[i][0][0], row[i][0][1], row[i][1][0], row[i][1][1])\n",
    "            if i in (Headers.Left_kvbm_kcbm, Headers.Right_kvbm_kcbm) else\n",
    "        fmt.format(row[i])\n",
    "            if isinstance(row[i],float) else\n",
    "        row[i]\n",
    "        for i in range(len(Headers))\n",
    "    )\n",
    "\n",
    "print(tabulate( # Print a table of the best pairs\n",
    "    [pretty(row,(\"GW\",\"HSE\",\"PBE\"),1) for row in best],\n",
    "    [header.name for header in Headers],\n",
    "    colalign=(\"left\",\"right\",\"left\",\"right\")\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from operator import itemgetter\n",
    "\n",
    "table_headers = itemgetter( # Which headers to display in the table\n",
    "    Headers.Left,\n",
    "    Headers.Right,\n",
    "    Headers.Type,\n",
    "    Headers.ΔE,\n",
    "    Headers.R,\n",
    "    Headers.Fit\n",
    ")\n",
    "\n",
    "def bands_with_table(pairs,calcs,figs): # Plot each pair of candidates, with bars for the band gaps, and a table for other stats\n",
    "    ax = plt.subplot(label=str(id(pairs)))\n",
    "    ax.margins(x=0)\n",
    "\n",
    "    flatx = range(len(pairs)*2)\n",
    "\n",
    "    if(not flatx): # Empty graph\n",
    "        return\n",
    "\n",
    "    [leftvbm, leftcbm, rightvbm, rightcbm] = np.transpose([\n",
    "        pair[Headers.Bands_vbm_cbm][pair[Headers.Calc].index(\n",
    "            next(calc for calc in calcs if calc in pair[Headers.Calc])\n",
    "        )]\n",
    "        for pair in pairs\n",
    "    ])\n",
    "\n",
    "    emin = np.amin(leftvbm) - .5\n",
    "    emax = np.amax(rightcbm) + .5\n",
    "\n",
    "    leftx = flatx[::2]\n",
    "    ax.bar(leftx, leftvbm-emin, bottom=emin, color='C0')\n",
    "    ax.bar(leftx, leftcbm-emax, bottom=emax, color='C1')\n",
    "    #ax.bar(leftx, leftvbm-leftcbm, bottom=leftcbm)\n",
    "\n",
    "    rightx = flatx[1::2]\n",
    "    ax.bar(rightx, rightvbm-emin, bottom=emin)\n",
    "    ax.bar(rightx, rightcbm-emax, bottom=emax)\n",
    "    #ax.bar(rightx, rightvbm-rightcbm, bottom=rightcbm)\n",
    "\n",
    "    ax.set_xlim(-.5,len(flatx)-.5)\n",
    "    ax.set_ylim(emin,emax)\n",
    "\n",
    "    plt.xticks([],[])\n",
    "\n",
    "    table = plt.table(\n",
    "        cellText=np.transpose([table_headers(tuple(pretty(pair,calcs,figs))) for pair in pairs]),\n",
    "        rowLabels=table_headers(tuple(header.name for header in Headers)),\n",
    "        loc='bottom'\n",
    "    )\n",
    "    table.auto_set_font_size(False)\n",
    "    table.set_fontsize('medium')\n",
    "\n",
    "    plt.table(\n",
    "        cellText=[['' for x in leftx]],\n",
    "        bbox=[0,0,1,1]\n",
    "    )\n",
    "\n",
    "    plt.ylabel('Band gap, Blue=VBM, Orange=CBM (' + ','.join(calcs) + ' '*bool(calcs) + 'eV)')\n",
    "\n",
    "plt.figure(figsize=(17,7),dpi=100)\n",
    "bands_with_table(best,(\"GW\",\"HSE\",\"PBE\"),9)\n",
    "plt.title(\"Top candidates (\" + str(len(spgs)) + \" space groups)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Label(value='Calculations:')",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "48db1a6b354c4cf988bb0314ef9344d3"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "VBox(children=(Checkbox(value=False, description='GW'), Checkbox(value=False, description='HSE'), Checkbox(val…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8bd0aec42d0a4bce8fdc1444879a0649"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Dropdown(description='Space group:', options=('P-6m2', 'P-1', 'P-4m2', 'P4/nmm', 'P-3m1', 'P2_1/m', 'P-31m', '…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "212b3822c3b24eb3a98985dd660082c5"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "IntRangeSlider(value=(0, 20), description='Candidate indices:', max=443, style=SliderStyle(description_width='…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "446f5ac85e9147608ef1e9656abd0ee7"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Button(description='VBM, CBM', style=ButtonStyle())",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "16a8403b682043b6bcbe9b59f2768734"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Button(description='ΔE, R', style=ButtonStyle())",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f17884863188434a9fde01b7bc758191"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Output()",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3f3d3fcc2a894076957d92969e08080c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "HTML(value='')",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fd86713e254646138d7dde9b3019f48f"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Combobox(value='', description='Espresso input:', options=('MgBr2-bee1987fb4e6', 'HfI2-05a69240794c', 'Hg2Se2-…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "74cc1b1b845c42aaaac459a375f06227"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Textarea(value='', disabled=True, placeholder='pwi')",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3c4f99e278b348658f86189bc4901bc1"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "label = widgets.Label(value=\"Calculations:\")\n",
    "cvbox = widgets.VBox([\n",
    "    widgets.Checkbox(description=desc)\n",
    "    for desc in ('GW','HSE','PBE')\n",
    "])\n",
    "def desc_value_vbox(vbox):\n",
    "    return (child.description for child in vbox.children if child.value)\n",
    "\n",
    "cvbox.children[1].value = True\n",
    "cvbox.children[2].value = True\n",
    "\n",
    "ddown = widgets.Dropdown(\n",
    "    options=spgs,\n",
    "    description='Space group:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "slide = widgets.IntRangeSlider(\n",
    "    value=(0,20),\n",
    "    description='Candidate indices:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "def update_slide_max(event):\n",
    "    slide.max = len(spacegroup)\n",
    "    lower, upper = slide.value\n",
    "    slide.value = (min(lower,slide.max),min(upper,slide.max))\n",
    "\n",
    "spacegroup = None\n",
    "def update_spacegroup(event):\n",
    "    global spacegroup\n",
    "    spacegroup = [row for row in functional if row[Headers.Group] == ddown.value] # Rows in this group\n",
    "    update_slide_max(event)\n",
    "\n",
    "functional = None\n",
    "def update_functional(event):\n",
    "    global functional\n",
    "    functional = [\n",
    "        row for row in typeIII\n",
    "        if set(desc_value_vbox(cvbox)) & set(row[Headers.Calc])\n",
    "    ]\n",
    "    old = ddown.value\n",
    "    ddown.options = pd.unique([row[Headers.Group] for row in functional])\n",
    "    if old in ddown.options:\n",
    "        ddown.value = old\n",
    "    update_spacegroup(event)\n",
    "\n",
    "update_functional(None)\n",
    "ddown.observe(update_spacegroup, names='value')\n",
    "for child in cvbox.children:\n",
    "    child.observe(update_functional, names='value')\n",
    "\n",
    "bands = widgets.Button(description='VBM, CBM')\n",
    "ratio = widgets.Button(description='ΔE, R')\n",
    "combo = widgets.Combobox(\n",
    "    placeholder='uid',\n",
    "    options=tuple(qeinput.keys()),\n",
    "    description=\"Espresso input:\",\n",
    "    style={'description_width': 'initial'},\n",
    ")\n",
    "\n",
    "qebox = widgets.Textarea(\n",
    "    placeholder='pwi',\n",
    "    disabled=True\n",
    ")\n",
    "\n",
    "def update_qebox(event):\n",
    "    if(combo.value in qeinput.keys()):\n",
    "        qebox.value = qeinput[combo.value]\n",
    "        qebox.rows = qebox.value.count('\\n') + 1\n",
    "\n",
    "combo.observe(update_qebox, names='value')\n",
    "matpl = widgets.Output()\n",
    "table = widgets.HTML()\n",
    "\n",
    "def update_matpl_table(button):\n",
    "    with matpl:\n",
    "        matpl.clear_output(wait=True)\n",
    "        top = spacegroup[slide.value[0]:slide.value[1]] # Top 20 rows in this space group\n",
    "        plt.figure(figsize=(17,7),dpi=100)\n",
    "        calcs = tuple(desc_value_vbox(cvbox))\n",
    "        if button==bands:\n",
    "            bands_with_table(top,calcs,6)\n",
    "        if button==ratio:\n",
    "            plt.scatter(\n",
    "                x=[pair[Headers.ΔE][pair[Headers.Calc].index(next(calc for calc in calcs if calc in pair[Headers.Calc]))] for pair in top],\n",
    "                y=[pair[Headers.R] for pair in top]\n",
    "            )\n",
    "            for pair in top: # Label scatter plot points with formulas\n",
    "                plt.annotate(\n",
    "                    pair[Headers.Left]+\",\"+pair[Headers.Right],\n",
    "                    (pair[Headers.ΔE][pair[Headers.Calc].index(next(calc for calc in calcs if calc in pair[Headers.Calc]))],\n",
    "                    pair[Headers.R])\n",
    "                )\n",
    "            plt.xlabel(Headers(Headers.ΔE).name + \" (eV)\")\n",
    "            plt.ylabel(Headers(Headers.R).name + \" (1)\")\n",
    "\n",
    "        if ddown.value:\n",
    "            plt.title(ddown.value + \" (top \" + str(slide.value[0]) + \"-\" + str(slide.value[1]) + \" of \" + str(len(spacegroup)) + \" candidates)\")\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        for row in range(len(top)):\n",
    "            pair = list(top[row])\n",
    "\n",
    "            pair[Headers.Left_uid] = '<a target=\"_blank\" href=\"https://cmrdb.fysik.dtu.dk/c2db/row/' + \\\n",
    "                pair[Headers.Left_uid] + '\">' + \\\n",
    "                pair[Headers.Left_uid] + '</a>'\n",
    "\n",
    "            pair[Headers.Right_uid] = '<a target=\"_blank\" href=\"https://cmrdb.fysik.dtu.dk/c2db/row/' + \\\n",
    "                pair[Headers.Right_uid] + '\">' + \\\n",
    "                pair[Headers.Right_uid] + '</a>'\n",
    "            top[row] = pair\n",
    "\n",
    "        table.value = \\\n",
    "            \"\"\"\n",
    "            <style>\n",
    "            tr:hover {\n",
    "                background-color: gainsboro !important;\n",
    "            }\n",
    "            tr {\n",
    "                background-color: white !important;\n",
    "            }\n",
    "            table tr td {\n",
    "                border-left: 1px solid gainsboro !important;\n",
    "                border-right: 1px solid gainsboro !important;\n",
    "                font-family: monospace !important;\n",
    "            }\n",
    "             table {\n",
    "              border-spacing: 0 !important;\n",
    "            }\n",
    "            thead tr th {\n",
    "              border-bottom: 1px solid gainsboro !important;\n",
    "            }\n",
    "            </style>\n",
    "            \"\"\" + tabulate(\n",
    "                [pretty(pair,calcs,3) for pair in top],\n",
    "                [header.name for header in Headers],\n",
    "                tablefmt=\"unsafehtml\"\n",
    "            )\n",
    "\n",
    "bands.on_click(update_matpl_table)\n",
    "ratio.on_click(update_matpl_table)\n",
    "\n",
    "display(label, cvbox, ddown, slide, bands, ratio, matpl, table, combo, qebox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}