diff --git "a/data/dataset_Homo_Lumo.csv" "b/data/dataset_Homo_Lumo.csv"
new file mode 100644--- /dev/null
+++ "b/data/dataset_Homo_Lumo.csv"
@@ -0,0 +1,4665 @@
+"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
+"Homo-Lumo","setten/GW100","docs/index.md",".md","40","4","test
+
+[name](../pages/compare_all.html)
+","Markdown"
+"Homo-Lumo","setten/GW100","scripts/datasetclass.ipynb",".ipynb","3056","126","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""import json""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""class GW100DataSet(object):\n"",
+    ""    \n"",
+    ""    def __init__(self):\n"",
+    ""        self.data = {'data': {}}\n"",
+    ""    \n"",
+    ""    @classmethod\n"",
+    ""    def from_txt(cls, file_name):\n"",
+    ""        set = GW100DataSet()\n"",
+    ""        set.read_txt(file_name)\n"",
+    ""        return set\n"",
+    ""    \n"",
+    ""    def name(self, post_fix=''):\n"",
+    ""        return '%s_%s_%sv%s_%s%s' % (self.data['calc_type'], self.data['orbital'], self.data['code'][0], \n"",
+    ""                            self.data['code_version'], self.data['basis_name'], post_fix)\n"",
+    ""    \n"",
+    ""    def read_txt(self, file_name):\n"",
+    ""        with open(file_name, 'r') as f:\n"",
+    ""            lines = f.readlines()\n"",
+    ""        for line in lines:\n"",
+    ""            if '=' in line:\n"",
+    ""                d = line.strip().split('=')\n"",
+    ""                if d[1].startswith('{'):\n"",
+    ""                    self.data[d[0]] = json.loads(d[1])\n"",
+    ""                else:\n"",
+    ""                    self.data[d[0]] = d[1]\n"",
+    ""            else:\n"",
+    ""                d = line.strip().split( )\n"",
+    ""                try:\n"",
+    ""                    self.data['data'][d[0]]=float(d[1])\n"",
+    ""                except (IndexError, ValueError):\n"",
+    ""                    self.data['data'][d[0]]='null'\n"",
+    ""                    \n"",
+    ""    def dump_json(self, post):\n"",
+    ""        with open(self.name(post)+'.json','w') as fp:\n"",
+    ""            json.dump(self.data, fp, indent=4)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""'G0W0@PBE_HOMO_Tv7.0_def2-TQZVP_cbas'""
+      ]
+     },
+     ""execution_count"": 8,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""dataset = GW100DataSet.from_txt('test_data/tmcbastqex.txt')\n"",
+    ""dataset.name(\""_cbas\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""dataset.dump_json(\""_cbas\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""ll\n""
+   ]
+  }
+ ],
+ ""metadata"": {
+  ""anaconda-cloud"": {},
+  ""kernelspec"": {
+   ""display_name"": ""Python [default]"",
+   ""language"": ""python"",
+   ""name"": ""python2""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 2
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython2"",
+   ""version"": ""2.7.12""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 1
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","setup.py",".py","740","25","#!/usr/bin/env python
+
+#from distutils.core import setup
+from setuptools import setup
+
+with open('README.md') as readme_file:
+    long_description = readme_file.read()
+
+setup(name='Psikit',
+      version='0.2.0',
+      description='A thin wrapper library for Psi4 and RDKit',
+      long_description=long_description,
+      long_description_content_type='text/markdown',
+      author='Kazufumi Ohkawa, Takayuki Serizawa',
+      author_email='kerolinq@gmail.com, seritaka@gmail.com',
+      url='https://github.com/Mishima-syk/psikit',
+      packages=['psikit'],
+      install_requires=['debtcollector'],
+      license='MIT',
+    classifiers = [
+        ""Programming Language :: Python"",
+        ""Programming Language :: Python :: 3""]
+     )
+
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/pymol_helper.py",".py","3380","80","from glob import glob
+import os
+
+def run_pymol_server(tmpdir, target='FRONTIER', maprange=0.05):
+    '''
+    To use the function, user need to install pymol and run the pymol for server mode
+    The command is pymol -R
+    target ['ESP', 'FRONTIER', 'DUAL']
+    '''
+    targetlist = ['ESP', 'FRONTIER', 'DUAL']
+    if target not in targetlist:
+        raise Exception(f'Please set target from ESP, FRONTIER, DENSITY!!')
+    import sys
+    import xmlrpc.client as xmlrpc
+    srv = xmlrpc.ServerProxy('http://localhost:9123')
+    srv.do('delete *')
+    srv.do('load '+os.path.join(tmpdir, 'target.mol'))
+    srv.do('as sticks, target')
+    if target == 'FRONTIER':
+        homof = glob(os.path.join(tmpdir, 'Psi*_HOMO.cube'))[0]
+        lumof = glob(os.path.join(tmpdir, 'Psi*_LUMO.cube'))[0]
+        srv.do('load ' + homof + ',HOMO')
+        srv.do('load ' + lumof + ',LUMO')
+        srv.do(f'isosurface HOMO_A, HOMO, -0.02')
+        srv.do(f'isosurface HOMO_B, HOMO, 0.02')
+        srv.do(f'isosurface LUMO_A, LUMO, -0.02')
+        srv.do(f'isosurface LUMO_B, LUMO, 0.02')
+        srv.do('color blue, HOMO_A')
+        srv.do('color red, HOMO_B')
+        srv.do('color green, LUMO_A')
+        srv.do('color yellow, LUMO_B')
+        srv.do('set transparency, 0.2')
+        srv.do('disable HOMO_A')
+        srv.do('disable HOMO_B')
+        abb = 'frontier_'
+    elif target == 'ESP':
+        srv.do('load '+ 'ESP.cube' + ', ESP')
+        srv.do('show surface, target')
+        srv.do(f'ramp_new cmap, ESP, [-{maprange}, {maprange}]')
+        srv.do('color cmap, target')
+        srv.do('set transparency, 0.2')
+        abb = 'esp_'
+    elif target == 'DUAL':
+        dualf = glob.glob('DUAL*.cube')[0]
+        srv.do('load '+ dualf + ', DUAL_DESC')
+        srv.do('show surface, target')
+        srv.do(f'ramp_new cmap, DUAL_DESC, [-{maprange}, {maprange}]')
+        srv.do('color cmap, target')
+        srv.do('set transparency, 0.2')
+        abb = 'dual_'
+
+    outputpath = abb + 'mo.pse'
+    srv.do(f'save {outputpath}')
+    print('finished !')
+
+
+def save_pyscript(tmpdir, isotype=""isosurface""):
+    homof = glob(os.path.join(tmpdir, 'Psi*_HOMO.cube'))[0]
+    lumof = glob(os.path.join(tmpdir, 'Psi*_LUMO.cube'))[0]
+    with open(""frontier.py"", ""w"") as f:
+        f.write('from pymol import *\n')
+        f.write('cmd.load(""{0}"", ""HOMO"")\n'.format(homof))
+        f.write('cmd.load(""{0}"", ""LUMO"")\n'.format(lumof))
+        f.write('cmd.load(""{0}"")\n'.format(os.path.join(tmpdir, ""target.mol"")))
+        if isotype == ""isomesh"":        
+            f.write('cmd.isomesh(""HOMO_A"", ""HOMO"", -0.02)\n')
+            f.write('cmd.isomesh(""HOMO_B"", ""HOMO"", 0.02)\n')
+            f.write('cmd.isomesh(""LUMO_A"", ""LUMO"", 0.02)\n')
+            f.write('cmd.isomesh(""LUMO_B"", ""LUMO"", -0.02)\n')
+        else:
+            f.write('cmd.isosurface(""HOMO_A"", ""HOMO"", -0.02)\n')
+            f.write('cmd.isosurface(""HOMO_B"", ""HOMO"", 0.02)\n')
+            f.write('cmd.isosurface(""LUMO_A"", ""LUMO"", 0.02)\n')
+            f.write('cmd.isosurface(""LUMO_B"", ""LUMO"", -0.02)\n')            
+        f.write('cmd.color(""blue"", ""HOMO_A"")\n')       
+        f.write('cmd.color(""red"", ""HOMO_B"")\n')       
+        f.write('cmd.color(""blue"", ""LUMO_A"")\n')
+        f.write('cmd.color(""red"", ""LUMO_B"")\n')
+        f.write('cmd.disable(""LUMO_A"")\n')              
+        f.write('cmd.disable(""LUMO_B"")\n')       ","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/sapt.py",".py","11353","254","from rdkit import Chem
+from psikit import Psikit
+import numpy as np
+import os
+from .util import mol2xyz
+
+class Sapt():
+    def __init__(self, threads=4, memory=4, debug=False):
+        import psi4
+        from . import helper_SAPT
+
+        self.psi4 = psi4
+        self.helper_SAPT = helper_SAPT
+        self.psi4.set_memory(""{} GB"".format(memory))
+        #self.psi4.set_options({""save_jk"": True})  # for JK calculation
+        self.psi4.set_num_threads(threads)
+        self.wfn = None
+        self.monomer1 = None
+        self.monomer2 = None
+        self.dimer = None
+        self.debug = debug
+        if self.debug:
+            self.psi4.core.set_output_file(""psikit_out.dat"", True)
+        else:
+            self.psi4.core.be_quiet()
+
+    def monomer1_from_molfile(self, molfile, opt=True, removeHs=False):
+        self.monomer1 = Chem.MolFromMolFile(molfile, removeHs=removeHs)
+
+    def monomer2_from_molfile(self, molfile, opt=True, removeHs=False):
+        self.monomer2 = Chem.MolFromMolFile(molfile, removeHs=removeHs)
+
+    def make_dimer(self):
+        xyz1 = mol2xyz(self.monomer1)
+        xyz2 = mol2xyz(self.monomer2)
+        self.dimer = ""{}--\n{}"".format(xyz1, xyz2)
+        self.dimer += ""no_reorient\n""
+        self.dimer += ""no_com\n""
+        self.dimer += ""units angstrom\n""
+        
+ 
+    def run_sapt(self, basis='aug-cc-pvdz', e_convergence=1e-8, d_convergence=1e-8, memory=4):
+        self.psi4.set_options({'basis':basis,
+                               'e_convergence':e_convergence,
+                               'd_convergence':d_convergence})
+        dimer = self.psi4.geometry(self.dimer)
+        sapt = self.helper_SAPT.helper_SAPT(dimer, memory=memory)
+        ### Start E100 Electostatics
+        elst_timer = self.helper_SAPT.sapt_timer('electrostatics')
+        Elst10 = 4 * np.einsum('abab', sapt.vt('abab'))
+        elst_timer.stop()
+        ### End E100 Electrostatics
+        
+        ### Start E100 Exchange
+        exch_timer =self.helper_SAPT.sapt_timer('exchange')
+        vt_abba = sapt.vt('abba')
+        vt_abaa = sapt.vt('abaa')
+        vt_abbb = sapt.vt('abbb')
+        vt_abab = sapt.vt('abab')
+        s_ab = sapt.s('ab')
+        
+        Exch100 = np.einsum('abba', vt_abba)
+        
+        tmp = 2 * vt_abaa - vt_abaa.swapaxes(2, 3)
+        Exch100 += np.einsum('Ab,abaA', s_ab, tmp)
+        
+        tmp = 2 * vt_abbb - vt_abbb.swapaxes(2, 3)
+        Exch100 += np.einsum('Ba,abBb', s_ab.T, tmp)
+        
+        Exch100 -= 2 * np.einsum('Ab,BA,abaB', s_ab, s_ab.T, vt_abab)
+        Exch100 -= 2 * np.einsum('AB,Ba,abAb', s_ab, s_ab.T, vt_abab)
+        Exch100 += np.einsum('Ab,Ba,abAB', s_ab, s_ab.T, vt_abab)
+        
+        Exch100 *= -2
+        exch_timer.stop()
+        ### End E100 (S^2) Exchange
+        
+        ### Start E200 Disp
+        disp_timer = self.helper_SAPT.sapt_timer('dispersion')
+        v_abrs = sapt.v('abrs')
+        v_rsab = sapt.v('rsab')
+        e_rsab = 1/(-sapt.eps('r', dim=4) - sapt.eps('s', dim=3) + sapt.eps('a', dim=2) + sapt.eps('b'))
+        
+        Disp200 = 4 * np.einsum('rsab,rsab,abrs->', e_rsab, v_rsab, v_abrs)
+        ### End E200 Disp
+        
+        ### Start E200 Exchange-Dispersion
+        
+        # Build t_rsab
+        t_rsab = np.einsum('rsab,rsab->rsab', v_rsab, e_rsab)
+        
+        # Build h_abrs
+        vt_abar = sapt.vt('abar')
+        vt_abra = sapt.vt('abra')
+        vt_absb = sapt.vt('absb')
+        vt_abbs = sapt.vt('abbs')
+        
+        tmp = 2 * vt_abar - vt_abra.swapaxes(2, 3)
+        h_abrs = np.einsum('as,AbAr->abrs', sapt.s('as'), tmp)
+        
+        tmp = 2 * vt_abra - vt_abar.swapaxes(2, 3)
+        h_abrs += np.einsum('As,abrA->abrs', sapt.s('as'), tmp)
+        
+        tmp = 2 * vt_absb - vt_abbs.swapaxes(2, 3)
+        h_abrs += np.einsum('br,aBsB->abrs', sapt.s('br'), tmp)
+        
+        tmp = 2 * vt_abbs - vt_absb.swapaxes(2, 3)
+        h_abrs += np.einsum('Br,abBs->abrs', sapt.s('br'), tmp)
+        
+        # Build q_abrs
+        vt_abas = sapt.vt('abas')
+        q_abrs =      np.einsum('br,AB,aBAs->abrs', sapt.s('br'), sapt.s('ab'), vt_abas)
+        q_abrs -= 2 * np.einsum('Br,AB,abAs->abrs', sapt.s('br'), sapt.s('ab'), vt_abas)
+        q_abrs -= 2 * np.einsum('br,aB,ABAs->abrs', sapt.s('br'), sapt.s('ab'), vt_abas)
+        q_abrs += 4 * np.einsum('Br,aB,AbAs->abrs', sapt.s('br'), sapt.s('ab'), vt_abas)
+        
+        vt_abrb = sapt.vt('abrb')
+        q_abrs -= 2 * np.einsum('as,bA,ABrB->abrs', sapt.s('as'), sapt.s('ba'), vt_abrb)
+        q_abrs += 4 * np.einsum('As,bA,aBrB->abrs', sapt.s('as'), sapt.s('ba'), vt_abrb)
+        q_abrs +=     np.einsum('as,BA,AbrB->abrs', sapt.s('as'), sapt.s('ba'), vt_abrb)
+        q_abrs -= 2 * np.einsum('As,BA,abrB->abrs', sapt.s('as'), sapt.s('ba'), vt_abrb)
+        
+        vt_abab = sapt.vt('abab')
+        q_abrs +=     np.einsum('Br,As,abAB->abrs', sapt.s('br'), sapt.s('as'), vt_abab)
+        q_abrs -= 2 * np.einsum('br,As,aBAB->abrs', sapt.s('br'), sapt.s('as'), vt_abab)
+        q_abrs -= 2 * np.einsum('Br,as,AbAB->abrs', sapt.s('br'), sapt.s('as'), vt_abab)
+        
+        vt_abrs = sapt.vt('abrs')
+        q_abrs +=     np.einsum('bA,aB,ABrs->abrs', sapt.s('ba'), sapt.s('ab'), vt_abrs)
+        q_abrs -= 2 * np.einsum('bA,AB,aBrs->abrs', sapt.s('ba'), sapt.s('ab'), vt_abrs)
+        q_abrs -= 2 * np.einsum('BA,aB,Abrs->abrs', sapt.s('ba'), sapt.s('ab'), vt_abrs)
+        
+        # Sum it all together
+        xd_absr = sapt.vt('absr')
+        xd_absr += h_abrs.swapaxes(2, 3)
+        xd_absr += q_abrs.swapaxes(2, 3)
+        ExchDisp20 = -2 * np.einsum('absr,rsab->', xd_absr, t_rsab)
+        
+        disp_timer.stop()
+        ### End E200 Exchange-Dispersion
+        
+        
+        ### Start E200 Induction and Exchange-Induction
+        
+        # E200Induction and CPHF orbitals
+        ind_timer = self.helper_SAPT.sapt_timer('induction')
+        
+        CPHF_ra, Ind20_ba = sapt.chf('B', ind=True)
+        self.helper_SAPT.sapt_printer('Ind20,r (A<-B)', Ind20_ba)
+        
+        CPHF_sb, Ind20_ab = sapt.chf('A', ind=True)
+        self.helper_SAPT.sapt_printer('Ind20,r (A->B)', Ind20_ab)
+        
+        Ind20r = Ind20_ba + Ind20_ab
+        
+        # Exchange-Induction
+        
+        # A <- B
+        vt_abra = sapt.vt('abra')
+        vt_abar = sapt.vt('abar')
+        ExchInd20_ab  =     np.einsum('ra,abbr', CPHF_ra, sapt.vt('abbr'))
+        ExchInd20_ab += 2 * np.einsum('rA,Ab,abar', CPHF_ra, sapt.s('ab'), vt_abar)
+        ExchInd20_ab += 2 * np.einsum('ra,Ab,abrA', CPHF_ra, sapt.s('ab'), vt_abra)
+        ExchInd20_ab -=     np.einsum('rA,Ab,abra', CPHF_ra, sapt.s('ab'), vt_abra)
+        
+        vt_abbb = sapt.vt('abbb')
+        vt_abab = sapt.vt('abab')
+        ExchInd20_ab -=     np.einsum('ra,Ab,abAr', CPHF_ra, sapt.s('ab'), vt_abar)
+        ExchInd20_ab += 2 * np.einsum('ra,Br,abBb', CPHF_ra, sapt.s('br'), vt_abbb)
+        ExchInd20_ab -=     np.einsum('ra,Br,abbB', CPHF_ra, sapt.s('br'), vt_abbb)
+        ExchInd20_ab -= 2 * np.einsum('rA,Ab,Br,abaB', CPHF_ra, sapt.s('ab'), sapt.s('br'), vt_abab)
+        
+        vt_abrb = sapt.vt('abrb')
+        ExchInd20_ab -= 2 * np.einsum('ra,Ab,BA,abrB', CPHF_ra, sapt.s('ab'), sapt.s('ba'), vt_abrb)
+        ExchInd20_ab -= 2 * np.einsum('ra,AB,Br,abAb', CPHF_ra, sapt.s('ab'), sapt.s('br'), vt_abab)
+        ExchInd20_ab -= 2 * np.einsum('rA,AB,Ba,abrb', CPHF_ra, sapt.s('ab'), sapt.s('ba'), vt_abrb)
+        
+        ExchInd20_ab +=     np.einsum('ra,Ab,Br,abAB', CPHF_ra, sapt.s('ab'), sapt.s('br'), vt_abab)
+        ExchInd20_ab +=     np.einsum('rA,Ab,Ba,abrB', CPHF_ra, sapt.s('ab'), sapt.s('ba'), vt_abrb)
+        
+        ExchInd20_ab *= -2
+        self.helper_SAPT.sapt_printer('Exch-Ind20,r (A<-B)', ExchInd20_ab)
+        
+        # B <- A
+        vt_abbs = sapt.vt('abbs')
+        vt_absb = sapt.vt('absb')
+        ExchInd20_ba  =     np.einsum('sb,absa', CPHF_sb, sapt.vt('absa'))
+        ExchInd20_ba += 2 * np.einsum('sB,Ba,absb', CPHF_sb, sapt.s('ba'), vt_absb)
+        ExchInd20_ba += 2 * np.einsum('sb,Ba,abBs', CPHF_sb, sapt.s('ba'), vt_abbs)
+        ExchInd20_ba -=     np.einsum('sB,Ba,abbs', CPHF_sb, sapt.s('ba'), vt_abbs)
+        
+        vt_abaa = sapt.vt('abaa')
+        vt_abab = sapt.vt('abab')
+        ExchInd20_ba -=     np.einsum('sb,Ba,absB', CPHF_sb, sapt.s('ba'), vt_absb)
+        ExchInd20_ba += 2 * np.einsum('sb,As,abaA', CPHF_sb, sapt.s('as'), vt_abaa)
+        ExchInd20_ba -=     np.einsum('sb,As,abAa', CPHF_sb, sapt.s('as'), vt_abaa)
+        ExchInd20_ba -= 2 * np.einsum('sB,Ba,As,abAb', CPHF_sb, sapt.s('ba'), sapt.s('as'), vt_abab)
+        
+        vt_abas = sapt.vt('abas')
+        ExchInd20_ba -= 2 * np.einsum('sb,Ba,AB,abAs', CPHF_sb, sapt.s('ba'), sapt.s('ab'), vt_abas)
+        ExchInd20_ba -= 2 * np.einsum('sb,BA,As,abaB', CPHF_sb, sapt.s('ba'), sapt.s('as'), vt_abab)
+        ExchInd20_ba -= 2 * np.einsum('sB,BA,Ab,abas', CPHF_sb, sapt.s('ba'), sapt.s('ab'), vt_abas)
+        
+        ExchInd20_ba +=     np.einsum('sb,Ba,As,abAB', CPHF_sb, sapt.s('ba'), sapt.s('as'), vt_abab)
+        ExchInd20_ba +=     np.einsum('sB,Ba,Ab,abAs', CPHF_sb, sapt.s('ba'), sapt.s('ab'), vt_abas)
+        
+        ExchInd20_ba *= -2
+        self.helper_SAPT.sapt_printer('Exch-Ind20,r (A->B)', ExchInd20_ba)
+        ExchInd20r = ExchInd20_ba + ExchInd20_ab
+        
+        ind_timer.stop()
+        ### End E200 Induction and Exchange-Induction
+        
+        print('\nSAPT0 Results')
+        print('-' * 70)
+        self.helper_SAPT.sapt_printer('Exch10 (S^2)', Exch100)
+        self.helper_SAPT.sapt_printer('Elst10', Elst10)
+        self.helper_SAPT.sapt_printer('Disp20', Disp200)
+        self.helper_SAPT.sapt_printer('Exch-Disp20', ExchDisp20)
+        self.helper_SAPT.sapt_printer('Ind20,r', Ind20r)
+        self.helper_SAPT.sapt_printer('Exch-Ind20,r', ExchInd20r)
+        
+        print('-' * 70)
+        sapt0 = Exch100 + Elst10 + Disp200 + ExchDisp20 + Ind20r + ExchInd20r
+        self.helper_SAPT.sapt_printer('Total SAPT0', sapt0)        
+        return sapt0, Exch100, Elst10, Disp200, ExchDisp20, Ind20r, ExchInd20r
+
+    def run_fisapt(self, basis='jun-cc-pvdz', scf_type='df', d_convergence=1e-8, memory=4, fisapt_path='fsapt/', return_wfn=False):
+        import shutil
+        from distutils.dir_util import copy_tree
+        from . import fsapt_helper
+        self.psi4.set_options({'basis':basis,
+                               'scf_type':scf_type,
+                               'd_convergence':d_convergence,
+                               'FISAPT_FSAPT_FILEPATH':fisapt_path,
+                               'FISAPT_DO_PLOT': 'true'
+                               })
+        self.psi4.geometry(self.dimer)
+        res = self.psi4.energy('fisapt0', return_wfn=return_wfn)
+        copy_tree(self.psi4.core.get_datadir()+'/fsapt', fisapt_path)
+        #sapt = self.helper_SAPT.helper_SAPT(dimer, memory=memory)
+        feats1 = fsapt_helper.make_feat_data(self.monomer1, 1)
+        feats2 = fsapt_helper.make_feat_data(self.monomer2, 1 + self.monomer1.GetNumAtoms())
+        with open(os.path.join(fisapt_path, 'fA.dat'), 'w') as fA:
+            for feat1 in feats1:
+                fA.write(' '.join(feat1) + '\n')
+
+        with open(os.path.join(fisapt_path,'fB.dat'), 'w') as fB:
+            for feat2 in feats2:
+                fB.write(' '.join(feat2) + '\n')
+
+        return res
+    
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/psikit.py",".py","8801","242","# -*- coding: utf-8 -*-
+import rdkit
+from rdkit import Chem
+from rdkit.Chem import AllChem
+import numpy as np
+import glob
+import os
+import uuid
+import warnings
+from collections import defaultdict
+from tempfile import mkdtemp
+from shutil import rmtree
+from debtcollector import moves
+from .util import mol2xyz
+from .pymol_helper import run_pymol_server, save_pyscript
+warnings.simplefilter('ignore')
+
+
+class Psikit(object):
+    def __init__(self, threads=4, memory=4, debug=False):
+        import psi4
+        self.psi4 = psi4
+        self.psi4.set_memory(""{} GB"".format(memory))
+        #self.psi4.set_options({""save_jk"": True})  # for JK calculation
+        self.psi4.set_num_threads(threads)
+        self.wfn = None
+        self.mol = None
+        self.debug = debug
+        self.tempdir = mkdtemp()
+        if self.debug:
+            self.psi4.core.set_output_file(""psikit_out.dat"", True)
+        else:
+            self.psi4.core.be_quiet()
+
+    def clean(self):
+        rmtree(self.tempdir)
+    
+    def read_from_smiles(self, smiles_str, opt=True):
+        self.mol = Chem.MolFromSmiles(smiles_str)
+        if opt:
+            self.rdkit_optimize()   
+
+    def read_from_molfile(self, molfile, opt=True, removeHs=False):
+        self.mol = Chem.MolFromMolFile(molfile, removeHs=removeHs)
+        if opt:
+            self.rdkit_optimize()   
+
+    def rdkit_optimize(self, addHs=True):
+        if addHs:
+            self.mol = Chem.AddHs(self.mol)
+        AllChem.EmbedMolecule(self.mol, useExpTorsionAnglePrefs=True,useBasicKnowledge=True)
+        AllChem.UFFOptimizeMolecule(self.mol)
+
+    def geometry(self, multiplicity=1):
+        xyz = self.mol2xyz(multiplicity=multiplicity)
+        self.psi4.geometry(xyz)
+
+    def energy(self, basis_sets= ""scf/6-31g**"", return_wfn=True, multiplicity=1):
+        self.geometry(multiplicity=multiplicity)
+        scf_energy, wfn = self.psi4.energy(basis_sets, return_wfn=return_wfn)
+        self.psi4.core.clean()
+        self.wfn = wfn
+        self.mol = self.xyz2mol()
+        return scf_energy
+
+    def optimize(self, basis_sets= ""scf/6-31g**"", return_wfn=True, name=None, multiplicity=1, maxiter=50):
+        if not name:
+            name = uuid.uuid4().hex
+        self.psi4.core.IO.set_default_namespace(name)
+        self.geometry(multiplicity=multiplicity)
+        self.psi4.set_options({'GEOM_MAXITER':maxiter})
+        try:
+            scf_energy, wfn = self.psi4.optimize(basis_sets, return_wfn=return_wfn)
+            self.wfn = wfn
+        except self.psi4.OptimizationConvergenceError as cError:
+            print('Convergence error caught: {0}'.format(cError))
+            self.wfn = cError.wfn
+            scf_energy = self.wfn.energy()
+            self.psi4.core.clean()
+        self.mol = self.xyz2mol()
+
+        if not self.debug:
+            self.psi4.core.opt_clean() # Seg fault will occured when the function is called before optimize.
+        return scf_energy
+
+    def set_options(self, **kwargs):
+        """"""
+        http://www.psicode.org/psi4manual/1.2/psiapi.html
+        IV. Analysis of Intermolecular Interactions
+        and 
+        http://forum.psicode.org/t/how-can-i-change-max-iteration-in-energy-method/1238/2
+        """"""
+        self.psi4.set_options(kwargs)
+
+    def mol2xyz(self, multiplicity=1):
+        return mol2xyz(self.mol)
+
+    def xyz2mol(self, confId=0):
+        natom = self.wfn.molecule().natom()
+        mol_array_bohr = self.wfn.molecule().geometry().to_array()
+        mol_array = mol_array_bohr * 0.52917721092
+        nmol = Chem.Mol(self.mol)
+        conf = nmol.GetConformer(confId)
+        for i in range(natom):
+            conf.SetAtomPosition(i, tuple(mol_array[i]))
+        return nmol
+
+    
+    def clone_mol(self):
+        return Chem.Mol(self.mol)
+
+    def create_cube_files(self, gridspace=0.3):
+        if self.wfn == None:
+            print('wfn not found. run optimze()/energy()')
+            return None
+        else:
+            a = self.wfn.nalpha()  # HOMO
+            b = a + 1  # LUMO
+            self.psi4.set_options({""cubeprop_tasks"": ['ESP', 'FRONTIER_ORBITALS', 'Density', 'DUAL_DESCRIPTOR'],
+                                   ""cubic_grid_spacing"": [gridspace, gridspace, gridspace],
+                                   ""cubeprop_filepath"": self.tempdir
+                                   })
+            Chem.MolToMolFile(self.mol, os.path.join(self.tempdir, 'target.mol'))
+            self.psi4.cubeprop(self.wfn)
+    
+    getMOview = moves.moved_function(create_cube_files, 'getMOview', __name__)
+
+    def view_on_pymol(self, target='FRONTIER', maprange=0.05, gridspace=0.3):
+        self.create_cube_files(gridspace=gridspace)
+        run_pymol_server(self.tempdir, target=target, maprange=maprange)
+
+    def save_frontier(self, gridspace=0.3, isotype=""isosurface""):
+        self.create_cube_files(gridspace=gridspace)
+        save_pyscript(self.tempdir, isotype=isotype)  
+
+    def save_fchk(self, filename=""output.fchk""):
+        fchk_writer = self.psi4.core.FCHKWriter(self.wfn)
+        fchk_writer.write(filename)
+
+    def save_cube(self):
+        self.psi4.cubeprop(self.wfn)
+
+    def calc_resp_charges(self, constrain_symmetric_atoms=False):
+        if self.wfn.molecule() == None:
+            print('please run optimze() at first!')
+            return None
+        try:
+            import resp
+        except:
+            print('please install resp at first')
+            print('conda install -c psi4 resp')
+            return None
+        # https://www.cgl.ucsf.edu/chimerax/docs/user/radii.html
+        options = {'VDW_SCALE_FACTORS' : [1.4, 1.6, 1.8, 2.0],
+                   'VDW_POINT_DENSITY'  : 1.0,
+                   'RESP_A'             : 0.0005,
+                   'RESP_B'             : 0.1,
+                   'RESTRAINT'          : True,
+                   'RADIUS'             : {'Br':1.98, 'I':2.09,}
+                   }
+
+        if constrain_symmetric_atoms:
+            ranks = Chem.CanonicalRankAtoms(self.mol, breakTies=False)
+            groups = defaultdict(list)
+            for idx, rank in enumerate(ranks):
+                groups[rank].append(idx + 1)  # as RESP atoms are 1-indexed but RDKit 0-indexed
+            constraint_groups = [constraint_group for constraint_group in groups.values()]
+            options['CONSTRAINT_GROUP'] = constraint_groups
+
+        charges = resp.resp([self.wfn.molecule()], options)
+        #breakpoint()
+
+        options['resp_a'] = 0.001
+        resp.set_stage2_constraint(self.wfn.molecule(), charges[1], options)
+        options['grid']=['%i_%s_grid.dat'%(1, self.wfn.molecule().name())]
+        options['esp']=['%i_%s_grid_esp.dat'%(1, self.wfn.molecule().name())]
+
+        charges2 = resp.resp([self.wfn.molecule()], options)
+
+        atoms = self.mol.GetAtoms()
+        for idx, atom in enumerate(atoms):
+            atom.SetDoubleProp(""EP"", charges2[0][idx])
+            atom.SetDoubleProp(""RESP"", charges2[1][idx])
+        return charges2[1]
+
+
+    def calc_mulliken_charges(self):
+        '''
+        Compute Mulliken Charges
+        And return the results as numpy array.
+        '''
+        if self.wfn.molecule() == None:
+            print('please run optimze() at first!')
+            return None
+        self.psi4.oeprop(self.wfn, 'MULLIKEN_CHARGES')
+        mulliken_acp = self.wfn.atomic_point_charges()
+        atoms = self.mol.GetAtoms()
+        for idx, atom in enumerate(atoms):
+            atom.SetDoubleProp(""MULLIKEN"", mulliken_acp.np[idx])
+        return mulliken_acp.np
+
+    def calc_lowdin_charges(self):
+        '''
+        Compute Lowdin Charges
+        And return the results as numpy array.
+        '''
+        if self.wfn.molecule() == None:
+            print('please run optimze() at first!')
+            return None
+        self.psi4.oeprop(self.wfn, 'LOWDIN_CHARGES')
+        lowdin_acp = self.wfn.atomic_point_charges()
+        atoms = self.mol.GetAtoms()
+        for idx, atom in enumerate(atoms):
+            atom.SetDoubleProp(""LOWDIN"", lowdin_acp.np[idx])
+        return lowdin_acp.np
+
+
+    @property
+    def dipolemoment(self, basis_sets=""scf/6-31g**"", return_wfn=True):
+        #  The three components of the SCF dipole [Debye]
+        x = self.psi4.get_variable('SCF DIPOLE X')
+        y = self.psi4.get_variable('SCF DIPOLE Y')
+        z = self.psi4.get_variable('SCF DIPOLE Z')
+        total = np.sqrt(x * x + y * y + z * z)
+        return (x, y, z, total)
+
+    @property
+    def HOMO(self):
+        return self.wfn.epsilon_a_subset('AO', 'ALL').np[self.wfn.nalpha()-1]
+
+    @property
+    def LUMO(self):
+        return self.wfn.epsilon_a_subset('AO', 'ALL').np[self.wfn.nalpha()]
+
+    @property
+    def coulomb_matrix(self):
+        return self.wfn.jk().J[0].to_array()
+
+    @property
+    def exchange_matrix(self):
+        return self.wfn.jk().K[0].to_array()
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/__init__.py",".py","75","5","# -*- coding: utf-8 -*-
+from .psikit import Psikit
+from .sapt import Sapt
+
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/util.py",".py","356","9","from rdkit import Chem
+
+def mol2xyz(mol, multiplicity=1):
+    charge = Chem.GetFormalCharge(mol)
+    xyz_string = ""\n{} {}\n"".format(charge, multiplicity)
+    for atom in mol.GetAtoms():
+        pos = mol.GetConformer().GetAtomPosition(atom.GetIdx())
+        xyz_string += ""{} {} {} {}\n"".format(atom.GetSymbol(), pos.x, pos.y, pos.z)
+    return xyz_string","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/helper_SAPT.py",".py","16733","442","""""""
+Helper classes and functions for the SAPT directory.
+
+References:
+- Equations and algorithms from [Szalewicz:2005:43], [Jeziorski:1994:1887],
+[Szalewicz:2012:254], and [Hohenstein:2012:304]
+""""""
+
+__authors__   = ""Daniel G. A. Smith""
+__credits__   = [""Daniel G. A. Smith""]
+
+__copyright__ = ""(c) 2014-2018, The Psi4NumPy Developers""
+__license__   = ""BSD-3-Clause""
+__date__      = ""2015-12-01""
+
+import numpy as np
+import time
+import psi4
+
+class helper_SAPT(object):
+
+    def __init__(self, dimer, memory=8, algorithm='MO', reference='RHF'):
+        print(""\nInitializing SAPT object...\n"")
+        tinit_start = time.time()
+
+        # Set a few crucial attributes
+        self.alg = algorithm.upper()
+        self.reference = reference.upper()
+        dimer.reset_point_group('c1')
+        dimer.fix_orientation(True)
+        dimer.fix_com(True)
+        dimer.update_geometry()
+        nfrags = dimer.nfragments()
+        if nfrags != 2:
+            psi4.core.clean()
+            raise Exception(""Found %d fragments, must be 2."" % nfrags)
+
+        # Grab monomers in DCBS
+        monomerA = dimer.extract_subsets(1, 2)
+        monomerA.set_name('monomerA')
+        monomerB = dimer.extract_subsets(2, 1)
+        monomerB.set_name('monomerB')
+        self.mult_A = monomerA.multiplicity()
+        self.mult_B = monomerB.multiplicity()
+
+        # Compute monomer properties
+
+        tstart = time.time()
+        self.rhfA, self.wfnA = psi4.energy('SCF', return_wfn=True, molecule=monomerA)
+        self.V_A = np.asarray(psi4.core.MintsHelper(self.wfnA.basisset()).ao_potential())
+        print(""RHF for monomer A finished in %.2f seconds."" % (time.time() - tstart))
+
+        tstart = time.time()
+        self.rhfB, self.wfnB = psi4.energy('SCF', return_wfn=True, molecule=monomerB)
+        self.V_B = np.asarray(psi4.core.MintsHelper(self.wfnB.basisset()).ao_potential())
+        print(""RHF for monomer B finished in %.2f seconds."" % (time.time() - tstart))
+
+        # Setup a few variables
+        self.memory = memory
+        self.nmo = self.wfnA.nmo()
+
+        # Monomer A
+        self.nuc_rep_A = monomerA.nuclear_repulsion_energy()
+        self.ndocc_A = self.wfnA.doccpi()[0]
+        self.nvirt_A = self.nmo - self.ndocc_A
+        if reference == 'ROHF':
+          self.idx_A = ['i', 'a', 'r']
+          self.nsocc_A = self.wfnA.soccpi()[0]
+          occA = self.ndocc_A + self.nsocc_A
+        else:
+          self.idx_A = ['a', 'r']
+          self.nsocc_A = 0
+          occA = self.ndocc_A 
+
+        self.C_A = np.asarray(self.wfnA.Ca())
+        self.Co_A = self.C_A[:, :self.ndocc_A]
+        self.Ca_A = self.C_A[:, self.ndocc_A:occA]
+        self.Cv_A = self.C_A[:, occA:]
+        self.eps_A = np.asarray(self.wfnA.epsilon_a())
+
+        # Monomer B
+        self.nuc_rep_B = monomerB.nuclear_repulsion_energy()
+        self.ndocc_B = self.wfnB.doccpi()[0]
+        self.nvirt_B = self.nmo - self.ndocc_B
+        if reference == 'ROHF':
+          self.idx_B = ['j', 'b', 's']
+          self.nsocc_B = self.wfnB.soccpi()[0]
+          occB = self.ndocc_B + self.nsocc_B
+        else:
+          self.idx_B = ['b', 's']
+          self.nsocc_B = 0
+          occB = self.ndocc_B 
+
+        self.C_B = np.asarray(self.wfnB.Ca())
+        self.Co_B = self.C_B[:, :self.ndocc_B]
+        self.Ca_B = self.C_B[:, self.ndocc_B:occB]
+        self.Cv_B = self.C_B[:, occB:]
+        self.eps_B = np.asarray(self.wfnB.epsilon_a())
+
+        # Dimer
+        self.nuc_rep = dimer.nuclear_repulsion_energy() - self.nuc_rep_A - self.nuc_rep_B
+        self.vt_nuc_rep = self.nuc_rep / ((2 * self.ndocc_A + self.nsocc_A)
+                                           * (2 * self.ndocc_B + self.nsocc_B))
+
+        # Make slice, orbital, and size dictionaries
+        if reference == 'ROHF':
+          self.slices = {
+                       'i': slice(0, self.ndocc_A),
+                       'a': slice(self.ndocc_A, occA),
+                       'r': slice(occA, None),
+                       'j': slice(0, self.ndocc_B),
+                       'b': slice(self.ndocc_B, occB),
+                       's': slice(occB, None)
+                      }
+
+          self.orbitals = {'i': self.Co_A,
+                           'a': self.Ca_A,
+                           'r': self.Cv_A,
+                           'j': self.Co_B,
+                           'b': self.Ca_B,
+                           's': self.Cv_B
+                        }
+
+          self.sizes = {'i': self.ndocc_A,
+                        'a': self.nsocc_A,
+                        'r': self.nvirt_A,
+                        'j': self.ndocc_B,
+                        'b': self.nsocc_B,
+                        's': self.nvirt_B}
+
+        else:
+          self.slices = {
+                       'a': slice(0, self.ndocc_A),
+                       'r': slice(occA, None),
+                       'b': slice(0, self.ndocc_B),
+                       's': slice(occB, None)
+                      }
+
+          self.orbitals = {'a': self.Co_A,
+                           'r': self.Cv_A,
+                           'b': self.Co_B,
+                           's': self.Cv_B
+                        }
+
+          self.sizes = {'a': self.ndocc_A,
+                        'r': self.nvirt_A,
+                        'b': self.ndocc_B,
+                        's': self.nvirt_B}
+
+        # Compute size of ERI tensor in GB
+        self.dimer_wfn = psi4.core.Wavefunction.build(dimer, psi4.core.get_global_option('BASIS'))
+        mints = psi4.core.MintsHelper(self.dimer_wfn.basisset())
+        self.mints = mints
+        ERI_Size = (self.nmo ** 4) * 8.e-9
+        memory_footprint = ERI_Size * 4
+        if memory_footprint > self.memory:
+            psi4.core.clean()
+            raise Exception(""Estimated memory utilization (%4.2f GB) exceeds numpy_memory \
+                            limit of %4.2f GB."" % (memory_footprint, self.memory))
+
+        # Integral generation from Psi4's MintsHelper
+        print('Building ERI tensor...')
+        tstart = time.time()
+        # Leave ERI as a Psi4 Matrix
+        self.I = np.asarray(self.mints.ao_eri()).swapaxes(1,2)
+        print('...built ERI tensor in %.3f seconds.' % (time.time() - tstart))
+        print(""Size of the ERI tensor is %4.2f GB, %d basis functions."" % (ERI_Size, self.nmo))
+        self.S = np.asarray(self.mints.ao_overlap())
+
+        # Save additional rank 2 tensors
+        self.V_A_BB = np.einsum('ui,vj,uv->ij', self.C_B, self.C_B, self.V_A)
+        self.V_A_AB = np.einsum('ui,vj,uv->ij', self.C_A, self.C_B, self.V_A)
+        self.V_B_AA = np.einsum('ui,vj,uv->ij', self.C_A, self.C_A, self.V_B)
+        self.V_B_AB = np.einsum('ui,vj,uv->ij', self.C_A, self.C_B, self.V_B)
+
+        self.S_AB = np.einsum('ui,vj,uv->ij', self.C_A, self.C_B, self.S)
+
+        if self.alg == ""AO"":
+            tstart = time.time()
+            aux_basis = psi4.core.BasisSet.build(self.dimer_wfn.molecule(), ""DF_BASIS_SCF"",
+                                            psi4.core.get_option(""SCF"", ""DF_BASIS_SCF""),
+                                            ""JKFIT"", psi4.core.get_global_option('BASIS'),
+                                            puream=self.dimer_wfn.basisset().has_puream())
+
+            self.jk = psi4.core.JK.build(self.dimer_wfn.basisset(), aux_basis)
+            self.jk.set_memory(int(memory * 1e9))
+            self.jk.initialize()
+            print(""\n...initialized JK objects in %5.2f seconds."" % (time.time() - tstart))
+
+        print(""\n...finished initializing SAPT object in %5.2f seconds."" % (time.time() - tinit_start))
+
+    # Compute MO ERI tensor (v) on the fly
+    def v(self, string):
+        if len(string) != 4:
+            psi4.core.clean()
+            raise Exception('v: string %s does not have 4 elements' % string)
+
+        # ERI's from mints are of type (11|22) - need <12|12>
+        V = np.einsum('pA,pqrs->Aqrs', self.orbitals[string[0]], self.I)
+        V = np.einsum('qB,Aqrs->ABrs', self.orbitals[string[1]], V)
+        V = np.einsum('rC,ABrs->ABCs', self.orbitals[string[2]], V)
+        V = np.einsum('sD,ABCs->ABCD', self.orbitals[string[3]], V)
+        return V
+
+    # Grab MO overlap matrices
+    def s(self, string):
+        if len(string) != 2:
+            psi4.core.clean()
+            raise Exception('S: string %s does not have 2 elements.' % string)
+
+        for alpha in 'ijab':
+            if (alpha in string) and (self.sizes[alpha] == 0):
+                return np.array([0]).reshape(1,1)
+
+        s1 = string[0]
+        s2 = string[1]
+
+        # Compute on the fly
+        return (self.orbitals[string[0]].T).dot(self.S).dot(self.orbitals[string[1]])
+        #return np.einsum('ui,vj,uv->ij', self.orbitals[string[0]], self.orbitals[string[1]], self.S)
+
+    # Grab epsilons, reshape if requested
+    def eps(self, string, dim=1):
+        if len(string) != 1:
+            psi4.core.clean()
+            raise Exception('Epsilon: string %s does not have 1 element.' % string)
+
+        shape = (-1,) + tuple([1] * (dim - 1))
+
+        if (string == 'i') or (string == 'a') or (string == 'r'):
+            return self.eps_A[self.slices[string]].reshape(shape)
+        elif (string == 'j') or (string == 'b') or (string == 's'):
+            return self.eps_B[self.slices[string]].reshape(shape)
+        else:
+            psi4.core.clean()
+            raise Exception('Unknown orbital type in eps: %s.' % string)
+
+    # Grab MO potential matrices
+    def potential(self, string, side):
+        if len(string) != 2:
+            psi4.core.clean()
+            raise Exception('Potential: string %s does not have 2 elements.' % string)
+
+        s1 = string[0]
+        s2 = string[1]
+
+        # Two separate cases
+        if side == 'A':
+            # Compute on the fly
+            return (self.orbitals[string[0]].T).dot(self.V_A).dot(self.orbitals[string[1]])
+            #return np.einsum('ui,vj,uv->ij', self.orbitals[s1], self.orbitals[s2], self.V_A)
+
+        elif side == 'B':
+            # Compute on the fly
+            return (self.orbitals[string[0]].T).dot(self.V_B).dot(self.orbitals[string[1]])
+            #return np.einsum('ui,vj,uv->ij', self.orbitals[s1], self.orbitals[s2], self.V_B)
+        else:
+            psi4.core.clean()
+            raise Exception('helper_SAPT.potential side must be either A or B, not %s.' % side)
+
+    # Compute V tilde, Index as V_{1,2}^{3,4}
+    def vt(self, string):
+        if len(string) != 4:
+            psi4.core.clean()
+            raise Exception('Compute tilde{V}: string %s does not have 4 elements' % string)
+
+        for alpha in 'ijab':
+            if (alpha in string) and (self.sizes[alpha] == 0):
+                return np.array([0]).reshape(1,1,1,1)
+
+        # Grab left and right strings
+        s_left = string[0] + string[2]
+        s_right = string[1] + string[3]
+
+        # ERI term
+        V = self.v(string)
+        # Potential A
+        S_A = self.s(s_left)
+        V_A = self.potential(s_right, 'A') / (2 * self.ndocc_A + self.nsocc_A)
+        V += np.einsum('ik,jl->ijkl', S_A, V_A)
+
+        # Potential B
+        S_B = self.s(s_right)
+        V_B = self.potential(s_left, 'B') / (2 * self.ndocc_B + self.nsocc_B)
+        #print s_right, np.abs(V_B).sum()
+        V += np.einsum('ik,jl->ijkl', V_B, S_B)
+
+        # Nuclear
+        V += np.einsum('ik,jl->ijkl', S_A, S_B) * self.vt_nuc_rep
+
+        return V
+
+    # Compute CPHF orbitals
+    def chf(self, monomer, ind=False):
+        if monomer not in ['A', 'B']:
+            psi4.core.clean()
+            raise Exception('%s is not a valid monomer for CHF.' % monomer)
+
+        if self.reference == 'ROHF':
+            psi4.core.clean()
+            raise Exception('CPHF for a ROHF reference not implemented yet.')
+
+        if monomer == 'A':
+            # Form electrostatic potential
+            w_n = 2 * np.einsum('saba->bs', self.v('saba'))
+            w_n += self.V_A_BB[self.slices['b'], self.slices['s']]
+            eps_ov = (self.eps('b', dim=2) - self.eps('s'))
+
+            # Set terms
+            v_term1 = 'sbbs'
+            v_term2 = 'sbsb'
+            no, nv = self.ndocc_B, self.nvirt_B
+
+        if monomer == 'B':
+            w_n = 2 * np.einsum('rbab->ar', self.v('rbab'))
+            w_n += self.V_B_AA[self.slices['a'], self.slices['r']]
+            eps_ov = (self.eps('a', dim=2) - self.eps('r'))
+            v_term1 = 'raar'
+            v_term2 = 'rara'
+            no, nv = self.ndocc_A, self.nvirt_A
+
+        # Form A matrix (LHS)
+        voov = self.v(v_term1)
+        v_vOoV = 2 * voov - self.v(v_term2).swapaxes(2, 3)
+        v_ooaa = voov.swapaxes(1, 3)
+        v_vVoO = 2 * v_ooaa - v_ooaa.swapaxes(2, 3)
+        A_ovOV = np.einsum('vOoV->ovOV', v_vOoV + v_vVoO.swapaxes(1, 3))
+
+        # Mangled the indices so badly with strides we need to copy back to C contiguous
+        nov = nv * no
+        A_ovOV = A_ovOV.reshape(nov, nov).copy(order='C')
+        A_ovOV[np.diag_indices_from(A_ovOV)] -= eps_ov.ravel()
+
+        # Call DGESV, need flat ov array
+        B_ov = -1 * w_n.ravel()
+        t = np.linalg.solve(A_ovOV, B_ov)
+        # Our notation wants vo array
+        t = t.reshape(no, nv).T
+
+        if ind:
+            # E200 Induction energy is free at the point
+            e20_ind = 2 * np.einsum('vo,ov->', t, w_n)
+            return (t, e20_ind)
+        else:
+            return t
+
+    def compute_sapt_JK(self, Cleft, Cright, tensor=None):
+
+        if self.alg != ""AO"":
+            raise Exception(""Attempted a call to JK builder in an MO algorithm"")
+
+        if self.reference == ""ROHF"":
+            raise Exception(""AO algorithm not yet implemented for ROHF reference."")
+
+        return_single = False
+        if not isinstance(Cleft, (list, tuple)):
+            Cleft = [Cleft]
+            return_single = True
+        if not isinstance(Cright, (list, tuple)):
+            Cright = [Cright]
+            return_single = True
+        if (not isinstance(tensor, (list, tuple))) and (tensor is not None):
+            tensor = [tensor]
+            return_single = True
+
+        if len(Cleft) != len(Cright):
+            raise Exception(""Cleft list is not the same length as Cright list"")
+
+        zero_append = []
+        num_compute = 0
+
+        for num in range(len(Cleft)):
+            Cl = Cleft[num]
+            Cr = Cright[num]
+
+            if (Cr.shape[1] == 0) or (Cl.shape[1] == 0):
+                zero_append.append(num)
+                continue
+
+            if tensor is not None:
+                mol = Cl.shape[1]
+                mor = Cr.shape[1]
+                
+                if (tensor[num].shape[0] != mol) or (tensor[num].shape[1] != mor):
+                    raise Exception(""compute_sapt_JK: Tensor size does not match Cl (%d) /Cr (%d) : %s"" %
+                                                            (mol, mor, str(tensor[num].shape)))
+                if mol < mor:
+                    Cl = np.dot(Cl, tensor[num])
+                else:
+                    Cr = np.dot(Cr, tensor[num].T)
+
+            Cl = psi4.core.Matrix.from_array(Cl)
+            Cr = psi4.core.Matrix.from_array(Cr)
+
+            self.jk.C_left_add(Cl)
+            self.jk.C_right_add(Cr)
+            num_compute += 1
+        
+        self.jk.compute() 
+
+        J_list = []
+        K_list = []
+        for num in range(num_compute):
+            J_list.append(np.array(self.jk.J()[num])) 
+            K_list.append(np.array(self.jk.K()[num])) 
+
+        self.jk.C_clear()
+
+        z = np.zeros((self.nmo, self.nmo))
+        for num in zero_append:
+            J_list.insert(num, z)
+            K_list.insert(num, z)
+
+        if return_single:
+            return J_list[0], K_list[0]
+        else:
+            return J_list, K_list
+
+    def chain_dot(self, *dot_list):
+        result = dot_list[0]
+        for x in range(len(dot_list) - 1):
+            result = np.dot(result, dot_list[x + 1])
+        return result
+
+# End SAPT helper
+
+class sapt_timer(object):
+    def __init__(self, name):
+        self.name = name
+        self.start = time.time()
+        print('\nStarting %s...' % name)
+
+    def stop(self):
+        t = time.time() - self.start
+        print('...%s took a total of % .2f seconds.' % (self.name, t))
+
+
+def sapt_printer(line, value):
+    spacer = ' ' * (20 - len(line))
+    print(line + spacer + '% 16.8f mH  % 16.8f kcal/mol' % (value * 1000, value * 627.509))
+# End SAPT helper
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","psikit/fsapt_helper.py",".py","1697","47","from rdkit import Chem
+from rdkit.Chem import AllChem
+from rdkit.Chem import Recap
+from rdkit.Chem import rdChemReactions
+import os
+    
+Recap.reactions += tuple([rdChemReactions.ReactionFromSmarts('[c:1]-[X:2]>>[c:1]*.*[X:2]'), rdChemReactions.ReactionFromSmarts('[c:1]-[OH1:2]>>[c:1]*.*[OH1:2]')])
+
+def get_neighbor_h(atom_idx, mol):
+    atom = mol.GetAtomWithIdx(atom_idx)
+    neis = atom.GetNeighbors()
+    res = []
+    for nei in neis:
+        if nei.GetSymbol() == 'H':
+            res.append(nei.GetIdx())
+    return res
+
+def make_feat_data(mol, offset=1):
+    res = []
+    check_atom = set()
+    nohmol = Chem.RemoveHs(mol)
+    recap_res = Recap.RecapDecompose(nohmol)
+    leaves = [key.replace('*','').replace('()','') for key in recap_res.GetLeaves().keys()]
+    leaves = [leave.replace('[H]', '') for leave in leaves if leave != '[H]']
+    leaves = sorted(leaves, key=lambda x: Chem.MolFromSmarts(x).GetNumAtoms(), reverse=True)
+    if len(leaves) == 0:
+
+        line = [i for i in range(mol.GetNumAtoms())]
+        line = [str(n + offset) for n in line]
+        line = [Chem.MolToSmiles(mol)] + line
+        return [line]
+    for leavsmi in leaves:
+        leav = Chem.MolFromSmarts(leavsmi)
+        matches = mol.GetSubstructMatches(leav)
+        for i, match in enumerate(matches):
+            line = list(match)
+            if len(check_atom & set(line)) > 0:
+                continue
+            check_atom = check_atom|set(line)
+            for idx in match:
+                nei = get_neighbor_h(idx, mol)
+                line += nei
+            line = [str(j + offset) for j in line]
+            line = [leavsmi + '_' + str(i)] + line
+            res.append(line)
+    return res
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/Torsion_scan/torsional_scan.ipynb",".ipynb","63377","332","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 65,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""%matplotlib inline\n"",
+    ""from rdkit import Chem\n"",
+    ""from rdkit.Chem import rdMolTransforms\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole\n"",
+    ""from psikit import Psikit\n"",
+    ""import numpy as np\n"",
+    ""import seaborn as sns\n"",
+    ""import pandas as pd\n"",
+    ""import warnings\n"",
+    ""warnings.filterwarnings(\""ignore\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""pk = Psikit()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""pk.read_from_smiles(\""CNS(=O)(=O)CC\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 10,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-711.7748942789913""
+      ]
+     },
+     ""execution_count"": 10,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.optimize(basis_sets=\""scf/sto-3g\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 11,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-723.0626131232355""
+      ]
+     },
+     ""execution_count"": 11,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.optimize(basis_sets=\""B3LYP/6-31G*\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 12,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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w+yHxyeXZp6TyZLJ3NFkybluTpedLKSjyG64hEG2fO/NHK6jGF4nRZ2cLu7soBJ9jYzPfy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\ng2CMIoSQQTBGEULIIBijCCFkkP8HNyhcpre+9QsAAAAASUVORK5CYII=\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x7f6804111ce0>""
+      ]
+     },
+     ""execution_count"": 12,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 13,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""['C',\n"",
+       "" 'N',\n"",
+       "" 'S',\n"",
+       "" 'O',\n"",
+       "" 'O',\n"",
+       "" 'C',\n"",
+       "" 'C',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H',\n"",
+       "" 'H']""
+      ]
+     },
+     ""execution_count"": 13,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""[a.GetSymbol() for a in pk.mol.GetAtoms()]""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 14,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""conformer = pk.mol.GetConformer(0)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 26,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""0 -723.056921763491\n"",
+      ""10 -723.0574009035583\n"",
+      ""20 -723.0583845883023\n"",
+      ""30 -723.0595485772963\n"",
+      ""40 -723.0605034078015\n"",
+      ""50 -723.0609938370956\n"",
+      ""60 -723.0608832849458\n"",
+      ""70 -723.0600488840882\n"",
+      ""80 -723.0585385255836\n"",
+      ""90 -723.0567086747076\n"",
+      ""100 -723.0551682176562\n"",
+      ""110 -723.054408816703\n"",
+      ""120 -723.0545318226641\n"",
+      ""130 -723.0554506590202\n"",
+      ""140 -723.0570179572535\n"",
+      ""150 -723.0589412650767\n"",
+      ""160 -723.0607819309749\n"",
+      ""170 -723.0620693038213\n"",
+      ""180 -723.0625640298554\n"",
+      ""190 -723.062178569547\n"",
+      ""200 -723.0611117138934\n"",
+      ""210 -723.0596917366915\n"",
+      ""220 -723.0583669682219\n"",
+      ""230 -723.0574853343633\n"",
+      ""240 -723.0572859520408\n"",
+      ""250 -723.0577599957417\n"",
+      ""260 -723.058744956759\n"",
+      ""270 -723.0599360174974\n"",
+      ""280 -723.0610146943501\n"",
+      ""290 -723.061727957017\n"",
+      ""300 -723.0618940554882\n"",
+      ""310 -723.0614391811523\n"",
+      ""320 -723.0604675894782\n"",
+      ""330 -723.0592105713769\n"",
+      ""340 -723.0579701713469\n"",
+      ""350 -723.0571320334551\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""dihedral_energies = []\n"",
+    ""for dihedral_degree in range(0, 360, 10):\n"",
+    ""    rdMolTransforms.SetDihedralDeg(conformer, 3, 2, 5, 6, dihedral_degree)\n"",
+    ""    degy = pk.energy(basis_sets=\""b3lyp/6-31g*\"")\n"",
+    ""    print(dihedral_degree, degy)\n"",
+    ""    dihedral_energies.append(degy)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 28,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""de = np.array(dihedral_energies)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 67,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""de_kcal = (de - de.min()) * 627.51 # convert hartree -> kcal/mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 68,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""array([3.54057857, 3.23991338, 2.62264137, 1.89222664, 1.29306094,\n"",
+       ""       0.98531166, 1.05468424, 1.57827912, 2.52604419, 3.67429391,\n"",
+       ""       4.64094611, 5.11747781, 5.04029033, 4.46371133, 3.48021602,\n"",
+       ""       2.27332113, 1.11828487, 0.31044553, 0.        , 0.2418802 ,\n"",
+       ""       0.91134279, 1.80239268, 2.63369815, 3.18693221, 3.31204661,\n"",
+       ""       3.01457945, 2.39650656, 1.64910403, 0.97222352, 0.52464407,\n"",
+       ""       0.42041562, 0.70585381, 1.3155373 , 2.10432873, 2.88269215,\n"",
+       ""       3.40863206])""
+      ]
+     },
+     ""execution_count"": 68,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""de_kcal""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 69,
+   ""metadata"": {
+    ""collapsed"": true
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""tps = np.array(range(0, 360, 10))""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 70,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""df = pd.DataFrame({\""timepoint\"":tps, \""relative_energy\"":de_kcal})""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 71,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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ERERGR1Kx6NbZ//34EBgYiNTUVJ06cMDu/paXLmuEQkQXqW7qw+0S5OF4+YxzaW7vRLmFM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\nYPFGRERERERkBv4fiP7PPdppBvQAAAAASUVORK5CYII=\n"",
+      ""text/plain"": [
+       ""<Figure size 1080x360 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""sns.set(rc={\""figure.figsize\"":(15,5)})\n"",
+    ""g = sns.pointplot(x=\""timepoint\"", y=\""relative_energy\"",data=df)""
+   ]
+  }
+ ],
+ ""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"": ""3.6.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/Rendering_Orbital/Render_orbital.ipynb",".ipynb","73704","437","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from psikit import Psikit\n"",
+    ""from IPython.display import Image, display_png""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk = Psikit()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk.read_from_smiles(\""c1ccccc1\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-230.71352354216""
+      ]
+     },
+     ""execution_count"": 4,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.optimize()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk.save_cube()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""find_vmd\n"",
+      ""Parameters:\n"",
+      ""  Cubefile Directory   .\n"",
+      ""  Font size            20\n"",
+      ""  Gzip Cube Files      False\n"",
+      ""  Image height         500\n"",
+      ""  Image width          500\n"",
+      ""  Interactive Mode     False\n"",
+      ""  Isosurface Color(s)  [3, 23]\n"",
+      ""  Isosurface Value(s)  [0.05, -0.05]\n"",
+      ""  Label MOs            True\n"",
+      ""  Montage              True\n"",
+      ""  Opacity              1.0\n"",
+      ""  X-axis Rotation      30.0\n"",
+      ""  Y-axis Rotation      40.0\n"",
+      ""  Z-axis Rotation      15.0\n"",
+      ""  Scaling Factor       1.0\n"",
+      ""  X-axis Translation   0.0\n"",
+      ""  Y-axis Translation   0.0\n"",
+      ""  Z-axis Translation   0.0\n"",
+      ""  VMD Path             /Applications/VMD1.9.4.app/Contents/vmd/vmd_MACOSXX86\n"",
+      ""read options\n"",
+      ""setup done\n"",
+      ""cube done\n"",
+      ""Plotting Psi_a_100_100-A.cube with isosurface values [0.0529853, -0.0529793]\n"",
+      ""Plotting Psi_a_101_101-A.cube with isosurface values [0.0646591, -0.0646653]\n"",
+      ""Plotting Psi_a_102_102-A.cube with isosurface values [0.0527656, -0.0527723]\n"",
+      ""Plotting Psi_a_103_103-A.cube with isosurface values [0.0435506, -0.0435516]\n"",
+      ""Plotting Psi_a_104_104-A.cube with isosurface values [0.0439361, -0.0439659]\n"",
+      ""Plotting Psi_a_105_105-A.cube with isosurface values [0.0546034, -0.0546044]\n"",
+      ""Plotting Psi_a_106_106-A.cube with isosurface values [0.0600706, -0.0600712]\n"",
+      ""Plotting Psi_a_107_107-A.cube with isosurface values [0.0380527, -0.0380539]\n"",
+      ""Plotting Psi_a_108_108-A.cube with isosurface values [0.0471048, -0.0470534]\n"",
+      ""Plotting Psi_a_109_109-A.cube with isosurface values [0.0546187, -0.0546171]\n"",
+      ""Plotting Psi_a_10_10-A.cube with isosurface values [0.0404385, -0.0404382]\n"",
+      ""Plotting Psi_a_110_110-A.cube with isosurface values [0.0526063, -0.0526048]\n"",
+      ""Plotting Psi_a_111_111-A.cube with isosurface values [0.0560259, -0.0560205]\n"",
+      ""Plotting Psi_a_112_112-A.cube with isosurface values [0.0535457, -0.0535405]\n"",
+      ""Plotting Psi_a_113_113-A.cube with isosurface values [0.0536658, -0.0536515]\n"",
+      ""Plotting Psi_a_114_114-A.cube with isosurface values [0.0530359, -0.053055]\n"",
+      ""Plotting Psi_a_115_115-A.cube with isosurface values [0.0416529, -0.0416623]\n"",
+      ""Plotting Psi_a_116_116-A.cube with isosurface values [0.0404025, -0.0404031]\n"",
+      ""Plotting Psi_a_117_117-A.cube with isosurface values [0.0447573, -0.0447494]\n"",
+      ""Plotting Psi_a_118_118-A.cube with isosurface values [0.0435566, -0.0435481]\n"",
+      ""Plotting Psi_a_119_119-A.cube with isosurface values [0.0462539, -0.0462196]\n"",
+      ""Plotting Psi_a_11_11-A.cube with isosurface values [0.0404038, -0.0403994]\n"",
+      ""Plotting Psi_a_120_120-A.cube with isosurface values [0.0461635, -0.0461516]\n"",
+      ""Plotting Psi_a_12_12-A.cube with isosurface values [0.0334224, -0.033353]\n"",
+      ""Plotting Psi_a_13_13-A.cube with isosurface values [0.0379054, -0.0379054]\n"",
+      ""Plotting Psi_a_14_14-A.cube with isosurface values [0.0508558, -0.0508547]\n"",
+      ""Plotting Psi_a_15_15-A.cube with isosurface values [0.0357363, -0.0357361]\n"",
+      ""Plotting Psi_a_16_16-A.cube with isosurface values [0.0391251, -0.0391396]\n"",
+      ""Plotting Psi_a_17_17-A.cube with isosurface values [0.0352166, -0.0352204]\n"",
+      ""Plotting Psi_a_18_18-A.cube with isosurface values [0.0392106, -0.039208]\n"",
+      ""Plotting Psi_a_19_19-A.cube with isosurface values [0.0408659, -0.040882]\n"",
+      ""Plotting Psi_a_1_1-A.cube with isosurface values [0.28033, 0.0]\n"",
+      ""Plotting Psi_a_20_20-A.cube with isosurface values [0.0360412, -0.0360367]\n"",
+      ""Plotting Psi_a_21_21-A.cube with isosurface values [0.036461, -0.0364741]\n"",
+      ""Plotting Psi_a_22_22-A.cube with isosurface values [0.0329912, -0.0329921]\n"",
+      ""Plotting Psi_a_23_23-A.cube with isosurface values [0.0330877, -0.0330881]\n"",
+      ""Plotting Psi_a_24_24-A.cube with isosurface values [0.0224977, -0.0224989]\n"",
+      ""Plotting Psi_a_25_25-A.cube with isosurface values [0.0235961, -0.023596]\n"",
+      ""Plotting Psi_a_26_26-A.cube with isosurface values [0.0240074, -0.0240074]\n"",
+      ""Plotting Psi_a_27_27-A.cube with isosurface values [0.0233761, -0.0233829]\n"",
+      ""Plotting Psi_a_28_28-A.cube with isosurface values [0.0244246, -0.0244241]\n"",
+      ""Plotting Psi_a_29_29-A.cube with isosurface values [0.0240182, -0.0240184]\n"",
+      ""Plotting Psi_a_2_2-A.cube with isosurface values [0.289922, -0.289918]\n"",
+      ""Plotting Psi_a_30_30-A.cube with isosurface values [0.031347, -0.0313509]\n"",
+      ""Plotting Psi_a_31_31-A.cube with isosurface values [0.0251724, -0.0251759]\n"",
+      ""Plotting Psi_a_32_32-A.cube with isosurface values [0.0250444, -0.0250439]\n"",
+      ""Plotting Psi_a_33_33-A.cube with isosurface values [0.0303727, -0.0303724]\n"",
+      ""Plotting Psi_a_34_34-A.cube with isosurface values [0.0306925, -0.0306934]\n"",
+      ""Plotting Psi_a_35_35-A.cube with isosurface values [0.0301311, -0.0301336]\n"",
+      ""Plotting Psi_a_36_36-A.cube with isosurface values [0.031564, -0.0315457]\n"",
+      ""Plotting Psi_a_37_37-A.cube with isosurface values [0.0288736, -0.0288735]\n"",
+      ""Plotting Psi_a_38_38-A.cube with isosurface values [0.0297631, -0.0297685]\n"",
+      ""Plotting Psi_a_39_39-A.cube with isosurface values [0.0269562, -0.0269527]\n"",
+      ""Plotting Psi_a_3_3-A.cube with isosurface values [0.338179, -0.338176]\n"",
+      ""Plotting Psi_a_40_40-A.cube with isosurface values [0.0248251, -0.0248248]\n"",
+      ""Plotting Psi_a_41_41-A.cube with isosurface values [0.030073, -0.0300728]\n"",
+      ""Plotting Psi_a_42_42-A.cube with isosurface values [0.0300686, -0.0300691]\n"",
+      ""Plotting Psi_a_43_43-A.cube with isosurface values [0.0325552, -0.0325515]\n"",
+      ""Plotting Psi_a_44_44-A.cube with isosurface values [0.0325553, -0.032558]\n"",
+      ""Plotting Psi_a_45_45-A.cube with isosurface values [0.0315161, -0.0315217]\n"",
+      ""Plotting Psi_a_46_46-A.cube with isosurface values [0.0294245, -0.0294298]\n"",
+      ""Plotting Psi_a_47_47-A.cube with isosurface values [0.0286684, -0.0286684]\n"",
+      ""Plotting Psi_a_48_48-A.cube with isosurface values [0.028968, -0.0289678]\n"",
+      ""Plotting Psi_a_49_49-A.cube with isosurface values [0.0281508, -0.0281492]\n"",
+      ""Plotting Psi_a_4_4-A.cube with isosurface values [0.29907, -0.289467]\n"",
+      ""Plotting Psi_a_50_50-A.cube with isosurface values [0.0255125, -0.025512]\n"",
+      ""Plotting Psi_a_51_51-A.cube with isosurface values [0.023336, -0.023336]\n"",
+      ""Plotting Psi_a_52_52-A.cube with isosurface values [0.0234729, -0.023473]\n"",
+      ""Plotting Psi_a_53_53-A.cube with isosurface values [0.0235727, -0.0235727]\n"",
+      ""Plotting Psi_a_54_54-A.cube with isosurface values [0.0236941, -0.0236941]\n"",
+      ""Plotting Psi_a_55_55-A.cube with isosurface values [0.021238, -0.0212395]\n"",
+      ""Plotting Psi_a_56_56-A.cube with isosurface values [0.0265318, -0.0265317]\n"",
+      ""Plotting Psi_a_57_57-A.cube with isosurface values [0.026403, -0.0264055]\n"",
+      ""Plotting Psi_a_58_58-A.cube with isosurface values [0.0293796, -0.0293822]\n"",
+      ""Plotting Psi_a_59_59-A.cube with isosurface values [0.0278522, -0.0278522]\n"",
+      ""Plotting Psi_a_5_5-A.cube with isosurface values [0.342107, -0.335833]\n"",
+      ""Plotting Psi_a_60_60-A.cube with isosurface values [0.0279467, -0.0279452]\n"",
+      ""Plotting Psi_a_61_61-A.cube with isosurface values [0.0299558, -0.0299556]\n"",
+      ""Plotting Psi_a_62_62-A.cube with isosurface values [0.0304382, -0.0304383]\n"",
+      ""Plotting Psi_a_63_63-A.cube with isosurface values [0.0299731, -0.0299732]\n"",
+      ""Plotting Psi_a_64_64-A.cube with isosurface values [0.063641, -0.0636315]\n"",
+      ""Plotting Psi_a_65_65-A.cube with isosurface values [0.0516425, -0.0516627]\n"",
+      ""Plotting Psi_a_66_66-A.cube with isosurface values [0.0526899, -0.0526737]\n"",
+      ""Plotting Psi_a_67_67-A.cube with isosurface values [0.0553581, -0.0553531]\n"",
+      ""Plotting Psi_a_68_68-A.cube with isosurface values [0.0276088, -0.0276111]\n"",
+      ""Plotting Psi_a_69_69-A.cube with isosurface values [0.0291093, -0.0291048]\n"",
+      ""Plotting Psi_a_6_6-A.cube with isosurface values [0.281277, -0.281279]\n"",
+      ""Plotting Psi_a_70_70-A.cube with isosurface values [0.0537655, -0.0537729]\n"",
+      ""Plotting Psi_a_71_71-A.cube with isosurface values [0.0552311, -0.0552725]\n"",
+      ""Plotting Psi_a_72_72-A.cube with isosurface values [0.0435948, -0.0436224]\n"",
+      ""Plotting Psi_a_73_73-A.cube with isosurface values [0.0328853, -0.0328849]\n"",
+      ""Plotting Psi_a_74_74-A.cube with isosurface values [0.0430291, -0.043043]\n"",
+      ""Plotting Psi_a_75_75-A.cube with isosurface values [0.0257321, -0.0257337]\n"",
+      ""Plotting Psi_a_76_76-A.cube with isosurface values [0.0458547, -0.0458552]\n"",
+      ""Plotting Psi_a_77_77-A.cube with isosurface values [0.0494258, -0.049419]\n"",
+      ""Plotting Psi_a_78_78-A.cube with isosurface values [0.0400556, -0.040052]\n"",
+      ""Plotting Psi_a_79_79-A.cube with isosurface values [0.0401515, -0.0401424]\n"",
+      ""Plotting Psi_a_7_7-A.cube with isosurface values [0.0437048, -0.0426561]\n"",
+      ""Plotting Psi_a_80_80-A.cube with isosurface values [0.0359129, -0.0359138]\n"",
+      ""Plotting Psi_a_81_81-A.cube with isosurface values [0.0357842, -0.0357842]\n"",
+      ""Plotting Psi_a_82_82-A.cube with isosurface values [0.0336639, -0.033663]\n"",
+      ""Plotting Psi_a_83_83-A.cube with isosurface values [0.0388873, -0.0388891]\n"",
+      ""Plotting Psi_a_84_84-A.cube with isosurface values [0.0382899, -0.0382862]\n""
+     ]
+    },
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Plotting Psi_a_85_85-A.cube with isosurface values [0.0525889, -0.0525902]\n"",
+      ""Plotting Psi_a_86_86-A.cube with isosurface values [0.0518022, -0.0517836]\n"",
+      ""Plotting Psi_a_87_87-A.cube with isosurface values [0.0471893, -0.0471904]\n"",
+      ""Plotting Psi_a_88_88-A.cube with isosurface values [0.0433201, -0.0433132]\n"",
+      ""Plotting Psi_a_89_89-A.cube with isosurface values [0.0429296, -0.042935]\n"",
+      ""Plotting Psi_a_8_8-A.cube with isosurface values [0.0419202, -0.0419122]\n"",
+      ""Plotting Psi_a_90_90-A.cube with isosurface values [0.0486204, -0.0486207]\n"",
+      ""Plotting Psi_a_91_91-A.cube with isosurface values [0.0498826, -0.0498727]\n"",
+      ""Plotting Psi_a_92_92-A.cube with isosurface values [0.052563, -0.0525392]\n"",
+      ""Plotting Psi_a_93_93-A.cube with isosurface values [0.0286104, -0.0286095]\n"",
+      ""Plotting Psi_a_94_94-A.cube with isosurface values [0.0286184, -0.0286164]\n"",
+      ""Plotting Psi_a_95_95-A.cube with isosurface values [0.0457728, -0.0458067]\n"",
+      ""Plotting Psi_a_96_96-A.cube with isosurface values [0.0456521, -0.0456488]\n"",
+      ""Plotting Psi_a_97_97-A.cube with isosurface values [0.0368242, -0.0368248]\n"",
+      ""Plotting Psi_a_98_98-A.cube with isosurface values [0.0278935, -0.0278908]\n"",
+      ""Plotting Psi_a_99_99-A.cube with isosurface values [0.0519307, -0.0519572]\n"",
+      ""Plotting Psi_a_9_9-A.cube with isosurface values [0.0422579, -0.0422601]\n"",
+      ""Plotting Psi_b_100_100-A.cube with isosurface values [0.0529853, -0.0529793]\n"",
+      ""Plotting Psi_b_101_101-A.cube with isosurface values [0.0646591, -0.0646653]\n"",
+      ""Plotting Psi_b_102_102-A.cube with isosurface values [0.0527656, -0.0527723]\n"",
+      ""Plotting Psi_b_103_103-A.cube with isosurface values [0.0435506, -0.0435516]\n"",
+      ""Plotting Psi_b_104_104-A.cube with isosurface values [0.0439361, -0.0439659]\n"",
+      ""Plotting Psi_b_105_105-A.cube with isosurface values [0.0546034, -0.0546044]\n"",
+      ""Plotting Psi_b_106_106-A.cube with isosurface values [0.0600706, -0.0600712]\n"",
+      ""Plotting Psi_b_107_107-A.cube with isosurface values [0.0380527, -0.0380539]\n"",
+      ""Plotting Psi_b_108_108-A.cube with isosurface values [0.0471048, -0.0470534]\n"",
+      ""Plotting Psi_b_109_109-A.cube with isosurface values [0.0546187, -0.0546171]\n"",
+      ""Plotting Psi_b_10_10-A.cube with isosurface values [0.0404385, -0.0404382]\n"",
+      ""Plotting Psi_b_110_110-A.cube with isosurface values [0.0526063, -0.0526048]\n"",
+      ""Plotting Psi_b_111_111-A.cube with isosurface values [0.0560259, -0.0560205]\n"",
+      ""Plotting Psi_b_112_112-A.cube with isosurface values [0.0535457, -0.0535405]\n"",
+      ""Plotting Psi_b_113_113-A.cube with isosurface values [0.0536658, -0.0536515]\n"",
+      ""Plotting Psi_b_114_114-A.cube with isosurface values [0.0530359, -0.053055]\n"",
+      ""Plotting Psi_b_115_115-A.cube with isosurface values [0.0416529, -0.0416623]\n"",
+      ""Plotting Psi_b_116_116-A.cube with isosurface values [0.0404025, -0.0404031]\n"",
+      ""Plotting Psi_b_117_117-A.cube with isosurface values [0.0447573, -0.0447494]\n"",
+      ""Plotting Psi_b_118_118-A.cube with isosurface values [0.0435566, -0.0435481]\n"",
+      ""Plotting Psi_b_119_119-A.cube with isosurface values [0.0462539, -0.0462196]\n"",
+      ""Plotting Psi_b_11_11-A.cube with isosurface values [0.0404038, -0.0403994]\n"",
+      ""Plotting Psi_b_120_120-A.cube with isosurface values [0.0461635, -0.0461516]\n"",
+      ""Plotting Psi_b_12_12-A.cube with isosurface values [0.0334224, -0.033353]\n"",
+      ""Plotting Psi_b_13_13-A.cube with isosurface values [0.0379054, -0.0379054]\n"",
+      ""Plotting Psi_b_14_14-A.cube with isosurface values [0.0508558, -0.0508547]\n"",
+      ""Plotting Psi_b_15_15-A.cube with isosurface values [0.0357363, -0.0357361]\n"",
+      ""Plotting Psi_b_16_16-A.cube with isosurface values [0.0391251, -0.0391396]\n"",
+      ""Plotting Psi_b_17_17-A.cube with isosurface values [0.0352166, -0.0352204]\n"",
+      ""Plotting Psi_b_18_18-A.cube with isosurface values [0.0392106, -0.039208]\n"",
+      ""Plotting Psi_b_19_19-A.cube with isosurface values [0.0408659, -0.040882]\n"",
+      ""Plotting Psi_b_1_1-A.cube with isosurface values [0.28033, 0.0]\n"",
+      ""Plotting Psi_b_20_20-A.cube with isosurface values [0.0360412, -0.0360367]\n"",
+      ""Plotting Psi_b_21_21-A.cube with isosurface values [0.036461, -0.0364741]\n"",
+      ""Plotting Psi_b_22_22-A.cube with isosurface values [0.0329912, -0.0329921]\n"",
+      ""Plotting Psi_b_23_23-A.cube with isosurface values [0.0330877, -0.0330881]\n"",
+      ""Plotting Psi_b_24_24-A.cube with isosurface values [0.0224977, -0.0224989]\n"",
+      ""Plotting Psi_b_25_25-A.cube with isosurface values [0.0235961, -0.023596]\n"",
+      ""Plotting Psi_b_26_26-A.cube with isosurface values [0.0240074, -0.0240074]\n"",
+      ""Plotting Psi_b_27_27-A.cube with isosurface values [0.0233761, -0.0233829]\n"",
+      ""Plotting Psi_b_28_28-A.cube with isosurface values [0.0244246, -0.0244241]\n"",
+      ""Plotting Psi_b_29_29-A.cube with isosurface values [0.0240182, -0.0240184]\n"",
+      ""Plotting Psi_b_2_2-A.cube with isosurface values [0.289922, -0.289918]\n"",
+      ""Plotting Psi_b_30_30-A.cube with isosurface values [0.031347, -0.0313509]\n"",
+      ""Plotting Psi_b_31_31-A.cube with isosurface values [0.0251724, -0.0251759]\n"",
+      ""Plotting Psi_b_32_32-A.cube with isosurface values [0.0250444, -0.0250439]\n"",
+      ""Plotting Psi_b_33_33-A.cube with isosurface values [0.0303727, -0.0303724]\n"",
+      ""Plotting Psi_b_34_34-A.cube with isosurface values [0.0306925, -0.0306934]\n"",
+      ""Plotting Psi_b_35_35-A.cube with isosurface values [0.0301311, -0.0301336]\n"",
+      ""Plotting Psi_b_36_36-A.cube with isosurface values [0.031564, -0.0315457]\n"",
+      ""Plotting Psi_b_37_37-A.cube with isosurface values [0.0288736, -0.0288735]\n"",
+      ""Plotting Psi_b_38_38-A.cube with isosurface values [0.0297631, -0.0297685]\n"",
+      ""Plotting Psi_b_39_39-A.cube with isosurface values [0.0269562, -0.0269527]\n"",
+      ""Plotting Psi_b_3_3-A.cube with isosurface values [0.338179, -0.338176]\n"",
+      ""Plotting Psi_b_40_40-A.cube with isosurface values [0.0248251, -0.0248248]\n"",
+      ""Plotting Psi_b_41_41-A.cube with isosurface values [0.030073, -0.0300728]\n"",
+      ""Plotting Psi_b_42_42-A.cube with isosurface values [0.0300686, -0.0300691]\n"",
+      ""Plotting Psi_b_43_43-A.cube with isosurface values [0.0325552, -0.0325515]\n"",
+      ""Plotting Psi_b_44_44-A.cube with isosurface values [0.0325553, -0.032558]\n"",
+      ""Plotting Psi_b_45_45-A.cube with isosurface values [0.0315161, -0.0315217]\n"",
+      ""Plotting Psi_b_46_46-A.cube with isosurface values [0.0294245, -0.0294298]\n"",
+      ""Plotting Psi_b_47_47-A.cube with isosurface values [0.0286684, -0.0286684]\n"",
+      ""Plotting Psi_b_48_48-A.cube with isosurface values [0.028968, -0.0289678]\n"",
+      ""Plotting Psi_b_49_49-A.cube with isosurface values [0.0281508, -0.0281492]\n"",
+      ""Plotting Psi_b_4_4-A.cube with isosurface values [0.29907, -0.289467]\n"",
+      ""Plotting Psi_b_50_50-A.cube with isosurface values [0.0255125, -0.025512]\n"",
+      ""Plotting Psi_b_51_51-A.cube with isosurface values [0.023336, -0.023336]\n"",
+      ""Plotting Psi_b_52_52-A.cube with isosurface values [0.0234729, -0.023473]\n"",
+      ""Plotting Psi_b_53_53-A.cube with isosurface values [0.0235727, -0.0235727]\n"",
+      ""Plotting Psi_b_54_54-A.cube with isosurface values [0.0236941, -0.0236941]\n"",
+      ""Plotting Psi_b_55_55-A.cube with isosurface values [0.021238, -0.0212395]\n"",
+      ""Plotting Psi_b_56_56-A.cube with isosurface values [0.0265318, -0.0265317]\n"",
+      ""Plotting Psi_b_57_57-A.cube with isosurface values [0.026403, -0.0264055]\n"",
+      ""Plotting Psi_b_58_58-A.cube with isosurface values [0.0293796, -0.0293822]\n"",
+      ""Plotting Psi_b_59_59-A.cube with isosurface values [0.0278522, -0.0278522]\n"",
+      ""Plotting Psi_b_5_5-A.cube with isosurface values [0.342107, -0.335833]\n"",
+      ""Plotting Psi_b_60_60-A.cube with isosurface values [0.0279467, -0.0279452]\n"",
+      ""Plotting Psi_b_61_61-A.cube with isosurface values [0.0299558, -0.0299556]\n"",
+      ""Plotting Psi_b_62_62-A.cube with isosurface values [0.0304382, -0.0304383]\n"",
+      ""Plotting Psi_b_63_63-A.cube with isosurface values [0.0299731, -0.0299732]\n"",
+      ""Plotting Psi_b_64_64-A.cube with isosurface values [0.063641, -0.0636315]\n"",
+      ""Plotting Psi_b_65_65-A.cube with isosurface values [0.0516425, -0.0516627]\n"",
+      ""Plotting Psi_b_66_66-A.cube with isosurface values [0.0526899, -0.0526737]\n"",
+      ""Plotting Psi_b_67_67-A.cube with isosurface values [0.0553581, -0.0553531]\n"",
+      ""Plotting Psi_b_68_68-A.cube with isosurface values [0.0276088, -0.0276111]\n"",
+      ""Plotting Psi_b_69_69-A.cube with isosurface values [0.0291093, -0.0291048]\n"",
+      ""Plotting Psi_b_6_6-A.cube with isosurface values [0.281277, -0.281279]\n"",
+      ""Plotting Psi_b_70_70-A.cube with isosurface values [0.0537655, -0.0537729]\n"",
+      ""Plotting Psi_b_71_71-A.cube with isosurface values [0.0552311, -0.0552725]\n"",
+      ""Plotting Psi_b_72_72-A.cube with isosurface values [0.0435948, -0.0436224]\n"",
+      ""Plotting Psi_b_73_73-A.cube with isosurface values [0.0328853, -0.0328849]\n"",
+      ""Plotting Psi_b_74_74-A.cube with isosurface values [0.0430291, -0.043043]\n"",
+      ""Plotting Psi_b_75_75-A.cube with isosurface values [0.0257321, -0.0257337]\n"",
+      ""Plotting Psi_b_76_76-A.cube with isosurface values [0.0458547, -0.0458552]\n"",
+      ""Plotting Psi_b_77_77-A.cube with isosurface values [0.0494258, -0.049419]\n"",
+      ""Plotting Psi_b_78_78-A.cube with isosurface values [0.0400556, -0.040052]\n"",
+      ""Plotting Psi_b_79_79-A.cube with isosurface values [0.0401515, -0.0401424]\n"",
+      ""Plotting Psi_b_7_7-A.cube with isosurface values [0.0437048, -0.0426561]\n"",
+      ""Plotting Psi_b_80_80-A.cube with isosurface values [0.0359129, -0.0359138]\n"",
+      ""Plotting Psi_b_81_81-A.cube with isosurface values [0.0357842, -0.0357842]\n"",
+      ""Plotting Psi_b_82_82-A.cube with isosurface values [0.0336639, -0.033663]\n"",
+      ""Plotting Psi_b_83_83-A.cube with isosurface values [0.0388873, -0.0388891]\n"",
+      ""Plotting Psi_b_84_84-A.cube with isosurface values [0.0382899, -0.0382862]\n"",
+      ""Plotting Psi_b_85_85-A.cube with isosurface values [0.0525889, -0.0525902]\n""
+     ]
+    },
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Plotting Psi_b_86_86-A.cube with isosurface values [0.0518022, -0.0517836]\n"",
+      ""Plotting Psi_b_87_87-A.cube with isosurface values [0.0471893, -0.0471904]\n"",
+      ""Plotting Psi_b_88_88-A.cube with isosurface values [0.0433201, -0.0433132]\n"",
+      ""Plotting Psi_b_89_89-A.cube with isosurface values [0.0429296, -0.042935]\n"",
+      ""Plotting Psi_b_8_8-A.cube with isosurface values [0.0419202, -0.0419122]\n"",
+      ""Plotting Psi_b_90_90-A.cube with isosurface values [0.0486204, -0.0486207]\n"",
+      ""Plotting Psi_b_91_91-A.cube with isosurface values [0.0498826, -0.0498727]\n"",
+      ""Plotting Psi_b_92_92-A.cube with isosurface values [0.052563, -0.0525392]\n"",
+      ""Plotting Psi_b_93_93-A.cube with isosurface values [0.0286104, -0.0286095]\n"",
+      ""Plotting Psi_b_94_94-A.cube with isosurface values [0.0286184, -0.0286164]\n"",
+      ""Plotting Psi_b_95_95-A.cube with isosurface values [0.0457728, -0.0458067]\n"",
+      ""Plotting Psi_b_96_96-A.cube with isosurface values [0.0456521, -0.0456488]\n"",
+      ""Plotting Psi_b_97_97-A.cube with isosurface values [0.0368242, -0.0368248]\n"",
+      ""Plotting Psi_b_98_98-A.cube with isosurface values [0.0278935, -0.0278908]\n"",
+      ""Plotting Psi_b_99_99-A.cube with isosurface values [0.0519307, -0.0519572]\n"",
+      ""Plotting Psi_b_9_9-A.cube with isosurface values [0.0422579, -0.0422601]\n"",
+      ""run vmd\n"",
+      ""call montage\n"",
+      ""zip\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# https://github.com/fevangelista/vmd_cube\n"",
+    ""!python vmd_cube.py --imagew=500 --imageh=500""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {},
+   ""source"": [
+    ""## tga2png\n"",
+    ""\n"",
+    ""    #!/usr/bin/env python\n"",
+    ""    \n"",
+    ""    from PIL import Image\n"",
+    ""    import sys\n"",
+    ""    \n"",
+    ""    tgafile = sys.argv[1]\n"",
+    ""    pngfile = tgafile.split(\"".\"")[0] + \"".png\""\n"",
+    ""    \n"",
+    ""    im = Image.open(tgafile)\n"",
+    ""    im.save(pngfile)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 10,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""!~/bin/tga2png Psi_a_21_21-A.tga""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 11,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n""
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""display_png(Image(\""Psi_a_21_21-A.png\""))""
+   ]
+  },
+  {
+   ""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"": ""3.6.8""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/Rendering_RESP_charge/example.ipynb",".ipynb","31538","342","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from rdkit import Chem\n"",
+    ""from rdkit.Chem import Draw\n"",
+    ""from rdkit.Chem import AllChem\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from psikit import Psikit""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": ""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\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x112bc4620>""
+      ]
+     },
+     ""execution_count"": 4,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""smi = 'CC(=O)O'\n"",
+    ""mol = Chem.MolFromSmiles(smi)\n"",
+    ""AllChem.Compute2DCoords(mol)\n"",
+    ""mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n"",
+      ""CPU times: user 21.9 s, sys: 996 ms, total: 22.9 s\n"",
+      ""Wall time: 6.31 s\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-227.82082530457998""
+      ]
+     },
+     ""execution_count"": 5,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""# load smiles and perform optimzation\n"",
+    ""pk = Psikit()\n"",
+    ""pk.read_from_smiles(smi)\n"",
+    ""%time pk.optimize()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""-0.447525061770846 HOMO\n"",
+      ""0.19145171932070656 LUMO\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""print('{} HOMO'.format(pk.HOMO))\n"",
+    ""print('{} LUMO'.format(pk.LUMO))""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""[]\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Get Atoms from pk.mol object and check atom properties\n"",
+    ""atoms = pk.mol.GetAtoms()\n"",
+    ""print(list(atoms[0].GetPropNames()))""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""charges = pk.resp_charge""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""['EP_C', 'RESP_C']\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""atoms = pk.mol.GetAtoms()\n"",
+    ""print(list(atoms[0].GetPropNames()))""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 10,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x10d11dc00>""
+      ]
+     },
+     ""execution_count"": 10,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 11,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from rdkit.Chem.Draw import MolDrawOptions\n"",
+    ""from rdkit.Chem.Draw import rdMolDraw2D\n"",
+    ""from IPython.display import SVG""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 12,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""view = rdMolDraw2D.MolDraw2DSVG(600,800)\n"",
+    ""op = view.drawOptions()\n"",
+    ""for idx, atom in enumerate(pk.mol.GetAtoms()):\n"",
+    ""    num = float(atom.GetProp('RESP_C'))\n"",
+    ""    op.atomLabels[idx] = \""{}({:,.2f})\"".format(atom.GetSymbol(), num)\n"",
+    ""AllChem.Compute2DCoords(pk.mol)\n"",
+    ""view.DrawMolecule(pk.mol)\n"",
+    ""view.FinishDrawing()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 13,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/svg+xml"": [
+       ""<svg baseProfile=\""full\"" height=\""800px\"" version=\""1.1\"" width=\""600px\"" xml:space=\""preserve\"" xmlns=\""http://www.w3.org/2000/svg\"" xmlns:rdkit=\""http://www.rdkit.org/xml\"" xmlns:xlink=\""http://www.w3.org/1999/xlink\"">\n"",
+       ""<rect height=\""800\"" style=\""opacity:1.0;fill:#FFFFFF;stroke:none\"" width=\""600\"" x=\""0\"" y=\""0\""> </rect>\n"",
+       ""<path d=\""M 348.052,405.509 315.114,395.716\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 398.501,420.509 431.439,430.303\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 371.047,420.509 350.713,488.897\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 375.506,405.509 395.84,337.122\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 296.558,378.694 288.239,343.887\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 288.239,343.887 279.921,309.081\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 279.635,382.738 271.317,347.932\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 271.317,347.932 262.999,313.125\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 281.969,395.716 258.306,418.125\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 258.306,418.125 234.643,440.534\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 201.499,440.534 185.03,435.638\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 185.03,435.638 168.561,430.741\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""340.26\"" y=\""420.509\""><tspan>C(-0.33)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""259.871\"" y=\""395.716\""><tspan>C(0.84)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"" x=\""236.147\"" y=\""311.103\""><tspan>O(-0.62)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"" x=\""193.203\"" y=\""455.534\""><tspan>O(-0.66)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""426.646\"" y=\""445.303\""><tspan>H(0.10)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""318.465\"" y=\""503.897\""><tspan>H(0.10)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""368.052\"" y=\""337.122\""><tspan>H(0.11)</tspan></text>\n"",
+       ""<text style=\""font-size:14px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""113.318\"" y=\""430.741\""><tspan>H(0.45)</tspan></text>\n"",
+       ""</svg>""
+      ],
+      ""text/plain"": [
+       ""<IPython.core.display.SVG object>""
+      ]
+     },
+     ""execution_count"": 13,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""svg = view.GetDrawingText().replace('svg:', '')\n"",
+    ""SVG(svg)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": ""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\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x110ea9080>""
+      ]
+     },
+     ""execution_count"": 4,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""smi = 'COc1cccnc1'\n"",
+    ""mol = Chem.MolFromSmiles(smi)\n"",
+    ""AllChem.Compute2DCoords(mol)\n"",
+    ""mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n"",
+      ""CPU times: user 1min 54s, sys: 3.97 s, total: 1min 58s\n"",
+      ""Wall time: 31.5 s\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-360.58490537900167""
+      ]
+     },
+     ""execution_count"": 5,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk = Psikit()\n"",
+    ""pk.read_from_smiles(smi)\n"",
+    ""%time pk.optimize()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Done!\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# get MO view!\n"",
+    ""pk.getMOview()""
+   ]
+  },
+  {
+   ""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"": ""3.6.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/Rendering_RESP_charge/RESP charge of the tetrazole.ipynb",".ipynb","24473","260","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from psikit import Psikit\n"",
+    ""from rdkit import Chem\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole\n"",
+    ""from IPython.display import SVG\n"",
+    ""from rdkit.Chem.Draw import rdMolDraw2D""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk = Psikit()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 19,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""{'Electrostatic Potential Charges': array([-0.3384504 ,  0.9525741 , -0.85518224, -0.86392646,  0.0387593 ,\n"",
+       ""         0.03824675,  0.02797894]),\n"",
+       "" 'Restrained Electrostatic Potential Charges': array([-0.19605235,  0.87815436, -0.83637101, -0.84554228,  0.00486571,\n"",
+       ""         0.00417476, -0.00922919])}""
+      ]
+     },
+     ""execution_count"": 19,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.read_from_smiles(\""CC(=O)[O-]\"")\n"",
+    ""pk.optimize()\n"",
+    ""pk.resp_charge""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 20,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/svg+xml"": [
+       ""<svg baseProfile=\""full\"" height=\""200px\"" version=\""1.1\"" width=\""400px\"" xml:space=\""preserve\"" xmlns=\""http://www.w3.org/2000/svg\"" xmlns:rdkit=\""http://www.rdkit.org/xml\"" xmlns:xlink=\""http://www.w3.org/1999/xlink\"">\n"",
+       ""<rect height=\""200\"" style=\""opacity:1.0;fill:#FFFFFF;stroke:none\"" width=\""400\"" x=\""0\"" y=\""0\""> </rect>\n"",
+       ""<path d=\""M 156.311,105.956 237.204,102.646\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 105.261,114.073 95.4988,116.777\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 126.81,99.5001 93.774,37.1813\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 126.626,114.5 94.5235,172.374\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 256.542,112.579 271.68,143.521\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 271.68,143.521 286.817,174.463\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 270.259,105.869 285.396,136.811\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 285.396,136.811 300.534,167.753\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 264.318,94.2238 284.684,60.9251\"" style=\""fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<path d=\""M 284.684,60.9251 305.05,27.6264\"" style=\""fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\""/>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""105.261\"" y=\""114.5\""><tspan>-0.196</tspan></text>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""237.204\"" y=\""109.224\""><tspan>0.878</tspan></text>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"" x=\""271.819\"" y=\""186.108\""><tspan>-0.836</tspan></text>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"" x=\""284.111\"" y=\""27.6264\""><tspan>-0.846</tspan></text>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""95.4988\"" y=\""118.036\""><tspan>0.005</tspan></text>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""67.2712\"" y=\""37.1813\""><tspan>0.004</tspan></text>\n"",
+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""64.8381\"" y=\""187.374\""><tspan>-0.009</tspan></text>\n"",
+       ""</svg>""
+      ],
+      ""text/plain"": [
+       ""<IPython.core.display.SVG object>""
+      ]
+     },
+     ""execution_count"": 20,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""drawer = rdMolDraw2D.MolDraw2DSVG(400,200)\n"",
+    ""opts = drawer.drawOptions()\n"",
+    ""\n"",
+    ""for i, atom in enumerate(pk.mol.GetAtoms()):\n"",
+    ""    opts.atomLabels[i] = \""{0:.3f}\"".format(float(atom.GetProp(\""RESP_C\"")))\n"",
+    ""\n"",
+    ""drawer.DrawMolecule(pk.mol)\n"",
+    ""drawer.FinishDrawing()\n"",
+    ""svg = drawer.GetDrawingText().replace('svg:','')\n"",
+    ""SVG(svg)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk.read_from_smiles(\""CC1=NN=N[N-]1\"")""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x1217a4da0>""
+      ]
+     },
+     ""execution_count"": 5,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""{'Electrostatic Potential Charges': array([-0.98211149,  1.18464533, -0.67803762, -0.21994535, -0.19810641,\n"",
+       ""        -0.6954824 ,  0.19644875,  0.19970103,  0.19288816]),\n"",
+       "" 'Restrained Electrostatic Potential Charges': array([-0.78229709,  1.09017898, -0.65778538, -0.22054976, -0.19647406,\n"",
+       ""        -0.67770262,  0.14870634,  0.15198418,  0.1439394 ])}""
+      ]
+     },
+     ""execution_count"": 6,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.optimize()\n"",
+    ""pk.resp_charge""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/svg+xml"": [
+       ""<svg baseProfile=\""full\"" height=\""200px\"" version=\""1.1\"" width=\""400px\"" xml:space=\""preserve\"" xmlns=\""http://www.w3.org/2000/svg\"" xmlns:rdkit=\""http://www.rdkit.org/xml\"" xmlns:xlink=\""http://www.w3.org/1999/xlink\"">\n"",
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+       ""<text style=\""font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"" x=\""44.9457\"" y=\""164.277\""><tspan>0.144</tspan></text>\n"",
+       ""</svg>""
+      ],
+      ""text/plain"": [
+       ""<IPython.core.display.SVG object>""
+      ]
+     },
+     ""execution_count"": 7,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""drawer = rdMolDraw2D.MolDraw2DSVG(400,200)\n"",
+    ""opts = drawer.drawOptions()\n"",
+    ""\n"",
+    ""for i, atom in enumerate(pk.mol.GetAtoms()):\n"",
+    ""    opts.atomLabels[i] = \""{0:.3f}\"".format(float(atom.GetProp(\""RESP_C\"")))\n"",
+    ""\n"",
+    ""drawer.DrawMolecule(pk.mol)\n"",
+    ""drawer.FinishDrawing()\n"",
+    ""svg = drawer.GetDrawingText().replace('svg:','')\n"",
+    ""SVG(svg)""
+   ]
+  },
+  {
+   ""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"": ""3.6.8""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/fsapt_ex.ipynb",".ipynb","18906","115","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F57CA013208>""
+      ]
+     },
+     ""execution_count"": 1,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""from rdkit import Chem\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole\n"",
+    ""from rdkit.Chem import Draw\n"",
+    ""from psikit import Sapt\n"",
+    ""\n"",
+    ""p1 = Chem.MolFromMolFile('phenol1.mol', removeHs=False)\n"",
+    ""p2 = Chem.MolFromMolFile('phenol2.mol', removeHs=False)\n"",
+    ""Draw.MolsToGridImage([p1,p2])""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""sapt = Sapt()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""sapt.monomer1_from_molfile('phenol1.mol')\n"",
+    ""sapt.monomer2_from_molfile('phenol2.mol')\n"",
+    ""sapt.make_dimer()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""done\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""res = sapt.run_fisapt()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""-0.011385703499910926\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""print(res)""
+   ]
+  },
+  {
+   ""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"": ""3.7.3""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/fisapt0.py",".py","170","8","import psikit
+sapt = psikit.Sapt()
+sapt.monomer1_from_molfile('phenol1.mol')
+sapt.monomer2_from_molfile('phenol2.mol')
+sapt.make_dimer()
+p = sapt.dimer
+sapt.run_fisapt()
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/sapt0.py",".py","436","15","from psikit import Sapt
+sapt=Sapt()
+sapt.monomer1_from_molfile('water1.mol')
+sapt.monomer2_from_molfile('water2.mol')
+sapt.make_dimer()
+print(sapt.dimer)
+sapt0, Exch100, Elst10, Disp200, ExchDisp20, Ind20r, ExchInd20r = sapt.run_sapt()
+print('total sapt0', sapt0)
+print('Exch10 (S^2)', Exch100)
+print('Elst10', Elst10)
+print('Disp20', Disp200)
+print('Exch-Disp20', ExchDisp20)
+print('Ind20,r', Ind20r)
+print('Exch-Ind20,r', ExchInd20r)
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/sapt_ex.ipynb",".ipynb","9922","151","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": ""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\n"",
+      ""text/plain"": [
+       ""<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F96442EFB70>""
+      ]
+     },
+     ""execution_count"": 1,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""from rdkit import Chem\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole\n"",
+    ""from rdkit.Chem import Draw\n"",
+    ""from psikit import Sapt\n"",
+    ""\n"",
+    ""w1 = Chem.MolFromMolFile('water1.mol', removeHs=False)\n"",
+    ""w2 = Chem.MolFromMolFile('water2.mol', removeHs=False)\n"",
+    ""Draw.MolsToGridImage([w1,w2])""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""sapt = Sapt()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""sapt.monomer1_from_molfile('water1.mol')\n"",
+    ""sapt.monomer2_from_molfile('water2.mol')\n"",
+    ""sapt.make_dimer()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""\n"",
+      ""Initializing SAPT object...\n"",
+      ""\n"",
+      ""RHF for monomer A finished in 0.55 seconds.\n"",
+      ""RHF for monomer B finished in 0.52 seconds.\n"",
+      ""Building ERI tensor...\n"",
+      ""...built ERI tensor in 3.180 seconds.\n"",
+      ""Size of the ERI tensor is 0.36 GB, 82 basis functions.\n"",
+      ""\n"",
+      ""...finished initializing SAPT object in  4.49 seconds.\n"",
+      ""\n"",
+      ""Starting electrostatics...\n"",
+      ""...electrostatics took a total of  0.17 seconds.\n"",
+      ""\n"",
+      ""Starting exchange...\n"",
+      ""...exchange took a total of  0.63 seconds.\n"",
+      ""\n"",
+      ""Starting dispersion...\n"",
+      ""...dispersion took a total of  8.92 seconds.\n"",
+      ""\n"",
+      ""Starting induction...\n"",
+      ""Ind20,r (A<-B)           -2.95216090 mH       -1.85250753 kcal/mol\n"",
+      ""Ind20,r (A->B)           -1.37852223 mH       -0.86503511 kcal/mol\n"",
+      ""Exch-Ind20,r (A<-B)       1.41545280 mH        0.88820937 kcal/mol\n"",
+      ""Exch-Ind20,r (A->B)       0.88580457 mH        0.55585034 kcal/mol\n"",
+      ""...induction took a total of  15.90 seconds.\n"",
+      ""\n"",
+      ""SAPT0 Results\n"",
+      ""----------------------------------------------------------------------\n"",
+      ""Exch10 (S^2)             10.53844851 mH        6.61297129 kcal/mol\n"",
+      ""Elst10                  -13.02830646 mH       -8.17537956 kcal/mol\n"",
+      ""Disp20                   -3.42996225 mH       -2.15233218 kcal/mol\n"",
+      ""Exch-Disp20               0.61399531 mH        0.38528758 kcal/mol\n"",
+      ""Ind20,r                  -4.33068313 mH       -2.71754264 kcal/mol\n"",
+      ""Exch-Ind20,r              2.30125737 mH        1.44405971 kcal/mol\n"",
+      ""----------------------------------------------------------------------\n"",
+      ""Total SAPT0              -7.33525065 mH       -4.60293580 kcal/mol\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""res = sapt.run_sapt()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""(-0.007335250653651768, 0.010538448513184471, -0.013028306455483696, -0.0034299622542766023, 0.0006139953072061937, -0.004330683131344517, 0.0023012573670623832)\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""print(res)""
+   ]
+  },
+  {
+   ""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"": ""3.7.3""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/fsapt/copy_pymol2.py",".py","1339","51","#!/usr/bin/env python
+
+#
+# @BEGIN LICENSE
+#
+# Psi4: an open-source quantum chemistry software package
+#
+# Copyright (c) 2007-2019 The Psi4 Developers.
+#
+# The copyrights for code used from other parties are included in
+# the corresponding files.
+#
+# This file is part of Psi4.
+#
+# Psi4 is free software; you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, version 3.
+#
+# Psi4 is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with Psi4; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#
+# @END LICENSE
+#
+
+import sys, os
+
+root = os.path.dirname(os.path.realpath(__file__))
+
+# => Driver Code <= #
+
+if __name__ == '__main__':
+
+    # > Working Dirname < #
+
+    if len(sys.argv) == 1:
+        dirname = '.'
+    elif len(sys.argv) == 2:
+        dirname = sys.argv[1]
+    else:
+        raise Exception('Usage: fsapt.py [dirname]')
+
+    # > Copy Files < #
+
+    os.system('cp %s/pymol2/*pymol %s' % (root, dirname))
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/fsapt/copy_pymol.py",".py","1338","51","#!/usr/bin/env python
+
+#
+# @BEGIN LICENSE
+#
+# Psi4: an open-source quantum chemistry software package
+#
+# Copyright (c) 2007-2019 The Psi4 Developers.
+#
+# The copyrights for code used from other parties are included in
+# the corresponding files.
+#
+# This file is part of Psi4.
+#
+# Psi4 is free software; you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, version 3.
+#
+# Psi4 is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with Psi4; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#
+# @END LICENSE
+#
+
+import sys, os
+
+root = os.path.dirname(os.path.realpath(__file__))
+
+# => Driver Code <= #
+
+if __name__ == '__main__':
+
+    # > Working Dirname < #
+
+    if len(sys.argv) == 1:
+        dirname = '.'
+    elif len(sys.argv) == 2:
+        dirname = sys.argv[1]
+    else:
+        raise Exception('Usage: fsapt.py [dirname]')
+
+    # > Copy Files < #
+
+    os.system('cp %s/pymol/*pymol %s' % (root, dirname))
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/fsapt/fsaptdiff.py",".py","4127","134","#!/usr/bin/env python
+
+#
+# @BEGIN LICENSE
+#
+# Psi4: an open-source quantum chemistry software package
+#
+# Copyright (c) 2007-2019 The Psi4 Developers.
+#
+# The copyrights for code used from other parties are included in
+# the corresponding files.
+#
+# This file is part of Psi4.
+#
+# Psi4 is free software; you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, version 3.
+#
+# Psi4 is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with Psi4; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#
+# @END LICENSE
+#
+
+import sys, os
+
+sys.path.append('%s/fsapt' % os.environ['PSIDATADIR'])
+from fsapt import *
+
+# => Driver Code <= #
+
+if __name__ == '__main__':
+
+    # > Working Dirname < #
+
+    if len(sys.argv) == 3:
+        dirA = sys.argv[1]
+        dirB = sys.argv[2]
+        dirD = '.'
+    elif len(sys.argv) == 4:
+        dirA = sys.argv[1]
+        dirB = sys.argv[2]
+        dirD = sys.argv[3]
+    else:
+        raise Exception('Usage: fsapt.py dirnameA dirnameB [dirnameD]')
+
+    # Make dirD if needed
+    if not os.path.exists(dirD):
+        os.makedirs(dirD)
+
+    # > Order-2 Analysis < #
+
+    fh = open('%s/fsapt.dat' % dirA, 'w')
+    fh, sys.stdout = sys.stdout, fh
+    print('  ==> F-ISAPT: Links by Charge <==\n')
+    stuffA = computeFsapt(dirA, False)
+    print('   => Full Analysis <=\n')
+    printOrder2(stuffA['order2'], stuffA['fragkeys']) 
+    print('   => Reduced Analysis <=\n')
+    printOrder2(stuffA['order2r'], stuffA['fragkeysr']) 
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    fh = open('%s/fsapt.dat' % dirB, 'w')
+    fh, sys.stdout = sys.stdout, fh
+    print('  ==> F-ISAPT: Links by Charge <==\n')
+    stuffB = computeFsapt(dirB, False)
+    print('   => Full Analysis <=\n')
+    printOrder2(stuffB['order2'], stuffB['fragkeys']) 
+    print('   => Reduced Analysis <=\n')
+    printOrder2(stuffB['order2r'], stuffB['fragkeysr']) 
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    fh = open('%s/fsapt.dat' % dirD, 'w')
+    fh, sys.stdout = sys.stdout, fh
+    print('  ==> F-ISAPT: Links by Charge <==\n')
+    order2D = diffOrder2(stuffA['order2r'], stuffB['order2r'])
+    print('   => Reduced Analysis <=\n')
+    printOrder2(order2D, stuffB['fragkeysr']) 
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    fh = open('%s/fsapt.dat' % dirA, 'a')
+    fh, sys.stdout = sys.stdout, fh
+    print('  ==> F-ISAPT: Links 50-50 <==\n')
+    stuffA = computeFsapt(dirA, True)
+    print('   => Full Analysis <=\n')
+    printOrder2(stuffA['order2'], stuffA['fragkeys']) 
+    print('   => Reduced Analysis <=\n')
+    printOrder2(stuffA['order2r'], stuffA['fragkeysr']) 
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    fh = open('%s/fsapt.dat' % dirB, 'a')
+    fh, sys.stdout = sys.stdout, fh
+    print('  ==> F-ISAPT: Links 50-50 <==\n')
+    stuffB = computeFsapt(dirB, True)
+    print('   => Full Analysis <=\n')
+    printOrder2(stuffB['order2'], stuffB['fragkeys']) 
+    print('   => Reduced Analysis <=\n')
+    printOrder2(stuffB['order2r'], stuffB['fragkeysr']) 
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    fh = open('%s/fsapt.dat' % dirD, 'a')
+    fh, sys.stdout = sys.stdout, fh
+    print('  ==> F-ISAPT: Links 50-50 <==\n')
+    order2D = diffOrder2(stuffA['order2r'], stuffB['order2r'])
+    print('   => Reduced Analysis <=\n')
+    printOrder2(order2D, stuffB['fragkeysr']) 
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    # > Order-1 PBD Files < #
+
+    pdbA = PDB.fromGeom(stuffA['geom'])
+    printOrder1(dirA, stuffA['order2r'], pdbA, stuffA['frags'])
+
+    pdbB = PDB.fromGeom(stuffB['geom'])
+    printOrder1(dirB, stuffB['order2r'], pdbB, stuffB['frags'])
+
+    # Using A geometry
+    printOrder1(dirD, order2D, pdbA, stuffA['frags'])
+
+
+
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/example_sapt/fsapt/fsapt.py",".py","24945","839","#!/usr/bin/env python
+
+#
+# @BEGIN LICENSE
+#
+# Psi4: an open-source quantum chemistry software package
+#
+# Copyright (c) 2007-2019 The Psi4 Developers.
+#
+# The copyrights for code used from other parties are included in
+# the corresponding files.
+#
+# This file is part of Psi4.
+#
+# Psi4 is free software; you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, version 3.
+#
+# Psi4 is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with Psi4; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#
+# @END LICENSE
+#
+
+from __future__ import print_function
+import sys, os, re, math, copy
+
+# => Global Data <= #
+
+# H to kcal constant
+H_to_kcal_ = 627.5095
+
+# SAPT Keys
+saptkeys_ = [
+    'Elst',
+    'Exch',
+    'IndAB',
+    'IndBA',
+    'Disp',
+    'Total',
+]
+
+# Map from atom name to charge, vDW radius, nfrozen
+atom_data_ = {
+'H'  :  [1.0,  0.402, 0],
+'HE' :  [2.0,  0.700, 0],
+'LI' :  [3.0,  1.230, 1],
+'BE' :  [4.0,  0.900, 1],
+'B'  :  [5.0,  1.000, 1],
+'C'  :  [6.0,  0.762, 1],
+'N'  :  [7.0,  0.676, 1],
+'O'  :  [8.0,  0.640, 1],
+'F'  :  [9.0,  0.630, 1],
+'NE' :  [10.0, 0.700, 1],
+'NA' :  [11.0, 1.540, 5],
+'MG' :  [12.0, 1.360, 5],
+'AL' :  [13.0, 1.180, 5],
+'SI' :  [14.0, 1.300, 5],
+'P'  :  [15.0, 1.094, 5],
+'S'  :  [16.0, 1.253, 5],
+'CL' :  [17.0, 1.033, 5],
+'AR' :  [18.0, 1.740, 5],
+}
+
+# => Standard Order-2 Analysis <= #
+
+def readXYZ(filename):
+
+    fh = open(filename,'r')
+    lines = fh.readlines()
+    lines = lines[2:]
+    fh.close()
+
+    re_xyz = re.compile(r'^\s*(\S+)\s+(\S+)\s+(\S+)\s+(\S+)\s*$')
+
+    val = []
+    for line in lines:
+        mobj = re.match(re_xyz, line)
+        val.append([mobj.group(1), float(mobj.group(2)), float(mobj.group(3)), float(mobj.group(4))])
+
+    return val
+
+def writeXYZ(filename, geom):
+
+    fh = open(filename, 'w')
+    fh.write('%d\n\n' % len(geom))
+    for line in geom:
+        fh.write('%6s %14.10f %14.10f %14.10f\n' % (line[0], line[1], line[2], line[3]))
+    fh.close();
+
+def readList(filename, factor = 1.0):
+
+    fh = open(filename, 'r')
+    lines = fh.readlines()
+    fh.close()
+
+    val = [float(x) * factor for x in lines]
+
+    return val
+
+def readBlock(filename, factor = 1.0):
+
+    fh = open(filename, 'r')
+    lines = fh.readlines()
+    fh.close()
+
+    val = []
+    for line in lines:
+        val.append([factor * float(x) for x in re.split(r'\s+',line.strip())])
+
+    return val
+
+def readFrags(filename):
+
+    frags = {}
+    fragkeys = []
+
+    fh = open(filename, 'r')
+    lines = fh.readlines()
+    fh.close()
+
+    for line in lines:
+        tokens = re.split(r'\s+', line.strip())
+        key = tokens[0]
+        val = [int(token)-1 for token in tokens[1:]]
+        frags[key] = val
+        fragkeys.append(key)
+
+    return [frags, fragkeys]
+
+def collapseRows(vals):
+    vals2 = [0.0 for x in vals[0]]
+    for row in vals:
+        for k in range(len(row)):
+            vals2[k] += row[k]
+
+    return vals2
+
+def collapseCols(val):
+    vals2 = [sum(x) for x in vals]
+    return vals2
+
+def checkFragments(geom, Zs, frags):
+
+    # Uniqueness
+    taken = []
+    for key, value in frags.items():
+        for index in value:
+            if index in taken:
+                raise Exception('Atom %d is duplicated' % (index+1))
+            taken.append(index)
+
+    # Cover/Exclusion
+    for ind in range(len(geom)):
+        if (ind in taken) and (Zs[ind] == 0.0):
+            raise Exception('Atom %d has charge 0.0, should not be in fragments.' % (ind+1))
+        elif (ind not in taken) and (Zs[ind] != 0.0):
+            raise Exception('Atom %d has charge >0.0, should be in fragments.' % (ind+1))
+
+def partitionFragments(fragkeys,frags,Z,Q,completeness = 0.85):
+
+    nA = len(Q)
+    na = len(Q[0])
+
+    nuclear_ws = {}
+    orbital_ws = {}
+    for key in fragkeys:
+        nuclear_ws[key] = [0.0 for x in range(nA)]
+        orbital_ws[key] = [0.0 for x in range(na)]
+
+    for A in range(nA):
+        for key in fragkeys:
+            if A in frags[key]:
+                nuclear_ws[key][A] = 1.0
+
+    linkas = []
+    for a in range(na):
+        assigned = False
+        for key in fragkeys:
+            sum = 0.0
+            for A in frags[key]:
+                sum += Q[A][a]
+            if sum > completeness:
+                assigned = True
+                orbital_ws[key][a] = 1.0
+                break
+        if not assigned:
+            linkas.append(a)
+
+    linkkeys = []
+    links = {}
+    link_nuclear_ws = {}
+    link_orbital_ws = {}
+    linkindex = 0;
+
+    for a in linkas:
+        sums = []
+        for key in fragkeys:
+            sum = 0.0
+            for A in frags[key]:
+                sum += Q[A][a]
+            sums.append(sum)
+
+        inds = sorted(range(len(sums)),key=lambda x:-sums[x])
+        sum = sums[inds[0]] + sums[inds[1]]
+        if sum <= completeness:
+            raise Exception(""Orbital %d is not complete over two fragments. "" 
+                            ""To avoid this error, please try to avoid cutting ""
+                            ""multiple bonds, aromatic rings, etc., in your ""
+                            ""definitions of fragments."" % (a+1))
+        key1 = fragkeys[inds[0]]
+        key2 = fragkeys[inds[1]]
+
+        Ainds = sorted(range(nA),key = lambda x:-Q[x][a])
+        sum = Q[Ainds[0]][a] + Q[Ainds[1]][a]
+        if sum <= completeness:
+            raise Exception(""Orbital %d is not complete over two link atoms. ""
+                            ""To avoid this error, please try to avoid cutting ""
+                            ""multiple bonds, aromatic rings, etc., in your ""
+                            ""definitions of fragments."" % (a+1))
+        A1 = Ainds[0]
+        A2 = Ainds[1]
+
+        nuclear_ws[key1][A1] -= 1.0 / Z[A1]
+        nuclear_ws[key2][A2] -= 1.0 / Z[A2]
+
+        linkname = 'Link-%d' % (linkindex+1);
+        linkindex+=1;
+
+        linkkeys.append(linkname)
+        links[linkname] = [A1, A2]
+        link_nuclear_ws[linkname] = [0.0 for x in range(nA)]
+        link_orbital_ws[linkname] = [0.0 for x in range(na)]
+
+        link_nuclear_ws[linkname][A1] += 1.0 / Z[A1]
+        link_nuclear_ws[linkname][A2] += 1.0 / Z[A2]
+        link_orbital_ws[linkname][a] = 1.0
+
+    fragkeys = fragkeys + linkkeys
+    for key in linkkeys:
+        frags[key] = links[key]
+        orbital_ws[key] = link_orbital_ws[key]
+        nuclear_ws[key] = link_nuclear_ws[key]
+
+    total_ws = {}
+    for key in fragkeys:
+        total_ws[key] = nuclear_ws[key] + orbital_ws[key]
+
+    return [fragkeys, frags, nuclear_ws, orbital_ws, total_ws]
+
+def printFrag(geom, Z, Q, fragkeys, frags, nuclear_ws, orbital_ws, filename):
+
+    fh = open(filename, 'w')
+
+    fh.write('   => Geometry <=\n\n')
+    for k in range(len(geom)):
+        fh.write('%4d %4s %11.3f %11.3f %11.3f\n' % (k+1, geom[k][0], geom[k][1], geom[k][2], geom[k][3]))
+    fh.write('\n')
+
+    fh.write('   => Fragments <=\n\n')
+    for key in fragkeys:
+        if len(key) > 4 and key[0:4] == 'Link':
+            continue
+        fh.write('%10s: ' % (key),)
+        for val in frags[key]:
+            fh.write('%3d ' % (val+1),)
+        fh.write('\n')
+    fh.write('\n')
+
+    fh.write('   => Links <=\n\n')
+    for key in fragkeys:
+        if not (len(key) > 4 and key[0:4] == 'Link'):
+            continue
+        fh.write('%10s: ' % (key),)
+        for val in frags[key]:
+            fh.write('%3d ' % (val+1),)
+        fh.write('\n')
+    fh.write('\n')
+
+    fh.write('   => Orbitals <=\n\n')
+    for key in fragkeys:
+        fh.write('%10s: ' % (key),)
+        for k in range(len(orbital_ws[key])):
+            if orbital_ws[key][k] != 0.0:
+                fh.write('%3d ' % (k+1),)
+        fh.write('\n')
+    fh.write('\n')
+
+
+    fh.write('   =>  Nuclear Weights <=\n\n')
+    for key in fragkeys:
+        fh.write('%10s: ' % (key),)
+        for k in range(len(nuclear_ws[key])):
+            if nuclear_ws[key][k] != 0.0:
+                fh.write('%3d (%11.3f) ' % ((k+1), nuclear_ws[key][k] * Z[k]))
+        fh.write('\n')
+    fh.write('\n')
+
+    fh.write('  => Charges <=\n\n')
+    for key in fragkeys:
+        Zval = sum([nuclear_ws[key][k] * Z[k] for k in range(len(Z))])
+        Yval = 2.0 * sum(orbital_ws[key])
+        fh.write('%10s: Z = %11.3f, Y = %11.3f, Q = %11.3f\n' % (key, Zval, Yval, Zval - Yval))
+    fh.write('\n')
+
+    fh.write('   => Orbital Check (Loss in Docc) <=\n\n')
+
+    for key in fragkeys:
+        fh.write('    Fragment: %s:\n' % key)
+        for k in range(len(orbital_ws[key])):
+            if orbital_ws[key][k] == 0.0:
+                continue
+            occ = 0.0
+            for atom in frags[key]:
+                occ += Q[atom][k]
+            loss = 1.0 - occ
+            fh.write('    %4d: %11.3f\n' % (k+1, loss))
+    fh.write('\n')
+
+    fh.close()
+
+def extractOsaptData(filepath):
+
+    vals = {}
+    vals['Elst']  = readBlock('%s/Elst.dat'  % filepath, H_to_kcal_)
+    vals['Exch']  = readBlock('%s/Exch.dat'  % filepath, H_to_kcal_)
+    vals['IndAB'] = readBlock('%s/IndAB.dat' % filepath, H_to_kcal_)
+    vals['IndBA'] = readBlock('%s/IndBA.dat' % filepath, H_to_kcal_)
+    vals['Disp']  = readBlock('%s/Disp.dat'  % filepath, H_to_kcal_)
+    vals['Total'] = [[0.0 for x in vals['Elst'][0]] for x2 in vals['Elst']]
+    for key in ['Elst', 'Exch', 'IndAB', 'IndBA', 'Disp']:
+        for k in range(len(vals['Total'])):
+            for l in range(len(vals['Total'][0])):
+                vals['Total'][k][l] += vals[key][k][l]
+
+    return vals
+
+def extractOrder2Fsapt(osapt, wsA, wsB):
+
+    vals = {}
+    for key, value in osapt.items():
+        vals[key] = {}
+        for keyA, valueA in wsA.items():
+            vals[key][keyA] = {}
+            for keyB, valueB in wsB.items():
+                val = 0.0
+                for k in range(len(valueA)):
+                    for l in range(len(valueB)):
+                        val += valueA[k] * valueB[l] * value[k][l]
+                vals[key][keyA][keyB] = val
+
+    return vals
+
+def collapseLinks(order2, frags, Qs, orbital_ws, links5050):
+
+    vals = {}
+    for key in order2.keys():
+        vals[key] = {}
+        for keyA in frags['A'].keys():
+            if len(keyA) > 4 and keyA[:4] == 'Link':
+                continue
+            vals[key][keyA] = {}
+            for keyB in frags['B'].keys():
+                if len(keyB) > 4 and keyB[:4] == 'Link':
+                    continue
+                vals[key][keyA][keyB] = order2[key][keyA][keyB]
+
+    for key in order2.keys():
+        for keyA in frags['A'].keys():
+            if not (len(keyA) > 4 and keyA[:4] == 'Link'):
+                continue
+            for keyB in frags['B'].keys():
+                if (len(keyB) > 4 and keyB[:4] == 'Link'):
+                    continue
+
+                energy = order2[key][keyA][keyB]
+
+                atom1A = frags['A'][keyA][0]
+                atom2A = frags['A'][keyA][1]
+
+                orbA = orbital_ws['A'][keyA].index(1.0)
+
+                if links5050:
+                    Q1A = 0.5
+                    Q2A = 0.5
+                else:
+                    Q1A = Qs['A'][atom1A][orbA]
+                    Q2A = Qs['A'][atom2A][orbA]
+
+                V1A = Q1A / (Q1A + Q2A)
+                V2A = Q2A / (Q1A + Q2A)
+
+                key1A = ''
+                for keyT, value in frags['A'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom1A in value:
+                        key1A = keyT
+                        break
+
+                key2A = ''
+                for keyT, value in frags['A'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom2A in value:
+                        key2A = keyT
+                        break
+
+                vals[key][key1A][keyB] += V1A * energy
+                vals[key][key2A][keyB] += V2A * energy
+
+    for key in order2.keys():
+        for keyA in frags['A'].keys():
+            if (len(keyA) > 4 and keyA[:4] == 'Link'):
+                continue
+            for keyB in frags['B'].keys():
+                if not (len(keyB) > 4 and keyB[:4] == 'Link'):
+                    continue
+
+                energy = order2[key][keyA][keyB]
+
+                atom1B = frags['B'][keyB][0]
+                atom2B = frags['B'][keyB][1]
+
+                orbB = orbital_ws['B'][keyB].index(1.0)
+
+                if links5050:
+                    Q1B = 0.5
+                    Q2B = 0.5
+                else:
+                    Q1B = Qs['B'][atom1B][orbB]
+                    Q2B = Qs['B'][atom2B][orbB]
+
+                V1B = Q1B / (Q1B + Q2B)
+                V2B = Q2B / (Q1B + Q2B)
+
+                key1B = ''
+                for keyT, value in frags['B'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom1B in value:
+                        key1B = keyT
+                        break
+
+                key2B = ''
+                for keyT, value in frags['B'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom2B in value:
+                        key2B = keyT
+                        break
+
+                vals[key][keyA][key1B] += V1B * energy
+                vals[key][keyA][key2B] += V2B * energy
+
+    for key in order2.keys():
+        for keyA in frags['A'].keys():
+            if not (len(keyA) > 4 and keyA[:4] == 'Link'):
+                continue
+            for keyB in frags['B'].keys():
+                if not (len(keyB) > 4 and keyB[:4] == 'Link'):
+                    continue
+
+                energy = order2[key][keyA][keyB]
+
+                atom1A = frags['A'][keyA][0]
+                atom2A = frags['A'][keyA][1]
+                atom1B = frags['B'][keyB][0]
+                atom2B = frags['B'][keyB][1]
+
+                orbA = orbital_ws['A'][keyA].index(1.0)
+                orbB = orbital_ws['B'][keyB].index(1.0)
+
+                if links5050:
+                    Q1A = 0.5
+                    Q2A = 0.5
+                    Q1B = 0.5
+                    Q2B = 0.5
+                else:
+                    Q1A = Qs['A'][atom1A][orbA]
+                    Q2A = Qs['A'][atom2A][orbA]
+                    Q1B = Qs['B'][atom1B][orbB]
+                    Q2B = Qs['B'][atom2B][orbB]
+
+                V1A = Q1A / (Q1A + Q2A)
+                V2A = Q2A / (Q1A + Q2A)
+                V1B = Q1B / (Q1B + Q2B)
+                V2B = Q2B / (Q1B + Q2B)
+
+                key1A = ''
+                for keyT, value in frags['A'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom1A in value:
+                        key1A = keyT
+                        break
+
+                key2A = ''
+                for keyT, value in frags['A'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom2A in value:
+                        key2A = keyT
+                        break
+
+                key1B = ''
+                for keyT, value in frags['B'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom1B in value:
+                        key1B = keyT
+                        break
+
+                key2B = ''
+                for keyT, value in frags['B'].items():
+                    if len(keyT) > 4 and keyT[:4] == 'Link':
+                        continue
+                    if atom2B in value:
+                        key2B = keyT
+                        break
+
+                vals[key][key1A][key1B] += V1A * V1B * energy
+                vals[key][key1A][key2B] += V1A * V2B * energy
+                vals[key][key2A][key1B] += V2A * V1B * energy
+                vals[key][key2A][key2B] += V2A * V2B * energy
+
+    return vals
+
+def printOrder2(order2, fragkeys):
+
+    order1A = {}
+    order1B = {}
+    for saptkey in saptkeys_:
+        order1A[saptkey] = {}
+        order1B[saptkey] = {}
+        for keyA in fragkeys['A']:
+            val = 0.0
+            for keyB in fragkeys['B']:
+                val += order2[saptkey][keyA][keyB]
+            order1A[saptkey][keyA] = val
+        for keyB in fragkeys['B']:
+            val = 0.0
+            for keyA in fragkeys['A']:
+                val += order2[saptkey][keyA][keyB]
+            order1B[saptkey][keyB] = val
+
+    order0 = {}
+    for saptkey in saptkeys_:
+        val = 0.0
+        for keyA in fragkeys['A']:
+            val += order1A[saptkey][keyA]
+        order0[saptkey] = val
+
+    print('%-9s %-9s ' % ('Frag1', 'Frag2'), end='')
+    for saptkey in saptkeys_:
+        print('%8s ' % (saptkey), end='')
+    print('')
+    for keyA in fragkeys['A']:
+        for keyB in fragkeys['B']:
+            print('%-9s %-9s ' % (keyA, keyB), end='')
+            for saptkey in saptkeys_:
+                print('%8.3f ' % (order2[saptkey][keyA][keyB]), end='')
+            print('')
+
+    for keyA in fragkeys['A']:
+        print('%-9s %-9s ' % (keyA, 'All'), end='')
+        for saptkey in saptkeys_:
+            print('%8.3f ' % (order1A[saptkey][keyA]), end='')
+        print('')
+
+    for keyB in fragkeys['B']:
+        print('%-9s %-9s ' % ('All', keyB), end='')
+        for saptkey in saptkeys_:
+            print('%8.3f ' % (order1B[saptkey][keyB]), end='')
+        print('')
+
+    print('%-9s %-9s ' % ('All', 'All'), end='')
+    for saptkey in saptkeys_:
+        print('%8.3f ' % (order0[saptkey]), end='')
+    print('')
+
+    print('')
+
+def diffOrder2(order2P, order2M):
+
+    vals = {}
+    for key in order2P.keys():
+        vals[key] = {}
+        for keyA in order2P[key].keys():
+            vals[key][keyA] = {}
+            for keyB in order2P[key][keyA].keys():
+                vals[key][keyA][keyB] = order2P[key][keyA][keyB] - order2M[key][keyA][keyB]
+
+    return vals
+
+def computeFsapt(dirname, links5050, completeness = 0.85):
+
+    geom = readXYZ('%s/geom.xyz' % dirname)
+
+    Zs = {}
+    Zs['A'] = readList('%s/ZA.dat' % dirname)
+    Zs['B'] = readList('%s/ZB.dat' % dirname)
+
+    holder = {}
+    holder['A'] = readFrags('%s/fA.dat' % dirname)
+    holder['B'] = readFrags('%s/fB.dat' % dirname)
+
+    fragkeys = {}
+    fragkeys['A'] = holder['A'][1]
+    fragkeys['B'] = holder['B'][1]
+
+    frags = {}
+    frags['A'] = holder['A'][0]
+    frags['B'] = holder['B'][0]
+
+    checkFragments(geom, Zs['A'], frags['A'])
+    checkFragments(geom, Zs['B'], frags['B'])
+
+    Qs = {}
+    Qs['A'] = readBlock('%s/QA.dat' % dirname)
+    Qs['B'] = readBlock('%s/QB.dat' % dirname)
+
+    holder1 = partitionFragments(fragkeys['A'], frags['A'], Zs['A'], Qs['A'], completeness)
+    holder2 = partitionFragments(fragkeys['B'], frags['B'], Zs['B'], Qs['B'], completeness)
+
+    fragkeysr = {}
+    fragkeysr['A'] = fragkeys['A']
+    fragkeysr['B'] = fragkeys['B']
+
+    fragkeys['A'] = holder1[0]
+    fragkeys['B'] = holder2[0]
+
+    frags['A']    = holder1[1]
+    frags['B']    = holder2[1]
+
+    nuclear_ws = {}
+    nuclear_ws['A'] = holder1[2]
+    nuclear_ws['B'] = holder2[2]
+
+    orbital_ws = {}
+    orbital_ws['A'] = holder1[3]
+    orbital_ws['B'] = holder2[3]
+
+    total_ws = {}
+    total_ws['A'] = holder1[4]
+    total_ws['B'] = holder2[4]
+
+    printFrag(geom, Zs['A'], Qs['A'], fragkeys['A'], frags['A'], nuclear_ws['A'], orbital_ws['A'], '%s/fragA.dat' % dirname)
+    printFrag(geom, Zs['B'], Qs['B'], fragkeys['B'], frags['B'], nuclear_ws['B'], orbital_ws['B'], '%s/fragB.dat' % dirname)
+
+    osapt = extractOsaptData(dirname)
+
+    order2  = extractOrder2Fsapt(osapt, total_ws['A'], total_ws['B'])
+    order2r = collapseLinks(order2, frags, Qs, orbital_ws, links5050)
+
+    stuff = {}
+    stuff['order2'] = order2
+    stuff['fragkeys'] = fragkeys
+    stuff['order2r'] = order2r
+    stuff['fragkeysr'] = fragkeysr
+    stuff['frags'] = frags
+    stuff['geom'] = geom
+    return stuff
+
+# => Extra Order-2 Analysis <= #
+
+class PDBAtom:
+
+    def __init__(self, I, Z, x, y, z, T = 0.0):
+
+        key_key        = 'HETATM'
+        key_serial     = I
+        key_name       = Z
+        key_altLoc     = ''
+        key_resName    = '001'
+        key_chainID    = 'A'
+        key_resSeq     = 1
+        key_iCode      = ''
+        key_x          = x
+        key_y          = y
+        key_z          = z
+        key_occupancy  = 1.0
+        key_tempFactor = T
+        key_element    = Z
+        key_charge     = 0
+
+        self.key = key_key.strip()
+        self.serial = int(key_serial)
+        self.name = key_name.strip()
+        self.altLoc = key_altLoc
+        self.resName = key_resName.strip()
+        self.chainID = key_chainID
+        self.resSeq = int(key_resSeq)
+        self.iCode = key_iCode
+        self.x = float(key_x)
+        self.y = float(key_y)
+        self.z = float(key_z)
+        self.occupancy = float(key_occupancy)
+        self.tempFactor = float(key_tempFactor)
+        self.element = key_element.strip()
+        self.charge = int(key_charge)
+
+    def __str__(self):
+
+        if self.charge == 0:
+            chargeStr = ''
+        else:
+            chargeStr = str(abs(self.charge))
+            if self.charge > 0:
+                chargeStr += '+'
+            else:
+                chargeStr += '-'
+
+        return '%-6s%5d %-4s%1s%3s %1s%4d%1s   %8.3f%8.3f%8.3f%6.2f%6.2f          %2s%2s\n' % (self.key, self.serial, self.name, self.altLoc, self.resName, self.chainID, self.resSeq, self.iCode, self.x, self.y, self.z, self.occupancy, self.tempFactor, self.element, chargeStr)
+
+    def xyzLine(self, form = '%8.3f '):
+        contents = '%-2s ' + form + form + form + '\n'
+        return contents % (self.element, self.x, self.y, self.z)
+
+    def frozen(self):
+        return atom_data_[self.element.upper()][2]
+
+class PDB:
+
+    def __init__(self, atoms, name):
+
+        self.name = name
+        self.atoms = atoms
+
+    @classmethod
+    def fromGeom(cls,geom):
+
+        name = ''
+        atoms = []
+
+        for A in range(len(geom)):
+            atoms.append(PDBAtom(
+                A+1,
+                geom[A][0],
+                geom[A][1],
+                geom[A][2],
+                geom[A][3],
+                ))
+
+        return cls(atoms, name)
+
+    def __str__(self):
+
+        strval = '  --> PDB Object %s <--\n\n' % (self.name)
+        for atom in self.atoms:
+            strval += str(atom)
+        strval += '\n'
+        return strval
+
+    def write(self,filename):
+        fh = open(filename,'w')
+        for atom in self.atoms:
+            fh.write('%s' % str(atom))
+        fh.close()
+
+    def charge(self):
+        charge = 0
+        for atom in self.atoms:
+            charge += atom.charge
+        return charge
+
+    def frozen(self):
+        frozen = 0
+        for atom in self.atoms:
+            frozen += atom.frozen()
+        return frozen
+
+def printOrder1(dirname, order2, pdb, frags, reA = r'\S+', reB = r'\S+'):
+
+    for saptkey in saptkeys_:
+        E = [0.0 for x in pdb.atoms]
+        for keyA in order2[saptkey].keys():
+            if not re.match(reA, keyA):
+                continue
+            for keyB in order2[saptkey][keyA].keys():
+                if not re.match(reB, keyB):
+                    continue
+                val = order2[saptkey][keyA][keyB]
+                for k in frags['A'][keyA]:
+                    E[k] += val
+                for l in frags['B'][keyB]:
+                    E[l] += val
+
+        pdb2 = copy.deepcopy(pdb)
+        for A in range(len(pdb.atoms)):
+            pdb2.atoms[A].tempFactor = E[A]
+        pdb2.write('%s/%s.pdb' % (dirname, saptkey))
+
+# => Driver Code <= #
+
+if __name__ == '__main__':
+
+    # > Working Dirname < #
+
+    if len(sys.argv) == 1:
+        dirname = '.'
+    elif len(sys.argv) == 2:
+        dirname = sys.argv[1]
+    else:
+        raise Exception('Usage: fsapt.py [dirname]')
+
+    # > Order-2 Analysis < #
+
+    fh = open('%s/fsapt.dat' % dirname, 'w')
+    fh, sys.stdout = sys.stdout, fh
+
+    print('  ==> F-ISAPT: Links by Charge <==\n')
+    stuff = computeFsapt(dirname, False)
+    print('   => Full Analysis <=\n')
+    printOrder2(stuff['order2'], stuff['fragkeys'])
+    print('   => Reduced Analysis <=\n')
+    printOrder2(stuff['order2r'], stuff['fragkeysr'])
+
+    print('  ==> F-ISAPT: Links 50-50 <==\n')
+    stuff = computeFsapt(dirname, True)
+    print('   => Full Analysis <=\n')
+    printOrder2(stuff['order2'], stuff['fragkeys'])
+    print('   => Reduced Analysis <=\n')
+    printOrder2(stuff['order2r'], stuff['fragkeysr'])
+
+    fh, sys.stdout = sys.stdout, fh
+    fh.close()
+
+    # > Order-1 PBD Files < #
+
+    pdb = PDB.fromGeom(stuff['geom'])
+    printOrder1(dirname, stuff['order2r'], pdb, stuff['frags'])
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/Rendering_Orbital_in_PyMol/frontier.py",".py","519","17","from pymol import *
+cmd.load(""Psi_a_126_126-A.cube"")
+cmd.load(""Psi_b_126_126-A.cube"")
+cmd.load(""Psi_a_127_127-A.cube"")
+cmd.load(""Psi_b_127_127-A.cube"")
+cmd.load(""target.mol"")
+cmd.isomesh(""HOMO_A"", ""Psi_a_126_126-A"", -0.02)
+cmd.isomesh(""HOMO_B"", ""Psi_b_126_126-A"", 0.02)
+cmd.isomesh(""LUMO_A"", ""Psi_a_127_127-A"", 0.02)
+cmd.isomesh(""LUMO_B"", ""Psi_b_127_127-A"", -0.02)
+cmd.color(""blue"", ""HOMO_A"")
+cmd.color(""red"", ""HOMO_B"")
+cmd.color(""blue"", ""LUMO_A"")
+cmd.color(""red"", ""LUMO_B"")
+cmd.disable(""LUMO_A"")
+cmd.disable(""LUMO_B"")
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","examples/Rendering_Orbital_in_PyMol/Visualize_MO_in_PyMol.ipynb",".ipynb","344546","135","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from rdkit.Chem import rdMolTransforms\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole\n"",
+    ""from psikit import Psikit\n"",
+    ""from IPython.display import Image, display_png""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x130cb6ad0>""
+      ]
+     },
+     ""execution_count"": 4,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk = Psikit()\n"",
+    ""smi = 'CCCC1=NN(C)C2=C1NC(=NC2=O)C1=C(OCC)C=CC(=C1)S(=O)(=O)N1CCN(C)CC1' # Sildenafil\n"",
+    ""pk.read_from_smiles(smi)\n"",
+    ""pk.mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-1850.9895916913435""
+      ]
+     },
+     ""execution_count"": 5,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.energy(basis_sets=\""scf/sto-3g\"") # It is better to optimize although it is single point calculation due to time""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk.save_frontier()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {},
+   ""source"": [
+    ""## PyMol\n"",
+    ""\n"",
+    ""1. open PyMol\n"",
+    ""2. run \""frontier.py\"" from File -> Run Script""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n""
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""display_png(Image(\""SIL_HOMO.png\""))""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 10,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n""
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""display_png(Image(\""SIL_LUMO.png\""))""
+   ]
+  }
+ ],
+ ""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"": ""3.7.3""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","examples/CHARGE_COMPARISON/charge_comparison.ipynb",".ipynb","84034","456","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""import os\n"",
+    ""import sys\n"",
+    ""import pandas as pd\n"",
+    ""import numpy as np\n"",
+    ""from rdkit import Chem\n"",
+    ""from rdkit.Chem.Draw import IPythonConsole\n"",
+    ""sys.path.append('/Users/iwatobipen/develop/chemoenv/psikit/psikit/')""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""from psikit import Psikit""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk = Psikit()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""pk.read_from_smiles('OC(=O)c1cnccc1')""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizer: Optimization complete!\n""
+     ]
+    },
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""-434.31618866070147""
+      ]
+     },
+     ""execution_count"": 5,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.optimize()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<rdkit.Chem.rdchem.Mol at 0x113272688>""
+      ]
+     },
+     ""execution_count"": 6,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""pk.mol""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""[-0.58253784  0.69011262 -0.50822101 -0.21638198  0.3090496  -0.58764823\n"",
+      ""  0.35116267 -0.29441912  0.00119611  0.40735602  0.06348114  0.06372305\n"",
+      ""  0.15143447  0.15169252]\n"",
+      ""[-0.60451003  0.82714117 -0.53442304 -0.24069704  0.13815438 -0.54392709\n"",
+      ""  0.1459902  -0.21533841 -0.04066254  0.35929545  0.15532705  0.16754771\n"",
+      ""  0.17394675  0.21215544]\n"",
+      ""[-0.27235014  0.34136197 -0.32165514 -0.12349265  0.00183538 -0.19122938\n"",
+      ""  0.02154698 -0.14202575 -0.0189029   0.22605628  0.10476005  0.11484402\n"",
+      ""  0.11953793  0.13971336]\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""#caculate chages\n"",
+    ""print(pk.calc_resp_charges())\n"",
+    ""print(pk.calc_mulliken_charges())\n"",
+    ""print(pk.calc_lowdin_charges())""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""mol = pk.mol\n"",
+    ""atoms = mol.GetAtoms()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""data = {'Symbol':[atom.GetSymbol() for atom in atoms],\n"",
+    ""        'RESP': [float(atom.GetProp('RESP')) for atom in atoms],\n"",
+    ""        'MULLIKEN': [float(atom.GetProp('MULLIKEN')) for atom in atoms],\n"",
+    ""       'LOWDIN':  [float(atom.GetProp('LOWDIN')) for atom in atoms]}""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 10,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""df = pd.DataFrame(data)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 11,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/html"": [
+       ""<div>\n"",
+       ""<style scoped>\n"",
+       ""    .dataframe tbody tr th:only-of-type {\n"",
+       ""        vertical-align: middle;\n"",
+       ""    }\n"",
+       ""\n"",
+       ""    .dataframe tbody tr th {\n"",
+       ""        vertical-align: top;\n"",
+       ""    }\n"",
+       ""\n"",
+       ""    .dataframe thead th {\n"",
+       ""        text-align: right;\n"",
+       ""    }\n"",
+       ""</style>\n"",
+       ""<table border=\""1\"" class=\""dataframe\"">\n"",
+       ""  <thead>\n"",
+       ""    <tr style=\""text-align: right;\"">\n"",
+       ""      <th></th>\n"",
+       ""      <th>LOWDIN</th>\n"",
+       ""      <th>MULLIKEN</th>\n"",
+       ""      <th>RESP</th>\n"",
+       ""      <th>Symbol</th>\n"",
+       ""    </tr>\n"",
+       ""  </thead>\n"",
+       ""  <tbody>\n"",
+       ""    <tr>\n"",
+       ""      <th>0</th>\n"",
+       ""      <td>-0.272</td>\n"",
+       ""      <td>-0.605</td>\n"",
+       ""      <td>-0.583</td>\n"",
+       ""      <td>O</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>1</th>\n"",
+       ""      <td>0.341</td>\n"",
+       ""      <td>0.827</td>\n"",
+       ""      <td>0.690</td>\n"",
+       ""      <td>C</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>2</th>\n"",
+       ""      <td>-0.322</td>\n"",
+       ""      <td>-0.534</td>\n"",
+       ""      <td>-0.508</td>\n"",
+       ""      <td>O</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>3</th>\n"",
+       ""      <td>-0.123</td>\n"",
+       ""      <td>-0.241</td>\n"",
+       ""      <td>-0.216</td>\n"",
+       ""      <td>C</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>4</th>\n"",
+       ""      <td>0.002</td>\n"",
+       ""      <td>0.138</td>\n"",
+       ""      <td>0.309</td>\n"",
+       ""      <td>C</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>5</th>\n"",
+       ""      <td>-0.191</td>\n"",
+       ""      <td>-0.544</td>\n"",
+       ""      <td>-0.588</td>\n"",
+       ""      <td>N</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>6</th>\n"",
+       ""      <td>0.022</td>\n"",
+       ""      <td>0.146</td>\n"",
+       ""      <td>0.351</td>\n"",
+       ""      <td>C</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>7</th>\n"",
+       ""      <td>-0.142</td>\n"",
+       ""      <td>-0.215</td>\n"",
+       ""      <td>-0.294</td>\n"",
+       ""      <td>C</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>8</th>\n"",
+       ""      <td>-0.019</td>\n"",
+       ""      <td>-0.041</td>\n"",
+       ""      <td>0.001</td>\n"",
+       ""      <td>C</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>9</th>\n"",
+       ""      <td>0.226</td>\n"",
+       ""      <td>0.359</td>\n"",
+       ""      <td>0.407</td>\n"",
+       ""      <td>H</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>10</th>\n"",
+       ""      <td>0.105</td>\n"",
+       ""      <td>0.155</td>\n"",
+       ""      <td>0.063</td>\n"",
+       ""      <td>H</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>11</th>\n"",
+       ""      <td>0.115</td>\n"",
+       ""      <td>0.168</td>\n"",
+       ""      <td>0.064</td>\n"",
+       ""      <td>H</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>12</th>\n"",
+       ""      <td>0.120</td>\n"",
+       ""      <td>0.174</td>\n"",
+       ""      <td>0.151</td>\n"",
+       ""      <td>H</td>\n"",
+       ""    </tr>\n"",
+       ""    <tr>\n"",
+       ""      <th>13</th>\n"",
+       ""      <td>0.140</td>\n"",
+       ""      <td>0.212</td>\n"",
+       ""      <td>0.152</td>\n"",
+       ""      <td>H</td>\n"",
+       ""    </tr>\n"",
+       ""  </tbody>\n"",
+       ""</table>\n"",
+       ""</div>""
+      ],
+      ""text/plain"": [
+       ""    LOWDIN  MULLIKEN   RESP Symbol\n"",
+       ""0   -0.272    -0.605 -0.583      O\n"",
+       ""1    0.341     0.827  0.690      C\n"",
+       ""2   -0.322    -0.534 -0.508      O\n"",
+       ""3   -0.123    -0.241 -0.216      C\n"",
+       ""4    0.002     0.138  0.309      C\n"",
+       ""5   -0.191    -0.544 -0.588      N\n"",
+       ""6    0.022     0.146  0.351      C\n"",
+       ""7   -0.142    -0.215 -0.294      C\n"",
+       ""8   -0.019    -0.041  0.001      C\n"",
+       ""9    0.226     0.359  0.407      H\n"",
+       ""10   0.105     0.155  0.063      H\n"",
+       ""11   0.115     0.168  0.064      H\n"",
+       ""12   0.120     0.174  0.151      H\n"",
+       ""13   0.140     0.212  0.152      H""
+      ]
+     },
+     ""execution_count"": 11,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    }
+   ],
+   ""source"": [
+    ""df.round(3)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 12,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""%matplotlib inline\n"",
+    ""import matplotlib.pyplot as plt""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 13,
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""plt.style.use('ggplot')""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 14,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""[<matplotlib.lines.Line2D at 0x13bf93940>]""
+      ]
+     },
+     ""execution_count"": 14,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    },
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""# RESP charge and MULLIKEN charge shows high correlation\n"",
+    ""plt.scatter(df.RESP, df.MULLIKEN)\n"",
+    ""plt.xlabel('RESP')\n"",
+    ""plt.ylabel('MULLIKEN')\n"",
+    ""plt.plot(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01), c='b')""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 15,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""[<matplotlib.lines.Line2D at 0x13c0db8d0>]""
+      ]
+     },
+     ""execution_count"": 15,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    },
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""plt.scatter(df.RESP, df.LOWDIN)\n"",
+    ""plt.xlabel('RESP')\n"",
+    ""plt.ylabel('LOWDIN')\n"",
+    ""plt.plot(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01), c='b')""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 16,
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""[<matplotlib.lines.Line2D at 0x13c1ef1d0>]""
+      ]
+     },
+     ""execution_count"": 16,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    },
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""plt.scatter(df.MULLIKEN, df.LOWDIN)\n"",
+    ""plt.xlabel('MULLIKEN')\n"",
+    ""plt.ylabel('LOWDIN')\n"",
+    ""plt.plot(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01), c='b')""
+   ]
+  }
+ ],
+ ""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"": ""3.6.8""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 2
+}
+","Unknown"
+"Homo-Lumo","Mishima-syk/psikit","tests/test_multiplicity.py",".py","535","23","import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+from psikit import Psikit
+from psikit import Sapt
+import rdkit
+import pytest
+
+
+def test_mul1_energy():
+    pk = Psikit()
+    pk.read_from_smiles(""CO"")
+    energy = pk.energy(multiplicity=1)
+    assert pytest.approx(-115.0421871159, 0.000000005) == energy
+
+@pytest.mark.skip
+def test_mul3_energy():
+    pk = Psikit()
+    pk.read_from_smiles(""CO"")
+    energy = pk.energy(multiplicity=4)
+    assert pytest.approx(-40.19996313, 0.000000005) == energy
+
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","tests/test_init.py",".py","929","37","import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+from psikit import Psikit
+from psikit import Sapt
+import rdkit
+import pytest
+
+
+def test_read_from_smiles():
+    pk = Psikit()
+    pk.read_from_smiles(""C"")
+    assert type(pk.mol) is rdkit.Chem.rdchem.Mol
+
+def test_read_from_molfiles():
+    pk = Psikit()
+    pk.read_from_molfile(""tests/test.mol"")
+    assert type(pk.mol) is rdkit.Chem.rdchem.Mol
+
+def test_energy():
+    pk = Psikit()
+    pk.read_from_smiles(""C"")
+    energy = pk.energy()
+    assert pytest.approx(-40.19996313, 0.000000005) == energy
+
+def test_energy_sto3g():
+    pk = Psikit()
+    pk.read_from_smiles(""C"")
+    energy = pk.energy(basis_sets=""scf/sto-3g"")
+    assert pytest.approx(-39.724747932, 0.000000005) == energy
+
+def test_optimize():
+    pk = Psikit()
+    pk.read_from_smiles(""C"")
+    energy = pk.optimize()
+    assert pytest.approx(-40.20171733, 0.000000005) == energy
+","Python"
+"Homo-Lumo","Mishima-syk/psikit","tests/test_sapt.py",".py","821","27","import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+from psikit import Psikit
+from psikit.sapt import Sapt
+import rdkit
+import pytest
+
+def test_sapt():
+    sapt = Sapt()
+    sapt.psi4.core.clean()
+    sapt.monomer1_from_molfile('tests/saptex/water1.mol')
+    sapt.monomer2_from_molfile('tests/saptex/water2.mol')
+    sapt.make_dimer()
+    sapt0, Exch100, Elst10, Disp200, ExchDisp20, Ind20r, ExchInd20r = sapt.run_sapt()
+    assert pytest.approx(-0.007335250653651525, 0.000000005) == sapt0
+
+def test_fisapt():
+    sapt = Sapt()
+    sapt.psi4.core.clean()
+    sapt.monomer1_from_molfile('tests/saptex/water1.mol')
+    sapt.monomer2_from_molfile('tests/saptex/water2.mol')
+    sapt.make_dimer()
+    e = sapt.run_fisapt()
+    assert pytest.approx(-0.00822262799944366, 0.000000005) == e
+
+","Python"