EH_notebook_201020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An Extended Huckel notebook for generating MO diagrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run all cells to load the codeblocks, then run/re-run the programme blocks at the bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>$(\"div.input\").hide()</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d918143809148409100ddbe388a5fc7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ToggleButton(value=False, description='Show code')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "javascript_functions = {False: \"hide()\", True: \"show()\"}\n",
    "button_descriptions  = {False: \"Show code\", True: \"Hide code\"}\n",
    "\n",
    "\n",
    "def toggle_code(state):\n",
    "\n",
    "    \"\"\"\n",
    "    Toggles the JavaScript show()/hide() function on the div.input element.\n",
    "    \"\"\"\n",
    "\n",
    "    output_string = \"<script>$(\\\"div.input\\\").{}</script>\"\n",
    "    output_args   = (javascript_functions[state],)\n",
    "    output        = output_string.format(*output_args)\n",
    "\n",
    "    display(HTML(output))\n",
    "\n",
    "\n",
    "def button_action(value):\n",
    "\n",
    "    \"\"\"\n",
    "    Calls the toggle_code function and updates the button description.\n",
    "    \"\"\"\n",
    "\n",
    "    state = value.new\n",
    "\n",
    "    toggle_code(state)\n",
    "\n",
    "    value.owner.description = button_descriptions[state]\n",
    "\n",
    "\n",
    "state = False\n",
    "toggle_code(state)\n",
    "\n",
    "button = widgets.ToggleButton(state, description = button_descriptions[state])\n",
    "button.observe(button_action, \"value\")\n",
    "\n",
    "display(button)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hideCode": true
   },
   "outputs": [],
   "source": [
    "from scipy import linalg  #scipy.linalg needed to solve generalised eigenvalue problem\n",
    "import math as m\n",
    "import numpy as np   #numpy package contains various numerical routines\n",
    "import os\n",
    "import copy as copy\n",
    "import time\n",
    "import sys as sys\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import itertools\n",
    "import warnings    #turn off warnings (these can occur in contour plots if plot plane is a node)\n",
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "#some defaults\n",
    "localtime = time.asctime( time.localtime(time.time()) )\n",
    "A0=0.52917721092;dist_cutoff=6.0/A0;WH=1.75;\n",
    "no_symmetry=False;separate_symmetry=True;plt.close(\"all\");\n",
    "\n",
    "\n",
    "##Parameters for various standard calculations\n",
    "\n",
    "#these are Hoffmann's parameters - appropriate for molecular calculations but will lead to convergence issues\n",
    "#if used in the solid state\n",
    "#note the tuple (exponent,coefficient) as the last entry. For s and p this is redundant for Hoffmann parameters\n",
    "#as coefficient is always 1, but it is in place here to be compatible with Cerda's parameters where the\n",
    "#p orbitals are sometimes double zeta as well as d.\n",
    "Hoffmann={1:[1,0,-13.6,(1.3,1.)],                                           #H\n",
    "       2:[2,0,-23.4,(1.688,1.)],                                              #He\n",
    "       3:[[2,0,-5.4,(0.650,1.)],[2,1,-3.5,(0.65,1.)]],                             #Li\n",
    "       4:[[2,0,-10.0,(0.975,1.)],[2,1,-6.0,(0.975,1.)]],                           #Be\n",
    "       5:[[2,0,-15.2,(1.300,1.)],[2,1,-8.5,(1.300,1.)]],                           #B\n",
    "       6:[[2,0,-21.4,(1.625,1.)],[2,1,-11.4,(1.625,1.)]],                          #C\n",
    "       7:[[2,0,-26.0,(2.140,1.)],[2,1,-13.4,(1.950,1.)]],                          #N\n",
    "       8:[[2,0,-32.3,(2.275,1.)],[2,1,-14.8,(2.275,1.)]],                          #O\n",
    "       9:[[2,0,-40.0,(2.425,1.)],[2,1,-18.1,(2.425,1.)]],                          #F\n",
    "       10:[[2,0,-43.2,(2.879,1.)],[2,1,-20.0,(2.879,1.)]],                         #Ne\n",
    "       11:[[3,0,-5.1,(0.733,1.)],[3,1,-3.0,(0.733,1.)]],                           #Na\n",
    "       12:[[3,0,-9.0,(1.100,1.)],[3,1,-4.5,(1.100,1.)]],                           #Mg\n",
    "       13:[[3,0,-12.3,(1.167,1.)],[3,1,-6.5,(1.167,1.)]],                          #Al\n",
    "       14:[[3,0,-17.3,(1.383,1.)],[3,1,-9.2,(1.383,1.)]],                          #Si\n",
    "       15:[[3,0,-18.6,(1.750,1.)],[3,1,-14.0,(1.600,1.)]],                         #P\n",
    "       16:[[3,0,-20.0,(2.122,1.)],[3,1,-13.3,(1.827,1.)]],                         #S\n",
    "       17:[[3,0,-26.3,(2.183,1.)],[3,1,-14.2,(1.733,1.)]],                         #Cl\n",
    "       18:[[3,0,-27.63,(2.585,1.)],[3,1,-15.76,(2.255,1.)]],                       #Ar\n",
    "       19:[[4,0,-4.34,(0.874,1.)],[4,1,-2.73,(0.874,1.)]],                         #K\n",
    "       20:[[4,0,-7.0,(1.2,1.)],[4,1,-4.0,(1.2,1.)]],                               #Ca\n",
    "       21:[[4,0,-8.87,(1.3,1.)],[4,1,-2.75,(1.3,1.)],[3,2,-8.51,((4.35,0.422783),(1.70,0.72757))]],            #Sc 3d\n",
    "       22:[[4,0,-8.97,(1.075,1.)],[4,1,-5.44,(0.675,1.)],[3,2,-10.81,((4.55,0.420607),(1.40,0.783913))]],       #Ti 3d\n",
    "       23:[[4,0,-8.81,(1.3,1.)],[4,1,-5.52,(1.30,1.)],[3,2,-11.00,((4.75,0.475509),(1.70,0.705213))]],     #     #V 3d\n",
    "       24:[[4,0,-8.66,(1.7,1.)],[4,1,-5.24,(1.7,1.)],[3,2,-11.22,((4.95,0.505792),(1.80,0.674723))]],           #Cr 3d\n",
    "       25:[[4,0,-9.75,(1.8,1.)],[4,1,-5.89,(1.80,1.)],[3,2,-11.67,((5.15,0.513906),(1.70,0.692909))]],          #Mn 3d\n",
    "       26:[[4,0,-9.10,(1.9,1.)],[4,1,-5.32,(1.90,1.)],[3,2,-12.60,((5.35,0.5505),(2.00,0.626))]],         #Fe 3d\n",
    "       27:[[4,0,-9.21,(2.00,1.)],[4,1,-5.29,(2.00,1.)],[3,2,-13.18,((5.55,0.567865),(2.10,0.605856))]],         #Co 3d\n",
    "       28:[[4,0,-9.17,(1.825,1.)],[4,1,-5.15,(1.125,1.)],[3,2,-13.49,((5.75,0.549306),(2.30,0.60817))]],        #Ni 3d\n",
    "       29:[[4,0,-11.40,(2.20,1.)],[4,1,-6.06,(2.20,1.)],[3,2,-14.00,((5.95,0.593322),(2.30,0.574421))]],        #Cu 3d\n",
    "       30:[[4,0,-12.96,(2.37,1.)],[4,1,-9.23,(1.63,0.42)],[3,2,-18.03,((3.565,0.87781),(2.01,0.1554))]],                          #Zn 4p\n",
    "       31:[[4,0,-14.58,(1.77,1.)],[4,1,-6.75,(1.55,1.)]],                          #Ga 4p\n",
    "       32:[[4,0,-16.00,(2.16,1.)],[4,1,-9.00,(1.85,1.)]],                          #Ge 4p\n",
    "       33:[[4,0,-16.22,(2.23,1.)],[4,1,-12.16,(1.89,1.)]],                         #As 4p\n",
    "       34:[[4,0,-20.50,(2.44,1.)],[4,1,-14.40,(2.07,1.)]],                         #Se 4p\n",
    "       35:[[4,0,-22.07,(2.588,1.)],[4,1,-13.10,(2.131,1.)]],                       #Br 4p\n",
    "       36:[[4,0,-24.36,(2.829,1.)],[4,1,-13.99,(2.442,1.)]],                       #Kr 4p\n",
    "       37:[[5,0,-4.180,(0.997,1.)],[5,1,-2.60,(0.997,1.)]],                        #Rb 5s\n",
    "       38:[[5,0,-6.620,(1.214,1.)],[5,1,-3.92,(1.214,1.)]],                        #Sr 5s\n",
    "       39:[[5,0,-5.337,(1.279,1.)],[5,1,-3.515,(1.079,1.)],[4,2,-6.799,((2.554,0.601963),(1.068,0.577964))]],          #Y  4d  Muller (nb energies rather diff from Hoffman)\n",
    "       40:[[5,0,-9.87,(1.817,1.)],[5,1,-6.76,(1.776,1.)],[4,2,-11.18,((3.835,0.622416),(1.505,0.578216))]],      #Zr 4d\n",
    "       41:[[5,0,-10.10,(1.890,1.)],[5,1,-6.86,(1.85,1.)],[4,2,-12.10,((4.08,0.640101),(1.64,0.551601))]],\n",
    "       42:[[5,0,-8.340,(1.96,1.)],[5,1,-5.24,(1.90,1.)],[4,2,-10.50,((4.54,0.589879),(1.9,0.589879))]],        #Mo 4d\n",
    "       43:[[5,0,-10.07,(2.018,1.)],[5,1,-5.40,(1.984,1.)],[4,2,-12.82,((4.9,0.5715),(2.094,0.6012))]],      #Tc 4d\n",
    "       44:[[5,0,-10.40,(2.08,1.)],[5,1,-6.87,(2.04,1.)],[4,2,-14.90,((5.38,0.534242),(2.3,0.636789))]],        #Ru 4d\n",
    "       45:[[5,0,-8.090,(2.135,1.)],[5,1,-4.57,(2.10,1.)],[4,2,-12.50,((4.29,0.580698),(1.97,0.568498))]],       #Rh 4d\n",
    "       46:[[5,0,-7.320,(2.19,1.)],[5,1,-3.75,(2.152,1.)],[4,2,-12.02,((5.983,0.526436),(2.613,0.637334))]],      #Pd 4d\n",
    "       47:[[5,0,-7.054,(1.662,1.)],[5,1,-4.133,(1.397,1.)],[4,2,-19.24,((3.912,0.824441),(1.545,0.328777))]],    #Ag 4d  taken from Muller parameterisation in ATK\n",
    "       48:[[5,0,-7.242,(1.706,1.)],[5,1,-4.196,(1.432,1.)],[4,2,-20.78,((4.094,0.823957),(1.64,0.324983))]],      #Cd  Muller                                               #Cd 4d\n",
    "       78:[[6,0,-9.077,(2.554,1.)],[6,1,-5.475,(2.554,1.)],[5,2,-12.59,((6.013,0.633378),(2.696,0.551281))]],\n",
    "       79:[[6,0,-10.92,(2.6020,1.)],[6,1,-5.50,(2.5040,1.)],[5,2,-15.07,((6.163,0.644177),(2.794,0.535576))]],   #nb p exponent slightly different from literature to avoid singularities in linear chain\n",
    "       179:[6,0,-10.92,(2.6020,1.)], #special Au 6s only for electrodes\n",
    "       } \n",
    "\n",
    "#Cerda's parameters specifically for solid state\n",
    "#Accurate and transferable EHT tight binding parameters, PRB, 2000, 61, 7965. J Cerda and F. Soria\n",
    "#(only available for the TMs and selected main group). Otherwise I have used the Hoffmann parameters\n",
    "#note that coefficients < 1.0 for some of the s and p states even though there is only one component. \n",
    "#This is justified because they are in fact 'double zeta' in character even though only one component is given. \n",
    "#However the other (more contracted) component makes no contribution to diatomic overlap as it is too contracted,\n",
    "#so its coefficient is in any case irrelevant. So it is ignored here. \n",
    "\n",
    "Cerda={1:[1,0,-17.84,(0.87223,0.50494)],                                           #H\n",
    "       2:[2,0,-23.4,(1.688,1.)],                                              #He\n",
    "       3:[[2,0,-5.4,(0.650,1.)],[2,1,-3.5,(0.65,1.)]],                             #Li\n",
    "       4:[[2,0,-10.0,(0.975,1.)],[2,1,-6.0,(0.975,1.)]],                           #Be\n",
    "       5:[[2,0,-21.7174,(1.60645,0.66811)],[2,1,-14.1676,(1.63627,0.99022)]],                           #B                          #C\n",
    "       6:[[2,0,-19.8892,(2.0249,0.76422)],[2,1,-13.08,((2.17687,0.7389),(1.269,0.177))]],  \n",
    "       7:[[2,0,-26.4216,(2.74251,1.)],[2,1,-17.0156,(2.26145,1.)]],                          #N\n",
    "       8:[[2,0,-27.06782,(2.56634,1.)],[2,1,-13.25162,(2.0,1.)]],                          #O\n",
    "       9:[[2,0,-40.0,(2.425,1.)],[2,1,-18.1,(2.425,1.)]],                          #F\n",
    "       10:[[2,0,-43.2,(2.879,1.)],[2,1,-20.0,(2.879,1.)]],                         #Ne\n",
    "       11:[[3,0,-5.1,(1.40,0.7)],[3,1,-3.0,(1.15,0.75)]],                           #Na\n",
    "       12:[[3,0,-9.0,(1.50,0.7)],[3,1,-4.5,(1.20,0.75)]],                           #Mg\n",
    "       13:[[3,0,-13.133,(1.657,0.749)],[3,1,-8.525,((1.312,0.750),(4.679,0.492))]], #Al #,[3,2,-5.141,((4.245,0.717),(0.860,0.609))]],                         #Al\n",
    "       #14:[[3,0,-18.137,(1.864,0.720)],[3,1,-11.277,((1.813,0.705),(1.470,0.303))],[3,2,-5.336,((1.705,0.485),(0.675,0.671))]],\n",
    "       14:[[3,0,-18.029,(1.780,0.657)],[3,1,-11.820,(1.614,0.818)]],                       #Si\n",
    "       15:[[3,0,-20.24,(2.131,0.769)],[3,1,-12.437,(1.859,0.633)]],#,(2.479,0.384))]],                         #P\n",
    "       16:[[3,0,-20.0,(2.122,0.8)],[3,1,-13.3,(1.827,0.70)]],                         #S\n",
    "       17:[[3,0,-26.3,(2.183,1.)],[3,1,-14.2,(1.733,1.)]],                         #Cl\n",
    "       18:[[3,0,-27.63,(2.585,1.)],[3,1,-15.76,(2.255,1.)]],                       #Ar\n",
    "       19:[[4,0,-4.34,(0.874,0.65)],[4,1,-2.73,(0.874,0.65)]],                         #K  #changed weights from 1 to 0.65 (K not given in Cerda's tables)\n",
    "       20:[[4,0,-7.0,(1.2,1.)],[4,1,-4.0,(1.2,1.)]],                               #Ca\n",
    "       21:[[4,0,-8.419,(1.325,0.575)],[4,1,-5.989,(1.111,0.624)],[3,2,-8.391,((4.172,0.690),(1.332,0.527))]],            #Sc 3d\n",
    "       22:[[4,0,-7.879,(1.337,0.707)],[4,1,-5.950,(1.135,0.617)],[3,2,-8.943,((4.760,0.695),(1.464,0.532))]],       #Ti 3d\n",
    "       23:[[4,0,-8.40,(1.37,0.72)],[4,1,-6.1,(1.18,0.620)],[3,2,-9.8,((4.70,0.70),(1.56,0.52))]], #V 3d nb V not given in Cerda's paper - I have interpolated between Ti and Cr \n",
    "       24:[[4,0,-8.492,(1.426,0.753)],[4,1,-6.269,(1.210,0.632)],[3,2,-10.635,((4.688,0.683),(1.676,0.502))]],           #Cr 3d\n",
    "       25:[[4,0,-8.906,(1.468,0.725)],[4,1,-6.630,(1.233,0.625)],[3,2,-11.162,((4.534,0.720),(1.683,0.447))]],          #Mn 3d\n",
    "       26:[[4,0,-8.994,(1.594,0.777)],[4,1,-6.177,(1.315,0.658)],[3,2,-11.323,((4.845,0.730),(1.755,0.442))]],         #Fe 3d\n",
    "       27:[[4,0,-9.543,(1.561,0.665)],[4,1,-6.685,(1.282,0.616)],[3,2,-11.689,((4.765,0.769),(1.724,0.393))]],         #Co 3d\n",
    "       28:[[4,0,-9.523,(1.616,0.691)],[4,1,-6.553,(1.307,0.630)],[3,2,-11.744,((5.238,0.785),(1.775,0.392))]],        #Ni 3d\n",
    "       29:[[4,0,-10.563,(1.704,0.614)],[4,1,-6.78,(1.340,0.648)],[3,2,-12.896,((6.770,0.842),(1.855,0.367))]],        #Cu 3d\n",
    "       30:[[4,0,-12.96,(2.366,1.)],[4,1,-9.23,(1.63,0.4167)]],                          #Zn 4p\n",
    "       31:[[4,0,-17.629,(2.041,0.662)],[4,1,-10.766,((5.835,0.596),(1.616,0.705))]],   #Ga 4p \n",
    "       32:[[4,0,-16.00,(2.16,1.)],[4,1,-9.00,(1.85,1.)]],                          #Ge 4p\n",
    "       33:[[4,0,-21.764,(2.344,0.634)],[4,1,-12.822,((7.588,0.539),(2.102,0.753))]],                        #As 4p\n",
    "       34:[[4,0,-20.50,(2.44,1.)],[4,1,-14.40,(2.07,1.)]],                         #Se 4p\n",
    "       35:[[4,0,-22.07,(2.588,1.)],[4,1,-13.10,(2.131,1.)]],                       #Br 4p\n",
    "       36:[[4,0,-24.36,(2.829,1.)],[4,1,-13.99,(2.442,1.)]],                       #Kr 4p\n",
    "       37:[[5,0,-4.180,(0.997,1.)],[5,1,-2.60,(0.997,1.)]],                        #Rb 5s\n",
    "       38:[[5,0,-7.0,(1.50,0.69)],[5,1,-3.0,(1.16,0.50)]],                        #Sr 5s\n",
    "       39:[[5,0,-7.8,(1.50,0.69)],[5,1,-3.515,(1.20,0.50)],[4,2,-6.799,((4.20,0.60),(1.60,0.56))]],          #Y  4d  extrapolated from Zr\n",
    "       40:[[5,0,-8.017,(1.539,0.706)],[5,1,-6.033,(1.263,0.530)],[4,2,-9.022,((4.478,0.622416),(1.646,0.578216))]],      #Zr 4d\n",
    "       41:[[5,0,-7.768,(1.615,0.772)],[5,1,-5.246,(1.285,0.602)],[4,2,-9.318,((4.335,0.640101),(1.761,0.551601))]],       #Nb 4d\n",
    "       42:[[5,0,-8.365,(1.662,0.789)],[5,1,-5.666,(1.334,0.638)],[4,2,-10.855,((4.693,0.589879),(1.900,0.589879))]],        #Mo 4d\n",
    "       43:[[5,0,-8.406,(1.647,0.803)],[5,1,-5.804,(1.365,0.623)],[4,2,-11.386,((4.870,0.5715),(1.962,0.6012))]],      #Tc 4d\n",
    "       44:[[5,0,-8.953,(1.729,0.769)],[5,1,-5.778,(1.408,0.667)],[4,2,-12.352,((5.500,0.534242),(2.048,0.636789))]],        #Ru 4d\n",
    "       45:[[5,0,-8.412,(1.697,0.786)],[5,1,-5.854,(1.415,0.638)],[4,2,-12.388,((4.857,0.580698),(2.042,0.568498))]],       #Rh 4d\n",
    "       46:[[5,0,-8.781,(1.816,0.741)],[5,1,-5.627,(1.452,0.695)],[4,2,-12.346,((6.622,0.526436),(2.120,0.637334))]],      #Pd 4d\n",
    "       47:[[5,0,-9.925,(1.846,0.589)],[5,1,-6.437,(1.475,0.610)],[4,2,-14.705,((4.907,0.824441),(2.076,0.328777))]],    #Ag 4d  \n",
    "       48:[[5,0,-10.743,(1.703,0.570)],[5,1,-7.779,(1.281,0.508)],[5,2,-6.441,((1.237,0.453),(1.,0.0))]],      #Cd                  #note Cd 5d, not 4d as in Hoffmann params. Need to change core electrons\n",
    "       74:[[6,0,-10.959,(2.216,0.673)],[6,1,-5.944,(1.642,0.603)],[5,2,-11.337,((5.139,0.665),(2.132,0.546))]],   #Re       \n",
    "       75:[[6,0,-11.013,(2.253,0.723)],[6,1,-6.062,(1.615,0.703)],[5,2,-10.806,((4.926,0.667),(2.194,0.517))]],   #Re\n",
    "       77:[[6,0,-11.548,(2.252,0.651)],[6,1,-6.650,(1.712,0.631)],[5,2,-13.089,((4.929,0.727),(2.287,0.432))]],   #Ir\n",
    "       78:[[6,0,-11.851,(2.294,0.625)],[6,1,-6.304,(1.715,0.681)],[5,2,-13.058,((5.327,0.749),(2.318,0.429))]],   #Au\n",
    "       79:[[6,0,-12.131,(2.309,0.6)],[6,1,-6.760,(1.740,0.622)],[5,2,-14.000,((5.065,0.799),(2.291,0.349))]],   #nb p exponent slightly different from literature to avoid singularities in linear chain\n",
    "       179:[6,0,-12.131,(2.309,0.6)], #special Au 6s only for electrodes\n",
    "       } \n",
    "\n",
    "\n",
    "#these identify a label for an atomic number\n",
    "atno_to_element={1:\"H\",2:\"He\",3:\"Li\",4:\"Be\",5:\"B\",6:\"C\",7:\"N\",8:\"O\",9:\"F\",10:\"Ne\",11:\"Na\",12:\"Mg\",13:\"Al\",14:\"Si\",15:\"P\",16:\"S\",17:\"Cl\",18:\"Ar\",19:\"K\",20:\"Ca\",21:\"Sc\",22:\"Ti\",23:\"V\",\\\n",
    "        24:\"Cr\",25:\"Mn\",26:\"Fe\",27:\"Co\",28:\"Ni\",29:\"Cu\",30:\"Zn\",31:\"Ga\",32:\"Ge\",33:\"As\",34:\"Se\",35:\"Br\",36:\"Kr\",37:\"Rb\",38:\"Sr\",39:\"Y\",40:\"Zr\",41:\"Nb\",42:\"Mo\",43:\"Tc\",44:\"Ru\",45:\"Rh\",\\\n",
    "        46:\"Pd\",47:\"Ag\",48:\"Cd\",49:\"In\",50:\"Sn\",51:\"Sb\",52:\"Te\",53:\"I\",54:\"Xe\",55:\"Cs\",56:\"Ba\",47:\"La\",58:\"Ce\",59:\"Pr\",60:\"Nd\",61:\"Pm\",62:\"Sm\",63:\"Eu\",64:\"Gd\",65:\"Tb\",66:\"Dy\",67:\"Ho\",68:\"Er\",69:\"Tm\",70:\"Yb\",\\\n",
    "        71:\"Lu\",72:\"Hf\",73:\"Ta\",74:\"W\",75:\"Re\",77:\"Ir\",78:\"Pt\",79:\"Au\",179:\"Au\",100:\"XX\"\n",
    "}\n",
    "#atom colors taken from jmol  http://jmol.sourceforge.net/jscolors/\n",
    "atom_colours={1:(255,255,255),2:(217,255,255),3:(204,128,255),4:(194,255,0),5:(255,181,181),6:(144,144,144),7:(48,80,248),8:(255,13,13),9:(144,224,80),10:(179,227,245),\\\n",
    "        11:(171,92,242),12:(138,255,0),13:(191,166,166),14:(240,200,160),15:(255,128,0),16:(255,255,48),17:(31,240,31),18:(128,209,227),19:(143,64,212),20:(61,255,0),\\\n",
    "        21:(230,230,230),22:(191,194,199),23:(166,166,171),24:(138,153,199),25:(156,122,199),\\\n",
    "        26:(224,102,51),27:(240,144,160),28:(80,208,80),29:(200,128,51),30:(125,128,176),\\\n",
    "        31:(194,143,143),32:(102,143,143),33:(189,128,227),34:(255,161,0),35:(166,41,41),\\\n",
    "        36:(92,184,209),37:(112,46,176),38:(0,255,0),39:(148,255,255),40:(148,224,224),\\\n",
    "        41:(115,194,201),42:(84,181,181),43:(59,158,158),44:(36,143,143),45:(10,125,140),\\\n",
    "        46:(0,105,133),47:(192,192,192),48:(255,217,143),\\\n",
    "        49:(166,117,115),50:(102,128,128),51:(158,99,181),52:(212,122,0),53:(148,0,148),\\\n",
    "        54:(66,158,176),55:(87,23,143),56:(0,201,0),57:(112,212,255),\\\n",
    "        58:(255,255,199),59:(217,255,199),60:(199,255,199),61:(163,255,199),\\\n",
    "        62:(143,255,199),63:(97,255,199),64:(69,255,199),65:(48,255,199),66:(31,255,199),\\\n",
    "        67:(0,255,156),68:(0,230,117),69:(0,212,82),70:(0,191,56),71:(0,171,36),\\\n",
    "        72:(77,194,255),73:(77,166,255),74:(33,148,214),75:(38,125,171),76:(38,102,150),\\\n",
    "        77:(23,84,135),78:(208,208,224),79:(255,209,35),\\\n",
    "        179:(255,209,35)\n",
    "}\n",
    "\n",
    "\n",
    "covalent_radii={1:0.31,2:0.6,3:1.28,4:0.96,5:0.84,6:0.79,7:0.71,8:0.66,9:0.57,10:0.58,11:1.66,12:1.41,13:1.21,14:1.11,15:1.07,16:1.05,17:1.02,18:1.06,19:2.03,20:1.76,21:1.70,22:1.60,23:1.53,\\\n",
    "        24:1.39,25:1.61,26:1.52,27:1.50,28:1.24,29:1.32,30:1.22,31:1.22,32:1.20,33:1.19,34:1.20,35:1.20,36:1.16,37:2.20,38:1.95,39:1.90,40:1.75,41:1.64,42:1.54,43:1.47,44:1.46,45:1.42,\\\n",
    "        46:1.39,47:1.45,48:1.44,74:1.51,75:1.44,77:1.41,78:1.36,79:1.36,179:1.36\n",
    "}\n",
    "ionic_radii={1:0.6,2:0.6,3:0.9,4:0.59,5:0.84,6:0.79,7:0.71,8:0.66,9:0.57,10:0.58,11:1.66,12:0.86,13:0.67,14:1.11,15:1.07,16:1.3,17:1.67,18:1.06,19:1.52,20:1.14,21:0.89,22:0.75,23:0.68,\\\n",
    "        24:0.70,25:1.0,26:1.0,27:1.0,28:1.0,29:1.0,30:1.0,31:1.22,32:1.20,33:1.19,34:1.20,35:1.20,36:1.16,37:2.20,38:1.20,39:1.90,40:1.75,41:1.64,42:1.54,43:1.47,44:1.46,45:1.42,\\\n",
    "        46:1.50,47:1.45,48:1.44,74:1.51,75:1.44,77:1.41,78:1.56,79:1.36,179:1.36\n",
    "}\n",
    "\n",
    "atomic_masses={1:1.008,2:4.003,3:6.941,4:9.012,5:10.811,6:12.011,7:14.007,8:15.999,9:18.998,10:20.180,11:22.990,12:24.305,13:26.982,14:28.086,15:30.974,16:32.066,17:35.453,18:39.948,19:39.098\\\n",
    ",20:40.078,21:44.956,22:47.867,23:50.942,24:51.996,25:53.938,26:55.845,27:58.933,28:59.693,29:63.546,30:65.390\\\n",
    ",31:69.723,32:72.61,33:74.922,34:78.96,35:79.904,36:83.80,37:85.468,38:87.62,39:88.906,40:91.223,41:92.906,42:95.94,43:98,44:101.07,45:102.906,46:106.42,47:107.868,48:112.411,70:132.905,71:137.327,\\\n",
    "72:178.49,73:190.948,74:183.84,75:186.207,76:190.23,77:192.217,78:195.078,79:196.967,}\n",
    "\n",
    "#these identify a label for a particular set of quantum numbers.\n",
    "#used only in print out\n",
    "identifiers={(1,0,0):\"1s     \",(2,0,0):\"2s     \",(2,1,1):\"2px    \",(2,1,0):\"2pz    \",(2,1,-1):\"2py    \",\\\n",
    "(3,0,0):\"3s     \",(3,1,1):\"3px    \",(3,1,0):\"3pz    \",(3,1,-1):\"3py    \",(3,2,2):\"3dx2-y2\",(3,2,1):\"3dxz   \",\\\n",
    "(3,2,0):\"3dz2   \",(3,2,-1):\"3dyz   \",(3,2,-2):\"3dxy   \",(4,0,0):\"4s     \",(4,1,1):\"4px    \",(4,1,0):\"4pz    \",\\\n",
    "(4,1,-1):\"4py    \",(4,2,2):\"4dx2-y2\",(4,2,1):\"4dxz   \",(4,2,0):\"4dz2   \",(4,2,-1):\"4dyz   \",(4,2,-2):\"4dxy   \",\\\n",
    "(5,0,0):\"5s     \",(5,1,1):\"5px    \",(5,1,0):\"5pz    \",(5,1,-1):\"5py    \",(5,2,2):\"5dx2-y2\",(5,2,1):\"5dxz   \",\\\n",
    "(5,2,0):\"5dz2   \",(5,2,-1):\"5dyz   \",(5,2,-2):\"5dxy   \", (6,0,0):\"6s     \",(6,1,1):\"6px    \",(6,1,0):\"6pz    \",\\\n",
    "(6,1,-1):\"6py    \",(6,2,2):\"6dx2-y2\",(6,2,1):\"6dxz   \",(6,2,0):\"6dz2   \",(6,2,-1):\"6dyz   \",(6,2,-2):\"6dxy   \"\n",
    "       } \n",
    "\n",
    "l_identifiers={0:\"s\",1:\"p\",2:\"d\"} \n",
    "\n",
    "\n",
    "\n",
    "identifiers2={\n",
    "    (1,0):'1s',\n",
    "    (2,0):'2s', (2,1):'2p',\n",
    "    (3,0):'3s', (3,1):'3p', (3,2):'3d',\n",
    "    (4,0):'4s', (4,1):'4p', (4,2):'4d',\n",
    "    (5,0):'5s', (5,1):'5p', (5,2):'5d',\n",
    "    (6,0):'6s', (6,1):'6p', (6,2):'6d',\n",
    "}\n",
    "\n",
    "#A library of coordinates for common molecules\n",
    "\n",
    "# FORMAT:\n",
    "# ([Coords (2d array)],[Atoms (list of atomic numbers corresponding to coords)],charge,'point group',\n",
    "#MO_list,window,connection_threshold,plane_offset,levels)\n",
    "# Point group optional!\n",
    "#items 4 (MO_list) and 5 (window) define the list of MOs to be plotted and the energy window for the MO diagram. \n",
    "#In some cases I have pre-defined sensible values. In cases where they are blank, the programme will select\n",
    "#default values (6 MOs either side of HOMO-LUMO gap and total energy range +-5 eV, resp). They can be over-ridden \n",
    "#in the input stack.\n",
    "#item 6 is the connection threshold - if the AO contributes more than this to the MO, a blue connection \n",
    "#line will be plotted: higher value = fewer lines\n",
    "#item 7 is the plane offset. By default the MO is plotted in the xz plane, but often this means that some\n",
    "#important aspect of the MO is missed (if it has a node in this plane, for example). The offset plots the\n",
    "#MO at the specified distance above the xz plane\n",
    "#item 8 defines the values of the contours to be plotted. A smaller value for the 3rd number = more contours  \n",
    "\n",
    "coords_dict = {\n",
    "    \n",
    "'H2':([\n",
    " [0., 0., -0.385],\n",
    " [0., 0., 0.385]] , \n",
    " [1,1],\n",
    " 0,\n",
    " 'Dinfh',[0,1],[-20.,5.],0.4,0.0,np.linspace(-2.0,2.0,11),'Hoffmann'),\n",
    "\n",
    "'HHe+':([\n",
    " [0., 0., -0.50],\n",
    " [0., 0., 0.50]] , \n",
    " [1,2],\n",
    " 1,\n",
    " 'Dinfh',[0,1],[-30.,10.],0.3,0.0,np.linspace(-2.0,2.0,11),'Hoffmann'),\n",
    "\n",
    "'B2':([\n",
    " [0., 0., -0.67],\n",
    " [0., 0., 0.67]] , \n",
    " [5,5],\n",
    " 0,\n",
    " 'Dinfh',[0,1,3,4,5,7],[-20.,20.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'C2':([\n",
    " [0., 0., -0.6],\n",
    " [0., 0., 0.6]] , \n",
    " [6,6],\n",
    " 0,\n",
    " 'Dinfh',[0,1,3,4,5,7],[-25.,20.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    " \n",
    " 'N2':([\n",
    " [0., 0., -0.55],\n",
    " [0., 0., 0.55]] , \n",
    " [7,7],\n",
    " 0,\n",
    " 'Dinfh',[0,1,3,4,5,7],[-25.,20],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    " \n",
    " 'O2':([\n",
    " [0., 0., -0.6],\n",
    " [0., 0., 0.6]] , \n",
    " [8,8],\n",
    " 0,\n",
    " 'Dinfh',[0,1,3,4,5,7],[-35.,10.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'F2':([\n",
    " [0., 0., -0.75],\n",
    " [0., 0., 0.75]] , \n",
    " [9,9],\n",
    " 0,\n",
    " 'Dinfh',[0,1,3,4,5,7],[-45,-10.],0.4,0.0,np.linspace(-1.0,1.0,11),'Hoffmann'),\n",
    "\n",
    " '[H3]+':( [\n",
    "   [-0.51297, 0., 0.0],\n",
    "   [np.sin(np.pi/6)*0.51297, 0., np.cos(np.pi/6)*0.51297],\n",
    "   [np.sin(np.pi/6)*0.51297, 0., -np.cos(np.pi/6)*0.51297]],[1,1,1],1,'D3h',[0,1,2],[-20.,0.],0.4,0.0,np.linspace(-1.0,1.0,11),'Hoffmann'),\n",
    "\n",
    " 'Li3+':( [\n",
    "   [-1.72, 0., 0.0],\n",
    "   [np.sin(np.pi/6)*1.72, 0., np.cos(np.pi/6)*1.72],\n",
    "   [np.sin(np.pi/6)*1.72, 0., -np.cos(np.pi/6)*1.72]],[3,3,3],1,'D3h',[0,1,2],[-8.,-4],0.2,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "    \n",
    " '[H3]-':([\n",
    " [0., 0., -0.9],\n",
    " [0., 0., 0.0],\n",
    "[0., 0., 0.9]] , \n",
    " [1,1,1],\n",
    " -1,\n",
    " 'Dinfh',[0,1,2],[-20,0],0.4,0.0,np.linspace(-1.0,1.0,11),'Hoffmann'),\n",
    " \n",
    " 'HF':([\n",
    " [0., 0., 0.0],\n",
    " [0., 0., 0.93],] , \n",
    " [9,1],\n",
    " 0,\n",
    " 'Cinfv',[1,2,4,5],[-45.,10.],0.2,0.0,np.linspace(-1.0,1.0,11),'Hoffmann'), \n",
    "\n",
    " 'HB':([\n",
    " [0., 0., 0.0],\n",
    " [0., 0., 1.2],] , \n",
    " [1,5],\n",
    " 0,\n",
    " 'Cinfv',[1,2,4,5],[-25.,0.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),  \n",
    " \n",
    " 'HCN':([\n",
    " [0., 0., -1.07],\n",
    " [0., 0., 0.0],\n",
    "[0., 0., 1.16]] , \n",
    " [1,6,7],\n",
    " 0,\n",
    " 'Cinfv',[1,2,4,5,7,8,9],[-35.,15],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'CO2':([\n",
    "    [ 0.000000,     0.000000,     0.000000],\n",
    "    [ 0.000000,     0.000000,     1.17000],\n",
    "    [ 0.000000,     0.000000,    -1.17000]],\n",
    "    [6,8,8,],\n",
    "    0,\n",
    "    'DinfH',[2,3,5,6,7,9,11,12],[-40.,20.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'KrF2':([\n",
    "    [ 0.000000,     0.000000,     0.000000],\n",
    "    [ 0.000000,     0.000000,     1.98000],\n",
    "    [ 0.000000,     0.000000,    -1.98000]],\n",
    "    [36,9,9,],\n",
    "    0,\n",
    "    'DinfH',[3,4,6,8,9,11],[-25.,-5.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "   \n",
    "    \n",
    "'BeH2':([\n",
    "    [  0.,0.,0.],\n",
    "    [  0.,0.,-1.34],\n",
    "    [  0.,0.,1.34]\n",
    "    ],[4,1,1],0,'Cinfv',[0,1,2,3,4,5],[-25.,10.],0.4,0.01,np.linspace(-0.50,0.50,11),'Hoffmann'),\n",
    "    \n",
    "'H2O':([\n",
    "    [  0.,0.,0.],\n",
    "    [  0.9584,0.,-0.5867],\n",
    "    [  -0.9584,0.,-0.5867]\n",
    "    ],[8,1,1],0,'C2v',[0,1,2,3,4,5],[-35.,0.],0.4,0.00,np.linspace(-0.50,0.50,11),'Hoffmann'),\n",
    "    \n",
    "'H2S':([\n",
    "    [0.0, 0.0, 0.0], \n",
    "    [0.0, 0.0, -1.34], \n",
    "    [1.3391000476633994, 0.0, 0.049102569666785244]\n",
    "    ],[16,1,1],0,'C2v',[0,1,2,3,4,5],[-25.,10.],0.4,0.00,np.linspace(-0.50,0.50,11),'Hoffmann'),\n",
    "    \n",
    "'BH3':( [\n",
    "   [0., 0., 0.],\n",
    "   [-1.20, 0., 0.0],\n",
    "   [0.6, 0., np.cos(np.pi/6)*1.20],\n",
    "   [0.6, 0., -np.cos(np.pi/6)*1.20]],[5,1,1,1],0,'D3h',[0,1,2,3,4,5,6],[-25.,0],0.4,0.2,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'NH3':( [\n",
    "   [0., 0., 0.],\n",
    "   [-0.9457, 0., 0.3821],\n",
    "   [0.47285,0.81900022,0.3821],\n",
    "   [0.47285,-0.81900022,0.3821]],\n",
    "   [7,1,1,1],0,'C3v',[0,1,2,3,4,5,6],[-35.,12.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'PH3':( [\n",
    "   [ 0.   ,  0.   ,  0.   ],\n",
    "     [-1.095 , 0.   , -0.919],\n",
    "     [ 0.548 , 0.949, -0.919],\n",
    "     [ 0.548 ,-0.949, -0.919]],\n",
    "   [15,1,1,1],0,'C3v',[0,1,2,3,4,5,6],[-18.,15.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "        \n",
    "'CH4':([\n",
    "    [ 0.000000,     0.000000,     0.000000],\n",
    "    [ 0.,     0.8953,     0.6331],\n",
    "    [ 0.,     -0.8953,    0.6331],\n",
    "    [0.8953,     0.,    -0.6331],\n",
    "    [-0.8953,     0.0,    -0.6331]],\n",
    "    [6,1,1,1,1,],\n",
    "    0,\n",
    "    'Td',[0,1,2,3,4,5,6,7],[-30.,30.],0.4,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),   \n",
    "    \n",
    "'KrOF4':([\n",
    " [0., 0., 0.],\n",
    " [0., 0., 1.5],\n",
    " [1.61, 0., 0.],\n",
    " [-1.61,0., 0.],\n",
    " [0., 1.6100, 0.],\n",
    " [0.,-1.6100, 0.],\n",
    " ] , [36,8,9,9,9,9],0,'Oh',[19,20,21,22,23,24],[-22.,-17.],0.2,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "    \n",
    "'SF6':([\n",
    " [0., 0., 0.],\n",
    " [1.61, 0., 0.],\n",
    " [-1.61,0., 0.],\n",
    " [0., 1.61001, 0.],\n",
    " [0.,-1.61001, 0.],\n",
    " [0., 0., 1.61001],\n",
    " [0., 0.,-1.61001]\n",
    " ] , [16,9,9,9,9,9,9],0,'Oh',[19,20,21,22,23,24],[-22.,-17.],0.2,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    "\n",
    "'SF4':([\n",
    " [0., 0., -0.44],\n",
    " [1.7, 0., -0.36],\n",
    " [-1.7,0., -0.36],\n",
    " [0., 1.24, 0.58],\n",
    " [0.,-1.24, 0.58],\n",
    " ] , [16,9,9,9,9],0,'C2v',[13,14,15,16,17,18],[-22.,5.],0.2,0.0,np.linspace(-0.25,0.25,11),'Hoffmann'),\n",
    " \n",
    "'HGeGeH_trans_bent':(\n",
    "    [[ 0.  ,  0.  ,  1.15  ],\n",
    " [ 0.   , 0. ,  -1.15 ],\n",
    " [-0.11 , 0. ,   2.60],\n",
    " [ 0.11 , 0. ,  -2.60]],\n",
    "[32,32,1,1],0,'C2h'\n",
    ",[],[-22.,5.],0.4,0.0,np.linspace(-1.0,1.0,21),'Hoffmann'),\n",
    "\n",
    "'HGeGeH_linear':(\n",
    "[[    1.04,.00,  0.  ],\n",
    "[    -1.04, .00,  0.  ],\n",
    "[     2.56, 0., 0.0],\n",
    "[    -2.56, 0., 0.0],\n",
    "],\n",
    "[32,32,1,1],0,'C2h'\n",
    ",[0,1,2,3,4],[-22.,5.],0.4,0.8,np.linspace(-1.0,1.0,21),'Hoffmann'),    \n",
    "    \n",
    "'B2H6':([\n",
    "[  0.,0.,-0.88],\n",
    "[  0.,0.0,0.88],\n",
    "[  0.98,0.,0.],\n",
    "[  -0.98,0.,0.],\n",
    "[  0.,-1.05,1.46],\n",
    "[  0.,1.05,1.46],\n",
    "[  0.,-1.05,-1.46],\n",
    "[  0.,1.05,-1.46],\n",
    "],\n",
    "[5,5,1,1,1,1,1,1],0,'D2h',[1,2,4,6,7,8],[-25.,3.],0.3,0.0,np.linspace(-1.0,1.0,21),'Hoffmann'),\n",
    "\n",
    "'[B6H6]2-':([\n",
    "[  1.23,0.0,0.],\n",
    "[  -1.23,0.,0.],\n",
    "[  0.0,1.23,0.],\n",
    "[  0.0,-1.23,0.],\n",
    "[  0.0,0.,1.23],\n",
    "[  0.0,0.,-1.23],\n",
    "[  2.46,0.0,0.],\n",
    "[  -2.46,0.,0.],\n",
    "[  0.0,2.46,0.],\n",
    "[  0.0,-2.46,0.],\n",
    "[  0.0,0.,2.46],\n",
    "[  0.0,0.,-2.46]\n",
    "],[5,5,5,5,5,5,1,1,1,1,1,1],-2,'Oh',[7,10,11,12,13],[-25.,20.],0.2,0.0,np.linspace(-1.0,1.0,21),'Hoffmann'),\n",
    "\n",
    "'[NiCN4]2-':([\n",
    "[  0.,0.,0.],\n",
    "[  2.00,0.0,0.],\n",
    "[  0.0,0.,2.0],\n",
    "[  0.0,0.,-2.0],\n",
    "[  -2.00,0.,0.],\n",
    "[  3.10,0.0,0.],\n",
    "[  0.0,0.,3.1],\n",
    "[  0.0,0.,-3.1],\n",
    "[  -3.10,0.,0.],\n",
    "],[28,6,6,6,6,7,7,7,7],-2,'D4h',[18,19,20,21,22,25],[-14.,-8.],0.4,0.3,np.linspace(-4.0,4.0,17),'Cerda'),\n",
    "\n",
    "'[PdCl4]2-':([\n",
    "[  0.,0.,0.],\n",
    "[  2.40,0.0,0.],\n",
    "[  -2.40,0.,0.],\n",
    "[  0.0,2.40,0.],\n",
    "[  0.0,-2.40,0.],\n",
    "],[46,17,17,17,17],-2,'D4h',[16,17,18,19,20,21],[-15.,-5.],0.4,0.05,np.linspace(-4.0,4.0,17),'Cerda'),\n",
    "\n",
    "    \n",
    "'[NiF6]4-':([\n",
    "[  0.,0.,0.],\n",
    "[  2.0701,0.0,0.],\n",
    "[  -2.0701,0.,0.],\n",
    "[  0.0,2.0701,0.],\n",
    "[  0.0,-2.0701,0.],\n",
    "[  0.0,0.,2.0701],\n",
    "[  0.0,0.,-2.0701]\n",
    "],[28,9,9,9,9,9,9],-4,'Oh',[24,25,26,27,28,29],[-20.,-12.],0.4,0.05,np.linspace(-4.0,4.0,17),'Cerda'),\n",
    "\n",
    "'[FeCN6]4-':([\n",
    "[  0.,0.,0.],\n",
    "[  1.94,0.0,0.],\n",
    "[  -1.94,0.,0.],\n",
    "[  0.0,1.9401,0.],\n",
    "[  0.0,-1.9401,0.],\n",
    "[  0.0,0.,1.9402],\n",
    "[  0.0,0.,-1.9402],\n",
    "[  3.12,0.0,0.],\n",
    "[  -3.12,0.,0.],\n",
    "[  0.0,3.1201,0.],\n",
    "[  0.0,-3.1201,0.],\n",
    "[  0.0,0.,3.1202],\n",
    "[  0.0,0.,-3.1202]\n",
    "],[26,6,6,6,6,6,6,7,7,7,7,7,7],-4,'Oh',[27,28,29,30],[-14,-4],0.4,0.0,np.linspace(-4.0,4.0,17),'Hoffmann'),\n",
    "\n",
    "'CrCO6':([\n",
    "[  0.,0.,0.],\n",
    "[  1.93,0.0,0.],\n",
    "[  -1.93,0.,0.],\n",
    "[  0.0,1.93,0.],\n",
    "[  0.0,-1.93,0.],\n",
    "[  0.0,0.,1.93],\n",
    "[  0.0,0.,-1.93],\n",
    "[  3.08,0.0,0.],\n",
    "[  -3.08,0.,0.],\n",
    "[  0.0,3.08,0.],\n",
    "[  0.0,-3.08,0.],\n",
    "[  0.0,0.,3.08],\n",
    "[  0.0,0.,-3.08]\n",
    "],[24,6,6,6,6,6,6,8,8,8,8,8,8],0,'Oh',[25,26,30,42,45,46],[-16,0],0.3,0.0,np.linspace(-4.0,4.0,17),'Hoffmann'),\n",
    "\n",
    "'ferrocene':(\n",
    "[[   0.,0.,0.],\n",
    " [   1.214056  ,  0.000000  , -1.636090],\n",
    "          [  0.375164  ,  1.154636  , -1.636090],\n",
    "          [  0.375164  , -1.154636  , -1.636090],\n",
    "         [  1.214056  ,  0.000000  ,  1.636090],\n",
    "          [ -0.982192  ,  0.713604  , -1.636090],\n",
    "         [ -0.982192  , -0.713604  , -1.636090],\n",
    "          [  0.375164  , -1.154636  ,  1.636090],\n",
    "          [  0.375164  ,  1.154636  ,  1.636090],\n",
    "        [ -0.982192  , -0.713604  ,  1.636090],\n",
    "         [ -0.982192  ,  0.713604  ,  1.636090],\n",
    "        [  2.302230  ,  0.000000  , -1.621820],\n",
    "         [  0.711428  ,  2.189550  , -1.621820],\n",
    "         [  0.711428  , -2.189550  , -1.621820],\n",
    "         [ -1.862543  ,  1.353217  , -1.621820],\n",
    "        [ -1.862543  , -1.353217  , -1.621820],\n",
    "         [  2.302230  ,  0.000000  ,  1.621820],\n",
    "         [  0.711428  , -2.189550  ,  1.621820],\n",
    "         [  0.711428  ,  2.189550  ,  1.621820],\n",
    "       [ -1.862543  , -1.353217  ,  1.62182],\n",
    "       [ -1.862543  ,  1.353217  ,  1.621820]],\n",
    "[26,6,6,6,6,6,6,6,6,6,6,1,1,1,1,1,1,1,1,1,1],0,'D5h'\n",
    ",[22,24,26,28,29],[-15.,-5.],0.4,0.0,np.linspace(-4.0,4.0,17),'Hoffmann'),\n",
    "\n",
    "'CrSi12':(\n",
    "[[   0.,0.,0.],\n",
    "[   -2.3754,0.,1.1854],\n",
    "[   2.3754,0.,1.1854],\n",
    "[  -1.1877,2.0572,1.1854],\n",
    "[  -1.1877,-2.0572,1.1854],\n",
    "[  1.1877,2.0572,1.1854],\n",
    "[  1.1877,-2.0572,1.1854],\n",
    "[   -2.3754,0.,-1.1854],\n",
    "[   2.3754,0.,-1.1854],\n",
    "[  -1.1877,2.0572,-1.1854],\n",
    "[  -1.1877,-2.0572,-1.1854],\n",
    "[  1.1877,2.0572,-1.1854],\n",
    "[  1.1877,-2.0572,-1.1854],],\n",
    "[24,14,14,14,14,14,14,14,14,14,14,14,14],0,'D6h'\n",
    ",[13,23,27,32],[-12.,-6.],0.4,0.0,np.linspace(-4.0,4.0,17),'Hoffmann'),\n",
    "\n",
    "'ethene':(\n",
    "[[    .00,  0.,   .667],\n",
    "[     .00,  0.,  -.667],\n",
    "[     .923, 0.,   1.238],\n",
    "[    -.923, 0.,   1.238],\n",
    "[     .923, 0.,  -1.238],\n",
    "[    -.923, 0.,  -1.238]],\n",
    "[6,6,1,1,1,1],0,'D2h'\n",
    ",[5,6,7,8],[-22.,5.],0.4,0.2,np.linspace(-1.0,1.0,21),'Hoffmann'),\n",
    "\n",
    "'benzene':(\n",
    "[[0.00000, 1.396,0.],\n",
    "[1.209,0.698,0.],\n",
    "[1.209,-0.698,0.],\n",
    "[0.,-1.396,0.],\n",
    "[-1.209, -0.698,0.],\n",
    "[-1.209,0.698,0.],\n",
    "[0.00000, 2.483,0.],\n",
    "[2.150, 1.241,0.],\n",
    "[2.150,-1.241,0.],\n",
    "[0.,-2.483,0.],\n",
    "[-2.150, -1.241,0.],\n",
    "[-2.150,1.241,0.],\n",
    "],\n",
    " [6,6,6,6,6,6,1,1,1,1,1,1],0,'D6h',[9,13,14,15,16,17],[-16.,-4.]\n",
    ",0.3,0.2,np.linspace(-1.0,1.0,21),'Hoffmann'),\n",
    "\n",
    "'[Co(NH2)(NH3)4]+':(   \n",
    "[[0.000000000   ,     -0.014621406, 0.],\n",
    "[        1.732145892   ,     0.985213884, 0.],\n",
    "[        2.463801353   ,     0.516694057, 0.],\n",
    "[        1.879039330   ,     1.515428498, 0.669],\n",
    "[        1.879039330   ,     1.515428498, -0.669],\n",
    "[        0.000000000   ,    -0.014621406, -2.],\n",
    "[       -0.850945449   ,     0.476521324, -2.398],\n",
    "[        0.000000000   ,    -0.996906868, -2.398],\n",
    "[        0.850945449   ,     0.476521324, -2.398],\n",
    "[       -1.732145892   ,      0.985213884, 0.],\n",
    "[       -1.879039330   ,     1.515428498, -0.669],\n",
    "[       -1.879039330   ,      1.515428498, 0.669],\n",
    "[       -2.463801353   ,      0.516694057, 0.],\n",
    "[        0.000000000   ,     -1.914621406, 0.],\n",
    "[        0.000000000   ,     -2.522015410, 0.915],\n",
    "[        0.000000000   ,    -2.522015410, -0.915],\n",
    "[        0.000000000   ,     -0.014621406, 2.],\n",
    "[        0.850945449   ,      0.476521324, 2.398],\n",
    "[        0.000000000   ,     -0.996906868, 2.398],\n",
    "[       -0.850945449   ,      0.476521324, 2.398 ]   \n",
    "],\n",
    "[27,7,1,1,1,7,1,1,1,7,1,1,1,7,1,1,7,1,1,1],1,'C2V',[16,17],[-16.,-8.]\n",
    ",0.3,0.2,np.linspace(-1.0,1.0,21),'Hoffmann')\n",
    "}\n",
    "\n",
    "#FUNCTIONS TO CREATE NEW MOLECULES\n",
    "\n",
    "#create a new molecule using cartesians\n",
    "#function if you want to pick up all the coordinates etc from a pre-defined library\n",
    "def select_from_list(coords_dict):\n",
    "    print(\"The list of available molecules is:\",'\\n')\n",
    "    molecule_list=[]\n",
    "    for key,value in coords_dict.items():\n",
    "        molecule_list.append(key)\n",
    "    for i in range(len(molecule_list)):\n",
    "        print(\"%-5i\"%(i),\"%-15s\"%str(molecule_list[i]))\n",
    "    print('\\n')\n",
    "    mol=input(\"which molecule do you want to look at? type a formula from the list  \")\n",
    "\n",
    "    ##get the molecular parameters from the molecular_library. \n",
    "    title=mol     \n",
    "    coords=coords_dict[mol][0];atoms=coords_dict[mol][1];Q=coords_dict[mol][2]#;parameters=coords_dict[mol][9]\n",
    "\n",
    "    if coords_dict[mol][9]=='Hoffmann':\n",
    "        parameters=Hoffmann;params='Hoffmann'\n",
    "    elif coords_dict[mol][9]=='Cerda':\n",
    "        parameters=Cerda;params='Cerda'  \n",
    "\n",
    "    print(\"molecule is \",mol)\n",
    "    print(\"the standard settings are:\",'\\n')\n",
    "    print(\"Parameter set:\",coords_dict[mol][9])\n",
    "    print(\"Charge\",coords_dict[mol][2])\n",
    "    print(\"Atoms and Coordinates (in Angstroms)\")\n",
    "    for i in range(len(coords)):\n",
    "        print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]) ) \n",
    "\n",
    "    change_molecule=input(\"Do you want to edit any of these values?  Y/N  \")\n",
    "\n",
    "    while change_molecule=='Y':\n",
    "        print('\\n')\n",
    "        print(\"the options to change are:  \")\n",
    "        print(\"1  list of atoms\")\n",
    "        print(\"2  list of coordinates\")\n",
    "        print(\"3  title\")\n",
    "        print(\"4  parameter set\")\n",
    "        print('\\n')\n",
    "        what_to_change_molecule=int(input(\"what do you want to change? enter 1, 2, 3 or 4 \"))\n",
    "        if what_to_change_molecule==1:\n",
    "            new_atoms=input(\"input a list of atomic numbers, 8 1 1 for H2O, for example \")\n",
    "            atoms=[int(x) for x in new_atoms.split()]\n",
    "            print(\"updated coordinates\")\n",
    "            for i in range(len(atoms)):\n",
    "                print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==2:\n",
    "            updated_coords=[]\n",
    "            which_atom=int(input(\"which atom's coordinates do you want to change? remember to start from 0  \"))\n",
    "            new_atom_coords=input(\"input x, y and z coordinates, 0. 0. 2. , for example \")\n",
    "            new_atom_coords_temp=[float(x) for x in new_atom_coords.split()]\n",
    "            coords[which_atom]=new_atom_coords_temp\n",
    "            print('\\n')\n",
    "            print(\"updated coordinates\")\n",
    "            for i in range(len(atoms)):\n",
    "                print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==3:\n",
    "            title=str(input(\"new title  \"))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==4:\n",
    "            params=input(\"type parameter set:  Hoffmann or Cerda  \")\n",
    "            print('\\n')\n",
    "            print(\"switched to the \",params,\" parameter set\")\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "\n",
    "    return title,mol,params,coords,atoms,Q\n",
    "\n",
    "def create_new_molecule():\n",
    "    print(\"Creating a new molecule\",'\\n')\n",
    "    print('\\n')\n",
    "    mol=input(\"Title?  \")\n",
    "    Q=int(input(\"What is the charge on the molecule?   \"))\n",
    "\n",
    "    title=mol\n",
    "    \n",
    "    coords=[]\n",
    "    #atoms=input(\"input a list of atomic numbers, 8 1 1 for H2O, for example \")\n",
    "    #atoms=[int(x) for x in atoms.split()]\n",
    "    atoms=[]\n",
    "    atom_number=0\n",
    "    add_atom=True\n",
    "    while add_atom:\n",
    "        print('\\n')\n",
    "        new_atom=int(input(\"input atomic number for new atom   \"))\n",
    "        atoms.append(new_atom)\n",
    "        new_coords=input(\"input x, y and z coordinates, 0. 0. 2. , for example.   \")\n",
    "        new_coords=[float(x) for x in new_coords.split()]\n",
    "        if new_coords in coords:\n",
    "            print(\"there is already an atom at this point\")\n",
    "            new_coords=input(\"input another set of x, y and z coordinates, 0. 0. 2. , for example.   \")\n",
    "            new_coords=[float(x) for x in new_coords.split()]\n",
    "        coords.append(new_coords) \n",
    "        print('\\n')\n",
    "        print(\"updated coordinates\")\n",
    "        print('\\n')\n",
    "        for i in range(len(coords)):\n",
    "            print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "        print('\\n')\n",
    "        add_atom=str(input(\"Add another atom? Y or hit return to stop\"))\n",
    "                     \n",
    "    print('\\n')\n",
    "    params=str(input(\"which parameter set would you like to use, Hoffmann or Cerda?  \"))\n",
    "    print('\\n')\n",
    "    print(\"molecule is \",mol)\n",
    "    print(\"the standard settings are:\",'\\n')\n",
    "    print(\"Parameter set:\",params)\n",
    "    print(\"Charge\",Q)\n",
    "    print(\"Atoms and Coordinates (in Angstroms)\")\n",
    "    for i in range(len(coords)):\n",
    "        print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]) ) \n",
    "\n",
    "    change_molecule=input(\"Do you want to edit any of these values?  Y/N  \")#\n",
    "\n",
    "    while change_molecule=='Y':\n",
    "        print('\\n')\n",
    "        print(\"the options to change are:  \")\n",
    "        print(\"1  list of atoms\")\n",
    "        print(\"2  list of coordinates\")\n",
    "        print(\"3  title\")\n",
    "        print(\"4  parameter set\")\n",
    "        print('\\n')\n",
    "        what_to_change_molecule=int(input(\"what do you want to change? enter 1, 2, 3 or 4 \"))\n",
    "        if what_to_change_molecule==1:\n",
    "            new_atoms=input(\"input a list of atomic numbers, 8 1 1 for H2O, for example \")\n",
    "            atoms=[int(x) for x in new_atoms.split()]\n",
    "            print(\"updated coordinates\")\n",
    "            for i in range(len(atoms)):\n",
    "                print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==2:\n",
    "            updated_coords=[]\n",
    "            which_atom=int(input(\"which atom's coordinates do you want to change? remember to start from 0  \"))\n",
    "            new_atom_coords=input(\"input x, y and z coordinates, 0. 0. 2. , for example \")\n",
    "            new_atom_coords_temp=[float(x) for x in new_atom_coords.split()]\n",
    "            coords[which_atom]=new_atom_coords_temp\n",
    "            print('\\n')\n",
    "            print(\"updated coordinates\")\n",
    "            for i in range(len(atoms)):\n",
    "                print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==3:\n",
    "            title=str(input(\"new title  \"))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==4:\n",
    "            params=input(\"type parameter set:  Hoffmann or Cerda  \")\n",
    "            print('\\n')\n",
    "            print(\"switched to the \",params,\" parameter set\")\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "\n",
    "    return title,mol,params,coords,atoms,Q\n",
    "\n",
    "\n",
    "def zmatrix_to_cartesian(zmatrix):\n",
    "    cartesians=[]\n",
    "    #add first atom at origin\n",
    "    cartesians.append([0.,0.,0.])\n",
    "    #add second along z\n",
    "    r1=zmatrix[1][3] #bond length\n",
    "    cartesians.append([0.,0.,zmatrix[1][3]])\n",
    "    #print(\"step 1\",cartesians)\n",
    "    #identify which atom the third atom is bonded to (atom a)\n",
    "    new_origin=zmatrix[2][0]\n",
    "    #print(\"new origin\",new_origin)\n",
    "    #by elimination, the bonded atom (b) must be the other one\n",
    "    if new_origin==1:\n",
    "        bonded_atom=2\n",
    "    else:\n",
    "        bonded_atom=1\n",
    "#shift both vectors such that atom a sits at the origin\n",
    "    shifted_cartesians=[]\n",
    "    for vector in cartesians:\n",
    "        new_vector=[a-b for a,b in zip(vector,cartesians[new_origin-1])]\n",
    "        shifted_cartesians.append(new_vector)\n",
    "#update master list\n",
    "    cartesians=shifted_cartesians\n",
    "    #print(\"step 2\",cartesians)\n",
    "#need to have the second atom aligned along -ve z. This will happen automatically if the 3rd atom is bonded\n",
    "#to atom 2, but not of it is bonded to atom 1. In case it is aligned along +ve z, flip it.\n",
    "    vector_to_rotate=cartesians[bonded_atom-1]\n",
    "    if vector_to_rotate[2]>0:       \n",
    "        flipped_cartesians=[]        \n",
    "        for vector in cartesians:\n",
    "            flipped_cartesians.append(flip_matrix_xy().dot(vector).tolist())\n",
    "        cartesians=flipped_cartesians\n",
    "\n",
    "#first 2 should now be in the standard orientation now: \n",
    "    #1) atom a to form the bond to at the origin\n",
    "    #2) bond vector ab aligned along negative z axis\n",
    "\n",
    "#now add third atom in xz plane\n",
    "#identify bond length and angle\n",
    "    r1=zmatrix[2][3];a1=(zmatrix[2][4]*np.pi)/180.\n",
    "\n",
    "#create new vector by rotating a vector with length r1 along -z by angle a1 \n",
    "    new_vector=rotation_matrix_y(a1).dot([0.,0.,-r1]).tolist()\n",
    "    cartesians.append(new_vector)\n",
    "\n",
    "#shift origin back to first atom\n",
    "    origin_cartesians=[]\n",
    "    for vector in cartesians:\n",
    "        origin_cartesians.append([a-b for a,b in zip(vector,cartesians[0])])\n",
    "\n",
    "#update master list\n",
    "    cartesians=origin_cartesians\n",
    "    #print(\"step 3\",cartesians)\n",
    "#now add all further atoms\n",
    "    for i in range(3,len(zmatrix)):\n",
    "    #identify origin atom, bonded atom and torsion atom\n",
    "        origin_atom=zmatrix[i][0];  #atom a\n",
    "        bonded_atom=zmatrix[i][1];  #atom b\n",
    "        torsion_atom=zmatrix[i][2]  #atom c\n",
    "    \n",
    "        #print(origin_atom,bonded_atom,torsion_atom)\n",
    "    #identify bond length, angle and torsion    \n",
    "        r1=zmatrix[i][3];a1=(zmatrix[i][4]*np.pi)/180.;phi1=(zmatrix[i][5]*np.pi)/180.\n",
    "    #shift origin to atom a of the triad\n",
    "        shifted_cartesians=[]\n",
    "        for vector in cartesians:\n",
    "            shifted_cartesians.append([a-b for a,b in zip(vector,cartesians[origin_atom-1])])\n",
    "    #update master list\n",
    "        cartesians=shifted_cartesians\n",
    "    #work out the vector between atom a and atom b    \n",
    "        bond_vector=cartesians[bonded_atom-1]    \n",
    "\n",
    "    #rotate bond vector about y to bring it onto the negative z axis    \n",
    "    #work out rotation angle\n",
    "        theta=np.arccos(np.dot(bond_vector,[0,0,-1])/np.linalg.norm(bond_vector))  \n",
    "    #then generate list of rotated cartesians\n",
    "        rotated_cartesians=[]\n",
    "        for vector in cartesians:\n",
    "            rotated_cartesians.append(rotation_matrix_y(theta).dot(vector).tolist())\n",
    "\n",
    "    #update master list\n",
    "        cartesians=rotated_cartesians\n",
    "    #note origin atom and bonded atom are now on the z axis.\n",
    "    \n",
    "    #now rotate the torsion atom about the z axis to bring it into the xz plane   \n",
    "    #work out angle to rotate by\n",
    "        torsion_vector=cartesians[torsion_atom-1] \n",
    "        phi=np.arcsin(np.dot(torsion_vector,[0,1,0])/np.linalg.norm(torsion_vector))   \n",
    "    \n",
    "    #generate list of rotated coordinates\n",
    "        rotated_cartesians=[]\n",
    "        for vector in cartesians:\n",
    "            rotated_cartesians.append(rotation_matrix_z(phi).dot(vector).tolist())\n",
    "    #update master list\n",
    "        cartesians=rotated_cartesians\n",
    "    #origin atom, bonded atom and torsion atom are now in the xz plane\n",
    "    #now make sure torsion atom lies in positive x direction:\n",
    "        torsion_vector=cartesians[torsion_atom-1] \n",
    "        if torsion_vector[0]<0:\n",
    "            flipped_cartesians=[]        \n",
    "            for vector in cartesians:\n",
    "                flipped_cartesians.append(flip_matrix_yz().dot(vector).tolist())\n",
    "            cartesians=flipped_cartesians\n",
    "\n",
    "    #finally, generate the new vector, first placed in the xz plane\n",
    "        vector_to_rotate=cartesians[bonded_atom-1]    \n",
    "    #if bond vector is aligned along positive z, flip molecule in xy plane    \n",
    "        if vector_to_rotate[2]>0:\n",
    "            flipped_cartesians=[]        \n",
    "            for vector in cartesians:\n",
    "                flipped_cartesians.append(flip_matrix_xy().dot(vector).tolist())\n",
    "            cartesians=flipped_cartesians\n",
    "\n",
    "    #everything should be in the standard orientation now: \n",
    "    #1) atom a to form the bond to at the origin\n",
    "    #2) bond vector ab aligned along negative z axis\n",
    "    #3) torsion atom c in the xz plane\n",
    "    #4) bc vector aligned in the positive x direction\n",
    "   \n",
    "    #create new vector by rotating a vector with length along -z by angle a1 \n",
    "        new_vector=rotation_matrix_y(a1).dot([0.,0.,-r1]).tolist()\n",
    "    #then rotate it about z according t torsion angle phi1\n",
    "        new_vector_rotated=rotation_matrix_z(phi1).dot(new_vector).tolist()\n",
    "    #then add it to the list\n",
    "        cartesians.append(new_vector_rotated)\n",
    "    #now restore the original orientation\n",
    "        rotated_cartesians=[];origin_cartesians=[]    \n",
    "    #rotate the bond vector back to its original position\n",
    "        for vector in cartesians:\n",
    "            rotated_cartesians.append(rotation_matrix_y(-theta).dot(vector).tolist())\n",
    "    #shift origin back to first atom    \n",
    "        for vector in rotated_cartesians:\n",
    "            origin_cartesians.append([a-b for a,b in zip(vector,rotated_cartesians[0])])\n",
    "\n",
    "    #update master list\n",
    "        cartesians=origin_cartesians         \n",
    "    return cartesians\n",
    "\n",
    "#create a new molecule using zmatrix\n",
    "def create_new_molecule_zmatrix():\n",
    "    print(\"Creating a new molecule using a zmatrix\",'\\n')\n",
    "    print('\\n')\n",
    "    mol=input(\"Title?  \")\n",
    "    Q=int(input(\"What is the charge on the molecule?   \"))\n",
    "\n",
    "    title=mol\n",
    "    \n",
    "    zmatrix=[];zmatrix_to_print=[]\n",
    "    #print(\"For dummy atoms, use the atomic number 1000\",'\\n')\n",
    "    #atoms=input(\"input a list of atomic numbers, 8 1 1 for H2O, for example \")\n",
    "    #atoms=[int(x) for x in atoms.split()]\n",
    "\n",
    "    atoms=[];atom_number=0\n",
    "    print('\\n')\n",
    "    print(\"For dummy atoms, use atomic number 100\",'\\n')\n",
    "    atom1=int(input(\"Input atomic number of atom 1   \"))\n",
    "    atoms.append(atom1)\n",
    "    print(\"Placing first atom at the origin\")\n",
    "    zmatrix.append([0,0,0,0.,0.,0.])\n",
    "    #label=str(atno_to_element[atom1])\n",
    "    zmatrix_to_print.append([1,atno_to_element[atom1],0,0,0,0.,0.,0.])\n",
    "    print('\\n')\n",
    "    atom2=int(input(\"Input atomic number of atom 2   \"))\n",
    "    atoms.append(atom2)\n",
    "    r1=float(input(\"Input bond length from atom 1 to atom 2, in Angstrom.   \"))\n",
    "    zmatrix.append([1,0,0,r1,0.,0.])\n",
    "    zmatrix_to_print.append([2,atno_to_element[atom2],1,0,0,r1,0.,0.])\n",
    "    print(\"Placing second atom along z\")\n",
    "    print('\\n')\n",
    "    print(\"updated zmatrix\")\n",
    "    print('\\t'.join(map(str, [\" \",\"atom\",\" \",\" \",\" \",\"r\",\"a\",\"d\"])))\n",
    "    for item in zmatrix_to_print:\n",
    "        print('\\t'.join(map(str, item)))\n",
    "    print('\\n')\n",
    "\n",
    "    third_atom=str(input(\"add a third atom? Y/N  \"))\n",
    "    if third_atom=='Y':\n",
    "        atom3=int(input(\"Input atomic number of atom 3   \"))\n",
    "        atoms.append(atom3)\n",
    "        print('\\n')\n",
    "        print(\"for the third atom, input the two connection numbers\")\n",
    "        print(\"these are: the atom to which the new atom is bonded, the atom to which the angle is defined\")\n",
    "        print(\"numbering starts from 1\")\n",
    "        print('\\n')\n",
    "        connection2=input(\"input 2 connection numbers, 1 2  or 2 1   \")\n",
    "        connection2=[int(x) for x in connection2.split()]\n",
    "        print('\\n')\n",
    "        distance_and_angle=input(\"input bond length, and bond angle, 1.1 109.5 for example   \")\n",
    "        distance_and_angle=[float(x) for x in distance_and_angle.split()]\n",
    "        zmatrix.append(connection2+[0]+distance_and_angle+[0.])\n",
    "        zmatrix_to_print.append([3]+[atno_to_element[atom3]]+connection2+[0]+distance_and_angle+[0.])\n",
    "        print('\\n')\n",
    "        print(\"updated zmatrix\")\n",
    "        print('\\t'.join(map(str, [\" \",\"atom\",\" \",\" \",\" \",\"r\",\"a\",\"d\"])))\n",
    "        for item in zmatrix_to_print:\n",
    "            print('\\t'.join(map(str, item)))\n",
    "\n",
    "        #now set up all further atoms:\n",
    "        print('\\n')\n",
    "        print(\"for every additional atom, input the three connection numbers\")\n",
    "        print(\"these are: the atom to which the new atom is bonded, the atom to which the angle is defined and the atom to which the dihedral is defined\")\n",
    "        print('\\n')\n",
    "    \n",
    "        counter=4\n",
    "        more_atoms=str(input(\"add another atom? Y/N   \"))\n",
    "        while more_atoms=='Y':\n",
    "            print('\\n')\n",
    "            atom_number=int(input(\"Input atomic number of the new atom      \"))\n",
    "            atoms.append(atom_number)\n",
    "            connections=input(\"input three connection numbers, 1 2 3 , for example   \")\n",
    "            distances_and_angles=input(\"input bond length, bond angle and dihedral, 1.1 109.5 120. for example   \")\n",
    "            connections=[int(x) for x in connections.split()]\n",
    "            distances_and_angles=[float(x) for x in distances_and_angles.split()]\n",
    "            zmatrix.append(connections+distances_and_angles)\n",
    "            zmatrix_to_print.append([counter]+[atno_to_element[atom_number]]+connections+distances_and_angles)\n",
    "            print('\\n')\n",
    "            print(\"updated zmatrix\")\n",
    "            print('\\t'.join(map(str, [\" \",\"atom\",\" \",\" \",\" \",\"r\",\"a\",\"d\"])))\n",
    "            for item in zmatrix_to_print:\n",
    "                print('\\t'.join(map(str, item)))\n",
    "            print('\\n')\n",
    "            counter+=1\n",
    "            #print(\"zm\",zmatrix)\n",
    "            more_atoms=str(input(\"add another atom? Y/N\"))\n",
    "    \n",
    "    coords=zmatrix_to_cartesian(zmatrix)\n",
    "    \n",
    "    #remove dummy atoms\n",
    "    new_atoms=[];new_coords=[]\n",
    "    for i in range(len(atoms)):\n",
    "        if atoms[i]!=100:\n",
    "            new_atoms.append(atoms[i]);new_coords.append(coords[i])\n",
    "        else:\n",
    "            print(\"removed dummy atom\",i)\n",
    "    atoms=new_atoms;coords=new_coords\n",
    "\n",
    "    params=str(input(\"which parameter set would you like to use, Hoffmann or Cerda?  \"))\n",
    "\n",
    "    print('\\n')\n",
    "    print(\"molecule is \",mol)\n",
    "    print(\"the standard settings are:\",'\\n')\n",
    "    print(\"Parameter set:\",params)\n",
    "    print(\"Charge\",Q)\n",
    "    print(\"Atoms and Coordinates (in Angstroms)\")\n",
    "    for i in range(len(coords)):\n",
    "        print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]) ) \n",
    "\n",
    "    change_molecule=input(\"Do you want to edit any of these values?  Y/N  \")#\n",
    "\n",
    "    while change_molecule=='Y':\n",
    "        print('\\n')\n",
    "        print(\"the options to change are:  \")\n",
    "        print(\"1  list of atoms\")\n",
    "        print(\"2  list of coordinates\")\n",
    "        print(\"3  title\")\n",
    "        print(\"4  parameter set\")\n",
    "        print('\\n')\n",
    "        what_to_change_molecule=int(input(\"what do you want to change? enter 1, 2, 3 or 4 \"))\n",
    "        if what_to_change_molecule==1:\n",
    "            new_atoms=input(\"input a list of atomic numbers, 8 1 1 for H2O, for example \")\n",
    "            atoms=[int(x) for x in new_atoms.split()]\n",
    "            print(\"updated coordinates\")\n",
    "            for i in range(len(atoms)):\n",
    "                print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==2:\n",
    "            updated_coords=[]\n",
    "            which_atom=int(input(\"which atom's coordinates do you want to change? remember to start from 0  \"))\n",
    "            new_atom_coords=input(\"input x, y and z coordinates, 0. 0. 2. , for example \")\n",
    "            new_atom_coords_temp=[float(x) for x in new_atom_coords.split()]\n",
    "            coords[which_atom]=new_atom_coords_temp\n",
    "            print('\\n')\n",
    "            print(\"updated coordinates\")\n",
    "            for i in range(len(atoms)):\n",
    "                print(\"%-5s\"%(str(atoms[i])),\"%10.2f\"%(coords[i][0]),\"%10.2f\"%(coords[i][1]),\"%10.2f\"%(coords[i][2]))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==3:\n",
    "            title=str(input(\"new title  \"))\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        elif what_to_change_molecule==4:\n",
    "            params=input(\"type parameter set:  Hoffmann or Cerda  \")\n",
    "            print('\\n')\n",
    "            print(\"switched to the \",params,\" parameter set\")\n",
    "            print('\\n')\n",
    "            change_molecule=input(\"do you want to change anything else?  Y/N  \")        \n",
    "\n",
    "    return title,mol,params,coords,atoms,Q\n",
    "\n",
    "#CHARACTER TABLEs\n",
    "\n",
    "##Character Tables\n",
    "\n",
    "pg_irreps = { #String lengths should be equal to keep printout neat!\n",
    "'C1':('a1',),\n",
    "'Cs':('a\\'','a\"'),\n",
    "'Ci':('ag','au'),\n",
    "'Cinfv':('sigma+','sigma-','pi','delta'),\n",
    "'Cinfv_print':('sigma+','sigma-','pi','delta'),\n",
    "'Dinfh':('sigmag+','sigmag-','piu','deltag','sigmau+','sigmau-','pig','deltau'),\n",
    "'Dinfh_print':('sigmag+','sigmag-','piu','deltag','sigmau+','sigmau-','pig','deltau'),\n",
    "'C2v':('a1','a2','b2','b1'),\n",
    "'C3v':('a1','a2','e '),\n",
    "'C4v':('a1','a2','b1', 'b2', 'e '),\n",
    "'C5v':('a1','a2','e1','e2'),\n",
    "'C6v':('a1','a2','b1', 'b2', 'e1','e2'),\n",
    "'C2h':('ag','bg','au','bu'),\n",
    "'C3h':('a\\' ','e\\'','a\"','e\"'),\n",
    "'Td':('a1','a2','e','t1','t2'),\n",
    "'Oh':('a1g','a2g','eg ','t1g','t2g','a1u','a2u','eu ','t1u','t2u'),\n",
    "'Ih':('ag','t1g','t2g ','gg','hg','au','t1u','t2u ','gu','hu'),\n",
    "'I':('a','t1','t2 ','g','h'),\n",
    "'D2':('a', 'b1', 'b2', 'b3'),\n",
    "'D3':('a1', 'a2', 'e'),\n",
    "'D4':('a1', 'a2', 'b1', 'b2', 'e'),\n",
    "'D5':('a1', 'a2', 'e1', 'e2'),\n",
    "'D2h':('ag', 'b1g', 'b2g', 'b3g', 'au', 'b1u','b2u','b3u'),\n",
    "'D3h':('a1\\'', 'a2\\'', 'e\\'', 'a1\"', 'a2\"', 'e\"'),\n",
    "'D4h':( 'a1g', 'a2g', 'b1g', 'b2g', 'eg', 'a1u', 'a2u', 'b1u', 'b2u', 'eu'),\n",
    "'D5h':('a1\\' ', 'a2\\' ', 'e1\\' ', 'e2\\' ', 'a1\"', 'a2\"', 'e1\"', 'e2\"'),\n",
    "'D6h':( 'a1g', 'a2g', 'b1g', 'b2g', 'e1g', 'e2g', 'a1u', 'a2u', 'b1u', 'b2u', 'e1u', 'e2u'),\n",
    "'D2d':('a1', 'a2', 'b1', 'b2', 'e'),\n",
    "'D3d':('a1g', 'a2g', 'eg', 'a1u', 'a2u', 'eu'),\n",
    "'D4d':('a1', 'a2', 'b1', 'b2', 'e1', 'e2','e3'),\n",
    "'D5d':('a1g', 'a2g', 'e1g', 'e2g', 'a1u', 'a2u','e1u','e2u'),\n",
    "'D6d':('a1', 'a2', 'b1', 'b2', 'e1', 'e2','e3','e4','e5'),\n",
    "}\n",
    "\n",
    "#below is a dictionary of lists giving the symmetry types of d orbitals (in order dxy,dx2-y2,dxz,dyz,dz2) on invariant points\n",
    "#this is a short-term fix until I fully implement d orbitals.\n",
    "#ml=0: dz2; ml=+2: dx2-y2; ml=-2: dxy; ml=+1: dxz; ml=-1: dyz\n",
    "#order in which orbitals appear in basis set list 2,0,-2,1,-1\n",
    "invariant_pts = {\n",
    "'C1':(0,0,0,0,0),\n",
    "'Cs':(0,0,0,1,1),\n",
    "'Ci':(0,0,0,0,0),\n",
    "'Cinfv':(3,0,3,2,2),\n",
    "'Dinfh':(3,0,3,6,6),\n",
    "'C2v':(0,0,1,2,3),\n",
    "'C3v':(2,0,2,2,2),\n",
    "'C4v':(2,0,3,4,4),\n",
    "'C5v':(3,0,3,2,2),\n",
    "'C6v':(5,0,5,4,4),\n",
    "'C2h':(0,0,0,1,1),\n",
    "'C3h':(1,0,1,3,3),\n",
    "'Oh':(2,2,4,4,4),\n",
    "'Ih':(4,4,4,4,4),\n",
    "'I':(4,4,4,4,4),\n",
    "'Td':(2,2,4,4,4),\n",
    "'D2':(0,0,1,2,3),\n",
    "'D3':(2,0,2,2,2),\n",
    "'D4':(2,0,3,4,4),\n",
    "'D4':(2,0,3,4,4),\n",
    "'D2h':(0,0,1,2,3),\n",
    "'D3h':(2,0,2,5,5),\n",
    "'D4h':(2,0,3,4,4),\n",
    "'D5h':(3,0,3,6,6),\n",
    "'D6h':(5,0,5,4,4),\n",
    "'D2d':(2,0,3,4,4),\n",
    "'D3d':(2,0,2,2,2),\n",
    "'D4d':(5,0,5,6,6),\n",
    "'D5d':(3,0,3,2,2),\n",
    "'D6d':(5,0,5,8,8),\n",
    "}\n",
    "\n",
    "pg_operations = {\n",
    "# FORMAT:\n",
    "# ('Operator type', [ARGS])\n",
    "# E - Identity      [Class]; class is an index corresponding to class of the symmetry operation\n",
    "# C - Rotation -    [Class,X,Y,Z,a] x,y,z describe the axis, angle = 360°/a\n",
    "# R - reflection -  [Class,X,Y,Z], describe plane perpendicular to axis\n",
    "# I - inversion -   [Class],\n",
    "# S - improper rotation -    [Class,X,Y,Z,a] x,y,z describe the axis and perpendicular plane, angle = 360°/a\n",
    "\n",
    "'cx':(  ('E',[0]), #Dummy point group used to identify primary axis\n",
    "        ('C',[0,0.,0.,1.,2]),\n",
    "        ('C',[0,0.,0.,1.,3]),\n",
    "        ('C',[0,0.,0.,1.,4]),\n",
    "        ('C',[0,0.,0.,1.,5]),\n",
    "        ('C',[0,0.,0.,1.,6]),\n",
    "        ('C',[0,0.,0.,1.,7]),\n",
    "        ('C',[0,0.,0.,1.,8]),\n",
    "        ('C',[0,0.,0.,1.,9]),\n",
    "        ('C',[0,0.,0.,1.,10])),\n",
    "\n",
    "\n",
    "'Dinfh':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),('C',[1,0.,0.,1.,5/4]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),  ('C',[2,0.,0.,1.,5/3]),\n",
    "        ('C',[3, 0.,  1.,  0., 2],), ('C',[3, 0.95105652,  0.30901699,  0., 2],), ('C',[3, 0.58778525, -0.80901699,  0., 2],), ('C',[3,-0.58778525, -0.80901699,  0., 2],), ('C',[3,-0.95105652,  0.30901699,  0., 2],),\n",
    "        ('R',[4,0.,0.,1.]),\n",
    "        ('S',[5,0.,0.,1.,5]),        ('S',[5,0.,0.,1.,5/4]),\n",
    "        ('S',[6,0.,0.,1.,5/2]),        ('S',[6,0.,0.,1.,5/3]),\n",
    "        ('R', [7,  1.0,  0.,  0.,5  ]),('R', [7,  0.30901699, -0.95105652,  0.,5/1]),('R', [7, -0.80901699, -0.58778525,  0.,5/2]),('R', [7, -0.80901699,  0.58778525,  0.,5/3]),('R', [7,  0.30901699,  0.95105652,  0.,5/4])\n",
    "        ),\n",
    "#same as D5h\n",
    "\n",
    "'Cinfv':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),('C',[1,0.,0.,1.,5/4]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),('C',[2,0.,0.,1.,5/3]),\n",
    "        ('R', [3,  1.0,  0.,  0.,5  ]),('R', [3,  0.30901699, -0.95105652,  0.,5/1]),('R', [3, -0.80901699, -0.58778525,  0.,5/2]),('R', [3, -0.80901699,  0.58778525,  0.,5/3]),('R', [3,  0.30901699,  0.95105652,  0.,5/4])),\n",
    "#just using the same as C5v here\n",
    "    \n",
    "    \n",
    "'C1':(   ('E', [0]),),\n",
    "'C2':(('E',[0]),('C',[1,0.,0.,1.,2])),\n",
    "'C3':(('E',[0]),('C',[1,0.,0.,1.,3]),('C',[2,0.,0.,1.,3/2,])),\n",
    "'C4':(('E',[0]),('C',[1,0.,0.,1.,4]),('C',[2,0.,0.,1.,2]),  ('C',[3,0.,0.,1.,4/3]),),\n",
    "'C5':(('E',[0]),('C',[1,0.,0.,1.,5]),('C',[2,0.,0.,1.,5/2]),('C',[3,0.,0.,1.,5/3]),('C',[4,0.,0.,1.,5/4])),\n",
    "'C6':(  ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,6]),('C',[2,0.,0.,1.,6/2]),('C',[3,0.,0.,1.,6/3]),('C',[4,0.,0.,1.,6/4]),('C',[5,0.,0.,1.,6/5])),\n",
    "\n",
    "'Cs':(('E',[0]),('R',[1,0.,0.,1.])),   #standard orientation, plane perpendicular to z\n",
    "'Ci':(('E',[0]),('I',[1])),\n",
    "\n",
    "###Cnv\n",
    "'C2v':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,2]),\n",
    "        ('R',[2,0.,1.,0.]),\n",
    "        ('R',[3,1.,0.,0.])),\n",
    "'C3v':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,3]),('C',[1,0.,0.,1.,3/2]),\n",
    "        ('R',[2,1,3**0.5,0.]),('R',[2,1,0.,0.]),('R',[2,1,-3**0.5,0.])\n",
    "        ),\n",
    "'C4v':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,4]),('C',[1,0.,0.,1.,4/3]),\n",
    "        ('C',[2,0.,0.,1.,2]),\n",
    "        ('R',[3,1.,0.,0.]),('R',[3,0.,1.,0.]),\n",
    "        ('R',[4,1.,1.,0.]),('R',[4,-1.,1.,0.]),\n",
    "        ),\n",
    "'C5v':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),('C',[1,0.,0.,1.,5/4]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),('C',[2,0.,0.,1.,5/3]),\n",
    "        ('R', [3,  1.0,  0.,  0.,5  ]),('R', [3,  0.30901699, -0.95105652,  0.,5/1]),('R', [3, -0.80901699, -0.58778525,  0.,5/2]),('R', [3, -0.80901699,  0.58778525,  0.,5/3]),('R', [3,  0.30901699,  0.95105652,  0.,5/4])),\n",
    "\n",
    "'C6v':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,6]),('C',[1,0.,0.,1.,6/5]),\n",
    "        ('C',[2,0.,0.,1.,3]),('C',[2,0.,0.,1.,3/2]),\n",
    "        ('C',[3,0.,0.,1.,2]),\n",
    "        ('R',[4,1,3**0.5,0.]),('R',[4,1,0.,0.]),('R',[4,1,-3**0.5,0.]),\n",
    "        ('R',[5,0.,1.,0.]),('R',[5,3**0.5,1.,0.]),('R',[5,-3**0.5,1.,0.])),\n",
    "\n",
    "###Cnh\n",
    "\n",
    "'C2h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,2]),\n",
    "        ('I',[2]),\n",
    "        ('R',[3,0.,0.,1.])\n",
    "        ),\n",
    "'C3h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,3]),('C',[1,0.,0.,1.,3/2,]),\n",
    "        ('R',[2,0.,0.,1.]),\n",
    "        ('S',[3,0.,0.,1.,3]),('S',[3,0.,0.,1.,3/2,])\n",
    "        ),\n",
    "'C4h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,4]),\n",
    "        ('C',[2,0.,0.,1.,2]),\n",
    "        ('C',[3,0.,0.,1.,4/3]),\n",
    "        ('I',[4]),\n",
    "        ('S',[5,0.,0.,1.,4/3]),\n",
    "        ('R',[6,0.,0.,1.]),\n",
    "        ('S',[7,0.,0.,1.,4])\n",
    "        ),\n",
    "'C5h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),\n",
    "        ('C',[3,0.,0.,1.,5/3]),\n",
    "        ('C',[4,0.,0.,1.,5/4]),\n",
    "        ('R',[5,0.,0.,1.]),\n",
    "        ('S',[6,0.,0.,1.,5]),\n",
    "        ('S',[7,0.,0.,1.,5/2]),\n",
    "        ('S',[8,0.,0.,1.,5/3]),\n",
    "        ('S',[9,0.,0.,1.,5/4])\n",
    "        ),\n",
    "'C6h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,6]),\n",
    "        ('C',[2,0.,0.,1.,6/2]),\n",
    "        ('C',[3,0.,0.,1.,6/3]),\n",
    "        ('C',[4,0.,0.,1.,6/4]),\n",
    "        ('C',[5,0.,0.,1.,6/5]),\n",
    "        ('I',[6]),\n",
    "        ('S',[7,0.,0.,1.,6/4]),\n",
    "        ('S',[8,0.,0.,1.,6/5]),\n",
    "        ('R',[9,0.,0.,1.]),\n",
    "        ('S',[10,0.,0.,1.,6]),\n",
    "        ('S',[10,0.,0.,1.,6/2])\n",
    "        ),\n",
    "###Dn\n",
    "'D2':(  ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,2]),\n",
    "        ('C',[2,0.,1.,0.,2]),\n",
    "        ('C',[3,1.,0.,0.,2]),\n",
    "),\n",
    "\n",
    "'D3':(  ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,3]),('C',[1,0.,0.,1.,3/2]),\n",
    "        ('C',[2,1,3**0.5,0.,2]),('C',[2,1,0.,0.,2]),('C',[2,1,-3**0.5,0.,2]),\n",
    "),\n",
    "\n",
    "'D4':(  ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,4]),('C',[1,0.,0.,1.,4/3]),\n",
    "        ('C',[2,0.,0.,1.,2]),\n",
    "        ('C',[3,1.,0.,0.,2]), ('C',[3,0.,1.,0.,2]),\n",
    "        ('C',[4,1.,1.,0.,2]), ('C',[4,1.,-1.,0.,2]),\n",
    "),\n",
    "\n",
    "'D5':(  ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),('C',[1,0.,0.,1.,5/4]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),  ('C',[2,0.,0.,1.,5/3]),\n",
    "        ('C',[3, 0.,  1.,  0., 2],), ('C',[3, 0.95105652,  0.30901699,  0., 2],), ('C',[3, 0.58778525, -0.80901699,  0., 2],), ('C',[3,-0.58778525, -0.80901699,  0., 2],), ('C',[3,-0.95105652,  0.30901699,  0., 2],),\n",
    "\n",
    "),\n",
    "\n",
    "'D6':(  ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,6]),('C',[1,0.,0.,1.,6/5]),\n",
    "        ('C',[2,0.,0.,1.,3]),('C',[2,0.,0.,1.,3/2]),\n",
    "        ('C',[3,0.,0.,1.,2]),\n",
    "        ('C',[4,0.,1.,0., 2]),('C',[4,3**0.5,1.,0., 2]),('C',[4,-3**0.5,1.,0., 2]),\n",
    "        ('C',[5,1,3**0.5, 0., 2]),('C',[5,1,0., 0., 2]),('C',[5,1,-3**0.5, 0., 2]),\n",
    "),\n",
    "\n",
    "###Dnh\n",
    "'D2h':( ('E',[0]),\n",
    "        ('C',[1, 0.,0.,1.,2]),\n",
    "        ('C',[2, 0.,1.,0.,2]),\n",
    "        ('C',[3, 1.,0.,0.,2]),\n",
    "        ('I',[4]),\n",
    "        ('R',[5, 0.,0.,1.]),\n",
    "        ('R',[6, 0.,1.,0.]),\n",
    "        ('R',[7, 1.,0.,0.])\n",
    "        ),\n",
    "\n",
    "'D3h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,3]),('C',[1,0.,0.,1.,3/2]),\n",
    "        ('C',[2,1,3**0.5,0.,2]),('C',[2,1,0.,0.,2]),('C',[2,1,-3**0.5,0.,2]),\n",
    "        ('R',[3,0.,0.,1.]),\n",
    "        ('S',[4,0.,0.,1.,3]),('S',[4,0.,0.,1.,3/2]),\n",
    "        ('R',[5,0.,1.,0.]),('R',[5,3**0.5,1.,0.]),('R',[5,-3**0.5,1.,0.])\n",
    "        ),\n",
    "\n",
    "'D4h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,4]),('C',[1,0.,0.,1.,4/3]),\n",
    "        ('C',[2,0.,0.,1.,2]),\n",
    "        ('C',[3,1.,0.,0.,2]), ('C',[3,0.,1.,0.,2]),\n",
    "        ('C',[4,1.,1.,0.,2]), ('C',[4,1.,-1.,0.,2]),\n",
    "        ('I',[5]),\n",
    "        ('S',[6,0.,0.,1.,4/3]),('S',[6,0.,0.,1.,4]),\n",
    "        ('R',[7,0.,0.,1.]),\n",
    "        ('R',[8,1.,0.,0.]), ('R',[8,0.,1.,0.]),\n",
    "        ('R',[9,1.,1.,0.]), ('R',[9,1.,-1.,0.])\n",
    "        ),\n",
    "\n",
    "'D5h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),('C',[1,0.,0.,1.,5/4]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),  ('C',[2,0.,0.,1.,5/3]),\n",
    "        ('C',[3, 0.,  1.,  0., 2],), ('C',[3, 0.95105652,  0.30901699,  0., 2],), ('C',[3, 0.58778525, -0.80901699,  0., 2],), ('C',[3,-0.58778525, -0.80901699,  0., 2],), ('C',[3,-0.95105652,  0.30901699,  0., 2],),\n",
    "        ('R',[4,0.,0.,1.]),\n",
    "        ('S',[5,0.,0.,1.,5]),\n",
    "        ('S',[5,0.,0.,1.,5/4]),\n",
    "        ('S',[6,0.,0.,1.,5/2]),\n",
    "        ('S',[6,0.,0.,1.,5/3]),\n",
    "        ('R', [7,  1.0,  0.,  0.,5  ]),('R', [7,  0.30901699, -0.95105652,  0.,5/1]),('R', [7, -0.80901699, -0.58778525,  0.,5/2]),('R', [7, -0.80901699,  0.58778525,  0.,5/3]),('R', [7,  0.30901699,  0.95105652,  0.,5/4])\n",
    "        ),\n",
    "\n",
    "'D6h':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,6]),('C',[1,0.,0.,1.,6/5]),\n",
    "        ('C',[2,0.,0.,1.,3]),('C',[2,0.,0.,1.,3/2]),\n",
    "        ('C',[3,0.,0.,1.,2]),\n",
    "        ('C',[4,0.,1.,0., 2]),('C',[4,3**0.5,1.,0., 2]),('C',[4,-3**0.5,1.,0., 2]),\n",
    "        ('C',[5,1,3**0.5, 0., 2]),('C',[5,1,0., 0., 2]),('C',[5,1,-3**0.5, 0., 2]),\n",
    "        ('I',[6]),\n",
    "        ('S',[7,0.,0.,1.,3]),('S',[7,0.,0.,1.,3/2]),\n",
    "        ('S',[8,0.,0.,1.,6]),('S',[8,0.,0.,1.,6/5]),\n",
    "        ('R',[9,0.,0.,1.]),\n",
    "        ('R',[10,0.,1.,0.]),('R',[10,3**0.5,1.,0.]),('R',[10,-3**0.5,1.,0.]),\n",
    "        ('R',[11,1,3**0.5,0.]),('R',[11,1,0.,0.]),('R',[11,1,-3**0.5,0.]),\n",
    "        ),\n",
    "###Dnd\n",
    "\n",
    "'D2d':( ('E',[0]),\n",
    "        ('S',[1, 0.,0.,1.,4]),\n",
    "        ('S',[1, 0.,0.,1.,4/3]),\n",
    "        ('C',[2, 0.,0.,1.,2]),\n",
    "        ('C',[3, 1.,1.,0.,2]),\n",
    "        ('C',[3, 1.,-1.,0.,2]),\n",
    "        ('R',[4, 1.,0.,0.]),\n",
    "        ('R',[4, 0.,1.,0.])\n",
    "        ),\n",
    "\n",
    "'D3d':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,3]),('C',[1,0.,0.,1.,3/2]),\n",
    "        ('C',[2,1,3**0.5,0.,2]),('C',[2,1,0.,0.,2]),('C',[2,1,-3**0.5,0.,2]),\n",
    "        ('I',[3, 1.,-1.,0.,2]),\n",
    "        ('S',[4,0.,0.,1.,6]),('S',[4,0.,0.,1.,6/5]),\n",
    "        ('R',[5,1.,0.,0.]),('R',[5,1.,3**0.5,0.]),('R',[5,1.,-3**0.5,0.])\n",
    "        ),\n",
    "\n",
    "'D4d':( ('E',[0]),\n",
    "        ('S',[1,0.,0.,1.,8]),('S',[1,0.,0.,1.,8/7]),\n",
    "        ('C',[2,0.,0.,1.,4]),('C',[2,0.,0.,1.,4/3]),\n",
    "        ('S',[3,0.,0.,1.,8/3]),('S',[3,0.,0.,1.,8/5]),\n",
    "        ('C',[4,0.,0.,1.,2]),\n",
    "        ('C',[5,np.cos(np.pi/8),np.sin(np.pi/8),0.,2]),\n",
    "        ('C',[5,np.cos(3*np.pi/8),np.sin(3*np.pi/8),0.,2]),\n",
    "        ('C',[5,np.cos(5*np.pi/8),np.sin(5*np.pi/8),0.,2]),\n",
    "        ('C',[5,np.cos(7*np.pi/8),np.sin(7*np.pi/8),0.,2]),\n",
    "        ('R',[6,1.,0.,0.]),('R',[6,0.,1.,0.]),('R',[6,1.,1.,0.]),('R',[6,1.,-1.,0.]),\n",
    "        ),\n",
    "\n",
    "'D5d':( ('E',[0]),\n",
    "        ('C',[1,0.,0.,1.,5]),('C',[1,0.,0.,1.,5/4]),\n",
    "        ('C',[2,0.,0.,1.,5/2]),  ('C',[2,0.,0.,1.,5/3]),\n",
    "        ('C',[3, np.cos(np.pi/10), np.sin(np.pi/10), 0., 2]),('C',[3, np.cos(3*np.pi/10), np.sin(3*np.pi/10), 0., 2]),\n",
    "        ('C',[3, np.cos(5*np.pi/10), np.sin(5*np.pi/10), 0., 2]),('C',[3, np.cos(7*np.pi/10), np.sin(7*np.pi/10), 0., 2]),\n",
    "        ('C',[3, np.cos(9*np.pi/10), np.sin(9*np.pi/10), 0., 2]),\n",
    "        ('I',[4]),\n",
    "        ('S',[5,0.,0.,1.,10/3]),('S',[5,0.,0.,1.,10/7]),\n",
    "        ('S',[6,0.,0.,1.,10]),('S',[6,0.,0.,1.,10/9]),\n",
    "        ('R', [7,  0.0,  1.,  0.,5  ]),('R',[7, np.sin(np.pi/5), np.cos(np.pi/5), 0.]),\n",
    "        ('R',[7, np.sin(2*np.pi/5), np.cos(2*np.pi/5), 0.]),('R',[7, np.sin(3*np.pi/5), np.cos(3*np.pi/5), 0.]),\n",
    "        ('R',[7, np.sin(4*np.pi/5), np.cos(4*np.pi/5), 0.]),\n",
    "        ),\n",
    "\n",
    "\n",
    "'D6d':( ('E',[0]),\n",
    "        ('S',[1,0.,0.,1.,12]),('S',[1,0.,0.,1.,12/11]),\n",
    "        ('C',[2,0.,0.,1.,6]),  ('C',[2,0.,0.,1.,6/5]),\n",
    "        ('S',[3,0.,0.,1.,4]),('S',[3,0.,0.,1.,4/3]),\n",
    "        ('C',[4,0.,0.,1.,3]),  ('C',[4,0.,0.,1.,3/2]),\n",
    "        ('S',[5,0.,0.,1.,12/5]),('S',[5,0.,0.,1.,12/7]),\n",
    "        ('C',[6,0.,0.,1.,2]),  \n",
    "        ('C',[7, np.cos(np.pi/12), np.sin(np.pi/12), 0., 2]),('C',[7, np.cos(3*np.pi/12), np.sin(3*np.pi/12), 0., 2]),\n",
    "        ('C',[7, np.cos(5*np.pi/12), np.sin(5*np.pi/12), 0., 2]),('C',[7, np.cos(7*np.pi/12), np.sin(7*np.pi/12), 0., 2]),\n",
    "        ('C',[7, np.cos(9*np.pi/12), np.sin(9*np.pi/12), 0., 2]),('C',[7, np.cos(11*np.pi/12), np.sin(11*np.pi/12), 0., 2]),\n",
    "        ('R',[8,  1.0,  0.,  0.,5  ]),('R',[8, np.cos(np.pi/6), np.sin(np.pi/6), 0.]),\n",
    "        ('R',[8, np.cos(np.pi/3), np.sin(np.pi/3), 0.]),('R',[8, np.cos(np.pi/2), np.sin(np.pi/2), 0.]),\n",
    "        ('R',[8, np.cos(2*np.pi/3), np.sin(2*np.pi/3), 0.]),('R',[8, np.cos(5*np.pi/6),np.sin(5*np.pi/6),0.])\n",
    "        ),\n",
    "\n",
    "#Td standard orientation places C2 and S4 axes along x,y,z axes, with C3 axes aligned\n",
    "#along bisectors of diagonals (i.e. 1,1,1,   -1,-1,1,   -1,1,-1  and 1,-1,-1)\n",
    "'Td':(\n",
    "    ('E',[0]),\n",
    "    ('C',[1,1.,1.,1.,3]),                              ('C',[1,1.,1.,1.,3/2]),\n",
    "    ('C',[1,-1.,-1.,1.,3]),                              ('C',[1,-1.,-1.,1.,3/2]),\n",
    "    ('C',[1,1.,-1.,-1.,3]),                              ('C',[1,1.,-1.,-1.,3/2]),\n",
    "    ('C',[1,-1.,1.,-1.,3]),                              ('C',[1,-1.,1.,-1.,3/2]),\n",
    "    ('C',[2,0.,0.,1, 2]), ('C',[2,1.,0.,0., 2]), ('C',[2,0.,1.,0., 2]),\n",
    "    ('S',[3,0.,0.,1, 4]), ('S',[3,1.,0.,0., 4]), ('S',[3,0.,1.,0., 4]),\n",
    "    ('S',[3,0.,0.,1, 4/3]), ('S',[3,1.,0.,0., 4/3]), ('S',[3,0.,1.,0., 4/3]),\n",
    "    ('R',[4, 1.,1.,0.]),('R',[4, -1.,1.,0.]),\n",
    "    ('R',[4, 1.,0.,1.]),('R',[4, -1.,0.,1.]),\n",
    "    ('R',[4, 0.,1.,1.]),('R',[4, 0.,1.,-1.]),\n",
    "),\n",
    "\n",
    "'Oh':(\n",
    "    ('E',[0]),\n",
    "    ('C',[1,1.,1.,1.,3]),   ('C',[1, 1.,1.,1.,3/2]),\n",
    "    ('C',[1,-1.,1.,1.,3]),  ('C',[1, -1.,1.,1.,3/2]),\n",
    "    ('C',[1,1.,-1.,1.,3]),  ('C',[1, 1.,-1.,1.,3/2]),\n",
    "    ('C',[1,-1.,-1.,1.,3]), ('C',[1, -1.,-1.,1.,3/2]),\n",
    "    ('C',[2,1.,1.,0.,2]),   ('C',[2,1.,0.,1.,2]),\n",
    "    ('C',[2,0.,1.,1.,2]),   ('C',[2,-1.,1.,0.,2]),\n",
    "    ('C',[2,-1.,0.,1.,2]),  ('C',[2,0.,-1.,1.,2]),\n",
    "    ('C',[3, 1.,0.,0.,4]),  ('C',[3, 1.,0.,0.,4/3]),\n",
    "    ('C',[3, 0.,1.,0.,4]),  ('C',[3, 0.,1.,0.,4/3]),\n",
    "    ('C',[3, 0.,0.,1.,4]),  ('C',[3, 0.,0.,1.,4/3]),\n",
    "    ('C',[4, 1.,0.,0.,4/2]), ('C',[4, 0.,1.,0.,4/2]),\n",
    "    ('C',[4, 0.,0.,1.,4/2]),\n",
    "    ('I',[5]),\n",
    "    ('S',[6, 1.,0.,0.,4]),('S',[6, 1.,0.,0.,4/3]),\n",
    "    ('S',[6, 0.,1.,0.,4]),('S',[6, 0.,1.,0.,4/3]),\n",
    "    ('S',[6, 0.,0.,1.,4]),('S',[6, 0.,0.,1.,4/3]),\n",
    "    ('S',[7, 1.,1.,1.,6]),  ('S',[7,1.,1.,1.,6/5]),\n",
    "    ('S',[7, -1.,1.,1.,6]), ('S',[7,-1.,1.,1.,6/5]),\n",
    "    ('S',[7, 1.,-1.,1.,6]), ('S',[7,1.,-1.,1.,6/5]),\n",
    "    ('S',[7, -1.,-1.,1.,6]),('S',[7,-1.,-1.,1.,6/5]),\n",
    "    ('R',[8, 1.,0.,0.]),    ('R',[8, 0.,1.,0.]),\n",
    "    ('R',[8, 0.,0.,1.]),    ('R',[9, 1.,1.,0.]),\n",
    "    ('R',[9, 1.,0.,1.]),    ('R',[9, 0.,1.,1.]),\n",
    "    ('R',[9, -1.,1.,0.]),   ('R',[9, -1.,0.,1.]),\n",
    "    ('R',[9, 0.,-1.,1.])),\n",
    "\n",
    "'Ih':(\n",
    "    ('E',[0]),\n",
    "\n",
    "    ('C',[1,0.,0.,1.,5]),   ('C',[1, 0.,0.,1.,5/4]),\n",
    "    ('C',[1,2.,0.,1,5]),  ('C',[1, 2.,0.,1,5/4]),\n",
    "    ('C',[1,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1.,5]),  ('C',[1,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1.,5/4]),\n",
    "    ('C',[1,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1.,5]), ('C',[1,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1.,5/4]),\n",
    "    ('C',[1,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1.,5]),  ('C',[1,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1.,5/4]),\n",
    "    ('C',[1,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1.,5]), ('C',[1,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1.,5/4]),\n",
    "\n",
    "    ('C',[2,0.,0.,1.,5/2]),   ('C',[2, 0.,0.,1.,5/3]),\n",
    "    ('C',[2,2.,0.,1,5/2]),  ('C',[2, 2.,0.,1,5/3]),\n",
    "    ('C',[2,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1.,5/2]),  ('C',[2,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1.,5/3]),\n",
    "    ('C',[2,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1.,5/2]), ('C',[2,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1.,5/3]),\n",
    "    ('C',[2,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1.,5/2]),  ('C',[2,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1.,5/3]),\n",
    "    ('C',[2,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1.,5/2]), ('C',[2,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1.,5/3]),\n",
    "\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(1*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(1*np.pi/5),np.cos(37.377*np.pi/180),3]),   \n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(1*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(1*np.pi/5),np.cos(37.377*np.pi/180),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(3*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(3*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(3*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(3*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(5*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(5*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(5*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(5*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(7*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(7*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(7*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(7*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(9*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(9*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(9*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(9*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(1*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(1*np.pi/5),np.cos(79.188*np.pi/180),3]),   \n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(1*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(1*np.pi/5),np.cos(79.188*np.pi/180),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(3*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(3*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(3*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(3*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(5*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(5*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(5*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(5*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(7*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(7*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(7*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(7*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(9*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(9*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(9*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(9*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),   \n",
    "    \n",
    "\n",
    "    ('C',[4,np.sin(31.717*np.pi/180.),0.,np.cos(31.717*np.pi/180.),2]),\n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(2*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(2*np.pi/5),np.cos(31.717*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(4*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(4*np.pi/5),np.cos(31.717*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(6*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(6*np.pi/5),np.cos(31.717*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(8*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(8*np.pi/5),np.cos(31.717*np.pi/180.),2]),     \n",
    "\n",
    "    ('C',[4,np.cos(np.pi/10),np.sin(np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(3*np.pi/10),np.sin(3*np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(5*np.pi/10),np.sin(5*np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(7*np.pi/10),np.sin(7*np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(9*np.pi/10),np.sin(9*np.pi/10),0.,2]),\n",
    "\n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(1*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(1*np.pi/5),np.cos(58.283*np.pi/180.),2]),\n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(3*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(3*np.pi/5),np.cos(58.283*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(5*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(5*np.pi/5),np.cos(58.283*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(7*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(7*np.pi/5),np.cos(58.283*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(9*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(9*np.pi/5),np.cos(58.283*np.pi/180.),2]),     \n",
    "\n",
    "    ('I',[5]),\n",
    "\n",
    "    ('S',[6,0.,0.,1.,10]),   ('S',[6, 0.,0.,1.,10/9]),\n",
    "    ('S',[6,2.,0.,1,10]),  ('S',[6, 2.,0.,1,10/9]),\n",
    "    ('S',[6,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1,10]),  ('S',[6,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1,10/9]),\n",
    "    ('S',[6,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1,10]),  ('S',[6,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1,10/9]),\n",
    "    ('S',[6,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1,10]),  ('S',[6,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1,10/9]),\n",
    "    ('S',[6,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1,10]),  ('S',[6,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1,10/9]),\n",
    "\n",
    "\n",
    "    ('S',[7,0.,0.,1.,10/3]),   ('S',[7, 0.,0.,1.,10/7]),\n",
    "    ('S',[7,2.,0.,1,10/3]),  ('S',[7, 2.,0.,1,10/7]),\n",
    "    ('S',[7,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1,10/3]),  ('S',[7,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1,10/7]),\n",
    "    ('S',[7,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1,10/3]), ('S',[7,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1,10/7]),\n",
    "    ('S',[7,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1,10/3]),  ('S',[7,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1,10/7]),\n",
    "    ('S',[7,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1,10/3]), ('S',[7,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1,10/7]),\n",
    "\n",
    "    \n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.),0.,np.cos(37.377*np.pi/180),6]),   \n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.),0.,np.cos(37.377*np.pi/180),6/5]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(2*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(2*np.pi/5),np.cos(37.377*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(2*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(2*np.pi/5),np.cos(37.377*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(4*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(4*np.pi/5),np.cos(37.377*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(4*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(4*np.pi/5),np.cos(37.377*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(6*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(6*np.pi/5),np.cos(37.377*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(6*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(6*np.pi/5),np.cos(37.377*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(8*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(8*np.pi/5),np.cos(37.377*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(37.377*np.pi/180.)*np.cos(8*np.pi/5),-np.sin(37.377*np.pi/180.)*np.sin(8*np.pi/5),np.cos(37.377*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.),0.,np.cos(79.188*np.pi/180),6]),   \n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.),0.,np.cos(79.188*np.pi/180),6/5]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(2*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(2*np.pi/5),np.cos(79.188*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(2*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(2*np.pi/5),np.cos(79.188*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(4*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(4*np.pi/5),np.cos(79.188*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(4*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(4*np.pi/5),np.cos(79.188*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(6*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(6*np.pi/5),np.cos(79.188*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(6*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(6*np.pi/5),np.cos(79.188*np.pi/180.),6/5]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(8*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(8*np.pi/5),np.cos(79.188*np.pi/180.),6]),\n",
    "    ('S',[8,-np.sin(79.188*np.pi/180.)*np.cos(8*np.pi/5),-np.sin(79.188*np.pi/180.)*np.sin(8*np.pi/5),np.cos(79.188*np.pi/180.),6/5]),\n",
    "   \n",
    "\n",
    "    ('R',[9,-np.sin(58.283*np.pi/180.),0.,np.cos(58.283*np.pi/180.)]),\n",
    "    ('R',[9,-np.sin(58.283*np.pi/180.)*np.cos(2*np.pi/5),-np.sin(58.283*np.pi/180.)*np.sin(2*np.pi/5),np.cos(58.283*np.pi/180.)]),  \n",
    "    ('R',[9,-np.sin(58.283*np.pi/180.)*np.cos(4*np.pi/5),-np.sin(58.283*np.pi/180.)*np.sin(4*np.pi/5),np.cos(58.283*np.pi/180.)]),  \n",
    "    ('R',[9,-np.sin(58.283*np.pi/180.)*np.cos(6*np.pi/5),-np.sin(58.283*np.pi/180.)*np.sin(6*np.pi/5),np.cos(58.283*np.pi/180.)]),  \n",
    "    ('R',[9,-np.sin(58.283*np.pi/180.)*np.cos(8*np.pi/5),-np.sin(58.283*np.pi/180.)*np.sin(8*np.pi/5),np.cos(58.283*np.pi/180.)]),     \n",
    "\n",
    "    ('R',[9,0.,1.,0.]),\n",
    "    ('R',[9,np.sin(2*np.pi/5),np.cos(2*np.pi/5),0.]),\n",
    "    ('R',[9,np.sin(4*np.pi/5),np.cos(4*np.pi/5),0.]),\n",
    "    ('R',[9,np.sin(6*np.pi/5),np.cos(6*np.pi/5),0.]),\n",
    "    ('R',[9,np.sin(8*np.pi/5),np.cos(8*np.pi/5),0.]),\n",
    "\n",
    "    ('R',[9,np.sin(31.717*np.pi/180.),0.,np.cos(31.717*np.pi/180.)]),\n",
    "    ('R',[9,np.sin(31.717*np.pi/180.)*np.cos(2*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(2*np.pi/5),np.cos(31.717*np.pi/180.)]),  \n",
    "    ('R',[9,np.sin(31.717*np.pi/180.)*np.cos(4*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(4*np.pi/5),np.cos(31.717*np.pi/180.)]),  \n",
    "    ('R',[9,np.sin(31.717*np.pi/180.)*np.cos(6*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(6*np.pi/5),np.cos(31.717*np.pi/180.)]),  \n",
    "    ('R',[9,np.sin(31.717*np.pi/180.)*np.cos(8*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(8*np.pi/5),np.cos(31.717*np.pi/180.)])\n",
    "\n",
    "),\n",
    "\n",
    "\n",
    "'I':(\n",
    "    ('E',[0]),\n",
    "    ('C',[1,0.,0.,1.,5]),   ('C',[1, 0.,0.,1.,5/4]),\n",
    "    ('C',[1,2.,0.,1.,5]),   ('C',[1, 2.,0.,1.,5/4]),\n",
    "    ('C',[1,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1,5]),  ('C',[1,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1,5/4]),\n",
    "    ('C',[1,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1,5]),  ('C',[1,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1,5/4]),\n",
    "    ('C',[1,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1,5]),  ('C',[1,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1,5/4]),\n",
    "    ('C',[1,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1,5]),  ('C',[1,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1,5/4]),\n",
    "\n",
    "    ('C',[2,0.,0.,1.,5/2]),   ('C',[2, 0.,0.,1.,5/3]),\n",
    "    ('C',[2,2.,0.,1.,5/2]),  ('C',[2, 2.,0.,1.,5/3]),\n",
    "    ('C',[2,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1.,5/2]),  ('C',[2,2*np.cos(2*np.pi/5),2*np.sin(2*np.pi/5),1.,5/3]),\n",
    "    ('C',[2,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1.,5/2]),  ('C',[2,2*np.cos(4*np.pi/5),2*np.sin(4*np.pi/5),1.,5/3]),\n",
    "    ('C',[2,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1.,5/2]),  ('C',[2,2*np.cos(6*np.pi/5),2*np.sin(6*np.pi/5),1.,5/3]),\n",
    "    ('C',[2,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1.,5/2]),  ('C',[2,2*np.cos(8*np.pi/5),2*np.sin(8*np.pi/5),1.,5/3]),\n",
    "\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(1*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(1*np.pi/5),np.cos(37.377*np.pi/180),3]),   \n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(1*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(1*np.pi/5),np.cos(37.377*np.pi/180),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(3*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(3*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(3*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(3*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(5*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(5*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(5*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(5*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(7*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(7*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(7*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(7*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(9*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(9*np.pi/5),np.cos(37.377*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(37.377*np.pi/180.)*np.cos(9*np.pi/5),np.sin(37.377*np.pi/180.)*np.sin(9*np.pi/5),np.cos(37.377*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(1*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(1*np.pi/5),np.cos(79.188*np.pi/180),3]),   \n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(1*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(1*np.pi/5),np.cos(79.188*np.pi/180),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(3*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(3*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(3*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(3*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(5*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(5*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(5*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(5*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(7*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(7*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(7*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(7*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(9*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(9*np.pi/5),np.cos(79.188*np.pi/180.),3]),\n",
    "    ('C',[3,np.sin(79.188*np.pi/180.)*np.cos(9*np.pi/5),np.sin(79.188*np.pi/180.)*np.sin(9*np.pi/5),np.cos(79.188*np.pi/180.),3/2]),   \n",
    "\n",
    "    ('C',[4,np.sin(31.717*np.pi/180.),0.,np.cos(31.717*np.pi/180.),2]),\n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(2*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(2*np.pi/5),np.cos(31.717*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(4*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(4*np.pi/5),np.cos(31.717*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(6*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(6*np.pi/5),np.cos(31.717*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(31.717*np.pi/180.)*np.cos(8*np.pi/5),np.sin(31.717*np.pi/180.)*np.sin(8*np.pi/5),np.cos(31.717*np.pi/180.),2]),     \n",
    "\n",
    "    ('C',[4,np.cos(np.pi/10),np.sin(np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(3*np.pi/10),np.sin(3*np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(5*np.pi/10),np.sin(5*np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(7*np.pi/10),np.sin(7*np.pi/10),0.,2]),\n",
    "    ('C',[4,np.cos(9*np.pi/10),np.sin(9*np.pi/10),0.,2]),\n",
    "\n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(1*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(1*np.pi/5),np.cos(58.283*np.pi/180.),2]),\n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(3*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(3*np.pi/5),np.cos(58.283*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(5*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(5*np.pi/5),np.cos(58.283*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(7*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(7*np.pi/5),np.cos(58.283*np.pi/180.),2]),  \n",
    "    ('C',[4,np.sin(58.283*np.pi/180.)*np.cos(9*np.pi/5),np.sin(58.283*np.pi/180.)*np.sin(9*np.pi/5),np.cos(58.283*np.pi/180.),2]),     \n",
    "\n",
    ")\n",
    "}\n",
    "\n",
    "#Dictionary of point group characters; Each column corresponds to a class of operator:\n",
    "pg_characters = {\n",
    "'C1':[[1],],\n",
    "\n",
    "'Cs':[\n",
    "[1.,1.],            #a'\n",
    "[1.,-1.],          #a\"\n",
    "],\n",
    "\n",
    "'Ci':[\n",
    "[1.,1.],            #ag\n",
    "[1.,-1.],          #au\n",
    "],\n",
    "\n",
    "    \n",
    "'Dinfh':[\n",
    "    [1,    1,            1,            1,    1,    1,            1,            1],   #sigmag+\n",
    "    [1,    1,            1,            -1,    1,    1,            1,           -1],  #sigmag-\n",
    "    [2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0,    2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0],  #pig\n",
    "    [2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0,    2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0],  #deltag\n",
    "    [1,    1,            1,            1,    -1,    -1,            -1,           -1],   #sigmau+\n",
    "    [1,    1,            1,            -1,    -1,    -1,            -1,            1],  #sigmau-\n",
    "    [2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0,    -2,    -2 * np.cos(2 * np.pi/5),-2 * np.cos(4 * np.pi/5),0],   #piu\n",
    "    [2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0,    -2,    -2 * np.cos(4 * np.pi/5),-2 * np.cos(2 * np.pi/5),0],   #deltau\n",
    "],\n",
    "#just using D5h\n",
    "\n",
    "#the version below just for printing    \n",
    "'Dinfh_print':[\n",
    "    [1,    1,                        1,    1,    1,                 1],   #sigmag+\n",
    "    [1,    1,                        -1,    1,    1,                  -1],  #sigmag-\n",
    "    [2,    2 * np.cos(2 * np.pi/5),     0,    2,    2 * np.cos(2 * np.pi/5),    0],  #pig\n",
    "    [2,    2 * np.cos(4 * np.pi/5),     0,    2,    2 * np.cos(4 * np.pi/5),    0],  #deltag\n",
    "    [1,    1,            1,           -1,    -1,                       -1],   #sigmau+\n",
    "    [1,    1,            1,           -1,    -1,                        1],  #sigmau-\n",
    "    [2,    2 * np.cos(2 * np.pi/5),     0,    -2,    -2 * np.cos(2 * np.pi/5),0],   #piu\n",
    "    [2,    2 * np.cos(4 * np.pi/5),     0,    -2,    -2 * np.cos(4 * np.pi/5),0],   #deltau\n",
    "],\n",
    "    \n",
    "'Cinfv':[\n",
    "[1.,1.,1.,1.],                    #sigma+\n",
    "[1.,1.,1.,-1.],                    #sigma-\n",
    "[2.,2*np.cos(2*np.pi/5),2*np.cos(4*np.pi/5),0],    #pi\n",
    "[2.,2*np.cos(4*np.pi/5),2*np.cos(2*np.pi/5),0]], #delta\n",
    "#just using C5v here\n",
    "\n",
    "'Cinfv_print':[\n",
    "[1.,1.,1.],                    #sigma+\n",
    "[1.,1.,-1.],                    #sigma-\n",
    "[2.,2*np.cos(2*np.pi/5),0],    #pi\n",
    "[2.,2*np.cos(4*np.pi/5),0]], #delta\n",
    "#just using C5v here\n",
    "\n",
    "    \n",
    "    \n",
    "'C2v':[\n",
    "[1.,1.,1.,1.],            #A1\n",
    "[1.,1.,-1.,-1.],          #A2\n",
    "[1.,-1.,1.,-1.],          #B1\n",
    "[1.,-1.,-1.,1.],          #A2\n",
    "],\n",
    "\n",
    "'C3v':[\n",
    "    [1.,  1.,  1.],       #A1\n",
    "    [1.,  1., -1.],       #A2\n",
    "    [2.,  -1., 0.],       #E\n",
    "],\n",
    "\n",
    "'C4v':[\n",
    "    [1.,  1.,  1.,  1.,  1,],       #A1\n",
    "    [1.,  1.,  1., -1., -1,],       #A2\n",
    "    [1., -1.,  1.,  1., -1,],       #B1\n",
    "    [1., -1.,  1., -1.,  1,],       #B1\n",
    "    [2.,  -0.,-2.,  0.,  0,],       #E\n",
    "],\n",
    "\n",
    "'C5v':[\n",
    "    [1,    1,            1,            1],\n",
    "    [1,    1,            1,            -1],\n",
    "    [2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0],\n",
    "    [2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0]],\n",
    "\n",
    "'C6v':[\n",
    "    [1.,  1.,  1.,  1.,  1,  1],       #A1\n",
    "    [1.,  1.,  1.,  1,  -1., -1,],       #A2\n",
    "    [1., -1.,  1., -1,   1., -1,],       #B1\n",
    "    [1., -1.,  1., -1,  -1.,  1,],       #B1\n",
    "    [2.,  1., -1., -2.,  0,   0,],       #E1\n",
    "    [2., -1., -1.,  2.,  0,   0,]],       #E2\n",
    "\n",
    "\n",
    "'C2h':[\n",
    "[1.,1.,1.,1.],            #ag\n",
    "[1.,-1.,1.,-1.],          #bg\n",
    "[1.,1.,-1.,-1.],          #au\n",
    "[1.,-1.,-1.,1.],          #bu\n",
    "],\n",
    "\n",
    "'C3h':[\n",
    "[1.,1.,1.,1.],            #ag\n",
    "[2.,-1.,2.,-1.],          #bg\n",
    "[1.,1.,-1.,-1.],          #au\n",
    "[2.,-1.,-2.,-1.],          #bu\n",
    "],\n",
    "\n",
    "'D2':[\n",
    "    [1,    1,    1,  1],   #a1\n",
    " [1,    1,     -1,   -1],  #b1\n",
    " [1,    -1,    1,   -1],   #b2\n",
    " [1,    -1,     -1,  1]],  #b3\n",
    "\n",
    "'D3':[[1,    1,    1],  #a1\n",
    " [1,    1,     -1],   #a2\n",
    " [2,    -1,    0]],   #e\n",
    "\n",
    "'D4':[\n",
    "    [1,    1,    1,  1,    1],\n",
    " [1,    1,     1,   -1,    -1],\n",
    " [1,    -1,    1,   1,    -1],\n",
    " [1,    -1,     1,  -1,    1],\n",
    " [2,    0,    -2,  0,    0]],\n",
    "\n",
    "'D5':[\n",
    "    [1,    1,    1,  1],\n",
    " [1,    1,     1,   -1],\n",
    " [2,    2*np.cos(2*np.pi/5),    2*np.cos(4*np.pi/5),   0],\n",
    " [2,    2*np.cos(4*np.pi/5),     2*np.cos(2*np.pi/5),  0]],\n",
    "\n",
    "'D2h':[\n",
    "    [1,    1,    1,  1,    1,    1,  1,  1],\n",
    " [1,    1,     -1,   -1,    1,    1,  -1,  -1],\n",
    " [1,    -1,     1,   -1,    1,    -1,  1,  -1],\n",
    " [1,    -1,     -1,   1,    1,    -1,  -1,  1],\n",
    " [1,    1,     1,   1,    -1,    -1,  -1,  -1],\n",
    " [1,    1,     -1,   -1,    -1,    -1,  1,  1],\n",
    " [1,    -1,     1,   -1,    -1,    1,  -1,  1],\n",
    " [1,    -1,     -1,   1,    -1,    1,  1,  -1]\n",
    "],  \n",
    "\n",
    "'D3h':[\n",
    "    [1,    1,    1,  1,    1,    1],\n",
    " [1,    1,     -1,   1,    1,    -1],\n",
    " [2,    -1,    0,   2,    -1,    0],\n",
    " [1,    1,     1,  -1,    -1,    -1],\n",
    " [1,    1,    -1,  -1,    -1,    1],\n",
    " [2,    -1,   0,   -2,    1,    0]],\n",
    "\n",
    "'D4h':[\n",
    "[1,  1,  1,  1,  1,    1,  1,  1,  1,  1],\n",
    "[1,  1,  1,  -1,  -1,  1,  1,  1,  -1,  -1],\n",
    "[1,  -1,  1,  1,  -1,  1,  -1,  1,  1,  -1],\n",
    "[1,  -1,  1,  -1,  1,  1,  -1,  1,  -1,  1],\n",
    "[2,  0,  -2,  0,  0,   2,  0,  -2,  0,  0],\n",
    "[1,  1,  1,  1,  1,    -1,  -1,  -1,  -1,  -1],\n",
    "[1,  1,  1,  -1,  -1,  -1,  -1,  -1,  1,  1],\n",
    "[1,  -1,  1,  1,  -1,  -1,  1,  -1,  -1,  1],\n",
    "[1,  -1,  1,  -1,  1,  -1,  1,  -1,  1,  -1],\n",
    "[2,  0,  -2,  0,  0,   -2,  0,  2,  0,  0],\n",
    "],\n",
    "\n",
    "'D5h':[\n",
    "    [1,    1,            1,            1,    1,    1,            1,            1],\n",
    "    [1,    1,            1,            -1,    1,    1,            1,           -1],\n",
    "    [2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0,    2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0],\n",
    "    [2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0,    2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0],\n",
    "    [1,    1,            1,            1,    -1,    -1,            -1,           -1],\n",
    "    [1,    1,            1,            -1,    -1,    -1,            -1,            1],\n",
    "    [2,    2 * np.cos(2 * np.pi/5),    2 * np.cos(4 * np.pi/5),    0,    -2,    -2 * np.cos(2 * np.pi/5),-2 * np.cos(4 * np.pi/5),0],\n",
    "    [2,    2 * np.cos(4 * np.pi/5),    2 * np.cos(2 * np.pi/5),    0,    -2,    -2 * np.cos(4 * np.pi/5),-2 * np.cos(2 * np.pi/5),0],\n",
    "],\n",
    "\n",
    "\n",
    "'D6h':[\n",
    "    [1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,],      #A1g\n",
    "    [1,   1,   1,   1,  -1,  -1,   1,   1,   1,   1,  -1,  -1,],      #A2g\n",
    "    [1,  -1,   1,  -1,   1,  -1,   1,  -1,   1,  -1,   1,  -1,],      #B1g\n",
    "    [1,  -1,   1,  -1,  -1,   1,   1,  -1,   1,  -1,  -1,   1,],      #B2g\n",
    "    [2,   1,  -1,  -2,   0,   0,   2,   1,  -1,  -2,   0,   0,],      #E1g\n",
    "    [2,  -1,  -1,   2,   0,   0,   2,  -1,  -1,   2,   0,   0,],      #E2g\n",
    "    [1,   1,   1,   1,   1,   1,  -1,  -1,  -1,  -1,  -1,  -1,],      #A1u\n",
    "    [1,   1,   1,   1,  -1,  -1,  -1,  -1,  -1,  -1,   1,   1,],      #A2u\n",
    "    [1,  -1,   1,  -1,   1,  -1,  -1,   1,  -1,   1,  -1,   1,],      #B1u\n",
    "    [1,  -1,   1,  -1,  -1,   1,  -1,   1,  -1,   1,   1,  -1,],      #B2u\n",
    "    [2,   1,  -1,  -2,   0,   0,  -2,  -1,   1,   2,   0,   0,],      #E1u\n",
    "    [2,  -1,  -1,   2,   0,   0,  -2,   1,   1,  -2,   0,   0,],      #E2u\n",
    "],\n",
    "\n",
    "'D2d':[\n",
    " [1,    1,    1,  1,    1],\n",
    " [1,    1,     1,   -1,    -1],\n",
    " [1,    -1,    1,   1,    -1],\n",
    " [1,    -1,     1,  -1,    1],\n",
    " [2,    0,    -2,  0,    0]],\n",
    "\n",
    "'D3d':[\n",
    " [1,    1,    1,  1,    1,       1],\n",
    " [1,    1,     -1,   1,    1,    -1],\n",
    " [2,    -1,    0,   2,    -1,    0],\n",
    " [1,    1,     1,  -1,    -1,    -1],\n",
    " [1,    1,    -1,  -1,    -1,    1],\n",
    " [2,    -1,   0,   -2,    1,    0]],\n",
    "\n",
    "'D4d':[\n",
    " [1,  1,  1,  1,  1,  1,  1],\n",
    " [1,  1,  1,  1,  1,  -1,  -1],\n",
    " [1,  -1,  1,  -1,  1,  1,  -1],\n",
    " [1,  -1,  1,  -1,  1,  -1,  1],\n",
    " [2,  2**0.5,  0,  -2**0.5,  -2,  0,  0],\n",
    " [2,  0,  -2,  0,  2,  0,  0,],\n",
    " [2,  -2**0.5,  0,  2**0.5,  -2,  0,  0]],\n",
    "\n",
    "'D5d':[\n",
    "    [1,    1,            1,            1,    1,    1,            1,       1],\n",
    "    [1,    1,            1,            -1,    1,    1,         1,        -1],\n",
    "    [2,    2*np.cos(2*np.pi/5),    2*np.cos(4*np.pi/5),    0,    2,2*np.cos(2*np.pi/5),2*np.cos(4*np.pi/5),    0],\n",
    "    [2,    2*np.cos(4*np.pi/5),    2*np.cos(2*np.pi/5),    0,    2,2*np.cos(4*np.pi/5),2*np.cos(2*np.pi/5),    0],\n",
    "    [1,    1,            1,            1,    -1,    -1,            -1,    -1],\n",
    "    [1,    1,            1,            -1,    -1,    -1,            -1,     1],\n",
    "    [2,    2*np.cos(2*np.pi/5),    2*np.cos(4*np.pi/5),    0,-2,-2*np.cos(2*np.pi/5),-2*np.cos(4*np.pi/5),0],\n",
    "    [2,    2*np.cos(4*np.pi/5),    2*np.cos(2*np.pi/5),    0,-2,-2*np.cos(4*np.pi/5),-2*np.cos(2*np.pi/5),0],\n",
    "],\n",
    "\n",
    "'D6d':[\n",
    " [1,  1,  1,  1,  1,  1,  1,  1,  1],\n",
    "[1,  1,  1,  1,  1,  1,  1,  -1,  -1],\n",
    "[1,  -1,  1,  -1,  1,  -1,  1,  1,  -1],\n",
    "[1,  -1,  1,  -1,  1,  -1,  1,  -1,  1],\n",
    "[2,  3**0.5,  1,  0,  -1,  -3**0.5,  -2,  0,  0],\n",
    "[2,  1,  -1,  -2,  -1,  1,  2,  0,  0],\n",
    "[2,  0,  -2,  0,  2,  0,  -2,  0,  0],\n",
    "[2,  -1,  -1,  2,  -1,  -1,  2,  0,  0],\n",
    "[2,  -3**0.5,  1,  0,  -1,  3**0.5,  -2,  0,  0]],\n",
    "\n",
    "'Td':[\n",
    "    [1.,  1.,   1.,   1.,   1.],       #A1\n",
    "    [1.,  1.,   1.,  -1.,  -1.],       #A2\n",
    "    [2.,  -1.,  2.,   0.,   0.],       #E\n",
    "    [3.,  0.,  -1.,   1.,  -1.],       #T1\n",
    "    [3.,  0.,  -1.,  -1.,   1.],       #T2\n",
    "],\n",
    "\n",
    "'Oh':[\n",
    "    [1,    1,    1,    1,    1,    1,    1,    1,    1,    1],\n",
    "    [1,    1,   -1,   -1,    1,    1,   -1,    1,    1,   -1],\n",
    "    [2,   -1,    0,    0,    2,    2,    0,   -1,    2,    0],\n",
    "    [3,    0,   -1,    1,   -1,    3,    1,    0,   -1,   -1],\n",
    "    [3,    0,    1,   -1,   -1,    3,   -1,    0,   -1,    1],\n",
    "    [1,    1,    1,    1,    1,   -1,   -1,   -1,   -1,   -1],\n",
    "    [1,    1,   -1,   -1,    1,   -1,    1,   -1,   -1,    1],\n",
    "    [2,   -1,    0,    0,    2,   -2,    0,    1,   -2,    0],\n",
    "    [3,    0,   -1,    1,   -1,   -3,   -1,    0,    1,    1],\n",
    "    [3,    0,    1,   -1,   -1,   -3,    1,    0,    1,   -1],\n",
    "],\n",
    "\n",
    "'Ih':[\n",
    "    [1,    1,    1,    1,    1,    1,    1,    1,    1,    1],\n",
    "    [3,    -2*np.cos(4*np.pi/5),   -2*np.cos(2*np.pi/5),   0,    -1,    3,   -2*np.cos(2*np.pi/5),    -2*np.cos(4*np.pi/5),    0,   -1],\n",
    "    [3,    -2*np.cos(2*np.pi/5),   -2*np.cos(4*np.pi/5),   0,    -1,    3,   -2*np.cos(4*np.pi/5),    -2*np.cos(2*np.pi/5),    0,   -1],\n",
    "    [4,    -1,   -1,    1,   0,    4,    -1,    -1,   1,   0],\n",
    "    [5,    0,    0,   -1,   1,    5,   0,    0,   -1,    1],\n",
    "    [1,    1,    1,    1,    1,   -1,   -1,   -1,   -1,   -1],\n",
    "    [3,    -2*np.cos(4*np.pi/5),   -2*np.cos(2*np.pi/5),   0,    -1,    -3,   2*np.cos(2*np.pi/5),    2*np.cos(4*np.pi/5),    0,   1],\n",
    "    [3,    -2*np.cos(2*np.pi/5),   -2*np.cos(4*np.pi/5),   0,    -1,    -3,   2*np.cos(4*np.pi/5),    2*np.cos(2*np.pi/5),    0,   1],\n",
    "    [4,    -1,   -1,    1,   0,    -4,    1,    1,   -1,   0],\n",
    "    [5,    0,    0,   -1,   1,    -5,   0,    0,   1,    -1]],\n",
    "\n",
    "'I':[\n",
    "    [1,    1,    1,    1,    1],\n",
    "    [3,    -2*np.cos(4*np.pi/5),   -2*np.cos(2*np.pi/5),   0,    -1],\n",
    "    [3,    -2*np.cos(2*np.pi/5),   -2*np.cos(4*np.pi/5),   0,    -1],\n",
    "    [4,    -1,   -1,    1,   0],\n",
    "    [5,    0,    0,   -1,   1]],\n",
    "}\n",
    "\n",
    "pg_labels = {\n",
    "\n",
    "'Dinfh_print':(['E','2Cinf','infsv','i','2Sinf','infC2'] ),\n",
    "'Cinfv_print':(['E','2Cinf','infsv'] ),\n",
    "    \n",
    "'Ci':(['E','i']),   #standard orientation, plane perpendicular to z\n",
    "'Cs':(['E','s']),   #standard orientation, plane perpendicular to z    \n",
    "    \n",
    "'C1':(['E']),\n",
    "'C2':(['E','C2','i','sh']),\n",
    "'C3':(['E','C3','C32']),\n",
    "'C4':(['E','C4','C2','C43']),\n",
    "'C5':(['E','C5','C52','C53','C54']),\n",
    "'C6':(['E','C6','C62','C63','C64']),\n",
    "\n",
    "'C2h':(['E','C2']),\n",
    "'C4h':(['E','C4','C2','C43','i','S43','sh','S4']),\n",
    "\n",
    "'C2v':( ['E','C2','sv','sv\\'']),\n",
    "'C3v':( ['E','2C3','3sv']),\n",
    "'C4v':( ['E','2C4','C2','sv','sd']),\n",
    "'C5v':( ['E','2C5','2C52','5sv']),\n",
    "'C6v':( ['E','2C6','2C3','C2','3sv','3sd']),\n",
    "\n",
    "'D2':( ['E','C2z','C2y','C2x']),\n",
    "'D3':( ['E','2C3','3C2']),\n",
    "'D4':( ['E','2C4','C42','2C2\\'','2C2\\\"']),\n",
    "'D5':( ['E','2C5','2C52','5C2']),\n",
    "'D6':( ['E','2C6','2C3','C2','3C2\\'','3C2\\\"']),\n",
    "    \n",
    "'D2d':( ['E','2S4','C2','2C2\\'','2sd']),\n",
    "'D3d':( ['E','2C3','3C2','i','2S6','3sd']),\n",
    "'D4d':( ['E','2S8','2C4','2S83','C2','4C2\\'','4sd']),\n",
    "'D5d':( ['E','2C5','2C52','5C2','i','2S102','2S10','5sd']),\n",
    "'D6d':( ['E','2S12','2C6','2S4','2C3','2S125','C2','6C2\\'','6sd']),\n",
    "\n",
    "'D2h':( ['E','C2z','C2y','C2x','i','sxy','sxz','syz']),\n",
    "'D3h':( ['E','2C3','3C2','sh','2S3','3sv']),\n",
    "'D4h':( ['E','2C4','C2','2C2\\'','2C2\\\"','i','2S4','sh','2sd','2sd\\'']),\n",
    "'D5h':( ['E','2C5','2C52','5C2','sh','2S5','2S52','5sv']),\n",
    "'D6h':( ['E','2C6','2C3','C2','3C2\\'','3C2\\\"','i','2S3','2S6','sh','3sd','3sd\\'']),\n",
    "\n",
    "'Td':( ['E','8C3','3C2','6S4','6sd']),\n",
    "'Oh':( ['E','8C3','6C2\\'','6C4','3C2\\'','i','8S6','6S4','3sh','6sd']),\n",
    "'Ih':( ['E','12C5','12C52','20C3','15C2','i','12S10','12S103','20S6','15s']),\n",
    "\n",
    "         }\n",
    "\n",
    "\n",
    "#symmetry-based functions\n",
    "\n",
    "def rotate_about_axis(angle,coords,axis): #rotates by theta about the z-axis\n",
    "    l = (axis[0]**2 + axis[1]**2 + axis[2]**2)**0.5 # normalising axis vector…\n",
    "    a = axis[0]/l\n",
    "    b = axis[1]/l\n",
    "    c = axis[2]/l\n",
    "    #matrix from https://en.wikipedia.org/wiki/Rotation_matrix#General_rotations\n",
    "    #print(rot_matrix)\n",
    "    cos = np.cos(angle)\n",
    "    sin = np.sin(angle)\n",
    "    rot_matrix = [\n",
    "        [cos + a**2 * (1-cos), a * b * (1 - cos) - c * sin,a * c * (1 - cos) + b * sin],\n",
    "        [b * a * (1 - cos) + c * sin, cos + b**2 * ( 1 - cos), b * c * (1 - cos) - a * sin],\n",
    "        [c * a * (1 - cos) - b * sin, c * b * (1 - cos) + a * sin, cos + c**2 * (1 - cos)]]\n",
    "\n",
    "    #print('final rotation matrix\\n', np.round(np.array(rot_matrix),decimals=4), '\\n\\n\\n')\n",
    "    coordsrot = coord_matrix_multiplication(coords,rot_matrix)\n",
    "    return coordsrot\n",
    "\n",
    "def translation(vector,coords):\n",
    "    coordsprod = np.array(coords)\n",
    "    #print(coordsprod)\n",
    "    for counter in range (0, len(coords)):\n",
    "        coordsprod[counter][0] += vector[0]\n",
    "        coordsprod[counter][1] += vector[1]\n",
    "        coordsprod[counter][2] += vector[2]\n",
    "    return coordsprod\n",
    "\n",
    "def reflection(plane,coords): #reflection through plane ax + by + cz = 0 - this plane passes through the origin (plane given in format [a,b,c]).\n",
    "                              #e.g. xz plane written as [0.,1.,0.]\n",
    "    #ensure plane is normalised (ie a^2 + b^2 + c^2 = 0)\n",
    "    l = (plane[0]**2 + plane[1]**2 + plane[2]**2)**0.5\n",
    "    a = plane[0]/l\n",
    "    b = plane[1]/l\n",
    "    c = plane[2]/l\n",
    "    refl_matrix = [\n",
    "        [1.-2*a**2,-2*a*b,-2*a*c],\n",
    "        [-2*a*b,1.-2*b**2,-2*b*c],\n",
    "        [-2*a*c,-2*b*c,1.-2*c**2]\n",
    "        ]\n",
    "    #print('\\n\\nreflection matrix\\n', np.round(np.array(refl_matrix).transpose(),decimals=4), '\\n')\n",
    "    coordsrefl = coord_matrix_multiplication(coords,refl_matrix)\n",
    "    return coordsrefl\n",
    "\n",
    "def inversion(coords): #inversion about the origin\n",
    "    coordsprod = coords.tolist()\n",
    "    for counter in range (0, len(coords)):\n",
    "        coordsprod[counter][0] *= -1\n",
    "        coordsprod[counter][1] *= -1\n",
    "        coordsprod[counter][2] *= -1\n",
    "    return coordsprod\n",
    "\n",
    "def improper_rotation(angle,coords,axis):\n",
    "    coordsrot = rotate_about_axis(angle,coords,axis)\n",
    "    coordsimp = reflection(axis,coordsrot) #the rotation axis defines the mirror plane\n",
    "    return coordsimp\n",
    "\n",
    "def coord_matrix_multiplication(coords,matrix): #multiplies all coordinates in list by supplied matrix\n",
    "    coordsprod = np.array(coords)\n",
    "    for counter in range (0, len(coords)):\n",
    "        #coordsprod[counter] = np.matrix(coords[counter]) * (np.matrix(matrix).transpose())\n",
    "        coordsprod[counter] = np.dot(np.array(matrix).transpose(),coords[counter])\n",
    "    return coordsprod\n",
    "\n",
    "def symmetry_operation(coords,operation_arguments): #feed in a list of coordinates and arguments to describe the symmetry operation in format ('X',(ARGS)\n",
    "    # X gives symmetry operation type; E (identity), C (rotation), R (reflection), I (inversion), S (improper rotation)\n",
    "    #(ARGS) is a tuple to describe the symmetry operation; for rotation, reflection and improper rotation this is the axis to rotate about/plane perpendicular to this axis to reflect through, plus the angle to rotate by\n",
    "    #ARGS = 0 for identity, inverse. NB all symmetry operations axes/planes/points include the origin\n",
    "    if operation_arguments[0] == 'E':     #identity\n",
    "        coords_operator = coords\n",
    "    elif operation_arguments[0] == 'C':   #rotation:\n",
    "        coords_operator = rotate_about_axis(2*np.pi / operation_arguments[1][4],coords,operation_arguments[1][1:4])\n",
    "    elif operation_arguments[0] == 'R':   #reflection\n",
    "        coords_operator = reflection(operation_arguments[1][1:4],coords)\n",
    "    elif operation_arguments[0] == 'I':   #inversion\n",
    "        coords_operator = inversion(coords)\n",
    "    elif operation_arguments[0] == 'S':   #improper rotation\n",
    "        coords_operator = improper_rotation(2*np.pi / operation_arguments[1][4],    coords,operation_arguments[1][1:4])\n",
    "    return coords_operator\n",
    "\n",
    "\n",
    "def gram_schmidt(vectors): #Gram Schmidt orthonormalisation on a 2D array of row vectors.\n",
    "    def Project(u,v):\n",
    "        uv = np.dot(u,v)\n",
    "        uu = np.dot(u,u)\n",
    "        if uu == 0:\n",
    "            return 0\n",
    "        proj = uv / uu\n",
    "        return proj\n",
    "    gsvectors = np.array(vectors)\n",
    "    for vCounter in range (0,len(vectors)):\n",
    "        for projCounter in range (0,vCounter):\n",
    "            gsvectors[vCounter] -= Project(gsvectors[projCounter],vectors[vCounter]) * gsvectors[projCounter]\n",
    "        #print(np.round(gsvectors[vCounter],decimals=3))\n",
    "        gs_length = (np.dot(gsvectors[vCounter],gsvectors[vCounter]))**0.5\n",
    "        if round(gs_length,6) < 0.0001: #To get rid of orbitals that get through because of rounding errors!\n",
    "            for x in range(0, len(gsvectors[vCounter])):\n",
    "                gsvectors[vCounter][x]  = 0\n",
    "        else:\n",
    "            for x in range(0, len(gsvectors[vCounter])): gsvectors[vCounter][x]  /= gs_length\n",
    "    return gsvectors\n",
    "\n",
    "\n",
    "def set_function(atoms,coords,eh_basis_functs): #Generates 4 sets or lists of sets as described below\n",
    "    set_elements_in_mol = set() #Gives a set of elements in the molecule\n",
    "\n",
    "    for counter in range (0,len(atoms)):\n",
    "        set_elements_in_mol.add(atoms[counter])\n",
    "\n",
    "    coordinate_sets = [] #Gives a list atomic numbers in the molecule with corresponding coordinate indices\n",
    "    i=0\n",
    "    for e in set_elements_in_mol:\n",
    "        coordinate_sets.append([0,set()])\n",
    "        coordinate_sets[i][0] = e\n",
    "        coordinate_sets[i][1] = set()\n",
    "        for allInList in range (0,len(atoms)):\n",
    "            if e == atoms[allInList]:\n",
    "                coordinate_sets[i][1].add(allInList)\n",
    "        i+=1\n",
    "\n",
    "    set_AOs_in_mol = set() #Gives a set of tuples for each orbital type in the molecule, format (Z, n, l), e.g. (8,2,1) => Oxygen 2p\n",
    "    for counter in range (0,len(eh_basis_functs)):\n",
    "        set_AOs_in_mol.add(tuple(eh_basis_functs[counter][x] for x in [0,2,3]))\n",
    "\n",
    "\n",
    "    orbital_sets = [] #Gives a list of sets; for each AO type a number corresponding to the AO in eh_basis_functs\n",
    "    i=0\n",
    "    for e in set_AOs_in_mol:\n",
    "        orbital_sets.append([0,set()])\n",
    "        orbital_sets[i][0] = e\n",
    "        orbital_sets[i][1] = set()\n",
    "        for allInList in range (0,len(eh_basis_functs)):\n",
    "            if e == tuple(eh_basis_functs[allInList][x] for x in [0,2,3]):\n",
    "                orbital_sets[i][1].add(allInList)\n",
    "        i+=1\n",
    "\n",
    "    orbitals_on_atom_sets = [] #A list of sets, which gives the AOs on each coordinate\n",
    "    for counter in range(0,len(coords)):\n",
    "        orbitals_on_atom_sets.append([0,set()])\n",
    "        orbitals_on_atom_sets[counter][0] = counter\n",
    "        orbitals_on_atom_sets[counter][1] = set()\n",
    "        for counter1 in range(0,len(eh_basis_functs)):\n",
    "            if eh_basis_functs[counter1][1] == counter:\n",
    "                orbitals_on_atom_sets[counter][1].add(counter1)\n",
    "        i+=1\n",
    "    return set_elements_in_mol, coordinate_sets, set_AOs_in_mol, orbital_sets, orbitals_on_atom_sets\n",
    "    \n",
    "    \n",
    "    #this is redundant now because atom table is generated in corre_table\n",
    "    \n",
    "def corre_table_atoms(atoms,coords,pg): #Used in identifying the point group where only how the coordinates transform is important\n",
    "    symmtol=1   #number of decimals controls degree of match required - larger number = more precise #is am integer  \n",
    "    corr_table_atoms = np.zeros((len(coords), len(pg_operations[pg]))) #This will describe the action of the symmetry operations on each coordinate.\n",
    "    for alloperators in range(0, len(pg_operations[pg])): #generates table to describe action of operator on coordinate.\n",
    "        coords_operator = symmetry_operation(coords,pg_operations[pg][alloperators])\n",
    "        for counter1 in range(0, len(coords)): #looks for equivalent coordinates and generates a correlation table for ATOMS\n",
    "            for counter2 in range(0, len(coords)):\n",
    "                corr_table_atoms[counter1][alloperators] = -1\n",
    "                if np.array_equal(np.around(coords_operator[counter1],decimals = symmtol),np.around(coords[counter2],decimals=symmtol))== True and atoms[counter1] == atoms[counter2] :\n",
    "                    corr_table_atoms[counter1][alloperators] = counter2\n",
    "                    break #since a coordinate can't be transformed to 2 coordinates by a symmetry operation!\n",
    "    return corr_table_atoms\n",
    "\n",
    "def corre_table(atoms,coords,eh_basis_functs,pg,set_elements_in_mol, coordinate_sets, set_AOs_in_mol, orbital_sets, orbitals_on_atom_sets):\n",
    "    #First we generate a condensed matrix that contains all coordinates in the atom, plus x, y, z vectors on each atom  \n",
    "    symmtol=1    \n",
    "    coords_xyz = np.zeros((4,len(coords),3))\n",
    "\n",
    "    coords_xyz[0] = coords_xyz[1] = coords_xyz[2] = coords_xyz[3] = np.array(coords) #4 2D arrays; [0] is coords……\n",
    "    #set_elements_in_mol, coordinate_sets, set_AOs_in_mol, orbital_sets, orbitals_on_atom_sets = set_function(atoms,coords,eh_basis_functs)\n",
    "    for counter in range(0,len(coords)):\n",
    "        coords_xyz[1][counter][0] = coords[counter][0] + 1 #[1] is coords translated by 1 in x,\n",
    "        coords_xyz[2][counter][1] = coords[counter][1] + 1 #[2] is coords translated by 1 in y,\n",
    "        coords_xyz[3][counter][2] = coords[counter][2] + 1 #[3] is coords translated by 1 in z.\n",
    "\n",
    "    corr_table_atoms = np.zeros((len(coords), len(pg_operations[pg]))) #This will describe the action of the symmetry operations on each coordinate.\n",
    "    corr_table_AO = np.zeros((len(eh_basis_functs), len(pg_operations[pg]),3)) #This describes how a, e.g. px orbital is transformed. TO SUPPORT nd orbitals, 3 would need to be set to 5.\n",
    "\n",
    "    for alloperators in range(0, len(pg_operations[pg])): #generates table to describe action of operator on coordinate.\n",
    "        coords_operator = np.zeros((4,len(coords),3))\n",
    "        for xyzcounter in range(0,4):\n",
    "            coords_operator[xyzcounter] = symmetry_operation(coords_xyz[xyzcounter],pg_operations[pg][alloperators])\n",
    "            if xyzcounter != 0:\n",
    "                coords_operator[xyzcounter] -= coords_operator[0] #so coords_operator[1,2,3] are given relative to\n",
    "        for counter1 in range(0, len(coords)): #looks for equivalent coordinates and generates a correlation table for ATOMS\n",
    "                                               #we're only worried about how the coordinates transform, so only run over coords_xyz[0]\n",
    "            for counter2 in range(0, len(coords)):\n",
    "                if np.array_equal(np.round(coords_operator[0][counter1],decimals = symmtol),np.round(coords_xyz[0][counter2],decimals=symmtol))== True:\n",
    "                    corr_table_atoms[counter1][alloperators] = counter2\n",
    "                    break #since a coordinate can't be transformed to 2 coordinates by a symmetry operation\n",
    "        for counter1 in range(0, len(orbital_sets)):\n",
    "            for e in orbital_sets[counter1][1]:\n",
    "                for f in orbital_sets[counter1][1]:\n",
    "                    if orbital_sets[counter1][0][2] == 0: #s orbitals\n",
    "                        corr_table_AO[e][alloperators][0] = 1.\n",
    "                    if orbital_sets[counter1][0][2] == 1: #p orbitals\n",
    "                        if np.array_equal(np.round(coords_operator[0][[eh_basis_functs[e][1]]],decimals=symmtol), np.round(coords_xyz[0][[eh_basis_functs[f][1]]],decimals=symmtol))== True:\n",
    "                            if eh_basis_functs[e][4] == 1:  #Originally a 2px orbital\n",
    "                                corr_table_AO[e][alloperators][0] = coords_operator[1][eh_basis_functs[e][1]][0]\n",
    "                                corr_table_AO[e][alloperators][1] = coords_operator[1][eh_basis_functs[e][1]][1]\n",
    "                                corr_table_AO[e][alloperators][2] = coords_operator[1][eh_basis_functs[e][1]][2]\n",
    "                            elif eh_basis_functs[e][4] == -1: #Originally a 2py orbital\n",
    "                                corr_table_AO[e][alloperators][0] = coords_operator[2][eh_basis_functs[e][1]][0]\n",
    "                                corr_table_AO[e][alloperators][1] = coords_operator[2][eh_basis_functs[e][1]][1]\n",
    "                                corr_table_AO[e][alloperators][2] = coords_operator[2][eh_basis_functs[e][1]][2]\n",
    "                            elif eh_basis_functs[e][4] == 0: #Originally a 2pz orbital\n",
    "                                corr_table_AO[e][alloperators][0] = coords_operator[3][eh_basis_functs[e][1]][0]\n",
    "                                corr_table_AO[e][alloperators][1] = coords_operator[3][eh_basis_functs[e][1]][1]\n",
    "                                corr_table_AO[e][alloperators][2] = coords_operator[3][eh_basis_functs[e][1]][2]\n",
    "    return corr_table_AO,corr_table_atoms,coords_operator\n",
    "\n",
    "def corr_table_print(corr_table,pg): #Prints correlation tables neatly\n",
    "    for counter in range(0, len(corr_table)):\n",
    "        print(counter, ' ', pg_operations[pg][counter][0],  sep='', end='')\n",
    "        if pg_operations[pg][counter][0] == 'E':     #identity\n",
    "            print(' ',end='')\n",
    "        elif pg_operations[pg][counter][0] == 'C':   #rotation:\n",
    "            print(str(pg_operations[pg][counter][1][4])[0],end='')\n",
    "        elif pg_operations[pg][counter][0] == 'R':   #reflection\n",
    "            print(' ',end='')\n",
    "        elif pg_operations[pg][counter][0] == 'I':   #inversion\n",
    "            print(' ',end='')\n",
    "        elif pg_operations[pg][counter][0] == 'S':   #improper rotation\n",
    "            print(str(pg_operations[pg][counter][1][4])[0],end='')\n",
    "        print('',*corr_table[counter].astype(int), sep='\\t')\n",
    "    print('\\n')\n",
    "\n",
    "def proj_operator(coords, pg,eh_basis_functs, corr_table_atoms, corr_table_AO, orbitals_on_atom_sets, orbital_sets, set_AOs_in_mol):\n",
    "    symmtol2=4    \n",
    "    MOindexMax = len(eh_basis_functs) * len( pg_irreps[pg])\n",
    "    SALC_basis_set = np.zeros( (MOindexMax, len(eh_basis_functs)+2 )).tolist()\n",
    "    MOindex = 0\n",
    "    for orbitalTypeIndex in range(0, len(orbital_sets)): #Orbital type is, e.g. C 2p. orbital_sets is a list, orbitalsets[x][0] gives the xth type in format (Z,n,l)\n",
    "        orbital_l = orbital_sets[orbitalTypeIndex][0][2] #Sets angular momentum number for current type\n",
    "        for atomCounter in range(0, len(coords)):\n",
    "            orbitals_on_atom = list(orbitals_on_atom_sets[atomCounter][1].intersection(orbital_sets[orbitalTypeIndex][1])) #Finds all orbitals of current type on an atom - if the current atom is a different element, this will return an empty set so the if below will fail.\n",
    "            #orbitals_on_atom = a set of indices corresponding to AOs in eh_basis_functs, for current orbital type and atom\n",
    "            if orbitals_on_atom:\n",
    "                for orbitalIndex in orbitals_on_atom: #orbitalIndex'th row in eh_basis_functs is the current AO\n",
    "                    for irrepIndex in range (0,len(pg_irreps[pg])):\n",
    "                        irrep = pg_irreps[pg][irrepIndex]                        \n",
    "                        SALC_basis_set[MOindex][0] = irrepIndex\n",
    "                        SALC_basis_set[MOindex][1] = orbitalTypeIndex\n",
    "                        for operatorCounter in range(0, len(pg_operations[pg])):\n",
    "                            operator_type = pg_operations[pg][operatorCounter][0]\n",
    "                            operator_args = pg_operations[pg][operatorCounter][1]\n",
    "                            operator_class = pg_operations[pg][operatorCounter][1][0]\n",
    "                            new_atom = int(corr_table_atoms[atomCounter][operatorCounter]) #Pulls corresponding atom from corr_table_atoms\n",
    "                            orbitals_on_new_atom = list(orbitals_on_atom_sets[new_atom][1].intersection(orbital_sets[orbitalTypeIndex][1])) #finds the indices of orbitals on new atom\n",
    "                            if orbital_l == 0:\n",
    "                                SALC_basis_set[MOindex][orbitals_on_new_atom[0] + 2] += pg_characters[pg][irrepIndex][operator_class]\n",
    "                            elif orbital_l == 1:\n",
    "                                for norbital in orbitals_on_new_atom:\n",
    "                                    if eh_basis_functs[norbital][4] == 1:\n",
    "                                        nxyz = 0         #Identifying px character\n",
    "                                    elif eh_basis_functs[norbital][4] == -1:\n",
    "                                        nxyz = 1          # py\n",
    "                                    elif eh_basis_functs[norbital][4] == 0:\n",
    "                                        nxyz = 2         # pz\n",
    "                                    SALC_basis_set[MOindex][norbital+2] += pg_characters[pg][irrepIndex][operator_class] * corr_table_AO[orbitalIndex][operatorCounter][nxyz]\n",
    "                        MOindex += 1\n",
    "\n",
    "    SALC_basis_set2 = []\n",
    "    for MOCounter in range(0,len(SALC_basis_set)):\n",
    "        allZero = True\n",
    "        for AOCounter in range(2,len(SALC_basis_set[0])):\n",
    "            if np.round(SALC_basis_set[MOCounter][AOCounter],decimals=symmtol2) != 0:\n",
    "                allZero = False\n",
    "        if allZero == False:\n",
    "            SALC_basis_set2.append(SALC_basis_set[MOCounter])\n",
    "    set_of_SALC_types = set()\n",
    "    for SALCs in SALC_basis_set2:\n",
    "        set_of_SALC_types.add(tuple(SALCs[0:2]))\n",
    "    SALC_types_sets = []\n",
    "    counter = 0\n",
    "    for SALC_types in set_of_SALC_types:\n",
    "        SALC_types_sets.append([SALC_types,set()])\n",
    "        for SALCCounter in range(0,len(SALC_basis_set2)):\n",
    "            if SALC_types == tuple(SALC_basis_set2[SALCCounter][0:2]):\n",
    "                SALC_types_sets[counter][1].add(SALCCounter)\n",
    "        counter += 1\n",
    "\n",
    "    #Gram-Schmidt orthogonolisation\n",
    "    SALC_basis_set3 = []\n",
    "    SALC_basis_set_types = []\n",
    "    for typeCounter in range(0, len(SALC_types_sets)):\n",
    "        typeVectors = []\n",
    "        for SALCs in SALC_types_sets[typeCounter][1]:\n",
    "            typeVectors.append(SALC_basis_set2[SALCs][2:len(eh_basis_functs)+2])\n",
    "        gs_typeVectors = gram_schmidt(typeVectors)\n",
    "        gs_typeVectors[np.isnan(gs_typeVectors)]=0\n",
    "        for SALCs in gs_typeVectors: #Again, getting rid of empty SALCs. If we don't do it twice rounding errors crop up.\n",
    "            allZero = True\n",
    "            for counter in range(0,len(SALCs)):\n",
    "                if np.round(SALCs[counter],decimals=symmtol2) != 0:\n",
    "                    allZero = False\n",
    "            if allZero == False:\n",
    "                nml = list(orbital_sets[SALC_types_sets[typeCounter][0][1]][0])\n",
    "                SALC_basis_set3.append(SALCs)\n",
    "                SALC_basis_set_types.append([SALC_types_sets[typeCounter][0][0],nml[0],nml[1],nml[2]])\n",
    "    SALC_basis_set4 = np.append(SALC_basis_set_types,SALC_basis_set3, axis=1)\n",
    "    SALC_basis_set4 = sorted(SALC_basis_set4, key=lambda x:x[0])\n",
    "    SALC_basis_set4 = sorted(SALC_basis_set4, key=lambda x:x[3])\n",
    "    SALC_basis_set4 = sorted(SALC_basis_set4, key=lambda x:x[2])\n",
    "    SALC_basis_set4 = sorted(SALC_basis_set4, key=lambda x:x[1]) #Sorts by; Z, n, l, irrep\n",
    "    \n",
    "    for element in eh_basis_functs:\n",
    "        if element[3]==2:\n",
    "            bf=np.zeros(len(SALC_basis_set4[0]))\n",
    "            bf[1]=element[0];bf[2]=element[2];bf[3]=2\n",
    "            if element[4]==2:\n",
    "                bf[0]=invariant_pts[pg][0];bf[8]=1.0\n",
    "                SALC_basis_set4.append(bf)    \n",
    "            elif element[4]==0:\n",
    "                bf[0]=invariant_pts[pg][1];bf[9]=1.0\n",
    "                SALC_basis_set4.append(bf)  \n",
    "            elif element[4]==-2:\n",
    "                bf[0]=invariant_pts[pg][2];bf[10]=1.0\n",
    "                SALC_basis_set4.append(bf) \n",
    "            elif element[4]==1:\n",
    "                bf[0]=invariant_pts[pg][3];bf[11]=1.0\n",
    "                SALC_basis_set4.append(bf) \n",
    "            elif element[4]==-1:\n",
    "                bf[0]=invariant_pts[pg][4];bf[12]=1.0\n",
    "                SALC_basis_set4.append(bf) \n",
    "    return SALC_basis_set4\n",
    "\n",
    "def translate_to_COM(atoms,coords):\n",
    "    M = 0\n",
    "    for atom in atoms: M += atomic_masses[atom]\n",
    "    COM = np.zeros((3))\n",
    "    for coord in range (0, len(coords)): COM[0] += coords[coord][0] * atoms[coord]\n",
    "    for coord in range (0, len(coords)): COM[1] += coords[coord][1] * atoms[coord]\n",
    "    for coord in range (0, len(coords)): COM[2] += coords[coord][2] * atoms[coord]\n",
    "    COM[0] /= -M\n",
    "    COM[1] /= -M\n",
    "    COM[2] /= -M\n",
    "    return translation(COM,coords)\n",
    "\n",
    "def inertia_matrix(atoms,coords): #from ???\n",
    "    inertia = np.zeros((3,3))\n",
    "    for coordCounter in range (0, len(coords)):\n",
    "        x = coords[coordCounter][0]\n",
    "        y = coords[coordCounter][1]\n",
    "        z = coords[coordCounter][2]\n",
    "        if atoms[coordCounter]==1:\n",
    "            m=5.    #set mass of H arbitrarily to higher value to emphasise small differences in MoI for nearly linear molecules\n",
    "        else:\n",
    "            m = atomic_masses[atoms[coordCounter]]\n",
    "        inertia += [\n",
    "            [(y**2 + z**2)*m, - x * y * m, - x * z * m],\n",
    "            [- x * y * m, (x**2 + z**2) * m, - y * z * m ],\n",
    "            [- x * z * m, - y * z * m, (x**2 + y**2) * m]\n",
    "\n",
    "        ]\n",
    "    return inertia\n",
    "\n",
    "def symmetric_top_evec_assign(evals,evecs,average_eval): #function to label the principal axis, used numerous times when aligning a symmetric top\n",
    "    evalsR = np.round(evals,decimals=1)\n",
    "    if abs((evals[0]-evalsR[1])/average_eval) < 0.01:\n",
    "        principal_axis = evecs[2]\n",
    "        v1 = evecs[0]\n",
    "        v2 = evecs[1]\n",
    "    elif abs((evals[0]-evalsR[2])/average_eval) < 0.01:\n",
    "        principal_axis = evecs[1]\n",
    "        v1 = evecs[0]\n",
    "        v2 = evecs[2]\n",
    "    elif abs((evals[1]-evalsR[2])/average_eval) < 0.01:\n",
    "        v1 = evecs[1]\n",
    "        v2 = evecs[2]\n",
    "        principal_axis = evecs[0]\n",
    "\n",
    "    return v1,v2,principal_axis\n",
    "\n",
    "def assymetric_top_evec_assign(evecs): #'principal axis' means the one already aligned along x, y or z for an assymetric top!\n",
    "    if np.array_equal(np.round(evecs[0], decimals=8), [1,0,0]) or np.array_equal(np.round(evecs[0], decimals=8), [0,1,0]) or np.array_equal(np.round(evecs[0], decimals=8), [0,0,1]):\n",
    "        principal_axis = evecs[0]\n",
    "        v1 = evecs[1]\n",
    "        v2 = evecs[2]\n",
    "    if np.array_equal(np.round(evecs[1], decimals=8), [1,0,0]) or np.array_equal(np.round(evecs[1], decimals=8), [0,1,0]) or np.array_equal(np.round(evecs[1], decimals=8), [0,0,1]):\n",
    "        principal_axis = evecs[1]\n",
    "        v1 = evecs[0]\n",
    "        v2 = evecs[2]\n",
    "    if np.array_equal(np.round(evecs[2], decimals=8), [1,0,0]) or np.array_equal(np.round(evecs[2], decimals=8), [0,1,0]) or np.array_equal(np.round(evecs[2], decimals=8), [0,0,1]):\n",
    "        principal_axis = evecs[2]\n",
    "        v1 = evecs[1]\n",
    "        v2 = evecs[0]\n",
    "    return v1,v2,principal_axis\n",
    "\n",
    "def right_pg(pg,coords,atoms): #Returns true if the supplied coordinates & atoms satisfy the given point group. Prints the correlation table, and prints the first incorrect operator if its the wrong point group.\n",
    "    corr_table_atoms= corre_table_atoms(atoms,coords,pg).transpose()\n",
    "    corr_table_sets2 = []\n",
    "    for counter in range(0,len(corr_table_atoms)):\n",
    "        corr_table_sets2.append([counter, set()])\n",
    "        for e in corr_table_atoms[counter]:\n",
    "            corr_table_sets2[counter][1].add(e)\n",
    "    rightpg = True\n",
    "    for counter in range(0,len(corr_table_atoms)):\n",
    "        if corr_table_sets2[0][1] != corr_table_sets2[counter][1]:\n",
    "            rightpg = False\n",
    "            break\n",
    "    return rightpg\n",
    "\n",
    "\n",
    "def align(coords,atoms):\n",
    "    set_elements_in_mol = set() #Gives a set of elements in the molecule\n",
    "    for counter in range (0,len(atoms)):\n",
    "        set_elements_in_mol.add(atoms[counter])\n",
    "    coordinate_sets = [] #Gives a list of sets; for each element there's a set of numbers corresponding to coordinates in coords[]\n",
    "    i=0\n",
    "    for e in set_elements_in_mol:\n",
    "        coordinate_sets.append([0,set()])\n",
    "        coordinate_sets[i][0] = e\n",
    "        coordinate_sets[i][1] = set()\n",
    "        for allInList in range (0,len(atoms)):\n",
    "            if e == atoms[allInList]:\n",
    "                coordinate_sets[i][1].add(allInList)\n",
    "        i+=1\n",
    "    pg = 'cx'\n",
    "    coords = translate_to_COM(atoms,coords) #Translates coordinates to centre of mass\n",
    "    inertia = inertia_matrix(atoms,coords)\n",
    "    evals,evecs= np.linalg.eig(inertia)\n",
    "    evecs = np.swapaxes(evecs,0,1)\n",
    "    #Generates inertia matrix, and diagonalises it\n",
    "    evalsR = np.round(evals,decimals=4)\n",
    "    average_eval=np.average(evalsR)#(evalsR[0]+evalsR[1]+evalsR[2])/3\n",
    "    evalsR2=evalsR/average_eval\n",
    "    ordered_evals=np.sort(evalsR)\n",
    "    RMSDP=((evalsR[0]-average_eval)**2+(evalsR[2]-average_eval)**2+(evalsR[2]-average_eval)**2)**0.5\n",
    "\n",
    "###################################\n",
    "#          Spherical Top          #\n",
    "###################################\n",
    "    #need to evaluate whether the three eigenvalues are the same. ROunding and comparing is a bit dangerous because the rounding may cause very\n",
    "    #similar numbers to appear different (.449 and .451, for example). \n",
    "    if RMSDP<0.01:\n",
    "        distance_from_origin=[]        \n",
    "        for i in range (len(coords)):\n",
    "            dist=np.round((coords[i][0]**2+coords[i][1]**2+coords[i][2]**2)**0.5,decimals=3)\n",
    "            if dist>0:\n",
    "                distance_from_origin.append(np.round(dist,decimals=2))\n",
    "        no_of_occurrences=[]\n",
    "        for element in distance_from_origin:\n",
    "            no_of_occurrences.append(distance_from_origin.count(element))\n",
    "        symmetry_related_atoms=min(no_of_occurrences)\n",
    "        #print('********************************\\n* Spherical Top - x and y axis * \\n*   must be properly aligned   * \\n********************************\\n')\n",
    "        \n",
    "        if symmetry_related_atoms==4:\n",
    "            #now make sure everything is aligned in the standard way (z along bisector of 2 bonds, bonds lying along bisectors of x and y axes)\n",
    "            #first, find the bisector of two bonds\n",
    "            for i in range(len(coords)):\n",
    "                for j in range(len(coords)):\n",
    "                    if j>i and abs(np.round(np.dot(coords[i],coords[i]),decimals=3))>0. and abs(np.round(np.dot(coords[j],coords[j]),decimals=3))>0.:\n",
    "                        bisector=[a+b for a,b in zip(coords[i],coords[j])]\n",
    "                        break \n",
    "            #now align bisector with z axis\n",
    "            if abs(np.dot(bisector,[0,0,1]))!=np.dot(bisector,bisector)**0.5:\n",
    "            #else:\n",
    "                theta = np.arccos(np.dot(bisector,[0,0,1]/(np.dot(bisector,bisector))**0.5))\n",
    "                axis = np.cross(bisector,[0,0,1])\n",
    "                coords = rotate_about_axis(-theta, coords, axis)\n",
    "                principal_axis=bisector\n",
    "            #now rotate bonds to lie along bisectors of x and y\n",
    "            for i in range(len(coords)):\n",
    "                if np.round(np.dot(coords[i],coords[i]),decimals=3)>0.:\n",
    "                    if abs(coords[i][0])!=abs(coords[i][1]):\n",
    "                        new_angle = np.arccos(np.dot([coords[i][0],coords[i][1]],[1.,1.])/(2**0.5*np.dot([coords[i][0],coords[i][1]],[coords[i][0],coords[i][1]])**0.5))\n",
    "                        coords = rotate_about_axis(-new_angle, coords, [0,0,1])\n",
    "                        break\n",
    "            possible_pgs = ['Th','Td','T','C1']\n",
    "            \n",
    "        elif symmetry_related_atoms==6:\n",
    "        #now make sure everything is aligned in the standard way\n",
    "            #align first bond along z            \n",
    "            for i in range(len(coords)):\n",
    "                if np.dot(coords[i],coords[i])>0.:\n",
    "                    if abs(np.dot(coords[i],[0.,0.,1.]))!=np.dot(coords[i],coords[i])**0.5: \n",
    "                        theta = np.arccos(np.dot(coords[i],[0,0,1]/(np.dot(coords[i],coords[i]))**0.5))\n",
    "                        axis = np.cross(coords[i],[0,0,1])\n",
    "                        coords = rotate_about_axis(-theta, coords, axis)\n",
    "                        principal_axis=coords[i]\n",
    "                        index_of_principal_axis=i                        \n",
    "                        break\n",
    "            for i in range(index_of_principal_axis,len(coords)):\n",
    "                if np.dot(coords[i],coords[i])>0. and np.dot(coords[i],principal_axis)==0.:\n",
    "                    if np.array_equal(np.round(coords[i],decimals=8),np.array([1.,0.,0.])) == False and np.array_equal(np.round(coords[i],decimals=8),np.array([-1.,0.,0.])) == False:           \n",
    "                        new_angle = np.arctan2(coords[i][0],coords[i][1])\n",
    "                        coords = rotate_about_axis(-new_angle, coords, [0,0,1])\n",
    "                        break\n",
    "        \n",
    "            possible_pgs = ['Oh','O','C1']\n",
    "            #print('Possible point groups:',*possible_pgs,sep='   ')\n",
    "\n",
    "        elif symmetry_related_atoms==12:\n",
    "            for i in range(len(coords)):\n",
    "                if np.dot(coords[i],coords[i])>0.:\n",
    "                    if abs(np.dot(coords[i],[0.,0.,1.]))!=np.dot(coords[i],coords[i])**0.5: \n",
    "                        theta = np.arccos(np.dot(coords[i],[0,0,1]/(np.dot(coords[i],coords[i]))**0.5))\n",
    "                        axis = np.cross(coords[i],[0,0,1])\n",
    "                        coords = rotate_about_axis(-theta, coords, axis)\n",
    "                        principal_axis=coords[i]\n",
    "                        index_of_principal_axis=i                        \n",
    "                        break\n",
    "            #now align second atom along x\n",
    "            #first, check to see whether one is already aligned along x\n",
    "            coords_aligned=False            \n",
    "            for i in range(len(coords)):\n",
    "                if np.dot(coords[i],coords[i])>0.01 and abs(np.dot(np.round([coords[i][0],coords[i][1]],decimals=8),np.array([1.,0.]))/np.dot([coords[i][0],coords[i][1]],[coords[i][0],coords[i][1]])**0.5)>0.999:# and np.dot([coords[i][0],coords[i][1]],[principal_axis[0],principal_axis[1]])<0.01:\n",
    "                    coords_aligned=True\n",
    "                    break\n",
    "            #if not, align one\n",
    "            if not coords_aligned:\n",
    "                for i in range(len(coords)):\n",
    "                    if coords[i][0]>0.1 and coords[i][1]>0.1 and coords[i][2]>0.1:                        \n",
    "                        new_angle = np.arctan2(coords[i][1],coords[i][0])\n",
    "                        coords = rotate_about_axis(new_angle, coords, [0,0,1])\n",
    "                        break   \n",
    "            #finally, work out whether the coordinate along x axis is in upper quadrant or lower (needs to be in the upper)\n",
    "            reflectcoords=False\n",
    "            for i in range(len(coords)):\n",
    "                if abs(coords[i][1])<0.01 and coords[i][0]>0.01 and coords[i][2]<0.01:\n",
    "                    reflectcoords=True\n",
    "                    break            \n",
    "            if reflectcoords:\n",
    "                for i in range(len(coords)):\n",
    "                    coords[i][2]=-1*coords[i][2]\n",
    "            possible_pgs = ['I','D5d', 'I','C1']\n",
    "            #print('Possible point groups:',*possible_pgs,sep='   ')\n",
    "            \n",
    "            \n",
    "        #now everything us aligned, double check the point group is right\n",
    "        for pg in possible_pgs:\n",
    "            if pg_operations.get(pg) and pg_irreps.get(pg) and pg_characters.get(pg):\n",
    "                rightpg = right_pg(pg,coords,atoms)\n",
    "                if rightpg == True: break\n",
    "            #else: print('Point group',pg,'not supported')\n",
    "#        print('********************************\\n* Point group identified: ',pg,'* \\n********************************\\n')\n",
    "        \n",
    "        \n",
    "        element_coord=[]        \n",
    "        for a in range(len(coords)):        \n",
    "            element_coord.append((atoms[a],coords[a][0],coords[a][1],coords[a][2]))        \n",
    "\n",
    "        return pg, coords, element_coord\n",
    "\n",
    "###################################\n",
    "#          linear          #\n",
    "###################################\n",
    "    elif evalsR[0] == 0 or evalsR[1] == 0 or evalsR[2] == 0:\n",
    " #       print('********************************\\n* Linear Molecule   * \\n********************************\\n')\n",
    "        possible_pgs = ['Dinfh', 'Cinfv']\n",
    "        #print('Possible point groups:',*possible_pgs,sep='   ')\n",
    "\n",
    "        for pg in possible_pgs:\n",
    "            if pg_operations.get(pg) and pg_irreps.get(pg) and pg_characters.get(pg):\n",
    "                rightpg = right_pg(pg,coords,atoms)\n",
    "                if rightpg == True: break\n",
    "#        print('********************************\\n* Point group identified: ',pg,'* \\n********************************\\n')\n",
    "        \n",
    "        element_coord=[]        \n",
    "        for a in range(len(coords)):        \n",
    "            element_coord.append((atoms[a],coords[a][0],coords[a][1],coords[a][2]))        \n",
    "        \n",
    "        return pg, coords, element_coord\n",
    "\n",
    "\n",
    "##################################\n",
    "#          Symmetric Top         #\n",
    "##################################\n",
    "\n",
    "    elif abs((ordered_evals[0]-ordered_evals[1])/average_eval) <0.001 or abs((ordered_evals[1]-ordered_evals[2])/average_eval) <0.001:\n",
    "        v1,v2,principal_axis = symmetric_top_evec_assign(evals,evecs,average_eval)      \n",
    "        #first, align the molecular frame along the principal axis (if it is not already so)        \n",
    "        if np.array_equal(np.round(principal_axis,decimals=8),np.array([0.,0.,1.])) == False and np.array_equal(np.round(principal_axis,decimals=8),np.array([0.,0.,-1.])) == False:           \n",
    "            theta = np.arccos(principal_axis[2] / (np.dot(principal_axis,principal_axis))**0.5)\n",
    "            axis = np.cross(principal_axis,[0,0,1])\n",
    "            coords = rotate_about_axis(-theta, coords, axis)\n",
    "            inertia = inertia_matrix(atoms,coords)\n",
    "            evals,evecs= np.linalg.eig(inertia)\n",
    "            evecs = np.swapaxes(evecs,0,1)\n",
    "            v1,v2,principal_axis = symmetric_top_evec_assign(evals,evecs,average_eval)        \n",
    "        corr_table_atoms = corre_table_atoms(atoms,coords,pg)\n",
    "        corr_table_atoms= corr_table_atoms.transpose()\n",
    "\n",
    "        #now identify order of rotation about principal axis\n",
    "        corr_table_sets = []\n",
    "        for counter in range(0,len(corr_table_atoms)):\n",
    "            corr_table_sets.append([counter, set()])\n",
    "            for e in corr_table_atoms[counter]:\n",
    "                corr_table_sets[counter][1].add(e)\n",
    "        for counter in range(0,len(corr_table_atoms)):\n",
    "            if corr_table_sets[0][1] == corr_table_sets[counter][1]:\n",
    "                max_rotational_symmetry = counter+1\n",
    "\n",
    "        #now align one atom (not at an invariant point) along the y axis\n",
    "        for i in range(len(corr_table_atoms[max_rotational_symmetry-1])):\n",
    "            if corr_table_atoms[2][i]!=i:\n",
    "                atom_to_align=i\n",
    "                break\n",
    "        new_angle = np.arctan2(coords[atom_to_align][0],coords[atom_to_align][1])\n",
    "        coords = rotate_about_axis(-new_angle, coords, [0,0,1])                \n",
    "        \n",
    "        #now everything is aligned, identify the point group        \n",
    "        possible_pgs = ['Dnh', 'Dnd', 'Dn', 'Cnh', 'Cnv', 'Sn2', 'Cn', 'C1']\n",
    "        pgs2 = []\n",
    "        for pgs in possible_pgs:\n",
    "            pglist = list(pgs)\n",
    "            pglist[1] = str(max_rotational_symmetry)\n",
    "            pg = ''\n",
    "            for e in pglist: pg += e\n",
    "            if pgs == 'C1': pg = 'C1'\n",
    "            pgs2.append(pg)\n",
    "        possible_pgs = pgs2\n",
    "        #print('Possible point groups:',*possible_pgs,sep='   ')\n",
    "\n",
    "        for pg in possible_pgs:\n",
    "            if pg_operations.get(pg) and pg_irreps.get(pg) and pg_characters.get(pg):\n",
    "                rightpg = right_pg(pg,coords,atoms)\n",
    "                if rightpg == True: \n",
    "                    break\n",
    "                coordsz = rotate_about_axis(-np.pi/2,coords,[0,0,1])\n",
    "                rightpg = right_pg(pg,coordsz,atoms)\n",
    "                if rightpg == True:\n",
    "                    coords = coordsz\n",
    "                    break\n",
    "        #print('  ********************************\\n  * Point group identified: ',pg,'* \\n  ********************************\\n')\n",
    "        \n",
    "        element_coord=[]        \n",
    "        for a in range(len(coords)):        \n",
    "            element_coord.append((atoms[a],coords[a][0],coords[a][1],coords[a][2]))        \n",
    "        \n",
    "        return pg, coords, element_coord\n",
    "\n",
    "\n",
    "###################################\n",
    "#          Asymmetric Top         #\n",
    "###################################\n",
    "    else:\n",
    "        #print('eigenvalues!',np.round(evals,decimals=3),'\\neigenvectors\\n',np.round(evecs,decimals=3))\n",
    "        if np.array_equal(np.round(evecs[0],decimals=8),np.array([0.,0.,1.])) == False and np.array_equal(np.round(evecs[0],decimals=8),np.array([0.,0.,-1.])) == False:\n",
    "            theta = np.arccos(evecs[0][2] / (np.dot(evecs[0],evecs[0]))**0.5)\n",
    "            axis = np.cross(evecs[0],[0,0,1])\n",
    "            coords = rotate_about_axis(-theta, coords, axis)\n",
    "            inertia = inertia_matrix(atoms,coords)\n",
    "            evals,evecs= np.linalg.eig(inertia)\n",
    "            evecs = np.swapaxes(evecs,0,1)\n",
    "            v1,v2,principal_axis = assymetric_top_evec_assign(evecs)\n",
    "        if np.array_equal(np.round(v1,decimals=3),np.array([0.,1.,0.])) == False and np.array_equal(np.round(v1,decimals=3),np.array([1.,0.,0.])) == False and np.array_equal(np.round(v1,decimals=3),np.array([-1.,0.,0.])) == False and np.array_equal(np.round(v1,decimals=3),np.array([0.,-1.,0.])) == False:\n",
    "                phi = np.arctan2(v1[1],v1[0])\n",
    "                coords = rotate_about_axis(phi, coords, [0,0,1])\n",
    "                inertia = inertia_matrix(atoms,coords)\n",
    "                evals,evecs= np.linalg.eig(inertia)\n",
    "                evecs = np.swapaxes(evecs,0,1)\n",
    "                v1,v2,principal_axis = assymetric_top_evec_assign(evecs)\n",
    "        if np.array_equal(np.round(v2,decimals=3),np.array([0.,1.,0.])) == False and np.array_equal(np.round(v2,decimals=3),np.array([1.,0.,0.])) == False and np.array_equal(np.round(v2,decimals=3),np.array([-1.,0.,0.])) == False and np.array_equal(np.round(v2,decimals=3),np.array([0.,-1.,0.])) == False:\n",
    "                phi = np.arctan2(v2[1],v2[0])\n",
    "                coords = rotate_about_axis(phi, coords, [0,0,1])\n",
    "                inertia = inertia_matrix(atoms,coords)\n",
    "                evals,evecs= np.linalg.eig(inertia)\n",
    "                evecs = np.swapaxes(evecs,0,1)\n",
    "                v1,v2,principal_axis = assymetric_top_evec_assign(evecs)\n",
    "        possible_pgs = ['D2h','C2h','C2v','C2','Cs','Ci','C1']\n",
    "        for pg in possible_pgs:\n",
    "            if pg_operations.get(pg) and pg_irreps.get(pg) and pg_characters.get(pg):\n",
    "                rightpg = right_pg(pg,coords,atoms)\n",
    "                if rightpg == True: break\n",
    "                coordsy = rotate_about_axis(-np.pi/2,coords,[0,1,0])\n",
    "                rightpg = right_pg(pg,coordsy,atoms)\n",
    "                if rightpg == True:\n",
    "                    coords = coordsy\n",
    "                    break\n",
    "                coordsx = rotate_about_axis(-np.pi/2,coords,[1,0,0])\n",
    "                rightpg = right_pg(pg,coordsx,atoms)\n",
    "                if rightpg == True:\n",
    "                    coords = coordsx\n",
    "                    break\n",
    "\n",
    "        #print('********************************\\n* Point group identified: ',pg,'* \\n********************************\\n')\n",
    "        \n",
    "        element_coord=[]        \n",
    "        for a in range(len(coords)):        \n",
    "            element_coord.append((atoms[a],coords[a][0],coords[a][1],coords[a][2]))        \n",
    "        \n",
    "        #if pg == 'C1': print('Warning: Alignment likely failed - C1 symmetry identified!') #C1 included in each list of point groups to check - so if C1 symmetry is identified it's likely alignment hasn't worked.\n",
    "\n",
    "        return pg, coords, element_coord\n",
    "\n",
    "        return 0\n",
    " \n",
    "\n",
    "#functions associated with generating coordinates from zmatrices\n",
    "\n",
    "\n",
    "#define functions to perform rotations\n",
    "def rotation_matrix_z(phi):  \n",
    "    return(np.array([[np.cos(phi),-np.sin(phi),0.],\n",
    "                [np.sin(phi),np.cos(phi),0.],\n",
    "                [0.,0.,1.]]))\n",
    "        \n",
    "def rotation_matrix_y(theta):\n",
    "    return(np.array([[np.cos(theta),0.,-np.sin(theta)],\n",
    "                 [0.,1.,0.],\n",
    "                [np.sin(theta),0.,np.cos(theta)]]))\n",
    "\n",
    "def flip_matrix_yz():\n",
    "    return(np.array([[-1,0.,0],\n",
    "                 [0.,1.,0.],\n",
    "                [0,0,1]]))\n",
    "\n",
    "def flip_matrix_xy():\n",
    "    return(np.array([[1,0.,0],\n",
    "                 [0.,1.,0.],\n",
    "                [0,0,-1]]))\n",
    "\n",
    "\n",
    "\n",
    "def zmatrix_to_cartesian(zmatrix):\n",
    "    cartesians=[]\n",
    "    #add first atom at origin\n",
    "    cartesians.append([0.,0.,0.])\n",
    "    #add second along z\n",
    "    r1=zmatrix[1][3] #bond length\n",
    "    cartesians.append([0.,0.,zmatrix[1][3]])\n",
    "    #identify which atom the third atom is bonded to (atom a)\n",
    "    new_origin=zmatrix[2][0]\n",
    "    #by elimination, the bonded atom (b) must be the other one\n",
    "    if new_origin==1:\n",
    "        bonded_atom=2\n",
    "    else:\n",
    "        bonded_atom=1\n",
    "#shift both vectors such that atom a sits at the origin\n",
    "    shifted_cartesians=[]\n",
    "    for vector in cartesians:\n",
    "        new_vector=[a-b for a,b in zip(vector,cartesians[new_origin-1])]\n",
    "        shifted_cartesians.append(new_vector)\n",
    "#update master list\n",
    "    cartesians=shifted_cartesians\n",
    "\n",
    "#need to have the second atom aligned along -ve z. This will happen automatically if the 3rd atom is bonded\n",
    "#to atom 2, but not of it is bonded to atom 1. In case it is aligned along +ve z, flip it.\n",
    "    vector_to_rotate=cartesians[bonded_atom-1]\n",
    "    if vector_to_rotate[2]>0:       \n",
    "        flipped_cartesians=[]        \n",
    "        for vector in cartesians:\n",
    "            flipped_cartesians.append(flip_matrix_xy().dot(vector).tolist())\n",
    "        cartesians=flipped_cartesians\n",
    "\n",
    "#first 2 should now be in the standard orientation now: \n",
    "    #1) atom a to form the bond to at the origin\n",
    "    #2) bond vector ab aligned along negative z axis\n",
    "\n",
    "#now add third atom in xz plane\n",
    "#identify bond length and angle\n",
    "    r1=zmatrix[2][3];a1=(zmatrix[2][4]*np.pi)/180.\n",
    "\n",
    "#create new vector by rotating a vector with length r1 along -z by angle a1 \n",
    "    new_vector=rotation_matrix_y(a1).dot([0.,0.,-r1]).tolist()\n",
    "    cartesians.append(new_vector)\n",
    "\n",
    "#shift origin back to first atom\n",
    "    origin_cartesians=[]\n",
    "    for vector in cartesians:\n",
    "        origin_cartesians.append([a-b for a,b in zip(vector,cartesians[0])])\n",
    "\n",
    "#update master list\n",
    "    cartesians=origin_cartesians\n",
    "\n",
    "#now add all further atoms\n",
    "    for i in range(3,len(zmatrix)):\n",
    "    #identify origin atom, bonded atom and torsion atom\n",
    "        origin_atom=zmatrix[i][0];  #atom a\n",
    "        bonded_atom=zmatrix[i][1];  #atom b\n",
    "        torsion_atom=zmatrix[i][2]  #atom c\n",
    "    \n",
    "        #print(origin_atom,bonded_atom,torsion_atom)\n",
    "    #identify bond length, angle and torsion    \n",
    "        r1=zmatrix[i][3];a1=(zmatrix[i][4]*np.pi)/180.;phi1=(zmatrix[i][5]*np.pi)/180.\n",
    "    #shift origin to atom a of the triad\n",
    "        shifted_cartesians=[]\n",
    "        for vector in cartesians:\n",
    "            shifted_cartesians.append([a-b for a,b in zip(vector,cartesians[origin_atom-1])])\n",
    "    #update master list\n",
    "        cartesians=shifted_cartesians\n",
    "    #work out the vector between atom a and atom b    \n",
    "        bond_vector=cartesians[bonded_atom-1]    \n",
    "\n",
    "    #rotate bond vector about y to bring it onto the negative z axis    \n",
    "    #work out rotation angle\n",
    "        theta=np.arccos(np.dot(bond_vector,[0,0,-1])/np.linalg.norm(bond_vector))  \n",
    "    #then generate list of rotated cartesians\n",
    "        rotated_cartesians=[]\n",
    "        for vector in cartesians:\n",
    "            rotated_cartesians.append(rotation_matrix_y(theta).dot(vector).tolist())\n",
    "\n",
    "    #update master list\n",
    "        cartesians=rotated_cartesians\n",
    "    #note origin atom and bonded atom are now on the z axis.\n",
    "    \n",
    "    #now rotate the torsion atom about the z axis to bring it into the xz plane   \n",
    "    #work out angle to rotate by\n",
    "        torsion_vector=cartesians[torsion_atom-1] \n",
    "        phi=np.arcsin(np.dot(torsion_vector,[0,1,0])/np.linalg.norm(torsion_vector))   \n",
    "    \n",
    "    #generate list of rotated coordinates\n",
    "        rotated_cartesians=[]\n",
    "        for vector in cartesians:\n",
    "            rotated_cartesians.append(rotation_matrix_z(phi).dot(vector).tolist())\n",
    "    #update master list\n",
    "        cartesians=rotated_cartesians\n",
    "    #origin atom, bonded atom and torsion atom are now in the xz plane\n",
    "    #now make sure torsion atom lies in positive x direction:\n",
    "        torsion_vector=cartesians[torsion_atom-1] \n",
    "        if torsion_vector[0]<0:\n",
    "            flipped_cartesians=[]        \n",
    "            for vector in cartesians:\n",
    "                flipped_cartesians.append(flip_matrix_yz().dot(vector).tolist())\n",
    "            cartesians=flipped_cartesians\n",
    "\n",
    "    #finally, generate the new vector, first placed in the xz plane\n",
    "        vector_to_rotate=cartesians[bonded_atom-1]    \n",
    "    #if bond vector is aligned along positive z, flip molecule in xy plane    \n",
    "        if vector_to_rotate[2]>0:\n",
    "            flipped_cartesians=[]        \n",
    "            for vector in cartesians:\n",
    "                flipped_cartesians.append(flip_matrix_xy().dot(vector).tolist())\n",
    "            cartesians=flipped_cartesians\n",
    "\n",
    "    #everything should be in the standard orientation now: \n",
    "    #1) atom a to form the bond to at the origin\n",
    "    #2) bond vector ab aligned along negative z axis\n",
    "    #3) torsion atom c in the xz plane\n",
    "    #4) bc vector aligned in the positive x direction\n",
    "   \n",
    "    #create new vector by rotating a vector with length along -z by angle a1 \n",
    "        new_vector=rotation_matrix_y(a1).dot([0.,0.,-r1]).tolist()\n",
    "    #then rotate it about z according t torsion angle phi1\n",
    "        new_vector_rotated=rotation_matrix_z(phi1).dot(new_vector).tolist()\n",
    "    #then add it to the list\n",
    "        cartesians.append(new_vector_rotated)\n",
    "    #now restore the original orientation\n",
    "        rotated_cartesians=[];origin_cartesians=[]    \n",
    "    #rotate the bond vector back to its original position\n",
    "        for vector in cartesians:\n",
    "            rotated_cartesians.append(rotation_matrix_y(-theta).dot(vector).tolist())\n",
    "    #shift origin back to first atom    \n",
    "        for vector in rotated_cartesians:\n",
    "            origin_cartesians.append([a-b for a,b in zip(vector,rotated_cartesians[0])])\n",
    "\n",
    "    #update master list\n",
    "        cartesians=origin_cartesians         \n",
    "    return cartesians\n",
    "\n",
    "#generates coordinates using zmatrices (for Walsh diagrams)\n",
    "\n",
    "def coordinate_generator(angle_max,angle_min,npts,bond_length,atoms,symmetry):\n",
    "#set up a list of coordinates.\n",
    "    angle_list=np.linspace(angle_max,angle_min,npts)\n",
    "    coordinate_list=[]\n",
    "    \n",
    "#note this is very clumsy - it is set up for specific types (AH2 linear-bent, AH3 planar-pyramidal, AH4 tetrahedral-square planar and ML4 tetrahedral-square planar).\n",
    "#it won't work for more generic structure types. Need to find a way to read coordinate generating function from the dictionary\n",
    "\n",
    "    if len(atoms)==3:\n",
    "        r1=bond_length\n",
    "        for theta in angle_list:\n",
    "            coordinate_list.append(zmatrix_to_cartesian(c2v_zmatrix(theta,r1)))  \n",
    "\n",
    "    elif len(atoms)==4 and symmetry=='C2h':\n",
    "        for theta in angle_list:\n",
    "            r1=bond_length[0];r2=bond_length[1]\n",
    "            temp_coords=zmatrix_to_cartesian(c2h_zmatrix(theta,r1,r2))\n",
    "            del temp_coords[0]\n",
    "            del temp_coords[0]\n",
    "            coordinate_list.append(temp_coords)\n",
    "\n",
    "    elif len(atoms)==4 and symmetry=='C3v':\n",
    "        for theta in angle_list:\n",
    "            r1=bond_length\n",
    "            temp_coords=zmatrix_to_cartesian(c3v_zmatrix(theta,r1))\n",
    "            del temp_coords[1]\n",
    "            del temp_coords[1]\n",
    "            coordinate_list.append(temp_coords)#remove dummy at position 1\n",
    "\n",
    "    elif len(atoms)==5:\n",
    "        r1=bond_length\n",
    "        for theta in angle_list:\n",
    "            temp_coords=zmatrix_to_cartesian(td_d4h_zmatrix(theta,r1))\n",
    "            del temp_coords[1]\n",
    "            del temp_coords[1]\n",
    "            coordinate_list.append(temp_coords)#remove dummy at position 1\n",
    "\n",
    "    elif len(atoms)>5:\n",
    "        plot_plane='xy';plane_offset=0.2\n",
    "        for theta in angle_list:\n",
    "            temp_coords=\\\n",
    "            [[0.,0.,0.],\n",
    "             [bond_length*np.sin(np.pi*theta/180.),0.,bond_length*np.cos(np.pi*theta/180.)],\n",
    "            [-bond_length*np.sin(np.pi*theta/180.),0.,bond_length*np.cos(np.pi*theta/180.)],\n",
    "            [0.,bond_length*np.sin(np.pi*theta/180.),-bond_length*np.cos(np.pi*theta/180.)],\n",
    "            [0.,-bond_length*np.sin(np.pi*theta/180.),-bond_length*np.cos(np.pi*theta/180.)],\n",
    "            [(bond_length+1.1)*np.sin(np.pi*theta/180.),0.,(bond_length+1.1)*np.cos(np.pi*theta/180.)],\n",
    "            [-(bond_length+1.1)*np.sin(np.pi*theta/180.),0.,(bond_length+1.1)*np.cos(np.pi*theta/180.)],\n",
    "            [0.,(bond_length+1.1)*np.sin(np.pi*theta/180.),-(bond_length+1.1)*np.cos(np.pi*theta/180.)],\n",
    "            [0.,-(bond_length+1.1)*np.sin(np.pi*theta/180.),-(bond_length+1.1)*np.cos(np.pi*theta/180.)]]\n",
    "            coordinate_list.append(temp_coords)\n",
    "    return coordinate_list,angle_list\n",
    "\n",
    "#basis functions, overlap matrix, Hamiltonian matrix, diagonalisation\n",
    "\n",
    "\n",
    "#this checks that we have a set of coordinates for each atom\n",
    "def check_lengths(coords,atoms):    \n",
    "    if len(atoms)!=len(coords):\n",
    "        print(\"number of atoms\",len(atoms), \"doesn't equal number of coordinates\", len(coords))\n",
    "\n",
    "#function to convert coordinates to atomic units\n",
    "def coord_con_EHT(coords,atoms):\n",
    "    coord_tuple=[];coords_new=coords.copy()    \n",
    "    for a in range(len(coords)):        #convert angstrom to atomic units\n",
    "        coords_new[a]=[x/A0 for x in coords[a]]\n",
    "        coord_tuple.append((atoms[a],coords_new[a][0],coords_new[a][1],coords_new[a][2]))\n",
    "    return coords_new,coord_tuple\n",
    "\n",
    "\n",
    "def eh_basis_setupb(atoms,parameters):\n",
    "    basis_functs=[]         #set up matrix of basis functions\n",
    "    #keep count of how many basis functions we have    \n",
    "    count=0\n",
    "    for atom_index,atomic_number in enumerate(atoms):\n",
    "        if atomic_number<=2:             # H and He (ns)\n",
    "\n",
    "            n=parameters[atomic_number][0]   #principal qu no\n",
    "            l=parameters[atomic_number][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][3] #exponent\n",
    "\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])  #the zero is ml\n",
    "            count+=1\n",
    "\n",
    "        elif 20>=atomic_number>2 or 38>=atomic_number>29:        # main group (ns+np)\n",
    "        \n",
    "            #first deal with the s orbital: note this is the first item in a nested list, hence the extra [0]\n",
    "            n=parameters[atomic_number][0][0]   #principal qu no\n",
    "            l=parameters[atomic_number][0][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][0][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][0][3] #exponent\n",
    "            \n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])\n",
    "            \n",
    "           #now the p orbitals. We generate 3 from each entry in parameters: px,py,pz which differ only in ml\n",
    "            n=parameters[atomic_number][1][0]   #principal qu no\n",
    "            l=parameters[atomic_number][1][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][1][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][1][3] #exponent\n",
    "            \n",
    "            basis_functs.append([atomic_number,atom_index,n,l,1,Hii,zeta])   #1,-1,0 are ml\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,-1,Hii,zeta])\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])\n",
    "            count+=4\n",
    "            \n",
    "#            #now the d orbitals    #in the case of Cerda\n",
    "#            n=parameters[atomic_number][2][0]   #principal qu no\n",
    "#            l=parameters[atomic_number][2][1]   #angular mom qu no                         \n",
    "#            Hii=parameters[atomic_number][2][2]  #ionisation energy\n",
    "#            zeta=parameters[atomic_number][2][3] #exponent\n",
    "#            \n",
    "#            basis_functs.append([atomic_number,atom_index,n,l,2,Hii,zeta])   #2,1,0,-1,-2 are ml\n",
    "#            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])\n",
    "#            basis_functs.append([atomic_number,atom_index,n,l,-2,Hii,zeta])\n",
    "#            basis_functs.append([atomic_number,atom_index,n,l,1,Hii,zeta])\n",
    "#            basis_functs.append([atomic_number,atom_index,n,l,-1,Hii,zeta])\n",
    "#            count=count+9\n",
    "            \n",
    "        elif 30>atomic_number>20 or 48>atomic_number>38 or atomic_number>=74:           # transition metals ((n+1)s+(n+1)p+nd) order chosen to be consistent with iconc (pz,px,py: dx2-y2,dz2,dxy,dxz,dyz)\n",
    "            #first deal with the s orbital: note this is the first item in a nested list, hence the extra [0]\n",
    "            n=parameters[atomic_number][0][0]   #principal qu no\n",
    "            l=parameters[atomic_number][0][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][0][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][0][3] #exponent\n",
    "            \n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])  \n",
    "         \n",
    "            #now the p orbitals. We generate 3 from each entry in parameters: px,py,pz which differ only in ml\n",
    "            n=parameters[atomic_number][1][0]   #principal qu no\n",
    "            l=parameters[atomic_number][1][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][1][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][1][3] #exponent\n",
    "            \n",
    "            basis_functs.append([atomic_number,atom_index,n,l,1,Hii,zeta])   #1,-1,0 are ml\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,-1,Hii,zeta])\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])\n",
    "            \n",
    "            #now the d orbitals\n",
    "            n=parameters[atomic_number][2][0]   #principal qu no\n",
    "            l=parameters[atomic_number][2][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][2][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][2][3] #exponent\n",
    "            \n",
    "            basis_functs.append([atomic_number,atom_index,n,l,2,Hii,zeta])   #2,1,0,-1,-2 are ml\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,-2,Hii,zeta])\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,1,Hii,zeta])\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,-1,Hii,zeta])\n",
    "            count+=9\n",
    "        elif atomic_number==179:             # special 6s-only Au for electrodes\n",
    "            n=parameters[atomic_number][0]   #principal qu no\n",
    "            l=parameters[atomic_number][1]   #angular mom qu no                         \n",
    "            Hii=parameters[atomic_number][2]  #ionisation energy\n",
    "            zeta=parameters[atomic_number][3] #exponent\n",
    "\n",
    "            basis_functs.append([atomic_number,atom_index,n,l,0,Hii,zeta])  #the zero is ml\n",
    "            count+=1\n",
    "    return basis_functs\n",
    "\n",
    "def count_electrons(atoms,Q,parameter_file):\n",
    "        #function to work out how many VALENCE electrons are present    \n",
    "        no_electrons=-Q   #initial the number of electrons as -Q\n",
    "        #loop over the list of atoms (length len(atoms))\n",
    "        for i in range(len(atoms)):\n",
    "            if atoms[i]<=2:      #H\n",
    "                    no_electrons+=atoms[i]\n",
    "            elif 2<atoms[i]<=10:   #He to F\n",
    "                    no_electrons+=atoms[i]-2    #atoms[i] is the atomic number. \n",
    "            elif 10<atoms[i]<=18:  #Ne to Cl    #We need to subtract off number of core electrons\n",
    "                    no_electrons+=atoms[i]-10\n",
    "            elif 18<atoms[i]<=29:  #Ar to Cu\n",
    "                    no_electrons+=atoms[i]-18\n",
    "            elif atoms[i]==30:  #Zn\n",
    "                if parameter_file=='Hoffmann':\n",
    "                    no_electrons+=atoms[i]-18\n",
    "                elif parameter_file=='Cerda':\n",
    "                    no_electrons+=atoms[i]-28\n",
    "            elif 30<atoms[i]<=36:  #Ga to Br\n",
    "                    no_electrons+=atoms[i]-28\n",
    "            elif 36<atoms[i]<=48:\n",
    "                    no_electrons+=atoms[i]-36    \n",
    "            elif atoms[i]>=74:\n",
    "                    no_electrons+=atoms[i]-68   \n",
    "        return no_electrons\n",
    "\n",
    "#calculates direction cosines needed for angular dependence (see wikipidea entry for parameters)            \n",
    "def dircos(i,j,coords,basis_functs):\n",
    "        atomindexi=basis_functs[i][1]\n",
    "        atomindexj=basis_functs[j][1]\n",
    "        coordsi=coords[atomindexi]\n",
    "        coordsj=coords[atomindexj]\n",
    "        vecij=[(coordsj[0]-coordsi[0]),(coordsj[1]-coordsi[1]),(coordsj[2]-coordsi[2])]\n",
    "        distij=np.linalg.norm(vecij)\n",
    "        if distij==0:           #create exception in case bfs are on the same atom, where vector == null\n",
    "                dcl=0.0\n",
    "                dcm=0.0\n",
    "                dcn=0.0\n",
    "        else:\n",
    "                dcn=vecij[2]/distij\n",
    "                dcm=vecij[1]/distij\n",
    "                dcl=vecij[0]/distij\n",
    "\n",
    "        return distij,dcl,dcm,dcn\n",
    "\n",
    "\n",
    "# functions for Mulliken's A and B coefficients\n",
    "# formulae from Mulliken, Rieke, Orloff and Orloff, J Chem Phys 1949, 17, 1248\n",
    "\n",
    "def A_funct(k,pij):\n",
    "        A=0\n",
    "        for i in range(1,k+2):\n",
    "                A+=np.exp(-pij)*m.factorial(k)*(m.factorial(k-i+1)*(pij**i))**-1\n",
    "        return A\n",
    "\n",
    "def B_funct(k,pij,tij):\n",
    "        if tij==0.:         #exception in case exponents are identical, in which case it reduces to a trivial integral of x**n.\n",
    "            if k%2==0.:     #test for k even, int(x**k)=2/(k+1)\n",
    "                B=2/(k+1)\n",
    "            else:           #if k odd, integral of x**k is zero\n",
    "                B=0\n",
    "        else:               #full function\n",
    "            B=0\n",
    "            for i in range(1,k+2):\n",
    "                B+=-np.exp(-pij*tij)*m.factorial(k)*(m.factorial(k-i+1)*(pij*tij)**i)**-1-np.exp(pij*tij)*m.factorial(k)*((-1.0)**(k-i)*(m.factorial(k-i+1)*(pij*tij)**i)**-1)\n",
    "        return B\n",
    "\n",
    "#Formulae from Stewart, Encyclopedia of Computational Chemistry  (\"Overlap\" chapter)\n",
    "\n",
    "#table of premultipliers that depend only on i,j,ja/jb (from Stewart in Encyclopedia of Computational Chemistry (\"overlap integrals\"))\n",
    "radial_coeff={(0,0,0):1.,                                                                               \n",
    "              (1,0,0):1,\n",
    "              (1,1,0):0.5**0.5,\n",
    "              (2,0,0):1.5,\n",
    "              (2,0,1):-0.5,\n",
    "              (2,1,0):1.5**0.5,\n",
    "              (2,2,0):0.375**0.5}  \n",
    "\n",
    "def new_overlap(n_i,n_j,l_i,l_j,zeta_i,zeta_j,pij,tij,distij,mz):   #function for overlap between any pair of Slater functions (Stewart)\n",
    "    #following Stewart.....\n",
    "    prefactor=0.5*zeta_i**(n_i+0.5)*zeta_j**(n_j+0.5)*((2*l_i+1)*(2*l_j+1)/(m.factorial(2*n_i)*m.factorial(2*n_j)))**0.5*distij**(n_i+n_j+1)\n",
    "    #initialise the cumulative overlap (there are many contributions to this which I am going to sum up)            \n",
    "    sumovlp=0.\n",
    "    for ja,jb,ka,kb in itertools.product(range(int(0.5*(l_i-mz))+1), range(int(0.5*(l_j-mz))+1), range(int(mz+1)), range(int(mz+1))):\n",
    "        for Pa,Pb,qa,qb in itertools.product(range(int(n_i-l_i+2*ja)+1),range(int(n_j-l_j+2*jb)+1),range(int(l_i-mz-2*ja)+1),range(int(l_j-mz-2*jb)+1)):\n",
    "            sumovlp+=prefactor*radial_coeff[(l_i,mz,ja)]*radial_coeff[(l_j,mz,jb)]\\\n",
    "            *m.factorial(l_i-mz-2*ja)*m.factorial(l_j-mz-2*jb)*m.factorial(n_i-l_i+2*ja)*m.factorial(n_j-l_j+2*jb)*(-1)**(ka+kb+mz+Pb+qb)*m.factorial(mz)**2\\\n",
    "            *B_funct((2*ka+Pa+Pb+qa+qb),pij,tij)*A_funct((2*kb+n_i-l_i+2*ja+n_j-l_j+2*jb-Pa-Pb+qa+qb),pij)\\\n",
    "            /(m.factorial(l_i-mz-2*ja-qa)*m.factorial(qa)*m.factorial(l_j-mz-2*jb-qb)*m.factorial(qb)*m.factorial(n_i-l_i+2*ja-Pa)*m.factorial(Pa)\\\n",
    "            *m.factorial(n_j-l_j+2*jb-Pb)*m.factorial(Pb)*m.factorial(mz-ka)*m.factorial(ka)*m.factorial(mz-kb)*m.factorial(kb))\n",
    "    return sumovlp\n",
    "\n",
    "    \n",
    "#constructs the overlap matrix:\n",
    "#first compute the overlap along the z axis (using one of newoverlap/b/c), then multiply by the angular factor from direction cosines\n",
    "def overlap_matrix_new(basis_functs,coords,dist_cutoff):    \n",
    "    dcovlp=np.identity(len(basis_functs))    # automatically sets diagonal atoms to 1.0 \n",
    "    overlaps_dictionary={}\n",
    "    # note \"dcovlp\" used to remind me I use direction cosines\n",
    "    # now compute all off diagonal elements    \n",
    "    skip=0.;keep=0.\n",
    "    for i in range(len(basis_functs)):     \n",
    "        for j in range(len(basis_functs)):\n",
    "           #so we loop over pairs of basis functions, i and j\n",
    "            if j>i:  #only do the upper half of the matrix - we will simply copy the top half to the bottom at the end as it must be symmetric\n",
    "                sigma_ovlp=0.;pi_ovlp=0.;delta_ovlp=0.   #initialise all overlaps to zero\n",
    "                    #now calculate the direction cosines. see https://en.wikipedia.org/wiki/Tight_binding\n",
    "                    #if v = vx(x) + vy(y) + vz(z) then l=v.x/|v| = the cosine of the angle between v and x axis\n",
    "                    #distij is the distance between the two centres, i and j\n",
    "                distij,dcl,dcm,dcn=dircos(i,j,coords,basis_functs)\n",
    "                #if 1!=1:                \n",
    "                if distij>dist_cutoff:\n",
    "                        dcovlp[i,j]=0.0\n",
    "                        skip+=1\n",
    "                else:\n",
    "                    keep+=1\n",
    "                    #now identify the 2 atoms:\n",
    "                    atom_index_i=basis_functs[i][1];atom_index_j=basis_functs[j][1]\n",
    "                    atomic_number_i=basis_functs[i][0];atomic_number_j=basis_functs[i][0]\n",
    "                    #identify the quantum numbers\n",
    "                    n_i=basis_functs[i][2];n_j=basis_functs[j][2]\n",
    "                    l_i=basis_functs[i][3];l_j=basis_functs[j][3]\n",
    "                    ml_i=basis_functs[i][4];ml_j=basis_functs[j][4]\n",
    "                    Hii_i=basis_functs[i][5];Hii_j=basis_functs[j][5]\n",
    "                    zeta_i_temp=basis_functs[i][6];zeta_j_temp=basis_functs[j][6]    #mistake was here (i instead of j)\n",
    "                    \n",
    "                    if atom_index_i==atom_index_j:                      \n",
    "                            # same atom, different basis functions, set ovlp to 0\n",
    "                            dcovlp[i,j]=0.0\n",
    "                            \n",
    "                    #ss overlaps\n",
    "                    elif l_i==l_j==0:                 \n",
    "                    #no point in setting up a distionary entry for this as there is only ever 1 ss overlap between 2 atoms\n",
    "                            zeta_i=zeta_i_temp[0];zeta_j=zeta_j_temp[0];coeff_i=zeta_i_temp[1];coeff_j=zeta_j_temp[1]\n",
    "                        #set the pij,Bpij and tij parameters\n",
    "                            pij=0.5*(zeta_i+zeta_j)*distij\n",
    "                            Bpij=0.5*(zeta_i-zeta_j)*distij\n",
    "                            tij=(zeta_i-zeta_j)/(zeta_i+zeta_j)      \n",
    "                            #calculate the overlap using \"new_overlap\" function\n",
    "                            ss_ovlp=new_overlap(n_i,n_j,l_i,l_j,zeta_i,zeta_j,pij,tij,distij,0) \n",
    "                            #set the total overlap: trivial here because there is no angular factor to worry about \n",
    "                            dcovlp[i,j]=coeff_i*coeff_j*ss_ovlp     \n",
    "                    #pp overlaps (9 permutations of pxpy etc)        \n",
    "                    elif l_i==l_j==1:\n",
    "                        sigma_key=(1,atom_index_i,atom_index_j,1,1)\n",
    "                        pi_key=(2,atom_index_i,atom_index_j,1,1)\n",
    "                        if sigma_key not in overlaps_dictionary: \n",
    "                            if type(zeta_i_temp[0]) is float and type(zeta_j_temp[0]) is float: #both p's are single zeta\n",
    "                                zeta_i=zeta_i_temp[0];zeta_j=zeta_j_temp[0];coeff_i=zeta_i_temp[1];coeff_j=zeta_j_temp[1]\n",
    "                                pij=0.5*(zeta_i+zeta_j)*distij\n",
    "                                Bpij=0.5*(zeta_i-zeta_j)*distij\n",
    "                                tij=(zeta_i-zeta_j)/(zeta_i+zeta_j) \n",
    "                            #calculate the pure sigma and pi overlaps. \n",
    "                            #Note the negative sign: 2 p orbitals aligned along the same axis have negative overlap                             \n",
    "                                ppsigma_ovlp=-coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,zeta_i,zeta_j,pij,tij,distij,0) #m=0\n",
    "                                pppi_ovlp=coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,zeta_i,zeta_j,pij,tij,distij,1)     #m=1\n",
    "                            elif type(zeta_i_temp[0]) is float and type(zeta_j_temp[0]) is tuple:  #first is single second is double\n",
    "                                ppsigma_ovlp=0.;pppi_ovlp=0.\n",
    "                                for l in [0,1]:    \n",
    "                                    zeta_i=zeta_i_temp[0];coeff_i=zeta_i_temp[1]\n",
    "                                    exponent_j=zeta_j_temp[l][0];coeff_j=zeta_j_temp[l][1]\n",
    "                                    pij=0.5*(zeta_i+exponent_j)*distij\n",
    "                                    Bpij=0.5*(zeta_i-exponent_j)*distij\n",
    "                                    tij=(zeta_i-exponent_j)/(zeta_i+exponent_j)\n",
    "                                    ppsigma_ovlp+=-coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,zeta_i,exponent_j,pij,tij,distij,0) \n",
    "                                    pppi_ovlp+=coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,zeta_i,exponent_j,pij,tij,distij,1)\n",
    "                            elif type(zeta_j_temp[0]) is float and type(zeta_i_temp[0]) is tuple:   #first is double, second is single\n",
    "                                ppsigma_ovlp=0.;pppi_ovlp=0.\n",
    "                                for l in [0,1]:    \n",
    "                                    zeta_j=zeta_j_temp[0];coeff_j=zeta_j_temp[1]\n",
    "                                    exponent_i=zeta_i_temp[l][0];coeff_i=zeta_i_temp[l][1]\n",
    "                                    pij=0.5*(zeta_j+exponent_i)*distij\n",
    "                                    Bpij=0.5*(zeta_j-exponent_i)*distij\n",
    "                                    tij=(zeta_j-exponent_i)/(zeta_j+exponent_i)\n",
    "                                    ppsigma_ovlp+=-coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,zeta_j,pij,tij,distij,0) \n",
    "                                    pppi_ovlp+=coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,zeta_j,pij,tij,distij,1)\n",
    "                            elif type(zeta_j_temp[0]) is tuple and type(zeta_i_temp[0]) is tuple:  #both are double\n",
    "                                ppsigma_ovlp=0.;pppi_ovlp=0.\n",
    "                                for l in [0,1]:\n",
    "                                    for m in [0,1]:\n",
    "                                        exponent_i=zeta_i_temp[l][0];coeff_i=zeta_i_temp[l][1]\n",
    "                                        exponent_j=zeta_j_temp[m][0];coeff_j=zeta_j_temp[m][1]\n",
    "                                        pij=0.5*(exponent_j+exponent_i)*distij\n",
    "                                        Bpij=0.5*(exponent_j-exponent_i)*distij\n",
    "                                        tij=(exponent_j-exponent_i)/(exponent_j+exponent_i)\n",
    "                                        ppsigma_ovlp+=-coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,exponent_j,pij,tij,distij,0) \n",
    "                                        pppi_ovlp+=coeff_i*coeff_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,exponent_j,pij,tij,distij,1)\n",
    "                            overlaps_dictionary[sigma_key]=ppsigma_ovlp\n",
    "                            overlaps_dictionary[pi_key]=pppi_ovlp\n",
    "                        else:\n",
    "                            ppsigma_ovlp=overlaps_dictionary[sigma_key]\n",
    "                            pppi_ovlp=overlaps_dictionary[pi_key]\n",
    "                            \n",
    "                            #The angular overlap parameters (in terms of direction cosines) are derived in  \n",
    "                            #https://en.wikipedia.org/wiki/Tight_binding\n",
    "                        if ml_i==1:                         #px on i\n",
    "                            if ml_j==1:                 #pxpx\n",
    "                                dcovlp[i,j]=dcl**2*ppsigma_ovlp+(1-dcl**2)*pppi_ovlp\n",
    "                            elif ml_j==0:               #pxpz\n",
    "                                dcovlp[i,j]=dcl*dcn*(ppsigma_ovlp-pppi_ovlp)\n",
    "                            else:                                     #pxpy\n",
    "                                dcovlp[i,j]=dcl*dcm*(ppsigma_ovlp-pppi_ovlp)\n",
    "                        elif ml_i==0:                       #pz on i\n",
    "                            if ml_j==1:                 #pzpx\n",
    "                                dcovlp[i,j]=dcn*dcl*(ppsigma_ovlp-pppi_ovlp)\n",
    "                            elif ml_j==0:               #pzpz\n",
    "                                dcovlp[i,j]=dcn**2*ppsigma_ovlp+(1-dcn**2)*pppi_ovlp\n",
    "                            else:                                     #pzpy\n",
    "                                dcovlp[i,j]=dcn*dcm*(ppsigma_ovlp-pppi_ovlp)\n",
    "                        else:                                             #py on i\n",
    "                            if ml_j==1:                 #pypx\n",
    "                                dcovlp[i,j]=dcm*dcl*(ppsigma_ovlp-pppi_ovlp)\n",
    "                            elif ml_j==0:               #pypz\n",
    "                                dcovlp[i,j]=dcm*dcn*(ppsigma_ovlp-pppi_ovlp)\n",
    "                            else:                                     #pypy\n",
    "                                dcovlp[i,j]=dcm**2*ppsigma_ovlp+(1-dcm**2)*pppi_ovlp\n",
    "                                          \n",
    "                    elif (l_i==1 and l_j==0) or (l_i==0 and l_j==1):           #ps overlap\n",
    "                        if basis_functs[i][3]==1:\n",
    "                            itemp=j;jtemp=i\n",
    "                        else:\n",
    "                            itemp=i;jtemp=j\n",
    "                        ml_p=basis_functs[jtemp][4];ml_s=basis_functs[itemp][4]\n",
    "                        zeta_p_temp=basis_functs[jtemp][6]\n",
    "                        zeta_s_temp=basis_functs[itemp][6]\n",
    "                        n_p=basis_functs[jtemp][2];l_p=basis_functs[jtemp][3]\n",
    "                        n_s=basis_functs[itemp][2];l_s=basis_functs[itemp][3]\n",
    "                        sigma_key=(1,atom_index_i,atom_index_j,l_i,l_j)\n",
    "                        pi_key=(2,atom_index_i,atom_index_j,l_i,l_j)\n",
    "                        if sigma_key not in overlaps_dictionary:\n",
    "                            ps_ovlp=0.\n",
    "                            if type(zeta_s_temp[0]) is float and type(zeta_p_temp[0]) is float:                            \n",
    "                                zeta_s=zeta_s_temp[0];zeta_p=zeta_p_temp[0];coeff_s=zeta_s_temp[1];coeff_p=zeta_p_temp[1]\n",
    "                                pij=0.5*(zeta_s+zeta_p)*distij\n",
    "                                Bpij=0.5*(zeta_s-zeta_p)*distij\n",
    "                                tij=(zeta_s-zeta_p)/(zeta_s+zeta_p) \n",
    "                            \n",
    "                                ps_ovlp=coeff_i*coeff_j*new_overlap(n_s,n_p,l_s,l_p,zeta_s,zeta_p,pij,tij,distij,0)                # function for angle independent ps overlaps (sigma) \n",
    "                            elif type(zeta_s_temp[0]) is tuple:\n",
    "                                exponent_p=zeta_p_temp[0];coeff_p=zeta_p_temp[1]\n",
    "                                for l in [0,1]:    \n",
    "                                    exponent_s=zeta_s_temp[l][0];coeff_s=zeta_s_temp[l][1]\n",
    "                                    pij=0.5*(exponent_s+exponent_p)*distij\n",
    "                                    Bpij=0.5*(exponent_s-exponent_p)*distij\n",
    "                                    tij=(exponent_s-exponent_p)/(exponent_s+exponent_p)\n",
    "                                    ps_ovlp+=coeff_s*coeff_p*new_overlap(n_s,n_p,l_s,l_p,zeta_s,zeta_p,pij,tij,distij,0) \n",
    "                            elif type(zeta_p_temp[0]) is tuple:  \n",
    "                                exponent_s=zeta_s_temp[0];coeff_s=zeta_s_temp[1]\n",
    "                                for l in [0,1]:    \n",
    "                                    exponent_p=zeta_p_temp[l][0];coeff_p=zeta_p_temp[l][1]\n",
    "                                    pij=0.5*(exponent_s+exponent_p)*distij\n",
    "                                    Bpij=0.5*(exponent_s-exponent_p)*distij\n",
    "                                    tij=(exponent_s-exponent_p)/(exponent_s+exponent_p)\n",
    "                                    ps_ovlp+=coeff_s*coeff_p*new_overlap(n_s,n_p,l_s,l_p,zeta_s,zeta_p,pij,tij,distij,0) \n",
    "                            \n",
    "                            overlaps_dictionary[sigma_key]=ps_ovlp\n",
    "                            #if the atoms are the same, then it doesn't matter which order l_i and l_j come in.\n",
    "                            if atomic_number_i==atomic_number_j:\n",
    "                                complementary_sigma_key=(1,atom_index_j,atom_index_i,l_j,l_i)\n",
    "                                overlaps_dictionary[complementary_sigma_key]=ps_ovlp                            \n",
    "                        else:\n",
    "                            ps_ovlp=overlaps_dictionary[sigma_key]\n",
    "                        #work out which centre the p orbital is on\n",
    "                        if l_i==1:\n",
    "                            mlp=ml_i\n",
    "                        if l_j==1:\n",
    "                            mlp=ml_j\n",
    "                            ps_ovlp=-ps_ovlp                #this is the switch - along z axis, sp overlap is negative but ps is positive (pz defined positive along +z)\n",
    "                        if ml_p==-1:                        #pzs\n",
    "                            dcovlp[i,j]=dcm*ps_ovlp\n",
    "                        elif ml_p==0:                       #pys\n",
    "                            dcovlp[i,j]=dcn*ps_ovlp\n",
    "                        elif ml_p==1:                       #px1s\n",
    "                            dcovlp[i,j]=dcl*ps_ovlp\n",
    "                    \n",
    "                    elif l_i==l_j==2:                 #dd overlap  (loop over two zeta components of d)\n",
    "                         #note in this case zeta_i, zeta_j are lists rather than just numbers\n",
    "                            sigma_key=(1,atom_index_i,atom_index_j,2,2)\n",
    "                            pi_key=(2,atom_index_i,atom_index_j,2,2)\n",
    "                            delta_key=(3,atom_index_i,atom_index_j,2,2)\n",
    "                            if sigma_key not in overlaps_dictionary:  \n",
    "                                ddsigma_ovlp=0.;ddpi_ovlp=0.;delta_ovlp=0.\n",
    "                                for k in [0,1]:    #the range is always 2 as it is a double zeta basis\n",
    "                                #extract the exponents and coefficients for each component of the double zeta basis - \n",
    "                                #these are labelled 'exponent_i, rather than 'zeta_i')\n",
    "                                    exponent_i=zeta_i_temp[k][0];coefficient_i=zeta_i_temp[k][1]                            \n",
    "                                    for l in [0,1]:    \n",
    "                                        exponent_j=zeta_j_temp[l][0];coefficient_j=zeta_j_temp[l][1]\n",
    "                                        pij=0.5*(exponent_i+exponent_j)*distij\n",
    "                                        Bpij=0.5*(exponent_i-exponent_j)*distij\n",
    "                                        tij=(exponent_i-exponent_j)/(exponent_i+exponent_j) \n",
    "                                        ddsigma_ovlp+=coefficient_i*coefficient_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,exponent_j,pij,tij,distij,0)\n",
    "                                        ddpi_ovlp-=coefficient_i*coefficient_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,exponent_j,pij,tij,distij,1)\n",
    "                                        delta_ovlp+=coefficient_i*coefficient_j*new_overlap(n_i,n_j,l_i,l_j,exponent_i,exponent_j,pij,tij,distij,2)\n",
    "                                overlaps_dictionary[sigma_key]=ddsigma_ovlp\n",
    "                                overlaps_dictionary[pi_key]=ddpi_ovlp\n",
    "                                overlaps_dictionary[delta_key]=delta_ovlp\n",
    "                            else:\n",
    "                                ddsigma_ovlp=overlaps_dictionary[sigma_key]\n",
    "                                ddpi_ovlp=overlaps_dictionary[pi_key]\n",
    "                                delta_ovlp=overlaps_dictionary[delta_key]\n",
    "                            if ml_i==0:                         #dz2 on i\n",
    "                                if ml_j==0:\n",
    "                                    dcovlp[i,j]=(dcn**2-0.5*(dcl**2+dcm**2))**2*ddsigma_ovlp+3*dcn**2*(dcl**2+dcm**2)*ddpi_ovlp+0.75*(dcl**2+dcm**2)**2*delta_ovlp\n",
    "                                elif ml_j==2:\n",
    "                                    dcovlp[i,j]=0.5*3**0.5*(dcl**2-dcm**2)*(dcn**2-0.5*(dcl**2+dcm**2))*ddsigma_ovlp+3**0.5*dcn**2*(dcm**2-dcl**2)*ddpi_ovlp+0.25*3**0.5*(1+dcn**2)*(dcl**2-dcm**2)*delta_ovlp\n",
    "                                elif ml_j==-2:\n",
    "                                    dcovlp[i,j]=3**0.5*(dcl*dcm*(dcn**2-(dcl**2+dcm**2)*0.5)*ddsigma_ovlp-2*dcl*dcm*dcn**2*ddpi_ovlp+0.5*dcl*dcm*(1+dcn**2)*delta_ovlp)\n",
    "                                elif ml_j==1:\n",
    "                                    dcovlp[i,j]=3**0.5*(dcl*dcn*(dcn**2-(dcl**2+dcm**2)*0.5)*ddsigma_ovlp+dcl*dcn*(dcl**2+dcm**2-dcn**2)*ddpi_ovlp-0.5*dcl*dcn*(dcl**2+dcm**2)*delta_ovlp)\n",
    "                                elif ml_j==-1:\n",
    "                                    dcovlp[i,j]=3**0.5*(dcm*dcn*(dcn**2-(dcl**2+dcm**2)*0.5)*ddsigma_ovlp+dcm*dcn*(dcl**2+dcm**2-dcn**2)*ddpi_ovlp-0.5*dcm*dcn*(dcl**2+dcm**2)*delta_ovlp)\n",
    "                            elif ml_i==1:\n",
    "                                if ml_j==1:\n",
    "                                    dcovlp[i,j]=3*dcl**2*dcn**2*ddsigma_ovlp+(dcl**2+dcn**2-4*dcl**2*dcn**2)*ddpi_ovlp+(dcm**2+dcl**2*dcn**2)*delta_ovlp\n",
    "                                elif ml_j==-2:\n",
    "                                    dcovlp[i,j]=3*dcm*dcl**2*dcn*ddsigma_ovlp+dcm*dcn*(1-4*dcl**2)*ddpi_ovlp+dcm*dcn*(dcl**2-1)*delta_ovlp\n",
    "                                elif ml_j==2:\n",
    "                                    dcovlp[i,j]=1.5*dcn*dcl*(dcl**2-dcm**2)*ddsigma_ovlp+dcn*dcl*(1-2*(dcl**2-dcm**2))*ddpi_ovlp-dcn*dcl*(1-0.5*(dcl**2-dcm**2))*delta_ovlp\n",
    "                                elif ml_j==0:\n",
    "                                    dcovlp[i,j]=3**0.5*dcl*dcn*(dcn**2-0.5*(dcl**2+dcm**2))*ddsigma_ovlp+3**0.5*dcl*dcn*(dcl**2+dcm**2-dcn**2)*ddpi_ovlp-0.5*3**0.5*dcl*dcn*(dcl**2+dcm**2)*delta_ovlp\n",
    "                                elif ml_j==-1:\n",
    "                                    dcovlp[i,j]=3*dcm*dcn**2*dcl*ddsigma_ovlp+dcm*dcl*(1-4*dcn**2)*ddpi_ovlp+dcm*dcl*(dcn**2-1)*delta_ovlp\n",
    "                            elif ml_i==-1:\n",
    "                                if ml_j==-1:\n",
    "                                    dcovlp[i,j]=3*dcm**2*dcn**2*ddsigma_ovlp+(dcm**2+dcn**2-4*dcm**2*dcn**2)*ddpi_ovlp+(dcl**2+dcm**2*dcn**2)*delta_ovlp\n",
    "                                elif ml_j==-2:\n",
    "                                    dcovlp[i,j]=3*dcl*dcm**2*dcn*ddsigma_ovlp+dcl*dcn*(1-4*dcm**2)*ddpi_ovlp+dcl*dcn*(dcm**2-1)*delta_ovlp\n",
    "                                elif ml_j==2:\n",
    "                                    dcovlp[i,j]=1.5*dcm*dcn*(dcl**2-dcm**2)*ddsigma_ovlp-dcm*dcn*(1+2*(dcl**2-dcm**2))*ddpi_ovlp+dcm*dcn*(1+0.5*(dcl**2-dcm**2))*delta_ovlp\n",
    "                                elif ml_j==0:\n",
    "                                    dcovlp[i,j]=3**0.5*(dcm*dcn*(dcn**2-(dcl**2+dcm**2)*0.5)*ddsigma_ovlp+dcm*dcn*(dcl**2+dcm**2-dcn**2)*ddpi_ovlp-0.5*dcm*dcn*(dcl**2+dcm**2)*delta_ovlp)\n",
    "                                elif ml_j==1:\n",
    "                                    dcovlp[i,j]=3*dcm*dcn**2*dcl*ddsigma_ovlp+dcm*dcl*(1-4*dcn**2)*ddpi_ovlp+dcm*dcl*(dcn**2-1)*delta_ovlp\n",
    "                            elif ml_i==-2:\n",
    "                                if ml_j==-2:\n",
    "                                    dcovlp[i,j]=3*dcl**2*dcm**2*ddsigma_ovlp+(dcl**2+dcm**2-4*dcl**2*dcm**2)*ddpi_ovlp+(dcn**2+dcl**2*dcm**2)*delta_ovlp\n",
    "                                elif ml_j==-1:\n",
    "                                    dcovlp[i,j]=3*dcl*dcm**2*dcn*ddsigma_ovlp+dcl*dcn*(1-4*dcm**2)*ddpi_ovlp+dcl*dcn*(dcm**2-1)*delta_ovlp\n",
    "                                elif ml_j==1:\n",
    "                                    dcovlp[i,j]=3*dcm*dcl**2*dcn*ddsigma_ovlp+dcm*dcn*(1-4*dcl**2)*ddpi_ovlp+dcm*dcn*(dcl**2-1)*delta_ovlp\n",
    "                                elif ml_j==0:\n",
    "                                    dcovlp[i,j]=3**0.5*(dcl*dcm*(dcn**2-(dcl**2+dcm**2)/2)*ddsigma_ovlp-2*dcl*dcm*dcn**2*ddpi_ovlp+0.5*dcl*dcm*(1+dcn**2)*delta_ovlp)\n",
    "                                elif ml_j==2:\n",
    "                                    dcovlp[i,j]=1.5*dcl*dcm*(dcl**2-dcm**2)*ddsigma_ovlp+2*dcl*dcm*(dcm**2-dcl**2)*ddpi_ovlp+0.5*dcl*dcm*(dcl**2-dcm**2)*delta_ovlp\n",
    "                            elif ml_i==2:\n",
    "                                if ml_j==2:\n",
    "                                    dcovlp[i,j]=0.75*(dcl**2-dcm**2)**2*ddsigma_ovlp+(dcl**2+dcm**2-(dcl**2-dcm**2)**2)*ddpi_ovlp+(dcn**2+0.25*(dcl**2-dcm**2)**2)*delta_ovlp\n",
    "                                elif ml_j==-2:\n",
    "                                    dcovlp[i,j]=1.5*dcl*dcm*(dcl**2-dcm**2)*ddsigma_ovlp+2*dcl*dcm*(dcm**2-dcl**2)*ddpi_ovlp+0.5*dcl*dcm*(dcl**2-dcm**2)*delta_ovlp\n",
    "                                elif ml_j==-1:\n",
    "                                    dcovlp[i,j]=1.5*dcm*dcn*(dcl**2-dcm**2)*ddsigma_ovlp-dcm*dcn*(1+2*(dcl**2-dcm**2))*ddpi_ovlp+dcm*dcn*(1+0.5*(dcl**2-dcm**2))*delta_ovlp\n",
    "                                elif ml_j==1:\n",
    "                                    dcovlp[i,j]=1.5*dcn*dcl*(dcl**2-dcm**2)*ddsigma_ovlp+dcn*dcl*(1-2*(dcl**2-dcm**2))*ddpi_ovlp-dcn*dcl*(1-0.5*(dcl**2-dcm**2))*delta_ovlp\n",
    "                                elif ml_j==0:\n",
    "                                    dcovlp[i,j]=0.5*3**0.5*(dcl**2-dcm**2)*(dcn**2-0.5*(dcl**2+dcm**2))*ddsigma_ovlp+3**0.5*dcn**2*(dcm**2-dcl**2)*ddpi_ovlp+0.25*3**0.5*(1+dcn**2)*(dcl**2-dcm**2)*delta_ovlp\n",
    "                                \n",
    "                    elif (l_i==0 and l_j==2) or (l_i==2 and l_j==0):\n",
    "                            #(need to test to see which of the two is the d orbital - use temp labels to avoid messing up i and j for later) \n",
    "                            if basis_functs[i][3]==2:\n",
    "                                itemp=j;jtemp=i\n",
    "                            else:\n",
    "                                itemp=i;jtemp=j\n",
    "                            #identify the quantum numbers and exponents (note again zeta_d is a list, not a number)\n",
    "                            ml_d=basis_functs[jtemp][4]   #ml for the d orbital \n",
    "                            zeta_d=basis_functs[jtemp][6];zeta_s_temp=basis_functs[itemp][6]\n",
    "                            n_d=basis_functs[jtemp][2];l_d=basis_functs[jtemp][3]\n",
    "                            n_s=basis_functs[itemp][2];l_s=basis_functs[itemp][3]\n",
    "                            zeta_s=zeta_s_temp[0];coeff_s=zeta_s_temp[1]\n",
    "                            #loop over components of d orbital]\n",
    "                            sigma_key=(1,atom_index_i,atom_index_j,l_i,l_j)\n",
    "                            if sigma_key not in overlaps_dictionary:\n",
    "                                sdsigma_ovlp=0.\n",
    "                                for k in [0,1]:\n",
    "                                    exponent_d=zeta_d[k][0];coefficient_d=zeta_d[k][1]\n",
    "                                    pij=0.5*(zeta_s+exponent_d)*distij\n",
    "                                    Bpij=0.5*(zeta_s-exponent_d)*distij\n",
    "                                    tij=(zeta_s-exponent_d)/(zeta_s+exponent_d)\n",
    "                                    sdsigma_ovlp+=coefficient_d*new_overlap(n_s,n_d,l_s,l_d,zeta_s,exponent_d,pij,tij,distij,0) #m=0\n",
    "                                overlaps_dictionary[sigma_key]=sdsigma_ovlp\n",
    "                                if atomic_number_i==atomic_number_j:\n",
    "                                    complementary_sigma_key=(1,atom_index_j,atom_index_i,l_j,l_i)\n",
    "                                    overlaps_dictionary[complementary_sigma_key]=sdsigma_ovlp   \n",
    "                            else:\n",
    "                                sdsigma_ovlp=overlaps_dictionary[sigma_key]                                             \n",
    "                            \n",
    "                            #deal with angular dependence\n",
    "                            if ml_d==0:\n",
    "                                dcovlp[i,j]=sdsigma_ovlp*(dcn**2-0.5*(dcl**2+dcm**2))\n",
    "                            elif ml_d==2:\n",
    "                                dcovlp[i,j]=3**0.5*(dcl**2-dcm**2)*sdsigma_ovlp/2\n",
    "                            elif ml_d==-2:\n",
    "                                dcovlp[i,j]=3**0.5*dcl*dcm*sdsigma_ovlp\n",
    "                            elif ml_d==1:\n",
    "                                dcovlp[i,j]=3**0.5*dcl*dcn*sdsigma_ovlp\n",
    "                            elif ml_d==-1:\n",
    "                                dcovlp[i,j]=3**0.5*dcm*dcn*sdsigma_ovlp\n",
    "                            dcovlp[i,j]=coeff_s*dcovlp[i,j]   \n",
    "                            \n",
    "                    #pd overlaps    note we need switches again to change sign of ovlp depending on which comes first            \n",
    "                    elif (l_i==2 and l_j==1) or (l_i==1 and l_j==2):\n",
    "                            #(need to test to see which of the two is the d orbital - use temp labels to avoid messing up i and j for later)    \n",
    "                        if basis_functs[i][3]==2:\n",
    "                            itemp=j;jtemp=i\n",
    "                        else:\n",
    "                            itemp=i;jtemp=j\n",
    "                        ml_d=basis_functs[jtemp][4];ml_p=basis_functs[itemp][4]   \n",
    "                        zeta_d=basis_functs[jtemp][6]\n",
    "                        zeta_p_temp=basis_functs[itemp][6]\n",
    "                        n_d=basis_functs[jtemp][2];l_d=basis_functs[jtemp][3]\n",
    "                        n_p=basis_functs[itemp][2];l_p=basis_functs[itemp][3]\n",
    "                        sigma_key=(1,atom_index_i,atom_index_j,l_i,l_j)\n",
    "                        pi_key=(2,atom_index_i,atom_index_j,l_i,l_j)\n",
    "                        if sigma_key not in overlaps_dictionary:\n",
    "                            pdsigma_ovlp=0.;pdpi_ovlp=0.\n",
    "                            if type(zeta_p_temp[0]) is float:   \n",
    "                                zeta_p=zeta_p_temp[0];coeff_p=zeta_p_temp[1]\n",
    "                            #loop over components of d orbital\n",
    "                                for k in [0,1]:\n",
    "                                    exponent_d=zeta_d[k][0];coeff_d=zeta_d[k][1]\n",
    "                                    pij=0.5*(zeta_p+exponent_d)*distij\n",
    "                                    Bpij=0.5*(zeta_p-exponent_d)*distij\n",
    "                                    tij=(zeta_p-exponent_d)/(zeta_p+exponent_d)\n",
    "                                    pdsigma_ovlp+=coeff_p*coeff_d*new_overlap(n_p,n_d,l_p,l_d,zeta_p,exponent_d,pij,tij,distij,0) #m=0\n",
    "                                    pdpi_ovlp+=coeff_p*coeff_d*new_overlap(n_p,n_d,l_p,l_d,zeta_p,exponent_d,pij,tij,distij,1) #m=0\n",
    "                            elif type(zeta_p_temp[0]) is tuple:\n",
    "                                for k in [0,1]:\n",
    "                                    for l in [0,1]:\n",
    "                                        exponent_d=zeta_d[k][0];coeff_d=zeta_d[k][1]\n",
    "                                        zeta_p=zeta_p_temp[l][0];coeff_p=zeta_p_temp[l][1]\n",
    "                                        pij=0.5*(zeta_p+exponent_d)*distij\n",
    "                                        Bpij=0.5*(zeta_p-exponent_d)*distij\n",
    "                                        tij=(zeta_p-exponent_d)/(zeta_p+exponent_d)\n",
    "                                        pdsigma_ovlp+=coeff_p*coeff_d*new_overlap(n_p,n_d,l_p,l_d,zeta_p,exponent_d,pij,tij,distij,0) #m=0\n",
    "                                        pdpi_ovlp+=coeff_p*coeff_d*new_overlap(n_p,n_d,l_p,l_d,zeta_p,exponent_d,pij,tij,distij,1) #m=0    \n",
    "                            overlaps_dictionary[sigma_key]=pdsigma_ovlp\n",
    "                            overlaps_dictionary[pi_key]=pdpi_ovlp\n",
    "                            if atomic_number_i==atomic_number_j:\n",
    "                                complementary_sigma_key=(1,atom_index_j,atom_index_i,l_j,l_i)\n",
    "                                complementary_pi_key=(2,atom_index_j,atom_index_i,l_j,l_i)\n",
    "                                overlaps_dictionary[complementary_sigma_key]=pdsigma_ovlp\n",
    "                                overlaps_dictionary[complementary_pi_key]=pdpi_ovlp \n",
    "                        else:\n",
    "                            pdsigma_ovlp=overlaps_dictionary[sigma_key]\n",
    "                            pdpi_ovlp=overlaps_dictionary[pi_key]\n",
    "                        if l_i==2:  \n",
    "                            pdsigma_ovlp=-pdsigma_ovlp          #this is the switch - along z axis, dp sigma overlap is negative but pd is positive (pz defined positive along +z)\n",
    "                        if l_j==2:         #swap ml indices if d comes second in list                               \n",
    "                            pdpi_ovlp=-pdpi_ovlp                #this is the switch - along z axis, dp pi overlap is positive but pd is negative (pz defined positive along +z)                        \n",
    "                            #now deal with angular variations                            \n",
    "                            #5 x 3 = 15 possible combinations of d x p\n",
    "                        if ml_d==0 and ml_p==0:\n",
    "                            dcovlp[i,j]=dcn*(dcn**2-0.5*(dcl**2+dcm**2))*pdsigma_ovlp+3**0.5*dcn*(dcl**2+dcm**2)*pdpi_ovlp\n",
    "                        elif ml_d==0 and ml_p==1:\n",
    "                            dcovlp[i,j]=dcl*(dcn**2-0.5*(dcl**2+dcm**2))*pdsigma_ovlp-3**0.5*dcl*dcn**2*pdpi_ovlp\n",
    "                        elif ml_d==0 and ml_p==-1:\n",
    "                            dcovlp[i,j]=dcm*(dcn**2-0.5*(dcl**2+dcm**2))*pdsigma_ovlp-3**0.5*dcm*dcn**2*pdpi_ovlp\n",
    "                        elif ml_d==1 and ml_p==0:\n",
    "                            dcovlp[i,j]=3**0.5*dcn**2*dcl*pdsigma_ovlp+dcl*(1-2*dcn**2)*pdpi_ovlp\n",
    "                        elif ml_d==1 and ml_p==1:\n",
    "                            dcovlp[i,j]=3**0.5*dcl**2*dcn*pdsigma_ovlp+dcn*(1-2*dcl**2)*pdpi_ovlp\n",
    "                        elif ml_d==1 and ml_p==-1:\n",
    "                            dcovlp[i,j]=3**0.5*dcl*dcm*dcn*pdsigma_ovlp-2*dcl*dcm*dcn*pdpi_ovlp\n",
    "                        elif ml_d==-1 and ml_p==0:\n",
    "                            dcovlp[i,j]=3**0.5*dcn**2*dcm*pdsigma_ovlp+dcm*(1-2*dcn**2)*pdpi_ovlp\n",
    "                        elif ml_d==-1 and ml_p==1:\n",
    "                            dcovlp[i,j]=3**0.5*dcl*dcm*dcn*pdsigma_ovlp-2*dcl*dcm*dcn*pdpi_ovlp\n",
    "                        elif ml_d==-1 and ml_p==-1:\n",
    "                            dcovlp[i,j]=3**0.5*dcm**2*dcn*pdsigma_ovlp+dcn*(1-2*dcm**2)*pdpi_ovlp\n",
    "                        elif ml_d==2 and ml_p==0:\n",
    "                            dcovlp[i,j]=3**0.5*dcn*(dcl**2-dcm**2)*pdsigma_ovlp/2-dcn*(dcl**2-dcm**2)*pdpi_ovlp\n",
    "                        elif ml_d==2 and ml_p==1:\n",
    "                            dcovlp[i,j]=3**0.5*dcl*(dcl**2-dcm**2)*pdsigma_ovlp/2+dcl*(1-dcl**2+dcm**2)*pdpi_ovlp\n",
    "                        elif ml_d==2 and ml_p==-1:\n",
    "                            dcovlp[i,j]=3**0.5*dcm*(dcl**2-dcm**2)*pdsigma_ovlp/2-dcm*(1-dcm**2+dcl**2)*pdpi_ovlp\n",
    "                        elif ml_d==-2 and ml_p==0:\n",
    "                            dcovlp[i,j]=3**0.5*dcl*dcm*dcn*pdsigma_ovlp-2*dcl*dcm*dcn*pdpi_ovlp\n",
    "                        elif ml_d==-2 and ml_p==1:           \n",
    "                            dcovlp[i,j]=3**0.5*dcl**2*dcm*pdsigma_ovlp+dcm*(1-2*dcl**2)*pdpi_ovlp\n",
    "                        elif ml_d==-2 and ml_p==-1:\n",
    "                            dcovlp[i,j]=3**0.5*dcm**2*dcl*pdsigma_ovlp+dcl*(1-2*dcm**2)*pdpi_ovlp\n",
    "                        dcovlp[i,j]=dcovlp[i,j]\n",
    "\n",
    "    for i in range(len(basis_functs)):                                      #copy the upper half to the lower half of ovlp\n",
    "        for j in range(len(basis_functs)):\n",
    "                if j<i:\n",
    "                        dcovlp[i,j]=dcovlp[j,i]\n",
    "    return dcovlp\n",
    "\n",
    "def Hamiltonian_matrix(basis_functs,ovlp,WH): \n",
    "    H=np.identity(len(basis_functs))    #   initialise Hamiltonian matrix\n",
    "    for i in range(len(basis_functs)):\n",
    "        for j in range(len(basis_functs)):\n",
    "            Hii_i=basis_functs[i][5];Hii_j=basis_functs[j][5]\n",
    "            if i==j:\n",
    "                H[i,i]=Hii_i\n",
    "            else:\n",
    "                H[i,j]=0.5*WH*(Hii_i+Hii_j)*ovlp[i,j]                   #Wolfsberg Helmoltz formula with K = 1.75\n",
    "    return H\n",
    " \n",
    "def EH_mulliken(eigsort,occupations,ovlp,no_electrons):     #Mulliken population analysis\n",
    "    Mulliken=np.zeros((len(eigsort),len(eigsort)),dtype=float)  # initialise Mulliken matrix\n",
    "    reduced_Mulliken=np.zeros((len(eigsort),len(eigsort)),dtype=float)  # initialise Mulliken matrix\n",
    "    for k in range(int(no_electrons/2)):                             #sum over MOs\n",
    "        for i in range(len(eigsort)):                     # sum over first index\n",
    "                for j in range(len(eigsort)):             #sum over second index\n",
    "                        reduced_Mulliken[i,j]='{:3.5f}'.format(Mulliken[i,j]+eigsort[i,k]*eigsort[j,k]*ovlp[i,j])\n",
    "                        Mulliken[i,j]='{:3.5f}'.format(Mulliken[i,j]+eigsort[i,k]*eigsort[j,k]*ovlp[i,j])\n",
    "\n",
    "    for i in range(len(eigsort)):\n",
    "        for j in range(len(eigsort)):\n",
    "                if j!=i:\n",
    "                        reduced_Mulliken[i,i]='{:3.5f}'.format(reduced_Mulliken[i,i]+Mulliken[i,j])\n",
    "    \n",
    "    Mullikenb=np.array(occupations).dot(np.transpose(eigsort)).dot(ovlp).dot(eigsort)\n",
    "    total_pop=np.trace(reduced_Mulliken)\n",
    "    return Mulliken,reduced_Mulliken,total_pop\n",
    "\n",
    "def diagonalise(H,ovlp,no_electrons):\n",
    "    e,v=linalg.eigh(H,ovlp)    \n",
    "    #generate occupation numbers\n",
    "    occupations=[2*i for i in np.ones(int(no_electrons/2)).tolist()]+np.zeros(len(ovlp)-int(no_electrons/2)).tolist()\n",
    "    return v,e,occupations\n",
    "\n",
    "#symmetry-adapted overlap, Hamiltonian and diagonalisation routines.\n",
    "\n",
    "def SALC_overlap_matrix(basis_set_SALC,basis_functs,ovlp): #converts a_matrix (w. atomic basis) to the new basis set (SALC_matrix), as given in basis_set_SALC; (atom index, atomic orbital index, MO index, contrib. of AO to MO). Same function used for ovlp and H.\n",
    "    #first up, normalise the SALCS\n",
    "    #strip out coefficients    \n",
    "    coefficients=[];symmetry_list=[]\n",
    "    for i in range(len(basis_functs)):\n",
    "        coefficients.append(basis_set_SALC[i][4:])\n",
    "        symmetry_list.append(int(basis_set_SALC[i][0]))\n",
    "    for i in range(len(coefficients)):\n",
    "        norm=0\n",
    "        for j in range(len(coefficients)):\n",
    "            for k in range(len(coefficients)):\n",
    "                norm+=coefficients[i][j]*coefficients[i][k]*ovlp[j][k]\n",
    "        coefficients[i]=coefficients[i]/norm**0.5\n",
    "    #coefficients are now the appropriately normalised SALCS\n",
    "\n",
    "    #now calculate overlap matrix\n",
    "    SALC_overlaps=np.zeros((len(coefficients),len(coefficients)))\n",
    "    \n",
    "    for i in range(len(coefficients)):\n",
    "        for j in range(len(coefficients)):\n",
    "            if basis_set_SALC[i][0]==basis_set_SALC[j][0]:   #only calculate S if they have same symmetry\n",
    "                for k in range(len(coefficients)):\n",
    "                    for l in range(len(coefficients)):\n",
    "                        SALC_overlaps[i][j]+=coefficients[i][k]*coefficients[j][l]*ovlp[k][l]\n",
    "\n",
    "    return coefficients,SALC_overlaps,symmetry_list\n",
    "\n",
    "\n",
    "def SALC_H_matrix(basis_set_SALC,basis_functs,ovlp,coefficients): #converts a_matrix (w. atomic basis) to the new basis set (SALC_matrix), as given in basis_set_SALC; (atom index, atomic orbital index, MO index, contrib. of AO to MO). Same function used for ovlp and H.    \n",
    "    #now calculate off diagonal elements of the H matrix\n",
    "    SALC_H_matrix=np.zeros((len(coefficients),len(coefficients)))\n",
    "    \n",
    "    for i in range(len(coefficients)):\n",
    "        for j in range(len(coefficients)):\n",
    "            for k in range(len(coefficients)):\n",
    "                for l in range(len(coefficients)):                    \n",
    "                    if i>j:\n",
    "                        SALC_H_matrix[i][j]+=0.5*1.75*coefficients[i][k]*coefficients[j][l]*ovlp[k][l]*(basis_functs[k][5]+basis_functs[l][5])\n",
    "            SALC_H_matrix[j][i]=SALC_H_matrix[i][j]\n",
    "            \n",
    "    #now add the diagonal elements\n",
    "    for i in range(len(coefficients)):\n",
    "        for j in range(len(coefficients)):\n",
    "            for k in range(len(coefficients)):\n",
    "                if j==k:\n",
    "                    SALC_H_matrix[i][i]+=coefficients[i][j]**2*basis_functs[j][5]*ovlp[j][j]\n",
    "                else:\n",
    "                    SALC_H_matrix[i][i]+=0.5*1.75*coefficients[i][j]*coefficients[i][k]*ovlp[j][k]*(basis_functs[j][5]+basis_functs[k][5])\n",
    "    return SALC_H_matrix\n",
    "\n",
    "    \n",
    "#this breaks up the S and H matrices into their symmetry blocks.    \n",
    "def separate_by_symmetry(basis_set_SALC,SALC_Hamiltonianb,SALC_overlaps,no_electrons):\n",
    "    symmetry_labels=[]\n",
    "    for i in range(len(basis_set_SALC)):\n",
    "        symmetry_labels.append(int(basis_set_SALC[i][0]))\n",
    "    S_matrices=[];H_matrices=[]    \n",
    "    for i in range(max(symmetry_labels)+1):\n",
    "        S_matrices.append(np.zeros((symmetry_labels.count(i),symmetry_labels.count(i))))\n",
    "        H_matrices.append(np.zeros((symmetry_labels.count(i),symmetry_labels.count(i))))\n",
    "    \n",
    "    position_tracker=[]    \n",
    "    for i in range(max(symmetry_labels)+1):\n",
    "        position_tracker.append([])\n",
    "        for j in range(len(symmetry_labels)):\n",
    "            if symmetry_labels[j]==i:\n",
    "                position_tracker[i].append(j)\n",
    "        \n",
    "    position_tracker_without_nulls=[]\n",
    "    for item in position_tracker:\n",
    "        if item:\n",
    "            position_tracker_without_nulls.append(item)\n",
    "    \n",
    "    #generate S and H matrices for each symmetry type (collected in a list)       \n",
    "    for j in range(len(position_tracker)):\n",
    "         for i in range(len(position_tracker[j])):\n",
    "            for k in range(len(position_tracker[j])):\n",
    "                    S_matrices[j][i][k]=SALC_overlaps[position_tracker[j][i]][position_tracker[j][k]]\n",
    "                    H_matrices[j][i][k]=SALC_Hamiltonianb[position_tracker[j][i]][position_tracker[j][k]]\n",
    "                    S_matrices[j][k][i]=S_matrices[j][i][k]\n",
    "                    H_matrices[j][k][i]=H_matrices[j][i][k]\n",
    "    \n",
    "    #now diagonalise each symmetry type separately        \n",
    "    eigenvalues=[];eigenvectors=[]    \n",
    "    no_of_symmetry_types=0   \n",
    "    for i in range(len(position_tracker)):\n",
    "        if len(S_matrices[i])>0:       #note this eliminates symmetry blocks with no irreps, so dimensions may be reduced\n",
    "            no_of_symmetry_types+=1        \n",
    "            e,v=linalg.eigh(H_matrices[i],S_matrices[i])\n",
    "            eigenvalues.append(e),eigenvectors.append(v) \n",
    "\n",
    "    #collect all eigenvalues together in one list\n",
    "    all_eigenvalues=[]\n",
    "    for i in range(len(eigenvalues)):\n",
    "        for j in range(len(eigenvalues[i])):\n",
    "            all_eigenvalues.append(eigenvalues[i][j])\n",
    "\n",
    "    #before collecting the eigenvectors, note that they are shorter than the full length: I need to add zeros to get them back to the original dimension (len(basis functions))\n",
    "    #these will be in the form of a block diagonal matrix with symmetry types 0, 1, 2 etc appearing in that order\n",
    "    \n",
    "    #first, identify the start point of the various symmetry blocks \n",
    "    start_points=[0]\n",
    "    count=0\n",
    "    for i in range(len(position_tracker_without_nulls)-1):        \n",
    "        start_points.append(len(position_tracker_without_nulls[i])+count)\n",
    "        count+=len(position_tracker_without_nulls[i])\n",
    "    #initialise eigenvector matrix to zeros\n",
    "    stretched_eigenvectors=np.zeros_like(SALC_overlaps)\n",
    "\n",
    "    #fill in the non-zero elements by copying from the different eigenvectors\n",
    "    for i in range(len(start_points)):\n",
    "        for j in range(len(eigenvectors[i][0])):\n",
    "            for k in range(len(eigenvectors[i][0])):\n",
    "                stretched_eigenvectors[start_points[i]+k][start_points[i]+j]=eigenvectors[i][j][k]\n",
    "    #vectors are in row frmat: need to transpose them to columns\n",
    "    stretched_eigenvectors=np.transpose(stretched_eigenvectors)\n",
    "    #now the eigenvectors are back in standard form (with zeros for bfs with different symmetry) but\n",
    "\n",
    "    # but don't forget the order of bfs has been changed.    \n",
    "    #reorder basis functions so they are grouped by symmetry\n",
    "    reordered_basis_set_SALC=[]\n",
    "    for i in range(len(position_tracker_without_nulls)):\n",
    "        for j in range(len(position_tracker_without_nulls[i])):\n",
    "            reordered_basis_set_SALC.append(basis_set_SALC[position_tracker_without_nulls[i][j]])\n",
    "    #symmetry list including nulls\n",
    "    symmetry_list=[]\n",
    "    for i in range(len(position_tracker)):\n",
    "        for j in range(len(position_tracker[i])):\n",
    "            symmetry_list.append(i)\n",
    "\n",
    "    #now sort the eigenvalues and eigenvectors    \n",
    "    eref=np.argsort(all_eigenvalues)                                      #indices for sorting eigenvectors \n",
    "    all_eigenvalues=np.sort(all_eigenvalues)                                            # sort eigenvalues\n",
    "    for i in range (len(all_eigenvalues)):                                # format eigenvalues\n",
    "        all_eigenvalues[i]='{:4.3f}'.format(all_eigenvalues[i])\n",
    "   \n",
    "    eigsort=np.zeros((len(all_eigenvalues),len(all_eigenvalues)),dtype=float)           #generate list of eigenvectors in ascending order of eigenvalue\n",
    "    revised_symmetry_list=[]   #and an associated list of symmetries in ascending order of eigenvalues\n",
    "    for i in range(len(all_eigenvalues)):\n",
    "        revised_symmetry_list.append(symmetry_list[eref[i]])\n",
    "        for j in range(len(all_eigenvalues)):\n",
    "                eigsort[i,j]='{:6.5f}'.format(stretched_eigenvectors[i,eref[j]])\n",
    "\n",
    "    #generate occupation numbers\n",
    "    occupations=[2*i for i in np.ones(int(no_electrons/2)).tolist()]+np.zeros(len(ovlp)-int(no_electrons/2)).tolist()\n",
    " \n",
    "    return eigsort,all_eigenvalues,occupations,reordered_basis_set_SALC,revised_symmetry_list   \n",
    "\n",
    "#PRINT FUNCTIONS\n",
    "def EH_print_symmetry(params,title,element_coord,Q,eh_basis_functs,no_electrons,ovlp,H,C,e,occupations,reduced_Mulliken,total_pop,WH,pg,SALC_basis,new_SALC_basis,pg_irreps,identifiers,identifiers2,revised_symmetry_list,pg_labels):\n",
    "    e=e.real\n",
    "    if pg=='Dinfh':\n",
    "        pg='Dinfh_print'     #switch to the version for printing (actual calculation uses D5h)\n",
    "    if pg=='Cinfv':\n",
    "        pg='Cinfv_print'     #switch to the version for printing (actual calculation uses D5h)\n",
    "        \n",
    "    class color:\n",
    "        PURPLE = '\\033[95m'\n",
    "        CYAN = '\\033[96m'\n",
    "        DARKCYAN = '\\033[36m'\n",
    "        BLUE = '\\033[94m'\n",
    "        GREEN = '\\033[92m'\n",
    "        YELLOW = '\\033[93m'\n",
    "        RED = '\\033[91m'\n",
    "        BOLD = '\\033[1m'\n",
    "        UNDERLINE = '\\033[4m'\n",
    "        END = '\\033[0m'\n",
    "        \n",
    "    print(color.BOLD, end='')\n",
    "\n",
    "    print(\"%-35s\"%(\"Extended Huckel Calculation\"),'\\n')    \n",
    "    print(\"%-15s\"%(\"title\"),title,'\\n')  \n",
    "    print(\"number of electrons = \",no_electrons,\" charge = \",Q)\n",
    "    print(\"number of basis functions = \",len(eh_basis_functs))\n",
    "    print(\"Using\",WH,\"as the Wolfsberg-Helmholtz Parameter\") \n",
    "    if params=='Hoffmann':\n",
    "        print(\"Using Hoffmann's new parameter set\")\n",
    "    elif params=='Cerda':\n",
    "        print(\"Using Cerda's parameter set\")\n",
    "\n",
    "    print(\"%-10s\"%('Point group: '),pg,'\\n')\n",
    "    print(\"%-10s\"%('Character table: '),'\\n')\n",
    "    print('\\t','\\t'.join([\"%-6s\"%(str(a)) for a in pg_labels[pg]]))\n",
    "    for i in range(len(pg_characters[pg])):\n",
    "        line=[pg_irreps[pg][i]] + [\"%6.2f\"% member for member in pg_characters[pg][i]]\n",
    "        print('\\t'.join([\"%-6s\"%(str(a)) for a in line]))   \n",
    "    print('\\n')\n",
    "    print(\"%-35s\"%(\"Symmetry-adapted Coordinates\"),'\\n')\n",
    "    for i in range(len(element_coord)):\n",
    "        print(\"%-10s\"%(atno_to_element[element_coord[i][0]]),(\"%10.2f\"% (element_coord[i][1])),(\"%10.2f\"% (element_coord[i][2])),(\"%10.2f\"% (element_coord[i][3])))    \n",
    "\n",
    "    print('\\n')\n",
    "    C=np.round(C,decimals=2);e=np.round(e,decimals=3)\n",
    "    \n",
    "    atomic_basis_functions_list = []\n",
    "    for funct in eh_basis_functs:\n",
    "        label = atno_to_element[funct[0]] + identifiers[tuple(funct[2:5])]\n",
    "        atomic_basis_functions_list.append(label)\n",
    "    \n",
    "    print('\\n')\n",
    "    print(\"Symmetry-adapted basis set:\")\n",
    "    print(\"%-15s\"%('*'),'\\t'.join([\"%-10s\"%(str(a)) for a in atomic_basis_functions_list]))\n",
    "\n",
    "    #print SALCS in symmetry groups\n",
    "    counter = 0\n",
    "    for SALC in new_SALC_basis:\n",
    "        print(color.BOLD, end='')\n",
    "        label=str(counter)+' '+pg_irreps[pg][int(SALC[0])]+' '+atno_to_element[SALC[1]]+' '+identifiers2[int(SALC[2]),int(SALC[3])]\n",
    "        SALC=np.around(SALC[4:len(SALC)],decimals=3)\n",
    "        print(\"%-15s\"%(label),'\\t'.join([\"%-10s\"%(str(a)) for a in SALC]))\n",
    "        counter+=1\n",
    "        print(color.END, end='')\n",
    "\n",
    "\n",
    "    SALC_basis_functions_list = []\n",
    "    for SALC in SALC_basis:\n",
    "        label = pg_irreps[pg][int(SALC[0])] + ' ' + atno_to_element[SALC[1]] + identifiers2[(int(SALC[2]),int(SALC[3]))]\n",
    "        SALC_basis_functions_list.append(label)\n",
    "    new_SALC_basis_functions_list = []\n",
    "    for SALC in new_SALC_basis:\n",
    "        label = pg_irreps[pg][int(SALC[0])] + ' ' + atno_to_element[SALC[1]] + identifiers2[(int(SALC[2]),int(SALC[3]))]\n",
    "        new_SALC_basis_functions_list.append(label)\n",
    "\n",
    "    print(color.BOLD,\"\\n\\noverlap matrix:\")\n",
    "    print(\"%10s\"%(''),'\\t'.join([\"%10s\"%(str(a)) for a in SALC_basis_functions_list]))\n",
    "    print('\\n')\n",
    "    for counter in range(0,len(ovlp)):\n",
    "        print(color.BOLD, end='')\n",
    "        print(\"%-10s\"%(SALC_basis_functions_list[counter]), end= '')\n",
    "        matrix_elements=[\"%.2f\" % member for member in ovlp[counter]]\n",
    "        print('\\t'.join([\"%10s\"%(str(a)) for a in matrix_elements]))\n",
    "        print(color.END, end='')\n",
    "\n",
    "    print(color.BOLD,\"\\n\\nHamiltonian matrix:\")\n",
    "    print(\"%10s\"%(''),'\\t'.join([\"%10s\"%(str(a)) for a in SALC_basis_functions_list]))\n",
    "    print('\\n')\n",
    "    for counter in range(0,len(ovlp)):\n",
    "        print(color.BOLD, end='')\n",
    "        print(\"%-10s\"%(SALC_basis_functions_list[counter]), end= '')\n",
    "        matrix_elements=[\"%.2f\" % member for member in H[counter]]\n",
    "        print('\\t'.join([\"%10s\"%(str(a)) for a in matrix_elements]))\n",
    "    \n",
    "    print('****************************************************************************************************************')\n",
    "    print('\\n\\n','                                                  RESULTS')\n",
    "    print('****************************************************************************************************************')\n",
    "\n",
    "    nlines=int(len(e)/6)\n",
    "    residue=len(e)-6*(nlines)\n",
    "    for i in range(nlines):     \n",
    "        print('Orbital Energies (eV), occupations and symmetries')\n",
    "        print('****************************************************************************************************************')\n",
    "        elist=[];occlist=[];symm_list=[]       \n",
    "        for j in range(6):\n",
    "            elist.append(\"% 10.3f\"%(e.real[6*i+j]))\n",
    "            occlist.append(\"% 10.1f\"%(occupations[6*i+j]))\n",
    "            symm_list.append(\"% 10s\"%(pg_irreps[pg][revised_symmetry_list[6*i+j]]))##\n",
    "\n",
    "        print(\"%-15s\"%'E','\\t'.join(elist))\n",
    "        print(\"%-15s\"%'Occup','\\t'.join(occlist))  \n",
    "        print(\"%-15s\"%'Symm','\\t'.join(symm_list),'\\n')        \n",
    "        \n",
    "        for m in range(len(e)):\n",
    "            coeff=[]\n",
    "            for j in range(6):     \n",
    "                coeff.append(\"% 10.2f\"% (C[:,6*i+j][m]))\n",
    "                coeff=[str(a) for a in coeff]\n",
    "            print(\"%-15s\"%(str(m)+'  '+str(new_SALC_basis_functions_list[m][:8])),'\\t'.join(coeff))  \n",
    "    \n",
    "        print('\\n')\n",
    "    if residue>0:       #print the residual energies and coefficients if no. of orbitals is not a multiple of 6\n",
    "            elist=[];occlist=[];symm_list=[]         \n",
    "            for m in range((len(e)-residue),len(e)):\n",
    "                elist.append(\"% 10.3f\"%(e.real[m]))\n",
    "                occlist.append(\"% 10.1f\"%(occupations[m]))\n",
    "                symm_list.append(\"% 10s\"%(pg_irreps[pg][revised_symmetry_list[m]]))##\n",
    "\n",
    "            print(\"%-15s\"%'E','\\t'.join(elist))\n",
    "            print(\"%-15s\"%'Occup','\\t'.join(occlist))  \n",
    "            print(\"%-15s\"%'Symm','\\t'.join(symm_list),'\\n')        \n",
    "        \n",
    "            for m in range(len(e)):\n",
    "                coeff=[]\n",
    "                for k in range((len(e)-residue),len(e)):     \n",
    "                    coeff.append(\"% 10.2f\"% (C[:,k][m]))\n",
    "                    coeff=[str(a) for a in coeff]\n",
    "                print(\"%-15s\"%(str(m)+'  '+str(new_SALC_basis_functions_list[m][:8])),'\\t'.join(coeff))\n",
    "            print('\\n')\n",
    "            print('****************************************************************************************************************')\n",
    "\n",
    "#FUNCTION FOR PLOTTING MO DIAGRAM AND CONTOURS\n",
    "\n",
    "def MO_plot2(e,eh_basis_functs,C,occupations,title,atoms,coords,window,no_electrons,connection_threshold,revised_symmetry_list,pg_irreps,pg):\n",
    "    #def MO_plot2(e,eh_basis_functs,C,occupations,title,atoms,window,coords,no_electrons,MO_list,connection_threshold,plane_offset,levels,plot_plane,revised_symmetry_list,pg_irreps,pg): \n",
    "    homonuclear=False\n",
    "    plt.figure(figsize=(16,12))     \n",
    "    #now decide how many unique atoms we have so we know what to plot:\n",
    "    #unique atoms tells me what the atoms are, and the index tells me where they first appear in the atom list \n",
    "    #(only relevant for more than 2 atoms)\n",
    "    unique_atoms=[];unique_atom_index=[]    \n",
    "    for i in range(len(atoms)):\n",
    "        if atoms[i] not in unique_atoms:\n",
    "            unique_atoms.append(atoms[i])\n",
    "            unique_atom_index.append(i)\n",
    "    #identify whether it is a homonuclear diatomic\n",
    "    if len(unique_atoms)==1:\n",
    "        homonuclear=True\n",
    "        if len(atoms)>2:\n",
    "            polyatomic=True\n",
    "        #if it is a disatomic, add an extra one onto unique_atoms (and index) as I'm going to plot atoms on LHS and RHS\n",
    "        else:\n",
    "            unique_atoms.append(unique_atoms[0]);unique_atom_index.append(1)     \n",
    "    #work out degeneracies of molecular levels\n",
    "    degeneracy=[]\n",
    "    e=np.round(e,decimals=1).tolist()#;occupations=occupations[0].tolist()\n",
    "    for energy in e:\n",
    "        degeneracy.append(e.count(energy))\n",
    "    box=plt.subplot(121)\n",
    "    expanded_title='MO diagram for '+title\n",
    "    plt.title(expanded_title)\n",
    "    box.set_xlim(-4,4) \n",
    "    box.set_xticks([])\n",
    "    box.set_ylabel('Energy / eV')\n",
    "    #if window isn't set in the input, go 10 above and below total range    \n",
    "    if window:\n",
    "        energy_min=window[0];energy_max=window[1]\n",
    "    else:\n",
    "        energy_min=min(e)-10;energy_max=max(e)+10\n",
    "    box.set_ylim(energy_min,energy_max)\n",
    "\n",
    "    #now, plot the MO energies\n",
    "\n",
    "    #first I need a list that keeps track of sequential numbering of orbitals of a given symmetry\n",
    "    symmetry_tracker=np.zeros_like(revised_symmetry_list)\n",
    "    for i in range(len(pg_irreps[pg])):\n",
    "        count=0\n",
    "        for j in range(len(revised_symmetry_list)):\n",
    "            if revised_symmetry_list[j]==i:\n",
    "                symmetry_tracker[j]=count+1\n",
    "                count+=1/degeneracy[j]   #this increments count by 1/2 per doubly degenerate orbital, 1/3 per triply degenerate etc\n",
    "\n",
    "    i=0\n",
    "    while i<len(e):\n",
    "        label=str(symmetry_tracker[i])+pg_irreps[pg][revised_symmetry_list[i]]\n",
    "        if degeneracy[i]==3:\n",
    "            box.plot([-0.2,0.2],[e[i],e[i]], color=\"black\",lw=2) \n",
    "            box.plot([-1.0,-0.6],[e[i],e[i]], color=\"black\",lw=2)\n",
    "            box.plot([0.6,1.0],[e[i],e[i]], color=\"black\",lw=2)\n",
    "            #label=str(i+1)+'+'+str(i+2)+'+'+str(i+3)\n",
    "            if energy_max>e[i]>energy_min:\n",
    "                box.text(1.1, e[i],label,fontsize=12)\n",
    "            i+=3\n",
    "        elif degeneracy[i]==2:\n",
    "            box.plot([-0.6,-0.2],[e[i],e[i]], color=\"black\",lw=2)\n",
    "            box.plot([0.2,0.6],[e[i],e[i]], color=\"black\",lw=2)\n",
    "            #label=str(i+1)+'+'+str(i+2)\n",
    "            if energy_max>e[i]>energy_min:\n",
    "                box.text(0.7,e[i],label,fontsize=12)\n",
    "            i+=2\n",
    "        else:\n",
    "            box.plot([-0.2,0.2],[e[i],e[i]], color=\"black\",lw=2) \n",
    "            if energy_max>e[i]>energy_min:    \n",
    "                box.text(0.3, e[i],label,fontsize=12)\n",
    "            i+=1\n",
    "            \n",
    "    #now work out the atomic energies part\n",
    "    #first deal with the first atom (which we will put on the left)    \n",
    "    atomic_energies_left=[]\n",
    "    for basis_funct in eh_basis_functs:\n",
    "        if basis_funct[1]==unique_atom_index[0]:\n",
    "            atomic_energies_left.append(basis_funct[5])\n",
    "    atomic_degeneracy_left=[]\n",
    "    for energy in atomic_energies_left:\n",
    "        atomic_degeneracy_left.append(atomic_energies_left.count(energy))\n",
    "    \n",
    "    for i in range(len(atomic_energies_left)):\n",
    "        if atomic_degeneracy_left[i]==5:\n",
    "            box.plot([-4.2,-3.8],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "            box.plot([-3.6,-3.2],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "            box.plot([-3.0,-2.6],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "            box.plot([-2.4,-2.0],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "            box.plot([-1.8,-1.4],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "        elif atomic_degeneracy_left[i]==3:\n",
    "            box.plot([-3.6,-3.2],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "            box.plot([-3.0,-2.6],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "            box.plot([-2.4,-2.0],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2)\n",
    "        else:\n",
    "            box.plot([-3.0,-2.6],[atomic_energies_left[i],atomic_energies_left[i]], color=\"black\",lw=2) \n",
    "\n",
    "    #now deal with the second atom (which we will put on the right)    \n",
    "    if len(unique_atoms)>1:   \n",
    "        atomic_energies_right=[]\n",
    "        for basis_funct in eh_basis_functs:\n",
    "            if basis_funct[1]==unique_atom_index[1]:\n",
    "                atomic_energies_right.append(basis_funct[5])\n",
    "        atomic_degeneracy_right=[]\n",
    "        for energy in atomic_energies_right:\n",
    "            atomic_degeneracy_right.append(atomic_energies_right.count(energy))\n",
    "        for i in range(len(atomic_energies_right)):\n",
    "            if atomic_degeneracy_right[i]==5:\n",
    "                box.plot([4.2,3.8],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "                box.plot([3.6,3.2],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "                box.plot([3.0,2.6],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "                box.plot([2.4,2.0],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "                box.plot([1.8,1.4],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "            elif atomic_degeneracy_right[i]==3:\n",
    "                box.plot([3.6,3.2],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "                box.plot([3.0,2.6],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "                box.plot([2.4,2.0],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)\n",
    "            else:\n",
    "                box.plot([3.0,2.6],[atomic_energies_right[i],atomic_energies_right[i]], color=\"black\",lw=2)  \n",
    "\n",
    "\n",
    "    #now deal with the third and additional atoma, if they exists (which we will also put on the right but in green)    \n",
    "    if len(unique_atoms)>2:   \n",
    "        atomic_energies_rightb=[]\n",
    "        for i in range(2,len(unique_atoms)):\n",
    "            for basis_funct in eh_basis_functs:\n",
    "                if basis_funct[1]==unique_atom_index[i]:\n",
    "                    atomic_energies_rightb.append(basis_funct[5])\n",
    "            atomic_degeneracy_rightb=[]\n",
    "            for energy in atomic_energies_rightb:\n",
    "                atomic_degeneracy_rightb.append(atomic_energies_rightb.count(energy))\n",
    "            for i in range(len(atomic_energies_rightb)):\n",
    "                if atomic_degeneracy_rightb[i]==5:\n",
    "                    box.plot([4.2,3.8],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                    box.plot([3.6,3.2],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                    box.plot([3.0,2.6],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                    box.plot([2.4,2.0],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                    box.plot([1.8,1.4],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                elif atomic_degeneracy_rightb[i]==3:\n",
    "                    box.plot([3.6,3.2],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                    box.plot([3.0,2.6],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                    box.plot([2.4,2.0],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2)\n",
    "                else:\n",
    "                    box.plot([3.0,2.6],[atomic_energies_rightb[i],atomic_energies_rightb[i]], color=\"green\",lw=2) \n",
    "\n",
    "    #now work out the connecting lines\n",
    "    for i in range(len(e)):\n",
    "        MO=C[:,i]   #slice out the MO of interest\n",
    "        MO_energy=e[i]\n",
    "        #work out where to end plot line\n",
    "        if degeneracy[i]==3:\n",
    "            end_left=-1.0;end_right=1.0\n",
    "        if degeneracy[i]==2:\n",
    "            end_left=-0.6;end_right=0.6\n",
    "        elif degeneracy[i]==1:\n",
    "            end_left=-0.2;end_right=0.2\n",
    "        if homonuclear:\n",
    "            for j in range(len(MO)):\n",
    "                if abs(MO[j])>connection_threshold:\n",
    "                    AO_energy=eh_basis_functs[j][5]\n",
    "                    for k in range(len(atomic_energies_left)):\n",
    "                        if AO_energy==atomic_energies_left[k]:\n",
    "                            deg=atomic_degeneracy_left[k]\n",
    "                            if deg==5:\n",
    "                                start_left=-1.4;start_right=1.4\n",
    "                            if deg==3:\n",
    "                                start_left=-2.0;start_right=2.0\n",
    "                            elif deg==1:\n",
    "                                start_left=-2.6;start_right=2.6\n",
    "                            box.plot([start_left,end_left],[AO_energy,MO_energy], '--',color=\"blue\",lw=0.5)\n",
    "                            if len(atoms)==2:                            \n",
    "                                box.plot([start_right,end_right],[AO_energy,MO_energy], '--',color=\"blue\",lw=0.5)\n",
    "        else:\n",
    "            for j in range(len(MO)):\n",
    "                RHS=False;LHS=False\n",
    "                if abs(MO[j])>connection_threshold:\n",
    "                    AO_energy=eh_basis_functs[j][5]\n",
    "                    #now I need to decide if it is on right or left hand atom\n",
    "                    atom_AO_is_on=eh_basis_functs[j][0]\n",
    "                    if atom_AO_is_on==unique_atoms[0]:\n",
    "                        LHS=True\n",
    "                    elif atom_AO_is_on==unique_atoms[1]:\n",
    "                        RHS=True\n",
    "                    else:\n",
    "                        RHSb=True\n",
    "                    #elif atom_AO_is_on==unique_atoms[2]:\n",
    "                    #    RHSb=True \n",
    "                    if LHS:\n",
    "                        for k in range(len(atomic_energies_left)):\n",
    "                            if AO_energy==atomic_energies_left[k]:\n",
    "                                deg=atomic_degeneracy_left[k]\n",
    "                                if deg==5:\n",
    "                                    start_left=-1.4\n",
    "                                elif deg==3:\n",
    "                                    start_left=-2.0\n",
    "                                elif deg==1:\n",
    "                                    start_left=-2.6\n",
    "                                box.plot([start_left,end_left],[AO_energy,MO_energy], '--',color=\"blue\",lw=0.5)\n",
    "                    elif RHS:\n",
    "                        for k in range(len(atomic_energies_right)):\n",
    "                            if AO_energy==atomic_energies_right[k]:\n",
    "                                deg=atomic_degeneracy_right[k]\n",
    "                                if deg==5:\n",
    "                                    start_right=1.4\n",
    "                                elif deg==3:\n",
    "                                    start_right=2.0\n",
    "                                elif deg==1:\n",
    "                                    start_right=2.6\n",
    "                                box.plot([start_right,end_right],[AO_energy,MO_energy], '--',color=\"blue\",lw=0.5)\n",
    "                    elif RHSb:\n",
    "                        for k in range(len(atomic_energies_rightb)):\n",
    "                            if AO_energy==atomic_energies_rightb[k]:\n",
    "                                deg=atomic_degeneracy_rightb[k]\n",
    "                                if deg==5:\n",
    "                                    start_right=1.4\n",
    "                                elif deg==3:\n",
    "                                    start_right=2.0\n",
    "                                elif deg==1:\n",
    "                                    start_right=2.6\n",
    "                                box.plot([start_right,end_right],[AO_energy,MO_energy], '--',color=\"blue\",lw=0.5)\n",
    "    #plot some red dots to represent electrons \n",
    "    dotsx=[];dotsy=[]\n",
    "    for i in range(len(e)):\n",
    "        if occupations[i]==2:\n",
    "            if degeneracy[i]==3:\n",
    "                dotsx.append(-0.1);dotsy.append(e[i])\n",
    "                dotsx.append(0.1);dotsy.append(e[i])\n",
    "                dotsx.append(-0.9);dotsy.append(e[i])\n",
    "                dotsx.append(0.9);dotsy.append(e[i])\n",
    "                dotsx.append(-0.7);dotsy.append(e[i])\n",
    "                dotsx.append(0.7);dotsy.append(e[i])\n",
    "            elif degeneracy[i]==2:\n",
    "                dotsx.append(-0.5);dotsy.append(e[i])\n",
    "                dotsx.append(0.5);dotsy.append(e[i])\n",
    "                if occupations[i+1]==2:   #only add\n",
    "                    dotsx.append(-0.3);dotsy.append(e[i])\n",
    "                    dotsx.append(0.3);dotsy.append(e[i])\n",
    "            elif degeneracy[i]==1.:\n",
    "                dotsx.append(-0.1);dotsy.append(e[i])\n",
    "                dotsx.append(0.1);dotsy.append(e[i])\n",
    "    box.plot(dotsx,dotsy,'ro')\n",
    "    \n",
    "    #work out the atom labels  \n",
    "    offset=(energy_max-energy_min)*0.05   #works out where to place labels\n",
    "    box.text(-0.1, energy_min-offset, title, fontsize=12,color='red')          \n",
    "    atom_label_Left=atno_to_element[unique_atoms[0]]       \n",
    "    box.text(-2.9, energy_min-offset, atom_label_Left, fontsize=12,color='red')         \n",
    "    if len(unique_atoms)>1: \n",
    "        atom_label_Right=atno_to_element[unique_atoms[1]]\n",
    "        box.text(2.9, energy_min-offset, atom_label_Right, fontsize=12,color='red')\n",
    "    if len(unique_atoms)>2: \n",
    "        atom_label_Rightb=str('/'+atno_to_element[unique_atoms[2]])\n",
    "        box.text(3.2, energy_min-offset, atom_label_Rightb, fontsize=12,color='green')\n",
    "                \n",
    "    ax3=plt.subplot(222,projection='3d')\n",
    "    xcoords=[];ycoords=[];zcoords=[]\n",
    "    for coordinate in coords:\n",
    "        xcoords.append(coordinate[0]);ycoords.append(coordinate[1]);zcoords.append(coordinate[2])\n",
    "    xmax=2*max(xcoords);xmin=2*min(xcoords);ymax=2*max(ycoords);ymin=2*min(ycoords);zmax=2*max(zcoords);zmin=2*min(zcoords)       \n",
    "    #below is a work around to try to set the axis ratios equal.  \n",
    "    max_range = np.array([xmax-xmin, ymax-ymin, zmax-zmin]).max() / 2.0\n",
    "    xcoords=np.asarray(xcoords);ycoords=np.asarray(ycoords);zcoords=np.asarray(zcoords) \n",
    "    mean_x = xcoords.mean();mean_y = ycoords.mean();mean_z = zcoords.mean()\n",
    "    ax3.set_xlim(mean_x - max_range, mean_x + max_range)\n",
    "    ax3.set_ylim(mean_y - max_range, mean_y + max_range)\n",
    "    ax3.set_zlim(mean_z - max_range, mean_z + max_range)     \n",
    "    \n",
    "    for i in range(len(coords)):\n",
    "        if atoms[i]==1:\n",
    "            atom_colour='k'\n",
    "        else:\n",
    "            atom_colour=[x/255 for x in atom_colours[atoms[i]]]\n",
    "        #atom_colour=[x/255 for x in atom_colours[atoms[i]]]\n",
    "        size=covalent_radii[atoms[i]]*100\n",
    "        ax3.scatter(coords[i][0],coords[i][1],coords[i][2],color=atom_colour,s=size)\n",
    "    for x in range(len(coords)):\n",
    "        for y in range(x,len(coords)):\n",
    "            if x<y:\n",
    "                vecab=[(coords[y][0]-coords[x][0]),(coords[y][1]-coords[x][1]),(coords[y][2]-coords[x][2])]\n",
    "                distab=np.linalg.norm(vecab)\n",
    "                sum_of_radii=(covalent_radii[atoms[x]]+covalent_radii[atoms[y]])/A0 \n",
    "                if distab<1.2*sum_of_radii:  #define bonded cutoff as 110% of sum of covalent radii                                         \n",
    "                    ax3.plot([coords[y][0],coords[x][0]],[coords[y][1],coords[x][1]],[coords[y][2],coords[x][2]], color=\"black\",lw=1)  \n",
    "    ax3.set_title(title)\n",
    "        \n",
    "    \n",
    "    plt.show()\n",
    "        \n",
    "def MO_contour(e,eh_basis_functs,C,title,atoms,coords,MO_list,plane_offset,levels,plot_plane,revised_symmetry_list,pg_irreps,pg):    \n",
    "    plt.figure(figsize=(16,12))\n",
    "    if not MO_list: \n",
    "        HOMO=int(no_electrons/2);LUMO=HOMO+1\n",
    "        for i in range(HOMO-3,LUMO+2):\n",
    "            if 0<=i<len(e):\n",
    "                MO_list.append(i)  \n",
    "    if len(MO_list)%2==0:   #if the number of MOs is even\n",
    "        columns=int(len(MO_list)/2)+1\n",
    "    else:     #if number of MOs is odd\n",
    "        columns=int(len(MO_list)/2)+2\n",
    "    #choose which orbitals to plot (later) if they are not specified in input\n",
    "  \n",
    "    #else:\n",
    "    #    for i in range(len(MO_list)):\n",
    "    #        MO_list[i]-=1  #subtract one to start the list at zero    \n",
    "    #work out numbering of levels for plots\n",
    "    #work out degeneracies of molecular levels\n",
    "    degeneracy=[]\n",
    "    e=np.round(e,decimals=2).tolist()\n",
    "    for energy in e:\n",
    "        degeneracy.append(e.count(energy))\n",
    "    #set up figure structure\n",
    "                    \n",
    "    #first I need a list that keeps track of sequential numbering of orbitals of a given symmetry\n",
    "    symmetry_tracker=np.zeros_like(revised_symmetry_list)\n",
    "    for i in range(len(pg_irreps[pg])):\n",
    "        count=0.\n",
    "        for j in range(len(revised_symmetry_list)):\n",
    "            if revised_symmetry_list[j]==i:\n",
    "                symmetry_tracker[j]=count+1\n",
    "                count+=1/degeneracy[j]   #this increments count by 1/2 per doubly degenerate orbital, 1/3 per triply degenerate etc\n",
    "\n",
    "    #Calculate normalisation constants\n",
    "    norms=[]\n",
    "    for basis_funct in eh_basis_functs:\n",
    "        if type(basis_funct[6][0]) is tuple:\n",
    "            n=basis_funct[2]\n",
    "            exponent1=basis_funct[6][0][0];exponent2=basis_funct[6][1][0]\n",
    "            coeff1=basis_funct[6][0][1];coeff2=basis_funct[6][1][1]\n",
    "            norm1=(2*exponent1)**n*np.sqrt(2*exponent1/m.factorial(2*n))\n",
    "            norm2=(2*exponent1)**n*np.sqrt(2*exponent2/m.factorial(2*n))            \n",
    "            norms.append([norm1,coeff1,norm2,coeff2])\n",
    "        else:\n",
    "            exponent=basis_funct[6][0];n=basis_funct[2]\n",
    "            norm=(2*exponent)**n*np.sqrt(2*exponent/m.factorial(2*n))\n",
    "            norms.append([norm])\n",
    "    xcoords=[];ycoords=[];zcoords=[]\n",
    "    for coordinate in coords:\n",
    "        xcoords.append(coordinate[0]);ycoords.append(coordinate[1]);zcoords.append(coordinate[2])\n",
    "    \n",
    "    xmax=max(xcoords);xmin=min(xcoords);ymax=max(ycoords);ymin=min(ycoords);zmax=max(zcoords);zmin=min(zcoords)       \n",
    "    #set up the grid\n",
    "    x=np.linspace(xmin-3,xmax+3,int((xmax-xmin+1)*50));y=np.linspace(ymin-3,ymax+3,int((ymax-ymin+1)*50));z=np.linspace(zmin-3,zmax+3,int((zmax-zmin+1)*50))\n",
    "    if plot_plane=='xz':    \n",
    "        [X,Z] = np.meshgrid(x,z)\n",
    "    elif plot_plane=='xy':    \n",
    "        [X,Y] = np.meshgrid(x,y)\n",
    "    elif plot_plane=='yz':    \n",
    "        [Y,Z] = np.meshgrid(y,z)\n",
    "    #[X1,Y1,Z1]=np.meshgrid(x1,y1,z1)\n",
    "    count=0;count2=1;count2_temp=1\n",
    "    for MO in MO_list:\n",
    "        MO1=C[:,MO].tolist()\n",
    "        if plot_plane=='xz':        \n",
    "            psi=0*X+0*Z\n",
    "        elif plot_plane=='xy':        \n",
    "            psi=0*X+0*Y\n",
    "        elif plot_plane=='yz':        \n",
    "            psi=0*Y+0*Z\n",
    "        X1,Y1,Z1=np.mgrid[-5:5:64j,-5:5:64j,-5:5:64j]\n",
    "        psi3d=0*X1+0*Y1+0*Z1\n",
    "        for k in range(len(MO1)):            \n",
    "            atomindex=eh_basis_functs[k][1];exponent=eh_basis_functs[k][6][0];n=eh_basis_functs[k][2]\n",
    "            coordk=coords[atomindex]            \n",
    "            if plot_plane=='xz':\n",
    "                vecij=[(X-coordk[0]),-coordk[1]-plane_offset,(Z-coordk[2])]\n",
    "            #elif plot_plane=='yz':\n",
    "            #    vecij=[(-coordk[0]-plane_offset),(Y-coordk[1]),(Z-coordk[2])]\n",
    "            elif plot_plane=='xy':\n",
    "                vecij=[(X-coordk[0]),(Y-coordk[1]),-coordk[2]-plane_offset]\n",
    "            elif plot_plane=='yz':\n",
    "                vecij=[-coordk[0]-plane_offset,(Y-coordk[1]),(Z-coordk[2])]\n",
    "            vec3d=[(X1-coordk[0]),(Y1-coordk[1]),(Z1-coordk[2])]\n",
    "            distij=np.linalg.norm(vecij)\n",
    "            dist3d=np.linalg.norm(vec3d)           \n",
    "            #set angular factors for the projection onto the plane. Note that these are identical to the angular factors for overlap\n",
    "            #with an s orbital at the same point, so I can re-use the direction cosines used in the calculation of the overlap.\n",
    "            dcn=vecij[2]/distij\n",
    "            dcm=vecij[1]/distij\n",
    "            dcl=vecij[0]/distij\n",
    "            dcn3d=vec3d[2]/dist3d\n",
    "            dcm3d=vec3d[1]/dist3d\n",
    "            dcl3d=vec3d[0]/dist3d\n",
    "            angular_factor=1.    #1 for an s orbitals    \n",
    "            angular_factor3d=1.\n",
    "            if eh_basis_functs[k][3]==1 and eh_basis_functs[k][4]==0:     #p orbitals\n",
    "                angular_factor=dcn\n",
    "                angular_factor3d=dcn3d\n",
    "            elif eh_basis_functs[k][3]==1 and eh_basis_functs[k][4]==1:  \n",
    "                angular_factor=dcl\n",
    "                angular_factor3d=dcn3d\n",
    "            elif eh_basis_functs[k][3]==1 and eh_basis_functs[k][4]==-1:\n",
    "                angular_factor=dcm\n",
    "                angular_factor3d=dcn3d\n",
    "            elif eh_basis_functs[k][3]==2 and eh_basis_functs[k][4]==-2:   #d orbitals\n",
    "                angular_factor=3**0.5*dcl*dcm\n",
    "                angular_factor3d=3**0.5*dcl3d*dcm3d\n",
    "            elif eh_basis_functs[k][3]==2 and eh_basis_functs[k][4]==-1:\n",
    "                angular_factor=3**0.5*dcm*dcn\n",
    "                angular_factor3d=3**0.5*dcm3d*dcn3d\n",
    "            elif eh_basis_functs[k][3]==2 and eh_basis_functs[k][4]==0:\n",
    "                angular_factor=dcn**2-0.5*(dcl**2+dcm**2)\n",
    "                angular_factor3d=dcn3d**2-0.5*(dcl3d**2+dcm3d**2)\n",
    "            elif eh_basis_functs[k][3]==2 and eh_basis_functs[k][4]==1:\n",
    "                angular_factor=3**0.5*dcl*dcn\n",
    "                angular_factor3d=3**0.5*dcl3d*dcn3d\n",
    "            elif eh_basis_functs[k][3]==2 and eh_basis_functs[k][4]==2:\n",
    "                angular_factor=3**0.5*(dcl**2-dcm**2)\n",
    "                angular_factor3d=3**0.5*(dcl3d**2-dcm3d**2)\n",
    "            if len(norms[k])==1:   #if it is single zeta\n",
    "                psi+=norms[k][0]*MO1[k]*np.exp(-exponent*distij)*distij**(n-1)*angular_factor\n",
    "                psi3d+=norms[k][0]*MO1[k]*np.exp(-exponent*dist3d)*dist3d**(n-1)*angular_factor3d\n",
    "            else:                #if it is double zeta\n",
    "                psi+=MO1[k]*angular_factor\\\n",
    "                *norms[k][0]*norms[k][1]*np.exp(-exponent1*distij)*distij**(n-1)\\\n",
    "                *norms[k][2]*norms[k][3]*np.exp(-exponent2*distij)*distij**(n-1)\n",
    "                psi3d+=MO1[k]*angular_factor3d\\\n",
    "                *norms[k][0]*norms[k][1]*np.exp(-exponent1*dist3d)*dist3d**(n-1)\\\n",
    "                *norms[k][2]*norms[k][3]*np.exp(-exponent2*dist3d)*dist3d**(n-1)\n",
    "\n",
    "        box2=plt.subplot2grid((2,columns),(count,count2_temp))\n",
    "        if plot_plane=='xz':\n",
    "            CS=box2.contour(X,Z,psi, colors='k',levels=levels)\n",
    "            plt.axis('equal')\n",
    "        elif plot_plane=='xy':\n",
    "            CS=box2.contour(X,Y,psi, colors='k',levels=levels)\n",
    "            plt.axis('equal')\n",
    "        elif plot_plane=='yz':\n",
    "            CS=box2.contour(Y,Z,psi, colors='k',levels=levels)\n",
    "        #set colors for +ve and -ve contours\n",
    "        for line in box2.collections:\n",
    "            if line.get_linestyle() == [(None, None)]:\n",
    "                line.set_color('blue')\n",
    "            else:\n",
    "                line.set_color('red')\n",
    "        box2.clabel(CS,inline=1, fontsize=10,levels=levels[1::2])\n",
    "        label=str(symmetry_tracker[MO])+pg_irreps[pg][revised_symmetry_list[MO]]  #3a2, 2b1 etc    \n",
    "        box2.text(0.95,0.95,label,horizontalalignment='right',verticalalignment='top',transform = box2.transAxes,fontsize=12,color='blue')\n",
    "        for i in range(len(coords)):\n",
    "            if atoms[i]==1:\n",
    "                atom_colour='k'\n",
    "            else:\n",
    "                atom_colour=[x/255 for x in atom_colours[atoms[i]]]\n",
    "            if plot_plane=='xz':\n",
    "                box2.set_xlim(xmin-3,xmax+3);box2.set_ylim(zmin-3,zmax+3)\n",
    "                box2.plot(coords[i][0],coords[i][2],'o',color=atom_colour,markersize=covalent_radii[atoms[i]]*10)\n",
    "            elif plot_plane=='xy':\n",
    "                box2.plot(coords[i][0],coords[i][1],'o',color=atom_colour,markersize=covalent_radii[atoms[i]]*10)\n",
    "                box2.set_xlim(xmin-3,xmax+3);box2.set_ylim(ymin-3,ymax+3)\n",
    "            elif plot_plane=='yz':\n",
    "                box2.plot(coords[i][1],coords[i][2],'o',color=atom_colour,markersize=covalent_radii[atoms[i]]*10)\n",
    "                box2.set_xlim(ymin-3,ymax+3);box2.set_ylim(zmin-3,zmax+3)\n",
    "                \n",
    "        for a in range(len(coords)):\n",
    "            for b in range(a,len(coords)):\n",
    "                if a<b:\n",
    "                    vecab=[(coords[b][0]-coords[a][0]),(coords[b][1]-coords[a][1]),(coords[b][2]-coords[a][2])]\n",
    "                    distab=np.linalg.norm(vecab)\n",
    "                    sum_of_radii=(covalent_radii[atoms[a]]+covalent_radii[atoms[b]])/A0 \n",
    "                    #print(distab,sum_of_radii)\n",
    "                    if distab<1.2*sum_of_radii:  #define bonded cutoff as 110% of sum of covalent radii                                         \n",
    "                        if plot_plane=='xz':\n",
    "                            box2.plot([coords[b][0],coords[a][0]],[coords[b][2],coords[a][2]], color=\"black\",lw=2) \n",
    "                        elif plot_plane=='xy':\n",
    "                            box2.plot([coords[b][0],coords[a][0]],[coords[b][1],coords[a][1]], color=\"black\",lw=2) \n",
    "                        elif plot_plane=='yz':\n",
    "                            box2.plot([coords[b][1],coords[a][1]],[coords[b][2],coords[a][2]], color=\"black\",lw=2) \n",
    "\n",
    "                            #    elif plot_plane=='yz':\n",
    "        #        box2.plot(coord[1],coord[2],'ro')\n",
    "        count+=1;count2+=0.5;count2_temp=int(count2)\n",
    "        if count==2:\n",
    "            count=0    #reset count to zero every two loops through\n",
    "\n",
    "    plt.show()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the MO programme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hideCode": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PYTHON SCRIPT TO GENERATE MO DIAGRAMS USING EXTENDED HUCKEL THEORY\n",
      "\n",
      "\n",
      "Do you want to create a new molecule (1) or choose one from the library (2)?     2\n",
      "The list of available molecules is: \n",
      "\n",
      "0     H2             \n",
      "1     HHe+           \n",
      "2     B2             \n",
      "3     C2             \n",
      "4     N2             \n",
      "5     O2             \n",
      "6     F2             \n",
      "7     [H3]+          \n",
      "8     Li3+           \n",
      "9     [H3]-          \n",
      "10    HF             \n",
      "11    HB             \n",
      "12    HCN            \n",
      "13    CO2            \n",
      "14    KrF2           \n",
      "15    BeH2           \n",
      "16    H2O            \n",
      "17    H2S            \n",
      "18    BH3            \n",
      "19    NH3            \n",
      "20    PH3            \n",
      "21    CH4            \n",
      "22    KrOF4          \n",
      "23    SF6            \n",
      "24    SF4            \n",
      "25    HGeGeH_trans_bent\n",
      "26    HGeGeH_linear  \n",
      "27    B2H6           \n",
      "28    [B6H6]2-       \n",
      "29    [NiCN4]2-      \n",
      "30    [PdCl4]2-      \n",
      "31    [NiF6]4-       \n",
      "32    [FeCN6]4-      \n",
      "33    CrCO6          \n",
      "34    ferrocene      \n",
      "35    CrSi12         \n",
      "36    ethene         \n",
      "37    benzene        \n",
      "38    [Co(NH2)(NH3)4]+\n",
      "\n",
      "\n",
      "which molecule do you want to look at? type a formula from the list  benzene\n",
      "molecule is  benzene\n",
      "the standard settings are: \n",
      "\n",
      "Parameter set: Hoffmann\n",
      "Charge 0\n",
      "Atoms and Coordinates (in Angstroms)\n",
      "6           0.00       1.40       0.00\n",
      "6           1.21       0.70       0.00\n",
      "6           1.21      -0.70       0.00\n",
      "6           0.00      -1.40       0.00\n",
      "6          -1.21      -0.70       0.00\n",
      "6          -1.21       0.70       0.00\n",
      "1           0.00       2.48       0.00\n",
      "1           2.15       1.24       0.00\n",
      "1           2.15      -1.24       0.00\n",
      "1           0.00      -2.48       0.00\n",
      "1          -2.15      -1.24       0.00\n",
      "1          -2.15       1.24       0.00\n",
      "Do you want to edit any of these values?  Y/N  N\n",
      "\n",
      "\u001b[1mExtended Huckel Calculation         \n",
      "\n",
      "title           benzene \n",
      "\n",
      "number of electrons =  30  charge =  0\n",
      "number of basis functions =  30\n",
      "Using 1.75 as the Wolfsberg-Helmholtz Parameter\n",
      "Using Hoffmann's new parameter set\n",
      "Point group:  D6h \n",
      "\n",
      "Character table:  \n",
      "\n",
      "\t E     \t2C6   \t2C3   \tC2    \t3C2'  \t3C2\"  \ti     \t2S3   \t2S6   \tsh    \t3sd   \t3sd'  \n",
      "a1g   \t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\n",
      "a2g   \t  1.00\t  1.00\t  1.00\t  1.00\t -1.00\t -1.00\t  1.00\t  1.00\t  1.00\t  1.00\t -1.00\t -1.00\n",
      "b1g   \t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\n",
      "b2g   \t  1.00\t -1.00\t  1.00\t -1.00\t -1.00\t  1.00\t  1.00\t -1.00\t  1.00\t -1.00\t -1.00\t  1.00\n",
      "e1g   \t  2.00\t  1.00\t -1.00\t -2.00\t  0.00\t  0.00\t  2.00\t  1.00\t -1.00\t -2.00\t  0.00\t  0.00\n",
      "e2g   \t  2.00\t -1.00\t -1.00\t  2.00\t  0.00\t  0.00\t  2.00\t -1.00\t -1.00\t  2.00\t  0.00\t  0.00\n",
      "a1u   \t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t  1.00\t -1.00\t -1.00\t -1.00\t -1.00\t -1.00\t -1.00\n",
      "a2u   \t  1.00\t  1.00\t  1.00\t  1.00\t -1.00\t -1.00\t -1.00\t -1.00\t -1.00\t -1.00\t  1.00\t  1.00\n",
      "b1u   \t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\n",
      "b2u   \t  1.00\t -1.00\t  1.00\t -1.00\t -1.00\t  1.00\t -1.00\t  1.00\t -1.00\t  1.00\t  1.00\t -1.00\n",
      "e1u   \t  2.00\t  1.00\t -1.00\t -2.00\t  0.00\t  0.00\t -2.00\t -1.00\t  1.00\t  2.00\t  0.00\t  0.00\n",
      "e2u   \t  2.00\t -1.00\t -1.00\t  2.00\t  0.00\t  0.00\t -2.00\t  1.00\t  1.00\t -2.00\t  0.00\t  0.00\n",
      "\n",
      "\n",
      "Symmetry-adapted Coordinates        \n",
      "\n",
      "C                0.00       2.64       0.00\n",
      "C                2.28       1.32       0.00\n",
      "C                2.28      -1.32       0.00\n",
      "C                0.00      -2.64       0.00\n",
      "C               -2.28      -1.32       0.00\n",
      "C               -2.28       1.32       0.00\n",
      "H                0.00       4.69       0.00\n",
      "H                4.06       2.35       0.00\n",
      "H                4.06      -2.35       0.00\n",
      "H                0.00      -4.69       0.00\n",
      "H               -4.06      -2.35       0.00\n",
      "H               -4.06       2.35       0.00\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Symmetry-adapted basis set:\n",
      "*               C2s       \tC2px      \tC2py      \tC2pz      \tC2s       \tC2px      \tC2py      \tC2pz      \tC2s       \tC2px      \tC2py      \tC2pz      \tC2s       \tC2px      \tC2py      \tC2pz      \tC2s       \tC2px      \tC2py      \tC2pz      \tC2s       \tC2px      \tC2py      \tC2pz      \tH1s       \tH1s       \tH1s       \tH1s       \tH1s       \tH1s       \n",
      "\u001b[1m0 a1g H 1s      0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.408     \t0.408     \t0.408     \t0.408     \t0.408     \t0.408     \n",
      "\u001b[0m\u001b[1m1 a1g C 2s      0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m2 a1g C 2p      0.0       \t0.0       \t-0.408    \t-0.0      \t0.0       \t-0.354    \t-0.204    \t0.0       \t0.0       \t-0.354    \t0.204     \t-0.0      \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.354     \t0.204     \t-0.0      \t0.0       \t0.354     \t-0.204    \t-0.0      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m3 a2g C 2p      0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t-0.204    \t0.354     \t0.0       \t0.0       \t0.204     \t0.354     \t0.0       \t0.0       \t0.408     \t0.0       \t-0.0      \t0.0       \t0.204     \t-0.354    \t0.0       \t0.0       \t-0.204    \t-0.354    \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m4 b2g C 2p      0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m5 e1g C 2p      0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t0.289     \t0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t0.289     \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t-0.577    \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m6 e1g C 2p      0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m7 e2g H 1s      0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.577     \t-0.289    \t-0.289    \t0.577     \t-0.289    \t-0.289    \n",
      "\u001b[0m\u001b[1m8 e2g H 1s      0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.5       \t-0.5      \t0.0       \t0.5       \t-0.5      \n",
      "\u001b[0m\u001b[1m9 e2g C 2s      -0.289    \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m10 e2g C 2s     0.5       \t0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m11 e2g C 2p     0.0       \t-0.25     \t-0.144    \t0.0       \t0.0       \t-0.25     \t0.144     \t0.0       \t0.0       \t0.0       \t-0.577    \t0.0       \t0.0       \t0.25      \t0.144     \t0.0       \t0.0       \t0.25      \t-0.144    \t0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m12 e2g C 2p     0.0       \t0.065     \t-0.559    \t0.0       \t0.0       \t0.323     \t0.112     \t0.0       \t0.0       \t0.258     \t0.0       \t0.0       \t0.0       \t-0.065    \t0.559     \t0.0       \t0.0       \t-0.323    \t-0.112    \t0.0       \t0.0       \t-0.258    \t-0.0      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m13 e2g C 2p     0.0       \t0.316     \t0.0       \t0.0       \t0.0       \t0.0       \t0.548     \t0.0       \t0.0       \t-0.316    \t-0.0      \t0.0       \t0.0       \t-0.316    \t0.0       \t0.0       \t0.0       \t0.0       \t-0.548    \t0.0       \t0.0       \t0.316     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m14 e2g C 2p     0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t-0.0      \t0.0       \t0.0       \t0.408     \t-0.0      \t0.0       \t0.0       \t-0.408    \t-0.0      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m15 a2u C 2p     0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t-0.0      \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t-0.0      \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m16 b1u H 1s     0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.408     \t-0.408    \t0.408     \t-0.408    \t0.408     \t-0.408    \n",
      "\u001b[0m\u001b[1m17 b1u C 2s     0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.408     \t0.0       \t0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m18 b1u C 2p     0.0       \t0.0       \t-0.408    \t0.0       \t0.0       \t0.354     \t0.204     \t0.0       \t0.0       \t-0.354    \t0.204     \t0.0       \t0.0       \t0.0       \t-0.408    \t-0.0      \t0.0       \t0.354     \t0.204     \t0.0       \t0.0       \t-0.354    \t0.204     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m19 b2u C 2p     0.0       \t-0.408    \t0.0       \t0.0       \t0.0       \t0.204     \t-0.354    \t-0.0      \t0.0       \t0.204     \t0.354     \t0.0       \t0.0       \t-0.408    \t-0.0      \t-0.0      \t0.0       \t0.204     \t-0.354    \t-0.0      \t0.0       \t0.204     \t0.354     \t-0.0      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m20 e1u H 1s     0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.577     \t0.289     \t-0.289    \t-0.577    \t-0.289    \t0.289     \n",
      "\u001b[0m\u001b[1m21 e1u H 1s     0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.5       \t0.5       \t0.0       \t-0.5      \t-0.5      \n",
      "\u001b[0m\u001b[1m22 e1u C 2s     -0.289    \t0.0       \t0.0       \t0.0       \t0.289     \t0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t0.289     \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t-0.577    \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m23 e1u C 2s     -0.5      \t0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m24 e1u C 2p     0.0       \t0.144     \t0.25      \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t0.144     \t-0.25     \t0.0       \t0.0       \t0.144     \t0.25      \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t0.144     \t-0.25     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m25 e1u C 2p     0.0       \t-0.069    \t0.52      \t0.0       \t0.0       \t-0.0      \t0.16      \t0.0       \t0.0       \t-0.347    \t0.28      \t0.0       \t0.0       \t-0.069    \t0.52      \t0.0       \t0.0       \t-0.0      \t0.16      \t0.0       \t0.0       \t-0.347    \t0.28      \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m26 e1u C 2p     0.0       \t0.456     \t0.0       \t0.0       \t0.0       \t-0.0      \t0.088     \t0.0       \t0.0       \t0.304     \t0.439     \t0.0       \t0.0       \t0.456     \t0.0       \t0.0       \t0.0       \t-0.0      \t0.088     \t0.0       \t0.0       \t0.304     \t0.439     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m27 e1u C 2p     0.0       \t-0.316    \t-0.0      \t0.0       \t0.0       \t0.0       \t0.548     \t0.0       \t0.0       \t0.316     \t0.0       \t0.0       \t0.0       \t-0.316    \t-0.0      \t0.0       \t0.0       \t-0.0      \t0.548     \t0.0       \t0.0       \t0.316     \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m28 e2u C 2p     0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t0.577     \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t-0.289    \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m29 e2u C 2p     0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t-0.5      \t0.0       \t0.0       \t0.0       \t0.5       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \t0.0       \n",
      "\u001b[0m\u001b[1m \n",
      "\n",
      "overlap matrix:\n",
      "              a1g H1s\t   e2g H1s\t   e2g H1s\t   b1u H1s\t   e1u H1s\t   e1u H1s\t   a1g C2s\t   e2g C2s\t   e2g C2s\t   b1u C2s\t   e1u C2s\t   e1u C2s\t   a1g C2p\t   a2g C2p\t   b2g C2p\t   e1g C2p\t   e1g C2p\t   e2g C2p\t   e2g C2p\t   e2g C2p\t   e2g C2p\t   a2u C2p\t   b1u C2p\t   b2u C2p\t   e1u C2p\t   e1u C2p\t   e1u C2p\t   e1u C2p\t   e2u C2p\t   e2u C2p\n",
      "\n",
      "\n",
      "\u001b[1ma1g H1s         1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.49\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.32\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g H1s         0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.27\t      0.46\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.03\t     -0.63\t      0.13\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g H1s         0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.46\t     -0.27\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.29\t      0.10\t      0.37\t     -0.21\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1mb1u H1s         0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.57\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.66\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u H1s         0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.25\t     -0.44\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.11\t      0.49\t      0.17\t      0.16\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u H1s         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.44\t     -0.25\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.43\t     -0.21\t      0.27\t      0.14\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1ma1g C2s         0.49\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.35\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g C2s         0.00\t     -0.27\t     -0.46\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.15\t      0.15\t     -0.66\t     -0.23\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g C2s         0.00\t      0.46\t     -0.27\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.56\t     -0.53\t      0.10\t     -0.13\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1mb1u C2s         0.00\t      0.00\t      0.00\t      0.57\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.63\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u C2s         0.00\t      0.00\t      0.00\t      0.00\t     -0.25\t      0.44\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.26\t     -0.10\t      0.28\t     -0.54\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u C2s         0.00\t      0.00\t      0.00\t      0.00\t     -0.44\t     -0.25\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.19\t     -0.05\t     -0.68\t     -0.17\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1ma1g C2p        -0.32\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.35\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1ma2g C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1mb2g C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1g C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1g C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g C2p         0.00\t      0.03\t     -0.29\t      0.00\t      0.00\t      0.00\t      0.00\t      0.15\t      0.56\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t     -0.15\t      0.12\t     -0.18\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g C2p         0.00\t     -0.63\t      0.10\t      0.00\t      0.00\t      0.00\t      0.00\t      0.15\t     -0.53\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.15\t      1.00\t     -0.05\t      0.06\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g C2p         0.00\t      0.13\t      0.37\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.66\t      0.10\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.12\t     -0.05\t      1.00\t      0.23\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2g C2p         0.00\t     -0.00\t     -0.21\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.23\t     -0.13\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.18\t      0.06\t      0.23\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1ma2u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1mb1u C2p         0.00\t      0.00\t      0.00\t     -0.66\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.63\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1mb2u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.11\t      0.43\t      0.00\t      0.00\t      0.00\t      0.00\t      0.26\t      0.19\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.11\t     -0.12\t      0.13\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.49\t     -0.21\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.10\t     -0.05\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.11\t      1.00\t     -0.05\t     -0.17\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.17\t      0.27\t      0.00\t      0.00\t      0.00\t      0.00\t      0.28\t     -0.68\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.12\t     -0.05\t      1.00\t     -0.05\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me1u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.16\t      0.14\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.54\t     -0.17\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.13\t     -0.17\t     -0.05\t      1.00\t      0.00\t      0.00\n",
      "\u001b[0m\u001b[1me2u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\n",
      "\u001b[0m\u001b[1me2u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\n",
      "\u001b[0m\u001b[1m \n",
      "\n",
      "Hamiltonian matrix:\n",
      "              a1g H1s\t   e2g H1s\t   e2g H1s\t   b1u H1s\t   e1u H1s\t   e1u H1s\t   a1g C2s\t   e2g C2s\t   e2g C2s\t   b1u C2s\t   e1u C2s\t   e1u C2s\t   a1g C2p\t   a2g C2p\t   b2g C2p\t   e1g C2p\t   e1g C2p\t   e2g C2p\t   e2g C2p\t   e2g C2p\t   e2g C2p\t   a2u C2p\t   b1u C2p\t   b2u C2p\t   e1u C2p\t   e1u C2p\t   e1u C2p\t   e1u C2p\t   e2u C2p\t   e2u C2p\n",
      "\n",
      "\n",
      "\u001b[1ma1g H1s       -14.45\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t    -14.91\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      6.89\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g H1s         0.00\t    -13.12\t      0.00\t      0.00\t      0.00\t      0.00\t      0.01\t      8.13\t    -14.08\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.68\t     13.89\t     -2.80\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g H1s         0.00\t      0.00\t    -13.12\t      0.00\t      0.00\t      0.00\t      0.00\t     14.08\t      8.13\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      6.40\t     -2.10\t     -8.18\t      4.63\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1mb1u H1s        -0.00\t      0.00\t      0.00\t    -12.65\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t    -17.34\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     14.43\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1me1u H1s        -0.00\t      0.00\t      0.00\t     -0.00\t    -14.01\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      7.75\t     13.43\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -2.37\t    -10.79\t     -3.67\t     -3.40\t      0.00\t      0.00\n",
      "\u001b[1me1u H1s        -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t    -14.01\t      0.00\t      0.00\t     -0.00\t      0.00\t    -13.43\t      7.76\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -9.35\t      4.51\t     -5.85\t     -3.08\t      0.00\t      0.00\n",
      "\u001b[1ma1g C2s       -14.91\t      0.01\t      0.00\t      0.00\t     -0.00\t      0.00\t    -29.56\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -9.94\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g C2s        -0.00\t      8.13\t     14.08\t     -0.00\t     -0.00\t      0.00\t     -0.00\t     -8.18\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -4.36\t     -4.29\t     19.02\t      6.57\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g C2s         0.00\t    -14.08\t      8.13\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -8.18\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t    -16.10\t     15.20\t     -2.73\t      3.79\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1mb1u C2s         0.00\t      0.00\t     -0.00\t    -17.34\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     13.81\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     18.17\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1me1u C2s         0.00\t      0.00\t      0.00\t     -0.00\t      7.75\t    -13.43\t     -0.00\t     -0.00\t      0.00\t      0.00\t    -24.89\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     -7.39\t      2.87\t     -7.91\t     15.48\t      0.00\t      0.00\n",
      "\u001b[1me1u C2s         0.00\t     -0.00\t     -0.00\t     -0.00\t     13.43\t      7.76\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t    -24.89\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -5.52\t      1.45\t     19.39\t      4.80\t      0.00\t      0.00\n",
      "\u001b[1ma1g C2p         6.89\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -9.94\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t    -15.31\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1ma2g C2p         0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     15.22\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1mb2g C2p        -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -4.73\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\n",
      "\u001b[1me1g C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t    -12.80\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\n",
      "\u001b[1me1g C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t    -12.80\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\n",
      "\u001b[1me2g C2p         0.00\t     -0.68\t      6.40\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -4.36\t    -16.10\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t    -12.77\t      2.99\t     -2.46\t      3.67\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g C2p        -0.00\t     13.89\t     -2.10\t      0.00\t     -0.00\t      0.00\t      0.00\t     -4.29\t     15.20\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      2.99\t     -8.35\t      1.04\t     -1.20\t     -0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g C2p         0.00\t     -2.80\t     -8.18\t      0.00\t     -0.00\t     -0.00\t      0.00\t     19.02\t     -2.73\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -2.46\t      1.04\t    -12.63\t     -4.68\t      0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\n",
      "\u001b[1me2g C2p        -0.00\t      0.00\t      4.63\t      0.00\t     -0.00\t      0.00\t      0.00\t      6.57\t      3.79\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      3.67\t     -1.20\t     -4.68\t    -11.48\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1ma2u C2p         0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t    -14.53\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\u001b[1mb1u C2p         0.00\t      0.00\t     -0.00\t     14.43\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     18.17\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t     -6.07\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t     -0.00\n",
      "\u001b[1mb2u C2p        -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t    -14.29\t     -0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\n",
      "\u001b[1me1u C2p        -0.00\t      0.00\t      0.00\t      0.00\t     -2.37\t     -9.35\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -7.39\t     -5.52\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t    -11.37\t     -2.13\t      2.45\t     -2.58\t      0.00\t      0.00\n",
      "\u001b[1me1u C2p        -0.00\t      0.00\t      0.00\t      0.00\t    -10.79\t      4.51\t      0.00\t      0.00\t      0.00\t      0.00\t      2.87\t      1.45\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -2.13\t    -11.30\t      1.07\t      3.33\t      0.00\t      0.00\n",
      "\u001b[1me1u C2p         0.00\t     -0.00\t     -0.00\t      0.00\t     -3.67\t     -5.85\t     -0.00\t      0.00\t     -0.00\t      0.00\t     -7.91\t     19.39\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      2.45\t      1.07\t     -8.54\t      0.96\t      0.00\t      0.00\n",
      "\u001b[1me1u C2p        -0.00\t      0.00\t      0.00\t      0.00\t     -3.40\t     -3.08\t     -0.00\t     -0.00\t     -0.00\t      0.00\t     15.48\t      4.80\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -2.58\t      3.33\t      0.96\t    -10.90\t      0.00\t      0.00\n",
      "\u001b[1me2u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -8.32\t      0.00\n",
      "\u001b[1me2u C2p         0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -8.32\n",
      "****************************************************************************************************************\n",
      "\n",
      "\n",
      "                                                   RESULTS\n",
      "****************************************************************************************************************\n",
      "Orbital Energies (eV), occupations and symmetries\n",
      "****************************************************************************************************************\n",
      "E                  -29.583\t   -25.672\t   -25.671\t   -19.893\t   -19.892\t   -17.138\n",
      "Occup                  2.0\t       2.0\t       2.0\t       2.0\t       2.0\t       2.0\n",
      "Symm                   a1g\t       e1u\t       e1u\t       e2g\t       e2g\t       b1u \n",
      "\n",
      "0  a1g H1s            0.03\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "1  a1g C2s            0.99\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "2  a1g C2p           -0.02\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "3  a2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "4  b2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "5  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "6  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "7  e2g H1s            0.00\t      0.00\t      0.00\t      0.00\t      0.39\t      0.00\n",
      "8  e2g H1s            0.00\t      0.00\t      0.00\t      0.39\t     -0.00\t      0.00\n",
      "9  e2g C2s            0.00\t      0.00\t      0.00\t     -0.35\t     -0.20\t      0.00\n",
      "10  e2g C2s           0.00\t      0.00\t      0.00\t     -0.20\t      0.35\t      0.00\n",
      "11  e2g C2p           0.00\t      0.00\t      0.00\t     -0.24\t      0.21\t      0.00\n",
      "12  e2g C2p           0.00\t      0.00\t      0.00\t      0.05\t     -0.21\t      0.00\n",
      "13  e2g C2p           0.00\t      0.00\t      0.00\t      0.30\t      0.22\t      0.00\n",
      "14  e2g C2p           0.00\t      0.00\t      0.00\t      0.09\t     -0.00\t      0.00\n",
      "15  a2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "16  b1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.60\n",
      "17  b1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.22\n",
      "18  b1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.32\n",
      "19  b2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "20  e1u H1s           0.00\t     -0.00\t     -0.17\t      0.00\t      0.00\t      0.00\n",
      "21  e1u H1s           0.00\t      0.17\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "22  e1u C2s           0.00\t      0.72\t      0.42\t      0.00\t      0.00\t      0.00\n",
      "23  e1u C2s           0.00\t     -0.42\t      0.72\t      0.00\t      0.00\t      0.00\n",
      "24  e1u C2p           0.00\t      0.01\t      0.06\t      0.00\t      0.00\t      0.00\n",
      "25  e1u C2p           0.00\t     -0.00\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "26  e1u C2p           0.00\t      0.07\t     -0.06\t      0.00\t      0.00\t      0.00\n",
      "27  e1u C2p           0.00\t     -0.07\t     -0.07\t      0.00\t      0.00\t      0.00\n",
      "28  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "29  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\n",
      "\n",
      "Orbital Energies (eV), occupations and symmetries\n",
      "****************************************************************************************************************\n",
      "E                  -16.579\t   -14.914\t   -14.914\t   -14.525\t   -14.295\t   -13.376\n",
      "Occup                  2.0\t       2.0\t       2.0\t       2.0\t       2.0\t       2.0\n",
      "Symm                   a1g\t       e1u\t       e1u\t       a2u\t       b2u\t       e2g \n",
      "\n",
      "0  a1g H1s           -0.49\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "1  a1g C2s            0.02\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "2  a1g C2p            0.73\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "3  a2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "4  b2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "5  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "6  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "7  e2g H1s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.00\n",
      "8  e2g H1s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.48\n",
      "9  e2g C2s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.06\n",
      "10  e2g C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.03\n",
      "11  e2g C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.10\n",
      "12  e2g C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.02\n",
      "13  e2g C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.12\n",
      "14  e2g C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.76\n",
      "15  a2u C2p           0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\n",
      "16  b1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "17  b1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "18  b1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "19  b2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\n",
      "20  e1u H1s           0.00\t     -0.50\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "21  e1u H1s           0.00\t      0.00\t     -0.50\t      0.00\t      0.00\t      0.00\n",
      "22  e1u C2s           0.00\t     -0.08\t      0.14\t      0.00\t      0.00\t      0.00\n",
      "23  e1u C2s           0.00\t     -0.14\t     -0.08\t      0.00\t      0.00\t      0.00\n",
      "24  e1u C2p           0.00\t     -0.37\t     -0.41\t      0.00\t      0.00\t      0.00\n",
      "25  e1u C2p           0.00\t     -0.51\t      0.20\t      0.00\t      0.00\t      0.00\n",
      "26  e1u C2p           0.00\t      0.12\t      0.11\t      0.00\t      0.00\t      0.00\n",
      "27  e1u C2p           0.00\t      0.14\t     -0.45\t      0.00\t      0.00\t      0.00\n",
      "28  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "29  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\n",
      "\n",
      "Orbital Energies (eV), occupations and symmetries\n",
      "****************************************************************************************************************\n",
      "E                  -13.375\t   -12.802\t   -12.802\t    -8.316\t    -8.316\t    -4.728\n",
      "Occup                  2.0\t       2.0\t       2.0\t       0.0\t       0.0\t       0.0\n",
      "Symm                   e2g\t       e1g\t       e1g\t       e2u\t       e2u\t       b2g \n",
      "\n",
      "0  a1g H1s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "1  a1g C2s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "2  a1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "3  a2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "4  b2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.00\n",
      "5  e1g C2p            0.00\t     -0.87\t     -0.50\t      0.00\t      0.00\t      0.00\n",
      "6  e1g C2p            0.00\t      0.50\t     -0.87\t      0.00\t      0.00\t      0.00\n",
      "7  e2g H1s           -0.48\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "8  e2g H1s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "9  e2g C2s           -0.03\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "10  e2g C2s           0.06\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "11  e2g C2p           0.64\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "12  e2g C2p           0.25\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "13  e2g C2p           0.44\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "14  e2g C2p          -0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "15  a2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "16  b1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "17  b1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "18  b1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "19  b2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "20  e1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "21  e1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "22  e1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "23  e1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "24  e1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "25  e1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "26  e1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "27  e1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "28  e2u C2p           0.00\t      0.00\t      0.00\t      1.00\t      0.00\t      0.00\n",
      "29  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      1.00\t      0.00\n",
      "\n",
      "\n",
      "Orbital Energies (eV), occupations and symmetries\n",
      "****************************************************************************************************************\n",
      "E                    3.565\t     3.580\t     8.110\t    10.341\t    10.359\t    14.091\n",
      "Occup                  0.0\t       0.0\t       0.0\t       0.0\t       0.0\t       0.0\n",
      "Symm                   e1u\t       e1u\t       a1g\t       e2g\t       e2g\t       b1u \n",
      "\n",
      "0  a1g H1s            0.00\t      0.00\t     -1.33\t      0.00\t      0.00\t      0.00\n",
      "1  a1g C2s            0.00\t      0.00\t      1.04\t      0.00\t      0.00\t      0.00\n",
      "2  a1g C2p            0.00\t      0.00\t     -1.10\t      0.00\t      0.00\t      0.00\n",
      "3  a2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "4  b2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "5  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "6  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "7  e2g H1s            0.00\t      0.00\t      0.00\t     -1.13\t      0.00\t      0.00\n",
      "8  e2g H1s            0.00\t      0.00\t      0.00\t     -0.00\t     -1.13\t      0.00\n",
      "9  e2g C2s            0.00\t      0.00\t      0.00\t     -0.03\t     -0.04\t      0.00\n",
      "10  e2g C2s           0.00\t      0.00\t      0.00\t      0.04\t     -0.03\t      0.00\n",
      "11  e2g C2p           0.00\t      0.00\t      0.00\t     -0.25\t     -0.77\t      0.00\n",
      "12  e2g C2p           0.00\t      0.00\t      0.00\t     -1.23\t      0.16\t      0.00\n",
      "13  e2g C2p           0.00\t      0.00\t      0.00\t      0.12\t      0.96\t      0.00\n",
      "14  e2g C2p           0.00\t      0.00\t      0.00\t     -0.00\t     -0.90\t      0.00\n",
      "15  a2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "16  b1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -1.21\n",
      "17  b1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.22\n",
      "18  b1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -1.13\n",
      "19  b2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "20  e1u H1s          -1.06\t     -0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "21  e1u H1s          -0.00\t      1.06\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "22  e1u C2s          -0.04\t     -0.07\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "23  e1u C2s          -0.07\t      0.04\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "24  e1u C2p           0.05\t     -0.93\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "25  e1u C2p           1.04\t      0.45\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "26  e1u C2p           0.44\t     -0.65\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "27  e1u C2p           0.52\t     -0.07\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "28  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "29  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\n",
      "\n",
      "Orbital Energies (eV), occupations and symmetries\n",
      "****************************************************************************************************************\n",
      "E                   15.220\t    27.634\t    27.644\t    43.431\t    43.455\t    64.258\n",
      "Occup                  0.0\t       0.0\t       0.0\t       0.0\t       0.0\t       0.0\n",
      "Symm                   a2g\t       e1u\t       e1u\t       e2g\t       e2g\t       b1u \n",
      "\n",
      "0  a1g H1s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "1  a1g C2s            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "2  a1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "3  a2g C2p            1.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "4  b2g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "5  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "6  e1g C2p            0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "7  e2g H1s            0.00\t      0.00\t      0.00\t     -0.53\t      0.00\t      0.00\n",
      "8  e2g H1s            0.00\t      0.00\t      0.00\t     -0.00\t     -0.53\t      0.00\n",
      "9  e2g C2s            0.00\t      0.00\t      0.00\t     -0.74\t     -1.28\t      0.00\n",
      "10  e2g C2s           0.00\t      0.00\t      0.00\t      1.28\t     -0.74\t      0.00\n",
      "11  e2g C2p           0.00\t      0.00\t      0.00\t     -0.58\t      0.63\t      0.00\n",
      "12  e2g C2p           0.00\t      0.00\t      0.00\t      0.52\t     -0.13\t      0.00\n",
      "13  e2g C2p           0.00\t      0.00\t      0.00\t     -0.59\t     -0.78\t      0.00\n",
      "14  e2g C2p           0.00\t      0.00\t      0.00\t     -0.00\t     -0.29\t      0.00\n",
      "15  a2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "16  b1u H1s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t     -0.26\n",
      "17  b1u C2s           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      1.30\n",
      "18  b1u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.88\n",
      "19  b2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "20  e1u H1s           0.00\t     -0.78\t     -0.00\t      0.00\t      0.00\t      0.00\n",
      "21  e1u H1s           0.00\t      0.00\t     -0.78\t      0.00\t      0.00\t      0.00\n",
      "22  e1u C2s           0.00\t     -0.72\t      1.25\t      0.00\t      0.00\t      0.00\n",
      "23  e1u C2s           0.00\t     -1.25\t     -0.72\t      0.00\t      0.00\t      0.00\n",
      "24  e1u C2p           0.00\t      0.76\t     -0.14\t      0.00\t      0.00\t      0.00\n",
      "25  e1u C2p           0.00\t     -0.04\t      0.07\t      0.00\t      0.00\t      0.00\n",
      "26  e1u C2p           0.00\t     -0.77\t     -0.97\t      0.00\t      0.00\t      0.00\n",
      "27  e1u C2p           0.00\t     -0.89\t      0.93\t      0.00\t      0.00\t      0.00\n",
      "28  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "29  e2u C2p           0.00\t      0.00\t      0.00\t      0.00\t      0.00\t      0.00\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"PYTHON SCRIPT TO GENERATE MO DIAGRAMS USING EXTENDED HUCKEL THEORY\")\n",
    "print('\\n')\n",
    "create_molecule=int(input(\"Do you want to create a new molecule (1) or choose one from the library (2)?     \"))\n",
    "if create_molecule==2:\n",
    "    title,mol,params,coords,atoms,Q=select_from_list(coords_dict)\n",
    "elif create_molecule==1:\n",
    "    zmatrix_or_cartesians=int(input(\"Do you want to use cartesians (0) or a zmatrix (1) to define the geometry?      \"))\n",
    "    if zmatrix_or_cartesians==0:\n",
    "        title,mol,params,coords,atoms,Q=create_new_molecule()\n",
    "    elif zmatrix_or_cartesians==1:\n",
    "        title,mol,params,coords,atoms,Q=create_new_molecule_zmatrix()\n",
    "if params=='Hoffmann':\n",
    "    parameters=Hoffmann\n",
    "elif params=='Cerda':\n",
    "    parameters=Cerda\n",
    "#print(\"parameters\",parameters)     \n",
    "check_lengths(coords,atoms)\n",
    "coords,element_coord=coord_con_EHT(coords,atoms)   #convert coordinates\n",
    "eh_basis_functs=eh_basis_setupb(atoms,parameters) # set up a list of basis functions\n",
    "no_electrons=count_electrons(atoms,Q,Hoffmann)  # count the electrons (just to work out what's occupied at the end - has no impact on eigenvalues)\n",
    "pg, coords, element_coord = align(coords, atoms) #Aligns along principal axes, identifies point groups. NB alignment doesn't work for spherical tops!\n",
    "#print(pg)\n",
    "#pg='C1'\n",
    "if pg == 'C1': print('Warning: Alignment likely failed - C1 symmetry identified!') #C1 included in each list of point groups to check - so if C1 symmetry is identified it's likely alignment hasn't worked.\n",
    "set_elements_in_mol, coordinate_sets, set_AOs_in_mol, orbital_sets, orbitals_on_atom_sets = set_function(atoms,coords,eh_basis_functs) #Various sets used in later functions - each is described in the function itself (symmetry.py)\n",
    "corr_table_AO,corr_table_atoms,coords_operator = corre_table(atoms,coords,eh_basis_functs,pg,set_elements_in_mol, coordinate_sets, set_AOs_in_mol, orbital_sets, orbitals_on_atom_sets) #Generates correlation table for coordinates (describing the action of symmetry operations on each coordinate) and corr_table_AO, which describes how p orbitals transform\n",
    "SALC_basis = proj_operator(coords,pg,eh_basis_functs,corr_table_atoms,corr_table_AO,orbitals_on_atom_sets,orbital_sets,set_AOs_in_mol) #Applies the projection operator, then runs Gram-Schmidt orthonormalisation\n",
    "    #recompute the overlap matrix in the new coordinate system    \n",
    "ovlp=overlap_matrix_new(eh_basis_functs,coords,dist_cutoff)  #calculates overlap matrix    \n",
    "    #first, use a symmetry-adapted basis    \n",
    "coefficients,SALC_overlaps,revised_symmetry_list=SALC_overlap_matrix(SALC_basis,eh_basis_functs,ovlp)\n",
    "SALC_Hamiltonianb = SALC_H_matrix(SALC_basis,eh_basis_functs,ovlp,coefficients) #calculates symmetrised Hamiltonian matrix\n",
    "    #separate by symmetry splits the overlap and Hamiltonian matrices into sub-blocks for each symmetry, then diagonalises each in turn and then reconstructs the eigenvectors. It's not worth the bother unless the\n",
    "    #molecule is so big that the diagonalisation step becomes a bottleneck. \n",
    "C_SALC,e,occupations,new_SALC_basis,revised_symmetry_list=separate_by_symmetry(SALC_basis,SALC_Hamiltonianb,SALC_overlaps,no_electrons)   \n",
    "C_SALC=np.round(C_SALC,decimals=2);e=e.real\n",
    "    #then shift to the atomic basis for plots   \n",
    "H=Hamiltonian_matrix(eh_basis_functs,ovlp,WH)   #calculates Hamiltonian matrix\n",
    "C,e,occupations=diagonalise(H,ovlp,no_electrons)  #Solves generalised eigenvalue equation\n",
    "Mulliken,reduced_Mulliken,total_pop=EH_mulliken(C,occupations,ovlp,no_electrons)  #calculates Mulliken populations\n",
    "\n",
    "print()\n",
    "EH_print_symmetry(params,title,element_coord,Q,eh_basis_functs,no_electrons,SALC_overlaps,SALC_Hamiltonianb,C_SALC,e,occupations,reduced_Mulliken,total_pop,WH,pg,SALC_basis,new_SALC_basis,pg_irreps,identifiers,identifiers2,revised_symmetry_list,pg_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the MO diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hideCode": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plot the MO diagram?  Y/N  Y\n",
      "plotting a molecular orbital diagram: \n",
      "\n",
      "current settings are:\n",
      "energy window from:  -16.0  to  -4.0 \n",
      "\n",
      "connection threshold:  0.3\n",
      "Do you want to change either of these parameters?  Y/N  Y\n",
      "the options to change are:  \n",
      "1  energy window\n",
      "2  connection threshold\n",
      "what do you want to change? enter 1 or 2 1\n",
      "input the lower and upper bounds of the new window: -10. 10. , for example -30. -4.\n",
      "do you want to change anything else?  Y/N  N\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_MO=input(\"plot the MO diagram?  Y/N  \")\n",
    "if plot_MO=='Y':\n",
    "    molecule_from_list=False\n",
    "    if create_molecule==2:    #load default values\n",
    "        window=coords_dict[mol][5];connection_threshold=coords_dict[mol][6]\n",
    "    elif create_molecule==1:\n",
    "        window=[-20.,+20.];connection_threshold=0.4\n",
    "    print(\"plotting a molecular orbital diagram:\",'\\n')\n",
    "    print(\"current settings are:\")\n",
    "    print(\"%-20s\"%(\"energy window from: \"),window[0],\" to \",window[1],'\\n')\n",
    "    if create_molecule==1:\n",
    "        print(\"This is a guess - it will probably need to be adjusted to suite the positions of the frontier orbitals in your molecule. Carry on and look at your plot, then re-run this cell if you want to make an adjustment\",'\\n')\n",
    "    print(\"%-20s\"%(\"connection threshold: \"),connection_threshold)\n",
    "    change_MO_diagram=input(\"Do you want to change either of these parameters?  Y/N  \")\n",
    "    while change_MO_diagram=='Y':\n",
    "        print(\"the options to change are:  \")\n",
    "        print(\"1  energy window\")\n",
    "        print(\"2  connection threshold\")\n",
    "        what_to_change_MO=int(input(\"what do you want to change? enter 1 or 2 \"))\n",
    "        if what_to_change_MO==1:\n",
    "            new_window=input(\"input the lower and upper bounds of the new window: -10. 10. , for example \")\n",
    "            window = [float(x) for x in new_window.split()]\n",
    "            change_MO_diagram=input(\"do you want to change anything else?  Y/N  \")        \n",
    "        if what_to_change_MO==2:\n",
    "            connection_threshold=float(input(\"input the value for connection threshold: 0.2, for example  \"))\n",
    "            change_MO_diagram=input(\"do you want to change anything else?  Y/N  \")        \n",
    "    MO_plot2(e,eh_basis_functs,C,occupations,title,atoms,coords,window,no_electrons,connection_threshold,revised_symmetry_list,pg_irreps,pg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot contours of individual MOs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hideCode": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plot contours of some MOs?  Y/N  Y\n",
      "plotting MOs:   9 13 14 15 16 17 \n",
      "\n",
      "the full list of available MOs is (selected MOs in bold):\n",
      "\n",
      "0     -29.58        2.0        a1g\n",
      "1     -25.67        2.0        e1u\n",
      "2     -25.67        2.0        e1u\n",
      "3     -19.89        2.0        e2g\n",
      "4     -19.89        2.0        e2g\n",
      "5     -17.14        2.0        b1u\n",
      "6     -16.58        2.0        a1g\n",
      "7     -14.91        2.0        e1u\n",
      "8     -14.91        2.0        e1u\n",
      "\u001b[1m9     -14.53        2.0        a2u\n",
      "\u001b[0m10     -14.29        2.0        b2u\n",
      "11     -13.38        2.0        e2g\n",
      "12     -13.38        2.0        e2g\n",
      "\u001b[1m13     -12.80        2.0        e1g\n",
      "\u001b[0m\u001b[1m14     -12.80        2.0        e1g\n",
      "\u001b[0m\u001b[1m15      -8.32        0.0        e2u\n",
      "\u001b[0m\u001b[1m16      -8.32        0.0        e2u\n",
      "\u001b[0m\u001b[1m17      -4.73        0.0        b2g\n",
      "\u001b[0m18       3.56        0.0        e1u\n",
      "19       3.58        0.0        e1u\n",
      "20       8.11        0.0        a1g\n",
      "21      10.34        0.0        e2g\n",
      "22      10.36        0.0        e2g\n",
      "23      14.09        0.0        b1u\n",
      "24      15.22        0.0        a2g\n",
      "25      27.63        0.0        e1u\n",
      "26      27.64        0.0        e1u\n",
      "27      43.43        0.0        e2g\n",
      "28      43.45        0.0        e2g\n",
      "29      64.26        0.0        b1u\n",
      "Do you want to change this list?  Y/N  Y\n",
      "\n",
      "\n",
      "input a list of MOs identified by number, 0 2 4 etc. Don't forget to start at 0!  10 11 12 13 14 15\n",
      "\n",
      "\n",
      "updated list: 10 11 12 13 14 15\n",
      "Do you want to change this list?  Y/N  N\n",
      "default plotting parameters are:                             \n",
      "\n",
      "plot plane: current choice    xz\n",
      "offset - shifts the plot plane up or down relative to axes:   0.2\n",
      "contours values:     -1.00 -0.90 -0.80 -0.70 -0.60 -0.50 -0.40 -0.30 -0.20 -0.10  0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.70  0.80  0.90  1.00 \n",
      "\n",
      "Do you want to change anything on this list?  Y/N  Y\n",
      "\n",
      "\n",
      "the options to change are:   \n",
      "\n",
      "1  plot plane\n",
      "2  offset\n",
      "3  contour levels\n",
      "\n",
      "\n",
      "what do you want to change? enter 1, 2 or 3 1\n",
      "\n",
      "\n",
      "input the new plane: xz, yz or xy  xy\n",
      "\n",
      "\n",
      "do you want to change anything else?  Y/N  N\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_contours=input(\"plot contours of some MOs?  Y/N  \")\n",
    "class color:\n",
    "    BOLD = '\\033[1m'\n",
    "    RED = '\\033[91m'\n",
    "    END = '\\033[0m'\n",
    "    \n",
    "if plot_contours=='Y':\n",
    "    if create_molecule==1:\n",
    "        plane_offset=0.05;plot_plane='xz';levels=np.linspace(-0.5,0.5,10)\n",
    "        print(\"The full list of available MOs is:\"'\\n')\n",
    "        for i in range(len(e)):\n",
    "            print(color.BOLD, end='')\n",
    "            print(i,\"%10.2f\"%(e[i]),\"%10.1f\"%(occupations[i]),\"% 10s\"%(pg_irreps[pg][revised_symmetry_list[i]]))\n",
    "            print(color.END, end='')        \n",
    "        MO_list=input(\"Type in a list of the molecular orbitals you would like to plot 0 1 4 6. for example - the count starts at zero for the lowest\")\n",
    "        MO_list=[int(x) for x in MO_list.split()]\n",
    "    elif create_molecule==2:\n",
    "        plane_offset=coords_dict[mol][7];MO_list=coords_dict[mol][4];levels=coords_dict[mol][8];plot_plane='xz'\n",
    "    print(\"plotting MOs:  \",\" \".join([\"%i\"%(MO) for MO in MO_list]),'\\n')\n",
    "    print(\"the full list of available MOs is (selected MOs in bold):\"'\\n')\n",
    "    for i in range(len(e)):\n",
    "        if i in MO_list:\n",
    "            print(color.BOLD, end='')\n",
    "            print(i,\"%10.2f\"%(e[i]),\"%10.1f\"%(occupations[i]),\"% 10s\"%(pg_irreps[pg][revised_symmetry_list[i]]))\n",
    "            print(color.END, end='')\n",
    "        else:\n",
    "            print(i,\"%10.2f\"%(e[i]),\"%10.1f\"%(occupations[i]),\"% 10s\"%(pg_irreps[pg][revised_symmetry_list[i]]))            \n",
    "    if len(MO_list)>len(e):\n",
    "        print(\"Warning, the molecule does not have this many orbitals\",'\\n')\n",
    "    change_orbitals=input(\"Do you want to change this list?  Y/N  \")\n",
    "    print('\\n')\n",
    "    while change_orbitals=='Y':\n",
    "        new_MO_temp=input(\"input a list of MOs identified by number, 0 2 4 etc. Don't forget to start at 0!  \")\n",
    "        MO_list = [int(x) for x in new_MO_temp.split()]\n",
    "        print('\\n')\n",
    "        print(\"updated list:\",\" \".join([\"%i\"%(MO) for MO in MO_list]))\n",
    "        if len(MO_list)>len(e):\n",
    "            print(\"Warning, the molecule does not have this many orbitals\",'\\n')\n",
    "        change_orbitals=input(\"Do you want to change this list?  Y/N  \")\n",
    "\n",
    "    print(\"%-60s\"%(\"default plotting parameters are:  \"),'\\n')\n",
    "    print(\"%-20s\"%(\"plot plane: current choice   \"),plot_plane)\n",
    "    print(\"%-60s\"%(\"offset - shifts the plot plane up or down relative to axes:  \"),plane_offset)\n",
    "    print(\"%-20s\"%(\"contours values:  \"),\" \".join([\"%5.2f\"%(level) for level in levels]),'\\n')\n",
    "\n",
    "    change_parameters=input(\"Do you want to change anything on this list?  Y/N  \")\n",
    "    print('\\n')\n",
    "    while change_parameters=='Y':\n",
    "        print(\"the options to change are:  \",'\\n')\n",
    "        print(\"1  plot plane\")\n",
    "        print(\"2  offset\")\n",
    "        print(\"3  contour levels\")\n",
    "        print('\\n')\n",
    "        what_to_change=input(\"what do you want to change? enter 1, 2 or 3 \")\n",
    "        what_to_change=int(what_to_change)\n",
    "        print('\\n')\n",
    "        if what_to_change==1:\n",
    "            new_plane=input(\"input the new plane: xz, yz or xy  \")\n",
    "            if str(new_plane)=='xy':\n",
    "                plot_plane='xy'\n",
    "            elif str(new_plane)=='xz':\n",
    "                plot_plane='xz'\n",
    "            elif str(new_plane)=='yz':\n",
    "                plot_plane='yz'\n",
    "            print('\\n')\n",
    "            change_parameters=input(\"do you want to change anything else?  Y/N  \") \n",
    "            print('\\n')\n",
    "        if what_to_change==2:\n",
    "            offset=float(input(\"input the value for offset: 0.2, for example  \"))\n",
    "            print('\\n')\n",
    "            change_parameters=input(\"do you want to change anything else?  Y/N  \")\n",
    "            print('\\n')\n",
    "        if what_to_change==3:\n",
    "            new_levels=input(\"input a list of contour values: -0.2 -0.1 0.0 0.1 0.2, for example  \")\n",
    "            levels = [float(x) for x in new_levels.split()]\n",
    "            print('\\n')\n",
    "            change_parameters=input(\"do you want to change anything else?  Y/N  \")\n",
    "            print('\\n')\n",
    "\n",
    "    MO_contour(e,eh_basis_functs,C,title,atoms,coords,MO_list,plane_offset,levels,plot_plane,revised_symmetry_list,pg_irreps,pg)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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