diff --git a/Wavefunctions/MolecularOrbitals.ipynb b/Wavefunctions/MolecularOrbitals.ipynb index f7204fc..1643f67 100644 --- a/Wavefunctions/MolecularOrbitals.ipynb +++ b/Wavefunctions/MolecularOrbitals.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,32 +13,32 @@ "from sympy import *\n", "init_printing()\n", "import matplotlib.pyplot as plt\n", - "%matplotlib notebook" + "%matplotlib inline" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\psi_{j} = \\sum_{i=1}^{N} c_{i,j} \\chi_{i}$$" + "$\\displaystyle {\\psi}_{j} = \\sum_{i=1}^{N} {c}_{i,j} {\\chi}_{i}$" ], "text/plain": [ " N \n", " ___ \n", " ╲ \n", - " ╲ c[i, j]⋅chi[i]\n", - "psi[j] = ╱ \n", + " ╲ \n", + "psi[j] = ╱ c[i, j]⋅chi[i]\n", " ╱ \n", " ‾‾‾ \n", " i = 1 " ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ "# - GTO_centers - multiple atoms of different kinds at different locations\n", "\n", "import gaussian_orbitals\n", - "reload(gaussian_orbitals)\n", + "#reload(gaussian_orbitals)\n", "from gaussian_orbitals import GTO, CG_basis, GTO_centers\n", "\n", "from read_qmcpack import parse_qmc_wf, read_structure_file" @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -143,7 +143,7 @@ " -0. , -0. , -0. ]])" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -152,14 +152,14 @@ "# For Neon with DEF2-SVP\n", "ne_basis_set, ne_MO_matrix = parse_qmc_wf('ne_def2_svp.wfnoj.xml',['Ne'])\n", "for cg in ne_basis_set:\n", - " print cg\n", + " print(cg)\n", " \n", "ne_MO_matrix" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -175,12 +175,12 @@ "source": [ "ne_gto = GTO(ne_basis_set['Ne'])\n", "ne_one_eval = ne_gto.eval_v(0.2, 0.3, 0.1)\n", - "print ne_one_eval" + "print(ne_one_eval)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -188,8 +188,8 @@ "output_type": "stream", "text": [ "(15, 80)\n", - "CPU times: user 50.2 ms, sys: 4.53 ms, total: 54.7 ms\n", - "Wall time: 37.8 ms\n" + "CPU times: user 26.6 ms, sys: 0 ns, total: 26.6 ms\n", + "Wall time: 26.8 ms\n" ] } ], @@ -197,34 +197,36 @@ "%%time\n", "rvals = np.arange(-4.0, 4.0, .1)\n", "ne_atomic_orbs = np.zeros( (len(ne_one_eval), rvals.shape[0]))\n", - "print ne_atomic_orbs.shape\n", + "print(ne_atomic_orbs.shape)\n", "for i,x in enumerate(rvals):\n", " ne_atomic_orbs[:,i] = ne_gto.eval_v(x, 0.0, 0.0)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -236,19 +238,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 39.5 s, sys: 185 ms, total: 39.6 s\n", - "Wall time: 39.2 s\n" + "CPU times: user 30.2 s, sys: 0 ns, total: 30.2 s\n", + "Wall time: 30.2 s\n" ] } ], "source": [ + "# This cell may take a while to execute\n", "%%time\n", "n = 50\n", "xsp = np.linspace(-1, 1, n)\n", @@ -322,12 +325,12 @@ } ], "source": [ - "print xyzvals" + "print(xyzvals)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -337,24 +340,23 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4992fd3f1ade4a9791f1d656c02d868c", + "model_id": "148c213d86e041bcaad816d0d4b6464a", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "VkJveChjaGlsZHJlbj0oVkJveChjaGlsZHJlbj0oSEJveChjaGlsZHJlbj0oTGFiZWwodmFsdWU9dSdsZXZlbHM6JyksIEZsb2F0U2xpZGVyKHZhbHVlPTAuMSwgbWF4PTEuMCwgc3RlcD0wLjDigKY=\n" + "Container(children=[VBox(children=(HBox(children=(Label(value='levels:'), FloatSlider(value=0.1, max=1.0, step…" ] }, + "execution_count": 14, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -377,9 +379,9 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\left ( 14, \\quad 80\\right )$$" + "$\\displaystyle \\left( 14, \\ 80\\right)$" ], "text/plain": [ "(14, 80)" @@ -402,7 +404,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -411,12 +413,14 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -448,7 +452,7 @@ "hcn_basis_sets, hcn_MO_matrix = parse_qmc_wf(\"hcn.wfnoj.xml\", hcn_elements)\n", "\n", "hcn_gtos = GTO_centers(hcn_pos_list, hcn_elements, hcn_basis_sets)\n", - "print hcn_pos_list" + "print(hcn_pos_list)" ] }, { @@ -471,7 +475,7 @@ "hcn_one_eval = hcn_gtos.eval_v(0.2, 0.3, 0.1)\n", "\n", "hcn_atomic_orbs = np.zeros( (len(hcn_one_eval), rvals.shape[0]))\n", - "print hcn_atomic_orbs.shape\n", + "print(hcn_atomic_orbs.shape)\n", "for i,x in enumerate(rvals):\n", " hcn_atomic_orbs[:,i] = hcn_gtos.eval_v(x, 0.0, 0.0)" ] @@ -493,7 +497,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -502,12 +506,14 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -519,6 +525,13 @@ "plt.legend()\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -529,23 +542,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Wavefunctions/read_qmcpack.py b/Wavefunctions/read_qmcpack.py index 3422cab..8c99ef0 100644 --- a/Wavefunctions/read_qmcpack.py +++ b/Wavefunctions/read_qmcpack.py @@ -23,12 +23,9 @@ def read_basis_groups(atomic_basis_set): basis_set = [] for basis_group in basis_groups: if basis_group.attrib['type'] != 'Gaussian': - print 'Expecting Gaussian type basisGroup' - #print basis_group.attrib['n'] + print('Expecting Gaussian type basisGroup') n = int(basis_group.attrib['n']) - #print basis_group.attrib['l'] ang_mom_l = int(basis_group.attrib['l']) - #print basis_group.attrib['type'] zeta_list = [] coef_list = [] radfuncs = basis_group.findall('radfunc') @@ -65,11 +62,11 @@ def parse_qmc_wf(fname, element_list): MO_coeff_node = tree.find('.//determinant/coefficient') MO_matrix = None if MO_coeff_node is None: - print 'Molecular orbital coefficients not found' + print('Molecular orbital coefficients not found') else: - #print 'MO_coeff = ',MO_coeff_node + #print('MO_coeff = ',MO_coeff_node) MO_size = int(MO_coeff_node.attrib['size']) - print 'MO coeff size = ',MO_size + print('MO coeff size = ',MO_size) MO_text = MO_coeff_node.text MO_text_entries = MO_text.split() @@ -77,7 +74,7 @@ def parse_qmc_wf(fname, element_list): #MO_matrix = np.array(MO_values).reshape( (basis_size, MO_size) ) MO_matrix = np.array(MO_values).reshape( (MO_size, basis_size) ) - #print 'MO_values = ',MO_values + #print('MO_values = ',MO_values) return basis_sets, MO_matrix @@ -129,21 +126,21 @@ def eval_vgl(self, x, y, z): #basis_set, MO_matrix = parse_qmc_wf('he_sto3g.wfj.xml',['He']) #basis_set, MO_matrix = parse_qmc_wf('ne_def2_svp.wfnoj.xml',['Ne']) basis_sets, MO_matrix = parse_qmc_wf('hcn.wfnoj.xml',['He']) - #print basis_set + #print(basis_set) pos_list, elements = read_structure_file("hcn.structure.xml") gtos = GTO_centers(pos_list, elements, basis_sets) atomic_orbs = gtos.eval_v(1.0, 0.0, 0.0) - print np.dot(MO_matrix, atomic_orbs) + print(np.dot(MO_matrix, atomic_orbs)) #gto_list = [] #for pos, element in zip(pos_list, elements): # gto = gaussian_orbitals.GTO(basis_sets[element], pos) # gto_list.append(gto) - #print gto_list + #print(gto_list) #gto = gaussian_orbitals.GTO(basis_set) #atomic_orbs = gto.eval_v(1.0, 0.0, 0.0) - #print 'atomic orbs = ', atomic_orbs - #print 'shape = ',MO_matrix.shape + #print('atomic orbs = ', atomic_orbs) + #print('shape = ',MO_matrix.shape) #print np.dot(MO_matrix, atomic_orbs)