Development

.github/workflows/build.yml

@@ -1,49 +1,48 @@
 name: build
 
-on: [push]
+on:
+  push:
+    branches:
+      - master
+      - development
+  pull_request:
+    branches:
+      - master
+      - development
 
 jobs:
   build:
     name: build and test
     runs-on: ubuntu-latest
 
     strategy:
-      fail-fast: false
+      fail-fast: true
       matrix:
-        version: ['3.8', '3.10']
+        version: ['3.9']
 
     steps:
     - name: Cancel Previous Runs
       uses: styfle/[email protected]
       with:
           access_token: ${{ github.token }}
     - uses: actions/checkout@v4
-    - name: Setup conda
-      uses: s-weigand/setup-conda@v1
-      with:
-        update-conda: true
-        python-version: ${{ matrix.version }}
-        conda-channels: anaconda
     - name: Install essential
       run: |
         sudo apt update
-        sudo apt install build-essential
-    - name: Install conda packages
+        sudo apt install build-essential pandoc
+    - name: Install specific packages
       run: |
-        conda install -c anaconda cmake
-        conda install mpi4py h5py pytorch==2.0.0 torchvision==0.15.0 cpuonly -c pytorch -c conda-forge
-        conda install -c conda-forge libstdcxx-ng
-        conda install -c anaconda gxx_linux-64
-
+        python -m pip install torch==2.4.1 --index-url https://download.pytorch.org/whl/cpu
+        python -m pip install dgl -f https://data.dgl.ai/wheels/torch-2.4/repo.html
     - name: Install the package
-      run: python -m pip install .[test,hpc]
+      run: python -m pip install .[test,doc]
       env:
         CONDA_PREFIX: /usr/share/miniconda
 
-    - name: Test with multithreading
-      env:
-        CONDA_PREFIX: /usr/share/miniconda
-      run: mpirun -np 2 coverage run -m pytest tests_hvd
+    # - name: Test with multithreading
+    #   env:
+    #     CONDA_PREFIX: /usr/share/miniconda
+    #   run: mpirun -np 2 coverage run -m pytest tests_hvd
 
     - name: Test with single thread
       env:

.prospector.yml

@@ -26,4 +26,5 @@ pep257:
         D212,  # Multi-line docstring summary should start at the first line
         D213,  # Multi-line docstring summary should start at the second line
         D404,  # First word of the docstring should not be This
+        R0913, # too many arguments
     ]

CHANGELOG.rst

@@ -2,6 +2,32 @@
 Change Log
 ##########
 
+
+0.4.0 [Unreleased]
+******************
+
+Change
+---------
+
+* Change sugnature of Jastrow and Baclflow modules (#174)
+* Limit testing to Python 3.8 (#174)
+
+
+0.3.3 [Released]
+******************
+
+Change
+-----------
+
+* Fig bug in the AO norm (#172)
+
+0.3.2 [Released]
+******************
+
+Change
+----------
+* Fix a minor OSX bug regarding torch type cast to int
+
 0.3.1 [Released]
 *****************
 

H2.xyz

@@ -0,0 +1,4 @@
+2
+Properties=species:S:1:pos:R:3 pbc="F F F"
+H        0.00000000       0.00000000      -0.35000000
+H        0.00000000       0.00000000       0.35000000

README.md

@@ -3,11 +3,12 @@
 Pytorch Implementation of Real Space Quantum Monte Carlo Simulations of Molecular Systems
 
 [![PyPI version](https://badge.fury.io/py/qmctorch.svg)](https://badge.fury.io/py/qmctorch)
-[![Build Status](https://github.com/NLESC-JCER/QMCTorch/workflows/build/badge.svg)](https://github.com/NLESC-JCER/QMCTorch/actions)
+[![Build Status](https://github.com/NLESC-JCER/QMCTorch/workflows/build/badge.svg?branch=master)](https://github.com/NLESC-JCER/QMCTorch/actions)
 [![Coverage Status](https://coveralls.io/repos/github/NLESC-JCER/QMCTorch/badge.svg?branch=master)](https://coveralls.io/github/NLESC-JCER/QMCTorch?branch=master)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/1c52407422a7428083968833341b5945)](https://app.codacy.com/gh/NLESC-JCER/QMCTorch/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3780094.svg)](https://doi.org/10.5281/zenodo.3780094)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.05472/status.svg)](https://doi.org/10.21105/joss.05472)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 ## Installation
 

bin/qmctorch_energy_plotter

@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+import h5py
+import matplotlib.pyplot as plt
+import argparse
+
+
+def get_energy(mol):
+    with h5py.File(mol, 'r') as f5:
+        data = f5['wf_opt']['energy'][()]
+    return data
+
+
+def get_correlation_energy(e, e0, ehf):
+    return 1 - (e-e0)/(ehf-e0)
+
+
+def plot_percent_correlation_energy(args):
+
+    nepoch = args.num_epoch
+    energy = []
+    percent_correlation_energy = []
+    for mol in args.filename:
+        e = get_energy(mol)[:nepoch]
+        print(e[-1])
+        energy.append(e)
+        percent_correlation_energy.append(
+            get_correlation_energy(e, args.exact_energy, args.hf_energy))
+
+    plt_fn = plt.plot
+    if args.semi_logy:
+        plt_fn = plt.semilogy
+
+    for ec in percent_correlation_energy:
+        plt_fn(ec)
+    plt.show()
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('filename', nargs='+',
+                        help='name of the files')
+    parser.add_argument('-l', '--labels', nargs='+',
+                        help='label of the data')
+    parser.add_argument('-ne', '--num_epoch', type=int,
+                        default=-1, help='Number of epcoh to plot')
+    parser.add_argument('-e0', '--exact_energy', type=float,
+                        default=None, help='True exact energy of thre system')
+    parser.add_argument('-ehf', '--hf_energy', type=float,
+                        default=None, help='Hartree Fock energy of thre system')
+    parser.add_argument('-log', '--semi_logy', action='store_true',
+                        help='plot on semilog y axis')
+    args = parser.parse_args()
+
+    plot_percent_correlation_energy(args)

docs/conf.py

@@ -262,4 +262,5 @@
 
 autoclass_content = 'init'
 autodoc_member_order = 'bysource'
-nbsphinx_allow_errors = True
+nbsphinx_allow_errors = True
+nbsphinx_execute = 'never'

docs/example/ase/H2.xyz

@@ -0,0 +1,4 @@
+2
+Properties=species:S:1:pos:R:3 pbc="F F F"
+H        0.00000000       0.00000000      -0.35000000
+H        0.00000000       0.00000000       0.35000000

docs/example/ase/HLi.xyz

@@ -0,0 +1,4 @@
+2
+Properties=species:S:1:pos:R:3 pbc="F F F"
+Li       0.00000000       0.00000000       0.00000000
+H        0.00000000       0.00000000       3.14000000

docs/example/ase/h2.py

@@ -0,0 +1,59 @@
+from qmctorch.ase import QMCTorch     
+from qmctorch.ase.optimizer import TorchOptimizer
+from qmctorch.sampler.symmetry import Cinfv, Dinfh
+from ase import Atoms 
+from ase.optimize import GoodOldQuasiNewton, FIRE
+from ase.io import write
+import torch
+import numpy as np
+from qmctorch.utils.plot_data import plot_walkers_traj, plot_correlation_coefficient
+
+torch.random.manual_seed(0)
+np.random.seed(0)
+
+
+configs = (torch.tensor([[0],[1]]), torch.tensor([[0],[1]]))
+
+d = 0.70
+h2 = Atoms('H2', positions=[(0, 0, -d/2), (0, 0, d/2)])
+
+h2.calc = QMCTorch()
+
+# SCF options
+h2.calc.scf_options.calculator = 'adf'
+h2.calc.scf_options.basis = 'dzp'
+
+# WF options
+# h2.calc.wf_options.configs = 'ground_state'
+h2.calc.wf_options.configs = 'single_double(2,4)'
+# h2.calc.wf_options.configs = configs
+h2.calc.wf_options.mix_mo = False
+h2.calc.wf_options.orthogonalize_mo = False
+# h2.calc.wf_options.gto2sto = True
+h2.calc.wf_options.jastrow.kernel_kwargs = {'w':1.0}
+
+# sampler options
+h2.calc.sampler_options.nwalkers = 10000
+h2.calc.sampler_options.nstep  = 500
+h2.calc.sampler_options.step_size = 0.5
+h2.calc.sampler_options.ntherm = -1
+h2.calc.sampler_options.ndecor = 10
+h2.calc.sampler_options.symmetry = Dinfh(axis='z')
+
+# solver options
+h2.calc.solver_options.freeze = []
+h2.calc.solver_options.niter = 50
+h2.calc.solver_options.tqdm = True
+h2.calc.solver_options.grad = 'manual'
+
+# options for the resampling
+h2.calc.solver_options.resampling.mode = 'update'
+h2.calc.solver_options.resampling.resample_every = 1
+h2.calc.solver_options.resampling.ntherm_update = 100
+
+
+# Optimize the wave function
+h2.calc.initialize()
+
+h2.get_potential_energy()
+

docs/example/ase/h2_cc.py

@@ -0,0 +1,16 @@
+from pyscf import gto, scf, cc
+import numpy as np
+import matplotlib.pyplot as plt 
+
+mol = gto.M(atom="H 0 0 0; H 0 0 0.74")
+cc_scanner = cc.CCSD(scf.RHF(mol)).nuc_grad_method().as_scanner()
+
+dist = np.linspace(0.25,1.5,15)
+energies = []
+for d in dist:
+    atom = 'H 0 0 0; H 0 0 %f' %d
+    e,g = cc_scanner(gto.M(atom=atom))
+    energies.append(e)    
+
+plt.plot(dist, energies)
+plt.show()

docs/example/ase/lih.py

@@ -0,0 +1,59 @@
+from qmctorch.ase import QMCTorch     
+from qmctorch.ase.optimizer import TorchOptimizer
+from qmctorch.sampler.symmetry import Cinfv, Dinfh
+from ase import Atoms 
+from ase.optimize import GoodOldQuasiNewton, FIRE
+from ase.io import write
+import torch
+import numpy as np
+from qmctorch.utils.plot_data import plot_walkers_traj, plot_correlation_coefficient
+
+torch.random.manual_seed(0)
+np.random.seed(0)
+
+
+configs = (torch.tensor([[0],[1]]), torch.tensor([[0],[1]]))
+
+d = 0.70
+h2 = Atoms('LiH', positions=[(0, 0, 0), (0, 0, 3.14)])
+
+h2.calc = QMCTorch()
+
+# SCF options
+h2.calc.scf_options.calculator = 'adf'
+h2.calc.scf_options.basis = 'dzp'
+
+# WF options
+# h2.calc.wf_options.configs = 'ground_state'
+h2.calc.wf_options.configs = 'single_double(2,4)'
+# h2.calc.wf_options.configs = configs
+h2.calc.wf_options.mix_mo = False
+h2.calc.wf_options.orthogonalize_mo = False
+# h2.calc.wf_options.gto2sto = True
+h2.calc.wf_options.jastrow.kernel_kwargs = {'w':1.0}
+
+# sampler options
+h2.calc.sampler_options.nwalkers = 10000
+h2.calc.sampler_options.nstep  = 500
+h2.calc.sampler_options.step_size = 0.5
+h2.calc.sampler_options.ntherm = -1
+h2.calc.sampler_options.ndecor = 10
+h2.calc.sampler_options.symmetry = None
+
+# solver options
+h2.calc.solver_options.freeze = []
+h2.calc.solver_options.niter = 50
+h2.calc.solver_options.tqdm = True
+h2.calc.solver_options.grad = 'manual'
+
+# options for the resampling
+h2.calc.solver_options.resampling.mode = 'update'
+h2.calc.solver_options.resampling.resample_every = 1
+h2.calc.solver_options.resampling.ntherm_update = 100
+
+
+# Optimize the wave function
+h2.calc.initialize()
+
+h2.get_potential_energy()
+

docs/example/autocorrelation/h2.py

@@ -0,0 +1,53 @@
+import torch
+from torch import optim
+
+from qmctorch.sampler import Metropolis
+from qmctorch.scf import Molecule
+from qmctorch.solver import Solver
+from qmctorch.utils.plot_data import plot_correlation_coefficient, plot_integrated_autocorrelation_time
+from qmctorch.wavefunction.slater_jastrow import SlaterJastrow
+from qmctorch.wavefunction.jastrows.elec_elec import JastrowFactor, PadeJastrowKernel
+torch.manual_seed(0)
+
+# molecule
+mol = Molecule(
+    atom='H 0 0 -0.69; H 0 0 0.69',
+    unit='bohr',
+    calculator='pyscf',
+    basis='sto-3g')
+
+
+# jastrow
+jastrow = JastrowFactor(mol, PadeJastrowKernel)
+
+# wave funtion
+wf = SlaterJastrow(mol, kinetic='auto',
+                   jastrow=jastrow,
+                   configs='single(2,2)')
+
+# sampler
+sampler = Metropolis(
+    nwalkers=10,
+    nstep=1000,
+    ntherm=0,
+    ndecor=1,
+    step_size=0.5,
+    ndim=wf.ndim,
+    nelec=wf.nelec,
+    init=mol.domain('normal'),
+    move={
+        'type': 'all-elec',
+        'proba': 'normal'})
+
+opt = optim.Adam(wf.parameters(), lr=0.01)
+
+solver = Solver(wf=wf, sampler=sampler, optimizer=opt)
+
+pos = solver.sampler(wf.pdf)
+obs = solver.sampling_traj(pos)
+
+rho, tau = plot_correlation_coefficient(obs.local_energy)
+print(f'fit exp(-x/tau), tau={tau}')
+iat = plot_integrated_autocorrelation_time(
+    obs.local_energy, rho=rho, C=5)
+print(f"integrated autocorrelation time: {iat}")