Merge pull request #3 from vanderhe/continuousIntegration

Set up regression tests and continuous integration workflow

Author: vanderhe

.github/workflows/ci.yml

@@ -0,0 +1,29 @@
+name: CI
+on: [push, pull_request]
+
+jobs:
+  ci:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - uses: actions/setup-python@v1
+      with:
+        python-version: '3.9'
+
+    - name: Install requirements (hdf5-tools)
+      run: |
+        sudo apt-get update -y
+        sudo apt-get install -y hdf5-tools
+
+    - name: Install requirements (python)
+      run: pip3 install pytest numpy scipy h5py ase
+
+    - name: Setup up PYTHONPATH
+      run: echo "PYTHONPATH=${PWD}/src" >> $GITHUB_ENV
+
+    - name: Run regression tests
+      run: python3 -m pytest --basetemp=Testing

.gitignore

@@ -7,3 +7,6 @@ build
 _build
 _downloads
 __pycache__
+*.egg-info
+dist
+Testing

README.rst

@@ -16,7 +16,9 @@ results obtained by the neural network implementation
 Installation
 ============
 
-The package can be installed via conda-forge::
+|build status|
+
+The package can be installed via conda-forge (available with first release)::
 
   conda install fortnet-python
 
@@ -37,7 +39,7 @@ Documentation
 Consult following resources for documentation:
 
 * `Step-by-step instructions with selected examples (Fortnet Recipes)
-  <https://fortnet.readthedocs.io/>`_
+  <https://fortnet.readthedocs.io/en/latest/fortformat/index.html>`_
 
 Contributing
 ============
@@ -81,5 +83,5 @@ fortnet-python is released under the BSD 2-clause license. See the included
 .. |issues| image:: https://img.shields.io/github/issues/vanderhe/fortnet-python.svg
     :target: https://github.com/vanderhe/fortnet-python/issues/
 
-.. |build status| image:: https://img.shields.io/github/workflow/status/vanderhe/fortnet-python/Build%20and%20Test
+.. |build status| image:: https://img.shields.io/github/workflow/status/vanderhe/fortnet-python/CI
     :target: https://github.com/vanderhe/fortnet-python/actions/

pyproject.toml

@@ -1,3 +1,3 @@
 [build-system]
-requires = ['setuptools', 'wheel', 'numpy', 'h5py']
+requires = ['setuptools', 'wheel', 'pytest', 'numpy', 'h5py', 'ase']
 build-backend = 'setuptools.build_meta'

src/MANIFEST.in

@@ -4,3 +4,8 @@
 #                                                                              #
 #  See the LICENSE file for terms of usage and distribution.                   #
 #------------------------------------------------------------------------------#
+
+
+# Pull main classes up to the fortformat top level domain namespace
+from .fnetdata import Fnetdata
+from .fnetout import Fnetout

src/README.rst

@@ -7,11 +7,10 @@
 
 
 '''
-Basic Fortnet IO Format Classes
+Basic Fortnet Input Format Class
 
-These basic Python classes implement the Fortnet input and output file format.
-The Fnetdata class enables to create compatible HDF5 datasets, whereas the
-Fnetout class extracts certain properties of the HDF5 output for later analysis.
+This basic Python class implements the Fortnet input
+format and enables to create compatible HDF5 datasets.
 '''
 
 
@@ -538,227 +537,5 @@ def hdf_append_targets(root, data):
     targets[...] = tmp
 
 
-class Fnetout:
-    '''Basic Fortnet Output Format Class.'''
-
-
-    def __init__(self, fname):
-        '''Initializes a Fnetout object.
-
-        Args:
-
-            fname (str): filename to extract data from
-
-        '''
-
-        self._fname = fname
-
-        with h5py.File(self._fname, 'r') as fnetoutfile:
-            fnetout = fnetoutfile['fnetout']
-            self._mode = fnetout.attrs.get('mode').decode('UTF-8').strip()
-            if not self._mode in ('validate', 'predict'):
-                raise FnetoutError('Invalid running mode specification.')
-
-            output = fnetoutfile['fnetout']['output']
-            self._ndatapoints = output.attrs.get('ndatapoints')
-            if len(self._ndatapoints) == 1:
-                # number of datapoints stored in array of size 1
-                self._ndatapoints = self._ndatapoints[0]
-            else:
-                msg = "Error while reading fnetout file '" + self._fname + \
-                    "'. Unrecognized number of datapoints obtained."
-                raise FnetoutError(msg)
-            self._tforces = output.attrs.get('tforces')
-            if len(self._tforces) == 1:
-                # booleans stored in integer arrays of size 1
-                self._tforces = bool(self._tforces[0])
-            else:
-                msg = "Error while reading fnetout file '" + self._fname + \
-                    "'. Unrecognized force specification obtained."
-                raise FnetoutError(msg)
-
-            self._targettype = \
-                output.attrs.get('targettype').decode('UTF-8').strip()
-            if not self._targettype in ('atomic', 'global'):
-                raise FnetoutError('Invalid running mode obtained.')
-
-            # get number of atomic or global predictions/targets
-            self._npredictions = np.shape(
-                np.array(output['datapoint1']['output']))[1]
-
-            if self._mode == 'validate':
-                self._npredictions = int(self._npredictions / 2)
-
-
-    @property
-    def mode(self):
-        '''Defines property, providing the mode of the Fortnet run.
-
-        Returns:
-
-            mode (str): mode of the run that produced the Fnetout file
-
-        '''
-
-        return self._mode
-
-
-    @property
-    def ndatapoints(self):
-        '''Defines property, providing the number of datapoints.
-
-        Returns:
-
-            ndatapoints (int): total number of datapoints of the training
-
-        '''
-
-        return self._ndatapoints
-
-
-    @property
-    def targettype(self):
-        '''Defines property, providing the target type.
-
-        Returns:
-
-            targettype (str): type of targets the network was trained on
-
-        '''
-
-        return self._targettype
-
-
-    @property
-    def tforces(self):
-        '''Defines property, providing hint whether atomic forces are present.
-
-        Returns:
-
-            tforces (bool): true, if atomic forces are supplied
-
-        '''
-
-        return self._tforces
-
-
-    @property
-    def predictions(self):
-        '''Defines property, providing the predictions of Fortnet.
-
-        Returns:
-
-            predictions (list or 2darray): predictions of the network
-
-        '''
-
-        with h5py.File(self._fname, 'r') as fnetoutfile:
-            output = fnetoutfile['fnetout']['output']
-            if self._targettype == 'atomic':
-                predictions = []
-                for idata in range(self._ndatapoints):
-                    dataname = 'datapoint' + str(idata + 1)
-                    if self._mode == 'validate':
-                        predictions.append(
-                            np.array(output[dataname]['output'],
-                                     dtype=float)[:, :self._npredictions])
-                    else:
-                        predictions.append(
-                            np.array(output[dataname]['output'], dtype=float))
-            else:
-                predictions = np.empty(
-                    (self._ndatapoints, self._npredictions), dtype=float)
-                for idata in range(self._ndatapoints):
-                    dataname = 'datapoint' + str(idata + 1)
-                    if self._mode == 'validate':
-                        predictions[idata, :] = \
-                            np.array(output[dataname]['output'],
-                                     dtype=float)[0, :self._npredictions]
-                    else:
-                        predictions[idata, :] = \
-                            np.array(output[dataname]['output'],
-                                     dtype=float)[0, :]
-
-        return predictions
-
-
-    @property
-    def targets(self):
-        '''Defines property, providing the targets during training.
-
-        Returns:
-
-            targets (list or 2darray): targets during training
-
-        '''
-
-        if self._mode == 'predict':
-            return None
-
-        with h5py.File(self._fname, 'r') as fnetoutfile:
-            output = fnetoutfile['fnetout']['output']
-            if self._targettype == 'atomic':
-                targets = []
-                for idata in range(self._ndatapoints):
-                    dataname = 'datapoint' + str(idata + 1)
-                    targets.append(
-                        np.array(output[dataname]['output'],
-                                 dtype=float)[:, self._npredictions:])
-            else:
-                targets = np.empty(
-                    (self._ndatapoints, self._npredictions), dtype=float)
-                for idata in range(self._ndatapoints):
-                    dataname = 'datapoint' + str(idata + 1)
-                    targets[idata, :] = \
-                        np.array(output[dataname]['output'],
-                                 dtype=float)[0, self._npredictions:]
-
-        return targets
-
-
-    @property
-    def forces(self):
-        '''Defines property, providing the atomic forces, if supplied.
-
-        Returns:
-
-            forces (list): atomic forces on atoms
-
-        '''
-
-        tmp1 = []
-
-        if self._targettype == 'atomic':
-            msg = "Error while extracting forces from fnetout file '" \
-                + self._fname + \
-                "'. Forces only supplied for global property targets."
-            raise FnetoutError(msg)
-
-        with h5py.File(self._fname, 'r') as fnetoutfile:
-            output = fnetoutfile['fnetout']['output']
-            for idata in range(self._ndatapoints):
-                dataname = 'datapoint' + str(idata + 1)
-                tmp1.append(np.array(output[dataname]['forces'], dtype=float))
-
-        # convert to shape np.shape(forces[iData][iTarget]) = (iAtom, 3)
-        forces = []
-        for tmp2 in tmp1:
-            entry = []
-            if not np.shape(tmp2)[1]%3 == 0:
-                msg = "Error while extracting forces from fnetout file '" \
-                    + self._fname + \
-                    "'. Expected three force components and global target."
-                raise FnetoutError(msg)
-            for jj in range(int(np.shape(tmp2)[1] / 3)):
-                entry.append(tmp2[:, 3 * jj:3 * (jj + 1)])
-            forces.append(entry)
-
-        return forces
-
-
 class FnetdataError(Exception):
     '''Exception thrown by the Fnetdata class.'''
-
-
-class FnetoutError(Exception):
-    '''Exception thrown by the Fnetout class.'''