Add farhadh/zasdfgbnm changes

.github/workflows/deploy-pypi.yml

@@ -28,5 +28,5 @@ jobs:
     - name: Deploy
       run: |
         rm -rf dist/*
-        python setup.py bdist bdist_wheel
+        python setup.py sdist bdist_wheel
         twine upload -u zasdfgbnm-bot -p ${{secrets.zasdfgbnm_bot_pypi_password}} dist/*

.github/workflows/deploy-test-pypi.yml

@@ -9,7 +9,7 @@ on:
     - cron: '0 0 * * *'
 
 jobs:
-  build:
+  deploy-test:
 
     runs-on: ubuntu-latest
     strategy:
@@ -29,8 +29,7 @@ jobs:
         pip install twine wheel
     - name: Deploy
       run: |
-        [ ! -z "$GITHUB_HEAD_REF" ] && exit
         rm -rf dist/*
         git tag $(date +'v%Y.%m.%d.%H.%M.%S')
-        python setup.py bdist bdist_wheel
+        python setup.py sdist bdist_wheel
         twine upload --repository-url https://test.pypi.org/legacy/ -u zasdfgbnm-bot -p ${{secrets.zasdfgbnm_bot_test_pypi_password}} dist/*

.github/workflows/mypy.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.7, 3.8]
 
     steps:
     - uses: actions/checkout@v1

.gitignore

@@ -30,9 +30,6 @@ dist
 *.swo
 /download
 /download.tar.xz
-/torchani/resources/*.info
-/torchani/resources/ani-1ccx_8x
-/torchani/resources/ani-2x_8x
 *.qdrep
 *.qdstrm
 *.zip

README.md

@@ -69,7 +69,9 @@ To run the tests and examples, you must manually download a data package
 
 Please cite the following paper if you use TorchANI 
 
-* Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, and Adrian E. Roitberg. *TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials*. Journal of Chemical Information and Modeling Just Accepted Manuscript, [![DOI for Citing](https://img.shields.io/badge/DOI-10.1021%2Facs.jcim.0c00451-green.svg)](https://doi.org/10.1021/acs.jcim.0c00451)
+* Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, and Adrian E. Roitberg. *TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials*. Journal of Chemical Information and Modeling 2020 60 (7), 3408-3415, [![DOI for Citing](https://img.shields.io/badge/DOI-10.1021%2Facs.jcim.0c00451-green.svg)](https://doi.org/10.1021/acs.jcim.0c00451)
+
+[![JCIM Cover](https://pubs.acs.org/na101/home/literatum/publisher/achs/journals/content/jcisd8/2020/jcisd8.2020.60.issue-7/jcisd8.2020.60.issue-7/20200727/jcisd8.2020.60.issue-7.largecover.jpg)](https://pubs.acs.org/toc/jcisd8/60/7)
 
 * Please refer to [isayev/ASE_ANI](https://github.com/isayev/ASE_ANI) for ANI model references.
 

torchani/aev.py

@@ -3,7 +3,15 @@
 from torch import Tensor
 import math
 from typing import Tuple, Optional, NamedTuple
-from torch.jit import Final
+import sys
+
+if sys.version_info[:2] < (3, 7):
+    class FakeFinal:
+        def __getitem__(self, x):
+            return x
+    Final = FakeFinal()
+else:
+    from torch.jit import Final
 
 
 class SpeciesAEV(NamedTuple):

torchani/neurochem/parse_resources.py

@@ -1,9 +1,8 @@
 import os
 import io
 import requests
-import glob
 import zipfile
-import shutil
+from distutils import dir_util
 from pathlib import Path
 
 
@@ -18,13 +17,13 @@ def parse_neurochem_resources(info_file_path):
     resource_path = os.path.join(torchani_dir, 'resources/')
     local_dir = os.path.expanduser('~/.local/torchani/')
 
-    if os.path.isfile(os.path.join(resource_path, info_file_path)):
-        # No action needed if the info file can be located in the default path
+    if os.stat(os.path.join(resource_path, info_file_path)).st_size != 0:
+        # No action needed if the info file located in the default path is not zero-sized
         pass
 
     elif os.path.isfile(os.path.join(local_dir, info_file_path)):
-        # if the info file is not located in the default path, ~/.local/torchani
-        # is tried as an alternative
+        # if a non-zero sized info file is not located in the default path,
+        # ~/.local/torchani is tried as an alternative
         resource_path = local_dir
 
     else:
@@ -40,18 +39,19 @@ def parse_neurochem_resources(info_file_path):
             resource_res = requests.get(url)
             resource_zip = zipfile.ZipFile(io.BytesIO(resource_res.content))
             try:
+                # by default try to extract inside resources and overwrite the
+                # zero sized files
                 resource_zip.extractall(resource_path)
             except PermissionError:
+                # if this fails then files are extracted in ~/.local/torchani
                 resource_zip.extractall(local_dir)
                 resource_path = local_dir
-            files = glob.glob(os.path.join(resource_path, extracted_name, "resources", "*"))
-            for f in files:
-                try:
-                    shutil.move(f, resource_path)
-                except shutil.Error:
-                    pass
-            shutil.rmtree(os.path.join(resource_path, extracted_name))
 
+            # copy all files inside the extracted directory and remove
+            # the directory itself
+            source = os.path.join(resource_path, extracted_name, "resources")
+            dir_util.copy_tree(source, resource_path)
+            dir_util.remove_tree(os.path.join(resource_path, extracted_name))
         else:
             raise ValueError('File {0} could not be found either in {1} or {2}\n'
                              'It is also not one of the supported builtin info files:'