Merge pull request #12 from janosh/filter-warnings-examples

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
   - repo: https://github.com/psf/black
     rev: 22.3.0
     hooks:
-      - id: black
+      - id: black-jupyter
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.1.0
@@ -46,3 +46,8 @@ repos:
     rev: v0.960
     hooks:
       - id: mypy
+
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.3.1
+    hooks:
+      - id: nbqa-isort

CHANGES.md

@@ -1,12 +1,16 @@
-# v0.0.4
-- Fix model download 
+# Changelog
+
+## v0.0.4
+
+- Fix model download
 - More documentation
 - Bug fixes
 
-# v0.0.2
+## v0.0.2
+
 - Fix edge cases for triple bonds
 - Enable model weights download within code
 
-# v0.0.1
+## v0.0.1
 
-- Initial release.
+- Initial release.

m3gnet/graph/_batch.py

@@ -124,7 +124,7 @@ def _concatenate(list_of_arrays: List, name: AnyStr) -> Optional[np.ndarray]:
 
     Returns: concatenated array
     """
-    num_none = sum([i is None for i in list_of_arrays])
+    num_none = sum(i is None for i in list_of_arrays)
 
     if num_none == len(list_of_arrays):
         return None

m3gnet/graph/_converters.py

@@ -86,7 +86,6 @@ def convert(self, structure: StructureOrMolecule, **kwargs) -> MaterialGraph:
         Returns:
 
         """
-        pass
 
     def convert_many(self, structures: List[StructureOrMolecule], **kwargs) -> MaterialGraph:
         """

m3gnet/graph/_types.py

@@ -113,7 +113,6 @@ def _check_graph(self) -> None:
         """
         Check if the graph is valid
         """
-        pass
 
     def replace(self, **kwargs):
         """

m3gnet/layers/_core.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Core layers provide basic operations, e.g., MLP
 """

m3gnet/layers/tests/data/test_NaCl.json

@@ -729,4 +729,4 @@
       ]
     ]
   ]
-]
+]

m3gnet/layers/tests/test_aggregate.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

m3gnet/layers/tests/test_atom_ref.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

m3gnet/layers/tests/test_base.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

m3gnet/layers/tests/test_basis.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

m3gnet/layers/tests/test_bond.py

@@ -1,11 +1,10 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np
 from pymatgen.core import Lattice, Molecule, Structure
 
-from m3gnet.graph import Index, RadiusCutoffGraphConverter, tf_compute_distance_angle
-from m3gnet.layers import ConcatAtoms, PairRadialBasisExpansion, ThreeDInteraction
+from m3gnet.graph import Index, RadiusCutoffGraphConverter
+from m3gnet.layers import PairRadialBasisExpansion
 
 
 class TestAgg(unittest.TestCase):

m3gnet/layers/tests/test_core.py

@@ -4,7 +4,7 @@
 import tensorflow as tf
 from pymatgen.core import Lattice, Structure
 
-from m3gnet.graph import MaterialGraph, RadiusCutoffGraphConverter
+from m3gnet.graph import RadiusCutoffGraphConverter
 from m3gnet.layers import MLP, Embedding, GatedMLP, Pipe
 
 

m3gnet/layers/tests/test_gn.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

m3gnet/layers/tests/test_readout.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import json
 import os
 import unittest

m3gnet/models/_m3gnet.py

@@ -329,7 +329,7 @@ def from_dir(cls, dirname: str, custom_objects: Optional[dict] = None) -> "M3GNe
         fname = os.path.join(dirname, MODEL_NAME + ".json")
         if not os.path.isfile(fname):
             raise ValueError("Model does not exists")
-        with open(fname, "r") as f:
+        with open(fname) as f:
             model_serialized = json.load(f)
         # model_serialized = _replace_compatibility(model_serialized)
         model = tf.keras.models.model_from_json(model_serialized, custom_objects=custom_objects)

m3gnet/models/tests/test_model.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import os
 import unittest
 

m3gnet/trainers/__init__.py

@@ -1,5 +1,4 @@
 """M3GNet trainers"""
-# -*- coding: utf-8 -*-
 from ._potential import PotentialTrainer
 from ._property import Trainer
 

m3gnet/trainers/_property.py

@@ -167,7 +167,7 @@ def train(
             pbar.set_params({"verbose": verbose, "epochs": epochs})
             callbacks.append(pbar)
 
-        if len(callbacks) > len(set(i.__class__ for i in callbacks)):
+        if len(callbacks) > len({i.__class__ for i in callbacks}):
             logger.warning("Duplicated callbacks found")
 
         callback_list = tf.keras.callbacks.CallbackList(callbacks)

m3gnet/trainers/tests/test_trainer.py

@@ -1,8 +1,6 @@
-# -*- coding: utf-8 -*-
 import json
 import os
 import unittest
-from shutil import rmtree
 
 import numpy as np
 import tensorflow as tf

m3gnet/utils/__init__.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Utilities for m3gnet
 """

m3gnet/utils/_math.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 math functions and miscellaneous calculations
 """

m3gnet/utils/_tf.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Tensorflow related utility
 """

m3gnet/utils/tests/test_math.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

m3gnet/utils/tests/test_tf.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 
 import numpy as np

pretrained/MP-2021.2.8-EFS/m3gnet.json

@@ -1 +1 @@
-"{\"class_name\": \"M3GNet\", \"config\": {\"name\": \"m3g_net\", \"max_n\": 3, \"max_l\": 3, \"n_blocks\": 3, \"units\": 64, \"cutoff\": 5.0, \"threebody_cutoff\": 4.0, \"include_states\": false, \"readout\": \"weighted_atom\", \"n_atom_types\": 94, \"task_type\": \"regression\", \"is_intensive\": false, \"mean\": 0.0, \"std\": 32.17109834917642, \"element_refs\": [0.0, -3.4843261911561028, -0.7929797687431355, -3.4573625172171165, -4.707765870771016, -8.011108016668768, -8.394581462422844, -7.750094492197886, -7.387677478973862, -4.946528796048113, -0.029288391672177685, -2.466164931685727, -2.0318155603403856, -5.122487716146491, -7.875463903751272, -6.908196189228034, -4.611218804437086, -3.027152379064371, -0.06279713634418577, -2.3532963902733193, -4.778241420229052, -8.066454839808204, -11.401003587685803, -8.927171971061405, -8.508408434441172, -8.170571408379823, -6.59277195296443, -5.234141518172957, -4.465330755509164, -3.296725580820661, -1.3526280480990915, -3.545950773762615, -4.6323977966864, -4.138259405833041, -3.6809167595262324, -2.840390787871695, 6.470248022224444, -2.230039686972397, -4.28467191958041, -10.288419163253234, -11.646994569088832, -11.897257027375439, -8.629010559078154, -9.868963036382208, -7.529920274034659, -5.566108785366125, -4.989987934464926, -1.8907720720017154, -0.7166445477628792, -2.7783827902612814, -3.8250237734025108, -3.3872574311610446, -2.540821374472112, -1.9316853365228492, 9.327723590340101, -2.631982538856448, -4.863763303187234, -8.941340884330346, -9.007528604842154, -7.925494962422752, -8.063533560475317, -6.535237679123891, -8.225441352062779, -12.392082038520687, -17.33760321554992, -7.479752317415126, -8.264982305581707, -8.248225450889509, -7.0921249481053525, -8.415686698628432, -3.419624756529247, -7.574452251703786, -12.67851552979807, -13.501372548858408, -9.474060607688976, -11.789707376091997, -9.746879324605413, -7.55581267880792, -5.675347011209681, -2.470084079873828, 0.3532971230155427, -2.3394321085720198, -3.5180003513248814, -3.1732941043540257, 1.0547455480334031e-16, -1.8536706833505882e-16, -5.78493996176134e-16, 1.3591855871025166e-15, 2.991253288696013e-28, -5.688410264859977, -10.404066708232437, -11.41133300856, -14.291848396754993, -14.633848842020615, -15.398398562127586]}, \"keras_version\": \"2.7.0\", \"backend\": \"tensorflow\"}"
+"{\"class_name\": \"M3GNet\", \"config\": {\"name\": \"m3g_net\", \"max_n\": 3, \"max_l\": 3, \"n_blocks\": 3, \"units\": 64, \"cutoff\": 5.0, \"threebody_cutoff\": 4.0, \"include_states\": false, \"readout\": \"weighted_atom\", \"n_atom_types\": 94, \"task_type\": \"regression\", \"is_intensive\": false, \"mean\": 0.0, \"std\": 32.17109834917642, \"element_refs\": [0.0, -3.4843261911561028, -0.7929797687431355, -3.4573625172171165, -4.707765870771016, -8.011108016668768, -8.394581462422844, -7.750094492197886, -7.387677478973862, -4.946528796048113, -0.029288391672177685, -2.466164931685727, -2.0318155603403856, -5.122487716146491, -7.875463903751272, -6.908196189228034, -4.611218804437086, -3.027152379064371, -0.06279713634418577, -2.3532963902733193, -4.778241420229052, -8.066454839808204, -11.401003587685803, -8.927171971061405, -8.508408434441172, -8.170571408379823, -6.59277195296443, -5.234141518172957, -4.465330755509164, -3.296725580820661, -1.3526280480990915, -3.545950773762615, -4.6323977966864, -4.138259405833041, -3.6809167595262324, -2.840390787871695, 6.470248022224444, -2.230039686972397, -4.28467191958041, -10.288419163253234, -11.646994569088832, -11.897257027375439, -8.629010559078154, -9.868963036382208, -7.529920274034659, -5.566108785366125, -4.989987934464926, -1.8907720720017154, -0.7166445477628792, -2.7783827902612814, -3.8250237734025108, -3.3872574311610446, -2.540821374472112, -1.9316853365228492, 9.327723590340101, -2.631982538856448, -4.863763303187234, -8.941340884330346, -9.007528604842154, -7.925494962422752, -8.063533560475317, -6.535237679123891, -8.225441352062779, -12.392082038520687, -17.33760321554992, -7.479752317415126, -8.264982305581707, -8.248225450889509, -7.0921249481053525, -8.415686698628432, -3.419624756529247, -7.574452251703786, -12.67851552979807, -13.501372548858408, -9.474060607688976, -11.789707376091997, -9.746879324605413, -7.55581267880792, -5.675347011209681, -2.470084079873828, 0.3532971230155427, -2.3394321085720198, -3.5180003513248814, -3.1732941043540257, 1.0547455480334031e-16, -1.8536706833505882e-16, -5.78493996176134e-16, 1.3591855871025166e-15, 2.991253288696013e-28, -5.688410264859977, -10.404066708232437, -11.41133300856, -14.291848396754993, -14.633848842020615, -15.398398562127586]}, \"keras_version\": \"2.7.0\", \"backend\": \"tensorflow\"}"

pyproject.toml

@@ -29,4 +29,4 @@ exclude = '''
     | test_files
   )/
 )
-'''
+'''

requirements-ci.txt

@@ -8,4 +8,4 @@ pydocstyle
 black
 flake8
 types-tabulate==0.8.3
-pylint
+pylint

setup.cfg

@@ -16,4 +16,4 @@ ignore = BLK999, W503, BLK100
 
 [mypy]
 ignore_missing_imports = True
-exclude = pretrained, m3gnet.data-*, *.index, *.json
+exclude = pretrained, m3gnet.data-*, *.index, *.json

tasks.py

@@ -7,7 +7,6 @@
 import json
 import os
 import re
-import subprocess
 
 import requests
 from invoke import task
@@ -40,9 +39,7 @@ def make_doc(ctx):
                         else:
                             if not clean.endswith("tests"):
                                 suboutput.append(line)
-                            if clean.startswith("m3gnet") and not clean.endswith(
-                                "tests"
-                            ):
+                            if clean.startswith("m3gnet") and not clean.endswith("tests"):
                                 newoutput.extend(suboutput)
                                 subpackage = False
                                 suboutput = []
@@ -91,23 +88,6 @@ def release_github(ctx):
     print(response.text)
 
 
-@task
-def update_changelog(ctx):
-    output = subprocess.check_output(
-        ["git", "log", "--pretty=format:%s", "v%s..HEAD" % CURRENT_VER]
-    )
-    lines = ["* " + l for l in output.decode("utf-8").strip().split("\n")]
-    with open("CHANGES.rst") as f:
-        contents = f.read()
-    l = "=========="
-    toks = contents.split(l)
-    head = "\n\nv%s\n" % NEW_VER + "-" * (len(NEW_VER) + 1) + "\n"
-    toks.insert(-1, head + "\n".join(lines))
-    with open("CHANGES.rst", "w") as f:
-        f.write(toks[0] + l + "".join(toks[1:]))
-    ctx.run("open CHANGES.rst")
-
-
 @task
 def release(ctx, notest=False):
     ctx.run("rm -r dist build m3gnet.egg-info", warn=True)