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https://api.github.com/repos/materialsproject/pymatgen/issues/1201
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| 341,103,335
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MDExOlB1bGxSZXF1ZXN0MjAxMzU2Mzc2
| 1,201
|
change gb location
|
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[] | 2018-07-13T17:42:11
| 2018-07-14T14:22:34
|
2018-07-14T14:22:34Z
|
CONTRIBUTOR
|
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Move gb generator to pymatgen.io.gb.
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| 341,196,515
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MDExOlB1bGxSZXF1ZXN0MjAxNDI4MzA3
| 1,202
|
add get_fermi_interextrapolated method to FermiDos
|
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[] | 2018-07-14T01:09:26
| 2018-07-14T14:22:52
|
2018-07-14T14:22:52Z
|
CONTRIBUTOR
|
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## Summary
when the value of doping concentration, c, is too low or too high, the get_fermi method may not converge in which case it raises error which is expected. To support a wider range of c even for a coarse CompleteDos with an energy range that is not wide enough, get_fermi_interextrapolated is introduced that supports c=0 or c=1e26 or c=-1e24, etc (all in 1/cm3).
|
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| 341,285,842
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MDU6SXNzdWUzNDEyODU4NDI=
| 1,203
|
spacegroup error in rhombohedral lattice
|
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[] | null |
[
"Sorry for the late reply. To force rhom setting, set your spacegroup to 'R32:R'. This will result in no errors and the right structure.",
"It works! Thank you.",
"Hi, I also have the similar error when I constructed the structure based on the groupspace, \r\n\r\n\r\nCould you give me some suggestions?",
"Your supplied lattice is NOT a rhombohedral lattice. The angles are 90, 125, 90 - that is not rhombohedral. Please review your spacegroup symmetry and lattices and correct accordingly (wikipedia page on spacegroup)."
] | 2018-07-15T02:15:46
| 2023-10-25T15:31:52
|
2018-07-16T22:35:09Z
|
CONTRIBUTOR
|
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|
Hello,
I encountered spacegroup issue about rhombohedral lattice as follows.
I read the source code of `pymatgen.symmetry.groups` for a while, but I don't know about what should I do about this.
Do you have any good ideas?
## System
* Pymatgen version: 2018.6.27
* Python version: 3.6.2
* OS version: Ubuntu 16.04.4 LTS
## Summary
Since pymatgen assumes hexagonal lattice notation for rhombohedral lattice,
we can not input crystal structure with rhombohedral lattice notation.
If possible, I'd like to input rhombohedral lattice into pymatgen without lattice conversion to hexagonal.
## Example code
```python
import pymatgen as mg
# Ni3 S2 http://www.crystallography.net/cod/9007640.html?cif=9007640
lattice = mg.Lattice.rhombohedral(4.0718, 89.459)
species = [{'S': 1.0}, {'Ni': 1.0}]
coodinate = [(0.252100, 0.252100, 0.252100), (0.500000, 0.244900, -0.244900)]
structure = mg.Structure.from_spacegroup('R32', lattice, species, coodinate )
```
## Error message
pymatgen interprets space group R32 as R32H and returns value error.
```
ValueError: Supplied lattice with parameters ((4.0717999999999996, 4.0717999999999988, 4.0717999999999996), (89.459000000000017, 89.459000000000017, 89.459000000000017)) is incompatible with supplied spacegroup R32H!
```
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MDExOlB1bGxSZXF1ZXN0MjAxNzE4NzIy
| 1,204
|
Set Matplotlib backend in CircleCI
|
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[] | null |
[
"Already did it."
] | 2018-07-16T18:21:56
| 2018-07-24T00:39:45
|
2018-07-17T14:29:44Z
|
NONE
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## Summary
Set the matplotlib backend in circleCi to fix DISPLAY variable issues
|
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MDExOlB1bGxSZXF1ZXN0MjAxNzg3NzE3
| 1,205
|
Optimize the symmetry analyzer
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[] | 2018-07-16T23:34:37
| 2018-07-17T14:29:18
|
2018-07-17T14:29:18Z
|
CONTRIBUTOR
|
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Use pymatgen Spacegroup to analyze the symmetry of the structure.
Uses can provide any cell they want (swap a, b, c or angles in tetragonal or hexagonal systems).
|
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| 342,026,657
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MDExOlB1bGxSZXF1ZXN0MjAyMDI0Mzgy
| 1,206
|
Add FunctionalGroupExtractor
|
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[
"This broke code. Can you fix and submit a new PR?",
"Can you tell me what it broke? I'd be happy to fix whatever went wrong.",
"Go to CircleCI and look at the latest build.",
"I'm sorry, I'm not super used to these build tools. I see that Travis-CI failed for a timeout, and AppVeyor succeeded. Where can I find the latest CircleCI build?",
"https://circleci.com/gh/materialsproject/pymatgen/7929",
"Thank you. Working on a fix now, and will send another pull request shortly."
] | 2018-07-17T18:07:34
| 2018-07-19T16:39:02
|
2018-07-19T12:37:01Z
|
CONTRIBUTOR
|
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## Summary
Added a new file (functional_groups.py) and class (FunctionalGroupExtractor) in pymatgen.analysis. This new class uses an algorithm developed by Ertl (2017) to identify atoms of interest (heteroatoms and certain special carbons) and then combine connected atoms of interest to form functional groups. It is highly flexible; other than phenyl and methyl groups, no groups are hard-coded.
## Additional dependencies introduced (if any)
* None (although networkx is required for functional_groups.py)
## TODO (if any)
* Some other functional groups could be hard-coded. For instance, ethyl groups will not be caught because the carbons do not fit any of the requirements laid out by the algorithm.
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Testbranch
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[
"What is this about?",
"I'm sorry, I thought I was working on my own fork. Closing this now."
] | 2018-07-17T19:18:36
| 2018-07-17T19:45:24
|
2018-07-17T19:45:24Z
|
CONTRIBUTOR
|
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## Summary
Short few sentences, and summary of the major changes in bullet
points
* Feature 1
* Feature 2
* Fix 1
* Fix 2
## Additional dependencies introduced (if any)
* List all new dependencies needed. While adding dependencies that bring
significantly useful functionality is perfectly fine, adding ones that
add trivial functionality, e.g., to use one single easily implementable
function, is frowned upon. Provide a justification why that dependency is needed.
Especially frowned upon are circular dependencies, e.g., depending on derivative
modules of pymatgen such as custodian or Fireworks.
## TODO (if any)
If this is a work-in-progress, write something about what else needs
to be done
* Feature 1 supports a, but not b.
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|
QChem Refactor and Update
|
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[
"There are still failing tests. Pls fix. Thanks.",
"Pretty sure this will fix the appveyor / travis-ci build, but it required that I put an unused attempted import openbabel just to know whether or not to include that test, which now makes codacy upset. If this isn't acceptable, please advise. Thanks!"
] | 2018-07-18T20:43:34
| 2018-07-19T12:36:26
|
2018-07-19T12:36:26Z
|
CONTRIBUTOR
|
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* QChem IO functionalities have been refactored to be named consistently with other IO packages
* Default sets have been improved, including changing the default SCF strategy from DIIS to GDM
* Multiple new functionalities have been added to the output parser, including the ability to classify different types of structural changes encountered while optimizing
* Tests have been udpated and supplemented accordingly
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MDExOlB1bGxSZXF1ZXN0MjAyNDAyNDg4
| 1,209
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Add new feature to GB generator
|
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[] | 2018-07-18T22:57:24
| 2018-07-19T12:36:45
|
2018-07-19T12:36:45Z
|
CONTRIBUTOR
|
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In this new version,
A new class method 'get_ratio' is added to help user obtain the axial ratio from the structure needed for GB generator. Users can also supply the ratio directly by making their own approximations.
In addition, a new tag 'max_search' is added. This is to avoid crash for large GB with large sigmas. The very low speed for large GB with large sigmas is mainly due to the very large max_search, which is to search for the 'best' lattice vectors for GB. The previous version sets 'max_search' as the value that can guarantee to find the 'best'. But most of the time, it is not necessary to use that big number. So this new tag can control this value, and speed up the process a lot.
One more thing that related to the speed for large GB is to label the coincidence sites. When the top/bottom grain is very big (e.g. exceeds 10000 atoms), this labeling process is slow since our code needs to compute a N by N distance matrix, N the number of atoms. If no need to label the coincidence sites, I think there is no bottleneck for the speed even with large GBs.
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| 342,805,880
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MDExOlB1bGxSZXF1ZXN0MjAyNjE5ODAy
| 1,210
|
Fixed broken tests for MoleculeGraph
|
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[] | 2018-07-19T16:41:04
| 2018-07-19T16:41:58
|
2018-07-19T16:41:58Z
|
CONTRIBUTOR
|
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## Summary
Previous pull request left one test for pymatgen.analysis.graphs.MoleculeGraph broken. This pull request fixes those broken tests.
|
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| 342,902,109
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MDU6SXNzdWUzNDI5MDIxMDk=
| 1,211
|
Obtaining what materials-project ids are available
|
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[] | null |
[
"The method you are looking for is `MPRester.get_materials_ids`. Mids is simply a running number based on time of calculation. There is no system of assignment.\r\n\r\nIn future, please post questions on the Google groups. Github Issues are meant for reporting issues/bugs/feature requests. Thanks.",
"Questions regarding Materials Project data in particular should be directed to the [forum](https://discuss.materialsproject.org) linked to at the bottom of every materialsproject.org page. [Your question](https://discuss.materialsproject.org/t/what-is-resource-to-access-and-manage-a-list-of-materials/293) was asked and answered previously."
] | 2018-07-19T21:51:32
| 2018-07-19T22:04:19
|
2018-07-19T21:58:37Z
|
NONE
|
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|
Are there any queries for obtaining what materials-project ids ("mp-1234" and so on) are available? I don't really understand the system by which they are assigned...
|
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| 343,110,387
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MDU6SXNzdWUzNDMxMTAzODc=
| 1,212
|
Cythonized linear assignment algorithm very slow ONLY with negative costs
|
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[] | 2018-07-20T13:36:29
| 2023-08-08T21:26:32
|
2023-08-08T21:26:31Z
|
NONE
|
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Unexpectedly, in `pymatgen.optimization`, the cythonized linear assignment algorithm LAPJV, inside `linear_assignment.pyx`, becomes extremely slow when the cost matrix has some negative values. Adding a constant to the cost matrix does not change the solution and when the constant is high enough, the equivalent matrix becomes non-negative. In that case the algorithm finds the (same) solution in a much shorter time (10x - 100x). This issue does not affect the NumPy implementation of LAPJV. Example code:
```python
import numpy as np
import time
try:
from pymatgen.optimization.linear_assignment import LinearAssignment
except ImportError:
print("WARNING: Cythonized LAPJV not available. Falling back to Python/NumPy.")
from pymatgen.optimization.linear_assignment_numpy import LinearAssignment
# matrix with some negative values
matrix = np.random.normal(size=(1000, 3000))
# matrix with all non-negative values
matrix_nonnegative = matrix - matrix.min()
t0 = time.time()
assignment = LinearAssignment(matrix).solution
print("Linear Assignment with some negative values: %s seconds" %(time.time()-t0))
t0 = time.time()
assignment_nonnegative = LinearAssignment(matrix_nonnegative).solution
print("Linear Assignment with non-negative values: %s seconds" %(time.time()-t0))
print("Are the solutions the same? %s" %(assignment == assignment_nonnegative).all())
```
Output with cythonized LAPJV, showing a 50x slower solution when negative costs are present:
```
Linear Assignment with some negative values: 5.35161304474 seconds
Linear Assignment with non-negative values: 0.124813079834 seconds
Are the solutions the same? True
```
Output with NumPy LAPJV, showing no difference between the timing of non-negative vs some-negative costs:
```
WARNING: Cythonized LAPJV not available. Falling back to Python/NumPy.
Linear Assignment with some negative values: 213.514488935 seconds
Linear Assignment with non-negative values: 209.583215952 seconds
Are the solutions the same? True
```
I suspect that there is a bug in the Cython implementation of LAPJV. A short-term solution is to force the cost matrix to be non-negative, or to make it positive adding a constant and then subtracting its effect from the final optimized cost.
|
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| 343,181,346
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MDExOlB1bGxSZXF1ZXN0MjAyOTA1OTk0
| 1,213
|
Create GB object
|
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[] | null |
[] | 2018-07-20T17:04:32
| 2018-07-23T20:08:38
|
2018-07-23T20:08:37Z
|
CONTRIBUTOR
|
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|
Similar to Slab object, we create a Gb object, which stores additional information compared with structure object, e.g. rotation axis, angle, gb_plane, etc., all are gb properties.
Gb object generator also adds ab_shift, which is the in-plane shift of two grains, particularly useful for twist GB.
|
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MDU6SXNzdWUzNDM3MTkxOTY=
| 1,214
|
Band structure checks if HF in order to determine if self-consistent
|
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[
"Thanks for the report @ajjackson !"
] | 2018-07-23T17:10:28
| 2018-07-31T18:36:27
|
2018-07-31T18:36:27Z
|
CONTRIBUTOR
|
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## System
* Pymatgen version: 2018.5.14
* Python version: 3.6.3
* OS version: Linux Mint 17.3
## Summary
* I am a developer of the `sumo` package which uses pymatgen's Vasp parsers for band stucture plotting
* When attempting to plot self-consistent band structures, the undesired k-points are included in the band structure data unless a hybrid XC functional was used.
* As a result, the band structure parsing in Pymatgen is currently not suitable for meta-GGA calculations:
- meta-GGA band structures (e.g. SCAN, TB09) require the same workflow as hybrid band structures: a self-consistent calculation with weighted k-points for density convergence and unweighted k-points for the band paths
- _pymatgen.io.vasp.outputs.Vasprun.get_band_structure_ determines whether a self-consistent band-structure calculation was performed by checking if `LHFCALC = True`.
## Suggested solution
* Instead of checking if a band-structure calculation included Hartree-Fock energy, check whether there is a mixture of weighted and zero-weighted k-points.
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MDExOlB1bGxSZXF1ZXN0MjAzMzA5ODQ1
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Elastic Revision
|
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[
"Need to fix a few tests. Will work through and update when done.",
"I think this is ready on my end. The travis tests seem to be failing because of an enumlib issue which I believe is unrelated to this PR."
] | 2018-07-23T18:50:18
| 2019-04-04T02:45:10
|
2018-07-25T18:52:13Z
|
CONTRIBUTOR
|
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* Tensors are MSONable, with optional flag for using voigt notation dict representation (this stores them more efficiently, but would only be used if the user explicitly chose to do so, as information may be lost)
* Revises determination of coefficient of linear expansion to use compliance tensor rather than inverse of bulk modulus, this is more rigorous and provides more directional information
* Fixes minor issue in finite-difference fitting of TOECs related to division by zero if equilibrium strains are included in fitting data
* Adds a set method for tensor mapping, primarily for dealing with tensor-paired values
* Fills in a few missing methods for tensor expansion objects that present for tensors
* Adds associated tests for prior bug, MSONable functionality, new alpha
|
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Added ability to determine which Voronoi neighs are adjacent
|
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[] | 2018-07-24T18:50:36
| 2018-07-25T02:24:24
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2018-07-25T02:24:24Z
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CONTRIBUTOR
|
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## Summary
Adds the ability to determine which neighbors in VoronoiNN are adjacent.
## Additional dependencies introduced (if any)
None
## TODO (if any)
None
|
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Building Travis and Appveyor with OpenBabel
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[
"Doesn't work. I tried the conda install openbabel before. There are too many issues with that package and compatibility with different systems.",
"OK... I'm going to try a couple more things because I'm curious, and because I did manage to get it working with custodian, but unless I have a major breakthrough, I'll close this pull request shortly."
] | 2018-07-24T23:52:12
| 2018-07-25T18:15:28
|
2018-07-25T18:15:28Z
|
CONTRIBUTOR
|
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All tests that require OpenBabel are currently skipped. Having figured out how to avoid this and successfully run Babel-based tests in Custodian, I'm now trying to do the same in Pymatgen.
|
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MDExOlB1bGxSZXF1ZXN0MjAzODkyNjgz
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Pourbaix patch 1
|
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[] | 2018-07-25T16:22:36
| 2019-04-04T02:45:11
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2018-07-25T18:52:35Z
|
CONTRIBUTOR
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A legacy portion of the pourbaix code filtered ion-including MultiEntries from pourbaix diagrams when their reaction coefficients were lower than their attached concentration attribute, which I can't think of a good reason to do. This resulted in pourbaix diagrams with high ion concentrations (e. g. > 0.5) filtering out ion entries.
* Changes pourbaix processing of multi-entries to filter ion entries based on reaction coefficient weights that are fixed, rather than concentration-based.
* Adds associated unit test to ensure that high concentration ion entries are not filtered arbitrarily
|
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MDExOlB1bGxSZXF1ZXN0MjA0MjQ2NzEw
| 1,219
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Add GrainBoundaryTransformation and test in advanced_transformations
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[] | 2018-07-26T19:42:51
| 2018-07-26T22:02:31
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2018-07-26T22:02:31Z
|
CONTRIBUTOR
|
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## Summary
GrainBoundaryTransformation is used to generate grain boundary structure, which will be used in atomate grain boundary workflow.
|
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MDExOlB1bGxSZXF1ZXN0MjA0NjE3NTYz
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added spin polarization support
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[
"Thanks!",
"Would you like me to fix that one assertion that caused the Codacy/PR Quality Review check to fail?",
"that's fine. It is nto a big deal."
] | 2018-07-29T00:37:06
| 2018-07-29T17:55:00
|
2018-07-29T02:22:11Z
|
CONTRIBUTOR
|
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## Summary
Added support for spin polarized WAVECARs.
## Additional dependencies introduced (if any)
N/A
## TODO (if any)
N/A
|
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MDExOlB1bGxSZXF1ZXN0MjA0NjE4MTM0
| 1,221
|
check for 0 weight kpoints for hybrid-type band structures
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[] | 2018-07-29T00:59:19
| 2018-07-29T17:55:19
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2018-07-29T02:22:33Z
|
CONTRIBUTOR
|
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## Summary
Added a check for 0 weight kpoints in pymatgen.io.vasp.outputs.Vasprun.get_band_structure. This is useful for meta-GGA calculations. Addresses Issue #1214 .
## Additional dependencies introduced (if any)
N/A
## TODO (if any)
N/A
|
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MDExOlB1bGxSZXF1ZXN0MjA0NjYxNzA1
| 1,222
|
Changes to JMolNN class
|
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[
"tests are failing.",
"@jdagdelen there was a query from Hillary/Anubhav recently about whether the Jmol emulation in pymatgen faithfully matched the behavior in Jmol itself; were you able to find a good reference for Jmol's algorithm? If so, would be good to add a reference to the docstring.\r\n\r\nThe best I was able to find was this memo, but I don't know if it's up to date: https://sourceforge.net/p/jmol/mailman/message/5402874/",
"Oh man, I forgot to refactor the name in the rest of pymatgen. That was a dumb oversight, will fix and submit another PR."
] | 2018-07-29T21:13:57
| 2018-08-08T23:07:29
|
2018-08-08T23:07:29Z
|
MEMBER
|
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## Summary
Jmol's spelling was incorrect (the developer explicitly says not to capitalize "mol") and there was a redundant constant used to determine bond lengths.
* JMol -> Jmol
* One tolerance instead of two.
|
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Fail to import pymatgen after master branch install
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[
"Why not use pip install?",
"I would like to use the new part of analysis particularly local_env and it doesn't seem to be available on the pip install version.",
"local_env is available on the pip installed version, try `pip install pymatgen --upgrade` ? We recommend using Python 3+ also since Python 2.x support is being dropped, so check what version of pip you're using.",
"I got the same error and fixed it with CC=gcc python setup.py develop.\r\n",
"@xivh I need a more detailed outline of the exact procedure that you did and error message for us to debug. This is an extremely old issue. We have not heard of anyone having this issue in the past four years.",
"I got the same import error as Sanjeev when installing from master and importing pymatgen.io.vasp.inputs from inside the pymatgen directory. Running `CC=gcc python setup.py develop` got rid of the error.\r\n\r\nBut, I just found out that everything works fine if I leave the directory before importing."
] | 2018-07-30T19:49:08
| 2022-06-13T23:58:33
|
2018-09-29T20:09:53Z
|
NONE
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When reporting bugs/issues, please supply the following information. If
this is a feature request, please simply state the requested feature.
## System
* Pymatgen version: <master>
* Python version: <Only 2.7.* and 3.* supported.>
* OS version: <Ubuntu 16.04>
## Summary
* 1-2 short bullet points stating the problem.
After running python setup.py install, I attempt to import pymatgen and it seems that pymatgen cannot import the coord_cython module. Is there a dependency that I am missing?
## Example code
```python
# Example code generating error
```
## Error message
----> 1 import pymatgen
/home/sanjeev/pymatgen/pymatgen/__init__.py in <module>()
54 # Allows from pymatgen import <class> for quick usage.
55
---> 56 from pymatgen.core import *
57 from .electronic_structure.core import Spin, Orbital
58 from .ext.matproj import MPRester
/home/sanjeev/pymatgen/pymatgen/core/__init__.py in <module>()
7 from .periodic_table import Element, Specie, DummySpecie
8 from .composition import Composition
----> 9 from .structure import Structure, IStructure, Molecule, IMolecule
10 from .lattice import Lattice
11 from .sites import Site, PeriodicSite
/home/sanjeev/pymatgen/pymatgen/core/structure.py in <module>()
28
29 from pymatgen.core.operations import SymmOp
---> 30 from pymatgen.core.lattice import Lattice
31 from pymatgen.core.periodic_table import Element, Specie, get_el_sp, DummySpecie
32 from monty.json import MSONable
/home/sanjeev/pymatgen/pymatgen/core/lattice.py in <module>()
14
15 from monty.json import MSONable
---> 16 from pymatgen.util.coord import pbc_shortest_vectors
17 from pymatgen.util.num import abs_cap
18
/home/sanjeev/pymatgen/pymatgen/util/coord.py in <module>()
9 import numpy as np
10 import math
---> 11 from . import coord_cython as cuc
12
13 """
ImportError: cannot import name coord_cython
```
# Error message
```
## Suggested solution (if any)
* If you have a suggestion on how to solve the issue, you may write it
here.
## Files (if any)
* If input files are needed to reproduce the error, please provide
either links (e.g., Dropbox or GDrive) for large files or simply paste
the file below for small files.
```
<contents of file 1>
```
|
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MDExOlB1bGxSZXF1ZXN0MjA0OTUzMjcw
| 1,224
|
Incorrect charge for repeated species in POTCAR
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[] | 2018-07-30T22:59:33
| 2018-07-30T23:05:24
|
2018-07-30T23:02:24Z
|
CONTRIBUTOR
|
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## Summary
Occasionally, it makes sense to have a repeated atomic specie in the POTCAR (e.g., defect calculations). This will cause the charge to be calculated incorrectly with the past implementation. This PR handles this edge case and adds a test to check for it.
## Additional dependencies introduced (if any)
N/A
## TODO (if any)
N/A
|
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A small number of band structures have doubled k-point labels.
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[
"Sorry for the extremely late reply. I can no longer reproduce this (even using the old `MPRester`). Either the issue was fixed in the meantime or I'm not looking in the right place.\r\n\r\n```py\r\nfor mp_id in (\"mp-8601\", \"mp-4100\", \"mp-853153\", \"mp-765720\"):\r\n bandstructure = MPRester().get_bandstructure_by_material_id(mp_id)\r\n print(f\"{bandstructure.branches=}\")\r\n\r\n>>> bandstructure.branches=[{'start_index': 0, 'end_index': 74, 'name': '\\\\Gamma-P'},\r\n {'start_index': 75, 'end_index': 78, 'name': 'P-Z'},\r\n {'start_index': 79, 'end_index': 164, 'name': 'Z-Q'},\r\n {'start_index': 165, 'end_index': 239, 'name': 'Q-\\\\Gamma'},\r\n {'start_index': 240, 'end_index': 301, 'name': '\\\\Gamma-F'},\r\n {'start_index': 302, 'end_index': 345, 'name': 'F-P_1'},\r\n {'start_index': 346, 'end_index': 349, 'name': 'P_1-Q_1'},\r\n {'start_index': 350, 'end_index': 409, 'name': 'Q_1-L'},\r\n {'start_index': 410, 'end_index': 471, 'name': 'L-Z'}]\r\n...\r\n```\r\n\r\nDo you have a code snippet to reproduce this?"
] | 2018-07-30T23:49:26
| 2023-08-08T21:26:52
|
2023-08-08T21:26:52Z
|
NONE
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Hi again Materials Project folks,
Not a major concern, but while we've been running through the electronic structures in the database, we've found that a small number of them have doubled labels for high-symmetry k-points. For example, the k-path will be \Gamma -> X -> X -> \Sigma instead of \Gamma -> X -> \Sigma.
Structures that have this problem in both the band structure objects downloaded from the API and on the website band structure visualization include: mp-8601, mp-4100, mp-853153, and mp-765720. We are not using the entire group of electronic structure calculations, so there may be more similar band structure label errors.
Thanks!
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io.lammps updates
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[
"Good. But are there supposed to be some sample templates/scripts? I don't seem to see them in there.",
"Sure, there will be examples on the usage of input script template updated in matgenb separately. ",
"This is better, but still not what I meant. THink about it from a new user perspective - I am clueless about LAMMPS and all I want to do is to use pymatgen to generate an input file for me to run a simple structure. At the minimum, the example templates should allow for specification fo arbitrary structures.\r\n\r\nWhat I want is basically something that allows the following code:\r\n\r\n```\r\nlr = LammpsRun.md(structure, various parameters...)\r\nlr.write(output_dir)\r\n```\r\n\r\nYou just need to support a few basic examples such as MD by default, and the template files should be provided in the code itself as files. For more examples, those can be in matgenb.materialsvirtuallab.org.",
"Personally, I am not a fan of this setting. The transferability (in many aspects) for lammps simulations is highly limited. Finding a proper potential and coding it right in lammps is already non-trivial. It might help users with completely no knowledge, but is hardly useful for users starting to do production runs. \r\n\r\nAnyway, @shyuep let me know if this is the thing in your mind. And we surely can add more in the future. ",
"Doesn't LAMMPS itself come with some potentials? The argument cannot be that \"this is non-trivial\" and so we do not need to provide any help or gudiance for users. By that logic, I can argue that VASP runs are very difficult, with so many parameters like smearing, energy cutoff, kpoints, psuedopotentials, etc. So you might as well work on your own input files. That is now how it works.\r\n\r\nSo let me lay this out simply. I am a new LAMMPS user. I want to generate a LAMMPS MD calculation and analyze it. I am not very sophisticated - so I will just live with the LJ or buckingham potential that is built into LAMMPS. Can I generate the input file and do the analysis in a few lines of code?\r\n\r\nOf course you also need to cater to the highly sophisticated user who wants to use their custom potential.",
"I agree that the interface for LAMMPS should be designed similar with the one for VASP here. The milestone achieved here for LAMMPS io is mainly the components for handling various files, like `Incar` and `Vasprun` for VASP. \r\n\r\nThe `VaspInputSet` counterpart is effectively the input script in LAMMPS. I have been more than grateful for all these input sets, allowing me to do calculations from a structure without any DFT knowledge a few years ago. \r\n\r\nBut the fundamental difference is the **transferability**, not only on the level of potential energy surface, but also software features. As I have shown you earlier, a script for LAMMPS can have infinite possibilities and can hardly be handled with coded rules. Similarly, analyses to be performed rely on the availability of output data, which is again highly customizable in the script. And that is why `lammps.outputs` might look way too simple at first glance. It has to be general for all the possibilities. \r\n\r\nThe template is definitely not perfect, but enough as a simple example. I will leave it as is. Improvements and addition of templates will be long-term based on more feedbacks from others, as I am just a solely LAMMPS user only familiar with my own day-to-day tasks."
] | 2018-08-01T07:41:32
| 2018-08-03T22:31:00
|
2018-08-03T22:31:00Z
|
CONTRIBUTOR
|
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## Summary
Major updates to `io.lammps`. The entire module is now flexible enough to handle most lammps related tasks.
* Simplified code for handling input script, which should just behave just like a string template.
* Revised log parser, and added many tests.
* Minor changes to dump parser. Now dump data for a single snapshot is a `LammpsDump` instance.
* Removed several badly designed modules (`lammps.sets` is no different than `lammps.inputs`) and classes (`LammpsRun` is very specific for certain type of runs).
## Additional dependencies introduced (if any)
None
## TODO (if any)
None
|
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MDExOlB1bGxSZXF1ZXN0MjA1NTU4NjQ0
| 1,227
|
Minor refactoring in DFPT part.
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[] | 2018-08-01T21:28:24
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2018-08-01T22:46:01Z
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MDU6SXNzdWUzNDczNDQ5ODg=
| 1,228
|
Saving to .mcif fails if oxidation_state is not set
|
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[
"@RoberTnf Thanks for fixing!\r\n\r\nThis bug actually comes partly from the fact that in pymatgen we can specify magnetic moments in two different ways: a `magmom` site property, or adding `spin` to `Specie`. So, for your first example, the line `s.add_spin_by_site([1, -1])` could have been replaced by `s.add_site_property('magmom', [1, -1])` and then it would work as intended.\r\n\r\nIndeed, it used to be that `oxidation_state` was mandatory for `Specie`, allowing None (so that we have the ability to only set `spin`) is relatively new, so I suspect there might be a few similar bugs lurking."
] | 2018-08-03T10:30:51
| 2018-08-04T17:34:33
|
2018-08-04T16:20:28Z
|
CONTRIBUTOR
|
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## System
* Pymatgen version: 2018.7.23
* Python version: 3.6.6
* OS version: Manjaro
## Summary
* Trying to save to .mcif will fail if oxidation_state is not set
## Example code
```python
import pymatgen as mg
s = mg.Structure(mg.Lattice.cubic(4.2), ["Cs", "Cl"],[[0, 0, 0], [0.5, 0.5, 0.5]])
s.add_spin_by_site([1, -1])
s.to(filename="s.mcif")
```
## Error message
```
# Error message
Traceback (most recent call last):
File "mcif_test.py", line 5, in <module>
s.to(filename="s.mcif")
File "/home/roberto/git/pymatgen/pymatgen/core/structure.py", line 1521, in to
writer = CifWriter(self, write_magmoms=True)
File "/home/roberto/git/pymatgen/pymatgen/io/cif.py", line 1216, in __init__
for el in sorted(comp.elements)])
File "/home/roberto/git/pymatgen/pymatgen/io/cif.py", line 1216, in <listcomp>
for el in sorted(comp.elements)])
TypeError: float() argument must be a string or a number, not 'NoneType'
```
## This works
```python
import pymatgen as mg
s = mg.Structure(mg.Lattice.cubic(4.2), ["Cs", "Cl"],[[0, 0, 0], [0.5, 0.5, 0.5]])
s.add_oxidation_state_by_site([1, 1])
s.add_spin_by_site([1, -1])
s.to(filename="s.mcif")
```
## Suggested solution (if any)
* I've already fixed, I will submit a PR
|
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MDExOlB1bGxSZXF1ZXN0MjA1OTkxMzkx
| 1,229
|
Use TypeError as oxi_state is set to None, not unset
|
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[] | null |
[
"The tests were failing as loading from a .mcif file would not set oxi_state to None. I'm checking for both `AtributeError` and `TypeError`.\r\n\r\nAlso I added new tests to check for this issue.",
"Travis was failing a test that in my machine passed, it was a timeout related one, so it might be a hardware difference problem. I decreased the timeout parameter in the test, let's see if it fixes it.",
"Also, the tests in `test_enumlib_caller` are skipped if this fails:\r\n\r\n```enum_cmd = which('enum.x') or which('multienum.x')\r\nmakestr_cmd = which('makestr.x') or which('makeStr.x') or which('makeStr.py')\r\nenumlib_present = enum_cmd and makestr_cmd\r\ntest_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\", \"..\",\r\n 'test_files')\r\n\r\n\r\n@unittest.skipIf(not enumlib_present, \"enum_lib not present.\")\r\n```\r\n\r\nHowever, `enum.x` and `makestr.x` are already in: https://github.com/materialsproject/pymatgen/tree/master/cmd_line/enum\r\n\r\nInstead of running `which` and skipping if it's not found, maybe we should just hardcode the position of those files?",
"@RoberTnf enum.x is machine-specific (it's a compiled code), so not everyone cloning the repo would be able to run those tests, which is why `which` is called"
] | 2018-08-03T10:36:05
| 2018-08-04T17:31:30
|
2018-08-04T12:54:33Z
|
CONTRIBUTOR
|
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## Summary
Fixing #1228
* Change to TypeError, as this is the one we want to catch, `oxi_state` is set to `None`, not unset
* Remove unnecessary code
|
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MDU6SXNzdWUzNDc5OTQzNzI=
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|
Unintuitive behavior when saving to .cif related to space groups
|
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[
"My personal opinion on this is that Structure.to is a convenience method and if you want more customization you should use CifWriter directly.\r\n\r\nYour proposed solution of adding a symprec kwarg to Structure.to is fairly harmless, but it’s also very specific to just CIF output. This could also be confusing if people expect the symprec arg to have an effect when they use Structure.to to output a different file format. It might also be awkward because you could imagine adding kwargs for many other options for the other supported output formats too, which would lead to Structure.to becoming over-complicated.\r\n\r\nAlso, I’d argue that this is not unexpected behavior because pymatgen’s Structure is implicitly P1. Symmetry information isn’t stored; it’s computed on demand. Outputting a symmetrized CIF is actually lossy depending on your symmetry precision. Perhaps SymmetrizedStructure should output a symmetrized CIF by default though.",
"I see, makes sense. Then what's the purpose of `_symmetry_space_group_name_H-M` in this case? Wouldn't it be better to not write that line, to avoid people trusting it (like I was).\r\n\r\nEDIT: same for `_symmetry_Int_Tables_number`",
"Yeah, I don't know the answer to that for sure. I can definitely see why this would be confusing; you're not the first person to flag this.\r\n\r\nI would say that the purpose of these tags is to tell the software parsing the CIF file what symmetry operations it needs to apply to the list of atomic positions to construct the crystal. This is important to many tools, e.g. visualization tools, etc.\r\n\r\nIn this sense, supplying P1 is technically correct, it says: \"we've supplied *all* the atomic positions, the only symmetry operation you need to apply is the identity.\" I'm not sure if this is strictly required by the standard or if P1 is assumed by default if symmetry information is not supplied, but in any case I'd expect many third-party tools that parse CIF to break if we didn't include this line.\r\n\r\nPerhaps a remedy to this would be to add a human-readable comment to the file, explaining it's been outputted in a P1 setting but also providing the calculated symmetry within the given symmetry tolerance.",
"I see. Maybe even just expanding the docstring, indicating that there are more advanced options would suffice. Now I realize it does says so in the online documentation, so I'm probably the one to blame here!",
"Yes, if you wanted to change it I would propose a change here:\r\n\r\nhttps://github.com/materialsproject/pymatgen/blob/0c989cad54ca1ac729aeb173bcbfd35e2faab0cc/pymatgen/io/cif.py#L1309\r\n\r\nThe default comment is `# generated using pymatgen`. But we could add an additional line iff `symprec is None`, something like `# crystal has been written in a P1 setting but pymatgen detects crystal symmetry as {...} within default symmetry tolerances\\n# use pymatgen.io.cif.CifWriter to output a symmetrized version of this file in a {...} setting` or something along those lines. And pass that comment to the line`CifFile(comment=...)`.\r\n\r\n"
] | 2018-08-06T16:29:40
| 2018-08-07T02:48:16
|
2018-08-07T02:48:16Z
|
CONTRIBUTOR
|
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## Summary
* Space group information lost when saving to cif
## Example code
```
import pymatgen as mg
from pymatgen import Lattice, Structure, Molecule
# Set up a cubic structure and write to a CIF.
lattice = mg.Lattice.cubic(4.2)
s1 = mg.Structure(lattice, ["Cs", "Cl"],[[0, 0, 0], [0.5, 0.5, 0.5]])
s1.to(filename="s1.cif")
```
# In the cif file:
```
_symmetry_space_group_name_H-M 'P 1'
```
# It should be
```
_symmetry_space_group_name_H-M Pm-3m
```
# This works as intended
```python
import pymatgen as mg
from pymatgen import Lattice, Structure, Molecule
from pymatgen.io.cif import CifWriter
# Set up a cubic structure and write to a CIF.
lattice = mg.Lattice.cubic(4.2)
s1 = mg.Structure(lattice, ["Cs", "Cl"],[[0, 0, 0], [0.5, 0.5, 0.5]])
writer = CifWriter(s1, symprec=0.01)
writer.write_file("s1.cif")
```
## Suggested solution
Maybe `symprec` should be accessible when saving a structure using the method `to`? I'm submitting a PR for it.
|
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| 347,998,644
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MDExOlB1bGxSZXF1ZXN0MjA2NDU1OTgx
| 1,231
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add 'symprec' to method 'to' of 'Structure'
|
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[] | null |
[
"Pls do not introduce a new keyword symprec to Structure.to. You can simply pass the **kwargs to where you are putting symprec in CifWriter, and that would allow the symprec keyword to work.",
"I fixed it in https://github.com/materialsproject/pymatgen/commit/5f0f80984d54b8d2549bf829342608c8587721e1"
] | 2018-08-06T16:42:58
| 2018-08-07T00:47:52
|
2018-08-07T00:47:51Z
|
CONTRIBUTOR
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Possible solution to #1230
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Improved performance of dumps parsing
|
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[] | 2018-08-06T23:52:19
| 2018-08-07T00:32:30
|
2018-08-07T00:32:30Z
|
CONTRIBUTOR
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## Summary
A memory friendly version of dump parser, reading a file line by line instead of reading the entire file into memory. Computers used to hang when dealing with GB size file, now parsing can be done in a few minutes w/ `pandas.read_csv`, tested to be faster than `numpy.loadtxt`.
## Additional dependencies introduced (if any)
None
## TODO (if any)
None
|
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|
from_file: recognize ".vasp" extension as POSCAR-type
|
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[] | null |
[
"Unittest pls. I know this change seems trivial, but I will still need tests for it."
] | 2018-08-09T20:39:25
| 2018-08-10T12:47:01
|
2018-08-10T12:47:01Z
|
CONTRIBUTOR
|
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POSCAR-type files with `.vasp` extension can be read by VESTA but not by pymatgen. One has to rename the file because the `from_file` method doesn't provide a `fmt=` flag.
This change makes life easier.
|
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Added shortcut if structures is equal to self
|
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[
"I'm guessing the timeout failure in the enumlib test is unrelated?"
] | 2018-08-09T23:31:27
| 2018-08-10T02:00:39
|
2018-08-10T02:00:39Z
|
CONTRIBUTOR
|
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## Summary
Determining whether a structure is equal to itself can be very expensive for large structures. A structure with 100 atoms can take seconds to verify, and the cost scales with the square of the number of atoms. This change adds a check in the equality operation if the "other" class refers to the same object in memory to drastically lower the cost of "x == x"
This problem has implications for the caching methods we use in matminer.
## Additional dependencies introduced (if any)
None
## TODO (if any)
None
|
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MDExOlB1bGxSZXF1ZXN0MjA3NDg2NzI5
| 1,235
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add init to gb
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[] | 2018-08-10T01:45:11
| 2019-04-04T02:45:12
|
2018-08-10T01:50:33Z
|
CONTRIBUTOR
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`__init__.py` is generally unnecessary for including packages in editable installs of pymatgen in python 3, but seems to be necessary for non-editable installs (e. g. with pip or `python setup.py install`). I think this should fix the issue. Currently the pip-installed release doesn't include the analysis.gb module, which breaks transformations. Might want to re-release as well.
|
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MDU6SXNzdWUzNDk0ODU2MTg=
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|
Band structure fails to load if vasprun is gzipped and KPOINTS isn't
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[
"Not sure why this is assigned to me (pretty sure I had no part in the original code)?\r\n\r\nBut anyway, I am not sure what os.path.pardir refers to in the above example.\r\n\r\nI would suggest to just use monty zpath (https://pythonhosted.org/monty/_modules/monty/os/path.html) \r\n\r\n\r\nMaybe something like this:\r\n\r\n```\r\nfrom monty.os.path import zpath\r\n\r\nif not kpoints_filename:\r\n kpoints_filename = zpath(os.path.join(os.path.dirname(self.filename), \"KPOINTS\"))\r\n```\r\n\r\nIt's a bit convoluted but should work (?)\r\n",
"nevermind just understood the os.path.pardir as \"..\" - but anyway would probably suggest the code snippet above if it works",
"@ajjackson would you be happy to submit a PR to fix using monty's `zpath` ? (monty is already a dependency of pymatgen, zpath handles a range of compressed formats so would be a more general solution)",
"Can put that together, sure. This seems to be exactly what `zpath` is for.",
"Many thanks :)",
"Fixed in in #1272 "
] | 2018-08-10T11:48:33
| 2018-12-17T16:22:18
|
2018-12-17T16:22:17Z
|
CONTRIBUTOR
|
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## System
* Pymatgen version: 2018.7.23
* Python version: 3.6
* OS version: Linux Mint 17.3 64-bit
## Summary
* Using the `BSVasprun` class to import a band structure calculation
* If vasprun.xml and KPOINTS files are present, it loads ok
* If both vasprun.xml and KPOINTS files have been gzipped (i.e. vasprun.xml.gz), it loads ok
* If only one file has been gzipped, an error is raised complaining about lack of KPOINTS file. This is quite disorientating if there is in fact a KPOINTS file...
## Example code
```bash
gzip vasprun.xml
```
```python
from pymatgen.io.vasp.outputs import BSVasprun
vr = BSVasprun('vasprun.xml.gz')
vr.get_band_structure(line_mode=True)
```
## Error message
```
File "/home/adam/.local/lib/python3.6/site-packages/pymatgen/io/vasp/outputs.py", line 749, in get_band_structure
raise VaspParserError('KPOINTS needed to obtain band structure '
pymatgen.io.vasp.outputs.VaspParserError: KPOINTS needed to obtain band structure along symmetry lines.
```
## Suggested solution
* This behaviour is caused by the following line in _pymatgen.io.vasp.outputs_:
```python
if not kpoints_filename:
kpoints_filename = self.filename.replace('vasprun.xml', 'KPOINTS')
```
Presumably the "replace" expression is intended to maintain the first part of the path (i.e. the parent directory). However, if `self.filename` was _/some/path/to/vasprun.xml.gz_ then the `replace` operation will leave _/some/path/to/KPOINTS.gz_.
* Suggested change
```python
if not kpoints_filename:
vasprun_dir = os.path.abspath(os.path.join(self.filename,
os.path.pardir))
for kpoints_filename in ('KPOINTS', 'KPOINTS.gz'):
if os.path.exists(os.path.join(vasprun_dir, kpoints_filename)):
kpoints_filename = os.path.join(vasprun_dir,
kpoints_filename)
break
else:
kpoints_filename = ''
```
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MDU6SXNzdWUzNDk1NDQ0NjQ=
| 1,237
|
Documentation: Slab surface normal explanation seems fishy to me
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[
"The documentation is correct as is. When you generate a slab, the c vector is out of the a-b plane, which is defined to be parallel to the plane of interest. It does not mean that c is perpendicular to the a-b plane. It will be perpendicular for certain planes, e.g., the (100) plane of the cubic structure or (001) of the hexagonal, tetragonal structures, etc. But it is not true for any general triclinic structure. Pls consult the publication linked to this piece of code for a detailed explanation why c is not always parallel to a and b.",
"I think @smheidrich has a point here, they're highlighting this statement in the documentation specifically:\r\n\r\n> Note that all Slabs have the surface normal oriented in the c-direction.\r\n\r\nI interpret this as \"the surface normal should be parallel to the c-vector in all cases.\" As you both are pointing out, this is untrue in general. @smheidrich why don't you try making a small edit and submitting a PR?",
"@montoyjh Thanks, done.\r\n\r\n@shyuep I agree with everything in your post (except for the first sentence of course), I just don't see how the statement that @montoyjh cited can be compatible with it. Or do we mean different things by a, b and c perhaps? I thought they were referring to the lattice vectors of the returned slab structure. Very sorry for wasting your time if it turns out I'm wrong. ",
"Thanks. Sorry I misread what you wrote earlier. I have merged the PR.",
"No problem, thanks for merging! :+1: "
] | 2018-08-10T14:47:33
| 2018-08-11T15:18:55
|
2018-08-10T19:23:52Z
|
CONTRIBUTOR
|
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## System
* Pymatgen version: v2018.8.10-4-g10cf55a00 (10cf55a00)
* Python version: 3.5.3
* OS version: Debian GNU/Linux 9 (stretch)
## Summary
In the documentation for the [pymatgen.core.surface.Slab](http://pymatgen.org/pymatgen.core.surface.html#pymatgen.core.surface.Slab) class, it says:
> Note that all Slabs have the surface normal oriented in the c-direction. This means the lattice vectors a and b are in the surface plane and the c vector is out of the surface plane (though not necessary perpendicular to the surface.)
Do I have a knot in my brain or does that make no sense? If the surface normal is really oriented in the c-direction, then the c-vector is **necessarily** perpendicular to the surface, isn't it? So either the first or the second sentence is wrong. From the tests below, it seems to me that (at least in this case with these settings) the first sentence doesn't hold.
## Example code
This demonstrates that the slab normal doesn't necessarily point in the direction of the c lattice vector, but it does seem to always point in the direction perpendicular to the a and b lattice vectors:
```python
import numpy as np
from pkg_resources import resource_filename
from pymatgen.io.vasp.inputs import Poscar
from pymatgen.core.surface import generate_all_slabs
# works only if you installed from the git repository in editable mode; if not,
# just plug in the path to the test file manually
pc_path = resource_filename("pymatgen", "../test_files/POSCAR.Al12O18")
pc = Poscar.from_file(pc_path)
structure = pc.structure
print("***** ORIGINAL LATTICE: *****")
print(structure.lattice)
# generate slabs with more or less default settings:
print("Generating slabs; this might take a while...")
slabs = generate_all_slabs(structure=structure, max_index=2, min_slab_size=5.0,
min_vacuum_size=5.0)
print("")
print("***** SLABS: *****")
for slab in slabs:
print("Lattice:")
print(slab.lattice)
print("Slab normal: {}".format(slab.normal))
ab_normal = np.cross(slab.lattice.matrix[0], slab.lattice.matrix[1])
ab_normal /= np.linalg.norm(ab_normal)
print("(a,b)-normal: {}".format(ab_normal))
print("")
```
## Output
```
***** ORIGINAL LATTICE: *****
2.397450 -4.152505 0.000000
2.397450 4.152505 0.000000
0.000000 0.000000 13.099500
Generating slabs; this might take a while...
***** SLABS: *****
Lattice:
4.152505 0.000000 -7.192350
-0.000000 13.099500 -4.794900
0.000000 0.000000 28.769399
Slab normal: [0.8518758 0.18002818 0.49183075]
(a,b)-normal: [0.8518758 0.18002818 0.49183075]
...
```
## Suggested solution
From the tests in the example code above, I would say that the first sentence should instead read:
> Note that all Slabs have the surface normal oriented perpendicular to the a and b lattice vectors.
Or, perhaps even simpler:
> Note that all Slab surfaces are oriented parallel to the plane spanned by the a and b lattice vectors.
But maybe that's just down to the specific settings I used, or maybe I made a mistake or misunderstood something?
## Files
[``POSCAR.Al12O18``]( https://github.com/materialsproject/pymatgen/blob/master/test_files/POSCAR.Al12O18), which ships with pymatgen (in the ``test_files`` folder).
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MDExOlB1bGxSZXF1ZXN0MjA3NzMxNzM5
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|
Fix #1237 (docs: slab surface normal explanation)
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[] | 2018-08-10T21:59:38
| 2018-08-11T00:19:09
|
2018-08-11T00:19:09Z
|
CONTRIBUTOR
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This aims to fix #1237, but let's first see what the decision will be on whether that's a valid issue in the first place.
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| 349,973,827
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MDExOlB1bGxSZXF1ZXN0MjA3OTM0MTE2
| 1,239
|
Include reading and evaluation of DOSCAR.lobster
|
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[
"What else do I need to do so that one can merge this? The Travis CI check seems to fail because of a time limit.\r\nThanks in advance."
] | 2018-08-13T10:31:51
| 2018-08-17T09:57:51
|
2018-08-17T09:57:51Z
|
MEMBER
|
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## Summary
I have included a class to read "DOSCAR.lobster" created with Lobster and VASP and another class to evaluate the results so that one could plot "DOSCAR.lobster" with the help of the class DosPlotter.
I have also written tests for both classes.
|
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MDExOlB1bGxSZXF1ZXN0MjA4MTA3NTUy
| 1,240
|
Changing JMolNN -> JmolNN and fixing algorithm to be consistent with Jmol.
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[
"@shyuep All tests pass now. Ready to be merged. "
] | 2018-08-13T21:20:56
| 2018-08-26T04:32:14
|
2018-08-26T04:32:14Z
|
MEMBER
|
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## Summary
This PR fixes a naming issue with the Jmol-like NN tool in local_env and makes the algorithm work more consistently with Jmol's algorithm.
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MDExOlB1bGxSZXF1ZXN0MjA4NzAzMjky
| 1,241
|
get_cohesive_energy() added to MPRester() and other updates
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[] | 2018-08-15T22:54:35
| 2018-08-16T00:31:12
|
2018-08-16T00:31:12Z
|
CONTRIBUTOR
|
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- Added missing metallic radii to periodic table. See:
http://www.wiredchemist.com/chemistry/data/metallic-radii
- WulffShape objects now returns total number of corners and edges on the shape. Useful for studies into nanoparticle catalysis.
- Minor changes to surface_analysis.py to make it less dependent on sympy
- get_entries() from MPRester() has the option to sort entries by e_above_hull. (For a future update, do we want to have an option to sort the entries in such a way that the experimentally known ground state is first in the list of entries?
- MPRester() has get_cohesive_energy() method. Takes in a mpid and can return cohesive energy per formula unit or per atom.
- Made symmetrically_remove_atoms() and symmetrically_add_atoms() to Slab class.
|
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MDU6SXNzdWUzNTExMjgwMzQ=
| 1,242
|
Why the BandStructure and BandStructureSymmLine classes have different format of ["bands"] in as_dict()
|
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[
"Seems like this issue has been defunct for many years. Closing.... Feel free to reopen if still valid."
] | 2018-08-16T09:35:00
| 2023-08-08T21:27:13
|
2023-08-08T21:27:13Z
|
NONE
|
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Hello,
I have found the following difference handling the band structure data:
https://github.com/materialsproject/pymatgen/blob/2e03585538072b9e71adaaaa659029f4beaa0a68/pymatgen/electronic_structure/bandstructure.py#L855
https://github.com/materialsproject/pymatgen/blob/2e03585538072b9e71adaaaa659029f4beaa0a68/pymatgen/electronic_structure/bandstructure.py#L551
Could you please explain why they are converted to different format? Thanks.
|
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| 351,610,040
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MDExOlB1bGxSZXF1ZXN0MjA5MTYwMDU5
| 1,243
|
Fixes bug in VoronoiNN where atoms in unit cell are not bonded
|
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[] | null |
[] | 2018-08-17T14:10:31
| 2018-08-17T20:49:33
|
2018-08-17T20:49:33Z
|
CONTRIBUTOR
|
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## Summary
Fixes a bug with VoronoiNN. Under cases where an atom is not bonded to any other atoms in its own image of a unit cell, VoronoiNN will fail to analyze the NN environment of that atom. To fix this problem, we ensure that all sites are in the list of atoms to include in a tessellation
- Added a unit test that fails before this fix, and now passes
## Additional dependencies introduced (if any)
None
## TODO (if any)
None
|
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MDU6SXNzdWUzNTE3MTk3NzQ=
| 1,244
|
Possible error in strain.Deformation.apply_to_structure
|
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[] | null |
[
"@montoyjh ",
"The cited lattice isn't cubic, the second vector has a nonzero x-component, which is deformed the same way as the first vector.",
"Put differently, the deformation gradient is a vector mapping, it's applied to each of the three lattice vectors independently.",
"Sounds good, thanks for the explanation @montoyjh!"
] | 2018-08-17T19:56:46
| 2018-08-20T12:52:22
|
2018-08-20T12:52:22Z
|
CONTRIBUTOR
|
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Possibly related to this commit: https://github.com/materialsproject/pymatgen/commit/2c3a8dc72dddc72c72ed0dd0b417edc29a76a844
In the test_apply_to_structure unit test (https://github.com/materialsproject/pymatgen/blob/master/pymatgen/analysis/elasticity/tests/test_strain.py#L43) lattice is defined as:
```
lattice = Lattice([[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603]])
```
The deformation matrix is (from the deformation: self.norm_defo = Deformation.from_index_amount((0, 0), 0.02)):
```
[[ 1.02 0. 0. ]
[ 0. 1. 0. ]
[ 0. 0. 1. ]]
```
I expect that only first row of the lattice is modified, however, the lattice in self.assertArrayAlmostEqual is:
```
[[3.9170018886, 0, 0],
[1.958500946136, 3.32571019, 0],
[0, -2.21713849, 3.13550906]]
```
The first element of the second row is modified too (1.9200989668 vs 1.958500946136) what is the reason for that?
|
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| 352,390,115
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MDExOlB1bGxSZXF1ZXN0MjA5NzExNzEy
| 1,245
|
Pourbaix revision 2
|
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[] | null |
[] | 2018-08-21T05:25:24
| 2019-04-04T02:45:13
|
2018-08-21T23:19:51Z
|
CONTRIBUTOR
|
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## Added pourbaix functionality for parallel processing of multientries and serialization
* Adds functionality for parallel processing of MultiEntry generation, activated via a `nproc` argument supplied to the PourbaixDiagram constructor. This enables ternary diagrams to be generated much more quickly and quaternary diagrams to become tractable in some cases.
* Adds serialization functionality by which previously calculated pourbaix diagrams can be reconstructed relatively efficiently.
Previous pourbaix functionality should be unaffected.
|
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MDU6SXNzdWUzNTI5MjI0OTM=
| 1,246
|
Description of method `is_valid` is wrong
|
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[] | null |
[
"Thanks for the report @bonfus! If you spot any other docstring errors you're also welcome to submit a fix directly (GItHub has an integrated text editor which makes this easy to do)."
] | 2018-08-22T12:02:03
| 2018-08-22T16:40:38
|
2018-08-22T16:40:38Z
|
NONE
|
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Line 291 misleadingly states that the default value of tol is 0.01
https://github.com/materialsproject/pymatgen/blob/8cb71acda73b1544a08a087351edf30d68ed18da/pymatgen/core/structure.py#L283-L291
while it is actually defined as `DISTANCE_TOLERANCE=0.5` above.
|
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MDExOlB1bGxSZXF1ZXN0MjEwMTQyMTc3
| 1,247
|
Improve Functional Group Substitution/Replacement, aiming to resolve Issue #1072
|
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[] | null |
[
"No errors have arisen, but it seems that Travis timed out. What is the best way to move forward and resolve this problem?"
] | 2018-08-22T13:55:11
| 2018-08-22T20:17:26
|
2018-08-22T20:17:25Z
|
CONTRIBUTOR
|
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|
## Summary
Previously, functional group substitution was only possible for Molecules, not Structures. In addition, functional group replacements (substituting a functional group for another functional group, rather than an atom for a functional group) was impossible across both classes. This pull request adds functional group substitution to Structures as well as StructureGraphs, and adds functional group replacement to MoleculeGraphs. In addition, this establishes near feature parity between the MoleculeGraph and StructureGraph classes.
## TODO
Functional Group replacement could be added to StructureGraph, if there is a significant need. It was not added yet because it would likely require a significantly different algorithm than that used for MoleculeGraph in order to identify the extent of the functional group.
|
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MDExOlB1bGxSZXF1ZXN0MjEwMzA4NDA4
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|
Fix Outcar's MSONable pattern
|
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[
"note the travis-ci test failed due to a timeout. Should we increase this to get all the tests to complete?",
"This is tricky, because this breaks the MSONable contract. ~~I'm assuming you want to keep the MSONable subclass so that dumpfn will correctly use its as_dict() method.~~ (Edit: I realize MontyEncoder will try calling as_dict() even if you don't subclass MSONable...)\r\n\r\nNote that I believe MontyEncoder will automatically add the `@class` and `@module` if they're not present anyway, so I'm not sure if the fix proposed in this PR would even work.\r\n\r\nIdeally we'd just change Outcar to be properly MSONable but I imagine that would break a lot of other code at this point.",
"Question: Why does Outcar need to be MSONAble? The idea behind an MSONable is that you can convert to and from json/yaml format. Here, the conversion is one way.",
"Yes, I agree -- we could just make Outcar not subclass MSONable. This would fix @shyamd's problem of loadfn not working, while keeping the current as_dict() behavior (though the as_dict would have to be renamed to summary_dict or similar, otherwise loadfn would still not work).",
"@shyamd what's your desired behavior here? Are you trying to use dumpfn/loadfn on a set of Outcars?",
"@mkhorton That's right. I want to dump and load files that contain Outcar dicts somewhere in them. @shyuep I'm fine with just changing Outcar to just inherit from object. In which case, It might be easiest if we just got rid of this PR and you just made the simple change. ",
"Done."
] | 2018-08-23T01:43:55
| 2020-05-12T15:47:25
|
2018-08-27T02:37:38Z
|
NONE
|
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## Summary
Outcar is subclassed from MSONable, but is not intended to fully implement this pattern as its not designed to instantiated from the dictionary it generates. This ensures that Monty doesn't try to auto convert the dictionary into an Outcar object, for instance when using loadfn from a jsonified Outcar or task document.
- Ensures Outcar as_dict dictionaries don't have "@class" or "@module" keys
- Ensures Outcar from_dict throws an error
|
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MDExOlB1bGxSZXF1ZXN0MjEwMzExMzUx
| 1,249
|
bug fix for exporting structure out of LammpsData
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[] | 2018-08-23T02:08:15
| 2018-08-24T02:35:32
|
2018-08-24T02:35:32Z
|
CONTRIBUTOR
|
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## Summary
Fixed a bug where exported structure has Ni or Co in the composition. Thanks to the report by @ucsdlxg
## Additional dependencies introduced (if any)
None
## TODO (if any)
None
|
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MDU6SXNzdWUzNTMzMTk2MTA=
| 1,250
|
suggestion in periodic.py
|
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[] | 2018-08-23T10:27:12
| 2018-08-24T20:38:46
|
2018-08-24T20:38:46Z
|
NONE
|
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## Suggested solution (if any)
In the Element class, there are plenty of grouping functions like
* is_transition_metal
* is_noble_gas
...
These are very good functions for one who wants to quickly obtain the select elements by properties. However, there was a special group of 'post_transition_metal' missing here. I suggest the developers add the followings in periodic.py
@property
def is_post_transition_metal(self):
"""
True if element is a post-transition or poor metal.
"""
return self.symbol in ("Al", "Ga", "In", "Tl", "Sn", "Pb", "Bi")
I used the following reference for the classification of elements.
http://www.elementsdatabase.com
|
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MDExOlB1bGxSZXF1ZXN0MjExNjc3NTQz
| 1,251
|
Boltztrap2 interface
|
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[
"Pls fix failing tests.",
"The main error is due to the missing package boltztrap2 that is required to make the interface work.\r\nHow can we do?",
"@fraricci Add it to requirements-optional.txt and then the test should install it.",
"I added BoltzTraP2 to requirements-optional.txt but it seems not working yet.\r\nCould someone please have a look ?",
"@fraricci It looks like its failing during the pip install. I just tried pip installing boltztrap2 on a fresh environment and it failed to properly compile as well. I think there is some trick to installing it, but that needs to be put into the travis script for it to work out right. Do you remember what these tricks where?",
"@shyamd could you try this? \r\nCFLAGS=\"-std=gnu++11\" python3 setup.py install",
"Hi @shyuep , I solved the problem with the compilation of BoltzTrap2 editing the .travis.yml.\r\nNow it pass the test but it exceed the time limit during testing pymatgen.\r\nCould you have a look? thanks",
"@shyuep thanks for merging.\r\nI also have a notebook to show how to use this interface.\r\nWhere can I add it now that the example folder is not anymore included?",
"You can add it to matgenb.materialsvirtuallab.org . You can form the repo and contribute your notebook.\r\n\r\nBTW, tests are failing on CircleCI because they are not able to install Boltztrap. See https://circleci.com/gh/materialsproject/pymatgen/8001",
"ok I'll add the notebook there.\r\nwell I added the trick to install boltztrap2 only in travis.yml.\r\nDo you want me to add it in CircleCi too?",
"yes pls\r\n"
] | 2018-08-29T08:59:00
| 2018-09-07T15:58:05
|
2018-09-07T14:02:37Z
|
CONTRIBUTOR
|
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Here a first version of the interface to BoltzTraP2.
The interface provides the user with three main classes:
- BztInterpolator: it takes care of the interpolation of the bands, giving access to coefficients of the interpolation, eigenvalues on the new denser grid, and others properties. In the same class get_band_structure() and get_dos() functions have place. The first returns a BandStructureSymmLine object interpolating bands along a high symmetry path, the second return a Dos object interpolating bands. Partial doses are also possible.
- BztTransportProperties: it deals with the calculation of the Fermi integrals that give the transport properties: Seebeck, conductivity, electrical part of thermal conductivity and Hall coefficient, conductivity effective mass tensors, Power Factor w.r.t. the chemical potential and temperatures. To create it, a BztInterpolator obj is needed as input. Upon creation, it contains transport properties tensors as properties of the object (i.e. BztTransportProperties.Seebeck_mu as a numpy array). The function compute_properties_doping() calculates all the properties w.r.t. the doping levels in input. When executed, it adds the variables "property"_doping to the BztTransportProperties object (BztTransportProperties.Seebeck_doping as a dictionary {'n':array, 'p':array}).
- BztPlotter: this is in charge of plotting the transport properties w.r.t. chemical potential, the temperature and/or the doping using the dedicated function plot_props(). Other two functions are available: plot_bands() to plot directly a band structure on symmetry line; plot_dos() to plot the total Dos. To create an instance of this object, it needs as input both a BztInterpolator and a BztTransportProperties objects.
Also, a class VasprunLoader is provided. It allows to read all the information needed for the interpolation from a Vasprun object, or from a vasprun.xml file via the from_file() function. A similar class BandstructureLoader performs the same task starting from a BS obj instead (in addition a structure object is needed in this case).
A notebook is available and shows how to use this interface works.
This a first version that tries to reproduce most of the capabilities of the current BoltzTraP interface of pmg. Also, some of the new features of BoltzTraP2, like the use of the first derivatives of energy eigenvalues, are not yet implemented in this version.
|
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MDU6SXNzdWUzNTUzODg1ODE=
| 1,252
|
Bug in API related to tasks?
|
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[
"@dwinston Can you check on this bug and fix it (with a unittest)? Thanks.",
"@shyuep bug is fixed on MP backend, and #1254 guards against regression. Can close this."
] | 2018-08-30T01:21:42
| 2018-08-30T21:41:30
|
2018-08-30T21:41:30Z
|
CONTRIBUTOR
|
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## Summary
* `GET https://www.materialsproject.org/rest/v2/tasks/` faild with a error
* the error states `NoneType object is not iterable`
## Example code
```python
url = "https://www.materialsproject.org/rest/v2/tasks/mp-1"
r=requests.get(url, {"API_KEY" : api_key})
```
or
```python
with MPRester(api_key) as m:
m.get_task_data("mp-48","energy")
```
## Error message
```
Traceback (most recent call last):
File "/var/www/python/matgen_prod/materials_django/rest/rest.py", line 94, in wrapped
d = func(*args, **kwargs)
File "/var/www/python/matgen_prod/materials_django/tasks/rest.py", line 47, in get_property
entries = qe.get_entries(crit, False, supported_task_properties)
File "/var/www/miniconda3/envs/mpprod3/lib/python3.6/site-packages/matgendb/query_engine.py", line 305, in get_entries
symbols = ["{} {}".format(func, label) for label in labels]
TypeError: 'NoneType' object is not iterable
```
## Env
`requests.__version__ = 2.18.4`
|
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MDExOlB1bGxSZXF1ZXN0MjEyMTk1NTQ0
| 1,253
|
Methods for tensors
|
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[] | 2018-08-30T20:06:01
| 2018-08-30T21:42:13
|
2018-08-30T21:42:13Z
|
CONTRIBUTOR
|
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Adds a few methods to the base tensor class for projection, integration/averaging, and summaries that are useful for higher-order tensors.
* Tensor.project projects a given tensor into a provided direction
* Tensor.average_over_unit_sphere averages the projection over the unit sphere
* Tensor.get_grouped_indices groups equivalent indices of the tensor
* Tensor.get_symbol_dict creates a dictionary of unique tensor values with their associated symbols, useful for data aggregation in a more traditional format typically found in literature
* Associated unit tests are also added.
|
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| 355,762,970
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MDExOlB1bGxSZXF1ZXN0MjEyMjE3ODgy
| 1,254
|
Add test for MPRester.get_task_data
|
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[] | 2018-08-30T21:34:55
| 2018-08-30T21:41:57
|
2018-08-30T21:41:57Z
|
MEMBER
|
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* test getting supported properties
* test aliasing
Guards against #1252
|
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MDExOlB1bGxSZXF1ZXN0MjEyMzY2NzM1
| 1,255
|
New Lobster version
|
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[] | 2018-08-31T12:24:04
| 2018-09-01T05:15:59
|
2018-09-01T05:15:59Z
|
MEMBER
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## Adapted the classes to the new Lobster version (see www.cohp.de).
*New labels for the icohps and cohps from lobster were necessary since more than one bond per atom pair is possible now
*Added a class to read in "CHARGE.lobster"
*Fixed a bug in Cohpcar class that occured when there were f orbitals in Cohpcar.lobster
*new IcohpCollection class that makes handling of the data in Icohplist.lobster easier
*new IcohpValue class that offers addional methods to handle icohpvalues (e.g. summing up the icohpvalues of both spin channels)
*Updated all unit tests
*Added new unit tests and added test data
*Included myself in the author list: is this okay or how should I deal with this?
TODO:
Wait for the results of the automatic tests and code review.
|
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| 356,014,533
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MDExOlB1bGxSZXF1ZXN0MjEyNDA5OTkw
| 1,256
|
Add converters to and from dictionary
|
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[] | 2018-08-31T14:57:30
| 2018-09-01T05:15:43
|
2018-09-01T05:15:43Z
|
CONTRIBUTOR
|
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Implement PWInput.as_dict() and PWInput.from_dict(pwinput_dict) methods to the PWInput class.
## Summary
* as_dict method returns a dictionary with a dictionary-encoded Structure object, pseudopotential dictionary, input sections, and k points information
* from_dict method takes a dictionary from as_dict and returns a PWInput object
|
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MDExOlB1bGxSZXF1ZXN0MjEyNTEzNTMz
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|
Refactor tensors into core
|
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[] | 2018-08-31T23:28:17
| 2018-09-01T05:13:51
|
2018-09-01T05:13:51Z
|
CONTRIBUTOR
|
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## Refactor tensors into core
* refactor analysis.elasticity.tensors into core.tensors
* stub added to analysis.elasticity.tensors with a deprecation warning
* adds a rounding functionality that preserves class identity (numpy's default round attribute casts ndarray subclasses back into ndarrays).
* Cleans up a few unused imports and tests
Wait on integration tests for this one, there's a lot that's been moved around and I wanna be sure everything that's using this functionality passes.
|
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MDU6SXNzdWUzNTYzNjA2OTA=
| 1,258
|
bug in electronic_structure/bandstructure.py
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[] | null |
[
"@computron I think this is yours?",
"No I think this was all written by Geoffroy, with the last update to that line from you according to \"git blame\". I really didn't have much to do with writing the band structure classes apart from writing the specific BSDosPlotter and maybe a bugfix here or there",
"I thought this error is related to BSDosPlotter? see last two lines?",
"@weixie4 can you attach the relevant files (vasprun.xml, KPOINTS, POTCAR) so we can debug?\r\n\r\n@shyuep I am pretty sure one would get the same error just by calling ``bs.get_projection_on_elements`` without invoking BSDosPlotter at all, but I would need the raw VASP files to confirm",
"Sorry my Vasprun.xml file for band structure is huge (several GBs) and I confirm it’s not an error in BSDosPlotter function—see my suggested fix to module bandstructure.py",
"I pushed a more permanent fix. Thanks for reporting the bug.",
"Thanks @shyuep. I think this bug may be related to issue 1242 https://github.com/materialsproject/pymatgen/issues/1242 because tolist() is called in the end."
] | 2018-09-03T05:17:27
| 2018-09-03T18:45:40
|
2018-09-03T17:39:43Z
|
NONE
|
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## System
* Pymatgen version: 2018.9.1
* Python version: 2.7.14 and 3.6.3
* OS version: CentOS 7.4.1708
## Summary
* On line 251, v is a Python list but called as a Numpy array
## Example code
```python
#!/usr/bin/env python
from pymatgen.io.vasp.outputs import Vasprun
from pymatgen.io.vasp.outputs import BSVasprun
from pymatgen.electronic_structure.plotter import BSDOSPlotter
dosv = Vasprun("dos/vasprun.xml")
dos = dosv.complete_dos
bsv = BSVasprun("bs/vasprun.xml", parse_projected_eigen=True,parse_potcar_file=True)
bs = bsv.get_band_structure(kpoints_filename="KPOINTS",
efermi=dos.get_cbm_vbm()[1],
line_mode=True)
bsdosp = BSDOSPlotter(bs_projection='elements', dos_projection='orbitals')
plt=bsdosp.get_plot(bs,dos)
```
## Error message
```
File "/opt/intel/intelpython3/lib/python3.6/site-packages/pymatgen/electronic_structure/plotter.py", line 2177, in get_plot
not bs.get_projection_on_elements()):
File "/opt/intel/intelpython3/lib/python3.6/site-packages/pymatgen/electronic_structure/bandstructure.py", line 252, in get_projection_on_elements
v[i, j, :, k])
TypeError: list indices must be integers or slices, not tuple
```
## Suggested solution (if any)
convert v to a numpy array before calling it. For example adding the following to line 245:
v=np.array(v)
Note this same bug exists not only in function get_projection_on_elements but also at least in function get_projections_on_elements_and_orbitals. Please convert v to a numpy array in all similar functions.
|
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| 356,444,077
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MDExOlB1bGxSZXF1ZXN0MjEyNzAzMDI5
| 1,259
|
Minor bug fix in wulff.py
|
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[] | 2018-09-03T10:18:31
| 2019-04-04T02:45:14
|
2018-09-03T15:33:11Z
|
CONTRIBUTOR
|
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Got an error when trying to plot wulff shapes today. I think there's a typo in one of the method calls in the get_plot method, this should fix. Not sure why the unit test isn't throwing an error.
|
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MDExOlB1bGxSZXF1ZXN0MjEzMjkxMzMy
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|
Change default value of fix_qcritical
|
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[] | 2018-09-05T13:19:50
| 2018-09-06T22:11:51
|
2018-09-06T22:11:51Z
|
CONTRIBUTOR
|
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## Summary
* Change default value of fix_qcritical in scheduler (False instead of True)
* Pass `**kwargs` to plt.figure in get_ax_fix_plot and get_ax3d_fig_plt
* Ignore sendmail return code in scheduler
|
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| 357,417,188
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MDU6SXNzdWUzNTc0MTcxODg=
| 1,261
|
MPRester query for band_gap does not return full dict
|
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[] | null |
[
"This is an issue with the new builders on emmet, not directly an MPRester issue. Previously gaps from all tasks were included, now only the 'highest-ranked' gap is included:\r\n\r\nhttps://github.com/materialsproject/emmet/blob/master/emmet/vasp/settings/materials_settings.json#L12",
"Will raise an issue on emmet to fix this."
] | 2018-09-05T21:49:21
| 2023-08-08T21:27:23
|
2023-08-08T21:27:23Z
|
NONE
|
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## System
* Pymatgen version: master
* Python version: 3
## Summary
* MPRester query output for 'band_gap' is not consistent with mapidoc
* expect a full dictionary of band gap information, but returns only a single band gap value (presumably from search_gap)
## Example code
```python
mpr.query({'material_id': 'mp-555599'}, ['band_gap'])
```
## Expected output
(note: code from mapidoc)
```
[{
"optimize_structure_gap": {
"band_gap": 0.013900000000000468,
"is_direct": true
},
"GGA_NSCF_gap": {
"direct_gap": 0.0,
"transition": null,
"band_gap": 0.0,
"is_direct": false
},
"search_gap": {
"direct_gap": 0.0,
"transition": null,
"band_gap": 0.0,
"is_direct": false
},
"static_gap": {
"band_gap": 0.02880000000000038,
"is_direct": false
}
}]
```
## Actual output
```
[{'band_gap': 0.0}]
```
|
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| 357,566,811
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MDExOlB1bGxSZXF1ZXN0MjEzNTQ4ODkz
| 1,262
|
TensorMapping
|
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[] | 2018-09-06T09:18:16
| 2019-04-04T02:45:16
|
2018-09-06T22:11:31Z
|
CONTRIBUTOR
|
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Replaces some of the ad-hoc tensor mapping procedures with a more explicit TensorMapping class. These are often useful for cases when one has tensor datasets (e. g. stress-strain pairs) and when organizing symmetry-equivalent tensors. Also revises Strain objects to not include deformation matrix attributes, because this adds a significant amount of overhead to the initialization.
* Replaces the functionality contained in get and set_tkd_value, which are used for manipulating tensor-keyed dictionaries, with an explicit TensorMapping class
* Adds numpy-vectorized accessor methods which significantly increase the efficiency of tensor mapping lookup relative to the prior version (by a factor of ~8)
* Adds deprecation messages to the old methods, and revises the unit-tests accordingly.
* Strain no longer includes a deformation matrix attribute or property, rather, it is refactored to be a callable
|
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MDU6SXNzdWUzNTc2NDM1MTM=
| 1,263
|
missing pymatgen.io version 2018.9.1
|
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[] | null |
[
"I don't think there is a bug. Try `python -c \"import pymatgen.io.vasp;print(dir(pymatgen.io.vasp))\"`",
"That did work!\r\n\r\nI don't fully understand why. for most packages and submodules I should be able to import the entire package (parent directory), i.e `import pymatgen` and subsequently be able to navigate down through sub-modules in the form of directories/python_scripts. \r\n\r\nwhereas in this case the submodules are only available when directly called. i.e ` import pymatgen.io.vasp`\r\n\r\nIs this a feature or a bug",
"It is probably because io conflicts with a built in python package."
] | 2018-09-06T12:59:29
| 2018-09-06T15:11:03
|
2018-09-06T13:04:26Z
|
NONE
|
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## System
* Pymatgen version: 2018.9.1
* Python version: 3.5, 3.6
* OS version: ubuntu 14.04
* environment: using conda python
* installed via pip
## Summary
* Missing the **io** submodule, i.e pymatgen.io
* I want to interact with vasp. Therefore I must use `import pymatgen.io.vasp.inputs`
* navigating to the site-packages I can **clearly** see `~/anaconda3/envs/py36/lib/python3.6/site-packages/pymatgen/io/vasp/inputs.py` but is not view-able through python
## example code
`
python -c "import pymatgen;print(dir(pymatgen))"
`
### output
> ['ArrayWithUnit', 'Composition', 'DummySpecie', 'Element', 'FloatWithUnit', 'IMolecule', 'IStructure', 'Lattice', 'MPRester', 'MSONable', 'Molecule', 'MontyDecoder', 'MontyEncoder', 'Orbital', 'PeriodicSite', 'SETTINGS', 'SETTINGS_FILE', 'Site', 'Specie', 'Spin', 'Structure', 'SymmOp', 'Unit', '\__author__', '\__builtins__', '\__cached__', '\__doc__', '\__email__', '\__file__', '\__loader__', '\__maintainer__', '\__maintainer_email__', '\__name__', '\__package__', '\__path__', '\__spec__', '\__version__', '_load_pmg_settings', 'analysis', 'bonds', 'composition', 'core', 'electronic_structure', 'entries', 'ext', 'fnmatch', 'get_structure_from_mp', 'lattice', 'loadfn', 'operations', 'os', 'periodic_table', 'sites', 'structure', 'symmetry', 'sys', 'unicode_literals', 'units', 'util', 'warnings', 'yaml']
## any assistance would be greatly appreciated
|
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MDExOlB1bGxSZXF1ZXN0MjEzNjg3MTY3
| 1,264
|
Minor bug fix in bandstructure
|
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[] | 2018-09-06T17:03:12
| 2019-04-04T02:45:15
|
2018-09-06T17:06:36Z
|
CONTRIBUTOR
|
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Fixes a bug in the initialization of projections in bandstructure.py. This was causing an error in the boltztrap module test.
|
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MDExOlB1bGxSZXF1ZXN0MjE0MTY1NDY1
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Minor CIF Parser Fixes
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[] | 2018-09-09T21:19:02
| 2018-12-20T18:46:03
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2018-09-12T15:40:29Z
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NONE
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Fixes some minor issues with CIF Parser's get_bibtex_string when dealing with ICSD CIFs
- ensure bibtex gets single strings and not arrays when dealing with _loop fields
- properly formats author field when not supplied via _loop
- simple test for get_bibtex_string so we can monitor incase it breaks
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MDExOlB1bGxSZXF1ZXN0MjE0NDMwMzYz
| 1,266
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Add function to remove atoms when atoms are too close
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[
"Structure already has a merge_sites method. Is this different? There are so many changes in this code that I don't see where the specific code is.",
"I read the merge_site code in structure.py. It seems like that it is still different from what I want if I understand it correctly. e.g. if we have three atoms a, b, c, distance of ab and bc are within the threshold. Then the merge_site code will merge these three as one atom. In my case, I just need to remove b, since ac is still normal. The pairs have closer distance are always one from bottom and one from top grain. So ab belong to different grains. While ac belong to the same grain. We should not merge these three to one.\r\n\r\nFor the many changes, \r\n1) I also add one more gb properties, 'sigma_from_site_prop'. This is to determine the sigma value from site properties. This is help to determine the site_properties determined from this code is correct or not. The accurate sigma value is from the property of 'sigma', which are determined from the volume ratio of the oriented cell and primitive cell. If the 'sigma_from_site_prop' is different from 'sigma', then the site_properties are wrong. The user may need to adjust the 'tol_coi' to find the correct coincident sites.\r\n2) I also fix a bug in the previous code in the function reduce_mat.",
"@ucsdlxg would it also be possible to rename this class to something more descriptive, e.g. `GrainBoundaryGenerator` or `GrainBoundary` instead of `Gb` ? (likewise for the submodule name)",
"`Structure.merge_sites` currently drops any site properties, which would cause problems in the Gb class (we'd lose the grain labels), so in this instance I think this PR is ok. Ideally, `Structure.merge_sites` should work for this though.",
"How about we fix merge_sites to preserve site_properties? That said, we need some way to deciding what site properties to preserve. Maybe we take average of values or something. In either case, we need to issue a warning when this is done.",
"Yes, if @ucsdlxg has the time to fix `merge_sites` I'd do that -- if site properties are the same in both merged sites, keep them, otherwise set the site property value for that label to None and issue warning. (I'm not sure I see the problem with a, b, c, merging all a, b, c -- presumably if the tolerance is small enough it would just merge a, b or b, c)",
"@ucsdlxg \r\nPlease use the template in the future and write out what you're changing and why. For instance, it looks like you're only changing GB when you're initial description sounded like you were changing structure.\r\n\r\nExample for this PR:\r\n## Summary\r\n\r\nAdded some features to GB class:\r\n* Add tolerance to remove sites rather just merging clusters using structure.merge_site\r\n This prevents groups of sites : a,b,c from being merged into one site and instead will remove just one site to satisfy the distance tolerances\r\n* Calculate Sigma from site properties",
"Hi @mkhorton, I have changed the merge_sites in structure.py to keep the site properties, and use it in GB generator. I also changed the class names.\r\n\r\nOne issue I am still a little worried is that if we use merge_sites, it need the distance matrix for the whole sites. But the overlap atoms only locate near the interface of the two grains. Therefore, if the GB is super large (e.g. tens of thousands atoms), this maybe time-consuming. ",
"Thanks for the changes @ucsdlxg. Regarding the scaling of merge_sites, have you tested it? It looks like it uses some algorithms from scipy.spatial which will hopefully be quite efficient.",
"Hi @mkhorton, I modified the structure.py to enable the multiple site_properties.\r\n\r\nIn addition, I add the joining plane information to the GB object. In principle, a GB has five freedoms. With the rotation axis (2 freedoms), angle (1), and the plane (2 freedoms) of one grain (GB plane), one can determine the GB structure (That is how the code generate the GB structure). But people usually also want to know the plane of the other grain (joining plane), so I also add that information to the GB object. For twist and symmetrical tilt, the GB plane and joining plane are the same. For a general GB, these two planes can be different.",
"@ucsdlxg Can you also fix some of the deprecation. See ..../pymatgen/pymatgen/analysis/gb/gb.py:1067: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.\r\n\r\nnp.matrix should not be used anymore.",
"@shyuep , Sure I will fix it. I also plan to write a function so that it can enumerate all possible 'twist', 'symmetric tilt', 'normal tilt', 'mixed' GBs for a given rotation axis and angle with a plane miller index cutoff for cubic system at least. This is very useful for high-throughput calculations.",
"Ok, note that the GBmaker tests also takes a relatively long time to run. So try to reduce time taken for tests. The naming also does not follow Python style conventions.\r\n\r\nSee \r\n\r\n```\r\n[success] 20.10% pymatgen.io.qchem.tests.test_outputs.TestQCOutput.test_all: 153.4455s\r\n[success] 6.81% test_gb.Test_GBGenerator.test_gb_from_parameters: 52.0188s\r\n```",
"There are a number of ops that could be vectorized by using numpy. There's at least 4 sets of nested loops that you could vectorize with numpy and get significant speed up. ",
"I cleaned up some of the gb code to remove uses of np.matrix. You should pull the latest changes before continuing.\r\n\r\nI also notice a lot of print statements. That should not be the case. You should use a proper logging and allow people to specify the verbosity level. By default, all the logs should be at info level and turned off. Otherwise, it is very noisy. This is the system used in most of pymatgen.",
"@mkhorton, @shyuep, I am trying to speed up my code. Right now, one particular slow part is the merge_site function if the number of atoms is big (e.g. tens of thousands atoms). But for the GB, the region we need to merge_site is only located near the interface region(we donot need to calculate the distance matrix for the whole lattice). Obviously, the merge_sites function cannot limit the region. Maybe we should write a new function.",
"Or maybe we can divide the GB into two pseudo structures. One use merge_site, the other keep the same.",
"Theres nothing wrong with overriding merge_sites in GB so that its specific for the GB. You might want to do something like build a temp structure with just the relevant sites. Call Structure.merge_sites there. Then copy the resulting sites back over. \r\n",
"@shyamd, yes, that's what I thought now.",
"Is this ready for merging? If so, @mkhorton can do a final check before doing so.",
"@shyuep, I think there is still one more issue I am trying to solve now. Thas is not related the GB structure but about how to determine the coincident sites. Right now, I just check the overlap sites between the top and bottom grains. But I am thinking we should also need to consider the symmetry operations. If I use Spacegroupanalyzer to obtain the symmetry operations, for some big systems, it is really slow. Do you have any suggestions? ",
"Hi @mkhorton, I have optimized the GB codes thoroughly and ready to merge. Can you help do a final check? Thanks!",
"Will do, thanks @ucsdlxg "
] | 2018-09-10T20:14:06
| 2018-09-22T00:45:04
|
2018-09-22T00:45:04Z
|
CONTRIBUTOR
|
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The user now can set a criteria to remove atoms if the atoms are too close with each other.
This function is very useful for tilt GB generation.
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MDU6SXNzdWUzNTg4NDcxOTM=
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Is there a way that I can get a band structure projected on atom?s
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[
"@hautierg Can you answer pls.",
"any updates ?",
"Viet Anh, could you answer this?\r\n\r\nThanks,\r\n\r\nGeoffroy\r\n\r\nOn 20 Sep 2018, at 15:32, Shuyang Yang <notifications@github.com<mailto:notifications@github.com>> wrote:\r\n\r\n\r\nany updates ?\r\n\r\n—\r\nYou are receiving this because you were mentioned.\r\nReply to this email directly, view it on GitHub<https://github.com/materialsproject/pymatgen/issues/1267#issuecomment-423184076>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AA3l94e63Kp1kuZ3MFJ_TbGAfZb07hluks5uc5jXgaJpZM4WiakU>.\r\n\r\n",
"BTW, by “atom”, I guess you mean a site?\r\n\r\nGeoffroy\r\n\r\nOn 20 Sep 2018, at 15:47, Geoffroy Hautier <geoffroy.hautier@uclouvain.be<mailto:geoffroy.hautier@uclouvain.be>> wrote:\r\n\r\nViet Anh, could you answer this?\r\n\r\nThanks,\r\n\r\nGeoffroy\r\n\r\nOn 20 Sep 2018, at 15:32, Shuyang Yang <notifications@github.com<mailto:notifications@github.com>> wrote:\r\n\r\n\r\nany updates ?\r\n\r\n—\r\nYou are receiving this because you were mentioned.\r\nReply to this email directly, view it on GitHub<https://github.com/materialsproject/pymatgen/issues/1267#issuecomment-423184076>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AA3l94e63Kp1kuZ3MFJ_TbGAfZb07hluks5uc5jXgaJpZM4WiakU>.\r\n\r\n\r\n",
"Yes, I want to know how different layers of slab contribute to the band structure (which is local band structure in p4vasp) Thanks so much !\n\n> On Sep 20, 2018, at 9:48 AM, Geoffroy Hautier <notifications@github.com> wrote:\n> \n> BTW, by “atom”, I guess you mean a site? \n> \n> Geoffroy \n> \n> On 20 Sep 2018, at 15:47, Geoffroy Hautier <geoffroy.hautier@uclouvain.be<mailto:geoffroy.hautier@uclouvain.be>> wrote: \n> \n> Viet Anh, could you answer this? \n> \n> Thanks, \n> \n> Geoffroy \n> \n> On 20 Sep 2018, at 15:32, Shuyang Yang <notifications@github.com<mailto:notifications@github.com>> wrote: \n> \n> \n> any updates ? \n> \n> — \n> You are receiving this because you were mentioned. \n> Reply to this email directly, view it on GitHub<https://github.com/materialsproject/pymatgen/issues/1267#issuecomment-423184076>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AA3l94e63Kp1kuZ3MFJ_TbGAfZb07hluks5uc5jXgaJpZM4WiakU>. \n> \n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub <https://github.com/materialsproject/pymatgen/issues/1267#issuecomment-423189811>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AiDD7ZDY4Xi5GStb_23cPV2AxvgsP9-sks5uc5yygaJpZM4WiakU>.\n> \n\n",
"@CMUShuyang In pymatgen/electronic_structure/plotter.py, we supported a method to plot projection for given atoms (as listed in POSCAR 1, 2, 3, or 1+2, 3+4, ...), given orbitals (s, p, d, f or px, py, pz, px+py, and so on) and given branches of the electronic band structure (e. g. \\Gamma-X). It's named as \"get_projected_plots_dots_patom_pmorb\".\r\n\r\nP/S: More details about this method are in its docstring. If you get any problem when using this, feel free to contact us. ",
"Thanks so much ! I tried with this method, it can only plot a different band structure with just the selected site. Can I also plot graph like this in pymatgen ?\r\n\r\n\r\nAnother question is , is there a same way to plot the partial dos for different site ? Thanks again !\r\n",
"Hi,\r\n\r\nI did not completely understand your first question? Do you want to play with color and plot projection of different atoms in one band structure? In plotter.py, there exists a method named \"get_elt_projected_plots_color\" allowing you to plot projections on maximum there types atoms but you cannot select specific atoms (the projection for each sort of atom is sum over all the same-type atoms).\r\n\r\nFor projected DOS on specific atoms, I haven't try with this. You can see \"class BSDOSPlotter\"."
] | 2018-09-11T00:57:21
| 2023-08-08T21:27:35
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2023-08-08T21:27:35Z
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NONE
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I can only get projected on element or orbitals when plotting the band structure. How can I project on atoms ?
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| 359,276,029
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MDU6SXNzdWUzNTkyNzYwMjk=
| 1,268
|
Is there a method to parse a formula containing "@" ?
|
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[
"Hi @tamuhey , this isn't supported right now but you'd be welcome to submit a pull request to add this feature.\r\n\r\nYou'd want to strip out any '@' symbols present in the formula here:\r\nhttps://github.com/materialsproject/pymatgen/blob/master/pymatgen/core/composition.py#L468\r\n\r\nAnd then it should work as expected.",
"pull request #1269",
"Thanks for this @tamuhey !"
] | 2018-09-12T00:56:07
| 2018-09-12T16:43:43
|
2018-09-12T16:43:43Z
|
CONTRIBUTOR
|
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I am searching a parser of chemical formula that can handle [Metallofullerene](https://en.wikipedia.org/wiki/Metallofullerene), whose formula contains "@" symbol.
For example, I tried to handle it with `pymatgen.core.composition.Composition` class, like below
```python
Composition("Dy3N@C80")
```
but this causes an error:
```
CompositionError: @ is an invalid formula!
```
I know I can deal with it by dropping "@" symbol in the formula, but I want to know cool way.
|
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MDExOlB1bGxSZXF1ZXN0MjE0ODQ0MzM0
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|
Update composition parser able to handle "@" #1268
|
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[
"Thanks."
] | 2018-09-12T06:02:26
| 2018-09-12T16:40:08
|
2018-09-12T16:39:48Z
|
CONTRIBUTOR
|
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## Summary
See the issue #1268
* Update Composition._parse_formula able to handle "@", that is usually used in Metallofullerene formula (e.g. Y3N@C80)
* Add test_Metallofullerene to test the updated parser
|
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circleci fixed
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[
"Would it be possible to notify the BoltzTrap2 maintainers of this as well? A colleague was trying to install BoltzTrap2 recently via pip and was unable to do so / didn't realize additional CFLAGS had to be set.",
"Yes, it's a better solution actually. It can be useful in general.\r\nI'll do that so. Thanks\r\n",
"@fraricci I think you need to fix the tests. Right now, the boltztrap2 tests take so long that they fail on CircleCI. Tests need to be reasonably quick (less than a minute preferably, but certainly no more than 10 mins).",
"I'll check that. It sounds weird because in my computer test for btz2 run quickly.",
"> Would it be possible to notify the BoltzTrap2 maintainers of this as well? A colleague was trying to install BoltzTrap2 recently via pip and was unable to do so / didn't realize additional CFLAGS had to be set.\r\n\r\nMy request of adding CFLAGS in BoltzTrap2 has been accepted.",
"Thanks!\n\nOn Mon, Sep 17, 2018 at 08:23, fraricci <notifications@github.com> wrote:\n\n> Would it be possible to notify the BoltzTrap2 maintainers of this as well?\n> A colleague was trying to install BoltzTrap2 recently via pip and was\n> unable to do so / didn't realize additional CFLAGS had to be set.\n>\n> My request of adding CFLAGS in BoltzTrap2 has been accepted.\n>\n> —\n> You are receiving this because you modified the open/close state.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/materialsproject/pymatgen/pull/1270#issuecomment-421993008>,\n> or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/AC1rREb1Qkd8TcDF2cmBCQXAN70xE4g-ks5ub5Q-gaJpZM4WlmhJ>\n> .\n>\n"
] | 2018-09-12T15:12:33
| 2018-09-17T20:49:05
|
2018-09-12T16:12:48Z
|
CONTRIBUTOR
|
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- circleci edited to compile boltztrap
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MDExOlB1bGxSZXF1ZXN0MjE1MDE3NjMx
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BSDOSPlotter bug fix
|
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[] | 2018-09-12T16:08:50
| 2018-09-15T15:23:12
|
2018-09-15T15:23:12Z
|
CONTRIBUTOR
|
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I found a bug in plotting the dos.
In case the densities array have zeros
in the first and last elements, the fill_between()
function fill the outer part of the dos and not the
inner one as it should.
In any case, since the dos plot has densities in the
x-axis, the proper function to use should be fill_betweenx().
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MDExOlB1bGxSZXF1ZXN0MjE1MDQzNTYx
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Allow mixed (un)compressed files from VASP band structure
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[
"Can you pls include a unittest with the code? Thanks.",
"A meaningful test will need to open files called \"KPOINTS\" and \"vasprun.xml.gz\". Where should these be located? Is there a convention in this project for making temporary files/folders during testing?",
"try using monty's ScratchDir\r\n```\r\nfrom monty.tempdir import ScratchDir\r\n\r\nwith ScratchDir(\"./\"):\r\n Copy files\r\n Test\r\n Gzip some files\r\n Test more\r\n```",
"Thanks!"
] | 2018-09-12T17:45:27
| 2018-09-19T15:18:51
|
2018-09-19T15:18:51Z
|
CONTRIBUTOR
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## Summary
Pull request corresponding to Issue #1236
Monty's `zpath` is used to find (un)compressed KPOINT files corresponding to (un)cimpressed vasprun path. This was the solution suggested by @computron and it seems to cover all the permutations; you can even use gzip for one file and bzip2 for the other.
## Additional dependencies introduced (if any)
* None. Uses existing dependency Monty
## TODO (if any)
I looked at adding a test but the existing tests rely on passing specific filenames and to test this we need to either
a) add some redundant test data files or
b) copy/zip some files to a temporary directory and delete them. Probably overkill?
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Make NearNeighbor an abstract base class
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[
"I completely second the idea of restructuring for molecules/disordered structures. Currently, any function in MoleculeGraph which interfaces with the local_env NearNeighbor strategies has to have reorder and extend_structure kwargs to play nicely with the NearNeighbor classes; most NearNeighbor classes will wrap a Structure around the MoleculeGraph's Molecule, but NearNeighbor strategies that are more designed for Molecules (OpenBabelNN, CovalentBondNN) don't have to. I think the Structure-wrapping should be handled within the NearNeighbor classes, rather than in MoleculeGraph and other external codes.\r\n\r\nWould be happy to chip in with this effort if you need, @mkhorton.",
"Adding a small note here that MinimumDistanceNN will fail (empty list) if cutoff too small (e.g. very large cells, lots of vacuum), should increase cutoff dynamically; cutoff not relevant to algorithm output as long as it's large enough, it's just for optimization.",
"Also adding a note that JmolNN can be refactored to use CutoffDictNN.",
"Also adding a note that we need to check DummySpecie are handled correctly with local_env (which is to say, DummySpecie can be ignored by local_env, I just want to make sure it returns no neighbors rather than raising an exception).",
"Is this done?",
"Kind of, the way I want to do this would break backwards compatibility, `NearNeighbour().get_nn_info(struct, n)` would become `NearNeighbour(struct).get_nn_info(n)`. I'm going to do this because I think it's worthwhile, but I want to give people who use it a heads up that this will break some code (I think in matminer in particular).",
"Implement it with deprecation for old methods. I would also suggest that method naming be as short as possible. E.g., NN.get_nn_info is redundant. Just get_info would do since NN is implied by the fact it is part of the NN class.",
"Sure, fine with the rename. Difficult to implement with deprecation warnings without also including a lot of redundant code, but maybe I'll just do that for now."
] | 2018-09-12T22:57:55
| 2023-08-08T21:28:01
|
2023-08-08T21:28:01Z
|
MEMBER
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A colleague had issues when trying to use analysis.local_env.NearNeighbor directly, which is not possible. This should be an abstract base class.
NearNeighbor could probably also be restructured a little to be friendlier to molecules and disordered structures, and avoid repeating any expensive structure operations (e.g. to avoid the issue with VIRE #1169 where BVAnalyzer is re-run on the whole structure for every single site).
Assigning myself to this for now, but tagging @nisse3000 @espottesmith in case you have any ideas/inputs or want to implement.
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- reduced excecution time for test
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[
"There are still errors. The vasprun test file provided is a bad one - the xml is invalid. See https://ci.appveyor.com/project/shyuep/pymatgen/build/1.0.2756"
] | 2018-09-13T12:13:55
| 2018-09-13T14:21:32
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- vasprun test corrected
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[
"There are still failing tests. It says VasprunLoader is not defined.",
"weird... Is it loading boltztrap2 package?",
"I'm not sure how to set a variable in windows.\r\nCould you take care of that?\r\nWe could also skip installing boltztrap2 on windows, I added an IF that should skip the test in case\r\nit's not installed"
] | 2018-09-13T14:27:14
| 2018-09-14T09:52:17
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| 360,016,793
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MDExOlB1bGxSZXF1ZXN0MjE1Mzc2Njk2
| 1,276
|
Several defect bug fixes and modification to Incar.from_string
|
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[] | null |
[
"Thre are failing tests related to the defect package. Pls fix first.",
"Apologies. Tests are fixed now."
] | 2018-09-13T18:39:13
| 2018-09-17T19:02:50
|
2018-09-17T19:02:50Z
|
CONTRIBUTOR
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**Non-defects related**
1) Add ability for Incar.from_string to import multiple settings in a single line separated by a semi-colon (Ex. "LHFCALC= True ; HFSCREEN = 0.2"), also add unit test to ensure this works
**Defects related**
2) Enforce lattice matching between defect site and structure inputs to Defect objects. Also added a unit test to ensure this works.
3) Modify defect supercell generation routine for Defect objects to allow for rotational supercell transformations. Also added a unit test to ensure this works.
4) Fix bug in bandfilling correction and modify related unit test.
|
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Chemenv uses deprecated np.matrix
|
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[] | 2018-09-16T14:25:38
| 2018-09-19T13:39:29
|
2018-09-19T13:39:29Z
|
MEMBER
|
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|
When reporting bugs/issues, please supply the following information. If
this is a feature request, please simply state the requested feature.
## System
* Pymatgen version: "master"
* Python version: 3.7
* OS version: Mac
## Summary
* Chemenv uses lots of np.matrix, resulting in the following deprecation message.
```
PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
```
## Suggested solution (if any)
* See suggested solution from scipy docs.
@davidwaroquiers @hautierg Can fix?
|
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| 1,278
|
Pourbaix tests are slow
|
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[] | null |
[
"rofl @shyuep not following his own issue template :) \r\n@montoyjh \r\n\r\nEdit: I'm bad at reading. Expected the bottom bit of text at the top in place of the default block. ",
"Erm, I did...."
] | 2018-09-16T14:54:26
| 2023-08-08T21:28:41
|
2023-08-08T21:28:41Z
|
MEMBER
|
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|
When reporting bugs/issues, please supply the following information. If
this is a feature request, please simply state the requested feature.
## System
* Pymatgen version: "Master"
## Summary
* See output from nose-timer below.
```
[success] 68.23% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixDiagramTest.test_mpr_pipeline: 33.9371s
[success] 12.18% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixDiagramTest.test_multicomponent: 6.0564s
[success] 8.52% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixDiagramTest.test_solid_filter: 4.2371s
[success] 5.51% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixDiagramTest.test_multielement_parallel: 2.7406s
[success] 3.21% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixPlotterTest.test_plot_entry_stability: 1.5974s
```
I am not sure why the MPR pipeline test needs to be done by pymatgen. That is a MP thing, not a pymatgen thing. At the very least, it needs to be simplified and made much faster. Similarly for other tests.
|
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| 1,279
|
Qchem tests are slow.
|
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[] | null |
[
"@samblau ",
"I sped up the QCOutput tests by about an order of magnitude a couple weeks ago. They now take ~16 seconds. A large QChem-centric pull request will be coming soon which will include those improvements along with many other updates. ",
"Thanks for fixing these in #1293 @samblau !"
] | 2018-09-16T15:40:21
| 2018-10-04T19:48:47
|
2018-10-04T19:48:47Z
|
MEMBER
|
{
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|
## System
* Pymatgen version: "Master"
## Summary
* QCOutput tests take so much longer than the entire VASP test suite, including CHGCARs.
```
[success] 71.43% pymatgen.io.qchem.tests.test_outputs.TestQCOutput.test_all: 159.0792s
[success] 4.17% pymatgen.io.vasp.tests.test_outputs.OutcarTest.test_init: 9.2843s
[success] 3.37% pymatgen.io.vasp.tests.test_outputs.VasprunTest.test_properties: 7.5099s
[success] 2.02% pymatgen.io.lammps.tests.test_data.LammpsDataTest.test_disassemble: 4.4909s
[success] 1.75% pymatgen.io.vasp.tests.test_outputs.OutcarTest.test_avg_core_poten: 3.9014s
[success] 1.54% pymatgen.io.vasp.tests.test_outputs.OutcarTest.test_read_piezo_tensor: 3.4306s
[success] 1.45% pymatgen.io.vasp.tests.test_outputs.ChgcarTest.test_soc_chgcar: 3.2245s
[success] 1.43% pymatgen.io.vasp.tests.test_outputs.ChgcarTest.test_init: 3.1949s
```
|
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MDU6SXNzdWUzNjA5NjI2MDI=
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Ferroelectric classes use np.matrix
|
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[
"Tagging @blondegeek for this issue. This is news to me, but looks like numpy is deprecating np.matrix:\r\n\r\n> It is no longer recommended to use this class, even for linear algebra. Instead use regular arrays. The class may be removed in the future.",
"I really need this to be dealt with soon. The visual noise from this deprecated use of np.matrix is overwhelming. @blondegeek When can we expect a fix?",
"I took a quick look at fixing this, diff here:\r\n\r\nhttps://github.com/materialsproject/pymatgen/compare/master...mkhorton:ferroelectric-deprecation-fix\r\n\r\nAll tests pass except for `test_get_polarization_change_norm()` with:\r\n\r\n```\r\n(shapes (3,), (1, 3) mismatch)\r\n x: array([-5.7944874e-03, -4.4122660e-03, 4.6288752e+01])\r\n y: array([[-5.7944874e-03, -4.4122660e-03, 4.6288752e+01]])\r\n```\r\n\r\nThis seems like a trivial fix, but it's not immediately obvious to me where the extra dimension came from.",
"Sorry for the delayed reply. I agree with @mkhorton's changes. The difference in shapes is I believe due to the following difference between `np.matrix` and `np.array` during subtraction:\r\n\r\n```\r\na = np.random.randn(1,5)\r\nb = np.random.randn(1,5)\r\na_m = np.matrix(a)\r\nb_m = np.matrix(b)\r\nprint('np.array:\\t', (a[0]-b[0]).shape)\r\nprint('np.matrix:\\t',(a_m[0] - b_m[0]).shape)\r\n>> np.array:\t (5,)\r\n>> np.matrix:\t (1, 5)\r\n```\r\n\r\nFor backward compatibility with the ferroelectric workflow in atomate, I would recommend a reshape that adds the additional dimension along axis=0. Otherwise, I would change the test to drop the axis=0 dimension.",
"Ok, can we fix and close this issue?"
] | 2018-09-17T17:13:01
| 2018-11-19T02:54:47
|
2018-11-19T02:54:47Z
|
MEMBER
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## System
* Pymatgen version: Master
## Summary
* Currently the polarization module uses np.matrix, which will be deprecated from py3.8 due to the introduction of the matrix multiplication operator (@).
* This leads of excessive deprecation messages.
## Suggested solution (if any)
* Replace all np.matrix with np.array and use np.dot for not. @espottesmith
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MDU6SXNzdWUzNjEwMzQ3MTQ=
| 1,281
|
VoronoiNN causes Python to crash
|
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[
"Looking at `VoronoiNN().get_voronoi_polyhedra()` in more detail. The code actually mentions the potential for a segfault to occur if the cutoff used is too small.\r\n\r\n```python\r\n while True:\r\n try:\r\n neighbors = structure.get_sites_in_sphere(\r\n center.coords, cutoff)\r\n neighbors = [i[0] for i in\r\n sorted(neighbors, key=lambda s: s[1])]\r\n\r\n # Run the Voronoi tessellation\r\n qvoronoi_input = [s.coords for s in neighbors]\r\n voro = Voronoi(\r\n qvoronoi_input) # can give seg fault if cutoff is too small\r\n break\r\n\r\n except RuntimeError:\r\n if cutoff >= max_cutoff:\r\n raise RuntimeError(\"Error in Voronoi neighbor finding; max \"\r\n \"cutoff exceeded\")\r\n cutoff = min(cutoff * 2, max_cutoff + 0.001)\r\n```\r\n\r\nIncreasing the cutoff in the above example prevents the crash occurring. It's not ideal that this is happening with the default cutoff. Is there some way to predict whether the cutoff will be too small and instead raise an error?\r\n\r\nEDIT: I see @mkhorton has already raised this over at scipy/scipy#8929. ",
"Yes, I did raise it at the scipy repo. No fix yet though :(\r\n\r\nI don't really understand what specific set of conditions is causing this bug (hence the rather long bug report, because I couldn't find a more minimal example).\r\n\r\nIf you can find a sensible cutoff where this never happens, that would be ideal -- otherwise (as a temporary fix) I'm not sure if it might be possible to use multiprocessing, and run the Voronoi in a child process, so if it crashes it won't bring down the parent process?",
"@utf I tried the following snippet with a known bad array:\r\n\r\n```\r\nfrom scipy.spatial import Voronoi\r\nimport multiprocessing\r\ndef get_voronoi(arr):\r\n with multiprocessing.Pool(1) as pool:\r\n result = pool.apply_async(Voronoi, (arr, ))\r\n value = result.get(timeout=1)\r\n return value\r\n```\r\n\r\nThis will raise a TimeoutError if the array is bad instead of completely crashing the parent process, so can be recovered from more gracefully.\r\n\r\nCould you investigate if this approach could be integrated as a temporary fix, until the scipy bug is resolved, and submit a PR as appropriate?",
"@mkhorton Nice idea. Will give it a go.",
"I've implemented a version where this works nicely (e.g. it increases the timeout value/cutoff depending on the crash type) which correctly calculates the data for the difficult case I mentioned above.\r\n\r\nUnfortunately, when I tried to use this with matminer to run over all the materials in the MP everything fell apart. Matminer already uses `Pool` to speed up featurization, and in the standard Python `multiprocessing` library Pools cannot create their own Pools :(.\r\n\r\nI'm looking into the option of using `ThreadPool` in matminer (as threads can create their own process Pools) but I think this might have a heavy toll on featurization time.",
"That's a shame about matminer, but from the pymatgen-point-of-view we can't really be responsible for other libraries that might also want to use multiprocessing, since we use multiprocessing elsewhere in pymatgen to speed up several operations.\r\n\r\nThat said, it does look like creating pools-of-pools might be possible. Could you use concurrent.futures for this instead? https://stackoverflow.com/a/17229030/637562\r\n\r\nThanks very much for trying this out btw. This Voronoi bug is holding up some other database building we're trying to do too, so it'd be really nice to get it fixed (we can't use CrystalNN as a default bonding strategy until this is resolved).",
"I tried implementation a number of ideas based on your suggestion. All worked equally well at catching the python crash but also caused issues with `matminer`. Unfortunately, when you start a process from within another process (either through `ProcessPoolExecutor` or by hacking `multiprocessing.Pool` to spawn non-daemonic processes), you get a severe performance loss due to extra overhead, which I think must be cython's fault.\r\n\r\nAs an alternative, I tried setting the default cutoff to 13.0 instead of 10.0. Using this method I was able to process all 85,000 structures in the materials project database without a fatal segfault occurring. Unfortunately, this also results in a slight increase in running time as more atoms will be considered.\r\n\r\nThe timings for how long it took the different approaches to process all 85,000 structures are:\r\n\r\n```\r\nPool, default cutoff=8.0: 15 h 40 min\r\nNo Pool, default cutoff=13.0: 13 h 15 min\r\n```\r\n\r\nIt seems like, for now, setting the default cutoff to 13 is a workable solution that is best for `pymatgen` and `matminer`. Unfortunately, this might not be robust to new materials being added to the MP database.\r\n\r\nIf you're happy to go down this route, I'll submit a pull request with the changes (I also fixed another bug which means that we can now get Voronoi polyhedra for all materials).",
"I'm happy with a 13 cut-off over 10, especially since the Pool solution was a bit ugly anyway. A PR would be great, thank you.\r\n\r\nThanks for the timings, the 2 hrs wouldn't have been a deal breaker since we wouldn't do a full database re-build anyway, and we can build in parallel. But even so, I'd still prefer the faster (no pool/increased cut-off) solution."
] | 2018-09-17T20:39:30
| 2018-09-26T21:27:49
|
2018-09-26T21:27:49Z
|
MEMBER
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Calling `pymatgen.analysis.local_env.VoronoiNN.get_voronoi_polyhedra()` on a structure obtained via `MPRester` is causing Python to crash. Note that Python itself is crashing, not just returning an error message.
The structure in question is: https://materialsproject.org/materials/mp-978085/. It is a relatively small primitive cell with only 4 sites.
## System
* Pymatgen version: 2018.9.12
* Python version: Tested on 3.6.2 and 3.7.0
* OS version: macOS 10.13.6 (High Sierra)
## Example code
```python
from pymatgen.analysis.local_env import VoronoiNN
from pymatgen.ext.matproj import MPRester
mpr = MPRester()
struct = mpr.get_structure_by_material_id('mp-978085')
voro = VoronoiNN()
voro.get_voronoi_polyhedra(struct, 2) # sites 0, 1, and 3 work fine
```
## Error message
macOS crash reporter produces an error message saying:
```
malloc: *** error for object 0x1062909e8: pointer being freed was not allocated
*** set a breakpoint in malloc_error_break to debug
Python quit unexpectedly.
```
This appears to be a memory allocation error. Obviously Python itself should not crash under any circumstances but I wonder if we can do something on our end to circumvent this issue.
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Structure Predictor Lambda.json not included with package
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[] | 2018-09-18T19:01:57
| 2018-09-18T19:55:20
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2018-09-18T19:55:20Z
|
NONE
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lambda.json in pymatgen.analysis.structure_predictor.data doesn't seem to be installed with the package from pypi or conda.
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Non-integer subscripts in MolecularOrbitals class
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[
"Can you implement some unittests to confirm the new functionality pls? Thanks.",
"My apologies, tests are now added."
] | 2018-09-18T20:24:15
| 2018-09-19T18:02:25
|
2018-09-19T18:02:24Z
|
CONTRIBUTOR
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## Summary
* Semantically, `MolecularOrbitals` should only handle compositions with integer-valued subscripts.
* The doc strings for this class have been updated to reflect the intended use.
* A feature has been added that raises a `ValueError` when non-integer subscripts are used.
(the issue of non-integer subscripts propagated into _matminer_, where this issue was first identified)
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MDExOlB1bGxSZXF1ZXN0MjE2NTQ4MDQx
| 1,284
|
Bug fixes and extension of chemenv + tests.
|
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[] | null |
[] | 2018-09-19T08:03:54
| 2018-09-19T13:39:28
|
2018-09-19T13:39:27Z
|
CONTRIBUTOR
|
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## Summary
* New WeightedNbSetChemenvStrategy (accepts an arbitrary number of "weights")
* New weights DistanceNbSetWeight and DeltaDistanceNbSetWeight
* Fix of func_utils (smoothstep/smootherstep problem)
* Additional tests for new functionalities
* Removed unnecessary and unused code
|
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MDExOlB1bGxSZXF1ZXN0MjE3MTEyMTg4
| 1,285
|
added parsing of volumetric data as dictionaries
|
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[
"Thanks @jmmshn, will take a look at this when tests pass."
] | 2018-09-20T21:27:46
| 2018-09-21T01:40:22
|
2018-09-21T01:40:22Z
|
CONTRIBUTOR
|
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Changed
VolumetricData(object) -> VolumetricData(MSONable)
added tests for as_dict and from_dict
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MDU6SXNzdWUzNjI3OTYyNTU=
| 1,286
|
EnumerateStructureTransformation does not return structures with correct stoichiometries
|
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[] | null |
[
"For reference, the [topic on the pymatgen help group](https://groups.google.com/forum/#!topic/pymatgen/uK6kRiSqFEg)",
"I'm a bit confused by this too. It seems clear the first ordering sets one `Cu:0.4` site to 0, and the other `Cu:0.4` site to 1, which is not completely unreasonable but obviously the overall composition is wrong.\r\n\r\nI see this is @computron's code/Anubhav has already commented on the Google Groups thread, do you have any ideas where the rounding from 0.4->0.5 occupancy is happening before I take a closer look at this? Seems like this must be happening in EnumlibAdaptor.",
"The ``max_disordered_sites`` option is supposed to be a way to help you find a good ``max_cell_size`` parameter. Rather than a single ``max_cell_size``, it keeps incrementing ``max_cell_size`` (starting small and getting higher) until EnumlibAdapter returns a solution. If EnumlibAdapter finds any solution, the iteration will quit. Thus, it tries to find the minimum ``max_cell_size`` that produces some solution from EnumlibAdapter, thus saving large computation time from having max_cell_size cranked up too high.\r\n\r\nFor this structure, you need a cell size of at least 5 in order to get the occupancies to work out. However, EnumlibAdapter already starts returning structures (with the wrong composition, Cu3Te2) at max_cell_size=2. e.g. running the code with ``EnumerateStructureTransformation(max_cell_size=2)`` will produce the same results.\r\n\r\nClearly, the assumption that the point at which EnumlibAdapter produces *any* solution is a good \"stopping\" point for the ``max_cell_size`` parameter is incorrect. Actually, I have no idea why EnumlibAdapter doesn't produce any solution for max_cell_size=1 but does so for max_cell_size=2.\r\n\r\nAnyone with more idea of the inner workings of when EnumlibAdapter does and doesn't return a solution could probably help in designing a better solution to this problem ...\r\n\r\n",
"Note also that the intention of ``max_disordered_sites`` was to help make EnumerateStructureTransformation a bit more automatic.\r\n\r\nThat said, I think this needs a rework. Maybe a separate wrapper for the automatic function. Also the automatic function can:\r\n- turn the individual site occupancies into fractions, with some tolerance. e.g. 1/4, 1/2, 1/3 occupied.\r\n- take the least common multiple of these fraction denominators. In the case above, the LCM is 12 - meaning you need at least 12 cells to get all these ordered correctly.\r\n- set the minimum and maximum cell size to be that LCM. There can be a parameter to make sure the LCM isn't too big (e.g., LCM of 1000) and you don't have too many cells. This parameter could either set a ceiling on the max cell size or (probably more relevant) set an upper ceiling on the number of disordered sites that are being ordered (which indirectly sets an upper ceiling on the max cell size).\r\n\r\n",
"I think the problem is in enumlib itself. I did some basic debugging and the structure supplied to enumlib is not changing between loops. So somehow, enumlib is assuming that a 0.4 occupancy can be satisified by a Cu3Te2.",
"We could apply some sanity checks on the output / enforce a 'strict' mode to filter these incorrect composition structs out.\r\n\r\nAgreed the ultimate issue is enumlib. It's too much of a black box for my liking.\r\n\r\nWould also be happy to adopt the LCM approach suggested, I've tried something similar previously, possibly in MagOrdering, would have to check. I know we have `DiscretizeOccupanciesTransformation` now too which would take care of part of that approach.",
"We could apply sanity checks, but I think let's confirm that the problem is indeed in enumlib, and not in some weird issue in pymatgen's generation of enumlib input files. If there is indeed a problem with enumlib, we should post an issue on enumlib itself and let Gus Hart know. On our end, we should probably implement a tolerance check that the compositions returned matches the intended ones to within a certain tolerance.",
"I found the issue. The problem in this case is that the base (which is multipled by conc to generate composition ranges) is too small a number. That resulted in rounding precision errors which causes an overlap between the species and vacancy concentration ranges when generating the enumlib input file. For example, with base 8 and a supercell size of 2, the input file generated has the range of 2-4/8 for Cu, and 3-5/8 for vacancies. That resulted in a 4/8 4/8 split satisfying the enumeration, which is a Cu3Te2. \r\n\r\nThe solution is to make sure the base is large enough. I basically just applied a 10x factor to the base. This is an arbitrary factor in any case, so it does not make all that much of a difference.\r\n\r\nThe issue is closed for now. But someone should implement the aforementioned sanity checks with composition tolerances for the generated structure as well.\r\n\r\nPMG dictator #1 has now semi-retired....",
"Nice catch, and duly noted! Will add it to the list ...",
"Note that the fixed EnumlibAdaptor caused the max_disordered test to fail. The problem is that the max disordered test was actually wrongly coded. There was a primitive cubic cell with Li 0.2, Na 0.2 and K 0.6 + 1O. If you order this with max_ordered = 5, you obviously must have a cell with 10 atoms. The test was checking that it has only 8 atoms!! The above fix actually solves this problem.",
"A quick postscript: @utf will be looking into coding an alternate enumerator in pymatgen using the bsym (https://github.com/bjmorgan/bsym) library. According to Alex, this library might improve upon common problems with enumlib."
] | 2018-09-21T22:46:48
| 2018-09-25T17:30:12
|
2018-09-25T00:13:52Z
|
CONTRIBUTOR
|
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**Describe the bug**
Using `EnumerateStructureTransformation` with the `max_disordered_sites` parameter specified does not return structures with correct stoichometry. For example, with the attached cif file for Cu2.8Te2 the output structures should be Cu7Te5; however, the output structures are Cu3Te2.
**To Reproduce**
```python
from pymatgen import Structure
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation
from pymatgen.alchemy.transmuters import StandardTransmuter
s = Structure.from_file("cu7te5.cif")
print("Original Structure")
print(s)
orderer = EnumerateStructureTransformation(max_cell_size=None, max_disordered_sites=100)
stm = StandardTransmuter.from_structures([s], [orderer], extend_collection=10)
structures = stm.transformed_structures
for s in structures:
print(s.final_structure)
```
The original structure is:
```
Original Structure
Full Formula (Cu2.8 Te2)
Reduced Formula: Cu2.8Te2
abc : 3.980000 3.980000 6.120000
angles: 90.000000 90.000000 90.000000
Sites (6)
# SP a b c
--- -------- ---- ---- -----
0 Cu 0.75 0.25 0
1 Cu 0.25 0.75 0
2 Cu:0.400 0.25 0.25 0.27
3 Cu:0.400 0.75 0.75 0.73
4 Te 0.25 0.25 0.715
5 Te 0.75 0.75 0.285
```
The output structures are:
```
Full Formula (Cu3 Te2)
Reduced Formula: Cu3Te2
abc : 3.980000 3.980000 6.120000
angles: 90.000000 90.000000 90.000000
Sites (5)
# SP a b c
--- ---- ---- ---- -----
0 Cu 0.75 0.25 1
1 Cu 0.25 0.75 0
2 Cu 0.25 0.25 0.27
3 Te 0.25 0.25 0.715
4 Te 0.75 0.75 0.285
Full Formula (Cu6 Te4)
Reduced Formula: Cu3Te2
abc : 5.628570 5.628570 7.300329
angles: 67.325145 67.325145 90.000000
Sites (10)
# SP a b c
--- ---- ------ ------ -----
0 Cu 0.25 0 0
1 Cu 0.75 0.5 1
2 Cu 0.75 0 0
3 Cu 0.25 0.5 1
4 Cu 0.865 0.615 0.27
5 Cu 0.635 0.885 0.73
6 Te 0.1425 0.8925 0.715
7 Te 0.6425 0.3925 0.715
8 Te 0.3575 0.6075 0.285
9 Te 0.8575 0.1075 0.285
...
```
**Expected behavior**
The final structures should be Cu7Te5, not Cu3Te2. Note that using `EnumerateStructureTransformation(max_cell_size=5)` returns correct stoichiometries but the code above does not.
**Desktop (please complete the following information):**
- OS: OSX 10.13.6
- Python: 3.6.2
- Pymatgen version: 2018.9.12
**Additional context**
The cu7te5.cif file is:
```
##############################################################################
# #
# Cu-Te # Cu1.4Te ht1 # 2040845 #
# #
##############################################################################
# #
# Pearson's Crystal Data #
# Crystal Structure Database for Inorganic Compounds (on DVD) #
# Release 2017/18 #
# Editors: Pierre Villars and Karin Cenzual #
# #
# Copyright (c) ASM International & Material Phases Data System (MPDS), #
# Switzerland & National Institute for Materials Science (NIMS), Japan, 2017 #
# All rights reserved. Version 2017.08 #
# #
# This copy of Pearson's Crystal Data is licensed to: #
# University of Alberta, Chemistry Department, 1-5 Installations License #
# #
##############################################################################
data_2040845
_audit_creation_date 2018-09-07
_audit_creation_method
;
Pearson's Crystal Data browser
;
#_database_code_PCD 2040845
# Entry summary
_chemical_formula_structural 'Cu~1.4~ Te'
_chemical_formula_sum 'Cu1.40 Te'
_chemical_name_mineral rickardite
_chemical_compound_source synthetic
_chemical_name_structure_type Cu~2~Sb,tP6,129
_chemical_formula_weight 216.6
# Bibliographic data
_publ_section_title
'Crystal structure of rickardite, Cu~4-x~Te~2~'
_journal_coden_ASTM AMMIAY
_journal_name_full 'Am. Mineral.'
_journal_year 1949
_journal_volume 34
_journal_page_first 441
_journal_page_last 451
_journal_language English
loop_
_publ_author_name
_publ_author_address
'Forman S.A.'
;
Toronto University
Toronto
Canada
;
'Peacock M.A.'
;
Toronto University
Toronto
Canada
;
# Standardized crystallographic data
_cell_length_a 3.98
_cell_length_b 3.98
_cell_length_c 6.12
_cell_angle_alpha 90
_cell_angle_beta 90
_cell_angle_gamma 90
_cell_volume 96.94
_cell_formula_units_Z 2
_space_group_IT_number 129
_space_group_name_H-M_alt 'P 4/n m m (origin choice 2)'
loop_
_space_group_symop_id
_space_group_symop_operation_xyz
1 'x, y, z'
2 '1/2-x, 1/2-y, z'
3 '1/2-x, y, z'
4 '-x, -y, -z'
5 '-x, 1/2+y, -z'
6 '1/2-y, 1/2-x, z'
7 '1/2-y, x, z'
8 '-y, -x, -z'
9 '-y, 1/2+x, -z'
10 '1/2+x, -y, -z'
11 '1/2+x, 1/2+y, -z'
12 'x, 1/2-y, z'
13 '1/2+y, -x, -z'
14 '1/2+y, 1/2+x, -z'
15 'y, 1/2-x, z'
16 'y, x, z'
loop_
_atom_type_symbol
Cu
Te
loop_
_atom_site_label
_atom_site_type_symbol
_atom_site_symmetry_multiplicity
_atom_site_Wyckoff_symbol
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
_atom_site_occupancy
Cu2 Cu 2 c 0.25 0.25 0.27 0.4
Te Te 2 c 0.25 0.25 0.715 1
Cu1 Cu 2 a 0.75 0.25 0 1
_exptl_crystal_colour 'purple red'
_exptl_crystal_density_meas ?
_exptl_crystal_density_diffrn 7.42
_cell_measurement_temperature ?
_cell_measurement_radiation 'X-rays, Cu Ka1'
_cell_measurement_wavelength 1.5405
_pd_proc_wavelength 1.5405
_cell_measurement_reflns_used ?
_diffrn_ambient_temperature ?
_diffrn_measurement_device film
_diffrn_measurement_device_type ?
_diffrn_radiation_type 'X-rays, Cu Ka1'
_diffrn_radiation_wavelength 1.5405
_diffrn_reflns_number ?
_exptl_absorpt_coefficient_mu ?
_exptl_absorpt_correction_type ?
_computing_structure_solution 'starting values from the literature'
_refine_ls_number_parameters ?
_refine_ls_number_reflns ?
_refine_ls_R_factor_gt ?
_refine_ls_wR_factor_gt ?
_pd_proc_ls_proof_R_factor ?
_pd_proc_ls_proof_wR_factor ?
_refine_ls_R_I_factor ?
# End of data set 2040845
```
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MDExOlB1bGxSZXF1ZXN0MjE3NTI2MDgx
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Improvements to CifParser (finite precision, implicit hydrogens) + tests
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[
"Some of the CIF fixes inadvertently change FEFF tests, will close until FEFF tests updated.",
"Hi @czhengsci ,\r\n\r\nI have some improvements to CIF that break the FEFF tests, would it be possible to update the FEFF tests?\r\n\r\nBefore the CIF changes, CoO19128.cif parses as:\r\n\r\n```\r\nStructure Summary\r\nLattice\r\n abc : 3.297078325 3.29707832413 5.254212582\r\n angles : 90.0 90.0 119.999999999\r\n volume : 49.46486182849051\r\n A : 3.297078325 0.0 2.0188782086246834e-16\r\n B : -1.6485391620151644 2.8553535869923765 2.018878208091962e-16\r\n C : 0.0 0.0 5.254212582\r\nPeriodicSite: Co (1.6485, 0.9518, 2.6078) [0.6667, 0.3333, 0.4963]\r\nPeriodicSite: Co (-0.0000, 1.9036, 5.2349) [0.3333, 0.6667, 0.9963]\r\nPeriodicSite: O (1.6485, 0.9518, 4.6168) [0.6667, 0.3333, 0.8787]\r\nPeriodicSite: O (-0.0000, 1.9036, 1.9896) [0.3333, 0.6667, 0.3787]\r\n```\r\n\r\nand after we have:\r\n\r\n```\r\nStructure Summary\r\nLattice\r\n abc : 3.297078325 3.29707832413 5.254212582\r\n angles : 90.0 90.0 119.999999999\r\n volume : 49.46486182849051\r\n A : 3.297078325 0.0 2.0188782086246834e-16\r\n B : -1.6485391620151644 2.8553535869923765 2.018878208091962e-16\r\n C : 0.0 0.0 5.254212582\r\nPeriodicSite: Co (1.6485, 0.9518, 2.6078) [0.6667, 0.3333, 0.4963]\r\nPeriodicSite: Co (0.0000, 1.9036, 5.2349) [0.3333, 0.6667, 0.9963]\r\nPeriodicSite: O (1.6485, 0.9518, 4.6168) [0.6667, 0.3333, 0.8787]\r\nPeriodicSite: O (0.0000, 1.9036, 1.9896) [0.3333, 0.6667, 0.3787]\r\n```\r\n\r\nNote that these structures are virtually identical, except `-0.0000` has become `0.0000` in the Co/O fractional co-ordinates. This seems to break the FEFF distances/Atoms file in the tests however.",
"I think u can just go ahead and fix the tests.\n\nShyue Ping\nOn Sep 24, 2018, 09:59 -0700, Matthew Horton <notifications@github.com>, wrote:\n> Hi @czhengsci ,\n> I have some improvements to CIF that break the FEFF tests, would it be possible to update the FEFF tests?\n> Before the CIF changes, CoO19128.cif parses as:\n> Structure Summary\n> Lattice\n> abc : 3.297078325 3.29707832413 5.254212582\n> angles : 90.0 90.0 119.999999999\n> volume : 49.46486182849051\n> A : 3.297078325 0.0 2.0188782086246834e-16\n> B : -1.6485391620151644 2.8553535869923765 2.018878208091962e-16\n> C : 0.0 0.0 5.254212582\n> PeriodicSite: Co (1.6485, 0.9518, 2.6078) [0.6667, 0.3333, 0.4963]\n> PeriodicSite: Co (-0.0000, 1.9036, 5.2349) [0.3333, 0.6667, 0.9963]\n> PeriodicSite: O (1.6485, 0.9518, 4.6168) [0.6667, 0.3333, 0.8787]\n> PeriodicSite: O (-0.0000, 1.9036, 1.9896) [0.3333, 0.6667, 0.3787]\n> and after we have:\n> Structure Summary\n> Lattice\n> abc : 3.297078325 3.29707832413 5.254212582\n> angles : 90.0 90.0 119.999999999\n> volume : 49.46486182849051\n> A : 3.297078325 0.0 2.0188782086246834e-16\n> B : -1.6485391620151644 2.8553535869923765 2.018878208091962e-16\n> C : 0.0 0.0 5.254212582\n> PeriodicSite: Co (1.6485, 0.9518, 2.6078) [0.6667, 0.3333, 0.4963]\n> PeriodicSite: Co (0.0000, 1.9036, 5.2349) [0.3333, 0.6667, 0.9963]\n> PeriodicSite: O (1.6485, 0.9518, 4.6168) [0.6667, 0.3333, 0.8787]\n> PeriodicSite: O (0.0000, 1.9036, 1.9896) [0.3333, 0.6667, 0.3787]\n> Note that these structures are virtually identical, except -0.0000 has become 0.0000 in the Co/O fractional co-ordinates. This seems to break the FEFF distances/Atoms file in the tests however.\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub, or mute the thread.\n"
] | 2018-09-23T19:21:30
| 2018-09-25T00:49:06
|
2018-09-25T00:49:06Z
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MEMBER
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## Summary
* Fixes issue with finite precision in frac co-ords (0.6667 vs 0.6666666667) causing errors in resulting structure.
* Will warn/store warning if attached hydrogens detected.
## Additional dependencies introduced (if any)
* None
## TODO (if any)
* None
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MDU6SXNzdWUzNjI5OTA2OTg=
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Are there any plans on adding plotly based plotting codes to pymatgen (or to the optional requirements) in the future?
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[
"I am all for adding plot.ly based GUIs. But as a longer term strategy, we probably cannot be implementing GUIs on a regular basis as frameworks and code changes.\r\n\r\nMy suggestion is to think of a \"universal\" plot data container, e.g., a Pandas Dataframe. Then we implement a few things that translate the universal container to matplotlib or plot.ly/Dash. Of course, matplotlib and Dash already supports most of Dataframe by default. Then we require all pymatgen plot generators (Phase Diagrams, DOS, Bandstructure, etc.) to basically return a pandas dataframe, possibly with some metadata (beyond what pandas already implements).\r\n\r\n@mkhorton Thoughts?",
"Yes, I have two specific thoughts on this:\r\n\r\n1. The current limitation with a lot of the pymatgen plotting methods is that they output a matplotlib plot directly rather than an intermediate format. This makes re-plotting with alternative tools difficult. So I agree, a common plot data container will be convenient -- whether pandas or otherwise.\r\n\r\n2. For plot.ly specifically, plot.ly graph objects *are* a JSON-format, so in principle they could work as this intermediate format. For an example, see [here](https://plot.ly/~Dreamshot/8248.json).",
"@richardtran415 with regards to the periodic heatmap plotter, I saw the [plot.ly example here](https://plot.ly/python/annotated_heatmap/#custom-hovertext). Was there another good example you've seen of this in another code?",
"The interactive periodic heatmap was just something I have heard about previously in some of the MP meetings. I just thought it might be nifty to have an interactive version of the heatmap on util/plotting.py since it was brought up a couple of times",
"I agree. The one advantage of making plot.ly an explicit dependency is that we could have interactive plots in Jupyter -- not just periodic table heatmaps, but phase diagrams, band structures, etc. Will have a think of the best way we could structure this. (@montoyjh reminded me of the dataframe plotting functionality in matminer too, perhaps there's a way we could structure this into a common library)",
"I definitely agree there should be a common plot containner for all plotting devices on pymatgen instead of just a pyplot object. This not only allows users to go back and forth between a static and interactive plot as said, but it also allows for better user customization of certain plots. \r\n\r\nI was just wondering since there was a lot of interest over interactive plotting with hotlinks and such for datapoints on the website, it might be easier to transition to such utilities if there were some plot.ly implementations on pymatgen first.",
"Ok, I may prototype something out ... I'm considering a `Plottable` mixin that may do some of what we've talked about.\r\n\r\n> I was just wondering since there was a lot of interest over interactive plotting with hotlinks and such for datapoints on the website, it might be easier to transition to such utilities if there were some plot.ly implementations on pymatgen first.\r\n\r\nYes, completely agreed with this!",
"A brief update on this: we're continuing to prototype Plotly versions of pymatgen plots in the new crystal toolkit (in collab with @mattmcdermott), once these are stable + look nice the intent is to merge them back into pymatgen via a new standardized plot API (`Plottable`).\r\n\r\nTo re-iterate, advantage of Plotly is: (1) it has a canonical JSON representation, so it's easy for us to store, and means we can write adaptors for other plotting tools in future if we want to (2) they have nice auto-matplotlib conversion so we can still support matplotlib for people who want matplotlib without extra effort (3) it produces interactive plots, which is especially nice in Jupyter.",
"Another (late) update on this: I am working on finishing an overhaul of phase diagram plotting (using Plotly) and will be making a WIP pull request in the upcoming days!",
"@mattmcdermott Did your PR solve this issue? can it be closed? ",
"@jdagdelen I'm not sure we ever truly overhauled plotting in pymatgen, but I think we're getting there slowly? Not sure I can say whether this issue should be closed or not.",
"I'm fine with closing it. It sounds like this is going to be an ongoing\neffort in the long run anyways, and besides, the installation of plotly in\nthe requirements.txt means we can just add on more data containers\ncompatible with plotly and matplotlib in the future if need be.\n\nOn Thu, Jul 8, 2021 at 3:02 PM Matthew McDermott ***@***.***>\nwrote:\n\n> @jdagdelen <https://github.com/jdagdelen> I'm not sure we ever truly\n> overhauled plotting in pymatgen, but I think we're getting there slowly?\n> Not sure I can comment on whether this issue should be closed or not.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/materialsproject/pymatgen/issues/1288#issuecomment-876673575>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AB5J5VQUHXU45NE4MKJY4LTTWXY4ZANCNFSM4FWXZBYA>\n> .\n>\n\n\n-- \n\nRichard Tran\nPhone: (415) 425-7660\nEmail: r ***@***.***\n",
"I can offer [this matplotlib version of a periodic table heatmap plot](https://github.com/janosh/ml-matrics#elements). it's not interactive of course and I've been considering writing a Plotly version for a while. Wasn't aware of this thread before but if there's still interest, I could tackle that sooner.",
"That looks nice. We have a matplotlib periodic plotting already in `pymatgen.utils` but I'm sure it could be improved.",
"There's a new interactive [`ptable_heatmap_plotly()`](https://github.com/janosh/pymatviz/commit/ef043fd1e9f#L335) in `pymatviz` now ([ef043fd1e](https://github.com/janosh/ml-matrics/commit/ef043fd1e9f152c05a3a7a511eb2c016acdb4cf3)).\r\n\r\n\r\n\r\n\r\n",
"`pymatgen` now uses the interactive `plotly` periodic table heatmap in [`pymatviz`](https://github.com/janosh/pymatviz) if it's installed. See #3180."
] | 2018-09-24T00:18:18
| 2023-08-08T21:33:27
|
2023-08-08T21:30:58Z
|
CONTRIBUTOR
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This might be useful for future plotting codes such as the periodic heatmap plotter. It may be useful to have interactive features for the periodic heatmap to compete with some similar codes outside of pymatgen.
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MDExOlB1bGxSZXF1ZXN0MjE4NDQ3OTQy
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|
Change default VoronoiNN cutoff
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[
"Excellent, thanks @utf !"
] | 2018-09-26T20:40:45
| 2018-09-26T21:41:22
|
2018-09-26T21:27:49Z
|
MEMBER
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## Summary
Change default cutoff of `VoronoiNN` from 10.0 to 13.0. This fixes #1281 for all 85,000 materials currently in the materials project database.
I have also changed the way the max_cutoff is calculated. Previously, the max cutoff was incorrect for cells with angles not equal to 90 deg (e.g. for the example in #1281). The max cutoff is now set to the largest of the 4 cell diagonals. I'm not confident that my implementation is the cleanest way to do this but it works.
Lastly, I moved the `self._extract_cell_info()` call to within the try-catch block, as I found that just increasing the cutoff fixed this error in most cases (maybe all cases). Just to be sure, I altered the error handling to make sure the right error message is thrown.
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MDExOlB1bGxSZXF1ZXN0MjE4ODQwNjcw
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[Waiting on tests] add BXSF support
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[
"Hi @specter119, thanks for your contribution! We will definitely need a test.\r\n\r\nAn appropriate place for this test would be `pymatgen.io.tests.test_xcrysden`, you can copy the existing `XSFTest` as a template to create a `BXSFTest`. To test your `from_vasprun()` method, you can reference one of the existing vasprun.xml files in the pymatgen test_files folder.\r\n\r\nYou can test your tests locally using `nose`, or you can just commit your tests to your fork and our CI will run the tests automatically, and leave a check-mark next to your commit if they pass.",
"Actually, you will probably have to add an example bxsf_vasprun.xml *and* an example BXSF to the test_files folder.",
"@mkhorton thanks, I will add the test.",
"Hi @specter119, any updates on the test files? Would be happy to merge this once tests are present :) ",
"His @specter119, do you have any example BXSF test files (.bxsf and the vasprun.xmls) to share so we can write a test? Then we can merge your code.",
"Closing as stale (and conflicting with current `pymatgen/io/xcrysden.py`)."
] | 2018-09-28T02:58:33
| 2022-10-10T16:58:52
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2022-10-10T16:56:48Z
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CONTRIBUTOR
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## Summary
vasp need to run with previous `CHGCAR`
start with nscf vis updated by
```python
user_incar_settings={
'SYM': -1,
'ICHARG': 11,
'ISMEAR': 0
}
```
and a [general grid](http://www.xcrysden.org/doc/XSF.html#genper) kpoints
for example:
```python
from itertools import product
import numpy as np
from pymatgen.io.vasp.inputs import Kpoints
subdivisions = [9] * 3
kpts = [
list(i) for i in product(*[np.linspace(0, 1, j) for j in subdivisions])
]
kpoints_xcrysden = Kpoints(
comment='Kponits for general grids {} x {} x {}.'.format(*subdivisions),
num_kpts=len(kpts),
style=Kpoints.supported_modes.Reciprocal,
kpts=kpts,
kpts_weights=[1] * len(kpts))
```
but I have no idea how to write a test.
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New StructureGraph analysis methods + speed up extract_molecules test
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[] | 2018-09-30T21:39:25
| 2018-09-30T21:44:28
|
2018-09-30T21:44:28Z
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MEMBER
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## Summary
* Adds three new methods to StructureGraph:
* `types_and_weights_of_connections` returns e.g. for MoS2 `{'Mo-S': [2.4169, 2.4169, 2.4169, 2.4169, 2.4169, 2.4169]`
* `weight_statistics` returns a dict summarizing statistics of weights in the graph (e.g. min, max, mean, variance)
* `types_of_coordination_environments` returns a list of co-ordination environments of the form e.g. `['Mo-S(6)', 'S-Mo(3)']` or (anonymously) `'A-B(3)', 'A-B(6)'`
All the above are intended for easier searching/aggregation in a database.
Also included is a very much faster test for `extract_molecules` (saner choice of test cell) for #1292
## Additional dependencies introduced (if any)
* None
## TODO (if any)
* None
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MDU6SXNzdWUzNjUyNjQzNTI=
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|
Longest running tests
|
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[
"I basically halved the time for tests by using multi-processing. Some of the io bound tests (e.g., getting data from MP / reading from files) are sped up enormously. But I sitll prefer to get total test time down below 10mins. Right now it is 20mins, down from 40mins.",
"This is great! I just re-ran nose-timer on my own machine, and found a few hot-spots to address next (including one of my own). Also in discussion with Joey about the Pourbaix tests."
] | 2018-09-30T21:41:54
| 2019-05-23T13:50:03
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2019-05-23T13:50:03Z
|
MEMBER
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[success] 20.10% pymatgen.io.qchem.tests.test_outputs.TestQCOutput.test_all: 153.4455s
[success] 6.81% test_gb.Test_GBGenerator.test_gb_from_parameters: 52.0188s
[success] 3.77% pymatgen.ext.tests.test_matproj.MPResterTest.test_get_pourbaix_entries: 28.8016s
[success] 2.90% test_elastic.DiffFitTest.test_fit: 22.1355s
[success] 2.81% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixDiagramTest.test_mpr_pipeline: 21.4254s
[success] 2.13% test_elastic.ElasticTensorExpansionTest.test_get_yield_stress: 16.2279s
[success] 2.01% pymatgen.core.tests.test_surface.MillerIndexFinderTests.test_generate_all_slabs: 15.3802s
[success] 2.00% pymatgen.analysis.tests.test_graphs.StructureGraphTest.test_extract_molecules: 15.3037s
[success] 2.00% pymatgen.core.tests.test_surface.SlabTest.test_symmetrization: 15.2751s
[success] 1.97% pymatgen.analysis.defects.tests.test_generators.InterstitialGeneratorTest.test_int_gen: 15.0360s
[success] 1.8
|
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Updates to QChem IO and molecule graphs, new fragmenter and bond dissociation analysis modules
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[
"Thanks. I will merge this for this time. But in future, pls submit PRs in bite-size morsels. Otherwise, it makes review extremely difficult.",
"Will do - sorry about that!",
"The fragmenter tests are taking way too long. Can you fix them pls? Pls try to get tests down to less than 10s.\r\n\r\n[None] 16.61% pymatgen.analysis.tests.test_fragmenter.TestFragmentMolecule.test_babel_PC_with_RO_depth_0_vs_depth_10: 247.0780s\r\n[None] 2.65% pymatgen.command_line.tests.test_bader_caller.BaderAnalysisTest.test_automatic_runner: 39.4394s\r\n[None] 2.56% pymatgen.analysis.tests.test_pourbaix_diagram.PourbaixDiagramTest.test_mpr_pipeline: 38.0186s\r\n[None] 2.38% pymatgen.analysis.tests.test_structure_analyzer.MiscFunctionTest.test_average_coordination_number: 35.3898s\r\n[None] 2.15% test_elastic.DiffFitTest.test_fit: 31.9953s\r\n[None] 2.13% pymatgen.core.tests.test_surface.MillerIndexFinderTests.test_generate_all_slabs: 31.6770s\r\n[None] 1.98% pymatgen.ext.tests.test_matproj.MPResterTest.test_get_pourbaix_entries: 29.4893s\r\n[None] 1.80% pymatgen.analysis.defects.tests.test_generators.InterstitialGeneratorTest.test_int_gen: 26.8336s\r\n[None] 1.60% pymatgen.command_line.tests.test_bader_caller.BaderAnalysisTest.test_from_path: 23.8605s\r\n[None] 1.37% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_complexity_factor: 20.4286s\r\n[None] 1.32% pymatgen.analysis.tests.test_fragmenter.TestFragmentMolecule.test_edges_given_TFSI: 19.6061s\r\n[None] 1.30% pymatgen.analysis.tests.test_fragmenter.TestFragmentMolecule.test_babel_TFSI: 19.3955s\r\n[None] 1.30% test_elastic.ElasticTensorExpansionTest.test_get_yield_stress: 19.3319s\r\n[None] 1.20% pymatgen.command_line.tests.test_bader_caller.BaderAnalysisTest.test_atom_parsing: 17.8511s\r\n[None] 1.14% pymatgen.analysis.tests.test_surface_analysis.WorkfunctionAnalyzerTest.test_attributes: 17.0007s\r\n[None] 1.11% pymatgen.analysis.tests.test_fragmenter.TestFragmentMolecule.test_PC_depth_0_vs_depth_10: 16.5224s\r\n[None] 1.06% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_seebeck_eff_mass: 15.7539s\r\n[None] 1.02% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_average_eff_mass: 15.2203s\r\n[None] 1.02% pymatgen.core.tests.test_surface.SlabTest.test_symmetrization: 15.1031s\r\n[None] 1.01% pymatgen.io.vasp.tests.test_outputs.OutcarTest.test_stopped: 14.9695s\r\n[None] 0.97% pymatgen.analysis.tests.test_path_finder.PathFinderTest.test_image_num: 14.4032s\r\n[None] 0.96% pymatgen.analysis.tests.test_graphs.MoleculeGraphTest.test_build_unique_fragments: 14.3393s\r\n[None] 0.96% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_power_factor: 14.2802s\r\n[None] 0.91% pymatgen.io.qchem.tests.test_outputs.TestQCOutput.test_all: 13.5214s\r\n[None] 0.90% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_seebeck: 13.3679s\r\n[None] 0.88% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_carrier_concentration: 13.1203s\r\n[None] 0.87% pymatgen.symmetry.tests.test_kpaths.HighSymmKpathTest.test_kpath_generation: 12.9847s\r\n[None] 0.83% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_conductivity: 12.3571s\r\n[None] 0.81% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_complete_dos: 12.0780s\r\n[None] 0.80% pymatgen.electronic_structure.tests.test_boltztrap.BoltztrapAnalyzerTest.test_get_hall_carrier_concentration: 11.9374s",
"Hi Shyue - that specific test is slow because it involves many calls to OpenBabel's UFF energy minimization tool that I depend on for ring openings. It also calls molecule graph's isomorphic_to many, many times, which is quite computationally intensive. However, this specific test revealed multiple bugs during development, which is why I decided to keep it despite it being so slow. @mkhorton and I were talking about it just yesterday and trying to think of a smaller test system that would still be comprehensive, but fundamentally, it needs to be an asymmetric ring with fragments from both normal bond breaking and ring bond breaking that are isomorphic. Thus far, I've been unable to think up a molecule smaller than our 13 atom test case that would still test everything covered at present.",
"I could certainly turn off the other depth_0 vs depth_10 test which I only added as a mediocre replacement for the extremely long test when babel is not present, or at least turn it off when babel is present. I could also get rid of one or both of the TFSI tests that look to be just outside of the top 10 shown above given that they likely aren't testing anything that the very long test isn't."
] | 2018-10-01T19:27:05
| 2018-10-05T19:19:39
|
2018-10-02T23:46:06Z
|
CONTRIBUTOR
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## Summary
* New molecule fragmenter in analysis/fragmentation.py. Can find all possible fragments of a molecule or only fragments obtained within a specific number of bond breakages, including ring openings. Relies heavily on molecule graphs, networkx, and open babel. Essential to a new fragmentation firetask in Atomate which is used in a new fragmentation workflow to automatically calculate the energies of all fragments necessary to obtain the bond dissociation energies of a molecule. Tests present in analysis/tests/test_fragmenter.py
* Bond dissociation calculator in analysis/bond_dissociation.py. Takes entries of a molecule and its fragments and returns the bond dissociation energies of the molecule. Typically used following the new fragmentation workflow in Atomate. Tests present in analysis/tests/test_bond_dissociation.py
* Updates to analysis/graphs.py in order to facilitate fragmentation as well as additional tests in analysis/tests/test_graphs.py
* Updates to io/qchem/outputs.py including better parsing of PCM calculations, single point calculations, enthalpy and entropy values, and some new machinery around fragmentation. Tests in io/qchem/tests/test_outputs.py updated and sped up by an order of magnitude.
* Updates to io/qchem/inputs.py and io/qchem/sets.py and their accompanying tests in order to allow the smd_solvent method to be used.
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Slab generator doesn't find all surfaces, possible bug in symmetry routines
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[
"Thank you for the comprehensive report. However, I don't think you are doing the slabs right in VESTA. First of all, you can logically reason (without any code) that it is an impossibility that (102) is not equivalent to (012) in hexagonal systems. By definition, hexagonal systems have a 3/6-fold rotational symmetry. So anything that involves permutation of just the first two miller indices must result in symmetrically equivalent planes. In your case, you can simply think of it as the a direction in a hexagonal crystal is the same as the b direction.\r\n\r\nI took the liberty of generating the (102) and (012) planes in the crystal, setting the d = 1. Download and open https://www.dropbox.com/s/wp1e0ii5zc8iskb/LNO.vesta?dl=0\r\n\r\nYou can quite clearly see that the two planes are equivalent.",
"Dear shyuep, \r\n\r\nthank you for your time and your kind reply. \r\nFirst of all, I am aware of the fact that in hexagonal systems the 4-index notation should be valid and I would conclude that (102) and (012) are equivalent. The exact crystal system based on the spacegroup analysis of pymatgen is trigonal. But also for trigonal systems the 4-index notation should be valid (please correct me if I am wrong). This is also one of the reasons why I was surprised by the differences I've seen between the (102) and (012) surface. \r\n\r\nI think I prepared the surfaces in VESTA correctly, I just used a slightly higher \"d\" and I repeated the cell several times in all directions to get a better view on the surfaces. I also took a look into your VESTA file (together with a colleague) and unfortunately we do not agree that the equivalence is \" quite clear\". To us both planes are not equally occupied with atoms, even if the plane is shifted along its normal direction I can't clearly see it. \r\n\r\nI would really appreciate if you could go more into detail, how you concluded to \"equivalent\" planes based on the visual inspection in VESTA. \r\n\r\nIf you are experienced with pymatgen you could also generate some slabs in (102) and (012) direction and they look different. \r\n\r\nIn the meanwhile we also compared with another software that is used in our group and it also returns two different surfaces. \r\n\r\nWith best regards, \r\nLars\r\n\r\n ",
"Sorry, you are right - the two planes are not equivalent. Let me ponder the problem a bit more.",
"Lars, I did some basic investigations (note: I was the one who coded the slab generators). It seems to me that the hex setting of the rhom system does not preserve all symmetries. If you instead use the primitive rhom cell of LiNiO2 to generate the slabs, you have 35 slabs within a max miller index of 2 instead of 31 generated for the hexagonal non-primitive cell. If you map the (102) and (012) miller indices to the rhom miller indices, they are quite clearly non-equivalent in the rhom setting (which should follow similar permutation symmetries as the cubic system). I need to think about this more deeply before giving you a definite answer.\r\n\r\nFor now, can you try using the prim cell to generate the slabs and see if it reproduces the two non-equivalent slabs?",
"Dear Shyue Ping Ong, \r\n\r\nis there a simple way in pymatgen to find the corresponding Miller indices in the primitive cell when the indices are known in the conventional cell? \r\nFurthermore, I am not able to reproduce the \"35 slabs within a max miller index of 2\", since it also depends on the settings of the slab generator, like \"symmetrize\" etc. \r\nIf I would know which Miller index is (012) but in the rhombohedral system it would be easier to check. \r\n\r\nBest regards, \r\nLars",
"Yes. See solution to Q5 of my crystallography exam. You can perform a coordinate transformation to map Miller indices.\r\n\r\n[Spring 2017 Final Solutions.pdf](https://github.com/materialsproject/pymatgen/files/2442167/Spring.2017.Final.Solutions.pdf)\r\n",
"Just a note that (012) in hex should map to (110) in rhom. (102) in hex should map to (411).",
"Today I confirmed that with the primitive cell as a starting point both surfaces are reproduced. ",
"Hi Lars,\n\nYou may also be interested in this recent publication.\n\nhttps://www.sciencedirect.com/science/article/pii/S0039602817307537\n\nThere is a pymatgen implementation in the SI, but I haven't merged it into\npymatgen yet.\n\nBest,\nWenhao Sun\n\nOn Thu, Oct 4, 2018 at 3:54 AM MLars <notifications@github.com> wrote:\n\n> Today I confirmed that with the primitive cell as a starting point both\n> surfaces are reproduced.\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/materialsproject/pymatgen/issues/1294#issuecomment-426974205>,\n> or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/ABvGo3-Fr4y3F4VoUTF-zPjhEhTQmwhFks5uhejkgaJpZM4XD2FF>\n> .\n>\n\n\n-- \nCeder Research Group <http://ceder.berkeley.edu/> | Postdoctoral Fellow\nLawrence Berkeley National Laboratory | Materials Sciences Division\n1 Cyclotron Rd | Bldg 33-338F | Berkeley, CA 94704\n",
"Dear wenhaosun, \r\n\r\nI am also aware of this publication and I also sent you an email regarding the stability of the program. I almost forgot about it, because I believe I fixed it for the time being, at least the results seemed reasonable. \r\n\r\nBest regards, \r\nLars",
"Dear Shyue Ping Ong, \r\n\r\nis somebody already working on a fix for the bug or is there something I can/should do? \r\n\r\nBest regards, \r\nLars",
"Yes we have some idea of a solution. @richardtran415 is working on it.",
"This is fixed.",
"Thanks a lot. I will test it tomorrow. "
] | 2018-10-02T11:33:29
| 2018-10-23T14:45:53
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2018-10-23T14:21:50Z
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Dear community,
first of all I would like to thank you for the great work you put into the project. I started working with pymatgen a few months ago. I stumbled across the "bug" by accident and did quite some tests for verification.
The intention to use pymatgen was the availability of a slab generator for crystals in order to study surfaces of hexagonal crystals, namely LiNiO2, which is a common material for batteries. I am using VASP, so I attached a POSCAR file and also minimal script which is able to reproduce the bug.
**Description of the bug**
Surfaces of crystals are characterized by their Miller index in the conventional standard cell. Some crystal surfaces and thus, also the corresponding Miller indices, are related to each other by symmetry operations. The provided script uses pymatgen to determine all distinct Miller indices of crystal surfaces up to a maximum index of 2. This is the result (just grep for "Miller" in the output and remove the Miller indices occurring twice):
Miller index: (0, 0, 1)
Miller index: (1, 0, 0)
Miller index: (1, 0, 1)
Miller index: (1, 1, 0)
Miller index: (1, 1, 1)
Miller index: (1, 0, 2)
Miller index: (2, 0, 1)
Miller index: (2, 1, 0)
Miller index: (2, 1, 1)
Miller index: (2, 2, 1)
Miller index: (2, 1, 2)
Miller index: (2, -1, 2)
I was particularly interested in the plane with Miller index (1,0,2) and (0,1,2). By cleaving the crystal along these planes (use e.g. VESTA and the conventional cell!) it is clearly visible, that both planes are completely different. (1,0,2) is kind of a vicinal surface, whereas (0,1,2) is terminated with either a (Li-Ni) plane or an Oxygen plane.
(1,0,2) surface in the top left (green plane):
(0,1,2) surface in the top left (green plane):
So my basic question is, why the (0,1,2) surface is not found by the SlabGenerator?
**Possible root cause of the faulty behavior:**
I did some more investigations and found out, that there are some symmetry operations that do not map the crystal onto itself. With the commands (to be used in the script attached to this report)
`sga=SpacegroupAnalyzer(pin.structure)`
'conv=sga.get_conventional_standard_structure()'
`symm_ops = SpacegroupAnalyzer(conv).get_symmetry_operations()`
it is possible to analyze the symmetry operations. In particular the symmetry operation in real space ((1,-1,0),(1,0,0),(0,0,1)) (with some finite translation tau), which is in reciprocal space ((0,-1,0),(1,1,0),(0,0,1)) would map the Miller index (1,0,2) to (0,1,2). However, as stated above, these planes are not equivalent. So it might be possible that I (and maybe also the programmers of the SlabGenerator) do not fully understand how symmetry works or is implemented, or there is some kind of bug in the symmetry routines of pymatgen or, in the worst case, even in spglib.
**POSCAR file:**
[POSCAR_LiNiO2.txt](https://github.com/materialsproject/pymatgen/files/2437618/POSCAR_LiNiO2.txt)
**script.py to generate slabs from crystals (please remove .txt):**
[surface_minimal.py.txt](https://github.com/materialsproject/pymatgen/files/2437622/surface_minimal.py.txt)
I would be very thankful for any feedback.
Best regards,
Lars
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Possible issue with DLSVolumePredictor
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[
"Also tagging @tschaume here",
"Increasing cutoff by 10-20x (tried up to 80.0) also doesn't seem to change the result.",
"@computron Incidentally, why are we using DLS? In most instances, the RLS should work fine. As far as I know, we don't have any method of structure prediction from completely unknown crystals.",
"1. I don't know where DLS is being used by MP (or why it was chosen instead of RLS)\r\n2. Sanity checks would be good\r\n3. Crazy failures do happen with DLS, we've seen it before. DLS tries to make sure that no two atoms are \"too close\". If _any_ two atoms happen to be closer than DLS expects, the entire volume of the cell is expanded to bring the atoms to an \"expected\" distance. Two enhancements to mitigate this behavior would be to (i) look more at the \"average\" distance between sites rather than only the worst case scenario and (ii) allow for adjustments to the cell that are not wholesale full volume expansions.\r\n\r\nFor example, let's say you have a layered compound, and two atoms within a layer are \"too close\". We'll expand the entire cell, including the inter-layer vacuum distance, to bring those atoms further apart. Ideally in this situation, however, we could just shift along axes parallel to the layers and keep the inter-layer distance the same.\r\n\r\nThere are definitely problems with DLS, but the problem is we had an undergrad working one summer on it and I really didn't have much capacity to fix the issues myself. Most fixes ended up resembling some kind of energy model. Honestly, I think with some ML we could do much better than the published DLS model while retaining the \"no reference structure\" criterion which I think is nice to have.\r\n\r\nNote that some limitations, like the one about only having the volume to expand on and not individual cell parameters, is also true for RLS. But I suspect RLS will have much fewer crazy failures.",
"For MP, I would suggest we use RLS *unless* there is no parent structure. Since RLS always starts from a known parent, it is very unlikely you have problems such as atoms being too close together.\r\n\r\nI am by no means suggesting we fix the DLS algo - I think we have more important things we are all going after. ",
"Given that DLS can give very bad results (even if sporadic, this method is often used in high-throughput), should we add a deprecation or warning to this technique? We might also add a warning if the final volume differs significantly from the initial volume.\r\n\r\nIt'd be nice if the code was still around somewhere, since it's referred to in a paper and anyone who is using it semi-carefully (e.g., with a check on the volume expansion) might still use it.",
"I would also suggest trying some of the crazy failures with DLS using RLS to see if this problem is isolated to DLS or also happens with RLS. Both of them do have the problem that they scale volume in all directions, but it's possible / likely that the RLS error will be more controlled.",
"I would suggest just a warning. Technically, it is not a deprecation given that we are not substituting it for a newer method. It is just a different method for a different use case.",
"I recommend closing this based on PR #1392 as I don't think we'll fix it any further than this anytime soon"
] | 2018-10-03T06:13:37
| 2019-02-12T21:01:24
|
2019-02-12T21:01:24Z
|
MEMBER
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**Describe the bug**
In a very small number of cases, `DLSVolumePredictor` seems to be giving non-sensical volumes.
**To Reproduce**
This structure almost made it to MP (though way off the hull!):
```
{'@module': 'pymatgen.core.structure',
'@class': 'Structure',
'charge': None,
'lattice': {'matrix': [[17.05509494, 0.0, 0.0],
[0.0, 21.75339345, -0.74777563],
[0.0, -0.74777563, 21.75339345]]},
'sites': [{'species': [{'element': 'V', 'occu': 1}],
'abc': [-0.0, -0.0, -0.0],
'properties': {'magmom': 3.454}},
{'species': [{'element': 'Sb', 'occu': 1}],
'abc': [0.5, 0.5, 0.5],
'properties': {'magmom': 0.106}},
{'species': [{'element': 'O', 'occu': 1}],
'abc': [-0.0, 0.36862268, 0.36862268],
'properties': {'magmom': 1.402}},
{'species': [{'element': 'O', 'occu': 1}],
'abc': [0.5, 0.55918021, 0.44081979],
'properties': {'magmom': 0.327}},
{'species': [{'element': 'O', 'occu': 1}],
'abc': [0.5, 0.44081979, 0.55918021],
'properties': {'magmom': 0.327}},
{'species': [{'element': 'O', 'occu': 1}],
'abc': [-0.0, 0.63137732, 0.63137732],
'properties': {'magmom': 1.402}}]}
```
I haven't got the exact input structure to hand, but the input was sensible, but the DLS predicted volume is 8786.83 Å^3, or 9226.18 Å^3 with icsd_vol=True.
**Expected behavior**
Perhaps some sanity checks? e.g. a plausible density range or % vol change? We're currently running DLSVolumePredictor against MP inputs by default, but in many cases the input volumes are not far off and may only need to change by a few %. Very large changes seem like they'd be relatively rare.
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MDU6SXNzdWUzNjY4NzI5Mzk=
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pip version 2018.9.30 is broken
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[
"Interesting, thanks for the report. I tested the release and it installs fine on my machine, both with the macOS wheel and direct from source. There is no Linux wheel at present which means your computer has to compile the Cython coord utils itself, so this is likely a compiler issue on your own machine.\r\n\r\nIf you're using the Intel compilers, are you also using the Intel Python? e.g. see a similar issue here https://stackoverflow.com/questions/49524083/importing-any-module-in-cython-file-gives-undefined-symbol-error\r\n\r\nWe have recently changed the release process, so it is also possible 2018.9.30 is bad somehow, so I will investigate further to double-check.",
"Thanks for your response. It might very well be it's something to do with\nmy system but in order to solve the problem, I carefully installed last few\nreleases one at a time and the lat at version only causes this issue. So I\nthink there must be something in the latest release that caused this. See\nif you fix it someway, otherwise, I am sure building from source instead of\nusing pip would solve this problem for me. Just wanted to report as I saw\nit was just released 3 days back.\n\n\nOn Thu, 4 Oct 2018, 21:47 Matthew Horton, <notifications@github.com> wrote:\n\n> Interesting, thanks for the report. I tested the release and it installs\n> fine on my machine, both with the macOS wheel and direct from source. There\n> is no Linux wheel at present which means your computer has to compile the\n> Cython coord utils itself, so this is likely a compiler issue on your own\n> machine.\n>\n> If you're using the Intel compilers, are you also using the Intel Python?\n> e.g. see a similar issue here\n> https://stackoverflow.com/questions/49524083/importing-any-module-in-cython-file-gives-undefined-symbol-error\n>\n> We have recently changed the release process, so it is also possible\n> 2018.9.30 is bad somehow, so I will investigate further to double-check.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/materialsproject/pymatgen/issues/1296#issuecomment-427146335>,\n> or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/AKytCcOc_soGQvyRGW4gFOdBKBvz1WEQks5uhmXJgaJpZM4XIeeJ>\n> .\n>\n",
"I'm still not seeing the issue myself, but I'll leave this issue open for a while in case other people have the same problem so we can investigate further.\r\n\r\n@shyuep issues with people installing from source and Cython/coord utils seem fairly common, e.g. also see: #1172 #1223 and [here](https://groups.google.com/forum/#!msg/pymatgen/KFS3oIFS-t0/20YCbhN0AwAJ), thoughts on having a pure Python fallback ?\r\n\r\nIn pymatgen.util.coord we could have something like:\r\n\r\n```\r\ntry:\r\n from . import coord_cython as cuc\r\nexcept ImportError:\r\n warnings.warn(\"pymatgen's optimized co-ordinate utils not installed correctly,\"\r\n \"performance of certain operations will be slower than expected.\")\r\n cuc = None\r\n```\r\n\r\nand then just update coord.py as appropriate ... it seems like only `coord_list_mapping_pbc` and `pbc_shortest_vectors` need the Cython utils anyway.\r\n\r\nIn long-term future, we may want to consider something like Cython's [pure Python mode](https://cython.readthedocs.io/en/latest/src/tutorial/pure.html) which would allow us to offer Cython-optimized and pure Python versions of the same functions.",
"I just want to add that if anyone else comes upon this issue, using the Intel distribution of Python resolved it completely.",
"Installing via pip is still leading to errors when I try to import the Lattice object from pymatgen.core. I am working on a cluster system.\r\n\r\nTraceback (most recent call last):\r\n File \"***/home/***/test/test_pymatgen.py\", line 7, in <module>\r\n from pymatgen.core import Lattice\r\n File \"/home/***/.local/lib/python3.9/site-packages/pymatgen/core/__init__.py\", line 21, in <module>\r\n from .lattice import Lattice # noqa\r\n File \"/home/***/.local/lib/python3.9/site-packages/pymatgen/core/lattice.py\", line 23, in <module>\r\n from pymatgen.util.coord import pbc_shortest_vectors\r\n File \"/home/***/.local/lib/python3.9/site-packages/pymatgen/util/coord.py\", line 17, in <module>\r\n from . import coord_cython as cuc\r\nImportError: /home/***/.local/lib/python3.9/site-packages/pymatgen/util/coord_cython.cpython-39-x86_64-linux-gnu.so: undefined symbol: __intel_sse2_strlen",
"Pls see previous comment. This sounds like there is a mismatch between your system's gcc version and that required by pymatgen. I would suggest you use a conda environment to do the installation first."
] | 2018-10-04T16:31:10
| 2021-07-01T15:50:08
|
2018-11-17T00:15:24Z
|
NONE
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**Describe the bug**
upgrading pymatgen to latest version breaks the build. The previous version works okay.
system details: python3.6 with intel
<ipython-input-2-ad23870f0a12> in <module>()
1 import numpy as np
----> 2 from pymatgen import Structure
~/.local/lib/python3.6/site-packages/pymatgen/__init__.py in <module>()
54 # Allows from pymatgen import <class> for quick usage.
55
---> 56 from pymatgen.core import *
57 from .electronic_structure.core import Spin, Orbital
58 from .ext.matproj import MPRester
~/.local/lib/python3.6/site-packages/pymatgen/core/__init__.py in <module>()
7 from .periodic_table import Element, Specie, DummySpecie
8 from .composition import Composition
----> 9 from .structure import Structure, IStructure, Molecule, IMolecule
10 from .lattice import Lattice
11 from .sites import Site, PeriodicSite
~/.local/lib/python3.6/site-packages/pymatgen/core/structure.py in <module>()
28
29 from pymatgen.core.operations import SymmOp
---> 30 from pymatgen.core.lattice import Lattice
31 from pymatgen.core.periodic_table import Element, Specie, get_el_sp, DummySpecie
32 from monty.json import MSONable
~/.local/lib/python3.6/site-packages/pymatgen/core/lattice.py in <module>()
14
15 from monty.json import MSONable
---> 16 from pymatgen.util.coord import pbc_shortest_vectors
17 from pymatgen.util.num import abs_cap
18
~/.local/lib/python3.6/site-packages/pymatgen/util/coord.py in <module>()
9 import numpy as np
10 import math
---> 11 from . import coord_cython as cuc
12
13 """
ImportError: /gpfs/backup/users/home/desa/.local/lib/python3.6/site-packages/pymatgen/util/coord_cython.cpython-36m-x86_64-linux-gnu.so: undefined symbol: __intel_sse2_strchr
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Test ieee
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[] | 2018-10-04T21:38:53
| 2019-04-04T02:45:17
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2018-10-04T23:19:42Z
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CONTRIBUTOR
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## Summary
* Fixes bug in triclinic IEEE tensor conversion where c axis was being rotated into x instead of z axis.
* Adds test for triclinic conversion.
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Insertion batt js
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[
"Test for a case that previously wasn't working?"
] | 2018-10-08T21:09:47
| 2018-10-13T05:49:08
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2018-10-13T05:49:08Z
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CONTRIBUTOR
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## Small changes to the insertion battery module
* updated the artificial shift in energy to the non-working-ion elements so that we can always have a convex hull of with just the working-ion changing
* made the dicts for adj_pairs and all_paris return the actual list and not just the iterator.
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| 368,187,266
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MDExOlB1bGxSZXF1ZXN0MjIxNDIwODE4
| 1,299
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Removes build_MoleculeGraph, replaces with MoleculeGraph constructors
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[
"Thanks for this!"
] | 2018-10-09T12:33:10
| 2018-10-09T23:21:03
|
2018-10-09T16:11:15Z
|
CONTRIBUTOR
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Recently, a build_MoleculeGraph function was added to pymatgen.analysis.graphs. This function allowed the user to create a MoleculeGraph either by specifying a pymatgen.analysis.local_env strategy, or by specifying the edges between nodes explicitly. This function was deemed unnecessary, as it effectively replicated the functionality of the existing MoleculeGraph constructors.
This pull request removes all references to build_MoleculeGraph and replaces them either with MoleculeGraph.with_local_env_strategy, or with the new MoleculeGraph.with_edges constructor. An analogous constructor has been added to StructureGraph (StructureGraph.with_edges).
This PR also fixes a small bug with pymatgen.analysis.functional_groups.FunctionalGroupExtractor that caused failures on some ring groups.
|
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MDExOlB1bGxSZXF1ZXN0MjIyNjE2NjY5
| 1,300
|
Chgcar insertion
|
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[
"What's the difference between this method and Chgcar.get_integrated_diff?",
"This is meant to look at the data['total'] and just suggest the best site to insert an interstitial atom.\r\nThe Chgcar.get_integrated_diff is really meant for analysis of the data['diff'].\r\nThere is mention of get_integrated_diff being time-consuming in the documentation, I assume that is for the binning process. We do not need to do the binning here.\r\nOther than that, I think I can replace the masking parts of my routines with the `lattice.get_points_in_sphere` so there is less redundant code.",
"Hi @jmmshn, I think this is similar to `ChargeDensityAnalyzer` class, which locates local minima using charge density read from CHGCAR or similar files. See the example code below.\r\n\r\n```python\r\nfrom pymatgen.analysis.defects.point_defects import ChargeDensityAnalyzer as CDA\r\n\r\nca = CDA.from_file(\"CHGCAR_sum\") # or CHGCAR\r\ns = ca.get_structure_with_nodes()\r\ns.to(filename=\"output.cif\")\r\n```",
"Hi @HanmeiTang ,\r\nThat is actually the issue I'm trying to address.\r\nWhen I initially used\r\n```\r\nget_structure_with_nodes \r\n```\r\nTo get a updated structure I ran into problems because the exact value of the charge density is not a good indicator of how much space there is for a new atom at that point. By integrating around the possible site, I was able to get a better guess at which site has the most amount of \"free space\" for the interstitial atom.\r\n\r\nPlease let me know if I'm mistaken on something.\r\n\r\nBest,\r\n-Jimmy",
"@jmmshn, so this method actually re-sorts all local minima using integrated charge density at a specific radius instead of charge density. Thanks for the clarification. ",
"Hi @shyuep and @HanmeiTang thanks for your feedback and sorry for the late reply.\r\nI tried to make it work with `lattice.get_points_in_sphere` but that routine works slightly differently:\r\n`lattice.get_points_in_sphere` it creates the supercell first and then tries to fit the sphere in \r\nSince I just need to get the smallest distance for the set of points in my unit cell much of what that routine does is not needed.\r\nSo I have kept the implementation mostly the same and cleaned up the code a little.\r\nPlease let me know if there is anything else you need."
] | 2018-10-13T00:46:20
| 2018-11-05T03:06:59
|
2018-11-05T03:06:59Z
|
CONTRIBUTOR
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added capability to identify site by their integrated charge density
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