Source code for asr.c2db.dimensionality

from asr.core import command, option, AtomsFile, ASRResult
from pathlib import Path
from ase import Atoms
from ase.db.row import AtomsRow
import numpy as np

def get_dimtypes():
    """Create a list of all dimensionality types."""
    from itertools import product
    s = set(product([0, 1], repeat=4))
    s2 = sorted(s, key=lambda x: (sum(x), *[-t for t in x]))[1:]
    string = "0123"
    return ["".join(x for x, y in zip(string, s3) if y) + "D" for s3 in s2]

def webpanel(result, context):
    from asr.database.browser import table, fig
    dimtable = table(result, 'Dimensionality scores',
                     [f'dim_score_{dimtype}' for dimtype in get_dimtypes()],
                     context.descriptions, 2)
    panel = {'title': 'Dimensionality analysis',
             'columns': [[dimtable], [fig('dimensionality-histogram.png')]]}
    return [panel]

def plot_dimensionality_histogram(row: AtomsRow, path: Path) -> None:
    from matplotlib import pyplot as plt
    dimtypes = get_dimtypes()
    vs = []
    for dimtype in dimtypes:
        v = row.get(f'dim_score_{dimtype}', 0)
    x = np.arange(dimtypes)
    fig, ax = plt.subplots(), vs)
    prettykeys = [f'$S_{{{dimtype}}}$' for dimtype in dimtypes]
    ax.set_xticklabels(prettykeys, rotation=90)

[docs]@command('asr.c2db.dimensionality') @option('--atoms', type=AtomsFile(), default='structure.json') def main(atoms: Atoms) -> ASRResult: """Make cluster and dimensionality analysis of the input structure. Analyzes the primary dimensionality of the input structure and analyze clusters following Mahler, et. al. Physical Review Materials 3 (3), 034003. """ from ase.geometry.dimensionality import analyze_dimensionality k_intervals = [dict(interval._asdict()) for interval in analyze_dimensionality(atoms, merge=False)] dim_scores = {} dim_thresholds = {i: None for i in range(4)} # 1000 is arbitrary # Fix for numpy.int64 in cdim which is not jsonable. for interval in k_intervals: cdim = {int(key): value for key, value in interval['cdim'].items()} interval['cdim'] = cdim dim_scores[interval['dimtype']] = interval['score'] for nd in range(4): if interval['h'][nd]: dim_thresholds[nd] = min(dim_thresholds[nd] or 1000, interval['a']) results = {'k_intervals': k_intervals} primary_interval = k_intervals[0] dim_primary = primary_interval['dimtype'] dim_primary_score = primary_interval['score'] results['dim_primary'] = dim_primary results['dim_primary_score'] = dim_primary_score for dimtype in get_dimtypes(): results[f'dim_score_{dimtype}'] = dim_scores.get(dimtype, 0) for nd in range(4): results[f'dim_nclusters_{nd}D'] = primary_interval['h'][nd] if dim_thresholds[nd] is not None: results[f'dim_threshold_{nd}D'] = dim_thresholds[nd] return results