17694421918

Committed 13 Sep 2025 09:00AM UTC coverage: 50.459% (+9.6%) from 40.81%

Build # 17694421918

Build Type

Pull #136

github

Committed by

web-flow

Commit Message

Merge 31195e228 into ba206def0

Pull Request Pull Request #136: Prepare release

Run Details

26 of 88 branches covered (29.55%)

Branch coverage included in aggregate %.

2 of 19 new or added lines in 5 files covered. (10.53%)

21 existing lines in 4 files now uncovered.

359 of 675 relevant lines covered (53.19%)

1.6 hits per line

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27.78

/src/quant_met/cli/q_analysis.py

"""Functions to run self-consistent calculation for the order parameter."""

import logging
from pathlib import Path

import h5py
import pandas as pd
import sisl

from quant_met import routines
from quant_met.parameters import Parameters
from quant_met.parameters.control import QAnalysis

logger = logging.getLogger(__name__)


def q_analysis(parameters: Parameters) -> None:
    """Self-consistent calculation for the order parameter.

    Parameters
    ----------
    parameters: Parameters
        An instance of Parameters containing control settings, the model,
        and k-point specifications for the T_C calculation.
    """
    if not isinstance(parameters.control, QAnalysis):
        err_msg = "Wrong parameters for q-loop."
        raise TypeError(err_msg)

    result_path = Path(parameters.control.outdir)
    result_path.mkdir(exist_ok=True, parents=True)

    q_data: dict[str, pd.DataFrame] = {}
    with h5py.File(f"{parameters.control.q_data}") as f:
        for key in f:
            q_data.update({key: pd.DataFrame()})

    for key in q_data:
        data: pd.DataFrame = pd.read_hdf(f"{parameters.control.q_data}", key=key)
        q_data[key] = data

    hamiltonian = sisl.get_sile(parameters.control.hamiltonian_file).read_hamiltonian()

    (
        lengths_vs_temp,
        gap_and_current_fig,
    ) = routines.get_lengths_vs_temp(q_data=q_data, hamiltonian=hamiltonian)

    result_file = result_path / f"{parameters.control.prefix}_sc_lengths.hdf5"
    if result_file.exists():
        result_file.unlink()
    lengths_vs_temp.to_hdf(result_file, key="lengths_vs_temp")
    gap_and_current_fig.savefig(
        result_path / f"{parameters.control.prefix}_gap_and_current_vs_q.pdf"
    )

    zero_temp_lengths, length_vs_temp_fig = routines.get_zero_temperature_values(
        hamiltonian=hamiltonian,
        lengths_vs_temp=lengths_vs_temp,
    )
    zero_temp_lengths.to_hdf(result_file, key="zero_temp_lengths")
    length_vs_temp_fig.savefig(
        f"{parameters.control.outdir}/{parameters.control.prefix}_lengths_vs_temperature.pdf",
    )

1	"""Functions to run self-consistent calculation for the order parameter."""
2
3	import logging	3✔
4	from pathlib import Path	3✔
5
6	import h5py	3✔
7	import pandas as pd	3✔
8	import sisl	3✔
9
10	from quant_met import routines	3✔
11	from quant_met.parameters import Parameters	3✔
12	from quant_met.parameters.control import QAnalysis	3✔
13
14	logger = logging.getLogger(__name__)	3✔
15
16
17	def q_analysis(parameters: Parameters) -> None:	3✔
18	"""Self-consistent calculation for the order parameter.
19
20	Parameters
21	----------
22	parameters: Parameters
23	An instance of Parameters containing control settings, the model,
24	and k-point specifications for the T_C calculation.
25	"""
26	if not isinstance(parameters.control, QAnalysis):	×
27	err_msg = "Wrong parameters for q-loop."	×
28	raise TypeError(err_msg)	×
29
NEW 30	result_path = Path(parameters.control.outdir)	×
NEW 31	result_path.mkdir(exist_ok=True, parents=True)	×
32
33	q_data: dict[str, pd.DataFrame] = {}	×
34	with h5py.File(f"{parameters.control.q_data}") as f:	×
35	for key in f:	×
36	q_data.update({key: pd.DataFrame()})	×
37
38	for key in q_data:	×
39	data: pd.DataFrame = pd.read_hdf(f"{parameters.control.q_data}", key=key)	×
40	q_data[key] = data	×
41
42	hamiltonian = sisl.get_sile(parameters.control.hamiltonian_file).read_hamiltonian()	×
43
44	(	×
45	lengths_vs_temp,
46	gap_and_current_fig,
47	) = routines.get_lengths_vs_temp(q_data=q_data, hamiltonian=hamiltonian)
48
NEW 49	result_file = result_path / f"{parameters.control.prefix}_sc_lengths.hdf5"	×
50	if result_file.exists():	×
51	result_file.unlink()	×
52	lengths_vs_temp.to_hdf(result_file, key="lengths_vs_temp")	×
53	gap_and_current_fig.savefig(	×
54	result_path / f"{parameters.control.prefix}_gap_and_current_vs_q.pdf"
55	)
56
57	zero_temp_lengths, length_vs_temp_fig = routines.get_zero_temperature_values(	×
58	hamiltonian=hamiltonian,
59	lengths_vs_temp=lengths_vs_temp,
60	)
61	zero_temp_lengths.to_hdf(result_file, key="zero_temp_lengths")	×
62	length_vs_temp_fig.savefig(	×
63	f"{parameters.control.outdir}/{parameters.control.prefix}_lengths_vs_temperature.pdf",
64	)

Ruberhauptmann / quant-met / 17694421918

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