uppasd_tools.collect

Collect averaged output columns from run directories matching a name template.

Parameters:

root – Directory containing run subdirectories.
name_template – Folder name template with {field} placeholders used to extract variables into columns (e.g., “run_T{T}_P{P}”).
simid – Optional UppASD simid to select a specific output set within each run directory. When None, UppOut auto-detects the simid.
start – Start index for row slicing (inclusive) before averaging.
end – End index for row slicing (exclusive) before averaging.
step – Step for row slicing before averaging.
strict – When True, raise on any bad run; when False, skip with a warning. Missing output files are always skipped with an error log.
progress – When True, show a progress bar during collection.

Returns:

DataFrame containing one row per run directory. Columns include the extracted template variables and the averaged values for Mx, My, Mz, M, and M_std.

Collect averaged cumulants columns from run directories matching a name template.

Parameters:

root – Directory containing run subdirectories.
name_template – Folder name template with {field} placeholders used to extract variables into columns (e.g., “run_T{T}_P{P}”).
simid – Optional UppASD simid to select a specific output set within each run directory. When None, UppOut auto-detects the simid.
start – Start index for row slicing (inclusive) before averaging.
end – End index for row slicing (exclusive) before averaging.
step – Step for row slicing before averaging.
strict – When True, raise on any bad run; when False, skip with a warning. Missing output files are always skipped with an error log.
progress – When True, show a progress bar during collection.

Returns:

DataFrame containing one row per run directory. Columns include the extracted template variables and the averaged values for M, M2, M4, Binder, chi, Cv, E, E_exch, and E_lsf.

Collect averaged energy columns from run directories matching a name template.

Parameters:

root – Directory containing run subdirectories.
name_template – Folder name template with {field} placeholders used to extract variables into columns (e.g., “run_T{T}_P{P}”).
simid – Optional UppASD simid to select a specific output set within each run directory. When None, UppOut auto-detects the simid.
start – Start index for row slicing (inclusive) before averaging.
end – End index for row slicing (exclusive) before averaging.
step – Step for row slicing before averaging.
strict – When True, raise on any bad run; when False, skip with a warning. Missing output files are always skipped with an error log.
progress – When True, show a progress bar during collection.

Returns:

DataFrame containing one row per run directory. Columns include the extracted template variables and the averaged values for tot, exch, aniso, DM, PD, BiqDM, BQ, dip, Zeeman, LSF, and chir.

uppasd_tools.collect.collect_projavgs(root: str | Path, name_template: str, simid: str | None = None, start: int | None = None, end: int | None = None, step: int | None = None, strict: bool = True, progress: bool = True) → dict[int, DataFrame]

Collect averaged projavgs columns from run directories matching a name template.

Parameters:

root – Directory containing run subdirectories.
name_template – Folder name template with {field} placeholders used to extract variables into columns (e.g., “run_T{T}_P{P}”).
simid – Optional UppASD simid to select a specific output set within each run directory. When None, UppOut auto-detects the simid.
start – Start index for row slicing (inclusive) before averaging.
end – End index for row slicing (exclusive) before averaging.
step – Step for row slicing before averaging.
strict – When True, raise on any bad run; when False, skip with a warning. Missing output files are always skipped with an error log.
progress – When True, show a progress bar during collection.

Returns:

Dictionary of DataFrames, one per projection index. Columns include the extracted template variables and the averaged values for M, M_std, Mx, My, and Mz.

uppasd_tools.collect.collect_projcumulants(root: str | Path, name_template: str, simid: str | None = None, start: int | None = None, end: int | None = None, step: int | None = None, strict: bool = True, progress: bool = True) → dict[int, DataFrame]

Collect averaged projcumulants columns from run directories matching a name template.

Parameters:

root – Directory containing run subdirectories.
name_template – Folder name template with {field} placeholders used to extract variables into columns (e.g., “run_T{T}_P{P}”).
simid – Optional UppASD simid to select a specific output set within each run directory. When None, UppOut auto-detects the simid.
start – Start index for row slicing (inclusive) before averaging.
end – End index for row slicing (exclusive) before averaging.
step – Step for row slicing before averaging.
strict – When True, raise on any bad run; when False, skip with a warning. Missing output files are always skipped with an error log.
progress – When True, show a progress bar during collection.

Returns:

Dictionary of DataFrames, one per projection index. Columns include the extracted template variables and the averaged values for M, M2, M4, Binder, and chi.

uppasd_tools.collect.get_matching_directories(root: str | Path, name_template: str, *, sort: bool = True) → dict[str, list[Path] | list[float | int | str]]

Return run subdirectories matching a folder-name template.

Parameters:

root – Directory containing run subdirectories.
name_template – Folder name template with {field} placeholders used for matching (e.g., “run_T{T}_P{P}”).
sort – When True, return paths sorted by directory name.

Returns:

Dictionary with - directories: list of matching run directory paths. - one key per placeholder field: list of values in directory order.