psweep

CALC_DIR	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
DATABASE_BASENAME	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
FileTemplate(filename[, target_suffix])
GIT_ADD_ALL	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Machine(machine_dir[, jobscript_name])
PANDAS_DEFAULT_ORIENT	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
PANDAS_TIME_UNIT	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
PSET_HASH_ALG	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
PsweepHashError
_get_col_filter([skip_prefix_cols, ...])	Implement the package-wide prefix/postfix default convention.
_setup_simulate_dir(*, calc_dir, ...)
capture_logs_wrapper(pset, func, capture_logs)	Capture and redirect stdout and stderr produced in func().
check_calc_dir(calc_dir, df)	Check calc dir for consistency with database.
df_ensure_dtypes(df[, fill_value])	Make sure that df's dtype is object.
df_extract_dicts(df[, py_types])	Convert df's rows to dicts.
df_extract_params(df[, py_types])	Extract params (list of psets) from df.
df_extract_pset(df, pset_id[, py_types])	Extract a single pset dict for pset_id from df.
df_filter_conds(df, conds[, op])	Filter DataFrame using bool arrays/Series/DataFrames in conds.
df_print(df[, index, special_cols, ...])	Print DataFrame, by default without the index and prefix columns such as _pset_id.
df_read(fn[, fmt])	Read DataFrame from file fn.
df_to_json(df, **kwds)	Like df.to_json but with defaults for orient, date_unit, date_format, double_precision.
df_update_pset_cols(df, pset_cols[, ...])	Make sure that df has at least pset_cols columns.
df_update_pset_hash(df[, copy])	Add or update _pset_hash column.
df_write(fn, df[, fmt])	Write DataFrame to disk.
file_read(fn)
file_write(fn, txt[, mode])
filter_cols(cols[, kind])
filter_params_dup_hash(params, hashes)	Return params with psets whose hash is not in hashes.
filter_params_unique(params)	Reduce params to unique psets.
flatten(seq)
fullpath(path)
func_wrapper(pset, func, *[, tmpsave, ...])	Add those prefix fields (e.g. _time_utc) to pset which can be determined at call time.
gather_calc_templates(calc_templ_dir)
gather_machines(machine_templ_dir)
get_many_uuids(num[, retry, existing])
get_uuid([retry, existing])
git_clean()
git_enter(use_git[, always_commit])
git_exit(use_git, df)
in_git_repo()
intspace(*args[, dtype])	Like np.linspace but round to integers.
is_seq(seq)
itr(func)	Wrap func to allow passing args not as sequence.
itr2params(loops)	Transform the (possibly nested) result of a loop over plists (or whatever has been used to create psets) to a proper list of psets by flattening and merging dicts.
json_read(fn)
json_write(fn, obj)
logspace(start, stop[, num, offset, log_func])	Like numpy.logspace but
makedirs(path)	Create path recursively, no questions asked.
merge_dicts(args)	Start with an empty dict and update with each arg dict left-to-right.
pgrid(plists)	Convenience function for the most common loop: nested loops with itertools.product: ps.itr2params(itertools.product(a,b,c,...)).
pickle_read(fn)
pickle_write(fn, obj)
plist(name, seq)	Create a list of single-item dicts holding the parameter name and a value.
prep_batch(params, *[, calc_templ_dir, ...])	Write files based on templates.
pset_hash(dct[, method, raise_error])	Reproducible hash of a dict for usage in database (hash of a pset).
run(func, params[, df, poolsize, ...])	Call func for each pset in params.
stargrid(const, vary[, vary_labels, ...])	Helper to create a specific param sampling pattern.
system(cmd, **kwds)	Call shell command.