Contrast¶

class passengersim.contrast.Contrast[source]¶

Bases: dict

A named collection of SimulationTables for side-by-side comparison.

Contrast is a dict subclass that maps string labels to SimulationTables instances. It provides methods for aggregating, visualizing, and reporting results across multiple PassengerSim simulation runs.

Module-level functions whose names begin with fig_ are automatically accessible as instance methods, with the first summaries argument pre-filled with this object.

Methods

__init__(*args, **kwargs)
apply(func[, axis, warn_if_missing])	Apply a callable to each non-None entry and concatenate the results.
clear()
copy()
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
subset([keys, regex])	Subset the contrast to only include the specified keys.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
write_report(filename, **kwargs)	Write an HTML report summarizing this contrast to a file.

apply(func: Callable, axis: int | 'index' | 'columns' | 'rows' = 0, warn_if_missing: bool = False) → DataFrame | Series[source]¶

Apply a callable to each non-None entry and concatenate the results.

Parameters:

func : callable¶

A function that accepts a SimulationTables instance and returns a pandas Series or DataFrame.

axis : int or {'index', 'columns', 'rows'}, default 0¶

The axis along which to concatenate the results. Passed directly to pandas.concat().

warn_if_missing : bool, default False¶

If True, emit a warning for any key whose value is None.

Returns:

pd.DataFrame or pd.Series – Concatenated results with a "source" level prepended to the index.

write_report(filename: str | bytes | PathLike, **kwargs) → Path[source]¶

Write an HTML report summarizing this contrast to a file.

Parameters:

filename : path-like¶

Destination path for the HTML report.

**kwargs¶

Additional keyword arguments forwarded to to_html().

Returns:

pathlib.Path – The path of the written report file.

subset(keys=None, *, regex: str | None = None) → Self[source]¶

Subset the contrast to only include the specified keys.

Parameters:

keys : str or list of str, optional¶

The keys to include in the subset. If None, use the regex filter.

regex : str, optional¶

A regular expression to filter the keys. If None, use the keys filter.

Returns:

Contrast

clear() → None.  Remove all items from D.¶

copy() → a shallow copy of D¶

classmethod fromkeys(iterable, value=None, /)¶

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()¶

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from mapping/iterable E and F.¶

If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values¶