124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# IMPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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from src.thirdparty.types import *;
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from src.thirdparty.maths import *;
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from src.hirschberg.constants import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# EXPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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__all__ = [
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'represent_cost_matrix',
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'display_cost_matrix',
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'display_cost_matrix_halves',
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];
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# METHODS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def represent_cost_matrix(
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Costs: np.ndarray, # NDArray[(Any, Any), int],
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path: List[Tuple[int, int]],
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X: str,
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Y: str,
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verb: VerboseMode,
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pad: bool = False,
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) -> np.ndarray: # NDArray[(Any, Any), Any]:
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m = len(X); # display vertically
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n = len(Y); # display horizontally
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# erstelle string-Array:
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if pad:
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table = np.full(shape=(3 + m + 3, 3 + n + 1), dtype=object, fill_value='');
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else:
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table = np.full(shape=(3 + m, 3 + n), dtype=object, fill_value='');
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# topmost rows:
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table[0, 3:(3+n)] = [str(j) for j in range(n)];
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table[1, 3:(3+n)] = [y for y in Y];
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table[2, 3:(3+n)] = '--';
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# leftmost columns:
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table[3:(3+m), 0] = [str(i) for i in range(m)];
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table[3:(3+m), 1] = [x for x in X];
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table[3:(3+m), 2] = '|';
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if pad:
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table[-3, 3:(3+n)] = '--';
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table[3:(3+m), -1] = '|';
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match verb:
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case VerboseMode.MOVES:
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table[3:(3+m), 3:(3+n)] = '.';
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for (i, j) in path:
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table[3 + i, 3 + j] = '*';
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case VerboseMode.COSTS | VerboseMode.COSTS_AND_MOVES:
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table[3:(3+m), 3:(3+n)] = Costs.copy();
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if verb == VerboseMode.COSTS_AND_MOVES:
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for (i, j) in path:
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table[3 + i, 3 + j] = f'\x1b[31;4;1m{table[3 + i, 3 + j]}\x1b[0m';
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return table;
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def display_cost_matrix(
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Costs: np.ndarray, # NDArray[(Any, Any), int],
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path: List[Tuple[int, int]],
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X: str,
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Y: str,
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verb: VerboseMode,
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) -> str:
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'''
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Zeigt Kostenmatrix + optimalen Pfad.
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@inputs
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- `Costs` - Kostenmatrix
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- `Moves` - Kodiert die optimalen Schritte
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- `X`, `Y` - Strings
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@returns
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- eine 'printable' Darstellung der Matrix mit den Strings X, Y + Indexes.
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'''
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table = represent_cost_matrix(Costs=Costs, path=path, X=X, Y=Y, verb=verb);
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# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
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repr = tabulate(pd.DataFrame(table), showindex=False, stralign='center', tablefmt='plain');
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return repr;
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def display_cost_matrix_halves(
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Costs1: np.ndarray, # NDArray[(Any, Any), int],
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Costs2: np.ndarray, # NDArray[(Any, Any), int],
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path1: List[Tuple[int, int]],
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path2: List[Tuple[int, int]],
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X1: str,
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X2: str,
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Y1: str,
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Y2: str,
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verb: VerboseMode,
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) -> str:
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'''
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Zeigt Kostenmatrix + optimalen Pfad für Schritt im D & C Hirschberg-Algorithmus
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@inputs
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- `Costs1`, `Costs2` - Kostenmatrizen
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- `Moves1`, `Moves2` - Kodiert die optimalen Schritte
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- `X1`, `X2`, `Y1`, `Y2` - Strings
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@returns
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- eine 'printable' Darstellung der Matrix mit den Strings X, Y + Indexes.
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'''
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table1 = represent_cost_matrix(Costs=Costs1, path=path1, X=X1, Y=Y1, verb=verb, pad=True);
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table2 = represent_cost_matrix(Costs=Costs2, path=path2, X=X2, Y=Y2, verb=verb, pad=True);
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# merge Taellen:
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table = np.concatenate([table1[:, :-1], table2[::-1, ::-1]], axis=1);
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# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
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repr = tabulate(pd.DataFrame(table), showindex=False, stralign='center', tablefmt='plain');
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return repr;
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