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1 | 1 | import logging
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2 | 2 |
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3 | 3 | from collections.abc import Callable, Sequence
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4 |
| -from typing import Any |
| 4 | +from typing import Any, Literal |
5 | 5 |
|
6 | 6 | import numpy as np
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7 | 7 | import pandas as pd
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@@ -1109,7 +1109,7 @@ def _sample_conditional(
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1109 | 1109 | group: str,
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1110 | 1110 | random_seed: RandomState | None = None,
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1111 | 1111 | data: pt.TensorLike | None = None,
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1112 |
| - mvn_method: str = "svd", |
| 1112 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1113 | 1113 | **kwargs,
|
1114 | 1114 | ):
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1115 | 1115 | """
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@@ -1230,7 +1230,7 @@ def _sample_unconditional(
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1230 | 1230 | steps: int | None = None,
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1231 | 1231 | use_data_time_dim: bool = False,
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1232 | 1232 | random_seed: RandomState | None = None,
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1233 |
| - mvn_method: str = "svd", |
| 1233 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1234 | 1234 | **kwargs,
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1235 | 1235 | ):
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1236 | 1236 | """
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@@ -1349,7 +1349,7 @@ def sample_conditional_prior(
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1349 | 1349 | self,
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1350 | 1350 | idata: InferenceData,
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1351 | 1351 | random_seed: RandomState | None = None,
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1352 |
| - mvn_method: str = "svd", |
| 1352 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1353 | 1353 | **kwargs,
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1354 | 1354 | ) -> InferenceData:
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1355 | 1355 | """
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@@ -1390,7 +1390,7 @@ def sample_conditional_posterior(
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1390 | 1390 | self,
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1391 | 1391 | idata: InferenceData,
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1392 | 1392 | random_seed: RandomState | None = None,
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1393 |
| - mvn_method: str = "svd", |
| 1393 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1394 | 1394 | **kwargs,
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1395 | 1395 | ):
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1396 | 1396 | """
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@@ -1432,7 +1432,7 @@ def sample_unconditional_prior(
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1432 | 1432 | steps: int | None = None,
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1433 | 1433 | use_data_time_dim: bool = False,
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1434 | 1434 | random_seed: RandomState | None = None,
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1435 |
| - mvn_method: str = "svd", |
| 1435 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1436 | 1436 | **kwargs,
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1437 | 1437 | ) -> InferenceData:
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1438 | 1438 | """
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@@ -1497,7 +1497,7 @@ def sample_unconditional_posterior(
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1497 | 1497 | steps: int | None = None,
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1498 | 1498 | use_data_time_dim: bool = False,
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1499 | 1499 | random_seed: RandomState | None = None,
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1500 |
| - mvn_method: str = "svd", |
| 1500 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1501 | 1501 | **kwargs,
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1502 | 1502 | ) -> InferenceData:
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1503 | 1503 | """
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@@ -1994,7 +1994,7 @@ def forecast(
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1994 | 1994 | filter_output="smoothed",
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1995 | 1995 | random_seed: RandomState | None = None,
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1996 | 1996 | verbose: bool = True,
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1997 |
| - mvn_method: str = "svd", |
| 1997 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
1998 | 1998 | **kwargs,
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1999 | 1999 | ) -> InferenceData:
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2000 | 2000 | """
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@@ -2194,7 +2194,7 @@ def impulse_response_function(
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2194 | 2194 | shock_trajectory: np.ndarray | None = None,
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2195 | 2195 | orthogonalize_shocks: bool = False,
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2196 | 2196 | random_seed: RandomState | None = None,
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2197 |
| - mvn_method: str = "svd", |
| 2197 | + mvn_method: Literal["cholesky", "eigh", "svd"] = "svd", |
2198 | 2198 | **kwargs,
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2199 | 2199 | ):
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2200 | 2200 | """
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