From 20a5e8c77d250549b6483c806444e8772bc663ee Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 9 Oct 2026 15:11:51 -0700 Subject: [PATCH 1/7] chore(deps): bump scipy to >=1.17.1 and require Python 3.11+ scipy 1.17+ does not support Python 3.10; raise the project floor and refresh the lockfile. Co-authored-by: Cursor --- .github/workflows/ci.yaml | 2 +- README.md | 4 +- pyproject.toml | 6 +- uv.lock | 2275 ++++++++++--------------------------- 4 files changed, 605 insertions(+), 1682 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 07f0a16d..a38d3393 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -17,7 +17,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.10", "3.11", "3.12", "3.13"] + python-version: ["3.11", "3.12", "3.13"] env: UV_PYTHON: ${{ matrix.python-version }} diff --git a/README.md b/README.md index 72839b8d..9c5bd407 100644 --- a/README.md +++ b/README.md @@ -122,8 +122,8 @@ TimeCopilot is available on PyPI as [`timecopilot`](https://pypi.org/project/tim and that's it! !!! Important - - TimeCopilot requires Python 3.10+. Additionally, it currently does not support macOS running on Intel processors (x86_64). If you’re using this setup, you may encounter installation issues with some dependencies like PyTorch. If you need support for this architecture, please create a new issue. - - If on Windows, Python 3.10 is recommended due to some of the packages' current architecture. + - TimeCopilot requires Python 3.11+. Additionally, it currently does not support macOS running on Intel processors (x86_64). If you’re using this setup, you may encounter installation issues with some dependencies like PyTorch. If you need support for this architecture, please create a new issue. + - If on Windows, Python 3.11 is recommended due to some of the packages' current architecture. --- diff --git a/pyproject.toml b/pyproject.toml index dc71f8ec..09e93b51 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,7 +44,6 @@ classifiers = [ "Operating System :: OS Independent", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", @@ -57,6 +56,7 @@ classifiers = [ # - transformers>=5 (CVE-2026-1839 fix) requires huggingface-hub>=1; the upper # bounds were widened together (transformers<6, huggingface-hub<2). Reaching # transformers 5 also requires tfc-t0>=0.2 (older tfc-t0 capped hub<1). +# - scipy>=1.17.1 requires Python>=3.11 (see requires-python). # - lightning and pytorch-lightning are the same library # NOTE: dependencies pinned for faster pip installs # versions chosen from uv's dependency resolution @@ -93,7 +93,7 @@ dependencies = [ "prophet>=1.1.7", "pydantic-ai>=0.7.0", "pytorch-lightning==2.4.0", - "scipy<=1.15.3", + "scipy>=1.17.1", "statsforecast>=2.0.2", "tensorboard>=2.20.0", "torch>=2.10,<2.11 ; python_full_version >= '3.11'", @@ -108,7 +108,7 @@ description = "The GenAI Forecasting Agent · LLMs × Time Series Foundation Mod license = "MIT" name = "timecopilot" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.11" version = "0.0.36" [project.optional-dependencies] diff --git a/uv.lock b/uv.lock index 06fbeb46..fe0fbcdd 100644 --- a/uv.lock +++ b/uv.lock @@ -1,22 +1,15 @@ version = 1 revision = 2 -requires-python = ">=3.10" +requires-python = ">=3.11" 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Co-authored-by: Cursor --- .github/workflows/ci.yaml | 2 +- README.md | 4 +- pyproject.toml | 8 +- uv.lock | 1589 ++++++++++++++++++++++++++++++------- 4 files changed, 1317 insertions(+), 286 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index a38d3393..07f0a16d 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -17,7 +17,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.11", "3.12", "3.13"] + python-version: ["3.10", "3.11", "3.12", "3.13"] env: UV_PYTHON: ${{ matrix.python-version }} diff --git a/README.md b/README.md index 9c5bd407..72839b8d 100644 --- a/README.md +++ b/README.md @@ -122,8 +122,8 @@ TimeCopilot is available on PyPI as [`timecopilot`](https://pypi.org/project/tim and that's it! !!! Important - - TimeCopilot requires Python 3.11+. Additionally, it currently does not support macOS running on Intel processors (x86_64). If you’re using this setup, you may encounter installation issues with some dependencies like PyTorch. If you need support for this architecture, please create a new issue. - - If on Windows, Python 3.11 is recommended due to some of the packages' current architecture. + - TimeCopilot requires Python 3.10+. Additionally, it currently does not support macOS running on Intel processors (x86_64). If you’re using this setup, you may encounter installation issues with some dependencies like PyTorch. If you need support for this architecture, please create a new issue. + - If on Windows, Python 3.10 is recommended due to some of the packages' current architecture. --- diff --git a/pyproject.toml b/pyproject.toml index 09e93b51..4b9eea52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ classifiers = [ "Operating System :: OS Independent", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", @@ -56,7 +57,7 @@ classifiers = [ # - transformers>=5 (CVE-2026-1839 fix) requires huggingface-hub>=1; the upper # bounds were widened together (transformers<6, huggingface-hub<2). Reaching # transformers 5 also requires tfc-t0>=0.2 (older tfc-t0 capped hub<1). -# - scipy>=1.17.1 requires Python>=3.11 (see requires-python). +# - scipy>=1.17.1 on Python>=3.11; scipy<=1.15.3 remains on 3.10 (no 1.17 wheels). # - lightning and pytorch-lightning are the same library # NOTE: dependencies pinned for faster pip installs # versions chosen from uv's dependency resolution @@ -93,7 +94,8 @@ dependencies = [ "prophet>=1.1.7", "pydantic-ai>=0.7.0", "pytorch-lightning==2.4.0", - "scipy>=1.17.1", + "scipy<=1.15.3 ; python_full_version < '3.11'", + "scipy>=1.17.1 ; python_full_version >= '3.11'", "statsforecast>=2.0.2", "tensorboard>=2.20.0", "torch>=2.10,<2.11 ; python_full_version >= '3.11'", @@ -108,7 +110,7 @@ description = "The GenAI Forecasting Agent · LLMs × Time Series Foundation Mod license = "MIT" name = "timecopilot" readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.10" version = "0.0.36" [project.optional-dependencies] diff --git a/uv.lock 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Co-authored-by: Cursor --- docs/examples/tabpfn-family.ipynb | 4 ++-- docs/examples/timesfm-family.ipynb | 4 ++-- docs/examples/tirex-family.ipynb | 2 +- tests/models/foundation/test_tabpfn.py | 2 +- tests/models/test_models.py | 3 +-- tests/test_distributed.py | 6 ------ 6 files changed, 7 insertions(+), 14 deletions(-) diff --git a/docs/examples/tabpfn-family.ipynb b/docs/examples/tabpfn-family.ipynb index bda32bdf..38536555 100644 --- a/docs/examples/tabpfn-family.ipynb +++ b/docs/examples/tabpfn-family.ipynb @@ -304,7 +304,7 @@ "source": [ "## Generate forecast\n", "\n", - "TabPFN outputs nine fixed quantiles (0.1 through 0.9). Pass `level=[0, 20, 40, 60, 80]` to request all supported prediction intervals.\n" + "TabPFN outputs nine fixed quantiles (0.1 through 0.9). Pass `level=[20, 40, 60, 80]` for common decile-style prediction intervals.\n" ] }, { @@ -480,7 +480,7 @@ } ], "source": [ - "level = [0, 20, 40, 60, 80]\n", + "level = [20, 40, 60, 80]\n", "cv_df = tcf.cross_validation(df=df, h=12, freq=\"MS\", level=level)\n" ] }, diff --git a/docs/examples/timesfm-family.ipynb b/docs/examples/timesfm-family.ipynb index 7dedfcda..37111d9c 100644 --- a/docs/examples/timesfm-family.ipynb +++ b/docs/examples/timesfm-family.ipynb @@ -23,7 +23,7 @@ "\n", "In this example we compare all four generations on event pageview data.\n", "\n", - "**Prediction intervals:** TimesFM outputs nine fixed quantiles (0.1 through 0.9). Pass `level=[0, 20, 40, 60, 80]` to request all supported prediction intervals; custom levels or quantiles are not supported." + "**Prediction intervals:** TimesFM outputs nine fixed quantiles (0.1 through 0.9). Pass `level=[20, 40, 60, 80]` for common decile-style prediction intervals; custom levels or quantiles are not supported." ] }, { @@ -360,7 +360,7 @@ } ], "source": [ - "level = [0, 20, 40, 60, 80] # required to use all TimesFM-supported quantiles\n", + "level = [20, 40, 60, 80]\n", "cv_df = tcf.cross_validation(df=df, h=12, level=level)" ] }, diff --git a/docs/examples/tirex-family.ipynb b/docs/examples/tirex-family.ipynb index 5e5e7d8f..ad567290 100644 --- a/docs/examples/tirex-family.ipynb +++ b/docs/examples/tirex-family.ipynb @@ -254,7 +254,7 @@ "metadata": {}, "outputs": [], "source": [ - "level = [0, 20, 40, 60, 80] # we need to pass 0 level since specific quantiles are required by TiRex\n", + "level = [20, 40, 60, 80]\n", "cv_df = tcf.cross_validation(df=df, h=12, level=level)\n" ] }, diff --git a/tests/models/foundation/test_tabpfn.py b/tests/models/foundation/test_tabpfn.py index 33e1497b..6ba7a4b6 100644 --- a/tests/models/foundation/test_tabpfn.py +++ b/tests/models/foundation/test_tabpfn.py @@ -24,7 +24,7 @@ pytestmark = pytest.mark.models DEFAULT_QUANTILES = [round(i * 0.1, 1) for i in range(1, 10)] -DEFAULT_LEVEL = [0, 20, 40, 60, 80] +DEFAULT_LEVEL = [20, 40, 60, 80] TABPFN_CASES = [ pytest.param(TABPFN_V2_MODEL, 4096, "TabPFN-2", id="v2"), diff --git a/tests/models/test_models.py b/tests/models/test_models.py index b15abb26..8b05100d 100644 --- a/tests/models/test_models.py +++ b/tests/models/test_models.py @@ -282,7 +282,7 @@ def test_using_quantiles(model): @pytest.mark.parametrize("model", models) def test_using_level(model): - level = [0, 20, 40, 60, 80] # corresponds to qs [0.1, 0.2, ..., 0.9] + level = [20, 40, 60, 80] # decile-style levels (maps to native quantile knots) df = generate_series(n_series=2, freq="D") _level_unsupported = { "AutoLGBM", @@ -323,7 +323,6 @@ def test_using_level(model): assert all(col in fcst_df.columns for col in exp_lv_cols) assert not any(("-q-" in col) for col in fcst_df.columns) # test monotonicity of levels - exp_lv_cols = exp_lv_cols[2:] # remove level 0 for c1, c2 in zip(exp_lv_cols[:-1:2], exp_lv_cols[1::2], strict=False): if model.alias == "ZeroModel": # ZeroModel is a constant model, so all levels should be the same diff --git a/tests/test_distributed.py b/tests/test_distributed.py index 63635a08..9916ab0b 100644 --- a/tests/test_distributed.py +++ b/tests/test_distributed.py @@ -178,7 +178,6 @@ def test_cross_validation_spark(simple_models, spark_df): @pytest.mark.distributed def test_using_level_spark(foundation_model, spark_session): - # level = [0, 20, 40, 60, 80] # corresponds to qs [0.1, 0.2, ..., 0.9] level: list[int | float] = [20, 80] df = generate_series( n_series=2, freq="D", max_length=100, static_as_categorical=False @@ -217,7 +216,6 @@ def test_using_level_spark(foundation_model, spark_session): assert all(col in fcst_df_pd.columns for col in exp_lv_cols) assert not any(("-q-" in col) for col in fcst_df_pd.columns) # test monotonicity of levels - exp_lv_cols = exp_lv_cols[2:] # remove level 0 for c1, c2 in zip(exp_lv_cols[:-1:2], exp_lv_cols[1::2], strict=False): for model in foundation_model: if model.alias == "ZeroModel": @@ -312,7 +310,6 @@ def test_cross_validation_dask(simple_models, dask_df): def test_using_level_dask(foundation_model): import dask.dataframe as dd - # level = [0, 20, 40, 60, 80] # corresponds to qs [0.1, 0.2, ..., 0.9] level: list[int | float] = [20, 80] n_partitions = 2 df = generate_series( @@ -352,7 +349,6 @@ def test_using_level_dask(foundation_model): assert all(col in fcst_df_pd.columns for col in exp_lv_cols) assert not any(("-q-" in col) for col in fcst_df_pd.columns) # test monotonicity of levels - exp_lv_cols = exp_lv_cols[2:] # remove level 0 for c1, c2 in zip(exp_lv_cols[:-1:2], exp_lv_cols[1::2], strict=False): for model in foundation_model: if model.alias == "ZeroModel": @@ -425,7 +421,6 @@ def test_cross_validation_ray(simple_models, ray_dataset): def test_using_level_ray(foundation_model): import ray - # level = [0, 20, 40, 60, 80] # corresponds to qs [0.1, 0.2, ..., 0.9] level: list[int | float] = [20, 80] df = generate_series( n_series=2, freq="D", max_length=100, static_as_categorical=False @@ -473,7 +468,6 @@ def test_using_level_ray(foundation_model): assert all(col in fcst_df_pd.columns for col in exp_lv_cols) assert not any(("-q-" in col) for col in fcst_df_pd.columns) # test monotonicity of levels - exp_lv_cols = exp_lv_cols[2:] # remove level 0 for c1, c2 in zip(exp_lv_cols[:-1:2], exp_lv_cols[1::2], strict=False): for model in foundation_model: if model.alias == "ZeroModel": From 0abdf69325ba0219ad8d6e4fe0562ae79a5e26d0 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 9 Oct 2026 16:32:25 -0700 Subject: [PATCH 4/7] chore(deps): require foundationforecast>=0.1.12 Align with quantile interpolation and TimesFM level support; fixes level forecast tests on CI with foundationforecast 0.1.8. 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{ name = "scipy", version = "1.18.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, - { name = "tensorboard" }, - { name = "torch" }, + { name = "scipy", version = "1.18.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' and python_full_version < '3.14'" }, + { name = "tensorboard", marker = "python_full_version < '3.14'" }, + { name = "torch", marker = "python_full_version < '3.14'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/63/99/e2b3d93a82734103282a9f8f2460d881d6c34f764911de4f52047a59f5a5/timecopilot_uni2ts-0.1.3.tar.gz", hash = "sha256:352b08af9fcd001399f36a8c6c398c1f80ee84801c2b3d7bc36bf9634ecefffc", size = 102981, upload-time = "2026-08-10T17:59:11.628Z" } wheels = [ @@ -8204,8 +8204,8 @@ wheels = [ [package.optional-dependencies] torch = [ - { name = "accelerate", marker = "python_full_version >= '3.11'" }, - { name = "torch", marker = "python_full_version >= '3.11'" }, + { name = "accelerate", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, + { name = "torch", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, ] [[package]] From cb83abef3bdd2592a9279a6164c166b04dab86c1 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 12 Oct 2026 10:28:17 -0700 Subject: [PATCH 5/7] fix(forecaster): initialize exog_strategy for FoundationForecast 0.1.12+ TimeCopilot stats/ML wrappers inherit cross_validation from FoundationForecast but often skip base __init__; default exog_strategy=False and ensure state before CV. Align TimeCopilotForecaster.cross_validation signature with futr_exog_list. Co-authored-by: Cursor --- timecopilot/forecaster.py | 9 ++++++ timecopilot/models/utils/forecaster.py | 45 ++++++++++++++++++++++++++ 2 files changed, 54 insertions(+) diff --git a/timecopilot/forecaster.py b/timecopilot/forecaster.py index cf160464..dc228060 100644 --- a/timecopilot/forecaster.py +++ b/timecopilot/forecaster.py @@ -269,6 +269,8 @@ def _cross_validation_pandas( step_size: int | None = None, level: list[int | float] | None = None, quantiles: list[float] | None = None, + *, + futr_exog_list: list[str] | None = None, ) -> pd.DataFrame: """ Internal pandas-only cross-validation implementation. @@ -285,6 +287,7 @@ def _cross_validation_pandas( step_size=step_size, level=level, quantiles=quantiles, + futr_exog_list=futr_exog_list, ) def _cross_validation_distributed( @@ -296,6 +299,8 @@ def _cross_validation_distributed( step_size: int | None = None, level: list[int | float] | None = None, quantiles: list[float] | None = None, + *, + futr_exog_list: list[str] | None = None, num_partitions: int | None = None, ) -> DistributedDataFrame: """ @@ -368,6 +373,8 @@ def cross_validation( step_size: int | None = None, level: list[int | float] | None = None, quantiles: list[float] | None = None, + *, + futr_exog_list: list[str] | None = None, num_partitions: int | None = None, ) -> AnyDataFrame: """ @@ -443,6 +450,7 @@ def cross_validation( step_size=step_size, level=level, quantiles=quantiles, + futr_exog_list=futr_exog_list, num_partitions=num_partitions, ) @@ -455,6 +463,7 @@ def cross_validation( step_size=step_size, level=level, quantiles=quantiles, + futr_exog_list=futr_exog_list, ) def _detect_anomalies_pandas( diff --git a/timecopilot/models/utils/forecaster.py b/timecopilot/models/utils/forecaster.py index c3e2da35..10a3326a 100644 --- a/timecopilot/models/utils/forecaster.py +++ b/timecopilot/models/utils/forecaster.py @@ -1,4 +1,8 @@ import pandas as pd +from foundationforecast.core.exog.covariates import ( + ExogStrategyConfig, + normalize_exog_strategy, +) from foundationforecast.core.forecaster import Forecaster as _Forecaster from foundationforecast.core.forecaster import ( QuantileConverter, @@ -21,6 +25,47 @@ class Forecaster(_Forecaster): """TimeCopilot Forecaster base (extends foundationforecast).""" + def __init__( + self, + *, + reuse_loaded_model: bool = True, + exog_strategy: ExogStrategyConfig = False, + ) -> None: + super().__init__( + reuse_loaded_model=reuse_loaded_model, + exog_strategy=exog_strategy, + ) + + def _ensure_forecaster_state(self) -> None: + if not hasattr(self, "exog_strategy"): + self.exog_strategy = normalize_exog_strategy(False) + if not hasattr(self, "reuse_loaded_model"): + self.reuse_loaded_model = True + + def cross_validation( + self, + df: pd.DataFrame, + h: int, + freq: str | None = None, + n_windows: int = 1, + step_size: int | None = None, + level: list[int | float] | None = None, + quantiles: list[float] | None = None, + *, + futr_exog_list: list[str] | None = None, + ) -> pd.DataFrame: + self._ensure_forecaster_state() + return super().cross_validation( + df, + h, + freq=freq, + n_windows=n_windows, + step_size=step_size, + level=level, + quantiles=quantiles, + futr_exog_list=futr_exog_list, + ) + @staticmethod def _maybe_infer_freq(df: pd.DataFrame, freq: str | None) -> str: return maybe_infer_freq(df, freq) From 81a398410706ba168d50eae669da65c4bfc42cac Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 12 Oct 2026 10:33:40 -0700 Subject: [PATCH 6/7] fix(mypy): drop Forecaster.__init__ override on TimeCopilot mixin Keep cross_validation exog_strategy guard only; explicit __init__ broke call-arg typing for multiply-inherited foundation wrappers in mypy. Co-authored-by: Cursor --- timecopilot/models/utils/forecaster.py | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/timecopilot/models/utils/forecaster.py b/timecopilot/models/utils/forecaster.py index 10a3326a..0c1be767 100644 --- a/timecopilot/models/utils/forecaster.py +++ b/timecopilot/models/utils/forecaster.py @@ -1,8 +1,5 @@ import pandas as pd -from foundationforecast.core.exog.covariates import ( - ExogStrategyConfig, - normalize_exog_strategy, -) +from foundationforecast.core.exog.covariates import normalize_exog_strategy from foundationforecast.core.forecaster import Forecaster as _Forecaster from foundationforecast.core.forecaster import ( QuantileConverter, @@ -25,17 +22,6 @@ class Forecaster(_Forecaster): """TimeCopilot Forecaster base (extends foundationforecast).""" - def __init__( - self, - *, - reuse_loaded_model: bool = True, - exog_strategy: ExogStrategyConfig = False, - ) -> None: - super().__init__( - reuse_loaded_model=reuse_loaded_model, - exog_strategy=exog_strategy, - ) - def _ensure_forecaster_state(self) -> None: if not hasattr(self, "exog_strategy"): self.exog_strategy = normalize_exog_strategy(False) From 4acf5a26fac134974c5a95ab47b72a352389aee7 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 12 Oct 2026 11:02:16 -0700 Subject: [PATCH 7/7] test(forecaster): drop level=0 from QuantileConverter parametrization FoundationForecast>=0.1.12 rejects level 0; use [20, 80] instead of [0, 80]. Co-authored-by: Cursor --- tests/models/utils/test_forecaster.py | 35 +++++++++++---------------- 1 file changed, 14 insertions(+), 21 deletions(-) diff --git a/tests/models/utils/test_forecaster.py b/tests/models/utils/test_forecaster.py index 4344a5de..d9b82333 100644 --- a/tests/models/utils/test_forecaster.py +++ b/tests/models/utils/test_forecaster.py @@ -142,7 +142,7 @@ def test_maybe_convert_level_to_quantiles(n_models, quantiles): "n_models,level", [ (1, [80]), - (2, [0, 80]), + (2, [20, 80]), (2, [60, 80]), ], ) @@ -167,26 +167,19 @@ def test_maybe_convert_quantiles_to_level(n_models, level): assert result_df.shape[1] == exp_n_cols for model in models: for lv in level: - if lv == 0: - pd.testing.assert_series_equal( - result_df[model], - df[f"{model}-q-50"], - check_names=False, - ) - else: - alpha = round(1 - lv / 100, 2) - q_lo = int((alpha / 2) * 100) - q_hi = int((1 - alpha / 2) * 100) - pd.testing.assert_series_equal( - result_df[f"{model}-lo-{lv}"], - df[f"{model}-q-{q_lo}"], - check_names=False, - ) - pd.testing.assert_series_equal( - result_df[f"{model}-hi-{lv}"], - df[f"{model}-q-{q_hi}"], - check_names=False, - ) + alpha = round(1 - lv / 100, 2) + q_lo = int((alpha / 2) * 100) + q_hi = int((1 - alpha / 2) * 100) + pd.testing.assert_series_equal( + result_df[f"{model}-lo-{lv}"], + df[f"{model}-q-{q_lo}"], + check_names=False, + ) + pd.testing.assert_series_equal( + result_df[f"{model}-hi-{lv}"], + df[f"{model}-q-{q_hi}"], + check_names=False, + ) # check that maybe convert level to quantiles returns the same result pd.testing.assert_frame_equal( df,