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Merged
joshuabmoore merged 1 commit into from
Jun 9, 2026
Merged

drop calc.summary #47

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2 changes: 0 additions & 2 deletions README.md
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Expand Up @@ -60,8 +60,6 @@ res = calc.extract(data)
Note that each time-series instances does *not* have to be the same length to compute a vector of features.
The results of the extraction will be returned in a pandas dataframe of shape $N \times F$, where $N$ is the number of time-series instances and $F$ is the number of time-series features.

You can also inspect the quality of the extracted feature values by calling ```calc.summary()```.

# 📘 Tutorials
New to _pyhctsa_? Step-by-step tutorials and example workflows are available in the repository
👉 [`/tutorials`](./tutorials)
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3 changes: 1 addition & 2 deletions pyhctsa/calculator.py
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Expand Up @@ -184,7 +184,6 @@ class FeatureCalculator:
>>> fc = FeatureCalculator() # Load default configuration
>>> x = np.random.randn(1000)
>>> df = fc.extract(x)
>>> print(fc.summary())
"""
def __init__(self, config_path: Union[str, None] = None):
"""
Expand Down Expand Up @@ -359,7 +358,7 @@ def extract(self, data: Union[ArrayLike, list[ArrayLike]],
df = pd.json_normalize(rows)
# assign row names
df.index = pd.Index(labels_list, name="instance")
# meta data for summary
# metadata
self._last_elapsed = elapsed
self._errors = df.map(classify_output)

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