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Small improvements to timeseriesaggregation #82

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Small improvements to timeseriesaggregation #82

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74cf93d
updated inplace=True pandas command to silence warning
Jan 25, 2024
0c5ba67
deleted unused import
Jan 25, 2024
4f64c52
changed two exceptions slightly to preserve original traceback
Jan 25, 2024
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13 changes: 6 additions & 7 deletions tsam/timeseriesaggregation.py
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Expand Up @@ -8,7 +8,6 @@
import numpy as np

from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.metrics.pairwise import euclidean_distances
from sklearn import preprocessing

from tsam.periodAggregation import aggregatePeriods
Expand Down Expand Up @@ -388,21 +387,21 @@ def _check_init_args(self):
try:
timedelta = self.timeSeries.index[1] - self.timeSeries.index[0]
self.resolution = float(timedelta.total_seconds()) / 3600
except AttributeError:
except AttributeError as exc:
raise ValueError(
"'resolution' argument has to be nonnegative float or int"
+ " or the given timeseries needs a datetime index"
)
) from exc
except TypeError:
try:
self.timeSeries.index = pd.to_datetime(self.timeSeries.index)
timedelta = self.timeSeries.index[1] - self.timeSeries.index[0]
self.resolution = float(timedelta.total_seconds()) / 3600
except:
except Exception as exc:
raise ValueError(
"'resolution' argument has to be nonnegative float or int"
+ " or the given timeseries needs a datetime index"
)
) from exc

if not (isinstance(self.resolution, int) or isinstance(self.resolution, float)):
raise ValueError("resolution has to be nonnegative float or int")
Expand Down Expand Up @@ -870,9 +869,9 @@ def _rescaleClusterPeriods(self, clusterOrder, clusterPeriods, extremeClusterIdx
)

# reset values higher than the upper sacle or less than zero
typicalPeriods[column].clip(lower=0, upper=scale_ub, inplace=True)
typicalPeriods[column] = typicalPeriods[column].clip(lower=0, upper=scale_ub)

typicalPeriods[column].fillna(0.0, inplace=True)
typicalPeriods[column] = typicalPeriods[column].fillna(0.0)

# calc new sum and new diff to orig data
sum_clu_wo_peak = np.sum(
Expand Down