minor things

portfolyo/tools/intersect.py

@@ -214,8 +214,7 @@ def frames(
 
     Returns
     -------
-    list of series and/or dataframes
-        As input, but trimmed to their intersection.
+    As input, but trimmed to their intersection.
 
     Notes
     -----
@@ -228,4 +227,4 @@ def frames(
         ignore_tz=ignore_tz,
         ignore_start_of_day=ignore_start_of_day,
     )
-    return [fr.loc[idx] for idx, fr in zip(new_idxs, frames)]
+    return [fr.loc[i] for i, fr in zip(new_idxs, frames)]

portfolyo/tools/product.py

@@ -1,7 +1,6 @@
 """Utilities for calculating / manipulating price data."""
 
 import datetime as dt
-import warnings
 from typing import Tuple
 
 import pandas as pd
@@ -14,31 +13,11 @@
 
 
 def is_peak_hour(i: pd.DatetimeIndex) -> pd.Series:
-    """
-    Calculate if a timestamp is in a peak period or not.
-
-    Parameters
-    ----------
-    i : pd.DatetimeIndex
-        Timestamps for which to calculate if it falls in a peak period.
-
-    More precisely: if timestamp lies in one of the (left-closed) time intervals that
-    define the peak hour periods.
-
-    Returns
-    -------
-    Series
-        with boolean values and same index.
-    """
-    if isinstance(i, pd.Timestamp):
-        raise TypeError("no longer supported")
-        warnings.warn(
-            "Calling this function with a single timestamp is deprecated and will be removed in a future version",
-            FutureWarning,
-        )
-        return is_peak_hour(pd.DatetimeIndex([i], freq="15T"))[0]
-
-    return germanpower_peakfn(i)
+    raise DeprecationWarning(
+        "``pf.is_peak_hour`` has been deprecated and will be removed in a future version."
+        " Use ``pf.germanpower_peakfn`` instead, or create your own peak function with"
+        " ``pf.create_peakfn()``."
+    )
 
 
 def delivery_period(
@@ -59,7 +38,7 @@ def delivery_period(
     front_count : int, optional (default: 1)
         1 = next/coming (full) period, 2 = period after that, etc.
     start_of_day : dt.time, optional (default: midnight)
-        Start of day for delivery periods with a longer-than-daily frequency.
+        Start of day for delivery periods with a daily-or-longer frequency.
 
     Returns
     -------

portfolyo/tools2/intersect.py

@@ -1,21 +1,21 @@
 from typing import List
 
-from ..tools.intersect import indices_flex
-from .types import NDFrameLike
+from ..tools import intersect as tools_intersect
+from .types import Indexable
 
 
 def indexable(
-    *frames: NDFrameLike,
+    *objs: Indexable,
     ignore_freq: bool = False,
     ignore_tz: bool = False,
     ignore_start_of_day: bool = False,
-) -> List[NDFrameLike]:
+) -> List[Indexable]:
     """Intersect several dataframes and/or series.
 
     Parameters
     ----------
-    *frames : pd.Series and/or pd.DataFrame and/or PfLines and/or PfStates
-        The frames to intersect.
+    *objs : pd.Series and/or pd.DataFrame and/or PfLines and/or PfStates
+        The indexable objects to intersect.
     ignore_freq: bool, optional (default: False)
         If True, do the intersection even if the frequencies do not match; drop the
         time periods that do not (fully) exist in either of the frames.
@@ -28,18 +28,17 @@ def indexable(
 
     Returns
     -------
-    list of series and/or dataframes
-        As input, but trimmed to their intersection.
+    As input, but trimmed to their intersection.
 
     Notes
     -----
     The indices must have equal frequency, timezone, start-of-day. Otherwise, an error
     is raised. If there is no overlap, empty frames are returned.
     """
-    new_idxs = indices_flex(
-        *[fr.index for fr in frames],
+    new_idxs = tools_intersect.indices_flex(
+        *[o.index for o in objs],
         ignore_freq=ignore_freq,
         ignore_tz=ignore_tz,
         ignore_start_of_day=ignore_start_of_day,
     )
-    return [fr.loc[idx] for idx, fr in zip(new_idxs, frames)]
+    return [o.loc[i] for i, o in zip(new_idxs, objs)]

portfolyo/tools2/types.py

@@ -7,4 +7,6 @@
 from ..core.pfline import PfLine
 from ..core.pfstate import PfState
 
-NDFrameLike = TypeVar("NDFrameLike", pd.Series, pd.DataFrame, PfLine, PfState)
+Indexable = TypeVar(
+    "Series_DataFrame_PfLine_PfState", pd.Series, pd.DataFrame, PfLine, PfState
+)