'weighted' method no longer works for Hourly data acquisition

[kumpakri]

Hello, I have been pulling hourly meteostat data using 'weighted' method without problem until yesterday. When I rerun code that used to be working, without any changes to the code, I am now getting a MaskError. And the error is pointing to the internal meteostat code. But I didn't even updated the meteostat package before I started running into this error. If I switch the method to 'nearest' I do not run into this error.

It is true that I write code on a cloud based jupyter server and somebody else from my organization might have mess with some python configurations but I have no idea what might happen and how to fix it.

Can somebody provide any insight, please?

---

MaskError Traceback (most recent call last)
Cell In[1], line 11
9 start_date = date_object = datetime.strptime('2024-07-01', '%Y-%m-%d')
10 end_date = date_object = datetime.strptime('2024-07-02', '%Y-%m-%d')
---> 11 data = meteostat.Hourly(coord_point, start_date, end_date)
12 data = data.fetch()
13 df_dealer = data.reset_index()

File ~/.local/lib/python3.11/site-packages/meteostat/interface/hourly.py:147, in Hourly.init(self, loc, start, end, timezone, model, flags)
144 self._set_time(start, end, timezone)
146 # Initialize time series
--> 147 self._init_time_series(loc, start, end, model, flags)

File ~/.local/lib/python3.11/site-packages/meteostat/interface/timeseries.py:221, in TimeSeries._init_time_series(self, loc, start, end, model, flags)
218 # Interpolate data spatially if requested
219 # location is a geographical point
220 if isinstance(loc, Point):
--> 221 self._resolve_point(loc.method, stations, loc.alt, loc.adapt_temp)
223 # Clear cache if auto cleaning is enabled
224 if self.max_age > 0 and self.autoclean:

File ~/.local/lib/python3.11/site-packages/meteostat/interface/meteodata.py:184, in MeteoData._resolve_point(self, method, stations, alt, adapt_temp)
181 pass
183 else:
--> 184 data = data.groupby(pd.Grouper(level="time", freq=self._freq)).apply(
185 weighted_average
186 )
188 # Drop RangeIndex
189 data.index = data.index.droplevel(1)

File ~/.local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1824, in GroupBy.apply(self, func, include_groups, *args, **kwargs)
1822 with option_context("mode.chained_assignment", None):
1823 try:
-> 1824 result = self._python_apply_general(f, self._selected_obj)
1825 if (
1826 not isinstance(self.obj, Series)
1827 and self._selection is None
1828 and self._selected_obj.shape != self._obj_with_exclusions.shape
1829 ):
1830 warnings.warn(
1831 message=_apply_groupings_depr.format(
1832 type(self).name, "apply"
(...)
1835 stacklevel=find_stack_level(),
1836 )

File ~/.local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1885, in GroupBy._python_apply_general(self, f, data, not_indexed_same, is_transform, is_agg)
1850 @Final
1851 def _python_apply_general(
1852 self,
(...)
1857 is_agg: bool = False,
1858 ) -> NDFrameT:
1859 """
1860 Apply function f in python space
1861
(...)
1883 data after applying f
1884 """
-> 1885 values, mutated = self._grouper.apply_groupwise(f, data, self.axis)
1886 if not_indexed_same is None:
1887 not_indexed_same = mutated

File ~/.local/lib/python3.11/site-packages/pandas/core/groupby/ops.py:919, in BaseGrouper.apply_groupwise(self, f, data, axis)
917 # group might be modified
918 group_axes = group.axes
--> 919 res = f(group)
920 if not mutated and not _is_indexed_like(res, group_axes, axis):
921 mutated = True

File ~/.local/lib/python3.11/site-packages/meteostat/utilities/aggregations.py:20, in weighted_average(step)
15 def weighted_average(step: pd.DataFrame) -> pd.DataFrame:
16 """
17 Calculate weighted average from grouped data
18 """
---> 20 data = np.ma.masked_array(step, np.isnan(step))
21 data = np.ma.average(data, axis=0, weights=data[:, -2])
22 data = data.filled(np.nan)

File /usr/local/lib/python3.11/site-packages/numpy/ma/core.py:2911, in MaskedArray.new(cls, data, mask, dtype, copy, subok, ndmin, fill_value, keep_mask, hard_mask, shrink, order)
2908 else:
2909 msg = "Mask and data not compatible: data size is %i, " +
2910 "mask size is %i."
-> 2911 raise MaskError(msg % (nd, nm))
2912 copy = True
2913 # Set the mask to the new value

MaskError: Mask and data not compatible: data size is 52, mask size is 0.