def _resample_periods(self, obj):
axlabels = obj._get_axis(self.axis)
if len(axlabels) == 0:
new_index = PeriodIndex(data=[], freq=self.freq)
return obj.reindex(new_index)
else:
start = axlabels[0].asfreq(self.freq, how=self.convention)
end = axlabels[-1].asfreq(self.freq, how='end')
new_index = period_range(start, end, freq=self.freq)
# Start vs. end of period
memb = axlabels.asfreq(self.freq, how=self.convention)
if is_subperiod(axlabels.freq, self.freq) or self.how is not None:
# Downsampling
rng = np.arange(memb.values[0], memb.values[-1] + 1)
bins = memb.searchsorted(rng, side='right')
grouper = BinGrouper(bins, new_index)
grouped = obj.groupby(grouper, axis=self.axis)
return grouped.aggregate(self._agg_method)
elif is_superperiod(axlabels.freq, self.freq):
# Get the fill indexer
indexer = memb.get_indexer(new_index,
method=self.fill_method,
limit=self.limit)
return _take_new_index(obj, indexer, new_index, axis=self.axis)
else:
raise ValueError('Frequency %s cannot be resampled to %s' %
(axlabels.freq, self.freq))
def _get_binner(self):
"""
create the BinGrouper, assume that self.set_grouper(obj)
has already been called
"""
binner, bins, binlabels = self._get_binner_for_time()
bin_grouper = BinGrouper(bins, binlabels)
return binner, bin_grouper
def _get_time_grouper(self, obj):
axis = obj._get_axis(self.axis)
if self.kind is None or self.kind == 'timestamp':
binner, bins, binlabels = self._get_time_bins(axis)
else:
binner, bins, binlabels = self._get_time_period_bins(axis)
grouper = BinGrouper(bins, binlabels)
return binner, grouper
def _get_time_grouper(self, obj):
obj = self._ensure_sortedness(obj)
ax = obj._get_axis(self.axis)
if self.kind is None or self.kind == 'timestamp':
binner, bins, binlabels = self._get_time_bins(ax)
else:
binner, bins, binlabels = self._get_time_period_bins(ax)
grouper = BinGrouper(bins, binlabels)
return binner, grouper, obj
def _get_binner_for_resample(self):
# create the BinGrouper
# assume that self.set_grouper(obj) has already been called
ax = self.ax
if self.kind is None or self.kind == 'timestamp':
self.binner, bins, binlabels = self._get_time_bins(ax)
else:
self.binner, bins, binlabels = self._get_time_period_bins(ax)
self.grouper = BinGrouper(bins, binlabels)
return self.binner, self.grouper, self.obj
def anchor_downsample(obj, freq, axis=None):
"""
Point of this is to fix the freq to regular intervals like 9:30, 9:45, 10:00
and not 9:13, 9:28: 9:43
"""
if axis is None:
axis = 0
if isinstance(obj, Panel):
axis = 1
index = obj._get_axis(axis)
ind = get_anchor_index(index, freq)
bins = lib.generate_bins_dt64(index.asi8, ind.asi8, closed='right')
labels = ind[1:]
grouper = BinGrouper(bins, labels)
return obj.groupby(grouper)
def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
# we may need to actually resample as if we are timestamps
if self.kind == 'timestamp':
return super(PeriodIndexResampler, self)._downsample(how, **kwargs)
how = self._is_cython_func(how) or how
ax = self.ax
new_index = self._get_new_index()
# Start vs. end of period
memb = ax.asfreq(self.freq, how=self.convention)
if is_subperiod(ax.freq, self.freq):
# Downsampling
if len(new_index) == 0:
bins = []
else:
i8 = memb.asi8
rng = np.arange(i8[0], i8[-1] + 1)
bins = memb.searchsorted(rng, side='right')
grouper = BinGrouper(bins, new_index)
return self._groupby_and_aggregate(how, grouper=grouper)
elif is_superperiod(ax.freq, self.freq):
return self.asfreq()
elif ax.freq == self.freq:
return self.asfreq()
raise IncompatibleFrequency(
'Frequency {} cannot be resampled to {}, as they are not '
'sub or super periods'.format(ax.freq, self.freq))
def _downsample(self, how, **kwargs):
"""
Downsample the cython defined function
Parameters
----------
how : string / cython mapped function
**kwargs : kw args passed to how function
"""
# we may need to actually resample as if we are timestamps
if self.kind == 'timestamp':
return super(PeriodIndexResampler, self)._downsample(how, **kwargs)
how = self._is_cython_func(how) or how
ax = self.ax
new_index = self._get_new_index()
if len(new_index) == 0:
return self._wrap_result(self._selected_obj.reindex(new_index))
# Start vs. end of period
memb = ax.asfreq(self.freq, how=self.convention)
if is_subperiod(ax.freq, self.freq):
# Downsampling
rng = np.arange(memb.values[0], memb.values[-1] + 1)
bins = memb.searchsorted(rng, side='right')
grouper = BinGrouper(bins, new_index)
return self._groupby_and_aggregate(how, grouper=grouper)
elif is_superperiod(ax.freq, self.freq):
return self.asfreq()
elif ax.freq == self.freq:
return self.asfreq()
raise ValueError('Frequency {axfreq} cannot be '
'resampled to {freq}'.format(
axfreq=ax.freq,
freq=self.freq))
