def commit(self):
self.Error.seasonal_decompose_fail.clear()
data = self.data
if not data or not self.selected:
self.Outputs.time_series.send(data)
return
selected_subset = Timeseries.from_table(
Domain(self.selected, source=data.domain), data)
# FIXME: might not pass selected interpolation method
with self.progressBar(len(self.selected)) as progress:
try:
adjusted_data = seasonal_decompose(
selected_subset,
self.DECOMPOSITION_MODELS[self.decomposition],
self.n_periods,
callback=lambda *_: progress.advance())
except ValueError as ex:
self.Error.seasonal_decompose_fail(str(ex))
adjusted_data = None
if adjusted_data is not None:
new_domain = Domain(
data.domain.attributes + adjusted_data.domain.attributes,
data.domain.class_vars, data.domain.metas)
ts = Timeseries.from_numpy(new_domain,
X=hstack((data.X, adjusted_data.X)),
Y=data.Y,
metas=data.metas)
ts.time_variable = data.time_variable
else:
ts = None
self.Outputs.time_series.send(ts)
def finance_data(symbol, since=None, until=None, granularity='d'):
"""Fetch Yahoo Finance data for stock or index `symbol` within the period
after `since` and before `until` (both inclusive).
Parameters
----------
symbol: str
A stock or index symbol, as supported by Yahoo Finance.
since: date
A start date (default: 1900-01-01).
until: date
An end date (default: today).
granularity: 'd' or 'w' or 'm' or 'v'
What data to get: daily, weekly, monthly, or dividends.
Returns
-------
data : Timeseries
"""
if since is None:
since = date(1900, 1, 1)
if until is None:
until = date.today()
f = web.DataReader(symbol, 'yahoo', since, until)
data = Timeseries.from_data_table(table_from_frame(f))
# Make Adjusted Close a class variable
attrs = [var.name for var in data.domain.attributes]
attrs.remove('Adj Close')
data = Timeseries.from_table(
Domain(attrs, [data.domain['Adj Close']], None, source=data.domain),
data)
data.name = symbol
data.time_variable = data.domain['Date']
return data
def on_changed(self):
self.commit()
def commit(self):
self.Warning.no_transforms_added.clear()
data = self.data
if not data:
self.Outputs.time_series.send(None)
return
if not len(self.table_model):
self.Warning.no_transforms_added()
self.Outputs.time_series.send(None)
return
ts = moving_transform(data, self.table_model, self.non_overlapping and self.fixed_wlen)
self.Outputs.time_series.send(ts)
if __name__ == "__main__":
data = Timeseries.from_file('airpassengers')
attrs = [var.name for var in data.domain.attributes]
if 'Adj Close' in attrs:
# Make Adjusted Close a class variable
attrs.remove('Adj Close')
data = Timeseries.from_table(
Domain(attrs, [data.domain['Adj Close']], None, source=data.domain),
data)
WidgetPreview(OWMovingTransform).run(data)
