label_name = line['label']
color = line['color']
line = Lines(x=input_df['datetime'][::stride],
y=input_df[col_name][::stride],
scales={
'x': date_co,
'y': linear_co
},
colors=[color],
labels=[label_name],
display_legend=True)
lines.append(line)
new_fig = Figure(marks=lines,
axes=[yax, xax],
title=self.conf['title'],
interaction=panzoom_main)
return new_fig
if __name__ == "__main__":
from gquant.dataloader.csvStockLoader import CsvStockLoader
from gquant.transform.averageNode import AverageNode
from gquant.analysis.outCsvNode import OutCsvNode
loader = CsvStockLoader("id0", {}, True, False)
df = loader([])
vf = AverageNode("id1", {"column": "volume"})
df2 = vf([df])
o = OutCsvNode("id3", {"path": "o.csv"})
o([df2])
class AssetFilterNode(Node):
def process(self, inputs):
"""
select the asset based on asset id, which is defined in `asset` in the
nodes' conf
Arguments
-------
inputs: list
list of input dataframes.
Returns
-------
dataframe
"""
input_df = inputs[0]
output_df = input_df.query('asset==%s' % self.conf["asset"])
return output_df
def columns_setup(self):
self.required = {"asset": "int64"}
if __name__ == "__main__":
from gquant.dataloader.csvStockLoader import CsvStockLoader
loader = CsvStockLoader("node_csvdata", {}, True, False)
df = loader([])
sf = AssetFilterNode("id2", {"asset": 22123})
df2 = sf([df])
