def run(self, data):
index, col, lamb = (self.cfg.get(p) for p in ["index", "col", "lamb"])
df = build_data(data, self.cfg)
cycle, trend = hpfilter(df, lamb=1600)
df = pd.concat([df, cycle, trend], axis=1, keys=[col, "cycle", "trend"])
df = df.reset_index()
return_data, _code = build_base_chart(
df.fillna(0), index, [col, "cycle", "trend"], agg="raw"
)
return return_data
def run(self, data):
index, col, low, high, K = (
self.cfg.get(p) for p in ["index", "col", "low", "high", "K"]
)
df = build_data(data, self.cfg)
cycle = bkfilter(df, low, high, K)
df = pd.concat([df, cycle], axis=1, keys=[col, "cycle"])
df = df.reset_index()
return_data, _code = build_base_chart(
df.fillna(0), index, [col, "cycle"], agg="raw"
)
return return_data
def run(self, data):
index, col, model = (self.cfg.get(p) for p in ["index", "col", "model"])
df = build_data(data, self.cfg)
sd_df = seasonal_decompose(df, model=model)
df = pd.concat(
[df, sd_df.seasonal, sd_df.trend, sd_df.resid],
axis=1,
keys=[col, "seasonal", "trend", "resid"],
)
df = df.reset_index()
return_data, _code = build_base_chart(
df.fillna(0), index, [col, "seasonal", "trend", "resid"], agg="raw"
)
return return_data
def run(self, data):
from statsmodels.tsa.seasonal import STL
index, col = (self.cfg.get(p) for p in ["index", "col"])
df = build_data(data, self.cfg)
sd_df = STL(df).fit()
df = pd.concat(
[df, sd_df.seasonal, sd_df.trend, sd_df.resid],
axis=1,
keys=[col, "seasonal", "trend", "resid"],
)
df = df.reset_index()
return_data, _code = build_base_chart(
df.fillna(0), index, [col, "seasonal", "trend", "resid"], agg="raw"
)
return return_data