def ta_inverse_gasf(df: Typing.PatchedPandas):
if has_indexed_columns(df):
# todo implement recursion
pass
def gaf_decode(x):
values = x._.values
values = values.reshape(values.shape[1:])
return _np.sqrt(((_np.diag(values) + 1) / 2)).tolist()
return df.to_frame().apply(gaf_decode, axis=1, result_type='expand')
def ta_z_norm(df: Typing.PatchedPandas, period=200, ddof=1, demean=True, lag=0):
if has_indexed_columns(df):
return pd.DataFrame(
{col: ta_z_norm(df[col], period, ddof, demean) for col in df.columns},
index=df.index
)
# (value - mean) / std
s = df.rolling(period).std(ddof=ddof)
a = (df - df.rolling(period).mean().shift(lag)) if demean else df
return (a / s / 4).rename(df.name)
def ta_ncdf_compress(df: Typing.PatchedPandas, period=200, lower_percentile=25, upper_percentile=75) -> Typing.PatchedPandas:
if has_indexed_columns(df):
return pd.DataFrame(
{col: ta_ncdf_compress(df[col], period, lower_percentile, upper_percentile) for col in df.columns},
index=df.index
)
f50 = df.rolling(period).mean().rename("f50")
fup = df.rolling(period).apply(lambda x: np.percentile(x, upper_percentile)).rename("fup")
flo = df.rolling(period).apply(lambda x: np.percentile(x, lower_percentile)).rename("flo")
return pd.Series(norm.cdf(0.5 * (df - f50) / (fup - flo)) - 0.5, index=df.index, name=df.name)
def ta_inverse_gasf(df: Typing.PatchedPandas):
if has_indexed_columns(df):
res = _pd.DataFrame({}, index=df.index)
for col in df.columns:
sdf = ta_inverse_gasf(df[col])
sdf.columns = _pd.MultiIndex.from_product([[col], sdf.columns])
res = res.join(sdf)
return res
inv = np_inverse_gaf(df._.values)
if inv.ndim == 2:
return _pd.DataFrame(inv, index=df.index)
else:
if inv.shape[1] == 1:
return _pd.DataFrame(inv[:, 0, :], index=df.index)
else:
raise ValueError("should never get here ..")