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Optimise Ticker._fix_unit_mixups()

pull/1400/head
ValueRaider 2023-02-05 15:15:56 +00:00
parent 36ace8017d
commit 7c6742a60a
1 changed files with 13 additions and 8 deletions
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21
yfinance/base.py
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@ -1169,7 +1169,7 @@ class TickerBase:
if df.index.tz is None:
df2.index = df2.index.tz_localize(tz_exchange)
else:
elif df2.index.tz != tz_exchange:
df2.index = df2.index.tz_convert(tz_exchange)
# Only import scipy if users actually want function. To avoid
@ -1178,7 +1178,7 @@ class TickerBase:
data_cols = ["High", "Open", "Low", "Close", "Adj Close"] # Order important, separate High from Low
data_cols = [c for c in data_cols if c in df2.columns]
f_zeroes = (df2[data_cols]==0).any(axis=1)
f_zeroes = (df2[data_cols]==0).any(axis=1).to_numpy()
if f_zeroes.any():
df2_zeroes = df2[f_zeroes]
df2 = df2[~f_zeroes]
@ -1186,8 +1186,9 @@ class TickerBase:
df2_zeroes = None
if df2.shape[0] <= 1:
return df
median = _ndimage.median_filter(df2[data_cols].values, size=(3, 3), mode="wrap")
ratio = df2[data_cols].values / median
df2_data = df2[data_cols].to_numpy()
median = _ndimage.median_filter(df2_data, size=(3, 3), mode="wrap")
ratio = df2_data / median
ratio_rounded = (ratio / 20).round() * 20 # round ratio to nearest 20
f = ratio_rounded == 100
if not f.any():
@ -1200,14 +1201,15 @@ class TickerBase:
c = data_cols[i]
df2.loc[fi, c] = tag
n_before = (df2[data_cols].to_numpy()==tag).sum()
n_before = df2_data.sum()
df2 = self._reconstruct_intervals_batch(df2, interval, prepost, tag, silent)
df2_tagged = df2[data_cols].to_numpy()==tag
n_after = (df2[data_cols].to_numpy()==tag).sum()
if n_after > 0:
# This second pass will *crudely* "fix" any remaining errors in High/Low
# simply by ensuring they don't contradict e.g. Low = 100x High.
f = df2[data_cols].to_numpy()==tag
f = df2_tagged
for i in range(f.shape[0]):
fi = f[i,:]
if not fi.any():
@ -1239,7 +1241,10 @@ class TickerBase:
if fi[j]:
df2.loc[idx, c] = df2.loc[idx, ["Open", "Close"]].min()
n_after_crude = (df2[data_cols].to_numpy()==tag).sum()
df2_tagged = df2[data_cols].to_numpy()==tag
n_after_crude = df2_tagged.sum()
else:
n_after_crude = n_after
n_fixed = n_before - n_after_crude
n_fixed_crudely = n_after - n_after_crude
@ -1251,7 +1256,7 @@ class TickerBase:
print(report_msg)
# Restore original values where repair failed
f = df2[data_cols].values==tag
f = df2_tagged
for j in range(len(data_cols)):
fj = f[:,j]
if fj.any():