def _plot_superimposed_ohlc():
"""Superimposed, downsampled vbars"""
resample_rule = (superimpose if isinstance(superimpose, str) else dict(
day='W', hour='D', minute='H', second='T',
millisecond='S').get(time_resolution))
if not resample_rule:
warnings.warn(
"'Can't superimpose OHLC data with rule '{}' (index datetime resolution: '{}'). "
"Skipping.".format(resample_rule, time_resolution),
stacklevel=4)
return
orig_df['_width'] = 1
from .lib import OHLCV_AGG
df2 = orig_df.resample(resample_rule, label='left').agg(
dict(OHLCV_AGG, _width='count'))
# Check if resampling was downsampling; error on upsampling
orig_freq = _data_period(orig_df)
resample_freq = _data_period(df2)
if resample_freq < orig_freq:
raise ValueError(
'Invalid value for `superimpose`: Upsampling not supported.')
if resample_freq == orig_freq:
warnings.warn(
'Superimposed OHLC plot matches the original plot. Skipping.',
stacklevel=4)
return
if omit_missing:
width2 = '_width'
df2.index = df2['_width'].cumsum().shift(1).fillna(0)
df2.index += df2['_width'] / 2 - .5
df2['_width'] -= .1 # Candles don't touch
else:
del df['_width']
width2 = dict(day=86400 * 5, hour=86400, minute=3600,
second=60)[time_resolution] * 1000
df2.index += pd.Timedelta(
width2 / 2 +
(width2 /
5 if resample_rule == 'W' else 0), # Sunday week start
unit='ms')
df2['inc'] = (df2.Close >= df2.Open).astype(np.uint8).astype(str)
df2.index.name = None
source2 = ColumnDataSource(df2)
fig_ohlc.segment('index',
'High',
'index',
'Low',
source=source2,
color='#bbbbbb')
fig_ohlc.vbar('index',
width2,
'Open',
'Close',
source=source2,
line_color=None,
fill_color=factor_cmap('inc', COLORS_LIGHT, ['0', '1']))
def _plot_superimposed_ohlc():
"""Superimposed, downsampled vbars"""
time_resolution = pd.DatetimeIndex(df['datetime']).resolution
resample_rule = (superimpose if isinstance(superimpose, str) else dict(
day='M', hour='D', minute='H', second='T',
millisecond='S').get(time_resolution))
if not resample_rule:
warnings.warn(
f"'Can't superimpose OHLC data with rule '{resample_rule}'"
f"(index datetime resolution: '{time_resolution}'). Skipping.",
stacklevel=4,
)
return
df2 = df.assign(_width=1).set_index('datetime').resample(
resample_rule, label='left').agg(dict(OHLCV_AGG, _width='count'))
# Check if resampling was downsampling; error on upsampling
orig_freq = _data_period(df['datetime'])
resample_freq = _data_period(df2.index)
if resample_freq < orig_freq:
raise ValueError(
'Invalid value for `superimpose`: Upsampling not supported.')
if resample_freq == orig_freq:
warnings.warn(
'Superimposed OHLC plot matches the original plot. Skipping.',
stacklevel=4,
)
return
df2.index = df2['_width'].cumsum().shift(1).fillna(0)
df2.index += df2['_width'] / 2 - 0.5
df2['_width'] -= 0.1 # Candles don't touch
df2['inc'] = (df2.Close >= df2.Open).astype(int).astype(str)
df2.index.name = None
source2 = ColumnDataSource(df2)
fig_ohlc.segment('index',
'High',
'index',
'Low',
source=source2,
color='#bbbbbb')
colors_lighter = [
lightness(BEAR_COLOR, 0.92),
lightness(BULL_COLOR, 0.92)
]
fig_ohlc.vbar(
'index',
'_width',
'Open',
'Close',
source=source2,
line_color=None,
fill_color=factor_cmap('inc', colors_lighter, ['0', '1']),
)
def _round_timedelta(value, _period=_data_period(df)):
if not isinstance(value, pd.Timedelta):
return value
resolution = getattr(_period, 'resolution_string',
None) or _period.resolution
return value.ceil(resolution)
