def downsample(self, x, y, d, plotid=0, series=0):
# x = self.get_data(plotid, series)
# y = self.get_data(plotid, series, axis=1)
self.set_data(downsample_1d(array(x), d), plotid, series)
self.set_data(downsample_1d(array(y), d), plotid, series, axis=1)
self.redraw()
def downsample(self, x, y, d, plotid=0, series=0):
# x = self.get_data(plotid, series)
# y = self.get_data(plotid, series, axis=1)
self.set_data(downsample_1d(array(x), d), plotid, series)
self.set_data(downsample_1d(array(y), d), plotid, series, axis=1)
self.redraw()
def new_series(self, x=None, y=None, plotid=0, normalize=False,
time_series=True, timescale=False, downsample=None,
use_smooth=False, scale=None, ** kw):
'''
'''
if not time_series:
return super(TimeSeriesGraph, self).new_series(x=x, y=y, plotid=plotid, **kw)
xd = x
if x is not None:
if isinstance(x[0], str):
# convert the time stamp into seconds since the Epoch
# Epoch = 12:00 am 1/1/1970
fmt = "%Y-%m-%d %H:%M:%S"
args = x[0].split(' +')
timefunc = lambda xi, fmt: time.mktime(time.strptime(xi, fmt))
if len(args) > 1:
xd = [timefunc(xi.split(' +')[0], fmt) + float(xi.split(' +')[1]) / 1000.0 for xi in x]
else:
fmt = '%a %b %d %H:%M:%S %Y'
xd = [timefunc(xi, fmt) for xi in x]
if downsample:
xd = downsample_1d(x, downsample)
y = downsample_1d(y, downsample)
if use_smooth:
y = smooth(y)
if xd is not None:
xd = array(xd)
if normalize:
xd = xd - xd[0]
if scale:
xd = xd * scale
plot, names, rd = self._series_factory(xd, y, plotid=plotid, **kw)
if 'type' in rd:
if rd['type'] == 'line_scatter':
plot.plot(names, type='scatter', marker_size=2,
marker='circle')
rd['type'] = 'line'
plota = plot.plot(names, **rd)[0]
# plota.unified_draw = True
# plota.use_downsampling = True
# if the plot is not visible dont remove the underlays
if plota.visible:
self._set_bottom_axis(plota, plot, plotid, timescale=timescale)
return plota, plot
def new_series(self, x=None, y=None, plotid=0, normalize=False,
time_series=True, timescale=False, downsample=None,
use_smooth=False, scale=None, ** kw):
'''
'''
if not time_series:
return super(TimeSeriesGraph, self).new_series(x=x, y=y, plotid=plotid, **kw)
xd = x
if x is not None:
if isinstance(x[0], str):
# convert the time stamp into seconds since the Epoch
# Epoch = 12:00 am 1/1/1970
fmt = "%Y-%m-%d %H:%M:%S"
args = x[0].split(' +')
timefunc = lambda xi, fmt: time.mktime(time.strptime(xi, fmt))
if len(args) > 1:
xd = [timefunc(xi.split(' +')[0], fmt) + float(xi.split(' +')[1]) / 1000.0 for xi in x]
else:
fmt = '%a %b %d %H:%M:%S %Y'
xd = [timefunc(xi, fmt) for xi in x]
if downsample:
xd = downsample_1d(x, downsample)
y = downsample_1d(y, downsample)
if use_smooth:
y = smooth(y)
if xd is not None:
xd = array(xd)
if normalize:
xd = xd - xd[0]
if scale:
xd = xd * scale
plot, names, rd = self._series_factory(xd, y, plotid=plotid, **kw)
if 'type' in rd:
if rd['type'] == 'line_scatter':
plot.plot(names, type='scatter', marker_size=2,
marker='circle')
rd['type'] = 'line'
plota = plot.plot(names, **rd)[0]
# plota.unified_draw = True
# plota.use_downsampling = True
# if the plot is not visible dont remove the underlays
if plota.visible:
self._set_bottom_axis(plota, plot, plotid, timescale=timescale)
return plota, plot
