def get_trial_avg(trial_data,step=1):
from numpy import concatenate,array,transpose
from plastk.utils import stats
trial_data = get_trial_data(trial_data,step)
indices = trial_data[0][:,0]
data = array([x[:,1] for x in trial_data])
mean,var,stderr = stats(data)
return transpose(array((indices, mean,stderr)))
def plot_avg(self,x=None,y=None,title=None,replot=False,step=1,
errorbars='conf',lw=1):
"""
Plot the average over a set of Y values with error bars
indicating the 95% confidence interval of the sample mean at
each point. (i.e. stderr * 1.96)
y = A sequence of sequences of Y values to average.
If not all sequences are of equal length, the length
of the shortest sequence is used for all.
x = (optional) A single sequence of X values corresponding to
the Ys.
title = The title of the average plot.
replot = Keep the old contents of the plot window.
default = False
step = Plot the average at every Nth point. (default = 1)
errorbars = What statistic to use for error bars, one of:
'conf' -> 95% confidence interval (stderr * 1.96)
'stderr' -> Standard error
'stddev -> Standard deviation
'var' -> Variance
"""
from numpy import concatenate as join
N = min(map(len,y))
mean,var,stderr = utils.stats(join([array([a[:N]]) for a in y],axis=0))
if replot:
self.current_style += 1
else:
self.current_style = 1
self.plot(x=x,y=mean,
#title=title,
with='lines lt %d lw %d'%(self.current_style,lw),
step=step,replot=replot)
if not x:
x = range(len(mean))
if errorbars == 'conf':
bars = stderr * 1.96
elif errorbars == 'stderr':
bars = stderr
elif errorbars == 'stddev':
bars = sqrt(var)
elif errorbars == 'var':
bars = var
else:
raise 'Unknown error bar type: "%s"' % errorbars
self.plot(pts=zip(x,mean,bars),
title=title,
with='errorbars lt %d lw %d'%(self.current_style,lw),
step=step,replot=1)
def plot_avg(self,x=None,y=None,title=None,replot=False,step=1,
errorbars='conf'):
"""
Plot the average over a set of Y values with error bars
indicating the 95% confidence interval of the sample mean at
each point. (i.e. stderr * 1.96)
y = A sequence of sequences of Y values to average.
If not all sequences are of equal length, the length
of the shortest sequence is used for all.
x = (optional) A single sequence of X values corresponding to
the Ys.
title = The title of the average plot.
replot = Keep the old contents of the plot window.
default = False
step = Plot the average at every Nth point. (default = 1)
errorbars = What statistic to use for error bars, one of:
'conf' -> 95% confidence interval (stderr * 1.96)
'stderr' -> Standard error
'stddev -> Standard deviation
'var' -> Variance
"""
from Numeric import concatenate as join
N = min(map(len,y))
mean,var,stderr = utils.stats(join([array([a[:N]]) for a in y],axis=0))
if replot:
self.current_style += 1
else:
self.current_style = 1
self.plot(x=x,y=mean,title=title,
with='lines %d'%self.current_style,
step=step,replot=replot)
if not x:
x = range(len(mean))
if errorbars == 'conf':
bars = stderr * 1.96
elif errorbars == 'stderr':
bars = stderr
elif errorbars == 'stddev':
bars = sqrt(var)
elif errorbars == 'var':
bars = var
else:
raise 'Unknown error bar type: "%s"' % errorbars
self.plot(pts=zip(x,mean,bars),with='errorbars %d'%self.current_style,
step=step,replot=1)
class GPlot(Gp.Gnuplot):
"""
A subclass of Gnuplot.Gnuplot that makes some common kinds of
plotting easier.
Notably, it's easy to plot a single y variable against the
integers, without haveing explicitly specify the data as a
sequence of (x,y) pairs. It also allows the plotting of multiple
curves in a single call, automatic plotting of averages with error
bars, and plotting only everyt Nth point in a data set. See the
individual method docs for more details.
Using GPlot, it's not necessary to create separate GPlot.Data
objects for data.
"""
def __init__(self,*args,**kw):
Gp.Gnuplot.__init__(self,*args,**kw)
self.current_style = 0
def plot(self,x=None, y=None, pts=None, cmd=None,
replot=False, queue_only=False, clear_queue=False,
step=1,
**params):
"""
General data plotting. Uses keywords to specify data:
y -- The Y values to plot
x -- The X values for the plotted points. By
default, x = range(0,len(y)).
pts -- A sequence of points to plot, similar to the
standard arg to Gnuplot.Data(), overrides x and y.
replot -- Keep the old plot contents. Default = False.
queue_only -- adds data to PlotItem queue, but doesn't plot it
overrides replot=True. Default=False.
step -- Only plot every nth data point, default = 1.
**params -- Keyword arguments that will be passed to
Gnuplot.Data(), e.g. with='lines'
"""
if clear_queue:
self._clear_queue()
if queue_only:
plot_fn = lambda s,d:Gp.Gnuplot._add_to_queue(s,[d])
elif replot:
plot_fn = Gp.Gnuplot.replot
else:
plot_fn = Gp.Gnuplot.plot
if pts:
if step > 1:
pts = step_select(pts,step)
data = Gp.Data(pts,inline=1,**params)
elif y:
if not x:
x = range(len(y))
pts = step_select(zip(x,y),step)
data = Gp.Data(pts,inline=1,**params)
elif cmd:
data = cmd
else:
Gp.Gnuplot.replot(self)
return
plot_fn(self,data)
def plot_multi(self,x=None,y=None,pts=None,title=None,
replot=False,**params):
"""
Plot several data sets.
y -- A sequence of sequences of Y coordinates to plot.
x -- A *single* sequence of X coordinates to match the Ys,
(can be blank)
pts -- A sequence of sequences of (x,y) pairs to
plot. (overrides x and y).
replot -- Keep the old plot contents. Default False
title -- A sequence of strings as titles for the respective
data sets.
**params -- keyword args to pass to Gnuplot.Data()
"""
if pts:
if not title:
title = [''] * len(pts)
self.plot(pts=pts[0],title=title[0],replot=replot,**params)
for p,t in zip(pts,title):
self.plot(pts=p,title=t,replot=1,**params)
elif y:
if not title:
title = [''] * len(y)
self.plot(x=x,y=y[0],title=title[0],replot=replot,**params)
for p,t in zip(y,title):
self.plot(x=x,y=p,title=t,replot=1,**params)
else:
raise "plot_multi requires either pts, or y as an argument"
def plot_avg(self,x=None,y=None,title=None,replot=False,step=1,
errorbars='conf'):
"""
Plot the average over a set of Y values with error bars
indicating the 95% confidence interval of the sample mean at
each point. (i.e. stderr * 1.96)
y = A sequence of sequences of Y values to average.
If not all sequences are of equal length, the length
of the shortest sequence is used for all.
x = (optional) A single sequence of X values corresponding to
the Ys.
title = The title of the average plot.
replot = Keep the old contents of the plot window.
default = False
step = Plot the average at every Nth point. (default = 1)
errorbars = What statistic to use for error bars, one of:
'conf' -> 95% confidence interval (stderr * 1.96)
'stderr' -> Standard error
'stddev -> Standard deviation
'var' -> Variance
"""
from Numeric import concatenate as join
N = min(map(len,y))
mean,var,stderr = utils.stats(join([array([a[:N]]) for a in y],axis=0))
if replot:
self.current_style += 1
else:
self.current_style = 1
self.plot(x=x,y=mean,title=title,
with='lines %d'%self.current_style,
step=step,replot=replot)
if not x:
x = range(len(mean))
if errorbars == 'conf':
bars = stderr * 1.96
elif errorbars == 'stderr':
bars = stderr
elif errorbars == 'stddev':
bars = sqrt(var)
elif errorbars == 'var':
bars = var
else:
raise 'Unknown error bar type: "%s"' % errorbars
self.plot(pts=zip(x,mean,bars),with='errorbars %d'%self.current_style,
step=step,replot=1)
def plot_stddev(self,x=None,y=None,title=None,replot=False,step=1,with='lines'):
from Numeric import concatenate as join
N = min(map(len,y))
mean,var,stderr = utils.stats(join([array([a[:N]]) for a in y],axis=0))
self.plot(x=x,y=sqrt(var),title=title,with=with,replot=replot,step=step)
def replot(self,**params):
self.plot(replot=True,**params)
