def addData(self,
xvals, yvals,
xlabel, ylabel,
nplotted,
yerrors=None):
xxvals, yyvals = Stats.filterNone((xvals, yvals))
color, linestyle, marker = self.getFormat(nplotted)
self.bokeh_figure.line(
xxvals,
yyvals,
color=color,
line_width=2)
def render(self, dataframe, path):
if len(dataframe.columns) < 2:
raise ValueError(
"requiring two coordinates, only got %s" %
str(dataframe.columns))
plts, legend = [], []
blocks = ResultBlocks()
for xcolumn, ycolumn in itertools.combinations(dataframe.columns, 2):
# remove missing data points
xvalues, yvalues = Stats.filterMissing(
(dataframe[xcolumn], dataframe[ycolumn]))
# remove columns with all NaN
if len(xvalues) == 0 or len(yvalues) == 0:
continue
# apply log transformation on data not on plot
if self.logscale:
if "x" in self.logscale:
xvalues = R.log10(xvalues)
if "y" in self.logscale:
yvalues = R.log10(yvalues)
self.startPlot()
# wrap, as pandas series can not
# passed through rpy2.
R.smoothScatter(numpy.array(xvalues, dtype=numpy.float),
numpy.array(yvalues, dtype=numpy.float),
xlab=xcolumn,
ylab=ycolumn,
nbin=self.nbins)
blocks.extend(self.endPlot(dataframe, path))
return blocks
def apply(self, xvals, yvals):
xx = xvals[~numpy.isnan(xvals)]
yy = yvals[~numpy.isnan(yvals)]
r = Stats.doMannWhitneyUTest(xx, yy)
return r
def apply(self, xvals, yvals):
return Stats.doCorrelationTest(xvals, yvals,
method=self.method)
def smooth_histogram(self, data):
if len(data) <= self.smooth_window_size:
return data
r = Stats.smooth(data, window_len=self.smooth_window_size)
return r[:len(data)]