def makeScatter(x, y, ax, fh):
""" Plot a scatter plot of x vs y.
:Arguments:
:type x: pandas.Series
:param x: Series of first sample, treated as independent variable.
:type y: pandas.Series
:param y: Series of second sample, treated as dependent variables.
:type ax: matplotlib.axis
:param ax: Axis which to plot.
:type fh: figureHandler
:param fh: figure to draw BA plots onto.
:Returns:
:rtype: matplotlib.axis
:returns: A matplotlib axis with a scatter plot.
"""
#logger.info('{0}, {1}'.format(x.name, y.name))
# Get Upper and Lower CI from regression
lower, upper, fitted, resid, infl = runRegression(x, y)
# Plot scatter
scatter.scatter2D(x=x, y=y, ax=ax, colorList=list("b"))
# Plot regression lines
# If there are missing data, x and the result vectors won't have the same
# dimensions. First filter x by the index of the fitted values then plot.
x2 = x.loc[fitted.index]
lines.drawCutoff(x=x2, y=lower, ax=ax)
lines.drawCutoff(x=x2, y=fitted, ax=ax)
lines.drawCutoff(x=x2, y=upper, ax=ax)
# Adjust plot
fh.formatAxis(axnum=0,
xTitle=x.name,
yTitle=y.name,
axTitle='Scatter plot',
grid=False)
def plotSignificantROR(data, pdf, palette):
"""
Plot a scatter plot of x vs y.
:Arguments:
:type row:
:param row:
:type pdf: PdfPages
:param pdf: pdf object to store scatterplots
:type des: pandas DataFrame
:param des: design file
:type groupName: string
:param groupName: name of group
"""
# Iterates over all rows in the dataframe
# Make scatter plot if p-pvalue is less than 0.05
for index, row in data.iterrows():
if row["pval"] > 0.05: continue
#plotSignificantROR(row,pdf,dat.design,args.group)
# Get 95% CI
prstd, lower, upper = wls_prediction_std(row["res"])
# Sort CIs for Plotting
toPlot = pd.DataFrame({"x": row["x"], "lower": lower, "upper": upper})
toPlot.sort_values(by="x", inplace=True)
# Create plot
fh = figureHandler(proj="2d", figsize=(14, 8))
#Plot scatterplot
scatter.scatter2D(ax=fh.ax[0],
x=row["x"],
y=row["y"],
colorList=palette.list_colors)
# Plot cutoffs
lines.drawCutoff(ax=fh.ax[0], x=row["x"], y=row["fitted"], c="c")
lines.drawCutoff(ax=fh.ax[0], x=toPlot["x"], y=toPlot["lower"], c="r")
lines.drawCutoff(ax=fh.ax[0], x=toPlot["x"], y=toPlot["upper"], c="r")
# Formatting
ymin, ymax = fh.ax[0].get_ylim()
fh.formatAxis(xTitle="Run Order", yTitle="Value", ylim=(ymin,ymax*1.2),
figTitle=u"{} Scatter plot (fitted regression line and prediction bands"\
" included)".format(row["name"]))
# Shrink figure
fh.shrink()
# Add legend to figure
fh.makeLegend(ax=fh.ax[0],
ucGroups=palette.ugColors,
group=palette.combName)
#Add text to the ax
fh.ax[0].text(.7, .85, u"Slope= {0:.4f}\n(p-value = {1:.4f})\n"\
"$R^2$ = {2:4f}".format(round(row["slope"],4), round(row["pval"],4),
round(row["rsq"],4)),transform=fh.ax[0].transAxes, fontsize=12)
# Save to PDF
fh.addToPdf(pdf)