def plotDensity(data, name, pdf):
"""
This function takes a pandas dataframe and plots a
density plot and a boxplot.
"""
# Stablishing figure layout (x,y,colspan,rowspan)
axisLayout = [(0, 0, 1, 3), (3, 0, 1, 1)]
# Creating a figure template
figure = figureHandler(proj='2d',
numAx=2,
numRow=4,
numCol=1,
figsize=(8, 13),
arrangement=axisLayout)
# Adding figure Title
figure.formatAxis(figTitle="Distribution by Features {0}".format(name),
xlim="ignore",
ylim="ignore",
axnum=0,
showX=False)
#Creting list of len(wide.T) maximu 50 with the colors for each index
colors = [palette.ugColors[name]] * len(data.index)
# Plotting boxPlot
box.boxDF(ax=figure.ax[0], colors=colors, dat=data.T, vert=False, rot=0)
# Plotting density plot
density.plotDensityDF(data=data.T.unstack(),
ax=figure.ax[1],
colors=colors[0])
# Adding figure to pdf object
figure.addToPdf(pdf)
def plotDistributions(data, cutoff, palette, pdf):
# Open new figureHandler instance
fh = figureHandler(proj='2d', figsize=(14, 8))
#Get xmin and xmax
xmin = -np.nanpercentile(data['cv'].values, 99) * 0.2
xmax = np.nanpercentile(data['cv'].values, 99) * 1.5
# Split design file by treatment group and plot density plot
for name, group in palette.design.groupby(palette.combName):
dist.plotDensityDF(data=data["cv_" + name],
ax=fh.ax[0],
colors=palette.ugColors[name],
lb="{0}".format(name))
# Plot legend
fh.makeLegendLabel(ax=fh.ax[0])
# Give format to the axis
fh.formatAxis(
yTitle="Density",
xlim=(xmin, xmax),
ylim="ignore",
figTitle="Density Plot of Coefficients of Variation by {0}".format(
palette.combName))
# Shrink figure
fh.shrink()
# Add figure to PDF
fh.addToPdf(pdfPages=pdf)
def plotDensityDistribution(pdf, wide, palette):
# Instanciating figureHandler object
figure = figureHandler(proj="2d", figsize=(12, 7))
# Formating axis
figure.formatAxis(figTitle="Distribution by Samples Density",
xlim="ignore",
ylim="ignore",
grid=False)
# Plotting density plot
density.plotDensityDF(colors=palette.design["colors"],
ax=figure.ax[0],
data=wide)
# Add legend to the plot
figure.makeLegend(ax=figure.ax[0],
ucGroups=palette.ugColors,
group=palette.combName)
# Shrinking figure
figure.shrink()
# Adding to PDF
figure.addToPdf(pdf, dpi=600)
def plotCVplots(data, cutoff, palette, pdf):
#Iterate over groups
for name, group in palette.design.groupby(palette.combName):
# Open figure handler
fh = figureHandler(proj='2d', figsize=(14, 8))
# Get xmin and xmax
xmin = -np.nanpercentile(data['cv_' + name].values, 99) * 0.2
xmax = np.nanpercentile(data['cv_' + name].values, 99) * 1.5
# Plot histogram
hist.serHist(ax=fh.ax[0],
dat=data['cv_' + name],
color='grey',
normed=1,
range=(xmin, xmax),
bins=15)
# Plot density plot
dist.plotDensityDF(data=data['cv_' + name],
ax=fh.ax[0],
lb="CV density",
colors=palette.ugColors[name])
# Plot cutoff
lines.drawCutoffVert(ax=fh.ax[0],
x=cutoff[name],
lb="Cutoff at: {0}".format(cutoff[name]))
# Plot legend
fh.makeLegendLabel(ax=fh.ax[0])
# Give format to the axis
fh.formatAxis(
yTitle='Density',
xlim=(xmin, xmax),
ylim="ignore",
figTitle="Density Plot of Coefficients of Variation in {0}".format(
name))
# Shrink figure to fit legend
fh.shrink()
# Add plot to PDF
fh.addToPdf(pdfPages=pdf)