def plotMRMCsv(df, samples = None, sampleField='File Name', colors=None, labelHash=None, yAxisLabel=None, legendLoc = 'upper center', areaField = 'Area',
num = ['light '], den = ['heavy '], proteins = None, alpha=1.0, markersize=10, title=None, legendBBox = (0.5, 1.0), proteinField = 'Protein Name',
median = True, medianMarker = '-', legend=True, legendCols=5, normProtein=None, medianMarkerInc = 2.5, normSample=None,
yAxis=None, yMin = 0.0, yMax = 10.0, yTicks=2, figSize=(15,5), grid=False, scaleProt=None, markeredgewidth=1.0):
df.loc[:,'currentCalc'] = mrmTools.calcValue(df, num, den, field=areaField)
#print 'running code'
if scaleProt is None:
scaleProt = {}
if samples is None:
samples = qMS.sort_nicely(list(df[sampleField].unique()))
if colors is None:
colors = pylab.cm.jet([float(i)/float(len(samples)) for i in range(len(samples))])
if proteins is None:
proteins = qMS.sort_nicely(list(df[proteinField].unique()))
if yAxis is None:
interval = (yMax*1.0-yMin)/(yTicks+1.0)
yAxis = [yMin+i*interval for i in range(0,yTicks+2)]
if labelHash is None:
labelHash = {i:i for i in samples}
for i in proteins:
if i not in scaleProt.keys():
scaleProt[i] = 1.0
if not normSample is None:
sampDF = df[df[sampleField]==normSample]
normValue = 1.0
if not normProtein is None:
normValue = sampDF[sampDF[proteinField]==normProtein]['currentCalc'].median()
for p in proteins:
scaleProt[p] = (sampDF[sampDF[proteinField]==p]['currentCalc']/normValue).median()
xOffset = 1.0/(len(samples)+1)
f = pylab.figure(figsize=figSize)
ax = f.add_subplot(111)
for i, s in enumerate(samples):
sampDF = df[df[sampleField]==s]
ax.plot(numpy.NaN, numpy.NaN, marker = 'o', color=colors[i], label=labelHash[s], markersize=markersize*1.5, markeredgewidth=markeredgewidth)
if not normProtein is None:
normValue = sampDF[sampDF[proteinField]==normProtein]['currentCalc'].median()
else:
normValue = 1.0
for j, p in enumerate(proteins):
ax.plot([float(j+(i+1)*xOffset)]*len(sampDF[sampDF[proteinField]==p]),
sampDF[sampDF[proteinField]==p]['currentCalc']/(normValue*scaleProt[p]),
'o', alpha=alpha, color=colors[i], markersize=markersize, markeredgewidth=markeredgewidth)
if median:
try:
ax.plot(float(j+(i+1)*xOffset), sampDF[sampDF[proteinField]==p]['currentCalc'].median()/(normValue*scaleProt[p]), marker='_',
mew=markersize/3, color='black', markersize=markersize*medianMarkerInc)
#print str(s) + '\t' + str(p) + '\t' + str(sampDF[sampDF[proteinField]==p]['currentCalc'].median()/(normValue*scaleProt[p]))
except:
print 'error'
if legend:
ax.legend(ncol=legendCols, loc=legendLoc, bbox_to_anchor=legendBBox,
fancybox=True, shadow=False)
ax.set_ylim(yMin, yMax)
ax.set_yticks(yAxis)
pylab.xticks(range(len(proteins)), proteins, rotation=45)
pylab.xlim(0, len(proteins))
if grid:
pylab.grid()
cleanAxis(ax, ticksOnly=True)
if not title is None:
ax.set_title(title)
if not yAxisLabel is None:
ax.set_ylabel(yAxisLabel)
return ax
def plotMRMScatterPlot(df, samples=None, labelHash=None, yAxisLabel=None, proteins=None, alpha=1.0, legend=True, legendCols=5, median=True,
grid=True, yAxis=None, num=['light '], den=['heavy '], yTicks=4, sampleLocations=None, sampleField='File Name', ax=None,
medianMarkerInc=1.25, colors=None, figSize=(10,10), markersize=10, fitLine=False, fitR2=False, medianColor='black',
yMin=0, yMax=10, xMin=None, xMax=None, title=None, zOrder=None, medianOnly=None, proteinNameHeader = 'Protein Name'):
df.loc[:,'currentCalc'] = mrmTools.calcValue(df, num, den)
if samples is None:
samples = qMS.sort_nicely(list(df[sampleField].unique()))
if proteins is None:
proteins = qMS.sort_nicely(list(df[proteinNameHeader].unique()))
if colors is None:
colors = pylab.cm.jet([float(i)/float(len(proteins)) for i in range(len(proteins))])
if yAxis is None:
interval = (yMax*1.0-yMin)/(yTicks+1.0)
yAxis = [yMin+i*interval for i in range(0,yTicks+2)]
if labelHash is None:
labelHash = {i:i for i in proteins}
if sampleLocations is None:
sampleLocations = {samples[i]:i for i in range(len(samples))}
if xMin is None:
xMin = min(sampleLocations.values())
if xMax is None:
xMax = max(sampleLocations.values())
if zOrder is None:
zOrder = [i*2+1 for i in range(len(proteins))]
if medianOnly is None:
medianOnly = [False]*len(proteins)
if ax is None:
f = pylab.figure(figsize=figSize)
ax = f.add_subplot(111)
for i, p in enumerate(proteins):
protDF = df[df[proteinNameHeader] == p]
ax.plot(numpy.NaN, numpy.NaN, marker = 'o', color=colors[i], label=labelHash[p], markersize=markersize*1.5)
ysFit = []
xsFit = []
for j, s in enumerate(samples):
if not medianOnly[i]:
ax.plot([float(sampleLocations[s])]*(len(protDF[protDF[sampleField] == s])),
protDF[protDF[sampleField]==s]['currentCalc'],
'o', alpha=alpha, color=colors[i], markersize=markersize, zorder=zOrder[i])
if median:
marker='_'
mew=markersize/3
zorder=100
if medianColor is None:
medianColor = colors[i]
if medianOnly[i]:
marker='o'
medianColor=colors[i]
mew = 0
zorder=zOrder[i]
ax.plot(sampleLocations[s], protDF[protDF[sampleField]==s]['currentCalc'].median(), marker=marker,
mew=mew, color=medianColor, markersize=markersize*medianMarkerInc, zorder=zorder)
ysFit.append(protDF[protDF[sampleField]==s]['currentCalc'].median())
xsFit.append(sampleLocations[s])
if fitLine:
pFit = numpy.polyfit(xsFit, ysFit, 1)
xsFit.append(xMin)
xsFit.append(xMax)
ax.plot(numpy.array(xsFit), pFit[0]*numpy.array(xsFit)+pFit[1], '-', color=colors[i], zorder=zOrder[i]-1)
if legend:
ax.legend(ncol=legendCols, loc='upper center', bbox_to_anchor=(0.5, 1.0),
fancybox=True, shadow=False)
ax.set_ylim(yMin, yMax)
ax.set_yticks(yAxis)
pylab.xticks(sampleLocations.values())
ax.set_xlim(xMin, xMax)
if grid:
pylab.grid()
cleanAxis(ax, ticksOnly=True)
if not title is None:
ax.set_title(title)
if not yAxisLabel is None:
ax.set_ylabel(yAxisLabel)
return ax
