def makePlotWithFileList(isoFileList, numerator, denominator, subunits=None, normProtein=None, yMax=1.5, title=None, legendCols=4,
median=False, names=None, colors=None, figSize=(22,5), markerSize=None, noFill=False, legend=False,
mew=1, yMin=-0.05, highlightMed=False, hms=2, hmFilled=True, yAxis=None, alpha=1.0, adjProt=None):
"""makePlotWithFileList is a helper function that plots massage-style data from a list of files
:param isoFileList: a list of the files to be ploted (shoudl be full path to _iso.csv files)
:type isoFileList: list of strings
:param numerator: strings of the keys in the numerator (ampu, ampl, amps)
:type numerator: list of strings
:param denominator: strings of the keys in the denominator (ampu, ampl, amps)
:type denominator: list of strings
:param subunits: a list of the proteins to be plotted - strings must match the keys of the 'proteins' field in _iso.csv
:type subunits: list
:param normProtein: string of the protein to normalize to for all datasets (defaults to None)
:type normProtein: string
:param yMax: float of maximum y value
:type yMax: float
:param median: bool to plot only the median values
:type median: bool
:param names: a list of the names to be listed in the legend; must be same length as isoFileList
:type names: list of strings
:param colors: a list of the colors to be used in plotting, again must be same length as isoFileList
:type colors: list of strings
:param figSize: the figure size (list - (10,10))
:type figSize: list of floats
:param noFill: a bool if you want open circles
:type noFill: bool
:param mew: a float of the marker edge width
:type mew: float
:returns: the plotted axis
"""
if names is None:
names = isoFileList
namesList = [(isoFileList[i], names[i]) for i in range(len(isoFileList))]
allStats = qMS.multiStatsDict(isoFileList, numerator, denominator, normalization=1.0, offset=0.0, normProtein=normProtein, noProcess=True, adjProt=adjProt)
return makePlotWithStatsDictDict(allStats, subunits=subunits, yMax=yMax, title=title, legend=legend, legendCols=legendCols, yAxis=yAxis,
median=median, namesList=namesList, colors=colors, figSize=figSize, markerSize=markerSize,
noFill=noFill, mew=mew, yMin=yMin, highlightMed=highlightMed, hms=hms, hmFilled=hmFilled, alpha=alpha)
def plotPeptideCount(files, labels=None, colors=None, proteins=None, num=['AMP_U'], den=['AMP_U'], legend=True, figSize=(20,10)):
if labels is None:
labels=files
if colors is None:
colors = [pylab.cm.jet(float(i)/float(len(files))) for i in range(len(files))]
numFiles=len(files)
offset = 1.0/(numFiles+1)
msd = qMS.multiStatsDict(files, num, den)
f = pylab.figure(figsize=figSize)
a = f.add_subplot(111)
for j in range(0,numFiles):
a.bar([i+offset*j for i in range(len(proteins))], [len(msd[files[j]][i]) for i in proteins], align='edge', width=1.0/(numFiles+2), color=colors[j], label=labels[j])
a.set_xticks([i+0.4 for i in range(0,len(proteins))])
a.set_xticklabels(proteins, size='medium', rotation='90')
a.set_ylabel('number of peptides')
cleanAxis(a, ticksOnly=True)
if legend:
a.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=5, mode="expand", borderaxespad=0.)
return a
'''
labels1000 = labels1000[0:3]
times1000 = times1000[0:3]
names1000 = names1000[0:3]
'''
num = ['AMP_L']
den = ['AMP_U', 'AMP_L']
##################Extract the data###########################
csvs = {}
dataByProtein = {}
allProteins = set()
dataByProtein = qMS.multiStatsDict(names10+names1000, num, den)
##################Plot the datasets###########################
'''
yMax=1.25
pylab.close('all')
ax = vizLib.makePlotWithFileList(names10, num, den, AllProteins=AllSubunits, yMax=yMax, names=labels10, colors=reds, figSize=(22,7),median=False)
pylab.legend(loc='upper left', ncol=3)
pylab.xticks(numpy.arange(1,len(AllSubunits)+1,1), [item[4:] for item in AllSubunits], rotation=45)
ax.set_ylim([0,1])
ax.set_title('non-permissive conditions : 45S\nfraction labeled', multialignment='center')
ax.set_ylabel('Labeled/[Labeled+Unlabeled]')
pylab.tight_layout()
#pylab.savefig('10uM_fracLab_med.pdf')
#pylab.savefig('10uM_fracLab_med.png')
names50SIF2 = qMS.sort_nicely([i.split('/')[-1] for i in glob.glob(rootPath+'45Sfiltered/*.csv')])
names45S = qMS.sort_nicely([i.split('/')[-1] for i in glob.glob(rootPath+'50Sfiltered/*.csv')])
namesMy70S = qMS.sort_nicely([i.split('/')[-1] for i in glob.glob(rootPath+'my70Sfiltered/*.csv')])
namesMy50S = qMS.sort_nicely([i.split('/')[-1] for i in glob.glob(rootPath+'my50Sfiltered/*.csv')])
names50SIF2Pulse = qMS.sort_nicely([i.split('/')[-1] for i in glob.glob(rootPath+'45Sfiltered/pulse/*.csv')])
names45SPulse = qMS.sort_nicely([i.split('/')[-1] for i in glob.glob(rootPath+'50Sfiltered/pulse/*.csv')])
reds = ['#fee5d9', '#fcbba1', '#fc9272', '#fb6a4a', '#de2d26', '#a50f15']
blues = ['#eff3ff', '#c6dbef', '#93cae1', '#6baed6', '#3182bd', '#08519c']
##################Extract the data###########################
num = ['AMP_U']
den = ['AMP_U', 'AMP_S']
dataByProtein70S = qMS.multiStatsDict(files45S, num, den)
##################Plot the datasets###########################
pylab.close('all')
yMax=1.5
figSize=(figWidth,figWidth/3.0)
median=False
num = ['AMP_U']
den = ['AMP_U', 'AMP_S']
filtPlots_45S = plotDataSets(files45S, names45S, num, den, AllSubunits, '45S', 'U/[U+S]', reds,
saveName=None, yMax=yMax, figSize=figSize, median=median, legendLoc='upper left', legendCols=2, normProtein='BSubL24')
filtPlots_50SIF2 = plotDataSets(files50SIF2, names50SIF2, num, den, AllSubunits, '50S IF2', 'U/[U+S]', blues,
times10 = [22, 49, 66, 88, 110, 132]
times1000 = [11, 22, 33, 44, 55, 66]
labels10 = ['10 $\mu$M, 22 min', '10 $\mu$M, 49 min', '10 $\mu$M, 66 min', '10 $\mu$M, 88 min', '10 $\mu$M, 110 min', '10 $\mu$M, 132 min']
labels1000 = ['1 mM, 11 min', '1 mM, 22 min', '1 mM, 33 min', '1 mM, 44 min', '1 mM, 55 min', '1 mM, 66 min']
labels1000 = labels1000[0:3]
times1000 = times1000[0:3]
names70S_1000 = names70S_1000[0:3]
namesInter_1000 = namesInter_1000[0:3]
##################Extract the data###########################
num = ['AMP_L']
den = ['AMP_U', 'AMP_L']
dataByProtein70S = qMS.multiStatsDict(names70S_10+names70S_1000, num, den)
dataByProteinInter = qMS.multiStatsDict(namesInter_10+namesInter_1000, num, den)
##################Find the medians###########################
medDict70S_10 = {}
medDict70S_1000 = {}
medDictInter_10 = {}
medDictInter_1000 = {}
ntDict70S_10 = {'names': names70S_10, 'times': times10}
ntDict70S_1000 = {'names': names70S_1000, 'times': times1000}
ntDictInter_10 = {'names': namesInter_10, 'times': times10}
ntDictInter_1000 = {'names': namesInter_1000, 'times': times1000}
