def drawSprings(corrMatrix, kclusters, initialPosFileName, mult=1.1, kMeansRuns=1000):
"""drawSprings draws two figures, one of the spring evolution at 9 timepoints, one of the initial vs. final.
:param corrMatrix: a data object generated by kmeans cluster - has the correlations between objects.
:type corrMatrix: dict - must have 'data' and 'proteins'
:param kclusters: the number of kclusters used in the analysis (what was passed to kmeans)
:type kclusters: int
:param initialPosFileName: a string pointing to the initial positions of each node - see nierhausPositions.txt
:type initialPosFileName: string
:param mult: a float of the base of the exponent to exapand on in each spring interation
:type mult: float
:returns: an array of figures - first is the evolution, second is the start and end.
"""
G = nx.Graph()
positions = qMS.readDataFile(initialPosFileName)
initialNodePos = {positions['proteins'][i]: [float(positions['data'][i,0])/4.0, float(positions['data'][i,1])/5.0] for i in range(len(positions['proteins']))}
[G.add_node(x) for x in corrMatrix['proteins']]
connection = (lambda x,y: corrMatrix['data'][x][y])
[G.add_edge(corrMatrix['proteins'][x],corrMatrix['proteins'][y], weight=connection(x,y)) for x in range(len(corrMatrix['proteins'])) for y in range(len(corrMatrix['proteins'])) if (connection(x,y)!=0)]
weights = [G.get_edge_data(x[0],x[1])['weight'] for x in G.edges()]
notes = pylab.figure()
notes.add_subplot(1,2,1, title="nierhaus positions_kclusters=" + str(kclusters))
drawNodePos = initialNodePos
yDiff = [initialNodePos[x][1] - drawNodePos[x][1] for x in drawNodePos.keys()]
nx.draw_networkx(G, pos = drawNodePos,
node_size=600, node_color = yDiff, cmap = pylab.cm.RdBu, vmin=-1, vmax=1,
edge_color=weights, edge_vmin = 0, edge_vmax = kMeansRuns, edge_cmap = pylab.cm.autumn_r, width=2,
font_size=10, font_weight='bold')
iters = int(mult**8)
notes.add_subplot(1,2,2, title="spring iteration=" + str(iters) + "_kclusters=" + str(kclusters))
drawNodePos = nx.spring_layout(G, pos=initialNodePos, iterations=iters)
yDiff = [initialNodePos[x][1] - drawNodePos[x][1] for x in drawNodePos.keys()]
nx.draw_networkx(G, pos = drawNodePos,
node_size=600, node_color = yDiff, cmap = pylab.cm.RdBu, vmin=-1, vmax=1,
edge_color=weights, edge_vmin = 0, edge_vmax = kMeansRuns, edge_cmap = pylab.cm.autumn_r, width=2,
font_size=10, font_weight='bold')
cb1ax = notes.add_axes([0.025, 0.1, 0.05, 0.8])
pylab.colorbar(cmap=pylab.cm.autumn_r, cax=cb1ax)
cb2ax = notes.add_axes([0.925, 0.1, 0.05, 0.8])
norm = mpl.colors.Normalize(vmin=-1, vmax=1)
mpl.colorbar.ColorbarBase(cb2ax, cmap=pylab.cm.RdBu_r, norm=norm, orientation='vertical')
notes2 = pylab.figure()
drawNodePos = initialNodePos
for i in range(9):
notes2.add_subplot(3,3,i+1, title="iterations=" + str(int(mult**i)) + "_k=" + str(kclusters))
drawNodePos = nx.spring_layout(G, pos=drawNodePos, iterations=int(mult**i))
yDiff = [initialNodePos[x][1] - drawNodePos[x][1] for x in drawNodePos.keys()]
nx.draw_networkx(G, pos = drawNodePos,
node_size=600, node_color = yDiff, cmap = pylab.cm.RdBu, vmin=-1, vmax=1,
edge_color=weights, edge_vmin = 0, edge_vmax = kMeansRuns, edge_cmap = pylab.cm.autumn_r, width=2,
font_size=10, font_weight='bold')
correctedDFsDict, files1hrGrad, smallSubunit, num, den,
normalization=1.0, offset=0.0)
qMS.outputDataMatrixFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/1hrLarge.csv',
correctedDFsDict, files1hrGrad, largeSubunit, num, den,
normalization=1.0, offset=0.0)
correctedDFsDict = qMS.correctListOfFiles(filesRefSet[1], files6hrGrad)
qMS.outputDataMatrixFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/6hrSmall.csv',
correctedDFsDict, files6hrGrad, smallSubunit, num, den,
normalization=1.0, offset=0.0)
qMS.outputDataMatrixFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/6hrLarge.csv',
correctedDFsDict, files6hrGrad, largeSubunit, num, den,
normalization=1.0, offset=0.0)
'''
rawData = qMS.readDataFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/wtSmall.csv', 1.0)
rawMap = vizLib.drawHeatMap(rawData, "wtGradient", nameList=wtFracNames, colors=pylab.cm.autumn, scale='14N/[14N+15N]', saveName='wtSmallHeat.png')
rawData = qMS.readDataFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/wtLarge.csv', 1.0)
rawMap = vizLib.drawHeatMap(rawData, "wtGradient", nameList=wtFracNames, colors=pylab.cm.autumn, scale='14N/[14N+15N]', saveName='wtLargeHeat.png')
rawData = qMS.readDataFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/1hrSmall.csv', 1.0)
rawMap = vizLib.drawHeatMap(rawData, "1hrGradient", nameList=fracNames1hr, colors=pylab.cm.autumn, scale='14N/[14N+15N]', saveName='1hrSmallHeat.png')
rawData = qMS.readDataFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/1hrLarge.csv', 1.0)
rawMap = vizLib.drawHeatMap(rawData, "1hrGradient", nameList=fracNames1hr, colors=pylab.cm.autumn, scale='14N/[14N+15N]', saveName='1hrLargeHeat.png')
rawData = qMS.readDataFile('/home/jhdavis/data/2013_08_08-JStokesESITOFAnalysis/6hrSmall.csv', 1.0)
rawMap = vizLib.drawHeatMap(rawData, "6hrGradient", nameList=fracNames6hr, colors=pylab.cm.autumn, scale='14N/[14N+15N]', saveName='6hrSmallHeat.png')
def drawSprings(corrMatrix,
kclusters,
initialPosFileName,
mult=1.1,
kMeansRuns=1000):
"""drawSprings draws two figures, one of the spring evolution at 9 timepoints, one of the initial vs. final.
:param corrMatrix: a data object generated by kmeans cluster - has the correlations between objects.
:type corrMatrix: dict - must have 'data' and 'proteins'
:param kclusters: the number of kclusters used in the analysis (what was passed to kmeans)
:type kclusters: int
:param initialPosFileName: a string pointing to the initial positions of each node - see nierhausPositions.txt
:type initialPosFileName: string
:param mult: a float of the base of the exponent to exapand on in each spring interation
:type mult: float
:returns: an array of figures - first is the evolution, second is the start and end.
"""
G = nx.Graph()
positions = qMS.readDataFile(initialPosFileName)
initialNodePos = {
positions['proteins'][i]: [
float(positions['data'][i, 0]) / 4.0,
float(positions['data'][i, 1]) / 5.0
]
for i in range(len(positions['proteins']))
}
[G.add_node(x) for x in corrMatrix['proteins']]
connection = (lambda x, y: corrMatrix['data'][x][y])
[
G.add_edge(corrMatrix['proteins'][x],
corrMatrix['proteins'][y],
weight=connection(x, y))
for x in range(len(corrMatrix['proteins']))
for y in range(len(corrMatrix['proteins'])) if (connection(x, y) != 0)
]
weights = [G.get_edge_data(x[0], x[1])['weight'] for x in G.edges()]
notes = pylab.figure()
notes.add_subplot(1,
2,
1,
title="nierhaus positions_kclusters=" + str(kclusters))
drawNodePos = initialNodePos
yDiff = [
initialNodePos[x][1] - drawNodePos[x][1] for x in drawNodePos.keys()
]
nx.draw_networkx(G,
pos=drawNodePos,
node_size=600,
node_color=yDiff,
cmap=pylab.cm.RdBu,
vmin=-1,
vmax=1,
edge_color=weights,
edge_vmin=0,
edge_vmax=kMeansRuns,
edge_cmap=pylab.cm.autumn_r,
width=2,
font_size=10,
font_weight='bold')
iters = int(mult**8)
notes.add_subplot(1,
2,
2,
title="spring iteration=" + str(iters) + "_kclusters=" +
str(kclusters))
drawNodePos = nx.spring_layout(G, pos=initialNodePos, iterations=iters)
yDiff = [
initialNodePos[x][1] - drawNodePos[x][1] for x in drawNodePos.keys()
]
nx.draw_networkx(G,
pos=drawNodePos,
node_size=600,
node_color=yDiff,
cmap=pylab.cm.RdBu,
vmin=-1,
vmax=1,
edge_color=weights,
edge_vmin=0,
edge_vmax=kMeansRuns,
edge_cmap=pylab.cm.autumn_r,
width=2,
font_size=10,
font_weight='bold')
cb1ax = notes.add_axes([0.025, 0.1, 0.05, 0.8])
pylab.colorbar(cmap=pylab.cm.autumn_r, cax=cb1ax)
cb2ax = notes.add_axes([0.925, 0.1, 0.05, 0.8])
norm = mpl.colors.Normalize(vmin=-1, vmax=1)
mpl.colorbar.ColorbarBase(cb2ax,
cmap=pylab.cm.RdBu_r,
norm=norm,
orientation='vertical')
notes2 = pylab.figure()
drawNodePos = initialNodePos
for i in range(9):
notes2.add_subplot(3,
3,
i + 1,
title="iterations=" + str(int(mult**i)) + "_k=" +
str(kclusters))
drawNodePos = nx.spring_layout(G,
pos=drawNodePos,
iterations=int(mult**i))
yDiff = [
initialNodePos[x][1] - drawNodePos[x][1]
for x in drawNodePos.keys()
]
nx.draw_networkx(G,
pos=drawNodePos,
node_size=600,
node_color=yDiff,
cmap=pylab.cm.RdBu,
vmin=-1,
vmax=1,
edge_color=weights,
edge_vmin=0,
edge_vmax=kMeansRuns,
edge_cmap=pylab.cm.autumn_r,
width=2,
font_size=10,
font_weight='bold')