def vecPlot2(self, row, col, r_m, mWx, mWy, data1, data2, dmr, frmSize, winSize):
pl.clf()
pl.ion()
pWx = []
l = dmr - row - col
for f in range(l):
pWx.append(mWx[row+f, col+f,:,0])
pWx = np.array(pWx)
sqWx = np.sqrt(pWx * pWx)
print sqWx
r,c = sqWx.shape
x = pl.arange(c+1)
y = pl.arange(r+1)
X, Y = pl.meshgrid(x, y)
pl.subplot2grid((1,2),(0,0))
pl.pcolor(X, Y, sqWx, vmin=0, vmax=1)
pl.xlim(0,c)
pl.ylim(0,r)
pl.colorbar()
pl.title("user_1 (t:"+str(row)+")")
pl.gray()
pWy = []
l = dmr - row - col
for f in range(l):
pWy.append(mWy[row+f, col+f,:,0])
pWy = np.array(pWy)
sqWy = np.sqrt(pWy * pWy)
print sqWy
r,c = sqWy.shape
x = pl.arange(c+1)
y = pl.arange(r+1)
X, Y = pl.meshgrid(x, y)
pl.subplot2grid((1,2),(0,1))
pl.pcolor(X, Y, sqWy, vmin=0, vmax=1)
pl.xlim(0,c)
pl.ylim(0,r)
pl.colorbar()
pl.title("user_2 (t:"+str(col)+")")
pl.gray()
pl.tight_layout()
pl.draw()
def _render_bar(self, key_word_sep=" ", title=None, **kwargs):
"""Generates a pylab horizaontal barchart from the result set.
``matplotlib`` must be installed, and in an
IPython Notebook, inlining must be on::
%%matplotlib inline
First column is x-axis, and can be text, datetime or numeric.
Other columns are numeric, displayed as horizontal strips.
Parameters
----------
key_word_sep: string used to separate column values
from each other in pie labels
title: Plot title, defaults to name of value column
Any additional keyword arguments will be passsed
through to ``matplotlib.pylab.pie``.
"""
import matplotlib.pylab as plt
self.build_columns()
quantity_columns = [c for c in self.columns[1:] if c.is_quantity]
ylabel = ", ".join([c.name for c in quantity_columns])
xlabel = self.columns[0].name
# print("xlabel: {}".format(xlabel))
# print("ylabel: {}".format(ylabel))
dim = len(quantity_columns)
w = 0.8
dimw = w / dim
ax = plt.subplot(111)
x = plt.arange(len(self.columns[0]))
xpos = -dimw * (len(quantity_columns) / 2)
for y in quantity_columns:
columnchart = plt.bar(x + xpos,
y,
width=dimw,
align="center",
**kwargs)
xpos += dimw
plt.xticks(range(len(self.columns[0])), self.columns[0], rotation=45)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title or ylabel)
plt.show()
return columnchart
def PlotCodonMutNTComposition(allmutations, plotfile, title):
"""Plots nucleotide composition of mutant codons and mtuated.
For each site containing a mutated codon, looks at the nucleotide
composition of all three sites at the original and new codon. Plots the
overall frequency of each nucleotide at these sites.
allmutations -> list of all mutations as tuples (wtcodon, r, mutcodon)
plotfile -> name of the plot file we create.
title -> string giving the plot title.
"""
nts = ['A', 'T', 'C', 'G']
wtntcounts = dict([(nt, 0) for nt in nts])
mutntcounts = dict([(nt, 0) for nt in nts])
ntot = float(3 * len(allmutations))
for (wtcodon, r, mutcodon) in allmutations:
for nt in wtcodon:
wtntcounts[nt] += 1 / ntot
for nt in mutcodon:
mutntcounts[nt] += 1 / ntot
pylab.figure(figsize=(3.5, 2.25))
(lmargin, rmargin, bmargin, tmargin) = (0.16, 0.01, 0.21, 0.07)
pylab.axes([lmargin, bmargin, 1.0 - lmargin - rmargin, 1.0 - bmargin - tmargin])
barwidth = 0.35
xs = [i for i in range(len(nts))]
nwt = pylab.bar([x - barwidth for x in xs], [wtntcounts[nt] for nt in nts], width=barwidth, color='blue')
nmut = pylab.bar([x for x in xs], [mutntcounts[nt] for nt in nts], width=barwidth, color='red')
#pred = pylab.plot(xs, nexpected, 'rx', markersize=6, mew=3)
pylab.gca().set_xlim([-0.5, 3.5])
pylab.gca().set_ylim([0, max(wtntcounts.values() + mutntcounts.values()) * 1.35])
#pylab.gca().xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(4))
pylab.gca().yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(5))
pylab.xlabel('nucleotide')
pylab.ylabel('codon composition')
pylab.legend((nwt[0], nmut[0]), ('parent', 'mutant'), loc='upper center', numpoints=1, handlelength=1, ncol=2, borderaxespad=0.01, columnspacing=1.1, handletextpad=0.4)
pylab.title(title, fontsize=12)
pylab.xticks(pylab.arange(0, 4, 1), tuple(nts))
pylab.savefig(plotfile)
time.sleep(0.5)
pylab.show()
def make_plot_observed(fname, bins=None, norm=None):
d = pl.genfromtxt(fname, names=True, invalid_raise=False)
# observed quantities
Z = pl.array([chargeNumber(id) for id in d['ID'].astype(int)]) # element
A = pl.array([massNumber(id)
for id in d['ID'].astype(int)]) # atomic mass number
lE = pl.log10(d['E']) + 18 # energy in log10(E/eV))
if bins == None:
lEbins = pl.arange(18, 20.51, 0.01) # logarithmic bins
lEcens = (lEbins[1:] + lEbins[:-1]) / 2 # logarithmic bin centers
else:
lEbins = pl.arange(17.5, 20.2, 0.1) # logarithmic bins
lEcens = (lEbins[1:] + lEbins[:-1]) / 2 # logarithmic bin centers
dE = 10**lEbins[1:] - 10**lEbins[:-1] # bin widths
# identify mass groups
EE = (10.0**lEcens)**3
J1 = EE * pl.histogram(lE, bins=lEbins)[0] / dE
J = np.zeros_like(J1)
Jn = np.zeros([len(J1, 58)])
for iz in range(1, 57):
idx = (Z == iz)
J += pl.histogram(lE[idx], bins=lEbins)[0] / dE
# idx1 = A == 1
# idx2 = (A > 1) * (A < 5)
# idx3 = (A > 4) * (A < 23)
# idx4 = (A > 22) * (A < 39)
# # calculate spectrum: J(E) = dN/dE, we want E^3 J(E)
# EE = (10.0 ** lEcens) ** 3
#lum = np.sum(J * dE)
# J = EE * pl.histogram(lE, bins=lEbins)[0] / dE
# J1 = EE * pl.histogram(lE[idx1], bins=lEbins)[0] / dE
# J2 = EE * pl.histogram(lE[idx2], bins=lEbins)[0] / dE
# J3 = EE * pl.histogram(lE[idx3], bins=lEbins)[0] / dE
# J4 = EE * pl.histogram(lE[idx4], bins=lEbins)[0] / dE
# normalize the histograms
# if norm == None:
# norm = 1.0 / J[0]
# else:
# norm = norm / J[10]
norm = 1.0 / J[0]
J1 *= norm
# J2 *= norm
# J3 *= norm
# J4 *= norm
J *= norm
pl.plot(lEcens, smooth(J), color='k', linewidth=3, label="Total")
pl.plot(lEcens, smooth(J1), color='#e84118', label='A = 1')
pl.plot(lEcens, smooth(J2), color="#7f8fa6", label='A = 2-4')
pl.plot(lEcens, smooth(J3), color="#44bd32", label='A = 5-22')
pl.plot(lEcens, smooth(J4), color="#00a8ff", label='A = 23-38')
pl.legend(fontsize=20, frameon=True, loc=3)
pl.semilogy()
#pl.ylim(1e-5)
pl.grid()
pl.ylabel('$E^3 J(E)$ [a.u.]')
pl.xlabel('$\log_{10}$(E/eV)')
def drawPlot(self, row, col, mWx, mWy, ccaMat, data1, data2, dataMaxRange, dataDimen, winSize):
#def drawPlot(self, row, col):
print "draw:"+str(row)+", "+str(col)
#test
pWx = mWx[row,col:,0,:]
print "pWx:",pWx
print "shape:",pWx.shape
#データの取得
U1 = []
U2 = []
for w in range(winSize):
U1.append(data1[row+w])
U2.append(data2[col+w])
nU1 = CCA().stdn(U1)
nU2 = CCA().stdn(U2)
#pl.clf()
pl.ion()
Wx = mWx[row][col]
Wy = mWy[row][col]
X, Y = pl.meshgrid(pl.arange(dataDimen+1), pl.arange(dataDimen+1))
strCorU1s = []
strCorU2s = []
for i in range(dataDimen):
fU1 = np.dot(nU1.T, Wx[:,i:i+1]).T
fU2 = np.dot(nU2.T, Wy[:,i:i+1]).T
strU1 = np.corrcoef(fU1, nU1)
strU2 = np.corrcoef(fU2, nU2)
strCorU1 = np.squeeze(np.asarray(strU1[0:1,1:]))
strCorU2 = np.squeeze(np.asarray(strU2[0:1,1:]))
strCorU1s.append(strCorU1)
strCorU2s.append(strCorU2)
#print np.dot(Wx[:,i:i+1].T,Wx[:,i:i+1]).mean()
sWx = np.array(np.matrix(strCorU1s))
sWy = np.array(np.matrix(strCorU2s))
#print Wx.shape
#print Wx
#print sWx.shape
#print sWx
pl.subplot2grid((3,2),(0,0),colspan=2)
pl.xlim(0,dataDimen)
pl.ylim(0,1)
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.plot(ccaMat[row][col])
pl.title("eigen value (user 1:"+str(row)+", user 2:"+str(col)+")",fontsize=11)
pl.subplot2grid((3,2),(1,0))
pl.pcolor(X, Y, sWx)
pl.gca().set_aspect('equal')
pl.colorbar()
pl.gray()
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.title("user 1:"+str(row),fontsize=11)
pl.subplot2grid((3,2),(1,1))
pl.pcolor(X, Y, sWy)
pl.gca().set_aspect('equal')
pl.colorbar()
pl.gray()
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.title("user 2:"+str(col),fontsize=11)
x = np.linspace(0, winSize-1, winSize)
#forで回して第三位の正準相関までとる？
pl.subplot2grid((3,2),(2,0),colspan=2)
ls = ["-","--","-."]
rhos = ""
order = 3
for i in range(order):
fU1 = np.dot(nU1.T, Wx[:,i:i+1]).T
fU2 = np.dot(nU2.T, Wy[:,i:i+1]).T
fU1 = np.squeeze(np.asarray(fU1))
fU2 = np.squeeze(np.asarray(fU2))
rho = round(ccaMat[row][col][i],5)
pl.plot(x, fU1, label="user1:"+str(rho), linestyle=ls[i])
pl.plot(x, fU2, label="user2:"+str(rho), linestyle=ls[i])
rhos += str(rho)+", "
leg = pl.legend(prop={'size':9})
leg.get_frame().set_alpha(0.7)
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.title("canonical variate (eig val:"+rhos.rstrip(", ")+")",fontsize=11)
pl.tight_layout()
pl.draw()
def vecPlot(self, row, col, r_m, mWx, mWy, data1, data2, frmSize, winSize):
pl.clf()
pl.ion()
#pWy = mWy[row][col]
#まずはx方向のWx
pWx = mWx[row,col:(frmSize-winSize+1)+row,:,0]
#print np.mean(pWx*pWx, axis=0)
sqWx = np.sqrt(pWx * pWx)/1.1
print sqWx
r,c = sqWx.shape
x = pl.arange(c+1)
y = pl.arange(r+1)
X, Y = pl.meshgrid(x, y)
pl.subplot2grid((2,2),(0,0))
pl.pcolor(X, Y, sqWx, vmin=0, vmax=1)
pl.xlim(0,c)
pl.ylim(0,r)
pl.colorbar()
pl.title("user_1 (t:"+str(row)+")")
pl.gray()
pWy = mWy[row,col:(frmSize-winSize+1)+row,:,0]
#print "pWy sum:",np.sum(pWy[0,:],axis=1)
#print np.sum(pWy*pWy,axis=1)
#print np.mean(pWy*pWy, axis=0)
sqWy = np.sqrt(pWy * pWy)
r,c = sqWx.shape
x = pl.arange(c+1)
y = pl.arange(r+1)
X, Y = pl.meshgrid(x, y)
pl.subplot2grid((2,2),(0,1))
pl.pcolor(X, Y, sqWy,vmin=0, vmax=1)
pl.xlim(0,c)
pl.ylim(0,r)
pl.colorbar()
pl.title("user_2 (t:"+str(col)+")")
pl.gray()
"""
pl.subplot2grid((2,2),(1,0),colspan=2)
pr, pc = pWx.shape
for i in range(pc):
pl.plot(pWx[:,i], color="r")
"""
xl = np.arange(c)
pl.subplot2grid((2,2),(1,0))
#subx1 = np.delete(sqWx,0,0)
#subx2 = np.delete(sqWx,r-1,0)
#print "sum sub:",sum(subx2-subx1)
#pl.bar(xl, np.fabs(np.mean(subx2-subx1,axis=0)))
pl.bar(xl, np.mean(sqWx, axis=0))
pl.xlim(0,c)
pl.ylim(0,1)
pl.subplot2grid((2,2),(1,1))
#subx1 = np.delete(sqWy,0,0)
#subx2 = np.delete(sqWy,r-1,0)
#pl.bar(xl, np.fabs(np.mean(subx2-subx1,axis=0)))
pl.bar(xl, np.mean(sqWy, axis=0))
pl.xlim(0,c)
pl.ylim(0,1)
"""
pl.subplot2grid((2,2),(1,0),colspan=2)
U1 = []
U2 = []
od = 0
for w in range(winSize):
U1.append(data1[row+w][od])
U2.append(data2[col+w][od])
pl.plot(U1, color="r")
pl.plot(U2, color="b")
"""
"""
#正準相関変量の表示
U1 = []
U2 = []
for w in range(winSize):
U1.append(data1[row+w])
U2.append(data2[col+w])
U1 = np.array(U1)
U2 = np.array(U2)
U1 = U1 - U1.mean(axis=0)
U2 = U2 - U2.mean(axis=0)
print "u1 s:",U1.shape
print "wx s:",mWx[row,col,0,:].shape
ls = ["-","--","-."]
rhos = ""
order = 3
xl = np.linspace(0, winSize-1, winSize)
for i in range(order):
fU1 = np.dot(U1, mWx[row,col,i,:]).T
fU2 = np.dot(U2, mWy[row,col,i,:]).T
fU1 = np.squeeze(np.asarray(fU1))
fU2 = np.squeeze(np.asarray(fU2))
rho = round(r_m[row][col][i],5)
pl.plot(xl, fU1, label="user1:"+str(rho), linestyle=ls[i])
pl.plot(xl, fU2, label="user2:"+str(rho), linestyle=ls[i])
rhos += str(rho)+", "
leg = pl.legend(prop={'size':9})
leg.get_frame().set_alpha(0.7)
pl.title("canonical variate (eig val:"+rhos.rstrip(", ")+")",fontsize=11)
"""
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.tight_layout()
pl.draw()
