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 neh_contourplot(dictionary, X_key, Y_key, Z_key, color_lims, color_map, xlabel='xlabel', ylabel='ylabel', title='title', size=[10, 7], sub=0):
# Don't generate a new figure if it's being called from neh_subplot
if sub != 1: fig, ax = plt.subplots(figsize=size)
# fig = plt.figure()
# ax = fig.add_subplot(111)
X, Y = plt.meshgrid(dictionary[X_key], dictionary[Y_key])
# Check that the vector lengths conform to the plot matrix, and if not try to reverse the transpose
Z = dictionary[Z_key]
if Z.shape != X.shape:
Z = plt.transpose(Z)
# pdb.set_trace()
plt.pcolor(X, Y, Z, vmin=color_lims[0], vmax=color_lims[1], cmap=color_map, norm=MidpointNormalize(midpoint=0))
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=14)
# Axis tick font (rotate the x ticks if they are dates - too long to be horizontal)
plt.setp(ax.get_xticklabels(), rotation='horizontal', fontsize=10)
plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=10)
# Axis labels
if xlabel != '': plt.xlabel(xlabel, fontsize=12, fontweight='bold')
if ylabel != '': plt.ylabel(ylabel, fontsize=12, fontweight='bold')
# Plot title (smaller if part of a subplot)
plt.title(title, fontsize=14 if sub != 1 else 12, fontweight='bold')
# Don't show the figure if it's being called from neh_subplot
if sub != 1: plt.show()
# class MidpointNormalize(colors.Normalize):
# def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
# self.midpoint = midpoint
# colors.Normalize.__init__(self, vmin, vmax, clip)
#
# def __call__(self, value, clip=None):
# # I'm ignoring masked values and all kinds of edge cases to make a
# # simple example...
# x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
# return nplt.ma.masked_array(nplt.interp(value, x, y))
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
alpha = 0.7
phi_ext = 2 * np.pi * 0.5
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) - alpha * cos(phi_ext - 2*phi_p)
phi_m = np.linspace(0, 2*np.pi, 100)
phi_p = np.linspace(0, 2*np.pi, 100)
X,Y = plt.meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
fig, ax = subplots()
p = ax.pcolor(X/(2*np.pi), Y/(2*np.pi), Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p)
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()
def Lineas_Campo():
os.system("cls")
def Phi(r, rp, q):
phi = 1 / (4 * np.pi * eps0) * q / np.linalg.norm(r - rp)
return phi
def Campo_Electrico(r, rp, q):
E = 1 / (4 * np.pi * eps0) * q * (r - rp) / np.linalg.norm(r - rp)**3
return E
eps0 = 8.85418781762e-12
Nres = 25
Xarray = np.linspace(0, 10, Nres)
Yarray = np.linspace(0, 10, Nres)
X, Y = plt.meshgrid(Xarray, Yarray)
q1 = float(input("Ingrese el el Valor de la Carga 1 : "))
rp1 = np.array([4, 4])
phi1 = np.zeros((Nres, Nres))
E1x = np.ones((Nres, Nres))
E1y = np.ones((Nres, Nres))
for i in range(Nres):
for j in range(Nres):
r = np.array([Xarray[i], Yarray[j]])
phi1[i, j] = Phi(r, rp1, q1)
E = Campo_Electrico(r, rp1, q1)
E1x[i, j], E1y[i, j] = E / np.linalg.norm(E)
q2 = float(input("\nIngrese el el Valor de la Carga 2 : "))
os.system("cls")
rp2 = np.array([6, 6])
phi2 = np.zeros((Nres, Nres))
E2x = np.ones((Nres, Nres))
E2y = np.ones((Nres, Nres))
for i in range(Nres):
for j in range(Nres):
r = np.array([Xarray[i], Yarray[j]])
phi2[i, j] = Phi(r, rp2, q2)
E = Campo_Electrico(r, rp2, q2)
E2x[i, j], E2y[i, j] = E / np.linalg.norm(E)
os.system("cls")
phi_tot = phi1 + phi2
Ex_tot = np.ones((Nres, Nres))
Ey_tot = np.ones((Nres, Nres))
for i in range(Nres):
for j in range(Nres):
E = np.array( [E1x[i,j] + E2x[i,j], \
E1y[i,j] + E2y[i,j]] )
E = E / np.linalg.norm(E)
Ex_tot[i, j], Ey_tot[i, j] = E
os.system("cls")
plt.contour(X, Y, phi_tot, 100)
plt.quiver(X, Y, Ey_tot, Ex_tot)
plt.xlim((0, 10))
plt.ylim((0, 10))
plt.legend()
plt.title('Lineas de campo y Equipotenciales de Cargas Puntuales\n')
os.system("cls")
plt.show()
