if not X:

X = range(Y.shape[0])

w = pywt.Wavelet('sym2')

L = pywt.dwt_max_level(Y.shape[0],w)

#L = pywt.swt_max_level(Y.shape[0])

Zavg = zeros((L,len(X)))

Zavgabs = zeros((L,len(X)))

if reshape:

e = [0,L,0,L]

else:

e = [X[0],X[-1],0,L]

sp = 1

if not Names:

Names = ["Series "+str(y+1) for y in range(Y.shape[1])]

if Title:

pylab.suptitle(Title)

if plot_xy:

tp = str(Y.shape[1]+3)

pylab.subplot(int(''.join([tp,'1',str(sp)])))

P = pylab.plot(X,Y)

pylab.legend(P,Names,prop={'size':6})

pylab.xlim([0,Y.shape[0]-1])

pylab.title("Relative Abundance Time Series")

sp += 1

else:

tp = str(Y.shape[1]+2)

for y in range(Y.shape[1]):

C = pywt.wavedec(Y[:,y],w,level=L)

#C = pywt.swt(Y[:,y],w,level=L)

#C = ['dummy'] + [c[1] for c in C]

Z = wavelet_matrix(C,L,len(X))

pylab.subplot(int(''.join([tp,'1',str(sp)])))

pylab.imshow(abs(Z),extent=e)

pylab.title(Names[y])

sp += 1

Zavg += Z

Zavgabs += abs(Z)

Zavg /= Y.shape[1]

Zavg[Zavg<zero_thresh] = 0

pylab.subplot(int(''.join([tp,'1',str(sp)])))

pylab.imshow(abs(Zavg),extent=e)

pylab.title("Decomposition Avg")

sp += 1

Zavgabs /= Y.shape[1]

Zavgabs[Zavgabs<zero_thresh] = 0

pylab.subplot(int(''.join([tp,'1',str(sp)])))

pylab.imshow(abs(Zavgabs),extent=e)

pylab.title("abs(Decomposition) Sum")

sp += 1

pylab.show()

Z = zeros((r,c))

C1 = C[1:]

for i in range(r):

yp = partition(range(c),len(C1[i]))

for yj in range(len(yp)):

for j in yp[yj]:

Z[i,j] += C1[i][yj]

X = linspace(1,n,n)

Y = zeros((len(X),2))

Y[:p,0] = random.rand(1,p)

Y[:p,1] = Y[0,0]

for x in range(p,len(X)):

Y[x,0] = Y[x-p,0]*(3.8 - 3.8*Y[x-p,0] - b1*Y[x-p,1])

Y[x,1] = Y[x-p,1]*(3.5 - 3.5*Y[x-p,1] - b1*Y[x-p,0])

x = linspace(0,1,n/(reflections+1))

y = sqrt(x*(1-x))*sin((2.1*pi)/(x+.05))

y += -y.min()

y /= y.max()

if reflections%2 == 1:

y = array((list(y) + list(y)[::-1])*((reflections+1)/2))

elif reflections%2 == 0:

y = list(y) + (list(y)[::-1] + list(y))*(reflections/2)

y += list(y[:n-len(y)])

y = array(y)

ym = zeros((n,m))

ym[:,0] = y

for j in range(1,m):

b = random.rand()*(bmax - bmin) + bmin

ym[:,j] = ym[:,0]*b + random.rand(1,n)*(1-b)

Y = zeros((len(y0),n+1))

Y[:,0] = y0

for i in range(Y.shape[0]):

r = random.rand(1,n)

Y[i,1:] = r*(1-Y[i,0])/r.sum()

w = pywt.Wavelet('sym2')

if use_swt:

L = pywt.swt_max_level(Y.shape[0])

C = [pywt.swt(Y[:,i],w,level=L) for i in range(Y.shape[1])]

C = [[list(reshape(l[0],-1)) + list(reshape(l[1],-1)) for l in c] for c in C]

else:

L = pywt.dwt_max_level(Y.shape[0],w)

C = [pywt.wavedec(Y[:,i],w,level=L) for i in range(Y.shape[1])]

if level_weights:

if use_swt:

raise NameError('No level weights with SWT')

Wc = [1. for x in range(1,L+1)]

D = zeros((len(C),len(C)))

for i in range(len(C)):

for j in range(i+1,len(C)):

d = sum([distance.cosine(C[i][x],C[j][x])*Wc[x] for x in range(L)])/sum(Wc)

D[i,j] = d

D[j,i] = d

return D

else:

Cn = []

for c in C:

cn = []

for l in c:

cn += list(l)

Cn.append(cn)

if weights:

D = abs(c_dists(Y,level_weights=True,use_swt=False))

Dr = []

for i in range(D.shape[0]-1):

Dr += list(D[i,i+1:])

else:

Dr = c_dists(Y,use_swt=swt)

if return_clusters:

L = linkage(Dr,method='single',metric='cosine')

C = fcluster(L,ct,criterion='distance')

return cluster_sets(C)

D = pdist(transpose(Y),'correlation')

D = abs(D)

if return_clusters:

L = linkage(D,method='single',metric='cosine')

C = fcluster(L,ct,criterion='distance')

return cluster_sets(C)

Y = []

for t in types:

if t[0] == 'nonlinear':

x,y = nonlinear_ab(n,b1=random.rand()*.2,b2=random.rand()*.75,p=random.randint(1,1+rand_periods))

elif t[0] == 'doppler':

x,y = doppler(n,t[1],reflections=random.randint(0,rand_periods))

elif t[0] == 'rand':

y = random.rand(n,t[1])

if len(Y) > 0:

Y = concatenate((Y,y),axis=1)

else:

Y = y

s = 0

matched_sets = []

for rs in result_sets:

for ans in answer_sets:

if len(rs & ans)/float(len(rs)) > .7:

s += len(rs)

matched_sets.append(rs)

break

cluster_array = sorted(enumerate(cluster_array),key=itemgetter(1))

rs = [[cluster_array[0][0]]]

for i in range(1,len(cluster_array)):

if cluster_array[i][1] != cluster_array[i-1][1]:

rs.append([])

rs[-1].append(cluster_array[i][0])

A = {'nonlinear': [],'doppler': []}

i = 0

for t in types:

if t[0] != 'rand':

A[t[0]] += range(i,i+t[1])

i += t[1]

A0 = [set(A['nonlinear'] + A['doppler'])]

A = [set(v) for v in A.values() if len(v)>1]

ta = sum([len(s) for s in A])

print ta

tsum = 0

wsum = 0

if swt:

wct = 0.1

else:

wct = 0.08

for i in range(n):

Y = create_population(types,m,rand_periods=r)

t = time_series_clusters(Y,ct=0.5,return_clusters=True)

t0 = check_clusters(t,A)

t = wavelet_clusters(Y,ct=wct,return_clusters=True,swt=swt)

t1 = check_clusters(t,A0)

if (t0[0] < ta) and (t1[0] < ta):

print t0[0],t1[0]

tsum += t0[0]

wsum += t1[0]

print tsum,wsum