def makeMinPickle(gon,vertex,modeType, maxItter):
test = PlanarLayout(gon = gon, vertex = vertex, side =1, radius_method = 'lin', modeType = modeType)
test.populate(maxItter = maxItter)
pickleDict = {}
pickleDict['gon'] = test.gon
pickleDict['vertex'] = test.vertex
pickleDict['itter'] = test.itter
pickleDict['modeType'] = test.modeType
pickleDict['Es'] = test.Es
pickleDict['Eorder'] = test.Eorder
pickleName = str(test.gon) + 'gon_' + str(test.vertex) + 'vertex_' + str(test.itter) + '_' + str(test.modeType) + '_DOSminimal'
pickleDict['name'] = pickleName
pickle.dump(pickleDict, open(pickleName + '.pkl', 'wb'))
def makeMinPickle(gon, vertex, modeType, maxItter):
'''
Old function used for making minimal pickles of the energies for effective graphs.
'''
test = PlanarLayout(gon=gon,
vertex=vertex,
side=1,
radius_method='lin',
modeType=modeType)
test.populate(maxItter=maxItter)
pickleDict = {}
pickleDict['gon'] = test.gon
pickleDict['vertex'] = test.vertex
pickleDict['itter'] = test.itter
pickleDict['modeType'] = test.modeType
pickleDict['Es'] = test.Es
pickleDict['Eorder'] = test.Eorder
pickleName = str(test.gon) + 'gon_' + str(test.vertex) + 'vertex_' + str(
test.itter) + '_' + str(test.modeType) + '_DOSminimal'
pickleDict['name'] = pickleName
pickle.dump(pickleDict, open(pickleName + '.pkl', 'wb'))
#minimal = False
if showHardWall:
if minimal:
# testDict = pickle.load( open('8gon_3vertex_4_FW_DOSminimal.pkl', "rb"))
# testDict = pickle.load( open('8gon_3vertex_5_FW_DOSminimal.pkl', "rb"))
# testDict = pickle.load( open('7gon_3vertex_6_FW_DOSminimal.pkl', "rb"))
testDict = pickle.load( open('7gon_3vertex_6_HW_DOSminimal.pkl', "rb"))
else:
# test = PlanarLayout(file_path = '7gon_3vertex_ 3.pkl')
# test = PlanarLayout(file_path = '7gon_3vertex_ 4.pkl')
# test = PlanarLayout(file_path = '7gon_3vertex_ 5.pkl')
#test = PlanarLayout(file_path = '7gon_3vertex_ 6.pkl')
test = PlanarLayout(file_path = '8gon_3vertex_ 3.pkl')
# test = PlanarLayout(file_path = '8gon_3vertex_ 4.pkl')
######
#trying DOS comparison versus sytem size instead
######
#set up frequency sweep
freq_range = 4.01
freq_res = 0.04
# freq_res = 0.06
#freq_res = 0.12
freqs = scipy.arange(-freq_range/2, freq_range, freq_res) + freq_res/2.
freq_bins = scipy.arange(-freq_range/2, freq_range+freq_res, freq_res)
def makeMinLayoutPickle_hyperbolic(
gon,
vertex,
maxItter,
modeType='FW',
verbose=True,
saveFolder=r'/Users/kollar2/Documents/HouckLab/HyperbolicPlanning/layoutDOSs',
sparse=True,
flags=5):
'''
Old function used for making minimal pickles of the energies for effective graphs.
'''
test = PlanarLayout(gon=gon,
vertex=vertex,
side=1,
radius_method='lin',
modeType=modeType)
test.populate(maxItter=maxItter, resonatorsOnly=True)
if sparse:
Hmat = generate_layout_Hamiltonian(test,
verbose=verbose,
sparse=True,
flags=flags)
# Es,Psis = scipy.sparse.linalg.eigsh(Hmat, k = test.coords.shape[0]-1 + flags) #will get all but one eigenvalue
Es = scipy.sparse.linalg.eigsh(
Hmat,
k=test.coords.shape[0] - 1 + flags,
return_eigenvectors=False) #will get all but one eigenvalue
Eorder = numpy.argsort(Es)
if Es[Eorder[flags - 1]] < 7.4:
#all the flag values are present, so an actual eigenvalue is missing
print 'actual eigenvalue missing'
Es = Es[flags:]
else:
print 'all real eigenvalues found'
Es = Es[flags - 1:]
else:
Hmat = generate_layout_Hamiltonian(test, verbose=verbose, sparse=False)
Es, Psis = scipy.linalg.eigh(Hmat)
Eorder = numpy.argsort(Es)
if verbose:
print 'max eigenvalue = ' + str(numpy.max(Es))
pickleDict = {}
pickleDict['gon'] = test.gon
pickleDict['vertex'] = test.vertex
pickleDict['itter'] = test.itter
pickleDict['modeType'] = test.modeType
pickleDict['Es'] = Es
pickleDict['Eorder'] = Eorder
if sparse:
pickleDict['sparse'] = True
else:
pickleDict['sparse'] = False
# pickleName = str(test.gon) + 'gon_' + str(test.vertex) + 'vertex_' + str(test.itter) + '_' + str(test.modeType) + '_layoutDOSminimal'
pickleName1 = str(test.gon) + 'gon_' + str(test.vertex) + 'vertex_' + str(
test.itter) + '_' + str(test.modeType)
if sparse:
pickleName = pickleName1 + '_splayoutDOSminimal'
else:
pickleName = pickleName1 + '_layoutDOSminimal'
pickleDict['name'] = pickleName
# pickle.dump(pickleDict, open(pickleName + '.pkl', 'wb'))
filePath = os.path.join(saveFolder, pickleName + '.pkl')
pickle.dump(pickleDict, open(filePath, 'wb'))
return Es
# pickleName = str(test.gon) + 'gon_' + str(test.vertex) + 'vertex_' + str(test.itter) + '_' + str(test.modeType) + '_layoutDOSminimal'
pickleName1 = str(test.gon) + 'gon_' + str(test.vertex) + 'vertex_' + str(
test.itter) + '_' + str(test.modeType)
if sparse:
pickleName = pickleName1 + '_splayoutDOSminimal'
else:
pickleName = pickleName1 + '_layoutDOSminimal'
pickleDict['name'] = pickleName
# pickle.dump(pickleDict, open(pickleName + '.pkl', 'wb'))
filePath = os.path.join(saveFolder, pickleName + '.pkl')
pickle.dump(pickleDict, open(filePath, 'wb'))
return Es
def compile_lambda_extrema(maxItt, gon = 7, tree = False, degree = 3, roundDepth = 6, save = True, saveFolder = '', secondEnergy = False):
lambdaMax_vals = numpy.zeros(maxItt)
lambdaMin_vals = numpy.zeros(maxItt)
if secondEnergy:
lambda2Max_vals = numpy.zeros(maxItt)
lambda2Min_vals = numpy.zeros(maxItt)
sizes = numpy.zeros(maxItt)
if tree:
stepSize = 2
itts = scipy.arange(1,maxItt+1, 1)*stepSize #go up ins steps of 3 generations at a time
maxItt = maxItt*stepSize
for ind in range(0, len(itts)):
itt = itts[ind]
base = TreeResonators(degree = degree, iterations = itt)
baseName = '3tree'
print itt
nameStr = baseName + '_' + str(itt)
test = GeneralLayout(resonators = base.get_all_resonators(), resonatorsOnly = True, modeType = 'FW', name = nameStr, roundDepth = roundDepth)
Hsparse = generate_layout_Hamiltonian(test, roundDepth = roundDepth, t = 1, verbose = False, sparse = True, flags = 0)
almostMaxs = scipy.sparse.linalg.eigsh(Hsparse, sigma = 3, return_eigenvectors = False)
almostMins = scipy.sparse.linalg.eigsh(Hsparse, sigma = -3, return_eigenvectors = False)
lambdaMax_vals[ind] = numpy.max(almostMaxs)
lambdaMin_vals[ind] = numpy.min(almostMins)
if secondEnergy:
tempMaxs = almostMaxs
tempMins = almostMins
flagmax = numpy.where(tempMaxs == numpy.max(almostMaxs))[0][0]
flagmin = numpy.where(tempMins == numpy.min(almostMins))[0][0]
tempMaxs[flagmax] = -200
tempMins[flagmin] = 200
lambda2Max_vals[ind] = numpy.max(tempMaxs)
lambda2Min_vals[ind] = numpy.min(tempMins)
sizes[ind] = test.coords.shape[0]
else:
itts = scipy.arange(1,maxItt+1, 1)
base = PlanarLayout(gon = gon, vertex = degree, side =1, radius_method = 'exp', modeType = 'FW')
base.populate(maxItter = 1, resonatorsOnly = True)
baseName = str(gon) + 'gon_' + str(degree) + 'vertex'
for itt in itts:
if itt > 1:
base.itter_generate()
base.generate_semiduals()
print itt
nameStr = baseName + '_' + str(itt)
test = GeneralLayout(resonators = base.get_all_resonators(), resonatorsOnly = True, modeType = 'FW', name = nameStr, roundDepth = roundDepth)
Hsparse = generate_layout_Hamiltonian(test, roundDepth = roundDepth, t = 1, verbose = False, sparse = True, flags = 0)
almostMaxs = scipy.sparse.linalg.eigsh(Hsparse, sigma = 3, return_eigenvectors = False)
almostMins = scipy.sparse.linalg.eigsh(Hsparse, sigma = -3, return_eigenvectors = False)
lambdaMax_vals[itt-1] = numpy.max(almostMaxs)
lambdaMin_vals[itt-1] = numpy.min(almostMins)
if secondEnergy:
tempMaxs = almostMaxs
tempMins = almostMins
flagmax = numpy.where(tempMaxs == numpy.max(almostMaxs))[0][0]
flagmin = numpy.where(tempMins == numpy.min(almostMins))[0][0]
tempMaxs[flagmax] = -200
tempMins[flagmin] = 200
lambda2Max_vals[itt-1] = numpy.max(tempMaxs)
lambda2Min_vals[itt-1] = numpy.min(tempMins)
sizes[itt-1] = test.coords.shape[0]
pickleDict = {}
pickleDict['itts'] = itts
pickleDict['lambdaMax'] = lambdaMax_vals
pickleDict['lambdaMin'] = lambdaMin_vals
pickleDict['name'] = baseName
pickleDict['systemSizes'] = sizes
if secondEnergy:
pickleDict['lambda2Max'] = lambda2Max_vals
pickleDict['lambda2Min'] = lambda2Min_vals
if save:
if secondEnergy:
saveName = baseName + '_layoutLambdaExtremaAndSecond_' + str(maxItt) + '.pkl'
else:
saveName = baseName + '_layoutLambdaExtrema_' + str(maxItt) + '.pkl'
save_path = os.path.join(saveFolder, saveName)
pickle.dump(pickleDict, open(save_path, 'wb'))
print save_path
fig1 = pylab.figure(1)
pylab.clf()
ax = pylab.subplot(1,2,1)
pylab.plot(itts, lambdaMax_vals, 'b.-', label = 'sparse')
if secondEnergy:
pylab.plot(itts, lambda2Max_vals, color = 'dodgerblue', marker = '.', linestyle = '-', label = 'sparse, 2nd')
# pylab.plot(itts_full[2:], lambdaMax_vals_full[2:], color = 'deepskyblue', linestyle = '-', marker = '.', label = 'full')
ax.legend(loc = 'lower right')
pylab.title('$\lambda_{max}$')
pylab.xlabel('itterations')
ax = pylab.subplot(1,2,2)
pylab.plot(itts, lambdaMin_vals, 'r.-', label = 'sparse')
if secondEnergy:
pylab.plot(itts, lambda2Min_vals, color = 'firebrick', marker = '.', linestyle = '-', label = 'sparse, 2nd')
# pylab.plot(itts_full[2:], lambdaMin_vals_full[2:], color = 'deepskyblue', linestyle = '-', marker = '.', label = 'full')
ax.legend(loc = 'upper right')
pylab.title('$\lambda_{min}$')
pylab.xlabel('itterations')
pylab.suptitle(baseName)
fig1.set_size_inches([9.7,5.1])
pylab.show()
if save:
fig_saveName = baseName + '_layoutLambdaExtremaFig_' + str(maxItt) + '.png'
fig_save_path = os.path.join(saveFolder, fig_saveName)
fig1.savefig(fig_save_path,transparent= False, dpi = 200)
return lambdaMax_vals, lambdaMin_vals, itts
def compile_lambda_target_sigma(maxItt, gon = 7, target = 1.51, numVals = 20, roundDepth = 6, save = True, saveFolder = ''):
lambda_vals = numpy.zeros((maxItt, numVals))
sizes = numpy.zeros(maxItt)
itts = scipy.arange(1,maxItt+1, 1)
base = PlanarLayout(gon = gon, vertex = 3, side =1, radius_method = 'exp', modeType = 'FW')
base.populate(maxItter = 1, resonatorsOnly = True)
baseName = str(gon) + 'gon_3vertex'
for itt in itts:
if itt > 1:
base.itter_generate()
base.generate_semiduals()
print itt
nameStr = baseName + '_' + str(itt)
test = GeneralLayout(resonators = base.get_all_resonators(), resonatorsOnly = True, modeType = 'FW', name = nameStr, roundDepth = roundDepth)
Hsparse = generate_layout_Hamiltonian(test, roundDepth = roundDepth, t = 1, verbose = False, sparse = True, flags = 0)
almostMaxs = scipy.sparse.linalg.eigsh(Hsparse, sigma = target, return_eigenvectors = False, k = numVals)
lambda_vals[itt-1,: ] = almostMaxs
sizes[itt-1] = test.coords.shape[0]
pickleDict = {}
pickleDict['itts'] = itts
pickleDict['lambdas'] = lambda_vals
pickleDict['target'] = target
pickleDict['name'] = baseName
pickleDict['systemSizes'] = sizes
saveName_0 = baseName + '_' + str(maxItt) + '_layoutLambdaTarget_' + str(target) + '_' +str(numVals)
if save:
saveName = saveName_0 + '.pkl'
save_path = os.path.join(saveFolder, saveName)
pickle.dump(pickleDict, open(save_path, 'wb'))
fig1 = pylab.figure(1)
pylab.clf()
ax = pylab.subplot(1,1,1)
for ind in range(0, numVals):
pylab.plot(itts, lambda_vals[:, ind], 'b.-', label = 'sparse')
# ax.legend(loc = 'lower right')
pylab.title('$\lambda$_' + str(target))
pylab.xlabel('itterations')
# ax = pylab.subplot(1,2,2)
# pylab.plot(itts, lambdaMin_vals, 'r.-', label = 'sparse')
## pylab.plot(itts_full[2:], lambdaMin_vals_full[2:], color = 'deepskyblue', linestyle = '-', marker = '.', label = 'full')
# ax.legend(loc = 'upper right')
# pylab.title('$\lambda_{min}$')
# pylab.xlabel('itterations')
pylab.suptitle(saveName_0)
fig1.set_size_inches([5.1,5.1])
pylab.show()
if save:
# fig_saveName = baseName + '_layoutLambdaExtremaFig_' + str(maxItt) + '.png'
fig_saveName = saveName_0 +'.png'
fig_save_path = os.path.join(saveFolder, fig_saveName)
fig1.savefig(fig_save_path,transparent= False, dpi = 200)
return lambda_vals, itts
def compile_lambda_extrema_gonSequence(maxItt, firstGon = 7, lastGon = 10, degree = 3, roundDepth = 6, save = True, saveFolder = ''):
lambdaMax_vals = numpy.zeros(lastGon - firstGon + 1)
lambdaMin_vals = numpy.zeros(lastGon - firstGon + 1)
sizes = numpy.zeros(lastGon - firstGon + 1)
gons = scipy.arange(firstGon,lastGon+1, 1)
# baseName = str(gon) + 'gon_' + str(degree) + 'vertex'
for ind in range(0, len(gons)):
gon = gons[ind]
base = PlanarLayout(gon = gon, vertex = degree, side =1, radius_method = 'exp', modeType = 'FW')
base.populate(maxItter = maxItt, resonatorsOnly = True)
nameStr = str(gon) + 'gon_' + str(degree) + 'vertex' + '_' + str(maxItt)
test = GeneralLayout(resonators = base.get_all_resonators(), resonatorsOnly = True, modeType = 'FW', name = nameStr, roundDepth = roundDepth)
Hsparse = generate_layout_Hamiltonian(test, roundDepth = roundDepth, t = 1, verbose = False, sparse = True, flags = 0)
almostMaxs = scipy.sparse.linalg.eigsh(Hsparse, sigma = degree, return_eigenvectors = False)
almostMins = scipy.sparse.linalg.eigsh(Hsparse, sigma = -degree, return_eigenvectors = False)
lambdaMax_vals[ind] = numpy.max(almostMaxs)
lambdaMin_vals[ind] = numpy.min(almostMins)
sizes[ind] = test.coords.shape[0]
baseName = str(firstGon) + '_' + str(lastGon) + 'gon_' + str(degree) + 'vertex'
pickleDict = {}
pickleDict['gons'] = gons
pickleDict['lambdaMax'] = lambdaMax_vals
pickleDict['lambdaMin'] = lambdaMin_vals
pickleDict['name'] = baseName
pickleDict['systemSizes'] = sizes
if save:
saveName = baseName + '_layoutLambdaExtrema_' + str(maxItt) + '.pkl'
save_path = os.path.join(saveFolder, saveName)
pickle.dump(pickleDict, open(save_path, 'wb'))
fig1 = pylab.figure(1)
pylab.clf()
ax = pylab.subplot(1,2,1)
pylab.plot(gons, lambdaMax_vals, 'b.-', label = 'sparse')
ax.legend(loc = 'upper right')
pylab.title('$\lambda_{max}$')
pylab.xlabel('k')
ax = pylab.subplot(1,2,2)
pylab.plot(gons, lambdaMin_vals, 'r.-', label = 'sparse')
ax.legend(loc = 'upper right')
pylab.title('$\lambda_{min}$')
pylab.xlabel('k')
pylab.suptitle(baseName)
fig1.set_size_inches([9.7,5.1])
pylab.show()
if save:
fig_saveName = baseName + '_layoutLambdaExtremaFig_' + str(maxItt) + '.png'
fig_save_path = os.path.join(saveFolder, fig_saveName)
fig1.savefig(fig_save_path,transparent= False, dpi = 200)
return lambdaMax_vals, lambdaMin_vals, gons