def analyze_diamond(data_func,
N,
omega_larmor,
kernel="rbf",
verbose=False,
plots=False):
tau = choose_tau_params(N)
data = data_func(N, tau)
dip_inds, windows = find_resonances(data, fit_dips_below=None)
lower_ind_cutoff = 3220 # corresponds to 15 microseconds
if verbose:
print "number of dips to measure: ", sum(dip_inds >= lower_ind_cutoff)
spin_dict = {}
for dip_ind in dip_inds:
if dip_ind >= lower_ind_cutoff: # 15 microseconds and on
spin_dict = analyze_dip(dip_ind,
tau,
data_func,
omega_larmor,
spin_dict,
N_vals=np.arange(0, 256, 2),
error_tol=.1 / 64,
verbose=verbose,
plots=plots)
# load the classifiers
guess_scaler = learning.load_obj("classifiers/scaler_svm_" + kernel +
"_di3220_29diamonds_cxABe")
guess_clf = learning.load_obj("classifiers/clf_svm_" + kernel +
"_di3220_29diamonds_cxABe")
def guess_classifier(features):
return guess_clf.predict(guess_scaler.transform([features]))
guess_As, guess_Bs, dataerrs, all_guess_As, all_guess_Bs, select_As, select_Bs = choose_spin_guesses(
spin_dict,
N,
omega_larmor,
tau,
data,
guess_classifier,
x_min=1,
error_fun=squared_error)
cluster_As, cluster_Bs, cluster_dataerrs = cluster_spin_guesses(
guess_As, guess_Bs, dataerrs, eps=.075, min_samples=1)
As, Bs, num_remove = cluster_As, cluster_Bs, 1
while num_remove <= 2:
As, Bs, num_remove = remove_spins(As,
Bs,
N,
omega_larmor,
tau,
data,
num_remove=num_remove,
error_fun=squared_error,
verbose=verbose)
return As, Bs, all_guess_As, all_guess_Bs, select_As, select_Bs, guess_As, guess_Bs, cluster_As, cluster_Bs
def analyze_diamond(data_func, N, omega_larmor, kernel = "rbf", verbose = False, plots = False):
tau = choose_tau_params(N)
data = data_func(N, tau)
dip_inds, windows = find_resonances(data, fit_dips_below = None)
lower_ind_cutoff = 3220 # corresponds to 15 microseconds
if verbose:
print "number of dips to measure: ", sum(dip_inds >= lower_ind_cutoff)
spin_dict = {}
for dip_ind in dip_inds:
if dip_ind >= lower_ind_cutoff: # 15 microseconds and on
spin_dict = analyze_dip(dip_ind, tau, data_func, omega_larmor, spin_dict, N_vals = np.arange(0,256,2),
error_tol = .1/64, verbose = verbose, plots = plots)
# load the classifiers
guess_scaler = learning.load_obj("classifiers/scaler_svm_" + kernel + "_di3220_29diamonds_cxABe")
guess_clf = learning.load_obj("classifiers/clf_svm_" + kernel + "_di3220_29diamonds_cxABe")
def guess_classifier(features):
return guess_clf.predict(guess_scaler.transform([features]))
guess_As, guess_Bs, dataerrs, all_guess_As, all_guess_Bs, select_As, select_Bs = choose_spin_guesses(spin_dict, N, omega_larmor, tau, data, guess_classifier, x_min = 1, error_fun = squared_error)
cluster_As, cluster_Bs, cluster_dataerrs = cluster_spin_guesses(guess_As, guess_Bs, dataerrs, eps = .075, min_samples = 1)
As, Bs, num_remove = cluster_As, cluster_Bs, 1
while num_remove <= 2:
As, Bs, num_remove = remove_spins(As, Bs, N, omega_larmor, tau, data, num_remove = num_remove, error_fun = squared_error, verbose=verbose)
return As, Bs, all_guess_As, all_guess_Bs, select_As, select_Bs, guess_As, guess_Bs, cluster_As, cluster_Bs
if k >= 0:
label_inds = np.where(labels == k)[0]
best_guess = np.argmin(dataerrs[label_inds])
cluster_As.append(guess_As[label_inds][best_guess])
cluster_Bs.append(guess_Bs[label_inds][best_guess])
cluster_dataerrs.append(dataerrs[label_inds][best_guess])
return np.array(cluster_As), np.array(cluster_Bs), np.array(
cluster_dataerrs)
# an approximation of the background due to the weakly coupled spins
#A_background = 4 * mag * (np.random.rand(400) - .5)
#B_background = 2 * mag * (np.random.rand(400))
#background_dict = {"A_background" : A_background, "B_background" : B_background}
#learning.store_obj(background_dict, "background_A_B")
background_dict = learning.load_obj("background_A_B")
A_background, B_background = background_dict["A_background"], background_dict[
"B_background"]
# given guess_As and guess_Bs, this function considers all ways of removing num_remove spins from the guess list
# it compares all of these possibilities along with not taking anything out in terms of the error from this subset to the data
# the error function considers the spins along with 400 weakly coupled spins which approximates the background.
# it returns the optimal subset as well as a number to remove next time: the same number if a spin was removed, otherwise the number
# considered for removal this time plus one (considering taking several out at the same time)
def remove_spins(guess_As,
guess_Bs,
N,
omega_larmor,
tau,
data,
cluster_As, cluster_Bs, cluster_dataerrs = [], [], []
for k in np.unique(labels):
if k >= 0:
label_inds = np.where(labels == k)[0]
best_guess = np.argmin(dataerrs[label_inds])
cluster_As.append(guess_As[label_inds][best_guess])
cluster_Bs.append(guess_Bs[label_inds][best_guess])
cluster_dataerrs.append(dataerrs[label_inds][best_guess])
return np.array(cluster_As), np.array(cluster_Bs), np.array(cluster_dataerrs)
# an approximation of the background due to the weakly coupled spins
#A_background = 4 * mag * (np.random.rand(400) - .5)
#B_background = 2 * mag * (np.random.rand(400))
#background_dict = {"A_background" : A_background, "B_background" : B_background}
#learning.store_obj(background_dict, "background_A_B")
background_dict = learning.load_obj("background_A_B")
A_background, B_background = background_dict["A_background"], background_dict["B_background"]
# given guess_As and guess_Bs, this function considers all ways of removing num_remove spins from the guess list
# it compares all of these possibilities along with not taking anything out in terms of the error from this subset to the data
# the error function considers the spins along with 400 weakly coupled spins which approximates the background.
# it returns the optimal subset as well as a number to remove next time: the same number if a spin was removed, otherwise the number
# considered for removal this time plus one (considering taking several out at the same time)
def remove_spins(guess_As, guess_Bs, N, omega_larmor, tau, data, num_remove = 1, error_fun = squared_error, verbose = False):
# creates all subsets of s with size between lower and upper
def subset_size_range(s, lower, upper):
ans = []
for i in range(lower, upper+1):
for j in itertools.combinations(s, i):
ans.append(j)
return ans
