def get_next_cost_dict(self, params_dict):
# Get parameters from the provided dictionary
params = params_dict['params']
#wave = self.gaussian_interpolation(params=x)
wave = sf.wave_normalization(params)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=wave)
signal = [-1]
gate = sf.convey_params_get_cost(signal)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
df_SQ = pd.read_csv(filename_SQ, header=None).values
df_ASQ = pd.read_csv(filename_ASQ, header=None).values
res = df_SQ - df_ASQ
res = res[np.where(np.abs(res - np.mean(res)) < 3 * gate)]
varian = np.var(res) / np.mean(df_SQ)
print('var is:', varian)
cost = 100 * np.log10(varian)
uncer = 0
bad = False
cost_dict = {'cost': cost, 'uncer': uncer, 'bad': bad}
return cost_dict
def run(self, csv_file=None, signal=[1]):
if not csv_file:
if self.init_wave == 'g':
wave = self.gauss_wave()
elif self.init_wave == 't':
wave = self.triangle_wave()
elif self.init_wave == 's':
wave = self.square_wave()
else:
while not os.path.isfile(csv_file):
csv_file = input('Please input the path of Arbitary Wave:')
wave = self.AWG(csv_file)
wave = sf.wave_normalization(wave)
sf.array_to_csv(array=wave, filename=self.filename)
if self.path_today:
sf.array_to_csv(array=wave,
filename=self.path_today + 'initwave' + '.csv')
for i in range(self.nums_iter):
print('Run:', i + 1)
self.signal = signal[0]
cost = sf.convey_params_get_cost(params=signal)
if self.signal < 0:
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename_ASQ = 'D:/shot noise/' + sf.today_str(
) + '/try/ASQ.csv'
df_SQ = pd.read_csv(filename_SQ, header=None).values
df_ASQ = pd.read_csv(filename_ASQ, header=None).values
res = df_SQ - df_ASQ
gate = cost
res = res[np.where(np.abs(res - np.mean(res)) < 3 * gate)]
var_diff = 10 * np.log10(np.var(res) / np.mean(df_SQ))
print('var_diff is:', var_diff)
self.cost_list.append(var_diff)
self.generate_wave(count=i + 1)
if self.path_today:
filename = self.path_today + 'Gain_vs_run' + '.csv'
sf.array_to_csv(array=self.cost_list, filename=filename)
filename = self.path_today + 'Seed intensity_vs_run' + '.csv'
sf.array_to_csv(array=self.seed_intensity, filename=filename)
self.plot_costs_vs_run()
def get_next_cost_dict(self, params_dict):
# Get parameters from the provided dictionary
params = params_dict['params']
#wave = self.gaussian_interpolation(params=x)
wave = sf.wave_normalization(params)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=wave)
siganl = [1]
gain = sf.convey_params_get_cost(siganl)
if gain < 0.002 or np.max(wave) >= 2:
bad = True
cost = 1
uncer = 0
else:
wave = wave / abs(gain) * self.paras_scale
sf.array_to_csv(filename=filename, array=wave)
signal = [-1]
gate = sf.convey_params_get_cost(signal)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
df_SQ = pd.read_csv(filename_SQ, header=None).values
df_ASQ = pd.read_csv(filename_ASQ, header=None).values
res = df_SQ - df_ASQ
df_SQ = df_SQ[np.where(np.abs(res - np.mean(res)) < (3 * gate))]
res = res[np.where(np.abs(res - np.mean(res)) < (3 * gate))]
varian = np.var(res) / np.mean(df_SQ)
print('var is:', varian)
cost = 10 * np.log10(varian)
bad = False
uncer = sf.estim_noise_uncer(res) * 10 / np.var(res) / np.log(10)
cost_dict = {'cost': cost, 'uncer': uncer, 'bad': bad}
return cost_dict
def stokes_test():
wave = generate_wave()
wave = sf.wave_normalization(wave)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(array=wave, filename=filename)
signal = [-1]
gate = sf.convey_params_get_cost(signal)
if signal[0] < 0:
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
filename_excited = 'D:/shot noise/' + sf.today_str(
) + '/try/excited.csv'
df_SQ = pd.read_csv(filename_SQ, header=None).values
df_ASQ = pd.read_csv(filename_ASQ, header=None).values
df_excited = pd.read_csv(filename_excited, header=None).values
df_SQ = df_SQ - np.min(df_excited) * len(df_excited)
inten_SQ = np.mean(df_SQ)
std_SQ = np.var(df_SQ)
print('intensity of SQ and var of SQ are:', inten_SQ, std_SQ)
return inten_SQ, std_SQ
def seedlight_test():
wave = generate_wave()
wave = sf.wave_normalization(wave)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(array=wave, filename=filename)
signal = [-1]
gate = sf.convey_params_get_cost(signal)
if signal[0] < 0:
filename_seed = 'D:/shot noise/' + sf.today_str() + '/try2/seed.csv'
filename_everyseed = 'D:/shot noise/' + sf.today_str(
) + '/try2/everyseed.csv'
df_seed = pd.read_csv(filename_seed, header=None).values
df_everyseed = pd.read_csv(filename_everyseed, header=None).values
df_everyseed = df_everyseed - np.min(df_seed) * len(df_seed)
inten_seed = np.mean(df_everyseed)
var_seed = np.std(df_everyseed)
print('intensity of seedlight and std of seedlight are:', inten_seed,
var_seed)
return inten_seed, var_seed
def generate_wave(self, count):
filename_excited = 'D:/shot noise/' + sf.today_str(
) + '/try/excited.csv'
filename_seed = 'D:/shot noise/' + sf.today_str() + '/try2/seed.csv'
df = pd.read_csv(filename_excited, header=None)
df_seed = pd.read_csv(filename_seed, header=None)
wave_seed = df_seed.values.flatten()
self.seed_intensity.append(np.sum(wave_seed))
print('seedlight:', np.sum(wave_seed))
wave = df.values.flatten()
wave = sf.wave_normalization(wave)
wave = np.maximum(wave, 0)
sf.array_to_csv(filename=self.filename, array=wave)
if self.path_today:
savefile_name = self.path_today + str(count) + '.csv'
seedfile = self.path_today + 'seedlight' + str(count) + '.csv'
sf.array_to_csv(filename=savefile_name, array=wave)
sf.array_to_csv(filename=seedfile, array=wave_seed)
def get_next_cost_dict(self, params_dict):
# Get parameters from the provided dictionary
params = params_dict['params']
# input_inter = s.gaussian_interpolation(x) # GPR interpolation
input_inter = sf.wave_normalization(params)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename,
array=input_inter) # save wave into a csv file
# To optimize the gain, signal=[1] is to tell labview the optimizing target is gain
signal = [1]
gain = sf.convey_params_get_cost(signal)
'''
filename_seedlight = 'D:/shot noise/' + today_str() + '/try2/SQ.csv'
df = pd.read_csv(filename_seedlight, header=None)
wave = np.array(df)
array_to_csv(filename=filename,array=wave)
signal = [1]
cost = sf.convey_params_get_cost(signal)
'''
if gain > 0:
cost = 10 * np.log10(
1 / gain
) # the target is the minimum value of the cost, also is the maximum value of the gain
uncer = 0
bad = False
else:
cost = 10 * np.log10(-1 / gain)
uncer = 0
bad = True
cost_dict = {'cost': cost, 'uncer': uncer, 'bad': bad}
return cost_dict
def main():
# create a file folder to save today's data
today = str(datetime.date.today())
path_today = 'E:/python/code/data/' + today
if not os.path.exists(path_today):
os.makedirs(path_today)
n = 7
interface_2 = CustomInterface_2(n)
controller = mlc.create_controller(
interface_2,
#training_type='differential_evolution',
controller_type='neural_net',
max_num_runs=100,
keep_prob=0.8,
num_params=n,
min_boundary=[0.2] * n,
max_boundary=[1] * n)
# start the second optimization
controller.optimize()
# create the file folder of this optimization
start_datetime = str(controller.start_datetime)[11:19]
start_time = sf.change_colon_into_dash(start_datetime)
path_today += '/' + str(start_time)
os.makedirs(path_today)
# save the best wave
res = sf.add_zero(controller.best_params)
filename = path_today + '/best_paras_2_' + str(start_time) + '.csv'
sf.array_to_csv(filename=filename, array=res)
# save the predicted wave
res = sf.add_zero(controller.predicted_best_parameters)
filename = path_today + '/pre_best_paras_2_' + str(start_time) + '.csv'
sf.array_to_csv(filename=filename, array=res)
# save the seed light of the predicted wave
res = sf.wave_normalization(res)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=res)
siganl = [1]
gain = sf.convey_params_get_cost(siganl)
res = res / gain * interface_2.paras_scale
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=res)
signal = [-1]
gate = sf.convey_params_get_cost(signal)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
df_SQ = pd.read_csv(filename_SQ, header=None).values
df_ASQ = pd.read_csv(filename_ASQ, header=None).values
res = df_SQ - df_ASQ
df_SQ = df_SQ[np.where(np.abs(res - np.mean(res)) < 3 * gate)]
res = res[np.where(np.abs(res - np.mean(res)) < 3 * gate)]
cost = np.var(res) / np.mean(df_SQ)
print('predicted best cost_2 is:', controller.predicted_best_cost)
print('actual cost_2 is:', 10 * np.log10(cost))
filename_seedlight = 'D:/shot noise/' + sf.today_str() + '/try2/seed.csv'
filename = path_today + '/seedlight_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_seedlight, header=None)
df.to_csv(filename, header=0, index=0)
# save the excited stokes light
filename_excited = 'D:/shot noise/' + sf.today_str() + '/try/excited.csv'
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename = path_today + '/excited_light_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_excited, header=None)
df.to_csv(filename, header=0, index=0)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename = path_today + '/SQ_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_SQ, header=None)
df.to_csv(filename, header=0, index=0)
# save the anti-stokes light
filename_excited = 'D:/shot noise/' + sf.today_str(
) + '/try/antistokes.csv'
filename = path_today + '/antistokes_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_excited, header=None)
df.to_csv(filename, header=0, index=0)
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
filename = path_today + '/ASQ_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_ASQ, header=None)
df.to_csv(filename, header=0, index=0)
# show visualizations
mlv.show_all_default_visualizations(controller)