def params_from_experiment(can_params_file, params_cfg):
'''
Generates a simulation-compatible configuration given a Cantilever Parameters file
(typically acquired in the experiment) and a Params.cfg file saved with FFtrEFM data
can_params : string
Path to cantilever parameters file (from Force Calibration tab)
params_cfg : string
Path to parameters.cfg file (from FFtrEFM experiment, in the data folder)
'''
can = cantilever_params(can_params_file)
_, par = configuration(params_cfg)
if isinstance(params_cfg, dict):
par = params_cfg
can_params = {}
can_params['amp_invols'] = can['Initial']['AMPINVOLS']
can_params['def_invols'] = can['Initial']['DEFINVOLS']
can_params['soft_amp'] = 0.3
can_params['drive_freq'] = par['drive_freq']
can_params['res_freq'] = par['drive_freq']
can_params['k'] = can['Initial']['SpringConstant']
can_params['q_factor'] = can['Initial']['Q']
force_params = {}
force_params['es_force'] = can['Differential']['ElectroForce']
force_params['ac_force'] = can['Initial']['DrivingForce']
force_params['dc_force'] = 0 # only for GKPFM
force_params['delta_freq'] = can['Differential']['ResFrequency']
force_params['tau'] = 1e-5
force_params['dFdz'] = can['Differential']['dFdZ']
force_params['lift_height'] = can['Initial']['LiftHeight']
sim_params = {}
sim_params['trigger'] = par['trigger']
sim_params['total_time'] = par['total_time']
sim_params['sampling_rate'] = par['sampling_rate']
return can_params, force_params, sim_params, can, par
def generate_tf(self, can_params_dict={}, plot=False):
"""
Uses the cantilever simulation to generate a tune as the transfer function
:param can_params_dict: use ffta.pixel_utils.load.cantilever_params()
:type can_params_dict: Dict
:param plot: Plots the time-dependent tune
:type plot: bool
"""
if isinstance(can_params_dict, str):
can_params_dict = cantilever_params(can_params_dict)
can_params_dict = can_params_dict['Initial']
can_params = {
'amp_invols': 7.5e-08,
'def_invols': 6.88e-08,
'soft_amp': 0.3,
'drive_freq': 309412.0,
'res_freq': 309412.0,
'k': 43.1,
'q_factor': 340.0
}
force_params = {
'es_force': 1e-10,
'ac_force': 6e-07,
'dc_force': 3e-09,
'delta_freq': -170.0,
'tau': 0.001,
'v_dc': 3.0,
'v_ac': 2.0,
'v_cpd': 1.0,
'dCdz': 1e-10,
'v_step': 1.0
}
sim_params = {'trigger': 0.02, 'total_time': 0.05}
for k, v in can_params_dict.items():
can_params.update(k=v)
# Update from GKPixel class
sim_params['trigger'] = self.trigger
sim_params['sampling_rate'] = self.sampling_rate
sim_params['total_time'] = self.total_time
sim_params['sampling_rate'] = self.sampling_rate
can_params['drive_freq'] = self.drive_freq
can_params['res_freq'] = self.drive_freq
force_keys = ['es_force']
can_keys = {
'amp_invols': ['amp_invols', 'AMPINVOLS'],
'q': ['q_factor', 'Q'],
'k': ['SpringConstant', 'k']
}
for f in force_keys:
if 'Force' in can_params_dict:
force_params.update(es_force=can_params_dict['Force'])
elif 'es_force' in can_params_dict:
force_params.update(es_force=can_params_dict['es_force'])
for c in ['amp_invols', 'q', 'k']:
for l in can_keys[c]:
if l in can_params_dict:
can_params.update(l=can_params_dict[l])
if can_params['k'] < 1e-3:
can_params['k'] *= 1e9 # old code had this off by 1e9
cant = Cantilever(can_params, force_params, sim_params)
cant.trigger = cant.total_time # don't want a trigger
Z, _ = cant.simulate()
Z = Z.flatten()
if plot:
plt.figure()
plt.plot(Z)
plt.title('Tip response)')
TF = np.fft.fftshift(np.fft.fft(Z))
Q = can_params['q_factor']
mid = int(len(self.f_ax) / 2)
drive_bin = np.searchsorted(self.f_ax[mid:], self.drive_freq) + mid
TFmax = np.abs(TF[drive_bin])
TF_norm = Q * (TF - np.min(np.abs(TF))) / (TFmax - np.min(np.abs(TF)))
self.tf = Z
self.TF = TF
self.TF_norm = TF_norm
self.tf_f_ax = np.linspace(-self.sampling_rate / 2,
self.sampling_rate / 2,
num=self.tf.shape[0])
self.tf_exc = gen_chirp(sampling_rate=self.sampling_rate,
length=self.tf.shape / self.sampling_rate)
self.TF_EXC = np.fft.fftshift(np.fft.fft(self.tf_exc))
return