def _def_params(self):
if self.start_pkl_name is not None:
myparams = SFLR_TMM.load_params(self.start_pkl_name)
elif os.path.exists(self.pkl_name):
myparams = SFLR_TMM.load_params(self.pkl_name)
else:
myparams = SFLR_TMM.new_def_params()
myparams.p_act1 = 15*2*pi
return myparams
def _def_params(self):
if self.start_pkl_name is not None:
myparams = SFLR_TMM.load_params(self.start_pkl_name)
elif os.path.exists(self.pkl_name):
myparams = SFLR_TMM.load_params(self.pkl_name)
else:
myparams = SFLR_TMM.new_def_params()
myparams.k_spring = 10.0
myparams.c_spring = 0.1
return myparams
def _def_params(self):
if self.start_pkl_name is not None:
return SFLR_TMM.load_params(self.start_pkl_name)
case = 1
if case == 1:
myparams = SFLR_TMM.load_params('model2b_bf_min.pkl')
elif case == 2 and os.path.exists(self.pkl_name):
myparams = SFLR_TMM.load_params(self.pkl_name)
else:
myparams = SFLR_TMM.new_def_params()
myparams.k_spring = 10.0
myparams.c_spring = 0.1
return myparams
def spreadsheet_row_to_params(rowin, labels, old=False, \
pklname=None):
keys = labels[0:-3]
if pklname is not None:
params = SFLR_TMM.load_params(pklname)
else:
if old:
params = SFLR_TMM.SFLR_params()
else:
params = SFLR_TMM.new_def_params()
for key, val in zip(keys, rowin[0:-3]):
setattr(params, key, val)
return params
def calc_and_massage_a_th_Bode(f, th_bode, a_bode, \
**kwargs):
a_th_bode = a_bode/th_bode
a_th_bode.seedfreq = 10.0
a_th_bode.seedphase = 0.0
a_th_bode.PhaseMassage(f)
a_th_bode = SFLR_TMM.a_massage(a_th_bode, f, **kwargs)
return a_th_bode
def build_Gth_TMM_model(pklpathin=None):
if pklpathin is None:
pklpathin = pkl_path
import Gth
reload(Gth)
Gth_TMM = SFLR_TMM.model_w_bm_with_theta_feedback_comp(pklpathin, \
p=Gth.p, \
z=Gth.z, \
gain=Gth.gain)
return Gth_TMM
def run_simulation(pkl_name, mod_name, param_dict={},
f=f2, plot=True, startfi=1, **kwargs):
params = SFLR_TMM.load_params('temp_params.pkl')
params.update(param_dict)
print('params.c_clamp = %s' % params.c_clamp)
mymod = my_import(mod_name)
myfunc = mymod.Bodes
th_bode, a_bode = calc_and_massage_Bodes(f, params, func=myfunc)
a_th_bode = calc_and_massage_a_th_Bode(f, th_bode, a_bode)
if plot:
#plot_exp(startfi=startfi)
plot_Bodes(f, th_bode, a_bode, a_th_bode, startfi=startfi, \
**kwargs)
return th_bode, a_bode, a_th_bode
def calc_and_massage_Bodes(f, params, func=mod.Bodes, \
**kwargs):
s = 2.0j*pi*f
th_comp, a_comp = func(s, params)
a_bode = rwkbode.rwkbode(compin=a_comp)
th_bode = rwkbode.rwkbode(compin=th_comp)
a_bode.seedfreq = 2.0
a_bode.seedphase = 30.0
a_bode.PhaseMassage(f)
a_bode = SFLR_TMM.a_massage(a_bode, f, **kwargs)
th_bode.seedfreq = 1.2
th_bode.seedphase = -90.0
th_bode.PhaseMassage(f)
return th_bode, a_bode
def build_Accel_TMM_model(pklpathin=None, Ga=None):
if pklpathin is None:
pklpathin = pkl_path
import Gth
reload(Gth)
import Ga5
reload(Ga5)
import Unc_row
reload(Unc_row)
if Ga is None:
Ga = Ga5.Ga5
Gth_Ga_TMM = SFLR_TMM.model_w_bm_accel_and_theta_FB(pkl_path, \
Gth=Gth.Gth, \
Ga=Ga, \
Unc_func=Unc_row.Unc_row0)
return Gth_Ga_TMM
def save_params(self, pklname, params=None):
if params is None:
params = self.params
SFLR_TMM.save_params(params, pklname)
def save_minrow(self, pklname):
bfparams = self.row_to_params()
SFLR_TMM.save_params(bfparams, pklname)
def _save_params(self, params=None, pkl_name=None):
if pkl_name is None:
pkl_name = self.pkl_name
if params is None:
params = self.opt_params
SFLR_TMM.save_params(params, pkl_name)
def build_OL_TMM_model(pklpathin=None):
if pklpathin is None:
pklpathin = pkl_path
OL_TMM_model = SFLR_TMM.model_w_bm(pklpathin)
return OL_TMM_model
