def _load_mod(self): try: self.mod = my_import(self.mod_name) reload(self.mod) self.func = self.mod.Bodes except ImportError: self.mod = None self.func = None
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 LoadDatafromSavedModules(filelist,deschannels,xlabel='t'): """Builds matrices for each channel assuming that each filename has been saved to a module using scipy.io.save. filelist is a list of the modules that will be imported using my_import, so that need to be somewhere on sys.path. deschannels are the keys from the saved modules that will be kept.""" outdict=_initializedict(deschannels) for modname in filelist: mod=my_import(modname) mydict=ConvertSaveModuletoDict(mod) for chn in deschannels: outdict[chn].append(mydict[chn]) for key, value in outdict.iteritems(): if key != xlabel: # print('key='+str(key)) # print('type(value)='+str(type(value))) # print('shape(value)='+str(shape(value))) outdict[key]=mystack(value) myN=max(outdict[key].shape) outdict[xlabel]=mydict[xlabel][0:myN] return outdict
def BodeFromModname(modname,expbode,f,funcname=None,ucv=[],optargs=(),PhaseMassage=True): """Generate one Bode from its module name, i.e. the name of a module that contains the symbolic Bode function funcname. If funcname is not given, it is assumed to be the same as modname. expbode is used to determine the inputs and outputs as well as the seedfreq and seedphase.""" s=2.0j*pi*f if not modname: bodefunc=funcname elif callable(modname): bodefunc=modname else: bodemod=my_import(modname) if funcname: bodefunc=getattr(bodemod,funcname) else: bodefunc=getattr(bodemod,modname) # if ucv: if ucv==[]: compv=bodefunc(s,*optargs) else: compv=bodefunc(s,ucv,*optargs) mybode=BodeFromComp(compv, expbode, f, PhaseMassage) return mybode
from scipy import optimize from rwkmisc import my_import import time, copy import SFLR_TMM reload(SFLR_TMM) import rwkbode #import sympy_bodes #reload(sympy_bodes) mod_name = 'sympy_bodes_tau' mod = my_import(mod_name) reload(mod) import exp_data f2 = exp_data.f2 fexp = exp_data.f def plot_exp(startfi=1): exp_data.plot_exp(startfi=startfi) def calc_and_massage_a_th_Bode(f, th_bode, a_bode, \ **kwargs): a_th_bode = a_bode/th_bode
'"Exceeds expectation (5)"', \ '"Meets expectation (3)"', \ '"Does not meet expectation (1)"', \ '"Ave. Score"', \ '"Exceeds"', \ '"Meets"', \ '"Does Not Meet"', \ '"Previous Year Ave. Score"', \ ] #last_years_ave = last_years_sheet.ave_score.astype('float') #make a dictionary with the number of members in each group import rwkmisc #modname = 'spring_%s_484' % year_str modname = 'fall_%s_482' % year_str mymod = rwkmisc.my_import(modname) group_list = mymod.group_list alts = mymod.alts group_names = mymod.group_list[1] team_names = group_names member_list = mymod.group_list[2] number_of_members = [0] * len(group_names) for i, cur_members in enumerate(member_list): cur_list = cur_members.split(',') cur_num = len(cur_list) number_of_members[i] = cur_num group_members_dict = dict(zip(group_names, number_of_members))
'"Exceeds expectation (5)"', \ '"Meets expectation (3)"', \ '"Does not meet expectation (1)"', \ '"Ave. Score"', \ '"Exceeds"', \ '"Meets"', \ '"Does Not Meet"', \ '"Previous Year Ave. Score"', \ ] #last_years_ave = last_years_sheet.ave_score.astype('float') #make a dictionary with the number of members in each group import rwkmisc #modname = 'spring_%s_484' % year_str modname = 'fall_%s_482' % year_str mymod = rwkmisc.my_import(modname) group_list = mymod.group_list alts = mymod.alts group_names = mymod.group_list[1] team_names = group_names member_list = mymod.group_list[2] number_of_members = [0]*len(group_names) for i, cur_members in enumerate(member_list): cur_list = cur_members.split(',') cur_num = len(cur_list) number_of_members[i] = cur_num group_members_dict = dict(zip(group_names, number_of_members))