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))
