den = [1,2*z*wn,wn**2]
# create some data
tf = TransferFunction(num,den)
t = arange(0,2,.01)
u = create_step_vector(t, step_time=.5, amp=1.0)
y = tf.lsim(u,t)
#################################################
# start systemid
num_dict = {'0':'g*wn**2'}
den_dict = {'2':'1','1':'2*z*wn','0':'wn**2'}
var_dict = {'g':1,'z':z,'wn':10}
to_opt_dict = {'g':5,'z':z,'wn':10}
data = time_data(t,u,y)
###############################
model = Model(num_dict,den_dict,var_dict,to_opt_dict)
opt_dict = fit_time(model,data)
opt_model = Model(num_dict,den_dict,opt_dict)
fig = figure(1)
model.plot_resp(data.t,data.input,fig=fig,clear=True,linelabel='Initial Guess')
opt_model.plot_resp(data.t,data.input,fig=fig,clear=False,title='Model vs. Exp',linelabel='Opt',linewidth=5,linestyle=':')
data.plot_output(fig=fig,clear=False,linelabel='Output',linestyle='--')
data.plot_input(fig=fig,clear=False,linelabel='Input',linestyle='-',linewidth=1)
show()
usecols = (0,2,4) datafile = 'step.txt' data = time_data_file(datafile) data.read(usecols,skiprows=3) #data.resample(2) #data.scale_t(1.0/250) #data.scale_output(1.0/128/4*2*pi) ############################## # remove steady state stuff inds = data.t<.7 new_data = time_data(data.t[inds],data.input[inds],data.output[inds]) ############################### model = Model(num_dict,den_dict,var_dict,to_opt_dict) opt_dict = fit_time(model,new_data) opt_model = Model(num_dict,den_dict,opt_dict) fig = figure(1) model.plot_resp(new_data.t,new_data.input,fig=fig,clear=True,linelabel='Initial Guess') opt_model.plot_resp(new_data.t,new_data.input,fig=fig,clear=False,title='Model vs. Exp',linelabel='Opt') new_data.plot_output(fig=fig,clear=False,linelabel='Output') new_data.plot_input(fig=fig,clear=False,linelabel='Input') show()