**args)
ax9.set_ylabel('$\\chi_{DC}$ (MHz)')
ax8.plot(SWP, [r[ID][1, 2] for r in RES],
label='$\\chi_{BC}$',
c='r',
**args)
ax8.set_ylabel('$\\chi_{BC}$ (MHz)')
ax9.set_xlabel(swpvar)
ax7.set_title('cross-Kerr')
#ax7.axhspan(85,150, alpha =0.4, color= 'b')
ax8.axhspan(5.5, 6.5, alpha=0.4, color='b')
ax9.axhline(0.5, alpha=0.4, color='b')
fig.tight_layout()
#%%
if 0:
variation = '0'
pJ_method = 'J_surf_mag'
#pJ_mj_series = bbp.calc_Pjs_from_I_for_mode(variation, bbp.U_H,bbp.U_E, bbp.LJs, junc_rects, junc_lens, method = pJ_method) # to be implemented
res = bbp.calc_avg_current_J_surf_mag(variation, junc_rects[0],
junc_lens[0])
if 0: # for debug
variation = '0'
junc_rect = 'juncV'
print_color(' Setup: ' + bbp.setup.name)
lv = bbp.get_lv(variation)
calc = CalcObject([], bbp.setup)
calc = calc.getQty("Jsurf").mag().integrate_surf(name=junc_rect)
#bbp.calc_avg_current_J_surf_mag('0','juncV',1)
#%%==============================================================================
# Plot results for sweep
#==============================================================================
if 1:
swpvar='LJ'
use_1st_order = True # use 1st O PT to identify correct eigenvectors in ND
RES = []; SWP = [];
for key, s in sol.iteritems():
print '\r Analyzing ', key,
try:
varz = hfss_variables[key]
SWP += [ ureg.Quantity(varz['_'+swpvar]).magnitude ]
RES += [ eBBQ_Pmj_to_H_params(s, meta_datas[key], cos_trunc = cos_trunc, fock_trunc = fock_trunc, use_1st_order = False) ]
except Exception as e:
print_color(" !ERROR %s" % e)
import matplotlib.gridspec as gridspec;
#%%
fig = plt.figure(num = 1, figsize=(19,5))
gs1 = gridspec.GridSpec(1, 4, width_ratios=[2,2,2,1]); gs1.update(left=0.05, right=0.98) # wspace=0.05
ax1 = plt.subplot(gs1[0]); ax2 = plt.subplot(gs1[1]); ax3 = plt.subplot(gs1[2]); ax3b = plt.subplot(gs1[3])
ax = ax1; ID = 1;
args = {'lw':0,'marker':'o','ms':5}
ax.plot(SWP, [r[ID][0,1]for r in RES], label = '$\\chi_{DB}$', **args)
ax.plot(SWP, [r[ID][0,2]for r in RES], label = '$\\chi_{DC}$', **args)
ax.plot(SWP, [r[ID][1,2]for r in RES], label = '$\\chi_{BC}$', **args)
ax.set_ylim([0.01,10**2]); ax.set_xlabel(swpvar); ax.set_title('cross-Kerr'); ax.set_ylabel('$\\chi$ (MHz)'); ax.legend(loc = 0)
ax.set_yscale('log'); ax.set_ylim(0.1,100)
ax1.axhspan(5.5,6.5, alpha =0.4, color= 'b')
ax1.axhline(0.5, alpha =0.4, color= 'b')
hfss_variables = bba.hfss_variables
sols = bba.sols
meta_datas = bba.meta_data
#%%
if analyze_BBQ:
RES = []; SWP = [];
for key, s in sols.iteritems():
print '\r Analyzing ', key,
try:
varz = hfss_variables[key]
SWP += [ ureg.Quantity(varz['_'+swp_var]).magnitude ]
RES += [ eBBQ_Pmj_to_H_params(s, meta_datas[key], cos_trunc = cos_trunc, fock_trunc = fock_trunc) ]
except Exception as e:
print_color(" !ERROR %s" % e)
#%%==============================================================================
# Plot Chis
#==============================================================================
if plot_Fs:
#Fs = bba.get_Fs(swp_var=swp_var)
Fs = []
for i in range(len(RES)):
Fs.append(RES[i][9])
plt.plot(sorted(SWP), np.array(Fs).transpose()[0]*10**-9, label='D mode freq', marker = 'o', lw =1)
plt.plot(sorted(SWP), np.array(Fs).transpose()[1]*10**-9, label='B mode freq', marker = 'o', lw =1)
plt.title('D and B mode frequencies (nonlinear)');
plt.xlabel(swp_var);
plt.ylabel('F (GHz)');
ax.set_ylabel('Participation'); ax.legend(loc = 0)
ax = ax5
ax.plot(SWP, [r[ID][0,1] for r in RES], label = '$P_{DV}$', **args)
ax.plot(SWP, [r[ID][1,0] for r in RES], label = '$P_{BH}$', **args)
ax.set_xlabel(swpvar); ax.set_ylabel('Participation'); ax.legend(loc = 0)
ax = ax6
ax.plot(SWP, [r[ID][2,0] for r in RES], label = '$P_{CH}$', **args)
ax.plot(SWP, [r[ID][2,1] for r in RES], label = '$P_{CV}$', **args)
ax.set_yscale('log'); ax.set_xlabel(swpvar); ax.set_ylabel('Participation'); ax.legend(loc = 0)
ax = plt.subplot(gs1[1,0]); ID =1;
chiDC = np.array([r[ID][0,2] for r in RES])
chiDB = np.array([r[ID][0,1] for r in RES])
#print_color("chiDB/chiDC ratios:"); print chiDB/chiDC
ax.plot(SWP, chiDB/chiDC, **args); ax.locator_params(nbins=4); ax.grid(); ax.set_ylabel('$\\chi_{DB}/\\chi_{DC}$')
ax.set_xlabel(swpvar);
#%%
if 0:
variation = '0'; pJ_method = 'J_surf_mag';
#pJ_mj_series = bbp.calc_Pjs_from_I_for_mode(variation, bbp.U_H,bbp.U_E, bbp.LJs, junc_rects, junc_lens, method = pJ_method) # to be implemented
res = bbp.calc_avg_current_J_surf_mag(variation,junc_rects[0], junc_lens[0])
if 0: # for debug
variation = '0'; junc_rect = 'juncV';
print_color(' Setup: ' + bbp.setup.name)
lv = bbp.get_lv(variation)
calc = CalcObject([],bbp.setup)
calc = calc.getQty("Jsurf").mag().integrate_surf(name = junc_rect)
#bbp.calc_avg_current_J_surf_mag('0','juncV',1)
if 1:
proj_name = r'mesh_analysis_project'
project_path = 'C:\\Users\rslqulab\Desktop\Lysander\\'
app, desktop, project = load_HFSS_project(proj_name, project_path)
design = project.get_design("20pass_7k_seed- 11. SHANTANU FAB SM22 Col 1 Row 9 Measured [May 6 2016]")
junc_rects = ['juncV', 'juncH']
junc_lines = ['juncV_line','juncH_line']
junc_LJ_names = ['LJ1', 'LJ2'];
junc_lens = [0.0001]*2
setup_names = design.get_setup_names()
setup = design.get_setup(setup_names[0])
setup.delta_f = '0.00001' # set max delta f convergence
### Set min # passes = 1 & min conv passes = 1
# wrap in function / class eventually
#delte all data?
optimtrc_nms = design._optimetrics.GetSetupNames()
optimtrc_nms = [optimtrc_nms[i] for i in range(optimtrc_nms.Count)]
#assert len(optimtrc_nms) == 1, "THis was made for optimetric sweeps which have only 1 optimetric swep "
optimtrc_name = optimtrc_nms[0] #'scale_factor_tiny' # optimetrics sweep -- doesnt have to be
for max_passes in range(1, 20, 2):
print_color("Running optimetric sweep '%s' up to %d passes" % (optimtrc_name, max_passes))
setup.passes = max_passes
design._optimetrics.SolveSetup(optimtrc_name) # wait for the optimetrics to finish --> would be nice to add a timer here and call asynch
bbp = bbq.Bbq(project, design, append_analysis=False)
bbp.do_eBBQ(junc_rect=junc_rects, junc_lines = junc_lines, junc_len = junc_lens, junc_LJ_var_name = junc_LJ_names, save_mesh_stats=True)
bba = bbp.bbq_analysis
