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__init__.py
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__init__.py
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from pyinstruments import CurveDB
import matplotlib.pylab as plt
import matplotlib.colors as mcolors
import numpy as np
import os.path as osp
import time, os
from scipy.optimize import leastsq
from scipy.constants import k, hbar, h
from pyinstruments.datastore.settings import MEDIA_ROOT
class OMIT(object):
def __init__(self, curve_id_s, large_curve_id, portion=False):
self.curve_id_s = curve_id_s
self.curves=[CurveDB.objects.get(id=curve_id) for curve_id in
self.curve_id_s]
self.curves_data = [child for curve
in self.curves for child in curve.childs.all() ]
self.n_curves = np.sum([len(curve.childs.all()) for curve in
self.curves])
self.large_curve_id = large_curve_id
self.large_curve = CurveDB.objects.get(id=large_curve_id)
if portion:
self.portion_of_large_curve = self.large_curve.childs.first()
else:
self.portion_of_large_curve=self.large_curve
fit=None
for child in self.portion_of_large_curve.childs.all():
if 'fit_microwave_cavity' in child.name:
fit = child
break
if fit is None:
fit = self.portion_of_large_curve.fit('microwave_cavity', autosave=True)
self.portion_of_large_curve=fit
self.CAVITY_FREQUENCY = self.portion_of_large_curve.params["x0"]
self.cavity_Qi = self.portion_of_large_curve.params["Q_i"]
self.cavity_Qc = self.portion_of_large_curve.params["Q_c"]
self.cavity_Q = self.portion_of_large_curve.params["Q"]
self.ETA_C = self.cavity_Qi / (self.cavity_Qc + self.cavity_Qi)
self.KAPPA_HZ = self.CAVITY_FREQUENCY / self.cavity_Q
self.KAPPA_C_HZ = self.CAVITY_FREQUENCY / self.cavity_Qc
self.normalization = np.conjugate(0.5*(np.mean(
self.large_curve.data.get_values()[
:10])+np.mean(
self.large_curve.data.get_values()[-10:])))
self.PUMP_FREQUENCIES = [curve.params["pump_frequency"] for curve in
self.curves]
assert (np.array(self.PUMP_FREQUENCIES)-self.PUMP_FREQUENCIES[
0]).any()==False
self.PUMP_FREQUENCY = self.PUMP_FREQUENCIES[0]
del self.PUMP_FREQUENCIES
self.DELTA_PUMP = self.PUMP_FREQUENCY - self.CAVITY_FREQUENCY
self.MECHANICAL_FREQUENCIES = [curve.params['mechanical_frequency']
for curve in self.curves]
assert (np.array(self.MECHANICAL_FREQUENCIES)-self.MECHANICAL_FREQUENCIES[
0]).any()==False
self.MECHANICAL_FREQUENCY = self.MECHANICAL_FREQUENCIES[0]
del self.MECHANICAL_FREQUENCIES
self.datas = []
self.cbar_ax = None
self.re_ax=None
self.im_ax=None
self.abs_ax=None
self.re_ax_large=None
self.im_ax_large=None
self.abs_ax_large=None
self.fig_subplots = None
self.fig_large = None
self.dir=None
self.dir_large=None
self.filename_large=None
self.filename=None
self.one_plot=False
def transmission(self, nu_probe, n, nu_pump, nu_cav,
kappa_cav_hz,
K_coop, Q_m, nu_m, eta_c):
coop = n * np.abs(K_coop)
omega = 2. * np.pi * (nu_probe - nu_pump)
kappa = kappa_cav_hz * 2. * np.pi
omega_m = nu_m * 2. * np.pi
gamma_m = omega_m/np.abs(Q_m)
delta = 2. * np.pi * (nu_pump -
nu_cav)+0.5*coop*kappa*gamma_m*omega_m/(
omega_m**2+0.25*gamma_m**2)
f = coop / ((2 * Q_m * (1. - omega / omega_m) - 1j) * (
1 + 2j * (delta - omega) / kappa))
# #coop / (-(2 * Q_m * (1. - omega / omega_m) - 1j) * (
#1 + 2j * (delta - omega) / kappa))
#results from Sam's paper
t = 1. - 0.5 * (1. + 1j * f) * eta_c * kappa / (
-1j * (delta + omega) + kappa / 2. + 2. * delta * f)
#1. - 0.5 * (1. + 1j * f) * eta_c * kappa / (
#1j * (delta + omega) + kappa / 2. + 2. * delta * f)
#results from handmade computations
t_bis = 1.-eta_c/(-2j*(
delta+omega)/kappa+1-coop/(2j*(
omega-omega_m)/gamma_m-1))
return t_bis
def fit_func(self, probes, n, params):
K_coop, Q_m, *nu_m_s_and_nu_0 = params
nu_m_s = nu_m_s_and_nu_0[:int(len(nu_m_s_and_nu_0)/2)]
nu_0_s = nu_m_s_and_nu_0[int(len(nu_m_s_and_nu_0)/2):]
if len(nu_m_s)>1:
nu_m = nu_m_s[self.ns.index(n)]
nu_0 = nu_0_s[self.ns.index(n)]
else:
nu_m = self.MECHANICAL_FREQUENCY
nu_0 = self.CAVITY_FREQUENCY
return [self.transmission(nu_probe,
n,
self.PUMP_FREQUENCY,
nu_0,
self.KAPPA_HZ,
K_coop,
Q_m,
nu_m,
self.ETA_C) for nu_probe in probes]
def fit_global(self, guess):
self.fitted_params, self.flag = leastsq(self.to_minimize,
guess,
(self.ns_all,self.probes_all,
self.datas_all))
def fit(self, guess, n, datas):
self.fitted_params, self.flag = leastsq(self.to_minimize,
guess,
([n for i in datas],
self.probes,
datas))
def to_minimize(self, params, ns, probes, datas):
K_coop, Q_m, *nu_m_s_and_nu_0 = params
nu_m_s = nu_m_s_and_nu_0[:int(len(nu_m_s_and_nu_0)/2)]
nu_0_s = nu_m_s_and_nu_0[int(len(nu_m_s_and_nu_0)/2):]
nu_m_s_all = [nu_m for nu_m in nu_m_s for i in range(int(len(
datas)/self.n_curves))]
nu_0_s_all = [nu_0 for nu_0 in nu_0_s for i in range(int(len(
datas)/self.n_curves))]
return np.array([np.abs(self.transmission(nu_probe, n,
self.PUMP_FREQUENCY,
nu_0,
self.KAPPA_HZ, K_coop, Q_m,
nu_m,self.ETA_C)-data/self.normalization)
for
nu_probe, n, data, nu_m, nu_0 in zip(probes, ns,
datas, nu_m_s_all,
nu_0_s_all)]).flatten()
def plot_global_fit(self, one_plot=False, plot_guess=True, plot_fit=True,
save=False, abs=False, title=None, curves=None,
large=False, plot_color_bar=True, plot_title=True,
xlabel=True):
self.one_plot=one_plot
if curves is not None:
ind_s = [np.int(curve*(len(self.ns)-1)) for curve in
curves]
else:
ind_s = range(len(self.ns))
for ind, tuple in enumerate(zip(self.datas, self.ns, self.probes)) :
if ind in ind_s:
datas, n, probes = tuple
if large:
offset_freq = self.CAVITY_FREQUENCY
scale_freq = 1e-6
else:
offset_freq = \
self.PUMP_FREQUENCY+self.MECHANICAL_FREQUENCY
scale_freq=1.
if not one_plot:
self.fig_subplots = plt.figure()
if self.fig_subplots is None:
if (ind==0 and one_plot):
self.fig_subplots = plt.figure()
self.probes_fit = np.linspace(np.min(self.probes), np.max(
self.probes), 10000)
if plot_fit:
self.plot(scale_freq*(np.array(self.probes_fit)
-offset_freq),
self.fit_func(self.probes_fit, n,
self.fitted_params),abs,
label='fit')
if plot_guess:
self.plot(scale_freq*(np.array(self.probes_fit)
-offset_freq),
self.fit_func(self.probes_fit, n,
self.guess()),
abs,label='guess')
self.plot(scale_freq*(np.array(probes)
-offset_freq),
datas/self.normalization, abs, label='raw data',
color=plt.cm.viridis(ind/len(list(zip(self.datas, self.ns)))))
if not one_plot:
plt.legend()
if plot_title:
if title is None:
plt.suptitle(r'OMIT signal, $\nu_0=${:.2f} GHz, $\nu_m=${'
r':.2f} '
r'kHz, '
r'n={:}'.format(
self.CAVITY_FREQUENCY / 1e9,
self.MECHANICAL_FREQUENCY / 1e3,
int(n)))
else:
plt.suptitle(title)
if save:
if self.dir is None:
self.dir = self.curve.get_or_create_dir()
plt.savefig(osp.join(self.dir, 'fit_n={:}.png'.format(
int(n))),dpi=200)
plt.savefig(osp.join(self.dir, 'fit_n={:}.pdf'.format(
int(n))),dpi=200)
if one_plot:
if xlabel:
if abs:
self.abs_ax.set_xlabel(r'Detuning from $\nu_{pump}+\nu_m$ (Hz)')
else:
self.im_ax.set_xlabel(r'Detuning from $\nu_{pump}+\nu_m$ (Hz)')
if not abs:
if self.re_ax is None:
self.re_ax=self.fig_subplots.add_subplot(211)
self.re_ax.set_ylabel(r'Re($\mathcal{T}$) (a.u.)')
if self.im_ax is None:
self.im_ax = self.fig_subplots.add_subplot(212)
self.im_ax.set_ylabel(r'Im($\mathcal{T}$) (a.u.)')
else:
if self.abs_ax is None:
self.abs_ax = self.fig_subplots.add_subplot(
111)
self.abs_ax.set_ylabel(r'|$\mathcal{T}$| (a.u.)')
if title is None:
plt.suptitle(r'$\nu_0=${:.2f} GHz, $\nu_m=${:.2f} kHz'.format(
self.CAVITY_FREQUENCY/1e9, self.MECHANICAL_FREQUENCY/1e3))
else:
plt.suptitle(title)
if plot_color_bar:
self.plot_color_bar()
if save and one_plot:
if self.dir is None:
self.dir = MEDIA_ROOT+time.strftime("/%Y/%m/%d",
time.gmtime())
if not osp.exists(self.dir):
os.makedirs(self.dir)
if self.filename is None:
self.filename = 'plot.png'
self.fig_subplots.savefig(self.dir+'/'+
self.filename)
self.fig_subplots.savefig((self.dir+'/'+
self.filename).replace('.png',
'.pdf'))
self.fig_subplots.savefig(osp.join(self.curves[0].get_or_create_dir(), 'display.png'),
dpi=200)
def plot_color_bar(self):
nValues = np.array(self.ns)
normalize = mcolors.LogNorm(vmin=nValues.min(), vmax=nValues.max())
# setup the colorbar
self.scalarmappaple = plt.cm.ScalarMappable(norm=normalize,
cmap=plt.cm.viridis)
self.scalarmappaple.set_array(nValues)
if self.cbar_ax is None:
self.cbar_ax = self.fig_subplots.add_axes([0.85, 0.15, 0.05, 0.7])
self.cb = plt.colorbar(self.scalarmappaple, cax=self.cbar_ax)
plt.subplots_adjust(right=0.8, top=0.9)
self.cb.set_label('n', labelpad=-10)
def plot(self, x, y, abs=False, **kwds):
if abs:
self.plot_abs(x,y,**kwds)
else:
self.plot_re_im(x,y,**kwds)
def plot_re_im(self, x, z, **kwds):
assert self.fig_subplots is not None
if (self.re_ax is None and self.one_plot)or (not self.one_plot) :
self.re_ax= self.fig_subplots.add_subplot(211)
self.re_ax.plot(x, np.real(z), **kwds)
#plt.ylim([-1,1])
#self.im_ax = plt.subplot(212, sharex=self.re_ax)
if (self.im_ax is None and self.one_plot) or (not self.one_plot):
self.im_ax= self.fig_subplots.add_subplot(212)
self.im_ax.plot(x, np.imag(z), **kwds)
#plt.ylim([-1, 1])
def plot_abs(self, x, z, **kwds):
assert self.fig_subplots is not None
if (self.abs_ax is None and self.one_plot) or (not self.one_plot):
self.abs_ax=self.fig_subplots.add_subplot(111)
self.abs_ax.plot(x, np.abs(z), **kwds)
def plot_large(self, x, z, abs, **kwds):
if abs:
self.plot_abs_large(x,z,**kwds)
else:
self.plot_re_im_large(x,z,**kwds)
def plot_abs_large(self, x, z, **kwds):
assert self.fig_large is not None
if self.abs_ax_large is None:
self.abs_ax_large = self.fig_large.add_subplot(111)
self.abs_ax_large.plot(x, z, **kwds)
def plot_re_im_large(self, x, z, **kwds):
assert self.fig_large is not None
if self.re_ax_large is None:
self.re_ax_large = self.fig_large.add_subplot(211)
self.re_ax_large.plot(x, np.real(z), **kwds)
plt.ylim([-1,1])
if self.im_ax_large is None:
self.im_ax_large = self.fig_large.add_subplot(212,
sharex=self.re_ax_large)
self.im_ax_large.plot(x, np.imag(z), **kwds)
plt.ylim([-1, 1])
def get_number_of_photons(self, power, attenuation):
P0=1e-3 # 1dBm == 1mW
a_squared=((P0*10**(0.1*(power+attenuation)))/(h*self.CAVITY_FREQUENCY))
return a_squared*4*self.KAPPA_C_HZ/(
self.KAPPA_HZ**2+4.*self.DELTA_PUMP**2)
def get_data_from_scans(self, get_bandwidths = False,
get_frequencies=False, get_normalization = False,
power_threshold = 15, get_mechanical_frequencies
= False):
ns_all = []
ns = []
self.probes_all = []
self.probes = []
datas_all = []
datas = []
if get_bandwidths:
self.bandwidths = []
self.bandwidths_microwave = []
if get_frequencies:
self.cavity_frequencies = []
if get_normalization:
self.normalizations = []
if get_mechanical_frequencies:
self.mechanical_frequencies = []
for curve in self.curves:
for child in curve.childs.all():
power = child.params["Sideband_power"]
if power<power_threshold:
if not "attenuation_pump" in child.params.keys():
ATTENUATION_PUMP = child.params["attenuation"] + 40
child.params["attenuation_pump"] = ATTENUATION_PUMP
child.save()
else:
ATTENUATION_PUMP = child.params["attenuation_pump"]
if get_bandwidths:
fit = None
for grandchild in child.childs.all():
if 'lorentz_complex_sam' in grandchild.name:
fit=grandchild
break
if fit is None:
fit = child.fit('lorentz_complex_sam', autosave=True)
self.bandwidths.append(fit.params['bandwidth'])
large = None
for grandchild in curve.childs.all():
if 'large_curve' or 'na_curve' in grandchild.name:
large = grandchild
if 'na_curve' in grandchild.name:
large.name='large_curve'
large.save()
break
assert large is not None
fit_large = large.fit('lorentz_complex_sam', autosave=True)
self.bandwidths_microwave.append(fit_large.params['bandwidth'])
fit_large.delete()
if get_frequencies:
large = None
for grandchild in curve.childs.all():
if 'large_curve' or 'na_curve' in grandchild.name:
large=grandchild
if 'na_curve' in grandchild.name:
large.name='large_curve'
large.save()
break
assert large is not None
fit = None
for grandgrandchild in large.childs.all():
if 'fit' in grandgrandchild.name:
fit=grandgrandchild
break
if fit is None:
fit = large.fit('microwave_cavity', autosave=True)
self.cavity_frequencies.append(fit.params['x0'])
if get_mechanical_frequencies:
fit = None
for grandchild in child.childs.all():
if 'lorentz_complex_sam' in grandchild.name:
fit = grandchild
break
if fit is None:
fit = child.fit('lorentz_complex_sam',
autosave=True)
self.mechanical_frequencies.append(fit.params[
'x0']-self.PUMP_FREQUENCY)
if get_normalization:
large = None
for grandchild in curve.childs.all():
if 'large_curve' or 'na_curve' in grandchild.name:
large = grandchild
if 'na_curve' in grandchild.name:
large.name = 'large_curve'
large.save()
break
assert large is not None
self.normalizations.append(0.5*(np.mean(
large.data.get_values()[:10]+large.data.get_values()[
-10:])))
data = np.conjugate(np.array(child.data.get_values()))
probe = np.array(child.data.index)
n = self.get_number_of_photons(power, ATTENUATION_PUMP)
ns.append(n)
datas.append(data)
self.probes.append(probe)
for t, f in zip(data, probe):
datas_all.append(t)
self.probes_all.append(f)
ns_all.append(n)
self.ns_all = ns_all
self.ns = ns
self.datas_all = datas_all
#self.probes = self.probes_all[0]
#1/0
self.datas = datas
def plot_large_guess(self, abs=False, plot_fit=True, legend=True,
save=False, plot_fit_Geerlings=False):
if self.fig_large is None:
self.fig_large=plt.figure()
freqs = (self.large_curve.data.index-self.CAVITY_FREQUENCY)/1e6
self.plot_large(freqs,
np.conjugate(self.large_curve.data.get_values()) /
self.normalization, abs,
label='raw')
params_large_guess = 0,1000,1000
if plot_fit:
self.plot_large(freqs,
self.fit_func(np.array(self.large_curve.data.index),
0,
params_large_guess)
,abs, label="fit 0 coop")
if plot_fit_Geerlings:
fit = None
for child in self.large_curve.childs.all():
if 'fit_microwave_cavity' in child.name:
fit=child
break
if fit is None:
fit = self.large_curve.fit('microwave_cavity', autosave=True)
self.plot_large(freqs,fit.data.get_values()/self.normalization, abs,
label="fit")
if legend:
plt.legend()
self.fig_large.suptitle(r'$\nu_0=${:.2f} GHz, $\nu_m=${:.2f} '
r'kHz'.format(
self.CAVITY_FREQUENCY / 1e9, self.MECHANICAL_FREQUENCY / 1e3))
if save:
if self.dir_large is None:
self.dir_large = MEDIA_ROOT+time.strftime("/%Y/%m/%d",
time.gmtime())
if not osp.exists(self.dir_large):
os.makedirs(self.dir_large)
if self.filename_large is None:
self.filename_large='large_plot.png'
self.fig_large.savefig(self.dir_large+'/'+
self.filename_large)
self.fig_large.savefig((self.dir_large + '/' +
self.filename_large).replace('.png', '.pdf'))
def guess(self):
Q_m = 1e6
ind = int(np.argmax(self.ns))
grandchild = self.curves_data[ind]
for fits in grandchild.childs.all():
fits.delete()
fit = grandchild.fit('lorentz_complex_sam', autosave=True)
n = self.ns[ind]
x0 = fit.params['x0']
bandwidth = fit.params['bandwidth']
nu_m = x0-self.PUMP_FREQUENCY
natural_bandwidth = nu_m/Q_m
coop = bandwidth/natural_bandwidth
K_coop = coop/n
self.MECHANICAL_FREQUENCY=nu_m
return [K_coop, Q_m, *self.mechanical_frequencies,
*self.cavity_frequencies]