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artiq_experiments
Commit 6e4b9258 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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todo

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analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py
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......@@ -1129,16 +1129,20 @@ de que en el punto optimo el ion no esta en el centro de la trampa
sino que esta a una distancia d
"""
def hiperbola(x,a,b,c,x0):
return a*np.sqrt(((x-x0)**2+c**2))+b
#b=0
return np.sqrt((a*(x-x0)**2+c**2))+b
medfin = 11
voltages_dcA = Voltages[0][1:medfin]
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[:10],Betas_vec[:10],p0=(100,0.1,1,-0.15))
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[:10],Betas_vec[:10],p0=(20,-0.2,2e-2,-0.13))
errorhip = np.sqrt(np.diag(pcovhip))
xhip = np.linspace(-0.23,0.005,200)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec[:medfin-1],yerr=ErrorBetas_vec[:medfin-1],fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xhip,hiperbola(xhip,*popthip))
......@@ -1147,11 +1151,17 @@ plt.ylabel('Modulation factor')
#plt.yscale('log')
plt.grid()
betacero = popthip[2]+popthip[1]
error_betacero = np.sqrt((errorhip[1])**2+(errorhip[2])**2+2*pcovhip[1,2])
print(f'Beta_0 = {betacero} pm {error_betacero}')
#%%
from scipy.special import jv
import seaborn as sns
paleta = sns.color_palette("rocket")
"""
Por si no quiero correr todo de nuevo:
......@@ -1170,19 +1180,6 @@ def expo(x,tau,A,B):
def cuadratica(x,a,c):
return a*(x**2)+c
def InverseMicromotionSpectra(beta, A, det, x0, gamma, B):
ftrap=22.1
#gamma=30
P = ((jv(0, beta)**2)/((((det-x0)**2)+(0.5*gamma)**2)**2))*(-2*(det-x0))
i = 1
#print(P)
while i <= 2:
P = P + (-2*(det-x0))*((jv(i, beta))**2)/(((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det-x0))*(((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
#return 1/(A*P+B)
return 1/(A*P+B)
def MicromotionSpectra(beta,det, gamma):
ftrap=2*np.pi*22.1e6
......@@ -1194,13 +1191,13 @@ def MicromotionSpectra(beta,det, gamma):
P = P + ((jv(i, beta))**2)/((((det)+i*ftrap)**2)+(0.5*gamma)**2) + ((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)
i = i + 1
#print(P)
return P
return (gamma/2*np.pi)*P
#return P
def polynomial(x,a,b,c,d,e):
b=0
d=0
return a+b*x+c*x*x+d*x*x*x+e*x*x*x*x
def polynomial(x,a,b,c,d,e):
b=0
d=0
return a+b*x+c*x*x+d*x*x*x+e*x*x*x*x
def InverseDerivMicromotionSpectra(beta, det, gamma):
ftrap=2*np.pi*22.1e6
......@@ -1213,12 +1210,14 @@ def InverseDerivMicromotionSpectra(beta, det, gamma):
P = P + (-2*(det))*((jv(i, beta))**2)/(((((det)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det))*(((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
return 1/P
return 1/((gamma/2*np.pi)*P)
#return 1/P
def FinalTemp(beta,det, C,D):
"""
En mK
Este deja libre un factor C para corregir la primera parte pero
si ajustamos con esto, C da casi 1 entonces es al pedo.
"""
gamma = 23e6
hbar = 1.05e-34
......@@ -1228,45 +1227,44 @@ def FinalTemp(beta,det, C,D):
#det = -12e6
#print(MicromotionSpectra(beta,det,gamma))
#D=0
#C=1
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
def FinalTemp_fixedall(beta, det, D):
def FinalTemp_withoutC(beta,det, D):
"""
En mK
Este anda barbaro y es el que hay que usar
"""
gamma = 23e6
#det=-11
#D=-0.8
#C = 1.68656122e-03
#D = 6.64227010e-02
#D=1e-29
hbar = 1.05e-34
kb = 1.38e-23
D = 2e-9
C = 1
det = -11e6
#D=0.5e-27
#det = -12e6
#print(MicromotionSpectra(beta,det,gamma))
return 1e3*((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*beta**2)*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#D=0
#C=1
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
"""
Temperatura vs beta con un ajuste exponencial
"""
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(np.arange(0,0.3,0.001))+list(Betas_vec[:9]), list(0.5e-3*np.ones(len(np.arange(0,0.3,0.001))))+[t for t in Temp_vec[:9]],p0=(-10e6, 1,1e-27), bounds=((-30e6,0,0),(0e6,2,1e-25))) #esto ajusta muy bien
popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:9]), [t for t in Temp_vec[:9]],p0=(-11e6,0.8,4e-28)) #esto ajusta muy bien
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp_fixedall,list(Betas_vec[:9]), [t*1e3 for t in Temp_vec[:9]],p0=(-10,1e-20), bounds=((-30,0),(0,1e6))) #esto ajusta muy bien
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:9]), [t for t in Temp_vec[:9]],p0=(-11e6,0.8,4e-28)) #esto ajusta muy bien
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,0.8,5e-20)) #esto ajusta muy bien
popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp_withoutC,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,5e-20)) #esto ajusta muy bien
betaslong = np.arange(0,2.8,0.01)
#print(f'Min temp predicted: {FinalTemp_fixedall(betaslong,*popt_rho22_balance)[0]}')
print(f'Min temp predicted: {1e3*FinalTemp(betaslong,*popt_rho22_balance)[0]} mK')
print(f'Min temp predicted: {FinalTemp_withoutC(betaslong,*popt_rho22_balance)[0]}')
#print(f'Min temp predicted: {1e3*FinalTemp(betaslong,*popt_rho22_balance)[0]} mK')
print(f'Detuning: {1e-6*popt_rho22_balance[0]} MHz')
......@@ -1278,11 +1276,11 @@ k_plot = 9
plt.figure()
plt.errorbar(Betas_vec[:k_plot],[t*1e3 for t in Temp_vec[:k_plot]],xerr=ErrorBetas_vec[:k_plot], yerr=[t*1e3 for t in ErrorTemp_vec[:k_plot]],fmt='o',capsize=5,markersize=5,color=paleta[3])
plt.plot(betaslong,[t*1e3 for t in FinalTemp(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
#plt.plot(betaslong,[t*1e3 for t in FinalTemp(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
plt.plot(betaslong,[t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
#plt.plot(betaslong,FinalTemp_fixedall(betaslong,*popt_rho22_balance),label='Ajuste con espectro modulado')
# plt.xlim(-0.1,1.1)
#plt.ylim(0,1)
#plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
#plt.axhline(0.538)
plt.xlabel('Modulation factor')
......@@ -1290,13 +1288,69 @@ plt.ylabel('Temperature (mK)')
plt.title('With RF heating')
plt.grid()
#%%
"""
Convierto D a RF heating rate.
Analizo como es la derivada de n (fonones) respecto a la temperatura
y veo en que regimenes puedo convertir n a T facilmente y cuando no.
"""
hbar = 1.05e-34
kb = 1.38e-23
bigfactor = hbar*((2*np.pi/0.397e-6)**2)*2*np.pi*23e6*kb/6.6e-26
Tdot = 2*(popt_rho22_balance[1]*bigfactor)/kb #en unidades de k/s
ErrorTdot = 2*(np.sqrt(pcov_rho22_balance[1,1])*bigfactor)/kb
print()
wtrap = 2*np.pi*1e6
Temp = 1e-6
ndot_factor_vec = []
Tempvec = np.logspace(-6,-2,1000)
for Temp in Tempvec:
ndot_factor = (hbar*wtrap/kb)*np.exp(hbar*wtrap/(kb*Temp))/((Temp**2)*(np.exp(hbar*wtrap/(kb*Temp))-1)**2)
ndot_factor_vec.append(ndot_factor)
plt.figure()
plt.semilogx(Tempvec,ndot_factor_vec)
plt.xlabel('Temperature (K)')
plt.ylabel('dn/dT')
plt.grid()
print(ndot_factor*Tdot)
"""
se ve que para temperaturas de mas de 0.2 mK es recontra constante esa funcion y no depende de T
"""
#%%
"""
Calculo el heating rate en unidades de fonones/s, para beta = 1. Para otro beta hay que multiplicar por beta**2
"""
Temp = 0.5e-3
wtrap = 2*np.pi*1e6
ndot_factor_dopplerlimit = (hbar*wtrap/kb)*np.exp(hbar*wtrap/(kb*Temp))/((Temp**2)*(np.exp(hbar*wtrap/(kb*Temp))-1)**2)
ndot = ndot_factor_dopplerlimit*Tdot
errorndot = ndot_factor_dopplerlimit*ErrorTdot
print(ndot)
#%%
hbar = 1.05e-34
kb = 1.38e-23
beta_vector = np.arange(0,1.5,0.01)
beta_vector = np.arange(0,2,0.01)
comparison = []
......@@ -1306,10 +1360,11 @@ for beta_target in beta_vector:
comparison.append(factor2/factor1)
plt.figure()
plt.plot(beta_vector,comparison)
plt.xlabel('Beta')
plt.ylabel('Ratio RF heat/Normal heat')
plt.axhline(1)
plt.axhline(1, color=paleta[4],linestyle='dashed')
plt.plot(beta_vector,comparison,color=paleta[1],linewidth=3)
plt.xlabel('Modulation factor beta')
plt.ylabel('RF heating/Recoil heating')
plt.grid()
#%%
......
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