limits = np.arange(0.90, 0.99, 0.01)
avers = []
parms = [[], []]
for lim in limits:
position = np.argwhere(data >= lim)
pos_zero = np.argwhere(data >= 0)
pos_tp = int(position[-1]) + 1
pos_zero = int(pos_zero[-1])
summ = []
parm1, parm2 = cv(exp_mod, time[0:pos_tp], data[0:pos_tp])
sig = parm1
parm3, parm4 = cv(parab_mod, time[pos_tp:pos_zero],
data[pos_tp:pos_zero])
te = parm1 + parm3
parm2 = np.sqrt(np.diag(parm2))
aver = [parm2[0], parm2[1]]
avers.append(parm2[0] * 2 + parm2[1])
parms[0].append(parm1)
parms[1].append(parm3)
#position = int(np.argwhere(avers == np.amin(avers)))
return modelo1
def modelo2(x, A, B, C, mu, sigma):
modelo2 = recta(x, A, B) - lorentz(x, C, mu, sigma)
return modelo2
# Main Setup
x = sp.loadtxt('espectro.dat')[:, 0] # long de onda
y = sp.loadtxt('espectro.dat')[:, 1] # Flujo
sigma = sp.std(x)
mu = sp.mean(x)
A, B = sp.polyfit(x, y, 1)
C = 1 * 10**(-16) # valor mas pequeno de y
a1, b1 = cv(modelo1, x, y, [A, B, C, mu, sigma])
a2, b2 = cv(modelo2, x, y, [A, B, C, mu, sigma])
modelo1v = modelo1(x, *a1)
modelo2v = modelo2(x, *a2)
# Grafico modelo 1
fig1 = plt.figure(1)
fig1.clf()
plt.plot(x, y, 'b-')
plt.plot(x, modelo1v, 'r-')
plt.xlabel(r'Longitud de onda [$\AA$]')
plt.ylabel(r'Flujo [$erg s^{-1} Hz^{-1}cm^{-2}$]')
plt.title('Curva y modelamiento de Espectro ' +
'por Gaussiana')
plt.grid(True)
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit as cv
#Import the data from the Text File
data = np.genfromtxt("Draft4.txt", skip_header=3, delimiter="\t")
print(data)
time = data[:, 0]
V_out = data[:, 1]
def func(x, b):
#we know V0 is o.5 so only have program find b in the function
return 0.5 * np.exp(-b * x)
popt1, pcov1 = cv(func, time, V_out)
print(popt1)
bVal1 = str(round(popt1[0], 3))
eq1 = " 0.5 * EXP(-" + bVal1 + "x)"
plt.xlabel("Time / s")
plt.ylabel("Voltage / V")
plt.plot(time, V_out, "r", label="V_out, " + eq1)
plt.title("Time against Voltage at Different Parts of the Circuit")
plt.legend()
limits = np.arange(0.2, 1, 0.01)
avers = []
sizes = [[], []]
alphas = []
limas = [[], []]
for lim in limits:
#get cond_cool and rad_cool
position = np.argwhere(data >= lim)
pos_zero = np.argwhere(data >= 0)
pos_tp = int(position[-1]) + 1
parm1, parm2 = cv(cond_cool,
time[0:pos_tp],
data[0:pos_tp],
bounds=(0, 20))
parm3, parm4 = cv(rad_cool,
time[pos_tp:-1],
data[pos_tp:-1],
bounds=(0, 20))
size_c = parm1
size_r = parm3[1]
alpha = parm3[0]
sizes[0].append(size_c)
sizes[1].append(size_r)
alphas.append(alpha)
time2 = np.arange(0, 15, 0.01)
for lim_timea in time2[2:-1]:
data = data[maximum: -1]
time = time[maximum: -1]
limits = np.arange(0.1, 0.99, 0.01)
avers = []
parms = [[],[]]
for lim in limits:
position = np.argwhere(data >= lim)
pos_zero = np.argwhere(data >= 0)
pos_tp = int(position[-1]) + 1
pos_zero = int(pos_zero[-1])
parm1, parm2 = cv(fl.A_exp, time[0 : pos_tp], data[0 : pos_tp])
sig = parm1[0]
parm3, parm4 = cv(fl.A_parab, time[pos_tp : pos_zero], data[pos_tp : pos_zero])
alpha = parm3[0]
parms[0].append(sig)
parms[1].append(alpha)
aver = 0.0
ind = 0
for i in time:
aver += (fl.A_full(i, parm1[0], parm3[0], lim) - data[ind])**2
ind += 1
aver = np.float(np.sqrt(aver / len(time)))
avers.append(aver)
def modelo2(x, A, B, C, mu, sigma):
modelo2 = recta(x, A, B) - lorentz(x, C, mu, sigma)
return modelo2
# Main Setup
x = sp.loadtxt('espectro.dat')[:, 0] # long de onda
y = sp.loadtxt('espectro.dat')[:, 1] # Flujo
sigma = sp.std(x)
mu = sp.mean(x)
A, B = sp.polyfit(x, y, 1)
C = 1 * 10**(-16) # valor mas pequeno de y
a1, b1 = cv(modelo1, x, y, [A, B, C, mu, sigma])
a2, b2 = cv(modelo2, x, y, [A, B, C, mu, sigma])
modelo1v = modelo1(x, *a1)
modelo2v = modelo2(x, *a2)
# Grafico modelo 1
fig1 = plt.figure(1)
fig1.clf()
plt.plot(x, y, 'b-')
plt.plot(x, modelo1v, 'r-')
plt.xlabel(r'Longitud de onda [$\AA$]')
plt.ylabel(r'Flujo [$erg s^{-1} Hz^{-1}cm^{-2}$]')
plt.title('Curva y modelamiento de Espectro ' + 'por Gaussiana')
plt.grid(True)
fig1.savefig('gauss')