def count_peaks(params, min_prominence=0., base_mag=19.):
"""
Compute the number of peaks in the parallax event curve
Parameters
----------
params : dict
Dictionary containing the parameters to compute the parallax curve
min_prominence : float
Minimum prominence of a peak to be taken into account, in magnitude
base_mag : float
Base magnitude of the unlensed source
Returns
-------
int
Number of peaks detected
"""
t = np.arange(params['t0'] - 2 * np.abs(params['tE']),
params['t0'] + 2 * np.abs(params['tE']), 1)
cpara = microlens_parallax(t, **params)
peaks, infos = find_peaks(cpara - base_mag, prominence=min_prominence)
if len(peaks):
return len(peaks) #np.max(infos["prominences"])
else:
return 0
def simplemax_distance(params, dt=1):
t = np.arange(params['t0'] - 200, params['t0'] + 200, dt)
return np.max(
np.abs(
np.max(
np.abs(
microlens_simple(t, **params) -
microlens_parallax(t, **params)))))
def update_plot(xk, convergence): convergence=0 u0, t0, tE = xk global curr_max global i i+=1 axs[1].set_title(str(i)) pdif1.set_ydata(np.abs((microlens_parallax(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'],p1['theta']) - microlens_simple(t, 19., 0., u0, t0, tE, 0., 0.)))) ppar1.set_ydata(-(microlens_parallax(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'], p1['theta']))) pnop1.set_ydata(-(microlens_simple(t, 19, 0, u0, t0, tE, p1['delta_u'], p1['theta']))) l1.set_xdata(t0 - 6*tE) l2.set_xdata(t0 + 6*tE) plt.pause(0.000001) hl2.set_ydata(-convergence) if convergence>curr_max: curr_max = convergence hl.set_ydata(-convergence) axs[1].relim() axs[1].autoscale_view() fig.canvas.draw()
def fastfit_simplemax_distance(params, init_dt=0.5):
t = np.arange(params['t0'] - 200, params['t0'] + 200, init_dt)
init_t = t[(-np.abs(
microlens_parallax(t, **params) - microlens_simple(t, **params))
).argmin()]
m = Minuit(absdiff2,
t=init_t,
t0=params['t0'],
u0=params['u0'],
tE=params['tE'],
delta_u=params['delta_u'],
theta=params['theta'],
fix_t0=True,
fix_u0=True,
fix_tE=True,
fix_delta_u=True,
fix_theta=True,
error_t=10,
errordef=1,
print_level=0)
m.migrad()
return [m.get_fmin().fval, dict(m.values)]
def curvefit_func(t, u0, t0, tE, pu0, pt0, ptE, pdu, ptheta): return (microlens_parallax(t, 19, 0, pu0, pt0, ptE, pdu, ptheta) - microlens_simple(t, 19., 0., u0, t0, tE, 0., 0.))**2
def max_fitter(t, u0, t0, tE, pu0, pt0, ptE, pdu, ptheta): return -np.abs((microlens_parallax(t, 19, 0, pu0, pt0, ptE, pdu, ptheta) - microlens_simple(t, 19., 0., u0, t0, tE, 0., 0.)))
def microlens_parallax_inv(t, u0, t0, tE, delta_u, theta): return -microlens_parallax(np.array([t]), 19., 0., u0, t0, tE, delta_u, theta) mmin = Minuit(microlens_parallax_inv,
def drydiff(t, u0, t0, tE, pu0, pt0, ptE, pdu, ptheta): return microlens_parallax(t, 19, 0, pu0, pt0, ptE, pdu, ptheta) - microlens_simple(t, 19., 0., u0, t0, tE, 0., 0.)
def max_fitter(t): t = np.array([t]) return -np.abs((microlens_parallax(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'], p1['theta']) - microlens_simple(t, 19., 0., u0, t0, tE, 0., 0.)))
print(df.idx.nunique())
tmin = 48928
tmax = 52697
# df = df[(df.mass == 30) & (df.fitted_u0<2.0)]
p2 = df.sort_values(by='distance', ascending=False).iloc[1000].to_dict()
# p1 = df.iloc[np.random.randint(0, len(df))].to_dict()
print(p2)
p2['blend']=0.
p2['mag']=19.
p1 = {key: p2[key] for key in ['mag', 'blend', 'u0', 'tE', 't0', 'theta', 'delta_u']}
t = np.arange(tmin, tmax, 0.5)
cnopa = microlens_simple(t, **p1)
cpara = microlens_parallax(t, **p1)
fig, axs = plt.subplots(ncols=1, nrows=2, sharex='col')
pdif1, = axs[1].plot(t, np.abs((microlens_parallax(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'],p1['theta']) - microlens_simple(t, 19., 0., p1['u0'], p1['t0'], p1['tE'], 0., 0.))))
ppar1, = axs[0].plot(t, -(microlens_parallax(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'], p1['theta'])))
pnop1, = axs[0].plot(t, -(microlens_simple(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'], p1['theta'])))
axs[0].plot(t, -(microlens_simple(t, 19, 0, p1['u0'], p1['t0'], p1['tE'], p1['delta_u'], p1['theta'])), ls='--')
hl = axs[1].axhline(0, color='black', linewidth=0.5)
hl2 = axs[1].axhline(0, color='red', linewidth=0.5)
l1 = axs[1].axvline(tmin, color='red', linewidth=0.5)
l2 = axs[1].axvline(tmax, color='red', linewidth=0.5)
pts, = axs[1].plot([], [], ls='', marker='+')
# plt.xlim(51500, 52500)
curr_max=-np.inf
i=0
explored_parameters=[]
def absdiff2(t, u0, t0, tE, delta_u, theta):
t = np.array([t])
return -np.abs((microlens_parallax(t, 19, 0, u0, t0, tE, delta_u, theta) -
microlens_simple(t, 19., 0., u0, t0, tE, 0., 0.)))
def absdiff_dict(t, params):
return -np.abs(
(microlens_parallax(t, 19, 0, params['u0'], params['t0'], params['tE'],
params['delta_u'], params['theta']) -
microlens_simple(t, 19., 0., params['u0'], params['t0'], params['tE'],
0., 0.)))
def minus_parallax(t):
t = np.array([t])
return microlens_parallax(t, 19, 0, params['u0'], params['t0'],
params['tE'], params['delta_u'],
params['theta'])
def distance1(t, params):
return np.max(
np.abs(
microlens_simple(t, **params) - microlens_parallax(t, **params)))
