def update_pos_rk4(pos, psi, x, y, dt, dx):
#Runge-Kutta 4th order to update the position.
#We find the indices closest to our double value (x,y).
xcoord = find_nearest(x, pos[0])
ycoord = find_nearest(y, pos[1])
#Nan to num is used here to avoid potential division by zero.
#This part updates the x coordinate.
k1 = np.nan_to_num(
(x_deriv(psi, xcoord, ycoord, dx) / psi[xcoord, ycoord]).imag)
k2 = np.nan_to_num((x_deriv(psi + 0.5 * dt * k1, xcoord, ycoord, dx) /
(psi[xcoord, ycoord] + 0.5 * dt * k1)).imag)
k3 = np.nan_to_num((x_deriv(psi + 0.5 * dt * k2, xcoord, ycoord, dx) /
(psi[xcoord, ycoord] + 0.5 * dt * k2)).imag)
k4 = np.nan_to_num((x_deriv(psi + dt * k3, xcoord, ycoord, dx) /
(psi[xcoord, ycoord] + dt * k3)).imag)
xnew = pos[0] + dt * 1. / 6. * (k1 + 2. * k2 + 2. * k3 + k4)
#Updating the y part with RK4..
k1 = np.nan_to_num(
(y_deriv(psi, xcoord, ycoord, dx) / psi[xcoord, ycoord]).imag)
k2 = np.nan_to_num((y_deriv(psi + 0.5 * dt * k1, xcoord, ycoord, dx) /
(psi[xcoord, ycoord] + 0.5 * dt * k1)).imag)
k3 = np.nan_to_num((y_deriv(psi + 0.5 * dt * k2, xcoord, ycoord, dx) /
(psi[xcoord, ycoord] + 0.5 * dt * k2)).imag)
k4 = np.nan_to_num((y_deriv(psi + dt * k3, xcoord, ycoord, dx) /
(psi[xcoord, ycoord] + dt * k3)).imag)
ynew = pos[1] + dt * 1. / 6. * (k1 + 2. * k2 + 2. * k3 + k4)
return [xnew, ynew]
def create_mom0(self, vel1, vel2, mask=None):
'vel1, vel2, mask=None'
idx1, idx2 = ut.find_nearest(self.vels, vel1)[1], ut.find_nearest(self.vels, vel2)[1]
if mask:
self.mom0 = np.sum(self.cubedat[idx1:idx2,mask], axis=0)
else:
self.mom0 = np.sum(self.cubedat[idx1:idx2,:,:], axis=0)
self.mom_props = [vel1, vel2, mask]
def lnprior(params, vsin_obs, sig_vsin_obs, dist_pc, sig_dist_pc, ranges,
model, stellar_prior, npy_star, pdf_mas, pdf_obl, pdf_age, pdf_dis,
pdf_ebv, grid_mas, grid_obl, grid_age, grid_dis, grid_ebv):
if model == 'befavor':
Mstar, oblat, Hfrac, cosi, dist, ebv = params[0], params[1],\
params[2], params[3], params[4], params[5]
if model == 'aara' or model == 'acol' or model == 'bcmi':
Mstar, oblat, Hfrac, cosi, dist, ebv = params[0], params[1],\
params[2], params[6], params[7], params[8]
if model == 'beatlas':
Mstar, oblat, Hfrac, cosi, dist, ebv = params[0], params[1],\
0.3, params[4], params[5], params[6]
# Reading Stellar Priors
if stellar_prior is True:
temp, idx_mas = find_nearest(grid_mas, value=Mstar)
temp, idx_obl = find_nearest(grid_obl, value=oblat)
temp, idx_age = find_nearest(grid_age, value=Hfrac)
temp, idx_dis = find_nearest(grid_dis, value=dist)
temp, idx_ebv = find_nearest(grid_ebv, value=ebv)
chi2_stellar_prior = Mstar * pdf_mas[idx_mas] +\
oblat * pdf_obl[idx_obl] + \
Hfrac * pdf_age[idx_age] + \
dist * pdf_dis[idx_dis] + \
ebv * pdf_ebv[idx_ebv]
else:
chi2_stellar_prior = 0.0
# Rpole, Lstar, Teff = vkg.geneve_par(Mstar, oblat, Hfrac, folder_tables)
t = np.max(np.array([hfrac2tms(Hfrac), 0.]))
Rpole, logL = geneva_interp_fast(Mstar,
oblat,
t,
neighbours_only=True,
isRpole=False)
wcrit = np.sqrt(8. / 27. * G * Mstar * Msun / (Rpole * Rsun)**3)
vsini = oblat2w(oblat) * wcrit * (Rpole * Rsun * oblat) *\
np.sin(np.arccos(cosi)) * 1e-5
chi2_vsi = (vsin_obs - vsini)**2 / sig_vsin_obs**2.
chi2_dis = (dist_pc - dist)**2 / sig_dist_pc**2.
chi2_prior = chi2_vsi + chi2_dis + chi2_stellar_prior
return -0.5 * chi2_prior
def jump(self, event):
"""Handle frequency change from the marker TxtCtrl."""
evt_obj = event.GetEventObject()
temp_freq = evt_obj.GetValue()
try:
# MHz to Hz. Will raise ValueError if not a number
temp_freq = float(temp_freq) * 1e6
except ValueError:
if temp_freq == "":
# Let the user remove the marker
self.unplot()
return
else:
temp_freq = self.freq # reset to last known good value
if temp_freq is None:
return
bin_freqs = self.frame.tb.cfg.bin_freqs
idx = utils.find_nearest(bin_freqs, temp_freq)
freq = bin_freqs[idx]
if freq != self.freq:
self.bin_idx = idx
self.freq = freq
self.plot()
evt_obj.SetValue(self.get_freq_str())
def update_bin_indices(self):
"""Update common indices used in cropping and overlaying DFTs"""
self.bin_start = int(self.fft_size * (self.overlap / 2))
self.bin_stop = int(self.fft_size - self.bin_start)
self.max_plotted_bin = utils.find_nearest(self.bin_freqs,
self.max_freq) + 1
self.bin_offset = (self.bin_stop - self.bin_start) / 2
def lnprior(params, vsin_obs, sig_vsin_obs, plx_obs, sig_plx_obs, ranges,
model, stellar_prior, npy_star, pdf_mas, pdf_obl, pdf_age, pdf_plx,
pdf_ebv, grid_mas, grid_obl, grid_age, grid_plx, grid_ebv):
if model == 'befavor':
Mstar, oblat, Hfrac, cosi, plx, ebv = params[0], params[1],\
params[2], params[3], params[4], params[5]
if model == 'aara' or model == 'acol' or model == 'bcmi':
Mstar, oblat, Hfrac, cosi, plx, ebv = params[0], params[1],\
params[2], params[6], params[7], params[8]
if model == 'beatlas':
Mstar, oblat, Hfrac, cosi, plx, ebv = params[0], params[1],\
0.3, params[4], params[5], params[6]
# Reading Stellar Priors
if stellar_prior is True:
temp, idx_mas = find_nearest(grid_mas, value=Mstar)
temp, idx_obl = find_nearest(grid_obl, value=oblat)
temp, idx_age = find_nearest(grid_age, value=Hfrac)
temp, idx_plx = find_nearest(grid_plx, value=plx)
temp, idx_ebv = find_nearest(grid_ebv, value=ebv)
chi2_stellar_prior = Mstar * pdf_mas[idx_mas] +\
oblat * pdf_obl[idx_obl] + \
Hfrac * pdf_age[idx_age] + \
plx * pdf_plx[idx_plx] + \
ebv * pdf_ebv[idx_ebv]
else:
chi2_stellar_prior = 0.0
t = hfrac2tms(Hfrac)
Rpole, logL, _ = geneva_interp_fast(Mstar, oblat, t)
wcrit = np.sqrt(8. / 27. * G * Mstar * Msun / (Rpole * Rsun)**3)
vsini = oblat2w(oblat) * wcrit * (Rpole * Rsun * oblat) *\
np.sin(np.arccos(cosi)) * 1e-5
chi2_vsi = (vsin_obs - vsini)**2 / sig_vsin_obs**2.
chi2_plx = (plx_obs - plx)**2 / sig_plx_obs**2.
chi2_prior = chi2_vsi + chi2_plx + chi2_stellar_prior
return -0.5 * chi2_prior
def animate(i):
t_detail = 1.0 * i / fps
__, detail_idx = find_nearest(t_ods, t_detail)
tf_line.set_xdata([t_detail, t_detail])
t_line.set_xdata([t_detail, t_detail])
detail.set_xdata(np.abs(TF[:, detail_idx]))
print(" {0:.2f}/{1:.2f}".format(t_detail, max(t_ods)), end="\r")
sys.stdout.flush()
return tf_line, t_line, detail
def peak_search(self, event, txtctrl):
"""Find the point of max power in the whole plot or within a span."""
bin_freqs = self.frame.tb.cfg.bin_freqs
if self.frame.span_left and self.frame.span_right:
left_idx = utils.find_nearest(bin_freqs, self.frame.span_left)
right_idx = utils.find_nearest(bin_freqs, self.frame.span_right)
power_data = self.frame.line.get_ydata()[left_idx:right_idx]
else:
left_idx = 0
right_idx = utils.find_nearest(bin_freqs,
self.frame.tb.cfg.max_freq)
power_data = self.frame.line.get_ydata()[:right_idx]
try:
relative_idx = np.where(power_data == np.amax(power_data))[0][0]
except ValueError:
# User selected an area with no data in it; do nothing
return
# add the left index offset to get the absolute index
self.bin_idx = relative_idx + left_idx
self.freq = self.frame.tb.cfg.bin_freqs[self.bin_idx]
txtctrl.SetValue(self.get_freq_str())
self.plot()
def __init__(self, initial_frame_time, final_frame_time, dt, scan_times):
self.initial_frame_time = initial_frame_time
self.final_frame_time = final_frame_time
self.dt = dt # Temporal resolution
self.frame_times = np.arange(self.initial_frame_time,
self.final_frame_time, self.dt) # for now
self.scan_times = scan_times
self.n_scans = len(scan_times)
idx_scans_within_frames = np.zeros(self.n_scans)
for k in range(self.n_scans):
idx_scans_within_frames[k] = utils.find_nearest(
self.frame_times, self.scan_times[k])
self.idx_scans_within_frames = idx_scans_within_frames.astype(int)
def update_georef(vgeo, v, vref_level):
"""
Return vgeo after updating geostrophic reference depth by constraining
velocities in vgeo to match those in v at the specified depth.
"""
vgeodat = vgeo.data.filled(0)
vdat = v.data.filled(0)
zind = utils.find_nearest(v.z, vref_level)
vadj = np.ones_like(vgeodat) * (vdat[:, zind, :] - vgeodat[:, zind, :])
vgeo.data = np.ma.MaskedArray(vgeo.data + vadj, mask=vgeo.mask)
return vgeo
def animate(i):
global psi
global pos
for q in range(rk4_steps_per_frame):
psinew = update_psi_rk4(psi, timestep)
posnew = update_pos_rk4(pos, psi, x, y, timestep, dx)
psi = psinew
pos = posnew
ax.patches = []
#Our particle!
electron = plt.Circle((find_nearest(x, pos[1]), find_nearest(x, pos[0])),
2,
color="black")
ax.add_patch(electron)
currentnorm = ts.get_norm(psi, Npoints, dx)
#If the norm changes from 1 significantly, the simulation is probably in trouble.
print(i, currentnorm)
plotted = abs(psi)**2 + V.real
line.set_data(plotted) # update the data
line.set_clim(vmax=np.amax(np.abs(psi)**2))
line.set_clim(vmin=0)
return line
def process_nearest(update):
user_id = update["message"]["from"]["id"]
if user_id in users_locations:
stations = scraper.scrape_bikes()
nearest_station = utils.find_nearest(
list(stations.values()), users_locations[user_id]["latitude"],
users_locations[user_id]["longitude"])
tc.send_simple_message(user_id, str(nearest_station))
tc.send_location(user_id, {
'lat': nearest_station.lat,
'lng': nearest_station.lng
})
else:
log.info(users_locations)
process_start(update)
def resample(x, y, delta_x=0.01, max_delta_y=400):
#Create x array with desired spacing
x_desired = np.arange(0, 14, delta_x)
x_resampled = [0]
y_resampled = [0]
#Find value in x closest to each value in x_desired, then resample the y accordingly
for val in x_desired:
nearest_val, nearest_index = find_nearest(x, val)
if nearest_val not in x_resampled:
x_resampled.append(nearest_val)
y_resampled.append(y[nearest_index])
custom_filter(x_resampled, y_resampled)
return x_resampled, y_resampled
def polesFunc(S, pole_posns, Smin, Smax, coeffs):
"""
"""
if S <= Smin or S >= Smax:
return 0.0
#print coeffs
#print S,idx,Snearest#,pole_posns[0],pole_posns[-1]
#sys.exit(0)
#print idx,pole_posns[idx],coeffs[idx]
# counts=calculateDnByDs(pole_posns,coeffs[:-1],eucl=False,verbose=False,\
# idl_style=True,errors=False,bright=False,bright_errors=False,
# return_all=False)
idx, Snearest = find_nearest(pole_posns[:-1], S)
#if idx >= len(coeffs)-1: return counts[idx-1]
#return counts[idx]
return coeffs[idx] #/(pole_posns[idx+1]-pole_posns[idx])
def find_value_for_state(self, state, values, possible_states):
effective_resource = round(
utils.find_nearest(self.get_possible_resources(), state.resource),
4)
if not state.is_valid():
result = 0
else:
result = None
for value, iter_state in zip(values, possible_states):
if (iter_state.price == state.price
and iter_state.resource == effective_resource):
result = value
break
if result is None:
utils.print_state(state)
raise Exception("State was not found!")
return result
def custom_filter(x, y, delta_y=50):
x_avg = np.average(x)
to_delete = []
_, temp_i = find_nearest(x, x_avg / 1.5)
y_prev = np.average(y[temp_i - 2:temp_i + 2]) #Initializing y_prev
#Simple filter based on difference with previous value
for i, y_val in enumerate(y):
# Remove points that have x > x_avg/2 and where the difference in values between sequential y is bigger than chosen delta_y value
if abs(y_val - y_prev) > delta_y and x[i] > x_avg / 1.5:
to_delete.append(i)
else:
y_prev = y_val
to_delete.reverse()
for i in to_delete:
del x[i]
del y[i]
return
def find_bc(profile, mc_input):
s = read_mesa(profile)
r = np.flipud(s.radius)
m = np.flipud(s.mass)
rho = np.flipud(s.rho)
p = np.flipud(s.pressure)
drhodr = np.gradient(rho,r)
drhodr_smooth = smooth(drhodr)
dpdr = np.gradient(p,r)
dpdr_smooth = smooth(dpdr)
ic = find_nearest(m, mc_input)
bc = {'r' : r[ic],
'm' : m[ic],
'rho' : rho[ic],
'p' : p[ic],
'drhodr' : drhodr[ic],
'dpdr' : dpdr[ic]}
return bc
def calc_vgeo(v, dh, georef=4750.):
"""
Return ZonalSections containing geostrophic velocities
relative to specified reference level.
"""
vgeo = copy.deepcopy(v) # Copy velocity data structure
for nprof in range(len(vgeo.x)): # Loop through profiles
if not v.mask[:, :, nprof].all():
# Extract depth and dynamic height profiles at bounds
z = dh.z
z1 = dh.z_as_bounds_data[:, :, nprof]
z2 = dh.z_as_bounds_data[:, :, nprof + 1]
dh1 = dh.bounds_data[:, :, nprof]
dh2 = dh.bounds_data[:, :, nprof + 1]
# Coriolis parameter at profile location
corf = 2 * ROT * np.sin(np.pi * (vgeo.y[nprof] / 180.))
# cell width along section
dx = vgeo.cell_widths[nprof]
# Clip reference depth using ocean floor.
maxz = np.min([z1.max(), z2.max()])
zref = min(georef, maxz)
# Adjust dh to new reference level
zind = utils.find_nearest(z, zref)
dh1 -= dh1[:, zind]
dh2 -= dh2[:, zind]
# Calculate geostrophic velocity
vgeo_profile = (-1. * (G / corf) * ((dh2 - dh1) / dx))
vgeo.data[:, :, nprof] = vgeo_profile
return vgeo
def __init__(self, octave, overlap, fft_len, delta_f, sample_rate):
"""Calculate and cache frequencies used in stitching FFT segments"""
self.start, self.stop = octave
# Check invariants
assert self.start < self.stop # low freq is lower than high freq
assert 0 <= overlap < 1 # overlap is percentage
assert math.log(fft_len, 2) % 2 == 0 # fft_len is power of 2
self.span = self.stop - self.start
self.nvalid_bins = int((fft_len - (fft_len * overlap))) // 2 * 2
usable_bw = sample_rate * (1 - overlap)
self.step = int(round(usable_bw / delta_f) * delta_f)
self.center_freqs = self.cache_center_freqs()
self.nsegments = len(self.center_freqs)
self.bin_freqs = self.cache_bin_freqs(delta_f)
self.bin_start = int(fft_len * (overlap / 2))
self.bin_stop = int(fft_len - self.bin_start)
self.max_plotted_bin = utils.find_nearest(self.bin_freqs, self.stop) + 1
self.bin_offset = (self.bin_stop - self.bin_start) / 2
def NCA(self,t,C):
AUClist = []
AUMClist = []
Cminlist = []
Cavglist = []
Cmaxlist = []
Tmaxlist = []
Tminlist = []
Ndoses=len(self.drugSource.parsedDoseList)
for ndose in range(0,max(Ndoses-1,1)):
tperiod0 = self.drugSource.parsedDoseList[ndose].t0
if ndose+1<Ndoses:
tperiodF = self.drugSource.parsedDoseList[ndose+1].t0-self.model.deltaT
else:
tperiodF = np.max(t)-1
idx0 = find_nearest(t,tperiod0)
idxF = find_nearest(t,tperiodF)
AUC0t = 0
AUMC0t = 0
t0 = t[idx0+1]
for idx in range(idx0,idxF+1):
dt = (t[idx+1]-t[idx])
if C[idx+1]>=C[idx]: # Trapezoidal in the raise
AUC0t += 0.5*dt*(C[idx]+C[idx+1])
AUMC0t += 0.5*dt*(C[idx]*t[idx]+C[idx+1]*t[idx+1])
else: # Log-trapezoidal in the decay
decrement = C[idx]/C[idx+1]
K = math.log(decrement)
B = K/dt
AUC0t += dt*(C[idx]-C[idx+1])/K
AUMC0t += (C[idx]*(t[idx]-tperiod0)-C[idx+1]*(t[idx+1]-tperiod0))/B-(C[idx+1]-C[idx])/(B*B)
if idx==idx0:
Cmax=C[idx]
Tmax=t[idx]-t0
Cmin=C[idx]
Tmin=t[idx]-t0
else:
if C[idx]<Cmin:
Cmin=C[idx]
Tmin=t[idx]-t0
elif C[idx]>Cmax:
Cmax=C[idx]
Tmax=t[idx]-t0
if ndose==0:
Cmin=C[idx]
Tmin=t[idx]-t0
AUClist.append(AUC0t)
AUMClist.append(AUMC0t)
Cminlist.append(Cmin)
Cmaxlist.append(Cmax)
Tmaxlist.append(Tmax)
Tminlist.append(Tmin)
Cavglist.append(AUC0t/(t[idxF]-t[idx0]))
print("Fluctuation = Cmax/Cmin")
print("Accumulation(1) = Cavg(n)/Cavg(1) %")
print("Accumulation(n) = Cavg(n)/Cavg(n-1) %")
print("Steady state fraction(n) = Cavg(n)/Cavg(last) %")
for ndose in range(0,len(AUClist)):
fluctuation = Cmaxlist[ndose]/Cminlist[ndose]
if ndose>0:
accumn = Cavglist[ndose]/Cavglist[ndose-1]
else:
accumn = 0
print("Dose #%d: Cavg= %f [%s] Cmin= %f [%s] Tmin= %d [min] Cmax= %f [%s] Tmax= %d [min] Fluct= %f %% Accum(1)= %f %% Accum(n)= %f %% SSFrac(n)= %f %% AUC= %f [%s] AUMC= %f [%s]"%\
(ndose+1,Cavglist[ndose], strUnit(self.Cunits.unit), Cminlist[ndose],strUnit(self.Cunits.unit), int(Tminlist[ndose]), Cmaxlist[ndose], strUnit(self.Cunits.unit),
int(Tmaxlist[ndose]), fluctuation*100, Cavglist[ndose]/Cavglist[0]*100, accumn*100, Cavglist[ndose]/Cavglist[-1]*100, AUClist[ndose],strUnit(self.AUCunits),
AUMClist[ndose],strUnit(self.AUMCunits)))
self.AUC0t = AUClist[-1]
self.AUMC0t = AUMClist[-1]
self.MRT = self.AUMC0t/self.AUC0t
self.Cmin = Cminlist[-1]
self.Cmax = Cmaxlist[-1]
self.Cavg = Cavglist[-1]
self.fluctuation = self.Cmax/self.Cmin
self.percentageAccumulation = Cavglist[-1]/Cavglist[0]
print(" AUC0t=%f [%s]"%(self.AUC0t,strUnit(self.AUCunits)))
print(" AUMC0t=%f [%s]"%(self.AUMC0t,strUnit(self.AUMCunits)))
print(" MRT=%f [min]"%self.MRT)
def t_tms_from_Xc(M, savefig=None, plot_fig=None, ttms_true=None, Xc=None):
'''
Calculates the t(tms) for a given Xc and mass
Xc: float
M: float
'''
# ------------------------------------------------------------------------------
# Parameters from the models
mass = np.array([14.6, 12.5, 10.8, 9.6, 8.6, 7.7, 6.4, 5.5, 4.8,
4.2, 3.8, 3.4])
nm = len(mass)
str_mass = ['M14p60', 'M12p50', 'M10p80', 'M9p600', 'M8p600', 'M7p700',
'M6p400', 'M5p500', 'M4p800', 'M4p200', 'M3p800', 'M3p400']
st = ['B0.5', 'B1', 'B1.5', 'B2', 'B2.5', 'B3', 'B4', 'B5', 'B6', 'B7',
'B8', 'B9']
zsun = 'Z01400'
str_vel = ['V60000', 'V70000', 'V80000', 'V90000', 'V95000']
Hfracf = 0. # end of main sequence
# ****
folder_data = 'tables/models/models_bes/'
if plot_fig is True:
plt.xlabel(r'$t/t_{MS}$')
plt.ylabel(r'$X_c$')
plt.ylim([0.0, 0.8])
plt.xlim([0.0, 1.0])
# ------------------------------------------------------------------------------
# Loop (reading the models)
typ = (1, 3, 16, 21) # Age, Lum versus Teff versus Hfrac
arr_age = []
arr_Hfr = []
arr_t_tc = []
cor = phc.gradColor(np.arange(len(st)), cmapn='inferno')
iv = 2 # O que eh isto?
arr_Xc = []
for i in range(nm):
file_data = folder_data + str_mass[i] + zsun + str_vel[iv] + '.dat'
age, lum, Teff, Hfrac = np.loadtxt(file_data, usecols=typ,
unpack=True, skiprows=2)
arr_age.append(age)
arr_Hfr.append(Hfrac)
iMS = np.where(abs(Hfrac - Hfracf) == min(abs(Hfrac - Hfracf)))
X_c = Hfrac[0:iMS[0][0]]
arr_Xc.append(X_c)
t_tc = age[0:iMS[0][0]] / max(age[0:iMS[0][0]])
arr_t_tc.append(t_tc)
if plot_fig is True:
plt.plot(t_tc, X_c, color=cor[i], label=('%s' % st[i]))
# ------------------------------------------------------------------------------
# Interpolation
k = find_nearest(mass, M)[1]
if plot_fig is True:
plt.plot(ttms_true, Xc, 'o')
plt.autoscale()
plt.minorticks_on()
plt.legend(fontsize=10, ncol=2, fancybox=False, frameon=False)
# ------------------------------------------------------------------------------
if savefig is True:
pdfname = 'Xc_vs_Tsp.png'
plt.savefig(pdfname)
return k, arr_t_tc, arr_Xc
def read_befavor_xdr_complete():
folder_models = 'models/'
dims = ['M', 'ob', 'Hfrac', 'sig0', 'Rd', 'mr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
ctrlarr = [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]
tmp = 0
cont = 0
while tmp < len(ctrlarr):
if math.isnan(ctrlarr[tmp]) is True:
cont = cont + 1
tmp = tmp + 1
else:
tmp = tmp + 1
# Read the grid models, with the interval of parameters.
xdrPL = folder_models + 'aara_sed.xdr' # 'PL.xdr'
# xdrPL = folder_models + 'aara_final.xdr' # 'PL.xdr'
# xdrPL = folder_models + 'aara_acs.xdr' # 'PL.xdr'
# xdrPL = folder_models + 'disk_flx.xdr' # 'PL.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrPL, quiet=False)
# F(lbd)] = 10^-4 erg/s/cm2/Ang
for i in range(np.shape(minfo)[0]):
for j in range(np.shape(minfo)[1]):
if minfo[i][j] < 0:
minfo[i][j] = 0.
for i in range(np.shape(models)[0]):
for j in range(np.shape(models)[1]):
if models[i][j] < 0. or models[i][j] == 0.:
models[i][j] = (models[i][j + 1] + models[i][j - 1]) / 2.
# n0 to logn0
listpar[4] = np.log10(listpar[4])
listpar[4].sort()
minfo[:, 4] = np.log10(minfo[:, 4])
if True:
mask = []
tmp, idx = find_nearest(lbdarr, 1000)
for i in range(len(models)):
if models[i][idx] > 2.21834e-10:
mask.append(i)
# print(i)
# plt.plot(lbdarr, models[i], alpha=0.1)
tmp, idx = find_nearest(lbdarr, 80)
for i in range(len(models)):
if models[i][idx] > 2e-8:
mask.append(i)
# print(i)
# # plt.plot(lbdarr, models[i], alpha=0.1)
tmp, idx = find_nearest(lbdarr, 850)
for i in range(len(models)):
if models[i][idx] > 7e-11:
mask.append(i)
# print(i)
# plt.plot(lbdarr, models[i], alpha=0.1)
# plt.yscale('log')
# plt.xscale('log')
# plt.show()
new_models = np.delete(models, mask, axis=0)
new_minfo = np.delete(minfo, mask, axis=0)
models = np.copy(new_models)
minfo = np.copy(new_minfo)
# delete columns of fixed par
cols2keep = [0, 1, 3, 4, 5, 7, 8]
cols2delete = [2, 6]
listpar = [listpar[i] for i in cols2keep]
minfo = np.delete(minfo, cols2delete, axis=1)
listpar[3].sort()
# for i in range(len(models)):
# plt.plot(lbdarr, models[i], alpha=0.1)
# plt.yscale('log')
# plt.xscale('log')
# plt.show()
return ctrlarr, minfo, models, lbdarr, listpar, dims, isig
def train_bdt(config):
# Trains BDT with given hyperparams and returns max Z_A (as calculated on bkg MC), requiring at least 4 signal events
if config["invert_test_and_train"]:
config["input_file"] = config["input_file_2"]
else:
config["input_file"] = config["input_file_1"]
f = h5py.File(config["input_file"], "r")
feature_names = utils.load_array(f, 'feature_names')
training_feature_names = utils.load_array(f, 'training_feature_names')
print(("Training with the following features: ", training_feature_names))
#if config["invert_test_and_train"]:
#print "Inverting test and train splits"
#if config["sideband"]:
# print "Not yet implemented how to handle inverting the test/train set when training on data sidebands, exiting"
# return -1
#global_features = utils.load_array(f, 'global_validation')
#label = utils.load_array(f, 'label_validation')
#multi_label = utils.load_array(f, 'multi_label_validation')
#weights = utils.load_array(f, 'weights_validation')
#mass = utils.load_array(f, 'mass_validation')
#global_features_validation = utils.load_array(f, 'global')
#label_validation = utils.load_array(f, 'label')
#multi_label_validation = utils.load_array(f, 'multi_label')
#weights_validation = utils.load_array(f, 'weights')
#mass_validation = utils.load_array(f, 'mass')
#else:
global_features = utils.load_array(f, 'global')
label = utils.load_array(f, 'label')
multi_label = utils.load_array(f, 'multi_label')
weights = utils.load_array(f, 'weights')
mass = utils.load_array(f, 'mass')
global_features_validation = utils.load_array(f, 'global_validation')
label_validation = utils.load_array(f, 'label_validation')
multi_label_validation = utils.load_array(f, 'multi_label_validation')
weights_validation = utils.load_array(f, 'weights_validation')
mass_validation = utils.load_array(f, 'mass_validation')
if config["sideband"]:
global_features = utils.load_array(f, 'global_data_sideband')
label = utils.load_array(f, 'label_data_sideband')
multi_label = utils.load_array(f, 'multi_label_data_sideband')
weights = utils.load_array(f, 'weights_data_sideband')
mass = utils.load_array(f, 'mass_data_sideband')
global_features_data = utils.load_array(f, 'global_data')
label_data = utils.load_array(f, 'label_data')
multi_label_data = utils.load_array(f, 'multi_label_data')
weights_data = utils.load_array(f, 'weights_data')
mass_data = utils.load_array(f, 'mass_data')
print((global_features.shape))
print((label.shape))
print((weights.shape))
print((global_features_validation.shape))
print((label_validation.shape))
print((weights_validation.shape))
print((global_features_data.shape))
print((label_data.shape))
print((weights_data.shape))
x_train, y_train, y_train_multi, weights_train = global_features, label, multi_label, weights
x_test, y_test, y_test_multi, weights_test = global_features_validation, label_validation, multi_label_validation, weights_validation
X_train = pandas.DataFrame(data=x_train, columns=training_feature_names)
X_test = pandas.DataFrame(data=x_test, columns=training_feature_names)
X_data = pandas.DataFrame(data=global_features_data,
columns=training_feature_names)
if config["multiclassifier"]:
Y_train = y_train_multi
Y_test = y_test_multi
else:
Y_train = y_train
Y_test = y_test
sum_neg_weights = utils.sum_of_weights_v2(weights_train, label, 0)
sum_pos_weights = utils.sum_of_weights_v2(weights_train, label, 1)
print((sum_pos_weights, sum_neg_weights))
d_train = xgboost.DMatrix(X_train, label=Y_train, weight=weights_train)
d_test = xgboost.DMatrix(X_test, label=Y_test)
d_data = xgboost.DMatrix(X_data)
param = {
'max_depth': config["max_depth"],
'eta': config["eta"],
'subsample': config["subsample"],
'colsample_bytree': config["colsample_bytree"],
'min_child_weight': config["min_child_weight"],
'gamma': config["gamma"],
'reg_alpha': config["reg_alpha"],
'reg_lambda': config["reg_lambda"],
'scale_pos_weight': sum_neg_weights / sum_pos_weights,
'objective': 'binary:logistic',
'nthread': 16,
}
if config["multiclassifier"]:
param["num_class"] = config["n_class"]
param["objective"] = "multi:softprob"
param["scale_pos_weight"] = 1
evallist = [(d_train, 'train'), (d_test, 'test')]
progress = {}
n_round = config["n_round"]
print((param, n_round))
# train
bdt = xgboost.train(param,
d_train,
n_round,
evallist,
evals_result=progress)
bdt.save_model(config["tag"] + "_bdt.xgb")
model = bdt.get_dump()
input_variables = []
for name in feature_names:
input_variables.append((name, 'F'))
#tmva_utils.convert_model(model, input_variables = input_variables, output_xml = config["tag"] + '_bdt.xml')
# predict
pred_train = bdt.predict(d_train, output_margin=config["multiclassifier"])
pred_test = bdt.predict(d_test, output_margin=config["multiclassifier"])
pred_data = bdt.predict(d_data, output_margin=config["multiclassifier"])
fpr_train, tpr_train, thresh_train = metrics.roc_curve(
y_train, pred_train, pos_label=1, sample_weight=weights_train)
fpr_test, tpr_test, thresh_test = metrics.roc_curve(
y_test, pred_test, pos_label=1, sample_weight=weights_test)
auc_train, auc_train_unc = utils.auc_and_unc(y_train, pred_train,
weights_train, 100)
auc_test, auc_test_unc = utils.auc_and_unc(y_test, pred_test, weights_test,
100)
#auc_train = metrics.auc(fpr_train, tpr_train, reorder = True)
#auc_test = metrics.auc(fpr_test , tpr_test , reorder = True)
print(("Training AUC: %.3f" % auc_train))
print(("Testing AUC: %.3f" % auc_test))
# estimate z_a w/at least 4 signal events
n_quantiles = 25
signal_mva_scores = {
"bdt_score": ks_test.logical_vector(pred_test, y_test, 1)
}
bkg_mva_scores = {
"bdt_score": ks_test.logical_vector(pred_test, y_test, 0)
}
data_mva_scores = {"bdt_score": pred_data}
signal_mass = ks_test.logical_vector(mass_validation, y_test, 1)
bkg_mass = ks_test.logical_vector(mass_validation, y_test, 0)
signal_weights = ks_test.logical_vector(weights_validation, y_test, 1)
bkg_weights = ks_test.logical_vector(weights_validation, y_test, 0)
optimization_vars = config["optimization_vars"].split(
",") if config["optimization_vars"] else []
for var in optimization_vars:
signal_mva_scores[var] = ks_test.logical_vector(
utils.load_array(f, var + '_validation'), y_test, 1)
bkg_mva_scores[var] = ks_test.logical_vector(
utils.load_array(f, var + '_validation'), y_test, 0)
data_mva_scores[var] = utils.load_array(f, var + '_data')
signal_events = {
"mass": signal_mass,
"weights": signal_weights,
"mva_score": signal_mva_scores
}
bkg_events = {
"mass": bkg_mass,
"weights": bkg_weights,
"mva_score": bkg_mva_scores
}
data_events = {
"mass": mass_data,
"weights": weights_data,
"mva_score": data_mva_scores
}
za, za_unc, s, b, sigma_eff = significance_utils.za_scores(
n_quantiles, signal_events, bkg_events, False)
za_data, za_unc_data, s_data, b_data, sigma_eff_data = significance_utils.za_scores(
n_quantiles, signal_events, data_events, True)
za = numpy.asarray(za)
za_data = numpy.asarray(za_data)
if numpy.all(za == 0) or numpy.all(za_data == 0):
return 0.0, 0.0, 0.0, 0.0
max_za_mc = numpy.max(za[numpy.where(numpy.asarray(s) >= 4.)])
max_za_data = numpy.max(za_data[numpy.where(numpy.asarray(s_data) >= 4.)])
max_za_mc, max_za_mc_idx = utils.find_nearest(za, max_za_mc)
max_za_data, max_za_data_idx = utils.find_nearest(za_data, max_za_data)
max_za_mc_unc = za_unc[max_za_mc_idx]
max_za_data_unc = za_unc_data[max_za_data_idx]
print(("Max Z_A on MC: %.4f +/- %.4f" % (max_za_mc, max_za_mc_unc)))
print(("Max Z_A on data: %.4f +/- %.4f" % (max_za_data, max_za_data_unc)))
return max_za_mc, max_za_mc_unc, max_za_data, max_za_data_unc, auc_train, auc_train_unc, auc_test, auc_test_unc
alpha *= cm_Rsun
r *= cm_Rsun
m *= g_Msun
m_cut *= g_Msun
input_mc *= g_Msun
drhodr = np.gradient(rho, r)
drhodr_smooth = smooth(drhodr)
dpdr = np.gradient(p, r)
dpdr_smooth = smooth(dpdr)
kernel.set_hsoft(input_h * cm_Rsun)
ic = find_nearest(m, m_cut)
rc = r[ic]
mc = m[
ic] #mass m(r) at transition (cutoff) radius, different from mc which is mass of central point particle
rhoc = rho[ic]
pc = p[ic]
X_mle = np.flipud(s.x_mass_fraction_H)[ic]
Y_mle = np.flipud(s.y_mass_fraction_He)[ic]
Z_mle = np.flipud(s.z_mass_fraction_metals)[ic]
r_MLE = alpha * xi
len_MLE = len(r_MLE)
rprofile = np.concatenate((r_MLE[:-1], r[ic:]))
print(alpha, rho0)
def read_data(dirName, varName, latName, lonName, userVariables, filelist=None):
'''
Purpose::
Read gridded data into (t, lat, lon) arrays for processing
Inputs::
dirName: a string representing the directory to the MERG files in NETCDF format
varName: a string representing the variable name to use from the file
latName: a string representing the latitude from the file's metadata
lonName: a string representing the longitude from the file's metadata
filelist (optional): a list of strings representing the filenames betweent the start and end dates provided
Returns:
Outputs::
A 3D masked array (t,lat,lon) with only the variables which meet the minimum temperature
criteria for each frame
Assumptions::
(1) All the files requested to extract data are from the same instrument/model, and thus have the same
metadata properties (varName, latName, lonName) as entered
(2) Assumes rectilinear grids for input datasets i.e. lat, lon will be 1D arrays
'''
global LAT
global LON
timeName = 'time'
filelistInstructions = dirName+'/*'
if filelist == None:
filelist = glob.glob(filelistInstructions)
inputData = []
timelist = []
time2store = None
tempMaskedValueNp = []
filelist.sort()
nfiles = len(filelist)
# Crash nicely if there are no netcdf files
if nfiles == 0:
print 'Error: no files in this directory! Exiting elegantly'
sys.exit()
else:
# Open the first file in the list to read in lats, lons and generate the grid for comparison
tmp = Dataset(filelist[0], 'r+', format='NETCDF4')
alllatsraw = tmp.variables[latName][:]
alllonsraw = tmp.variables[lonName][:]
alllonsraw[alllonsraw > 180] = alllonsraw[alllonsraw > 180] - 360. # convert to -180,180 if necessary
#get the lat/lon info data (different resolution)
latminNETCDF = utils.find_nearest(alllatsraw, float(userVariables.LATMIN))
latmaxNETCDF = utils.find_nearest(alllatsraw, float(userVariables.LATMAX))
lonminNETCDF = utils.find_nearest(alllonsraw, float(userVariables.LONMIN))
lonmaxNETCDF = utils.find_nearest(alllonsraw, float(userVariables.LONMAX))
latminIndex = (np.where(alllatsraw == latminNETCDF))[0][0]
latmaxIndex = (np.where(alllatsraw == latmaxNETCDF))[0][0]
lonminIndex = (np.where(alllonsraw == lonminNETCDF))[0][0]
lonmaxIndex = (np.where(alllonsraw == lonmaxNETCDF))[0][0]
#subsetting the data
latsraw = alllatsraw[latminIndex: latmaxIndex]
lonsraw = alllonsraw[lonminIndex:lonmaxIndex]
LON, LAT = np.meshgrid(lonsraw, latsraw)
latsraw = []
lonsraw = []
tmp.close
for files in filelist:
try:
thisFile = Dataset(files, 'r', format='NETCDF4')
#clip the dataset according to user lat,lon coordinates
#mask the data and fill with zeros for later
tempRaw = thisFile.variables[varName][:, latminIndex:latmaxIndex, lonminIndex:lonmaxIndex].astype('int16')
tempMask = ma.masked_array(tempRaw, mask=(tempRaw > userVariables.T_BB_MAX), fill_value=0)
#get the actual values that the mask returned
tempMaskedValue = ma.zeros((tempRaw.shape)).astype('int16')
for index, value in utils.maenumerate(tempMask):
timeIndex, latIndex, lonIndex = index
tempMaskedValue[timeIndex, latIndex, lonIndex] = value
xtimes = thisFile.variables[timeName]
#convert this time to a python datastring
time2store, _ = get_model_times(xtimes, timeName)
#extend instead of append because get_model_times returns a list already and we don't
#want a list of list
timelist.extend(time2store)
inputData.extend(tempMaskedValue)
thisFile.close
thisFile = None
except:
print 'bad file! ', files
inputData = ma.array(inputData)
return inputData, timelist, LAT, LON
ax.yaxis.set_ticks_position('both')
ax.grid(True)
plt.plot(fpr_re, tpr_re, color='darkred', lw=2, label='RelIso')
#plt.plot(fpr_bdt, tpr_bdt, color='blue', lw=2, label='BDT')
plt.plot(fpr_nn, tpr_nn, color='darkorange', lw=2, label='DNN')
plt.xscale('log')
plt.grid()
plt.xlim([0.005, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate (bkg. eff.)')
plt.ylabel('True Positive Rate (sig. eff.)')
plt.legend(loc='lower right')
plt.savefig('plot.pdf')
value1, idx1 = utils.find_nearest(tpr_nn, 0.90)
value2, idx2 = utils.find_nearest(tpr_nn, 0.99)
value3, idx3 = utils.find_nearest(fpr_nn, 0.01)
value4, idx4 = utils.find_nearest(fpr_nn, 0.1)
#value1BDT, idx1BDT = utils.find_nearest(tpr_bdt, 0.90)
#value2BDT, idx2BDT = utils.find_nearest(tpr_bdt, 0.99)
#value3BDT, idx3BDT = utils.find_nearest(fpr_bdt, 0.01)
#value4BDT, idx4BDT = utils.find_nearest(fpr_bdt, 0.1)
value1RE, idx1RE = utils.find_nearest(tpr_re, 0.90)
value2RE, idx2RE = utils.find_nearest(tpr_re, 0.99)
value3RE, idx3RE = utils.find_nearest(fpr_re, 0.01)
value4RE, idx4RE = utils.find_nearest(fpr_re, 0.1)
print('Neural net FPR, TPR: (%.3f, %.3f)' % (fpr_nn[idx1], tpr_nn[idx1]))
def tf_detail(TF, t, f, title=None, t_detail=None, x=None, display_op=np.abs,
figsize=None, cmap='binary', vmin=None, vmax=None):
"""
Detailed time-frequency representation (TFR).
Show the TFR in the top plot. Also show the frequency representation at a
specific time instants (last time by default) on the plot on the right. If
specified, the original time signal ``x`` is shown the bottom plot.
Args:
TF (:class:`numpy.ndarray`): time-frequency representation
t (:class:`numpy.ndarray`): time vector
f (:class:`numpy.ndarray`): frequency vector
title (``string``): title of the plot
t_detail (``float`` or ``list``): time instant(s) to be detailed
x (:class:`numpy.ndarray`): original time domain signal. If *None*, not
time domain plot is shown
display_op (``function``): operator to apply to the TF representation
(e.g. :func:`numpy.angle`)
figsize (``tuple``): matplotlib's figure size (optional)
cmap (``string``): colormap to use in the TF representation
vmin (``float``): lower limit of the colormap
vmax (``float``): upper limit of the colormap
Returns:
``(handles, ...)``: tuple of handles to plotted elements. They can be
used to create animations
Note:
``vmin`` and ``vmax`` are useful when comparing different time-frequency
representations, so they all share the same color scale.
Note:
Is the caller's responsibility to issue :func:`matplotlib.pyplot.show()`
if necessary.
"""
if figsize is not None:
fig = plt.figure(figsize=figsize)
else:
fig = plt.figure()
opTF = display_op(TF)
if t_detail is None:
wr = [1, 2, 20]
detail = None
else:
wr = [1, 2, 20, 6]
if x is None:
hr = [1]
axOnset = None
else:
hr = [3, 1]
gs = gridspec.GridSpec(len(hr), len(wr),
width_ratios=wr,
height_ratios=hr
)
gs.update(wspace=0.0, hspace=0.0) # set the spacing between axes.
axCB = fig.add_subplot(gs[0])
axTF = fig.add_subplot(gs[2])
if x is not None:
axOnset = fig.add_subplot(gs[len(wr)+2], sharex=axTF)
if t_detail is not None:
axF = fig.add_subplot(gs[3], sharey=axTF)
nice_freqs = nice_log_values(f)
# TF image
# im = axTF.pcolormesh(t, f, opTF, cmap=cmap)
im = axTF.imshow(opTF,
extent=[min(t), max(t), min(f), max(f)],
cmap=cmap,
vmin=vmin,
vmax=vmax,
origin='lower'
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
axTF.set_yscale('log')
axTF.set_yticks(nice_freqs)
axTF.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
axTF.invert_yaxis()
if title is not None:
axTF.set_title(title)
# Add colorbar
cb = plt.colorbar(im, ax=axTF, cax=axCB)
cb.ax.yaxis.set_ticks_position('left')
# TF detail
# find detail index
tf_line = None
tf_x_min, tf_x_max = 0, np.max(opTF)
if vmin is not None:
tf_x_min = vmin
if vmax is not None:
tf_x_max = vmax
if t_detail is not None:
if isinstance(t_detail, np.ndarray):
t_detail = t_detail.tolist()
elif not isinstance(t_detail, list):
t_detail = [t_detail]
t_detail, idx = find_nearest(t, t_detail)
# axF.invert_xaxis()
detail = axF.semilogy(opTF[:, idx], f)
axF.set_yticks(nice_freqs)
axF.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
axF.xaxis.set_ticks_position('top')
axF.axis('tight')
axF.set_xlim(tf_x_min, tf_x_max)
axF.yaxis.set_ticks_position('right')
plt.setp(axF.get_xaxis().get_ticklabels(), rotation=-90 )
axTF.hold(True)
tf_line = axTF.plot([t_detail, t_detail], [np.min(f), np.max(f)])
# tf_line = [axTF.axvline(td) for td in t_detail]
axTF.hold(False)
axTF.axis('tight')
# onset signal
t_line = None
if axOnset is not None:
plt.setp(axTF.get_xticklabels(), visible=False)
axOnset.plot(t, x, color='k')
if t_detail is not None:
t_line = axOnset.plot([t_detail, t_detail], [np.min(x), np.max(x)])
# t_line = [axOnset.axvline(td) for td in t_detail]
axOnset.yaxis.set_ticks_position('right')
axOnset.axis('tight')
# plt.show()
return (fig, im, tf_line, t_line, detail)
def plot_connections(connection, title=None, f_detail=None, display_op=np.abs,
detail_type='polar', cmap='binary', vmin=None, vmax=None):
"""plot_connections(connection, t_detail=None, display_op=np.abs,
detail_type='polar')
Args:
connection (:class:`.Connection`): connection object
title (``string``): Title to be displayed
f_detail (``float``): frequency of the detail plot
display_op (``function``): operator to apply to the connection
matrix (e.g. :func:`numpy.abs`)
detail_type (``string``): detail complex display type (``'cartesian',
'polar', 'magnitude'`` or ``'phase'``)
cmap (``string``): colormap to use in the TF representation
vmin (``float``): lower limit of the colormap
vmax (``float``): upper limit of the colormap
Note:
Is the caller's responsibility to issue :func:`matplotlib.pyplot.show()`
if necessary.
"""
fig = plt.figure()
if f_detail is not None:
gs = gridspec.GridSpec(2, 1,
width_ratios=[1],
height_ratios=[3, 1]
)
gs.update(wspace=0.0, hspace=0.0) # set the spacing between axes.
axConn = fig.add_subplot(gs[0])
axDetail = fig.add_subplot(gs[1])
else:
axConn = fig.add_subplot(1, 1, 1)
f_source = connection.source.f
f_dest = connection.destination.f
matrix = connection.matrix
opMat = display_op(matrix)
# axConn.pcolormesh(f_source, f_dest, opMat, cmap=cmap)
axConn.imshow(opMat,
extent=[min(f_source), max(f_source),
min(f_dest), max(f_dest)],
cmap=cmap,
vmin=vmin,
vmax=vmax,
origin='lower'
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# axConn.invert_yaxis()
axConn.set_xscale('log')
axConn.set_xticks(nice_log_values(f_source))
axConn.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
axConn.set_yscale('log')
axConn.set_yticks(nice_log_values(f_dest))
axConn.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
axConn.set_ylabel(r'$f_{\mathrm{dest}}$')
if title is not None:
axConn.set_title(title)
if f_detail is None:
axConn.set_xlabel(r'$f_{\mathrm{source}}$')
else:
(f_detail, idx) = find_nearest(f_dest, f_detail)
conn = matrix[idx, :]
axConn.hold(True)
axConn.plot([np.min(f_source), np.max(f_source)],
[f_detail, f_detail],
color='r')
axConn.hold(False)
scalar_formatter = mpl.ticker.ScalarFormatter()
if detail_type is 'polar':
axDetail.semilogx(f_source, np.abs(conn))
axDetailb = axDetail.twinx()
axDetailb.semilogx(f_source, np.angle(conn), color='r')
axDetailb.set_xticks(nice_log_values(f_source))
axDetailb.get_xaxis().set_major_formatter(scalar_formatter)
axDetailb.set_ylim([-np.pi, np.pi])
axDetail.axis('tight')
elif detail_type is 'magnitude':
y_min, y_max = 0, np.abs(conn)
if vmin is not None:
y_min = vmin
if vmax is not None:
y_max = vmax
axDetail.semilogx(f_source, np.abs(conn))
axDetail.set_xticks(nice_log_values(f_source))
axDetail.get_xaxis().set_major_formatter(scalar_formatter)
# axDetail.axis('tight')
axDetail.set_ylim([y_min, y_max])
elif detail_type is 'phase':
axDetail.semilogx(f_source, np.angle(conn), color='r')
axDetail.set_xticks(nice_log_values(f_source))
axDetail.get_xaxis().set_major_formatter(scalar_formatter)
axDetail.set_ylim([-np.pi, np.pi])
else:
axDetail.semilogx(f_source, np.real(conn))
axDetailb = axDetail.twinx()
axDetailb.semilogx(f_source, np.imag(conn), color='r')
axDetailb.set_xticks(nice_log_values(f_source))
axDetailb.get_xaxis().set_major_formatter(scalar_formatter)
axDetail.axis('tight')
axDetail.set_xlabel(r'$f_{\mathrm{dest}}$')
axConn.set(aspect=1, adjustable='box-forced')
def simulate(family,params,paramsList,bins,\
seed=None,N=None,noise=None,output=None,\
dump=None,version=2,verbose=False,area=None,\
skadsf=None,pole_posns=None,simarrayf=None,\
simdocatnoise=True):
"""
Based on lumfunc.simtable()
Specify family + parameters
Specify number of sources
Build CDF (or set up function)
Draw deviates
Sample CDF given deviates
Add noise (None or some value)
Bin
Write out
Return
Look at simulate.ipynb for an example run
Need to add normalization capability
Families:
========
skads:
-----
r=countUtils.simulate('skads',[0.01,85.0],['S0','S1'],numpy.linspace(-60.0,100.0,26),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
ppl:
---
r=countUtils.simulate('ppl',[1000.0,5.0,75.0,-1.6],['C','S0','S1','a0'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,75.0,-1.6,-2.5],['C','S0','S1','S2','a0','a1'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,40.0,75.0,-1.6,-2.5,-1.0],['C','S0','S1','S2','S3','a0','a1','a2'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,40.0,75.0,90.0,-1.6,-2.5,-1.0,2.0],['C','S0','S1','S2','S3','S4','a0','a1','a2','a3'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
poly:
----
r=countUtils.simulate('poly',[5.0,75.0,1.0],['S0','S1','p0'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('poly',[5.0,75.0,1.0,-1.0],['S0','S1','p0','p1'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('poly',[5.0,75.0,1.0,-1.0,5.0],['S0','S1','p0','p1','p2'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
bins:
----
test:
----
array:
-----
"""
# Initialize seed for variates AND any noise
if seed is not None:
numpy.random.seed(seed=SEED_SIM)
if family == 'ppl':
C = alpha = Smin = Smax = beta = S0 = gamma = S1 = delta = S2 = -99.0
nlaws = int(0.5 * len(paramsList) - 1)
C = params[paramsList.index('C')]
Smin = params[paramsList.index('S0')]
alpha = params[paramsList.index('a0')]
if nlaws > 1:
beta = params[paramsList.index('a1')]
S0 = params[paramsList.index('S1')]
if nlaws > 2:
gamma = params[paramsList.index('a2')]
S1 = params[paramsList.index('S2')]
if nlaws > 3:
delta = params[paramsList.index('a3')]
S2 = params[paramsList.index('S3')]
iSmax = int([i for i in paramsList if i.startswith('S')][-1][-1])
Smax = params[paramsList.index('S%i' % iSmax)]
function = lambda S:powerLawFuncWrap(nlaws,S,C,alpha,-99.0,beta,\
Smin/1e6,Smax/1e6,S0/1e6,gamma,S1/1e6,delta,S2/1e6,1.0)
elif family == 'test':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
function = lambda S: S**2
elif family == 'poly':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
coeffs = [
params[paramsList.index(p)] for p in paramsList
if p.startswith('p')
]
S_1 = 1.0
function = lambda S: polyFunc(S, S_1, Smin, Smax, coeffs)
elif family == 'bins':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
coeffs = [
params[paramsList.index(p)] for p in paramsList
if p.startswith('b')
]
if pole_posns is None:
pole_posns = numpy.logspace(numpy.log10(Smin), numpy.log10(Smax),
len(coeffs) + 1)
assert (len(coeffs) == len(pole_posns) -
1), '***Mismatch in number of poles!!'
Smin = pole_posns[0]
Smax = pole_posns[-1]
function = lambda S: polesFunc(S, pole_posns, Smin, Smax, coeffs)
elif family == 'array':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
assert (simarrayf
is not None), '***Need to specify an input simulation!'
print 'Reading %s...' % simarrayf
dataMatrix = numpy.genfromtxt(simarrayf)
dndsInArr = dataMatrix[:, 4]
binsDogleg = numpy.concatenate((dataMatrix[:, 0], [dataMatrix[-1, 1]]))
binsMedian = dataMatrix[:, 2]
assert ((
medianArray(binsDogleg) == binsMedian).all()), '***bin mismatch!'
Smin = binsDogleg[0]
Smax = binsDogleg[-1]
if not simdocatnoise:
Smin = -5.01 #-2.01 # binsMedian[0]
print dndsInArr
function = lambda S: arrayFunc(S, binsMedian, dndsInArr, Smin, Smax)
#function2=lambda S:arrayFunc(S,binsMedian,dndsInArr,Smin,Smax)
#for x in numpy.linspace(-10.0,100.0,500):
# print x,function(x),function2(x)
#sys.exit(0)
elif family == 'skads':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
function = None
assert (skadsf is not None), '***Need to specify input SKADS file!'
print 'Reading %s...' % skadsf
R = Jy2muJy * 10**numpy.genfromtxt(skadsf)
numpy.ndarray.sort(R)
iRmin, Rmin = find_nearest(R, Smin)
iRmax, Rmax = find_nearest(R, Smax)
F = R[iRmin:iRmax]
print '%i/%i sources ingested after Smin/Smax cuts' % (len(F), len(R))
if N is not None:
F = numpy.random.choice(F, size=N, replace=False)
N = len(F)
print 'NSKADS = %i' % N
elif family == 'Lrad':
Smin = params[paramsList.index('LoptMIN')]
Smax = params[paramsList.index('LoptMAX')]
A = params[paramsList.index('A')]
B = params[paramsList.index('B')]
sigma_Lrad = params[paramsList.index('sigma_Lrad')]
#print Loptmin,Loptmax
print 'Doing LF simulation'
inta = None
#intg = integrate.quad(lambda Lopt:Lopt2Lrad(Lopt,A=A,B=B,flux=False),Loptmin,Loptmax,epsabs=0.)[0]
function = lambda Lopt: Lopt2Lrad(Lopt, A=A, B=B, flux=False)
elif family in ['LFsch', 'LFdpl']:
redshift = 0.325
z_min = 0.2
z_max = 0.45
Lmin = params[paramsList.index('LMIN')]
Lmax = params[paramsList.index('LMAX')]
[Smin, Smax] = SMIN_SIM, SMAX_SIM
print Smin, Smax
[Smin, Smax] = get_sbins([10**Lmin, 10**Lmax], redshift, dl) * 1e6
print Smin, Smax, Lmin, Lmax
print 'Doing LF simulation'
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(redshift, dl)
inta = None
intg = integrate.quad(lambda S:LF(S,redshift,dsdl,Vmax,dl,params=params,paramsList=paramsList,\
inta=inta,area=area,family=family),Smin*1e-6,Smax*1e-6,epsabs=0.)[0]
print intg * Vmax
print Vmax
area = N / (Vmax * intg)
area1 = area
print N, area
function = lambda S:dNdS_LF(S,z_min,redshift,z_max,dl,params=params,paramsList=paramsList,\
area=area,family=family)
if family != 'skads':
# Set up the 'rough' array
gridlength = 10000 # Good enough to prevent bleeding at the edges
Ss = numpy.linspace(Smin, Smax, gridlength)
print Smin, Smax
print 'checking for one sample'
kl = function(20 / 1e6)
print kl
#sys.exit()
values = numpy.array([function(ix / 1e6) for ix in Ss])
print values[:10]
# Build the CDF
CDF = buildCDF(values)
plt.plot(CDF, Ss)
#plt.xscale('log')
#plt.yscale('log')
plt.ylabel('Flux')
plt.xlabel('CDF')
plt.show()
print CDF.max()
# Create the interpolant object
sampler = interp1d(CDF, Ss)
plt.plot(Ss, values, '.')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Flux')
plt.ylabel('LF')
plt.show()
x = numpy.linspace(0., 1., 10000)
z = numpy.logspace(0, 1, 1000) / 10.
f = sampler(z)
y = sampler(x)
plt.yscale('log')
#plt.xscale('log')
plt.axhline(Smin)
plt.axhline(Smax)
plt.xlabel('R')
plt.ylabel('Sampler(R) [flux]')
#plt.plot(x,y)
plt.plot(z, f)
plt.show()
#sys.exit()
# Test that the sampler extrema match
print Smin, sampler(0.0), 'you know wa mean'
print Smax, sampler(0.99999)
# assert(numpy.isclose(sampler(0.0),Smin)[0])
# assert(numpy.isclose(sampler(0.99999),Smax,atol=1.0e-3)[0])
# Draw the random deviates
R = numpy.random.rand(N)
print len(R)
F = sampler(R)
Nt = 0.
for f in F:
if f < 1.:
Nt += 1.
F = F[F > 1.]
print N, Nt
print len(F)
Nt = len(F)
#sys.exit()
# Normalize here - this is N2C
# EITHER N is specified explicitly
# BOTH N2C and C2N are useful
# Integrate the original function
#intg = integrate.quad(lambda S:LF(S,redshift,dsdl,Vmax,dl,params=params,paramsList=paramsList,\
# inta=inta,area=area,family=family),Smin*1e-6,Smax*1e-6,epsabs=0.)[0]
#print intg*Vmax
#print Vmax
#area = Nt/(Vmax*intg)
#print N,area,area1
#plt.show()
#sys.exit()
A = integrate.quad(function, Smin, Smax)[0]
# print A,N
# Bin the random samples
bbins = numpy.linspace(Smin, Smax, 100)
E = numpy.histogram(F, bins=bbins)[0]
# And calculate their area
G = integrate.trapz(E, x=medianArray(bbins))
# print G
# print G/A
# Gunpowder, treason and....
if False:
plt.xlim(0.0, 100.0)
plt.xlabel('S / $\mu$Jy')
plt.hist(F, bins=bbins)
plt.plot(Ss, values * G / A, 'r')
plt.savefig('N2C.pdf')
plt.close()
# Want: C given N, to compare to original C
numbins = 1000
if family == 'ppl':
C_calc = N / N2C(function, F, Smin, Smax, numbins)
#print N2C(function,F,Smin,Smax,numbins),C
print 'For %i sources, C is %e (should be %e)' % (N, C_calc, C)
elif family == 'poly':
C_calc = log10(N / N2C(function, F, Smin, Smax, numbins))
print 'For %i sources, C is %e (should be %e)' % (N, C_calc, coeffs[0])
# Dump noiseless fluxes to file
puredumpf = dump
idl_style = False
numpy.savetxt(puredumpf, F)
print 'Draws (noiseless) are in %s' % puredumpf
writeCountsFile(output[1],
bins,
F,
area,
idl_style=idl_style,
verbose=verbose)
print output[1]
# Now add noise if requested
if simdocatnoise:
numpy.random.seed(seed=SEED_SIM)
poln = False
if poln:
F += rice.rvs(F / noise, size=N)
else:
F += numpy.random.normal(0.0, noise, Nt)
# Dump noisy fluxes to file
if dump is not None:
noisydumpf = '%s_noisy.txt' % puredumpf.split('.')[0]
numpy.savetxt(noisydumpf, F)
print 'Draws (noisy) are in %s' % noisydumpf
print 'Minimum flux in catalogue = %f' % F.min()
print 'Maximum flux in catalogue = %f' % F.max()
# Write counts file
print output[0]
writeCountsFile(output[0],
bins,
F,
area,
idl_style=idl_style,
verbose=verbose)
print N, area #,area1
return F
def train_with_early_stopping(self):
best_auc = 0.5
keep_training = True
max_batch_size = 10000
epochs = 1
bad_epochs = 0
while keep_training:
auc_train, auc, rocs = self.train(epochs, self.batch_size_train)
improvement = ((1-best_auc)-(1-auc))/(1-best_auc)
overfit = (auc_train - auc) / auc_train
if improvement > 0.01:
print(("Improvement in (1-AUC) of %.3f percent! Keeping batch size the same" % (improvement*100.)))
best_auc = auc
bad_epochs = 0
elif self.batch_size_train * 4 < max_batch_size:
print(("Improvement in (1-AUC) of %.3f percent. Increasing batch size" % (improvement*100.)))
self.batch_size_train *= 4
bad_epochs = 0
if auc > best_auc:
best_auc = auc
elif self.batch_size_train < max_batch_size:
print(("Improvement in (1-AUC) of %.3f percent. Increasing batch size" % (improvement*100.)))
self.batch_size_train = max_batch_size
bad_epochs = 0
if auc > best_auc:
best_auc = auc
elif improvement > 0:
print(("Improvement in (1-AUC) of %.3f percent. Can't increase batch size anymore" % (improvement*100.)))
bad_epochs = 0
best_auc = auc
#elif improvement < 0 and overfit < 0.01 and bad_epochs < 3:
# print (("Overfitting by less than 1%, continue training"))
# bad_epochs += 1
else:
print("AUC did not improve and we can't increase batch size anymore. Stopping training.")
keep_training = False
if self.n_epochs >= self.max_epochs:
print("Have already trained for 25 epochs. Stopping training.")
keep_training = False
if self.curriculum_learn:
value, idx = utils.find_nearest(rocs["tpr_train"], 0.90)
cut = rocs["thresh_train"][idx]
good_indices = numpy.where(self.predictions["train"] > cut)
self.features_train.features[0] = self.features_train.features[0][good_indices]
self.features_train.features[1] = self.features_train.features[1][good_indices]
self.features_train.global_features = self.features_train.global_features[good_indices]
self.features_train.objects = self.features_train.objects[good_indices]
self.features_train.label = self.features_train.label[good_indices]
self.features_train.weights = self.features_train.weights[good_indices]
self.prepped = False
auc, auc_unc, fpr, tpr, thresh = utils.auc_and_unc(self.features_validation.label, self.predictions["validation"], self.features_validation.weights, 50)
auc_train, auc_unc_train, fpr_train, tpr_train, threshd_train = utils.auc_and_unc(self.features_train.label, self.predictions["train"], self.features_train.weights, 50)
self.auc_unc["validation"] = auc_unc
self.auc_unc["train"] = auc_unc_train
self.model.save_weights("dnn_weights/" + self.tag + "_weights.hdf5")
with open("dnn_weights/" + self.tag + "_model_architecture.json", "w") as f_out:
f_out.write(self.model.to_json())
return
#signal_effs = [0.95, 0.97, 0.98, 0.99]
effs = {}
for i in range(len(files)):
if not "dnn_roc" in inputs[i]:
fpr = files[i]["fpr_test"]
tpr = files[i]["tpr_test"]
else:
fpr = files[i]["fpr_validation"]
tpr = files[i]["tpr_validation"]
tprs = []
fprs = []
for eff in signal_effs:
sig_eff, idx = utils.find_nearest(tpr, eff)
tprs.append(tpr[idx])
fprs.append(fpr[idx])
auc = metrics.auc(fpr, tpr, reorder=True)
effs[labels[i]] = {"fpr": fprs, "tpr": tprs, "auc": auc}
ax1.plot(fpr,
tpr,
label=labels[i] + " [AUC = %.3f]" % (auc),
color=colors[i])
if "tpr_unc_test" in files[i].files:
tpr_unc = files[i]["tpr_unc_test"]
ax1.fill_between(fpr,
tpr - 1 * (tpr_unc / 2.),
tpr + 1 * (tpr_unc / 2.),
def household_ss_olg(params, w, tau, d):
"""Solves the household's problem for a given real wage, interest rate, and social security policy."""
# Construct labor income
iret = 1 * (params['jvec'] >= params['Tr']) # Retirement indicator
y_js = (1 - tau) * w * params['y_eps'][
np.newaxis, :] * params['h'][:, np.newaxis] + d * iret[:, np.newaxis]
# allocate arrays to store results
uc, c, a, D = (np.empty((params['T'] + 1, params['N_eps'], params['N_a']))
for _ in range(4))
a[:params['Tw']], c[:params['Tw']], D[:params[
'Tw']] = 0.0, 0.0, 0.0 # Make sure = 0 before age Tw
# Backward iteration to obtain policy function
for j in reversed(range(params['Tw'], params['T'] + 1)):
if j == params['T']:
# Compute cash-on-hand and bequest factor
coh_T = get_coh(y_js[j, ], params['r'], params['phi'][j],
params['a'])
# Call backward iteration function
a[j, ], c[j, ] = constrained(coh_T, params['a'])
uc[j, ] = c[j, ]**(-params['sigma'])
# Compute cash-on-hand and bequest factor
coh = get_coh(y_js[j - 1, ], params['r'], params['phi'][j - 1],
params['a'])
# Call backward iteration function
a[j-1,], c[j-1,], uc[j-1,] = \
backward_iterate_olg(uc[j,], params['a'], params['Pi_eps'], coh, params['r'], params['beta'], params['sigma'])
# initialize age-Tw distribution: point mass at 0
Dst_start = np.zeros((params['N_eps'], params['N_a']))
Dst_start[:, find_nearest(params['a'], 0.0)] = 1.0
# to make matrix multiplication more efficient, make separate copy of Pi transpose
Pi_T = params['Pi_eps'].T.copy()
# forward iteration to obtain distributions at each future age
for j in range(params['Tw'], params['T']):
if j == params['Tw']:
D_temp = Dst_start
D[j, ] = D_temp
else:
D_temp = D[j, ]
# get interpolated rule corresponding to a and iterate forward
a_pol_i, a_pol_pi = interpolate_coord(params['a'], a[j, ])
D[j + 1, ] = forward_step(D_temp, Pi_T, a_pol_i, a_pol_pi)
# Assets
A_j = np.einsum('jsa,jsa->j', D, a) # by age j
A = np.einsum('j,j', params['pi'], A_j) # Aggregate assets
# Consumption
C_j = np.einsum('jsa,jsa->j', D, c) # by age j
C = np.einsum('j,j', params['pi'], C_j) # Aggregate consumption
return {
'D': D, # Distribution of agents
'a': a,
'A_j': A_j,
'A': A, # Asset policy, by age, aggregate
'c': c,
'C_j': C_j,
'C': C, # Consumption policy, by age, aggregate
}
def create_netcdf(config, rapid_trans, model_trans, fc_trans, wbw_trans,
int_trans, ek_trans):
"""
Return diagnostics for comparison with RAPID-MOCHA observations
in netcdf object for plotting/output.
"""
# Configuration options
zind = utils.find_nearest(rapid_trans.z, 1000)
fc_minlon = config.getfloat('options', 'fc_minlon')
fc_maxlon = config.getfloat('options', 'fc_maxlon')
wbw_maxlon = config.getfloat('options', 'wbw_maxlon')
int_maxlon = config.getfloat('options', 'int_maxlon')
georef = config.getfloat('options', 'georef_level')
ek_level = config.getfloat('options', 'ekman_depth')
try:
vref_level = config.getfloat('options', 'vref_level')
except:
vref_level = 'None'
# Create netcdf file and add dimensions
dataset = open_ncfile(config, rapid_trans.dates)
zdim = dataset.createDimension('depth', rapid_trans.z.size)
tdim = dataset.createDimension('time', None)
# Create time coordinate
time = dataset.createVariable('time', np.float64, (tdim.name, ))
time.units = 'hours since 0001-01-01 00:00:00.0'
time.calendar = 'gregorian'
time[:] = date2num(rapid_trans.dates, time.units, calendar=time.calendar)
# Create depth coordinate
z = dataset.createVariable('depth', np.float64, (zdim.name, ))
z.units = 'm'
z[:] = rapid_trans.z
# Create depth coordinate
dz = dataset.createVariable('level_thickness', np.float64, (zdim.name, ))
dz.units = 'm'
dz[:] = rapid_trans.dz
# Global attributes
dataset.geostrophic_reference_level = georef
dataset.rhocp = rapid_trans.rhocp
dataset.ekman_level = ek_level
dataset.reference_to_model_velocity = vref_level
dataset.contact = '*****@*****.**'
dataset.code_reference = 'Roberts, C. D., et al. (2013), Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5N, Geophys. Res. Lett., 40, 5164-5170 doi:10.1002/grl.50930.'
dataset.method_references = '(1) McCarthy, G. D., and Coauthors, 2015: Measuring the Atlantic Meridional Overturning Circulation at 26 degrees N. Progress in Oceanography, 130, 91-111. (2) Johns, W.E., M.O. Baringer, L.M. Beal, S.A. Cunningham, T. Kanzow, H.L. Bryden, J.J. Hirschi, J. Marotzke, C.S. Meinen, B. Shaw, and R. Curry, 2011: Continuous, Array-Based Estimates of Atlantic Ocean Heat Transport at 26.5N. J. Climate, 24, 2429-2449, doi: 10.1175/2010JCLI3997.1.'
# Basinwide potential temperature profile
t_basin = dataset.createVariable('t_basin', np.float64,
(tdim.name, zdim.name))
t_basin.units = 'degC'
t_basin.minimum_longitude = fc_minlon
t_basin.maximum_longitude = int_maxlon
t_basin.comment = 'Basinwide zonal mean potential temperature profile'
t_basin[:] = rapid_trans.zonal_avg_t
# Florida current flow-weighted potential temperature
t_fc_fwt = dataset.createVariable('t_fc_fwt', np.float64, (tdim.name))
t_fc_fwt.units = 'degC'
t_fc_fwt.minimum_longitude = fc_minlon
t_fc_fwt.maximum_longitude = fc_maxlon
t_fc_fwt.comment = 'Florida current flow-weighted potential temperature'
t_fc_fwt[:] = fc_trans.oht_total / (fc_trans.rhocp *
fc_trans.net_transport)
# Basinwide transport profile - RAPID approx
v_basin_rapid = dataset.createVariable('v_basin_rapid', np.float64,
(tdim.name, zdim.name))
v_basin_rapid.units = 'Sv/m'
v_basin_rapid.minimum_longitude = fc_minlon
v_basin_rapid.maximum_longitude = int_maxlon
v_basin_rapid.comment = 'Basinwide transport profile using RAPID approximations'
v_basin_rapid[:] = rapid_trans.zonal_sum_v / 1e6
# Basinwide transport profile - model v
v_basin_model = dataset.createVariable('v_basin_model', np.float64,
(tdim.name, zdim.name))
v_basin_model.units = 'Sv/m'
v_basin_model.minimum_longitude = fc_minlon
v_basin_model.maximum_longitude = int_maxlon
v_basin_model.comment = 'Basinwide transport profile using model velocities'
v_basin_model[:] = model_trans.zonal_sum_v / 1e6
# Florida current transport profile
v_fc = dataset.createVariable('v_fc', np.float64, (tdim.name, zdim.name))
v_fc.units = 'Sv/m'
v_fc.minimum_longitude = fc_minlon
v_fc.maximum_longitude = fc_maxlon
v_fc.comment = 'Florida current transport profile'
v_fc[:] = fc_trans.zonal_sum_v / 1e6
# Ekman transport time series
ekman = dataset.createVariable('ekman', np.float64, (tdim.name))
ekman.units = 'Sv'
ekman.minimum_longitude = wbw_maxlon
ekman.maximum_longitude = int_maxlon
ekman.comment = 'Ekman transport time series (streamfunction at 1000m)'
ekman[:] = ek_trans.streamfunction[:, zind] / 1e6
# Gyre interior transport time series
geoint = dataset.createVariable('geoint', np.float64, (tdim.name))
geoint.units = 'Sv'
geoint.minimum_longitude = wbw_maxlon
geoint.maximum_longitude = int_maxlon
geoint.comment = 'Geostrophic interior transport time series (streamfunction at 1000m).'
geoint[:] = int_trans.streamfunction[:, zind] / 1e6
# Western-boundary wedge transport time series
wbw = dataset.createVariable('wbw', np.float64, (tdim.name))
wbw.units = 'Sv'
wbw.minimum_longitude = fc_maxlon
wbw.maximum_longitude = wbw_maxlon
wbw.comment = 'Western boundary wedge transport time series (streamfunction at 1000m).'
wbw[:] = wbw_trans.streamfunction[:, zind] / 1e6
# Florida current transport time series
fc = dataset.createVariable('fc', np.float64, (tdim.name))
fc.units = 'Sv'
fc.minimum_longitude = fc_minlon
fc.maximum_longitude = fc_maxlon
fc.comment = 'Florida current transport time series (streamfunction at 1000m).'
fc[:] = fc_trans.streamfunction[:, zind] / 1e6
# Upper mid ocean transport time series
umo = dataset.createVariable('umo', np.float64, (tdim.name))
umo.units = 'Sv'
umo.minimum_longitude = fc_maxlon
umo.maximum_longitude = int_maxlon
umo.comment = 'Upper mid-ocean transport time series (streamfunction at 1000m). umo = wbw + geoint'
umo[:] = wbw[:] + geoint[:]
# Meridional overturning transport time series - RAPID approx
moc_rapid = dataset.createVariable('moc_rapid', np.float64, (tdim.name))
moc_rapid.units = 'Sv'
moc_rapid.minimum_longitude = fc_minlon
moc_rapid.maximum_longitude = int_maxlon
moc_rapid.comment = 'Time series of meridional overturning transport using RAPID approximation (streamfunction at 1000m)'
moc_rapid[:] = rapid_trans.streamfunction[:, zind] / 1e6
# Meridional overturning transport time series - model v
moc_model = dataset.createVariable('moc_model', np.float64, (tdim.name))
moc_model.units = 'Sv'
moc_model.minimum_longitude = fc_minlon
moc_model.maximum_longitude = int_maxlon
moc_model.comment = 'Time series of meridional overturning transport using model velocities (streamfunction at 1000m)'
moc_model[:] = model_trans.streamfunction[:, zind] / 1e6
# Meridional overturning transport maxima time series - RAPID approx
mocmax_rapid = dataset.createVariable('mocmax_rapid', np.float64,
(tdim.name))
mocmax_rapid.units = 'Sv'
mocmax_rapid.minimum_longitude = fc_minlon
mocmax_rapid.maximum_longitude = int_maxlon
mocmax_rapid.comment = 'Time series of meridional overturning transport using RAPID approximation (streamfunction maxima)'
mocmax_rapid[:] = rapid_trans.streamfunction.max(axis=1) / 1e6
# Meridional overturning transport maxima time series - model v
mocmax_model = dataset.createVariable('mocmax_model', np.float64,
(tdim.name))
mocmax_model.units = 'Sv'
mocmax_model.minimum_longitude = fc_minlon
mocmax_model.maximum_longitude = int_maxlon
mocmax_model.comment = 'Time series of meridional overturning transport using model velocities (streamfunction maxima)'
mocmax_model[:] = model_trans.streamfunction.max(axis=1) / 1e6
# Overturning streamfunctions - RAPID approx
sf_rapid = dataset.createVariable('sf_rapid', np.float64,
(tdim.name, zdim.name))
sf_rapid.units = 'Sv'
sf_rapid.minimum_longitude = fc_minlon
sf_rapid.maximum_longitude = int_maxlon
sf_rapid.comment = 'Overturning streamfunctions using RAPID approximation.'
sf_rapid[:] = rapid_trans.streamfunction / 1e6
# Meridional overturning transport time series - model v
sf_model = dataset.createVariable('sf_model', np.float64,
(tdim.name, zdim.name))
sf_model.units = 'Sv'
sf_model.minimum_longitude = fc_minlon
sf_model.maximum_longitude = int_maxlon
sf_model.comment = 'Overturning streamfunctions using model velocities.'
sf_model[:] = model_trans.streamfunction / 1e6
# Florida current stream function
sf_fc = dataset.createVariable('sf_fc', np.float64, (tdim.name, zdim.name))
sf_fc.units = 'Sv'
sf_fc.minimum_longitude = fc_minlon
sf_fc.maximum_longitude = fc_maxlon
sf_fc.comment = 'Florida current overturning streamfunction.'
sf_fc[:] = fc_trans.streamfunction / 1e6
# Ekman stream function
sf_ek = dataset.createVariable('sf_ek', np.float64, (tdim.name, zdim.name))
sf_ek.units = 'Sv'
sf_ek.minimum_longitude = wbw_maxlon
sf_ek.maximum_longitude = int_maxlon
sf_ek.comment = 'Ekman overturning streamfunction.'
sf_ek[:] = ek_trans.streamfunction / 1e6
# Wbw stream function
sf_wbw = dataset.createVariable('sf_wbw', np.float64,
(tdim.name, zdim.name))
sf_wbw.units = 'Sv'
sf_wbw.minimum_longitude = fc_minlon
sf_wbw.maximum_longitude = wbw_maxlon
sf_wbw.comment = 'Western boundary wedge overturning streamfunction.'
sf_wbw[:] = wbw_trans.streamfunction / 1e6
# Geostrophic interior stream function
sf_geoint = dataset.createVariable('sf_geoint', np.float64,
(tdim.name, zdim.name))
sf_geoint.units = 'Sv'
sf_geoint.minimum_longitude = wbw_maxlon
sf_geoint.maximum_longitude = int_maxlon
sf_geoint.comment = 'Geostrophic interior overturning streamfunction.'
sf_geoint[:] = int_trans.streamfunction / 1e6
# mid ocean stream function
sf_mo = dataset.createVariable('sf_mo', np.float64, (tdim.name, zdim.name))
sf_mo.units = 'Sv'
sf_mo.minimum_longitude = fc_maxlon
sf_mo.maximum_longitude = int_maxlon
sf_mo.comment = 'Mid ocean overturning streamfunction (sf_mo = sf_wbw + sf_int).'
sf_mo[:] = sf_geoint[:] + sf_wbw[:]
# Total heat transport - RAPID approx
q_sum_rapid = dataset.createVariable('q_sum_rapid', np.float64,
(tdim.name))
q_sum_rapid.units = 'PW'
q_sum_rapid.minimum_longitude = fc_minlon
q_sum_rapid.maximum_longitude = int_maxlon
q_sum_rapid.comment = 'Total heat transport across section calculated using RAPID approximations (q_sum_rapid = q_fc + q_ek + q_mo = q_ot_rapid + q_gyre_rapid + q_net_rapid)'
q_sum_rapid[:] = rapid_trans.oht_total / 1e15
# Gyre heat transport - RAPID approx
q_gyre_rapid = dataset.createVariable('q_gyre_rapid', np.float64,
(tdim.name))
q_gyre_rapid.units = 'PW'
q_gyre_rapid.minimum_longitude = fc_minlon
q_gyre_rapid.maximum_longitude = int_maxlon
q_gyre_rapid.comment = 'Heat transport by the horizontal circulation calculated using RAPID approximations '
q_gyre_rapid[:] = rapid_trans.oht_by_horizontal / 1e15
# Overturning heat transport - RAPID approx
q_ot_rapid = dataset.createVariable('q_ot_rapid', np.float64, (tdim.name))
q_ot_rapid.units = 'PW'
q_ot_rapid.minimum_longitude = fc_minlon
q_ot_rapid.maximum_longitude = int_maxlon
q_ot_rapid.comment = 'Heat transport by the overturning circulation calculated using RAPID approximations'
q_ot_rapid[:] = rapid_trans.oht_by_overturning / 1e15
# Heat transport by net throughflow - RAPID approx
q_net_rapid = dataset.createVariable('q_net_rapid', np.float64,
(tdim.name))
q_net_rapid.units = 'PW'
q_net_rapid.minimum_longitude = fc_minlon
q_net_rapid.maximum_longitude = int_maxlon
q_net_rapid.comment = 'Heat transport referenced to 0C by the net flow through the section using RAPID approximations'
q_net_rapid[:] = rapid_trans.oht_by_net / 1e15
# Total heat transport - model v
q_sum_model = dataset.createVariable('q_sum_model', np.float64,
(tdim.name))
q_sum_model.units = 'PW'
q_sum_model.minimum_longitude = fc_minlon
q_sum_model.maximum_longitude = int_maxlon
q_sum_model.comment = 'Total heat transport across section calculated using model velocities (q_sum_model = q_gyre_model + q_ot_model + q_net_model)'
q_sum_model[:] = model_trans.oht_total / 1e15
# Gyre heat transport -model v
q_gyre_model = dataset.createVariable('q_gyre_model', np.float64,
(tdim.name))
q_gyre_model.units = 'PW'
q_gyre_model.minimum_longitude = fc_minlon
q_gyre_model.maximum_longitude = int_maxlon
q_gyre_model.comment = 'Heat transport by the horizontal circulation calculated using model velocities'
q_gyre_model[:] = model_trans.oht_by_horizontal / 1e15
# Overturning heat transport - model v
q_ot_model = dataset.createVariable('q_ot_model', np.float64, (tdim.name))
q_ot_model.units = 'PW'
q_ot_model.minimum_longitude = fc_minlon
q_ot_model.maximum_longitude = int_maxlon
q_ot_model.comment = 'Heat transport by the overturning circulation calculated using model velocities'
q_ot_model[:] = model_trans.oht_by_overturning / 1e15
# Heat transport by net throughflow - model v
q_net_model = dataset.createVariable('q_net_model', np.float64,
(tdim.name))
q_net_model.units = 'PW'
q_net_model.minimum_longitude = fc_minlon
q_net_model.maximum_longitude = int_maxlon
q_net_model.comment = 'Heat transport referenced to 0C by the net flow through the section using model velocities'
q_net_model[:] = model_trans.oht_by_net / 1e15
# Heat transport by florida current
q_fc = dataset.createVariable('q_fc', np.float64, (tdim.name))
q_fc.units = 'PW'
q_fc.minimum_longitude = fc_minlon
q_fc.maximum_longitude = fc_maxlon
q_fc.comment = 'Heat transport referenced to 0C by the Florida current'
q_fc[:] = fc_trans.oht_total / 1e15
# Heat transport by ekman
q_ek = dataset.createVariable('q_ek', np.float64, (tdim.name))
q_ek.units = 'PW'
q_ek.minimum_longitude = wbw_maxlon
q_ek.maximum_longitude = int_maxlon
q_ek.comment = 'Heat transport referenced to 0C by Ekman transport'
q_ek[:] = ek_trans.oht_total / 1e15
# Heat transport by wbw
q_wbw = dataset.createVariable('q_wbw', np.float64, (tdim.name))
q_wbw.units = 'PW'
q_wbw.minimum_longitude = fc_maxlon
q_wbw.maximum_longitude = wbw_maxlon
q_wbw.comment = 'Heat transport referenced to 0C by western boundary wedge transport'
q_wbw[:] = wbw_trans.oht_total / 1e15
# Heat transport by zonal mean geostrophic interior
q_geoint = dataset.createVariable('q_geoint', np.float64, (tdim.name))
q_geoint.units = 'PW'
q_geoint.minimum_longitude = wbw_maxlon
q_geoint.maximum_longitude = int_maxlon
q_geoint.comment = 'Heat transport referenced to 0C by zonal mean of geostrophic interior transport'
q_geoint[:] = (int_trans.oht_total - int_trans.oht_by_horizontal) / 1e15
# Heat transport by standing "eddy" component of geostrophic interior
q_eddy = dataset.createVariable('q_eddy', np.float64, (tdim.name))
q_eddy.units = 'PW'
q_eddy.minimum_longitude = wbw_maxlon
q_eddy.maximum_longitude = int_maxlon
q_eddy.comment = 'Heat transport referenced to 0C by standing eddy component of geostrophic interior transport'
q_eddy[:] = (int_trans.oht_by_horizontal) / 1e15
# Heat transport by mid ocean
q_mo = dataset.createVariable('q_mo', np.float64, (tdim.name))
q_mo.units = 'PW'
q_mo.minimum_longitude = wbw_maxlon
q_mo.maximum_longitude = int_maxlon
q_mo.comment = 'Heat transport referenced to 0C by mid-ocean transport (q_mo = q_geoint + q_wbw + q_eddy)'
q_mo[:] = q_geoint[:] + q_wbw[:] + q_eddy[:]
return dataset
def NCA(self, t, C):
AUClist = []
AUMClist = []
Cminlist = []
Cavglist = []
Cmaxlist = []
Tmaxlist = []
Tminlist = []
Ndoses = len(self.drugSource.parsedDoseList)
for ndose in range(0, max(Ndoses - 1, 1)):
tperiod0 = self.drugSource.parsedDoseList[ndose].t0
if ndose + 1 < Ndoses:
tperiodF = self.drugSource.parsedDoseList[
ndose + 1].t0 - self.model.deltaT
else:
tperiodF = np.max(t) - 1
idx0 = find_nearest(t, tperiod0)
idxF = find_nearest(t, tperiodF)
AUC0t = 0
AUMC0t = 0
t0 = t[idx0 + 1]
for idx in range(idx0, idxF + 1):
dt = (t[idx + 1] - t[idx])
if C[idx + 1] >= C[idx]: # Trapezoidal in the raise
AUC0t += 0.5 * dt * (C[idx] + C[idx + 1])
AUMC0t += 0.5 * dt * (C[idx] * t[idx] +
C[idx + 1] * t[idx + 1])
else: # Log-trapezoidal in the decay
decrement = C[idx] / C[idx + 1]
K = math.log(decrement)
B = K / dt
AUC0t += dt * (C[idx] - C[idx + 1]) / K
AUMC0t += (C[idx] * (t[idx] - tperiod0) - C[idx + 1] *
(t[idx + 1] - tperiod0)) / B - (
C[idx + 1] - C[idx]) / (B * B)
if idx == idx0:
Cmax = C[idx]
Tmax = t[idx] - t0
Cmin = C[idx]
Tmin = t[idx] - t0
else:
if C[idx] < Cmin:
Cmin = C[idx]
Tmin = t[idx] - t0
elif C[idx] > Cmax:
Cmax = C[idx]
Tmax = t[idx] - t0
if ndose == 0:
Cmin = C[idx]
Tmin = t[idx] - t0
AUClist.append(AUC0t)
AUMClist.append(AUMC0t)
Cminlist.append(Cmin)
Cmaxlist.append(Cmax)
Tmaxlist.append(Tmax)
Tminlist.append(Tmin)
Cavglist.append(AUC0t / (t[idxF] - t[idx0]))
print("Fluctuation = Cmax/Cmin")
print("Accumulation(1) = Cavg(n)/Cavg(1) %")
print("Accumulation(n) = Cavg(n)/Cavg(n-1) %")
print("Steady state fraction(n) = Cavg(n)/Cavg(last) %")
for ndose in range(0, len(AUClist)):
fluctuation = Cmaxlist[ndose] / Cminlist[ndose]
if ndose > 0:
accumn = Cavglist[ndose] / Cavglist[ndose - 1]
else:
accumn = 0
print("Dose #%d: Cavg= %f [%s] Cmin= %f [%s] Tmin= %d [min] Cmax= %f [%s] Tmax= %d [min] Fluct= %f %% Accum(1)= %f %% Accum(n)= %f %% SSFrac(n)= %f %% AUC= %f [%s] AUMC= %f [%s]"%\
(ndose+1,Cavglist[ndose], strUnit(self.Cunits.unit), Cminlist[ndose],strUnit(self.Cunits.unit), int(Tminlist[ndose]), Cmaxlist[ndose], strUnit(self.Cunits.unit),
int(Tmaxlist[ndose]), fluctuation*100, Cavglist[ndose]/Cavglist[0]*100, accumn*100, Cavglist[ndose]/Cavglist[-1]*100, AUClist[ndose],strUnit(self.AUCunits),
AUMClist[ndose],strUnit(self.AUMCunits)))
self.AUC0t = AUClist[-1]
self.AUMC0t = AUMClist[-1]
self.MRT = self.AUMC0t / self.AUC0t
self.Cmin = Cminlist[-1]
self.Cmax = Cmaxlist[-1]
self.Cavg = Cavglist[-1]
self.fluctuation = self.Cmax / self.Cmin
self.percentageAccumulation = Cavglist[-1] / Cavglist[0]
print(" AUC0t=%f [%s]" % (self.AUC0t, strUnit(self.AUCunits)))
print(" AUMC0t=%f [%s]" % (self.AUMC0t, strUnit(self.AUMCunits)))
print(" MRT=%f [min]" % self.MRT)
def emcee_inference(star, Ndim, ranges, lbdarr, wave, logF, dlogF, minfo,
listpar, logF_grid, vsin_obs, sig_vsin_obs, incl, dist_pc,
sig_dist_pc, isig, dims, include_rv, a_parameter,
af_filter, tag, plot_fits, long_process, log_scale, model,
acrux, pool, Nproc, stellar_prior, npy_star, pdf_mas,
pdf_obl, pdf_age, grid_mas, grid_obl, grid_age, band,
lbd_range):
#star, Ndim, ranges, lbdarr, wave, logF, dlogF, minfo,
# listpar, logF_grid, vsin_obs, sig_vsin_obs, dist_pc,
# sig_dist_pc, isig, dims, include_rv, a_parameter,
# af_filter, tag, plot_fits, long_process, log_scale,
# model, acrux, pool, Nproc, stellar_prior, npy_star,
# pdf_mas, pdf_obl, pdf_age, grid_mas,
# grid_obl, grid_age, band):
if long_process is True:
#Nwalk = 2500 #500 # 200 # 500
#nint_burnin = 500 #100 # 50
#nint_mcmc = 5000 # 500 # 1000
Nwalk = 500 # 200 # 500
nint_burnin = 100 # 50
nint_mcmc = 1000 # 500 # 1000
else:
Nwalk = 50
nint_burnin = 20
nint_mcmc = 100
p0 = [
np.random.rand(Ndim) * (ranges[:, 1] - ranges[:, 0]) + ranges[:, 0]
for i in range(Nwalk)
]
start_time = time.time()
with Pool() as pool:
if acrux is True:
sampler = emcee.EnsembleSampler(
Nwalk,
Ndim,
lnprob,
args=[
incl, wave, logF, dlogF, minfo, listpar, logF_grid,
vsin_obs, sig_vsin_obs, dist_pc, sig_dist_pc, isig, ranges,
dims, include_rv, model, stellar_prior, npy_star, pdf_mas,
pdf_obl, pdf_age, grid_mas, grid_obl, grid_age
],
a=a_parameter,
pool=pool)
else:
sampler = emcee.EnsembleSampler(
Nwalk,
Ndim,
lnprob,
args=[
incl, wave, logF, dlogF, minfo, listpar, logF_grid,
vsin_obs, sig_vsin_obs, dist_pc, sig_dist_pc, isig, ranges,
dims, include_rv, model, stellar_prior, npy_star, pdf_mas,
pdf_obl, pdf_age, grid_mas, grid_obl, grid_age
],
a=a_parameter)
sampler_tmp = run_emcee(p0,
sampler,
nint_burnin,
nint_mcmc,
Ndim,
file_name=star)
print("--- %s minutes ---" % ((time.time() - start_time) / 60))
sampler, params_fit, errors_fit, maxprob_index,\
minprob_index, af = sampler_tmp
if include_rv is True:
mass_true, obt_true, xc_true,\
cos_true, ebv_true, dist_true, rv_true = params_fit
else:
if model == 'befavor' or model == 'befavor_new':
mass_true, obt_true, xc_true,\
cos_true, ebv_true, dist_true = params_fit
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
mass_true, obt_true, xc_true, n0, Rd, n_true,\
cos_true, ebv_true, dist_true = params_fit
if model == 'bcmi_pol' or model == 'aara_pol':
mass_true, obt_true, xc_true, n0, Rd, n_true,\
cos_true = params_fit
if model == 'beatlas':
mass_true, obt_true, rh0_true, nix_true,\
inc_true, dis_true, ebv_true = params_fit
# if model is False:
# angle_in_rad = np.arccos(params_fit[6])
# params_fit[6] = (np.arccos(params_fit[6])) * (180. / np.pi)
# errors_fit[6] = (errors_fit[6] / (np.sqrt(1. -
# (np.cos(angle_in_rad)**2.)))) * (180. / np.pi)
chain = sampler.chain
if af_filter is True:
acceptance_fractions = sampler.acceptance_fraction
chain = chain[(acceptance_fractions >= 0.20)
& (acceptance_fractions <= 0.50)]
af = acceptance_fractions[(acceptance_fractions >= 0.20)
& (acceptance_fractions <= 0.50)]
af = np.mean(af)
af = str('{0:.2f}'.format(af))
# Saving first sample
# file_npy = 'figures/' + str(star) + '/' + 'Walkers_' +\
# str(Nwalk) + '_Nmcmc_' + str(nint_mcmc) +\
# '_af_' + str(af) + '_a_' + str(a_parameter) +\
# tag + ".npy"
# np.save(file_npy, chain)
current_folder = 'figures/' + str(star) + '/' + str(lbd_range) + '/'
file_npy = current_folder + str(star) + ".npy"
np.save(file_npy, chain)
# plot results
mpl.rcParams['mathtext.fontset'] = 'stix'
mpl.rcParams['font.family'] = 'STIXGeneral'
mpl.rcParams['font.size'] = 16
# Loading first dataframe
chain_1 = np.load(file_npy)
Ndim_1 = np.shape(chain_1)[-1]
flatchain_1 = chain_1.reshape((-1, Ndim_1))
if model == 'befavor' or 'befavor_new':
mas_1 = flatchain_1[:, 0]
obl_1 = flatchain_1[:, 1]
age_1 = flatchain_1[:, 2]
inc_1 = flatchain_1[:, 3]
dis_1 = flatchain_1[:, 4]
ebv_1 = flatchain_1[:, 5]
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
mas_1 = flatchain_1[:, 0]
obl_1 = flatchain_1[:, 1]
age_1 = flatchain_1[:, 2]
rh0_1 = flatchain_1[:, 3]
rdk_1 = flatchain_1[:, 4]
nix_1 = flatchain_1[:, 5]
inc_1 = flatchain_1[:, 6]
dis_1 = flatchain_1[:, 7]
ebv_1 = flatchain_1[:, 8]
if model == 'bcmi_pol' or model == 'aara_pol':
mas_1 = flatchain_1[:, 0]
obl_1 = flatchain_1[:, 1]
age_1 = flatchain_1[:, 2]
rh0_1 = flatchain_1[:, 3]
rdk_1 = flatchain_1[:, 4]
nix_1 = flatchain_1[:, 5]
inc_1 = flatchain_1[:, 6]
if model == 'beatlas':
mas_1 = flatchain_1[:, 0]
obl_1 = flatchain_1[:, 1]
rh0_1 = flatchain_1[:, 2]
nix_1 = flatchain_1[:, 3]
inc_1 = flatchain_1[:, 4]
dis_1 = flatchain_1[:, 5]
ebv_1 = flatchain_1[:, 6]
if include_rv is True:
rv_1 = flatchain_1[:, 6]
par_list = np.zeros([len(mas_1), 7])
else:
if model == 'befavor' or model == 'befavor_new':
par_list = np.zeros([len(mas_1), 6])
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
par_list = np.zeros([len(mas_1), 9])
if model == 'bcmi_pol' or model == 'aara_pol':
par_list = np.zeros([len(mas_1), 7])
if model == 'beatlas':
par_list = np.zeros([len(mas_1), 7])
for i in range(len(mas_1)):
if include_rv is True:
par_list[i] = [
mas_1[i], obl_1[i], age_1[i], inc_1[i], dis_1[i], ebv_1[i],
rv_1[i]
]
else:
if model == 'befavor' or model == 'befavor_new':
par_list[i] = [
mas_1[i], obl_1[i], age_1[i], inc_1[i], dis_1[i], ebv_1[i]
]
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
par_list[i] = [
mas_1[i], obl_1[i], age_1[i], rh0_1[i], rdk_1[i], nix_1[i],
inc_1[i], dis_1[i], ebv_1[i]
]
if model == 'bcmi_pol' or model == 'aara_pol':
par_list[i] = [
mas_1[i], obl_1[i], age_1[i], rh0_1[i], rdk_1[i], nix_1[i],
inc_1[i]
]
if model == 'beatlas':
par_list[i] = [
mas_1[i], obl_1[i], rh0_1[i], nix_1[i], inc_1[i], dis_1[i],
ebv_1[i]
]
# plot corner
if include_rv is True:
samples = np.vstack((mas_1, obl_1, age_1, inc_1, dis_1, ebv_1, rv_1)).T
else:
if model == 'befavor' or model == 'befavor_new':
samples = np.vstack((mas_1, obl_1, age_1, inc_1, dis_1, ebv_1)).T
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
samples = np.vstack((mas_1, obl_1, age_1, rh0_1, rdk_1, nix_1,
inc_1, dis_1, ebv_1)).T
if model == 'bcmi_pol' or model == 'aara_pol':
samples = np.vstack(
(mas_1, obl_1, age_1, rh0_1, rdk_1, nix_1, inc_1)).T
if model == 'beatlas':
samples = np.vstack(
(mas_1, obl_1, rh0_1, nix_1, inc_1, dis_1, ebv_1)).T
k, arr_t_tc, arr_Xc = t_tms_from_Xc(mass_true,
savefig=False,
plot_fig=False)
ttms_ip = np.arange(0.001, 1., 0.001)
Xc_ip = np.interp(ttms_ip, arr_t_tc[k], arr_Xc[k])
for i in range(len(samples)):
if model == 'befavor' or model == 'aara' or\
model == 'acol' or model == 'bcmi' or\
model == 'befavor_new' or model == 'bcmi_pol' or\
model == 'aara_pol':
# Calculating logg for the non_rotating case
Mstar, oblat, Hfrac = samples[i][0], samples[i][1],\
samples[i][2]
else:
Mstar, oblat, Hfrac = samples[i][0], samples[i][1], 0.3
if model != 'befavor_new':
t = np.max(np.array([hfrac2tms(Hfrac), 0.]))
else:
t = np.copy(Hfrac)
Rpole, logL = geneva_interp_fast(Mstar,
oblat,
t,
neighbours_only=True,
isRpole=False)
# Converting oblat to W
samples[i][1] = obl2W(samples[i][1])
if model == 'befavor':
# Converting angles to degrees
samples[i][3] = (np.arccos(samples[i][3])) * (180. / np.pi)
# Converting Xc to t(tms)
samples[i][2] = ttms_ip[find_nearest(Xc_ip, samples[i][2])[1]]
if model == 'befavor_new':
# Converting angles to degrees
samples[i][3] = (np.arccos(samples[i][3])) * (180. / np.pi)
if model == 'aara':
# Converting Xc to t(tms)
samples[i][2] = ttms_ip[find_nearest(Xc_ip, samples[i][2])[1]]
samples[i][5] = samples[i][5] + 1.5
samples[i][6] = (np.arccos(samples[i][6])) * (180. / np.pi)
if model == 'acol' or model == 'bcmi' or model == 'bcmi_pol' or\
model == 'aara_pol':
# Converting Xc to t(tms)
samples[i][2] = ttms_ip[find_nearest(Xc_ip, samples[i][2])[1]]
samples[i][6] = (np.arccos(samples[i][6])) * (180. / np.pi)
if model == 'beatlas':
samples[i][4] = (np.arccos(samples[i][4])) * (180. / np.pi)
if model == 'aara_pol':
samples[i][5] = samples[i][5] + 1.5
# plot corner
quantiles = [0.16, 0.5, 0.84]
if include_rv is True:
labels = [
r'$M\,[\mathrm{M_\odot}]$', r'$W$', r"$t/t_\mathrm{ms}$",
r'$i[\mathrm{^o}]$', r'$d\,[pc]$', r'E(B-V)', r'$R_\mathrm{V}$'
]
else:
if model == 'befavor' or model == 'befavor_new':
labels = [
r'$M\,[\mathrm{M_\odot}]$', r'$W$', r"$t/t_\mathrm{ms}$",
r'$i[\mathrm{^o}]$', r'$d\,[\mathrm{pc}]$', r'E(B-V)'
]
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
labels = [
r'$M\,[\mathrm{M_\odot}]$', r'$W$', r"$t/t_\mathrm{ms}$",
r'$\log \, n_0 \, [\mathrm{cm^{-3}}]$',
r'$R_\mathrm{D}\, [R_\star]$', r'$n$', r'$i[\mathrm{^o}]$',
r'$d\,[\mathrm{pc}]$', r'E(B-V)'
]
if model == 'bcmi_pol' or model == 'aara_pol':
labels = [
r'$M\,[\mathrm{M_\odot}]$', r'$W$', r"$t/t_\mathrm{ms}$",
r'$\log \, n_0$', r'$R_\mathrm{D}\, [R_\star]$', r'$n$',
r'$i[\mathrm{^o}]$'
]
if model == 'beatlas':
labels = [
r'$M\,[\mathrm{M_\odot}]$', r'$W$', r'$\Sigma_0$', r'$n$',
r'$i[\mathrm{^o}]$', r'$d\,[pc]$', r'E(B-V)'
]
if model == 'befavor':
ranges[1] = obl2W(ranges[1])
ranges[2][0] = ttms_ip[find_nearest(Xc_ip, ranges[2][1])[1]]
ranges[2][1] = ttms_ip[find_nearest(Xc_ip, ranges[2][0])[1]]
ranges[3] = (np.arccos(ranges[3])) * (180. / np.pi)
ranges[3] = np.array([ranges[3][1], ranges[3][0]])
if model == 'befavor_new':
ranges[1] = obl2W(ranges[1])
ranges[3] = (np.arccos(ranges[3])) * (180. / np.pi)
ranges[3] = np.array([ranges[3][1], ranges[3][0]])
if model == 'aara':
ranges[1] = obl2W(ranges[1])
ranges[2][0] = ttms_ip[find_nearest(Xc_ip, ranges[2][1])[1]]
ranges[2][1] = ttms_ip[find_nearest(Xc_ip, ranges[2][0])[1]]
ranges[3] = np.array([ranges[3][1], ranges[3][0]])
ranges[5] = ranges[5] + 1.5
ranges[6] = (np.arccos(ranges[6])) * (180. / np.pi)
ranges[6] = np.array([ranges[6][1], ranges[6][0]])
if model == 'acol' or model == 'bcmi' or model == 'bcmi_pol' or\
model == 'aara_pol':
ranges[1] = obl2W(ranges[1])
ranges[2][0] = ttms_ip[find_nearest(Xc_ip, ranges[2][1])[1]]
ranges[2][1] = ttms_ip[find_nearest(Xc_ip, ranges[2][0])[1]]
ranges[3] = np.array([ranges[3][1], ranges[3][0]])
ranges[6] = (np.arccos(ranges[6])) * (180. / np.pi)
ranges[6] = np.array([ranges[6][1], ranges[6][0]])
if model == 'beatlas':
ranges[1] = obl2W(ranges[1])
ranges[3] = np.array([ranges[3][-1], ranges[3][0]])
ranges[4] = (np.arccos(ranges[4])) * (180. / np.pi)
ranges[4] = np.array([ranges[4][1], ranges[4][0]])
corner(samples,
labels=labels,
range=ranges,
quantiles=quantiles,
plot_contours=True,
smooth=2.,
smooth1d=False,
plot_datapoints=True,
label_kwargs={'fontsize': 17},
title_kwargs={'fontsize': 17},
truths=None,
show_titles=True,
color_hist='black',
plot_density=True,
fill_contours=False,
no_fill_contours=False,
normed=True)
if plot_fits is True:
plot_fit_last(params_fit, wave, logF, dlogF, minfo, listpar, lbdarr,
logF_grid, isig, dims, Nwalk, nint_mcmc, ranges,
include_rv, file_npy, log_scale, model)
fig_name = 'Walkers_' + np.str(Nwalk) + '_Nmcmc_' +\
np.str(nint_mcmc) + '_af_' + str(af) + '_a_' +\
str(a_parameter) + tag
plot_residuals(params_fit, wave, logF, dlogF, minfo, listpar, lbdarr,
logF_grid, isig, dims, Nwalk, nint_mcmc, ranges, include_rv,
file_npy, log_scale, model)
plt.savefig(current_folder + fig_name + '_' + lbd_range + '.png', dpi=100)
plot_convergence(file_npy, file_npy[:-4] + '_convergence', model)
# Saving the chord diagram
if model == 'aara' or model == 'bcmi' or model == 'acol':
chord_plot_complete(folder='figures/', file=str(star))
elif model == 'bcmi_pol' or model == 'aara_pol':
chord_plot_pol(folder='figures/', file=str(star))
else:
chord_plot(folder='figures/', file=str(star))
return
