def simulate(self, plot=False, **kwargs):
"""
TODO Different run parameters can be passed through kwargs
"""
waves, pdf = self.source_spectrum[0], self.source_spectrum[1]
pdf_tot = scipy.integrate.simps(pdf, waves)
t0 = time.time()
d0 = time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime())
# START SIMULATION
log.info("Beginning simulation, %s", d0)
for m in self.orders:
# pre sample wavelengths
mwaves, mpdf = wf.feed_spectrum(self, m, waves, pdf)
if mwaves.size == 0 or mpdf.size == 0:
log.warning("Order %s failed. Source spectrum does not have wavelengths in this range.", m)
continue
pdf_ratio = scipy.integrate.simps(mpdf, mwaves) / pdf_tot
mnrays = int(pdf_ratio * self.nrays)
# just in case we are given 0 rays to simulate
while mnrays <= 0:
mnrays = np.random.poisson(mnrays)
waves_in = wf.sample_cdf(mwaves, mpdf, mnrays)
self.nrays_per_order.append(mnrays)
# pre sample slit
slit_x, slit_y = self.slitfunc(mnrays)
# normalize to unity
slit_x /= self.slit_height
slit_y /= self.slit_height
# input through model
assert slit_x.size == slit_y.size == waves_in.size
self.modelfunc(m, waves_in, slit_x, slit_y)
self.sim_time = time.time() - t0
inds = np.where(self.outarr != 0)
self.mean_rays_per_pixel = np.mean(self.outarr[inds])
self.med_rays_per_pixel = np.median(self.outarr[inds])
self.min_rays_per_pixel = np.min(self.outarr[inds])
self.max_rays_per_pixel = np.max(self.outarr[inds])
self.nrays_tot = np.sum(self.outarr[inds])
def simulate(self, plot=False, **kwargs):
"""
TODO Different run parameters can be passed through kwargs
"""
waves, pdf = self.source_spectrum[0], self.source_spectrum[1]
pdf_tot = scipy.integrate.simps(pdf, waves)
t0 = time.time()
d0 = time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime())
self.mwaves_mpdfs = []
# START SIMULATION
log.info("Beginning simulation, %s", d0)
for m in self.orders:
# pre sample wavelengths
mwaves, mpdf = wf.feed_spectrum(self, m, waves, pdf)
# Blaze function
if self.blaze:
blaze_eff, lambda_blaze = blazefunc.blaze_func(mwaves, m, self.echang, self.sigma_ech_inv, self.gamma_ech, self.blaze_ech)
# New probability distribution with blaze efficiency embedded
mpdf = np.multiply(mpdf, blaze_eff)
# save mwaves and mwaves_mpdfs
self.mwaves_mpdfs.append((mwaves,mpdf))
if mwaves.size == 0 or mpdf.size == 0:
log.warning("Order %s failed. Source spectrum does not have wavelengths in this range.", m)
continue
pdf_ratio = scipy.integrate.simps(mpdf, mwaves) / pdf_tot
mnrays = int(pdf_ratio * self.nrays)
waves_in = wf.sample_cdf(mwaves, mpdf, mnrays)
self.nrays_per_order.append(mnrays)
# pre sample slit
slit_x, slit_y = self.slitfunc(mnrays)
# polarimeter
if self.polarimeter:
log.info("Adding polarimeter shift to ray coordinates")
slit_x, slit_y = self.polarimeter_shift(m, waves_in, slit_x, slit_y)
# normalize to unity
slit_x /= self.slit_height
slit_y /= self.slit_height
# input through model
assert slit_x.size == slit_y.size == waves_in.size
self.modelfunc(m, waves_in, slit_x, slit_y)
self.sim_time = time.time() - t0
inds = np.where(self.outarr != 0)
# self.mean_rays_per_pixel = np.mean(self.outarr[inds])
# self.med_rays_per_pixel = np.median(self.outarr[inds])
# self.min_rays_per_pixel = np.min(self.outarr[inds])
# self.max_rays_per_pixel = np.max(self.outarr[inds])
# self.nrays_tot = np.sum(self.outarr[inds])
#self.outwaves[inds] /= self.outarr[inds]
# Because we get errors from inds if no rays hit the detectors.
# For wavemap, overwrite outarray with wavelengths
if (self.wavemap==True):
self.outwaves /= self.outarr # Normalise wavelengths
self.outwaves[np.isnan(self.outwaves)]=0 # Remove eventual NaNs
self.outarr = self.outwaves
