def parallel_bulkfit(path, num_splits=20, ncores=8, start_pt=0):
'''
Run bulk fitting in parallel. Results are outputted in chunks to make
restarting easier.
'''
spectra = [f for f in os.listdir(path) if f[-4:] == 'fits']
split_at = len(spectra) / num_splits
splits = [split_at * i for i in range(1, num_splits)]
splits.append(len(spectra))
splits = splits[start_pt:]
prev_split = 0
for i, split in enumerate(splits):
print("On split " + str(i + 1) + " of " + str(len(splits)))
print(str(datetime.now()))
split_spectra = spectra[prev_split:split]
pool = Pool(processes=ncores)
output = pool.map(do_specfit, split_spectra)
pool.close()
pool.join()
df = DataFrame(output[0], columns=split_spectra[:1])
for out, spec in zip(output[1:], split_spectra[1:]):
df[spec[:-5]] = out
df.to_csv("spectral_fitting_" + str(i + 1) + ".csv")
prev_split = split
def parallel_bulkfit(path, num_splits=20, ncores=8, start_pt=0):
'''
Run bulk fitting in parallel. Results are outputted in chunks to make
restarting easier.
'''
spectra = [f for f in os.listdir(path) if f[-4:] == 'fits']
split_at = len(spectra) / num_splits
splits = [split_at*i for i in range(1, num_splits)]
splits.append(len(spectra))
splits = splits[start_pt:]
prev_split = 0
for i, split in enumerate(splits):
print("On split " + str(i+1) + " of " + str(len(splits)))
print(str(datetime.now()))
split_spectra = spectra[prev_split:split]
pool = Pool(processes=ncores)
output = pool.map(do_specfit, split_spectra)
pool.close()
pool.join()
df = DataFrame(output[0], columns=split_spectra[:1])
for out, spec in zip(output[1:], split_spectra[1:]):
df[spec[:-5]] = out
df.to_csv("spectral_fitting_"+str(i+1)+".csv")
prev_split = split
e = (ei-ef)
es.append(e)
ei = ef
es = np.array(es)
print integrator + " done."
return [es]
Nsteps = 10000
dt = 0.00102839712987319
integrators = ["wh","whfast-nocor"]
parameters = [(i) for i in integrators]
pool = InterruptiblePool()
res = np.array(pool.map(simulation,parameters)).reshape(len(integrators),Nsteps)
import matplotlib; matplotlib.use("pdf")
import matplotlib.pyplot as plt
from matplotlib import ticker
from matplotlib.colors import LogNorm
f,axarr = plt.subplots(1,1,figsize=(10,7))
x = np.linspace(0,Nsteps*dt,Nsteps)
extent=[x.min(),x.max(), res.min(), res.max()]
axarr.set_xlim(extent[0], extent[1])
axarr.set_ylim(extent[2], extent[3])
axarr.set_xlabel(r"time [year ~ 4300]")
axarr.set_ylabel(r"rel energy error")
rebound.integrate(50000.*np.pi)
return [rebound.get_megno(), rebound.get_t()]
#I always set the (osculating) semimajor axis to 1, you can pass different initial e values
e0 = 0.9 # Rauch uses 0.9 for Fig 4
Scrit = 0.25 # always true if you use G=M=a=1
N = 80
dts = np.linspace(0.1,2.,N)
Ss = np.linspace(0,0.5,N)
parameters = [(Ss[i]*Scrit,dts[j]*2.*np.pi,e0) for i in range(N) for j in range(N)]
pool = InterruptiblePool()
res = pool.map(simulation,parameters)
res = np.nan_to_num(res)
megno = np.clip(res[:,0].reshape((N,N)),1.8,4.)
lyaptime = np.clip(np.absolute(res[:,1].reshape((N,N))),1.,1.e5)/2./np.pi # divide by 2pi to get in units of orbital period
import matplotlib; matplotlib.use("pdf")
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
f,axarr = plt.subplots(2)
extent=[dts.min(), dts.max(), Ss.min(), Ss.max()]
for ax in axarr:
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.set_xlabel(r"$\Delta t / t_{orb}$")
offsets = [
31.697, 14.437, 85.452, 26.216, 9.330, -879.063, 23.698, 21.273,
20.728, 32.616, 35.219, 9.005, 10.124, 14.678
]
beamwidth_250 = [18.2] * len(fits250)
beamwidth_350 = [24.9] * len(fits350)
# Inputs (adjust to desired wavelength)
beamwidths = beamwidth_350 # + beamwidth_350
distances = distances # + distances
fits_files = fits350 # + fits350
print "Started at " + str(datetime.now())
if not MULTICORE:
for i, filename in enumerate(fits_files):
wrapper(filename,
distances[i],
beamwidths[i],
offsets[i],
verbose=False)
else:
pool = Pool(processes=NCORES)
pool.map(single_input, izip(fits_files, distances, beamwidths,
offsets))
pool.close()
# pool.join()
"california_west-250_normed.fits"]
fits350 = ["pipeCenterB59-350.fits", "lupusI-350.fits", "aquilaM2-350.fits", "orionB-350.fits", "polaris-350.fits",
"chamaeleonI-350.fits", "perseus04-350.fits", "taurusN3-350.fits", "ic5146-350.fits",
"orionA-C-350.fits", "orionA-S-350.fits", "california_cntr-350.fits", "california_east-350.fits",
"california_west-350.fits"]
distances = [145., 150., 260., 400., 150., 170., 235.,
140., 460., 400., 400., 450., 450., 450.] # pc
offsets = [31.697, 14.437, 85.452, 26.216, 9.330, -879.063, 23.698,
21.273, 20.728, 32.616, 35.219, 9.005, 10.124, 14.678]
beamwidth_250 = [18.2] * len(fits250)
beamwidth_350 = [24.9] * len(fits350)
# Inputs (adjust to desired wavelength)
beamwidths = beamwidth_350 # + beamwidth_350
distances = distances # + distances
fits_files = fits350 # + fits350
print "Started at " + str(datetime.now())
if not MULTICORE:
for i, filename in enumerate(fits_files):
wrapper(filename, distances[i], beamwidths[i], offsets[i], verbose=False)
else:
pool = Pool(processes=NCORES)
pool.map(single_input, izip(fits_files, distances, beamwidths, offsets))
pool.close()
# pool.join()
