def read_array(ftype,
directory,
tag,
dataset,
numThreads=1,
noH=False,
physicalUnits=False,
CGS=False,
verbose=True):
"""
Args:
ftype (str)
directory (str)
tag (str)
dataset (str)
numThreads (int)
noH (bool)
physicalUnits (bool)
"""
start = timeit.default_timer()
files = get_files(ftype, directory, tag)
if numThreads == 1:
pool = schwimmbad.SerialPool()
elif numThreads == -1:
pool = schwimmbad.MultiPool()
else:
pool = schwimmbad.MultiPool(processes=numThreads)
lg = partial(read_hdf5, dataset=dataset)
dat = np.concatenate(list(pool.map(lg, files)), axis=0)
pool.close()
stop = timeit.default_timer()
print("Reading in '{}' for z = {} using {} thread(s) took {}s".format(
dataset,
np.round(read_header(ftype, directory, tag, dataset='Redshift'), 3),
numThreads, np.round(stop - start, 6)))
if noH:
dat = apply_hfreeUnits_conversion(files[0],
dataset,
dat,
verbose=verbose)
if physicalUnits:
dat = apply_physicalUnits_conversion(files[0],
dataset,
dat,
verbose=verbose)
if CGS:
dat = apply_CGSUnits_conversion(files[0],
dataset,
dat,
verbose=verbose)
return dat
def get_age(arr, z, numThreads=4):
if numThreads == 1:
pool = schwimmbad.SerialPool()
elif numThreads == -1:
pool = schwimmbad.MultiPool()
else:
pool = schwimmbad.MultiPool(processes=numThreads)
calc = partial(get_SFT, redshift=z)
Age = np.array(list(pool.map(calc, arr)))
return Age
def get_age(self, arr, z, numThreads=4):
if numThreads == 1:
pool = schwimmbad.SerialPool()
elif numThreads == -1:
pool = schwimmbad.MultiPool()
else:
pool = schwimmbad.MultiPool(processes=numThreads)
Age = self.cosmo.age(z).value - np.array(
list(pool.map(self.get_star_formation_time, arr)))
pool.close()
return Age
def test_freqmap_multi(cache_file):
with FreqMap(cache_file) as exp:
with schwimmbad.MultiPool() as pool:
pool.map(exp, list(range(16)), callback=exp.callback)
exp.status()
def proceed2(self, subd, dates):
assert isinstance(dates, list)
nworkers = len(dates)
assert nworkers < 127
tiles = [t for t, s in self.tg.subdmap.items() if s == subd]
for date in dates:
self.create_destdir(date, subd)
self.allocate_empty_binfile(date, subd)
tasks = [(date, subd, t) for t in tiles for date in dates]
pool = schwimmbad.MultiPool(processes=nworkers + 1)
tic = time.time()
pool.map(self.task_extract_convert, tasks)
toc = time.time()
elapsed = toc - tic
print(
f"time to convert {len(tasks)} tiles with {nworkers} workers: {elapsed:.4} s"
)
pool.close()
for date in dates:
self.cleantar(date, subd)
def get_flux_all(kappa, tag, BC_fac, IMF='Chabrier_300', inp='FLARES',
filters=flare.filters.NIRCam, Type='Total', log10t_BC=7.,
extinction='default', orientation="sim", numThreads=8):
print(f"Getting fluxes for tag {tag} with kappa={kappa}")
if inp == 'FLARES':
df = pd.read_csv('../weight_files/weights_grid.txt')
weights = np.array(df['weights'])
sims = np.arange(0, len(weights))
calc = partial(get_flux, kappa=kappa, tag=tag, BC_fac=BC_fac, IMF=IMF,
inp=inp, filters=filters, Type=Type,
log10t_BC=log10t_BC, extinction=extinction,
orientation=orientation)
pool = schwimmbad.MultiPool(processes=numThreads)
out = np.array(list(pool.map(calc, sims)))
pool.close()
else:
out = get_flux(00, kappa=kappa, tag=tag, BC_fac=BC_fac, IMF=IMF,
inp=inp, filters=filters, Type=Type,
log10t_BC=log10t_BC, extinction=extinction,
orientation=orientation)
return out
def get_data(tiles, date, hour):
if ismaster:
print(f"read HIST {date}:{hour:02} #tiles: {len(tiles)} in {subds}",
flush=True)
tasks = [(tile, hour, date) for tile in tiles]
pool = schwimmbad.MultiPool(processes=nthreads)
data = pool.map(proceed_tile, tasks)
pool.close()
return data
def do_frame(self, domain_args, data_args):
task = (self.hmaps[self.index], domain_args, data_args)
self.videotasks += [task] # data_args]
self.index += 1
if self.index == self.nthreads:
pool = schwimmbad.MultiPool(processes=self.nthreads+1)
pool.map(proceed_frame, self.videotasks)
pool.close()
self.index = 0
self.videotasks = []
def get_allcorners():
import schwimmbad
pool = schwimmbad.MultiPool()
res = pool.map(get_corner, alltiles)
pool.close()
corners = {}
for tile in alltiles:
corners[tile] = res[tile]
return corners
def setup_pool(self):
""" If threads > 1, setup a MultiPool, else run in serial mode """
if self.threads > 1:
import schwimmbad
logger.info("Creating MultiPool with {} processes".format(self.threads))
self.pool = schwimmbad.MultiPool(
self.threads, initializer=init, initargs=(self.likelihood, self.priors)
)
else:
self.pool = None
def pread(self, args):
varname, tiles = args
# if not self.has_threads:
# self.pool = schwimmbad.MultiPool(processes=self.nthreads+1)
# self.has_treads = True
tasks = [(self.readers[self.subdmap[tile]], varname, tile)
for tile in tiles]
pool = schwimmbad.MultiPool(processes=self.nthreads)
data = pool.map(read_grid, tasks)
pool.close()
return data
def get_all(dataset, tag):
df = pd.read_csv('weight_files/weights_grid.txt')
weights = np.array(df['weights'])
sims = np.arange(0,len(weights))
calc = partial(get_data, tag = tag, dataset = dataset)
pool = schwimmbad.MultiPool(processes=12)
dat = np.array(list(pool.map(calc, sims)))
pool.close()
return dat
def get_line_all(tag,
line,
inp='FLARES',
LF=True,
bins=np.arange(40, 46, 0.5),
Type='DustModelI'):
if inp == 'FLARES':
df = pd.read_csv('weight_files/weights_grid.txt')
weights = np.array(df['weights'])
sims = np.arange(0, len(weights))
calc = partial(get_line,
tag=tag,
line=line,
inp=inp,
LF=LF,
bins=bins,
Type=Type)
pool = schwimmbad.MultiPool(processes=12)
dat = np.array(list(pool.map(calc, sims)))
pool.close()
if LF:
hist = np.sum(dat, axis=0)
out = np.zeros(len(bins) - 1)
err = np.zeros(len(bins) - 1)
for ii, sim in enumerate(sims):
out += dat[ii] * weights[ii]
err += np.square(np.sqrt(dat[ii]) * weights[ii])
return out, hist, np.sqrt(err)
else:
return dat
else:
out = get_line(00,
tag=tag,
line=line,
inp=inp,
LF=LF,
bins=bins,
Type=Type)
return out
def mypool(func, tasks, nworkers=0, maxworkers=31):
if nworkers == 0:
nworkers = min(len(tasks), maxworkers)
# https://stackoverflow.com/questions/62186218/python-multiprocessing-attributeerror-cant-pickle-local-object
# if isinstance(tasks[0], tuple):
# f = Unpack(func)
# else:
# f = func
pool = schwimmbad.MultiPool(processes=nworkers+1)
data = pool.map(Unpack(func), tasks)
pool.close()
return data
def convert_fulltar(date, subd):
tasks = [(date, subd, t) for t, s in giga.subdmap.items() if s == subd]
create_destdir(date, subd)
allocate_empty_binfile(date, subd)
pool = schwimmbad.MultiPool(processes=nworkers + 1)
tic = time.time()
pool.map(task_extract_convert, tasks)
toc = time.time()
elapsed = toc - tic
print(
f"time to convert {len(tasks)} tiles with {nworkers} workers: {elapsed:.4} s"
)
pool.close()
cleantar(date, subd)
def get_all(tag, inp='FLARES', filter='FUV', Luminosity='DustModelI'):
if inp == 'FLARES':
sims = np.arange(0, 40)
calc = partial(get_data,
tag=tag,
inp=inp,
filter=filter,
Luminosity=Luminosity)
pool = schwimmbad.MultiPool(processes=12)
dat = np.array(list(pool.map(calc, sims)))
pool.close()
return dat
def multiproc_vca(subcube_locs, channels, output_loc, fig_loc, dimensions):
""" subcube_locs must be a list containing the string preceding the dimensions and subcube details.
e.g. everything before '..._7x7_x1_y2.fits' can be a list of multiple prefixes if needed.
arrayloc=/priv/myrtle1/gaskap/nickill/smc/vca/turbustatoutput/simcube_him_7x7_avatar
channels should be input as a list and be factors of the total channel range in decreasing order e.g. [32,16,8,4,2,1] """
with schwimmbad.MultiPool() as pool:
print('started multi processing')
print(datetime.datetime.now())
#create the lists for multiprocessing
#vcacube=[f'{subcube_locs}_{dimensions}x{dimensions}_x{i}_y{j}.fits' for j in np.arange(0,dimensions) for i in np.arange(0,dimensions)]
vcacube = [
f'{k}_{dimensions}x{dimensions}_x{i}_y{j}.fits'
for k in subcube_locs for j in np.arange(0, dimensions)
for i in np.arange(0, dimensions)
]
chansamps = [
channels for j in np.arange(0, dimensions) for k in subcube_locs
for i in np.arange(0, dimensions)
]
#arrayloc=[f'{output_loc}_{dimensions}x{dimensions}_x{i}_y{j}' for j in np.arange(0,dimensions) for i in np.arange(0,dimensions)]
arrayloc = [
f'{k}_{dimensions}x{dimensions}_x{i}_y{j}' for k in output_loc
for j in np.arange(0, dimensions)
for i in np.arange(0, dimensions)
]
#figloc=[f'{fig_loc}_{dimensions}x{dimensions}_x{i}_y{j}' for j in np.arange(0,dimensions) for i in np.arange(0,dimensions)]
figloc = [
f'{k}_{dimensions}x{dimensions}_x{i}_y{j}' for k in fig_loc
for j in np.arange(0, dimensions)
for i in np.arange(0, dimensions)
]
inputs = list(zip(vcacube, chansamps, arrayloc, figloc))
print(f'THESE ARE THE INPUTS FOR MULTIPROCESSING:{inputs}')
out = list(pool.map(do_vca, inputs))
print('finished multiprocessing')
print(datetime.datetime.now())
print(out)
def get_lum_all(kappa, tag, BC_fac, IMF='Chabrier_300',
bins=np.arange(-24, -16, 0.5), inp='FLARES', LF=True,
filters=('FAKE.TH.FUV'), Type='Total', log10t_BC=7.,
extinction='default', orientation="sim", numThreads=8,
masslim=None):
print(f"Getting luminosities for tag {tag} with kappa={kappa}")
if inp == 'FLARES':
df = pd.read_csv('../weight_files/weights_grid.txt')
weights = np.array(df['weights'])
sims = np.arange(0, len(weights))
calc = partial(get_lum, kappa=kappa, tag=tag, BC_fac=BC_fac, IMF=IMF,
bins=bins, inp=inp, LF=LF, filters=filters, Type=Type,
log10t_BC=log10t_BC, extinction=extinction,
orientation=orientation, masslim=masslim)
pool = schwimmbad.MultiPool(processes=numThreads)
dat = np.array(list(pool.map(calc, sims)))
pool.close()
if LF:
hist = np.sum(dat, axis=0)
out = np.zeros(len(bins) - 1)
err = np.zeros(len(bins) - 1)
for ii, sim in enumerate(sims):
err += np.square(np.sqrt(dat[ii]) * weights[ii])
out += dat[ii] * weights[ii]
return out, hist, np.sqrt(err)
else:
return dat
else:
out = get_lum(00, kappa=kappa, tag=tag, BC_fac=BC_fac, IMF=IMF,
bins=bins, inp=inp, LF=LF, filters=filters, Type=Type,
log10t_BC=log10t_BC, extinction=extinction,
orientation=orientation, masslim=masslim)
return out
def choose_pool(parallel):
"""
This is a remake of schwimmbad.choose_pool with a single argument.
"""
# TODO: This function should be refactored as a factory class with methods
# to enable more thorough testing of imports, MPI behaviour, etc.
if hasattr(parallel, 'map'):
# user-defined pool
pool = parallel
elif parallel is None:
# serial calculation - define dummy pool
pool = NonePool()
elif NO_SCHWIMMBAD:
raise DependencyMissing(
'schwimmbad',
"To perform inference calculations in parallel, install schwimmbad"
" with \'conda install -c conda-forge schwimmbad\' or define your "
"Strategy object with a 'parallel' keyword argument that is a "
"multiprocessing.Pool object. To run serial calculations instead, "
"pass in parallel=None.")
elif isinstance(parallel, int):
pool = schwimmbad.MultiPool(parallel)
elif parallel is 'all':
threads = os.cpu_count()
pool = choose_pool(threads)
elif parallel is 'mpi':
pool = schwimmbad.MPIPool()
# need to kill all non-master instances of currently running script
if not pool.is_master():
pool.wait()
sys.exit(0)
elif parallel is 'auto':
# try mpi, otherwise go for multiprocessing
if schwimmbad.MPIPool.enabled():
pool = choose_pool('mpi')
else:
pool = choose_pool('all')
else:
raise TypeError("Could not interpret 'parallel' argument. Use an "
"integer, 'mpi', 'all', 'auto', None or pass a pool "
"object with 'map' method.")
return pool
def get_data_all(tag,
dataset='Mstar_30',
bins=np.arange(7.5, 12, 0.5),
inp='FLARES',
DF=False):
if inp == 'FLARES':
df = pd.read_csv('weight_files/weights_grid.txt')
weights = np.array(df['weights'])
sims = np.arange(0, len(weights))
calc = partial(get_data,
tag=tag,
dataset=dataset,
bins=bins,
inp=inp,
DF=DF)
pool = schwimmbad.MultiPool(processes=12)
dat = np.array(list(pool.map(calc, sims)))
pool.close()
if DF:
hist = np.sum(dat, axis=0)
out = np.zeros(len(bins) - 1)
err = np.zeros(len(bins) - 1)
for ii, sim in enumerate(sims):
out += dat[ii] * weights[ii]
err += np.square(np.sqrt(dat[ii]) * weights[ii])
return out, hist, np.sqrt(err)
else:
return dat
else:
out = get_data(00, tag=tag, bins=bins, inp=inp, DF=DF)
return out
def partial(self, subd, tiles, dates, nworkers=31):
assert isinstance(dates, list)
assert isinstance(tiles, list)
for date in dates:
self.create_destdir(date, subd)
self.allocate_empty_binfile(date, subd)
tasks = [(date, subd, t) for t in tiles for date in dates]
pool = schwimmbad.MultiPool(processes=nworkers + 1)
tic = time.time()
pool.map(self.task_extract_convert, tasks)
toc = time.time()
elapsed = toc - tic
print(
f"time to convert {len(tasks)} tiles with {nworkers} workers: {elapsed:.4} s"
)
pool.close()
for date in dates:
self.cleantar(date, subd)
def readalltiles(reader, varname, hour, date, parallel=False):
ny, nx = 140, 105
data = np.zeros((ny * 100, nx * 100))
ntiles = len(reader.tiles)
if parallel:
nworkers = 16
pool = schwimmbad.MultiPool(processes=nworkers + 1)
tasks = [(varname, tile, hour, date) for tile in reader.tiles]
chunk = pool.map(reader.read, tasks)
pool.close()
else:
chunk = np.zeros((ntiles, ny, nx))
for k, tile in enumerate(reader.tiles):
print(f"\rsubd:{subd:02} tile:{tile}", end="")
chunk[k] = reader.read(varname, tile, hour, date)
for k, tile in enumerate(reader.tiles):
j0, i0 = ny * (tile // 100), nx * (tile % 100)
data[j0:j0 + ny, i0:i0 + nx] = chunk[k]
return data
def get_all(tag,
bins=np.arange(-25, -16, 0.5),
inp='GEAGLE',
filter='FUV',
Luminosity='Dustcorr'):
if inp == 'GEAGLE':
sims = np.arange(0, 38)
df = pd.read_csv('weight_files/weights_grid.txt')
weights = np.array(df['weights'])
calc = partial(get_hist,
tag=tag,
bins=bins,
inp=inp,
filter=filter,
Luminosity=Luminosity)
pool = schwimmbad.MultiPool(processes=12)
dat = np.array(list(pool.map(calc, sims)))
pool.close()
hist = np.sum(dat, axis=0)
out = np.zeros(len(bins) - 1)
for ii, sim in enumerate(sims):
out += dat[ii] * weights[ii]
return out, hist
else:
out = get_hist(00, tag, bins, inp='REF')
return out
import schwimmbad
import numpy as np
def func(i):
'''
A useless function
'''
print(str(i + 1))
return i
# Use multipool - same as multiprocessing
with schwimmbad.MultiPool() as pool:
inputs = [i for i in np.arange(0, 10, 2)]
out1 = list(pool.map(func, inputs))
# Use serial pool
with schwimmbad.SerialPool() as pool:
inputs = [i for i in np.arange(10, 20, 2)]
out2 = list(pool.map(func, inputs))
print(out1, out2)
except:
print (F"No data available in {ii}")
return np.array([]), np.array([]), np.array([]), np.array([]), np.array([]), np.array([])
tag='010_z005p000'
z = float(tag[5:].replace('p','.'))
sim_type='FLARES'
inp = int(sys.argv[1])
# mstar, lfuv, lfuv_int, att, begin, end, slen = get_data(0, tag, inp = 'FLARES')
func = partial(get_data, tag=tag, inp = 'FLARES')
pool = schwimmbad.MultiPool(processes=8)
dat = np.array(list(pool.map(func, np.arange(0,40))))
pool.close()
for ii in range(40):
if ii==0:
mstar = dat[ii][0]
lfuv = dat[ii][1]
lfuv_int = dat[ii][2]
slen = dat[ii][-1]
num = np.append(np.array([0]), len(dat[ii][0]))
part_num = np.arange(0, len(dat[ii][0]))
else:
mstar = np.append(mstar, dat[ii][0])
lfuv = np.append(lfuv, dat[ii][1])
lfuv_int = np.append(lfuv_int, dat[ii][2])
def featurize_data(pbmap, pbnames, lcdata, metadata, nobjects, featurefile):
"""
***Feature extractor for PLaSTiCC***
Extracts features from data by some Cesium library functions.
Builds a timeseries dictionary and for each time series extracts
features. Features described in file: feature_sets.
Created on Mon Apr 29 19:30:52 2019
@author: luisarribas
"""
print("")
print("EXTRACTING FEATURES")
print("===================")
print("")
print("Building Timeseries....wait")
print("===========================")
#**********************BUILD TIME SERIES**********************************
tsdict = OrderedDict()
for i in range(nobjects):
row = metadata[i]
thisid = row['object_id']
target = row['target']
meta = {'zBand':row['zBand'],\
'z':row['hostgal_photoz'],\
'zerr':row['hostgal_photoz_err'],\
'mag':row['magnitude'],\
'u-b':row['u-b'],\
'b-v':row['b-v']
}
ind = (lcdata['object_id'] == thisid)
thislc = lcdata[ind]
pbind = [(thislc['passband'] == pb) for pb in pbmap]
t = [thislc['mjd'][mask].data for mask in pbind]
m = [thislc['flux'][mask].data for mask in pbind]
e = [thislc['flux_err'][mask].data for mask in pbind]
tsdict[thisid] = TimeSeries(t=t, m=m, e=e,\
label=target, name=thisid, meta_features=meta,\
channel_names=pbnames )
print("")
print("OK!")
print(" ")
#***********************FEATURE EXTRACTION WITH CESIUM********************
warnings.simplefilter('ignore')
if os.path.exists(featurefile):
print("")
print("Loading features from file....wait")
print("==================================")
featuretable, _ = featurize.load_featureset(featurefile)
print("")
print("OK!")
print(" ")
else:
features_list = []
print("")
print("Computing features....wait")
print("==========================")
with schwimmbad.MultiPool() as pool:
results = pool.imap(worker, list(tsdict.values()))
for res in results:
features_list.append(res)
featuretable = featurize.assemble_featureset(features_list=features_list,\
time_series=tsdict.values())
featurize.impute_featureset(fset=featuretable,
strategy='constant',
value=0,
max_value=18446744073709551000,
inplace=True)
featurize.save_featureset(fset=featuretable, path=featurefile)
print("")
print("OK!")
print(" ")
#*******Build Pandas dataframe output*************************************
old_names = featuretable.columns.values
new_names = ['{}_{}'.format(x, pbmap.get(y, 'meta')) for x, y in old_names]
cols = [featuretable[col] for col in old_names]
allfeats = Table(cols, names=new_names, masked=False)
allfeats['target'] = metadata['target']
allfeats = allfeats.to_pandas()
allfeats = np.nan_to_num(allfeats)
new_names.append('target')
allfeats = Table(allfeats, names=new_names, masked=False)
allfeats = allfeats.to_pandas()
print("")
print("Extracted features = ", len(allfeats.columns))
print("==========================")
print("")
print("Nan Values detected = ", sum(len(allfeats) - allfeats.count()))
print("==========================")
return allfeats
def line_write_out(num, lines, tag, kappa, BC_fac, label, inp = 'FLARES', LF = False, log10t_BC = 7., Type = 'Total', extinction = 'default', data_folder = 'data', aperture='30'):
if inp == 'FLARES':
num = str(num)
if len(num) == 1:
num = '0'+num
filename = F"./{data_folder}/FLARES_{num}_sp_info.hdf5"
sim_type = inp
elif (inp == 'REF') or (inp == 'AGNdT9'):
filename = F"./{data_folder}/EAGLE_{inp}_sp_info.hdf5"
sim_type = 'PERIODIC'
num='00'
else:
ValueError(F"No input option of {inp}")
calc = partial(get_lines, sim=num, kappa = kappa, tag = tag, BC_fac = BC_fac, inp = inp, IMF = 'Chabrier_300', LF = False, log10t_BC = log10t_BC, Type = Type, extinction = extinction, data_folder = data_folder, aperture = aperture)
pool = schwimmbad.MultiPool(processes=8)
dat = np.array(list(pool.map(calc, lines)))
pool.close()
for ii, line in enumerate(lines):
out_lum = dat[:,0][ii]
out_EW = dat[:,1][ii]
if Type == 'Total':
fl = flares.flares(fname = filename, sim_type = sim_type)
fl.create_group(F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/DustModelI", verbose=True)
print (F'{line} is being written to disk')
fl.create_dataset(values = out_lum, name = F"{line}/Luminosity",
group = F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/DustModelI",
desc = F"Dust corrected luminosity (using ModelI) of the galaxy with a birth cloud factor of {BC_fac} following {extinction} curve", unit = "ergs/s", overwrite=True)
fl.create_dataset(values = out_EW, name = F"{line}/EW",
group = F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/DustModelI",
desc = F"EW (using ModelI) of the galaxy with a birth cloud factor of {BC_fac} following {extinction} curve", unit = "Angstrom", overwrite=True)
elif Type=='Intrinsic':
fl = flares.flares(fname = filename, sim_type = sim_type)
fl.create_group(F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{label}", verbose=True)
print (F'{line} is being written to disk')
fl.create_dataset(values = out_lum, name = F"{line}/Luminosity",
group = F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{label}",
desc = F"Intrinsic line luminosity", unit = "ergs/s", overwrite=True)
fl.create_dataset(values = out_EW, name = F"{line}/EW",
group = F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{label}",
desc = F"Intrinsic EW", unit = "Angstrom", overwrite=True)
elif Type=='Only-BC':
fl = flares.flares(fname = filename, sim_type = sim_type)
fl.create_group(F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{label}", verbose=True)
print (F'{line} is being written to disk')
fl.create_dataset(values = out_lum, name = F"{line}/Luminosity",
group = F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{label}",
desc = F"Intrinsic line luminosity with birth cloud factor {BC_fac} following {extinction} curve", unit = "ergs/s", overwrite=True)
fl.create_dataset(values = out_EW, name = F"{line}/EW",
group = F"{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{label}",
desc = F"Intrinsic EW with birth cloud factor {BC_fac} following {extinction} curve", unit = "Angstrom", overwrite=True)
err = []
# Initialize three list objects for generating RVData.
for x in range(len(data)):
# Assign values in corresponding columns to individual lists.
t.append(data[x][0])
rv.append(data[x][1])
err.append(data[x][2])
rv = rv * u.km/u.s
err = err * u.km/u.s
return(t,rv,err)
t,rv,err = readAndGenerateData()
data = RVData(t=t, rv=rv, stddev=err)
params = JokerParams(P_min=8*u.day, P_max=512*u.day)
pool = schwimmbad.MultiPool()
joker = TheJoker(params, pool=pool)
samples = joker.rejection_sample(data, n_prior_samples=SAMPLE_SIZE)
fig, ax = plt.subplots(1, 1, figsize=(6,6)) # doctest: +SKIP
ax.scatter(samples['P'].value, samples['K'].to(u.km/u.s).value,
marker='.', color='k', alpha=0.45) # doctest: +SKIP
ax.set_xlabel("$P$ [day]")
ax.set_ylabel("$K$ [km/s]")
ax.set_xlim(-10, 200)
ax.set_ylim(0.75,12.)
ax.scatter(61.942, 1.3959, marker='o', color='#31a354', zorder=-100)
fig, ax = plt.subplots(1, 1, figsize=(8,5)) # doctest: +SKIP
def main():
p0 = [
cl_c.phi2.degree, cl_c.distance.kpc, cl_c.pm_phi1_cosphi2.value,
cl_c.pm_phi2.value, cl_c.radial_velocity.value,
np.log(mw['halo'].parameters['m'].value), 1,
np.log(mw['disk'].parameters['m'].value)
]
# 11.1, 232.24, 7.25,
# HACK: optimized values from below:
p0 = np.array([
3.64098650e-02, 20.3, 3.83442589e+00, 6.92904214e-01, -5.58127521e+01,
2.76771005e+01, 1.11980719e+00, 2.49729735e+01
])
data_units = {
'phi1': u.deg,
'phi2': u.deg,
'distance': u.kpc,
'pm_phi1_cosphi2': u.mas / u.yr,
'pm_phi2': u.mas / u.yr,
'radial_velocity': u.km / u.s
}
extra_var = dict()
extra_var['phi2'] = (0.05 * u.deg)**2
extra_var['distance'] = (0.2 * u.kpc)**2
extra_var['pm_phi1_cosphi2'] = (0.1 * u.mas / u.yr)**2
extra_var['pm_phi2'] = (0.1 * u.mas / u.yr)**2
extra_var['radial_velocity'] = (1 * u.km / u.s)**2
frame_comp_names = (
list(cl_c.get_representation_component_names().keys()) +
list(cl_c.get_representation_component_names('s').keys()))
_extra_var = dict()
for k in extra_var:
_extra_var[k] = extra_var[k].to_value(data_units[k]**2)
args = (cl_c.phi1, mw, data, data_units, frame_comp_names, _extra_var)
# Test likelihood call:
ln_likelihood(p0, *args)
# Run emcee
nwalkers = 128 * len(p0)
# p0s = emcee.utils.sample_ball(res.x)
p0s = emcee.utils.sample_ball(p0, std=[1e-3] * len(p0), size=nwalkers)
with schwimmbad.MultiPool() as pool:
sampler = emcee.EnsembleSampler(nwalkers,
len(p0),
ln_likelihood,
args=args,
pool=pool)
for sample in sampler.sample(p0s, iterations=1024, progress=True):
# Update plot every 32 steps
if sampler.iteration % 32:
continue
fig = trace_plot(sampler)
fig.savefig('../plots/pal5_emcee_trace.png', dpi=250)
plt.close(fig)
#hisdates = [giga.hisdates[i+1+idate] for i in range(ndates)]
hisdates = giga.hisdates[idate:]
hisdates = ["2008-09-26"]
regions = list(range(11, 14))
for date in hisdates:
nworkers = 31
tasks = []
for subd in regions:
tasks += [(date, subd, t) for t, s in giga.subdmap.items()
if s == subd]
create_destdir(date, subd)
allocate_empty_binfile(date, subd)
pool = schwimmbad.MultiPool(processes=nworkers + 1)
tic = time.time()
pool.map(task_extract_convert, tasks)
toc = time.time()
elapsed = toc - tic
print(
f"time to convert {len(tasks)} tiles with {nworkers} workers: {elapsed:.4} s"
)
pool.close()
cleantar(date, subd)
exit()
# time to extract all tiles from region 10 with 20 workers: 500.4 s (task_extract)
nworkers = 20
tasks = []