def plot_lf_with_stages(target, trilegal_output):
outfile_dir = snap_src + '/models/varysfh/match-hmc/'
vSFH, vsfh_kws = sfh_tests.prepare_vsfh_run([target], ['cmd_input_CAF09_S_NOV13.dat'], 50,
vsfh_kw={'outfile_loc': outfile_dir,
'extra_str': ''},
default_kw=None)
pl = sfh_tests.Plotting(vSFH[0])
cols = color_scheme
cols.append('#9966cc')
del cols[2]
del cols[3]
kw = {'trilegal_output': trilegal_output,
'narratio': False,
'add_stage_lfs': 'all',
'plot_data': False,
'plot_models': False,
'cols': cols,
'stage_lf_kw': {'lw': 3, 'label': translate_model_name('nov13')}}
ax1, ax2 = pl.compare_to_gal(target, **kw)
lims = load_plot_limits()
row = lims[np.nonzero(lims['target'] == target)[0]]
for ax, band in zip([ax1, ax2], ['opt', 'ir']):
ax.set_xlim(row['%s_cmdmin' % band], row['%s_cmdmax' % band])
ax.set_ylim(row['%s_lfmin' % band], row['%s_lfmax' % band])
base_dir = snap_src + '/models/varysfh/match-hmc'
model = 'caf09_s_nov13'
file_loc = os.path.join(base_dir, target, model, 'mc')
best_tri_out, = rsp.fileIO.get_files(file_loc, '*opt_best*')
filter1 = sfh_tests.get_filter1(target)
sgal = rsp.Galaxies.simgalaxy(best_tri_out, filter1=filter1,
filter2='F814W')
return ax1, ax2
def all_stats(targets, cmd_inputs, nsfhs, outfile_dir="default", extra_str="", default_kw=None):
vSFHs, vsfh_kws = sfh_tests_multi_proc.prepare_vsfh_run(
targets, cmd_inputs, nsfhs, vsfh_kw={"outfile_loc": outfile_dir, "extra_str": extra_str}, default_kw=default_kw
)
chi2_files = stats.write_chi2_table(targets, cmd_inputs, outfile_loc=outfile_dir, extra_str=extra_str)
narratio_files = rsp.fileIO.get_files(outfile_dir, "*narratio*dat")
chi2_dicts = stats.result2dict(chi2_files)
stats.narratio_table(narratio_files)
# axs = stats.chi2plot(chi2_dicts, outfile_loc=outfile_dir)
axs = stats.chi2plot2(chi2_dicts, outfile_loc=outfile_dir)
chi2_files = stats.write_chi2_table(
targets, cmd_inputs, outfile_loc=outfile_dir, extra_str=extra_str, just_gauss=True
)
pl = [sfh_tests_multi_proc.Plotting(v) for v in vSFHs]
_ = [pl[i].compare_to_gal(extra_str=extra_str) for i in range(len(pl))]
# plt.close('all')
return
def lf_figs(targets, cmd_inputs, nsfhs, outfile_dir='default', extra_str='',
default_kw=None, comp_corr=False, example=True):
import os
if comp_corr is True:
table_file = 'comp_corr'
else:
table_file = 'default'
vSFHs, vsfh_kws = sfh_tests.prepare_vsfh_run(targets, cmd_inputs, nsfhs,
vsfh_kw={'outfile_loc': outfile_dir,
'extra_str': extra_str,
'table_file': table_file},
default_kw=default_kw)
if comp_corr is True:
extra_str += '_comp'
for i in range(len(vSFHs)):
pl = sfh_tests.Plotting(vSFHs[i])
pl.compare_to_gal(extra_str=extra_str,
completeness_correction=comp_corr)
# example LF from the model
if example is True:
for i in range(len(vSFHs)):
pl = sfh_tests.Plotting(vSFHs[i])
best = rsp.fileIO.get_files(os.path.join(outfile_dir, vSFHs[i].target,
vSFHs[i].agb_mod, 'mc'),
'*best.dat')
if len(best) == 0:
continue
pl.compare_to_gal(narratio=False, add_stage_lfs='all',
extra_str='no_data', plot_data=False,
completeness_correction=comp_corr,
plot_models=False,
trilegal_output=best[0])
return
def main(targets, cmd_inputs, nsfhs, dry_run=False, comp_corr=False):
'''
calls sfh_tests_multi_proc.sfh_tests_multi_proc in most basic way possible
for up to 2 * available processors. Target & cmd_inputs are distributed and
nsfhs are all done per processor. So with 12 processors, you can do up to
24 target and cmd_inputs in one call, or code something better for a change.
'''
clients = parallel.Client()
clients.block = False
clients[:].execute('cd ~/research/TP-AGBcalib/code/TPAGB-calib/')
clients[:].execute('import sfh_tests')
if comp_corr is True:
table_file = 'comp_corr'
else:
table_file = 'default'
vSFHs, vsfh_kws = sfh_tests.prepare_vsfh_run(targets, cmd_inputs, nsfhs,
dry_run=dry_run,
table_file=table_file)
if dry_run is True:
timeout = 10
else:
timeout = 900
# find a better way to run this all at once, what if I need three times
# through?
nprocs = len(clients)
nvsfhs = len(vSFHs)
ntimes = np.min([nprocs, nvsfhs])
ndiff = np.abs(nvsfhs - nprocs)
if ndiff > nprocs:
print 'need a for loop, too many processes code code code man'
import sys
sys.exit()
print 'calling first set'
res = [clients[i].apply(caller, vSFHs[i], vsfh_kws,)
for i in range(ntimes)]
while False in [r.ready() for r in res]:
print 'waiting on first set'
time.sleep(timeout)
print 'checking first set...'
print 'writing first set'
[vSFHs[i].write_results(res[i].result) for i in range(ntimes)]
print 'calling second set'
res2 = [clients[i].apply(caller, vSFHs[i+ntimes], vsfh_kws,)
for i in range(ndiff)]
while False in [r.ready() for r in res2]:
print 'waiting on second set'
time.sleep(timeout)
print 'checking second set...'
print 'writing second set'
[vSFHs[i+ntimes].write_results(res2[i].result) for i in range(ndiff)]
print 'done.'
