def age_vs_plot(track, infile, ycol='logl', ax=None, annotate=True, xlabels=True,
save_plot=True, ylabels=True):
agb_mix = infile.agb_mix
set_name = infile.set_name
if ycol == 'logl':
ydata= track.get_col('L_star')
majL = MultipleLocator(.2)
minL = MultipleLocator(.1)
ylab = '$\log\ L_{\odot}$'
elif ycol == 'logt':
ydata = track.get_col('T_star')
majL = MultipleLocator(.1)
minL = MultipleLocator(.05)
ylab = '$\log\ Te$'
elif ycol == 'C/O':
ydata = track.get_col('CO')
majL = MaxNLocator(4)
minL = MaxNLocator(2)
ylab = '$C/O$'
else:
print 'logl, logt, C/O only choices for y.'
return
age = track.get_col('ageyr')
addpt = track.addpt
Qs = list(track.Qs)
if ax is None:
fig, ax = plt.subplots()
ax.plot(age, ydata, color='black')
ax.plot(age[Qs], ydata[Qs], 'o', color='green')
if len(addpt) > 0:
ax.plot(age[addpt], ydata[addpt], 'o', color='purple')
ax.yaxis.set_major_locator(majL)
ax.yaxis.set_minor_locator(minL)
majorFormatter = ScalarFormatter()
majorFormatter.set_powerlimits((-3, 4))
ax.xaxis.set_major_formatter(majorFormatter)
if annotate is True:
ax.text(0.06, 0.87, '${\\rm %s}$' % agb_mix.replace('_', '\ '),
transform=ax.transAxes)
ax.text(0.06, 0.77,'${\\rm %s}$' % set_name.replace('_', '\ '),
transform=ax.transAxes)
ax.text(0.06, 0.67, '$M=%.2f$' % track.mass,
transform=ax.transAxes)
if ylabels is True:
ax.set_ylabel('$%s$' % ylab, fontsize=20)
if xlabels is True:
ax.set_xlabel('$\rm{Age (yr)}$', fontsize=20)
if save_plot is True:
plotpath = os.path.join(infile.diagnostic_dir, 'age_v/')
fileIO.ensure_dir(plotpath)
fname = os.path.split(track.name)[1].replace('.dat', '')
fig_name = os.path.join(plotpath, '_'.join(('diag', fname)))
plt.savefig('%s_age_v.png' % fig_name, dpi=300)
plt.close()
return
def diag_plots(track, infile):
agb_mix = infile.agb_mix
set_name = infile.set_name
ext = '.png'
logt_lim = (3.75, 3.35)
logl_lim = (2.4, 4.8)
lage_lim = (1., 1e7)
co_lim = (0, 5)
logl = track.get_col('L_star')
logt = track.get_col('T_star')
addpt = track.addpt
Qs = list(track.Qs)
# HRD
fig = plt.figure()
ax = plt.axes()
plotpath = os.path.join(infile.diagnostic_dir, 'HRD/')
fileIO.ensure_dir(plotpath)
ax.annotate(r'$%s$' % agb_mix.replace('_', '\ '), xy=(3.43, 2.8))
ax.annotate(r'$%s$' % set_name, xy=(3.43, 2.7))
ax.annotate(r'$M=%.2f$' % track.mass, xy=(3.43, 2.6))
ax.plot(logt, logl, color='black')
ax.plot(logt[Qs], logl[Qs], color='green', lw=2)
ax.plot(logt[Qs], logl[Qs], 'o', color='green')
if len(addpt) > 0:
ax.plot(logt[addpt], logl[addpt], 'o', color='purple')
ax.set_xlim(logt_lim)
ax.set_ylim(logl_lim)
ax.set_xlabel(r'$\log\ Te$')
ax.set_ylabel(r'$\log\ L_{\odot}$')
fname = os.path.split(track.name)[1].replace('.dat', '')
fig_name = os.path.join(plotpath, '_'.join(('diag', fname)))
plt.savefig('%s%s' % (fig_name, ext))
plt.close()
fig, (axs) = plt.subplots(nrows=3)
ycols = ['logl', 'logt', 'C/O']
annotate = [False, False, True]
save_plot = [False, False, True]
xlabels = [False, False, True]
for i in range(len(ycols)):
age_vs_plot(track, infile, ycol=ycols[i], ax=axs[i], annotate=annotate[i],
xlabels=xlabels[i], ylabels=True, save_plot=save_plot[i])
def attach_experiment(self,experiment_dir,error_key='dev_mae',verbose=True):
"""
Overide this to suit your purposes!
The purpose of this function is to go over each experiment in the supplied experiment_dir and aggregate the best results.
the results into a nice dictionary object. I encourage this to be overidden in ones own implementation if their experiment structure
is different. Alternativley, if an aggregation can be supplied to attach_aggregation, your life will be easier.
:param experiment_dir: String pointing to valid experiment directory as defined by experimentGenerator
:returns: 0 on success, <0 for any error.
.. side-effects:: Upon success, the class variable results will be populated.
.. note:: A valid experiment directory (if using this as is) should have sufficient permissions set and contain a var directory. The var directory should contain sub folders for each experiment, labeled like: alpha_1__gamma_2/. Underneath each sub directory there should be the folder logs underwhich there should be a file runtime.txt like this logs/runtime.txt.
"""
dirs = []
results_dir = '%s/%s' %(experiment_dir,'results')
var_dir = '%s/%s' %(experiment_dir,'var')
if not os.path.isdir(var_dir):
print('There is no var directory. This directory should have all of the sub experiments beneath it.')
return -1
if not os.path.isdir(results_dir):
if ensure_dir(results_dir) < 0:
print('There is no results directory. Failed attempt to create directory here: %s.' % (results_dir))
return -2
print('Building list of directories...')
dirs = list()
num_sep = var_dir.count(os.path.sep)
level = 1
for root, ds, fs in os.walk(var_dir):
num_sep_this = root.count(os.path.sep)
if num_sep + level <= num_sep_this:
del ds[:]
continue
dirs = ds
num_experiments = len(dirs)
results = {}
# ---------------------------------
# Go over each experiment directory
# -----------------------
print('Aggregating results.')
num_dirs = len(dirs)
param_count = None
last_params = None
for p,d in enumerate(dirs):
runtime_fn = '%s/var/%s/logs/runtime.txt' % (experiment_dir,d)
if not os.path.isfile(runtime_fn):
continue
d_end = d.split('/')[-1]
# Hyper parameters are in the name of a directory
hyper_params = self.parse_key_values(d_end,assignment='_',delimeter='__')
runtime = open(runtime_fn,'r').read().split('\n')
min_error = float('inf')
min_error_exp= None
# -----------------------------------------
# Gather the results for a given experiment
# -------------------
for line in runtime:
if not line:
continue
rt = self.parse_key_values(line)
# ----
# See if this current line in the runtime log had the best error.
try:
error = float(rt[error_key])
if error < min_error:
min_error = error
min_error_exp = copy.deepcopy(rt)
except ValueError:
raise ValueError('Expecting error key %s to evaluate to a float.' % (error_key))
except KeyError:
print('ERROR: Parsing file:',runtime_fn)
print('- Error key %s does not apear on all lines.' % error_key)
break
# -------
# -------
if not min_error_exp:
if verbose:
print('WARNING: Unable to find best result in experiment %s.' % (runtime_fn))
continue
# Add all the hyper params to the best experiment found
for key in hyper_params:
min_error_exp[key] = float(hyper_params[key])
# Update the values pointed to by every key in min_error_exp
if not param_count:
param_count = len(min_error_exp)
last_params = min_error_exp
elif len(min_error_exp) != param_count:
print("Error in file:",runtime_fn)
print("Irregular number of outputs per line.")
continue
else:
param_count = len(min_error_exp)
last_params = min_error_exp
for key in min_error_exp:
if not key in results:
results[key] = dict()
results[key]['values'] = list()
results[key]['__longest_sequence__'] = -1
val = min_error_exp[key]
try:
val = float(val)
except:
pass
max_ = max(len(str(val)),len(key),results[key]['__longest_sequence__'])
results[key]['__longest_sequence__'] = max_
results[key]['values'].append(val)
if( p % 200 == 0):
progress(p,num_experiments)
# -------
progress(num_experiments,num_experiments)
print('(%d) results' % (len(results)))
self.results = results
return 0