def process_remainder(cat, number, stage, success):
"""
Optional processing remainder. All sources are split as a multi-group.
:param cat: Inputted catalogue of sources as yet unsolved.
:param number: The number of epochs included.
:param stage: The name of the stage so output can be sorted.
:param success: The percentage of sources solved by the beginning of this stage.
:return: stage information including catalogues of sources solved and unsolved.
"""
regroup_start_time = time.time() # record start time
islands = [cat]
bug_count = 0
eps = 0
for t in range(len(islands)):
print(len(islands[t]))
total_count = len(islands)
print("%d islands created\n" % total_count)
goodies = []
badies = []
islands[0] = sorted(islands[0])
seperated_group = complete_island_splitting(islands[0], number, stage)
goodies.extend(seperated_group)
for i in range(int(len(seperated_group))):
light_curve(seperated_group[i], stage, number)
badies.extend([item for item in islands[0] if item not in np.ravel(seperated_group)])
goodies = sorted(goodies)
for i in range(len(goodies)):
print(goodies[i])
goodies = np.ravel(goodies)
badies = np.ravel(badies)
badies = sorted(badies)
write_catalog("./results/goodies/%d_epochs/goodies_%s_%depochs.csv" % (number, stage, number), goodies, fmt='csv')
write_catalog("./results/badies/%d_epochs/badies_%s_%depochs.csv" %(number, stage, number), badies, fmt='csv')
goodies_cat = sorted(goodies)
print(goodies_cat)
print(badies)
percentage_solved = 100*(success/100 + (1-success/100)*(len(goodies)/(len(goodies)+len(badies))))
print("\nSuccess rate = %f%%" % percentage_solved)
run_time = (time.time() - regroup_start_time)
print("%s --- %f seconds ---" % (stage, run_time))
return {'eps': eps, 'goodies': goodies, 'badies': badies, 'percentage_solved': percentage_solved, 'time': run_time, 'bug_count': bug_count}
def multigroup_plot(island, stage, num, group_size):
"""
Plot the combined light curve of multiple objects.
:param island: The combined catalogue of sources for multiple sources.
:param stage: The name of the current stage.
:param num: The number of epochs included.
:param group_size: Number of objects included in the multi plot.
:return: Nothing. Only plots combined light curve and generates multi-group csv.
"""
x = []
y = []
flux_error = []
for i in range(len(island)):
x.append(island[i].island)
y.append(island[i].peak_flux)
flux_error.append(island[i].err_peak_flux)
which_srcs = []
for k in range(group_size):
which_srcs.append(island[k].source)
if (len(island) / num) < 30:
plot_name = str(which_srcs).strip('[]')
else:
plot_name = str(len(island))
plt.plot(x, y, linestyle='none', marker='o', color='red')
plt.xlabel('Epoch number')
plt.ylabel('peak_flux (Jy)')
plt.xlim(-1, num)
plt.ylim(0, max(y[0], y[1], y[2], y[3]) + 2)
plt.suptitle('Light Curve %s' % plot_name)
plt.savefig('./results/plots/%d_epochs/%s/multiplot_%s.png' %
(num, stage, plot_name))
plt.gcf().clear()
write_catalog('./results/plots/%d_epochs/%s/plot_%s.csv' %
(num, stage, plot_name),
island,
fmt='csv')
def light_curve(island, stage, num):
"""
Plot the light for a inputted group.
:param island: The group of sources that have been associated and deemed good.
:param stage: The name of the current stage.
:param num: The number of epochs included.
:return: Nothing. Only plots light curve and generates group csv.
"""
x = []
y = []
flux_error = []
for i in range(len(island)):
x.append(island[i].island)
y.append(island[i].peak_flux)
flux_error.append(island[i].err_peak_flux)
which_src = island[0].source
which_first_island = island[0].island
plt.errorbar(x,
y,
yerr=flux_error,
marker='o',
color='red',
ecolor='blue',
capsize=2,
elinewidth=1)
plt.xlabel('Epoch number')
plt.ylabel('peak_flux (Jy)')
plt.xlim(-1, num)
plt.ylim(0, y[0] + 1)
plt.suptitle('Light Curve %d' % which_src)
plt.savefig('./results/plots/%d_epochs/%s/plot_%d - %d.png' %
(num, stage, which_src, which_first_island))
plt.gcf().clear()
write_catalog('./results/plots/%d_epochs/%s/plot_%d - %d.csv' %
(num, stage, which_src, which_first_island),
island,
fmt='csv')
def process_regrouping_fractionislands(cat, number, eps, stage, dist_func, success, looseness=1):
"""
Processing for each stage of layer three of the program. Where partial groups can be accepted and processed.
:param cat: Inputted catalogue of sources as yet unsolved.
:param number: The number of epochs included.
:param eps: The set range parameter for grouping for this stage.
:param stage: The name of the stage so output can be sorted.
:param dist_func: The name of the separation function to be used in regrouping.
:param success: The percentage of sources solved by the beginning of this stage.
:param looseness: Looseness selection for scaling of allowance for partial islands.
:return: stage information including catalogues of sources solved and unsolved.
"""
regroup_start_time = time.time() # record start time
regroup_return = regroup(cat, eps, number, far=None, dist=dist_func, partial= True)
islands = regroup_return['islands']
bug_count = regroup_return['bug_counter']
for t in range(len(islands)):
print(len(islands[t]))
total_count = len(islands)
print("%d islands created\n" % total_count)
goodies = []
badies = []
for i in range(len(islands)):
islands[i] = sorted(islands[i])
flux_sum = 0
local_rms_sum = 0
err_peak_flux_sum = 0
for k in range(len(islands[i])):
flux_sum += islands[i][k].peak_flux
local_rms_sum += islands[i][k].local_rms
if math.isnan(islands[i][k].err_peak_flux) or islands[i][k].err_peak_flux > 1000:
err_peak_flux_sum += 0.04
else:
err_peak_flux_sum += islands[i][k].err_peak_flux
average_flux = flux_sum/(len(islands[i]))
average_local_rms = local_rms_sum/(len(islands[i]))
average_err_peak_flux = err_peak_flux_sum/(len(islands[i]))
allowance = math.ceil(looseness*number*(1/((average_flux-average_local_rms)/average_err_peak_flux)))
if allowance < 0:
allowance = 2*number/3
if len(islands[i]) <= number and len(islands[i]) > (number - allowance) and len(islands[i]) > number/3:
goodies.extend(islands[i])
print(islands[i])
light_curve(islands[i], stage, number)
else:
badies.extend(islands[i])
badies = np.ravel(badies)
badies = sorted(badies)
write_catalog("./results/goodies/%d_epochs/goodies_%s_%depochs.csv" % (number, stage, number), goodies, fmt='csv')
write_catalog("./results/badies/%d_epochs/badies_%s_%depochs.csv" %(number, stage, number), badies, fmt='csv')
goodies_cat = sorted(goodies)
print(goodies_cat)
print(badies)
percentage_solved = 100*(success/100 + (1-success/100)*(len(goodies)/(len(goodies)+len(badies))))
print("\nSuccess rate = %f%%" % percentage_solved)
run_time = (time.time() - regroup_start_time)
print("%s --- %f seconds ---" % (stage, run_time))
return {'eps': eps, 'goodies': goodies, 'badies': badies, 'percentage_solved': percentage_solved, 'time': run_time, 'bug_count': bug_count}
def process_regrouping_allislands(cat, number, eps, stage, dist_func, success):
"""
Processing for each stage of layer two of the program. Regrouping followed by flux splitting.
:param cat: Inputted catalogue of sources as yet unsolved.
:param number: The number of epochs included.
:param eps: The set range parameter for grouping for this stage.
:param stage: The name of the stage so output can be sorted.
:param dist_func: The name of the separation function to be used in regrouping.
:param success: The percentage of sources solved by the beginning of this stage.
:return: stage information including catalogues of sources solved and unsolved.
"""
regroup_start_time = time.time() # record start time
regroup_return = regroup(cat, eps, number, far=None, dist=dist_func)
islands = regroup_return['islands']
bug_count = regroup_return['bug_counter']
for t in range(len(islands)):
print(len(islands[t]))
total_count = len(islands)
print("%d islands created\n" % total_count)
goodies = []
badies = []
for i in range(len(islands)):
islands[i] = sorted(islands[i])
if len(islands[i]) == number:
goodies.append(islands[i])
light_curve(islands[i], stage, number)
elif len(islands[i])% number == 0:
seperated_group = complete_island_splitting(islands[i], number, stage)
goodies.extend(seperated_group)
for i in range(int(len(seperated_group))):
light_curve(seperated_group[i], stage, number)
else:
badies.extend(islands[i])
goodies = sorted(goodies)
for i in range(len(goodies)):
print(goodies[i])
goodies = np.ravel(goodies)
badies = np.ravel(badies)
badies = sorted(badies)
write_catalog("./results/goodies/%d_epochs/goodies_%s_%depochs.csv" % (number, stage, number), goodies, fmt='csv')
write_catalog("./results/badies/%d_epochs/badies_%s_%depochs.csv" %(number, stage, number), badies, fmt='csv')
goodies_cat = sorted(goodies)
print(goodies_cat)
print(badies)
percentage_solved = 100*(success/100 + (1-success/100)*(len(goodies)/(len(goodies)+len(badies))))
print("\nSuccess rate = %f%%" % percentage_solved)
run_time = (time.time() - regroup_start_time)
print("%s --- %f seconds ---" % (stage, run_time))
return {'eps': eps, 'goodies': goodies, 'badies': badies, 'percentage_solved': percentage_solved, 'time': run_time, 'bug_count': bug_count}