def plot_difference_tot(filename):
N = {}
for flagged in [True, False]:
N["flagged" if flagged else "unflagged"] = []
Nnew = []
for i, cat in enumerate(sn_cats):
catarr = np.load(updated_sn_catalogue_name(cat, flagged=flagged))
N["flagged" if flagged else "unflagged"].append(len(catarr))
newcat = np.load(
updated_sn_catalogue_name(cat, pdf_name="missed_objects"))
Nnew.append(len(newcat))
fig, ax = plt.subplots()
x = np.arange(len(sn_cats))
width = 0.4
r1 = ax.bar(x + width / 2, N["unflagged"], width, label="new catalogue")
r2 = ax.bar(x - width / 2, N["flagged"], width, label="old catalogue")
r1new = ax.bar(
x + width / 2,
Nnew,
width,
bottom=np.array(N["unflagged"]) - np.array(Nnew),
hatch="//",
color=r1[0].get_facecolor(),
label="previously not included",
)
ax.set_ylabel("number of objects")
ax.set_xlabel("SN types")
ax.set_xticks(x)
ax.set_xticklabels(sn_cats)
ax.set_ylim(np.array(ax.get_ylim()) * [1, 1.1])
ax.set_title("number of objects in catalogues")
ax.legend()
autolabel(r1, ax)
autolabel(r2, ax)
fig.savefig(filename)
plt.close()
def plot_difference_individual(sn_types, filename):
fig, axs = plt.subplots(len(sn_types))
for i, sn_type in enumerate(sn_types):
ax = axs[i]
N = {}
cats = []
for pdf_type in sn_times[sn_type]:
for pdf_time in sn_times[sn_type][pdf_type]:
cats.append(pdf_names(pdf_type, pdf_time))
for flagged in [True, False]:
N["flagged" if flagged else "unflagged"] = []
for cat in cats:
catarr = np.load(
updated_sn_catalogue_name(sn_type,
pdf_name=cat,
flagged=flagged))
N["flagged" if flagged else "unflagged"].append(len(catarr))
x = np.arange(len(cats))
width = 0.4
r1 = ax.bar(x + width / 2, N["unflagged"], width, label="new")
r2 = ax.bar(x - width / 2, N["flagged"], width, label="old")
ax.set_ylabel(f"SN type {sn_type}")
ax.set_xticks(x)
ax.set_xticklabels(cats)
ax.set_ylim(np.array(ax.get_ylim()) * [1, 1.1])
autolabel(r1, ax)
autolabel(r2, ax)
axs[-1].set_xlabel("PDF type")
axs[0].set_title("catalogues with wrong classifications")
axs[-1].legend()
fig.savefig(filename)
plt.close()
inj_dict = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
}
llh_dict = {
"llh_name": "standard_matrix",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
}
catalogue = np.load(updated_sn_catalogue_name("IIn"))
catalogue["distance_mpc"] = np.array([1] * len(catalogue))
dir_path = os.path.dirname(os.path.realpath(__file__))
temp_save_catalogue_to = f"{dir_path}/temp_check_stack_bias_equal_dist_cat.npy"
np.save(temp_save_catalogue_to, catalogue)
mh_dict = {
"name": "examples/crosscheck_stacking_equal_dist/",
"mh_name": "large_catalogue",
"dataset": ps_v002_p01.get_seasons(),
"catalogue": temp_save_catalogue_to,
# "catalogue": ps_stack_catalogue_name(0.1, 0.3),
# "catalogue": tde_catalogue_name("jetted"),
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"scale": 10.0,
# get the time pdfs for this catalogue
time_pdfs = sn_time_pdfs(cat, pdf_type=pdf_type)
# Loop over time PDFs
for llh_time in time_pdfs:
# set up an empty results array for this time pdf
time_res = dict()
logging.debug(f'time_pdf is {llh_time}')
time_key = str(llh_time['decay_time'])
pdf_time = llh_time['decay_time'] / 364.25
pdf_name = pdf_names(pdf_type, pdf_time)
cat_path = updated_sn_catalogue_name(cat) # , pdf_name=pdf_name)
logging.debug('catalogue path: ' + str(cat_path))
# load catalogue and select the closest source
# that serves for estimating a good injection scale later
catalogue = np.load(cat_path)
logging.debug('catalogue dtype: ' + str(catalogue.dtype))
closest_src = np.sort(catalogue, order="distance_mpc")[0]
time_name = name + time_key + "/"
# set up the likelihood dictionary
llh_dict = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
)
from flarestack.core.unblinding import create_unblinder
from flarestack.utils.custom_dataset import custom_dataset
name_root = "analyses/ccsn/necker_2019/unblind_ccsn"
bkg_ts_root = "analyses/ccsn/necker_2019/calculate_sensitivity/fit_weights"
llh_energy = {
"energy_pdf_name": "power_law",
}
res_dict = dict()
for cat in sn_cats:
cat_path = updated_sn_catalogue_name(cat)
catalogue = np.load(cat_path)
for pdf_type in sn_times[cat]:
llh_times = sn_time_pdfs(cat, pdf_type=pdf_type)
name = f"{name_root}/{pdf_type}/{cat}"
bkg_ts = f"{bkg_ts_root}/{pdf_type}/{cat}"
for llh_time in llh_times:
time = (llh_time["decay_time"] if "decay" in pdf_type else
llh_time["pre_window"] + llh_time["post_window"])
unblind_llh = {
inj_dict = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
}
llh_dict = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
}
old_path = updated_sn_catalogue_name("IIn")
new_cat_path = os.path.join(os.path.dirname(old_path), "cut_IIn.npy")
old_cat = np.load(old_path)
old_cat = old_cat[old_cat["dec_rad"] > 0.0]
np.save(new_cat_path, old_cat)
mh_dict = {
"name": "analyses/general/crosscheck_stacking",
"mh_name": "fixed_weights",
# "dataset": ps_v002_p01.get_seasons(),
"dataset": diffuse_8_year.get_seasons(),
# "catalogue": updated_sn_catalogue_name("IIn"),
"catalogue": new_cat_path,
# "catalogue": ps_stack_catalogue_name(0.1, 0.3),
# "catalogue": tde_catalogue_name("jetted"),
inj_dict = {
'injection_energy_pdf': injection_energy,
'injection_sig_time_pdf': injection_time
}
llh_dict = {
"llh_name": "standard_matrix",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
}
}
catalogue = np.load(updated_sn_catalogue_name('IIn'))
catalogue['distance_mpc'] = np.array([1] * len(catalogue))
dir_path = os.path.dirname(os.path.realpath(__file__))
temp_save_catalogue_to = f'{dir_path}/temp_check_stack_bias_equal_dist_cat.npy'
np.save(temp_save_catalogue_to, catalogue)
mh_dict = {
"name": "examples/crosscheck_stacking_equal_dist/",
"mh_name": 'large_catalogue',
"dataset": ps_v002_p01.get_seasons(),
"catalogue": temp_save_catalogue_to,
# "catalogue": ps_stack_catalogue_name(0.1, 0.3),
# "catalogue": tde_catalogue_name("jetted"),
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"scale": 10.,
for pdf_type in sn_times[sn_type]:
logging.info(f"pdf type: {pdf_type}")
for pdf_time in sn_times[sn_type][pdf_type]:
# get catalogues for individual PDFs
pdf_name = pdf_names(pdf_type, pdf_time)
logging.info(f"pdf time: {pdf_time}")
catalogue = load_catalogue(sn_type,
pdf_name,
include_flagged=flag,
z_add=z_add)
savename = updated_sn_catalogue_name(
sn_type,
pdf_name,
flagged=flag,
z_conservative=z_add)
np.save(savename, catalogue)
# combine with previous PDF-catalogues
catalogue_red = catalogue[columns_out[keep_inds_out]]
if start:
combined_catalogue = catalogue_red
start = False
new_mask = np.invert([
name in combined_catalogue["source_name"]
for name in catalogue_red["source_name"]
])
new_objects = catalogue_red[new_mask]
