def main(input_files, verbose, output):
setup_logging(verbose=verbose)
eventlist_list = []
for f in input_files:
eventlist_list.append(EventList.read(f))
events = eventlist_list[0]
for e in eventlist_list[1:]:
events.stack(e)
figures = []
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
events.plot_energy(ax=ax)
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
events.plot_energy_offset(ax=ax)
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
events.plot_offset2_distribution(ax=ax)
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
events.plot_time(ax=ax)
if output is None:
plt.show()
else:
with PdfPages(output) as pdf:
for fig in figures:
fig.tight_layout(pad=0, h_pad=1.08, w_pad=1.08)
pdf.savefig(fig)
from cta_tools.plotting import preliminary
import matplotlib.pyplot as plt
import click
from pathlib import Path
from gammapy.analysis import Analysis, AnalysisConfig
from gammapy.modeling.models import create_crab_spectral_model
from gammapy.estimators import LightCurveEstimator
import astropy.units as u
from cta_tools.logging import setup_logging
log = setup_logging()
@click.command()
@click.argument("config_path", type=click.Path(exists=True, dir_okay=False))
@click.argument("models_path", type=click.Path(exists=True, dir_okay=False))
@click.option("-o", "--output", type=click.Path(dir_okay=False))
@click.option("-r", "--reference", type=str, help="Crab Reference Spectrum. Crab Only!")
def main(config_path, models_path, output, reference):
config = AnalysisConfig.read(config_path)
analysis = Analysis(config)
log.info(config)
analysis.get_observations()
log.info(analysis)
log.info(dir(analysis))
log.info(analysis.datasets)
log.info(analysis.datasets[0].counts)
analysis.get_datasets()
analysis.read_models(models_path)
def main(infile, verbose, output):
setup_logging(verbose=verbose)
figures = []
try:
magic = QTable.read("magic_sensitivity_2014.ecsv")
except Exception as e:
log.warning(e)
magic = None
sens = QTable.read(infile, hdu="SENSITIVITY")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_sensitivity(sens[1:-1], ax, label="LST-1")
if magic:
for k in filter(
lambda k: k.startswith("sensitivity_") or k.startswith("e_"), magic.colnames
):
magic[k].info.format = ".3g"
magic["reco_energy_low"] = magic["e_min"]
magic["reco_energy_high"] = magic["e_max"]
magic["reco_energy_center"] = magic["e_center"]
magic["flux_sensitivity"] = magic["sensitivity_lima_5off"]
plot_sensitivity(magic, ax, label="MAGIC")
ax.legend()
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_sensitivity_gp(sens[1:-1], ax)
aeff = QTable.read(infile, hdu="EFFECTIVE_AREA")
aeff_only_gh = QTable.read(infile, hdu="EFFECTIVE_AREA_ONLY_GH")
aeff_no_cuts = QTable.read(infile, hdu="EFFECTIVE_AREA_NO_CUTS")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_aeff(aeff[0], ax, label="gh + theta")
plot_aeff(aeff_only_gh[0], ax, label="Only GH")
plot_aeff(aeff_no_cuts[0], ax, label="No Cuts")
ax.legend()
edisp = QTable.read(infile, hdu="ENERGY_DISPERSION")
edisp_only_gh = QTable.read(infile, hdu="ENERGY_DISPERSION_ONLY_GH")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_edisp(edisp[0], ax, label="gh + theta")
plot_edisp(edisp_only_gh[0], ax, label="only gh")
angres = QTable.read(infile, hdu="ANGULAR_RESOLUTION")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_angular_resolution(angres, ax)
energres = QTable.read(infile, hdu="ENERGY_BIAS_RESOLUTION")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_energy_bias_resolution(energres, ax)
bkg = QTable.read(infile, hdu="BACKGROUND")[0]
rad_max = QTable.read(infile, hdu="RAD_MAX")[0]
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_background(bkg, rad_max, ax)
thetacuts = QTable.read(infile, hdu="THETA_CUTS")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_theta_cuts(thetacuts, ax)
ghcuts = QTable.read(infile, hdu="GH_CUTS")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_gh_cuts(ghcuts, ax)
signal = QTable.read(infile, hdu="SIGNAL")
signal_gh = QTable.read(infile, hdu="SIGNAL_GH")
signal_cuts = QTable.read(infile, hdu="SIGNAL_CUTS")
figures.append(plt.figure())
ax = figures[-1].add_subplot(1, 1, 1)
plot_efficiency({"NO CUTS": signal, "ONLY GH": signal_gh, "CUTS": signal_cuts}, ax)
ax.legend()
if output is None:
plt.show()
else:
with PdfPages(output) as pdf:
for fig in figures:
fig.tight_layout(pad=0, h_pad=1.08, w_pad=1.08)
pdf.savefig(fig)
def main(
input_files,
protons,
electrons,
source_gammas,
binsizes_config,
cuts,
verbose,
output,
):
setup_logging(verbose=verbose)
observations = {}
if binsizes_config:
with open(binsizes_config) as f:
binsizes = yaml.safe_load(f)
else:
binsizes = None
if cuts:
with open(cuts) as f:
selection = yaml.safe_load(f).get("selection")
else:
selection = None
cache = Path(output).with_suffix(".h5")
if cache.exists():
plot_data = read_plot_data(cache)
else:
plot_data = {}
observations, obstime = read_lst_dl2_runs(input_files)
combined = vstack(list(observations.values()))
log.info(combined.keys())
if protons:
protons = load_mc(protons, obstime, IRFDOC_PROTON_SPECTRUM)
if electrons:
electrons = load_mc(electrons, obstime, IRFDOC_ELECTRON_SPECTRUM)
if electrons and protons:
background = vstack([protons, electrons])
elif protons:
background = protons
elif electrons:
background = electrons
else:
background = None
if source_gammas:
gammas = load_mc(source_gammas, obstime, CRAB_MAGIC_JHEAP2015)
else:
gammas = None
if selection:
mask = create_mask_selection(combined, selection)
combined = combined[mask]
if background:
mask = create_mask_selection(background, selection)
background = background[mask]
if gammas:
mask = create_mask_selection(gammas, selection)
gammas = gammas[mask]
for feature in combined.keys():
plot_data[feature] = {}
plot_data[feature]["bins"] = pd.Series(dtype=np.float64)
plot_data[feature]["values"] = pd.DataFrame()
log.info(feature)
if binsizes:
if feature in binsizes:
min_, max_, n = binsizes[feature]
else:
min_ = get_value(combined, feature).min()
max_ = get_value(combined, feature).max()
n = 30
else:
min_ = get_value(combined, feature).min()
max_ = get_value(combined, feature).max()
n = 30
log.debug(min_)
log.debug(max_)
if feature in logx:
bins = np.logspace(np.log10(min_), np.log10(max_), n)
else:
bins = np.linspace(min_, max_, n)
plot_data[feature]["bins"] = pd.Series(bins)
feature_df = pd.DataFrame()
feature_df["observations"], _ = np.histogram(
get_value(combined, feature), bins=bins
)
if background:
feature_df["background"], _ = np.histogram(
get_value(background, feature),
weights=background["weights"],
bins=bins,
)
if gammas:
feature_df["gammas"], _ = np.histogram(
get_value(gammas, feature), weights=gammas["weights"], bins=bins
)
for run_data in observations:
run_id = run_data[0]["obs_id"]
log.info(f"Filling feature df for run {run_id}")
# why would that happen?
if feature not in run_data.keys():
log.debug(f"{feature} missing in keys: {run_data.keys()}")
continue
feature_df[run_id], _ = np.histogram(
get_value(run_data, feature), bins=bins
)
plot_data[feature]["values"] = feature_df
save_plot_data(cache, plot_data)
figs = []
datamc = ["observations"]
if protons or electrons:
datamc.append("background")
if source_gammas:
datamc.append("gammas")
for feature in combined.keys():
log.debug(f"Plotting feature: {feature}")
if not plot_data[feature]["values"].empty:
log.debug("And indeed there is data")
figs.append(
compare_datasets(plot_data, feature, datamc, logx=feature in logx)
)
figs.append(
compare_datasets(
plot_data,
feature,
set(plot_data[feature]["values"].keys()) - set(datamc),
logx=feature in logx,
)
)
if output is None:
matplotlib.pyplot.show()
else:
with PdfPages(output) as pdf:
for fig in figs:
fig.tight_layout(pad=0)
pdf.savefig(fig)