Theta2 next-day results

cfg/sequencer.cfg

@@ -24,6 +24,7 @@ DATACHECK_DIR: %(BASE)s/DL1
 DL2_DIR: %(BASE)s/DL2
 DL3_DIR: %(BASE)s/DL3
 CLOSER_DIR: %(BASE)s/OSA/Closer
+HIGHLEVEL_DIR: %(BASE)s/OSA/HighLevel
 LONGTERM_DIR: %(BASE)s/OSA/DL1DataCheck_LongTerm
 
 # To be set by the user. Using PROD-ID will overcome the automatic

osa/high_level/significance.py

@@ -0,0 +1,322 @@
+"""
+Functions to compute significance and build the theta2 plots for each source.
+
+Based on Explore_DL2_data.ipynb from LST-1 analysis 2022 school.
+"""
+
+import logging
+import subprocess
+from collections import defaultdict
+from datetime import date, timedelta
+from pathlib import Path
+
+import astropy
+import astropy.units as u
+import click
+import numpy as np
+import pandas as pd
+from ctapipe.containers import EventType
+from gammapy.stats import WStatCountsStatistic
+from lstchain.io.io import dl2_params_lstcam_key
+from lstchain.reco.utils import (
+    get_effective_time, extract_source_position, compute_theta2
+)
+from matplotlib import pyplot as plt
+
+from osa.configs import options
+from osa.configs.config import cfg
+from osa.nightsummary.extract import extractruns, extractsequences, extractsubruns
+from osa.nightsummary.nightsummary import run_summary_table
+from osa.utils.cliopts import (
+    set_default_directory_if_needed,
+    get_prod_id,
+    get_dl2_prod_id
+)
+from osa.utils.logging import myLogger
+from osa.utils.utils import DATACHECK_BASEDIR
+from osa.utils.utils import lstdate_to_dir
+
+log = myLogger(logging.getLogger(__name__))
+
+DEFAULT_CFG = Path(__file__).parent / '../../cfg/sequencer.cfg'
+
+THETA2_GLOBAL_CUT = 0.04
+THETA2_RANGE = (0, 1)
+NORM_RANGE_THETA2_MIN = 0.5
+NORM_RANGE_THETA2_MAX = 1
+
+mpl_linewidth = 1.6
+mpl_rc = {
+    "figure.autolayout": True,
+    "figure.dpi": 150,
+    "font.size": 12,
+    "lines.linewidth": mpl_linewidth,
+    "axes.grid": True,
+    "grid.linestyle": ":",
+    "grid.linewidth": 1.4,
+    "axes.linewidth": mpl_linewidth,
+    "xtick.major.size": 7,
+    "xtick.major.width": mpl_linewidth,
+    "xtick.minor.size": 4,
+    "xtick.minor.width": mpl_linewidth,
+    "xtick.minor.visible": False,
+    "ytick.major.size": 7,
+    "ytick.major.width": mpl_linewidth,
+    "ytick.minor.size": 4,
+    "ytick.minor.width": mpl_linewidth,
+    "ytick.minor.visible": False,
+}
+plt.style.use(mpl_rc)
+
+
+def list_of_source(date_obs: str) -> dict:
+    """
+    Get the list of sources from the sequences' information.
+
+    Parameters
+    ----------
+    date_obs : str
+
+    Returns
+    -------
+    sources : Dict[str, list]
+    """
+
+    # Build the sequences
+    summary_table = run_summary_table(date_obs)
+    subrun_list = extractsubruns(summary_table)
+    run_list = extractruns(subrun_list)
+    sequence_list = extractsequences(run_list)
+
+    # Create a dictionary of sources and their corresponding sequences
+    source_dict = {sequence.run: sequence.source_name for sequence in sequence_list}
+
+    source_dict_grouped = defaultdict(list)
+    for key, val in sorted(source_dict.items()):
+        source_dict_grouped[val].append(key)
+
+    return dict(source_dict_grouped)
+
+
+def create_hist(theta2_on, theta2_off):
+    nbins = round((THETA2_RANGE[1] / THETA2_GLOBAL_CUT) * 2)
+    hist_on, bin_edges_on = np.histogram(
+        theta2_on, density=False, bins=nbins, range=THETA2_RANGE
+    )
+    hist_off, bin_edges_off = np.histogram(
+        theta2_off, density=False, bins=nbins, range=THETA2_RANGE
+    )
+
+    bin_width = bin_edges_on[1] - bin_edges_off[0]
+    bin_center = bin_edges_on[:-1] + (bin_width / 2)
+
+    return hist_on, hist_off, bin_edges_on, bin_edges_off, bin_center
+
+
+def lima_significance(hist_on, hist_off, bin_edges_on, bin_edges_off, eff_time):
+    N_on = np.sum(hist_on[bin_edges_on[1:] <= THETA2_GLOBAL_CUT])
+    N_off = np.sum(hist_off[bin_edges_off[1:] <= THETA2_GLOBAL_CUT])
+
+    idx_min = (np.abs(bin_edges_on - NORM_RANGE_THETA2_MIN)).argmin()
+    idx_max = (np.abs(bin_edges_on - NORM_RANGE_THETA2_MAX)).argmin()
+
+    Non_norm = np.sum(hist_on[idx_min:idx_max])
+    Noff_norm = np.sum(hist_off[idx_min:idx_max])
+
+    alpha = Noff_norm / Non_norm
+
+    stat = WStatCountsStatistic(n_on=N_on, n_off=N_off, alpha=alpha)
+    significance_lima = stat.sqrt_ts
+
+    text_statistics = (
+        f'N$_{{\\rm on}}$ = {N_on:.0f}\n'
+        f'N$_{{\\rm off}}$ = {N_off:.0f}\n'
+        f'Livetime = {eff_time.to(u.h):.1f}\n'
+        f'Li&Ma significance = {significance_lima:.1f} $\\sigma$'
+    )
+    log.info(text_statistics)
+
+    box_color = 'yellow' if significance_lima > 5 else 'white'
+
+    return text_statistics, box_color
+
+
+def select_data(data):
+
+    t_effective, _ = get_effective_time(data)
+    gammaness = np.array(data.gammaness)
+    leakage_intensity_width_2 = np.array(data.leakage_intensity_width_2)
+    intensity = np.array(data.intensity)
+    wl = np.array(data.wl)
+    event_type = np.array(data.event_type)
+    t_effective.to(u.min)
+
+    # Cuts. TODO: Use a config file.
+    gammaness_cut = 0.7
+    intensity_cut = 50
+    wl_cut = 0.0
+    log.info(
+        f'Gammaness cut: {gammaness_cut}\n'
+        f'Intensity cut: {intensity_cut}\n'
+        f'Width/Length cut: {wl_cut}'
+    )
+
+    # Mask for data selection
+    condition = (
+        (gammaness > gammaness_cut) &
+        (intensity_cut < intensity) &
+        (wl > wl_cut) &
+        (event_type != EventType.FLATFIELD.value) &
+        (event_type != EventType.SKY_PEDESTAL.value) &
+        (leakage_intensity_width_2 < 0.2)
+    )
+    return data[condition]
+
+
+def plot_theta2(
+        bin_center,
+        hist_on,
+        hist_off,
+        legend_text,
+        box_color,
+        source_name,
+        date_obs,
+        runs,
+        highlevel_dir,
+):
+    fig, ax = plt.subplots()
+
+    ax.errorbar(
+        bin_center,
+        hist_on,
+        yerr=np.sqrt(hist_on),
+        fmt='o',
+        label='ON data',
+    )
+    ax.errorbar(
+        bin_center,
+        hist_off,
+        yerr=np.sqrt(hist_off),
+        fmt='s',
+        label='Background',
+    )
+    ax.set_xlim(0, 0.5)
+    ax.axvline(THETA2_GLOBAL_CUT, color='black', ls='--', alpha=0.75)
+    ax.set_xlabel("$\\theta^{2}$ [deg$^{2}$]")
+    ax.set_ylabel("Counts")
+    ax.legend(
+        title=legend_text, facecolor=box_color, loc='upper right'
+    )._legend_box.align='left'
+    ax.set_title(
+        f"Source: {source_name}. Date: {date_obs.strftime('%Y-%m-%d')}\n Runs: {runs}"
+    )
+    plot_path = (
+        highlevel_dir / f"Theta2_{source_name}_{date_obs.strftime('%Y-%m-%d')}.png"
+    )
+    plt.savefig(plot_path, dpi=300)
+    return plot_path
+
+
+@click.command()
+@click.argument('telescope', type=click.Choice(['LST1', 'LST2']))
+@click.option(
+    '-d',
+    '--date-obs',
+    type=click.DateTime(formats=["%Y_%m_%d"]),
+    default=(date.today() - timedelta(days=1)).strftime("%Y_%m_%d")
+)
+@click.option(
+    '-c', '--config',
+    type=click.Path(dir_okay=False),
+    default=DEFAULT_CFG,
+    help='Read option defaults from the specified cfg file',
+)
+def main(date_obs, telescope, config):
+
+    log.setLevel(logging.INFO)
+
+    # Initial setup of global parameters
+    options.date = date_obs.strftime('%Y_%m_%d')
+    night_dir = lstdate_to_dir(options.date)
+    options.tel_id = telescope
+    options.prod_id = get_prod_id()
+    options.dl2_prod_id = get_dl2_prod_id()
+    options.directory = set_default_directory_if_needed()
+    dl2_directory = Path(cfg.get('LST1', 'DL2_DIR'))
+    highlevel_directory = (
+        Path(cfg.get('LST1', 'HIGHLEVEL_DIR')) / night_dir / options.prod_id
+    )
+    highlevel_directory.mkdir(parents=True, exist_ok=True)
+    host = cfg.get('WEBSERVER', 'HOST')
+
+    # Create high-level directory in the webserver
+    dest_directory = (
+        DATACHECK_BASEDIR /
+        "high_level" /
+        options.prod_id /
+        date_obs.strftime('%Y-%m-%d')
+    )
+    cmd = ["ssh", host, "mkdir", "-p", dest_directory]
+    subprocess.run(cmd, capture_output=True, check=True)
+    cmd = ["scp", cfg.get("WEBSERVER", "INDEXPHP"), f"{host}:{dest_directory}/."]
+    subprocess.run(cmd, capture_output=True, check=True)
+
+    sources = list_of_source(options.date)
+    log.info(f"Sources: {sources}")
+
+    for source in sources:
+        if source is not None:
+            data = pd.DataFrame()
+            runs = sources[source]
+            log.info(f"Source: {source}, runs: {runs}")
+
+            for run in runs:
+                input_file = (
+                    dl2_directory / night_dir / options.prod_id / options.dl2_prod_id /
+                    f"dl2_LST-1.Run{run:05d}.h5"
+                )
+                data = pd.concat([data, pd.read_hdf(input_file, key=dl2_params_lstcam_key)])
+
+            selected_events = select_data(data)
+
+            try:
+                true_source_position = extract_source_position(
+                    data=selected_events,
+                    observed_source_name=source
+                )
+                off_source_position = [element * -1 for element in true_source_position]
+
+                theta2_on = np.array(compute_theta2(selected_events, true_source_position))
+                theta2_off = np.array(compute_theta2(selected_events, off_source_position))
+
+                hist_on, hist_off, bin_edges_on, bin_edges_off, bin_center = create_hist(
+                    theta2_on, theta2_off
+                )
+                text, box_color = lima_significance(
+                    hist_on=hist_on,
+                    hist_off=hist_off,
+                    bin_edges_on=bin_edges_on,
+                    bin_edges_off=bin_edges_off,
+                    eff_time=get_effective_time(data)[0],
+                )
+                pdf_file = plot_theta2(
+                    bin_center=bin_center,
+                    hist_on=hist_on,
+                    hist_off=hist_off,
+                    legend_text=text,
+                    box_color=box_color,
+                    source_name=source,
+                    date_obs=date_obs,
+                    runs=runs,
+                    highlevel_dir=highlevel_directory,
+                )
+                cmd = ["scp", pdf_file, f"{host}:{dest_directory}/."]
+                subprocess.run(cmd, capture_output=True, check=True)
+
+            except astropy.coordinates.name_resolve.NameResolveError:
+                log.warning(f"Source {source} not found in the catalog. Skipping.")
+                # TODO: get ra/dec from the TCU database instead
+
+
+if __name__ == "__main__":
+    main()

setup.py

@@ -48,6 +48,7 @@
     install_requires=[
         "astropy~=4.2",
         "lstchain~=0.8.4",
+        "ctapipe~=0.11.0",
         "matplotlib~=3.4.3",
         "pyparsing~=2.4",
         "numpy<1.22.0a0",
@@ -58,7 +59,8 @@
         "pydotplus",
         "psutil",
         "click",
-        "pymongo"
+        "pymongo",
+        "gammapy~=0.18.2",
     ],
     package_data={
         'osa': [