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Merge dbb1b67 into 956b8ca
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@msackerm
msackerm committed Nov 23, 2023
2 parents 956b8ca + dbb1b67 commit b6aac1c
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Showing 16 changed files with 1,633 additions and 42 deletions.
25 changes: 22 additions & 3 deletions .github/workflows/continous_integration.yml
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Expand Up @@ -33,27 +33,44 @@ jobs:
# Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
- uses: actions/checkout@v3

# Set up the python versions
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
# Set up the python versions and install conda dependencies
- name: Install conda dependencies
uses: conda-incubator/setup-miniconda@v2
with:
python-version: ${{ matrix.python-version }}
environment-file: conda_env.yml
activate-environment: flarestack
auto-activate-base: false

# Display the python version and conda info
- name: Display Python version and Conda info
shell: bash -el {0}
run: |
python --version
conda --version
conda config --show-sources
conda config --show
conda list
conda info -a
# Setting up dependencies
- name: Install dependencies
shell: bash -el {0}
run: |
python -m pip install --upgrade pip
python -m pip install poetry
python -m poetry install
# Runs a set of commands using the runners shell
- name: Test the code
shell: bash -el {0}
run: |
poetry run coverage run --concurrency=multiprocessing -m unittest discover tests/
poetry run coverage combine
- name: Run Coveralls
if: ${{ success() }}
shell: bash -el {0}
run: |
poetry run coveralls --service=github
env:
Expand All @@ -63,10 +80,12 @@ jobs:
run: echo "Tag is ${{ github.ref }}, Deploy is ${{ startsWith(github.ref, 'refs/tags/') && matrix.python-version == 3.9}}"

- name: Build a binary wheel and a source tarball
shell: bash -el {0}
run: python -m poetry build

- name: Publish distribution 📦 to PyPI
if: ${{ startsWith(github.ref, 'refs/tags/') && success() && matrix.python-version == 3.9}}
env:
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
shell: bash -el {0}
run: poetry publish
3 changes: 2 additions & 1 deletion .gitignore
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@@ -1,3 +1,4 @@
flarestack.egg-info
*.pyc
docs/build
docs/build
scratch/
6 changes: 6 additions & 0 deletions conda_env.yml
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@@ -0,0 +1,6 @@
name: flarestack
channels:
- conda-forge
- defaults
dependencies:
- photospline=2.1.0
36 changes: 21 additions & 15 deletions flarestack/analyses/benchmarks/benchmark_point_source_sensitivity_10yr.py
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Expand Up @@ -24,28 +24,33 @@
"time_pdf_name": "steady",
}

llh_time = {
"time_pdf_name": "steady",
injection_spatial = {
"spatial_pdf_name": "circular_gaussian",
}

inj_kwargs = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
"injection_spatial_pdf": injection_spatial,
}


llh_time = injection_time
llh_spatial = injection_spatial
llh_energy = injection_energy

llh_kwargs = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_spatial_pdf": llh_spatial,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {"time_pdf_name": "steady"},
"negative_ns_bool": True,
}

name = "analyses/benchmarks/ps_sens_10yr"

# sindecs = np.linspace(0.90, -0.90, 3)
# sindecs = np.linspace(0.90, 0.00, 10)
sindecs = np.linspace(0.90, -0.90, 19)
# sindecs = np.linspace(0.5, -0.5, 3)
#
Expand All @@ -60,7 +65,7 @@

subname = name + "/sindec=" + "{0:.2f}".format(sindec) + "/"

scale = flux_to_k(reference_sensitivity(sindec)) * 3
scale = (flux_to_k(reference_sensitivity(sindec)) * 3)[0]

mh_dict = {
"name": subname,
Expand All @@ -70,22 +75,23 @@
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 5000,
"n_trials": 1000,
"trials_per_task": 50,
"n_steps": 15,
}

job_id = analyse(
mh_dict,
cluster=cluster,
n_cpu=1 if cluster else 32,
h_cpu="23:59:59",
ram_per_core="8.0G",
)
job_ids.append(job_id)
# job_id = analyse(
# mh_dict,
# cluster=cluster,
# n_cpu=1 if cluster else 16,
# h_cpu="23:59:59",
# ram_per_core="8.0G",
# )
# job_ids.append(job_id)

analyses.append(mh_dict)

wait_cluster(job_ids)
# wait_cluster(job_ids)

sens = []
sens_err = []
Expand Down Expand Up @@ -136,7 +142,7 @@
ax1.set_xlim(xmin=-1.0, xmax=1.0)
# ax1.set_ylim(ymin=1.e-13, ymax=1.e-10)
ax1.grid(True, which="both")
ax1.semilogy(nonposy="clip")
ax1.semilogy(nonpositive="clip")
ax1.set_ylabel(r"Flux Strength [ GeV$^{-1}$ cm$^{-2}$ s$^{-1}$ ]", fontsize=12)

ax2 = plt.subplot2grid((4, 1), (3, 0), colspan=3, rowspan=1, sharex=ax1)
Expand Down
180 changes: 180 additions & 0 deletions flarestack/analyses/benchmarks/benchmark_point_source_sensitivity_10yr_KDE.py
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@@ -0,0 +1,180 @@
import numpy as np
from flarestack import ResultsHandler, MinimisationHandler
from flarestack.data.icecube import ps_v003_p02
from flarestack.shared import plot_output_dir, flux_to_k
from flarestack.utils.prepare_catalogue import ps_catalogue_name
from flarestack.icecube_utils.reference_sensitivity import (
reference_sensitivity,
reference_discovery_potential,
)
import matplotlib.pyplot as plt
from flarestack import analyse, wait_cluster
import logging

logging.basicConfig(level=logging.INFO)

# Initialise Injectors/LLHs

injection_energy = {
"energy_pdf_name": "power_law",
"gamma": 2.0,
}

injection_time = {
"time_pdf_name": "steady",
}

injection_spatial = {
"spatial_pdf_name": "northern_tracks_kde",
"spatial_pdf_index": None, # if 'None', use 3D spline with fixed gamma
"spatial_pdf_data": "/lustre/fs22/group/icecube/data_mirror/northern_tracks/version-005-p01/KDE_PDFs_v007/IC86_pass2/2.02/sig_E_psi_photospline.fits",
}

inj_kwargs = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
"injection_spatial_pdf": injection_spatial,
}


llh_time = injection_time
llh_spatial = injection_spatial
llh_energy = injection_energy

llh_kwargs = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_spatial_pdf": llh_spatial,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {"time_pdf_name": "steady"},
"negative_ns_bool": True,
}

name = "analyses/benchmarks/ps_sens_10yr_KDE"

# sindecs = np.linspace(0.90, -0.90, 3)
sindecs = np.linspace(0.90, -0.90, 19)
# sindecs = np.linspace(0.5, -0.5, 3)
#
analyses = []

cluster = True

job_ids = []

for sindec in sindecs:
cat_path = ps_catalogue_name(sindec)

subname = name + "/KDEsindec=" + "{0:.2f}".format(sindec) + "/"

scale = (flux_to_k(reference_sensitivity(sindec)) * 3)[0]

mh_dict = {
"name": subname,
"mh_name": "fixed_weights",
"dataset": ps_v003_p02,
"catalogue": cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 1000,
"trials_per_task": 50,
"n_steps": 15,
}

# job_id = analyse(
# mh_dict,
# cluster=cluster,
# n_cpu=1 if cluster else 16,
# h_cpu="23:59:59",
# ram_per_core="8.0G",
# )
# job_ids.append(job_id)

analyses.append(mh_dict)

# wait_cluster(job_ids)

sens = []
sens_err = []
disc_pots = []

for rh_dict in analyses:
rh = ResultsHandler(rh_dict)
sens.append(rh.sensitivity)
sens_err.append(rh.sensitivity_err)
disc_pots.append(rh.disc_potential)

sens_err = np.array(sens_err).T

# sens = reference_sensitivity(sindecs, sample="7yr")
# disc_pots = reference_discovery_potential(sindecs, sample="7yr")
# sens_err = 0.1*sens

plot_range = np.linspace(-0.99, 0.99, 1000)

plt.figure()
ax1 = plt.subplot2grid((4, 1), (0, 0), colspan=3, rowspan=3)
ax1.plot(
sindecs,
reference_sensitivity(sindecs, sample="10yr"),
color="blue",
label=r"10-year Point Source analysis",
)
# ax1.plot(sindecs, reference_sensitivity(sindecs, sample="7yr"), color="green",
# label=r"7-year Point Source analysis")

# ax1.plot(sindecs, sens, color='orange', label="Flarestack")

ax1.errorbar(
sindecs, sens, yerr=sens_err, color="orange", label="Sensitivity", marker="o"
)

ax1.plot(
sindecs,
reference_discovery_potential(sindecs, sample="10yr"),
color="blue",
linestyle="--",
)

ax1.plot(
sindecs, disc_pots, color="orange", linestyle="--", label="Discovery Potential"
)

ax1.set_xlim(xmin=-1.0, xmax=1.0)
# ax1.set_ylim(ymin=1.e-13, ymax=1.e-10)
ax1.grid(True, which="both")
ax1.semilogy(nonpositive="clip")
ax1.set_ylabel(r"Flux Strength [ GeV$^{-1}$ cm$^{-2}$ s$^{-1}$ ]", fontsize=12)

ax2 = plt.subplot2grid((4, 1), (3, 0), colspan=3, rowspan=1, sharex=ax1)

sens_ratios = np.array(sens) / reference_sensitivity(sindecs, sample="10yr")
sens_ratio_errs = sens_err / reference_sensitivity(sindecs, sample="10yr")

disc_ratios = np.array(disc_pots) / reference_discovery_potential(
sindecs, sample="10yr"
)

ax2.errorbar(sindecs, sens_ratios, yerr=sens_ratio_errs, color="red", marker="o")
ax2.scatter(sindecs, disc_ratios, color="k")
ax2.plot(sindecs, disc_ratios, color="k", linestyle="--")
ax2.set_ylabel(r"ratio", fontsize=12)
ax2.set_xlabel(r"sin($\delta$)", fontsize=12)

plt.suptitle("Point Source Sensitivity (10 year)")
#
ax1.set_xlim(xmin=-1.0, xmax=1.0)

xticklabels = ax1.get_xticklabels()
plt.setp(xticklabels, visible=False)
plt.subplots_adjust(hspace=0.001)

ax1.legend(loc="upper right", fancybox=True, framealpha=1.0)

savefile = plot_output_dir(name) + "/PS10yrKDE.pdf"

logging.info(f"Saving to {savefile}")

plt.savefig(savefile)
plt.close()

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