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applying_neos.md

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@@ -22,4 +22,3 @@ I've just released [`relaxed` v0.3.0](https://github.com/gradhep/relaxed), which
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## reaching out
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If you're interested in working on this, please reach out to me through [Mattermost](https://mattermost.web.cern.ch/signup_user_complete/?id=zf7w5rb1miy85xsfjqm68q9hwr&md=link&sbr=su), or by email.
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examples/binning.ipynb

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"num_bins = 4\n",
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"pars = jnp.linspace(0, 8, num_bins + 1)\n",
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"\n",
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"\n",
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"# differentiable significance pipeline\n",
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"def asimov_pipe(bins, s, b, bandwidth=1e-2):\n",
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" # restrict bin edges so they don't overlap during optimisation\n",
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"\n",
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"from copy import deepcopy\n",
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"\n",
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"\n",
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"# specify the workspace for a simple model with a three-point background systematic\n",
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"def correlated_background(signal, bkg, bkg_up, bkg_down):\n",
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" spec = {\n",
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"\n",
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"from pyhf import interpolators\n",
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"\n",
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"\n",
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"# pipeline to calculate the full CLs for the model, given a number of bins\n",
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"def pipe(num_bins):\n",
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" def cls_pipe(bins, s, b, bup, bdown, bandwidth=1e-2, skeleton_model=None):\n",

examples/cuts.ipynb

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"from jaxopt import OptaxSolver\n",
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"from optax import adam\n",
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"\n",
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"\n",
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"# define something to minimise (1/significance)\n",
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"def loss(cut):\n",
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" s_weights = relaxed.cut(sig, cut, slope) + 1e-4\n",

setup.cfg

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@@ -9,7 +9,7 @@ author_email = [email protected]
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maintainer = Nathan Simpson
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maintainer_email = [email protected]
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license = BSD-3-Clause
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license_file = LICENSE
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license_files = LICENSE
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platforms =
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Any
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classifiers =
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Programming Language :: Python
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Programming Language :: Python :: 3
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Programming Language :: Python :: 3 :: Only
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Programming Language :: Python :: 3.6
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Programming Language :: Python :: 3.7
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Programming Language :: Python :: 3.8
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Programming Language :: Python :: 3.9
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Programming Language :: Python :: 3.10
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Topic :: Scientific/Engineering
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project_urls =
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Documentation = https://neos.readthedocs.io/
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relaxed>=0.2.0
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sklearn
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typing-extensions>=3.7;python_version<'3.8'
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python_requires = >=3.6
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python_requires = >=3.8
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include_package_data = True
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package_dir =
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=src

src/neos/losses.py

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import jax.numpy as jnp
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import pyhf
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import relaxed
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Array = jnp.ndarray

src/neos/top_level.py

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import jax.numpy as jnp
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import pyhf
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import relaxed
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from neos.losses import (
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cls_value,
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discovery_significance,

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