enh: fix ruff comments

scikit-hep · Apr 16, 2024 · 3679b65 · 3679b65
1 parent 95a3ac5
commit 3679b65
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -58,11 +58,9 @@ repos:
   - repo: https://github.com/nbQA-dev/nbQA
     rev: 1.8.5
     hooks:
-      - id: nbqa-isort
-        additional_dependencies: [ isort==5.6.4 ]
 
       - id: nbqa-pyupgrade
-        additional_dependencies: [ pyupgrade==2.7.4 ]
+        additional_dependencies: [ pyupgrade ]
         args: [ --py38-plus ]
 
 

diff --git a/docs/conf.py b/docs/conf.py
@@ -5,9 +5,11 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 # -- Path setup --------------------------------------------------------------
+from __future__ import annotations
 
 import sys
 from pathlib import Path
+
 from hepstats import __version__ as version
 
 project_dir = Path(__file__).parents[1]

diff --git a/notebooks/hypotests/FC_interval_asy.ipynb b/notebooks/hypotests/FC_interval_asy.ipynb
@@ -23,19 +23,19 @@
    },
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import zfit\n",
-    "from zfit.loss import UnbinnedNLL\n",
-    "from zfit.minimize import Minuit\n",
-    "\n",
-    "zfit.settings.set_seed(10)\n",
-    "\n",
-    "\n",
     "from hepstats.hypotests import ConfidenceInterval\n",
     "from hepstats.hypotests.calculators import AsymptoticCalculator\n",
     "from hepstats.hypotests.exceptions import POIRangeError\n",
-    "from hepstats.hypotests.parameters import POIarray"
+    "from hepstats.hypotests.parameters import POIarray\n",
+    "from zfit.loss import UnbinnedNLL\n",
+    "from zfit.minimize import Minuit\n",
+    "\n",
+    "zfit.settings.set_seed(10)"
    ]
   },
   {
@@ -102,7 +102,6 @@
     "# minimisation of the loss function\n",
     "minimum = minimizer.minimize(loss=nll)\n",
     "minimum.hesse()\n",
-    "print(minimum)\n",
     "\n",
     "x_err = minimum.params[mean][\"hesse\"][\"error\"]"
    ]

diff --git a/notebooks/hypotests/FC_interval_freq.ipynb b/notebooks/hypotests/FC_interval_freq.ipynb
@@ -21,21 +21,21 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
+    "from __future__ import annotations\n",
+    "\n",
+    "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import zfit\n",
-    "from zfit.loss import UnbinnedNLL\n",
-    "from zfit.minimize import Minuit\n",
-    "\n",
-    "zfit.settings.set_seed(10)\n",
-    "\n",
-    "\n",
     "from hepstats.hypotests import ConfidenceInterval\n",
     "from hepstats.hypotests.calculators import FrequentistCalculator\n",
     "from hepstats.hypotests.exceptions import POIRangeError\n",
-    "from hepstats.hypotests.parameters import POIarray"
+    "from hepstats.hypotests.parameters import POIarray\n",
+    "from zfit.loss import UnbinnedNLL\n",
+    "from zfit.minimize import Minuit\n",
+    "\n",
+    "zfit.settings.set_seed(10)"
    ]
   },
   {
@@ -102,7 +102,6 @@
     "# minimisation of the loss function\n",
     "minimum = minimizer.minimize(loss=nll)\n",
     "minimum.hesse()\n",
-    "print(minimum)\n",
     "\n",
     "x_err = minimum.params[mean][\"hesse\"][\"error\"]"
    ]
@@ -171,7 +170,7 @@
     "\n",
     "        zfit.settings.set_seed()  # randomized\n",
     "\n",
-    "        if not os.path.isfile(toys_fname):\n",
+    "        if not Path(toys_fname).is_file():\n",
     "            calculator = FrequentistCalculator(minimum, minimizer)\n",
     "\n",
     "            x_min = results_n[\"x\"] - results_n[\"x_err\"] * 3\n",
@@ -193,7 +192,7 @@
     "        else:\n",
     "            calculator = FrequentistCalculator.from_yaml(toys_fname, minimum, minimizer)\n",
     "\n",
-    "            keys = np.unique([k[0].value for k in calculator.keys()])\n",
+    "            keys = np.unique([k[0].value for k in calculator])\n",
     "            keys.sort()\n",
     "            poinull = POIarray(mean, keys)\n",
     "\n",

diff --git a/notebooks/hypotests/Simultaneous_fit_discovery_splot.ipynb b/notebooks/hypotests/Simultaneous_fit_discovery_splot.ipynb
@@ -45,6 +45,8 @@
    },
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import hepunits as u\n",
     "import matplotlib.pyplot as plt\n",
     "import mplhep\n",
@@ -93,9 +95,7 @@
     "# create some data\n",
     "signal_np = np.random.normal(loc=mu_true, scale=sigma_true, size=n_sig_rare)\n",
     "bkg_np_raw = np.random.exponential(size=20000, scale=700)\n",
-    "bkg_np = (\n",
-    "    bkg_np_raw[bkg_np_raw < 1000][:n_bkg_rare] + 5000\n",
-    ")  # just cutting right, but zfit could also cut"
+    "bkg_np = bkg_np_raw[bkg_np_raw < 1000][:n_bkg_rare] + 5000  # just cutting right, but zfit could also cut"
    ]
   },
   {
@@ -150,14 +150,10 @@
     "sigma = zfit.Parameter(\"sigma\", 20, 1, 200)\n",
     "signal = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obs)\n",
     "\n",
-    "lam = zfit.Parameter(\n",
-    "    \"lambda\", -0.002, -0.1, -0.00001, step_size=0.001\n",
-    ")  # floating, also without limits\n",
+    "lam = zfit.Parameter(\"lambda\", -0.002, -0.1, -0.00001, step_size=0.001)  # floating, also without limits\n",
     "comb_bkg = zfit.pdf.Exponential(lam, obs=obs)\n",
     "\n",
-    "sig_yield = zfit.Parameter(\n",
-    "    \"sig_yield\", n_sig_rare + 30, step_size=3\n",
-    ")  # step size: default is small, use appropriate\n",
+    "sig_yield = zfit.Parameter(\"sig_yield\", n_sig_rare + 30, step_size=3)  # step size: default is small, use appropriate\n",
     "bkg_yield = zfit.Parameter(\"bkg_yield\", n_bkg_rare - 40, step_size=1)\n",
     "# Create extended PDFs\n",
     "extended_sig = signal.create_extended(sig_yield)\n",
@@ -180,9 +176,7 @@
    },
    "outputs": [],
    "source": [
-    "constraint = zfit.constraint.GaussianConstraint(\n",
-    "    mu, observation=5275 * u.MeV, sigma=15 * u.MeV\n",
-    ")"
+    "constraint = zfit.constraint.GaussianConstraint(mu, observation=5275 * u.MeV, sigma=15 * u.MeV)"
    ]
   },
   {
@@ -216,9 +210,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "print(result.params)"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -282,9 +274,7 @@
     "\n",
     "# load data into zfit\n",
     "obs_reso = zfit.Space(\"Bmass_reso\", (5000, 6000))\n",
-    "data_reso = zfit.Data.from_numpy(\n",
-    "    obs=obs_reso, array=np.concatenate([signal_np_reso, bkg_np_reso], axis=0)\n",
-    ")"
+    "data_reso = zfit.Data.from_numpy(obs=obs_reso, array=np.concatenate([signal_np_reso, bkg_np_reso], axis=0))"
    ]
   },
   {
@@ -465,9 +455,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "print(result_simultaneous.params)"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -524,11 +512,9 @@
     "    # Line plots of the total pdf and the sub-pdfs.\n",
     "    y = model.ext_pdf(x) * binwidth\n",
     "    ax.plot(x, y, label=\"total\", color=\"royalblue\")\n",
-    "    for m, l, c in zip(\n",
-    "        model.get_models(), [\"background\", \"signal\"], [\"forestgreen\", \"crimson\"]\n",
-    "    ):\n",
-    "        ym = m.ext_pdf(x) * binwidth\n",
-    "        ax.plot(x, ym, label=l, color=c)\n",
+    "    for mod, label, color in zip(model.get_models(), [\"background\", \"signal\"], [\"forestgreen\", \"crimson\"]):\n",
+    "        ym = mod.ext_pdf(x) * binwidth\n",
+    "        ax.plot(x, ym, label=label, color=color)\n",
     "\n",
     "    ax.set_title(data.data_range.obs[0])\n",
     "    ax.set_xlim(lower, upper)\n",
@@ -539,9 +525,7 @@
     "\n",
     "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n",
     "\n",
-    "for mod, dat, ax, nb in zip(\n",
-    "    nll_simultaneous.model, nll_simultaneous.data, axs, [30, 60]\n",
-    "):\n",
+    "for mod, dat, ax, nb in zip(nll_simultaneous.model, nll_simultaneous.data, axs, [30, 60]):\n",
     "    plot_fit_projection(mod, dat, nbins=nb, ax=ax)"
    ]
   },
@@ -580,9 +564,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "print(result_simultaneous.params[sig_yield])"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -804,13 +786,10 @@
    },
    "outputs": [],
    "source": [
-    "calculator_low_sig = AsymptoticCalculator(\n",
-    "    input=nll_simultaneous_low_sig, minimizer=minimizer, asimov_bins=100\n",
-    ")\n",
+    "calculator_low_sig = AsymptoticCalculator(input=nll_simultaneous_low_sig, minimizer=minimizer, asimov_bins=100)\n",
     "\n",
     "discovery_low_sig = Discovery(calculator=calculator_low_sig, poinull=sig_yield_poi)\n",
-    "discovery_low_sig.result()\n",
-    "print(f\"\\n {calculator_low_sig.bestfit.params} \\n\")"
+    "discovery_low_sig.result()"
    ]
   },
   {
@@ -990,8 +969,7 @@
     "sigma_scaling.floating = False\n",
     "\n",
     "# Minimizes the loss.\n",
-    "result_sw = minimizer.minimize(nll_sw)\n",
-    "print(result_sw.params)"
+    "result_sw = minimizer.minimize(nll_sw)"
    ]
   },
   {
@@ -1053,9 +1031,7 @@
    "source": [
     "from hepstats.splot import compute_sweights\n",
     "\n",
-    "weights = compute_sweights(model_reso, data_sw)\n",
-    "\n",
-    "print(weights)"
+    "weights = compute_sweights(model_reso, data_sw)"
    ]
   },
   {
@@ -1070,10 +1046,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "print(\"Sum of signal sWeights: \", np.sum(weights[reso_sig_yield]))\n",
-    "print(\"Sum of background sWeights: \", np.sum(weights[reso_bkg_yield]))"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -1159,9 +1132,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "print(f\"Correlation between m and t: {np.corrcoef(np_m_sw, np_t_sw)[0, 1]}\")"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",

diff --git a/notebooks/hypotests/confidenceinterval_asy_zfit.ipynb b/notebooks/hypotests/confidenceinterval_asy_zfit.ipynb
@@ -21,16 +21,17 @@
    },
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import zfit\n",
-    "from utils import one_minus_cl_plot, plotfitresult, pltdist\n",
-    "from zfit.loss import ExtendedUnbinnedNLL\n",
-    "from zfit.minimize import Minuit\n",
-    "\n",
     "from hepstats.hypotests import ConfidenceInterval\n",
     "from hepstats.hypotests.calculators import AsymptoticCalculator\n",
-    "from hepstats.hypotests.parameters import POIarray"
+    "from hepstats.hypotests.parameters import POIarray\n",
+    "from utils import one_minus_cl_plot, plotfitresult, pltdist\n",
+    "from zfit.loss import ExtendedUnbinnedNLL\n",
+    "from zfit.minimize import Minuit"
    ]
   },
   {
@@ -169,8 +170,7 @@
    "source": [
     "# minimisation of the loss function\n",
     "minimum = minimizer.minimize(loss=nll)\n",
-    "minimum.hesse()\n",
-    "print(minimum)"
+    "minimum.hesse()"
    ]
   },
   {

diff --git a/notebooks/hypotests/confidenceinterval_freq_zfit.ipynb b/notebooks/hypotests/confidenceinterval_freq_zfit.ipynb
@@ -21,16 +21,17 @@
    },
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import zfit\n",
-    "from utils import one_minus_cl_plot, plotfitresult, pltdist\n",
-    "from zfit.loss import ExtendedUnbinnedNLL\n",
-    "from zfit.minimize import Minuit\n",
-    "\n",
     "from hepstats.hypotests import ConfidenceInterval\n",
     "from hepstats.hypotests.calculators import FrequentistCalculator\n",
-    "from hepstats.hypotests.parameters import POIarray"
+    "from hepstats.hypotests.parameters import POIarray\n",
+    "from utils import one_minus_cl_plot, plotfitresult, pltdist\n",
+    "from zfit.loss import ExtendedUnbinnedNLL\n",
+    "from zfit.minimize import Minuit"
    ]
   },
   {
@@ -154,8 +155,7 @@
    "outputs": [],
    "source": [
     "# minimisation of the loss function\n",
-    "minimum = minimizer.minimize(loss=nll)\n",
-    "print(minimum)"
+    "minimum = minimizer.minimize(loss=nll)"
    ]
   },
   {
@@ -200,9 +200,7 @@
    "source": [
     "# instantation of the calculator\n",
     "# calculator = FrequentistCalculator(nll, minimizer, ntoysnull=100)\n",
-    "calculator = FrequentistCalculator.from_yaml(\n",
-    "    \"toys/ci_freq_zfit_toys.yml\", nll, minimizer, ntoysnull=2000\n",
-    ")\n",
+    "calculator = FrequentistCalculator.from_yaml(\"toys/ci_freq_zfit_toys.yml\", nll, minimizer, ntoysnull=2000)\n",
     "calculator.bestfit = minimum  # optionnal"
    ]
   },