feat: Add nbQA to pre-commit hooks (#1165)

* Add nbqa to pre-commit hooks - Apply nbqa-black and nbqa-pyupgrade * Add nbqa config options to pyproject.toml * Run nbqa over all notebooks * Add --py36-plus option as arg to pyupgrade pre-commit hook - Amends PR #1164
scikit-hep · Nov 6, 2020 · e4011ff · e4011ff
1 parent a3b34a5
commit e4011ff
Show file tree

Hide file tree

Showing 25 changed files with 1,198 additions and 897 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -7,4 +7,10 @@ repos:
 -   repo: https://github.com/asottile/pyupgrade
     rev: v2.7.3
     hooks:
-    -   id: pyupgrade
+    - id: pyupgrade
+      args: ["--py36-plus"]
+-   repo: https://github.com/nbQA-dev/nbQA
+    rev: 0.4.0
+    hooks:
+    - id: nbqa-black
+    - id: nbqa-pyupgrade
diff --git a/docs/examples/experiments/edwardpyhf.ipynb b/docs/examples/experiments/edwardpyhf.ipynb
@@ -16,11 +16,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nuispar = tf.constant([3.])\n",
+    "nuispar = tf.constant([3.0])\n",
     "\n",
-    "x = Poisson(rate = tf.ones(1)*nuispar)\n",
-    "n = Normal(loc = nuispar, scale = tf.ones(1))\n",
-    "joined = tf.concat([x,n], axis=0)   # p(n, x | nuispar) = Pois(n|nuispar) * Normal(x |mu = nuispar, sigma = 1)"
+    "x = Poisson(rate=tf.ones(1) * nuispar)\n",
+    "n = Normal(loc=nuispar, scale=tf.ones(1))\n",
+    "joined = tf.concat(\n",
+    "    [x, n], axis=0\n",
+    ")  # p(n, x | nuispar) = Pois(n|nuispar) * Normal(x |mu = nuispar, sigma = 1)"
    ]
   },
   {
@@ -30,7 +32,7 @@
    "outputs": [],
    "source": [
     "results = []\n",
-    "for i in range(1000): #thee is probably a batched evaluation version .. this is stupid\n",
+    "for i in range(1000):  # thee is probably a batched evaluation version .. this is stupid\n",
     "    with tf.Session() as sess:\n",
     "        r = sess.run(joined)\n",
     "        results.append(r)"
@@ -85,11 +87,11 @@
    "source": [
     "%pylab inline\n",
     "r = np.array(results)\n",
-    "f, axarr = plt.subplots(1,3)\n",
-    "f.set_size_inches(12,4)\n",
-    "axarr[0].scatter(r[:,0],r[:,1])\n",
-    "axarr[1].hist(r[:,0], bins = np.linspace(0,10,11))\n",
-    "axarr[2].hist(r[:,1])"
+    "f, axarr = plt.subplots(1, 3)\n",
+    "f.set_size_inches(12, 4)\n",
+    "axarr[0].scatter(r[:, 0], r[:, 1])\n",
+    "axarr[1].hist(r[:, 0], bins=np.linspace(0, 10, 11))\n",
+    "axarr[2].hist(r[:, 1])"
    ]
   },
   {

diff --git a/docs/examples/notebooks/ImpactPlot.ipynb b/docs/examples/notebooks/ImpactPlot.ipynb
@@ -12,6 +12,7 @@
     "import jsonpatch\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "%matplotlib inline"
    ]
   },
@@ -82,17 +83,21 @@
     "    _, model, data = make_model([\"CRtt_meff\"])\n",
     "\n",
     "    pyhf.set_backend(\"numpy\", pyhf.optimize.minuit_optimizer(verbose=True))\n",
-    "    \n",
+    "\n",
     "    constraints = constraints or []\n",
     "    init_pars = model.config.suggested_init()\n",
     "    fixed_params = model.config.suggested_fixed()\n",
-    "    \n",
-    "    for idx,fixed_val in constraints:\n",
+    "\n",
+    "    for idx, fixed_val in constraints:\n",
     "        init_pars[idx] = fixed_val\n",
     "        fixed_params[idx] = True\n",
-    "    \n",
+    "\n",
     "    result = pyhf.infer.mle.fit(\n",
-    "        data, model, init_pars=init_pars, fixed_params=fixed_params, return_uncertainties=True\n",
+    "        data,\n",
+    "        model,\n",
+    "        init_pars=init_pars,\n",
+    "        fixed_params=fixed_params,\n",
+    "        return_uncertainties=True,\n",
     "    )\n",
     "    bestfit = result[:, 0]\n",
     "    errors = result[:, 1]\n",
@@ -151,9 +156,7 @@
     "    )\n",
     "    labels = np.asarray(\n",
     "        [\n",
-    "            \"{}[{:02}]\".format(k, i)\n",
-    "            if model.config.param_set(k).n_parameters > 1\n",
-    "            else k\n",
+    "            f\"{k}[{i:02}]\" if model.config.param_set(k).n_parameters > 1 else k\n",
     "            for k in model.config.par_order\n",
     "            if model.config.param_set(k).constrained\n",
     "            for i in range(model.config.param_set(k).n_parameters)\n",

diff --git a/docs/examples/notebooks/Recast.ipynb b/docs/examples/notebooks/Recast.ipynb
@@ -39,6 +39,7 @@
     "import jsonpatch\n",
     "import pyhf\n",
     "import pyhf.contrib.viz.brazil as brazil\n",
+    "\n",
     "%pylab inline"
    ]
   },
@@ -124,7 +125,7 @@
     "fig, ax = plt.subplots(1, 1)\n",
     "ax.set_title(\"Hypothesis Tests\")\n",
     "ax.set_ylabel(\"CLs\")\n",
-    "ax.set_xlabel(u\"µ\")\n",
+    "ax.set_xlabel(\"µ\")\n",
     "brazil.plot_results(ax, test_mus, results, test_size=0.05)"
    ]
   },
@@ -149,7 +150,9 @@
    "source": [
     "new_signal = [20.0, 10.0]\n",
     "patch = jsonpatch.JsonPatch(\n",
-    "    [{\"op\": \"replace\", \"path\": \"/channels/0/samples/0/data\", \"value\": new_signal},]\n",
+    "    [\n",
+    "        {\"op\": \"replace\", \"path\": \"/channels/0/samples/0/data\", \"value\": new_signal},\n",
+    "    ]\n",
     ")"
    ]
   },
@@ -231,7 +234,7 @@
     "fig, ax = plt.subplots(1, 1)\n",
     "ax.set_title(\"Hypothesis Tests\")\n",
     "ax.set_ylabel(\"CLs\")\n",
-    "ax.set_xlabel(u\"µ\")\n",
+    "ax.set_xlabel(\"µ\")\n",
     "brazil.plot_results(ax, test_mus, results, test_size=0.05)"
    ]
   }

diff --git a/docs/examples/notebooks/ShapeFactor.ipynb b/docs/examples/notebooks/ShapeFactor.ipynb
@@ -36,7 +36,7 @@
     "import pyhf\n",
     "from pyhf import Model\n",
     "\n",
-    "logging.basicConfig(level = logging.INFO)"
+    "logging.basicConfig(level=logging.INFO)"
    ]
   },
   {
@@ -46,7 +46,7 @@
    "outputs": [],
    "source": [
     "def prep_data(sourcedata):\n",
-    "    spec =  {\n",
+    "    spec = {\n",
     "        'channels': [\n",
     "            {\n",
     "                'name': 'signal',\n",
@@ -55,12 +55,8 @@
     "                        'name': 'signal',\n",
     "                        'data': sourcedata['signal']['bindata']['sig'],\n",
     "                        'modifiers': [\n",
-    "                            {\n",
-    "                                'name': 'mu',\n",
-    "                                'type': 'normfactor',\n",
-    "                                'data': None\n",
-    "                            }\n",
-    "                        ]\n",
+    "                            {'name': 'mu', 'type': 'normfactor', 'data': None}\n",
+    "                        ],\n",
     "                    },\n",
     "                    {\n",
     "                        'name': 'bkg1',\n",
@@ -69,11 +65,11 @@
     "                            {\n",
     "                                'name': 'coupled_shapefactor',\n",
     "                                'type': 'shapefactor',\n",
-    "                                'data': None\n",
+    "                                'data': None,\n",
     "                            }\n",
-    "                        ]\n",
-    "                    }\n",
-    "                ]\n",
+    "                        ],\n",
+    "                    },\n",
+    "                ],\n",
     "            },\n",
     "            {\n",
     "                'name': 'control',\n",
@@ -85,15 +81,15 @@
     "                            {\n",
     "                                'name': 'coupled_shapefactor',\n",
     "                                'type': 'shapefactor',\n",
-    "                                'data': None\n",
+    "                                'data': None,\n",
     "                            }\n",
-    "                        ]\n",
+    "                        ],\n",
     "                    }\n",
-    "                ]\n",
-    "            }\n",
+    "                ],\n",
+    "            },\n",
     "        ]\n",
     "    }\n",
-    "    pdf  = Model(spec)\n",
+    "    pdf = Model(spec)\n",
     "    data = []\n",
     "    for c in pdf.spec['channels']:\n",
     "        data += sourcedata[c['name']]['bindata']['data']\n",
@@ -126,27 +122,24 @@
    ],
    "source": [
     "source = {\n",
-    "  \"channels\": {\n",
-    "    \"signal\": {\n",
-    "      \"binning\": [2,-0.5,1.5],\n",
-    "      \"bindata\": {\n",
-    "        \"data\":     [220.0, 230.0],\n",
-    "        \"bkg1\":     [100.0, 70.0],\n",
-    "        \"sig\":      [ 20.0, 20.0]\n",
-    "      }\n",
-    "    },\n",
-    "    \"control\": {\n",
-    "      \"binning\": [2,-0.5,1.5],\n",
-    "      \"bindata\": {\n",
-    "        \"data\":    [200.0, 300.0],\n",
-    "        \"bkg1\":    [100.0, 100.0]\n",
-    "      }\n",
+    "    \"channels\": {\n",
+    "        \"signal\": {\n",
+    "            \"binning\": [2, -0.5, 1.5],\n",
+    "            \"bindata\": {\n",
+    "                \"data\": [220.0, 230.0],\n",
+    "                \"bkg1\": [100.0, 70.0],\n",
+    "                \"sig\": [20.0, 20.0],\n",
+    "            },\n",
+    "        },\n",
+    "        \"control\": {\n",
+    "            \"binning\": [2, -0.5, 1.5],\n",
+    "            \"bindata\": {\"data\": [200.0, 300.0], \"bkg1\": [100.0, 100.0]},\n",
+    "        },\n",
     "    }\n",
-    "  }\n",
     "}\n",
     "\n",
     "data, pdf = prep_data(source['channels'])\n",
-    "print('data: {}'.format(data))\n",
+    "print(f'data: {data}')\n",
     "\n",
     "init_pars = pdf.config.suggested_init()\n",
     "print('expected data: {}'.format(pdf.expected_data(init_pars)))\n",
@@ -177,10 +170,10 @@
     }
    ],
    "source": [
-    "print('initialization parameters: {}'.format(pdf.config.suggested_init()))\n",
+    "print(f'initialization parameters: {pdf.config.suggested_init()}')\n",
     "\n",
     "unconpars = pyhf.infer.mle.fit(data, pdf)\n",
-    "print('parameters post unconstrained fit: {}'.format(unconpars))"
+    "print(f'parameters post unconstrained fit: {unconpars}')"
    ]
   },
   {
@@ -225,12 +218,13 @@
     }
    ],
    "source": [
-    "def plot_results(testmus, cls_obs, cls_exp, poi_tests, test_size = 0.05):\n",
-    "    plt.plot(poi_tests,cls_obs, c = 'k')\n",
-    "    for i,c in zip(range(5),['grey','grey','grey','grey','grey']):\n",
-    "        plt.plot(poi_tests, cls_exp[i], c = c)\n",
-    "    plt.plot(testmus,[test_size]*len(testmus), c = 'r')\n",
-    "    plt.ylim(0,1)\n",
+    "def plot_results(testmus, cls_obs, cls_exp, poi_tests, test_size=0.05):\n",
+    "    plt.plot(poi_tests, cls_obs, c='k')\n",
+    "    for i, c in zip(range(5), ['grey', 'grey', 'grey', 'grey', 'grey']):\n",
+    "        plt.plot(poi_tests, cls_exp[i], c=c)\n",
+    "    plt.plot(testmus, [test_size] * len(testmus), c='r')\n",
+    "    plt.ylim(0, 1)\n",
+    "\n",
     "\n",
     "def invert_interval(test_mus, cls_obs, cls_exp, test_size=0.05):\n",
     "    crossing_test_stats = {'exp': [], 'obs': None}\n",
@@ -247,8 +241,12 @@
     "\n",
     "\n",
     "poi_tests = np.linspace(0, 5, 61)\n",
-    "tests = [pyhf.infer.hypotest(poi_test, data, pdf, init_pars, par_bounds, return_expected_set=True) \n",
-    "         for poi_test in poi_tests]\n",
+    "tests = [\n",
+    "    pyhf.infer.hypotest(\n",
+    "        poi_test, data, pdf, init_pars, par_bounds, return_expected_set=True\n",
+    "    )\n",
+    "    for poi_test in poi_tests\n",
+    "]\n",
     "cls_obs = np.array([test[0] for test in tests]).flatten()\n",
     "cls_exp = [np.array([test[1][i] for test in tests]).flatten() for i in range(5)]\n",
     "\n",