diff --git a/docs/examples/notebooks/custom_modifiers.ipynb b/docs/examples/notebooks/custom_modifiers.ipynb
index 6a91e4f5ca..118b9d5a94 100644
--- a/docs/examples/notebooks/custom_modifiers.ipynb
+++ b/docs/examples/notebooks/custom_modifiers.ipynb
@@ -1 +1,184 @@
-{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pyhf\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import scipy.stats"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[],"source":["class custom_modifier(object):\n","    op_code = 'addition'\n","    def __init__(self,name = 'name'):\n","        self.required_parsets = {\n","            'mean': {\n","                'paramset_type': pyhf.parameters.paramsets.unconstrained,\n","                'n_parameters': 1,\n","                'is_constrained': False,\n","                'is_shared': True,\n","                'inits': (0.0,),\n","                'bounds': ((-5, 5),),\n","                'fixed': False\n","        }}\n","    def apply(self,pars):\n","        base = np.zeros((1,len(m.config.samples),1,sum(m.config.channel_nbins.values())))\n","\n","        bins = np.linspace(-5,5,20+1)\n","        mean = pars[self.config.par_slice('mean')][0]\n","        yields = 100*(scipy.stats.norm(loc = mean).cdf(bins[1:]) - scipy.stats.norm(loc = mean).cdf(bins[:-1]))\n","        base[0,self.config.samples.index('signal'),0,:] = yields\n","        return base\n","\n","\n","\n","m = pyhf.Model(\n","{'channels': [{'name': 'singlechannel',\n","   'samples': [{'name': 'signal',\n","     'data': [0]*20,\n","     'modifiers': [{'name': 'mu', 'type': 'normfactor', 'data': None}]},\n","    {'name': 'background',\n","     'data': [300]*20,\n","     'modifiers': []}]}]},\n","    custom_modifiers=[custom_modifier()]\n",")\n","bp = pyhf.tensorlib.astensor(m.config.suggested_init())\n","bp[m.config.poi_index] = 5\n","bp[m.config.par_slice('mean')] = [3.0]\n","d = m.make_pdf(bp).sample()"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["[3.06386254]\n[5.17062475]\n"]}],"source":["bestfit = pyhf.infer.mle.fit(d,m)\n","print(bestfit[m.config.par_slice('mean')])\n","print(bestfit[m.config.par_slice('mu')])\n"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x12e671ba8>"]},"metadata":{},"execution_count":30},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 380.425271 248.518125\" width=\"380.425271pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" 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alpha = 1.0, facecolor = None, edgecolor = 'k', label = 'fit')\n","plt.scatter(np.arange(20),d[:m.config.nmaindata], alpha = 1.0, marker = 'o', c='k', label = 'data', zorder=99)\n","plt.errorbar(np.arange(20),d[:m.config.nmaindata],yerr=np.sqrt(d[:m.config.nmaindata]), marker='o', c='k', linestyle = '')\n","plt.legend()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["s"]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.2-final"},"orig_nbformat":2,"kernelspec":{"name":"python3","display_name":"Python 3"}}}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyhf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class custom_modifier:\n",
+    "    op_code = 'addition'\n",
+    "\n",
+    "    def __init__(self, name='name'):\n",
+    "        self.required_parsets = {\n",
+    "            'mean': {\n",
+    "                'paramset_type': pyhf.parameters.paramsets.unconstrained,\n",
+    "                'n_parameters': 1,\n",
+    "                'is_constrained': False,\n",
+    "                'is_shared': True,\n",
+    "                'inits': (0.0,),\n",
+    "                'bounds': ((-5, 5),),\n",
+    "                'fixed': False,\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "    def apply(self, pars):\n",
+    "        base = np.zeros(\n",
+    "            (1, len(m.config.samples), 1, sum(m.config.channel_nbins.values()))\n",
+    "        )\n",
+    "\n",
+    "        bins = np.linspace(-5, 5, 20 + 1)\n",
+    "        mean = pars[self.config.par_slice('mean')][0]\n",
+    "        yields = 100 * (\n",
+    "            scipy.stats.norm(loc=mean).cdf(bins[1:])\n",
+    "            - scipy.stats.norm(loc=mean).cdf(bins[:-1])\n",
+    "        )\n",
+    "        base[0, self.config.samples.index('signal'), 0, :] = yields\n",
+    "        return base\n",
+    "\n",
+    "\n",
+    "m = pyhf.Model(\n",
+    "    {\n",
+    "        'channels': [\n",
+    "            {\n",
+    "                'name': 'singlechannel',\n",
+    "                'samples': [\n",
+    "                    {\n",
+    "                        'name': 'signal',\n",
+    "                        'data': [0] * 20,\n",
+    "                        'modifiers': [\n",
+    "                            {'name': 'mu', 'type': 'normfactor', 'data': None}\n",
+    "                        ],\n",
+    "                    },\n",
+    "                    {'name': 'background', 'data': [300] * 20, 'modifiers': []},\n",
+    "                ],\n",
+    "            }\n",
+    "        ]\n",
+    "    },\n",
+    "    custom_modifiers=[custom_modifier()],\n",
+    ")\n",
+    "bp = pyhf.tensorlib.astensor(m.config.suggested_init())\n",
+    "bp[m.config.poi_index] = 5\n",
+    "bp[m.config.par_slice('mean')] = [3.0]\n",
+    "d = m.make_pdf(bp).sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3.06386254]\n[5.17062475]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bestfit = pyhf.infer.mle.fit(d, m)\n",
+    "print(bestfit[m.config.par_slice('mean')])\n",
+    "print(bestfit[m.config.par_slice('mu')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x12e671ba8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 380.425271 248.518125\" width=\"380.425271pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2020-11-20T14:04:05.443973</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.0, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.bar(\n",
+    "    np.arange(20),\n",
+    "    m.expected_actualdata(bestfit),\n",
+    "    alpha=1.0,\n",
+    "    facecolor=None,\n",
+    "    edgecolor='k',\n",
+    "    label='fit',\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    np.arange(20),\n",
+    "    d[: m.config.nmaindata],\n",
+    "    alpha=1.0,\n",
+    "    marker='o',\n",
+    "    c='k',\n",
+    "    label='data',\n",
+    "    zorder=99,\n",
+    ")\n",
+    "plt.errorbar(\n",
+    "    np.arange(20),\n",
+    "    d[: m.config.nmaindata],\n",
+    "    yerr=np.sqrt(d[: m.config.nmaindata]),\n",
+    "    marker='o',\n",
+    "    c='k',\n",
+    "    linestyle='',\n",
+    ")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s"
+   ]
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  }
+ }
+}
diff --git a/src/pyhf/pdf.py b/src/pyhf/pdf.py
index a25b9aff71..e5fbecc7da 100644
--- a/src/pyhf/pdf.py
+++ b/src/pyhf/pdf.py
@@ -66,8 +66,8 @@ def _paramset_requirements_from_modelspec(spec, channel_nbins, custom_modifiers_
         spec, channel_nbins
     )
     for req in custom_modifiers_params:
-        for k,v in req.items():
-            _paramsets_requirements.setdefault(k,[]).append(v)
+        for k, v in req.items():
+            _paramsets_requirements.setdefault(k, []).append(v)
 
     # build up a dictionary of the parameter configurations provided by the user
     _paramsets_user_configs = {}
@@ -211,7 +211,7 @@ class _ModelConfig(_ChannelSummaryMixin):
     def __init__(self, spec, **config_kwargs):
         super().__init__(channels=spec['channels'])
         _required_paramsets = _paramset_requirements_from_modelspec(
-            spec, self.channel_nbins,config_kwargs.pop('custom_modifiers_params')
+            spec, self.channel_nbins, config_kwargs.pop('custom_modifiers_params')
         )
         poi_name = config_kwargs.pop('poi_name', 'mu')
 
@@ -398,7 +398,9 @@ def logpdf(self, auxdata, pars):
 class _MainModel:
     """Factory class to create pdfs for the main measurement."""
 
-    def __init__(self, config, mega_mods, nominal_rates, custom_modifiers = None,batch_size = None):
+    def __init__(
+        self, config, mega_mods, nominal_rates, custom_modifiers=None, batch_size=None
+    ):
         self.config = config
         self._factor_mods = [
             modtype
@@ -426,13 +428,12 @@ def __init__(self, config, mega_mods, nominal_rates, custom_modifiers = None,bat
             )
             for k, c in modifiers.combined.items()
         }
-        for i,custom in enumerate(custom_modifiers):
+        for i, custom in enumerate(custom_modifiers):
             name = f'custom_mod_{str(i).zfill(3)}'
             self.modifiers_appliers[name] = custom
             if custom.op_code == 'addition':
                 self._delta_mods.append(name)
 
-
         self._precompute()
         events.subscribe('tensorlib_changed')(self._precompute)
 
@@ -515,9 +516,7 @@ def expected_data(self, pars, return_by_sample=False):
         pars = tensorlib.astensor(pars)
         deltas, factors = self._modifications(pars)
 
-        allsum = tensorlib.concatenate(
-            deltas + [self.nominal_rates]
-        )
+        allsum = tensorlib.concatenate(deltas + [self.nominal_rates])
 
         nom_plus_delta = tensorlib.sum(allsum, axis=0)
         nom_plus_delta = tensorlib.reshape(
@@ -542,7 +541,7 @@ def expected_data(self, pars, return_by_sample=False):
 class Model:
     """The main pyhf model class."""
 
-    def __init__(self, spec, custom_modifiers = None, batch_size=None, **config_kwargs):
+    def __init__(self, spec, custom_modifiers=None, batch_size=None, **config_kwargs):
         """
         Construct a HistFactory Model.
 
@@ -563,9 +562,11 @@ def __init__(self, spec, custom_modifiers = None, batch_size=None, **config_kwar
         log.info(f"Validating spec against schema: {self.schema:s}")
         utils.validate(self.spec, self.schema, version=self.version)
         # build up our representation of the specification
-        self.config = _ModelConfig(self.spec, custom_modifiers_params = [
-            c.required_parsets for c in custom_modifiers
-        ], **config_kwargs)
+        self.config = _ModelConfig(
+            self.spec,
+            custom_modifiers_params=[c.required_parsets for c in custom_modifiers],
+            **config_kwargs,
+        )
 
         mega_mods, _nominal_rates = _nominal_and_modifiers_from_spec(
             self.config, self.spec