fix: typo 2

_website/tutorials/components/61 - Custom Binned PDF.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating your own pdf\n",
+    "\n",
+    "A core feature of zfit is the ability to create custom pdfs and functions in an simple and straightforward way.\n",
+    "\n",
+    "In this tutorial, we will show how to create a custom binned PDF.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import zfit\n",
+    "import zfit.z.numpy as znp\n",
+    "from zfit import z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first way is the most simple and should only be used for the trivial cases, i.e. if you're not familiar with Python classes (especially not with the `__init__` method)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyGauss(zfit.pdf.BaseBinnedPDF):  # TODO from here\n",
+    "    _N_OBS = 1  # dimension, can be omitted\n",
+    "    _PARAMS = ['mean', 'std']  # the name of the parameters\n",
+    "\n",
+    "    @zfit.supports()\n",
+    "    def _unnormalized_pdf(self, x, params):\n",
+    "        x0 = x[0]  # using the 0th axis\n",
+    "        mean = params['mean']\n",
+    "        std = params['std']\n",
+    "        return z.exp(- ((x0 - mean) / std) ** 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Done. Now we can use our pdf already!\n",
+    "\n",
+    "The slightly more general way involves overwritting the `__init__` and gives you all the possible flexibility: to use custom parameters, to preprocess them etc.\n",
+    "\n",
+    "Here we inherit from `BasePDF`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyGauss(zfit.pdf.BasePDF):\n",
+    "\n",
+    "    def __init__(self, mean, std, obs, extended=None, norm=None, name=None, label=None):\n",
+    "        params = {'mean': mean,  # 'mean' is the name as it will be named in the PDF, mean is just the parameter to create the PDF\n",
+    "                  'std': std\n",
+    "                  }\n",
+    "        super().__init__(obs=obs, params=params, extended=extended, norm=norm,\n",
+    "                         name=name, label=label)\n",
+    "\n",
+    "    @zfit.supports()\n",
+    "    def _unnormalized_pdf(self, x, params):\n",
+    "        x0 = x[0]  # using the 0th axis\n",
+    "        mean = params['mean']\n",
+    "        std = params['std']\n",
+    "        return z.exp(- ((x0 - mean) / std) ** 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = zfit.Space('obs1', -3, 6)\n",
+    "\n",
+    "data_np = np.random.random(size=1000)\n",
+    "data = zfit.Data(data_np, obs=obs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create two parameters and an instance of your own pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean = zfit.Parameter(\"mean\", 1.)\n",
+    "std = zfit.Parameter(\"std\", 1.)\n",
+    "my_gauss = MyGauss(obs=obs, mean=mean, std=std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "probs = my_gauss.pdf(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(probs[:20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to make sure it's a numpy array, we can use `zfit.run`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could improve our PDF by registering an integral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gauss_integral_from_any_to_any(limits, params, model):\n",
+    "    lower, upper = limits.v1.limits\n",
+    "    mean = params['mean']\n",
+    "    std = params['std']\n",
+    "    # write your integral here\n",
+    "    return 42.  # dummy integral, must be a scalar!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limits = zfit.Space(axes=0, lower=zfit.Space.ANY_LOWER, upper=zfit.Space.ANY_UPPER)\n",
+    "MyGauss.register_analytic_integral(func=gauss_integral_from_any_to_any, limits=limits)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More advanced custom PDFs are introduced in the guide on [custom PDFs](custom_pdfs.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}