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fix: typo 2
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Diff for: ‎_website/tutorials/components/61 - Custom Binned PDF.ipynb

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

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