{ "cells": [ { "cell_type": "markdown", "id": "c9c9b53f", "metadata": {}, "source": [ "# rf409_NumPyPandasToRooFit\n", "Convert between NumPy arrays or Pandas DataFrames and RooDataSets.\n", "\n", "This tutorials first how to export a RooDataSet to NumPy arrays or a Pandas\n", "DataFrame, and then it shows you how to create a RooDataSet from a Pandas\n", "DataFrame.\n", "\n", "\n", "\n", "\n", "**Author:** Jonas Rembser \n", "This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 19, 2026 at 08:32 PM." ] }, { "cell_type": "code", "execution_count": 1, "id": "108357e2", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:03.494013Z", "iopub.status.busy": "2026-05-19T20:32:03.493895Z", "iopub.status.idle": "2026-05-19T20:32:04.546192Z", "shell.execute_reply": "2026-05-19T20:32:04.545470Z" } }, "outputs": [], "source": [ "import ROOT\n", "\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "b44016dd", "metadata": {}, "source": [ "The number of events that we use for the datasets created in this tutorial." ] }, { "cell_type": "code", "execution_count": 2, "id": "3d2ce94d", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:04.548182Z", "iopub.status.busy": "2026-05-19T20:32:04.547988Z", "iopub.status.idle": "2026-05-19T20:32:04.662430Z", "shell.execute_reply": "2026-05-19T20:32:04.661652Z" } }, "outputs": [], "source": [ "n_events = 10000" ] }, { "cell_type": "markdown", "id": "4ea814f4", "metadata": {}, "source": [ "Creating a RooDataSet and exporting it to the Python ecosystem\n", "--------------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "6fb9a04c", "metadata": {}, "source": [ "Define the observable." ] }, { "cell_type": "code", "execution_count": 3, "id": "c66aeb03", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:04.664539Z", "iopub.status.busy": "2026-05-19T20:32:04.664406Z", "iopub.status.idle": "2026-05-19T20:32:04.818561Z", "shell.execute_reply": "2026-05-19T20:32:04.817485Z" } }, "outputs": [], "source": [ "x = ROOT.RooRealVar(\"x\", \"x\", -10, 10)" ] }, { "cell_type": "markdown", "id": "afa5fe4a", "metadata": {}, "source": [ "Define a Gaussian model with its parameters." ] }, { "cell_type": "code", "execution_count": 4, "id": "21a9a5c5", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:04.820229Z", "iopub.status.busy": "2026-05-19T20:32:04.820094Z", "iopub.status.idle": "2026-05-19T20:32:04.952372Z", "shell.execute_reply": "2026-05-19T20:32:04.951780Z" } }, "outputs": [], "source": [ "mean = ROOT.RooRealVar(\"mean\", \"mean of gaussian\", 1, -10, 10)\n", "sigma = ROOT.RooRealVar(\"sigma\", \"width of gaussian\", 1, 0.1, 10)\n", "gauss = ROOT.RooGaussian(\"gauss\", \"gaussian PDF\", x, mean, sigma)" ] }, { "cell_type": "markdown", "id": "d58ecdae", "metadata": {}, "source": [ "Create a RooDataSet." ] }, { "cell_type": "code", "execution_count": 5, "id": "dc29a51f", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:04.954483Z", "iopub.status.busy": "2026-05-19T20:32:04.954356Z", "iopub.status.idle": "2026-05-19T20:32:05.123472Z", "shell.execute_reply": "2026-05-19T20:32:05.122342Z" } }, "outputs": [], "source": [ "data = gauss.generate(ROOT.RooArgSet(x), 10000)" ] }, { "cell_type": "markdown", "id": "b7631402", "metadata": {}, "source": [ "Use RooDataSet.to_numpy() to export dataset to a dictionary of NumPy arrays.\n", "Real values will be of type `double`, categorical values of type `int`." ] }, { "cell_type": "code", "execution_count": 6, "id": "b8a60812", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:05.125157Z", "iopub.status.busy": "2026-05-19T20:32:05.125030Z", "iopub.status.idle": "2026-05-19T20:32:05.323596Z", "shell.execute_reply": "2026-05-19T20:32:05.322330Z" } }, "outputs": [], "source": [ "arrays = data.to_numpy()" ] }, { "cell_type": "markdown", "id": "0410cc3e", "metadata": {}, "source": [ "We can verify that the mean and standard deviation matches our model specification." ] }, { "cell_type": "code", "execution_count": 7, "id": "08566951", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:05.325260Z", "iopub.status.busy": "2026-05-19T20:32:05.325134Z", "iopub.status.idle": "2026-05-19T20:32:05.429804Z", "shell.execute_reply": "2026-05-19T20:32:05.428888Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean of numpy array: 1.0066466535473986\n", "Standard deviation of numpy array: 0.997349967781135\n" ] } ], "source": [ "print(\"Mean of numpy array:\", np.mean(arrays[\"x\"]))\n", "print(\"Standard deviation of numpy array:\", np.std(arrays[\"x\"]))" ] }, { "cell_type": "markdown", "id": "2bf5edcc", "metadata": {}, "source": [ "It is also possible to create a Pandas DataFrame directly from the numpy arrays:" ] }, { "cell_type": "code", "execution_count": 8, "id": "2ed064dd", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:05.431319Z", "iopub.status.busy": "2026-05-19T20:32:05.431192Z", "iopub.status.idle": "2026-05-19T20:32:05.672970Z", "shell.execute_reply": "2026-05-19T20:32:05.671864Z" } }, "outputs": [], "source": [ "df = data.to_pandas()" ] }, { "cell_type": "markdown", "id": "971d138b", "metadata": {}, "source": [ "Now you can use the DataFrame e.g. for plotting. You can even combine this\n", "with the RooAbsReal.bins PyROOT function, which returns the binning from\n", "RooFit as a numpy array!" ] }, { "cell_type": "code", "execution_count": 9, "id": "a7061c05", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:05.674709Z", "iopub.status.busy": "2026-05-19T20:32:05.674496Z", "iopub.status.idle": "2026-05-19T20:32:06.202566Z", "shell.execute_reply": "2026-05-19T20:32:06.201612Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " import matplotlib.pyplot as plt\n", "\n", " df.hist(column=\"x\", bins=x.bins())\n", "except Exception:\n", " print(\n", " 'Skipping `df.hist(column=\"x\", bins=x.bins())` because matplotlib could not be imported or was not able to display the plot.'\n", " )\n", "\n", "del data\n", "del arrays\n", "del df" ] }, { "cell_type": "markdown", "id": "fc21a665", "metadata": {}, "source": [ "Creating a dataset with NumPy and importing it to a RooDataSet\n", "--------------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "f9a8396d", "metadata": {}, "source": [ "Now we create some Gaussian toy data with numpy, this time with a different\n", "mean." ] }, { "cell_type": "code", "execution_count": 10, "id": "f39f6572", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:06.204250Z", "iopub.status.busy": "2026-05-19T20:32:06.204067Z", "iopub.status.idle": "2026-05-19T20:32:06.307925Z", "shell.execute_reply": "2026-05-19T20:32:06.307364Z" } }, "outputs": [], "source": [ "x_arr = np.random.normal(-1.0, 1.0, (n_events,))" ] }, { "cell_type": "markdown", "id": "9ed52a57", "metadata": {}, "source": [ "Import the data to a RooDataSet, passing a dictionary of arrays and the\n", "corresponding RooRealVars just like you would pass to the RooDataSet\n", "constructor." ] }, { "cell_type": "code", "execution_count": 11, "id": "8eba4a0b", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:06.310166Z", "iopub.status.busy": "2026-05-19T20:32:06.310031Z", "iopub.status.idle": "2026-05-19T20:32:06.588242Z", "shell.execute_reply": "2026-05-19T20:32:06.587082Z" } }, "outputs": [], "source": [ "data = ROOT.RooDataSet.from_numpy({\"x\": x_arr}, [x])" ] }, { "cell_type": "markdown", "id": "9d1dc6c3", "metadata": {}, "source": [ "Let's fit the Gaussian to the data. The mean is updated accordingly." ] }, { "cell_type": "code", "execution_count": 12, "id": "7ce055d3", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:06.589952Z", "iopub.status.busy": "2026-05-19T20:32:06.589806Z", "iopub.status.idle": "2026-05-19T20:32:06.814850Z", "shell.execute_reply": "2026-05-19T20:32:06.813953Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Fitting -- RooAbsPdf::fitTo(gauss_over_gauss_Int[x]) fixing normalization set for coefficient determination to observables in data\n", "[#1] INFO:Fitting -- using generic CPU library compiled with no vectorizations\n", "[#1] INFO:Fitting -- Creation of NLL object took 9.23545 ms\n", "[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_gauss_over_gauss_Int[x]_) Summation contains a RooNLLVar, using its error level\n", "[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " RooFitResult: minimized FCN value: 14152.2, estimated distance to minimum: 6.36827e-09\n", " covariance matrix quality: Full, accurate covariance matrix\n", " Status : MINIMIZE=0 HESSE=0 \n", "\n", " Floating Parameter FinalValue +/- Error \n", " -------------------- --------------------------\n", " mean -9.9420e-01 +/- 9.96e-03\n", " sigma 9.9629e-01 +/- 7.04e-03\n", "\n" ] } ], "source": [ "fit_result = gauss.fitTo(data, PrintLevel=-1, Save=True)\n", "fit_result.Print()" ] }, { "cell_type": "markdown", "id": "d0c54a54", "metadata": {}, "source": [ "We can now plot the model and the dataset with RooFit." ] }, { "cell_type": "code", "execution_count": 13, "id": "2e854545", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:06.816397Z", "iopub.status.busy": "2026-05-19T20:32:06.816268Z", "iopub.status.idle": "2026-05-19T20:32:06.993996Z", "shell.execute_reply": "2026-05-19T20:32:06.992915Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xframe = x.frame(Title=\"Gaussian pdf\")\n", "data.plotOn(xframe)\n", "gauss.plotOn(xframe)" ] }, { "cell_type": "markdown", "id": "dc42add4", "metadata": {}, "source": [ "Draw RooFit plot on a canvas." ] }, { "cell_type": "code", "execution_count": 14, "id": "065f4e09", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:06.995654Z", "iopub.status.busy": "2026-05-19T20:32:06.995504Z", "iopub.status.idle": "2026-05-19T20:32:07.199113Z", "shell.execute_reply": "2026-05-19T20:32:07.198413Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Info in : png file rf409_NumPyPandasToRooFit.png has been created\n" ] } ], "source": [ "c = ROOT.TCanvas(\"rf409_NumPyPandasToRooFit\", \"rf409_NumPyPandasToRooFit\", 800, 400)\n", "xframe.Draw()\n", "c.SaveAs(\"rf409_NumPyPandasToRooFit.png\")" ] }, { "cell_type": "markdown", "id": "6cd2cb83", "metadata": {}, "source": [ "Exporting a RooDataHist to NumPy arrays for histogram counts and bin edges\n", "--------------------------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": 15, "id": "2b4cb99d", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.201078Z", "iopub.status.busy": "2026-05-19T20:32:07.200950Z", "iopub.status.idle": "2026-05-19T20:32:07.304593Z", "shell.execute_reply": "2026-05-19T20:32:07.304018Z" } }, "outputs": [], "source": [ "def print_histogram_output(histogram_output):\n", " counts, bin_edges = histogram_output\n", " print(np.array(counts, dtype=int))\n", " print(bin_edges[0])" ] }, { "cell_type": "markdown", "id": "a99b86e3", "metadata": {}, "source": [ "Create a binned clone of the dataset to show RooDataHist to NumPy export." ] }, { "cell_type": "code", "execution_count": 16, "id": "54bb28ce", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.306538Z", "iopub.status.busy": "2026-05-19T20:32:07.306414Z", "iopub.status.idle": "2026-05-19T20:32:07.420041Z", "shell.execute_reply": "2026-05-19T20:32:07.419479Z" } }, "outputs": [], "source": [ "datahist = data.binnedClone()" ] }, { "cell_type": "markdown", "id": "0ed8fb93", "metadata": {}, "source": [ "You can also export a RooDataHist to numpy arrays with\n", "RooDataHist.to_numpy(). As output, you will get a multidimensional array with\n", "the histogram counts and a list of arrays with bin edges. This is comparable\n", "to the output of numpy.histogram (or numpy.histogramdd for the\n", "multidimensional case)." ] }, { "cell_type": "code", "execution_count": 17, "id": "dd0483fb", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.422102Z", "iopub.status.busy": "2026-05-19T20:32:07.421967Z", "iopub.status.idle": "2026-05-19T20:32:07.602415Z", "shell.execute_reply": "2026-05-19T20:32:07.601801Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts and bin edges from RooDataHist.to_numpy:\n", "[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 2 1 8 13 19 39 60 83 131\n", " 187 248 331 453 506 633 668 789 791 785 797 710 637 532 458 362 229 180\n", " 115 84 49 46 19 12 10 7 4 1 0 0 0 1 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0]\n", "[-1.00000000e+01 -9.80000000e+00 -9.60000000e+00 -9.40000000e+00\n", " -9.20000000e+00 -9.00000000e+00 -8.80000000e+00 -8.60000000e+00\n", " -8.40000000e+00 -8.20000000e+00 -8.00000000e+00 -7.80000000e+00\n", " -7.60000000e+00 -7.40000000e+00 -7.20000000e+00 -7.00000000e+00\n", " -6.80000000e+00 -6.60000000e+00 -6.40000000e+00 -6.20000000e+00\n", " -6.00000000e+00 -5.80000000e+00 -5.60000000e+00 -5.40000000e+00\n", " -5.20000000e+00 -5.00000000e+00 -4.80000000e+00 -4.60000000e+00\n", " -4.40000000e+00 -4.20000000e+00 -4.00000000e+00 -3.80000000e+00\n", " -3.60000000e+00 -3.40000000e+00 -3.20000000e+00 -3.00000000e+00\n", " -2.80000000e+00 -2.60000000e+00 -2.40000000e+00 -2.20000000e+00\n", " -2.00000000e+00 -1.80000000e+00 -1.60000000e+00 -1.40000000e+00\n", " -1.20000000e+00 -1.00000000e+00 -8.00000000e-01 -6.00000000e-01\n", " -4.00000000e-01 -2.00000000e-01 5.55111512e-16 2.00000000e-01\n", " 4.00000000e-01 6.00000000e-01 8.00000000e-01 1.00000000e+00\n", " 1.20000000e+00 1.40000000e+00 1.60000000e+00 1.80000000e+00\n", " 2.00000000e+00 2.20000000e+00 2.40000000e+00 2.60000000e+00\n", " 2.80000000e+00 3.00000000e+00 3.20000000e+00 3.40000000e+00\n", " 3.60000000e+00 3.80000000e+00 4.00000000e+00 4.20000000e+00\n", " 4.40000000e+00 4.60000000e+00 4.80000000e+00 5.00000000e+00\n", " 5.20000000e+00 5.40000000e+00 5.60000000e+00 5.80000000e+00\n", " 6.00000000e+00 6.20000000e+00 6.40000000e+00 6.60000000e+00\n", " 6.80000000e+00 7.00000000e+00 7.20000000e+00 7.40000000e+00\n", " 7.60000000e+00 7.80000000e+00 8.00000000e+00 8.20000000e+00\n", " 8.40000000e+00 8.60000000e+00 8.80000000e+00 9.00000000e+00\n", " 9.20000000e+00 9.40000000e+00 9.60000000e+00 9.80000000e+00\n", " 1.00000000e+01]\n" ] } ], "source": [ "counts, bin_edges = datahist.to_numpy()\n", "\n", "print(\"Counts and bin edges from RooDataHist.to_numpy:\")\n", "print_histogram_output((counts, bin_edges))" ] }, { "cell_type": "markdown", "id": "369509b7", "metadata": {}, "source": [ "Let's compare the output to the counts and bin edges we get with\n", "numpy.histogramdd when we pass it the original samples:" ] }, { "cell_type": "code", "execution_count": 18, "id": "4f588165", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.603978Z", "iopub.status.busy": "2026-05-19T20:32:07.603851Z", "iopub.status.idle": "2026-05-19T20:32:07.709040Z", "shell.execute_reply": "2026-05-19T20:32:07.708448Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts and bin edges from np.histogram:\n", "[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 2 1 8 13 19 39 60 83 131\n", " 187 248 331 453 506 633 668 789 791 785 797 710 637 532 458 362 229 180\n", " 115 84 49 46 19 12 10 7 4 1 0 0 0 1 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0]\n", "[-1.00000000e+01 -9.80000000e+00 -9.60000000e+00 -9.40000000e+00\n", " -9.20000000e+00 -9.00000000e+00 -8.80000000e+00 -8.60000000e+00\n", " -8.40000000e+00 -8.20000000e+00 -8.00000000e+00 -7.80000000e+00\n", " -7.60000000e+00 -7.40000000e+00 -7.20000000e+00 -7.00000000e+00\n", " -6.80000000e+00 -6.60000000e+00 -6.40000000e+00 -6.20000000e+00\n", " -6.00000000e+00 -5.80000000e+00 -5.60000000e+00 -5.40000000e+00\n", " -5.20000000e+00 -5.00000000e+00 -4.80000000e+00 -4.60000000e+00\n", " -4.40000000e+00 -4.20000000e+00 -4.00000000e+00 -3.80000000e+00\n", " -3.60000000e+00 -3.40000000e+00 -3.20000000e+00 -3.00000000e+00\n", " -2.80000000e+00 -2.60000000e+00 -2.40000000e+00 -2.20000000e+00\n", " -2.00000000e+00 -1.80000000e+00 -1.60000000e+00 -1.40000000e+00\n", " -1.20000000e+00 -1.00000000e+00 -8.00000000e-01 -6.00000000e-01\n", " -4.00000000e-01 -2.00000000e-01 5.55111512e-16 2.00000000e-01\n", " 4.00000000e-01 6.00000000e-01 8.00000000e-01 1.00000000e+00\n", " 1.20000000e+00 1.40000000e+00 1.60000000e+00 1.80000000e+00\n", " 2.00000000e+00 2.20000000e+00 2.40000000e+00 2.60000000e+00\n", " 2.80000000e+00 3.00000000e+00 3.20000000e+00 3.40000000e+00\n", " 3.60000000e+00 3.80000000e+00 4.00000000e+00 4.20000000e+00\n", " 4.40000000e+00 4.60000000e+00 4.80000000e+00 5.00000000e+00\n", " 5.20000000e+00 5.40000000e+00 5.60000000e+00 5.80000000e+00\n", " 6.00000000e+00 6.20000000e+00 6.40000000e+00 6.60000000e+00\n", " 6.80000000e+00 7.00000000e+00 7.20000000e+00 7.40000000e+00\n", " 7.60000000e+00 7.80000000e+00 8.00000000e+00 8.20000000e+00\n", " 8.40000000e+00 8.60000000e+00 8.80000000e+00 9.00000000e+00\n", " 9.20000000e+00 9.40000000e+00 9.60000000e+00 9.80000000e+00\n", " 1.00000000e+01]\n" ] } ], "source": [ "print(\"Counts and bin edges from np.histogram:\")\n", "print_histogram_output(np.histogramdd([x_arr], bins=[x.bins()]))" ] }, { "cell_type": "markdown", "id": "f135ad79", "metadata": {}, "source": [ "The array values should be the same!" ] }, { "cell_type": "markdown", "id": "ebad92f9", "metadata": {}, "source": [ "Importing a RooDataHist from NumPy arrays with histogram counts and bin edges\n", "-----------------------------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "c8b0590a", "metadata": {}, "source": [ "There is also a `RooDataHist.from_numpy` function, again with an interface\n", "inspired by `numpy.histogramdd`. You need to pass at least the histogram\n", "counts and the list of variables. The binning is optional: the default\n", "binning of the RooRealVars is used if not explicitly specified." ] }, { "cell_type": "code", "execution_count": 19, "id": "d8e533e6", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.710845Z", "iopub.status.busy": "2026-05-19T20:32:07.710719Z", "iopub.status.idle": "2026-05-19T20:32:07.842690Z", "shell.execute_reply": "2026-05-19T20:32:07.842091Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RooDataHist imported with default binning and exported back to numpy:\n", "[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 2 1 8 13 19 39 60 83 131\n", " 187 248 331 453 506 633 668 789 791 785 797 710 637 532 458 362 229 180\n", " 115 84 49 46 19 12 10 7 4 1 0 0 0 1 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0]\n", "[-1.00000000e+01 -9.80000000e+00 -9.60000000e+00 -9.40000000e+00\n", " -9.20000000e+00 -9.00000000e+00 -8.80000000e+00 -8.60000000e+00\n", " -8.40000000e+00 -8.20000000e+00 -8.00000000e+00 -7.80000000e+00\n", " -7.60000000e+00 -7.40000000e+00 -7.20000000e+00 -7.00000000e+00\n", " -6.80000000e+00 -6.60000000e+00 -6.40000000e+00 -6.20000000e+00\n", " -6.00000000e+00 -5.80000000e+00 -5.60000000e+00 -5.40000000e+00\n", " -5.20000000e+00 -5.00000000e+00 -4.80000000e+00 -4.60000000e+00\n", " -4.40000000e+00 -4.20000000e+00 -4.00000000e+00 -3.80000000e+00\n", " -3.60000000e+00 -3.40000000e+00 -3.20000000e+00 -3.00000000e+00\n", " -2.80000000e+00 -2.60000000e+00 -2.40000000e+00 -2.20000000e+00\n", " -2.00000000e+00 -1.80000000e+00 -1.60000000e+00 -1.40000000e+00\n", " -1.20000000e+00 -1.00000000e+00 -8.00000000e-01 -6.00000000e-01\n", " -4.00000000e-01 -2.00000000e-01 5.55111512e-16 2.00000000e-01\n", " 4.00000000e-01 6.00000000e-01 8.00000000e-01 1.00000000e+00\n", " 1.20000000e+00 1.40000000e+00 1.60000000e+00 1.80000000e+00\n", " 2.00000000e+00 2.20000000e+00 2.40000000e+00 2.60000000e+00\n", " 2.80000000e+00 3.00000000e+00 3.20000000e+00 3.40000000e+00\n", " 3.60000000e+00 3.80000000e+00 4.00000000e+00 4.20000000e+00\n", " 4.40000000e+00 4.60000000e+00 4.80000000e+00 5.00000000e+00\n", " 5.20000000e+00 5.40000000e+00 5.60000000e+00 5.80000000e+00\n", " 6.00000000e+00 6.20000000e+00 6.40000000e+00 6.60000000e+00\n", " 6.80000000e+00 7.00000000e+00 7.20000000e+00 7.40000000e+00\n", " 7.60000000e+00 7.80000000e+00 8.00000000e+00 8.20000000e+00\n", " 8.40000000e+00 8.60000000e+00 8.80000000e+00 9.00000000e+00\n", " 9.20000000e+00 9.40000000e+00 9.60000000e+00 9.80000000e+00\n", " 1.00000000e+01]\n" ] } ], "source": [ "datahist_new_1 = ROOT.RooDataHist.from_numpy(counts, [x])\n", "\n", "print(\"RooDataHist imported with default binning and exported back to numpy:\")\n", "print_histogram_output(datahist_new_1.to_numpy())" ] }, { "cell_type": "markdown", "id": "c403edf4", "metadata": {}, "source": [ "It's also possible to pass custom bin edges to `RooDataHist.from_numpy`, just\n", "like you pass them to `numpy.histogramdd` when you get the counts to fill the\n", "RooDataHist with:" ] }, { "cell_type": "code", "execution_count": 20, "id": "cea4277d", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.844550Z", "iopub.status.busy": "2026-05-19T20:32:07.844422Z", "iopub.status.idle": "2026-05-19T20:32:07.956290Z", "shell.execute_reply": "2026-05-19T20:32:07.955709Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RooDataHist imported with linspace binning and exported back to numpy:\n", "[ 0 0 0 0 0 11 214 1350 3387 3461 1344 210 22 1\n", " 0 0 0 0 0 0]\n", "[-10. -9. -8. -7. -6. -5. -4. -3. -2. -1. 0. 1. 2. 3.\n", " 4. 5. 6. 7. 8. 9. 10.]\n" ] } ], "source": [ "bins = [np.linspace(-10, 10, 21)]\n", "counts, _ = np.histogramdd([x_arr], bins=bins)\n", "datahist_new_2 = ROOT.RooDataHist.from_numpy(counts, [x], bins=bins)\n", "\n", "print(\"RooDataHist imported with linspace binning and exported back to numpy:\")\n", "print_histogram_output(datahist_new_2.to_numpy())" ] }, { "cell_type": "markdown", "id": "0fad7c94", "metadata": {}, "source": [ "Alternatively, you can specify only the number of bins and the range if your\n", "binning is uniform. This is preferred over passing the full list of bin\n", "edges, because RooFit will know that the binning is uniform and do some\n", "optimizations." ] }, { "cell_type": "code", "execution_count": 21, "id": "02185a8a", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:07.958276Z", "iopub.status.busy": "2026-05-19T20:32:07.958104Z", "iopub.status.idle": "2026-05-19T20:32:08.069179Z", "shell.execute_reply": "2026-05-19T20:32:08.068622Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RooDataHist imported with uniform binning and exported back to numpy:\n", "[ 0 0 0 0 0 11 214 1350 3387 3461 1344 210 22 1\n", " 0 0 0 0 0 0]\n", "[-10. -9. -8. -7. -6. -5. -4. -3. -2. -1. 0. 1. 2. 3.\n", " 4. 5. 6. 7. 8. 9. 10.]\n" ] } ], "source": [ "bins = [20]\n", "ranges = [(-10, 10)]\n", "counts, _ = np.histogramdd([x_arr], bins=bins, range=ranges)\n", "datahist_new_3 = ROOT.RooDataHist.from_numpy(counts, [x], bins=bins, ranges=ranges)\n", "\n", "print(\"RooDataHist imported with uniform binning and exported back to numpy:\")\n", "print_histogram_output(datahist_new_3.to_numpy())" ] }, { "cell_type": "markdown", "id": "4ee224a2", "metadata": {}, "source": [ "Draw all canvases " ] }, { "cell_type": "code", "execution_count": 22, "id": "fea71dbd", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:32:08.071017Z", "iopub.status.busy": "2026-05-19T20:32:08.070889Z", "iopub.status.idle": "2026-05-19T20:32:08.264004Z", "shell.execute_reply": "2026-05-19T20:32:08.263418Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "
\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ROOT import gROOT \n", "gROOT.GetListOfCanvases().Draw()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }