UHepp

This notebook illustrates how FreeForestML integrates with uhepp.

import pandas as pd
import seaborn as sns

from freeforestml import Variable, Process, Cut, hist, McStack, DataStack, Stack
from freeforestml import toydata, example_style
from freeforestml.plot import hist
example_style()

import uhepp

2023-08-02 16:41:31.455133: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-02 16:41:31.595309: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-08-02 16:41:31.595341: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2023-08-02 16:41:32.451346: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-08-02 16:41:32.451458: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-08-02 16:41:32.451470: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

df = toydata.get()

df.compute()

	jet_1_pt	jet_1_eta	jet_1_phi	jet_2_pt	jet_2_eta	jet_2_phi	met_phi	met_pt	tau_phi	tau_eta	...	higgs_pt	higgs_eta	higgs_phi	higgs_m	m_jj	lep_centrality	tau_centrality	random	fpid	weight
0	88.594422	2.227779	5.447955	41.690209	2.551271	0.837869	3.421552	8.111048	4.885477	-1.005320	...	176.643326	-1.067341	-1.511005	80.330013	92.365255	8.546637e-201	1.600036e-96	0.692583	0	1
1	112.332084	3.689913	5.009235	88.663452	2.571029	0.240387	5.901392	18.987162	6.185564	-1.392806	...	170.961698	-1.164432	-0.135802	105.554097	180.611149	4.808119e-01	4.168943e-27	0.484160	0	1
2	75.818592	-0.688960	4.217875	26.745473	0.173188	1.702686	5.571233	10.456192	4.205480	-0.640830	...	41.179120	-2.048499	-2.060285	88.839000	94.573758	2.911326e-02	8.633178e-01	0.561923	0	1
3	167.919679	-4.106876	2.664687	39.265503	0.872895	5.447550	0.222917	49.302641	4.527605	-0.201476	...	18.004806	-2.177271	-1.638312	138.509621	985.571563	5.101841e-01	9.830257e-01	0.695018	1	1
4	211.198986	1.722085	5.363762	60.079350	3.617989	2.822620	2.986224	20.536071	4.570506	-0.073213	...	113.577391	-0.082111	-1.477354	104.931391	327.616481	9.670123e-01	9.583673e-01	0.144974	1	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	179.536895	-2.198896	5.824548	44.455686	0.217464	6.009975	3.558790	8.675338	2.185412	0.297409	...	61.431004	0.614294	1.550631	90.480296	272.864556	8.434026e-01	9.422578e-01	0.727435	0	1
9996	126.324773	-0.266018	6.057800	109.739485	-0.275967	4.999130	4.737586	27.654352	5.569802	-0.307197	...	185.914270	-0.404378	-1.054940	91.512900	118.914089	0.000000e+00	2.401191e-122	0.342996	0	1
9997	109.171854	-1.575872	0.212045	46.863079	0.288242	0.142182	3.250009	36.009504	4.196136	-0.312703	...	215.453970	-0.160157	-2.606385	95.198448	153.577498	9.979729e-01	9.544165e-01	0.838124	0	1
9998	314.678913	0.803816	1.355712	38.399009	1.614987	2.319213	0.653931	31.069108	5.298284	1.312493	...	65.500176	1.167349	-0.376793	83.755828	137.012721	5.652720e-05	2.228233e-07	0.094694	0	1
9999	99.152141	-1.644103	1.775950	45.133586	-1.181621	1.986518	2.716860	11.789360	4.567087	-0.430057	...	37.335738	-0.916864	1.077127	88.458963	34.235184	2.973423e-04	1.003417e-03	0.595205	0	1

10000 rows × 24 columns

p_ztt = Process(r"$Z\rightarrow\tau\tau$", range=(0, 0))
p_sig = Process(r"Signal", range=(1, 1))

p_asimov = Process(r"Asimov", selection=lambda d: d.fpid >= 0)

colors = ["windows blue", "amber", "greyish", "faded green", "dusty purple"]
palette = sns.xkcd_palette(colors)

s_bkg = McStack(p_ztt, p_sig, palette=palette)
s_data = DataStack(p_asimov)

v_higgs_m = Variable(r"$m^H$", "higgs_m", "GeV")

Pass the return_uhepp=True argument to hist().

h = hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM", return_uhepp=True)
h.render()

(<Figure size 500x500 with 2 Axes>,
 (<AxesSubplot:ylabel='Events / 10 GeV'>,
  <AxesSubplot:xlabel='$m^H$ / GeV', ylabel='Data / SM'>))

json_string = h.to_jsons()
print(json_string)

{"badge": {"brand": null, "subtext": "FreeForestML Example"}, "bins": {"edges": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0]}, "metadata": {"date": "2023-08-02T16:41:33.389677+00:00", "filename": "unnamed", "producer": "freeforestml", "tags": {}}, "ratio": [{"denominator": ["den"], "error": "stat", "keep_zero": false, "numerator": ["num_0"], "style": {"color": "#000000"}, "type": "points", "x_errorbar": true}], "ratio_axis": {"diff": false, "label": "Data / SM", "log": false}, "stacks": [{"content": [{"label": "$Z\\rightarrow\\tau\\tau$", "style": {"color": "#3778bf"}, "yield": ["Z_rightarrow_tau_tau__s0_p0"]}, {"label": "Signal", "style": {"color": "#feb308"}, "yield": ["Signal__s0_p1"]}], "error": "stat", "keep_zero": false, "type": "stepfilled", "x_errorbar": true}, {"content": [{"label": "Asimov", "style": {"color": "#000000"}, "yield": ["Asimov__s1_p0"]}], "error": "stat", "keep_zero": false, "type": "points", "x_errorbar": true}], "type": "histogram", "variable": {"symbol": "$m^H$", "unit": "GeV"}, "version": "0.4", "y_axis": {}, "yields": {"Asimov__s1_p0": {"base": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.0, 146.0, 1046.0, 2398.0, 2760.0, 1634.0, 654.0, 516.0, 436.0, 273.0, 99.0, 27.0, 4.0, 0.0, 0.0, 0.0], "stat": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.6457513110645907, 12.083045973594572, 32.341923257592455, 48.96937818678118, 52.53570214625479, 40.422765862815474, 25.573423705088842, 22.715633383201094, 20.8806130178211, 16.522711641858304, 9.9498743710662, 5.196152422706632, 2.0, 0.0, 0.0, 0.0]}, "Signal__s0_p1": {"base": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 8.0, 61.0, 222.0, 367.0, 502.0, 436.0, 273.0, 99.0, 27.0, 4.0, 0.0, 0.0, 0.0], "stat": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.8284271247461903, 7.810249675906654, 14.89966442575134, 19.157244060668017, 22.40535650240808, 20.8806130178211, 16.522711641858304, 9.9498743710662, 5.196152422706632, 2.0, 0.0, 0.0, 0.0]}, "Z_rightarrow_tau_tau__s0_p0": {"base": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.0, 146.0, 1045.0, 2390.0, 2699.0, 1412.0, 287.0, 14.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "stat": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.6457513110645907, 12.083045973594572, 32.32645975048923, 48.88762624632127, 51.95190083144215, 37.57658845611187, 16.941074346097416, 3.7416573867739413, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "den": {"base": [0, 0, 0, 0, 0, 0, 7, 146, 1046, 2398, 2760, 1634, 654, 516, 436, 273, 99, 27, 4, 0, 0, 0], "stat": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.6457513110645907, 12.083045973594572, 32.341923257592455, 48.96937818678118, 52.53570214625479, 40.422765862815474, 25.573423705088842, 22.715633383201094, 20.8806130178211, 16.522711641858304, 9.9498743710662, 5.196152422706632, 2.0, 0.0, 0.0, 0.0]}, "num_0": {"base": [0, 0, 0, 0, 0, 0, 7, 146, 1046, 2398, 2760, 1634, 654, 516, 436, 273, 99, 27, 4, 0, 0, 0], "stat": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.6457513110645907, 12.083045973594572, 32.341923257592455, 48.96937818678118, 52.53570214625479, 40.422765862815474, 25.573423705088842, 22.715633383201094, 20.8806130178211, 16.522711641858304, 9.9498743710662, 5.196152422706632, 2.0, 0.0, 0.0, 0.0]}}}

You could now save the json_string as a file or push it to uhepp.org via h.push(collection_id). Here will will simply restore the plot from the string.

h = uhepp.from_jsons(json_string)
h.render()

(<Figure size 500x500 with 2 Axes>,
 (<AxesSubplot:ylabel='Events / 10 GeV'>,
  <AxesSubplot:xlabel='$m^H$ / GeV', ylabel='Data / SM'>))

h.rebin_edges = [0, 70, 80, 90, 100, 110, 120, 130, 140, 150, 200]
h.subtext = "Hello"
h.brand = None
h.render()

(<Figure size 500x500 with 2 Axes>,
 (<AxesSubplot:ylabel='Events / Bin'>,
  <AxesSubplot:xlabel='$m^H$ / GeV', ylabel='Data / SM'>))