{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot function using ROOT"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<ROOT.TCanvas object (\"icanvas\") at 0x7fb58c078b20>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import ROOT\n",
"from rep.plotting import default_canvas\n",
"c1 = default_canvas()\n",
"fun1 = ROOT.TF1( 'fun1', 'abs(sin(x)/x)', 0, 10)\n",
"c1.SetGridx()\n",
"c1.SetGridy()\n",
"fun1.Draw()\n",
"c1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot histogram using ROOT for branch in root file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"F = ROOT.TFile(\"toy_datasets/toyMC_bck_mass.root\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"T = F.Get(\"Tau23Mu\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"/ezRJwAAACRRb93sDfhs1obT6dS2bdiMNHsjQqu3hmGIGrds5ohw39nzsX199ZYPbqgMAEBG/P1w\n",
"rGVrxY1BUQUAALACuv8AANidi31nVVVlO8fmblFUAQCwO3S3HRHdfwAAACtgSgUAAIAVUFQBAACs\n",
"gKIKAABgBRRVAAAAK6CoAgAAWAFFFQAAwAooqgAAAFZAUQUAALACiioAAIAVUFQBAACsgKIKAABg\n",
"BRRVAAAAK6CoAgAAWAFFFQAAwAooqgAAAFbw/wHDeINHbRYYXAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<ROOT.TCanvas object (\"icanvas\") at 0x7fb58c078b20>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"T.Draw(\"min_DOCA\")\n",
"c1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## use histogram settings"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"h1 = ROOT.TH1F(\"h1\",\"hist from tree\",50, -0.25, 0.25)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<ROOT.TCanvas object (\"icanvas\") at 0x7fb58c078b20>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"T.Draw(\"min_DOCA>>h1\")\n",
"c1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# root_numpy way\n",
"\n",
"There are two comfortable libraries to read and work with root files\n",
"\n",
"* root_numpy http://rootpy.github.io/root_numpy/\n",
"* rootpy http://www.rootpy.org"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import root_numpy\n",
"# read root data to numpy.array\n",
"data = root_numpy.root2array(\"toy_datasets/toyMC_bck_mass.root\", 'Tau23Mu', branches=['min_DOCA', 'mass'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy\n",
"data2 = numpy.array(data[:10], dtype=[('1', numpy.float32), ('2', numpy.float32)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### convert to pandas"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>min_DOCA</th>\n",
" <th>mass</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 0.015175</td>\n",
" <td> 1628.509784</td>\n",
" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <td> 0.056992</td>\n",
" <td> 1397.421265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
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" <td> 0.037431</td>\n",
" <td> 1914.717285</td>\n",
" </tr>\n",
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" <th>10 </th>\n",
" <td> 0.000503</td>\n",
" <td> 1681.143799</td>\n",
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" <td> 1914.717285</td>\n",
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" <td> 2056.731136</td>\n",
" </tr>\n",
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" <td> 1914.717285</td>\n",
" </tr>\n",
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" <td> 1844.825271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 0.002454</td>\n",
" <td> 1674.145226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 0.028302</td>\n",
" <td> 1578.553626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> 0.052585</td>\n",
" <td> 1783.390566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 0.039515</td>\n",
" <td> 2130.677002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 0.004157</td>\n",
" <td> 1610.112183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 0.051618</td>\n",
" <td> 1968.904289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 0.057749</td>\n",
" <td> 1398.062700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 0.056992</td>\n",
" <td> 1397.421265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 0.010571</td>\n",
" <td> 2079.312230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 0.046581</td>\n",
" <td> 1481.461555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 0.034090</td>\n",
" <td> 1938.229590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 0.039960</td>\n",
" <td> 1941.380579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 0.041143</td>\n",
" <td> 1686.872633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 0.050269</td>\n",
" <td> 1918.156982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20431</th>\n",
" <td> 0.006256</td>\n",
" <td> 1442.303467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20432</th>\n",
" <td> 0.000893</td>\n",
" <td> 1960.582031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20433</th>\n",
" <td> 0.012074</td>\n",
" <td> 1684.759155</td>\n",
" </tr>\n",
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" <td> 0.001444</td>\n",
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" <th>20437</th>\n",
" <td> 0.041552</td>\n",
" <td> 1938.861206</td>\n",
" </tr>\n",
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" <td> 0.012459</td>\n",
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" <th>20439</th>\n",
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" <td> 1764.710571</td>\n",
" </tr>\n",
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" <td> 0.022338</td>\n",
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" <tr>\n",
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" <td> 0.001849</td>\n",
" <td> 1866.240967</td>\n",
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" <td> 0.040196</td>\n",
" <td> 1411.917358</td>\n",
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" <th>20443</th>\n",
" <td> 0.057608</td>\n",
" <td> 2063.528320</td>\n",
" </tr>\n",
" <tr>\n",
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" <td> 0.009527</td>\n",
" <td> 1492.762329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20445</th>\n",
" <td> 0.102680</td>\n",
" <td> 2062.338379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20446</th>\n",
" <td> 0.027973</td>\n",
" <td> 2074.640381</td>\n",
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" <th>20447</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>20448</th>\n",
" <td> 0.002519</td>\n",
" <td> 2144.978027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20449</th>\n",
" <td> 0.000272</td>\n",
" <td> 1376.617798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20450</th>\n",
" <td> 0.019663</td>\n",
" <td> 2172.513672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20451</th>\n",
" <td> 0.016306</td>\n",
" <td> 1402.403809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20452</th>\n",
" <td> 0.001617</td>\n",
" <td> 1668.644165</td>\n",
" </tr>\n",
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" <th>20453</th>\n",
" <td> 0.133691</td>\n",
" <td> 1703.245483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20454</th>\n",
" <td> 0.001939</td>\n",
" <td> 1854.070801</td>\n",
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" <th>20455</th>\n",
" <td> 0.011345</td>\n",
" <td> 2170.454346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20456</th>\n",
" <td> 0.030052</td>\n",
" <td> 1802.026733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20457</th>\n",
" <td> 0.039883</td>\n",
" <td> 1943.062012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20458</th>\n",
" <td> 0.061589</td>\n",
" <td> 1861.949951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20459</th>\n",
" <td> 0.016136</td>\n",
" <td> 1615.272217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20460</th>\n",
" <td> 0.070077</td>\n",
" <td> 1541.635254</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20461 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" min_DOCA mass\n",
"0 0.015175 1628.509784\n",
"1 0.000997 1679.370983\n",
"2 0.016812 2128.668736\n",
"3 0.006014 2089.925634\n",
"4 0.004157 1610.112183\n",
"5 0.056992 1397.421265\n",
"6 0.029978 1613.646622\n",
"7 0.050269 1918.156982\n",
"8 0.082375 1497.562059\n",
"9 0.037431 1914.717285\n",
"10 0.000503 1681.143799\n",
"11 0.037431 1914.717285\n",
"12 0.037431 1914.717285\n",
"13 0.003043 2056.731136\n",
"14 0.037431 1914.717285\n",
"15 0.034449 1844.825271\n",
"16 0.002454 1674.145226\n",
"17 0.028302 1578.553626\n",
"18 0.052585 1783.390566\n",
"19 0.039515 2130.677002\n",
"20 0.004157 1610.112183\n",
"21 0.051618 1968.904289\n",
"22 0.057749 1398.062700\n",
"23 0.056992 1397.421265\n",
"24 0.010571 2079.312230\n",
"25 0.046581 1481.461555\n",
"26 0.034090 1938.229590\n",
"27 0.039960 1941.380579\n",
"28 0.041143 1686.872633\n",
"29 0.050269 1918.156982\n",
"... ... ...\n",
"20431 0.006256 1442.303467\n",
"20432 0.000893 1960.582031\n",
"20433 0.012074 1684.759155\n",
"20434 0.001444 2159.516846\n",
"20435 0.084172 1805.777466\n",
"20436 0.004888 1837.179199\n",
"20437 0.041552 1938.861206\n",
"20438 0.012459 1876.531860\n",
"20439 0.035683 1764.710571\n",
"20440 0.022338 2019.067627\n",
"20441 0.001849 1866.240967\n",
"20442 0.040196 1411.917358\n",
"20443 0.057608 2063.528320\n",
"20444 0.009527 1492.762329\n",
"20445 0.102680 2062.338379\n",
"20446 0.027973 2074.640381\n",
"20447 0.027973 1765.936768\n",
"20448 0.002519 2144.978027\n",
"20449 0.000272 1376.617798\n",
"20450 0.019663 2172.513672\n",
"20451 0.016306 1402.403809\n",
"20452 0.001617 1668.644165\n",
"20453 0.133691 1703.245483\n",
"20454 0.001939 1854.070801\n",
"20455 0.011345 2170.454346\n",
"20456 0.030052 1802.026733\n",
"20457 0.039883 1943.062012\n",
"20458 0.061589 1861.949951\n",
"20459 0.016136 1615.272217\n",
"20460 0.070077 1541.635254\n",
"\n",
"[20461 rows x 2 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"pandas.DataFrame(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histograms "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10e45e910>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e3df810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure(figsize=(9, 7))\n",
"hist(data['min_DOCA'], bins=50)\n",
"xlabel('min_doca')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10e5b9d90>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e6c1d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure(figsize=(9, 7))\n",
"hist(data['mass'], bins=50)\n",
"xlabel('mass')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read data using some preselections"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = root_numpy.root2array(\"toy_datasets/toyMC_bck_mass.root\", 'Tau23Mu', branches=['mass'], selection='mass > 1700')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10e7e1510>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e8d1490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure(figsize=(9, 7))\n",
"hist(data['mass'], bins=50)\n",
"xlabel('mass')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}