{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Welcome to JupyROOT 6.10/02\n"
]
}
],
"source": [
"import ROOT\n",
"\n",
"in_file = ROOT.TFile(\"~/work/sti-profiling/inputs_joinTwo.root\")\n",
"#in_file = ROOT.TFile(\"/home/smirnovd/my-tests-data/inputs_joinTwo.root\")\n",
"\n",
"t = in_file.Get(\"t\")\n",
"\n",
"ROOT.enableJSVis()\n",
"ROOT.gStyle.SetPadRightMargin(0.3)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly\n",
"import plotly.figure_factory as ff\n",
"import plotly.graph_objs as go\n",
"\n",
"plotly.offline.init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define some functions\n",
"\n",
"g_h_counter = 1\n",
"\n",
"# Fill 1D histogram from the tree\n",
"def project1D(expression, x_bins, x_first, x_last, h_title = \"\", selection = \"\"):\n",
"\n",
" global g_h_counter\n",
"\n",
" h_name = 'h_{:03d}'.format(g_h_counter)\n",
" g_h_counter += 1\n",
"\n",
" if h_title == \"\":\n",
" h_title = expression\n",
"\n",
" h1D = ROOT.TH1D(h_name, h_title, x_bins, x_first, x_last)\n",
"\n",
" t.Project(h_name, expression, selection)\n",
"\n",
" return h1D\n",
"\n",
"\n",
"\n",
"# Fill 2D histogram from the tree\n",
"def project2D(expression, x_bins, x_first, x_last, y_bins, y_first, y_last,\n",
" h_title = \"\", selection = \"\", draw_options = \"\"):\n",
"\n",
" global g_h_counter\n",
"\n",
" h_name = 'h_{:03d}'.format(g_h_counter)\n",
" g_h_counter += 1\n",
"\n",
" if h_title == \"\":\n",
" h_title = expression\n",
"\n",
" h2D = ROOT.TH2D(h_name, h_title, x_bins, x_first, x_last, y_bins, y_first, y_last)\n",
" h2D.SetOption(draw_options)\n",
"\n",
" t.Project(h_name, expression, selection, draw_options)\n",
"\n",
" return h2D\n",
"\n",
"\n",
"\n",
"def drawOnCanvases(hists, width = 800, height = 600):\n",
"\n",
" canvases = []\n",
"\n",
" for hist in hists:\n",
" canvas = ROOT.TCanvas(\"canvas_\" + hist.GetName(), \"ROOT Canvas\", width, height)\n",
" hist.Draw( hist.GetOption() )\n",
" canvas.Draw()\n",
" canvases.append(canvas)\n",
"\n",
" return canvases\n",
"\n",
"\n",
"\n",
"def getCumulative(hists):\n",
"\n",
" hists_cumul = []\n",
"\n",
" for hist in hists:\n",
" hist_cumul = hist\n",
" hist_cumul.Scale( 1./hist_cumul.Integral() )\n",
" hist_cumul = hist_cumul.GetCumulative()\n",
" hists_cumul.append(hist_cumul)\n",
"\n",
" return hists_cumul\n",
"\n",
"\n",
"\n",
"def plotly1D(h1D):\n",
"\n",
" y_values_ptr = h1D.GetArray()\n",
" y_values = []\n",
" x_values = []\n",
"\n",
" for i_bin in range( 1, h1D.GetNbinsX() + 1 ):\n",
" y_values.append( y_values_ptr[i_bin] )\n",
" x_values.append( h1D.GetBinCenter(i_bin) )\n",
"\n",
" #return [ go.Bar(y = y_values) ]\n",
" plotly.offline.iplot( [ go.Bar(y = y_values) ] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distributions for each component of the input vectors"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"source": [
"hists = []\n",
"\n",
"# Create and plot input distributions\n",
"hists.append( project1D(\"P1[0]\", 100, -20, 200, \"; x; events\", \"nP1 == 6\") )\n",
"hists.append( project1D(\"P1[1]\", 100, -60, 60, \"; y; events\", \"nP1 == 6\") )\n",
"hists.append( project1D(\"P1[2]\", 100, -250, 250, \"; z; events\", \"nP1 == 6\") )\n",
"hists.append( project1D(\"P1[3]\", 100, -1.5, 1.5, \"; #phi; events\", \"nP1 == 6\") )\n",
"hists.append( project1D(\"P1[4]\", 100, -100, 100, \"; q/Pt; events\", \"nP1 == 6\") )\n",
"hists.append( project1D(\"P1[5]\", 100, -5, 5, \"; tan(#Lambda); events\", \"nP1 == 6\") )\n",
"\n",
"canvases = drawOnCanvases(hists)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation between the corresponding components of the input vectors"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"text/html": [
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"source": [
"hists = []\n",
"\n",
"# Create and plot input distributions\n",
"hists.append( project2D(\"P1[0]:P2[0]\", 200, -20, 200, 200, -20, 200, \"; P2[x]; P1[x];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[1]:P2[1]\", 200, -60, 60, 200, -60, 60, \"; P2[y]; P1[y];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[2]:P2[2]\", 200, -250, 250, 200, -250, 250, \"; P2[z]; P1[z];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[3]:P2[3]\", 200, -1.5, 1.5, 200, -1.5, 1.5, \"; P2[#phi]; P1[#phi];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[4]:P2[4]\", 200, -100, 100, 200, -100, 100, \"; P2[q/Pt]; P1[q/Pt];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[5]:P2[5]\", 200, -5, 5, 200, -5, 5, \"; P2[tan(#Lambda)]; P1[tan(#Lambda)];\", \"nP1 == 6\", \"colz logz\") )\n",
"\n",
"canvases = drawOnCanvases(hists)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation between the corresponding components of the input and output vectors"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"source": [
"hists = []\n",
"\n",
"# Create and plot input distributions\n",
"hists.append( project2D(\"P1[0]:PJ[0]\", 200, -20, 200, 200, -20, 200, \"; PJ[x]; P1[x];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[1]:PJ[1]\", 200, -60, 60, 200, -60, 60, \"; PJ[y]; P1[y];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[2]:PJ[2]\", 200, -250, 250, 200, -250, 250, \"; PJ[z]; P1[z];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[3]:PJ[3]\", 200, -1.5, 1.5, 200, -1.5, 1.5, \"; PJ[#phi]; P1[#phi];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[4]:PJ[4]\", 200, -100, 100, 200, -100, 100, \"; PJ[q/Pt]; P1[q/Pt];\", \"nP1 == 6\", \"colz logz\") )\n",
"hists.append( project2D(\"P1[5]:PJ[5]\", 200, -5, 5, 200, -5, 5, \"; PJ[tan(#Lambda)]; P1[tan(#Lambda)];\", \"nP1 == 6\", \"colz logz\") )\n",
"\n",
"canvases = drawOnCanvases(hists)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Difference between individual components in input and output vectors"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"source": [
"hists = []\n",
"\n",
"hists.append( project1D(\"fabs(P2[0] - PJ[0]) / sqrt(E2[1])\", 50, 0, 10, \"; #chi[x]; prob.\" , \"nP2 == 6\") )\n",
"hists.append( project1D(\"fabs(P2[1] - PJ[1]) / sqrt(E2[2])\", 50, 0, 10, \"; #chi[y]; prob.\" , \"nP2 == 6\") )\n",
"hists.append( project1D(\"fabs(P2[2] - PJ[2]) / sqrt(E2[5])\", 50, 0, 10, \"; #chi[z]; prob.\" , \"nP2 == 6\") )\n",
"hists.append( project1D(\"fabs(P2[3] - PJ[3]) / sqrt(E2[9])\", 50, 0, 10, \"; #chi[#phi]; prob.\" , \"nP2 == 6\") )\n",
"hists.append( project1D(\"fabs(P2[4] - PJ[4]) / sqrt(E2[14])\", 50, 0, 10, \"; #chi[q/Pt]; prob.\" , \"nP2 == 6\") )\n",
"hists.append( project1D(\"fabs(P2[5] - PJ[5]) / sqrt(E2[20])\", 50, 0, 10, \"; #chi[tan(#Lambda)]; prob.\", \"nP2 == 6\") )\n",
"\n",
"#canvases = drawOnCanvases(hists)\n",
"hists_cumul = getCumulative(hists)\n",
"canvases = drawOnCanvases(hists_cumul)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Difference between input and output vectors for all/inner/outer nodes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
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"source": [
"diff_P2_PJ_chi2 = (\n",
" \" (P2[1] - PJ[1]) * (P2[1] - PJ[1]) / E2[2] + \"\n",
" \" (P2[2] - PJ[2]) * (P2[2] - PJ[2]) / E2[5] + \"\n",
" \" (P2[3] - PJ[3]) * (P2[3] - PJ[3]) / E2[9] + \"\n",
" \" (P2[4] - PJ[4]) * (P2[4] - PJ[4]) / E2[14] + \"\n",
" \" (P2[5] - PJ[5]) * (P2[5] - PJ[5]) / E2[20] \"\n",
")\n",
"\n",
"hists = []\n",
"\n",
"hists.append( project1D(diff_P2_PJ_chi2, 50, 0, 50, \"; #chi^2; \", \"nP1 == 6\") )\n",
"hists.append( project1D(diff_P2_PJ_chi2, 50, 0, 50, \"; #chi^2; \", \"nP1 == 6 && ino == 1\") )\n",
"hists.append( project1D(diff_P2_PJ_chi2, 50, 0, 50, \"; #chi^2; \", \"nP1 == 6 && ino == -1\") )\n",
"\n",
"hists_cumul = getCumulative(hists)\n",
"canvases = drawOnCanvases(hists_cumul)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Auxilary plots\n",
"\n",
"1 nP2 vs nP1\n",
"2 inner/middle/outer = -1/0/1. More outer nodes than inner ones?\n",
"3 Direction of the fit. The fit is done only in one direction (0 = outside in)"
]
},
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"cell_type": "code",
"execution_count": 9,
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"hists = []\n",
"\n",
"hists.append( project2D(\"nP2:nP1\", 10, 0, 10, 10, 0, 10, \"; nP1; nP2\", \"\", \"colz\") )\n",
"hists.append( project1D(\"ino\", 5, -2, 3, \"; inner/middle/outer; \", \"nP1 == 6\") )\n",
"hists.append( project1D(\"dir\", 4, -2, 2, \"; fit direction; \", \"\") )\n",
"hists.append( project1D(\"dir\", 4, -2, 2, \"; fit direction; \", \"nP1 == 6\") )\n",
"\n",
"canvases = drawOnCanvases(hists)"
]
},
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"metadata": {},
"source": [
""
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"The raw code for this IPython notebook is by default hidden for easier reading.\n",
"To toggle on/off the raw code, click here."
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