#!/usr/bin/env python
# coding: utf-8

# # rf303_conditional
# 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #303
# Use of tailored p.d.f as conditional p.d.fs.s
# 
# pdf = gauss(x,f(y),sx | y ) with f(y) = a0 + a1*y
# 
# 
# 
# 
# **Author:**  Clemens Lange, Wouter Verkerke (C version)  
# <i><small>This notebook tutorial was automatically generated with <a href= "https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Wednesday, May 15, 2024 at 09:50 AM.</small></i>

# In[1]:


import ROOT


def makeFakeDataXY():
    x = ROOT.RooRealVar("x", "x", -10, 10)
    y = ROOT.RooRealVar("y", "y", -10, 10)
    coord = {x, y}

    d = ROOT.RooDataSet("d", "d", coord)

    for i in range(10000):
        tmpy = ROOT.gRandom.Gaus(0, 10)
        tmpx = ROOT.gRandom.Gaus(0.5 * tmpy, 1)
        if (abs(tmpy) < 10) and (abs(tmpx) < 10):
            x.setVal(tmpx)
            y.setVal(tmpy)
            d.add(coord)

    return d


# Set up composed model gauss(x, m(y), s)
# -----------------------------------------------------------------------

# Create observables

# In[2]:


x = ROOT.RooRealVar("x", "x", -10, 10)
y = ROOT.RooRealVar("y", "y", -10, 10)


# Create function f(y) = a0 + a1*y

# In[3]:


a0 = ROOT.RooRealVar("a0", "a0", -0.5, -5, 5)
a1 = ROOT.RooRealVar("a1", "a1", -0.5, -1, 1)
fy = ROOT.RooPolyVar("fy", "fy", y, [a0, a1])


# Creat gauss(x,f(y),s)

# In[4]:


sigma = ROOT.RooRealVar("sigma", "width of gaussian", 0.5, 0.1, 2.0)
model = ROOT.RooGaussian("model", "Gaussian with shifting mean", x, fy, sigma)


# Obtain fake external experimental dataset with values for x and y

# In[5]:


expDataXY = makeFakeDataXY()


# Generate data from conditional p.d.f. model(x|y)
# ---------------------------------------------------------------------------------------------

# Make subset of experimental data with only y values

# In[6]:


expDataY = expDataXY.reduce({y})


# Generate 10000 events in x obtained from _conditional_ model(x|y) with y
# values taken from experimental data

# In[7]:


data = model.generate({x}, ProtoData=expDataY)
data.Print()


# Fit conditional p.d.f model(x|y) to data
# ---------------------------------------------------------------------------------------------

# In[8]:


model.fitTo(expDataXY, ConditionalObservables={y}, PrintLevel=-1)


# Project conditional p.d.f on x and y dimensions
# ---------------------------------------------------------------------------------------------

# Plot x distribution of data and projection of model x = 1/Ndata
# sum(data(y_i)) model(x;y_i)

# In[9]:


xframe = x.frame()
expDataXY.plotOn(xframe)
model.plotOn(xframe, ProjWData=expDataY)


# Speed up (and approximate) projection by using binned clone of data for
# projection

# In[10]:


binnedDataY = expDataY.binnedClone()
model.plotOn(xframe, ProjWData=binnedDataY, LineColor="c", LineStyle=":")


# Show effect of projection with too coarse binning

# In[11]:


(expDataY.get().find("y")).setBins(5)
binnedDataY2 = expDataY.binnedClone()
model.plotOn(xframe, ProjWData=binnedDataY2, LineColor="r")


# Make canvas and draw ROOT.RooPlots

# In[12]:


c = ROOT.TCanvas("rf303_conditional", "rf303_conditional", 600, 460)
ROOT.gPad.SetLeftMargin(0.15)
xframe.GetYaxis().SetTitleOffset(1.2)
xframe.Draw()

c.SaveAs("rf303_conditional.png")


# Draw all canvases 

# In[13]:


from ROOT import gROOT 
gROOT.GetListOfCanvases().Draw()