****************
ProSper Tutorial 
****************

This tutorial is one of our top priorities. Unfortunately, we are only able to provide examples at this point.
The following example is a simple script that runs a Binary Sparse Coding model on an artificial dataset.
It is ready for use in a parallel programming environment with data logging capabilities::

	#!/usr/bin/env python
	import sys

	import numpy as np
	from mpi4py import MPI

	from prosper.utils import create_output_path 
	from prosper.utils.parallel import pprint, stride_data
	from prosper.utils.barstest import generate_bars_dict

	from prosper.utils.datalog import dlog, StoreToH5, TextPrinter, StoreToTxt

	from prosper.em import EM
	from prosper.em.annealing import LinearAnnealing
	from prosper.em.camodels.bsc_et import BSC_ET


	#=============================================================================
	# Parameters

	D2     = 5
	N      = 1000
	Hprime = 6
	gamma  = 5

	#=============================================================================
	# Main
	comm = MPI.COMM_WORLD

	pprint("="*70)
	pprint(" Running %d parallel processes" % comm.size) 
	pprint("="*70)
	 
	H = 2*D2      # number of latent units
	D = D2**2     # total size of image in pixels

	my_N = N // comm.size

	# Some sanity checks
	assert Hprime <= H
	assert gamma <= Hprime
	assert D == D2**2

	# Configure DataLogger
	print_list = ('T', 'pi', 'sigma')
	dlog.set_handler(print_list, TextPrinter)

	# Invent some ground truth parameter models
	params_gt = {
	    'W'     :  10*generate_bars_dict(H),  
	    'pi'    :  2. / H,
	    'sigma' :  1.0
	}

	# Use model to generate data 
	model = BSC_ET(D, H, Hprime, gamma)
	my_data = model.generate_data(params_gt, my_N)

	model_params = model.standard_init(my_data)
	    
	# Choose annealing schedule
	anneal = LinearAnnealing(50)
	anneal['T'] = [(15, 1.), (-10, 1.)]
	anneal['Ncut_factor'] = [(0,0.),(2./3,1.)]
	anneal['anneal_prior'] = False
	    
	# Create and start EM annealing
	em = EM(model=model, anneal=anneal)
	em.data = my_data
	em.lparams = model_params
	em.run()

	dlog.close()
	pprint("Done")