#!/usr/bin/env python

import sys #,hotshot

from cogent.evolve.substitution_model import Nucleotide, Dinucleotide, Codon
from cogent import LoadSeqs, LoadTree
from cogent.maths import optimisers
from cogent.util import parallel

__author__ = "Peter Maxwell and  Gavin Huttley"
__copyright__ = "Copyright 2007-2009, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.3"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Production"

ALIGNMENT = LoadSeqs(filename="data/brca1.fasta")
TREE = LoadTree(filename="data/murphy.tree")

def subtree(size):
    names = ALIGNMENT.getSeqNames()[:size]
    assert len(names) == size
    tree = TREE.getSubTree(names)  #.balanced()
    return names, tree

def brca_test(subMod, names, tree, length, par_rules, **kw):
    #names = ALIGNMENT.getSeqNames()[:taxa]
    #assert len(names) == taxa
    tree = TREE.getSubTree(names)  #.balanced()
    aln = ALIGNMENT.takeSeqs(names).omitGapPositions()[:length]
    assert len(aln) == length, (len(aln), length)
    #the_tree_analysis = LikelihoodFunction(treeobj = tree, submodelobj = subMod, alignobj = aln)
    par_controller = subMod.makeParamController(tree, **kw)
    for par_rule in par_rules:
        par_controller.setParamRule(**par_rule)
    #lf = par_controller.makeCalculator(aln)
    return (par_controller, aln)

def measure_evals_per_sec(pc, aln):
    pc.setAlignment(aln)
    lf = pc.real_par_controller.makeCalculator()
    return lf.measureEvalsPerSecond(time_limit=2.0, wall=False)

def profile(pc, aln):
    pc.makeCalculator(aln)
    p = hotshot.Profile('cogent.profile')
    lf.measureEvalsPerSecond(time_limit=1.0, wall=False)

def makePC(modelClass, parameterisation, length, taxa, tree, opt_mprobs, **kw):
    modelClass = eval(modelClass)
    if parameterisation is not None:
        predicates = {'silly': silly_predicate}
        par_rules = [{'par_name':'silly', 'is_independent':parameterisation}]
    else:
        predicates = {}
        par_rules = []
    subMod = modelClass(equal_motif_probs=True, optimise_motif_probs=opt_mprobs,
            predicates=predicates, recode_gaps=True)
    (pc, aln) = brca_test(subMod, taxa, tree, length, par_rules, **kw)
    return (pc, aln)

def quiet(f, *args, **kw):
        import sys, cStringIO
        temp = cStringIO.StringIO()
        _stdout = sys.stdout
        try:
                sys.stdout = temp
                result = f(*args, **kw)
        finally:
                #pass
                sys.stdout = _stdout
        return result

def evals_per_sec(*args):
    pc, aln = makePC(*args) #quiet(makeLF, *args)
    speed1 = measure_evals_per_sec(pc, aln)
    speed = str(int(speed1))
    return speed

class CompareImplementations(object):
    def __init__(self, switch):
        self.switch = switch
    
    def __call__(self, *args):
        self.switch(0)
        (pc,aln) = quiet(makePC, *args)
        speed1 = measure_evals_per_sec(pc,aln)
        self.switch(1)
        (pc,aln) = quiet(makePC, *args)
        speed2 = measure_evals_per_sec(pc,aln)
        if speed1 < speed2:
            speed = '+%2.1f' % (speed2/speed1)
        else:
            speed = '-%2.1f' % (speed1/speed2)
        if speed in ['+1.0', '-1.0']:
            speed = ''
        return speed

def benchmarks(test):
    alphabets = ["Nucleotide", "Dinucleotide", "Codon"]
    sequence_lengths = [18, 2004]
    treesizes = [5, 20]
    
    for (optimise_motifs, parameterisation) in [
            (False, 'global'), (False, 'local'), (True, 'global')]:
        print parameterisation, ['', 'opt motifs'][optimise_motifs]
        print ' ' * 14,
        wcol = 5*len(sequence_lengths) + 2
        for alphabet in alphabets:
            print str(alphabet).ljust(wcol),
        print
        print '%-15s' % "",    # "length"
        for alphabet in alphabets:
            for sequence_length in sequence_lengths:
                print "%4s" % sequence_length,
            print '  ',
        print
        print ' '*12 + (' | '.join(['']+['-'*(len(sequence_lengths)*5) for alphabet in alphabets]+['']))
        for treesize in treesizes:
            print ("%4s taxa    | " % treesize),
            (taxa, tree) = subtree(treesize)
            for alphabet in alphabets:
                for sequence_length in sequence_lengths:
                    speed = test(alphabet, parameterisation=='local',
                            sequence_length, taxa, tree, optimise_motifs)
                    print "%4s" % speed,
                print '| ',
            print
        print
    print

def silly_predicate(a,b):
    return a.count('A') > a.count('T') or b.count('A') > b.count('T')

#def asym_predicate((a,b)):
#    print a, b, 'a' in a
#    return 'a' in a
#mA = Codon()
#mA.setPredicates({'asym': asym_predicate})

def exponentiator_switch(switch):
    import cogent.evolve.substitution_calculation
    cogent.evolve.substitution_calculation.use_new = switch

import sys
if 'relative' in sys.argv:
    test = CompareImplementations(exponentiator_switch)
else:
    test = evals_per_sec

parallel.inefficiency_forgiven = True

if parallel.getCommunicator().rank > 0:
    #benchmarks(test)
    quiet(benchmarks, test)
else:
    try:
        benchmarks(test)
    except KeyboardInterrupt:
        print ' OK'

