import sys

try:
    import wx
    WX_VERSION = int(wx.version()[0])
    hasWx = True

except Exception as e:
    hasWx = False
    WX_VERSION = 0

if hasWx:
    import wx.xrc
    from wx.lib.buttons import GenBitmapTextButton
    from pubsub import pub

import os
import time
import math
import random
import numpy
import scipy.stats
import datetime

import base
import pytransit.transit_tools as transit_tools
import pytransit.tnseq_tools as tnseq_tools
import pytransit.norm_tools as norm_tools
import pytransit.stat_tools as stat_tools

#method_name = "binomial"

############# GUI ELEMENTS ##################

short_name = "binomial"
long_name = "Binomial"
short_desc = "Hierarchical binomial model of essentiality with individual frequencies."
long_desc = """Hierarchical bayesian model of essentiality based on the binomial distribution. Estimates individual probabilities for insertion, leading to more conservative predictions.

Reference: DeJesus and Ioerger (2014; IEEE TCBB)
"""
transposons = ["himar1"]
columns = ["Orf","Name","Description","Mean Insertion","Sites per Replicate","Total Insertions","Total Sites","thetabar", "zbar", "Call"]

############# Analysis Method ##############

class BinomialAnalysis(base.TransitAnalysis):
    def __init__(self):
        base.TransitAnalysis.__init__(self, short_name, long_name, short_desc, long_desc, transposons, BinomialMethod, BinomialGUI, [BinomialFile])


################## FILE ###################

class BinomialFile(base.TransitFile):

    def __init__(self):
        base.TransitFile.__init__(self, "#Binomial", columns)

    def getHeader(self, path):
        ess=0; unc=0; non=0; short=0
        for line in open(path):
            if line.startswith("#"): continue
            tmp = line.strip().split("\t")
            if tmp[-1] == "Essential": ess+=1
            if tmp[-1] == "Uncertain": unc+=1
            if tmp[-1] == "Non-Essential": non+=1

        text = """Results:
    Essentials: %s
    Uncertain: %s
    Non-Essential: %s
            """ % (ess, unc, non)
        return text


################## GUI ###################

class BinomialGUI(base.AnalysisGUI):

    def definePanel(self, wxobj):
        self.wxobj = wxobj
        binomialPanel = wx.Panel( self.wxobj.optionsWindow, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, wx.TAB_TRAVERSAL )

        binomialSection = wx.BoxSizer( wx.VERTICAL )

        binomialLabel = wx.StaticText( binomialPanel, wx.ID_ANY, u"Binomial Options", wx.DefaultPosition, (130, -1), 0 )
        binomialLabel.SetFont( wx.Font( 10, wx.DEFAULT, wx.NORMAL, wx.BOLD) )
        binomialSection.Add( binomialLabel, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )

        binomialSizer1 = wx.BoxSizer( wx.HORIZONTAL )
        binomialSizer2 = wx.BoxSizer( wx.HORIZONTAL )
        binomialLabelSizer = wx.BoxSizer( wx.VERTICAL )
        binomialControlSizer = wx.BoxSizer( wx.VERTICAL )
    
        #Samples 
        binomialSampleLabel = wx.StaticText( binomialPanel, wx.ID_ANY, u"Samples", wx.DefaultPosition, wx.DefaultSize, 0 )
        binomialSampleLabel.Wrap(-1)
        binomialLabelSizer.Add(binomialSampleLabel, 1, wx.ALL, 5)
        self.wxobj.binomialSampleText = wx.TextCtrl( binomialPanel, wx.ID_ANY, u"10000", wx.DefaultPosition, wx.DefaultSize, 0 )
        binomialControlSizer.Add(self.wxobj.binomialSampleText, 0, wx.ALL|wx.EXPAND, 5)

        #Burnin 
        binomialBurnLabel = wx.StaticText( binomialPanel, wx.ID_ANY, u"Burn-in", wx.DefaultPosition, wx.DefaultSize, 0 )
        binomialBurnLabel.Wrap(-1)
        binomialLabelSizer.Add(binomialBurnLabel, 1, wx.ALL, 5)
        self.wxobj.binomialBurnText = wx.TextCtrl( binomialPanel, wx.ID_ANY, u"500", wx.DefaultPosition, wx.DefaultSize, 0 )
        binomialControlSizer.Add(self.wxobj.binomialBurnText, 0, wx.ALL|wx.EXPAND, 5)


        binomialSizer2.Add(binomialLabelSizer, 1, wx.EXPAND, 5)
        binomialSizer2.Add(binomialControlSizer, 1, wx.EXPAND, 5)
        binomialSizer1.Add(binomialSizer2, 1, wx.EXPAND, 5 )


        binomialSection.Add( binomialSizer1, 1, wx.EXPAND, 5 )

        binomialButton = wx.Button( binomialPanel, wx.ID_ANY, u"Run binomial", wx.DefaultPosition, wx.DefaultSize, 0 )
        binomialSection.Add( binomialButton, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )

        binomialPanel.SetSizer( binomialSection )
        binomialPanel.Layout()
        binomialSection.Fit( binomialPanel )

        #Connect events
        binomialButton.Bind( wx.EVT_BUTTON, self.wxobj.RunMethod )

        self.panel = binomialPanel





########## CLASS #######################

class BinomialMethod(base.SingleConditionMethod):
    """   
    binomial
 
    """
    def __init__(self,
                ctrldata,
                annotation_path,
                output_file,
                samples=10000,
                burnin=500,
                replicates="Sum",
                normalization=None,
                LOESS=False,
                ignoreCodon=True,
                NTerminus=0.0,
                CTerminus=0.0,
                pi0=0.5,
                pi1=0.5,
                M0=1.0,
                M1=1.0,
                a0=10.0,
                a1=10.0,
                b0=1.0,
                b1=1.0,
                alpha_w=0.5,
                beta_w=0.5,
                wxobj=None):

        base.SingleConditionMethod.__init__(self, short_name, long_name, short_desc, long_desc, ctrldata, annotation_path, output_file, replicates=replicates, normalization=normalization, LOESS=LOESS, NTerminus=NTerminus, CTerminus=CTerminus, wxobj=wxobj)

        self.samples = samples
        self.burnin = burnin

        self.pi0 = pi0
        self.pi1 = pi1
        self.M0 = M0
        self.M1 = M1
        self.a0 = a0
        self.a1 = a1
        self.b0 = b0
        self.b1 = b1
        self.alpha_w = alpha_w
        self.beta_w = beta_w


    @classmethod
    def fromGUI(self, wxobj):
        """ """

        #Get Annotation file
        annotationPath = wxobj.annotation
        if not transit_tools.validate_annotation(annotationPath):
            return None

        #Get selected files
        ctrldata = wxobj.ctrlSelected()
        if not transit_tools.validate_control_datasets(ctrldata):
            return None

        #Validate transposon types
        if not transit_tools.validate_transposons_used(ctrldata, transposons):
            return None



        #Read the parameters from the wxPython widgets
        samples = int(wxobj.binomialSampleText.GetValue())
        burnin = int(wxobj.binomialBurnText.GetValue())
        ignoreCodon = True
        NTerminus = float(wxobj.globalNTerminusText.GetValue())
        CTerminus = float(wxobj.globalCTerminusText.GetValue())
        replicates = "Sum"
        normalization = None
        LOESS = False



        #Get output path
        name = transit_tools.basename(ctrldata[0])
        defaultFileName = "binomial_output.dat"
        defaultDir = os.getcwd()
        output_path = wxobj.SaveFile(defaultDir, defaultFileName)
        if not output_path: return None
        output_file = open(output_path, "w")


        return self(ctrldata,
                annotationPath,
                output_file,
                samples=samples,
                burnin=burnin,
                replicates=replicates,
                normalization=normalization,
                LOESS=LOESS,
                ignoreCodon=ignoreCodon,
                NTerminus=NTerminus,
                CTerminus=CTerminus,
                wxobj=wxobj)


    @classmethod
    def fromargs(self, rawargs): 
        (args, kwargs) = transit_tools.cleanargs(rawargs)


        ctrldata = args[0].split(",")
        annotationPath = args[1]
        outpath = args[2]
        output_file = open(outpath, "w")

        samples = int(kwargs.get("s", 10000))
        burnin = int(kwargs.get("b", 500))
        replicates = "Sum"
        normalization = None
        LOESS = False
        ignoreCodon = True
        NTerminus = float(kwargs.get("iN", 0.0))
        CTerminus = float(kwargs.get("iC", 0.0))

        pi0 = float(kwargs.get("pi0", 0.5))
        pi1 = float(kwargs.get("pi1", 0.5))
        M0 = float(kwargs.get("M0", 1.0))
        M1 = float(kwargs.get("M1", 1.0))
        a0 = float(kwargs.get("a0", 10.0))
        a1 = float(kwargs.get("a1", 10.0))
        b0 = float(kwargs.get("b0", 1.0))
        b1 = float(kwargs.get("b1", 1.0))
        alpha_w = float(kwargs.get("aw", 0.5))
        beta_w = float(kwargs.get("bw", 0.5))


        return self(ctrldata,
                annotationPath,
                output_file,
                samples=samples,
                burnin=burnin,
                replicates=replicates,
                normalization=normalization,
                LOESS=LOESS,
                ignoreCodon=ignoreCodon,
                NTerminus=NTerminus,
                CTerminus=CTerminus,
                pi0=pi0,
                pi1=pi1,
                M0=M0,
                M1=M1,
                a0=a0,
                a1=a1,
                b0=b0,
                b1=b1,
                alpha_w=alpha_w,
                beta_w=beta_w)


    def Run(self):

        self.transit_message("Starting Binomial Method")
        start_time = time.time()
        
        self.progress_range(self.samples+self.burnin)

        #Get orf data
        #self.transit_message("Getting Data")
        #G = tnseq_tools.Genes(self.ctrldata, self.annotation_path, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus)

        self.transit_message("Getting Data")
        (data, position) = transit_tools.get_validated_data(self.ctrldata, wxobj=self.wxobj)
        (K,N) = data.shape

        if self.normalization and self.normalization != "nonorm":
            self.transit_message("Normalizing using: %s" % self.normalization)
            (data, factors) = norm_tools.normalize_data(data, self.normalization, self.ctrldata, self.annotation_path)

        G = tnseq_tools.Genes(self.ctrldata, self.annotation_path, minread=1, reps=self.replicates, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus, data=data, position=position)



        #Parameters
        self.transit_message("Setting Parameters")
        w1 = 0.15
        w0 = 1.0 - w1
        mu_c = 0

        Ngenes = len(G)
        sample_size = self.samples+self.burnin
        numReps = len(self.ctrldata)

        theta = numpy.zeros((Ngenes, sample_size))
        theta[:,0] = 0.10

        rho0 = numpy.zeros(sample_size); rho0[0] = 0.5;  Kp0 = numpy.zeros(sample_size); Kp0[0] = 10;
        rho1 = numpy.zeros(sample_size); rho1[0] = 0.10; Kp1 = numpy.zeros(sample_size); Kp1[0] = 3;

        Z = numpy.zeros((Ngenes, sample_size))
        pz1 = numpy.zeros(sample_size);
        n1 = 0

        w1 = scipy.stats.beta.rvs(self.alpha_w, self.beta_w)
        W1 = numpy.zeros(sample_size); W1[0] = w1



        #
        self.transit_message("Setting Initial Values")
        K = numpy.array([sum([1 for x in gene.reads.flatten() if x> 0]) for gene in G])
        N = numpy.array([len(gene.reads.flatten()) for gene in G])

        for g,gene in enumerate(G):
            if N[g] == 0: theta[g][0] = 0.5
            elif K[g]/float(N[g]) == 0: theta[g][0] = 0.001
            elif K[g]/float(N[g]) == 1: theta[g][0] = 0.001
            else: theta[g][0] = K[g]/float(N[g])

            #print g, ORF[g], K[g], N[g], theta[g][0]
            Z[g][0] = scipy.stats.bernoulli.rvs(1-theta[g][0])


        acc_p0 = 0; acc_k0 = 0;
        acc_p1 = 0; acc_k1 = 0;


        rho0c_std = 0.010
        kp0c_std = 1.40
        rho1c_std = 0.009
        kp1c_std = 1.1


        numpy.seterr(divide='ignore')
        for i in range(1, sample_size):

            i0 = Z[:,i-1] == 0; n0 = numpy.sum(i0);
            i1 = Z[:,i-1] == 1; n1 = numpy.sum(i1);

            theta[i0,i] = scipy.stats.beta.rvs(Kp0[i-1]*rho0[i-1] + K[i0],  Kp0[i-1]*(1-rho0[i-1]) + N[i0] - K[i0])
            theta[i1,i] = scipy.stats.beta.rvs(Kp1[i-1]*rho1[i-1] + K[i1],  Kp1[i-1]*(1-rho1[i-1]) + N[i1] - K[i1])
            
            rho0_c = rho0[i-1] + scipy.stats.norm.rvs(0, rho0c_std)
            Kp0_c = Kp0[i-1] + scipy.stats.norm.rvs(0, kp0c_std)


            if rho0_c <= 0: rho0[i] = rho0[i-1]
            else:
                fc = numpy.log(scipy.stats.beta.pdf(rho0_c, self.M0*self.pi0, self.M0*(1.0-self.pi0)))
                f0 = numpy.log(scipy.stats.beta.pdf(rho0[i-1], self.M0*self.pi0, self.M0*(1.0-self.pi0)))
                fc += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i0,i], Kp0[i-1]*rho0_c, Kp0[i-1]*(1-rho0_c))))
                f0 += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i0,i], Kp0[i-1]*rho0[i-1], Kp0[i-1]*(1-rho0[i-1]))))
    
                if numpy.log(scipy.stats.uniform.rvs()) < fc - f0:
                    rho0[i] = rho0_c
                    acc_p0+=1
                else: rho0[i] = rho0[i-1]


            if Kp0_c <= 0: Kp0[i] = Kp0[i-1]
            else:
                fc = numpy.log(scipy.stats.gamma.pdf(Kp0_c, self.a0, self.b0));
                f0 = numpy.log(scipy.stats.gamma.pdf(Kp0[i-1], self.a0, self.b0));
                fc += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i0,i], Kp0_c*rho0[i], Kp0_c*(1-rho0[i]))))
                f0 += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i0,i], Kp0[i-1]*rho0[i], Kp0[i-1]*(1-rho0[i]))))
    
                if numpy.log(scipy.stats.uniform.rvs()) < fc - f0:
                    Kp0[i] = Kp0_c
                    acc_k0+=1
                else: Kp0[i] = Kp0[i-1]

            rho1_c = rho1[i-1] + scipy.stats.norm.rvs(0, rho1c_std)
            Kp1_c = Kp1[i-1] + scipy.stats.norm.rvs(0, kp1c_std)


            if rho1_c <= 0:
                rho1[i] = rho1[i-1]
            else:
                fc = numpy.log(scipy.stats.beta.pdf(rho1_c, self.M1*self.pi1, self.M1*(1-self.pi1)))
                f1 = numpy.log(scipy.stats.beta.pdf(rho1[i-1], self.M1*self.pi1, self.M1*(1-self.pi1)))
                fc += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i1,i], Kp1[i-1]*rho1_c, Kp1[i-1]*(1-rho1_c))))
                f1 += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i1,i], Kp1[i-1]*rho1[i-1], Kp1[i-1]*(1-rho1[i-1]))))
    
                if numpy.log(scipy.stats.uniform.rvs()) < fc - f1:
                    rho1[i] = rho1_c
                    acc_p1+=1
                else: rho1[i] = rho1[i-1]

            if Kp1_c <= 0: Kp1[i] = Kp1[i-1]
            else:
                
                fc = numpy.log(scipy.stats.gamma.pdf(Kp1_c, self.a1, self.b1));
                f1 = numpy.log(scipy.stats.gamma.pdf(Kp1[i-1], self.a1, self.b1));
                fc += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i1,i], Kp1_c*rho1[i], Kp1_c*(1-rho1[i]))))
                f1 += numpy.sum(numpy.log(scipy.stats.beta.pdf(theta[i1,i], Kp1[i-1]*rho1[i], Kp1[i-1]*(1-rho1[i]))))

                if numpy.log(scipy.stats.uniform.rvs()) < fc - f1:
                    Kp1[i] = Kp1_c
                    acc_k1+=1
                else: Kp1[i] = Kp1[i-1]


            g0 = scipy.stats.beta.pdf(theta[:,i], Kp0[i]*rho0[i], Kp0[i]*(1-rho0[i])) * (1-w1)
            g1 = scipy.stats.beta.pdf(theta[:,i], Kp1[i]*rho1[i], Kp1[i]*(1-rho1[i])) * (w1)
            p1 = g1/(g0+g1)
            p1 = numpy.nan_to_num(p1)

            
            try:
                Z[:,i] = scipy.stats.bernoulli.rvs(p1)
            except:
                inan = numpy.isnan(p1)
                print >> sys.stderr, "K=\t", K[inan]
                print >> sys.stderr, "N=\t", N[inan]
                print >> sys.stderr, "theta=", theta[inan,i]
                sys.exit()
            pz1[i] = p1[0]


            i1 = Z[:,i] == 1; n1 = numpy.sum(i1);
            #w1 = 0.15
            w1 = scipy.stats.beta.rvs(self.alpha_w + n1, self.beta_w + Ngenes - n1)
            W1[i] = w1


            #Update progress
            text = "Running Binomial Method... %5.1f%%" % (100.0*(i+1)/(sample_size))
            self.progress_update(text, i)

        numpy.seterr(divide='warn')

        z_bar = numpy.apply_along_axis(numpy.mean, 1, Z[:, self.burnin:])
        theta_bar = numpy.apply_along_axis(numpy.mean, 1, theta[:, self.burnin:])
        #(ess_threshold, noness_threshold) = stat_tools.fdr_post_prob(z_bar)
        (ess_threshold, noness_threshold) = stat_tools.bayesian_ess_thresholds(z_bar)

        self.output.write("#Binomial\n")
        #output.write("#Command: %s\n" % " ".join(["%s=%s" %(key,val) for (key,val) in kwargs.items()]))
        if self.wxobj:
            members = sorted([attr for attr in dir(self) if not callable(getattr(self,attr)) and not attr.startswith("__")])
            memberstr = ""
            for m in members:
                memberstr += "%s = %s, " % (m, getattr(self, m))
            self.output.write("#GUI with: ctrldata=%s, annotation=%s, output=%s, samples=%s, burnin=%s\n" % (",".join(self.ctrldata).encode('utf-8'), self.annotation_path.encode('utf-8'), self.output.name.encode('utf-8'), self.samples, self.burnin))
        else:
            self.output.write("#Console: python %s\n" % " ".join(sys.argv))

        self.output.write("#Thresholds: (%1.5f, %1.5f)\n" % (ess_threshold,noness_threshold))
        self.output.write("#rho0 Acceptance Rate:\t%f%%\n" % ((100.0*acc_p0)/sample_size))
        self.output.write("#Kp0  Acceptance Rate:\t%f%%\n" % ((100.0*acc_k0)/sample_size))
        self.output.write("#rho1 Acceptance Rate:\t%f%%\n" % ((100.0*acc_p1)/sample_size))
        self.output.write("#Kp1  Acceptance Rate:\t%f%%\n" % ((100.0*acc_k1)/sample_size))
        self.output.write("#Hyperparameters rho: \t%1.2f\t%3.1f\t%1.2f\t%3.1f\n" % (self.pi0, self.M0, self.pi1, self.M1))
        self.output.write("#Hyperparameters Kp: \t%3.1f\t%3.1f\t%3.1f\t%3.1f\n" % (self.a0, self.b0, self.a1, self.b1))
        self.output.write("#Hyperparameters W: \t%1.3f\t%1.3f\n" % (self.alpha_w, self.beta_w))


        self.output.write("#%s\n" % "\t".join(columns))

        data = []
        for g,gene in enumerate(G):
            c = "Uncertain"
            if z_bar[g] > ess_threshold:
                c = "Essential"
            if z_bar[g] < noness_threshold:
                c = "Non-Essential"    
            data.append("%s\t%s\t%s\t%1.1f\t%d\t%d\t%d\t%f\t%f\t%s" % (gene.orf, gene.name, gene.desc, K[g]/float(numReps), N[g]/numReps, K[g], N[g], theta_bar[g], z_bar[g], c))

        data.sort()
        for row in data:
            self.output.write("%s\n" % row)
        self.output.close()

        self.transit_message("") # Printing empty line to flush stdout 
        self.transit_message("Adding File: %s" % (self.output.name))
        self.add_file(filetype="Binomial")
        self.finish()
        self.transit_message("Finished Binomial Method")


 

    @classmethod
    def usage_string(self):
        return """python %s binomial <comma-separated .wig files> <annotation .prot_table or GFF3> <output file> [Optional Arguments]

        Optional Arguments:
            -s <int>        :=  Number of samples to take. Default: -s 10000
            -b <int>        :=  Number of burn-in samples to take. Default: -b 500
            -iN <float>     :=  Ignore TAs occuring at given fraction of the N terminus. Default: -iN 0.0
            -iC <float>     :=  Ignore TAs occuring at given fraction of the C terminus. Default: -iC 0.0

            Hyper-parameters:
            -pi0 <float>     :=  Hyper-parameters for rho, non-essential genes. Default: -pi0 0.5
            -pi1 <float>     :=  Hyper-parameters for rho, essential genes. Default: -pi1 0.5
            -M0 <float>     :=  Hyper-parameters for rho, non-essential genes. Default: -M0 1.0
            -M1 <float>     :=  Hyper-parameters for rho, essential genes. Default: -M1 1.0

            -a0 <float>     :=  Hyper-parameters for kappa, non-essential genes. Default: -a0 10
            -a1 <float>     :=  Hyper-parameters for kappa, essential genes. Default: -a1 10
            -b0 <float>     :=  Hyper-parameters for kappa, non-essential genes. Default: -b0 1.0
            -b1 <float>     :=  Hyper-parameters for kappa, essential genes. Default: -b1 1.0

            -aw <float>     :=  Hyper-parameters for prior prob of gene being essential. Default: -aw 0.5
            -bw <float>     :=  Hyper-parameters for prior prob of gene being essential. Default: -bw 0.5


            """ % (sys.argv[0])


if __name__ == "__main__":

    (args, kwargs) = transit_tools.cleanargs(sys.argv)

    G = BinomialMethod.fromargs(sys.argv[1:])

    G.console_message("Printing the member variables:")   
    G.print_members()

    print ""
    print "Running:"

    G.Run()


