import numpy

from Pycluster.cluster import *

def _savetree(jobname, tree, order, transpose):
    # Save the hierarchical clustering solution given by the tree, following
    # the specified order, in a file whose name is based on jobname.
    if transpose == 0:
        extension = ".gtr"
        keyword = "GENE"
    else:
        extension = ".atr"
        keyword = "ARRY"
    index = tree.sort(order)
    nnodes = len(tree)
    with open(jobname + extension, "w") as outputfile:
        nodeID = [''] * nnodes
        nodedist = numpy.array([node.distance for node in tree])
        for nodeindex in range(nnodes):
            min1 = tree[nodeindex].left
            min2 = tree[nodeindex].right
            nodeID[nodeindex] = "NODE%dX" % (nodeindex + 1)
            outputfile.write(nodeID[nodeindex])
            outputfile.write("\t")
            if min1 < 0:
                index1 = -min1 - 1
                outputfile.write(nodeID[index1] + "\t")
                nodedist[nodeindex] = max(nodedist[nodeindex], nodedist[index1])
            else:
                outputfile.write("%s%dX\t" % (keyword, min1))
            if min2 < 0:
                index2 = -min2 - 1
                outputfile.write(nodeID[index2] + "\t")
                nodedist[nodeindex] = max(nodedist[nodeindex], nodedist[index2])
            else:
                outputfile.write("%s%dX\t" % (keyword, min2))
            outputfile.write(str(1.0 - nodedist[nodeindex]))
            outputfile.write("\n")
    return index


class Record(object):
    """Store gene expression data.

    A Record stores the gene expression data and related information contained
    in a data file following the file format defined for Michael Eisen's
    Cluster/TreeView program.

    Attributes:
     - data:     a matrix containing the gene expression data
     - mask:     a matrix containing only 1's and 0's, denoting which values
       are present (1) or missing (0). If all elements of mask are
       one (no missing data), then mask is set to None.
     - geneid:   a list containing a unique identifier for each gene
       (e.g., ORF name)
     - genename: a list containing an additional description for each gene
       (e.g., gene name)
     - gweight:  the weight to be used for each gene when calculating the
       distance
     - gorder:   an array of real numbers indicating the preferred order of the
       genes in the output file
     - expid:    a list containing a unique identifier for each experimental
       condition
     - eweight:  the weight to be used for each experimental condition when
       calculating the distance
     - eorder:   an array of real numbers indication the preferred order in the
       output file of the experimental conditions
     - uniqid:   the string that was used instead of UNIQID in the input file.

    """

    def __init__(self, handle=None):
        """Read gene expression data from the file handle and return a Record.

        The file should be in the format defined for Michael Eisen's
        Cluster/TreeView program.
        """
        self.data = None
        self.mask = None
        self.geneid = None
        self.genename = None
        self.gweight = None
        self.gorder = None
        self.expid = None
        self.eweight = None
        self.eorder = None
        self.uniqid = None
        if not handle:
            return
        line = handle.readline().strip("\r\n").split("\t")
        n = len(line)
        self.uniqid = line[0]
        self.expid = []
        cols = {0: "GENEID"}
        for word in line[1:]:
            if word == "NAME":
                cols[line.index(word)] = word
                self.genename = []
            elif word == "GWEIGHT":
                cols[line.index(word)] = word
                self.gweight = []
            elif word == "GORDER":
                cols[line.index(word)] = word
                self.gorder = []
            else:
                self.expid.append(word)
        self.geneid = []
        self.data = []
        self.mask = []
        needmask = 0
        for line in handle:
            line = line.strip("\r\n").split("\t")
            if len(line) != n:
                raise ValueError("Line with %d columns found (expected %d)" %
                                 (len(line), n))
            if line[0] == "EWEIGHT":
                i = max(cols) + 1
                self.eweight = numpy.array(line[i:], float)
                continue
            if line[0] == "EORDER":
                i = max(cols) + 1
                self.eorder = numpy.array(line[i:], float)
                continue
            rowdata = []
            rowmask = []
            n = len(line)
            for i in range(n):
                word = line[i]
                if i in cols:
                    if cols[i] == "GENEID":
                        self.geneid.append(word)
                    if cols[i] == "NAME":
                        self.genename.append(word)
                    if cols[i] == "GWEIGHT":
                        self.gweight.append(float(word))
                    if cols[i] == "GORDER":
                        self.gorder.append(float(word))
                    continue
                if not word:
                    rowdata.append(0.0)
                    rowmask.append(0)
                    needmask = 1
                else:
                    rowdata.append(float(word))
                    rowmask.append(1)
            self.data.append(rowdata)
            self.mask.append(rowmask)
        self.data = numpy.array(self.data)
        if needmask:
            self.mask = numpy.array(self.mask, int)
        else:
            self.mask = None
        if self.gweight:
            self.gweight = numpy.array(self.gweight)
        if self.gorder:
            self.gorder = numpy.array(self.gorder)

    def treecluster(self, transpose=0, method='m', dist='e'):
        """Apply hierarchical clustering and return a Tree object.

        The pairwise single, complete, centroid, and average linkage
        hierarchical clustering methods are available.

        Arguments:
         - transpose: if equal to 0, genes (rows) are clustered;
           if equal to 1, microarrays (columns) are clustered.
         - dist     : specifies the distance function to be used:

           - dist=='e': Euclidean distance
           - dist=='b': City Block distance
           - dist=='c': Pearson correlation
           - dist=='a': absolute value of the correlation
           - dist=='u': uncentered correlation
           - dist=='x': absolute uncentered correlation
           - dist=='s': Spearman's rank correlation
           - dist=='k': Kendall's tau

         - method   : specifies which linkage method is used:

           - method=='s': Single pairwise linkage
           - method=='m': Complete (maximum) pairwise linkage (default)
           - method=='c': Centroid linkage
           - method=='a': Average pairwise linkage

        See the description of the Tree class for more information about
        the Tree object returned by this method.
        """
        if transpose == 0:
            weight = self.eweight
        else:
            weight = self.gweight
        return treecluster(self.data, self.mask, weight, transpose, method,
                           dist)

    def kcluster(self, nclusters=2, transpose=0, npass=1, method='a', dist='e',
                 initialid=None):
        """Apply k-means or k-median clustering.

        This method returns a tuple (clusterid, error, nfound).

        Arguments:
         - nclusters: number of clusters (the 'k' in k-means)
         - transpose: if equal to 0, genes (rows) are clustered;
           if equal to 1, microarrays (columns) are clustered.
         - npass    : number of times the k-means clustering algorithm is
           performed, each time with a different (random) initial condition.
         - method   : specifies how the center of a cluster is found:

           - method=='a': arithmetic mean
           - method=='m': median

         - dist     : specifies the distance function to be used:

             - dist=='e': Euclidean distance
             - dist=='b': City Block distance
             - dist=='c': Pearson correlation
             - dist=='a': absolute value of the correlation
             - dist=='u': uncentered correlation
             - dist=='x': absolute uncentered correlation
             - dist=='s': Spearman's rank correlation
             - dist=='k': Kendall's tau

         - initialid: the initial clustering from which the algorithm should
           start. If initialid is None, the routine carries out npass
           repetitions of the EM algorithm, each time starting from a different
           random initial clustering. If initialid is given, the routine
           carries out the EM algorithm only once, starting from the given
           initial clustering and without randomizing the order in which items
           are assigned to clusters (i.e., using the same order as in the data
           matrix). In that case, the k-means algorithm is fully deterministic.

        Return values:
         - clusterid: array containing the number of the cluster to which each
           gene/microarray was assigned in the best k-means clustering
           solution that was found in the npass runs;
         - error:     the within-cluster sum of distances for the returned
           k-means clustering solution;
         - nfound:    the number of times this solution was found.

        """
        if transpose == 0:
            weight = self.eweight
        else:
            weight = self.gweight
        return kcluster(self.data, nclusters, self.mask, weight, transpose,
                        npass, method, dist, initialid)

    def somcluster(self, transpose=0, nxgrid=2, nygrid=1, inittau=0.02,
                   niter=1, dist='e'):
        """Calculate a self-organizing map on a rectangular grid.

        The somcluster method returns a tuple (clusterid, celldata).

        Arguments:
         - transpose: if equal to 0, genes (rows) are clustered;
           if equal to 1, microarrays (columns) are clustered.
         - nxgrid   : the horizontal dimension of the rectangular SOM map
         - nygrid   : the vertical dimension of the rectangular SOM map
         - inittau  : the initial value of tau (the neighborbood function)
         - niter    : the number of iterations
         - dist     : specifies the distance function to be used:

           - dist=='e': Euclidean distance
           - dist=='b': City Block distance
           - dist=='c': Pearson correlation
           - dist=='a': absolute value of the correlation
           - dist=='u': uncentered correlation
           - dist=='x': absolute uncentered correlation
           - dist=='s': Spearman's rank correlation
           - dist=='k': Kendall's tau

        Return values:
         - clusterid: array with two columns, while the number of rows is equal
           to the number of genes or the number of microarrays depending on
           whether genes or microarrays are being clustered. Each row in
           the array contains the x and y coordinates of the cell in the
           rectangular SOM grid to which the gene or microarray was assigned.
         - celldata:  an array with dimensions (nxgrid, nygrid, number of
           microarrays) if genes are being clustered, or (nxgrid, nygrid,
           number of genes) if microarrays are being clustered. Each element
           [ix][iy] of this array is a 1D vector containing the gene
           expression data for the centroid of the cluster in the SOM grid
           cell with coordinates (ix, iy).

        """
        if transpose == 0:
            weight = self.eweight
        else:
            weight = self.gweight
        return somcluster(self.data, self.mask, weight, transpose,
                          nxgrid, nygrid, inittau, niter, dist)

    def clustercentroids(self, clusterid=None, method='a', transpose=0):
        """Calculate the cluster centroids and return a tuple (cdata, cmask).

        The centroid is defined as either the mean or the median over all
        elements for each dimension.

        Arguments:
         - data     : nrows x ncolumns array containing the expression data
         - mask     : nrows x ncolumns array of integers, showing which data
           are missing. If mask[i][j]==0, then data[i][j] is missing.
         - transpose: if equal to 0, gene (row) clusters are considered;
           if equal to 1, microarray (column) clusters are considered.
         - clusterid: array containing the cluster number for each gene or
           microarray. The cluster number should be non-negative.
         - method   : specifies how the centroid is calculated:

           - method=='a': arithmetic mean over each dimension. (default)
           - method=='m': median over each dimension.

        Return values:
         - cdata    : 2D array containing the cluster centroids. If transpose==0,
           then the dimensions of cdata are nclusters x ncolumns. If
           transpose==1, then the dimensions of cdata are nrows x nclusters.
         - cmask    : 2D array of integers describing which elements in cdata,
           if any, are missing.

        """
        return clustercentroids(self.data, self.mask, clusterid, method,
                                transpose)

    def clusterdistance(self, index1=0, index2=0, method='a', dist='e',
                        transpose=0):
        """Calculate the distance between two clusters.

        Arguments:
         - index1   : 1D array identifying which genes/microarrays belong to the
           first cluster. If the cluster contains only one gene, then
           index1 can also be written as a single integer.
         - index2   : 1D array identifying which genes/microarrays belong to the
           second cluster. If the cluster contains only one gene, then
           index2 can also be written as a single integer.
         - transpose: if equal to 0, genes (rows) are clustered;
           if equal to 1, microarrays (columns) are clustered.
         - dist     : specifies the distance function to be used:

           - dist=='e': Euclidean distance
           - dist=='b': City Block distance
           - dist=='c': Pearson correlation
           - dist=='a': absolute value of the correlation
           - dist=='u': uncentered correlation
           - dist=='x': absolute uncentered correlation
           - dist=='s': Spearman's rank correlation
           - dist=='k': Kendall's tau

         - method   : specifies how the distance between two clusters is defined:

           - method=='a': the distance between the arithmetic means of the
             two clusters
           - method=='m': the distance between the medians of the two clusters
           - method=='s': the smallest pairwise distance between members of
             the two clusters
           - method=='x': the largest pairwise distance between members of
             the two clusters
           - method=='v': average of the pairwise distances between members
             of the clusters

         - transpose: if equal to 0: clusters of genes (rows) are considered;
           if equal to 1: clusters of microarrays (columns) are considered.

        """
        if transpose == 0:
            weight = self.eweight
        else:
            weight = self.gweight
        return clusterdistance(self.data, self.mask, weight,
                               index1, index2, method, dist, transpose)

    def distancematrix(self, transpose=0, dist='e'):
        """Calculate the distance matrix and return it as a list of arrays.

        Arguments:
         - transpose: if equal to 0: calculate the distances between genes
           (rows); if equal to 1: calculate the distances beteeen microarrays
           (columns).
         - dist     : specifies the distance function to be used:

           - dist=='e': Euclidean distance
           - dist=='b': City Block distance
           - dist=='c': Pearson correlation
           - dist=='a': absolute value of the correlation
           - dist=='u': uncentered correlation
           - dist=='x': absolute uncentered correlation
           - dist=='s': Spearman's rank correlation
           - dist=='k': Kendall's tau

        Return value:

        The distance matrix is returned as a list of 1D arrays containing the
        distance matrix between the gene expression data. The number of columns
        in each row is equal to the row number. Hence, the first row has zero
        elements. An example of the return value is:

            matrix = [[],
                      array([1.]),
                      array([7., 3.]),
                      array([4., 2., 6.])]

        This corresponds to the distance matrix:

            [0., 1., 7., 4.]
            [1., 0., 3., 2.]
            [7., 3., 0., 6.]
            [4., 2., 6., 0.]

        """
        if transpose == 0:
            weight = self.eweight
        else:
            weight = self.gweight
        return distancematrix(self.data, self.mask, weight, transpose, dist)

    def save(self, jobname, geneclusters=None, expclusters=None):
        """Save the clustering results.

        The saved files follow the convention for the Java TreeView program,
        which can therefore be used to view the clustering result.

        Arguments:
         - jobname:   The base name of the files to be saved. The filenames
           are jobname.cdt, jobname.gtr, and jobname.atr for hierarchical
           clustering, and jobname-K*.cdt, jobname-K*.kgg, jobname-K*.kag
           for k-means clustering results.
         - geneclusters=None:  For hierarchical clustering results,
           geneclusters is a Tree object as returned by the treecluster
           method. For k-means clustering results, geneclusters is a vector
           containing ngenes integers, describing to which cluster a given
           gene belongs. This vector can be calculated by kcluster.
         - expclusters=None:  For hierarchical clustering results, expclusters
           is a Tree object as returned by the treecluster method. For k-means
           clustering results, expclusters is a vector containing nexps
           integers, describing to which cluster a given experimental
           condition belongs. This vector can be calculated by kcluster.

        """
        (ngenes, nexps) = numpy.shape(self.data)
        if self.gorder is None:
            gorder = numpy.arange(ngenes)
        else:
            gorder = self.gorder
        if self.eorder is None:
            eorder = numpy.arange(nexps)
        else:
            eorder = self.eorder
        if geneclusters is not None and expclusters is not None and \
           type(geneclusters) != type(expclusters):
            raise ValueError("found one k-means and one hierarchical "
                             "clustering solution in geneclusters and "
                             "expclusters")
        gid = 0
        aid = 0
        filename = jobname
        postfix = ""
        if isinstance(geneclusters, Tree):
            # This is a hierarchical clustering result.
            geneindex = _savetree(jobname, geneclusters, gorder, 0)
            gid = 1
        elif geneclusters is not None:
            # This is a k-means clustering result.
            filename = jobname + "_K"
            k = max(geneclusters) + 1
            kggfilename = "%s_K_G%d.kgg" % (jobname, k)
            geneindex = self._savekmeans(kggfilename, geneclusters, gorder, 0)
            postfix = "_G%d" % k
        else:
            geneindex = numpy.argsort(gorder)
        if isinstance(expclusters, Tree):
            # This is a hierarchical clustering result.
            expindex = _savetree(jobname, expclusters, eorder, 1)
            aid = 1
        elif expclusters is not None:
            # This is a k-means clustering result.
            filename = jobname + "_K"
            k = max(expclusters) + 1
            kagfilename = "%s_K_A%d.kag" % (jobname, k)
            expindex = self._savekmeans(kagfilename, expclusters, eorder, 1)
            postfix += "_A%d" % k
        else:
            expindex = numpy.argsort(eorder)
        filename = filename + postfix
        self._savedata(filename, gid, aid, geneindex, expindex)

    def _savekmeans(self, filename, clusterids, order, transpose):
        # Save a k-means clustering solution
        if transpose == 0:
            label = self.uniqid
            names = self.geneid
        else:
            label = "ARRAY"
            names = self.expid
        with open(filename, "w") as outputfile:
            outputfile.write(label + "\tGROUP\n")
            index = numpy.argsort(order)
            n = len(names)
            sortedindex = numpy.zeros(n, int)
            counter = 0
            cluster = 0
            while counter < n:
                for j in index:
                    if clusterids[j] == cluster:
                        outputfile.write("%s\t%s\n" % (names[j], cluster))
                        sortedindex[counter] = j
                        counter += 1
                cluster += 1
        return sortedindex

    def _savedata(self, jobname, gid, aid, geneindex, expindex):
        # Save the clustered data.
        if self.genename is None:
            genename = self.geneid
        else:
            genename = self.genename
        (ngenes, nexps) = numpy.shape(self.data)
        with open(jobname + '.cdt', 'w') as outputfile:
            if self.mask is not None:
                mask = self.mask
            else:
                mask = numpy.ones((ngenes, nexps), int)
            if self.gweight is not None:
                gweight = self.gweight
            else:
                gweight = numpy.ones(ngenes)
            if self.eweight is not None:
                eweight = self.eweight
            else:
                eweight = numpy.ones(nexps)
            if gid:
                outputfile.write('GID\t')
            outputfile.write(self.uniqid)
            outputfile.write('\tNAME\tGWEIGHT')
            # Now add headers for data columns.
            for j in expindex:
                outputfile.write('\t%s' % self.expid[j])
            outputfile.write('\n')
            if aid:
                outputfile.write("AID")
                if gid:
                    outputfile.write('\t')
                outputfile.write("\t\t")
                for j in expindex:
                    outputfile.write('\tARRY%dX' % j)
                outputfile.write('\n')
            outputfile.write('EWEIGHT')
            if gid:
                outputfile.write('\t')
            outputfile.write('\t\t')
            for j in expindex:
                outputfile.write('\t%f' % eweight[j])
            outputfile.write('\n')
            for i in geneindex:
                if gid:
                    outputfile.write('GENE%dX\t' % i)
                outputfile.write("%s\t%s\t%f" %
                                 (self.geneid[i], genename[i], gweight[i]))
                for j in expindex:
                    outputfile.write('\t')
                    if mask[i, j]:
                        outputfile.write(str(self.data[i, j]))
                outputfile.write('\n')


def read(handle):
    """Read gene expression data from the file handle and return a Record.

    The file should be in the file format defined for Michael Eisen's
    Cluster/TreeView program.
    """
    return Record(handle)
