Untitled

from ete2 import Tree
import numpy as np
import matplotlib.pyplot as plt

# SUB-FUNCTIONS:
def mean(array):
    return sum(array)/float(len(array))

def cache_distances(tree):
    ''' precalculate distances of all nodes to the root'''
    node2rootdist = {tree:0}
    for node in tree.iter_descendants('preorder'):
        node2rootdist[node] = node.dist + node2rootdist[node.up]
    return node2rootdist

def collapse(tree, min_dist):
    # cache the tip content of each node to reduce the number of times the tree is traversed
    node2tips       = tree.get_cached_content()
    root_distance   = cache_distances(tree)
    for node in tree.get_descendants('preorder'):
        if not node.is_leaf():
            avg_distance_to_tips = mean([root_distance[tip]-root_distance[node] for tip in node2tips[node]])
            if avg_distance_to_tips < min_dist:
                # do whatever, ete support node annotation, deletion, labeling, etc.
                # rename
                node.name += ' COLLAPSED avg_d:%g {%s}' %(avg_distance_to_tips,','.join([tip.name for tip in node2tips[node]]))
                # label
                node.add_features(collapsed=True)
                # set drawing attribute so they look collapsed when displayed with tree.show()
                node.img_style['draw_descendants'] = False
                # etc...

# MAIN FUNCTION
def exploreCutoffs( dataPath, filename_in, d_from = 0, d_to = 1, d_step = 0.05):
    """
    Description:
        Given a Newick format tree, collapses clades whose lengths to tips are on average < d, where d is here incremented from e.g. 0 to 1 in 0.05 increments

    Arguments:
        dataPath = '../../data/stamp_data/SWU_SSD/'  # directory of tree
        filename_in = 'out.tree'                        # Newick format tree
        d_from      = 0.00                              # distance cut-off start
        d_to        = 1.00                              # distance cut-off end
        d_step      = 0.05                              # increment of distance cut-off
    """

    # Statistics and data vars
    clusterTree_arr         = []
    clusterSize_dist_arr    = []
    clusterSize_mean_arr    = []
    clusterSize_median_arr  = []
    clusterSize_std_arr     = []
    clusterSize_max_arr     = []
    distances               = np.arange(float(d_from),float(d_to),float(d_step))# generate distance vector
    t                       = Tree(dataPath+filename_in)    # Load a tree structure from a newick file.

    # Collapse for each d
    print 'Trees are collapsing...'
    for count,d in enumerate(distances):     # explore distance threshold params
        # if count%10 == 0:
        #     print(count)
        print('Tree: '+str(count)+'  Distance: '+str(d))
        t = Tree(dataPath+filename_in)           # load tree
        collapse(t, d)                              # collapse /u threshold d
        for n in t.search_nodes(collapsed=True):    # re-annotate via collapses
            for ch in n.get_children():
                ch.detach()

    # node distances
        dist_dict = cache_distances(t)
    # unite nested list of collapsed motifs
        clusters_multiple   = [i.name.split('{')[1][:-1].split(',') for i in dist_dict.keys() if "COLLAPSED" in i.name] # list of clusters, where each cluster is a list of motifs)
        clusters_single     = [[i.name] for i in dist_dict.keys() if not 'NoName' in i.name]
        clusters_all        = clusters_single+clusters_multiple
    # ranked distribution of cluster sizes, largest-to-smallest
        clusterTree         = t
        clusterSize_dist    = np.array(sorted([float(len(i)) for i in clusters_all],reverse=True))
        clusterSize_mean    = np.mean(clusterSize_dist)
        clusterSize_median  = np.median(clusterSize_dist)
        clusterSize_std     = np.std(clusterSize_dist)
        clusterSize_max     = np.max(clusterSize_dist)
    # store
        clusterTree_arr.append(clusterTree)
        clusterSize_dist_arr.append(clusterSize_dist)
        clusterSize_mean_arr.append(clusterSize_mean)
        clusterSize_median_arr.append(clusterSize_median)
        clusterSize_std_arr.append(clusterSize_std)
        clusterSize_max_arr.append(clusterSize_max)

    # print stats
    #clusterStats = [clusterSize_dist_arr,clusterSize_mean_arr,clusterSize_std_arr]
    #print(clusterStats)

    # write collapsed newicks

    for j,i in enumerate(clusterTree_arr):
        file_out = open(dataPath+'/collapsed_d_'+str(distances[j])+'.tre','w')
        file_out.write(i.write())
        file_out.close()
        # print 'd: '+str(distances[j])+'  '+str(i.write())
        # print '   '

    # Vectors to plot
    x   = distances
    y   = clusterSize_mean_arr
    z   = clusterSize_median_arr
    a   = clusterSize_max_arr
    yerr= [0]*len(distances)
    xerr= clusterSize_std_arr

    # diagram
    plt.errorbar(x, y, xerr, yerr, capsize=0, ls='none', color='gray', elinewidth=1)
    plt.annotate('Std deviation bars', (0.55, 58), xytext=(-10, 60), va='bottom',textcoords='offset points',arrowprops=dict(facecolor='gray', shrink=0.05),color='Gray')

    # Text to annotate on plot
    ydescrip = [str(int(round(i))) for i in y] # means
    zdescrip = [str(int(round(i))) for i in z] # medians
    adescrip = [str(int(round(i))) for i in a] # max

    # means
    for xpos, ypos, yname in zip(x, y, ydescrip):
        plt.annotate(yname, (xpos, ypos), xytext=(0, 0), va='bottom',textcoords='offset points')
    plt.annotate('Means (black)', (0.75, 50), xytext=(-10, 60), va='bottom',textcoords='offset points',arrowprops=dict(facecolor='black', shrink=0.05))

    # medians (red)
    for xpos, zpos, zname in zip(x, z, zdescrip):
        plt.annotate(zname, (xpos, zpos), xytext=(0, 0), va='bottom',textcoords='offset points',color='Red')
    plt.annotate('Medians (red)', (0.75, 15), xytext=(-10, -60), va='bottom',textcoords='offset points',color='Red',arrowprops=dict(facecolor='Red', shrink=0.05))

    # max
    # for xpos, apos, aname in zip(x, a, adescrip):
    #     plt.annotate(aname, (xpos, apos), xytext=(0, 0), va='bottom',textcoords='offset points')
    # plt.annotate('Max (blue)', (0.75, 50), xytext=(-10, 60), va='bottom',textcoords='offset points',arrowprops=dict(facecolor='black', shrink=0.05))

    # plotting
    plt.xlabel('Collapse clades with average distance to leaves < X (e.g. 0.2)')
    plt.ylabel('Mean size of clusters after collapse')
    plt.show()

#-----------------------------------------------------------------

#################
#   Example     #
#################

# change to current directory
import os
os.chdir('./')

# generate a tree file (using my example in stackoverflow)
file_out_tree = open('out.tree','w')
file_out_tree.write('((((((DMEL_CGTATRCGY:0.007557,DMEL_CGCMTGCGC:0.007557):0.359666,((((((((AALB_CRCACGCAC:0.000001,(ADAR_CRCACGCAC:0.000001,(ADIR_CRCACGCAC:0.000001,AFUN_CRCACGCAC:0.000001):0.000000):0.000000):0.000002,((AFAR_CKCACGCAC:0.000001,ASTE_CKCACGCAC:0.000001):0.000001,(AGAM_CGYACGCAC:0.000001,AMEL_CGMACGCAC:0.000001):0.000001):0.000001):0.000004,((AARA_CGCRCGCAC:0.000001,AEPI_CGCRCGCAC:0.000001):0.000000,(AMER_CGCRCGCAC:0.000001,AQUA_CGCRCGCAC:0.000001):0.000000):0.000007):0.000011,ACUL_CDCACKCAC:0.000018):0.000672,AMIN_ACGCACGCA:0.000690):0.012285,(ACHR_CACGCWCGC:0.000410,ADAR_CGTGCGTGC:0.000410):0.012565):0.000661,(ADAR_CGCGCGCRC:0.000013,AMEL_GCGCGCGCK:0.000013):0.013623):0.174649,(((((AARA_CGTACG:0.000018,ASTE_CGTACG:0.000018):0.000078,ADIR_CGTACGH:0.000096):0.000386,((AFAR_CGWWCG:0.000019,AMER_CGWWCG:0.000019):0.000001,AQUA_CGWWCG:0.000019):0.000462):0.000772,(AARA_CTCGWACGA:0.000819,ACHR_CGTACSA:0.000819):0.000434):0.017848,((((AATR_SGAACGR:0.000047,AMER_SGAACGRA:0.000047):0.000126,(AGAM_SGWACGA:0.000019,AMEL_CGWACGA:0.000019):0.000153):0.000063,AEPI_CGTWCS:0.000236):0.006357,((AMEL_CGMACSG:0.000029,ASTE_CGRACSG:0.000029):0.000461,ASIN_CCSTTCG:0.000490):0.006102):0.012509):0.169184):0.178938):0.662820,(((((DMEL_AAAGTATGC:0.000003,AGAM_AAAGKATGC:0.000003):0.000435,(AEPI_GCATACATW:0.000006,AFAR_AAWGTATGC:0.000006):0.000433):0.001079,AARA_AAGKTATGC:0.001517):0.103829,(((ACHR_GTATGVAA:0.000008,(AFUN_GTATGSAA:0.000005,ASTE_GTATGCAA:0.000005):0.000004):0.000025,((ACUL_GTATGCAAW:0.000001,AMIN_GTATGCAAW:0.000001):0.000002,AMER_ATTGCATAC:0.000003):0.000031):0.018355,(ADIR_ADGTATGCA:0.000425,(((AFAR_ATGTATGHA:0.000002,(AMAC_ATGTATGYA:0.000001,AQUA_ATGTATGYA:0.000001):0.000001):0.000015,AFUN_ATRTATGTA:0.000017):0.000032,AMIN_TGTATGYA:0.000049):0.000376):0.017964):0.086957):0.525619,(AAEG_ACTCTATGC:0.174506,((AMAC_CACTATGGG:0.000002,ASTE_CACTAYGGG:0.000002):0.015808,AMAC_ACTATGCGG:0.015810):0.158696):0.456460):0.399078):0.590745,(((((DMEL_CAGTCWGTC:0.001236,ASTE_GRCTGACTA:0.001236):0.051820,DMEL_AACTAACTR:0.053056):0.763670,(((((DMEL_CTCGCTCK:0.005427,(((((((AAEG_CTCACTCWC:0.000001,AMER_CTCACTCHC:0.000001):0.000000,((AALB_CTCACTCWC:0.000001,AMAC_CTCACTCWC:0.000001):0.000000,(ACHR_CTCACTCWC:0.000001,ASTE_CTCACTCWC:0.000001):0.000000):0.000000):0.000001,AFAR_CTCACYCWC:0.000002):0.000004,(AEPI_CTCRCTCDC:0.000001,AFUN_CTCRCTCDC:0.000001):0.000005):0.000001,AATR_CTCRCTCAC:0.000007):0.000035,ACUL_CTCRCTCTC:0.000042):0.000496,ADAR_GMGAGHGA:0.000538):0.004888):0.031451,((((DMEL_CBCTCTCB:0.000332,((DMEL_CTCTCK:0.000070,AMER_CTCTCY:0.000070):0.000137,(AAEG_GCTCTCK:0.000070,(ADIR_AGAGAGM:0.000033,AMAC_KCTCTCK:0.000033):0.000036):0.000137):0.000125):0.002933,(((ACUL_CKCTCS:0.000034,(AEPI_CKCTCB:0.000019,AQUA_CKCTCB:0.000019):0.000015):0.000241,ADIR_CTCYCS:0.000275):0.001025,AMIN_ASCGAGMG:0.001300):0.001965):0.006633,(AARA_GAGAGRR:0.001081,(AFUN_CTCYCYC:0.000069,ASIN_CTCNCTC:0.000069):0.001012):0.008817):0.007219,(((((AALB_CKAGWG:0.000019,ADAR_CKAGWG:0.000019):0.000000,ASIN_CKAGWG:0.000019):0.000024,ASTE_CGAGWG:0.000043):0.000046,AFAR_CGRGWG:0.000089):0.000703,AATR_CGAGDGCGA:0.000791):0.016325):0.019761):0.030461,(((AARA_CACTCACTC:0.000003,AGAM_CWCTCACTC:0.000003):0.000010,(ADIR_CWCTCACWC:0.000001,AMIN_CWCTCACWC:0.000001):0.000011):0.000835,((ADAR_AGTGAGYGA:0.000001,AQUA_AGTGAGYGA:0.000001):0.000001,AMEL_AGTGAGTGW:0.000002):0.000845):0.066491):0.243983,(AAEG_GCTCTGTCC:0.029988,(AFUN_CTCTGTCTS:0.002360,AQUA_AGAAAGAG:0.002360):0.027629):0.281333):0.041049,(((((((AALB_ANCGAD:0.000030,(((AARA_ANCGAW:0.000019,(AMAC_ANCGAW:0.000019,AMIN_ANCGAW:0.000019):0.000000):0.000000,AFAR_ANCGAW:0.000019):0.000000,((ADIR_ANCGAW:0.000019,ASIN_ANCGAW:0.000019):0.000000,ASTE_ANCGAW:0.000019):0.000000):0.000011):0.000047,ADAR_ANCGRW:0.000077):0.000313,(ACHR_AGCGAW:0.000030,AFUN_AGCGAD:0.000030):0.000359):0.000227,ACHR_GARCGAR:0.000616):0.007189,(AATR_AGCGAAAS:0.001310,(ACUL_AVCGAAMC:0.000283,AGAM_AACGAAASG:0.000283):0.001027):0.006496):0.026708,((((AARA_AGMGAGA:0.000021,ASTE_AGMGAGM:0.000021):0.000194,((AEPI_AGMGADA:0.000007,AMER_AGMGADA:0.000007):0.000026,AQUA_AGCGADA:0.000034):0.000181):0.000917,((AATR_CWCTCGCTC:0.000009,AMAC_CTCTCVCTC:0.000009):0.000087,AMEL_GAGCGAKA:0.000096):0.001036):0.008630,ADIR_ASCGAGAAC:0.009762):0.024751):0.190816,((((AALB_AHAGAGAG:0.000019,ADAR_AMAGAGAGA:0.000019):0.002387,AARA_AWAGAAAGA:0.002406):0.017754,(AEPI_ACAGAAAR:0.000417,(AMER_AWAGAAAR:0.000014,ASTE_ARAGAAAA:0.000014):0.000403):0.019744):0.098822,(AGAM_CACTGAMAG:0.000262,AQUA_ACTGAMAGC:0.000262):0.118720):0.106347):0.127041):0.464355):0.170924,((((DMEL_ASTCG:0.010110,AMIN_ACCGAGTAG:0.010110):0.013588,AAEG_CCVCTCGGC:0.023698):0.196372,((AAEG_GCCACWCGA:0.001877,AATR_MCACTCGA:0.001877):0.047324,AFUN_CGTGTGGAC:0.049202):0.170869):0.470403,(((DMEL_CACTB:0.006689,(AAEG_CACTTCAM:0.000293,(((AARA_AGTGVAGTG:0.000001,AMER_AGTGMAGTG:0.000001):0.000003,(AMEL_AGTGAAGTG:0.000002,AQUA_AGTGRAGTG:0.000002):0.000002):0.000028,(((ADIR_CACTDCAC:0.000004,ASTE_CACTDCAC: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file_out_tree.close()

# collapsed at distances,d, from d_from to d_to, with d_step increments
exploreCutoffs( './', 'out.tree', d_from = 0, d_to = 1, d_step = 0.05)