view MotifFinderPlot.py @ 4:6e657d323d6c draft default tip

planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/master/tools/GraphClust/Plotting commit 4406735e44aba20859c252be39f4e99df28c7a92

#!/usr/bin/env python

import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
from collections import defaultdict
import glob
import pandas as pd
import itertools
import seaborn as sns
import numpy as np

def plot_bar(ranges, colors, orig_names, cluster_nums):
    fig, ax = plt.subplots()
    for i, k in enumerate(sorted(ranges.keys())):
        ax.broken_barh(ranges[k], (i-0.25, 0.5), facecolors=colors[k])

    ax.set_xlim(0)
    ax.set_xlabel('position in sequence')
    ax.set_yticks(np.arange(-1, len(ranges)))
    ax.set_yticklabels(['']+[k+'-'+orig_names[k] for k in sorted(ranges.keys())])
    ax.grid(True)
    fig.suptitle('Structure motif prediction\nRegions with same color are prediticted to have similar structures')
    # Add the legend
    patches = [mpatches.Patch(color=cluster_nums[lab], label=lab) for lab in sorted(cluster_nums)]
    ax.legend(handles=patches, loc='best', bbox_to_anchor=(1.2, 1.05))#, loc='center left')
    plt.savefig("motif_plot.png", bbox_inches='tight')


def parse_clusters():
    currentdir_files = sorted(list(glob.glob('*')))
    print ("currentdir_files are: ", currentdir_files)
    print ("RESULTS_files are: ", sorted(list(glob.glob('RESULTS/*'))))

    cluster_files = sorted(list(glob.glob('RESULTS/*.cluster.all')))
    if len(cluster_files) == 0:
        raise RuntimeError('Expected cluster.all search path is empty:{}'.format(cluster_files))
    palette = itertools.cycle(sns.color_palette("Set2", len(cluster_files)))

    ranges = defaultdict(list)
    colors = defaultdict(list)
    orig_names = defaultdict(list)
    cluster_nums = defaultdict(list)
    for cluster_file in cluster_files:
        cluster_color = next(palette)
        df_cluster = pd.read_csv(cluster_file, sep='\s+', header=None)
        for irow, row in df_cluster.iterrows():
            seq, start, end, strand = row[0].split("#")
            ranges[seq].append((int(start), int(end)-int(start)+1))
            colors[seq].append(cluster_color)
            assert row[1] == 'RESULT'
            cluster_nums['cluster-{}'.format(row[2])] = cluster_color
            assert row[9] == 'ORIGHEAD'
            orig_names[seq] = row[10]
    return ranges, colors, orig_names, cluster_nums


my_ranges, my_colors, my_orig_names, my_cluster_nums = parse_clusters()
plot_bar(my_ranges, my_colors, my_orig_names, my_cluster_nums)