#!/usr/bin/env python import sys, os, time, math from JJnetstat import stataggregate from JJnetstat import plothelper as nxplot from DurandDB import blastq if __name__ == "__main__": date = time.strftime('%Y%m%d') set_id = 91 br_id = 87 # 8306 edges? # br_id = 89 low density? 0.0008 13810 edges # br_id = 90 60983 edges #br_id = 113 60983 edges #(stype, run_id, set_id_filter, symmetric, 'descr') networks = [ ('nc_score', 731, set_id, True, 'Scer'), #('nc_score', 732, set_id, 'pre_post'), #('nc_score', 733, set_id, 'post_post'), ('nc_score', 734, set_id, True, 'ygob_four'), ('nc_score', 735, set_id, True, 'ygob_nine'), #('nc_score', 718, None, 'Scer_SP'), ('bit_score', br_id, set_id, True, 'Scer'), #('bit_score', 84, None, 'Scer_SP'), ] thresholds = { 'nc_score': (0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99 ), 'bit_score': (0, 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900, 1000) } # Calculate statistics for each network net_set = stataggregate.stat_set_debug( networks, thresholds) net_set.calculate_statistics( omit_spl=True, cacheq=True, verbose=False) g_plot = nxplot.nxplot( net_set.net_stat) #ftype = 'png' ftype = 'pdf' equiv_density_trans = g_plot.build_equiv_transform( 'graph_density', equiv_runs=[('bit_score', br_id, set_id, True), ('nc_score', 735, set_id, True)]) runs = net_set.net_stat.get_runs( stype_run_id=('bit_score', br_id, set_id, True)) runs += net_set.net_stat.get_runs( stype_run_id=('nc_score', 731, set_id, True)) runs += net_set.net_stat.get_runs( stype_run_id=('nc_score', 734, set_id, True)) runs += net_set.net_stat.get_runs( stype_run_id=('nc_score', 735, set_id, True)) # Draw plots for stat_name in ('ccomp_num', 'ccomp_num_single', 'ccomp_avg_size', 'ccomp_avg_density_w', 'graph_mean_cc', 'graph_density', # 'ccomp_avg_spl' ): #g_plot.plot_statistic( stat_name, ftype=ftype, fprefix='figures/%s_ygob2' % date, # base_stype='nc_score') #g_plot.plot_statistic( stat_name, ftype=ftype, fprefix='figures/%s_ygob2_log' % date, # base_stype='nc_score', # top_transform=lambda a: math.log(a)) if stat_name=='ccomp_num': legend_position='bottom' else: legend_position='bottomright' g_plot.plot_statistic( stat_name, ftype=ftype, fprefix='figures/%s_ygob2_eqd' % date, base_stype='nc_score', top_transform=equiv_density_trans, absolute=True, runs=runs, draw_title=False, ylab=False, ylog = stat_name == 'ccomp_avg_size', #legend_position=legend_position, legend_position='topright', draw_legend = stat_name == 'ccomp_avg_size') #g_plot.plot_equivalent_x(('graph_density', #'ccomp_avg_size', 'ccomp_num_single', # #'graph_mean_cc', 'ccomp_num', 'ccomp_avg_density_w' # ), # equiv_runs=[('bit_score', br_id, set_id, True), # ('nc_score', 735, set_id, True)], # fprefix='figures/%s_ygob2' % date, # xlimit=(29,1000), ylimit=(0,1)) # # Select Density vs component size scatter plots # fprefix='figures/%s_sizedens' % date # for (bit_score, xlimit) in ((35, (0,800)), # (35, (0,3000)), # (60, (0,200)), # (80, (0,150)), # (100, (0,100))): # run_bit = g_plot.net_stats.get_run( 'bit_score', 89, bit_score, 91) # g_plot.plot_cc_scatter( run_bit, fprefix=fprefix+'_%s' % str(xlimit), # xlimit=xlimit) # # find the nearest equivalent nc_score # equiv_nc = equiv_density_trans(bit_score) # # if equiv_nc is None: # # print "no equivalent score for bit_score", bit_score # # continue # print equiv_nc # run_best = None # runs = g_plot.net_stats.get_runs( stype_run_id=('nc_score', 735)) # for run in runs: # if run.set_id_filter != 91: continue # if run_best is None or abs(run.min_score - equiv_nc) < abs(run_best.min_score - equiv_nc): # run_best = run # g_plot.plot_cc_scatter( run_best, fprefix=fprefix+'_%s' % str(xlimit), # xlimit=xlimit) #for nc_id in net_stats: # fprefix = # # nxplot.plot_graph_statistics(net_stats[nc_id], plot_stats, # fprefix=fprefix, ftype='png') # # nxplot.plot_graph_cc_histogram( net_stats[nc_id], 'ccomp_size', # 'nc_score', 0.3, # xlimit=(0,600), ylimit=(0,10), # fprefix=fprefix, ftype=ftype)# # # nxplot.plot_graph_cc_histogram( net_stats[nc_id], 'ccomp_density', # 'nc_score', 0.3, # xlimit=(0,600), ylimit=(0,10), # fprefix=fprefix, ftype=ftype) # # nxplot.plot_graph_cc_histograms(net_stats[nc_id], 'ccomp_size', # bitthresh=225, ncthresh=0.975, # xlimit=(0,600), ylimit=(0,10), # fprefix=fprefix, ftype=ftype) #nxplot.plot_graph_cc_histograms(net_stats, 'ccomp_density', # bitthresh=225, ncthresh=0.975, # fprefix=fprefix, ftype=ftype) #for (bitthresh,ncthresh) in ((29,0.05),(35,0.25),(40,0.4),(45,0.55),(50,0.65), # (55,0.7),(60,0.75),(65,0.8),(70,0.85),(80,0.85), # (90,0.9),(100,0.925),(125,0.95),(150,0.95), # (175,0.975),(200,0.975),(225,0.975),(250,0.975), # (300,0.999),(350,0.999),(400,0.999),(500,0.999), # (600,0.999),(700,0.999),(800,0.999),(900,0.999), # (1000,0.999)): # nxplot.plot_graph_k_histograms(net_stats, 'degree_hist', bitthresh=bitthresh, # ncthresh=ncthresh, fprefix=fprefix,ftype='png') # nxplot.plot_graph_k_histograms(net_stats, 'cc_hist', bitthresh=bitthresh, # ncthresh=ncthresh, fprefix=fprefix,ftype='png')