def main(args): copybook = load.csv_(args.copybook.readlines(), strip_=True)[1:] field_lengths = [ int(i[2]) for i in copybook ] struct_fmt = 's'.join([ str(i) for i in field_lengths ]) + 's' if args.struct: print struct_fmt else: for record in parse_data(struct_fmt, load.lines(args.datafile)): print record
def main(args): copybook = load.csv_(args.copybook.readlines(), strip_=True)[1:] field_lengths = [int(i[2]) for i in copybook] struct_fmt = 's'.join([str(i) for i in field_lengths]) + 's' if args.struct: print struct_fmt else: for record in parse_data(struct_fmt, load.lines(args.datafile)): print record
def main(args): fields = load.csv_(args.copybook, strip="right", prune=True) stop = None if args.recnum: stop = Splice().get_values(args.recnum)[1] if stop < 0: stop = None records = load.lines(args.datafile, stop_at_line=stop) Data(fields, records, args).parse()
def main(args): fields = load.csv_(args.copybook, strip_="right", prune=True) datetime_output_fmt = FormatDateTimeOutput( date_fmt = '%Y-%m-%d', time_fmt = '%H:%M:%S.%f') data = Data(fields, args, datetime_output_fmt) record = True record_num = 1 while record: line = args.datafile.readline() if args.debug and line: sys.stdout.write('%s\n' % DBL_HORIZ_LINE) sys.stdout.write('RECORD NUMBER: %d\n' % record_num) sys.stdout.write('%s%s%s' % (HORIZ_LINE, line, HORIZ_LINE)) record = data.parse_record(record_num, line, args.debug) if record: print record record_num += 1
out = open('results_real.csv', 'w') out.write( 'conn,rec,graph_type,parameters,susc_dist,eig,vvv,eee,VU,diam,clust,rob,rob_max,I,II,III,IV,V,resistance,death,transmission_rate\n' ) out.close() modes = [ 'equi' ] # select the distribution from which the individual node susceptibility is drawn; alternatives choices are: 'norm','lognorm','lognorm_rev','deg','deg_rev','unif' RES = [] fff = listdir('./vole_networks') while len(RES) < 10000: ff = sample(fff, 1)[0] g = csv_('./vole_networks/' + ff) f_rec = [i[::-1] for i in g] g += f_rec g = [map(int, i) for i in g] b = Graph.TupleList(g, directed=True) vvv, eee, diam, clust = b.vcount(), b.ecount(), b.diameter( ), b.transitivity_undirected() eig = GH(b) rob, rob_max = R_pr(g) rec = 1 conn = eee / (comb(vvv, 2) * 2.0) l = make_list(g) susc_mode = sample(modes, 1)[0] susc_ = make_susc(l, susc_mode) resistance = randrange(99) / 100.0 death = randrange(1, 101) / 100.0
nnn = g.neighbors(i, mode='ALL') for k in nnn: if g.vs[k]['S'] == 'H' and random() < pi: g.vs[k]['S'] = 'I' tot_infs |= set([k]) if random() < pr: g.vs[i]['S'] = 'R' infs = [i.index for i in g.vs.select(S='I')] return len(set(tot_infs)) / N fff = listdir('./vole_networks') y = [] for ff in fff: a = csv_('./vole_networks/' + ff, ',') g = Graph.TupleList(a, directed=False) g.simplify() vvv = len(g.vs()) a = [list(i.tuple) for i in g.es()] rob = ROB(a, mode='bet') y.append(rob) y = array(y).argsort().argsort() out = open('RANK_sir.csv', 'w') out.close() for p1 in arange(0.1, 1.1, 0.1): for p2 in arange(0.1, 1.1, 0.1): x = []
xx_m, yy_m = m(xx, yy) # Convert latlong coordinates to basemap proj im = m.pcolormesh(xx_m, yy_m, data.T, cmap=color_map) plt.title(sp[:-4].split('_')[0]) plt.savefig("./primate_range_reduction_figures/" + sp[:-3] + "png", dpi=300) clear = [plt.clf() for i in range(100000)] ########### from load import csv_ from numpy import mean fff = [] for scen in [ 'vulnerability', 'suitability', 'accessibility', 'carbon', 'random' ]: fff.append( csv_('./results/' + scen + '_primate_range_loss_food_biofuel_half_africa.csv')) spp = sorted(list(set([i[0] for i in fff[0] if float(i[2]) > 0]))) res = [] for sp in spp: ROW = [] for ff in range(5): row = [] for j in fff[ff]: if sp == j[0]: status = j[1] row.append(1 - float(j[3]) / float(j[2])) ROW.append(mean(row)) res.append([sp, status] + ROW) print res[-1]
from load import csv_ sim = csv_('results_sim.csv') real = csv_('results_real.csv') #conn,rec,ty,pars,susc_mode,eig,vvv,eee,VU,diam,clust,rob,rob_max,I,II,III,IV,V,resistance,death,tr pca_file = open('for_pca.csv','w') pca_file.write('conn,eig,vvv,eee,diam,clust,rob,type\n') sc=0 for i in sim[1:]: row = [str(sc),i[0]]+i[5:8]+i[9:12]+['simulated'] mds_file.write(','.join(row)+'\n') sc+=1 done = [] for i in real[1:]: if [i[6],i[7],i[9],i[10]] not in done: done.append([i[6],i[7],i[9],i[10]]) row = [str(sc),i[0]]+i[5:8]+i[9:12]+['real'] pca_file.write(','.join(row)+'\n') sc+=1 pca_file.close()