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()
