def master():
pars1 = np.linspace(0.0, 0.03, nstep) # alpha_max
pars2 = np.linspace(0.0, 0.3, nstep) # thresh_geo
for index in xrange(nstep): # loop over alpha_max
par1 = pars1[index]
# Submit a job which will calculate partial sum
mpi.submit_call("loop", (par1, pars2), id=index)
for i in xrange(nstep):
print i
a = mpi.get_result(id=i)
if i == 0:
a_alpha_max_thresh_geo = a
else:
a_alpha_max_thresh_geo = np.vstack([a_alpha_max_thresh_geo, a])
print a_alpha_max_thresh_geo
np.save('/save/a_alpha_max_thresh_geo_c_max=0.2', a_alpha_max_thresh_geo)
def master():
regions = ['nism']
prefix = ''
prefix2 = '/p/tmp/boers/'
data = 'trmm7'
lat = np.load(prefix + '%s_lat_global.npy' % data)
lon = np.load(prefix + '%s_lon_global.npy' % data)
c = 0
for perc in [80, 85, 90, 94, 95, 96]:
print perc
for tm in [3, 10, 30]:
print "tm = ", tm
for region in regions:
print "region: ", region
if region == 'sam2':
j = 0
else:
j = 2
events = np.load(
prefix +
'%s_global_wd_nob_cor_seasonal_rain_perc%d_season%d.npy' %
(data, perc, j))
deg = np.load(
prefix +
'degree01_%s_global_wd_perc%d_tm%d_season%d_2_nb_sig005.npy'
% (data, perc, tm, j))
cu_deg = np.load(
prefix +
'cu_degree01_%s_global_wd_perc%d_tm%d_season%d_2_nb_sig005.npy'
% (data, perc, tm, j))
nlist = np.load(
prefix2 +
'nlist01_%s_global_wd_perc%d_tm%d_season%d_2_nb_sig005.npy'
% (data, perc, tm, j))
links = np.array([], dtype='int')
index = np.array([], dtype='int')
for lat_t in [27.5, 27.75, 28., 28.25, 28.5]:
for lon_t in [78.5, 78.75, 79., 79.25, 79.5]:
index = np.concatenate(
(index,
get_index_from_coord(lat, lon, lat_t, lon_t)))
lat_t = 28
lon_t = 79
index = np.unique(index)
links = np.load(
'%s_geo_links_samples_perc%d_tm%d_season%d_%s_nb_sig005.npy'
% (data, perc, tm, j, region))
print "nol = ", links.shape[0]
index = get_index_from_coord(lat, lon, lat_t, lon_t)
from itertools import product
coords = np.array(list(product(lat, lon)))
lat0 = coords[index, 0]
lon0 = coords[index, 1]
lat1 = coords[links, 0]
lon1 = coords[links, 1]
nol = links.shape[0]
values = np.vstack([coords[links, 0], coords[links, 1]]).T
values *= np.pi / 180.
X, Y = np.meshgrid(lon, lat)
xy = np.vstack([Y.ravel(), X.ravel()]).T
xy *= np.pi / 180.
bw_opt = .2 * values.shape[0]**(-1. / (2 + 4))
print ".2 * bw_scott = ", bw_opt
nos = 15
ser = 35
dats = np.zeros((nos * ser, X.shape[0], X.shape[1]))
for k in xrange(ser):
for i in xrange(nos):
noel2 = np.where(events > 2)[0]
values = np.vstack([
np.random.choice(coords[noel2, 0], nol),
np.random.choice(coords[noel2, 1], nol)
]).T
values *= np.pi / 180.
mpi.submit_call("shperical_kde", (values, xy, bw_opt),
id=c * nos * ser + k * nos + i)
print "bash %d submitted" % (k * nos + i)
for i in xrange(nos):
datss = mpi.get_result(id=c * nos * ser + k * nos + i)
dats[k * nos + i] = datss.reshape(X.shape)
print "bash %d picked up" % (k * nos + i)
np.save(
prefix2 +
'%s_geo_link_tc_sphkde_nm_perc%d_tm%d_season%d_%s_bw02_nb'
% (data, perc, tm, j, region), dats)
mean = np.mean(dats, axis=0)
std = np.std(dats, axis=0)
perc90 = st.scoreatpercentile(dats, 90, axis=0)
perc95 = st.scoreatpercentile(dats, 95, axis=0)
perc99 = st.scoreatpercentile(dats, 99, axis=0)
perc995 = st.scoreatpercentile(dats, 99.5, axis=0)
perc999 = st.scoreatpercentile(dats, 99.9, axis=0)
stats = np.array(
[mean, std, perc90, perc95, perc99, perc995, perc999])
np.save(
'%s_geo_link_tc_sphkde_stats_perc%d_tm%d_season%d_%s_bw02_nb.npy'
% (data, perc, tm, j, region), stats)
c += 1