def render_slp_component_element_values():
with open(FILE_NAME_COMPS, 'r') as f:
d = cPickle.load(f)
mn = d['mean']
cid = np.amax(np.abs(mn), axis=1)[:, np.newaxis]
gf = GeoField()
gf.load('data/pres.mon.mean.nc', 'pres')
gf.slice_spatial(None, [20, 89])
mnd = gf.reshape_flat_field(cid)
f = render_component_single(mnd[0, :, :], gf.lats, gf.lons, (0, 0.15),
None,
'NH Extratropical Components - max values')
f.savefig('figs/slp_nh_component_maxima_sameaxis.pdf')
f = render_component_single(mnd[0, :, :], gf.lats, gf.lons, None, None,
'NH Extratropical Components - max values')
f.savefig('figs/slp_nh_component_maxima.pdf')
f = plt.figure()
plt.hist(cid, bins=40)
plt.title('Histogram of max values @ grid points across components')
plt.xlabel('Maximum value [-]')
plt.ylabel('Frequency [-]')
f.savefig('figs/slp_nh_compmax_hist.pdf')
return f
def render_slp_component_element_values():
with open(FILE_NAME_COMPS, 'r') as f:
d = cPickle.load(f)
mn = d['mean']
cid = np.amax(np.abs(mn), axis = 1)[:, np.newaxis]
gf = GeoField()
gf.load('data/pres.mon.mean.nc', 'pres')
gf.slice_spatial(None, [20, 89])
mnd = gf.reshape_flat_field(cid)
f = render_component_single(mnd[0, :, :], gf.lats, gf.lons, (0, 0.15), None,
'NH Extratropical Components - max values')
f.savefig('figs/slp_nh_component_maxima_sameaxis.pdf')
f = render_component_single(mnd[0, :, :], gf.lats, gf.lons, None, None,
'NH Extratropical Components - max values')
f.savefig('figs/slp_nh_component_maxima.pdf')
f = plt.figure()
plt.hist(cid, bins = 40)
plt.title('Histogram of max values @ grid points across components')
plt.xlabel('Maximum value [-]')
plt.ylabel('Frequency [-]')
f.savefig('figs/slp_nh_compmax_hist.pdf')
return f
def plot_components_slp():
gf = GeoField()
gf.load('data/pres.mon.mean.nc', 'pres')
gf.slice_spatial(None, [20, 89])
with open('results/slp_nh_var_bootstrap_results_b1000_cosweights.bin', 'r') as f:
d = cPickle.load(f)
mn = d['mean']
mn_gf = gf.reshape_flat_field(mn)
t_start = datetime.now()
thr = 1.0 / mn.shape[0]**0.5
pool = Pool(4)
render_list_triples = [ ([mn_gf[i, ...],
mn_gf[i, ...] * (np.abs(mn_gf[i,...]) > thr)],
[ 'Mean', 'Mean:Thr'],
gf.lats, gf.lons, False,
# 'figs/nhemi_comp%02d_varimax_mn.png' % (i+1),
'figs/nhemi_comp%02d_varimax_mn.pdf' % (i+1),
'Component %d' % (i+1))
for i in range(mn.shape[1])]
# use less nodes to render the maps due to memory constraints
pool.map(render_set_stub, render_list_triples)
pool.close()
# clean up memory
del render_list_triples
print("DONE after [%s]." % (datetime.now() - t_start))
def render_slp_components():
with open('results/slp_nh_var_bootstrap_results_b1000.bin', 'r') as f:
d = cPickle.load(f)
mn = d['mean']
mn_mask = (np.abs(mn) > 1.0 / mn.shape[0]**0.5)
mn_thr = mn * mn_mask
print np.sum(np.sum(mn_mask, axis= 1) == 0)
cid = np.argmax(np.abs(mn) * mn_mask, axis = 1)[:, np.newaxis] + 1
gf = GeoField()
gf.load('data/pres.mon.mean.nc', 'pres')
gf.slice_spatial(None, [20, 89])
mnd = gf.reshape_flat_field(cid)
# plt.figure()
# plt.hist(cid, bins = 43)
# plt.show()
f = render_component_single(mnd[0, :, :], gf.lats, gf.lons, False, None,
'NH Extratropical Components',
cmap = plt.get_cmap('gist_ncar'))
plt.show()
def render_slp_component_element_values():
with open('results/slp_nh_var_bootstrap_results_b1000_cosweights.bin', 'r') as f:
d = cPickle.load(f)
mn = d['mean']
cid = np.amax(np.abs(mn), axis = 1)[:, np.newaxis]
gf = GeoField()
gf.load('data/pres.mon.mean.nc', 'pres')
gf.slice_spatial(None, [20, 89])
mnd = gf.reshape_flat_field(cid)
f = render_component_single(mnd[0, :, :], gf.lats, gf.lons, False, None,
'NH Extratropical Components - max values')
plt.show()
db = t2 - t1
# print progress
print(
"PROGRESS [%s]: %d/%d complete (sorting %g percent), predicted completion at %s."
% (str(t2), bsmp_done, LNO_COUNT,
int((da.seconds + da.microseconds / 10e6) /
(db.seconds + db.microseconds / 10e6) * 100), str(t2 + dt)))
var_comp /= (bsmp_done - 1)
print("DONE at %s after %s" %
(str(datetime.now()), str(datetime.now() - t_start)))
# reshape all the fields back into correct spatial dimensions to match lon/lat of the original geofield
max_comp = gf.reshape_flat_field(max_comp[BULK_STEP, :, :])
min_comp = gf.reshape_flat_field(min_comp[EXTREMA_MEMORY, :, :])
mean_comp = gf.reshape_flat_field(mean_comp)
var_comp = gf.reshape_flat_field(var_comp)
# Ud and Ur are reshaped into new variables as the old ones may be reused in a new computation
Ud_gf = gf.reshape_flat_field(Ud)
Ur_gf = gf.reshape_flat_field(Ur)
# save the results to file
with open('results/lno05_results.bin', 'w') as f:
cPickle.dump(
{
'Ud': Ud_gf,
'Ur': Ur_gf,
'max': max_comp,
if __name__ == '__main__':
# load geo-field
gf = GeoField()
gf.load('data/pres.mon.mean.nc', 'pres')
gf.transform_to_anomalies()
gf.normalize_monthly_variance()
gf.slice_spatial(None, [20, 89])
gf.slice_date_range(date(1950, 1, 1), date(2012, 3, 1))
# load the components
with open('results/slp_nh_var_bootstrap_results_b1000_cosweights.bin', 'r') as f:
d = cPickle.load(f)
# convert to unit vectors
mn = d['mean']
mn = mn / np.sum(mn**2, axis = 0) ** 0.5
# mark maxima
mx_pos = np.argmax(mn**2, axis = 0)
print mx_pos
mx_loc = np.zeros_like(mn)
mx_loc[(mx_pos, np.arange(mn.shape[1]))] = 1.0
mx_loc_gf = gf.reshape_flat_field(mx_loc)
sio.savemat("results/component_maxima_locations.mat", { 'mx_loc' : mx_loc_gf })
da = t2 - t1a
db = t2 - t1
# print progress
print("PROGRESS [%s]: %d/%d complete (sorting %g percent), predicted completion at %s." %
(str(t2), bsmp_done, LNO_COUNT,
int((da.seconds + da.microseconds/10e6) / (db.seconds + db.microseconds/10e6) * 100),
str(t2 + dt)))
var_comp /= (bsmp_done - 1)
print("DONE at %s after %s" % (str(datetime.now()), str(datetime.now() - t_start)))
# reshape all the fields back into correct spatial dimensions to match lon/lat of the original geofield
max_comp = gf.reshape_flat_field(max_comp[BULK_STEP, :, :])
min_comp = gf.reshape_flat_field(min_comp[EXTREMA_MEMORY, :, :])
mean_comp = gf.reshape_flat_field(mean_comp)
var_comp = gf.reshape_flat_field(var_comp)
# Ud and Ur are reshaped into new variables as the old ones may be reused in a new computation
Ud_gf = gf.reshape_flat_field(Ud)
Ur_gf = gf.reshape_flat_field(Ur)
# save the results to file
with open('results/lno05_results.bin', 'w') as f:
cPickle.dump({ 'Ud' : Ud_gf, 'Ur' : Ur_gf, 'max' : max_comp, 'min' : min_comp,
'mean' : mean_comp, 'var' : var_comp}, f)
t_start = datetime.now()
