def extend_fine(idir, ifile, extend_aggregation, aggregation, ofile):
r'''
'''
dic = calculate_change_factor(idir, ifile)
fmin = scipy.optimize.fmin_l_bfgs_b
fmin = scipy.optimize.fmin
# Changed factors original data
dsf = dic['dsf']
mean = dic['mean']
var = dic['var']
skew = dic['skew']
correl_ori = []
skew_ori = []
# Find the original
# -----------------
dic = wg_io.load(ifile)
ts = 1
lag = 1
accumulate = True
group_type = 'm'
dic_stat = wg_tool.series_statistics(dic, ts, extend_aggregation, lag=lag,
group_type=group_type, accumulate=accumulate)
# [Ey, VARy, CVy, SKy, Rly, DSFy]
dicdic = dic_stat['period']
for g in range(12):
skew_index = 3
skew_ori.append(dicdic[skew_index][g])
correl_index = 4
correl_ori.append(dicdic[correl_index][g])
# Find indexes to use extension values for bellow 24 hours and calculates
# values for over 24 hours
n_group = len(dsf)
pre_index = []
post_index = []
for i in range(len(extend_aggregation)):
if not(extend_aggregation[i] in aggregation):
pre_index.append(i)
for i in range(len(aggregation)):
if (aggregation[i] in extend_aggregation):
post_index.append(i)
h = numpy.array(aggregation)
h_ext = numpy.array(extend_aggregation)
# Extend mean: E(h) = A*h. Is linear and cuts in 0. Either one value of future
# is used, or a regression is made...
# ----------------------------------------------------------------------
mean_new = []
for g in range(n_group):
mean_h = mean[g]
Xo = [1] # A
Xf = fmin(mean_min, Xo, args=(h, mean_h), disp=0)
A = Xf[0]
mean_adj = A * h_ext
mean_res = [mean_adj[i] for i in pre_index] + [mean_h[j] for j in post_index]
mean_new.append(numpy.array(mean_res))
# matplotlib.pyplot.plot(h_ext, mean_adj, 'o--')
# matplotlib.pyplot.plot(h, mean_h)
# matplotlib.pyplot.show()
# Extend dry spell fraction: dsf(h) = A*(e^h*B)
# ---------------------------------------------
# TODO: include the different groupings
dsf_new = []
for g in range(n_group):
dsf_h = dsf[g]
Xo = [-1] # A
if False:
# Only use 24 72
hh = [h[0], h[1]]
dsf_hh = [dsf_h[0], dsf_h[1]]
else:
hh = h
dsf_hh = dsf_h
#print fmin(dsf_min, Xo, args=(h, dsf_h), approx_grad=True, bounds=bounds, maxfun=3000)
Xf = fmin(dsf_min, Xo, args=(hh, dsf_hh), disp=0)
A = Xf[0]
dsf_adj = numpy.exp(A * h_ext)
dsf_res = [dsf_adj[i] for i in pre_index] + [dsf_h[j] for j in post_index]
dsf_new.append(numpy.array(dsf_res))
# matplotlib.pyplot.plot(h_ext, dsf_adj, 'o--')
# matplotlib.pyplot.plot(h, dsf_h)
# matplotlib.pyplot.show()
# Skewness.... hmmmm difficult so far so only observed is downscaled
# ---------------------------------------------
# TODO: include the different groupings
skew_new = []
for g in range(n_group):
skew_h = skew[g]
skew_adj = skew_ori[g]
skew_res = [skew_adj[i] for i in pre_index] + [skew_h[j] for j in post_index]
skew_new.append(numpy.array(skew_res))
# Autocorrelation.... very difficult indeed not taken into account
# ---------------------------------------------
correl_new = correl_ori
# Variance
# -------------------------------------------------
var_new = []
for g in range(n_group):
var_h = var[g]
Xo = [0.5, 1, 0.8]
Xf = fmin(var_min, Xo, args=(aggregation, var_h), disp=0)
eps =Xf[0] ## [h]
Sig2i = Xf[1] ## [mm^2]
alp = Xf[2] ## []
var_adj = variance_t_downscaling(extend_aggregation, eps, Sig2i, alp)
var_res = [var_adj[i] for i in pre_index] + [var_h[j] for j in post_index]
var_new.append(numpy.array(var_res))
# Coefficient of variance
#-----------------------
cv_new = [(var_new[i])**0.5 / mean_new[i] for i in range(len(mean_new))]
dic = {'aggregation': extend_aggregation,
'dsf': dsf_new,
'mean': mean_new,
'var': var_new,
'skew': skew_new,
'correl': correl_new,
'cv': cv_new}
# Plot test
# for g in range(n_group):
# for key in dic:
# x = dic['aggregation']
# if not(key == 'aggregation'):
# y = dic[key][g]
#
# fig = matplotlib.pyplot.figure()
## fig.subplots_adjust(left=0.10, bottom=0.10, wspace=0.40, hspace=0.20)
# fig.suptitle(key)
# ax = fig.add_subplot(111)
# ax.set_axisbelow(True)
# ax.plot(x, y, 'b-', label='period statistics')
# matplotlib.pyplot.show()
wg_io.save(dic, ofile)
return dic
def extend_fine(idir, ifile, extend_aggregation, aggregation, ofile):
r'''
'''
dic = calculate_change_factor(idir, ifile)
fmin = scipy.optimize.fmin_l_bfgs_b
fmin = scipy.optimize.fmin
# Changed factors original data
dsf = dic['dsf']
mean = dic['mean']
var = dic['var']
skew = dic['skew']
correl_ori = []
skew_ori = []
# Find the original
# -----------------
dic = wg_io.load(ifile)
ts = 1
lag = 1
accumulate = True
group_type = 'm'
dic_stat = wg_tool.series_statistics(dic,
ts,
extend_aggregation,
lag=lag,
group_type=group_type,
accumulate=accumulate)
# [Ey, VARy, CVy, SKy, Rly, DSFy]
dicdic = dic_stat['period']
for g in range(12):
skew_index = 3
skew_ori.append(dicdic[skew_index][g])
correl_index = 4
correl_ori.append(dicdic[correl_index][g])
# Find indexes to use extension values for bellow 24 hours and calculates
# values for over 24 hours
n_group = len(dsf)
pre_index = []
post_index = []
for i in range(len(extend_aggregation)):
if not (extend_aggregation[i] in aggregation):
pre_index.append(i)
for i in range(len(aggregation)):
if (aggregation[i] in extend_aggregation):
post_index.append(i)
h = numpy.array(aggregation)
h_ext = numpy.array(extend_aggregation)
# Extend mean: E(h) = A*h. Is linear and cuts in 0. Either one value of future
# is used, or a regression is made...
# ----------------------------------------------------------------------
mean_new = []
for g in range(n_group):
mean_h = mean[g]
Xo = [1] # A
Xf = fmin(mean_min, Xo, args=(h, mean_h), disp=0)
A = Xf[0]
mean_adj = A * h_ext
mean_res = [mean_adj[i]
for i in pre_index] + [mean_h[j] for j in post_index]
mean_new.append(numpy.array(mean_res))
# matplotlib.pyplot.plot(h_ext, mean_adj, 'o--')
# matplotlib.pyplot.plot(h, mean_h)
# matplotlib.pyplot.show()
# Extend dry spell fraction: dsf(h) = A*(e^h*B)
# ---------------------------------------------
# TODO: include the different groupings
dsf_new = []
for g in range(n_group):
dsf_h = dsf[g]
Xo = [-1] # A
if False:
# Only use 24 72
hh = [h[0], h[1]]
dsf_hh = [dsf_h[0], dsf_h[1]]
else:
hh = h
dsf_hh = dsf_h
#print fmin(dsf_min, Xo, args=(h, dsf_h), approx_grad=True, bounds=bounds, maxfun=3000)
Xf = fmin(dsf_min, Xo, args=(hh, dsf_hh), disp=0)
A = Xf[0]
dsf_adj = numpy.exp(A * h_ext)
dsf_res = [dsf_adj[i]
for i in pre_index] + [dsf_h[j] for j in post_index]
dsf_new.append(numpy.array(dsf_res))
# matplotlib.pyplot.plot(h_ext, dsf_adj, 'o--')
# matplotlib.pyplot.plot(h, dsf_h)
# matplotlib.pyplot.show()
# Skewness.... hmmmm difficult so far so only observed is downscaled
# ---------------------------------------------
# TODO: include the different groupings
skew_new = []
for g in range(n_group):
skew_h = skew[g]
skew_adj = skew_ori[g]
skew_res = [skew_adj[i]
for i in pre_index] + [skew_h[j] for j in post_index]
skew_new.append(numpy.array(skew_res))
# Autocorrelation.... very difficult indeed not taken into account
# ---------------------------------------------
correl_new = correl_ori
# Variance
# -------------------------------------------------
var_new = []
for g in range(n_group):
var_h = var[g]
Xo = [0.5, 1, 0.8]
Xf = fmin(var_min, Xo, args=(aggregation, var_h), disp=0)
eps = Xf[0] ## [h]
Sig2i = Xf[1] ## [mm^2]
alp = Xf[2] ## []
var_adj = variance_t_downscaling(extend_aggregation, eps, Sig2i, alp)
var_res = [var_adj[i]
for i in pre_index] + [var_h[j] for j in post_index]
var_new.append(numpy.array(var_res))
# Coefficient of variance
#-----------------------
cv_new = [(var_new[i])**0.5 / mean_new[i] for i in range(len(mean_new))]
dic = {
'aggregation': extend_aggregation,
'dsf': dsf_new,
'mean': mean_new,
'var': var_new,
'skew': skew_new,
'correl': correl_new,
'cv': cv_new
}
# Plot test
# for g in range(n_group):
# for key in dic:
# x = dic['aggregation']
# if not(key == 'aggregation'):
# y = dic[key][g]
#
# fig = matplotlib.pyplot.figure()
## fig.subplots_adjust(left=0.10, bottom=0.10, wspace=0.40, hspace=0.20)
# fig.suptitle(key)
# ax = fig.add_subplot(111)
# ax.set_axisbelow(True)
# ax.plot(x, y, 'b-', label='period statistics')
# matplotlib.pyplot.show()
wg_io.save(dic, ofile)
return dic
else:
day_normal.append(numpy.int(numpy.round(tail, 0)))
day_leap.append(numpy.int(numpy.round(tail + 1, 0)))
#10 + [(365-14*10)]
dic = parameter_NSRP(rain_grouped, ts, aggregation, lag, stats=stats,
weights=weights, change_factors=change_factors, DEBUG=True,
MY_METHOD=MY_METHOD)
dic['day_normal'] = day_normal
dic['day_leap'] = day_leap
# Here is to save
print 'Saving: ' + str(ofile)
wg_io.save(dic, ofile)
return dic
def run_gen(ifile, ofile, start_year, end_year):
r'''
Parameters
----------
ifile : String
TODO
save_as : String
TODO
start_year : Integer
Inclusive
end_year : Integer