def validate(year, month, hrv):
tst_hour_step = 1/24.
root = nc_process_create(getfilename(year, month, hrv))
globalradiation = nc_process_var(root, 'radiacion_global')
clearskyradiation = nc_process_var(root, 'radiacion_global_despejado')
dt_int = nc_process_var(root, 'datetime')
timestamp = np.array([date2num(getdatetimefromint(i)) for i in dt_int])
tst_hour = nc_process_var(root, 'tiempo_tst')
nc_process_ready(root)
stations = pgs.getmeasuresinstations(year, month)
for s in stations:
l,c = dp.getpos(float(s['latitude']), float(s['longitude']), hrv)
globalradiationinposition = globalradiation[:,l,c]
tst_datehour = gettstdatetime(timestamp,tst_hour[:,l,c])
estimated = []
measured = []
for m in s['measures']:
cond = ((tst_datehour > m['timestamp'] - tst_hour_step) & (tst_datehour <= m['timestamp']))
tst_filtered = tst_datehour[cond]
if tst_filtered.size >= 4 and m['ghi'] > 0:
estimated.append(globalradiationinposition[cond].mean())
measured.append(m['ghi'])
measured = np.array(measured)
estimated = np.array(estimated)
diff = estimated - measured
ghi_mean = measured.mean()
ghi_ratio = 100 / ghi_mean
show("----------")
show("Length:", measured.size)
show("mean(GHI)", ghi_mean)
show("mean(estimated)", estimated.mean())
show(s['Name'])
bias = diff.mean()
show("BIAS:", bias, "(", bias * ghi_ratio, "%)")
rmse = np.sqrt((diff**2).mean())
show("RMSE:", rmse, "(", rmse * ghi_ratio, "%)")
mae = np.absolute(diff).mean()
show("MAE:", mae, "(", mae * ghi_ratio, "%)")
def process_validate(year, month, times, root):
tst_hour_step = 1/24.
globalradiation = nc.getvar(root, 'globalradiation')
clearskyradiation = nc.getvar(root, 'clearskyglobalradiation')
timestamp = np.array([date2num(dt) for dt in times])
tst_hour = nc.getvar(root, 'tst_hour')
stations = pgs.getmeasuresinstations(year, month)
for s in stations:
l,c = dp.getpos(float(s['latitude']), float(s['longitude']))
globalradiationinposition = globalradiation[:,l,c]
tst_datehour = gettstdatetime(timestamp,tst_hour[:,l,c])
estimated = []
measured = []
for m in s['measures']:
cond = ((tst_datehour > m['timestamp'] - tst_hour_step) & (tst_datehour <= m['timestamp']))
tst_filtered = tst_datehour[cond]
if tst_filtered.size >= 4 and m['ghi'] > 0:
estimated.append(globalradiationinposition[cond].mean())
measured.append(m['ghi'])
measured = np.array(measured)
estimated = np.array(estimated)
diff = estimated - measured
ghi_mean = measured.mean()
ghi_ratio = 100 / ghi_mean
show("----------")
show("Length:", measured.size)
show("mean(GHI)", ghi_mean)
show("mean(estimated)", estimated.mean())
show(s['Name'])
bias = diff.mean()
show("BIAS:", bias, "(", bias * ghi_ratio, "%)")
rmse = np.sqrt((diff**2).mean())
show("RMSE:", rmse, "(", rmse * ghi_ratio, "%)")
mae = np.absolute(diff).mean()
show("MAE:", mae, "(", mae * ghi_ratio, "%)")