def rmse(root, index):
times = [
datetime.utcfromtimestamp(int(t)) for t in nc.getvar(root, 'time')[:]
]
days = [t.date() for t in times]
days.sort()
days_index = [d.day for d in set(days)]
days_amount = len(days_index)
nc.getdim(root, 'diarying', days_amount)
nc.sync(root)
measurements = nc.getvar(root, 'measurements')
estimated = nc.getvar(root, 'globalradiation')
error_diff = nc.getvar(root, 'errordiff', 'f4', (
'time',
'yc_cut',
'xc_cut',
), 4)
error = nc.getvar(root, 'error', 'f4', (
'time',
'yc_cut',
'xc_cut',
), 4)
diary_error = nc.getvar(root, 'diaryerror', 'f4', (
'diarying',
'yc_cut',
'xc_cut',
), 4)
error_diff[:] = np.zeros(estimated.shape)
error[:] = np.zeros(estimated.shape)
diary_error[:] = np.zeros(
(days_amount, estimated.shape[1], estimated.shape[2]))
nc.sync(root)
#the_max = measurements[:].max()
error_diff[:,
index, :] = measurements[:, index, :] - estimated[:, index, :]
error[:, index, :] = np.abs(error_diff[:, index, :])
nc.sync(root)
max_value_in_day = np.zeros([days_amount]) + 1
for i in range(len(days)):
d_i = days_index.index(days[i].day)
max_value_in_day[d_i] = measurements[
i, index, 0] if max_value_in_day[d_i] < measurements[
i, index, 0] else max_value_in_day[d_i]
diary_error[d_i, index, :] += np.array([error_diff[i, index, 0]**2, 1])
count = diary_error[:, index, 1]
count[count == 0] = 1
diary_error[:, index, 0] = np.sqrt(diary_error[:, index, 0] / count)
diary_error[:, index,
1] = diary_error[:, index, 0] / max_value_in_day * 100
show("\rDiary RMS error: %.2f" % (diary_error[:, index, 1]).mean())
for i in range(len(days)):
d_i = days_index.index(days[i].day)
error[i, index, 1] = error[i, index, 1] / max_value_in_day[d_i] * 100
result = np.sum(error[:, index, 1]**2)
result = np.sqrt(result / error.shape[0])
show("Half-hour RMS error: %.2f \n" % result)
#diary_error[:, index,1] = diary_error[:, index,0]
nc.sync(root)
nc.close(root)
def dailyerrors(root, stations):
times = [ datetime.fromtimestamp(int(t)) for t in nc.getvar(root, 'time')[:] ]
days = [ t.date() for t in times ]
days.sort()
days_index = [d.day for d in set(days)]
days_index.sort()
days_amount = len(days_index)
nc.getdim(root, 'diarying', days_amount)
nc.sync(root)
measurements = nc.getvar(root, 'measurements')
estimated = nc.getvar(root, 'globalradiation')
error_diff = nc.getvar(root, 'errordiff', 'f4', ('time', 'yc_cut', 'xc_cut',), 4)
RMS_daily_error = nc.getvar(root, 'RMSdailyerror', 'f4', ('diarying', 'yc_cut', 'xc_cut',), 4)
BIAS_daily_error = nc.getvar(root, 'BIASdailyerror', 'f4', ('diarying', 'yc_cut', 'xc_cut',), 4)
error_diff[:] = np.zeros(estimated.shape)
RMS_daily_error[:] = np.zeros((days_amount, estimated.shape[1], estimated.shape[2]))
BIAS_daily_error[:] = np.zeros((days_amount, estimated.shape[1], estimated.shape[2]))
nc.sync(root)
for s in stations:
index = stations.index(s)
show('Station: %s \n' % stations[index])
error_diff[:, index, :] = measurements[:, index, :] - estimated[:, index, :]
nc.sync(root)
sum_value_in_day = np.zeros((days_amount))
for i in range(len(days)):
d_i = days_index.index(days[i].day)
if not measurements[i, index, 0] == 0.0:
sum_value_in_day[d_i] += measurements[i,index ,0]
RMS_daily_error[d_i, index ,:] += np.array([ error_diff[i, index ,0] ** 2,1])
BIAS_daily_error[d_i, index ,:] += error_diff[i, index ,0]
count = RMS_daily_error[:, index, 1]
count[count == 0] = 1
RMS_daily_error[:, index,0] = np.sqrt(RMS_daily_error[:, index, 0] / count) / sum_value_in_day * 100
BIAS_daily_error[:, index,0] = (BIAS_daily_error[:, index, 0] / count) / sum_value_in_day * 100
RMS_daily_error[:, index,1] = RMS_daily_error[:, index,0]
BIAS_daily_error[:, index,1] = BIAS_daily_error[:, index,0]
print 'RMS :', RMS_daily_error[:, index, 0]
print 'BIAS', BIAS_daily_error[:, index, 0]
print 'sum value in day: ', sum_value_in_day[:]
show("\rDiary RMS daily error: %.2f\n" % (RMS_daily_error[:, index, 0]).mean())
nc.sync(root)
nc.close(root)
def rmse(root, index):
times = [ datetime.utcfromtimestamp(int(t)) for t in nc.getvar(root, 'time')[:] ]
days = [ t.date() for t in times ]
days.sort()
days_index = [d.day for d in set(days)]
days_amount = len(days_index)
nc.getdim(root, 'diarying', days_amount)
nc.sync(root)
measurements = nc.getvar(root, 'measurements')
estimated = nc.getvar(root, 'globalradiation')
error_diff = nc.getvar(root, 'errordiff', 'f4', ('time', 'yc_cut', 'xc_cut',), 4)
error = nc.getvar(root, 'error', 'f4', ('time','yc_cut','xc_cut',), 4)
diary_error = nc.getvar(root, 'diaryerror', 'f4', ('diarying', 'yc_cut', 'xc_cut',), 4)
error_diff[:] = np.zeros(estimated.shape)
error[:] = np.zeros(estimated.shape)
diary_error[:] = np.zeros((days_amount, estimated.shape[1], estimated.shape[2]))
nc.sync(root)
#the_max = measurements[:].max()
error_diff[:, index, :] = measurements[:,index,:] - estimated[:,index,:]
error[:, index, :] = np.abs(error_diff[:, index, :])
nc.sync(root)
max_value_in_day = np.zeros([days_amount]) + 1
for i in range(len(days)):
d_i = days_index.index(days[i].day)
max_value_in_day[d_i] = measurements[i,index,0] if max_value_in_day[d_i] < measurements[i,index,0] else max_value_in_day[d_i]
diary_error[d_i, index,:] += np.array([ error_diff[i,index,0] ** 2,1])
count = diary_error[:, index, 1]
count[count == 0] = 1
diary_error[:, index,0] = np.sqrt(diary_error[:, index, 0] / count)
diary_error[:, index,1] = diary_error[:, index,0] / max_value_in_day * 100
show("\rDiary RMS error: %.2f" % (diary_error[:, index, 1]).mean())
for i in range(len(days)):
d_i = days_index.index(days[i].day)
error[i,index,1] = error[i,index,1] / max_value_in_day[d_i] * 100
result = np.sum(error[:, index, 1] ** 2)
result = np.sqrt(result / error.shape[0])
show("Half-hour RMS error: %.2f \n" % result)
#diary_error[:, index,1] = diary_error[:, index,0]
nc.sync(root)
nc.close(root)
y = core.new_randint(1, x, numbers_log)
z = x - y
else:
# Take one random pair from generated list
pair = random.choice(multipliers_list)
# And remove
multipliers_list.remove(pair)
# Random order of multipliers
(x, y) = pair if random.getrandbits(1) == 0 else (pair[1], pair[0])
z = x * y
if operation == '/':
(x, z) = (z, x)
# Strings with example by numbers and words
example_numbers = f'{x} {operation} {y} = {z}'
example_words = int_to_german(
x) + ' ' + operation_german + ' ' + int_to_german(
y) + ' gleich ' + int_to_german(z)
operations_log.append(operation)
# Turn off this boolean to not repeat generator's running
first_run = False
# Send these strings to interface script
return example_numbers, example_words
# Run simple console interface which show random content with user's parameters
core.show(print_examples, 'examples', True, 'Deutsche Wörter:')
def cli(profile, show):
print core.show(profile, show)
def dailyerrors(root, stations):
times = [
datetime.fromtimestamp(int(t)) for t in nc.getvar(root, 'time')[:]
]
days = [t.date() for t in times]
days.sort()
days_index = [d.day for d in set(days)]
days_index.sort()
days_amount = len(days_index)
nc.getdim(root, 'diarying', days_amount)
nc.sync(root)
measurements = nc.getvar(root, 'measurements')
estimated = nc.getvar(root, 'globalradiation')
error_diff = nc.getvar(root, 'errordiff', 'f4', (
'time',
'yc_cut',
'xc_cut',
), 4)
RMS_daily_error = nc.getvar(root, 'RMSdailyerror', 'f4', (
'diarying',
'yc_cut',
'xc_cut',
), 4)
BIAS_daily_error = nc.getvar(root, 'BIASdailyerror', 'f4', (
'diarying',
'yc_cut',
'xc_cut',
), 4)
error_diff[:] = np.zeros(estimated.shape)
RMS_daily_error[:] = np.zeros(
(days_amount, estimated.shape[1], estimated.shape[2]))
BIAS_daily_error[:] = np.zeros(
(days_amount, estimated.shape[1], estimated.shape[2]))
nc.sync(root)
for s in stations:
index = stations.index(s)
show('Station: %s \n' % stations[index])
error_diff[:,
index, :] = measurements[:, index, :] - estimated[:,
index, :]
nc.sync(root)
sum_value_in_day = np.zeros((days_amount))
for i in range(len(days)):
d_i = days_index.index(days[i].day)
if not measurements[i, index, 0] == 0.0:
sum_value_in_day[d_i] += measurements[i, index, 0]
RMS_daily_error[d_i, index, :] += np.array(
[error_diff[i, index, 0]**2, 1])
BIAS_daily_error[d_i, index, :] += error_diff[i, index, 0]
count = RMS_daily_error[:, index, 1]
count[count == 0] = 1
RMS_daily_error[:, index, 0] = np.sqrt(
RMS_daily_error[:, index, 0] / count) / sum_value_in_day * 100
BIAS_daily_error[:, index, 0] = (BIAS_daily_error[:, index, 0] /
count) / sum_value_in_day * 100
RMS_daily_error[:, index, 1] = RMS_daily_error[:, index, 0]
BIAS_daily_error[:, index, 1] = BIAS_daily_error[:, index, 0]
print 'RMS :', RMS_daily_error[:, index, 0]
print 'BIAS', BIAS_daily_error[:, index, 0]
print 'sum value in day: ', sum_value_in_day[:]
show("\rDiary RMS daily error: %.2f\n" %
(RMS_daily_error[:, index, 0]).mean())
nc.sync(root)
nc.close(root)
year_second_part = int(str(year)[2:])
year_words = int_to_german(
year_first_part) + 'hundert' + int_to_german(year_second_part)
# DD.MM.YYYY
short_date = f'{day}.{str_month}.{year}'
# Russian format
russian_date = f'{day} {months_dict["russian"][month - 1]} {year} г.'
# American format
english_date = f'{months_dict["english"][month - 1]} {day}, {year}'
# German format with numbers
german_date = f'{day}. {months_dict["german"][month - 1]} {year}'
# The entire date is written by german words
german_date_words = f'{day_words} {months_dict["german"][month - 1]} {year_words}'
# Output
result = f'{short_date} / {english_date}\n' \
f' {russian_date}\n' \
f' German:\n' \
f' {german_date}\n' \
f' {german_date_words}\n'
return result
# Run simple console interface which show random content with user's parameters
core.show(print_dates, 'dates')
diary_error[:] = np.zeros((days_amount, estimated.shape[1], estimated.shape[2]))
nc.sync(root)
#the_max = measurements[:].max()
error_diff[:, index, :] = measurements[:,index,:] - estimated[:,index,:]
error[:, index, :] = np.abs(error_diff[:, index, :])
nc.sync(root)
max_value_in_day = np.zeros([days_amount]) + 1
for i in range(len(days)):
d_i = days_index.index(days[i].day)
max_value_in_day[d_i] = measurements[i,index,0] if max_value_in_day[d_i] < measurements[i,index,0] else max_value_in_day[d_i]
diary_error[d_i, index,:] += np.array([ error_diff[i,index,0] ** 2,1])
count = diary_error[:, index, 1]
count[count == 0] = 1
diary_error[:, index,0] = np.sqrt(diary_error[:, index, 0] / count)
diary_error[:, index,1] = diary_error[:, index,0] / max_value_in_day * 100
show("\rDiary RMS error: {:.2f}".format(diary_error[:, index, 1].mean()))
for i in range(len(days)):
d_i = days_index.index(days[i].day)
error[i,index,1] = error[i,index,1] / max_value_in_day[d_i] * 100
result = np.sum(error[:, index, 1] ** 2)
result = np.sqrt(result / error.shape[0])
show("Half-hour RMS error: {:.2f} \n".format(result))
#diary_error[:, index,1] = diary_error[:, index,0]
nc.sync(root)
nc.close(root)
def dailyerrors(root, stations):
times = [ datetime.fromtimestamp(int(t)) for t in nc.getvar(root, 'time')[:] ]
days = [ t.date() for t in times ]
days.sort()
days_index = [d.day for d in set(days)]
