from SunSpot import autocorrelations as bbl
### load data (loaded automatically with package)
data_path = pkg.resource_filename(pkg.Requirement.parse("SunSpot"), 'data')
with open(data_path + '/data_1981', 'rb') as file:
#with open('data/data_1981', 'rb') as file: #local
my_depickler = pickle.Unpickler(file)
Ns = my_depickler.load() #number of spots
Ng = my_depickler.load() #number of sunspot groups
Nc = my_depickler.load() #composite: Ns+10Ng
station_names = my_depickler.load() #codenames of the stations
time = my_depickler.load() #time
### compute the long-term errors
mu2 = err.long_term_error(Ng, period_rescaling=14, wdw=27)
### discard stations with no values
ind_nan = []
for i in range(mu2.shape[1]):
if not np.all(np.isnan(mu2[:, i])):
ind_nan.append(i)
mu2 = mu2[:, ind_nan]
station_names = [
station_names[i] for i in range(len(station_names)) if i in ind_nan
]
(n_obs, n_series) = mu2.shape
### apply preprocessing
dataNs = pre.PreProcessing(mu2)
dataNs.level_removal(wdw=4000) #remove intrisic levels
station_names = my_depickler.load() #codenames of the stations
time = my_depickler.load() #time
n_data = len(Nc[~np.isnan(Nc)])
n_perc = n_data * 100 / (Nc.shape[0] * Nc.shape[1])
### add new data to station KS (not included in database)
data_ks = np.loadtxt(data_path + '\kisl_wolf.txt',
usecols=(0, 1, 2, 3),
skiprows=1)
#data_ks = np.loadtxt('data/kisl_wolf.txt', usecols=(0,1,2,3), skiprows=1) #local
Nc_ks = data_ks[9670:23914, 3]
Nc[:, 24] = Nc_ks
### compute the long-term errors
mu2 = err.long_term_error(Nc, period_rescaling=10, wdw=365)
### discard stations with no values
ind_nan = []
for i in range(mu2.shape[1]):
if not np.all(np.isnan(mu2[:, i])):
ind_nan.append(i)
mu2 = mu2[:, ind_nan]
station_names = [
station_names[i] for i in range(len(station_names)) if i in ind_nan
]
(n_obs, n_series) = mu2.shape
### apply preprocessing
dataNs = pre.PreProcessing(mu2)
dataNs.level_removal(wdw=4000) #remove intrisic levels
from SunSpot import autocorrelations as bbl
### load data (loaded automatically with package)
data_path = pkg.resource_filename(pkg.Requirement.parse("SunSpot"), 'data')
with open(data_path + '/data_1981', 'rb') as file:
#with open('data/data_1981', 'rb') as file: #local
my_depickler = pickle.Unpickler(file)
Ns = my_depickler.load() #number of spots
Ng = my_depickler.load() #number of sunspot groups
Nc = my_depickler.load() #composite: Ns+10Ng
station_names = my_depickler.load() #codenames of the stations
time = my_depickler.load() #time
### compute the long-term errors
mu2 = err.long_term_error(Ns, period_rescaling=8, wdw=365)
### discard stations with no values
ind_nan = []
for i in range(mu2.shape[1]):
if not np.all(np.isnan(mu2[:, i])):
ind_nan.append(i)
mu2 = mu2[:, ind_nan]
station_names = [
station_names[i] for i in range(len(station_names)) if i in ind_nan
]
(n_obs, n_series) = mu2.shape
### apply preprocessing
dataNs = pre.PreProcessing(mu2)
dataNs.level_removal(wdw=4000) #remove intrisic levels