def load_CR_climax_daily_data(fname, start_date, end_date, anom=False):
from dateutil.relativedelta import relativedelta
raw = np.loadtxt(fname)
time = []
datenow = date(1994, 1, 1)
delta = timedelta(days=1)
for t in range(raw.shape[0]):
time.append(datenow.toordinal())
datenow += delta
print raw.shape
print len(time)
g = DataField(data=np.array(raw), time=np.array(time))
g.location = 'Climax, CO cosmic data'
g.select_date(start_date, end_date)
if anom:
g.anomalise()
if NUM_SURR != 0:
g_surr = SurrogateField()
seasonality = g.get_seasonality(True)
g_surr.copy_field(g)
g.return_seasonality(seasonality[0], seasonality[1], seasonality[2])
else:
g_surr, seasonality = None, None
return g, g_surr, seasonality
def load_neutron_NESDIS_data(fname, start_date, end_date, anom=True):
raw = np.loadtxt(fname, skiprows=2)
data = []
time = []
for year in range(raw.shape[0]):
for month in range(1, 13):
dat = float(raw[year, month])
if dat == 9999.:
dat = (float(raw[year, month - 2]) + float(
raw[year, month - 1]) + float(raw[year, month + 1]) +
float(raw[year, month + 2])) / 4.
data.append(dat)
time.append(date(int(raw[year, 0]), month, 1).toordinal())
g = DataField(data=np.array(data), time=np.array(time))
g.location = ('%s cosmic data' % (fname[32].upper() + fname[33:-4]))
g.select_date(start_date, end_date)
if anom:
g.anomalise()
if NUM_SURR != 0:
g_surr = SurrogateField()
seasonality = g.get_seasonality()
g_surr.copy_field(g)
g.return_seasonality(seasonality[0], seasonality[1], None)
else:
g_surr, seasonality = None, None
return g, g_surr, seasonality
def load_CR_climax_daily_data(fname, start_date, end_date, anom = False):
from dateutil.relativedelta import relativedelta
raw = np.loadtxt(fname)
time = []
datenow = date(1994, 1, 1)
delta = timedelta(days = 1)
for t in range(raw.shape[0]):
time.append(datenow.toordinal())
datenow += delta
print raw.shape
print len(time)
g = DataField(data = np.array(raw), time = np.array(time))
g.location = 'Climax, CO cosmic data'
g.select_date(start_date, end_date)
if anom:
g.anomalise()
if NUM_SURR != 0:
g_surr = SurrogateField()
seasonality = g.get_seasonality(True)
g_surr.copy_field(g)
g.return_seasonality(seasonality[0], seasonality[1], seasonality[2])
else:
g_surr, seasonality = None, None
return g, g_surr, seasonality
def load_neutron_NESDIS_data(fname, start_date, end_date, anom = True):
raw = np.loadtxt(fname, skiprows = 2)
data = []
time = []
for year in range(raw.shape[0]):
for month in range(1,13):
dat = float(raw[year, month])
if dat == 9999.:
dat = (float(raw[year, month-2]) + float(raw[year, month-1]) + float(raw[year, month+1]) + float(raw[year, month+2])) / 4.
data.append(dat)
time.append(date(int(raw[year,0]), month, 1).toordinal())
g = DataField(data = np.array(data), time = np.array(time))
g.location = ('%s cosmic data' % (fname[32].upper() + fname[33:-4]))
g.select_date(start_date, end_date)
if anom:
g.anomalise()
if NUM_SURR != 0:
g_surr = SurrogateField()
seasonality = g.get_seasonality()
g_surr.copy_field(g)
g.return_seasonality(seasonality[0], seasonality[1], None)
else:
g_surr, seasonality = None, None
return g, g_surr, seasonality
def load_cosmic_data(fname,
start_date,
end_date,
anom=True,
daily=False,
corrected=True):
# corrected stands for if use corrected data or not
from dateutil.relativedelta import relativedelta
raw = open(fname).read()
lines = raw.split('\n')
data = []
time = []
d = date(int(lines[0][:4]), int(lines[0][5:7]), 1)
if not daily:
delta = relativedelta(months=+1)
elif daily:
delta = timedelta(days=1)
for line in lines:
row = line.split(' ')
if len(row) < 6:
continue
time.append(d.toordinal())
if corrected:
data.append(float(row[4]))
else:
data.append(float(row[5]))
d += delta
g = DataField(data=np.array(data), time=np.array(time))
g.location = 'Oulu cosmic data'
g.select_date(start_date, end_date)
if anom:
g.anomalise()
g.data = X[:, 0].copy()
if NUM_SURR != 0:
g_surr = SurrogateField()
seasonality = g.get_seasonality(True)
g_surr.copy_field(g)
g.return_seasonality(seasonality[0], seasonality[1], seasonality[2])
else:
g_surr, seasonality = None, None
return g, g_surr, seasonality
def load_cosmic_data(fname, start_date, end_date, anom = True, daily = False, corrected = True):
# corrected stands for if use corrected data or not
from dateutil.relativedelta import relativedelta
raw = open(fname).read()
lines = raw.split('\n')
data = []
time = []
d = date(int(lines[0][:4]), int(lines[0][5:7]), 1)
if not daily:
delta = relativedelta(months = +1)
elif daily:
delta = timedelta(days = 1)
for line in lines:
row = line.split(' ')
if len(row) < 6:
continue
time.append(d.toordinal())
if corrected:
data.append(float(row[4]))
else:
data.append(float(row[5]))
d += delta
g = DataField(data = np.array(data), time = np.array(time))
g.location = 'Oulu cosmic data'
g.select_date(start_date, end_date)
if anom:
g.anomalise()
g.data = X[:, 0].copy()
if NUM_SURR != 0:
g_surr = SurrogateField()
seasonality = g.get_seasonality(True)
g_surr.copy_field(g)
g.return_seasonality(seasonality[0], seasonality[1], seasonality[2])
else:
g_surr, seasonality = None, None
return g, g_surr, seasonality
