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_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_nino34_wavelet_phase(start_date, end_date, anom=True):
raw = np.loadtxt('/home/nikola/Work/phd/data/nino34monthly.txt')
data = []
time = []
for y in range(raw.shape[0]):
for m in range(1, 13):
dat = float(raw[y, m])
data.append(dat)
time.append(date(int(raw[y, 0]), m, 1).toordinal())
g = DataField(data=np.array(data), time=np.array(time))
g.location = "NINO3.4"
g.select_date(start_date, end_date)
if anom:
g.anomalise()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
per = PERIOD * 12 # frequency of interest
s0 = per / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g.data,
1,
False,
wvlt.morlet,
dj=0,
s0=s0,
j1=0,
k0=6.)
phase = np.arctan2(np.imag(wave), np.real(wave))[0, :]
return phase
def load_nino34_wavelet_phase(start_date, end_date, anom = True):
raw = np.loadtxt('/home/nikola/Work/phd/data/nino34monthly.txt')
data = []
time = []
for y in range(raw.shape[0]):
for m in range(1,13):
dat = float(raw[y, m])
data.append(dat)
time.append(date(int(raw[y, 0]), m, 1).toordinal())
g = DataField(data = np.array(data), time = np.array(time))
g.location = "NINO3.4"
g.select_date(start_date, end_date)
if anom:
g.anomalise()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
per = PERIOD * 12 # frequency of interest
s0 = per / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g.data, 1, False, wvlt.morlet, dj = 0, s0 = s0, j1 = 0, k0 = 6.)
phase = np.arctan2(np.imag(wave), np.real(wave))[0, :]
return phase
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
GRID_POINTS = [[50, 15], [50, 12.5], [52.5, 12.5], [52.5, 15]]
for lat, lon in GRID_POINTS:
g = load_NCEP_data_monthly(
"../data/air.mon.mean.levels.nc",
"air",
date(1948, 1, 1),
date(2014, 1, 1),
[lat - 1, lat + 1],
[lon - 1, lon + 1],
level=LEVEL,
anom=False,
)
print g.data.shape
lat_arg = np.argmin(np.abs(LAT - g.lats))
lon_arg = np.argmin(np.abs(LON - g.lons))
ts = g.data[:, lat_arg, lon_arg].copy()
time = g.time.copy()
loc = "GRID | lat: %.1f, lon: %.1f" % (g.lats[lat_arg], g.lons[lon_arg])
g_grid = DataField(data=ts, time=time)
g_grid.location = loc
with open("%s_time_series_%.1fN_%.1fE.bin" % ("NCEP30hPa", lat, lon), "wb") as f:
cPickle.dump({"g": g_grid}, f, protocol=cPickle.HIGHEST_PROTOCOL)
print ("[%s] Dumped time-series from %.1f N and %.1f E." % (str(datetime.now()), g.lats[lat_arg], g.lons[lon_arg]))
# [LON - 5, LON + 5], False, parts = 3)
GRID_POINTS = [[50, 15], [50, 12.5], [52.5, 12.5], [52.5, 15]]
for lat, lon in GRID_POINTS:
g = load_NCEP_data_monthly('../data/air.mon.mean.levels.nc',
'air',
date(1948, 1, 1),
date(2014, 1, 1), [lat - 1, lat + 1],
[lon - 1, lon + 1],
level=LEVEL,
anom=False)
print g.data.shape
lat_arg = np.argmin(np.abs(LAT - g.lats))
lon_arg = np.argmin(np.abs(LON - g.lons))
ts = g.data[:, lat_arg, lon_arg].copy()
time = g.time.copy()
loc = ("GRID | lat: %.1f, lon: %.1f" % (g.lats[lat_arg], g.lons[lon_arg]))
g_grid = DataField(data=ts, time=time)
g_grid.location = loc
with open("%s_time_series_%.1fN_%.1fE.bin" % ('NCEP30hPa', lat, lon),
'wb') as f:
cPickle.dump({'g': g_grid}, f, protocol=cPickle.HIGHEST_PROTOCOL)
print("[%s] Dumped time-series from %.1f N and %.1f E." %
(str(datetime.now()), g.lats[lat_arg], g.lons[lon_arg]))
