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