from src.data_class import load_station_data
from src.mutual_information import mutual_information
import numpy as np
from datetime import date
import matplotlib.pyplot as plt
g = load_station_data('../data/TG_STAID000027.txt', date(1958, 1, 1),
date(2015, 11, 1), False)
g.get_data_of_precise_length(14976,
start_date=date(1958, 1, 1),
apply_to_data=True)
mi = [mutual_information(g.data, g.data)]
for d in np.arange(1, 401, 1):
mi.append(mutual_information(g.data[d:], g.data[:-d]))
plt.plot(mi)
plt.show()
# # g.wavelet(1, period_unit = 'y')
# # reconstruction = g.amplitude * np.cos(g.phase)
# # fit_x = np.vstack([reconstruction, np.ones(reconstruction.shape[0])]).T
# # m, c = np.linalg.lstsq(fit_x, g.data)[0]
# # amplitude = m * g.amplitude + c
# g.wavelet(8, period_unit = 'y')
# g.anomalise()
# def get_equidistant_bins():
# return np.array(np.linspace(-np.pi, np.pi, 9))
def running_mean(arr, aver):
out = np.zeros((arr.shape[0] - aver + 1,))
for i in range(out.shape[0]):
out[i] = np.mean(arr[i : i+aver])
return out
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
BINS_COND = True
g = load_station_data('TG_STAID000027.txt', date(1834, 4, 28), date(2013, 10, 1), False)
g_max = load_station_data('TX_STAID000027.txt', date(1834, 4, 28), date(2013, 10, 1), False)
g_min = load_station_data('TN_STAID000027.txt', date(1834, 4, 28), date(2013, 10, 1), False)
if BINS_COND:
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = 8 * y # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wavelet_analysis.continous_wavelet(g.data, 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave))
ndx = g.select_date(date(1838,4,28), date(2009,10,1)) # data as with temporal evolution (of anything studied)
g_max.select_date(date(1838,4,28), date(2009,10,1))
g_min.select_date(date(1838,4,28), date(2009,10,1))
mons = {1: 'J', 2: 'F', 3: 'M', 4: 'A', 5: 'M', 6: 'J', 7: 'J', 8: 'A', 9: 'S', 10: 'O', 11: 'N', 12: 'D'}
NUM_SURRS = 1000
WINDOW_LENGTH = 36 # years
# SEASON = [12,1,2]
# SEASON = None
# param_window = 32 # years
SEASONS = [None, [3,4,5], [6,7,8], [9,10,11], [12, 1, 2]]
for SEASON in SEASONS:
print ''.join([mons[s] for s in SEASON]) if SEASON is not None else 'overall'
# prg = load_station_data('../data/ECAstation-TG/TG_STAID000027.txt', date(1775, 1, 1), date(2016, 5, 1),
# anom = False, offset = 1)
prg = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1), date(2016, 5, 1),
anom = False, offset = 1)
bins = get_equidistant_bins()
ndxs, dates = prg.get_sliding_window_indexes(window_length = WINDOW_LENGTH, window_shift = 1, unit = 'y', return_half_dates = True)
n_windows = len(ndxs)
amp_windows = np.zeros((n_windows))
effect_windows = np.zeros((n_windows))
mean_amp_windows = np.zeros((n_windows))
mean_ampAAC_windows = np.zeros((n_windows))
amp_windows_surrs = np.zeros((NUM_SURRS, n_windows))
effect_windows_surrs = np.zeros((NUM_SURRS, n_windows))
mean_amp_windows_surrs = np.zeros((NUM_SURRS, n_windows))
mean_ampAAC_windows_surrs = np.zeros((NUM_SURRS, n_windows))
for i, ndx in zip(range(len(ndxs)), ndxs):
from src import wavelet_analysis as wvlt
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from scipy.signal import argrelextrema
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
PLOT = True
SEASON = 'JJA'
PERCENTIL = 80
g = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1), date(2014, 1, 1), False)
g_amp = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1), date(2014, 1, 1), False)
# g_sun = load_sunspot_data('monthssn.dat', date(1775, 1, 1), date(2014, 1, 1), False)
tg = g.copy_data()
g.anomalise()
# tg is SAT, g.data is SATA
k0 = 6. # wavenumber of Morlet wavelet used in analysis
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = 8 * 365.25 # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g.data, 1, False, wvlt.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave)) # get phases from oscillatory modes
period = 1 * 365.25 # frequency of interest
ANOMALISE = True
PERIOD = 8 # years, period of wavelet
WINDOW_LENGTH = 16384
WINDOW_SHIFT = 1 # years, delta in the sliding window analysis
MEANS = True # if True, compute conditional means, if False, compute conditional variance
WORKERS = 16
NUM_SURR = 100 # how many surrs will be used to evaluate
SURR_TYPE = 'AR' # MF, FT, AR
diff_ax = (0, 2) # means -> 0, 2, var -> 1, 8
mean_ax = (18, 22) # means -> -1, 1.5, var -> 9, 18
PLOT_PHASE = False
PHASE_ANALYSIS_YEAR = None # year of detailed analysis - phase and bins, or None
AMPLITUDE = True
## loading data
g = load_station_data('TG_STAID000027.txt', date(1834, 7, 28),
date(2014, 1, 1), ANOMALISE)
sg = SurrogateField()
if AMPLITUDE:
g_amp = load_station_data('TG_STAID000027.txt', date(1834, 7, 28),
date(2014, 1, 1), False)
sg_amp = SurrogateField()
print(
"[%s] Wavelet analysis in progress with %d year window shifted by %d year(s)..."
% (str(datetime.now()), WINDOW_LENGTH, WINDOW_SHIFT))
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
PERIOD = 8
PAST_UNTIL = 1930 # from which segment the average extremes should be computed
WINDOW_LENGTH = 13462 # 13462, 16384 for surrogates only 13462
PLOT = True
USE_SURR = True
NUM_SURR = 100
# load whole data - load SAT data
if USE_SURR:
g = load_station_data('../data/TG_STAID000027.txt', date(1834, 7, 28),
date(2014, 1, 1),
False) # 1834-7-28 till 2014-1-1 = 64k
g_for_avg = load_station_data('../data/TG_STAID000027.txt',
date(1840, 4, 14), date(1930, 1, 1),
False) # 1840-4-14 till 1930-1-1 = 32k
else:
g = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1),
date(2014, 1, 1), False)
g_for_avg = load_station_data('../data/TG_STAID000027.txt',
date(1775, 1, 1), date(2014, 1, 1), False)
if not USE_SURR:
# save SAT data
tg_sat = g.copy_data()
tg_avg_sat = g_for_avg.copy_data()
# anomalise to obtain SATA data
PERIOD = 8 # years, period of wavelet
WINDOW_LENGTH = 16384
WINDOW_SHIFT = 1 # years, delta in the sliding window analysis
MEANS = True # if True, compute conditional means, if False, compute conditional variance
WORKERS = 16
NUM_SURR = 100 # how many surrs will be used to evaluate
SURR_TYPE = 'AR' # MF, FT, AR
diff_ax = (0, 2) # means -> 0, 2, var -> 1, 8
mean_ax = (18, 22) # means -> -1, 1.5, var -> 9, 18
PLOT_PHASE = False
PHASE_ANALYSIS_YEAR = None # year of detailed analysis - phase and bins, or None
AMPLITUDE = True
## loading data
g = load_station_data('TG_STAID000027.txt', date(1834,7,28), date(2014,1,1), ANOMALISE)
sg = SurrogateField()
if AMPLITUDE:
g_amp = load_station_data('TG_STAID000027.txt', date(1834,7,28), date(2014, 1, 1), False)
sg_amp = SurrogateField()
print("[%s] Wavelet analysis in progress with %d year window shifted by %d year(s)..." % (str(datetime.now()), WINDOW_LENGTH, WINDOW_SHIFT))
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
cond_means = np.zeros((8,))
import numpy as np
from surrogates.surrogates import SurrogateField
from datetime import date
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
PERIOD = 8
BINS = 8
NUM_SURR = 1000
def get_equidistant_bins(num):
return np.array(np.linspace(-np.pi, np.pi, num+1))
g = load_station_data('TG_STAID000027.txt', date(1924,1,14), date(2013,10,1), True)
g_amp = load_station_data('TG_STAID000027.txt', date(1924,1,14), date(2013, 10, 1), False)
g_data = DataField()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wavelet_analysis.continous_wavelet(g.data, 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave)) # get phases from oscillatory modes
period = 1 * 365.25 # frequency of interest
s0_amp = period / fourier_factor # get scale
wave, _, _, _ = wavelet_analysis.continous_wavelet(g_amp.data, 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0_amp, j1 = 0, k0 = k0) # perform wavelet
import matplotlib.pyplot as plt
ANOMALISE = True
PERIOD = 8
START_DATE = None #date(1970, 1, 1)
END_DATE = date(2013,12,31)
MEANS = True
NUM_SURR = 100
WORKERS = 3
MF_SURR = True
SAVE_FILE = False
## load data
g = load_station_data('TG_STAID000027.txt', date(1950,1,1), date(2014,1,1), ANOMALISE)
sg = SurrogateField()
print("[%s] Wavelet analysis and conditional mean computation in progress..." % (str(datetime.now())))
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
# get 16k length of the time series because of surrogates
return output, m
WINDOW = 32 # years
# load AMO smoothed version
amo_sm_raw = np.loadtxt('../data/amon.sm.long.data', comments="#",
skiprows=1) # no. of years x 13
# load AMO unsmoothed version
amo_un_raw = np.loadtxt('../data/amon.us.long.data', comments="#", skiprows=1)
missing_val = -99.99
## loading data ##
start_date = date(1856, 1, 1)
end_date = date(2014, 1, 1) # exclusive
g = load_station_data('TG_STAID000027.txt', start_date, end_date, True)
# data var
data_var = np.zeros((end_date.year - 1 - start_date.year - WINDOW + 1))
start_idx = g.find_date_ndx(start_date)
year = start_date.year
end_idx = g.find_date_ndx(date(year + WINDOW, 1, 1))
coefs = []
print data_var.shape[0]
for i in range(data_var.shape[0]):
# detrended_data, coef = detrend_with_coefs(g.data[start_idx:end_idx])
# coefs.append(coef)
s_idx = start_idx
e_idx = g.find_date_ndx(date(year + 1, 1, 1))
var = []
WINDOW = 32 # years
# load AMO smoothed version
amo_sm_raw = np.loadtxt('../data/amon.sm.long.data', comments = "#", skiprows = 1) # no. of years x 13
# load AMO unsmoothed version
amo_un_raw = np.loadtxt('../data/amon.us.long.data', comments = "#", skiprows = 1)
missing_val = -99.99
## loading data ##
start_date = date(1856,1,1)
end_date = date(2014, 1, 1) # exclusive
g = load_station_data('TG_STAID000027.txt', start_date, end_date, True)
# data var
data_var = np.zeros((end_date.year - 1 - start_date.year - WINDOW + 1))
start_idx = g.find_date_ndx(start_date)
year = start_date.year
end_idx = g.find_date_ndx(date(year + WINDOW, 1, 1))
coefs = []
print data_var.shape[0]
for i in range(data_var.shape[0]):
# detrended_data, coef = detrend_with_coefs(g.data[start_idx:end_idx])
# coefs.append(coef)
s_idx = start_idx
e_idx = g.find_date_ndx(date(year+1,1,1))
var = []
from src.data_class import load_station_data, DataField
import numpy as np
from datetime import datetime, date
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
AMPLITUDE = True
PERIOD = 8
BINS = 8
SEASON = None#[[12, 1, 2], [6, 7, 8]]
STATIONS = None # ['TG_STAID000047.txt', 'TG_STAID000054.txt']
if STATIONS == None:
g = load_station_data('../data/TG_STAID000027.txt', date(1958, 1, 1), date(2013, 11, 10), True)
if AMPLITUDE:
g_amp = load_station_data('../data/TG_STAID000027.txt', date(1958, 1, 1), date(2013, 11, 10), False)
g_data = DataField()
else:
for i in range(len(STATIONS)):
locals()['g' + str(i)] = load_station_data(STATIONS[i], date(1924,1,15), date(2013,10,1), True)
if AMPLITUDE:
locals()['g_amp' + str(i)] = load_station_data(STATIONS[i], date(1924,1,15), date(2013, 10, 1), False)
locals()['g_data' + str(i)] = DataField()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
amplitude2 = amplitude2[idx[0] : idx[1]]
# amp_to_plot_surr = amp_to_plot_surr[idx[0] : idx[1]]
phase_amp = phase_amp[idx[0] : idx[1]]
sg.surr_data = sg.surr_data[idx[0] : idx[1]]
cond_temp = np.zeros((BINS,2))
for i in range(cond_means.shape[0]):
ndx = ((phase_amp >= phase_bins[i]) & (phase_amp <= phase_bins[i+1]))
cond_temp[i,1] = np.mean(sg.surr_data[ndx])
data_diff = cond_temp[:, 1].max() - cond_temp[:, 1].min()
resq.put([data_diff, np.mean(amplitude2)])
g = load_station_data('../data/TG_STAID000027.txt', date(1958, 1, 1), date(2013, 11, 10), True)
g_amp = load_station_data('../data/TG_STAID000027.txt', date(1958, 1, 1), date(2013, 11, 10), True)
g_data = DataField()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
# wavelet - data
wave, _, _, _ = wavelet_analysis.continous_wavelet(g.data, 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave)) # get phases from oscillatory modes
"""
created on May 7, 2014
@author: Nikola Jajcay
"""
import numpy as np
from src.data_class import load_station_data
from datetime import date
import matplotlib.pyplot as plt
g = load_station_data("TG_STAID000027.txt", date(1775, 1, 1), date(2014, 1, 1), True)
g.get_monthly_data()
# seasons as MAM, JJA, SON, DJF
seasons_data = []
for i in range(g.data.shape[0] / 4 - 1):
seasons_data.append(np.mean(g.data[2 + i * 3 : 2 + i * 3 + 3], axis=0))
g.data = np.array(seasons_data)
scaling_mean = []
scaling_max = []
for diff in range(1, g.data.shape[0]):
difs = []
for i in range(g.data.shape[0] - diff):
difs.append(np.abs(g.data[i + diff] - g.data[i]))
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
PERIOD = 8
WINDOW_LENGTH = 13462 # 13462, 16384
MIDDLE_YEAR = 1965 # year around which the window will be deployed
DIFFS_LIM = [10,25]
SAT = False
# load whole data - load SAT data
g = load_station_data('TG_STAID000027.txt', date(1775, 1, 1), date(2014, 1, 1), False)
# save SAT data
tg_sat = g.copy_data()
# anomalise to obtain SATA data
g.anomalise()
g_temp = DataField()
# starting month and day
sm = 7
sd = 28
# starting year of final window
y = 365.25
sy = int(MIDDLE_YEAR - (WINDOW_LENGTH/y)/2)
import matplotlib.pyplot as plt
import scipy.stats as sts
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
PERIOD = 8
WINDOW_LENGTH = 13462 # 13462, 16384
MIDDLE_YEAR = 1965 # year around which the window will be deployed
DIFFS_LIM = [10, 25]
SAT = False
# load whole data - load SAT data
g = load_station_data('TG_STAID000027.txt', date(1775, 1, 1), date(2014, 1, 1),
False)
# save SAT data
tg_sat = g.copy_data()
# anomalise to obtain SATA data
g.anomalise()
g_temp = DataField()
# starting month and day
sm = 7
sd = 28
# starting year of final window
y = 365.25
sy = int(MIDDLE_YEAR - (WINDOW_LENGTH / y) / 2)
from src.data_class import load_station_data
from src.mutual_information import mutual_information
import numpy as np
from datetime import date
import matplotlib.pyplot as plt
g = load_station_data('../data/TG_STAID000027.txt', date(1958,1,1), date(2015,11,1), False)
g.get_data_of_precise_length(14976, start_date = date(1958, 1, 1), apply_to_data = True)
mi = [mutual_information(g.data, g.data)]
for d in np.arange(1,401,1):
mi.append(mutual_information(g.data[d:], g.data[:-d]))
plt.plot(mi)
plt.show()
# # g.wavelet(1, period_unit = 'y')
# # reconstruction = g.amplitude * np.cos(g.phase)
# # fit_x = np.vstack([reconstruction, np.ones(reconstruction.shape[0])]).T
# # m, c = np.linalg.lstsq(fit_x, g.data)[0]
# # amplitude = m * g.amplitude + c
# g.wavelet(8, period_unit = 'y')
# g.anomalise()
else:
plt.show()
PERIOD = 8
WINDOW_LENGTH = 13462 # 13462, 16384
MIDDLE_YEAR = 1965 # year around which the window will be deployed
JUST_SCALING = True
PLOT = True
WAVES_PERCENTIL = 80
DATA = 0 # 0 - original station, 1 - closest ERA, 2 - closest ECA&D
# load whole data - load SAT data
if DATA == 0:
g = load_station_data("../data/TG_STAID000027.txt", date(1775, 1, 1), date(2014, 1, 1), False)
elif DATA == 1:
g = load_bin_data("../data/ERA_time_series_50.0N_15.0E.bin", date(1940, 1, 1), date(2014, 1, 1), False)
elif DATA == 2:
g = load_bin_data("../data/ECA&D_time_series_50.1N_14.4E.bin", date(1940, 1, 1), date(2014, 1, 1), False)
# save SAT data
tg_sat = g.copy_data()
# anomalise to obtain SATA data
g.anomalise()
if DATA == 0:
g_max = load_station_data("../data/TX_STAID000027.txt", date(1775, 1, 1), date(2014, 1, 1), False)
g_min = load_station_data("../data/TN_STAID000027.txt", date(1775, 1, 1), date(2014, 1, 1), False)
elif DATA == 1:
g_max = load_bin_data("../data/ERA_time_series_50.0N_15.0E.bin", date(1940, 1, 1), date(2014, 1, 1), False)
g_min = load_bin_data("../data/ERA_time_series_50.0N_15.0E.bin", date(1940, 1, 1), date(2014, 1, 1), False)
diff_ax = (0, 1.5) # means -> 0, 2, var -> 1, 8
mean_ax = (18, 22) # means -> -1, 1.5, var -> 9, 18
PLOT = True
PLOT_PHASE = False
BEGIN = True # if True, phase will be rewritten as in the beggining, otherwise as in the end
PHASE_ANALYSIS_YEAR = None # year of detailed analysis - phase and bins, or None
AA = False
SAME_BINS = False
CONDITION = False
SEASON = None
AMPLITUDE = False
## loading data
## PRG
g = load_station_data(
"../data/TG_STAID000027.txt", date(1775, 1, 1), date(2015, 1, 1), ANOMALISE
) # 15-01-1924 if 32k, 28-04-1834 if 64k
if AMPLITUDE:
g_amp = load_station_data("../data/TG_STAID000027.txt", date(1775, 1, 1), date(2015, 1, 1), False)
## HAMBURG -- TG_STAID000047, POTSDAM -- TG_STAID000054
# g = load_station_data('../data/TG_STAID000054.txt', date(1893,1,1), date(2014,1,1), ANOMALISE) # 15-01-1924 if 32k, 28-04-1834 if 64k
# if AMPLITUDE:
# g_amp = load_station_data('../data/TG_STAID000054.txt', date(1893,1,1), date(2014, 1, 1), False)
# ERA
# g = load_bin_data('../data/ERA_time_series_50.0N_15.0E.bin', date(1958,4,28), date(2013,10,1), ANOMALISE)
# ECA
# g = load_bin_data('../data/ECA&D_time_series_50.1N_14.4E.bin', date(1950,4,28), date(2013,10,1), ANOMALISE)
g_working = DataField()
g_surrs = DataField()
PERIOD = 8
PAST_UNTIL = 1930 # from which segment the average extremes should be computed
WINDOW_LENGTH = 13462 # 13462, 16384 for surrogates only 13462
PLOT = True
USE_SURR = True
NUM_SURR = 100
# load whole data - load SAT data
if USE_SURR:
g = load_station_data('../data/TG_STAID000027.txt', date(1834, 7, 28), date(2014, 1, 1), False) # 1834-7-28 till 2014-1-1 = 64k
g_for_avg = load_station_data('../data/TG_STAID000027.txt', date(1840, 4, 14), date(1930, 1, 1), False) # 1840-4-14 till 1930-1-1 = 32k
else:
g = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1), date(2014, 1, 1), False)
g_for_avg = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1), date(2014, 1, 1), False)
if not USE_SURR:
# save SAT data
tg_sat = g.copy_data()
tg_avg_sat = g_for_avg.copy_data()
# anomalise to obtain SATA data
g.anomalise()
g_for_avg.anomalise()
g_for_avg.select_date(date(1775, 1, 1), date(PAST_UNTIL, 1, 1))
PERIOD = 8
AMP_PERIOD = 8
BINS = 8
ANOMALISE = True # amplitude from SAT / SATA
SURR = True
NUM_SURR = 1000
WORKERS = 3
SURR_TYPE = 'FT'
# 65k
# g = load_station_data('TG_STAID000027.txt', date(1834,4,27), date(2013,10,1), True)
# g_amp = load_station_data('TG_STAID000027.txt', date(1834,4,27), date(2013,10,1), ANOMALISE)
# 32k
g = load_station_data('TG_STAID000027.txt', date(1958, 1,
1), date(2013, 11, 10),
True) # date(1924,1,14), date(2013,10,1)
g_amp = load_station_data('TG_STAID000027.txt', date(1958, 1, 1),
date(2013, 11, 10), ANOMALISE)
g_data = DataField()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
# wavelet - data
wave, _, _, _ = wavelet_analysis.continous_wavelet(g.data,
1,
False,
wavelet_analysis.morlet,
plt.savefig(fname)
else:
plt.show()
PERIOD = 8
WINDOW_LENGTH = 13462 # 13462, 16384
MIDDLE_YEAR = 1965 # year around which the window will be deployed
JUST_SCALING = True
PLOT = True
WAVES_PERCENTIL = 80
DATA = 0 # 0 - original station, 1 - closest ERA, 2 - closest ECA&D
# load whole data - load SAT data
if DATA == 0:
g = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1),
date(2014, 1, 1), False)
elif DATA == 1:
g = load_bin_data('../data/ERA_time_series_50.0N_15.0E.bin',
date(1940, 1, 1), date(2014, 1, 1), False)
elif DATA == 2:
g = load_bin_data('../data/ECA&D_time_series_50.1N_14.4E.bin',
date(1940, 1, 1), date(2014, 1, 1), False)
# save SAT data
tg_sat = g.copy_data()
# anomalise to obtain SATA data
g.anomalise()
if DATA == 0:
g_max = load_station_data('../data/TX_STAID000027.txt', date(1775, 1, 1),
date(2014, 1, 1), False)
g_min = load_station_data('../data/TN_STAID000027.txt', date(1775, 1, 1),
diff_ax = (0, 1.5) # means -> 0, 2, var -> 1, 8
mean_ax = (18, 22) # means -> -1, 1.5, var -> 9, 18
PLOT = True
PLOT_PHASE = False
BEGIN = True # if True, phase will be rewritten as in the beggining, otherwise as in the end
PHASE_ANALYSIS_YEAR = None # year of detailed analysis - phase and bins, or None
AA = False
SAME_BINS = False
CONDITION = False
SEASON = None
AMPLITUDE = False
## loading data
## PRG
g = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1),
date(2015, 1, 1),
ANOMALISE) # 15-01-1924 if 32k, 28-04-1834 if 64k
if AMPLITUDE:
g_amp = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1),
date(2015, 1, 1), False)
## HAMBURG -- TG_STAID000047, POTSDAM -- TG_STAID000054
# g = load_station_data('../data/TG_STAID000054.txt', date(1893,1,1), date(2014,1,1), ANOMALISE) # 15-01-1924 if 32k, 28-04-1834 if 64k
# if AMPLITUDE:
# g_amp = load_station_data('../data/TG_STAID000054.txt', date(1893,1,1), date(2014, 1, 1), False)
# ERA
#g = load_bin_data('../data/ERA_time_series_50.0N_15.0E.bin', date(1958,4,28), date(2013,10,1), ANOMALISE)
# ECA
#g = load_bin_data('../data/ECA&D_time_series_50.1N_14.4E.bin', date(1950,4,28), date(2013,10,1), ANOMALISE)
g_working = DataField()
PERIOD = 8
AMP_PERIOD = 8
BINS = 8
ANOMALISE = True # amplitude from SAT / SATA
SURR = True
NUM_SURR = 1000
WORKERS = 3
SURR_TYPE = 'FT'
# 65k
# g = load_station_data('TG_STAID000027.txt', date(1834,4,27), date(2013,10,1), True)
# g_amp = load_station_data('TG_STAID000027.txt', date(1834,4,27), date(2013,10,1), ANOMALISE)
# 32k
g = load_station_data('TG_STAID000027.txt', date(1958, 1, 1), date(2013, 11, 10), True) # date(1924,1,14), date(2013,10,1)
g_amp = load_station_data('TG_STAID000027.txt', date(1958, 1, 1), date(2013, 11, 10), ANOMALISE)
g_data = DataField()
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * y # frequency of interest
s0 = period / fourier_factor # get scale
# wavelet - data
wave, _, _, _ = wavelet_analysis.continous_wavelet(g.data, 1, False, wavelet_analysis.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave)) # get phases from oscillatory modes
period = AMP_PERIOD * 365.25 # frequency of interest
from src import wavelet_analysis
from src.data_class import load_station_data
import numpy as np
from datetime import date
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
AMP_PERIODS = [1, 8]
# g_amp = load_station_data('TG_STAID000027.txt', date(1924,1,14), date(2013,10,1), False)
# g_amp_sata = load_station_data('TG_STAID000027.txt', date(1924,1,14), date(2013,10,1), True)
g_amp = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1),
date(2014, 1, 1), False)
g_amp_sata = load_station_data('../data/TG_STAID000027.txt', date(1775, 1, 1),
date(2014, 1, 1), True)
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
result = dict()
start_cut = date(1779, 1, 1)
# _, _, idx = g_amp.get_data_of_precise_length('16k', start_cut, None, False)
idx = g_amp.select_date(start_cut, date(2010, 1, 1), apply_to_data=False)
fields = [g_amp, g_amp_sata]
for i in range(len(AMP_PERIODS)):
period = AMP_PERIODS[i] * 365.25 # frequency of interest
s0_amp = period / fourier_factor # get scale
wave, _, _, _ = wavelet_analysis.continous_wavelet(
def running_mean(arr, aver):
out = np.zeros((arr.shape[0] - aver + 1, ))
for i in range(out.shape[0]):
out[i] = np.mean(arr[i:i + aver])
return out
def get_equidistant_bins():
return np.array(np.linspace(-np.pi, np.pi, 9))
BINS_COND = True
g = load_station_data('TG_STAID000027.txt', date(1834, 4, 28),
date(2013, 10, 1), False)
g_max = load_station_data('TX_STAID000027.txt', date(1834, 4, 28),
date(2013, 10, 1), False)
g_min = load_station_data('TN_STAID000027.txt', date(1834, 4, 28),
date(2013, 10, 1), False)
if BINS_COND:
k0 = 6. # wavenumber of Morlet wavelet used in analysis
y = 365.25 # year in days
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
period = 8 * y # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wavelet_analysis.continous_wavelet(
g.data, 1, False, wavelet_analysis.morlet, dj=0, s0=s0, j1=0,
k0=k0) # perform wavelet
phase = np.arctan2(np.imag(wave), np.real(wave))
