start_cut = date(start_year + cnt * WINDOW_SHIFT, sm, sd)
idx = g_working.get_data_of_precise_length(WINDOW_LENGTH, start_cut,
None, True)
print 'data ', g.get_date_from_ndx(
start_idx), ' - ', g.get_date_from_ndx(end_idx)
print 'cut from ', start_cut, ' to ', g_working.get_date_from_ndx(-1)
#last_mid_year = date.fromordinal(g_working.time[WINDOW_LENGTH/2]).year
last_mid_year += 1
print last_mid_year
phase = phase[0, idx[0]:idx[1]]
if AMPLITUDE:
amplitude = amplitude[idx[0]:idx[1]]
if PLOT_PHASE and BEGIN:
phase_till = date(start_year + (cnt + 1) * WINDOW_SHIFT, sm, sd)
ndx = g_working.find_date_ndx(phase_till)
if ndx != None and cnt < 125:
phase_total.append(phase[:ndx])
else:
phase_total.append(phase)
if PLOT_PHASE and not BEGIN:
ndx = g_working.find_date_ndx(last_day)
phase_total.append(phase[ndx:])
last_day = g_working.get_date_from_ndx(-1)
# season
if SEASON != None:
ndx_season = g_working.select_months(SEASON)
phase = phase[ndx_season]
if AMPLITUDE:
amplitude = amplitude[ndx_season]
else:
if filename != None:
plt.savefig(filename)
else:
plt.show()
g = DataField()
g.load('tg_0.25deg_reg_v9.0.nc', 'tg')
means = True
daily = False
if means:
idx = 0
y = 1950
while idx < g.data.shape[0]:
idx2 = g.find_date_ndx(date(y, 1, 1))
render_geo_field(g.data[idx:idx2, ...], g.lats, g.lons, None, None,
False, 'Yearly mean temperature %s' % str(y),
'temperature [$^{\circ}C$]',
'imgs/temp_mean%s.png' % str(y))
y += 1
idx = idx2
if daily:
idx = g.find_date_ndx(date(2012, 1, 1))
for i in range(idx, idx + 59):
render_geo_field(g.data[i, ...], g.lats, g.lons, None, None, False,
'Temperature %s' % str(date.fromordinal(g.time[i])),
'temperature [$^{\circ}C$]',
'imgs/temp%s.png' % str(i + 1))
m, c = np.linalg.lstsq(fit_x, g_working_amp.data)[0]
amplitude = m * amplitude + c
start_cut = date(start_year + cnt * WINDOW_SHIFT, sm, sd)
idx = g_working.get_data_of_precise_length(WINDOW_LENGTH, start_cut, None, True)
print "data ", g.get_date_from_ndx(start_idx), " - ", g.get_date_from_ndx(end_idx)
print "cut from ", start_cut, " to ", g_working.get_date_from_ndx(-1)
# last_mid_year = date.fromordinal(g_working.time[WINDOW_LENGTH/2]).year
last_mid_year += 1
print last_mid_year
phase = phase[0, idx[0] : idx[1]]
if AMPLITUDE:
amplitude = amplitude[idx[0] : idx[1]]
if PLOT_PHASE and BEGIN:
phase_till = date(start_year + (cnt + 1) * WINDOW_SHIFT, sm, sd)
ndx = g_working.find_date_ndx(phase_till)
if ndx != None and cnt < 125:
phase_total.append(phase[:ndx])
else:
phase_total.append(phase)
if PLOT_PHASE and not BEGIN:
ndx = g_working.find_date_ndx(last_day)
phase_total.append(phase[ndx:])
last_day = g_working.get_date_from_ndx(-1)
# season
if SEASON != None:
ndx_season = g_working.select_months(SEASON)
phase = phase[ndx_season]
if AMPLITUDE:
amplitude = amplitude[ndx_season]
else:
result_temp_surr = np.zeros((NUM_SURR, 8, 2))
for surr in range(NUM_SURR):
sg.construct_surrogates_with_residuals()
sg.add_seasonality(mean[:-1], var[:-1], trend[:-1]) # so SAT data
g.data = sg.surr_data.copy()
tg_sat = g.copy_data()
g.time = g.time[:-1]
g.anomalise()
g_temp = DataField()
tg_temp = tg_sat.copy()
sy = int(MIDDLE_YEAR - (WINDOW_LENGTH / year) / 2)
g_temp.data = g.data.copy()
g_temp.time = g.time.copy()
start = g_temp.find_date_ndx(date(sy - 4, sm, sd))
end = start + 16384 if WINDOW_LENGTH < 16000 else start + 32768
g_temp.data = g_temp.data[start:end]
g_temp.time = g_temp.time[start:end]
tg_temp = tg_temp[start:end]
k0 = 6. # wavenumber of Morlet wavelet used in analysis
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0, 2)))
period = PERIOD * year # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g_temp.data,
1,
False,
wvlt.morlet,
dj=0,
result_temp_surr = np.zeros((NUM_SURR, 8,2))
for surr in range(NUM_SURR):
sg.construct_surrogates_with_residuals()
sg.add_seasonality(mean[:-1], var[:-1], trend[:-1]) # so SAT data
g.data = sg.surr_data.copy()
tg_sat = g.copy_data()
g.time = g.time[:-1]
g.anomalise()
g_temp = DataField()
tg_temp = tg_sat.copy()
sy = int(MIDDLE_YEAR - (WINDOW_LENGTH/year)/2)
g_temp.data = g.data.copy()
g_temp.time = g.time.copy()
start = g_temp.find_date_ndx(date(sy - 4, sm, sd))
end = start + 16384 if WINDOW_LENGTH < 16000 else start + 32768
g_temp.data = g_temp.data[start : end]
g_temp.time = g_temp.time[start : end]
tg_temp = tg_temp[start : end]
k0 = 6. # wavenumber of Morlet wavelet used in analysis
fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))
period = PERIOD * year # frequency of interest
s0 = period / fourier_factor # get scale
wave, _, _, _ = wvlt.continous_wavelet(g_temp.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
idx = g_temp.get_data_of_precise_length(WINDOW_LENGTH, date(sy, sm, sd), None, True)
phase = phase[0, idx[0] : idx[1]]
plt.title(title)
cbar = plt.colorbar(format = r"%2.2f", shrink = 0.75, ticks = np.arange(mi, ma + step, (ma-mi)/8),
aspect = 25, drawedges = False)
cbar.set_label(cbar_label)
cbar_obj = plt.getp(cbar.ax.axes, 'yticklabels')
plt.setp(cbar_obj, fontsize = 10, color = (.1, .1, .1))
if filename != None:
plt.savefig(filename)
else:
plt.show()
g = DataField()
g.load('tg_0.25deg_reg_v9.0.nc', 'tg')
means = True
daily = False
if means:
idx = 0
y = 1950
while idx < g.data.shape[0]:
idx2 = g.find_date_ndx(date(y,1,1))
render_geo_field(g.data[idx:idx2, ...], g.lats, g.lons, None, None, False, 'Yearly mean temperature %s' % str(y), 'temperature [$^{\circ}C$]', 'imgs/temp_mean%s.png' % str(y))
y += 1
idx = idx2
if daily:
idx = g.find_date_ndx(date(2012,1,1))
for i in range(idx,idx+59):
render_geo_field(g.data[i, ...], g.lats, g.lons, None, None, False, 'Temperature %s' % str(date.fromordinal(g.time[i])), 'temperature [$^{\circ}C$]', 'imgs/temp%s.png' % str(i+1))
