def run_LSP(vals,x):
print obs_refs[x]
#check obs vals are valid
valid = vals >= 0
vals = vals[valid]
valid_times = obs_ref_time[valid]
#full_times = modules.date_process(obs_dates,obs_times,start_year)
full_times_year = obs_ref_time[:8766]
full_times_day = obs_ref_time[:24]
#make time start from 0
valid_times_from0 = modules.phase_start_correct(valid_times)
samp_step = 1./24
f = interpolate.interp1d(valid_times_from0, vals)
valid_times_from0 = np.arange(np.min(valid_times_from0),np.max(valid_times_from0),samp_step)
vals = f(valid_times_from0)
site_lon = obs_lons[x]
#convert site_lon to 0 to 360 degs
if site_lon < 0:
site_lon = 360-np.abs(site_lon)
#transform from UTC time to solar time
sun_time = lon_step_time*site_lon
time_diff = sun_time - 0
if time_diff > 12:
time_diff = time_diff-24
#take obs lsp
ofac = 1
periodic_periods = [1./10.,1./9.,1./8.,1./7.,1./6.,1./5.,1./4.,1./3.,1./2.,1.,365.25/4.,365.25/3.,365.25/2.,365.25]
#periods,mag,ph,fr,fi,amp_corr = modules.take_lomb(valid_times_from0,vals,ofac,samp_step,w=True,kp=periodic_periods)
periods,mag,ph,fr,fi,fft_array,amp_corr = modules.take_fft(valid_times_from0,vals,ofac,samp_step,w=True,kp=periodic_periods)
#convert mag to normalised psd
psd_mag = mag**2
freq = 1./periods
diff = freq[1] - freq[0]
psd_mag = psd_mag/diff
#get info of weather regimes through model fit.
grad1,grad2,bp1,line1_periods,line1_mag,line2_periods,line2_mag,ave1,ave2,med1,med2,sum1,sum2,line1_s,line1_e,line2_s,line2_e = modules.spectra_fit_fixed_piecewise(periods,psd_mag,ofac,3.0,100.0,10.0)
#get mean of values
mean_array = np.average(vals)
#correct all phases for start point (not actually being from 0 - just corrected to be)
ph = modules.phase_start_point_correct_all(periods,ph,valid_times)
#convert phase to time(days)
ph = modules.convert_phase_units_actual_all(ph,periods)
return (x,periods,psd_mag,ph,grad1,grad2,bp1,line1_periods,line1_mag,line2_periods,line2_mag,ave1,ave2,med1,med2,sum1,sum2,line1_s,line1_e,line2_s,line2_e)
def run_LSP(vals,x):
lat_i = lat_indices[x]
lon_i = lon_indices[x]
print lat_i,lon_i
current_lat = lat_c[lat_i]
current_lon = lon_c[lon_i]
site_lon = lon_c[lon_i]
valid = vals >= 0
vals = vals[valid]
valid_times = model_ref_time[valid]
full_times_year = model_ref_time[:8766]
full_times_day = model_ref_time[:24]
#convert site_lon to 0 to 360 degs
if site_lon < 0:
site_lon = 360-np.abs(site_lon)
#transform from UTC time to solar time
sun_time = lon_step_time*site_lon
time_diff = sun_time - 0
if time_diff > 12:
time_diff = time_diff-24
#make time start from 0
valid_times_from0 = modules.phase_start_correct(valid_times)
ofac = 1
samp_step = 1./24
periodic_periods = [1./10.,1./9.,1./8.,1./7.,1./6.,1./5.,1./4.,1./3.,1./2.,1.,365.25/4.,365.25/3.,365.25/2.,365.25]
#periods,mag,ph,fr,fi,amp_corr = modules.take_lomb(valid_times_from0,vals,ofac,samp_step,w=True,kp=periodic_periods)
periods,mag,ph,fr,fi,fft_array,amp_corr = modules.take_fft(valid_times_from0,vals,ofac,samp_step,w=True,kp=periodic_periods)
#convert mag to normalised psd
psd_mag = mag**2
freq = 1./periods
diff = freq[1] - freq[0]
psd_mag = psd_mag/diff
#get info of weather regimes through model fit.
grad1,grad2,bp1,line1_periods,line1_mag,line2_periods,line2_mag,ave1,ave2,med1,med2,sum1,sum2,line1_s,line1_e,line2_s,line2_e = modules.spectra_fit_fixed_piecewise(periods,psd_mag,ofac,3.0,100.0,10.0)
#get mean of values
mean_array = np.average(vals)
#correct all phases for start point (not actually being from 0 - just corrected to be)
ph = modules.phase_start_point_correct_all(periods,ph,valid_times)
#convert phase to time(days)
ph = modules.convert_phase_units_actual_all(ph,periods)
return (x,periods,psd_mag,ph,grad1,grad2,bp1,line1_periods,line1_mag,line2_periods,line2_mag,ave1,ave2,med1,med2,sum1,sum2,line1_s,line1_e,line2_s,line2_e)
lsp5_diff_p2.append(diff_p2)
#print '\nLOMB WINDOWED, SPECIFIC FREQUENCIES'
#print 'Est. Amplitude 1 = ',mag1
#print 'Est. Phase 1 = ',phase1
#print 'Est. Amplitude 2 = ',mag2
#print 'Est. Phase 2 = ',phase2
#print '------------------------------------------\n'
#FFT - OUT OF BOX
#----------------------------------------------------------------
ofac=1
periods,mag,ph,fr,fi,fft_array,amp_corr = modules.take_fft(b,vals,hourly_step,ofac,w=False)
closest_period_1 = min(range(len(periods)), key=lambda i: abs(periods[i]-p1))
closest_period_2 = min(range(len(periods)), key=lambda i: abs(periods[i]-p2))
mag1 = mag[closest_period_1]
phase1 = ph[closest_period_1]
mag2 = mag[closest_period_2]
phase2 = ph[closest_period_2]
diff_a1 = (mag1/amp1)*100.
diff_a2 = (mag2/amp2)*100.
diff_p1 = phase_1 - phase1
if diff_p1 > np.pi:
diff_p1 = np.abs(-np.pi + (diff_p1 - np.pi))
elif diff_p1 < -np.pi:
diff_p1 = np.abs(np.pi - (np.abs(diff_p1) - np.pi))
daily_phase1 = ph[closest_period_1]
daily_mag2 = mag[closest_period_2]
daily_phase2 = ph[closest_period_2]
print '\nLOMB WINDOWED, SPECIFIC FREQUENCIES'
print 'Est. Amplitude 1 = ', daily_mag1
print 'Est. Phase 1 = ', daily_phase1
print 'Est. Amplitude 2 = ', daily_mag2
print 'Est. Phase 2 = ', daily_phase2
print '------------------------------------------\n'
#FFT - OUT OF BOX
#----------------------------------------------------------------
ofac = 1
periods, mag, ph, fr, fi, fft_array, amp_corr = modules.take_fft(
b, vals, hourly_step, ofac, w=False)
closest_period_1 = min(range(len(periods)),
key=lambda i: abs(periods[i] - p1))
closest_period_2 = min(range(len(periods)),
key=lambda i: abs(periods[i] - p2))
daily_mag1 = mag[closest_period_1]
daily_phase1 = ph[closest_period_1]
daily_mag2 = mag[closest_period_2]
daily_phase2 = ph[closest_period_2]
print '\nFFT OUT OF BOX'
print 'Est. Amplitude 1 = ', daily_mag1
print 'Est. Phase 1 = ', daily_phase1
print 'Est. Amplitude 2 = ', daily_mag2
print 'Est. Phase 2 = ', daily_phase2
print vals valid = vals > 0 vals = vals[valid] current_time = current_time[valid] print current_time all_hours = np.arange(np.min(current_time),np.max(current_time)+1./48.,1./24.) f = interpolate.interp1d(current_time, vals) vals = f(all_hours) ofac=4 periods,mag,ph,fr,fi,fft_array,amp_corr = modules.take_fft(all_hours,vals,1./24.,ofac,w=False) print len(fft_array) print len(mag) print len(fr) print len(fi) print fr plt.loglog(periods,mag) #remove points n around fifth diurnal peak diurnal_i = min(range(len(periods)), key=lambda i: abs(periods[i]-0.2)) n_points = int((170*ofac)+np.floor(ofac/2.)) rm_points = range(diurnal_i-n_points,(diurnal_i+n_points)+1) mag[rm_points] = 0 fr[rm_points] = 0
#print 'Est. Amplitude 1 = ',daily_mag1
#print 'Est. Phase 1 = ',daily_phase1
#print 'Est. Amplitude 2 = ',daily_mag2
#print 'Est. Phase 2 = ',daily_phase2
#print '------------------------------------------\n'
#FFT - windowed
#----------------------------------------------------------------
f = interpolate.interp1d(b, vals)
new_b = np.arange(np.min(b), np.max(b), hourly_step)
interp_vals = f(new_b)
periods, mag, ph, fr, fi, fft_array = modules.take_fft(new_b,
interp_vals,
hourly_step,
w=True)
closest_period_1 = min(range(len(periods)),
key=lambda i: abs(periods[i] - p1))
closest_period_2 = min(range(len(periods)),
key=lambda i: abs(periods[i] - p2))
daily_mag1 = mag[closest_period_1]
daily_phase1 = ph[closest_period_1]
daily_mag2 = mag[closest_period_2]
daily_phase2 = ph[closest_period_2]
fft_diff = percent * np.abs(daily_mag1 - amp1)
all_fft_diff.append(fft_diff)