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)
Esempio n. 3
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        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)) 
Esempio n. 4
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    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
Esempio n. 5
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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
Esempio n. 6
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    #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)