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)
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] #check model vals are valid 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 = 4 samp_step = 1./24 periods,mag,ph,fr,fi,amp_corr = modules.take_lomb(valid_times_from0,vals,ofac,samp_step,w=True,kp=[1./4.,1./3.,1./2.,1.,365.25/4.,365.25/3.,365.25/2.,365.25]) #get info of weather regimes through model fit. grad1,grad2,grad3,bp1,bp2,bp_periods,bp_mag = modules.spectra_fit(periods,mag,ofac) #get mean of values mean_array = np.average(vals) #correct all phases for start point 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,mag,ph,grad1,grad2,grad3,bp1,bp2,bp_mag)
def run_LSP(model_data, x): print obs_refs[x] vals = model_data #check obs vals are valid valid = vals >= 0 vals = vals[valid] model_time_val = model_time[valid] model_date_val = model_date[valid] full_times = modules.date_process(model_date, model_time, start_year) if timeres == 'M': full_times_year = full_times[:12] else: full_times_year = full_times[:8766] full_times_day = full_times[:24] valid_times = modules.date_process(model_date_val, model_time_val, start_year) 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 #make time start from 0 valid_times_from0 = modules.phase_start_correct(valid_times) periodic_periods = [ 1. / 4., 1. / 3., 1. / 2., 1., 365.25 / 4., 365.25 / 3., 365.25 / 2., 365.25 ] periods, mag, ph, fr, fi = modules.take_lomb_spec( valid_times_from0, vals, w=True, key_periods=periodic_periods) #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(periodic_periods, ph, valid_times) key_diurnal_periods = [1. / 4., 1. / 3., 1. / 2., 1.] key_seasonal_periods = [365.25 / 4., 365.25 / 3., 365.25 / 2., 365.25] diurnal_mags = mag[:4] seasonal_mags = mag[4:] seasonal_phs = ph[4:] #get individual mags and phases daily_h3_mag = mag[0] daily_h2_mag = mag[1] daily_h1_mag = mag[2] orig_daily_mag = mag[3] daily_h3_ph = ph[0] daily_h2_ph = ph[1] daily_h1_ph = ph[2] orig_daily_ph = ph[3] seasonal_h3_mag = mag[4] seasonal_h2_mag = mag[5] seasonal_h1_mag = mag[6] annual_mag = mag[7] seasonal_h3_ph = ph[4] seasonal_h2_ph = ph[5] seasonal_h1_ph = ph[6] annual_ph = ph[7] #convert sub diurnal phases from UTC to solar time daily_h3_ph = modules.solar_time_phase_corrector(daily_h3_ph, 6, time_diff) daily_h2_ph = modules.solar_time_phase_corrector(daily_h2_ph, 24. / 3., time_diff) daily_h1_ph = modules.solar_time_phase_corrector(daily_h1_ph, 12, time_diff) orig_daily_ph = modules.solar_time_phase_corrector(orig_daily_ph, 24, time_diff) diurnal_phs = [daily_h3_ph, daily_h2_ph, daily_h1_ph, orig_daily_ph] #convolve annual cycle and harmonics to seasonal waveform for 1 year seasonal_mag, seasonal_min_ph, seasonal_max_ph, seasonal_waveform, seasonal_ff = modules.period_convolution( key_seasonal_periods, full_times_year, seasonal_mags, seasonal_phs, mean_array) #convolve diurnal cycle and harmonics to diurnal waveform for 1 day diurnal_mag, diurnal_min_ph, diurnal_max_ph, diurnal_waveform, diurnal_ff = modules.period_convolution( key_diurnal_periods, full_times_day, diurnal_mags, diurnal_phs, mean_array) #convolve all full_mag, full_min_ph, full_max_ph, full_waveform, full_ff = modules.period_convolution( periodic_periods, full_times, mag, ph, mean_array) #convert phase to time daily_h3_ph = modules.convert_phase_units_actual_single(daily_h3_ph, 6.) daily_h2_ph = modules.convert_phase_units_actual_single( daily_h2_ph, 24. / 3.) daily_h1_ph = modules.convert_phase_units_actual_single(daily_h1_ph, 12.) orig_daily_ph = modules.convert_phase_units_actual_single( orig_daily_ph, 24.) diurnal_min_ph = modules.convert_phase_units_actual_single( diurnal_min_ph, 24.) diurnal_max_ph = modules.convert_phase_units_actual_single( diurnal_max_ph, 24.) seasonal_h3_ph = modules.convert_phase_units_actual_single( seasonal_h3_ph, 3.) seasonal_h2_ph = modules.convert_phase_units_actual_single( seasonal_h2_ph, 4.) seasonal_h1_ph = modules.convert_phase_units_actual_single( seasonal_h1_ph, 6.) annual_ph = modules.convert_phase_units_actual_single(annual_ph, 12.) seasonal_min_ph = modules.convert_phase_units_actual_single( seasonal_min_ph, 12.) seasonal_max_ph = modules.convert_phase_units_actual_single( seasonal_max_ph, 12.) return (x, daily_h3_mag, daily_h3_ph, daily_h2_mag, daily_h2_ph, daily_h1_mag, daily_h1_ph, orig_daily_mag, orig_daily_ph, diurnal_mag, diurnal_min_ph, diurnal_max_ph, seasonal_h3_mag, seasonal_h3_ph, seasonal_h2_mag, seasonal_h2_ph, seasonal_h1_mag, seasonal_h1_ph, annual_mag, annual_ph, seasonal_mag, seasonal_min_ph, seasonal_max_ph, mean_array, diurnal_waveform, seasonal_waveform, full_waveform)
def run_LSP(obs_time,site_lon,x): data_valid = True #print 'lev %s'%(x) full_times_year = np.arange(0,365,1.) #check obs vals are valid valid = vals >= 0 vals = vals[valid] valid_times = obs_time[valid] #if length of vals is zero then class as invalid immediately if len(vals) == 0: data_valid = False else: #test if there if data is valid to process at each height for each site #data should not have gaps > 1 year or time_gaps = np.diff(valid_times) inv_count = 0 max_count = round(n_years/2.) for i in time_gaps: if i > 90: inv_count+=1 if inv_count >= max_count: data_valid = False print 'Persisent Data gap > 3 months' break if i > 365: data_valid = False print 'Data gap > 1 Year' break if data_valid == True: #convert site_lon to 0 to 360 degs if site_lon < 0: site_lon = 360-np.abs(site_lon) #make time start from 0 valid_times_from0 = modules.phase_start_correct(valid_times) periodic_periods = [365.25/4.,365.25/3.,365.25/2.,365.25] periods,mag,ph,fr,fi = modules.take_lomb_spec(valid_times_from0,vals,w=True,key_periods=periodic_periods) #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(periodic_periods,ph,valid_times) key_seasonal_periods = [365.25/4.,365.25/3.,365.25/2.,365.25] seasonal_mags = mag[:] seasonal_phs = ph[:] seasonal_h3_mag = mag[0] seasonal_h2_mag = mag[1] seasonal_h1_mag = mag[2] annual_mag = mag[3] seasonal_h3_ph = ph[0] seasonal_h2_ph = ph[1] seasonal_h1_ph = ph[2] annual_ph = ph[3] #convolve annual cycle and harmonics to seasonal waveform for 1 year seasonal_mag,seasonal_min_ph,seasonal_max_ph,seasonal_waveform,seasonal_ff = modules.period_convolution(key_seasonal_periods,full_times_year,seasonal_mags,seasonal_phs,mean_array) #convert phase to time seasonal_h3_ph = modules.convert_phase_units_actual_single(seasonal_h3_ph,3.) seasonal_h2_ph = modules.convert_phase_units_actual_single(seasonal_h2_ph,4.) seasonal_h1_ph = modules.convert_phase_units_actual_single(seasonal_h1_ph,6.) annual_ph = modules.convert_phase_units_actual_single(annual_ph,12.) seasonal_min_ph = modules.convert_phase_units_actual_single(seasonal_min_ph,12.) seasonal_max_ph = modules.convert_phase_units_actual_single(seasonal_max_ph,12.) else: seasonal_h3_mag = -99999 seasonal_h2_mag = -99999 seasonal_h1_mag = -99999 annual_mag = -99999 seasonal_mag = -99999 seasonal_h3_ph = -99999 seasonal_h2_ph = -99999 seasonal_h1_ph = -99999 annual_ph = -99999 seasonal_max_ph = -99999 seasonal_min_ph = -99999 seasonal_waveform = np.array([-99999]*len(full_times_year)) mean_array = -99999 return x,seasonal_h3_mag,seasonal_h3_ph,seasonal_h2_mag,seasonal_h2_ph,seasonal_h1_mag,seasonal_h1_ph,annual_mag,annual_ph,seasonal_mag,seasonal_max_ph,seasonal_min_ph,seasonal_waveform,mean_array
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] if timeres == 'H': full_times_year = obs_ref_time[:8766] elif timeres == 'D': full_times_year = obs_ref_time[:365] elif timeres == 'M': full_times_year = obs_ref_time[:12] full_times_day = obs_ref_time[:24] 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 #make time start from 0 valid_times_from0 = modules.phase_start_correct(valid_times) print valid_times_from0 periodic_periods = [1./4.,1./3.,1./2.,1.,365.25/4.,365.25/3.,365.25/2.,365.25] periods,mag,ph,fr,fi = modules.take_lomb_spec(valid_times_from0,vals,w=True,key_periods=periodic_periods) #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(periodic_periods,ph,valid_times) key_diurnal_periods = [1./4.,1./3.,1./2.,1.] key_seasonal_periods = [365.25/4.,365.25/3.,365.25/2.,365.25] diurnal_mags = mag[:4] seasonal_mags = mag[4:] seasonal_phs = ph[4:] #get individual mags and phases daily_h3_mag = mag[0] daily_h2_mag = mag[1] daily_h1_mag = mag[2] orig_daily_mag = mag[3] daily_h3_ph = ph[0] daily_h2_ph = ph[1] daily_h1_ph = ph[2] orig_daily_ph = ph[3] seasonal_h3_mag = mag[4] seasonal_h2_mag = mag[5] seasonal_h1_mag = mag[6] annual_mag = mag[7] seasonal_h3_ph = ph[4] seasonal_h2_ph = ph[5] seasonal_h1_ph = ph[6] annual_ph = ph[7] #convert sub diurnal phases from UTC to solar time daily_h3_ph = modules.solar_time_phase_corrector(daily_h3_ph,6,time_diff) daily_h2_ph = modules.solar_time_phase_corrector(daily_h2_ph,24./3.,time_diff) daily_h1_ph = modules.solar_time_phase_corrector(daily_h1_ph,12,time_diff) orig_daily_ph = modules.solar_time_phase_corrector(orig_daily_ph,24,time_diff) diurnal_phs = [daily_h3_ph,daily_h2_ph,daily_h1_ph,orig_daily_ph] #convolve annual cycle and harmonics to seasonal waveform for 1 year seasonal_mag,seasonal_min_ph,seasonal_max_ph,seasonal_waveform,seasonal_ff = modules.period_convolution(key_seasonal_periods,full_times_year,seasonal_mags,seasonal_phs,mean_array) #convolve diurnal cycle and harmonics to diurnal waveform for 1 day diurnal_mag,diurnal_min_ph,diurnal_max_ph,diurnal_waveform,diurnal_ff = modules.period_convolution(key_diurnal_periods,full_times_day,diurnal_mags,diurnal_phs,mean_array) #convolve all full_mag,full_min_ph,full_max_ph,full_waveform,full_ff = modules.period_convolution(periodic_periods,obs_ref_time,mag,ph,mean_array) #convert phase to time daily_h3_ph = modules.convert_phase_units_actual_single(daily_h3_ph,6.) daily_h2_ph = modules.convert_phase_units_actual_single(daily_h2_ph,24./3.) daily_h1_ph = modules.convert_phase_units_actual_single(daily_h1_ph,12.) orig_daily_ph = modules.convert_phase_units_actual_single(orig_daily_ph,24.) diurnal_min_ph = modules.convert_phase_units_actual_single(diurnal_min_ph,24.) diurnal_max_ph = modules.convert_phase_units_actual_single(diurnal_max_ph,24.) seasonal_h3_ph = modules.convert_phase_units_actual_single(seasonal_h3_ph,3.) seasonal_h2_ph = modules.convert_phase_units_actual_single(seasonal_h2_ph,4.) seasonal_h1_ph = modules.convert_phase_units_actual_single(seasonal_h1_ph,6.) annual_ph = modules.convert_phase_units_actual_single(annual_ph,12.) seasonal_min_ph = modules.convert_phase_units_actual_single(seasonal_min_ph,12.) seasonal_max_ph = modules.convert_phase_units_actual_single(seasonal_max_ph,12.) return (x,daily_h3_mag,daily_h3_ph,daily_h2_mag,daily_h2_ph,daily_h1_mag,daily_h1_ph,orig_daily_mag,orig_daily_ph,diurnal_mag,diurnal_min_ph,diurnal_max_ph,seasonal_h3_mag,seasonal_h3_ph,seasonal_h2_mag,seasonal_h2_ph,seasonal_h1_mag,seasonal_h1_ph,annual_mag,annual_ph,seasonal_mag,seasonal_min_ph,seasonal_max_ph,mean_array,diurnal_waveform,seasonal_waveform,full_waveform,diurnal_ff,seasonal_ff,full_ff)