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 main_arg(x, y, grid_count):
print grid_count
ofac = 4
#average dt of entire time series
diffs = [x[i + 1] - x[i] for i in range(len(x) - 1)]
avgdt = np.average(diffs)
#make time start from 0
x_from0 = modules.phase_start_correct(x)
periods, mag, ph, fr, fi, amp_corr = modules.take_lomb(
x_from0, y, ofac, avgdt)
#get mean of values
mean_array = np.average(y)
#correct magnitude and phase for spectral leakage
zoomfact = 1000
half_annual_mag, half_annual_phase = modules.periodic_interp(
fr, fi, zoomfact, periods, 365.25 / 2., len(y), amp_corr)
annual_mag, annual_phase = modules.periodic_interp(fr, fi, zoomfact,
periods, 365.25, len(y),
amp_corr)
#correct for phase shift as data starts in Oct 2004
n_off = 273.25
if n_off > 365.25 / 2:
n_off = n_off - (365.25 / 2)
offset = ((np.pi * 2) / (365.25 / 2)) * n_off
half_annual_phase = half_annual_phase + offset
if half_annual_phase > np.pi:
half_annual_phase = -np.pi + (half_annual_phase - np.pi)
n_off = 273.25
offset = ((np.pi * 2) / (365.25)) * n_off
annual_phase = annual_phase + offset
if annual_phase > np.pi:
annual_phase = -np.pi + (annual_phase - np.pi)
#convert phase to time
half_annual_phase = modules.convert_phase_units_actual_single(
half_annual_phase, 6)
annual_phase = modules.convert_phase_units_actual_single(annual_phase, 12)
#np.save('mags_phases/mag_spectrums/%i'%(grid_count),mag)
#np.save('mags_phases/phase_spectrums/%i'%(grid_count),ph)
#np.save('mags_phases/periods',periods)
return (half_annual_mag, half_annual_phase, annual_mag, annual_phase,
mean_array)
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 main_arg(x,y,grid_count):
print grid_count
ofac = 4
#average dt of entire time series
diffs = [x[i+1]-x[i] for i in range(len(x)-1)]
avgdt = np.average(diffs)
#make time start from 0
x_from0 = modules.phase_start_correct(x)
periods,mag,ph,fr,fi,amp_corr = modules.take_lomb(x_from0,y,ofac,avgdt)
#get mean of values
mean_array = np.average(y)
#correct magnitude and phase for spectral leakage
zoomfact = 1000
half_annual_mag,half_annual_phase = modules.periodic_interp(fr,fi,zoomfact,periods,365.25/2.,len(y),amp_corr)
annual_mag,annual_phase = modules.periodic_interp(fr,fi,zoomfact,periods,365.25,len(y),amp_corr)
#correct for phase shift as data starts in Oct 2004
n_off = 273.25
if n_off > 365.25/2:
n_off = n_off-(365.25/2)
offset = ((np.pi*2)/(365.25/2))*n_off
half_annual_phase = half_annual_phase + offset
if half_annual_phase > np.pi:
half_annual_phase = -np.pi + (half_annual_phase - np.pi)
n_off = 273.25
offset = ((np.pi*2)/(365.25))*n_off
annual_phase = annual_phase + offset
if annual_phase > np.pi:
annual_phase = -np.pi + (annual_phase - np.pi)
#convert phase to time
half_annual_phase = modules.convert_phase_units_actual_single(half_annual_phase,6)
annual_phase = modules.convert_phase_units_actual_single(annual_phase,12)
#np.save('mags_phases/mag_spectrums/%i'%(grid_count),mag)
#np.save('mags_phases/phase_spectrums/%i'%(grid_count),ph)
#np.save('mags_phases/periods',periods)
return (half_annual_mag,half_annual_phase,annual_mag,annual_phase,mean_array)
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)
model_var = model_data[gridbox_n::gridbox_count]
model_var = model_var*1e9
#----------------------------------------
model_time = model_time[valids]
model_var = model_var[valids]
#take lomb for obs and model
ofac = raw_input('\nChoose oversampling factor. (Typically 4.)\n')
#shift times to start from 0
obs_time = np.array(obs_time)
model_time = np.array(model_time)
obs_time_from0 = modules.phase_start_correct(obs_time)
model_time_from0 = modules.phase_start_correct(model_time)
#obs lomb
obs_periods,obs_mag,obs_ph,obs_fr,obs_fi,obs_amp_corr = modules.take_lomb(obs_time_from0,obs_var,ofac,1./24)
#model lomb
model_periods,model_mag,model_ph,model_fr,model_fi,model_amp_corr = modules.take_lomb(model_time_from0,model_var,ofac,1./24)
obs_time = np.array(obs_time)
obs_var = np.array(obs_var)
lon_step_time = 24./360.
#convert site_lon to 0 to 360 degs
if obs_lon < 0:
minute_val.append(int(time[-2:]))
full_obs_datetimes = [
datetime.datetime(year=year_val[i],
month=month_val[i],
day=day_val[i],
hour=hour_val[i],
minute=minute_val[i]) for i in range(len(year_val))
]
full_obs_datetimes = np.array(full_obs_datetimes)
obs_datetimes = full_obs_datetimes[valids]
obs_ave = np.mean(obs_var)
#make time start from 0
obs_times_from0 = modules.phase_start_correct(obs_times)
#remove invalid data
periods, mag, ph, fr, fi, amp_corr = modules.take_lomb(obs_times_from0,
obs_var, 4, 1. / 24)
zoomfact = 1000
daily_amp, daily_phase = modules.periodic_interp(fr, fi, zoomfact, periods, 1.,
len(obs_var), amp_corr)
ha_amp, ha_phase = modules.periodic_interp(fr, fi, zoomfact, periods,
365.25 / 2., len(obs_var), amp_corr)
annual_amp, annual_phase = modules.periodic_interp(fr, fi,
zoomfact, periods, 365.25,
len(obs_var), amp_corr)
daily_period = 1 annual_period = 365.25 hourly_step = 1. / 24 b = np.arange(200.75, 500, hourly_step) pi2 = np.pi * 2 daily_amplitude = 10 half_annual_amplitude = 1 annual_amplitude = 1 #shift times to start from 0 b = modules.phase_start_correct(b) print 'First time = ', b[0] #b = b-b[0] print b cos_waveform1 = daily_amplitude * (np.cos((pi2 * b / 1.1) - (np.pi / 2))) #cos_waveform2 = half_annual_amplitude*(np.cos((pi2*b/(365.25/2))-0)) cos_waveform3 = annual_amplitude * (np.cos((pi2 * b / 365.25) - (0))) #cos_waveform4 = daily_amplitude*(np.cos((pi2*b/1.0001)-(np.pi))) #vals = 50 + (cos_waveform1+cos_waveform2+cos_waveform3) vals = 50 + (cos_waveform1 + cos_waveform3) total_len = len(vals)