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(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)
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, run_type, x):
if run_type == 'model':
lat_i = lat_indices[x]
lon_i = lon_indices[x]
site_lon = lon_c[lon_i]
site_lat = lat_c[lat_i]
if run_type == 'obs':
site_lon = obs_lons[x]
site_lat = obs_lats[x]
print obs_refs[x], x, site_lat, site_lon
#check obs vals are valid
valid = vals >= 0
vals = vals[valid]
if run_type == 'obs':
orig_time = obs_ref_time
valid_times = obs_ref_time[valid]
orig_datetimes = obs_datetime_time
valid_datetimes = obs_datetime_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]
if run_type == 'model':
orig_time = model_ref_time
valid_times = model_ref_time[valid]
orig_datetimes = model_datetime_time
valid_datetimes = model_datetime_time[valid]
if timeres == 'H':
full_times_year = model_ref_time[:8766]
elif timeres == 'D':
full_times_year = model_ref_time[:365]
elif timeres == 'M':
full_times_year = model_ref_time[:12]
full_times_day = model_ref_time[:24]
#convert site_lon to 0 to 360 degs
#keep -180 - 180 lon as orig_lon
orig_lon = site_lon
if site_lon < 0:
site_lon = 360 - np.abs(site_lon)
#transform factor for conversion from UTC time to solar time
time_diff = lon_step_time * site_lon
if time_diff > 12:
time_diff = time_diff - 24
#cut vals into seasons
valid_times_spring, vals_spring, valid_inds_spring, valid_times_summer, vals_summer, valid_inds_summer, valid_times_autumn, vals_autumn, valid_inds_autumn, valid_times_winter, vals_winter, valid_inds_winter = modules.cut_season(
valid_datetimes, valid_times, vals)
#cut vals into day/night (use -180 - 180 lons)
valid_times_day, vals_day, valid_times_night, vals_night = modules.cut_daynight(
valid_datetimes, valid_times, vals, site_lat, orig_lon, timeres)
#make time start from 0
valid_times_from0 = modules.time_from0(valid_times)
valid_times_from0_spring = modules.time_from0(valid_times_spring)
valid_times_from0_summer = modules.time_from0(valid_times_summer)
valid_times_from0_autumn = modules.time_from0(valid_times_autumn)
valid_times_from0_winter = modules.time_from0(valid_times_winter)
valid_times_from0_day = modules.time_from0(valid_times_day)
valid_times_from0_night = modules.time_from0(valid_times_night)
key_diurnal_periods = [
1. / 12., 1. / 11., 1. / 10., 1. / 9., 1. / 8., 1. / 7., 1. / 6.,
1. / 5., 1. / 4., 1. / 3., 1. / 2., 1.
]
#key_seasonal_periods = [365.25/12.,365.25/11.,365.25/10.,365.25/9.,365.25/8.,365.25/7.,365.25/6.,365.25/5.,365.25/4.,365.25/3.,365.25/2.,365.25]
key_seasonal_periods = [365.25 / 4., 365.25 / 3., 365.25 / 2., 365.25]
#periodic_periods = [1./12.,1./11.,1./10.,1./9.,1./8.,1./7.,1./6.,1./5.,1./4.,1./3.,1./2.,1.,365.25/12.,365.25/11.,365.25/10.,365.25/9.,365.25/8.,365.25/7.,365.25/6.,365.25/5.,365.25/4.,365.25/3.,365.25/2.,365.25]
periodic_periods = [
1. / 12., 1. / 11., 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 = modules.lomb_specific(
valid_times_from0, vals, w=True, key_periods=periodic_periods)
periods_spring, mag_spring, ph_spring, fr_spring, fi_spring = modules.lomb_specific(
valid_times_from0_spring,
vals_spring,
w=True,
key_periods=key_diurnal_periods)
periods_summer, mag_summer, ph_summer, fr_summer, fi_summer = modules.lomb_specific(
valid_times_from0_summer,
vals_summer,
w=True,
key_periods=key_diurnal_periods)
periods_autumn, mag_autumn, ph_autumn, fr_autumn, fi_autumn = modules.lomb_specific(
valid_times_from0_autumn,
vals_autumn,
w=True,
key_periods=key_diurnal_periods)
periods_winter, mag_winter, ph_winter, fr_winter, fi_winter = modules.lomb_specific(
valid_times_from0_winter,
vals_winter,
w=True,
key_periods=key_diurnal_periods)
periods_day, mag_day, ph_day, fr_day, fi_day = modules.lomb_specific(
valid_times_from0_day,
vals_day,
w=True,
key_periods=key_seasonal_periods)
periods_night, mag_night, ph_night, fr_night, fi_night = modules.lomb_specific(
valid_times_from0_night,
vals_night,
w=True,
key_periods=key_seasonal_periods)
#process RAW STATS
#STANDARD
mean, p1, p5, p25, p50, p75, p95, p99 = modules.raw_stats(vals)
#SEASON
mean_spring, p1_spring, p5_spring, p25_spring, p50_spring, p75_spring, p95_spring, p99_spring = modules.raw_stats(
vals_spring)
mean_summer, p1_summer, p5_summer, p25_summer, p50_summer, p75_summer, p95_summer, p99_summer = modules.raw_stats(
vals_summer)
mean_autumn, p1_autumn, p5_autumn, p25_autumn, p50_autumn, p75_autumn, p95_autumn, p99_autumn = modules.raw_stats(
vals_autumn)
mean_winter, p1_winter, p5_winter, p25_winter, p50_winter, p75_winter, p95_winter, p99_winter = modules.raw_stats(
vals_winter)
#DAY/NIGHT
mean_day, p1_day, p5_day, p25_day, p50_day, p75_day, p95_day, p99_day = modules.raw_stats(
vals_day)
mean_night, p1_night, p5_night, p25_night, p50_night, p75_night, p95_night, p99_night = modules.raw_stats(
vals_night)
#correct all phases for start point (not actually being from 0 - just corrected to be)
ph = modules.phase_start_time_relative_correct(periodic_periods, ph,
valid_times)
ph_spring = modules.phase_start_time_relative_correct(
key_diurnal_periods, ph_spring, valid_times_spring)
ph_summer = modules.phase_start_time_relative_correct(
key_diurnal_periods, ph_summer, valid_times_summer)
ph_autumn = modules.phase_start_time_relative_correct(
key_diurnal_periods, ph_autumn, valid_times_autumn)
ph_winter = modules.phase_start_time_relative_correct(
key_diurnal_periods, ph_winter, valid_times_winter)
ph_day = modules.phase_start_time_relative_correct(key_seasonal_periods,
ph_day, valid_times_day)
ph_night = modules.phase_start_time_relative_correct(
key_seasonal_periods, ph_night, valid_times_night)
#put mags and phases into appropriate arrays
diurnal_mags = mag[:12]
diurnal_phs = ph[:12]
diurnal_mags_spring = mag_spring[:]
diurnal_phs_spring = ph_spring[:]
diurnal_mags_summer = mag_summer[:]
diurnal_phs_summer = ph_summer[:]
diurnal_mags_autumn = mag_autumn[:]
diurnal_phs_autumn = ph_autumn[:]
diurnal_mags_winter = mag_winter[:]
diurnal_phs_winter = ph_winter[:]
seasonal_mags = mag[12:]
seasonal_mags_day = mag_day[:]
seasonal_mags_night = mag_night[:]
seasonal_phs = ph[12:]
seasonal_phs_day = ph_day[:]
seasonal_phs_night = ph_night[:]
all_mags_spring = np.concatenate((diurnal_mags_spring, seasonal_mags))
all_mags_summer = np.concatenate((diurnal_mags_summer, seasonal_mags))
all_mags_autumn = np.concatenate((diurnal_mags_autumn, seasonal_mags))
all_mags_winter = np.concatenate((diurnal_mags_winter, seasonal_mags))
all_phs_spring = np.concatenate((diurnal_phs_spring, seasonal_phs))
all_phs_summer = np.concatenate((diurnal_phs_summer, seasonal_phs))
all_phs_autumn = np.concatenate((diurnal_phs_autumn, seasonal_phs))
all_phs_winter = np.concatenate((diurnal_phs_winter, seasonal_phs))
all_mags_day = np.concatenate((diurnal_mags, seasonal_mags_day))
all_mags_night = np.concatenate((diurnal_mags, seasonal_mags_night))
#set key individual mags and phases
daily_h11_mag = mag[0]
daily_h10_mag = mag[1]
daily_h9_mag = mag[2]
daily_h8_mag = mag[3]
daily_h7_mag = mag[4]
daily_h6_mag = mag[5]
daily_h5_mag = mag[6]
daily_h4_mag = mag[7]
daily_h3_mag = mag[8]
daily_h2_mag = mag[9]
daily_h1_mag = mag[10]
orig_daily_mag = mag[11]
#seasonal_h11_mag = mag[12]
#seasonal_h10_mag = mag[13]
#seasonal_h9_mag = mag[14]
#seasonal_h8_mag = mag[15]
#seasonal_h7_mag = mag[16]
#seasonal_h6_mag = mag[17]
#seasonal_h5_mag = mag[18]
#seasonal_h4_mag = mag[12]
seasonal_h3_mag = mag[12]
seasonal_h2_mag = mag[13]
seasonal_h1_mag = mag[14]
annual_mag = mag[15]
#convolve annual cycle and harmonics to seasonal waveform for 1 year
seasonal_mag, seasonal_ph, seasonal_waveform = modules.period_convolution(
key_seasonal_periods, full_times_year, seasonal_mags, seasonal_phs,
mean)
seasonal_mag_day, seasonal_ph_day, seasonal_waveform_day = modules.period_convolution(
key_seasonal_periods, full_times_year, seasonal_mags_day,
seasonal_phs_day, mean_day)
seasonal_mag_night, seasonal_ph_night, seasonal_waveform_night = modules.period_convolution(
key_seasonal_periods, full_times_year, seasonal_mags_night,
seasonal_phs_night, mean_night)
remainder = np.mod(len(orig_time), len(seasonal_waveform))
if remainder > 0:
seasonal_waveform_extended = np.append(
list(seasonal_waveform) *
(len(orig_time) // len(seasonal_waveform)),
seasonal_waveform[-remainder:])
else:
seasonal_waveform_extended = np.array(
list(seasonal_waveform) *
(len(orig_time) // len(seasonal_waveform)))
#convolve diurnal cycle and harmonics to diurnal waveform for 1 day
diurnal_mag, diurnal_ph, diurnal_ave_waveform = modules.period_convolution(
key_diurnal_periods, full_times_day, diurnal_mags, diurnal_phs, mean)
diurnal_mag_spring, diurnal_ph_spring, diurnal_waveform_spring = modules.period_convolution(
key_diurnal_periods, full_times_day, diurnal_mags_spring,
diurnal_phs_spring, mean_spring)
diurnal_mag_summer, diurnal_ph_summer, diurnal_waveform_summer = modules.period_convolution(
key_diurnal_periods, full_times_day, diurnal_mags_summer,
diurnal_phs_summer, mean_summer)
diurnal_mag_autumn, diurnal_ph_autumn, diurnal_waveform_autumn = modules.period_convolution(
key_diurnal_periods, full_times_day, diurnal_mags_autumn,
diurnal_phs_autumn, mean_autumn)
diurnal_mag_winter, diurnal_ph_winter, diurnal_waveform_winter = modules.period_convolution(
key_diurnal_periods, full_times_day, diurnal_mags_winter,
diurnal_phs_winter, mean_winter)
remainder = np.mod(len(orig_time), len(diurnal_ave_waveform))
if remainder > 0:
diurnal_ave_waveform_extended = np.append(
list(diurnal_ave_waveform) *
(len(orig_time) // len(diurnal_ave_waveform)),
diurnal_ave_waveform[-remainder:])
else:
diurnal_ave_waveform_extended = np.array(
list(diurnal_ave_waveform) *
(len(orig_time) // len(diurnal_ave_waveform)))
#convert diurnal phase and waveforms from UTC to local solar time (in radians)
diurnal_ph = modules.solar_time_phase_corrector(diurnal_ph, 24., time_diff)
diurnal_ph_spring = modules.solar_time_phase_corrector(
diurnal_ph_spring, 24., time_diff)
diurnal_ph_summer = modules.solar_time_phase_corrector(
diurnal_ph_summer, 24., time_diff)
diurnal_ph_autumn = modules.solar_time_phase_corrector(
diurnal_ph_autumn, 24., time_diff)
diurnal_ph_winter = modules.solar_time_phase_corrector(
diurnal_ph_winter, 24., time_diff)
diurnal_ave_waveform = np.append(diurnal_ave_waveform[-time_diff:],
diurnal_ave_waveform[:-time_diff])
diurnal_waveform_spring = np.append(diurnal_waveform_spring[-time_diff:],
diurnal_waveform_spring[:-time_diff])
diurnal_waveform_summer = np.append(diurnal_waveform_summer[-time_diff:],
diurnal_waveform_summer[:-time_diff])
diurnal_waveform_autumn = np.append(diurnal_waveform_autumn[-time_diff:],
diurnal_waveform_autumn[:-time_diff])
diurnal_waveform_winter = np.append(diurnal_waveform_winter[-time_diff:],
diurnal_waveform_winter[:-time_diff])
diurnal_ave_waveform_extended = np.append(
diurnal_ave_waveform_extended[-time_diff:],
diurnal_ave_waveform_extended[:-time_diff])
#convert phase from radians to time
#make sure diurnal phases are on hour by rounding - some numerical rounding issues previous
diurnal_ph = round(modules.radians_to_time(diurnal_ph, 24.))
diurnal_ph_spring = round(modules.radians_to_time(diurnal_ph_spring, 24.))
diurnal_ph_summer = round(modules.radians_to_time(diurnal_ph_summer, 24.))
diurnal_ph_autumn = round(modules.radians_to_time(diurnal_ph_autumn, 24.))
diurnal_ph_winter = round(modules.radians_to_time(diurnal_ph_winter, 24.))
seasonal_ph = modules.radians_to_time(seasonal_ph, 12.)
seasonal_ph_day = modules.radians_to_time(seasonal_ph_day, 12.)
seasonal_ph_night = modules.radians_to_time(seasonal_ph_night, 12.)
#make extended waveform for diurnal cycle putting together diurnal waveforms from seasons
#also make full waveforms combining both seasonal varying diurnal cycles: full_season_waveform & average diurnal cycles: full_ave_waveform
#take mean from seasonal_waveform_extended
seasonal_waveform_extended_nomean = seasonal_waveform_extended - mean
if timeres == 'H':
diurnal_season_waveform_extended = []
full_ave_waveform = []
full_season_waveform = []
n_days = len(orig_datetimes) / 24
start_n = 0
end_n = 24
for i in range(n_days):
if (int(orig_datetimes[start_n].month) == 3) or (int(
orig_datetimes[start_n].month) == 4) or (int(
orig_datetimes[start_n].month) == 5):
if site_lat >= 0:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_spring)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_spring - mean_spring) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_spring)
else:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_autumn)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_autumn - mean_autumn) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_autumn)
elif (int(orig_datetimes[start_n].month) == 6) or (int(
orig_datetimes[start_n].month) == 7) or (int(
orig_datetimes[start_n].month) == 8):
if site_lat >= 0:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_summer)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_summer - mean_summer) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_summer)
else:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_winter)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_winter - mean_winter) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_winter)
elif (int(orig_datetimes[start_n].month) == 9) or (int(
orig_datetimes[start_n].month) == 10) or (int(
orig_datetimes[start_n].month) == 11):
if site_lat >= 0:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_autumn)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_autumn - mean_autumn) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_autumn)
else:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_spring)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_spring - mean_spring) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_spring)
elif (int(orig_datetimes[start_n].month) == 12) or (int(
orig_datetimes[start_n].month) == 1) or (int(
orig_datetimes[start_n].month) == 2):
if site_lat >= 0:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_winter)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_winter - mean_winter) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_winter)
else:
diurnal_season_waveform_extended = np.append(
diurnal_season_waveform_extended,
diurnal_waveform_summer)
full_ave_waveform = np.append(
full_ave_waveform,
((diurnal_ave_waveform - mean) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean)
full_season_waveform = np.append(
full_season_waveform,
((diurnal_waveform_summer - mean_summer) +
seasonal_waveform_extended_nomean[start_n:end_n]
)) #+mean_summer)
start_n += 24
end_n += 24
full_ave_waveform = full_ave_waveform + mean
full_season_waveform = full_season_waveform + mean
elif (timeres == 'D') or (timeres == 'M'):
diurnal_season_waveform_extended = diurnal_ave_waveform_extended
full_ave_waveform = seasonal_waveform_extended
full_season_waveform = seasonal_waveform_extended
#get % variability of periodic waveform with raw time series
#also get total variance and noise variance %
pc_var_daily, na, na = modules.periodic_variance_percent(
vals, full_ave_waveform, mag, mag[:12], valid)
pc_var_seasonal, na, na = modules.periodic_variance_percent(
vals, full_ave_waveform, mag, mag[12:], valid)
pc_var_full, pc_var_noise, total_var = modules.periodic_variance_percent(
vals, full_ave_waveform, mag, mag, valid)
#remove mean from standard diurnal and seasonal waveforms
#diurnal_ave_waveform = diurnal_ave_waveform - mean
#seasonal_waveform = seasonal_waveform - mean
#diurnal_waveform_spring = diurnal_waveform_spring - mean_spring
#diurnal_waveform_summer = diurnal_waveform_summer - mean_summer
#diurnal_waveform_autummn = diurnal_waveform_autumn - mean_autumn
#diurnal_waveform_winter = diurnal_waveform_winter - mean_winter
#seasonal_waveform_day = seasonal_waveform_day - mean_day
#seasonal_waveform_night = seasonal_waveform_night - mean_night
return (x, diurnal_mag, diurnal_ph, seasonal_mag, seasonal_ph, mean, p1,
p5, p25, p50, p75, p95, p99, diurnal_ave_waveform,
seasonal_waveform, full_ave_waveform, pc_var_daily,
pc_var_seasonal, pc_var_full, pc_var_noise, total_var,
diurnal_mag_spring, diurnal_ph_spring, mean_spring, p1_spring,
p5_spring, p25_spring, p50_spring, p75_spring, p95_spring,
p99_spring, diurnal_waveform_spring, diurnal_mag_summer,
diurnal_ph_summer, mean_summer, p1_summer, p5_summer, p25_summer,
p50_summer, p75_summer, p95_summer, p99_summer,
diurnal_waveform_summer, diurnal_mag_autumn, diurnal_ph_autumn,
mean_autumn, p1_autumn, p5_autumn, p25_autumn, p50_autumn,
p75_autumn, p95_autumn, p99_autumn, diurnal_waveform_autumn,
diurnal_mag_winter, diurnal_ph_winter, mean_winter, p1_winter,
p5_winter, p25_winter, p50_winter, p75_winter, p95_winter,
p99_winter, diurnal_waveform_winter, seasonal_mag_day,
seasonal_ph_day, mean_day, p1_day, p5_day, p25_day, p50_day,
p75_day, p95_day, p99_day, seasonal_waveform_day,
seasonal_mag_night, seasonal_ph_night, mean_night, p1_night,
p5_night, p25_night, p50_night, p75_night, p95_night, p99_night,
seasonal_waveform_night, daily_h11_mag, daily_h10_mag,
daily_h9_mag, daily_h8_mag, daily_h7_mag, daily_h6_mag,
daily_h5_mag, daily_h4_mag, daily_h3_mag, daily_h2_mag,
daily_h1_mag, orig_daily_mag, seasonal_h3_mag, seasonal_h2_mag,
seasonal_h1_mag, annual_mag)
