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(valid_times, vals, start_point, end_point, x, time_diff):
vals = vals[start_point:end_point]
valid_times = valid_times[start_point:end_point]
if len(valid_times) > 0:
#change times to go from 0 - for accurate phase
first_time = valid_times[0]
valid_times = valid_times - first_time
#window
window = np.hamming(len(vals))
mean = np.mean(vals)
vals = vals - mean
vals = vals * window
NOUT = 0.5 * 4 * 1 * len(vals)
NOUT = int(NOUT)
#take lomb
fa, fb, mag, ph = lomb_phase.lomb(valid_times, vals, NOUT)
periods = 1. / fa
amp_corr = 1. / (sum(window) / len(window))
mag = mag * amp_corr
#calculations for mags and phases of key periods
closest_daily_period_index = min(
range(len(periods)), key=lambda i: abs(periods[i] - daily_period))
daily_mag = mag[closest_daily_period_index]
daily_phase = ph[closest_daily_period_index]
#adjust daily_phase to solar time
correction_f = ((2 * np.pi) / 24) * time_diff
daily_phase = daily_phase + (correction_f)
if daily_phase < -np.pi:
diff = np.abs(daily_phase) - np.pi
daily_phase = np.pi - diff
if daily_phase > np.pi:
diff = np.abs(daily_phase) - np.pi
daily_phase = -np.pi + diff
#convert phase to hours
daily_phase = modules.convert_phase_units_actual_single(
daily_phase, 24)
else:
print 'nan'
daily_mag = float('NaN')
daily_phase = float('NaN')
return (daily_mag, daily_phase, x)
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)
daily_model_mag,daily_model_phase = modules.periodic_interp(model_fr,model_fi,zoomfact,model_periods,1.,len(model_var),model_amp_corr) ha_obs_mag,ha_obs_phase = modules.periodic_interp(obs_fr,obs_fi,zoomfact,obs_periods,(365.25/2.),len(obs_var),obs_amp_corr) ha_model_mag,ha_model_phase = modules.periodic_interp(model_fr,model_fi,zoomfact,model_periods,(365.25/2.),len(model_var),model_amp_corr) annual_obs_mag,annual_obs_phase = modules.periodic_interp(obs_fr,obs_fi,zoomfact,obs_periods,365.25,len(obs_var),obs_amp_corr) annual_model_mag,annual_model_phase = modules.periodic_interp(model_fr,model_fi,zoomfact,model_periods,365.25,len(model_var),model_amp_corr) #correct for phase shift from sites where raw times do not start from 0 daily_obs_phase = modules.phase_start_point_correct(1.,daily_obs_phase,obs_time) daily_model_phase = modules.phase_start_point_correct(1.,daily_model_phase,model_time) ha_obs_phase = modules.phase_start_point_correct((365.25/2.),ha_obs_phase,obs_time) ha_model_phase = modules.phase_start_point_correct((365.25/2.),ha_model_phase,model_time) annual_obs_phase = modules.phase_start_point_correct(365.25,annual_obs_phase,obs_time) annual_model_phase = modules.phase_start_point_correct(365.25,annual_model_phase,model_time) #convert phase to time daily_obs_phase = modules.convert_phase_units_actual_single(daily_obs_phase,24) daily_model_phase = modules.convert_phase_units_actual_single(daily_model_phase,24) ha_obs_phase = modules.convert_phase_units_actual_single(ha_obs_phase,6) ha_model_phase = modules.convert_phase_units_actual_single(ha_model_phase,6) annual_obs_phase = modules.convert_phase_units_actual_single(annual_obs_phase,12) annual_model_phase = modules.convert_phase_units_actual_single(annual_model_phase,12) #Maske daily phase SolaR time from UTC daily_obs_phase = daily_obs_phase + time_diff daily_model_phase = daily_model_phase + time_diff if daily_obs_phase >= 24: daily_obs_phase = daily_obs_phase-24 if daily_obs_phase < 0:
periods = 1. / fa
amp_corr = 1. / (sum(window) / len(window))
mag = mag * amp_corr
closest_daily_period_index = min(
range(len(periods)), key=lambda i: abs(periods[i] - daily_period))
daily_mag = mag[closest_daily_period_index]
daily_phase = ph[closest_daily_period_index]
print np.min(ph)
print np.max(ph)
#print vals
print 'daily phase = ', daily_phase
print 'daily phase = ', modules.convert_phase_units_actual_single(
daily_phase, 24)
#daily_phase = modules.correct_select_daily_phase_actual_single(daily_phase,lon_c,lat_c,lon_e,lat_e, site_lon)
#daily_phase = modules.convert_phase_units_actual_single(daily_phase,24)
print 'sun time =', sun_time
print 'time diff = ', time_diff
#adjust daily_phase to solar time
correction_f = ((2 * np.pi) / 24) * time_diff
daily_phase = daily_phase + (correction_f)
if daily_phase < -np.pi:
diff = np.abs(daily_phase) - np.pi
daily_phase = np.pi - diff
if daily_phase > np.pi:
diff = np.abs(daily_phase) - np.pi
#transform from UTC time to solar time
sun_time = lon_step_time * obs_lon
time_diff = sun_time - 0
if time_diff > 12:
time_diff = time_diff - 24
#convert daily phase from UTC to solar time
daily_phase = daily_phase + ((pi2 / 24.) * time_diff)
if daily_phase >= np.pi:
daily_phase = -np.pi + (daily_phase - np.pi)
if daily_phase < -np.pi:
daily_phase = np.pi - (np.abs(daily_phase) - np.pi)
#convert phase to time
daily_phase_time = modules.convert_phase_units_actual_single(daily_phase, 24)
ha_phase_time = modules.convert_phase_units_actual_single(ha_phase, 6)
annual_phase_time = modules.convert_phase_units_actual_single(annual_phase, 12)
daily_wave = daily_amp * (np.cos((pi2 * obs_times_full / 1.) - (daily_phase)))
ha_wave = ha_amp * (np.cos((pi2 * obs_times_full / (365.25 / 2.)) -
(ha_phase)))
annual_wave = annual_amp * (np.cos((pi2 * obs_times_full / 365.25) -
(annual_phase)))
big_wave = daily_wave + ha_wave + annual_wave
big_wave = big_wave + obs_ave
daily_wave = daily_wave + obs_ave
ha_wave = ha_wave + obs_ave
annual_wave = annual_wave + obs_ave
def main_arg(x, y, grid_count):
print grid_count
ofac = 4
orig_len = len(y)
print np.min(y)
#cut valid data
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
if len(y) > (orig_len / 2):
#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)
else:
half_annual_mag = -99999
half_annual_phase = -99999
annual_mag = -99999
annual_phase = -99999
mean_array = -99999
#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)
ax2.xaxis.set_major_formatter(dt.DateFormatter('%d/%m/%Y'))
ax2.plot(obs_datetimes, obs_var, color='black', alpha=0.3)
ax2.plot(model_datetimes, model_var, color='red', alpha=0.3)
ax2.plot(model_datetimes,
obs_wave,
color='black',
label='Obs. Sinusoidal Waveform',
linewidth=2)
ax2.plot(model_datetimes,
model_wave,
color='red',
label='GEOS 2x2.5 Sinusoidal Waveform',
linewidth=2)
#convert phase to time
obs_phase = modules.convert_phase_units_actual_single(obs_annual_phase, 12)
model_phase = modules.convert_phase_units_actual_single(model_annual_phase, 12)
print obs_phase
print model_phase
ax2.grid(True)
leg = ax2.legend(loc=1, prop={'size': 21})
leg.get_frame().set_alpha(0.4)
ax2.set_xlabel('Time', fontsize=21)
ax2.set_ylabel('Concentration (ppb)', fontsize=21)
#ax.set_title(r'Time Series of Surface $O_3$ at %s, for Obs. & Model'%(site),fontsize=18)
ax.tick_params(axis='both', which='major', labelsize=18)
ax2.tick_params(axis='both', which='major', labelsize=18)
#for tick in ax.get_xaxis().get_major_ticks():
