def cache_omni_interval_wrapper(dt):
#print("Cached OMNI called for {}".format(dt))
if 'omni_interval' in cache:
cached_oi = cache['omni_interval']
need_new_oi = not _dt_within_range(dt, cached_oi)
else:
need_new_oi = True
if need_new_oi:
startdt = dt - datetime.timedelta(days=new_interval_days_before_dt)
enddt = dt + datetime.timedelta(days=new_interval_days_after_dt)
oi = omnireader.omni_interval(startdt,
enddt,
_ovation_prime_omni_cadence,
silent=True)
#Save to cache
cache['omni_interval'] = oi
# print("Created new solar wind interval: {}-{}".format(oi.startdt,
# oi.enddt))
else:
#Load from cache
oi = cache['omni_interval']
# print("Using cached solar wind interval: {}-{}".format(oi.startdt,
# oi.enddt))
return func(dt, oi)
def get_f107_ap(dt, silent=False):
"""
Get F10.7 for day of interest (noon), and day previous and 81-day centered average
Also get AP index for day of interest (noon)
"""
jd = special_datetime.datetime2jd(dt)
oi = omnireader.omni_interval(dt - datetime.timedelta(days=41),
dt + datetime.timedelta(days=41), 'hourly')
odt = oi['Epoch']
ojd = special_datetime.datetimearr2jd(odt).flatten()
f107_81 = oi['F10_INDEX']
ap_81 = oi['AP_INDEX']
f107a = np.nanmean(f107_81)
today = np.logical_and(ojd > np.floor(jd), ojd < np.ceil(jd))
yesterday = np.logical_and(ojd > np.floor(jd - 1), ojd < np.ceil(jd - 1))
f107p = np.nanmean(f107_81[yesterday]) # Previous day f10.7
f107 = np.nanmean(f107_81[today])
ap = np.nanmean(ap_81[today])
if not silent:
print((dt.strftime('%Y%m%d %H:%M')))
print(('Yesterday F107 %.2f' % (f107p)))
print(('Today F107 %.2f' % (f107)))
print(('81-day avg F107: %.2f' % (f107a)))
return f107, ap, f107p, f107a
def example_omni_interval(request):
"""
A fixture that can have tests written around it,
and each test will be executed for each parameter,
that is, for each possible cadence
"""
cadence = request.param
dt = datetime.datetime(2006, 3, 14)
return omnireader.omni_interval(dt, dt + datetime.timedelta(days=1),
cadence)
def omni_interval_txtcdf_comparison(request):
"""
A fixture that can have tests write around it,
and each test will be executed for each parameter,
that is, for each possible cadence
"""
cadence = request.param
dt = datetime.datetime(2006, 3, 14)
oiformats = {
'cdf':
omnireader.omni_interval(dt,
dt + datetime.timedelta(days=1),
cadence,
cdf_or_txt='cdf'),
'txt':
omnireader.omni_interval(dt,
dt + datetime.timedelta(days=1),
cadence,
cdf_or_txt='txt')
}
return oiformats
def __init__(self, year, month, day, hour, minute, use_igrf, *args,
**kwargs):
from geopack import t01
# epoch time since Jan. 1 1970 00:00 UT
# datetime.timedelta
dt = datetime.datetime(year, month, day, hour,
minute) - datetime.datetime(1970, 1, 1)
# seconds from 1970/1/1 00:00 UT
self.dt_seconds = dt.total_seconds()
self.ps = geopack.recalc(self.dt_seconds)
self.use_igrf = use_igrf
from geospacepy import omnireader
sTimeIMF = datetime.datetime(year, month, day, hour, minute)
eTimeIMF = datetime.datetime(year, month, day, hour,
minute) + datetime.timedelta(
0, 0, 0, 0, 1, 0)
omniInt = omnireader.omni_interval(sTimeIMF, eTimeIMF, '1min')
t = omniInt['Epoch'] #datetime timestamps
By = omniInt['BY_GSM']
Bz = omniInt['BZ_GSM']
Pdyn = omniInt['Pressure']
SYM_H = omniInt['SYM_H']
self.parmod = np.array([Pdyn, SYM_H, By, Bz, 0., 0., 0., 0., 0., 0.],
dtype=float)
super(T01, self).__init__(*args, **kwargs)
# parmod=np.zeros(10,dtype=float)
# t89.tsyganenko.init_t89(year,month,day,hour,minute,use_igrf,0,parmod)
# t89.tsyganenko.init_t89(int(year),int(month),int(day),int(hour),int(minute),int(use_igrf))
bounds_error = kwargs.get('bounds_error', False)
fill_value = kwargs.get('missing_value', np.nan)
self.citation = 'Kamodo.T01 by Lutz Rastaetter (2020), Geopack/Tsyganenko by Sheng Tian (2019) and geospacepy-lite by Liam Kilkommons (2019)'
self.x = np.linspace(-30., 10., 40) # make sure to avoid (0,0,0)
self.y = np.linspace(-10., 10., 20)
self.z = np.linspace(-10., 10., 20)
self.variables = dict(b_x=dict(units='nT', data=None),
b_y=dict(units='nT', data=None),
b_z=dict(units='nT', data=None),
bvec=dict(units='nT', data=None))
for varname in self.variables:
units = self.variables[varname]['units']
self.register_variable(varname, units)
def __init__(self, year, month, day, hour, minute, use_igrf, *args,
**kwargs):
from geopack import t89
super(T89, self).__init__(*args, **kwargs)
# time since Jan. 1 1970 00:00 UT as datetime.timedelta
dt = datetime.datetime(year, month, day, hour,
minute) - datetime.datetime(1970, 1, 1)
# seconds from 1970/1/1 00:00 UT
self.dt_seconds = dt.total_seconds()
self.ps = geopack.recalc(self.dt_seconds)
self.use_igrf = use_igrf
from geospacepy import omnireader
sTimeIMF = datetime.datetime(year, month, day, hour)
eTimeIMF = datetime.datetime(
year, month, day, hour) + datetime.timedelta(0, 0, 0, 1, 0, 0)
omniInt = omnireader.omni_interval(sTimeIMF, eTimeIMF, 'hourly')
t = omniInt['Epoch'] #datetime timestamps
By, Bz = omniInt['BY_GSM'], omniInt['BZ_GSM']
kp = omniInt['KP']
self.iopt = int(kp[0]) + 1
if self.iopt > 7: self.iopt = 7
bounds_error = kwargs.get('bounds_error', False)
fill_value = kwargs.get('missing_value', np.nan)
self.citation = 'Kamodo.T89 by Lutz Rastaetter (2020), Geopack/Tsyganenko by Sheng Tian (2019) and geospacepy-lite by Liam Kilkommons (2019)'
self.unit = 'nT'
self.x = np.linspace(-30., 10., 20)
self.y = np.linspace(-10., 10., 10)
self.z = np.linspace(-10., 10., 10)
self.variables = dict(b_x=dict(units='nT', data=None),
b_y=dict(units='nT', data=None),
b_z=dict(units='nT', data=None),
bvec=dict(units='nT', data=None))
for varname in self.variables:
units = self.variables[varname]['units']
self.register_variable(varname, units)
def __init__(self, year, month, day, hour, minute, use_igrf, *args,
**kwargs):
from geopack import t04
# epoch time since Jan. 1 1970 00:00 UT
# datetime.timedelta
dt = datetime.datetime(year, month, day, hour,
minute) - datetime.datetime(1970, 1, 1)
# seconds from 1970/1/1 00:00 UT
self.dt_seconds = dt.total_seconds()
qin_denton_url = 'https://rbsp-ect.newmexicoconsortium.org/data_pub/QinDenton/%d/' % (
year)
qin_denton_file = 'QinDenton_%d%d%d_1min.txt' % (year, month, day)
# fetch file
qin_denton_local_file = './data/QinDenton/%d/%s' % (year,
qin_denton_file)
# import requests
# response=requests.get(qin_denton_url+qin-denton_file
import pandas as pd
qindenton_frame = pd.read_json(qin_denton_local_file)
self.ps = geopack.recalc(self.dt_seconds)
self.use_igrf = use_igrf
from geospacepy import omnireader
sTimeIMF = datetime.datetime(year, month, day, hour, minute)
eTimeIMF = datetime.datetime(year, month, day, hour,
minute) + datetime.timedelta(
0, 0, 0, 0, 1, 0)
omniInt = omnireader.omni_interval(sTimeIMF, eTimeIMF, '1min')
t = omniInt['Epoch'] #datetime timestamps
By = omniInt['BY_GSM']
Bz = omniInt['BZ_GSM']
Pdyn = omniInt['Pressure']
SYM_H = omniInt['SYM_H']
# need Qin-Denton parameters
w1, w2, w3, w4, w5, w6 = np.zeros(6, dtype=float)
# import pandas as pd
# pd.read_json(qin_denton_path
# end Qin-Dention acquisition
self.parmod = np.array([Pdyn, SYM_H, By, Bz, w1, w2, w3, w4, w5, w6],
dtype=float)
super(T04, self).__init__(*args, **kwargs)
# parmod=np.zeros(10,dtype=float)
# t89.tsyganenko.init_t89(year,month,day,hour,minute,use_igrf,0,parmod)
# t89.tsyganenko.init_t89(int(year),int(month),int(day),int(hour),int(minute),int(use_igrf))
bounds_error = kwargs.get('bounds_error', False)
fill_value = kwargs.get('missing_value', np.nan)
self.units = 'nT'
self.citation = 'Kamodo.T89 by Lutz Rastaetter (2020), Geopack/Tsyganenko by Sheng Tian (2019) and geospacepy-lite by Liam Kilkommons (2019)'
self.x = np.linspace(-30., 10., 40) # make sure to avoid (0,0,0)
self.y = np.linspace(-10., 10., 20)
self.z = np.linspace(-10., 10., 20)
self.variables = dict(b_x=dict(units='nT', data=None),
b_y=dict(units='nT', data=None),
b_z=dict(units='nT', data=None),
bvec=dict(units='nT', data=None))
for varname in self.variables:
units = self.variables[varname]['units']
self.register_variable(varname, units)
def plot_comparison_data(all_data,outfile='ssm_comparison.png',outdir='/tmp',offset=9):
from matplotlib.ticker import FuncFormatter,MaxNLocator
fig = pp.figure(figsize=(6,6))
gs = mpl.gridspec.GridSpec(7,2)
a1 = pp.subplot(gs[:2,0])
a2 = pp.subplot(gs[:2,1])
a3 = fig.add_subplot(gs[3,:])
a4 = fig.add_subplot(gs[4,:])
a5 = fig.add_subplot(gs[5,:])
a6 = fig.add_subplot(gs[6,:])
gs.update(hspace=.35,wspace=.35)
axletter_x,axletter_y = -.13,1.0
axletter_xh,axletter_yh = -.33,1.15
axlabel_x,axlabel_y = 0.,1.0
textkwargs = {'fontweight':'bold'}
jd_cdf = all_data[:,0]
db_inds = np.array([4,5,6])
dbav_inds = np.array([7,8,9])
sod_diff_noaa = all_data[:,1]-all_data[:,(1+offset)]
sod_diff_mad = all_data[:,1]-all_data[:,(1+2*offset)]
glat_cdf,glon_cdf = all_data[:,2],all_data[:,3]
glat_noaa,glon_noaa = all_data[:,(2+offset)],all_data[:,(3+offset)]
glat_mad,glon_mad = all_data[:,(2+2*offset)],all_data[:,(3+2*offset)]
glon_cdf[glon_cdf<0] += 360.
glon_cdf[glon_cdf>360.] -= 360.
glon_noaa[glon_noaa<0] += 360.
glon_noaa[glon_noaa>360.] -= 360.
glon_mad[glon_mad<0.] += 360.
glon_mad[glon_mad>360.] -= 360.
glon_diff_noaa = glon_cdf-glon_noaa
glon_diff_mad = glon_cdf-glon_mad
gc_diff_noaa = satplottools.greatCircleDist(np.column_stack((glat_cdf,glon_cdf)),
np.column_stack((glat_noaa,glon_noaa)),
lonorlt='lon')
gc_diff_mad = satplottools.greatCircleDist(np.column_stack((glat_cdf,glon_cdf)),
np.column_stack((glat_mad,glon_mad)),
lonorlt='lon')
magnitude = lambda x: np.sqrt(x[:,0]**2+x[:,1]**2+x[:,2]**2)
db_cdf,db_noaa,db_mad = all_data[:,dbav_inds],all_data[:,(dbav_inds+offset)],all_data[:,(dbav_inds+2*offset)]
db_diff_noaa = db_cdf-db_noaa
db_diff_mad = db_cdf-db_mad
db_diff_noaa_mag = magnitude(db_diff_noaa)
db_diff_mad_mag = magnitude(db_diff_mad)
#for a in [a1,a2,a3,a4,a5,a6]:
# a.cla()
sod_bins = np.arange(-2.,2.,.1)
sns.distplot(sod_diff_noaa[np.isfinite(sod_diff_noaa)],kde=False,bins=sod_bins,color='g',label='CDF-MFR',ax=a1)
sns.distplot(sod_diff_mad[np.isfinite(sod_diff_mad)],kde=False,bins=sod_bins,color='r',label='CDF-MAD',ax=a1)
#a1.plot(jd_cdf,sod_diff_noaa,'go')
#a1.plot(jd_cdf,sod_diff_mad,'ro')
#a1.set_yscale('log')
a1.set_ylabel('Equator Crossings')
a1.legend()
a1.set_xlabel('Seconds')
a1.text(axletter_xh,axletter_yh, 'a)',transform=a1.transAxes,**textkwargs)
R = 6371.2+850.
gc_bins = np.arange(0,.1,.01)*R/180.*np.pi
sns.distplot(gc_diff_noaa[np.isfinite(gc_diff_noaa)]*R,kde=False,bins=gc_bins,color='g',label='CDF-MFR',ax=a2)
sns.distplot(gc_diff_mad[np.isfinite(gc_diff_mad)]*R,kde=False,bins=gc_bins,color='r',label='CDF-MAD',ax=a2)
#a2.plot(jd_cdf,glon_diff_noaa,'go')
#a2.plot(jd_cdf,glon_diff_mad,'ro')
#a2.set_yscale('log')
a2.legend()
a2.set_ylabel('Equator Crossings')
a2.set_xlabel('Km')
a2.text(axletter_xh,axletter_yh, 'b)',transform=a2.transAxes,**textkwargs)
"""
a3.plot(jd_cdf,glon_cdf,'bo')
a3.plot(jd_cdf,glon_noaa,'go')
a3.plot(jd_cdf,glon_mad,'ro')
#a2.set_yscale('log')
a3.set_ylabel('Degrees')
a3.set_title('Longitude at Equator Crossing')
a4.plot(jd_cdf,glat_cdf,'bo')
a4.plot(jd_cdf,glat_noaa,'go')
a4.plot(jd_cdf,glat_mad,'ro')
#a2.set_yscale('log')
a4.set_ylabel('Degrees')
a4.set_title('Latitude at Equator Crossing')
"""
#alpha=.8
a3.plot(jd_cdf,db_mad[:,0],'r^',label='MAD')
a3.plot(jd_cdf,db_noaa[:,0],'g.',label='MFR')
a3.plot(jd_cdf,db_cdf[:,0],'b.',label='CDF')
a3.set_title('Magnetic Perturbation @ GEO Equator\n',fontweight='bold')
a3.text(axletter_x,axletter_y, 'c)',
transform=a3.transAxes,**textkwargs)
a3.text(axlabel_x,axlabel_y, 'X (Down) [nT]',
transform=a3.transAxes,**textkwargs)
a4.plot(jd_cdf,db_mad[:,1],'r^',label='MAD')
a4.plot(jd_cdf,db_noaa[:,1],'g.',label='MFR')
a4.plot(jd_cdf,db_cdf[:,1],'b.',label='CDF')
a4.text(axletter_x,axletter_y, 'd)',
transform=a4.transAxes,**textkwargs)
a4.text(axlabel_x,axlabel_y, 'Y (Along) [nT]',
transform=a4.transAxes,**textkwargs)
#a4.set_title('Y (Along-Track)')
a5.plot(jd_cdf,db_mad[:,2],'r^',label='MAD')
a5.plot(jd_cdf,db_noaa[:,2],'g.',label='MFR')
a5.plot(jd_cdf,db_cdf[:,2],'b.',label='CDF')
a5.text(axletter_x,axletter_y, 'e)',
transform=a5.transAxes,**textkwargs)
a5.text(axlabel_x,axlabel_y, 'Z (Across) [nT]',
transform=a5.transAxes,**textkwargs)
#a5.set_title('Z (Across-Track)')
a3.legend(bbox_to_anchor=(0.,.8, 1., .102),ncol=3,loc=4)
for a in [a3,a4,a5]:
a.xaxis.set_ticklabels([])
a.yaxis.set_major_locator(MaxNLocator(5))
#a.set_ylim([-250.,250.])
#a.set_xlim((jd_cdf[0],jd_cdf[-1]))
#a5.set_xlabel('Time of Equator Crossing (from CDF data)')
startdt,enddt = special_datetime.jd2datetime(jd_cdf[0]),special_datetime.jd2datetime(jd_cdf[-1])
oi = omnireader.omni_interval(startdt,enddt,'hourly')
dt_omni = oi['Epoch']
jd_omni = special_datetime.datetimearr2jd(oi['Epoch'])
dst = oi['DST']
a6.plot(jd_omni,dst,'m.')
a6.text(axletter_x,axletter_y, 'f)',
transform=a6.transAxes,**textkwargs)
a6.text(axlabel_x,axlabel_y, 'DST [nT]',
transform=a6.transAxes,**textkwargs)
#a6.set_title('Hourly OMNIWeb DST',fontweight='bold',ha='right')
jd2str = lambda x,pos: special_datetime.jd2datetime(x).strftime('%m/%d\n%H:%M')
a6.xaxis.set_major_formatter(FuncFormatter(jd2str))
a6.set_xlabel('Date & Universal Time (2010)',fontweight='bold')
fig.savefig(os.path.join(outdir,outfile),dpi=300.)
def download_omni_text(input_datetime):
t_start = input_datetime - datetime.timedelta(1)
t_end = input_datetime + datetime.timedelta(1) + datetime.timedelta(
minutes=10)
t_start_day = input_datetime
t_end_day = input_datetime + datetime.timedelta(minutes=1439)
#--------------------------------------------------------#
# OMNI Data - includes solar wind, and geomag params #
#--------------------------------------------------------#
#get OMNI data
omniInt = omnireader.omni_interval(t_start,
t_end,
'5min',
cdf_or_txt='txt')
#print(omniInt.cdfs[0].vars) #prints all the variables available on omni
epochs = omniInt['Epoch'] #time array for omni 5min data
By, Bz, AE, SymH = omniInt['BY_GSM'], omniInt['BZ_GSM'], omniInt[
'AE_INDEX'], omniInt['SYM_H']
vsw, psw = omniInt['flow_speed'], omniInt['Pressure']
borovsky_reader = omnireader.borovsky(omniInt)
borovsky = borovsky_reader()
#newell_reader = omnireader.newell(omniInt)
#newell = newell_reader()
def NewellCF_calc(v, bz, by):
# v expected in km/s
# b's expected in nT
NCF = np.zeros_like(v)
NCF.fill(np.nan)
bt = np.sqrt(by**2 + bz**2)
bztemp = bz
bztemp[bz == 0] = .001
#Caculate clock angle (theta_c = t_c)
tc = np.arctan2(by, bztemp)
neg_tc = bt * np.cos(tc) * bz < 0
tc[neg_tc] = tc[neg_tc] + np.pi
sintc = np.abs(np.sin(tc / 2.))
NCF = (v**1.33333) * (sintc**2.66667) * (bt**0.66667)
return NCF
newell = NewellCF_calc(vsw, Bz, By)
proton_flux_10MeV, proton_flux_30MeV, proton_flux_60MeV = omniInt[
'PR-FLX_10'], omniInt['PR-FLX_30'], omniInt['PR-FLX_60']
#calculate clock angle
clock_angle = np.degrees(np.arctan2(By, Bz))
clock_angle[clock_angle < 0] = clock_angle[clock_angle < 0] + 360.
print('Got 5 minutes data')
omniInt_1hr = omnireader.omni_interval(t_start,
t_end,
'hourly',
cdf_or_txt='txt')
epochs_1hr = omniInt_1hr['Epoch'] #datetime timestamps
F107, KP = omniInt_1hr['F10_INDEX'], omniInt_1hr['KP']
print('Got hour data')
#--------------------------------------------------------#
# GOES X-ray data - Channel 1-8A, defines flare class #
#--------------------------------------------------------#
results = Fido.search(a.Time(t_start, t_end), a.Instrument('XRS'))
files = Fido.fetch(results)
goes = TimeSeries(files, concatenate=True)
goes_l = goes.data['xrsb']
print('Got GOES data')
#--------------------------------------------------------#
# Resample data to 1min to match GNSS CHAIN network #
#--------------------------------------------------------#
#resample OMNI Solar Wind Data
By_data = pd.Series(By, index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
Bz_data = pd.Series(Bz, index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
AE_data = pd.Series(AE, index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
SymH_data = pd.Series(SymH, index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
vsw_data = pd.Series(vsw, index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
psw_data = pd.Series(psw, index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
borovsky_data = pd.Series(borovsky,
index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
newell_data = pd.Series(newell,
index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
proton_10_data = pd.Series(proton_flux_10MeV,
index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
proton_30_data = pd.Series(proton_flux_30MeV,
index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
proton_60_data = pd.Series(proton_flux_60MeV,
index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
clock_angle_data = pd.Series(clock_angle,
index=epochs).resample('1T').pad().truncate(
t_start_day, t_end_day)
F107data = pd.Series(F107, index=epochs_1hr).resample('1T').pad().truncate(
t_start_day, t_end_day)
KPdata = pd.Series(KP, index=epochs_1hr).resample('1T').pad().truncate(
t_start_day, t_end_day)
#function to find data at previous time intervals
def roll_back(data, minutes=1):
ts = t_start_day - datetime.timedelta(minutes=minutes)
te = t_end_day - datetime.timedelta(minutes=minutes)
data = pd.Series(data, index=epochs).resample('1T').pad()
new_data = data.truncate(ts, te)
rolled_data = pd.Series(np.array(new_data), index=By_data.index)
return rolled_data
#calculate rolled back timeseries - 15 and 30 minutes previous
By_15 = roll_back(By, minutes=15)
By_30 = roll_back(By, minutes=30)
Bz_15 = roll_back(Bz, minutes=15)
Bz_30 = roll_back(Bz, minutes=30)
AE_15 = roll_back(AE, minutes=15)
AE_30 = roll_back(AE, minutes=30)
SymH_15 = roll_back(SymH, minutes=15)
SymH_30 = roll_back(SymH, minutes=30)
vsw_15 = roll_back(vsw, minutes=15)
vsw_30 = roll_back(vsw, minutes=30)
psw_15 = roll_back(psw, minutes=15)
psw_30 = roll_back(psw, minutes=30)
borovsky_15 = roll_back(borovsky, minutes=15)
borovsky_30 = roll_back(borovsky, minutes=30)
newell_15 = roll_back(newell, minutes=15)
newell_30 = roll_back(newell, minutes=30)
clock_angle_15 = roll_back(clock_angle, minutes=15)
clock_angle_30 = roll_back(clock_angle, minutes=30)
#resample GOES X-ray flux
goes_data = goes_l.resample('1T').mean().truncate(t_start_day, t_end_day)
#put all in a dataframe and save
dataframe = pd.DataFrame()
dataframe['Bz - 0min [nT]'] = Bz_data
dataframe['Bz - 15min [nT]'] = Bz_15
dataframe['Bz - 30min [nT]'] = Bz_30
dataframe['By - 0min [nT]'] = By_data
dataframe['By - 15min [nT]'] = By_15
dataframe['By - 30min [nT]'] = By_30
dataframe['Vsw - 0min [km/s]'] = vsw_data
dataframe['Vsw - 15min [km/s]'] = vsw_15
dataframe['Vsw - 30min [km/s]'] = vsw_30
dataframe['Psw - 0min [nPa]'] = psw_data
dataframe['Psw - 15min [nPa]'] = psw_15
dataframe['Psw - 30min [nPa]'] = psw_30
dataframe['AE - 0min [nT]'] = AE_data
dataframe['AE - 15min [nT]'] = AE_15
dataframe['AE - 30min [nT]'] = AE_30
dataframe['SymH - 0min [nT]'] = SymH_data
dataframe['SymH - 15min [nT]'] = SymH_15
dataframe['SymH - 30min [nT]'] = SymH_30
dataframe['Clock Angle - 0min [deg]'] = clock_angle_data
dataframe['Clock Angle - 15min [deg]'] = clock_angle_15
dataframe['Clock Angle - 30min [deg]'] = clock_angle_30
dataframe['Newell CF - 0min [m/s^(4/3) T^(2/3)]'] = newell_data
dataframe['Newell CF - 15min [m/s^(4/3) T^(2/3)]'] = newell_15
dataframe['Newell CF - 30min [m/s^(4/3) T^(2/3)]'] = newell_30
dataframe['Borovsky CF - 0min [nT km/s]'] = borovsky_data
dataframe['Borovsky CF - 15min [nT km/s]'] = borovsky_15
dataframe['Borovsky CF - 30min [nT km/s]'] = borovsky_30
dataframe['Kp [dimensionless]'] = KPdata
dataframe['F107 [sfu=10^-22 W/m^2/hz]'] = F107data
dataframe['Proton 10MeV'] = proton_10_data
dataframe['Proton 30MeV'] = proton_30_data
dataframe['Proton 60MeV'] = proton_60_data
dataframe['GOES X-ray Wm^-2'] = goes_data
dataframe_nan = dataframe.replace(9999.99,
np.nan) #replace 9999.99 with nans
filepath = '/Users/ryanmcgranaghan/Documents/Conferences/ISSI_2018/ISSI_geospaceParticles/solar_data/'
filename = filepath + 'solardata' + input_datetime.strftime(
'%Y') + '_' + input_datetime.strftime('%j') + '.csv'
print('output solardata file location = {}'.format(filename))
dataframe_nan.to_csv(filename, index_label='Datetime')
