def _merged_fromascii(probe, year, doy, try_download):
"""
Read in a single day of merged data.
Data is loaded from orignal ascii files. and saved to a hdf file for faster
access after first read in.
Parameters
----------
probe : int, string
Helios probe to import data from. Must be 1 or 2.
year : int
Year.
doy : int
Day of year.
Returns
-------
data : DataFrame
Merged data set.
"""
probe = _check_probe(probe)
local_dir = os.path.join(helios_dir, 'helios' + probe, 'merged',
'he' + probe + '_40sec/')
remote_url = 'ftp://cdaweb.gsfc.nasa.gov/pub/data/helios/helios' +\
probe + '/' + 'merged/he' + probe + '_40sec'
filename = 'H' + probe + str(year - 1900) + '_' +\
str(doy).zfill(3) + '.dat'
asciiloc = os.path.join(local_dir, filename)
# Make sure file is downloaded
helper.load(filename, local_dir, remote_url, try_download=try_download)
# Load data
data = pd.read_table(asciiloc, delim_whitespace=True)
# Process data
data['year'] = data['year'].astype(int)
# Convert date info to datetime
data['Time'] = pd.to_datetime(data['year'], format='%Y') + \
pd.to_timedelta(data['day'] - 1, unit='d') + \
pd.to_timedelta(data['hour'], unit='h') + \
pd.to_timedelta(data['min'], unit='m') + \
pd.to_timedelta(data['sec'], unit='s')
data['ordinal'] = pd.DatetimeIndex(data['Time']).astype(np.int64)
data = data.drop(['year', 'day', 'hour', 'min', 'sec', 'dechr'], axis=1)
# Set zero values to nans
data.replace(0.0, np.nan, inplace=True)
# Save data to a hdf store
saveloc = os.path.join(local_dir, filename[:-4] + '.h5')
data.to_hdf(saveloc, 'table', format='fixed', mode='w')
return (data)
def mitplasma_h0(probe, starttime, endtime):
"""
Import mit h0 plasma data.
Parameters
----------
probe : string
Probe number.
starttime : datetime
Start of interval.
endtime : datetime
End of interval.
Returns
-------
data : DataFrame
Requested data.
"""
data = []
dtimes = spacetime.daysplitinterval(starttime, endtime)
# Loop through years
for dtime in dtimes:
date = dtime[0]
intervalstring = str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2)
filename = 'i' + probe + '_h0_mitplasma_' + intervalstring + '_v01.cdf'
# Location of file relative to local directory or remote url
relative_loc = 'imp' + probe + '/plasma_mit/mitplasma_h0/' +\
str(date.year)
local_dir = os.path.join(imp_dir, relative_loc)
remote_url = imp_url + relative_loc
cdf = helper.load(filename, local_dir, remote_url)
keys = {
'EW_flowangle_best': 'EW_flowangle_best',
'EW_flowangle_mom': 'EW_flowangle_best',
'Epoch': 'Time',
'Flow_elevation_thresh': 'Flow_elevation_thresh',
'Flow_elevation_threshsp': 'Flow_elevation_threshsp',
'Region': 'Region',
'V_fit': 'V_fit',
'V_mom': 'V_fit',
'mode': 'mode',
'protonV_thermal_fit': 'protonV_thermal_fit',
'protonV_thermal_mom': 'protonV_thermal_fit',
'proton_density_fit': 'proton_density_fit',
'proton_density_mom': 'proton_density_mom',
'xyzgse': ['x_gse', 'y_gse', 'z_gse'],
'ygsm': 'ygsm',
'zgsm': 'zgsm'
}
thisdata = helper.cdf2df(cdf, 'Epoch', keys)
data.append(thisdata)
data = pd.concat(data)
data = data[(data['Time'] > starttime) & (data['Time'] < endtime)]
return data
def mag_rtn(starttime, endtime):
"""
Import magnetic field in RTN coordinates from Messenger.
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = 'rtn'
keys = {'B_normal': 'Bn',
'B_radial': 'Br',
'B_tangential': 'Bt',
'Epoch': 'Time',
'azimuth_ecliptic': 'sc_Az',
'latitude_ecliptic': 'sc_Lat',
'radialDistance': 'sc_r'}
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
this_relative_dir = os.path.join(relative_dir, str(date.year))
hdffile = 'messenger_mag_rtn_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v01.hdf'
hdfloc = os.path.join(mess_dir, this_relative_dir, hdffile)
# Try to load hdf file
if os.path.isfile(hdfloc):
df = pd.read_hdf(hdfloc)
data.append(df)
continue
filename = hdffile[:-4] + '.cdf'
# Absolute path to local directory for this data file
local_dir = os.path.join(mess_dir, this_relative_dir)
helper.checkdir(local_dir)
remote_url = os.path.join(remote_mess_dir, this_relative_dir)
cdf = helper.load(filename, local_dir, remote_url, guessversion=True)
df = helper.cdf2df(cdf, index_key='Epoch', keys=keys)
if use_hdf:
hdffile = filename[:-4] + '.hdf'
df.to_hdf(hdfloc, key='data', mode='w')
data.append(df)
return helper.timefilter(data, starttime, endtime)
def threedp_pm(starttime, endtime):
"""
Import 'pm' wind data.
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = os.path.join('3dp', '3dp_pm')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
this_relative_dir = os.path.join(relative_dir, str(day[0].year))
# Absolute path to local directory for this data file
local_dir = os.path.join(wind_dir, this_relative_dir)
filename = 'wi_pm_3dp_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v05.cdf'
hdfname = filename[:-4] + 'hdf'
hdfloc = os.path.join(local_dir, hdfname)
if os.path.isfile(hdfloc):
df = pd.read_hdf(hdfloc)
data.append(df)
continue
helper.checkdir(local_dir)
remote_url = remote_wind_dir + this_relative_dir
cdf = helper.load(filename, local_dir, remote_url, guessversion=True)
keys = {
'A_DENS': 'n_a',
'A_TEMP': 'T_a',
'A_VELS': ['va_x', 'va_y', 'va_z'],
'P_DENS': 'n_p',
'P_TEMP': 'T_p',
'P_VELS': ['vp_x', 'vp_y', 'vp_z'],
'Epoch': 'Time'
}
df = helper.cdf2df(cdf, index_key='Epoch', keys=keys)
if use_hdf:
df.to_hdf(hdfloc, 'pm', mode='w', format='f')
data.append(df)
return helper.timefilter(data, starttime, endtime)
def fgm_hires(starttime, endtime):
"""
Import high resolution fluxgate magnetometer data.
Parameters
----------
starttime : datetime
Start of interval
endtime : datetime
End of interval
Returns
-------
data : DataFrame
Requested data
"""
fgm_options = url_options
readargs = {
'names':
['year', 'doy', 'hour', 'minute', 'second', 'Bx', 'By', 'Bz', '|B|'],
'delim_whitespace':
True
}
data = []
dtimes = heliotime.daysplitinterval(starttime, endtime)
# Loop through years
for dtime in dtimes:
date = dtime[0]
yearstr = date.strftime('%Y')
fgm_options['FILE_NAME'] = ('U' + yearstr[-2:] + date.strftime('%j') +
'SH.ASC')
# Local locaiton to download to
local_dir = os.path.join(ulysses_dir, 'fgm', 'hires', yearstr)
local_file = os.path.join(local_dir, fgm_options['FILE_NAME'])
local_hdf = local_file[:-4] + '.hdf'
# If we have already saved a hdf file
if os.path.exists(local_hdf):
thisdata = pd.read_hdf(local_hdf)
else:
# Put together remote url
fgm_options['FILE_PATH'] = '/ufa/HiRes/VHM-FGM/' + yearstr
remote_url = ulysses_url
for key in fgm_options:
remote_url += key + '=' + fgm_options[key] + '&'
f = helper.load(fgm_options['FILE_NAME'], local_dir, remote_url)
# Read in data
thisdata = pd.read_table(f, **readargs)
# Process data/time
thisdata = _convert_ulysses_time(thisdata)
if use_hdf:
thisdata.to_hdf(local_hdf, 'fgm_hires')
data.append(thisdata)
return helper.timefilter(data, starttime, endtime)
def fgm_survey(probe, starttime, endtime):
"""
Import fgm survey mode data.
Parameters
----------
probe : string
Probe number, must be 1, 2, 3, or 4
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
Imported data.
"""
# Directory relative to main MMS data directory
relative_dir = os.path.join('mms' + probe,
'fgm',
'srvy',
'l2')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
this_relative_dir = os.path.join(relative_dir,
str(date.year),
str(date.month).zfill(2))
filename = 'mms' + probe + '_fgm_srvy_l2_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v4.18.0.cdf'
# Absolute path to local directory for this data file
local_dir = os.path.join(mms_dir, this_relative_dir)
helper.checkdir(local_dir)
remote_url = remote_mms_dir + this_relative_dir
# Load cdf file
cdf = helper.load(filename, local_dir, remote_url)
# Convert cdf to dataframe
keys = {'mms' + probe + '_fgm_b_gsm_srvy_l2': ['Bx', 'By', 'Bz', 'Br'],
'Epoch': 'Time'}
df = helper.cdf2df(cdf, 'Epoch', keys)
data.append(df)
data = pd.concat(data)
data = data[(data['Time'] > starttime) & (data['Time'] < endtime)]
return data
def swe_h3(starttime, endtime):
"""
Import 'h3' solar wind electron data product from WIND.
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = os.path.join('swe', 'swe_h3')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
filename = 'wi_h3_swe_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v01.cdf'
this_relative_dir = os.path.join(relative_dir, str(day[0].year))
# Absolute path to local directory for this data file
local_dir = os.path.join(wind_dir, this_relative_dir)
helper.checkdir(local_dir)
remote_url = remote_wind_dir + this_relative_dir
cdf = helper.load(filename, local_dir, remote_url)
distkeys = []
for i in range(0, 13):
distkeys.append('f_pitch_E' + str(i).zfill(2))
anglelabels = []
for i in range(0, 30):
anglelabels.append((i + 0.5) * np.pi / 30)
timekey = 'Epoch'
energykey = 'Ve'
df = helper.pitchdist_cdf2df(cdf, distkeys, energykey, timekey,
anglelabels)
data.append(df)
data = pd.concat(data)
data = data[(data.index.get_level_values('Time') > starttime)
& (data.index.get_level_values('Time') < endtime)]
return data
def mfi_h0(starttime, endtime):
"""
Import 'mfi_h0' magnetic field data product from ACE.
This data set has 16 second cadence.
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = os.path.join('mag', 'level_2_cdaweb', 'mfi_h0')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
filename = 'ac_h0_mfi_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v06.cdf'
this_relative_dir = os.path.join(relative_dir, str(day[0].year))
# Absolute path to local directory for this data file
local_dir = os.path.join(ace_dir, this_relative_dir)
helper.checkdir(local_dir)
remote_url = remote_ace_dir + this_relative_dir
cdf = helper.load(filename, local_dir, remote_url, guessversion=True)
keys = {
'BGSEc': ['Bx_gse', 'By_gse', 'Bz_gse'],
'Magnitude': '|B|',
'SC_pos_GSE': ['sc_gse_x', 'sc_gse_y', 'sc_gse_z'],
'Epoch': 'Time'
}
badvalues = {}
df = helper.cdf2df(cdf,
index_key='Epoch',
keys=keys,
badvalues=badvalues)
data.append(df)
data = pd.concat(data)
data = data[(data['Time'] > starttime) & (data['Time'] < endtime)]
return data
def mfi_h0(starttime, endtime):
"""
Import 'mfi_h0' magnetic field data product from WIND.
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = os.path.join('mfi', 'mfi_h0')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
this_relative_dir = os.path.join(relative_dir, str(day[0].year))
# Absolute path to local directory for this data file
local_dir = os.path.join(wind_dir, this_relative_dir)
filename = 'wi_h0_mfi_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v05.cdf'
hdfname = filename[:-4] + 'hdf'
hdfloc = os.path.join(local_dir, hdfname)
if os.path.isfile(hdfloc):
df = pd.read_hdf(hdfloc)
data.append(df)
continue
helper.checkdir(local_dir)
remote_url = remote_wind_dir + this_relative_dir
cdf = helper.load(filename, local_dir, remote_url, guessversion=True)
keys = {'B3GSE': ['Bx_gse', 'By_gse', 'Bz_gse'], 'Epoch3': 'Time'}
badvalues = {'Bx_gse': -1e+31, 'By_gse': -1e+31, 'Bz_gse': -1e+31}
df = helper.cdf2df(cdf,
index_key='Epoch3',
keys=keys,
badvalues=badvalues)
if use_hdf:
df.to_hdf(hdfloc, 'mag', mode='w', format='f')
data.append(df)
return helper.timefilter(data, starttime, endtime)
def mag320ms(probe, startTime, endTime):
"""
Import 320ms cadence magnetic field data.
Parameters
----------
probe : string
Probe number.
starttime : datetime
Start of interval.
endtime : datetime
End of interval.
Returns
-------
data : DataFrame
Requested data.
"""
data = []
dtimes = spacetime.daysplitinterval(startTime, endTime)
# Loop through years
for dtime in dtimes:
date = dtime[0]
intervalstring = str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2)
filename = 'i8_320msec_mag_' + intervalstring + '_v01.cdf'
# Location of file relative to local directory or remote url
relative_loc = 'imp' + probe + '/mag/mag_320msec_cdaweb/' +\
str(date.year)
local_dir = os.path.join(imp_dir, relative_loc)
remote_url = imp_url + relative_loc
cdf = helper.load(filename, local_dir, remote_url)
keys = {
'B': '|B|',
'BX': 'Bx',
'BY': 'By',
'BZ': 'Bz',
'Epoch': 'Time'
}
thisdata = helper.cdf2df(cdf, 'Epoch', keys)
data.append(thisdata)
data = pd.concat(data)
data = data[(data['Time'] > startTime) & (data['Time'] < endTime)]
return data
def swoops_ions(starttime, endtime):
"""
Import SWOOPS ion data.
Parameters
----------
starttime : datetime
Start of interval
endtime : datetime
End of interval
Returns
-------
data : DataFrame
Requested data
"""
swoops_options = url_options
readargs = {
'names': [
'year', 'doy', 'hour', 'minute', 'second', 'r', 'hlat', 'hlon',
'n_p', 'n_a', 'T_p_large', 'T_p_small', 'v_r', 'v_t', 'v_n',
'iqual'
],
'delim_whitespace':
True
}
data = []
months_loaded = []
dtimes = heliotime.daysplitinterval(starttime, endtime)
# Loop through individual days
for dtime in dtimes:
thisdate = dtime[0]
# Get first day of the month
first_day = date(thisdate.year, thisdate.month, 1)
# Check if this month's data already loaded
if first_day in months_loaded:
continue
doy = first_day.strftime('%j')
swoops_options['FILE_NAME'] = ('u' + first_day.strftime('%y') + doy +
'bam.dat')
swoops_options['FILE_PATH'] =\
('/ufa/stageIngestArea/swoops/ions/bamion' +
first_day.strftime('%y') + '.zip_files')
# Put together url for this days data
remote_url = ulysses_url
for key in swoops_options:
remote_url += key + '=' + swoops_options[key] + '&'
# Local locaiton to download to
local_dir = os.path.join(ulysses_dir, 'swoops', 'ions',
first_day.strftime('%Y'))
# Load data
try:
f = helper.load(swoops_options['FILE_NAME'], local_dir, remote_url)
except HTTPError:
print('No SWOOPS ion data available for date %s' % first_day)
continue
# Read in data
thisdata = pd.read_table(f, **readargs)
# Process data/time
thisdata = _convert_ulysses_time(thisdata)
data.append(thisdata)
months_loaded.append(first_day)
return helper.timefilter(data, starttime, endtime)
def fgm(probe, rate, coords, starttime, endtime):
"""
Import fgm magnetic field data from THEMIS.
Parameters
----------
probe : string
Alowed values are [a, b, c, d, e].
rate : string
Date rate to return. Allowed values are [e, h, l, s].
coords : string
Magnetic field co-ordinate system. Allowed values are
[dsl, gse, gsm, ssl]. NOTE: Add link to co-ordinate system
descriptions.
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
valid_rates = ['e', 'h', 'l', 's']
valid_coords = ['dsl', 'gse', 'gsm', 'ssl']
_validate_probe(probe)
if rate not in valid_rates:
raise ValueError(('rate argument %s is not in list of allowed'
'rates: %s') % (rate, valid_rates))
if coords not in valid_coords:
raise ValueError(('coords argument %s is not in list of allowed'
'co-ordinate systems: %s') % (rate, valid_rates))
# Directory relative to main THEMIS data directory
relative_dir = os.path.join('th' + probe, 'l2', 'fgm')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
this_relative_dir = os.path.join(relative_dir, str(date.year))
filename = 'th' + probe + '_l2_fgm_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
'_v01.cdf'
# Absolute path to local directory for this data file
local_dir = os.path.join(themis_dir, this_relative_dir)
helper.checkdir(local_dir)
remote_url = remote_themis_dir + this_relative_dir
cdf = helper.load(filename, local_dir, remote_url)
probestr = 'th' + probe
ratestr = '_fg' + rate + '_'
keys = {
probestr + ratestr + 'btotal':
'|B|',
probestr + ratestr + coords:
['Bx_' + coords, 'By_' + coords, 'Bz_' + coords],
probestr + ratestr + 'time':
'Time'
}
df = helper.cdf2df(cdf,
probestr + ratestr + 'time',
keys,
dtimeindex=False)
df = df.set_index(pd.to_datetime(df.index.values, unit='s'))
df['Time'] = df.index.values
data.append(df)
data = pd.concat(data)
data = data[(data['Time'] > starttime) & (data['Time'] < endtime)]
return data
def fpi_dis_moms(probe, mode, starttime, endtime):
"""
Import fpi distribution moment data.
Parameters
----------
probe : string
Probe number, must be 1, 2, 3, or 4
mode : string
Data mode, must be 'fast' or 'brst'
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
Imported data.
"""
valid_modes = ['fast', 'brst']
if mode not in valid_modes:
raise RuntimeError('Mode must be either fast or brst')
# Directory relative to main MMS data directory
relative_dir = os.path.join('mms' + probe,
'fpi',
mode,
'l2',
'dis-moms')
daylist = spacetime.daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
starthour = day[1].hour
endhour = day[2].hour + 1
# fips fast data product has files every two hours, so get nearest two
# hour stamps
starthour -= np.mod(starthour, 2)
endhour += np.mod(endhour, 2)
for h in range(starthour, endhour, 2):
this_relative_dir = os.path.join(relative_dir,
str(date.year),
str(date.month).zfill(2))
filename = 'mms' + probe + '_fpi_' + mode + '_l2_dis-moms_' +\
str(date.year) +\
str(date.month).zfill(2) +\
str(date.day).zfill(2) +\
str(h).zfill(2) + '0000' + \
'_v3.1.1.cdf'
# Absolute path to local directory for this data file
local_dir = os.path.join(mms_dir, this_relative_dir)
helper.checkdir(local_dir)
remote_url = remote_mms_dir + this_relative_dir
# Load cdf file
try:
cdf = helper.load(filename, local_dir, remote_url)
except urllib.error.HTTPError as e:
if str(e) == 'HTTP Error 404: Not Found':
print('No data available for hours', str(h) + '-' +
str(h + 2), 'on', date.strftime('%d/%m/%Y'))
continue
else:
raise
probestr = 'mms' + probe + '_'
# Convert cdf to dataframe
keys = {'Epoch': 'Time',
probestr + 'dis_bulkv_gse_fast': ['bulkv_x',
'bulkv_y',
'bulkv_z'],
probestr + 'dis_heatq_gse_fast': ['heatq_x',
'heatq_y',
'heatq_z'],
probestr + 'dis_numberdensity_fast': 'n',
probestr + 'dis_temppara_fast': 'T_par',
probestr + 'dis_tempperp_fast': 'T_perp'}
df = helper.cdf2df(cdf, 'Epoch', keys)
data.append(df)
data = pd.concat(data)
data = data[(data['Time'] > starttime) & (data['Time'] < endtime)]
return data
