def download_func(remote_base_url, local_base_dir,
directory, fname, remote_fname, extension):
remote_url = remote_base_url + str(directory)
# Now load remotely
util.load(fname + extension,
local_base_dir / directory,
remote_url)
def download_func(remote_base_url, local_base_dir, directory, fname,
extension):
remote_url = '{}{}'.format(remote_base_url, directory)
util.load(fname + extension,
local_base_dir / directory,
remote_url,
guessversion=True)
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 = _merged_localdir(probe)
remote_url = ('ftp://cdaweb.gsfc.nasa.gov/pub/data/helios/'
'helios{}/merged/he{}_40sec'.format(probe, probe))
filename = _merged_fname(probe, year, doy) + '.dat'
asciiloc = os.path.join(local_dir, filename)
# Make sure file is downloaded
util.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 = 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
if use_hdf:
_save_hdf(data, _merged_localdir(probe),
_merged_fname(probe, year, doy))
return (data)
def _mag_hires_helper(year, doy, local_dir, url, coords):
fname = str(year)[2:] + doy + '_FGM_' + coords
hdf_fname = '{}_{}.hdf'.format(year, doy)
hdfloc = local_dir / hdf_fname
if hdfloc.exists():
return pd.read_hdf(hdfloc)
f = util.load(fname + '.TAB', local_dir, url)
if f is None:
raise RuntimeError(
'No file named {} exits on remote server'.format(fname))
elif 'error_message' in f.readline():
location = f.name
f.close()
os.remove(f.name)
raise RuntimeError(
'No file named {} exits on remote server'.format(fname))
df = pd.read_table(f,
names=['Time', 'Bx', 'By', 'Bz'],
delim_whitespace=True,
parse_dates=[0],
index_col=0)
f.close()
if use_hdf:
df.to_hdf(hdfloc, key='data', mode='w')
return df
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 = util._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 = util.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 = util.cdf2df(cdf, 'Epoch', keys)
data.append(thisdata)
data = pd.concat(data)
data = data[(data['Time'] > starttime) & (data['Time'] < endtime)]
data.index.name = 'Time'
return data
def download_func(remote_base_url, local_base_dir,
directory, fname, extension):
def check_exists():
# Because the version might be different to the one we guess, work
# out the downloaded filename
for f in os.listdir(os.path.join(local_base_dir, directory)):
if (f[:-6] == (fname + extension)[:-6]):
# Return filename with '.cdf' stripped off the end
return f[:-4]
if check_exists() is not None:
return check_exists()
# Now load remotely
util.load(fname + extension,
os.path.join(local_base_dir, directory),
remote_base_url + directory, guessversion=True)
if check_exists() is not None:
return check_exists()
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 = util._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'
hdffname = filename[:-3] + '.hdf'
# 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)
hdffile = os.path.join(local_dir, hdffname)
if os.path.exists(hdffile):
thisdata = pd.read_hdf(hdffile)
data.append(thisdata)
continue
remote_url = imp_url + relative_loc
cdf = util.load(filename, local_dir, remote_url)
keys = {
'B': '|B|',
'BX': 'Bx',
'BY': 'By',
'BZ': 'Bz',
'Epoch': 'Time'
}
thisdata = util.cdf2df(cdf, 'Epoch', keys)
data.append(thisdata)
if use_hdf:
thisdata.to_hdf(hdffile, key='merged', mode='w')
data = pd.concat(data)
data = data[(data['Time'] > startTime) & (data['Time'] < endTime)]
data = data.drop(columns='Time')
data.index.name = 'Time'
return data
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 = util._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 = ulysses_dir / 'fgm' / 'hires' / yearstr
local_file = local_dir / fgm_options['FILE_NAME']
local_hdf = local_file.with_suffix('.hdf')
# If we have already saved a hdf file
if local_hdf.exists():
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 = util.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 util.timefilter(data, starttime, endtime)
def download_func(remote_base_url, local_base_dir, directory, fname,
extension):
def check_exists():
# Because the version might be different to the one we guess, work
# out the downloaded filename
for f in (local_base_dir / directory).iterdir():
fstr = str(f.name)
if (fstr[:-6] == (fname + extension)[:-6]):
# Return filename with '.cdf' stripped off the end
return fstr[:-4]
if check_exists() is not None:
return check_exists()
# Now load remotely
util.load(fname + extension,
local_base_dir / directory,
remote_base_url + str(directory),
guessversion=True)
return check_exists()
def _mag_ness_fromascii(probe, year, doy, try_download=True):
"""
Read in a single day of 6 second magnetic field data.
Data is read from orignal ascii files, and saved to a hdf file for faster
access after the first read.
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
6 second magnetic field data set
"""
probe = _check_probe(probe)
local_dir = _ness_localdir(probe, year)
remote_url = ('ftp://spdf.sci.gsfc.nasa.gov/pub/data/helios/helios' +
probe + '/mag/6sec_ness/' + str(year) + '/')
fname = _ness_fname(probe, year, doy) + '.asc'
f = util.load(fname, local_dir, remote_url, try_download=try_download)
# Read in data
headings = [
'probe', 'year', 'doy', 'hour', 'minute', 'second', 'naverage', 'Bx',
'By', 'Bz', '|B|', 'sigma_Bx', 'sigma_By', 'sigma_Bz'
]
colspecs = [(1, 2), (2, 4), (4, 7), (7, 9), (9, 11), (11, 13), (13, 15),
(15, 22), (22, 29), (29, 36), (36, 42), (42, 48), (48, 54),
(54, 60)]
data = pd.read_fwf(f, names=headings, header=None, colspecs=colspecs)
# Process data
data['year'] += 1900
# Convert date info to datetime
data['Time'] = pd.to_datetime(data['year'], format='%Y') + \
pd.to_timedelta(data['doy'] - 1, unit='d') + \
pd.to_timedelta(data['hour'], unit='h') + \
pd.to_timedelta(data['minute'], unit='m') + \
pd.to_timedelta(data['second'], unit='s')
data = data.drop(['year', 'doy', 'hour', 'minute', 'second'], axis=1)
data = data.set_index('Time', drop=False)
# Save data to a hdf store
if use_hdf:
_save_hdf(data, local_dir, _ness_fname(probe, year, doy))
return (data)
def processing_func(local_dir, fname):
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'
cdf = util.load(fname, local_dir, '')
df = util.pitchdist_cdf2df(cdf, distkeys, energykey, timekey,
anglelabels)
return df
def processing_func(directory, fname):
cdf = util.load(fname, directory, '')
epoch_dict = {'h0': 'Epoch3', 'h2': 'Epoch'}
mag_dict = {'h0': 'B3GSE', 'h2': 'BGSE'}
epoch_key = epoch_dict[version]
mag_key = mag_dict[version]
keys = {mag_key: ['Bx_gse', 'By_gse', 'Bz_gse'], epoch_key: 'Time'}
badvalues = {'Bx_gse': -1e+31, 'By_gse': -1e+31, 'Bz_gse': -1e+31}
df = util.cdf2df(cdf,
index_key=epoch_key,
keys=keys,
badvalues=badvalues)
return df
def fgm_survey(probe, starttime, endtime):
"""
Import fgm survey mode magnetic field 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 = util._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{}_fgm_srvy_l2_{}{:02}{:02}_v4.18.0.cdf'.format(
probe, date.year, date.month, date.day)
# Absolute path to local directory for this data file
local_dir = os.path.join(mms_dir, this_relative_dir)
util._checkdir(local_dir)
remote_url = remote_mms_dir + this_relative_dir
# Load cdf file
cdf = util.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 = util.cdf2df(cdf, 'Epoch', keys)
data.append(df)
return util.timefilter(data, starttime, endtime)
def _swics(starttime, endtime, names, product):
swics_options = url_options
readargs = {'names': names, 'delim_whitespace': True}
data = []
dtimes = util._daysplitinterval(starttime, endtime)
# Loop through years
for year in range(starttime.year, endtime.year + 1):
swics_options['FILE_NAME'] = '{}{}.dat'.format(product, str(year)[-2:])
# Local locaiton to download to
local_dir = os.path.join(ulysses_dir, 'swics')
local_file = os.path.join(local_dir, swics_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
swics_options['FILE_PATH'] = '/ufa/HiRes/data/swics'
remote_url = ulysses_url
for key in swics_options:
remote_url += key + '=' + swics_options[key] + '&'
f = util.load(swics_options['FILE_NAME'], local_dir, remote_url)
if f is None:
print('File {}/{} not available\n'.format(
remote_url, filename))
continue
# 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, 'swics')
data.append(thisdata)
return util.timefilter(data, starttime, endtime)
def threedp_sfpd(starttime, endtime):
"""
Import 'sfpd' wind data.
12 second energetic electron pitch-angle energy spectra from the foil SST
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = path.Path('3dp') / '3dp_sfpd'
daylist = util._daysplitinterval(starttime, endtime)
data = []
mag = []
for (date, _, _) in daylist:
this_relative_dir = relative_dir / str(date.year)
# Absolute path to local directory for this data file
local_dir = wind_dir / this_relative_dir
filename = 'wi_sfpd_3dp_{:{dfmt}}_v02'.format(date, dfmt='%Y%m%d')
hdfname = filename + '.hdf'
hdfloc = local_dir / hdfname
if hdfloc.exists():
df = pd.read_hdf(hdfloc)
data.append(df)
continue
util._checkdir(local_dir)
remote_url = remote_wind_dir + str(this_relative_dir)
cdf = util.load(filename + '.cdf',
local_dir,
remote_url,
guessversion=True)
if cdf is None:
print('File {}/{} not available\n'.format(remote_url, filename))
continue
data_today = []
# Loop through each timestamp to build up fluxes
for i, time in enumerate(cdf['Epoch'][...]):
energies = cdf['ENERGY'][i, :]
angles = cdf['PANGLE'][i, :]
fluxes = cdf['FLUX'][i, :, :]
magfield = cdf['MAGF'][i, :]
index = pd.MultiIndex.from_product(
([time], energies, angles),
names=['Time', 'Energy', 'Pitch angle'])
df = pd.DataFrame(fluxes.ravel(), index=index, columns=['Flux'])
df['Bx'] = magfield[0]
df['By'] = magfield[1]
df['Bz'] = magfield[2]
data_today.append(df)
data_today = pd.concat(data_today)
data_today = data_today.sort_index()
if use_hdf:
data_today.to_hdf(hdfloc, 'sfpd', mode='w')
data.append(data_today)
data = util.timefilter(data, starttime, endtime)
data = data.reset_index(level=['Energy', 'Pitch angle'])
return data
def threedp_pm(starttime, endtime):
"""
Import 'pm' wind data.
3 second time resolution solar wind proton and alpha particle moments from
the PESA LOW sensor, computed on-board the spacecraft
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
Returns
-------
data : DataFrame
"""
# Directory relative to main WIND data directory
relative_dir = path.Path('3dp') / '3dp_pm'
daylist = util._daysplitinterval(starttime, endtime)
data = []
for day in daylist:
date = day[0]
this_relative_dir = relative_dir / str(day[0].year)
# Absolute path to local directory for this data file
local_dir = 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 = local_dir / hdfname
if hdfloc.exists():
df = pd.read_hdf(hdfloc)
data.append(df)
continue
util._checkdir(local_dir)
remote_url = remote_wind_dir + str(this_relative_dir)
cdf = util.load(filename, local_dir, remote_url, guessversion=True)
if cdf is None:
print('File {}/{} not available\n'.format(remote_url, filename))
continue
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 = util.cdf2df(cdf, index_key='Epoch', keys=keys)
if use_hdf:
df.to_hdf(hdfloc, 'pm', mode='w')
data.append(df)
return util.timefilter(data, starttime, endtime)
def fpi_dis_moms(probe, mode, starttime, endtime):
"""
Import fpi ion 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 = util._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{}_fpi_{}_l2_dis-moms_'
'{}{:02}{:02}{:02}0000_v3.3.0.cdf').format(
probe, mode, date.year, date.month, date.day, h)
# Absolute path to local directory for this data file
local_dir = os.path.join(mms_dir, this_relative_dir)
util._checkdir(local_dir)
remote_url = remote_mms_dir + this_relative_dir
# Load cdf file
try:
cdf = util.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 = util.cdf2df(cdf, 'Epoch', keys)
data.append(df)
return util.timefilter(data, starttime, endtime)
def processing_func(directory, fname):
cdf = util.load(fname, directory, '')
return util.cdf2df(cdf, 'Epoch', badvalues=badvalues)
def mag_1min(starttime, endtime, coords):
"""
Import 1 minute magnetic field from Cassini.
See http://pds-ppi.igpp.ucla.edu/search/view/?f=yes&id=pds://PPI/CO-E_SW_J_S-MAG-4-SUMM-1MINAVG-V1.0
for more information.
Cassini Orbiter Magnetometer Calibrated MAG data in 1 minute averages
available covering the period 1999-08-16 (DOY 228) to 2016-12-31 (DOY 366).
The data are provided in RTN coordinates throughout the mission, with
Earth, Jupiter, and Saturn centered coordinates for the respective
flybys of those planets.
Parameters
----------
starttime : datetime
Interval start time.
endtime : datetime
Interval end time.
coords : strings
Requested coordinate system. Must be one of
``['KRTP', 'KSM', 'KSO', 'RTN']``
Returns
-------
data : DataFrame
Requested data
"""
valid_coords = ['KRTP', 'KSM', 'KSO', 'RTN']
if coords not in valid_coords:
raise ValueError('coords must be one of {}'.format(valid_coords))
base_url = ('http://pds-ppi.igpp.ucla.edu/ditdos/download?'
'id=pds://PPI/CO-E_SW_J_S-MAG-4-SUMM-1MINAVG-V1.0/DATA')
data = []
for year in starttime.year, endtime.year:
url = '{}/{}'.format(base_url, year)
local_dir = os.path.join(cassini_dir, 'mag', '1min')
fname = '{}_FGM_{}_1M'.format(year, coords)
hdfloc = os.path.join(local_dir, fname + '.hdf')
if os.path.isfile(hdfloc):
df = pd.read_hdf(hdfloc)
data.append(df)
continue
f = util.load(fname + '.TAB', local_dir, url)
if 'error_message' in f.readline():
f.close()
os.remove(os.path.join(local_dir, fname + '.TAB'))
continue
df = pd.read_table(f,
names=[
'Time', 'Bx', 'By', 'Bz', '|B|', 'X', 'Y', 'Z',
'Local hour', 'n points'
],
delim_whitespace=True,
parse_dates=[0],
index_col=0)
data.append(df)
if use_hdf:
df.to_hdf(hdfloc, key='data', mode='w')
return util.timefilter(data, starttime, endtime)
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'
daylist = util._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)
util._checkdir(local_dir)
remote_url = os.path.join(remote_mess_dir, this_relative_dir)
cdf = util.load(filename, local_dir, remote_url, guessversion=True)
if cdf is None:
print('File {}/{} not available\n'.format(remote_url, filename))
continue
keys = {
'B_normal': 'Bn',
'B_radial': 'Br',
'B_tangential': 'Bt',
'Epoch': 'Time',
'azimuth_ecliptic': 'sc_Az',
'latitude_ecliptic': 'sc_Lat',
'radialDistance': 'sc_r',
'MissionElapsedTime': 'mission_time'
}
df = util.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 util.timefilter(data, starttime, endtime)
def download_func(remote_base_url, local_base_dir, directory, fname,
extension):
remote_url = '{}{}'.format(remote_base_url, directory)
util.load(fname + extension, os.path.join(local_base_dir, directory),
remote_url)
def processing_func(local_dir, local_fname):
cdf = util.load(local_fname, local_dir, '')
return util.cdf2df(cdf,
index_key='Epoch',
keys=keys,
badvalues=badvalues)
def _fourHz_fromascii(probe, year, doy, try_download=True):
"""
Read in a single day of 4Hz magnetic field data.
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
4Hz magnetic field data set
"""
probe = _check_probe(probe)
local_dir = _4hz_localdir(probe)
fname_prefix = _4hz_filename(probe, year, doy)
# For some reason the last number in the filename is the hour at which
# data starts from on that day... this means a loop to check each hour
asciiloc = None
fname = None
for i in range(0, 24):
testloc = os.path.join(local_dir,
fname_prefix + str(i).zfill(2) + '.asc')
if os.path.isfile(testloc):
asciiloc = testloc
break
if asciiloc is not None:
if os.name == 'nt':
splitchar = '\\'
else:
splitchar = '/'
fname = asciiloc.split(splitchar)[-1]
remote_url = None
elif try_download is not False:
ftpsite = 'apollo.ssl.berkeley.edu'
remote_dir = ('pub/helios-data/E2_experiment/'
'Data_Cologne_Nov2016_bestdata/'
'HR/helios{}'.format(probe))
remote_url = 'ftp://' + ftpsite + '/' + remote_dir
fname = None
with FTP(ftpsite) as ftp:
ftp.login()
filenames = ftp.nlst(remote_dir)
for filename in filenames:
if fname_prefix in filename:
fname = filename
break
if fname is None:
raise ValueError('No mag data available locally or remotely')
asciiloc = util.load(fname, local_dir, remote_url)
# Read in data
headings = ['Time', 'Bx', 'By', 'Bz', '|B|']
cols = [0, 4, 5, 6, 7]
data = pd.read_table(asciiloc,
names=headings,
header=None,
usecols=cols,
delim_whitespace=True)
# Convert date info to datetime
data['Time'] = pd.to_datetime(data['Time'], format='%Y-%m-%dT%H:%M:%S')
data = data.set_index('Time', drop=False)
# Save data to a hdf store
if use_hdf:
fname = _4hz_filename(probe, year, doy)
_save_hdf(data, local_dir, fname)
return (data)
def merged(probe, starttime, endtime, verbose=False):
"""
Import merged plasma data. See
ftp://cdaweb.gsfc.nasa.gov/pub/data/imp/imp8/merged/00readme.txt
for information on variables.
Parameters
----------
probe : string
Probe number.
starttime : datetime
Start of interval.
endtime : datetime
End of interval.
verbose : bool, optional
If ``True``, print information whilst loading. Default is
``False``.
Returns
-------
data : DataFrame
Requested data.
"""
_check_probe(probe, ['8'])
data = []
startyear = starttime.year
endyear = endtime.year
# Loop through years
for year in range(startyear, endyear + 1):
if year == startyear:
startmonth = starttime.month
else:
startmonth = 1
if year == endyear:
endmonth = endtime.month
else:
endmonth = 12
# Loop through months
for month in range(startmonth, endmonth + 1):
if verbose:
print('Loading IMP merged probe {}, {:02d}/{}'.format(
probe, month, year))
intervalstring = str(year) + str(month).zfill(2)
filename = 'imp_min_merge' + intervalstring + '.asc'
# Location of file relative to local directory or remote url
relative_loc = os.path.join('imp' + probe, 'merged')
local_dir = os.path.join(imp_dir, relative_loc)
hdffile = os.path.join(local_dir, filename[:-4] + '.hdf')
if os.path.isfile(hdffile):
data.append(pd.read_hdf(hdffile))
continue
remote_url = imp_url + relative_loc
f = util.load(filename, local_dir, remote_url)
if f is None:
print('File {}/{} not available\n'.format(
remote_url, filename))
continue
readargs = {
'names': [
'Year', 'doy', 'Hour', 'Minute', 'sw_flag', 'x_gse',
'y_gse', 'z_gse', 'y_gsm', 'z_gsm', 'Nm', 'FCm', 'DWm',
'<|B|>', '|<B>|', '<B_lat>', '<B_long>', 'Bx_gse',
'By_gse', 'Bz_gse', 'By_gsm', 'Bz_gsm', 'sigma|B|',
'sigma B', 'sigma B_x', 'sigma B_y', 'sigma B_z',
'plas_reg', 'Npp', 'FCp', 'DWp', 'v_fit', 'vx_fit_gse',
'vy_fit_gse', 'vz_fit_gse', 'vlong_fit', 'vlat_fit',
'np_fit', 'Tp_fit', 'v_mom', 'vx_mom_gse', 'vy_mom_gse',
'vz_mom_gse', 'vlong_mom', 'vlat_mom', 'np_mom', 'Tp_mom'
],
'na_values': [
'9999', '999', '99', '99', '9', '9999.99', '9999.99',
'9999.99', '9999.99', '9999.99', '9', '99', '9.99',
'9999.99', '9999.99', '9999.99', '9999.99', '9999.99',
'9999.99', '9999.99', '9999.99', '9999.99', '9999.99',
'9999.99', '9999.99', '9999.99', '9999.99', '9', '9', '99',
'9.99', '9999.9', '9999.9', '9999.9', '9999.9', '9999.9',
'9999.9', '9999.9', '9999999.', '9999.9', '9999.9',
'9999.9', '9999.9', '9999.9', '9999.9', '9999.9',
'9999999.'
],
'delim_whitespace':
True
}
# Read in data
thisdata = pd.read_table(f, **readargs)
thisdata['Time'] = (
pd.to_datetime(thisdata['Year'], format='%Y') +
pd.to_timedelta(thisdata['doy'] - 1, unit='d') +
pd.to_timedelta(thisdata['Hour'], unit='h') +
pd.to_timedelta(thisdata['Minute'], unit='m'))
if use_hdf:
thisdata.to_hdf(hdffile, key='distparams', mode='w')
data.append(thisdata)
return util.timefilter(data, starttime, endtime)
def mag15s(probe, starttime, endtime, verbose=False):
"""
Import 15s cadence magnetic field data.
Parameters
----------
probe : string
Probe number.
starttime : datetime
Start of interval.
endtime : datetime
End of interval.
verbose : bool, optional
If ``True``, print information whilst loading. Default is ``False``.
Returns
-------
data : DataFrame
Requested data.
"""
data = []
dtimes = util._daysplitinterval(starttime, endtime)
# Loop through years
for dtime in dtimes:
startdt = dtime[0]
year = startdt.year
doy = util.dtime2doy(startdt)
if verbose:
print('Loading IMP 15s mag probe {}, {:03d}/{}'.format(
probe, doy, year))
filename = '{}{:03d}_imp{}_mag_15s_v3.asc'.format(year, doy, probe)
hdffname = filename[:-3] + 'hdf'
# Location of file relative to local directory or remote url
relative_loc = os.path.join('imp{}'.format(probe), 'mag',
'15s_ascii_v3', str(year))
local_dir = os.path.join(imp_dir, relative_loc)
hdffile = os.path.join(local_dir, hdffname)
if os.path.exists(hdffile):
thisdata = pd.read_hdf(hdffile)
data.append(thisdata)
continue
remote_url = imp_url + relative_loc
f = util.load(filename, local_dir, remote_url)
readargs = {
'names': [
'Year', 'doy', 'Second', 'Source flag', 'n points', 'x gse',
'y gse', 'z gse', 'y gsm', 'z gsm', '|B|', 'Bx gse', 'By gse',
'Bz gse', 'By gsm', 'Bz gsm', 'Bxx gse', 'Byy gse', 'Bzz gse',
'Byx gse', 'Bzx gse', 'Bzy gse', 'Time shift', 'sw flag'
],
'na_values': [
'9999', '999', '99', '9', '999', '99.99', '99.99', '99.99',
'99.99', '99.99', '9999.99', '9999.99', '9999.99', '9999.99',
'9999.99', '9999.99', '9999.99', '9999.99', '9999.99',
'9999.99', '9999.99', '9999.99', '999.9', '9'
],
'delim_whitespace':
True
}
# Read in data
thisdata = pd.read_table(f, **readargs)
thisdata['Time'] = (pd.to_datetime(thisdata['Year'], format='%Y') +
pd.to_timedelta(thisdata['doy'] - 1, unit='d') +
pd.to_timedelta(thisdata['Second'], unit='s'))
thisdata = thisdata.set_index('Time', drop=False)
thisdata = thisdata.drop(['Year', 'doy', 'Second'], 1)
if use_hdf:
thisdata.to_hdf(hdffile, key='distparams', mode='w')
data.append(thisdata)
return util.timefilter(data, starttime, endtime)
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 = util._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 = util.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 util.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 = util._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)
util._checkdir(local_dir)
remote_url = remote_themis_dir + this_relative_dir
cdf = util.load(filename, local_dir, remote_url)
if cdf is None:
print('File {}/{} not available\n'.format(remote_url, filename))
continue
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 = util.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
