def burst_selections(file, type, tstart=None, tstop=None, **kwargs):
'''
Retrieve burst selections and write them to a file.
Parameters
----------
file : str
Name of the file to be written. If the file exists,
`tstart` will be amended and new data will be appended
to the end.
type : str
Type of selections to retrieve. Options include:
'abs', 'sitl+back', or 'gls'. See
`pymms.selections.selections` for more.
tstart : `datetime.datetime`
Start of the data interval. If `file` exists, the
start time will default to the end of the last
interval recorded. The end of the data interval
is the current time.
\*\*kwargs
Any keyword accepted by `pymms.selections.selections`
'''
file = pathlib.Path(file)
old_data = []
if not file.parent.exists():
file.parent.mkdir()
if file.exists():
old_data = get_start_time(file)
tstart = old_data[-1].tstop
if tstop is None:
tstop = dt.datetime.now()
# Get the data interval. If the file exists, start from
# the last time in the file. Go through the current time.
# Read the data
new_data = sel.selections(type, tstart, tstop, **kwargs)
i = 0
while new_data[i].tstart < tstart:
i += 1
old_data.extend(new_data[i:])
# Write the data
sel.write_csv(file, old_data)
def combined_dataframe(self):
'''
Combines all dataframes, downsamples to DES, which as the time index with the longest sampling period (`4.5s`).
After that, multi-instrument metafeatures are calculated.
Returns:
df
'''
afg_df = self.afg_data()
edp_df = self.edp_data()
dis_df = self.dis_data()
des_df = self.des_data()
# Resample data
afg_df = afg_df.reindex(des_df.index, method='nearest')
edp_df = edp_df.reindex(des_df.index, method='nearest')
dis_df = dis_df.reindex(des_df.index, method='nearest')
# Merge dataframes
df = des_df
df = df.join(dis_df, how='outer')
df = df.join(afg_df, how='outer')
df = df.join(edp_df, how='outer')
# Metafeatures
df['T_ratio'] = df['Ti_scalar'] / df['Te_scalar']
df['plasma_beta'] = (df['Pe_scalar'] + df['Pi_scalar']) / df['P_B']
if self.include_selections:
"""Download SITL selections"""
selections_path = Path(
tempfile.gettempdir()) / 'all_selections.csv'
data = selections_api.selections('sitl+back', self.start_date,
self.end_date)
selections_api.write_csv(selections_path, data)
"""Mark datapoints selected by a SITL as selected"""
selections = pd.read_csv(selections_path,
infer_datetime_format=True,
parse_dates=[0, 1])
selections.dropna()
if self.include_partials:
selections = selections[selections['discussion'].str.
contains("MP", na=False)
| selections['discussion'].str.
contains("magnetopause", na=False)]
else:
selections = selections[selections['discussion'].str.
contains("MP", na=False)
| selections['discussion'].str.
contains("magnetopause", na=False)
& ~selections['discussion'].str.
contains("magnetopause", na=False)]
# Create column to denote whether an observation is selected by SITLs
df['selected'] = False
# Set selected to be True if the observation is in a date range of a selection
date_col = df.index
cond_series = df['selected']
for start, end in zip(selections['start_time'],
selections['stop_time']):
cond_series |= (start <= date_col) & (date_col <= end)
if self.verbose:
print(df.loc[cond_series, 'selected'])
df.loc[cond_series, 'selected'] = True
return df
def plot_burst_selections(sc, start_date, end_date,
figsize=(5.5, 7)):
mode = 'srvy'
level = 'l2'
# FGM
b_vname = '_'.join((sc, 'fgm', 'b', 'gse', mode, level))
mms = api.MrMMS_SDC_API(sc, 'fgm', mode, level,
start_date=start_date, end_date=end_date)
files = mms.download_files()
files = api.sort_files(files)[0]
fgm_data = from_cdflib(files, b_vname,
start_date, end_date)
fgm_data[fgm_data['LABL_PTR_1']]['data'] = ['Bx', 'By', 'Bz', '|B|']
# FPI DIS
fpi_mode = 'fast'
ni_vname = '_'.join((sc, 'dis', 'numberdensity', fpi_mode))
espec_i_vname = '_'.join((sc, 'dis', 'energyspectr', 'omni', fpi_mode))
mms = api.MrMMS_SDC_API(sc, 'fpi', fpi_mode, level,
optdesc='dis-moms',
start_date=start_date, end_date=end_date)
files = mms.download_files()
files = api.sort_files(files)[0]
ni_data = from_cdflib(files, ni_vname,
start_date, end_date)
especi_data = from_cdflib(files, espec_i_vname,
start_date, end_date)
# FPI DES
ne_vname = '_'.join((sc, 'des', 'numberdensity', fpi_mode))
espec_e_vname = '_'.join((sc, 'des', 'energyspectr', 'omni', fpi_mode))
mms = api.MrMMS_SDC_API(sc, 'fpi', fpi_mode, level,
optdesc='des-moms',
start_date=start_date, end_date=end_date)
files = mms.download_files()
files = api.sort_files(files)[0]
ne_data = from_cdflib(files, ne_vname,
start_date, end_date)
espece_data = from_cdflib(files, espec_e_vname,
start_date, end_date)
# Grab selections
abs_data = sel.selections('abs', start_date, end_date)
sitl_data = sel.selections('sitl+back', start_date, end_date)
gls_data = sel.selections('mp-dl-unh', start_date, end_date)
# SITL data time series
t_abs = []
x_abs = []
for selection in abs_data:
t_abs.extend([selection.tstart, selection.tstart,
selection.tstop, selection.tstop])
x_abs.extend([0, selection.fom, selection.fom, 0])
if len(abs_data) == 0:
t_abs = [start_date, end_date]
x_abs = [0, 0]
abs = {'data': x_abs,
'DEPEND_0': 't',
't': {'data': t_abs}}
t_sitl = []
x_sitl = []
for selection in sitl_data:
t_sitl.extend([selection.tstart, selection.tstart,
selection.tstop, selection.tstop])
x_sitl.extend([0, selection.fom, selection.fom, 0])
if len(sitl_data) == 0:
t_sitl = [start_date, end_date]
x_sitl = [0, 0]
sitl = {'data': x_sitl,
'DEPEND_0': 't',
't': {'data': t_sitl}}
t_gls = []
x_gls = []
for selection in gls_data:
t_gls.extend([selection.tstart, selection.tstart,
selection.tstop, selection.tstop])
x_gls.extend([0, selection.fom, selection.fom, 0])
if len(gls_data) == 0:
t_gls = [start_date, end_date]
x_gls = [0, 0]
gls = {'data': x_gls,
'DEPEND_0': 't',
't': {'data': t_gls}}
# Setup plot
nrows = 7
ncols = 1
fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
figsize=figsize, squeeze=False)
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
# Plot FGM
plot_2D(especi_data, axes[0,0])
axes[0,0].set_title(sc.upper())
fig.axes[-1].set_label('DEF')
axes[0,0].set_ylabel('$E_{ion}$\n(eV)')
axes[0,0].set_xticks([])
axes[0,0].set_xlabel('')
plot_2D(espece_data, axes[1,0])
fig.axes[-1].set_label('DEF\nLog_{10}(keV/(cm^2 s sr keV))')
axes[1,0].set_ylabel('$E_{e-}$\n(eV)')
axes[1,0].set_xticks([])
axes[1,0].set_xlabel('')
axes[1,0].set_title('')
plot_1D(fgm_data, axes[2,0])
axes[2,0].set_ylabel('B\n(nT)')
axes[2,0].set_xticks([])
axes[2,0].set_xlabel('')
axes[2,0].set_title('')
plot_1D(ni_data, axes[3,0])
axes[3,0].set_ylabel('$N_{i}$\n($cm^{-3}$)')
axes[3,0].set_xticks([])
axes[3,0].set_xlabel('')
axes[3,0].set_title('')
plot_1D(abs, axes[4,0])
axes[4,0].set_ylabel('ABS')
axes[4,0].set_xticks([])
axes[4,0].set_xlabel('')
axes[4,0].set_title('')
plot_1D(gls, axes[5,0])
axes[5,0].set_ylabel('GLS')
axes[5,0].set_ylim(0, 200)
axes[5,0].set_xticks([])
axes[5,0].set_xlabel('')
axes[5,0].set_title('')
plot_1D(sitl, axes[6,0])
axes[6,0].set_ylabel('SITL')
axes[6,0].set_title('')
axes[6,0].xaxis.set_major_locator(locator)
axes[6,0].xaxis.set_major_formatter(formatter)
for tick in axes[6,0].get_xticklabels():
tick.set_rotation(45)
# Set a common time range
plt.setp(axes, xlim=mdates.date2num([start_date, end_date]))
plt.subplots_adjust(left=0.15, right=0.85, top=0.93)
return fig, axes