def load_data(sc='mms1', mode='srvy', level='l2', optdesc='efield',
start_date=None, end_date=None, rename_vars=True,
**kwargs):
"""
Load EDI data.
CDF variable names are renamed to something easier to remember and
use. Original CDF variable names are kept as an attribute "cdf_name"
in each individual variable.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('slow', 'srvy', 'fast', 'brst').
level : str
Data quality level ('l1a', 'l2pre', 'l2')
optdesc : str
Optional descriptor. Options are: {'efield' | 'amb' | 'amb-pm2' |
'amb-alt-cc', 'amb-alt-oc', 'amb-alt-oob', 'amb-perp-c',
'amb-perp-ob'}
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
\*\*kwargs : dict
Any keyword accepted by *pymms.data.util.load_data*
Returns
-------
dist : `xarray.Dataset`
EDI data.
"""
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Load the data
data = util.load_data(sc=sc, instr='edi', mode=mode, level=level,
optdesc=optdesc, start_date=start_date,
end_date=end_date, **kwargs)
# Verify some things
import pdb
pdb.set_trace()
if len(w) > 0:
data = prune_time_overlap(data, start_date, end_date)
# EDI generates empty efield files when in ambient mode. These files
# have a version number of '0.0.0' and get read in as empty datasets,
# which fail the concatenation in util.load_data. Remove the datasets
# associated with empty files and concatenate the datasets with data
if isinstance(data, list):
# Remove empty datasets
data = [ds
for ds in data
if api.parse_file_name(ds.attrs['filename'])[-1] != '0.0.0']
# Concatenate again
data = xr.concat(data, dim='Epoch')
# Rename data variables to something simpler
if rename_vars:
data = rename(data, sc, mode, level, optdesc)
# Add data descriptors to attributes
data.attrs['sc'] = sc
data.attrs['instr'] = 'edi'
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
return data
def load_data(sc='mms1',
mode='srvy',
level='l2',
optdesc='efield',
start_date=None,
end_date=None,
**kwargs):
"""
Load EDI data.
CDF variable names are renamed to something easier to remember and
use. Original CDF variable names are kept as an attribute "cdf_name"
in each individual variable.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('slow', 'srvy', 'fast', 'brst').
optdesc : str
Optional descriptor. Options are: {'efield' | 'amb' | 'amb-pm2' |
'amb-alt-cc', 'amb-alt-oc', 'amb-alt-oob', 'amb-perp-c',
'amb-perp-ob'}
\*\*kwargs : dict
Any keyword accepted by *pymms.data.util.load_data*
Returns
-------
dist : `xarray.Dataset`
EDI data.
"""
# Load the data
data = util.load_data(sc=sc,
instr='edi',
mode=mode,
level=level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
**kwargs)
# Rename data variables to something simpler
if optdesc == 'efield':
v_dsl_vname = '_'.join((sc, 'edi', 'vdrift', 'dsl', mode, level))
v_gse_vname = '_'.join((sc, 'edi', 'vdrift', 'gse', mode, level))
v_gsm_vname = '_'.join((sc, 'edi', 'vdrift', 'gsm', mode, level))
e_dsl_vname = '_'.join((sc, 'edi', 'e', 'dsl', mode, level))
e_gse_vname = '_'.join((sc, 'edi', 'e', 'gse', mode, level))
e_gsm_vname = '_'.join((sc, 'edi', 'e', 'gsm', mode, level))
v_labl_vname = 'v_labls'
e_labl_vname = 'e_labls'
names = {
v_dsl_vname: 'V_DSL',
v_gse_vname: 'V_GSE',
v_gsm_vname: 'V_GSM',
e_dsl_vname: 'E_DSL',
e_gse_vname: 'E_GSE',
e_gsm_vname: 'E_GSM',
v_labl_vname: 'V_index',
e_labl_vname: 'E_index'
}
names = {key: val for key, val in names.items() if key in data}
data = data.rename(names)
return data
def load_data(sc='mms1',
mode='srvy',
level='l2',
start_date=None,
end_date=None,
rename_vars=True,
**kwargs):
"""
Load SCM data.
CDF variable names are renamed to something easier to remember and
use. Original CDF variable names are kept as an attribute "cdf_name"
in each individual variable.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('slow', 'srvy', 'fast', 'brst').
level : str
Data quality level ('l1a', 'l2pre', 'l2')
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
\*\*kwargs : dict
Any keyword accepted by *pymms.data.util.load_data*
Returns
-------
data : `xarray.Dataset`
SCM data.
"""
if mode == 'srvy':
optcesc = 'scsrvy'
elif mode == 'slow':
optdesc = 'scs'
elif mode == 'fast':
optdesc = 'scf'
elif mode == 'brst':
optdesc = 'scb'
else:
raise ValueError('SCM data rate mode {0} invalid. Try {1}'.format(
mode, ('slow', 'fast', 'srvy', 'brst')))
# Load the data
# - R is concatenated along Epoch, but depends on Epoch_state
data = util.load_data(sc=sc,
instr='scm',
mode=mode,
level=level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
**kwargs)
# Rename data variables to something simpler
if rename_vars:
data = rename(data, sc, mode, level, optdesc)
return data
def load_data(sc='mms1',
mode='brst',
level='l3',
optdesc='8khz',
start_date=None,
end_date=None,
rename_vars=True,
coords='gse',
product='b',
**kwargs):
"""
Load EDI data.
CDF variable names are renamed to something easier to remember and
use. Original CDF variable names are kept as an attribute "cdf_name"
in each individual variable.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('fast', 'brst'). If 'srvy' is given, it is
automatically changed to 'fast'.
level : str
Data quality level ('l1a', 'l2pre', 'l2')
optdesc : str
Optional descriptor. Options are: ('8khz',)
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
coords : str, list
Data coordinate system ('gse', 'gsm')
product : str
Data product to be loaded ('b', 'r')
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
\*\*kwargs : dict
Any keyword accepted by *pymms.data.util.load_data*
Returns
-------
dist : `xarray.Dataset`
FSM data.
"""
if isinstance(coords, str):
coords = [coords]
# Select either magnetic field or position data
if product in ('b', 'b-field'):
product = 'b'
elif product in ('r', 'ephemeris', 'state'):
product = 'r'
else:
raise ValueError(
'Invalid data product {0}. Choose from (b, r)'.format(product))
if product == 'b':
coords += ['mag']
varformat = '_' + product + '_(' + '|'.join(coords) + ')_'
# Load the data
# - R is concatenated along Epoch, but depends on Epoch_state
data = util.load_data(sc=sc,
instr='fsm',
mode=mode,
level=level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
team_site=True,
varformat=varformat,
**kwargs)
# The FSM CDFs do not have a DEPEND_0 attribute for the time delta
# variable. Coordinates have to be assigned and its index reset
if isinstance(data, list):
t_delta_vname = '_'.join((sc, 'fsm', 'epoch', 'delta', mode, level))
for idx, ds in enumerate(data):
data[idx] = (ds.assign_coords({
t_delta_vname: ds['Epoch']
}).reset_coords(t_delta_vname))
# Concatenate the dat
data = xr.concat(data, dim='Epoch')
# Rename data variables to something simpler
if rename_vars:
data = rename(data, sc, mode, level, optdesc, product=product)
# Add attributes about the data request
data.attrs['sc'] = sc
data.attrs['instr'] = 'fsm'
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
return data
def load_data(sc='mms1',
mode='fast',
level='l2',
optdesc='dce',
start_date=None,
end_date=None,
rename_vars=True,
**kwargs):
"""
Load EDP data.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('slow', 'srvy', 'fast', 'brst').
level : str
Data quality level ('l1a', 'l2pre', 'l2')
optdesc : str
Optional descriptor ('dce', 'scpot', 'hmfe')
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
\*\*kwargs : dict
Any keyword accepted by *pymms.data.util.load_data*
Returns
-------
data : `xarray.Dataset`
EDP data.
"""
# Check the inputs
check_spacecraft(sc)
mode = check_mode(mode)
# Load the data
t_vname = '_'.join((sc, 'edp', 'epoch', mode, level))
data = util.load_data(sc=sc,
instr='edp',
mode=mode,
level=level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
record_dim=t_vname,
**kwargs)
# Trim time interval
data = data.sel({t_vname: slice(start_date, end_date)})
# Rename variables
if rename_vars:
data = rename(data, sc, mode, level, optdesc)
# Add data descriptors to attributes
data.attrs['sc'] = sc
data.attrs['instr'] = 'fpi'
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
return data
def load_data(sc='mms1',
instr='fgm',
mode='srvy',
level='l2',
start_date=None,
end_date=None,
rename_vars=True,
coords='gse',
product='b-field',
**kwargs):
"""
Load FGM data.
Notes
-----
Ephemeris data and magnetic field data are stored in the same file
but have different DEPEND_0 variables. When concatenating along
Epoch (the B-field DEPEND_0), a new Epoch dimension is added to the
ephemeris data. Particularly for survey data, the array dimensions
are too large to hold in memory and xarray/python crashes. For now,
load ephemeris and b-field data separately. See the *product*
keyword.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
instr : str
Instrument ID: ('afg', 'dfg', 'fgm')
mode : str
Instrument mode: ('fast', 'brst'). If 'srvy' is given, it is
automatically changed to 'fast'.
level : str
Data quality level ('l1a', 'l2pre', 'l2')
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
product : str
Data product to be loaded ('b', 'b-field', 'ephemeris', 'state')
coords : str, list
Data coordinate system ('gse', 'gsm', 'dmpa', 'bcs')
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
\*\*kwargs : dict
Keywords for `pymms.data.util.load_data`
Returns
-------
dist : `metaarray.metaarray`
Particle distribution function.
"""
# Check the inputs
check_spacecraft(sc)
mode = check_mode(mode, level=level)
check_level(level, instr=instr)
if isinstance(coords, str):
coords = [coords]
# Select either magnetic field or position data
if product in ('b', 'b-field'):
product = 'b'
elif product in ('r', 'ephemeris', 'state'):
product = 'r'
else:
raise ValueError(
'Invalid data product {0}. Choose from (b, r)'.format(product))
varformat = '_' + product + '_(' + '|'.join(coords) + ')_'
# Load the data
# - R is concatenated along Epoch, but depends on Epoch_state
data = util.load_data(sc=sc,
instr=instr,
mode=mode,
level=level,
start_date=start_date,
end_date=end_date,
varformat=varformat,
**kwargs)
if rename_vars:
data = rename(data, sc, instr, mode, level, product=product)
# Add data descriptors to attributes
data.attrs['sc'] = sc
data.attrs['instr'] = instr
data.attrs['mode'] = mode
data.attrs['level'] = level
return data
def plot_photocurrent():
# Download the data
scpot = edp.load_scpot(sc=sc,
mode=mode,
start_date=orbit_t0,
end_date=orbit_t1)
dis_moms = fpi.load_moms(sc=sc,
mode=mode,
optdesc='dis-moms',
start_date=orbit_t0,
end_date=orbit_t1)
des_moms = fpi.load_moms(sc=sc,
mode=mode,
optdesc='des-moms',
start_date=orbit_t0,
end_date=orbit_t1)
aspoc = mms_util.load_data(sc=sc,
instr='aspoc',
mode='srvy',
level='l2',
start_date=orbit_t0,
end_date=orbit_t1)
# Bin data into FPI time stamps
t_bins = np.append(
dis_moms['time'].data - dis_moms['Epoch_minus_var'].data,
dis_moms['time'].data[-1] + dis_moms['Epoch_plus_var'].data)
Vsc, edges, binnum = binned_statistic(scpot['time'].astype('i8'),
scpot['Vsc'],
statistic='mean',
bins=t_bins.astype('i8'))
asp1, edges, binnum = binned_statistic(aspoc['Epoch'].astype('i8'),
aspoc[sc + '_asp1_ionc'],
statistic='mean',
bins=t_bins.astype('i8'))
idx = 0
data_bin = []
asp2 = np.empty_like(asp1)
asp_on = np.zeros(asp1.shape, dtype='?')
ref_idx = binnum[0]
for aspoc_idx, bin_idx in enumerate(binnum):
if (bin_idx == 0) | (bin_idx == len(aspoc[sc + '_asp1_ionc'])):
continue
elif ref_idx == 0:
ref_idx = bin_idx
if bin_idx == ref_idx:
data_bin.append(aspoc_idx)
else:
asp2[idx] = aspoc[sc + '_asp2_ionc'][data_bin].mean()
asp_on[idx] = aspoc[sc + '_aspoc_status'][data_bin, 3].max() > 0
idx += 1
ref_idx = bin_idx
data_bin = [aspoc_idx]
Vsc = xr.DataArray(Vsc, dims='time', coords={'time': dis_moms['time']})
asp = xr.Dataset(
{
'asp1': (['time'], asp1),
'asp2': (['time'], asp2),
'asp': (['time'], asp1 + asp2)
},
coords={'time': dis_moms['time']})
# Electron current
Ie = electron_current(des_moms['density'], des_moms['t'], Vsc)
# Flag the data
flag = xr.DataArray(asp_on.astype('int'),
dims='time',
coords={'time': dis_moms['time']})
flag += (((Ie < 1e-11) & (Vsc > 8) & (Vsc < 11)) * 2**1)
flag += (((Vsc > 14.75) & (Ie > 7.9e-12) & (Ie < 2e-11)) * 2**2)
flag += (((Vsc > 12.6) & (Vsc <= 14.75) & (Ie > 9.2e-12) & (Ie < 2e-11)) *
2**2)
#
# Fit the data
#
# Ie = Iph0 exp(-Vsc/Vph0)
# y = a exp(b * x)
# log(y) = log(a) + b*x
b, a = np.polyfit(Vsc[flag == 0],
np.log(Ie[flag == 0]),
1,
w=np.sqrt(Ie[flag == 0]))
Ie_fit = np.exp(a) * np.exp(b * Vsc[flag == 0])
str_fit = ('Ie = {0:0.3e} exp(-Vsc/{1:0.3e})'.format(
np.exp(a), 1 / np.exp(b)))
# Fit density to the spacecraft potential
c, b, a = np.polyfit(Vsc[flag == 0], des_moms['density'][flag == 0], 2)
Ne_fit2 = c * Vsc[flag == 0]**2 + b * Vsc[flag == 0] + a
str_Ne_fit2 = ('Ne = {0:0.3e}Vsc^2 + {1:0.3e}Vsc^1 + {2:0.3e})'.format(
c, b, a))
e, d, c, b, a = np.polyfit(Vsc[flag == 0], des_moms['density'][flag == 0],
4)
Ne_fit4 = e * Vsc[flag == 0]**4 + d * Vsc[flag == 0]**3 + c * Vsc[
flag == 0]**2 + b * Vsc[flag == 0] + a
str_Ne_fit4 = (
'Ne = {0:0.3e}Vsc^4 + {1:0.3e}Vsc^3 + {2:0.3e}Vsc^2 + {3:0.3e}Vsc + {4:0.3e})'
.format(e, d, c, b, a))
# Fit density to the inverse of the spacecraft potential
c, b, a = np.polyfit(1 / Vsc[flag == 0], des_moms['density'][flag == 0], 2)
invV_fit2 = c / Vsc[flag == 0]**2 + b / Vsc[flag == 0] + a
str_invV_fit2 = ('Ne = {0:0.3e}/Vsc^2 + {1:0.3e}/Vsc + {2:0.3e})'.format(
c, b, a))
e, d, c, b, a = np.polyfit(1 / Vsc[flag == 0],
des_moms['density'][flag == 0], 4)
invV_fit4 = e / Vsc[flag == 0]**4 + d / Vsc[flag == 0]**3 + c / Vsc[
flag == 0]**2 + b / Vsc[flag == 0] + a
str_invV_fit4 = (
'Ne = {0:0.3e}/Vsc^4 + {1:0.3e}/Vsc^3 + {2:0.3e}/Vsc^2 + {3:0.3e}/Vsc + {4:0.3e})'
.format(e, d, c, b, a))
#
# Plot: Time Series
#
# Plot current-related data
fig1, axes1 = plt.subplots(nrows=8,
ncols=1,
sharex=True,
figsize=(5, 6.5),
squeeze=False)
plt.subplots_adjust(left=0.17, right=0.85, bottom=0.14, top=0.95)
# Ion energy spectrogram
nt = dis_moms['omnispectr'].shape[0]
nE = dis_moms['omnispectr'].shape[1]
x0 = mdates.date2num(dis_moms['time'])[:, np.newaxis].repeat(nE, axis=1)
x1 = dis_moms['energy']
y = np.where(dis_moms['omnispectr'] == 0, 1, dis_moms['omnispectr'])
y = np.log(y[0:-1, 0:-1])
ax = axes1[0, 0]
im = ax.pcolorfast(x0, x1, y, cmap='nipy_spectral')
ax.images.append(im)
ax.set_title(sc.upper())
ax.set_yscale('log')
ax.set_ylabel('E (ion)\n(eV)')
util.format_axes(ax, xaxis='off')
cb = util.add_colorbar(ax, im)
cb.set_label('$log_{10}$DEF\nkeV/($cm^{2}$ s sr keV)')
# Electron energy spectrogram
nt = des_moms['omnispectr'].shape[0]
nE = des_moms['omnispectr'].shape[1]
x0 = mdates.date2num(des_moms['time'])[:, np.newaxis].repeat(nE, axis=1)
x1 = des_moms['energy']
y = np.where(des_moms['omnispectr'] == 0, 1, des_moms['omnispectr'])
y = np.log(y[0:-1, 0:-1])
ax = axes1[1, 0]
im = ax.pcolorfast(x0, x1, y, cmap='nipy_spectral')
ax.images.append(im)
ax.set_yscale('log')
ax.set_ylabel('E (e-)\n(eV)')
util.format_axes(ax, xaxis='off')
cb = util.add_colorbar(ax, im)
cb.set_label('$log_{10}$DEF\nkeV/($cm^{2}$ s sr keV)')
# Density
ax = axes1[2, 0]
l1 = dis_moms['density'].plot(ax=ax, label='$N_{i}$')
l2 = des_moms['density'].plot(ax=ax, label='$N_{e}$')
ax.set_title('')
ax.set_ylabel('N\n($cm^{3}$)')
ax.set_yscale('log')
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0]])
# Temperature
ax = axes1[3, 0]
l1 = dis_moms['t'].plot(ax=ax, label='$T_{i}$')
l2 = des_moms['t'].plot(ax=ax, label='$T_{e}$')
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0]])
ax.set_title('')
ax.set_ylabel('T\n(eV)')
ax.set_yscale('log')
# Spacecraft potential
ax = axes1[4, 0]
Vsc[~asp_on].plot(ax=ax)
Vsc[asp_on].plot(ax=ax, color='red')
util.format_axes(ax, xaxis='off')
ax.set_title('')
ax.set_ylabel('$V_{S/C}$\n(V)')
# Electron current
ax = axes1[5, 0]
Ie.plot(ax=ax)
ax.set_title('')
ax.set_ylabel('$I_{e}$\n($\mu A$)')
ax.set_yscale('log')
ax.set_ylim([1e-12, 1e-10])
util.format_axes(ax, xaxis='off')
# Aspoc Status
ax = axes1[6, 0]
l1 = asp['asp1'].plot(ax=ax, label='$ASP_{1}$')
l2 = asp['asp2'].plot(ax=ax, label='$ASP_{2}$')
l3 = asp['asp'].plot(ax=ax, label='$ASP_{tot}$')
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0], l3[0]])
ax.set_title('')
ax.set_xlabel('')
ax.set_ylabel('$I_{ASPOC}$\n($\mu A$)')
# Flag
ax = axes1[7, 0]
flag.plot(ax=ax)
util.format_axes(ax)
ax.set_title('')
ax.set_xlabel('')
ax.set_ylabel('Flag')
#
# Plot: Photocurrent Fit
#
# Plot the data
fig2, axes = plt.subplots(nrows=3,
ncols=1,
figsize=[5, 5.5],
squeeze=False)
plt.subplots_adjust(left=0.15, right=0.95, top=0.94)
# I_ph = I_ph0 exp(Vsc/Vph0)
ax = axes[0, 0]
ax.scatter(Vsc[flag == 0], Ie[flag == 0], marker='o')
ax.scatter(Vsc[np.bitwise_and(flag, 2**0) > 0],
Ie[np.bitwise_and(flag, 2**0) > 0],
marker='o',
color='red')
ax.scatter(Vsc[np.bitwise_and(flag, 2**1) > 0],
Ie[np.bitwise_and(flag, 2**1) > 0],
marker='o',
color='purple')
ax.scatter(Vsc[np.bitwise_and(flag, 2**2) > 0],
Ie[np.bitwise_and(flag, 2**2) > 0],
marker='o',
color='green')
ax.plot(Vsc[flag == 0], Ie_fit, color='black')
ax.set_title(
'Photocurrent Parameters $I_{e} = -I_{ph0} \exp(V_{S/C}/V_{ph0})$')
ax.set_xlabel('$V_{S/C}$ (V)')
ax.set_ylabel('$I_{e}$\n($\mu A$)')
ax.set_yscale('log')
ax.set_ylim([
10**np.floor(np.log10(Ie.min().values)),
10**np.ceil(np.log10(Ie.max().values))
])
ax.text(0.9 * ax.get_xlim()[1],
0.7 * ax.get_ylim()[1],
str_fit,
horizontalalignment='right',
verticalalignment='bottom',
color='black')
# Ne = \Sum a_i * V^i
ax = axes[1, 0]
ax.scatter(Vsc[flag == 0], des_moms['density'][flag == 0], marker='o')
ax.scatter(Vsc[np.bitwise_and(flag, 2**0) > 0],
des_moms['density'][np.bitwise_and(flag, 2**0) > 0],
marker='o',
color='red')
ax.scatter(Vsc[np.bitwise_and(flag, 2**1) > 0],
des_moms['density'][np.bitwise_and(flag, 2**1) > 0],
marker='o',
color='purple')
ax.scatter(Vsc[np.bitwise_and(flag, 2**2) > 0],
des_moms['density'][np.bitwise_and(flag, 2**2) > 0],
marker='o',
color='green')
ax.plot(Vsc[flag == 0], Ne_fit2, color='black')
ax.plot(Vsc[flag == 0], Ne_fit4, color='grey')
ax.set_title('')
ax.set_xlabel('$V_{S/C}$ (V)')
ax.set_ylabel('$N_{e}$\n($cm^{-3}$)')
ax.text(0.98 * ax.get_xlim()[1],
0.85 * ax.get_ylim()[1],
str_Ne_fit2,
horizontalalignment='right',
verticalalignment='bottom',
color='black')
ax.text(0.98 * ax.get_xlim()[1],
0.75 * ax.get_ylim()[1],
str_Ne_fit4,
horizontalalignment='right',
verticalalignment='bottom',
color='grey')
# Ne = \Sum a_i * V^-i
ax = axes[2, 0]
ax.scatter(1 / Vsc[flag == 0], des_moms['density'][flag == 0], marker='o')
ax.scatter(1 / Vsc[np.bitwise_and(flag, 2**0) > 0],
des_moms['density'][np.bitwise_and(flag, 2**0) > 0],
marker='o',
color='red')
ax.scatter(1 / Vsc[np.bitwise_and(flag, 2**1) > 0],
des_moms['density'][np.bitwise_and(flag, 2**1) > 0],
marker='o',
color='purple')
ax.scatter(1 / Vsc[np.bitwise_and(flag, 2**2) > 0],
des_moms['density'][np.bitwise_and(flag, 2**2) > 0],
marker='o',
color='green')
ax.plot(Vsc[flag == 0], invV_fit2, color='black')
ax.plot(Vsc[flag == 0], invV_fit4, color='grey')
ax.set_title('')
ax.set_xlabel('1/$V_{S/C}$ ($V^{-1}$)')
ax.set_ylabel('$N_{e}$\n($cm^{-3}$)')
ax.text(0.98 * ax.get_xlim()[1],
0.85 * ax.get_ylim()[1],
str_invV_fit2,
horizontalalignment='right',
verticalalignment='bottom',
color='black')
ax.text(0.98 * ax.get_xlim()[1],
0.75 * ax.get_ylim()[1],
str_invV_fit4,
horizontalalignment='right',
verticalalignment='bottom',
color='grey')
return [fig1, fig2], ax
def load_sunpulse(sc='mms1',
start_date=None, end_date=None, rename_vars=True):
"""
Load Digital Sun Sensor data.
CDF variable names are renamed to something easier to remember and
use. Original CDF variable names are kept as an attribute "cdf_name"
in each individual variable.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
Returns
-------
dist : `xarray.Dataset`
EDI data.
"""
instr = 'fields'
mode = 'hk'
level = 'l1b'
optdesc = '101'
sunsps_vname = '_'.join((sc, '101', 'sunssps'))
sunper_vname = '_'.join((sc, '101', 'iifsunper'))
sunpulse_vname = '_'.join((sc, '101', 'sunpulse'))
sunpulse_uniq_vname = '_'.join((sc, '101', 'sunpulse', 'uniq'))
# Load the data
data = util.load_data(sc=sc, instr=instr, mode=mode, level=level,
optdesc=optdesc, start_date=start_date,
end_date=end_date, team_site=True, data_type='hk',
variables=[sunsps_vname, sunper_vname,
sunpulse_vname, sunpulse_uniq_vname])
# Convert the sun pulse period to a timedelta64
data[sunper_vname] = data[sunper_vname].astype('timedelta64[us]')
# Select only the unique sun pulse times
# - Spin period is 20 sec
# - HK is reported once every 10 sec
attrs = data.attrs
pulse, idx = np.unique(data[sunpulse_vname], return_index=True, axis=0)
data = data.isel(Epoch=idx)
# Rename data variables to something simpler
if rename_vars:
data = rename(data, sc, optdesc)
# Add data descriptors to attributes
data.attrs['sc'] = sc
data.attrs['instr'] = instr
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
return data