def mms_eis_set_metadata(tplotnames,
data_rate='srvy',
datatype='extof',
suffix=''):
"""
This function updates the metadata for the EIS data products
Parameters:
tplotnames : list of str
list of tplot variables loaded by the load routine
data_rate : str
Data rate
datatype : str
EIS datatype loaded (extof or phxtof)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
"""
if datatype == 'extof':
options(tnames('*_extof_proton_flux_omni*'), 'yrange', [55, 1000])
options(tnames('*_extof_alpha_flux_omni*'), 'yrange', [80, 650])
options(tnames('*_extof_oxygen_flux_omni*'), 'yrange', [145, 950])
def tcopy(names_in, names_out=None, suffix=None):
names_in = pyspedas.tnames(names_in)
if len(names_in) < 1:
print('tcopy error: No pytplot variables found.')
return
if suffix is None:
suffix = '-copy'
if names_out is None:
names_out = [s + suffix for s in names_in]
if isinstance(names_out, str):
names_out = [names_out]
if len(names_in) != len(names_out):
print('tcopy error: List with the names_in does not match list\
with the names out.')
return
for i in range(len(names_in)):
n = names_in[i]
o = names_out[i]
if len(pyspedas.tnames(n)) == 1:
tcopy_one(n, o)
else:
print('tplot name not found: ' + n)
def mms_hpca_set_metadata(probe='1', fov=[0, 360], suffix=''):
"""
This function sets the plot metadata for HPCA data products, and is meant
to be called from the HPCA load routine
Parameters:
fov : list of int
field of view, in angles, from 0-360
probe : str
probe #, e.g., '4' for MMS4
suffix: str
suffix of the loaded data
Returns:
None
"""
prefix = 'mms'+str(probe)
valid_density = prefix+'*_number_density'+suffix
valid_vel = prefix+'*_ion_bulk_velocity*'+suffix
valid_temp = prefix+'*_scalar_temperature'+suffix
for var in tnames(valid_density):
if var == prefix+'_hpca_hplus_number_density'+suffix: options(var, 'ytitle', 'H+ density')
if var == prefix+'_hpca_heplus_number_density'+suffix: options(var, 'ytitle', 'He+ density')
if var == prefix+'_hpca_heplusplus_number_density'+suffix: options(var, 'ytitle', 'He++ density')
if var == prefix+'_hpca_oplus_number_density'+suffix: options(var, 'ytitle', 'O+ density')
if var == prefix+'_hpca_oplusplus_number_density'+suffix: options(var, 'ytitle', 'O++ density')
for var in tnames(valid_vel):
if var == prefix+'_hpca_hplus_ion_bulk_velocity'+suffix:
options(var, 'legend_names', ['Vx (H+)', 'Vy (H+)', 'Vz (H+)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'H+ velocity')
if var == prefix+'_hpca_heplus_ion_bulk_velocity'+suffix:
options(var, 'legend_names', ['Vx (He+)', 'Vy (He+)', 'Vz (He+)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'He+ velocity')
if var == prefix+'_hpca_heplusplus_ion_bulk_velocity'+suffix:
options(var, 'legend_names', ['Vx (He++)', 'Vy (He++)', 'Vz (He++)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'He++ velocity')
if var == prefix+'_hpca_oplus_ion_bulk_velocity'+suffix:
options(var, 'legend_names', ['Vx (O+)', 'Vy (O+)', 'Vz (O+)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'O+ velocity')
if var == prefix+'_hpca_oplusplus_ion_bulk_velocity'+suffix:
options(var, 'legend_names', ['Vx (O++)', 'Vy (O++)', 'Vz (O++)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'O++ velocity')
for var in tnames(valid_temp):
if var == prefix+'_hpca_hplus_scalar_temperature'+suffix: options(var, 'ytitle', 'H+ temp')
if var == prefix+'_hpca_heplus_scalar_temperature'+suffix: options(var, 'ytitle', 'He+ temp')
if var == prefix+'_hpca_heplusplus_scalar_temperature'+suffix: options(var, 'ytitle', 'He++ temp')
if var == prefix+'_hpca_oplus_scalar_temperature'+suffix: options(var, 'ytitle', 'O+ temp')
if var == prefix+'_hpca_oplusplus_scalar_temperature'+suffix: options(var, 'ytitle', 'O++ temp')
def subtract_average(names, new_names=None, suffix=None, overwrite=None,
median=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('Subtract Average error: No pytplot names were provided.')
return
if suffix is None:
if median:
suffix = '-m'
else:
suffix = '-d'
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
old_names = pyspedas.tnames(names)
for i, old in enumerate(old_names):
new = n_names[i]
if new != old:
pyspedas.tcopy(old, new)
data = pytplot.data_quants[new].values
dim = data.shape
if median:
if len(dim) == 1:
data -= numpy.median(data, axis=0)
else:
for i in range(dim[1]):
data[:, i] -= numpy.median(data[:, i], axis=0)
ptype = 'Median'
else:
if len(dim) == 1:
data -= numpy.mean(data, axis=0)
else:
for i in range(dim[1]):
data[:, i] -= numpy.mean(data[:, i], axis=0)
ptype = 'Mean'
pytplot.data_quants[new].values = data
print('Subtract ' + ptype + ' was applied to: ' + new)
def mms_eis_set_metadata(tplotnames,
data_rate='srvy',
datatype='extof',
suffix=''):
"""
"""
if datatype == 'extof':
options(tnames('*_extof_proton_flux_omni*'), 'yrange', [55, 1000])
options(tnames('*_extof_alpha_flux_omni*'), 'yrange', [80, 650])
options(tnames('*_extof_oxygen_flux_omni*'), 'yrange', [145, 950])
def mms_hpca_set_metadata(probe='1', fov=[0, 360], suffix=''):
prefix = 'mms' + str(probe)
valid_density = prefix + '*_number_density' + suffix
valid_vel = prefix + '*_ion_bulk_velocity*' + suffix
valid_temp = prefix + '*_scalar_temperature' + suffix
for var in tnames(valid_density):
if var == prefix + '_hpca_hplus_number_density' + suffix:
options(var, 'ytitle', 'H+ density')
if var == prefix + '_hpca_heplus_number_density' + suffix:
options(var, 'ytitle', 'He+ density')
if var == prefix + '_hpca_heplusplus_number_density' + suffix:
options(var, 'ytitle', 'He++ density')
if var == prefix + '_hpca_oplus_number_density' + suffix:
options(var, 'ytitle', 'O+ density')
if var == prefix + '_hpca_oplusplus_number_density' + suffix:
options(var, 'ytitle', 'O++ density')
for var in tnames(valid_vel):
if var == prefix + '_hpca_hplus_ion_bulk_velocity' + suffix:
options(var, 'legend_names', ['Vx (H+)', 'Vy (H+)', 'Vz (H+)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'H+ velocity')
if var == prefix + '_hpca_heplus_ion_bulk_velocity' + suffix:
options(var, 'legend_names', ['Vx (He+)', 'Vy (He+)', 'Vz (He+)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'He+ velocity')
if var == prefix + '_hpca_heplusplus_ion_bulk_velocity' + suffix:
options(var, 'legend_names',
['Vx (He++)', 'Vy (He++)', 'Vz (He++)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'He++ velocity')
if var == prefix + '_hpca_oplus_ion_bulk_velocity' + suffix:
options(var, 'legend_names', ['Vx (O+)', 'Vy (O+)', 'Vz (O+)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'O+ velocity')
if var == prefix + '_hpca_oplusplus_ion_bulk_velocity' + suffix:
options(var, 'legend_names', ['Vx (O++)', 'Vy (O++)', 'Vz (O++)'])
options(var, 'color', ['b', 'g', 'r'])
options(var, 'ytitle', 'O++ velocity')
for var in tnames(valid_temp):
if var == prefix + '_hpca_hplus_scalar_temperature' + suffix:
options(var, 'ytitle', 'H+ temp (eV)')
if var == prefix + '_hpca_heplus_scalar_temperature' + suffix:
options(var, 'ytitle', 'He+ temp (eV)')
if var == prefix + '_hpca_heplusplus_scalar_temperature' + suffix:
options(var, 'ytitle', 'He++ temp (eV)')
if var == prefix + '_hpca_oplus_scalar_temperature' + suffix:
options(var, 'ytitle', 'O+ temp (eV)')
if var == prefix + '_hpca_oplusplus_scalar_temperature' + suffix:
options(var, 'ytitle', 'O++ temp (eV)')
def mms_hpca_calc_anodes(fov=[0, 360], probe='1', suffix=''):
"""
This function will sum (or average, for flux) the HPCA data over the requested field-of-view (fov)
Parameters:
fov : list of int
field of view, in angles, from 0-360
probe : str
probe #, e.g., '4' for MMS4
suffix: str
suffix of the loaded data
Returns:
List of tplot variables created.
"""
sum_anodes = [a+suffix for a in ['*_count_rate', '*_RF_corrected', '*_bkgd_corrected', '*_norm_counts']]
avg_anodes = ['*_flux'+suffix]
output_vars = []
fov_str = '_elev_'+str(fov[0])+'-'+str(fov[1])
for sum_anode in sum_anodes:
vars_to_sum = tnames(sum_anode)
for var in vars_to_sum:
times, data, angles, energies = get_data(var)
updated_spectra = mms_hpca_sum_fov(times, data, angles, energies, fov=fov)
store_data(var+fov_str, data={'x': times, 'y': updated_spectra, 'v': energies})
options(var+fov_str, 'spec', True)
options(var+fov_str, 'ylog', True)
options(var+fov_str, 'zlog', True)
options(var+fov_str, 'Colormap', 'jet')
output_vars.append(var+fov_str)
for avg_anode in avg_anodes:
vars_to_avg = tnames(avg_anode)
for var in vars_to_avg:
times, data, angles, energies = get_data(var)
updated_spectra = mms_hpca_avg_fov(times, data, angles, energies, fov=fov)
store_data(var+fov_str, data={'x': times, 'y': updated_spectra, 'v': energies})
options(var+fov_str, 'spec', True)
options(var+fov_str, 'ylog', True)
options(var+fov_str, 'zlog', True)
options(var+fov_str, 'Colormap', 'jet')
output_vars.append(var+fov_str)
def tinterpol(names, interp_names=None, method=None, new_names=None,
suffix=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('tinterpol error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-itrp'
if method is None:
method = 'linear'
if new_names is None:
n_names = [s + suffix for s in old_names]
elif new_names == '':
n_names = old_names
else:
n_names = new_names
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i in range(len(old_names)):
time, data = pytplot.get_data(old_names[i])
new_time, data1 = pytplot.get_data(interp_names[i])
data = numpy.asarray(data).squeeze()
f2 = interp1d(time, data, kind=method)
new_data = f2(new_time)
pytplot.store_data(n_names[i], data={'x': new_time, 'y': new_data})
print('tinterpol (' + method + ') was applied to: ' + n_names[i])
def deriv_data(names, new_names=None, suffix=None, overwrite=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('avg_data error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-der'
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
if new != old:
pyspedas.tcopy(old, new)
data = pytplot.data_quants[new]
data_new = data.differentiate('time').copy()
pytplot.data_quants[new].values = data_new.values
print('deriv_data was applied to: ' + new)
def tdeflag(names, method=None, new_names=None, suffix=None, overwrite=None):
"""
Remove NaNs from tplot variables.
Parameters
----------
names: str/list of str
List of pytplot names.
method: str, optional
Method to apply. Default is 'remove_nan.
Other options 'repeat' (repeat last good value).
new_names: str/list of str, optional
List of new_names for pytplot variables.
If '', then pytplot variables are replaced.
If not given, then a suffix is applied.
suffix: str, optional
A suffix to apply. Default is '-clip'.
overwrite: bool, optional
Replace the existing tplot name.
Returns
-------
None.
"""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('tdeflag error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-deflag'
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i in range(len(old_names)):
alldata = pytplot.get_data(old_names[i])
time = alldata[0]
data = alldata[1]
new_time = []
new_data = []
for j in range(len(time)):
if not numpy.isnan(data[j]):
new_time.append(time[j])
new_data.append(data[j])
pytplot.store_data(n_names[i], data={'x': new_time, 'y': new_data})
print('tdeflag was applied to: ' + n_names[i])
def subtract_median(names, new_names=None, suffix=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('Subtract Median error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-m'
if new_names is None:
n_names = [s + suffix for s in old_names]
elif new_names == '':
n_names = old_names
else:
n_names = new_names
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i in range(len(old_names)):
alldata = pytplot.get_data(old_names[i])
time = alldata[0]
data = alldata[1]
new_data = data-numpy.median(data, axis=0)
pytplot.store_data(n_names[i], data={'x': time, 'y': new_data})
print('Subtract Median was applied to: ' + n_names[i])
def tclip(names, ymin, ymax, flag=None, new_names=None, suffix=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('tclip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-clip'
if flag is None:
flag = float('nan')
if new_names is None:
n_names = [s + suffix for s in old_names]
elif new_names == '':
n_names = old_names
else:
n_names = new_names
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i in range(len(old_names)):
alldata = pytplot.get_data(old_names[i])
time = alldata[0]
data = alldata[1]
new_data = numpy.array(data)
new_data[new_data <= ymin] = flag
new_data[new_data >= ymax] = flag
pytplot.store_data(n_names[i], data={'x': time, 'y': new_data})
print('tclip was applied to: ' + n_names[i])
def yclip(names, ymin, ymax, flag=None, new_names=None, suffix=None,
overwrite=None):
"""
Clip data values.
Parameters
----------
names: str/list of str
List of pytplot names.
ymin: float
Minimum value to clip.
ymax: float
Maximum value to clip.
flag: float, optional
Values outside (ymin, ymax) are replaced with flag.
Default is float('nan').
new_names: str/list of str, optional
List of new_names for pytplot variables.
If not given, then a suffix is applied.
suffix: str, optional
A suffix to apply. Default is '-clip'.
overwrite: bool, optional
Replace the existing tplot name.
Returns
-------
None.
"""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('yclip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-clip'
if flag is None:
flag = np.nan
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
data = pytplot.clip(old, ymin, ymax, new)
print('yclip was applied to: ' + new)
def tdeflag(names, method=None, flag=None, new_names=None, suffix=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('tdeflag error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-deflag'
if flag is None:
flag = float('nan')
if new_names is None:
n_names = [s + suffix for s in old_names]
elif new_names == '':
n_names = old_names
else:
n_names = new_names
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i in range(len(old_names)):
time, data = pytplot.get_data(old_names[i])
new_time = []
new_data = []
for j in range(len(time)):
if not numpy.isnan(data[j]):
new_time.append(time[j])
new_data.append(data[j])
pytplot.store_data(n_names[i], data={'x': new_time, 'y': new_data})
print('tdeflag was applied to: ' + n_names[i])
def ex_gmag(plot=True):
"""Demonstrate how to use gmag functions."""
# Delete any existing pytplot variables
pytplot.del_data()
# Define a time rage as a list
trange = ['2015-12-31', '2015-12-31']
# Get a list of EPO gmag stations
sites = pyspedas.themis.ground.gmag.gmag_list('epo')
# Download gmag files and load data into pytplot variables
pyspedas.themis.gmag(sites=sites, trange=trange)
# Get a list of loaded sites
sites_loaded = pyspedas.tnames()
# Subtract mean values
pyspedas.subtract_average(sites_loaded, '')
# Download AE index data
# pyspedas.load_data('gmag', time_list, ['idx'], '', '')
pyspedas.themis.gmag(sites='idx', trange=trange)
# Plot
sites_loaded = pytplot.tplot_names()
pytplot.tplot_options('title', 'EPO GMAG 2015-12-31')
if plot:
pytplot.tplot(sites_loaded)
# Return 1 as indication that the example finished without problems.
return 1
def mms_fgm_remove_flags(probe, data_rate, level, instrument, suffix=''):
"""
This function removes data flagged by the FGM 'flag' variable (flags > 0),
in order to only show science quality data by default.
Parameters:
probe : str or list of str
probe or list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for FGM include 'brst' 'fast' 'slow' 'srvy'. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
instrument : str
instrument; probably 'fgm'
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
"""
if not isinstance(probe, list): probe = [probe]
if not isinstance(data_rate, list): data_rate = [data_rate]
if not isinstance(level, list): level = [level]
tplot_vars = set(tnames())
for this_probe in probe:
for this_dr in data_rate:
for this_lvl in level:
if level == 'ql':
flag_var = 'mms' + str(
this_probe
) + '_' + instrument + '_' + this_dr + '_' + this_lvl + '_flag' + suffix
else:
flag_var = 'mms' + str(
this_probe
) + '_' + instrument + '_flag_' + this_dr + '_' + this_lvl + suffix
flagged = get_data(flag_var)
if flagged == None:
continue
times, flags = flagged
flagged_data = np.where(flags != 0.0)[0]
for var_specifier in [
'_b_gse_', '_b_gsm_', '_b_dmpa_', '_b_bcs_'
]:
var_name = 'mms' + str(
this_probe
) + '_' + instrument + var_specifier + this_dr + '_' + this_lvl + suffix
if var_name in tplot_vars:
times, var_data = get_data(var_name)
var_data[flagged_data] = np.nan
store_data(var_name, data={'x': times, 'y': var_data})
def tcopy(names_in, names_out=None, suffix=None):
"""
Copy a list of pytplot variables.
Parameters
----------
names_in : str/list of str
List of pytplot names.
names_out : str/list of str, optional
List of pytplot names. The default is None.
suffix : str, optional
Suffix to apply to names_in. The default is '-copy'.
Returns
-------
None.
"""
names_in = pyspedas.tnames(names_in)
if len(names_in) < 1:
print('tcopy error: No pytplot variables found.')
return
if suffix is None:
suffix = '-copy'
if names_out is None:
names_out = [s + suffix for s in names_in]
if isinstance(names_out, str):
names_out = [names_out]
if len(names_in) != len(names_out):
print('tcopy error: List with the names_in does not match list\
with the names out.')
return
for i in range(len(names_in)):
n = names_in[i]
o = names_out[i]
if len(pyspedas.tnames(n)) == 1:
tcopy_one(n, o)
else:
print('tplot name not found: ' + n)
def yclip(names,
ymin,
ymax,
flag=None,
new_names=None,
suffix=None,
overwrite=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('yclip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-clip'
if flag is None:
flag = np.nan
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
if new != old:
pyspedas.tcopy(old, new)
data = pytplot.data_quants[new].values
try:
for j, v in enumerate(data):
try:
iterator = enumerate(v)
except TypeError:
if not np.isnan(v) and (v <= ymin or v >= ymax):
data[j] = flag
else:
for k, s in iterator:
if not np.isnan(s) and (s <= ymin or s >= ymax):
data[j][k] = flag
except TypeError: # data Not itterable
print("Cannot clip data.")
print('yclip was applied to: ' + new)
def tinterpol(names, interp_to, method=None, newname=None, suffix=None):
if not isinstance(names, list):
names = [names]
if not isinstance(newname, list):
newname = [newname]
old_names = tnames(names)
if len(old_names) < 1:
print('tinterpol error: No pytplot names were provided.')
return
if suffix == None:
suffix = '-itrp'
if method == None:
method = 'linear'
if (newname == None) or (len(newname) == 1 and newname[0] == None):
n_names = [s + suffix for s in old_names]
elif newname == '':
n_names = old_names
else:
n_names = newname
interp_to_data = get_data(interp_to)
if interp_to_data == None:
print('Error, tplot variable: ' + interp_to + ' not found.')
return
interp_to_times = interp_to_data[0]
for name_idx, name in enumerate(old_names):
xdata = get_data(name, xarray=True)
xdata_interpolated = xdata.interp({'time': interp_to_times},
method=method)
if 'spec_bins' in xdata.coords:
store_data(n_names[name_idx],
data={
'x': interp_to_times,
'y': xdata_interpolated.values,
'v': xdata_interpolated.coords['spec_bins'].values
})
else:
store_data(n_names[name_idx],
data={
'x': interp_to_times,
'y': xdata_interpolated.values
})
print('tinterpol (' + method + ') was applied to: ' +
n_names[name_idx])
def deriv_data(names, new_names=None, suffix=None, overwrite=None):
"""
Compute the derivative.
Parameters
----------
names: str/list of str
List of pytplot names.
new_names: str/list of str, optional
List of new_names for pytplot variables.
If not given, then a suffix is applied.
suffix: str, optional
A suffix to apply. Default is '-avg'.
overwrite: bool, optional
Replace the existing tplot name.
Returns
-------
None.
"""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('deriv_data error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-der'
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
if new != old:
pyspedas.tcopy(old, new)
data = pytplot.data_quants[new]
data_new = data.differentiate('time').copy()
pytplot.data_quants[new].values = data_new.values
print('deriv_data was applied to: ' + new)
def remove_duplicated_tframe(tvars=[]):
tvars = tnames(tvars)
if len(tvars) < 1:
return
for tvar in tvars:
input_attr_dict = get_data(tvar, metadata=True)
get_data_vars = get_data(tvar)
unique_array, counts_array = np.unique(
get_data_vars[0], return_counts=True)
duplicate_time_indices = np.where(counts_array > 1)[0]
if duplicate_time_indices.shape[0] > 0: # duplication check
delete_indices_array = np.array(
[np.where(get_data_vars[0] == unique_array[index])[0][0]
for index in duplicate_time_indices])
input_data_dictionary = {}
input_data_dictionary['x'] = np.delete(
get_data_vars[0], delete_indices_array, axis=0)
input_data_dictionary['y'] = np.delete(
get_data_vars[1], delete_indices_array, axis=0)
# for v or v1, v2.. elements. not tested yet.
if len(get_data_vars) >= 3:
element_counts = len(get_data_vars)
if element_counts == 3:
if get_data_vars[2].ndim >= 2:
input_data_dictionary['v'] = np.delete(
get_data_vars[2], delete_indices_array, axis=0)
elif get_data_vars[2].ndim == 1:
input_data_dictionary['v'] = get_data_vars[2]
elif element_counts > 3:
for element_index in range(2, element_counts):
v_element_name = f'v{element_index-1}'
if get_data_vars[element_index].ndim >= 2:
input_data_dictionary[v_element_name] = np.delete(
get_data_vars[element_index], delete_indices_array,
axis=0)
elif get_data_vars[element_index].ndim == 1:
input_data_dictionary[v_element_name] = get_data_vars[element_index]
store_data(tvar, data=input_data_dictionary,
attr_dict=input_attr_dict)
return
def mms_fgm_set_metadata(probe, data_rate, level, instrument, suffix=''):
"""
This function updates the metadata for FGM data products
Parameters:
probe : str or list of str
probe or list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for FGM include 'brst' 'fast' 'slow' 'srvy'. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
instrument : str
instrument; probably 'fgm'
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
"""
if not isinstance(probe, list): probe = [probe]
if not isinstance(data_rate, list): data_rate = [data_rate]
if not isinstance(level, list): level = [level]
tvars = set(tnames())
for this_probe in probe:
for this_dr in data_rate:
for this_lvl in level:
if 'mms'+str(this_probe)+'_'+instrument+'_b_gse_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_b_gse_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' FGM')
options('mms'+str(this_probe)+'_'+instrument+'_b_gse_'+this_dr+'_'+this_lvl+suffix, 'color', ['b', 'g', 'r', '#000000'])
options('mms'+str(this_probe)+'_'+instrument+'_b_gse_'+this_dr+'_'+this_lvl+suffix, 'legend_names', ['Bx GSE', 'By GSE', 'Bz GSE', 'B total'])
if 'mms'+str(this_probe)+'_'+instrument+'_b_gsm_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_b_gsm_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' FGM')
options('mms'+str(this_probe)+'_'+instrument+'_b_gsm_'+this_dr+'_'+this_lvl+suffix, 'color', ['b', 'g', 'r', '#000000'])
options('mms'+str(this_probe)+'_'+instrument+'_b_gsm_'+this_dr+'_'+this_lvl+suffix, 'legend_names', ['Bx GSM', 'By GSM', 'Bz GSM', 'B total'])
if 'mms'+str(this_probe)+'_'+instrument+'_b_dmpa_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_b_dmpa_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' FGM')
options('mms'+str(this_probe)+'_'+instrument+'_b_dmpa_'+this_dr+'_'+this_lvl+suffix, 'color', ['b', 'g', 'r', '#000000'])
options('mms'+str(this_probe)+'_'+instrument+'_b_dmpa_'+this_dr+'_'+this_lvl+suffix, 'legend_names', ['Bx DMPA', 'By DMPA', 'Bz DMPA', 'B total'])
if 'mms'+str(this_probe)+'_'+instrument+'_b_bcs_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_b_bcs_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' FGM')
options('mms'+str(this_probe)+'_'+instrument+'_b_bcs_'+this_dr+'_'+this_lvl+suffix, 'color', ['b', 'g', 'r', '#000000'])
options('mms'+str(this_probe)+'_'+instrument+'_b_bcs_'+this_dr+'_'+this_lvl+suffix, 'legend_names', ['Bx BCS', 'By BCS', 'Bz BCS', 'B total'])
def mms_edp_set_metadata(probe, data_rate, level, suffix=''):
"""
This function updates the metadata for EDP data products
Parameters:
probe : str or list of str
probe or list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rate for EDP
level : str
indicates level of data processing. the default if no level is specified is 'l2'
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
"""
if not isinstance(probe, list): probe = [probe]
if not isinstance(data_rate, list): data_rate = [data_rate]
if not isinstance(level, list): level = [level]
instrument = 'edp'
tvars = set(tnames())
for this_probe in probe:
for this_dr in data_rate:
for this_lvl in level:
if 'mms'+str(this_probe)+'_'+instrument+'_dce_gse_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_dce_gse_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' EDP DCE [mV/m]')
options('mms'+str(this_probe)+'_'+instrument+'_dce_gse_'+this_dr+'_'+this_lvl+suffix, 'color', ['b', 'g', 'r'])
options('mms'+str(this_probe)+'_'+instrument+'_dce_gse_'+this_dr+'_'+this_lvl+suffix, 'legend_names', ['Ex GSE', 'Ey GSE', 'Ez GSE'])
if 'mms'+str(this_probe)+'_'+instrument+'_dce_dsl_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_dce_dsl_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' EDP DCE [mV/m]')
options('mms'+str(this_probe)+'_'+instrument+'_dce_dsl_'+this_dr+'_'+this_lvl+suffix, 'color', ['b', 'g', 'r'])
options('mms'+str(this_probe)+'_'+instrument+'_dce_dsl_'+this_dr+'_'+this_lvl+suffix, 'legend_names', ['Ex DSL', 'Ey DSL', 'Ez DSL'])
if 'mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix in tvars:
options('mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix, 'ytitle', 'MMS'+str(this_probe)+' EDP HFesp [Hz]')
options('mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix, 'ztitle', '(V/m)^2/Hz')
options('mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix, 'ylog', True)
options('mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix, 'zlog', True)
options('mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix, 'spec', True)
options('mms'+str(this_probe)+'_'+instrument+'_hfesp_'+this_dr+'_'+this_lvl+suffix, 'Colormap', 'jet')
def tsmooth(names, width=10, median=None, preserve_nans=None,
new_names=None, suffix=None, overwrite=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('tsmooth error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-s'
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
if new != old:
pyspedas.tcopy(old, new)
data = pytplot.data_quants[new].values
dim = data.shape
if len(dim) == 1:
data = smooth(data, width=width, preserve_nans=preserve_nans)
else:
for k in range(dim[1]):
data[:, k] = smooth(data[:, k], width=width,
preserve_nans=preserve_nans)
pytplot.data_quants[new].values = data
print('tsmooth was applied to: ' + new)
def mms_feeps_correct_energies(probes, data_rate, level='l2', suffix=''):
types = ['top', 'bottom']
sensors = range(1, 13)
units_types = ['intensity', 'count_rate']
for probe in probes:
for sensor_type in types:
for sensor in sensors:
if sensor >= 6 and sensor <= 8:
species = 'ion'
else:
species = 'electron'
for units in units_types:
var_name = tnames('mms' + probe + '_epd_feeps_' +
data_rate + '_' + level + '_' + species +
'_' + sensor_type + '_' + units +
'_sensorid_' + str(sensor))
if var_name == []:
continue
else:
var_name = var_name[0]
var_data = get_data(var_name + suffix)
if var_data is not None:
times, data, energies = var_data
else:
continue
energy_map = mms_feeps_energy_table(
probe, sensor_type[0:3], sensor)
try:
store_data(var_name + suffix,
data={
'x': times,
'y': data,
'v': energy_map
})
except:
continue
def mms_edi_set_metadata(probe, data_rate, level, suffix=''):
"""
This function updates the metadata for EDI data products
Parameters
----------
probe : str or list of str
probe or list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rate for EDI
level : str
indicates level of data processing. the default if no level is specified is 'l2'
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
"""
if not isinstance(probe, list): probe = [probe]
if not isinstance(data_rate, list): data_rate = [data_rate]
if not isinstance(level, list): level = [level]
instrument = 'edi'
tvars = set(tnames())
for this_probe in probe:
for this_dr in data_rate:
for this_lvl in level:
if 'mms' + str(
this_probe
) + '_' + instrument + '_vdrift_dsl_' + this_dr + '_' + this_lvl + suffix in tvars:
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_dsl_' + this_dr + '_' + this_lvl + suffix,
'ytitle',
'MMS' + str(this_probe) + ' EDI drift velocity')
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_dsl_' + this_dr + '_' + this_lvl + suffix,
'color', ['b', 'g', 'r'])
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_dsl_' + this_dr + '_' + this_lvl + suffix,
'legend_names', ['Vx DSL', 'Vy DSL', 'Vz DSL'])
if 'mms' + str(
this_probe
) + '_' + instrument + '_vdrift_gse_' + this_dr + '_' + this_lvl + suffix in tvars:
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_gse_' + this_dr + '_' + this_lvl + suffix,
'ytitle',
'MMS' + str(this_probe) + ' EDI drift velocity')
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_gse_' + this_dr + '_' + this_lvl + suffix,
'color', ['b', 'g', 'r'])
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_gse_' + this_dr + '_' + this_lvl + suffix,
'legend_names', ['Vx GSE', 'Vy GSE', 'Vz GSE'])
if 'mms' + str(
this_probe
) + '_' + instrument + '_vdrift_gsm_' + this_dr + '_' + this_lvl + suffix in tvars:
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_gsm_' + this_dr + '_' + this_lvl + suffix,
'ytitle',
'MMS' + str(this_probe) + ' EDI drift velocity')
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_gsm_' + this_dr + '_' + this_lvl + suffix,
'color', ['b', 'g', 'r'])
options(
'mms' + str(this_probe) + '_' + instrument +
'_vdrift_gsm_' + this_dr + '_' + this_lvl + suffix,
'legend_names', ['Vx GSM', 'Vy GSM', 'Vz GSM'])
if 'mms' + str(
this_probe
) + '_' + instrument + '_e_dsl_' + this_dr + '_' + this_lvl + suffix in tvars:
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_dsl_' + this_dr + '_' + this_lvl + suffix,
'ytitle', 'MMS' + str(this_probe) + ' EDI e-field')
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_dsl_' + this_dr + '_' + this_lvl + suffix, 'color',
['b', 'g', 'r'])
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_dsl_' + this_dr + '_' + this_lvl + suffix,
'legend_names', ['Ex DSL', 'Ey DSL', 'Ez DSL'])
if 'mms' + str(
this_probe
) + '_' + instrument + '_e_gse_' + this_dr + '_' + this_lvl + suffix in tvars:
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_gse_' + this_dr + '_' + this_lvl + suffix,
'ytitle', 'MMS' + str(this_probe) + ' EDI e-field')
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_gse_' + this_dr + '_' + this_lvl + suffix, 'color',
['b', 'g', 'r'])
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_gse_' + this_dr + '_' + this_lvl + suffix,
'legend_names', ['Ex GSE', 'Ey GSE', 'Ez GSE'])
if 'mms' + str(
this_probe
) + '_' + instrument + '_e_gsm_' + this_dr + '_' + this_lvl + suffix in tvars:
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_gsm_' + this_dr + '_' + this_lvl + suffix,
'ytitle', 'MMS' + str(this_probe) + ' EDI e-field')
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_gsm_' + this_dr + '_' + this_lvl + suffix, 'color',
['b', 'g', 'r'])
options(
'mms' + str(this_probe) + '_' + instrument +
'_e_gsm_' + this_dr + '_' + this_lvl + suffix,
'legend_names', ['Ex GSM', 'Ey GSM', 'Ez GSM'])
def mms_hpca_calc_anodes(fov=[0, 360], probe='1', suffix=''):
"""
This function will sum (or average, for flux) the HPCA data over the requested field-of-view (fov)
Parameters
----------
fov : list of int
field of view, in angles, from 0-360
probe : str
probe #, e.g., '4' for MMS4
suffix: str
suffix of the loaded data
Returns
----------
List of tplot variables created.
"""
sum_anodes = [a+suffix for a in ['*_count_rate', '*_RF_corrected', '*_bkgd_corrected', '*_norm_counts']]
avg_anodes = ['*_flux'+suffix]
output_vars = []
species_map = {'hplus': 'H+', 'oplus': 'O+', 'heplus': 'He+', 'heplusplus': 'He++', 'oplusplus': 'O++'}
fov_str = '_elev_'+str(fov[0])+'-'+str(fov[1])
for sum_anode in sum_anodes:
vars_to_sum = tnames(sum_anode)
for var in vars_to_sum:
var_species = var.split('_')[2]
times, data, angles, energies = get_data(var)
updated_spectra = mms_hpca_sum_fov(times, data, angles, energies, fov=fov)
store_data(var+fov_str, data={'x': times, 'y': updated_spectra, 'v': energies})
options(var+fov_str, 'spec', True)
options(var+fov_str, 'ylog', True)
options(var+fov_str, 'zlog', True)
options(var+fov_str, 'ztitle', species_map[var_species] + ' ' + var.split('_')[3] + ' (cm^2-s-sr-eV)^-1')
options(var+fov_str, 'ytitle', species_map[var_species] + ' Energy (eV)')
options(var+fov_str, 'Colormap', 'spedas')
output_vars.append(var+fov_str)
for avg_anode in avg_anodes:
vars_to_avg = tnames(avg_anode)
for var in vars_to_avg:
var_species = var.split('_')[2]
times, data, angles, energies = get_data(var)
updated_spectra = mms_hpca_avg_fov(times, data, angles, energies, fov=fov)
store_data(var+fov_str, data={'x': times, 'y': updated_spectra, 'v': energies})
options(var+fov_str, 'spec', True)
options(var+fov_str, 'ylog', True)
options(var+fov_str, 'zlog', True)
options(var+fov_str, 'ztitle', species_map[var_species] + ' ' + var.split('_')[3] + ' (cm^2-s-sr-eV)^-1')
options(var+fov_str, 'ytitle', species_map[var_species] + ' Energy (eV)')
options(var+fov_str, 'Colormap', 'spedas')
output_vars.append(var+fov_str)
return output_vars
def mms_feeps_correct_energies(probes, data_rate, level='l2', suffix=''):
"""
This function modifies the energy table in FEEPS spectra (intensity, count_rate, counts) variables
using the function: mms_feeps_energy_table (which is s/c, sensor head and sensor ID dependent)
Parameters:
probes: list of str
list of probes #, e.g., '4' for MMS4
data_rate: str
instrument data rate, e.g., 'srvy' or 'brst'
level: str
data level (default: 'l2')
suffix: str
suffix of the loaded data
Notes:
BAD EYES are replaced by NaNs
"""
types = ['top', 'bottom']
sensors = range(1, 13)
units_types = ['intensity', 'count_rate']
for probe in probes:
for sensor_type in types:
for sensor in sensors:
if sensor >= 6 and sensor <= 8:
species = 'ion'
else:
species = 'electron'
for units in units_types:
var_name = tnames('mms' + probe + '_epd_feeps_' +
data_rate + '_' + level + '_' + species +
'_' + sensor_type + '_' + units +
'_sensorid_' + str(sensor) + suffix)
if var_name == []:
continue
else:
var_name = var_name[0]
var_data = get_data(var_name)
if var_data is not None:
times, data, energies = var_data
else:
continue
energy_map = mms_feeps_energy_table(
probe, sensor_type[0:3], sensor)
try:
store_data(var_name,
data={
'x': times,
'y': data,
'v': energy_map
})
except:
continue
def avg_data(names,
width=60,
noremainder=True,
new_names=None,
suffix=None,
overwrite=None):
"""Get a new tplot variable with averaged data."""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('avg_data error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-avg'
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
d = pytplot.data_quants[old].copy()
data = d.values
time = d.time.values
dim = data.shape
dim0 = dim[0]
if len(dim) < 2:
dim1 = 1
else:
dim1 = dim[1]
new_data = []
new_time = []
for i in range(0, dim0, width):
last = (i + width) if (i + width) < dim0 else dim0
idx = int(i + width / 2)
if idx > dim0 - 1:
if noremainder: # Skip the last part of data
continue
idx = dim0 - 1 # Include the last part of data
new_time.append(time[idx])
if dim1 < 2:
nd0 = np.average(data[i:last])
else:
nd0 = []
for j in range(dim1):
nd0.append(np.average(data[i:last, j]))
new_data.append(nd0)
store_data(new, data={'x': new_time, 'y': new_data})
# copy attributes
pytplot.data_quants[new].attrs = d.attrs.copy()
print('avg_data was applied to: ' + new)
from pytplot import tplot_names
tplot_names()
from pyspedas.mms import mms_load_edp
# load some burst mode electric field data
mms_load_edp(probe='4',
data_rate='brst',
trange=['2015-10-16/13:06', '2015-10-16/13:07'],
time_clip=True)
from pyspedas import tnames
# the tnames function supports filtering with wild cards, e.g.,
# to find the E-field variables:
dce_vars = tnames('*_edp_dce_*')
# trange also accepts datetime objects
# note: be aware of potential time zone issues
from pyspedas.mms import mms_load_fpi
from datetime import datetime
from datetime import timezone as tz
start_time = datetime(year=2015,
month=10,
day=16,
hour=13,
minute=6,
tzinfo=tz.utc)
end_time = datetime(year=2015,
month=10,
