def tdpwrspc(varname,
newname=None,
nboxpoints=256,
nshiftpoints=128,
binsize=3,
nohanning=False,
noline=False,
notperhz=False,
notmvariance=False):
if newname is None:
newname = varname + '_dpwrspc'
data_tuple = get_data(varname)
if data_tuple is not None:
if data_tuple[1][0].shape != ():
split_vars = split_vec(varname)
out_vars = []
for var in split_vars:
out_vars.append(
tdpwrspc(var,
newname=var + '_dpwrspc',
nboxpoints=nboxpoints,
nshiftpoints=nshiftpoints))
return out_vars
else:
pwrspc = dpwrspc(data_tuple[0],
data_tuple[1],
nboxpoints=nboxpoints,
nshiftpoints=nshiftpoints,
binsize=binsize,
nohanning=nohanning,
noline=noline,
notperhz=notperhz,
notmvariance=notmvariance)
if pwrspc != None:
store_data(newname,
data={
'x': pwrspc[0],
'y': pwrspc[2],
'v': pwrspc[1]
})
options(newname, 'spec', True)
options(newname, 'ylog', True)
options(newname, 'zlog', True)
options(newname, 'Colormap', 'jet')
# options(newname, 'yrange', [0.01, 16])
return newname
def wavelet(names,
new_names=None,
suffix='_pow',
wavename='morl',
scales=None,
method='fft',
sampling_period=1.0):
"""
Find the wavelet transofrmation of a tplot variable.
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 '_pow'.
wavename: str, optional
The name of the continous wavelet function to apply.
Examples: 'gaus1', 'morl', 'cmorlB-C'.
scales: list of float, optional
The wavelet scales to use.
method: str, optional
Either ‘fft’ for frequency domain convolution,
or 'conv' for numpy.convolve.
sampling_period: float, optional
The sampling period for the frequencies output.
Returns
-------
A list of pytplot variables that contain the wavelet power.
"""
varnames = pytplot.split_vec(names)
powervar = []
if len(varnames) < 1:
print('wavelet error: No pytplot names were provided.')
return
if scales is None:
scales = np.arange(1, 128)
for i, old in enumerate(varnames):
old = varnames[i]
if (new_names is not None) and (len(new_names) == len(varnames)):
new = new_names[i]
else:
new = old + suffix
alldata = pytplot.get_data(old)
time = alldata[0]
len_time = len(time)
data = alldata[1]
if len_time < 2:
print('wavelet error: Not enought data points for ' + old)
continue
coef, freqs = pywt.cwt(data,
scales=scales,
wavelet=wavename,
method=method,
sampling_period=sampling_period)
power = np.abs(coef)**2
power = power.transpose()
pytplot.store_data(new, data={'x': time, 'y': power, 'v': freqs})
pytplot.options(new, 'spec', 1)
powervar.append(new)
print('wavelet was applied to: ' + new)
return powervar
def tdpwrspc(varname,
newname=None,
nboxpoints=256,
nshiftpoints=128,
binsize=3,
nohanning=False,
noline=False,
notperhz=False,
notmvariance=False):
"""
Compute power spectra for a tplot variable.
Parameters
----------
varname: str
Name of pytplot variable.
newname: str, optional
Name of new pytplot variable to save data to.
nboxpoints: int, optional
The number of points to use for the hanning window.
The default is 256.
nshiftpoints: int, optional
The number of points to shift for each spectrum.
The default is 128.
binsize: int, optional
Size for binning of the data along the frequency domain.
The default is 3.
nohanning: bool, optional
If True, no hanning window is applied to the input.
The default is False.
noline: bool, optional
If True, no straight line is subtracted.
The default is False.
notperhz: bool, optional
If True, the output units are the square of the input units.
The default is False.
notmvariance: bool, optional
If True, replace output spectrum for any windows that have variable.
cadence with NaNs.
The default is False.
Returns
-------
str
Name of new pytplot variable.
"""
if newname is None:
newname = varname + '_dpwrspc'
data_tuple = get_data(varname)
if data_tuple is not None:
if data_tuple[1][0].shape != ():
split_vars = split_vec(varname)
out_vars = []
for var in split_vars:
out_vars.append(
tdpwrspc(var,
newname=var + '_dpwrspc',
nboxpoints=nboxpoints,
nshiftpoints=nshiftpoints))
return out_vars
else:
pwrspc = dpwrspc(data_tuple[0],
data_tuple[1],
nboxpoints=nboxpoints,
nshiftpoints=nshiftpoints,
binsize=binsize,
nohanning=nohanning,
noline=noline,
notperhz=notperhz,
notmvariance=notmvariance)
if pwrspc != None:
store_data(newname,
data={
'x': pwrspc[0],
'y': pwrspc[2],
'v': pwrspc[1]
})
options(newname, 'spec', True)
options(newname, 'ylog', True)
options(newname, 'zlog', True)
options(newname, 'Colormap', 'jet')
# options(newname, 'yrange', [0.01, 16])
return newname
def test_math():
pytplot.cdf_to_tplot(os.path.dirname(os.path.realpath(__file__)) + "/testfiles/mvn_euv_l2_bands_20170619_v09_r03.cdf")
pytplot.tplot_names()
pytplot.tplot_math.split_vec('mvn_euv_calib_bands')
pytplot.tplot('mvn_euv_calib_bands_x', testing=True)
pytplot.tplot_math.subtract('mvn_euv_calib_bands_x', 'mvn_euv_calib_bands_y', new_tvar='s')
pytplot.tplot('s', testing=True)
pytplot.tplot_math.add('s', 'mvn_euv_calib_bands_x', new_tvar='a')
pytplot.tplot(['mvn_euv_calib_bands_x', 'a'], testing=True)
pytplot.tplot_math.subtract('mvn_euv_calib_bands_x', 'mvn_euv_calib_bands_z', new_tvar='m')
pytplot.tplot('m', testing=True)
pytplot.tplot_math.divide('m', 'mvn_euv_calib_bands_z', new_tvar='d')
pytplot.tplot('d', testing=True)
pytplot.add_across('mvn_euv_calib_bands', new_tvar='data_summed')
pytplot.tplot('mvn_euv_calib_bands', testing=True)
pytplot.avg_res_data('data_summed', res=120)
pytplot.tplot('data_summed', testing=True)
pytplot.deflag('mvn_euv_calib_bands', 0, new_tvar='deflagged')
pytplot.tplot('deflagged', testing=True)
pytplot.flatten('mvn_euv_calib_bands')
pytplot.tplot('data_flattened', testing=True)
pytplot.join_vec(['mvn_euv_calib_bands_x', 'mvn_euv_calib_bands_y', 'mvn_euv_calib_bands_z'], new_tvar='data2')
pytplot.tplot('data2', testing=True)
pytplot.pwr_spec('mvn_euv_calib_bands_x')
pytplot.tplot('mvn_euv_calib_bands_x_pwrspec', testing=True)
pytplot.derive('mvn_euv_calib_bands_x')
pytplot.store_data("data3", data=['mvn_euv_calib_bands_x', 'mvn_euv_calib_bands_y', 'mvn_euv_calib_bands_z'])
pytplot.tplot('data3', testing=True)
pytplot.cdf_to_tplot(os.path.dirname(os.path.realpath(__file__))+ "/testfiles/mvn_swe_l2_svyspec_20170619_v04_r04.cdf")
pytplot.resample('mvn_euv_calib_bands_y', pytplot.data_quants['diff_en_fluxes'].coords['time'].values, new_tvar='data_3_resampled')
pytplot.tplot('data_3_resampled', testing=True)
pytplot.options('diff_en_fluxes', 'spec', 1)
pytplot.spec_mult('diff_en_fluxes')
pytplot.add_across('diff_en_fluxes_specmult', new_tvar='tot_en_flux', column_range=[[0, 10], [10, 20], [20, 30]])
pytplot.options('diff_en_fluxes', 'ylog', 1)
pytplot.options('diff_en_fluxes', 'zlog', 1)
pytplot.options('tot_en_flux', 'ylog', 1)
pytplot.ylim('tot_en_flux', 1, 100)
pytplot.tplot(['diff_en_fluxes', 'tot_en_flux'], testing=True)
pytplot.split_vec('tot_en_flux')
pytplot.add('tot_en_flux_x', 'mvn_euv_calib_bands_y', new_tvar='weird_data')
pytplot.tplot('weird_data', testing=True)
pytplot.join_vec(['d', 'e', 'g'], 'deg')
print(pytplot.data_quants['deg'].data)
print('multiply')
pytplot.multiply('a', 'c', 'ac', interp='linear')
print(pytplot.data_quants['ac'].data)
print('resample')
# pytplot.resample('d',[3,4,5,6,7,18],'d_resampled')
# print(pytplot.data_quants['d_resampled'].data)
pytplot.resample('h', [3, 4, 5, 6, 7, 18], 'h_resampled')
print(pytplot.data_quants['h_resampled'].data)
print(pytplot.data_quants['h_resampled'].spec_bins)
print('spec_mult')
pytplot.spec_mult('h', 'h_specmult')
print(pytplot.data_quants['h_specmult'].data)
print('split_vec')
pytplot.split_vec('b', ['b1', 'b2', 'b3'], [0, [1, 3], 4])
print(pytplot.data_quants['b1'].data)
print(pytplot.data_quants['b2'].data)
print(pytplot.data_quants['b3'].data)
defaultlist = pytplot.split_vec('b')
print(pytplot.data_quants['b'].data)
print(pytplot.data_quants['data_3'].data)
print(defaultlist)
print('subtract')
pytplot.subtract('a', 'c', 'a-c')
print(pytplot.data_quants['a-c'].data)