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 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 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 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 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 test_tcopy(self):
store_data('test', data={'x': [1, 2, 3], 'y': [5, 5, 5]})
tcopy('test')
tcopy('test', 'another-copy')
t, d = get_data('test-copy')
self.assertTrue(t.tolist() == [1, 2, 3])
self.assertTrue(d.tolist() == [5, 5, 5])
t, d = get_data('another-copy')
self.assertTrue(t.tolist() == [1, 2, 3])
self.assertTrue(d.tolist() == [5, 5, 5])
# the following should gracefully error
tcopy('doesnt exist', 'another-copy')
tcopy(['another-copy', 'test'], 'another-copy')
def time_clip(names,
time_start,
time_end,
new_names=None,
suffix=None,
overwrite=None):
"""
Clip data from time_start to time_end.
Parameters:
names: str/list of str
List of pytplot names.
time_start : float
Start time.
time_end : float
End time.
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 '-m'.
overwrite: bool, optional
Replace the existing tplot name.
Returns
-------
None.
"""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('Time clip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-tclip'
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 j in range(len(old_names)):
if old_names[j] != n_names[j]:
pyspedas.tcopy(old_names[j], n_names[j])
alldata = pytplot.get_data(n_names[j])
time = alldata[0]
data = alldata[1]
index_start = 0
index_end = len(time)
if index_end < 1:
print('Time clip found empty list.')
continue
new_time = pyspedas.time_float(time)
new_time_start = pyspedas.time_float(time_start)
new_time_end = pyspedas.time_float(time_end)
if new_time_start > new_time_end:
print('Error: Start time is larger than end time.')
continue
if (new_time_start > new_time[-1]) or (new_time_end < new_time[0]):
print('Time clip returns empty data.')
continue
if (new_time_start <= new_time[0]) and (new_time_end >= new_time[-1]):
print('Time clip returns full data set.')
continue
for i in range(index_end):
if new_time[i] >= new_time_start:
index_start = i
break
found_end = index_end
for i in range(index_start, index_end):
if new_time[i] > new_time_end:
found_end = i
break
index_end = found_end
tmp_q = data_quants[n_names[j]]
if 'v1' in tmp_q.coords.keys():
if len(tmp_q.coords['v1'].values.shape) == 2:
v1_data = tmp_q.coords['v1'].values[index_start:index_end, :]
else:
v1_data = tmp_q.coords['v1'].values
if 'v2' in tmp_q.coords.keys():
if len(tmp_q.coords['v2'].values.shape) == 2:
v2_data = tmp_q.coords['v2'].values[index_start:index_end, :]
else:
v2_data = tmp_q.coords['v2'].values
if 'v3' in tmp_q.coords.keys():
if len(tmp_q.coords['v3'].values.shape) == 2:
v3_data = tmp_q.coords['v3'].values[index_start:index_end, :]
else:
v3_data = tmp_q.coords['v3'].values
if 'v' in tmp_q.coords.keys():
if len(tmp_q.coords['v'].values.shape) == 2:
v_data = tmp_q.coords['v'].values[index_start:index_end, :]
else:
v_data = tmp_q.coords['v'].values
if 'spec_bins' in tmp_q.coords.keys():
if len(tmp_q.coords['spec_bins'].values.shape) == 2:
v_data = tmp_q.coords['spec_bins']\
.values[index_start:index_end, :]
else:
v_data = tmp_q.coords['spec_bins'].values
try:
if 'v1' in tmp_q.coords.keys() and\
'v2' in tmp_q.coords.keys() and\
'v3' in tmp_q.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y':
data[index_start:index_end, :, :, :],
'v1': v1_data,
'v2': v2_data,
'v3': v3_data
})
elif 'v1' in tmp_q.coords.keys() and\
'v2' in tmp_q.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :, :],
'v1': v1_data,
'v2': v2_data
})
elif 'v1' in tmp_q.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :],
'v1': v1_data
})
elif 'spec_bins' in tmp_q.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :],
'v': v_data
})
elif 'v' in tmp_q.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :],
'v': v_data
})
elif data.ndim == 1:
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end]
})
else:
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end]
})
except:
print('Problem time clipping: ' + n_names[j])
continue
print('Time clip was applied to: ' + n_names[j])
def subtract_average(names,
new_names=None,
suffix=None,
overwrite=None,
median=None):
"""
Subtracts the average or the median from data.
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 '-d'.
overwrite: bool, optional
If set, then pytplot variables are replaced.
median: float, optional
If it is 0 or not set, then it computes the mean.
Otherwise, it computes the median.
Returns
-------
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 clean_spikes(names,
nsmooth=10,
thresh=0.3,
sub_avg=False,
new_names=None,
suffix=None,
overwrite=None):
"""
Clean spikes from data.
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.
nsmooth: int, optional
the number of data points for smoothing
thresh: float, optional
threshold value
sub_avg: bool, optional
if set, subtract the average value of the data
prior to checking for spikes
Returns
-------
None.
"""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('clean_spikes error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-despike'
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 old_idx, old in enumerate(old_names):
new = n_names[old_idx]
tmp = new + '_tmp_data'
# Create new
if old != new:
pyspedas.tcopy(old, new)
# Perform subtract_average or just copy the values
if sub_avg:
subtract_average(new, new_names=tmp)
else:
pyspedas.tcopy(new, tmp)
# Find spikes
tmps = tmp + '-s'
tsmooth(tmp, new_names=tmps, width=nsmooth)
ds0 = pytplot.get_data(tmps) # smoothed out values
ds = ds0[1]
dor0 = pytplot.get_data(tmp) # original values
d0 = dor0[1]
dn = d0.copy() # final values
dim = dn.shape
if len(dim) == 1:
# One dim data.
for i in range(dim[0]):
# compare smoothed out values to original values
if abs(d0[i] - ds[i]) > thresh * abs(ds[i]):
dn[i] = np.NaN # for spikes, set to NaN
else:
# More than one dim data.
for j in range(dim[1]):
# print("j = ", j)
for i in range(dim[0]):
# compare smoothed out values to original values
if abs(d0[i, j] - ds[i, j]) > thresh * abs(ds[i, j]):
dn[i, j] = np.NaN # for spikes, set to NaN
# pytplot.data_quants[new] = d
pytplot.replace_data(new, dn)
# remove temp data
del pytplot.data_quants[tmp]
del pytplot.data_quants[tmps]
print('clean_spikes was applied to: ' + new)
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]
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 time_clip(names, time_start, time_end, new_names=None, suffix=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('Time clip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-tclip'
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 j in range(len(old_names)):
if old_names[j] != n_names[j]:
pyspedas.tcopy(old_names[j], n_names[j])
time, data = pytplot.get_data(n_names[j])
index_start = 0
index_end = len(time)
if index_end < 1:
print('Time clip found empty list.')
continue
new_time = pyspedas.time_float(time)
new_time_start = pyspedas.time_float(time_start)
new_time_end = pyspedas.time_float(time_end)
if new_time_start > new_time_end:
print('Error: Start time is larger than end time.')
continue
if (new_time_start > new_time[-1]) or (new_time_end < new_time[0]):
print('Time clip returns empty data.')
continue
if (new_time_start <= new_time[0]) and (new_time_end >= new_time[-1]):
print('Time clip returns full data set.')
continue
for i in range(index_end):
if new_time[i] >= new_time_start:
index_start = i
break
found_end = index_end
for i in range(index_start, index_end):
if new_time[i] > new_time_end:
found_end = i
break
index_end = found_end
pytplot.store_data(n_names[j], data={'x': time[index_start:index_end],
'y': data[index_start:index_end, :]})
print('Time clip was applied to: ' + n_names[j])
def time_clip(names, time_start, time_end, new_names=None, suffix=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('Time clip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-tclip'
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 j in range(len(old_names)):
if old_names[j] != n_names[j]:
pyspedas.tcopy(old_names[j], n_names[j])
alldata = pytplot.get_data(n_names[j])
time = alldata[0]
data = alldata[1]
index_start = 0
index_end = len(time)
if index_end < 1:
print('Time clip found empty list.')
continue
new_time = pyspedas.time_float(time)
new_time_start = pyspedas.time_float(time_start)
new_time_end = pyspedas.time_float(time_end)
if new_time_start > new_time_end:
print('Error: Start time is larger than end time.')
continue
if (new_time_start > new_time[-1]) or (new_time_end < new_time[0]):
print('Time clip returns empty data.')
continue
if (new_time_start <= new_time[0]) and (new_time_end >= new_time[-1]):
print('Time clip returns full data set.')
continue
for i in range(index_end):
if new_time[i] >= new_time_start:
index_start = i
break
found_end = index_end
for i in range(index_start, index_end):
if new_time[i] > new_time_end:
found_end = i
break
index_end = found_end
tmp_dquant = data_quants[n_names[j]]
if 'v1' in tmp_dquant.coords.keys():
if len(tmp_dquant.coords['v1'].values.shape) is 2:
v1_data = tmp_dquant.coords['v1'].values[
index_start:index_end, :]
else:
v1_data = tmp_dquant.coords['v1'].values
if 'v2' in tmp_dquant.coords.keys():
if len(tmp_dquant.coords['v2'].values.shape) is 2:
v2_data = tmp_dquant.coords['v2'].values[
index_start:index_end, :]
else:
v2_data = tmp_dquant.coords['v2'].values
if 'v3' in tmp_dquant.coords.keys():
if len(tmp_dquant.coords['v3'].values.shape) is 2:
v3_data = tmp_dquant.coords['v3'].values[
index_start:index_end, :]
else:
v3_data = tmp_dquant.coords['v3'].values
if 'v' in tmp_dquant.coords.keys():
if len(tmp_dquant.coords['v'].values.shape) is 2:
v_data = tmp_dquant.coords['v'].values[
index_start:index_end, :]
else:
v_data = tmp_dquant.coords['v'].values
if 'spec_bins' in tmp_dquant.coords.keys():
if len(tmp_dquant.coords['spec_bins'].values.shape) is 2:
v_data = tmp_dquant.coords['spec_bins'].values[
index_start:index_end, :]
else:
v_data = tmp_dquant.coords['spec_bins'].values
try:
if 'v1' in tmp_dquant.coords.keys(
) and 'v2' in tmp_dquant.coords.keys(
) and 'v3' in tmp_dquant.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y':
data[index_start:index_end, :, :, :],
'v1': v1_data,
'v2': v2_data,
'v3': v3_data
})
elif 'v1' in tmp_dquant.coords.keys(
) and 'v2' in tmp_dquant.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :, :],
'v1': v1_data,
'v2': v2_data
})
elif 'v1' in tmp_dquant.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :],
'v1': v1_data
})
elif 'spec_bins' in tmp_dquant.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :],
'v': v_data
})
elif 'v' in tmp_dquant.coords.keys():
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, :],
'v': v_data
})
elif data.ndim == 1:
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end]
})
else:
pytplot.store_data(n_names[j],
data={
'x': time[index_start:index_end],
'y': data[index_start:index_end, ...]
})
except IndexError:
print('Problem time clipping: ' + n_names[j])
continue
print('Time clip was applied to: ' + n_names[j])
def clean_spikes(names,
nsmooth=10,
thresh=0.3,
sub_avg=False,
new_names=None,
suffix=None,
overwrite=None):
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('clean_spikes error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-despike'
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]
tmp = new + '_tmp_data'
# Create new
if old != new:
pyspedas.tcopy(old, new)
# Perform subtract_average or just copy the values
if sub_avg:
subtract_average(new, new_names=tmp)
else:
pyspedas.tcopy(new, tmp)
# Find spikes
tmps = tmp + '-s'
tsmooth(tmp, new_names=tmps, width=nsmooth)
ds = pytplot.data_quants[tmps].values # smoothed out values
d0 = pytplot.data_quants[tmp].values # original values
d = pytplot.data_quants[new] # final values
dim = d.shape
for j in range(dim[1]):
# print("j = ", j)
for i in range(dim[0]):
# compare smoothed out values to original values
if abs(d0[i, j] - ds[i, j]) > thresh * abs(ds[i, j]):
d[i, j] = np.NaN # for spikes, set to NaN
pytplot.data_quants[new] = d
# remove temp data
del pytplot.data_quants[tmp]
del pytplot.data_quants[tmps]
print('clean_spikes was applied to: ' + new)
def tsmooth(names, width=10, median=None, preserve_nans=None,
new_names=None, suffix=None, overwrite=None):
"""
Smooths data.
Parameters
----------
names: str/list of str
List of pytplot names.
width: int, optional
Data window to use for smoothing. The default is 10.
median: bool, optional
Apply the median as well.
preserve_nans: bool, optional
If None, then replace NaNs.
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 '-s'.
overwrite: bool, optional
Replace the existing tplot name.
Returns
-------
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)
