def ex_create():
# Delete any existing pytplot variables
pytplot.del_data()
# Create a sin wave plot
a = list(range(0, 101))
b = [2.0 / 100.0 * numpy.pi * s for s in a]
c = pyspedas.time_float('2017-01-01')
x = list()
y = list()
for i in range(len(b)):
x.append(c + 60.0 / (2 * numpy.pi) * 60.0 * b[i])
y.append(1000.0 * numpy.sin(b[i]))
# Store data
pytplot.store_data('sinx', data={'x': x, 'y': y})
# Apply tclip
pyspedas.tclip('sinx', -800.0, 800.0)
# Remove NaN values
pyspedas.tdeflag('sinx-clip')
# Interpolate
pyspedas.tinterpol(['sinx-clip-deflag'], ['sinx'], 'quadratic')
# Plot
pytplot.ylim('sinx', -1100.0, 1100.0)
pytplot.ylim('sinx-clip', -1100.0, 1100.0)
pytplot.ylim('sinx-clip-deflag', -1100.0, 1100.0)
pytplot.ylim('sinx-clip-deflag-itrp', -1100.0, 1100.0)
pytplot.tplot_options('title', 'Interpolation example')
pytplot.tplot(
['sinx', 'sinx-clip', 'sinx-clip-deflag', 'sinx-clip-deflag-itrp'])
def ex_wavelet(plot=True):
"""Demonstrates how to use wavelets with pyspedas."""
# Delete any existing pytplot variables
pytplot.del_data()
# Create a pytplot variable.
t = np.arange(4000.)
y = np.sin(2 * np.pi * t / 32.)
y2 = np.sin(2 * np.pi * t / 64.)
y[1000:3000] = y2[1000:3000]
var = 'sin_wav'
time = time_float('2010-01-01') + 10 * t
pytplot.store_data(var, data={'x': time, 'y': y})
# Complex wavelet transformation.
powervar = wavelet(var, wavename='cmorl0.5-1.0')
# Also try the following and compare:
# powervar = wavelet(var, wavename='gaus1')
pvar = powervar[0]
# Plot.
pytplot.options(pvar, 'colormap', 'jet')
pytplot.options(pvar, 'ylog', True)
pytplot.options(pvar, 'ytitle', pvar)
pytplot.ylim(pvar, 0.001, 1.0)
if plot:
pytplot.tplot([var, pvar])
return 1
def ex_deriv2():
"""Find the derivative of sinx."""
# Delete any existing pytplot variables
pytplot.del_data()
# Create a sin wave plot
a = list(range(0, 101))
b = [2.0 / 100.0 * np.pi * s for s in a]
c = pyspedas.time_float('2017-01-01')
x = list()
y = list()
for i in range(len(b)):
x.append(c + 60.0 / (2 * np.pi) * 60.0 * b[i])
y.append(1000.0 * np.sin(b[i]))
# Store data
pytplot.store_data('sinx', data={'x': x, 'y': y})
var = 'sinx'
deriv_data(var)
pytplot.tplot([var, var + '-der'])
return 1
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 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 avg_data(names,
dt=None,
width=60,
noremainder=False,
new_names=None,
suffix=None,
overwrite=None):
"""
Get a new tplot variable with averaged data.
Parameters
----------
names: str/list of str
List of pytplot names.
dt: float, optional
Time window in seconds for averaging data. It can be less than 1 sec.
width: int, optional
Number of values for the averaging window.
Default is 60 points (usually this means 60 seconds).
If dt is set, then width is ignored.
noremainder: boolean, optional
If True, the remainter (last part of data) will not be included.
If False. the remainter will be included.
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('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 old_idx, old in enumerate(old_names):
new = n_names[old_idx]
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 = []
if dt is None:
# Use width
width = int(width)
# print(dim0, width)
for i in range(0, dim0, width):
last = (i + width) if (i + width) < dim0 else dim0
# idx = int(i + width/2) # redefined below before it's ever used?
if (i + width > dim0) and noremainder:
continue # Skip the last part of data.
else:
idx = int((i + last - 1) / 2) # Include the last part.
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)
else:
# Use dt
dt = float(dt)
timedbl = np.array(pyspedas.time_float(time))
alldt = timedbl[-1] - timedbl[0]
if not dt > 0.0:
print("avg_data: Time interval dt<=0.0. Exiting.")
return
if dt > alldt:
print("avg_data: Time interval dt is too large. Exiting.")
return
# Find bins for time: equal bins of length dt.
bincount = int(alldt / dt)
if alldt % dt > 0.0 and not noremainder: # residual bin
# Include the last bin which might not be the same size.
bincount += 1
time0 = timedbl[0]
maxtime = timedbl[-1]
for i in range(bincount):
time1 = time0 + dt
bintime = time0 + dt / 2.0
if bintime > maxtime:
bintime = maxtime
new_time.append(bintime)
# Find all indexes between time0 and time1.
idx = np.where((timedbl >= time0) & (timedbl < time1))
# Check if idx is empty, ie. there is a gap in data.
idx_is_empty = False
if not idx:
idx_is_empty = True
elif len(idx) == 1:
if len(idx[0]) == 0:
idx_is_empty = True
if dim1 < 2:
if idx_is_empty: # Empty list.
nd0 = np.nan
else:
nd0 = np.average(data[idx])
else:
nd0 = []
for j in range(dim1):
if idx_is_empty: # Empty list.
nd0.append(np.nan)
else:
nd0.append(np.average(data[idx, j]))
new_data.append(nd0)
time0 = time1
pytplot.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)
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])
