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signal1d.py
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signal1d.py
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# -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HyperSpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with HyperSpy. If not, see <http://www.gnu.org/licenses/>.
import logging
import math
import matplotlib.pyplot as plt
import numpy as np
import dask.array as da
import scipy.interpolate
import scipy as sp
from scipy.signal import savgol_filter
from scipy.ndimage.filters import gaussian_filter1d
try:
from statsmodels.nonparametric.smoothers_lowess import lowess
statsmodels_installed = True
except BaseException:
statsmodels_installed = False
from hyperspy.signal import BaseSignal
from hyperspy._signals.common_signal1d import CommonSignal1D
from hyperspy.signal_tools import SpikesRemoval
from hyperspy.models.model1d import Model1D
from hyperspy.misc.utils import signal_range_from_roi
from hyperspy.defaults_parser import preferences
from hyperspy.signal_tools import (
Signal1DCalibration,
SmoothingSavitzkyGolay,
SmoothingLowess,
SmoothingTV,
ButterworthFilter)
from hyperspy.ui_registry import DISPLAY_DT, TOOLKIT_DT
from hyperspy.misc.tv_denoise import _tv_denoise_1d
from hyperspy.signal_tools import BackgroundRemoval
from hyperspy.decorators import interactive_range_selector
from hyperspy.signal_tools import IntegrateArea
from hyperspy import components1d
from hyperspy._signals.lazy import LazySignal
from hyperspy.docstrings.signal1d import CROP_PARAMETER_DOC
from hyperspy.docstrings.signal import SHOW_PROGRESSBAR_ARG, PARALLEL_ARG
_logger = logging.getLogger(__name__)
def find_peaks_ohaver(y, x=None, slope_thresh=0., amp_thresh=None,
medfilt_radius=5, maxpeakn=30000, peakgroup=10,
subchannel=True,):
"""Find peaks along a 1D line.
Function to locate the positive peaks in a noisy x-y data set.
Detects peaks by looking for downward zero-crossings in the first
derivative that exceed 'slope_thresh'.
Returns an array containing position, height, and width of each peak.
Sorted by position.
'slope_thresh' and 'amp_thresh', control sensitivity: higher values
will neglect wider peaks (slope) and smaller features (amp),
respectively.
Parameters
----------
y : array
1D input array, e.g. a spectrum
x : array (optional)
1D array describing the calibration of y (must have same shape as y)
slope_thresh : float (optional)
1st derivative threshold to count the peak;
higher values will neglect broader features;
default is set to 0.
amp_thresh : float (optional)
intensity threshold below which peaks are ignored;
higher values will neglect smaller features;
default is set to 10% of max(y).
medfilt_radius : int (optional)
median filter window to apply to smooth the data
(see scipy.signal.medfilt);
if 0, no filter will be applied;
default is set to 5.
peakgroup : int (optional)
number of points around the "top part" of the peak that
are taken to estimate the peak height; for spikes or
very narrow peaks, keep PeakGroup=1 or 2; for broad or
noisy peaks, make PeakGroup larger to reduce the effect
of noise;
default is set to 10.
maxpeakn : int (optional)
number of maximum detectable peaks;
default is set to 30000.
subchannel : bool (optional)
default is set to True.
Returns
-------
P : structured array of shape (npeaks)
contains fields: 'position', 'width', and 'height' for each peak.
Examples
--------
>>> x = np.arange(0,50,0.01)
>>> y = np.cos(x)
>>> peaks = find_peaks_ohaver(y, x, 0, 0)
Notes
-----
Original code from T. C. O'Haver, 1995.
Version 2 Last revised Oct 27, 2006 Converted to Python by
Michael Sarahan, Feb 2011.
Revised to handle edges better. MCS, Mar 2011
"""
if x is None:
x = np.arange(len(y), dtype=np.int64)
if not amp_thresh:
amp_thresh = 0.1 * y.max()
peakgroup = np.round(peakgroup)
if medfilt_radius:
d = np.gradient(scipy.signal.medfilt(y, medfilt_radius))
else:
d = np.gradient(y)
n = np.round(peakgroup / 2 + 1)
peak_dt = np.dtype([('position', np.float),
('height', np.float),
('width', np.float)])
P = np.array([], dtype=peak_dt)
peak = 0
for j in range(len(y) - 4):
if np.sign(d[j]) > np.sign(d[j + 1]): # Detects zero-crossing
if np.sign(d[j + 1]) == 0:
continue
# if slope of derivative is larger than slope_thresh
if d[j] - d[j + 1] > slope_thresh:
# if height of peak is larger than amp_thresh
if y[j] > amp_thresh:
# the next section is very slow, and actually messes
# things up for images (discrete pixels),
# so by default, don't do subchannel precision in the
# 1D peakfind step.
if subchannel:
xx = np.zeros(peakgroup)
yy = np.zeros(peakgroup)
s = 0
for k in range(peakgroup):
groupindex = int(j + k - n + 1)
if groupindex < 1:
xx = xx[1:]
yy = yy[1:]
s += 1
continue
elif groupindex > y.shape[0] - 1:
xx = xx[:groupindex - 1]
yy = yy[:groupindex - 1]
break
xx[k - s] = x[groupindex]
yy[k - s] = y[groupindex]
avg = np.average(xx)
stdev = np.std(xx)
xxf = (xx - avg) / stdev
# Fit parabola to log10 of sub-group with
# centering and scaling
yynz = yy != 0
coef = np.polyfit(
xxf[yynz], np.log10(np.abs(yy[yynz])), 2)
c1 = coef[2]
c2 = coef[1]
c3 = coef[0]
with np.errstate(invalid='ignore'):
width = np.linalg.norm(stdev * 2.35703 /
(np.sqrt(2) * np.sqrt(-1 *
c3)))
# if the peak is too narrow for least-squares
# technique to work well, just use the max value
# of y in the sub-group of points near peak.
if peakgroup < 7:
height = np.max(yy)
position = xx[np.argmin(np.abs(yy - height))]
else:
position = - ((stdev * c2 / (2 * c3)) - avg)
height = np.exp(c1 - c3 * (c2 / (2 * c3)) ** 2)
# Fill results array P. One row for each peak
# detected, containing the
# peak position (x-value) and peak height (y-value).
else:
position = x[j]
height = y[j]
# no way to know peak width without
# the above measurements.
width = 0
if (not np.isnan(position) and 0 < position < x[-1]):
P = np.hstack((P,
np.array([(position, height, width)],
dtype=peak_dt)))
peak += 1
# return only the part of the array that contains peaks
# (not the whole maxpeakn x 3 array)
if len(P) > maxpeakn:
minh = np.sort(P['height'])[-maxpeakn]
P = P[P['height'] >= minh]
# Sorts the values as a function of position
P.sort(0)
return P
def interpolate1D(number_of_interpolation_points, data):
ip = number_of_interpolation_points
ch = len(data)
old_ax = np.linspace(0, 100, ch)
new_ax = np.linspace(0, 100, ch * ip - (ip - 1))
interpolator = scipy.interpolate.interp1d(old_ax, data)
return interpolator(new_ax)
def _estimate_shift1D(data, **kwargs):
mask = kwargs.get('mask', None)
ref = kwargs.get('ref', None)
interpolate = kwargs.get('interpolate', True)
ip = kwargs.get('ip', 5)
data_slice = kwargs.get('data_slice', slice(None))
if bool(mask):
# asarray is required for consistensy as argmax
# returns a numpy scalar array
return np.asarray(np.nan)
data = data[data_slice]
if interpolate is True:
data = interpolate1D(ip, data)
return np.argmax(np.correlate(ref, data, 'full')) - len(ref) + 1
def _shift1D(data, **kwargs):
shift = kwargs.get('shift', 0.)
original_axis = kwargs.get('original_axis', None)
fill_value = kwargs.get('fill_value', np.nan)
kind = kwargs.get('kind', 'linear')
offset = kwargs.get('offset', 0.)
scale = kwargs.get('scale', 1.)
size = kwargs.get('size', 2)
if np.isnan(shift) or shift == 0:
return data
axis = np.linspace(offset, offset + scale * (size - 1), size)
si = sp.interpolate.interp1d(original_axis,
data,
bounds_error=False,
fill_value=fill_value,
kind=kind)
offset = float(offset - shift)
axis = np.linspace(offset, offset + scale * (size - 1), size)
return si(axis)
class Signal1D(BaseSignal, CommonSignal1D):
"""
"""
_signal_dimension = 1
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.axes_manager.signal_dimension != 1:
self.axes_manager.set_signal_dimension(1)
def _spikes_diagnosis(self, signal_mask=None,
navigation_mask=None):
"""Plots a histogram to help in choosing the threshold for
spikes removal.
Parameters
----------
signal_mask : boolean array
Restricts the operation to the signal locations not marked
as True (masked)
navigation_mask : boolean array
Restricts the operation to the navigation locations not
marked as True (masked).
See also
--------
spikes_removal_tool
"""
self._check_signal_dimension_equals_one()
dc = self.data
if signal_mask is not None:
dc = dc[..., ~signal_mask]
if navigation_mask is not None:
dc = dc[~navigation_mask, :]
der = np.abs(np.diff(dc, 1, -1))
n = ((~navigation_mask).sum() if navigation_mask else
self.axes_manager.navigation_size)
# arbitrary cutoff for number of spectra necessary before histogram
# data is compressed by finding maxima of each spectrum
tmp = BaseSignal(der) if n < 2000 else BaseSignal(
np.ravel(der.max(-1)))
# get histogram signal using smart binning and plot
tmph = tmp.get_histogram()
tmph.plot()
# Customize plot appearance
plt.gca().set_title('')
plt.gca().fill_between(tmph.axes_manager[0].axis,
tmph.data,
facecolor='#fddbc7',
interpolate=True,
color='none')
ax = tmph._plot.signal_plot.ax
axl = tmph._plot.signal_plot.ax_lines[0]
axl.set_line_properties(color='#b2182b')
plt.xlabel('Derivative magnitude')
plt.ylabel('Log(Counts)')
ax.set_yscale('log')
ax.set_ylim(10 ** -1, plt.ylim()[1])
ax.set_xlim(plt.xlim()[0], 1.1 * plt.xlim()[1])
plt.draw()
def spikes_removal_tool(self, signal_mask=None,
navigation_mask=None, display=True, toolkit=None):
"""Graphical interface to remove spikes from EELS spectra.
Parameters
----------
signal_mask: boolean array or signal of bool
Restricts the operation to the signal locations not marked
as True (masked)
navigation_mask: boolean array or signal of bool
Restricts the operation to the navigation locations not
marked as True (masked)
%s
%s
See also
--------
`_spikes_diagnosis`
"""
self._check_signal_dimension_equals_one()
self._check_signal_mask(signal_mask)
if isinstance(signal_mask, BaseSignal):
signal_mask = signal_mask.data
self._check_navigation_mask(navigation_mask)
if isinstance(navigation_mask, BaseSignal):
navigation_mask = navigation_mask.data
sr = SpikesRemoval(self,
navigation_mask=navigation_mask,
signal_mask=signal_mask)
return sr.gui(display=display, toolkit=toolkit)
spikes_removal_tool.__doc__ %= (DISPLAY_DT, TOOLKIT_DT)
def create_model(self, dictionary=None):
"""Create a model for the current data.
Returns
-------
model : `Model1D` instance.
"""
model = Model1D(self, dictionary=dictionary)
return model
def shift1D(self,
shift_array,
interpolation_method='linear',
crop=True,
expand=False,
fill_value=np.nan,
parallel=None,
show_progressbar=None):
"""Shift the data in place over the signal axis by the amount specified
by an array.
Parameters
----------
shift_array : numpy array
An array containing the shifting amount. It must have
`axes_manager._navigation_shape_in_array` shape.
interpolation_method : str or int
Specifies the kind of interpolation as a string ('linear',
'nearest', 'zero', 'slinear', 'quadratic, 'cubic') or as an
integer specifying the order of the spline interpolator to
use.
%s
expand : bool
If True, the data will be expanded to fit all data after alignment.
Overrides `crop`.
fill_value : float
If crop is False fill the data outside of the original
interval with the given value where needed.
%s
%s
Raises
------
SignalDimensionError
If the signal dimension is not 1.
"""
if not np.any(shift_array):
# Nothing to do, the shift array if filled with zeros
return
if show_progressbar is None:
show_progressbar = preferences.General.show_progressbar
self._check_signal_dimension_equals_one()
axis = self.axes_manager.signal_axes[0]
# Figure out min/max shifts, and translate to shifts in index as well
minimum, maximum = np.nanmin(shift_array), np.nanmax(shift_array)
if minimum < 0:
ihigh = 1 + axis.value2index(
axis.high_value + minimum,
rounding=math.floor)
else:
ihigh = axis.high_index + 1
if maximum > 0:
ilow = axis.value2index(axis.offset + maximum,
rounding=math.ceil)
else:
ilow = axis.low_index
if expand:
if self._lazy:
ind = axis.index_in_array
pre_shape = list(self.data.shape)
post_shape = list(self.data.shape)
pre_chunks = list(self.data.chunks)
post_chunks = list(self.data.chunks)
pre_shape[ind] = axis.high_index - ihigh + 1
post_shape[ind] = ilow - axis.low_index
for chunks, shape in zip((pre_chunks, post_chunks),
(pre_shape, post_shape)):
maxsize = min(np.max(chunks[ind]), shape[ind])
num = np.ceil(shape[ind] / maxsize)
chunks[ind] = tuple(len(ar) for ar in
np.array_split(np.arange(shape[ind]),
num))
pre_array = da.full(tuple(pre_shape),
fill_value,
chunks=tuple(pre_chunks))
post_array = da.full(tuple(post_shape),
fill_value,
chunks=tuple(post_chunks))
self.data = da.concatenate((pre_array, self.data, post_array),
axis=ind)
else:
padding = []
for i in range(self.data.ndim):
if i == axis.index_in_array:
padding.append((axis.high_index - ihigh + 1,
ilow - axis.low_index))
else:
padding.append((0, 0))
self.data = np.pad(self.data, padding, mode='constant',
constant_values=(fill_value,))
axis.offset += minimum
axis.size += axis.high_index - ihigh + 1 + ilow - axis.low_index
self._map_iterate(_shift1D, (('shift', shift_array.ravel()),),
original_axis=axis.axis,
fill_value=fill_value,
kind=interpolation_method,
offset=axis.offset,
scale=axis.scale,
size=axis.size,
show_progressbar=show_progressbar,
parallel=parallel,
ragged=False)
if crop and not expand:
_logger.debug("Cropping %s from index %i to %i"
% (self, ilow, ihigh))
self.crop(axis.index_in_axes_manager,
ilow,
ihigh)
self.events.data_changed.trigger(obj=self)
shift1D.__doc__ %= (CROP_PARAMETER_DOC, SHOW_PROGRESSBAR_ARG, PARALLEL_ARG)
def interpolate_in_between(self, start, end, delta=3, parallel=None,
show_progressbar=None, **kwargs):
"""Replace the data in a given range by interpolation.
The operation is performed in place.
Parameters
----------
start, end : int or float
The limits of the interval. If int they are taken as the
axis index. If float they are taken as the axis value.
delta : int or float
The windows around the (start, end) to use for interpolation
%s
%s
All extra keyword arguments are passed to
`scipy.interpolate.interp1d`. See the function documentation
for details.
Raises
------
SignalDimensionError
If the signal dimension is not 1.
"""
if show_progressbar is None:
show_progressbar = preferences.General.show_progressbar
self._check_signal_dimension_equals_one()
axis = self.axes_manager.signal_axes[0]
i1 = axis._get_index(start)
i2 = axis._get_index(end)
if isinstance(delta, float):
delta = int(delta / axis.scale)
i0 = int(np.clip(i1 - delta, 0, np.inf))
i3 = int(np.clip(i2 + delta, 0, axis.size))
def interpolating_function(dat):
dat_int = sp.interpolate.interp1d(
list(range(i0, i1)) + list(range(i2, i3)),
dat[i0:i1].tolist() + dat[i2:i3].tolist(),
**kwargs)
dat[i1:i2] = dat_int(list(range(i1, i2)))
return dat
self._map_iterate(interpolating_function, ragged=False,
parallel=parallel, show_progressbar=show_progressbar)
self.events.data_changed.trigger(obj=self)
interpolate_in_between.__doc__ %= (SHOW_PROGRESSBAR_ARG, PARALLEL_ARG)
def _check_navigation_mask(self, mask):
if mask is not None:
if not isinstance(mask, BaseSignal):
raise ValueError("mask must be a BaseSignal instance.")
elif mask.axes_manager.signal_dimension not in (0, 1):
raise ValueError("mask must be a BaseSignal "
"with signal_dimension equal to 1")
super()._check_navigation_mask(mask)
def estimate_shift1D(self,
start=None,
end=None,
reference_indices=None,
max_shift=None,
interpolate=True,
number_of_interpolation_points=5,
mask=None,
show_progressbar=None,
parallel=None):
"""Estimate the shifts in the current signal axis using
cross-correlation.
This method can only estimate the shift by comparing
unidimensional features that should not change the position in
the signal axis. To decrease the memory usage, the time of
computation and the accuracy of the results it is convenient to
select the feature of interest providing sensible values for
`start` and `end`. By default interpolation is used to obtain
subpixel precision.
Parameters
----------
start, end : int, float or None
The limits of the interval. If int they are taken as the
axis index. If float they are taken as the axis value.
reference_indices : tuple of ints or None
Defines the coordinates of the spectrum that will be used
as eference. If None the spectrum at the current
coordinates is used for this purpose.
max_shift : int
"Saturation limit" for the shift.
interpolate : bool
If True, interpolation is used to provide sub-pixel
accuracy.
number_of_interpolation_points : int
Number of interpolation points. Warning: making this number
too big can saturate the memory
mask : `BaseSignal` of bool.
It must have signal_dimension = 0 and navigation_shape equal to the
current signal. Where mask is True the shift is not computed
and set to nan.
%s
%s
Returns
-------
An array with the result of the estimation in the axis units. \
Although the computation is performed in batches if the signal is \
lazy, the result is computed in memory because it depends on the \
current state of the axes that could change later on in the workflow.
Raises
------
SignalDimensionError
If the signal dimension is not 1.
"""
if show_progressbar is None:
show_progressbar = preferences.General.show_progressbar
self._check_signal_dimension_equals_one()
ip = number_of_interpolation_points + 1
axis = self.axes_manager.signal_axes[0]
self._check_navigation_mask(mask)
# we compute for now
if isinstance(start, da.Array):
start = start.compute()
if isinstance(end, da.Array):
end = end.compute()
i1, i2 = axis._get_index(start), axis._get_index(end)
if reference_indices is None:
reference_indices = self.axes_manager.indices
ref = self.inav[reference_indices].data[i1:i2]
if interpolate is True:
ref = interpolate1D(ip, ref)
iterating_kwargs = ()
if mask is not None:
iterating_kwargs += (('mask', mask),)
shift_signal = self._map_iterate(
_estimate_shift1D,
iterating_kwargs=iterating_kwargs,
data_slice=slice(i1, i2),
ref=ref,
ip=ip,
interpolate=interpolate,
ragged=False,
parallel=parallel,
inplace=False,
show_progressbar=show_progressbar,)
shift_array = shift_signal.data
if max_shift is not None:
if interpolate is True:
max_shift *= ip
shift_array.clip(-max_shift, max_shift)
if interpolate is True:
shift_array = shift_array / ip
shift_array *= axis.scale
if self._lazy:
# We must compute right now because otherwise any changes to the
# axes_manager of the signal later in the workflow may result in
# a wrong shift_array
shift_array = shift_array.compute()
return shift_array
estimate_shift1D.__doc__ %= (SHOW_PROGRESSBAR_ARG, PARALLEL_ARG)
def align1D(self,
start=None,
end=None,
reference_indices=None,
max_shift=None,
interpolate=True,
number_of_interpolation_points=5,
interpolation_method='linear',
crop=True,
expand=False,
fill_value=np.nan,
also_align=None,
mask=None,
show_progressbar=None):
"""Estimate the shifts in the signal axis using
cross-correlation and use the estimation to align the data in place.
This method can only estimate the shift by comparing
unidimensional
features that should not change the position.
To decrease memory usage, time of computation and improve
accuracy it is convenient to select the feature of interest
setting the `start` and `end` keywords. By default interpolation is
used to obtain subpixel precision.
Parameters
----------
start, end : int, float or None
The limits of the interval. If int they are taken as the
axis index. If float they are taken as the axis value.
reference_indices : tuple of ints or None
Defines the coordinates of the spectrum that will be used
as eference. If None the spectrum at the current
coordinates is used for this purpose.
max_shift : int
"Saturation limit" for the shift.
interpolate : bool
If True, interpolation is used to provide sub-pixel
accuracy.
number_of_interpolation_points : int
Number of interpolation points. Warning: making this number
too big can saturate the memory
interpolation_method : str or int
Specifies the kind of interpolation as a string ('linear',
'nearest', 'zero', 'slinear', 'quadratic, 'cubic') or as an
integer specifying the order of the spline interpolator to
use.
%s
expand : bool
If True, the data will be expanded to fit all data after alignment.
Overrides `crop`.
fill_value : float
If crop is False fill the data outside of the original
interval with the given value where needed.
also_align : list of signals, None
A list of BaseSignal instances that has exactly the same
dimensions as this one and that will be aligned using the shift map
estimated using the this signal.
mask : `BaseSignal` or bool data type.
It must have signal_dimension = 0 and navigation_shape equal to the
current signal. Where mask is True the shift is not computed
and set to nan.
%s
Returns
-------
An array with the result of the estimation.
Raises
------
SignalDimensionError
If the signal dimension is not 1.
See also
--------
`estimate_shift1D`
"""
if also_align is None:
also_align = []
self._check_signal_dimension_equals_one()
if self._lazy:
_logger.warning('In order to properly expand, the lazy '
'reference signal will be read twice (once to '
'estimate shifts, and second time to shift '
'appropriatelly), which might take a long time. '
'Use expand=False to only pass through the data '
'once.')
shift_array = self.estimate_shift1D(
start=start,
end=end,
reference_indices=reference_indices,
max_shift=max_shift,
interpolate=interpolate,
number_of_interpolation_points=number_of_interpolation_points,
mask=mask,
show_progressbar=show_progressbar)
signals_to_shift = [self] + also_align
for signal in signals_to_shift:
signal.shift1D(shift_array=shift_array,
interpolation_method=interpolation_method,
crop=crop,
fill_value=fill_value,
expand=expand,
show_progressbar=show_progressbar)
align1D.__doc__ %= (CROP_PARAMETER_DOC, SHOW_PROGRESSBAR_ARG)
def integrate_in_range(self, signal_range='interactive',
display=True, toolkit=None):
"""Sums the spectrum over an energy range, giving the integrated
area.
The energy range can either be selected through a GUI or the command
line.
Parameters
----------
signal_range : a tuple of this form (l, r) or "interactive"
l and r are the left and right limits of the range. They can be
numbers or None, where None indicates the extremes of the interval.
If l and r are floats the `signal_range` will be in axis units (for
example eV). If l and r are integers the `signal_range` will be in
index units. When `signal_range` is "interactive" (default) the
range is selected using a GUI.
Returns
--------
integrated_spectrum : `BaseSignal` subclass
See Also
--------
`integrate_simpson`
Examples
--------
Using the GUI
>>> s = hs.signals.Signal1D(range(1000))
>>> s.integrate_in_range() #doctest: +SKIP
Using the CLI
>>> s_int = s.integrate_in_range(signal_range=(560,None))
Selecting a range in the axis units, by specifying the
signal range with floats.
>>> s_int = s.integrate_in_range(signal_range=(560.,590.))
Selecting a range using the index, by specifying the
signal range with integers.
>>> s_int = s.integrate_in_range(signal_range=(100,120))
"""
from hyperspy.misc.utils import deprecation_warning
msg = (
"The `Signal1D.integrate_in_range` method is deprecated and will "
"be removed in v2.0. Use a `roi.SpanRoi` followed by `integrate1D` "
"instead.")
deprecation_warning(msg)
signal_range = signal_range_from_roi(signal_range)
if signal_range == 'interactive':
self_copy = self.deepcopy()
ia = IntegrateArea(self_copy, signal_range)
ia.gui(display=display, toolkit=toolkit)
integrated_signal1D = self_copy
else:
integrated_signal1D = self._integrate_in_range_commandline(
signal_range)
return integrated_signal1D
def _integrate_in_range_commandline(self, signal_range):
signal_range = signal_range_from_roi(signal_range)
e1 = signal_range[0]
e2 = signal_range[1]
integrated_signal1D = self.isig[e1:e2].integrate1D(-1)
return integrated_signal1D
def calibrate(self, display=True, toolkit=None):
"""
Calibrate the spectral dimension using a gui.
It displays a window where the new calibration can be set by:
* setting the offset, units and scale directly
* selecting a range by dragging the mouse on the spectrum figure
and setting the new values for the given range limits
Parameters
----------
%s
%s
Notes
-----
For this method to work the output_dimension must be 1.
Raises
------
SignalDimensionError
If the signal dimension is not 1.
"""
self._check_signal_dimension_equals_one()
calibration = Signal1DCalibration(self)
return calibration.gui(display=display, toolkit=toolkit)
calibrate.__doc__ %= (DISPLAY_DT, TOOLKIT_DT)
def smooth_savitzky_golay(self,
polynomial_order=None,
window_length=None,
differential_order=0,
parallel=None, display=True, toolkit=None):
"""
Apply a Savitzky-Golay filter to the data in place.
If `polynomial_order` or `window_length` or `differential_order` are
None the method is run in interactive mode.
Parameters
----------
polynomial_order : int, optional
The order of the polynomial used to fit the samples.
`polyorder` must be less than `window_length`.
window_length : int, optional
The length of the filter window (i.e. the number of coefficients).
`window_length` must be a positive odd integer.
differential_order: int, optional
The order of the derivative to compute. This must be a
nonnegative integer. The default is 0, which means to filter
the data without differentiating.
%s
%s
%s
Notes
-----
More information about the filter in `scipy.signal.savgol_filter`.
"""
self._check_signal_dimension_equals_one()
if (polynomial_order is not None and
window_length is not None):
axis = self.axes_manager.signal_axes[0]
self.map(savgol_filter, window_length=window_length,
polyorder=polynomial_order, deriv=differential_order,
delta=axis.scale, ragged=False, parallel=parallel)
else:
# Interactive mode
smoother = SmoothingSavitzkyGolay(self)
smoother.differential_order = differential_order
if polynomial_order is not None:
smoother.polynomial_order = polynomial_order
if window_length is not None:
smoother.window_length = window_length
return smoother.gui(display=display, toolkit=toolkit)
smooth_savitzky_golay.__doc__ %= (PARALLEL_ARG, DISPLAY_DT, TOOLKIT_DT)
def smooth_lowess(self,
smoothing_parameter=None,
number_of_iterations=None,
show_progressbar=None,
parallel=None, display=True, toolkit=None):
"""
Lowess data smoothing in place.
If `smoothing_parameter` or `number_of_iterations` are None the method
is run in interactive mode.
Parameters
----------
smoothing_parameter: float or None
Between 0 and 1. The fraction of the data used
when estimating each y-value.
number_of_iterations: int or None
The number of residual-based reweightings
to perform.
%s
%s
%s
%s
Raises
------
SignalDimensionError
If the signal dimension is not 1.
ImportError
If statsmodels is not installed.
Notes
-----
This method uses the lowess algorithm from the `statsmodels` library,
which needs to be installed to use this method.
"""
if not statsmodels_installed:
raise ImportError("statsmodels is not installed. This package is "
"required for this feature.")
self._check_signal_dimension_equals_one()
if smoothing_parameter is None or number_of_iterations is None:
smoother = SmoothingLowess(self)
if smoothing_parameter is not None:
smoother.smoothing_parameter = smoothing_parameter
if number_of_iterations is not None:
smoother.number_of_iterations = number_of_iterations
return smoother.gui(display=display, toolkit=toolkit)
else:
self.map(lowess,
exog=self.axes_manager[-1].axis,
frac=smoothing_parameter,
it=number_of_iterations,
is_sorted=True,
return_sorted=False,
show_progressbar=show_progressbar,
ragged=False,
parallel=parallel)
smooth_lowess.__doc__ %= (SHOW_PROGRESSBAR_ARG, PARALLEL_ARG, DISPLAY_DT,
TOOLKIT_DT)
def smooth_tv(self, smoothing_parameter=None, show_progressbar=None,
parallel=None, display=True, toolkit=None):
"""
Total variation data smoothing in place.
Parameters
----------
smoothing_parameter: float or None
Denoising weight relative to L2 minimization. If None the method
is run in interactive mode.
%s
%s
%s
%s
Raises
------
SignalDimensionError
If the signal dimension is not 1.
"""
self._check_signal_dimension_equals_one()
if smoothing_parameter is None:
smoother = SmoothingTV(self)
return smoother.gui(display=display, toolkit=toolkit)
else:
self.map(_tv_denoise_1d, weight=smoothing_parameter,
ragged=False,
show_progressbar=show_progressbar,
parallel=parallel)