diff --git a/LICENSE b/LICENSE
index b743e04..261eeb9 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,201 @@
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diff --git a/README.rst b/README.rst
index 4f05db7..716bbc9 100644
--- a/README.rst
+++ b/README.rst
@@ -11,8 +11,8 @@ photoplethysmography, or electrocardiogram (ECG/EKG) monitors.
:target: https://codecov.io/gh/rmarkello/peakdet
.. image:: https://readthedocs.org/projects/peakdet/badge/?version=latest
:target: http://peakdet.readthedocs.io/en/latest
-.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
- :target: https://www.gnu.org/licenses/gpl-3.0
+.. image:: https://img.shields.io/badge/license-Apache%202-blue.svg
+ :target: http://www.apache.org/licenses/LICENSE-2.0
.. _overview:
@@ -55,31 +55,12 @@ anything you might like to work on. Alternatively, if you've found a bug, are
experiencing a problem, or have a question, create a new issue with some
information about it!
-.. _acknowledgments:
-
-Acknowledgments
----------------
-
-While this package was initially created in 2016, some of the behind-the-scenes
-functions in the project were adopted from the `PhysIO `_ MATLAB toolbox. As such,
-if you use this code it would be good to (1) provide a link back to the
-``peakdet`` repository with the version of the code used, and (2) cite `their
-paper `_
-([1]_):
-
-.. [1] Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J.,
- Iglesias, S., ... & Stephan, K. E. (2017). The PhysIO toolbox for modeling
- physiological noise in fMRI data. Journal of Neuroscience Methods, 276,
- 56-72.
-
.. _licensing:
License Information
-------------------
-This codebase is licensed under the GNU General Public License version 3.
-The full license can be found in the `LICENSE `_ file in the ``peakdet`` distribution.
-
-All trademarks referenced herein are property of their respective holders.
+This codebase is licensed under the Apache License, Version 2.0. The full
+license can be found in the `LICENSE `_ file in the ``peakdet`` distribution. You may also
+obtain a copy of the license at: http://www.apache.org/licenses/LICENSE-2.0.
diff --git a/docs/conf.py b/docs/conf.py
index ea0f0d0..3242e78 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -101,7 +101,6 @@
'matplotlib': ('https://matplotlib.org', None),
'numpy': ('https://docs.scipy.org/doc/numpy', None),
'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
- 'sklearn': ('http://scikit-learn.org/stable', None),
}
doctest_global_setup = """
diff --git a/docs/user_guide/loading.rst b/docs/user_guide/loading.rst
index 2a2e8b8..705c4fc 100644
--- a/docs/user_guide/loading.rst
+++ b/docs/user_guide/loading.rst
@@ -33,7 +33,7 @@ This way, the loading of the data is retained in the object's history:
.. doctest::
>>> ecg.history
- [('load_physio', {'data': 'ECG.csv', 'dtype': None, 'fs': 1000.0, 'history': None})]
+ [('load_physio', {'allow_pickle': False, 'data': 'ECG.csv', 'dtype': None, 'fs': 1000.0, 'history': None})]
There are also a number of functions for loading data from "standard" formats.
For example, if your data were collected using the `rtpeaks >> from peakdet import HRV
>>> metrics = HRV(data)
>>> print(f'{metrics.rmssd:.2f} ms')
- 27.75 ms
+ 26.61 ms
The :py:class:`~.HRV` class contains many common heart rate variability metrics
including the root mean square of successive differences, as shown above. It
diff --git a/docs/user_guide/physio.rst b/docs/user_guide/physio.rst
index b1d5c0a..e15af86 100644
--- a/docs/user_guide/physio.rst
+++ b/docs/user_guide/physio.rst
@@ -68,9 +68,9 @@ store the indices of the detected :py:attr:`~.Physio.peaks` and
>>> phys = operations.peakfind_physio(phys)
>>> phys.peaks
- array([ 477, 2120, 3253, 4128])
+ array([ 477, 2120, 3253])
>>> phys.troughs
- array([1413, 2611, 3756])
+ array([1413, 2611])
Next, we'll move on to how you can load your data into a :py:class:`~.Physio`
object in a more reproducible manner. Feel free to refer to the :ref:`api_ref`
diff --git a/docs/user_guide/saving.rst b/docs/user_guide/saving.rst
index d2086e7..506513a 100644
--- a/docs/user_guide/saving.rst
+++ b/docs/user_guide/saving.rst
@@ -31,13 +31,16 @@ If later on you want to reload the processed data you can do so:
.. doctest::
>>> from peakdet import load_physio
- >>> data = load_physio('out.phys')
+ >>> data = load_physio('out.phys', allow_pickle=True)
-.. note::
+.. warning::
:py:func:`peakdet.save_physio` uses :py:func:`numpy.savez` to save the data
objects, meaning the generated files can actually be quite a bit larger
- than the original data, themselves!
+ than the original data, themselves! Moreover, you NEED to pass the
+ ``allow_pickle=True`` parameter; this is turned off by default for safety
+ reasons, as you should never load pickle files not generated by a trusted
+ source.
Saving history
^^^^^^^^^^^^^^
diff --git a/peakdet/editor.py b/peakdet/editor.py
index 293c957..49358fa 100644
--- a/peakdet/editor.py
+++ b/peakdet/editor.py
@@ -112,19 +112,18 @@ def undo(self):
func, peaks = self.data._history.pop()
if func == 'reject_peaks':
- self.data._metadata.reject = np.setdiff1d(
- self.data._metadata.reject, peaks['remove']
+ self.data._metadata['reject'] = np.setdiff1d(
+ self.data._metadata['reject'], peaks['remove']
)
self.rejected.difference_update(peaks['remove'])
elif func == 'delete_peaks':
- self.data._metadata.peaks = np.insert(
- self.data._metadata.peaks,
- np.searchsorted(self.data._metadata.peaks, peaks['remove']),
+ self.data._metadata['peaks'] = np.insert(
+ self.data._metadata['peaks'],
+ np.searchsorted(self.data._metadata['peaks'], peaks['remove']),
peaks['remove']
)
self.deleted.difference_update(peaks['remove'])
- self.data._metadata.troughs = utils.check_troughs(self.data,
- self.data.peaks,
- self.data.troughs)
+ self.data._metadata['troughs'] = utils.check_troughs(self.data,
+ self.data.peaks)
self.plot_signals()
diff --git a/peakdet/info.py b/peakdet/info.py
index b268158..d48af12 100644
--- a/peakdet/info.py
+++ b/peakdet/info.py
@@ -5,7 +5,7 @@
MAINTAINER = 'Ross Markello'
EMAIL = 'rossmarkello@gmail.com'
VERSION = __version__
-LICENSE = 'GPLv3'
+LICENSE = 'Apache-2.0'
DESCRIPTION = """\
A toolbox for reproducible physiological data analysis\
"""
@@ -18,7 +18,6 @@
INSTALL_REQUIRES = [
'matplotlib',
'numpy',
- 'scikit-learn',
'scipy',
]
@@ -49,6 +48,6 @@
CLASSIFIERS = [
'Development Status :: 3 - Alpha',
'Intended Audience :: Science/Research',
- 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
+ 'License :: OSI Approved :: Apache Software License',
'Programming Language :: Python :: 3.6',
]
diff --git a/peakdet/io.py b/peakdet/io.py
index 0868ac7..b3f0fc5 100644
--- a/peakdet/io.py
+++ b/peakdet/io.py
@@ -12,7 +12,8 @@
EXPECTED = ['data', 'fs', 'history', 'metadata']
-def load_physio(data, *, fs=None, dtype=None, history=None):
+def load_physio(data, *, fs=None, dtype=None, history=None,
+ allow_pickle=False):
"""
Returns `Physio` object with provided data
@@ -26,6 +27,9 @@ def load_physio(data, *, fs=None, dtype=None, history=None):
Data type to convert `data` to, if conversion needed. Default: None
history : list of tuples, optional
Functions that have been performed on `data`. Default: None
+ allow_pickle : bool, optional
+ Whether to allow loading if `data` contains pickled objects. Default:
+ False
Returns
-------
@@ -42,7 +46,7 @@ def load_physio(data, *, fs=None, dtype=None, history=None):
# load it as a plain text file and instantiate a history
if isinstance(data, str):
try:
- inp = dict(np.load(data))
+ inp = dict(np.load(data, allow_pickle=allow_pickle))
for attr in EXPECTED:
try:
inp[attr] = inp[attr].dtype.type(inp[attr])
@@ -53,7 +57,7 @@ def load_physio(data, *, fs=None, dtype=None, history=None):
# fix history, which needs to be list-of-tuple
if inp['history'] is not None:
inp['history'] = list(map(tuple, inp['history']))
- except IOError:
+ except (IOError, OSError, ValueError):
inp = dict(data=np.loadtxt(data),
history=[utils._get_call(exclude=[])])
phys = physio.Physio(**inp)
@@ -86,33 +90,33 @@ def load_physio(data, *, fs=None, dtype=None, history=None):
return phys
-def save_physio(file, data):
+def save_physio(fname, data):
"""
Saves `data` to `fname`
Parameters
----------
- file : str
+ fname : str
Path to output file; .phys will be appended if necessary
data : Physio_like
Data to be saved to file
Returns
-------
- file : str
+ fname : str
Full filepath to saved output
"""
from peakdet.utils import check_physio
data = check_physio(data)
- file += '.phys' if not file.endswith('.phys') else ''
- with open(file, 'wb') as dest:
+ fname += '.phys' if not fname.endswith('.phys') else ''
+ with open(fname, 'wb') as dest:
hist = data.history if data.history != [] else None
np.savez_compressed(dest, data=data.data, fs=data.fs,
history=hist, metadata=data._metadata)
- return file
+ return fname
def load_history(file, verbose=False):
diff --git a/peakdet/modalities.py b/peakdet/modalities.py
index ac71c20..828ef82 100644
--- a/peakdet/modalities.py
+++ b/peakdet/modalities.py
@@ -12,14 +12,14 @@ def iHR(self, step=1, start=0, end=None, TR=None):
time = np.arange(start - mod, end + mod + 1, self.TR, dtype='int')
HR = np.zeros(len(time) - step)
- for l in range(step, time.size):
- inds = np.logical_and(self.rrtime >= time[l - step],
- self.rrtime < time[l])
+ for tpoint in range(step, time.size):
+ inds = np.logical_and(self.rrtime >= time[tpoint - step],
+ self.rrtime < time[tpoint])
relevant = self.rrint[inds]
if relevant.size == 0:
continue
- HR[l - step] = (60 / relevant).mean()
+ HR[tpoint - step] = (60 / relevant).mean()
return HR
diff --git a/peakdet/operations.py b/peakdet/operations.py
index 0aa7ca6..2563947 100644
--- a/peakdet/operations.py
+++ b/peakdet/operations.py
@@ -5,8 +5,7 @@
import matplotlib.pyplot as plt
import numpy as np
-from scipy import signal
-from scipy.interpolate import InterpolatedUnivariateSpline
+from scipy import interpolate, signal
from peakdet import editor, utils
@@ -63,7 +62,7 @@ def filter_physio(data, cutoffs, method, *, order=3):
@utils.make_operation()
-def interpolate_physio(data, target_fs):
+def interpolate_physio(data, target_fs, *, kind='cubic'):
"""
Interpolates `data` to desired sampling rate `target_fs`
@@ -73,6 +72,9 @@ def interpolate_physio(data, target_fs):
Input physiological data to be interpolated
target_fs : float
Desired sampling rate for `data`
+ kind : str or int, optional
+ Type of interpolation to perform. Must be one of available kinds in
+ :func:`scipy.interpolate.interp1d`. Default: 'cubic'
Returns
-------
@@ -89,7 +91,7 @@ def interpolate_physio(data, target_fs):
t_new = np.linspace(0, len(data) / data.fs, int(len(data) * factor))
# interpolate data and generate new Physio object
- interp = InterpolatedUnivariateSpline(t_orig, data[:])(t_new)
+ interp = interpolate.interp1d(t_orig, data, kind=kind)(t_new)
interp = utils.new_physio_like(data, interp, fs=target_fs)
return interp
@@ -122,12 +124,16 @@ def peakfind_physio(data, *, thresh=0.2, dist=None):
# first pass peak detection to get approximate distance between peaks
cdist = data.fs // 4 if dist is None else dist
- locs = utils.find_peaks(data, dist=cdist, thresh=thresh)
- cdist = np.diff(locs).mean() // 2
+ thresh = np.squeeze(np.diff(np.percentile(data, [5, 95]))) * thresh
+ locs, heights = signal.find_peaks(data[:], distance=cdist, height=thresh)
+
# second, more thorough peak detection
- data._metadata.peaks = utils.find_peaks(data, dist=cdist, thresh=thresh)
+ cdist = np.diff(locs).mean() // 2
+ heights = np.percentile(heights['peak_heights'], 1)
+ locs, heights = signal.find_peaks(data[:], distance=cdist, height=heights)
+ data._metadata['peaks'] = locs
# perform trough detection based on detected peaks
- data._metadata.troughs = utils.check_troughs(data, data.peaks, [])
+ data._metadata['troughs'] = utils.check_troughs(data, data.peaks)
return data
@@ -148,9 +154,8 @@ def delete_peaks(data, remove):
"""
data = utils.check_physio(data, ensure_fs=False, copy=True)
- data._metadata.peaks = np.setdiff1d(data._metadata.peaks, remove)
- data._metadata.troughs = utils.check_troughs(data, data.peaks,
- data.troughs)
+ data._metadata['peaks'] = np.setdiff1d(data._metadata['peaks'], remove)
+ data._metadata['troughs'] = utils.check_troughs(data, data.peaks)
return data
@@ -171,9 +176,8 @@ def reject_peaks(data, remove):
"""
data = utils.check_physio(data, ensure_fs=False, copy=True)
- data._metadata.reject = np.append(data._metadata.reject, remove)
- data._metadata.troughs = utils.check_troughs(data, data.peaks,
- data.troughs)
+ data._metadata['reject'] = np.append(data._metadata['reject'], remove)
+ data._metadata['troughs'] = utils.check_troughs(data, data.peaks)
return data
diff --git a/peakdet/physio.py b/peakdet/physio.py
index 5bcddcb..a5e4cec 100644
--- a/peakdet/physio.py
+++ b/peakdet/physio.py
@@ -4,7 +4,6 @@
"""
import numpy as np
-from sklearn.utils import Bunch
class Physio():
@@ -63,11 +62,11 @@ def __init__(self, data, fs=None, history=None, metadata=None):
except TypeError:
raise TypeError('Provided metadata must be dict-like'
'with integer array entries.')
- self._metadata = Bunch(**metadata)
+ self._metadata = dict(**metadata)
else:
- self._metadata = Bunch(peaks=np.empty(0, dtype=int),
- troughs=np.empty(0, dtype=int),
- reject=np.empty(0, dtype=int))
+ self._metadata = dict(peaks=np.empty(0, dtype=int),
+ troughs=np.empty(0, dtype=int),
+ reject=np.empty(0, dtype=int))
def __array__(self):
return self.data
@@ -110,10 +109,10 @@ def peaks(self):
@property
def troughs(self):
""" Indices of detected troughs in `data` """
- return self._metadata.troughs
+ return self._metadata['troughs']
@property
def _masked(self):
- return np.ma.masked_array(self._metadata.peaks,
- mask=np.isin(self._metadata.peaks,
- self._metadata.reject))
+ return np.ma.masked_array(self._metadata['peaks'],
+ mask=np.isin(self._metadata['peaks'],
+ self._metadata['reject']))
diff --git a/peakdet/tests/data/ECG.phys b/peakdet/tests/data/ECG.phys
index 5ddd3b0..3a61be3 100644
Binary files a/peakdet/tests/data/ECG.phys and b/peakdet/tests/data/ECG.phys differ
diff --git a/peakdet/tests/test_editor.py b/peakdet/tests/test_editor.py
index 95d292c..d3dee6d 100644
--- a/peakdet/tests/test_editor.py
+++ b/peakdet/tests/test_editor.py
@@ -1,31 +1,41 @@
# -*- coding: utf-8 -*-
+from collections import namedtuple
+
import pytest
-from sklearn.utils import Bunch
+
from peakdet import editor
from peakdet.tests.utils import get_peak_data
-PEAKS = get_peak_data()
+
+wheel = namedtuple('wheel', ('step'))
+key = namedtuple('key', ('key',))
def test_PhysioEditor():
edits = editor._PhysioEditor(get_peak_data())
+
# test scroll functionality
- edits.on_wheel(Bunch(step=10))
+ edits.on_wheel(wheel(10))
+
# test reject / delete functionality
for m in range(2):
edits.on_remove(0, 10, reject=True)
edits.on_remove(10, 20, reject=False)
+
# undo delete + reject
for m in range(2):
edits.undo()
+
# test key undo (and undo when history doesn't exist)
- edits.on_key(Bunch(key='ctrl+z'))
+ edits.on_key(key('ctrl+z'))
+
# redo so that there is history on quit
edits.on_remove(0, 10, reject=True)
edits.on_remove(10, 20, reject=False)
+
# quit editor and clean up edits
- edits.on_key(Bunch(key='ctrl+q'))
+ edits.on_key(key('ctrl+z'))
with pytest.raises(TypeError):
editor._PhysioEditor([0, 1, 2])
diff --git a/peakdet/tests/test_io.py b/peakdet/tests/test_io.py
index 8896229..db02ebd 100644
--- a/peakdet/tests/test_io.py
+++ b/peakdet/tests/test_io.py
@@ -10,19 +10,22 @@
def test_load_physio():
# try loading pickle file (from io.save_physio)
- pckl = io.load_physio(get_test_data_path('ECG.phys'))
+ pckl = io.load_physio(get_test_data_path('ECG.phys'), allow_pickle=True)
assert isinstance(pckl, physio.Physio)
assert pckl.data.size == 44611
assert pckl.fs == 1000.
with pytest.warns(UserWarning):
- pckl = io.load_physio(get_test_data_path('ECG.phys'), fs=500.)
+ pckl = io.load_physio(get_test_data_path('ECG.phys'), fs=500.,
+ allow_pickle=True)
assert pckl.fs == 500.
+
# try loading CSV file
csv = io.load_physio(get_test_data_path('ECG.csv'))
assert isinstance(csv, physio.Physio)
assert np.allclose(csv, pckl)
assert np.isnan(csv.fs)
assert csv.history[0][0] == 'load_physio'
+
# try loading array
with pytest.warns(UserWarning):
arr = io.load_physio(np.loadtxt(get_test_data_path('ECG.csv')))
@@ -30,6 +33,7 @@ def test_load_physio():
arr = io.load_physio(np.loadtxt(get_test_data_path('ECG.csv')),
history=[('np.loadtxt', {'fname': 'ECG.csv'})])
assert isinstance(arr, physio.Physio)
+
# try loading physio object (and resetting dtype)
out = io.load_physio(arr, dtype='float32')
assert out.data.dtype == np.dtype('float32')
@@ -40,10 +44,10 @@ def test_load_physio():
def test_save_physio(tmpdir):
- pckl = io.load_physio(get_test_data_path('ECG.phys'))
+ pckl = io.load_physio(get_test_data_path('ECG.phys'), allow_pickle=True)
out = io.save_physio(tmpdir.join('tmp').purebasename, pckl)
assert os.path.exists(out)
- assert isinstance(io.load_physio(out), physio.Physio)
+ assert isinstance(io.load_physio(out, allow_pickle=True), physio.Physio)
def test_load_history(tmpdir):
diff --git a/peakdet/tests/test_operations.py b/peakdet/tests/test_operations.py
index 8060cd3..4257e53 100644
--- a/peakdet/tests/test_operations.py
+++ b/peakdet/tests/test_operations.py
@@ -67,14 +67,14 @@ def test_peakfind_physio():
def test_delete_peaks():
- to_delete = [192, 24685, 44169]
+ to_delete = [24685, 44169]
peaks = operations.peakfind_physio(WITHFS)
deleted = operations.delete_peaks(peaks, to_delete)
assert len(deleted.peaks) == len(peaks.peaks) - len(to_delete)
def test_reject_peaks():
- to_reject = [192, 24685, 44169]
+ to_reject = [24685, 44169]
peaks = operations.peakfind_physio(WITHFS)
rejected = operations.reject_peaks(peaks, to_reject)
assert len(rejected.peaks) == len(peaks.peaks) - len(to_reject)
diff --git a/peakdet/tests/test_utils.py b/peakdet/tests/test_utils.py
index 1d859f5..fa15da7 100644
--- a/peakdet/tests/test_utils.py
+++ b/peakdet/tests/test_utils.py
@@ -66,7 +66,7 @@ def test_new_physio_like():
fname = testutils.get_test_data_path('ECG.csv')
data = physio.Physio(np.loadtxt(fname), fs=1000.)
data._history = [('does history', 'copy?')]
- data._metadata.peaks = np.array([1, 2, 3])
+ data._metadata['peaks'] = np.array([1, 2, 3])
# assert all copies happen by default
new_data = utils.new_physio_like(data, data[:])
assert np.allclose(data, utils.new_physio_like(data, data[:]))
@@ -85,85 +85,6 @@ def test_new_physio_like():
assert v.size == 0
-def test_get_extrema():
- # check that peak detection works
- assert_array_equal(utils.get_extrema(DATA), PEAKS)
- # check that threshold modulates detection
- assert_array_equal(utils.get_extrema(DATA, thresh=0.6), np.array([]))
- # check that trough detection works, too
- assert_array_equal(utils.get_extrema(DATA, False), TROUGHS)
- # check threshold bounds
- with pytest.raises(ValueError):
- utils.get_extrema(DATA, thresh=-0.1)
- with pytest.raises(ValueError):
- utils.get_extrema(DATA, thresh=1.1)
-
-
-def test_min_peak_dist():
- # test peaks w/ and w/o removal due to `dist`
- assert_array_equal(utils.min_peak_dist(DATA, PEAKS, dist=10),
- PEAKS)
- assert_array_equal(utils.min_peak_dist(DATA, PEAKS, dist=20),
- np.array([3, 28]))
- # test troughs w/ and w/o removal due to `dist`
- assert_array_equal(utils.min_peak_dist(DATA, TROUGHS, False, dist=10),
- TROUGHS)
- assert_array_equal(utils.min_peak_dist(DATA, TROUGHS, False, dist=20),
- np.array([9, 34]))
-
-
-def test_find_peaks():
- # check that `dist` parameter modulates detected peaks
- assert_array_equal(utils.find_peaks(DATA, dist=10), PEAKS)
- assert_array_equal(utils.find_peaks(DATA, dist=20), np.array([3, 28]))
- # ensure returned array is of correct type
- assert utils.find_peaks(DATA).dtype == np.int64
-
-
-def test_find_troughs():
- # check that `dist` parameter modulates detected troughs
- assert_array_equal(utils.find_troughs(DATA, dist=10), TROUGHS)
- assert_array_equal(utils.find_troughs(DATA, dist=20), np.array([9, 34]))
- # ensure returned array is of correct type
- assert utils.find_peaks(DATA).dtype == np.int64
-
-
def test_check_troughs():
true = np.array([9, 21])
- # check that func fills in when no troughs provided
- assert_array_equal(utils.check_troughs(DATA, PEAKS, []), true)
- # check that func disregard troughs outside of peak bounds
- assert_array_equal(utils.check_troughs(DATA, PEAKS, TROUGHS), true)
- # check that func removes when two points are "troughs" inside peaks
- assert_array_equal(utils.check_troughs(DATA, PEAKS, np.array([9, 10, 21])),
- true)
-
-
-def test_corr():
- x = np.random.rand(10, 1)
- # works both as 2D and 1D vectors (i.e., squeezeable)
- assert np.allclose(utils.corr(x, x), 1)
- assert np.allclose(utils.corr(x.squeeze(), x.squeeze()), 1)
- # error raised when inputs aren't the same size
- with pytest.raises(ValueError):
- utils.corr(x, np.random.rand(9, 1))
- # error raised when one of the inputs is not squeezeable
- with pytest.raises(ValueError):
- utils.corr(x, np.random.rand(10, 2))
- # zscore only first
- utils.corr(x, x, zscored=[False, True])
- # zscore only second
- utils.corr(x, x, zscored=[True, False])
- # zscore both
- utils.corr(x, x, zscored=[True, True])
-
-
-def test_gen_temp():
- peaks = testutils.get_peak_data()
- utils.gen_temp(peaks.data, peaks.peaks)
-
-
-def test_corr_template():
- peaks = testutils.get_peak_data()
- temp = utils.gen_temp(peaks.data, peaks.peaks)
- assert utils.corr_template(temp).size == temp.shape[-1]
+ assert_array_equal(utils.check_troughs(DATA, PEAKS), true)
diff --git a/peakdet/utils.py b/peakdet/utils.py
index 6588468..578b3f7 100644
--- a/peakdet/utils.py
+++ b/peakdet/utils.py
@@ -7,8 +7,6 @@
from functools import wraps
import inspect
import numpy as np
-from scipy.stats import zscore
-from sklearn.utils import Bunch
from peakdet import physio
@@ -177,7 +175,7 @@ def new_physio_like(ref_physio, data, *, fs=None, dtype=None,
if dtype is None:
dtype = ref_physio.data.dtype
history = list(ref_physio.history) if copy_history else []
- metadata = Bunch(**ref_physio._metadata) if copy_metadata else None
+ metadata = dict(**ref_physio._metadata) if copy_metadata else None
# make new class
out = ref_physio.__class__(np.array(data, dtype=dtype),
@@ -185,140 +183,7 @@ def new_physio_like(ref_physio, data, *, fs=None, dtype=None,
return out
-def get_extrema(data, peaks=True, thresh=0.4):
- """
- Find extrema in `data` by changes in sign of first derivative
-
- Parameters
- ----------
- data : array_like or Physio_like
- Input data on which to perform extrema detection
- peaks : bool, optional
- Whether to look for peaks instead of troughs. Default: True
- thresh : float (0, 1), optional
- Amplitude based threshold
-
- Returns
- -------
- locs : np.ndarray
- Indices of extrema from `data`
- """
-
- if thresh < 0 or thresh > 1:
- raise ValueError('Provided threshold {} is not in range [0, 1]. '
- 'Please provide a valid threshold.'
- .format(thresh))
-
- if peaks:
- uthresh = (thresh * np.diff(np.percentile(data, [5, 95])))
- Indx = np.argwhere(data > uthresh).squeeze()
- else:
- uthresh = (thresh * np.diff(np.percentile(data, [95, 5])))
- Indx = np.argwhere(data < uthresh).squeeze()
-
- trend = np.sign(np.diff(data))
- idx = np.argwhere(trend == 0).squeeze()
-
- for i in range(idx.size - 1, -1, -1):
- gtz = trend[min(idx[i] + 1, trend.size - 1)] >= 0
- trend[idx[i]] = 1 if gtz else -1
-
- idx = np.argwhere(np.diff(trend) == (-2 if peaks else 2)).squeeze() + 1
-
- return np.intersect1d(Indx, idx)
-
-
-def min_peak_dist(data, locs, peaks=True, dist=250):
- """
- Ensures `locs` in `data` are separated by at least `dist`
-
- Parameters
- ----------
- data : array_like or Physio_like
- Input data for which `locs` were detected
- locs : array_like
- Extrema in `data`, typically from `get_extrema()`
- peaks : bool, optional
- Whether to look for peaks instead of troughs. Default: True
- dist : int, optional
- Minimum required distance (in datapoints) b/w `locs`. Default: 250
-
- Returns
- -------
- locs : np.ndarray
- Extrema separated by at least `dist`
- """
-
- if not any(np.diff(sorted(locs)) <= dist):
- return locs
-
- idx = data[locs].argsort()
- if peaks:
- idx = idx[::-1]
- locs = locs[idx]
- idelete = np.zeros_like(locs, dtype=bool)
-
- for i in range(locs.size):
- if not idelete[i]:
- dist_diff = np.logical_and(locs >= locs[i] - dist,
- locs <= locs[i] + dist)
- idelete = np.logical_or(idelete, dist_diff)
- idelete[i] = 0
-
- return np.sort(locs[~idelete])
-
-
-def find_peaks(data, thresh=0.4, dist=250):
- """
- Finds peaks in `data`
-
- Parameters
- ----------
- data : array_like or Physio_like
- Input data on which to perform peak detection
- thresh : float (0, 1), optional
- Amplitude based threshold
- dist : int
- Minimum required distance (in datapoints) b/w peaks in `data`
-
- Returns
- -------
- peaks : np.ndarray
- Indices of peak locations in `data`
- """
-
- extrema = get_extrema(data, thresh=thresh)
- peaks = min_peak_dist(data, extrema, dist=dist)
-
- return peaks.astype(int)
-
-
-def find_troughs(data, thresh=0.4, dist=250):
- """
- Finds troughs in `data`
-
- Parameters
- ----------
- data : array_like or Physio_like
- Input data on which to perform trough detection
- thresh : float (0, 1), optional
- Amplitude based threshold
- dist : int
- Minimum required distance (in datapoints) b/w troughs in `data`
-
- Returns
- -------
- troughs : np.ndarray
- Indices of trough locations in `data`
- """
-
- extrema = get_extrema(data, peaks=False, thresh=thresh)
- troughs = min_peak_dist(data, extrema, peaks=False, dist=dist)
-
- return troughs.astype(int)
-
-
-def check_troughs(data, peaks, troughs):
+def check_troughs(data, peaks):
"""
Confirms that `troughs` exists between every set of `peaks` in `data`
@@ -328,8 +193,6 @@ def check_troughs(data, peaks, troughs):
Input data for which `troughs` and `peaks` were detected
peaks : array-like
Indices of suspected peak locations in `data`
- troughs : array-like
- Indices of suspected trough locations in `data`
Returns
-------
@@ -340,121 +203,8 @@ def check_troughs(data, peaks, troughs):
all_troughs = np.zeros(peaks.size - 1, dtype=int)
for f in range(peaks.size - 1):
- curr = np.logical_and(troughs > peaks[f],
- troughs < peaks[f + 1])
- if not np.any(curr):
- dp = data[peaks[f]:peaks[f + 1]]
- idx = peaks[f] + np.argwhere(dp == dp.min())[0]
- else:
- idx = troughs[curr]
- if idx.size > 1:
- idx = idx[0]
+ dp = data[peaks[f]:peaks[f + 1]]
+ idx = peaks[f] + np.argwhere(dp == dp.min())[0]
all_troughs[f] = idx
return all_troughs
-
-
-def gen_temp(data, locs, factor=0.5):
- """
- Generate waveform template array from `data`
-
- Waveforms are taken from around peak locations in `locs`
-
- Parameters
- ----------
- data : array_like
- locs : arrray_like
- Indices of suspected peak locations
- factor: float (0, 1), optional
-
- Returns
- -------
- array : peak waveforms
- """
-
- avgrate = round(np.diff(locs).mean())
- THW = int(np.ceil(factor * (avgrate / 2)))
- nsamptemp = (THW * 2) + 1
- npulse = locs.size
- template = np.zeros([npulse - 2, nsamptemp])
-
- for n in range(1, npulse - 1):
- template[n - 1] = data[locs[n] - THW:locs[n] + THW + 1]
- template[n - 1] = template[n - 1] - template[n - 1].mean()
- template[n - 1] = template[n - 1] / max(abs(template[n - 1]))
-
- return template
-
-
-def corr(x, y, zscored=[False, False]):
- """
- Potentially faster correlation of `x` and `y`
-
- Will z-transform data before correlation.
-
- Parameters
- ----------
- x : array, n x 1
- y : array, n x 1
- zscored : [bool, bool]
- Whether x and y, respectively, have been z-transformed
-
- Returns
- -------
- float : [0,1] correlation between `x` and `y`
- """
-
- x, y = np.asarray(x).squeeze(), np.asarray(y).squeeze()
-
- if x.ndim > 1 or y.ndim > 1:
- raise ValueError('Input arrays must have only one dimension.')
- if x.size != y.size:
- raise ValueError('Input array dimensions must be same size.')
-
- # numpy corrcoef is faster if both variables need to be z-scored
- if not np.any(zscored):
- return np.corrcoef(x, y)[0, 1]
-
- if not zscored[0]:
- x = zscore(x, ddof=1)
- if not zscored[1]:
- y = zscore(y, ddof=1)
-
- return np.dot(x.T, y) * (1. / (x.size - 1))
-
-
-def corr_template(temp, sim=0.95):
- """
- Generates single waveform template from output of `gen_temp`.
-
- Correlates each row of `temp` to averaged template and selects rows with
- correlation >=`sim` for use in final, averaged template.
-
- Parameters
- ----------
- temp : array of waveforms
- sim : float (0,1)
- Cutoff for correlation of waveforms to average template
-
- Returns
- -------
- array : template waveform
- """
-
- npulse = temp.shape[0]
-
- mean_temp = zscore(temp.mean(axis=0), ddof=1)
- sim_to_temp = np.zeros((temp.shape[0], 1))
-
- for n in range(temp.shape[0]):
- sim_to_temp[n] = corr(temp[n], mean_temp, [False, True])
-
- good_temp_ind = np.where(sim_to_temp > sim)[0]
- if good_temp_ind.shape[0] >= np.ceil(npulse * 0.1):
- clean_temp = temp[good_temp_ind]
- else:
- comp = 1 - np.ceil(npulse * 0.1) / npulse
- new_temp_ind = np.where(sim_to_temp > comp)[0]
- clean_temp = np.atleast_2d(temp[new_temp_ind]).T
-
- return clean_temp.mean(axis=0)
diff --git a/requirements.txt b/requirements.txt
index a3f358f..74fa65e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,3 @@
matplotlib
numpy
-scikit-learn
scipy