BasicPulseExtractor.ungated_conv_deriv() - Code Metrics - Inspection of "Pulsed 3.0" - Ulm-IQO/qudi - Measure and Improve Code Quality continuously with Scrutinizer

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created 2018-06-26 12:52 UTC

BasicPulseExtractor.ungated_conv_deriv() F

↳ Parent: BasicPulseExtractor

Complexity

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Size

Total Lines

170

Duplication

Lines	5
Ratio	2.94 %

Importance

Changes	2
Bugs	0	Features	0

Metric	Value
cc	20
dl	5
loc	170
rs	0
c	2
b	0
f	0

How to fix Long Method Complexity

# -*- coding: utf-8 -*-
"""
This file contains basic pulse extraction methods for Qudi.

Qudi 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.

Qudi 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 Qudi. If not, see <http://www.gnu.org/licenses/>.

Copyright (c) the Qudi Developers. See the COPYRIGHT.txt file at the
top-level directory of this distribution and at <https://github.com/Ulm-IQO/qudi/>
"""

import numpy as np
from scipy import ndimage

from logic.pulsed.pulse_extractor import PulseExtractorBase


class BasicPulseExtractor(PulseExtractorBase):
    """

    """
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def gated_conv_deriv(self, count_data, conv_std_dev=20.0):
        """
        Detects the rising flank in the gated timetrace data and extracts just the laser pulses.
        The flank detection is based on an image edge detection technique performed in 1D.
        There is no information about the data needed.
        Only the gaussian filter width to reduce shot noise can be given as parameter.

        @param 2D numpy.ndarray count_data: the raw timetrace data from a gated fast counter
                                            dim 0: gate number; dim 1: time bin
        @param float conv_std_dev: The standard deviation of the gaussian filter used for smoothing

        @return dict: The extracted laser pulses of the timetrace as well as the indices for rising
                      and falling flanks.
        """
        # Create return dictionary
        return_dict = {'laser_counts_arr': np.zeros(0, dtype='int64'),
                       'laser_indices_rising': -1,
                       'laser_indices_falling': -1}

        # sum up all gated timetraces to ease flank detection
        timetrace_sum = np.sum(count_data, 0)

        # apply gaussian filter to remove noise and compute the gradient of the timetrace sum
        try:
            conv = ndimage.filters.gaussian_filter1d(timetrace_sum.astype(float), conv_std_dev)
        except:
            conv = np.zeros(timetrace_sum.size)
        try:
            conv_deriv = np.gradient(conv)
        except:
            conv_deriv = np.zeros(conv.size)

        # get indices of rising and falling flank
        rising_ind = conv_deriv.argmax()
        falling_ind = conv_deriv.argmin()

        # If gaussian smoothing or derivative failed, the returned array only contains zeros.
        # Check for that and return also only zeros to indicate a failed pulse extraction.
        if len(conv_deriv.nonzero()[0]) == 0:
            laser_arr = np.zeros(count_data.shape, dtype='int64')
        else:
            # slice the data array to cut off anything but laser pulses
            laser_arr = count_data[:, rising_ind:falling_ind]

        return_dict['laser_counts_arr'] = laser_arr.astype('int64')
        return_dict['laser_indices_rising'] = rising_ind
        return_dict['laser_indices_falling'] = falling_ind

        return return_dict

    def ungated_conv_deriv(self, count_data, conv_std_dev=20.0):
        """ Detects the laser pulses in the ungated timetrace data and extracts
            them.

        @param numpy.ndarray count_data: 1D array the raw timetrace data from an ungated fast counter
        @param dict measurement_settings: The measurement settings of the currently running measurement.
        @param float conv_std_dev: The standard deviation of the gaussian used for smoothing

        @return 2D numpy.ndarray:   2D array, the extracted laser pulses of the timetrace.
                                    dimensions: 0: laser number, 1: time bin

        Procedure:
            Edge Detection:
            ---------------

            The count_data array with the laser pulses is smoothed with a
            gaussian filter (convolution), which used a defined standard
            deviation of 10 entries (bins). Then the derivation of the convolved
            time trace is taken to obtain the maxima and minima, which
            corresponds to the rising and falling edge of the pulses.

            The convolution with a gaussian removes nasty peaks due to count
            fluctuation within a laser pulse and at the same time ensures a
            clear distinction of the maxima and minima in the derived convolved
            trace.

            The maxima and minima are not found sequentially, pulse by pulse,
            but are rather globally obtained. I.e. the convolved and derived
            array is searched iteratively for a maximum and a minimum, and after
            finding those the array entries within the 4 times
            self.conv_std_dev (2*self.conv_std_dev to the left and
            2*self.conv_std_dev) are set to zero.

            The crucial part is the knowledge of the number of laser pulses and
            the choice of the appropriate std_dev for the gauss filter.

            To ensure a good performance of the edge detection, you have to
            ensure a steep rising and falling edge of the laser pulse! Be also
            careful in choosing a large conv_std_dev value and using a small
            laser pulse (rule of thumb: conv_std_dev < laser_length/10).
        """
        # Create return dictionary
        return_dict = {'laser_counts_arr': np.empty(0, dtype='int64'),
                       'laser_indices_rising': np.empty(0, dtype='int64'),
                       'laser_indices_falling': np.empty(0, dtype='int64')}

        number_of_lasers = self.measurement_settings.get('number_of_lasers')
        if not isinstance(number_of_lasers, int):
            return return_dict

        # apply gaussian filter to remove noise and compute the gradient of the timetrace sum
        try:
            conv = ndimage.filters.gaussian_filter1d(count_data.astype(float), conv_std_dev)
        except:
            conv = np.zeros(count_data.size)
        try:
            conv_deriv = np.gradient(conv)
        except:
            conv_deriv = np.zeros(conv.size)

        # if gaussian smoothing or derivative failed, the returned array only contains zeros.
        # Check for that and return also only zeros to indicate a failed pulse extraction.
        if len(conv_deriv.nonzero()[0]) == 0:
            return_dict['laser_counts_arr'] = np.zeros((number_of_lasers, 10), dtype='int64')
            return return_dict

        # use a reference for array, because the exact position of the peaks or dips
        # (i.e. maxima or minima, which are the inflection points in the pulse) are distorted by
        # a large conv_std_dev value.
        try:
            conv = ndimage.filters.gaussian_filter1d(count_data.astype(float), 10)
        except:
            conv = np.zeros(count_data.size)
        try:
            conv_deriv_ref = np.gradient(conv)
        except:
            conv_deriv_ref = np.zeros(conv.size)

        # initialize arrays to contain indices for all rising and falling
        # flanks, respectively
        rising_ind = np.empty(number_of_lasers, dtype='int64')
        falling_ind = np.empty(number_of_lasers, dtype='int64')

        # Find as many rising and falling flanks as there are laser pulses in
        # the trace:
        for i in range(number_of_lasers):
            # save the index of the absolute maximum of the derived time trace
            # as rising edge position
            rising_ind[i] = np.argmax(conv_deriv)

            # refine the rising edge detection, by using a small and fixed
            # conv_std_dev parameter to find the inflection point more precise
            start_ind = int(rising_ind[i] - conv_std_dev)
            if start_ind < 0:
                start_ind = 0

            stop_ind = int(rising_ind[i] + conv_std_dev)
            if stop_ind > len(conv_deriv):
                stop_ind = len(conv_deriv)

            if start_ind == stop_ind:
                stop_ind = start_ind + 1

            rising_ind[i] = start_ind + np.argmax(conv_deriv_ref[start_ind:stop_ind])

            # set this position and the surrounding of the saved edge to 0 to
            # avoid a second detection
            if rising_ind[i] < 2 * conv_std_dev:
                del_ind_start = 0
            else:
                del_ind_start = rising_ind[i] - int(2 * conv_std_dev)
            if (conv_deriv.size - rising_ind[i]) < 2 * conv_std_dev:
                del_ind_stop = conv_deriv.size - 1
            else:
                del_ind_stop = rising_ind[i] + int(2 * conv_std_dev)
                conv_deriv[del_ind_start:del_ind_stop] = 0

            # save the index of the absolute minimum of the derived time trace
            # as falling edge position
            falling_ind[i] = np.argmin(conv_deriv)

            # refine the falling edge detection, by using a small and fixed
            # conv_std_dev parameter to find the inflection point more precise
            start_ind = int(falling_ind[i] - conv_std_dev)
            if start_ind < 0:
                start_ind = 0

            stop_ind = int(falling_ind[i] + conv_std_dev)
            if stop_ind > len(conv_deriv):
                stop_ind = len(conv_deriv)

            if start_ind == stop_ind:
                stop_ind = start_ind + 1

            falling_ind[i] = start_ind + np.argmin(conv_deriv_ref[start_ind:stop_ind])

            # set this position and the sourrounding of the saved flank to 0 to
            #  avoid a second detection
            if falling_ind[i] < 2 * conv_std_dev:
                del_ind_start = 0
            else:
                del_ind_start = falling_ind[i] - int(2 * conv_std_dev)
            if (conv_deriv.size - falling_ind[i]) < 2 * conv_std_dev:
                del_ind_stop = conv_deriv.size - 1
            else:
                del_ind_stop = falling_ind[i] + int(2 * conv_std_dev)
            conv_deriv[del_ind_start:del_ind_stop] = 0

        # sort all indices of rising and falling flanks
        rising_ind.sort()
        falling_ind.sort()

        # find the maximum laser length to use as size for the laser array
        laser_length = np.max(falling_ind - rising_ind)

        # initialize the empty output array
        laser_arr = np.zeros((number_of_lasers, laser_length), dtype='int64')
        # slice the detected laser pulses of the timetrace and save them in the
        # output array according to the found rising edge
        for i in range(number_of_lasers):
            if rising_ind[i] + laser_length > count_data.size:

                lenarr = count_data[rising_ind[i]:].size
                laser_arr[i, 0:lenarr] = count_data[rising_ind[i]:]
            else:
                laser_arr[i] = count_data[rising_ind[i]:rising_ind[i] + laser_length]

        return_dict['laser_counts_arr'] = laser_arr.astype('int64')
        return_dict['laser_indices_rising'] = rising_ind
        return_dict['laser_indices_falling'] = falling_ind
        return return_dict

    def ungated_threshold(self, count_data, count_threshold=10, min_laser_length=200e-9,
                          threshold_tolerance=20e-9):
        """

        @param count_data:

        @return:
        """
        """
        Detects the laser pulses in the ungated timetrace data and extracts them.
    
        @param numpy.ndarray count_data: 1D array the raw timetrace data from an ungated fast counter
        @param measurement_settings: 
        @param fast_counter_settings: 
        @param count_threshold: 
        @param min_laser_length: 
        @param threshold_tolerance: 
        
        @return 2D numpy.ndarray:   2D array, the extracted laser pulses of the timetrace.
                                    dimensions: 0: laser number, 1: time bin
    
        Procedure:
            Threshold detection:
            ---------------
    
            All count data from the time trace is compared to a threshold value.
            Values above the threshold are considered to belong to a laser pulse.
            If the length of a pulse would be below the minimum length the pulse is discarded.
            If a number of bins which are below the threshold is smaller than the number of bins making the
            threshold_tolerance then they are still considered to belong to a laser pulse.
        """
        return_dict = dict()

        number_of_lasers = self.measurement_settings.get('number_of_lasers')
        counter_bin_width = self.fast_counter_settings.get('bin_width')

        if not isinstance(number_of_lasers, int):
            return_dict['laser_indices_rising'] = np.zeros(1, dtype='int64')
            return_dict['laser_indices_falling'] = np.zeros(1, dtype='int64')
            return_dict['laser_counts_arr'] = np.zeros((1, 3000), dtype='int64')
            return return_dict
        else:
            return_dict['laser_indices_rising'] = np.zeros(number_of_lasers, dtype='int64')
            return_dict['laser_indices_falling'] = np.zeros(number_of_lasers, dtype='int64')
            return_dict['laser_counts_arr'] = np.zeros((number_of_lasers, 3000), dtype='int64')

        # Convert length in seconds into length in time bins
        threshold_tolerance = round(threshold_tolerance / counter_bin_width)
        min_laser_length = round(min_laser_length / counter_bin_width)

        # get all bin indices with counts > threshold value
        bigger_indices = np.where(count_data >= count_threshold)[0]

        # get all indices with consecutive numbering (bin chains not interrupted by values < threshold
        index_list = np.split(bigger_indices,
                              np.where(np.diff(bigger_indices) >= threshold_tolerance)[0] + 1)
        for i, index_group in enumerate(index_list):
            if index_group.size > 0:
                start, end = index_list[i][0], index_list[i][-1]
                index_list[i] = np.arange(start, end + 1)
        consecutive_indices_unfiltered = index_list

        # sort out all groups shorter than minimum laser length
        consecutive_indices = [item for item in consecutive_indices_unfiltered if
                               len(item) > min_laser_length]

        # Check if the number of lasers matches the number of remaining index groups
        if number_of_lasers != len(consecutive_indices):
            return return_dict

        # determine max length of laser pulse and initialize laser array
        max_laser_length = max([index_array.size for index_array in consecutive_indices])
        return_dict['laser_counts_arr'] = np.zeros((number_of_lasers, max_laser_length), dtype='int64')

        # fill laser array with slices of raw data array. Also populate the rising/falling index arrays
        for i, index_group in enumerate(consecutive_indices):
            return_dict['laser_indices_rising'][i] = index_group[0]
            return_dict['laser_indices_falling'][i] = index_group[-1]
            return_dict['laser_counts_arr'][i, :index_group.size] = count_data[index_group]

        return return_dict


1		# -- coding: utf-8 --
2		"""
3		This file contains basic pulse extraction methods for Qudi.
4
5		Qudi is free software: you can redistribute it and/or modify
6		it under the terms of the GNU General Public License as published by
7		the Free Software Foundation, either version 3 of the License, or
8		(at your option) any later version.
9
10		Qudi is distributed in the hope that it will be useful,
11		but WITHOUT ANY WARRANTY; without even the implied warranty of
12		MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
13		GNU General Public License for more details.
14
15		You should have received a copy of the GNU General Public License
16		along with Qudi. If not, see <http://www.gnu.org/licenses/>.
17
18		Copyright (c) the Qudi Developers. See the COPYRIGHT.txt file at the
19		top-level directory of this distribution and at <https://github.com/Ulm-IQO/qudi/>
20		"""
21
22		import numpy as np
23		from scipy import ndimage
24
25		from logic.pulsed.pulse_extractor import PulseExtractorBase
26
27
28		class BasicPulseExtractor(PulseExtractorBase):
29		"""
30
31		"""
32		def __init__(self, args, *kwargs):
33		super().__init__(args, *kwargs)
34
35		def gated_conv_deriv(self, count_data, conv_std_dev=20.0):
36		"""
37		Detects the rising flank in the gated timetrace data and extracts just the laser pulses.
38		The flank detection is based on an image edge detection technique performed in 1D.
39		There is no information about the data needed.
40		Only the gaussian filter width to reduce shot noise can be given as parameter.
41
42		@param 2D numpy.ndarray count_data: the raw timetrace data from a gated fast counter
43		dim 0: gate number; dim 1: time bin
44		@param float conv_std_dev: The standard deviation of the gaussian filter used for smoothing
45
46		@return dict: The extracted laser pulses of the timetrace as well as the indices for rising
47		and falling flanks.
48		"""
49		# Create return dictionary
50		return_dict = {'laser_counts_arr': np.zeros(0, dtype='int64'),
51		'laser_indices_rising': -1,
52		'laser_indices_falling': -1}
53
54		# sum up all gated timetraces to ease flank detection
55		timetrace_sum = np.sum(count_data, 0)
56
57		# apply gaussian filter to remove noise and compute the gradient of the timetrace sum
58		try:
59		conv = ndimage.filters.gaussian_filter1d(timetrace_sum.astype(float), conv_std_dev)
60		except:
61		conv = np.zeros(timetrace_sum.size)
62		try:
63		conv_deriv = np.gradient(conv)
64		except:
65		conv_deriv = np.zeros(conv.size)
66
67		# get indices of rising and falling flank
68		rising_ind = conv_deriv.argmax()
69		falling_ind = conv_deriv.argmin()
70
71		# If gaussian smoothing or derivative failed, the returned array only contains zeros.
72		# Check for that and return also only zeros to indicate a failed pulse extraction.
73		if len(conv_deriv.nonzero()[0]) == 0:
74		laser_arr = np.zeros(count_data.shape, dtype='int64')
75		else:
76		# slice the data array to cut off anything but laser pulses
77		laser_arr = count_data[:, rising_ind:falling_ind]
78
79		return_dict['laser_counts_arr'] = laser_arr.astype('int64')
80		return_dict['laser_indices_rising'] = rising_ind
81		return_dict['laser_indices_falling'] = falling_ind
82
83		return return_dict
84
85		def ungated_conv_deriv(self, count_data, conv_std_dev=20.0):
86		""" Detects the laser pulses in the ungated timetrace data and extracts
87		them.
88
89		@param numpy.ndarray count_data: 1D array the raw timetrace data from an ungated fast counter
90		@param dict measurement_settings: The measurement settings of the currently running measurement.
91		@param float conv_std_dev: The standard deviation of the gaussian used for smoothing
92
93		@return 2D numpy.ndarray: 2D array, the extracted laser pulses of the timetrace.
94		dimensions: 0: laser number, 1: time bin
95
96		Procedure:
97		Edge Detection:
98		---------------
99
100		The count_data array with the laser pulses is smoothed with a
101		gaussian filter (convolution), which used a defined standard
102		deviation of 10 entries (bins). Then the derivation of the convolved
103		time trace is taken to obtain the maxima and minima, which
104		corresponds to the rising and falling edge of the pulses.
105
106		The convolution with a gaussian removes nasty peaks due to count
107		fluctuation within a laser pulse and at the same time ensures a
108		clear distinction of the maxima and minima in the derived convolved
109		trace.
110
111		The maxima and minima are not found sequentially, pulse by pulse,
112		but are rather globally obtained. I.e. the convolved and derived
113		array is searched iteratively for a maximum and a minimum, and after
114		finding those the array entries within the 4 times
115		self.conv_std_dev (2*self.conv_std_dev to the left and
116		2*self.conv_std_dev) are set to zero.
117
118		The crucial part is the knowledge of the number of laser pulses and
119		the choice of the appropriate std_dev for the gauss filter.
120
121		To ensure a good performance of the edge detection, you have to
122		ensure a steep rising and falling edge of the laser pulse! Be also
123		careful in choosing a large conv_std_dev value and using a small
124		laser pulse (rule of thumb: conv_std_dev < laser_length/10).
125		"""
126		# Create return dictionary
127		return_dict = {'laser_counts_arr': np.empty(0, dtype='int64'),
128		'laser_indices_rising': np.empty(0, dtype='int64'),
129		'laser_indices_falling': np.empty(0, dtype='int64')}
130
131		number_of_lasers = self.measurement_settings.get('number_of_lasers')
132		if not isinstance(number_of_lasers, int):
133		return return_dict
134
135		# apply gaussian filter to remove noise and compute the gradient of the timetrace sum
136		try:
137		conv = ndimage.filters.gaussian_filter1d(count_data.astype(float), conv_std_dev)
138		except:
139		conv = np.zeros(count_data.size)
140		try:
141		conv_deriv = np.gradient(conv)
142		except:
143		conv_deriv = np.zeros(conv.size)
144
145		# if gaussian smoothing or derivative failed, the returned array only contains zeros.
146		# Check for that and return also only zeros to indicate a failed pulse extraction.
147		if len(conv_deriv.nonzero()[0]) == 0:
148		return_dict['laser_counts_arr'] = np.zeros((number_of_lasers, 10), dtype='int64')
149		return return_dict
150
151		# use a reference for array, because the exact position of the peaks or dips
152		# (i.e. maxima or minima, which are the inflection points in the pulse) are distorted by
153		# a large conv_std_dev value.
154		try:
155		conv = ndimage.filters.gaussian_filter1d(count_data.astype(float), 10)
156		except:
157		conv = np.zeros(count_data.size)
158		try:
159		conv_deriv_ref = np.gradient(conv)
160		except:
161		conv_deriv_ref = np.zeros(conv.size)
162
163		# initialize arrays to contain indices for all rising and falling
164		# flanks, respectively
165		rising_ind = np.empty(number_of_lasers, dtype='int64')
166		falling_ind = np.empty(number_of_lasers, dtype='int64')
167
168		# Find as many rising and falling flanks as there are laser pulses in
169		# the trace:
170		for i in range(number_of_lasers):
171		# save the index of the absolute maximum of the derived time trace
172		# as rising edge position
173		rising_ind[i] = np.argmax(conv_deriv)
174
175		# refine the rising edge detection, by using a small and fixed
176		# conv_std_dev parameter to find the inflection point more precise
177		start_ind = int(rising_ind[i] - conv_std_dev)
178		if start_ind < 0:
179		start_ind = 0
180
181		stop_ind = int(rising_ind[i] + conv_std_dev)
182		if stop_ind > len(conv_deriv):
183		stop_ind = len(conv_deriv)
184
185		if start_ind == stop_ind:
186		stop_ind = start_ind + 1
187
188		rising_ind[i] = start_ind + np.argmax(conv_deriv_ref[start_ind:stop_ind])
189
190		# set this position and the surrounding of the saved edge to 0 to
191		# avoid a second detection
192		if rising_ind[i] < 2 * conv_std_dev:
193		del_ind_start = 0
194		else:
195		del_ind_start = rising_ind[i] - int(2 * conv_std_dev)
196		if (conv_deriv.size - rising_ind[i]) < 2 * conv_std_dev:
197		del_ind_stop = conv_deriv.size - 1
198		else:
199		del_ind_stop = rising_ind[i] + int(2 * conv_std_dev)
200		conv_deriv[del_ind_start:del_ind_stop] = 0
201
202		# save the index of the absolute minimum of the derived time trace
203		# as falling edge position
204		falling_ind[i] = np.argmin(conv_deriv)
205
206		# refine the falling edge detection, by using a small and fixed
207		# conv_std_dev parameter to find the inflection point more precise
208		start_ind = int(falling_ind[i] - conv_std_dev)
209		if start_ind < 0:
210		start_ind = 0
211
212		stop_ind = int(falling_ind[i] + conv_std_dev)
213		if stop_ind > len(conv_deriv):
214		stop_ind = len(conv_deriv)
215
216		if start_ind == stop_ind:
217		stop_ind = start_ind + 1
218
219		falling_ind[i] = start_ind + np.argmin(conv_deriv_ref[start_ind:stop_ind])
220
221		# set this position and the sourrounding of the saved flank to 0 to
222		# avoid a second detection
223		if falling_ind[i] < 2 * conv_std_dev:
224		del_ind_start = 0
225		else:
226		del_ind_start = falling_ind[i] - int(2 * conv_std_dev)
227		if (conv_deriv.size - falling_ind[i]) < 2 * conv_std_dev:
228		del_ind_stop = conv_deriv.size - 1
229		else:
230		del_ind_stop = falling_ind[i] + int(2 * conv_std_dev)
231		conv_deriv[del_ind_start:del_ind_stop] = 0
232
233		# sort all indices of rising and falling flanks
234		rising_ind.sort()
235		falling_ind.sort()
236
237		# find the maximum laser length to use as size for the laser array
238		laser_length = np.max(falling_ind - rising_ind)
239
240		# initialize the empty output array
241		laser_arr = np.zeros((number_of_lasers, laser_length), dtype='int64')
242		# slice the detected laser pulses of the timetrace and save them in the
243		# output array according to the found rising edge
244		for i in range(number_of_lasers):
245	View Code Duplication	if rising_ind[i] + laser_length > count_data.size:
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246		lenarr = count_data[rising_ind[i]:].size
247		laser_arr[i, 0:lenarr] = count_data[rising_ind[i]:]
248		else:
249		laser_arr[i] = count_data[rising_ind[i]:rising_ind[i] + laser_length]
250
251		return_dict['laser_counts_arr'] = laser_arr.astype('int64')
252		return_dict['laser_indices_rising'] = rising_ind
253		return_dict['laser_indices_falling'] = falling_ind
254		return return_dict
255
256		def ungated_threshold(self, count_data, count_threshold=10, min_laser_length=200e-9,
257		threshold_tolerance=20e-9):
258		"""
259
260		@param count_data:
261
262		@return:
263		"""
264		"""
265		Detects the laser pulses in the ungated timetrace data and extracts them.
266
267		@param numpy.ndarray count_data: 1D array the raw timetrace data from an ungated fast counter
268		@param measurement_settings:
269		@param fast_counter_settings:
270		@param count_threshold:
271		@param min_laser_length:
272		@param threshold_tolerance:
273
274		@return 2D numpy.ndarray: 2D array, the extracted laser pulses of the timetrace.
275		dimensions: 0: laser number, 1: time bin
276
277		Procedure:
278		Threshold detection:
279		---------------
280
281		All count data from the time trace is compared to a threshold value.
282		Values above the threshold are considered to belong to a laser pulse.
283		If the length of a pulse would be below the minimum length the pulse is discarded.
284		If a number of bins which are below the threshold is smaller than the number of bins making the
285		threshold_tolerance then they are still considered to belong to a laser pulse.
286		"""
287		return_dict = dict()
288
289		number_of_lasers = self.measurement_settings.get('number_of_lasers')
290		counter_bin_width = self.fast_counter_settings.get('bin_width')
291
292		if not isinstance(number_of_lasers, int):
293		return_dict['laser_indices_rising'] = np.zeros(1, dtype='int64')
294		return_dict['laser_indices_falling'] = np.zeros(1, dtype='int64')
295		return_dict['laser_counts_arr'] = np.zeros((1, 3000), dtype='int64')
296		return return_dict
297		else:
298		return_dict['laser_indices_rising'] = np.zeros(number_of_lasers, dtype='int64')
299		return_dict['laser_indices_falling'] = np.zeros(number_of_lasers, dtype='int64')
300		return_dict['laser_counts_arr'] = np.zeros((number_of_lasers, 3000), dtype='int64')
301
302		# Convert length in seconds into length in time bins
303		threshold_tolerance = round(threshold_tolerance / counter_bin_width)
304		min_laser_length = round(min_laser_length / counter_bin_width)
305
306		# get all bin indices with counts > threshold value
307		bigger_indices = np.where(count_data >= count_threshold)[0]
308
309		# get all indices with consecutive numbering (bin chains not interrupted by values < threshold
310		index_list = np.split(bigger_indices,
311		np.where(np.diff(bigger_indices) >= threshold_tolerance)[0] + 1)
312		for i, index_group in enumerate(index_list):
313		if index_group.size > 0:
314		start, end = index_list[i][0], index_list[i][-1]
315		index_list[i] = np.arange(start, end + 1)
316		consecutive_indices_unfiltered = index_list
317
318		# sort out all groups shorter than minimum laser length
319		consecutive_indices = [item for item in consecutive_indices_unfiltered if
320		len(item) > min_laser_length]
321
322		# Check if the number of lasers matches the number of remaining index groups
323		if number_of_lasers != len(consecutive_indices):
324		return return_dict
325
326		# determine max length of laser pulse and initialize laser array
327		max_laser_length = max([index_array.size for index_array in consecutive_indices])
328		return_dict['laser_counts_arr'] = np.zeros((number_of_lasers, max_laser_length), dtype='int64')
329
330		# fill laser array with slices of raw data array. Also populate the rising/falling index arrays
331		for i, index_group in enumerate(consecutive_indices):
332		return_dict['laser_indices_rising'][i] = index_group[0]
333		return_dict['laser_indices_falling'][i] = index_group[-1]
334		return_dict['laser_counts_arr'][i, :index_group.size] = count_data[index_group]
335
336		return return_dict
337

Ulm-IQO / qudi

Pull Request — master (#384)

BasicPulseExtractor.ungated_conv_deriv() F

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