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# -*- coding: utf-8 -*- |
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""" |
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This file contains basic pulse analysis methods for Qudi. |
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Qudi is free software: you can redistribute it and/or modify |
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it under the terms of the GNU General Public License as published by |
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the Free Software Foundation, either version 3 of the License, or |
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(at your option) any later version. |
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Qudi is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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GNU General Public License for more details. |
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You should have received a copy of the GNU General Public License |
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along with Qudi. If not, see <http://www.gnu.org/licenses/>. |
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Copyright (c) the Qudi Developers. See the COPYRIGHT.txt file at the |
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top-level directory of this distribution and at <https://github.com/Ulm-IQO/qudi/> |
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""" |
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import numpy as np |
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from logic.pulsed.pulse_analyzer import PulseAnalyzerBase |
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class BasicPulseAnalyzer(PulseAnalyzerBase): |
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""" |
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""" |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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def analyse_mean_norm(self, laser_data, signal_start=0.0, signal_end=200e-9, norm_start=300e-9, |
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norm_end=500e-9): |
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""" |
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@param laser_data: |
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@param signal_start: |
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@param signal_end: |
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@param norm_start: |
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@param norm_end: |
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@return: |
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""" |
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# Get number of lasers |
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num_of_lasers = laser_data.shape[0] |
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# Get counter bin width |
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bin_width = self.fast_counter_settings.get('bin_width') |
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if not isinstance(bin_width, float): |
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return np.zeros(num_of_lasers), np.zeros(num_of_lasers) |
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# Convert the times in seconds to bins (i.e. array indices) |
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signal_start_bin = round(signal_start / bin_width) |
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signal_end_bin = round(signal_end / bin_width) |
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norm_start_bin = round(norm_start / bin_width) |
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norm_end_bin = round(norm_end / bin_width) |
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# initialize data arrays for signal and measurement error |
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signal_data = np.empty(num_of_lasers, dtype=float) |
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error_data = np.empty(num_of_lasers, dtype=float) |
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# loop over all laser pulses and analyze them |
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for ii, laser_arr in enumerate(laser_data): |
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# calculate the sum and mean of the data in the normalization window |
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tmp_data = laser_arr[norm_start_bin:norm_end_bin] |
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reference_sum = np.sum(tmp_data) |
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reference_mean = (reference_sum / len(tmp_data)) if len(tmp_data) != 0 else 0.0 |
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# calculate the sum and mean of the data in the signal window |
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tmp_data = laser_arr[signal_start_bin:signal_end_bin] |
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signal_sum = np.sum(tmp_data) |
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signal_mean = (signal_sum / len(tmp_data)) if len(tmp_data) != 0 else 0.0 |
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# Calculate normalized signal while avoiding division by zero |
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if reference_mean > 0 and signal_mean >= 0: |
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signal_data[ii] = signal_mean / reference_mean |
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else: |
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signal_data[ii] = 0.0 |
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# Calculate measurement error while avoiding division by zero |
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if reference_sum > 0 and signal_sum > 0: |
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# calculate with respect to gaussian error 'evolution' |
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error_data[ii] = signal_data[ii] * np.sqrt(1 / signal_sum + 1 / reference_sum) |
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else: |
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error_data[ii] = 0.0 |
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return signal_data, error_data |
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def analyse_mean(self, laser_data, signal_start=0.0, signal_end=200e-9): |
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""" |
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@param laser_data: |
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@param signal_start: |
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@param signal_end: |
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@return: |
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""" |
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# Get number of lasers |
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num_of_lasers = laser_data.shape[0] |
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# Get counter bin width |
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bin_width = self.fast_counter_settings.get('bin_width') |
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if not isinstance(bin_width, float): |
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return np.zeros(num_of_lasers), np.zeros(num_of_lasers) |
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# Convert the times in seconds to bins (i.e. array indices) |
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signal_start_bin = round(signal_start / bin_width) |
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signal_end_bin = round(signal_end / bin_width) |
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# initialize data arrays for signal and measurement error |
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signal_data = np.empty(num_of_lasers, dtype=float) |
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error_data = np.empty(num_of_lasers, dtype=float) |
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# loop over all laser pulses and analyze them |
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for ii, laser_arr in enumerate(laser_data): |
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# calculate the sum and mean of the data in the signal window |
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signal_mean = laser_arr[signal_start_bin:signal_end_bin].mean() |
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# Avoid numpy C type variables overflow and NaN values |
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if signal_mean < 0 or signal_mean != signal_mean: |
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signal_data[ii] = 0.0 |
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error_data[ii] = 0.0 |
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else: |
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signal_data[ii] = signal_mean |
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error_data[ii] = np.sqrt(signal_mean) |
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return signal_data, error_data |
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Ulm-IQO /
qudi
