Metadata-Version: 2.1
Name: neurokit2
Version: 0.1.4.1
Summary: The Python Toolbox for Neurophysiological Signal Processing.
Home-page: https://github.com/neuropsychology/NeuroKit
Author: Dominique Makowski
Author-email: dom.makowski@gmail.com
License: MIT license
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        **The Python Toolbox for Neurophysiological Signal Processing**
        
        **NeuroKit2** is a user-friendly package providing easy access to advanced biosignal processing routines.
        Researchers and clinicians without extensive knowledge of programming or biomedical signal processing
        can **analyze physiological data with only two lines of code**.
        
        
        Quick Example
        ------------------
        
        .. code-block:: python
        
            import neurokit2 as nk
        
            # Download example data
            data = nk.data("bio_eventrelated_100hz")
        
            # Preprocess the data (filter, find peaks, etc.)
            processed_data, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)
        
            # Compute relevant features
            results = nk.bio_analyze(processed_data, sampling_rate=100)
        
        And **boom** 💥 your analysis is done 😎
        
        Installation
        -------------
        
        You can install NeuroKit2 from `PyPI <https://pypi.org/project/neurokit2/>`_
        
        .. code-block::
        
            pip install neurokit2
        
        or `conda-forge <https://anaconda.org/conda-forge/neurokit2>`_
        
        .. code-block::
        
            conda install -c conda-forge neurokit2
        
        If you're not sure what to do, read our `installation guide <https://neurokit2.readthedocs.io/en/latest/installation.html>`_.
        
        Contributing
        -------------
        
        .. image:: https://img.shields.io/badge/License-MIT-blue.svg
                :target: https://github.com/neuropsychology/NeuroKit/blob/master/LICENSE
                :alt: License
        
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        NeuroKit2 is the most `welcoming <https://github.com/neuropsychology/NeuroKit#popularity>`_ project with a large community of contributors with all levels of programming expertise. **But the package is still far from being perfect!** Thus, if you have some ideas for **improvement**, **new features**, or just want to **learn Python** and do something useful at the same time, do not hesitate and check out the following guides:
        
        - `Understanding NeuroKit <https://neurokit2.readthedocs.io/en/latest/contributing/understanding.html>`_
        - `Contributing guide <https://neurokit2.readthedocs.io/en/latest/contributing/contributing.html>`_
        - `Ideas for first contributions <https://neurokit2.readthedocs.io/en/latest/contributing/first_contribution.html>`_
        
        Also, if you have developped new signal processing methods or algorithms and you want to **increase its usage, popularity and citations**, get in touch with us to eventually add it to NeuroKit. A great opportunity for the users as well as the original developpers! 
        
        
        Documentation
        ----------------
        
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        Click on the links above and check out our tutorials:
        
        General
        ^^^^^^^^^^
        
        -  `Get familiar with Python in 10 minutes <https://neurokit2.readthedocs.io/en/latest/tutorials/learnpython.html>`_
        -  `Recording good quality signals <https://neurokit2.readthedocs.io/en/latest/tutorials/recording.html>`_
        -  `What software for physiological signal processing <https://neurokit2.readthedocs.io/en/latest/tutorials/software.html>`_
        -  `Install Python and NeuroKit <https://neurokit2.readthedocs.io/en/latest/installation.html>`_
        -  `Included datasets <https://neurokit2.readthedocs.io/en/latest/datasets.html>`_
        -  `Additional Resources <https://neurokit2.readthedocs.io/en/latest/tutorials/resources.html>`_
        
        
        Examples
        ^^^^^^^^^^
        
        -  `Simulate Artificial Physiological Signals <https://neurokit2.readthedocs.io/en/latest/examples/simulation.html>`_
        -  `Customize your Processing Pipeline <https://neurokit2.readthedocs.io/en/latest/examples/custom.html>`_
        -  `Event-related Analysis <https://neurokit2.readthedocs.io/en/latest/examples/eventrelated.html>`_
        -  `Interval-related Analysis <https://neurokit2.readthedocs.io/en/latest/examples/intervalrelated.html>`_
        -  `Analyze Electrodermal Activity (EDA) <https://neurokit2.readthedocs.io/en/latest/examples/eda.html>`_
        -  `Analyze Respiratory Rate Variability (RRV) <https://neurokit2.readthedocs.io/en/latest/examples/rrv.html>`_
        -  `Extract and Visualize Individual Heartbeats <https://neurokit2.readthedocs.io/en/latest/examples/heartbeats.html>`_
        -  `Locate P, Q, S and T waves in ECG <https://neurokit2.readthedocs.io/en/latest/examples/ecg_delineate.html>`_
        -  `Complexity Analysis of Physiological Signals <https://neurokit2.readthedocs.io/en/latest/examples/complexity.html>`_
        -  `Analyze Electrooculography EOG data <https://neurokit2.readthedocs.io/en/latest/examples/eog.html>`_
        -  `Fit a function to a signal <https://neurokit2.readthedocs.io/en/latest/examples/fit_function.html>`_
        
        *You can try out these examples directly* `in your browser <https://github.com/neuropsychology/NeuroKit/tree/master/docs/examples#cloud-based-interactive-examples>`_.
        
        **Don't know which tutorial is suited for your case?** Follow this flowchart:
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/workflow.png
                :target: https://neurokit2.readthedocs.io/en/latest/?badge=latest
        
        Citation
        ---------
        
        .. image:: https://zenodo.org/badge/218212111.svg
           :target: https://zenodo.org/badge/latestdoi/218212111
        
        .. image:: https://img.shields.io/badge/details-authors-purple.svg?colorB=9C27B0
           :target: https://neurokit2.readthedocs.io/en/latest/authors.html
        
        
        The **NeuroKit2** paper can be found `here <https://doi.org/10.3758/s13428-020-01516-y>`_ 🎉 Additionally, you can get the reference directly from Python by running:
        
        .. code-block:: python
        
            nk.cite()
        
        
        .. code-block:: tex
        
            You can cite NeuroKit2 as follows:
        
            - Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H.,
            Schölzel, C., & Chen, S. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing.
            Behavior Research Methods, 53(4), 1689–1696. https://doi.org/10.3758/s13428-020-01516-y
        
            Full bibtex reference:
        
            @article{Makowski2021neurokit,
                author = {Dominique Makowski and Tam Pham and Zen J. Lau and Jan C. Brammer and Fran{\c{c}}ois Lespinasse and Hung Pham and Christopher Schölzel and S. H. Annabel Chen},
                title = {{NeuroKit}2: A Python toolbox for neurophysiological signal processing},
                journal = {Behavior Research Methods},
                volume = {53},
                number = {4},
                pages = {1689--1696},
                publisher = {Springer Science and Business Media {LLC}},
                doi = {10.3758/s13428-020-01516-y},
                url = {https://doi.org/10.3758%2Fs13428-020-01516-y},
                year = 2021,
                month = {feb}
            }
        
        
        **Let us know if you used NeuroKit2 in a publication!** Open a new `discussion <https://github.com/neuropsychology/NeuroKit/discussions>`_ (select the *NK in publications* category) and link the paper. The community would be happy to know about how you used it and learn about your research. We could also feature it once we have a section on the website for papers that used the software.
        
        ..
            Design
            --------
        
            *NeuroKit2* is designed to provide a **consistent**, **accessible** yet **powerful** and **flexible** API.
        
            - **Consistency**: For each type of signals (ECG, RSP, EDA, EMG...), the same function names are called (in the form :code:`signaltype_functiongoal()`) to achieve equivalent goals, such as :code:`*_clean()`, :code:`*_findpeaks()`, :code:`*_process()`, :code:`*_plot()` (replace the star with the signal type, e.g., :code:`ecg_clean()`).
            - **Accessibility**: Using NeuroKit2 is made very easy for beginners through the existence of powerful high-level "master" functions, such as :code:`*_process()`, that performs cleaning, preprocessing and processing with sensible defaults.
            - **Flexibility**: However, advanced users can very easily build their own custom analysis pipeline by using the mid-level functions (such as :code:`*_clean()`, :code:`*_rate()`), offering more control and flexibility over their parameters.
        
        
        Physiological Data Preprocessing
        ---------------------------------
        
        Simulate physiological signals
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            import numpy as np
            import pandas as pd
            import neurokit2 as nk
        
            # Generate synthetic signals
            ecg = nk.ecg_simulate(duration=10, heart_rate=70)
            ppg = nk.ppg_simulate(duration=10, heart_rate=70)
            rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
            eda = nk.eda_simulate(duration=10, scr_number=3)
            emg = nk.emg_simulate(duration=10, burst_number=2)
        
            # Visualise biosignals
            data = pd.DataFrame({"ECG": ecg,
                                 "PPG": ppg,
                                 "RSP": rsp,
                                 "EDA": eda,
                                 "EMG": emg})
            nk.signal_plot(data, subplots=True)
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_simulation.png
                :target: https://neurokit2.readthedocs.io/en/latest/examples/simulation.html
        
        
        Electrodermal Activity (EDA/GSR)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Generate 10 seconds of EDA signal (recorded at 250 samples / second) with 2 SCR peaks
            eda = nk.eda_simulate(duration=10, sampling_rate=250, scr_number=2, drift=0.01)
        
            # Process it
            signals, info = nk.eda_process(eda, sampling_rate=250)
        
            # Visualise the processing
            nk.eda_plot(signals, sampling_rate=250)
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_eda.png
                :target: https://neurokit2.readthedocs.io/en/latest/examples/eda.html
        
        
        Cardiac activity (ECG)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Generate 15 seconds of ECG signal (recorded at 250 samples / second)
            ecg = nk.ecg_simulate(duration=15, sampling_rate=250, heart_rate=70)
        
            # Process it
            signals, info = nk.ecg_process(ecg, sampling_rate=250)
        
            # Visualise the processing
            nk.ecg_plot(signals, sampling_rate=250)
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_ecg.png
                :target: https://neurokit2.readthedocs.io/en/latest/examples/heartbeats.html
        
        
        Respiration (RSP)
        ^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Generate one minute of respiratory (RSP) signal (recorded at 250 samples / second)
            rsp = nk.rsp_simulate(duration=60, sampling_rate=250, respiratory_rate=15)
        
            # Process it
            signals, info = nk.rsp_process(rsp, sampling_rate=250)
        
            # Visualise the processing
            nk.rsp_plot(signals, sampling_rate=250)
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_rsp.png
                :target: https://neurokit2.readthedocs.io/en/latest/examples/rrv.html
        
        
        Electromyography (EMG)
        ^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Generate 10 seconds of EMG signal (recorded at 250 samples / second)
            emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
        
            # Process it
            signal, info = nk.emg_process(emg, sampling_rate=250)
        
            # Visualise the processing
            nk.emg_plot(signals, sampling_rate=250)
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_emg.png
        
        
        Photoplethysmography (PPG/BVP)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Generate 15 seconds of PPG signal (recorded at 250 samples / second)
            ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70)
        
            # Process it
            signals, info = nk.ppg_process(ppg, sampling_rate=250)
        
            # Visualize the processing
            nk.ppg_plot(signals, sampling_rate=250)
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_ppg.png
        
        
        Electrooculography (EOG)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Import EOG data
            eog_signal = nk.data("eog_100hz")
        
            # Process it
            signals, info = nk.eog_process(eog_signal, sampling_rate=100)
        
            # Plot
            plot = nk.eog_plot(signals, info, sampling_rate=100)
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_eog.png
        
        
        
        Electrogastrography (EGG)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Consider `helping us develop it <https://neurokit2.readthedocs.io/en/latest/tutorials/contributing.html>`_!
        
        
        Physiological Data Analysis
        ----------------------------
        
        The analysis of physiological data usually comes in two types, **event-related** or **interval-related**.
        
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/features.png
        
        
        Event-related
        ^^^^^^^^^^^^^^
        
        This type of analysis refers to physiological changes immediately occurring in response to an event.
        For instance, physiological changes following the presentation of a stimulus (e.g., an emotional stimulus) indicated by
        the dotted lines in the figure above. In this situation the analysis is epoch-based.
        An epoch is a short chunk of the physiological signal (usually < 10 seconds), that is locked to a specific stimulus and hence
        the physiological signals of interest are time-segmented accordingly. This is represented by the orange boxes in the figure above.
        In this case, using `bio_analyze()` will compute features like rate changes, peak characteristics and phase characteristics.
        
        - `Event-related example <https://neurokit2.readthedocs.io/en/latest/examples/eventrelated.html>`_
        
        Interval-related
        ^^^^^^^^^^^^^^^^^
        
        This type of analysis refers to the physiological characteristics and features that occur over
        longer periods of time (from a few seconds to days of activity). Typical use cases are either
        periods of resting-state, in which the activity is recorded for several minutes while the participant
        is at rest, or during different conditions in which there is no specific time-locked event
        (e.g., watching movies, listening to music, engaging in physical activity, etc.). For instance,
        this type of analysis is used when people want to compare the physiological activity under different
        intensities of physical exercise, different types of movies, or different intensities of
        stress. To compare event-related and interval-related analysis, we can refer to the example figure above.
        For example, a participant might be watching a 20s-long short film where particular stimuli of
        interest in the movie appears at certain time points (marked by the dotted lines). While
        event-related analysis pertains to the segments of signals within the orange boxes (to understand the physiological
        changes pertaining to the appearance of stimuli), interval-related analysis can be
        applied on the entire 20s duration to investigate how physiology fluctuates in general.
        In this case, using `bio_analyze()` will compute features such as rate characteristics (in particular,
        variability metrics) and peak characteristics.
        
        - `Interval-related example <https://neurokit2.readthedocs.io/en/latest/examples/intervalrelated.html>`_
        
        
        Heart Rate Variability (HRV)
        ----------------------------
        .. image:: https://img.shields.io/badge/Tutorial-HRV-green
                :target: https://www.mdpi.com/1424-8220/21/12/3998
        
        Check-out our **Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial** `paper <https://doi.org/10.3390/s21123998>`_ for:
        
        - a comprehensive review of the most up-to-date HRV indices
        - a discussion of their significance in psychological research and practices
        - a step-by-step guide for HRV analysis using **NeuroKit2**
        
        
        .. code-block:: tex
        
            You can cite the paper as follows:
        
            - Pham, T., Lau, Z. J., Chen, S. H. A., & Makowski, D. (2021).
            Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial.
            Sensors, 21(12), 3998. https://doi:10.3390/s21123998
        
        
        - **Compute HRV indices using Python**
        
          - **Time domain**: RMSSD, MeanNN, SDNN, SDSD, CVNN etc.
          - **Frequency domain**: Spectral power density in various frequency bands (Ultra low/ULF, Very low/VLF, Low/LF, High/HF, Very high/VHF), Ratio of LF to HF power, Normalized LF (LFn) and HF (HFn), Log transformed HF (LnHF).
          - **Nonlinear domain**: Spread of RR intervals (SD1, SD2, ratio between SD2 to SD1), Cardiac Sympathetic Index (CSI), Cardial Vagal Index (CVI), Modified CSI, Sample Entropy (SampEn).
        
        
        .. code-block:: python
        
            # Download data
            data = nk.data("bio_resting_8min_100hz")
        
            # Find peaks
            peaks, info = nk.ecg_peaks(data["ECG"], sampling_rate=100)
        
            # Compute HRV indices
            nk.hrv(peaks, sampling_rate=100, show=True)
            >>>    HRV_RMSSD  HRV_MeanNN   HRV_SDNN  ...   HRV_CVI  HRV_CSI_Modified  HRV_SampEn
            >>> 0  69.697983  696.395349  62.135891  ...  4.829101        592.095372    1.259931
        
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_hrv.png
        
        Miscellaneous
        ----------------------------
        
        ECG Delineation
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        - Delineate the QRS complex of an electrocardiac signal (ECG) including P-peaks, T-peaks, as well as their onsets and offsets.
        
        
        .. code-block:: python
        
        
            # Download data
            ecg_signal = nk.data(dataset="ecg_3000hz")['ECG']
        
            # Extract R-peaks locations
            _, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=3000)
        
            # Delineate
            signal, waves = nk.ecg_delineate(ecg_signal, rpeaks, sampling_rate=3000, method="dwt", show=True, show_type='all')
        
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_delineate.png
               :target: https://neurokit2.readthedocs.io/en/latest/examples/ecg_delineate.html
        
        
        
        Signal Processing
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        - **Signal processing functionalities**
        
          - **Filtering**: Using different methods.
          - **Detrending**: Remove the baseline drift or trend.
          - **Distorting**: Add noise and artifacts.
        
        .. code-block:: python
        
            # Generate original signal
            original = nk.signal_simulate(duration=6, frequency=1)
        
            # Distort the signal (add noise, linear trend, artifacts etc.)
            distorted = nk.signal_distort(original,
                                          noise_amplitude=0.1,
                                          noise_frequency=[5, 10, 20],
                                          powerline_amplitude=0.05,
                                          artifacts_amplitude=0.3,
                                          artifacts_number=3,
                                          linear_drift=0.5)
        
            # Clean (filter and detrend)
            cleaned = nk.signal_detrend(distorted)
            cleaned = nk.signal_filter(cleaned, lowcut=0.5, highcut=1.5)
        
            # Compare the 3 signals
            plot = nk.signal_plot([original, distorted, cleaned])
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_signalprocessing.png
        
        
        Complexity (Entropy, Fractal Dimensions, ...)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        - **Optimize complexity parameters** (delay *tau*, dimension *m*, tolerance *r*)
        
        .. code-block:: python
        
            # Generate signal
            signal = nk.signal_simulate(frequency=[1, 3], noise=0.01, sampling_rate=100)
        
            # Find optimal time delay, embedding dimension and r
            parameters = nk.complexity_optimize(signal, show=True)
        
        
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_complexity_optimize.png
                :target: https://neurokit2.readthedocs.io/en/latest/tutorials/complexity.html
        
        
        
        - **Compute complexity features**
        
          - **Entropy**: Sample Entropy (SampEn), Approximate Entropy (ApEn), Fuzzy Entropy (FuzzEn), Multiscale Entropy (MSE), Shannon Entropy (ShEn)
          - **Fractal dimensions**: Correlation Dimension D2, ...
          - **Detrended Fluctuation Analysis**
        
        .. code-block:: python
        
            nk.entropy_sample(signal)
            nk.entropy_approximate(signal)
        
        
        Signal Decomposition
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            # Create complex signal
            signal = nk.signal_simulate(duration=10, frequency=1)  # High freq
            signal += 3 * nk.signal_simulate(duration=10, frequency=3)  # Higher freq
            signal += 3 * np.linspace(0, 2, len(signal))  # Add baseline and linear trend
            signal += 2 * nk.signal_simulate(duration=10, frequency=0.1, noise=0)  # Non-linear trend
            signal += np.random.normal(0, 0.02, len(signal))  # Add noise
        
            # Decompose signal using Empirical Mode Decomposition (EMD)
            components = nk.signal_decompose(signal, method='emd')
            nk.signal_plot(components)  # Visualize components
        
            # Recompose merging correlated components
            recomposed = nk.signal_recompose(components, threshold=0.99)
            nk.signal_plot(recomposed)  # Visualize components
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_decomposition.png
                :target: https://neurokit2.readthedocs.io/en/latest/
        
        Signal Power Spectrum Density (PSD)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        .. code-block:: python
        
            # Generate complex signal
            signal = nk.signal_simulate(duration=20, frequency=[0.5, 5, 10, 15], amplitude=[2, 1.5, 0.5, 0.3], noise=0.025)
        
            # Get the PSD using different methods
            welch = nk.signal_psd(signal, method="welch", min_frequency=1, max_frequency=20, show=True)
            multitaper = nk.signal_psd(signal, method="multitapers", max_frequency=20, show=True)
            lomb = nk.signal_psd(signal, method="lomb", min_frequency=1, max_frequency=20, show=True)
            burg = nk.signal_psd(signal, method="burg", min_frequency=1, max_frequency=20, order=10, show=True)
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_psd.png
                :target: https://neurokit2.readthedocs.io/en/latest/
        
        Statistics
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        - **Highest Density Interval (HDI)**
        
        .. code-block:: python
        
            x = np.random.normal(loc=0, scale=1, size=100000)
        
            ci_min, ci_max = nk.hdi(x, ci=0.95, show=True)
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_hdi.png
        
        Popularity
        ---------------------
        
        .. image:: https://img.shields.io/pypi/dd/neurokit2
                :target: https://pypi.python.org/pypi/neurokit2
        
        .. image:: https://img.shields.io/github/stars/neuropsychology/NeuroKit
                :target: https://github.com/neuropsychology/NeuroKit/stargazers
        
        .. image:: https://img.shields.io/github/forks/neuropsychology/NeuroKit
                :target: https://github.com/neuropsychology/NeuroKit/network
        
        
        NeuroKit2 is one of the most welcoming package for new contributors and users, as well as the fastest growing package. So stop hesitating and hop onboard 🤗
        
        .. image:: https://raw.github.com/neuropsychology/NeuroKit/master/docs/readme/README_popularity.png
                :target: https://pypi.python.org/pypi/neurokit2
        
        
        
        Notes
        -------
        
        *The authors do not provide any warranty. If this software causes your keyboard to blow up, your brain to liquefy, your toilet to clog or a zombie plague to break loose, the authors CANNOT IN ANY WAY be held responsible.*
        
        
        News
        -----
        
        
        
        0.1.4.1
        ^^^^^^^^^^^^^^^^^^^
        
        Fixes
        +++++++++++++
        * Adjust `kmax` parameter in `fractal_higuchi()` according to signal length as having `kmax` more than half of signal length leads to division by zero error
        * Ensure that sanitization of input in `_hrv_dfa()` is done before windows for `DFA_alpha2` is computed
        * `np.seterr` is added to `fractal_dfa()` to avoid returning division by zero warning which is an expected behaviour
        
        
        0.1.4
        ^^^^^^^^^^^^^^^^^^^
        
        Breaking Changes
        +++++++++++++++++
        
        * `fractal_df()` now returns a dictionary of windows, fluctuations and the slope value (see documentation for more information. If `multifractal` is True, the dictionary additionally contains the parameters of the singularity spectrum (see `singularity_spectrum()` for more information)
        
        New Features
        +++++++++++++
        
        * Add convenience function `intervals_to_peaks()` useful for RRI or BBI conversion to peak indices
        * `hrv_nonlinear()` and `rrv_rsp()` now return the parameters of singularity spectrum for multifractal DFA analysis
        * Add new complexity measures in `fractal_higuchi()`, `fractal_katz()` and `fractal_lempelziv()`
        * Add new time-domain measures in `hrv_time()`: `SDANN` and `SDNNI`
        * Add new non-linear measures in `hrv_nonlinear()`: `ShanEn`, `FuzzyEn`, `HFD`, `KFD` and `LZC`
        
        Fixes
        +++++++++++++
        
        * Add path argument in `mne_data()` and throw warning to download mne datasets if data folder is not present
        * The implementation of `TTIN` in `hrv_time()` is amended to its correct formulation.
        * The default binsize used for RRI histogram in the computation of geometric HRV indices is set to 1 / 128 seconds
        
        
        0.1.3
        ^^^^^^^^^^^^^^^^^^^
        
        Breaking Changes
        +++++++++++++++++
        
        * None
        
        New Features
        +++++++++++++
        
        * Add internal function for detecting missing data points and forward filling missing values in `nk.*_clean()` functions
        * Add computation of standard deviation in `eventrelated()` functions for *ECG_Rate_SD*, *EMG_Amplitude_SD*, *EOG_Rate_SD*, *PPG_Rate_SD*, *RSP_Rate_SD*, *RSP_Amplitude_SD*
        * Add labelling for interval related features if a dictionary of dataframes is passed
        * Retrun Q peaks and S Peaks information for wavelet-based methods in `nk.ecg_delineate()`
        
        Fixes
        +++++++++++++
        
        * Fix epochs columns with `dtype: object` generated by `nk.epochs_create()`
        * Bug fix ecg_findpeaks_rodrigues for array out of bounds bug
        
        
        0.1.2
        ^^^^^^^^^^^^^^^^^^^
        
        New Features
        +++++++++++++
        
        * Additional features for `nk.rsp_intervalrelated()`: average inspiratory and expiratory durations, inspiratory-to-expiratory (I/E) time ratio
        * Add multiscale entropy measures (MSE, CMSE, RCMSE) and fractal methods (Detrended Fluctuation Analysis, Correlation Dimension) into `nk.hrv_nonlinear()`
        * Allow for data resampling in `nk.read_bitalino()`
        * Add `bio_resting_8min_200hz` into database for reading with `nk.data()`
        * Reading of url links in `nk.data()`
        * Allow for `nk.hrv()` to compute RSA indices if respiratory data is present
        * All `hrv` functions to automatically detect correct sampling rate if tuple or dict is passed as input
        * Add support for PPG analysis: `nk.ppg_eventrelated()`, `nk.ppg_intervalrelated()`, `nk.ppg_analyze()`
        * Add Zhao et al. (2018) method for `nk.ecg_quality()`
        * Add tests for `epochs` module
        * Add sub-epoch option for ECG and RSP event-related analysis:
        	* users can create a smaller sub-epoch within the event-related epoch
        	* the rate-related features of ECG and RSP signals are calculated over the sub-epoch
        	* the remaining features are calculated over the original epoch, not the sub-epoch
        
        Fixes
        +++++++++++++
        
        * Fix propagation of values in `nk.signal_formatpeaks()` for formatting SCR column outputs generated by `eda_peaks()`
        * Fix docstrings of `nk.rsp_phase()`, from "RSP_Inspiration" to "RSP_Phase"
        * Update `signal_filter()` method for `rsp_clean()`: to use `sos` form, instead of `ba` form of butterworth (similar to `eda_clean()`)
        
        
        
        
        
        0.1.1
        ^^^^^^^^^^^^^^^^^^^
        
        New Features
        +++++++++++++
        
        * Use duration from `nk.events_find()` as `epochs_end` in `nk.epochs_create()`
        * Allow customized subsets of epoch lengths in `nk.bio_analyze()` with `window_lengths` argument
        * Add `nk.find_outliers()` to identify outliers (abnormal values)
        * Add utility function - `nk.check_type()` to return appropriate boolean values of input (integer, list, ndarray, pandas dataframe or pandas series)
        * (experimental) Add error bars in the summary plot method to illustrate standard error of each bin
        
        
        Fixes
        +++++++++++++
        
        * Fix type of value in `nk.signal_formatpeaks()` to ensure slice assignment is done on the same type
        
        
        0.0.1 (2019-10-29)
        ^^^^^^^^^^^^^^^^^^^
        
        * First release on PyPI.
        
        
        
        
Keywords: NeuroKit2,physiology,bodily signals,Python,ECG,EDA,EMG,PPG
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/x-rst
Provides-Extra: test
