mne-python | MNE : Magnetoencephalography | Dataset library

 by   mne-tools Python Version: v1.4.2 License: BSD-3-Clause

kandi X-RAY | mne-python Summary

kandi X-RAY | mne-python Summary

mne-python is a Python library typically used in Artificial Intelligence, Dataset applications. mne-python has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install mne-python' or download it from GitHub, PyPI.

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
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              mne-python has a highly active ecosystem.
              It has 2264 star(s) with 1199 fork(s). There are 86 watchers for this library.
              There were 3 major release(s) in the last 12 months.
              There are 453 open issues and 4000 have been closed. On average issues are closed in 170 days. There are 50 open pull requests and 0 closed requests.
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              It has a negative sentiment in the developer community.
              The latest version of mne-python is v1.4.2

            kandi-Quality Quality

              mne-python has 0 bugs and 0 code smells.

            kandi-Security Security

              mne-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              mne-python code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              mne-python is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

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              mne-python releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed mne-python and discovered the below as its top functions. This is intended to give you an instant insight into mne-python implemented functionality, and help decide if they suit your requirements.
            • Plot evokeds .
            • Read measurement information from a FIFF file .
            • Get the information about the header .
            • Plot volume sources .
            • Plot an alignment .
            • Get information from a KIT file .
            • Read the GDF header .
            • Reads the configuration file
            • Plot a figure .
            • Plot an epoch .
            Get all kandi verified functions for this library.

            mne-python Key Features

            No Key Features are available at this moment for mne-python.

            mne-python Examples and Code Snippets

            copy iconCopy
            library(devtools)
            devtools::install_github("mne-tools/mne-r")
            
            library(mne)  # load mne and get the mne object
            #> Importing MNE version=0.18.dev0, path='/Users/dengeman/github/mne-python/mne'
            
            # the mne object wraps the loaded mne module inside Py  
            bss_ica_eeg
            Pythondot img2Lines of Code : 21dot img2no licencesLicense : No License
            copy iconCopy
            For our purpose it is Biosemi128 data format (csv).
            
            ICA artifacts removal consists of two consecutive phases:
                1. Extract and plot indepentent components.
                *** Decide which of the components to remove ***
                2. Remove components.
            
            In this pac  
            copy iconCopy
            triggers = {'condition1': 1, 'condition2': 4,
                        'condition3': 8, 'condition4': 16}
            
            subject = 'A0001'
            
            exp = MnePreproc(subject, 'MyExp', '/Users/user/Experiments/MyExp', triggers)
            
            exp.load_raw()
            exp.filter_raw(low_bound=1)
            exp.find_events  
            Is there a MNE Python function that can enable me to use only part of the EEG data?
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            tmin = 10
            tmax = 30
            raw.crop(tmin=tmin, tmax=tmax)
            
            error install PyQt5 on Yocto Linux - board Variscite
            Pythondot img5Lines of Code : 2dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            IMAGE_INSTALL_append = " python3-pyqt5"
            
            How to create subplot from prerendered image with Matplotlib
            Pythondot img6Lines of Code : 110dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            
            
                from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
                canvas = FigureCanvas ( fig )
                plot_connectivity_circle ( conmat, all_ch, n_lines=300,
                                                node_angles=node_angles,
              
            Python: How do I add a While time loop code to a EEG CSV export for better clarity
            Pythondot img7Lines of Code : 5dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import mne
            edf = mne.io.read_raw_edf(r"Path name from external drive\PN00\PN00-1.edf")
            data = edf.to_data_frame(index="time", time_format="datetime")
            data.to_csv("PN00-1Testv3.csv")
            
            Generate interactive plot using mne package in google colab
            Pythondot img8Lines of Code : 10dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import altair as alt
            from vega_datasets import data
            cars = data.cars()
            
            alt.Chart(cars).mark_point().encode(
                x='Horsepower',
                y='Miles_per_Gallon',
                color='Origin',
            ).interactive()
            
            How to keep plotting window open in MNE with Python?
            Pythondot img9Lines of Code : 13dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import mne
            import matplotlib
            matplotlib.use('TkAgg')
            
            def get_plot():
                sample_data_folder = mne.datasets.sample.data_path()
                sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
                                               
            How to properly get the results of a tkinter file dialog when implemented using tkinter button?
            Pythondot img10Lines of Code : 19dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            window = Tk()
            
            def open_file():
            
                window.filename = filedialog.askopenfilename(initialdir = "Users/fishbacp/Desktop",title 
                = "Select file",filetypes = (("EDF files","*.edf"),("all files","*.*")))
                raw = mn.io.read_raw_edf(window

            Community Discussions

            QUESTION

            Why does mne resample method does not sample the data point to point?
            Asked 2020-Jul-24 at 02:09

            My understanding of downsampling is that it is an operation to decrease the sample rate of x by keeping the first sample and then every nth sample after the first. The example provided from the resample method of scipy package clearly illustrated about this operation as depicted the picture which is accessible from the link (https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html) or as extracted below

            In an enlarged view, it is evident that the original data points were resampled point by point.

            However, using the mne example of downsampling which accessible via the link : https://mne.tools/dev/auto_examples/preprocessing/plot_resample.html , I notice that the data points were not resampled point by point as illustrated visually below

            This given that, mne resample is based on the resample method of scipy package as indicated from mne resample function as shown: https://github.com/mne-tools/mne-python/blob/607fb4613fb5a80dd225132a4a53fe43b8fde0fb/mne/filter.py#L1342

            May I know whether this issue is due to the ringing artifacts or due to other problems?

            Also, are there remedies to mitigate this problem.

            Thanks for any insight. Appreciate it

            The same question has been asked in mne discussion repo, but un-answered as of the time of writing

            ...

            ANSWER

            Answered 2020-Jul-24 at 02:09

            My understanding of downsampling is that it is an operation to decrease the sample rate of x by keeping the first sample and then every nth sample after the first.

            Resampling typically consists of two steps: low-pass filtering to avoid aliasing, then sample rate reduction (subselecting samples from the resulting signal). The low-passing actually changes the values, so the subselection-of-filtered-data step will not necessarily yield points that were "on" the original signal.

            May I know whether this issue is due to the ringing artifacts or due to other problems?

            In this case it's likely due to the (implicit) low-pass filtering in the frequency-domain resampling of the signal. It looks pretty reasonable to me. If you want to play around with it a bit, you can

            1. Call scipy.signal.resample directly on your data and see how closely it matches.
            2. Pad your signal, call scipy.signal.resample, and remove the (now reduced-length) padding -- this is what MNE does internally.
            3. Use scipy.signal.resample_poly directly on your data.
            4. Manually low-pass filter and then directly subselect samples from the low-passed signal, which is what resample_poly does internally.

            Also, scipy.signal.resample does frequency-domain resampling, so implicitly uses a brick-wall filter at Nyquist when downsampling (unless you specify something for the window argument, which gets applied in the frequency domain in addition to the effective brick-wall filter).

            p.s. The answer provided is an extract from the discussion with the folk at mne, namely Eric Larson,Brunner Clemens,Phillip Alday. Credit should be given to them

            Source https://stackoverflow.com/questions/63046994

            QUESTION

            Using Mayavi mlab over VNC Throws OpenGL error
            Asked 2020-May-07 at 16:16

            I'm having some difficulty using Mayavi mlab in python with PyCharm IDE from the MNE-Python environment. I access the Conda environment with Mayavi and VTK over a VNC session using a xvnc server from my local MacOS to a Linux cluster machine.

            The error I get when opening a mlab window is:

            ...

            ANSWER

            Answered 2020-Apr-25 at 14:15

            I am unable to reproduce. Have you made sure that the machine you are accessing has available and loaded up-to-date graphics drivers and/or Mesa software? As an example, I am using TurboVNC to access a remote Ubuntu 18.04 machine, and I am able to produce the Spherical Harmonics Gallery Example both through the regular and envisage Mayavi backends. However, using the default settings of TurboVNC, the rendered scene has artefacts and saving the scene as png yields a black image, which I guess is a consequence of Mesa being used (llvmpipe). If I either start TurboVNC with -extension GLX or if I simply prepend vglrun to the python3 command, which calls VirtualGL, then OpenGL is used and the rendered scene is flawless. I attach the screenshot and saved figures below.

            Source https://stackoverflow.com/questions/61114076

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install mne-python

            You can install using 'pip install mne-python' or download it from GitHub, PyPI.
            You can use mne-python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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