mne-bids-pipeline | Automatically process entire electrophysiological datasets

by mne-tools Python Version: 1.9.0 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | mne-bids-pipeline Summary

mne-bids-pipeline is a Python library typically used in Data Science applications. mne-bids-pipeline has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However mne-bids-pipeline build file is not available. You can install using 'pip install mne-bids-pipeline' or download it from GitHub, PyPI.

Automatically process entire electrophysiological datasets using MNE-Python.

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Security

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Support

mne-bids-pipeline has a low active ecosystem.

It has 91 star(s) with 52 fork(s). There are 17 watchers for this library.

There were 1 major release(s) in the last 6 months.

There are 54 open issues and 177 have been closed. On average issues are closed in 93 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of mne-bids-pipeline is 1.9.0

Quality

mne-bids-pipeline has 0 bugs and 0 code smells.

Security

mne-bids-pipeline has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

mne-bids-pipeline code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

mne-bids-pipeline 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.

Reuse

mne-bids-pipeline releases are available to install and integrate.

Deployable package is available in PyPI.

mne-bids-pipeline has no build file. You will be need to create the build yourself to build the component from source.

It has 8270 lines of code, 164 functions and 56 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed mne-bids-pipeline and discovered the below as its top functions. This is intended to give you an instant insight into mne-bids-pipeline implemented functionality, and help decide if they suit your requirements.

Perform an IICA analysis
Detect bad components
Generate log kwargs
Filter raw data
Run a Miller filter
Handler for drop_channels
Load raw data
Import MEG channel data
Create a config namespace
Extract the list of runs
Create a run
Initialize the script module
Run time - decoding
Generate a grand average report
Generate epochs for each run
Return the filenames of the input files
Extract the list of runs from MNE
Run evoked data based on experimental conditions
Get input files for covariance
Get input filenames for filtering
Create a BIDSP file
Run inverse operator
Run epochs decoding
Decorator to wrap a failsafe run
Compute SSP
Apply ICA artifact removal
Get script modules
Drops a PEP dataset

Get all kandi verified functions for this library.

mne-bids-pipeline Key Features

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

mne-bids-pipeline Examples and Code Snippets

No Code Snippets are available at this moment for mne-bids-pipeline.

Community Discussions

Trending Discussions on Data Science

What does stopping the runtime while uploading a dataset to Hub cause?

Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?

split geometric progression efficiently in Python (Pythonic way)

is there any effective or efficient way to find net position of numbers from a data frame in python

Is there a way to return float or integer from a conditional True/False

Generate the all possible unique peptides (permutants) in Python/Biopython

Getting Error 524 while running jupyter lab in google cloud platform

TypeError: import_optional_dependency() got an unexpected keyword argument 'errors'

HUGGINGFACE TypeError: '>' not supported between instances of 'NoneType' and 'int'

How to identify what features affect predictions result?

QUESTION

What does stopping the runtime while uploading a dataset to Hub cause?

Asked 2022-Mar-24 at 01:06

I am getting the following error while trying to upload a dataset to Hub (dataset format for AI) S3SetError: Connection was closed before we received a valid response from endpoint URL: "<...>".

So, I tried to delete the dataset and it is throwing this error below.

CorruptedMetaError: 'boxes/tensor_meta.json' and 'boxes/chunks_index/unsharded' have a record of different numbers of samples. Got 0 and 6103 respectively.

Using Hub version: v2.3.1

...

ANSWER

Answered 2022-Mar-24 at 01:06

Seems like when you were uploading the dataset the runtime got interrupted which led to the corruption of the data you were trying to upload. Using force=True while deleting should allow you to delete it.

For more information feel free to check out the Hub API basics docs for details on how to delete datasets in Hub.

If you stop uploading a Hub dataset midway through your dataset will be only partially uploaded to Hub. So, you will need to restart the upload. If you would like to re-create the dataset, you can use the overwrite = True flag in hub.empty(overwrite = True). If you are making updates to an existing dataset, you should use version control to checkpoint the states that are in good shape.

Source https://stackoverflow.com/questions/71595867

QUESTION

Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?

Asked 2022-Mar-19 at 16:28

I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.

I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.

I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).

...

ANSWER

Answered 2022-Mar-19 at 16:28

Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.

You can find more information about storage authentication in the Hub docs.

Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.

Source https://stackoverflow.com/questions/71539946

QUESTION

split geometric progression efficiently in Python (Pythonic way)

Asked 2022-Jan-22 at 10:09

I am trying to achieve a calculation involving geometric progression (split). Is there any effective/efficient way of doing it. The data set has millions of rows. I need the column "Traded_quantity"

Marker Action Traded_quantity 2019-11-05 09:25 0 0 09:35 2 BUY 3 09:45 0 0 09:55 1 BUY 4 10:05 0 0 10:15 3 BUY 56 10:24 6 BUY 8128

turtle = 2 (User defined)

base_quantity = 1 (User defined)

...

ANSWER

Answered 2022-Jan-22 at 10:09

This should work

Source https://stackoverflow.com/questions/70810857

QUESTION

is there any effective or efficient way to find net position of numbers from a data frame in python

Asked 2022-Jan-21 at 01:04

I have a multi index df, with column "Turtle"

...

ANSWER

Answered 2022-Jan-21 at 01:02

There is a simple formula that maps Turtle to Net Pos. The calculation can be expressed as a sum of geometric series times base_quantity, yielding the function f below.

Source https://stackoverflow.com/questions/70795029

QUESTION

Is there a way to return float or integer from a conditional True/False

Asked 2022-Jan-16 at 14:28

n_level = range(1, steps + 2)

...

ANSWER

Answered 2022-Jan-16 at 14:22

this can be achieved easily using binary search, there are many ways to apply that(NumPy, bisect). I would recommend the library bisect.

Added Buu for the Crest and See for the Trough, so that code and differentiate the segments. You can choose anything

Source https://stackoverflow.com/questions/70601323

QUESTION

Generate the all possible unique peptides (permutants) in Python/Biopython

Asked 2021-Dec-01 at 07:07

I have a scenario in which I have a peptide frame having 9 AA. I want to generate all possible peptides by replacing a maximum of 3 AA on this frame ie by replacing only 1 or 2 or 3 AA.

The frame is CKASGFTFS and I want to see all the mutants by replacing a maximum of 3 AA from the pool of 20 AA.

we have a pool of 20 different AA (A,R,N,D,E,G,C,Q,H,I,L,K,M,F,P,S,T,W,Y,V).

I am new to coding so Can someone help me out with how to code for this in Python or Biopython.

output is supposed to be a list of unique sequences like below:

CKASGFTFT, CTTSGFTFS, CTASGKTFS, CTASAFTWS, CTRSGFTFS, CKASEFTFS ....so on so forth getting 1, 2, or 3 substitutions from the pool of AA without changing the existing frame.

...

ANSWER

Answered 2021-Dec-01 at 07:07

Ok, so after my code finished, I worked the calculations backwards,

Case1, is 9c1 x 19 = 171

Case2, is 9c2 x 19 x 19 = 12,996

Case3, is 9c3 x 19 x 19 x 19 = 576,156

That's a total of 589,323 combinations.

Here is the code for all 3 cases, you can run them sequentially.

You also requested to join the array into a single string, I have updated my code to reflect that.

Source https://stackoverflow.com/questions/70178355

QUESTION

Getting Error 524 while running jupyter lab in google cloud platform

Asked 2021-Oct-15 at 02:14

I am not able to access jupyter lab created on google cloud

I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

I tried the diagnostic tool, which gave the following result:

but didn't fix it.

Thanks in advance.

...

ANSWER

Answered 2021-Aug-20 at 14:00

You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error

Source https://stackoverflow.com/questions/68862621

QUESTION

TypeError: import_optional_dependency() got an unexpected keyword argument 'errors'

Asked 2021-Oct-08 at 03:00

I am trying to work with Featuretools to develop an automated feature engineering workflow for the customer churn dataset. The end outcome is a function that takes in a dataset and label times for customers and builds a feature matrix that can be used to train a machine learning model.

As part of this exercise I am trying to execute the below code for plotting a histogram and got "TypeError: import_optional_dependency() got an unexpected keyword argument 'errors' ". Please help resolve this TypeError.

...

ANSWER

Answered 2021-Sep-14 at 20:32

Try to upgrade pandas:

Source https://stackoverflow.com/questions/69148495

QUESTION

HUGGINGFACE TypeError: '>' not supported between instances of 'NoneType' and 'int'

Asked 2021-Sep-12 at 16:55

I am working on Fine-Tuning Pretrained Model on custom (using HuggingFace) dataset I will copy all code correctly from the one youtube video everything is ok but in this cell/code:

...

ANSWER

Answered 2021-Sep-12 at 16:55

Seems to be an issue with the new version of transformers.

Installing version 4.6.0 worked for me.

Source https://stackoverflow.com/questions/68875496

QUESTION

How to identify what features affect predictions result?

Asked 2021-Aug-11 at 15:55

I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

Table example:

...

ANSWER

Answered 2021-Aug-11 at 15:55

You could build a model like this.

x = features you have. y = true_lable

from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).

Source https://stackoverflow.com/questions/68744565

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

Vulnerabilities

No vulnerabilities reported

Install mne-bids-pipeline

You can install using 'pip install mne-bids-pipeline' or download it from GitHub, PyPI.
You can use mne-bids-pipeline 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.