Changes for page SGA2 SP3 UC002 KR3.2 - Slow Wave Analysis Pipeline
Last modified by robing on 2022/03/25 09:55
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... ... @@ -78,8 +78,6 @@ 78 78 79 79 === ii) Local execution === 80 80 81 -//tested only with Linux!// 82 - 83 83 * **Get the code** 84 84 The source code of the pipeline is available via Github: [[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]] and can be cloned to your machine ([[how to get started with Github>>https://guides.github.com/activities/hello-world/]]). 85 85 ... ... @@ -88,10 +88,6 @@ 88 88 In the wavescalephant git repository, there is an environment file ([[pipeline/environment.yaml>>https://drive.ebrains.eu/smart-link/1a0b15bb-be87-46ee-b838-4734bc320d20/]]) specifying the required packages and versions. To build the environment, we recommend using conda ([[how to get started with conda>>https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html]]). 89 89 ##conda env create ~-~-file environment.yaml 90 90 conda activate wavescalephant_env## 91 - 92 -Make sure that neo and elephant were installed as their Github development version, and if necessary add them manually to the environment. 93 -##pip install git+https:~/~/github.com/NeuralEnsemble/elephant.git 94 -pip install git+https:~/~/github.com/NeuralEnsemble/python-neo.git## 95 95 96 96 ))) 97 97 * **Edit the settings** ... ... @@ -119,20 +119,8 @@ 119 119 120 120 All results are stored in the path specified in the //settings.py// file. The folder structure reflects the structuring of the pipeline into stages and blocks. All intermediate results are stored as //.nix// files using the [[Neo data format>>https://neo.readthedocs.io/en/stable/]] and can be loaded with ##neo.NixIO('/path/to/file.nix').read_block()##. Additionally, most blocks produce a figure, and each stage a report file, to give an overview of the execution log, parameters, intermediate results, and to help with debugging. The final stage (//stage05_wave_characterization//) stores the results as[[ //pandas.DataFrames//>>https://pandas.pydata.org/]] in //.csv// files, separately for each measure as well as in a combined dataframe for all measures. 121 121 122 - **Examplesof the output figures (forIDIBAPS dataset)**116 +== Outlook == 123 123 124 -* Stage 01 - [[example signal traces and metadata>>https://drive.ebrains.eu/smart-link/cf2fa914-260d-4d61-a2da-03ea07b7f9be/]] 125 -* Stage 02 - [[background substraction>>https://drive.ebrains.eu/smart-link/586d2f3c-591b-4dfb-94ee-8c0e28050dc4/]] 126 -* Stage 02 - [[logMUA estimation>>https://drive.ebrains.eu/smart-link/c92e4b0c-0938-44e8-9f8d-00522796b2fd/]] 127 -* Stage 02 - [[processed signal trace>>https://drive.ebrains.eu/smart-link/26ed27c6-de56-4b48-a57b-f70aab629197/]] 128 -* Stage 03 - [[amplitude distribution>>https://drive.ebrains.eu/smart-link/8ba80293-ba75-4a37-8a8f-05d44cf6f65c/]] 129 -* Stage 03 - [[UP state detection>>https://drive.ebrains.eu/smart-link/ab172be0-178e-4153-a3e6-b4bace32dd50/]] 130 -* Stage 04 - [[trigger clustering>>https://drive.ebrains.eu/smart-link/4a1f0169-8b43-49ce-80c8-f2fa0f4d50d3/]] 131 -* Stage 05 - [[planar velocities>>https://drive.ebrains.eu/smart-link/f4de8073-cb40-47a7-bc82-f97d36dbae25/]] 132 -* Stage 05 - [[directionality>>https://drive.ebrains.eu/smart-link/5485032d-0121-4cde-9ea2-3e0af3f12178/]] 133 - 134 -=== Outlook === 135 - 136 136 * Using the **KnowledgeGraph API **to insert data directly from the Knowledge Graph into the pipeline and also register and store the corresponding results as Analysis Objects. Such Analysis Objects are to incorporate **Provenance Tracking, **using [[fairgraph>>https://github.com/HumanBrainProject/fairgraph]],** **to record the details of the processing and analysis steps. 137 137 * Adding support for the pipeline to make use of **HPC** resources when running on the collab. 138 138 * Further extending the available **methods** to address a wider variety of analysis objectives and support the processing of other datatypes. Additional documentation and guides should also make it easier for non-developers to contribute new method blocks.
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