Last modified by robing on 2022/03/25 09:55
From version 73.1
edited by denker
on 2020/05/08 15:19
Change comment: There is no comment for this version
To version 81.1
edited by robing
on 2020/05/28 15:12
Change comment: There is no comment for this version

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73 73  Each stage has config files (//pipeline/<stage_name>/configs/config_<profile>.yaml//) to specify which analysis/processing blocks to execute and which parameters to use. General and specific information about the blocks and parameters can be found in the README and config files of each stage. There are preset configuration profiles for the benchmark datasets IDIBAPS ([[ECoG, anesthetized mouse>>https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=sanchez-vives#Dataset/2ead029b-bba5-4611-b957-bb6feb631396]]) and LENS ([[Calcium Imaging, anesthetized mouse>>https://kg.ebrains.eu/search/instances/Dataset/71285966-8381-48f7-bd4d-f7a66afa9d79]]).
74 74  
75 75  * **Run the notebook**
76 -In the jupyter hub, navigate to //drive/My Libraries/My Library/pipeline/showcase_notebooks/run_snakemake_in_collab.ipynb//, or where you copied the //pipeline// folder to.
77 -Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake
76 +In the jupyter hub, navigate to //drive/My Libraries/My Library/run_snakemake_in_collab.ipynb//, or where you copied the //pipeline// folder to.
77 +Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
78 78  
79 79  === ii) Local execution ===
80 80  
81 +//tested only with Mac OS and Linux!//
82 +
81 81  * **Get the code**
82 82  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/]]).
83 83  
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86 86  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]]).
87 87  ##conda env create ~-~-file environment.yaml
88 88  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##
89 89  
90 90  )))
91 91  * **Edit the settings**
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113 113  
114 114  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.
115 115  
116 -== Outlook ==
122 +**Examples of the output figures (for IDIBAPS dataset)**
117 117  
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 +
118 118  * 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.
119 119  * Adding support for the pipeline to make use of **HPC** resources when running on the collab.
120 120  * 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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