Last modified by robing on 2022/03/25 09:55
From version 63.1
edited by robing
on 2020/04/28 10:48
Change comment: There is no comment for this version
To version 66.1
edited by robing
on 2020/04/30 14:23
Change comment: There is no comment for this version

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71 71  In another browser tab, open [[https:~~/~~/lab.ebrains.eu>>https://lab.ebrains.eu]]
72 72  
73 73  * **Edit the config files**
74 -Each stage has a config file (//pipeline/<stage_name>/config.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. The default values are set for an example dataset (ECoG, anesthetized mouse, [[IDIBAPS>>https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=sanchez-vives#Dataset/2ead029b-bba5-4611-b957-bb6feb631396]]]).
74 +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]]).
75 75  
76 76  * **Run the notebook**
77 77  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.
78 -Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
79 -
80 -* **Coming soon**
81 -** Use of KnowledgeGraph API
82 -** Provenance Tracking
83 -** HPC support
78 +Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake
84 84  
85 85  === ii) Local execution ===
86 86  
87 87  * **Get the code**
88 -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 Github>>https://guides.github.com/activities/hello-world/]]).
83 +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/]]).
89 89  
90 90  * (((
91 91  **Build the Python environment**
92 -In the wavescalephant git repository, there is an environment file ([[pipeline/envs/wavescalephant_env.yml>>https://drive.ebrains.eu/lib/905d7321-a16b-4147-8cca-31d710d1f946/file/pipeline/envs/wavescalephant_env.yml]]) 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]]).
93 -##conda env create ~-~-file /envs/wavescalephant_env.yml##
87 +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]]).
88 +##conda env create ~-~-file environment.yaml
89 +conda activate wavescalephant_env##
94 94  
95 95  )))
96 96  * **Edit the settings**
... ... @@ -97,14 +97,14 @@
97 97  The settings file specifies the path to the output folder, where results are saved to. Open the template file //[[pipeline/settings_template.py>>https://drive.ebrains.eu/lib/905d7321-a16b-4147-8cca-31d710d1f946/file/pipeline/settings_template.py]]//, set the ##output_path## to the desired path, and save it as //pipeline/settings.py//.
98 98  
99 99  * **Edit the config files**
100 -Each stage has a config file to specify which analysis/processing blocks to execute and which parameters to use. Edit the config template files //pipeline/stageXX_<stage_name>/config_template.yaml// according to your dataset and analysis goal, and save them as //pipeline/stageXX_<stage_name>/config.yaml//. A detailed description of the available parameter settings and their meaning is commented in the template files, and a more general description of the working mechanism of each stage can be found in the respective README file //pipeline/stageXX_<stage_name>/README.md//.
96 +Each stage uses a config file to specify which analysis/processing blocks to execute and which parameters to use. Edit the config template files //pipeline/stageXX_<stage_name>/configs/config_template.yaml// according to your dataset and analysis goal, and save them as //pipeline/stageXX_<stage_name>/configs/config_<profile>.yaml//. A detailed description of the available parameter settings and their meaning is commented in the template files, and a more general description of the working mechanism of each stage can be found in the respective README file //pipeline/stageXX_<stage_name>/README.md//.
101 101  //Links are view-only//
102 102  ** full pipeline: [[README.md>>https://drive.ebrains.eu/smart-link/d2e93a2a-09f6-4dce-982d-0370953a4da8/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/7948fbb3-bf8a-4785-9b28-d5c15a1aafa7/]]
103 -** stage01_data_entry: [[README.md>>https://drive.ebrains.eu/smart-link/896f8880-a7d1-4a30-adbf-98759860fed5/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/d429639d-b76e-4093-8fad-a25463d41edc/]]
104 -** stage02_processing: [[README.md>>https://drive.ebrains.eu/smart-link/01f21fa5-94f7-4883-8388-cc50957f9c81/]], [[config.yaml>>https://drive.ebrains.eu/f/b1607671f6f2468ahttps://drive.ebrains.eu/smart-link/02a3f92c-dc7d-4b33-94f5-91b00db060d5/a43c/]]
105 -** stage03_trigger_detection: [[README.md>>https://drive.ebrains.eu/smart-link/18d276cd-a691-4ee1-81c6-7978cef9c1b4/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/76adbb12-7cb4-42df-9fd5-735927ea3ba8/]]
106 -** stage04_wavefront_detection: [[README.md>>https://drive.ebrains.eu/smart-link/a8e80096-06a0-4ff4-b645-90e134e46ac5/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/6b0b233f-30b7-4bbd-8564-1abebd27ea6d/]]
107 -** stage05_wave_characterization: [[README.md>>https://drive.ebrains.eu/smart-link/3009a214-a11f-424c-8a6e-13e7506545eb/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/471001d5-33f5-488e-a9a4-f03b190e3da7/]]
99 +** stage01_data_entry: [[README.md>>https://drive.ebrains.eu/smart-link/896f8880-a7d1-4a30-adbf-98759860fed5/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/9bef8f59-1007-48c4-b5ba-30de4ff18f34/]]
100 +** stage02_processing: [[README.md>>https://drive.ebrains.eu/smart-link/01f21fa5-94f7-4883-8388-cc50957f9c81/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/7e75caf6-e2d6-4393-a97c-4f481c908cf8/]]
101 +** stage03_trigger_detection: [[README.md>>https://drive.ebrains.eu/smart-link/18d276cd-a691-4ee1-81c6-7978cef9c1b4/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/dfa375c0-cc80-4f95-b3ed-40140acbd96b/]]
102 +** stage04_wavefront_detection: [[README.md>>https://drive.ebrains.eu/smart-link/a8e80096-06a0-4ff4-b645-90e134e46ac5/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/3a54be8c-b9f4-4698-a85d-6ad97990b40a/]]
103 +** stage05_wave_characterization: [[README.md>>https://drive.ebrains.eu/smart-link/3009a214-a11f-424c-8a6e-13e7506545eb/]], [[config.yaml>>https://drive.ebrains.eu/smart-link/83f68955-0ca8-4123-9734-6e93349ca3e3/]]
108 108  
109 109  * **Enter a dataset**
110 110  There are two test datasets in the collab drive (IDIBAPS and LENS) for which there are also corresponding config files and scripts in the data_entry stage. So, these datasets are ready to be used and analyzed.
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111 111  For adding new datasets see //[[pipeline/stage01_data_entry/README.md>>https://drive.ebrains.eu/smart-link/d2e93a2a-09f6-4dce-982d-0370953a4da8/]]//
112 112  
113 113  * **Run the pipeline (-stages)**
114 -To run the pipeline with [[snakemake>>https://snakemake.readthedocs.io/en/stable/]]), activate the Python environment ##conda activate wavescalephant_env,## make sure you are in the working directory (pipeline/), and call ##snakemake## to run the entire pipeline.
115 -To (re-)execute an individual stage, you can navigate to the corresponding stage folder and call the ##snakemake## command there. For running an individual stage, you may need to manually set the path for input file for the stage (i.e. the output file of the previous stage) in the config file ##INPUT: /path/to/file##.
110 +To run the pipeline with [[snakemake>>https://snakemake.readthedocs.io/en/stable/]]), activate the Python environment ##conda activate wavescalephant_env,## make sure you are in the working directory (//pipeline/)//, and call ##snakemake## to run the entire pipeline.
111 +For a more detailed executed guide and how to execute individual stages and blocks see the pipeline [[Readme>>https://drive.ebrains.eu/smart-link/3009a214-a11f-424c-8a6e-13e7506545eb/]].
116 116  
117 117  == Accessing and using the results ==
118 118  
119 119  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.
120 120  
117 +== Outlook ==
118 +
119 +* 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.
120 +* Adding support for the pipeline to make use of **HPC** resources when running on the collab.
121 +* 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.
122 +* Extending the **application** of the pipeline to the analysis of other types of activity waves and oscillations.
123 +* Integrating and co-developing new features of the underlying **software tools **[[Elephant>>https://elephant.readthedocs.io/en/latest/]], [[Neo>>https://neo.readthedocs.io/en/stable/]], [[Nix>>https://github.com/G-Node/nix]], [[Snakemake>>https://snakemake.readthedocs.io/en/stable/]].
124 +
121 121  == References ==
122 122  
123 123  * [[Celotto, Marco, et al. "Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques." //Methods and Protocols// 3.1 (2020): 14.>>https://doi.org/10.3390/mps3010014]]
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128 128  
129 129  == License (to discuss) ==
130 130  
131 -All text and example data in this collab is licensed under Creative Commons CC-BY 4.0 license. Software code is licensed under a modified BSD license.
135 +All text and example data in this collab is licensed under Creative Commons CC-BY 4.0 license. Software code is licensed under GNU General Public License v3.0.
132 132  
133 133  [[image:https://i.creativecommons.org/l/by/4.0/88x31.png||style="float:left"]]
134 134