This Python project aims to create a simple Python package to produce data packages of cell migration tracking files. The final goal is to have a uniform, standardized way to represent these data, as in Frictionless Data and Data Packages .
Steps to follow to use the package:
- step 1 - Install the package (note it's Python 3 only at the moment):
python setup.py install
- step 2 - create a
biotracks.iniconfiguration file and place it in the same directory as your tracking file. The file must be structured as follows:
[TOP_LEVEL_INFO] author = the author of the dp title = a title describing the dp name = a name for the dp author_institute = the insitute of the author author_email = a valid email address [TRACKING_DATA] x_coord_cmso = the column name pointing to the x coordinate y_coord_cmso = the column name pointing to the y coordinate z_coord_cmso = the column name pointing to the z coordinate frame_cmso = the column name pointing to the frame information object_id_cmso = the object identifier link_id_cmso = the link identifier
- step 3 - move to the
scriptsdirectory and run:
python create_dpkg.py your_tracking_filethis will create a dp directory containing:
- a csv file for the objects (i.e. cells)
- a csv file for the links (i.e. a linear collection of objects)
- and a dp.json file containing the json schemas of the two csv files.
This last file will look something like this:
{
"resources": [{
"name": "objects_table",
"schema": {
"primaryKey": "SPOT_ID",
"fields": [{
"name": "SPOT_ID",
"title": "",
"description": "",
"constraints": {
"unique": true
},
"type": "integer",
"format": "default"
}, {
"type": "integer",
"name": "FRAME",
"title": "",
"format": "default",
"description": ""
}, {
"type": "number",
"name": "POSITION_X",
"title": "",
"format": "default",
"description": ""
}, {
"type": "number",
"name": "POSITION_Y",
"title": "",
"format": "default",
"description": ""
}]
},
"path": "objects.csv"
}, {
"name": "links_table",
"schema": {
"foreignKeys": [{
"fields": "SPOT_ID",
"reference": {
"resource": "objects_table",
"fields": "SPOT_ID",
"datapackage": ""
}
}],
"fields": [{
"type": "integer",
"name": "LINK_ID",
"title": "",
"format": "default",
"description": ""
}, {
"type": "integer",
"name": "SPOT_ID",
"title": "",
"format": "default",
"description": ""
}]
},
"path": "links.csv"
}],
"name": "CMSO_tracks",
"title": "A CMSO data package representation of cell tracking data",
"author_email": "[email protected]",
"author_institute": "VIB",
"author": "paola masuzzo"
}Then, the datapackage is pushed to a pandas dataframe. At the moment, this dataframe is used to create simple visualizations of links and turning angles.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent