Previous: Intro ::: Next pipeline.experiment

The Big Picture



In [ ]:

    
%pylab inline
import datajoint as dj
from pipeline import mice, experiment, vis, preprocess, tuning
pylab.rcParams['figure.figsize'] = (8, 8)  # make figures bigger

Below is the outline of processing chain.

Each schema corresponds to a module in the pipeline package and the corresponding database on the server.

Each node represents a class in the module and a table in the database.

Lines represents dependencies.

The pipeline starts with the manual information entered in the schemas mice and experiment.
The visual stimulus information is stored in the vis schema.
The schema preprocess extracts traces, infers spikes, and synchronizes two-photon data to the visual stimulus.



In [ ]:

    
erd = dj.ERD(preprocess.Sync) - 2
erd += dj.ERD(preprocess.Spikes) - 3 
erd += experiment.Session 
erd += dj.ERD(tuning.MonetRF.Map) - 1
erd.draw()

The color code:

Manual (e.g. experiment.Scan) is data entered manually or by external software.
Imported (e.g. preprocess.Prepare) is data imported from external files by datajoint, requires access to local drives in the lab.
Computed (e.g. preprocess.ComputeTraces) is computed by datajoint from data already in the database without access to external files, can be repopulated in the cloud.
Lookup (e.g. preprocess.Method) are lookup tables containing general information that is not specific to any one experiment. These often contain processing settings and general knowledge.

For any table, you can see its definition (attributes, dependencies, comments) by using the show_definition method.



In [ ]:

    
experiment.Session().show_definition();

For any tables or relational expressions derived from them, you may see all the attributes using the heading attribute.



In [ ]:

    
experiment.Session().heading

Note that the attributes above the dividing line --- comprise the primary key, i.e. identifying information used for matching data across different tables.

To preview the contents of a table or of a derived relational expression, simply type it at the IPython prompt.



In [ ]:

    
experiment.Session()

To fetch the contents of a table or a derived relational expression into a numpy record array, use the fetch method.



In [ ]:

    
data = experiment.Session().fetch()

Next: pipeline.experiment