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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Previous
Step1:  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.
Step2:  The color code
Step3:  For any tables or relational expressions derived from them, you may see all the attributes using the heading attribute.
Step4:  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.
Step5:  To fetch the contents of a table or a derived relational expression into a numpy record array, use the fetch method.
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Python Code:
%pylab inline
import datajoint as dj
from pipeline import mice, experiment, vis, preprocess, tuning
pylab.rcParams['figure.figsize'] = (8, 8)  # make figures bigger
Explanation: Previous: Intro  ::: Next pipeline.experiment
The Big Picture
End of explanation
erd = dj.ERD(preprocess.Sync) - 2
erd += dj.ERD(preprocess.Spikes) - 3 
erd += experiment.Session 
erd += dj.ERD(tuning.MonetRF.Map) - 1
erd.draw()
Explanation: 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.
End of explanation
experiment.Session().show_definition();
Explanation: The color code: 
* <span style="background-color: #BBDDBB">Manual</span> (e.g. experiment.Scan) is data entered manually or by external software.
* <span style="background-color: #BBBBDD">Imported</span> (e.g. preprocess.Prepare) is data imported from external files by datajoint, requires access to local drives in the lab.
* <span style="background-color: #FFBBBB">Computed</span> (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.
* <span style="background-color: #DDDDDD">Lookup</span> (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.
End of explanation
experiment.Session().heading
Explanation: For any tables or relational expressions derived from them, you may see all the attributes using the heading attribute.
End of explanation
experiment.Session()
Explanation: 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.
End of explanation
data = experiment.Session().fetch()
Explanation: To fetch the contents of a table or a derived relational expression into a numpy record array, use the fetch method.
End of explanation
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