### Complete DataJoint Element Session Workflow Example

Source: https://context7.com/datajoint/element-session/llms.txt

Illustrates a full workflow for setting up and using DataJoint Element Session. This includes defining upstream tables (Subject, Experimenter, Project), activating the session schema, inserting data, creating a session with associated metadata (directory, experimenter, notes, project), querying the complete session information, and exporting it to NWB format. Key dependencies include 'datajoint', 'element_session', and 'element_session.export.nwb'.

```python
import datajoint as dj
from element_session import session_with_datetime as session
from datetime import datetime

# Step 1: Define and activate schema with dependencies
schema = dj.schema()

@schema
class Subject(dj.Manual):
    definition = """
    subject: varchar(32)
    ---
    species: varchar(32)
    sex: enum('M', 'F', 'U')
    """

@schema
class Experimenter(dj.Manual):
    definition = """
    user: varchar(32)
    """

@schema
class Project(dj.Manual):
    definition = """
    project: varchar(64)
    ---
    project_description: varchar(1024)
    """

# Create module for dependencies
import sys
current_module = sys.modules[__name__]
current_module.Subject = Subject
current_module.Experimenter = Experimenter
current_module.Project = Project

# Activate session schema
session.activate('neuroscience_db', linking_module=current_module)

# Step 2: Insert upstream data
Subject.insert1({'subject': 'mouse_alpha', 'species': 'Mus musculus', 'sex': 'M'})
Experimenter.insert1({'user': 'researcher_a'})
Project.insert1({
    'project': 'spatial_navigation',
    'project_description': 'Study of hippocampal place cells during navigation'
})

# Step 3: Create session with metadata
session_key = {
    'subject': 'mouse_alpha',
    'session_datetime': datetime(2023, 12, 1, 10, 0, 0)
}

session.Session.insert1({**session_key, 'session_id': 1})

session.SessionDirectory.insert1({
    **session_key,
    'session_dir': '/mnt/data/spatial_navigation/mouse_alpha/session_001'
})

session.SessionExperimenter.insert1({**session_key, 'user': 'researcher_a'})

session.SessionNote.insert1({
    **session_key,
    'session_note': 'Baseline recording in familiar environment'
})

session.ProjectSession.insert1({**session_key, 'project': 'spatial_navigation'})

# Step 4: Query complete session information
complete_session = (
    session.Session *
    session.SessionDirectory *
    session.SessionNote & session_key
).fetch1()

# Step 5: Export to NWB
from element_session.export.nwb import session_to_nwb
nwbfile = session_to_nwb(
    session_key=session_key,
    project_key={'project': 'spatial_navigation'}
)
```

--------------------------------

### Link Sessions to Projects - Python

Source: https://context7.com/datajoint/element-session/llms.txt

This Python code demonstrates how to link multiple experimental sessions to a specific project. It involves inserting new sessions, creating project-session associations, and then querying to retrieve all sessions belonging to a project or all projects associated with a given session. The 'element_session' library is required.

```python
from element_session import session
from datetime import datetime

# Define project and sessions
project_key = {'project': 'memory_consolidation_2023'}

session_keys = [
    {'subject': 'mouse_101', 'session_datetime': datetime(2023, 11, 25, 10, 0, 0)},
    {'subject': 'mouse_102', 'session_datetime': datetime(2023, 11, 25, 11, 30, 0)},
    {'subject': 'mouse_103', 'session_datetime': datetime(2023, 11, 25, 14, 0, 0)}
]

# Insert sessions
session.Session.insert(session_keys)

# Link sessions to project
project_sessions = [
    {**project_key, **sess_key}
    for sess_key in session_keys
]
session.ProjectSession.insert(project_sessions)

# Query all sessions for a project
project_sessions = (
    session.Session * session.ProjectSession & project_key
).fetch(as_dict=True)

# Query all projects for a specific session
session_projects = (
    session.ProjectSession & session_keys[0]
).fetch('project')

# Output: ['memory_consolidation_2023']
```

--------------------------------

### Manage Sessions, Experimenters, and Notes - Python

Source: https://context7.com/datajoint/element-session/llms.txt

This code snippet illustrates how to manage sessions, associate experimenters with them, and add notes. It covers inserting a new session, linking multiple experimenters, adding a descriptive note, and querying for sessions involving a specific experimenter or any session with notes. It requires the 'element_session' library.

```python
from element_session import session
from datetime import datetime

session_key = {
    'subject': 'mouse_003',
    'session_datetime': datetime(2023, 11, 20, 16, 0, 0)
}

# Insert session
session.Session.insert1(session_key)

# Associate experimenters with session
session.SessionExperimenter.insert([
    {**session_key, 'user': 'john_doe'},
    {**session_key, 'user': 'jane_smith'}
])

# Add session notes
session.SessionNote.insert1({
    **session_key,
    'session_note': 'Subject was alert and responsive. All electrodes showed good signal quality.'
})

# Query experimenter sessions
experimenter_sessions = (session.SessionExperimenter & {'user': 'john_doe'}).fetch(
    as_dict=True
)

# Query sessions with notes
sessions_with_notes = (session.Session * session.SessionNote).fetch(as_dict=True)

# Output for sessions_with_notes:
# [
#   {
#     'subject': 'mouse_003',
#     'session_datetime': datetime(2023, 11, 20, 16, 0, 0),
#     'session_id': None,
#     'session_note': 'Subject was alert and responsive. All electrodes showed good signal quality.'
#   }
# ]
```

--------------------------------

### Query and Filter Sessions with DataJoint Element Session

Source: https://context7.com/datajoint/element-session/llms.txt

Demonstrates various methods to query and filter session data. It covers fetching all sessions for a subject, sessions within a date range, sessions with directory information, and sessions associated with a specific experimenter. It also shows how to count sessions per subject. Dependencies include the 'element_session' library and Python's 'datetime'.

```python
from element_session import session
from datetime import datetime, timedelta

# Query all sessions for a subject
subject_sessions = (session.Session & {'subject': 'mouse_001'}).fetch(as_dict=True)

# Query sessions within date range
start_date = datetime(2023, 11, 1)
end_date = datetime(2023, 11, 30)

sessions_in_range = (
    session.Session &
    f'session_datetime >= "{start_date}"' &
    f'session_datetime <= "{end_date}"'
).fetch(as_dict=True)

# Query sessions with directory information
sessions_with_dirs = (
    session.Session * session.SessionDirectory
).fetch(as_dict=True)

# Query sessions by experimenter
experimenter_sessions = (
    session.Session &
    (session.SessionExperimenter & {'user': 'jane_smith'})
).fetch(as_dict=True)

# Get session count per subject
session_counts = (
    session.Session &
    {'subject': ['mouse_001', 'mouse_002', 'mouse_003']}
).proj().fetch()
```

--------------------------------

### Export Session to NWB Format - Python

Source: https://context7.com/datajoint/element-session/llms.txt

This Python code demonstrates exporting session data to the NWB (Neurodata Without Borders) format using the 'element_session.export.nwb' module. It shows how to export basic session information, include lab, project, and protocol details, and add custom metadata. The output is an NWBFile object, which can then be saved to disk using 'pynwb'. Dependencies include 'element_session', 'datetime', and 'pynwb'.

```python
from element_session.export.nwb import session_to_nwb
from datetime import datetime
import pynwb

# Define session key
session_key = {
    'subject': 'mouse_001',
    'session_datetime': datetime(2023, 11, 15, 14, 30, 0)
}

# Export basic session to NWB
nwbfile = session_to_nwb(session_key)

# Export with lab, project, and protocol information
lab_key = {'lab': 'Neuroscience_Lab_A'}
project_key = {'project': 'visual_cortex_study'}
protocol_key = {'protocol': 'two_photon_imaging'}

nwbfile = session_to_nwb(
    session_key=session_key,
    lab_key=lab_key,
    project_key=project_key,
    protocol_key=protocol_key
)

# Add custom metadata
additional_kwargs = {
    'institution': 'University Research Institute',
    'lab': 'Visual Neuroscience Lab',
    'keywords': ['visual cortex', 'calcium imaging', 'behaving animal']
}

nwbfile = session_to_nwb(
    session_key=session_key,
    additional_nwbfile_kwargs=additional_kwargs
)

# Save NWB file to disk
from pynwb import NWBHDF5IO
with NWBHDF5IO('session_mouse_001.nwb', 'w') as io:
    io.write(nwbfile)

# Output: NWBFile object with session metadata
# nwbfile.session_id = 'mouse_001_2023-11-15T14:30:00'
# nwbfile.session_start_time = datetime(2023, 11, 15, 14, 30, 0, tzinfo=timezone.utc)
# nwbfile.identifier = '550e8400-e29b-41d4-a716-446655440000' (UUID)
```

--------------------------------

### Add Session Attributes - Python

Source: https://context7.com/datajoint/element-session/llms.txt

Demonstrates how to insert and query attributes associated with a session. Attributes can be simple text values or binary blobs, and multiple attributes can be added in a single operation. This functionality requires the 'element_session' library and 'pickle' for binary data.

```python
from element_session import session
from datetime import datetime
import pickle

session_key = {
    'subject': 'mouse_001',
    'session_datetime': datetime(2023, 11, 15, 14, 30, 0)
}

# Insert text attribute
session.Session.Attribute.insert1({
    **session_key,
    'attribute_name': 'room_temperature',
    'attribute_value': '22.5'
})

# Insert attribute with binary blob
behavior_data = {'trials': 120, 'success_rate': 0.85}
session.Session.Attribute.insert1({
    **session_key,
    'attribute_name': 'behavior_summary',
    'attribute_blob': pickle.dumps(behavior_data)
})

# Insert multiple attributes
attributes = [
    {**session_key, 'attribute_name': 'light_cycle', 'attribute_value': 'dark'},
    {**session_key, 'attribute_name': 'weight_grams', 'attribute_value': '28.3'}
]
session.Session.Attribute.insert(attributes)

# Query attributes
attrs = (session.Session.Attribute & session_key).fetch(as_dict=True)
# Output:
# [
#   {'subject': 'mouse_001', 'session_datetime': datetime(2023,11,15,14,30,0),
#    'attribute_name': 'room_temperature', 'attribute_value': '22.5'},
#   {'subject': 'mouse_001', 'session_datetime': datetime(2023,11,15,14,30,0),
#    'attribute_name': 'behavior_summary', 'attribute_blob': b'...'} 
# ]
```

--------------------------------

### Activate Session Schema with DateTime-based Tracking

Source: https://context7.com/datajoint/element-session/llms.txt

Activates the DataJoint session schema using a datetime-based approach for session tracking. Requires upstream dependencies like Subject, Experimenter, and Project tables. Creates schema and tables within the specified database name.

```python
import datajoint as dj
from element_session import session_with_datetime as session

# Define upstream dependencies
schema = dj.schema()

@schema class Subject(dj.Manual):
    definition = """
    subject: varchar(32)
    ---    
    sex: enum('M', 'F', 'U')
    subject_birth_date: date
    """

@schema class Experimenter(dj.Manual):
    definition = """
    user: varchar(32)
    ---    
    user_email: varchar(64)
    """

@schema class Project(dj.Manual):
    definition = """
    project: varchar(64)
    ---    
    project_description: varchar(1024)
    """

# Create linking module with dependencies
class LinkingModule:
    Subject = Subject
    Experimenter = Experimenter
    Project = Project

# Activate the session schema
session.activate(
    schema_name='my_session_db',
    create_schema=True,
    create_tables=True,
    linking_module=LinkingModule
)

# Output: Schema 'my_session_db' created with Session tables
```

--------------------------------

### Activate Session Schema with ID-based Tracking

Source: https://context7.com/datajoint/element-session/llms.txt

Activates the DataJoint session schema using an ID-based approach for session tracking. Requires upstream dependencies like Subject and Experimenter tables. Uses the current module for linking and creates the schema and tables.

```python
import datajoint as dj
from element_session import session_with_id as session

# Define upstream dependencies
@dj.schema() class Subject(dj.Manual):
    definition = """
    subject: varchar(32)
    ---    
    species: varchar(32)
    """

@dj.schema() class Experimenter(dj.Manual):
    definition = """
    user: varchar(32)
    """

# Create linking module
import sys
current_module = sys.modules[__name__]
current_module.Subject = Subject
current_module.Experimenter = Experimenter

# Activate using module name
session.activate(
    schema_name='experiment_sessions',
    create_schema=True,
    create_tables=True,
    linking_module=current_module
)

# Output: Schema activated with session_id as primary key
```

--------------------------------

### Insert Session Records using DateTime-based Schema

Source: https://context7.com/datajoint/element-session/llms.txt

Inserts session records into a DataJoint database using the datetime-based session schema. Demonstrates inserting basic sessions, sessions with optional IDs, and associated session directory paths. Requires the `session` module to be activated.

```python
from datetime import datetime
from element_session import session

# Insert a basic session
session_key = {
    'subject': 'mouse_001',
    'session_datetime': datetime(2023, 11, 15, 14, 30, 0)
}
session.Session.insert1(session_key)

# Insert session with optional session_id
session_data = {
    'subject': 'mouse_002',
    'session_datetime': datetime(2023, 11, 16, 10, 15, 0),
    'session_id': 42
}
session.Session.insert1(session_data)

# Add session directory path
session.SessionDirectory.insert1({
    'subject': 'mouse_001',
    'session_datetime': datetime(2023, 11, 15, 14, 30, 0),
    'session_dir': '/data/recordings/mouse_001/2023-11-15'
})

# Output: Session records created in database
# Query: session.Session()
# +----------+---------------------+------------+
# | subject  | session_datetime    | session_id |
# +----------+---------------------+------------+
# | mouse_001| 2023-11-15 14:30:00 | None       |
# | mouse_002| 2023-11-16 10:15:00 | 42         |
# +----------+---------------------+------------+
```

--------------------------------

### Insert Session Records using ID-based Schema

Source: https://context7.com/datajoint/element-session/llms.txt

Inserts session records into a DataJoint database using the ID-based session schema. Demonstrates inserting sessions with numeric IDs as primary keys, including optional datetimes, and performing bulk inserts. Requires the `session_with_id` module to be activated.

```python
from datetime import datetime
from element_session import session_with_id as session

# Insert session with numeric ID (primary key)
session_key = {
    'subject': 'rat_007',
    'session_id': 1
}
session.Session.insert1(session_key)

# Insert session with optional datetime
session_data = {
    'subject': 'rat_007',
    'session_id': 2,
    'session_datetime': datetime(2023, 12, 1, 9, 0, 0)
}
session.Session.insert1(session_data)

# Bulk insert multiple sessions
sessions = [
    {'subject': 'rat_007', 'session_id': 3},
    {'subject': 'rat_008', 'session_id': 1},
    {'subject': 'rat_008', 'session_id': 2}
]
session.Session.insert(sessions)

# Output: Sessions created with session_id as identifier
# Query: session.Session()
# +---------+------------+---------------------+
# | subject | session_id | session_datetime    |
# +---------+------------+---------------------+
```

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