Breaking: merge instrument and rig, make rig accept components: List[Union[]]

README.md

@@ -115,7 +115,7 @@ If you'd like to propose a large change or addition, or generally have a questio
 
 ## Controlled Vocabularies
 
-Controlled vocabularies and other enumerated lists are maintained in a separate repository: [aind-data-schema-models](https://github.com/AllenNeuralDynamics/aind-data-schema-models). This allows us to specify these lists without changing aind-data-schema. Controlled vocabularies include lists of organizations, manufacturers, species, modalities, platforms, units, harp devices, and registries.
+Controlled vocabularies and other enumerated lists are maintained in a separate repository: [aind-data-schema-models](https://github.com/AllenNeuralDynamics/aind-data-schema-models). This allows us to specify these lists without changing aind-data-schema. Controlled vocabularies include lists of organizations, manufacturers, species, modalities, units, harp devices, and registries.
 
 To upgrade to the latest data models version:
 ```

docs/source/conf.py

@@ -18,7 +18,6 @@
     metadata,
     procedures,
     processing,
-    rig,
     session,
     subject,
     quality_control,
@@ -31,7 +30,7 @@
     metadata,
     procedures,
     processing,
-    rig,
+    instrument,
     session,
     subject,
     quality_control,
@@ -88,7 +87,6 @@
     "metadata.nd",
     "procedures",
     "processing",
-    "rig",
     "session",
     "subject",
     "quality_control",

docs/source/data_description.rst

@@ -11,26 +11,9 @@ data modalities, dates of collection, and more.
 The data description is created during data transfer based on information you provide to that service and pulling 
 information from internal resources.
 
-**Q: What is the difference between modality and platform?**
-
-Modalities are types of data being collected. A platform is a standardized way of collecting one or more modalities of 
-data that we give a name. Platform standardization -- of file formats, hardware setup, etc -- enables us automatically 
-and reliably process data with centrally managed data pipelines. 
-
-    Example 1: the behavior platform leverages Harp and Bonsai to run behavioral experiments and acquire multiple 
-    modalities of data (behavior videos, electrophysiology, photometry, etc). 
-
-    Example 2: We use SmartSPIM lightsheet microscopes to collect whole-mesoscale whole brain neuroanatomy data. This 
-    is a single-modality (SPIM) platform (mesoscale anatomy, SmartSPIM colloquially).
-
 Questions for AIND users
 ------------------------
 
-**Q: What platform should I use?**
-
-There is a controlled vocabulary in (aind-data-schema-models)[https://github.com/AllenNeuralDynamics/aind-data-schema-models]. 
-Pick the one that most closely aligns with how you have collected data. If none exists, talk to Saskia de Vries or David Feng. 
-
 **Q: This data is for a AIND project and not part of a grant. Shouldn’t the funder be AIND?**
 
 No. The funding for internally funded AIND or AIBS work is listed as “Allen Institute”.
@@ -44,16 +27,3 @@ to make sure your grant is on that sheet.
 
 In the future we may need to tag cloud resources based on the originating 
 group, which may or may not be in AIND, in order to track usage and spending. 
-
-
-**Q: What happened to the “experiment type” asset label? Why are we using platform names instead?**
-
-Formerly we used a short label called “experiment type” in asset names instead of platform 
-names. This concept was confusing because it was difficult to distinguish from a “modality”. 
-Most of our data contains multiple modalities. A recording session may contain trained behavior
-event data (e.g. lick times), behavior videos (e.g. face camera), neuropixels recordings, and 
-fiber photometry recordings.  
-
-Anchoring browsing on data collection platforms is clearer. We will tag sessions in our metadata 
-database to indicate which modalities are present in which sessions.  
-

docs/source/data_organization.rst

@@ -86,9 +86,7 @@ name be unique, but we should not use this name to encode essential metadata.
 
 All primary data assets have the following naming convention: 
 
-    <platform-abbreviation>_<subject-id>_<acquisition-date>_<acquisition-time>
-
-A platform is a standardized system for collecting one or more modalities of data. 
+    <subject-id>_<acquisition-date>_<acquisition-time>
 
 A few points: 
 
@@ -97,8 +95,7 @@ A few points:
 - Acquisition date and time are essential for uniqueness
 - Acquisition date and time are in local time zone 
 - Time-zone is documented in metadata 
-- All tokens (e.g. ``<platform-abbreviation>``, ``<subject-id>``) must not contain underscores or illegal filename characters. 
-- ``<platform-abbreviation>``: a less-than 10 character shorthand for a data acquisition platform 
+- All tokens (e.g. ``<subject-id>``) must not contain underscores or illegal filename characters. 
 
 Again, this name is strictly for uniqueness. We could use a GUID, but choose 
 to have a relatively simple naming convention to facilitate casual browsing. 
@@ -123,7 +120,7 @@ Primary data assets are organized as follows:
         - logs (general log files generated by the instrument or rig that are not modality-specific) 
             - <list of files> 
 
-Platform abbreviation and modality terms come from controlled vocabularies in aind-data-schema-models. 
+Modality terms come from controlled vocabularies in aind-data-schema-models. 
 
 Example for simultaneous electrophysiology with optotagging and fiber photometry:
 
@@ -145,9 +142,9 @@ Example for simultaneous electrophysiology with optotagging and fiber photometry
             - face_camera.mp4 
             - body_camera.mp4 
 
-Example for lightsheet microscopy data acquired on the ExaSPIM platform:
+Example for lightsheet microscopy data:
 
-    - exaSPIM_655568_2022-04-26_11-48-09
+    - 655568_2022-04-26_11-48-09
         - <metadata JSON files> 
         - SPIM 
             - SPIM.ome.zarr 
@@ -200,10 +197,9 @@ File name guidelines
 When naming files, we should: 
 
 - use terms from vocabularies defined in aind-data-schema, e.g. 
-    - platform names and modalities behavior video file names 
+    - modalities, etc
+    - use isoformat datetimes, e.g. "YYYY-MM-DDThhmmss"
     - use “yyyy-mm-dd" and “hh-mm-ss" in local time zone for dates and times 
 - separate tokens with underscores, and not include underscores in tokens, e.g. 
-    - Do this: ``EFIP_655568_2022-04-26_11-48-09``
-    - Not this: ``EFIP-655568-2022_04_26-11_48_09``
+    - Do this: ``EFIP_655568_2022-04-26T114809``
 - Do not include illegal filename characters in tokens 
-

docs/source/example_workflow/example_workflow.py

@@ -3,7 +3,6 @@
 import pandas as pd
 from aind_data_schema_models.modalities import Modality
 from aind_data_schema_models.organizations import Organization
-from aind_data_schema_models.platforms import Platform
 
 from aind_data_schema.core.data_description import Funding, RawDataDescription
 from aind_data_schema.core.procedures import NanojectInjection, Perfusion, Procedures, Surgery, ViralMaterial
@@ -31,13 +30,13 @@
 for session_idx, session in sessions_df.iterrows():
     # our data always contains planar optical physiology and behavior videos
     d = RawDataDescription(
-        modality=[Modality.POPHYS, Modality.BEHAVIOR_VIDEOS],
-        platform=Platform.BEHAVIOR,
+        modalities=[Modality.POPHYS, Modality.BEHAVIOR_VIDEOS],
         subject_id=str(session["mouse_id"]),
         creation_time=session["end_time"].to_pydatetime(),
         institution=Organization.OTHER,
         experimenters=[experimenter],
         funding_source=[Funding(funder=Organization.NIMH)],
+        investigators=[experimenter)],
     )
 
     # we will store our json files in a directory named after the session