New ingestion_schemas + MANY minor fixes and improvements

aeon/dj_pipeline/__init__.py

@@ -1,4 +1,5 @@
 import hashlib
+import logging
 import os
 import uuid
 
@@ -30,11 +31,17 @@ def dict_to_uuid(key) -> uuid.UUID:
     return uuid.UUID(hex=hashed.hexdigest())
 
 
-def fetch_stream(query, drop_pk=True):
+def fetch_stream(query, drop_pk=True, round_microseconds=True):
     """Fetches data from a Stream table based on a query and returns it as a DataFrame.
 
     Provided a query containing data from a Stream table,
     fetch and aggregate the data into one DataFrame indexed by "time"
+
+    Args:
+        query (datajoint.Query): A query object containing data from a Stream table
+        drop_pk (bool, optional): Drop primary key columns. Defaults to True.
+        round_microseconds (bool, optional): Round timestamps to microseconds. Defaults to False.
+            (this is important as timestamps in mysql is only accurate to microseconds)
     """
     df = (query & "sample_count > 0").fetch(format="frame").reset_index()
     cols2explode = [
@@ -47,6 +54,10 @@ def fetch_stream(query, drop_pk=True):
     df.set_index("time", inplace=True)
     df.sort_index(inplace=True)
     df = df.convert_dtypes(convert_string=False, convert_integer=False, convert_boolean=False, convert_floating=False)
+    if not df.empty and round_microseconds:
+        logging.warning("Rounding timestamps to microseconds is now enabled by default."
+                        " To disable, set round_microseconds=False.")
+        df.index = df.index.round("us")
     return df
 
 

aeon/dj_pipeline/acquisition.py

@@ -12,7 +12,7 @@
 from aeon.dj_pipeline.utils import paths
 from aeon.io import api as io_api
 from aeon.io import reader as io_reader
-from aeon.schema import schemas as aeon_schemas
+from aeon.schema import ingestion_schemas as aeon_schemas
 
 logger = dj.logger
 schema = dj.schema(get_schema_name("acquisition"))
@@ -646,10 +646,14 @@ def _match_experiment_directory(experiment_name, path, directories):
 
 def create_chunk_restriction(experiment_name, start_time, end_time):
     """Create a time restriction string for the chunks between the specified "start" and "end" times."""
+    exp_key = {"experiment_name": experiment_name}
     start_restriction = f'"{start_time}" BETWEEN chunk_start AND chunk_end'
     end_restriction = f'"{end_time}" BETWEEN chunk_start AND chunk_end'
-    start_query = Chunk & {"experiment_name": experiment_name} & start_restriction
-    end_query = Chunk & {"experiment_name": experiment_name} & end_restriction
+    start_query = Chunk & exp_key & start_restriction
+    end_query = Chunk & exp_key & end_restriction
+    if not end_query:
+        # No chunk contains the end time, so we need to find the last chunk that starts before the end time
+        end_query = Chunk & exp_key & f'chunk_end BETWEEN "{start_time}" AND "{end_time}"'
     if not (start_query and end_query):
         raise ValueError(f"No Chunk found between {start_time} and {end_time}")
     time_restriction = (

aeon/dj_pipeline/analysis/block_analysis.py

@@ -53,6 +53,8 @@ class BlockDetection(dj.Computed):
     -> acquisition.Environment
     """
 
+    key_source = acquisition.Environment - {"experiment_name": "social0.1-aeon3"}
+
     def make(self, key):
         """On a per-chunk basis, check for the presence of new block, insert into Block table.
 
@@ -88,8 +90,7 @@ def make(self, key):
         blocks_df = block_state_df[block_state_df.pellet_ct == 0]
         # account for the double 0s - find any 0s that are within 1 second of each other, remove the 2nd one
         double_0s = blocks_df.index.to_series().diff().dt.total_seconds() < 1
-        # find the indices of the 2nd 0s and remove
-        double_0s = double_0s.shift(-1).fillna(False)
+        # keep the first 0s
         blocks_df = blocks_df[~double_0s]
 
         block_entries = []
@@ -144,8 +145,8 @@ class Patch(dj.Part):
         wheel_timestamps: longblob
         patch_threshold: longblob
         patch_threshold_timestamps: longblob
-        patch_rate: float
-        patch_offset: float
+        patch_rate=null: float
+        patch_offset=null: float
         """
 
     class Subject(dj.Part):
@@ -181,17 +182,27 @@ def make(self, key):
             streams.UndergroundFeederDepletionState,
             streams.UndergroundFeederDeliverPellet,
             streams.UndergroundFeederEncoder,
-            tracking.SLEAPTracking,
         )
         for streams_table in streams_tables:
             if len(streams_table & chunk_keys) < len(streams_table.key_source & chunk_keys):
                 raise ValueError(
                     f"BlockAnalysis Not Ready - {streams_table.__name__} not yet fully ingested for block: {key}. Skipping (to retry later)..."
                 )
 
+        # Check if SLEAPTracking is ready, if not, see if BlobPosition can be used instead
+        use_blob_position = False
+        if len(tracking.SLEAPTracking & chunk_keys) < len(tracking.SLEAPTracking.key_source & chunk_keys):
+            if len(tracking.BlobPosition & chunk_keys) < len(tracking.BlobPosition.key_source & chunk_keys):
+                raise ValueError(
+                    f"BlockAnalysis Not Ready - SLEAPTracking (and BlobPosition) not yet fully ingested for block: {key}. Skipping (to retry later)..."
+                )
+            else:
+                use_blob_position = True
+
         # Patch data - TriggerPellet, DepletionState, Encoder (distancetravelled)
         # For wheel data, downsample to 10Hz
-        final_encoder_fs = 10
+        final_encoder_hz = 10
+        freq = 1 / final_encoder_hz * 1e3  # in ms
 
         maintenance_period = get_maintenance_periods(key["experiment_name"], block_start, block_end)
 
@@ -233,51 +244,51 @@ def make(self, key):
                 encoder_df, maintenance_period, block_end, dropna=True
             )
 
-            if depletion_state_df.empty:
-                raise ValueError(f"No depletion state data found for block {key} - patch: {patch_name}")
-
-            encoder_df["distance_travelled"] = -1 * analysis_utils.distancetravelled(encoder_df.angle)
+            # if all dataframes are empty, skip
+            if pellet_ts_threshold_df.empty and depletion_state_df.empty and encoder_df.empty:
+                continue
 
-            if len(depletion_state_df.rate.unique()) > 1:
-                # multiple patch rates per block is unexpected, log a note and pick the first rate to move forward
-                AnalysisNote.insert1(
-                    {
-                        "note_timestamp": datetime.utcnow(),
-                        "note_type": "Multiple patch rates",
-                        "note": f"Found multiple patch rates for block {key} - patch: {patch_name} - rates: {depletion_state_df.rate.unique()}",
-                    }
-                )
+            if encoder_df.empty:
+                encoder_df["distance_travelled"] = 0
+            else:
+                encoder_df["distance_travelled"] = -1 * analysis_utils.distancetravelled(encoder_df.angle)
+                encoder_df = encoder_df.resample(f"{freq}ms").first()
 
-            patch_rate = depletion_state_df.rate.iloc[0]
-            patch_offset = depletion_state_df.offset.iloc[0]
-            # handles patch rate value being INF
-            patch_rate = 999999999 if np.isinf(patch_rate) else patch_rate
+            if not depletion_state_df.empty:
+                if len(depletion_state_df.rate.unique()) > 1:
+                    # multiple patch rates per block is unexpected, log a note and pick the first rate to move forward
+                    AnalysisNote.insert1(
+                        {
+                            "note_timestamp": datetime.utcnow(),
+                            "note_type": "Multiple patch rates",
+                            "note": f"Found multiple patch rates for block {key} - patch: {patch_name} - rates: {depletion_state_df.rate.unique()}",
+                        }
+                    )
 
-            encoder_fs = (
-                1 / encoder_df.index.to_series().diff().dt.total_seconds().median()
-            )  # mean or median?
-            wheel_downsampling_factor = int(encoder_fs / final_encoder_fs)
+                patch_rate = depletion_state_df.rate.iloc[0]
+                patch_offset = depletion_state_df.offset.iloc[0]
+                # handles patch rate value being INF
+                patch_rate = 999999999 if np.isinf(patch_rate) else patch_rate
+            else:
+                logger.warning(f"No depletion state data found for block {key} - patch: {patch_name}")
+                patch_rate = None
+                patch_offset = None
 
             block_patch_entries.append(
                 {
                     **key,
                     "patch_name": patch_name,
                     "pellet_count": len(pellet_ts_threshold_df),
                     "pellet_timestamps": pellet_ts_threshold_df.pellet_timestamp.values,
-                    "wheel_cumsum_distance_travelled": encoder_df.distance_travelled.values[
-                        ::wheel_downsampling_factor
-                    ],
-                    "wheel_timestamps": encoder_df.index.values[::wheel_downsampling_factor],
+                    "wheel_cumsum_distance_travelled": encoder_df.distance_travelled.values,
+                    "wheel_timestamps": encoder_df.index.values,
                     "patch_threshold": pellet_ts_threshold_df.threshold.values,
                     "patch_threshold_timestamps": pellet_ts_threshold_df.index.values,
                     "patch_rate": patch_rate,
                     "patch_offset": patch_offset,
                 }
             )
 
-            # update block_end if last timestamp of encoder_df is before the current block_end
-            block_end = min(encoder_df.index[-1], block_end)
-
         # Subject data
         # Get all unique subjects that visited the environment over the entire exp;
         # For each subject, see 'type' of visit most recent to start of block
@@ -288,27 +299,50 @@ def make(self, key):
             & f'chunk_start <= "{chunk_keys[-1]["chunk_start"]}"'
         )[:block_start]
         subject_visits_df = subject_visits_df[subject_visits_df.region == "Environment"]
+        subject_visits_df = subject_visits_df[~subject_visits_df.id.str.contains("Test", case=False)]
         subject_names = []
         for subject_name in set(subject_visits_df.id):
             _df = subject_visits_df[subject_visits_df.id == subject_name]
             if _df.type.iloc[-1] != "Exit":
                 subject_names.append(subject_name)
 
+        if use_blob_position and len(subject_names) > 1:
+            raise ValueError(
+                f"Without SLEAPTracking, BlobPosition can only handle single-subject block. Found {len(subject_names)} subjects."
+            )
+
         block_subject_entries = []
         for subject_name in subject_names:
             # positions - query for CameraTop, identity_name matches subject_name,
-            pos_query = (
-                streams.SpinnakerVideoSource
-                * tracking.SLEAPTracking.PoseIdentity.proj("identity_name", part_name="anchor_part")
-                * tracking.SLEAPTracking.Part
-                & key
-                & {
-                    "spinnaker_video_source_name": "CameraTop",
-                    "identity_name": subject_name,
-                }
-                & chunk_restriction
-            )
-            pos_df = fetch_stream(pos_query)[block_start:block_end]
+            if use_blob_position:
+                pos_query = (
+                    streams.SpinnakerVideoSource
+                    * tracking.BlobPosition.Object
+                    & key
+                    & chunk_restriction
+                    & {
+                        "spinnaker_video_source_name": "CameraTop",
+                        "identity_name": subject_name
+                    }
+                )
+                pos_df = fetch_stream(pos_query)[block_start:block_end]
+                pos_df["likelihood"] = np.nan
+                # keep only rows with area between 0 and 1000
+                pos_df = pos_df[(pos_df.area > 0) & (pos_df.area < 1000)]
+            else:
+                pos_query = (
+                    streams.SpinnakerVideoSource
+                    * tracking.SLEAPTracking.PoseIdentity.proj("identity_name", part_name="anchor_part")
+                    * tracking.SLEAPTracking.Part
+                    & key
+                    & {
+                        "spinnaker_video_source_name": "CameraTop",
+                        "identity_name": subject_name,
+                    }
+                    & chunk_restriction
+                )
+                pos_df = fetch_stream(pos_query)[block_start:block_end]
+
             pos_df = filter_out_maintenance_periods(pos_df, maintenance_period, block_end)
 
             if pos_df.empty:
@@ -345,8 +379,8 @@ def make(self, key):
             {
                 **key,
                 "block_duration": (block_end - block_start).total_seconds() / 3600,
-                "patch_count": len(patch_keys),
-                "subject_count": len(subject_names),
+                "patch_count": len(block_patch_entries),
+                "subject_count": len(block_subject_entries),
             }
         )
         self.Patch.insert(block_patch_entries)
@@ -423,6 +457,17 @@ def make(self, key):
         )
         subjects_positions_df.set_index("position_timestamps", inplace=True)
 
+        # Ensure wheel_timestamps are of the same length across all patches
+        wheel_lens = [len(p["wheel_timestamps"]) for p in block_patches]
+        if len(set(wheel_lens)) > 1:
+            max_diff = max(wheel_lens) - min(wheel_lens)
+            if max_diff > 10:
+                # if diff is more than 10 samples, raise error, this is unexpected, some patches crash?
+                raise ValueError(f"Wheel data lengths are not consistent across patches ({max_diff} samples diff)")
+            for p in block_patches:
+                p["wheel_timestamps"] = p["wheel_timestamps"][: min(wheel_lens)]
+                p["wheel_cumsum_distance_travelled"] = p["wheel_cumsum_distance_travelled"][: min(wheel_lens)]
+
         self.insert1(key)
 
         in_patch_radius = 130  # pixels
@@ -541,7 +586,7 @@ def make(self, key):
                     | {
                         "patch_name": patch["patch_name"],
                         "subject_name": subject_name,
-                        "in_patch_timestamps": subject_in_patch.index.values,
+                        "in_patch_timestamps": subject_in_patch[in_patch[subject_name]].index.values,
                         "in_patch_time": subject_in_patch_cum_time[-1],
                         "pellet_count": len(subj_pellets),
                         "pellet_timestamps": subj_pellets.index.values,
@@ -1521,10 +1566,10 @@ def make(self, key):
         foraging_bout_df = get_foraging_bouts(key)
         foraging_bout_df.rename(
             columns={
-                "subject_name": "subject",
-                "bout_start": "start",
-                "bout_end": "end",
-                "pellet_count": "n_pellets",
+                "subject": "subject_name",
+                "start": "bout_start",
+                "end": "bout_end",
+                "n_pellets": "pellet_count",
                 "cum_wheel_dist": "cum_wheel_dist",
             },
             inplace=True,
@@ -1540,7 +1585,7 @@ def make(self, key):
 @schema
 class AnalysisNote(dj.Manual):
     definition = """  # Generic table to catch all notes generated during analysis
-    note_timestamp: datetime
+    note_timestamp: datetime(6)
     ---
     note_type='': varchar(64)
     note: varchar(3000)

aeon/dj_pipeline/populate/worker.py

@@ -99,12 +99,32 @@ def ingest_epochs_chunks():
 )
 
 analysis_worker(block_analysis.BlockAnalysis, max_calls=6)
-analysis_worker(block_analysis.BlockPlots, max_calls=6)
 analysis_worker(block_analysis.BlockSubjectAnalysis, max_calls=6)
-analysis_worker(block_analysis.BlockSubjectPlots, max_calls=6)
+analysis_worker(block_analysis.BlockForaging, max_calls=6)
+analysis_worker(block_analysis.BlockPatchPlots, max_calls=6)
+analysis_worker(block_analysis.BlockSubjectPositionPlots, max_calls=6)
 
 
 def get_workflow_operation_overview():
     from datajoint_utilities.dj_worker.utils import get_workflow_operation_overview
 
     return get_workflow_operation_overview(worker_schema_name=worker_schema_name, db_prefixes=[db_prefix])
+
+
+def retrieve_schemas_sizes(schema_only=False, all_schemas=False):
+    schema_names = [n for n in dj.list_schemas() if n != "mysql"]
+    if not all_schemas:
+        schema_names = [n for n in schema_names
+                        if n.startswith(db_prefix) and not n.startswith(f"{db_prefix}archived")]
+
+    if schema_only:
+        return {n: dj.Schema(n).size_on_disk / 1e9 for n in schema_names}
+
+    schema_sizes = {n: {} for n in schema_names}
+    for n in schema_names:
+        vm = dj.VirtualModule(n, n)
+        schema_sizes[n]["schema_gb"] = vm.schema.size_on_disk / 1e9
+        schema_sizes[n]["tables_gb"] = {n: t().size_on_disk / 1e9
+                                        for n, t in vm.__dict__.items()
+                                        if isinstance(t, dj.user_tables.TableMeta)}
+    return schema_sizes