Clear all CI checks on all platforms

aeon/analysis/block_plotting.py

@@ -1,17 +1,7 @@
-import os
-import pathlib
 from colorsys import hls_to_rgb, rgb_to_hls
-from contextlib import contextmanager
-from pathlib import Path
 
-import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 import plotly
-import plotly.express as px
-import plotly.graph_objs as go
-import seaborn as sns
-from numpy.lib.stride_tricks import as_strided
 
 """Standardize subject colors, patch colors, and markers."""
 
@@ -35,27 +25,21 @@
     "star",
 ]
 patch_markers_symbols = ["●", "⧓", "■", "⧗", "♦", "✖", "×", "▲", "★"]
-patch_markers_dict = {
-    marker: symbol for marker, symbol in zip(patch_markers, patch_markers_symbols)
-}
+patch_markers_dict = dict(zip(patch_markers, patch_markers_symbols, strict=False))
 patch_markers_linestyles = ["solid", "dash", "dot", "dashdot", "longdashdot"]
 
 
 def gen_hex_grad(hex_col, vals, min_l=0.3):
     """Generates an array of hex color values based on a gradient defined by unit-normalized values."""
     # Convert hex to rgb to hls
-    h, l, s = rgb_to_hls(
-        *[int(hex_col.lstrip("#")[i: i + 2], 16) / 255 for i in (0, 2, 4)]
-    )
+    h, l, s = rgb_to_hls(*[int(hex_col.lstrip("#")[i : i + 2], 16) / 255 for i in (0, 2, 4)])
     grad = np.empty(shape=(len(vals),), dtype="<U10")  # init grad
     for i, val in enumerate(vals):
-        cur_l = (l * val) + (
-            min_l * (1 - val)
-        )  # get cur lightness relative to `hex_col`
+        cur_l = (l * val) + (min_l * (1 - val))  # get cur lightness relative to `hex_col`
         cur_l = max(min(cur_l, l), min_l)  # set min, max bounds
         cur_rgb_col = hls_to_rgb(h, cur_l, s)  # convert to rgb
-        cur_hex_col = "#%02x%02x%02x" % tuple(
-            int(c * 255) for c in cur_rgb_col
+        cur_hex_col = "#{:02x}{:02x}{:02x}".format(
+            *tuple(int(c * 255) for c in cur_rgb_col)
         )  # convert to hex
         grad[i] = cur_hex_col
 

aeon/analysis/movies.py

@@ -8,8 +8,7 @@
 
 
 def gridframes(frames, width, height, shape=None):
-    """Arranges a set of frames into a grid layout with the specified
-    pixel dimensions and shape.
+    """Arranges a set of frames into a grid layout with the specified pixel dimensions and shape.
 
     :param list frames: A list of frames to include in the grid layout.
     :param int width: The width of the output grid image, in pixels.
@@ -65,7 +64,7 @@ def groupframes(frames, n, fun):
             i = i + 1
 
 
-def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
+def triggerclip(data, events, before=None, after=None):
     """Split video data around the specified sequence of event timestamps.
 
     :param DataFrame data:
@@ -76,10 +75,16 @@ def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
     :return:
     A pandas DataFrame containing the frames, clip and sequence numbers for each event timestamp.
     """
-    if before is not pd.Timedelta:
+    if before is None:
+        before = pd.Timedelta(0)
+    elif before is not pd.Timedelta:
         before = pd.Timedelta(before)
-    if after is not pd.Timedelta:
+
+    if after is None:
+        after = pd.Timedelta(0)
+    elif after is not pd.Timedelta:
         after = pd.Timedelta(after)
+
     if events is not pd.Index:
         events = events.index
 
@@ -107,8 +112,7 @@ def collatemovie(clipdata, fun):
 
 
 def gridmovie(clipdata, width, height, shape=None):
-    """Collates a set of video clips into a grid movie with the specified pixel dimensions
-    and grid layout.
+    """Collates a set of video clips into a grid movie with the specified pixel dimensions and grid layout.
 
     :param DataFrame clipdata:
     A pandas DataFrame where each row specifies video path, frame number, clip and sequence number.

aeon/analysis/plotting.py

@@ -6,7 +6,7 @@
 from matplotlib import colors
 from matplotlib.collections import LineCollection
 
-from aeon.analysis.utils import *
+from aeon.analysis.utils import rate, sessiontime
 
 
 def heatmap(position, frequency, ax=None, **kwargs):
@@ -60,16 +60,17 @@ def rateplot(
     ax=None,
     **kwargs,
 ):
-    """Plot the continuous event rate and raster of a discrete event sequence, given the specified
-    window size and sampling frequency.
+    """Plot the continuous event rate and raster of a discrete event sequence.
+
+    The window size and sampling frequency can be specified.
 
     :param Series events: The discrete sequence of events.
     :param offset window: The time period of each window used to compute the rate.
     :param DateOffset, Timedelta or str frequency: The sampling frequency for the continuous rate.
     :param number, optional weight: A weight used to scale the continuous rate of each window.
     :param datetime, optional start: The left bound of the time range for the continuous rate.
     :param datetime, optional end: The right bound of the time range for the continuous rate.
-    :param datetime, optional smooth: The size of the smoothing kernel applied to the continuous rate output.
+    :param datetime, optional smooth: The size of the smoothing kernel applied to the rate output.
     :param DateOffset, Timedelta or str, optional smooth:
     The size of the smoothing kernel applied to the continuous rate output.
     :param bool, optional center: Specifies whether to center the convolution kernels.
@@ -108,8 +109,8 @@ def colorline(
     x,
     y,
     z=None,
-    cmap=plt.get_cmap("copper"),
-    norm=plt.Normalize(0.0, 1.0),
+    cmap=None,
+    norm=None,
     ax=None,
     **kwargs,
 ):
@@ -128,6 +129,10 @@ def colorline(
         ax = plt.gca()
     if z is None:
         z = np.linspace(0.0, 1.0, len(x))
+    if cmap is None:
+        cmap = plt.get_cmap("copper")
+    if norm is None:
+        norm = plt.Normalize(0.0, 1.0)
     z = np.asarray(z)
     points = np.array([x, y]).T.reshape(-1, 1, 2)
     segments = np.concatenate([points[:-1], points[1:]], axis=1)

aeon/analysis/utils.py

@@ -3,8 +3,9 @@
 
 
 def distancetravelled(angle, radius=4.0):
-    """Calculates the total distance travelled on the wheel, by taking into account
-    its radius and the total number of turns in both directions across time.
+    """Calculates the total distance travelled on the wheel.
+
+    Takes into account the wheel radius and the total number of turns in both directions across time.
 
     :param Series angle: A series of magnetic encoder measurements.
     :param float radius: The radius of the wheel, in metric units.
@@ -22,10 +23,11 @@ def distancetravelled(angle, radius=4.0):
 
 
 def visits(data, onset="Enter", offset="Exit"):
-    """Computes duration, onset and offset times from paired events. Allows for missing data
-    by trying to match event onset times with subsequent offset times. If the match fails,
-    event offset metadata is filled with NaN. Any additional metadata columns in the data
-    frame will be paired and included in the output.
+    """Computes duration, onset and offset times from paired events.
+
+    Allows for missing data by trying to match event onset times with subsequent offset times.
+    If the match fails, event offset metadata is filled with NaN. Any additional metadata columns
+    in the data frame will be paired and included in the output.
 
     :param DataFrame data: A pandas data frame containing visit onset and offset events.
     :param str, optional onset: The label used to identify event onsets.
@@ -69,16 +71,17 @@ def visits(data, onset="Enter", offset="Exit"):
 
 
 def rate(events, window, frequency, weight=1, start=None, end=None, smooth=None, center=False):
-    """Computes the continuous event rate from a discrete event sequence, given the specified
-    window size and sampling frequency.
+    """Computes the continuous event rate from a discrete event sequence.
+
+    The window size and sampling frequency can be specified.
 
     :param Series events: The discrete sequence of events.
     :param offset window: The time period of each window used to compute the rate.
     :param DateOffset, Timedelta or str frequency: The sampling frequency for the continuous rate.
     :param number, optional weight: A weight used to scale the continuous rate of each window.
     :param datetime, optional start: The left bound of the time range for the continuous rate.
     :param datetime, optional end: The right bound of the time range for the continuous rate.
-    :param datetime, optional smooth: The size of the smoothing kernel applied to the continuous rate output.
+    :param datetime, optional smooth: The size of the smoothing kernel applied to the rate output.
     :param DateOffset, Timedelta or str, optional smooth:
     The size of the smoothing kernel applied to the continuous rate output.
     :param bool, optional center: Specifies whether to center the convolution kernels.

aeon/dj_pipeline/__init__.py

@@ -31,9 +31,8 @@ def dict_to_uuid(key) -> uuid.UUID:
 
 
 def fetch_stream(query, drop_pk=True):
-    """
-    Provided a query containing data from a Stream table,
-     fetch and aggregate the data into one DataFrame indexed by "time"
+    """Provided a query containing data from a Stream table,
+    fetch and aggregate the data into one DataFrame indexed by "time"
     """
     df = (query & "sample_count > 0").fetch(format="frame").reset_index()
     cols2explode = [

aeon/dj_pipeline/acquisition.py

@@ -604,9 +604,7 @@ 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
-    """
+    """Create a time restriction string for the chunks between the specified "start" and "end" times"""
     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

aeon/dj_pipeline/create_experiments/insert_experiment_directory.ipynb

@@ -301,12 +301,12 @@
     "computer = \"AEON3\"\n",
     "\n",
     "acquisition.Experiment.Directory.insert1(\n",
-    "        {\n",
-    "            \"experiment_name\": experiment_name,\n",
-    "            \"repository_name\": \"ceph_aeon\",\n",
-    "            \"directory_type\": \"raw\",\n",
-    "            \"directory_path\": f\"aeon/data/raw/{computer}/social0.1\"\n",
-    "        },\n",
+    "    {\n",
+    "        \"experiment_name\": experiment_name,\n",
+    "        \"repository_name\": \"ceph_aeon\",\n",
+    "        \"directory_type\": \"raw\",\n",
+    "        \"directory_path\": f\"aeon/data/raw/{computer}/social0.1\",\n",
+    "    },\n",
     "    skip_duplicates=True,\n",
     ")\n",
     "\n",
@@ -315,12 +315,12 @@
     "computer = \"AEON4\"\n",
     "\n",
     "acquisition.Experiment.Directory.insert1(\n",
-    "        {\n",
-    "            \"experiment_name\": experiment_name,\n",
-    "            \"repository_name\": \"ceph_aeon\",\n",
-    "            \"directory_type\": \"raw\",\n",
-    "            \"directory_path\": f\"aeon/data/raw/{computer}/social0.1\"\n",
-    "        },\n",
+    "    {\n",
+    "        \"experiment_name\": experiment_name,\n",
+    "        \"repository_name\": \"ceph_aeon\",\n",
+    "        \"directory_type\": \"raw\",\n",
+    "        \"directory_path\": f\"aeon/data/raw/{computer}/social0.1\",\n",
+    "    },\n",
     "    skip_duplicates=True,\n",
     ")\n",
     "\n",

aeon/dj_pipeline/docs/notebooks/diagram.ipynb

@@ -44,7 +44,7 @@
     }
    ],
    "source": [
-    "_db_prefix = 'aeon_'\n",
+    "_db_prefix = \"aeon_\"\n",
     "\n",
     "lab = dj.create_virtual_module(\"lab\", _db_prefix + \"lab\")\n",
     "subject = dj.create_virtual_module(\"subject\", _db_prefix + \"subject\")\n",
@@ -488,7 +488,7 @@
     "    + acquisition.FoodPatchWheel\n",
     "    + acquisition.FoodPatchEvent\n",
     "    + acquisition.WheelState\n",
-    "    + acquisition.EventType \n",
+    "    + acquisition.EventType\n",
     "    + acquisition.WeightMeasurement\n",
     "    + qc.CameraQC\n",
     "    + tracking.CameraTracking.Object\n",
@@ -857,7 +857,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "datajoint_analysis_diagram.svg  datajoint_overview_diagram.svg  \u001B[0m\u001B[01;34mnotebooks\u001B[0m/\r\n"
+      "datajoint_analysis_diagram.svg  datajoint_overview_diagram.svg  \u001b[0m\u001b[01;34mnotebooks\u001b[0m/\r\n"
      ]
     }
    ],

aeon/dj_pipeline/docs/notebooks/social_experiments_block_analysis.ipynb

@@ -28,7 +28,7 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "analysis_vm = dj.create_virtual_module('aeon_block_analysis', 'aeon_block_analysis')"
+    "analysis_vm = dj.create_virtual_module(\"aeon_block_analysis\", \"aeon_block_analysis\")"
    ],
    "metadata": {
     "collapsed": false,
@@ -83,7 +83,7 @@
    "outputs": [],
    "source": [
     "# Pick a block of interest\n",
-    "block_key = {'experiment_name': 'social0.1-aeon3', 'block_start': '2023-11-30 18:49:05.001984'}"
+    "block_key = {\"experiment_name\": \"social0.1-aeon3\", \"block_start\": \"2023-11-30 18:49:05.001984\"}"
    ],
    "metadata": {
     "collapsed": false,

aeon/dj_pipeline/populate/worker.py

@@ -48,7 +48,6 @@ def ingest_environment_visits():
     """Extract and insert complete visits for experiments specified in AutomatedExperimentIngestion."""
     experiment_names = AutomatedExperimentIngestion.fetch("experiment_name")
     # analysis.ingest_environment_visits(experiment_names)
-    pass
 
 
 # ---- Define worker(s) ----
@@ -111,6 +110,8 @@ def ingest_environment_visits():
 analysis_worker(block_analysis.BlockSubjectAnalysis, max_calls=6)
 analysis_worker(block_analysis.BlockSubjectPlots, 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])

aeon/dj_pipeline/qc.py

@@ -77,7 +77,7 @@ def make(self, key):
                 "devices_schema_name"
             ),
         )
-        stream_reader = getattr(getattr(devices_schema, device_name), "Video")
+        stream_reader = getattr(devices_schema, device_name).Video
 
         videodata = io_api.load(
             root=data_dirs,

aeon/dj_pipeline/scripts/clone_and_freeze_exp01.py

@@ -1,6 +1,7 @@
 """March 2022
 Cloning and archiving schemas and data for experiment 0.1.
 """
+
 import os
 
 import datajoint as dj

aeon/dj_pipeline/scripts/clone_and_freeze_exp02.py

@@ -3,6 +3,7 @@
 The pipeline code associated with this archived data pipeline is here
 https://github.com/SainsburyWellcomeCentre/aeon_mecha/releases/tag/dj_exp02_stable
 """
+
 import os
 import inspect
 import datajoint as dj
@@ -57,11 +58,10 @@ def data_copy(restriction, table_block_list, batch_size=None):
 
 
 def validate():
-    """
-    Validation of schemas migration
-        1. for the provided list of schema names - validate all schemas have been migrated
-        2. for each schema - validate all tables have been migrated
-        3. for each table, validate all entries have been migrated
+    """Validation of schemas migration
+    1. for the provided list of schema names - validate all schemas have been migrated
+    2. for each schema - validate all tables have been migrated
+    3. for each table, validate all entries have been migrated
     """
     missing_schemas = []
     missing_tables = {}

aeon/dj_pipeline/scripts/update_timestamps_longblob.py

@@ -1,6 +1,7 @@
 """July 2022
 Upgrade all timestamps longblob fields with datajoint 0.13.7.
 """
+
 from datetime import datetime
 
 import datajoint as dj

aeon/dj_pipeline/tracking.py

@@ -162,7 +162,7 @@ def make(self, key):
                 "devices_schema_name"
             ),
         )
-        stream_reader = getattr(getattr(devices_schema, device_name), "Pose")
+        stream_reader = getattr(devices_schema, device_name).Pose
 
         pose_data = io_api.load(
             root=data_dirs,