documentation

With this we separate reading and handling of tracking data. io classes handle reading

.gitignore

@@ -5,3 +5,4 @@ requires.txt
 SOURCES.txt
 dependency_links.txt
 top_level.txt
+.DS_Store

README.md

@@ -28,7 +28,7 @@ If  you leave away the ```--user``` the package will be installed system-wide.
 
 ## TrackingResults
 
-Is a class that wraps around the *.h5 files written by DeppLabCut
+Is a class that wraps around the *.h5 files written by DeepLabCut
 
 ## ImageMarker
 

build_docs.sh

@@ -0,0 +1,74 @@
+#!/bin/bash
+
+die() { echo "ERROR: $*"; exit 2; }
+warn() { echo "WARNING: $*"; }
+
+for cmd in mkdocs pdoc3 genbadge; do
+    command -v "$cmd" >/dev/null ||
+        warn "missing $cmd: run \`pip install $cmd\`"
+done
+
+PACKAGE="etrack"
+PACKAGESRC="src/$PACKAGE"
+PACKAGEROOT="$(dirname "$(realpath "$0")")"
+BUILDROOT="$PACKAGEROOT/site"
+
+# check for code coverage report:
+# need to call nosetest with --with-coverage --cover-html --cover-xml
+HAS_COVER=false
+test -d cover && HAS_COVER=true
+
+echo
+echo "Clean up documentation of $PACKAGE"
+echo
+
+rm -rf "$BUILDROOT" 2> /dev/null || true
+mkdir -p "$BUILDROOT"
+
+if command -v mkdocs >/dev/null; then
+    echo
+    echo "Building general documentation for $PACKAGE"
+    echo
+
+    cd "$PACKAGEROOT"
+    cp .mkdocs.yml mkdocs-tmp.yml
+    if $HAS_COVER; then
+	echo "        - Coverage: 'cover/index.html'" >> mkdocs-tmp.yml
+    fi
+    mkdir -p docs
+    sed -e 's|docs/||; /\[Documentation\]/d; /\[API Reference\]/d' README.md > docs/index.md
+    mkdocs build --config-file mkdocs.yml --site-dir "$BUILDROOT"
+    rm mkdocs-tmp.yml docs/index.md
+    cd - > /dev/null
+fi
+
+if $HAS_COVER; then
+    echo
+    echo "Copy code coverage report and generate badge for $PACKAGE"
+    echo
+
+    cd "$PACKAGEROOT"
+    cp -r cover "$BUILDROOT/"
+    genbadge coverage -i coverage.xml
+    # https://smarie.github.io/python-genbadge/
+    mv coverage-badge.svg site/coverage.svg
+    cd - > /dev/null
+fi
+
+if command -v pdoc3 >/dev/null; then
+    echo
+    echo "Building API reference docs for $PACKAGE"
+    echo
+
+    cd "$PACKAGEROOT"
+    pdoc3 --html --config latex_math=True --config sort_identifiers=False --output-dir "$BUILDROOT/api-tmp" $PACKAGESRC
+    mv "$BUILDROOT/api-tmp/$PACKAGE" "$BUILDROOT/api"
+    rmdir "$BUILDROOT/api-tmp"
+    cd - > /dev/null
+fi
+
+echo
+echo "Done. Docs in:"
+echo
+echo "    file://$BUILDROOT/index.html"
+echo

docs/etrack.md

@@ -0,0 +1,35 @@
+# E-Fish tracking
+
+Tool for easier handling of tracking results.
+
+## Installation
+
+### 1. Clone git repository
+
+```shell
+git clone https://whale.am28.uni-tuebingen.de/git/jgrewe/efish_tracking.git
+```
+
+### 2. Change into directory
+
+```shell
+cd efish_tracking
+````
+
+### 3. Install with pip
+
+```shell
+pip3 install -e . --user
+```
+
+The ```-e``` installs the package in an *editable* model that you do not need to reinstall whenever you pull upstream changes.
+
+If  you leave away the ```--user``` the package will be installed system-wide.
+
+## TrackingResults
+
+Is a class that wraps around the *.h5 files written by DeepLabCut
+
+## ImageMarker
+
+Class that allows for creating MarkerTasks to get specific positions in a video.

docs/trackingdata.md

@@ -0,0 +1,4 @@
+# TrackingData
+
+Class that represents the position data associated with one noe/bodypart.
+

etrack/__init__.py

@@ -1,2 +0,0 @@
-from .image_marker import ImageMarker, MarkerTask
-from .tracking_result import TrackingResult

mkdocs.yml

@@ -0,0 +1,17 @@
+site_name: etrack
+
+repo_url: https://github.com/bendalab/etrack/
+
+edit_uri: ""
+
+site_author: Jan Grewe jan.grewe@g-node.org
+
+theme: readthedocs
+    
+nav:
+    - Home: 'index.md'
+    - 'User guide':
+        - 'etrack': 'etrack.md'
+        - 'TrackingData' : 'trackingdata.md'
+    - 'Code':
+        - API reference: 'api/index.html'

pyproject.toml

@@ -0,0 +1,48 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "etrack"
+dynamic = ["version"]
+dependencies = [
+  "hdf5",
+  "nixtrack",
+  "numpy",
+  "matplotlib",
+  "opencv-python",
+  "pandas",
+  "scikit-image",
+]
+requires-python = ">=3.6"
+authors = [
+  {name = "Jan Grewe", email = "jan.grewe@g-node.org"},
+]
+maintainers = [
+  {name = "Jan Grewe", email = "jan.grewe@g-node.org"},
+]
+description = "Goodies for working with tracking data of efishes."
+readme = "README.md"
+license = {file = "LICENSE"}
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: BSD-2-Clause",
+    "Natural Language :: English",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.12",
+    "Operating System :: OS Independent",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+]
+
+[project.urls]
+Repository    = "https://github.com/bendalab/etrack"
+
+[tool.setuptools.dynamic]
+version = {attr = "etrack.info.VERSION"}
+
+[tool.pytest.ini_options]
+pythonpath = "src"

setup.py

@@ -1,33 +0,0 @@
-from setuptools import setup
-
-NAME = "etrack"
-VERSION = 0.5
-AUTHOR = "Jan Grewe"
-CONTACT = "jan.grewe@g-node.org"
-CLASSIFIERS = "science"
-DESCRIPTION = "helpers for handling depp lab cut tracking results"
-
-README = "README.md"
-with open(README) as f:
-    description_text = f.read()
-
-packages = [
-    "etrack",
-]
-
-install_req = ["h5py", "pandas", "matplotlib", "numpy", "opencv-python"]
-
-setup(
-    name=NAME,
-    version=VERSION,
-    description=DESCRIPTION,
-    author=AUTHOR,
-    author_email=CONTACT,
-    packages=packages,
-    install_requires=install_req,
-    include_package_data=True,
-    long_description=description_text,
-    long_description_content_type="text/markdown",
-    classifiers=CLASSIFIERS,
-    license="BSD"
-)

src/etrack/__init__.py

@@ -0,0 +1,14 @@
+# -*- coding: utf-8 -*-
+""" etrack package for easier reading and handling of efish tracking data.
+
+Copyright © 2024, Jan Grewe
+
+Redistribution and use in source and binary forms, with or without modification, are permitted under the terms of the BSD License. See LICENSE file in the root of the Project.
+"""
+from .image_marker import ImageMarker, MarkerTask
+from .tracking_result import TrackingResult, coordinate_transformation
+from .arena import Arena, Region
+from .tracking_data import TrackingData
+from .io.dlc_data import DLCReader
+from .io.nixtrack_data import NixtrackData
+from .util import RegionShape, AnalysisType

src/etrack/arena.py

@@ -0,0 +1,527 @@
+"""
+Classes to construct the arena in which the animals were tracked.
+"""
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+from skimage.draw import disk
+from .util import RegionShape, AnalysisType, Illumination
+
+
+class Region(object):
+    """
+    Class representing a region (of interest). Regions can be either circular or rectangular. 
+    A Region can have a parent, i.e. it is contained inside a parent region. It can also have children.
+
+    Coordinates are given in absolute coordinates. The extent is treated depending on the shape. In case of a circular
+    shape, it is the radius and the origin is the center of the circle. Otherwise the origin is the bottom, or top-left corner, depending on the y-axis orientation, if inverted, then it is top-left. FIXME: check this
+    
+    """
+    def __init__(self, origin, extent, inverted_y=True, name="", region_shape=RegionShape.Rectangular, parent=None) -> None:
+        """Region constructor.
+        Parameters
+        ----------
+        origin : 2-tuple
+            x, and y coordinates
+        extent : scalar or 2-tuple, scalar only allowed to circular regions, 2-tuple for rectangular.
+        inverted_y : bool, optional
+            _description_, by default True
+        name : str, optional
+            _description_, by default ""
+        region_shape : _type_, optional
+            _description_, by default RegionShape.Rectangular
+        parent : _type_, optional
+            _description_, by default None
+
+        Returns
+        -------
+        _type_
+            _description_
+
+        Raises
+        ------
+        ValueError
+            Raises Value error when origin or extent are invalid
+        """
+        logging.debug(
+            f"etrack.Region: Create {str(region_shape)} region {name} with props origin {origin}, extent {extent} and parent {parent}"
+        )
+        if len(origin) != 2:
+            raise ValueError("Region: origin must be 2-tuple!")
+        self._parent = parent
+        self._name = name
+        self._shape_type = region_shape
+        self._origin = origin
+        self._check_extent(extent)
+        self._extent = extent
+        self._inverted_y = inverted_y
+
+    @staticmethod
+    def circular_mask(width, height, center, radius):
+        assert center[1] + radius < width and center[1] - radius > 0
+        assert center[0] + radius < height and center[0] - radius > 0
+
+        mask = np.zeros((height, width), dtype=np.uint8)
+        rr, cc = disk(reversed(center), radius)
+        mask[rr, cc] = 1
+
+        return mask
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def inverted_y(self):
+        return self._inverted_y
+
+    @property
+    def _max_extent(self):
+        if self._shape_type == RegionShape.Rectangular:
+            max_extent = (
+                self._origin[0] + self._extent[0],
+                self._origin[1] + self._extent[1],
+            )
+        else:
+            max_extent = (
+                self._origin[0] + self._extent,
+                self._origin[1] + self._extent,
+            )
+        return np.asarray(max_extent)
+
+    @property
+    def _min_extent(self):
+        if self._shape_type == RegionShape.Rectangular:
+            min_extent = self._origin
+        else:
+            min_extent = (
+                self._origin[0] - self._extent,
+                self._origin[1] - self._extent,
+            )
+        return np.asarray(min_extent)
+
+    @property
+    def xmax(self):
+        return self._max_extent[0]
+
+    @property
+    def xmin(self):
+        return self._min_extent[0]
+
+    @property
+    def ymin(self):
+        return self._min_extent[1]
+
+    @property
+    def ymax(self):
+        return self._max_extent[1]
+
+    @property
+    def position(self):
+        """
+        Get the position of the arena.
+
+        Returns
+        -------
+        tuple
+            A tuple containing the x-coordinate, y-coordinate, width, and height of the arena.
+        """
+        x = self._min_extent[0]
+        y = self._min_extent[1]
+        width = self._max_extent[0] - self._min_extent[0]
+        height = self._max_extent[1] - self._min_extent[1]
+        return x, y, width, height
+
+    def _check_extent(self, ext):
+        """Checks whether the extent matches the shape. i.e. if the shape is Rectangular, extent must be a length 2 list, tuple, otherwise, if the region is circular, extent must be a single numerical value.
+
+        Parameters
+        ----------
+        ext : tuple, or numeric scalar
+        """
+        if self._shape_type == RegionShape.Rectangular:
+            if not isinstance(ext, (list, tuple, np.ndarray)) and len(ext) != 2:
+                raise ValueError(
+                    "Extent must be a length 2 list or tuple for rectangular regions!"
+                )
+        elif self._shape_type == RegionShape.Circular:
+            if not isinstance(ext, (int, float)):
+                raise ValueError(
+                    "Extent must be a numerical scalar for circular regions!"
+                )
+        else:
+            raise ValueError(f"Invalid ShapeType, {self._shape_type}!")
+
+    def fits(self, other) -> bool:
+        """
+        Checks if the given region fits into the current region.
+
+        Args:
+            other (Region): The region to check if it fits.
+
+        Returns:
+            bool: True if the given region fits into the current region, False otherwise.
+        """
+        assert isinstance(other, Region)
+        does_fit = all(
+            (
+                other._min_extent[0] >= self._min_extent[0],
+                other._min_extent[1] >= self._min_extent[1],
+                other._max_extent[0] <= self._max_extent[0],
+                other._max_extent[1] <= self._max_extent[1],
+            )
+        )
+        if not does_fit:
+            m = (
+                f"Region {other.name} does not fit into {self.name}. "
+                f"min x: {other._min_extent[0] >= self._min_extent[0]},",
+                f"min y: {other._min_extent[1] >= self._min_extent[1]},",
+                f"max x: {other._max_extent[0] <= self._max_extent[0]},",
+                f"max y: {other._max_extent[1] <= self._max_extent[1]}",
+            )
+            logging.debug(m)
+        return does_fit
+
+    @property
+    def is_child(self):
+        """
+        Check if the current instance is a child.
+
+        Returns:
+            bool: True if the instance has a parent, False otherwise.
+        """
+        return self._parent is not None
+
+    def points_in_region(self, x, y, analysis_type=AnalysisType.Full):
+        """Returns the indices of the points specified by 'x' and 'y' that fall into this region.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            the x positions
+        y : np.ndarray
+            the y positions
+        analysis_type : AnalysisType, optional
+            defines how the positions are evaluated, by default AnalysisType.Full
+            FIXME: some of this can probably be solved using linear algebra, what with multiple exact same points?
+        """
+        if self._shape_type == RegionShape.Rectangular or (
+            self._shape_type == RegionShape.Circular
+            and analysis_type != AnalysisType.Full
+        ):
+            if analysis_type == AnalysisType.Full:
+                indices = np.where(
+                    ((y >= self._min_extent[1]) & (y <= self._max_extent[1]))
+                    & ((x >= self._min_extent[0]) & (x <= self._max_extent[0]))
+                )[0]
+                indices = np.array(indices, dtype=int)
+            elif analysis_type == AnalysisType.CollapseX:
+                x_indices = np.where(
+                    (x >= self._min_extent[0]) & (x <= self._max_extent[0])
+                )[0]
+                indices = np.asarray(x_indices, dtype=int)
+            else:
+                y_indices = np.where(
+                    (y >= self._min_extent[1]) & (y <= self._max_extent[1])
+                )[0]
+                indices = np.asarray(y_indices, dtype=int)
+        else:
+            if self.is_child:
+                mask = self.circular_mask(
+                    self._parent.position[2],
+                    self._parent.position[3],
+                    self._origin,
+                    self._extent,
+                )
+            else:
+                mask = self.circular_mask(
+                    self.position[2], self.position[3], self._origin, self._extent
+                )
+            img = np.zeros_like(mask)
+            img[np.asarray(y, dtype=int), np.asarray(x, dtype=int)] = 1
+            temp = np.where(img & mask)
+            indices = []
+            for i, j in zip(list(temp[1]), list(temp[0])):
+                matches = np.where((x == i) & (y == j))
+                if len(matches[0]) == 0:
+                    continue
+                indices.append(matches[0][0])
+            indices = np.array(indices)
+        return indices
+
+    def time_in_region(self, x, y, time, analysis_type=AnalysisType.Full):
+        """Returns the entering and leaving times at which the animal entered
+        and left a region. In case the animal was not observed after entering
+        this region (for example when hidden in a tube) the leaving time is
+        the maximum time entry.
+        Whether the full position, or only the x- or y-position should be considered 
+        is controlled with the analysis_type parameter.
+
+        Parameters
+        ----------
+        x : np.ndarray
+        The animal's x-positions
+        y : np.ndarray
+            the animal's y-positions
+        time : np.ndarray
+            the time array
+        analysis_type : AnalysisType, optional
+            The type of analysis, by default AnalysisType.Full
+
+        Returns
+        -------
+        np.ndarray
+            The entering times
+        np.ndarray
+            The leaving times
+        """
+        indices = self.points_in_region(x, y, analysis_type)
+        if len(indices) == 0:
+            return np.array([]), np.array([])
+
+        diffs = np.diff(indices)
+        if len(diffs) == sum(diffs):
+            entering = [time[indices[0]]]
+            leaving = [time[indices[-1]]]
+        else:
+            entering = []
+            leaving = []
+            jumps = np.where(diffs > 1)[0]
+            start = time[indices[0]]
+            for i in range(len(jumps)):
+                end = time[indices[jumps[i]]]
+                entering.append(start)
+                leaving.append(end)
+                start = time[indices[jumps[i] + 1]]
+
+            end = time[indices[-1]]
+            entering.append(start)
+            leaving.append(end)
+        return np.array(entering), np.array(leaving)
+
+    def patch(self, **kwargs):
+        """
+        Create and return a matplotlib patch object based on the shape type of the arena.
+
+        Parameters:
+        - kwargs: Additional keyword arguments to customize the patch object.
+
+        Returns:
+        - A matplotlib patch object representing the arena shape.
+
+        If the 'fc' (facecolor) keyword argument is not provided, it will default to None.
+        If the 'fill' keyword argument is not provided, it will default to False.
+
+        For rectangular arenas, the patch object will be a Rectangle with width and height
+        based on the arena's position.
+        For circular arenas, the patch object will be a Circle with radius based on the
+        arena's extent.
+
+        Example usage:
+        ```
+        arena = Arena()
+        patch = arena.patch(fc='blue', fill=True)
+        ax.add_patch(patch)
+        ```
+        """
+        if "fc" not in kwargs:
+            kwargs["fc"] = None
+            kwargs["fill"] = False
+        if self._shape_type == RegionShape.Rectangular:
+            w = self.position[2]
+            h = self.position[3]
+            return patches.Rectangle(self._origin, w, h, **kwargs)
+        else:
+            return patches.Circle(self._origin, self._extent, **kwargs)
+
+    def __repr__(self):
+        return f"Region: '{self._name}' of {self._shape_type} shape."
+
+
+class Arena(Region):
+    """
+    Class to represent the experimental arena. Arena is derived from Region and can be either rectangular or circular. 
+    An arena can not have a parent.
+    See Region for more details.
+    """
+    def __init__(self, origin, extent, inverted_y=True, name="", arena_shape=RegionShape.Rectangular, 
+                 illumination=Illumination.Backlight) -> None:
+        """ Construct a new Area with a given origin and extent.
+
+
+        Returns
+        -------
+        _type_
+            _description_
+
+        Raises
+        ------
+        ValueError
+            _description_
+        """
+        super().__init__(origin, extent, inverted_y, name, arena_shape)
+        self._illumination = illumination
+        self.regions = {}
+
+    def add_region(
+        self, name, origin, extent, shape_type=RegionShape.Rectangular, region=None
+    ):
+        if name is None or name in self.regions.keys():
+            raise ValueError(
+                "Region name '{name}' is invalid. The name must not be None and must be unique among the regions."
+            )
+        if region is None:
+            region = Region(
+                origin, extent, name=name, region_shape=shape_type, parent=self
+            )
+        else:
+            region._parent = self
+        doesfit = self.fits(region)
+        if not doesfit:
+            logging.warn(
+                f"Warning! Region {region.name} with size {region.position} does fit into {self.name} with size {self.position}!"
+            )
+        self.regions[name] = region
+
+    def remove_region(self, name):
+        """
+        Remove a region from the arena.
+
+        Parameter:
+            name : str
+            The name of the region to remove.
+
+        Returns:
+            None
+        """
+        if name in self.regions:
+            self.regions.pop(name)
+
+    def __repr__(self):
+        return f"Arena: '{self._name}' of {self._shape_type} shape."
+
+    def plot(self, axis=None):
+        """
+        Plots the arena on the given axis.
+
+        Parameters
+        ----------
+        - axis (matplotlib.axes.Axes, optional): The axis on which to plot the arena. If not provided, a new figure and axis will be created.
+
+        Returns
+        -------
+        - matplotlib.axes.Axes: The axis on which the arena is plotted.
+        """
+        if axis is None:
+            fig = plt.figure()
+            axis = fig.add_subplot(111)
+        axis.add_patch(self.patch())
+        axis.set_xlim([self._origin[0], self._max_extent[0]])
+
+        if self.inverted_y:
+            axis.set_ylim([self._max_extent[1], self._origin[1]])
+        else:
+            axis.set_ylim([self._origin[1], self._max_extent[1]])
+        for r in self.regions:
+            axis.add_patch(self.regions[r].patch())
+        return axis
+
+    def region_vector(self, x, y):
+        """Returns a vector that contains the region names within which the agent was found.
+            FIXME: This does not work well with overlapping regions!@!
+        Parameters
+        ----------
+        x : np.array
+            the x-positions
+        y : np.ndarray
+            the y-positions
+
+        Returns
+        -------
+        np.array
+            vector of the same size as x and y. Each entry is the region to which the position is assigned to. If the point is not assigned to a region, the entry will be empty.
+        """
+        if not isinstance(x, np.ndarray):
+            x = np.asarray(x)
+        if not isinstance(y, np.ndarray):
+            y = np.asarray(y)
+        rv = np.empty(x.shape, dtype=str)
+        for r in self.regions:
+            indices = self.regions[r].points_in_region(x, y)
+            rv[indices] = r
+        return rv
+
+    def in_region(self, x, y):
+        """
+        Determines if the given coordinates (x, y) are within any of the defined regions in the arena.
+
+        Parameters
+        ----------
+        x : float
+        The x-coordinate of the point to check.
+        y : float
+        The y-coordinate of the point to check.
+
+        Returns
+        -------
+        dict: 
+        A dictionary containing the region names as keys and a list of indices of points within each region as values.
+        """
+        tmp = {}
+        for r in self.regions:
+            print(r)
+            indices = self.regions[r].points_in_region(x, y)
+            tmp[r] = indices
+        return tmp
+
+    def __getitem__(self, key):
+        if isinstance(key, (str)):
+            return self.regions[key]
+        else:
+            return self.regions[self.regions.keys()[key]]
+
+
+if __name__ == "__main__":
+    a = Arena((0, 0), (1024, 768), name="arena", arena_shape=RegionShape.Rectangular)
+    a.add_region("small rect1", (0, 0), (100, 300))
+    a.add_region("small rect2", (150, 0), (100, 300))
+    a.add_region("small rect3", (300, 0), (100, 300))
+    a.add_region("circ", (600, 400), 150, shape_type=RegionShape.Circular)
+    axis = a.plot()
+    x = np.linspace(a.position[0], a.position[0] + a.position[2] - 1, 100, dtype=int)
+    y = np.asarray(
+        (np.sin(x * 0.01) + 1) * a.position[3] / 2 + a.position[1] - 1, dtype=int
+    )
+    # y = np.linspace(a.position[1], a.position[1] + a.position[3] - 1, 100, dtype=int)
+    axis.scatter(x, y, c="k", s=2)
+
+    ind = a.regions[3].points_in_region(x, y)
+    if len(ind) > 0:
+        axis.scatter(x[ind], y[ind], label="circ full")
+
+    ind = a.regions[3].points_in_region(x, y, AnalysisType.CollapseX)
+    if len(ind) > 0:
+        axis.scatter(x[ind], y[ind] - 10, label="circ collapseX")
+
+    ind = a.regions[3].points_in_region(x, y, AnalysisType.CollapseY)
+    if len(ind) > 0:
+        axis.scatter(x[ind], y[ind] + 10, label="circ collapseY")
+
+    ind = a.regions[0].points_in_region(x, y, AnalysisType.CollapseX)
+    if len(ind) > 0:
+        axis.scatter(x[ind], y[ind] - 10, label="rect collapseX")
+
+    ind = a.regions[1].points_in_region(x, y, AnalysisType.CollapseY)
+    if len(ind) > 0:
+        axis.scatter(x[ind], y[ind] + 10, label="rect collapseY")
+
+    ind = a.regions[2].points_in_region(x, y, AnalysisType.Full)
+    if len(ind) > 0:
+        axis.scatter(x[ind], y[ind] + 20, label="rect full")
+    axis.legend()
+    plt.show()
+
+    a.plot()
+    plt.show()

src/etrack/image_marker.py

@@ -1,8 +1,11 @@
-import matplotlib.pyplot as plt 
+"""
-import cv2
+Module that defines the ImageMarker and MarkerTask classes to manually mark things in individual images.
+"""
 import os
+import cv2
 import sys
-from IPython import embed
+import matplotlib.pyplot as plt 
+
 
 class ImageMarker:
 
@@ -30,6 +33,7 @@ class ImageMarker:
             print("Reading frame: %i" % frame_counter, end="\r")
             success, frame = video.read()
             frame_counter += 1
+
         if success:
            self._fig.gca().imshow(frame)
         else:
@@ -139,13 +143,14 @@ class MarkerTask():
 
 
 if __name__ == "__main__":
+    print("Hello Jan!")
     tank_task = MarkerTask("tank limits", ["bottom left corner", "top left corner", "top right corner", "bottom right corner"], "Mark tank corners")
-    feeder_task = MarkerTask("Feeder positions", list(map(str, range(1, 2))), "Mark feeder positions")
+    #feeder_task = MarkerTask("Feeder positions", list(map(str, range(1, 2))), "Mark feeder positions")
-    tasks = [tank_task, feeder_task]
+    #tasks = [tank_task, feeder_task]
-    im = ImageMarker(tasks)
+    im = ImageMarker([tank_task])
-    # vid1 = "2020.12.11_lepto48DLC_resnet50_boldnessDec11shuffle1_200000_labeled.mp4"
+    vid1 = "/data/personality/secondhome/fischies/lepto_03/position/lepto03_position_2021.06.07_60.mp4"
-    print(sys.argv[0])
+    # print(sys.argv[0])
-    print (sys.argv[1])
+    # print (sys.argv[1])
-    vid1 = sys.argv[1]
+    # vid1 = sys.argv[1]
-    marker_positions = im.mark_movie(vid1, 10)
+    marker_positions = im.mark_movie(vid1, 00)
     print(marker_positions)

src/etrack/info.json

@@ -0,0 +1,10 @@
+{
+    "VERSION": "0.5.0",
+    "STATUS": "Release",
+    "RELEASE": "0.5.0 Release",
+    "AUTHOR": "Jan Grewe",
+    "COPYRIGHT": "2024, University of Tuebingen, Neuroethology, Jan Grewe",
+    "CONTACT": "jan.grewe@g-node.org",
+    "BRIEF": "Efish tracking helpers for handling tracking data.",
+    "HOMEPAGE": "https://github.com/G-Node/nixpy"
+}

src/etrack/info.py

@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Copyright © 2024, Jan Grewe
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted under the terms of the BSD License. See
+# LICENSE file in the root of the Project.
+"""
+Package info.
+"""
+import os
+import json
+
+here = os.path.dirname(__file__)
+
+with open(os.path.join(here, "info.json")) as infofile:
+    infodict = json.load(infofile)
+
+
+VERSION = infodict["VERSION"]
+STATUS = infodict["STATUS"]
+RELEASE = infodict["RELEASE"]
+AUTHOR = infodict["AUTHOR"]
+COPYRIGHT = infodict["COPYRIGHT"]
+CONTACT = infodict["CONTACT"]
+BRIEF = infodict["BRIEF"]
+HOMEPAGE = infodict["HOMEPAGE"]

src/etrack/io/__init__.py

@@ -0,0 +1,3 @@
+"""
+Reader classes for DeepLabCut, or SLEAP written data files.
+"""

src/etrack/io/dlc_data.py

@@ -0,0 +1,78 @@
+import os
+import numpy as np
+import pandas as pd 
+import numbers as nb
+
+from ..tracking_data import TrackingData
+
+
+class DLCReader(object):
+    """Class that represents the tracking data stored in a DeepLabCut hdf5 file."""
+    def __init__(self, results_file, crop=(0, 0)) -> None:
+        """
+        If the video data was cropped before tracking and the tracked positions are with respect to the cropped images, we may want to correct for this to convert the data back to absolute positions in the video frame.
+
+        Parameters
+        ----------
+        crop : 2-tuple
+            tuple of (xoffset, yoffset)
+
+        Raises
+        ------
+        ValueError if crop value is not a 2-tuple
+        """
+        if not os.path.exists(results_file):
+            raise ValueError("File %s does not exist!" % results_file)
+        if not isinstance(crop, tuple) or len(crop) < 2:
+            raise ValueError("Cropping info must be a 2-tuple of (x, y)")
+        self._file_name = results_file
+        self._crop = crop
+        self._data_frame = pd.read_hdf(results_file)
+        self._level_shape = self._data_frame.columns.levshape
+        self._scorer = self._data_frame.columns.levels[0].values
+        self._bodyparts = self._data_frame.columns.levels[1].values if self._level_shape[1] > 0 else []
+        self._positions = self._data_frame.columns.levels[2].values if self._level_shape[2] > 0 else []
+
+    @property
+    def filename(self):
+        return self._file_name
+
+    @property
+    def dataframe(self):
+        return self._data_frame
+
+    @property
+    def scorer(self):
+        return self._scorer
+
+    @property
+    def bodyparts(self):
+        return self._bodyparts
+    
+    def _correct_cropping(self, orgx, orgy):
+        x = orgx + self._crop[0]
+        y = orgy + self._crop[1]
+        return x, y
+
+    def track(self, scorer=0, bodypart=0, framerate=30):
+        if isinstance(scorer, nb.Number):
+            sc = self._scorer[scorer]
+        elif isinstance(scorer, str) and scorer in self._scorer:
+            sc = scorer
+        else:
+            raise ValueError(f"Scorer {scorer} is not in dataframe!")
+        if  isinstance(bodypart, nb.Number):
+            bp = self._bodyparts[bodypart]
+        elif isinstance(bodypart, str) and bodypart in self._bodyparts:
+            bp = bodypart
+        else:
+            raise ValueError(f"Body part {bodypart} is not in dataframe!")
+
+        x = np.asarray(self._data_frame[sc][bp]["x"] if "x" in self._positions else [])
+        y = np.asarray(self._data_frame[sc][bp]["y"] if "y" in self._positions else [])
+        x, y = self._correct_cropping(x, y)
+        l = np.asarray(self._data_frame[sc][bp]["likelihood"] if "likelihood" in self._positions else [])
+
+        time = np.arange(len(x))/framerate
+
+        return TrackingData(x, y, time, l, bp, fps=framerate)

src/etrack/io/nixtrack_data.py

@@ -0,0 +1,137 @@
+import os
+import numpy as np
+import pandas as pd 
+import numbers as nb
+import nixtrack as nt
+
+from ..tracking_data import TrackingData
+from IPython import embed
+
+
+class NixtrackData(object):
+    """Wrapper around a nix data file that has been written accorind to the nixtrack model (https://github.com/bendalab/nixtrack)
+    """
+    def __init__(self, filename, crop=(0, 0)) -> None:
+        """
+        If the video data was cropped before tracking and the tracked positions are with respect to the cropped images, we may want to correct for this to convert the data back to absolute positions in the video frame.
+
+        Parameters
+        ----------
+        filename : str
+            full filename
+        crop : 2-tuple
+            tuple of (xoffset, yoffset)
+
+        Raises
+        ------
+        ValueError if crop value is not a 2-tuple
+        """
+        if not os.path.exists(filename):
+            raise ValueError("File %s does not exist!" % filename)
+        if not isinstance(crop, tuple) or len(crop) < 2:
+            raise ValueError("Cropping info must be a 2-tuple of (x, y)")
+        self._file_name = filename
+        self._crop = crop
+        self._dataset = nt.Dataset(self._file_name)
+        if not self._dataset.is_open:
+            raise ValueError(f"An error occurred opening file {self._file_name}! File is not open!")
+
+    @property
+    def filename(self):
+        """
+        Returns the name of the file associated with the NixtrackData object.
+        
+        Returns:
+            str: The name of the file.
+        """
+        return self._file_name
+
+    @property
+    def bodyparts(self):
+        """
+        Returns the bodyparts of the dataset.
+
+        Returns:
+            list: A list of bodyparts.
+        """
+        return self._dataset.nodes
+    
+    def _correct_cropping(self, orgx, orgy):
+        """
+        Corrects the coordinates based on the cropping values, If it cropping was done during tracking.
+
+        Args:
+            orgx (int): The original x-coordinate.
+            orgy (int): The original y-coordinate.
+
+        Returns:
+            tuple: A tuple containing the corrected x and y coordinates.
+        """
+        x = orgx + self._crop[0]
+        y = orgy + self._crop[1]
+        return x, y
+
+    @property
+    def fps(self):
+        """Property that holds frames per second of the original video.
+        Returns
+        -------
+        int : the frames of second
+        """
+        return self._dataset.fps
+
+    @property
+    def tracks(self):
+        """
+        Returns a list of tracks from the dataset.
+
+        Returns:
+            list: A list of tracks.
+        """
+        return [t[0] for t in self._dataset.tracks]
+
+    def track_data(self, bodypart=0, track=-1, fps=None):
+        """
+        Retrieve tracking data for a specific body part and track.
+
+        Parameters
+        ----------
+            bodypart : int or str
+            Index or name of the body part to retrieve tracking data for.
+            track : int or str
+            Index of the track to retrieve tracking data for.
+            fps : float
+            Frames per second of the tracking data. If not provided, it will be retrieved from the dataset.
+
+        Returns
+        -------
+            TrackingData: An object containing the x and y positions, time, score, body part name, and frames per second.
+
+        Raises
+        ------
+            ValueError: If the body part or track is not valid.
+        """
+        if isinstance(bodypart, nb.Number):
+            bp = self.bodyparts[bodypart]
+        elif isinstance(bodypart, (str)) and bodypart in self.bodyparts:
+            bp = bodypart
+        else:
+            raise ValueError(f"Body part {bodypart} is not a tracked node!")
+
+        if track not in self.tracks:
+            raise ValueError(f"Track {track} is not a valid track name!")
+        if not isinstance(track, (list, tuple)):
+            track = [track]
+        elif isinstance(track, int):
+            track = [self.tracks[track]]
+
+        if fps is None:
+            fps = self._dataset.fps
+
+        positions, time, _, nscore = self._dataset.positions(node=bp, axis_type=nt.AxisType.Time)
+        valid = ~np.isnan(positions[:, 0])
+        positions = positions[valid,:]
+        time = time[valid]
+        score = nscore[valid]
+
+        return TrackingData(positions[:, 0], positions[:, 1], time, score, bp, fps=fps)

src/etrack/tracking_data.py

@@ -0,0 +1,267 @@
+"""
+Module that defines the TrackingData class that wraps the position data for a given node/bodypart that has been tracked.
+"""
+
+import numpy as np
+
+
+class TrackingData(object):
+    """Class that represents tracking data, i.e. positions of an agent tracked in an environment.
+    These data are the x, and y-positions, the time at which the agent was detected, and the quality associated with the position estimation.
+    TrackingData contains these data and offers a few functions to work with it.
+    Using the 'quality_threshold', 'temporal_limits', or the 'position_limits' data can be filtered (see filter_tracks function).
+    The 'interpolate' function allows to fill up the gaps that may result from filtering with linearly interpolated data points.
+
+    More may follow...
+    """
+
+    def __init__(self, x, y, time, quality, node="", fps=None, 
+                 quality_threshold=None, temporal_limits=None, position_limits=None) -> None:
+        """
+        Initialize a TrackingData object.
+
+        Parameters
+        ----------
+        x : float
+            The x-coordinates of the tracking data.
+        y : float
+            The y-coordinates of the tracking data.
+        time : float
+            The time vector associated with the x-, and y-coordinates.
+        quality : float
+            The quality score associated with the position estimates.
+        node : str, optional
+            The node name associated with the data. Default is an empty string.
+        fps : float, optional
+            The frames per second of the tracking data. Default is None.
+        quality_threshold : float, optional
+            The quality threshold for the tracking data. Default is None.
+        temporal_limits : tuple, optional
+            The temporal limits for the tracking data. Default is None.
+        position_limits : tuple, optional
+            The position limits for the tracking data. Default is None.
+        """
+        self._orgx = x
+        self._orgy = y
+        self._orgtime = time
+        self._orgquality = quality
+        self._x = x
+        self._y = y
+        self._time = time
+        self._quality = quality
+        self._node = node
+        self._threshold = quality_threshold
+        self._position_limits = position_limits
+        self._time_limits = temporal_limits
+        self._fps = fps
+
+    @property
+    def original_positions(self):
+        return self._orgx, self._orgy
+
+    @property
+    def original_quality(self):
+        return self._orgquality
+
+    def interpolate(self, start_time=None, end_time=None, min_count=5):
+        if len(self._x) < min_count:
+            print(
+                f"{self._node} data has less than {min_count} data points with sufficient quality ({len(self._x)})!"
+            )
+            return None, None, None
+        start = self._time[0] if start_time is None else start_time
+        end = self._time[-1] if end_time is None else end_time
+        time = np.arange(start, end, 1.0 / self._fps)
+        x = np.interp(time, self._time, self._x)
+        y = np.interp(time, self._time, self._y)
+
+        return x, y, time
+
+    @property
+    def quality_threshold(self):
+        """Property that holds the quality filter setting.
+        
+        Returns
+        -------
+            float : the quality threshold
+        """
+        return self._threshold
+
+    @quality_threshold.setter
+    def quality_threshold(self, new_threshold):
+        """Setter of the quality threshold that should be applied when filtering the data. Setting this to None removes the quality filter.
+        
+        Data points that have a quality score below the given threshold are discarded.
+
+        Parameters
+        ----------
+        new_threshold : float
+
+        """
+        self._threshold = new_threshold
+
+    @property
+    def position_limits(self):
+        """
+        Get the position limits of the tracking data.
+
+        Returns:
+            tuple: A 4-tuple containing the start x, and y positions, width and height limits.
+        """
+        return self._position_limits
+
+    @position_limits.setter
+    def position_limits(self, new_limits):
+        """Sets the limits for the position filter. 'new_limits' must be a 4-tuple of the form (x0, y0, width, height). If None, the limits will be removed.
+        Data points outside the position limits are discarded.
+
+        Parameters
+        ----------
+        new_limits: 4-tuple
+            tuple of x-position, y-position, the width and the height. Passing None removes the filter
+
+        Raises
+        ------
+        ValueError, if new_value is not a 4-tuple
+        """
+        if new_limits is not None and not (
+            isinstance(new_limits, (tuple, list)) and len(new_limits) == 4
+        ):
+            raise ValueError(
+                f"The new_limits vector must be a 4-tuple of the form (x, y, width, height)"
+            )
+        self._position_limits = new_limits
+
+    @property
+    def temporal_limits(self):
+        """
+        Get the temporal limits of the tracking data.
+
+        Returns:
+            tuple: A tuple containing the start and end time of the tracking data.
+        """
+        return self._time_limits
+
+    @temporal_limits.setter
+    def temporal_limits(self, new_limits):
+        """Limits for temporal filter. The limits must be a 2-tuple of start and end time. Setting this to None removes the filter.
+        Data points the are associated with times outside the limits are discarded.
+        
+        Parameters
+        ----------
+        new_limits : 2-tuple
+            The new limits in the form (start, end) given in seconds.
+        Returns
+        -------
+        None
+        
+        Raises
+        ------
+            ValueError if the limits are not valid.
+        """
+        if new_limits is not None and not (
+            isinstance(new_limits, (tuple, list)) and len(new_limits) == 2
+        ):
+            raise ValueError(
+                f"The new_limits vector must be a 2-tuple of the form (start, end). "
+            )
+        self._time_limits = new_limits
+
+    def filter_tracks(self, align_time=True):
+        """Applies the filters to the tracking data. All filters will be applied sequentially, i.e. an AND connection.
+        To change the filter settings use the setters for 'quality_threshold', 'temporal_limits', 'position_limits'. Setting them to None disables the respective filter discarding the setting.
+
+        Parameters
+        ----------
+        align_time: bool
+            Controls whether the time vector is aligned to the first time point at which the agent is within the positional_limits. Default = True
+        """
+        self._x = self._orgx.copy()
+        self._y = self._orgy.copy()
+        self._time = self._orgtime.copy()
+        self._quality = self.original_quality.copy()
+
+        if self.position_limits is not None:
+            x_max = self.position_limits[0] + self.position_limits[2]
+            y_max = self.position_limits[1] + self.position_limits[3]
+            indices = np.where(
+                (self._x >= self.position_limits[0])
+                & (self._x < x_max)
+                & (self._y >= self.position_limits[1])
+                & (self._y < y_max)
+            )
+            self._x = self._x[indices]
+            self._y = self._y[indices]
+            self._time = self._time[indices] - self._time[0] if align_time else 0.0
+            self._quality = self._quality[indices]
+
+        if self.temporal_limits is not None:
+            indices = np.where(
+                (self._time >= self.temporal_limits[0])
+                & (self._time < self.temporal_limits[1])
+            )
+            self._x = self._x[indices]
+            self._y = self._y[indices]
+            self._time = self._time[indices]
+            self._quality = self._quality[indices]
+
+        if self.quality_threshold is not None:
+            indices = np.where((self._quality >= self.quality_threshold))
+            self._x = self._x[indices]
+            self._y = self._y[indices]
+            self._time = self._time[indices]
+            self._quality = self._quality[indices]
+
+    def positions(self):
+        """Returns the filtered data (if filters have been applied, otherwise the original data).
+
+        Returns
+        -------
+        np.ndarray
+            The x-positions
+        np.ndarray
+            The y-positions
+        np.ndarray
+            The time vector
+        np.ndarray
+            The detection quality
+        """
+        return self._x, self._y, self._time, self._quality
+
+    def speed(self, x=None, y=None, t=None):
+        """ Returns the agent's speed as a function of time and position. The speed estimation is associated to the time/position between two sample points. If any of the arguments is not provided, the function will use the x,y coordinates that are stored within the object, otherwise, if all are provided, the user-provided values will be used.
+        
+        Since the velocities are estimated from the difference between two sample points the returned velocities and positions are assigned to positions and times between the respective sampled positions/times. 
+
+        Parameters
+        ----------
+        x: np.ndarray
+            The x-coordinates, defaults to None
+        y: np.ndarray
+            The y-coordinates, defaults to None
+        t: np.ndarray
+            The time vector, defaults to None
+        Returns
+        -------
+        np.ndarray:
+            The time vector.
+        np.ndarray:
+            The speed.
+        tuple of np.ndarray
+            The position
+        """
+        if x is None or y is None or t is None:
+            x = self._x.copy()
+            y = self._y.copy()
+            t = self._time.copy()
+        dt = np.diff(t)
+        speed = np.sqrt(np.diff(x)**2 + np.diff(y)**2) / dt
+        t = t[:-1] + dt / 2
+        x = x[:-1] + np.diff(x) / 2
+        y = y[:-1] + np.diff(y) / 2
+
+        return t, speed, (x, y)
+
+    def __repr__(self) -> str:
+        s = f"Tracking data of node '{self._node}'!"
+        return s

src/etrack/tracking_result.py

@@ -14,6 +14,10 @@ y_factor = 0.81/height  # Einheit m/px
 center = (np.round(x_0 + width/2), np.round(y_0 + height/2))
 center_meter = ((center[0] - x_0) * x_factor, (center[1] - y_0) * y_factor)
 """
+def coordinate_transformation(position,x_0, y_0, x_factor, y_factor):
+        x = (position[0] - x_0) * x_factor
+        y = (position[1] - y_0) * y_factor
+        return (x, y) #in m 
 
 class TrackingResult(object):
 
@@ -105,7 +109,11 @@ class TrackingResult(object):
             bp string: the body part
             [type]: [description]
         """
+        time, x, y, l, bp = self.pixel_positions(scorer, bodypart, framerate, interpolate, min_likelihood)
+        x, y = self._to_meter(x, y)
+        return time, x, y, l, bp
 
+    def pixel_positions(self, scorer=0, bodypart=0, framerate=30, interpolate=True, min_likelihood=0.95):
         if  isinstance(scorer, nb.Number):
             sc = self._scorer[scorer]
         elif isinstance(scorer, str) and scorer in self._scorer:
@@ -119,25 +127,37 @@ class TrackingResult(object):
         else:
             raise ValueError("Bodypart %s is not in dataframe!" % bodypart)
 
-        x = self._data_frame[sc][bp]["x"] if "x" in self._positions else []
+        x = np.asarray(self._data_frame[sc][bp]["x"] if "x" in self._positions else [])
-        x = (np.asarray(x) - self.x_0) * self.x_factor
+        y = np.asarray(self._data_frame[sc][bp]["y"] if "y" in self._positions else [])
-        y = self._data_frame[sc][bp]["y"] if "y" in self._positions else []
+        l = np.asarray(self._data_frame[sc][bp]["likelihood"] if "likelihood" in self._positions else [])
-        y = (np.asarray(y) - self.y_0) * self.y_factor
+
-        l = self._data_frame[sc][bp]["likelihood"] if "likelihood" in self._positions else []
+        time = np.arange(len(x))/framerate
-
+        if interpolate:
-        time = np.arange(len(self._data_frame))/framerate
+            x, y = self.interpolate(time, x, y, l, min_likelihood)
-        time2 = time[l > min_likelihood]
+        return time, x, y, l, bp
-        if len(l[l > min_likelihood]) < 100:
+
-            print("%s has not datapoints with likelihood larger than %.2f" % (self._file_name, min_likelihood) )
+    def _to_meter(self, x, y):
-            return None, None, None, None, None
+        new_x = (np.asarray(x) - self.x_0) * self.x_factor
+        new_y = (np.asarray(y) - self.y_0) * self.y_factor
+        return new_x, new_y
+
+    def _speed(self, t, x, y):
+        speed = np.sqrt(np.diff(x)**2 + np.diff(y)**2) / np.diff(t)
+        return speed
+
+    def interpolate(self, t, x, y, l, min_likelihood=0.9):
+        time2 = t[l > min_likelihood]
+        if len(l[l > min_likelihood]) < 10:
+            print("%s has less than 10 datapoints with likelihood larger than %.2f" % (self._file_name, min_likelihood) )
+            return None, None
         x2 = x[l > min_likelihood]
         y2 = y[l > min_likelihood]
-        x3 = np.interp(time, time2, x2)
+        x3 = np.interp(t, time2, x2)
-        y3 = np.interp(time, time2, y2)
+        y3 = np.interp(t, time2, y2)
-        return time, x3, y3, l, bp
+        return x3, y3
 
     def plot(self, scorer=0, bodypart=0, threshold=0.9, framerate=30):
-        t, x, y, l, name  = self.position_values(scorer=scorer, bodypart=bodypart, framerate=framerate)
+        t, x, y, l, name  = self.position_values(scorer=scorer, bodypart=bodypart, framerate=framerate, min_likelihood=threshold)
         plt.scatter(x[l > threshold], y[l > threshold], c=t[l > threshold], label=name)
         plt.scatter(self.center_meter[0], self.center_meter[1], marker="*")
         plt.plot(x[l > threshold], y[l > threshold])
@@ -148,7 +168,6 @@ class TrackingResult(object):
         bar.set_label("time [s]")
         plt.legend()
         plt.show()
-        from IPython import embed
 
 
 if __name__ == '__main__':
@@ -166,12 +185,12 @@ if __name__ == '__main__':
     x3 = np.interp(time, time2, x2)
     y3 = np.interp(time, time2, y2)
 
-
     fig, axes = plt.subplots(3,1, sharex=True)
     axes[0].plot(time, x)
     axes[0].plot(time, x3)
     axes[1].plot(time, y)
     axes[1].plot(time, y3)
+
     axes[2].plot(time, l)
     plt.show()
 

src/etrack/util.py

@@ -0,0 +1,52 @@
+"""
+Module containing utility functions and enum classes.
+"""
+from enum import Enum
+
+class Illumination(Enum):
+    Backlight = 0
+    Incident = 1
+
+
+class RegionShape(Enum):
+    """
+    Enumeration representing the shape of a region.
+
+    Attributes:
+        Circular: Represents a circular region.
+        Rectangular: Represents a rectangular region.
+    """
+
+    Circular = 0
+    Rectangular = 1
+
+    def __str__(self) -> str:
+        return self.name
+
+
+class AnalysisType(Enum):
+    """
+    Enumeration representing different types of analysis used when analyzing whether
+    positions fall into a given region.
+    
+    Possible types:
+        AnalysisType.Full: considers both, the x- and the y-coordinates
+        AnalysisType.CollapseX: consider only the x-coordinates
+        AnalysisType.CollapseY: consider only the y-coordinates
+    """
+    Full = 0
+    CollapseX = 1
+    CollapseY = 2
+
+    def __str__(self) -> str:
+        """
+        Returns the string representation of the analysis type.
+        
+        Returns:
+            str: The name of the analysis type.
+        """
+        return self.name
+
+class PositionType(Enum):
+    Absolute = 0
+    Cropped = 1

test/test_arena.py

@@ -0,0 +1,82 @@
+import pytest
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mp
+
+from etrack import Arena, Region, RegionShape
+
+
+def test_region():
+    # Create a parent region
+    parent_region = Region((0, 0), (100, 100), name="parent", region_shape=RegionShape.Rectangular)
+
+    # Create a child region
+    child_region = Region((10, 10), (50, 50), name="child", region_shape=RegionShape.Rectangular, parent=parent_region)
+
+    # Test properties
+    assert child_region.name == "child"
+    assert child_region.inverted_y == True
+    assert (child_region._max_extent == np.array((60, 60))).all()
+    assert (child_region._min_extent == np.array((10, 10))).all()
+    assert child_region.xmax == 60
+    assert child_region.xmin == 10
+    assert child_region.ymin == 10
+    assert child_region.ymax == 60
+    assert child_region.position == (10, 10, 50, 50)
+    assert child_region.is_child == True
+
+    # Test fits method
+    assert parent_region.fits(child_region) == True
+
+    # Test points_in_region method
+    x = [15, 20, 25, 30, 35, 5]
+    y = [15, 20, 25, 30, 35, 5]
+    assert (child_region.points_in_region(x, y) == np.array([0, 1, 2, 3, 4])).all()
+
+    # Test time_in_region method
+    x = [5, 15, 20, 25, 30, 35, 35]
+    y = [5, 15, 20, 25, 30, 35, 65]
+    time = np.arange(0, len(x), 1)
+    enter, leave = child_region.time_in_region(x, y, time)
+    assert enter[0] == 1
+    assert leave[0] == 5
+
+    # Test patch method
+    patch = child_region.patch(color='red')
+    assert isinstance(patch, mp.Patch)
+
+    # Test __repr__ method
+    assert repr(child_region) == "Region: 'child' of Rectangular shape."
+
+
+def test_arena():
+    # Create an arena
+    arena = Arena((0, 0), (100, 100), name="arena", arena_shape=RegionShape.Rectangular)
+    # Test add_region method
+    arena.add_region("small rect1", (0, 0), (50, 50))
+    assert len(arena.regions) == 1
+    assert arena.regions["small rect1"].name == "small rect1"
+    # Test remove_region method
+    arena.remove_region("small rect1")
+    assert len(arena.regions) == 0
+    # Test plot method
+    axis = arena.plot()
+    assert isinstance(axis, plt.Axes)
+    # Test region_vector method
+    x = [10, 20, 30]
+    y = [10, 20, 30]
+    assert (arena.region_vector(x, y) == "").all()
+
+    # Test in_region method
+    # assert len(arena.in_region(10, 10)) > 0
+    # print(arena.in_region(10, 10))
+
+    # print(arena.in_region(110, 110))
+    # assert arena.in_region(110, 110) == False
+    # Test __getitem__ method
+    arena.add_region("small rect2", (0, 0), (50, 50))
+    assert arena["small rect2"].name == "small rect2"
+
+
+if __name__ == "__main__":
+    pytest.main()

test/test_nixtrackio.py

@@ -0,0 +1,32 @@
+import pytest
+import etrack as et
+
+from IPython import embed
+
+dataset = "test/2022lepto01_converted_2024.03.27_0.mp4.nix"
+
+
+@pytest.fixture
+def nixtrack_data():
+    # Create a NixTrackData object with some test data
+    return et.NixtrackData(dataset)
+
+
+def test_basics(nixtrack_data):
+    assert nixtrack_data.filename == dataset
+    assert len(nixtrack_data.bodyparts) == 5
+    assert len(nixtrack_data.tracks) == 2
+    assert nixtrack_data.fps == 25
+
+
+def test_trackingdata(nixtrack_data):
+    with pytest.raises(ValueError):
+        nixtrack_data.track_data(bodypart="test")
+        nixtrack_data.track_data(track="fish")
+
+    assert nixtrack_data.track_data("center") is not None
+    assert nixtrack_data.track_data("center", "none") is not None
+
+
+if __name__ == "__main__":
+    pytest.main()