diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..d645695
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/NOTICE b/NOTICE
new file mode 100644
index 0000000..9523f34
--- /dev/null
+++ b/NOTICE
@@ -0,0 +1,6 @@
+Automated Camera Trapping Identification and Organization Network (ACTION)
+Copyright 2023 Morgan Humphrey, David Humphrey
+
+This software contains code derived from https://github.com/parlaynu/megadetector-v5-onnx, used under the MIT License (https://github.com/parlaynu/megadetector-v5-onnx/blob/main/LICENSE), and https://github.com/Tianxiaomo/pytorch-YOLOv4, used under the Apache-2.0 License (https://github.com/Tianxiaomo/pytorch-YOLOv4/blob/master/License.txt).
+
+This software uses derived machine learning models (ONNX formatted) from https://github.com/tamim662/YOLO-Fish (GPL-3.0 - https://github.com/tamim662/YOLO-Fish/blob/main/LICENSE) and https://github.com/microsoft/CameraTraps (MIT - https://github.com/microsoft/CameraTraps/blob/main/LICENSE), which are also made available.
diff --git a/README.md b/README.md
index 79b8bfd..75ba67a 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,29 @@
 # Automated Camera Trapping Identification and Organization Network (ACTION)
 
+## Overview
+
+Action is a Python-based tool designed to bring the power of AI computer vision models to camera trap video analysis.  Using Action, it's possible to process hours of raw video into clip segments where animals or fish appear.
+
+Whether you're monitoring aquatic life with underwater cameras or tracking terrestrial wildlife, Action can help you save time tediously scanning footage manually.
+
+## How it Works
+
+Action takes one or more video files as input, along with several optional parameters to customize the process, and analyzes each video.  Depending on the environment specified by the user, an appropriate object detection model is used: [YOLO-Fish v4](https://github.com/tamim662/YOLO-Fish) for aquatic videos, or [Megadetector v5](https://github.com/microsoft/CameraTraps/blob/main/megadetector.md) for terrestrial. The videos are then processed using the AI model, and whenever animals or fish are detected, a clip is created.  At the end of the process, the clips represent all the detections in the raw footage.
+
 ## Setup
 
 Action is written in Python and requires a number of dependencies and large machine learning models (~778M) to be installed and downloaded.
 
 The easiest way to use it is with the [pixi](https://prefix.dev/docs/pixi/overview) package manager.  Pixi installs everything you need into a local `.pixi` folder (i.e., at the root of the project), without needing to modify your system.
 
+### Installation Steps
+
 1. Clone this repo using `git clone https://github.com/humphrem/action.git`. NOTE: the repo includes large `.onnx` model files, which will get downloaded if you have [`git-lfs`](https://git-lfs.com/) installed (otherwise we'll get them as part of the setup below).
 2. [Install pixi](https://prefix.dev/docs/pixi/overview#installation)
 3. Start a terminal and navigate to the root of the Action project folder you just cloned, `cd action`
 4. Enter the command `pixi run setup` to download, install, and setup everything you'll need
 
-## Using Action
-
-### Pixi Shell
+### Using the Pixi Shell Environment
 
 Each time you want to use Action, you need to open a terminal and navigate to the Action folder, then start a shell with `pixi`:
 
@@ -23,8 +33,80 @@ pixi shell
 
 This will make all of the dependencies installed with `pixi run setup` available.
 
-You can exit the pixi shell by using:
+When you are done, you can exit the pixi shell by using:
 
 ```sh
 exit
 ```
+
+## Running Action
+
+With all dependencies installed, and the models downloaded to the `models/` directory, you can now run action:
+
+```sh
+pixi shell
+python3 action.py
+
+usage: action.py [-h] [-e {terrestrial,aquatic}] [-b BUFFER] [-c CONFIDENCE]
+                 [-m MIN_DURATION] [-f SKIP_FRAMES] [-d] [-o OUTPUT_DIR] [-s]
+                 [--log-level {DEBUG,INFO,WARNING,ERROR}]
+                 filename [filename ...]
+action.py: error: the following arguments are required: filename
+```
+
+> [!NOTE]
+> On Unix systems you can also use `./action.py` without `python3`.
+
+### Options
+
+Action can be configured to run in different ways using various arguments and flags.
+
+| Option | Description | Example |
+| --- | --- | --- |
+| `filename` | Path to a video file, multiple video files, or a glob pattern | `./video/*.mov` |
+| `-e`, `--environment` | Type of camera environment, either aquatic or terrestrial. Defaults to `aquatic` | `--environment terrestrial` |
+| `-b`, `--buffer` | Number of seconds to add before and after detection. Cannot be negative | `--buffer 1.0` |
+| `-c`, `--confidence` | Confidence threshold for detection. Must be greater than 0.0 and less than 1.0 | `--confidence 0.45` |
+| `-m`, `--minimum-duration` | Minimum duration for clips in seconds. Must be greater than 0.0 | `--minimum-duration 2.0` |
+| `-f`, `--frames-to-skip` | Number of frames to skip when detecting. Cannot be negative, defaults to half the frame rate | `--frames-to-skip 15` |
+| `-d`, `--delete-previous-clips` | Whether to delete previous clips before processing video | `--delete-previous-clips` |
+| `-o`, `--output-dir` | Output directory to use for all clips | `--output-dir ./output` |
+| `-s`, `--show-detections` | Whether to show detection frames with bounding boxes | `--show-detections` |
+| `--log-level` | Logging level. Can be `DEBUG`, `INFO`, `WARNING`, or `ERROR`. Defaults to `INFO` | `--log-level DEBUG` |
+
+> [!NOTE]
+> The options with `-` or `--` are optional, while `filename` is a required argument.
+
+### Examples
+
+To process a video named `recording.mov` using all the default settings, specify only the filename:
+
+```sh
+python3 action.py recording.mov
+```
+
+You can also include multiple filenames:
+
+```sh
+python3 action.py recording1.mov recording2.mov recording3.mov
+```
+
+Or use a file pattern:
+
+```sh
+python3 action.py ./videos/*.avi
+```
+
+Many other options can be altered (see above) to process videos in specific ways.  For example:
+
+```sh
+python3 action.py ./video/aquatic.mov -c 0.60 -m 3.0 -s -b 1.0 -d -e aquatic
+```
+
+This would process the file `./video/aquatic.mov`, deleting all previous detections, use YOLO-Fish for detections, set a confidence threshold of `0.60` (i.e., include fish detections with confidence `0.60` and higher), make all clips `3.0` seconds minimum with a `1.0` second buffer added to the start and end of the clip (i.e., `1.0` + `3.0` + `1.0` = `5.0` seconds), and show each initial detection visual (i.e., bounding boxes on the video frame).
+
+```sh
+python3 action.py ./video/terrestrial.mov -c 0.45 -m 8.0 -b 2.0 -e terrestrial -f 25
+```
+
+This would process the file `./video/terrestrial.mov`, use Megadetector for detections, set a confidence threshold of `0.45` (i.e., include animal detections with confidence `0.45` and higher), make all clips `8.0` seconds minimum with a `2.0` second buffer added to the start and end of the clip (i.e., `2.0` + `8.0` + `2.0` = `12.0` seconds), and run detections on every 25th frame in the video.
diff --git a/action.py b/action.py
index 899f528..773189e 100755
--- a/action.py
+++ b/action.py
@@ -1,376 +1,9 @@
-"""
-This is the main module for ACTION. It handles parsing arguments from the user,
-loading and managing resources, and processing detections into clips.
-"""
+#!/usr/bin/env python3
 
-import os
-import sys
-import time
-import logging
 import argparse
+from src.action import main
 
-from clip_manager import (
-    ClipManager,
-    remove_clips_dir,
-    remove_output_dir,
-    create_output_dir,
-)
-from yolo_fish_detector import YoloFishDetector
-from megadetector_detector import MegadetectorDetector
-from utils import format_time, format_percent, get_video_paths
-
-import cv2
-
-
-# We use converted ONNX models for YOLO-Fish (https://github.com/tamim662/YOLO-Fish)
-# and Megadetector (https://github.com/microsoft/CameraTraps)
-def load_detector(environment, min_duration, buffer, confidence, logger):
-    """
-    Load the appropriate detector based on the environment provided.
-
-    Args:
-        environment (str): The type of detector to load. Must be either "aquatic" or "terrestrial".
-        min_duration (float): The minimum duration of a generated clip
-        buffer (float): An optional number of seconds to add before/after a clip
-        confidence (float): The confidence level to use
-        logger (logging.Logger): The logger to use for logging messages.
-
-    Returns:
-        detector (object): An instance of the appropriate detector.
-
-    Raises:
-        TypeError: If the any args are not of the correct type
-    """
-
-    # Make sure any user-provided flags for detection are valid before we use them
-    if buffer and buffer < 0.0:
-        raise TypeError("Error: minimum buffer cannot be negative")
-
-    if min_duration and min_duration <= 0.0:
-        raise TypeError("Error: minimum duration must be greater than 0.0")
-
-    if confidence and (confidence <= 0.0 or confidence > 1.0):
-        raise TypeError("Error: confidence must be greater than 0.0 and less than 1.0")
-
-    detector = None
-    if environment == "terrestrial":
-        detector = MegadetectorDetector(logger, min_duration, buffer, confidence)
-    elif environment == "aquatic":
-        detector = YoloFishDetector(logger, min_duration, buffer, confidence)
-    else:
-        raise TypeError("environment must be one of aquatic or terrestrial")
-
-    detector.load()
-    return detector
-
-
-# Defining the function process_frames, called in main
-def process_frames(
-    video_path,
-    cap,
-    detector,
-    clips,
-    fps,
-    total_frames,
-    frames_to_skip,
-    logger,
-    args,
-):
-    """
-    Process frames from a video file and create clips based on detections.
-
-    Args:
-        video_path (str): The path to the video file.
-        cap (cv2.VideoCapture): The video capture object.
-        detector (object): The detector to use for detecting objects in frames.
-        clips (ClipManager): The clip manager for managing clips.
-        fps (int): The frames per second of the video.
-        total_frames (int): The total number of frames in the video.
-        frames_to_skip (int): The number of frames to skip between detections
-        logger (logging.Logger): The logger to use for logging messages.
-        args (argparse.Namespace): The command line arguments.
-
-    Returns:
-        None
-    """
-    buffer_seconds = detector.buffer
-    min_detection_duration = detector.min_duration
-    show_detections = args.show_detections
-
-    # Number of frames per minute of video time
-    frames_per_minute = 60 * fps
-    # Frame number for the next progress message
-    next_progress_frame = frames_per_minute
-
-    # Track when there are is something in frame as detection events
-    detection_start_time = None
-    detection_highest_confidence = 0
-    detection_event = False
-
-    frame_count = 0
-
-    # Loop over all frames in the video
-    while cap.isOpened():
-        ret, frame = cap.read()
-        # If there isn't another frame, we're done
-        if not ret:
-            break
-
-        # If a detection is already happening, then we know we're going to
-        # record for a given period, so we can skip ahead to the frame where
-        # the detection period ends. However, we then need to check *all* the
-        # frames after this within the buffer period (i.e., so we don't overlap
-        # with the end buffer period in the previous clip).  If we detect something
-        # in these frames, we should extend this detection period; otherwise
-        # we end it and create a new clip.
-        if detection_event:
-            # Calculate the number of frames to skip ahead. We know that we
-            # want to record for a minimum duration, and potentially we add
-            # a buffer period in seconds.
-            skip_ahead_frames = int((min_detection_duration + buffer_seconds) * fps)
-            logger.debug(f"Detection event, skipping ahead {skip_ahead_frames} frames")
-
-            # Skip ahead the number of frames that will be in the clip
-            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count + skip_ahead_frames)
-            frame_count += skip_ahead_frames
-
-            # Check some frames within the buffer period for a detection, to see if
-            # we should extend this detection period, or if it's OK to end it. NOTE:
-            # if `buffer_seconds` is 0, at least check the next frame.
-            for i in range(max(1, int(buffer_seconds * fps))):
-                ret, frame = cap.read()
-                if not ret:
-                    break
-                frame_count += 1
-
-                # Only process frames that are multiples of frames_to_skip
-                # or the last frame in the video.
-                if i % frames_to_skip == 0 or frame_count == total_frames - 1:
-                    logger.debug(
-                        f"Checking frame {frame_count} before ending detection event"
-                    )
-                    # If a detection is made, extend the current detection period
-                    boxes = detector.detect(frame)
-                    if len(boxes) > 0:
-                        detection_highest_confidence = max(
-                            detection_highest_confidence, max(box[4] for box in boxes)
-                        )
-                        logger.info(
-                            f"{detector.class_name} detected, extending detection event: {format_time(frame_count / fps + buffer_seconds)} (max confidence={format_percent(detection_highest_confidence)})"
-                        )
-                        if show_detections:
-                            detector.draw_detections(frame, boxes, video_path)
-                        break
-            else:
-                # If no detection was made within the buffer period, and we didn't
-                # extend, end the detection period now and create a new clip.
-                detection_end_time = frame_count / fps + buffer_seconds
-                logger.info(
-                    f"Detection period ended: {format_time(detection_end_time)} (duration={format_time(detection_end_time - detection_start_time)}, max confidence={format_percent(detection_highest_confidence)})"
-                )
-                clips.create_clip(
-                    detection_start_time,
-                    detection_end_time,
-                    video_path,
-                )
-
-                # Reset the detection period
-                detection_event = False
-                detection_highest_confidence = 0
-
-                # Start again reading the next frame
-                continue
-
-        # If we're not already in a detection event, process every n frames
-        # vs. every frame for speed (e.g., every 15 of 30fps). We also check
-        # the last frame, so we don't miss anything at the edge.
-        if frame_count % frames_to_skip == 0 or frame_count == total_frames - 1:
-            boxes = detector.detect(frame)
-
-            # If there are one ore more detections
-            if len(boxes) > 0:
-                detection_highest_confidence = max(
-                    detection_highest_confidence, max(box[4] for box in boxes)
-                )
-
-                # If we're not already in a detection event, start one
-                if not detection_event:
-                    detection_start_time = max(0, frame_count / fps - buffer_seconds)
-                    logger.info(
-                        f"{detector.class_name} detected, starting detection event: {format_time(frame_count / fps)} (max confidence={format_percent(detection_highest_confidence)})"
-                    )
-                    if show_detections:
-                        detector.draw_detections(frame, boxes, video_path)
-                    detection_event = True
-
-        # We've finished processing this frame
-        frame_count += 1
-
-        # Print a progress message every minute of video time so we know what's going on
-        if frame_count >= next_progress_frame:
-            logger.info(
-                f"\nProgress: {format_percent(frame_count / total_frames)} processed ({frame_count}/{total_frames} frames, {format_time(frame_count / fps)})\n"
-            )
-            next_progress_frame += frames_per_minute
-
-    # Before we finish the program, check if there's a detection event in progress
-    # and if there is, end it now so we don't lose the final clip.
-    if detection_event:
-        detection_end_time = frame_count / fps + buffer_seconds
-        logger.info(
-            f"Detection period ended: {format_time(detection_end_time)} (duration={format_time(detection_end_time - detection_start_time)}, max confidence={format_percent(detection_highest_confidence)})"
-        )
-        clips.create_clip(
-            detection_start_time,
-            detection_end_time,
-            video_path,
-        )
-
-
-# Main part of program to do setup and start processing frames in each file
-def main(args):
-    """
-    The main function of the program. Sets up the logger, validates arguments,
-    loads the detector, and processes frames from each video file.
-
-    Args:
-        args (argparse.Namespace): The command line arguments.
-    """
-
-    # Create a logger for this module and set the log level
-    logger = logging.getLogger(__name__)
-    logging.basicConfig(level=args.log_level, format="%(message)s")
-
-    delete_clips = args.delete_clips
-    output_dir = args.output_dir
-    video_paths = get_video_paths(args.filename)
-    logger.debug(f"Got input files: {video_paths}")
-
-    # Validate argument parameters from user before using them
-    if len(video_paths) < 1:
-        logger.error("Error: you must specify one or more video filenames to process")
-        sys.exit(1)
-
-    # Load YOLO-Fish or Megadetector, based on `-e` value
-    detector = None
-    try:
-        detector = load_detector(
-            args.environment, args.min_duration, args.buffer, args.confidence, logger
-        )
-    except Exception as e:
-        logger.error(f"There was an error: {e}")
-        sys.exit(1)
-
-    cap = None
-    clips = None
-
-    # Initialize the output_dir if specified
-    if output_dir:
-        # If `-d`` was specified, delete old clips first
-        if delete_clips:
-            remove_output_dir(output_dir, logger)
-        # Create the output directory if it doesn't exist
-        create_output_dir(output_dir)
-
-    try:
-        # Create a queue manager for clips to be processed by ffmpeg
-        clips = ClipManager(logger, output_dir)
-
-        # Keep track of total time to process all files, recording start time
-        total_time_start = time.time()
-
-        # Loop over all the video file paths and process each one
-        for i, video_path in enumerate(video_paths, start=1):
-            # Make sure this video path actually exists before we try to use it
-            if not os.path.exists(video_path):
-                logger.info(f"Video path {video_path} does not exist, skipping.")
-                continue
-
-            file_start_time = time.time()
-
-            # If the user requests it via -d flag, and isn't using a common output_dir
-            # remove old clips first
-            if not output_dir and delete_clips:
-                remove_clips_dir(video_path, logger)
-
-            # Setup video capture for this video file
-            cap = cv2.VideoCapture(video_path)
-            fps = cap.get(cv2.CAP_PROP_FPS)
-            frames_to_skip = args.skip_frames or int(fps / 2.0)
-            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-            duration = total_frames / fps
-
-            logger.info(
-                f"\nStarting file {i} of {len(video_paths)}: {video_path} - {format_time(duration)} - {total_frames} frames at {fps} fps, skipping every {frames_to_skip} frames"
-            )
-            logger.info(
-                f"Using confidence threshold {detector.confidence}, minimum clip duration of {detector.min_duration} seconds, and {detector.buffer} seconds of buffer."
-            )
-
-            # If we're not using a common clips dir, reset the counter for future clips
-            if not output_dir:
-                clips.reset_clip_count()
-
-            clip_count_before = clips.get_clip_count()
-
-            # Process the video's frames into clips
-            process_frames(
-                video_path,
-                cap,
-                detector,
-                clips,
-                fps,
-                total_frames,
-                frames_to_skip,
-                logger,
-                args,
-            )
-
-            clip_count_after = clips.get_clip_count()
-            clips_processed = clip_count_after - clip_count_before
-
-            file_end_time = time.time()
-            logger.info(
-                f"Finished file {i} of {len(video_paths)}: {video_path} (total time to process file {format_time(file_end_time - file_start_time)}). Processed {total_frames} frames into {clips_processed} clips"
-            )
-
-            # Clean-up the resources we have open, if necessary
-            if cap is not None:
-                cap.release()
-
-            cv2.destroyAllWindows()
-            cv2.waitKey(1)
-
-        # Keep track of total time to process all files, recording end time
-        total_time_end = time.time()
-        logger.info(
-            f"\nFinished. Total time for {len(video_paths)} files: {format_time(total_time_end - total_time_start)}"
-        )
-
-    except KeyboardInterrupt:
-        logger.warning("Interrupted by user, cleaning up...")
-        clips.stop()
-    except Exception as e:
-        logger.error(f"There was an error: {e}")
-    finally:
-        # Clean-up the resources we have open, if necessary
-        if cap is not None:
-            cap.release()
-
-        cv2.destroyAllWindows()
-        cv2.waitKey(1)
-
-        # Wait for the ffmpeg clip queue to complete before we exit
-        if clips is not None:
-            clips.cleanup()
-
-
-# Define the command line arguments
 if __name__ == "__main__":
-    """
-    The entry point of the program. Defines the command line arguments and
-    calls the main function.
-    """
     parser = argparse.ArgumentParser(
         description="Automated Camera Trapping Identification and Organization Network (ACTION)"
     )
diff --git a/pixi.lock b/pixi.lock
index cd6ea48..7ea9aa2 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -27,9 +27,9 @@ package:
     openssl: '>=3.0.7,<4.0a0'
     readline: '>=8.1.2,<9.0a0'
     libffi: '>=3.4,<4.0a0'
-    libsqlite: '>=3.40.0,<4.0a0'
     libgcc-ng: '>=12'
     libnsl: '>=2.0.0,<2.1.0a0'
+    libsqlite: '>=3.40.0,<4.0a0'
     libzlib: '>=1.2.13,<1.3.0a0'
     bzip2: '>=1.0.8,<2.0a0'
     libuuid: '>=2.32.1,<3.0a0'
@@ -115,25 +115,6 @@ package:
   noarch: python
   size: 1386212
   timestamp: 1690024763393
-- name: git-lfs
-  version: 3.4.0
-  manager: conda
-  platform: linux-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/linux-64/git-lfs-3.4.0-ha770c72_0.conda
-  hash:
-    md5: 8b8aef0a35f5b98937a65b67b7d3b536
-    sha256: 77c871ba307297dab92ef9a2923ad44bd5d7dcf429b92c45cdbea2bc85209fb3
-  optional: false
-  category: main
-  build: ha770c72_0
-  arch: x86_64
-  subdir: linux-64
-  build_number: 0
-  license: MIT
-  license_family: MIT
-  size: 3783189
-  timestamp: 1690413607828
 - name: git
   version: 2.42.0
   manager: conda
@@ -141,11 +122,11 @@ package:
   dependencies:
     openssl: '>=3.1.2,<4.0a0'
     pcre2: '>=10.40,<10.41.0a0'
-    libiconv: '>=1.17,<2.0a0'
-    libgcc-ng: '>=12'
     libzlib: '>=1.2.13,<1.3.0a0'
-    libexpat: '>=2.5.0,<3.0a0'
+    libiconv: '>=1.17,<2.0a0'
     curl: '*'
+    libexpat: '>=2.5.0,<3.0a0'
+    libgcc-ng: '>=12'
     gettext: '*'
     perl: 5.*
   url: https://conda.anaconda.org/conda-forge/linux-64/git-2.42.0-pl5321h86e50cf_0.conda
@@ -212,6 +193,46 @@ package:
   license_family: MIT
   size: 4902978
   timestamp: 1696896633112
+- name: libstdcxx-ng
+  version: 13.2.0
+  manager: conda
+  platform: linux-64
+  dependencies: {}
+  url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_2.conda
+  hash:
+    md5: 9172c297304f2a20134fc56c97fbe229
+    sha256: ab22ecdc974cdbe148874ea876d9c564294d5eafa760f403ed4fd495307b4243
+  optional: false
+  category: main
+  build: h7e041cc_2
+  arch: x86_64
+  subdir: linux-64
+  build_number: 2
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
+  size: 3842773
+  timestamp: 1695219454837
+- name: python_abi
+  version: '3.11'
+  manager: conda
+  platform: linux-64
+  dependencies: {}
+  url: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda
+  hash:
+    md5: d786502c97404c94d7d58d258a445a65
+    sha256: 0be3ac1bf852d64f553220c7e6457e9c047dfb7412da9d22fbaa67e60858b3cf
+  optional: false
+  category: main
+  build: 4_cp311
+  arch: x86_64
+  subdir: linux-64
+  build_number: 4
+  constrains:
+  - python 3.11.* *_cpython
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 6385
+  timestamp: 1695147338551
 - name: libgcc-ng
   version: 13.2.0
   manager: conda
@@ -296,46 +317,6 @@ package:
   license_family: GPL
   size: 421133
   timestamp: 1695219303065
-- name: libstdcxx-ng
-  version: 13.2.0
-  manager: conda
-  platform: linux-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_2.conda
-  hash:
-    md5: 9172c297304f2a20134fc56c97fbe229
-    sha256: ab22ecdc974cdbe148874ea876d9c564294d5eafa760f403ed4fd495307b4243
-  optional: false
-  category: main
-  build: h7e041cc_2
-  arch: x86_64
-  subdir: linux-64
-  build_number: 2
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
-  size: 3842773
-  timestamp: 1695219454837
-- name: python_abi
-  version: '3.11'
-  manager: conda
-  platform: linux-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda
-  hash:
-    md5: d786502c97404c94d7d58d258a445a65
-    sha256: 0be3ac1bf852d64f553220c7e6457e9c047dfb7412da9d22fbaa67e60858b3cf
-  optional: false
-  category: main
-  build: 4_cp311
-  arch: x86_64
-  subdir: linux-64
-  build_number: 4
-  constrains:
-  - python 3.11.* *_cpython
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 6385
-  timestamp: 1695147338551
 - name: libcblas
   version: 3.9.0
   manager: conda
@@ -605,12 +586,12 @@ package:
   platform: linux-64
   dependencies:
     libssh2: '>=1.11.0,<2.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
+    krb5: '>=1.21.2,<1.22.0a0'
     libcurl: ==8.4.0 hca28451_0
     openssl: '>=3.1.3,<4.0a0'
-    zstd: '>=1.5.5,<1.6.0a0'
-    krb5: '>=1.21.2,<1.22.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
     libgcc-ng: '>=12'
+    zstd: '>=1.5.5,<1.6.0a0'
   url: https://conda.anaconda.org/conda-forge/linux-64/curl-8.4.0-hca28451_0.conda
   hash:
     md5: 2bcf7689cae931dd35d9a45626f49fce
@@ -647,40 +628,18 @@ package:
   license_family: BSD
   size: 271133
   timestamp: 1685837707056
-- name: zstd
-  version: 1.5.5
-  manager: conda
-  platform: linux-64
-  dependencies:
-    libzlib: '>=1.2.13,<1.3.0a0'
-    libgcc-ng: '>=12'
-    libstdcxx-ng: '>=12'
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.5-hfc55251_0.conda
-  hash:
-    md5: 04b88013080254850d6c01ed54810589
-    sha256: 607cbeb1a533be98ba96cf5cdf0ddbb101c78019f1fda063261871dad6248609
-  optional: false
-  category: main
-  build: hfc55251_0
-  arch: x86_64
-  subdir: linux-64
-  build_number: 0
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 545199
-  timestamp: 1693151163452
 - name: libcurl
   version: 8.4.0
   manager: conda
   platform: linux-64
   dependencies:
     libssh2: '>=1.11.0,<2.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
+    krb5: '>=1.21.2,<1.22.0a0'
     libnghttp2: '>=1.52.0,<2.0a0'
     openssl: '>=3.1.3,<4.0a0'
-    zstd: '>=1.5.5,<1.6.0a0'
-    krb5: '>=1.21.2,<1.22.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
     libgcc-ng: '>=12'
+    zstd: '>=1.5.5,<1.6.0a0'
   url: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.4.0-hca28451_0.conda
   hash:
     md5: 1158ac1d2613b28685644931f11ee807
@@ -760,6 +719,28 @@ package:
   license_family: BSD
   size: 106190
   timestamp: 1598867915
+- name: zstd
+  version: 1.5.5
+  manager: conda
+  platform: linux-64
+  dependencies:
+    libzlib: '>=1.2.13,<1.3.0a0'
+    libgcc-ng: '>=12'
+    libstdcxx-ng: '>=12'
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.5-hfc55251_0.conda
+  hash:
+    md5: 04b88013080254850d6c01ed54810589
+    sha256: 607cbeb1a533be98ba96cf5cdf0ddbb101c78019f1fda063261871dad6248609
+  optional: false
+  category: main
+  build: hfc55251_0
+  arch: x86_64
+  subdir: linux-64
+  build_number: 0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 545199
+  timestamp: 1693151163452
 - name: krb5
   version: 1.21.2
   manager: conda
@@ -1246,9 +1227,9 @@ package:
   manager: conda
   platform: linux-64
   dependencies:
-    gettext: '>=0.21.1,<1.0a0'
     libgcc-ng: '>=12'
     libunistring: '>=0,<1.0a0'
+    gettext: '>=0.21.1,<1.0a0'
   url: https://conda.anaconda.org/conda-forge/linux-64/libidn2-2.3.4-h166bdaf_0.tar.bz2
   hash:
     md5: 7440fbafd870b8bab68f83a064875d34
@@ -1474,10 +1455,10 @@ package:
   manager: conda
   platform: linux-64
   dependencies:
-    libgcc-ng: '>=12'
+    libzlib: '>=1.2.13,<1.3.0a0'
     libuuid: '>=2.32.1,<3.0a0'
     freetype: '>=2.12.1,<3.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
+    libgcc-ng: '>=12'
     expat: '>=2.5.0,<3.0a0'
   url: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda
   hash:
@@ -1519,13 +1500,13 @@ package:
   manager: conda
   platform: linux-64
   dependencies:
-    graphite2: '*'
+    libgcc-ng: '>=12'
     libstdcxx-ng: '>=12'
     icu: '>=73.2,<74.0a0'
+    graphite2: '*'
+    freetype: '>=2.12.1,<3.0a0'
     libglib: '>=2.78.0,<3.0a0'
     cairo: '>=1.16.0,<2.0a0'
-    libgcc-ng: '>=12'
-    freetype: '>=2.12.1,<3.0a0'
   url: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.2.1-h3d44ed6_0.conda
   hash:
     md5: 98db5f8813f45e2b29766aff0e4a499c
@@ -1540,18 +1521,38 @@ package:
   license_family: MIT
   size: 1526592
   timestamp: 1695089914042
+- name: graphite2
+  version: 1.3.13
+  manager: conda
+  platform: linux-64
+  dependencies:
+    libgcc-ng: '>=7.5.0'
+    libstdcxx-ng: '>=7.5.0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h58526e2_1001.tar.bz2
+  hash:
+    md5: 8c54672728e8ec6aa6db90cf2806d220
+    sha256: 65da967f3101b737b08222de6a6a14e20e480e7d523a5d1e19ace7b960b5d6b1
+  optional: false
+  category: main
+  build: h58526e2_1001
+  arch: x86_64
+  subdir: linux-64
+  build_number: 1001
+  license: LGPLv2
+  size: 104701
+  timestamp: 1604365484436
 - name: libglib
   version: 2.78.0
   manager: conda
   platform: linux-64
   dependencies:
-    libgcc-ng: '>=12'
-    gettext: '>=0.21.1,<1.0a0'
+    libffi: '>=3.4,<4.0a0'
     libstdcxx-ng: '>=12'
+    gettext: '>=0.21.1,<1.0a0'
     pcre2: '>=10.40,<10.41.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
-    libffi: '>=3.4,<4.0a0'
     libiconv: '>=1.17,<2.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
+    libgcc-ng: '>=12'
   url: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.78.0-hebfc3b9_0.conda
   hash:
     md5: e618003da3547216310088478e475945
@@ -1937,26 +1938,6 @@ package:
   license_family: MIT
   size: 385309
   timestamp: 1695736061006
-- name: graphite2
-  version: 1.3.13
-  manager: conda
-  platform: linux-64
-  dependencies:
-    libgcc-ng: '>=7.5.0'
-    libstdcxx-ng: '>=7.5.0'
-  url: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h58526e2_1001.tar.bz2
-  hash:
-    md5: 8c54672728e8ec6aa6db90cf2806d220
-    sha256: 65da967f3101b737b08222de6a6a14e20e480e7d523a5d1e19ace7b960b5d6b1
-  optional: false
-  category: main
-  build: h58526e2_1001
-  arch: x86_64
-  subdir: linux-64
-  build_number: 1001
-  license: LGPLv2
-  size: 104701
-  timestamp: 1604365484436
 - name: fonts-conda-forge
   version: '1'
   manager: conda
@@ -2384,25 +2365,6 @@ package:
   noarch: python
   size: 1386212
   timestamp: 1690024763393
-- name: git-lfs
-  version: 3.4.0
-  manager: conda
-  platform: osx-arm64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/git-lfs-3.4.0-hce30654_0.conda
-  hash:
-    md5: c96c89fe47e4a4cb14929a04ba78488f
-    sha256: d4ecce07f6aa93f319d267460068b4dcf5058b665b4530ef2c5be9b7ca047cdb
-  optional: false
-  category: main
-  build: hce30654_0
-  arch: aarch64
-  subdir: osx-arm64
-  build_number: 0
-  license: MIT
-  license_family: MIT
-  size: 3741778
-  timestamp: 1690414069499
 - name: git
   version: 2.42.0
   manager: conda
@@ -3553,11 +3515,11 @@ package:
   dependencies:
     libexpat: '>=2.5.0,<3.0a0'
     fontconfig: '>=2.14.2,<3.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
-    fonts-conda-ecosystem: '*'
-    freetype: '>=2.12.1,<3.0a0'
     fribidi: '>=1.0.10,<2.0a0'
+    fonts-conda-ecosystem: '*'
     harfbuzz: '>=8.1.1,<9.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
+    freetype: '>=2.12.1,<3.0a0'
   url: https://conda.anaconda.org/conda-forge/osx-arm64/libass-0.17.1-hf7da4fe_1.conda
   hash:
     md5: 53fff6fc379154382f5121325c5fe2f5
@@ -3594,63 +3556,24 @@ package:
   license_family: MIT
   size: 237668
   timestamp: 1674829263740
-- name: fonts-conda-ecosystem
-  version: '1'
+- name: harfbuzz
+  version: 8.2.1
   manager: conda
   platform: osx-arm64
   dependencies:
-    fonts-conda-forge: '*'
-  url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+    icu: '>=73.2,<74.0a0'
+    libcxx: '>=15.0.7'
+    graphite2: '*'
+    cairo: '>=1.16.0,<2.0a0'
+    freetype: '>=2.12.1,<3.0a0'
+    libglib: '>=2.78.0,<3.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-8.2.1-hf1a6348_0.conda
   hash:
-    md5: fee5683a3f04bd15cbd8318b096a27ab
-    sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61
+    md5: 001c9b64c94fb7066525bff0dc0f30c2
+    sha256: 4f1a8d3f2968d9d76ae1b75e021426f7bc53e88bd1efab82b22eb7fff15ace81
   optional: false
   category: main
-  build: '0'
-  arch: aarch64
-  subdir: osx-arm64
-  build_number: 0
-  license: BSD-3-Clause
-  license_family: BSD
-  noarch: generic
-  size: 3667
-  timestamp: 1566974674465
-- name: fribidi
-  version: 1.0.10
-  manager: conda
-  platform: osx-arm64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/fribidi-1.0.10-h27ca646_0.tar.bz2
-  hash:
-    md5: c64443234ff91d70cb9c7dc926c58834
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-  optional: false
-  category: main
-  build: h27ca646_0
-  arch: aarch64
-  subdir: osx-arm64
-  build_number: 0
-  license: LGPL-2.1
-  size: 60255
-  timestamp: 1604417405528
-- name: harfbuzz
-  version: 8.2.1
-  manager: conda
-  platform: osx-arm64
-  dependencies:
-    icu: '>=73.2,<74.0a0'
-    libcxx: '>=15.0.7'
-    graphite2: '*'
-    cairo: '>=1.16.0,<2.0a0'
-    freetype: '>=2.12.1,<3.0a0'
-    libglib: '>=2.78.0,<3.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-8.2.1-hf1a6348_0.conda
-  hash:
-    md5: 001c9b64c94fb7066525bff0dc0f30c2
-    sha256: 4f1a8d3f2968d9d76ae1b75e021426f7bc53e88bd1efab82b22eb7fff15ace81
-  optional: false
-  category: main
-  build: hf1a6348_0
+  build: hf1a6348_0
   arch: aarch64
   subdir: osx-arm64
   build_number: 0
@@ -3684,11 +3607,11 @@ package:
   dependencies:
     icu: '>=73.2,<74.0a0'
     libpng: '>=1.6.39,<1.7.0a0'
+    __osx: '>=10.9'
+    freetype: '>=2.12.1,<3.0a0'
     libglib: '>=2.78.0,<3.0a0'
     libzlib: '>=1.2.13,<1.3.0a0'
     zlib: '*'
-    freetype: '>=2.12.1,<3.0a0'
-    __osx: '>=10.9'
     fontconfig: '>=2.14.2,<3.0a0'
     libcxx: '>=16.0.6'
     fonts-conda-ecosystem: '*'
@@ -3711,9 +3634,9 @@ package:
   manager: conda
   platform: osx-arm64
   dependencies:
-    pcre2: '>=10.40,<10.41.0a0'
     gettext: '>=0.21.1,<1.0a0'
     libcxx: '>=15.0.7'
+    pcre2: '>=10.40,<10.41.0a0'
     libffi: '>=3.4,<4.0a0'
     libiconv: '>=1.17,<2.0a0'
     libzlib: '>=1.2.13,<1.3.0a0'
@@ -3752,6 +3675,27 @@ package:
   license_family: Other
   size: 79577
   timestamp: 1686575471024
+- name: fonts-conda-ecosystem
+  version: '1'
+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    fonts-conda-forge: '*'
+  url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+  hash:
+    md5: fee5683a3f04bd15cbd8318b096a27ab
+    sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61
+  optional: false
+  category: main
+  build: '0'
+  arch: aarch64
+  subdir: osx-arm64
+  build_number: 0
+  license: BSD-3-Clause
+  license_family: BSD
+  noarch: generic
+  size: 3667
+  timestamp: 1566974674465
 - name: pixman
   version: 0.42.2
   manager: conda
@@ -3772,6 +3716,24 @@ package:
   license_family: MIT
   size: 213843
   timestamp: 1695736518800
+- name: fribidi
+  version: 1.0.10
+  manager: conda
+  platform: osx-arm64
+  dependencies: {}
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fribidi-1.0.10-h27ca646_0.tar.bz2
+  hash:
+    md5: c64443234ff91d70cb9c7dc926c58834
+    sha256: 4b37ea851a2cf85edf0a63d2a63266847ec3dcbba4a31156d430cdd6aa811303
+  optional: false
+  category: main
+  build: h27ca646_0
+  arch: aarch64
+  subdir: osx-arm64
+  build_number: 0
+  license: LGPL-2.1
+  size: 60255
+  timestamp: 1604417405528
 - name: fonts-conda-forge
   version: '1'
   manager: conda
@@ -3982,9 +3944,9 @@ package:
     tzdata: '*'
     openssl: '>=3.0.5,<4.0a0'
     libzlib: '>=1.2.13,<1.3.0a0'
-    libffi: '>=3.4.2,<3.5.0a0'
     vc: '>=14.1,<15'
     libsqlite: '>=3.39.4,<4.0a0'
+    libffi: '>=3.4.2,<3.5.0a0'
     tk: '>=8.6.12,<8.7.0a0'
     bzip2: '>=1.0.8,<2.0a0'
     xz: '>=5.2.6,<5.3.0a0'
@@ -4062,25 +4024,6 @@ package:
   noarch: python
   size: 1386212
   timestamp: 1690024763393
-- name: git-lfs
-  version: 3.4.0
-  manager: conda
-  platform: win-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/win-64/git-lfs-3.4.0-h57928b3_0.conda
-  hash:
-    md5: d8b2d400f3762539ccdf4138b18a61ef
-    sha256: 89acdfc86aac146263cc562fd41fed4d2fceefd099e93a2afc837bf8dda73ebc
-  optional: false
-  category: main
-  build: h57928b3_0
-  arch: x86_64
-  subdir: win-64
-  build_number: 0
-  license: MIT
-  license_family: MIT
-  size: 3650843
-  timestamp: 1690414029264
 - name: git
   version: 2.42.0
   manager: conda
@@ -4152,27 +4095,6 @@ package:
   license_family: MIT
   size: 4812037
   timestamp: 1696897314194
-- name: python_abi
-  version: '3.11'
-  manager: conda
-  platform: win-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/win-64/python_abi-3.11-4_cp311.conda
-  hash:
-    md5: 70513332c71b56eace4ee6441e66c012
-    sha256: 67c2aade3e2160642eec0742384e766b20c766055e3d99335681e3e05d88ed7b
-  optional: false
-  category: main
-  build: 4_cp311
-  arch: x86_64
-  subdir: win-64
-  build_number: 4
-  constrains:
-  - python 3.11.* *_cpython
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 6755
-  timestamp: 1695147711935
 - name: ucrt
   version: 10.0.22621.0
   manager: conda
@@ -4194,6 +4116,27 @@ package:
   license_family: PROPRIETARY
   size: 1283972
   timestamp: 1666630199266
+- name: python_abi
+  version: '3.11'
+  manager: conda
+  platform: win-64
+  dependencies: {}
+  url: https://conda.anaconda.org/conda-forge/win-64/python_abi-3.11-4_cp311.conda
+  hash:
+    md5: 70513332c71b56eace4ee6441e66c012
+    sha256: 67c2aade3e2160642eec0742384e766b20c766055e3d99335681e3e05d88ed7b
+  optional: false
+  category: main
+  build: 4_cp311
+  arch: x86_64
+  subdir: win-64
+  build_number: 4
+  constrains:
+  - python 3.11.* *_cpython
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 6755
+  timestamp: 1695147711935
 - name: vc
   version: '14.3'
   manager: conda
@@ -4356,10 +4299,10 @@ package:
   platform: win-64
   dependencies:
     ucrt: '>=10.0.20348.0'
-    libzlib: '>=1.2.13,<1.3.0a0'
-    libiconv: '>=1.17,<2.0a0'
     vc: '>=14.2,<15'
+    libzlib: '>=1.2.13,<1.3.0a0'
     vc14_runtime: '>=14.29.30139'
+    libiconv: '>=1.17,<2.0a0'
   url: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.11.5-hc3477c8_1.conda
   hash:
     md5: 27974f880a010b1441093d9f737a949f
@@ -4374,6 +4317,30 @@ package:
   license_family: MIT
   size: 1600640
   timestamp: 1692960798126
+- name: libzlib
+  version: 1.2.13
+  manager: conda
+  platform: win-64
+  dependencies:
+    ucrt: '>=10.0.20348.0'
+    vc: '>=14.2,<15'
+    vc14_runtime: '>=14.29.30139'
+  url: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.13-hcfcfb64_5.conda
+  hash:
+    md5: 5fdb9c6a113b6b6cb5e517fd972d5f41
+    sha256: c161822ee8130b71e08b6d282b9919c1de2c5274b29921a867bca0f7d30cad26
+  optional: false
+  category: main
+  build: hcfcfb64_5
+  arch: x86_64
+  subdir: win-64
+  build_number: 5
+  constrains:
+  - zlib 1.2.13 *_5
+  license: Zlib
+  license_family: Other
+  size: 55800
+  timestamp: 1686575452215
 - name: libiconv
   version: '1.17'
   manager: conda
@@ -4414,30 +4381,6 @@ package:
   license_family: BSD
   size: 17207
   timestamp: 1688020635322
-- name: libzlib
-  version: 1.2.13
-  manager: conda
-  platform: win-64
-  dependencies:
-    ucrt: '>=10.0.20348.0'
-    vc: '>=14.2,<15'
-    vc14_runtime: '>=14.29.30139'
-  url: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.13-hcfcfb64_5.conda
-  hash:
-    md5: 5fdb9c6a113b6b6cb5e517fd972d5f41
-    sha256: c161822ee8130b71e08b6d282b9919c1de2c5274b29921a867bca0f7d30cad26
-  optional: false
-  category: main
-  build: hcfcfb64_5
-  arch: x86_64
-  subdir: win-64
-  build_number: 5
-  constrains:
-  - zlib 1.2.13 *_5
-  license: Zlib
-  license_family: Other
-  size: 55800
-  timestamp: 1686575452215
 - name: pthreads-win32
   version: 2.9.1
   manager: conda
@@ -5018,27 +4961,6 @@ package:
   license: ISC
   size: 150013
   timestamp: 1690026269050
-- name: libffi
-  version: 3.4.2
-  manager: conda
-  platform: win-64
-  dependencies:
-    vc: '>=14.1,<15.0a0'
-    vs2015_runtime: '>=14.16.27012'
-  url: https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2
-  hash:
-    md5: 2c96d1b6915b408893f9472569dee135
-    sha256: 1951ab740f80660e9bc07d2ed3aefb874d78c107264fd810f24a1a6211d4b1a5
-  optional: false
-  category: main
-  build: h8ffe710_5
-  arch: x86_64
-  subdir: win-64
-  build_number: 5
-  license: MIT
-  license_family: MIT
-  size: 42063
-  timestamp: 1636489106777
 - name: libsqlite
   version: 3.43.2
   manager: conda
@@ -5060,6 +4982,27 @@ package:
   license: Unlicense
   size: 846363
   timestamp: 1696959271392
+- name: libffi
+  version: 3.4.2
+  manager: conda
+  platform: win-64
+  dependencies:
+    vc: '>=14.1,<15.0a0'
+    vs2015_runtime: '>=14.16.27012'
+  url: https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2
+  hash:
+    md5: 2c96d1b6915b408893f9472569dee135
+    sha256: 1951ab740f80660e9bc07d2ed3aefb874d78c107264fd810f24a1a6211d4b1a5
+  optional: false
+  category: main
+  build: h8ffe710_5
+  arch: x86_64
+  subdir: win-64
+  build_number: 5
+  license: MIT
+  license_family: MIT
+  size: 42063
+  timestamp: 1636489106777
 - name: tk
   version: 8.6.13
   manager: conda
@@ -5194,25 +5137,6 @@ package:
   noarch: python
   size: 1386212
   timestamp: 1690024763393
-- name: git-lfs
-  version: 3.4.0
-  manager: conda
-  platform: osx-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-64/git-lfs-3.4.0-h694c41f_0.conda
-  hash:
-    md5: 26e1938cad30e5ffbfd4a6e3b814aa9f
-    sha256: 7677cf73b481452625e71e9b29b0358d90ed56055a9fdabfff30c3e5c5f1936d
-  optional: false
-  category: main
-  build: h694c41f_0
-  arch: x86_64
-  subdir: osx-64
-  build_number: 0
-  license: MIT
-  license_family: MIT
-  size: 4049660
-  timestamp: 1690413813491
 - name: git
   version: 2.42.0
   manager: conda
@@ -6364,11 +6288,11 @@ package:
   dependencies:
     libexpat: '>=2.5.0,<3.0a0'
     fontconfig: '>=2.14.2,<3.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
     fonts-conda-ecosystem: '*'
+    freetype: '>=2.12.1,<3.0a0'
     fribidi: '>=1.0.10,<2.0a0'
     harfbuzz: '>=8.1.1,<9.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
-    freetype: '>=2.12.1,<3.0a0'
   url: https://conda.anaconda.org/conda-forge/osx-64/libass-0.17.1-h80904bb_1.conda
   hash:
     md5: 9ccad0aebe916aa3715fda9eefe92584
@@ -6405,6 +6329,45 @@ package:
   license_family: MIT
   size: 237068
   timestamp: 1674829100063
+- name: fonts-conda-ecosystem
+  version: '1'
+  manager: conda
+  platform: osx-64
+  dependencies:
+    fonts-conda-forge: '*'
+  url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+  hash:
+    md5: fee5683a3f04bd15cbd8318b096a27ab
+    sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61
+  optional: false
+  category: main
+  build: '0'
+  arch: x86_64
+  subdir: osx-64
+  build_number: 0
+  license: BSD-3-Clause
+  license_family: BSD
+  noarch: generic
+  size: 3667
+  timestamp: 1566974674465
+- name: fribidi
+  version: 1.0.10
+  manager: conda
+  platform: osx-64
+  dependencies: {}
+  url: https://conda.anaconda.org/conda-forge/osx-64/fribidi-1.0.10-hbcb3906_0.tar.bz2
+  hash:
+    md5: f1c6b41e0f56998ecd9a3e210faa1dc0
+    sha256: 4f6db86ecc4984cd4ac88ca52030726c3cfd11a64dfb15c8602025ee3001a2b5
+  optional: false
+  category: main
+  build: hbcb3906_0
+  arch: x86_64
+  subdir: osx-64
+  build_number: 0
+  license: LGPL-2.1
+  size: 65388
+  timestamp: 1604417213
 - name: harfbuzz
   version: 8.2.1
   manager: conda
@@ -6457,10 +6420,10 @@ package:
     icu: '>=73.2,<74.0a0'
     libpng: '>=1.6.39,<1.7.0a0'
     libglib: '>=2.78.0,<3.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
+    zlib: '*'
     freetype: '>=2.12.1,<3.0a0'
     __osx: '>=10.9'
-    zlib: '*'
-    libzlib: '>=1.2.13,<1.3.0a0'
     fontconfig: '>=2.14.2,<3.0a0'
     libcxx: '>=16.0.6'
     fonts-conda-ecosystem: '*'
@@ -6483,9 +6446,9 @@ package:
   manager: conda
   platform: osx-64
   dependencies:
-    libcxx: '>=15.0.7'
     pcre2: '>=10.40,<10.41.0a0'
     gettext: '>=0.21.1,<1.0a0'
+    libcxx: '>=15.0.7'
     libffi: '>=3.4,<4.0a0'
     libiconv: '>=1.17,<2.0a0'
     libzlib: '>=1.2.13,<1.3.0a0'
@@ -6524,27 +6487,6 @@ package:
   license_family: Other
   size: 90764
   timestamp: 1686575574678
-- name: fonts-conda-ecosystem
-  version: '1'
-  manager: conda
-  platform: osx-64
-  dependencies:
-    fonts-conda-forge: '*'
-  url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
-  hash:
-    md5: fee5683a3f04bd15cbd8318b096a27ab
-    sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61
-  optional: false
-  category: main
-  build: '0'
-  arch: x86_64
-  subdir: osx-64
-  build_number: 0
-  license: BSD-3-Clause
-  license_family: BSD
-  noarch: generic
-  size: 3667
-  timestamp: 1566974674465
 - name: pixman
   version: 0.42.2
   manager: conda
@@ -6565,24 +6507,6 @@ package:
   license_family: MIT
   size: 336190
   timestamp: 1695736270076
-- name: fribidi
-  version: 1.0.10
-  manager: conda
-  platform: osx-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-64/fribidi-1.0.10-hbcb3906_0.tar.bz2
-  hash:
-    md5: f1c6b41e0f56998ecd9a3e210faa1dc0
-    sha256: 4f6db86ecc4984cd4ac88ca52030726c3cfd11a64dfb15c8602025ee3001a2b5
-  optional: false
-  category: main
-  build: hbcb3906_0
-  arch: x86_64
-  subdir: osx-64
-  build_number: 0
-  license: LGPL-2.1
-  size: 65388
-  timestamp: 1604417213
 - name: fonts-conda-forge
   version: '1'
   manager: conda
diff --git a/pixi.toml b/pixi.toml
index ba7dfbe..29383d8 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -3,6 +3,9 @@ name = "action"
 version = "0.1.0"
 description = "Automated Camera Trapping Identification and Organization Network (ACTION)"
 repository = "https://github.com/humphrem/action"
+readme = "README.md"
+license = "Apache-2.0"
+license-file = "LICENSE"
 authors = [ "Morgan Humphrey", "David Humphrey"]
 channels = ["conda-forge"]
 platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"]
diff --git a/aquatic-demo.sh b/scripts/aquatic-demo.sh
similarity index 87%
rename from aquatic-demo.sh
rename to scripts/aquatic-demo.sh
index 2aa92ae..5dbe5e0 100755
--- a/aquatic-demo.sh
+++ b/scripts/aquatic-demo.sh
@@ -1,3 +1,3 @@
-#!/bin/zsh
+#!/bin/bash
 
 python3 action.py ./video/aquatic-demo.mov -c 0.45 -m 3.0 -s -b 1.0 -d -e aquatic
diff --git a/terrestrial-demo.sh b/scripts/terrestrial-demo.sh
similarity index 88%
rename from terrestrial-demo.sh
rename to scripts/terrestrial-demo.sh
index b2b76a3..d70d713 100755
--- a/terrestrial-demo.sh
+++ b/scripts/terrestrial-demo.sh
@@ -1,3 +1,3 @@
-#!/bin/zsh
+#!/bin/bash
 
 python3 action.py ./video/terrestrial-demo.mov -c 0.45 -m 3.0 -s -b 1.0 -d -e terrestrial
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..22a7d32
--- /dev/null
+++ b/src/__init__.py
@@ -0,0 +1 @@
+from .action import main  # noqa: F401
diff --git a/src/action.py b/src/action.py
new file mode 100755
index 0000000..b139ff6
--- /dev/null
+++ b/src/action.py
@@ -0,0 +1,364 @@
+"""
+This is the main module for ACTION. It handles parsing arguments from the user,
+loading and managing resources, and processing detections into clips.
+"""
+
+import os
+import sys
+import time
+import logging
+
+from .clip_manager import (
+    ClipManager,
+    remove_clips_dir,
+    remove_output_dir,
+    create_output_dir,
+)
+from .yolo_fish_detector import YoloFishDetector
+from .megadetector_detector import MegadetectorDetector
+from .utils import format_time, format_percent, get_video_paths
+
+import cv2
+
+
+# We use converted ONNX models for YOLO-Fish (https://github.com/tamim662/YOLO-Fish)
+# and Megadetector (https://github.com/microsoft/CameraTraps)
+def load_detector(environment, min_duration, buffer, confidence, logger):
+    """
+    Load the appropriate detector based on the environment provided.
+
+    Args:
+        environment (str): The type of detector to load. Must be either "aquatic" or "terrestrial".
+        min_duration (float): The minimum duration of a generated clip
+        buffer (float): An optional number of seconds to add before/after a clip
+        confidence (float): The confidence level to use
+        logger (logging.Logger): The logger to use for logging messages.
+
+    Returns:
+        detector (object): An instance of the appropriate detector.
+
+    Raises:
+        TypeError: If the any args are not of the correct type
+    """
+
+    # Make sure any user-provided flags for detection are valid before we use them
+    if buffer and buffer < 0.0:
+        raise TypeError("Error: minimum buffer cannot be negative")
+
+    if min_duration and min_duration <= 0.0:
+        raise TypeError("Error: minimum duration must be greater than 0.0")
+
+    if confidence and (confidence <= 0.0 or confidence > 1.0):
+        raise TypeError("Error: confidence must be greater than 0.0 and less than 1.0")
+
+    detector = None
+    if environment == "terrestrial":
+        detector = MegadetectorDetector(logger, min_duration, buffer, confidence)
+    elif environment == "aquatic":
+        detector = YoloFishDetector(logger, min_duration, buffer, confidence)
+    else:
+        raise TypeError("environment must be one of aquatic or terrestrial")
+
+    detector.load()
+    return detector
+
+
+# Defining the function process_frames, called in main
+def process_frames(
+    video_path,
+    cap,
+    detector,
+    clips,
+    fps,
+    total_frames,
+    frames_to_skip,
+    logger,
+    args,
+):
+    """
+    Process frames from a video file and create clips based on detections.
+
+    Args:
+        video_path (str): The path to the video file.
+        cap (cv2.VideoCapture): The video capture object.
+        detector (object): The detector to use for detecting objects in frames.
+        clips (ClipManager): The clip manager for managing clips.
+        fps (int): The frames per second of the video.
+        total_frames (int): The total number of frames in the video.
+        frames_to_skip (int): The number of frames to skip between detections
+        logger (logging.Logger): The logger to use for logging messages.
+        args (argparse.Namespace): The command line arguments.
+
+    Returns:
+        None
+    """
+    buffer_seconds = detector.buffer
+    min_detection_duration = detector.min_duration
+    show_detections = args.show_detections
+
+    # Number of frames per minute of video time
+    frames_per_minute = 60 * fps
+    # Frame number for the next progress message
+    next_progress_frame = frames_per_minute
+
+    # Track when there are is something in frame as detection events
+    detection_start_time = None
+    detection_highest_confidence = 0
+    detection_event = False
+
+    frame_count = 0
+
+    # Loop over all frames in the video
+    while cap.isOpened():
+        ret, frame = cap.read()
+        # If there isn't another frame, we're done
+        if not ret:
+            break
+
+        # If a detection is already happening, then we know we're going to
+        # record for a given period, so we can skip ahead to the frame where
+        # the detection period ends. However, we then need to check *all* the
+        # frames after this within the buffer period (i.e., so we don't overlap
+        # with the end buffer period in the previous clip).  If we detect something
+        # in these frames, we should extend this detection period; otherwise
+        # we end it and create a new clip.
+        if detection_event:
+            # Calculate the number of frames to skip ahead. We know that we
+            # want to record for a minimum duration, and potentially we add
+            # a buffer period in seconds.
+            skip_ahead_frames = int((min_detection_duration + buffer_seconds) * fps)
+            logger.debug(f"Detection event, skipping ahead {skip_ahead_frames} frames")
+
+            # Skip ahead the number of frames that will be in the clip
+            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count + skip_ahead_frames)
+            frame_count += skip_ahead_frames
+
+            # Check some frames within the buffer period for a detection, to see if
+            # we should extend this detection period, or if it's OK to end it. NOTE:
+            # if `buffer_seconds` is 0, at least check the next frame.
+            for i in range(max(1, int(buffer_seconds * fps))):
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                frame_count += 1
+
+                # Only process frames that are multiples of frames_to_skip
+                # or the last frame in the video.
+                if i % frames_to_skip == 0 or frame_count == total_frames - 1:
+                    logger.debug(
+                        f"Checking frame {frame_count} before ending detection event"
+                    )
+                    # If a detection is made, extend the current detection period
+                    boxes = detector.detect(frame)
+                    if len(boxes) > 0:
+                        detection_highest_confidence = max(
+                            detection_highest_confidence, max(box[4] for box in boxes)
+                        )
+                        logger.info(
+                            f"{detector.class_name} detected, extending detection event: {format_time(frame_count / fps + buffer_seconds)} (max confidence={format_percent(detection_highest_confidence)})"
+                        )
+                        if show_detections:
+                            detector.draw_detections(frame, boxes, video_path)
+                        break
+            else:
+                # If no detection was made within the buffer period, and we didn't
+                # extend, end the detection period now and create a new clip.
+                detection_end_time = frame_count / fps + buffer_seconds
+                logger.info(
+                    f"Detection period ended: {format_time(detection_end_time)} (duration={format_time(detection_end_time - detection_start_time)}, max confidence={format_percent(detection_highest_confidence)})"
+                )
+                clips.create_clip(
+                    detection_start_time,
+                    detection_end_time,
+                    video_path,
+                )
+
+                # Reset the detection period
+                detection_event = False
+                detection_highest_confidence = 0
+
+                # Start again reading the next frame
+                continue
+
+        # If we're not already in a detection event, process every n frames
+        # vs. every frame for speed (e.g., every 15 of 30fps). We also check
+        # the last frame, so we don't miss anything at the edge.
+        if frame_count % frames_to_skip == 0 or frame_count == total_frames - 1:
+            boxes = detector.detect(frame)
+
+            # If there are one ore more detections
+            if len(boxes) > 0:
+                detection_highest_confidence = max(
+                    detection_highest_confidence, max(box[4] for box in boxes)
+                )
+
+                # If we're not already in a detection event, start one
+                if not detection_event:
+                    detection_start_time = max(0, frame_count / fps - buffer_seconds)
+                    logger.info(
+                        f"{detector.class_name} detected, starting detection event: {format_time(frame_count / fps)} (max confidence={format_percent(detection_highest_confidence)})"
+                    )
+                    if show_detections:
+                        detector.draw_detections(frame, boxes, video_path)
+                    detection_event = True
+
+        # We've finished processing this frame
+        frame_count += 1
+
+        # Print a progress message every minute of video time so we know what's going on
+        if frame_count >= next_progress_frame:
+            logger.info(
+                f"\nProgress: {format_percent(frame_count / total_frames)} processed ({frame_count}/{total_frames} frames, {format_time(frame_count / fps)})\n"
+            )
+            next_progress_frame += frames_per_minute
+
+    # Before we finish the program, check if there's a detection event in progress
+    # and if there is, end it now so we don't lose the final clip.
+    if detection_event:
+        detection_end_time = frame_count / fps + buffer_seconds
+        logger.info(
+            f"Detection period ended: {format_time(detection_end_time)} (duration={format_time(detection_end_time - detection_start_time)}, max confidence={format_percent(detection_highest_confidence)})"
+        )
+        clips.create_clip(
+            detection_start_time,
+            detection_end_time,
+            video_path,
+        )
+
+
+# Main part of program to do setup and start processing frames in each file
+def main(args):
+    """
+    The main function of the program. Sets up the logger, validates arguments,
+    loads the detector, and processes frames from each video file.
+
+    Args:
+        args (argparse.Namespace): The command line arguments.
+    """
+
+    # Create a logger for this module and set the log level
+    logger = logging.getLogger(__name__)
+    logging.basicConfig(level=args.log_level, format="%(message)s")
+
+    delete_clips = args.delete_clips
+    output_dir = args.output_dir
+    video_paths = get_video_paths(args.filename)
+    logger.debug(f"Got input files: {video_paths}")
+
+    # Validate argument parameters from user before using them
+    if len(video_paths) < 1:
+        logger.error("Error: you must specify one or more video filenames to process")
+        sys.exit(1)
+
+    # Load YOLO-Fish or Megadetector, based on `-e` value
+    detector = None
+    try:
+        detector = load_detector(
+            args.environment, args.min_duration, args.buffer, args.confidence, logger
+        )
+    except Exception as e:
+        logger.error(f"There was an error: {e}")
+        sys.exit(1)
+
+    cap = None
+    clips = None
+
+    # Initialize the output_dir if specified
+    if output_dir:
+        # If `-d`` was specified, delete old clips first
+        if delete_clips:
+            remove_output_dir(output_dir, logger)
+        # Create the output directory if it doesn't exist
+        create_output_dir(output_dir)
+
+    try:
+        # Create a queue manager for clips to be processed by ffmpeg
+        clips = ClipManager(logger, output_dir)
+
+        # Keep track of total time to process all files, recording start time
+        total_time_start = time.time()
+
+        # Loop over all the video file paths and process each one
+        for i, video_path in enumerate(video_paths, start=1):
+            # Make sure this video path actually exists before we try to use it
+            if not os.path.exists(video_path):
+                logger.info(f"Video path {video_path} does not exist, skipping.")
+                continue
+
+            file_start_time = time.time()
+
+            # If the user requests it via -d flag, and isn't using a common output_dir
+            # remove old clips first
+            if not output_dir and delete_clips:
+                remove_clips_dir(video_path, logger)
+
+            # Setup video capture for this video file
+            cap = cv2.VideoCapture(video_path)
+            fps = cap.get(cv2.CAP_PROP_FPS)
+            frames_to_skip = args.skip_frames or int(fps / 2.0)
+            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+            duration = total_frames / fps
+
+            logger.info(
+                f"\nStarting file {i} of {len(video_paths)}: {video_path} - {format_time(duration)} - {total_frames} frames at {fps} fps, skipping every {frames_to_skip} frames"
+            )
+            logger.info(
+                f"Using confidence threshold {detector.confidence}, minimum clip duration of {detector.min_duration} seconds, and {detector.buffer} seconds of buffer."
+            )
+
+            # If we're not using a common clips dir, reset the counter for future clips
+            if not output_dir:
+                clips.reset_clip_count()
+
+            clip_count_before = clips.get_clip_count()
+
+            # Process the video's frames into clips
+            process_frames(
+                video_path,
+                cap,
+                detector,
+                clips,
+                fps,
+                total_frames,
+                frames_to_skip,
+                logger,
+                args,
+            )
+
+            clip_count_after = clips.get_clip_count()
+            clips_processed = clip_count_after - clip_count_before
+
+            file_end_time = time.time()
+            logger.info(
+                f"Finished file {i} of {len(video_paths)}: {video_path} (total time to process file {format_time(file_end_time - file_start_time)}). Processed {total_frames} frames into {clips_processed} clips"
+            )
+
+            # Clean-up the resources we have open, if necessary
+            if cap is not None:
+                cap.release()
+
+            cv2.destroyAllWindows()
+            cv2.waitKey(1)
+
+        # Keep track of total time to process all files, recording end time
+        total_time_end = time.time()
+        logger.info(
+            f"\nFinished. Total time for {len(video_paths)} files: {format_time(total_time_end - total_time_start)}"
+        )
+
+    except KeyboardInterrupt:
+        logger.warning("Interrupted by user, cleaning up...")
+        clips.stop()
+    except Exception as e:
+        logger.error(f"There was an error: {e}")
+    finally:
+        # Clean-up the resources we have open, if necessary
+        if cap is not None:
+            cap.release()
+
+        cv2.destroyAllWindows()
+        cv2.waitKey(1)
+
+        # Wait for the ffmpeg clip queue to complete before we exit
+        if clips is not None:
+            clips.cleanup()
diff --git a/base_detector.py b/src/base_detector.py
similarity index 100%
rename from base_detector.py
rename to src/base_detector.py
diff --git a/clip_manager.py b/src/clip_manager.py
similarity index 99%
rename from clip_manager.py
rename to src/clip_manager.py
index e051c45..8885ae9 100644
--- a/clip_manager.py
+++ b/src/clip_manager.py
@@ -12,7 +12,7 @@
 from multiprocessing import Process, Queue, Event
 from queue import Empty
 
-from utils import format_time
+from .utils import format_time
 
 
 def get_clips_dir(video_path):
diff --git a/megadetector_detector.py b/src/megadetector_detector.py
similarity index 99%
rename from megadetector_detector.py
rename to src/megadetector_detector.py
index de068f7..a6841b2 100644
--- a/megadetector_detector.py
+++ b/src/megadetector_detector.py
@@ -8,7 +8,7 @@
 import numpy as np
 from onnxruntime.capi._pybind_state import get_available_providers
 
-from base_detector import BaseDetector
+from .base_detector import BaseDetector
 
 # The Megadetector ONNX model should be in the ./models directory and needs to be pulled with git-lfs
 megadetector_model_path = "models/md_v5a_1_3_640_640_static.onnx"
diff --git a/utils.py b/src/utils.py
similarity index 100%
rename from utils.py
rename to src/utils.py
diff --git a/yolo_fish_detector.py b/src/yolo_fish_detector.py
similarity index 99%
rename from yolo_fish_detector.py
rename to src/yolo_fish_detector.py
index 1c545aa..ad28ad9 100644
--- a/yolo_fish_detector.py
+++ b/src/yolo_fish_detector.py
@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from base_detector import BaseDetector
+from .base_detector import BaseDetector
 
 # Code inspired by https://github.com/Tianxiaomo/pytorch-YOLOv4, used under Apache-2.0 License
 # https://github.com/Tianxiaomo/pytorch-YOLOv4/blob/master/License.txt