task-scheduler-x 1.0.20

A CLI-based Task Scheduling Application

Task Scheduler

A CLI-based task scheduling application with interactive mode and visualization capabilities.

Features

• Create/manage multiple independent schedulers
• Define time slots with start/end times
• Create tasks with deadlines, durations, and descriptions
• Hierarchical task management with subtasks
• Multiple visualization modes:
• Gantt chart view
• Calendar view
• progress bars
• deadline warnings
• Automatic task scheduling with deadline awareness
• Interactive terminal UI
• Docker container support
• JSON-based persistent storage

Installation

Local Installation (pip)

git clone https://github.com/yourusername/task_scheduler.git
cd task_scheduler
pip install -e .

• package is also availabole at PyPI and under the name task-scheduler-x
• it can be installed using

pip install task-scheduler-x

Docker Installation

# Build image
docker build -t task-scheduler .

# Run with default command
docker run -it task-scheduler

# Run with specific command
docker run -it task-scheduler view_schedule MySchedule

Basic Usage

CLI Commands

Create new scheduler

task-scheduler create --name MySchedule

Add time slot

task-scheduler add_time_slot MySchedule
--start_time 2024-03-01T09:00
--end_time 2024-03-01T11:00

Add task with subtasks

task-scheduler add_task MySchedule
--name "Project X"
--description "Main project"
--duration 300
--deadline 2024-03-15T17:00

Generate schedule

task-scheduler schedule_tasks MySchedule

View visualizations

task-scheduler view_gantt MySchedule task-scheduler view_calendar MySchedule --month 3

Interactive Mode

Launch with:

task-scheduler interactive MySchedule

Controls:

↑/↓ - Navigate tasks

Enter - Select task

a - Add new task

m - Move task mode

q - Quit

Features:

Visual task hierarchy

Vim-based task editing

Drag-and-drop reorganization

Real-time progress updates

Color-coded deadlines

Controls:

Navigation

↑/↓ - Navigate items in focused panel Tab - Switch between task/time slot panels clicking on a task - selects the task

Actions

Enter - Select task/time slot a - Add new task/time slot (depending on focused panel) m - Enter move mode (tasks) / Modify slot (time slots)

General

q - Quit application

Key Features:

Dual-Pane Interface

📋 Left Panel - Task Hierarchy:

• Visual tree structure with nested subtasks

⏱️ Right Panel - Time Slot Management:

• Chronological schedule view
• Duration calculations with time slot validation
• Enhanced Editing
• In-line time slot modification with instant validation
• Drag-and-drop reorganization (tasks)
• Keyboard-based time slot adjustments

New Features

• Split-screen workflow management
• Cross-panel task/time slot associations
• Real-time schedule validation
• Visual focus indicators (highlighted panel borders)
• Smart time slot sorting and gap detection

Feedback & Safety

• Instant save confirmation toasts
• Undo/redo stack for critical operations

Data Format

data format for information fully describing the scheduler instance - JSON

example:

{
  "schedule_name": "MySchedule",
  "time_slots": [
    {
      "start_time": "2024-03-01T09:00:00",
      "end_time": "2024-03-01T11:00:00"
    }
  ],
  "tasks": [
    {
      "name": "Design Phase",
      "description": "Initial design work",
      "deadline": "2024-03-05T17:00:00",
      "duration": 360,
      "completion": 45,
      "subtasks": [
        {
          "name": "UI Mockups",
          "duration": 120,
          "completion": 75
        }
      ]
    }
  ]
}

Visualisation Examples

Gantt Chart

task-scheduler view_gantt MySchedule

Calendar View

task-scheduler view_calendar MySchedule --month 3

Task Progress

task-scheduler view_task MySchedule "Design Phase"

output:

=== Task Details: Design Phase ===

Name: Design Phase Deadline: 2024-03-05T17:00:00 Completion: 45.0% Duration: 360 min Subtasks: ['UI Mockups'] Description: Initial design work

Testing

python -m pytest tests/ -v

Docker Support

The Docker image includes:

Pre-configured Python environment

Automatic dependency installation

Persistent data storage

Built-in test execution

To mount local data directory:

To build the docker image run:

docker build -t task-scheduler .

docker run -v $(pwd)/data:/app/data -it task-scheduler

Requirements

Python 3.7+

colorama

urwid

pytest (for testing)

Remarks and Recommendations for Users

• a colorful terminal application is required to get color-coded outputs in terminal

• each scheduler instance stores two data files: schedule_state.json (storing all information input by the user), schedule.json (storing the result of the latest scheduling).

  • rescheduling happens automatically after all kinds of edits and operations, not upon calling the command view_schedule however! This commands loads directly the schedule.json file, thus removing the need of schedule recalculation in series of view_schedule calls
  • you can use the command schedule_tasks to recalculate your schedule - this command also lists impossible-to-schedule tasks in the terminal assuming your current settings

• it may be a good idea to occasionally save a version of your scheduler. For this purpose the command merge can be used. Example: task-scheduler merge -n MySchedule_backup -ns MySchedule

• this application requires that vim be installed on your system. In ceratain scenarios, vim motions can be more efficient than editing your settings through interactive mode eg. adding multipe time slots. You can use the command update_time_slots to edit/add/remove your time slots using the vim interface.

• the application was so far tuned mainly for Unix-based operating systems (it works in windows as well but operations in the interactive mode are recommended to be carried mainly by mouse clicks)

• the pip installs the source files and the JSON data files in two separate directories. Thanks to that, after reinstalling the package your data are not deleted from the file system on your machine. If you want to uninstall for good the data files have to be deleted manually.

Contributing

Fork the repository

Create your feature branch (git checkout -b feature/awesome-feature)

Commit your changes (git commit -am 'Add awesome feature')

Push to the branch (git push origin feature/awesome-feature)

Open a Pull Request