Welcome to visualization_toolkit’s documentation!

The visualization_toolkit contains utilities to create interactive charts, and data applications based on plotly and dash while following company styling. The toolkit is the foundational package installed on every Atlas application to power analytics.

Getting Started

Installing in a python project

• The visualization_toolkit can be used in a python project and is commonly installed in a dash application

• If you are using the Atlas Repo Template, the dependencies are already in requirements.txt. Run make setup to install the library.

• For general python use (make sure Jfrog is configured in your environment):

Install via pip

pip install visualization-toolkit

• Import the library in python

Using the library in python

from visualization_toolkit.helpers.plotly import chart

fig = chart(...)

Installing from Databricks

• In Databricks notebooks, the visualization_toolkit operates the same way as a repo-based library

• Add this to the top of your notebook as a cell

Enabling repo access in a notebook

import sys
sys.path.append("/Workspace/Repos/ETL_Production/visualization_toolkit")

• Then in subsequent notebook cells, import the library in python and use

Using the library in a notebook cell

from visualization_toolkit.helpers.plotly import chart

fig = chart(...)

display(fig)

• The chart function returns a plotly go.Figure object. If this is returned in a cell it will automatically display the figure. In addition, you can call display(figure) in databricks to visualize inside a cell.

Using the documentation

Navigate to each module of the visualization_toolkit to learn about the functions provided and how to use them.

• plotly: Contains functions using plotly for generating charts in databricks or dash-based apps.

• dash: Contains standard functions to simplify dash application development. Used by Atlas applications.

• chart recipes: Contains many examples of how to create and tune charts with live outputs. Recommended to reference these examples when learning to build visualizations.

Contents:

• plotly module
  • Core Charting Functions
    • chart()
    • axis()
    • series()
    • shade_series()
    • annotation()
    • shade_x()
    • shade_y()
  • Advanced Charting Functions
    • heatmap_chart()
    • waterfall_chart()
  • Plotly Base Functions
    • generate_base_figure()
    • highlight_trace()
    • disable_hover_labels()
• theme module
  • Plotly Theme
    • COLORS
    • YD_CLASSIC_THEME_CONFIG
• dash module
  • Core Component Functions
    • chart_component()
    • custom_tooltip_callback()
  • Core Dash Functions
    • initialize_app()
    • get_triggered_id()
    • resolve_html_id()
  • Core Databricks Functions
    • execute_query()
    • get_spark_session()
  • Core Query Template Functions
    • sql_query_from_template()
  • Core Export Functions
    • export_chart_button_component()
    • setup_chart_exports()
  • Core Analytics Functions
    • user_store()
    • visibility_store()
    • control_store()
    • pendo_initialize_callback()
    • pendo_track_click_callback()
    • pendo_track_input_callback()
    • pendo_track_select_callback()
    • pendo_track_select_range_callback()
    • pendo_track_visible_elements_callback()
    • pendo_track_table_sort_callback()
    • pendo_track_table_filter_callback()
    • pendo_track_page_load_callback()
  • Core Celery Functions
    • setup_celery_app()
  • Core System Monitoring Functions
    • setup_sentry()
    • setup_datadog()
    • setup_healthcheck()
• chart recipes
  • Area Chart Examples
  • Axes Customization Examples
  • Bar Chart Examples
  • Chart Context Examples
  • Combo Chart Examples
  • Heatmap Chart Examples
  • Line Chart Examples
  • Pivot Chart Examples
  • Styling Chart Examples
  • Waterfall Chart Examples

Indices and tables

• Index

• Module Index

• Search Page