fcx-playground 1.0.1

A package that has a core fcx data processing module and a module to visualize the processed data in python interactive notebook environment (playground).

Documentation

Steps to Use

1. Installation:

pip install fcx-playground

2. Usage:

• To use data processing steps for NAV:

from fcx_playground.fcx_dataprocess.czml_nav import NavCZMLDataProcess
obj = NavCZMLDataProcess()

data = obj.ingest("<path_to_input>")
pre_processed_data = obj.preprocess(data)
czml_str = obj.prep_visualization(pre_processed_data)

• To use data processing steps for CRS rad-range:

from fcx_playground.fcx_dataprocess.tiles_rad_range import RadRangeTilesPointCloudDataProcess
obj = RadRangeTilesPointCloudDataProcess()

data = obj.ingest("<path_to_input>")
pre_processed_data = obj.preprocess(data)
point_clouds_tileset = obj.prep_visualization(pre_processed_data)

• To visualize NAV CZML:

from fcx_playground.fcx_cesium_viz.czml_viz import CZMLViz
czml_viz_obj = CZMLViz()
nav_czml_cesium_html = czml_viz_obj.generate_html("<path_to_saved_czml>")

# use the nav_czml_cesium_html in IPython.display.HTML to render it.

• To visualize CRS rad-range 3DTiles:

from fcx_playground.fcx_cesium_viz.tiles_viz import TilesViz
tileset_viz_obj = TilesViz()
point_clouds_tileset_html = tileset_viz_obj.generate_html("<path_to_saved_point_clouds_tileset>")

# use the point_clouds_tileset_html in IPython.display.HTML to render it.

Note:

ingest, preprocess, prep_visualization methods are inherited from DataProcess Abstract Class.
As per need, we can override or write custom methods for ingest, preprocess, prep_visualization, by maintaining consistency on the return type of the overrides.

Steps to use fcx playground from Source Code

Pre-requisites

1. General direction:

• Install python
• Install conda (optional but recommended)
• Use either pip or conda to install dependencies mentioned in requirements.txt
• Data are ingested from AWS S3. So, Setup AWS credentials
  • aws configure Preferred. This deployment configuration is assumed to be used.
  • Need aws_access_key_id and aws_secret_access_key key values; inside ~/.aws/credentials

2. Using Docker

• Install Docker
• Data are ingested from AWS S3. So, Setup AWS credentials
  • aws configure Preferred. This deployment configuration is assumed to be used.
  • Need aws_access_key_id and aws_secret_access_key key values; inside ~/.aws/credentials
• Run docker compose build (will take few minutes)
• Run docker compose up, and note down the token_id
• Use localhost:8888/tree?token=<token_id> to run Jupyter Notebook.

Usage:

• notebooks dir contains all the interactive python notebooks to get started with various visualization file generations.
• src dir contains modules that enables the visualization file generation.
  • Abstact classes defines the highlevel process on which the raw data are manupulated.
  • The concrete classes are implemented from abstract classes for detailed 3d visualization file generation processes.
  • There are utilities that help the visualization file generation.

Devloper guidelines:

• Clear Notebook outputs before commiting any changes to git; for clean changes tracking.