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Polaris

Project description

Polaris

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Python3 tool to analyze a satellite set of telemetry to understand links/dependencies among different subsystems. The telemetry is currently retrieved from the SatNOGS Network.

If you want to know more:

Project structure

contrib/               - code that is not directly dependent on Polaris, but is used in the project
docs/                  - Some documentation on the project (though more is in the wiki)
polaris/               - Project source code
    common/            - Modules common to all of Polaris
    fetch/             - Module to fetch and prepare data for the analysis
    viz/               - Module to visualize the analysis results
    learn/             - Module to perform the data analysis
    batch/             - Module to perform batch operations
    polaris.py         - Polaris entry point

tests/                 - Project unit tests
playground/            - Exploratory tests

Installation

$ pip3 install polaris-ml

We recommend to install it inside a Python virtual environment:

# Create the virtual env
$ python3 -m venv .venv

# Activate it
$ source .venv/bin/activate

# Install Polaris from Pypi
$ (.venv) pip install polaris-ml

Note: If you run into problems installing Polaris via pip, try using the new Pip resolver:

pip install polaris-ml --use-feature=2020-resolver

Running the code

$ (.venv) polaris --help
Usage: polaris [OPTIONS] COMMAND [ARGS]...

  Tool for analyzing satellite telemetry

Options:
  --version   Show the version and exit.
  --help      Show this message and exit.

Commands:
  batch     Run polaris commands in batch mode
  fetch     Download data set(s)
  learn     Analyze data
  viz       Displaying results

# To fetch and decode data from the SatNOGS network and space weather sources, run:
$ (.venv) polaris fetch -s 2019-08-10 -e 2019-10-5 --cache_dir /tmp/LightSail_2 LightSail-2 /tmp/normalized_frames.json
# Note: this may take some time.

# If the normalizer for your satellite does not exist, you can run polaris fetch
# with the --skip_normalizer flag. The result with and without the normalizer
# (without/with --skip-normalizer) are bound to be slightly different.
# The normalizer is mainly present to give you, the satellite operator, an
# intuitive (SI) value for fields (instead of arbitrarily scaled/shifted
# values). It is easy to create and you can get started with the process using
# the snippet at https://gitlab.com/librespacefoundation/polaris/polaris/-/snippets/2006696
$ (.venv) polaris fetch -s 2019-08-10 -e 2019-10-5 --cache_dir /tmp/LightSail_2 --skip_normalizer LightSail-2 /tmp/normalized_frames.json

# Data will be saved at /tmp/normalized_frames.json
$ (.venv) head /tmp/normalized_frames.json
[
    {
        "time": "2019-09-12 08:14:42",
        "measurement": "",
        "tags": {
            "satellite": "",
            "decoder": "Lightsail2",
            "station": "",
            "observer": "",
            "source": "",
[...]


# To learn from that data, run:
$ (.venv) polaris learn -g /tmp/new_graph.json /tmp/normalized_frames.json
# Note: depending on your hardware, this may take some time.

# Note: `polaris learn` uses your dedicated (CUDA enabled) GPU by default
#       to suppress this behaviour, you can utilise the --force-cpu flag.
$ (.venv) polaris learn -g /tmp/new_graph.json /tmp/normalized_frames.json --force_cpu

# To see a visualization of these results, run:
$ (.venv) polaris viz /tmp/new_graph.json
# Then visit http://localhost:8080 in your browser

Configuring Polaris

It is possible to override the default parameters used in the ai processes of Polaris by your own using configuration files.

  • Learn cross correlation process (generating graph) :
{
  "use_gridsearch": false,
  "random_state": 43,
  "test_size": 0.2,
  "gridsearch_scoring": "neg_mean_squared_error",
  "gridsearch_n_splits": 6,
  "dataset_cleaning_params": {
    "col_max_na_percentage": 100,
    "row_max_na_percentage": 100
	},
  "model_cpu_params": {
    "objective": "reg:squarederror",
    "n_estimators": 81,
    "learning_rate": 0.1,
    "n_jobs": 1,
    "predictor": "cpu_predictor",
    "tree_method": "auto",
    "max_depth": 8
  },
  "model_params": {
    "objective": "reg:squarederror",
    "n_estimators": 80,
    "learning_rate": 0.1,
    "n_jobs": 1,
    "max_depth": 8
	}
}

To use it, add the -l or learn_config_file command line parameter when calling learn :

$ polaris learn -g /tmp/graph.json /tmp/normalized_frames -l ../xcorr_cfg.json

## Batch operations

Batch operations allow automation of repeated steps.  For example:

- running `polaris fetch` so that it fetches the latest data for a particular satellite, then running `polaris learn` to update the model

- running `polaris fetch`, `polaris learn` and `polaris viz` as part of an integration test

The `polaris batch` command is controlled by a JSON configuration file; an example can be found at `polaris/common/polaris_config.json.EXAMPLE`.

```bash
$ (.venv) polaris batch --config_file polaris/common/polaris_config.json.EXAMPLE

InfluxDB

With the addition of space weather recently, influxdb support has been added to Polaris. To create the required docker-compose.yml file and start and stop the docker container, run:

$ python
>>> from vinvelivaanilai.storage import common

# To create the path
>>> common.create_docker_compose("/path/to/docker-compose.yml", "/path/to/storage")

# To start influxdb
>>> common.start_docker_compose("/path/to/docker-compose.yml")

# To stop influxdb 
>>> common.stop_docker_compose("/path/to/docker-compose.yml")

To store in and fetch from influxdb use the flags --store_in_influxdb and --fetch_from_influxdb respectively.

$ polaris fetch -s 2019-08-10 -e 2019-10-5 --cache_dir /tmp/LightSail_2 LightSail-2 /tmp/normalized_frames.json --store_in_influxdb
$ polaris fetch -s 2019-08-10 -e 2019-10-5 --cache_dir /tmp/LightSail_2 LightSail-2 /tmp/normalized_frames.json --fetch_from_influxdb

MLflow

Installing Polaris will install MLflow as a dependency. At this time Polaris is using MLflow during the cross check dependencies process, and the database is stored in the current working directory under the mlruns folder.

To view the logs in MLflow, run this command in the directory that holds the mlruns folder (by default, this is the project root directory):

$ mlflow ui

This command will start the tracking ui server at http://localhost:5000.

More info for developers

Building the package from the sources:

# Clone the repo
$ git clone https://gitlab.com/crespum/polaris.git

# Activate the virtual environment:
$ source .venv/bin/activate

# Build and install the package in editable mode; any changes
# to your code will be reflected when you run polaris.
$ (.venv) pip install -e .

Note: If you run into problems installing Polaris via pip, try using the new Pip resolver:

pip install -e . --use-feature=2020-resolver

It is important to format the code before commiting, otherwise the CI engine will fail. We have a tox command setup to run tests before committing so you will never have to push failing pipelines. Code linting is also done to ensure the code does not have any errors before committing.

First you will have to install Prettier. Be sure to have a node version equal or greater than version 10.13.0. In case you don't have a good node version here is how to install/update it:

$ curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.35.3/install.sh | bash

# Feel free to install any version you like, but >= 10.13.0
$ nvm install v13.8.0
$ nvm use v13.8.0

After the installation of node, you have to restart your terminal. Then, to install Prettier:

$ npm install -g prettier

You can learn more about npm here.

# Install tox to execute CI tasks
$ (.venv) pip install tox

# Auto-format the code
$ (.venv) tox -e yapf-apply -e isort-apply -e prettier-apply
______________________ summary______________________
  yapf-apply: commands succeeded
  isort: commands succeeded
  prettier-apply: commands succeeded
  congratulations :)

# Verify CI test passes
$ (.venv) tox
# If all goes well, you will get something like this:
______________________ summary______________________
  flake8: commands succeeded
  isort: commands succeeded
  yapf: commands succeeded
  pylint: commands succeeded
  build: commands succeeded
  pytest: commands succeeded
  prettier: commands succeeded
  congratulations :)

You can learn more about tox here.

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