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Master-repository for all code related to "A Novel Approach to Topological Graph Theory with R-K Diagrams and Gravitational Wave Analysis"

Home Page: https://arxiv.org/abs/2201.06923

TeX 9.50% Shell 0.01% Jupyter Notebook 88.92% Python 1.41% Emacs Lisp 0.16%
astronomy astrophysics data-science graph-algorithms graph-theory gravitational-waves ligo machine-learning topological-data-analysis topology-graph

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rk_toolkit_pipeline_diagrams's Issues

TODO:

TODO

  1. Better documentation of methods
  2. Merge graph2 and graph together
  3. Merge common from jupyter notebooks
  4. Write more Unit Tests. See tests folder in rk_toolkit for samples.
  5. Add docstrings to methods (already somewhat there). Serve sphinx docs
  6. General code cleanup and refactoring
  7. Write improved README on how to run everything together
  8. Remove unnecessary files.
  9. Update results and items from Animikh 1-4 on Ligo review.

Todo task board updated for reference with priorities and task distribution in the following link on Notion: https://dot-terrier-969.notion.site/686f90e5615445c5bdd7753d32e5028f?v=1256c9f6528142a4aa367ef1d1d90b23

Primary Analysis Python code needs to be added to rk_gw_mma under notebooks

@andorsk the following folder which is vitally important for our PR publication : rk_toolkit_pipeline_diagrams/pruned/02_notebooks/rk_gw_mma/
currently contains:
\ data
\ helpers.py
\ ligo.ipynb

however it needs to be updated as follows as a key component of our PR publication:

\ data
\ helpers.py
\ ligo_primary_analysis.ipynb
\ ligo_secondary_analysis.ipynb

the current file : \ ligo.ipynb only contains the secondary analysis part which will be updated with your results soon. However as discussed today, the primary analysis was already completed earlier on the strain data from gw_openscience and all the work on it needs to be added from andorsk a_novel_approach_toward_tda_paper/notebooks/ into a single .ipynb file titled :
\ ligo_primary_analysis.ipynb

R-K diagram based classification diagram of compact binaries with t-sne or any other technique as shown below

I believe you had attempted this initially as well but discussed about removing the redundant node clusters apart from mass, spin, q-ratio and redshift and redoing it with better spatial separation and distinction as shown in the diagram below. Everyone really appreciated this idea at Glasgow and gave their positive feedback and support so this will be the biggest validation of our application from the LIGO and compact binary POV for the physics community w.r.t the last section of our paper.

common.py across notebooks should be shared.

Import issue with common.py where it needs to be shared across notebooks, but based on feedback of structure from animikh, it needs to be in parent folder, which casued some import issues. Look into this.

add more tests to rk_toolkit

# TODO: Tests to be written here
# def test_filters
#   TODO:
#
# def test_htg
#   TODO
#
# def test_linkers
#   TODO
#
# def test_rk_io
#   TODO
#
# def test_rkmodels:
#   TODO
#
# def test_pipeline:
#   TODO
#
# def test_graph_checks:
#   TODO
#
# def test_visualizers:
#   TODO

R-K Workbench Missing

@andorsk upon review there's always been an important section of the paper involving the R-K Workbench that was referenced in multiple sections including the Novel Approach, Computational Pipeline and Case Studies. I have added this to the documentation and methods google doc (https://docs.google.com/document/d/1dB_YSAV-rW3i0Jk1aHKDU5fPjcrS1OZlqdVvUJiGWx0/edit?usp=sharing) that @ashxyz998 and I are currently preparing as follows:

#R-K Workbench:

In order to help users get started quicker, we provide a docker image called R-K Workbench (https://github.com/andorsk/rk-workbench) which wraps the ML-Workspace (https://github.com/ml-tooling/ml-workspace) with packages relevant to building R-K Models and R-K Diagrams built into the core image. The README file contains details on how to use it for the purpose of independent use by researchers and programmers to customize and apply the R-K Pipeline to various scientific and business use-cases.

However, I can't seem to find the relevant code with the docker image that was previously provided on this link: https://github.com/ml-tooling/ml-workspace as referenced in our paper and wanted to seek your help in locating that page with its corresponding files for adding it to the R-K Toolkit folder on this repo: https://github.com/animikhroy/rk_toolkit_pipeline_diagrams/

We can update the link on the documentation accordingly.

Improve Test Coverage

We are at 38% right now. Normally 80% is decent.

---------- coverage: platform darwin, python 3.9.2-final-0 -----------
Name                                          Stmts   Miss  Cover
-----------------------------------------------------------------
setup.py                                          5      5     0%
src/rktoolkit/__init__.py                         2      0   100%
src/rktoolkit/functions/__init__.py               0      0   100%
src/rktoolkit/functions/distance.py              14     10    29%
src/rktoolkit/functions/filters.py               31     18    42%
src/rktoolkit/functions/functions_test.py        16      0   100%
src/rktoolkit/functions/htg_transformers.py      56     44    21%
src/rktoolkit/functions/linkers.py               64     46    28%
src/rktoolkit/functions/localizers.py            35     14    60%
src/rktoolkit/models/__init__.py                  0      0   100%
src/rktoolkit/models/functions.py                24      8    67%
src/rktoolkit/models/graph.py                   275    181    34%
src/rktoolkit/models/pipeline.py                 60     43    28%
src/rktoolkit/models/pipeline_test.py             9      0   100%
src/rktoolkit/models/rkmodel.py                  13      7    46%
tests/visualizer_test.py                          0      0   100%
-----------------------------------------------------------------
TOTAL                                           604    376    38%

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