The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
sampler_name: string
kernel: struct<step_size: double, inverse_mass_matrix: null, adapt_step_size: bool, adapt_mass_matrix: bool, (... 179 chars omitted)
child 0, step_size: double
child 1, inverse_mass_matrix: null
child 2, adapt_step_size: bool
child 3, adapt_mass_matrix: bool
child 4, dense_mass: bool
child 5, target_accept_prob: double
child 6, max_tree_depth: list<item: int64>
child 0, item: int64
child 7, find_heuristic_step_size: bool
child 8, forward_mode_differentiation: bool
child 9, regularize_mass_matrix: bool
mcmc: struct<num_warmup: int64, num_samples: int64, thinning: int64, num_chains: int64, chain_method: stri (... 66 chars omitted)
child 0, num_warmup: int64
child 1, num_samples: int64
child 2, thinning: int64
child 3, num_chains: int64
child 4, chain_method: string
child 5, progress_bar: bool
child 6, progress_rate: null
child 7, jit_model_args: bool
regex: string
default_spin_prior: string
max_samples: int64
default_datasets: list<item: string>
child 0, item: string
default_distance_prior: string
default_mass_prior: string
to
{'regex': Value('string'), 'default_datasets': List(Value('string')), 'max_samples': Value('int64'), 'default_mass_prior': Value('string'), 'default_spin_prior': Value('string'), 'default_distance_prior': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
sampler_name: string
kernel: struct<step_size: double, inverse_mass_matrix: null, adapt_step_size: bool, adapt_mass_matrix: bool, (... 179 chars omitted)
child 0, step_size: double
child 1, inverse_mass_matrix: null
child 2, adapt_step_size: bool
child 3, adapt_mass_matrix: bool
child 4, dense_mass: bool
child 5, target_accept_prob: double
child 6, max_tree_depth: list<item: int64>
child 0, item: int64
child 7, find_heuristic_step_size: bool
child 8, forward_mode_differentiation: bool
child 9, regularize_mass_matrix: bool
mcmc: struct<num_warmup: int64, num_samples: int64, thinning: int64, num_chains: int64, chain_method: stri (... 66 chars omitted)
child 0, num_warmup: int64
child 1, num_samples: int64
child 2, thinning: int64
child 3, num_chains: int64
child 4, chain_method: string
child 5, progress_bar: bool
child 6, progress_rate: null
child 7, jit_model_args: bool
regex: string
default_spin_prior: string
max_samples: int64
default_datasets: list<item: string>
child 0, item: string
default_distance_prior: string
default_mass_prior: string
to
{'regex': Value('string'), 'default_datasets': List(Value('string')), 'max_samples': Value('int64'), 'default_mass_prior': Value('string'), 'default_spin_prior': Value('string'), 'default_distance_prior': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
GWKokab O4a Example Run
This repository provides a minimal working example demonstrating how to perform gravitational-wave population inference with GWKokab, a JAX-based framework for hierarchical Bayesian inference of compact binary populations.
The purpose of this repository is to help new users learn how to run GWKokab through the command-line interface (CLI) by reproducing an O4a population analysis setup. The repository includes example configuration files, scripts, and a complete workflow that can be used as a starting point for custom analyses.
Contents
- Example GWKokab configuration files
- O4a-style population inference setup
- Command-line execution examples
- Post-processing and plotting scripts
Quick Start
Run the population inference using the following bash script. It has all the required configs, you can read this file, names are self-explantory.
./run.sh
The inference results will be saved in:
inference_data.hdf5
Make plots to see the inference using following commnad line. It will create all the diagnostic plots in figs, and report.html.
gwk_report
To learn about any command line in GWkokab use help for instance gwk_report -h or discrete_subpopulation -h.
Purpose
This example is intended as a tutorial and reference workflow for users who want to:
- Learn the GWKokab CLI
- Understand GWKokab configuration files
- Reproduce an O4a-style population analysis
- Build their own population inference studies
GWKokab
GWKokab is an open-source framework for gravitational-wave population inference, supporting flexible population models for mass, spin, redshift, eccentricity, and multi-source populations.
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