diff --git a/bilby/gw/conversion.py b/bilby/gw/conversion.py index 6f8a0e0e38d4ad2e3391f37a28203370035cd71d..455c4e41478521460a91ba6807b264550f66b357 100644 --- a/bilby/gw/conversion.py +++ b/bilby/gw/conversion.py @@ -1,5 +1,7 @@ from __future__ import division +import sys +from tqdm import tqdm import numpy as np from pandas import DataFrame @@ -969,13 +971,13 @@ def compute_snrs(sample, likelihood): else: logger.info( - 'Computing SNRs for every sample, this may take some time.') + 'Computing SNRs for every sample.') matched_filter_snrs = { ifo.name: [] for ifo in likelihood.interferometers} optimal_snrs = {ifo.name: [] for ifo in likelihood.interferometers} - for ii in range(len(sample)): + for ii in tqdm(range(len(sample)), file=sys.stdout): signal_polarizations =\ likelihood.waveform_generator.frequency_domain_strain( dict(sample.iloc[ii])) @@ -1030,7 +1032,7 @@ def generate_posterior_samples_from_marginalized_likelihood( new_time_samples = list() new_distance_samples = list() new_phase_samples = list() - for ii in range(len(samples)): + for ii in tqdm(range(len(samples)), file=sys.stdout): sample = dict(samples.iloc[ii]).copy() likelihood.parameters.update(sample) new_sample = likelihood.generate_posterior_sample_from_marginalized_likelihood() diff --git a/requirements.txt b/requirements.txt index de58a6b16f36ea4614961dcdb0b54cce46167f8e..849fd3f93b800fb3f8fddde4ea2136e74c3c8463 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,4 @@ scipy>=0.16 pandas mock dill +tqdm diff --git a/setup.py b/setup.py index fe9889959deae7df47683206e5294a63e1ddb776..6c88272651f956f7acae8b0f6632249bfebe6ea3 100644 --- a/setup.py +++ b/setup.py @@ -84,7 +84,8 @@ setup(name='bilby', 'numpy>=1.9', 'matplotlib>=2.0', 'pandas', - 'scipy'], + 'scipy', + 'tqdm'], entry_points={'console_scripts': ['bilby_plot=cli_bilby.plot_multiple_posteriors:main', 'bilby_result=cli_bilby.bilby_result:main',