Note
Click here to download the full example code
Parameter tables¶
Display pandas tables of catalog meta data, detections, point estimates for their parameters, and summaries of posterior samples. This example also serves as a brief tutorial for using the lisacattools.catalog tools
import pandas as pd
from lisacattools.catalog import GWCatalogs
from lisacattools.catalog import GWCatalogType
Start by loading the main catalog files processed from GBMCMC outputs
catType = GWCatalogType.UCB # catalog type (UCB or MBH)
catPath = "../../tutorial/data/ucb" # path to catalog files
catName = "cat15728640_v2.h5" # name of specific catalog file
catPattern = "/cat**.h5" # pattern of main catalog file(s) cat[T]_v[i].h5
rejPattern = "/*chains*" # pattern of chain files cat[T]_v[i]_chains_[j]s.h5
# create catalogs object by searching for specifically-named files
catalogs = GWCatalogs.create(
catType, catPath, accepted_pattern=catPattern, rejected_pattern=rejPattern
)
# or create catalogs object for specific file name
catalogs_specified = GWCatalogs.create(catType, catPath, catName)
Get metadata of catalogs into DataFrame
catalogs.metadata
Compare metadata to specified catalog
catalogs_specified.metadata
GWCatalogs object¶
The GWCatalogs object can contain multiple catalogs (e.g. updated releases after more data are analyzed)
Out:
['aws_3mo_v3', 'aws_6mo_v2']
Select individual catelogs…
# ...by place in list (oldest)
cat_first = catalogs.get_first_catalog()
# ...by place in list (newest)
cat_last = catalogs.get_last_catalog()
# ...by name
cat_6mo = catalogs.get_catalog_by("aws_6mo_v2")
print(cat_first, cat_6mo, cat_last, sep="\n")
Out:
UcbCatalog: aws_3mo_v3 ../../tutorial/data/ucb/cat7864320_v3.h5
UcbCatalog: aws_6mo_v2 ../../tutorial/data/ucb/cat15728640_v2.h5
UcbCatalog: aws_6mo_v2 ../../tutorial/data/ucb/cat15728640_v2.h5
GWCatalog object¶
Once an individual catalog is selected, explore some of the metadata it contains
# Select the 6-months release
catalog = catalogs.get_catalog_by("aws_6mo_v2")
Get list of detections in catalog
detections_list = catalog.get_detections()
N_detections = len(detections_list)
print("\nlist of 1st 10 detections ({} total):\n".format(N_detections))
print(*detections_list[:10], sep="\n")
Out:
list of 1st 10 detections (6195 total):
LDC0081497609
LDC0081535331
LDC0081547837
LDC0081595365
LDC0081697901
LDC0081782737
LDC0081731061
LDC0081720817
LDC0081805652
LDC0081853812
Get list of attributes for each detection in catalog object
list_of_attributes = catalog.get_attr_detections()
print("list of attributes:\n")
print(*list_of_attributes, sep="\n")
Out:
list of attributes:
SNR
evidence
segment
chain file
parent
Frequency
Frequency Derivative
Amplitude
Ecliptic Longitude
coslat
cosinc
Initial Phase
Polarization
Ecliptic Latitude
Inclination
Get DataFrame of all detections, sorted by SNR
detections_df = catalog.get_dataset("detections")
detections_df.sort_values(by="SNR", ascending=False)
Individual sources¶
Select sources based on their observed properties
# catalog.get_median_source() returns pandas DataFrame of detection metadata
median_snr_source = catalog.get_median_source("SNR")
median_f0_source = catalog.get_median_source("Frequency")
median_A_source = catalog.get_median_source("Amplitude")
pd.concat([median_snr_source, median_A_source, median_f0_source]).style
Now pick a single source and investigate it’s parameters
# Choose median SNR source for deeper analysis
sourceID = median_snr_source.index[0]
# get list of samples attributes
sample_attr = catalog.get_attr_source_samples(sourceID)
print(
"\nlist of posterior sample parameters for source {}:\n".format(sourceID)
)
print(*sample_attr, sep="\n")
Out:
list of posterior sample parameters for source LDC0032278111:
Frequency
Frequency Derivative
Amplitude
Ecliptic Longitude
coslat
cosinc
Initial Phase
Polarization
SNR
entry match
waveform measure
Ecliptic Latitude
Inclination
Get all posterior samples in a pandas DataFrame, dropping redundant columns
samples = catalog.get_source_samples(sourceID).drop(
["coslat", "cosinc"], axis=1
)
samples.describe().loc[["mean", "std", "25%", "50%", "75%"]]
It is easy to pick out a subset of parameters (i.e. marginalize over all others)
# get subset (i.e. marginalized) of samples
parameters = [
"Frequency",
"Amplitude",
"Ecliptic Longitude",
"Ecliptic Latitude",
"Inclination",
]
marginalized_samples = catalog.get_source_samples(sourceID, parameters)
marginalized_samples.describe().loc[["mean", "std", "25%", "50%", "75%"]]
Total running time of the script: ( 0 minutes 0.929 seconds)