On the command-line
This page was generated with the following command-line call:
$ gw_summary day 20230505 -o /loscdata/summary_pages/detector_status --verbose --config-file /home/loscdetstat/src/ligo-summary-pages/configurations/public/public.ini
The install path used was /home/detchar/.conda/envs/ligo-summary-3.9
.
Configuration files
The following INI-format configuration file(s) were passed on the comand-line and are reproduced here in full:
[calendar]
start-of-week = sunday
start-date = 2015-09-14
[state-H1-science]
key = H1-science
name = Observing
definition = H1:DMT-ANALYSIS_READY:1
[state-L1-science]
key = L1-science
name = Observing
definition = L1:DMT-ANALYSIS_READY:1
[tab-home]
type = link
name = _Home
url = https://gw-openscience.org/detector_status/
[html]
issues = https://gw-openscience.org/contact
[tab-summary]
type = plots
name = Summary
foreword = The plots shown below characterize the sensitivity and
status of each of the LIGO interferometers as well as the
<a href='http://www.virgo-gw.eu/'>Virgo</a> detector in Cascina, Italy and the
<a href='http://www.geo600.org'>GEO600</a> detector in Hanover, Germany. <br>
For more information about the plots listed below, click on an image to read the caption.
Use the tabs in the navigation bar at the top of the screen for more detailed information about
the LIGO, Virgo, and GEO interferometers.
1 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/G1H1L1V1-OBSERVING_HOFT_SPECTRUM-%(gps-start-time)s-%(duration)s.png
1-caption = This plot represents the median noise of each interferometer measured over the course of the day. The measured output of each interferometer, calibrated to units of gravitational wave strain, is shown as a function of frequency. Since the amplitude of a gravitational wave signal changes with frequency, the shape of this curve determines each detector's sensitivity to incoming gravitational waves. This plot is often referred to as the "noise curve".
2 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/G1H1L1V1-SEGMENTS-%(gps-start-time)s-%(duration)s.png
2-caption = This plot shows the instrument status for each detector, represented by horizontal bars, over the course of the day. The tall opaque blocks indicate times when the interferometer is intended to be in observing mode and no tests are currently being performed. The narrow, transparent blocks indicate times when the interferometer was in an operational state, but not in observing mode. This state often means that testing or commissioning is in progress.
3 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1V1-OBSERVING_BNS_INSPIRAL_RANGE-%(gps-start-time)s-%(duration)s.png
3-caption = This plot shows average sensitivity of the Hanford, Livingston, and Virgo detectors to gravitational wave signals from binary neutron star mergers, as measured in megaparsecs (where 1 megaparsec = 3,262,000 light-years), averaged over all sky positions and source orientations. The value reported in this plot is determined by comparing the noise curve, as seen averaged over the entire day in the first plot, to a predicted gravitational wave signal from a binary neutron star merger. The sharp drops in this curve are typically due to transient noise in the interferometer limiting sensitivity to gravitational waves. The sharp upward peaks in the Virgo curve are artifacts caused by data dropouts.
layout = 1,2
afterword = This page is a product of the <a href='https://gw-openscience.org'>Gravitational Wave Open Science Center</a>. See <a href='https://gw-openscience.org'>gw-openscience.org</a> for more information.
Note that some information on these pages may be missing or incomplete.
[tab-segments]
type = plots
parent = Instrument performance
name = Analysis time
layout = 2
foreword = The plots shown below characterize the uptime of each of the LIGO detectors as well as the Virgo detector in Cascina, Italy and the GEO600 detector in Hanover, Germany. For more information about the plots listed below, click on an image to read the caption.
1 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/G1H1L1V1-SEGMENTS-%(gps-start-time)s-%(duration)s.png
1-caption = This plot shows the instrument status for each detector, represented by horizontal bars, over the course of the day. The tall opaque blocks indicate times when the interferometer is intended to be in observing mode and no tests are currently being performed. The narrow transparent blocks indicate times when the interferometer was in an operational state, but not in observing mode. This state often means that testing or commissioning is in progress.
2 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/G1H1L1V1-DUTY_FACTOR-%(gps-start-time)s-%(duration)s.png
2-caption = This plot shows the fractional observing time of each detector in one hour bins over the course of the day. The dotted line represents a rolling average of observing time as data are accumulated.
3 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1V1-COINC_OBSERVING_FACTOR-%(gps-start-time)s-%(duration)s.png
3-caption = This pie chart shows the fraction of single, double, and triple interferometer observing time for the Hanford, Livingston, and Virgo detectors.
4 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-COINC_OBSERVING_FACTOR-%(gps-start-time)s-%(duration)s.png
4-caption = This pie chart shows the fraction of single and double interferometer observing time for the Hanford and Livingston detectors.
[tab-range]
type = plots
parent = Instrument performance
name = Astrophysical range
layout = 2
foreword = The plots shown below characterize the astrophysical range of each of the LIGO detectors as well as the Virgo detector in Cascina, Italy and the GEO600 detector in Hanover, Germany. For more information about the plots listed below, click on an image to read the caption.
1 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1V1-OBSERVING_BNS_INSPIRAL_RANGE-%(gps-start-time)s-%(duration)s.png
1-caption = This plot shows the average sensitivity of the Hanford, Livingston, and Virgo detectors to gravitational wave signals from binary neutron star mergers, as measured in megaparsecs (where 1 megaparsec = 3,262,000 light-years), averaged over all sky positions and source orientations. The value reported in this plot is determined by comparing the noise curve to a predicted gravitational wave signal from a binary neutron star merger. The sharp drops in this curve are typically due to transient noise in the interferometer limiting sensitivity to gravitational waves. The sharp upward peaks in the Virgo curve are artifacts caused by data dropouts.
2 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1V1-OBSERVING_BNS_INSPIRAL_RANGE_HISTOGRAM-%(gps-start-time)s-%(duration)s.png
2-caption = This histogram shows the total amount of time spent at a given sensitive distance for the Hanford, Livingston, and Virgo detectors over the course of the day.
3 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/G1-OBSERVING_BNS_INSPIRAL_RANGE-%(gps-start-time)s-%(duration)s.png
3-caption = This plot shows the average sensitivity of the GEO600 detector to gravitational wave signals from binary neutron star mergers, as measured in megaparsecs.
4 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/G1-OBSERVING_4E4F4B_RANGE_HISTOGRAM-%(gps-start-time)s-%(duration)s.png
4-caption = This histogram shows the total amount of time spent at a given sensitive distance for the GEO600 interferometer over the course of the day.
[tab-env]
type = plots
name = Overview
parent = Environment
foreword = The environment at the LIGO sites is monitored by the Physical and Environmental Monitoring (PEM) system. This system is comprised of a network of sensitive instruments that are designed to precisely measure environmental disturbances. The diagram below shows the layout of the PEM sensors at the LIGO Livingston Observatory (LLO). For more information on the PEM system, please see <a href='http://pem.ligo.org'>pem.ligo.org</a>.
1=/s/summary_pages/LLOPEMMap.png
layout=1
afterword = This page is a product of the <a href='https://gw-openscience.org'>Gravitational Wave Open Science Center</a>.
[tab-blrms]
parent = Environment
shortname = Ground motion
name = Ground motion
type = plots
foreword = These plots display the ground motion at the LIGO Livingston and LIGO Hanford Observatories as measured by Streckeisen STS-2 seismometers at the corner station (where the X- and Y-arms meet). Each plot shows the root-mean-square ground motion in a different frequency band, which capture independent ground motion behavior.
1 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-BLRMS_30M_100M-%(gps-start-time)s-%(duration)s.png
1-caption = This plot shows the root-mean-square ground motion in the 0.03 - 0.1 Hz frequency band. This frequency band is sensitive to ground motion due to earthquakes, which is seen in all three measured degrees of freedom (X, Y, and Z), and wind, which tends to been seen largely in the horizontal degrees of freedom (X and Y).
2 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-BLRMS_100M_300M-%(gps-start-time)s-%(duration)s.png
2-caption = This plot shows the root-mean-square ground motion in the 0.1 - 0.3 Hz frequency band. This frequency band is sensitive to ground motion due to ocean waves beating up against the shore, dominated by the Pacific Ocean for the LIGO-Hanford detector and the Atlantic Ocean and Gulf of Mexico for the LIGO-Livingston detector. It is called 'micro-seismic' because elevated ground motion amplitude can exceed one micron in this frequency range.
3 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-BLRMS_300M_1-%(gps-start-time)s-%(duration)s.png
3-caption = This plot shows the root-mean-square ground motion in the 0.3 - 1 Hz frequency band. Ground motion in this frequency band often follows strong peaks and trends in the micro-seismic band.
4 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-BLRMS_1_3-%(gps-start-time)s-%(duration)s.png
4-caption = This plot shows the root-mean-square ground motion in the 1 - 3 Hz frequency band. This frequency band is sensitive to ground motion due to the activity of people and vehicles, such as heavy traffic on nearby roads, passing trains, and logging or construction close to a LIGO site.
5 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-BLRMS_3_10-%(gps-start-time)s-%(duration)s.png
5-caption = This plot shows the root-mean-square ground motion in the 3 - 10 Hz frequency band. This frequency band is sensitive to ground motion due to the movement of people and vehicles and by mechanical vibrations of equipment at the LIGO sites, such as the HVAC system.
6 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L1-BLRMS_10_30-%(gps-start-time)s-%(duration)s.png
6-caption = This plot shows the root-mean-square ground motion in the 10 - 30 Hz frequency band. This frequency band is sensitive to ground motion due to mechanical vibrations of equipment at the LIGO sites, such as the HVAC system.
[tab-temp]
parent = Environment
shortname = Temp
name = temperature
type = plots
foreword = These plots show the indoor and outdoor temperatures at the LIGO Livingston and LIGO Hanford Observatories as measured by thermometers located at the end of the X-arm (EX), the end of the Y-arm (EY), and the corner station (CS; near the beam splitter, where the X- and Y-arms meet). Any extreme dips in these plots are likely due to gaps in recorded data rather than rapid temperature swings.
1 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H1L0-OUTDOOR_TEMPERATURE-%(gps-start-time)s-%(duration)s.png
1-caption = This plot shows the outdoor temperature at the LIGO Observatories over the course of the day. LIGO Hanford is located in a desert climate that has hot summers, moderately cold winters, low humidity, and little precipitation. LIGO Livingston is located in a subtropical climate that has hot summers, mild winters, high humidity, and heavy precipitation.
2 = /s/summary_pages/detector_status/cache/day/%(yyyymmdd)s/H0L0-INDOOR_TEMPERATURE-%(gps-start-time)s-%(duration)s.png
2-caption = This plot shows the indoor temperature at the LIGO Observatories over the course of the day. Some of these traces will show fluctuations with a period of 30 - 60 minutes, these are typically caused by the HVAC system turning on and heating or cooling the building.
Environment
Name | Version | Channel | Build_String |
---|---|---|---|
_libgcc_mutex | 0.1 | conda-forge | conda_forge |
_openmp_mutex | 4.5 | conda-forge | 1_llvm |
argon2-cffi | 20.1.0 | conda-forge | py39hbd71b63_2 |
astropy | 4.2.1 | conda-forge | py39hce5d2b2_0 |
async_generator | 1.10 | conda-forge | py_0 |
attrs | 20.3.0 | conda-forge | pyhd3deb0d_0 |
backcall | 0.2.0 | conda-forge | pyh9f0ad1d_0 |
backports | 1.0 | conda-forge | py_2 |
backports.functools_lru_cache | 1.6.1 | conda-forge | py_0 |
beautifulsoup4 | 4.9.3 | conda-forge | pyhb0f4dca_0 |
bleach | 3.2.1 | conda-forge | pyh9f0ad1d_0 |
boost-cpp | 1.74.0 | conda-forge | h9d3c048_1 |
boto | 2.49.0 | conda-forge | py_0 |
brotlipy | 0.7.0 | conda-forge | py39h38d8fee_1001 |
bzip2 | 1.0.8 | conda-forge | h7f98852_4 |
c-ares | 1.17.1 | conda-forge | h36c2ea0_0 |
ca-certificates | 2021.10.8 | conda-forge | ha878542_0 |
cached-property | 1.5.1 | conda-forge | py_0 |
certifi | 2021.10.8 | conda-forge | py39hf3d152e_1 |
cffi | 1.14.4 | conda-forge | py39he32792d_1 |
cfitsio | 3.470 | conda-forge | hb418390_7 |
chardet | 4.0.0 | conda-forge | py39hf3d152e_1 |
click | 7.1.2 | conda-forge | pyh9f0ad1d_0 |
coloredlogs | 15.0 | conda-forge | py39hf3d152e_0 |
conda | 4.10.1 | conda-forge | py39hf3d152e_0 |
conda-package-handling | 1.7.3 | conda-forge | py39h3811e60_0 |
configobj | 5.0.6 | conda-forge | py_0 |
cryptography | 3.3.1 | conda-forge | py39h3da14fd_1 |
cycler | 0.10.0 | conda-forge | py_2 |
cyrus-sasl | 2.1.27 | conda-forge | h063b49f_1 |
dbus | 1.13.6 | conda-forge | hfdff14a_1 |
decorator | 4.4.2 | conda-forge | py_0 |
defusedxml | 0.6.0 | conda-forge | py_0 |
dqsegdb | 1.6.1 | conda-forge | py39hde42818_1 |
dqsegdb2 | 1.0.1 | conda-forge | py_0 |
entrypoints | 0.3 | conda-forge | pyhd8ed1ab_1003 |
expat | 2.2.9 | conda-forge | he1b5a44_2 |
fftw | 3.3.8 | conda-forge | nompi_h8cb7ab2_1114 |
flask | 0.12.4 | conda-forge | py_0 |
flask-cache | 0.13.1 | conda-forge | py_1000 |
flask-sqlalchemy | 2.4.0 | conda-forge | py_0 |
fontconfig | 2.13.1 | conda-forge | h736d332_1003 |
freetype | 2.10.4 | conda-forge | h7ca028e_0 |
future | 0.18.2 | conda-forge | py39hf3d152e_3 |
gettext | 0.19.8.1 | conda-forge | h0b5b191_1005 |
glib | 2.66.4 | conda-forge | hcd2ae1e_1 |
gsl | 2.6 | conda-forge | he838d99_1 |
gst-plugins-base | 1.14.5 | conda-forge | h0935bb2_2 |
gstreamer | 1.18.2 | conda-forge | h3560a44_1 |
gwdatafind | 1.0.4 | conda-forge | pyh9f0ad1d_1 |
gwdetchar | 2.0.5 | conda-forge | pyhd8ed1ab_0 |
gwosc | 0.5.6 | conda-forge | py_0 |
gwpy | 2.0.4 | conda-forge | pyhd8ed1ab_1 |
gwsumm | 2.1.0 | conda-forge | pyhd8ed1ab_0 |
gwtrigfind | 0.8.0 | conda-forge | py_0 |
gwvet | 1.0.0 | conda-forge | pyhd8ed1ab_0 |
h5py | 3.1.0 | conda-forge | nompi_py39h25020de_100 |
hdf5 | 1.10.6 | conda-forge | nompi_h6a2412b_1114 |
healpy | 1.14.0 | conda-forge | py39h5cc0c36_2 |
humanfriendly | 9.1 | conda-forge | py39hf3d152e_0 |
hveto | 2.0.1 | conda-forge | pyhd8ed1ab_0 |
icu | 68.1 | conda-forge | h58526e2_0 |
idna | 2.10 | conda-forge | pyh9f0ad1d_0 |
importlib-metadata | 3.4.0 | conda-forge | py39hf3d152e_0 |
importlib_metadata | 3.4.0 | conda-forge | hd8ed1ab_0 |
iniconfig | 1.1.1 | conda-forge | pyh9f0ad1d_0 |
ipykernel | 5.4.2 | conda-forge | py39hef51801_0 |
ipython | 7.24.1 | conda-forge | py39hef51801_0 |
ipython_genutils | 0.2.0 | conda-forge | py_1 |
ipywidgets | 7.6.3 | conda-forge | pyhd3deb0d_0 |
itsdangerous | 1.1.0 | conda-forge | py_0 |
jedi | 0.17.2 | conda-forge | py39hf3d152e_1 |
jinja2 | 2.11.2 | conda-forge | pyh9f0ad1d_0 |
joblib | 1.0.0 | conda-forge | pyhd8ed1ab_0 |
jpeg | 9d | conda-forge | h36c2ea0_0 |
jsonschema | 3.2.0 | conda-forge | py_2 |
jupyter | 1.0.0 | conda-forge | py39hf3d152e_7 |
jupyter_client | 6.1.11 | conda-forge | pyhd8ed1ab_1 |
jupyter_console | 6.2.0 | conda-forge | py_0 |
jupyter_core | 4.7.0 | conda-forge | py39hf3d152e_0 |
jupyterlab_pygments | 0.1.2 | conda-forge | pyh9f0ad1d_0 |
jupyterlab_widgets | 1.0.0 | conda-forge | pyhd8ed1ab_1 |
kiwisolver | 1.3.1 | conda-forge | py39h1a9c180_1 |
krb5 | 1.17.2 | conda-forge | h926e7f8_0 |
lal | 7.1.2 | conda-forge | mkl_py39he78072e_0 |
lalapps | 7.0.0 | conda-forge | py39h7a3d968_1 |
lalburst | 1.5.6 | conda-forge | h64fcd52_0 |
lalframe | 1.5.2 | conda-forge | h36c2ea0_0 |
lalinference | 2.0.5 | conda-forge | nompi_h4677115_100 |
lalinspiral | 2.0.0 | conda-forge | h64fcd52_0 |
lalmetaio | 2.0.0 | conda-forge | h36c2ea0_0 |
lalpulsar | 2.1.0 | conda-forge | h2914cb3_1 |
lalsimulation | 2.4.0 | conda-forge | h8832763_0 |
lalsuite | 6.57 | conda-forge | py_1 |
lcms2 | 2.11 | conda-forge | hcbb858e_1 |
ld_impl_linux-64 | 2.35.1 | conda-forge | hea4e1c9_1 |
ldas-tools-al | 2.6.5 | conda-forge | h37f9cb6_0 |
ldas-tools-framecpp | 2.7.1 | conda-forge | h7a4f57c_0 |
libblas | 3.9.0 | conda-forge | 7_openblas |
libcblas | 3.9.0 | conda-forge | 7_openblas |
libclang | 11.0.0 | conda-forge | default_ha5c780c_2 |
libcurl | 7.71.1 | conda-forge | hcdd3856_8 |
libedit | 3.1.20191231 | conda-forge | he28a2e2_2 |
libev | 4.33 | conda-forge | h516909a_1 |
libevent | 2.1.10 | conda-forge | hcdb4288_3 |
libffi | 3.3 | conda-forge | h58526e2_2 |
libframel | 8.40.1 | conda-forge | h516909a_2 |
libgcc-ng | 9.3.0 | conda-forge | h5dbcf3e_17 |
libgfortran-ng | 9.3.0 | conda-forge | he4bcb1c_17 |
libgfortran5 | 9.3.0 | conda-forge | he4bcb1c_17 |
libglib | 2.66.4 | conda-forge | h164308a_1 |
libgomp | 9.3.0 | conda-forge | h5dbcf3e_17 |
libiconv | 1.16 | conda-forge | h516909a_0 |
liblal | 7.1.2 | conda-forge | mkl_ha046c46_0 |
liblapack | 3.9.0 | conda-forge | 7_openblas |
libllvm11 | 11.0.0 | conda-forge | he513fc3_0 |
libnghttp2 | 1.41.0 | conda-forge | h8cfc5f6_2 |
libntlm | 1.4 | conda-forge | h7f98852_1002 |
libopenblas | 0.3.12 | conda-forge | pthreads_h4812303_1 |
libpng | 1.6.37 | conda-forge | h21135ba_2 |
libpq | 12.3 | conda-forge | h255efa7_3 |
libsodium | 1.0.18 | conda-forge | h36c2ea0_1 |
libssh2 | 1.9.0 | conda-forge | hab1572f_5 |
libstdcxx-ng | 9.3.0 | conda-forge | h2ae2ef3_17 |
libtiff | 4.2.0 | conda-forge | hdc55705_0 |
libuuid | 2.32.1 | conda-forge | h7f98852_1000 |
libwebp-base | 1.1.0 | conda-forge | h36c2ea0_3 |
libxcb | 1.13 | conda-forge | h7f98852_1003 |
libxkbcommon | 1.0.3 | conda-forge | he3ba5ed_0 |
libxml2 | 2.9.10 | conda-forge | h72842e0_3 |
libxslt | 1.1.33 | conda-forge | h15afd5d_2 |
ligo-common | 1.0.4 | conda-forge | py_0 |
ligo-gracedb | 2.7.6 | conda-forge | pyhd8ed1ab_0 |
ligo-segments | 1.2.0 | conda-forge | py39h07f9747_3 |
ligotimegps | 2.0.1 | conda-forge | py_0 |
llvm-openmp | 11.0.1 | conda-forge | h4bd325d_0 |
lscsoft-glue | 2.0.0 | conda-forge | py39h3811e60_4 |
lxml | 4.6.2 | conda-forge | py39h107f48f_1 |
lz4-c | 1.9.3 | conda-forge | h9c3ff4c_0 |
markdown | 3.3.3 | conda-forge | pyh9f0ad1d_0 |
markuppy | 1.14 | conda-forge | py_0 |
markupsafe | 1.1.1 | conda-forge | py39h3811e60_3 |
matplotlib-base | 3.3.3 | conda-forge | py39h98787fa_0 |
matplotlib-inline | 0.1.2 | conda-forge | pyhd8ed1ab_2 |
metaio | 8.5.1 | conda-forge | hed695b0_1001 |
mistune | 0.8.4 | conda-forge | py39h3811e60_1003 |
mkl | 2019.5 | conda-forge | 281 |
more-itertools | 8.6.0 | conda-forge | pyhd8ed1ab_0 |
mysql-common | 8.0.22 | conda-forge | ha770c72_1 |
mysql-libs | 8.0.22 | conda-forge | h1fd7589_1 |
nbclient | 0.5.1 | conda-forge | py_0 |
nbconvert | 6.0.7 | conda-forge | py39hf3d152e_3 |
nbformat | 5.0.8 | conda-forge | py_0 |
ncurses | 6.2 | conda-forge | h58526e2_4 |
nds2-client | 0.16.6 | conda-forge | hfdf2c5b_1 |
nest-asyncio | 1.4.3 | conda-forge | pyhd8ed1ab_0 |
notebook | 6.1.6 | conda-forge | py39hf3d152e_0 |
nspr | 4.29 | conda-forge | h9c3ff4c_1 |
nss | 3.60 | conda-forge | hb5efdd6_0 |
numpy | 1.19.5 | conda-forge | py39hdbf815f_1 |
olefile | 0.46 | conda-forge | pyh9f0ad1d_1 |
openssl | 1.1.1k | conda-forge | h7f98852_0 |
oyaml | 1.0 | conda-forge | pyhd8ed1ab_0 |
packaging | 20.8 | conda-forge | pyhd3deb0d_0 |
pamela | 1.0.0 | conda-forge | py_0 |
pandas | 1.2.0 | conda-forge | py39hde0f152_0 |
pandoc | 2.11.3.2 | conda-forge | h7f98852_0 |
pandocfilters | 1.4.2 | conda-forge | py_1 |
parso | 0.7.1 | conda-forge | pyh9f0ad1d_0 |
pcre | 8.44 | conda-forge | he1b5a44_0 |
pexpect | 4.8.0 | conda-forge | pyh9f0ad1d_2 |
pickleshare | 0.7.5 | conda-forge | py_1003 |
pillow | 8.1.0 | conda-forge | py39h2bb83ca_1 |
pip | 20.3.3 | conda-forge | pyhd8ed1ab_0 |
pluggy | 0.13.1 | conda-forge | py39hf3d152e_4 |
prometheus_client | 0.9.0 | conda-forge | pyhd3deb0d_0 |
prompt-toolkit | 3.0.10 | conda-forge | pyha770c72_0 |
prompt_toolkit | 3.0.10 | conda-forge | hd8ed1ab_0 |
psycopg2 | 2.8.6 | conda-forge | py39h3da14fd_1 |
pthread-stubs | 0.4 | conda-forge | h36c2ea0_1001 |
ptyprocess | 0.7.0 | conda-forge | pyhd3deb0d_0 |
py | 1.10.0 | conda-forge | pyhd3deb0d_0 |
pycondor | 0.5.0 | conda-forge | py_1 |
pycosat | 0.6.3 | conda-forge | py39h3811e60_1006 |
pycparser | 2.20 | conda-forge | pyh9f0ad1d_2 |
pyerfa | 1.7.1.1 | conda-forge | py39h3811e60_2 |
pygments | 2.7.3 | conda-forge | pyhd8ed1ab_0 |
pyopenssl | 20.0.1 | conda-forge | pyhd8ed1ab_0 |
pyparsing | 2.4.7 | conda-forge | pyh9f0ad1d_0 |
pyqt | 5.12.3 | conda-forge | py39hf3d152e_6 |
pyqt-impl | 5.12.3 | conda-forge | py39h0fcd23e_6 |
pyqt5-sip | 4.19.18 | conda-forge | py39he80948d_6 |
pyqtchart | 5.12 | conda-forge | py39h0fcd23e_6 |
pyqtwebengine | 5.12.1 | conda-forge | py39h0fcd23e_6 |
pyrsistent | 0.17.3 | conda-forge | py39h3811e60_2 |
pyrxp | 2.2.0 | conda-forge | py39h07f9747_1 |
pysocks | 1.7.1 | conda-forge | py39hf3d152e_3 |
pytest | 6.2.1 | conda-forge | py39hf3d152e_1 |
pytest-runner | 5.2 | conda-forge | py_0 |
python | 3.9.5 | pkgs/main | h12debd9_4 |
python-dateutil | 2.8.1 | conda-forge | py_0 |
python-lal | 7.1.2 | conda-forge | mkl_py39h5c75ae1_0 |
python-lalburst | 1.5.6 | conda-forge | py39h16ac069_0 |
python-lalframe | 1.5.2 | conda-forge | py39h16ac069_0 |
python-lalinference | 2.0.5 | conda-forge | nompi_py39hc61b08a_100 |
python-lalinspiral | 2.0.0 | conda-forge | py39h16ac069_0 |
python-lalmetaio | 2.0.0 | conda-forge | py39h16ac069_0 |
python-lalpulsar | 2.1.0 | conda-forge | py39h7f7d743_1 |
python-lalsimulation | 2.4.0 | conda-forge | py39hce5d2b2_0 |
python-ldas-tools-al | 2.6.8 | conda-forge | py39h1a9c180_0 |
python-ldas-tools-framecpp | 2.6.10 | conda-forge | py39hde0f152_0 |
python-ligo-lw | 1.7.1 | conda-forge | py39h3811e60_0 |
python-nds2-client | 0.16.8 | conda-forge | py39hd56b5f2_1 |
python-pegasus-wms | 4.9.3 | conda-forge | py_0 |
python_abi | 3.9 | conda-forge | 1_cp39 |
pytz | 2020.5 | conda-forge | pyhd8ed1ab_0 |
pyyaml | 5.3.1 | conda-forge | py39h3811e60_2 |
pyzmq | 20.0.0 | conda-forge | py39hea8fd45_1 |
qt | 5.12.9 | conda-forge | h9d6b050_2 |
qtconsole | 5.0.1 | conda-forge | pyhd8ed1ab_0 |
qtpy | 1.9.0 | conda-forge | py_0 |
readline | 8.0 | conda-forge | he28a2e2_2 |
requests | 2.25.1 | conda-forge | pyhd3deb0d_0 |
ruamel_yaml | 0.15.80 | conda-forge | py39h3811e60_1004 |
scikit-learn | 0.24.0 | conda-forge | py39h4dfa638_0 |
scipy | 1.6.0 | conda-forge | py39hee8e79c_0 |
send2trash | 1.5.0 | conda-forge | py_0 |
setuptools | 49.6.0 | conda-forge | py39hf3d152e_3 |
six | 1.15.0 | conda-forge | pyh9f0ad1d_0 |
soupsieve | 2.0.1 | conda-forge | py_1 |
sqlalchemy | 1.3.22 | conda-forge | py39h3811e60_1 |
sqlite | 3.35.5 | conda-forge | h74cdb3f_0 |
terminado | 0.9.2 | conda-forge | py39hf3d152e_0 |
testpath | 0.4.4 | conda-forge | py_0 |
threadpoolctl | 2.1.0 | conda-forge | pyh5ca1d4c_0 |
tk | 8.6.10 | conda-forge | h21135ba_1 |
toml | 0.10.2 | conda-forge | pyhd8ed1ab_0 |
tornado | 6.1 | conda-forge | py39h3811e60_1 |
tqdm | 4.56.0 | conda-forge | pyhd8ed1ab_0 |
traitlets | 5.0.5 | conda-forge | py_0 |
tzdata | 2020f | conda-forge | he74cb21_0 |
urllib3 | 1.26.2 | conda-forge | pyhd8ed1ab_0 |
wcwidth | 0.2.5 | conda-forge | pyh9f0ad1d_2 |
webencodings | 0.5.1 | conda-forge | py_1 |
werkzeug | 1.0.1 | conda-forge | pyh9f0ad1d_0 |
wheel | 0.36.2 | conda-forge | pyhd3deb0d_0 |
widgetsnbextension | 3.5.1 | conda-forge | py39hf3d152e_4 |
xorg-libxau | 1.0.9 | conda-forge | h7f98852_0 |
xorg-libxdmcp | 1.1.3 | conda-forge | h7f98852_0 |
xz | 5.2.5 | conda-forge | h516909a_1 |
yaml | 0.2.5 | conda-forge | h516909a_0 |
zeromq | 4.3.3 | conda-forge | h58526e2_3 |
zipp | 3.4.0 | conda-forge | py_0 |
zlib | 1.2.11 | conda-forge | h516909a_1010 |
zstd | 1.4.8 | conda-forge | ha95c52a_1 |