On the command-line

This page was generated with the following command-line call:

$ gw_summary day 20231121 -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&apos;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

Table of packages installed in the production environment
NameVersionChannelBuild_String
_libgcc_mutex0.1conda-forgeconda_forge
_openmp_mutex4.5conda-forge1_llvm
argon2-cffi20.1.0conda-forgepy39hbd71b63_2
astropy4.2.1conda-forgepy39hce5d2b2_0
async_generator1.10conda-forgepy_0
attrs20.3.0conda-forgepyhd3deb0d_0
backcall0.2.0conda-forgepyh9f0ad1d_0
backports1.0conda-forgepy_2
backports.functools_lru_cache1.6.1conda-forgepy_0
beautifulsoup44.9.3conda-forgepyhb0f4dca_0
bleach3.2.1conda-forgepyh9f0ad1d_0
boost-cpp1.74.0conda-forgeh9d3c048_1
boto2.49.0conda-forgepy_0
brotlipy0.7.0conda-forgepy39h38d8fee_1001
bzip21.0.8conda-forgeh7f98852_4
c-ares1.17.1conda-forgeh36c2ea0_0
ca-certificates2021.10.8conda-forgeha878542_0
cached-property1.5.1conda-forgepy_0
certifi2021.10.8conda-forgepy39hf3d152e_1
cffi1.14.4conda-forgepy39he32792d_1
cfitsio3.470conda-forgehb418390_7
chardet4.0.0conda-forgepy39hf3d152e_1
click7.1.2conda-forgepyh9f0ad1d_0
coloredlogs15.0conda-forgepy39hf3d152e_0
conda4.10.1conda-forgepy39hf3d152e_0
conda-package-handling1.7.3conda-forgepy39h3811e60_0
configobj5.0.6conda-forgepy_0
cryptography3.3.1conda-forgepy39h3da14fd_1
cycler0.10.0conda-forgepy_2
cyrus-sasl2.1.27conda-forgeh063b49f_1
dbus1.13.6conda-forgehfdff14a_1
decorator4.4.2conda-forgepy_0
defusedxml0.6.0conda-forgepy_0
dqsegdb1.6.1conda-forgepy39hde42818_1
dqsegdb21.0.1conda-forgepy_0
entrypoints0.3conda-forgepyhd8ed1ab_1003
expat2.2.9conda-forgehe1b5a44_2
fftw3.3.8conda-forgenompi_h8cb7ab2_1114
flask0.12.4conda-forgepy_0
flask-cache0.13.1conda-forgepy_1000
flask-sqlalchemy2.4.0conda-forgepy_0
fontconfig2.13.1conda-forgeh736d332_1003
freetype2.10.4conda-forgeh7ca028e_0
future0.18.2conda-forgepy39hf3d152e_3
gettext0.19.8.1conda-forgeh0b5b191_1005
glib2.66.4conda-forgehcd2ae1e_1
gsl2.6conda-forgehe838d99_1
gst-plugins-base1.14.5conda-forgeh0935bb2_2
gstreamer1.18.2conda-forgeh3560a44_1
gwdatafind1.0.4conda-forgepyh9f0ad1d_1
gwdetchar2.0.5conda-forgepyhd8ed1ab_0
gwosc0.5.6conda-forgepy_0
gwpy2.0.4conda-forgepyhd8ed1ab_1
gwsumm2.1.0conda-forgepyhd8ed1ab_0
gwtrigfind0.8.0conda-forgepy_0
gwvet1.0.0conda-forgepyhd8ed1ab_0
h5py3.1.0conda-forgenompi_py39h25020de_100
hdf51.10.6conda-forgenompi_h6a2412b_1114
healpy1.14.0conda-forgepy39h5cc0c36_2
humanfriendly9.1conda-forgepy39hf3d152e_0
hveto2.0.1conda-forgepyhd8ed1ab_0
icu68.1conda-forgeh58526e2_0
idna2.10conda-forgepyh9f0ad1d_0
importlib-metadata3.4.0conda-forgepy39hf3d152e_0
importlib_metadata3.4.0conda-forgehd8ed1ab_0
iniconfig1.1.1conda-forgepyh9f0ad1d_0
ipykernel5.4.2conda-forgepy39hef51801_0
ipython7.24.1conda-forgepy39hef51801_0
ipython_genutils0.2.0conda-forgepy_1
ipywidgets7.6.3conda-forgepyhd3deb0d_0
itsdangerous1.1.0conda-forgepy_0
jedi0.17.2conda-forgepy39hf3d152e_1
jinja22.11.2conda-forgepyh9f0ad1d_0
joblib1.0.0conda-forgepyhd8ed1ab_0
jpeg9dconda-forgeh36c2ea0_0
jsonschema3.2.0conda-forgepy_2
jupyter1.0.0conda-forgepy39hf3d152e_7
jupyter_client6.1.11conda-forgepyhd8ed1ab_1
jupyter_console6.2.0conda-forgepy_0
jupyter_core4.7.0conda-forgepy39hf3d152e_0
jupyterlab_pygments0.1.2conda-forgepyh9f0ad1d_0
jupyterlab_widgets1.0.0conda-forgepyhd8ed1ab_1
kiwisolver1.3.1conda-forgepy39h1a9c180_1
krb51.17.2conda-forgeh926e7f8_0
lal7.1.2conda-forgemkl_py39he78072e_0
lalapps7.0.0conda-forgepy39h7a3d968_1
lalburst1.5.6conda-forgeh64fcd52_0
lalframe1.5.2conda-forgeh36c2ea0_0
lalinference2.0.5conda-forgenompi_h4677115_100
lalinspiral2.0.0conda-forgeh64fcd52_0
lalmetaio2.0.0conda-forgeh36c2ea0_0
lalpulsar2.1.0conda-forgeh2914cb3_1
lalsimulation2.4.0conda-forgeh8832763_0
lalsuite6.57conda-forgepy_1
lcms22.11conda-forgehcbb858e_1
ld_impl_linux-642.35.1conda-forgehea4e1c9_1
ldas-tools-al2.6.5conda-forgeh37f9cb6_0
ldas-tools-framecpp2.7.1conda-forgeh7a4f57c_0
libblas3.9.0conda-forge7_openblas
libcblas3.9.0conda-forge7_openblas
libclang11.0.0conda-forgedefault_ha5c780c_2
libcurl7.71.1conda-forgehcdd3856_8
libedit3.1.20191231conda-forgehe28a2e2_2
libev4.33conda-forgeh516909a_1
libevent2.1.10conda-forgehcdb4288_3
libffi3.3conda-forgeh58526e2_2
libframel8.40.1conda-forgeh516909a_2
libgcc-ng9.3.0conda-forgeh5dbcf3e_17
libgfortran-ng9.3.0conda-forgehe4bcb1c_17
libgfortran59.3.0conda-forgehe4bcb1c_17
libglib2.66.4conda-forgeh164308a_1
libgomp9.3.0conda-forgeh5dbcf3e_17
libiconv1.16conda-forgeh516909a_0
liblal7.1.2conda-forgemkl_ha046c46_0
liblapack3.9.0conda-forge7_openblas
libllvm1111.0.0conda-forgehe513fc3_0
libnghttp21.41.0conda-forgeh8cfc5f6_2
libntlm1.4conda-forgeh7f98852_1002
libopenblas0.3.12conda-forgepthreads_h4812303_1
libpng1.6.37conda-forgeh21135ba_2
libpq12.3conda-forgeh255efa7_3
libsodium1.0.18conda-forgeh36c2ea0_1
libssh21.9.0conda-forgehab1572f_5
libstdcxx-ng9.3.0conda-forgeh2ae2ef3_17
libtiff4.2.0conda-forgehdc55705_0
libuuid2.32.1conda-forgeh7f98852_1000
libwebp-base1.1.0conda-forgeh36c2ea0_3
libxcb1.13conda-forgeh7f98852_1003
libxkbcommon1.0.3conda-forgehe3ba5ed_0
libxml22.9.10conda-forgeh72842e0_3
libxslt1.1.33conda-forgeh15afd5d_2
ligo-common1.0.4conda-forgepy_0
ligo-gracedb2.7.6conda-forgepyhd8ed1ab_0
ligo-segments1.2.0conda-forgepy39h07f9747_3
ligotimegps2.0.1conda-forgepy_0
llvm-openmp11.0.1conda-forgeh4bd325d_0
lscsoft-glue2.0.0conda-forgepy39h3811e60_4
lxml4.6.2conda-forgepy39h107f48f_1
lz4-c1.9.3conda-forgeh9c3ff4c_0
markdown3.3.3conda-forgepyh9f0ad1d_0
markuppy1.14conda-forgepy_0
markupsafe1.1.1conda-forgepy39h3811e60_3
matplotlib-base3.3.3conda-forgepy39h98787fa_0
matplotlib-inline0.1.2conda-forgepyhd8ed1ab_2
metaio8.5.1conda-forgehed695b0_1001
mistune0.8.4conda-forgepy39h3811e60_1003
mkl2019.5conda-forge281
more-itertools8.6.0conda-forgepyhd8ed1ab_0
mysql-common8.0.22conda-forgeha770c72_1
mysql-libs8.0.22conda-forgeh1fd7589_1
nbclient0.5.1conda-forgepy_0
nbconvert6.0.7conda-forgepy39hf3d152e_3
nbformat5.0.8conda-forgepy_0
ncurses6.2conda-forgeh58526e2_4
nds2-client0.16.6conda-forgehfdf2c5b_1
nest-asyncio1.4.3conda-forgepyhd8ed1ab_0
notebook6.1.6conda-forgepy39hf3d152e_0
nspr4.29conda-forgeh9c3ff4c_1
nss3.60conda-forgehb5efdd6_0
numpy1.19.5conda-forgepy39hdbf815f_1
olefile0.46conda-forgepyh9f0ad1d_1
openssl1.1.1kconda-forgeh7f98852_0
oyaml1.0conda-forgepyhd8ed1ab_0
packaging20.8conda-forgepyhd3deb0d_0
pamela1.0.0conda-forgepy_0
pandas1.2.0conda-forgepy39hde0f152_0
pandoc2.11.3.2conda-forgeh7f98852_0
pandocfilters1.4.2conda-forgepy_1
parso0.7.1conda-forgepyh9f0ad1d_0
pcre8.44conda-forgehe1b5a44_0
pexpect4.8.0conda-forgepyh9f0ad1d_2
pickleshare0.7.5conda-forgepy_1003
pillow8.1.0conda-forgepy39h2bb83ca_1
pip20.3.3conda-forgepyhd8ed1ab_0
pluggy0.13.1conda-forgepy39hf3d152e_4
prometheus_client0.9.0conda-forgepyhd3deb0d_0
prompt-toolkit3.0.10conda-forgepyha770c72_0
prompt_toolkit3.0.10conda-forgehd8ed1ab_0
psycopg22.8.6conda-forgepy39h3da14fd_1
pthread-stubs0.4conda-forgeh36c2ea0_1001
ptyprocess0.7.0conda-forgepyhd3deb0d_0
py1.10.0conda-forgepyhd3deb0d_0
pycondor0.5.0conda-forgepy_1
pycosat0.6.3conda-forgepy39h3811e60_1006
pycparser2.20conda-forgepyh9f0ad1d_2
pyerfa1.7.1.1conda-forgepy39h3811e60_2
pygments2.7.3conda-forgepyhd8ed1ab_0
pyopenssl20.0.1conda-forgepyhd8ed1ab_0
pyparsing2.4.7conda-forgepyh9f0ad1d_0
pyqt5.12.3conda-forgepy39hf3d152e_6
pyqt-impl5.12.3conda-forgepy39h0fcd23e_6
pyqt5-sip4.19.18conda-forgepy39he80948d_6
pyqtchart5.12conda-forgepy39h0fcd23e_6
pyqtwebengine5.12.1conda-forgepy39h0fcd23e_6
pyrsistent0.17.3conda-forgepy39h3811e60_2
pyrxp2.2.0conda-forgepy39h07f9747_1
pysocks1.7.1conda-forgepy39hf3d152e_3
pytest6.2.1conda-forgepy39hf3d152e_1
pytest-runner5.2conda-forgepy_0
python3.9.5pkgs/mainh12debd9_4
python-dateutil2.8.1conda-forgepy_0
python-lal7.1.2conda-forgemkl_py39h5c75ae1_0
python-lalburst1.5.6conda-forgepy39h16ac069_0
python-lalframe1.5.2conda-forgepy39h16ac069_0
python-lalinference2.0.5conda-forgenompi_py39hc61b08a_100
python-lalinspiral2.0.0conda-forgepy39h16ac069_0
python-lalmetaio2.0.0conda-forgepy39h16ac069_0
python-lalpulsar2.1.0conda-forgepy39h7f7d743_1
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python-ligo-lw1.7.1conda-forgepy39h3811e60_0
python-nds2-client0.16.8conda-forgepy39hd56b5f2_1
python-pegasus-wms4.9.3conda-forgepy_0
python_abi3.9conda-forge1_cp39
pytz2020.5conda-forgepyhd8ed1ab_0
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requests2.25.1conda-forgepyhd3deb0d_0
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sqlite3.35.5conda-forgeh74cdb3f_0
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tk8.6.10conda-forgeh21135ba_1
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urllib31.26.2conda-forgepyhd8ed1ab_0
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xz5.2.5conda-forgeh516909a_1
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zlib1.2.11conda-forgeh516909a_1010
zstd1.4.8conda-forgeha95c52a_1