<commit_before>:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

------------------------------------------------
Using Python with Smoke and JWST Mirrors
------------------------------------------------

.. class:: abstract

    We will describe how the Space Telescope Science Institute is
    using Python in support of the next large space telescope, the James Webb
    Space Telescope (JWST). We will briefly describe the 6.5 meter
    segmented-mirror infra-red telescope, currently planned for a
    2014 launch, and its science goals.  Our experience with Python
    has already been employed to study the variation of the mirror
    and instrument support structures during cyrogenic cool-down from
    ambient temperatures to 30 Kelvin with accuracies better than
    10 nanometers using a speckle interferometer. Python was used to
    monitor, process (initially in near real-time) and analyze over 15
    TB of data collected. We are currently planning a metrology test
    that will collect 10 TB of data in 7 minutes. 
    We will discuss the advantages of using
    Python for each of these projects.


Introduction
------------

.. figure:: ariane5launch_sm.png

  An Ariane 5 launch similar to the launcher that will be used to 
  place JWST into orbit about the L2 point. 

The James Webb Space Telescope (JWST) will be NASA's next Great
Observatory.  It will be an infrared-optimized telescope with a
6.5m primary mirror made up of 18-separate segments 
which will be launched in 2014<commit_msg>EDIT: Hack: Add ESA credit for Ariane photo, as per their webpage guideline.<commit_after>:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

:author: <NAME>
:email: <EMAIL>
:institution: Space Telescope Science Institute

------------------------------------------------
Using Python with Smoke and JWST Mirrors
------------------------------------------------

.. class:: abstract

    We will describe how the Space Telescope Science Institute is
    using Python in support of the next large space telescope, the James Webb
    Space Telescope (JWST). We will briefly describe the 6.5 meter
    segmented-mirror infra-red telescope, currently planned for a
    2014 launch, and its science goals.  Our experience with Python
    has already been employed to study the variation of the mirror
    and instrument support structures during cyrogenic cool-down from
    ambient temperatures to 30 Kelvin with accuracies better than
    10 nanometers using a speckle interferometer. Python was used to
    monitor, process (initially in near real-time) and analyze over 15
    TB of data collected. We are currently planning a metrology test
    that will collect 10 TB of data in 7 minutes. 
    We will discuss the advantages of using
    Python for each of these projects.


Introduction
------------

.. figure:: ariane5launch_sm.png

  An Ariane 5 launch similar to the launcher that will be used to 
  place JWST into orbit about the L2 point. Photo: ESA.

The James Webb Space Telescope (JWST) will be NASA's next Great
Observatory.  It will be an infrared-optimized telescope with a
6.5m primary mirror made up of 18-separate segments 
which will be launched in 2014
{'fraction_non_alphanumeric': 0.10399562123700054, 'fraction_numerical': 0.014230979748221127, 'mean_word_length': 3.034216335540839, 'pattern_counts': {'":': 0, '<': 19, '<?xml version=': 0, '>': 19, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5252688', 'n_tokens_mistral': 1001, 'n_tokens_neox': 886, 'n_words': 483}