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<title>atoolbox.git/astro/marx, branch master</title>
<subtitle>Personal toolbox
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<entry>
<title>Update marx/randpoints.py</title>
<updated>2016-01-18T05:38:57+00:00</updated>
<author>
<name>Aaron LI</name>
<email>aaronly.me@gmail.com</email>
</author>
<published>2016-01-18T05:38:57+00:00</published>
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<entry>
<title>Add `randpoints.py` to generate point source informations</title>
<updated>2015-12-12T15:01:02+00:00</updated>
<author>
<name>Aaron LI</name>
<email>aaronly.me@outlook.com</email>
</author>
<published>2015-12-12T15:01:02+00:00</published>
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<entry>
<title>Updated astro tools.</title>
<updated>2015-06-16T08:46:23+00:00</updated>
<author>
<name>Aaron LI</name>
<email>aaronly.me@outlook.com</email>
</author>
<published>2015-06-16T08:46:23+00:00</published>
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<id>9c7782686fd607fd4e38038ed44f0307da0cd8c7</id>
<content type='text'>
* fit_betasbp_cut.py
  - fixed 'dof' calculation with 'n-p-1'
  - fixed 'par_eps' calculation/configuration
  - added 'fit_model_bounds' using 'scipy.optimize.minimize' to
    perform function minimization with bounds
  - split the data cut section to function 'cut_data'
  - added argument 'options' to 'fit_model_bounds'
* marx_pntsrc.py
  Run MARX simulation on a given list of point sources, merge the
  output simulation results, and finally convert into FITS image.
* blanksky_add_time.py
  Add a time column for the chandra blanksky event file.
  The time data are generated with a uniform distribution
  between TSTART and TSTOP.
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<pre>
* fit_betasbp_cut.py
  - fixed 'dof' calculation with 'n-p-1'
  - fixed 'par_eps' calculation/configuration
  - added 'fit_model_bounds' using 'scipy.optimize.minimize' to
    perform function minimization with bounds
  - split the data cut section to function 'cut_data'
  - added argument 'options' to 'fit_model_bounds'
* marx_pntsrc.py
  Run MARX simulation on a given list of point sources, merge the
  output simulation results, and finally convert into FITS image.
* blanksky_add_time.py
  Add a time column for the chandra blanksky event file.
  The time data are generated with a uniform distribution
  between TSTART and TSTOP.
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