lsstdesc/lsslike

LSS Likelihood module

lsslike's Issues

Improve lss_theory

A possibly not complete list of things that should be implemented in lss_theory.py is:

• generalize to correlation functions. This is not currently in CCL, but will soon be
• convolve multipoles with window function (currently C_ell only computed at band centres)
• add flags for RSDs and magnification (magnification bias should then be passed as parameters too)
• implement a cleverer/more general way of passing parameters (?)
• make bias interpolation order a parameter (?)
• make sure we're happy with the way bias parameters are currently being passed
• do we want to start implementing other tracers (e.g. cmb, lensing...)?
• benchmark likelihood evaluation
• include simple photo-z systematics (?)

CCL compatibility

@slosar sorry to bother you again but can you say what is compatibility requirement between LSSLike and CCL? I am using the CCL version that comes with the DESC stack (v1.0.0), and I am getting errors (e.g. there's no Parameters objects in CCL but lss_theory.py calls it).

SACC compatibility

@slosar can you please say what branch of this repository works with the latest version of SACC? I am working with the current-repo-version of SACC and it doesn't appear to be working with master branch here (or hsc_fit), e.g. I get various errors like AttributeError: module 'sacc' has no attribute 'SACC', AttributeError: type object 'Sacc' has no attribute 'loadFromHDF', AttributeError: 'Sacc' object has no attribute 'precision'.

I can go through the main code and make it compatible with the latest version of SACC but I am wondering if its already been done. Can you please let me know?

Make yaml input to run_CosmoHammer

The interface with run_CosmoHammer needs to be cleaned up.

Add plotting notebooks

Add all notebooks needed to reproduce plots.

No README, LICENSE

We need a README for this project! It should say what the projects goals are, who is working on it, and how the ideas and code in this repository should be cited. We should provide a license, too - there's a good template for LSST DESC projects here.

Example sampler

Hi, so in my effort to get LSSLike to work, I am trying to get the example emcee sampler code to work. When I just try to read in the sim_sample/sims/sim_mean.sacc file, I get an error saying KeyError: "Unable to open object (object 'meta' doesn't exist)", which I assume is arising from a SACC incompatibility.

I then attempted to recreate the sims files using mk_sims.py. After a bit of work, I got it to create a sim_mean.sacc file (though not the rest of them .. yet). While I am able to read the new file, CCL throws an error saying

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-54-7e2c81c37fa2> in <module>
      6 p0[i_b2] = 1.0
      7 p0[i_b3] = 1.0
----> 8 print("This should be a small number: %lE"%(logprob(p0,lk)))

<ipython-input-51-740aec220aed> in logprob(p, lk)
     12                             p[i_b0],p[i_b1],p[i_b2],p[i_b3],
     13                             1.0]})
---> 14     cls=lt.get_prediction(dic)
     15     return lk(cls)

~/LSST/lsstRepos/LSSLike/desclss/lss_theory.py in get_prediction(self, dic_par)
     74         theory_out=np.zeros((self.s.size(),))
     75         cosmo=self.get_cosmo(dic_par)
---> 76         tr=self.get_tracers(cosmo,dic_par)
     77         for i1,i2,_,ells,ndx in self.s.sortTracers() :
     78             cls=ccl.angular_cl(cosmo,tr[i1],tr[i2],ells)

~/LSST/lsstRepos/LSSLike/desclss/lss_theory.py in get_tracers(self, cosmo, dic_par)
     33                                                        z = thistracer.z, n=(zbins, thistracer.Nz), bias = (z_b_arr, b_b_arr)))
     34             else :
---> 35                 raise ValueError("Only \"point\" tracers supported")
     36         return tr_out
     37 

ValueError: Only "point" tracers supported

I'd appreciate some insight into whats going wrong. At this point, I am basically trying to get a SACC object that actually works with LSSLike, and then I can proceed with creating the objects for my own analysis.