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pyfisher's Introduction

pyfisher

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Documentation Status

Fisher forecasting for cosmological surveys

pyfisher is a python package for calculating Fisher matrices and for forecasting parameter uncertainties for cosmological surveys.

๐ŸŸฅ Version 2 is a total revamp, so if you're used to what this software looked like before November 2020, you should switch to the ``legacy`` branch.

While the new version does not (yet) provide an interface for CMB lensing noise curves with iterative delensing like the old one did, it has a simplified API, lots of pre-calculated Fishers, and a tool to reparametrize into a ฯƒ8 parameterization.

Installation

Install in two steps:

  1. Git clone the repository
  2. cd into the repository and run pip install -e . --user.

The latter step just copies symbolic links to the relevant modules into a directory (managed by pip) that is in your python path.

Once this is done, you should be able to do things like

import pyfisher

from anywhere on your system.

Basic Usage

See and run python tests/test_lensing.py to reproduce Planck constraints and get a feel for how to use this package.

pyfisher's People

Contributors

honamnguyen avatar msyriac avatar nbatta avatar pyup-bot avatar sjforeman avatar ybh0822 avatar

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pyfisher's Issues

Initial Update

The bot created this issue to inform you that pyup.io has been set up on this repo.
Once you have closed it, the bot will open pull requests for updates as soon as they are available.

Does num_massive_neutrinos matter?

We need to check if num_massive_neutrinos of 1 (CAMB default) vs. 3 (Planck analysis) makes any significant difference to derivatives and/or Fisher forecasts. Easiest thing to do first would be to make new derivs with num_massive_neutrinos = 3 and see if you get the same constraints for a set of experiments that includes lensing Auto information from high-ell.

Compact parametrization doesn't exactly reproduce non-compact

I'm allowing for two ways to calculate the Fisher matrix, which under its current implementation should be identical for the case where no lensing data is added. The default is the compact = False version which uses FisherForecast.CovFromVecs. This is just an implementation of Eq.A.4 of 1509.07471 (or more explicitly, Eq 3 of 1109.6322). The compact = True option uses FisherForecast.CovFromVecsSmall, which is easier to extend to datasets involving cross-correlations with optical data (Eq.4 of 1402.4108). However, the numbers don't perfectly agree, so there's likely a bug somewhere.

PyFisher lensInterface Issue(s)

We noticed that there were discrepancies between polarization estimator noise curves from pyfisher/lensInterface.lensNoise() and orphics.lensing.lensing_noise() functions.

nltest

Here the dashed lines are from lensInterface.lensNoise and the solid lines are from lensing.lensing_noise(). I am opening this issue to track the changes needed to be made to lensInterface.py

  1. L min for Kappa is hard-coded (https://github.com/msyriac/pyfisher/blob/master/pyfisher/lensInterface.py#L51)

  2. L max for Kappa set in param.ini is not being used (https://github.com/msyriac/pyfisher/blob/master/pyfisher/lensInterface.py#L55)

  3. Wrong function signature for getNlIterative (https://github.com/msyriac/pyfisher/blob/master/pyfisher/lensInterface.py#L68)

  • call from lensInterface.py
    myNls.getNlIterative(pols,kellmin,kellmax,tellmax,pellmin,pellmax,dell=dell,halo=True,plot=plot)

  • function definition in orphics/lensing.py
    def getNlIterative(self,polCombs,pellmin,pellmax,dell=20,halo=True,dTolPercentage=1.,verbose=True,plot=False,max_iterations=np.inf,eff_at=60,kappa_min=0,kappa_max=np.inf):

  1. Polarization l min and l max are not used in getNlIterative (It is probably better to open an issue about this on orphics repo).
    (https://github.com/msyriac/orphics/blob/master/orphics/lensing.py#L1218)

Step sizes for r and w

We should add derivatives w.r.t. tensor-scalar-ratio r and the equation of state of dark energy w, and also its dynamical component w_a. It would be nice to have a plot of C_ell around each parameter for a few representative ells, with the final chosen step size clearly shown (showing both linearity and numerical stability).

Galaxy spectra

Add cosmic shear and CMB x shear spectra for forecasting.

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