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This repository contains data and code used in the analysis presented in Muir, Adhikari, and Huterer 2018, "The Covariance of CMB anomalies." Note that the directory structure and data presented here is arranged for transparancy, and is NOT in the format that the code was actually run. Because of that, the code will not work out of the box if you clone the repository. Rather, it is provided as a reference. ---------------------- PYTHON SCRIPT The script measure_anomalystats.py does most of the calculation. The general outline of this file is: - utility functions for handling healpix maps (downgrading maps & masks, filename handling, 2pt function measurement, map simulation using synfast, etc) - functions for measuring features associated with anomalies - functions for obtaining & handling feature meas. from many realizations - functions for plotting 1d hists and 2d triangle scatter - functions for measuring covariance matrix and doing PCA analysis - the main() function, which has calls to functions for generating data used in analysis. ---------------------- ANALYSIS PROCEDURE The anomaly covariance analysis used this code as follows: - The main() function in measure_anomalystats.py (at the bottom of this file) was used to generate & analyze simulated maps to get anomaly measurements for all desired realizations. There's a hack there, where bool switches "if 0:" or "if 1" are used to manually turn off and on parts of the calculation (if e.g. only one part needs to be rerun). -> The workhorse function used by that script is run_manystats which, given a list of stat names calls the necessary functions to compute them for all realizations, and handles file-naming. - Once the stat data is generated, a jupyter notebook (not included in this repo) was used to analyze data and do plotting. It imports this python file as a module, and so uses some of the covariance calculation and plotting functions. Again: The script is set up to expect specific filename structures, and expects folders to be in a specific arrangement (as they are on my [Jessie's] computer). Because of this, it won't work as a self-contained script, but the functions defined in it could be useful, either if it is imported as a module in another pythons script, or simply for reference to see how we've computed the various anomaly features. ---------------------- DATA PROVIDED ........................................ _stats directories: The various directories whose names and in 'stats' contain anomaly feature measurements obtained from our 100,000 synfast simulations, and 1000 SMICA separated FFP8.1 maps provided on the Planck Legacy Archive. In the directory names: - lcdm means synfast simulations (described in paper) - lcdm+DQ are the same set of synfast simulations, with a dopper quadrupole added according to Table 3 of Copi et al arXiv:1311.4562 - smicaffp means the SMICA separated FFP8.1 sims, downgraded to NSIDE=64 - fullsky means that the analysis is done with pseudo-cl measurements of the full sky - UT78 means that the UT78 mask was used, downgraded to NSIDE=64 In each directory, the data for each stat appear in text files, which have the realization number in the first column, and the stat measurement in the second column. They are split into several smaller files (due to how I handled multiprocessing when creating them); the numbers in each filename show the range of realizaiton numbers in each file is in the filename. For memory considerations, the full cl and c(theta) measurements are not included, nor are the actual fits files for the maps, but these could could be shared upon request. ........................................ COVMATS directory This directory contains the feature covariances measured from the data in the stats directories. In the covmats directory there are a number of files. - The files with the tags -0mean-nounits-unnorm are the covariance matrices. > 0mean means the mean over realizations for each quantity is subtracted off before computing covmat > nounits means the data is divided by the standard deviation over realizations for each quantity before computing covmat > unnorm just means that the numpy.cov function is used to compute the covariance matrix, rather than numpy.corrcoef. (Though, since the data are preprocessed by subtracting the mean and dividing by the standard deviation before computing the covariances, the covariance and correlation coefficients will actually be identical.) > the suffix 'lcdm' means they were computed using our fiducial synfast sims > the suffix 'ffp' means they were computed using the FFP8.1 sims > the suffix 'lcdm+DQ' means they were computed with the synfast sims with a doppler quadrupole contribution added - covmat-samplevar_lcdm_N01000-r00000-99999.dat - contains the sample variance of the covariance measured from 1000 sample realizations of the synfast (lcdm) sims. The 100 subsampled covmats used to compute this are in the directory lcdmcov_N1000_subsample. (Versions with +DQ mean Doppler quadrupole has been added) - covmat-eigendata_lcdm/ffp.dat - contains eigenvalues of corresponding covariance matrix in first row, eigen vectors (so PCs) below that. ---------------------- Feel free to contact me if you have questions about the data or code in this directory! Script maintained by Jessie Muir ([email protected] or [email protected]). Docstring last updated June 6, 2018.
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