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[ascl:1302.013]
NIFTY: A versatile Python library for signal inference

Selig, Marco; Bell, Michael R.; Junklewitz, Henrik; Oppermann, Niels; Reinecke, Martin; Greiner, Maksim; Pachajoa, Carlos; Ensslin, Torsten A.

NIFTY (Numerical Information Field TheorY) is a versatile library enables the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTY offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTY permits rapid prototyping of algorithms in 1D and then the application of the developed code in higher-dimensional settings of real world problems. NIFTY operates on point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those.

[ascl:1504.018]
D3PO: Denoising, Deconvolving, and Decomposing Photon Observations

D3PO (Denoising, Deconvolving, and Decomposing Photon Observations) addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. A hierarchical Bayesian parameter model is used to discriminate between morphologically different signal components, yielding a diffuse and a point-like signal estimate for the photon flux components.

[ascl:1601.020]
ProC: Process Coordinator

Hovest, Wolfgang; Knoche, Jörg; Hell, Reinhard; Doerl, Uwe; Riller, Thomas; Matthai, Frank; Ensslin, Torsten A.; Rachen, Jörg; Robbers, Georg; Adorf, Hans-Martin; Reinecke, Martin; Bartelmann, Matthias

ProC (short for Process Coordinator) is a versatile workflow engine that allows the user to build, run and manage workflows with just a few clicks. It automatically documents every processing step, making every modification to data reproducible. ProC provides a graphical user interface for constructing complex data processing workflows out of a given set of computer programs. The user can, for example, specify that only data products which are affected by a change in the input data are updated selectively, avoiding unnecessary computations. The ProC suite is flexible and satisfies basic needs of data processing centers that have to be able to restructure their data processing along with the development of a project.

[ascl:2203.010]
D2O: Distributed Data Object

D2O acts as a layer of abstraction between algorithm code and data-distribution logic to manage cluster-distributed multi-dimensional numerical arrays; this provides usability without losing numerical performance and scalability. D2O's global interface makes the cluster node's local data directly accessible for use in customized high-performance modules. D2O is written in Python; the code is portable and easy to use and modify. Expensive operations are carried out by dedicated external libraries like numpy and mpi4py and performance scales well when moving to an MPI cluster. In combination with NIFTy, D2O enables supercomputer based astronomical imaging via RESOLVE (ascl:1505.028) and D3PO (ascl:1504.018).

[ascl:1703.015]
Charm: Cosmic history agnostic reconstruction method

Charm (cosmic history agnostic reconstruction method) reconstructs the cosmic expansion history in the framework of Information Field Theory. The reconstruction is performed via the iterative Wiener filter from an agnostic or from an informative prior. The charm code allows one to test the compatibility of several different data sets with the LambdaCDM model in a non-parametric way.

[ascl:1805.009]
STARBLADE: STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission

STARBLADE (STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission) separates superimposed point-like sources from a diffuse background by imposing physically motivated models as prior knowledge. The algorithm can also be used on noisy and convolved data, though performing a proper reconstruction including a deconvolution prior to the application of the algorithm is advised; the algorithm could also be used within a denoising imaging method. STARBLADE learns the correlation structure of the diffuse emission and takes it into account to determine the occurrence and strength of a superimposed point source.

[ascl:1903.008]
NIFTy5: Numerical Information Field Theory v5

Arras, Philipp; Baltac, Mihai; Ensslin, Torsten A.; Frank, Philipp; Hutschenreuter, Sebastian; Knollmueller, Jakob; Leike, Reimar; Newrzella, Max-Niklas; Platz, Lukas; Reinecke, Martin; Stadler, Julia

NIFTy (Numerical Information Field Theory) facilitates the construction of Bayesian field reconstruction algorithms for fields being defined over multidimensional domains. A NIFTy algorithm can be developed for 1D field inference and then be used in 2D or 3D, on the sphere, or on product spaces thereof. NIFTy5 is a complete redesign of the previous framework (ascl:1302.013), and requires only the specification of a probabilistic generative model for all involved fields and the data in order to be able to recover the former from the latter. This is achieved via Metric Gaussian Variational Inference, which also provides posterior samples for all unknown quantities jointly.

[ascl:2312.004]
DENSe: Bayesian density estimation for Poisson data

DENSe enables Bayesian non-parametric inferences of densities of Poisson data counts. Its framework of stateless methods is written in Python, although it relies on NIFTy (ascl:1302.013, ascl:1903.008) for the heavy lifting. DENSe utilizes all available information in the data by modeling the inherent correlation structure using a Matérn kernel. The inference of the density from count data can be written in a single line of python code. The fitting method takes a multidimensional numpy array as input and returns multidimensional arrays of the same dimensions encoding the density field.