LabSpec: rezonans stochastyczny
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Updated
Jun 29, 2016 - Python
LabSpec: rezonans stochastyczny
To analyze physiology data with power spectra of average and residuals.
Python library for interstellar medium (ISM) analysis.
Accurate predictions for the clustering of galaxies in redshift-space in Python
Python functions to calculate the FFT and autocorrelation function using GPU (Cuda)
Libraries to analyze numerical simulations
Gaussian Process Inference
Overall process of speech signal processing (Mel-spectrogram & MFCCs) and loading data using Pytorch dataloader
BINGO: BI-spectra and Non-Gaussianity Operator is a FORTRAN 90 code that numerically evaluates the scalar bi-spectrum and the non-Gaussianity parameter fNL in single field inflationary models involving the canonical scalar field.
Εxercises for Advanced Signal Processing Methods course in Faculty of Engineering of Aristotle's University of Thessaloniki
Codes of some useful statistical quantities in astrophysics and cosmology
Python binding allowing to retrieve audio levels by frequency bands given audio samples (power spectrum in fact), on a raspberry pi, using GPU FFT
This is a modified version of the L-PICOLA code extending the COLA approach for simulating cosmological structure formation to theories that exhibit scale-dependent growth. It can compute matter power-spectra (CDM and total), redshift-space multipole power-spectra P0,P2,P4 and do halofinding on the fly. It can also include the effects of massive…
Precision Radio Interferometry Simulator (for radio astronomy applications)
Replication code for simulating and estimation by GMM of DSGE models with higher-order statistics
Replication code for checking identification in nonlinear pruned DSGE models with Gaussian or Student's t distributed errors
Colored/White Guassian Noise Removal via Adaptive Thresholding in Curvelet Domain
Power spectrum analysis on the LCMD cosmology, using CLASS
power spectra on the masked sky
This web app allows you to decompose your signal data or time series using FFT and gives the opportunity to interactively investigate the signal and its spectrum (frequency spectrum, power spectrum, periodogram, and its power spectral density) using the advantage of Plotly package.
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