Download the code from the
git clone https://github.com/CosmoStat/shear_bias
shear_bias is created. There, call the setup script to install the package.
cd shear_bias python setup.py install
The installation of PySAP has been extensively tested on Ubuntu and macOS, however we cannot guarantee it will work on every operating system (e.g. Windows).
If you encounter any installation issues be sure to go through the following steps before opening a new issue:
- Check that that all of the installed all the dependencies listed above have been installed.
- Read through all of the documentation provided, including the troubleshooting suggestions.
- Check if you problem has already been addressed in a previous issue.
Further instructions are available here.
To install PySAP simply run:
$ pip install python-pysap
Depending on your Python setup you may need to provide the
$ pip install --user python-pysap
To build PySAP locally, clone the repository:
$ git clone https://github.com/CEA-COSMIC/pysap.git
$ python setup.py install
$ python setup.py develop
As before, use the
--user option if needed.
Help with installation on macOS is available here.
Please refer to the PyQtGraph homepage for issues regarding the installation of
Space test of the Equivalence Principle: first results of the MICROSCOPE mission
|Authors:||P. Touboul, G. Metris, M. Rodrigues, Y. André, Q. Baghi, J. Bergé, D. Boulanger, S. Bremer, R. Chhun, B. Christophe, V. Cipolla, T. Damour, P. Danto, H. Dittus, P. Fayet, B. Foulon, P.-Y. Guidotti, E. Hardy, P.-A. Huynh, C. Lämmerzahl, V. Lebat, F. Liorzou, M. List, I. Panel, S. Pires, B. Pouilloux, P. Prieur, S. Reynaud, B. Rievers, A. Robert, H. Selig, L. Serron, T. Sumner, P. Viesser|
|Journal:||Classical and Quantum Gravity|
|Download:||ADS | arXiv | Fait Marquant|
The Weak Equivalence Principle (WEP), stating that two bodies of different compositions and/or mass fall at the same rate in a gravitational field (universality of free fall), is at the very foundation of General Relativity. The MICROSCOPE mission aims to test its validity to a precision of 10^-15, two orders of magnitude better than current on-ground tests, by using two masses of different compositions (titanium and platinum alloys) on a quasi-circular trajectory around the Earth. This is realised by measuring the accelerations inferred from the forces required to maintain the two masses exactly in the same orbit. Any significant difference between the measured accelerations, occurring at a defined frequency, would correspond to the detection of a violation of the WEP, or to the discovery of a tiny new type of force added to gravity. MICROSCOPE's first results show no hint for such a difference, expressed in terms of Eötvös parameter = [-1 +/- 9(stat) +/- 9 (syst)] x 10^-15 (both 1 uncertainties) for a titanium and platinum pair of materials. This result was obtained on a session with 120 orbital revolutions representing 7% of the current available data acquired during the whole mission. The quadratic combination of 1 uncertainties leads to a current limit on of about 1.3 x 10^-14.
|Authors:||K. Benabed, M. Kilbinger et al.|
|Description:||Population Monte-Carlo (PMC) sampling code, for fast end efficient parallel iterative importance sampling to compute integrals over the posterior including the Bayesian evidence.
|Notes:||Requires gsl and fftw libraries. A MPI C compiler is recommended.
This repository contains the code and data used to produce the results in A. Peel et al. (2018), arXiv:1810.11030.
The Convolutional Neural Network (CNN) is implemented in Keras using TensorFlow as backend. Since the DUSTGRAIN-pathfinder simulations are not yet public, we are not able to include the original convergence maps obtained from the various cosmological runs. We do provide, however, the wavelet PDF datacubes derived for the four models as described in the paper: one standard LCDM and three modified gravity f(R) models.
- Python 3
- Keras with Tensorflow as backend
$ python3 train_mgcnn.py -n0
The three options for the noise flag "-n" are (0, 1, 2), which correspond to noise standard deviations of sigma = (0, 0.35, 0.70) added to the original convergence maps. Additional options are "-i" and "-e" for the number of training iterations and epochs, respectively.
Confusion matrices and evaluation metrics (loss function and validation accuracy) are saved as numpy arrays in the generated output/ directory after each iteration.