From 27d8c4ea322a2a3be2e838d92788e2324ed271ad Mon Sep 17 00:00:00 2001 From: Justine Date: Tue, 5 Mar 2024 21:04:10 +0100 Subject: [PATCH 1/4] updt readme --- README.md | 84 ++++++++++++++++++++---------- img/compare_contour_plot.png | Bin 255894 -> 0 bytes img/convergence_map.png | Bin 789593 -> 0 bytes img/convergence_map_lognormal.png | Bin 0 -> 76937 bytes img/convergence_map_lpt.png | Bin 0 -> 66507 bytes 5 files changed, 56 insertions(+), 28 deletions(-) delete mode 100644 img/compare_contour_plot.png delete mode 100644 img/convergence_map.png create mode 100644 img/convergence_map_lognormal.png create mode 100644 img/convergence_map_lpt.png diff --git a/README.md b/README.md index 2169fd1..1c4f919 100644 --- a/README.md +++ b/README.md @@ -14,29 +14,15 @@
-`sbi_lens` provides a diferentiable log-normal mass map simulator with 5 tomographic redshift bins and 6 cosmological parameters to infer ($\Omega_c, \Omega_b, \sigma_8, n_s, w_0, h_0$). The shift parameter is computed with [CosMomentum](https://github.com/OliverFHD/CosMomentum) and depends on $\Omega_c, \sigma_8, w_0$. - -Note: only LSST year 10 implemented for the moment. - -
- -
- -Due to the non-linear growth of structures in the universe, the density cosmological field is expecteed to be highly non-Gaussian. Therefore log-normal fields which account for non-Guassianities, provide a more realistic representation of the late-time field. The figure below emphasizes this claim by comparing the posterior obtained from power spectrum analysis (which captures only the Gaussian signal) against the one obtained from full field analysis performed through HMC (which extracts the entire signal). - -

- -

+`sbi_lens` provides a range of lensing forward models, including Gaussian model, log-normal model and LPT model. Powered by JAX, our codebase is fast and fully differentiable. +It is specifically designed to match LSST Y10 survey setting, but it can be easily adapted.

- # Installation ```sh @@ -44,27 +30,27 @@ pip install git+https://github.com/DifferentiableUniverseInitiative/sbi_lens.git ``` # Quick example -``` python - +```python # load lsst year 10 settings from sbi_lens.config import config_lsst_y_10 -N = config_lsst_y_10.N -map_size = config_lsst_y_10.map_size sigma_e = config_lsst_y_10.sigma_e gals_per_arcmin2 = config_lsst_y_10.gals_per_arcmin2 nbins = config_lsst_y_10.nbins a = config_lsst_y_10.a b = config_lsst_y_10.b z0 = config_lsst_y_10.z0 +``` + +``` python # define lsst year 10 log normal model from sbi_lens.simulator.LogNormal_field import lensingLogNormal -model = partial( +model_log_normal = partial( lensingLogNormal, - N=N, - map_size=map_size, + N=60, + map_size=5, gal_per_arcmin2=gals_per_arcmin2, sigma_e=sigma_e, nbins=nbins, @@ -80,23 +66,65 @@ model = partial( from sbi_lens.simulator.utils import get_samples_and_scores (log_prob, samples), gradients = get_samples_and_scores( - model, + model_log_normal, + PRNGKey(0), + batch_size=1, + with_noise=False +) +map_example_log_normal = samples['y'] +``` + +``` python +for i in range(5): + subplot(1,5, i+1) + imshow(map_example_log_normal[0][...,i], cmap='cividis') + title('Bin %d'%(i+1)) + axis('off') +``` +

+ +

+ + +``` python +# define lsst year 10 lpt model +from sbi_lens.simulator.Lpt_field import lensingLpt +model_lpt = partial( + lensingLpt, + N=60, + map_size=5, + box_size=[400.0, 400.0, 4000.0], + box_shape=[300, 300, 128], + gal_per_arcmin2=gals_per_arcmin2, + sigma_e=sigma_e, + nbins=nbins, + a=a, + b=b, + z0=z0, + with_noise=False, +) + +# simulate one mass map +from sbi_lens.simulator.utils import get_samples_and_scores + +(log_prob, samples), gradients = get_samples_and_scores( + model_lpt, PRNGKey(0), batch_size=1, with_noise=False ) -map_example = samples['y'] +map_example_lpt = samples['y'] ``` ``` python for i in range(5): subplot(1,5, i+1) - imshow(map_example[0][...,i], cmap='cividis') + imshow(map_example_lpt[0][...,i], cmap='cividis') title('Bin %d'%(i+1)) axis('off') ```

- +

Check out a full example here: [![colab link](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pSjhrOJbVi80RQlsVz2oXhVAtxwBhSbn?usp=sharing) diff --git a/img/compare_contour_plot.png b/img/compare_contour_plot.png deleted file mode 100644 index d2090dfff62331b3b132ebb2969a0788681e2ce6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 255894 zcmeFY_d8r))IK~jdYvQ^y%Quv8N#TOL_~;^AWC#bi{3jUB#7RLK@vekP1GQakLW`5 z&M-mr-rIYIJkR@ne|!Ic?_8H_&Na?Cd#$xsx%a)-4u7hqNJY*_4g!Iwlpa6S0D%bM z_&-u&;Fpyvb}_)0q?>}So2H|so2RLZ1xVG@&B@Nu&CbS*-NVAg)yC05R8UM%RDj*u z&CSVGN=V55e{T?Ubg>fZ%6!-ZyoAi@v7Rdk1U1F~fj=YiY(QWTNa^8yEid%Sq^F<$ z;OY71aBumX{b=_T88`CUf4Q;3O?>4~rGolqD&3xX_601iRpr#aJhHV}rmo?e8>c0| z4zURNK^CI&&C^VO>dE%WSvAa=dHtreH-Zhyu{h=0bNV=0bxm=>FZ$~c=f4l^-S<8g zRM_RG?cS|Mj(`8i&b-F_Kld#C-=8l`@qcY{=@GF1KMuh=34XlJU`{E_YW>(#z(uPQ zyr*YtUqf*9;A(4I?z$HgvsHkImx$_rX5 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Y_+bs#*xK^uAK&W1eH)WTqi4VW2j& Date: Tue, 5 Mar 2024 21:07:27 +0100 Subject: [PATCH 2/4] minor change --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1c4f919..87ee167 100644 --- a/README.md +++ b/README.md @@ -42,8 +42,8 @@ b = config_lsst_y_10.b z0 = config_lsst_y_10.z0 ``` +**Log-Normal Simulation** ``` python - # define lsst year 10 log normal model from sbi_lens.simulator.LogNormal_field import lensingLogNormal @@ -86,6 +86,7 @@ for i in range(5):

+**LPT Simulation** ``` python # define lsst year 10 lpt model from sbi_lens.simulator.Lpt_field import lensingLpt From a41c23ee9b3e65f53239ad2ceedb407d517bea96 Mon Sep 17 00:00:00 2001 From: Justine Date: Tue, 5 Mar 2024 21:13:33 +0100 Subject: [PATCH 3/4] format --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 87ee167..b4281b8 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ `sbi_lens` provides a range of lensing forward models, including Gaussian model, log-normal model and LPT model. Powered by JAX, our codebase is fast and fully differentiable. -It is specifically designed to match LSST Y10 survey setting, but it can be easily adapted. +It is specifically designed to match LSST Y10 survey setting, but it can be easily adapted.

@@ -42,7 +42,7 @@ b = config_lsst_y_10.b z0 = config_lsst_y_10.z0 ``` -**Log-Normal Simulation** +**Log-Normal Simulation** ``` python # define lsst year 10 log normal model from sbi_lens.simulator.LogNormal_field import lensingLogNormal @@ -86,7 +86,7 @@ for i in range(5):

-**LPT Simulation** +**LPT Simulation** ``` python # define lsst year 10 lpt model from sbi_lens.simulator.Lpt_field import lensingLpt From b48eea3122502632b859148547d4c1648fe05a8e Mon Sep 17 00:00:00 2001 From: Justine Date: Tue, 5 Mar 2024 21:24:38 +0100 Subject: [PATCH 4/4] minor changes --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b4281b8..25558ce 100644 --- a/README.md +++ b/README.md @@ -18,8 +18,7 @@ -`sbi_lens` provides a range of lensing forward models, including Gaussian model, log-normal model and LPT model. Powered by JAX, our codebase is fast and fully differentiable. -It is specifically designed to match LSST Y10 survey setting, but it can be easily adapted. +`sbi_lens` provides a range of lensing forward models, including Gaussian model, log-normal model and LPT model. Powered by JAX, our codebase is fast and fully differentiable. It is specifically designed to match LSST Y10 survey setting, but it can be easily adapted.

@@ -42,6 +41,7 @@ b = config_lsst_y_10.b z0 = config_lsst_y_10.z0 ``` + **Log-Normal Simulation** ``` python # define lsst year 10 log normal model @@ -128,7 +128,7 @@ for i in range(5):

-Check out a full example here: [![colab link](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pSjhrOJbVi80RQlsVz2oXhVAtxwBhSbn?usp=sharing) +Check out an example of the cool things you can do with `sbi_lens` here: [![colab link](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pSjhrOJbVi80RQlsVz2oXhVAtxwBhSbn?usp=sharing) # Contributors