Update installation (#113)

* update explicit packages to work with cosmopower * update installation instructions * compressed parameters tutorial update * update hierarchy * update hierarchy * update hierarchy * Update README.md LaCE instead of Cup1d for notebooks * remove conda-forge * add metric information --------- Co-authored-by: Laura Cabayol Garcia <[email protected]> Co-authored-by: Andreu Font-Ribera <[email protected]> Co-authored-by: Laura Cabayol Garcia <[email protected]>
igmhub · Nov 22, 2024 · 8d043dd · 8d043dd
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diff --git a/README.md b/README.md
@@ -8,13 +8,19 @@ LaCE contains a set of emulators for the one-dimensional flux power spectrum of
 
 Please cite at least https://arxiv.org/abs/2305.19064 if you use this emulator in your research.
 
+There is a documentation website with installation instructions and relevant descriptions at [here](https://igmhub.github.io/LaCE/
+
 ## Installation
-(Last updated: Jan 19 2024)
+(Last updated: Nov 19 2024)
+
+LaCE contains a submodule to estimate compressed parameters from the power spectrum that uses cosmopower. The LaCE installation is slightly different depending on whether you want to use cosmopower or not.
+
+#### LaCE without cosmopower
 
 - Create a new conda environment. It is usually better to follow python version one or two behind. In January 2024, the latest is 3.12, so we recommend 3.11. If you want to use LaCE with cosmopower, as of November 2024 you need to install python 3.10. Please look at the cosmopower installation before proceeding with the LaCE installation.
 
 ```
-conda create -n lace -c conda-forge python=3.11 camb fdasrsf
+conda create -n lace -c conda-forge python=3.11 pip 
 conda activate lace
 pip install --upgrade pip
 ```
@@ -30,28 +36,36 @@ pip install -e .
 - If you find problems, please install LaCE as follows:
 
 ```
-pip install -e .[explicit]
+pip install -e ".[explicit]"
 ```
 
-- If you want to use cosmopower, you need to use python 3.10 and install the cosmopower package. You must install cosmopower before installing LaCE. You can do this by running:
+#### LaCE with cosmopower
+
+- Create a new conda environment. 
 
 ```
-pip install cosmopower
+conda create -n lace python=3.11 pip 
+conda activate lace
+pip install --upgrade pip
 ```
-and install:
+
+- Install cosmopower:
 ```
-pip install -e .
-pip install pyDOE2
-pip install git+https://github.com/igmhub/cup1d.git
+pip install cosmopower pyDOE
 ```
 
-#### Tests
-
-Please run the following script to check that the package is working properly.
+- Clone the repo into your machine and perform an *editable* installation:
+```
+git clone https://github.com/igmhub/LaCE.git
+cd LacE
+``` 
 
+- Install LaCE using the installation with explicit dependencies:
+```
+pip install -e ".[explicit]"
 ```
-=======
-#### Tests
+
+## Tests
 
 Please run the following script to check that the package is working properly.
 
@@ -82,7 +96,6 @@ python scripts/save_nyx_emu_cosmo.py
 python scripts/save_nyx_IGM.py
 ```
 
-
 ## Emulator parameters:
 
 These are the parameters that describe each individual P1D(k) power spectrum. We have detached these from redshift and traditional cosmology parameters.
@@ -110,7 +123,7 @@ The current version of the emulator, relased in this repo, does not emulate `alp
 `kF_Mpc` is the filtering length (or pressure smoothing scale) in inverse comoving units
 
 
-### Notebooks / tutorials
+## Notebooks / tutorials
 
 
 - All notebooks in the repository are in .py format. To generate the .ipynb version, run:
@@ -123,7 +136,7 @@ jupytext --to ipynb notebooks/*.py
 
 ```
 pip install ipykernel
-python -m ipykernel install --user --name cup1d --display-name lace
+python -m ipykernel install --user --name lace --display-name lace
 ```
 In the `Notebooks` folder, there are several tutorials one can run to learn how to use the archives and emulators.
 

diff --git a/docs/docs/installation.md b/docs/docs/installation.md
@@ -1,15 +1,14 @@
+# Installation
+(Last updated: Nov 19 2024)
 
-## Installation
+LaCE contains a submodule to estimate compressed parameters from the power spectrum that uses cosmopower. The LaCE installation is slightly different depending on whether you want to use cosmopower or not.
 
-We recommend using conda environment to install LaCE. To create a conda environment, one needs to specify the python version.
-In January 2024, the latest is 3.12. To use the emulators, there are no strong requirements on the python version. LaCE also contains a cosmology module to estimate compressed cosmological parameters using cosmopower, and this module requires python 3.10. 
+### LaCE without cosmopower
 
-The following instructions are for python 3.10 to ensure that all modules in the repository can run.
-
-To create a conda environment with python 3.10, run:
+- Create a new conda environment. It is usually better to follow python version one or two behind. In January 2024, the latest is 3.12, so we recommend 3.11. If you want to use LaCE with cosmopower, as of November 2025 you need to install python 3.10. Please look at the cosmopower installation before proceeding with the LaCE installation.
 
 ```
-conda create -n lace -c conda-forge python=3.10 camb fdasrsf cosmopower, pyDOE2
+conda create -n lace -c conda-forge python=3.11 pip 
 conda activate lace
 pip install --upgrade pip
 ```
@@ -21,3 +20,35 @@ git clone https://github.com/igmhub/LaCE.git
 cd LacE
 pip install -e .
 ``` 
+
+- If you find problems, please install LaCE as follows:
+
+```
+pip install -e ".[explicit]"
+```
+
+### LaCE with cosmopower
+
+- Create a new conda environment. 
+
+```
+conda create -n lace -c conda-forge python=3.11 pip 
+conda activate lace
+pip install --upgrade pip
+```
+- Install cosmopower:
+
+```
+pip install cosmopower pyDOE
+```
+
+- Clone the repo into your machine and perform an *editable* installation:
+
+```
+git clone https://github.com/igmhub/LaCE.git
+cd LacE
+``` 
+- Install LaCE using the installation with explicit dependencies:
+```
+pip install -e ".[explicit]"
+```
diff --git a/notebooks/Tutorial_compressedParams.py b/notebooks/Tutorial_compressedParams.py
@@ -19,6 +19,8 @@
 # ##### DESCLAIMER: Cosmopower is not installed by default in the LaCE package. You can install it as pip install cosmopower. 
 #
 
+# Additional documentation on how to use cosmopower can be found [here](https://igmhub.github.io/LaCE/compressedParameters/)
+
 # +
 import numpy as np
 import cosmopower as cp
@@ -37,9 +39,6 @@
 
 # -
 
-# ## TRAIN COSMOPOWER
-#
-
 # ## 1. ESTIMATE THE COMPRESSED PARAMETERS OF THE GADGET TEST SIMULATIONS
 
 # ### 1.1. LOAD THE GADGET TEST SIMULATION
@@ -122,7 +121,7 @@
     'mnu': [0, 2],}
 
 create_LH_sample(dict_params_ranges = dict_params_ranges,
-                     nsamples = 10_000,
+                     nsamples = 10,
                      filename = "LHS_params_test.npz")
 
 # ### 2.2 GENERATE THE TRAINING SPECTRA
@@ -155,7 +154,7 @@
 param_mapping = {
     'h': 'h',
     'm_ncdm': 'm_ncdm',
-    'omega_cdm': 'omega_cdm',
+    'omega_cdm': 'omega_c',
     'Omega_m': 'Omega_m',
     'ln_A_s_1e10': 'ln_A_s_1e10',
     'n_s': 'n_s'
@@ -165,8 +164,6 @@
 linP_cosmology_results = fitter_compressed_params.fit_linP_cosmology(chains_df = df, 
                                                                      param_mapping = param_mapping)
 
-#
-
 linP_cosmology_results
 
 
diff --git a/notebooks/nyx_central_z3.py b/notebooks/nyx_central_z3.py
@@ -35,7 +35,7 @@
 
 # ## Extract parameters
 
-z_test=3.4
+z_test=3
 
 take_pars = ['Delta2_p', 'n_p','alpha_p','mF', 'sigT_Mpc', 'gamma', 'kF_Mpc']
 nelem = len(training_data)
@@ -152,6 +152,8 @@
 
 # ## FIND NEAREST NEIGHBOURS TO CENTRAL AT z=3
 
+# The nearest neighbours are calculated using the minkowsky distance.
+
 # +
 from sklearn.neighbors import NearestNeighbors
 
@@ -182,7 +184,10 @@
     logger.info(f"Parameters: {dict(zip(take_pars, central_params))}")
     logger.info("\nNearest neighbors:")
     for neighbor_idx, neighbor_dist in zip(idx, dist):
-        logger.info(f"Simulation: {training_data[neighbor_idx]['sim_label']} at z={np.round(training_data[neighbor_idx]['z'],2)}, Distance: {neighbor_dist:.3f}, Parameters: {dict(zip(take_pars, pars_all_nyx[neighbor_idx]))}")
+        logger.info(f"Simulation: {training_data[neighbor_idx]['sim_label']} at z={np.round(training_data[neighbor_idx]['z'],2)}, Distance: {neighbor_dist:.3f}, Scalings: {training_data[neighbor_idx]['val_scaling']}")
+
+# -
+
 
 
 # +

diff --git a/pyproject.toml b/pyproject.toml
@@ -31,10 +31,9 @@ dependencies = [
     "matplotlib",
     "configobj",
     "camb",
-    "torch",
-    "scikit-fda",
-    "jupyterlab",
-    "jupytext",
+    "torch==2.0",
+    "scikit-fda==0.4",
+    "fdasrsf==2.3.10",
     "strenum",
     "pathlib"
 ]
@@ -58,19 +57,19 @@ gpy = [
     "GPy==1.13.1",
 ]
 explicit = [
-    "numpy==1.24.4",
-    "pandas==2.1.4",
-    "scipy==1.11.4",
-    "h5py==3.10.0",
-    "scikit-learn==1.3.2",
-    "matplotlib==3.8.2",
-    "configobj==5.0.8",
-    "camb==1.5.4",
-    "torch==2.1.2",
-    "scikit-fda==0.9",
-    "GPy==1.13.1",
-    "jupyterlab"    
-]
+    "numpy==1.24.3",
+    "pandas==2.2.3",
+    "scipy==1.12",
+    "h5py==3.12.1",
+    "scikit-learn==1.5.2",
+    "matplotlib==3.9.2",
+    "configobj==5.0.9",
+    "camb==1.5.8",
+    "torch==2.0",
+    "scikit-fda==0.4",
+    "fdasrsf==2.3.10",
+    "GPy==1.13.2"
+    ]
 
 [tool.setuptools.packages.find]
 include = ["lace","lace.*"]