Merge pull request #16 from CosmologicalEmulators/develop

Develop

.github/workflows/test.yml

@@ -8,8 +8,7 @@ on:
       - develop
   push:
     branches:
-      - main
-      - develop
+        - '**'
     tags: '*'
 jobs:
   test:

Project.toml

@@ -1,10 +1,13 @@
 name = "Capse"
 uuid = "994f66c3-d2c7-4ba6-88fb-4a10f50800ba"
 authors = ["marcobonici <[email protected]>"]
-version = "0.2.0"
+
+version = "0.2.1"
 
 [deps]
 AbstractCosmologicalEmulators = "c83c1981-e5c4-4837-9eb8-c9b1572acfc6"
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 
 [compat]
-AbstractCosmologicalEmulators = "0.3"
+AbstractCosmologicalEmulators = "0.3.2"
+Adapt = "3"

README.md

@@ -9,7 +9,9 @@
 ![size](https://img.shields.io/github/repo-size/CosmologicalEmulators/Capse.jl)
 
 
-Repo containing the CMB Angular Power Spectrum Emulator (`Capse.jl`).
+Repo containing the CMB Angular Power Spectrum Emulator, `Capse.jl`.
+
+`Capse.jl` is entirely written in `Julia`, but can be transparently called from `Python` using the [`pycapse`](https://github.com/CosmologicalEmulators/pycapse) wrapper.
 
 ## Citing
 

docs/Project.toml

@@ -1,6 +1,5 @@
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-Capse = "994f66c3-d2c7-4ba6-88fb-4a10f50800ba"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

docs/src/index.md

@@ -10,6 +10,9 @@ default(palette = palette(:tab10))
 benchmark = BenchmarkTools.load("./assets/capse_benchmark.json")
 path_json = "./assets/nn_setup.json"
 weights = rand(20000)
+ℓgrid = ones(2000)
+InMinMax_array = zeros(2,2000)
+OutMinMax_array = zeros(2,2000)
 ```
 
 `Capse.jl` is a Julia package designed to emulate the computation of the CMB Angular Power Spectrum, with a speedup of several orders of magnitude.
@@ -43,8 +46,8 @@ After this, you just have to pass the `NN_dict` and the `weights` array to the `
 
 In order to instantiate the emulator, just run
 
-```julia
-trained_emu = Capse.init_emulator(NN_dict, weights, Capse.SimpleChainsEmulator)
+```@example tutorial
+trained_emu = Capse.init_emulator(NN_dict, weights, Capse.SimpleChainsEmulator);
 ```
 
 `SimpleChains.jl` is faster expecially for small NN on the CPU. If you prefer to use `Lux.jl`, pass as last argument `Capse.LuxEmulator`.
@@ -54,20 +57,25 @@ Each trained emulator should be shipped with a description within the JSON file.
 ```@example tutorial
 Capse.get_emulator_description(NN_dict["emulator_description"])
 ```
-
 After instantiating the NN, we need:
 
 - the ``\ell``-grid used to train the emulator, `ℓgrid`
 - the arrays used to perform the minmax normalization of both input and output features, `InMinMax_array` and `OutMinMax_array`
 
 Now you can instantiate the emulator, using
 
-```julia
+```@example tutorial
 Cℓ_emu = Capse.CℓEmulator(TrainedEmulator = trained_emu, ℓgrid = ℓgrid,
-                          InMinMax = InMinMax_array, OutMinMax = OutMinMax_array)
+                          InMinMax = InMinMax_array, OutMinMax = OutMinMax_array);
+```
+
+Each trained emulator should be shipped with a description within the JSON file. In order to print the description, just runs:
+
+```@example tutorial
+Capse.get_emulator_description(Cℓ_emu)
 ```
 
-After loading a trained `CℓTT_emu`, feed it some input parameters `x`.
+After loading a trained emulator, feed it some input parameters `x` in order to get the emulated $C_\ell$'s
 
 ```julia
 x = rand(6) # generate some random input

src/Capse.jl

@@ -1,7 +1,11 @@
 module Capse
 
 using Base: @kwdef
+using Adapt
 using AbstractCosmologicalEmulators
+import AbstractCosmologicalEmulators.get_emulator_description
+
+export get_Cℓ, get_emulator_description
 
 abstract type AbstractCℓEmulators end
 
@@ -14,19 +18,21 @@ It contains:
 
 - TrainedEmulator::AbstractTrainedEmulators, the trained emulator
 
-- ℓgrid::Array, the ``\\ell``-grid the emulator has been trained on.
+- ℓgrid::AbstractVector, the ``\\ell``-grid the emulator has been trained on.
 
-- InMinMax::Matrix, the `Matrix` used for the MinMax normalization of the input features
+- InMinMax::AbstractMatrix, the `Matrix` used for the MinMax normalization of the input features
 
-- OutMinMax::Matrix, the `Matrix` used for the MinMax normalization of the output features
+- OutMinMax::AbstractMatrix, the `Matrix` used for the MinMax normalization of the output features
 """
 @kwdef mutable struct CℓEmulator <: AbstractCℓEmulators
     TrainedEmulator::AbstractTrainedEmulators
-    ℓgrid::Array
-    InMinMax::Matrix
-    OutMinMax::Matrix
+    ℓgrid::AbstractVector
+    InMinMax::AbstractMatrix
+    OutMinMax::AbstractMatrix
 end
 
+Adapt.@adapt_structure CℓEmulator
+
 """
     get_Cℓ(CℓEmulator::AbstractCℓEmulators)
 Computes and returns the ``C_\\ell``on the ``\\ell``-grid the emulator has been trained on.
@@ -47,4 +53,8 @@ function get_ℓgrid(CℓEmulator::AbstractCℓEmulators)
     return CℓEmulator.ℓgrid
 end
 
+function get_emulator_description(Clemu::AbstractCℓEmulators)
+    get_emulator_description(Clemu.TrainedEmulator)
+end
+
 end # module

test/runtests.jl

@@ -26,4 +26,5 @@ capse_emu = Capse.CℓEmulator(TrainedEmulator = emu, ℓgrid=ℓ_test, InMinMax
     output_vec = Capse.get_Cℓ(cosmo_vec, capse_emu)
     @test isapprox(output_vec[:,1], output)
     @test ℓ_test == Capse.get_ℓgrid(capse_emu)
+    @test_logs (:warn, "We do not know which parameters were included in the emulators training space. Use this trained emulator with caution!") Capse.get_emulator_description(capse_emu)
 end