fix: correct the domains when building the transformer function in si…

…mulate

src/datasets/spectraldata.jl

@@ -175,8 +175,8 @@ function objective_transformer(
     end
     R = R_folded[dataset.data_mask, :]
     ΔE = bin_widths(dataset)
-    E = response_energy(dataset.response)
-    _fold_transformer(T, layout, R, ΔE, E)
+    model_domain = response_energy(dataset.response)
+    _fold_transformer(T, layout, R, ΔE, model_domain)
 end
 
 

src/simulate.jl

@@ -96,18 +96,17 @@ function _make_simulation_fitting_config(
         response.matrix
     end
 
-    E = output_domain
-    ΔE = diff(E)
+    ΔE = diff(output_domain)
 
-    objective = zeros(eltype(E), length(E) - 1)
+    objective = zeros(eltype(output_domain), length(output_domain) - 1)
     variance = ones(eltype(objective), size(objective))
 
     cache = SpectralCache(
         layout,
         model,
         input_domain,
         objective,
-        _fold_transformer(T, layout, R, ΔE, E),
+        _fold_transformer(T, layout, R, ΔE, input_domain),
     )
 
     conf = FittingConfig(

test/simulation/test-sample-data-sim.jl

@@ -1 +1,21 @@
+using Test, SpectralFitting
 
+# path to the data directory
+data1 = SpectralFitting.XmmData(
+    SpectralFitting.XmmEPIC(),
+    joinpath(testdir, "xmm/pn_spec_grp.fits"),
+)
+
+
+# smoke test
+model = GaussianLine() + PowerLaw(a = FitParam(0.2))
+sim = simulate(
+    model,
+    data1.data.response,
+    data1.data.ancillary;
+    seed = 8,
+    exposure_time = 1e1,
+)
+
+# TODO: add a fit. can't do it at the moment as the simulated datasets don't
+# support masking the model domain, so we have singular values at high energies