Merge branch 'satakihino' into marcosertoli/satakihino

indica/workflows/example_bayes_opt_brems.py

@@ -11,7 +11,16 @@
 from indica.workflows.bayes_workflow import plot_bayes_result
 from indica.workflows.bayes_workflow import sample_with_autocorr
 
-# import matplotlib.pylab as plt
+from indica.bayesmodels import BayesModels
+from indica.bayesmodels import get_uniform
+from indica.models.helike_spectroscopy import Helike_spectroscopy
+from indica.models.interferometry import Interferometry
+from indica.models.plasma import Plasma
+from indica.readers.read_st40 import ReadST40
+from indica.workflows.bayes_workflow import plot_bayes_result
+from indica.workflows.bayes_workflow import sample_with_autocorr
+import indica.readers.read_st40 as read_st40
+from indica.models.diode_filters import BremsstrahlungDiode
 
 # TODO: allow conditional prior usage even when only
 #  one param is being optimisied i.e. 1 is constant
@@ -116,7 +125,6 @@ def run(
         ],
         priors=priors,
     )
-
     ndim = param_names.__len__()
     start_points = bm.sample_from_priors(param_names, size=nwalkers)
     move = [(emcee.moves.StretchMove(), 1.0), (emcee.moves.DEMove(), 0.0)]
@@ -158,7 +166,6 @@ def run(
     print(sampler.acceptance_fraction.sum())
     plot_bayes_result(**result, figheader=result_path)
 
-
 if __name__ == "__main__":
     phantom_profile_params = {
         "Ne_prof.y0": 5e19,
@@ -169,7 +176,7 @@ def run(
         "Ne_prof.wped": 2,
         "Nimp_prof.y0": 1e18,
         "Nimp_prof.y1": 1e17,
-        "Nimp_prof.peaking": 7,  # 2
+        "Nimp_prof.peaking": 7,  
         "Te_prof.y0": 3000,
         "Te_prof.peaking": 2,
         "Ti_prof.y0": 5000,