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Marcosertoli/satakihino #263

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Jul 17, 2023
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Marcosertoli/satakihino #263

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6b054e0
Temporarily stop caching (issues to be solved)
marcosertoli Jul 17, 2023
67c5d33
Copied Bremss bayes opt to new file
marcosertoli Jul 17, 2023
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16 changes: 8 additions & 8 deletions indica/models/plasma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -350,7 +350,6 @@ def initialize_variables(self, tstart: float, tend: float, dt: float):
self._pressure_th = assign_data(
self.data2d, ("pressure", "thermal"), "Pa $m^{-3}$"
)
self._ion_density = assign_data(self.data3d, ("density", "ion"), "$m^{-3}$")
self._pressure_tot = assign_data(
self.data2d, ("pressure", "total"), "Pa $m^{-3}$"
)
Expand Down Expand Up @@ -598,7 +597,7 @@ def wp(self):

@property
def fz(self):
return self.Fz()
return self.calc_fz() # self.Fz()

def calc_fz(self):
for elem in self.elements:
Expand All @@ -621,7 +620,7 @@ def calc_fz(self):

@property
def zeff(self):
return self.Zeff()
return self.calc_zeff() # Zeff()

def calc_zeff(self):
electron_density = self.electron_density
Expand All @@ -636,7 +635,7 @@ def calc_zeff(self):

@property
def ion_density(self):
return self.Ion_density()
return self.calc_ion_density() # self.Ion_density()

def calc_ion_density(self):
meanz = self.meanz
Expand All @@ -656,7 +655,7 @@ def calc_ion_density(self):

@property
def lz_tot(self):
return self.Lz_tot()
return self.calc_lz_tot() # self.Lz_tot()

def calc_lz_tot(self):
fz = self.fz
Expand All @@ -681,7 +680,7 @@ def calc_lz_tot(self):

@property
def lz_sxr(self):
return self.Lz_sxr()
return self.calc_lz_sxr() # self.Lz_sxr()

def calc_lz_sxr(self):
fz = self.fz
Expand All @@ -708,7 +707,7 @@ def calc_lz_sxr(self):

@property
def total_radiation(self):
return self.Total_radiation()
return self.calc_total_radiation() # self.Total_radiation()

def calc_total_radiation(self):
lz_tot = self.lz_tot
Expand All @@ -728,7 +727,7 @@ def calc_total_radiation(self):

@property
def sxr_radiation(self):
return self.Sxr_radiation()
return self.calc_sxr_radiation() # self.Sxr_radiation()

def calc_sxr_radiation(self):
if not hasattr(self, "power_loss_sxr"):
Expand Down Expand Up @@ -1238,6 +1237,7 @@ def __init__(self, operator: Callable, dependencies: list, verbose: bool = False

@lru_cache()
def __call__(self):
print("Recalculating")
if self.verbose:
print("Recalculating")
return self.operator()
Expand Down
89 changes: 43 additions & 46 deletions indica/workflows/example_bayes_opt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,6 @@
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
Expand All @@ -34,8 +32,8 @@ def run(
tend=tend,
dt=dt,
main_ion="h",
impurities=("c",), #impurities: tuple = ("c", "ar"), impurity_concentration: tuple = (0.02, 0.001),
impurity_concentration=(0.2,),
impurities=("ar",),
impurity_concentration=(0.001,),
full_run=False,
n_rad=10,
)
Expand All @@ -50,27 +48,25 @@ def run(
"impurity_density": plasma.Nimp_prof.yspl,
}

ST40 = read_st40.ReadST40(pulse)
ST40(["pi"])

data_to_read=ST40.binned_data["pi"]["spectra"]
los_transform = data_to_read.transform
data_to_read.transform.set_equilibrium(data_to_read.transform.equilibrium)
pi = BremsstrahlungDiode(name="pi", channel_mask=slice(18, 28))
pi.set_los_transform(los_transform)

pi.plasma = plasma
pi_data = pi()["brightness"]

#from indica.models.background_fit import Bremsstrahlung
#pi_data = Bremsstrahlung(pulse)[1]
#data_measured = Bremsstrahlung(pulse)[1].sel(channel=channels)
#data_modelled = example_run(pulse)[2]["brightness"].sel(channel=channels)
ST40 = ReadST40(pulse, tstart=tstart, tend=tend)
ST40(["xrcs", "smmh1"])

# Initialise Diagnostic Models
los_transform = ST40.binned_data["smmh1"]["ne"].transform
smmh1 = Interferometry(name="smmh1")
smmh1.set_los_transform(los_transform)
smmh1.plasma = plasma
los_transform = ST40.binned_data["xrcs"]["te_kw"].transform
xrcs = Helike_spectroscopy(name="xrcs", window_masks=[slice(0.3945, 0.3962)])
xrcs.set_los_transform(los_transform)
xrcs.plasma = plasma

flat_data = {}
flat_data["pi.brightness"] = (
pi_data.expand_dims(dim={"t": [plasma.time_to_calculate]})
flat_data["smmh1.ne"] = (
smmh1().pop("ne").expand_dims(dim={"t": [plasma.time_to_calculate]})
)
flat_data["xrcs.spectra"] = (
xrcs().pop("spectra").expand_dims(dim={"t": [plasma.time_to_calculate]})
)

priors = {
Expand All @@ -93,34 +89,36 @@ def run(
"Ti_prof.y0": get_uniform(2000, 10000),
"Ti_prof.peaking": get_uniform(1, 4),
}

# Setup Optimiser
param_names = [
"Ne_prof.y0",
# "Ne_prof.y1",
# "Ne_prof.peaking",
"Nimp_prof.y0",
# "Nimp_prof.y1",
#"Nimp_prof.peaking",
# "Nimp_prof.peaking",
"Te_prof.y0",
# "Te_prof.peaking",
# "Te_prof.peaking",
"Ti_prof.y0",
# "Ti_prof.peaking",
]

bm = BayesModels(
plasma=plasma,
data=flat_data,
diagnostic_models=[pi],
diagnostic_models=[smmh1, xrcs],
quant_to_optimise=[
"pi.brightness",
"smmh1.ne",
"xrcs.spectra",
],
priors=priors,
)

ndim = param_names.__len__()
nwalkers = 50
nwalkers = 20
start_points = bm.sample_from_priors(param_names, size=nwalkers)
move = [(emcee.moves.StretchMove(), 1.0), (emcee.moves.DEMove(), 0.0)]

sampler = emcee.EnsembleSampler(
nwalkers,
ndim,
Expand All @@ -143,22 +141,26 @@ def run(
)
for blob_name in blob_names
}

samples = sampler.get_chain(flat=True)

prior_samples = bm.sample_from_priors(param_names, size=int(1e5))

# TODO make sure xrcs bckc doesn't have dims t and channels
# save result
result = {
"blobs": blob_dict,
"diag_data": flat_data,
"samples": samples,
"prior_samples": prior_samples,
"param_names": param_names,
"phantom_profiles": phantom_profiles,
"plasma": plasma,
"autocorr": autocorr,
}
"blobs": blob_dict,
"diag_data": flat_data,
"samples": samples,
"prior_samples": prior_samples,
"param_names": param_names,
"phantom_profiles": phantom_profiles,
"plasma": plasma,
"autocorr": autocorr,
}
print(sampler.acceptance_fraction.sum())
plot_bayes_result(**result, figheader=result_path)



if __name__ == "__main__":
params = {
"Ne_prof.y0": 5e19,
Expand All @@ -175,9 +177,4 @@ def run(
"Ti_prof.y0": 5000,
"Ti_prof.peaking": 2,
}
from sys import platform
if platform == "linux" or platform == "linux2":
pathname="./plots/"
elif platform == "win32":
pathname="C:\\Users\\Aleksandra.Alieva\\Desktop\\Plots\\New\\"
run(10607, params, 200, pathname, burn_in=0)
run(10009, params, 10, "./results/test/", burn_in=0)
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