def from_dict(cls, data):
"""Create SkyModel from dict"""
from gammapy.modeling.models import (
SPATIAL_MODEL_REGISTRY,
SPECTRAL_MODEL_REGISTRY,
TEMPORAL_MODEL_REGISTRY,
)
model_class = SPECTRAL_MODEL_REGISTRY.get_cls(data["spectral"]["type"])
spectral_model = model_class.from_dict(data["spectral"])
spatial_data = data.get("spatial")
if spatial_data is not None:
model_class = SPATIAL_MODEL_REGISTRY.get_cls(spatial_data["type"])
spatial_model = model_class.from_dict(spatial_data)
else:
spatial_model = None
temporal_data = data.get("temporal")
if temporal_data is not None:
model_class = TEMPORAL_MODEL_REGISTRY.get_cls(temporal_data["type"])
temporal_model = model_class.from_dict(temporal_data)
else:
temporal_model = None
return cls(
name=data["name"],
spatial_model=spatial_model,
spectral_model=spectral_model,
temporal_model=temporal_model,
apply_irf=data.get("apply_irf", cls._apply_irf_default),
datasets_names=data.get("datasets_names"),
)
extent = [
np.log10(values["r_0"][0]) - log_dr / 2,
np.log10(values["r_0"][-1]) + log_dr / 2,
values["e"][0] - de / 2,
values["e"][-1] + de / 2,
]
# model tags and parameter names
tags = ["disk", "gauss-general", "gauss-general", "gauss", "shell2"]
par_x = ["r_0", "r_0", "r_0", "sigma", "r_0"]
par_y = ["e", "e", "e", "e", "eta"]
eta_val = [None, 0.1, 0.9, None, None]
# norm correction array
for k, tag in enumerate(tags):
class_ = SPATIAL_MODEL_REGISTRY.get_cls(tag)
class_.norm_correction = norm_correction
m = class_()
ngrid = np.zeros((nr, ne))
if eta_val[k] is not None:
m.eta.value = eta_val[k]
tag += f"_eta{eta_val[k]}"
for kr in range(nr):
for ke in range(ne):
m.parameters[par_x[k]].value = values[par_x[k]][kr]
m.parameters[par_y[k]].value = values[par_y[k]][ke]
ngrid[kr, ke] = m.norm_correction()
plt.figure(figsize=(12, 4), dpi=100)
plt.imshow(ngrid.T,
origin="lower",
extent=extent,