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Use lmfit parameter guessing

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This commit is contained in:
usov_i 2021-02-25 16:58:30 +01:00
parent 0b4f4b1ce9
commit 794d5c49d4
1 changed files with 11 additions and 8 deletions
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19
pyzebra/ccl_process.py
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@ -83,7 +83,10 @@ def merge_scans(scan1, scan2):
print(f'Merging scans: {scan1["idx"]} <-- {scan2["idx"]}')
def _create_fit_model(model_dict):
def fit_scan(scan, model_dict):
y_fit = scan["Counts"]
x_fit = scan[scan["scan_motor"]]
model = None
for model_index, (model_name, model_param) in enumerate(model_dict.items()):
model_name, _ = model_name.split("-")
@ -100,12 +103,17 @@ def _create_fit_model(model_dict):
else:
raise ValueError(f"Unknown model name: '{model_name}'")
_init_guess = _model.guess(y_fit, x=x_fit)
for param_index, param_name in enumerate(model_param["param"]):
param_hints = {}
for hint_name in ("value", "vary", "min", "max"):
tmp = model_param[hint_name][param_index]
if tmp is not None:
if tmp is None:
param_hints[hint_name] = getattr(_init_guess[prefix + param_name], hint_name)
else:
param_hints[hint_name] = tmp
_model.set_param_hint(param_name, **param_hints)
if model is None:
@ -113,9 +121,4 @@ def _create_fit_model(model_dict):
else:
model += _model
return model
def fit_scan(scan, model_dict):
model = _create_fit_model(model_dict)
scan["fit_result"] = model.fit(scan["Counts"], x=scan[scan["scan_motor"]])
scan["fit_result"] = model.fit(y_fit, x=x_fit)