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Basic fit refactoring

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This commit is contained in:
usov_i 2021-02-25 15:41:14 +01:00
parent 9e3466fcaa
commit 89ff0b15f5
4 changed files with 76 additions and 99 deletions
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2
pyzebra/__init__.py
View File

@ -4,6 +4,6 @@ from pyzebra.ccl_io import export_1D, load_1D, parse_1D
from pyzebra.fit2 import fitccl
from pyzebra.h5 import *
from pyzebra.xtal import *
from pyzebra.ccl_process import normalize_dataset, merge_duplicates, merge_datasets, merge_scans
from pyzebra.ccl_process import *
__version__ = "0.2.2"

67
pyzebra/app/panel_ccl_integrate.py
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@ -211,41 +211,23 @@ def create():
peak_pos_textinput_lock = False
fit = scan.get("fit")
if fit is not None:
x = scan["fit"]["x_fit"]
plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
params = fit["result"].params
fit_output_textinput.value = (
"Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
"background: slope = %9.4f, intercept = %9.4f \n"
"Int. area = %9.4f +/- %9.4f \n"
"fit area = %9.4f +/- %9.4f \n"
"ratio((fit-int)/fit) = %9.4f"
% (
params["g_cen"].value,
params["g_width"].value,
params["g_amp"].value,
params["slope"].value,
params["intercept"].value,
fit["int_area"].n,
fit["int_area"].s,
params["g_amp"].value,
params["g_amp"].stderr,
(params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
)
)
numfit_min, numfit_max = fit["numfit"]
if numfit_min is None:
numfit_min_span.location = None
else:
numfit_min_span.location = x[numfit_min]
fit_result = scan.get("fit_result")
if fit_result is not None:
comps = fit_result.eval_components()
plot_gauss_source.data.update(x=x, y=comps["f1_"])
plot_bkg_source.data.update(x=x, y=comps["f0_"])
fit_output_textinput.value = fit_result.fit_report()
if numfit_max is None:
numfit_max_span.location = None
else:
numfit_max_span.location = x[numfit_max]
# numfit_min, numfit_max = fit_result["numfit"]
# if numfit_min is None:
# numfit_min_span.location = None
# else:
# numfit_min_span.location = x[numfit_min]
# if numfit_max is None:
# numfit_max_span.location = None
# else:
# numfit_max_span.location = x[numfit_max]
else:
plot_gauss_source.data.update(x=[], y=[])
@ -505,22 +487,9 @@ def create():
peakfind_button = Button(label="Peak Find Current", default_size=145)
peakfind_button.on_click(peakfind_button_callback)
def _get_fit_params():
return dict(
value=fit_params["gauss-1"]["value"] + fit_params["background-0"]["value"],
vary=fit_params["gauss-1"]["vary"] + fit_params["background-0"]["vary"],
constraints_min=fit_params["gauss-1"]["min"] + fit_params["background-0"]["min"],
constraints_max=fit_params["gauss-1"]["max"] + fit_params["background-0"]["max"],
numfit_min=integ_from.value,
numfit_max=integ_to.value,
binning=bin_size_spinner.value,
)
def fit_all_button_callback():
fit_params = _get_fit_params()
for scan in det_data:
# fit_params are updated inplace within `fitccl`
pyzebra.fitccl(scan, **deepcopy(fit_params))
pyzebra.fit_scan(scan, fit_params)
_update_plot(_get_selected_scan())
_update_table()
@ -530,7 +499,7 @@ def create():
def fit_button_callback():
scan = _get_selected_scan()
pyzebra.fitccl(scan, **_get_fit_params())
pyzebra.fit_scan(scan, fit_params)
_update_plot(scan)
_update_table()

67
pyzebra/app/panel_param_study.py
View File

@ -226,41 +226,23 @@ def create():
peak_pos_textinput_lock = False
fit = scan.get("fit")
if fit is not None:
x = scan["fit"]["x_fit"]
plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
params = fit["result"].params
fit_output_textinput.value = (
"Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
"background: slope = %9.4f, intercept = %9.4f \n"
"Int. area = %9.4f +/- %9.4f \n"
"fit area = %9.4f +/- %9.4f \n"
"ratio((fit-int)/fit) = %9.4f"
% (
params["g_cen"].value,
params["g_width"].value,
params["g_amp"].value,
params["slope"].value,
params["intercept"].value,
fit["int_area"].n,
fit["int_area"].s,
params["g_amp"].value,
params["g_amp"].stderr,
(params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
)
)
numfit_min, numfit_max = fit["numfit"]
if numfit_min is None:
numfit_min_span.location = None
else:
numfit_min_span.location = x[numfit_min]
fit_result = scan.get("fit_result")
if fit_result is not None:
comps = fit_result.eval_components()
plot_gauss_source.data.update(x=x, y=comps["f1_"])
plot_bkg_source.data.update(x=x, y=comps["f0_"])
fit_output_textinput.value = fit_result.fit_report()
if numfit_max is None:
numfit_max_span.location = None
else:
numfit_max_span.location = x[numfit_max]
# numfit_min, numfit_max = fit_result["numfit"]
# if numfit_min is None:
# numfit_min_span.location = None
# else:
# numfit_min_span.location = x[numfit_min]
# if numfit_max is None:
# numfit_max_span.location = None
# else:
# numfit_max_span.location = x[numfit_max]
else:
plot_gauss_source.data.update(x=[], y=[])
@ -586,22 +568,9 @@ def create():
peakfind_button = Button(label="Peak Find Current", default_size=145)
peakfind_button.on_click(peakfind_button_callback)
def _get_fit_params():
return dict(
value=fit_params["gauss-1"]["value"] + fit_params["background-0"]["value"],
vary=fit_params["gauss-1"]["vary"] + fit_params["background-0"]["vary"],
constraints_min=fit_params["gauss-1"]["min"] + fit_params["background-0"]["min"],
constraints_max=fit_params["gauss-1"]["max"] + fit_params["background-0"]["max"],
numfit_min=integ_from.value,
numfit_max=integ_to.value,
binning=bin_size_spinner.value,
)
def fit_all_button_callback():
fit_params = _get_fit_params()
for scan in det_data:
# fit_params are updated inplace within `fitccl`
pyzebra.fitccl(scan, **deepcopy(fit_params))
pyzebra.fit_scan(scan, fit_params)
_update_plot()
_update_table()
@ -611,7 +580,7 @@ def create():
def fit_button_callback():
scan = _get_selected_scan()
pyzebra.fitccl(scan, **_get_fit_params())
pyzebra.fit_scan(scan, fit_params)
_update_plot()
_update_table()

39
pyzebra/ccl_process.py
View File

@ -1,6 +1,7 @@
import itertools
import numpy as np
from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
from .ccl_io import CCL_ANGLES
@ -80,3 +81,41 @@ def merge_scans(scan1, scan2):
scan2["active"] = False
print(f'Merging scans: {scan1["idx"]} <-- {scan2["idx"]}')
def _create_fit_model(model_dict):
model = None
for model_index, (model_name, model_param) in enumerate(model_dict.items()):
model_name, _ = model_name.split("-")
prefix = f"f{model_index}_"
if model_name == "background":
_model = LinearModel(prefix=prefix, name="background")
elif model_name == "gauss":
_model = GaussianModel(prefix=prefix, name="gauss")
elif model_name == "voigt":
_model = VoigtModel(prefix=prefix)
elif model_name == "pseudovoigt":
_model = PseudoVoigtModel(prefix=prefix)
else:
raise ValueError(f"Unknown model name: '{model_name}'")
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:
param_hints[hint_name] = tmp
_model.set_param_hint(param_name, **param_hints)
if model is None:
model = _model
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"]])