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Refactor handling of the result dict

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This commit is contained in:
usov_i
2020-10-16 14:46:19 +02:00
parent de0c1b1f4b
commit 306958b02a
3 changed files with 28 additions and 49 deletions
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30
pyzebra/app/panel_ccl_integrate.py
View File

@ -195,10 +195,10 @@ def create():
plot_circle_source = ColumnDataSource(dict(x=[], y=[]))
plot.add_glyph(plot_circle_source, Circle(x="x", y="y"))
numfit_min_span = Span(location=None, dimension='height', line_dash='dashed')
numfit_min_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(numfit_min_span)
numfit_max_span = Span(location=None, dimension='height', line_dash='dashed')
numfit_max_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(numfit_max_span)
# Measurement select
@ -296,10 +296,8 @@ def create():
fit_output_textinput = TextAreaInput(title="Fit results:", width=450, height=400)
def peakfind_all_button_callback():
nonlocal det_data
for meas in det_data["Measurements"]:
det_data = pyzebra.ccl_findpeaks(
det_data,
for meas in det_data["Measurements"].values():
pyzebra.ccl_findpeaks(
meas,
int_threshold=peak_int_ratio_spinner.value,
prominence=peak_prominence_spinner.value,
@ -317,12 +315,10 @@ def create():
peakfind_all_button.on_click(peakfind_all_button_callback)
def peakfind_button_callback():
nonlocal det_data
sel_ind = meas_table_source.selected.indices[-1]
meas = meas_table_source.data["measurement"][sel_ind]
det_data = pyzebra.ccl_findpeaks(
det_data,
meas,
pyzebra.ccl_findpeaks(
det_data["Measurements"][meas],
int_threshold=peak_int_ratio_spinner.value,
prominence=peak_prominence_spinner.value,
smooth=smooth_toggle.active,
@ -337,12 +333,10 @@ def create():
peakfind_button.on_click(peakfind_button_callback)
def fit_all_button_callback():
nonlocal det_data
for meas in det_data["Measurements"]:
num_of_peaks = det_data["Measurements"][meas].get("num_of_peaks")
for meas in det_data["Measurements"].values():
num_of_peaks = meas.get("num_of_peaks")
if num_of_peaks is not None and num_of_peaks == 1:
det_data = pyzebra.fitccl(
det_data,
pyzebra.fitccl(
meas,
guess=[
centre_guess.value,
@ -383,15 +377,13 @@ def create():
fit_all_button.on_click(fit_all_button_callback)
def fit_button_callback():
nonlocal det_data
sel_ind = meas_table_source.selected.indices[-1]
meas = meas_table_source.data["measurement"][sel_ind]
num_of_peaks = det_data["Measurements"][meas].get("num_of_peaks")
if num_of_peaks is not None and num_of_peaks == 1:
det_data = pyzebra.fitccl(
det_data,
meas,
pyzebra.fitccl(
det_data["Measurements"][meas],
guess=[
centre_guess.value,
sigma_guess.value,

27
pyzebra/ccl_findpeaks.py
View File

@ -5,11 +5,11 @@ from scipy.signal import savgol_filter
def ccl_findpeaks(
data, keys, int_threshold=0.8, prominence=50, smooth=False, window_size=7, poly_order=3
meas, int_threshold=0.8, prominence=50, smooth=False, window_size=7, poly_order=3
):
"""function iterates through the dictionary created by load_cclv2 and locates peaks for each measurement
args: data (dictionary from load_cclv2),
args: meas - a single measurement,
int_threshold - fraction of threshold_intensity/max_intensity, must be positive num between 0 and 1
i.e. will only detect peaks above 75% of max intensity
@ -29,11 +29,6 @@ def ccl_findpeaks(
'peak_heights': [90.], # height of the peaks (if data vere smoothed
its the heigh of the peaks in smoothed data)
"""
meas = data["Measurements"][keys]
if type(data) is not dict and data["file_type"] != "ccl":
print("Data is not a dictionary or was not made from ccl file")
if not 0 <= int_threshold <= 1:
int_threshold = 0.8
print(
@ -41,27 +36,27 @@ def ccl_findpeaks(
int_threshold,
)
if isinstance(window_size, int) is False or (window_size % 2) == 0 or window_size <= 1:
if not isinstance(window_size, int) or (window_size % 2) == 0 or window_size <= 1:
window_size = 7
print(
"Invalid value for window_size, select positive odd integer, new value set to!:",
window_size,
)
if isinstance(poly_order, int) is False or window_size < poly_order:
if not isinstance(poly_order, int) or window_size < poly_order:
poly_order = 3
print(
"Invalid value for poly_order, select positive integer smaller than window_size, new value set to:",
poly_order,
)
if isinstance(prominence, (int, float)) is False and prominence < 0:
if not isinstance(prominence, (int, float)) and prominence < 0:
prominence = 50
print("Invalid value for prominence, select positive number, new value set to:", prominence)
omega = meas["om"]
counts = np.array(meas["Counts"])
if smooth is True:
if smooth:
itp = interp1d(omega, counts, kind="linear")
absintensity = [abs(number) for number in counts]
lowest_intensity = min(absintensity)
@ -71,12 +66,10 @@ def ccl_findpeaks(
else:
smooth_peaks = counts
indexes = sc.signal.find_peaks(
peaks, properties = sc.signal.find_peaks(
smooth_peaks, height=int_threshold * max(smooth_peaks), prominence=prominence
)
meas["num_of_peaks"] = len(indexes[0])
meas["peak_indexes"] = indexes[0]
meas["peak_heights"] = indexes[1]["peak_heights"]
meas["num_of_peaks"] = len(peaks)
meas["peak_indexes"] = peaks
meas["peak_heights"] = properties["peak_heights"]
meas["smooth_peaks"] = smooth_peaks # smoothed curve
return data

20
pyzebra/fit2.py
View File

@ -32,8 +32,7 @@ def create_uncertanities(y, y_err):
def fitccl(
data,
keys,
meas,
guess,
vary,
constraints_min,
@ -43,8 +42,7 @@ def fitccl(
binning=None,
):
"""Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian and linear model combination
:param data: dictionary after peak fining
:param keys: name of the measurement in the data dict (i.e. M123)
:param meas: measurement in the data dict (i.e. M123)
:param guess: initial guess for the fitting, if none, some values are added automatically in order (see below)
:param vary: True if parameter can vary during fitting, False if it to be fixed
:param numfit_min: minimal value on x axis for numerical integration - if none is centre of gaussian minus 3 sigma
@ -61,8 +59,6 @@ def fitccl(
constraints_min = [23, None, 50, 0, 0]
constraints_min = [80, None, 1000, 0, 100]
"""
meas = data["Measurements"][keys]
if len(meas["peak_indexes"]) > 1:
# return in case of more than 1 peaks
print("More than 1 peak, measurement skipped")
@ -85,7 +81,7 @@ def fitccl(
x = bin_data(x, binning)
y = list(meas["Counts"])
y_err = list(np.sqrt(y)) if meas.get("sigma", None) is None else list(meas["sigma"])
combined = bin_data(create_uncertanities(y,y_err), binning)
combined = bin_data(create_uncertanities(y, y_err), binning)
y = [combined[i].n for i in range(len(combined))]
y_err = [combined[i].s for i in range(len(combined))]
@ -126,10 +122,10 @@ def fitccl(
params = Parameters()
params.add_many(
("g_cen", guess[0], bool(vary[0]), np.min(x), np.max(x), None, None),
("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None,),
("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None,),
("slope", guess[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None,),
("intercept", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None,),
("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None),
("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None),
("slope", guess[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None),
("intercept", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None),
)
# the weighted fit
result = mod.fit(
@ -225,5 +221,3 @@ def fitccl(
d["comps"] = comps
d["numfit"] = [numfit_min, numfit_max]
meas["fit"] = d
return data