def __init__(self):
super().__init__("XAS spectra")
# inputs
self.a_p_norm = StreamInput(self, "a_p_norm")
self.a_a_norm = StreamInput(self, "a_a_norm")
# parameter inputs
self.fit = choice_input(
self,
"fit",
"Do Nothing!!",
[
"linear", "polynomial", "exp decay", "2 point linear",
"Do Nothing!!"
],
)
self.background_start = int_input(self, "p_start_xas", self.a_p_norm,
770)
self.background_stop = int_input(self, "p_stop_xas", self.a_p_norm,
805)
# outputs
self.xas = ArrayOutput(self, "xas", self.read_xas)
self.xas_bg = ArrayOutput(self, "xas_bg", self.read_xas_bg)
self.xas_integral = ArrayOutput(self, "xas_integral",
self.read_xas_integral)
def __init__(self):
super().__init__("step_subtraction")
# Input Ports
self.a_p_norm = StreamInput(self, "a_p_norm")
self.a_a_norm = StreamInput(self, "a_a_norm")
self.apply_step = choice_input(self, "Apply", "off", ["off", "on"])
self.fit_type = choice_input(self, "fit_type", "Alpha",
["Alpha", "Beta"])
self.fit_function = choice_input(self, "fit_function", "Voight",
["Voight", "Arctan"])
self.step_start = int_input(self, "step_start", self.a_p_norm, None)
self.step_intermediate = int_input(self, "step_intermediate",
self.a_p_norm, None)
self.step_stop = int_input(self, "step_stop", self.a_p_norm, None)
# output ports
self.a_a_stepfunction = ArrayOutput(self, "a_a_stepfunction",
self.read_a_a_stepfunction)
self.a_p_stepfunction = ArrayOutput(self, "a_p_stepfunction",
self.read_a_p_stepfunction)
self.a_a_step_subtracted = ArrayOutput(self, "a_a_step_subtracted",
self.read_a_a_step_subtracted)
self.a_p_step_subtracted = ArrayOutput(self, "a_p_step_subtracted",
self.read_a_p_step_subtracted)
def __init__(self):
super().__init__("area")
# input ports
self.input = StreamInput(self, "inputarray")
# int_input takes arguments (fn, name, input_stream)
# The input_stream allows for limits to be calculated for GUI sliders.
self.start = int_input(self, "start", self.input, 770)
self.mid = int_input(self, "mid", self.input, 790)
self.end = int_input(self, "end", self.input, 800)
#
self.value = TextOutput(self, "value", self.read_value)
self.graph = ArrayOutput(self, "graph", self.read_graph)
def __init__(self):
super().__init__("step_subtraction")
# Input Ports
self.input_array = StreamInput(self, "input_array")
self.apply_step = choice_input(self, "Apply", "on", ["on", "off"])
self.fit_function = choice_input(self, "fit_function", "Voight",
["Voight", "Arctan"])
self.step_start = int_input(self, "step_start", self.input_array, None)
self.step_stop = int_input(self, "step_stop", self.input_array, None)
self.edge = int_input(self, "edge", self.input_array, None)
# output ports
self.stepfunction = ArrayOutput(self, "stepfunction",
self.read_stepfunction)
self.subtracted_step = ArrayOutput(self, "post_step_a",
self.read_subtracted_step)
def __init__(self):
super().__init__("background_subtraction")
# streamed inputs
# self.e = StreamInput(self, "e")
self.input_data = StreamInput(self, "input_data")
# input.changed += self.on_data
# parameter inputs
self.fit = choice_input(
fn=self,
name="fit",
default="No fit",
choices=[
"No fit",
"Polynomial fit inside limits",
"Polynomial fit outside limits",
"exp decay (fits for all e < 'Background_start' and e > ' Background_end')",
"2 point linear (straight line between 2 points)",
],
)
self.apply_offset = choice_input(
fn=self,
name="apply_offset",
default="off",
choices=[
"off",
"on (shifts post 'Background_end' by amount equal to e = 'Background_end' to e = 'Background_start' )",
],
)
# self.apply_offset = int_input(self, "apply_offset", 1, 1, 3)
self.p_start = int_input(self, "Background_start", self.input_data,
None)
self.p_end = int_input(self, "Background_end", self.input_data, None)
self.power = free_int_input(self, "power", 1, 1, 3)
# output ports
self.background = ArrayOutput(self, "background", self.read_background)
self.subtracted_background = ArrayOutput(
self, "subtracted_background", self.read_subtracted_background)
def __init__(self, name):
super().__init__(name)
# input ports
self.inputarray = StreamInput(self, "inputarray")
self.lookup = int_input(self, "lookup", self.inputarray, None)
# output ports
self.value = TextOutput(self, "value", self.read_value)
self.graph = ArrayOutput(self, "graph", self.read_graph)
def __init__(self):
super().__init__("Identify Peaks")
# Input Ports
self.input_array = StreamInput(self, "input_array")
self.number_of_peaks = free_int_input(self, "number_of_peaks", 1, 2, 10)
self.center_of_peaks = int_input(
self, "center_of_peaks", self.input_array, 5990
)
self.sigma_of_peaks = int_input(self, "sigma_of_peaks", self.input_array, 30)
self.height_of_peaks = int_input(
self, "height_of_peaks", self.input_array, 0.12
)
self.type_of_peaks = choice_input(
self, "GaussianModel", "GaussianModel", ["GaussianModel", "LorentzianModel"]
)
# output ports
self.fitted_peaks = ArrayOutput(self, "fitted_peaks", self.read_fitted_peaks)
def __init__(self):
super().__init__("Normalise")
# streamed inputs
self.t_p_all = StreamInput(self, "t_p_all")
self.t_a_all = StreamInput(self, "t_a_all")
# parameter inputs
self.action = choice_input(
self, "Action", "Do not Apply", ["Do not Apply", "Apply"]
)
self.normalise_point_1 = int_input(
self, "normalise_point_1", self.t_p_all, None
)
self.normalise_point_2 = int_input(
self, "normalise_point_2", self.t_p_all, None
)
# output ports
self.a_p_norm = ArrayOutput(self, "a_p_norm", self.read_a_p_norm)
self.a_a_norm = ArrayOutput(self, "a_a_norm", self.read_a_a_norm)
def __init__(self):
super().__init__("step_subtraction")
# Input Ports
self.input_array = StreamInput(self, "input_array")
self.apply_step = choice_input(self, "Apply", "off", ["off", "on"])
self.fit_function = choice_input(self, "fit_function", "Voight",
["Voight", "Arctan"])
self.pre_feature_min = int_input(self, "pre_feature_min",
self.input_array, None)
self.pre_feature_max = int_input(self, "pre_feature_max",
self.input_array, None)
self.post_feature_min = int_input(self, "post_feature_min",
self.input_array, None)
self.post_feature_max = int_input(self, "post_feature_max",
self.input_array, None)
# output ports
self.stepfunction = ArrayOutput(self, "stepfunction",
self.read_stepfunction)
self.subtracted_step = ArrayOutput(self, "subtracted_step",
self.read_subtracted_step)
def __init__(self):
super().__init__("Transpose")
# streamed inputs
self.spectra = StreamInput(self, "spectra")
# parameter inputs
self.action = choice_input(self, "Action", "on", ["off", "on"])
self.x_value_for_transpose = int_input(self, "x_value_for_transpose",
self.spectra, 771)
# output ports
self.transposed_data = ArrayOutput(self, "transposed_data",
self.read_transposed_data)
def __init__(self):
super().__init__("background_subtraction")
# streamed inputs
# self.e = StreamInput(self, "e")
self.t_p_all = StreamInput(self, "t_p_all")
self.t_a_all = StreamInput(self, "t_a_all")
# parameter inputs
self.fit = choice_input(
self,
"fit",
"No fit",
[
"No fit",
"Polynomial fit inside limits",
"Polynomial fit outside limits",
"exp decay (fits for all e < 'Background_start' and e > ' Background_end')",
"2 point linear (straight line between 2 points)",
],
)
self.apply_offset = choice_input(
self,
"apply_offset",
"off",
[
"off",
"on (shifts post 'Background_end' by amount equal to e = 'Background_end' to e = 'Background_start' )",
],
)
# self.apply_offset = int_input(self, "apply_offset", 1, 1, 3)
self.p_start = int_input(self, "Background_start", self.t_p_all, None)
self.p_end = int_input(self, "Background_end", self.t_p_all, None)
self.power = free_int_input(self, "power", 1, 1, 4)
# output ports
self.a_p_background_subtraction = ArrayOutput(
self, "a_p_background_subtraction",
self.read_a_p_background_subtraction)
self.a_a_background_subtraction = ArrayOutput(
self, "a_a_background_subtraction",
self.read_a_a_background_subtraction)
self.a_p_background_subtracted = ArrayOutput(
self, "a_p_background_subtracted",
self.read_ia_p_background_subtracted)
self.a_a_background_subtracted = ArrayOutput(
self, "a_a_background_subtracted",
self.read_ia_a_background_subtracted)
# self.e_out= ArrayOutput(self, "e_out", self.read_e_out)
# publish ports
self.inputs = [
# self.e,
self.t_p_all,
self.t_a_all,
self.fit,
self.apply_offset,
self.p_start,
self.p_end,
self.power,
]
self.outputs = [
self.a_p_background_subtraction,
self.a_a_background_subtraction,
self.a_p_background_subtracted,
self.a_a_background_subtracted,
# self.e_out,
]