def submit(self):
"""
Method that reads data from QLineEdits and saves it to a dictionary that is then passed to a DataBuffer object
If the input data does not pass validation (start and end have to be castable to a number) then this method
shows error message
:return: dict: {x: {start: "...", end: "...", name: "...", unit: "..."}, y: {}, z: {}}
start: first point on specified axis
end: last point on specified axis
name: name of the parameter measured and displayed on that axis
unit: measurement unit in which this parameter is measured
"""
x_axis = {"start": self.controls["x"]["start"].text(),
"end": self.controls["x"]["end"].text(),
"name": self.controls["x"]["name"].text(),
"unit": self.controls["x"]["unit"].text()}
y_axis = {"start": self.controls["y"]["start"].text(),
"end": self.controls["y"]["end"].text(),
"name": self.controls["y"]["name"].text(),
"unit": self.controls["y"]["unit"].text()}
z_axis = {"name": self.controls["z"]["name"].text(),
"unit": self.controls["z"]["unit"].text()}
data_dict = {"x": x_axis, "y": y_axis, "z": z_axis}
if not self.validate_input():
self.submitted.emit(data_dict)
self.close()
else:
title, msg = self.validate_input()
show_error_message(title, msg)
def calculate_matrix_dimensions(self):
"""
Takes first two columns of QtLab file (x and y) and looks for unique values.
:return: list: [len(x_axis_data), len(y_axis_data)]
"""
self.raw_data = np.loadtxt(self.location, dtype=float)
self.textual = np.array2string(self.raw_data)
if len(self.raw_data) < 2:
show_error_message(
"Warning", "Seems like data for file {} is incomplete".format(
self.location))
else:
if self.raw_data[0][1] == self.raw_data[1][1]:
y_axis = pd.unique([value[0] for value in self.raw_data])
x_axis = pd.unique([value[1] for value in self.raw_data])
self.legend[0] = "y"
self.legend[1] = "x"
elif self.raw_data[0][0] == self.raw_data[1][0]:
x_axis = pd.unique([value[0] for value in self.raw_data])
y_axis = pd.unique([value[1] for value in self.raw_data])
else:
x_axis = [value[0] for value in self.raw_data]
y_axis = [value[1] for value in self.raw_data]
self.data["x"] = x_axis
self.data["y"] = {0: y_axis}
return [len(x_axis)]
self.data["x"] = x_axis
self.data["y"] = {0: y_axis}
return [len(x_axis), len(y_axis)]
def get_axis_data(self):
"""
Function that gets a matrix file location as parameter, and looks for snapshot.json file within the same directory.
If such file exists then get data from it, otherwise show an error msg saying that there is no such a file
:return: array: [x, y, z] or [x, y]
"""
snapshot_file_location = os.path.dirname(
self.location) + "\\snapshot.json"
measured = {}
if os.path.exists(snapshot_file_location):
with open(snapshot_file_location) as file:
json_data = json.load(file)
data_dict = {"x": {"name": "", "unit": ""}, "y": {}, "z": {}}
for param, data in json_data["__dataset_metadata"]["arrays"].items(
):
if data["is_setpoint"]:
self.number_of_set_parameters += 1
if len(data["shape"]) == 1:
data_dict["x"] = data
else:
data_dict["y"][0] = data
else:
measured[self.number_of_measured_parameters] = data
self.number_of_measured_parameters += 1
if self.number_of_set_parameters == 1:
data_dict["y"] = measured
else:
data_dict["z"] = measured
for k, v in data_dict.items():
print(k, v)
return data_dict
else:
helpers.show_error_message(
"Warning",
"Aborted, snapshot.json file does not exist for this measurement"
)
return
def submit_data(self):
send_value = []
for i, numeric_required in enumerate(self.numeric):
if numeric_required:
if is_numeric(self.textboxes[i].text()):
send_value.append(self.textboxes[i].text())
else:
show_error_message("Warning",
"Input data has to be numeric")
return
else:
send_value.append(self.textboxes[i].text())
if self.dropdown:
send_value.append(self.combobox.currentData())
self.submitted.emit(send_value)
self.close()
def submit_data(self):
# do stuff
send_value = []
if is_numeric(self.resistance.text()):
send_value.append(self.resistance.text())
else:
show_error_message("Warning", "Input data has to be numeric")
return
if is_numeric(self.d_v.text()):
send_value.append(self.d_v.text())
else:
show_error_message("Warning", "Input data has to be numeric")
return
matrix = self.matrices_dropdown.currentData()
send_value.append(matrix)
self.submitted.emit(send_value)
self.close()
def gaussian_fit_action(self):
"""
As suggested by the name of the function, it tries to apply gaussian fit the the selected data. Data is then
added and displayed in the graph for fir curves.
Turning off the action that prompts this function removes curve from the graph
:return:
"""
def gauss(x, a, x0, sigma):
"""
:param x:
:param a:
:param x0:
:param sigma:
:return:
"""
return a * exp(-(x - x0)**2 / (2 * sigma**2))
if self.gauss.isChecked():
try:
x, y = self.get_selection_area()
mean = sum(x * y) / sum(
y
) # https://en.wikipedia.org/wiki/Weighted_arithmetic_mean
sigma = np.sqrt(sum(y * (x - mean)**2) / sum(y))
p0 = array([max(y), mean, sigma])
popt, pcov = curve_fit(gauss, x, y, p0=p0)
plot_item = pg.PlotDataItem(x, gauss(x, *popt))
plot_item.setPen(255, 0, 0)
self.fit_plot.addItem(plot_item)
self.fit_curves["gauss"] = plot_item
except Exception as e:
print(str(e))
show_error_message("Could not find fit", str(e))
else:
self.fit_plot.removeItem(self.fit_curves["gauss"])
del self.fit_curves["gauss"]
def get_axis_data(self):
"""
Function that gets a matrix file location as parameter, and looks for snapshot.json file within the same directory.
If such file exists then get data from it, otherwise show an error msg saying that there is no such a file
:return: array: [x, y, z] or [x, y]
"""
snapshot_file_location = os.path.dirname(self.location) + "\\snapshot.json"
data_list = []
if os.path.exists(snapshot_file_location):
with open(snapshot_file_location) as file:
data = json.load(file)
data_list = self.get_sweep_param_data(data) + self.get_action_param_data(data)
data_dict = {}
legend = {0: "x", 1: "y", 2: "z"}
for index in range(len(data_list)):
data_dict[legend[index]] = data_list[index]
return data_dict
else:
helpers.show_error_message("Warning", "Aborted, snapshot.json file does not exist for this measurement")
return
def hover_action(self, point: int, orientation: str):
"""
When user is hovering its mouse over one of the custom menu items, show a preview of how the line will be
aligned.
:param point: can be 0, 1, 2 or 3. 0 and 1 are the edges of the lineROI, 2 and 3 are used to represent the
diagonal previews
:param orientation: can be horizontal, vertical or diagonal
:return: NoneType
"""
if point in [0, 1]:
handle_point = self.getSceneHandlePositions()[point]
name, scene_coords = handle_point
coords = self.mapSceneToParent(scene_coords)
else:
if point == 2:
coords = [[self.edges[0], self.edges[2]],
[self.edges[1], self.edges[3]]]
elif point == 3:
coords = [[self.edges[0], self.edges[3]],
[self.edges[1], self.edges[2]]]
if orientation == "horizontal":
self.add_preview(coords.y(), orientation, 0)
elif orientation == "vertical":
self.add_preview(coords.x(), orientation, 90)
elif orientation == "diagonal":
angle = self.get_angle_from_points(coords[0], coords[1])
self.add_preview(coords, orientation, angle)
else:
show_error_message(
"Warning",
"Non existent orientation. HOW THE HELL DID U MANAGE TO DO THIS !?"
)
return
return
