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omiclib.py
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omiclib.py
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import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.lines import Line2D
from matplotlib.transforms import Affine2D
from matplotlib.markers import MarkerStyle
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
matplotlib.use("Qt5Agg")
from skimage.measure import profile_line
from qtrangeslider import QRangeSlider
from time import time
import scipy.fftpack as sfft
import numpy as np
import math
import access2thematrix
from PyQt5 import QtWidgets, QtGui, QtCore
'''
Functions in library:
- angle : Returns angle in degrees between +x-axis and points
- find_nearest : Returns the index of the closest element in an array to the given value
- format_file_names : Returns a string condensing an array of file names
Classes in library:
(Data Handlers)
- Data2D : handle 2D data from Scienta-Omicron mtrx files or user-generated .asc files
- Data3D : handle 3D data from Scienta-Omicrom mtrx files or user-generated .asc files
- DraggablePoint : Dynamically updating point that can be dragged by user around graph, in pairs will create a line
(Dynamically updating widgets)
- SliderAndFloatInput : Slider that updates with a float textbox and two buttons to increase and decrease by 1 increment
- RangeSliderAndFloatInputs : Ranged slider with two float textboxes that set the slider range
- PointSetter : 4 float textboxes that set the position of two DraggablePoints
(Canvases)
- LineCutGraph : Line cut performed on topo data, a topo plot with two dynamically updating DraggablePoints and a 1D line cut plot
- dIdVGraph : dI/dV map that can be updated when an updated energy and data is specified
- FFTGraph : FFT of dI/dV map of any given energy and data
- FFTLineCutGraph : Line cut performed on FFT of dI/dV map and QPI plot
- GlitchFixGraph : Interactive plot where user can select pixels in each row to shift data
'''
''' Standalone functions '''
def angle(x_0, y_0, x_f, y_f):
# Return the angle in degrees from the +x-axis between pt0 and pt1
deltaY = y_f - y_0
deltaX = x_f - x_0
return math.degrees(math.atan2(deltaY, deltaX))
def find_nearest(array, value):
# Return the index of the closest element in an array to the given value
idx = np.searchsorted(array, value, side="left")
if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])):
return idx - 1
else:
return idx
def format_file_names(file_names):
# Returns a string for an array of file names, showing the first and last 5 files and the last 25 characters of each
display_names = [f'...{name[-25:]}' if len(name) > 25 else name for name in file_names]
first_five = '\n'.join(name.rjust(25, '.') for name in display_names[:5])
last_five = '\n'.join(name.rjust(25, '.') for name in display_names[-5:])
if len(file_names) <= 10:
return '\n'.join(display_names) + f'\n\n{len(file_names)} selected files.'
return f'{first_five}\n...\n{last_five}\n\n{len(file_names)} selected files.'
''' Scienta-Omicron mtrx data handlers '''
class Data2D:
def __init__(self, file_name):
# Initialize the Data2D object by reading the data from the specified file.
self.valid = True
self.file_name = file_name
self.header = ''
try:
# Check the file extension to decide how to read the data.
if file_name[-3:] == 'asc' or file_name[-3:] == 'txt':
self.txt_to_array()
elif file_name[-4:] == 'mtrx':
self.mtrx_to_array()
else:
self.valid = False
except:
self.valid = False
def txt_to_array(self):
# Read data from a text file (ASCII format) and extract header information.
self.data = np.loadtxt(self.file_name, delimiter='\t')
with open(self.file_name) as f:
for line in f:
if line.startswith('#'):
self.header += line
# Extract x and y lengths from the header to compute coordinate arrays.
if 'x-length' in line:
first, remainder = line.split('x-length = ')
self.x = float(remainder.split()[0]) / 2
self.x = np.linspace(-self.x, self.x, self.data.shape[1])
if 'y-length' in line:
first, remainder = line.split('y-length = ')
self.y = float(remainder.split()[0]) / 2
self.y = np.linspace(-self.y, self.y, self.data.shape[0])
self.header = self.header[:-1]
def mtrx_to_array(self):
# Read data from a .mtrx file (matrix format) using the access2thematrix library.
mtrx_data = access2thematrix.MtrxData()
traces, message = mtrx_data.open(self.file_name)
selected_image, message = mtrx_data.select_image(traces[0])
# Convert the data to nanometers and create coordinate arrays.
self.data = selected_image.data * 1e9
self.x = selected_image.width * 0.5e9
self.y = selected_image.height * 0.5e9
self.x = np.linspace(-self.x, self.x, self.data.shape[1])
self.y = np.linspace(-self.y, self.y, self.data.shape[0])
# Create a header for the ASCII format.
self.header = f'File format = ASCII\nx-pixels = {selected_image.data.shape[1]}\ny-pixels = {selected_image.data.shape[0]}\nx-length = {selected_image.width*1e9}\ny-length = {selected_image.height*1e9}\nz-unit = nm\nStart of data:'
class Data3D:
def __init__(self, file_names):
self.valid = True
self.file_names = file_names
self.header = ''
self.energy_dict = {}
# Check the file extension to determine the file format and read the data accordingly
if self.file_names[0].endswith('.Aux2(V)_mtrx'):
self.read_mtrx_files()
elif self.file_names[0].endswith('.asc'):
self.read_asc_files()
else:
self.valid = False
def read_mtrx_files(self):
try:
# Read data from .Aux2(V)_mtrx files using the access2thematrix module
mtrx_data = access2thematrix.MtrxData()
mtrx_data.open(self.file_names[0])
self.data = mtrx_data.volume_scan['forward/up']['trace']
self.V = np.round(mtrx_data.scan[0], 2)
# Create a dictionary mapping energy values to 2D data arrays
self.energy_dict = {self.V[i]: self.data[:, :, i] for i in range(self.data.shape[2])}
except:
self.valid = False
def read_asc_files(self):
self.V = []
self.data = []
for f in self.file_names:
try:
data = np.loadtxt(f, delimiter='\t')
with open(f) as fi:
for line in fi:
if '# Energy = ' in line:
# Extract the energy value from the header
V = round(float(line.split('Energy = ')[1].split()[0]), 2)
self.V.append(V)
self.data.append(data)
break
except:
continue
if len(self.V) > 0:
# Sort the data and energy values based on energy in ascending order
sorted_indices = np.argsort(self.V)
self.V = np.array(self.V)[sorted_indices]
self.data = np.array(self.data)[sorted_indices]
# Create a dictionary mapping energy values to 2D data arrays
self.energy_dict = dict(zip(self.V, self.data))
else:
# If no valid data was found in the .asc files, set valid to False
self.valid = False
''' Draggable point class '''
from matplotlib import patches
from matplotlib.lines import Line2D
from matplotlib.transforms import Affine2D
from matplotlib.markers import MarkerStyle
class DraggablePoint:
# A class for creating draggable points on a Matplotlib plot. The points can be dragged interactively to update a line cut plot.
# https://stackoverflow.com/questions/28001655/draggable-line-with-draggable-points
lock = None # Only one point can be animated at a time
def __init__(self, parent, x=0.1, y=0.1, size=0.1):
# Initialize the DraggablePoint object.
self.parent = parent
self.point = patches.Ellipse((x, y), size, size, fc='deeppink', alpha=0.65, edgecolor='deeppink', zorder=10)
self.line = None
self.x = x
self.y = y
parent.fig.axes[0].add_patch(self.point)
self.press = None
# self.background = None
self.connect()
if self.parent.list_points:
line_x = [self.parent.list_points[0].x, self.x]
line_y = [self.parent.list_points[0].y, self.y]
t = Affine2D().rotate_deg(angle(line_x[0], line_y[0], line_x[1], line_y[1]))
m = MarkerStyle('>', 'left', transform=t)
self.line = Line2D(line_x, line_y, color='deeppink', alpha=0.65, zorder=10, marker=m, markevery=(1, 2))
parent.fig.axes[0].add_line(self.line)
def connect(self):
# Connect events
self.cidpress = self.point.figure.canvas.mpl_connect('button_press_event', self.on_press)
self.cidrelease = self.point.figure.canvas.mpl_connect('button_release_event', self.on_release)
self.cidmotion = self.point.figure.canvas.mpl_connect('motion_notify_event', self.on_motion)
def on_press(self, event):
# Handle the button press event for the draggable point.
if event.inaxes != self.point.axes:
return
if DraggablePoint.lock is not None:
return
contains, attrd = self.point.contains(event)
if not contains:
return
self.press = (self.point.center), event.xdata, event.ydata
DraggablePoint.lock = self
# Draw everything but the selected point and store the pixel buffer
canvas = self.point.figure.canvas
axes = self.point.axes
self.point.set_animated(True)
if self == self.parent.list_points[1]:
self.line.set_animated(True)
else:
self.parent.list_points[1].line.set_animated(True)
canvas.draw()
self.background = canvas.copy_from_bbox(self.point.axes.bbox)
# Redraw just the point
axes.draw_artist(self.point)
# Blit just the redrawn area
canvas.blit(axes.bbox)
def on_motion(self, event):
# Handle the mouse motion event when dragging the point.
if DraggablePoint.lock is not self:
return
if event.inaxes != self.point.axes:
return
self.point.center, xpress, ypress = self.press
dx = event.xdata - xpress
dy = event.ydata - ypress
self.point.center = (self.point.center[0] + dx, self.point.center[1] + dy)
# Update the line cut plot and redraw
self.parent.updateLineCut()
self.parent.draw()
canvas = self.point.figure.canvas
axes = self.point.axes
# Restore the background region
canvas.restore_region(self.background)
# Redraw the point and line
axes.draw_artist(self.point)
if self == self.parent.list_points[1]:
axes.draw_artist(self.line)
else:
axes.draw_artist(self.parent.list_points[1].line)
# Blit just the redrawn area
canvas.blit(axes.bbox)
self.x = self.point.center[0]
self.y = self.point.center[1]
try:
self.parent.parent.updateCoords()
except:
pass
if self == self.parent.list_points[1]:
line_x = [self.parent.list_points[0].x, self.x]
line_y = [self.parent.list_points[0].y, self.y]
t = Affine2D().rotate_deg(angle(line_x[0], line_y[0], line_x[1], line_y[1])) # Rotation to the angle of the line
self.line.set_marker(MarkerStyle('>', 'left', transform=t)) # Marker to turn the line into an arrow
self.line.set_data(line_x, line_y)
else:
line_x = [self.x, self.parent.list_points[1].x]
line_y = [self.y, self.parent.list_points[1].y]
t = Affine2D().rotate_deg(angle(line_x[0], line_y[0], line_x[1], line_y[1]))
self.parent.list_points[1].line.set_marker(MarkerStyle('>', 'left', transform=t))
self.parent.list_points[1].line.set_data(line_x, line_y)
# Blit just the redrawn area
canvas.blit(axes.bbox)
def on_release(self, event):
# Handle the button release event when dragging the point.
if DraggablePoint.lock is not self:
return
self.press = None
DraggablePoint.lock = None
# Turn off the animation property and reset the background
self.point.set_animated(False)
if self == self.parent.list_points[1]:
self.line.set_animated(False)
else:
self.parent.list_points[1].line.set_animated(False)
self.background = None
# Redraw the full figure
self.point.figure.canvas.draw()
self.x = self.point.center[0]
self.y = self.point.center[1]
def disconnect(self):
# Disconnect all the stored connection ids.
self.point.figure.canvas.mpl_disconnect(self.cidpress)
self.point.figure.canvas.mpl_disconnect(self.cidrelease)
self.point.figure.canvas.mpl_disconnect(self.cidmotion)
def delete(self):
# Remove DraggablePoint from the canvas.
if self.parent is None:
return
self.disconnect()
self.point.remove()
if self.line is not None:
self.line.remove()
# Redraw the figure
self.parent.draw_idle()
self.parent = None
''' Dynamically updating widgets '''
# Used for energy sliders, creates a slider and a textbox which takes float inputs that update dynamically
class SliderAndFloatInput(QtWidgets.QWidget):
def __init__(self, title, V, parent=None):
super().__init__()
self.parent = parent
self.V = np.sort(V)
self.V_step = np.round(np.abs(V[1] - V[0]), 2)
self.V_min = V[0]
self.V_max = V[-1]
# Create slider, textbox, and increase and decrease buttons
self.slider = QtWidgets.QSlider(QtCore.Qt.Horizontal)
self.slider.setRange(0, V.size - 1) # Set the range of the slider, can only take integer values
self.slider.setSingleStep(1) # Set the step size for slider movements
self.slider.setPageStep(1)
self.float_textbox = QtWidgets.QLineEdit()
self.float_textbox.setValidator(QtGui.QDoubleValidator()) # Allow only float values
self.float_textbox.setText('{:g}'.format(float('{:.2g}'.format(V[0])))) # Set the initial value of the input text
self.decrease_button = QtWidgets.QPushButton("<<")
self.increase_button = QtWidgets.QPushButton(">>")
# Connect signals and slots
self.float_textbox.textEdited.connect(self.updateSlider)
self.slider.valueChanged.connect(self.updateFloatTextbox)
self.decrease_button.clicked.connect(self.decreaseValue)
self.increase_button.clicked.connect(self.increaseValue)
# Arrange widget in layouts
layout = QtWidgets.QHBoxLayout()
layout.addWidget(self.decrease_button)
layout.addWidget(self.float_textbox)
layout.addWidget(self.increase_button)
main_layout = QtWidgets.QVBoxLayout(self)
main_layout.addWidget(QtWidgets.QLabel(title))
main_layout.addLayout(layout)
main_layout.addWidget(self.slider)
self.setLayout(main_layout)
def updateFloatTextbox(self, value):
float_value = self.V[value]
self.float_textbox.setText('{:g}'.format(float('{:.2g}'.format(float_value))))
self.parent.updatePlots(float_value)
def updateSlider(self, text):
try:
float_value = round(float(text), 2)
float_value = max(self.V_min, min(self.V_max, float_value)) # Clamp the value between V_min and V_max
slider_value = np.argmin(np.abs(self.V - float_value)) # Find index of V closest to the given value
self.slider.setValue(slider_value)
except ValueError:
pass
def decreaseValue(self):
current_value = self.float_textbox.text()
try:
float_value = float(current_value)
float_value -= self.V_step
float_value = round(max(self.V_min, float_value), 2) # Clamp the value to V_min
self.float_textbox.setText('{:g}'.format(float('{:.2g}'.format(float_value))))
self.updateSlider(str(float_value))
except ValueError:
pass
def increaseValue(self):
current_value = self.float_textbox.text()
try:
float_value = float(current_value)
float_value += self.V_step
float_value = round(min(self.V_max, float_value), 2) # Clamp the value to V_max
self.float_textbox.setText('{:g}'.format(float('{:.2g}'.format(float_value))))
self.updateSlider(str(float_value))
except ValueError:
pass
# Used for adjusting colormap, range slider with float inputs for max and min values
class RangeSliderAndFloatInputs(QtWidgets.QWidget):
def __init__(self, title, min_val, max_val, parent=None, slider_id=1):
super().__init__(parent)
self.main_window = parent
self.id = slider_id
# Create a linearly spaced array of values between min_val and max_val for mapping slider positions to float values
self.map = np.round(np.linspace(min_val, max_val, 100), 2)
# Create a range slider widget
self.range_slider = QRangeSlider(QtCore.Qt.Horizontal)
self.range_slider.setRange(0, 99)
self.range_slider.setValue((0, 49)) # Set the initial range of the slider
# Create text boxes to display the float values corresponding to the slider positions
self.left_textbox = QtWidgets.QLineEdit(str(self.map[0]))
self.right_textbox = QtWidgets.QLineEdit(str(self.map[49]))
# Connect signals and slots for handling value changes
self.range_slider.valueChanged.connect(self.updateTextboxes)
self.left_textbox.setValidator(QtGui.QDoubleValidator())
self.right_textbox.setValidator(QtGui.QDoubleValidator())
self.left_textbox.editingFinished.connect(self.updateRangeSliderFromTextbox)
self.right_textbox.editingFinished.connect(self.updateRangeSliderFromTextbox)
# Create the layout for the widget
layout = QtWidgets.QVBoxLayout()
layout.addWidget(QtWidgets.QLabel(title))
layout.addWidget(self.range_slider)
textbox_layout = QtWidgets.QHBoxLayout()
textbox_layout.addWidget(self.left_textbox)
textbox_layout.addWidget(self.right_textbox)
layout.addLayout(textbox_layout)
self.setLayout(layout)
def updateTextboxes(self, vals):
# Update the text boxes with the float values corresponding to the slider positions
lower = self.map[vals[0]]
upper = self.map[vals[1]]
self.left_textbox.setText(str(lower))
self.right_textbox.setText(str(upper))
# Notify the main window about the range change
self.main_window.updateCmap(lower, upper, self.id)
def updateRangeSliderFromTextbox(self):
# Update the range slider positions based on the values in the text boxes
lower = float(self.left_textbox.text())
upper = float(self.right_textbox.text())
# Find the closest values in the map array to the entered float values
lower_index = np.searchsorted(self.map, lower)
upper_index = np.searchsorted(self.map, upper)
# Set the slider positions based on the found indices
self.range_slider.setValue((lower_index, upper_index))
# Notify the main window about the range change
self.main_window.updateCmap(self.map[lower_index], self.map[upper_index], self.id)
def getRange(self):
# Return the current range as floats
return float(self.left_textbox.text()), float(self.right_textbox.text())
class PointSetter(QtWidgets.QWidget):
def __init__(self, graph):
super().__init__()
self.graph = graph
self.labels = ['Start X:', 'Start Y:', 'End X:', 'End Y:']
self.textboxes = [QtWidgets.QLineEdit() for _ in range(4)]
# Set validators and placeholder text for each textbox
for i, tb in enumerate(self.textboxes):
tb.setValidator(QtGui.QDoubleValidator())
tb.setPlaceholderText(self.labels[i].split(':')[0])
# Use a grid layout for arrangement of labels and textboxes
layout = QtWidgets.QGridLayout()
for row in range(2):
for col in range(2):
label = QtWidgets.QLabel(self.labels[row * 2 + col])
layout.addWidget(label, row, col * 2)
textbox = self.textboxes[row * 2 + col]
textbox.setObjectName(f'textbox_{self.labels[row * 2 + col]}')
layout.addWidget(textbox, row, col * 2 + 1)
self.setLayout(layout)
# Connect editingFinished signals to update functions
self.textboxes[0].editingFinished.connect(self.updatePoint)
self.textboxes[1].editingFinished.connect(self.updatePoint)
self.textboxes[2].editingFinished.connect(self.updatePoint)
self.textboxes[3].editingFinished.connect(self.updatePoint)
def updatePoint(self):
values = []
for textbox in self.textboxes:
text = textbox.text()
if not text:
values.append(None) # If a textbox is empty, use the corresponding point's center coordinates
else:
try:
values.append(float(text))
except ValueError:
return
# Check if any textbox is empty and update the values accordingly
pt1 = (values[0] if values[0] is not None else self.graph.list_points[0].point.center[0],
values[1] if values[1] is not None else self.graph.list_points[0].point.center[1])
pt2 = (values[2] if values[2] is not None else self.graph.list_points[1].point.center[0],
values[3] if values[3] is not None else self.graph.list_points[1].point.center[1])
self.graph.updatePlots(pt1, pt2)
self.graph.draw_idle()
''' Graphs '''
class LineCutGraph(FigureCanvas):
# Topo line cuts, one plot with topography and DraggablePoints as well as line cut plot
def __init__(self, parent=None, width=4, height=5, dpi=150):
super().__init__()
self.parent = parent
# Initialize the figure and subplot
self.fig = Figure(figsize=(width, height), dpi=dpi, tight_layout=True)
self.fig.subplots_adjust(hspace=0.5)
self.topo_ax = self.fig.add_subplot(3, 1, (1, 2), aspect='equal')
self.topo_ax.set_title('STM Topography', fontsize=8)
self.topo_ax.set_xlabel('x (nm)', fontsize=7)
self.topo_ax.set_ylabel('y (nm)', fontsize=7)
self.topo_ax.tick_params(axis='both', labelsize=7)
self.lc_ax = self.fig.add_subplot(313)
self.lc_ax.set_title('Line cut', fontsize=8)
self.lc_ax.set_xlabel('x (nm)', fontsize=7)
self.lc_ax.set_ylabel('z (nm)', fontsize=7)
self.lc_ax.tick_params(axis='both', labelsize=7)
self.line = Line2D([], [], c='deeppink', lw=1)
self.lc_ax.add_line(self.line)
self.im = None
self.c = self.fig.colorbar(self.im, ax=self.topo_ax, label='z (nm)', fraction=0.02)
self.c.ax.tick_params(labelsize=7)
self.data = None
self.x = None
self.y = None
self.header = None
self.list_points = []
# Initialize the FigureCanvas
FigureCanvas.__init__(self, self.fig)
FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
def updatePlots(self, pt1, pt2):
# Update the STM Topography and line cut plots
self.im = self.topo_ax.imshow(self.data, cmap='Blues_r', origin='lower',
extent=[self.x[0], self.x[-1], self.y[0], self.y[-1]], zorder=1)
self.c.mappable.set_clim(self.im.get_clim())
self.c.mappable.set_cmap('Blues_r')
self.plotDraggablePoints(pt1, pt2, self.x[-1] / 20)
self.draw_idle()
def plotDraggablePoints(self, xy1, xy2, size=0.1):
# Hide draggable points that are already shown
for l in self.list_points:
l.delete()
self.list_points = []
# Add new draggable points at desired coordinates
self.list_points.append(DraggablePoint(self, xy1[0], xy1[1], size))
self.list_points.append(DraggablePoint(self, xy2[0], xy2[1], size))
# Update line cut and QPI plots based on new line cut
self.updateLineCut()
def updateLineCut(self):
# Update the line cut based on the draggable points' position
if self.data is None:
return
pt1_xy = self.list_points[0].point.center
pt2_xy = self.list_points[1].point.center
# Find the indices of the x and y coordinates
pt1 = [find_nearest(self.y, self.list_points[0].point.center[1]),
find_nearest(self.x, self.list_points[0].point.center[0])]
pt2 = [find_nearest(self.y, self.list_points[1].point.center[1]),
find_nearest(self.x, self.list_points[1].point.center[0])]
line_cut_data = profile_line(self.data, pt1, pt2, order=1, mode='constant') # Returns interpolated data along given line
# Plot and resize
dist = math.dist(pt1_xy, pt2_xy)
self.line.set_data(np.linspace(0, dist, line_cut_data.size), line_cut_data)
self.lc_ax.set_xlim(0, dist)
self.lc_ax.set_ylim(min(line_cut_data), max(line_cut_data))
def savePlot(self):
# Save the LineCutGraph plot to a file
file_path, _ = QtWidgets.QFileDialog.getSaveFileName(None, 'Save Plot', '', 'EPS Files (*.eps);;PNG Files (*.png);;All Files (*)')
if file_path:
self.fig.savefig(file_path, dpi=300, facecolor='w')
file_path = file_path[:-3] + 'asc'
header = f'File format = ASCII\nStart point = {self.list_points[0].point.center}\nEnd point = {self.list_points[1].point.center}\n\n\nz-unit = nm\nStart of data:'
np.savetxt(file_path, list(zip(*self.line.get_data())), delimiter='\t', header=header, fmt='%.6f')
print('Plot saved!')
else:
print('No file path selected.')
class dIdVGraph(FigureCanvas):
# colormap of dI/dV map given data and energy
def __init__(self, data, energy, parent=None, dpi=150):
super().__init__()
self.fig = Figure(figsize=(4, 4), dpi=dpi, tight_layout=True)
self.ax = self.fig.add_subplot(111, aspect='equal')
self.ax.set_title(f'dI/dV Map {energy}V')
self.im = self.ax.imshow(data, origin='lower', cmap='Blues_r')
self.im.set_clim(np.mean(data) - 2 * np.std(data), np.mean(data) + 2 * np.std(data))
self.ax.set_xlabel('x (pixels)')
self.ax.set_ylabel('y (pixels)')
FigureCanvas.__init__(self, self.fig)
self.setParent(parent)
FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
def updateFigure(self, data, energy):
# Update the image data using set_data
self.im.set_data(data)
self.ax.set_title(f'dI/dV Map {energy}V')
self.im.set_clim(np.mean(data) - 2 * np.std(data), np.mean(data) + 2 * np.std(data))
self.draw_idle()
class FFTGraph(FigureCanvas):
# colormap of FFT of dI/dV map
def __init__(self, data, parent=None, dpi=150):
super().__init__()
self.fig = Figure(figsize=(4, 4), dpi=dpi, tight_layout=True)
self.ax = self.fig.add_subplot(111, aspect='equal')
self.ax.set_title('FFT')
self.ax.set_xlabel('$k_x$')
self.ax.set_ylabel('$k_y$')
# Take 2D-FFT and center around the origin
data_fft = sfft.fft2(data)
self.data_shift = np.abs(sfft.fftshift(data_fft))
x_fft = sfft.fftshift(np.fft.fftfreq(data.shape[1], d=1))
y_fft = sfft.fftshift(np.fft.fftfreq(data.shape[0], d=1))
self.im = self.ax.imshow(self.data_shift, origin='lower', cmap='bone_r', vmin=0, vmax=np.mean(self.data_shift) + 2 * np.std(self.data_shift), extent=[x_fft[0], x_fft[-1], y_fft[0], y_fft[-1]])
FigureCanvas.__init__(self, self.fig)
self.setParent(parent)
FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
def getMax(self):
# Return a reasonable maximum value for the colormap
return np.mean(self.data_shift) + 4 * np.std(self.data_shift)
def updateFigure(self, data):
# Update plot when data is changed
data_fft = sfft.fft2(data)
self.data_shift = np.abs(sfft.fftshift(data_fft))
self.im.set_data(self.data_shift)
self.draw_idle()
def updateClim(self, lower, upper):
# Update limits of colormap on the plot
self.im.set_clim(vmin=lower, vmax=upper)
self.draw_idle()
class FFTLineCutGraph(FigureCanvas):
# Canvas with FFT, line cut, and QPI plots
def __init__(self, data, current_energy, parent=None, dpi=150):
super().__init__()
self.parent = parent
# Take 2D-FFT of all energies and store in dictionary
self.fft_dict = {data.V[i]: np.abs(sfft.fftshift(sfft.fft2(data.data[:,:,i]))) for i in range(len(data.V))}
self.x_fft = sfft.fftshift(np.fft.fftfreq(data.data.shape[1], d=1))
self.y_fft = sfft.fftshift(np.fft.fftfreq(data.data.shape[0], d=1))
self.current_energy = current_energy
# Initialize axes
space = 0.07
self.fig = Figure(figsize=(4, 8), dpi=dpi)
self.fft_ax = self.fig.add_axes([0 + space, 0.4 + space, 0.6 - 2 * space, 0.6 - 2 * space], aspect='equal')
self.lc_ax = self.fig.add_axes([0 + 1.25 * space, 0 + 1.25 * space, 0.6 - 2.5 * space, 0.4 - 2.5 * space])
self.qpi_ax = self.fig.add_axes([0.6 + .25 * space, 0 + .75 * space, 0.4 - 1.5 * space, 1 - 1.5 * space], aspect='equal')
self.fig.subplots_adjust(hspace=0.5)
# Initialize FFT and line cut plots
self.fft_ax.set_title('FFT', fontsize=8)
self.fft_ax.set_xlabel('$k_x$', fontsize=7, labelpad=0)
self.fft_ax.set_ylabel('$k_y$', fontsize=7, labelpad=0)
self.fft_ax.tick_params(axis='both', labelsize=7)
self.lc_ax.set_title('Line cut', fontsize=8)
self.lc_ax.set_xlabel('$k_x$', fontsize=7, labelpad=0)
self.lc_ax.set_ylabel('FFT', fontsize=7, labelpad=0)
self.lc_ax.tick_params(axis='both', labelsize=7)
self.fft_im = self.fft_ax.imshow(self.fft_dict.get(self.current_energy), origin='lower', cmap='bone_r', extent=[self.x_fft[0], self.x_fft[-1], self.y_fft[0], self.y_fft[-1]], vmin=0, vmax=self.getMax() / 2)
self.line = Line2D([], [], c='deeppink', lw=1)
self.lc_ax.add_line(self.line)
self.lc_ax.set_ylim(self.fft_im.get_clim()[0], self.fft_im.get_clim()[1])
# Store draggable points and initialize the QPI plot
self.list_points = []
self.updatePlots([self.x_fft.max() * 2 / 3, self.y_fft.max() * 2 / 3],
[self.x_fft.min() * 2 / 3, self.y_fft.min() * 2 / 3])
self.c = self.fig.colorbar(self.qpi_im, ax=self.qpi_ax, label='FFT', fraction=0.025)
self.c.ax.tick_params(labelsize=7)
FigureCanvas.__init__(self, self.fig)
FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
self.draw_idle()
def updatePlots(self, pt1, pt2):
# Resets QPI plot which has to be redrawn, and calls a function that will plot new data
self.qpi_ax.clear()
data = self.fft_dict.get(self.current_energy)
self.fft_im.set_data(data)
self.qpi_ax.set_title('QPI', fontsize=8)
self.qpi_ax.set_xlabel(r'$\vec{q}$', fontsize=7, labelpad=0)
self.qpi_ax.set_ylabel('E', fontsize=7, labelpad=0)
self.qpi_ax.tick_params(axis='both', labelsize=7)
self.plotDraggablePoints(pt1, pt2, self.x_fft.max() / 20)
def plotDraggablePoints(self, xy1, xy2, size=0.1):
# Hide draggable points that are already shown
for l in self.list_points:
l.delete()
self.list_points = []
# Add new draggable points at desired coordinates
self.list_points.append(DraggablePoint(self, xy1[0], xy1[1], size))
self.list_points.append(DraggablePoint(self, xy2[0], xy2[1], size))
# Update line cut and QPI plots based on new line cut
self.updateLineCut()
def updateLineCut(self):
# Find points in FFT closest to given points
pt1_xy = self.list_points[0].point.center
pt2_xy = self.list_points[1].point.center
pt1 = [find_nearest(self.y_fft, self.list_points[0].point.center[1]),
find_nearest(self.x_fft, self.list_points[0].point.center[0])]
pt2 = [find_nearest(self.y_fft, self.list_points[1].point.center[1]),
find_nearest(self.x_fft, self.list_points[1].point.center[0])]
# Find the linear interpolation along line for all energies
self.all_lines = {}
for en, dat in self.fft_dict.items():
self.all_lines[en] = profile_line(dat, pt1, pt2, order=1, mode='constant')
# Update line cut plot
current_line = self.all_lines.get(self.current_energy)
dist = math.dist(pt1_xy, pt2_xy)
x = np.linspace(0, dist, current_line.size)
self.line.set_data(x, current_line)
self.lc_ax.set_xlim(0, dist)
# Update QPI plot
E, self.z = zip(*sorted(self.all_lines.items()))
self.qpi_ax.set_xlim(0, dist)
self.qpi_im = self.qpi_ax.pcolormesh(x, E, self.z, vmin=0, vmax=self.getMax() / 2)
def updateClimFFTLC(self, lower, upper):
# Update colormap limits for FFT and line cut plots
self.fft_im.set_clim(vmin=lower, vmax=upper)
self.lc_ax.set_ylim(lower, upper)
def updateClimQPI(self, lower, upper):
# Update colormap limits for QPI plot
self.qpi_im.set_clim(vmin=lower, vmax=upper)
self.c.mappable.set_clim(vmin=lower, vmax=upper)
def updateFigure(self, current_energy):
# If the energy is changed, updates all plots accordingly
vmin2, vmax2 = self.qpi_im.get_clim()
self.current_energy = current_energy
self.updatePlots(self.list_points[0].point.center, self.list_points[1].point.center)
self.updateClimQPI(vmin2, vmax2)
self.draw_idle()
def getMax(self):
# Returns reasonable maximum
data = list(self.fft_dict.values())
return np.mean(data) + 5 * np.std(data)
def saveData(self, folder, identifier=''):
# Save line cut and QPI data
lc = np.array(self.line.get_data(orig=False)).transpose() # Returns pairs of points
qpi = self.z
# Set headers and filenames
E = self.current_energy
lc_header = f'File format = ASCII\nStart of data:'
qpi_header = f'File format = ASCII\nx-pixels = {lc.shape[0]}\ny-pixels = {len(self.fft_dict)}\nx-length = {lc[:,0].max()}\nmin E (y) = {min(self.fft_dict)}\nmax E (y) = {max(self.fft_dict)}\nE (y) units = V\nStart of data:'
if len(identifier) > 0:
lc_filename = f'{identifier}_line_cut_{E}V.asc'
qpi_filename = f'{identifier}_qpi.asc'
else:
lc_filename = f'line_cut_{E}V.asc'
qpi_filename = 'qpi.asc'
# Save as '.asc' files
np.savetxt(f'{folder}\{lc_filename}', lc, delimiter='\t', header=lc_header, fmt='%.6f')
np.savetxt(f'{folder}\{qpi_filename}', qpi, delimiter='\t', header=qpi_header, fmt='%.6f')
class GlitchFixGraph(FigureCanvas):
# Interactive canvas to select pixels and shift data
def __init__(self, pts, parent=None):
# Initialize the figure and the canvas
self.fig = Figure()
super().__init__(self.fig)
self.ax = self.fig.add_subplot(111)
self.parent = parent
self.mpl_connect('button_press_event', self.on_double_click)
# Initialize the data and original_data attributes
self.data = self.parent.energy_dict[self.parent.current_energy]
self.original_data = self.data
self.im = self.ax.imshow(self.data, origin='lower', cmap='Blues_r') # Display the data as an image
self.ax.set_xlabel('x (pixels)')
self.ax.set_ylabel('y (pixels)')
self.ax.set_title(f'dI/dV Map {self.parent.current_energy}V')
# Initialize the selected_pixels attribute
self.selected_pixels = {}
if len(pts) > 0:
self.selected_pixels = {int(x[1]): int(x[0]) for x in pts}
# Initialize red_circles to store circle patches representing selected pixels
self.red_circles = {}
for y, x in self.selected_pixels.items():
circle = plt.Circle((x, y), 0.25, fc='red', alpha=0.75, edgecolor='red', zorder=10)
self.red_circles[y] = circle
self.ax.add_patch(circle) # Add the circle patch to the plot
self.ax.set_xlim(0, self.data.shape[1])
self.ax.set_ylim(0, self.data.shape[0])
def update_data(self, current_energy):
# Update the data and original_data attributes based on the current_energy
self.data = self.parent.energy_dict[current_energy]
self.original_data = self.parent.energy_dict[current_energy]
self.update_plot()
def update_plot(self):
# Update the plot based on the current mode (Edit or Shift)
self.ax.set_title(f'dI/dV Map {self.parent.current_energy}V')
if self.parent.mode == 'Edit':
# Update the image data and color limits in Edit mode
self.im.set_data(self.original_data)
self.im.set_clim(np.mean(self.original_data) - 2 * np.std(self.original_data), np.mean(self.original_data) + 2 * np.std(self.original_data))
# Remove red circles for deselected pixels
to_remove = set(self.red_circles.keys()).difference(self.selected_pixels)
for y in to_remove:
circle = self.red_circles.pop(y)
circle.remove() # Remove the circle patch from the plot
# Add or update red circles for selected pixels
for y, x in self.selected_pixels.items():
circle = self.red_circles.get(y)
if circle is None:
circle = plt.Circle((x, y), 0.25, fc='red', alpha=0.75, edgecolor='red', zorder=10)
self.red_circles[y] = circle
self.ax.add_patch(circle) # Add the circle patch to the plot
else:
circle.center = (x, y) # Update the circle's center position
else:
# Update the image data and color limits in Shift mode
self.im.set_data(self.data)
self.im.set_clim(np.mean(self.data) - 2 * np.std(self.data), np.mean(self.data) + 2 * np.std(self.data))
# Clear all red circles in Shift mode
self.clear_red_circles()
# Update the canvas
self.fig.canvas.draw_idle()
def on_double_click(self, event):
# Handle the double-click event
if not self.parent.accepts_double_click:
return
if event.inaxes != self.ax:
return
if event.dblclick:
x, y = int(event.xdata), int(event.ydata)
if self.parent.mode == 'Edit':
# Toggle selection of a pixel in Edit mode
if y in self.selected_pixels and self.selected_pixels[y] == x:
del self.selected_pixels[y]
else:
self.selected_pixels[y] = x
self.update_plot()
def shift_data(self):
# Shift the selected pixels' data
self.fill_selected_pixels()
self.data = self.original_data.copy()
for y, x in self.selected_pixels.items():
row = self.data[y].copy()
self.data[y] = np.roll(row, -x-1)
def fill_selected_pixels(self):
# Fill the selected pixels' data based on pixels with lower y-value
if not self.selected_pixels:
return
min_y = min(self.selected_pixels.keys())
for y in range(min_y + 1, self.data.shape[0]):
if y in self.selected_pixels:
continue
if y - 1 in self.selected_pixels:
self.selected_pixels[y] = self.selected_pixels[y - 1]
def clear_red_circles(self):
# Remove all red circles from the plot
for c in self.red_circles.values():
c.remove()
self.red_circles = {}