mirror of https://github.com/Dioptas/Dioptas.git
387 lines
13 KiB
Python
387 lines
13 KiB
Python
# -*- coding: utf-8 -*-
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# Dioptas - GUI program for fast processing of 2D X-ray data
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# Copyright (C) 2019 Clemens Prescher (clemens.prescher@gmail.com)
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# Institute for Geology and Mineralogy, University of Cologne
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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from qtpy import QtCore
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import numpy as np
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import re
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from xypattern import Pattern
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from .BatchModel import BatchModel
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class MapModel(BatchModel):
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"""
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Model for 2D maps from multiple pattern.
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"""
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map_changed = QtCore.Signal()
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map_cleared = QtCore.Signal()
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map_problem = QtCore.Signal()
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roi_problem = QtCore.Signal()
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def __init__(self, configuration):
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super(MapModel, self).__init__(configuration)
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self.map = Map()
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self.theta_center = 5.9
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self.theta_range = 0.
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self.rois = []
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self.roi_math = ''
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self.possible_dimensions = []
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self.dimension_index = 0
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# Background for image
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self.bg_image = np.zeros([1920, 1200])
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def reset(self):
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self.reset_data()
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self.map.reset()
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self.reset_rois()
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self.possible_dimensions = []
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self.map_cleared.emit()
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def load_img_map(self, filenames, callback_fn=None):
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self.set_image_files(filenames)
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self.integrate_raw_data(0, len(filenames), 1, use_all=True, callback_fn=callback_fn)
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self.configuration.img_model.blockSignals(True)
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for n in range(self.data.shape[0]):
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self.add_map_point(None, Pattern(self.binning, self.data[n, :]), img_filename=filenames[n])
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self.possible_dimensions = find_possible_dimensions(self.data.shape[0])
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self.map.set_manual_positions(0, 0, 1, 1, self.possible_dimensions[0][0], self.possible_dimensions[0][1], True)
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self.configuration.img_model.blockSignals(False)
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def add_map_point(self, pattern_filename, pattern, position=None, img_filename=None):
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"""
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Adds a Point to the map.
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:param pattern_filename: filename of the corresponding map
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:param pattern: Pattern object containing the x and y values of the integrated pattern
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:type pattern: Pattern
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:param position: tuple with x and y position
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:param img_filename: corresponding img filename
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"""
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self.map.add_point(pattern_filename, pattern, position, img_filename)
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def add_roi(self, start, end, name=''):
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self.rois.append(Roi(start, end, name))
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def reset_rois(self):
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self.rois = []
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self.roi_math = ''
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def calculate_map_data(self):
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"""
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Calculates the ROI math and creates the map image.
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"""
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self.map.prepare()
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for point in self.map:
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sum_roi = {}
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for roi in self.rois:
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indices_in_roi = roi.ind_in_roi(point.x_data)
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sum_roi[roi.name] = np.sum(point.y_data[indices_in_roi])
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try:
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current_math = self.calculate_roi_math(sum_roi)
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except SyntaxError: # needed in case of problem with math
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return
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self.map.set_image_intensity(point.position, current_math)
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self.map_changed.emit()
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def create_simple_summing_roi_math(self):
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"""
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Sets the roi_math to be summing of all ROIs.
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"""
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self.roi_math = '+'.join([roi.name for roi in self.rois])
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def check_roi_math(self):
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"""
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Returns: False if a ROI in the math string is missing from the Rois
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"""
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names_in_roi_math = re.findall('([a-zA-Z]+)', self.roi_math)
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for name in names_in_roi_math:
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if name not in [roi.name for roi in self.rois]:
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return False
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return True
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def calculate_roi_math(self, sum_int):
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"""
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Evaluates current_roi_math by replacing each ROI name with the sum of the values in that range
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:param sum_int: dictionary with ROI names as key and there respective integral sums as values
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:return: the result of the roi_math equation
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"""
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if self.roi_math == '':
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self.create_simple_summing_roi_math()
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current_roi_math = self.roi_math
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for roi_letter in sum_int:
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current_roi_math = current_roi_math.replace(roi_letter, str(sum_int[roi_letter]))
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return eval(current_roi_math)
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def is_empty(self):
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return len(self.map) == 0
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class Map:
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def __init__(self):
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self.points = [] # list of MapPoints
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self.sorted_points = [] # list of (points index, x, y)
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self.sorted_map = [] # list of point indices, xs, ys (has a length of 3)
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self.px_per_point_x = 100
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self.px_per_point_y = 100
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def add_point(self, pattern_filename, pattern, position=None, img_filename=None):
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"""
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Adds a Point to the map
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:param pattern_filename: filename of the corresponding map
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:param pattern: Pattern object containing the x and y values of the integrated pattern
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:type pattern: Pattern
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:param position: tuple with x and y position
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:param img_filename: corresponding img filename
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"""
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self.points.append(MapPoint(pattern_filename, pattern, position, img_filename))
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def prepare(self):
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"""
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Prepares the map for inserting intensities
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"""
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self._sort_points()
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self._get_map_dimensions()
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self._create_empty_map()
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def _sort_points(self):
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"""
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Sorts the current points according to x and y positions and saves them in the sorted_points variable.
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"""
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datalist = []
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for ind, point in enumerate(self.points):
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datalist.append([ind, point.position[0], point.position[1]])
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self.sorted_points = sorted(datalist, key=lambda x: (x[1], x[2]))
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self.sorted_map = [[row[i] for row in self.sorted_points] for i in range(len(self.sorted_points[1]))]
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def _get_map_dimensions(self):
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"""
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Uses the sorted points and map to estimate minimum x and y position, the differences between points
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"""
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self.min_x = self.sorted_map[1][0]
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self.min_y = self.sorted_map[2][0]
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self.num_x = self.sorted_map[2].count(self.min_y)
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self.num_y = self.sorted_map[1].count(self.min_x)
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self.diff_x = self.sorted_points[self.num_y][1] - self.sorted_points[0][1]
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self.diff_y = self.sorted_points[1][2] - self.sorted_points[0][2]
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def _create_empty_map(self):
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"""
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Uses the estimated map dimension to calculate
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"""
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self.hor_size = self.px_per_point_x * self.num_x
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self.ver_size = self.px_per_point_y * self.num_y
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self.um_per_px_in_x = self.diff_x / self.px_per_point_x
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self.um_per_px_in_y = self.diff_y / self.px_per_point_y
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self.new_image = np.zeros([self.hor_size, self.ver_size])
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def all_positions_defined(self):
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for point in self.points:
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if point.position is None:
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return False
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return True
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def sort_points_by_name(self):
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"""
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Returns:
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sorted_datalist: a list of all the map files, sorted by natural filename
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"""
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return sorted(self.points, key=lambda point: [int(t) if t.isdigit() else t.lower() for t in
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re.split('(\\d+)', point.pattern_filename)])
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def set_manual_positions(self, min_x, min_y, diff_x, diff_y, num_x, num_y, is_hor_first):
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"""
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Args:
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min_x: Horizontal minimum position
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min_y: Vertical minimum position
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diff_x: Step in horizontal
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diff_y: Step in vertical
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num_x: Number of horizontal positions
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num_y: Number of vertical positions
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is_hor_first: True of horizontal changes first between files, False if vertical
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"""
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x_grid = np.linspace(min_x, min_x + diff_x * (num_x - 1), num_x)
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y_grid = np.linspace(min_y, min_y + diff_y * (num_y - 1), num_y)
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ind = 0
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if is_hor_first:
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for y in y_grid:
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for x in x_grid:
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self.points[ind].position = (x, y)
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ind += 1
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else:
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for x in x_grid:
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for y in y_grid:
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self.points[ind].position = (x, y)
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ind += 1
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self.min_x = min_x
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self.min_y = min_y
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self.num_x = num_x
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self.num_y = num_y
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self.diff_x = diff_x
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self.diff_y = diff_y
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self._create_empty_map()
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self.positions_set_manually = True
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def set_image_intensity(self, position, intensity):
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range_hor = self.pos_to_range(position[0], self.min_x, self.px_per_point_x, self.diff_x)
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range_ver = self.pos_to_range(position[1], self.min_y, self.px_per_point_y, self.diff_y)
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self.new_image[range_hor, range_ver] = intensity
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def pos_to_range(self, pos, min_pos, px_per_point, diff_pos):
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"""
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Args:
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pos: x or y position of point
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min_pos: minimum x or y value map position
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px_per_point: pixels to draw for each map point in the corresponding direction
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diff_pos: difference in corresponding direction between subsequent map files
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Returns:
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pos_range: a slice with the start and end pixels for drawing the current map file
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"""
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range_start = round((pos - min_pos) / diff_pos * px_per_point)
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range_end = round(range_start + px_per_point)
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pos_range = slice(int(range_start), int(range_end))
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return pos_range
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def position_from_xy(self, x, y):
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"""gives the position in units for a point clicked in the x, y space"""
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hor = self.min_x + x // self.px_per_point_x * self.diff_x
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ver = self.min_y + y // self.px_per_point_y * self.diff_y
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return hor, ver
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def filenames_from_position(self, pos):
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"""
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Gives the filenames for a certain position in the map
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:param pos: tuple horizontal and vertical position
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:return: (pattern_filename, image_filename)
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"""
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for point in self.points:
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if abs(float(point.position[0]) - pos[0]) < 2E-4 and \
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abs(point.position[1] - pos[1]) < 2E-4:
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return point.pattern_filename, point.img_filename
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return None, None
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# dist_sqr = {}
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# for filename, filedata in self.map_model.map_data.items():
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# dist_sqr[filename] = abs(float(filedata['pos_hor']) - hor) ** 2 + abs(float(filedata['pos_ver']) - ver) ** 2
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#
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# return min(dist_sqr, key=dist_sqr.get)
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def is_empty(self):
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return len(self.points) == 0
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def __getitem__(self, index):
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return self.points[index]
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def __len__(self):
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return len(self.points)
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def reset(self):
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self.points = []
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self.sorted_points = []
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self.sorted_map = []
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class MapPoint:
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def __init__(self, pattern_filename, pattern, position=None, img_filename=None):
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"""
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Defines a point in a map.
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:param pattern_filename: corresponding pattern filename
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:param pattern: corresponding pattern
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:type pattern: Pattern
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:param position: tuple with the position of the map (x, y)
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:param img_filename: corresponding image filename
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"""
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self.pattern_filename = pattern_filename
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self.x_data = pattern.x
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self.y_data = pattern.y
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self.position = position
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self.img_filename = img_filename
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class Roi:
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def __init__(self, start, end, name=None):
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"""
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Defines a ROI
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:param start: start_value of the ROI
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:param end: end_value of the ROI
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:param name: name of the ROI
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"""
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self.start = start
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self.end = end
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self.name = name
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def is_in_roi(self, x):
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"""
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whether value x is in the ROI
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:param x: x-value
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:type x: float
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:return: True or False
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:rtype: bool
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"""
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return self.start < x < self.end
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def ind_in_roi(self, x_array):
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"""
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Gets the indices of a numpy array which are in the ROI
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:param x_array: a numpy array
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:return: list of indices
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"""
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return np.where((x_array > self.start) & (x_array < self.end))[0]
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@property
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def center(self):
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return self.range / 2 + self.start
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@property
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def range(self):
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return self.end - self.start
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def find_possible_dimensions(num_points):
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dimension_pairs = []
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for n in range(1, int(np.floor(np.sqrt(num_points + 1))) + 1):
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if num_points % n == 0:
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dim1 = n
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dim2 = num_points // n
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dimension_pairs.append((dim1, dim2))
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if dim1 != dim2:
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dimension_pairs.append((dim2, dim1))
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dimension_pairs.sort(key=lambda x: ((x[0]+x[1])/2 - np.sqrt(num_points)) ** 2)
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return dimension_pairs
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