def winBox(self):
MessageBox = QMessageBox()
MessageBox.setStyleSheet(
"QLabel{min-width: 150px; min-height: 50px; color: #FF8C00;} QPushButton{min-width: 120px; min-height: 40px;} QMessageBox { background-color: #323232; font-size: 24px;}"
)
MessageBox.setText('YOU WIN!')
MessageBox.setStandardButtons(QMessageBox.Ok)
buttonOK = MessageBox.button(QMessageBox.Ok)
buttonOK.setText('OK!')
buttonOK.setStyleSheet(
'background-color: #ff1e56; border-radius: 10px; font-size:20px')
MessageBox.exec()
if MessageBox.clickedButton() == buttonOK:
MessageBox.close()
def about(self):
icon = QIcon()
icon.addPixmap(QPixmap('../../../images/icons/icon.png'))
message_box = QMessageBox(parent=self)
message_box.setWindowTitle('Título da caixa de texto')
message_box.setWindowIcon(icon)
message_box.setText(
'Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do '
'eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim '
'ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut '
'aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit '
'in voluptate velit esse cillum dolore eu fugiat nulla pariatur. '
'Excepteur sint occaecat cupidatat non proident, sunt in culpa '
'qui officia deserunt mollit anim id est laborum.')
response = message_box.exec()
message_box.close()
def showDialog():
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText("Can't find that address")
msgBox.setWindowTitle("Error")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.buttonClicked.connect(lambda: msgBox.close())
returnValue = msgBox.exec()
if returnValue == QMessageBox.Ok:
print('OK clicked')
def not_work():
msg = QMessageBox()
msg.setIcon(QMessageBox.Information)
msg.setText("This is a message box")
msg.setInformativeText("This is additional information")
msg.setWindowTitle("MessageBox demo")
msg.setDetailedText("The details are as follows:")
msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)
msg.buttonClicked.connect(lambda: msg.close())
def uploadCode(self, codefilename):
# Create 'wait' dialog
msg = QMessageBox(
QMessageBox.Information, _("Boring..."),
_("Please wait while the game is being sent to the game console."),
QMessageBox.NoButton)
msg.setStandardButtons(QMessageBox.NoButton)
# it's supposed to be the default according to doc, but they lie, an ok button is added
# Display it
msg.show()
sleep(0.1) # Trick Qt, otherwise it wont display the dialog!$%!$!
QCoreApplication.processEvents() # update content
# upload game
return_value = call(AVRDUDE + " -U flash:w:%s" % codefilename,
shell=True)
msg.close()
msg = QMessageBox()
if return_value != 0:
msg.setIcon(QMessageBox.Critical)
msg.setText(_("Oops..!"))
msg.setInformativeText(
_("It didn't work. Are you sure all is well connected ?\nOtherwise.. please call daddy or mummy."
))
msg.setWindowTitle(_("Beware !"))
else:
msg.setIcon(QMessageBox.Information)
msg.setText(_("Yeah !"))
msg.setInformativeText(
_("The game is installed.\nYou can take your console back and play."
))
msg.setWindowTitle(_("Yeah !"))
msg.exec_()
class FittingResultViewer(QDialog):
PAGE_ROWS = 20
logger = logging.getLogger("root.QGrain.ui.FittingResultViewer")
result_marked = Signal(SSUResult)
def __init__(self, reference_viewer: ReferenceResultViewer, parent=None):
super().__init__(parent=parent, f=Qt.Window)
self.setWindowTitle(self.tr("SSU Fitting Result Viewer"))
self.__fitting_results = [] # type: list[SSUResult]
self.retry_tasks = {} # type: dict[UUID, SSUTask]
self.__reference_viewer = reference_viewer
self.init_ui()
self.boxplot_chart = BoxplotChart(parent=self, toolbar=True)
self.typical_chart = SSUTypicalComponentChart(parent=self,
toolbar=True)
self.distance_chart = DistanceCurveChart(parent=self, toolbar=True)
self.mixed_distribution_chart = MixedDistributionChart(
parent=self, toolbar=True, use_animation=True)
self.file_dialog = QFileDialog(parent=self)
self.async_worker = AsyncWorker()
self.async_worker.background_worker.task_succeeded.connect(
self.on_fitting_succeeded)
self.async_worker.background_worker.task_failed.connect(
self.on_fitting_failed)
self.update_page_list()
self.update_page(self.page_index)
self.normal_msg = QMessageBox(self)
self.remove_warning_msg = QMessageBox(self)
self.remove_warning_msg.setStandardButtons(QMessageBox.No
| QMessageBox.Yes)
self.remove_warning_msg.setDefaultButton(QMessageBox.No)
self.remove_warning_msg.setWindowTitle(self.tr("Warning"))
self.remove_warning_msg.setText(
self.tr("Are you sure to remove all SSU results?"))
self.outlier_msg = QMessageBox(self)
self.outlier_msg.setStandardButtons(QMessageBox.Discard
| QMessageBox.Retry
| QMessageBox.Ignore)
self.outlier_msg.setDefaultButton(QMessageBox.Ignore)
self.retry_progress_msg = QMessageBox()
self.retry_progress_msg.addButton(QMessageBox.Ok)
self.retry_progress_msg.button(QMessageBox.Ok).hide()
self.retry_progress_msg.setWindowTitle(self.tr("Progress"))
self.retry_timer = QTimer(self)
self.retry_timer.setSingleShot(True)
self.retry_timer.timeout.connect(
lambda: self.retry_progress_msg.exec_())
def init_ui(self):
self.data_table = QTableWidget(100, 100)
self.data_table.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.data_table.setSelectionBehavior(QAbstractItemView.SelectRows)
self.data_table.setAlternatingRowColors(True)
self.data_table.setContextMenuPolicy(Qt.CustomContextMenu)
self.main_layout = QGridLayout(self)
self.main_layout.addWidget(self.data_table, 0, 0, 1, 3)
self.previous_button = QPushButton(
qta.icon("mdi.skip-previous-circle"), self.tr("Previous"))
self.previous_button.setToolTip(
self.tr("Click to back to the previous page."))
self.previous_button.clicked.connect(self.on_previous_button_clicked)
self.current_page_combo_box = QComboBox()
self.current_page_combo_box.addItem(self.tr("Page {0}").format(1))
self.current_page_combo_box.currentIndexChanged.connect(
self.update_page)
self.next_button = QPushButton(qta.icon("mdi.skip-next-circle"),
self.tr("Next"))
self.next_button.setToolTip(self.tr("Click to jump to the next page."))
self.next_button.clicked.connect(self.on_next_button_clicked)
self.main_layout.addWidget(self.previous_button, 1, 0)
self.main_layout.addWidget(self.current_page_combo_box, 1, 1)
self.main_layout.addWidget(self.next_button, 1, 2)
self.distance_label = QLabel(self.tr("Distance"))
self.distance_label.setToolTip(
self.
tr("It's the function to calculate the difference (on the contrary, similarity) between two samples."
))
self.distance_combo_box = QComboBox()
self.distance_combo_box.addItems(built_in_distances)
self.distance_combo_box.setCurrentText("log10MSE")
self.distance_combo_box.currentTextChanged.connect(
lambda: self.update_page(self.page_index))
self.main_layout.addWidget(self.distance_label, 2, 0)
self.main_layout.addWidget(self.distance_combo_box, 2, 1, 1, 2)
self.menu = QMenu(self.data_table)
self.menu.setShortcutAutoRepeat(True)
self.mark_action = self.menu.addAction(
qta.icon("mdi.marker-check"),
self.tr("Mark Selection(s) as Reference"))
self.mark_action.triggered.connect(self.mark_selections)
self.remove_selection_action = self.menu.addAction(
qta.icon("fa.remove"), self.tr("Remove Selection(s)"))
self.remove_selection_action.triggered.connect(self.remove_selections)
self.remove_all_action = self.menu.addAction(qta.icon("fa.remove"),
self.tr("Remove All"))
self.remove_all_action.triggered.connect(self.remove_all_results)
self.plot_loss_chart_action = self.menu.addAction(
qta.icon("mdi.chart-timeline-variant"), self.tr("Plot Loss Chart"))
self.plot_loss_chart_action.triggered.connect(self.show_distance)
self.plot_distribution_chart_action = self.menu.addAction(
qta.icon("fa5s.chart-area"), self.tr("Plot Distribution Chart"))
self.plot_distribution_chart_action.triggered.connect(
self.show_distribution)
self.plot_distribution_animation_action = self.menu.addAction(
qta.icon("fa5s.chart-area"),
self.tr("Plot Distribution Chart (Animation)"))
self.plot_distribution_animation_action.triggered.connect(
self.show_history_distribution)
self.detect_outliers_menu = self.menu.addMenu(
qta.icon("mdi.magnify"), self.tr("Detect Outliers"))
self.check_nan_and_inf_action = self.detect_outliers_menu.addAction(
self.tr("Check NaN and Inf"))
self.check_nan_and_inf_action.triggered.connect(self.check_nan_and_inf)
self.check_final_distances_action = self.detect_outliers_menu.addAction(
self.tr("Check Final Distances"))
self.check_final_distances_action.triggered.connect(
self.check_final_distances)
self.check_component_mean_action = self.detect_outliers_menu.addAction(
self.tr("Check Component Mean"))
self.check_component_mean_action.triggered.connect(
lambda: self.check_component_moments("mean"))
self.check_component_std_action = self.detect_outliers_menu.addAction(
self.tr("Check Component STD"))
self.check_component_std_action.triggered.connect(
lambda: self.check_component_moments("std"))
self.check_component_skewness_action = self.detect_outliers_menu.addAction(
self.tr("Check Component Skewness"))
self.check_component_skewness_action.triggered.connect(
lambda: self.check_component_moments("skewness"))
self.check_component_kurtosis_action = self.detect_outliers_menu.addAction(
self.tr("Check Component Kurtosis"))
self.check_component_kurtosis_action.triggered.connect(
lambda: self.check_component_moments("kurtosis"))
self.check_component_fractions_action = self.detect_outliers_menu.addAction(
self.tr("Check Component Fractions"))
self.check_component_fractions_action.triggered.connect(
self.check_component_fractions)
self.degrade_results_action = self.detect_outliers_menu.addAction(
self.tr("Degrade Results"))
self.degrade_results_action.triggered.connect(self.degrade_results)
self.try_align_components_action = self.detect_outliers_menu.addAction(
self.tr("Try Align Components"))
self.try_align_components_action.triggered.connect(
self.try_align_components)
self.analyse_typical_components_action = self.menu.addAction(
qta.icon("ei.tags"), self.tr("Analyse Typical Components"))
self.analyse_typical_components_action.triggered.connect(
self.analyse_typical_components)
self.load_dump_action = self.menu.addAction(
qta.icon("fa.database"), self.tr("Load Binary Dump"))
self.load_dump_action.triggered.connect(self.load_dump)
self.save_dump_action = self.menu.addAction(
qta.icon("fa.save"), self.tr("Save Binary Dump"))
self.save_dump_action.triggered.connect(self.save_dump)
self.save_excel_action = self.menu.addAction(
qta.icon("mdi.microsoft-excel"), self.tr("Save Excel"))
self.save_excel_action.triggered.connect(
lambda: self.on_save_excel_clicked(align_components=False))
self.save_excel_align_action = self.menu.addAction(
qta.icon("mdi.microsoft-excel"),
self.tr("Save Excel (Force Alignment)"))
self.save_excel_align_action.triggered.connect(
lambda: self.on_save_excel_clicked(align_components=True))
self.data_table.customContextMenuRequested.connect(self.show_menu)
# necessary to add actions of menu to this widget itself,
# otherwise, the shortcuts will not be triggered
self.addActions(self.menu.actions())
def show_menu(self, pos: QPoint):
self.menu.popup(QCursor.pos())
def show_message(self, title: str, message: str):
self.normal_msg.setWindowTitle(title)
self.normal_msg.setText(message)
self.normal_msg.exec_()
def show_info(self, message: str):
self.show_message(self.tr("Info"), message)
def show_warning(self, message: str):
self.show_message(self.tr("Warning"), message)
def show_error(self, message: str):
self.show_message(self.tr("Error"), message)
@property
def distance_name(self) -> str:
return self.distance_combo_box.currentText()
@property
def distance_func(self) -> typing.Callable:
return get_distance_func_by_name(self.distance_combo_box.currentText())
@property
def page_index(self) -> int:
return self.current_page_combo_box.currentIndex()
@property
def n_pages(self) -> int:
return self.current_page_combo_box.count()
@property
def n_results(self) -> int:
return len(self.__fitting_results)
@property
def selections(self):
start = self.page_index * self.PAGE_ROWS
temp = set()
for item in self.data_table.selectedRanges():
for i in range(item.topRow(),
min(self.PAGE_ROWS + 1,
item.bottomRow() + 1)):
temp.add(i + start)
indexes = list(temp)
indexes.sort()
return indexes
def update_page_list(self):
last_page_index = self.page_index
if self.n_results == 0:
n_pages = 1
else:
n_pages, left = divmod(self.n_results, self.PAGE_ROWS)
if left != 0:
n_pages += 1
self.current_page_combo_box.blockSignals(True)
self.current_page_combo_box.clear()
self.current_page_combo_box.addItems(
[self.tr("Page {0}").format(i + 1) for i in range(n_pages)])
if last_page_index >= n_pages:
self.current_page_combo_box.setCurrentIndex(n_pages - 1)
else:
self.current_page_combo_box.setCurrentIndex(last_page_index)
self.current_page_combo_box.blockSignals(False)
def update_page(self, page_index: int):
def write(row: int, col: int, value: str):
if isinstance(value, str):
pass
elif isinstance(value, int):
value = str(value)
elif isinstance(value, float):
value = f"{value: 0.4f}"
else:
value = value.__str__()
item = QTableWidgetItem(value)
item.setTextAlignment(Qt.AlignCenter)
self.data_table.setItem(row, col, item)
# necessary to clear
self.data_table.clear()
if page_index == self.n_pages - 1:
start = page_index * self.PAGE_ROWS
end = self.n_results
else:
start, end = page_index * self.PAGE_ROWS, (page_index +
1) * self.PAGE_ROWS
self.data_table.setRowCount(end - start)
self.data_table.setColumnCount(7)
self.data_table.setHorizontalHeaderLabels([
self.tr("Resolver"),
self.tr("Distribution Type"),
self.tr("N_components"),
self.tr("N_iterations"),
self.tr("Spent Time [s]"),
self.tr("Final Distance"),
self.tr("Has Reference")
])
sample_names = [
result.sample.name for result in self.__fitting_results[start:end]
]
self.data_table.setVerticalHeaderLabels(sample_names)
for row, result in enumerate(self.__fitting_results[start:end]):
write(row, 0, result.task.resolver)
write(row, 1,
self.get_distribution_name(result.task.distribution_type))
write(row, 2, result.task.n_components)
write(row, 3, result.n_iterations)
write(row, 4, result.time_spent)
write(
row, 5,
self.distance_func(result.sample.distribution,
result.distribution))
has_ref = result.task.initial_guess is not None or result.task.reference is not None
write(row, 6, self.tr("Yes") if has_ref else self.tr("No"))
self.data_table.resizeColumnsToContents()
def on_previous_button_clicked(self):
if self.page_index > 0:
self.current_page_combo_box.setCurrentIndex(self.page_index - 1)
def on_next_button_clicked(self):
if self.page_index < self.n_pages - 1:
self.current_page_combo_box.setCurrentIndex(self.page_index + 1)
def get_distribution_name(self, distribution_type: DistributionType):
if distribution_type == DistributionType.Normal:
return self.tr("Normal")
elif distribution_type == DistributionType.Weibull:
return self.tr("Weibull")
elif distribution_type == DistributionType.SkewNormal:
return self.tr("Skew Normal")
else:
raise NotImplementedError(distribution_type)
def add_result(self, result: SSUResult):
if self.n_results == 0 or \
(self.page_index == self.n_pages - 1 and \
divmod(self.n_results, self.PAGE_ROWS)[-1] != 0):
need_update = True
else:
need_update = False
self.__fitting_results.append(result)
self.update_page_list()
if need_update:
self.update_page(self.page_index)
def add_results(self, results: typing.List[SSUResult]):
if self.n_results == 0 or \
(self.page_index == self.n_pages - 1 and \
divmod(self.n_results, self.PAGE_ROWS)[-1] != 0):
need_update = True
else:
need_update = False
self.__fitting_results.extend(results)
self.update_page_list()
if need_update:
self.update_page(self.page_index)
def mark_selections(self):
for index in self.selections:
self.result_marked.emit(self.__fitting_results[index])
def remove_results(self, indexes):
results = []
for i in reversed(indexes):
res = self.__fitting_results.pop(i)
results.append(res)
self.update_page_list()
self.update_page(self.page_index)
def remove_selections(self):
indexes = self.selections
self.remove_results(indexes)
def remove_all_results(self):
res = self.remove_warning_msg.exec_()
if res == QMessageBox.Yes:
self.__fitting_results.clear()
self.update_page_list()
self.update_page(0)
def show_distance(self):
results = [self.__fitting_results[i] for i in self.selections]
if results is None or len(results) == 0:
return
result = results[0]
self.distance_chart.show_distance_series(result.get_distance_series(
self.distance_name),
title=result.sample.name)
self.distance_chart.show()
def show_distribution(self):
results = [self.__fitting_results[i] for i in self.selections]
if results is None or len(results) == 0:
return
result = results[0]
self.mixed_distribution_chart.show_model(result.view_model)
self.mixed_distribution_chart.show()
def show_history_distribution(self):
results = [self.__fitting_results[i] for i in self.selections]
if results is None or len(results) == 0:
return
result = results[0]
self.mixed_distribution_chart.show_result(result)
self.mixed_distribution_chart.show()
def load_dump(self):
filename, _ = self.file_dialog.getOpenFileName(
self, self.tr("Select a binary dump file of SSU results"), None,
self.tr("Binary dump (*.dump)"))
if filename is None or filename == "":
return
with open(filename, "rb") as f:
results = pickle.load(f) # type: list[SSUResult]
valid = True
if isinstance(results, list):
for result in results:
if not isinstance(result, SSUResult):
valid = False
break
else:
valid = False
if valid:
if self.n_results != 0 and len(results) != 0:
old_classes = self.__fitting_results[0].classes_φ
new_classes = results[0].classes_φ
classes_inconsistent = False
if len(old_classes) != len(new_classes):
classes_inconsistent = True
else:
classes_error = np.abs(old_classes - new_classes)
if not np.all(np.less_equal(classes_error, 1e-8)):
classes_inconsistent = True
if classes_inconsistent:
self.show_error(
self.
tr("The results in the dump file has inconsistent grain-size classes with that in your list."
))
return
self.add_results(results)
else:
self.show_error(self.tr("The binary dump file is invalid."))
def save_dump(self):
if self.n_results == 0:
self.show_warning(self.tr("There is not any result in the list."))
return
filename, _ = self.file_dialog.getSaveFileName(
self, self.tr("Save the SSU results to binary dump file"), None,
self.tr("Binary dump (*.dump)"))
if filename is None or filename == "":
return
with open(filename, "wb") as f:
pickle.dump(self.__fitting_results, f)
def save_excel(self, filename, align_components=False):
if self.n_results == 0:
return
results = self.__fitting_results.copy()
classes_μm = results[0].classes_μm
n_components_list = [
result.n_components for result in self.__fitting_results
]
count_dict = Counter(n_components_list)
max_n_components = max(count_dict.keys())
self.logger.debug(
f"N_components: {count_dict}, Max N_components: {max_n_components}"
)
flags = []
if not align_components:
for result in results:
flags.extend(range(result.n_components))
else:
n_components_desc = "\n".join([
self.tr("{0} Component(s): {1}").format(n_components, count)
for n_components, count in count_dict.items()
])
self.show_info(
self.tr("N_components distribution of Results:\n{0}").format(
n_components_desc))
stacked_components = []
for result in self.__fitting_results:
for component in result.components:
stacked_components.append(component.distribution)
stacked_components = np.array(stacked_components)
cluser = KMeans(n_clusters=max_n_components)
flags = cluser.fit_predict(stacked_components)
# check flags to make it unique
flag_index = 0
for i, result in enumerate(self.__fitting_results):
result_flags = set()
for component in result.components:
if flags[flag_index] in result_flags:
if flags[flag_index] == max_n_components:
flags[flag_index] = max_n_components - 1
else:
flag_index[flag_index] += 1
result_flags.add(flags[flag_index])
flag_index += 1
flag_set = set(flags)
picked = []
for target_flag in flag_set:
for i, flag in enumerate(flags):
if flag == target_flag:
picked.append(
(target_flag,
logarithmic(classes_μm,
stacked_components[i])["mean"]))
break
picked.sort(key=lambda x: x[1])
flag_map = {flag: index for index, (flag, _) in enumerate(picked)}
flags = np.array([flag_map[flag] for flag in flags])
wb = openpyxl.Workbook()
prepare_styles(wb)
ws = wb.active
ws.title = self.tr("README")
description = \
"""
This Excel file was generated by QGrain ({0}).
Please cite:
Liu, Y., Liu, X., Sun, Y., 2021. QGrain: An open-source and easy-to-use software for the comprehensive analysis of grain size distributions. Sedimentary Geology 423, 105980. https://doi.org/10.1016/j.sedgeo.2021.105980
It contanins 4 + max(N_components) sheets:
1. The first sheet is the sample distributions of SSU results.
2. The second sheet is used to put the infomation of fitting.
3. The third sheet is the statistic parameters calculated by statistic moment method.
4. The fouth sheet is the distributions of unmixed components and their sum of each sample.
5. Other sheets are the unmixed end-member distributions which were discretely stored.
The SSU algorithm is implemented by QGrain.
""".format(QGRAIN_VERSION)
def write(row, col, value, style="normal_light"):
cell = ws.cell(row + 1, col + 1, value=value)
cell.style = style
lines_of_desc = description.split("\n")
for row, line in enumerate(lines_of_desc):
write(row, 0, line, style="description")
ws.column_dimensions[column_to_char(0)].width = 200
ws = wb.create_sheet(self.tr("Sample Distributions"))
write(0, 0, self.tr("Sample Name"), style="header")
ws.column_dimensions[column_to_char(0)].width = 16
for col, value in enumerate(classes_μm, 1):
write(0, col, value, style="header")
ws.column_dimensions[column_to_char(col)].width = 10
for row, result in enumerate(results, 1):
if row % 2 == 0:
style = "normal_dark"
else:
style = "normal_light"
write(row, 0, result.sample.name, style=style)
for col, value in enumerate(result.sample.distribution, 1):
write(row, col, value, style=style)
QCoreApplication.processEvents()
ws = wb.create_sheet(self.tr("Information of Fitting"))
write(0, 0, self.tr("Sample Name"), style="header")
ws.column_dimensions[column_to_char(0)].width = 16
headers = [
self.tr("Distribution Type"),
self.tr("N_components"),
self.tr("Resolver"),
self.tr("Resolver Settings"),
self.tr("Initial Guess"),
self.tr("Reference"),
self.tr("Spent Time [s]"),
self.tr("N_iterations"),
self.tr("Final Distance [log10MSE]")
]
for col, value in enumerate(headers, 1):
write(0, col, value, style="header")
if col in (4, 5, 6):
ws.column_dimensions[column_to_char(col)].width = 10
else:
ws.column_dimensions[column_to_char(col)].width = 10
for row, result in enumerate(results, 1):
if row % 2 == 0:
style = "normal_dark"
else:
style = "normal_light"
write(row, 0, result.sample.name, style=style)
write(row, 1, result.distribution_type.name, style=style)
write(row, 2, result.n_components, style=style)
write(row, 3, result.task.resolver, style=style)
write(row,
4,
self.tr("Default") if result.task.resolver_setting is None
else result.task.resolver_setting.__str__(),
style=style)
write(row,
5,
self.tr("None") if result.task.initial_guess is None else
result.task.initial_guess.__str__(),
style=style)
write(row,
6,
self.tr("None") if result.task.reference is None else
result.task.reference.__str__(),
style=style)
write(row, 7, result.time_spent, style=style)
write(row, 8, result.n_iterations, style=style)
write(row, 9, result.get_distance("log10MSE"), style=style)
ws = wb.create_sheet(self.tr("Statistic Moments"))
write(0, 0, self.tr("Sample Name"), style="header")
ws.merge_cells(start_row=1, start_column=1, end_row=2, end_column=1)
ws.column_dimensions[column_to_char(0)].width = 16
headers = []
sub_headers = [
self.tr("Proportion"),
self.tr("Mean [φ]"),
self.tr("Mean [μm]"),
self.tr("STD [φ]"),
self.tr("STD [μm]"),
self.tr("Skewness"),
self.tr("Kurtosis")
]
for i in range(max_n_components):
write(0,
i * len(sub_headers) + 1,
self.tr("C{0}").format(i + 1),
style="header")
ws.merge_cells(start_row=1,
start_column=i * len(sub_headers) + 2,
end_row=1,
end_column=(i + 1) * len(sub_headers) + 1)
headers.extend(sub_headers)
for col, value in enumerate(headers, 1):
write(1, col, value, style="header")
ws.column_dimensions[column_to_char(col)].width = 10
flag_index = 0
for row, result in enumerate(results, 2):
if row % 2 == 0:
style = "normal_light"
else:
style = "normal_dark"
write(row, 0, result.sample.name, style=style)
for component in result.components:
index = flags[flag_index]
write(row,
index * len(sub_headers) + 1,
component.fraction,
style=style)
write(row,
index * len(sub_headers) + 2,
component.logarithmic_moments["mean"],
style=style)
write(row,
index * len(sub_headers) + 3,
component.geometric_moments["mean"],
style=style)
write(row,
index * len(sub_headers) + 4,
component.logarithmic_moments["std"],
style=style)
write(row,
index * len(sub_headers) + 5,
component.geometric_moments["std"],
style=style)
write(row,
index * len(sub_headers) + 6,
component.logarithmic_moments["skewness"],
style=style)
write(row,
index * len(sub_headers) + 7,
component.logarithmic_moments["kurtosis"],
style=style)
flag_index += 1
ws = wb.create_sheet(self.tr("Unmixed Components"))
ws.merge_cells(start_row=1, start_column=1, end_row=1, end_column=2)
write(0, 0, self.tr("Sample Name"), style="header")
ws.column_dimensions[column_to_char(0)].width = 16
for col, value in enumerate(classes_μm, 2):
write(0, col, value, style="header")
ws.column_dimensions[column_to_char(col)].width = 10
row = 1
for result_index, result in enumerate(results, 1):
if result_index % 2 == 0:
style = "normal_dark"
else:
style = "normal_light"
write(row, 0, result.sample.name, style=style)
ws.merge_cells(start_row=row + 1,
start_column=1,
end_row=row + result.n_components + 1,
end_column=1)
for component_i, component in enumerate(result.components, 1):
write(row, 1, self.tr("C{0}").format(component_i), style=style)
for col, value in enumerate(
component.distribution * component.fraction, 2):
write(row, col, value, style=style)
row += 1
write(row, 1, self.tr("Sum"), style=style)
for col, value in enumerate(result.distribution, 2):
write(row, col, value, style=style)
row += 1
ws_dict = {}
flag_set = set(flags)
for flag in flag_set:
ws = wb.create_sheet(self.tr("Unmixed EM{0}").format(flag + 1))
write(0, 0, self.tr("Sample Name"), style="header")
ws.column_dimensions[column_to_char(0)].width = 16
for col, value in enumerate(classes_μm, 1):
write(0, col, value, style="header")
ws.column_dimensions[column_to_char(col)].width = 10
ws_dict[flag] = ws
flag_index = 0
for row, result in enumerate(results, 1):
if row % 2 == 0:
style = "normal_dark"
else:
style = "normal_light"
for component in result.components:
flag = flags[flag_index]
ws = ws_dict[flag]
write(row, 0, result.sample.name, style=style)
for col, value in enumerate(component.distribution, 1):
write(row, col, value, style=style)
flag_index += 1
wb.save(filename)
wb.close()
def on_save_excel_clicked(self, align_components=False):
if self.n_results == 0:
self.show_warning(self.tr("There is not any SSU result."))
return
filename, _ = self.file_dialog.getSaveFileName(
None, self.tr("Choose a filename to save SSU Results"), None,
"Microsoft Excel (*.xlsx)")
if filename is None or filename == "":
return
try:
self.save_excel(filename, align_components)
self.show_info(
self.tr("SSU results have been saved to:\n {0}").format(
filename))
except Exception as e:
self.show_error(
self.
tr("Error raised while save SSU results to Excel file.\n {0}"
).format(e.__str__()))
def on_fitting_succeeded(self, result: SSUResult):
result_replace_index = self.retry_tasks[result.task.uuid]
self.__fitting_results[result_replace_index] = result
self.retry_tasks.pop(result.task.uuid)
self.retry_progress_msg.setText(
self.tr("Tasks to be retried: {0}").format(len(self.retry_tasks)))
if len(self.retry_tasks) == 0:
self.retry_progress_msg.close()
self.logger.debug(
f"Retried task succeeded, sample name={result.task.sample.name}, distribution_type={result.task.distribution_type.name}, n_components={result.task.n_components}"
)
self.update_page(self.page_index)
def on_fitting_failed(self, failed_info: str, task: SSUTask):
# necessary to remove it from the dict
self.retry_tasks.pop(task.uuid)
if len(self.retry_tasks) == 0:
self.retry_progress_msg.close()
self.show_error(
self.tr("Failed to retry task, sample name={0}.\n{1}").format(
task.sample.name, failed_info))
self.logger.warning(
f"Failed to retry task, sample name={task.sample.name}, distribution_type={task.distribution_type.name}, n_components={task.n_components}"
)
def retry_results(self, indexes, results):
assert len(indexes) == len(results)
if len(results) == 0:
return
self.retry_progress_msg.setText(
self.tr("Tasks to be retried: {0}").format(len(results)))
self.retry_timer.start(1)
for index, result in zip(indexes, results):
query = self.__reference_viewer.query_reference(result.sample)
ref_result = None
if query is None:
nearby_results = self.__fitting_results[
index - 5:index] + self.__fitting_results[index + 1:index +
6]
ref_result = self.__reference_viewer.find_similar(
result.sample, nearby_results)
else:
ref_result = query
keys = ["mean", "std", "skewness"]
# reference = [{key: comp.logarithmic_moments[key] for key in keys} for comp in ref_result.components]
task = SSUTask(
result.sample,
ref_result.distribution_type,
ref_result.n_components,
resolver=ref_result.task.resolver,
resolver_setting=ref_result.task.resolver_setting,
# reference=reference)
initial_guess=ref_result.last_func_args)
self.logger.debug(
f"Retry task: sample name={task.sample.name}, distribution_type={task.distribution_type.name}, n_components={task.n_components}"
)
self.retry_tasks[task.uuid] = index
self.async_worker.execute_task(task)
def degrade_results(self):
degrade_results = [] # type: list[SSUResult]
degrade_indexes = [] # type: list[int]
for i, result in enumerate(self.__fitting_results):
for component in result.components:
if component.fraction < 1e-3:
degrade_results.append(result)
degrade_indexes.append(i)
break
self.logger.debug(
f"Results should be degrade (have a redundant component): {[result.sample.name for result in degrade_results]}"
)
if len(degrade_results) == 0:
self.show_info(
self.tr("No fitting result was evaluated as an outlier."))
return
self.show_info(
self.
tr("The results below should be degrade (have a redundant component:\n {0}"
).format(", ".join(
[result.sample.name for result in degrade_results])))
self.retry_progress_msg.setText(
self.tr("Tasks to be retried: {0}").format(len(degrade_results)))
self.retry_timer.start(1)
for index, result in zip(degrade_indexes, degrade_results):
reference = []
n_redundant = 0
for component in result.components:
if component.fraction < 1e-3:
n_redundant += 1
else:
reference.append(
dict(mean=component.logarithmic_moments["mean"],
std=component.logarithmic_moments["std"],
skewness=component.logarithmic_moments["skewness"]
))
task = SSUTask(
result.sample,
result.distribution_type,
result.n_components -
n_redundant if result.n_components > n_redundant else 1,
resolver=result.task.resolver,
resolver_setting=result.task.resolver_setting,
reference=reference)
self.logger.debug(
f"Retry task: sample name={task.sample.name}, distribution_type={task.distribution_type.name}, n_components={task.n_components}"
)
self.retry_tasks[task.uuid] = index
self.async_worker.execute_task(task)
def ask_deal_outliers(self, outlier_results: typing.List[SSUResult],
outlier_indexes: typing.List[int]):
assert len(outlier_indexes) == len(outlier_results)
if len(outlier_results) == 0:
self.show_info(
self.tr("No fitting result was evaluated as an outlier."))
else:
if len(outlier_results) > 100:
self.outlier_msg.setText(
self.
tr("The fitting results have the component that its fraction is near zero:\n {0}...(total {1} outliers)\nHow to deal with them?"
).format(
", ".join([
result.sample.name
for result in outlier_results[:100]
]), len(outlier_results)))
else:
self.outlier_msg.setText(
self.
tr("The fitting results have the component that its fraction is near zero:\n {0}\nHow to deal with them?"
).format(", ".join([
result.sample.name for result in outlier_results
])))
res = self.outlier_msg.exec_()
if res == QMessageBox.Discard:
self.remove_results(outlier_indexes)
elif res == QMessageBox.Retry:
self.retry_results(outlier_indexes, outlier_results)
else:
pass
def check_nan_and_inf(self):
if self.n_results == 0:
self.show_warning(self.tr("There is not any result in the list."))
return
outlier_results = []
outlier_indexes = []
for i, result in enumerate(self.__fitting_results):
if not result.is_valid:
outlier_results.append(result)
outlier_indexes.append(i)
self.logger.debug(
f"Outlier results with the nan or inf value(s): {[result.sample.name for result in outlier_results]}"
)
self.ask_deal_outliers(outlier_results, outlier_indexes)
def check_final_distances(self):
if self.n_results == 0:
self.show_warning(self.tr("There is not any result in the list."))
return
elif self.n_results < 10:
self.show_warning(self.tr("The results in list are too less."))
return
distances = []
for result in self.__fitting_results:
distances.append(result.get_distance(self.distance_name))
distances = np.array(distances)
self.boxplot_chart.show_dataset([distances],
xlabels=[self.distance_name],
ylabel=self.tr("Distance"))
self.boxplot_chart.show()
# calculate the 1/4, 1/2, and 3/4 postion value to judge which result is invalid
# 1. the mean squared errors are much higher in the results which are lack of components
# 2. with the component number getting higher, the mean squared error will get lower and finally reach the minimum
median = np.median(distances)
upper_group = distances[np.greater(distances, median)]
lower_group = distances[np.less(distances, median)]
value_1_4 = np.median(lower_group)
value_3_4 = np.median(upper_group)
distance_QR = value_3_4 - value_1_4
outlier_results = []
outlier_indexes = []
for i, (result,
distance) in enumerate(zip(self.__fitting_results, distances)):
if distance > value_3_4 + distance_QR * 1.5:
# which error too small is not outlier
# if distance > value_3_4 + distance_QR * 1.5 or distance < value_1_4 - distance_QR * 1.5:
outlier_results.append(result)
outlier_indexes.append(i)
self.logger.debug(
f"Outlier results with too greater distances: {[result.sample.name for result in outlier_results]}"
)
self.ask_deal_outliers(outlier_results, outlier_indexes)
def check_component_moments(self, key: str):
if self.n_results == 0:
self.show_warning(self.tr("There is not any result in the list."))
return
elif self.n_results < 10:
self.show_warning(self.tr("The results in list are too less."))
return
max_n_components = 0
for result in self.__fitting_results:
if result.n_components > max_n_components:
max_n_components = result.n_components
moments = []
for i in range(max_n_components):
moments.append([])
for result in self.__fitting_results:
for i, component in enumerate(result.components):
if np.isnan(component.logarithmic_moments[key]) or np.isinf(
component.logarithmic_moments[key]):
pass
else:
moments[i].append(component.logarithmic_moments[key])
# key_trans = {"mean": self.tr("Mean"), "std": self.tr("STD"), "skewness": self.tr("Skewness"), "kurtosis": self.tr("Kurtosis")}
key_label_trans = {
"mean": self.tr("Mean [φ]"),
"std": self.tr("STD [φ]"),
"skewness": self.tr("Skewness"),
"kurtosis": self.tr("Kurtosis")
}
self.boxplot_chart.show_dataset(
moments,
xlabels=[f"C{i+1}" for i in range(max_n_components)],
ylabel=key_label_trans[key])
self.boxplot_chart.show()
outlier_dict = {}
for i in range(max_n_components):
stacked_moments = np.array(moments[i])
# calculate the 1/4, 1/2, and 3/4 postion value to judge which result is invalid
# 1. the mean squared errors are much higher in the results which are lack of components
# 2. with the component number getting higher, the mean squared error will get lower and finally reach the minimum
median = np.median(stacked_moments)
upper_group = stacked_moments[np.greater(stacked_moments, median)]
lower_group = stacked_moments[np.less(stacked_moments, median)]
value_1_4 = np.median(lower_group)
value_3_4 = np.median(upper_group)
distance_QR = value_3_4 - value_1_4
for j, result in enumerate(self.__fitting_results):
if result.n_components > i:
distance = result.components[i].logarithmic_moments[key]
if distance > value_3_4 + distance_QR * 1.5 or distance < value_1_4 - distance_QR * 1.5:
outlier_dict[j] = result
outlier_results = []
outlier_indexes = []
for index, result in sorted(outlier_dict.items(), key=lambda x: x[0]):
outlier_indexes.append(index)
outlier_results.append(result)
self.logger.debug(
f"Outlier results with abnormal {key} values of their components: {[result.sample.name for result in outlier_results]}"
)
self.ask_deal_outliers(outlier_results, outlier_indexes)
def check_component_fractions(self):
outlier_results = []
outlier_indexes = []
for i, result in enumerate(self.__fitting_results):
for component in result.components:
if component.fraction < 1e-3:
outlier_results.append(result)
outlier_indexes.append(i)
break
self.logger.debug(
f"Outlier results with the component that its fraction is near zero: {[result.sample.name for result in outlier_results]}"
)
self.ask_deal_outliers(outlier_results, outlier_indexes)
def try_align_components(self):
if self.n_results == 0:
self.show_warning(self.tr("There is not any result in the list."))
return
elif self.n_results < 10:
self.show_warning(self.tr("The results in list are too less."))
return
import matplotlib.pyplot as plt
n_components_list = [
result.n_components for result in self.__fitting_results
]
count_dict = Counter(n_components_list)
max_n_components = max(count_dict.keys())
self.logger.debug(
f"N_components: {count_dict}, Max N_components: {max_n_components}"
)
n_components_desc = "\n".join([
self.tr("{0} Component(s): {1}").format(n_components, count)
for n_components, count in count_dict.items()
])
self.show_info(
self.tr("N_components distribution of Results:\n{0}").format(
n_components_desc))
x = self.__fitting_results[0].classes_μm
stacked_components = []
for result in self.__fitting_results:
for component in result.components:
stacked_components.append(component.distribution)
stacked_components = np.array(stacked_components)
cluser = KMeans(n_clusters=max_n_components)
flags = cluser.fit_predict(stacked_components)
figure = plt.figure(figsize=(6, 4))
cmap = plt.get_cmap("tab10")
axes = figure.add_subplot(1, 1, 1)
for flag, distribution in zip(flags, stacked_components):
plt.plot(x, distribution, c=cmap(flag), zorder=flag)
axes.set_xscale("log")
axes.set_xlabel(self.tr("Grain-size [μm]"))
axes.set_ylabel(self.tr("Frequency"))
figure.tight_layout()
figure.show()
outlier_results = []
outlier_indexes = []
flag_index = 0
for i, result in enumerate(self.__fitting_results):
result_flags = set()
for component in result.components:
if flags[flag_index] in result_flags:
outlier_results.append(result)
outlier_indexes.append(i)
break
else:
result_flags.add(flags[flag_index])
flag_index += 1
self.logger.debug(
f"Outlier results that have two components in the same cluster: {[result.sample.name for result in outlier_results]}"
)
self.ask_deal_outliers(outlier_results, outlier_indexes)
def analyse_typical_components(self):
if self.n_results == 0:
self.show_warning(self.tr("There is not any result in the list."))
return
elif self.n_results < 10:
self.show_warning(self.tr("The results in list are too less."))
return
self.typical_chart.show_typical(self.__fitting_results)
self.typical_chart.show()
