mirror of
https://github.com/shizand/statapp.git
synced 2025-04-01 23:23:45 +03:00
115 lines
4.9 KiB
Python
115 lines
4.9 KiB
Python
#
|
|
# Copyright (c) 2024 Maxim Slipenko, Eugene Lazurenko.
|
|
#
|
|
# This file is part of Statapp
|
|
# (see https://github.com/shizand/statapp).
|
|
#
|
|
# This program is free software: you can redistribute it and/or modify
|
|
# it under the terms of the GNU General Public License as published by
|
|
# the Free Software Foundation, either version 3 of the License, or
|
|
# (at your option) any later version.
|
|
#
|
|
# This program is distributed in the hope that it will be useful,
|
|
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
# GNU General Public License for more details.
|
|
#
|
|
# You should have received a copy of the GNU General Public License
|
|
# along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
#
|
|
import numpy as np
|
|
from PySide2.QtCore import Qt
|
|
from PySide2.QtWidgets import QDialog, QHeaderView
|
|
|
|
from statapp.calculations import linearPolynom, prediction
|
|
from statapp.combo_delegate import ComboDelegate
|
|
from statapp.mathtex_header_view import MathTexHeaderView
|
|
from statapp.models.prediction_table_model import PreditionTableModel
|
|
from statapp.models.transform_polynom_model import TransformPolynomModel, TRANSFORMS
|
|
from statapp.polynoms.polynom_window import MplCanvas
|
|
from statapp.ui.ui_polynom_window import Ui_PolynomWindow
|
|
from statapp.utils import addIcon
|
|
|
|
|
|
class TransformPolynomWindow(QDialog):
|
|
def __init__(self, data):
|
|
super().__init__()
|
|
self.ui = Ui_PolynomWindow()
|
|
self.ui.setupUi(self)
|
|
addIcon(self)
|
|
self.setWindowTitle("Преобразования")
|
|
|
|
self.data = data
|
|
result = linearPolynom(data)
|
|
|
|
predictionResult = prediction(data, result)
|
|
self.predictionModel = PreditionTableModel(predictionResult)
|
|
self.ui.predictionTableView.setModel(self.predictionModel)
|
|
header = self.ui.predictionTableView.horizontalHeader()
|
|
header.setSectionResizeMode(QHeaderView.ResizeMode.Stretch)
|
|
|
|
self.sc = MplCanvas(self, width=5, height=4, dpi=100)
|
|
xAxes = np.array(range(len(data[:, 0])))
|
|
realY = predictionResult[:, 0]
|
|
calculatedY = predictionResult[:, 1]
|
|
self.sc.axes.scatter(xAxes, realY)
|
|
self.sc.axes.plot(xAxes, calculatedY)
|
|
self.ui.plotContainer.addWidget(self.sc)
|
|
|
|
# Создание столбца из нулей
|
|
zeroCol = np.zeros((result.paramsAndImportance.shape[0], 1))
|
|
# Добавление столбца к исходному массиву
|
|
result.paramsAndImportance = np.column_stack((zeroCol, result.paramsAndImportance))
|
|
|
|
# self.ui.tableView.setItemDelegate(FloatDelegate())
|
|
self.ui.tableView.setItemDelegate(
|
|
ComboDelegate(
|
|
self.ui.tableView,
|
|
list(TRANSFORMS.keys()),
|
|
list(TRANSFORMS.keys()),
|
|
)
|
|
)
|
|
self.model = TransformPolynomModel(result)
|
|
self.ui.tableView.setModel(self.model)
|
|
self.ui.tableView.setVerticalHeader(MathTexHeaderView(self.ui.tableView))
|
|
header = self.ui.tableView.horizontalHeader()
|
|
header.setSectionResizeMode(QHeaderView.ResizeMode.Stretch)
|
|
|
|
self.ui.residualVarianceValueLabel.setText(str(result.residualVariance))
|
|
self.ui.scaledResidualVarianceValueLabel.setText(str(result.scaledResidualVariance))
|
|
self.ui.fStatisticValueLabel.setText(str(result.fStatistic))
|
|
self.ui.rSquaredValueLabel.setText(str(result.scaledResidualVariance))
|
|
|
|
self.model.dataChanged.connect(self.on_data_changed)
|
|
|
|
def on_data_changed(self):
|
|
data = np.copy(self.data)
|
|
print(len(data[0:]))
|
|
for i in range(len(data[0:])):
|
|
for j in range(1, len(data[i])):
|
|
tr = self.model.data(self.model.createIndex(j, 0), Qt.DisplayRole)
|
|
data[i][j] = TRANSFORMS[tr](data[i][j])
|
|
|
|
self.rebuildData(data)
|
|
|
|
def rebuildData(self, data):
|
|
result = linearPolynom(data)
|
|
predictionResult = prediction(data, result)
|
|
self.predictionModel.updateAllData(predictionResult)
|
|
self.ui.plotContainer.removeWidget(self.sc)
|
|
self.sc = MplCanvas(self, width=5, height=4, dpi=100)
|
|
xAxes = np.array(range(len(data[:, 0])))
|
|
realY = predictionResult[:, 0]
|
|
calculatedY = predictionResult[:, 1]
|
|
self.sc.axes.scatter(xAxes, realY)
|
|
self.sc.axes.plot(xAxes, calculatedY)
|
|
self.ui.plotContainer.addWidget(self.sc)
|
|
|
|
zeroCol = np.zeros((result.paramsAndImportance.shape[0], 1))
|
|
result.paramsAndImportance = np.column_stack((zeroCol, result.paramsAndImportance))
|
|
self.model.updateAllData(result)
|
|
self.ui.residualVarianceValueLabel.setText(str(result.residualVariance))
|
|
self.ui.scaledResidualVarianceValueLabel.setText(str(result.scaledResidualVariance))
|
|
self.ui.fStatisticValueLabel.setText(str(result.fStatistic))
|
|
self.ui.rSquaredValueLabel.setText(str(result.scaledResidualVariance))
|