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	fix: исправлены предупреждения Codacy
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		| @@ -20,7 +20,7 @@ class DistributionWindow(QDialog): | |||||||
|  |  | ||||||
|         self.comboBox = QComboBox() |         self.comboBox = QComboBox() | ||||||
|         self.comboBox.addItems(self.values) |         self.comboBox.addItems(self.values) | ||||||
|         self.comboBox.currentIndexChanged.connect(self.on_change) |         self.comboBox.currentIndexChanged.connect(self.onChange) | ||||||
|         self.sc = self.getSc(data[:, 0]) |         self.sc = self.getSc(data[:, 0]) | ||||||
|         self.layout.addWidget(self.comboBox) |         self.layout.addWidget(self.comboBox) | ||||||
|  |  | ||||||
| @@ -29,7 +29,7 @@ class DistributionWindow(QDialog): | |||||||
|         self.layout.addLayout(self.l) |         self.layout.addLayout(self.l) | ||||||
|  |  | ||||||
|  |  | ||||||
|     def on_change(self): |     def onChange(self): | ||||||
|         while ((child := self.l.takeAt(0)) != None): |         while ((child := self.l.takeAt(0)) != None): | ||||||
|             child.widget().deleteLater() |             child.widget().deleteLater() | ||||||
|         self.sc = self.getSc(self.data[:, self.comboBox.currentIndex()]) |         self.sc = self.getSc(self.data[:, self.comboBox.currentIndex()]) | ||||||
| @@ -43,16 +43,19 @@ class UniformDistributionWindow(DistributionWindow): | |||||||
|     def getSc(self, points): |     def getSc(self, points): | ||||||
|         sc = MplCanvas(self, width=5, height=4, dpi=100) |         sc = MplCanvas(self, width=5, height=4, dpi=100) | ||||||
|         points = np.sort(points) |         points = np.sort(points) | ||||||
|         unique_points = np.array([points[0]] + [pt for pt, next_pt in zip(points[:-1], points[1:]) if pt != next_pt]) |         points = np.array( | ||||||
|         differences = np.diff(unique_points) |             [points[0]] + | ||||||
|         inverse_differences = 1 / differences |             [pt for pt, next_pt in zip(points[:-1], points[1:]) if pt != next_pt] | ||||||
|         for i, (start, end) in enumerate(zip(unique_points[:-1], unique_points[1:])): |         ) | ||||||
|             sc.axes.hlines(inverse_differences[i], start, end, colors='r', linestyles='solid') |         differences = np.diff(points) | ||||||
|  |         inverseDifferences = 1 / differences | ||||||
|  |         for i, (start, end) in enumerate(zip(points[:-1], points[1:])): | ||||||
|  |             sc.axes.hlines(inverseDifferences[i], start, end, colors='r', linestyles='solid') | ||||||
|         return sc |         return sc | ||||||
|  |  | ||||||
|  |  | ||||||
| def normal_density(x, mu, sigma_squared): | def normalDensity(x, mu, sigmaSquared): | ||||||
|     return 1 / np.sqrt(2 * np.pi * sigma_squared) * np.exp(-(x - mu)**2 / (2 * sigma_squared)) |     return 1 / np.sqrt(2 * np.pi * sigmaSquared) * np.exp(-(x - mu) ** 2 / (2 * sigmaSquared)) | ||||||
|  |  | ||||||
|  |  | ||||||
| class NormalDistributionWindow(DistributionWindow): | class NormalDistributionWindow(DistributionWindow): | ||||||
| @@ -63,8 +66,8 @@ class NormalDistributionWindow(DistributionWindow): | |||||||
|         sc = MplCanvas(self, width=5, height=4, dpi=100) |         sc = MplCanvas(self, width=5, height=4, dpi=100) | ||||||
|         points = np.sort(points) |         points = np.sort(points) | ||||||
|         mu = np.mean(points) |         mu = np.mean(points) | ||||||
|         sigma_squared = np.var(points) |         sigmaSquared = np.var(points) | ||||||
|         y_values = normal_density(points, mu, sigma_squared) |         y_values = normalDensity(points, mu, sigmaSquared) | ||||||
|  |  | ||||||
|         sc.axes.plot(points, y_values) |         sc.axes.plot(points, y_values) | ||||||
|  |  | ||||||
| @@ -79,7 +82,7 @@ class ExponentialDistributionWindow(DistributionWindow): | |||||||
|         sc = MplCanvas(self, width=5, height=4, dpi=100) |         sc = MplCanvas(self, width=5, height=4, dpi=100) | ||||||
|         points = np.sort(points) |         points = np.sort(points) | ||||||
|         mu = np.mean(points) |         mu = np.mean(points) | ||||||
|         lambda_param = 1 / mu |         lambdaParam = 1 / mu | ||||||
|         y_values = lambda_param * np.exp(-lambda_param * points) |         y_values = lambdaParam * np.exp(-lambdaParam * points) | ||||||
|         sc.axes.plot(points, y_values) |         sc.axes.plot(points, y_values) | ||||||
|         return sc |         return sc | ||||||
|   | |||||||
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