From d05acbc2e2632135983898c841d3b87e0fe63eea Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
<laura.kuehle@uni-duesseldorf.de>
Date: Thu, 9 Mar 2023 20:16:46 +0100
Subject: [PATCH] Refactored Boxplot class.
---
Snakefile | 7 +-
scripts/tcd/Troubled_Cell_Detector.py | 95 +++++++++++++++++++--------
2 files changed, 72 insertions(+), 30 deletions(-)
diff --git a/Snakefile b/Snakefile
index fd059c4..615f4a7 100644
--- a/Snakefile
+++ b/Snakefile
@@ -23,7 +23,12 @@ TODO: Contemplate allowing vector input for ICs
TODO: Discuss how wavelet details should be plotted
Urgent:
-TODO: Move boundary condition to Mesh class
+TODO: Refactor Boxplot class -> Done
+TODO: Enforce boundary condition with decorator
+TODO: Enforce Boxplot bounds with decorator
+TODO: Enforce Boxplot folds with decorator
+TODO: Enforce boundary for initial condition in exact solution only
+TODO: Adapt number of ghost cells based on ANN stencil
TODO: Ensure exact solution is calculated in Equation class
TODO: Extract objects from UpdateScheme
TODO: Enforce num_ghost_cells to be positive integer for DG (not training)
diff --git a/scripts/tcd/Troubled_Cell_Detector.py b/scripts/tcd/Troubled_Cell_Detector.py
index 805fdd8..69c2162 100644
--- a/scripts/tcd/Troubled_Cell_Detector.py
+++ b/scripts/tcd/Troubled_Cell_Detector.py
@@ -3,8 +3,10 @@
@author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
"""
+from typing import Tuple
from abc import ABC, abstractmethod
import numpy as np
+from numpy import ndarray
import torch
from . import ANN_Model
@@ -344,25 +346,39 @@ class Boxplot(WaveletDetector):
super()._reset(config)
# Unpack necessary configurations
- self._fold_len = config.pop('fold_len', 16)
+ self._fold_len = min(self._mesh.num_cells,
+ config.pop('fold_len', 16))
self._whisker_len = config.pop('whisker_len', 3)
self._adjust_outer_fences = config.pop('adjust_outer_fences', True)
self._extreme_outlier_only = config.pop('extreme_outlier_only', True)
- if self._mesh.num_cells < self._fold_len:
- self._fold_len = self._mesh.num_cells
-
self._quantile_method = config.pop('quantile_method', 'weibull')
num_overlapping_cells = config.pop('num_overlapping_cells', 1)
+ self._fold_indices = self._compute_folds(num_overlapping_cells)
+
+ def _compute_folds(self, num_overlapping_cells: int) -> ndarray:
+ """Compute indices for all folds used in Boxplot calculation.
+
+ Parameters
+ ----------
+ num_overlapping_cells : int
+ Number of cells overlapping between adjacent folds.
+
+ Returns
+ -------
+ fold_indices : ndarray
+ Array of projection indices in each fold.
+
+ """
num_folds = self._mesh.num_cells//self._fold_len
- self._fold_indices = np.zeros([num_folds,
- self._fold_len + 2 *
- num_overlapping_cells]).astype(np.int32)
+ fold_indices = np.zeros([num_folds, self._fold_len + 2 *
+ num_overlapping_cells]).astype(np.int32)
for fold in range(num_folds):
- self._fold_indices[fold] = np.array(
+ fold_indices[fold] = np.array(
[i % self._mesh.num_cells for i in range(
fold * self._fold_len - num_overlapping_cells,
(fold+1) * self._fold_len + num_overlapping_cells)])
+ return fold_indices
def get_cells(self, projection):
"""Calculate troubled cells in a given projection.
@@ -378,29 +394,10 @@ class Boxplot(WaveletDetector):
List of indices for all detected troubled cells.
"""
- # Determine quartiles of folds
+ # Determine bounds
coeffs = self._select_degree(self._calculate_wavelet_coeffs(
projection))
- folds = coeffs[self._fold_indices]
- first_quartiles = np.quantile(folds, 0.25, axis=1,
- method=self._quantile_method)
- third_quartiles = np.quantile(folds, 0.75, axis=1,
- method=self._quantile_method)
-
- # Determine bounds based on quartiles of a boxplot
- lower_bounds = np.zeros(len(first_quartiles) + 2)
- upper_bounds = np.zeros(len(first_quartiles) + 2)
-
- lower_bounds[1:-1] = first_quartiles - self._whisker_len * (
- third_quartiles-first_quartiles)
- upper_bounds[1:-1] = third_quartiles + self._whisker_len * (
- third_quartiles-first_quartiles)
-
- # Adjust bounds to capture periodic boundary
- lower_bounds[0] = lower_bounds[-2]
- lower_bounds[-1] = lower_bounds[1]
- upper_bounds[0] = upper_bounds[-2]
- upper_bounds[-1] = upper_bounds[1]
+ lower_bounds, upper_bounds = self._compute_bounds(coeffs)
# Adjust outer fences if flag is set
if self._adjust_outer_fences:
@@ -426,6 +423,46 @@ class Boxplot(WaveletDetector):
return troubled_cells
+ def _compute_bounds(self, coeffs: ndarray) -> Tuple[ndarray, ndarray]:
+ """Compute lower and upper bound for Boxplot outliers.
+
+ Parameters
+ ----------
+ coeffs : ndarray
+ Matrix of multiwavelet coefficients of projection.
+
+ Returns
+ -------
+ lower_bounds : ndarray
+ Array of lower bounds for outlier.
+ upper_bounds : ndarray
+ Array of upper bounds for outlier.
+
+ """
+ # Determine quartiles of folds
+ folds = coeffs[self._fold_indices]
+ first_quartiles = np.quantile(folds, 0.25, axis=1,
+ method=self._quantile_method)
+ third_quartiles = np.quantile(folds, 0.75, axis=1,
+ method=self._quantile_method)
+
+ # Determine bounds based on quartiles of a boxplot
+ lower_bounds = np.zeros(len(first_quartiles) + 2)
+ upper_bounds = np.zeros(len(first_quartiles) + 2)
+
+ lower_bounds[1:-1] = first_quartiles - self._whisker_len * (
+ third_quartiles-first_quartiles)
+ upper_bounds[1:-1] = third_quartiles + self._whisker_len * (
+ third_quartiles-first_quartiles)
+
+ # Adjust bounds to capture periodic boundary
+ lower_bounds[0] = lower_bounds[-2]
+ lower_bounds[-1] = lower_bounds[1]
+ upper_bounds[0] = upper_bounds[-2]
+ upper_bounds[-1] = upper_bounds[1]
+
+ return lower_bounds, upper_bounds
+
class Theoretical(WaveletDetector):
"""Class for troubled-cell detection based on theoretical thresholding.
--
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