diff --git a/ANN_Training.py b/ANN_Training.py
index 2c5316bb6742c112a6990e0b022a7208a26c8c46..41c71d66800872d5c98b2aa701461401bc9beade 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -10,8 +10,6 @@ TODO: Add README for ANN training
"""
import numpy as np
import time
-import matplotlib
-from matplotlib import pyplot as plt
import os
import torch
import json
@@ -21,9 +19,6 @@ from sklearn.metrics import accuracy_score, precision_recall_fscore_support, \
roc_auc_score
import ANN_Model
-from Plotting import plot_classification_barplot, plot_classification_boxplot
-
-matplotlib.use('Agg')
class ModelTrainer:
@@ -347,64 +342,3 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
json_file.write(json.dumps(classification_stats))
toc = time.perf_counter()
print(f'Total runtime: {toc - tic:0.4f}s')
-
-
-def plot_evaluation_results(evaluation_file: str, directory: str,
- colors: dict = None) -> None:
- """Plots given evaluation results of model classifications.
-
- Plots evaluation results for all measures for which a color is given. If
- colors is set to None, all measures are plotted with a default color
- scheme.
-
- Parameters
- ----------
- evaluation_file: str
- Path to file containing evaluation results.
- directory : str
- Path to directory for saving resulting plots.
- colors : dict, optional
- Dictionary containing plotting colors. If None, set to default colors.
- Default: None.
-
- """
- tic = time.perf_counter()
-
- # Set colors if not given
- if colors is None:
- colors = {'Accuracy': 'magenta', 'Precision_Smooth': 'red',
- 'Precision_Troubled': '#8B0000', 'Recall_Smooth': 'blue',
- 'Recall_Troubled': '#00008B', 'F-Score_Smooth': 'green',
- 'F-Score_Troubled': '#006400', 'AUROC': 'yellow'}
-
- # Read evaluation results
- print('Reading evaluation results.')
- with open(evaluation_file) as json_file:
- classification_stats = json.load(json_file)
-
- # Plot data
- print('\nPlotting evaluation of trained models...')
- print('Plotting data in boxplot.')
- models = classification_stats[list(colors.keys())[0]].keys()
- plot_classification_boxplot(classification_stats, colors)
- print('Plotting averaged data in barplot.')
- classification_stats = {measure: {model: np.array(
- classification_stats[measure][model]).mean()
- for model in models}
- for measure in colors}
- plot_classification_barplot(classification_stats, colors)
- print('Finished plotting evaluation of trained models!\n')
-
- # Set paths for plot files if not existing already
- plot_dir = directory + '/model evaluation'
- if not os.path.exists(plot_dir):
- os.makedirs(plot_dir)
-
- # Save plots
- print('Saving plots.')
- file_name = evaluation_file.split('/')[-1].rstrip('.json')
- for identifier in plt.get_figlabels():
- plt.figure(identifier)
- plt.savefig(plot_dir + '/' + file_name + '.' + identifier + '.pdf')
- toc = time.perf_counter()
- print(f'Total runtime: {toc - tic:0.4f}s')
diff --git a/DG_Approximation.py b/DG_Approximation.py
index 58e86da77ef389a07c8c984eda4ec1078ad3c198..d15fb5902ab94634f7f9506d8428345bdb48bbfd 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -5,6 +5,8 @@
Discussion:
Urgent:
+TODO: Rename Vector to Basis
+TODO: Extract ndarray encoding and decoding
TODO: Hard-code simplification of cell average/reconstruction in basis
TODO: Make basis variables public (if feasible)
TODO: Contain polynomial degree in basis
@@ -13,7 +15,7 @@ TODO: Introduce Mesh class
TODO: Check whether ghost cells are handled/set correctly
TODO: Find error in centering for ANN training
TODO: Investigate g-mesh(?)
-TODO: Contain all plotting in Plotting
+TODO: Contain all plotting in Plotting -> Done
TODO: Remove use of DGScheme from ANN_Data_Generator
TODO: Clean up docstrings
TODO: Adapt TCD from Soraya
@@ -52,14 +54,11 @@ TODO: Discuss adding kwargs to attributes in documentation
TODO: Add type annotations to function heads
"""
-import os
import json
import numpy as np
from sympy import Symbol
import math
import seaborn as sns
-import matplotlib
-from matplotlib import pyplot as plt
import Troubled_Cell_Detector
import Initial_Condition
@@ -67,12 +66,8 @@ import Limiter
import Quadrature
import Update_Scheme
from Basis_Function import OrthonormalLegendre
-from projection_utils import calculate_cell_average, \
- calculate_exact_solution, calculate_approximate_solution
-from Plotting import plot_solution_and_approx, plot_semilog_error, \
- plot_error, plot_shock_tube, plot_details
+from projection_utils import calculate_cell_average
-matplotlib.use('Agg')
x = Symbol('x')
sns.set()
@@ -406,195 +401,3 @@ def do_initial_projection(initial_condition, basis, quadrature,
# print(np.array(output_matrix).shape)
return np.transpose(np.array(output_matrix))
-
-
-def plot_approximation_results(data_file, directory, plot_name, quadrature,
- init_cond, basis):
- """Plots given approximation results.
-
- Generates plots based on given data, sets plot directory if not
- already existing, and saves plots.
-
- Parameters
- ----------
- data_file: str
- Path to data file for plotting.
- directory: str
- Path to directory in which plots will be saved.
- plot_name : str
- Name of plot.
- basis: Vector object
- Basis used for calculation.
- quadrature: Quadrature object
- Quadrature used for evaluation.
- init_cond : InitialCondition object
- Initial condition used for calculation.
-
- """
- # Read approximation results
- with open(data_file + '.json') as json_file:
- approx_stats = json.load(json_file)
-
- # Decode all ndarrays by converting lists
- approx_stats = {key: decode_ndarray(approx_stats[key])
- for key in approx_stats.keys()}
-
- # Plot exact/approximate results, errors, shock tubes,
- # and any detector-dependant plots
- plot_results(quadrature=quadrature, basis=basis,
- init_cond=init_cond, **approx_stats)
-
- # Set paths for plot files if not existing already
- if not os.path.exists(directory):
- os.makedirs(directory)
-
- # Save plots
- for identifier in plt.get_figlabels():
- # Set path for figure directory if not existing already
- if not os.path.exists(directory + '/' + identifier):
- os.makedirs(directory + '/' + identifier)
-
- plt.figure(identifier)
- plt.savefig(directory + '/' + identifier + '/' +
- plot_name + '.pdf')
-
-
-def plot_results(projection, troubled_cell_history, time_history, mesh,
- num_grid_cells, polynomial_degree, wave_speed, final_time,
- left_bound, right_bound, basis, quadrature, init_cond,
- colors=None, coarse_projection=None,
- multiwavelet_coeffs=None):
- """Plots results and troubled cells of a projection.
-
- Plots exact and approximate solution, errors, and troubled cells of a
- projection given its evaluation history.
-
- If coarse grid and projection are given, solutions are displayed for
- both coarse and fine grid. Additionally, coefficient details are plotted.
-
- Parameters
- ----------
- projection : ndarray
- Matrix of projection for each polynomial degree.
- troubled_cell_history : list
- List of detected troubled cells for each time step.
- time_history : list
- List of value of each time step.
- mesh : ndarray
- List of mesh valuation points.
- num_grid_cells : int
- Number of cells in the mesh. Usually exponential of 2.
- polynomial_degree : int
- Polynomial degree.
- wave_speed : float
- Speed of wave in rightward direction.
- final_time : float
- Final time for which approximation is calculated.
- left_bound : float
- Left boundary of interval.
- right_bound : float
- Right boundary of interval.
- basis: Vector object
- Basis used for calculation.
- quadrature: Quadrature object
- Quadrature used for evaluation.
- init_cond : InitialCondition object
- Initial condition used for calculation.
- colors: dict
- Dictionary of colors used for plots.
- coarse_projection: ndarray, optional
- Matrix of projection on coarse grid for each polynomial degree.
- Default: None.
- multiwavelet_coeffs: ndarray, optional
- Matrix of wavelet coefficients. Default: None.
-
- """
- # Set colors
- if colors is None:
- colors = {}
- colors = _check_colors(colors)
-
- # Calculate needed variables
- interval_len = right_bound-left_bound
- cell_len = interval_len/num_grid_cells
-
- # Plot troubled cells
- plot_shock_tube(num_grid_cells, troubled_cell_history, time_history)
-
- # Determine exact and approximate solution
- grid, exact = calculate_exact_solution(
- mesh[2:-2], cell_len, wave_speed,
- final_time, interval_len, quadrature,
- init_cond)
- approx = calculate_approximate_solution(
- projection[:, 1:-1], quadrature.get_eval_points(),
- polynomial_degree, basis.get_basis_vector())
-
- # Plot multiwavelet solution (fine and coarse grid)
- if coarse_projection is not None:
- coarse_cell_len = 2*cell_len
- coarse_mesh = np.arange(left_bound - (0.5*coarse_cell_len),
- right_bound + (1.5*coarse_cell_len),
- coarse_cell_len)
-
- # Plot exact and approximate solutions for coarse mesh
- coarse_grid, coarse_exact = calculate_exact_solution(
- coarse_mesh[1:-1], coarse_cell_len, wave_speed,
- final_time, interval_len, quadrature,
- init_cond)
- coarse_approx = calculate_approximate_solution(
- coarse_projection, quadrature.get_eval_points(),
- polynomial_degree, basis.get_basis_vector())
- plot_solution_and_approx(
- coarse_grid, coarse_exact, coarse_approx, colors['coarse_exact'],
- colors['coarse_approx'])
-
- # Plot multiwavelet details
- num_coarse_grid_cells = num_grid_cells//2
- plot_details(projection[:, 1:-1], mesh[2:-2], coarse_projection,
- basis.get_basis_vector(),
- basis.get_wavelet_vector(), multiwavelet_coeffs,
- num_coarse_grid_cells,
- polynomial_degree)
-
- plot_solution_and_approx(grid, exact, approx,
- colors['fine_exact'],
- colors['fine_approx'])
- plt.legend(['Exact (Coarse)', 'Approx (Coarse)', 'Exact (Fine)',
- 'Approx (Fine)'])
- # Plot regular solution (fine grid)
- else:
- plot_solution_and_approx(grid, exact, approx, colors['exact'],
- colors['approx'])
- plt.legend(['Exact', 'Approx'])
-
- # Calculate errors
- pointwise_error = np.abs(exact-approx)
- max_error = np.max(pointwise_error)
-
- # Plot errors
- plot_semilog_error(grid, pointwise_error)
- plot_error(grid, exact, approx)
-
- print('p =', polynomial_degree)
- print('N =', num_grid_cells)
- print('maximum error =', max_error)
-
-
-def _check_colors(colors):
- """Checks plot colors.
-
- Checks whether colors for plots were given and sets them if required.
-
- """
- # Set colors for general plots
- colors['exact'] = colors.get('exact', 'k-')
- colors['approx'] = colors.get('approx', 'y')
-
- # Set colors for multiwavelet plots
- colors['fine_exact'] = colors.get('fine_exact', 'k-.')
- colors['fine_approx'] = colors.get('fine_approx', 'b-.')
- colors['coarse_exact'] = colors.get('coarse_exact', 'k-')
- colors['coarse_approx'] = colors.get('coarse_approx', 'y')
-
- return colors
diff --git a/Plotting.py b/Plotting.py
index 5c63e04dcce824b480b00d81ced9b58a2ca65940..43b854b6d6ffffac3805cdbe729291ef48c867e3 100644
--- a/Plotting.py
+++ b/Plotting.py
@@ -6,6 +6,9 @@ TODO: Give option to select plotting color
"""
+import os
+import time
+import json
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
@@ -13,6 +16,13 @@ import seaborn as sns
from numpy import ndarray
from sympy import Symbol
+from Quadrature import Quadrature
+from Initial_Condition import InitialCondition
+from Basis_Function import Vector
+from projection_utils import calculate_exact_solution,\
+ calculate_approximate_solution
+from DG_Approximation import decode_ndarray
+
matplotlib.use('Agg')
x = Symbol('x')
@@ -248,3 +258,270 @@ def plot_classification_boxplot(evaluation_dict: dict, colors: dict) -> None:
ax.legend([bp["boxes"][0] for bp in boxplots], evaluation_dict.keys(),
loc='center right', bbox_to_anchor=(1.4, 0.75), shadow=True,
ncol=1, fancybox=True, fontsize=8)
+
+
+def plot_evaluation_results(evaluation_file: str, directory: str,
+ colors: dict = None) -> None:
+ """Plots given evaluation results of model classifications.
+
+ Plots evaluation results for all measures for which a color is given. If
+ colors is set to None, all measures are plotted with a default color
+ scheme.
+
+ Parameters
+ ----------
+ evaluation_file: str
+ Path to file containing evaluation results.
+ directory : str
+ Path to directory for saving resulting plots.
+ colors : dict, optional
+ Dictionary containing plotting colors. If None, set to default colors.
+ Default: None.
+
+ """
+ tic = time.perf_counter()
+
+ # Set colors if not given
+ if colors is None:
+ colors = {'Accuracy': 'magenta', 'Precision_Smooth': 'red',
+ 'Precision_Troubled': '#8B0000', 'Recall_Smooth': 'blue',
+ 'Recall_Troubled': '#00008B', 'F-Score_Smooth': 'green',
+ 'F-Score_Troubled': '#006400', 'AUROC': 'yellow'}
+
+ # Read evaluation results
+ print('Reading evaluation results.')
+ with open(evaluation_file) as json_file:
+ classification_stats = json.load(json_file)
+
+ # Plot data
+ print('\nPlotting evaluation of trained models...')
+ print('Plotting data in boxplot.')
+ models = classification_stats[list(colors.keys())[0]].keys()
+ plot_classification_boxplot(classification_stats, colors)
+ print('Plotting averaged data in barplot.')
+ classification_stats = {measure: {model: np.array(
+ classification_stats[measure][model]).mean()
+ for model in models}
+ for measure in colors}
+ plot_classification_barplot(classification_stats, colors)
+ print('Finished plotting evaluation of trained models!\n')
+
+ # Set paths for plot files if not existing already
+ plot_dir = directory + '/model evaluation'
+ if not os.path.exists(plot_dir):
+ os.makedirs(plot_dir)
+
+ # Save plots
+ print('Saving plots.')
+ file_name = evaluation_file.split('/')[-1].rstrip('.json')
+ for identifier in plt.get_figlabels():
+ plt.figure(identifier)
+ plt.savefig(plot_dir + '/' + file_name + '.' + identifier + '.pdf')
+ toc = time.perf_counter()
+ print(f'Total runtime: {toc - tic:0.4f}s')
+
+
+def plot_approximation_results(data_file: str, directory: str, plot_name: str,
+ basis: Vector, quadrature: Quadrature,
+ init_cond: InitialCondition) -> None:
+ """Plots given approximation results.
+
+ Generates plots based on given data, sets plot directory if not
+ already existing, and saves plots.
+
+ Parameters
+ ----------
+ data_file: str
+ Path to data file for plotting.
+ directory: str
+ Path to directory in which plots will be saved.
+ plot_name : str
+ Name of plot.
+ basis: Vector object
+ Basis used for calculation.
+ quadrature: Quadrature object
+ Quadrature used for evaluation.
+ init_cond : InitialCondition object
+ Initial condition used for calculation.
+
+ """
+ # Read approximation results
+ with open(data_file + '.json') as json_file:
+ approx_stats = json.load(json_file)
+
+ # Decode all ndarrays by converting lists
+ approx_stats = {key: decode_ndarray(approx_stats[key])
+ for key in approx_stats.keys()}
+
+ # Plot exact/approximate results, errors, shock tubes,
+ # and any detector-dependant plots
+ plot_results(quadrature=quadrature, basis=basis,
+ init_cond=init_cond, **approx_stats)
+
+ # Set paths for plot files if not existing already
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
+ # Save plots
+ for identifier in plt.get_figlabels():
+ # Set path for figure directory if not existing already
+ if not os.path.exists(directory + '/' + identifier):
+ os.makedirs(directory + '/' + identifier)
+
+ plt.figure(identifier)
+ plt.savefig(directory + '/' + identifier + '/' +
+ plot_name + '.pdf')
+
+
+def plot_results(projection: ndarray, troubled_cell_history: list,
+ time_history: list, mesh: ndarray, num_grid_cells: int,
+ polynomial_degree: int, wave_speed: float, final_time: float,
+ left_bound: float, right_bound: float, basis: Vector,
+ quadrature: Quadrature, init_cond: InitialCondition,
+ colors: dict = None, coarse_projection: ndarray = None,
+ multiwavelet_coeffs: ndarray = None) -> None:
+ """Plots results and troubled cells of a projection.
+
+ Plots exact and approximate solution, errors, and troubled cells of a
+ projection given its evaluation history.
+
+ If coarse grid and projection are given, solutions are displayed for
+ both coarse and fine grid. Additionally, coefficient details are plotted.
+
+ Parameters
+ ----------
+ projection : ndarray
+ Matrix of projection for each polynomial degree.
+ troubled_cell_history : list
+ List of detected troubled cells for each time step.
+ time_history : list
+ List of value of each time step.
+ mesh : ndarray
+ List of mesh valuation points.
+ num_grid_cells : int
+ Number of cells in the mesh. Usually exponential of 2.
+ polynomial_degree : int
+ Polynomial degree.
+ wave_speed : float
+ Speed of wave in rightward direction.
+ final_time : float
+ Final time for which approximation is calculated.
+ left_bound : float
+ Left boundary of interval.
+ right_bound : float
+ Right boundary of interval.
+ basis: Vector object
+ Basis used for calculation.
+ quadrature: Quadrature object
+ Quadrature used for evaluation.
+ init_cond : InitialCondition object
+ Initial condition used for calculation.
+ colors: dict
+ Dictionary of colors used for plots.
+ coarse_projection: ndarray, optional
+ Matrix of projection on coarse grid for each polynomial degree.
+ Default: None.
+ multiwavelet_coeffs: ndarray, optional
+ Matrix of wavelet coefficients. Default: None.
+
+ """
+ # Set colors
+ if colors is None:
+ colors = {}
+ colors = _check_colors(colors)
+
+ # Calculate needed variables
+ interval_len = right_bound-left_bound
+ cell_len = interval_len/num_grid_cells
+
+ # Plot troubled cells
+ plot_shock_tube(num_grid_cells, troubled_cell_history, time_history)
+
+ # Determine exact and approximate solution
+ grid, exact = calculate_exact_solution(
+ mesh[2:-2], cell_len, wave_speed,
+ final_time, interval_len, quadrature,
+ init_cond)
+ approx = calculate_approximate_solution(
+ projection[:, 1:-1], quadrature.get_eval_points(),
+ polynomial_degree, basis.get_basis_vector())
+
+ # Plot multiwavelet solution (fine and coarse grid)
+ if coarse_projection is not None:
+ coarse_cell_len = 2*cell_len
+ coarse_mesh = np.arange(left_bound - (0.5*coarse_cell_len),
+ right_bound + (1.5*coarse_cell_len),
+ coarse_cell_len)
+
+ # Plot exact and approximate solutions for coarse mesh
+ coarse_grid, coarse_exact = calculate_exact_solution(
+ coarse_mesh[1:-1], coarse_cell_len, wave_speed,
+ final_time, interval_len, quadrature,
+ init_cond)
+ coarse_approx = calculate_approximate_solution(
+ coarse_projection, quadrature.get_eval_points(),
+ polynomial_degree, basis.get_basis_vector())
+ plot_solution_and_approx(
+ coarse_grid, coarse_exact, coarse_approx, colors['coarse_exact'],
+ colors['coarse_approx'])
+
+ # Plot multiwavelet details
+ num_coarse_grid_cells = num_grid_cells//2
+ plot_details(projection[:, 1:-1], mesh[2:-2], coarse_projection,
+ basis.get_basis_vector(),
+ basis.get_wavelet_vector(), multiwavelet_coeffs,
+ num_coarse_grid_cells,
+ polynomial_degree)
+
+ plot_solution_and_approx(grid, exact, approx,
+ colors['fine_exact'],
+ colors['fine_approx'])
+ plt.legend(['Exact (Coarse)', 'Approx (Coarse)', 'Exact (Fine)',
+ 'Approx (Fine)'])
+ # Plot regular solution (fine grid)
+ else:
+ plot_solution_and_approx(grid, exact, approx, colors['exact'],
+ colors['approx'])
+ plt.legend(['Exact', 'Approx'])
+
+ # Calculate errors
+ pointwise_error = np.abs(exact-approx)
+ max_error = np.max(pointwise_error)
+
+ # Plot errors
+ plot_semilog_error(grid, pointwise_error)
+ plot_error(grid, exact, approx)
+
+ print('p =', polynomial_degree)
+ print('N =', num_grid_cells)
+ print('maximum error =', max_error)
+
+
+def _check_colors(colors: dict) -> dict:
+ """Checks plot colors.
+
+ Checks whether colors for plots were given and sets them if required.
+
+ Parameters
+ ----------
+ colors: dict
+ Dictionary containing color strings for plotting.
+
+ Returns
+ -------
+ dict
+ Dictionary containing color strings for plotting.
+
+
+ """
+ # Set colors for general plots
+ colors['exact'] = colors.get('exact', 'k-')
+ colors['approx'] = colors.get('approx', 'y')
+
+ # Set colors for multiwavelet plots
+ colors['fine_exact'] = colors.get('fine_exact', 'k-.')
+ colors['fine_approx'] = colors.get('fine_approx', 'b-.')
+ colors['coarse_exact'] = colors.get('coarse_exact', 'k-')
+ colors['coarse_approx'] = colors.get('coarse_approx', 'y')
+
+ return colors
diff --git a/workflows/ANN_training.smk b/workflows/ANN_training.smk
index af8085efed9435e0011c0b1a8017a7430f7e8f8f..f4ccfc40753da3178ffecfdf141e03dd11c30384 100644
--- a/workflows/ANN_training.smk
+++ b/workflows/ANN_training.smk
@@ -3,6 +3,7 @@ import numpy as np
import ANN_Training
from ANN_Training import *
+from Plotting import plot_evaluation_results
configfile: 'config.yaml'
diff --git a/workflows/approximation.smk b/workflows/approximation.smk
index f5cc877d4c56d7b9075d18de37f633610aaeff6e..eb8a84c17ad7f67c964a110afedfe0cb212f24c5 100644
--- a/workflows/approximation.smk
+++ b/workflows/approximation.smk
@@ -2,10 +2,10 @@ import sys
import time
import numpy as np
-import Troubled_Cell_Detector
import Initial_Condition
import Quadrature
-from DG_Approximation import DGScheme, plot_approximation_results
+from DG_Approximation import DGScheme
+from Plotting import plot_approximation_results
from Basis_Function import OrthonormalLegendre
configfile: 'config.yaml'