From d122546171a128377a0298a343c465a20b8702a8 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: Wed, 19 Jan 2022 17:35:14 +0100
Subject: [PATCH] Fixed QObject error by using an non-interactive backend for
matplotlib.
---
ANN_Data_Generator.py | 6 +++---
ANN_Training.py | 17 ++++++++++++-----
DG_Approximation.py | 6 ++++--
Plotting.py | 4 +++-
Snakefile | 8 ++++----
Troubled_Cell_Detector.py | 4 +++-
Update_Scheme.py | 5 +++--
7 files changed, 32 insertions(+), 18 deletions(-)
diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 1c80916..1a799fb 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -4,7 +4,7 @@
"""
import os
-import timeit
+import time
import numpy as np
import DG_Approximation
@@ -89,13 +89,13 @@ class TrainingDataGenerator(object):
Dictionary containing input (normalized and non-normalized) and output data.
"""
- tic = timeit.default_timer()
+ tic = time.perf_counter()
print('Calculating training data...\n')
data_dict = self._calculate_data_set(num_samples)
print('Finished calculating training data!')
self._save_data(data_dict)
- toc = timeit.default_timer()
+ toc = time.perf_counter()
print('Total runtime:', toc-tic)
return data_dict
diff --git a/ANN_Training.py b/ANN_Training.py
index cd09c15..79131b8 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -2,17 +2,23 @@
"""
@author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
+Code-Style: E226, W503
+Docstring-Style: D200, D400
+
TODO: Test new ANN set-up with Soraya
-TODO: Integrate Main.py into Snakefile
+TODO: Integrate Main.py into Snakefile -> Done
+TODO: Generalize output for approximate_solution rule -> Done
TODO: Remove object set-up (for more flexibility)
TODO: Add documentation
-TODO: Improve log output (+ switch log: to correct pos after params:)
+TODO: Improve log output
+TODO: Fix logs in Snakefile -> Done
TODO: Throw exception for error due to missing classes
-TODO: Fix QObject timer error
+TODO: Fix QObject timer error -> Done
"""
import numpy as np
-import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import pyplot as plt
import os
import torch
from torch.utils.data import TensorDataset, DataLoader, random_split
@@ -22,6 +28,7 @@ from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc
import ANN_Model
from Plotting import plot_classification_accuracy, plot_boxplot
+matplotlib.use('Agg')
class ModelTrainer(object):
def __init__(self, config):
@@ -121,7 +128,7 @@ class ModelTrainer(object):
# Saving Model
name = self._model_name
- # Set paths for plot files if not existing already
+ # Set paths for files if not existing already
model_dir = self._dir + '/trained models'
if not os.path.exists(model_dir):
os.makedirs(model_dir)
diff --git a/DG_Approximation.py b/DG_Approximation.py
index 6336f78..a4114a3 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -27,7 +27,8 @@ import os
import numpy as np
from sympy import Symbol
import math
-import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import pyplot as plt
import Troubled_Cell_Detector
import Initial_Condition
@@ -36,6 +37,7 @@ import Quadrature
import Update_Scheme
from Basis_Function import OrthonormalLegendre
+matplotlib.use('Agg')
x = Symbol('x')
@@ -298,7 +300,7 @@ class DGScheme(object):
output_matrix[0] = output_matrix[self._num_grid_cells]
output_matrix.append(output_matrix[1])
- print(np.array(output_matrix).shape)
+ # print(np.array(output_matrix).shape)
return np.transpose(np.array(output_matrix))
def build_training_data(self, adjustment, stencil_length, initial_condition=None):
diff --git a/Plotting.py b/Plotting.py
index 16019dc..ff5e608 100644
--- a/Plotting.py
+++ b/Plotting.py
@@ -8,11 +8,13 @@ TODO: Adjust documentation for plot_classification_accuracy()
"""
import numpy as np
-import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import pyplot as plt
import seaborn as sns
from sympy import Symbol
+matplotlib.use('Agg')
x = Symbol('x')
z = Symbol('z')
sns.set()
diff --git a/Snakefile b/Snakefile
index ae46980..a170be8 100644
--- a/Snakefile
+++ b/Snakefile
@@ -1,5 +1,5 @@
import sys
-import timeit
+import time
import ANN_Data_Generator, Initial_Condition, ANN_Training
from ANN_Training import evaluate_models
@@ -37,7 +37,7 @@ rule approximate_solution:
sys.stdout = logfile
sys.stderr = logfile
- tic = timeit.default_timer()
+ tic = time.perf_counter()
print(params.dg_params)
dg_scheme = DGScheme(plot_dir=params.plot_dir, **params.dg_params)
@@ -45,8 +45,8 @@ rule approximate_solution:
dg_scheme.approximate()
dg_scheme.save_plots(params.plot_name)
- toc = timeit.default_timer()
- print('Time:',toc-tic)
+ toc = time.perf_counter()
+ print(f'Time: {toc - tic:0.4f}s')
rule test_model:
input:
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index ef91fcc..3028247 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -9,7 +9,8 @@ TODO: Load ANN state and config in reset
"""
import numpy as np
-import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import pyplot as plt
import seaborn as sns
import torch
from sympy import Symbol
@@ -18,6 +19,7 @@ import ANN_Model
from Plotting import plot_solution_and_approx, plot_semilog_error, plot_error, plot_shock_tube, \
plot_details, calculate_approximate_solution, calculate_exact_solution
+matplotlib.use('Agg')
x = Symbol('x')
z = Symbol('z')
diff --git a/Update_Scheme.py b/Update_Scheme.py
index f5dc8d8..5d91109 100644
--- a/Update_Scheme.py
+++ b/Update_Scheme.py
@@ -2,12 +2,13 @@
"""
@author: Laura C. Kühle
-TODO: Discuss descriptions (matrices, cfl number, right-hand side, etc.)
+TODO: Discuss descriptions (matrices, cfl number, right-hand side, limiting slope, basis, wavelet,
+ etc.)
TODO: Discuss referencing info on SSPRK3
"""
import numpy as np
-import timeit
+import time
class UpdateScheme(object):
--
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