diff --git a/DG_Approximation.py b/DG_Approximation.py
index ea826715fc6a3210ecefac5f6f73fc360097ec14..09050d1d47114d221d828c0935ee4da5397b2283 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -3,7 +3,8 @@
@author: Laura C. Kühle
Urgent:
-TODO: Move plotting into separate function
+TODO: Move plotting into separate function -> Done
+TODO: Move plotting into separate rule
TODO: Adapt TCD from Soraya
(Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
TODO: Add verbose output
@@ -201,22 +202,19 @@ class DGScheme:
self._polynomial_degree, self._num_grid_cells, self._detector,
self._limiter)
- def approximate(self, data_dir, data_name):
+ def approximate(self, data_file):
"""Approximates projection.
Initializes projection and evolves it in time. Each time step consists
of three parts: A projection update, a troubled-cell detection,
and limiting based on the detected cells.
- At final time, result and error plots are
- generated and, if verbose flag is set, also displayed.
+ At final time, results are saved in JSON file.
Attributes
----------
- data_dir: str
- Path to directory in which data is saved.
- data_name : str
- Name of data.
+ data_file: str
+ Path to file in which data will be saved.
"""
projection = self._do_initial_projection(self._init_cond)
@@ -254,12 +252,28 @@ class DGScheme:
for key in approx_stats.keys()}
# Save approximation results in JSON format
- with open(data_dir + '/' + data_name + '.json', 'w') \
+ with open(data_file + '.json', 'w') \
as json_file:
json_file.write(json.dumps(approx_stats))
+ def plot_approximation_results(self, data_file, directory, plot_name):
+ """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.
+
+ """
# Read approximation results
- with open(data_dir + '/' + data_name + '.json') as json_file:
+ with open(data_file + '.json') as json_file:
approx_stats = json.load(json_file)
# Decode all ndarrays by converting lists
@@ -270,32 +284,18 @@ class DGScheme:
# and any detector-dependant plots
self._detector.plot_results(**approx_stats)
- def save_plots(self, plot_dir, plot_name):
- """Saves plotted results.
-
- Sets plot directory, if not already existing, and saves plots
- generated during the last approximation.
-
- Parameters
- ----------
- plot_dir: str
- Path to directory in which plots are saved.
- plot_name : str
- Name of plot.
-
- """
# Set paths for plot files if not existing already
- if not os.path.exists(plot_dir):
- os.makedirs(plot_dir)
+ 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(plot_dir + '/' + identifier):
- os.makedirs(plot_dir + '/' + identifier)
+ if not os.path.exists(directory + '/' + identifier):
+ os.makedirs(directory + '/' + identifier)
plt.figure(identifier)
- plt.savefig(plot_dir + '/' + identifier + '/' +
+ plt.savefig(directory + '/' + identifier + '/' +
plot_name + '.pdf')
def _reset(self):
diff --git a/config.yaml b/config.yaml
index c260bc19457e4029a01046941d3ddbf86188acec..05e07455eee4aa73a5267772a12aa979f359bff4 100644
--- a/config.yaml
+++ b/config.yaml
@@ -1,9 +1,9 @@
-data_dir: 'model-Soraya_Mar02'
+data_dir: 'model-Mar14'
random_seed: 1234
# Parameter for Approximation with Troubled Cell Detection
Approximation:
- plot_dir: 'fig-Soraya_Mar02'
+ plot_dir: 'fig-Mar14'
schemes:
Separation_Test:
@@ -21,7 +21,7 @@ Approximation:
detector_config:
fold_len: 16
whisker_len: 3
- add_reconstructions: False
+ add_reconstructions: True
model_state: 'Adam.model.pt'
init_cond: 'Sine'
@@ -52,7 +52,7 @@ ANN_Data:
smooth_troubled_balance: 0.5
stencil_length: 3
- add_reconstructions : False
+ add_reconstructions : True
# Initial Conditions for Training Data
functions:
@@ -87,7 +87,7 @@ ANN_Training:
threshold: 1.0e-5
batch_size: 500
model: ThreeLayerReLu
- model_config: {input_size: 3}
+ model_config: {}
loss_function: BCELoss
optimizer: Adam
SGD:
@@ -95,7 +95,7 @@ ANN_Training:
threshold: 1.0e-5
batch_size: 500
model: ThreeLayerReLu
- model_config: {input_size: 3}
+ model_config: {}
loss_function: BCELoss
optimizer: SGD
diff --git a/workflows/approximation.smk b/workflows/approximation.smk
index 75f7982f33ea9e2ba28d864ac80aed08dce7fa34..13ae4e758464394e4342e2ddee32473fe4ee570c 100644
--- a/workflows/approximation.smk
+++ b/workflows/approximation.smk
@@ -48,10 +48,11 @@ rule approximate_solution:
print(params.dg_params)
dg_scheme = DGScheme(**params.dg_params)
- dg_scheme.approximate(data_dir=params.plot_dir,
- data_name=wildcards.scheme)
- dg_scheme.save_plots(plot_dir=params.plot_dir,
- plot_name=wildcards.scheme)
+ dg_scheme.approximate(
+ data_file=params.plot_dir+'/'+wildcards.scheme)
+ dg_scheme.plot_approximation_results(directory=params.plot_dir,
+ plot_name=wildcards.scheme,
+ data_file=params.plot_dir+'/'+wildcards.scheme)
toc = time.perf_counter()
print(f'Time: {toc - tic:0.4f}s')
\ No newline at end of file