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Commit 141632da authored by Laura Christine Kühle's avatar Laura Christine Kühle
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Removed completed TODOs.

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...@@ -6,15 +6,6 @@ Code-Style: E226, W503 ...@@ -6,15 +6,6 @@ Code-Style: E226, W503
Docstring-Style: D200, D400 Docstring-Style: D200, D400
TODO: Add README for ANN training TODO: Add README for ANN training
TODO: Fix random seed -> Done
TODO: Improve file structure and naming (e.g. use '.' instead of '__') -> Done
TODO: Write-protect all data and models -> Done
TODO: Put legend outside plot (bbox_to_anchor) -> Done
TODO: Improve legend layout -> Done
TODO: Put plotting into separate function -> Done
TODO: Reduce number of testing epochs to 50 -> Done
TODO: Rename 'data_directory' to 'data_dir' -> Done
TODO: Add comments -> Done
""" """
import numpy as np import numpy as np
......
...@@ -3,8 +3,6 @@ ...@@ -3,8 +3,6 @@
@author: Laura C. Kühle @author: Laura C. Kühle
Urgent: Urgent:
TODO: Add way of saving data (np.savez('data/' + name,
coefficients=projection, troubled_cells=troubled_cells)) -> Done
TODO: Move plotting into separate function TODO: Move plotting into separate function
TODO: Adapt TCD from Soraya TODO: Adapt TCD from Soraya
(Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector) (Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
...@@ -17,7 +15,6 @@ TODO: Adjust code to allow classes for all equations ...@@ -17,7 +15,6 @@ TODO: Adjust code to allow classes for all equations
(Burger, linear advection, 1D Euler) (Burger, linear advection, 1D Euler)
Currently not critical: Currently not critical:
TODO: Remove object inheritance from classes -> Done
TODO: Replace loops with list comprehension if feasible TODO: Replace loops with list comprehension if feasible
TODO: Check whether 'projection' is always a np.array() TODO: Check whether 'projection' is always a np.array()
TODO: Check whether all instance variables are sensible TODO: Check whether all instance variables are sensible
...@@ -375,7 +372,7 @@ class DGScheme: ...@@ -375,7 +372,7 @@ class DGScheme:
return np.transpose(np.array(output_matrix)) return np.transpose(np.array(output_matrix))
def build_training_data(self, adjustment, stencil_length, def build_training_data(self, adjustment, stencil_length,
initial_condition=None): add_reconstructions, initial_condition=None):
"""Builds training data set. """Builds training data set.
Initializes projection and calculates cell averages and Initializes projection and calculates cell averages and
......
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