Renamed 'adjustment' to 'shift'.

ANN_Data_Generator.py

@@ -218,13 +218,13 @@ class TrainingDataGenerator:
             mesh = self._mesh_list[int(np.random.randint(
                 3, high=12, size=1))-3].random_stencil(stencil_length)
 
-            # Induce adjustment to capture troubled cells
-            adjustment = 0 if initial_condition.is_smooth() \
+            # Induce shift to capture troubled cells
+            shift = 0 if initial_condition.is_smooth() \
                 else mesh.non_ghost_cells[stencil_length//2]
             if initial_condition.discontinuity_position == 'left':
-                adjustment -= mesh.cell_len/2
+                shift -= mesh.cell_len/2
             elif initial_condition.discontinuity_position == 'right':
-                adjustment += mesh.cell_len/2
+                shift += mesh.cell_len/2
 
             # Calculate basis coefficients for stencil
             polynomial_degree = np.random.randint(1, high=5)
@@ -232,7 +232,7 @@ class TrainingDataGenerator:
                 initial_condition=initial_condition, mesh=mesh,
                 basis=self._basis_list[polynomial_degree],
                 quadrature=self._quadrature_list[polynomial_degree],
-                adjustment=adjustment)
+                x_shift=shift)
             input_data[i] = self._basis_list[
                 polynomial_degree].calculate_cell_average(
                 projection=projection[:, 1:-1],

DG_Approximation.py

@@ -21,19 +21,21 @@ TODO: Discuss descriptions (matrices, cfl number, right-hand side,
 TODO: Discuss referencing info on SSPRK3
 
 TODO: Discuss why the left_factor is not subtracted for the second
-    discontinuity for the two-sided Heaviside
-TODO: Discuss order of addends for two-sided Heaviside
+    discontinuity for the two-sided Heaviside -> Done (is correct)
+TODO: Discuss order of addends for two-sided Heaviside -> Done (change)
 TODO: Discuss whether to enforce positive shift in the two-sided Heaviside
+    -> Done (change)
 TODO: Discuss reasoning behind minimum mesh size of 2^5 instead of 2^3 for
-    training data
+    training data -> Done (closer to real application)
 TODO: Discuss why basis degree can be as high as 6 if it is only defined up
-    to 4 so far (alternative calculation?)
+    to 4 so far -> Done (fine as we don't use wavelets)
 TODO: Discuss whether domain other than [-1, 1] is in any way useful for
-    training
+    training -> Done (not)
 
 Urgent:
-TODO: Replace induce_adjustment() with margin
-TODO: Rename 'adjustment' to 'shift'
+TODO: Fix bug not using initial condition configs for training -> Done
+TODO: Replace induce_adjustment() with margin -> Done
+TODO: Rename 'adjustment' to 'shift' -> Done
 TODO: Induce shift in IC class
 TODO: Move plot_approximation_results() into plotting script
 TODO: Improve file naming (e.g. use '.' instead of '__')
@@ -49,6 +51,11 @@ TODO: Unify print commands (f vs no f)
 TODO: Add method and error info to Mesh
 TODO: Add use of discontinuity position to all ICs
 TODO: Adjust definitions of each IC to be dependant on domain
+TODO: Change maximum training degree to 6
+TODO: Change minimum training mesh size to 2^5
+TODO: Enforce shift to be positive for two-sided Heaviside
+TODO: Remove option to choose training domain
+TODO: Fix VisibleDeprecationWarning for ndarray encoding
 
 Critical, but not urgent:
 TODO: Introduce env files for each SM rule
@@ -128,7 +135,7 @@ class DGScheme:
         Approximates projection.
     save_plots()
         Saves plots generated during approximation process.
-    build_training_data(adjustment, stencil_length, initial_condition=None)
+    build_training_data(x_shift, stencil_length, initial_condition=None)
         Builds training data set.
 
     """
@@ -310,7 +317,7 @@ class DGScheme:
 
 
 def do_initial_projection(initial_condition, mesh, basis, quadrature,
-                          adjustment=0):
+                          x_shift=0):
     """Calculates initial projection.
 
     Calculates a projection at time step 0 and adds ghost cells on both
@@ -326,9 +333,8 @@ def do_initial_projection(initial_condition, mesh, basis, quadrature,
         Basis used for calculation.
     quadrature: Quadrature object
         Quadrature used for evaluation.
-    adjustment: float, optional
-        Extent of adjustment of each evaluation point in x-direction.
-        Default: 0.
+    x_shift: float, optional
+        Shift in x-direction for each evaluation point. Default: 0.
 
     Returns
     -------
@@ -345,7 +351,7 @@ def do_initial_projection(initial_condition, mesh, basis, quadrature,
         for degree in range(basis.polynomial_degree + 1):
             new_row.append(np.float64(sum(initial_condition.calculate(
                 x=eval_point + mesh.cell_len/2
-                * quadrature.nodes[point] - adjustment, mesh=mesh)
+                * quadrature.nodes[point] - x_shift, mesh=mesh)
                 * basis.basis[degree].subs(
                     x, quadrature.nodes[point])
                 * quadrature.weights[point]

Initial_Condition.py

@@ -247,9 +247,9 @@ class FourPeakWave(InitialCondition):
     beta : float
         Factor used for the Gaussian function.
     a : float
-        Value for x-value adjustment for elliptic function.
+        Value for shift in x-direction for elliptic function.
     z : float
-        Value for x-value adjustment for Gaussian function.
+        Value for shift in x-direction for Gaussian function.
 
     Methods
     -------
@@ -317,7 +317,7 @@ class FourPeakWave(InitialCondition):
         x : float
             Evaluation point of function.
         z : float
-            Value for x-value adjustment.
+            Value for shift in x-direction.
 
         Returns
         -------
@@ -335,7 +335,7 @@ class FourPeakWave(InitialCondition):
         x : float
             Evaluation point of function.
         a : float
-            Value for x-value adjustment.
+            Value for shift in x-direction .
 
         Returns
         -------

Plotting.py

@@ -189,13 +189,13 @@ def plot_classification_barplot(evaluation_dict: dict, colors: dict) -> None:
     fig = plt.figure('barplot')
     ax = fig.add_axes([0.15, 0.3, 0.6, 0.6])
     step_len = 1
-    adjustment = -(len(model_names)//2)*step_len
+    shift = -(len(model_names)//2)*step_len
     for measure in evaluation_dict:
         model_eval = [evaluation_dict[measure][model]
                       for model in evaluation_dict[measure]]
-        ax.bar(pos + adjustment*width, model_eval, width, label=measure,
+        ax.bar(pos + shift*width, model_eval, width, label=measure,
                color=colors[measure])
-        adjustment += step_len
+        shift += step_len
     ax.set_xticks(pos)
     ax.set_xticklabels(model_names, rotation=50, ha='right',
                        fontsize=font_size)
@@ -226,19 +226,19 @@ def plot_classification_boxplot(evaluation_dict: dict, colors: dict) -> None:
     ax = fig.add_axes([0.15, 0.3, 0.6, 0.6])
     step_len = 1.5
     boxplots = []
-    adjustment = -(len(model_names)//2)*step_len
+    shift = -(len(model_names)//2)*step_len
     pos = np.arange(len(model_names))
     width = 1/(5*len(model_names))
     for measure in evaluation_dict:
         model_eval = [evaluation_dict[measure][model]
                       for model in evaluation_dict[measure]]
-        boxplot = ax.boxplot(model_eval, positions=pos + adjustment*width,
+        boxplot = ax.boxplot(model_eval, positions=pos + shift*width,
                              widths=width, meanline=True, showmeans=True,
                              patch_artist=True)
         for patch in boxplot['boxes']:
             patch.set(facecolor=colors[measure])
         boxplots.append(boxplot)
-        adjustment += step_len
+        shift += step_len
 
     ax.set_xticks(pos)
     ax.set_xticklabels(model_names, rotation=50, ha='right',