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Commit df25e735 authored by Laura Christine Kühle's avatar Laura Christine Kühle
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Adapted docstring to uniform standard.

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with 147 additions and 130 deletions
ANN_Data_Generator.py
+ 11
− 11
View file @ df25e735
......@@ -13,7 +13,7 @@ import DG_Approximation
class TrainingDataGenerator(object):
"""Class for training data generator.
Generate random training data for given initial conditions.
Generates random training data for given initial conditions.
Attributes
----------
......@@ -26,7 +26,7 @@ class TrainingDataGenerator(object):
Methods
-------
build_training_data()
build_training_data(num_samples)
Builds random training data.
"""
......@@ -151,14 +151,14 @@ class TrainingDataGenerator(object):
Number of training data samples to generate.
initial_conditions : list
List of names of initial conditions for training.
is_smooth : boolean
is_smooth : bool
Flag whether initial conditions are smooth.
Returns
-------
input_data : np.array
input_data : ndarray
Array containing input data.
output_data : np.array
output_data : ndarray
Array containing output data.
"""
......@@ -215,9 +215,9 @@ class TrainingDataGenerator(object):
Returns
-------
interval : np.array
interval : ndarray
List containing left and right bound of interval.
stencil : np.array
stencil : ndarray
List of cell centers in stencil.
grid_spacing : float
Length of cell in grid.
......@@ -244,16 +244,16 @@ class TrainingDataGenerator(object):
@staticmethod
def _normalize_data(input_data):
"""Normalize data.
"""Normalizes data.
Parameters
----------
input_data : np.array
input_data : ndarray
Array containing input data.
Returns
-------
np.array
ndarray
Array containing normalized input data.
"""
......
ANN_Model.py
+ 14
− 7
View file @ df25e735
......@@ -28,6 +28,13 @@ class ThreeLayerReLu(torch.nn.Module):
output_layer : torch.nn.Module
Activation layer for output calculation.
Methods
-------
forward(input_data)
Executes forward propagation.
get_name()
Returns string of model name.
"""
def __init__(self, config):
"""Initializes ThreeLayerReLu.
......
ANN_Training.py
+ 15
− 14
View file @ df25e735
......@@ -12,7 +12,8 @@ TODO: Write-protect all data and models
TODO: Put legend outside plot (bbox_to_anchor)
TODO: Put plotting into separate function
TODO: Reduce number of testing epochs to 50
TODO: Adapt docstring to uniform standard
TODO: Adapt docstring to uniform standard -> Done
TODO: Change maximal line length to 79 (as advised by PEP8)
"""
import numpy as np
......@@ -50,11 +51,11 @@ class ModelTrainer(object):
Methods
-------
epoch_training()
epoch_training(dataset, num_epochs, verbose)
Trains model for a given number of epochs.
test_model()
test_model(training_set, test_set)
Evaluates predictions of a model.
save_model()
save_model(directory, model_name)
Saves state and validation loss of a model.
"""
......@@ -121,7 +122,7 @@ class ModelTrainer(object):
dataset : torch.utils.data.dataset.TensorDataset
Training dataset.
num_epochs : int, optional
Number of epochs for training. If None, set to instance value. Default: None.
Number of epochs for training. Default: None (i.e. instance variable).
verbose : bool, optional
Flag whether commentary in console is wanted. Default: False.
......
Basis_Function.py
+ 13
− 13
View file @ df25e735
......@@ -17,9 +17,9 @@ class Vector(object):
Attributes
----------
basis : np.array
basis : ndarray
Array of basis.
wavelet : np.array
wavelet : ndarray
Array of wavelet.
Methods
......@@ -61,7 +61,7 @@ class Vector(object):
Returns
-------
np.array
ndarray
Vector containing basis evaluated at evaluation point.
"""
......@@ -81,7 +81,7 @@ class Vector(object):
Returns
-------
np.array
ndarray
Vector containing wavelet evaluated at evaluation point.
"""
......@@ -108,7 +108,7 @@ class Legendre(Vector):
Returns
-------
np.array
ndarray
Vector containing basis evaluated at evaluation point.
"""
......@@ -124,7 +124,7 @@ class Legendre(Vector):
Returns
-------
np.array
ndarray
Vector containing Legendre polynomial evaluated at evaluation point.
"""
......@@ -163,7 +163,7 @@ class OrthonormalLegendre(Legendre):
Returns
-------
np.array
ndarray
Vector containing basis evaluated at evaluation point.
"""
......@@ -181,7 +181,7 @@ class OrthonormalLegendre(Legendre):
Returns
-------
np.array
ndarray
Vector containing wavelet evaluated at evaluation point.
Notes
......@@ -227,7 +227,7 @@ class OrthonormalLegendre(Legendre):
Returns
-------
np.array
ndarray
Array containing the basis projection based on the integrals of the product
of two basis vectors for each degree combination.
......@@ -248,7 +248,7 @@ class OrthonormalLegendre(Legendre):
Returns
-------
np.array
ndarray
Matrix containing the integral of basis products.
"""
......@@ -268,7 +268,7 @@ class OrthonormalLegendre(Legendre):
Returns
-------
np.array
ndarray
Array containing the multiwavelet projection based on the integrals of the product
of a basis vector and a wavelet vector for each degree combination.
......@@ -286,12 +286,12 @@ class OrthonormalLegendre(Legendre):
First parameter.
second_param : float
Second parameter.
is_left_matrix : boolean
is_left_matrix : bool
Flag whether the left matrix is calculated.
Returns
-------
np.array
ndarray
Matrix containing the integral of products of a basis and a wavelet vector.
"""
......
DG_Approximation.py
+ 12
− 6
View file @ df25e735
......@@ -25,6 +25,7 @@ TODO: Add way of saving data (np.savez('data/' + name,
TODO: Outsource scripts into separate directory
TODO: Allow comparison between ANN training datasets
TODO: Add a default model state
TODO: Add type annotations to function heads
"""
import os
......@@ -59,9 +60,9 @@ class DGScheme(object):
Length of a cell in mesh.
basis : Basis object
Basis for calculation.
mesh : array
mesh : ndarray
List of mesh valuation points.
inv_mass : np.array
inv_mass : ndarray
Inverse mass matrix.
Methods
......@@ -98,8 +99,8 @@ class DGScheme(object):
Left boundary of interval. Default: -1.
right_bound : float, optional
Right boundary of interval. Default: 1.
verbose : boolean, optional
Flag whether commentary in console is wanted. Default: False
verbose : bool, optional
Flag whether commentary in console is wanted. Default: False.
plot_dir : str, optional
Path to directory in which plots are saved. Default: 'test'.
history_threshold : float, optional
......@@ -218,6 +219,11 @@ class DGScheme(object):
Sets plot directory, if not already existing, and saves plots generated during the last
approximation.
Parameters
----------
plot_name : str
Name of plot.
"""
# Set paths for plot files if not existing already
if not os.path.exists(self._plot_dir):
......@@ -273,7 +279,7 @@ class DGScheme(object):
Returns
-------
np.array
ndarray
Matrix containing projection of size (N+2, p+1) with N being the number of grid cells
and p being the polynomial degree.
......@@ -323,7 +329,7 @@ class DGScheme(object):
Returns
-------
np.array
ndarray
Matrix containing cell averages and reconstructions for initial projection.
"""
......
Initial_Condition.py
+ 0
− 2
View file @ df25e735
......@@ -2,8 +2,6 @@
"""
@author: Laura C. Kühle
TODO: Add is_smooth() to DiscontinuousConstant class -> Done
"""
import numpy as np
......
Limiter.py
+ 10
− 10
View file @ df25e735
......@@ -47,14 +47,14 @@ class Limiter(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cell
cell: int
Index of cell.
Returns
-------
np.array
ndarray
Matrix of updated projection for each polynomial degree.
"""
......@@ -117,14 +117,14 @@ class MinMod(Limiter):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cell : int
Index of cell.
Returns
-------
adapted_projection : np.array
adapted_projection : ndarray
Matrix of updated projection for each polynomial degree.
"""
......@@ -145,7 +145,7 @@ class MinMod(Limiter):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cell : int
Index of cell.
......@@ -154,7 +154,7 @@ class MinMod(Limiter):
Returns
-------
boolean
bool
Flag whether cell should be adjusted.
"""
......@@ -170,7 +170,7 @@ class MinMod(Limiter):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cell : int
Index of cell.
......@@ -237,7 +237,7 @@ class ModifiedMinMod(MinMod):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cell : int
Index of cell.
......@@ -246,7 +246,7 @@ class ModifiedMinMod(MinMod):
Returns
-------
boolean
bool
Flag whether cell should be adjusted.
"""
......
Plotting.py
+ 7
− 7
View file @ df25e735
Quadrature.py
+ 4
− 4
View file @ df25e735
......@@ -15,9 +15,9 @@ class Quadrature(object):
----------
num_eval_points : int
Number of evaluation points per cell used for approximation.
eval_points : np.array
eval_points : ndarray
Evaluation points per cell used for approximation.
weights : np.array
weights : ndarray
Weights used for approximation calculation.
Methods
......@@ -80,9 +80,9 @@ class Gauss(Quadrature):
----------
num_eval_points : int
Number of evaluation points per cell used for approximation.
eval_points : np.array
eval_points : ndarray
Evaluation points per cell used for approximation.
weights : np.array
weights : ndarray
Weights used for approximation calculation.
Methods
......
Troubled_Cell_Detector.py
+ 35
− 30
View file @ df25e735
......@@ -53,7 +53,7 @@ class TroubledCellDetector(object):
Parameters
----------
mesh : array
mesh : ndarray
List of mesh valuation points.
wave_speed : float
Speed of wave in rightward direction.
......@@ -123,7 +123,7 @@ class TroubledCellDetector(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
"""
......@@ -137,14 +137,14 @@ class TroubledCellDetector(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
stencil_length : int
Size of data array.
Returns
-------
np.array
ndarray
Matrix containing cell averages and reconstructions for initial projection.
"""
......@@ -164,11 +164,11 @@ class TroubledCellDetector(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
troubled_cell_history : list
List of detected troubled cells for each time step.
time_history:
time_history : list
List of value of each time step.
"""
......@@ -184,12 +184,12 @@ class TroubledCellDetector(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
-------
max_error
max_error : float
Maximum error between exact and approximate solution.
"""
......@@ -225,9 +225,14 @@ class NoDetection(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
-------
list
List of indices for all detected troubled cells.
"""
return []
......@@ -239,7 +244,7 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
----------
stencil_length : int
Size of input data array.
model : ANNModel object
model : torch.nn.Model
ANN model instance for evaluation.
Methods
......@@ -280,7 +285,7 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
......@@ -345,7 +350,7 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
......@@ -362,12 +367,12 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
-------
np.array
ndarray
Matrix of wavelet coefficients.
"""
......@@ -383,9 +388,9 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
multiwavelet_coeffs : np.array
multiwavelet_coeffs : ndarray
Matrix of multiwavelet coefficients.
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
......@@ -403,11 +408,11 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
troubled_cell_history : list
List of detected troubled cells for each time step.
time_history:
time_history : list
List of value of each time step.
"""
......@@ -424,12 +429,12 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
-------
np.array
ndarray
Matrix of projection on coarse grid for each polynomial degree.
"""
......@@ -453,12 +458,12 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
-------
max_error
max_error : float
Maximum error between exact and approximate solution.
"""
......@@ -487,7 +492,7 @@ class WaveletDetector(TroubledCellDetector):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
"""
......@@ -540,9 +545,9 @@ class Boxplot(WaveletDetector):
Parameters
----------
multiwavelet_coeffs : np.array
multiwavelet_coeffs : ndarray
Matrix of multiwavelet coefficients.
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
......@@ -619,9 +624,9 @@ class Theoretical(WaveletDetector):
Parameters
----------
multiwavelet_coeffs : np.array
multiwavelet_coeffs : ndarray
Matrix of multiwavelet coefficients.
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
Returns
......@@ -645,16 +650,16 @@ class Theoretical(WaveletDetector):
Parameters
----------
multiwavelet_coeffs : np.array
multiwavelet_coeffs : ndarray
Matrix of multiwavelet coefficients.
cell : int
Index of cell.
max_avg
max_avg : float
Maximum average of projection.
Returns
-------
boolean
bool
Flag whether cell is troubled.
"""
......
Update_Scheme.py
+ 26
− 26
View file @ df25e735
......@@ -16,9 +16,9 @@ class UpdateScheme(object):
Attributes
----------
stiffness_matrix : np.array
stiffness_matrix : ndarray
Matrix
boundary_matrix : np.array
boundary_matrix : ndarray
Matrix
Methods
......@@ -85,14 +85,14 @@ class UpdateScheme(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cfl_number : float
CFL number to ensure stability.
Returns
-------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
troubled_cells : list
List of indices for all detected troubled cells.
......@@ -107,14 +107,14 @@ class UpdateScheme(object):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cfl_number : float
CFL number to ensure stability.
Returns
-------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
troubled_cells : list
List of indices for all detected troubled cells.
......@@ -127,12 +127,12 @@ class UpdateScheme(object):
Parameters
----------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
Returns
-------
new_projection : np.array
new_projection : ndarray
Matrix of updated projection for each polynomial degree.
troubled_cells : list
List of indices for all detected troubled cells.
......@@ -153,12 +153,12 @@ class UpdateScheme(object):
Parameters
----------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
Returns
-------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
"""
......@@ -181,14 +181,14 @@ class SSPRK3(UpdateScheme):
Parameters
----------
projection : np.array
projection : ndarray
Matrix of projection for each polynomial degree.
cfl_number : float
CFL number to ensure stability.
Returns
-------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
troubled_cells : list
List of indices for all detected troubled cells.
......@@ -213,18 +213,18 @@ class SSPRK3(UpdateScheme):
return current_projection, troubled_cells
def _apply_first_step(self, original_projection, cfl_number):
"""Apply first step of SSPRK3.
"""Applies first step of SSPRK3.
Parameters
----------
original_projection : np.array
original_projection : ndarray
Matrix of original projection for each polynomial degree.
cfl_number : float
CFL number to ensure stability.
Returns
-------
np.array
ndarray
Matrix of updated projection for each polynomial degree.
"""
......@@ -232,20 +232,20 @@ class SSPRK3(UpdateScheme):
return original_projection + (cfl_number*right_hand_side)
def _apply_second_step(self, original_projection, current_projection, cfl_number):
"""Apply second step of SSPRK3.
"""Applies second step of SSPRK3.
Parameters
----------
original_projection : np.array
original_projection : ndarray
Matrix of original projection for each polynomial degree.
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
cfl_number : float
CFL number to ensure stability.
Returns
-------
np.array
ndarray
Matrix of updated projection for each polynomial degree.
"""
......@@ -253,20 +253,20 @@ class SSPRK3(UpdateScheme):
return 1/4 * (3*original_projection + (current_projection + cfl_number*right_hand_side))
def _apply_third_step(self, original_projection, current_projection, cfl_number):
"""Apply third step of SSPRK3.
"""Applies third step of SSPRK3.
Parameter
---------
original_projection : np.array
original_projection : ndarray
Matrix of original projection for each polynomial degree.
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
cfl_number : float
CFL number to ensure stability.
Returns
-------
np.array
ndarray
Matrix of updated projection for each polynomial degree.
"""
......@@ -274,16 +274,16 @@ class SSPRK3(UpdateScheme):
return 1/3 * (original_projection + 2*(current_projection + cfl_number*right_hand_side))
def _update_right_hand_side(self, current_projection):
"""Update right-hand side.
"""Updates right-hand side.
Parameter
---------
current_projection : np.array
current_projection : ndarray
Matrix of projection of current update step for each polynomial degree.
Returns
-------
np.array
ndarray
Matrix of right-hand side.
"""
......
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