From 89a530ddf10d7c56d04b52954282568db0d9c5fa Mon Sep 17 00:00:00 2001 From: "Melching, David" Date: Fri, 17 Mar 2023 08:14:04 +0000 Subject: [PATCH] Scientific colormaps, improved detection stability, bugfixes --- crackpy/__init__.py | 2 +- crackpy/crack_detection/data/interpolation.py | 2 +- .../deep_learning/attention.py | 4 +- crackpy/crack_detection/detection.py | 14 ++- crackpy/crack_detection/pipeline/pipeline.py | 43 +++++-- crackpy/crack_detection/utils/plot.py | 29 +++-- crackpy/fracture_analysis/analysis.py | 9 +- crackpy/fracture_analysis/data_processing.py | 49 +++++--- crackpy/fracture_analysis/optimization.py | 107 ++---------------- crackpy/fracture_analysis/plot.py | 28 ++--- crackpy/fracture_analysis/write.py | 66 +++++------ .../test_deep_learning/test_attention.py | 5 +- example_images/example_image_output.png | Bin 1656975 -> 408001 bytes example_images/explanation.gif | Bin 9213366 -> 483504 bytes requirements.txt | 2 +- scripts/crack_detection/cd_pipeline.py | 12 +- scripts/crack_detection/crack_detection.py | 7 ++ .../crack_detection/crack_tip_attention.py | 1 + .../fracture_analysis_dic.py | 13 ++- .../fracture_analysis_fem.py | 12 +- .../fracture_analysis_pipeline_dic.py | 14 ++- .../fracture_analysis_pipeline_fem.py | 12 +- test_scripts/test_fracture_analysis.py | 4 +- 23 files changed, 213 insertions(+), 222 deletions(-) mode change 100644 => 100755 example_images/explanation.gif diff --git a/crackpy/__init__.py b/crackpy/__init__.py index b89fced..5bcf815 100644 --- a/crackpy/__init__.py +++ b/crackpy/__init__.py @@ -5,4 +5,4 @@ import crackpy.structure_elements # package information -__version__ = "1.0.4" +__version__ = "1.1.0" diff --git a/crackpy/crack_detection/data/interpolation.py b/crackpy/crack_detection/data/interpolation.py index 77fb6a5..c3accb2 100644 --- a/crackpy/crack_detection/data/interpolation.py +++ b/crackpy/crack_detection/data/interpolation.py @@ -44,7 +44,7 @@ def interpolate_on_array(input_by_nodemap, interp_size, offset=(0, 0), pixels=25 return interp_coors_by_nodemap, interp_disps_by_nodemap, interp_eps_vm_by_nodemap -def interpolate(input_data_object: InputData, size: int, offset=(0, 0), pixels: int=256): +def interpolate(input_data_object: InputData, size: int or float, offset=(0, 0), pixels: int=256): """Extracts and interpolates Coors, Disps and EpsVonMises from an InputData object onto arrays of size pixels x pixels. diff --git a/crackpy/crack_detection/deep_learning/attention.py b/crackpy/crack_detection/deep_learning/attention.py index dd27cf9..be7dbc6 100644 --- a/crackpy/crack_detection/deep_learning/attention.py +++ b/crackpy/crack_detection/deep_learning/attention.py @@ -323,7 +323,7 @@ def plot(self, triang.set_mask(mask) plot = ax.tricontourf(triang, heatmap.flatten(), contour_vector, - extend='neither', cmap='jet') + extend='neither') ax.autoscale(False) # ax.axis('off') # uncomment to turn off axis labels and ticks @@ -431,7 +431,7 @@ def plot_overview(output, maps, side: str, scale: str = 'QUALITATIVE'): # plot heatmap plot = ax.tricontourf(coor_x.flatten(), coor_y.flatten(), heatmap.flatten(), contour_vector, - extend='neither', cmap='jet') + extend='neither') ax.autoscale(False) # calculate crack tip segmentation diff --git a/crackpy/crack_detection/detection.py b/crackpy/crack_detection/detection.py index fbcb4ef..d31466f 100644 --- a/crackpy/crack_detection/detection.py +++ b/crackpy/crack_detection/detection.py @@ -26,7 +26,7 @@ def __init__(self, side: str = 'right', detection_window_size: float = 70, offse Args: side: of the specimen ('left' or 'right') - window: size of the detection window in mm + detection_window_size: size of the detection window in mm offset: tuple of (x,y)-offset in mm angle_det_radius: radius in mm around crack tip considered for angle detection device: (torch.device) @@ -54,6 +54,13 @@ def preprocess(interp_disps: np.ndarray) -> torch.Tensor: """ input_ch = torch.tensor(interp_disps, dtype=torch.float32) + + # check if NaNs are present + if torch.isnan(input_ch).any(): + print('NaNs in displacement data! ' + 'Please make sure that no NaNs are present ' + 'E.g. make the detection_window_boundary smaller.') + input_ch = preprocess.normalize(input_ch).unsqueeze(0) return input_ch @@ -105,7 +112,8 @@ def calculate_position_in_mm(self, crack_tip_px: list): """ # Transform to global coordinate system crack_tip_x = crack_tip_px[1] * self.detection.detection_window_size / 255 - crack_tip_y = crack_tip_px[0] * self.detection.detection_window_size / 255 - self.detection.detection_window_size / 2 + crack_tip_y = crack_tip_px[0] * self.detection.detection_window_size / 255 \ + - self.detection.detection_window_size / 2 if self.detection.side == 'left': # mirror x-value of crack tip position to left-hand side crack_tip_x *= -1 crack_tip_x += self.detection.offset[0] @@ -159,6 +167,7 @@ class CrackPathDetection: * predict_path - predict the crack path and return the segmentation and skeletonized path """ + def __init__(self, detection: CrackDetection, path_detector: UNet): """Initialize class arguments. @@ -221,6 +230,7 @@ class CrackAngleEstimation: * predict_angle - estimate the crack tip angle by means of a linear regression """ + def __init__(self, detection: CrackDetection, crack_tip_in_px: list): """Initialize class arguments. diff --git a/crackpy/crack_detection/pipeline/pipeline.py b/crackpy/crack_detection/pipeline/pipeline.py index 3393313..9acdcfb 100644 --- a/crackpy/crack_detection/pipeline/pipeline.py +++ b/crackpy/crack_detection/pipeline/pipeline.py @@ -3,6 +3,7 @@ import numpy as np import torch +from crackpy.crack_detection.utils.plot import plot_prediction from crackpy.crack_detection.utils.utilityfunctions import get_nodemaps_and_stage_nums from crackpy.crack_detection.detection import CrackTipDetection, CrackPathDetection, CrackAngleEstimation, CrackDetection from crackpy.fracture_analysis.data_processing import InputData @@ -17,6 +18,7 @@ def __init__( sides: list = None, stage_nums: range = 'All', detection_window_size: float = None, + detection_boundary: tuple = None, start_offset: tuple = (0, 0), angle_det_radius: float = 10 ): @@ -28,6 +30,11 @@ def __init__( stage_nums: numbers of stages to predict the crack tip (Default: 'All' uses all files of later given path) detection_window_size: window size used to predict the crack tip (if None: detection_window_size is equal to specimen_size / 2 - 10) + detection_boundary: (x_min, x_max, y_min, y_max) hard boundary of the crack detection window (to avoid NaNs) + The 'left' side is mirrored to the 'right' side. + Example: (0, 70, -35, 35) for MT160 specimen + If None: detection_boundary is set to + (0, specimen_size / 2, -specimen_size / 4, specimen_size / 4) start_offset: (offset_x, offset_y) offset from the standard starting window adapted automatically during the pipeline angle_det_radius: radius (in mm) around the crack tip @@ -38,6 +45,10 @@ def __init__( self.window_size = detection_window_size if detection_window_size is not None else (specimen_size / 2 - 10) self.start_offset = start_offset + if detection_boundary is not None: + self.detection_boundary = detection_boundary + else: + self.detection_boundary = (0, specimen_size / 2, -specimen_size / 4, specimen_size / 4) self.angle_det_radius = angle_det_radius self.stage_nums = stage_nums @@ -155,9 +166,7 @@ def run_detection(self) -> dict: offset_x *= -1 # left side: offset in negative x-direction stages_to_results = {} - for i, stage in enumerate(self.detection_stages): - print(f'\r Progress... {i+1}/{len(self.detection_stages)}', end='') - + for stage in sorted(self.detection_stages): # Init results = {} @@ -176,7 +185,7 @@ def run_detection(self) -> dict: ) # Interpolate data on arrays (256 x 256 pixels) - interp_disps, _ = det.interpolate(data) + interp_disps, interp_eps_vm = det.interpolate(data) # Preprocess input input_ch = det.preprocess(interp_disps) @@ -189,6 +198,7 @@ def run_detection(self) -> dict: # Make prediction pred = ct_det.make_prediction(input_ch) + crack_tip_seg = ct_det.calculate_segmentation(pred) # Calculate segmentation and most likely crack tip position crack_tip_pixels = ct_det.find_most_likely_tip_pos(pred) @@ -203,7 +213,7 @@ def run_detection(self) -> dict: cp_det = CrackPathDetection(detection=det, path_detector=self.path_detector) # predict segmentation and path skeleton - cp_segmentation, _ = cp_det.predict_path(input_ch) + cp_segmentation, cp_skeleton = cp_det.predict_path(input_ch) ########################## # Crack angle estimation @@ -224,12 +234,13 @@ def run_detection(self) -> dict: # Adjust crack detection window ############################### - if np.abs(offset_x) < self.setup.specimen_size / 2 - self.setup.window_size - 10: + x_min, x_max, y_min, y_max = self.setup.detection_boundary + if np.abs(offset_x) < x_max - self.setup.window_size: if side == 'right' and crack_tip_x > offset_x + self.setup.window_size / 2: offset_x += (crack_tip_x - offset_x - self.setup.window_size / 2) if side == 'left' and crack_tip_x < offset_x - self.setup.window_size / 2: offset_x -= (offset_x - self.setup.window_size / 2 - crack_tip_x) - if np.abs(offset_y) < self.setup.specimen_size / 2 - self.setup.window_size / 2 - 10: + if np.abs(offset_y) < np.minimum(y_min, y_max) - self.setup.window_size / 2: if crack_tip_y > offset_y + self.setup.window_size / 4: offset_y += (crack_tip_y - offset_y - self.setup.window_size / 4) if crack_tip_y < offset_y - self.setup.window_size / 4: @@ -240,6 +251,24 @@ def run_detection(self) -> dict: results['crack_tip_y'] = crack_tip_y results['angle'] = angle stages_to_results[stage] = results + print(f'Stage {stage}/{sorted(self.detection_stages)[-1]}' + f' - crack tip: {crack_tip_x:.2f} mm, {crack_tip_y:.2f} mm, angle: {angle:.2f}°') + + # Plot crack detection + plot_prediction(background=interp_eps_vm * 100, + interp_size=self.setup.window_size, + offset=(offset_x, offset_y), + save_name=nodemap[:-4], + crack_tip_prediction=np.asarray([crack_tip_pixels]), + crack_tip_seg=crack_tip_seg, + crack_tip_label=None, + crack_path=cp_skeleton, + f_min=0, + f_max=0.68, + title=nodemap[:-4] + f' - {side} side', + path=os.path.join(self.output_path, "plots", f"{side}"), + label='Von Mises strain [%]') + sides_to_results[side] = stages_to_results self.sides_to_results = sides_to_results diff --git a/crackpy/crack_detection/utils/plot.py b/crackpy/crack_detection/utils/plot.py index efa658f..9fe9e37 100644 --- a/crackpy/crack_detection/utils/plot.py +++ b/crackpy/crack_detection/utils/plot.py @@ -8,17 +8,17 @@ def plot_prediction( background: np.array, interp_size: float or int, - offset: tuple=(0, 0), - crack_tip_prediction: np.array=None, - crack_tip_seg: np.array=None, - crack_tip_label: np.array=None, - crack_path: np.array=None, - f_min: float=None, - f_max: float=None, - save_name: str=None, - path: str=None, - title: str='', - label: str='Plot of Data [Unit]' + offset: tuple = (0, 0), + crack_tip_prediction: np.array = None, + crack_tip_seg: np.array = None, + crack_tip_label: np.array = None, + crack_path: np.array = None, + f_min: float = None, + f_max: float = None, + save_name: str = None, + path: str = None, + title: str = '', + label: str = 'Plot of Data [Unit]' ): """Plots crack tip labels and predictions over background. @@ -49,9 +49,6 @@ def plot_prediction( num_colors = 120 contour_vector = np.linspace(f_min, f_max, num_colors, endpoint=True) label_vector = np.linspace(f_min, f_max, 10, endpoint=True) - # Colormap similar to Aramis - cm_jet = cm.get_cmap('jet', 512) - my_cmap = ListedColormap(cm_jet(np.linspace(0.1, 0.9, 256))) fig = plt.figure(1) ax = fig.add_subplot(111) @@ -73,7 +70,7 @@ def plot_prediction( triang.set_mask(mask) plot = ax.tricontourf(triang, background.flatten(), contour_vector, - extend=extend, cmap=my_cmap) + extend=extend) ax.autoscale(False) # ax.axis('off') # uncomment to turn of axis and labels @@ -113,7 +110,7 @@ def plot_prediction( if path is not None: if not os.path.exists(path): os.makedirs(path) - plt.savefig(os.path.join(path, save_name + '.png'), bbox_inches='tight', dpi=300) + plt.savefig(os.path.join(path, save_name + '.png'), bbox_inches='tight') else: plt.show() diff --git a/crackpy/fracture_analysis/analysis.py b/crackpy/fracture_analysis/analysis.py index ff512d2..a076332 100644 --- a/crackpy/fracture_analysis/analysis.py +++ b/crackpy/fracture_analysis/analysis.py @@ -145,7 +145,7 @@ def run(self, progress=None, task_id=None): except: print('CJP optimization failed.') - self.res_cjp = None + self.res_cjp = {'Error': np.nan, 'K_F': np.nan, 'K_R': np.nan, 'K_S': np.nan, 'K_II': np.nan, 'T': np.nan} try: # calculate Williams coefficients with fitting method @@ -161,12 +161,13 @@ def run(self, progress=None, task_id=None): K_II = -np.sqrt(2 * np.pi) * self.williams_fit_b_n[1] / np.sqrt(1000) T = 4 * self.williams_fit_a_n[2] - self.sifs_fit = {'Error': williams_results.cost, - 'K_I': K_I, 'K_II': K_II, 'T': T} + self.sifs_fit = {'Error': williams_results.cost, 'K_I': K_I, 'K_II': K_II, 'T': T} except: print('Williams optimization failed.') - self.sifs_fit = None + self.williams_fit_a_n = {n: np.nan for index, n in enumerate(self.optimization.terms)} + self.williams_fit_b_n = {n: np.nan for index, n in enumerate(self.optimization.terms)} + self.sifs_fit = {'Error': np.nan, 'K_I': np.nan, 'K_II': np.nan, 'T': np.nan} if self.integral_properties is not None: # calculate Williams coefficients with Bueckner-Chen integral method diff --git a/crackpy/fracture_analysis/data_processing.py b/crackpy/fracture_analysis/data_processing.py index e282c9c..fc72daa 100644 --- a/crackpy/fracture_analysis/data_processing.py +++ b/crackpy/fracture_analysis/data_processing.py @@ -336,15 +336,23 @@ def _calculate_principal_stresses(self): self.sig_1 = principal_stresses[:, 0] self.sig_2 = principal_stresses[:, 1] - def _renumber_nodes(self, node_number: int, mapping: dict): + def _renumber_nodes(self, old_node_number: int, mapping: dict): """Renumber nodes according to mapping table. Args: - node_number: node number + old_node_number: node number mapping: mapping of old node numbers to new node numbers + Returns: + new_node_number: it is -1 if the node number is not in the mapping table + """ - return mapping[node_number] + try: + new_node_number = mapping[old_node_number] + except KeyError: + new_node_number = -1 + + return new_node_number def to_vtk(self, output_folder: str = None): """Returns a vtk file of the DIC data. @@ -357,7 +365,7 @@ def to_vtk(self, output_folder: str = None): PyVista mesh object """ # create node - nodes = np.stack((self.coor_x, self.coor_y, np.zeros_like(self.coor_x)), axis=1) + nodes = np.stack((self.coor_x, self.coor_y, self.coor_z), axis=1) # remap node numbers old_facet_id = self.facet_id - 1 @@ -365,22 +373,28 @@ def to_vtk(self, output_folder: str = None): mapping = dict(zip(old_facet_id, new_facet_id)) renumber_nodes_vec = np.vectorize(self._renumber_nodes) - # define elements - elements = self.connections[:, 1:5] - elements[:, 0] = 3 - elements[:, 1:] = renumber_nodes_vec(elements[:, 1:], mapping) + # create mesh + if self.connections is not None: + # define elements + elements = self.connections[:, 1:5] + elements[:, 0] = 3 + elements[:, 1:] = renumber_nodes_vec(elements[:, 1:], mapping) - # define cell types - cell_types = np.full(len(elements[:, 0]), fill_value=CellType.TRIANGLE, dtype=np.uint8) + # Check if there are any elements with -1 as node number + mask = np.any(elements[:, 1:] == -1, axis=1) + elements = elements[~mask] - # define mesh - mesh = pyvista.UnstructuredGrid(elements, cell_types, nodes) + # define cell types + cell_types = np.full(len(elements[:, 0]), fill_value=CellType.TRIANGLE, dtype=np.uint8) + + # define mesh + mesh = pyvista.UnstructuredGrid(elements, cell_types, nodes) + + else: + print(f'No connectivity data provided for {self.nodemap_name}. Reconstructing a mesh from the nodes using Delaunay triangulation.') + cloud = pyvista.wrap(nodes) + mesh = cloud.delaunay_2d(alpha=1.0) - # Check if input data is 2D - if self.coor_z is None: - self.coor_z = np.zeros_like(self.coor_x) - if self.disp_z is None: - self.disp_z = np.zeros_like(self.coor_x) # add data mesh.point_data['x [mm]'] = self.coor_x @@ -389,6 +403,7 @@ def to_vtk(self, output_folder: str = None): mesh.point_data['u_x [mm]'] = self.disp_x mesh.point_data['u_y [mm]'] = self.disp_y mesh.point_data['u_z [mm]'] = self.disp_z + mesh.point_data['u_sum [mm]'] = np.sqrt(self.disp_x ** 2 + self.disp_y ** 2 + self.disp_z ** 2) mesh.point_data['eps_x [%]'] = self.eps_x * 100.0 mesh.point_data['eps_y [%]'] = self.eps_y * 100.0 mesh.point_data['eps_xy [1]'] = self.eps_xy diff --git a/crackpy/fracture_analysis/optimization.py b/crackpy/fracture_analysis/optimization.py index 49ecc84..706d3ac 100644 --- a/crackpy/fracture_analysis/optimization.py +++ b/crackpy/fracture_analysis/optimization.py @@ -2,7 +2,7 @@ from scipy.interpolate import griddata from scipy import optimize -from crackpy.fracture_analysis.crack_tip import williams_stress_field, williams_displ_field, cjp_displ_field +from crackpy.fracture_analysis.crack_tip import williams_displ_field, cjp_displ_field from crackpy.fracture_analysis.data_processing import InputData from crackpy.structure_elements.material import Material @@ -83,20 +83,11 @@ def __init__(self, self.x_grid, self.y_grid = self.make_cartesian(self.r_grid, self.phi_grid) # map transformed data to cartesian grid + self._interpolate_data_on_grid() + + def _interpolate_data_on_grid(self): disp_x_0_0 = griddata((self.data.coor_x, self.data.coor_y), self.data.disp_x, (0, 0)).item() disp_y_0_0 = griddata((self.data.coor_x, self.data.coor_y), self.data.disp_y, (0, 0)).item() - self.interp_sigma_x = griddata(points=(self.data.coor_x, self.data.coor_y), - values=self.data.sig_x, - xi=(self.x_grid, self.y_grid), - method='linear') - self.interp_sigma_y = griddata(points=(self.data.coor_x, self.data.coor_y), - values=self.data.sig_y, - xi=(self.x_grid, self.y_grid), - method='linear') - self.interp_sigma_xy = griddata(points=(self.data.coor_x, self.data.coor_y), - values=self.data.sig_xy, - xi=(self.x_grid, self.y_grid), - method='linear') self.interp_disp_x = griddata(points=(self.data.coor_x, self.data.coor_y), values=self.data.disp_x - disp_x_0_0, xi=(self.x_grid, self.y_grid), @@ -108,7 +99,7 @@ def __init__(self, def optimize_cjp_displacements(self, method='lm', init_coeffs=None): """Optimizes CJP displacements. - (see Yang et al. (2021) New algorithm for optimised fitting of DIC data + (see Yang et al. (2021) New algorithm for optimized fitting of DIC data to crack tip plastic zone using the CJP model) Args: @@ -127,7 +118,7 @@ def optimize_cjp_displacements(self, method='lm', init_coeffs=None): def optimize_williams_displacements(self, method='lm', init_coeffs=None): """Optimizes Williams displacements. - (see Yang et al. (2021) New algorithm for optimised fitting of DIC data + (see Yang et al. (2021) New algorithm for optimized fitting of DIC data to crack tip plastic zone using the CJP model) Args: @@ -143,23 +134,6 @@ def optimize_williams_displacements(self, method='lm', init_coeffs=None): x0=init_coeffs, method=method) - def optimize_williams_stresses(self, method='lm', init_coeffs=None): - """Optimizes Williams stresses. - - Args: - method: method from scipy.optimize.least_squares, defaults to 'lm' - Levenberg-Marquardt iterative algorithm - init_coeffs: initial coefficients used for x0 in scipy.optimize.least_squares - - """ - if init_coeffs is None: - init_coeffs = np.random.rand(2*len(self.terms)) - else: - init_coeffs += np.random.rand(2*len(self.terms)) - - return optimize.least_squares(fun=self.residuals_williams_stresses, - x0=init_coeffs, - method=method) - def residuals_cjp_displacements(self, inp: list or np.array) -> np.ndarray: """Returns the residuals of CJP displacements. @@ -177,6 +151,8 @@ def residuals_cjp_displacements(self, inp: list or np.array) -> np.ndarray: residual = np.asarray([cjp_disp_x - self.interp_disp_x, cjp_disp_y - self.interp_disp_y]) residual = residual.reshape(-1) + # filter out nan values + residual = residual[~np.isnan(residual)] return residual def residuals_williams_displacements(self, inp: list or np.array) -> np.ndarray: @@ -196,73 +172,10 @@ def residuals_williams_displacements(self, inp: list or np.array) -> np.ndarray: residual = np.asarray([williams_disp_x - self.interp_disp_x, williams_disp_y - self.interp_disp_y]) residual = residual.reshape(-1) + # filter out nan values + residual = residual[~np.isnan(residual)] return residual - def residuals_williams_stresses(self, inp: list or np.array) -> np.ndarray: - """Returns the residuals of Williams stresses. - - Args: - inp: Williams coefficients for williams_stress_field - - Returns: - residual: between stresses calculated from approximated Williams field and actual results. - Calculated for x and y direction and xy shear stress. - - """ - a = inp[:len(self.terms)] - b = inp[len(self.terms):] - - williams_sig_x, williams_sig_y, williams_sig_xy = williams_stress_field(a, b, self.terms, - self.phi_grid, self.r_grid) - - residual = np.asarray([williams_sig_x - self.interp_sigma_x, - williams_sig_y - self.interp_sigma_y, - williams_sig_xy - self.interp_sigma_xy]) - residual = residual.reshape(-1) - return residual - - def sse_williams_displacements(self, inp: list or np.array) -> float: - """Returns the summed squared error of Williams displacements (sum over all entries and summed over - x and y directions). - - Args: - inp: Williams coefficients for williams_displ_field - - Returns: - err - - """ - a = inp[:len(self.terms)] - b = inp[len(self.terms):] - - williams_disp_x, williams_disp_y = williams_displ_field(a, b, self.terms, self.phi_grid, self.r_grid, - self.material) - - err = np.sum((williams_disp_x - self.interp_disp_x)**2 + - (williams_disp_y - self.interp_disp_y)**2) - return err - - def sse_williams_stresses(self, inp: list or np.array) -> float: - """Returns the summed squared error of Williams stresses (sum over x, y and xy directions). - - Args: - inp: Williams coefficients for williams_stress_field - - Returns: - err: - - """ - a = inp[:len(self.terms)] - b = inp[len(self.terms):] - - williams_sig_x, williams_sig_y, williams_sig_xy = williams_stress_field(a, b, self.terms, - self.phi_grid, self.r_grid) - - err = np.sum((williams_sig_x - self.interp_sigma_x)**2 + - (williams_sig_y - self.interp_sigma_y)**2 + - (williams_sig_xy - self.interp_sigma_xy)**2) - return err - @staticmethod def make_cartesian(r: float, phi: float): """Takes polar coordinates and maps onto cartesian coordinates.""" diff --git a/crackpy/fracture_analysis/plot.py b/crackpy/fracture_analysis/plot.py index 19b2fe4..a997492 100644 --- a/crackpy/fracture_analysis/plot.py +++ b/crackpy/fracture_analysis/plot.py @@ -10,8 +10,7 @@ class PlotSettings: def __init__(self, xlim_down: float = None, xlim_up: float = None, ylim_down: float = None, ylim_up: float = None, - background: str = 'eps_vm', min_value: float = None, max_value: float = None, extend: str = None, - cmap='jet', dpi: int = 300): + background: str = 'eps_vm', min_value: float = None, max_value: float = None, extend: str = None): """Define plot settings for Plotter class object. Args: @@ -23,8 +22,6 @@ def __init__(self, xlim_down: float = None, xlim_up: float = None, ylim_down: fl min_value: minimum value of background max_value: maximum value of background extend: extend of background (e.g. 'neither', 'min', 'max') - cmap: obj of the class ColorMap or str, color map for the plot - dpi: resolution of the plot """ self.ylim_down = ylim_down @@ -38,9 +35,6 @@ def __init__(self, xlim_down: float = None, xlim_up: float = None, ylim_down: fl self.extend = extend self.legend_label = self._keyword_to_legend_label(background) - self.cmap = cmap - self.dpi = dpi - def _keyword_to_legend_label(self, keyword: str = 'sig_vm') -> str: """Convert keyword to legend label. @@ -128,7 +122,7 @@ def plot(self): # Save figure save_path = os.path.join(self.path, self.filename) - plt.savefig(save_path + '.png', bbox_inches='tight', dpi=self.plot_sets.dpi) + plt.savefig(save_path + '.png', bbox_inches='tight') plt.clf() plt.close() @@ -176,7 +170,7 @@ def _plot_integration(self): plt.rcParams['font.size'] = '16' plot = self.ax_int.tricontourf(self.analysis.data.coor_x, self.analysis.data.coor_y, background, - contour_vector, extend=extend, cmap=self.plot_sets.cmap) + contour_vector, extend=extend) ct_plot = self.ax_int.scatter(0, 0, marker='x', color='black', s=100, linewidth=3) self.ax_int.legend(handles=[ct_plot], labels=[f'$x_{{tip}}$ = {self.analysis.crack_tip.crack_tip_x:.2f} mm\n' @@ -237,7 +231,7 @@ def _plot_results(self): if self.analysis.integral_properties is not None: # J integral result - props = dict(boxstyle='round', facecolor='green', alpha=0.2) + props = dict(boxstyle='round', facecolor='gray', alpha=0.4) text = "J-integral\n\n" + \ f"$J$ = {self.analysis.sifs_int['rej_out_mean']['j']:.2f} $N*mm^{{-1}}$\n" + \ f"$K_J$ = {self.analysis.sifs_int['rej_out_mean']['sif_j']:.2f} $MPa*m^{{1/2}}$" @@ -246,7 +240,7 @@ def _plot_results(self): verticalalignment='top', bbox=props) # Williams integration results - props = dict(boxstyle='round', facecolor='green', alpha=0.3) + props = dict(boxstyle='round', facecolor='gray', alpha=0.4) text = "Interaction integral\n\n" + \ f"$K_I$ = {self.analysis.sifs_int['rej_out_mean']['sif_k_i']:.2f} $MPa*m^{{1/2}}$\n" + \ f"$K_{{II}}$ = {self.analysis.sifs_int['rej_out_mean']['sif_k_ii']:.2f} $MPa*m^{{1/2}}$\n" + \ @@ -255,7 +249,7 @@ def _plot_results(self): transform=self.ax_results.transAxes, fontsize=14, verticalalignment='top', bbox=props) - props = dict(boxstyle='round', facecolor='green', alpha=0.4) + props = dict(boxstyle='round', facecolor='gray', alpha=0.4) text = "Bueckner integral\n\n" + \ f"$K_I$ = {self.analysis.sifs_int['rej_out_mean']['k_i_chen']:.2f} $MPa*m^{{1/2}}$\n" + \ f"$K_{{II}}$ = {self.analysis.sifs_int['rej_out_mean']['k_ii_chen']:.2f} $MPa*m^{{1/2}}$\n" + \ @@ -269,7 +263,7 @@ def _plot_results(self): and self.analysis.res_cjp is not None: # Williams fitting results - props = dict(boxstyle='round', facecolor='red', alpha=0.2) + props = dict(boxstyle='round', facecolor='gray', alpha=0.4) text = "Williams fitting\n\n" + \ f"$K_I$ = {self.analysis.sifs_fit['K_I']:.2f} $MPa*m^{{1/2}}$\n" + \ f"$K_{{II}}$ = {self.analysis.sifs_fit['K_II']:.2f} $MPa*m^{{1/2}}$\n" + \ @@ -279,7 +273,7 @@ def _plot_results(self): verticalalignment='top', bbox=props) # CJP fitting results - props = dict(boxstyle='round', facecolor='red', alpha=0.4) + props = dict(boxstyle='round', facecolor='gray', alpha=0.4) text = "CJP fitting\n\n" + \ f"$K_F$ = {self.analysis.res_cjp['K_F']:.2f} $MPa*m^{{1/2}}$\n" + \ f"$K_R$ = {self.analysis.res_cjp['K_R']:.2f} $MPa*m^{{1/2}}$\n" + \ @@ -301,8 +295,7 @@ def _plot_cjp_residuals(self): # Set general font size plt.rcParams['font.size'] = '16' - plot = self.ax_cjp_opt.scatter(opt.x_grid.flatten(), opt.y_grid.flatten(), c=error, - cmap='jet', marker='.') + plot = self.ax_cjp_opt.scatter(opt.x_grid.flatten(), opt.y_grid.flatten(), c=error, marker='.') # Set tick font size for label in (self.ax_cjp_opt.get_xticklabels() + self.ax_cjp_opt.get_yticklabels()): @@ -331,8 +324,7 @@ def _plot_williams_residuals(self): # Set general font size plt.rcParams['font.size'] = '16' - plot = self.ax_williams_opt.scatter(opt.x_grid.flatten(), opt.y_grid.flatten(), c=error, - cmap='jet', marker='.') + plot = self.ax_williams_opt.scatter(opt.x_grid.flatten(), opt.y_grid.flatten(), c=error, marker='.') # Set tick font size for label in (self.ax_williams_opt.get_xticklabels() + self.ax_williams_opt.get_yticklabels()): diff --git a/crackpy/fracture_analysis/write.py b/crackpy/fracture_analysis/write.py index d83ab86..fb74750 100644 --- a/crackpy/fracture_analysis/write.py +++ b/crackpy/fracture_analysis/write.py @@ -1,5 +1,7 @@ import os +import numpy as np + from crackpy.fracture_analysis.analysis import FractureAnalysis from crackpy.fracture_analysis.crack_tip import unit_of_williams_coefficients @@ -68,40 +70,38 @@ def write_results(self) -> None: if self.analysis.optimization_properties is not None: - if self.analysis.res_cjp is not None: - file.write('\n') - file.write("#############################\n") - file.write("# CJP model #\n") - file.write("#############################\n") - file.write('\n') - file.write('\n') - file.write(f'{"Param":>10}, {"Unit":>20}, {"Result":>20} \n') - file.write(f'{"Error":>10}, {"1":>20}, {self.analysis.res_cjp["Error"]:20.10f} \n') - file.write(f'{"K_F":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_F"]:20.10f} \n') - file.write(f'{"K_R":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_R"]:20.10f} \n') - file.write(f'{"K_S":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_S"]:20.10f} \n') - file.write(f'{"K_II":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_II"]:20.10f} \n') - file.write(f'{"T":>10}, {"MPa":>20}, {self.analysis.res_cjp["T"]:20.10f} \n') - file.write('\n') - file.write('\n') + file.write('\n') + file.write("#############################\n") + file.write("# CJP model #\n") + file.write("#############################\n") + file.write('\n') + file.write('\n') + file.write(f'{"Param":>10}, {"Unit":>20}, {"Result":>20} \n') + file.write(f'{"Error":>10}, {"1":>20}, {self.analysis.res_cjp["Error"]:20.10f} \n') + file.write(f'{"K_F":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_F"]:20.10f} \n') + file.write(f'{"K_R":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_R"]:20.10f} \n') + file.write(f'{"K_S":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_S"]:20.10f} \n') + file.write(f'{"K_II":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.res_cjp["K_II"]:20.10f} \n') + file.write(f'{"T":>10}, {"MPa":>20}, {self.analysis.res_cjp["T"]:20.10f} \n') + file.write('\n') + file.write('\n') - if self.analysis.sifs_fit is not None: - file.write("#############################\n") - file.write("# Williams fitting #\n") - file.write("#############################\n") - file.write('\n') - file.write('\n') - file.write(f'{"Param":>10}, {"Unit":>20}, {"Result":>20} \n') - file.write(f'{"Error":>10}, {"1":>20}, {self.analysis.sifs_fit["Error"]:20.10f} \n') - file.write(f'{"K_I":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.sifs_fit["K_I"]:20.10f} \n') - file.write(f'{"K_II":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.sifs_fit["K_II"]:20.10f} \n') - file.write(f'{"T":>10}, {"MPa":>20}, {self.analysis.sifs_fit["T"]:20.10f} \n') - for n, a in self.analysis.williams_fit_a_n.items(): - file.write(f'{f"a_{n}":>10}, {unit_of_williams_coefficients(n):>20}, {a:20.10f} \n') - for n, b in self.analysis.williams_fit_b_n.items(): - file.write(f'{f"b_{n}":>10}, {unit_of_williams_coefficients(n):>20}, {b:20.10f} \n') - file.write('\n') - file.write('\n') + file.write("#############################\n") + file.write("# Williams fitting #\n") + file.write("#############################\n") + file.write('\n') + file.write('\n') + file.write(f'{"Param":>10}, {"Unit":>20}, {"Result":>20} \n') + file.write(f'{"Error":>10}, {"1":>20}, {self.analysis.sifs_fit["Error"]:20.10f} \n') + file.write(f'{"K_I":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.sifs_fit["K_I"]:20.10f} \n') + file.write(f'{"K_II":>10}, {"MPa*m^{1/2}":>20}, {self.analysis.sifs_fit["K_II"]:20.10f} \n') + file.write(f'{"T":>10}, {"MPa":>20}, {self.analysis.sifs_fit["T"]:20.10f} \n') + for n, a in self.analysis.williams_fit_a_n.items(): + file.write(f'{f"a_{n}":>10}, {unit_of_williams_coefficients(n):>20}, {a:20.10f} \n') + for n, b in self.analysis.williams_fit_b_n.items(): + file.write(f'{f"b_{n}":>10}, {unit_of_williams_coefficients(n):>20}, {b:20.10f} \n') + file.write('\n') + file.write('\n') if self.analysis.integral_properties is not None: file.write("###################################\n") diff --git a/crackpy/tests/test_crack_detection/test_deep_learning/test_attention.py b/crackpy/tests/test_crack_detection/test_deep_learning/test_attention.py index 006ac63..46eda87 100644 --- a/crackpy/tests/test_crack_detection/test_deep_learning/test_attention.py +++ b/crackpy/tests/test_crack_detection/test_deep_learning/test_attention.py @@ -3,6 +3,7 @@ import shutil import tempfile +import numpy as np import torch import numpy import matplotlib.pyplot as plt @@ -47,8 +48,8 @@ def test_calculate_cam(self): # calculate output and features in forward pass act_output, act_heatmap = self.sgc(input_t) - self.assertTrue(torch.equal(exp_output.detach().to('cpu'), act_output.detach().to('cpu'))) - self.assertTrue(torch.equal(torch.from_numpy(exp_heatmap.copy()), torch.from_numpy(act_heatmap.copy()))) + self.assertTrue(torch.allclose(exp_output.detach().to('cpu'), act_output.detach().to('cpu'))) + self.assertTrue(np.allclose(exp_heatmap.copy(), act_heatmap.copy(), rtol=1e-2)) def test_plot_and_save(self): base_path = os.path.join( # '..', '..', '..', '..', diff --git a/example_images/example_image_output.png b/example_images/example_image_output.png index a28e9e41ee49441e1e8b7bf9d690015d48f965e6..9de24c4126327700e1bd142fd1f7b68f9e797c89 100644 GIT binary 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