""" Tow class example ================= This example shows how to use the Tow class in PolyTex package. It is designed to handle the parametrization and geometrical analysis of a fiber tow. A Tow instance is created by passing the point cloud of a tow, which consists only the points on the tow surface, to the constructor. """ ##################################################################### # Example dataset # --------------- import polytex as ptx import numpy as np # Load the surface points of fiber tow path = ptx.example("surface points") surf_points = ptx.read_explicit_data(path) resolution = 0.022 # mm/pixel surf_points = surf_points * resolution # convert to millimeters ##################################################################### # We clip the coordinates to discard the part of the tow that is necessary for # the modeling of the tow. Optional. mask = (surf_points[:, 0] > 1.1 - 0.2) & (surf_points[:, 0] < 11.55 + 0.2) surf_points_clip = surf_points[mask] ##################################################################### # Exchange the first and last column for geometry analysis. Note that # the last column is deemed as the label column. Namely, the points # with the same label are considered as belonging to the same slice and # are parametrized in the radial direction (theta). This data reorder # is necessary for the users. coordinates = surf_points_clip[:, [0, 1, 2]] ##################################################################### # We can filtering the points to remove noise or the points that are not necessary: # mask = abs(coordinates[:, -1] - 9.196) > 0.09 # coordinates = coordinates[mask] ##################################################################### # Create a Tow instance with PolyTex Tow class # -------------------------------------------- tow = ptx.Tow( surf_points=coordinates, order="xyz", rho_fiber=2550, radius_fiber=6.5e-6, length_scale="mm", tex=1100, name="weft_0") ##################################################################### # Get the parametric coordinates of the tow: The points on the same slice # are parametrized in the radial direction (theta) and stored in the # normalized distance column of the attribute, tow.coordinates, a pandas # DataFrame. df_coord = tow.coordinates # parametric coordinates of the tow ##################################################################### # Get the geometrical features of the tow: The geometrical features of the tow # are stored in the attribute, tow.geom_features, a pandas DataFrame. # For straight fiber tows, the geometrical features can be used as an approximation # of the actual tow geometry. But for wavy tows, such as binder, the geometrical # features are not accurate enough. We need to redo the geometrical analysis # after identifying the normal cross-sections of the tow. df_geom = tow.geom_features # geometrical features of the tow ################################################################################ # Resampling # ---------- # Resampling the control points of the tow with a uniform spacing in the # normalized distance direction. The resampling is necessary to create a # parametric representation based on dual kriging. # Equidistant resampling of the tow control points in the radial direction. theta_res = 40 # number of control points in the radial direction sample_position = np.linspace(0, 1, theta_res, endpoint=True) # equal spaced points (normalized distance) # # Resampling according to distribution density # cluster = tow.kde(bw=0.004) # sample_position = cluster["cluster centers"] pts_krig, expr_krig = tow.resampling(krig_config=("lin", "cub"), skip=5, sample_position=sample_position, smooth=0.00001) ##################################################################### # Save and reload the tow instance # -------------------------------- # tow.save("./tow/weft_0.tow") ##################################################################### # Plot the tow # ------------ mesh = tow.surf_mesh(plot=True, save_path="./test_data/weft_0.ply", end_closed=True) ##################################################################### # Smooth the tow trajectory with Kriging # -------------------------------------- trajectory_sm = tow.trajectory(smooth=0.0015, plot=False, save_path="./test_data/trajectory.ply", orientation=True) ##################################################################### # Axial and radial lines # ---------------------- # Get the axial lines of the tow (the lines connecting the parametrized control points in # the axial direction) line_axi = tow.axial_lines(plot=True) # Get the radial lines of the tow (the lines connecting the parametrized control points in # the radial direction) line_rad = tow.radial_lines(plot=True) ################################################################################ # Get the normal cross-sections of the tow # ---------------------------------------- # So far, we provide two methods to get the normal cross-sections of the tow. # The first method wraps the intersection function of plane and surface mesh # in the pyvista package. The second method is based on the intersection of # a parametric curve and an implicit plane. cross_section, planes, clipped = tow.normal_cross_section(algorithm="pyvista") ##################################################################### # Update the geometrical features of the tow # ------------------------------------------ # The geometrical features of the tow are stored in the attribute, tow.geom_features, a pandas DataFrame. # You have this information once the tow instance is created. However, that is calculated based on the vertical # cross-sections of the tow. A more accurate geometrical analysis can be done during the identification of # the normal cross-sections with the class method, Tow.normal_cross_section. # Acess the updated geometry features according to the normal cross-sections. df_geom_pv = tow.geom_features.copy() ################################################################################ # Get the normal cross-sections of the tow # ---------------------------------------- # The kriging method is based on the intersection of a parametric curve and an implicit plane. cross_section, plane, clipped = tow.normal_cross_section(algorithm="kriging", plot=True, i_size=2, j_size=3, skip=15) # Acess the updated geometry features according to the normal cross-sections. df_geom_krig = tow.geom_features ################################################################################ # Geometry features # ----------------- # as shwon above, the tow geometry features can be updated after the normal cross-sections # are identified using both method. However, the accuracy are different. The pyvista method # is faster but less accurate. In the kriging method, we transform the identified cross-sections # to a 2d plane. The geometry features are then calculated based on the 2d coordinates. Thus, the # geometry features are more accurate than the pyvista method. However, this also makes the # kriging method less efficient. The kriging method is recommended for wavy tows, such as binder.