def shell_properties_top_view( shells: List[Shell], prop_f: Optional[Callable[[Material], float]] = None, prop_units: Optional[str] = None, cmap: matplotlib.colors.Colormap = default_cmap, colorbar: bool = False, label: bool = False, outline: bool = True, ): """Top view of shell elements optionally coloured by material property.""" # Vertices of nodes for each shell. verts = [] # Min and max values for colour normalization. prop_min, prop_max = np.inf, -np.inf for shell in shells: verts.append([]) for node in shell.nodes(): verts[-1].append([node.x, node.z]) shell_prop = prop_f(shell.section) if prop_f is not None else 0 if shell_prop < prop_min: prop_min = shell_prop if shell_prop > prop_max: prop_max = shell_prop if prop_f is not None: norm = matplotlib.colors.Normalize(vmin=prop_min, vmax=prop_max) # Keep track of all values used for colours. # This is so we don't add duplicate labels. values = set() ax = plt.gca() for shell, shell_verts in zip(shells, verts): colour, label_str = "none", None if prop_f is not None: value = prop_f(shell.section) colour = cmap(norm(value)) if label and value not in values: values.add(value) label_str = f"{value} {prop_units}" ax.add_collection( matplotlib.collections.PolyCollection( [shell_verts], facecolors=colour, edgecolors="black" if outline else "none", linewidths=0.01 if outline else 0, label=label_str, )) if prop_f is not None: if label: plt.legend() if colorbar: mappable = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm) clb = plt.gcf().colorbar(mappable, shrink=0.7) clb.ax.set_title(prop_units)
def temperature_effect_date(c: Config, month: str, vert: bool): temp = __init__.load(name=month) point = Point(x=51, y=0, z=-8.4) plt.landscape() def plot_hours(): if not vert: return label_set = False for dt in temp["datetime"]: if np.isclose(float(dt.hour + dt.minute), 0): label = None if not label_set: label = "Time at vertical line = 00:00" label_set = True plt.axvline(x=dt, linewidth=1, color="black", label=label) # Plot the temperature. plt.subplot(2, 1, 1) plot_hours() plt.scatter( temp["datetime"], temp["temp"], c=temp["missing"], cmap=mpl.cm.get_cmap("bwr"), s=1, ) plt.ylabel("Temperature (°C)") plt.xlabel("Date") plt.gcf().autofmt_xdate() plt.title(f"Temperature in {str(month[0]).upper()}{month[1:]}") plt.legend() # Plot the effect at a point. response_type = ResponseType.YTranslation plt.subplot(2, 1, 2) plot_hours() effect = __init__.effect( c=c, response_type=response_type, points=[point], temps=temp["temp"] )[0] plt.scatter( temp["datetime"], effect * 1000, c=temp["missing"], cmap=mpl.cm.get_cmap("bwr"), s=1, ) plt.ylabel(f"{response_type.name()} (mm)") plt.xlabel("Date") plt.gcf().autofmt_xdate() plt.title(f"{response_type.name()} to unit thermal loading in {month}") # Save. plt.tight_layout() plt.savefig(c.get_image_path("classify/temperature", f"{month}.png")) plt.savefig(c.get_image_path("classify/temperature", f"{month}.pdf")) plt.close()
def make_boundary_plot(c: Config): """Top view of bridge with boundary conditions.""" plt.landscape() top_view_bridge(c.bridge, abutments=True, piers=True, compass=False) plt.vlines( [0, c.bridge.length], c.bridge.z_min, c.bridge.z_max, lw=5, color="orange", label=" Y = 1, Z = 1", ) for p_i, pier in enumerate(c.bridge.supports): z_min_top, z_max_top = pier.z_min_max_bottom() x_min, x_max = pier.x_min_max_top() x_center = x_min + ((x_max - x_min) / 2) plt.vlines( [x_center], z_min_top, z_max_top, lw=5, color="red" if (8 <= p_i <= 15) else "orange", label="X = 1, Y = 1, Z = 1" if p_i == 8 else None, ) legend_marker_size(plt.legend(), 50) plt.title("Bridge 705 boundary conditions of nodal supports") plt.tight_layout() plt.savefig(c.get_image_path("sensors", "boundary.pdf")) plt.close()
def make_available_sensors_plot(c: Config, pier_radius: float, track_radius: float, edge_radius: float): """Scatter plot of sensors used for classification.""" top_view_bridge(c.bridge, abutments=True, piers=True, compass=False) plot_deck_sensors( c=c, without=without.points( c=c, pier_radius=pier_radius, track_radius=track_radius, edge_radius=edge_radius, ), label=True, ) for l_i, load in enumerate([Point(x=21, z=-8.4), Point(x=33, z=-4)]): plt.scatter( [load.x], [load.z], color="red", marker="o", s=50, label="Sensor of interest" if l_i == 0 else None, ) legend_marker_size(plt.legend(), 50) plt.title(f"Sensors available for classification on Bridge 705") plt.tight_layout() plt.savefig(c.get_image_path("sensors", "unavailable-sensors.pdf")) plt.close()
def plot_distributions( response_array: List[float], response_type: ResponseType, titles: List[str], save: str, cols: int = 5, expected: List[List[float]] = None, xlim: Optional[Tuple[float, float]] = None, ): # Transpose so points are indexed first. response_array = response_array.T # response_array, unit_str = resize_and_units(response_array, response_type) num_points = response_array.shape[0] amax, amin = np.amax(response_array), np.amin(response_array) # Determine the number of rows. rows = int(num_points / cols) if rows != num_points / cols: print_w( f"Cols don't divide number of points {num_points}, cols = {cols}") rows += 1 # Plot fem. for i in range(num_points): plt.subplot(rows, cols, i + 1) plt.xlim((amin, amax)) plt.title(titles[i]) label = None if expected is not None: if response_array.shape != expected.shape: expected = expected.T expected, _ = resize_and_units(expected, response_type) assert response_array.shape == expected.shape label = chisquare(response_array[i], expected[i]) plt.hist(response_array[i], label=label) if label is not None: plt.legend() if xlim is not None: plt.xlim(xlim) plt.savefig(save) plt.close()
def plot_deck_sensors(c: Config, without: Callable[[Point], bool], label: bool = False): """Scatter plot of deck sensors.""" deck_nodes, _ = get_bridge_nodes(c.bridge) deck_nodes = det_nodes(deck_nodes) unavail_nodes = [] avail_nodes = [] for node in deck_nodes: if without(Point(x=node.x, y=node.y, z=node.z)): unavail_nodes.append(node) else: avail_nodes.append(node) X, Z, H = [], [], [] # 2D arrays, x and z coordinates, and height. for node in deck_nodes: X.append(node.x) Z.append(node.z) if without(Point(x=node.x, y=node.y, z=node.z)): H.append(1) else: H.append(0) plt.scatter( [node.x for node in avail_nodes], [node.z for node in avail_nodes], s=5, color="#1f77b4", ) plt.scatter( [node.x for node in unavail_nodes], [node.z for node in unavail_nodes], color="#ff7f0e", s=5, ) if label: plt.scatter( [avail_nodes[0].x], [avail_nodes[0].z], color="#1f77b4", label="Available", s=5, ) plt.scatter( [unavail_nodes[0].x], [unavail_nodes[0].z], color="#ff7f0e", label="Unavailable", s=5, ) legend = plt.legend() legend_marker_size(legend, 50)
def number_of_uls_plot(c: Config): """Plot error as a function of number of unit load simulations.""" if not c.shorten_paths: raise ValueError("This plot requires --shorten-paths true") response_type = ResponseType.YTranslation num_ulss = np.arange(100, 2000, 10) chosen_uls = 600 point = Point(x=c.bridge.x_max - (c.bridge.length / 2), y=0, z=-8.4) wagen1_time = truck1.time_at(x=point.x, bridge=c.bridge) print_i(f"Wagen 1 time at x = {point.x:.3f} is t = {wagen1_time:.3f}") # Determine the reference value. truck_loads = flatten( truck1.to_point_load_pw(time=wagen1_time, bridge=c.bridge), PointLoad) print_i(f"Truck loads = {truck_loads}") sim_responses = load_fem_responses( c=c, response_type=response_type, sim_runner=OSRunner(c), sim_params=SimParams(ploads=truck_loads, response_types=[response_type]), ) ref_value = sim_responses.at_deck(point, interp=True) * 1000 print_i(f"Reference value = {ref_value}") # Collect the data. total_load = [] num_loads = [] responses = [] for num_uls in num_ulss: c.il_num_loads = num_uls # Nested in here because it depends on the setting of 'il_num_loads'. truck_loads = flatten( truck1.to_wheel_track_loads(c=c, time=wagen1_time), PointLoad) num_loads.append(len(truck_loads)) total_load.append(sum(map(lambda l: l.kn, truck_loads))) sim_responses = load_fem_responses( c=c, response_type=response_type, sim_runner=OSRunner(c), sim_params=SimParams(ploads=truck_loads, response_types=[response_type]), ) responses.append(sim_responses.at_deck(point, interp=True) * 1000) # Plot the raw fem, then error on the second axis. plt.landscape() # plt.plot(num_ulss, fem) # plt.ylabel(f"{response_type.name().lower()} (mm)") plt.xlabel("ULS") error = np.abs(np.array(responses) - ref_value).flatten() * 100 # ax2 = plt.twinx() plt.plot(num_ulss, error) plt.ylabel("Error (%)") plt.title( f"Error in {response_type.name()} to Truck 1 as a function of ULS") # Plot the chosen number of ULS. chosen_error = np.interp([chosen_uls], num_ulss, error)[0] plt.axhline( chosen_error, label=f"At {chosen_uls} ULS, error = {np.around(chosen_error, 2)} %", color="black", ) plt.axhline(0, color="red", label="Response from direct simulation (no wheel tracks)") plt.legend() plt.tight_layout() plt.savefig(c.get_image_path("paramselection", "uls.pdf")) plt.close() # Additional verification plots. plt.plot(num_ulss, total_load) plt.savefig(c.get_image_path("paramselection", "uls-verify-total-load.pdf")) plt.close() plt.plot(num_ulss, num_loads) plt.savefig(c.get_image_path("paramselection", "uls-verify-num-loads.pdf")) plt.close()
def experiment_noise(c: Config): """Plot displacement and strain noise from dynamic test 1""" ################ # Displacement # ################ plt.portrait() # Find points of each sensor. displa_labels = ["U13", "U26", "U29"] displa_points = [] for displa_label in displa_labels: sensor_x, sensor_z = _displa_sensor_xz(displa_label) displa_points.append(Point(x=sensor_x, y=0, z=sensor_z)) # For each sensor plot and estimate noise. side = 700 for s_i, displa_label in enumerate(displa_labels): # First plot the signal, and smoothed signal. plt.subplot(len(displa_points), 2, (s_i * 2) + 1) with open(f"validation/experiment/D1a-{displa_label}.txt") as f: data = list(map(float, f.readlines())) # Find the center of the plot, minimum point in first 15000 points. data_center = 0 for i in range(15000): if data[i] < data[data_center]: data_center = i data = data[data_center - side:data_center + side] smooth = savgol_filter(data, 31, 3) plt.plot(data, linewidth=1) plt.plot(smooth, linewidth=1) plt.ylim(-0.8, 0.3) plt.title(f"{displa_label} in dynamic test") # Then plot subtraction of smoothed from noisey. plt.subplot(len(displa_points), 2, (s_i * 2) + 2) noise = data - smooth plt.plot(noise, label=f"σ = {np.around(np.std(noise), 4)}") plt.legend() plt.title(f"Noise from {displa_label}") plt.tight_layout() plt.savefig(c.get_image_path("params", "noise-displa.pdf")) plt.close() ########## # Strain # ########## plt.portrait() # Find points of each sensor. strain_labels = ["T1", "T10", "T11"] strain_points = [] for strain_label in strain_labels: sensor_x, sensor_z = _strain_sensor_xz(strain_label) strain_points.append(Point(x=sensor_x, y=0, z=sensor_z)) # For each sensor plot and estimate noise. side = 700 xmin, xmax = np.inf, -np.inf for s_i, strain_label in enumerate(strain_labels): # First plot the signal, and smoothed signal. plt.subplot(len(strain_points), 2, (s_i * 2) + 1) with open(f"validation/experiment/D1a-{strain_label}.txt") as f: data = list(map(float, f.readlines())) # Find the center of the plot, minimum point in first 15000 points. data_center = 0 for i in range(15000): if data[i] < data[data_center]: data_center = i data = data[data_center - side:data_center + side] smooth = savgol_filter(data, 31, 3) plt.plot(data, linewidth=1) plt.plot(smooth, linewidth=1) plt.title(f"{strain_label} in dynamic test") # Then plot subtraction of smoothed from noisey. plt.subplot(len(strain_points), 2, (s_i * 2) + 2) noise = data - smooth plt.plot(noise, label=f"σ = {np.around(np.std(noise), 4)}") plt.legend() plt.title(f"Noise from {strain_label}") plt.tight_layout() plt.savefig(c.get_image_path("params", "noise-strain.pdf")) plt.close()
def shell_properties_3d( shells: List[Shell], prop_f: Callable[[Material], float], prop_units: str, cmap: matplotlib.colors.Colormap = default_cmap, colorbar: bool = False, label: bool = False, outline: bool = True, new_fig: bool = True, ): """3D plot of shell elements coloured by material property.""" # Coordinates for rotating the plot perspective. xs, ys, zs = [], [], [] # Vertices of nodes for each shell. verts = [] # Min and max values for colour normalization. prop_min, prop_max = np.inf, -np.inf for shell in shells: verts.append([]) for node in shell.nodes(): xs.append(node.x) ys.append(node.y) zs.append(node.z) verts[-1].append([node.x, node.z, node.y]) shell_prop = prop_f(shell.section) if shell_prop < prop_min: prop_min = shell_prop if shell_prop > prop_max: prop_max = shell_prop xs, ys, zs = np.array(xs), np.array(ys), np.array(zs) norm = matplotlib.colors.Normalize(vmin=prop_min, vmax=prop_max) # Setup a new 3D landscape figure. if new_fig: fig, ax = ax_3d(xs=xs, ys=zs, zs=ys) else: fig = plt.gcf() ax = plt.gca() # Keep track of all values used for colours. # This is so we don't add duplicate labels. values = set() for i, verts_ in enumerate(verts): value = prop_f(shells[i].section) colour = cmap(norm(value)) label_str = None if label and value not in values: values.add(value) label_str = f"{value}{prop_units}" poly = Poly3DCollection( [verts_], facecolors=colour, edgecolors="black" if outline else "none", linewidths=0.01 if outline else 0, label=label_str, ) poly._facecolors2d = poly._facecolors3d poly._edgecolors2d = poly._edgecolors3d ax.add_collection3d(poly) if label: plt.legend() # Add a colorbar if requested. if colorbar: mappable = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm) clb = fig.colorbar(mappable, shrink=0.7) clb.ax.set_title(prop_units)
def plot_mmm_strain_convergence( c: Config, pier: int, df: pd.DataFrame, all_strains: Dict[float, Responses], title: str, without: Optional[Callable[[Point], bool]] = None, append: Optional[str] = None, ): """Plot convergence of given fem as model size grows.""" # A grid of points 1m apart, over which to calculate fem. grid = [ Point(x=x, y=0, z=z) for x, z in itertools.product( np.linspace(c.bridge.x_min, c.bridge.x_max, int(c.bridge.length)), np.linspace(c.bridge.z_min, c.bridge.z_max, int(c.bridge.width)), ) ] # If requested, remove some values from the fem. if without is not None: grid = [point for point in grid if not without(point)] for msl, strains in all_strains.items(): print(f"Removing points from strains with max_shell_len = {msl}") all_strains[msl] = strains.without(without) # Collect fem over all fem, and over the grid. Iterate by # decreasing max_shell_len. mins, maxes, means = [], [], [] gmins, gmaxes, gmeans = [], [], [] max_shell_lens = [] for msl, strains in sorted(all_strains.items(), key=lambda kv: -kv[0]): max_shell_lens.append(msl) print_i(f"Gathering strains with max_shell_len = {msl}", end="\r") grid_strains = np.array([strains.at_deck(point, interp=True) for point in grid]) gmins.append(scalar(np.min(grid_strains))) gmaxes.append(scalar(np.max(grid_strains))) gmeans.append(scalar(np.mean(grid_strains))) strains = np.array(list(strains.values())) mins.append(scalar(np.min(strains))) maxes.append(scalar(np.max(strains))) means.append(scalar(np.mean(strains))) print() # Normalize and plot the mins, maxes, and means. def normalize(ys): print(ys) return ys / np.mean(ys[-5:]) mins, maxes, means = normalize(mins), normalize(maxes), normalize(means) gmins, gmaxes, gmeans = normalize(gmins), normalize(gmaxes), normalize(gmeans) deck_nodes = [df.at[msl, "deck-nodes"] for msl in max_shell_lens] pier_nodes = [df.at[msl, "pier-nodes"] for msl in max_shell_lens] num_nodes = np.array(deck_nodes) + np.array(pier_nodes) print(f"MSLs = {max_shell_lens}") print(f"num_nodes = {num_nodes}") # Plot all lines, for debugging. plt.landscape() plt.plot(num_nodes, mins, label="mins") plt.plot(num_nodes, maxes, label="maxes") plt.plot(num_nodes, means, label="means") plt.plot(num_nodes, gmins, label="gmins") plt.plot(num_nodes, gmaxes, label="gmaxes") plt.plot(num_nodes, gmeans, label="gmeans") plt.grid(axis="y") plt.xlabel("Nodes in FEM") plt.ylabel("Strain") plt.title(title) plt.tight_layout() plt.legend() plt.savefig( c.get_image_path("convergence-pier-strain", f"mmm-{append}-all.pdf", acc=False) ) plt.close() # Only plot some lines, for the thesis. plt.landscape() plt.plot(num_nodes, gmins, label="Minimum") plt.plot(num_nodes, gmaxes, label="Maximum") plt.plot(num_nodes, gmeans, label="Mean") plt.grid(axis="y") plt.title(title) plt.xlabel("Nodes in FEM") plt.ylabel("Strain") plt.legend() plt.tight_layout() plt.savefig( c.get_image_path("convergence-pier-strain", f"mmm-{append}.pdf", acc=False) ) plt.close()
def comparison_plots_705(c: Config, run_only: bool, scatter: bool): """Make contour plots for all verification points on bridge 705.""" # from classify.scenario.bridge import transverse_crack # c = transverse_crack().use(c)[0] positions = [ # (52, -8.4, "a"), (34.95459, 26.24579 - 16.6, "a"), (51.25051, 16.6 - 16.6, "b"), (89.98269, 9.445789 - 16.6, "c"), (102.5037, 6.954211 - 16.6, "d"), # (34.95459, 29.22606 - 16.6, "a"), # (51.25051, 16.6 - 16.6, "b"), # (92.40638, 12.405 - 16.6, "c"), # (101.7649, 3.973938 - 16.6, "d"), ] diana_values = pd.read_csv("validation/diana-screenshots/min-max.csv") response_types = [ResponseType.YTranslation, ResponseType.Strain] # For each response type and loading position first create contour plots for # OpenSees. Then finally create subplots comparing to Diana. cmap = diana_cmap_r for load_x, load_z, label in positions: for response_type in response_types: # Setup the metadata. if response_type == ResponseType.YTranslation: rt_str = "displa" unit_str = "mm" elif response_type == ResponseType.Strain: rt_str = "strain" unit_str = "E-6" else: raise ValueError("Unsupported response type") row = diana_values[diana_values["name"] == f"{label}-{rt_str}"] dmin, dmax = float(row["dmin"]), float(row["dmax"]) omin, omax = float(row["omin"]), float(row["omax"]) amin, amax = max(dmin, omin), min(dmax, omax) levels = np.linspace(amin, amax, 16) # Create the OpenSees plot. loads = [ PointLoad( x_frac=c.bridge.x_frac(load_x), z_frac=c.bridge.z_frac(load_z), kn=100, ) ] fem_responses = load_fem_responses( c=c, response_type=response_type, sim_runner=OSRunner(c), sim_params=SimParams(ploads=loads, response_types=response_types), ) if run_only: continue title = ( f"{response_type.name()} from a {loads[0].kn} kN point load at" + f"\nx = {load_x:.3f}m, z = {load_z:.3f}m, with ") save = lambda prefix: c.get_image_path( "validation/diana-comp", safe_str(f"{prefix}{response_type.name()}") + ".pdf", ) top_view_bridge(c.bridge, piers=True, abutments=True) fem_responses = fem_responses.resize() sci_format = response_type == ResponseType.Strain plot_contour_deck( c=c, responses=fem_responses, ploads=loads, cmap=cmap, levels=levels, sci_format=sci_format, decimals=4, scatter=scatter, ) plt.title(title + "OpenSees") plt.tight_layout() plt.savefig(save(f"{label}-")) plt.close() # Finally create label/title the Diana plot. if label is not None: # First plot and clear, just to have the same colorbar. plot_contour_deck(c=c, responses=fem_responses, ploads=loads, cmap=cmap, levels=levels) plt.cla() # Then plot the bridge and top_view_bridge(c.bridge, piers=True, abutments=True) plt.imshow( mpimg.imread( f"validation/diana-screenshots/{label}-{rt_str}.png"), extent=( c.bridge.x_min, c.bridge.x_max, c.bridge.z_min, c.bridge.z_max, ), ) dmin_s = f"{dmin:.4e}" if sci_format else f"{dmin:.4f}" dmax_s = f"{dmax:.4e}" if sci_format else f"{dmax:.4f}" dabs_s = (f"{abs(dmin - dmax):.4e}" if sci_format else f"{abs(dmin - dmax):.4f}") for point, leg_label, color, alpha in [ ((load_x, load_z), f"{loads[0].kn} kN load", "r", 1), ((0, 0), f"min = {dmin_s} {fem_responses.units}", "r", 0), ((0, 0), f"max = {dmax_s} {fem_responses.units}", "r", 0), ((0, 0), f"|min-max| = {dabs_s} {fem_responses.units}", "r", 0), ]: plt.scatter( [point[0]], [point[1]], label=leg_label, marker="o", color=color, alpha=alpha, ) plt.legend() plt.title(title + "Diana") plt.xlabel("X position (m)") plt.ylabel("Z position (m)") plt.tight_layout() plt.savefig(save(f"{label}-diana-")) plt.close()
def piers_displaced(c: Config): """Contour plots of pier displacement for the given pier indices.""" pier_indices = [4, 5] response_types = [ResponseType.YTranslation, ResponseType.Strain] axis_values = pd.read_csv("validation/axis-screenshots/piers-min-max.csv") for r_i, response_type in enumerate(response_types): for p in pier_indices: # Run the simulation and collect fem. sim_responses = load_fem_responses( c=c, response_type=response_type, sim_runner=OSRunner(c), sim_params=SimParams(displacement_ctrl=PierSettlement( displacement=c.pd_unit_disp, pier=p), ), ) # In the case of stress we map from kn/m2 to kn/mm2 (E-6) and then # divide by 1000, so (E-9). assert c.pd_unit_disp == 1 if response_type == ResponseType.Strain: sim_responses.to_stress(c.bridge).map(lambda r: r * 1e-9) # Get min and max values for both Axis and OpenSees. rt_str = ("displa" if response_type == ResponseType.YTranslation else "stress") row = axis_values[axis_values["name"] == f"{p}-{rt_str}"] dmin, dmax = float(row["dmin"]), float(row["dmax"]) omin, omax = float(row["omin"]), float(row["omax"]) amin, amax = max(dmin, omin), min(dmax, omax) levels = np.linspace(amin, amax, 16) # Plot and save the image. If plotting strains use Axis values for # colour normalization. # norm = None from plot import axis_cmap_r cmap = axis_cmap_r top_view_bridge(c.bridge, abutments=True, piers=True) plot_contour_deck(c=c, cmap=cmap, responses=sim_responses, levels=levels) plt.tight_layout() plt.title( f"{sim_responses.response_type.name()} from 1mm pier settlement with OpenSees" ) plt.savefig( c.get_image_path( "validation/pier-displacement", safe_str(f"pier-{p}-{sim_responses.response_type.name()}") + ".pdf", )) plt.close() # First plot and clear, just to have the same colorbar. plot_contour_deck(c=c, responses=sim_responses, cmap=cmap, levels=levels) plt.cla() # Save the axis plots. axis_img = mpimg.imread( f"validation/axis-screenshots/{p}-{rt_str}.png") top_view_bridge(c.bridge, abutments=True) plt.imshow( axis_img, extent=( c.bridge.x_min, c.bridge.x_max, c.bridge.z_min, c.bridge.z_max, ), ) # Plot the load and min, max values. for point, leg_label, color in [ ((0, 0), f"min = {np.around(dmin, 3)} {sim_responses.units}", "r"), ((0, 0), f"max = {np.around(dmax, 3)} {sim_responses.units}", "r"), ( (0, 0), f"|min-max| = {np.around(abs(dmax - dmin), 3)} {sim_responses.units}", "r", ), ]: plt.scatter( [point[0]], [point[1]], label=leg_label, marker="o", color=color, alpha=0, ) if response_type == ResponseType.YTranslation: plt.legend() # Title and save. plt.title( f"{response_type.name()} from 1mm pier settlement with AxisVM") plt.xlabel("X position (m)") plt.ylabel("Z position (m)") plt.tight_layout() plt.savefig( c.get_image_path( "validation/pier-displacement", f"{p}-axis-{rt_str}.pdf", )) plt.close()