def wagen1_plot(c: Config):
"""Plot of wagen1 compared to given specification."""
plt.landscape()
wheel_print = (0.31, 0.25)
wheel_prints = []
for w_i in range(len(truck1.axle_distances) + 1):
if w_i in [1, 2]:
wheel_prints.append([wheel_print, wheel_print])
else:
wheel_prints.append([wheel_print])
plt.subplot(1, 2, 1)
xlim, ylim = topview_vehicle(truck1, wheel_prints=wheel_prints)
plt.title("Truck 1 specification")
plt.xlabel("Width (m)")
plt.ylabel("Length (m)")
plt.subplot(1, 2, 2)
topview_vehicle(truck1, xlim=xlim, ylim=ylim)
plt.title("Truck 1 in simulation")
plt.xlabel("Width (m)")
plt.ylabel("Length (m)")
plt.savefig(c.get_image_path("vehicles", "wagen-1", bridge=False) + ".pdf")
plt.close()
def top_view_bridge(
config: Config,
abutments: bool = False,
edges: bool = False,
piers: bool = False,
lanes: bool = False,
lane_fill: bool = False,
landscape: bool = True,
compass: bool = False,
):
"""Plot the top view of a bridge's geometry.
Args:
bridge: the bridge top to plot.
landscape: orient the plot in landscape (16 x 10) ?
abutments: plot the bridge's abutments?
edges: plot the longitudinal edges?
piers: plot where the piers connect to the deck?
lanes: plot lanes on the bridge?
lane_fill: plot fill or only outline?
compass: plot a compass rose?
"""
bridge = config.bridge
if landscape:
plt.landscape()
plt.axis("equal")
if edges:
plt.hlines([bridge.z_min, bridge.z_max], 0, bridge.length)
if abutments:
plt.vlines([0, bridge.length], bridge.z_min, bridge.z_max)
if piers:
for pier in bridge.supports:
z_min_top, z_max_top = pier.z_min_max_top()
x_min, x_max = pier.x_min_max_top()
plt.vlines([x_min, x_max], z_min_top, z_max_top)
if lanes:
for lane in bridge.lanes:
plt.gca().add_patch(
matplotlib.patches.Rectangle(
(0, lane.z_min),
bridge.length,
lane.z_max - lane.z_min,
facecolor="black" if lane_fill else "none",
edgecolor="black",
)
)
if compass:
ax = plt.gca() # Reference to the original axis.
dir_path = os.path.dirname(os.path.abspath(__file__))
compass_img = plt.imread(os.path.join(dir_path, "compass-rose.png"))
c_len = max(bridge.width, bridge.length) * 0.2
ax_c = ax.inset_axes(
[0, bridge.z_max + (c_len * 0.05), c_len, c_len], transform=ax.transData,
)
ax_c.imshow(compass_img)
ax_c.axis("off")
plt.sca(ax) # Return control to the original axis.
plt.xlabel("X position")
plt.ylabel("Z position")
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 plot_contour_deck(
c: Config,
responses: Responses,
point_loads: List[PointLoad] = [],
units: Optional[str] = None,
cmap=default_cmap,
norm=None,
scatter: bool = False,
levels: int = 14,
mm_legend: bool = True,
mm_legend_without_f: Optional[Callable[[Point], bool]] = None,
sci_format: bool = False,
decimals: int = 4,
y: float = 0,
):
"""Contour or scatter plot of simulation responses.
Args:
config: simulation configuration object.
responses: the simulation responses to plot.
units: optional units string for the colourbar.
cmap: Matplotlib colormap to use for colouring responses.
norm: Matplotlib norm to use for colouring responses.
scatter: scatter plot instead of contour plot?
levels: levels in the contour plot.
mm_legend: plot a legend of min and max values?
mm_legend_without_f: function to filter points considered in the legend.
sci_format: force scientific formatting (E) in the legend.
decimals: round legend values to this many decimals.
"""
amax, amax_x, amax_z = -np.inf, None, None
amin, amin_x, amin_z = np.inf, None, None
X, Z, H = [], [], [] # 2D arrays, x and z coordinates, and height.
# Begin structure data.
def structure_data(responses):
nonlocal amax
nonlocal amax_x
nonlocal amax_z
nonlocal amin
nonlocal amin_x
nonlocal amin_z
nonlocal X
nonlocal Z
nonlocal H
# First reset the maximums and minimums.
amax, amax_x, amax_z = -np.inf, None, None
amin, amin_x, amin_z = np.inf, None, None
X, Z, H = [], [], []
for x in responses.xs:
# There is a chance that no sensors exist at given y position for every
# x position, thus we must check.
if y in responses.zs[x]:
for z in responses.zs[x][y]:
X.append(x)
Z.append(z)
H.append(responses.responses[0][x][y][z])
if H[-1] > amax:
amax = H[-1]
amax_x, amax_z = X[-1], Z[-1]
if H[-1] < amin:
amin = H[-1]
amin_x, amin_z = X[-1], Z[-1]
print(f"amin, amax = {amin}, {amax}")
structure_data(responses)
if len(X) == 0:
raise ValueError(f"No fem for contour plot")
# Plot fem, contour or scatter plot.
if scatter:
cs = plt.scatter(
x=np.array(X).flatten(),
y=np.array(Z).flatten(),
c=np.array(H).flatten(),
cmap=cmap,
norm=norm,
s=1,
)
else:
cs = plt.tricontourf(X, Z, H, levels=levels, cmap=cmap, norm=norm)
# Colourbar, maybe using given norm.
clb = plt.colorbar(cs, norm=norm)
if units is not None:
clb.ax.set_title(units)
# Plot point loads.
for pload in point_loads:
x = pload.x
z = pload.z
plt.scatter(
[x],
[z],
label=f"{pload.load} kN load",
marker="o",
color="black",
)
# Begin: min, max legend.
if mm_legend or mm_legend_without_f is not None:
if mm_legend_without_f is not None:
structure_data(responses.without(mm_legend_without_f))
# Plot min and max fem.
amin_s = (f"{amin:.{decimals}g}"
if sci_format else f"{np.around(amin, decimals)}")
amax_s = (f"{amax:.{decimals}g}"
if sci_format else f"{np.around(amax, decimals)}")
aabs_s = (f"{amin - amax:.{decimals}g}" if sci_format else
f"{np.around(abs(amin - amax), decimals)}")
units_str = "" if units is None else f" {units}"
print(units_str)
for point, label, color, alpha in [
((amin_x, amin_z), f"min = {amin_s} {units_str}", "orange", 0),
((amax_x, amax_z), f"max = {amax_s} {units_str}", "green", 0),
((amin_x, amin_z), f"|min-max| = {aabs_s} {units_str}", "red", 0),
]:
plt.scatter(
[point[0]],
[point[1]],
label=label,
marker="o",
color=color,
alpha=alpha,
)
# End: min, max legend.
plt.xlabel("X position")
plt.ylabel("Z position")
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 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()
def time_series_plot(c: Config, n: float):
"""Plot 24min time series of cracking, for multiple cracked bridges.
For each bridge (hard-coded), a time series of strain fem is plotted.
For each bridge it is initially in healthy condition, and the crack occurs
halfway through.
Args:
n: float, meters in front of the crack zone where to place sensor.
"""
# First construct one day (24 minutes) of traffic.
total_mins = 24
total_seconds = total_mins * 60
traffic_scenario = normal_traffic(c=c, lam=5, min_d=2)
traffic_sequence, traffic, traffic_array = load_traffic(
c=c,
traffic_scenario=traffic_scenario,
max_time=total_seconds,
)
traffic_array.shape
# Temperatures for one day.
temps_day = temperature.from_to_mins(
temperature.load("holly-springs"),
datetime.fromisoformat(f"2019-07-03T00:00"),
datetime.fromisoformat(f"2019-07-03T23:59"),
)
print(f"len temps = {len(temps_day['solar'])}")
print(f"len temps = {len(temps_day['temp'])}")
# Then generate some cracking time series.
damages = [
HealthyDamage(),
transverse_crack(),
transverse_crack(length=14.0, at_x=48.0),
]
sensors = [
Point(x=52, z=-8.4), # Sensor in middle of lane.
Point(x=damages[1].crack_area(c.bridge)[0] - n,
z=-8.4), # Sensor in front of crack zone.
Point(x=damages[2].crack_area(c.bridge)[0] - n,
z=-8.4), # Sensor in front of crack zone.
]
[print(f"Sensor {i} = {sensors[i]}") for i in range(len(sensors))]
time_series = [
crack_time_series(
c=c,
traffic_array=traffic_array,
traffic_array_mins=total_mins,
sensor=sensor,
crack_frac=0.5,
damage=damage,
temps=temps_day["temp"],
solar=temps_day["solar"],
) for damage, sensor in zip(damages, sensors)
]
plt.portrait()
for i, (y_trans, strain) in enumerate(time_series):
x = np.arange(len(strain)) * c.sensor_hz / 60
x_m = sensors[i].x
damage_str = "Healthy Bridge"
if i == 1:
damage_str = "0.5 m crack zone"
if i == 2:
damage_str = "14 m crack zone"
plt.subplot(len(time_series), 2, i * 2 + 1)
plt.plot(x, y_trans * 1000, color="tab:blue")
if i < len(time_series) - 1:
plt.tick_params(axis="x", bottom=False, labelbottom=False)
else:
plt.xlabel("Hours")
plt.title(f"At x = {x_m} m\n{damage_str}")
plt.ylabel("Y trans. (mm)")
plt.subplot(len(time_series), 2, i * 2 + 2)
plt.plot(x, strain * 1e6, color="tab:orange")
if i < len(time_series) - 1:
plt.tick_params(axis="x", bottom=False, labelbottom=False)
else:
plt.xlabel("Hours")
plt.title(f"At x = {x_m} m,\n{damage_str}")
plt.ylabel("Microstrain XXB")
plt.tight_layout()
plt.savefig(c.get_image_path("crack", "time-series-q5.pdf"))
plt.close()