def main(comp_000, comp_090, output_dir):
os.makedirs(output_dir, exist_ok=True)
log_file = os.path.join(output_dir, "log")
logging.basicConfig(format="%(asctime)s %(message)s",
filename=log_file,
level=logging.DEBUG)
waveforms = {}
waveforms["000"], meta = read_ascii(comp_000, meta=True)
waveforms["090"] = read_ascii(comp_090)
dt = meta["dt"]
results = {}
for component in COMPONENT:
results[component] = {}
for a in ALPHA:
for c in C:
for period in T:
im_name = f"TM05_a{a}_c{c}_T{period}_storey"
logging.info(f"calculating {im_name}")
df = Taghavi_Miranda_2005(waveforms[component],
dt,
period,
a,
c,
storey=STORIES)
for i in range(STORIES + 1):
results[component][
f"{im_name}{i}_disp_peak"] = df.iloc[i].disp_peak
results[component][
f"{im_name}{i}_slope_peak"] = df.iloc[i].slope_peak
results[component][
f"{im_name}{i}_storey_shear_peak"] = df.iloc[
i].storey_shear_peak
results[component][
f"{im_name}{i}_total_accel_peak"] = df.iloc[
i].total_accel_peak
im_csv_fname = os.path.join(output_dir, "Taghavi_Miranda_2005.csv")
df = pd.DataFrame.from_dict(results, orient="index")
df.index.name = "component"
geom = pd.Series(get_geom(df.loc["000"], df.loc["090"]), name="geom")
df = df.append(geom)
df.to_csv(im_csv_fname)
def test_sed_im(component_name, expected_im):
try:
path = waveforms[components.index(component_name)]
except ValueError:
# missing test data
print(f"Missing waveform data for {component_name}.")
raise
waveform, meta = read_ascii(path, meta=True)
# waveform in g -> cm/s^2
velocity = acc2vel(waveform * 980.665, dt=meta["dt"])
times = np.arange(meta["nt"], dtype=np.float32)
times *= meta["dt"]
im = get_specific_energy_density_nd(velocity, times)
assert np.isclose(im, expected_im, rtol=0.005)
for i, statname in enumerate(statnames):
s0 = statname + GV[0]
s1 = statname + GV[1]
s2 = statname + GV[2]
# stat_data_0_S, num_pts, dt, shift = readGP_2(mainfolder,s0)
# stat_data_1_S, _, _, _ = readGP_2(mainfolder,s1)
# stat_data_2_S, _, _, _ = readGP_2(mainfolder,s2)
#
# stat_data_0_O, num_pts, dt, shift1 = readGP_2(mainfolder_o,s0)
# stat_data_1_O, _, _, _ = readGP_2(mainfolder_o,s1)
# stat_data_2_O, _, _, _ = readGP_2(mainfolder_o,s2)
stat_data_0_S = timeseries.read_ascii(mainfolder + s0)
stat_data_0_S = np.multiply(signal.tukey(int(num_pts), 0.1),
stat_data_0_S)
stat_data_1_S = timeseries.read_ascii(mainfolder + s1)
stat_data_1_S = np.multiply(signal.tukey(int(num_pts), 0.1),
stat_data_1_S)
stat_data_2_S = timeseries.read_ascii(mainfolder + s2)
stat_data_2_S = np.multiply(signal.tukey(int(num_pts), 0.1),
stat_data_2_S)
stat_data_0_O = timeseries.read_ascii(mainfolder_o + s0)
stat_data_0_O = np.multiply(signal.tukey(int(num_pts), 0.1),
stat_data_0_O)
stat_data_1_O = timeseries.read_ascii(mainfolder_o + s1)
stat_data_1_O = np.multiply(signal.tukey(int(num_pts), 0.1),
stat_data_1_O)
def plot_station(
output,
sources,
labels,
tmax,
verbose,
station,
):
"""Creates a waveform plot for a specific station."""
if verbose:
print("Plotting station: {}...".format(station))
timeseries = []
for source in sources:
if type(source).__name__ != "str":
# opened binary object
timeline = (np.arange(source.nt, dtype=np.float32) * source.dt +
source.start_sec)
timeseries.append(np.vstack((source.vel(station).T, timeline)))
else:
# text directory
meta = read_ascii(os.path.join(source,
f"{station}{extensions[0]}"),
meta=True)[1]
vals = np.array([
read_ascii(os.path.join(source, f"{station}{ext}"))
for ext in extensions
])
timeline = (np.arange(meta["nt"], dtype=np.float32) * meta["dt"] +
meta["sec"])
timeseries.append(np.vstack((vals, timeline)))
x_max = max([ts[-1, -1] for ts in timeseries])
if tmax is not None:
x_max = min(tmax, x_max)
all_y = np.concatenate([ts[:-1] for ts in timeseries], axis=1)
# get axis min/max
y_min, y_max = np.min(all_y), np.max(all_y)
y_diff = y_max - y_min
pgvs = np.max(np.abs(all_y), axis=1)
ppgvs = np.max(all_y, axis=1)
npgvs = np.min(all_y, axis=1)
scale_length = max(int(round(x_max / 25.0)) * 5, 5)
# start plot
f, axis = plt.subplots(1,
3,
sharex=True,
sharey=True,
figsize=(20, 4),
dpi=96)
f.subplots_adjust(left=0.08,
bottom=0.12,
right=0.96,
top=None,
wspace=0.08,
hspace=0)
plt.suptitle(
station,
fontsize=20,
x=0.02,
y=0.5,
horizontalalignment="left",
verticalalignment="center",
)
plt.xlim([0, x_max])
# subplots
for i, s in enumerate(timeseries):
for j in range(len(extensions)):
ax = axis[j]
ax.set_axis_off()
ax.set_ylim([y_min - y_diff * 0.15, y_max])
(line, ) = ax.plot(
s[len(extensions)],
s[j] * min(y_max / ppgvs[j], y_min / npgvs[j]),
color=colours[i % len(colours)],
linewidth=1,
)
if j == 2:
line.set_label(labels[i])
ax.legend()
if i == 1 and j == 0:
# Add scale
ax.plot(
[0, scale_length],
[y_min - y_diff * 0.1] * 2,
color="black",
linewidth=1,
)
ax.text(
0,
y_min - y_diff * 0.15,
"0",
size=12,
verticalalignment="top",
horizontalalignment="center",
)
ax.text(
scale_length,
y_min - y_diff * 0.15,
str(scale_length),
size=12,
verticalalignment="top",
horizontalalignment="center",
)
ax.text(
scale_length / 2.0,
y_min - y_diff * 0.225,
"sec",
size=12,
verticalalignment="top",
horizontalalignment="center",
)
if i == 0:
# Add component label
ax.set_title(extensions[j][1:], fontsize=18)
ax.text(x_max, y_max, "{:.1f}".format(pgvs[j]), fontsize=14)
plt.savefig(os.path.join(output, f"{station}.png"))
plt.close()