#!/usr/bin/env python import matplotlib.pyplot as plt from array_plotting import plotting fig = plt.figure() ax = fig.add_subplot(111) p = plotting(ax) help(p)
window=[t_min, t_max],
Boots=Boots,
epsilon=epsilon,
slow_vec_error=slow_vec_error,
Filter=False,
)
# df = pd.DataFrame({"Slow_x":All_Thresh_Peaks_arr[:,0], "Slow_y":All_Thresh_Peaks_arr[:,1], "Labels":new_labels})
# plot!
with PdfPages(Res_dir +
f"Clustering_Summary_Plot_{fmin:.2f}_{fmax:.2f}.pdf") as pdf:
# clusters
fig = plt.figure(figsize=(10, 8))
ax1 = fig.add_subplot(111)
p = plotting(ax=ax1)
p.plot_clusters_XY(
labels=new_labels,
tp=Lin_Mean,
peaks=All_Thresh_Peaks_arr,
sxmin=x_min,
sxmax=x_max,
symin=y_min,
symax=y_max,
sstep=s_space,
title="Clusters",
predictions=predictions,
ellipse=True,
std_devs=[1, 2, 3],
)
action="store",
)
args = parser.parse_args()
filepath = args.file_path
# read in the SAC files
st = obspy.read(filepath)
fig = plt.figure(figsize=(6, 6))
# need to give a projection for the cartopy package
# to work. See a list here:
# https://scitools.org.uk/cartopy/docs/latest/crs/projections.html
ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
p = plotting(ax=ax)
p.plot_stations(st)
ax.gridlines(
crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=0,
color="gray",
alpha=0.5,
linestyle="--",
)
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111, projection=ccrs.Robinson())
p = plotting(ax=ax)
p.plot_paths(st)
filepath=filepath,
st=st,
peaks=slow_vec_obs,
prediction=pred_file,
phase=phase,
time_window=window,
)
print(st)
# plot!
with PdfPages(Res_dir + f"TP_Summary_Plot_{fmin:.2f}_{fmax:.2f}.pdf") as pdf:
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
p = plotting(ax=ax)
p.plot_TP_XY(
tp=Plot_arr,
peaks=peaks,
sxmin=sx_min_plot,
sxmax=sx_max_plot,
symin=sy_min_plot,
symax=sy_max_plot,
sstep=s_space,
contour_levels=20,
title="%s Plot" % Stack_type,
predictions=predictions,
log=False,
)
pdf.savefig()
header = "event centroid_lo centroid_la, n_stations, stations\n"
with open(Res_file, 'w') as w_file:
w_file.write(header)
try:
st = obspy.read(filepath)
final_centroids, lats_lons_use, lats_lons_core, stations_use = c.break_sub_arrays(
st=st, min_stat=min_stat, min_dist=min_dist, spacing=spacing)
fig = plt.figure(figsize=(10, 8), tight_layout=True)
ax1 = fig.add_subplot(111, projection=ccrs.Robinson())
p = plotting(ax1)
p.plot_stations(st)
print(lats_lons_use)
use_tree = BallTree(lats_lons_use,
leaf_size=lats_lons_use.shape[0] / 2,
metric='haversine')
with open(Res_file, 'a') as a_file:
for i, centroid in enumerate(final_centroids):
lat_centre = np.around(centroid[0], 2)
lon_centre = np.around(centroid[1], 2)
sub_array = use_tree.query_radius(X=[centroid], r=min_dist)[0]
print(lon_centre, lat_centre)
mode='constant')
# Ok! Now find the top 2 peaks using the findpeaks function.
peaks_auto = c.findpeaks_XY(Array=smoothed_arr,
xmin=slow_x_min,
xmax=slow_x_max,
ymin=slow_y_min,
ymax=slow_y_max,
xstep=s_space,
ystep=s_space,
N=2)
print('peaks:\n', peaks_auto)
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(121)
p = plotting(ax)
# plot to see if they're any good.
p.plot_TP_XY(tp=PWS_arr,
peaks=peaks_auto,
sxmin=slow_x_min,
sxmax=slow_x_max,
symin=slow_y_min,
symax=slow_y_max,
sstep=s_space,
contour_levels=50,
title="PWS Plot",
predictions=predictions,
log=False)
slow_min = float(S) - 2