def nr_dt(t_shift_array, max_ts=180., width=1., num_bands=1, enum=0, leg='default', line_plot=False):
"""
histogram plot for all measured traveltime anomalies
"""
bins = np.arange(-int(max_ts), int(max_ts)+width, width)
for i in range(len(bins)):
bins[i] = round(bins[i], 6)
for i in range(len(t_shift_array)):
t_shift_array[i] = uf.round_to(t_shift_array[i], width)
digit = np.digitize(t_shift_array, bins)
digit_list = digit.tolist()
for i in range(len(digit_list)):
digit_list[i] -= 1
digit_count = {}
for i in range(0, len(bins)):
digit_count[str(i)] = digit_list.count(i)
dic_color = {'0': 'blue', '1': 'deepskyblue', '2': 'green', '3': 'darkorange', '4': 'brown', '5': 'olive',
'6': 'tan', '7': 'darkviolet'}
x_line = []
y_line = []
for i in range(0, len(bins)):
x_line.append(bins[i])
y_line.append(digit_count[str(i)])
# Calculating the percentage:
# Method: First we should run the code for xcorr_limit = -1000
# This can assure us that we consider all the source receiver pairs
# The next two lines should be uncomment first
# Second: continue after two lines....
#y_l = open(os.path.join('statistics_all', 'yband' + str(enum)), 'w')
#pickle.dump(y_line, y_l)
# (cont) in this part, first we load the data for all source receiver pairs...
# note that there is not difference between band01 to the last band! since we consider all
# Now uncomment the next lines (up to plt.plot)
# to load and calculate the percentage
# IMPORTANT: the two lines up should be commented out!
y_l = open(os.path.join('statistics_all', 'yband' + str(enum)))
y_l_all = pickle.load(y_l)
for i in range(len(y_l_all)-1, -1, -1):
if y_l_all[i] < 1:
del y_l_all[i]
del y_line[i]
del x_line[i]
#plt.plot(x_line, np.array(y_line, dtype=float)/np.array(y_l_all, dtype=float)*100., lw=3.0, label=leg,
# color=dic_color[str(enum)])
plt.plot(x_line, y_line, lw=3.0, label=leg, color=dic_color[str(enum)])
def nr_dt(t_shift_array, max_ts=30., width=0.1, num_bands=1, enum=0, leg='default', line_plot=False):
"""
histogram plot for all measured traveltime anomalies
EXAMPLES:
1) For nr_cc:
max_ts=2., width=0.1
* max_ts=2., width=0.01
2) For nr_dt:
max_ts=30., width=0.1
max_ts=30., width=0.5
"""
bins = np.arange(-int(max_ts), int(max_ts)+width, width)
for i in range(len(bins)):
bins[i] = round(bins[i], 6)
for i in range(len(t_shift_array)):
t_shift_array[i] = uf.round_to(t_shift_array[i], width)
digit = np.digitize(t_shift_array, bins)
digit_list = digit.tolist()
for i in range(len(digit_list)):
digit_list[i] -= 1
digit_count = {}
for i in range(0, len(bins)):
digit_count[str(i)] = digit_list.count(i)
dic_color = {'0': 'blue', '1': 'deepskyblue', '2': 'green', '3': 'darkorange', '4': 'brown', '5': 'olive',
'6': 'tan', '7': 'darkviolet'}
x_line = []
y_line = []
for i in range(0, len(bins)):
if not line_plot:
if i == 0:
plt.bar(left=bins[i]-width*(0.25*1.5-enum*1.5*0.5/num_bands), width=1.5*width/2./num_bands,
height=digit_count[str(i)], color=dic_color[str(enum)], edgecolor=dic_color[str(enum)],
label=leg)
else:
plt.bar(left=bins[i]-width*(0.25*1.5-enum*1.5*0.5/num_bands), width=1.5*width/2./num_bands,
height=digit_count[str(i)], color=dic_color[str(enum)], edgecolor=dic_color[str(enum)])
else:
x_line.append(bins[i])
y_line.append(digit_count[str(i)])
if line_plot:
plt.plot(x_line[9:-10], y_line[9:-10], lw=3.0, label=leg, color=dic_color[str(enum)])
passed_staev = uf.filters(all_staev, [bands[i]], xcorr_limit=xcorr_limit)
if not passed_staev[0]:
continue
magni = mag_finder(proc_ev_ls[j], ev_fi)
mag_all = np.append(mag_all, magni)
### FIND EACH EVENT WITH SPECIFIED MAGNITUDE AND THE NUMBER OF WAVEFORNS ASSOCIATED ???????
passed_staev_epi = []
for k in range(len(passed_staev[0])):
if min_epi <= passed_staev[0][k][6] < max_epi:
passed_staev_epi.append(passed_staev[0][k])
nw_all = np.append(nw_all, len(passed_staev_epi))
for j in range(len(mag_all)):
for k in mag_dic:
if abs(uf.round_to(mag_all[j], 0.5) - float(k)) < 0.1:
mag_dic[k][0] += nw_all[j]
mag_dic[k][1] += 1
break
mag_sta_list = []
for md in mag_dic:
mag_sta_list.append([float(md), mag_dic[md][0], mag_dic[md][1]])
mag_sta_list.sort()
import py2mat_mod
py2mat_mod.py2mat(mag_sta_list, 'mag_sta_%s' % bands[i], 'mag_sta_%s' % bands[i])
plt.ion()
plt.figure()
