def test_cumulative_detections(self):
dates = []
for i in range(3):
dates.append([dt.datetime(2012, 3, 26) + dt.timedelta(n)
for n in np.random.randn(100)])
fig = cumulative_detections(
dates, ['a', 'b', 'c'], show=False, return_figure=True)
return fig
def plot(self, plot_grouped=False):
"""
Plot the cumulative number of detections in time.
.. rubric:: Example
>>> from eqcorrscan import Template, Detection
>>> family = Family(
... template=Template(name='a'), detections=[
... Detection(template_name='a', detect_time=UTCDateTime(0) + 200,
... no_chans=8, detect_val=4.2, threshold=1.2,
... typeofdet='corr', threshold_type='MAD',
... threshold_input=8.0),
... Detection(template_name='a', detect_time=UTCDateTime(0),
... no_chans=8, detect_val=4.5, threshold=1.2,
... typeofdet='corr', threshold_type='MAD',
... threshold_input=8.0),
... Detection(template_name='a', detect_time=UTCDateTime(0) + 10,
... no_chans=8, detect_val=4.5, threshold=1.2,
... typeofdet='corr', threshold_type='MAD',
... threshold_input=8.0)])
>>> family.plot(plot_grouped=True) # doctest: +SKIP
.. plot::
from eqcorrscan.core.match_filter import Family, Template
from eqcorrscan.core.match_filter import Detection
from obspy import UTCDateTime
family = Family(
template=Template(name='a'), detections=[
Detection(template_name='a', detect_time=UTCDateTime(0) + 200,
no_chans=8, detect_val=4.2, threshold=1.2,
typeofdet='corr', threshold_type='MAD',
threshold_input=8.0),
Detection(template_name='a', detect_time=UTCDateTime(0),
no_chans=8, detect_val=4.5, threshold=1.2,
typeofdet='corr', threshold_type='MAD',
threshold_input=8.0),
Detection(template_name='a', detect_time=UTCDateTime(0) + 10,
no_chans=8, detect_val=4.5, threshold=1.2,
typeofdet='corr', threshold_type='MAD',
threshold_input=8.0)])
family.plot(plot_grouped=True)
"""
cumulative_detections(detections=self.detections,
plot_grouped=plot_grouped)
# Create two dataframes for each set of detections
df_sub = pd.read_csv('/home/chet/data/detections/2015_corr_groups/2015_all_det_corrgrps.csv', header=None)
df_all = pd.read_csv('/home/chet/data/detections/2015_alltemps_1sec/2015_all_det_1sec.csv', header=None)
df = pd.concat([df_sub, df_all])
det_dict = {}
for index, row in df.iterrows():
if row[0] in det_dict:
if UTCDateTime(row[1]) < UTCDateTime(2015, 11, 20) and row[4] > 5:
det_dict[row[0]].append(UTCDateTime(row[1]).datetime)
else:
if UTCDateTime(row[1]) < UTCDateTime(2015, 11, 20) and row[4] > 5:
det_dict[row[0]] = [UTCDateTime(row[1]).datetime]
for grp in corr_groups:
temp_names = []
temp_times = []
cat = read_events(grp)
if len(cat) > 1:
sub_dets = grp.split('/')[-1].rstrip('.xml')
subs = [sub_dets + '_2n', sub_dets + '_1st']
plot_file = '/media/chet/hdd/seismic/NZ/figs/cum_det_plots/' +\
sub_dets + '.png'
for sub in subs:
temp_names.append(sub)
temp_times.append(det_dict[sub])
for ev in cat:
ev_name = str(ev.resource_id).split('/')[-1]
temp_names.append(ev_name)
temp_times.append(det_dict[ev_name + '_1sec'])
plotting.cumulative_detections(temp_times, temp_names, show=True,
save=False, savefile=plot_file)
def plot(self,
plot_grouped=False,
dates=None,
min_dets=1,
rate=False,
**kwargs):
"""
Plot the cumulative detections in time.
:type plot_grouped: bool
:param plot_grouped:
Whether to plot all families together (plot_grouped=True), or each
as a separate line.
:type dates: list
:param dates: list of obspy.core.UTCDateTime objects bounding the
plot. The first should be the start date, the last the end date.
:type min_dets: int
:param min_dets: Plot only families with this number of detections
or more.
:type rate: bool
:param rate: Whether or not to plot the daily rate of detection as
opposed to cumulative number. Only works with plot_grouped=True.
:param \**kwargs: Any other arguments accepted by
:func:`eqcorrscan.utils.plotting.cumulative_detections`
.. rubric:: Examples
Plot cumulative detections for all templates individually:
>>> Party().read().plot() # doctest: +SKIP
Plot cumulative detections for all templates grouped together:
>>> Party().read().plot(plot_grouped=True) # doctest: +SKIP
Plot the rate of detection for all templates grouped together:
>>> Party().read().plot(plot_grouped=True, rate=True) # doctest: +SKIP
Plot cumulative detections for all templates with more than five
detections between June 1st, 2012 and July 31st, 2012:
>>> from obspy import UTCDateTime
>>> Party().read().plot(dates=[UTCDateTime(2012, 6, 1),
... UTCDateTime(2012, 7, 31)],
... min_dets=5) # doctest: +SKIP
"""
all_dets = []
if dates:
new_party = self.filter(dates=dates, min_dets=min_dets)
for fam in new_party.families:
all_dets.extend(fam.detections)
else:
for fam in self.families:
all_dets.extend(fam.detections)
fig = cumulative_detections(detections=all_dets,
plot_grouped=plot_grouped,
rate=rate,
**kwargs)
return fig
def plot_detections_rate(cat, temp_list='all', bbox=None, depth_thresh=None, cumulative=False, detection_rate=False):
"""
Plotting detections for some catalog of detected events
:type cat: obspy.core.Catalog
:param cat: Catalog containting detections. Should have detecting template info in the resource_id
:type temp_list: list
:param temp_list: list of template names which we want to consider for this plot
:type bbox: tuple of tuple
:param bbox: select only events within a certain geographic area bound by ((long, long), (lat, lat))
:type depth_thresh: float
:param depth_thresh: Depth cutoff for detections being plotted in km
:type cumulative: bool
:param cumulative: Whether or not to combine detections into
:type detection_rate: bool
:param detection_rate: Do we plot derivative of detections?
:return:
"""
# If specified, filter catalog to only events in geographic area
if bbox:
det_cat = bounding_box(cat, bbox, depth_thresh)
# Sort det_cat by origin time
else:
det_cat = cat
if not det_cat[0].origins[-1]:
det_cat.events.sort(key=lambda x: x.origins[0].time)
else:
det_cat.events.sort(key=lambda x: x.origins[-1].time)
# Put times and names into sister lists
if detection_rate and not cumulative:
cat_start = min([ev.origins[-1].time.datetime for ev in det_cat])
cat_end = max([ev.origins[-1].time.datetime for ev in det_cat])
det_rates = []
dates = []
if not det_cat[0].origins[-1]:
for date in date_generator(cat_start, cat_end):
dates.append(date)
det_rates.append(len([ev for ev in det_cat if ev.origins[0].time.datetime > date
and ev.origins[0].time.datetime < date + timedelta(days=1)]))
else:
for date in date_generator(cat_start, cat_end):
dates.append(date)
det_rates.append(len([ev for ev in det_cat if ev.origins[-1].time.datetime > date
and ev.origins[-1].time.datetime < date + timedelta(days=1)]))
fig, ax1 = plt.subplots()
# ax1.step(dates, det_rates, label='All templates', linewidth=1.0, color='black')
ax1 = plt.subplot(111)
ax1.bar(dates, det_rates)
ax1.xaxis_date()
ax1.xaxis.set_major_locator(mdates.MonthLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
ax1.set_xlabel('Date')
ax1.set_ylabel('Cumulative detection rate (events/day)')
plt.title('Cumulative detection rate for all templates')
else:
det_times = []
temp_names = []
temp_dict = {}
for ev in det_cat:
temp_name = str(ev.resource_id).split('/')[-1].split('_')[0]
o = ev.origins[-1] or ev.origins[0]
if temp_name not in temp_dict:
temp_dict[temp_name] = [o.time.datetime]
else:
temp_dict[temp_name].append(o.time.datetime)
for temp_name, det_time_list in temp_dict.iteritems():
if temp_name in temp_list:
det_times.append(det_time_list)
temp_names.append(temp_name)
elif temp_list == 'all':
det_times.append(det_time_list)
temp_names.append(temp_name)
if cumulative:
fig = plotting.cumulative_detections(dates=det_times, template_names=temp_names,
plot_grouped=True, show=False, plot_legend=False)
if detection_rate:
ax2 = fig.get_axes()[0].twinx()
cat_start = min([ev.origins[-1].time.datetime for ev in det_cat])
cat_end = max([ev.origins[-1].time.datetime for ev in det_cat])
det_rates = []
dates = []
for date in date_generator(cat_start, cat_end):
dates.append(date)
det_rates.append(len([ev for ev in det_cat if ev.origins[-1].time.datetime > date
and ev.origins[-1].time.datetime < date + timedelta(days=1)]))
ax2.step(dates, det_rates, label='All templates', linewidth=2.0, color='black')
ax2.set_ylabel('Cumulative detection rate (events/day)')
else:
fig = plotting.cumulative_detections(dates=det_times, template_names=temp_names)
return fig