Test all plotting functions

CHANGES.md

@@ -34,7 +34,11 @@
   correlation functions, gaps not always properly handled during long-period
   trends - variance threshold is now raised, and Python checks for low-variance
   and applies gain to stabilise correlations if needed.
-* Plotting functions are now tested and have a more consistent interface.
+* Plotting functions are now tested and have a more consistent interface:
+  * All plotting functions accept the keyword arguments `save`, `savefile`,
+    `show`, `return_figure` and `title`.
+  * All plotting functions return a figure.
+  * `SVD_plot` renamed to `svd_plot`
 
 ## 0.3.1
 * Cleaned imports in utils modules

eqcorrscan/doc/submodules/utils.plotting.rst

@@ -4,6 +4,9 @@ plotting
 .. currentmodule:: eqcorrscan.utils.plotting
 .. automodule:: eqcorrscan.utils.plotting
 
+All plotting functions accept the `save`, `savefile`, `title` and `show` keyword
+arguments.
+
     .. comment to end block
 
     Classes & Functions
@@ -31,5 +34,6 @@ plotting
        threeD_seismplot
        triple_plot
        xcorr_plot
+       noise_plot
 
     .. comment to end block

eqcorrscan/tests/picker_test.py

@@ -46,4 +46,4 @@ def test_cross_net(self):
 
 
 if __name__ == "__main__":
-    unittest.main()
+    unittest.main()

eqcorrscan/tests/plotting_test.py

@@ -14,20 +14,22 @@
 import os
 import glob
 
-from obspy import read, read_events, UTCDateTime, Catalog
+from obspy import read, read_events, UTCDateTime, Catalog, Stream, Trace
 from obspy.io.nordic.core import readwavename
+from obspy.signal import filter
 
 from eqcorrscan.utils.plotting import (
-    _check_save_args, chunk_data, xcorr_plot, triple_plot, peaks_plot,
+    chunk_data, xcorr_plot, triple_plot, peaks_plot,
     cumulative_detections, threeD_gridplot, threeD_seismplot,
     multi_event_singlechan, multi_trace_plot, detection_multiplot,
-    interev_mag, obspy_3d_plot, noise_plot, pretty_template_plot, plot_repicked,
-    NR_plot, SVD_plot, plot_synth_real, freq_mag, spec_trace,
+    interev_mag, obspy_3d_plot, noise_plot, pretty_template_plot,
+    plot_repicked, NR_plot, svd_plot, plot_synth_real, freq_mag, spec_trace,
     subspace_detector_plot, subspace_fc_plot, _match_filter_plot,
     _plotting_decimation)
 from eqcorrscan.utils.stacking import align_traces
 from eqcorrscan.utils import findpeaks
 from eqcorrscan.core.match_filter import normxcorr2
+from eqcorrscan.core import template_gen, subspace
 
 
 class MultiStreamMethods(unittest.TestCase):
@@ -53,15 +55,71 @@ def setUpClass(cls):
     def test_multi_event_singlechan(self):
         _, _, fig = multi_event_singlechan(
             streams=self.streams, catalog=self.catalog, station="GCSZ",
-            channel="EZ", show=False)
+            channel="EZ", show=False, return_figure=True)
         return fig
 
     @pytest.mark.mpl_image_compare
     def test_multi_event_singlechan_realign(self):
         _, _, fig = multi_event_singlechan(
             streams=self.streams, catalog=self.catalog, station="GCSZ",
             channel="EZ", show=False, realign=True, freqmin=2, freqmax=20,
-            PWS=True)
+            PWS=True, return_figure=True)
+        return fig
+
+
+class EventPlottingMethods(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        test_file = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), 'test_data', 'REA',
+            'TEST_', '01-0411-15L.S201309')
+        test_wavefile = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), 'test_data', 'WAV',
+            'TEST_', '2013-09-01-0410-35.DFDPC_024_00')
+        cls.event = read_events(test_file)[0]
+        cls.st = read(test_wavefile)
+        cls.st.detrend().filter("bandpass", freqmin=2, freqmax=20, corners=4)
+        for tr in cls.st:
+            tr = tr.trim(tr.stats.starttime + 30, tr.stats.endtime - 30)
+            # Hack around seisan 2-letter channel naming
+            tr.stats.channel = tr.stats.channel[0] + tr.stats.channel[-1]
+        cls.template = template_gen._template_gen(cls.event.picks, cls.st, 2)
+
+    @pytest.mark.mpl_image_compare
+    def test_detection_multiplot(self):
+        times = [min([pk.time - 0.05 for pk in self.event.picks])]
+        times.append(times[0] + 10)
+        fig = detection_multiplot(
+            stream=self.st, template=self.template, times=times,
+            show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_pretty_template_plot(self):
+        fig = pretty_template_plot(
+            self.template, background=self.st, picks=self.event.picks,
+            show=False, return_figure=True, title="test template")
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_pretty_template_plot_basic(self):
+        fig = pretty_template_plot(
+            self.template, show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_plot_repicked(self):
+        _picks = [pick.copy() for pick in self.event.picks]
+        picked_channels = []
+        picks = []
+        for pick in _picks:
+            if pick.waveform_id not in picked_channels:
+                pick.time += 3
+                picks.append(pick)
+                picked_channels.append(pick.waveform_id)
+        fig = plot_repicked(
+            template=self.template, picks=picks, det_stream=self.st,
+            show=False, return_figure=True, title="test_detection")
         return fig
 
 
@@ -70,21 +128,6 @@ class StreamPlottingMethods(unittest.TestCase):
     def setUpClass(cls):
         cls.st = read()
 
-    def test_chunk_data(self):
-        desired_rate = 10
-        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Mean")
-        assert trout.stats.sampling_rate == desired_rate
-        assert np.allclose(trout.data.mean(), self.st[0].data.mean(),
-                           atol=.00000000001)
-        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Min")
-        assert trout.data.mean() < self.st[0].data.mean()
-        assert trout.stats.sampling_rate == desired_rate
-        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Max")
-        assert trout.stats.sampling_rate == desired_rate
-        assert trout.data.mean() > self.st[0].data.mean()
-        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Maxabs")
-        assert trout.stats.sampling_rate == desired_rate
-
     @pytest.mark.mpl_image_compare
     def test_xcorr_plot(self):
         st = self.st.copy().detrend('simple').filter(
@@ -93,7 +136,7 @@ def test_xcorr_plot(self):
         shift = shifts[1] * st[1].stats.sampling_rate
         cc = ccs[1]
         fig = xcorr_plot(template=st[1].data, image=st[0].data, shift=shift,
-                         cc=cc, show=False)
+                         cc=cc, show=False, return_figure=True)
         return fig
 
     @pytest.mark.mpl_image_compare
@@ -106,20 +149,103 @@ def test_xcorr_plot_cc_vec(self):
         cc_vec = cc_vec[0]
         fig = xcorr_plot(
             template=st[1].data[40:-40], image=st[0].data, cc_vec=cc_vec,
-            show=False)
+            show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_noise_plot(self):
+        signal = self.st.copy().trim(
+            self.st[0].stats.starttime + 10,
+            self.st[0].stats.starttime + 15)
+        noise = self.st.copy().trim(self.st[0].stats.starttime + 20)
+        fig = noise_plot(
+            signal=signal, noise=noise, show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_noise_plot_normalized(self):
+        signal = self.st.copy().trim(
+            self.st[0].stats.starttime + 10,
+            self.st[0].stats.starttime + 15)
+        noise = self.st.copy().trim(self.st[0].stats.starttime + 20)
+        fig = noise_plot(
+            signal=signal, noise=noise, normalise=True, show=False,
+            return_figure=True)
         return fig
 
     @pytest.mark.mpl_image_compare
     def test_triple_plot(self):
         template = self.st[0].copy().trim(self.st[0].stats.starttime + 8,
                                           self.st[0].stats.starttime + 12)
-        tr = self.st[0]
+        tr = self.st[0].copy()
         ccc = normxcorr2(template=template.data, image=tr.data)
         tr.data = tr.data[0:len(ccc[0])]
         fig = triple_plot(cccsum=ccc[0], cccsum_hist=ccc[0], trace=tr,
-                          threshold=0.8, show=False)
+                          threshold=0.8, show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_network_response_plot(self):
+        detections = [(self.st[0].stats.starttime + 10).datetime, ]
+        false_detections = [(self.st[0].stats.starttime + 20).datetime, ]
+        _envelope = self.st.copy()
+        for tr in _envelope:
+            tr.data = filter.envelope(tr.data)
+        envelope = Stream(_envelope[0])
+        for tr in envelope[1:]:
+            envelope[0].data += tr.data
+        envelope.detrend()
+        fig = NR_plot(stream=self.st, NR_stream=envelope,
+                      detections=detections, false_detections=false_detections,
+                      show=False, return_figure=True)
         return fig
 
+    @pytest.mark.mpl_image_compare
+    def test_plot_synth_real(self):
+        from eqcorrscan.utils.synth_seis import seis_sim
+
+        synth = Stream(Trace(seis_sim(sp=100, flength=200)))
+        synth[0].stats.station = 'RJOB'
+        synth[0].stats.channel = 'EHZ'
+        synth[0].stats.sampling_rate = 100
+        synth = synth.filter('bandpass', freqmin=2, freqmax=8)
+        real = self.st.select(
+           station='RJOB', channel='EHZ').detrend('simple').filter(
+              'bandpass', freqmin=2, freqmax=8)
+        real.trim(starttime=real[0].stats.starttime + 4.9,
+                  endtime=real[0].stats.starttime + 6.9)
+        fig = plot_synth_real(real_template=real, synthetic=synth,
+                              size=(7, 4), show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_spec_trace(self):
+        fig = spec_trace(
+            traces=self.st, trc="white", show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_spec_trace_list(self):
+        traces = [tr for tr in self.st]
+        fig = spec_trace(
+            traces=traces, trc="white", show=False, return_figure=True)
+        return fig
+
+    def test_chunk_data(self):
+        desired_rate = 10
+        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Mean")
+        assert trout.stats.sampling_rate == desired_rate
+        assert np.allclose(trout.data.mean(), self.st[0].data.mean(),
+                           atol=.00000000001)
+        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Min")
+        assert trout.data.mean() < self.st[0].data.mean()
+        assert trout.stats.sampling_rate == desired_rate
+        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Max")
+        assert trout.stats.sampling_rate == desired_rate
+        assert trout.data.mean() > self.st[0].data.mean()
+        trout = chunk_data(self.st[0], samp_rate=desired_rate, state="Maxabs")
+        assert trout.stats.sampling_rate == desired_rate
+
 
 class DataPlottingMethods(unittest.TestCase):
     @classmethod
@@ -134,7 +260,8 @@ def test_peaks_plot(self):
         threshold = 10
         peaks = findpeaks.find_peaks2_short(data, threshold, 3)
         fig = peaks_plot(data=data, starttime=UTCDateTime("2008001"),
-                         samp_rate=10, peaks=peaks, show=False)
+                         samp_rate=10, peaks=peaks, show=False,
+                         return_figure=True)
         return fig
 
     @pytest.mark.mpl_image_compare
@@ -143,7 +270,8 @@ def test_cumulative_detections(self):
         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)
+        fig = cumulative_detections(
+            dates, ['a', 'b', 'c'], show=False, return_figure=True)
         return fig
 
     @pytest.mark.mpl_image_compare
@@ -152,17 +280,99 @@ def test_cumulative_detections_rate(self):
         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,
-                                    plot_grouped=True, rate=True)
+        fig = cumulative_detections(
+            dates, ['a', 'b', 'c'], show=False, plot_grouped=True, rate=True,
+            return_figure=True)
         return fig
 
     @pytest.mark.mpl_image_compare
     def test_threeD_gridplot(self):
         nodes = [(-43.5, 170.4, 4), (-43.3, 170.8, 12), (-43.4, 170.3, 8)]
-        fig = threeD_gridplot(nodes=nodes, show=False)
+        fig = threeD_gridplot(nodes=nodes, show=False, return_figure=True)
+        return fig
+
+
+class MultiStreamPlottingMethods(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        wavefiles = glob.glob(os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), 'test_data', 'WAV',
+            'TEST_', '*'))
+        streams = [read(w) for w in wavefiles[1:10]]
+        cls.stream_list = []
+        for st in streams:
+            tr = st.select(station='GCSZ', channel='EHZ')
+            tr = tr.detrend('simple').resample(100).filter(
+                'bandpass', freqmin=2, freqmax=8)
+            cls.stream_list.append(tr)
+        cls.detector = subspace.Detector().read(os.path.join(
+            os.path.abspath(os.path.dirname(__file__)),
+            '..', 'tests', 'test_data', 'subspace', 'stat_test_detector.h5'))
+
+    @pytest.mark.mpl_image_compare
+    def test_svd_plot(self):
+        from eqcorrscan.utils.clustering import svd, svd_to_stream
+        uvec, sval, svec, stachans = svd(stream_list=self.stream_list)
+        svstreams = svd_to_stream(uvectors=uvec, stachans=stachans, k=3,
+                                  sampling_rate=100)
+        fig = svd_plot(svstreams=svstreams, svalues=sval, stachans=stachans,
+                       show=False, return_figure=True)
+        return fig[0]
+
+    @pytest.mark.mpl_image_compare
+    def test_subspace_detector_plot(self):
+        fig = subspace_detector_plot(
+            detector=self.detector, stachans='all', size=(10, 7), show=False,
+            return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_subspace_fc_plot(self):
+        fig = subspace_fc_plot(
+            detector=self.detector, stachans='all', size=(10, 7), show=False,
+            return_figure=True)
+        return fig
+
+
+@pytest.mark.network
+class NetworkPlottingTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        from obspy.clients.fdsn import Client
+        client = Client('IRIS')
+        t1 = UTCDateTime('2012-03-26T00:00:00')
+        t2 = t1 + (3 * 86400)
+        cls.catalog = client.get_events(
+            starttime=t1, endtime=t2, latitude=0,
+            longitude=170, maxradius=40)
+        cls.inventory = client.get_stations(
+            starttime=t1, endtime=t2, latitude=0, longitude=170,
+            maxradius=40)
+
+    @pytest.mark.mpl_image_compare
+    def test_interev_mag(self):
+        magnitudes = [event.preferred_magnitude().mag
+                      for event in self.catalog]
+        times = [event.preferred_origin().time for event in self.catalog]
+        fig = interev_mag(times, magnitudes, show=False, return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_obspy_3d_plot(self):
+        fig = obspy_3d_plot(
+            inventory=self.inventory, catalog=self.catalog, show=False,
+            return_figure=True)
+        return fig
+
+    @pytest.mark.mpl_image_compare
+    def test_freq_mag(self):
+        magnitudes = [event.preferred_magnitude().mag
+                      for event in self.catalog]
+        fig = freq_mag(
+            magnitudes, completeness=3.5, max_mag=7, show=False,
+            return_figure=True)
         return fig
 
 
 if __name__ == "__main__":
     unittest.main()
-