Source code for plotting

"""
Utility code for most of the plots used as part of the EQcorrscan package.

Copyright 2015 Calum Chamberlain

This file is part of EQcorrscan.

    EQcorrscan is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    EQcorrscan is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with EQcorrscan.  If not, see <http://www.gnu.org/licenses/>.

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import matplotlib.pylab as plt


[docs]def chunk_data(tr, samp_rate, state='mean'): r"""Function to downsample data for plotting by computing the maximum of \ data within chunks, useful for plotting waveforms or cccsums, large \ datasets that would otherwise exceed the complexity allowed, and overflow. :type tr: obspy.Trace :param tr: Trace to be chunked :type samp_rate: float :param samp_rate: Desired sampling rate in Hz :type state: str :param state: Either 'Min', 'Max', 'Mean' or 'Maxabs' to return one of \ these for the chunks. Maxabs will return the largest (positive or \ negative) for that chunk. :returns: :class: obspy.Trace """ trout = tr.copy() # Don't do it inplace on data x = np.arange(len(tr.data)) y = tr.data chunksize = int(round(tr.stats.sampling_rate / samp_rate)) # Wrap the array into a 2D array of chunks, truncating the last chunk if # chunksize isn't an even divisor of the total size. # (This part won't use _any_ additional memory) numchunks = int(y.size // chunksize) ychunks = y[:chunksize*numchunks].reshape((-1, chunksize)) xchunks = x[:chunksize*numchunks].reshape((-1, chunksize)) # Calculate the max, min, and means of chunksize-element chunks... if state == 'Max': trout.data = ychunks.max(axis=1) elif state == 'Min': trout.data = ychunks.min(axis=1) elif state == 'Mean': trout.data = ychunks.mean(axis=1) elif state == 'Maxabs': max_env = ychunks.max(axis=1) min_env = ychunks.min(axis=1) indeces = np.argmax(np.vstack([np.abs(max_env), np.abs(min_env)]), axis=0) stack = np.vstack([max_env, min_env]).T trout.data = np.array([stack[i][indeces[i]] for i in xrange(len(stack))]) xcenters = xchunks.mean(axis=1) trout.stats.starttime = tr.stats.starttime + xcenters[0] /\ tr.stats.sampling_rate trout.stats.sampling_rate = samp_rate return trout
[docs]def triple_plot(cccsum, cccsum_hist, trace, threshold, save=False, savefile=''): r"""Main function to make a triple plot with a day-long seismogram, \ day-long correlation sum trace and histogram of the correlation sum to \ show normality. :type cccsum: numpy.ndarray :param cccsum: Array of the cross-channel cross-correlation sum :type cccsum_hist: numpy.ndarray :param cccsum_hist: cccsum for histogram plotting, can be the same as \ cccsum but included if cccsum is just an envelope. :type trace: obspy.Trace :param trace: A sample trace from the same time as cccsum :type threshold: float :param threshold: Detection threshold within cccsum :type save: bool, optional :param save: If True will svae and not plot to screen, vice-versa if False :type savefile: str, optional :param savefile: Path to save figure to, only required if save=True """ if len(cccsum) != len(trace.data): print('cccsum is: ' + str(len(cccsum))+' trace is: '+str(len(trace.data))) msg = ' '.join(['cccsum and trace must have the', 'same number of data points']) raise ValueError(msg) df = trace.stats.sampling_rate npts = trace.stats.npts t = np.arange(npts, dtype=np.float32) / (df * 3600) # Generate the subplot for the seismic data ax1 = plt.subplot2grid((2, 5), (0, 0), colspan=4) ax1.plot(t, trace.data, 'k') ax1.axis('tight') ax1.set_ylim([-15 * np.mean(np.abs(trace.data)), 15 * np.mean(np.abs(trace.data))]) # Generate the subplot for the correlation sum data ax2 = plt.subplot2grid((2, 5), (1, 0), colspan=4, sharex=ax1) # Plot the threshold values ax2.plot([min(t), max(t)], [threshold, threshold], color='r', lw=1, label="Threshold") ax2.plot([min(t), max(t)], [-threshold, -threshold], color='r', lw=1) ax2.plot(t, cccsum, 'k') ax2.axis('tight') ax2.set_ylim([-1.7 * threshold, 1.7 * threshold]) ax2.set_xlabel("Time after %s [hr]" % trace.stats.starttime.isoformat()) # ax2.legend() # Generate a small subplot for the histogram of the cccsum data ax3 = plt.subplot2grid((2, 5), (1, 4), sharey=ax2) ax3.hist(cccsum_hist, 200, normed=1, histtype='stepfilled', orientation='horizontal', color='black') ax3.set_ylim([-5, 5]) fig = plt.gcf() fig.suptitle(trace.id) fig.canvas.draw() if not save: plt.show() plt.close() else: plt.savefig(savefile) return
[docs]def peaks_plot(data, starttime, samp_rate, save=False, peaks=[(0, 0)], savefile=''): r"""Simple utility code to plot the correlation peaks to check that the \ peak finding routine is running correctly, used in debugging for the \ EQcorrscan module. :type data: numpy.array :param data: Numpy array of the data within which peaks have been found :type starttime: obspy.UTCDateTime :param starttime: Start time for the data :type samp_rate: float :param samp_rate: Sampling rate of data in Hz :type save: Boolean, optional :param save: Save figure or plot to screen (False) :type peaks: list of Tuple, optional :param peaks: List of peak locations and amplitudes (loc, amp) :type savefile: String, optional :param savefile: Path to save to, only used if save=True """ npts = len(data) t = np.arange(npts, dtype=np.float32) / (samp_rate * 3600) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(t, data, 'k') ax1.scatter(peaks[0][1] / (samp_rate * 3600), abs(peaks[0][0]), color='r', label='Peaks') for peak in peaks: ax1.scatter(peak[1] / (samp_rate * 3600), abs(peak[0]), color='r') ax1.legend() ax1.set_xlabel("Time after %s [hr]" % starttime.isoformat()) ax1.axis('tight') fig.suptitle('Peaks') if not save: plt.show() plt.close() else: plt.savefig(savefile) return
[docs]def cumulative_detections(dates, template_names, save=False, savefile=''): r"""Simple plotting function to take a list of datetime objects and plot \ a cumulative detections list. Can take dates as a list of lists and will \ plot each list seperately, e.g. if you have dates from more than one \ template it will overlay them in different colours. :type dates: list of lists of datetime.datetime :param dates: Must be a list of lists of datetime.datetime objects :type template_names: list of strings :param template_names: List of the template names in order of the dates :type save: bool :param save: Save figure or show to screen, optional :type savefile: str :param savefile: String to save to, optional """ # Set up a default series of parameters for lines colors = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'firebrick', 'purple', 'darkgoldenrod', 'gray'] linestyles = ['-', '-.', '--', ':'] # Check that dates is a list of lists if type(dates[0]) != list: dates = [dates] i = 0 j = 0 # This is an ugly way of looping through colours and linestyles, it would # be better with itertools functions... plothandles = [] for k, template_dates in enumerate(dates): template_dates.sort() counts = np.arange(0, len(template_dates)) print(str(i)+' '+str(j)+' '+str(k)) filename = plt.plot(template_dates, counts, linestyles[j], color=colors[i], label=template_names[k], linewidth=3.0) plothandles.append(filename) if i < len(colors) - 1: i += 1 else: i = 0 if j < len(linestyles) - 1: j += 1 else: j = 0 plt.xlabel('Date') plt.ylabel('Cumulative detections') plt.title('Cumulative detections for all templates') plt.legend(loc=2, prop={'size': 8}, ncol=2) # handles=plothandles) if save: plt.savefig(savefile) plt.close() else: plt.show() return
[docs]def threeD_gridplot(nodes, save=False, savefile=''): r"""Function to plot in 3D a series of grid points. :type nodes: list of tuples :param nodes: List of tuples of the form (lat, long, depth) :type save: bool :param save: if True will save without plotting to screen, if False \ (default) will plot to screen but not save :type savefile: str :param savefile: required if save=True, path to save figure to. """ lats = [] longs = [] depths = [] for node in nodes: lats.append(float(node[0])) longs.append(float(node[1])) depths.append(float(node[2])) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(lats, longs, depths) ax.set_ylabel("Latitude (deg)") ax.set_xlabel("Longitude (deg)") ax.set_zlabel("Depth(km)") ax.get_xaxis().get_major_formatter().set_scientific(False) ax.get_yaxis().get_major_formatter().set_scientific(False) if not save: plt.show() plt.close() else: plt.savefig(savefile) return
[docs]def multi_event_singlechan(streams, catalog, clip=10.0, pre_pick=2.0, freqmin=False, freqmax=False, realign=False, cut=(-3.0, 5.0), PWS=False, title=False): r"""Function to plot data from a single channel at a single station for \ multiple events - data will be alligned by their pick-time given in the \ picks. :type streams: list of :class:obspy.stream :param streams: List of the streams to use, can contain more traces than \ you plan on plotting :type catalog: obspy.core.event.Catalog :param catalog: Catalog of events, one for each trace, with a single pick :type clip: float :param clip: Length in seconds to plot, defaults to 10.0 :type pre_pick: float :param pre_pick: Length in seconds to extract and plot before the pick, \ defaults to 2.0 :type freqmin: float :param freqmin: Low cut for bandpass in Hz :type freqmax: float :param freqmax: High cut for bandpass in Hz :type realign: bool :param realign: To compute best alignement based on correlation or not. :type cut: tuple :param cut: tuple of start and end times for cut in seconds from the pick :type PWS: bool :param PWS: compute Phase Weighted Stack, if False, will compute linear \ stack. :type title: str :param title: Plot title. :returns: Alligned and cut traces, and new picks """ from eqcorrscan.utils import stacking import copy from eqcorrscan.core.match_filter import normxcorr2 from obspy import Stream import warnings fig, axes = plt.subplots(len(catalog)+1, 1, sharex=True, figsize=(7, 12)) axes = axes.ravel() traces = [] al_traces = [] # Keep input safe clist = copy.deepcopy(catalog) st_list = copy.deepcopy(streams) for i, event in enumerate(clist): if st_list[i].select(station=event.picks[0].waveform_id.station_code, channel='*' + event.picks[0].waveform_id.channel_code[-1]): tr = st_list[i].select(station=event.picks[0].waveforms_id. station_code, channel='*' + event.picks[0].waveform_id. channel_code[-1])[0] else: print('No data for '+event.pick[0].waveform_id) continue tr.detrend('linear') if freqmin: tr.filter('bandpass', freqmin=freqmin, freqmax=freqmax) if realign: tr_cut = tr.copy() tr_cut.trim(event.picks[0].time + cut[0], event.picks[0].time + cut[1], nearest_sample=False) if len(tr_cut.data) <= (0.5 * (cut[1] - cut[0]) * tr_cut.stats.sampling_rate): msg = ''.join(['Not enough in the trace for ', tr.stats.station, '.', tr.stats.channel, '\n', 'Suggest removing pick from sfile at time ', str(event.picks[0].time)]) warnings.warn(msg) else: al_traces.append(tr_cut) else: tr.trim(event.picks[0].time - pre_pick, event.picks[0].time + clip - pre_pick, nearest_sample=False) if len(tr.data) == 0: msg = ''.join(['No data in the trace for ', tr.stats.station, '.', tr.stats.channel, '\n', 'Suggest removing pick from sfile at time ', str(event.picks[0].time)]) warnings.warn(msg) continue traces.append(tr) if realign: shift_len = int(0.25 * (cut[1] - cut[0]) * al_traces[0].stats.sampling_rate) shifts = stacking.align_traces(al_traces, shift_len) for i in xrange(len(shifts)): print('Shifting by '+str(shifts[i])+' seconds') event.picks[0].time -= shifts[i] traces[i].trim(event.picks[0].time - pre_pick, event.picks[0].time + clip-pre_pick, nearest_sample=False) # We now have a list of traces traces = [(trace, trace.stats.starttime.datetime) for trace in traces] traces.sort(key=lambda tup: tup[1]) traces = [trace[0] for trace in traces] # Plot the traces for i, tr in enumerate(traces): y = tr.data x = np.arange(len(y)) x = x / tr.stats.sampling_rate # convert to seconds axes[i+1].plot(x, y, 'k', linewidth=1.1) axes[i+1].yaxis.set_ticks([]) traces = [Stream(trace) for trace in traces] if PWS: linstack = stacking.PWS_stack(traces) else: linstack = stacking.linstack(traces) tr = linstack.select(station=event[0].picks[0].waveform_id.station_code, channel='*' + event[0].picks[0].waveform_id.channel_code[-1])[0] y = tr.data x = np.arange(len(y)) x = x / tr.stats.sampling_rate axes[0].plot(x, y, 'r', linewidth=2.0) axes[0].set_ylabel('Stack', rotation=0) axes[0].yaxis.set_ticks([]) for i, slave in enumerate(traces): cc = normxcorr2(tr.data, slave[0].data) axes[i+1].set_ylabel('cc='+str(round(np.max(cc), 2)), rotation=0) axes[i+1].text(0.9, 0.15, str(round(np.max(slave[0].data))), bbox=dict(facecolor='white', alpha=0.95), transform=axes[i+1].transAxes) axes[i+1].text(0.7, 0.85, slave[0].stats.starttime.datetime. strftime('%Y/%m/%d %H:%M:%S'), bbox=dict(facecolor='white', alpha=0.95), transform=axes[i+1].transAxes) axes[-1].set_xlabel('Time (s)') if title: axes[0].set_title(title) plt.subplots_adjust(hspace=0) plt.show() return traces, clist
[docs]def detection_multiplot(stream, template, times, streamcolour='k', templatecolour='r'): r"""Function to plot the stream of data that has been detected in, with\ the template on top of it timed according to a list of given times, just\ a pretty way to show a detection! :type stream: obspy.Stream :param stream: Stream of data to be plotted as the base (black) :type template: obspy.Stream :param template: Template to be plotted on top of the base stream (red) :type times: list of datetime.datetime :param times: list of times of detections in the order of the channels in template. :type streamcolour: str :param streamcolour: String of matplotlib colour types for the stream :type templatecolour: str :param templatecolour: Colour to plot the template in. """ import datetime as dt from obspy import UTCDateTime fig, axes = plt.subplots(len(template), 1, sharex=True) axes = axes.ravel() mintime = min([tr.stats.starttime for tr in template]) for i, template_tr in enumerate(template): image = stream.select(station=template_tr.stats.station, channel='*'+template_tr.stats.channel[-1]) if not image: msg = ' '.join(['No data for', template_tr.stats.station, template_tr.stats.channel]) print(msg) continue image = image.merge()[0] # Downsample if needed if image.stats.sampling_rate > 20: image.decimate(int(image.stats.sampling_rate // 20)) if template_tr.stats.sampling_rate > 20: template_tr.decimate(int(template_tr.stats.sampling_rate // 20)) # Get a list of datetime objects image_times = [image.stats.starttime.datetime + dt.timedelta((j * image.stats.delta) / 86400) for j in range(len(image.data))] axes[i].plot(image_times, image.data / max(image.data), streamcolour, linewidth=1.2) for k, time in enumerate(times): lagged_time = UTCDateTime(time) + (template_tr.stats.starttime - mintime) lagged_time = lagged_time.datetime template_times = [lagged_time + dt.timedelta((j * template_tr.stats.delta) / 86400) for j in range(len(template_tr.data))] axes[i].plot(template_times, template_tr.data / max(template_tr.data), templatecolour, linewidth=1.2) ylab = '.'.join([template_tr.stats.station, template_tr.stats.channel]) axes[i].set_ylabel(ylab, rotation=0, horizontalalignment='right') axes[i].yaxis.set_ticks([]) axes[len(axes) - 1].set_xlabel('Time') plt.subplots_adjust(hspace=0, left=0.175, right=0.95, bottom=0.07) plt.show() return
[docs]def interev_mag_sfiles(sfiles): r"""Function to plot interevent-time versus magnitude for series of events. **thin** Wrapper for interev_mag. :type sfiles: list :param sfiles: List of sfiles to read from """ from eqcorrscan.utils import sfile_util times = [sfile_util.readheader(sfile)[0].origins[0].time for sfile in sfiles] mags = [sfile_util.readheader(sfile)[0].magnitudes[0].mag for sfile in sfiles] interev_mag(times, mags)
[docs]def interev_mag(times, mags): r"""Function to plot interevent times against magnitude for given times and magnitudes. :type times: list of datetime :param times: list of the detection times, must be sorted the same as mags :type mags: list of float :param mags: list of magnitudes """ l = [(times[i], mags[i]) for i in xrange(len(times))] l.sort(key=lambda tup: tup[0]) times = [x[0] for x in l] mags = [x[1] for x in l] # Make two subplots next to each other of time before and time after fig, axes = plt.subplots(1, 2, sharey=True) axes = axes.ravel() pre_times = [] post_times = [] for i in range(len(times)): if i > 0: pre_times.append((times[i] - times[i - 1]) / 60) if i < len(times) - 1: post_times.append((times[i + 1] - times[i]) / 60) axes[0].scatter(pre_times, mags[1:]) axes[0].set_title('Pre-event times') axes[0].set_ylabel('Magnitude') axes[0].set_xlabel('Time (Minutes)') plt.setp(axes[0].xaxis.get_majorticklabels(), rotation=30) axes[1].scatter(pre_times, mags[:-1]) axes[1].set_title('Post-event times') axes[1].set_xlabel('Time (Minutes)') plt.setp(axes[1].xaxis.get_majorticklabels(), rotation=30) plt.show()
[docs]def threeD_seismplot(stations, nodes): r"""Function to plot seismicity and stations in a 3D, movable, zoomable \ space using matplotlibs Axes3D package. :type stations: list of tuple :param stations: list of one tuple per station of (lat, long, elevation), \ with up positive. :type nodes: list of tuple :param nodes: list of one tuple per event of (lat, long, depth) with down \ positive. """ stalats, stalongs, staelevs = zip(*stations) evlats, evlongs, evdepths = zip(*nodes) evdepths = [-1 * depth for depth in evdepths] fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(evlats, evlongs, evdepths, marker="x", c="k") ax.scatter(stalats, stalongs, staelevs, marker="v", c="r") ax.set_ylabel("Latitude (deg)") ax.set_xlabel("Longitude (deg)") ax.set_zlabel("Depth(km)") ax.get_xaxis().get_major_formatter().set_scientific(False) ax.get_yaxis().get_major_formatter().set_scientific(False) plt.show() return
[docs]def pretty_template_plot(template, size=(18.5, 10.5), save=False, title=False, background=False, picks=False): r"""Function to make a pretty plot of a single template, designed to work \ better than the default obspy plotting routine for short data lengths. :type template: :class: obspy.Stream :param template: Template stream to plot :type size: tuple :param size: tuple of plot size :type save: bool :param save: if False will plot to screen, if True will save :type title: bool :param title: String if set will be the plot title :type background: :class: obspy.stream :param background: Stream to plot the template within. :type picks: list of obspy.core.event.pick :param picks: List of obspy type picks. """ fig, axes = plt.subplots(len(template), 1, sharex=True, figsize=size) if len(template) > 1: axes = axes.ravel() else: return if not background: mintime = template.sort(['starttime'])[0].stats.starttime else: mintime = background.sort(['starttime'])[0].stats.starttime template.sort(['network', 'station', 'starttime']) lengths = [] lines = [] labels = [] for i, tr in enumerate(template): delay = tr.stats.starttime - mintime y = tr.data x = np.linspace(0, (len(y)-1) * tr.stats.delta, len(y)) x += delay if background: btr = background.select(station=tr.stats.station, channel=tr.stats.channel)[0] bdelay = btr.stats.starttime - mintime by = btr.data bx = np.linspace(0, (len(by)-1) * btr.stats.delta, len(by)) bx += bdelay axes[i].plot(bx, by, 'k', linewidth=1) template_line, = axes[i].plot(x, y, 'r', linewidth=1.1, label='Template') if i == 0: lines.append(template_line) labels.append('Template') lengths.append(max(bx[-1], x[-1])) else: template_line, = axes[i].plot(x, y, 'k', linewidth=1.1, label='Template') if i == 0: lines.append(template_line) labels.append('Template') lengths.append(x[-1]) # print(' '.join([tr.stats.station, str(len(x)), str(len(y))])) axes[i].set_ylabel('.'.join([tr.stats.station, tr.stats.channel]), rotation=0, horizontalalignment='right') axes[i].yaxis.set_ticks([]) # Plot the picks if they are given if picks: tr_picks = [pick for pick in picks if pick.waveform_id.station_code == tr.stats.station and pick.waveform_id.channel_code[0] + pick.waveform_id.channel_code[-1] == tr.stats.channel[0] + tr.stats.channel[-1]] for pick in tr_picks: if 'P' in pick.phase_hint.upper(): pcolor = 'red' label = 'P-pick' elif 'S' in pick.phase_hint.upper(): pcolor = 'blue' label = 'S-pick' else: pcolor = 'k' label = 'Unknown pick' pdelay = pick.time - mintime # print(pdelay) line = axes[i].axvline(x=pdelay, color=pcolor, linewidth=2, linestyle='--', label=label) if label not in labels: lines.append(line) labels.append(label) # axes[i].plot([pdelay, pdelay], []) axes[i].set_xlim([0, max(lengths)]) axes[len(template)-1].set_xlabel('Time (s) from start of template') plt.figlegend(lines, labels, 'upper right') if title: axes[0].set_title(title) else: plt.subplots_adjust(top=0.98) plt.tight_layout() plt.subplots_adjust(hspace=0) if not save: plt.show() plt.close() else: plt.savefig(save)
[docs]def NR_plot(stream, NR_stream, detections, false_detections=False, size=(18.5, 10), save=False, title=False): r"""Function to plot the Network response alongside the streams used -\ highlights detection times in the network response. :type stream: :class: obspy.Stream :param stream: Stream to plot :type NR_stream: :class: obspy.Stream :param NR_stream: Stream for the network response :type detections: list of datetime objects :param detections: List of the detections :type false_detections: list of datetime :param false_detections: Either False (default) or list of false detection\ times :type size: tuple :param size: Size of figure, default is (18.5,10) :type save: bool :param save: Save figure or plot to screen, if not False, must be string\ of save path. :type title: str :param title: String for the title of the plot, set to False """ import datetime as dt import matplotlib.dates as mdates fig, axes = plt.subplots(len(stream)+1, 1, sharex=True, figsize=size) if len(stream) > 1: axes = axes.ravel() else: return mintime = stream.sort(['starttime'])[0].stats.starttime stream.sort(['network', 'station', 'starttime']) for i, tr in enumerate(stream): delay = tr.stats.starttime - mintime delay *= tr.stats.sampling_rate y = tr.data x = [tr.stats.starttime + dt.timedelta(seconds=s / tr.stats.sampling_rate) for s in xrange(len(y))] x = mdates.date2num(x) axes[i].plot(x, y, 'k', linewidth=1.1) axes[i].set_ylabel(tr.stats.station+'.'+tr.stats.channel, rotation=0) axes[i].yaxis.set_ticks([]) axes[i].set_xlim(x[0], x[-1]) # Plot the network response tr = NR_stream[0] delay = tr.stats.starttime - mintime delay *= tr.stats.sampling_rate y = tr.data x = [tr.stats.starttime + dt.timedelta(seconds=s / tr.stats.sampling_rate) for s in range(len(y))] x = mdates.date2num(x) axes[i].plot(x, y, 'k', linewidth=1.1) axes[i].set_ylabel(tr.stats.station+'.'+tr.stats.channel, rotation=0) axes[i].yaxis.set_ticks([]) axes[-1].set_xlabel('Time') axes[-1].set_xlim(x[0], x[-1]) # Plot the detections! ymin, ymax = axes[-1].get_ylim() if false_detections: for detection in false_detections: xd = mdates.date2num(detection) axes[-1].plot((xd, xd), (ymin, ymax), 'k--', linewidth=0.5, alpha=0.5) for detection in detections: xd = mdates.date2num(detection) axes[-1].plot((xd, xd), (ymin, ymax), 'r--', linewidth=0.75) # Set formatters for x-labels mins = mdates.MinuteLocator() timedif = tr.stats.endtime.datetime - tr.stats.starttime.datetime if timedif.total_seconds() >= 10800 and timedif.total_seconds() <= 25200: hours = mdates.MinuteLocator(byminute=[0, 15, 30, 45]) elif timedif.total_seconds() <= 1200: hours = mdates.MinuteLocator(byminute=range(0, 60, 2)) elif timedif.total_seconds > 25200 and timedif.total_seconds() <= 172800: hours = mdates.HourLocator(byhour=range(0, 24, 3)) elif timedif.total_seconds() > 172800: hours = mdates.DayLocator() else: hours = mdates.MinuteLocator(byminute=range(0, 60, 5)) hrFMT = mdates.DateFormatter('%Y/%m/%d %H:%M:%S') axes[-1].xaxis.set_major_locator(hours) axes[-1].xaxis.set_major_formatter(hrFMT) axes[-1].xaxis.set_minor_locator(mins) plt.gcf().autofmt_xdate() axes[-1].fmt_xdata = mdates.DateFormatter('%Y/%m/%d %H:%M:%S') plt.subplots_adjust(hspace=0) if title: axes[0].set_title(title) if not save: plt.show() plt.close() else: plt.savefig(save) return
[docs]def SVD_plot(SVStreams, SValues, stachans, title=False): r"""Function to plot the singular vectors from the clustering routines, one\ plot for each stachan :type SVStreams: list of :class:Obspy.Stream :param SVStreams: See clustering.SVD_2_Stream - will assume these are\ ordered by power, e.g. first singular vector in the first stream :type SValues: list of float :param SValues: List of the singular values corresponding to the SVStreams :type stachans: list :param stachans: List of station.channel """ for stachan in stachans: print(stachan) plot_traces = [SVStream.select(station=stachan.split('.')[0], channel=stachan.split('.')[1])[0] for SVStream in SVStreams] fig, axes = plt.subplots(len(plot_traces), 1, sharex=True) axes = axes.ravel() for i, tr in enumerate(plot_traces): y = tr.data x = np.linspace(0, len(y) * tr.stats.delta, len(y)) axes[i].plot(x, y, 'k', linewidth=1.1) ylab = 'SV '+str(i+1)+'='+str(round(SValues[i] / len(SValues), 2)) axes[i].set_ylabel(ylab, rotation=0) axes[i].yaxis.set_ticks([]) print(i) axes[-1].set_xlabel('Time (s)') plt.subplots_adjust(hspace=0) if title: axes[0].set_title(title) else: axes[0].set_title(stachan) plt.show() return
[docs]def plot_synth_real(real_template, synthetic, channels=False): r"""Plot multiple channels of data for real data and synthetic. :type real_template: obspy.Stream :param real_template: Stream of the real template :type synthetic: obspy.Stream :param synthetic: Stream of synthetic template :type channels: list of str :param channels: List of tuples of (station, channel) to plot, default is\ False, which plots all. """ from obspy.signal.cross_correlation import xcorr from obspy import Stream colours = ['k', 'r'] labels = ['Real', 'Synthetic'] if channels: real = [] synth = [] for stachan in channels: real.append(real_template.select(station=stachan[0], channel=stachan[1])) synth.append(synthetic.select(station=stachan[0], channel=stachan[1])) real_template = Stream(real) synthetic = Stream(synth) # Extract the station and channels stachans = list(set([(tr.stats.station, tr.stats.channel) for tr in real_template])) fig, axes = plt.subplots(len(stachans), 1, sharex=True, figsize=(5, 10)) axes = axes.ravel() for i, stachan in enumerate(stachans): real_tr = real_template.select(station=stachan[0], channel=stachan[1])[0] synth_tr = synthetic.select(station=stachan[0], channel=stachan[1])[0] shift, corr = xcorr(real_tr, synth_tr, 2) print('Shifting by: '+str(shift)+' samples') if corr < 0: synth_tr.data = synth_tr.data * -1 corr = corr * -1 if shift < 0: synth_tr.data = synth_tr.data[abs(shift):] real_tr.data = real_tr.data[0:len(synth_tr.data)] elif shift > 0: real_tr.data = real_tr.data[abs(shift):] synth_tr.data = synth_tr.data[0:len(real_tr.data)] for j, tr in enumerate([real_tr, synth_tr]): y = tr.data y = y / float(max(abs(y))) x = np.linspace(0, len(y) * tr.stats.delta, len(y)) axes[i].plot(x, y, colours[j], linewidth=2.0, label=labels[j]) axes[i].get_yaxis().set_ticks([]) ylab = stachan[0]+'.'+stachan[1]+' cc='+str(round(corr, 2)) axes[i].set_ylabel(ylab, rotation=0) plt.subplots_adjust(hspace=0) # axes[0].legend() axes[-1].set_xlabel('Time (s)') plt.show()
[docs]def freq_mag(magnitudes, completeness, max_mag, binsize=0.2): r"""Function to make a frequency-magnitude histogram and cumulative \ density plot. This can compute a b-value, but not a completeness at \ the moment. B-value is computed by linear fitting to section of curve \ between completeness and max_mag. :type magnitudes: list :param magnitudes: list of float of magnitudes :type completeness: float :param completeness: Level to compute the b-value above :type max_mag: float :param max_mag: Maximum magnitude to try and fit a b-value to :type binsize: float :param binsize: Width of histogram bins, defaults to 0.2 """ # Ensure magnitudes are sorted magnitudes.sort() fig, ax1 = plt.subplots() # Set up the bins, the bin-size could be a variables bins = np.arange(min(magnitudes), max(magnitudes), binsize) n, bins, patches = ax1.hist(magnitudes, bins, facecolor='Black', alpha=0.5, label='Magnitudes') ax1.set_ylabel('Frequency') ax1.set_ylim([0, max(n) + 0.5 * max(n)]) plt.xlabel('Magnitude') # Now make the cumulative density function cdf = np.arange(len(magnitudes)) / float(len(magnitudes)) cdf = ((cdf * -1.0) + 1.0) * len(magnitudes) ax2 = ax1.twinx() ax2.scatter(magnitudes, np.log10(cdf), c='k', marker='+', s=20, lw=2, label='Magnitude cumulative density') # Now we want to calculate the b-value and plot the fit x = [] y = [] for i, magnitude in enumerate(magnitudes): if magnitude >= completeness <= max_mag: x.append(magnitude) y.append(cdf[i]) fit = np.polyfit(x, np.log10(y), 1) fit_fn = np.poly1d(fit) ax2.plot(magnitudes, fit_fn(magnitudes), '--k', label='GR trend, b-value = ' + str(abs(fit[0]))[0:4] + '\n $M_C$ = ' + str(completeness)) ax2.set_ylabel('$Log_{10}$ of cumulative density') plt.xlim([min(magnitudes) - 0.5, max(np.log10(cdf)) + 0.2]) plt.ylim([min(magnitudes) - 0.5, max(np.log10(cdf)) + 1.0]) plt.legend(loc=2) plt.show()
[docs]def spec_trace(traces, cmap=None, wlen=0.4, log=False, trc='k', tralpha=0.9, size=(10, 13), Fig=None, title=None, show=True): r"""Wrapper for _spec_trace, take a stream or list of traces and plots \ the trace with the spectra beneath it - this just does the overseeing to \ work out if it needs to add subplots or not. :type traces: either stream or list of traces :param traces: Traces to be plotted, can be a single obspy.Stream, or a \ list of obspy.Trace :type cmap: str :param cmap: [Matplotlib colormap](http://matplotlib.org/examples/color/ \ colormaps_reference.html) :type wlen: float :param wlen: Window length for fft in seconds :type log: bool :param log: Use a log frequency scale :type trc: str :param trc: Color for the trace. :type tralpha: float :param tralpha: Opacity level for the seismogram, from transparent (0.0) \ to opaque (1.0). :type size: tuple :param size: Plot size, tuple of floats, inches :type Fig: matplotlib Fig :param axes: Figure to plot onto, defaults to self generating. :type show: bool :param show: To show plot or not, if false, will return Fig. """ from obspy import Stream if isinstance(traces, Stream): traces.sort(['station', 'channel']) if not Fig: Fig = plt.figure(figsize=size) for i, tr in enumerate(traces): if i == 0: ax = Fig.add_subplot(len(traces), 1, i+1) else: ax = Fig.add_subplot(len(traces), 1, i+1, sharex=ax) ax1, ax2 = _spec_trace(tr, wlen=wlen, log=log, trc=trc, tralpha=tralpha, axes=ax) ax2.set_yticks([]) if i < len(traces) - 1: plt.setp(ax1.get_xticklabels(), visible=False) if type(traces) == list: ax2.text(0.005, 0.85, tr.stats.starttime.datetime. strftime('%Y/%m/%d %H:%M:%S'), bbox=dict(facecolor='white', alpha=0.8), transform=ax2.transAxes) else: ax2.text(0.005, 0.85, '.'.join([tr.stats.station, tr.stats.channel]), bbox=dict(facecolor='white', alpha=0.8), transform=ax2.transAxes) ax2.text(0.005, 0.02, str(np.max(tr.data).round(1)), bbox=dict(facecolor='white', alpha=0.95), transform=ax2.transAxes) ax1.set_xlabel('Time (s)') Fig.subplots_adjust(hspace=0) Fig.text(0.04, 0.5, 'Frequency (Hz)', va='center', rotation='vertical') if title: plt.suptitle(title) if show: plt.show() else: return Fig
[docs]def _spec_trace(trace, cmap=None, wlen=0.4, log=False, trc='k', tralpha=0.9, size=(10, 2.5), axes=None, title=None): r"""Function to plot a trace over that traces spectrogram. Uses obspys spectrogram routine. :type trace: obspy.Trace :param trace: trace to plot :type cmap: str :param cmap: [Matplotlib colormap](http://matplotlib.org/examples/color/ colormaps_reference.html) :type wlen: float :param wlen: Window length for fft in seconds :type log: bool :param log: Use a log frequency scale :type trc: str :param trc: Color for the trace. :type tralpha: float :param tralpha: Opacity level for the seismogram, from transparent (0.0) \ to opaque (1.0). :type size: tuple :param size: Plot size, tuple of floats, inches :type axes: matplotlib axes :param axes: Axes to plot onto, defaults to self generating. :type title: str :param title: Title for the plot. """ if not axes: Fig = plt.figure(figsize=size) ax1 = Fig.add_subplot(111) else: ax1 = axes trace.spectrogram(wlen=wlen, log=log, show=False, cmap=cmap, axes=ax1) Fig = plt.gcf() ax2 = ax1.twinx() y = trace.data x = np.linspace(0, len(y) / trace.stats.sampling_rate, len(y)) ax2.plot(x, y, color=trc, linewidth=2.0, alpha=tralpha) ax2.set_xlim(min(x), max(x)) ax2.set_ylim(min(y) * 2, max(y) * 2) if title: ax1.set_title(' '.join([trace.stats.station, trace.stats.channel, trace.stats.starttime.datetime. strftime('%Y/%m/%d %H:%M:%S')])) if not axes: Fig.set_size_inches(size) Fig.show() else: return ax1, ax2
if __name__ == "__main__": import doctest doctest.testmod()