Source code for openquake.hmtk.plotting.mapping

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4

#
# LICENSE
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# Copyright (c) 2010-2017, GEM Foundation, G. Weatherill, M. Pagani,
# D. Monelli., L. E. Rodriguez-Abreu
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# The Hazard Modeller's Toolkit is free software: you can redistribute
# it and/or modify it under the terms of the GNU Affero General Public
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# The software Hazard Modeller's Toolkit (openquake.hmtk) provided herein
# is released as a prototype implementation on behalf of
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# Earthquake Model).
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# developers, as GEM's OpenQuake software suite.
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'''
Module openquake.hmtk.plotting.catalogue.map is a graphical
function for plotting the spatial distribution of events
'''
from builtins import range
import collections
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from openquake.hmtk.sources.area_source import mtkAreaSource
from openquake.hmtk.sources.point_source import mtkPointSource
from openquake.hmtk.plotting.beachball import Beach
from openquake.hmtk.plotting.plotting_utils import DISSIMILAR_COLOURLIST
from openquake.hmtk.sources.simple_fault_source import mtkSimpleFaultSource
from openquake.hmtk.sources.complex_fault_source import mtkComplexFaultSource


DEFAULT_SYMBOLOGY = [(-np.inf, 1., 'k.'),  # M < 1
                     (1., 2., 'g*'),  # 1 < M < 2
                     (2., 3., 'cx'),  # 2 < M < 3
                     (3., 4., 'yd'),  # 3 < M < 4
                     (4., 5., 'm^'),  # 4 < M < 5
                     (5., 6., 'go'),  # 5 < M < 6
                     (6., 7., 'yh'),  # 6 < M < 7
                     (7., 8., 'bs'),  # 7 < M < 8
                     (8., 9., 'k^'),  # 8 < M < 9
                     (9., np.inf, 'ro')]  # 9 < M < 10

LEGEND_OFFSET = (1.3, 1.0)
PORTRAIT_ASPECT = (6, 8)
LANDSCAPE_ASPECT = (8, 6)
NCOLS = len(DISSIMILAR_COLOURLIST)


def _fault_polygon_from_mesh(source):
    # Mesh
    upper_edge = np.column_stack([source.geometry.mesh.lons[1],
                                  source.geometry.mesh.lats[1],
                                  source.geometry.mesh.depths[1]])
    lower_edge = np.column_stack([source.geometry.mesh.lons[-1],
                                  source.geometry.mesh.lats[-1],
                                  source.geometry.mesh.depths[-1]])
    return np.vstack([upper_edge, np.flipud(lower_edge), upper_edge[0, :]])


[docs]class HMTKBaseMap(object): ''' Class to plot the spatial distribution of events based in the Catalogue imported from openquake.hmtk. ''' def __init__(self, config, title=None, dpi=300, ax=None, lat_lon_spacing=2.): """ :param dict config: Configuration parameters of the algorithm, containing the following information - 'min_lat' Minimum value of latitude (in degrees, float) 'max_lat' Minimum value of longitude (in degrees, float) (min_lat, min_lon) Defines the inferior corner of the map 'min_lon' Maximum value of latitude (in degrees, float) 'max_lon' Maximum value of longitude (in degrees, float) (min_lon, max_lon) Defines the upper corner of the map :param str title: Title string """ self.config = config self.title = title self.dpi = dpi self.lat_lon_spacing = lat_lon_spacing self.fig = None self.ax = ax self.m = None self._build_basemap() def _build_basemap(self): ''' Creates the map according to the input configuration ''' if self.config['min_lon'] >= self.config['max_lon']: raise ValueError('Upper limit of long is smaller than lower limit') if self.config['min_lon'] >= self.config['max_lon']: raise ValueError('Upper limit of long is smaller than lower limit') # Corners of the map lowcrnrlat = self.config['min_lat'] lowcrnrlon = self.config['min_lon'] uppcrnrlat = self.config['max_lat'] uppcrnrlon = self.config['max_lon'] if 'resolution' not in self.config.keys(): self.config['resolution'] = 'l' lat0 = lowcrnrlat + ((uppcrnrlat - lowcrnrlat) / 2) lon0 = lowcrnrlon + ((uppcrnrlon - lowcrnrlon) / 2) if (uppcrnrlat - lowcrnrlat) >= (uppcrnrlon - lowcrnrlon): fig_aspect = PORTRAIT_ASPECT else: fig_aspect = LANDSCAPE_ASPECT if self.ax is None: self.fig, self.ax = plt.subplots(figsize=fig_aspect, facecolor='w', edgecolor='k') else: self.fig = self.ax.get_figure() if self.title: self.ax.set_title(self.title, fontsize=16) parallels = np.arange(-90., 90., self.lat_lon_spacing) meridians = np.arange(0., 360., self.lat_lon_spacing) # Build Map # Do not import Basemap at top level since it's an optional feature # and it would break doctests from mpl_toolkits.basemap import Basemap self.m = Basemap( llcrnrlon=lowcrnrlon, llcrnrlat=lowcrnrlat, urcrnrlon=uppcrnrlon, urcrnrlat=uppcrnrlat, projection='stere', resolution=self.config['resolution'], area_thresh=1000.0, lat_0=lat0, lon_0=lon0, ax=self.ax) self.m.drawcountries() self.m.drawmapboundary() self.m.drawcoastlines() self.m.drawstates() self.m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=12) self.m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=12) self.m.fillcontinents(color='wheat')
[docs] def savemap(self, filename, filetype='png', papertype="a4"): """ Save the figure """ self.fig.savefig(filename, dpi=self.dpi, format=filetype, papertype=papertype)
[docs] def add_catalogue(self, catalogue, overlay=False): ''' :param catalogue: Earthquake catalogue as instance of :class:`openquake.hmtk.seismicity.catalogue.Catalogue` :param dict config: Configuration parameters of the algorithm, containing the following information: 'min_lat' Minimum value of latitude (in degrees, float) 'max_lat' Minimum value of longitude (in degrees, float) (min_lat, min_lon) Defines the inferior corner of the map 'min_lon' Maximum value of latitude (in degrees, float) 'max_lon' Maximum value of longitude (in degrees, float) (min_lon, max_lon) Defines the upper corner of the map :returns: Figure with the spatial distribution of the events. ''' # Magnitudes bins and minimum marrker size # min_mag = np.min(catalogue.data['magnitude']) # max_mag = np.max(catalogue.data['magnitude']) con_min = np.where(np.array([symb[0] for symb in DEFAULT_SYMBOLOGY]) < np.min(catalogue.data['magnitude']))[0] con_max = np.where(np.array([symb[1] for symb in DEFAULT_SYMBOLOGY]) > np.max(catalogue.data['magnitude']))[0] if len(con_min) == 1: min_loc = con_min[0] else: min_loc = con_min[-1] if len(con_max) == 1: max_loc = con_max[0] else: max_loc = con_max[1] # min_loc = np.where(np.array([symb[0] for symb in DEFAULT_SYMBOLOGY]) # < np.min(catalogue.data['magnitude']))[0][-1] # max_loc = np.where(np.array([symb[1] for symb in DEFAULT_SYMBOLOGY]) # > np.max(catalogue.data['magnitude']))[0][1] symbology = DEFAULT_SYMBOLOGY[min_loc:max_loc] for sym in symbology: # Create legend string if np.isinf(sym[0]): leg_str = 'M < %5.2f' % sym[1] elif np.isinf(sym[1]): leg_str = 'M >= %5.2f' % sym[0] else: leg_str = '%5.2f <= M < %5.2f' % (sym[0], sym[1]) idx = np.logical_and(catalogue.data['magnitude'] >= sym[0], catalogue.data['magnitude'] < sym[1]) mag_size = 1.2 * np.min([sym[0] + 0.5, sym[1] - 0.5]) x, y = self.m(catalogue.data['longitude'][idx], catalogue.data['latitude'][idx]) self.m.plot(x, y, sym[2], markersize=mag_size, label=leg_str) self.ax.legend(bbox_to_anchor=LEGEND_OFFSET) if self.title: self.ax.set_title(self.title, fontsize=16) if not overlay: plt.show()
def _plot_area_source(self, source, border='k-', border_width=1.0): """ Plots the area source :param source: Area source as instance of :class: mtkAreaSource :param str border: Line properties of border (see matplotlib documentation for detail) :param float border_width: Line width of border (see matplotlib documentation for detail) """ lons = np.hstack([source.geometry.lons, source.geometry.lons[0]]) lats = np.hstack([source.geometry.lats, source.geometry.lats[0]]) x, y = self.m(lons, lats) self.m.plot(x, y, border, linewidth=border_width) def _plot_point_source(self, source, point_marker='ks', point_size=2.0): """ Plots the area source :param source: Area source as instance of :class: mtkPointSource :param str point_marker: Marker style for point (see matplotlib documentation for detail) :param float marker size for point: Line width of border (see matplotlib documentation for detail) """ x, y = self.m(source.geometry.longitude, source.geometry.latitude) self.m.plot(x, y, point_marker, markersize=point_size) def _plot_simple_fault(self, source, border='k-', border_width=1.0): """ Plots the simple fault source as a composite of the fault trace and the surface projection of the fault. :param source: Fault source as instance of :class: mtkSimpleFaultSource :param str border: Line properties of border (see matplotlib documentation for detail) :param float border_width: Line width of border (see matplotlib documentation for detail) """ # Get the trace trace_lons = np.array([pnt.longitude for pnt in source.fault_trace.points]) trace_lats = np.array([pnt.latitude for pnt in source.fault_trace.points]) surface_projection = _fault_polygon_from_mesh(source) # Plot surface projection first x, y = self.m(surface_projection[:, 0], surface_projection[:, 1]) self.m.plot(x, y, border, linewidth=border_width) # Plot fault trace x, y = self.m(trace_lons, trace_lats) self.m.plot(x, y, border, linewidth=1.3 * border_width) def _plot_complex_fault(self, source, border='k-', border_width=1.0, min_depth=0., max_depth=None, alpha=1.0): """ Plots the simple fault source as a composite of the fault trace and the surface projection of the fault. :param source: Fault source as instance of :class: mtkSimpleFaultSource :param str border: Line properties of border (see matplotlib documentation for detail) :param float border_width: Line width of border (see matplotlib documentation for detail) """ if not max_depth: max_depth = 70. # Get outline top_edge = np.column_stack([source.geometry.mesh.lons[0], source.geometry.mesh.lats[0]]) bottom_edge = np.column_stack([source.geometry.mesh.lons[-1][::-1], source.geometry.mesh.lats[-1][::-1]]) outline = np.vstack([top_edge, bottom_edge, top_edge[0, :]]) lons = source.geometry.mesh.lons.flatten() lats = source.geometry.mesh.lats.flatten() depths = source.geometry.mesh.depths.flatten() norm = Normalize(vmin=min_depth, vmax=max_depth) x1, y1 = self.m(lons, lats) self.m.scatter(x1, y1, marker=".", s=20, c=depths, norm=norm, cmap="jet_r", alpha=alpha, linewidths=0.0, zorder=4) # Plot border x2, y2 = self.m(outline[:, 0], outline[:, 1]) self.m.plot(x2, y2, border, linewidth=border_width)
[docs] def add_source_model( self, model, area_border='k-', border_width=1.0, point_marker='ks', point_size=2.0, overlay=False, min_depth=0., max_depth=None, alpha=1.0): """ Adds a source model to the map :param model: Source model of mixed typologies as instance of :class: openquake.hmtk.sources.source_model.mtkSourceModel """ for source in model.sources: if isinstance(source, mtkAreaSource): self._plot_area_source(source, area_border, border_width) elif isinstance(source, mtkPointSource): self._plot_point_source(source, point_marker, point_size) elif isinstance(source, mtkComplexFaultSource): self._plot_complex_fault(source, area_border, border_width, min_depth, max_depth, alpha) elif isinstance(source, mtkSimpleFaultSource): self._plot_simple_fault(source, area_border, border_width) else: pass if not overlay: plt.show()
[docs] def add_colour_scaled_points(self, longitude, latitude, data, shape='s', alpha=1.0, size=20, norm=None, overlay=False): """ Overlays a set of points on a map with a fixed size but colour scaled according to the data :param np.ndarray longitude: Longitude :param np.ndarray latitude: Latitude :param np.ndarray data: Data for plotting :param str shape: Marker style :param float alpha: Sets the transparency of the marker (0 for transparent, 1 opaque) :param int size: Marker size :param norm: Normalisation as instance of :class: matplotlib.colors.Normalize """ if not norm: norm = Normalize(vmin=np.min(data), vmax=np.max(data)) x, y, = self.m(longitude, latitude) mappable = self.m.scatter(x, y, marker=shape, s=size, c=data, norm=norm, alpha=alpha, linewidths=0.0, zorder=4) self.m.colorbar(mappable=mappable, fig=self.fig, ax=self.ax) if not overlay: plt.show()
[docs] def add_size_scaled_points( self, longitude, latitude, data, shape='o', logplot=False, alpha=1.0, colour='b', smin=2.0, sscale=2.0, overlay=False): """ Plots a set of points with size scaled according to the data :param bool logplot: Choose to scale according to the logarithm (base 10) of the data :param float smin: Minimum scale size :param float sscale: Scaling factor """ if logplot: data = np.log10(data.copy()) x, y, = self.m(longitude, latitude) self.m.scatter(x, y, marker=shape, s=(smin + data ** sscale), c=colour, alpha=alpha, zorder=2) if not overlay: plt.show()
def _select_color_mag(self, mag): if (mag > 8.0): color = 'k' # color.append('k') elif (mag < 8.0) and (mag >= 7.0): color = 'b' # color.append('b') elif (mag < 7.0) and (mag >= 6.0): color = 'y' # color.append('y') elif (mag < 6.0) and (mag >= 5.0): color = 'g' # color.append('g') elif (mag < 5.0): color = 'm' # color.append('m') return color
[docs] def add_focal_mechanism(self, catalogue, magnitude=None, overlay=True): """ Plots a the the focal mechanism based on the beachball representation. The focal_menchanism flag must contain: strike, dip, rake. """ longitude = catalogue.data['longitude'] latitude = catalogue.data['latitude'] strike = catalogue.data['strike1'] dip = catalogue.data['dip1'] rake = catalogue.data['rake1'] if not magnitude or (magnitude < 0): magnitude = catalogue.data['magnitude'] for i, mag in enumerate(magnitude): color = self._select_color_mag(mag) focal_mechanism = [strike[i], dip[i], rake[i]] x, y = self.m(longitude[i], latitude[i]) self.m.plot(x, y) size = mag * 10000 beach = Beach(focal_mechanism, linewidth=1, xy=(x, y), width=size, zorder=size, facecolor=color) self.ax.add_collection(beach) if not overlay: plt.show() else: for i in range(0, catalogue.get_number_tensors()): x, y = self.m(longitude[i], latitude[i]) self.m.plot(x, y) focal_mechanism = [strike[i], dip[i], rake[i]] size = magnitude * 10000. beach = Beach(focal_mechanism, linewidth=1, xy=(x, y), width=size, zorder=size, facecolor='r') self.ax.add_collection(beach) if not overlay: plt.show()
[docs] def add_catalogue_cluster(self, catalogue, vcl, flagvector, cluster_id=None, overlay=True): """ Creates a plot of a catalogue showing where particular clusters exist """ # Create simple magnitude scaled point basemap self.add_size_scaled_points(catalogue.data['longitude'], catalogue.data['latitude'], catalogue.data['magnitude'], shape="o", alpha=0.8, colour=(0.5, 0.5, 0.5), smin=1.0, sscale=1.5, overlay=True) # If cluster ID is not specified just show mainshocks if cluster_id is None: idx = flagvector == 0 self.add_size_scaled_points(catalogue.data['longitude'][idx], catalogue.data['latitude'][idx], catalogue.data['magnitude'][idx], shape="o", colour="r", smin=1.0, sscale=1.5, overlay=overlay) return if not isinstance(cluster_id, collections.Iterable): cluster_id = [cluster_id] for iloc, clid in enumerate(cluster_id): if iloc == (len(cluster_id) - 1): # On last iteration set overlay to function overlay temp_overlay = overlay else: temp_overlay = True idx = vcl == clid self.add_size_scaled_points( catalogue.data["longitude"][idx], catalogue.data["latitude"][idx], catalogue.data["magnitude"][idx], shape="o", colour=DISSIMILAR_COLOURLIST[(iloc + 1) % NCOLS], smin=1.0, sscale=1.5, overlay=temp_overlay)