1# http://pyrocko.org - GPLv3
2#
3# The Pyrocko Developers, 21st Century
4# ---|P------/S----------~Lg----------
6import math
7import random
8import logging
10try:
11 from StringIO import StringIO as BytesIO
12except ImportError:
13 from io import BytesIO
15import numpy as num
17from pyrocko.guts import (Object, Float, Bool, Int, Tuple, String, List,
18 Unicode, Dict)
19from pyrocko.guts_array import Array
20from pyrocko.dataset import topo
21from pyrocko import orthodrome as od
22from . import gmtpy
23from . import nice_value
26points_in_region = od.points_in_region
28logger = logging.getLogger('pyrocko.plot.automap')
30earthradius = 6371000.0
31r2d = 180./math.pi
32d2r = 1./r2d
33km = 1000.
34d2m = d2r*earthradius
35m2d = 1./d2m
36cm = gmtpy.cm
39def darken(c, f=0.7):
40 return (c[0]*f, c[1]*f, c[2]*f)
43def corners(lon, lat, w, h):
44 ll_lat, ll_lon = od.ne_to_latlon(lat, lon, -0.5*h, -0.5*w)
45 ur_lat, ur_lon = od.ne_to_latlon(lat, lon, 0.5*h, 0.5*w)
46 return ll_lon, ll_lat, ur_lon, ur_lat
49def extent(lon, lat, w, h, n):
50 x = num.linspace(-0.5*w, 0.5*w, n)
51 y = num.linspace(-0.5*h, 0.5*h, n)
52 slats, slons = od.ne_to_latlon(lat, lon, y[0], x)
53 nlats, nlons = od.ne_to_latlon(lat, lon, y[-1], x)
54 south = slats.min()
55 north = nlats.max()
57 wlats, wlons = od.ne_to_latlon(lat, lon, y, x[0])
58 elats, elons = od.ne_to_latlon(lat, lon, y, x[-1])
59 elons = num.where(elons < wlons, elons + 360., elons)
61 if elons.max() - elons.min() > 180 or wlons.max() - wlons.min() > 180.:
62 west = -180.
63 east = 180.
64 else:
65 west = wlons.min()
66 east = elons.max()
68 return topo.positive_region((west, east, south, north))
71class NoTopo(Exception):
72 pass
75class OutOfBounds(Exception):
76 pass
79class FloatTile(Object):
80 xmin = Float.T()
81 ymin = Float.T()
82 dx = Float.T()
83 dy = Float.T()
84 data = Array.T(shape=(None, None), dtype=float, serialize_as='table')
86 def __init__(self, xmin, ymin, dx, dy, data):
87 Object.__init__(self, init_props=False)
88 self.xmin = float(xmin)
89 self.ymin = float(ymin)
90 self.dx = float(dx)
91 self.dy = float(dy)
92 self.data = data
93 self._set_maxes()
95 def _set_maxes(self):
96 self.ny, self.nx = self.data.shape
97 self.xmax = self.xmin + (self.nx-1) * self.dx
98 self.ymax = self.ymin + (self.ny-1) * self.dy
100 def x(self):
101 return self.xmin + num.arange(self.nx) * self.dx
103 def y(self):
104 return self.ymin + num.arange(self.ny) * self.dy
106 def get(self, x, y):
107 ix = int(round((x - self.xmin) / self.dx))
108 iy = int(round((y - self.ymin) / self.dy))
109 if 0 <= ix < self.nx and 0 <= iy < self.ny:
110 return self.data[iy, ix]
111 else:
112 raise OutOfBounds()
115class City(Object):
116 def __init__(self, name, lat, lon, population=None, asciiname=None):
117 name = str(name)
118 lat = float(lat)
119 lon = float(lon)
120 if asciiname is None:
121 asciiname = name.encode('ascii', errors='replace')
123 if population is None:
124 population = 0
125 else:
126 population = int(population)
128 Object.__init__(self, name=name, lat=lat, lon=lon,
129 population=population, asciiname=asciiname)
131 name = Unicode.T()
132 lat = Float.T()
133 lon = Float.T()
134 population = Int.T()
135 asciiname = String.T()
138class Map(Object):
139 lat = Float.T(optional=True)
140 lon = Float.T(optional=True)
141 radius = Float.T(optional=True)
142 width = Float.T(default=20.)
143 height = Float.T(default=14.)
144 margins = List.T(Float.T())
145 illuminate = Bool.T(default=True)
146 skip_feature_factor = Float.T(default=0.02)
147 show_grid = Bool.T(default=False)
148 show_topo = Bool.T(default=True)
149 show_scale = Bool.T(default=False)
150 show_topo_scale = Bool.T(default=False)
151 show_center_mark = Bool.T(default=False)
152 show_rivers = Bool.T(default=True)
153 show_plates = Bool.T(default=False)
154 show_plate_velocities = Bool.T(default=False)
155 show_plate_names = Bool.T(default=False)
156 show_boundaries = Bool.T(default=False)
157 illuminate_factor_land = Float.T(default=0.5)
158 illuminate_factor_ocean = Float.T(default=0.25)
159 color_wet = Tuple.T(3, Int.T(), default=(216, 242, 254))
160 color_dry = Tuple.T(3, Int.T(), default=(172, 208, 165))
161 color_boundaries = Tuple.T(3, Int.T(), default=(1, 1, 1))
162 topo_resolution_min = Float.T(
163 default=40.,
164 help='minimum resolution of topography [dpi]')
165 topo_resolution_max = Float.T(
166 default=200.,
167 help='maximum resolution of topography [dpi]')
168 replace_topo_color_only = FloatTile.T(
169 optional=True,
170 help='replace topo color while keeping topographic shading')
171 topo_cpt_wet = String.T(default='light_sea')
172 topo_cpt_dry = String.T(default='light_land')
173 axes_layout = String.T(optional=True)
174 custom_cities = List.T(City.T())
175 gmt_config = Dict.T(String.T(), String.T())
176 comment = String.T(optional=True)
177 approx_ticks = Int.T(default=4)
179 def __init__(self, gmtversion='newest', **kwargs):
180 Object.__init__(self, **kwargs)
181 self._gmt = None
182 self._scaler = None
183 self._widget = None
184 self._corners = None
185 self._wesn = None
186 self._minarea = None
187 self._coastline_resolution = None
188 self._rivers = None
189 self._dems = None
190 self._have_topo_land = None
191 self._have_topo_ocean = None
192 self._jxyr = None
193 self._prep_topo_have = None
194 self._labels = []
195 self._area_labels = []
196 self._gmtversion = gmtversion
198 def save(self, outpath, resolution=75., oversample=2., size=None,
199 width=None, height=None, psconvert=False, crop_eps_mode=False):
201 '''
202 Save the image.
204 Save the image to ``outpath``. The format is determined by the filename
205 extension. Formats are handled as follows: ``'.eps'`` and ``'.ps'``
206 produce EPS and PS, respectively, directly with GMT. If the file name
207 ends with ``'.pdf'``, GMT output is fed through ``gmtpy-epstopdf`` to
208 create a PDF file. For any other filename extension, output is first
209 converted to PDF with ``gmtpy-epstopdf``, then with ``pdftocairo`` to
210 PNG with a resolution oversampled by the factor ``oversample`` and
211 finally the PNG is downsampled and converted to the target format with
212 ``convert``. The resolution of rasterized target image can be
213 controlled either by ``resolution`` in DPI or by specifying ``width``
214 or ``height`` or ``size``, where the latter fits the image into a
215 square with given side length. To save transparency use
216 ``psconvert=True``. To crop the output image with a rectangle to the
217 nearest non-white element set ``crop_eps_mode=True``.
218 '''
220 gmt = self.gmt
221 self.draw_labels()
222 self.draw_axes()
223 if self.show_topo and self.show_topo_scale:
224 self._draw_topo_scale()
226 gmt.save(outpath, resolution=resolution, oversample=oversample,
227 crop_eps_mode=crop_eps_mode,
228 size=size, width=width, height=height, psconvert=psconvert)
230 @property
231 def scaler(self):
232 if self._scaler is None:
233 self._setup_geometry()
235 return self._scaler
237 @property
238 def wesn(self):
239 if self._wesn is None:
240 self._setup_geometry()
242 return self._wesn
244 @property
245 def widget(self):
246 if self._widget is None:
247 self._setup()
249 return self._widget
251 @property
252 def layout(self):
253 if self._layout is None:
254 self._setup()
256 return self._layout
258 @property
259 def jxyr(self):
260 if self._jxyr is None:
261 self._setup()
263 return self._jxyr
265 @property
266 def pxyr(self):
267 if self._pxyr is None:
268 self._setup()
270 return self._pxyr
272 @property
273 def gmt(self):
274 if self._gmt is None:
275 self._setup()
277 if self._have_topo_ocean is None:
278 self._draw_background()
280 return self._gmt
282 def _setup(self):
283 if not self._widget:
284 self._setup_geometry()
286 self._setup_lod()
287 self._setup_gmt()
289 def _setup_geometry(self):
290 wpage, hpage = self.width, self.height
291 ml, mr, mt, mb = self._expand_margins()
292 wpage -= ml + mr
293 hpage -= mt + mb
295 wreg = self.radius * 2.0
296 hreg = self.radius * 2.0
297 if wpage >= hpage:
298 wreg *= wpage/hpage
299 else:
300 hreg *= hpage/wpage
302 self._wreg = wreg
303 self._hreg = hreg
305 self._corners = corners(self.lon, self.lat, wreg, hreg)
306 west, east, south, north = extent(self.lon, self.lat, wreg, hreg, 10)
308 x, y, z = ((west, east), (south, north), (-6000., 4500.))
310 xax = gmtpy.Ax(mode='min-max', approx_ticks=self.approx_ticks)
311 yax = gmtpy.Ax(mode='min-max', approx_ticks=self.approx_ticks)
312 zax = gmtpy.Ax(mode='min-max', inc=1000., label='Height',
313 scaled_unit='km', scaled_unit_factor=0.001)
315 scaler = gmtpy.ScaleGuru(data_tuples=[(x, y, z)], axes=(xax, yax, zax))
317 par = scaler.get_params()
319 west = par['xmin']
320 east = par['xmax']
321 south = par['ymin']
322 north = par['ymax']
324 self._wesn = west, east, south, north
325 self._scaler = scaler
327 def _setup_lod(self):
328 w, e, s, n = self._wesn
329 if self.radius > 1500.*km:
330 coastline_resolution = 'i'
331 rivers = False
332 else:
333 coastline_resolution = 'f'
334 rivers = True
336 self._minarea = (self.skip_feature_factor * self.radius/km)**2
338 self._coastline_resolution = coastline_resolution
339 self._rivers = rivers
341 self._prep_topo_have = {}
342 self._dems = {}
344 cm2inch = gmtpy.cm/gmtpy.inch
346 dmin = 2.0 * self.radius * m2d / (self.topo_resolution_max *
347 (self.height * cm2inch))
348 dmax = 2.0 * self.radius * m2d / (self.topo_resolution_min *
349 (self.height * cm2inch))
351 for k in ['ocean', 'land']:
352 self._dems[k] = topo.select_dem_names(k, dmin, dmax, self._wesn)
353 if self._dems[k]:
354 logger.debug('using topography dataset %s for %s'
355 % (','.join(self._dems[k]), k))
357 def _expand_margins(self):
358 if len(self.margins) == 0 or len(self.margins) > 4:
359 ml = mr = mt = mb = 2.0
360 elif len(self.margins) == 1:
361 ml = mr = mt = mb = self.margins[0]
362 elif len(self.margins) == 2:
363 ml = mr = self.margins[0]
364 mt = mb = self.margins[1]
365 elif len(self.margins) == 4:
366 ml, mr, mt, mb = self.margins
368 return ml, mr, mt, mb
370 def _setup_gmt(self):
371 w, h = self.width, self.height
372 scaler = self._scaler
374 if gmtpy.is_gmt5(self._gmtversion):
375 gmtconf = dict(
376 MAP_TICK_PEN_PRIMARY='1.25p',
377 MAP_TICK_PEN_SECONDARY='1.25p',
378 MAP_TICK_LENGTH_PRIMARY='0.2c',
379 MAP_TICK_LENGTH_SECONDARY='0.6c',
380 FONT_ANNOT_PRIMARY='12p,1,black',
381 FONT_LABEL='12p,1,black',
382 PS_CHAR_ENCODING='ISOLatin1+',
383 MAP_FRAME_TYPE='fancy',
384 FORMAT_GEO_MAP='D',
385 PS_MEDIA='Custom_%ix%i' % (
386 w*gmtpy.cm,
387 h*gmtpy.cm),
388 PS_PAGE_ORIENTATION='portrait',
389 MAP_GRID_PEN_PRIMARY='thinnest,0/50/0',
390 MAP_ANNOT_OBLIQUE='6')
391 else:
392 gmtconf = dict(
393 TICK_PEN='1.25p',
394 TICK_LENGTH='0.2c',
395 ANNOT_FONT_PRIMARY='1',
396 ANNOT_FONT_SIZE_PRIMARY='12p',
397 LABEL_FONT='1',
398 LABEL_FONT_SIZE='12p',
399 CHAR_ENCODING='ISOLatin1+',
400 BASEMAP_TYPE='fancy',
401 PLOT_DEGREE_FORMAT='D',
402 PAPER_MEDIA='Custom_%ix%i' % (
403 w*gmtpy.cm,
404 h*gmtpy.cm),
405 GRID_PEN_PRIMARY='thinnest/0/50/0',
406 DOTS_PR_INCH='1200',
407 OBLIQUE_ANNOTATION='6')
409 gmtconf.update(
410 (k.upper(), v) for (k, v) in self.gmt_config.items())
412 gmt = gmtpy.GMT(config=gmtconf, version=self._gmtversion)
414 layout = gmt.default_layout()
416 layout.set_fixed_margins(*[x*cm for x in self._expand_margins()])
418 widget = layout.get_widget()
419 widget['P'] = widget['J']
420 widget['J'] = ('-JA%g/%g' % (self.lon, self.lat)) + '/%(width)gp'
421 scaler['R'] = '-R%g/%g/%g/%gr' % self._corners
423 # aspect = gmtpy.aspect_for_projection(
424 # gmt.installation['version'], *(widget.J() + scaler.R()))
426 aspect = self._map_aspect(jr=widget.J() + scaler.R())
427 widget.set_aspect(aspect)
429 self._gmt = gmt
430 self._layout = layout
431 self._widget = widget
432 self._jxyr = self._widget.JXY() + self._scaler.R()
433 self._pxyr = self._widget.PXY() + [
434 '-R%g/%g/%g/%g' % (0, widget.width(), 0, widget.height())]
435 self._have_drawn_axes = False
436 self._have_drawn_labels = False
438 def _draw_background(self):
439 self._have_topo_land = False
440 self._have_topo_ocean = False
441 if self.show_topo:
442 self._have_topo = self._draw_topo()
444 self._draw_basefeatures()
446 def _get_topo_tile(self, k):
447 t = None
448 demname = None
449 for dem in self._dems[k]:
450 t = topo.get(dem, self._wesn)
451 demname = dem
452 if t is not None:
453 break
455 if not t:
456 raise NoTopo()
458 return t, demname
460 def _prep_topo(self, k):
461 gmt = self._gmt
462 t, demname = self._get_topo_tile(k)
464 if demname not in self._prep_topo_have:
466 grdfile = gmt.tempfilename()
468 is_flat = num.all(t.data[0] == t.data)
470 gmtpy.savegrd(
471 t.x(), t.y(), t.data, filename=grdfile, naming='lonlat')
473 if self.illuminate and not is_flat:
474 if k == 'ocean':
475 factor = self.illuminate_factor_ocean
476 else:
477 factor = self.illuminate_factor_land
479 ilumfn = gmt.tempfilename()
480 gmt.grdgradient(
481 grdfile,
482 N='e%g' % factor,
483 A=-45,
484 G=ilumfn,
485 out_discard=True)
487 ilumargs = ['-I%s' % ilumfn]
488 else:
489 ilumargs = []
491 if self.replace_topo_color_only:
492 t2 = self.replace_topo_color_only
493 grdfile2 = gmt.tempfilename()
495 gmtpy.savegrd(
496 t2.x(), t2.y(), t2.data, filename=grdfile2,
497 naming='lonlat')
499 if gmt.is_gmt5():
500 gmt.grdsample(
501 grdfile2,
502 G=grdfile,
503 n='l',
504 I='%g/%g' % (t.dx, t.dy), # noqa
505 R=grdfile,
506 out_discard=True)
507 else:
508 gmt.grdsample(
509 grdfile2,
510 G=grdfile,
511 Q='l',
512 I='%g/%g' % (t.dx, t.dy), # noqa
513 R=grdfile,
514 out_discard=True)
516 gmt.grdmath(
517 grdfile, '0.0', 'AND', '=', grdfile2,
518 out_discard=True)
520 grdfile = grdfile2
522 self._prep_topo_have[demname] = grdfile, ilumargs
524 return self._prep_topo_have[demname]
526 def _draw_topo(self):
527 widget = self._widget
528 scaler = self._scaler
529 gmt = self._gmt
530 cres = self._coastline_resolution
531 minarea = self._minarea
533 JXY = widget.JXY()
534 R = scaler.R()
536 try:
537 grdfile, ilumargs = self._prep_topo('ocean')
538 gmt.pscoast(D=cres, S='c', A=minarea, *(JXY+R))
539 gmt.grdimage(grdfile, C=topo.cpt(self.topo_cpt_wet),
540 *(ilumargs+JXY+R))
541 gmt.pscoast(Q=True, *(JXY+R))
542 self._have_topo_ocean = True
543 except NoTopo:
544 self._have_topo_ocean = False
546 try:
547 grdfile, ilumargs = self._prep_topo('land')
548 gmt.pscoast(D=cres, G='c', A=minarea, *(JXY+R))
549 gmt.grdimage(grdfile, C=topo.cpt(self.topo_cpt_dry),
550 *(ilumargs+JXY+R))
551 gmt.pscoast(Q=True, *(JXY+R))
552 self._have_topo_land = True
553 except NoTopo:
554 self._have_topo_land = False
556 def _draw_topo_scale(self, label='Elevation [km]'):
557 dry = read_cpt(topo.cpt(self.topo_cpt_dry))
558 wet = read_cpt(topo.cpt(self.topo_cpt_wet))
559 combi = cpt_merge_wet_dry(wet, dry)
560 for level in combi.levels:
561 level.vmin /= km
562 level.vmax /= km
564 topo_cpt = self.gmt.tempfilename() + '.cpt'
565 write_cpt(combi, topo_cpt)
567 (w, h), (xo, yo) = self.widget.get_size()
568 self.gmt.psscale(
569 D='%gp/%gp/%gp/%gph' % (xo + 0.5*w, yo - 2.0*gmtpy.cm, w,
570 0.5*gmtpy.cm),
571 C=topo_cpt,
572 B='1:%s:' % label)
574 def _draw_basefeatures(self):
575 gmt = self._gmt
576 cres = self._coastline_resolution
577 rivers = self._rivers
578 minarea = self._minarea
580 color_wet = self.color_wet
581 color_dry = self.color_dry
583 if self.show_rivers and rivers:
584 rivers = ['-Ir/0.25p,%s' % gmtpy.color(self.color_wet)]
585 else:
586 rivers = []
588 fill = {}
589 if not self._have_topo_land:
590 fill['G'] = color_dry
592 if not self._have_topo_ocean:
593 fill['S'] = color_wet
595 if self.show_boundaries:
596 fill['N'] = '1/1p,%s,%s' % (
597 gmtpy.color(self.color_boundaries), 'solid')
599 gmt.pscoast(
600 D=cres,
601 W='thinnest,%s' % gmtpy.color(darken(gmtpy.color_tup(color_dry))),
602 A=minarea,
603 *(rivers+self._jxyr), **fill)
605 if self.show_plates:
606 self.draw_plates()
608 def _draw_axes(self):
609 gmt = self._gmt
610 scaler = self._scaler
611 widget = self._widget
613 if self.axes_layout is None:
614 if self.lat > 0.0:
615 axes_layout = 'WSen'
616 else:
617 axes_layout = 'WseN'
618 else:
619 axes_layout = self.axes_layout
621 scale_km = nice_value(self.radius/5.) / 1000.
623 if self.show_center_mark:
624 gmt.psxy(
625 in_rows=[[self.lon, self.lat]],
626 S='c20p', W='2p,black',
627 *self._jxyr)
629 if self.show_grid:
630 btmpl = ('%(xinc)gg%(xinc)g:%(xlabel)s:/'
631 '%(yinc)gg%(yinc)g:%(ylabel)s:')
632 else:
633 btmpl = '%(xinc)g:%(xlabel)s:/%(yinc)g:%(ylabel)s:'
635 if self.show_scale:
636 scale = 'x%gp/%gp/%g/%g/%gk' % (
637 6./7*widget.width(),
638 widget.height()/7.,
639 self.lon,
640 self.lat,
641 scale_km)
642 else:
643 scale = False
645 gmt.psbasemap(
646 B=(btmpl % scaler.get_params())+axes_layout,
647 L=scale,
648 *self._jxyr)
650 if self.comment:
651 font_size = self.gmt.label_font_size()
653 _, east, south, _ = self._wesn
654 if gmt.is_gmt5():
655 row = [
656 1, 0,
657 '%gp,%s,%s' % (font_size, 0, 'black'), 'BR',
658 self.comment]
660 farg = ['-F+f+j']
661 else:
662 row = [1, 0, font_size, 0, 0, 'BR', self.comment]
663 farg = []
665 gmt.pstext(
666 in_rows=[row],
667 N=True,
668 R=(0, 1, 0, 1),
669 D='%gp/%gp' % (-font_size*0.2, font_size*0.3),
670 *(widget.PXY() + farg))
672 def draw_axes(self):
673 if not self._have_drawn_axes:
674 self._draw_axes()
675 self._have_drawn_axes = True
677 def _have_coastlines(self):
678 gmt = self._gmt
679 cres = self._coastline_resolution
680 minarea = self._minarea
682 checkfile = gmt.tempfilename()
684 gmt.pscoast(
685 M=True,
686 D=cres,
687 W='thinnest,black',
688 A=minarea,
689 out_filename=checkfile,
690 *self._jxyr)
692 points = []
693 with open(checkfile, 'r') as f:
694 for line in f:
695 ls = line.strip()
696 if ls.startswith('#') or ls.startswith('>') or ls == '':
697 continue
698 plon, plat = [float(x) for x in ls.split()]
699 points.append((plat, plon))
701 points = num.array(points, dtype=float)
702 return num.any(points_in_region(points, self._wesn))
704 def have_coastlines(self):
705 self.gmt
706 return self._have_coastlines()
708 def project(self, lats, lons, jr=None):
709 onepoint = False
710 if isinstance(lats, float) and isinstance(lons, float):
711 lats = [lats]
712 lons = [lons]
713 onepoint = True
715 if jr is not None:
716 j, r = jr
717 gmt = gmtpy.GMT(version=self._gmtversion)
718 else:
719 j, _, _, r = self.jxyr
720 gmt = self.gmt
722 f = BytesIO()
723 gmt.mapproject(j, r, in_columns=(lons, lats), out_stream=f, D='p')
724 f.seek(0)
725 data = num.loadtxt(f, ndmin=2)
726 xs, ys = data.T
727 if onepoint:
728 xs = xs[0]
729 ys = ys[0]
730 return xs, ys
732 def _map_box(self, jr=None):
733 ll_lon, ll_lat, ur_lon, ur_lat = self._corners
735 xs_corner, ys_corner = self.project(
736 (ll_lat, ur_lat), (ll_lon, ur_lon), jr=jr)
738 w = xs_corner[1] - xs_corner[0]
739 h = ys_corner[1] - ys_corner[0]
741 return w, h
743 def _map_aspect(self, jr=None):
744 w, h = self._map_box(jr=jr)
745 return h/w
747 def _draw_labels(self):
748 points_taken = []
749 regions_taken = []
751 def no_points_in_rect(xs, ys, xmin, ymin, xmax, ymax):
752 xx = not num.any(la(la(xmin < xs, xs < xmax),
753 la(ymin < ys, ys < ymax)))
754 return xx
756 def roverlaps(a, b):
757 return (a[0] < b[2] and b[0] < a[2] and
758 a[1] < b[3] and b[1] < a[3])
760 w, h = self._map_box()
762 label_font_size = self.gmt.label_font_size()
764 if self._labels:
766 n = len(self._labels)
768 lons, lats, texts, sx, sy, colors, fonts, font_sizes, \
769 angles, styles = list(zip(*self._labels))
771 font_sizes = [
772 (font_size or label_font_size) for font_size in font_sizes]
774 sx = num.array(sx, dtype=float)
775 sy = num.array(sy, dtype=float)
777 xs, ys = self.project(lats, lons)
779 points_taken.append((xs, ys))
781 dxs = num.zeros(n)
782 dys = num.zeros(n)
784 for i in range(n):
785 dx, dy = gmtpy.text_box(
786 texts[i],
787 font=fonts[i],
788 font_size=font_sizes[i],
789 **styles[i])
791 dxs[i] = dx
792 dys[i] = dy
794 la = num.logical_and
795 anchors_ok = (
796 la(xs + sx + dxs < w, ys + sy + dys < h),
797 la(xs - sx - dxs > 0., ys - sy - dys > 0.),
798 la(xs + sx + dxs < w, ys - sy - dys > 0.),
799 la(xs - sx - dxs > 0., ys + sy + dys < h),
800 )
802 arects = [
803 (xs, ys, xs + sx + dxs, ys + sy + dys),
804 (xs - sx - dxs, ys - sy - dys, xs, ys),
805 (xs, ys - sy - dys, xs + sx + dxs, ys),
806 (xs - sx - dxs, ys, xs, ys + sy + dys)]
808 for i in range(n):
809 for ianch in range(4):
810 anchors_ok[ianch][i] &= no_points_in_rect(
811 xs, ys, *[xxx[i] for xxx in arects[ianch]])
813 anchor_choices = []
814 anchor_take = []
815 for i in range(n):
816 choices = [ianch for ianch in range(4)
817 if anchors_ok[ianch][i]]
818 anchor_choices.append(choices)
819 if choices:
820 anchor_take.append(choices[0])
821 else:
822 anchor_take.append(None)
824 def cost(anchor_take):
825 noverlaps = 0
826 for i in range(n):
827 for j in range(n):
828 if i != j:
829 i_take = anchor_take[i]
830 j_take = anchor_take[j]
831 if i_take is None or j_take is None:
832 continue
833 r_i = [xxx[i] for xxx in arects[i_take]]
834 r_j = [xxx[j] for xxx in arects[j_take]]
835 if roverlaps(r_i, r_j):
836 noverlaps += 1
838 return noverlaps
840 cur_cost = cost(anchor_take)
841 imax = 30
842 while cur_cost != 0 and imax > 0:
843 for i in range(n):
844 for t in anchor_choices[i]:
845 anchor_take_new = list(anchor_take)
846 anchor_take_new[i] = t
847 new_cost = cost(anchor_take_new)
848 if new_cost < cur_cost:
849 anchor_take = anchor_take_new
850 cur_cost = new_cost
852 imax -= 1
854 while cur_cost != 0:
855 for i in range(n):
856 anchor_take_new = list(anchor_take)
857 anchor_take_new[i] = None
858 new_cost = cost(anchor_take_new)
859 if new_cost < cur_cost:
860 anchor_take = anchor_take_new
861 cur_cost = new_cost
862 break
864 anchor_strs = ['BL', 'TR', 'TL', 'BR']
866 for i in range(n):
867 ianchor = anchor_take[i]
868 color = colors[i]
869 if color is None:
870 color = 'black'
872 if ianchor is not None:
873 regions_taken.append([xxx[i] for xxx in arects[ianchor]])
875 anchor = anchor_strs[ianchor]
877 yoff = [-sy[i], sy[i]][anchor[0] == 'B']
878 xoff = [-sx[i], sx[i]][anchor[1] == 'L']
879 if self.gmt.is_gmt5():
880 row = (
881 lons[i], lats[i],
882 '%i,%s,%s' % (font_sizes[i], fonts[i], color),
883 anchor,
884 texts[i])
886 farg = ['-F+f+j+a%g' % angles[i]]
887 else:
888 row = (
889 lons[i], lats[i],
890 font_sizes[i], angles[i], fonts[i], anchor,
891 texts[i])
892 farg = ['-G%s' % color]
894 self.gmt.pstext(
895 in_rows=[row],
896 D='%gp/%gp' % (xoff, yoff),
897 *(self.jxyr + farg),
898 **styles[i])
900 if self._area_labels:
902 for lons, lats, text, color, font, font_size, style in \
903 self._area_labels:
905 if font_size is None:
906 font_size = label_font_size
908 if color is None:
909 color = 'black'
911 if self.gmt.is_gmt5():
912 farg = ['-F+f+j']
913 else:
914 farg = ['-G%s' % color]
916 xs, ys = self.project(lats, lons)
917 dx, dy = gmtpy.text_box(
918 text, font=font, font_size=font_size, **style)
920 rects = [xs-0.5*dx, ys-0.5*dy, xs+0.5*dx, ys+0.5*dy]
922 locs_ok = num.ones(xs.size, dtype=bool)
924 for iloc in range(xs.size):
925 rcandi = [xxx[iloc] for xxx in rects]
927 locs_ok[iloc] = True
928 locs_ok[iloc] &= (
929 0 < rcandi[0] and rcandi[2] < w
930 and 0 < rcandi[1] and rcandi[3] < h)
932 overlap = False
933 for r in regions_taken:
934 if roverlaps(r, rcandi):
935 overlap = True
936 break
938 locs_ok[iloc] &= not overlap
940 for xs_taken, ys_taken in points_taken:
941 locs_ok[iloc] &= no_points_in_rect(
942 xs_taken, ys_taken, *rcandi)
944 if not locs_ok[iloc]:
945 break
947 rows = []
948 for iloc, (lon, lat) in enumerate(zip(lons, lats)):
949 if not locs_ok[iloc]:
950 continue
952 if self.gmt.is_gmt5():
953 row = (
954 lon, lat,
955 '%i,%s,%s' % (font_size, font, color),
956 'MC',
957 text)
959 else:
960 row = (
961 lon, lat,
962 font_size, 0, font, 'MC',
963 text)
965 rows.append(row)
967 regions_taken.append([xxx[iloc] for xxx in rects])
968 break
970 self.gmt.pstext(
971 in_rows=rows,
972 *(self.jxyr + farg),
973 **style)
975 def draw_labels(self):
976 self.gmt
977 if not self._have_drawn_labels:
978 self._draw_labels()
979 self._have_drawn_labels = True
981 def add_label(
982 self, lat, lon, text,
983 offset_x=5., offset_y=5.,
984 color=None,
985 font='1',
986 font_size=None,
987 angle=0,
988 style={}):
990 if 'G' in style:
991 style = style.copy()
992 color = style.pop('G')
994 self._labels.append(
995 (lon, lat, text, offset_x, offset_y, color, font, font_size,
996 angle, style))
998 def add_area_label(
999 self, lat, lon, text,
1000 color=None,
1001 font='3',
1002 font_size=None,
1003 style={}):
1005 self._area_labels.append(
1006 (lon, lat, text, color, font, font_size, style))
1008 def cities_in_region(self):
1009 from pyrocko.dataset import geonames
1010 cities = geonames.get_cities_region(region=self.wesn, minpop=0)
1011 cities.extend(self.custom_cities)
1012 cities.sort(key=lambda x: x.population)
1013 return cities
1015 def draw_cities(self,
1016 exact=None,
1017 include=[],
1018 exclude=[],
1019 nmax_soft=10,
1020 psxy_style=dict(S='s5p', G='black')):
1022 cities = self.cities_in_region()
1024 if exact is not None:
1025 cities = [c for c in cities if c.name in exact]
1026 minpop = None
1027 else:
1028 cities = [c for c in cities if c.name not in exclude]
1029 minpop = 10**3
1030 for minpop_new in [1e3, 3e3, 1e4, 3e4, 1e5, 3e5, 1e6, 3e6, 1e7]:
1031 cities_new = [
1032 c for c in cities
1033 if c.population > minpop_new or c.name in include]
1035 if len(cities_new) == 0 or (
1036 len(cities_new) < 3 and len(cities) < nmax_soft*2):
1037 break
1039 cities = cities_new
1040 minpop = minpop_new
1041 if len(cities) <= nmax_soft:
1042 break
1044 if cities:
1045 lats = [c.lat for c in cities]
1046 lons = [c.lon for c in cities]
1048 self.gmt.psxy(
1049 in_columns=(lons, lats),
1050 *self.jxyr, **psxy_style)
1052 for c in cities:
1053 try:
1054 text = c.name.encode('iso-8859-1').decode('iso-8859-1')
1055 except UnicodeEncodeError:
1056 text = c.asciiname
1058 self.add_label(c.lat, c.lon, text)
1060 self._cities_minpop = minpop
1062 def add_stations(self, stations, psxy_style=dict()):
1064 default_psxy_style = {
1065 'S': 't8p',
1066 'G': 'black'
1067 }
1068 default_psxy_style.update(psxy_style)
1070 lats, lons = zip(*[s.effective_latlon for s in stations])
1072 self.gmt.psxy(
1073 in_columns=(lons, lats),
1074 *self.jxyr, **default_psxy_style)
1076 for station in stations:
1077 self.add_label(
1078 station.effective_lat,
1079 station.effective_lon,
1080 '.'.join(x for x in (station.network, station.station) if x))
1082 def add_kite_scene(self, scene):
1083 tile = FloatTile(
1084 scene.frame.llLon,
1085 scene.frame.llLat,
1086 scene.frame.dLon,
1087 scene.frame.dLat,
1088 scene.displacement)
1090 return tile
1092 def add_gnss_campaign(self, campaign, psxy_style=None, offset_scale=None,
1093 labels=True, vertical=False, fontsize=10):
1095 stations = campaign.stations
1097 if offset_scale is None:
1098 offset_scale = num.zeros(campaign.nstations)
1099 for ista, sta in enumerate(stations):
1100 for comp in sta.components.values():
1101 offset_scale[ista] += comp.shift
1102 offset_scale = num.sqrt(offset_scale**2).max()
1104 size = math.sqrt(self.height**2 + self.width**2)
1105 scale = (size/10.) / offset_scale
1106 logger.debug('GNSS: Using offset scale %f, map scale %f',
1107 offset_scale, scale)
1109 lats, lons = zip(*[s.effective_latlon for s in stations])
1111 if self.gmt.is_gmt6():
1112 sign_factor = 1.
1113 arrow_head_placement = 'e'
1114 else:
1115 sign_factor = -1.
1116 arrow_head_placement = 'b'
1118 if vertical:
1119 rows = [[lons[ista], lats[ista],
1120 0., sign_factor * s.up.shift,
1121 (s.east.sigma + s.north.sigma) if s.east.sigma else 0.,
1122 s.up.sigma, 0.,
1123 s.code if labels else None]
1124 for ista, s in enumerate(stations)
1125 if s.up is not None]
1127 else:
1128 rows = [[lons[ista], lats[ista],
1129 sign_factor * s.east.shift, sign_factor * s.north.shift,
1130 s.east.sigma, s.north.sigma, s.correlation_ne,
1131 s.code if labels else None]
1132 for ista, s in enumerate(stations)
1133 if s.east is not None or s.north is not None]
1135 default_psxy_style = {
1136 'h': 0,
1137 'W': '2p,black',
1138 'A': '+p2p,black+{}+a40'.format(arrow_head_placement),
1139 'G': 'black',
1140 'L': True,
1141 'S': 'e%dc/0.95/%d' % (scale, fontsize),
1142 }
1144 if not labels:
1145 for row in rows:
1146 row.pop(-1)
1148 if psxy_style is not None:
1149 default_psxy_style.update(psxy_style)
1151 self.gmt.psvelo(
1152 in_rows=rows,
1153 *self.jxyr,
1154 **default_psxy_style)
1156 def draw_plates(self):
1157 from pyrocko.dataset import tectonics
1159 neast = 20
1160 nnorth = max(1, int(round(num.round(self._hreg/self._wreg * neast))))
1161 norths = num.linspace(-self._hreg*0.5, self._hreg*0.5, nnorth)
1162 easts = num.linspace(-self._wreg*0.5, self._wreg*0.5, neast)
1163 norths2 = num.repeat(norths, neast)
1164 easts2 = num.tile(easts, nnorth)
1165 lats, lons = od.ne_to_latlon(
1166 self.lat, self.lon, norths2, easts2)
1168 bird = tectonics.PeterBird2003()
1169 plates = bird.get_plates()
1171 color_plates = gmtpy.color('aluminium5')
1172 color_velocities = gmtpy.color('skyblue1')
1173 color_velocities_lab = gmtpy.color(darken(gmtpy.color_tup('skyblue1')))
1175 points = num.vstack((lats, lons)).T
1176 used = []
1177 for plate in plates:
1178 mask = plate.contains_points(points)
1179 if num.any(mask):
1180 used.append((plate, mask))
1182 if len(used) > 1:
1184 candi_fixed = {}
1186 label_data = []
1187 for plate, mask in used:
1189 mean_north = num.mean(norths2[mask])
1190 mean_east = num.mean(easts2[mask])
1191 iorder = num.argsort(num.sqrt(
1192 (norths2[mask] - mean_north)**2 +
1193 (easts2[mask] - mean_east)**2))
1195 lat_candis = lats[mask][iorder]
1196 lon_candis = lons[mask][iorder]
1198 candi_fixed[plate.name] = lat_candis.size
1200 label_data.append((
1201 lat_candis, lon_candis, plate, color_plates))
1203 boundaries = bird.get_boundaries()
1205 size = 1.
1207 psxy_kwargs = []
1209 for boundary in boundaries:
1210 if num.any(points_in_region(boundary.points, self._wesn)):
1211 for typ, part in boundary.split_types(
1212 [['SUB'],
1213 ['OSR', 'CRB'],
1214 ['OTF', 'CTF', 'OCB', 'CCB']]):
1216 lats, lons = part.T
1218 kwargs = {}
1220 kwargs['in_columns'] = (lons, lats)
1221 kwargs['W'] = '%gp,%s' % (size, color_plates)
1223 if typ[0] == 'SUB':
1224 if boundary.kind == '\\':
1225 kwargs['S'] = 'f%g/%gp+t+r' % (
1226 0.45*size, 3.*size)
1227 elif boundary.kind == '/':
1228 kwargs['S'] = 'f%g/%gp+t+l' % (
1229 0.45*size, 3.*size)
1231 kwargs['G'] = color_plates
1233 elif typ[0] in ['OSR', 'CRB']:
1234 kwargs_bg = {}
1235 kwargs_bg['in_columns'] = (lons, lats)
1236 kwargs_bg['W'] = '%gp,%s' % (
1237 size * 3, color_plates)
1238 psxy_kwargs.append(kwargs_bg)
1240 kwargs['W'] = '%gp,%s' % (size * 2, 'white')
1242 psxy_kwargs.append(kwargs)
1244 if boundary.kind == '\\':
1245 if boundary.plate_name2 in candi_fixed:
1246 candi_fixed[boundary.plate_name2] += \
1247 neast*nnorth
1249 elif boundary.kind == '/':
1250 if boundary.plate_name1 in candi_fixed:
1251 candi_fixed[boundary.plate_name1] += \
1252 neast*nnorth
1254 candi_fixed = [name for name in sorted(
1255 list(candi_fixed.keys()), key=lambda name: -candi_fixed[name])]
1257 candi_fixed.append(None)
1259 gsrm = tectonics.GSRM1()
1261 for name in candi_fixed:
1262 if name not in gsrm.plate_names() \
1263 and name not in gsrm.plate_alt_names():
1265 continue
1267 lats, lons, vnorth, veast, vnorth_err, veast_err, corr = \
1268 gsrm.get_velocities(name, region=self._wesn)
1270 fixed_plate_name = name
1272 if self.show_plate_velocities:
1273 self.gmt.psvelo(
1274 in_columns=(
1275 lons, lats, veast, vnorth, veast_err, vnorth_err,
1276 corr),
1277 W='0.25p,%s' % color_velocities,
1278 A='9p+e+g%s' % color_velocities,
1279 S='e0.2p/0.95/10',
1280 *self.jxyr)
1282 for _ in range(len(lons) // 50 + 1):
1283 ii = random.randint(0, len(lons)-1)
1284 v = math.sqrt(vnorth[ii]**2 + veast[ii]**2)
1285 self.add_label(
1286 lats[ii], lons[ii], '%.0f' % v,
1287 font_size=0.7*self.gmt.label_font_size(),
1288 style=dict(
1289 G=color_velocities_lab))
1291 break
1293 if self.show_plate_names:
1294 for (lat_candis, lon_candis, plate, color) in label_data:
1295 full_name = bird.full_name(plate.name)
1296 if plate.name == fixed_plate_name:
1297 full_name = '@_' + full_name + '@_'
1299 self.add_area_label(
1300 lat_candis, lon_candis,
1301 full_name,
1302 color=color,
1303 font='3')
1305 for kwargs in psxy_kwargs:
1306 self.gmt.psxy(*self.jxyr, **kwargs)
1309def rand(mi, ma):
1310 mi = float(mi)
1311 ma = float(ma)
1312 return random.random() * (ma-mi) + mi
1315def split_region(region):
1316 west, east, south, north = topo.positive_region(region)
1317 if east > 180:
1318 return [(west, 180., south, north),
1319 (-180., east-360., south, north)]
1320 else:
1321 return [region]
1324class CPTLevel(Object):
1325 vmin = Float.T()
1326 vmax = Float.T()
1327 color_min = Tuple.T(3, Float.T())
1328 color_max = Tuple.T(3, Float.T())
1331class CPT(Object):
1332 color_below = Tuple.T(3, Float.T(), optional=True)
1333 color_above = Tuple.T(3, Float.T(), optional=True)
1334 color_nan = Tuple.T(3, Float.T(), optional=True)
1335 levels = List.T(CPTLevel.T())
1337 def scale(self, vmin, vmax):
1338 vmin_old, vmax_old = self.levels[0].vmin, self.levels[-1].vmax
1339 for level in self.levels:
1340 level.vmin = (level.vmin - vmin_old) / (vmax_old - vmin_old) * \
1341 (vmax - vmin) + vmin
1342 level.vmax = (level.vmax - vmin_old) / (vmax_old - vmin_old) * \
1343 (vmax - vmin) + vmin
1345 def discretize(self, nlevels):
1346 colors = []
1347 vals = []
1348 for level in self.levels:
1349 vals.append(level.vmin)
1350 vals.append(level.vmax)
1351 colors.append(level.color_min)
1352 colors.append(level.color_max)
1354 r, g, b = num.array(colors, dtype=float).T
1355 vals = num.array(vals, dtype=float)
1357 vmin, vmax = self.levels[0].vmin, self.levels[-1].vmax
1358 x = num.linspace(vmin, vmax, nlevels+1)
1359 rd = num.interp(x, vals, r)
1360 gd = num.interp(x, vals, g)
1361 bd = num.interp(x, vals, b)
1363 levels = []
1364 for ilevel in range(nlevels):
1365 color = (
1366 float(0.5*(rd[ilevel]+rd[ilevel+1])),
1367 float(0.5*(gd[ilevel]+gd[ilevel+1])),
1368 float(0.5*(bd[ilevel]+bd[ilevel+1])))
1370 levels.append(CPTLevel(
1371 vmin=x[ilevel],
1372 vmax=x[ilevel+1],
1373 color_min=color,
1374 color_max=color))
1376 cpt = CPT(
1377 color_below=self.color_below,
1378 color_above=self.color_above,
1379 color_nan=self.color_nan,
1380 levels=levels)
1382 return cpt
1385class CPTParseError(Exception):
1386 pass
1389def read_cpt(filename):
1390 with open(filename) as f:
1391 color_below = None
1392 color_above = None
1393 color_nan = None
1394 levels = []
1395 try:
1396 for line in f:
1397 line = line.strip()
1398 toks = line.split()
1400 if line.startswith('#'):
1401 continue
1403 elif line.startswith('B'):
1404 color_below = tuple(map(float, toks[1:4]))
1406 elif line.startswith('F'):
1407 color_above = tuple(map(float, toks[1:4]))
1409 elif line.startswith('N'):
1410 color_nan = tuple(map(float, toks[1:4]))
1412 else:
1413 values = list(map(float, line.split()))
1414 vmin = values[0]
1415 color_min = tuple(values[1:4])
1416 vmax = values[4]
1417 color_max = tuple(values[5:8])
1418 levels.append(CPTLevel(
1419 vmin=vmin,
1420 vmax=vmax,
1421 color_min=color_min,
1422 color_max=color_max))
1424 except Exception:
1425 raise CPTParseError()
1427 return CPT(
1428 color_below=color_below,
1429 color_above=color_above,
1430 color_nan=color_nan,
1431 levels=levels)
1434def color_to_int(color):
1435 return tuple(max(0, min(255, int(round(x)))) for x in color)
1438def write_cpt(cpt, filename):
1439 with open(filename, 'w') as f:
1440 for level in cpt.levels:
1441 f.write(
1442 '%e %i %i %i %e %i %i %i\n' %
1443 ((level.vmin, ) + color_to_int(level.color_min) +
1444 (level.vmax, ) + color_to_int(level.color_max)))
1446 if cpt.color_below:
1447 f.write('B %i %i %i\n' % color_to_int(cpt.color_below))
1449 if cpt.color_above:
1450 f.write('F %i %i %i\n' % color_to_int(cpt.color_above))
1452 if cpt.color_nan:
1453 f.write('N %i %i %i\n' % color_to_int(cpt.color_nan))
1456def cpt_merge_wet_dry(wet, dry):
1457 levels = []
1458 for level in wet.levels:
1459 if level.vmin < 0.:
1460 if level.vmax > 0.:
1461 level.vmax = 0.
1463 levels.append(level)
1465 for level in dry.levels:
1466 if level.vmax > 0.:
1467 if level.vmin < 0.:
1468 level.vmin = 0.
1470 levels.append(level)
1472 combi = CPT(
1473 color_below=wet.color_below,
1474 color_above=dry.color_above,
1475 color_nan=dry.color_nan,
1476 levels=levels)
1478 return combi
1481if __name__ == '__main__':
1482 from pyrocko import util
1483 util.setup_logging('pyrocko.automap', 'info')
1485 import sys
1486 if len(sys.argv) == 2:
1488 n = int(sys.argv[1])
1490 for i in range(n):
1491 m = Map(
1492 lat=rand(-60., 60.),
1493 lon=rand(-180., 180.),
1494 radius=math.exp(rand(math.log(500*km), math.log(3000*km))),
1495 width=30., height=30.,
1496 show_grid=True,
1497 show_topo=True,
1498 color_dry=(238, 236, 230),
1499 topo_cpt_wet='light_sea_uniform',
1500 topo_cpt_dry='light_land_uniform',
1501 illuminate=True,
1502 illuminate_factor_ocean=0.15,
1503 show_rivers=False,
1504 show_plates=True)
1506 m.draw_cities()
1507 print(m)
1508 m.save('map_%02i.pdf' % i)