def draw_stations_map(pstreams, event, event_dir):
# draw map of stations and cities and stuff
lats = np.array(
[stream[0].stats.coordinates['latitude'] for stream in pstreams])
lons = np.array(
[stream[0].stats.coordinates['longitude'] for stream in pstreams])
map_width = event.magnitude
cy = event.latitude
cx = event.longitude
xmin = lons.min()
xmax = lons.max()
ymin = lats.min()
ymax = lats.max()
if xmax - xmin < map_width:
xmin = cx - map_width / 2
xmax = cx + map_width / 2
if ymax - ymin < map_width:
ymin = cy - map_width / 2
ymax = cy + map_width / 2
bounds = (xmin, xmax, ymin, ymax)
figsize = (10, 10)
cities = Cities.fromDefault()
mmap = MercatorMap(bounds, figsize, cities)
mmap.drawCities(draw_dots=True)
ax = mmap.axes
draw_scale(ax)
ax.plot(cx, cy, 'r*', markersize=16, transform=mmap.geoproj, zorder=8)
status = [
FAILED_COLOR
if np.any([trace.hasParameter("failure")
for trace in stream]) else PASSED_COLOR
for stream in pstreams
]
ax.scatter(lons,
lats,
c=status,
marker='^',
edgecolors='k',
transform=mmap.geoproj,
zorder=100,
s=48)
scale = '50m'
land = cfeature.NaturalEarthFeature(category='physical',
name='land',
scale=scale,
facecolor=LAND_COLOR)
ocean = cfeature.NaturalEarthFeature(category='physical',
name='ocean',
scale=scale,
facecolor=OCEAN_COLOR)
ax.add_feature(land)
ax.add_feature(ocean)
ax.coastlines(resolution=scale, zorder=10, linewidth=1)
mapfile = os.path.join(event_dir, 'stations_map.png')
plt.savefig(mapfile)
return mapfile
def test_draw_scale():
fig = plt.figure(figsize=(4, 4))
proj = ccrs.AlbersEqualArea(central_latitude=30,
central_longitude=-82.5,
standard_parallels=(30.0))
ax = fig.add_axes(
[0, 0, 1, 1],
projection=proj,
)
ax.coastlines()
ax.set_extent([-90, -75, 25, 35])
draw_scale(ax, pos='ll', units='m')
def mapSequence(self, sequence):
sequence_frame = self.getSequenceEvents(sequence['name'])
fig = plt.figure(figsize=[7, 7])
clon = sequence['center_lon']
clat = sequence['center_lat']
xmin = sequence['xmin'] - 1
xmax = sequence['xmax'] + 1
ymin = sequence['ymin'] - 1
ymax = sequence['ymax'] + 1
bounds = [xmin, xmax, ymin, ymax]
figsize = (7, 7)
cities = Cities.fromDefault()
dims = [0.1, 0.1, 0.8, 0.8]
mmap = MercatorMap(bounds, figsize, cities, dimensions=dims)
fig = mmap.figure
ax = mmap.axes
proj = mmap.proj
markersizes = list(MSIZES.values())
for idx, row in sequence_frame.iterrows():
elat = row['latitude']
elon = row['longitude']
emag = row['magnitude']
mdiff = np.abs(emag - np.array(list(MSIZES.keys())))
imin = mdiff.argmin()
markersize = markersizes[imin]
zorder = 1 / markersize
ax.plot([elon], [elat],
'g',
marker='o',
mec='k',
markersize=markersize,
zorder=zorder,
transform=ccrs.PlateCarree())
mmap.drawCities(draw_dots=True)
_draw_graticules(ax, xmin, xmax, ymin, ymax)
corner = 'll'
ax.coastlines(resolution='50m')
draw_scale(ax, corner, pady=0.05, padx=0.05, zorder=SCALE_ZORDER)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
plt.title('%s (N=%i)' % (sequence['name'], sequence['n_earthquakes']))
return ax, fig
def draw_contour(shakegrid,
popgrid,
oceanfile,
oceangridfile,
cityfile,
basename,
borderfile=None,
is_scenario=False):
"""Create a contour map showing MMI contours over greyscale population.
:param shakegrid:
ShakeGrid object.
:param popgrid:
Grid2D object containing population data.
:param oceanfile:
String path to file containing ocean vector data in a format compatible
with fiona.
:param oceangridfile:
String path to file containing ocean grid data .
:param cityfile:
String path to file containing GeoNames cities data.
:param basename:
String path containing desired output PDF base name, i.e.,
/home/pager/exposure. ".pdf" and ".png" files will
be made.
:param make_png:
Boolean indicating whether a PNG version of the file should also be
created in the same output folder as the PDF.
:returns:
Tuple containing:
- Name of PNG file created, or None if PNG output not specified.
- Cities object containing the cities that were rendered on the
contour map.
"""
gd = shakegrid.getGeoDict()
# Retrieve the epicenter - this will get used on the map
center_lat = shakegrid.getEventDict()['lat']
center_lon = shakegrid.getEventDict()['lon']
# load the ocean grid file (has 1s in ocean, 0s over land)
# having this file saves us almost 30 seconds!
oceangrid = read(oceangridfile,
samplegeodict=gd,
resample=True,
doPadding=True)
# load the cities data, limit to cities within shakemap bounds
allcities = Cities.fromDefault()
cities = allcities.limitByBounds((gd.xmin, gd.xmax, gd.ymin, gd.ymax))
# define the map
# first cope with stupid 180 meridian
height = (gd.ymax - gd.ymin) * DEG2KM
if gd.xmin < gd.xmax:
width = (gd.xmax - gd.xmin) * np.cos(np.radians(center_lat)) * DEG2KM
xmin, xmax, ymin, ymax = (gd.xmin, gd.xmax, gd.ymin, gd.ymax)
else:
xmin, xmax, ymin, ymax = (gd.xmin, gd.xmax, gd.ymin, gd.ymax)
xmax += 360
width = ((gd.xmax + 360) - gd.xmin) * \
np.cos(np.radians(center_lat)) * DEG2KM
aspect = width / height
# if the aspect is not 1, then trim bounds in x or y direction
# as appropriate
if width > height:
dw = (width - height) / 2.0 # this is width in km
xmin = xmin + dw / (np.cos(np.radians(center_lat)) * DEG2KM)
xmax = xmax - dw / (np.cos(np.radians(center_lat)) * DEG2KM)
width = (xmax - xmin) * np.cos(np.radians(center_lat)) * DEG2KM
if height > width:
dh = (height - width) / 2.0 # this is width in km
ymin = ymin + dh / DEG2KM
ymax = ymax - dh / DEG2KM
height = (ymax - ymin) * DEG2KM
aspect = width / height
figheight = FIGWIDTH / aspect
bbox = (xmin, ymin, xmax, ymax)
bounds = (xmin, xmax, ymin, ymax)
figsize = (FIGWIDTH, figheight)
# Create the MercatorMap object, which holds a separate but identical
# axes object used to determine collisions between city labels.
mmap = MercatorMap(bounds, figsize, cities, padding=0.5)
fig = mmap.figure
ax = mmap.axes
# this needs to be done here so that city label collision
# detection will work
fig.canvas.draw()
geoproj = mmap.geoproj
proj = mmap.proj
# project our population grid to the map projection
projstr = proj.proj4_init
popgrid_proj = popgrid.project(projstr)
popdata = popgrid_proj.getData()
newgd = popgrid_proj.getGeoDict()
# Use our GMT-inspired palette class to create population and MMI colormaps
popmap = ColorPalette.fromPreset('pop')
mmimap = ColorPalette.fromPreset('mmi')
# set the image extent to that of the data
img_extent = (newgd.xmin, newgd.xmax, newgd.ymin, newgd.ymax)
plt.imshow(popdata,
origin='upper',
extent=img_extent,
cmap=popmap.cmap,
vmin=popmap.vmin,
vmax=popmap.vmax,
zorder=POP_ZORDER,
interpolation='nearest')
# draw 10m res coastlines
ax.coastlines(resolution="10m", zorder=COAST_ZORDER)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax.add_feature(states_provinces, edgecolor='black', zorder=COAST_ZORDER)
# draw country borders using natural earth data set
if borderfile is not None:
borders = ShapelyFeature(
Reader(borderfile).geometries(), ccrs.PlateCarree())
ax.add_feature(borders,
zorder=COAST_ZORDER,
edgecolor='black',
linewidth=2,
facecolor='none')
# clip the ocean data to the shakemap
bbox = (gd.xmin, gd.ymin, gd.xmax, gd.ymax)
oceanshapes = _clip_bounds(bbox, oceanfile)
ax.add_feature(ShapelyFeature(oceanshapes, crs=geoproj),
facecolor=WATERCOLOR,
zorder=OCEAN_ZORDER)
# So here we're going to project the MMI data to
# our mercator map, then smooth and contour that
# projected grid.
# smooth the MMI data for contouring, themn project
mmi = shakegrid.getLayer('mmi').getData()
smoothed_mmi = gaussian_filter(mmi, FILTER_SMOOTH)
newgd = shakegrid.getGeoDict().copy()
smooth_grid = Grid2D(data=smoothed_mmi, geodict=newgd)
smooth_grid_merc = smooth_grid.project(projstr)
newgd2 = smooth_grid_merc.getGeoDict()
# project the ocean grid
oceangrid_merc = oceangrid.project(projstr)
# create masked arrays using the ocean grid
data_xmin, data_xmax = newgd2.xmin, newgd2.xmax
data_ymin, data_ymax = newgd2.ymin, newgd2.ymax
smooth_data = smooth_grid_merc.getData()
landmask = np.ma.masked_where(oceangrid_merc._data == 0.0, smooth_data)
oceanmask = np.ma.masked_where(oceangrid_merc._data == 1.0, smooth_data)
# contour the data
contourx = np.linspace(data_xmin, data_xmax, newgd2.nx)
contoury = np.linspace(data_ymin, data_ymax, newgd2.ny)
ax.contour(
contourx,
contoury,
np.flipud(oceanmask),
linewidths=3.0,
linestyles='solid',
zorder=1000,
cmap=mmimap.cmap,
vmin=mmimap.vmin,
vmax=mmimap.vmax,
levels=np.arange(0.5, 10.5, 1.0),
)
ax.contour(
contourx,
contoury,
np.flipud(landmask),
linewidths=2.0,
linestyles='dashed',
zorder=OCEANC_ZORDER,
cmap=mmimap.cmap,
vmin=mmimap.vmin,
vmax=mmimap.vmax,
levels=np.arange(0.5, 10.5, 1.0),
)
# the idea here is to plot invisible MMI contours at integer levels
# and then label them. clabel method won't allow text to appear,
# which is this case is kind of ok, because it allows us an
# easy way to draw MMI labels as roman numerals.
cs_land = plt.contour(
contourx,
contoury,
np.flipud(oceanmask),
linewidths=0.0,
levels=np.arange(0, 11),
alpha=0.0,
zorder=CLABEL_ZORDER,
)
clabel_text = ax.clabel(cs_land,
cs_land.cvalues,
colors='k',
fmt='%.0f',
fontsize=40)
for clabel in clabel_text:
x, y = clabel.get_position()
label_str = clabel.get_text()
roman_label = MMI_LABELS[label_str]
th = plt.text(x,
y,
roman_label,
zorder=CLABEL_ZORDER,
ha='center',
va='center',
color='black',
weight='normal',
size=16)
th.set_path_effects([
path_effects.Stroke(linewidth=2.0, foreground='white'),
path_effects.Normal()
])
cs_ocean = plt.contour(
contourx,
contoury,
np.flipud(landmask),
linewidths=0.0,
levels=np.arange(0, 11),
zorder=CLABEL_ZORDER,
)
clabel_text = ax.clabel(cs_ocean,
cs_ocean.cvalues,
colors='k',
fmt='%.0f',
fontsize=40)
for clabel in clabel_text:
x, y = clabel.get_position()
label_str = clabel.get_text()
roman_label = MMI_LABELS[label_str]
th = plt.text(x,
y,
roman_label,
ha='center',
va='center',
color='black',
weight='normal',
size=16)
th.set_path_effects([
path_effects.Stroke(linewidth=2.0, foreground='white'),
path_effects.Normal()
])
# draw meridians and parallels using Cartopy's functions for that
gl = ax.gridlines(draw_labels=True,
linewidth=2,
color=(0.9, 0.9, 0.9),
alpha=0.5,
linestyle='-',
zorder=GRID_ZORDER)
gl.xlabels_top = False
gl.xlabels_bottom = False
gl.ylabels_left = False
gl.ylabels_right = False
gl.xlines = True
# let's floor/ceil the edges to nearest half a degree
gxmin = np.floor(xmin * 2) / 2
gxmax = np.ceil(xmax * 2) / 2
gymin = np.floor(ymin * 2) / 2
gymax = np.ceil(ymax * 2) / 2
xlocs = np.linspace(gxmin, gxmax + 0.5, num=5)
ylocs = np.linspace(gymin, gymax + 0.5, num=5)
gl.xlocator = mticker.FixedLocator(xlocs)
gl.ylocator = mticker.FixedLocator(ylocs)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 15, 'color': 'black'}
gl.ylabel_style = {'size': 15, 'color': 'black'}
# TODO - figure out x/y axes data coordinates
# corresponding to 10% from left and 10% from top
# use geoproj and proj
dleft = 0.01
dtop = 0.97
proj_str = proj.proj4_init
merc_to_dd = pyproj.Proj(proj_str)
# use built-in transforms to get from axes units to data units
display_to_data = ax.transData.inverted()
axes_to_display = ax.transAxes
# these are x,y coordinates in projected space
yleft, t1 = display_to_data.transform(
axes_to_display.transform((dleft, 0.5)))
t2, xtop = display_to_data.transform(axes_to_display.transform(
(0.5, dtop)))
# these are coordinates in lon,lat space
yleft_dd, t1_dd = merc_to_dd(yleft, t1, inverse=True)
t2_dd, xtop_dd = merc_to_dd(t2, xtop, inverse=True)
# drawing our own tick labels INSIDE the plot, as
# Cartopy doesn't seem to support this.
yrange = ymax - ymin
xrange = xmax - xmin
ddlabelsize = 12
for xloc in gl.xlocator.locs:
outside = xloc < xmin or xloc > xmax
# don't draw labels when we're too close to either edge
near_edge = (xloc - xmin) < (xrange * 0.1) or (xmax - xloc) < (xrange *
0.1)
if outside or near_edge:
continue
xtext = r'$%.1f^\circ$W' % (abs(xloc))
ax.text(xloc,
xtop_dd,
xtext,
fontsize=ddlabelsize,
zorder=GRID_ZORDER,
ha='center',
fontname=DEFAULT_FONT,
transform=ccrs.Geodetic())
for yloc in gl.ylocator.locs:
outside = yloc < gd.ymin or yloc > gd.ymax
# don't draw labels when we're too close to either edge
near_edge = (yloc - gd.ymin) < (yrange * 0.1) or (gd.ymax - yloc) < (
yrange * 0.1)
if outside or near_edge:
continue
if yloc < 0:
ytext = r'$%.1f^\circ$S' % (abs(yloc))
else:
ytext = r'$%.1f^\circ$N' % (abs(yloc))
ax.text(yleft_dd,
yloc,
ytext,
fontsize=ddlabelsize,
zorder=GRID_ZORDER,
va='center',
fontname=DEFAULT_FONT,
transform=ccrs.Geodetic())
# draw cities
mapcities = mmap.drawCities(shadow=True, zorder=CITIES_ZORDER)
# draw the figure border thickly
# TODO - figure out how to draw map border
# bwidth = 3
# ax.spines['top'].set_visible(True)
# ax.spines['left'].set_visible(True)
# ax.spines['bottom'].set_visible(True)
# ax.spines['right'].set_visible(True)
# ax.spines['top'].set_linewidth(bwidth)
# ax.spines['right'].set_linewidth(bwidth)
# ax.spines['bottom'].set_linewidth(bwidth)
# ax.spines['left'].set_linewidth(bwidth)
# Get the corner of the map with the lowest population
corner_rect, filled_corner = _get_open_corner(popgrid, ax)
clat2 = round_to_nearest(center_lat, 1.0)
clon2 = round_to_nearest(center_lon, 1.0)
# draw a little globe in the corner showing in small-scale
# where the earthquake is located.
proj = ccrs.Orthographic(central_latitude=clat2, central_longitude=clon2)
ax2 = fig.add_axes(corner_rect, projection=proj)
ax2.add_feature(cfeature.OCEAN,
zorder=0,
facecolor=WATERCOLOR,
edgecolor=WATERCOLOR)
ax2.add_feature(cfeature.LAND, zorder=0, edgecolor='black')
ax2.plot([clon2], [clat2],
'w*',
linewidth=1,
markersize=16,
markeredgecolor='k',
markerfacecolor='r')
ax2.gridlines()
ax2.set_global()
ax2.outline_patch.set_edgecolor('black')
ax2.outline_patch.set_linewidth(2)
# Draw the map scale in the unoccupied lower corner.
corner = 'lr'
if filled_corner == 'lr':
corner = 'll'
draw_scale(ax, corner, pady=0.05, padx=0.05)
# Draw the epicenter as a black star
plt.sca(ax)
plt.plot(center_lon,
center_lat,
'k*',
markersize=16,
zorder=EPICENTER_ZORDER,
transform=geoproj)
if is_scenario:
plt.text(center_lon,
center_lat,
'SCENARIO',
fontsize=64,
zorder=WATERMARK_ZORDER,
transform=geoproj,
alpha=0.2,
color='red',
horizontalalignment='center')
# create pdf and png output file names
pdf_file = basename + '.pdf'
png_file = basename + '.png'
# save to pdf
plt.savefig(pdf_file)
plt.savefig(png_file)
return (pdf_file, png_file, mapcities)
def draw_map(adict, override_scenario=False):
"""If adict['imtype'] is MMI, draw a map of intensity draped over
topography, otherwise Draw IMT contour lines over hill-shaded topography.
Args:
adict (dictionary): A dictionary containing the following keys:
'imtype' (str): The intensity measure type
'topogrid' (Grid2d): A topography grid
'allcities' (Cities): A list of global cities,
'states_provinces' (Cartopy Feature): States/province boundaries.
'countries' (Cartopy Feature): Country boundaries.
'oceans' (Cartopy Feature): Oceans.
'lakes' (Cartopy Feature): Lakes.
'roads' (Shapely Feature): Roads.
'faults' (Shapely Feature): Fault traces
'datadir' (str): The path into which to deposit products
'operator' (str): The producer of this shakemap
'filter_size' (int): The size of the filter used before contouring
'info' (dictionary): The shakemap info structure
'component' (str): The intensity measure component being plotted
'imtdict' (dictionary): Dict containing the IMT grids
'rupdict' (dictionary): Dict containing the rupture data
'stationdict' (dictionary): Dict of station data
'config' (dictionary): The configuration data for this shakemap
'tdict' (dictionary): The text strings to be printed on the map
in the user's choice of language.
'license_text' (str): License text to display at bottom of map
'license_logo' (str): Path to license logo image to display
next to license text
override_scenario (bool): Turn off scenario watermark.
Returns:
Tuple of (Matplotlib figure, Matplotlib figure): Objects containing
the map generated by this function, and the intensity legend,
respectively. If the imtype of this map is not 'MMI', the second
element of the tuple will be None.
"""
imtype = adict['imtype']
imtdict = adict['imtdict'] # mmidict
imtdata = np.nan_to_num(imtdict['mean'], nan=0.0) # mmidata
gd = GeoDict(imtdict['mean_metadata'])
imtgrid = Grid2D(imtdata, gd) # mmigrid
gd = imtgrid.getGeoDict()
# Retrieve the epicenter - this will get used on the map
rupture = rupture_from_dict(adict['ruptdict'])
origin = rupture.getOrigin()
center_lat = origin.lat
center_lon = origin.lon
# load the cities data, limit to cities within shakemap bounds
cities = adict['allcities'].limitByBounds((gd.xmin, gd.xmax,
gd.ymin, gd.ymax))
# get the map boundaries and figure size
bounds, figsize, aspect = _get_map_info(gd)
# Note: dimensions are: [left, bottom, width, height]
dim_left = 0.1
dim_bottom = 0.19
dim_width = 0.8
dim_height = dim_width/aspect
if dim_height > 0.8:
dim_height = 0.8
dim_width = 0.8 * aspect
dim_left = (1.0 - dim_width) / 2
# Create the MercatorMap object, which holds a separate but identical
# axes object used to determine collisions between city labels.
mmap = MercatorMap(
bounds, figsize, cities, padding=0.5,
dimensions=[dim_left, dim_bottom, dim_width, dim_height])
fig = mmap.figure
ax = mmap.axes
# this needs to be done here so that city label collision
# detection will work
fig.canvas.draw()
# get the geographic projection object
geoproj = mmap.geoproj
# get the mercator projection object
proj = mmap.proj
# get the proj4 string - used by Grid2D project() method
projstr = proj.proj4_init
# get the projected IMT and topo grids
pimtgrid, ptopogrid = _get_projected_grids(imtgrid, adict['topogrid'],
projstr)
# get the projected geodict
proj_gd = pimtgrid.getGeoDict()
pimtdata = pimtgrid.getData()
ptopo_data = ptopogrid.getData()
mmimap = ColorPalette.fromPreset('mmi')
if imtype == 'MMI':
draped_hsv = _get_draped(pimtdata, ptopo_data, mmimap)
else:
# get the draped topo data
topo_colormap = ColorPalette.fromPreset('shaketopo')
draped_hsv = _get_shaded(ptopo_data, topo_colormap)
# convert units
if imtype == 'PGV':
pimtdata = np.exp(pimtdata)
else:
pimtdata = np.exp(pimtdata) * 100
plt.sca(ax)
ax.set_xlim(proj_gd.xmin, proj_gd.xmax)
ax.set_ylim(proj_gd.ymin, proj_gd.ymax)
img_extent = (proj_gd.xmin, proj_gd.xmax, proj_gd.ymin, proj_gd.ymax)
plt.imshow(draped_hsv, origin='upper', extent=img_extent,
zorder=IMG_ZORDER, interpolation='none')
config = adict['config']
gmice = get_object_from_config('gmice', 'modeling', config)
gmice_imts = gmice.DEFINED_FOR_INTENSITY_MEASURE_TYPES
gmice_pers = gmice.DEFINED_FOR_SA_PERIODS
oqimt = imt.from_string(imtype)
if imtype != 'MMI' and (not isinstance(oqimt, tuple(gmice_imts)) or
(isinstance(oqimt, imt.SA) and
oqimt.period not in gmice_pers)):
my_gmice = None
else:
my_gmice = gmice
if imtype != 'MMI':
# call the contour module in plotting to get the vertices of the
# contour lines
contour_objects = contour(imtdict, imtype, adict['filter_size'],
my_gmice)
# get a color palette for the levels we have
# levels = [c['properties']['value'] for c in contour_objects]
# cartopy shapely feature has some weird behaviors, so I had to go
# rogue and draw contour lines/labels myself.
# To choose which contours to label, we will keep track of the lengths
# of contours, grouped by isovalue
contour_lens = defaultdict(lambda: [])
def arclen(path):
"""
Compute the arclength of *path*, which should be a list of pairs
of numbers.
"""
x0, y0 = [np.array(c) for c in zip(*path)]
x1, y1 = [np.roll(c, -1) for c in (x0, y0)] # offset by 1
# don't include first-last vertices as an edge:
x0, y0, x1, y1 = [c[:-1] for c in (x0, y0, x1, y1)]
return np.sum(np.sqrt((x0 - x1)**2 + (y0 - y1)**2))
# draw dashed contours first, the ones over land will be overridden by
# solid contours
for contour_object in contour_objects:
props = contour_object['properties']
multi_lines = sShape(contour_object['geometry'])
pmulti_lines = proj.project_geometry(multi_lines, src_crs=geoproj)
for multi_line in pmulti_lines:
pmulti_line = mapping(multi_line)['coordinates']
x, y = zip(*pmulti_line)
contour_lens[props['value']].append(arclen(pmulti_line))
# color = imt_cmap.getDataColor(props['value'])
ax.plot(x, y, color=props['color'], linestyle='dashed',
zorder=DASHED_CONTOUR_ZORDER)
white_box = dict(
boxstyle="round",
ec=(0, 0, 0),
fc=(1., 1, 1),
color='k'
)
# draw solid contours next - the ones over water will be covered by
# ocean polygon
for contour_object in contour_objects:
props = contour_object['properties']
multi_lines = sShape(contour_object['geometry'])
pmulti_lines = proj.project_geometry(multi_lines, src_crs=geoproj)
# only label long contours (relative to others with the same
# isovalue)
min_len = np.array(contour_lens[props['value']]).mean()
for multi_line in pmulti_lines:
pmulti_line = mapping(multi_line)['coordinates']
x, y = zip(*pmulti_line)
# color = imt_cmap.getDataColor(props['value'])
ax.plot(x, y, color=props['color'], linestyle='solid',
zorder=CONTOUR_ZORDER)
if arclen(pmulti_line) >= min_len:
# try to label each segment with black text in a white box
xc = x[int(len(x)/3)]
yc = y[int(len(y)/3)]
if _label_close_to_edge(
xc, yc, proj_gd.xmin, proj_gd.xmax,
proj_gd.ymin, proj_gd.ymax):
continue
# TODO: figure out if box is going to go outside the map,
# if so choose a different point on the line.
# For small values, use scientific notation with 1 sig fig
# to avoid multiple contours labelled 0.0:
value = props['value']
fmt = '%.1g' if abs(value) < 0.1 else '%.1f'
ax.text(xc, yc, fmt % value, size=8,
ha="center", va="center",
bbox=white_box, zorder=AXES_ZORDER-1)
# make the border thicker
lw = 2.0
ax.outline_patch.set_zorder(BORDER_ZORDER)
ax.outline_patch.set_linewidth(lw)
ax.outline_patch.set_joinstyle('round')
ax.outline_patch.set_capstyle('round')
# Coastlines will get drawn when we draw the ocean edges
# ax.coastlines(resolution="10m", zorder=COAST_ZORDER, linewidth=3)
if adict['states_provinces']:
ax.add_feature(adict['states_provinces'], edgecolor='0.5',
zorder=COAST_ZORDER)
if adict['countries']:
ax.add_feature(adict['countries'], edgecolor='black',
zorder=BORDER_ZORDER)
if adict['oceans']:
ax.add_feature(adict['oceans'], edgecolor='black',
zorder=OCEAN_ZORDER)
if adict['lakes']:
ax.add_feature(adict['lakes'], edgecolor='black',
zorder=OCEAN_ZORDER)
if adict['faults'] is not None:
ax.add_feature(adict['faults'], edgecolor='firebrick',
zorder=ROAD_ZORDER)
if adict['roads'] is not None:
ax.add_feature(adict['roads'], edgecolor='dimgray',
zorder=ROAD_ZORDER)
# draw graticules, ticks, tick labels
_draw_graticules(ax, *bounds)
# is this event a scenario?
info = adict['info']
etype = info['input']['event_information']['event_type']
is_scenario = etype == 'SCENARIO'
if is_scenario and not override_scenario:
plt.text(
center_lon, center_lat,
adict['tdict']['title_parts']['scenario'],
fontsize=72,
zorder=SCENARIO_ZORDER, transform=geoproj,
alpha=WATERMARK_ALPHA, color=WATERMARK_COLOR,
horizontalalignment='center',
verticalalignment='center',
rotation=45,
path_effects=[
path_effects.Stroke(linewidth=1, foreground='black')]
)
# Draw the map scale in the unoccupied lower corner.
corner = 'll'
draw_scale(ax, corner, pady=0.05, padx=0.05, zorder=SCALE_ZORDER)
# draw cities
mmap.drawCities(shadow=True, zorder=CITIES_ZORDER, draw_dots=True)
# Draw the epicenter as a black star
plt.sca(ax)
plt.plot(center_lon, center_lat, 'k*', markersize=16,
zorder=EPICENTER_ZORDER, transform=geoproj)
# draw the rupture polygon(s) in black, if not point rupture
point_source = True
if not isinstance(rupture, PointRupture):
point_source = False
json_dict = rupture._geojson
for feature in json_dict['features']:
for coords in feature['geometry']['coordinates']:
for pcoords in coords:
poly2d = sLineString([xy[0:2] for xy in pcoords])
ppoly = proj.project_geometry(poly2d)
mppoly = mapping(ppoly)['coordinates']
for spoly in mppoly:
x, y = zip(*spoly)
ax.plot(x, y, 'k', lw=1, zorder=FAULT_ZORDER)
# draw the station data on the map
stations = adict['stationdict']
_draw_stations(ax, stations, imtype, mmimap, geoproj)
_draw_title(imtype, adict)
process_time = info['processing']['shakemap_versions']['process_time']
map_version = int(info['processing']['shakemap_versions']['map_version'])
if imtype == 'MMI':
_draw_mmi_legend(fig, mmimap, gmice, process_time,
map_version, point_source, adict['tdict'])
# make a separate MMI legend
fig2 = plt.figure(figsize=figsize)
_draw_mmi_legend(fig2, mmimap, gmice, process_time,
map_version, point_source, adict['tdict'])
else:
_draw_imt_legend(fig, mmimap, imtype, gmice, process_time, map_version,
point_source, adict['tdict'])
plt.draw()
fig2 = None
_draw_license(fig, adict)
return (fig, fig2)
def draw_contour(shakefile,
popfile,
oceanfile,
cityfile,
outfilename,
make_png=False):
"""Create a contour map showing population (greyscale) underneath contoured MMI.
:param shakefile:
String path to ShakeMap grid.xml file.
:param popfile:
String path to GDALGrid-compliant file containing population data.
:param oceanfile:
String path to file containing ocean vector data in a format compatible with fiona.
:param cityfile:
String path to file containing GeoNames cities data.
:param outfilename:
String path containing desired output PDF filename.
:param make_png:
Boolean indicating whether a PNG version of the file should also be created in the
same output folder as the PDF.
:returns:
Tuple containing:
- Name of PNG file created, or None if PNG output not specified.
- CartopyCities object containing the cities that were rendered on the contour map.
"""
#load the shakemap - for the time being, we're interpolating the
#population data to the shakemap, which would be important
#if we were doing math with the pop values. We're not, so I think it's ok.
shakegrid = ShakeGrid.load(shakefile, adjust='res')
gd = shakegrid.getGeoDict()
#retrieve the epicenter - this will get used on the map
clat = shakegrid.getEventDict()['lat']
clon = shakegrid.getEventDict()['lon']
#load the population data, sample to shakemap
popgrid = GDALGrid.load(popfile, samplegeodict=gd, resample=True)
popdata = popgrid.getData()
#smooth the MMI data for contouring
mmi = shakegrid.getLayer('mmi').getData()
smoothed_mmi = gaussian_filter(mmi, FILTER_SMOOTH)
#clip the ocean data to the shakemap
bbox = (gd.xmin, gd.ymin, gd.xmax, gd.ymax)
oceanshapes = _clip_bounds(bbox, oceanfile)
#load the cities data, limit to cities within shakemap bounds
allcities = CartopyCities.fromDefault()
cities = allcities.limitByBounds((gd.xmin, gd.xmax, gd.ymin, gd.ymax))
# Define ocean/land masks to do the contours, since we want different contour line styles over land and water.
oceangrid = Grid2D.rasterizeFromGeometry(oceanshapes,
gd,
burnValue=1.0,
fillValue=0.0,
mustContainCenter=False,
attribute=None)
oceanmask = np.ma.masked_where(oceangrid == 1.0, smoothed_mmi)
landmask = np.ma.masked_where(oceangrid == 0.0, smoothed_mmi)
# Use our GMT-inspired palette class to create population and MMI colormaps
popmap = ColorPalette.fromPreset('pop')
mmimap = ColorPalette.fromPreset('mmi')
#use the ShakeMap to determine the aspect ratio of the map
aspect = (gd.xmax - gd.xmin) / (gd.ymax - gd.ymin)
figheight = FIGWIDTH / aspect
fig = plt.figure(figsize=(FIGWIDTH, figheight))
# set up axes object with PlateCaree (non) projection.
ax = plt.axes([0.02, 0.02, 0.95, 0.95], projection=ccrs.PlateCarree())
#set the image extent to that of the data
img_extent = (gd.xmin, gd.xmax, gd.ymin, gd.ymax)
plt.imshow(popdata,
origin='upper',
extent=img_extent,
cmap=popmap.cmap,
vmin=popmap.vmin,
vmax=popmap.vmax,
zorder=9,
interpolation='none')
#define arrays of latitude and longitude we will use to plot MMI contours
lat = np.linspace(gd.ymin, gd.ymax, gd.ny)
lon = np.linspace(gd.xmin, gd.xmax, gd.nx)
#contour the masked land/ocean MMI data at half-integer levels
plt.contour(lon,
lat,
landmask,
linewidths=3.0,
linestyles='solid',
zorder=10,
cmap=mmimap.cmap,
vmin=mmimap.vmin,
vmax=mmimap.vmax,
levels=np.arange(0.5, 10.5, 1.0))
plt.contour(lon,
lat,
oceanmask,
linewidths=2.0,
linestyles='dashed',
zorder=13,
cmap=mmimap.cmap,
vmin=mmimap.vmin,
vmax=mmimap.vmax,
levels=np.arange(0.5, 10.5, 1.0))
#the idea here is to plot invisible MMI contours at integer levels and then label them.
#labeling part does not currently work.
cs = plt.contour(lon,
lat,
landmask,
linewidths=0.0,
levels=np.arange(0, 11),
zorder=10)
#clabel is not actually drawing anything, but it is blotting out a portion of the contour line. ??
ax.clabel(cs, np.arange(0, 11), colors='k', zorder=25)
#set the extent of the map to our data
ax.set_extent([lon.min(), lon.max(), lat.min(), lat.max()])
#draw the ocean data
if isinstance(oceanshapes[0], mPolygon):
for shape in oceanshapes[0]:
ocean_patch = PolygonPatch(shape,
zorder=10,
facecolor=WATERCOLOR,
edgecolor=WATERCOLOR)
ax.add_patch(ocean_patch)
else:
ocean_patch = PolygonPatch(oceanshapes[0],
zorder=10,
facecolor=WATERCOLOR,
edgecolor=WATERCOLOR)
ax.add_patch(ocean_patch)
# add coastlines with desired scale of resolution
ax.coastlines('10m', zorder=11)
#draw meridians and parallels using Cartopy's functions for that
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color=(0.9, 0.9, 0.9),
alpha=0.5,
linestyle='-',
zorder=20)
gl.xlabels_top = False
gl.xlabels_bottom = False
gl.ylabels_left = False
gl.ylabels_right = False
gl.xlines = True
xlocs = np.arange(np.floor(gd.xmin - 1), np.ceil(gd.xmax + 1))
ylocs = np.arange(np.floor(gd.ymin - 1), np.ceil(gd.ymax + 1))
gl.xlocator = mticker.FixedLocator(xlocs)
gl.ylocator = mticker.FixedLocator(ylocs)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 15, 'color': 'black'}
gl.ylabel_style = {'size': 15, 'color': 'black'}
#drawing our own tick labels INSIDE the plot, as Cartopy doesn't seem to support this.
yrange = gd.ymax - gd.ymin
xrange = gd.xmax - gd.xmin
for xloc in gl.xlocator.locs:
outside = xloc < gd.xmin or xloc > gd.xmax
#don't draw labels when we're too close to either edge
near_edge = (xloc - gd.xmin) < (xrange * 0.1) or (gd.xmax - xloc) < (
xrange * 0.1)
if outside or near_edge:
continue
if xloc < 0:
xtext = r'$%s^\circ$W' % str(abs(int(xloc)))
else:
xtext = r'$%s^\circ$E' % str(int(xloc))
ax.text(xloc,
gd.ymax - (yrange / 35),
xtext,
fontsize=14,
zorder=20,
ha='center',
fontname='Bitstream Vera Sans')
for yloc in gl.ylocator.locs:
outside = yloc < gd.ymin or yloc > gd.ymax
#don't draw labels when we're too close to either edge
near_edge = (yloc - gd.ymin) < (yrange * 0.1) or (gd.ymax - yloc) < (
yrange * 0.1)
if outside or near_edge:
continue
if yloc < 0:
ytext = r'$%s^\circ$S' % str(abs(int(yloc)))
else:
ytext = r'$%s^\circ$N' % str(int(yloc))
thing = ax.text(gd.xmin + (xrange / 100),
yloc,
ytext,
fontsize=14,
zorder=20,
va='center',
fontname='Bitstream Vera Sans')
#Limit the number of cities we show - we may not want to use the population size
#filter in the global case, but the map collision filter is a little sketchy right now.
mapcities = cities.limitByPopulation(25000)
mapcities = mapcities.limitByGrid()
mapcities = mapcities.limitByMapCollision(ax, shadow=True)
mapcities.renderToMap(ax, shadow=True, fontsize=12, zorder=11)
#Get the corner of the map with the lowest population
corner_rect, filled_corner = _get_open_corner(popgrid, ax)
clat = round_to_nearest(clat, 1.0)
clon = round_to_nearest(clon, 1.0)
#draw a little globe in the corner showing in small-scale where the earthquake is located.
proj = ccrs.Orthographic(central_latitude=clat, central_longitude=clon)
ax2 = fig.add_axes(corner_rect, projection=proj)
ax2.add_feature(cartopy.feature.OCEAN,
zorder=0,
facecolor=WATERCOLOR,
edgecolor=WATERCOLOR)
ax2.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')
ax2.plot([clon], [clat],
'w*',
linewidth=1,
markersize=16,
markeredgecolor='k',
markerfacecolor='r')
gh = ax2.gridlines()
ax2.set_global()
ax2.outline_patch.set_edgecolor('black')
ax2.outline_patch.set_linewidth(2)
#Draw the map scale in the unoccupied lower corner.
corner = 'lr'
if filled_corner == 'lr':
corner = 'll'
draw_scale(ax, corner, pady=0.05, padx=0.05)
plt.savefig(outfilename)
pngfile = None
if make_png:
fpath, fname = os.path.split(outfilename)
fbase, t = os.path.splitext(fname)
pngfile = os.path.join(fpath, fbase + '.png')
plt.savefig(pngfile)
return (pngfile, mapcities)
def draw_stations_map(pstreams, event, event_dir):
# draw map of stations and cities and stuff
lats = np.array(
[stream[0].stats.coordinates['latitude'] for stream in pstreams])
lons = np.array(
[stream[0].stats.coordinates['longitude'] for stream in pstreams])
cy = event.latitude
cx = event.longitude
xmin = lons.min()
xmax = lons.max()
ymin = lats.min()
ymax = lats.max()
diff_x = max(abs(cx - xmin), abs(cx - xmax))
diff_y = max(abs(cy - ymin), abs(cy - ymax))
xmax = cx + MAP_PADDING * diff_x
xmin = cx - MAP_PADDING * diff_x
ymax = cy + MAP_PADDING * diff_y
ymin = cy - MAP_PADDING * diff_y
bounds = (xmin, xmax, ymin, ymax)
figsize = (10, 10)
cities = Cities.fromDefault()
mmap = MercatorMap(bounds, figsize, cities)
mmap.drawCities(draw_dots=True)
ax = mmap.axes
draw_scale(ax)
ax.plot(cx, cy, 'r*', markersize=16, transform=mmap.geoproj, zorder=8)
status = [
FAILED_COLOR
if np.any([trace.hasParameter("failure")
for trace in stream]) else PASSED_COLOR
for stream in pstreams
]
ax.scatter(lons,
lats,
c=status,
marker='^',
edgecolors='k',
transform=mmap.geoproj,
zorder=100,
s=48)
passed_marker = mlines.Line2D([], [],
color=PASSED_COLOR,
marker='^',
markeredgecolor='k',
markersize=12,
label='Passed station',
linestyle='None')
failed_marker = mlines.Line2D([], [],
color=FAILED_COLOR,
marker='^',
markeredgecolor='k',
markersize=12,
label='Failed station',
linestyle='None')
earthquake_marker = mlines.Line2D([], [],
color='red',
marker='*',
markersize=12,
label='Earthquake Epicenter',
linestyle='None')
ax.legend(handles=[passed_marker, failed_marker, earthquake_marker],
fontsize=12)
scale = '50m'
land = cfeature.NaturalEarthFeature(category='physical',
name='land',
scale=scale,
facecolor=LAND_COLOR)
ocean = cfeature.NaturalEarthFeature(category='physical',
name='ocean',
scale=scale,
facecolor=OCEAN_COLOR)
ax.add_feature(land)
ax.add_feature(ocean)
ax.coastlines(resolution=scale, zorder=10, linewidth=1)
mapfile = os.path.join(event_dir, 'stations_map.png')
plt.savefig(mapfile)
return mapfile
def draw_stations_map(pstreams, event, event_dir):
# interactive html map is created first
lats = np.array(
[stream[0].stats.coordinates["latitude"] for stream in pstreams])
lons = np.array(
[stream[0].stats.coordinates["longitude"] for stream in pstreams])
stnames = np.array([stream[0].stats.station for stream in pstreams])
networks = np.array([stream[0].stats.network for stream in pstreams])
failed = np.array([
np.any([trace.hasParameter("failure") for trace in stream])
for stream in pstreams
])
failure_reasons = list(
pd.Series(
[
next(tr for tr in st if tr.hasParameter(
"failure")).getParameter("failure")["reason"]
for st in pstreams if not st.passed
],
dtype=str,
))
station_map = folium.Map(location=[event.latitude, event.longitude],
zoom_start=7,
control_scale=True)
failed_coords = zip(lats[failed], lons[failed])
failed_stations = stnames[failed]
failed_networks = networks[failed]
failed_station_df = pd.DataFrame({
"stnames": failed_stations,
"network": failed_networks,
"coords": failed_coords,
"reason": failure_reasons,
})
passed_coords = zip(lats[~failed], lons[~failed])
passed_stations = stnames[~failed]
passed_networks = networks[~failed]
passed_station_df = pd.DataFrame({
"stnames": passed_stations,
"network": passed_networks,
"coords": passed_coords,
})
# Plot the failed first
for i, r in failed_station_df.iterrows():
station_info = "NET: {} LAT: {:.2f} LON: {:.2f} REASON: {}".format(
r["network"], r["coords"][0], r["coords"][1], r["reason"])
folium.CircleMarker(
location=r["coords"],
tooltip=r["stnames"],
popup=station_info,
color=FAILED_COLOR,
fill=True,
radius=6,
).add_to(station_map)
for i, r in passed_station_df.iterrows():
station_info = "NET: {}\n LAT: {:.2f} LON: {:.2f}".format(
r["network"], r["coords"][0], r["coords"][1])
folium.CircleMarker(
location=r["coords"],
tooltip=r["stnames"],
popup=station_info,
color=PASSED_COLOR,
fill=True,
radius=10,
).add_to(station_map)
event_info = "MAG: {} LAT: {:.2f} LON: {:.2f} DEPTH: {:.2f}".format(
event.magnitude, event.latitude, event.longitude, event.depth)
folium.CircleMarker(
[event.latitude, event.longitude],
popup=event_info,
color="yellow",
fill=True,
radius=15,
).add_to(station_map)
html_mapfile = os.path.join(event_dir, "stations_map.html")
station_map.save(html_mapfile)
# now the static map for the report is created
# draw map of stations and cities and stuff
cy = event.latitude
cx = event.longitude
xmin = lons.min()
xmax = lons.max()
ymin = lats.min()
ymax = lats.max()
diff_x = max(abs(cx - xmin), abs(cx - xmax), 1)
diff_y = max(abs(cy - ymin), abs(cy - ymax), 1)
xmax = cx + MAP_PADDING * diff_x
xmin = cx - MAP_PADDING * diff_x
ymax = cy + MAP_PADDING * diff_y
ymin = cy - MAP_PADDING * diff_y
bounds = (xmin, xmax, ymin, ymax)
figsize = (10, 10)
cities = Cities.fromDefault()
mmap = MercatorMap(bounds, figsize, cities)
mmap.drawCities(draw_dots=True)
ax = mmap.axes
draw_scale(ax)
ax.plot(cx, cy, "r*", markersize=16, transform=mmap.geoproj, zorder=8)
failed = np.array([
np.any([trace.hasParameter("failure") for trace in stream])
for stream in pstreams
])
# Plot the failed first
ax.scatter(
lons[failed],
lats[failed],
c=FAILED_COLOR,
marker="v",
edgecolors="k",
transform=mmap.geoproj,
zorder=100,
s=48,
)
# Plot the successes above the failures
ax.scatter(
lons[~failed],
lats[~failed],
c=PASSED_COLOR,
marker="^",
edgecolors="k",
transform=mmap.geoproj,
zorder=101,
s=48,
)
passed_marker = mlines.Line2D(
[],
[],
color=PASSED_COLOR,
marker="^",
markeredgecolor="k",
markersize=12,
label="Passed station",
linestyle="None",
)
failed_marker = mlines.Line2D(
[],
[],
color=FAILED_COLOR,
marker="v",
markeredgecolor="k",
markersize=12,
label="Failed station",
linestyle="None",
)
earthquake_marker = mlines.Line2D(
[],
[],
color="red",
marker="*",
markersize=12,
label="Earthquake Epicenter",
linestyle="None",
)
ax.legend(handles=[passed_marker, failed_marker, earthquake_marker],
fontsize=12)
scale = "50m"
land = cfeature.NaturalEarthFeature(category="physical",
name="land",
scale=scale,
facecolor=LAND_COLOR)
ocean = cfeature.NaturalEarthFeature(category="physical",
name="ocean",
scale=scale,
facecolor=OCEAN_COLOR)
ax.add_feature(land)
ax.add_feature(ocean)
ax.coastlines(resolution=scale, zorder=10, linewidth=1)
png_mapfile = os.path.join(event_dir, "stations_map.png")
plt.savefig(png_mapfile)
return (png_mapfile, html_mapfile)