def sampleGrid(gridfile,geodict):
xdim,ydim = getFileResolution(gridfile)
resolution = np.mean([xdim,ydim])
xmin = geodict['xmin']
xmax = geodict['xmax']
ymin = geodict['ymin']
ymax = geodict['ymax']
#resolution = geodict['xdim']
bounds = (xmin-(resolution*2),xmax+(resolution*2),ymin-(resolution*2),ymax+(resolution*2))
if gridfile.endswith('grd'):
grid = GMTGrid(grdfile=gridfile,bounds=bounds)
else:
tmpgrid = EsriGrid(gridfile)
tmpgrid.load(bounds=bounds)
grid = GMTGrid()
grid.loadFromGrid(tmpgrid)
grid.interpolateToGrid(geodict)
return grid
def main(args):
#define location for config file
homedir = os.path.expanduser("~") #where is the user's home directory?
configfile = args.configFile
shakefile = args.shakefile
if not os.path.isfile(shakefile):
if isURL(shakefile):
shakefile = getGridURL(shakefile) #returns a file object
else:
print 'Could not find "%s" as a file or a url. Returning.' % (shakefile)
shakemap = ShakeGrid(shakefile)
#figure out the bounds that are greater than the biggest bounds
#of any of the grids
shakerange = shakemap.getRange()
lonrange = shakerange[1] - shakerange[0]
latrange = shakerange[3] - shakerange[2]
xmin = shakerange[0] - lonrange*0.1
xmax = shakerange[1] + lonrange*0.1
ymin = shakerange[2] - latrange*0.1
ymax = shakerange[3] + latrange*0.1
bigbounds = (xmin,xmax,ymin,ymax)
#
shakeheader = shakemap.getAttributes()
edict = {'mag':shakeheader['event']['magnitude'],
'time':shakeheader['event']['event_timestamp'],
'loc':shakeheader['event']['event_description'],
'epicenter':(shakeheader['event']['lat'],shakeheader['event']['lon']),
'version':int(shakeheader['shakemap_grid']['shakemap_version']),
'eventid':shakeheader['shakemap_grid']['event_id']}
config = ConfigParser.RawConfigParser()
config.read(configfile)
network = shakeheader['shakemap_grid']['shakemap_originator']
eventcode = shakeheader['shakemap_grid']['shakemap_id']
if eventcode.startswith(network):
eventid = eventcode
else:
eventid = network + eventcode
outfolder = os.path.join(config.get('OUTPUT','folder'),eventid)
if not os.path.isdir(outfolder):
os.makedirs(outfolder)
slopefile = config.get('MAPDATA','slope')
slopegrid = GMTGrid(slopefile,bounds=shakemap.getRange())
slopeout = os.path.join(outfolder,'slope.grd')
cityfile = config.get('MAPDATA','cityfile')
#get all of the colors that people want
colors = {}
for option in config.options('MAPDATA'):
if option.endswith('color'):
colors[option] = config.get('MAPDATA',option)
#if they have roads configured, go find the appropriate roads segments
hasRoads = config.has_option('MAPDATA','roadfolder')
roadslist = []
if hasRoads and args.roads:
roadroot = config.get('MAPDATA','roadfolder')
xmin,xmax,ymin,ymax = shakemap.getRange()
for folder in os.listdir(roadroot):
roadfolder = os.path.join(roadroot,folder)
shpfiles = glob.glob(os.path.join(roadfolder,'*.shp'))
if len(shpfiles):
shpfile = shpfiles[0]
f = fiona.open(shpfile)
shapes = list(f.items(bbox=(xmin,ymin,xmax,ymax)))
for shapeid,shapedict in shapes:
roadslist.append(shapedict)
f.close()
#get the thresholds for liquefaction/landslide model
slopemin = float(config.get('MAPDATA','slopemin'))*100
slopemax = float(config.get('MAPDATA','slopemax'))*100
probdict = {}
gridbounds = [999,-999,999,-999] #this will hold the smallest bounding box enclosing both models
for model in getModelNames(configfile):
lm = LogisticModel(configfile,shakefile,model)
colormaps = getColorMaps(configfile)
print 'Equation for %s model:' % model
print
print lm.getEquation()
print
P = lm.calculate()
probgrid = GMTGrid()
probgrid.griddata = P.copy()
probgrid.geodict = lm.layerdict[lm.layerdict.keys()[0]].geodict.copy()
#resample the slope grid to model
slopegrid2 = GMTGrid()
slopegrid2.loadFromGrid(slopegrid)
slopegrid2.interpolateToGrid(probgrid.geodict)
if model == 'liquefaction':
ithresh = slopegrid2.griddata > slopemax
else:
ithresh = slopegrid2.griddata < slopemin
probgrid.griddata[ithresh] = 0.0
xmin,xmax,ymin,ymax = probgrid.getRange()
if xmin < gridbounds[0]:
gridbounds[0] = xmin
if xmax > gridbounds[1]:
gridbounds[1] = xmax
if ymin < gridbounds[2]:
gridbounds[2] = ymin
if ymax > gridbounds[3]:
gridbounds[3] = ymax
probdict[model] = probgrid
probfile = os.path.join(outfolder,'%s.grd' % model)
print 'Saving %s model output to %s' % (model,probfile)
probgrid.save(probfile)
#renderPanel(lm,colormaps,outfolder,edict)
# for layername,layergrid in lm.layerdict.iteritems():
# layerfile = os.path.join(outfolder,layername+'.grd')
# print 'Saving input grid %s to %s...' % (layername,layerfile)
# layergrid.save(layerfile)
# renderLayer(layergrid,layername,outfolder,edict,model,colormaps)
topofile = config.get('MAPDATA','topo')
#bigbounds = shakemap.getRange()
xdim = shakemap.geodict['xdim']
ydim = shakemap.geodict['xdim']
#bigbounds = (bigbounds[0]-xdim*4,bigbounds[1]+xdim*4,bigbounds[2]-ydim*4,bigbounds[3]+ydim*4)
topogrid = GMTGrid(topofile,bounds=bigbounds)
topogrid = adjustTopoGrid(topogrid,bigbounds) #make this grid as big as bigbounds if we hit an upper or lower bound
topoout = os.path.join(outfolder,'topography.grd')
print 'Saving topography to %s' % topoout
topogrid.save(topoout)
print 'Saving slope to %s' % slopeout
slopegrid.save(slopeout)
isScenario = shakeheader['shakemap_grid']['shakemap_event_type'].lower() == 'scenario'
if args.noscenario:
isScenario = False
timestr = renderDate(shakeheader['event']['event_timestamp'])
location = shakeheader['event']['event_description']
#hillshfile = config.get('MAPDATA','hillshadefile')
#hillshgrid = GMTGrid(hillshfile,bounds=bigbounds)
makeDualMap(probdict['liquefaction'],probdict['landslide'],topogrid,slopegrid,edict,outfolder,isScenario=isScenario,roadslist=roadslist,colors=colors,cityfile=cityfile)
def makeDualMap(
lqgrid, lsgrid, topogrid, slopegrid, eventdict, outfolder, isScenario=False, roadslist=[], colors={}, cityfile=None
):
# create the figure and axes instances.
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
xmin, xmax, ymin, ymax = topogrid.getRange()
clat = ymin + (ymax - ymin) / 2.0
clon = xmin + (xmax - xmin) / 2.0
m = Basemap(
llcrnrlon=xmin,
llcrnrlat=ymin,
urcrnrlon=xmax,
urcrnrlat=ymax,
rsphere=(6378137.00, 6356752.3142),
resolution="h",
area_thresh=1000.0,
projection="lcc",
lat_1=clat,
lon_0=clon,
ax=ax,
)
clear_color = [0, 0, 0, 0.0]
# topoextent = getGridExtent(topogrid,m)
# rgb,topopalette = getTopoRGB(topogrid)
# color_tuple = rgb.transpose((1,0,2)).reshape((rgb.shape[0]*rgb.shape[1],rgb.shape[2]))/255.0
hillsh = hillshade(topogrid, 315, 50) # divide by 2 to mute colors
topogrid2 = GMTGrid()
topogrid2.loadFromGrid(topogrid)
topogrid2.interpolateToGrid(lqgrid.geodict)
# define what cells are below sea level
iwater = np.where(topogrid2.griddata == SEA_LEVEL)
lons = np.arange(xmin, xmax, topogrid.getGeoDict()["xdim"])
lons = lons[: topogrid.getGeoDict()["ncols"]] # make sure right length
lats = np.arange(ymax, ymin, -topogrid.getGeoDict()["ydim"]) # backwards so it plots right
lats = lats[: topogrid.getGeoDict()["nrows"]]
llons, llats = np.meshgrid(lons, lats) # make meshgrid
x, y = m(llons, llats) # get projection coordinates
im = m.pcolormesh(x, y, hillsh, cmap="Greys", lw=0, vmin=0.0, vmax=550.0, rasterized=True)
# im = m.imshow(rgb,cmap=topopalette,extent=topoextent)
# figure out the aspect ratio of the axes
print "Map width: %s" % commify(int(m.xmax - m.xmin))
print "Map height: %s" % commify(int(m.ymax - m.ymin))
aspect = (m.xmax - m.xmin) / (m.ymax - m.ymin)
slope = -0.33
intercept = 0.463
leftx = slope * aspect + intercept
print "Left edge of left colorbar is at: %.2f" % leftx
slope = 20.833
intercept = -0.833
titlex = slope * aspect + intercept
print "Title X of right colorbar is at: %.2f" % titlex
# this business apparently has to happen after something has been
# rendered on the map, which I guess makes sense.
# draw the map ticks on outside of all edges
fig.canvas.draw() # have to do this for tick stuff to show
xticks, xlabels, yticks, ylabels = getMapTicks(m, xmin, xmax, ymin, ymax)
plt.sca(ax)
plt.tick_params(axis="both", direction="in", right="on", colors="white")
plt.xticks(xticks, xlabels, size=8)
plt.yticks(yticks, ylabels, size=8)
for tick in ax.axes.yaxis.get_major_ticks():
tick.set_pad(-38)
tick.label2.set_horizontalalignment("right")
for tick in ax.axes.xaxis.get_major_ticks():
tick.set_pad(-15)
tick.label2.set_verticalalignment("top")
[i.set_color("white") for i in plt.gca().get_xticklabels()]
[i.set_color("white") for i in plt.gca().get_yticklabels()]
# render liquefaction
lqdat = lqgrid.getData().copy() * 100.0
palettelq = cm.autumn_r
i = np.where(lqdat < 2.0)
lqdat[i] = 0
lqdat[iwater] = 0
lqdatm = np.ma.masked_equal(lqdat, 0)
palettelq.set_bad(clear_color, alpha=0.0)
xmin, xmax, ymin, ymax = lqgrid.getRange()
lons = np.arange(xmin, xmax, lqgrid.getGeoDict()["xdim"])
lons = lons[
: lqgrid.getGeoDict()["ncols"]
] # make sure it's the right length, sometimes it is one too long, sometimes it isn't because of GMTGrid issue
lats = np.arange(ymax, ymin, -lqgrid.getGeoDict()["ydim"]) # backwards so it plots right side up
lats = lats[
: lqgrid.getGeoDict()["nrows"]
] # make sure it's the right length, sometimes it is one too long, sometimes it isn't because of GMTGrid issue
# make meshgrid
llons, llats = np.meshgrid(lons, lats)
x, y = m(llons, llats) # get projection coordinates
lqprobhandle = m.pcolormesh(x, y, lqdatm, lw=0, cmap=palettelq, vmin=2.0, vmax=20.0, alpha=ALPHA, rasterized=True)
# extent = getGridExtent(lqgrid,m)
# lqprobhandle = m.imshow(lqdatm,cmap=palettelq,vmin=2.0,vmax=20.0,alpha=ALPHA,origin='upper',extent=extent)
norm = mpl.colors.Normalize(vmin=2.0, vmax=20.0)
cbarlq = m.colorbar(mappable=lqprobhandle, norm=norm, cmap=palettelq)
cbarlq.solids.set_rasterized(True)
# cbarlq.solids.set_edgecolor("face")
# plt.draw()
cbarlq.set_ticks([2.0, 11.0, 19.0])
cbarlq.set_ticklabels(["Low", "Medium", "High"]) # vertically oriented colorbar
# render landslide
topogrid2 = GMTGrid()
topogrid2.loadFromGrid(topogrid)
topogrid2.interpolateToGrid(lsgrid.geodict)
lsdat = lsgrid.getData().copy() * 100.0
clear_color = [0, 0, 0, 0.0]
palettels = cm.cool
i = np.where(lsdat < 2.0)
lsdat[i] = 0
lsdat[iwater] = 0
lsdatm = np.ma.masked_equal(lsdat, 0)
palettels.set_bad(clear_color, alpha=0.0)
xmin, xmax, ymin, ymax = lsgrid.getRange()
lons = np.arange(xmin, xmax, lsgrid.getGeoDict()["xdim"])
lons = lons[
: lsgrid.getGeoDict()["ncols"]
] # make sure it's the right length, sometimes it is one too long, sometimes it isn't because of GMTGrid issue
lats = np.arange(ymax, ymin, -lsgrid.getGeoDict()["ydim"]) # backwards so it plots right side up
# make meshgrid
lats = lats[: lsgrid.getGeoDict()["nrows"]]
llons, llats = np.meshgrid(lons, lats)
x, y = m(llons, llats) # get projection coordinates
lsprobhandle = m.pcolormesh(x, y, lsdatm, lw=0, cmap=palettels, vmin=2.0, vmax=20.0, alpha=ALPHA, rasterized=True)
# extent = getGridExtent(lsgrid,m)
# lsprobhandle = plt.imshow(lsdatm,cmap=palettels,vmin=2.0,vmax=20.0,alpha=ALPHA,origin='upper',extent=extent)
# draw landslide colorbar on the left side
axleft = fig.add_axes([leftx, 0.1, 0.033, 0.8])
norm = mpl.colors.Normalize(vmin=2.0, vmax=20.0)
cbarls = mpl.colorbar.ColorbarBase(axleft, cmap=palettels, norm=norm, orientation="vertical")
cbarls.solids.set_rasterized(True)
cbarls.ax.yaxis.set_ticks_position("left")
cbarls.set_ticks([2.0, 11.0, 19.0])
cbarls.set_ticklabels(["Low", "Medium", "High"]) # vertically oriented colorbar
# draw roads on the map, if they were provided to us
roadcolor = ROADCOLOR
if colors.has_key("roadcolor"):
roadcolor = "#" + colors["roadcolor"]
for road in roadslist:
xy = list(road["geometry"]["coordinates"])
roadx, roady = zip(*xy)
mapx, mapy = m(roadx, roady)
m.plot(mapx, mapy, roadcolor, lw=1.0)
# add city names to map with population >50,000 (add option later)
if cityfile is not None:
dmin = 0.04 * (m.ymax - m.ymin)
xyplotted = []
cities = PagerCity(cityfile)
# Find cities within bounding box
boundcity = cities.findCitiesByRectangle(bounds=(xmin, xmax, ymin, ymax))
# Just keep 7 biggest cities
thresh = sorted([cit["pop"] for cit in boundcity])[-7]
plotcity = [cit for cit in boundcity if cit["pop"] >= thresh]
# For cities that are more than one xth of the xwidth apart, keep only the larger one
pass # do later
# Plot cities
for (
cit
) in (
plotcity
): # should sort so it plots them in order of population so larger cities are preferentially plotted - do later
xi, yi = m(cit["lon"], cit["lat"])
dist = [np.sqrt((xi - x0) ** 2 + (yi - y0) ** 2) for x0, y0 in xyplotted]
if not dist or np.min(dist) > dmin:
m.scatter(cit["lon"], cit["lat"], c="k", latlon=True, marker=".")
ax.text(xi, yi, cit["name"], ha="right", va="top", fontsize=8)
xyplotted.append((xi, yi))
# draw titles
cbartitle_ls = "Landslide\nProbability"
plt.text(-1.0, 1.03, cbartitle_ls, multialignment="left", axes=ax)
cbartitle_ls = "Liquefaction\nProbability"
plt.text(titlex, 1.03, cbartitle_ls, multialignment="left", axes=ax)
axwidth = 20 # where can I get this from?
# draw a map boundary, fill in oceans with water
# water_color = [.47,.60,.81] # too similar to a blue shade in ls colorbar
water_color = "#B8EEFF"
m.drawmapboundary(fill_color=water_color)
if isScenario:
title = eventdict["loc"]
else:
timestr = eventdict["time"].strftime("%b %d %Y")
title = "M%.1f %s v%i\n %s" % (eventdict["mag"], timestr, eventdict["version"], eventdict["loc"])
# draw the title on the plot
ax.set_title(title)
# draw star at epicenter
plt.sca(ax)
elat, elon = eventdict["epicenter"]
ex, ey = m(elon, elat)
plt.plot(ex, ey, "*", markeredgecolor="k", mfc="None", mew=1.5, ms=24)
# fill in the lakes and rivers
m.fillcontinents(color=clear_color, lake_color=water_color)
m.drawrivers(color=water_color)
# draw country boundaries
countrycolor = COUNTRYCOLOR
if colors.has_key("countrycolor"):
countrycolor = "#" + colors["countrycolor"]
m.drawcountries(color=countrycolor, linewidth=1.0)
# add map scale
m.drawmapscale(
(xmax + xmin) / 2,
(ymin + (ymax - ymin) / 150),
clon,
clat,
np.round((((xmax - xmin) * 110) / 5) / 10.0) * 10,
barstyle="fancy",
)
# draw coastlines
m.drawcoastlines(color="#476C91", linewidth=0.5)
# draw scenario watermark, if scenario
if isScenario:
plt.sca(ax)
cx, cy = m(clon, clat)
plt.text(cx, cy, "SCENARIO", rotation=45, alpha=0.10, size=72, ha="center", va="center", color="red")
# plt.title(ptitle,axes=ax)
outfile = os.path.join(outfolder, "%s.pdf" % eventdict["eventid"])
pngfile = os.path.join(outfolder, "%s.png" % eventdict["eventid"])
plt.savefig(outfile)
plt.savefig(pngfile)
print "Saving map output to %s" % outfile
print "Saving map output to %s" % pngfile