def readTimeGrid(timefile):
stkeys = ['TOTALROWBYTES','NBITS','LAYOUT','YDIM','NCOLS',
'BANDROWBYTES','PIXELTYPE','XDIM','NROWS',
'NBANDS','ULXMAP','ULYMAP','BYTEORDER']
src = rasterio.open(timefile,'r',driver='EHdr')
timedata, = src.read()
m,n = timedata.shape
aff = src.affine
xdim = aff[0]
xmin = aff[2]
ydim = -aff[4]
ymax = aff[5]
src.close()
timegrid = GMTGrid()
timegrid.griddata = timedata
timegrid.geodict = {'nrows':m,'ncols':n,'nbands':1,'bandnames':['Alert Time'],
'xmin':xmin,'xmax':xmin+n*xdim,'ymin':ymax-m*ydim,'ymax':ymax,
'xdim':xdim,'ydim':ydim}
timepath,timefile = os.path.split(timefile)
timebase,timext = os.path.splitext(timefile)
timehdr = os.path.join(timepath,timebase+'.hdr')
timedict = readTimeHeader(timehdr)
for key in stkeys:
timedict.pop(key)
for key,value in timedict.iteritems():
if isinstance(value,str):
timedict[key] = value.replace('"','')
return (timegrid,timedict)
def renderPanel(logmodel, colormaps, outfolder, edict):
nparray = "<type 'numpy.ndarray'>"
# first, figure out how many layers we have
layerdict = logmodel.layerdict
outfiles = []
for smterm in model.SM_TERMS:
for term in logmodel.terms.values():
if term.find(smterm) > -1 and not isinstance(logmodel.shakedict[smterm], float):
layerdict[smterm] = logmodel.shakedict[smterm]
for layername, layergrid in layerdict.iteritems():
fig = plt.figure(figsize=(8, 8))
ax = plt.gca()
renderLayer(layername, layergrid, outfolder, edict, fig, ax, logmodel.model, colormaps)
outfile = os.path.join(outfolder, "%s_%s.pdf" % (layername, logmodel.model))
print "Saving input layer %s to %s" % (layername, outfile)
plt.savefig(outfile)
outfiles.append(outfile)
outfile = os.path.join(outfolder, "%s_model.pdf" % logmodel.model)
fig = plt.figure(figsize=(8, 8))
ax = plt.gca()
P = logmodel.calculate()
pgrid = GMTGrid()
pgrid.griddata = P.copy()
pgrid.geodict = layergrid.geodict.copy()
renderLayer(logmodel.model, pgrid, outfolder, edict, fig, ax, logmodel.model, colormaps)
print "Saving %s model to %s" % (logmodel.model, outfile)
outfiles.append(outfile)
return outfiles
def makeCoverageGrid(covshp,geodict):
shapes = fiona.open(covshp)
geoms = []
for shape in shapes:
geoms.append(shape['geometry'])
shapes.close()
outshape = (geodict['nrows'],geodict['ncols'])
transform = Affine.from_gdal(geodict['xmin'],geodict['xdim'],0.0,geodict['ymax'],0.0,-geodict['ydim'])
img = features.rasterize(geoms,out_shape=outshape,fill=0,
transform=transform,all_touched=True,
default_value=1)
covgrid = GMTGrid()
covgrid.geodict = geodict
covgrid.griddata = np.int8(img.copy())
return covgrid
def readTimeGrid(timefile):
stkeys = [
'TOTALROWBYTES', 'NBITS', 'LAYOUT', 'YDIM', 'NCOLS', 'BANDROWBYTES',
'PIXELTYPE', 'XDIM', 'NROWS', 'NBANDS', 'ULXMAP', 'ULYMAP', 'BYTEORDER'
]
src = rasterio.open(timefile, 'r', driver='EHdr')
timedata, = src.read()
m, n = timedata.shape
aff = src.affine
xdim = aff[0]
xmin = aff[2]
ydim = -aff[4]
ymax = aff[5]
src.close()
timegrid = GMTGrid()
timegrid.griddata = timedata
timegrid.geodict = {
'nrows': m,
'ncols': n,
'nbands': 1,
'bandnames': ['Alert Time'],
'xmin': xmin,
'xmax': xmin + n * xdim,
'ymin': ymax - m * ydim,
'ymax': ymax,
'xdim': xdim,
'ydim': ydim
}
timepath, timefile = os.path.split(timefile)
timebase, timext = os.path.splitext(timefile)
timehdr = os.path.join(timepath, timebase + '.hdr')
timedict = readTimeHeader(timehdr)
for key in stkeys:
timedict.pop(key)
for key, value in timedict.iteritems():
if isinstance(value, str):
timedict[key] = value.replace('"', '')
return (timegrid, timedict)
def getTimeExposure(timegriddata,mmigrid,popfile,mmithresh):
timegrid = GMTGrid()
timegrid.griddata = timegriddata.copy()
timegrid.geodict = mmigrid.geodict.copy()
popgrid = EsriGrid(popfile)
popgrid.load(bounds=timegrid.getRange())
timegrid.interpolateToGrid(popgrid.geodict)
timegrid.griddata[mmigrid.griddata < mmithresh] = np.NaN
times = np.arange(MINTIME,MAXTIME+DTIME,DTIME)
exposure = []
mintime = MINTIME
ireal = np.isfinite(timegrid.griddata)
for time in times[1:]:
ipop = ((timegrid.griddata >= mintime) & (timegrid.griddata < time) & np.isfinite(timegrid.griddata))
exposum = int(np.sum(popgrid.griddata[ipop]))
exposure.append({'mintime':mintime,'maxtime':time,'exposure':exposum})
mintime = time
return (exposure,timegrid.griddata)
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
#!/usr/bin/env python
import sys
import os.path
from neicio.gmt import GMTGrid
from neicio.esri import EsriGrid
import numpy as np
if __name__ == '__main__':
folder = sys.argv[1]
popfile = sys.argv[2]
slidefile = os.path.join(folder,'landslide.grd')
slidegrid = GMTGrid(slidefile)
slidegrid.load()
bounds = slidegrid.getRange()
popgrid = EsriGrid(popfile)
popgrid.load(bounds=bounds)
slidegrid.interpolateToGrid(popgrid.geodict)
expsum = np.nansum(slidegrid.griddata * popgrid.griddata)
parts = folder.split(os.sep)
print 'Event %s exposure: %i' % (parts[-1],int(expsum))
def main(args):
#read in global config file
configfile = os.path.join(os.path.expanduser('~'),'.lsprocess','lsprocess.cfg')
hasconfig = os.path.isfile(configfile)
if not hasconfig:
print()
print('No config file "%s" found.' % configfile)
print()
sys.exit(1)
global_grids,outfolder = readConfig(configfile) #returns a dictionary just like global_config above
#read in event specific grid file
try:
covdict,predictors,ename = parseEvent(args.eventfile)
except Exception as msg:
print('There is something wrong with your event file. See errors below.')
print(msg)
sys.exit(1)
#construct output folder from global/event configs
outfolder = os.path.join(outfolder,ename)
if not os.path.isdir(outfolder):
os.mkdir(outfolder)
#look for bounding box and resolution in event config file, or get from shakemap
bbox = None
shakemap = ShakeGrid(predictors['shakemap'][0],'MMI')
if 'bbox' in covdict:
bbox = covdict['bbox']
else:
#bbox = shakemap.getRange()
#default to the bounding box of the coverage data
with fiona.open(covdict['filename']) as src:
tbbox = src.bounds
bbox = (tbbox[0],tbbox[2],tbbox[1],tbbox[3])
if 'resolution' in covdict:
resolution = covdict['resolution']
else:
resolution = shakemap.getGeoDict()['xdim']
#get input coverage projection from event config OR from .prj file
#projstr = covdict['projstr']
#get format of coverage, check against list of supported fiona formats, read in data
#we'll do other support later
#if necessary, project coverage into lat/lon
#skip projection for now as well
#determine what the grid shape and (potentially) new bbox is given bbox and resolution
nrows,ncols,bbox = getShape(bbox,resolution)
#if the coverage dataset is larger than the ShakeMap, we need to make sure our output grid
#is contained by the shakemap for interpolation purposes.
shakebounds = shakemap.getRange()
shakexdim,shakeydim = (shakemap.geodict['xdim'],shakemap.geodict['ydim'])
xmin = max(bbox[0],shakebounds[0]+shakexdim*2)
xmax = min(bbox[1],shakebounds[1]-shakexdim*2)
ymin = max(bbox[2],shakebounds[2]+shakeydim*2)
ymax = min(bbox[3],shakebounds[3]-shakeydim*2)
geodict = {'xdim':resolution,'ydim':resolution,
'xmin':xmin,'xmax':xmax,
'ymin':ymin,'ymax':ymax,
'nrows':nrows,'ncols':ncols}
#rasterize projected coverage defined bounding box and resolution
shpfile = covdict['filename']
print('Creating coverage grid...')
covgrid = makeCoverageGrid(shpfile,geodict)
outgridfile = os.path.join(outfolder,'coverage.grd')
print('Saving coverage to %s...' % outgridfile)
covgrid.save(outgridfile)
#make a grid of lat,lon values
row = np.arange(0,nrows)
col = np.arange(0,ncols)
rows = repmat(row,ncols,1).T
cols = repmat(col,nrows,1)
lat,lon = covgrid.getLatLon(rows,cols)
#create a list of arrays that we'll dump out to a text file when done
vardict = {}
vardict['coverage'] = covgrid.griddata.flatten()
vardict['lat'] = lat.flatten()
vardict['lon'] = lon.flatten()
#subset shakemap and global grids using defined bounding box and resolution
shakefile = predictors['shakemap'][0]
variables = predictors['shakemap'][1]
for var in variables:
shakemap = ShakeGrid(shakefile,var.upper())
shakemap.interpolateToGrid(geodict)
gmtshake = GMTGrid()
gmtshake.geodict = shakemap.geodict
gmtshake.griddata = shakemap.griddata
outshakefile = os.path.join(outfolder,'%s.grd' % var)
print('Saving %s to %s...' % (var,outshakefile))
gmtshake.save(outshakefile)
vardict[var] = gmtshake.griddata.flatten()
#write netcdf versions of coverage, shakemap, and global grids to output folder
for gridname,gridfile in global_grids.items():
if not os.path.isfile(gridfile):
pass
try:
grid = sampleGrid(gridfile,geodict)
except Exception as e:
print('There was an error while sampling the "%s" grid "%s". - "%s"' % (gridname,gridfile,str(e)))
outgridfile = os.path.join(outfolder,gridname+'.grd')
print('Saving %s to %s...' % (gridname,outgridfile))
grid.save(outgridfile)
vardict[gridname] = grid.griddata.flatten()
#create text file with columns of data for all predictor variables
firstcols = ['lat','lon','coverage']
outmat = np.zeros((nrows*ncols,len(vardict)))
for i in range(0,len(firstcols)):
col = firstcols[i]
outmat[:,i] = vardict[col]
colidx = i+1
colnames = []
for col,column in vardict.items():
if col in firstcols:
continue
outmat[:,colidx] = vardict[col]
colnames.append(col)
colidx += 1
colnames = firstcols + colnames
m,n = outmat.shape
datfile = os.path.join(outfolder,'%s.dat' % ename)
print('Saving all variables to data file %s...' % datfile)
f = open(datfile,'wt')
f.write(','.join(colnames)+'\n')
for i in range(0,m):
line = ','.join('%.4f' % col for col in outmat[i,:])
f.write(line+'\n')
f.close()
cols = repmat(col,nrows,1)
lat,lon = covgrid.getLatLon(rows,cols)
#create a list of arrays that we'll dump out to a text file when done
vardict = {}
vardict['coverage'] = covgrid.griddata.flatten()
vardict['lat'] = lat.flatten()
vardict['lon'] = lon.flatten()
#subset shakemap and global grids using defined bounding box and resolution
shakefile = predictors['shakemap'][0]
variables = predictors['shakemap'][1]
for var in variables:
shakemap = ShakeGrid(shakefile,var.upper())
shakemap.interpolateToGrid(geodict)
gmtshake = GMTGrid()
gmtshake.geodict = shakemap.geodict
gmtshake.griddata = shakemap.griddata
outshakefile = os.path.join(outfolder,'%s.grd' % var)
print 'Saving %s to %s...' % (var,outshakefile)
gmtshake.save(outshakefile)
vardict[var] = gmtshake.griddata.flatten()
#write netcdf versions of coverage, shakemap, and global grids to output folder
for gridname,gridfile in global_grids.iteritems():
if not os.path.isfile(gridfile):
pass
try:
grid = sampleGrid(gridfile,geodict)
except Exception,e:
print 'There was an error while sampling the "%s" grid "%s". - "%s"' % (gridname,gridfile,str(e))
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
