except:
data = nc.variables['z'][:] / zscale
lons = nc.variables['x'][:]
lats = nc.variables['y'][:]
# transform to metres
nx = int((m.xmax-m.xmin)/500.)+1
ny = int((m.ymax-m.ymin)/500.)+1
topodat = m.transform_scalar(data,lons,lats,nx,ny)
print('Getting colormap...')
# get colormap
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//mby_topo-bath.cpt'
#cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//gray.cpt'
cmap, zvals = cpt2colormap(cptfile, 256)
cmap = remove_last_cmap_colour(cmap)
# make shading
print('Making map...')
ls = LightSource(azdeg = 180, altdeg = 45)
norm = mpl.colors.Normalize(vmin=-8000/zscale, vmax=5000/zscale)#myb
rgb = ls.shade(topodat, cmap=cmap, norm=norm)
im = m.imshow(rgb, alpha=0.6)
'''
##########################################################################################
# add epicentres
##########################################################################################
ncols = 18
elif pltProb == '2':
probidx = 2
'''
altPlaces = False
pltLog = False
# get colours
if getcwd().startswith('/nas'):
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/gay-flag-1978.cpt'
else:
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//gay-flag-1978.cpt'
ncolours = 9
cmap, zvals = cpt2colormap(cptfile, ncolours)
cmap = remove_last_cmap_colour(cmap)
cs = (cmap(arange(ncolours - 1)))
###############################################################################
# parse uhs file
###############################################################################
lines = open(uhsfile).readlines()
headers = [x for x in lines[1].strip().split(',')]
# get keys from uhs file
keys = lines[1].strip().split(',')[2:]
# get peridos in keys
periods = []
zscale = 20. #colour
data = nc.variables['elevation'][:] / zscale
lons = nc.variables['lon'][:]
lats = nc.variables['lat'][:]
# transform to metres
nx = int((m.xmax - m.xmin) / 500.) + 1
ny = int((m.ymax - m.ymin) / 500.) + 1
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print('Getting colormap...')
# get colormap
try:
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//wiki-2.0.cpt'
cmap, zvals = cpt2colormap(cptfile, 30)
except:
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/wiki-2.0.cpt'
cmap, zvals = cpt2colormap(cptfile, 30)
cmap = remove_last_cmap_colour(cmap)
# make shading
print('Making map...')
ls = LightSource(azdeg=180, altdeg=5)
#norm = mpl.colors.Normalize(vmin=-8000/zscale, vmax=5000/zscale)#myb
norm = mpl.colors.Normalize(vmin=-1000 / zscale, vmax=1900 / zscale) #wiki
rgb = ls.shade(topodat.data, cmap=cmap, norm=norm)
im = m.imshow(rgb, alpha=1.0)
from gmt_tools import cpt2colormap, remove_last_cmap_colour
from os import getcwd
import matplotlib as mpl
from matplotlib import colors, colorbar
mpl.style.use('classic')
##########################
# get colours
##########################
if getcwd().startswith('/nas'):
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/mi_pop.cpt'
else:
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//mi_pop.cpt'
ncols = 10
cmap, zvals = cpt2colormap(cptfile, ncols + 1, rev=False)
cmap = remove_last_cmap_colour(cmap)
cs = (cmap(arange(ncols)))
##########################
# set mmi
##########################
rand_mmi = 10 * random.rand(49) / 2 + 1
rand_mmi[10] = 7.
rand_mmi[29] = 7.
idx = rand_mmi < 2.5
rand_mmi[idx] = 3.
rand_mmi[21] = 2.
rand_mmi[4] = 2.
lons = nc.variables['lon'][:]
lats = nc.variables['lat'][:]
'''
# transform to metres
nx = int((m.xmax - m.xmin) / 30.) + 1
ny = int((m.ymax - m.ymin) / 30.) + 1
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print('Getting colormap...')
# get colormap
if getcwd().startswith('/nas'):
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/wiki-2.0.cpt'
else:
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//wiki-2.0.cpt'
cmap, zvals = cpt2colormap(cptfile, 117)
print(len(zvals))
cmap = remove_last_cmap_colour(cmap)
# make shading
print('Making map...')
ls = LightSource(azdeg=180, altdeg=5)
#norm = mpl.colors.Normalize(vmin=-8000/zscale, vmax=5000/zscale)
norm = mpl.colors.Normalize(vmin=-1000 / zscale, vmax=1900 / zscale) #wiki
rgb = ls.shade(topodat.data, cmap=cmap, norm=norm)
im = m.imshow(rgb, alpha=1.)
##########################################################################################
# add simple faults
##########################################################################################
if getcwd().startswith('/nas'):
hazFactAS1170 = array(hazFactAS1170)
hazFactNSHA10 = array(hazFactNSHA10)
hazFactNSHA2 = array(hazFactNSHA2)
# set min AS1170.4 value to 0.08
idx = hazFactAS1170 < 0.08
hazFactAS1170[idx] = 0.08
##########################################################################################
# get cmap
##########################################################################################
ncols = 17
try:
cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//temp_18lev.cpt'
cmap, zvals = cpt2colormap(cptfile, ncols)
except:
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/temp_19lev.cpt'
cmap, zvals = cpt2colormap(cptfile, ncols)
cmap = remove_last_cmap_colour(cmap)
cs = (cmap(arange(ncols-1)))
##########################################################################################
# make first plot AS1170.4-2018 / NSHA 10%
##########################################################################################
llcrnrlat = -44
urcrnrlat = -8.
llcrnrlon = 107.
def get_basemap(llcrnrlon, urcrnrlon, llcrnrlat, urcrnrlat, res, fig,
useGEBCO):
'''
useGEBCO = True: use GEBCO data
useGEBCO = False: use shadedrelief
'''
from mpl_toolkits.basemap import Basemap
import matplotlib as mpl
from matplotlib.colors import LightSource
from netCDF4 import Dataset as NetCDFFile
from os import getcwd
cwd = getcwd()
lon_0 = mean([llcrnrlon, urcrnrlon])
lat_1 = percentile([llcrnrlat, urcrnrlat], 25)
lat_2 = percentile([llcrnrlat, urcrnrlat], 75)
plt.tick_params(labelsize=16)
ax = fig.add_subplot(111)
m = Basemap(projection='lcc',lat_1=lat_1,lat_2=lat_2,lon_0=lon_0,\
llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat, \
urcrnrlon=urcrnrlon,urcrnrlat=urcrnrlat,\
rsphere=6371200.,resolution=res,area_thresh=10.)
# draw coastlines, state and country boundaries, edge of map.
if useGEBCO == False:
m.shadedrelief()
m.drawcoastlines()
m.drawstates()
m.drawcountries()
m.drawparallels(arange(-90., 90., 1.),
labels=[1, 0, 0, 0],
fontsize=16,
dashes=[2, 2],
color='0.5',
linewidth=0.75)
m.drawmeridians(arange(0., 360., 2.),
labels=[0, 0, 0, 1],
fontsize=16,
dashes=[2, 2],
color='0.5',
linewidth=0.75)
#m.drawmapscale(144, -34.8, 146., -38.5, 400, fontsize = 16, barstyle='fancy', zorder=100)
##########################################################################################
# plot gebco
##########################################################################################
if useGEBCO == True:
print 'Reading netCDF file...'
if cwd.startswith('/nas'):
nc = NetCDFFile(
'//nas//gemd//ehp//georisk_earthquake//hazard//DATA//GEBCO//au_gebco.nc'
)
cptfile = '//nas//gemd//ehp//georisk_earthquake//hazard//DATA//cpt//mby_topo-bath.cpt'
#cptfile = '//nas//gemd//ehp//georisk_earthquake//hazard//DATA//cpt//gray.cpt'
#cptfile = '//nas//gemd//ehp//georisk_earthquake//hazard//DATA//cpt//mby_topo-bath_mod.cpt'
else:
nc = NetCDFFile(
'//Users//tallen//Documents//DATA//GMT//GEBCO//Australia_30c.nc'
)
cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//mby_topo-bath.cpt'
zscale = 20. #gray
zscale = 30. #colour
data = nc.variables['elevation'][:] / zscale
lons = nc.variables['lon'][:]
lats = nc.variables['lat'][:]
# transform to metres
nx = int((m.xmax - m.xmin) / 500.) + 1
ny = int((m.ymax - m.ymin) / 500.) + 1
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print 'Getting colormap...'
# get colormap
#cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//gray.cpt'
cmap, zvals = cpt2colormap(cptfile, 256)
cmap = remove_last_cmap_colour(cmap)
#cmap = remove_last_cmap_colour(cmap)
# make shading
print 'Making map...'
ls = LightSource(azdeg=300, altdeg=45)
norm = mpl.colors.Normalize(vmin=-8000 / zscale,
vmax=5000 / zscale) #myb
rgb = ls.shade(topodat, cmap=cmap, norm=norm) #norm=norm)
im = m.imshow(rgb, alpha=0.4)
return m, ax
zscale = 10. #colour
data = nc.variables['z'][:] / zscale
lons = nc.variables['x'][:]
lats = nc.variables['y'][:]
# transform to metres
nx = int((m.xmax - m.xmin) / 30.) + 1
ny = int((m.ymax - m.ymin) / 30.) + 1
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print 'Getting colormap...'
# get colormap
cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//mby_topo-bath_mod.cpt'
#cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//gray.cpt'
cmap, zvals = cpt2colormap(cptfile, 256)
#cmap = cm.get_cmap('terrain', 256)
# make shading
print 'Making map...'
ls = LightSource(azdeg=180, altdeg=45)
norm = mpl.colors.Normalize(vmin=-8000 / zscale, vmax=5000 / zscale) #myb
rgb = ls.shade(topodat, cmap=cmap, norm=norm)
im = m.imshow(rgb)
##########################################################################################
# plot epicentres
##########################################################################################
fig = plt.figure(1, figsize=(10, 10))
ncols = 10
#cmap = plt.cm.get_cmap('Spectral', ncols)
lats = nc.variables['lat'][:]
# transform to metres
nx = int((m.xmax - m.xmin) / 500.) + 1
ny = int((m.ymax - m.ymin) / 500.) + 1
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print('Getting colormap...')
# get colormap
try:
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//wiki-2.0.cpt'
except:
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/wiki-2.0.cpt'
cmap, zvals = cpt2colormap(cptfile, 30)
cmap = remove_last_cmap_colour(cmap)
# make shading
print('Making map...')
ls = LightSource(azdeg=180, altdeg=5)
#norm = mpl.colors.Normalize(vmin=-8000/zscale, vmax=5000/zscale)
norm = mpl.colors.Normalize(vmin=-1000 / zscale, vmax=1900 / zscale) #wiki
rgb = ls.shade(topodat.data, cmap=cmap, norm=norm)
im = m.imshow(rgb, alpha=1.0)
##########################################################################################
# add epicentres
##########################################################################################
# get colormap
ftypes = ['Intraslab', 'Outer Rise', 'Strike-Slip']
grads = [lengrd1, widgrd1, areagrd1, mxsgrd, avsgrd, lwgrd]
fmag = [intra_fmag, outer_fmag, trans_fmag]
flen = [intra_flen, outer_flen, trans_flen]
fwid = [intra_fwid, outer_fwid, trans_fwid]
farea = [intra_farea, outer_farea, trans_farea]
fmxs = [intra_fmxs, outer_fmxs, trans_fmxs]
favs = [intra_favs, outer_favs, trans_favs]
mrng = arange(7.2, 9.61, 0.01)
omrng = arange(7.0, 8.71, 0.01)
from gmt_tools import cpt2colormap
cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/GMT_no_green.cpt'
#cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/temperature.cpt'
#cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/gay-flag-1978.cpt'
cmap = cpt2colormap(cptfile, 6)[0]
#cmap = plt.cm.get_cmap('hsv', 5)
#cs = (cmap(arange(5)))
#cols = [cs[1], cs[2], cs[3]]
cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/temp-c.cpt'
ncolours = 6
cmap2, zvals = cpt2colormap(cptfile, ncolours, rev=False)
cs2 = (cmap2(arange(ncolours)))
#cs2 = vstack(([cs2[0], cs2[2:4], cs2[5:]]))
#cols = [cs2[1], cs2[2], cs2[3]]
ncolours = 6
cmap = plt.cm.get_cmap('Spectral', ncolours)
colours = (cmap(arange(ncolours)))
bounds = array([
0, 0.01, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.06, 0.08,
0.12, 0.16, 0.2, 0.3
])
else:
bounds = array([
0, 0.001, 0.002, 0.003, 0.004, 0.005, 0.007, 0.015, 0.03, 0.04,
0.05, 0.06, 0.1, 0.16
])
ncolours = 13
if getcwd().startswith('/nas'):
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/modelling/sandpits/tallen/NSHA2018/postprocessing/maps/cw1-013_mod.cpt'
else:
cptfile = '/Users/tallen/Documents/Geoscience_Australia/NSHA2018/postprocessing/maps/cw1-013_mod.cpt'
cmap, zvals = cpt2colormap(cptfile, ncolours, rev=True)
rgbTable = cmap2rgb(cmap, ncolours)[0] * 255
# make gdal cpt file
cpttxt = ''
for bound, rgb in zip(bounds, rgbTable):
cpttxt += '\t'.join(
(str(bound), str(rgb[0]), str(rgb[1]), str(rgb[2]))) + '\n'
f = open('gdal_cpt.dat', 'wb')
f.write(cpttxt)
f.close()
# recolour geoTiff
intiff = path.join('geotiff', '_'.join(('nsha18', period, p50 + '.tiff')))
outtiff = intiff[0:-4] + '_colour.tiff'
nx = int((m.xmax - m.xmin) / 500.) + 1
ny = int((m.ymax - m.ymin) / 500.) + 1
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print('Getting colormap...')
# get colormap
try:
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//wiki-2.0.cpt'
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//lightgrey.cpt'
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//grey_fade.cpt'
except:
cptfile = '/nas/active/ops/community_safety/ehp/georisk_earthquake/hazard/DATA/cpt/wiki-2.0.cpt'
#cmap, zvals = cpt2colormap(cptfile, 30) # wiki
cmap, zvals = cpt2colormap(cptfile, 31) # grey
#cmap = remove_last_cmap_colour(cmap)
#cmap = ListedColormap
# make shading
print('Making map...')
ls = LightSource(azdeg=135, altdeg=10)
#norm = mpl.colors.Normalize(vmin=-8000/zscale, vmax=5000/zscale)#myb
#norm = mpl.colors.Normalize(vmin=-1000/zscale, vmax=1900/zscale)#wiki
norm = mpl.colors.Normalize(vmin=-2000 / zscale, vmax=3500 / zscale) #wiki
rgb = ls.shade(topodat.data, cmap=cmap, norm=norm)
im = m.imshow(rgb, alpha=1.0)
##########################################################################################
# http://stackoverflow.com/questions/23477775/matplotlib-mixed-subplots-with-plot-and-pcolormesh-to-have-the-same-x-axis-len
import matplotlib.pyplot as plt
from numpy import array, arange, random, linspace, meshgrid, dstack, amax, \
percentile, shape, where, mean
from scipy.stats import multivariate_normal
from gmt_tools import cpt2colormap
import matplotlib
matplotlib.rc('xtick', labelsize=16)
matplotlib.rc('ytick', labelsize=16)
cptfile = 'U:\\DATA\\GMT\\cpt\\grays.cpt'
cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//grays.cpt'
ncolours = 11
cmap, zvals = cpt2colormap(cptfile, ncolours, rev=False)
#cmap = plt.cm.get_cmap('gray_r', ncolours)
flen = 400 # km
fwid = 200 # km
subfsize = 10 # km
#cmap = cm.rainbow
vmin = 0.
vmax = 33.
random.seed(1000)
# Generate some interesting-looking random data...
xnum = flen / subfsize + 1
ynum = fwid / subfsize + 1
rndgrid = random.rand(ynum, xnum)
x = linspace(0, flen, xnum)
y = linspace(0, fwid, ynum)
'''
import matplotlib.pyplot as plt
#import matplotlib as mpl
pltpga = [0.1, 0.4]
mpl.rcParams['pdf.fonttype'] = 42
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
numcols = 6
cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/temperature.cpt'
cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//qual-dark-06.cpt' # try me!
cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//humidity.cpt'
cmap, zvals = cpt2colormap(cptfile, numcols)
cmap = remove_last_cmap_colour(cmap)
cs = (cmap(arange(numcols)))
# remove yellow
#idx=array([0, 1, 3, 4, 5])
#cs = cs[idx]
legtxt = ['Site Class B (Ae)', 'Site Class B/C (Be)', 'Site Class C (Ce)', 'Site Class D (De)', \
'Site Class E (Ee)'] #, 'F(T)', 'Fa & Fv']
figure = plt.figure(1,figsize=(19,8))
# Boarcherdt 1994 for 0.1 and 0.4 g
Fa01 = [0.6, 0.7, 1.0, 1.5, 1.5]
Fa04 = [1.1, 1.0, 1.0, 0.9, 0.9]
'''
########################################################################################
'''
# export coeffs
f = open('interface_coeffs.csv', 'wb')
f.write(coeftxt)
f.close()
'''
########################################################################################
'''
#from gmt_tools import cpt2colormap
#cptfile = 'U:\\DATA\\GMT\\cpt\\GMT_no_green.cpt'
cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/GMT_no_green.cpt'
cmap = cpt2colormap(cptfile, 5)[0] # colormap for data
cs = (cmap(arange(5)))
cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/gay-flag-1979.cpt'
#cptfile = '/Users/tallen/Documents/DATA/GMT/cpt/GMT_rainbow.cpt'
ncolours = 7
cmap2, zvals = cpt2colormap(cptfile, ncolours, rev=False)
cs2 = (cmap2(arange(ncolours)))
#cs2 = vstack(([cs2[0:4], cs2[5:]]))
'''
########################################################################################
'''
# plot length
fig = plt.figure(1, figsize=(11.5, 17))
ax = plt.subplot(3, 2, 1, aspect=1)
plt.axis('equal')
