def test_bounds_within_meridian():
host = GeoDict({'xmin': -180,
'xmax': 150,
'ymin': -90,
'ymax': 90,
'dx': 30,
'dy': 45,
'nx': 12,
'ny': 5})
sample = GeoDict({'xmin': 75,
'xmax': -135,
'ymin': -67.5,
'ymax': 67.5,
'dx': 30,
'dy': 45,
'nx': 6,
'ny': 4})
result = GeoDict({'xmin': 90,
'xmax': -150,
'ymin': -45,
'ymax': 45,
'dx': 30,
'dy': 45,
'nx': 5,
'ny': 3})
inside = host.getBoundsWithin(sample)
assert inside == result
def test_grid_hdf_container():
f,fname = tempfile.mkstemp()
os.close(f)
try:
#test grid container
container = GridHDFContainer.create(fname)
# before we put anything in here, let's make sure we get empty lists from
# all of the methods that are supposed to return lists of stuff.
assert container.getGrids() == []
#test grid2d
geodict = GeoDict.createDictFromBox(-118.5,-114.5,32.1,36.7,0.01,0.02)
nrows,ncols = geodict.ny,geodict.nx
data = np.random.rand(nrows,ncols)
metadata = {'name':'Gandalf',
'color':'white',
'powers':'magic'}
grid = Grid2D(data,geodict)
container.setGrid('testgrid',grid,metadata=metadata)
outgrid,outmetadata = container.getGrid('testgrid')
np.testing.assert_array_equal(outgrid.getData(),data)
assert outgrid.getGeoDict() == geodict
assert outmetadata == metadata
#set another grid without compression
geodict = GeoDict.createDictFromBox(-119.5,-115.5,32.3,37.7,0.01,0.02)
nrows,ncols = geodict.ny,geodict.nx
data = np.random.rand(nrows,ncols)
metadata = {'name':'Legolas',
'color':'green',
'powers':'stealth'}
grid2 = Grid2D(data,geodict)
container.setGrid('testgrid2',grid2,metadata=metadata,compression=False)
outgrid2,outmetadata2 = container.getGrid('testgrid2')
np.testing.assert_array_equal(outgrid2.getData(),data)
assert outgrid2.getGeoDict() == geodict
assert outmetadata2 == metadata
#test getGridNames()
names = container.getGrids()
assert sorted(names) == ['testgrid','testgrid2']
#test looking for a grid that does not exist
try:
container.getGrid('foo')
except LookupError as le:
pass
#test dropping a grid
container.dropGrid('testgrid2')
container.close()
container2 = GridHDFContainer.load(fname)
names = container2.getGrids()
assert sorted(names) == ['testgrid']
except:
assert 1==2
finally:
os.remove(fname)
def test_contains():
fxmin, fxmax = (-179.995833333333, 179.99583333189372)
fymin, fymax = (-89.99583333333332, 89.9958333326134)
fdx, fdy = (0.0083333333333, 0.0083333333333)
fnx, fny = (43200, 21600)
xmin, xmax = (-179.996, -177.496)
ymin, ymax = (-21.89175, -19.55425)
dx, dy = (0.025, 0.02513440860215052)
nx, ny = (101, 94)
host = GeoDict({'xmin': fxmin,
'xmax': fxmax,
'ymin': fymin,
'ymax': fymax,
'dx': fdx,
'dy': fdy,
'nx': fnx,
'ny': fny})
sample = GeoDict({'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'dx': dx,
'dy': dy,
'nx': nx,
'ny': ny})
assert host.contains(sample)
def test_bounds_within_again():
fxmin, fxmax = (-179.995833333333, 179.99583333189372)
fymin, fymax = (-89.99583333333332, 89.9958333326134)
fdx, fdy = (0.0083333333333, 0.0083333333333)
fnx, fny = (43200, 21600)
xmin, xmax = (97.233, 99.733)
ymin, ymax = (84.854, 85.074)
dx, dy = (0.025, 0.024444444444444317)
nx, ny = (101, 10)
host = GeoDict({'xmin': fxmin,
'xmax': fxmax,
'ymin': fymin,
'ymax': fymax,
'dx': fdx,
'dy': fdy,
'nx': fnx,
'ny': fny})
sample = GeoDict({'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'dx': dx,
'dy': dy,
'nx': nx,
'ny': ny})
inside = host.getBoundsWithin(sample)
def test_bounds_meridian2():
host = {'ny': 797, 'dx': 0.0083333333,
'ymin': -21.445972496439, 'dy': 0.0083333333,
'xmin': 178.33735967776101, 'nx': 841,
'xmax': -174.66264035023897, 'ymax': -14.812639189639}
sample = {'ny': 773, 'dx': 0.008333333333333333,
'ymin': -21.35, 'dy': 0.008333333333333333,
'xmin': 178.43333333333334, 'nx': 817,
'xmax': -174.76666666666665, 'ymax': -14.916666666666666}
host_geodict = GeoDict(host)
sample_geodict = GeoDict(sample)
within = host_geodict.getBoundsWithin(sample_geodict)
assert within.xmin == 178.437359677361
assert within.xmax == -174.770973683139
def test_write():
data = np.arange(0, 25).reshape(5, 5).astype(np.float32)
gdict = {
'xmin': 5.0,
'xmax': 9.0,
'ymin': 4.0,
'ymax': 8.0,
'dx': 1.0,
'dy': 1.0,
'nx': 5,
'ny': 5
}
gd = GeoDict(gdict)
grid = Grid2D(data, gd)
for format_type in ['netcdf', 'esri', 'hdf']:
tdir = tempfile.mkdtemp()
fname = os.path.join(tdir, 'tempfile.grd')
try:
write(grid, fname, format_type)
src = rasterio.open(fname, 'r')
tdata = src.read(1)
np.testing.assert_almost_equal(tdata, data)
except Exception as e:
raise e
finally:
shutil.rmtree(tdir)
def test_rasterize():
geodict = GeoDict({'xmin':0.5,'xmax':3.5,
'ymin':0.5,'ymax':3.5,
'dx':1.0,'dy':1.0,
'ny':4,'nx':4})
print('Testing rasterizeFromGeometry() burning in values from a polygon sequence...')
#Define two simple polygons and assign them to shapes
poly1 = [(0.25,3.75),(1.25,3.25),(1.25,2.25)]
poly2 = [(2.25,3.75),(3.25,3.75),(3.75,2.75),(3.75,1.50),(3.25,0.75),(2.25,2.25)]
shape1 = {'properties':{'value':5},'geometry':mapping(Polygon(poly1))}
shape2 = {'properties':{'value':7},'geometry':mapping(Polygon(poly2))}
shapes = [shape1,shape2]
print('Testing burning in values where polygons need not contain pixel centers...')
grid = Grid2D.rasterizeFromGeometry(shapes,geodict,fillValue=0,attribute='value',mustContainCenter=False)
output = np.array([[5,5,7,7],
[5,5,7,7],
[0,0,7,7],
[0,0,0,7]])
np.testing.assert_almost_equal(grid.getData(),output)
print('Passed burning in values where polygons need not contain pixel centers.')
print('Testing burning in values where polygons must contain pixel centers...')
grid2 = Grid2D.rasterizeFromGeometry(shapes,geodict,fillValue=0,attribute='value',mustContainCenter=True)
output = np.array([[5,0,7,0],
[0,0,7,7],
[0,0,0,7],
[0,0,0,0]])
np.testing.assert_almost_equal(grid2.getData(),output)
print('Passed burning in values where polygons must contain pixel centers.')
def getNoDataGrid(predictors,xmin,xmax,ymin,ymax):
txmin = xmin
txmax = xmax
tymin = ymin
tymax = ymax
mindx = 9999999999
mindy = 9999999999
#figure out bounds enclosing all files
for predname,predfile in predictors.items():
if not os.path.isfile(predfile):
continue
ftype = getFileType(predfile)
if ftype == 'shapefile':
f = fiona.open(predfile,'r')
bxmin,bymin,bxmax,bymax = f.bounds
f.close()
if bxmin < txmin:
txmin = bxmin
if bxmax > txmax:
txmax = bxmax
if bymin < tymin:
tymin = bymin
if bymax > tymax:
tymax = bymax
elif ftype == 'grid':
gridtype = getGridType(predfile)
if gridtype is None:
raise Exception('File "%s" does not appear to be either a GMT grid or an ESRI grid.' % gridfile)
fdict = getFileGeoDict(predfile,gridtype)
if fdict.dx < mindx:
mindx = fdict.dx
if fdict.dy < mindy:
mindy = fdict.dy
if fdict.xmin < txmin:
txmin = fdict.xmin
if fdict.xmax > txmax:
txmax = txmax
if fdict.ymin < tymin:
tymin = tymin
if fdict.ymax > tymax:
tymax = tymax
sdict = GeoDict.createDictFromBox(txmin,txmax,tymin,tymax,mindx,mindy)
nanarray = np.zeros((sdict.ny,sdict.nx),dtype=np.int8)
for predname,predfile in predictors.items():
if not os.path.isfile(predfile):
continue
ftype = getFileType(predfile)
if ftype == 'shapefile':
shapes = list(fiona.open(predfile,'r'))
grid = Grid2D.rasterizeFromGeometry(shapes,sdict)
else:
gridtype = getGridType(predfile)
if gridtype == 'gmt':
grid = GMTGrid.load(predfile,samplegeodict=sdict,resample=True,method='nearest',doPadding=True)
else:
grid = GDALGrid.load(predfile,samplegeodict=sdict,resample=True,method='nearest',doPadding=True)
nangrid = np.isnan(grid.getData())
nanarray = nanarray | nangrid
nangrid = Grid2D(data=nanarray,geodict=sdict)
return nangrid
def make_overlay(adict):
"""
Make a transparent PNG of intensity and a world file
Args:
adict (dict): The usual dictionary for the mapping functions.
Returns:
nothing: Nothing.
"""
mmidict = adict['imtdict']
mmi_array = mmidict['mean']
geodict = GeoDict(mmidict['mean_metadata'])
palette = ColorPalette.fromPreset('mmi')
mmi_rgb = palette.getDataColor(mmi_array, color_format='array')
img = Image.fromarray(mmi_rgb)
pngfile = os.path.join(adict['datadir'], 'intensity_overlay.png')
img.save(pngfile, "PNG")
# write out a world file
# https://en.wikipedia.org/wiki/World_file
worldfile = os.path.join(adict['datadir'], 'intensity_overlay.pngw')
with open(worldfile, 'wt') as f:
f.write('%.4f\n' % geodict.dx)
f.write('0.0\n')
f.write('0.0\n')
f.write('-%.4f\n' % geodict.dy)
f.write('%.4f\n' % geodict.xmin)
f.write('%.4f\n' % geodict.ymax)
return
def _trim_grid(ingrid):
outgrid = Grid2D.copyFromGrid(ingrid)
while np.isnan(outgrid._data).any():
nrows, ncols = outgrid._data.shape
top = outgrid._data[0, :]
bottom = outgrid._data[-1, :]
left = outgrid._data[:, 0]
right = outgrid._data[:, -1]
ftop = np.isnan(top).sum() / ncols
fbottom = np.isnan(bottom).sum() / ncols
fleft = np.isnan(left).sum() / nrows
fright = np.isnan(right).sum() / nrows
side = np.argmax([ftop, fbottom, fleft, fright])
gdict = outgrid.getGeoDict().asDict()
if side == 0: # removing top row
outgrid._data = outgrid._data[1:, :]
gdict['ymax'] -= gdict['dy']
gdict['ny'] -= 1
elif side == 1: # removing bottom row
outgrid._data = outgrid._data[0:-1, :]
gdict['ymin'] += gdict['dy']
gdict['ny'] -= 1
elif side == 2: # removing left column
outgrid._data = outgrid._data[:, 1:]
gdict['xmin'] += gdict['dx']
gdict['nx'] -= 1
elif side == 3: # removing right column
outgrid._data = outgrid._data[:, 0:-1]
gdict['xmax'] -= gdict['dx']
gdict['nx'] -= 1
geodict = GeoDict(gdict)
outgrid = Grid2D(data=outgrid._data, geodict=geodict)
return outgrid
def test_setData():
data = np.arange(0, 16).astype(np.float32).reshape(4, 4)
geodict = GeoDict({
'xmin': 0.5,
'xmax': 3.5,
'ymin': 0.5,
'ymax': 3.5,
'dx': 1.0,
'dy': 1.0,
'ny': 4,
'nx': 4
})
grid1 = Grid2D(data, geodict)
x = np.ones((4, 4))
try:
grid1.setData(x) #this should pass
print('setData test passed.')
except DataSetException as dse:
print('setData test failed.')
try:
x = np.ones((5, 5))
grid1.setData(x)
print('setData test did not fail when it should have.')
except DataSetException as dse:
print('setData test failed as expected.')
try:
x = 'fred'
grid1.setData(x)
print('setData test did not fail when it should have.')
except DataSetException as dse:
print('setData test failed as expected.')
def test_mapmaker_intensity():
homedir = os.path.dirname(
os.path.abspath(__file__)) # where is this script?
shakedir = os.path.abspath(os.path.join(homedir, '..', '..', '..'))
out_file = os.path.join(shakedir, 'tests', 'data', 'containers',
'northridge', 'shake_result.hdf')
container = ShakeMapOutputContainer.load(out_file)
topofile = os.path.join(homedir, '..', '..', 'data', 'install', 'data',
'mapping', 'CA_topo.grd')
info = container.getMetadata()
xmin = info['output']['map_information']['min']['longitude']
xmax = info['output']['map_information']['max']['longitude']
ymin = info['output']['map_information']['min']['latitude']
ymax = info['output']['map_information']['max']['latitude']
xmin = float(xmin) - 0.1
xmax = float(xmax) + 0.1
ymin = float(ymin) - 0.1
ymax = float(ymax) + 0.1
dy = float(info['output']['map_information']['grid_spacing']['latitude'])
dx = float(info['output']['map_information']['grid_spacing']['longitude'])
sampledict = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
topogrid = GMTGrid.load(topofile, samplegeodict=sampledict, resample=False)
outpath = mkdtemp()
model_config = container.getConfig()
comp = container.getComponents('MMI')[0]
textfile = os.path.join(get_data_path(), 'mapping', 'map_strings.en')
text_dict = get_text_strings(textfile)
cities = Cities.fromDefault()
d = {
'imtype': 'MMI',
'topogrid': topogrid,
'allcities': cities,
'states_provinces': None,
'countries': None,
'oceans': None,
'lakes': None,
'roads': None,
'faults': None,
'datadir': outpath,
'operator': 'NEIC',
'filter_size': 10,
'info': info,
'component': comp,
'imtdict': container.getIMTGrids('MMI', comp),
'ruptdict': copy.deepcopy(container.getRuptureDict()),
'stationdict': container.getStationDict(),
'config': model_config,
'tdict': text_dict
}
try:
fig1, fig2 = draw_map(d)
except Exception:
assert 1 == 2
finally:
shutil.rmtree(outpath)
def test_bounds_within_real():
fxmin, fxmax = (-179.995833333333, 179.99583333189372)
fymin, fymax = (-89.99583333333332, 89.9958333326134)
fdx, fdy = (0.0083333333333, 0.0083333333333)
fnx, fny = (43200, 21600)
xmin, xmax = (177.75, -179.75)
ymin, ymax = (50.41625, 51.98375)
dx, dy = (0.025, 0.02488095238095242)
nx, ny = (101, 64)
host = GeoDict({'xmin': fxmin,
'xmax': fxmax,
'ymin': fymin,
'ymax': fymax,
'dx': fdx,
'dy': fdy,
'nx': fnx,
'ny': fny})
sample = GeoDict({'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'dx': dx,
'dy': dy,
'nx': nx,
'ny': ny})
result = GeoDict({'xmin': 60,
'xmax': -120,
'ymin': -30,
'ymax': 30,
'dx': 60,
'dy': 30,
'nx': 4,
'ny': 3})
inside = host.getBoundsWithin(sample)
ixmin, ixmax = (177.75416666523603, -179.7541666666673)
iymin, iymax = (50.4208333327717, 51.9791666660988)
idx, idy = (0.0083333333333, 0.0083333333333)
inx, iny = (300, 188)
result = GeoDict({'xmin': ixmin,
'xmax': ixmax,
'ymin': iymin,
'ymax': iymax,
'dx': idx,
'dy': idy,
'nx': inx,
'ny': iny})
assert inside == result
def test_copy():
data = np.arange(0,16).astype(np.float32).reshape(4,4)
geodict = GeoDict({'xmin':0.5,'xmax':3.5,'ymin':0.5,'ymax':3.5,'dx':1.0,'dy':1.0,'ny':4,'nx':4})
grid1 = Grid2D(data,geodict)
grid2 = grid1.copyFromGrid(grid1)
grid1._data[0,0] = np.nan
print(grid2._data)
print(grid2._geodict)
def createFromCenter(cls,
cx,
cy,
xspan,
yspan,
dx,
dy,
defaultVs30=686.0,
vs30File=None,
vs30measured_grid=None,
backarc=False,
padding=False,
resample=False):
"""
Create a Sites object by defining a center point, resolution, extent,
and Vs30 values.
:param cx:
X coordinate of desired center point.
:param cy:
Y coordinate of desired center point.
:param xspan:
Width of desired grid.
:param yspan:
Height of desired grid.
:param dx:
Resolution of desired grid in X direction.
:param dy:
Resolution of desired grid in Y direction.
:param defaultVs30:
Default Vs30 value to use if vs30File not specified.
:param vs30File:
Name of GMT or GDAL format grid file containing Vs30 values.
:param vs30measured_grid:
Boolean grid indicating whether Vs30 values were measured or derived
(i.e., from slope)
:param backarc:
Boolean indicating whether event is on the backarc as defined
`here <http://earthquake.usgs.gov/learn/glossary/?term=backarc>`__.
:param padding:
Boolean indicating whether or not to pad resulting Vs30 grid out to
edges of input bounds. If False, grid will be clipped to the extent
of the input file.
:param resample:
Boolean indicating whether or not the grid should be resampled.
"""
geodict = GeoDict.createDictFromCenter(cx, cy, dx, dy, xspan, yspan)
if vs30File is not None:
vs30grid = cls._create(geodict, defaultVs30, vs30File, padding,
resample)
else:
griddata = np.ones(
(geodict.ny, geodict.nx), dtype=np.float64) * defaultVs30
vs30grid = Grid2D(griddata, geodict)
return cls(vs30grid,
vs30measured_grid=vs30measured_grid,
backarc=backarc,
defaultVs30=defaultVs30)
def getRegions(self, lat, lon, depth):
"""Get information about the tectonic region of a given hypocenter.
Args:
lat (float): Earthquake hypocentral latitude.
lon (float): Earthquake hypocentral longitude.
depth (float): Earthquake hypocentral depth.
Returns:
Series: Pandas series object containing labels:
- TectonicRegion: Subduction, Active, Stable, or Volcanic.
- DistanceToStable: Distance in km to nearest stable region.
- DistanceToActive: Distance in km to nearest active region.
- DistanceToSubduction: Distance in km to nearest subduction
region.
- DistanceToVolcanic: Distance in km to nearest volcanic
region.
- Oceanic: Boolean indicating if epicenter is in the ocean.
- DistanceToOceanic: Distance in km to nearest oceanic region.
- DistanceToContinental: Distance in km to nearest continental
region.
"""
regions = OrderedDict()
gd = GeoDict.createDictFromCenter(lon, lat, DX, DY, XSPAN, YSPAN)
tec_grid = read(self._tectonic_grid, samplegeodict=gd)
region_dict = get_dist_to_type(lon, lat, tec_grid, TECTONIC_REGIONS)
ocean_grid = read(self._oceanic_grid, samplegeodict=gd)
ocean_dict = get_dist_to_type(lon, lat, ocean_grid, OCEANIC_REGIONS)
if region_dict['DistanceToActive'] == 0:
region_dict['TectonicRegion'] = 'Active'
elif region_dict['DistanceToStable'] == 0:
region_dict['TectonicRegion'] = 'Stable'
elif region_dict['DistanceToSubduction'] == 0:
region_dict['TectonicRegion'] = 'Subduction'
else:
region_dict['TectonicRegion'] = 'Volcanic'
region_dict['DistanceToOceanic'] = ocean_dict['DistanceToOceanic']
region_dict['DistanceToContinental'] = ocean_dict['DistanceToContinental']
region_dict['Oceanic'] = False
if ocean_dict['DistanceToOceanic'] == 0:
region_dict['Oceanic'] = True
regions = pd.Series(region_dict, index=['TectonicRegion',
'DistanceToStable',
'DistanceToActive',
'DistanceToSubduction',
'DistanceToVolcanic',
'Oceanic',
'DistanceToOceanic',
'DistanceToContinental'])
return regions
def getRegions(self, lat, lon, depth):
"""Get information about the tectonic region of a given hypocenter.
Args:
lat (float): Earthquake hypocentral latitude.
lon (float): Earthquake hypocentral longitude.
depth (float): Earthquake hypocentral depth.
Returns:
Series: Pandas series object containing labels:
- TectonicRegion: Subduction, Active, Stable, or Volcanic.
- DistanceToStable: Distance in km to nearest stable region.
- DistanceToActive: Distance in km to nearest active region.
- DistanceToSubduction: Distance in km to nearest subduction
region.
- DistanceToVolcanic: Distance in km to nearest volcanic
region.
- Oceanic: Boolean indicating if epicenter is in the ocean.
- DistanceToOceanic: Distance in km to nearest oceanic region.
- DistanceToContinental: Distance in km to nearest continental
region.
"""
regions = OrderedDict()
gd = GeoDict.createDictFromCenter(lon, lat, DX, DY, XSPAN, YSPAN)
tec_grid = read(self._tectonic_grid, samplegeodict=gd)
region_dict = get_dist_to_type(lon, lat, tec_grid, TECTONIC_REGIONS)
ocean_grid = read(self._oceanic_grid, samplegeodict=gd)
ocean_dict = get_dist_to_type(lon, lat, ocean_grid, OCEANIC_REGIONS)
if region_dict['DistanceToActive'] == 0:
region_dict['TectonicRegion'] = 'Active'
elif region_dict['DistanceToStable'] == 0:
region_dict['TectonicRegion'] = 'Stable'
elif region_dict['DistanceToSubduction'] == 0:
region_dict['TectonicRegion'] = 'Subduction'
else:
region_dict['TectonicRegion'] = 'Volcanic'
region_dict['DistanceToOceanic'] = ocean_dict['DistanceToOceanic']
region_dict['DistanceToContinental'] = ocean_dict[
'DistanceToContinental']
region_dict['Oceanic'] = False
if ocean_dict['DistanceToOceanic'] == 0:
region_dict['Oceanic'] = True
regions = pd.Series(region_dict,
index=[
'TectonicRegion', 'DistanceToStable',
'DistanceToActive', 'DistanceToSubduction',
'DistanceToVolcanic', 'Oceanic',
'DistanceToOceanic', 'DistanceToContinental'
])
return regions
def test_basics():
geodict = GeoDict({
'xmin': 0.5,
'xmax': 3.5,
'ymin': 0.5,
'ymax': 3.5,
'dx': 1.0,
'dy': 1.0,
'ny': 4,
'nx': 4
})
data = np.arange(0, 16).reshape(4, 4).astype(np.float32)
grid = Grid2D(data, geodict)
print(
'Testing basic Grid2D functionality (retrieving data, lat/lon to pixel coordinates, etc...'
)
np.testing.assert_almost_equal(grid.getData(), data)
assert grid.getGeoDict() == geodict
assert grid.getBounds() == (geodict.xmin, geodict.xmax, geodict.ymin,
geodict.ymax)
lat, lon = grid.getLatLon(0, 0)
assert lat == 3.5 and lon == 0.5
row, col = grid.getRowCol(lat, lon)
assert row == 0 and col == 0
value = grid.getValue(lat, lon)
assert value == 0
frow, fcol = grid.getRowCol(1.0, 3.0, returnFloat=True)
assert frow == 2.5 and fcol == 2.5
irow, icol = grid.getRowCol(1.0, 3.0, returnFloat=False)
assert irow == 2 and icol == 2
#test getting values in and outside of the grid bounds
lat = np.array([0.0, 0.5, 2.5, 4.0])
lon = np.array([0.0, 0.5, 2.5, 4.0])
default = np.nan
output = np.array([np.nan, 12, 6, np.nan])
value = grid.getValue(lat, lon, default=default)
np.testing.assert_almost_equal(value, output)
print(
'Passed basic Grid2D functionality (retrieving data, lat/lon to pixel coordinates, etc...'
)
def sampleGridFile(gridfile, xypoints, method='nearest'):
"""Sample grid file (ESRI or GMT format) at each of a set of XY (decimal degrees) points.
:param gridfile:
Name of ESRI or GMT grid format file from which to sample values.
:param xypoints:
2D numpy array of XY points, decimal degrees.
:param method:
Interpolation method, either 'nearest' or 'linear'.
:returns:
1D numpy array of grid values at each of input XY points.
"""
xmin = np.min(xypoints[:, 0])
xmax = np.max(xypoints[:, 0])
ymin = np.min(xypoints[:, 1])
ymax = np.max(xypoints[:, 1])
gridtype = None
try:
fdict = GMTGrid.getFileGeoDict(gridfile)
gridtype = 'gmt'
except Exception as error:
try:
fdict = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
if gridtype is None:
raise Exception(
'File "%s" does not appear to be either a GMT grid or an ESRI grid.'
% gridfile)
xmin = xmin - fdict.dx * 3
xmax = xmax + fdict.dx * 3
ymin = ymin - fdict.dy * 3
ymax = ymax + fdict.dy * 3
#bounds = (xmin, xmax, ymin, ymax)
if gridtype == 'gmt':
fgeodict = GMTGrid.getFileGeoDict(gridfile)
else:
fgeodict = GDALGrid.getFileGeoDict(gridfile)
dx, dy = (fgeodict.dx, fgeodict.dy)
sdict = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
if gridtype == 'gmt':
grid = GMTGrid.load(gridfile,
samplegeodict=sdict,
resample=False,
method=method,
doPadding=True)
else:
grid = GDALGrid.load(gridfile,
samplegeodict=sdict,
resample=False,
method=method,
doPadding=True)
return sampleFromGrid(grid, xypoints)
def test_intersection():
fxmin, fxmax = (178.311, -179.189)
fymin, fymax = (50.616, 52.176)
fdx, fdy = (0.025, 0.02516129032258068)
fnx, fny = (101, 63)
host = GeoDict({'xmin': fxmin,
'xmax': fxmax,
'ymin': fymin,
'ymax': fymax,
'dx': fdx,
'dy': fdy,
'nx': fnx,
'ny': fny})
sxmin, sxmax = (178.31249999999858, -179.19583333333335)
symin, symax = (50.62083333333279, 52.17083333333278)
sdx, sdy = (0.0083333333333333, 0.0083333333333333)
snx, sny = (300, 187)
sample = GeoDict({'xmin': sxmin,
'xmax': sxmax,
'ymin': symin,
'ymax': symax,
'dx': sdx,
'dy': sdy,
'nx': snx,
'ny': sny})
ixmin, ixmax = (178.31249999999858, -179.19583333333478)
iymin, iymax = (50.62083333333278, 52.17083333333278)
idx, idy = (0.0083333333333333, 0.0083333333333333)
inx, iny = (300, 187)
result = GeoDict({'xmin': ixmin,
'xmax': ixmax,
'ymin': iymin,
'ymax': iymax,
'dx': idx,
'dy': idy,
'nx': inx,
'ny': iny})
intersection = host.getIntersection(sample)
np.testing.assert_allclose(intersection.xmin, ixmin)
np.testing.assert_allclose(intersection.xmax, ixmax)
np.testing.assert_allclose(intersection.ymin, iymin)
np.testing.assert_allclose(intersection.ymax, iymax)
def test_interpolate():
geodict = GeoDict({'xmin': 0.5, 'xmax': 6.5, 'ymin': 1.5,
'ymax': 6.5, 'dx': 1.0, 'dy': 1.0, 'ny': 6, 'nx': 7})
data = np.arange(14, 56).reshape(6, 7)
for method in ['nearest', 'linear', 'cubic']:
print('Testing interpolate with method "%s"...' % method)
grid = Grid2D(data, geodict)
sampledict = GeoDict({'xmin': 3.0, 'xmax': 4.0,
'ymin': 3.0, 'ymax': 4.0,
'dx': 1.0, 'dy': 1.0,
'ny': 2, 'nx': 2})
grid = grid.interpolateToGrid(sampledict, method=method)
tgrid = grid.interpolate2(sampledict, method=method)
if method == 'nearest':
output = np.array([[30.0, 31.0], [37.0, 38.0]])
elif method == 'linear':
output = np.array([[34., 35.], [41., 42.]])
elif method == 'cubic':
output = np.array([[34., 35.], [41., 42.]])
else:
pass
np.testing.assert_almost_equal(grid.getData(), output)
print('Passed interpolate with method "%s".' % method)
np.testing.assert_almost_equal(tgrid.getData(), output)
print('Passed interpolate2 with method "%s".' % method)
# speed test of interpolateToGrid and interpolate2
geodict = GeoDict.createDictFromBox(0, 10, 0, 10, 0.01, 0.01)
data = np.random.rand(geodict.ny, geodict.nx)
grid = Grid2D(data, geodict)
sampledict = GeoDict.createDictFromBox(2, 8, 2, 8, 0.098, 0.098)
t1 = time.time()
grid2 = grid.interpolateToGrid(sampledict, method='linear')
t2 = time.time()
grid3 = grid.interpolate2(sampledict, method='linear')
t3 = time.time()
# np.testing.assert_almost_equal(grid2._data.sum(),grid3._data.sum())
print('scipy method: %.3f seconds' % (t2-t1))
print('gdal method: %.3f seconds' % (t3-t2))
def test_cut():
geodict = GeoDict({'xmin': 0.5, 'xmax': 4.5, 'ymin': 0.5,
'ymax': 4.5, 'dx': 1.0, 'dy': 1.0, 'ny': 5, 'nx': 5})
data = np.arange(0, 25).reshape(5, 5)
print('Testing data extraction...')
grid = Grid2D(data, geodict)
xmin, xmax, ymin, ymax = (2.5, 3.5, 2.5, 3.5)
newgrid = grid.cut(xmin, xmax, ymin, ymax)
output = np.array([[7, 8], [12, 13]])
np.testing.assert_almost_equal(newgrid.getData(), output)
print('Passed data extraction...')
print('Testing data trimming with resampling...')
# make a more complicated test using getboundswithin
data = np.arange(0, 84).reshape(7, 12)
geodict = GeoDict({'xmin': -180, 'xmax': 150,
'ymin': -90, 'ymax': 90,
'dx': 30, 'dy': 30,
'nx': 12, 'ny': 7})
grid = Grid2D(data, geodict)
sampledict = GeoDict.createDictFromBox(-75,
45, -45, 75, geodict.dx, geodict.dy)
cutdict = geodict.getBoundsWithin(sampledict)
newgrid = grid.cut(cutdict.xmin, cutdict.xmax, cutdict.ymin, cutdict.ymax)
output = np.array([[16, 17, 18, 19],
[28, 29, 30, 31],
[40, 41, 42, 43],
[52, 53, 54, 55]])
np.testing.assert_almost_equal(newgrid.getData(), output)
print('Passed data trimming with resampling...')
print('Test cut with self-alignment...')
geodict = GeoDict({'xmin': 0.5, 'xmax': 4.5,
'ymin': 0.5, 'ymax': 6.5,
'dx': 1.0, 'dy': 1.0,
'nx': 5, 'ny': 7})
data = np.arange(0, 35).astype(np.float32).reshape(7, 5)
grid = Grid2D(data, geodict)
cutxmin = 1.7
cutxmax = 3.7
cutymin = 1.7
cutymax = 5.7
cutgrid = grid.cut(cutxmin, cutxmax, cutymin, cutymax, align=True)
output = np.array([[7, 8],
[12, 13],
[17, 18],
[22, 23]])
np.testing.assert_almost_equal(cutgrid.getData(), output)
print('Passed cut with self-alignment.')
def test_interpolate():
geodict = GeoDict({'xmin':0.5,'xmax':6.5,'ymin':1.5,'ymax':6.5,'dx':1.0,'dy':1.0,'ny':6,'nx':7})
data = np.arange(14,56).reshape(6,7)
for method in ['nearest','linear','cubic']:
print('Testing interpolate with method "%s"...' % method)
grid = Grid2D(data,geodict)
sampledict = GeoDict({'xmin':3.0,'xmax':4.0,
'ymin':3.0,'ymax':4.0,
'dx':1.0,'dy':1.0,
'ny':2,'nx':2})
grid = grid.interpolateToGrid(sampledict,method=method)
if method == 'nearest':
output = np.array([[30.0,31.0],[37.0,38.0]])
elif method == 'linear':
output = np.array([[34.,35.],[41.,42.]])
elif method == 'cubic':
output = np.array([[34.,35.],[41.,42.]])
else:
pass
np.testing.assert_almost_equal(grid.getData(),output)
print('Passed interpolate with method "%s".' % method)
def big_test():
xmin = -180
xmax = -170
ymin = 30
ymax = 40
dx = 0.0083
dy = 0.0083
gd = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
data = np.random.rand(gd.ny, gd.nx)
grid = Grid2D(data, gd)
fname = os.path.join(os.path.expanduser('~'), 'tempfile.grd')
write(grid, fname, 'hdf')
print(fname)
src = rasterio.open(fname, 'r')
def big_test():
xmin = -180
xmax = -170
ymin = 30
ymax = 40
dx = 0.0083
dy = 0.0083
gd = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
data = np.random.rand(gd.ny, gd.nx)
grid = Grid2D(data, gd)
fname = os.path.join(os.path.expanduser('~'), 'tempfile.grd')
write(grid, fname, 'hdf')
print(fname)
src = rasterio.open(fname, 'r')
def create_overlay_image(container, filename):
"""Create a semi-transparent PNG image of intensity.
Args:
container (ShakeMapOutputContainer): Results of model.conf.
filename (str): Path to desired output PNG file.
Returns:
GeoDict: GeoDict object for the intensity grid.
"""
# extract the intensity data from the container
comp = container.getComponents('MMI')
if len(comp) == 0:
return None
comp = comp[0]
imtdict = container.getIMTGrids('MMI', comp)
mmigrid = imtdict['mean']
gd = GeoDict(imtdict['mean_metadata'])
imtdata = mmigrid.copy()
rows, cols = imtdata.shape
# get the intensity colormap
palette = ColorPalette.fromPreset('mmi')
# map intensity values into
# RGBA array
rgba = palette.getDataColor(imtdata, color_format='array')
# set the alpha value to 255 wherever we have MMI 0
rgba[imtdata <= 1.5] = 0
if 'CALLED_FROM_PYTEST' not in os.environ:
# mask off the areas covered by ocean
oceans = shpreader.natural_earth(category='physical',
name='ocean',
resolution='10m')
bbox = (gd.xmin, gd.ymin, gd.xmax, gd.ymax)
with fiona.open(oceans) as c:
tshapes = list(c.items(bbox=bbox))
shapes = []
for tshp in tshapes:
shapes.append(shape(tshp[1]['geometry']))
if len(shapes):
oceangrid = Grid2D.rasterizeFromGeometry(shapes, gd,
fillValue=0.0)
rgba[oceangrid.getData() == 1] = 0
# save rgba image as png
img = Image.fromarray(rgba)
img.save(filename)
return gd
def test_project():
data = np.array([[0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [1, 1, 1, 1, 1],
[0, 0, 1, 0, 0], [0, 0, 1, 0, 0]],
dtype=np.int32)
geodict = {
'xmin': 50,
'xmax': 50.4,
'ymin': 50,
'ymax': 50.4,
'dx': 0.1,
'dy': 0.1,
'nx': 5,
'ny': 5
}
gd = GeoDict(geodict)
grid = GDALGrid(data, gd)
projstr = "+proj=utm +zone=40 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs "
newgrid = grid.project(projstr, method='nearest')
try:
tdir = tempfile.mkdtemp()
outfile = os.path.join(tdir, 'output.bil')
grid.save(outfile)
with rasterio.open(outfile) as src:
aff = get_affine(src)
data = src.read(1)
src_crs = CRS().from_string(GeoDict.DEFAULT_PROJ4).to_dict()
dst_crs = CRS().from_string(projstr).to_dict()
nrows, ncols = data.shape
left = aff.xoff
top = aff.yoff
right, bottom = aff * (ncols - 1, nrows - 1)
dst_transform, width, height = calculate_default_transform(
src_crs, dst_crs, ncols, nrows, left, bottom, right, top)
destination = np.zeros((height, width))
reproject(data,
destination,
src_transform=aff,
src_crs=src_crs,
dst_transform=dst_transform,
dst_crs=dst_crs,
src_nodata=src.nodata,
dst_nodata=np.nan,
resampling=Resampling.nearest)
x = 1
except:
pass
finally:
shutil.rmtree(tdir)
def fromBounds(cls,
xmin,
xmax,
ymin,
ymax,
dx,
dy,
defaultVs30=686.0,
vs30File=None,
vs30measured_grid=None,
backarc=None,
padding=False,
resample=False):
"""
Create a Sites object by defining a center point, resolution, extent,
and Vs30 values.
Args:
xmin: X coordinate of left edge of bounds.
xmax: X coordinate of right edge of bounds.
ymin: Y coordinate of bottom edge of bounds.
ymax: Y coordinate of top edge of bounds.
dx: Resolution of desired grid in X direction.
dy: Resolution of desired grid in Y direction.
defaultVs30: Default Vs30 value to use if vs30File not specified.
vs30File: Name of GMT or GDAL format grid file containing Vs30
values.
vs30measured_grid: Boolean grid indicating whether Vs30 values were
measured or derived (i.e., from slope).
backarc: Boolean array indicating whether site is in the subduction
`backarc <http://earthquake.usgs.gov/learn/glossary/?term=backarc>`__.
padding: Boolean indicating whether or not to pad resulting Vs30
grid out to edges of input bounds. If False, grid will be
clipped to the extent of the input file.
resample: Boolean indicating whether or not the grid should be
resampled.
""" # noqa
geodict = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
if vs30File is not None:
vs30grid = cls._create(geodict, defaultVs30, vs30File, padding,
resample)
else:
griddata = np.ones(
(geodict.ny, geodict.nx), dtype=np.float64) * defaultVs30
vs30grid = Grid2D(griddata, geodict)
return cls(vs30grid,
vs30measured_grid=vs30measured_grid,
backarc=backarc,
defaultVs30=defaultVs30)
def test_lat_lon_array():
gd = GeoDict({'xmin': 0, 'xmax': 4,
'ymin': 0, 'ymax': 3,
'dx': 1.0, 'dy': 1.0,
'nx': 5, 'ny': 4})
lat = np.array([0, 1, 2], dtype=np.float32)
lon = np.array([2, 2, 2], dtype=np.float32)
trow = np.array([3, 2, 1])
tcol = np.array([2, 2, 2])
row, col = gd.getRowCol(lat, lon)
np.testing.assert_almost_equal(trow, row)
np.testing.assert_almost_equal(tcol, col)
gd = GeoDict({'xmin': 176, 'xmax': -176,
'ymin': 0, 'ymax': 6,
'nx': 5, 'ny': 4,
'dx': 2, 'dy': 2})
lat = np.array([2, 4, 6], dtype=np.float32)
lon = np.array([178, 180, -178], dtype=np.float32)
row, col = gd.getRowCol(lat, lon)
trow = np.array([2, 1, 0])
tcol = np.array([1, 2, 3])
np.testing.assert_almost_equal(trow, row)
np.testing.assert_almost_equal(tcol, col)
def test_bounds_within_real():
fxmin, fxmax = (-179.995833333333, 179.99583333189372)
fymin, fymax = (-89.99583333333332, 89.9958333326134)
fdx, fdy = (0.0083333333333, 0.0083333333333)
fnx, fny = (43200, 21600)
xmin, xmax = (177.75, -179.75)
ymin, ymax = (50.41625, 51.98375)
dx, dy = (0.025, 0.02488095238095242)
nx, ny = (101, 64)
host = GeoDict({
'xmin': fxmin,
'xmax': fxmax,
'ymin': fymin,
'ymax': fymax,
'dx': fdx,
'dy': fdy,
'nx': fnx,
'ny': fny
})
sample = GeoDict({
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'dx': dx,
'dy': dy,
'nx': nx,
'ny': ny
})
result = GeoDict({
'xmin': 60,
'xmax': -120,
'ymin': -30,
'ymax': 30,
'dx': 60,
'dy': 30,
'nx': 4,
'ny': 3
})
inside = host.getBoundsWithin(sample)
ixmin, ixmax = (177.75416666523603, -179.7541666666673)
iymin, iymax = (50.4208333327717, 51.9791666660988)
idx, idy = (0.0083333333333, 0.0083333333333)
inx, iny = (300, 188)
result = GeoDict({
'xmin': ixmin,
'xmax': ixmax,
'ymin': iymin,
'ymax': iymax,
'dx': idx,
'dy': idy,
'nx': inx,
'ny': iny
})
assert inside == result
def test_read_meridian():
# where is this script?
homedir = os.path.dirname(os.path.abspath(__file__))
# this is an HDF 5 file
datafile = os.path.join(homedir, 'data', 'globalgrid_cdf.cdf')
sdict = {'xmin': 180,
'xmax': -120,
'ymin': 0,
'ymax': 30,
'nx': 2,
'ny': 2,
'dx': 60,
'dy': 30}
sampledict = GeoDict(sdict)
grid = read(datafile, samplegeodict=sampledict)
assert np.nansum(grid._data) == 50.0
def sampleGridFile(gridfile,xypoints,method='nearest'):
"""
Sample grid file (ESRI or GMT format) at each of a set of XY (decimal degrees) points.
:param gridfile:
Name of ESRI or GMT grid format file from which to sample values.
:param xypoints:
2D numpy array of XY points, decimal degrees.
:param method:
Interpolation method, either 'nearest' or 'linear'.
:returns:
1D numpy array of grid values at each of input XY points.
"""
if not len(xypoints):
return np.array([])
xmin = np.min(xypoints[:,0])
xmax = np.max(xypoints[:,0])
ymin = np.min(xypoints[:,1])
ymax = np.max(xypoints[:,1])
gridtype = None
try:
fdict,tmp = GMTGrid.getFileGeoDict(gridfile)
gridtype = 'gmt'
except Exception as error:
try:
fdict,tmp = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
if gridtype is None:
raise Exception('File "%s" does not appear to be either a GMT grid or an ESRI grid.' % gridfile)
xmin = xmin - fdict.dx*3
xmax = xmax + fdict.dx*3
ymin = ymin - fdict.dy*3
ymax = ymax + fdict.dy*3
bounds = (xmin,xmax,ymin,ymax)
if gridtype == 'gmt':
fgeodict,tmp = GMTGrid.getFileGeoDict(gridfile)
else:
fgeodict,tmp = GDALGrid.getFileGeoDict(gridfile)
dx,dy = (fgeodict.dx,fgeodict.dy)
sdict = GeoDict.createDictFromBox(xmin,xmax,ymin,ymax,dx,dy)
if gridtype == 'gmt':
grid = GMTGrid.load(gridfile,samplegeodict=sdict,resample=True,method=method,doPadding=True)
else:
grid = GDALGrid.load(gridfile,samplegeodict=sdict,resample=True,method=method,doPadding=True)
return sampleFromGrid(grid,xypoints)
def get_data_range_test():
# a standard global grid, going from -180 to 180
normal_dict = GeoDict({'xmin': -180, 'xmax': 120,
'ymin': -90, 'ymax': 90,
'dx': 60, 'dy': 45,
'nx': 6, 'ny': 5})
# test a simple example which does NOT cross the 180 meridian
sample1 = (-125, 65, -20, 20)
dict1 = Grid2D.getDataRange(normal_dict, sample1)
cdict1 = {'iulx1': 0, 'iuly1': 1,
'ilrx1': 6, 'ilry1': 4}
assert dict1 == cdict1
# test a less-simple example which DOES cross the 180 meridian
sample2 = (-235, -10, -20, 20)
dict2 = Grid2D.getDataRange(normal_dict, sample2)
cdict2 = {'iulx1': 5, 'iuly1': 1,
'ilrx1': 6, 'ilry1': 4,
'iulx2': 0, 'iuly2': 1,
'ilrx2': 4, 'ilry2': 4}
assert dict2 == cdict2
# test a less-simple example which DOES cross the 180 meridian, and xmin > xmax
sample3 = (125, -10, -20, 20)
dict3 = Grid2D.getDataRange(normal_dict, sample3)
cdict3 = {'iulx1': 5, 'iuly1': 1,
'ilrx1': 6, 'ilry1': 4,
'iulx2': 0, 'iuly2': 1,
'ilrx2': 4, 'ilry2': 4}
assert dict3 == cdict3
# test an example where the sample bounds are from 0 to 360
sample4 = (160, 200, -20, 20)
dict4 = Grid2D.getDataRange(normal_dict, sample4)
cdict4 = {'iulx1': 5, 'iuly1': 1,
'ilrx1': 6, 'ilry1': 4,
'iulx2': 0, 'iuly2': 1,
'ilrx2': 2, 'ilry2': 4}
assert dict4 == cdict4
# test an example where the sample bounds are from 0 to 360
sample5 = (220, 260, -20, 20)
dict5 = Grid2D.getDataRange(normal_dict, sample5)
cdict5 = {'iulx1': 0, 'iuly1': 1,
'ilrx1': 3, 'ilry1': 4}
assert dict5 == cdict5
def test_read_subset_no_resample():
# where is this script?
homedir = os.path.dirname(os.path.abspath(__file__))
# this is an HDF 5 file
datafile = os.path.join(homedir, 'data', 'samplegrid_cdf.cdf')
sdict = {'xmin': 6,
'xmax': 7,
'ymin': 5,
'ymax': 6,
'nx': 2,
'ny': 2,
'dx': 1,
'dy': 1}
sampledict = GeoDict(sdict)
grid = read(datafile, samplegeodict=sampledict)
tdata = np.array([[11, 12], [16, 17]])
np.testing.assert_almost_equal(grid._data, tdata)
def fromBounds(cls, xmin, xmax, ymin, ymax, dx, dy, defaultVs30=686.0,
vs30File=None, vs30measured_grid=None,
backarc=None, padding=False, resample=False):
"""
Create a Sites object by defining a center point, resolution, extent,
and Vs30 values.
:param xmin:
X coordinate of left edge of bounds.
:param xmax:
X coordinate of right edge of bounds.
:param ymin:
Y coordinate of bottom edge of bounds.
:param ymax:
Y coordinate of top edge of bounds.
:param dx:
Resolution of desired grid in X direction.
:param dy:
Resolution of desired grid in Y direction.
:param defaultVs30:
Default Vs30 value to use if vs30File not specified.
:param vs30File:
Name of GMT or GDAL format grid file containing Vs30 values.
:param vs30measured_grid:
Boolean grid indicating whether Vs30 values were measured or derived
(i.e., from slope)
:param backarc:
Boolean array indicating whether site is in the subduction
`backarc <http://earthquake.usgs.gov/learn/glossary/?term=backarc>`__.
:param padding:
Boolean indicating whether or not to pad resulting Vs30 grid out to
edges of input bounds. If False, grid will be clipped to the extent
of the input file.
:param resample:
Boolean indicating whether or not the grid should be resampled.
"""
geodict = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
if vs30File is not None:
vs30grid = cls._create(geodict, defaultVs30,
vs30File, padding, resample)
else:
griddata = np.ones((geodict.ny, geodict.nx),
dtype=np.float64) * defaultVs30
vs30grid = Grid2D(griddata, geodict)
return cls(vs30grid, vs30measured_grid=vs30measured_grid,
backarc=backarc, defaultVs30=defaultVs30)
def test_read_subset_with_padding():
# where is this script?
homedir = os.path.dirname(os.path.abspath(__file__))
# this is an HDF 5 file
datafile = os.path.join(homedir, 'data', 'samplegrid_cdf.cdf')
sdict = {'xmin': 4.5,
'xmax': 5.5,
'ymin': 7.5,
'ymax': 8.5,
'nx': 2,
'ny': 2,
'dx': 1,
'dy': 1}
sampledict = GeoDict(sdict)
grid = read(datafile, samplegeodict=sampledict,
resample=False, doPadding=True)
assert grid._data.shape == (3, 3)
assert grid._data[1, 1] == 0
def test_resample():
# this should fail
# where is this script?
homedir = os.path.dirname(os.path.abspath(__file__))
# this is an HDF 5 file
datafile = os.path.join(homedir, 'data', 'samplegrid_cdf.cdf')
sdict = {'xmin': 6.5,
'xmax': 7.5,
'ymin': 5.5,
'ymax': 6.5,
'nx': 2,
'ny': 2,
'dx': 1,
'dy': 1}
sampledict = GeoDict(sdict)
grid = read(datafile, samplegeodict=sampledict, resample=True)
tdata = np.array([[9, 10], [14, 15]])
np.testing.assert_almost_equal(grid._data, tdata)
def test_read_subset_with_resample_and_padding():
# where is this script?
homedir = os.path.dirname(os.path.abspath(__file__))
# this is an HDF 5 file
datafile = os.path.join(homedir, 'data', 'samplegrid_cdf.cdf')
sdict = {'xmin': 4.5,
'xmax': 5.5,
'ymin': 7.5,
'ymax': 8.5,
'nx': 2,
'ny': 2,
'dx': 1,
'dy': 1}
sampledict = GeoDict(sdict)
grid = read(datafile, samplegeodict=sampledict,
resample=True, doPadding=True)
atest = np.array([[np.nan, np.nan],
[np.nan, 3.]])
np.testing.assert_almost_equal(grid._data, atest)
def test_mapmaker_contour():
homedir = os.path.dirname(os.path.abspath(
__file__)) # where is this script?
shakedir = os.path.abspath(os.path.join(homedir, '..', '..', '..'))
out_file = os.path.join(shakedir, 'tests', 'data',
'containers', 'northridge',
'shake_result.hdf')
container = ShakeMapOutputContainer.load(out_file)
topofile = os.path.join(homedir, '..', '..', 'data', 'install', 'data',
'mapping', 'CA_topo.grd')
info = container.getMetadata()
xmin = info['output']['map_information']['min']['longitude']
xmax = info['output']['map_information']['max']['longitude']
ymin = info['output']['map_information']['min']['latitude']
ymax = info['output']['map_information']['max']['latitude']
xmin = float(xmin) - 0.1
xmax = float(xmax) + 0.1
ymin = float(ymin) - 0.1
ymax = float(ymax) + 0.1
dy = float(info['output']['map_information']
['grid_spacing']['latitude'])
dx = float(info['output']['map_information']
['grid_spacing']['longitude'])
sampledict = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
topogrid = GMTGrid.load(topofile,
samplegeodict=sampledict,
resample=False)
oceanfile = os.path.join(homedir, '..', '..', 'data', 'install', 'data',
'mapping', 'northridge_ocean.json')
outpath = mkdtemp()
filter_size = 10
try:
pdf, png = draw_contour(container, 'PGA', topogrid, oceanfile,
outpath, 'NEIC', filter_size)
print(pdf)
except Exception:
assert 1 == 2
finally:
shutil.rmtree(outpath)
def test_modify():
print('Testing ShakeGrid interpolate() method...')
geodict = GeoDict({
'xmin': 0.5,
'xmax': 6.5,
'ymin': 1.5,
'ymax': 6.5,
'dx': 1.0,
'dy': 1.0,
'ny': 6,
'nx': 7
})
data = np.arange(14, 56).reshape(6, 7)
layers = OrderedDict()
layers['pga'] = data
shakeDict = {
'event_id': 'usabcd1234',
'shakemap_id': 'usabcd1234',
'shakemap_version': 1,
'code_version': '4.0',
'process_timestamp': datetime.utcnow(),
'shakemap_originator': 'us',
'map_status': 'RELEASED',
'shakemap_event_type': 'ACTUAL'
}
eventDict = {
'event_id': 'usabcd1234',
'magnitude': 7.6,
'depth': 1.4,
'lat': 2.0,
'lon': 2.0,
'event_timestamp': datetime.utcnow(),
'event_network': 'us',
'event_description': 'sample event'
}
uncDict = {'pga': (0.0, 0)}
shake = ShakeGrid(layers, geodict, eventDict, shakeDict, uncDict)
rdata = np.random.rand(data.shape[0], data.shape[1])
shake.setLayer('pga', rdata)
newdata = shake.getLayer('pga').getData()
np.testing.assert_almost_equal(rdata, newdata)
def getGrid(self, name):
"""
Retrieve a Grid2D object and any associated metadata from the container.
Args:
name (str):
The name of the Grid2D object stored in the container.
Returns:
(tuple) Grid2D object, and a dictionary of metadata.
"""
array_name = '__grid_%s__' % name
if array_name not in self._hdfobj:
raise LookupError('Array %s not in %s' %
(name, self.getFileName()))
dset = self._hdfobj[array_name]
data = dset[()]
array_metadata, meta_metadata = _split_dset_attrs(dset)
geodict = GeoDict(array_metadata)
grid = Grid2D(data, geodict)
return grid, meta_metadata
def createFromCenter(cls,cx,cy,xspan,yspan,dx,dy,defaultVs30=686.0,vs30File=None,
vs30measured_grid=None,backarc=False,padding=False,resample=False):
"""Create a Sites object by defining a center point, resolution, extent, and Vs30 values.
:param cx:
X coordinate of desired center point.
:param cy:
X coordinate of desired center point.
:param xspan:
Width of desired grid.
:param yspan:
Height of desired grid.
:param dx:
Resolution of desired grid in X direction.
:param dy:
Resolution of desired grid in Y direction.
:param defaultVs30:
Default Vs30 value to use if vs30File not specified.
:param vs30File:
Name of GMT or GDAL format grid file containing Vs30 values.
:param vs30measured_grid:
Boolean grid indicating whether Vs30 values were measured or derived (i.e., from slope)
:param backarc:
Boolean indicating whether event is on the backarc as defined here:
http://earthquake.usgs.gov/learn/glossary/?term=backarc
:param padding:
Boolean indicating whether or not to pad resulting Vs30 grid out to edges of input
bounds. If False, grid will be clipped to the extent of the input file.
:param resample:
Boolean indicating whether or not the grid should be resampled.
"""
geodict = GeoDict.createDictFromCenter(cx,cy,dx,dy,xspan,yspan)
if vs30File is not None:
vs30grid = cls._create(geodict,defaultVs30,vs30File,padding,resample)
else:
griddata = np.ones((geodict.ny,geodict.nx), dtype=np.float64)*defaultVs30
vs30grid = Grid2D(griddata,geodict)
return cls(vs30grid,vs30measured_grid=vs30measured_grid,backarc=backarc,
defaultVs30=defaultVs30)
def test_getvalue():
array = np.arange(1, 26).reshape(5, 5)
gdict = GeoDict({'xmin': 1.0,
'xmax': 5.0,
'ymin': 1.0,
'ymax': 5.0,
'dx': 1.0,
'dy': 1.0,
'nx': 5,
'ny': 5})
grid = Grid2D(array, gdict)
assert grid.getValue(3.0, 3.0) == 13
lat = np.array([3.0, 4.0])
lon = np.array([3.0, 3.0])
test = grid.getValue(lat, lon)
np.testing.assert_almost_equal(test, np.array([13, 8]))
lat = np.array([[3.0, 4.0],
[4.0, 5.0]])
lon = np.array([[3.0, 3.0],
[4.0, 4.0]])
test = grid.getValue(lat, lon)
np.testing.assert_almost_equal(test, np.array([[13, 8], [9, 4]]))
def read_user_file_test(fname, xmin, xmax, ymin, ymax):
gd = get_file_geodict(fname)
sample = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, gd.dx, gd.dy)
t1 = time.time()
grid = read(fname, samplegeodict=sample)
t2 = time.time()
nrows, ncols = grid._data.shape
npixels = nrows*ncols
print('%.2f seconds to read %i pixels using h5py' % (t2-t1, npixels))
west, east, south, north = (-105.00416666665,
-102.98750000804999,
34.98750000805,
37.00416666665)
src = rasterio.open(fname, 'r')
window = src.window(west, south, east, north)
t1 = time.time()
data = src.read(window=window)
t2 = time.time()
print('%.2f seconds to read %i pixels using rasterio' % (t2-t1, npixels))
ratio = grid._data.sum()/data.sum()
print('Ratio of h5py data to rasterio data is %.4f' % ratio)
src.close()
def test_shapes():
gd = GeoDict.createDictFromBox(100.0, 102.0, 32.0, 34.0, 0.08, 0.08)
# pass in scalar values
inrow, incol = (10, 10)
lat, lon = gd.getLatLon(inrow, incol) # should get scalar results
assert np.isscalar(lat) and np.isscalar(lon)
# pass in array values
inrow = np.array([10, 11, 12])
incol = np.array([10, 11, 12])
lat, lon = gd.getLatLon(inrow, incol) # should get array results
c1 = isinstance(lat, np.ndarray) and lat.shape == inrow.shape
c2 = isinstance(lon, np.ndarray) and lon.shape == incol.shape
assert c1 and c2
# this should fail, because inputs are un-dimensioned numpy arrays
inrow = np.array(10)
incol = np.array(10)
try:
lat, lon = gd.getLatLon(inrow, incol) # should get array results
assert 1 == 0 # this should never happen
except DataSetException as dse:
pass
def test():
# these values taken from the shakemap header of:
# http://earthquake.usgs.gov/realtime/product/shakemap/ak12496371/ak/1453829475592/download/grid.xml
print('Testing various dictionaries for consistency...')
print('Testing consistent dictionary...')
# this should pass, and will serve as the comparison from now on
gdict = {'xmin': -160.340600, 'xmax': -146.340600,
'ymin': 54.104700, 'ymax': 65.104700,
'dx': 0.025000, 'dy': 0.025000,
'ny': 441, 'nx': 561}
gd = GeoDict(gdict)
print('Consistent dictionary passed.')
print('Testing dictionary with inconsistent resolution...')
# this should pass
gdict = {'xmin': -160.340600, 'xmax': -146.340600,
'ymin': 54.104700, 'ymax': 65.104700,
'dx': 0.026000, 'dy': 0.026000,
'ny': 441, 'nx': 561}
gd3 = GeoDict(gdict, adjust='res')
assert gd3 == gd
print('Dimensions modification passed.')
print('Testing dictionary with inconsistent lower right corner...')
# this should pass
gdict = {'xmin': -160.340600, 'xmax': -146.350600,
'ymin': 54.103700, 'ymax': 65.104700,
'dx': 0.025000, 'dy': 0.025000,
'ny': 441, 'nx': 561}
gd4 = GeoDict(gdict, adjust='bounds')
assert gd4 == gd
print('Corner modification passed.')
print('Testing to make sure lat/lon and row/col calculations are correct...')
# make sure the lat/lon row/col calculations are correct
ndec = int(np.abs(np.log10(GeoDict.EPS)))
lat, lon = gd.getLatLon(0, 0)
dlat = np.abs(lat-gd.ymax)
dlon = np.abs(lon-gd.xmin)
assert dlat < GeoDict.EPS and dlon < GeoDict.EPS
row, col = gd.getRowCol(lat, lon)
assert row == 0 and col == 0
lat, lon = gd.getLatLon(gd.ny-1, gd.nx-1)
dlat = np.abs(lat-gd.ymin)
dlon = np.abs(lon-gd.xmax)
assert dlat < GeoDict.EPS and dlon < GeoDict.EPS
row, col = gd.getRowCol(lat, lon)
assert row == (gd.ny-1) and col == (gd.nx-1)
print('lat/lon and row/col calculations are correct.')
print('Testing a dictionary for a global grid...')
# this is the file geodict for Landscan - should pass muster
globaldict = {'nx': 43200,
'ny': 20880,
'dx': 0.00833333333333,
'xmax': 179.99583333318935,
'xmin': -179.99583333333334,
'dy': 0.00833333333333,
'ymax': 83.99583333326376,
'ymin': -89.99583333333334}
gd5 = GeoDict(globaldict)
lat, lon = gd5.getLatLon(gd5.ny-1, gd5.nx-1)
dlat = np.abs(lat-gd5.ymin)
dlon = np.abs(lon-gd5.xmax)
assert dlat < GeoDict.EPS and dlon < GeoDict.EPS
print('Global grid is internally consistent.')
# Test class methods for creating a GeoDict
print('Testing whether GeoDict creator class methods work...')
xmin = -121.05333277776235
xmax = -116.03833388890432
ymin = 32.138334444506171
ymax = 36.286665555493826
dx = 0.0083333333333333332
dy = 0.0083333333333333332
gd6 = GeoDict.createDictFromBox(
xmin, xmax, ymin, ymax, dx, dy, inside=False)
assert gd6.xmax > xmax
assert gd6.ymin < ymin
print('Created dictionary (outside) is correct.')
gd7 = GeoDict.createDictFromBox(
xmin, xmax, ymin, ymax, dx, dy, inside=True)
assert gd7.xmax < xmax
assert gd7.ymin > ymin
print('Created dictionary (inside) is correct.')
xspan = 2.5
yspan = 2.5
gd8 = GeoDict.createDictFromCenter(xmin, ymin, dx, dy, xspan, yspan)
print('Created dictionary (from center point) is valid.')
print('Testing a geodict with dx/dy values that are NOT the same...')
xmin, xmax, ymin, ymax = (-121.06166611109568, -116.03000055557099,
32.130001111172838, 36.294998888827159)
dx, dy = (0.009999722214505959, 0.009999444413578534)
td = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
print('Passed testing a geodict with dx/dy values that are NOT the same...')
# test getBoundsWithin
# use global grid, and then a shakemap grid that we can get
print('Testing getBoundsWithin...')
grussia = {'xmin': 155.506400, 'xmax': 161.506400,
'ymin': 52.243000, 'ymax': 55.771000,
'dx': 0.016667, 'dy': 0.016642,
'nx': 361, 'ny': 213}
gdrussia = GeoDict(grussia, adjust='res')
sampledict = gd5.getBoundsWithin(gdrussia)
xSmaller = sampledict.xmin > grussia['xmin'] and sampledict.xmax < grussia['xmax']
ySmaller = sampledict.ymin > grussia['ymin'] and sampledict.ymax < grussia['ymax']
assert xSmaller and ySmaller
assert gd5.isAligned(sampledict)
print('getBoundsWithin returned correct result.')
print('Testing isAligned() method...')
gd = GeoDict({'xmin': 0.5, 'xmax': 3.5,
'ymin': 0.5, 'ymax': 3.5,
'dx': 1.0, 'dy': 1.0,
'nx': 4, 'ny': 4})
inside_aligned = GeoDict({'xmin': 1.5, 'xmax': 2.5,
'ymin': 1.5, 'ymax': 2.5,
'dx': 1.0, 'dy': 1.0,
'nx': 2, 'ny': 2})
inside_not_aligned = GeoDict({'xmin': 2.0, 'xmax': 3.0,
'ymin': 2.0, 'ymax': 3.0,
'dx': 1.0, 'dy': 1.0,
'nx': 2, 'ny': 2})
assert gd.isAligned(inside_aligned)
assert not gd.isAligned(inside_not_aligned)
print('Passed isAligned() method...')
print('Testing getAligned method...')
popdict = GeoDict({'dx': 0.00833333333333,
'dy': 0.00833333333333,
'nx': 43200,
'ny': 20880,
'xmax': 179.99583333318935,
'xmin': -179.99583333333334,
'ymax': 83.99583333326376,
'ymin': -89.99583333333334})
sampledict = GeoDict({'dx': 0.008333333333333333,
'dy': 0.008336693548387094,
'nx': 601,
'ny': 497,
'xmax': -116.046,
'xmin': -121.046,
'ymax': 36.2785,
'ymin': 32.1435})
aligndict = popdict.getAligned(sampledict)
assert popdict.isAligned(aligndict)
print('Testing geodict intersects method...')
gd1 = GeoDict({'xmin': 0.5, 'xmax': 3.5,
'ymin': 0.5, 'ymax': 3.5,
'dx': 1.0, 'dy': 1.0,
'nx': 4, 'ny': 4})
print('Testing geodict intersects method...')
gd2 = GeoDict({'xmin': 2.5, 'xmax': 5.5,
'ymin': 2.5, 'ymax': 5.5,
'dx': 1.0, 'dy': 1.0,
'nx': 4, 'ny': 4})
gd3 = GeoDict({'xmin': 4.5, 'xmax': 7.5,
'ymin': 4.5, 'ymax': 7.5,
'dx': 1.0, 'dy': 1.0,
'nx': 4, 'ny': 4})
gd4 = GeoDict({'xmin': 1.5, 'xmax': 2.5,
'ymin': 1.5, 'ymax': 2.5,
'dx': 1.0, 'dy': 1.0,
'nx': 2, 'ny': 2})
assert gd1.intersects(gd2)
assert not gd1.intersects(gd3)
print('Passed intersects method...')
print('Testing geodict intersects method with real geographic data...')
gda = GeoDict({'ymax': 83.62083333333263, 'nx': 43201,
'ny': 20835, 'dx': 0.00833333333333,
'dy': 0.00833333333333, 'xmin': -179.99583333333334,
'ymin': -89.99583333326461, 'xmax': -179.99583333347732})
gdb = GeoDict({'ymax': 28.729166666619193, 'nx': 300,
'ny': 264, 'dx': 0.00833333333333,
'dy': 0.00833333333333, 'xmin': 84.08749999989436,
'ymin': 26.537499999953404, 'xmax': 86.57916666656007})
assert gda.intersects(gdb)
print('Passed geodict intersects method with real geographic data.')
print('Testing geodict doesNotContain method...')
assert gd1.doesNotContain(gd3)
assert not gd1.doesNotContain(gd4)
print('Passed doesNotContain method...')
print('Testing geodict contains method...')
assert gd1.contains(gd4)
assert not gd1.contains(gd3)
print('Passed contains method...')
def test_project():
# test projecting a grid that wraps the 180 meridian
gd = GeoDict.createDictFromBox(175, -175, -5, 5, 1.0, 1.0)
ncells = gd.ny * gd.nx
data = np.arange(0.0, ncells).reshape(gd.ny, gd.nx)
grid = GDALGrid(data, gd)
projstr = "+proj=merc +lat_ts=55 +lon_0=180 +ellps=WGS84"
newgrid = grid.project(projstr, method='nearest')
proj = pyproj.Proj(projstr)
# what would the ul/lr corners be?
ulx, uly = proj(grid._geodict.xmin, grid._geodict.ymax)
lrx, lry = proj(grid._geodict.xmax, grid._geodict.ymin)
# what if we back-project?
newxmin, newymax = proj(newgrid._geodict.xmin,
newgrid._geodict.ymax, inverse=True)
newxmax, newymin = proj(newgrid._geodict.xmax,
newgrid._geodict.ymin, inverse=True)
x = 1
# test simple projection
data = np.array([[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[1, 1, 1, 1, 1],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0]], dtype=np.int32)
geodict = {'xmin': 50, 'xmax': 50.4, 'ymin': 50,
'ymax': 50.4, 'dx': 0.1, 'dy': 0.1, 'nx': 5, 'ny': 5}
gd = GeoDict(geodict)
grid = GDALGrid(data, gd)
projstr = "+proj=utm +zone=40 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs "
newgrid = grid.project(projstr, method='nearest')
try:
tdir = tempfile.mkdtemp()
outfile = os.path.join(tdir, 'output.bil')
grid.save(outfile)
with rasterio.open(outfile) as src:
aff = get_affine(src)
data = src.read(1)
src_crs = CRS().from_string(GeoDict.DEFAULT_PROJ4).to_dict()
dst_crs = CRS().from_string(projstr).to_dict()
nrows, ncols = data.shape
left = aff.xoff
top = aff.yoff
right, bottom = aff * (ncols-1, nrows-1)
dst_transform, width, height = calculate_default_transform(src_crs, dst_crs,
ncols, nrows,
left, bottom,
right, top)
destination = np.zeros((height, width))
reproject(data,
destination,
src_transform=aff,
src_crs=src_crs,
dst_transform=dst_transform,
dst_crs=dst_crs,
src_nodata=src.nodata,
dst_nodata=np.nan,
resampling=Resampling.nearest)
x = 1
except:
pass
finally:
shutil.rmtree(tdir)
def test_bounds_within():
host = GeoDict({'xmin': -180,
'xmax': 150,
'ymin': -90,
'ymax': 90,
'dx': 30,
'dy': 45,
'nx': 12,
'ny': 5})
sample = GeoDict({'xmin': -75,
'xmax': 45,
'ymin': -67.5,
'ymax': 67.5,
'dx': 30,
'dy': 45,
'nx': 5,
'ny': 4})
result = GeoDict({'xmin': -60,
'xmax': 30,
'ymin': -45,
'ymax': 45,
'dx': 30,
'dy': 45,
'nx': 4,
'ny': 3})
inside = host.getBoundsWithin(sample)
assert inside == result
# test degenerate case where first pass at getting inside bounds fails (xmax)
host = GeoDict({'ymax': 84.0,
'dx': 0.008333333333333333,
'ny': 16801,
'xmax': 179.99166666666667,
'xmin': -180.0,
'nx': 43200,
'dy': 0.008333333333333333,
'ymin': -56.0})
sample = GeoDict({'ymax': 18.933333333333334,
'dx': 0.008333333333333333,
'ny': 877,
'xmax': -90.28333333333333,
'xmin': -97.86666666666666,
'nx': 911,
'dy': 0.008333333333333333,
'ymin': 11.633333333333333})
inside = host.getBoundsWithin(sample)
assert sample.contains(inside)
# this tests some logic that I can't figure out
# a way to make happen inside getBoundsWithin().
newymin, ymin = (11.63333333333334, 11.633333333333333)
fdy = 0.008333333333333333
yminrow = 8684.0
fymax = 84.0
newymin -= fdy/2 # bump it down
while newymin <= ymin:
yminrow = yminrow - 1
newymin = fymax - yminrow*fdy
assert newymin > ymin
def test_output_container():
geodict = GeoDict.createDictFromBox(-118.5,-114.5,32.1,36.7,0.01,0.02)
nrows,ncols = geodict.ny,geodict.nx
#create MMI mean data for maximum component
mean_mmi_maximum_data = np.random.rand(nrows,ncols)
mean_mmi_maximum_metadata = {'name':'Gandalf',
'color':'white',
'powers':'magic'}
mean_mmi_maximum_grid = Grid2D(mean_mmi_maximum_data,geodict)
#create MMI std data for maximum component
std_mmi_maximum_data = mean_mmi_maximum_data/10
std_mmi_maximum_metadata = {'name':'Legolas',
'color':'green',
'powers':'good hair'}
std_mmi_maximum_grid = Grid2D(std_mmi_maximum_data,geodict)
#create MMI mean data for rotd50 component
mean_mmi_rotd50_data = np.random.rand(nrows,ncols)
mean_mmi_rotd50_metadata = {'name':'Gimli',
'color':'brown',
'powers':'axing'}
mean_mmi_rotd50_grid = Grid2D(mean_mmi_rotd50_data,geodict)
#create MMI std data for rotd50 component
std_mmi_rotd50_data = mean_mmi_rotd50_data/10
std_mmi_rotd50_metadata = {'name':'Aragorn',
'color':'white',
'powers':'scruffiness'}
std_mmi_rotd50_grid = Grid2D(std_mmi_rotd50_data,geodict)
#create PGA mean data for maximum component
mean_pga_maximum_data = np.random.rand(nrows,ncols)
mean_pga_maximum_metadata = {'name':'Pippin',
'color':'purple',
'powers':'rashness'}
mean_pga_maximum_grid = Grid2D(mean_pga_maximum_data,geodict)
#create PGA std data for maximum component
std_pga_maximum_data = mean_pga_maximum_data/10
std_pga_maximum_metadata = {'name':'Merry',
'color':'grey',
'powers':'hunger'}
std_pga_maximum_grid = Grid2D(std_pga_maximum_data,geodict)
f,datafile = tempfile.mkstemp()
os.close(f)
try:
container = ShakeMapOutputContainer.create(datafile)
container.setIMTGrids('mmi',
mean_mmi_maximum_grid,mean_mmi_maximum_metadata,
std_mmi_maximum_grid,std_mmi_maximum_metadata,
component='maximum')
container.setIMTGrids('mmi',
mean_mmi_rotd50_grid,mean_mmi_rotd50_metadata,
std_mmi_rotd50_grid,std_mmi_rotd50_metadata,
component='rotd50')
container.setIMTGrids('pga',
mean_pga_maximum_grid,mean_pga_maximum_metadata,
std_pga_maximum_grid,std_pga_maximum_metadata,
component='maximum')
#get the maximum MMI imt data
mmi_max_dict = container.getIMTGrids('mmi',component='maximum')
np.testing.assert_array_equal(mmi_max_dict['mean'].getData(),
mean_mmi_maximum_data)
np.testing.assert_array_equal(mmi_max_dict['std'].getData(),
std_mmi_maximum_data)
assert mmi_max_dict['mean_metadata'] == mean_mmi_maximum_metadata
assert mmi_max_dict['std_metadata'] == std_mmi_maximum_metadata
#get the rotd50 MMI imt data
mmi_rot_dict = container.getIMTGrids('mmi',component='rotd50')
np.testing.assert_array_equal(mmi_rot_dict['mean'].getData(),
mean_mmi_rotd50_data)
np.testing.assert_array_equal(mmi_rot_dict['std'].getData(),
std_mmi_rotd50_data)
assert mmi_rot_dict['mean_metadata'] == mean_mmi_rotd50_metadata
assert mmi_rot_dict['std_metadata'] == std_mmi_rotd50_metadata
#get list of maximum imts
max_imts = container.getIMTs(component='maximum')
assert sorted(max_imts) == ['mmi','pga']
#get list of components for mmi
mmi_comps = container.getComponents('mmi')
assert sorted(mmi_comps) == ['maximum','rotd50']
except Exception as e:
raise(e)
finally:
os.remove(datafile)
gridtype = 'esri'
except:
pass
if gridtype is None:
raise Exception('File "%s" does not appear to be either a GMT grid or an ESRI grid.' % gridfile)
xmin = xmin - fdict.dx*3
xmax = xmax + fdict.dx*3
ymin = ymin - fdict.dy*3
ymax = ymax + fdict.dy*3
bounds = (xmin,xmax,ymin,ymax)
if gridtype == 'gmt':
fgeodict = GMTGrid.getFileGeoDict(gridfile)
else:
fgeodict = GDALGrid.getFileGeoDict(gridfile)
dx,dy = (fgeodict.dx,fgeodict.dy)
sdict = GeoDict.createDictFromBox(xmin,xmax,ymin,ymax,dx,dy)
if gridtype == 'gmt':
grid = GMTGrid.load(gridfile,samplegeodict=sdict,resample=False,method=method,doPadding=True)
else:
grid = GDALGrid.load(gridfile,samplegeodict=sdict,resample=False,method=method,doPadding=True)
return sampleFromGrid(grid,xypoints)
def sampleFromGrid(grid,xypoints,method='nearest'):
"""
Sample 2D grid object at each of a set of XY (decimal degrees) points.
:param grid:
Grid2D object at which to sample data.
:param xypoints:
2D numpy array of XY points, decimal degrees.
:param method:
def test_rupture_depth(interactive=False):
DIP = 17.0
WIDTH = 20.0
GRIDRES = 0.1
names = ['single', 'double', 'triple',
'concave', 'concave_simple', 'ANrvSA']
means = [3.1554422780092461, 2.9224454569459781,
3.0381968625073563, 2.0522694624400271,
2.4805390352818755, 2.8740121776209673]
stds = [2.1895293825074575, 2.0506459673526174,
2.0244588429154402, 2.0112565876976416,
2.1599789955270019, 1.6156220309120068]
xp0list = [np.array([118.3]),
np.array([10.1, 10.1]),
np.array([10.1, 10.1, 10.3]),
np.array([10.9, 10.5, 10.9]),
np.array([10.9, 10.6]),
np.array([-76.483, -76.626, -76.757, -76.99, -77.024, -76.925,
-76.65, -76.321, -75.997, -75.958])]
xp1list = [np.array([118.3]),
np.array([10.1, 10.3]),
np.array([10.1, 10.3, 10.1]),
np.array([10.5, 10.9, 11.3]),
np.array([10.6, 10.9]),
np.array([-76.626, -76.757, -76.99, -77.024, -76.925, -76.65,
-76.321, -75.997, -75.958, -76.006])]
yp0list = [np.array([34.2]),
np.array([34.2, 34.5]),
np.array([34.2, 34.5, 34.8]),
np.array([34.2, 34.5, 34.8]),
np.array([35.1, 35.2]),
np.array([-52.068, -51.377, -50.729, -49.845, -49.192, -48.507,
-47.875, -47.478, -47.08, -46.422])]
yp1list = [np.array([34.5]),
np.array([34.5, 34.8]),
np.array([34.5, 34.8, 35.1]),
np.array([34.5, 34.8, 34.6]),
np.array([35.2, 35.4]),
np.array([-51.377, -50.729, -49.845, -49.192, -48.507, -47.875,
-47.478, -47.08, -46.422, -45.659])]
for i in range(0, len(xp0list)):
xp0 = xp0list[i]
xp1 = xp1list[i]
yp0 = yp0list[i]
yp1 = yp1list[i]
name = names[i]
mean_value = means[i]
std_value = stds[i]
zp = np.zeros(xp0.shape)
strike = azimuth(xp0[0], yp0[0], xp1[-1], yp1[-1])
widths = np.ones(xp0.shape) * WIDTH
dips = np.ones(xp0.shape) * DIP
strike = [strike]
origin = Origin({'eventsourcecode': 'test', 'lat': 0, 'lon': 0,
'depth': 5.0, 'mag': 7.0})
rupture = QuadRupture.fromTrace(
xp0, yp0, xp1, yp1, zp, widths, dips, origin, strike=strike)
# make a grid of points over both quads, ask for depths
ymin = np.nanmin(rupture.lats)
ymax = np.nanmax(rupture.lats)
xmin = np.nanmin(rupture.lons)
xmax = np.nanmax(rupture.lons)
xmin = np.floor(xmin * (1 / GRIDRES)) / (1 / GRIDRES)
xmax = np.ceil(xmax * (1 / GRIDRES)) / (1 / GRIDRES)
ymin = np.floor(ymin * (1 / GRIDRES)) / (1 / GRIDRES)
ymax = np.ceil(ymax * (1 / GRIDRES)) / (1 / GRIDRES)
geodict = GeoDict.createDictFromBox(
xmin, xmax, ymin, ymax, GRIDRES, GRIDRES)
nx = geodict.nx
ny = geodict.ny
depths = np.zeros((ny, nx))
for row in range(0, ny):
for col in range(0, nx):
lat, lon = geodict.getLatLon(row, col)
depth = rupture.getDepthAtPoint(lat, lon)
depths[row, col] = depth
np.testing.assert_almost_equal(np.nanmean(depths), mean_value)
np.testing.assert_almost_equal(np.nanstd(depths), std_value)
if interactive:
fig, axes = plt.subplots(nrows=2, ncols=1)
ax1, ax2 = axes
xdata = np.append(xp0, xp1[-1])
ydata = np.append(yp0, yp1[-1])
plt.sca(ax1)
plt.plot(xdata, ydata, 'b')
plt.sca(ax2)
im = plt.imshow(depths, cmap='viridis_r') # noqa
ch = plt.colorbar() # noqa
fname = os.path.join(os.path.expanduser('~'),
'quad_%s_test.png' % name)
print('Saving image for %s quad test... %s' % (name, fname))
plt.savefig(fname)
plt.close()
def modelMap(grids, shakefile=None, suptitle=None, inventory_shapefile=None,
plotorder=None, maskthreshes=None, colormaps=None, boundaries=None,
zthresh=0, scaletype='continuous', lims=None, logscale=False,
ALPHA=0.7, maproads=True, mapcities=True, isScenario=False,
roadfolder=None, topofile=None, cityfile=None, oceanfile=None,
roadcolor='#6E6E6E', watercolor='#B8EEFF', countrycolor='#177F10',
outputdir=None, savepdf=True, savepng=True, showplots=False,
roadref='unknown', cityref='unknown', oceanref='unknown',
printparam=False, ds=True, dstype='mean', upsample=False):
"""
This function creates maps of mapio grid layers (e.g. liquefaction or
landslide models with their input layers)
All grids must use the same bounds
TO DO change so that all input layers do not have to have the same bounds,
test plotting multiple probability layers, and add option so that if PDF and
PNG aren't output, opens plot on screen using plt.show()
:param grids: Dictionary of N layers and metadata formatted like:
maplayers['layer name']={
'grid': mapio grid2D object,
'label': 'label for colorbar and top line of subtitle',
'type': 'output or input to model',
'description': 'detailed description of layer for subtitle'}.
Layer names must be unique.
:type name: Dictionary or Ordered dictionary - import collections;
grids = collections.OrderedDict()
:param shakefile: optional ShakeMap file (url or full file path) to extract information for labels and folder names
:type shakefile: Shakemap Event Dictionary
:param suptitle: This will be displayed at the top of the plots and in the
figure names
:type suptitle: string
:param plotorder: List of keys describing the order to plot the grids, if
None and grids is an ordered dictionary, it will use the order of the
dictionary, otherwise it will choose order which may be somewhat random
but it will always put a probability grid first
:type plotorder: list
:param maskthreshes: N x 1 array or list of lower thresholds for masking
corresponding to order in plotorder or order of OrderedDict if plotorder
is None. If grids is not an ordered dict and plotorder is not specified,
this will not work right. If None (default), nothing will be masked
:param colormaps: List of strings of matplotlib colormaps (e.g. cm.autumn_r)
corresponding to plotorder or order of dictionary if plotorder is None.
The list can contain both strings and None e.g. colormaps = ['cm.autumn',
None, None, 'cm.jet'] and None's will default to default colormap
:param boundaries: None to show entire study area, 'zoom' to zoom in on the
area of action (only works if there is a probability layer) using zthresh
as a threshold, or a dictionary defining lats and lons in the form of
boundaries.xmin = minlon, boundaries.xmax = maxlon, boundaries.ymin =
min lat, boundaries.ymax = max lat
:param zthresh: threshold for computing zooming bounds, only used if
boundaries = 'zoom'
:type zthresh: float
:param scaletype: Type of scale for plotting, 'continuous' or 'binned' -
will be reflected in colorbar
:type scaletype: string
:param lims: None or Nx1 list of tuples or numpy arrays corresponding to
plotorder defining the limits for saturating the colorbar (vmin, vmax) if
scaletype is continuous or the bins to use (clev) if scaletype if binned.
The list can contain tuples, arrays, and Nones, e.g. lims = [(0., 10.),
None, (0.1, 1.5), np.linspace(0., 1.5, 15)]. When None is specified, the
program will estimate the limits, when an array is specified but the scale
type is continuous, vmin will be set to min(array) and vmax will be set
to max(array)
:param lims: None or Nx1 list of Trues and Falses corresponding to
plotorder defining whether to use a linear or log scale (log10) for
plotting the layer. This will be reflected in the labels
:param ALPHA: Transparency for mapping, if there is a hillshade that will
plot below each layer, it is recommended to set this to at least 0.7
:type ALPHA: float
:param maproads: Whether to show roads or not, default True, but requires
that roadfile is specified and valid to work
:type maproads: boolean
:param mapcities: Whether to show cities or not, default True, but requires
that cityfile is specified and valid to work
:type mapcities: boolean
:param isScenario: Whether this is a scenario (True) or a real event (False)
(default False)
:type isScenario: boolean
:param roadfolder: Full file path to folder containing road shapefiles
:type roadfolder: string
:param topofile: Full file path to topography grid (GDAL compatible) - this
is only needed to make a hillshade if a premade hillshade is not specified
:type topofile: string
:param cityfile: Full file path to Pager file containing city & population
information
:type cityfile: string
:param roadcolor: Color to use for roads, if plotted, default #6E6E6E
:type roadcolor: Hex color or other matplotlib compatible way of defining
color
:param watercolor: Color to use for oceans, lakes, and rivers, default
#B8EEFF
:type watercolor: Hex color or other matplotlib compatible way of defining
color
:param countrycolor: Color for country borders, default #177F10
:type countrycolor: Hex color or other matplotlib compatible way of defining
color
:param outputdir: File path for outputting figures, if edict is defined, a
subfolder based on the event id will be created in this folder. If None,
will use current directory
:param savepdf: True to save pdf figure, False to not
:param savepng: True to save png figure, False to not
:param ds: True to allow downsampling for display (necessary when arrays
are quite large, False to not allow)
:param dstype: What function to use in downsampling, options are 'min',
'max', 'median', or 'mean'
:param upsample: True to upsample the layer to the DEM resolution for better
looking hillshades
:returns:
* PDF and/or PNG of map
* Downsampled and trimmed version of input grids. If no
modification was needed for plotting, this will be identical to grids but
without the metadata
"""
if suptitle is None:
suptitle = ' '
plt.ioff()
defaultcolormap = cm.jet
if shakefile is not None:
edict = ShakeGrid.load(shakefile, adjust='res').getEventDict()
temp = ShakeGrid.load(shakefile, adjust='res').getShakeDict()
edict['eventid'] = temp['shakemap_id']
edict['version'] = temp['shakemap_version']
else:
edict = None
# Get output file location
if outputdir is None:
print('No output location given, using current directory for outputs\n')
outputdir = os.getcwd()
if edict is not None:
outfolder = os.path.join(outputdir, edict['event_id'])
else:
outfolder = outputdir
if not os.path.isdir(outfolder):
os.makedirs(outfolder)
# Get plotting order, if not specified
if plotorder is None:
plotorder = list(grids.keys())
# Get boundaries to use for all plots
cut = True
if boundaries is None:
cut = False
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
elif boundaries == 'zoom':
# Find probability layer (will just take the maximum bounds if there is
# more than one)
keytemp = list(grids.keys())
key1 = [key for key in keytemp if 'model' in key.lower()]
if len(key1) == 0:
print('Could not find model layer to use for zoom, using default boundaries')
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
else:
lonmax = -1.e10
lonmin = 1.e10
latmax = -1.e10
latmin = 1.e10
for key in key1:
# get lat lons of areas affected and add, if no areas affected,
# switch to shakemap boundaries
temp = grids[key]['grid']
xmin, xmax, ymin, ymax = temp.getBounds()
lons = np.linspace(xmin, xmax, temp.getGeoDict().nx)
lats = np.linspace(ymax, ymin, temp.getGeoDict().ny) # backwards so it plots right
row, col = np.where(temp.getData() > float(zthresh))
lonmin = lons[col].min()
lonmax = lons[col].max()
latmin = lats[row].min()
latmax = lats[row].max()
# llons, llats = np.meshgrid(lons, lats) # make meshgrid
# llons1 = llons[temp.getData() > float(zthresh)]
# llats1 = llats[temp.getData() > float(zthresh)]
# if llons1.min() < lonmin:
# lonmin = llons1.min()
# if llons1.max() > lonmax:
# lonmax = llons1.max()
# if llats1.min() < latmin:
# latmin = llats1.min()
# if llats1.max() > latmax:
# latmax = llats1.max()
boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100} # dummy fillers, only really care about bounds
if xmin < lonmin-0.15*(lonmax-lonmin):
boundaries1['xmin'] = lonmin-0.1*(lonmax-lonmin)
else:
boundaries1['xmin'] = xmin
if xmax > lonmax+0.15*(lonmax-lonmin):
boundaries1['xmax'] = lonmax+0.1*(lonmax-lonmin)
else:
boundaries1['xmax'] = xmax
if ymin < latmin-0.15*(latmax-latmin):
boundaries1['ymin'] = latmin-0.1*(latmax-latmin)
else:
boundaries1['ymin'] = ymin
if ymax > latmax+0.15*(latmax-latmin):
boundaries1['ymax'] = latmax+0.1*(latmax-latmin)
else:
boundaries1['ymax'] = ymax
boundaries = GeoDict(boundaries1, adjust='res')
else:
# SEE IF BOUNDARIES ARE SAME AS BOUNDARIES OF LAYERS
keytemp = list(grids.keys())
tempgdict = grids[keytemp[0]]['grid'].getGeoDict()
if np.abs(tempgdict.xmin-boundaries['xmin']) < 0.05 and \
np.abs(tempgdict.ymin-boundaries['ymin']) < 0.05 and \
np.abs(tempgdict.xmax-boundaries['xmax']) < 0.05 and \
np.abs(tempgdict.ymax - boundaries['ymax']) < 0.05:
print('Input boundaries are almost the same as specified boundaries, no cutting needed')
boundaries = tempgdict
cut = False
else:
try:
if boundaries['xmin'] > boundaries['xmax'] or \
boundaries['ymin'] > boundaries['ymax']:
print('Input boundaries are not usable, using default boundaries')
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
cut = False
else:
# Build dummy GeoDict
boundaries = GeoDict({'xmin': boundaries['xmin'],
'xmax': boundaries['xmax'],
'ymin': boundaries['ymin'],
'ymax': boundaries['ymax'],
'dx': 100.,
'dy': 100.,
'ny': 100.,
'nx': 100.},
adjust='res')
except:
print('Input boundaries are not usable, using default boundaries')
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
cut = False
# Pull out bounds for various uses
bxmin, bxmax, bymin, bymax = boundaries.xmin, boundaries.xmax, boundaries.ymin, boundaries.ymax
# Determine if need a single panel or multi-panel plot and if multi-panel,
# how many and how it will be arranged
fig = plt.figure()
numpanels = len(grids)
if numpanels == 1:
rowpan = 1
colpan = 1
# create the figure and axes instances.
fig.set_figwidth(5)
elif numpanels == 2 or numpanels == 4:
rowpan = np.ceil(numpanels/2.)
colpan = 2
fig.set_figwidth(13)
else:
rowpan = np.ceil(numpanels/3.)
colpan = 3
fig.set_figwidth(15)
if rowpan == 1:
fig.set_figheight(rowpan*6.0)
else:
fig.set_figheight(rowpan*5.3)
# Need to update naming to reflect the shakemap version once can get
# getHeaderData to work, add edict['version'] back into title, maybe
# shakemap id also?
fontsizemain = 14.
fontsizesub = 12.
fontsizesmallest = 10.
if rowpan == 1.:
fontsizemain = 12.
fontsizesub = 10.
fontsizesmallest = 8.
if edict is not None:
if isScenario:
title = edict['event_description']
else:
timestr = edict['event_timestamp'].strftime('%b %d %Y')
title = 'M%.1f %s v%i - %s' % (edict['magnitude'], timestr, edict['version'], edict['event_description'])
plt.suptitle(title+'\n'+suptitle, fontsize=fontsizemain)
else:
plt.suptitle(suptitle, fontsize=fontsizemain)
clear_color = [0, 0, 0, 0.0]
# Cut all of them and release extra memory
xbuff = (bxmax-bxmin)/10.
ybuff = (bymax-bymin)/10.
cutxmin = bxmin-xbuff
cutymin = bymin-ybuff
cutxmax = bxmax+xbuff
cutymax = bymax+ybuff
if cut is True:
newgrids = collections.OrderedDict()
for k, layer in enumerate(plotorder):
templayer = grids[layer]['grid']
try:
newgrids[layer] = {'grid': templayer.cut(cutxmin, cutxmax, cutymin, cutymax, align=True)}
except Exception as e:
print(('Cutting failed, %s, continuing with full layers' % e))
newgrids = grids
continue
del templayer
gc.collect()
else:
newgrids = grids
tempgdict = newgrids[list(grids.keys())[0]]['grid'].getGeoDict()
# Upsample layers to same as topofile if desired for better looking hillshades
if upsample is True and topofile is not None:
try:
topodict = GDALGrid.getFileGeoDict(topofile)
if topodict.dx >= tempgdict.dx or topodict.dy >= tempgdict.dy:
print('Upsampling not possible, resolution of results already smaller than DEM')
pass
else:
tempgdict1 = GeoDict({'xmin': tempgdict.xmin-xbuff,
'ymin': tempgdict.ymin-ybuff,
'xmax': tempgdict.xmax+xbuff,
'ymax': tempgdict.ymax+ybuff,
'dx': topodict.dx,
'dy': topodict.dy,
'nx': topodict.nx,
'ny': topodict.ny},
adjust='res')
tempgdict2 = tempgdict1.getBoundsWithin(tempgdict)
for k, layer in enumerate(plotorder):
newgrids[layer]['grid'] = newgrids[layer]['grid'].subdivide(tempgdict2)
except:
print('Upsampling failed, continuing')
# Downsample all of them for plotting, if needed, and replace them in
# grids (to save memory)
tempgrid = newgrids[list(grids.keys())[0]]['grid']
xsize = tempgrid.getGeoDict().nx
ysize = tempgrid.getGeoDict().ny
inchesx, inchesy = fig.get_size_inches()
divx = int(np.round(xsize/(500.*inchesx)))
divy = int(np.round(ysize/(500.*inchesy)))
xmin, xmax, ymin, ymax = tempgrid.getBounds()
gdict = tempgrid.getGeoDict() # Will be replaced if downsampled
del tempgrid
gc.collect()
if divx <= 1:
divx = 1
if divy <= 1:
divy = 1
if (divx > 1. or divy > 1.) and ds:
if dstype == 'max':
func = np.nanmax
elif dstype == 'min':
func = np.nanmin
elif dstype == 'med':
func = np.nanmedian
else:
func = np.nanmean
for k, layer in enumerate(plotorder):
layergrid = newgrids[layer]['grid']
dat = block_reduce(layergrid.getData().copy(),
block_size=(divy, divx),
cval=float('nan'),
func=func)
if k == 0:
lons = block_reduce(np.linspace(xmin, xmax, layergrid.getGeoDict().nx),
block_size=(divx,),
func=np.mean,
cval=float('nan'))
if math.isnan(lons[-1]):
lons[-1] = lons[-2] + (lons[1]-lons[0])
lats = block_reduce(np.linspace(ymax, ymin, layergrid.getGeoDict().ny),
block_size=(divy,),
func=np.mean,
cval=float('nan'))
if math.isnan(lats[-1]):
lats[-1] = lats[-2] + (lats[1]-lats[0])
gdict = GeoDict({'xmin': lons.min(),
'xmax': lons.max(),
'ymin': lats.min(),
'ymax': lats.max(),
'dx': np.abs(lons[1]-lons[0]),
'dy': np.abs(lats[1]-lats[0]),
'nx': len(lons),
'ny': len(lats)},
adjust='res')
newgrids[layer]['grid'] = Grid2D(dat, gdict)
del layergrid, dat
else:
lons = np.linspace(xmin, xmax, xsize)
lats = np.linspace(ymax, ymin, ysize) # backwards so it plots right side up
#make meshgrid
llons1, llats1 = np.meshgrid(lons, lats)
# See if there is an oceanfile for masking
bbox = PolygonSH(((cutxmin, cutymin), (cutxmin, cutymax), (cutxmax, cutymax), (cutxmax, cutymin)))
if oceanfile is not None:
try:
f = fiona.open(oceanfile)
oc = next(f)
f.close
shapes = shape(oc['geometry'])
# make boundaries into a shape
ocean = shapes.intersection(bbox)
except:
print('Not able to read specified ocean file, will use default ocean masking')
oceanfile = None
if inventory_shapefile is not None:
try:
f = fiona.open(inventory_shapefile)
invshp = list(f.items(bbox=(bxmin, bymin, bxmax, bymax)))
f.close()
inventory = [shape(inv[1]['geometry']) for inv in invshp]
except:
print('unable to read inventory shapefile specified, will not plot inventory')
inventory_shapefile = None
# # Find cities that will be plotted
if mapcities is True and cityfile is not None:
try:
mycity = BasemapCities.loadFromGeoNames(cityfile=cityfile)
bcities = mycity.limitByBounds((bxmin, bxmax, bymin, bymax))
#bcities = bcities.limitByPopulation(40000)
bcities = bcities.limitByGrid(nx=4, ny=4, cities_per_grid=2)
except:
print('Could not read in cityfile, not plotting cities')
mapcities = False
cityfile = None
# Load in topofile
if topofile is not None:
try:
topomap = GDALGrid.load(topofile, resample=True, method='linear', samplegeodict=gdict)
except:
topomap = GMTGrid.load(topofile, resample=True, method='linear', samplegeodict=gdict)
topodata = topomap.getData().copy()
# mask oceans if don't have ocean shapefile
if oceanfile is None:
topodata = maskoceans(llons1, llats1, topodata, resolution='h', grid=1.25, inlands=True)
else:
print('no hillshade is possible\n')
topomap = None
topodata = None
# Load in roads, if needed
if maproads is True and roadfolder is not None:
try:
roadslist = []
for folder in os.listdir(roadfolder):
road1 = os.path.join(roadfolder, folder)
shpfiles = glob.glob(os.path.join(road1, '*.shp'))
if len(shpfiles):
shpfile = shpfiles[0]
f = fiona.open(shpfile)
shapes = list(f.items(bbox=(bxmin, bymin, bxmax, bymax)))
for shapeid, shapedict in shapes:
roadslist.append(shapedict)
f.close()
except:
print('Not able to plot roads')
roadslist = None
val = 1
for k, layer in enumerate(plotorder):
layergrid = newgrids[layer]['grid']
if 'label' in list(grids[layer].keys()):
label1 = grids[layer]['label']
else:
label1 = layer
try:
sref = grids[layer]['description']['name']
except:
sref = None
ax = fig.add_subplot(rowpan, colpan, val)
val += 1
clat = bymin + (bymax-bymin)/2.0
clon = bxmin + (bxmax-bxmin)/2.0
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
m = Basemap(llcrnrlon=bxmin, llcrnrlat=bymin, urcrnrlon=bxmax, urcrnrlat=bymax,
rsphere=(6378137.00, 6356752.3142),
resolution='l', area_thresh=1000., projection='lcc',
lat_1=clat, lon_0=clon, ax=ax)
x1, y1 = m(llons1, llats1) # get projection coordinates
axsize = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
if k == 0:
wid, ht = axsize.width, axsize.height
if colormaps is not None and \
len(colormaps) == len(newgrids) and \
colormaps[k] is not None:
palette = colormaps[k]
else: # Find preferred default color map for each type of layer
if 'prob' in layer.lower() or 'pga' in layer.lower() or \
'pgv' in layer.lower() or 'cohesion' in layer.lower() or \
'friction' in layer.lower() or 'fs' in layer.lower():
palette = cm.jet
elif 'slope' in layer.lower():
palette = cm.gnuplot2
elif 'precip' in layer.lower():
palette = cm2.s3pcpn
else:
palette = defaultcolormap
if topodata is not None:
if k == 0:
ptopo = m.transform_scalar(
np.flipud(topodata), lons+0.5*gdict.dx,
lats[::-1]-0.5*gdict.dy, np.round(300.*wid),
np.round(300.*ht), returnxy=False, checkbounds=False,
order=1, masked=False)
#use lightsource class to make our shaded topography
ls = LightSource(azdeg=135, altdeg=45)
ls1 = LightSource(azdeg=120, altdeg=45)
ls2 = LightSource(azdeg=225, altdeg=45)
intensity1 = ls1.hillshade(ptopo, fraction=0.25, vert_exag=1.)
intensity2 = ls2.hillshade(ptopo, fraction=0.25, vert_exag=1.)
intensity = intensity1*0.5 + intensity2*0.5
#hillshm_im = m.transform_scalar(np.flipud(hillshm), lons, lats[::-1], np.round(300.*wid), np.round(300.*ht), returnxy=False, checkbounds=False, order=0, masked=False)
#m.imshow(hillshm_im, cmap='Greys', vmin=0., vmax=3., zorder=1, interpolation='none') # vmax = 3 to soften colors to light gray
#m.pcolormesh(x1, y1, hillshm, cmap='Greys', linewidth=0., rasterized=True, vmin=0., vmax=3., edgecolors='none', zorder=1);
# plt.draw()
# Get the data
dat = layergrid.getData().copy()
# mask out anything below any specified thresholds
# Might need to move this up to before downsampling...might give illusion of no hazard in places where there is some that just got averaged out
if maskthreshes is not None and len(maskthreshes) == len(newgrids):
if maskthreshes[k] is not None:
dat[dat <= maskthreshes[k]] = float('NaN')
dat = np.ma.array(dat, mask=np.isnan(dat))
if logscale is not False and len(logscale) == len(newgrids):
if logscale[k] is True:
dat = np.log10(dat)
label1 = r'$log_{10}$(' + label1 + ')'
if scaletype.lower() == 'binned':
# Find order of range to know how to scale
order = np.round(np.log(np.nanmax(dat) - np.nanmin(dat)))
if order < 1.:
scal = 10**-order
else:
scal = 1.
if lims is None or len(lims) != len(newgrids):
clev = (np.linspace(np.floor(scal*np.nanmin(dat)), np.ceil(scal*np.nanmax(dat)), 10))/scal
else:
if lims[k] is None:
clev = (np.linspace(np.floor(scal*np.nanmin(dat)), np.ceil(scal*np.nanmax(dat)), 10))/scal
else:
clev = lims[k]
# Adjust to colorbar levels
dat[dat < clev[0]] = clev[0]
for j, level in enumerate(clev[:-1]):
dat[(dat >= clev[j]) & (dat < clev[j+1])] = clev[j]
# So colorbar saturates at top
dat[dat > clev[-1]] = clev[-1]
#panelhandle = m.contourf(x1, y1, datm, clev, cmap=palette, linewidth=0., alpha=ALPHA, rasterized=True)
vmin = clev[0]
vmax = clev[-1]
else:
if lims is not None and len(lims) == len(newgrids):
if lims[k] is None:
vmin = np.nanmin(dat)
vmax = np.nanmax(dat)
else:
vmin = lims[k][0]
vmax = lims[k][-1]
else:
vmin = np.nanmin(dat)
vmax = np.nanmax(dat)
# Mask out cells overlying oceans or block with a shapefile if available
if oceanfile is None:
dat = maskoceans(llons1, llats1, dat, resolution='h', grid=1.25, inlands=True)
else:
#patches = []
if type(ocean) is PolygonSH:
ocean = [ocean]
for oc in ocean:
patch = getProjectedPatch(oc, m, edgecolor="#006280", facecolor=watercolor, lw=0.5, zorder=4.)
#x, y = m(oc.exterior.xy[0], oc.exterior.xy[1])
#xy = zip(x, y)
#patch = Polygon(xy, facecolor=watercolor, edgecolor="#006280", lw=0.5, zorder=4.)
##patches.append(Polygon(xy, facecolor=watercolor, edgecolor=watercolor, zorder=500.))
ax.add_patch(patch)
##ax.add_collection(PatchCollection(patches))
if inventory_shapefile is not None:
for in1 in inventory:
if 'point' in str(type(in1)):
x, y = in1.xy
x = x[0]
y = y[0]
m.scatter(x, y, c='m', s=50, latlon=True, marker='^',
zorder=100001)
else:
x, y = m(in1.exterior.xy[0], in1.exterior.xy[1])
xy = list(zip(x, y))
patch = Polygon(xy, facecolor='none', edgecolor='k', lw=0.5, zorder=10.)
#patches.append(Polygon(xy, facecolor=watercolor, edgecolor=watercolor, zorder=500.))
ax.add_patch(patch)
palette.set_bad(clear_color, alpha=0.0)
# Plot it up
dat_im = m.transform_scalar(
np.flipud(dat), lons+0.5*gdict.dx, lats[::-1]-0.5*gdict.dy,
np.round(300.*wid), np.round(300.*ht), returnxy=False,
checkbounds=False, order=0, masked=True)
if topodata is not None: # Drape over hillshade
#turn data into an RGBA image
cmap = palette
#adjust data so scaled between vmin and vmax and between 0 and 1
dat1 = dat_im.copy()
dat1[dat1 < vmin] = vmin
dat1[dat1 > vmax] = vmax
dat1 = (dat1 - vmin)/(vmax-vmin)
rgba_img = cmap(dat1)
maskvals = np.dstack((dat1.mask, dat1.mask, dat1.mask))
rgb = np.squeeze(rgba_img[:, :, 0:3])
rgb[maskvals] = 1.
draped_hsv = ls.blend_hsv(rgb, np.expand_dims(intensity, 2))
m.imshow(draped_hsv, zorder=3., interpolation='none')
# This is just a dummy layer that will be deleted to make the
# colorbar look right
panelhandle = m.imshow(dat_im, cmap=palette, zorder=0.,
vmin=vmin, vmax=vmax)
else:
panelhandle = m.imshow(dat_im, cmap=palette, zorder=3.,
vmin=vmin, vmax=vmax, interpolation='none')
#panelhandle = m.pcolormesh(x1, y1, dat, linewidth=0., cmap=palette, vmin=vmin, vmax=vmax, alpha=ALPHA, rasterized=True, zorder=2.);
#panelhandle.set_edgecolors('face')
# add colorbar
cbfmt = '%1.1f'
if vmax is not None and vmin is not None:
if (vmax - vmin) < 1.:
cbfmt = '%1.2f'
elif vmax > 5.: # (vmax - vmin) > len(clev):
cbfmt = '%1.0f'
#norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
if scaletype.lower() == 'binned':
cbar = fig.colorbar(panelhandle, spacing='proportional',
ticks=clev, boundaries=clev, fraction=0.036,
pad=0.04, format=cbfmt, extend='both')
#cbar1 = ColorbarBase(cbar.ax, cmap=palette, norm=norm, spacing='proportional', ticks=clev, boundaries=clev, fraction=0.036, pad=0.04, format=cbfmt, extend='both', extendfrac='auto')
else:
cbar = fig.colorbar(panelhandle, fraction=0.036, pad=0.04,
extend='both', format=cbfmt)
#cbar1 = ColorbarBase(cbar.ax, cmap=palette, norm=norm, fraction=0.036, pad=0.04, extend='both', extendfrac='auto', format=cbfmt)
if topodata is not None:
panelhandle.remove()
cbar.set_label(label1, fontsize=10)
cbar.ax.tick_params(labelsize=8)
parallels = m.drawparallels(getMapLines(bymin, bymax, 3),
labels=[1, 0, 0, 0], linewidth=0.5,
labelstyle='+/-', fontsize=9, xoffset=-0.8,
color='gray', zorder=100.)
m.drawmeridians(getMapLines(bxmin, bxmax, 3), labels=[0, 0, 0, 1],
linewidth=0.5, labelstyle='+/-', fontsize=9,
color='gray', zorder=100.)
for par in parallels:
try:
parallels[par][1][0].set_rotation(90)
except:
pass
#draw roads on the map, if they were provided to us
if maproads is True and roadslist is not None:
try:
for road in roadslist:
try:
xy = list(road['geometry']['coordinates'])
roadx, roady = list(zip(*xy))
mapx, mapy = m(roadx, roady)
m.plot(mapx, mapy, roadcolor, lw=0.5, zorder=9)
except:
continue
except Exception as e:
print(('Failed to plot roads, %s' % e))
#add city names to map
if mapcities is True and cityfile is not None:
try:
fontname = 'Arial'
fontsize = 8
if k == 0: # Only need to choose cities first time and then apply to rest
fcities = bcities.limitByMapCollision(
m, fontname=fontname, fontsize=fontsize)
ctlats, ctlons, names = fcities.getCities()
cxis, cyis = m(ctlons, ctlats)
for ctlat, ctlon, cxi, cyi, name in zip(ctlats, ctlons, cxis, cyis, names):
m.scatter(ctlon, ctlat, c='k', latlon=True, marker='.',
zorder=100000)
ax.text(cxi, cyi, name, fontname=fontname,
fontsize=fontsize, zorder=100000)
except Exception as e:
print('Failed to plot cities, %s' % e)
#draw star at epicenter
plt.sca(ax)
if edict is not None:
elat, elon = edict['lat'], edict['lon']
ex, ey = m(elon, elat)
plt.plot(ex, ey, '*', markeredgecolor='k', mfc='None', mew=1.0,
ms=15, zorder=10000.)
m.drawmapboundary(fill_color=watercolor)
m.fillcontinents(color=clear_color, lake_color=watercolor)
m.drawrivers(color=watercolor)
##m.drawcoastlines()
#draw country boundaries
m.drawcountries(color=countrycolor, linewidth=1.0)
#add map scale
m.drawmapscale((bxmax+bxmin)/2., (bymin+(bymax-bymin)/9.), clon, clat, np.round((((bxmax-bxmin)*111)/5)/10.)*10, barstyle='fancy', zorder=10)
# Add border
autoAxis = ax.axis()
rec = Rectangle((autoAxis[0]-0.7, autoAxis[2]-0.2), (autoAxis[1]-autoAxis[0])+1, (autoAxis[3]-autoAxis[2])+0.4, fill=False, lw=1, zorder=1e8)
rec = ax.add_patch(rec)
rec.set_clip_on(False)
plt.draw()
if sref is not None:
label2 = '%s\nsource: %s' % (label1, sref) # '%s\n' % label1 + r'{\fontsize{10pt}{3em}\selectfont{}%s}' % sref #
else:
label2 = label1
plt.title(label2, axes=ax, fontsize=fontsizesub)
#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')
#if ds: # Could add this to print "downsampled" on map
# plt.text()
if k == 1 and rowpan == 1:
# adjust single level plot
axsize = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
ht2 = axsize.height
fig.set_figheight(ht2*1.6)
else:
plt.tight_layout()
# Make room for suptitle - tight layout doesn't account for it
plt.subplots_adjust(top=0.92)
if printparam is True:
try:
fig = plt.gcf()
dictionary = grids['model']['description']['parameters']
paramstring = 'Model parameters: '
halfway = np.ceil(len(dictionary)/2.)
for i, key in enumerate(dictionary):
if i == halfway and colpan == 1:
paramstring += '\n'
paramstring += ('%s = %s; ' % (key, dictionary[key]))
print(paramstring)
fig.text(0.01, 0.015, paramstring, fontsize=fontsizesmallest)
plt.draw()
except:
print('Could not display model parameters')
if edict is not None:
eventid = edict['eventid']
else:
eventid = ''
time1 = datetime.datetime.utcnow().strftime('%d%b%Y_%H%M')
outfile = os.path.join(outfolder, '%s_%s_%s.pdf' % (eventid, suptitle, time1))
pngfile = os.path.join(outfolder, '%s_%s_%s.png' % (eventid, suptitle, time1))
if savepdf is True:
print('Saving map output to %s' % outfile)
plt.savefig(outfile, dpi=300)
if savepng is True:
print('Saving map output to %s' % pngfile)
plt.savefig(pngfile)
if showplots is True:
plt.show()
else:
plt.close(fig)
return newgrids
def getYesPoints(pshapes,proj,dx,nmax,touch_center=True):
"""
Collect x/y coordinates of all points within hazard coverage polygons at desired resolution.
:param pshapes:
Sequence of orthographically projected shapes.
:param proj:
PyProj projection object used to transform input shapes
:param dx:
Float resolution of grid at which to sample points, must be round number
:param nmax:
Threshold maximum number of points in total data mesh.
:param touch_center:
Boolean indicating whether presence of polygon in each grid cell is enough to turn that
into a yes pixel. Setting this to false presumes that the dx is relatively large, such
that creating a grid at that resolution will not tax the resources of the system.
:returns:
- numpy 2-D array of X/Y coordinates inside hazard polygons.
- number of rows of resulting mesh
- number of columns of resulting mesh
- numpy array of x coordinate centers of columns
- numpy array of y coordinate centers of rows
- 1D array of indices where yes pixels are located (use np.unravel_index to unpack to 2D array)
"""
mxmin = 9e10
mxmax = -9e10
mymin = 9e10
mymax = -9e10
for pshape in pshapes:
pxmin,pymin,pxmax,pymax = pshape.bounds
if pxmin < mxmin:
mxmin = pxmin
if pxmax > mxmax:
mxmax = pxmax
if pymin < mymin:
mymin = pymin
if pymax > mymax:
mymax = pymax
#
if not touch_center:
geodict = GeoDict.createDictFromBox(mxmin,mxmax,mymin,mymax,dx,dx)
img = rasterizeShapes(pshapes,geodict)
#now get the numpy array of x/y coordinates where covgrid == 1
idx = np.where(img == 1)[0]
x,y = np.unravel_index(idx,(geodict.ny,geodict.nx))
yespoints = list(zip(x.flatten(),y.flatten()))
nrows = geodict.ny
ncols = geodict.nx
#Create the sequence of column and row centers
xvar = np.arange(geodict.xmin,geodict.xmax+geodict.dx,geodict.dx)
yvar = np.arange(geodict.ymin,geodict.ymax+geodict.dy,geodict.dy)
else:
xvar = np.arange(mxmin,mxmax+dx,dx)
yvar = np.arange(mymin,mymax+dx,dx)
ncols = len(xvar)
nrows = len(yvar)
if nmax is not None:
if ncols*nrows > nmax:
aspect = ncols/nrows
ncols = np.sqrt(nmax*aspect)
nrows = nmax/ncols
ncols = int(ncols)
nrows = int(nrows)
#re-calculate dx here...
tdx = (mxmax-mxmin)/ncols
tdy = (mymax-mymin)/nrows
dx = np.max([tdx,tdy])
xvar = np.arange(mxmin,mxmax+dx,dx)
yvar = np.arange(mymin,mymax+dx,dx)
#Get the "yes" points to sample from
#here, we're in the situation
yespoints = []
idx = []
shapeidx = 0
if pshapes[0].type == 'Polygon':
#loop over shapes, projecting each one, then get the sample points
for pshape in pshapes:
if not shapeidx % 1000:
print('Searching polygon %i of %i' % (shapeidx,len(pshapes)))
shapeidx += 1
pxmin,pymin,pxmax,pymax = pshape.bounds
leftcol = np.where((pxmin - xvar) >= 0)
rightcol = np.where((xvar - pxmax) >= 0)
if len(leftcol[0]) and len(rightcol[0]):
leftcol = leftcol[0].argmax()
rightcol = rightcol[0].argmax()
bottomrow = np.where((pymin - yvar) >= 0)
toprow = np.where((yvar - pymax) >= 0)
if len(bottomrow[0]) and len(toprow[0]):
bottomrow = bottomrow[0].argmax()
toprow = toprow[0][0]
xp = np.arange(xvar[leftcol],xvar[rightcol]+dx,dx)
yp = np.arange(yvar[bottomrow],yvar[toprow]+dx,dx)
xmesh,ymesh = np.meshgrid(xp,yp)
xy = list(zip(xmesh.flatten(),ymesh.flatten()))
for point in xy:
ix = np.where(xvar == point[0])
iy = np.where(yvar == point[1])
if len(ix[0]) and len(iy[0]):
ix = ix[0][0]
try:
iy = iy[0][0]
except:
foo = 1
if pshape.contains(Point(point)):
yespoints.append(point)
idx.append(np.ravel_multi_index((iy,ix),(nrows,ncols),mode='raise',order='C'))
else:
yespoints = []
for pshape in pshapes:
yespoints.append(pshape.coords[0])
return (np.array(yespoints),nrows,ncols,xvar,yvar,idx)
