def test_output_arrays():
f, datafile = tempfile.mkstemp()
os.close(f)
try:
container = ShakeMapOutputContainer.create(datafile)
#
# Test that no data type is set
#
assert container.getDataType() is None
#
# Make some array data and metadata
#
mean = np.random.rand(100)
std = np.random.rand(100)
lats = np.random.rand(100)
lons = np.random.rand(100)
ids = np.array([randomword(4).encode('ascii') for x in range(100)])
metadata = {'units': '%g', 'digits': 4}
#
# Put the data in the container
#
container.setIMTArrays('PGA', lons, lats, ids, mean, metadata, std,
metadata, 'Larger')
#
# Now extract it and compare it to what we put in there
#
dout = container.getIMTArrays('PGA', 'Larger')
assert all(dout['lons'] == lons)
assert all(dout['lats'] == lats)
assert all(dout['ids'] == ids)
assert all(dout['mean'] == mean)
assert all(dout['std'] == std)
#
# Check the data type
#
assert container.getDataType() == 'points'
#
# Try raising some exceptions
#
# Shouldn't be able to find this IMT
with pytest.raises(LookupError):
container.getIMTArrays('JUNK', 'Larger')
# Shapes of inputs not the same
with pytest.raises(ValueError):
empty = np.array([])
container.setIMTArrays('PGV', empty, lats, ids, mean, metadata,
std, metadata, 'Larger')
# IMT already exists
with pytest.raises(ValueError):
container.setIMTArrays('PGA', lons, lats, ids, mean, metadata, std,
metadata, 'Larger')
# Trying to set a grid in a file with points
with pytest.raises(TypeError):
container.setIMTGrids('PGV', mean, metadata, std, metadata,
'Larger')
# Trying to get a grid in a file with points
with pytest.raises(TypeError):
container.getIMTGrids('PGA', 'Larger')
container.close()
except Exception as e:
raise (e)
finally:
os.remove(datafile)
def test_output_arrays():
f, datafile = tempfile.mkstemp()
os.close(f)
try:
container = ShakeMapOutputContainer.create(datafile)
#
# Test that no data type is set
#
assert container.getDataType() is None
#
# Make some array data and metadata
#
mean = np.random.rand(100)
std = np.random.rand(100)
lats = np.random.rand(100)
lons = np.random.rand(100)
ids = np.array([randomword(4).encode('ascii') for x in range(100)])
metadata = {'units': '%g',
'digits': 4}
#
# Put the data in the container
#
container.setIMTArrays('PGA', lons, lats, ids,
mean, metadata,
std, metadata, 'Larger')
#
# Now extract it and compare it to what we put in there
#
dout = container.getIMTArrays('PGA', 'Larger')
assert all(dout['lons'] == lons)
assert all(dout['lats'] == lats)
assert all(dout['ids'] == ids)
assert all(dout['mean'] == mean)
assert all(dout['std'] == std)
#
# Check the data type
#
assert container.getDataType() == 'points'
#
# Try raising some exceptions
#
# Shouldn't be able to find this IMT
with pytest.raises(LookupError):
junk = container.getIMTArrays('JUNK', 'Larger')
# Shapes of inputs not the same
with pytest.raises(ValueError):
empty = np.array([])
container.setIMTArrays('PGV', empty, lats, ids,
mean, metadata,
std, metadata, 'Larger')
# IMT already exists
with pytest.raises(ValueError):
container.setIMTArrays('PGA', lons, lats, ids,
mean, metadata,
std, metadata, 'Larger')
# Trying to set a grid in a file with points
with pytest.raises(TypeError):
container.setIMTGrids('PGV', mean, metadata,
std, metadata, 'Larger')
# Trying to get a grid in a file with points
with pytest.raises(TypeError):
container.getIMTGrids('PGA', 'Larger')
except Exception as e:
raise(e)
finally:
os.remove(datafile)
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
# Check repr method
assert repr(container) == '''Data type: grid
use "getIMTGrids" method to access interpolated IMTs
Rupture: None
Config: None
Stations: None
Metadata: None
Available IMTs (components):
mmi (maximum, rotd50)
pga (maximum)
'''
# get list of all imts
imts = container.getIMTs()
# 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']
# Test dropIMT
imts = container.getIMTs('maximum')
assert imts == ['mmi', 'pga']
container.dropIMT('mmi')
imts = container.getIMTs('maximum')
assert imts == ['pga']
container.close()
except Exception as e:
raise (e)
finally:
os.remove(datafile)
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)
