def _to_omf(self):
import omf
element = omf.PointSetElement(
name=self.title or '',
description=self.description or '',
geometry=self.mesh._to_omf(),
color=self.opts.color or 'random',
data=[],
textures=[tex._to_omf() for tex in self.textures]
)
for data in self.data:
location = 'vertices'
omf_data = data.data._to_omf()
omf_data.location = location
element.data.append(omf_data)
return element
def to_omf(self):
self.validate()
omf_point_set = omf.PointSetElement(
name=self.name or '',
description=self.description or '',
geometry=omf.PointSetGeometry(vertices=self.vertices.array, ),
data=[
attr.to_omf(cell_location='vertices') for attr in self.data
if not isinstance(attr, TextureProjection)
],
textures=[
tex.to_omf() for tex in self.data
if isinstance(tex, TextureProjection)
],
color=self.defaults.color.value,
)
return omf_point_set
def test_points():
proj = omf.Project(origin=[5., 5, 5])
pts = omf.PointSetElement(
name='my elem',
description='my desc',
geometry=omf.PointSetGeometry(
vertices=[[i / 3, i / 4, i / 5] for i in range(10)],
origin=[5., 5, 5],
),
color='red',
)
proj.elements = [pts]
proj.validate()
view = get_view_from_proj(proj)
view.validate()
assert len(view.contents) == 2
pointset = find(view, spatial.ElementPointSet)
defaults = pointset.defaults.serialize()
assert defaults['__class__'] == 'OptionsPoints'
assert defaults['color']['value'] == '#FF0000'
assert list(pointset.vertices.array[0]) == [10., 10, 10]
assert pointset.name == 'my elem'
assert pointset.description == 'my desc'
description='Just some assorted elements')
POINTSET = omf.PointSetElement(
name='Random Points',
description='Just random points',
geometry=omf.PointSetGeometry(vertices=np.random.rand(100, 3)),
data=[
omf.ScalarData(name='rand data',
array=np.random.rand(100),
location='vertices'),
omf.ScalarData(name='More rand data',
array=np.random.rand(100),
location='vertices')
],
# textures=[
# omf.ImageTexture(
# name='test image',
# image='test_image.png',
# origin=[0, 0, 0],
# axis_u=[1, 0, 0],
# axis_v=[0, 1, 0]
# ),
# omf.ImageTexture(
# name='test image',
# image='test_image.png',
# origin=[0, 0, 0],
# axis_u=[1, 0, 0],
# axis_v=[0, 0, 1]
# )
# ],
color='green')
LINESET = omf.LineSetElement(name='Random Line',
# ++++++++++++++++++++++
# Load the temperature data
_temp = pd.read_csv(gdc19.get_temperature_path('well_based_temperature.csv'))
_temp.head()
###############################################################################
temperature = omf.PointSetElement(
name='temperature',
description='cumulative record of one-dimensional temperature modeling '\
'based off of well data. Temperature log data were exampled and '\
'extrapolated below the bottom of a number of wells. Temperatures '\
'are in degrees Celsius, and all location data are georeferenced to '
'UTM, zone 12N, NAD 83, NAVD 88.',
subtype='point',
geometry=omf.PointSetGeometry(
vertices=_temp[['x', 'y', 'z']].values
),
data=[omf.ScalarData(
name='temperature (C)',
array=_temp['T'].values,
location='vertices'
),]
)
temperature.validate()
###############################################################################
omfvtk.wrap(temperature).plot(show_edges=False)
###############################################################################
# Geostatistical Model
def test_doc_ex(self):
dirname, _ = os.path.split(os.path.abspath(__file__))
pngfile = os.path.sep.join(dirname.split(os.path.sep)[:-1] +
['docs', 'images', 'PointSetGeometry.png'])
proj = omf.Project(
name='Test project',
description='Just some assorted elements'
)
pts = omf.PointSetElement(
name='Random Points',
description='Just random points',
geometry=omf.PointSetGeometry(
vertices=np.random.rand(100, 3)
),
data=[
omf.ScalarData(
name='rand data',
array=np.random.rand(100),
location='vertices'
),
omf.ScalarData(
name='More rand data',
array=np.random.rand(100),
location='vertices'
)
],
textures=[
omf.ImageTexture(
name='test image',
image=pngfile,
origin=[0, 0, 0],
axis_u=[1, 0, 0],
axis_v=[0, 1, 0]
),
omf.ImageTexture(
name='test image',
image=pngfile,
origin=[0, 0, 0],
axis_u=[1, 0, 0],
axis_v=[0, 0, 1]
)
],
color='green'
)
lin = omf.LineSetElement(
name='Random Line',
geometry=omf.LineSetGeometry(
vertices=np.random.rand(100, 3),
segments=np.floor(np.random.rand(50, 2)*100).astype(int)
),
data=[
omf.ScalarData(
name='rand vert data',
array=np.random.rand(100),
location='vertices'
),
omf.ScalarData(
name='rand segment data',
array=np.random.rand(50),
location='segments'
)
],
color='#0000FF'
)
surf = omf.SurfaceElement(
name='trisurf',
geometry=omf.SurfaceGeometry(
vertices=np.random.rand(100, 3),
triangles=np.floor(np.random.rand(50, 3)*100).astype(int)
),
data=[
omf.ScalarData(
name='rand vert data',
array=np.random.rand(100),
location='vertices'
),
omf.ScalarData(
name='rand face data',
array=np.random.rand(50),
location='faces'
)
],
color=[100, 200, 200]
)
grid = omf.SurfaceElement(
name='gridsurf',
geometry=omf.SurfaceGridGeometry(
tensor_u=np.ones(10).astype(float),
tensor_v=np.ones(15).astype(float),
origin=[50., 50., 50.],
axis_u=[1., 0, 0],
axis_v=[0, 0, 1.],
offset_w=np.random.rand(11, 16).flatten()
),
data=[
omf.ScalarData(
name='rand vert data',
array=np.random.rand(11, 16).flatten(),
location='vertices'
),
omf.ScalarData(
name='rand face data',
array=np.random.rand(10, 15).flatten(order='f'),
location='faces'
)
],
textures=[
omf.ImageTexture(
name='test image',
image=pngfile,
origin=[2., 2., 2.],
axis_u=[5., 0, 0],
axis_v=[0, 2., 5.]
)
]
)
vol = omf.VolumeElement(
name='vol',
geometry=omf.VolumeGridGeometry(
tensor_u=np.ones(10).astype(float),
tensor_v=np.ones(15).astype(float),
tensor_w=np.ones(20).astype(float),
origin=[10., 10., -10]
),
data=[
omf.ScalarData(
name='Random Data',
location='cells',
array=np.random.rand(10, 15, 20).flatten()
)
]
)
proj.elements = [pts, lin, surf, grid, vol]
assert proj.validate()
serial_file = os.path.sep.join([dirname, 'out.omf'])
omf.OMFWriter(proj, serial_file)
reader = omf.OMFReader(serial_file)
new_proj = reader.get_project()
assert new_proj.validate()
assert str(new_proj.elements[3].textures[0].uid) == \
str(proj.elements[3].textures[0].uid)
del reader
os.remove(serial_file)