def run_grid_pip(time,points,polygons,rotation_model,grid_dims):
reconstructed_polygons = []
pygplates.reconstruct(polygons,rotation_model,reconstructed_polygons,time)
rpolygons = []
for polygon in reconstructed_polygons:
if polygon.get_reconstructed_geometry():
rpolygons.append(polygon.get_reconstructed_geometry())
polygons_containing_points = points_in_polygons.find_polygons(points, rpolygons)
lat = []
lon = []
zval = []
for pcp,point in zip(polygons_containing_points,points):
lat.append(point.get_latitude())
lon.append(point.get_longitude())
if pcp is not None:
zval.append(1)
else:
zval.append(0)
bi = np.array(zval).reshape(grid_dims[0],grid_dims[1])
return bi
def run_grid_pip(recon_time,
points,
polygons,
rotation_model,
anchor_plate_id=0):
reconstructed_polygons = []
pygplates.reconstruct(polygons,
rotation_model,
reconstructed_polygons,
recon_time,
anchor_plate_id=anchor_plate_id)
rpolygons = []
for polygon in reconstructed_polygons:
if polygon.get_reconstructed_geometry():
rpolygons.append(polygon.get_reconstructed_geometry())
polygons_containing_points = points_in_polygons.find_polygons(
points, rpolygons)
lat = []
lon = []
zval = []
for pcp, point in zip(polygons_containing_points, points):
lat.append(point.get_latitude())
lon.append(point.get_longitude())
if pcp is not None:
zval.append(1)
else:
zval.append(0)
return zval
def run_grid_pnp(recon_time,
points,
spatial_tree_of_uniform_recon_points,
polygons,
rotation_model,
anchor_plate_id=0,
distance_threshold_radians=2):
reconstructed_polygons = []
pygplates.reconstruct(polygons,
rotation_model,
reconstructed_polygons,
recon_time,
anchor_plate_id=anchor_plate_id)
rpolygons = []
for polygon in reconstructed_polygons:
if polygon.get_reconstructed_geometry():
rpolygons.append(polygon.get_reconstructed_geometry())
res = find_closest_geometries_to_points_using_points_spatial_tree(
points,
spatial_tree_of_uniform_recon_points,
rpolygons,
distance_threshold_radians=distance_threshold_radians,
geometries_are_solid=True)
pnp_test = []
for index in res:
if index is not None:
pnp_test.append(1)
else:
pnp_test.append(0)
return pnp_test
def paleolithology(request):
anchor_plate_id = request.GET.get('pid', 0)
time = request.GET.get('time', 0)
model = request.GET.get('model', settings.MODEL_DEFAULT)
model_dict = get_reconstruction_model_dict(model)
rotation_model = pygplates.RotationModel([
str('%s/%s/%s' % (settings.MODEL_STORE_DIR, model, rot_file))
for rot_file in model_dict['RotationFile']
])
paleolithology_datafile = '%s/boucot_paleolithology_combined.shp' % settings.PALEO_STORE_DIR
static_polygons_filename = str(
'%s/%s/%s' %
(settings.MODEL_STORE_DIR, model, model_dict['StaticPolygons']))
#select points based on age before doing plate id assignment - should be quicker??
paleolithology_points = pygplates.FeatureCollection(
paleolithology_datafile)
valid_points = []
for point in paleolithology_points:
if point.get_valid_time()[0] > float(
time) and point.get_valid_time()[1] <= float(time):
valid_points.append(point)
print(valid_points)
assigned_features = pygplates.partition_into_plates(
static_polygons_filename,
rotation_model,
valid_points,
properties_to_copy=[
pygplates.PartitionProperty.reconstruction_plate_id
],
partition_method=pygplates.PartitionMethod.most_overlapping_plate)
reconstructed_paleolithology_points = []
pygplates.reconstruct(assigned_features,
rotation_model,
reconstructed_paleolithology_points,
float(time),
anchor_plate_id=anchor_plate_id)
ret = write_json_reconstructed_point_features(
reconstructed_paleolithology_points,
attributes=[('LithCode', 'string'), ('Period', 'string')])
#add header for CORS
#http://www.html5rocks.com/en/tutorials/cors/
response = HttpResponse(ret, content_type='application/json')
#TODO:
#The "*" makes the service wide open to anyone. We should implement access control when time comes.
response['Access-Control-Allow-Origin'] = '*'
return response
def reconstruct_coastlines(self):
#
#-----------Use pygplates to carry out the reconstruction
#
print 'Reconstructing coastlines...'
pygplates.reconstruct(coastlines_filename, rotation_filenames,
coastlines_output_basename + '.shp',
self.reconstruction_time, self.anchor_plate)
def reconstruct_features(rotation_model, topological_features,
reconstruction_time, output_filename_prefix,
output_filename_extension):
export_filename = 'reconstructed_{0!s}_{1}Ma.{2!s}'.format(
output_filename_prefix, reconstruction_time, output_filename_extension)
pygplates.reconstruct(topological_features, rotation_model,
export_filename, reconstruction_time)
def reconstruct_mineral_deposits(self):
pygplates.reconstruct(mineral_filename, rotation_filenames,
mineral_output_basename + '.shp',
self.reconstruction_time, self.anchor_plate)
pygplates.reconstruct(mineral_AU_filename, rotation_filenames,
mineral_AU_output_basename + '.shp',
self.reconstruction_time, self.anchor_plate)
def reconstruct_continental_polygons(self):
#
#-----------Use pygplates to carry out the reconstruction
#
print('Reconstructing continental polygons...')
pygplates.reconstruct(continental_polygons_filename,
rotation_filenames,
continental_polygons_output_basename + '.shp',
self.reconstruction_time, self.anchor_plate)
def tree_snapshot(polygons, rotation_model, recon_time):
reconstructed_polygons = []
pygplates.reconstruct(polygons,rotation_model,reconstructed_polygons,recon_time)
uniq_plates_from_polygons = get_unique_plate_ids_from_reconstructed_features(reconstructed_polygons)
reconstruction_tree = rotation_model.get_reconstruction_tree(recon_time)
chains = get_plate_chains(uniq_plates_from_polygons, reconstruction_tree)
return uniq_plates_from_polygons, chains, reconstruction_tree, reconstructed_polygons
def reconstruct_magnetic_picks(self):
sys.stdout.flush()
cnt = 0
for filename in magnetic_picks_filenames:
cnt += 1
# Use pygplates to carry out the reconstruction
print 'Reconstructing magnetic picks in {}'.format(filename)
sys.stdout.flush()
pygplates.reconstruct(
filename, rotation_filenames,
magnetic_picks_output_basename + '_{}.shp'.format(cnt),
self.reconstruction_time, self.anchor_plate)
def write_trees_to_file(input_features, rotation_model, filename,
reconstruction_time_range, time_step=1,
polygon_type='static', root_feature_filename=None):
reconstruction_times = np.arange(reconstruction_time_range[0], reconstruction_time_range[1]+time_step, time_step)
tree_features = None
root_centroid_features = []
for reconstruction_time in reconstruction_times:
print 'working on time %0.2f Ma' % reconstruction_time
reconstructed_polygons = []
if polygon_type is 'topological':
pygplates.resolve_topologies(input_features, rotation_model, reconstructed_polygons, reconstruction_time)
else:
pygplates.reconstruct(input_features, rotation_model, reconstructed_polygons, reconstruction_time)
uniq_plates_from_polygons = get_unique_plate_ids_from_reconstructed_features(reconstructed_polygons)
reconstruction_tree = rotation_model.get_reconstruction_tree(reconstruction_time)
chains = get_plate_chains(uniq_plates_from_polygons, reconstruction_tree)
tree_features = create_hierarchy_features(chains, reconstructed_polygons, tree_features,
valid_time=(reconstruction_time+time_step/2.,
reconstruction_time-time_step/2.))
if root_feature_filename is not None:
polygon_centroids = get_polygon_centroids(reconstructed_polygons)
root_plates = get_root_static_polygon_plate_ids(reconstruction_tree, uniq_plates_from_polygons)
for root_plate in root_plates:
p0 = polygon_centroids[root_plate]
feature = pygplates.Feature()
feature.set_geometry(pygplates.PointOnSphere(p0))
feature.set_name(str(root_plate))
feature.set_valid_time(reconstruction_time+time_step/2.,
reconstruction_time-time_step/2.)
root_centroid_features.append(feature)
tree_feature_collection = pygplates.FeatureCollection(tree_features)
tree_feature_collection.write(filename)
if root_feature_filename is not None:
tree_feature_collection = pygplates.FeatureCollection(root_centroid_features)
tree_feature_collection.write(root_feature_filename)
def GeneratePlateReferenceFrameXYZ(rotation_model, reconstruction_time,
multipoint_feature_collection,
InputGridFile):
data = Dataset(InputGridFile, 'r')
keys = [key for key in data.variables]
lon = np.copy(data.variables[keys[0][:]])
lat = np.copy(data.variables[keys[1][:]])
[lon, lat] = np.meshgrid(lon, lat)
Zg = data.variables['z'][:]
ithetas = np.radians(90. - lat)
iphis = np.radians(lon)
irs = np.ones(np.shape(ithetas))
nodes = []
ixyzs = rtp2xyz(irs.ravel(), ithetas.ravel(), iphis.ravel())
tree = spatial.cKDTree(ixyzs, 16)
with open('output', 'w') as output_file:
# Create an interpolation object for this grid
#f=RectBivariateSpline(lon,lat,data.variables['z'][:].T)
# Reconstruct the multipoint features into a list of pygplates.ReconstructedFeatureGeometry's.
reconstructed_feature_geometries = []
pygplates.reconstruct(multipoint_feature_collection, rotation_model,
reconstructed_feature_geometries,
reconstruction_time)
print('num reconstructed multipoint geometries: %d' %
len(reconstructed_feature_geometries))
for reconstructed_feature_geometry in reconstructed_feature_geometries:
reconstructed_lon_points, reconstructed_lat_points, present_day_lat_lon_points = GetReconstructedMultipoint(
reconstructed_feature_geometry)
num_points = len(reconstructed_lon_points)
# evaluate the current grid at the multipoint coordinates of the current feature
#gridZr = f.ev(reconstructed_lon_points, reconstructed_lat_points)
d, l = sampleOnSphere(lat,
lon,
Zg,
np.array(reconstructed_lat_points),
np.array(reconstructed_lon_points),
tree=tree)
gridZr = Zg.ravel()[l]
# append the interpolated points as lon,lat,Z triples to an ascii file
for point_index in range(0, num_points):
pdp = present_day_lat_lon_points[point_index]
output_file.write('%f %f %f\n' %
(pdp[1], pdp[0], gridZr[point_index]))
def add_reconstructed_points_to_xyz(points_file,
rotation_model,
reconstruction_time,
zval,
mask_file=None):
reconstructed_points = []
pygplates.reconstruct(points_file, rotation_model, reconstructed_points,
reconstruction_time)
#mask_file = './OrogenMasks.gpml'
mask_file = None
if mask_file is not None:
reconstructed_masks = []
pygplates.reconstruct(mask_file, rotation_model, reconstructed_masks,
reconstruction_time)
for reconstructed_point in reconstructed_points:
for reconstructed_mask in reconstructed_masks:
if reconstructed_mask.get_reconstructed_geometry(
).is_point_in_polygon(
reconstructed_point.get_reconstructed_geometry(
).get_centroid()):
reconstructed_point.get_feature().set_name(
reconstructed_mask.get_feature().get_name())
point_array = []
zval_array = []
for reconstructed_point in reconstructed_points:
feature_coordinates = reconstructed_point.get_reconstructed_geometry(
).to_lat_lon_array()
point_array.append(feature_coordinates)
#if reconstructed_point.get_feature().get_feature_type() == pygplates.FeatureType.create_gpml('OrogenicBelt'):
if reconstructed_point.get_feature().get_name() == 'OrogenicBelt':
zval_array.append(
np.ones((feature_coordinates.shape[0], 1)) * zval * 3)
elif reconstructed_point.get_feature().get_name() == 'Cordillera':
zval_array.append(
np.ones((feature_coordinates.shape[0], 1)) * zval * 2)
else:
zval_array.append(
np.ones((feature_coordinates.shape[0], 1)) * zval)
xy_array = np.vstack(point_array)
xyz_array = np.hstack((xy_array, np.vstack(zval_array)))
return xyz_array
def group_features_by_motion(features,
rotation_model,
time,
delta_time=1,
plot=False):
# given a list of features, a rotation model and a time range,
# this function will make a list of all the plate_ids based on the
# features, then determine groups of those plate ids within which
# there is no relative motion between the plates during the specified
# time range
rps = []
pygplates.reconstruct(features, rotation_model, rps, time)
pids = []
for rp in rps:
pids.append(rp.get_feature().get_reconstruction_plate_id())
pg_pids = list(set(pids))
plate_group_lists = get_plate_motion_groups(pg_pids, rotation_model, time,
delta_time)
#print 'Number of groups is %d' % len(plate_group_lists)
#for plate_group in plate_group_lists:
# print plate_group
if plot:
cmap = plt.get_cmap('hsv')
colors = cmap(np.linspace(0, 1, len(plate_group_lists)))
plt.figure(figsize=(20, 9))
for rp in rps:
for (plate_group, color) in zip(plate_group_lists, colors):
if rp.get_feature().get_reconstruction_plate_id(
) in plate_group:
plt.plot(
rp.get_reconstructed_geometry().to_lat_lon_array()[:,
1],
rp.get_reconstructed_geometry().to_lat_lon_array()[:,
0],
color=color)
plt.show()
return plate_group_lists
def reconstruct_points(request):
points = request.GET.get('points', None)
plate_id = request.GET.get('pid', None)
time = request.GET.get('time', None)
rotation_model = pygplates.RotationModel(
MODEL_DEFAULT+"Seton_etal_ESR2012_2012.1.rot")
static_polygons_filename = \
MODEL_DEFAULT+"Seton_etal_ESR2012_StaticPolygons_2012.1.gpmlz"
point_features = []
if points:
ps = points.split(',')
if len(ps)%2==0:
for lat,lon in zip(ps[1::2], ps[0::2]):
point_feature = pygplates.Feature()
point_feature.set_geometry(pygplates.PointOnSphere(float(lat),float(lon)))
point_features.append(point_feature)
#for f in point_features:
# f.set_reconstruction_plate_id(int(plate_id))
assigned_point_features = pygplates.partition_into_plates(
static_polygons_filename,
rotation_model,
point_features,
properties_to_copy = [
pygplates.PartitionProperty.reconstruction_plate_id,
pygplates.PartitionProperty.valid_time_period])
assigned_point_feature_collection = pygplates.FeatureCollection(assigned_point_features)
reconstructed_feature_geometries = []
pygplates.reconstruct(assigned_point_feature_collection, rotation_model, reconstructed_feature_geometries, float(time))
ret='{"coordinates":['
for g in reconstructed_feature_geometries:
ret+='[{0:5.2f},{1:5.2f}],'.format(
g.get_reconstructed_geometry().to_lat_lon()[1],
g.get_reconstructed_geometry().to_lat_lon()[0])
ret=ret[0:-1]
ret+=']}'
return HttpResponse(ret, content_type='application/json')
def run_grid_pnp(recon_time,
points,
spatial_tree_of_uniform_recon_points,
polygons,
rotation_model,
distance_threshold_radians=2):
reconstructed_polygons = []
pygplates.reconstruct(polygons, rotation_model, reconstructed_polygons,
recon_time)
rpolygons = []
for polygon in reconstructed_polygons:
if polygon.get_reconstructed_geometry():
rpolygons.append(polygon.get_reconstructed_geometry())
res1 = find_closest_geometries_to_points_using_points_spatial_tree(
points,
spatial_tree_of_uniform_recon_points,
rpolygons,
distance_threshold_radians=distance_threshold_radians,
geometries_are_solid=False)
distance_to_polygon_boundary = np.array(list(zip(*res1))[0])
# Make a copy of list of distances.
distance_to_polygon = list(distance_to_polygon_boundary)
# Set distance to zero for any points inside a polygon (leave other points unchanged).
res2 = points_in_polygons.find_polygons_using_points_spatial_tree(
points, spatial_tree_of_uniform_recon_points, rpolygons)
for point_index, rpolygon in enumerate(res2):
# If not inside any polygons then result will be None.
if rpolygon:
distance_to_polygon[point_index] = 0.0
distance_to_polygon = np.array(distance_to_polygon)
return distance_to_polygon, distance_to_polygon_boundary
def reconstruct_coastlines(time):
shp_path = settings.MODEL_STORE_DIR+'/'+settings.MODEL_DEFAULT+'/coastlines_low_res/Seton_etal_ESR2012_Coastlines_2012.shp'
import shapefile
sf = shapefile.Reader(shp_path)
features = []
for record in sf.shapeRecords():
if record.shape.shapeType != 5:
continue
for idx in range(len(record.shape.parts)):
start_idx = record.shape.parts[idx]
end_idx = len(record.shape.points)
if idx < (len(record.shape.parts) -1):
end_idx = record.shape.parts[idx+1]
polygon_feature = pygplates.Feature()
points = record.shape.points[start_idx:end_idx]
polygon_feature.set_geometry(
pygplates.PolygonOnSphere([(lat,lon) for lon, lat in points]))
polygon_feature.set_reconstruction_plate_id(int(record.record[0]))
features.append(polygon_feature)
break
feature_collection = pygplates.FeatureCollection(features)
reconstructed_polygons = []
model_dict = get_reconstruction_model_dict(settings.MODEL_DEFAULT)
rotation_model = pygplates.RotationModel([str('%s/%s/%s' %
(settings.MODEL_STORE_DIR,settings.MODEL_DEFAULT,rot_file)) for rot_file in model_dict['RotationFile']])
pygplates.reconstruct(
feature_collection,
rotation_model,
reconstructed_polygons,
float(time))
return reconstructed_polygons
def create_gmt(model, time, proj, anchor_plate_id, tmp_dir,cnt=0,fmt='png'):
data = get_reconstruction_model_dict(model)
shp_path = settings.MODEL_STORE_DIR+model+'/'+data['Coastlines']
input_rotation_filename = []
for f in data['RotationFile']:
input_rotation_filename.append((settings.MODEL_STORE_DIR+model+ '/'+f))
output_coastlines_filename = tmp_dir+'/coastlines.gmt'
pygplates.reconstruct(
shp_path,
input_rotation_filename,
output_coastlines_filename,
int(time),
int(anchor_plate_id))
#proj = '-JX20c/10c'
if proj.lower() == 'robinson':
projc = '-JN20c'
elif proj.lower() == 'mollweide':
projc = '-JW20c'
else:
projc = '-JQ20c'
file_base_name = 'img-{:04d}'.format(cnt)
os.system('gmt psbasemap -R-180/180/-90/90 {0} -K -Bafg -B+t\"{2}Ma\" > {1}/{3}.ps'.format(projc, tmp_dir, time, file_base_name))
os.system('gmt psxy -Rd {0} -W0.25p,grey10 -K -O -m {1}/coastlines.gmt -V >> {1}/{2}.ps'.format(projc, tmp_dir, file_base_name))
os.system('gmt psclip /usr/src/clip.txt -Rd {0} -O -V >> {1}/{2}.ps'.format(projc,tmp_dir, file_base_name))
os.system('cd {0} && gmt psconvert {0}/{1}.ps -A -E240 -Tg -P'.format(tmp_dir,file_base_name))
try:
with open(tmp_dir+'/{0}.png'.format(file_base_name), "rb") as image_file:
if fmt == 'base64':
response = HttpResponse('data:image/png;base64,' + urllib.parse.quote(base64.b64encode(image_file.read())))
else:
response = HttpResponse(image_file.read(),content_type='image/png')
response['Access-Control-Allow-Origin'] = '*'
return response
except Exception as e:
return HttpResponseServerError(str(e))
coastline_features = pygplates.FeatureCollection(
'../../../../sample_data/2.0/SampleData/FeatureCollections/Coastlines/Matthews_etal_GPC_2016_Coastlines.gpmlz'
)
rotation_model = pygplates.RotationModel(
'../../../../sample_data/2.0/SampleData/FeatureCollections/Rotations/Matthews_etal_GPC_2016_410-0Ma_GK07.rot'
)
print('Reconstructing coastline polygons...')
reconstruction_time = 0
coastline_reconstructed_feature_geometries = []
pygplates.reconstruct(coastline_features, rotation_model,
coastline_reconstructed_feature_geometries,
reconstruction_time)
polygons = []
polygon_features = []
for reconstructed_feature_geometry in coastline_reconstructed_feature_geometries:
polygons.append(
reconstructed_feature_geometry.get_reconstructed_geometry())
polygon_features.append(reconstructed_feature_geometry.get_feature())
# Create uniform lat/lon distribution of points.
def reconstruct_continent_ocean_boundaries(self):
# Use pygplates to carry out the reconstruction
pygplates.reconstruct(cob_filename, rotation_filenames,
cob_output_basename + '.shp',
self.reconstruction_time, self.anchor_plate)
def reconstruct_raster_stage(static_polygon_features,
rotation_model,
time_from,
time_to,
uniform_recon_points,
spatial_tree_of_uniform_recon_points,
anchor_plate_id=0):
print 'Reconstruct static polygons...'
# Reconstruct the multipoint feature.
recon_static_polygon_features = []
pygplates.reconstruct(static_polygon_features,
rotation_model,
recon_static_polygon_features,
time_to,
anchor_plate_id=anchor_plate_id)
# Extract the polygons and plate IDs from the reconstructed static polygons.
recon_static_polygons = []
recon_static_polygon_plate_ids = []
for recon_static_polygon_feature in recon_static_polygon_features:
recon_plate_id = recon_static_polygon_feature.get_feature(
).get_reconstruction_plate_id()
recon_polygon = recon_static_polygon_feature.get_reconstructed_geometry(
)
recon_static_polygon_plate_ids.append(recon_plate_id)
recon_static_polygons.append(recon_polygon)
print 'Find static polygons...'
# Find the reconstructed static polygon (plate IDs) containing the uniform (reconstructed) points.
#
# The order (and length) of 'recon_point_plate_ids' matches the order (and length) of 'uniform_recon_points'.
# Points outside all static polygons return a value of None.
recon_point_plate_ids = points_in_polygons.find_polygons_using_points_spatial_tree(
uniform_recon_points, spatial_tree_of_uniform_recon_points,
recon_static_polygons, recon_static_polygon_plate_ids)
print 'Group by polygons...'
# Group recon points with plate IDs so we can later create one multipoint per plate.
recon_points_grouped_by_plate_id = {}
for point_index, point_plate_id in enumerate(recon_point_plate_ids):
# Reject any points outside all reconstructed static polygons.
if point_plate_id is None:
continue
# Add empty list to dict if first time encountering plate ID.
if point_plate_id not in recon_points_grouped_by_plate_id:
recon_points_grouped_by_plate_id[point_plate_id] = []
# Add to list of points associated with plate ID.
recon_point = uniform_recon_points[point_index]
recon_points_grouped_by_plate_id[point_plate_id].append(recon_point)
print 'Reverse reconstruct points...'
# Reconstructed points.
recon_point_lons = []
recon_point_lats = []
# Present day points associated with reconstructed points.
point_lons = []
point_lats = []
# Create a multipoint feature for each plate ID and reverse-reconstruct it to get present-day points.
#
# Iterate over key/value pairs in dictionary.
for plate_id, recon_points_in_plate in recon_points_grouped_by_plate_id.iteritems(
):
# Reverse reconstructing a multipoint is much faster than individually reverse-reconstructing points.
multipoint_feature = pygplates.Feature()
multipoint_feature.set_geometry(
pygplates.MultiPointOnSphere(recon_points_in_plate))
multipoint_feature.set_reconstruction_plate_id(plate_id)
# Reverse reconstruct the multipoint feature.
pygplates.reverse_reconstruct(multipoint_feature,
rotation_model,
time_to,
anchor_plate_id=anchor_plate_id)
#Forward reconstruct multipoint to
multipoint_at_from_time = []
pygplates.reconstruct(multipoint_feature,
rotation_model,
multipoint_at_from_time,
time_from,
anchor_plate_id=anchor_plate_id)
# Extract reverse-reconstructed geometry.
multipoint = multipoint_at_from_time[0].get_reconstructed_geometry()
# Collect present day and associated reconstructed points.
for point_index, point in enumerate(multipoint):
lat, lon = point.to_lat_lon()
point_lons.append(lon)
point_lats.append(lat)
recon_point = recon_points_in_plate[point_index]
recon_lat, recon_lon = recon_point.to_lat_lon()
recon_point_lons.append(recon_lon)
recon_point_lats.append(recon_lat)
print 'Sample present-day grid...'
# Query present-day grid using present-day points.
#
# TODO: Note sure what happens in regions where there's no data in grid (need to ignore those points).
#data = data_grid.ev(point_lons, point_lats)
#data = [1.0] * len(recon_point_lons)
#data = sample_grid_using_scipy(point_lons,point_lats,grdfile)
return recon_point_lons, recon_point_lats, point_lons, point_lats
def GenerateReconstructedDeltaXYZ(rotation_model, reconstruction_time1,
reconstruction_time2,
multipoint_feature_collection,
InputGridFile1, InputGridFile2,
force_plate_frame):
data1 = Dataset(InputGridFile1, 'r')
data2 = Dataset(InputGridFile2, 'r')
keys = [key for key in data1.variables]
lon = np.copy(data1.variables[keys[0][:]])
lat = np.copy(data1.variables[keys[1][:]])
# lon = np.copy(data1.variables['x'][:])
# lat = np.copy(data1.variables['y'][:])
#except:
# lon = np.copy(data1.variables['lon'][:])
# lat = np.copy(data1.variables['lat'][:])
[lon, lat] = np.meshgrid(lon, lat)
Zg1 = data1.variables['z'][:]
Zg2 = data2.variables['z'][:]
ithetas = np.radians(90. - lat)
iphis = np.radians(lon)
irs = np.ones(np.shape(ithetas))
nodes = []
ixyzs = rtp2xyz(irs.ravel(), ithetas.ravel(), iphis.ravel())
tree = spatial.cKDTree(ixyzs, 16)
reconstruction_time_mid = 0.5 * (reconstruction_time1 +
reconstruction_time2)
with open('output', 'w') as output_file:
# Create interpolation objects for these grids
#f1=RectBivariateSpline(data1.variables['lon'][:],data1.variables['lat'][:],data1.variables['z'][:].T)
#f2=RectBivariateSpline(data2.variables['lon'][:],data2.variables['lat'][:],data2.variables['z'][:].T)
# Reconstruct the multipoint features into a list of pygplates.ReconstructedFeatureGeometry's.
reconstructed_feature_geometries1 = []
reconstructed_feature_geometries2 = []
reconstructed_feature_geometries_mid = []
pygplates.reconstruct(multipoint_feature_collection, rotation_model,
reconstructed_feature_geometries1,
reconstruction_time1)
pygplates.reconstruct(multipoint_feature_collection, rotation_model,
reconstructed_feature_geometries2,
reconstruction_time2)
pygplates.reconstruct(multipoint_feature_collection, rotation_model,
reconstructed_feature_geometries_mid,
reconstruction_time_mid)
print('num reconstructed multipoint geometries: %d' %
len(reconstructed_feature_geometries1))
for reconstructed_feature_geometry_index in range(
0, len(reconstructed_feature_geometries1)):
reconstructed_feature_geometry1 = reconstructed_feature_geometries1[
reconstructed_feature_geometry_index]
reconstructed_feature_geometry2 = reconstructed_feature_geometries2[
reconstructed_feature_geometry_index]
reconstructed_feature_geometry_mid = reconstructed_feature_geometries_mid[
reconstructed_feature_geometry_index]
reconstructed_lon_points1, reconstructed_lat_points1, present_day_lat_lon_points1 = \
GetReconstructedMultipoint(reconstructed_feature_geometry1)
reconstructed_lon_points2, reconstructed_lat_points2, present_day_lat_lon_points2 = \
GetReconstructedMultipoint(reconstructed_feature_geometry2)
reconstructed_lon_points_mid, reconstructed_lat_points_mid, present_day_lat_lon_points_mid = \
GetReconstructedMultipoint(reconstructed_feature_geometry_mid)
num_points = len(reconstructed_lon_points1)
# evaluate the current grids at the multipoint coordinates of the current feature
#gridZr1 = f1.ev(reconstructed_lon_points1, reconstructed_lat_points1)
#gridZr2 = f2.ev(reconstructed_lon_points2, reconstructed_lat_points2)
#gridZr1 = sample_grid_using_kdtree(np.array(reconstructed_lon_points1), np.array(reconstructed_lat_points1), InputGridFile1)
#gridZr2 = sample_grid_using_kdtree(np.array(reconstructed_lon_points2), np.array(reconstructed_lat_points2), InputGridFile2)
d, l = sampleOnSphere(lat,
lon,
Zg1,
np.array(reconstructed_lat_points1),
np.array(reconstructed_lon_points1),
tree=tree)
gridZr1 = Zg1.ravel()[l]
d, l = sampleOnSphere(lat,
lon,
Zg2,
np.array(reconstructed_lat_points1),
np.array(reconstructed_lon_points1),
tree=tree)
gridZr2 = Zg2.ravel()[l]
#print gridZr1
# append the interpolated points as lon,lat,Z triples to an ascii file
for point_index in range(0, num_points):
delta = (gridZr1[point_index] - gridZr2[point_index]) / (
reconstruction_time2 - reconstruction_time1)
if force_plate_frame:
output_lon = present_day_lat_lon_points_mid[point_index][1]
output_lat = present_day_lat_lon_points_mid[point_index][0]
else:
output_lon = reconstructed_lon_points_mid[point_index]
output_lat = reconstructed_lat_points_mid[point_index]
output_file.write('%f %f %f\n' %
(output_lon, output_lat, delta))
def reconstruct_fracture_zones(self):
# Use pygplates to carry out the reconstruction
print 'Reconstructing fracture zones...'
pygplates.reconstruct(fracture_zones_filename, rotation_filenames,
fracture_zones_output_basename + '.shp',
self.reconstruction_time, self.anchor_plate)
print now(
), 'frame_change_pygplates: Create RectBivariateSpline of input grid'
# Create an interpolation object for this grid
f = RectBivariateSpline(input_grid.variables['lon'][:],
input_grid.variables['lat'][:],
input_grid.variables['z'][:].T)
# Reconstruct the multipoint features into a list of pygplates.ReconstructedFeatureGeometry's.
reconstructed_feature_geometries = []
print now(
), 'frame_change_pygplates: pygplates.reconstruct multipoint_feature_collection'
pygplates.reconstruct(multipoint_feature_collection, rotation_model,
reconstructed_feature_geometries,
reconstruction_time)
print now(
), 'frame_change_pygplates: num reconstructed multipoint geometries: %d' % len(
reconstructed_feature_geometries)
# loop over RFGs
for reconstructed_feature_geometry in reconstructed_feature_geometries:
# Get the recon positions
reconstructed_lon_points, reconstructed_lat_points, present_day_lat_lon_points = GetReconstructedMultipoint(
reconstructed_feature_geometry)
num_points = len(reconstructed_lon_points)
# too verbose ?
rotation_model = pygplates.RotationModel(rotation_file)
proximity_features = list(pygplates.FeatureCollection(proximity_features_file))
print('{:<10} {:<10}'.format('Age(Ma)', 'Length(Kms)'))
# Time range 0-230Ma in 1My intervals.
for time in range(0, 231):
length_in_kms = 0
# Reconstruct the proximity features that exist at the current 'time'.
proximity_reconstructed_feature_geometries = []
pygplates.reconstruct(proximity_features, rotation_model, proximity_reconstructed_feature_geometries, time)
# Remove any 'exact' duplicate reconstructed geometries.
# This does not remove partially overlapping geometries, etc.
_remove_duplicate_reconstructed_feature_geometries(proximity_reconstructed_feature_geometries)
# Iterate over reconstructed geometries and add up their segment lengths.
for proximity_reconstructed_feature_geometry in proximity_reconstructed_feature_geometries:
proximity_reconstructed_geometry = proximity_reconstructed_feature_geometry.get_reconstructed_geometry()
try:
length_in_kms += proximity_reconstructed_geometry.get_arc_length() * pygplates.Earth.mean_radius_in_kms
except AttributeError:
# Only polylines and polygons have the 'get_arc_length()' method.
# Skip points and multipoints...
continue
def get_static_polygons(request):
"""
http GET request to retrieve reconstructed static polygons
**usage**
<http-address-to-gws>/reconstruct/static_polygons/plate_id=\ *anchor_plate_id*\&time=\ *reconstruction_time*\&model=\ *reconstruction_model*
**parameters:**
*anchor_plate_id* : integer value for reconstruction anchor plate id [default=0]
*time* : time for reconstruction [required]
*model* : name for reconstruction model [defaults to default model from web service settings]
**returns:**
json containing reconstructed coastline features
"""
anchor_plate_id = request.GET.get('pid', 0)
time = request.GET.get('time', 0)
model = request.GET.get('model',settings.MODEL_DEFAULT)
wrap = True
central_meridian = 0
if 'central_meridian' in request.GET:
try:
central_meridian = float(request.GET['central_meridian'])
wrap = True
except:
print('Invalid central meridian.')
avoid_map_boundary = False
if 'avoid_map_boundary' in request.GET:
avoid_map_boundary = True
model_dict = get_reconstruction_model_dict(model)
rotation_model = pygplates.RotationModel([str('%s/%s/%s' %
(settings.MODEL_STORE_DIR,model,rot_file)) for rot_file in model_dict['RotationFile']])
reconstructed_polygons = []
pygplates.reconstruct(
str('%s/%s/%s' % (settings.MODEL_STORE_DIR,model,model_dict['StaticPolygons'])),
rotation_model,
reconstructed_polygons,
float(time),
anchor_plate_id=anchor_plate_id)
data = process_reconstructed_polygons(reconstructed_polygons,
wrap,
central_meridian,
avoid_map_boundary)
ret = json.dumps(pretty_floats(data))
#add header for CORS
#http://www.html5rocks.com/en/tutorials/cors/
response = HttpResponse(ret, content_type='application/json')
#TODO:
response['Access-Control-Allow-Origin'] = '*'
return response
def reconstruct_files(request):
if not request.method == "POST":
return HttpResponseBadRequest('expecting post requests!')
try:
print((request.FILES))
if not len(list(request.FILES.items())):
return HttpResponseBadRequest("No File Received")
if not os.path.isdir(settings.MEDIA_ROOT + "/rftmp"):
os.makedirs(settings.MEDIA_ROOT + "/rftmp" )
reconstructable_files = []
for fs in request.FILES.lists():
for f in fs[1]:
print((f.name))
(name,ext) = os.path.splitext(f.name)
if ext in ['.shp', '.gpml','.gpmlz']:
reconstructable_files.append(f.name)
with open(settings.MEDIA_ROOT + "/rftmp/" + f.name, 'wb+') as fp:
for chunk in f.chunks():
fp.write(chunk)
time = request.POST.get('time', None)
model = request.POST.get('model',settings.MODEL_DEFAULT)
print(model)
model_dict = get_reconstruction_model_dict(model)
if not model_dict:
return HttpResponseBadRequest('The "model" ({0}) cannot be recognized.'.format(model))
rotation_model = pygplates.RotationModel([str('%s/%s/%s' %
(settings.MODEL_STORE_DIR,model,rot_file)) for rot_file in model_dict['RotationFile']])
static_polygons_filename = str('%s/%s/%s' % (settings.MODEL_STORE_DIR,model,model_dict['StaticPolygons']))
for f in reconstructable_files:
#print(f)
features = pygplates.partition_into_plates(
static_polygons_filename,
rotation_model,
(settings.MEDIA_ROOT + "/rftmp/" + f).encode("utf-8"),
partition_method = pygplates.PartitionMethod.most_overlapping_plate,
properties_to_copy = [pygplates.PartitionProperty.reconstruction_plate_id,
pygplates.PartitionProperty.valid_time_period]
)
feature_collection = pygplates.FeatureCollection(features)
feature_collection.write((settings.MEDIA_ROOT + "/rftmp/" + f+'.partitioned.gpml').encode("utf-8"))
if not os.path.isdir(settings.MEDIA_ROOT + "/rftmp/dst/"):
os.makedirs(settings.MEDIA_ROOT + "/rftmp/dst/" )
pygplates.reconstruct((settings.MEDIA_ROOT + "/rftmp/" + f+'.partitioned.gpml').encode("utf-8"),
rotation_model,
((settings.MEDIA_ROOT + "/rftmp/dst/" + f + '_' + model + '_' + str(time) + 'Ma').replace('.','_') + '.shp').encode("utf-8"),
float(time))
s = io.StringIO()
zf = zipfile.ZipFile(s, "w")
#settings.MEDIA_ROOT + '/rftmp/dst/reconstructed_files.zip', 'w', zipfile.ZIP_DEFLATED)
for r, d, files in os.walk(settings.MEDIA_ROOT + "/rftmp/dst/"):
if not files:
return HttpResponseServerError('No output files have been created!')
for f in files:
zf.write(os.path.join(r, f), 'reconstructed_files/'+f)
zf.close()
#f = StringIO(file(settings.MEDIA_ROOT + '/rftmp/dst/reconstructed_files.zip', "rb").read())
response = HttpResponse(s.getvalue(), content_type = 'application/x-zip-compressed')
response['Content-Disposition'] = 'attachment; filename=reconstructed_files.zip'
#clear_folder(settings.MEDIA_ROOT + "/rftmp" )
#clear_folder(settings.MEDIA_ROOT + "/rftmp/dst")
return response
except:
clear_folder(settings.MEDIA_ROOT + "/rftmp" )
clear_folder(settings.MEDIA_ROOT + "/rftmp/dst")
traceback.print_stack()
traceback.print_exc(file=sys.stdout)
err = traceback.format_exc()
return HttpResponseBadRequest(err)
def reconstruct_feature_collection(request):
if request.method == 'POST':
params = request.POST
elif request.method == 'GET':
params = request.GET
else:
return HttpResponseBadRequest('Unrecognized request type')
anchor_plate_id = params.get('pid', 0)
if 'time' in params:
time = params['time']
elif 'geologicage' in params:
time = params['geologicage']
else:
time = 140 #default reconstruction age
output_format = params.get('output', 'geojson')
fc_str = params.get('feature_collection')
model = str(params.get('model',settings.MODEL_DEFAULT))
if 'keep_properties' in params:
keep_properties = True
else:
keep_properties = False
try:
timef = float(time)
except:
return HttpResponseBadRequest('The "time" parameter is invalid ({0}).'.format(time))
try:
anchor_plate_id = int(anchor_plate_id)
except:
return HttpResponseBadRequest('The "pid" parameter is invalid ({0}).'.format(anchor_plate_id))
# Convert geojson input to gplates feature collection
features=[]
try:
fc = json.loads(fc_str)#load the input feature collection
for f in fc['features']:
geom = f['geometry']
feature = pygplates.Feature()
if geom['type'] == 'Point':
feature.set_geometry(pygplates.PointOnSphere(
float(geom['coordinates'][1]),
float(geom['coordinates'][0])))
if geom['type'] == 'LineString':
feature.set_geometry(
pygplates.PolylineOnSphere([(point[1],point[0]) for point in geom['coordinates']]))
if geom['type'] == 'Polygon':
feature.set_geometry(
pygplates.PolygonOnSphere([(point[1],point[0]) for point in geom['coordinates'][0]]))
if geom['type'] == 'MultiPoint':
feature.set_geometry(
pygplates.MultiPointOnSphere([(point[1],point[0]) for point in geom['coordinates']]))
if keep_properties and 'properties' in f:
for pk in f['properties']:
p = f['properties'][pk]
if isinstance(p, str):
p=str(p)
feature.set_shapefile_attribute(str(pk),p)
features.append(feature)
except Exception as e:
#print e
return HttpResponseBadRequest('Invalid input feature collection')
model_dict = get_reconstruction_model_dict(model)
if not model_dict:
return HttpResponseBadRequest('The "model" ({0}) cannot be recognized.'.format(model))
rotation_model = pygplates.RotationModel([str('%s/%s/%s' %
(settings.MODEL_STORE_DIR,model,rot_file)) for rot_file in model_dict['RotationFile']])
assigned_features = pygplates.partition_into_plates(
settings.MODEL_STORE_DIR+model+'/'+model_dict['StaticPolygons'],
rotation_model,
features,
properties_to_copy = [
pygplates.PartitionProperty.reconstruction_plate_id,
pygplates.PartitionProperty.valid_time_period],
partition_method = pygplates.PartitionMethod.most_overlapping_plate
)
reconstructed_geometries = []
pygplates.reconstruct(assigned_features,
rotation_model,
reconstructed_geometries,
timef,
anchor_plate_id=anchor_plate_id)
# convert feature collection back to geojson
data = {"type": "FeatureCollection"}
data["features"] = []
for g in reconstructed_geometries:
geom = g.get_reconstructed_geometry()
feature = {"type": "Feature"}
feature["geometry"] = {}
if isinstance(geom, pygplates.PointOnSphere):
feature["geometry"]["type"] = "Point"
p = geom.to_lat_lon_list()[0]
feature["geometry"]["coordinates"] = [p[1], p[0]]
elif isinstance(geom, pygplates.MultiPointOnSphere):
feature["geometry"]["type"] = 'MultiPoint'
feature["geometry"]["coordinates"] = [[lon,lat] for lat, lon in geom.to_lat_lon_list()]
elif isinstance(geom, pygplates.PolylineOnSphere):
feature["geometry"]["type"] = 'LineString'
feature["geometry"]["coordinates"] = [[lon,lat] for lat, lon in geom.to_lat_lon_list()]
elif isinstance(geom, pygplates.PolygonOnSphere):
feature["geometry"]["type"] = 'Polygon'
feature["geometry"]["coordinates"] = [[[lon,lat] for lat, lon in geom.to_lat_lon_list()]]
else:
return HttpResponseServerError('Unsupported Geometry Type.')
feature["properties"] = {}
if keep_properties:
for pk in g.get_feature().get_shapefile_attributes():
feature["properties"][pk] = g.get_feature().get_shapefile_attribute(pk)
#print feature["properties"]
data["features"].append(feature)
ret = json.dumps(pretty_floats(data))
#add header for CORS
#http://www.html5rocks.com/en/tutorials/cors/
response = HttpResponse(ret, content_type='application/json')
#TODO:
response['Access-Control-Allow-Origin'] = '*'
return response
def motion_path(request):
"""
http GET request to retrieve reconstructed static polygons
**usage**
<http-address-to-gws>/reconstruct/motion_path/seedpoints=\ *points*\×pec=\ *time_list*\&fixplate=\ *fixed_plate_id*\&movplate=\ *moving_plate_id*\&time=\ *reconstruction_time*\&model=\ *reconstruction_model*
:param seedpoints: integer value for reconstruction anchor plate id [required]
:param timespec: specification for times for motion path construction, in format 'mintime,maxtime,increment' [defaults to '0,100,10']
:param time: time for reconstruction [default=0]
:param fixplate: integer plate id for fixed plate [default=0]
:param movplate: integer plate id for moving plate [required]
:param model: name for reconstruction model [defaults to default model from web service settings]
:returns: json containing reconstructed motion path features
"""
seedpoints = request.GET.get('seedpoints', None)
times = request.GET.get('timespec', '0,100,10')
reconstruction_time = request.GET.get('time', 0)
RelativePlate = request.GET.get('fixplate', 0)
MovingPlate = request.GET.get('movplate', None)
model = request.GET.get('model',settings.MODEL_DEFAULT)
points = []
if seedpoints:
ps = seedpoints.split(',')
if len(ps)%2==0:
for lat,lon in zip(ps[1::2], ps[0::2]):
points.append((float(lat),float(lon)))
seed_points_at_digitisation_time = pygplates.MultiPointOnSphere(points)
if times:
ts = times.split(',')
if len(ts)==3:
times = np.arange(float(ts[0]),float(ts[1])+0.1,float(ts[2]))
model_dict = get_reconstruction_model_dict(model)
rotation_model = pygplates.RotationModel([str('%s/%s/%s' %
(settings.MODEL_STORE_DIR,model,rot_file)) for rot_file in model_dict['RotationFile']])
# Create the motion path feature
digitisation_time = 0
#seed_points_at_digitisation_time = pygplates.MultiPointOnSphere([SeedPoint])
motion_path_feature = pygplates.Feature.create_motion_path(
seed_points_at_digitisation_time,
times = times,
valid_time=(2000, 0),
relative_plate=int(RelativePlate),
reconstruction_plate_id = int(MovingPlate))
# Create the shape of the motion path
#reconstruction_time = 0
reconstructed_motion_paths = []
pygplates.reconstruct(
motion_path_feature, rotation_model, reconstructed_motion_paths, float(reconstruction_time),
reconstruct_type=pygplates.ReconstructType.motion_path)
data = {"type": "FeatureCollection"}
data["features"] = []
for reconstructed_motion_path in reconstructed_motion_paths:
Dist = []
for segment in reconstructed_motion_path.get_motion_path().get_segments():
Dist.append(segment.get_arc_length()*pygplates.Earth.mean_radius_in_kms)
feature = {"type": "Feature"}
feature["geometry"] = {}
feature["geometry"]["type"] = "LineString"
#### NOTE CODE TO FLIP COORDINATES TO
feature["geometry"]["coordinates"] = [(lon,lat) for lat,lon in reconstructed_motion_path.get_motion_path().to_lat_lon_list()]
feature["geometry"]["distance"] = Dist
feature["properties"] = {}
data["features"].append(feature)
ret = json.dumps(pretty_floats(data))
#add header for CORS
#http://www.html5rocks.com/en/tutorials/cors/
response = HttpResponse(ret, content_type='application/json')
#TODO:
response['Access-Control-Allow-Origin'] = '*'
return response
def reconstruct_points(request):
"""
http GET request to reconstruct points
**usage**
<http-address-to-gws>/reconstruct/reconstruct_points/points=\ *points*\&plate_id=\ *anchor_plate_id*\&time=\ *reconstruction_time*\&model=\ *reconstruction_model*
**parameters:**
*points* : list of points long,lat comma separated coordinates of points to be reconstructed [Required]
*anchor_plate_id* : integer value for reconstruction anchor plate id [default=0]
*time* : time for reconstruction [required]
*model* : name for reconstruction model [defaults to default model from web service settings]
**returns:**
json list of long,lat reconstructed coordinates
"""
if request.method == 'POST':
params = request.POST
elif request.method == 'GET':
params = request.GET
else:
return HttpResponseBadRequest('Unrecognized request type')
points = params.get('points', None)
anchor_plate_id = params.get('pid', 0)
time = params.get('time', None)
model = params.get('model',settings.MODEL_DEFAULT)
return_null_points = True if 'return_null_points' in params else False
return_feature_collection = True if 'fc' in params else False
is_reverse = True if 'reverse' in params else False
timef=0.0
if not time:
return HttpResponseBadRequest('The "time" parameter cannot be empty.')
else:
try:
timef = float(time)
except:
return HttpResponseBadRequest('The "time" parameter is invalid ({0}).'.format(time))
try:
anchor_plate_id = int(anchor_plate_id)
except:
return HttpResponseBadRequest('The "pid" parameter is invalid ({0}).'.format(anchor_plate_id))
model_dict = get_reconstruction_model_dict(model)
if not model_dict:
return HttpResponseBadRequest('The "model" ({0}) cannot be recognized.'.format(model))
rotation_model = pygplates.RotationModel([str('%s/%s/%s' %
(settings.MODEL_STORE_DIR,model,rot_file)) for rot_file in model_dict['RotationFile']])
static_polygons_filename = str('%s/%s/%s' % (settings.MODEL_STORE_DIR,model,model_dict['StaticPolygons']))
# create point features from input coordinates
p_index=0
point_features = []
if points:
ps = points.split(',')
if len(ps)%2==0:
try:
for lat,lon in zip(ps[1::2], ps[0::2]):
point_feature = pygplates.Feature()
point_feature.set_geometry(pygplates.PointOnSphere(float(lat),float(lon)))
point_feature.set_name(str(p_index))
point_features.append(point_feature)
p_index += 1
except pygplates.InvalidLatLonError as e:
return HttpResponseBadRequest('Invalid longitude or latitude ({0}).'.format(e))
else:
return HttpResponseBadRequest('The longitude and latitude should come in pairs ({0}).'.format(points))
else:
return HttpResponseBadRequest('The "points" parameter is missing.')
# assign plate-ids to points using static polygons
partition_time = timef if is_reverse else 0.
#LOOK HERE !!!!
#it seems when the partition_time is not 0
#the returned assigned_point_features contains reverse reconstructed present-day geometries.
#so, when doing reverse reconstruct, do not reverse reconstruct again later.
assigned_point_features = pygplates.partition_into_plates(
static_polygons_filename,
rotation_model,
point_features,
properties_to_copy = [
pygplates.PartitionProperty.reconstruction_plate_id,
pygplates.PartitionProperty.valid_time_period],
reconstruction_time = partition_time
)
# reconstruct the points
assigned_point_feature_collection = pygplates.FeatureCollection(assigned_point_features)
reconstructed_feature_geometries = []
if not is_reverse:
pygplates.reconstruct(
assigned_point_feature_collection,
rotation_model,
reconstructed_feature_geometries,
timef,
anchor_plate_id=anchor_plate_id)
rfgs = p_index*[None]
for rfg in reconstructed_feature_geometries:
rfgs[int(rfg.get_feature().get_name())] = rfg
assigned_fc = p_index*[None]
for f in assigned_point_feature_collection:
assigned_fc[int(f.get_name())] = f
# prepare the response to be returned
if not return_feature_collection:
ret='{"type":"MultiPoint","coordinates":['
for idx in range(p_index):
lon = None
lat = None
if not is_reverse and rfgs[idx]:
lon = rfgs[idx].get_reconstructed_geometry().to_lat_lon()[1]
lat = rfgs[idx].get_reconstructed_geometry().to_lat_lon()[0]
elif is_reverse and assigned_fc[idx]:
lon = assigned_fc[idx].get_geometry().to_lat_lon()[1]
lat = assigned_fc[idx].get_geometry().to_lat_lon()[0]
if lon is not None and lat is not None:
ret+='[{0:5.2f},{1:5.2f}],'.format(lon, lat)
elif return_null_points:
ret+='null,'
else:
ret='{"type":"FeatureCollection","features":['
for idx in range(p_index):
lon = None
lat = None
begin_time = None
end_time = None
if not is_reverse and rfgs[idx]:
lon = rfgs[idx].get_reconstructed_geometry().to_lat_lon()[1]
lat = rfgs[idx].get_reconstructed_geometry().to_lat_lon()[0]
elif is_reverse and assigned_fc[idx]:
lon = assigned_fc[idx].get_geometry().to_lat_lon()[1]
lat = assigned_fc[idx].get_geometry().to_lat_lon()[0]
if assigned_fc[idx]:
valid_time = assigned_fc[idx].get_valid_time(None)
if valid_time:
begin_time, end_time = valid_time
ret+='{"type":"Feature","geometry":'
if lon is not None and lat is not None:
ret+='{'+'"type":"Point","coordinates":[{0:5.2f},{1:5.2f}]'.format(lon, lat)+'},'
else:
ret+='null,'
if begin_time is not None and end_time is not None:
#write out begin and end time
if begin_time == pygplates.GeoTimeInstant.create_distant_past():
begin_time = '"distant past"'
if end_time == pygplates.GeoTimeInstant.create_distant_future():
end_time = '"distant future"'
ret+='"properties":{'+'"valid_time":[{0},{1}]'.format(begin_time,end_time)+'}},'
else:
ret+='"properties":{}'+'},'
ret=ret[0:-1]
ret+=']}'
#add header for CORS
#http://www.html5rocks.com/en/tutorials/cors/
response = HttpResponse(ret, content_type='application/json')
#TODO:
#The "*" makes the service wide open to anyone. We should implement access control when time comes.
response['Access-Control-Allow-Origin'] = '*'
return response
