def filt_stdev(coords, k=3, std_dev=2):
kDTree = KDTree(coords, leafsize = 5)
if pykdtree==1:
dx, idx_knn = kDTree.query(coords[:, :], k = k)
else:
dx, idx_knn = kDTree.query(coords[:, :], k = k, n_jobs=-1)
dx, idx_knn = dx[:,1:], idx_knn[:,1:]
distances = np.sum(dx, axis=1)/(k - 1.0)
valid_distances = np.shape(distances)[0]
#Estimate the mean and the standard deviation of the distance vector
sum = np.sum(distances)
sq_sum = np.sum(distances**2)
mean = sum / float(valid_distances)
variance = (sq_sum - sum * sum / float(valid_distances)) / (float(valid_distances) - 1)
stddev = np.sqrt (variance)
# a distance that is bigger than this signals an outlier
distance_threshold = mean + std_dev * stddev
idx = np.nonzero(distances < distance_threshold)
return idx, np.copy(coords[idx])
def relief(X, Y):
"""
Funcion para calcular los pesos mediante el algoritmo RELIEF.
:param X: Conjunto de vectores de caracteristicas.
:param Y: Conjunto de etiquetas.
:return Devuelve los pesos w que ponderan las caracteristicas.
"""
# Obtener numero de elementos de la muestra
N = Y.shape[0]
# Inicializar w a [0, 0, ... , 0]
w = np.zeros((X.shape[1], ), np.float64)
# Recorrer todos los elementos de la muestra
# Para cada elemento, obtener su amigo mas cercano y su enemigo
# mas cercano
# Actualizar w con la nueva informacion
for i in range(N):
# Obtener el elemento y su etiqueta
x, y = X[i], Y[i]
# Obtener la lista de amigos y enemigos
allies = X[Y == y]
enemies = X[Y != y]
# Construir los arboles de amigos y enemigos
kdtree_allies = KDTree(allies)
kdtree_enemies = KDTree(enemies)
# Obtener el aliado mas cercano mediante la tecnica de leave-one-out
# Se escoge el segundo vecino mas cercano a x, ya que el primero es
# el mismo (un indice al segundo vecino mas cercano)
closest_ally = kdtree_allies.query(x.reshape(1, -1), k=2)[1][0, 1]
# Obtener el enemigo mas cercano a x (se obtiene un indice)
closest_enemy = kdtree_enemies.query(x.reshape(1, -1), k=1)[1][0]
# Obtener el aliado y el enemigo correspondientes
ally = allies[closest_ally]
enemy = enemies[closest_enemy]
# Actualizar w
w += abs(x - enemy) - abs(x - ally)
# Obtener el maximo de w
w_max = w.max()
# Hacer que los elementos inferiores a 0 tengan como valor 0
w[w < 0.0] = 0.0
# Normalizar w con el maximo
w = w / w_max
return w
def _cleanDiscarded(self, discarded_X, discarded_y):
stm_tree = KDTree(np.array(self.STMX))
STMy = np.array(self.STMy)
clean_mask = np.zeros(discarded_X.shape[0], dtype=bool)
for x,y in zip(self.STMX, self.STMy):
# searching for points from the stm in the stm will also return that points, so we query one more to get k neighbours
dist, ind = stm_tree.query(np.array([x]), k=self.n_neighbors +1)
dist = dist[0]
ind = ind[0]
"""
find weighted maximum difference and max distance among next n neighbours in STM
"""
dist_max = np.amax(dist)
w_diff = self._clean_metric(STMy[ind] - y, dist, dist_max)
w_diff_max = np.amax(w_diff)
# print("w_diff_max:", w_diff_max)
"""
Query all points among the discarded that lie inside the maximum distance. Delete every point that has a greater weighted difference than the previously gathered maximum distance.
"""
discarded_tree = KDTree(discarded_X)
dist, ind = discarded_tree.query(
np.array([x]),
k=len(discarded_X),
distance_upper_bound=dist_max)
keep = ind < len(discarded_X)
ind = ind[keep]
dist = dist[keep]
disc_w_diff = self._clean_metric(discarded_y[ind] - y, dist, dist_max)
clean = ind[disc_w_diff < w_diff_max]
"""
We create a mask which us used to drop all values in the discarded
set whose weighted difference is to far from __all__ points.
E.g. it does not appear in neighbourhood of any other point or
is too different if it does.
"""
clean_mask[clean] = True
discarded_X = discarded_X[clean_mask]
discarded_y = discarded_y[clean_mask]
return discarded_X, discarded_y
def make_bud_neighbor_df(manual_bud_indices, auto_bud_indices, **kwargs):
"""
auto_bud_indices should be made using find_peaks from scipy.signal
as follows:
peak_indices, peak_dict = find_peaks(new_df.dsred_mean_local_mean_norm_meanfilt,
distance=min_cycle_frames)
"""
collection_interval = kwargs.get('collection_interval', 10)
death_cutoff_hr = (np.max(manual_bud_indices)*collection_interval)/60
df1 = pd.DataFrame({'manual_timepoint': manual_bud_indices[:]})
df2 = pd.DataFrame({'auto_timepoint': auto_bud_indices[auto_bud_indices <= death_cutoff_hr*6]})
comparison_df = pd.concat([df1, df2], ignore_index=True, axis=1)
# Find nearest manual frame for each auto frame
X = np.array(comparison_df.loc[:, 0])
Y = np.array(comparison_df.loc[:, 1])
tree = KDTree(X)
nearest_manual_dists, neighbor_indices = tree.query(Y)
nearest_manual_frame = []
for i in neighbor_indices:
try:
nearest_manual_frame.append(X[i])
except:
nearest_manual_frame.append(np.NaN)
# Find nearest auto frame for each manual frame
X = np.array(comparison_df.loc[:, 0]) # X is manual
Y = np.array(comparison_df.loc[:, 1]) # Y is auto
tree = KDTree(Y)
nearest_auto_dists, neighbor_indices = tree.query(X)
nearest_auto_frame = []
for i in neighbor_indices:
try:
nearest_auto_frame.append(Y[i])
except:
nearest_auto_frame.append(np.NaN)
neighbor_df = pd.DataFrame({'manual_bud_frame': X,
'auto_bud_frame': Y,
'nearest_manual_frame': nearest_manual_frame,
'nearest_auto_frame': nearest_auto_frame,
'nearest_auto_dist': nearest_auto_dists,
'nearest_manual_dist': nearest_manual_dists})
return neighbor_df
def compute_lfs(datadict, k=10):
from pykdtree.kdtree import KDTree
# collect all ma_coords that are not NaN
ma_coords = np.concatenate(
[datadict['ma_coords_in'], datadict['ma_coords_out']])
ma_coords = ma_coords[~np.isnan(ma_coords).any(axis=1)]
# build kd-tree of ma_coords to compute lfs
pykdtree = KDTree(ma_coords)
if k > 1:
datadict['lfs'] = np.sqrt(
np.median(pykdtree.query(datadict['coords'], k)[0], axis=1))
else:
datadict['lfs'] = np.sqrt(pykdtree.query(datadict['coords'], k)[0])
def match_cris_viirs(crisLos, crisPos, viirsPos, viirsMask):
"""
Match crisLos with viirsPos using the method by Wang et al. (2016)
Wang, L., D. A. Tremblay, B. Zhang, and Y. Han, 2016: Fast and Accurate
Collocation of the Visible Infrared Imaging Radiometer Suite
Measurements and Cross-track Infrared Sounder Measurements.
Remote Sensing, 8, 76; doi:10.3390/rs8010076.
"""
# Derive Satellite Postion
crisSat = crisPos - crisLos
# using KD-tree to find best matched points
# build kdtree to find match index
pytree_los = KDTree(viirsPos.reshape(viirsPos.size / 3, 3))
dist_los, idx_los = pytree_los.query(crisPos.reshape(crisPos.size / 3, 3),
sqr_dists=False)
my, mx = np.unravel_index(idx_los, viirsPos.shape[0:2])
idy, idx = find_match_index(crisLos.reshape(crisLos.size/3, 3),\
crisSat.reshape(crisSat.size/3, 3),\
viirsPos, viirsMask, mx, my)
idy = np.array(idy).reshape(crisLos.shape[0:crisLos.ndim - 1])
idx = np.array(idx).reshape(crisLos.shape[0:crisLos.ndim - 1])
return idy, idx
def findInCloud(self, cloud, cloudNormals):
sceneTree = KDTree(cloud)
indexes = list(range(len(cloud)))
r = self.Model.Radius
iterations = 0
while True:
iterations += 1
if iterations % 100 == 0:
print(iterations)
i = np.random.choice(indexes, 1)[0]
p1 = cloud[i]
n1 = cloudNormals[i]
neighborIdx = [
ii for ii in sceneTree.query(
p1.reshape((1, 3)), 50, distance_upper_bound=2 * r)[1][0]
if ii < len(cloud) and ii != i
]
j, k = np.random.choice(neighborIdx, 2, replace=False)
p2, p3 = cloud[j], cloud[k]
n2, n3 = cloudNormals[j], cloudNormals[k]
if np.linalg.cond(np.column_stack((n1, n2, n3))) > 1e5:
continue
for pose in self.Model.getPose(np.column_stack((p1, p2, p3)),
np.column_stack((n1, n2, n3))):
yield pose
return None
def _makeSparseness(self, features):
""" Sparseness is the mean dist to K nearest neighbors. """
feature_arr = np.array(features)
tree = KDTree(np.vstack((np.array(self.archive), feature_arr)))
dists, _ = tree.query(feature_arr, k=self.ns_K + 1)
sparseness = np.mean(dists[:, 1:], axis=1)
return sparseness
def distance_p2p(points_src, normals_src, points_tgt, normals_tgt):
"""Computes minimal distances of each point in points_src to points_tgt.
Arguments:
----------
points_src (numpy array): source points
normals_src (numpy array): source normals
points_tgt (numpy array): target points
normals_tgt (numpy array): target normals
"""
kdtree = KDTree(points_tgt)
dist, idx = kdtree.query(points_src)
if normals_src is not None and normals_tgt is not None:
normals_src = \
normals_src / np.linalg.norm(normals_src, axis=-1, keepdims=True)
normals_tgt = \
normals_tgt / np.linalg.norm(normals_tgt, axis=-1, keepdims=True)
normals_dot_product = (normals_tgt[idx] * normals_src).sum(axis=-1)
# Handle normals that point into wrong direction gracefully
# (mostly due to mehtod not caring about this in generation)
normals_dot_product = np.abs(normals_dot_product)
else:
normals_dot_product = np.array([np.nan] * points_src.shape[0],
dtype=np.float32)
return dist, normals_dot_product
class KDResampler:
def __init__(self, distance_limit=0.05, leafsize=32):
self.distance_limit = distance_limit
self.leafsize = leafsize
self._invalid_mask = None
self._indices = None
@staticmethod
def make_target_coords(georange, width, height, pad=0., ratio=1.02):
latmin, latmax, lonmin, lonmax = georange
image_width = int(width * ratio)
image_height = int(height * ratio)
ix = np.linspace(lonmin - pad, lonmax + pad, image_width)
iy = np.linspace(latmax + pad, latmin - pad, image_height)
return np.meshgrid(ix, iy), (lonmin - pad, lonmax + pad, latmin - pad,
latmax + pad)
def build_tree(self, lons, lats):
self.tree = KDTree(np.dstack((lons.ravel(), lats.ravel()))[0],
leafsize=self.leafsize)
def resample(self, data, target_x, target_y):
if self._indices is None:
target_coords = np.dstack((target_x.ravel(), target_y.ravel()))[0]
_, self._indices = self.tree.query(
target_coords, distance_upper_bound=self.distance_limit)
self._invalid_mask = self._indices == self.tree.n # beyond distance limit
self._indices[self._invalid_mask] = 0
remapped = np.array(data.reshape((-1, 3))[self._indices])
remapped[self._invalid_mask] = 0
remapped = remapped.reshape((*target_x.shape, 3))
return remapped
def distance_p2p(pointcloud_pred, pointcloud_gt, normals_pred, normals_gt):
''' Computes minimal distances of each point in points_src to points_tgt.
Args:
points_src (numpy array): source points
normals_src (numpy array): source normals
points_tgt (numpy array): target points
normals_tgt (numpy array): target normals
'''
kdtree = KDTree(pointcloud_gt)
dist, idx = kdtree.query(pointcloud_pred)
if normals_pred is None:
return dist, None
normals_pred = normals_pred / np.linalg.norm(
normals_pred, axis=-1, keepdims=True)
normals_gt = normals_gt / np.linalg.norm(
normals_gt, axis=-1, keepdims=True)
normals_dot_product = (normals_gt[idx] * normals_pred).sum(axis=-1)
# Handle normals that point into wrong direction gracefully
# (mostly due to mehtod not caring about this in generation)
normals_dot_product = np.abs(normals_dot_product)
return dist, normals_dot_product
def test3d_8n_ub_eps():
query_pts = np.array([[787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts,
k=8,
eps=0.1,
distance_upper_bound=10e3,
sqr_dists=False)
exp_dist = np.array([[
0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03,
8.17063009e+03, np.Inf, np.Inf, np.Inf
],
[
1.73205081e+00, 2.70216896e+03, 2.71431274e+03,
5.39537066e+03, 5.43793210e+03, 8.07855631e+03,
8.17119970e+03, np.Inf
],
[
1.41424892e+02, 3.25500021e+03, 3.44284958e+03,
6.58019346e+03, 6.81038455e+03, 9.89140135e+03,
np.Inf, np.Inf
]])
n = 100
exp_idx = np.array([[7, 8, 6, 9, 5, n, n, n],
[93, 94, 92, 95, 91, 96, 90, n],
[45, 46, 44, 47, 43, 48, n, n]])
assert np.array_equal(idx, exp_idx)
assert np.allclose(dist, exp_dist)
def fitness(self):
pos = np.asarray(self.node_pos[self.n_nodes])
kd_tree = KDTree(pos)
dist, _ = kd_tree.query(pos, k=30)
fitness = np.mean(dist)
return fitness
def test127d_ok():
pts = 2
dims = 127
data_pts = np.arange(pts * dims).reshape(pts, dims)
kdtree = KDTree(data_pts)
dist, idx = kdtree.query(data_pts)
assert np.all(dist == 0)
def test127d_ok():
pts = 2
dims = 127
data_pts = np.arange(pts * dims).reshape(pts, dims)
kdtree = KDTree(data_pts)
dist, idx = kdtree.query(data_pts)
assert np.all(dist == 0)
def closest_node_idx_to_sample_idx(
df,
axes_limits,
array_shape,
verbose = False):
"""
Purpose: To get the index of the closest node
point to a coordinate in the sampling
"""
limits_coords_by_axis = ctcu.axes_limits_coordinates(axes_limits,array_shape = array_shape)
from pykdtree.kdtree import KDTree
xi,yi,zi = np.meshgrid(*limits_coords_by_axis,indexing="ij")
limits_coords = np.vstack([k.ravel() for k in [xi,yi,zi]]).T
# if verbose:
# print(f"limits_coords=\n{limits_coords}")
limits_coords_kd = KDTree(df[["x","y","z"]].to_numpy())
dist,closest_nodes = limits_coords_kd.query(limits_coords)
if verbose:
print(f"closest_nodes = {closest_nodes}")
print(f"dist = {dist}")
return closest_nodes
def populate_indices(self):
"""Pre-populate guesses of particle xi/yi indices using a kdtree.
This is only intended for curvilinear grids, where the initial index search
may be quite expensive.
"""
if self.fieldset is None:
# we need to be attached to a fieldset to have a valid
# gridset to search for indices
return
if KDTree is None:
return
else:
for i, grid in enumerate(self.fieldset.gridset.grids):
if not isinstance(grid, CurvilinearGrid):
continue
tree_data = np.stack((grid.lon.flat, grid.lat.flat), axis=-1)
tree = KDTree(tree_data)
# stack all the particle positions for a single query
pts = np.stack((self._collection.data['lon'],
self._collection.data['lat']),
axis=-1)
# query datatype needs to match tree datatype
_, idx = tree.query(pts.astype(tree_data.dtype))
yi, xi = np.unravel_index(idx, grid.lon.shape)
self._collection.data['xi'][:, i] = xi
self._collection.data['yi'][:, i] = yi
def test3d_8n():
query_pts = np.array([[787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts, k=8)
exp_dist = np.array([[
0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03,
8.17063009e+03, 1.20904577e+04, 1.22902057e+04, 1.60775136e+04
],
[
1.73205081e+00, 2.70216896e+03, 2.71431274e+03,
5.39537066e+03, 5.43793210e+03, 8.07855631e+03,
8.17119970e+03, 1.07513693e+04
],
[
1.41424892e+02, 3.25500021e+03, 3.44284958e+03,
6.58019346e+03, 6.81038455e+03, 9.89140135e+03,
1.01918659e+04, 1.31892516e+04
]])
exp_idx = np.array([[7, 8, 6, 9, 5, 10, 4, 11],
[93, 94, 92, 95, 91, 96, 90, 97],
[45, 46, 44, 47, 43, 48, 42, 49]])
assert np.array_equal(idx, exp_idx)
assert np.allclose(dist, exp_dist)
def match_surface_precip(self, input_data):
"""
Match surface precipitation from .sim file to points in xarray
dataset.
Args:
input_data: xarray dataset containing the input data from
the preprocessor.
Return:
The input dataset but with the surface_precip field added.
"""
n_scans = input_data.scans.size
dx = 40
i_c = 110
ix_start = i_c - dx // 2
ix_end = i_c + 1 + dx // 2
lats_1c = input_data["latitude"][:,
ix_start:ix_end].data.reshape(-1, 1)
lons_1c = input_data["longitude"][:,
ix_start:ix_end].data.reshape(-1, 1)
z = np.zeros_like(lats_1c)
coords_1c = pyproj.transform(_LLA,
_ECEF,
lons_1c,
lats_1c,
z,
radians=False)
coords_1c = np.concatenate(coords_1c, axis=1)
lats = self.data["latitude"].reshape(-1, 1)
lons = self.data["longitude"].reshape(-1, 1)
z = np.zeros_like(lats)
coords_sim = pyproj.transform(_LLA,
_ECEF,
lons,
lats,
z,
radians=False)
coords_sim = np.concatenate(coords_sim, 1)
kdtree = KDTree(coords_1c)
dists, indices = kdtree.query(coords_sim)
print(dists, indices)
surface_precip = np.zeros(n_scans * (dx + 1))
surface_precip[:] = np.nan
surface_precip[indices] = self.data["surface_precip"]
surface_precip = surface_precip.reshape(n_scans, dx + 1)
surface_precip_full = np.zeros(input_data["latitude"].shape,
dtype=np.float32)
surface_precip_full[:] = np.nan
surface_precip_full[:, ix_start:ix_end] = surface_precip
input_data["surface_precip"] = (("scans", "pixels"),
surface_precip_full)
return input_data
def gll_2_exodus(gll_model,
exodus_model,
gll_order=4,
dimensions=3,
nelem_to_search=20,
parameters="TTI",
model_path="MODEL/data",
coordinates_path="MODEL/coordinates",
gradient=False):
"""
Interpolate parameters from gll file to exodus model. This will mostly be
used to interpolate gradients to begin with.
:param gll_model: path to gll_model
:param exodus_model: path_to_exodus_model
:param parameters: Currently not used but will be fixed later
"""
with h5py.File(gll_model, 'r') as gll_model:
gll_points = np.array(gll_model[coordinates_path][:], dtype=np.float64)
gll_data = gll_model[model_path][:]
params = gll_model[model_path].attrs.get(
"DIMENSION_LABELS")[1].decode()
parameters = params[2:-2].replace(" ", "").split("|")
centroids = _find_gll_centroids(gll_points, dimensions)
print("centroids", np.shape(centroids))
# Build a KDTree of the centroids to look for nearest elements
print("Building KDTree")
centroid_tree = KDTree(centroids)
print("Read in mesh")
exodus = Exodus(exodus_model, mode="a")
# Find nearest elements
print("Querying the KDTree")
print(exodus.points.shape)
# if exodus.points.shape[1] == 3:
# exodus.points = exodus.points[:, :-1]
_, nearest_element_indices = centroid_tree.query(exodus.points,
k=nelem_to_search)
npoints = exodus.npoint
# parameters = utils.pick_parameters(parameters)
values = np.zeros(shape=[npoints, len(parameters)])
print(parameters)
s = 0
for point in exodus.points:
if s == 0 or (s + 1) % 1000 == 0:
print(f"Now I'm looking at point number:"
f"{s+1}{len(exodus.points)}")
element, ref_coord = _check_if_inside_element(
gll_points, nearest_element_indices[s, :], point, dimensions)
coeffs = get_coefficients(4, 4, 0, ref_coord, dimensions)
values[s, :] = np.sum(gll_data[element, :, :] * coeffs, axis=1)
s += 1
i = 0
for param in parameters:
exodus.attach_field(param, np.zeros_like(values[:, i]))
exodus.attach_field(param, values[:, i])
i += 1
def gradient_2_cartesian_exodus(gradient, cartesian, params, first=False):
"""
Interpolate gradient from 2D smoothiesem and sum on top of
2D cartesian mesh. Using gll would be ideal but this function
is now only with exodus.
:param gradient: path to cartesian mesh to interpolate from
:param cartesian: path to smoothiesem mesh to interpolate to
:param params: list of parameters to interpolate
:param first: If this is the first gradient, it will overwrite fields
:return: Gradient interpolated to a cartesian mesh.
"""
lib = load_lib()
exodus_a = Exodus(gradient, mode="a")
print(exodus_a.points)
print(f"Exodus shape: {exodus_a.points.shape}")
points = np.array(exodus_a.points, dtype=np.float64)
exodus_a.points = points
a_centroids = exodus_a.get_element_centroid()
# The trilinear interpolator only works in 3D so we fool it to think
# we are working in 3D
a_centroids = np.concatenate(
(a_centroids, np.zeros(shape=(a_centroids.shape[0], 1))), axis=1)
centroid_tree = KDTree(a_centroids)
nelem_to_search = 20
exodus_b = Exodus(cartesian, mode="a")
_, nearest_element_indices = centroid_tree.query(exodus_b.points,
k=nelem_to_search)
nearest_element_indices = np.array(nearest_element_indices, dtype=np.int64)
npoints = exodus_b.npoint
enclosing_element_node_indices = np.zeros((npoints, 4), dtype=np.int64)
weights = np.zeros((npoints, 4))
connectivity = exodus_a.connectivity[:, :]
nfailed = lib.triLinearInterpolator(
nelem_to_search, npoints, nearest_element_indices,
np.ascontiguousarray(connectivity,
dtype=np.int64), enclosing_element_node_indices,
np.ascontiguousarray(exodus_a.points), weights,
np.ascontiguousarray(exodus_b.points))
assert nfailed is 0, f"{nfailed} points could not be interpolated"
for param in params:
param_a = exodus_a.get_nodal_field(param)
values = np.sum(param_a[enclosing_element_node_indices] * weights,
axis=1)
if not first:
param_b = exodus_b.get_nodal_field(
param) # Get pre-existing gradient
values += param_b # Add new gradient on top of old one
exodus_b.attach_field(param, np.zeros_like(values))
exodus_b.attach_field(param, values)
def test1d():
data_pts = np.arange(1000)
kdtree = KDTree(data_pts, leafsize=15)
query_pts = np.arange(400, 300, -10)
dist, idx = kdtree.query(query_pts)
assert idx[0] == 400
assert dist[0] == 0
assert idx[1] == 390
class MeshRasterize(RasterSource):
def __init__(self, lons, lats, face_nodes, img):
"""
lons, lats : 1d array in node index order
face_nodes: iterable of iterable of node index
img: 1d array of source data. Same length as face nodes
"""
# convert to 3d space
self._geocent = ccrs.Geocentric(globe=ccrs.Globe())
self.img = img
xyz = self._geocent.transform_points(ccrs.Geodetic(), lons, lats)
self._nodes_xyz = xyz
start = time.time()
self._kd = KDTree(xyz)
end = time.time()
logging.info('KD Construction time ({} points): {}'.format(
lons.size, end - start))
self._face_nodes = np.array(face_nodes)
self._node_faces = fmgc.create_node_faces_array(self._face_nodes,
num_nodes=len(lons))
def validate_projection(self, projection):
return True
def fetch_raster(self, projection, extent, target_resolution):
target_resolution = np.array(target_resolution, dtype=int)
x = np.linspace(extent[0], extent[1], target_resolution[0])
y = np.linspace(extent[2], extent[3], target_resolution[1])
xs, ys = np.meshgrid(x, y)
xyz_sample = self._geocent.transform_points(
projection, xs.flatten(), ys.flatten())
start = time.time()
_, node_indices = self._kd.query(xyz_sample, k=1)
end = time.time()
logging.info('Query of {} points: {}'.format(
np.prod(target_resolution), end - start))
# Clip to valid node indices: can get =N-points for NaN or inf. points.
n_points = self._node_faces.shape[0]
node_indices[(node_indices < 0) | (node_indices >= n_points)] = 0
start = time.time()
face_indices = fmgc.search_faces_for_points(
target_points_xyz=xyz_sample,
i_nodes_nearest=node_indices,
nodes_xyz_array=self._nodes_xyz,
face_nodes_array=self._face_nodes,
node_faces_array=self._node_faces
)
end = time.time()
logging.info('Face search of {} points: {}'.format(
np.prod(target_resolution), end - start))
result = self.img[face_indices].reshape(target_resolution[1],
target_resolution[0])
return [LocatedImage(result, extent)]
def test1d():
data_pts = np.arange(1000)
kdtree = KDTree(data_pts, leafsize=15)
query_pts = np.arange(400, 300, -10)
dist, idx = kdtree.query(query_pts)
assert idx[0] == 400
assert dist[0] == 0
assert idx[1] == 390
def test3d_float32_mismatch():
#7, 93, 45
query_pts = np.array([[787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]],
dtype=np.float32)
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts, sqr_dists=True)
def compute_lfs(self, k=10, only_interior=False):
from pykdtree.kdtree import KDTree
# collect all ma_coords that are not NaN
if only_interior:
ma_coords = self.D['ma_coords_in'][invert(self.filtered['in'])]
else:
ma_coords = concatenate([
self.D['ma_coords_in'][invert(self.filtered['in'])],
self.D['ma_coords_out'][invert(self.filtered['out'])]
])
ma_coords = ma_coords[~np.isnan(ma_coords).any(axis=1)]
kd_tree = KDTree(ma_coords)
# we can get *squared* distances for free, so take the square root
if k > 1:
self.D['lfs'] = np.sqrt(
np.median(kd_tree.query(self.D['coords'], k)[0], axis=1))
else:
self.D['lfs'] = np.sqrt(kd_tree.query(self.D['coords'], k)[0])
def test1d_all_masked():
data_pts = np.arange(1000)
np.random.shuffle(data_pts)
kdtree = KDTree(data_pts, leafsize=15)
query_pts = np.arange(400, 300, -10)
query_mask = np.ones(data_pts.shape[0]).astype(bool)
dist, idx = kdtree.query(query_pts, mask=query_mask)
# all invalid
assert np.all(i >= 1000 for i in idx)
assert np.all(d >= 1001 for d in dist)
def test3d_float32_mismatch():
#7, 93, 45
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]], dtype=np.float32)
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts, sqr_dists=True)
def test1d_all_masked():
data_pts = np.arange(1000)
np.random.shuffle(data_pts)
kdtree = KDTree(data_pts, leafsize=15)
query_pts = np.arange(400, 300, -10)
query_mask = np.ones(data_pts.shape[0]).astype(bool)
dist, idx = kdtree.query(query_pts, mask=query_mask)
# all invalid
assert np.all(i >= 1000 for i in idx)
assert np.all(d >= 1001 for d in dist)
def combine_meshes(meshes, max_dist='auto'):
"""Try combining (partially overlapping) meshes.
This function effectively works on the vertex graph and will not produce
meaningful faces.
"""
# Sort meshes by size
meshes = sorted(meshes, key=lambda x: len(x.vertices), reverse=True)
comb = tm.Trimesh(meshes[0].vertices.copy(), meshes[0].faces.copy())
comb.remove_unreferenced_vertices()
if max_dist == 'auto':
max_dist = comb.edges_unique_length.mean()
for m in config.tqdm(meshes[1:], desc='Combining',
disable=config.pbar_hide,
leave=config.pbar_leave):
# Generate a new up-to-date tree
tree = KDTree(comb.vertices)
# Offset faces
vertex_offset = comb.vertices.shape[0]
new_faces = m.faces + vertex_offset
# Find vertices that can be merged - note that we are effectively
# zippig the two meshes by making sure that each vertex can only be
# merged once
dist, ix = tree.query(m.vertices, distance_upper_bound=max_dist)
merged = set()
# Merge closest vertices first
for i in np.argsort(dist):
# Skip if no more within-distance
if dist[i] >= np.inf:
break
# Skip if target vertex has already been merged
if ix[i] in merged:
continue
# Remap this vertex
new_faces[new_faces == (i + vertex_offset)] = ix[i]
# Track that target vertex has already been seen
merged.add(ix[i])
# Merge vertices and faces
comb.vertices = np.append(comb.vertices, m.vertices, axis=0)
comb.faces = np.append(comb.faces, new_faces, axis=0)
# Drop unreferenced vertices (i.e. those that were remapped)
comb.remove_unreferenced_vertices()
return comb
def _cleanLTM(self, x, y):
LTMX = np.array(self.LTMX)
LTMy = np.array(self.LTMy)
STMX = np.array(self.STMX)
STMy = np.array(self.STMy)
stmtree = KDTree(STMX)#, self.leaf_size, metric='euclidean')
dist, ind = stmtree.query(np.array([x]), k=self.n_neighbors)
dist = dist[0] # only queriing one point
ind = ind[0] # ^
#dist = np.where(dist < 1, 1, dist) # TODO: what about really close points?
# print(dist)
dist_max = np.amax(dist)
qs = self._clean_metric(STMy[ind] - y, dist, dist_max)
w_diff_max = np.amax(qs)
ltmtree = KDTree(LTMX) #, self.leaf_size, metric='euclidean')
dist, ind = ltmtree.query(
np.array([x]),
k=len(LTMX),
distance_upper_bound=dist_max)
keep = ind < len(LTMX)
ind = ind[keep]
dist = dist[keep]
#dist = np.where(dist < 1, 1, dist) ^
qstest = self.LTM_clean_strictness * self._clean_metric(LTMy[ind] - y, dist, dist_max)
dirty = ind[qstest > w_diff_max]
if(dirty.size):
if (LTMX.shape[0] - len(dirty) < 5):
# print("LTM dirty but too small!")
return
self.LTMX = np.delete(LTMX, dirty, axis=0).tolist()
self.LTMy = np.delete(LTMy, dirty, axis=0).tolist()
def print_stationtable(sd, mode):
"""Print a table of 'mode' stations."""
import numpy as np
from pykdtree.kdtree import KDTree
cols = 5
header = '{| class="wikitable"'
subheader = """|-
! HSL name
! OSM name
! mode
! type
! delta"""
footer = "|}"
ost = sd["ostat"]
pst = sd["pstat"] # data from provider
stations = pst[mode]
stations.sort(key=keykey('name'))
ostats = ost[mode]
kd_tree = KDTree(np.array([e["x:latlon"] for e in ostats]))
linecounter = 0
statcounter = 0
probcounter = 0
wr(header)
wr(subheader)
for ind in range(len(stations)):
if linecounter > 19:
wr(subheader)
linecounter = 0
ps = stations[ind]
darr, iarr = kd_tree.query(np.array(ps["latlon"], ndmin=2))
oind = iarr[0]
os = ostats[oind]
nlines, isok = print_stationline(os, ps, cols)
#linecounter += nlines
linecounter += 1 # Makes diffs more stable
statcounter += 1
probcounter += 0 if isok else 1
wr(footer)
wr("")
wr("{} stations.\n".format(statcounter))
wr("{} stations with differences.\n".format(probcounter))
not_in = [s for s in ostats if not "x:matched" in s.keys()]
if not_in:
not_in.sort(key=keykey("name"))
wr("'''{} stations not in HSL'''\n".format(mode.capitalize()))
sgen = ("[{} {}]".format(osm.obj2url(s),
s.get("name", "<no name in OSM>"))
for s in not_in)
wr(" {}\n".format(", ".join(sgen)))
def PCA_normal(input_array):
kd_tree = KDTree(input_array)
neighbours = kd_tree.query(input_array, 10 + 1)[1]
neighbours = input_array[neighbours]
p = Pool()
normals = p.map(compute_normal, neighbours)
datadict = {}
datadict['coords'] = input_array
datadict['normals'] = np.array(normals, dtype=np.float32)
return datadict
def test3d_float32_mismatch2():
#7, 93, 45
query_pts = np.array([[787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real.astype(np.float32))
try:
dist, idx = kdtree.query(query_pts, sqr_dists=True)
assert False
except TypeError:
assert True
def test3d_float32_mismatch2():
#7, 93, 45
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real.astype(np.float32))
try:
dist, idx = kdtree.query(query_pts, sqr_dists=True)
assert False
except TypeError:
assert True
def test1d_mask():
data_pts = np.arange(1000)
# put the input locations in random order
np.random.shuffle(data_pts)
bad_idx = np.nonzero(data_pts == 400)
nearest_idx_1 = np.nonzero(data_pts == 399)
nearest_idx_2 = np.nonzero(data_pts == 390)
kdtree = KDTree(data_pts, leafsize=15)
# shift the query points just a little bit for known neighbors
# we want 399 as a result, not 401, when we query for ~400
query_pts = np.arange(399.9, 299.9, -10)
query_mask = np.zeros(data_pts.shape[0]).astype(bool)
query_mask[bad_idx] = True
dist, idx = kdtree.query(query_pts, mask=query_mask)
assert idx[0] == nearest_idx_1 # 399, would be 400 if no mask
assert np.isclose(dist[0], 0.9)
assert idx[1] == nearest_idx_2 # 390
assert np.isclose(dist[1], 0.1)
def test3d_mask():
#7, 93, 45
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real)
query_mask = np.zeros(data_pts_real.shape[0])
query_mask[6:10] = True
dist, idx = kdtree.query(query_pts, sqr_dists=True, mask=query_mask)
epsilon = 1e-5
assert idx[0] == 5 # would be 7 if no mask
assert idx[1] == 93
assert idx[2] == 45
# would be 0 if no mask
assert abs(dist[0] - 66759196.1053) < epsilon * dist[0]
assert abs(dist[1] - 3.) < epsilon * dist[1]
assert abs(dist[2] - 20001.) < epsilon * dist[2]
def test3d_float32():
#7, 93, 45
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]], dtype=np.float32)
kdtree = KDTree(data_pts_real.astype(np.float32))
dist, idx = kdtree.query(query_pts, sqr_dists=True)
epsilon = 1e-5
assert idx[0] == 7
assert idx[1] == 93
assert idx[2] == 45
assert dist[0] == 0
assert abs(dist[1] - 3.) < epsilon * dist[1]
assert abs(dist[2] - 20001.) < epsilon * dist[2]
assert kdtree.data_pts.dtype == np.float32
def test3d_large_query():
# Target idxs: 7, 93, 45
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
# Repeat the same points multiple times to get 60000 query points
n = 20000
query_pts = np.repeat(query_pts, n, axis=0)
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts, sqr_dists=True)
epsilon = 1e-5
assert np.all(idx[:n] == 7)
assert np.all(idx[n:2*n] == 93)
assert np.all(idx[2*n:] == 45)
assert np.all(dist[:n] == 0)
assert np.all(abs(dist[n:2*n] - 3.) < epsilon * dist[n:2*n])
assert np.all(abs(dist[2*n:] - 20001.) < epsilon * dist[2*n:])
def test3d_8n():
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts, k=8)
exp_dist = np.array([[ 0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03,
8.17063009e+03, 1.20904577e+04, 1.22902057e+04, 1.60775136e+04],
[ 1.73205081e+00, 2.70216896e+03, 2.71431274e+03, 5.39537066e+03,
5.43793210e+03, 8.07855631e+03, 8.17119970e+03, 1.07513693e+04],
[ 1.41424892e+02, 3.25500021e+03, 3.44284958e+03, 6.58019346e+03,
6.81038455e+03, 9.89140135e+03, 1.01918659e+04, 1.31892516e+04]])
exp_idx = np.array([[ 7, 8, 6, 9, 5, 10, 4, 11],
[93, 94, 92, 95, 91, 96, 90, 97],
[45, 46, 44, 47, 43, 48, 42, 49]])
assert np.array_equal(idx, exp_idx)
assert np.allclose(dist, exp_dist)
def test3d_8n_ub_eps():
query_pts = np.array([[ 787014.438, -340616.906, 6313018.],
[751763.125, -59925.969, 6326205.5],
[769957.188, -202418.125, 6321069.5]])
kdtree = KDTree(data_pts_real)
dist, idx = kdtree.query(query_pts, k=8, eps=0.1, distance_upper_bound=10e3, sqr_dists=False)
exp_dist = np.array([[ 0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03,
8.17063009e+03, np.Inf, np.Inf, np.Inf],
[ 1.73205081e+00, 2.70216896e+03, 2.71431274e+03, 5.39537066e+03,
5.43793210e+03, 8.07855631e+03, 8.17119970e+03, np.Inf],
[ 1.41424892e+02, 3.25500021e+03, 3.44284958e+03, 6.58019346e+03,
6.81038455e+03, 9.89140135e+03, np.Inf, np.Inf]])
n = 100
exp_idx = np.array([[ 7, 8, 6, 9, 5, n, n, n],
[93, 94, 92, 95, 91, 96, 90, n],
[45, 46, 44, 47, 43, 48, n, n]])
assert np.array_equal(idx, exp_idx)
assert np.allclose(dist, exp_dist)
def main(args):
if args.infile.endswith('ply'):
from masbpy import io_ply
datadict = io_ply.read_ply(args.infile)
elif args.infile.endswith('las'):
from masbpy import io_las
datadict = io_las.read_las(args.infile)
elif args.infile.endswith('npy'):
datadict = io_npy.read_npy(args.infile, ['coords'])
kd_tree = KDTree( datadict['coords'] )
neighbours = kd_tree.query( datadict['coords'], args.k+1 )[1]
neighbours = datadict['coords'][neighbours]
p = Pool()
t1 = time()
normals = p.map(compute_normal, neighbours)
t2 = time()
print "finished normal computation in {} s".format(t2-t1)
datadict['normals'] = np.array(normals, dtype=np.float32)
io_npy.write_npy(args.outfile, datadict)
def read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, model, calc_bearing, filt_bearing, chunk): #cog = 1,
'''
Read a .DAT and associated set of .SON files recorded by a Humminbird(R)
instrument.
Parse the data into a set of memory mapped files that will
subsequently be used by the other functions of the PyHum module.
Export time-series data and metadata in other formats.
Create a kml file for visualising boat track
Syntax
----------
[] = PyHum.read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, chunksize, model, calc_bearing, filt_bearing, chunk)
Parameters
------------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
c : float, *optional* [Default=1450.0]
speed of sound in water (m/s). Defaults to a value of freshwater
draft : float, *optional* [Default=0.3]
draft from water surface to transducer face (m)
doplot : float, *optional* [Default=1]
if 1, plots will be made
t : float, *optional* [Default=0.108]
length of transducer array (m).
Default value is that of the 998 series Humminbird(R)
bedpick : int, *optional* [Default=1]
if 1, bedpicking with be carried out automatically
if 0, user will be prompted to pick the bed location on screen
flip_lr : int, *optional* [Default=0]
if 1, port and starboard scans will be flipped
(for situations where the transducer is flipped 180 degrees)
model: int, *optional* [Default=998]
A 3 or 4 number code indicating the model number
Examples: 998, 997, 1198, 1199
calc_bearing : float, *optional* [Default=0]
if 1, bearing will be calculated from coordinates
filt_bearing : float, *optional* [Default=0]
if 1, bearing will be filtered
chunk : str, *optional* [Default='d100' (distance, 100 m)]
letter, followed by a number.
There are the following letter options:
'd' - parse chunks based on distance, then number which is distance in m
'p' - parse chunks based on number of pings, then number which is number of pings
'h' - parse chunks based on change in heading, then number which is the change in heading in degrees
'1' - process just 1 chunk
Returns
---------
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the port side
sidescan sonar (where present)
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the starboard side
sidescan sonar (where present)
sonpath+base+'_data_dwnhi.dat': memory-mapped file
contains the raw echogram from the high-frequency
echosounder (where present)
sonpath+base+'_data_dwnlow.dat': memory-mapped file
contains the raw echogram from the low-frequency
echosounder (where present)
sonpath+base+"trackline.kml": google-earth kml file
contains the trackline of the vessel during data
acquisition
sonpath+base+'rawdat.csv': comma separated value file
contains time-series data. columns corresponding to
longitude
latitude
easting (m)
northing (m)
depth to bed (m)
alongtrack cumulative distance (m)
vessel heading (deg.)
sonpath+base+'meta.mat': .mat file
matlab format file containing a dictionary object
holding metadata information. Fields are:
e : ndarray, easting (m)
n : ndarray, northing (m)
es : ndarray, low-pass filtered easting (m)
ns : ndarray, low-pass filtered northing (m)
lat : ndarray, latitude
lon : ndarray, longitude
shape_port : tuple, shape of port scans in memory mapped file
shape_star : tuple, shape of starboard scans in memory mapped file
shape_hi : tuple, shape of high-freq. scans in memory mapped file
shape_low : tuple, shape of low-freq. scans in memory mapped file
dep_m : ndarray, depth to bed (m)
dist_m : ndarray, distance along track (m)
heading : ndarray, heading of vessel (deg. N)
pix_m: float, size of 1 pixel in across-track dimension (m)
bed : ndarray, depth to bed (m)
c : float, speed of sound in water (m/s)
t : length of sidescan transducer array (m)
spd : ndarray, vessel speed (m/s)
time_s : ndarray, time elapsed (s)
caltime : ndarray, unix epoch time (s)
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Son files are in %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if draft:
draft = float(draft)
print('Draft: %s' % (str(draft)))
if c:
c = float(c)
print('Celerity of sound: %s m/s' % (str(c)))
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
if flip_lr:
flip_lr = int(flip_lr)
if flip_lr==1:
print("Port and starboard will be flipped")
if t:
t = np.asarray(t,float)
print('Transducer length is %s m' % (str(t)))
if bedpick:
bedpick = np.asarray(bedpick,int)
if bedpick==1:
print('Bed picking is auto')
elif bedpick==0:
print('Bed picking is manual')
else:
print('User will be prompted per chunk about bed picking method')
if chunk:
chunk = str(chunk)
if chunk[0]=='d':
chunkmode=1
chunkval = int(chunk[1:])
print('Chunks based on distance of %s m' % (str(chunkval)))
elif chunk[0]=='p':
chunkmode=2
chunkval = int(chunk[1:])
print('Chunks based on %s pings' % (str(chunkval)))
elif chunk[0]=='h':
chunkmode=3
chunkval = int(chunk[1:])
print('Chunks based on heading devation of %s degrees' % (str(chunkval)))
elif chunk[0]=='1':
chunkmode=4
chunkval = 1
print('Only 1 chunk will be produced')
else:
print("Chunk mode not understood - should be 'd', 'p', or 'h' - using defaults")
chunkmode=1
chunkval = 100
print('Chunks based on distance of %s m' % (str(chunkval)))
if model:
try:
model = int(model)
print("Data is from the %s series" % (str(model)))
except:
if model=='onix':
model=0
print("Data is from the ONIX series")
elif model=='helix':
model=1
print("Data is from the HELIX series")
elif model=='mega':
model=2
print("Data is from the MEGA series")
# if cog:
# cog = int(cog)
# if cog==1:
# print "Heading based on course-over-ground"
if calc_bearing:
calc_bearing = int(calc_bearing)
if calc_bearing==1:
print("Bearing will be calculated from coordinates")
if filt_bearing:
filt_bearing = int(filt_bearing)
if filt_bearing==1:
print("Bearing will be filtered")
## for debugging
#humfile = r"test.DAT"; sonpath = "test_data"
#cs2cs_args = "epsg:26949"; doplot = 1; draft = 0
#c=1450; bedpick=1; fliplr=1; chunk = 'd100'
#model=998; cog=1; calc_bearing=0; filt_bearing=0
#if model==2:
# f = 1000
#else:
f = 455
try:
print("Checking the epsg code you have chosen for compatibility with Basemap ... ")
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', llcrnrlon=10, llcrnrlat=10, urcrnrlon=30, urcrnrlat=30)
del m
print("... epsg code compatible")
except (ValueError):
print("Error: the epsg code you have chosen is not compatible with Basemap")
print("please choose a different epsg code (http://spatialreference.org/)")
print("program will now close")
sys.exit()
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
# get the SON files from this directory
sonfiles = glob.glob(sonpath+'*.SON')
if not sonfiles:
sonfiles = glob.glob(os.getcwd()+os.sep+sonpath+'*.SON')
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
print("WARNING: Because files have to be read in byte by byte,")
print("this could take a very long time ...")
#reading each sonfile in parallel should be faster ...
try:
o = Parallel(n_jobs = np.min([len(sonfiles), cpu_count()]), verbose=0)(delayed(getscans)(sonfiles[k], humfile, c, model, cs2cs_args) for k in range(len(sonfiles)))
X, Y, A, B = zip(*o)
for k in range(len(Y)):
if Y[k] == 'sidescan_port':
dat = A[k] #data.gethumdat()
metadat = B[k] #data.getmetadata()
if flip_lr==0:
data_port = X[k].astype('int16')
else:
data_star = X[k].astype('int16')
elif Y[k] == 'sidescan_starboard':
if flip_lr==0:
data_star = X[k].astype('int16')
else:
data_port = X[k].astype('int16')
elif Y[k] == 'down_lowfreq':
data_dwnlow = X[k].astype('int16')
elif Y[k] == 'down_highfreq':
data_dwnhi = X[k].astype('int16')
elif Y[k] == 'down_vhighfreq': #hopefully this only applies to mega systems
data_dwnhi = X[k].astype('int16')
del X, Y, A, B, o
old_pyread = 0
if 'data_port' not in locals():
data_port = ''
print("portside scan not available")
if 'data_star' not in locals():
data_star = ''
print("starboardside scan not available")
if 'data_dwnhi' not in locals():
data_dwnlow = ''
print("high-frq. downward scan not available")
if 'data_dwnlow' not in locals():
data_dwnlow = ''
print("low-frq. downward scan not available")
except: # revert back to older version if paralleleised version fails
print("something went wrong with the parallelised version of pyread ...")
try:
import pyread
except:
from . import pyread
data = pyread.pyread(sonfiles, humfile, c, model, cs2cs_args)
dat = data.gethumdat()
metadat = data.getmetadata()
old_pyread = 1
nrec = len(metadat['n'])
metadat['instr_heading'] = metadat['heading'][:nrec]
#metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, cog, metadat['lat'], metadat['lon'], metadat['instr_heading'])
try:
es = humutils.runningMeanFast(metadat['e'][:nrec],len(metadat['e'][:nrec])/100)
ns = humutils.runningMeanFast(metadat['n'][:nrec],len(metadat['n'][:nrec])/100)
except:
es = metadat['e'][:nrec]
ns = metadat['n'][:nrec]
metadat['es'] = es
metadat['ns'] = ns
try:
trans = pyproj.Proj(init=cs2cs_args)
except:
trans = pyproj.Proj(cs2cs_args.lstrip(), inverse=True)
lon, lat = trans(es, ns, inverse=True)
metadat['lon'] = lon
metadat['lat'] = lat
metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, metadat['lat'], metadat['lon'], metadat['instr_heading']) #cog
dist_m = humutils.get_dist(lat, lon)
metadat['dist_m'] = dist_m
if calc_bearing==1: # recalculate speed, m/s
ds=np.gradient(np.squeeze(metadat['time_s']))
dx=np.gradient(np.squeeze(metadat['dist_m']))
metadat['spd'] = dx[:nrec]/ds[:nrec]
# theta at 3dB in the horizontal
theta3dB = np.arcsin(c/(t*(f*1000)))
#resolution of 1 sidescan pixel to nadir
ft = (np.pi/2)*(1/theta3dB) #/ (f/455)
dep_m = humutils.get_depth(metadat['dep_m'][:nrec])
if old_pyread == 1: #older pyread version
# port scan
try:
if flip_lr==0:
data_port = data.getportscans().astype('int16')
else:
data_port = data.getstarscans().astype('int16')
except:
data_port = ''
print("portside scan not available")
if data_port!='':
Zt, ind_port = makechunks_scan(chunkmode, chunkval, metadat, data_port, 0)
del data_port
## create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port.dat', 'int16', Zt)
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port.dat', 'int16', shape_port)
if old_pyread == 1: #older pyread version
# starboard scan
try:
if flip_lr==0:
data_star = data.getstarscans().astype('int16')
else:
data_star = data.getportscans().astype('int16')
except:
data_star = ''
print("starboardside scan not available")
if data_star!='':
Zt, ind_star = makechunks_scan(chunkmode, chunkval, metadat, data_star, 1)
del data_star
# create memory mapped file for Z
shape_star = io.set_mmap_data(sonpath, base, '_data_star.dat', 'int16', Zt)
star_fp = io.get_mmap_data(sonpath, base, '_data_star.dat', 'int16', shape_star)
if 'star_fp' in locals() and 'port_fp' in locals():
# check that port and starboard are same size
# and trim if not
if np.shape(star_fp)!=np.shape(port_fp):
print("port and starboard scans are different sizes ... rectifying")
if np.shape(port_fp[0])[1] > np.shape(star_fp[0])[1]:
tmp = port_fp.copy()
tmp2 = np.empty_like(star_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(star_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port2.dat', 'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port2.dat', 'int16', shape_port)
ind_port = list(ind_port)
ind_port[-1] = np.shape(star_fp[0])[1]
ind_port = tuple(ind_port)
elif np.shape(port_fp[0])[1] < np.shape(star_fp[0])[1]:
tmp = star_fp.copy()
tmp2 = np.empty_like(port_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(port_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_star2.dat', 'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star2.dat', 'int16', shape_star)
ind_star = list(ind_star)
ind_star[-1] = np.shape(port_fp[0])[1]
ind_star = tuple(ind_star)
if old_pyread == 1: #older pyread version
# low-freq. sonar
try:
data_dwnlow = data.getlowscans().astype('int16')
except:
data_dwnlow = ''
print("low-freq. scan not available")
if data_dwnlow!='':
Zt, ind_low = makechunks_scan(chunkmode, chunkval, metadat, data_dwnlow, 2)
del data_dwnlow
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', Zt)
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', shape_low)
if old_pyread == 1: #older pyread version
# hi-freq. sonar
try:
data_dwnhi = data.gethiscans().astype('int16')
except:
data_dwnhi = ''
print("high-freq. scan not available")
if data_dwnhi!='':
Zt, ind_hi = makechunks_scan(chunkmode, chunkval, metadat, data_dwnhi, 3)
del data_dwnhi
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', Zt)
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', shape_hi)
if 'dwnhi_fp' in locals() and 'dwnlow_fp' in locals():
# check that low and high are same size
# and trim if not
if (np.shape(dwnhi_fp)!=np.shape(dwnlow_fp)) and (chunkmode!=4):
print("dwnhi and dwnlow are different sizes ... rectifying")
if np.shape(dwnhi_fp[0])[1] > np.shape(dwnlow_fp[0])[1]:
tmp = dwnhi_fp.copy()
tmp2 = np.empty_like(dwnlow_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(dwnlow_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', shape_hi)
ind_hi = list(ind_hi)
ind_hi[-1] = np.shape(dwnlow_fp[0])[1]
ind_hi = tuple(ind_hi)
elif np.shape(dwnhi_fp[0])[1] < np.shape(dwnlow_fp[0])[1]:
tmp = dwnlow_fp.copy()
tmp2 = np.empty_like(dwnhi_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(dwnhi_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', shape_low)
ind_low = list(ind_low)
ind_low[-1] = np.shape(dwnhi_fp[0])[1]
ind_low = tuple(ind_low)
if old_pyread == 1: #older pyread version
del data
if ('shape_port' in locals()) and (chunkmode!=4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][0] * metadat['shape_port'][2]
elif ('shape_port' in locals()) and (chunkmode==4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][1]
else:
metadat['shape_port'] = ''
if ('shape_star' in locals()) and (chunkmode!=4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][0] * metadat['shape_star'][2]
elif ('shape_star' in locals()) and (chunkmode==4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][1]
else:
metadat['shape_star'] = ''
if ('shape_hi' in locals()) and (chunkmode!=4):
metadat['shape_hi'] = shape_hi
#nrec = metadat['shape_hi'][0] * metadat['shape_hi'][2] * 2
elif ('shape_hi' in locals()) and (chunkmode==4):
metadat['shape_hi'] = shape_hi
else:
metadat['shape_hi'] = ''
if ('shape_low' in locals()) and (chunkmode!=4):
metadat['shape_low'] = shape_low
#nrec = metadat['shape_low'][0] * metadat['shape_low'][2] * 2
elif ('shape_low' in locals()) and (chunkmode==4):
metadat['shape_low'] = shape_low
else:
metadat['shape_low'] = ''
#make kml boat trackline
humutils.make_trackline(lon,lat, sonpath, base)
if 'port_fp' in locals() and 'star_fp' in locals():
#if not os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'meta.mat'))):
if 2>1:
if bedpick == 1: # auto
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp, c)
if len(dist_m)<len(bed):
dist_m = np.append(dist_m,dist_m[-1]*np.ones(len(bed)-len(dist_m)))
if doplot==1:
if chunkmode!=4:
for k in range(len(star_fp)):
plot_2bedpicks(port_fp[k], star_fp[k], bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], x[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft, shape_port, sonpath, 0, chunkmode)
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec],np.squeeze(x[:nrec]))),axis=0)
bed = humutils.runningMeanFast(bed, 3)
elif bedpick>1: # user prompt
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp, c)
if len(dist_m)<len(bed):
dist_m = np.append(dist_m,dist_m[-1]*np.ones(len(bed)-len(dist_m)))
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec],np.squeeze(x[:nrec]))),axis=0)
bed = humutils.runningMeanFast(bed, 3)
# manually intervene
fig = plt.figure()
ax = plt.gca()
if chunkmode !=4:
im = ax.imshow(np.hstack(port_fp), cmap = 'gray', origin = 'upper')
else:
im = ax.imshow(port_fp, cmap = 'gray', origin = 'upper')
plt.plot(bed,'r')
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
plt.close()
del fig
if x1 != []: # if x1 is not empty
tree = KDTree(zip(np.arange(1,len(bed)), bed))
try:
dist, inds = tree.query(zip(x1, y1), k = 100, eps=5, n_jobs=-1)
except:
dist, inds = tree.query(zip(x1, y1), k = 100, eps=5)
b = np.interp(inds,x1,y1)
bed2 = bed.copy()
bed2[inds] = b
bed = bed2
if doplot==1:
if chunkmode!=4:
for k in range(len(star_fp)):
plot_2bedpicks(port_fp[k], star_fp[k], bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], x[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft, shape_port, sonpath, 0, chunkmode)
else: #manual
beds=[]
if chunkmode!=4:
for k in range(len(port_fp)):
raw_input("Bed picking "+str(k+1)+" of "+str(len(port_fp))+", are you ready? 30 seconds. Press Enter to continue...")
bed={}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp[k], cmap = 'gray', origin = 'upper')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]],x1,y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp[k])[0]
bed = np.asarray(np.hstack(beds),'float')
else:
raw_input("Bed picking - are you ready? 30 seconds. Press Enter to continue...")
bed={}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp, cmap = 'gray', origin = 'upper')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]],x1,y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp)[1]
bed = np.asarray(np.hstack(beds),'float')
# now revise the depth in metres
dep_m = (1/ft)*bed
if doplot==1:
if chunkmode!=4:
for k in range(len(star_fp)):
plot_bedpick(port_fp[k], star_fp[k], (1/ft)*bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_bedpick(port_fp, star_fp, (1/ft)*bed, dist_m, ft, shape_port, sonpath, 0, chunkmode)
metadat['bed'] = bed[:nrec]
else:
metadat['bed'] = dep_m[:nrec]*ft
metadat['heading'] = metadat['heading'][:nrec]
metadat['lon'] = lon[:nrec]
metadat['lat'] = lat[:nrec]
metadat['dist_m'] = dist_m[:nrec]
metadat['dep_m'] = dep_m[:nrec]
metadat['pix_m'] = 1/ft
metadat['bed'] = metadat['bed'][:nrec]
metadat['c'] = c
metadat['t'] = t
if model==2:
metadat['f'] = f*2
else:
metadat['f'] = f
metadat['spd'] = metadat['spd'][:nrec]
metadat['time_s'] = metadat['time_s'][:nrec]
metadat['e'] = metadat['e'][:nrec]
metadat['n'] = metadat['n'][:nrec]
metadat['es'] = metadat['es'][:nrec]
metadat['ns'] = metadat['ns'][:nrec]
try:
metadat['caltime'] = metadat['caltime'][:nrec]
except:
metadat['caltime'] = metadat['caltime']
savemat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')), metadat ,oned_as='row')
f = open(os.path.normpath(os.path.join(sonpath,base+'rawdat.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth (m)', 'distance (m)', 'instr. heading (deg)', 'heading (deg.)' ) )
for i in range(0, nrec):
writer.writerow(( float(lon[i]),float(lat[i]),float(es[i]),float(ns[i]),float(dep_m[i]),float(dist_m[i]), float(metadat['instr_heading'][i]), float(metadat['heading'][i]) ))
f.close()
del lat, lon, dep_m #, dist_m
if doplot==1:
plot_pos(sonpath, metadat, es, ns)
if 'dwnlow_fp' in locals():
plot_dwnlow(dwnlow_fp, chunkmode, sonpath)
if 'dwnhi_fp' in locals():
plot_dwnhi(dwnhi_fp, chunkmode, sonpath)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+ str(elapsed) + "seconds to analyse")
print("Done!")
print("===================================================")
class MASB(object):
def __init__(self, datadict, max_r, denoise_absmin=None, denoise_delta=None, denoise_min=None, detect_planar=None):
self.D = datadict # dict of numpy arrays
self.pykdtree = KDTree(self.D['coords'])
self.m, self.n = datadict['coords'].shape
self.D['ma_coords_in'] = np.empty( (self.m,self.n), dtype=np.float32 )
self.D['ma_coords_in'][:] = np.nan
self.D['ma_coords_out'] = np.empty( (self.m,self.n), dtype=np.float32 )
self.D['ma_coords_out'][:] = np.nan
self.D['ma_q_in'] = np.zeros( (self.m), dtype=np.uint32 )
self.D['ma_q_in'][:] = np.nan
self.D['ma_q_out'] = np.zeros( (self.m), dtype=np.uint32 )
self.D['ma_q_out'][:] = np.nan
self.SuperR = max_r
self.delta_convergence = 0.001
self.iteration_limit = 30
if denoise_absmin is None:
self.denoise_absmin = None
else:
self.denoise_absmin = (math.pi/180)*denoise_absmin
if denoise_delta is None:
self.denoise_delta = None
else:
self.denoise_delta = (math.pi/180)*denoise_delta
if denoise_min is None:
self.denoise_min = None
else:
self.denoise_min = (math.pi/180)*denoise_min
if detect_planar is None:
self.detect_planar = None
else:
self.detect_planar = (math.pi/180)*detect_planar
def compute_balls(self):
for inner in [True, False]:
self.compute_balls_oneside(inner)
def compute_balls_oneside(self, inner=True):
"""Balls shrinking algorithm. Set `inner` to False when outer balls are wanted."""
# iterate over all point-normal pairs
for p_i in xrange(self.m):
p, n = self.D['coords'][p_i], self.D['normals'][p_i]
#-- p is the point along whose normal n we are shrinking a ball, its index is p_i
if not inner:
n = -n
# initialize some helper variables:
#-- q will represent the second point that defines a ball together with p and n
q = None
#-- q_i is the index of q
q_i = None
#-- r represents the ball radius found in the current iteration (i.e. of the while loop below)
r = None
#-- r_previous represents the ball radius found in the previous iteration
r_previous=self.SuperR
#-- c is the ball's center point in the current iteration
c = None
#-- c_previous is the ball's center point in the previous iteration
c_previous = None
#-- j counts the iterations
j = -1
while True:
# increment iteration counter
j+=1
# set r to last found radius if this isn't the first iteration
if j>0:
r_previous = r
# compute ball center
c = p - n*r_previous
# keep track of this for denoising purpose
q_i_previous = q_i
### FINDING NEAREST NEIGHBOR OF c
# find closest point to c and assign to q
dists, indices = self.pykdtree.query(np.array([c]), k=2)
try:
candidate_c = self.D['coords'][indices]
except IndexError as detail:
print detail, indices, dists
import pdb; pdb.set_trace()
raise
q = candidate_c[0][0]
q_i = indices[0][0]
# What to do if closest point is p itself?
if equal(q,p):
# 1) if r_previous==SuperR, apparantly no other points on the halfspace spanned by -n => that's an infinite ball
if r_previous == self.SuperR:
r = r_previous
break
# 2) otherwise just pick the second closest point
else:
q = candidate_c[0][1]
q_i = indices[0][1]
### END FINDING NEAREST NEIGHBOR OF c
# compute new candidate radius r
try:
r = compute_radius(p,n,q)
except ZeroDivisionError:
# this happens on some rare occasions, we'll just skip the point
# print 'ZeroDivisionError: excepting p: {} with n={} and q={}'.format(p,n,q)
break
### EXCEPTIONAL CASES
# if r < 0 closest point was on the wrong side of plane with normal n => start over with SuperRadius on the right side of that plane
if r < 0:
r = self.SuperR
# if r > SuperR, stop now because otherwise in case of planar surface point configuration, we end up in an infinite loop
elif r > self.SuperR:
r = self.SuperR
break
### END EXCEPTIONAL CASES
### DENOISING STUFF
# i.e. terminate iteration early if certain conditions are satisfied based on (history of) ball metrics
c_ = p - n*r
# this seems to work well against noisy ma points.
if self.denoise_absmin is not None:
if math.acos(cos_angle(p-c_, q-c_)) < self.denoise_absmin and j>0 and r>norm(q-p):
# keep previous radius:
r=r_previous
q_i = q_i_previous
break
if self.denoise_delta is not None and j>0:
theta_now = math.acos(cos_angle(p-c_, q-c_))
q_previous = self.D['coords'][q_i_previous]
theta_prev = math.acos(cos_angle(p-c_, q_previous-c_))
if theta_prev-theta_now > self.denoise_delta and theta_now < self.denoise_min and r>norm(q-p):
# keep previous radius:
r=r_previous
q_i = q_i_previous
break
if self.detect_planar != None:
if math.acos( cos_angle(q-p, -n) ) > self.detect_planar and j<2:
r= self.SuperR
break
### END DENOISING STUFF
# stop iteration if r has converged
if abs(r_previous - r) < self.delta_convergence:
break
# stop iteration if this looks like an infinite loop:
if j > self.iteration_limit:
# print "breaking for possible infinite loop"
break
# now store valid points in array (invalid points will be NaN)
if inner: inout = 'in'
else: inout = 'out'
if r >= self.SuperR:
pass
else:
# self.D['ma_radii_'+inout][p_i] = r
self.D['ma_coords_'+inout][p_i] = c
self.D['ma_q_'+inout][p_i] = q_i
class Grid(object):
def __init__(self, ncfile, latvarname, lonvarname):
"""Initialization function
Arguments:
latvar -- the netCDF latitude variable
lonvar -- the netCDF longitude variable
"""
self.latvar = ncfile.variables[latvarname]
self.lonvar = ncfile.variables[lonvarname]
self.time_var = utils.get_time_var(ncfile)
self.kdt = _data_cache.get(ncfile.filepath())
if self.kdt is None:
rad_factor = pi / 180.0
latvals = self.latvar[:] * rad_factor
lonvals = self.lonvar[:] * rad_factor
clat, clon = np.cos(latvals), np.cos(lonvals)
slat, slon = np.sin(latvals), np.sin(lonvals)
triples = np.array(list(zip(np.ravel(clat * clon),
np.ravel(clat * slon),
np.ravel(slat))))
self.kdt = KDTree(triples)
_data_cache[ncfile.filepath()] = self.kdt
self._shape = ncfile.variables[latvarname].shape
def find_index(self, lat0, lon0, n=1):
"""Finds the y,x indicies that are closest to a latitude, longitude
pair.
Arguments:
lat0 -- the target latitude
lon0 -- the target longitude
n -- the number of indicies to return
Returns:
y, x indicies
"""
if hasattr(lat0, "__len__"):
lat0 = np.array(lat0)
lon0 = np.array(lon0)
multiple = True
else:
multiple = False
rad_factor = pi / 180.0
lat0_rad = lat0 * rad_factor
lon0_rad = lon0 * rad_factor
clat0, clon0 = np.cos(lat0_rad), np.cos(lon0_rad)
slat0, slon0 = np.sin(lat0_rad), np.sin(lon0_rad)
q = [clat0 * clon0, clat0 * slon0, slat0]
if multiple:
q = np.array(q).transpose()
dist_sq_min, minindex_1d = self.kdt.query(np.float32(q), k=n)
iy_min, ix_min = np.unravel_index(minindex_1d, self._shape)
return iy_min, ix_min
def bounding_box(self, lat, lon, n=10):
y, x = self.find_index(np.array(lat).ravel(), np.array(lon).ravel(), n)
miny, maxy = np.amin(y), np.amax(y)
minx, maxx = np.amin(x), np.amax(x)
def fix_limits(data, limit):
max = np.amax(data)
min = np.amin(data)
delta = max - min
if delta < 2:
min -= 2
max += 2
min = int(min - delta / 4.0)
if min < 0:
min = 0
max = int(max + delta / 4.0)
if max > limit:
max = limit - 1
return min, max
miny, maxy = fix_limits(y, self.latvar.shape[0])
minx, maxx = fix_limits(x, self.latvar.shape[1])
return miny, maxy, minx, maxx
def interpolation_radius(self, lat, lon):
distance = VincentyDistance()
d = distance.measure(
(np.amin(lat), np.amin(lon)),
(np.amax(lat), np.amax(lon))
) * 1000 / 8.0
if d == 0:
d = 50000
d = np.clip(d, 20000, 50000)
return d
def _get_interpolation(self, interp, lat, lon):
method = interp.get('method')
neighbours = interp.get('neighbours')
if neighbours < 1:
neighbours = 1
radius = self.interpolation_radius(np.median(lat),
np.median(lon))
return method, neighbours, radius
def _transect(self, variable, points, timestep, depth=None, n=100,
interpolation={'method': 'inv_square', 'neighbours': 8}):
distances, times, target_lat, target_lon, b = _path_to_points(
points, n)
miny, maxy, minx, maxx = self.bounding_box(target_lat, target_lon, 10)
lat = self.latvar[miny:maxy, minx:maxx]
lon = self.lonvar[miny:maxy, minx:maxx]
if depth is None:
data = variable[timestep, :, miny:maxy, minx:maxx]
data = np.rollaxis(data, 0, 3)
else:
if len(variable.shape) == 4:
data = variable[timestep, depth, miny:maxy, minx:maxx]
else:
data = variable[timestep, miny:maxy, minx:maxx]
data = np.expand_dims(data, -1).view(np.ma.MaskedArray)
_fill_invalid_shift(data)
method, neighbours, radius = self._get_interpolation(interpolation,
target_lat,
target_lon)
resampled = []
for d in range(0, data.shape[-1]):
resampled.append(
resample(
lat,
lon,
np.array(target_lat),
np.array(target_lon),
data[:, :, d],
method=method,
neighbours=neighbours,
radius_of_influence=radius,
nprocs=4
)
)
resampled = np.ma.vstack(resampled)
return np.array([target_lat, target_lon]), distances, resampled, b
def transect(self, variable, points, timestep, n=100,
interpolation={'method': 'inv_square', 'neighbours': 8}):
latlon, distances, resampled, b = self._transect(
variable, points, timestep, None, n, interpolation)
return latlon, distances, resampled
def surfacetransect(self, variable, points, timestep, n=100,
interpolation={
'method': 'inv_square',
'neighbours': 8
}):
latlon, distances, resampled, b = self._transect(
variable, points, timestep, 0, n, interpolation)
return latlon, distances, resampled[0]
def velocitytransect(self, variablex, variabley,
points, timestep, n=100,
interpolation={
'method': 'inv_square',
'neighbours': 8
}):
latlon, distances, x, b = self._transect(variablex, points, timestep,
None, n, interpolation)
latlon, distances, y, b = self._transect(variabley, points, timestep,
None, n, interpolation)
r = np.radians(np.subtract(90, b))
theta = np.arctan2(y, x) - r
mag = np.sqrt(x ** 2 + y ** 2)
parallel = mag * np.cos(theta)
perpendicular = mag * np.sin(theta)
return latlon, distances, parallel, perpendicular
def path(self, variable, depth, points, times, n=100,
interpolation={'method': 'inv_square', 'neighbours': 8}):
target_lat = points[:, 0]
target_lon = points[:, 1]
miny, maxy, minx, maxx = self.bounding_box(target_lat, target_lon, 10)
lat = self.latvar[miny:maxy, minx:maxx]
lon = self.lonvar[miny:maxy, minx:maxx]
method, neighbours, radius = self._get_interpolation(interpolation,
target_lat,
target_lon)
ts = [
t.replace(tzinfo=pytz.UTC)
for t in
netcdftime.utime(self.time_var.units).num2date(self.time_var[:])
]
mintime, x = _take_surrounding(ts, times[0])
x, maxtime = _take_surrounding(ts, times[-1])
maxtime += 1
uniquetimes = range(mintime, maxtime + 1)
combined = []
for t in range(mintime, maxtime):
if len(variable.shape) == 3:
data = variable[t, miny:maxy, minx:maxx]
else:
data = variable[t, depth, miny:maxy, minx:maxx]
_fill_invalid_shift(np.ma.array(data))
combined.append(resample(lat,
lon,
np.array(target_lat),
np.array(target_lon),
data,
method=method,
neighbours=neighbours,
radius_of_influence=radius,
nprocs=4))
combined = np.ma.array(combined)
if mintime + 1 >= len(ts):
result = combined[0]
else:
t0 = ts[mintime]
td = (ts[mintime + 1] - t0).total_seconds()
deltas = np.ma.masked_array([t.total_seconds() / td
for t in np.subtract(times, t0)])
model_td = ts[1] - ts[0]
deltas[
np.where(np.array(times) > ts[-1] + model_td / 2)
] = np.ma.masked
deltas[
np.where(np.array(times) < ts[0] - model_td / 2)
] = np.ma.masked
print abcd
# This is a slight modification on scipy's interp1d
# https://github.com/scipy/scipy/blob/v0.17.1/scipy/interpolate/interpolate.py#L534-L561
x = np.array(range(0, len(uniquetimes) - 1))
new_idx = np.searchsorted(x, deltas)
new_idx = new_idx.clip(1, len(x) - 1).astype(int)
low = new_idx - 1
high = new_idx
if (high >= len(x)).any():
result = combined[0]
# result[:, np.where(deltas.mask)] = np.ma.masked
result[np.where(deltas.mask)] = np.ma.masked
else:
x_low = x[low]
x_high = x[high]
y_low = combined[low, range(0, len(times))]
y_high = combined[high, range(0, len(times))]
slope = (y_high - y_low) / (x_high - x_low)[None]
y_new = slope * (deltas - x_low)[None] + y_low
result = y_new[0]
return result
def map_texture(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs): #influence = 10,
'''
Create plots of the texture lengthscale maps made in PyHum.texture module
using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.map_texture(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0.5]
grid resolution of output gridded texture map
mode: int, *optional* [Default=3]
gridding mode. 1 = nearest neighbour
2 = inverse weighted nearest neighbour
3 = Gaussian weighted nearest neighbour
nn: int, *optional* [Default=64]
number of nearest neighbours for gridding (used if mode > 1)
numstdevs: int, *optional* [Default = 4]
Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept
Returns
-------
sonpath+'x_y_class'+str(p)+'.asc' : text file
contains the point cloud of easting, northing, and texture lengthscales
of the pth chunk
sonpath+'class_GroundOverlay'+str(p)+'.kml': kml file
contains gridded (or point cloud) texture lengthscale map for importing into google earth
of the pth chunk
sonpath+'class_map'+str(p)+'.png' :
image overlay associated with the kml file
sonpath+'class_map_imagery'+str(p)+'.png' : png image file
gridded (or point cloud) texture lengthscale map
overlain onto an image pulled from esri image server
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if res:
res = np.asarray(res,float)
print('Gridding resolution: %s' % (str(res)))
if mode:
mode = int(mode)
print('Mode for gridding: %s' % (str(mode)))
if nn:
nn = int(nn)
print('Number of nearest neighbours for gridding: %s' % (str(nn)))
#if influence:
# influence = int(influence)
# print 'Radius of influence for gridding: %s (m)' % (str(influence))
if numstdevs:
numstdevs = int(numstdevs)
print('Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept: %s' % (str(numstdevs)))
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
pix_m = np.squeeze(meta['pix_m'])*1.1
dep_m = np.squeeze(meta['dep_m'])
c = np.squeeze(meta['c'])
#dist_m = np.squeeze(meta['dist_m'])
theta = np.squeeze(meta['heading'])/(180/np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
if len(shape_star)>2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat', 'float32', tuple(shape))
#with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
# class_fp = np.memmap(ff, dtype='float32', mode='r', shape=tuple(shape))
else:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
class_fp = np.load(ff)
tvg = ((8.5*10**-5)+(3/76923)+((8.5*10**-5)/4))*c
dist_tvg = ((np.tan(np.radians(25)))*dep_m)-(tvg)
if len(shape_star)>2:
for p in range(len(class_fp)):
e = esi[shape_port[-1]*p:shape_port[-1]*(p+1)]
n = nsi[shape_port[-1]*p:shape_port[-1]*(p+1)]
t = theta[shape_port[-1]*p:shape_port[-1]*(p+1)]
d = dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)]
len_n = len(n)
merge = class_fp[p].copy()
merge[np.isnan(merge)] = 0
merge[np.isnan(np.vstack((np.flipud(port_fp[p]),star_fp[p])))] = 0
extent = shape_port[1]
R1 = merge[extent:,:]
R2 = np.flipud(merge[:extent,:])
merge = np.vstack((R2,R1))
del R1, R2
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.linspace(pix_m,extent*pix_m,extent)
X, Y = getXY(e,n,yvec,d,t,extent)
merge[merge==0] = np.nan
if len(merge.flatten()) != len(X):
merge = merge[:,:len_n]
merge = merge.T.flatten()
index = np.where(np.logical_not(np.isnan(merge)))[0]
X, Y, merge = trim_xys(X, Y, merge, index)
X = X.astype('float32')
Y = Y.astype('float32')
merge = merge.astype('float32')
# write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc'))
with open(outfile, 'w') as f:
np.savetxt(f, np.hstack((humutils.ascol(X),humutils.ascol(Y), humutils.ascol(merge))), delimiter=' ', fmt="%8.6f %8.6f %8.6f")
humlon, humlat = trans(X, Y, inverse=True)
#if dogrid==1:
orig_def, targ_def, grid_x, grid_y, res, shape = get_griddefs(np.min(X), np.max(X), np.min(Y), np.max(Y), res, humlon, humlat, trans)
grid_x = grid_x.astype('float32')
grid_y = grid_y.astype('float32')
sigmas = 1 #m
eps = 2
dat, res = get_grid(mode, orig_def, targ_def, merge, res*10, np.min(X), np.max(X), np.min(Y), np.max(Y), res, nn, sigmas, eps, shape, numstdevs, trans, humlon, humlat)
del merge
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
datm = np.ma.masked_invalid(dat)
del dat
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
vmin=np.nanmin(datm)+0.1
vmax=np.nanmax(datm)-0.1
if vmin > vmax:
vmin=np.nanmin(datm)
vmax=np.nanmax(datm)
print_map(cs2cs_args, glon, glat, datm, sonpath, p, vmin=vmin, vmax=vmax)
else: #just 1 chunk
e = esi
n = nsi
t = theta
d = dist_tvg
len_n = len(n)
merge = class_fp.copy()
merge[np.isnan(merge)] = 0
merge[np.isnan(np.vstack((np.flipud(port_fp),star_fp)))] = 0
extent = shape_port[0]
R1 = merge[extent:,:]
R2 = np.flipud(merge[:extent,:])
merge = np.vstack((R2,R1))
del R1, R2
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.linspace(pix_m,extent*pix_m,extent)
X, Y = getXY(e,n,yvec,d,t,extent)
merge[merge==0] = np.nan
if len(merge.flatten()) != len(X):
merge = merge[:,:len_n]
merge = merge.T.flatten()
index = np.where(np.logical_not(np.isnan(merge)))[0]
X, Y, merge = trim_xys(X, Y, merge, index)
# write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_class'+str(0)+'.asc'))
with open(outfile, 'w') as f:
np.savetxt(f, np.hstack((humutils.ascol(X),humutils.ascol(Y), humutils.ascol(merge))), delimiter=' ', fmt="%8.6f %8.6f %8.6f")
humlon, humlat = trans(X, Y, inverse=True)
#if dogrid==1:
if 2>1:
orig_def, targ_def, grid_x, grid_y, res, shape = get_griddefs(np.min(X), np.max(X), np.min(Y), np.max(Y), res, humlon, humlat, trans)
## create mask for where the data is not
tree = KDTree(np.c_[X.flatten(),Y.flatten()])
if pykdtree==1:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
else:
try:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1, n_jobs=cpu_count())
except:
#print ".... update your scipy installation to use faster kd-tree queries"
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
dist = dist.reshape(grid_x.shape)
sigmas = 1 #m
eps = 2
dat, res = get_grid(mode, orig_def, targ_def, merge, res*10, np.min(X), np.max(X), np.min(Y), np.max(Y), res, nn, sigmas, eps, shape, numstdevs, trans, humlon, humlat)
del merge
#if dogrid==1:
if 2>1:
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
dat[dist>res*2] = np.nan
del dist
datm = np.ma.masked_invalid(dat)
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
vmin=np.nanmin(datm)+0.1
vmax=np.nanmax(datm)-0.1
if vmin > vmax:
vmin=np.nanmin(datm)
vmax=np.nanmax(datm)
Parallel(n_jobs = 2, verbose=0)(delayed(doplots)(k, humlon, humlat, cs2cs_args, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax) for k in range(2))
#print_map(cs2cs_args, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax)
#print_contour_map(cs2cs_args, humlon, humlat, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+"seconds to analyse")
print("Done!")
print("===================================================")
def make_map(e, n, t, d, dat_port, dat_star, data_R, pix_m, res, cs2cs_args, sonpath, p, mode, nn, numstdevs, c, dx, use_uncorrected, scalemax): #dogrid, influence,dowrite,
thres=5
trans = pyproj.Proj(init=cs2cs_args)
mp = np.nanmean(dat_port)
ms = np.nanmean(dat_star)
if mp>ms:
merge = np.vstack((dat_port,dat_star*(mp/ms)))
else:
merge = np.vstack((dat_port*(ms/mp),dat_star))
del dat_port, dat_star
merge[np.isnan(merge)] = 0
merge = merge[:,:len(n)]
## actual along-track resolution is this: dx times dy = Af
tmp = data_R * dx * (c*0.007 / 2) #dx = np.arcsin(c/(1000*meta['t']*meta['f']))
res_grid = np.sqrt(np.vstack((tmp, tmp)))
del tmp
res_grid = res_grid[:np.shape(merge)[0],:np.shape(merge)[1]]
#if use_uncorrected != 1:
# merge = merge - 10*np.log10(res_grid)
res_grid = res_grid.astype('float32')
merge[np.isnan(merge)] = 0
merge[merge<0] = 0
merge = merge.astype('float32')
merge = denoise_tv_chambolle(merge.copy(), weight=.2, multichannel=False).astype('float32')
R = np.vstack((np.flipud(data_R),data_R))
del data_R
R = R[:np.shape(merge)[0],:np.shape(merge)[1]]
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.squeeze(np.linspace(np.squeeze(pix_m),extent*np.squeeze(pix_m),extent))
X, Y, D, h, t = getXY(e,n,yvec,np.squeeze(d),t,extent)
X = X.astype('float32')
Y = Y.astype('float32')
D = D.astype('float32')
h = h.astype('float32')
t = t.astype('float32')
X = X.astype('float32')
D[np.isnan(D)] = 0
h[np.isnan(h)] = 0
t[np.isnan(t)] = 0
X = X[np.where(np.logical_not(np.isnan(Y)))]
merge = merge.flatten()[np.where(np.logical_not(np.isnan(Y)))]
res_grid = res_grid.flatten()[np.where(np.logical_not(np.isnan(Y)))]
Y = Y[np.where(np.logical_not(np.isnan(Y)))]
D = D[np.where(np.logical_not(np.isnan(Y)))]
R = R.flatten()[np.where(np.logical_not(np.isnan(Y)))]
h = h[np.where(np.logical_not(np.isnan(Y)))]
t = t[np.where(np.logical_not(np.isnan(Y)))]
Y = Y[np.where(np.logical_not(np.isnan(X)))]
merge = merge.flatten()[np.where(np.logical_not(np.isnan(X)))]
res_grid = res_grid.flatten()[np.where(np.logical_not(np.isnan(X)))]
X = X[np.where(np.logical_not(np.isnan(X)))]
D = D[np.where(np.logical_not(np.isnan(X)))]
R = R.flatten()[np.where(np.logical_not(np.isnan(X)))]
h = h[np.where(np.logical_not(np.isnan(X)))]
t = t[np.where(np.logical_not(np.isnan(X)))]
X = X[np.where(np.logical_not(np.isnan(merge)))]
Y = Y[np.where(np.logical_not(np.isnan(merge)))]
merge = merge[np.where(np.logical_not(np.isnan(merge)))]
res_grid = res_grid.flatten()[np.where(np.logical_not(np.isnan(merge)))]
D = D[np.where(np.logical_not(np.isnan(merge)))]
R = R[np.where(np.logical_not(np.isnan(merge)))]
h = h[np.where(np.logical_not(np.isnan(merge)))]
t = t[np.where(np.logical_not(np.isnan(merge)))]
X = X[np.where(np.logical_not(np.isinf(merge)))]
Y = Y[np.where(np.logical_not(np.isinf(merge)))]
merge = merge[np.where(np.logical_not(np.isinf(merge)))]
res_grid = res_grid.flatten()[np.where(np.logical_not(np.isinf(merge)))]
D = D[np.where(np.logical_not(np.isinf(merge)))]
R = R[np.where(np.logical_not(np.isinf(merge)))]
h = h[np.where(np.logical_not(np.isinf(merge)))]
t = t[np.where(np.logical_not(np.isinf(merge)))]
print("writing point cloud")
#if dowrite==1:
## write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_ss_raw'+str(p)+'.asc'))
##write.txtwrite( outfile, np.hstack((humutils.ascol(X.flatten()),humutils.ascol(Y.flatten()), humutils.ascol(merge.flatten()), humutils.ascol(D.flatten()), humutils.ascol(R.flatten()), humutils.ascol(h.flatten()), humutils.ascol(t.flatten()) )) )
np.savetxt(outfile, np.hstack((humutils.ascol(X.flatten()),humutils.ascol(Y.flatten()), humutils.ascol(merge.flatten()), humutils.ascol(D.flatten()), humutils.ascol(R.flatten()), humutils.ascol(h.flatten()), humutils.ascol(t.flatten()) )) , fmt="%8.6f %8.6f %8.6f %8.6f %8.6f %8.6f %8.6f")
del D, R, h, t
sigmas = 0.1 #m
eps = 2
print("gridding ...")
#if dogrid==1:
if 2>1:
if res==99:
resg = np.min(res_grid[res_grid>0])/2
print('Gridding at resolution of %s' % str(resg))
else:
resg = res
tree = KDTree(np.c_[X.flatten(),Y.flatten()])
complete=0
while complete==0:
try:
grid_x, grid_y, res = getmesh(np.min(X), np.max(X), np.min(Y), np.max(Y), resg)
longrid, latgrid = trans(grid_x, grid_y, inverse=True)
longrid = longrid.astype('float32')
latgrid = latgrid.astype('float32')
shape = np.shape(grid_x)
## create mask for where the data is not
if pykdtree==1:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
else:
try:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1, n_jobs=cpu_count())
except:
#print ".... update your scipy installation to use faster kd-tree queries"
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
dist = dist.reshape(grid_x.shape)
targ_def = pyresample.geometry.SwathDefinition(lons=longrid.flatten(), lats=latgrid.flatten())
del longrid, latgrid
humlon, humlat = trans(X, Y, inverse=True)
orig_def = pyresample.geometry.SwathDefinition(lons=humlon.flatten(), lats=humlat.flatten())
del humlon, humlat
if 'orig_def' in locals():
complete=1
except:
print("memory error: trying grid resolution of %s" % (str(resg*2)))
resg = resg*2
if mode==1:
complete=0
while complete==0:
try:
try:
dat = pyresample.kd_tree.resample_nearest(orig_def, merge.flatten(), targ_def, radius_of_influence=res*20, fill_value=None, nprocs = cpu_count(), reduce_data=1)
except:
dat = pyresample.kd_tree.resample_nearest(orig_def, merge.flatten(), targ_def, radius_of_influence=res*20, fill_value=None, nprocs = 1, reduce_data=1)
try:
r_dat = pyresample.kd_tree.resample_nearest(orig_def, res_grid.flatten(), targ_def, radius_of_influence=res*20, fill_value=None, nprocs = cpu_count(), reduce_data=1)
except:
r_dat = pyresample.kd_tree.resample_nearest(orig_def, res_grid.flatten(), targ_def, radius_of_influence=res*20, fill_value=None, nprocs = 1, reduce_data=1)
stdev = None
counts = None
if 'dat' in locals():
complete=1
except:
del grid_x, grid_y, targ_def, orig_def
wf = None
humlon, humlat = trans(X, Y, inverse=True)
dat, stdev, counts, resg, complete, shape = getgrid_lm(humlon, humlat, merge, res*10, min(X), max(X), min(Y), max(Y), resg*2, mode, trans, nn, wf, sigmas, eps)
r_dat, stdev, counts, resg, complete, shape = getgrid_lm(humlon, humlat, res_grid, res*10, min(X), max(X), min(Y), max(Y), resg*2, mode, trans, nn, wf, sigmas, eps)
del humlon, humlat
elif mode==2:
# custom inverse distance
wf = lambda r: 1/r**2
complete=0
while complete==0:
try:
try:
dat, stdev, counts = pyresample.kd_tree.resample_custom(orig_def, merge.flatten(),targ_def, radius_of_influence=res*20, neighbours=nn, weight_funcs=wf, fill_value=None, with_uncert = True, nprocs = cpu_count(), reduce_data=1)
except:
dat, stdev, counts = pyresample.kd_tree.resample_custom(orig_def, merge.flatten(),targ_def, radius_of_influence=res*20, neighbours=nn, weight_funcs=wf, fill_value=None, with_uncert = True, nprocs = 1, reduce_data=1)
try:
r_dat = pyresample.kd_tree.resample_custom(orig_def, res_grid.flatten(), targ_def, radius_of_influence=res*20, neighbours=nn, weight_funcs=wf, fill_value=None, with_uncert = False, nprocs = cpu_count(), reduce_data=1)
except:
r_dat = pyresample.kd_tree.resample_custom(orig_def, res_grid.flatten(), targ_def, radius_of_influence=res*20, neighbours=nn, weight_funcs=wf, fill_value=None, with_uncert = False, nprocs = 1, reduce_data=1)
if 'dat' in locals():
complete=1
except:
del grid_x, grid_y, targ_def, orig_def
humlon, humlat = trans(X, Y, inverse=True)
dat, stdev, counts, resg, complete, shape = getgrid_lm(humlon, humlat, merge, res*2, min(X), max(X), min(Y), max(Y), resg*2, mode, trans, nn, wf, sigmas, eps)
r_dat, stdev, counts, resg, complete, shape = getgrid_lm(humlon, humlat, res_grid, res*2, min(X), max(X), min(Y), max(Y), resg*2, mode, trans, nn, wf, sigmas, eps)
del humlat, humlon
del stdev_null, counts_null
elif mode==3:
wf = None
complete=0
while complete==0:
try:
try:
dat, stdev, counts = pyresample.kd_tree.resample_gauss(orig_def, merge.flatten(), targ_def, radius_of_influence=res*20, neighbours=nn, sigmas=sigmas, fill_value=None, with_uncert = True, nprocs = cpu_count(), epsilon = eps, reduce_data=1)
except:
dat, stdev, counts = pyresample.kd_tree.resample_gauss(orig_def, merge.flatten(), targ_def, radius_of_influence=res*20, neighbours=nn, sigmas=sigmas, fill_value=None, with_uncert = True, nprocs = 1, epsilon = eps, reduce_data=1)
try:
r_dat = pyresample.kd_tree.resample_gauss(orig_def, res_grid.flatten(), targ_def, radius_of_influence=res*20, neighbours=nn, sigmas=sigmas, fill_value=None, with_uncert = False, nprocs = cpu_count(), epsilon = eps, reduce_data=1)
except:
r_dat = pyresample.kd_tree.resample_gauss(orig_def, res_grid.flatten(), targ_def, radius_of_influence=res*20, neighbours=nn, sigmas=sigmas, fill_value=None, with_uncert = False, nprocs = 1, epsilon = eps, reduce_data=1)
if 'dat' in locals():
complete=1
except:
del grid_x, grid_y, targ_def, orig_def
humlon, humlat = trans(X, Y, inverse=True)
dat, stdev, counts, resg, complete, shape = getgrid_lm(humlon, humlat, merge, res*10, min(X), max(X), min(Y), max(Y), resg*2, mode, trans, nn, wf, sigmas, eps)
r_dat, stdev_null, counts_null, resg, complete, shape = getgrid_lm(humlon, humlat, res_grid, res*10, min(X), max(X), min(Y), max(Y), resg*2, mode, trans, nn, wf, sigmas, eps)
del humlat, humlon
del stdev_null, counts_null
humlon, humlat = trans(X, Y, inverse=True)
del X, Y, res_grid, merge
dat = dat.reshape(shape)
dat[dist>res*30] = np.nan
del dist
r_dat = r_dat.reshape(shape)
r_dat[r_dat<1] = 1
r_dat[r_dat > 2*np.pi] = 1
r_dat[np.isnan(dat)] = np.nan
dat = dat + r_dat #np.sqrt(np.cos(np.deg2rad(r_dat))) #dat*np.sqrt(r_dat) + dat
del r_dat
if mode>1:
stdev = stdev.reshape(shape)
counts = counts.reshape(shape)
mask = dat.mask.copy()
dat[mask==1] = np.nan
#dat[mask==1] = 0
if mode>1:
dat[(stdev>numstdevs) & (mask!=0)] = np.nan
dat[(counts<nn) & (counts>0)] = np.nan
#if dogrid==1:
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
dat[dat<thres] = np.nan
datm = np.ma.masked_invalid(dat)
glon, glat = trans(grid_x, grid_y, inverse=True)
#del grid_x, grid_y
try:
from osgeo import gdal,ogr,osr
proj = osr.SpatialReference()
proj.ImportFromEPSG(int(cs2cs_args.split(':')[-1])) #26949)
datout = np.squeeze(np.ma.filled(dat))#.astype('int16')
datout[np.isnan(datout)] = -99
driver = gdal.GetDriverByName('GTiff')
#rows,cols = np.shape(datout)
cols,rows = np.shape(datout)
outFile = os.path.normpath(os.path.join(sonpath,'geotiff_map'+str(p)+'.tif'))
ds = driver.Create( outFile, rows, cols, 1, gdal.GDT_Float32, [ 'COMPRESS=LZW' ] )
if proj is not None:
ds.SetProjection(proj.ExportToWkt())
xmin, ymin, xmax, ymax = [grid_x.min(), grid_y.min(), grid_x.max(), grid_y.max()]
xres = (xmax - xmin) / float(rows)
yres = (ymax - ymin) / float(cols)
geotransform = (xmin, xres, 0, ymax, 0, -yres)
ds.SetGeoTransform(geotransform)
ss_band = ds.GetRasterBand(1)
ss_band.WriteArray(np.flipud(datout)) #datout)
ss_band.SetNoDataValue(-99)
ss_band.FlushCache()
ss_band.ComputeStatistics(False)
del ds
except:
print("error: geotiff could not be created... check your gdal/ogr install")
try:
# =========================================================
print("creating kmz file ...")
## new way to create kml file
pixels = 1024 * 10
fig, ax = humutils.gearth_fig(llcrnrlon=glon.min(),
llcrnrlat=glat.min(),
urcrnrlon=glon.max(),
urcrnrlat=glat.max(),
pixels=pixels)
cs = ax.pcolormesh(glon, glat, datm, vmax=scalemax, cmap='gray')
ax.set_axis_off()
fig.savefig(os.path.normpath(os.path.join(sonpath,'map'+str(p)+'.png')), transparent=True, format='png')
del fig, ax
# =========================================================
fig = plt.figure(figsize=(1.0, 4.0), facecolor=None, frameon=False)
ax = fig.add_axes([0.0, 0.05, 0.2, 0.9])
cb = fig.colorbar(cs, cax=ax)
cb.set_label('Intensity [dB W]', rotation=-90, color='k', labelpad=20)
fig.savefig(os.path.normpath(os.path.join(sonpath,'legend'+str(p)+'.png')), transparent=False, format='png')
del fig, ax, cs, cb
# =========================================================
humutils.make_kml(llcrnrlon=glon.min(), llcrnrlat=glat.min(),
urcrnrlon=glon.max(), urcrnrlat=glat.max(),
figs=[os.path.normpath(os.path.join(sonpath,'map'+str(p)+'.png'))],
colorbar=os.path.normpath(os.path.join(sonpath,'legend'+str(p)+'.png')),
kmzfile=os.path.normpath(os.path.join(sonpath,'GroundOverlay'+str(p)+'.kmz')),
name='Sidescan Intensity')
except:
print("error: map could not be created...")
#y1 = np.min(glat)-0.001
#x1 = np.min(glon)-0.001
#y2 = np.max(glat)+0.001
#x2 = np.max(glon)+0.001
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(humlon)-0.001, llcrnrlat=np.min(glat)-0.001,
urcrnrlon=np.max(humlon)+0.001, urcrnrlat=np.max(glat)+0.001)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
#finally:
# print "error: map could not be created..."
#if dogrid==1:
gx,gy = map.projtran(glon, glat)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
#if dogrid==1:
if 2>1:
if datm.size > 25000000:
print("matrix size > 25,000,000 - decimating by factor of 5 for display")
map.pcolormesh(gx[::5,::5], gy[::5,::5], datm[::5,::5], cmap='gray', vmin=np.nanmin(datm), vmax=scalemax) #vmax=np.nanmax(datm)
else:
map.pcolormesh(gx, gy, datm, cmap='gray', vmin=np.nanmin(datm), vmax=scalemax) #vmax=np.nanmax(datm)
del datm, dat
else:
## draw point cloud
x,y = map.projtran(humlon, humlat)
map.scatter(x.flatten(), y.flatten(), 0.5, merge.flatten(), cmap='gray', linewidth = '0')
#map.drawmapscale(x1+0.001, y1+0.001, x1, y1, 200., units='m', barstyle='fancy', labelstyle='simple', fontcolor='k') #'#F8F8FF')
#map.drawparallels(np.arange(y1-0.001, y2+0.001, 0.005),labels=[1,0,0,1], linewidth=0.0, rotation=30, fontsize=8)
#map.drawmeridians(np.arange(x1, x2, 0.002),labels=[1,0,0,1], linewidth=0.0, rotation=30, fontsize=8)
custom_save2(sonpath,'map_imagery'+str(p))
del fig
del humlat, humlon
return res #return the new resolution
class MASB(object):
def __init__(self, datadict, max_r, denoise=None, denoise_delta=None, detect_planar=None):
self.manager = Manager()
self.D = datadict # dict of numpy arrays
self.m, self.n = datadict['coords'].shape
if datadict.has_key('coords_in_buffer'):
self.kd_tree = KDTree(concatenate([self.D['coords'], self.D['coords_in_buffer']]))
else:
self.kd_tree = KDTree(self.D['coords'])
self.SuperR = max_r
self.denoise = denoise
self.denoise_delta = denoise_delta
self.detect_planar = detect_planar
def compute_sp(self):
from Queue import Queue
queue = Queue()
datalen = len(self.D['coords'])
self(queue,0,datalen, True, False)
self(queue,0,datalen, False, False)
return queue.get() + queue.get()
def compute_balls(self, num_processes=cpu_count()):
datalen = len(self.D['coords'])
n = num_processes/2 # we are spawning 2 processes (inner and outer ma) per n
batchsize = datalen/n
chunk_bounds = []
end=0
for i in xrange(n-1):
start = end
end = (i+1)* batchsize -1
chunk_bounds.append( (start, end) )
start, end = end, datalen
chunk_bounds.append((start, end))
print "chunking at:", chunk_bounds
jobs = []
queue = self.manager.Queue()
t1 = time()
for s,e in chunk_bounds:
p1 = Process(target=self, args=(queue,s,e, True, False))
p1.start()
jobs.append(p1)
p2 = Process(target=self, args=(queue,s,e, False, False))
p2.start()
jobs.append(p2)
result = []
for j in jobs:
j.join()
res = queue.get()
result.append(res)
t2 = time()
print "Finished ma computation in {} s".format(t2-t1)
result.sort(key=lambda item: (item[1], item[0]))
self.D['ma_coords_out'] = concatenate([ma_coords for start, inner, ma_coords, ma_f2 in result[:n] ])
# self.D['ma_radii_out'] = concatenate([ma_radii for start, inner, ma_coords, ma_radii, ma_f2, shrinkhist in result[:n] ])
self.D['ma_f2_out'] = concatenate([ma_f2 for start, inner, ma_coords, ma_f2 in result[:n] ])
# self.D['ma_shrinkhist_out'] = list(chain(*[shrinkhist for start, inner, ma_coords, ma_radii, ma_f2, shrinkhist in result[:n] ]))
self.D['ma_coords_in'] = concatenate([ma_coords for start, inner, ma_coords, ma_f2 in result[n:] ])
# self.D['ma_radii_in'] = concatenate([ma_radii for start, inner, ma_coords, ma_radii, ma_f2, shrinkhist in result[n:] ])
self.D['ma_f2_in'] = concatenate([ma_f2 for start, inner, ma_coords, ma_f2 in result[n:] ])
# self.D['ma_shrinkhist_in'] = list(chain(*[shrinkhist for start, inner, ma_coords, ma_radii, ma_f2, shrinkhist in result[n:] ]))
print "Finished datamerging in {} s".format(time()-t2)
# @autojit
# @profile
def __call__(self, queue, start, end, inner=True, verbose=False):
"""Balls shrinking algorithm. Set `inner` to False when outer balls are wanted."""
print 'processing', start, end, "inner:", inner#, hex(id(self.kd_tree)), hex(id(self.D))
m = end-start
ma_coords = zeros((m, self.n), dtype=np.float32)
ma_coords[:] = nan
# ma_radii = zeros(m)
# ma_radii[:] = nan
ma_f2 = zeros(m, dtype=np.uint32)
ma_f2[:] = nan
if self.denoise != None:
self.denoise = (math.pi/180)*self.denoise
if self.denoise_delta != None:
self.denoise_delta = (math.pi/180)*self.denoise_delta
if self.detect_planar != None:
self.detect_planar = (math.pi/180)*self.detect_planar
# q_history_list = []
ZeroDivisionError_cnt = 0
for i, pi in enumerate(xrange(start,end)):
p, n = self.D['coords'][pi], self.D['normals'][pi]
# print "for", p, n
if not inner:
n = -n
# use the previous point as initial estimate for q
q=p
# but, when approximating 1st point initialize q with random point not equal to p
# if i==0:
# while equal(q,p):
# random_index = int(rand(1)*self.D['coords'].shape[0])
# q = self.D['coords'][random_index]
# r = compute_radius(p,n,q)
# forget optimization of r:
r=self.SuperR
if verbose: print 'initial r: ' + str(r)
r_ = None
c = None
j = -1
q_i = None
q_history = []
while True:
j+=1
# initialize r on last found radius
if j>0:
r = r_
elif j==0 and i>0:
r = r
# compute ball center
c = p - n*r
q_i_previous = q_i
# find closest point to c and assign to q
dists, results = self.kd_tree.query(array([c]), k=2)
candidate_c = self.D['coords'][results]
q = candidate_c[0][0]
q_i = results[0][0]
# What to do if closest point is p itself?
if equal(q,p):
# 1) if r==SuperR, apparantly no other points on the halfspace spanned by -n => that's an infinite ball
if r == self.SuperR: break
# 2) otherwise just pick the second closest point
else:
q = candidate_c[0][1]
q_i = results[0][1]
# q_history.append(q_i)
# compute new candidate radius r_
try:
r_ = compute_radius(p,n,q)
except ZeroDivisionError:
ZeroDivisionError_cnt += 1
r_ = self.SuperR+1
# if r_ < 0 closest point was on the wrong side of plane with normal n => start over with SuperRadius on the right side of that plance
if r_ < 0.: r_ = self.SuperR
# if r_ > SuperR, stop now because otherwise in case of planar surface point configuration, we end up in an infinite loop
elif r_ > self.SuperR:
r_ = self.SuperR
break
if verbose: print 'current ball: ' + str(i) +' - ' + str(r_)
c_ = p - n*r_
if self.denoise != None:
if math.acos(cos_angle(p-c, q-c)) < self.denoise and j>0 and r_>norm(q-p):
r_=r
break
if self.denoise_delta != None and j>0:
theta_now = math.acos(cos_angle(p-c_, q-c_))
q_previous = self.D['coords'][q_i_previous]
theta_prev = math.acos(cos_angle(p-c_, q_previous-c_))
if theta_prev-theta_now > self.denoise_delta and r_>norm(q-p):
# keep previous radius:
r_=r
q_i = q_i_previous
break
if self.detect_planar != None:
if math.acos( cos_angle(q-p, -n) ) > self.detect_planar:
r_= self.SuperR
break
# stop iteration if r has converged
if abs(r - r_) < 0.01:
break
# stop iteration if this looks like an infinite loop:
if j > 30:
if verbose: print "breaking possible infinite loop at j=30"
break
if r_ >= self.SuperR or r_ == None:
pass
else:
# ma_radii[i] = r_
ma_coords[i] = c
ma_f2[i] = q_i
# q_history_list.append(q_history[:-1])
result = ( start, inner, ma_coords, ma_f2 )
queue.put( result )
print '{} ZeroDivisionErrors'.format(ZeroDivisionError_cnt)
print "done!", start, inner, "len:", ma_coords.shape