def SelectionUpdateWVT(event):
if event.final:
calculateCWT.button_type = "warning"
# Get x and y points of selection region
lassoRegionX = event.geometry['x']
lassoRegionY = event.geometry['y']
# Get current spectrogram data
wvpower = wvt_ds.data['image'][0]
# Find points in wavelet power spectrogram inside selection region
X, Y = np.meshgrid(np.arange(wvpower.shape[1]),
np.arange(wvpower.shape[0]))
powerPts = np.vstack((Y.flatten(), X.flatten())).T
polygon = [(int(row), int(col))
for row, col in zip(lassoRegionY, lassoRegionX)
] #flip X and Y for row/col orientation
path = matplotlib.path.Path(polygon)
selected = path.contains_points(powerPts)
selected = selected.reshape((wvpower.shape[0], wvpower.shape[1]))
# Update image data source
#wvt_ds.data = {'image': [np.multiply(selected, wvpower)], 'dw': [wvpower.shape[1]], 'dh': [wvpower.shape[0]]}
wvt_ds.data['image'] = [np.multiply(selected, wvpower)]
calculateCWT.button_type = "success"
def segment(self):
print('Segmenting plane: ', self.plane_ind)
x = np.arange(0, 1024)
y = np.arange(0, 1024)
xv, yv = np.meshgrid(x, y, indexing='xy')
points = np.hstack((xv.reshape((-1, 1)), yv.reshape((-1, 1))))
path = matplotlib.path.Path(self.img.pt_lst)
mask = path.contains_points(points)
mask = mask.reshape(1024, 1024).astype('float64')
self.data_masked[self.plane_ind, :, :][mask == 0] = 0
self.mask_arr[self.plane_ind] = mask
self.img.setImage(self.data_masked[self.plane_ind, :, :],
autoLevels=False,
lut=self.lut,
levels=[0, 255])
self.update_text_box()
self.img.pt_lst = []
def __getitem__(self, idx):
idx = idx % len(self.img_fns)
img_fn = self.img_fns[idx]
ann_fn = self.ann_fns[idx]
img = Image.open(self.data_root + img_fn)
with open(self.anns_root + ann_fn) as f:
# print(ann_fn)
data_dict = {}
annotations = json.load(f)
# print(len(annotations))
for annotation in annotations:
h, w = 1080, 1920
mask_attrs_list = list(
filter(
lambda x: True, #x['region_attributes']['name'].strip() == 'smoke',
annotation['regions']
)
)
# print(mask_attrs_list)
# print(annotation)
mask_attrs = mask_attrs_list[0]['shape_attributes']
pts = np.array(list(zip(mask_attrs['all_points_x'], mask_attrs['all_points_y'])))
x, y = np.meshgrid(np.arange(w), np.arange(h))
x, y = x.flatten(), y.flatten()
meshgrid = np.vstack((x,y)).T
path = Path(pts)
mask = path.contains_points(meshgrid)
mask = mask.reshape((h,w)).astype(np.float32)
data_dict['mask'] = mask
assert (data_dict['mask'].sum() > 0), "No points in mask"
mask = torch.from_numpy(mask)
mask = tf.to_pil_image(mask)
mask = tf.resize(mask, (512, 1024))
mask = tf.to_tensor(mask)
img = tf.resize(img, (512, 1024))
img = tf.to_tensor(img).squeeze()
cur_img_mean = torch.mean(img, axis=(1,2))
for c in range(3):
img[c] -= (self.other_data_mean[c]- cur_img_mean[c])
img = img
output_dict = {
"idx": idx,
"input_img": img,
"target_mask": mask
}
return output_dict
def __getitem__(self, idx):
idx = idx % len(self.img_fns)
img_fn = self.img_fns[idx]
ann_fn = self.ann_fns[idx]
img = Image.open(self.data_root + img_fn)
with open(self.anns_root + ann_fn) as f:
# print(img_fn)
# print(ann_fn)
data_dict = {}
annotations = json.load(f)
# print(len(annotations))
for annotation in annotations:
h, w = 1080, 1920
# h, w = self.image_size
mask_attrs_list = list(
filter(
lambda x: True,#'smoke' in x['region_attributes']['name'].strip(),
annotation['regions']
)
)
# print(mask_attrs_list)
# print(annotation)
mask_attrs = mask_attrs_list[0]['shape_attributes']
pts = np.array(list(zip(mask_attrs['all_points_x'], mask_attrs['all_points_y'])))
# print(len(mask_attrs['all_points_x']), len(mask_attrs['all_points_y']), pts.shape)
x, y = np.meshgrid(np.arange(w), np.arange(h))
x, y = x.flatten(), y.flatten()
meshgrid = np.vstack((x,y)).T
path = Path(pts)
mask = path.contains_points(meshgrid)
mask = mask.reshape((h,w)).astype(np.float32)
# mask = np.zeros((h,w))
# cv2.fillPoly(mask, [pts], (255))
data_dict['mask'] = mask
assert (np.count_nonzero(mask) > 0), "No points in mask"
mask = tf.to_pil_image(mask)
mask = tf.resize(mask, self.image_size)
mask = tf.to_tensor(mask)
img = tf.resize(img, self.image_size)
img = tf.to_tensor(img)
# print("img.shape after unsqueezing ",img.shape)
img = (img - img.min())*255/(img.max()-img.min())
img = img[[2,1,0]]
for c in range(3):
img[c,:,:] -= self.img_mean[c]
return img, mask, str(idx)
def mask_inpolygon(hdu, polygon, axis=('x', 'y')):
logger.info('(mask_inpolygon) polygon={polygon}, axis={axis}'.format(**locals()))
logger.info('(mask_inpolygon) start calculation')
wcs = astropy.wcs.WCS(hdu.header)
polygon_p = wcs.all_world2pix(polygon, 0)
path = matplotlib.path.Path(polygon_p)
ax1 = numpy.arange(hdu.data.shape[1])
ax2 = numpy.arange(hdu.data.shape[0])
ax12 = numpy.array([_.ravel() for _ in numpy.meshgrid(ax1, ax2)]).T
mask = path.contains_points(ax12).reshape(hdu.data.shape).astype(int)
logger.info('(mask_inpolygon) done')
new_header = hdu.header.copy()
new_hdu = astropy.io.fits.PrimaryHDU(mask, new_header)
return new_hdu
def reduced_bz_E_mask(data, S, e_cut, scale_zone=False):
symmetry_points, _ = data.S.symmetry_points()
symmetry = bz_symmetry(data.S.iter_own_symmetry_points)
point_names = _POINT_NAMES_FOR_SYMMETRY[symmetry]
# bz_dims = tuple(d for d in data.dims if d in list(symmetry_points.values())[0][0].keys())
symmetry_points, _ = data.S.symmetry_points()
points = {k: v[0] for k, v in symmetry_points.items() if k in point_names}
coords_by_point = {
k: np.array(
[v.get(d, 0) for d in data.dims if d in v.keys() or d == 'eV'])
for k, v in points.items()
}
dx_to = reduced_bz_axis_to(data, S, include_E=True)
if scale_zone:
dx_to = dx_to * 3
dE = np.array([0 if d != 'eV' else e_cut for d in data.dims])
poly_points = np.array([
coords_by_point['G'],
coords_by_point['G'] + dx_to,
coords_by_point['G'] + dx_to + dE,
coords_by_point['G'] + dE,
])
skip_col = None
for i in range(poly_points.shape[1]):
if np.all(poly_points[:, i] == poly_points[0, i]):
skip_col = i
assert skip_col is not None
selector_val = poly_points[0, skip_col]
poly_points = np.concatenate(
(poly_points[:, 0:skip_col], poly_points[:, skip_col + 1:]), axis=1)
selector = dict()
selector[data.dims[skip_col]] = selector_val
sdata = data.sel(**selector, method='nearest')
path = matplotlib.path.Path(poly_points)
grid = np.array([
a.ravel() for a in np.meshgrid(*[data.coords[d] for d in sdata.dims],
indexing='ij')
]).T
mask = path.contains_points(grid)
mask = np.reshape(mask, sdata.data.shape)
return mask
def mask_inpolygon(hdu, polygon, axis=('x', 'y')):
logger.info(
'(mask_inpolygon) polygon={polygon}, axis={axis}'.format(**locals()))
logger.info('(mask_inpolygon) start calculation')
wcs = astropy.wcs.WCS(hdu.header)
polygon_p = wcs.all_world2pix(polygon, 0)
path = matplotlib.path.Path(polygon_p)
ax1 = numpy.arange(hdu.data.shape[1])
ax2 = numpy.arange(hdu.data.shape[0])
ax12 = numpy.array([_.ravel() for _ in numpy.meshgrid(ax1, ax2)]).T
mask = path.contains_points(ax12).reshape(hdu.data.shape).astype(int)
logger.info('(mask_inpolygon) done')
new_header = hdu.header.copy()
new_hdu = astropy.io.fits.PrimaryHDU(mask, new_header)
return new_hdu
def query(self, args):
c, f, v = args
df = self._main_model.object_data
roi_class = False
if (c == "Size in pixels"):
try:
v = float(v)
except ValueError:
return None
if f == "<":
return df[operator.lt(df[c], v)]
elif f == ">":
return df[operator.gt(df[c], v)]
elif f == "<=":
return df[operator.le(df[c], v)]
elif f == ">=":
return df[operator.ge(df[c], v)]
elif f == "=":
return df[operator.eq(df[c], v)]
else:
if c in self._main_model.rois.keys():
path = self.roi_to_path(self._main_model.rois[c][1])
x = df['Center of the object_1'].values
y = df['Center of the object_0'].values
centers = np.transpose(np.vstack([x, y]))
contains_centers = path.contains_points(centers)
roi_class = True
if (f == "INCLUDE") or (f == "="):
if roi_class:
return df.loc[contains_centers]
else:
return df[df['Predicted Class'] == c]
elif f == "NOT INCLUDE":
if roi_class:
return df.loc[[not x for x in contains_centers]]
return df[df['Predicted Class'] != c]
return None
def pointsInsidePolygon(points, polygon):
"""
Return for each point if it lies inside the given polygon.
:param points: shape (n,2)
:param polygon: unclosed, shape (n,2)
:rtype: boolean ndarray of shape (n,)
"""
path = matplotlib.path.Path(polygon)
try:
# only in mpl >= 1.2.0
isInside = path.contains_points(points)
except AttributeError:
# we are on an older version, try nxutils instead
# nxutils is deprecated in mpl >= 1.2.0
isInside = matplotlib.nxutils.points_inside_poly(points, polygon)
return isInside
def pointsInsidePolygon(points, polygon):
"""
Return for each point if it lies inside the given polygon.
:param points: shape (n,2)
:param polygon: unclosed, shape (n,2)
:rtype: boolean ndarray of shape (n,)
"""
path = matplotlib.path.Path(polygon)
try:
# only in mpl >= 1.2.0
isInside = path.contains_points(points)
except AttributeError:
# we are on an older version, try nxutils instead
# nxutils is deprecated in mpl >= 1.2.0
isInside = matplotlib.nxutils.points_inside_poly(points, polygon)
return isInside
def reduced_bz_mask(data, **kwargs):
symmetry_points, _ = data.S.symmetry_points()
bz_dims = tuple(d for d in data.dims
if d in list(symmetry_points.values())[0][0].keys())
poly_points = reduced_bz_poly(data, **kwargs)
extra_dims_shape = tuple(
len(data.coords[d]) for d in data.dims if d in bz_dims)
path = matplotlib.path.Path(poly_points)
grid = np.array([
a.ravel()
for a in np.meshgrid(*[data.coords[d] for d in bz_dims], indexing='ij')
]).T
mask = path.contains_points(grid)
mask = np.reshape(mask, extra_dims_shape)
return mask
def place_on_subdomain(self, domain: np.ndarray):
"""
Places this shape in the center of the supplied (sub)domain
:param domain: 2D numpy array of type np.bool
:return: A copy of the domain, with this shape placed on it, centered horizontally and vertically
"""
geometry = self.get_shape_geometry(domain)
xy_stacked = np.array(geometry.boundary.xy).T
hull = scipy.spatial.ConvexHull(xy_stacked)
# Map hull onto a boolean array.
path = matplotlib.path.Path(xy_stacked[hull.vertices])
x, y = np.meshgrid(np.arange(domain.shape[0]),
np.arange(domain.shape[1]))
x, y = x.flatten(), y.flatten()
points = np.vstack((y, x)).T
mask = path.contains_points(points)
mask = mask.reshape(domain.shape)
return mask
def __getitem__(self, idx):
idx = idx % len(self.img_fns)
img_fn = self.img_fns[idx]
ann_fn = self.ann_fns[idx]
img = Image.open(self.data_root + img_fn)
with open(self.anns_root + ann_fn) as f:
data_dict = {}
annotations = json.load(f)
for annotation in annotations:
h, w = 1080, 1920
mask_attrs = list(filter(lambda x: x['region_attributes']['name'].strip() in ['smoke', 'smoke1', ''], annotation['regions']))[0]['shape_attributes']
pts = np.array(list(zip(mask_attrs['all_points_x'], mask_attrs['all_points_y'])))
x, y = np.meshgrid(np.arange(w), np.arange(h))
x, y = x.flatten(), y.flatten()
meshgrid = np.vstack((x,y)).T
path = Path(pts)
mask = path.contains_points(meshgrid)
mask = mask.reshape((h,w)).astype(np.float32)
data_dict['mask'] = mask
assert (data_dict['mask'].sum() > 0), "No points in mask"
mask = tf.to_pil_image(mask)
mask = tf.resize(mask, (512, 1024))
mask = tf.to_tensor(mask)
img = tf.resize(img, (512, 1024))
img = tf.to_tensor(img)
output_dict = {
"idx": idx,
"input_img": img,
"target_mask": mask
}
return output_dict
def pvtu_timeseries_flowline(x,y,DIR,fileprefix,variables,inputsdir='none',layer='surface',debug=False,t1=1,t2=np.Inf):
from scipy.interpolate import griddata
import numpy as np
import matplotlib.path
# First get number of timesteps
files = os.listdir(DIR)
totsteps = 0
for file in files:
if file.startswith(fileprefix) and file.endswith('.pvtu'):
timestep = int(file[len(fileprefix):-5])
numfilelen = len(file)-len('.pvtu')-len(fileprefix)
if timestep > totsteps:
totsteps = timestep
elif file.startswith(fileprefix) and file.endswith('.pvtu.tar.gz'):
timestep = int(file[-16:-12])
numfilelen = len(file)-len('.pvtu.tar.gz')-len(fileprefix)
if timestep > totsteps:
totsteps = timestep
if totsteps == 0:
sys.exit("Check that file "+DIR+fileprefix+" actually exists.")
if t2 > totsteps:
t2 = totsteps
print "Loading "+str(t2-t1+1)+" out of "+str(totsteps)+" timesteps"
if layer == 'surface':
freesurfacevar = 'zs top'
elif layer == 'bed' and 'zs bottom' not in variables:
freesurfacevar = 'zs bottom'
if freesurfacevar not in variables:
variables.append(freesurfacevar)
if not(inputsdir == 'none'):
mesh_extent_x = np.loadtxt(inputsdir+'/mesh_timeseries_x.dat')
mesh_extent_y = np.loadtxt(inputsdir+'/mesh_timeseries_y.dat')
for i in range(0,t2-t1+1):
t = i+t1
# Get filename
pvtufile = '{0}{2:0{1}d}{3}'.format(fileprefix,numfilelen,t,'.pvtu')
if debug:
print "Loading file "+pvtufile
# Get data
if i==0:
reader = 'none'
data,reader = pvtu_file(DIR+pvtufile,variables,reader=reader,returnreader=True)
surf = data[data[freesurfacevar] != 0]
del data
# If first timestep, set up output variable name
if i==0:
varnames = list(data.dtype.names)
varnames.remove('Node Number')
types = []
for var in varnames:
types.append(np.float64)
dataflow = np.zeros([len(x),t2-t1+1], dtype=zip(varnames,types))
for var in varnames:
dataflow[var][:,i] = float('nan')
if not(inputdirs == 'none'):
ind = np.where(mesh_extent_x[:,t-1] != 0)
path = matplotlib.path.Path(np.column_stack([mesh_extent_x[:,t-1],mesh_extent_y[:,t-1]]))
inmesh = path.contains_points(np.column_stack([x,y]))
else:
inmesh = np.arange(0,len(x))
for var in varnames:
dataflow[var][inmesh,i] = griddata((surf['x'],surf['y']),surf[var],(x[inmesh],y[inmesh]))
del surf
return dataflow
def streakMask(self, streak_file, addWidth=0., addLength=100., maxExtrapolate=0):
'''
Produce a list of pixels in the image that should be masked for
streaks in the input table. streaktab is the output table of new
streaks to add image is a FITS HDU, with header and image data
addWidth is additional number of pixels to add to half-width
addLength is length added to each end of streak (pixels)
Returns:
ypix, xpix: 1d arrays with indices of affected pixels
nStreaks: number of new streaks masked
'''
# Read the streaks table first
try:
tab = fitsio.FITS(streak_file)
streaktab = tab[1].read()
except:
logger.error('Could not read streak file {:s}'.format(streak_file))
sys.exit(1)
image_header = self.sci.header
image_data = self.sci.data
# Pixscale in degrees
pixscale = astrometry.get_pixelscale(image_header, units='arcsec') / 3600.
shape = image_data.shape
# # Due to a bug in fitsio 1.0.0rc1+0, we need to clean up the
# # header before feeding it to wcsutil and remove the 'None' and other problematic items
# for k in image_header:
# # Try to access the item, if failed we hace to remove it
# try:
# item = image_header[k]
# except:
# logger.info("Removing keyword: {:s} from header".format(k))
# image_header.delete(k)
w = wcsutil.WCS(image_header)
# WE NEED TO UPDATE THIS WHEN THE TABLE IS PER EXPNUM
use = np.logical_and(streaktab['expnum'] == image_header['EXPNUM'],
streaktab['ccdnum'] == image_header['CCDNUM'])
logger.info('{:d} streaks found to mask'.format(np.count_nonzero(use)))
nStreaks = 0
inside = None
for row in streaktab[use]:
if maxExtrapolate > 0:
if row['extrapolated'] and row['nearest'] > maxExtrapolate:
logger.info('Skipping extrapolated streak')
continue
width = row['width']
ra = np.array((row['ra1'], row['ra2']))
dec = np.array((row['dec1'], row['dec2']))
x, y = w.sky2image(ra, dec)
x1, x2, y1, y2 = x[0], x[1], y[0], y[1]
# Slope of the line, cos/sin form
mx = (x2 - x1) / np.hypot(x2 - x1, y2 -y1)
my = (y2 - y1) / np.hypot(x2 - x1, y2 -y1)
#displacement for width of streak:
wx = width / pixscale + addWidth
wy = wx * mx
wx = wx * -my
# grow length
x1 -= addLength * mx
x2 += addLength * mx
y1 -= addLength * my
y2 += addLength * my
# From Alex's immask routine: mark interior pixels
vertices = [(x1 + wx, y1 + wy), (x2 + wx, y2 + wy), (x2 - wx, y2 - wy), (x1 - wx, y1 - wy)]
vertices.append(vertices[0]) # Close the path
if inside is None:
# Set up coordinate arrays
yy, xx = np.indices(shape)
points = np.vstack((xx.flatten(), yy.flatten())).T
path = matplotlib.path.Path(vertices)
inside = path.contains_points(points)
else:
# use logical_and for additional streaks
path = matplotlib.path.Path(vertices)
inside = np.logical_or(inside, path.contains_points(points))
nStreaks = nStreaks + 1
logger.info('Masked {:d} new streaks'.format(nStreaks))
# Make the list of masked pixels
if inside is None:
ymask, xmask = np.array(0, dtype=int), np.array(0, dtype=int)
else:
ymask, xmask = np.nonzero(inside.reshape(shape))
logger.info('Setting bits in MSK image for STREAK: {:d}'.format(parse_badpix_mask('STREAK')))
self.sci.mask[ymask, xmask] |= parse_badpix_mask('STREAK')
def interpolate_from_unstructured(
self,
*,
fill_value=np.nan,
structured_output=True,
unstructured_dim_name="unstructured_dim",
**kwargs,
):
"""Interpolate DataArray onto new grids of some existing coordinates
Parameters
----------
**kwargs : (str, array)
Each keyword is the name of a coordinate in the DataArray, the argument is a
1d array giving the values of that coordinate on the output grid
fill_value : float, default np.nan
fill_value passed through to scipy.interpolation.griddata
structured_output : bool, default True
If True, treat output coordinates values as a structured grid.
If False, output coordinate values must all have the same length and are not
broadcast together.
unstructured_dim_name : str, default "unstructured_dim"
Name used for the dimension in the output that replaces the dimensions of
the interpolated coordinates. Only used if structured_output=False.
Returns
-------
DataArray
Data interpolated onto a new, structured grid
"""
da = self.data
if structured_output:
new_coords = {
name: xr.DataArray(values, dims=name)
for name, values in kwargs.items()
}
coord_arrays = tuple(
np.meshgrid(*[values for values in kwargs.values()],
indexing="ij"))
new_output_dims = [d for d in kwargs]
else:
new_coords = {
name: xr.DataArray(values, dims=unstructured_dim_name)
for name, values in kwargs.items()
}
coord_arrays = tuple(kwargs.values())
lengths = [len(c) for c in coord_arrays]
if np.any([x != lengths[0] for x in lengths[1:]]):
raise ValueError(
f"When structured_output=False, all the arrays of output coordinate"
f"values must have the same length. Got lengths "
f"{dict((name, len(coord)) for name, coord in kwargs.items())}"
)
new_output_dims = [unstructured_dim_name]
# Figure out number of dimensions in the coordinates to be interpolated
dims = set()
for coord in kwargs:
dims = dims.union(da[coord].dims)
dims = tuple(dims)
ndim = len(dims)
# dimensions that are not being interpolated
remaining_dims = tuple(d for d in da.dims if d not in dims)
# Select interpolation method
if ndim <= 2:
# "cubic" only available for 1d or 2d interpolation
method = "cubic"
else:
method = "linear"
# extend input coordinates to cover all dims, so we can flatten them
input_coords = []
for coord in kwargs:
data = da[coord]
missing_dims = tuple(set(dims) - set(data.dims))
expand = {dim: da.sizes[dim] for dim in missing_dims}
expand_positions = tuple(dims.index(d) for d in missing_dims)
da[coord] = data.expand_dims(expand, axis=expand_positions)
# scipy.interpolate.griddata requires the axis being interpolated to be the first
# one, so stack together 'dims', and then transpose so the resulting stacked
# dimension is the first
dims_name_list = [d for d in da.dims if d in dims]
stacked_dim_name = "stacked_" + "_".join(dims_name_list)
stacked = da.stack({stacked_dim_name: dims_name_list})
stacked = stacked.transpose(*((stacked_dim_name, ) + remaining_dims),
transpose_coords=True)
result = scipy_griddata(
tuple(stacked[coord] for coord in kwargs),
stacked,
coord_arrays,
method=method,
fill_value=fill_value,
)
# griddata only sets points outside the 'convex hull' to fill_value
# Nicer to set all points outside the grid boundaries to fill_value
###################################################################
boundaries = self.get_bounding_surfaces(coords=[c for c in kwargs])
points = np.stack(coord_arrays, axis=-1)
# boundaries[0] is the outer boundary
path = matplotlib.path.Path(boundaries[0], closed=True, readonly=True)
is_contained = path.contains_points(points.reshape([-1, 2]))
is_contained = is_contained.reshape(coord_arrays[0].shape +
(1, ) * len(remaining_dims))
result = np.where(is_contained, result, fill_value)
# boundaries[1] is the inner boundary if it exists
if len(boundaries) > 1:
path = matplotlib.path.Path(boundaries[1],
closed=True,
readonly=True)
is_contained = path.contains_points(points.reshape([-1, 2]))
is_contained = is_contained.reshape(coord_arrays[0].shape +
(1, ) * len(remaining_dims))
result = np.where(is_contained, fill_value, result)
if len(boundaries) > 2:
raise ValueError(
f"Found {len(boundaries)} boundaries, expected at most 2")
# Create DataArray to return, with as much metadata as possible retained
########################################################################
new_coords.update({
name: array
for name, array in stacked.coords.items()
if stacked_dim_name not in array.dims
})
result = xr.DataArray(
result,
dims=new_output_dims + list(remaining_dims),
coords=new_coords,
name=da.name,
attrs=da.attrs,
)
return result
# Load beds
x, y, zbed3 = elmerreadlib.input_file(DIR_bed3 + 'inputs/bedrock.xy')
x, y, zbed4 = elmerreadlib.input_file(DIR_bed4 + 'inputs/bedrock.xy')
x, y, zbed5 = elmerreadlib.input_file(DIR_bed5 + 'inputs/bedrock.xy')
x, y, zbed8 = elmerreadlib.input_file(DIR_bed8 + 'inputs/bedrock.xy')
xgrid, ygrid = np.meshgrid(x, y)
# Load mesh extents for cropping
mesh_extent = np.loadtxt(DIR_bed3 + 'inputs/mesh_extent.dat')
mesh_hole1 = np.loadtxt(DIR_bed3 + 'inputs/mesh_hole1.dat')
mesh_hole2 = np.loadtxt(DIR_bed3 + 'inputs/mesh_hole2.dat')
path = matplotlib.path.Path(
np.column_stack([mesh_extent[:, 0], mesh_extent[:, 1]]))
inmesh = path.contains_points(
np.column_stack([xgrid.flatten(), ygrid.flatten()]))
inmesh = inmesh.reshape(len(y), len(x))
path = matplotlib.path.Path(
np.column_stack([mesh_hole1[:, 0], mesh_hole1[:, 1]]))
inhole1 = path.contains_points(
np.column_stack([xgrid.flatten(), ygrid.flatten()]))
inhole1 = inhole1.reshape(len(y), len(x))
path = matplotlib.path.Path(
np.column_stack([mesh_hole2[:, 0], mesh_hole2[:, 1]]))
inhole2 = path.contains_points(
np.column_stack([xgrid.flatten(), ygrid.flatten()]))
inhole2 = inhole2.reshape(len(y), len(x))
del path
def weights(self):
"""(M,N) ndarray. Fractions of pixel areas within the aperture for
the MxN grid defined by `ApertureBase.extent`. Areas for partial
pixels along the aperture border are approximated by dividing each
border pixel into ``ApertureBase.nsub**2`` subpixels. Partial
pixels are not computed if `ApertureBase.nsub` is 1. Read only.
"""
# Pixels with centers within the aperture
x, y = self.grid
pix = np.vstack(np.broadcast_arrays(x, y)).reshape(2, -1).T
weights = (self.path.contains_points(pix)
.reshape(y.size, x.size).astype(float))
# Partial pixels
if self.nsub > 1:
# Shift the aperture to determine the border pixels.
#
# Ideally, additional contains tests would only have to be
# performed for a dialated and an eroded version of the
# aperture. Implementing a buffering algorithm for arbitrary
# polygons is not easy, however, and I don't want to add
# Shapely as a dependency for this module. Instead, the method
# below shifts the aperture in eight directions and records the
# changes in the pixels' coverage. The code is much simpler,
# but performing eight contains tests instead of two means that
# the code is also much slower.
shift_size = 0.5
shift_list = np.array(
[[1, 0], [0, 1], [-1, 0], [-1, 0],
[0, -1], [0, -1], [1, 0], [1, 0]]) * shift_size
w = weights.ravel().astype('bool')
bordertest = np.zeros(w.shape, dtype='bool')
xy = self._xy.copy()
path = matplotlib.path.Path(xy, closed=True)
for shift in shift_list:
path.vertices += shift
bordertest += bordertest | w != path.contains_points(pix)
bordertest = bordertest.reshape(y.size, x.size).astype(float)
# Lower-left corners of border pixels
i, j = np.where(bordertest)
x, y = self.extent[0] + j, self.extent[2] + i
# Centers of subpixels with respect to a generic pixel
subx = (np.arange(self.nsub).reshape(1, -1) + 0.5) / self.nsub
suby = (np.arange(self.nsub).reshape(-1, 1) + 0.5) / self.nsub
# Centers of subpixels in all border pixels
subx = x[:,None,None] + subx # (len(i), 1, nsub)
suby = y[:,None,None] + suby # (len(i), nsub, 1)
# Subpixels with centers within the aperture
subpix = np.vstack(np.broadcast_arrays(subx, suby)).reshape(2, -1).T
subweights = (self.path.contains_points(subpix)
.reshape(np.broadcast(subx, suby).shape))
# Refined pixel weights along the border
kwargs = dict(axis=(1, 2), dtype=float)
weights[i, j] = np.sum(subweights, **kwargs) / self.nsub**2
return weights
def maskPixels(self, path):
XY = np.dstack((self.grid_x, self.grid_y))
XY_flat = XY.reshape((-1, 2))
mask_flat = path.contains_points(XY_flat)
mask = mask_flat.reshape(self.grid_x.shape)
return mask
def pvtu_timeseries_grid(x,y,DIR,fileprefix,variables,inputsdir,layer='surface',debug=False,crop_mesh=True,t1=1,t2=np.Inf):
from scipy.interpolate import griddata
import numpy as np
import matplotlib.path
import gc
# First get number of timesteps
files = os.listdir(DIR)
xgrid,ygrid = np.meshgrid(x,y)
totsteps = 0
for file in files:
if file.startswith(fileprefix) and file.endswith('.pvtu'):
timestep = int(file[len(fileprefix):-5])
numfilelen = len(file)-len('.pvtu')-len(fileprefix)
if timestep > totsteps:
totsteps = timestep
elif file.startswith(fileprefix) and file.endswith('.pvtu.tar.gz'):
timestep = int(file[-16:-12])
numfilelen = len(file)-len('.pvtu.tar.gz')-len(fileprefix)
if timestep > totsteps:
totsteps = timestep
if totsteps == 0:
sys.exit("Check that file "+DIR+fileprefix+" actually exists.")
if t2 > totsteps:
t2 = totsteps
print "Loading "+str(t2-t1+1)+" out of "+str(totsteps)+" timesteps"
if layer == 'surface':
freesurfacevar = 'zs top'
elif layer == 'bed' and 'zs bottom' not in variables:
freesurfacevar = 'zs bottom'
if freesurfacevar not in variables:
variables.append(freesurfacevar)
if crop_mesh:
mesh_extent_x = np.loadtxt(inputsdir+'/mesh_timeseries_x.dat')
mesh_extent_y = np.loadtxt(inputsdir+'/mesh_timeseries_y.dat')
try:
mesh_extent_x = np.loadtxt(inputsdir+'/mesh_timeseries_x.dat')
mesh_extent_y = np.loadtxt(inputsdir+'/mesh_timeseries_y.dat')
path = matplotlib.path.Path(np.column_stack([mesh_extent_x[:,0],mesh_extent_y[:,0]]))
inmesh = path.contains_points(np.column_stack([xgrid.flatten(),ygrid.flatten()]))
inmesh = inmesh.reshape(len(y),len(x))
except:
mesh_extent = np.loadtxt(inputsdir+'/mesh_extent.dat')
path = matplotlib.path.Path(np.column_stack([mesh_extent_x[:,0],mesh_extent_y[:,1]]))
inmesh = path.contains_points(np.column_stack([xgrid.flatten(),ygrid.flatten()]))
inmesh = inmesh.reshape(len(y),len(x))
try:
mesh_hole1 = np.loadtxt(inputsdir+'/mesh_hole1.dat')
mesh_hole2 = np.loadtxt(inputsdir+'/mesh_hole2.dat')
holes = True
path = matplotlib.path.Path(np.column_stack([mesh_hole1[:,0],mesh_hole1[:,1]]))
inhole1 = path.contains_points(np.column_stack([xgrid.flatten(),ygrid.flatten()]))
inhole1 = inhole1.reshape(len(y),len(x))
path = matplotlib.path.Path(np.column_stack([mesh_hole2[:,0],mesh_hole2[:,1]]))
inhole2 = path.contains_points(np.column_stack([xgrid.flatten(),ygrid.flatten()]))
inhole2 = inhole2.reshape(len(y),len(x))
del path
except:
holes = False
for i in range(0,t2-t1+1):
t = i+t1
# Get filename
pvtufile = '{0}{2:0{1}d}{3}'.format(fileprefix,numfilelen,t,'.pvtu')
if debug:
print "Loading file "+pvtufile
# Get data
if i==0:
reader='none'
data,reader = pvtu_file(DIR+pvtufile,variables,reader=reader,returnreader=True)
surf = data[data[freesurfacevar] != 0]
del data
# If first timestep, set up output variable name
if i==0:
varnames = list(surf.dtype.names)
varnames.remove('Node Number')
varnames.append('dh')
types = []
for var in varnames:
types.append(np.float64)
datagrid = np.zeros([len(y),len(x),t2-t1+1], dtype=zip(varnames,types))
del types
if crop_mesh:
ind = np.where(mesh_extent_x[:,t-1] != 0)
path = matplotlib.path.Path(np.column_stack([mesh_extent_x[:,t-1],mesh_extent_y[:,t-1]]))
inmesh = path.contains_points(np.column_stack([xgrid.flatten(),ygrid.flatten()]))
inmesh = inmesh.reshape(len(y),len(x))
del path
for var in varnames:
if var == 'dh':
if i > 0:
z_old = griddata((surf_last['x'],surf_last['y']),surf_last['z'],(surf['x'],surf['y']))
dh = surf['z']-z_old
datagrid[var][:,:,i] = griddata((surf['x'],surf['y']),dh,(xgrid,ygrid))
del surf_last
surf_last = np.array(surf)
else:
datagrid[var][:,:,i] = griddata((surf['x'],surf['y']),surf[var],(xgrid,ygrid))
#if crop_mesh:
datagrid[var][~inmesh,i] = float('nan')
if holes:
datagrid[var][inhole1,i] = float('nan')
datagrid[var][inhole2,i] = float('nan')
del surf,pvtufile
if crop_mesh:
del inmesh,ind
gc.collect()
return datagrid
def intersection(self, points):
path = matplotlib.path.Path(self.ROI, closed=False)
return int(path.contains_points(points, radius=1e-9)) * 2 - 1