def _read_dfs2_header(self):
if not os.path.isfile(self._filename):
raise Exception(f"file {self._filename} does not exist!")
self._dfs = DfsFileFactory.Dfs2FileOpen(self._filename)
self._dx = self._dfs.SpatialAxis.Dx
self._dy = self._dfs.SpatialAxis.Dy
self._nx = self._dfs.SpatialAxis.XCount
self._ny = self._dfs.SpatialAxis.YCount
self._read_header()
def _read_dfs2_header(self):
if not os.path.isfile(self._filename):
raise Exception(f"file {self._filename} does not exist!")
self._dfs = DfsFileFactory.Dfs2FileOpen(self._filename)
self._dx = self._dfs.SpatialAxis.Dx
self._dy = self._dfs.SpatialAxis.Dy
self._nx = self._dfs.SpatialAxis.XCount
self._ny = self._dfs.SpatialAxis.YCount
if self._dfs.FileInfo.TimeAxis.TimeAxisType == 4:
self._is_equidistant = False
self._read_header()
def find_nearest_element(
self, lon, lat,
):
"""Find index of closest element
Parameters
----------
lon: float
longitude
lat: float
latitude
Returns
-------
(int,int): indexes in y, x
"""
dfs = DfsFileFactory.Dfs2FileOpen(self._filename)
self._dfs = dfs
projection = self._dfs.FileInfo.Projection
axis = self._dfs.SpatialAxis
cart = Cartography(
projection.WKTString,
projection.Longitude,
projection.Latitude,
projection.Orientation,
)
# C# out parameters must be handled in special way
(_, xx, yy) = cart.Geo2Xy(lon, lat, 0.0, 0.0)
j = int(xx / axis.Dx + 0.5)
k = axis.YCount - int(yy / axis.Dy + 0.5) - 1
j = min(max(0, j), axis.XCount - 1)
k = min(max(0, k), axis.YCount - 1)
return k, j
def reproject(
self,
filename,
projectionstring,
dx,
dy,
longitude_origin=None,
latitude_origin=None,
nx=None,
ny=None,
orientation=0.0,
interpolate=True,
):
"""
Reproject and write results to a new dfs2 file
Parameters
----------
filename: str
location to write the reprojected dfs2 file
projectionstring: str
WKT string of new projection
dx: float
length of each grid in the x direction (projection units)
dy: float
length of each grid in the y direction (projection units)
latitude_origin: float, optional
latitude at origin of new grid, default same as original
longitude_origin: float, optional
longitude at origin of new grid, default same as original
nx: int, optional
n grid points in x direction, default same as original
ny: int, optional
n grid points in y direction, default same as original
orientation: float, optional
rotated grid, default 0.0
interpolate: bool, optional
interpolate to new grid, default true
Examples
--------
>>> dfs = Dfs2("input.dfs")
>>> dfs.reproject("out.dfs2", projectionstring="UTM-33",
... dx=200.0, dy=200.0,
... longitude_origin=12.0, latitude_origin=55.0,
... nx=285, ny=612)
"""
if nx is None:
nx = self.shape[2]
if ny is None:
ny = self.shape[1]
if latitude_origin is None:
latitude_origin = self.latitude
if longitude_origin is None:
longitude_origin = self.longitude
dfs2File = DfsFileFactory.Dfs2FileOpen(self._filename)
tool = Dfs2Reprojector(dfs2File, filename)
tool.Interpolate = interpolate
tool.SetTarget(
projectionstring,
longitude_origin,
latitude_origin,
orientation,
nx,
0.0,
dx,
ny,
0.0,
dy,
)
tool.Process()
def _open(self):
self._dfs = DfsFileFactory.Dfs2FileOpen(self._filename)
self._source = self._dfs
def read(self, items=None, time_steps=None):
"""
Read data from a dfs2 file
Parameters
---------
items: list[int] or list[str], optional
Read only selected items, by number (0-based), or by name
time_steps: int or list[int], optional
Read only selected time_steps
Returns
-------
Dataset
A dataset with data dimensions [t,y,x]
"""
dfs = DfsFileFactory.Dfs2FileOpen(self._filename)
self._dfs = dfs
self._source = dfs
nt = dfs.FileInfo.TimeAxis.NumberOfTimeSteps
items, item_numbers, time_steps = get_valid_items_and_timesteps(
self, items, time_steps
)
# Determine the size of the grid
axis = dfs.SpatialAxis
yNum = axis.YCount
xNum = axis.XCount
if nt == 0:
raise ValueError("Static files (with no dynamic items) are not supported.")
for t in time_steps:
if t > (nt - 1):
raise ValueError(f"Trying to read timestep {t}: max timestep is {nt-1}")
deleteValue = dfs.FileInfo.DeleteValueFloat
self._n_items = len(item_numbers)
data_list = []
for item in range(self._n_items):
data = np.ndarray(shape=(len(time_steps), yNum, xNum), dtype=float)
data_list.append(data)
t_seconds = np.zeros(len(time_steps), dtype=float)
# startTime = dfs.FileInfo.TimeAxis.StartDateTime
for i in range(len(time_steps)):
it = time_steps[i]
for item in range(self._n_items):
itemdata = dfs.ReadItemTimeStep(item_numbers[item] + 1, it)
src = itemdata.Data
d = to_numpy(src)
d = d.reshape(yNum, xNum)
d = np.flipud(d)
d[d == deleteValue] = np.nan
data_list[item][i, :, :] = d
t_seconds[i] = itemdata.Time
start_time = from_dotnet_datetime(dfs.FileInfo.TimeAxis.StartDateTime)
time = [start_time + timedelta(seconds=tsec) for tsec in t_seconds]
items = get_item_info(dfs, item_numbers)
dfs.Close()
return Dataset(data_list, time, items)
def _read_dfs2_header(self):
dfs = DfsFileFactory.Dfs2FileOpen(self._filename)
self._dx = dfs.SpatialAxis.Dx
self._dy = dfs.SpatialAxis.Dy
self._read_header(dfs)
def read(self,
filename,
item_numbers=None,
item_names=None,
time_steps=None):
"""
Parameters
---------
filename: str
dfs2 filename
item_numbers: list[int], optional
Read only selected items, by number (0-based)
item_names: list[str], optional
Read only selected items, by name, takes precedence over item_numbers
time_steps: list[int], optional
Read only selected time_steps
Returns
-------
Dataset
A dataset with data dimensions [t,y,x]
"""
# NOTE. Item numbers are base 0 (everything else in the dfs is base 0)
# Open the dfs file for reading
dfs = DfsFileFactory.Dfs2FileOpen(filename)
self._dfs = dfs
if item_names is not None:
item_numbers = find_item(dfs, item_names)
if item_numbers is None:
n_items = safe_length(dfs.ItemInfo)
item_numbers = list(range(n_items))
nt = dfs.FileInfo.TimeAxis.NumberOfTimeSteps
if time_steps is None:
time_steps = list(range(nt))
# Determine the size of the grid
# axis = dfs.ItemInfo[0].SpatialAxis
axis = dfs.SpatialAxis
yNum = axis.YCount
xNum = axis.XCount
if nt == 0:
raise Warning(
"Static files (with no dynamic items) are not supported.")
nt = 1
for t in time_steps:
if t > (nt - 1):
raise ValueError(
f"Trying to read timestep {t}: max timestep is {nt-1}")
deleteValue = dfs.FileInfo.DeleteValueFloat
n_items = len(item_numbers)
data_list = []
for item in range(n_items):
# Initialize an empty data block
data = np.ndarray(shape=(len(time_steps), yNum, xNum), dtype=float)
data_list.append(data)
t_seconds = np.zeros(len(time_steps), dtype=float)
startTime = dfs.FileInfo.TimeAxis.StartDateTime
for i in range(len(time_steps)):
it = time_steps[i]
for item in range(n_items):
itemdata = dfs.ReadItemTimeStep(item_numbers[item] + 1, it)
src = itemdata.Data
d = to_numpy(src)
d = d.reshape(yNum, xNum)
d = np.flipud(d)
d[d == deleteValue] = np.nan
data_list[item][i, :, :] = d
t_seconds[i] = itemdata.Time
start_time = from_dotnet_datetime(dfs.FileInfo.TimeAxis.StartDateTime)
time = [start_time + timedelta(seconds=tsec) for tsec in t_seconds]
items = get_item_info(dfs, item_numbers)
dfs.Close()
return Dataset(data_list, time, items)
