def write(
self,
filename,
data,
start_time=None,
dt=None,
datetimes=None,
items=None,
dx=None,
dy=None,
coordinate=None,
title=None,
):
"""
Create a dfs2 file
Parameters
----------
filename: str
Location to write the dfs2 file
data: list[np.array] or Dataset
list of matrices, one for each item. Matrix dimension: time, y, x
start_time: datetime, optional
start date of type datetime.
dt: float, optional
The time step in seconds.
dt: datetime
The list of datetimes for the case of nonEquadistant Timeaxis.
items: list[ItemInfo], optional
List of ItemInfo corresponding to a variable types (ie. Water Level).
dx: float, optional
length of each grid in the x direction (projection units)
dy: float, optional
length of each grid in the y direction (projection units)
coordinate:
list of [projection, origin_x, origin_y, orientation]
e.g. ['LONG/LAT', 12.4387, 55.2257, 327]
title: str, optional
title of the dfs2 file. Default is blank.
"""
self._builder = Dfs2Builder.Create(title, "mikeio", 0)
if not self._dx:
self._dx = 1
if dx:
self._dx = dx
if not self._dy:
self._dy = 1
if dy:
self._dy = dy
self._write(filename, data, start_time, dt, datetimes, items,
coordinate, title)
def create(
self,
filename,
data,
start_time=None,
dt=1,
datetimes=None,
items=None,
length_x=1,
length_y=1,
x0=0,
y0=0,
coordinate=None,
timeseries_unit=TimeStep.SECOND,
title=None,
):
"""
Create a dfs2 file
Parameters
----------
filename: str
Location to write the dfs2 file
data: list[np.array]
list of matrices, one for each item. Matrix dimension: time, y, x
start_time: datetime, optional
start date of type datetime.
timeseries_unit: Timestep, optional
TimeStep default TimeStep.SECOND
dt: float, optional
The time step. Therefore dt of 5.5 with timeseries_unit of TimeStep.MINUTE
means 5 mins and 30 seconds. Default 1
datetimes: list[datetime], optional
datetimes, creates a non-equidistant calendar axis
items: list[ItemInfo], optional
List of ItemInfo corresponding to a variable types (ie. Water Level).
coordinate:
['UTM-33', 12.4387, 55.2257, 327] for UTM, Long, Lat, North to Y orientation. Note: long, lat in decimal degrees
x0: float, optional
Lower right position
x0: float, optional
Lower right position
length_x: float, optional
length of each grid in the x direction (projection units)
length_y: float, optional
length of each grid in the y direction (projection units)
title: str, optional
title of the dfs2 file. Default is blank.
"""
if title is None:
title = ""
n_time_steps = np.shape(data[0])[0]
number_y = np.shape(data[0])[1]
number_x = np.shape(data[0])[2]
n_items = len(data)
if start_time is None:
start_time = datetime.now()
if coordinate is None:
coordinate = ["LONG/LAT", 0, 0, 0]
if items is None:
items = [ItemInfo(f"temItem {i+1}") for i in range(n_items)]
if not all(np.shape(d)[0] == n_time_steps for d in data):
raise Warning(
"ERROR data matrices in the time dimension do not all match in the data list. "
"Data is list of matices [t,y,x]")
if not all(np.shape(d)[1] == number_y for d in data):
raise Warning(
"ERROR data matrices in the Y dimension do not all match in the data list. "
"Data is list of matices [t,y,x]")
if not all(np.shape(d)[2] == number_x for d in data):
raise Warning(
"ERROR data matrices in the X dimension do not all match in the data list. "
"Data is list of matices [t,y,x,]")
if len(items) != n_items:
raise Warning(
"number of items must correspond to the number of arrays in data list"
)
if datetimes is None:
equidistant = True
if not type(start_time) is datetime:
raise Warning("start_time must be of type datetime ")
else:
equidistant = False
start_time = datetimes[0]
# if not isinstance(timeseries_unit, int):
# raise Warning("timeseries_unit must be an integer. timeseries_unit: second=1400, minute=1401, hour=1402, "
# "day=1403, month=1405, year= 1404See dfsutil options for help ")
system_start_time = System.DateTime(
start_time.year,
start_time.month,
start_time.day,
start_time.hour,
start_time.minute,
start_time.second,
)
# Create an empty dfs2 file object
factory = DfsFactory()
builder = Dfs2Builder.Create(title, "mikeio", 0)
# Set up the header
builder.SetDataType(0)
if coordinate[0] == "LONG/LAT":
builder.SetGeographicalProjection(
factory.CreateProjectionGeoOrigin(coordinate[0], coordinate[1],
coordinate[2],
coordinate[3]))
else:
builder.SetGeographicalProjection(
factory.CreateProjectionProjOrigin(coordinate[0],
coordinate[1],
coordinate[2],
coordinate[3]))
if equidistant:
builder.SetTemporalAxis(
factory.CreateTemporalEqCalendarAxis(timeseries_unit,
system_start_time, 0, dt))
else:
builder.SetTemporalAxis(
factory.CreateTemporalNonEqCalendarAxis(
eumUnit.eumUsec, system_start_time))
builder.SetSpatialAxis(
factory.CreateAxisEqD2(eumUnit.eumUmeter, number_x, x0, length_x,
number_y, y0, length_y))
for i in range(n_items):
builder.AddDynamicItem(
items[i].name,
eumQuantity.Create(items[i].type, items[i].unit),
DfsSimpleType.Float,
DataValueType.Instantaneous,
)
try:
builder.CreateFile(filename)
except IOError:
print("cannot create dfs2 file: ", filename)
dfs = builder.GetFile()
deletevalue = dfs.FileInfo.DeleteValueFloat # -1.0000000031710769e-30
for i in range(n_time_steps):
for item in range(n_items):
d = data[item][i, :, :]
d[np.isnan(d)] = deletevalue
d = d.reshape(number_y, number_x)
d = np.flipud(d)
darray = Array[System.Single](np.array(
d.reshape(d.size, 1)[:, 0]))
if equidistant:
dfs.WriteItemTimeStepNext(0, darray)
else:
t = datetimes[i]
relt = (t - start_time).seconds
dfs.WriteItemTimeStepNext(relt, darray)
dfs.Close()
def create(self,
filename,
data,
start_time=None,
dt=1,
datetimes=None,
length_x=1,
length_y=1,
x0=0,
y0=0,
coordinate=None,
timeseries_unit=TimeStep.SECOND,
variable_type=None,
unit=None,
names=None,
title=None):
"""
Creates a dfs2 file
filename:
Location to write the dfs2 file
data:
list of matrices, one for each item. Matrix dimension: y, x, time
start_time:
start date of type datetime.
timeseries_unit:
TimeStep default TimeStep.SECOND
dt:
The time step. Therefore dt of 5.5 with timeseries_unit of TimeStep.MINUTE
means 5 mins and 30 seconds. Default 1
datetimes:
list of datetimes, creates a non-equidistant calendar axis
variable_type:
Array integers corresponding to a variable types (ie. Water Level). Use dfsutil type_list
to figure out the integer corresponding to the variable.
unit:
Array integers corresponding to the unit corresponding to the variable types The unit (meters, seconds),
use dfsutil unit_list to figure out the corresponding unit for the variable.
coordinate:
['UTM-33', 12.4387, 55.2257, 327] for UTM, Long, Lat, North to Y orientation. Note: long, lat in decimal degrees
x0:
Lower right position
x0:
Lower right position
length_x:
length of each grid in the x direction (projection units)
length_y:
length of each grid in the y direction (projection units)
names:
array of names (ie. array of strings). (can be blank)
title:
title of the dfs2 file. Default is blank.
"""
if title is None:
title = ""
n_time_steps = np.shape(data[0])[0]
number_y = np.shape(data[0])[1]
number_x = np.shape(data[0])[2]
n_items = len(data)
if start_time is None:
start_time = datetime.now()
if coordinate is None:
coordinate = ['LONG/LAT', 0, 0, 0]
if names is None:
names = [f"Item {i+1}" for i in range(n_items)]
if variable_type is None:
variable_type = [999] * n_items
if unit is None:
unit = [0] * n_items
if not all(np.shape(d)[0] == n_time_steps for d in data):
raise Warning(
"ERROR data matrices in the time dimension do not all match in the data list. "
"Data is list of matices [t,y,x]")
if not all(np.shape(d)[1] == number_y for d in data):
raise Warning(
"ERROR data matrices in the Y dimension do not all match in the data list. "
"Data is list of matices [t,y,x]")
if not all(np.shape(d)[2] == number_x for d in data):
raise Warning(
"ERROR data matrices in the X dimension do not all match in the data list. "
"Data is list of matices [t,y,x,]")
if len(names) != n_items:
raise Warning(
"names must be an array of strings with the same number as matrices in data list"
)
if len(variable_type) != n_items or not all(
isinstance(item, int) and 0 <= item < 1e15
for item in variable_type):
raise Warning(
"type if specified must be an array of integers (enuType) with the same number of "
"elements as data columns")
if len(unit) != n_items or not all(
isinstance(item, int) and 0 <= item < 1e15 for item in unit):
raise Warning(
"unit if specified must be an array of integers (enuType) with the same number of "
"elements as data columns")
if datetimes is None:
equidistant = True
if not type(start_time) is datetime:
raise Warning("start_time must be of type datetime ")
else:
equidistant = False
start_time = datetimes[0]
#if not isinstance(timeseries_unit, int):
# raise Warning("timeseries_unit must be an integer. timeseries_unit: second=1400, minute=1401, hour=1402, "
# "day=1403, month=1405, year= 1404See dfsutil options for help ")
system_start_time = System.DateTime(start_time.year, start_time.month,
start_time.day, start_time.hour,
start_time.minute,
start_time.second)
# Create an empty dfs2 file object
factory = DfsFactory()
builder = Dfs2Builder.Create(title, 'pydhi', 0)
# Set up the header
builder.SetDataType(0)
if coordinate[0] == 'LONG/LAT':
builder.SetGeographicalProjection(
factory.CreateProjectionGeoOrigin(coordinate[0], coordinate[1],
coordinate[2],
coordinate[3]))
else:
builder.SetGeographicalProjection(
factory.CreateProjectionProjOrigin(coordinate[0],
coordinate[1],
coordinate[2],
coordinate[3]))
if equidistant:
builder.SetTemporalAxis(
factory.CreateTemporalEqCalendarAxis(timeseries_unit,
system_start_time, 0, dt))
else:
builder.SetTemporalAxis(
factory.CreateTemporalNonEqCalendarAxis(
eumUnit.eumUsec, system_start_time))
builder.SetSpatialAxis(
factory.CreateAxisEqD2(eumUnit.eumUmeter, number_x, x0, length_x,
number_y, y0, length_y))
for i in range(n_items):
builder.AddDynamicItem(
names[i], eumQuantity.Create(variable_type[i], unit[i]),
DfsSimpleType.Float, DataValueType.Instantaneous)
try:
builder.CreateFile(filename)
except IOError:
print('cannot create dfs2 file: ', filename)
dfs = builder.GetFile()
deletevalue = dfs.FileInfo.DeleteValueFloat # -1.0000000031710769e-30
for i in range(n_time_steps):
for item in range(n_items):
d = data[item][i, :, :]
d[np.isnan(d)] = deletevalue
d = d.reshape(number_y, number_x)
d = np.flipud(d)
darray = Array[System.Single](np.array(
d.reshape(d.size, 1)[:, 0]))
if equidistant:
dfs.WriteItemTimeStepNext(0, darray)
else:
t = datetimes[i]
relt = (t - start_time).seconds
dfs.WriteItemTimeStepNext(relt, darray)
dfs.Close()
def write(
self,
filename,
data,
start_time=None,
dt=1,
datetimes=None,
items=None,
dx=None,
dy=None,
x0=0,
y0=0,
coordinate=None,
title=None,
):
"""
Create a dfs2 file
Parameters
----------
filename: str
Location to write the dfs2 file
data: list[np.array] or Dataset
list of matrices, one for each item. Matrix dimension: time, y, x
start_time: datetime, optional
start date of type datetime.
dt: float, optional
The time step in seconds.
datetimes: list[datetime], optional
datetimes, creates a non-equidistant calendar axis
items: list[ItemInfo], optional
List of ItemInfo corresponding to a variable types (ie. Water Level).
x0: float, optional
Lower right position
x0: float, optional
Lower right position
dx: float, optional
length of each grid in the x direction (projection units)
dy: float, optional
length of each grid in the y direction (projection units)
coordinate:
['UTM-33', 12.4387, 55.2257, 327] for UTM, Long, Lat, North to Y orientation. Note: long, lat in decimal degrees
title: str, optional
title of the dfs2 file. Default is blank.
"""
self._write_handle_common_arguments(
title, data, items, coordinate, start_time, dt
)
number_y = np.shape(data[0])[1]
number_x = np.shape(data[0])[2]
if dx is None:
if self._dx is not None:
dx = self._dx
else:
dx = 1
if dy is None:
if self._dy is not None:
dy = self._dy
else:
dy = 1
if not all(np.shape(d)[0] == self._n_time_steps for d in data):
raise ValueError(
"ERROR data matrices in the time dimension do not all match in the data list. "
"Data is list of matrices [t,y,x]"
)
if not all(np.shape(d)[1] == number_y for d in data):
raise ValueError(
"ERROR data matrices in the Y dimension do not all match in the data list. "
"Data is list of matrices [t,y,x]"
)
if not all(np.shape(d)[2] == number_x for d in data):
raise ValueError(
"ERROR data matrices in the X dimension do not all match in the data list. "
"Data is list of matrices [t,y,x]"
)
if datetimes is None:
self._is_equidistant = True
else:
self._is_equidistant = False
start_time = datetimes[0]
self._start_time = start_time
factory = DfsFactory()
builder = Dfs2Builder.Create(title, "mikeio", 0)
self._builder = builder
self._factory = factory
builder.SetSpatialAxis(
factory.CreateAxisEqD2(
eumUnit.eumUmeter, number_x, x0, dx, number_y, y0, dy
)
)
dfs = self._setup_header(filename)
# coordinate, start_time, dt, timeseries_unit, items, filename
# )
deletevalue = dfs.FileInfo.DeleteValueFloat # -1.0000000031710769e-30
for i in range(self._n_time_steps):
for item in range(self._n_items):
d = self._data[item][i, :, :]
d[np.isnan(d)] = deletevalue
d = d.reshape(number_y, number_x)
d = np.flipud(d)
darray = to_dotnet_float_array(d.reshape(d.size, 1)[:, 0])
if self._is_equidistant:
dfs.WriteItemTimeStepNext(0, darray)
else:
t = datetimes[i]
relt = (t - self._start_time).total_seconds()
dfs.WriteItemTimeStepNext(relt, darray)
dfs.Close()