def setupDfs0():
global shePath
global dfs
global dfsDataX
global dfsDataY
global nX
global nY
global nZ
import clr
global simStart
global simStart
now = MShePy.wm.currentTime()
clr.AddReference("DHI.Mike.Install, Version=1.0.0.0, Culture=neutral, PublicKeyToken=c513450b5d0bf0bf") # "fully qualified" name required!
from DHI.Mike.Install import MikeImport, MikeProducts
MikeImport.SetupLatest()
clr.AddReference("DHI.Generic.MikeZero.DFS")
clr.AddReference("DHI.Generic.MikeZero.EUM")
clr.AddReference("System")
import System
from System import Array
from DHI.Generic.MikeZero import eumUnit, eumItem, eumQuantity
from DHI.Generic.MikeZero.DFS import DfsFactory, DfsBuilder, DfsSimpleType, DataValueType
shePath = MShePy.wm.getSheFilePath()
sheDir = os.path.dirname(shePath)
filename = os.path.join(sheDir, 'BndFluxes.dfs2')
builder = DfsBuilder.Create(filename, "MSHE SZ boundary fluxes output per layer", 0)
builder.SetDataType(1)
factory = DfsFactory()
builder.SetGeographicalProjection(factory.CreateProjectionGeoOrigin("NON-UTM", 0, 0, 0))
simStart = now
nowSys = System.DateTime(now.year, now.month, now.day, now.hour, now.minute, now.second)
# note: time unit given here has to be used in WriteItemTimeStepNext
axis = factory.CreateTemporalNonEqCalendarAxis(eumUnit.eumUsec, nowSys)
builder.SetTemporalAxis(axis)
builder.DeleteValueFloat = -1e-30
(startTime, endTime, values) = MShePy.wm.getValues(MShePy.paramTypes.SZ_X_FLO) # just for the geometry
(nX, nY, nZ) = values.shape()
(x0, y0) = MShePy.wm.gridCellToCoord(0, 0)
(x1, y1) = MShePy.wm.gridCellToCoord(1, 1)
dfsDataX = Array.CreateInstance(System.Single, nX * nY)
dfsDataY = Array.CreateInstance(System.Single, nX * nY)
for x in range(nX):
for y in range(nY):
if(not MShePy.wm.gridIsInModel(x, y)):
dfsDataX[x + y * nX] = builder.DeleteValueFloat
dfsDataY[x + y * nX] = builder.DeleteValueFloat
dx = x1 - x0 # cell size, dx == dy
axis = factory.CreateAxisEqD2(eumUnit.eumUmeter, nX, x0 - dx / 2, dx, nY, y0 - dx / 2, dx)
itemBuilder = builder.CreateDynamicItemBuilder()
itemBuilder.SetValueType(DataValueType.MeanStepBackward)
itemBuilder.SetAxis(axis)
for iz in range(nZ):
for xy in ['x', 'y']:
itemBuilder.Set('Boundary inflow layer {0}, {1}-direction'.format(iz + 1, xy), eumQuantity.Create(eumItem.eumIDischarge, eumUnit.eumUm3PerSec), DfsSimpleType.Float)
builder.AddDynamicItem(itemBuilder.GetDynamicItemInfo())
builder.CreateFile(filename)
dfs = builder.GetFile()
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()