Convert *.asc file to *.dfs2

[RenewAndPlay]

Hi community,

I have tried to convert an ASC file to a dfs2 grid file, however I meet challenges whenever I need to include more than 1 timestep. Until now I have successfully created a dfs2 file based on a *.asc grid file with 1 time step. However, I want to simply add more time steps, for which the following time steps are just a copy of the imported *.asc file.

When I use mikeio.Grid2D, the specification of the data=array gives an error whenever the array has been transformed to a 3-d array.

I have provided the code below and hope that some of you might know how to get on with the task.

import numpy as np
import mikeio
import os
import pandas as pd
#%%


# Load data from the .asc file
data = np.loadtxt(asc_file_path, skiprows=6)

with open(asc_file_path, 'r') as file:
    ncols = int(file.readline().split()[1])
    nrows = int(file.readline().split()[1])
    xllcorner = float(file.readline().split()[1])
    yllcorner = float(file.readline().split()[1])
    cellsize = float(file.readline().split()[1])
    NODATA_value = float(file.readline().split()[1])

array = np.reshape(data, (nrows, -ncols))
array = array[::-1, :]
#%%Create a time series 
nt =73
time = pd.Series(pd.date_range(start_time, periods=nt, freq=dt))

#%% Make it into a 3d array, copying the data into all the dimensions
o_shape = array.shape
n_shape = (o_shape[0],o_shape[1],nt)
n_3d = np.zeros(n_shape)
n_3d[:, :, :] = array[:, :, np.newaxis]
print(n_3d)

array = n_3d
#%% Create geometry and dfs2 file
geometry = mikeio.Grid2D(nx=ncols, ny=nrows, dx=cellsize, dy=cellsize,
                         origin=(xllcorner + (cellsize / 2), yllcorner + (cellsize / 2)),
                         projection='PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]')


# Create the Wind speed DataArray (item 1)
wind_speed_da = mikeio.DataArray(data=array,
                                  item=mikeio.ItemInfo("Wind speed", mikeio.EUMType.Wind_speed),
                                  geometry=geometry,
                                  dims=("y", "x"))

# Create the Wind direction DataArray (item 2)
wind_direction_data = np.full((nrows, ncols), wind_direction, dtype=float)
wind_direction_da = mikeio.DataArray(data=wind_direction_data,
                                      item=mikeio.ItemInfo("Wind direction", mikeio.EUMType.Wind_Direction),
                                      geometry=geometry,
                                      dims=("y", "x"))

ds = wind_speed_da._to_dataset()
ds["Wdir"]=  wind_direction_da

# Save DFS2 file with multiple items
dfs2_filename = os.path.splitext(filename)[0] + ".dfs2"
dfs2_filepath = os.path.join(script_directory, dfs2_filename)

ds._to_dfs2(dfs2_filepath)

[RenewAndPlay]

I worked out the solution myself. The function mikeio.Dataarray needs the time dimension. See below solution.

#%%Create a time series 
        nt =73
        time = pd.Series(pd.date_range(start_time, periods=nt, freq=dt))

        #%% Make it into a 3d array, copying the data into all the dimensions
        #To add the time reliance, it needs to be 3d  with the dim(time, y, x)
        o_shape = array.shape
        n_shape = (nt,o_shape[0],o_shape[1])
        n_3d = np.zeros(n_shape)
        n_3d[:, :, :] = array[np.newaxis, :, :]
        
        array = n_3d
        #%% Create geometry and dfs2 file
        geometry = mikeio.Grid2D(nx=ncols, ny=nrows, dx=cellsize, dy=cellsize,
                                 origin=(xllcorner + (cellsize / 2), yllcorner + (cellsize / 2)),
                                 projection='PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]')

        # Create the Wind speed DataArray (item 1)
        wind_speed_da = mikeio.DataArray(data=array, time=time,
                                          item=mikeio.ItemInfo("Wind speed", mikeio.EUMType.Wind_speed),
                                          geometry=geometry,
                                          dims=("time", "y", "x"))

        # Create the Wind direction DataArray (item 2)
        wind_direction_data = np.full((nt, nrows, ncols), wind_direction, dtype=float)
        wind_direction_da = mikeio.DataArray(data=wind_direction_data, time=time,
                                              item=mikeio.ItemInfo("Wind direction", mikeio.EUMType.Wind_Direction),
                                              geometry=geometry,
                                              dims=("time", "y", "x"))

        ds = wind_speed_da._to_dataset()
        ds["Wdir"]=  wind_direction_da

        # Save DFS2 file with multiple items
        dfs2_filename = os.path.splitext(filename)[0] + ".dfs2"
        dfs2_filepath = os.path.join(script_directory, dfs2_filename)
        
        ds._to_dfs2(dfs2_filepath)

Maybe somebody else can benefit from the question.

Cheers.