From c750dd89f9ec472df8cf68f47a93c32813a3fef1 Mon Sep 17 00:00:00 2001 From: ecomodeller Date: Tue, 5 Mar 2024 07:33:39 +0000 Subject: [PATCH] deploy: 2bfaffd4bd83e88233eb766f3365965f2592429c --- api/DataArray.html | 6 ++ api/Dataset.html | 6 ++ api/Dfs0.html | 6 ++ api/Dfs1.html | 6 ++ api/Dfs2.html | 6 ++ api/Dfs3.html | 6 ++ api/Dfsu.html | 6 ++ api/EUMType.html | 6 ++ api/EUMUnit.html | 6 ++ api/Grid1D.html | 6 ++ api/Grid2D.html | 6 ++ api/Grid3D.html | 6 ++ api/ItemInfo.html | 6 ++ api/Mesh.html | 6 ++ api/PfsDocument.html | 6 ++ api/PfsSection.html | 6 ++ api/dataset._data_plot._DataArrayPlotter.html | 6 ++ ...ataset._data_plot._DataArrayPlotterFM.html | 6 ++ ...ot._DataArrayPlotterFMVerticalProfile.html | 6 ++ ...et._data_plot._DataArrayPlotterGrid1D.html | 6 ++ ...et._data_plot._DataArrayPlotterGrid2D.html | 6 ++ api/dfsu.Dfsu2DH.html | 6 ++ api/dfsu.Dfsu2DV.html | 6 ++ api/dfsu.Dfsu3D.html | 6 ++ api/dfsu.DfsuSpectral.html | 6 ++ api/generic.html | 6 ++ api/index.html | 6 ++ api/open.html | 6 ++ api/read.html | 6 ++ api/read_pfs.html | 6 ++ api/spatial.GeometryFM2D.html | 6 ++ api/spatial.GeometryFM3D.html | 6 ++ api/spatial.GeometryFMAreaSpectrum.html | 6 ++ api/spatial.GeometryFMLineSpectrum.html | 6 ++ api/spatial.GeometryFMPointSpectrum.html | 6 ++ api/spatial.GeometryFMVerticalColumn.html | 6 ++ api/spatial.GeometryFMVerticalProfile.html | 6 ++ ...atial._FM_geometry._GeometryFMPlotter.html | 6 ++ design.html | 6 ++ examples/Dfs2-Bathymetry.html | 50 ++++++----- examples/Dfsu-2D-interpolation.html | 64 +++++++------- examples/Generic.html | 72 ++++++++------- examples/Time-interpolation.html | 34 +++++--- examples/index.html | 14 ++- index.html | 8 +- search.json | 75 ++++++++++++++-- user-guide/data-structures.html | 6 ++ user-guide/dataarray.html | 26 +++--- user-guide/dataset.html | 6 ++ user-guide/dfs0.html | 12 ++- user-guide/dfs1.html | 10 ++- user-guide/dfs2.html | 16 ++-- user-guide/dfsu.html | 16 ++-- user-guide/eum.html | 30 ++++--- user-guide/generic.html | 6 ++ user-guide/getting-started.html | 16 ++-- user-guide/mesh.html | 87 ++++++++++++------- user-guide/pfs.html | 22 +++-- 58 files changed, 616 insertions(+), 188 deletions(-) diff --git a/api/DataArray.html b/api/DataArray.html index 03d4a944f..0dda8c185 100644 --- a/api/DataArray.html +++ b/api/DataArray.html @@ -238,6 +238,12 @@

Dfsu and Mesh Overview + +
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  • diff --git a/examples/Dfs2-Bathymetry.html b/examples/Dfs2-Bathymetry.html index ccb910b43..9fb94ab32 100644 --- a/examples/Dfs2-Bathymetry.html +++ b/examples/Dfs2-Bathymetry.html @@ -242,6 +242,12 @@

    Dfs2 - Bathymetric data

    Dfsu and Mesh Overview
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  • @@ -346,11 +352,11 @@

    Dfs2 - Bathymetric data

    GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9)

    -
    +
    import xarray
 import mikeio
    -
    +
    ds = xarray.open_dataset("../data/gebco_2020_n56.3_s55.2_w12.2_e13.1.nc")
 ds
    @@ -732,7 +738,7 @@

    Dfs2 - Bathymetric data

    history: Information on the development of the data set and the sour... references: DOI: 10.5285/a29c5465-b138-234d-e053-6c86abc040b9 comment: The data in the GEBCO_2020 Grid should not be used for navi... - node_offset: 1.0
  • Conventions :
    CF-1.6
    title :
    The GEBCO_2020 Grid - a continuous terrain model for oceans and land at 15 arc-second intervals
    institution :
    On behalf of the General Bathymetric Chart of the Oceans (GEBCO), the data are held at the British Oceanographic Data Centre (BODC).
    source :
    The GEBCO_2020 Grid is the latest global bathymetric product released by the General Bathymetric Chart of the Oceans (GEBCO) and has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and GEBCO. The Seabed 2030 Project aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor and make it available to all.
    history :
    Information on the development of the data set and the source data sets included in the grid can be found in the data set documentation available from https://www.gebco.net
    references :
    DOI: 10.5285/a29c5465-b138-234d-e053-6c86abc040b9
    comment :
    The data in the GEBCO_2020 Grid should not be used for navigation or any purpose relating to safety at sea.
    node_offset :
    1.0
    • -
    +
    ds.elevation.plot();
    @@ -763,7 +769,7 @@

    Dfs2 - Bathymetric data

    -
    +
    ds.elevation.sel(lon=12.74792, lat=55.865, method="nearest")
    @@ -1141,17 +1147,17 @@

    Dfs2 - Bathymetric data

    sdn_parameter_urn: SDN:P01::BATHHGHT sdn_parameter_name: Sea floor height (above mean sea level) {bathymetric... sdn_uom_urn: SDN:P06::ULAA - sdn_uom_name: Metres
    + sdn_uom_name: Metres

    Check ordering of dimensions, should be (y,x)

    -
    +
    ds.elevation.dims
    ('lat', 'lon')
    -
    +
    el = ds.elevation.values
 el.shape
    @@ -1159,37 +1165,37 @@

    Dfs2 - Bathymetric data

    Check that axes are increasing, S->N W->E

    -
    +
    ds.lat.values[0],ds.lat.values[-1] 
    (55.20208333333332, 56.29791666666665)
    -
    +
    ds.lat.values[0] < ds.lat.values[-1] 
    True
    -
    +
    ds.lon.values[0],ds.lon.values[-1] 
    (12.20208333333332, 13.097916666666663)
    -
    +
    el[0,0] # Bottom left
    -8
    -
    +
    el[-1,0] # Top Left
    -31
    -
    +
    geometry = mikeio.Grid2D(x=ds.lon.values, y=ds.lat.values, projection="LONG/LAT")
 geometry
    @@ -1199,7 +1205,7 @@

    Dfs2 - Bathymetric data

    projection: LONG/LAT
    -
    +
    da = mikeio.DataArray(data=el,
                item=mikeio.ItemInfo("Elevation", mikeio.EUMType.Total_Water_Depth),
                geometry=geometry,
@@ -1214,7 +1220,7 @@ 
    Dfs2 - Bathymetric data
    
 geometry: Grid2D (ny=264, nx=216)
    -
    +
    da.plot();
    @@ -1224,7 +1230,7 @@

    Dfs2 - Bathymetric data

    -
    +
    da.plot(cmap='coolwarm', vmin=-100, vmax=100);
    @@ -1234,10 +1240,10 @@

    Dfs2 - Bathymetric data

    -
    +
    da.to_dfs("gebco.dfs2")
    -
    +
    ds = mikeio.read("gebco.dfs2")
 ds.Elevation.plot()
    @@ -1250,7 +1256,7 @@

    Dfs2 - Bathymetric data

    Clean up

    -
    +
    import os
 
 os.remove("gebco.dfs2")
    diff --git a/examples/Dfsu-2D-interpolation.html b/examples/Dfsu-2D-interpolation.html index 82dc0532f..6ab12b115 100644 --- a/examples/Dfsu-2D-interpolation.html +++ b/examples/Dfsu-2D-interpolation.html @@ -241,6 +241,12 @@

    Dfsu - 2D interpolation

    Dfsu and Mesh Overview
    + +
  • +
    + + Mesh +
  • @@ -354,10 +360,10 @@

    Dfsu - 2D interpolation

    -
    +
    import mikeio
    -
    +
    ds = mikeio.read("../data/wind_north_sea.dfsu", items="Wind speed")
 ds
    @@ -369,7 +375,7 @@

    Dfsu - 2D interpolation

    0: Wind speed <Wind speed> (meter per sec)
    -
    +
    da = ds.Wind_speed
 da.plot();
    @@ -387,7 +393,7 @@

    Interpolate to gridThen interpolate all data to the new grid and plot.

  • The interpolated data is then saved to a dfs2 file.
    • -
    +
    g = da.geometry.get_overset_grid(dx=0.1)
 g
    @@ -397,7 +403,7 @@

    Interpolate to grid

    -
    +
    da_grid = da.interp_like(g)
 da_grid
    @@ -408,7 +414,7 @@

    Interpolate to grid

    -
    +
    da_grid.plot();
    @@ -421,14 +427,14 @@

    Interpolate to grid

    Save to dfs2 file

    -
    +
    da_grid.to_dfs("wind_north_sea_interpolated.dfs2")

    Save to NetCDF

    -
    +
    xr_da = da_grid.to_xarray()
 xr_da.to_netcdf("wind_north_sea_interpolated.nc")
    @@ -449,7 +455,7 @@

    Save to GeoTiff

    This section requires the rasterio package.

    -
    +
    import numpy as np
 import rasterio
 from rasterio.transform import from_origin
@@ -473,7 +479,7 @@ 
    Save to GeoTiff
    
 
    
 
    Interpolate to other mesh
    
 
    Interpolate the data from this coarse mesh onto a finer resolution mesh
    
-
    
+
    
 
    msh = mikeio.Mesh('../data/north_sea_2.mesh')
 msh
    
 
    
@@ -483,7 +489,7 @@ 
    Interpolate to other mesh
    
 projection: LONG/LAT
    -
    +
    dsi = da.interp_like(msh)
 dsi
    @@ -494,7 +500,7 @@

    Interpolate to other mesh

    geometry: Dfsu2D (2259 elements, 1296 nodes)
    -
    +
    da[0].plot(figsize=(9,7), show_mesh=True);
    @@ -504,7 +510,7 @@

    Interpolate to other mesh

    -
    +
    dsi[0].plot(figsize=(9,7), show_mesh=True);
    @@ -516,14 +522,14 @@

    Interpolate to other mesh

    Note: 3 of the new elements are outside the original mesh and data are therefore NaN by default

    -
    +
    nan_elements = np.where(np.isnan(dsi[0].to_numpy()))[0]
 nan_elements
    array([ 249,  451, 1546])
    -
    +
    da.geometry.contains(msh.element_coordinates[nan_elements,:2])
    array([False, False, False])
    @@ -532,10 +538,10 @@

    We can force extrapolation to avoid the NaN values

    -
    +
    dat_interp = da.interp_like(msh, extrapolate=True)
    -
    +
    n_nan_elements = np.sum(np.isnan(dat_interp.values))
 n_nan_elements
    @@ -548,14 +554,14 @@

    Interpola

    We want to interpolate scatter data onto an existing mesh and create a new dfsu with the interpolated data.

    This uses lower level private utility methods not part of the public API.

    Interpolating from scatter data will soon be possible in a simpler way.

    -
    +
    from mikeio.spatial._utils import dist_in_meters
 from mikeio._interpolation import get_idw_interpolant
    -
    +
    dfs = mikeio.open("../data/wind_north_sea.dfsu")
    -
    +
    dfs.geometry.plot.mesh();
    @@ -565,7 +571,7 @@

    Interpola

    -
    +
    # scatter data: x,y,value for 4 points
 scatter= np.array([[1,50,1], [4, 52, 3], [8, 55, 2], [-1, 55, 1.5]])
 scatter
    @@ -582,35 +588,35 @@

    Interpola
  • calc IDW interpolatant weights
  • Interpolate
    • -
    +
    dist = dist_in_meters(scatter[:,:2], dfs.element_coordinates[0,:2])
 dist
    array([4.00139539, 3.18881018, 6.58769411, 2.69722991])
    -
    +
    w = get_idw_interpolant(dist, p=2)
 w
    array([0.19438779, 0.30607974, 0.07171749, 0.42781498])
    -
    +
    np.dot(scatter[:,2], w) # interpolated value in element 0
    1.8977844597276883

    Let’s do the same for all points in the mesh and plot in the end

    -
    +
    dati = np.zeros((1,dfs.n_elements))
 for j in range(dfs.n_elements):
     dist = dist_in_meters(scatter[:,:2], dfs.element_coordinates[j,:2])
     w = get_idw_interpolant(dist, p=2)
     dati[0,j] = np.dot(scatter[:,2], w)
    -
    +
    da = mikeio.DataArray(data=dati, geometry=dfs.geometry, time=dfs.start_time)
 da
    @@ -621,7 +627,7 @@

    Interpola geometry: Dfsu2D (958 elements, 570 nodes)

    -
    +
    da.plot(title="Interpolated scatter data");
    @@ -631,13 +637,13 @@

    Interpola

    -
    +
    da.to_dfs("interpolated_scatter.dfsu")

    Clean up

    -
    +
    import os
 
 os.remove("wind_north_sea_interpolated.dfs2")
diff --git a/examples/Generic.html b/examples/Generic.html
index 36858c658..7e139be0a 100644
--- a/examples/Generic.html
+++ b/examples/Generic.html
@@ -243,6 +243,12 @@ 
    Generic dfs processing
    
   
  Dfsu and Mesh Overview
    + +
  • +
    + + Mesh +
  • @@ -365,7 +371,7 @@

    Generic dfs processing

  • quantile: Create temporal quantiles of dfs file
    • -
    +
    import matplotlib.pyplot as plt
 import mikeio
 import mikeio.generic
    @@ -373,7 +379,7 @@

    Generic dfs processing

    Concatenation

    Take a look at these two files with overlapping timesteps.

    -
    +
    t1 = mikeio.read("../data/tide1.dfs1")
 t1
    @@ -385,7 +391,7 @@

    Concatenation

    0: Level <Water Level> (meter)
    -
    +
    t2 = mikeio.read("../data/tide2.dfs1")
 t2
    @@ -398,7 +404,7 @@

    Concatenation

    Plot one of the points along the line.

    -
    +
    plt.plot(t1.time,t1[0].isel(x=1).values, label="File 1")
 plt.plot(t2.time,t2[0].isel(x=1).values,'k+', label="File 2")
 plt.legend()
    @@ -410,15 +416,15 @@

    Concatenation

    -
    +
    mikeio.generic.concat(infilenames=["../data/tide1.dfs1",
                                    "../data/tide2.dfs1"],
                      outfilename="concat.dfs1")
    -
      0%|          | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 527.62it/s]
    +
      0%|          | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 582.99it/s]
    -
    +
    c = mikeio.read("concat.dfs1")
 c[0].isel(x=1).plot()
 c
    @@ -442,16 +448,16 @@

    Concatenation

    Difference between two files

    Take difference between two dfs files with same structure - e.g. to see the difference in result between two calibration runs

    -
    +
    fn1 = "../data/oresundHD_run1.dfsu"
 fn2 = "../data/oresundHD_run2.dfsu"
 fn_diff = "oresundHD_difference.dfsu"
 mikeio.generic.diff(fn1, fn2, fn_diff)
    -
      0%|          | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2420.26it/s]
    +
      0%|          | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2686.93it/s]
    -
    +
    _, ax = plt.subplots(1,3, sharey=True, figsize=(12,5))
 da = mikeio.read(fn1, time=-1)[0]
 da.plot(vmin=0.06, vmax=0.27, ax=ax[0], title='run 1')
@@ -475,11 +481,11 @@ 
    Extract time s
 
  • time slice by specifying start and/or end
    • 
 
  • specific items
    • 
 
-
    
+
    
 
    infile = "../data/tide1.dfs1"
 mikeio.generic.extract(infile, "extracted.dfs1", start='2019-01-02')
    
 
    
-
    
+
    
 
    e = mikeio.read("extracted.dfs1")
 e
    
 
    
@@ -491,11 +497,11 @@ 
    Extract time s
   0:  Level <Water Level> (meter)
    -
    +
    infile = "../data/oresund_vertical_slice.dfsu"
 mikeio.generic.extract(infile, "extracted.dfsu", items='Salinity', end=-2)
    -
    +
    e = mikeio.read("extracted.dfsu")
 e
    @@ -516,7 +522,7 @@

    Extract time s

    Scaling

    Adding a constant e.g to adjust datum

    -
    +
    ds = mikeio.read("../data/gebco_sound.dfs2")
 ds.Elevation[0].plot();
    @@ -527,23 +533,23 @@

    Scaling

    -
    +
    ds['Elevation'][0,104,131].to_numpy()
    -1.0

    This is the processing step.

    -
    +
    mikeio.generic.scale("../data/gebco_sound.dfs2", 
                      "gebco_sound_local_datum.dfs2",
                      offset=-2.1
                      )
    -
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1586.95it/s]
    +
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1032.83it/s]
    -
    +
    ds2 = mikeio.read("gebco_sound_local_datum.dfs2")
 ds2['Elevation'][0].plot()
    @@ -554,7 +560,7 @@

    Scaling

    -
    +
    ds2['Elevation'][0,104,131].to_numpy()
    -3.1
    @@ -562,7 +568,7 @@

    Scaling

    Spatially varying correction

    -
    +
    import numpy as np
 factor = np.ones_like(ds['Elevation'][0].to_numpy())
 factor.shape
    @@ -571,7 +577,7 @@

    Spatially var

    Add some spatially varying factors, exaggerated values for educational purpose.

    -
    +
    factor[:,0:100] = 5.3
 factor[0:40,] = 0.1
 factor[150:,150:] = 10.7
@@ -586,7 +592,7 @@ 
    Spatially var

    The 2d array must first be flipped upside down and then converted to a 1d vector using numpy.ndarray.flatten to match how data is stored in dfs files.

    -
    +
    factor_ud = np.flipud(factor)
 factor_vec  = factor_ud.flatten()
 mikeio.generic.scale("../data/gebco_sound.dfs2", 
@@ -594,10 +600,10 @@ 
    Spatially var
                      factor=factor_vec
                      )
    -
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1259.55it/s]
    +
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1895.30it/s]
    -
    +
    ds3 = mikeio.read("gebco_sound_spatial.dfs2")
 ds3.Elevation[0].plot();
    @@ -612,15 +618,15 @@

    Spatially var

    Time average

    -
    +
    fn = "../data/NorthSea_HD_and_windspeed.dfsu"
 fn_avg = "Avg_NorthSea_HD_and_windspeed.dfsu"
 mikeio.generic.avg_time(fn, fn_avg)
    -
      0%|          | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 16045.91it/s]
    +
      0%|          | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 16490.38it/s]
    -
    +
    ds = mikeio.read(fn)
 ds.mean(axis=0).describe()   # alternative way of getting the time average
    @@ -684,7 +690,7 @@

    Time average

    -
    +
    ds_avg = mikeio.read(fn_avg)
 ds_avg.describe()
    @@ -752,12 +758,12 @@

    Time average

    Quantile

    Example that calculates the 25%, 50% and 75% percentile for all items in a dfsu file.

    -
    +
    fn = "../data/NorthSea_HD_and_windspeed.dfsu"
 fn_q = "Q_NorthSea_HD_and_windspeed.dfsu"
 mikeio.generic.quantile(fn, fn_q, q=[0.25,0.5,0.75])
    -
    +
    ds = mikeio.read(fn_q)
 ds
    @@ -774,7 +780,7 @@

    Quantile

    5: Quantile 0.75, Wind speed <Wind speed> (meter per sec)
    -
    +
    da_q75 = ds["Quantile 0.75, Wind speed"]
 da_q75.plot(title="75th percentile, wind speed", label="m/s")
    @@ -788,7 +794,7 @@

    Quantile

    Clean up

    -
    +
    import os
 os.remove("concat.dfs1")
 os.remove("oresundHD_difference.dfsu")
diff --git a/examples/Time-interpolation.html b/examples/Time-interpolation.html
index 210eb9eeb..de2fe9691 100644
--- a/examples/Time-interpolation.html
+++ b/examples/Time-interpolation.html
@@ -241,6 +241,12 @@ 
    Time interpolation
    
   
  Dfsu and Mesh Overview
    + +
  • +
    + + Mesh +
  • @@ -347,11 +353,11 @@

    Time interpolation

    -
    +
    import numpy as np
 import mikeio
    -
    +
    ds = mikeio.read("../data/waves.dfs2")
 ds
    @@ -368,7 +374,7 @@

    Time interpolation

    Interpolate to specific timestep

    A common use case is to interpolate to a shorter timestep, in this case 1h.

    -
    +
    ds_h = ds.interp_time(3600)
 ds_h
    @@ -383,14 +389,14 @@

    Interpola

    And to store the interpolated data in a new file.

    -
    +
    ds_h.to_dfs("waves_3h.dfs2")

    Interpolate to time axis of another dataset

    Read some non-equidistant data typically found in observed data.

    -
    +
    ts = mikeio.read("../data/waves.dfs0")
 ts
    @@ -405,10 +411,10 @@

    +
    dsi = ds.interp_time(ts)
    -
    +
    dsi.time
    DatetimeIndex(['2004-01-01 01:00:00', '2004-01-01 02:00:00',
@@ -426,13 +432,13 @@ 
    
+
    
 
    dsi["Sign. Wave Height"].shape
    
 
    
 
    (24, 31, 31)
    
 
    
 
    
-
    
+
    
 
    ax = dsi["Sign. Wave Height"].sel(x=250, y=1200).plot(marker='+')
 ts["Sign. Wave Height"].plot(ax=ax,marker='+')
    
 
    
@@ -450,7 +456,7 @@ 
    Model validation
    
 
    In the example below we calculate this metric using the model data interpolated to the observed times.
    
 
    For a more elaborate model validation library which takes care of these things for you as well as calculating a number of relevant metrics, take a look at `ModelSkill.
    
 
    Use np.nanmean to skip NaN.
    
-
    
+
    
 
    ts["Sign. Wave Height"]
    
 
    
 
    <mikeio.DataArray>
@@ -461,7 +467,7 @@ 
    Model validation
    
 values: [0.06521, 0.06771, ..., 0.0576]
    
 
    
 
    
-
    
+
    
 
    dsi["Sign. Wave Height"].sel(x=250, y=1200)
    
 
    
 
    <mikeio.DataArray>
@@ -472,7 +478,7 @@ 
    Model validation
    
 values: [0.0387, 0.03939, ..., nan]
    
 
    
 
    
-
    
+
    
 
    diff = (ts["Sign. Wave Height"]  - dsi["Sign. Wave Height"].sel(x=250, y=1200))
 diff.plot()
    
 
    
@@ -483,7 +489,7 @@ 
    Model validation
    
 
    
 
    
 
    
-
    
+
    
 
    mae = np.abs(diff).nanmean().to_numpy()
 mae
    
 
    
@@ -493,7 +499,7 @@ 
    Model validation

    Clean up

    -
    +
    import os
 os.remove("waves_3h.dfs2")
    diff --git a/examples/index.html b/examples/index.html index 47231f262..68afead08 100644 --- a/examples/index.html +++ b/examples/index.html @@ -266,6 +266,12 @@

    Examples

    Dfsu and Mesh Overview
    +
    • +
  • +
    + + Mesh +
  • @@ -391,7 +397,7 @@

    Examples

    - + Dfs2 - Bathymetric data @@ -399,7 +405,7 @@

    Examples

    Convert GEBCO 2020 NetCDF to dfs2 - + Dfsu - 2D interpolation @@ -407,7 +413,7 @@

    Examples

    Interpolate dfsu data to a grid, save as dfs2 and geotiff. Interpolate dfsu data to another mesh. - + Generic dfs processing @@ -415,7 +421,7 @@

    Examples

    Tools and methods that applies to any type of dfs files. - + Time interpolation diff --git a/index.html b/index.html index a15edbd99..17e7fd81c 100644 --- a/index.html +++ b/index.html @@ -238,6 +238,12 @@

    Dfsu and Mesh Overview
    +
    • +
  • +
    + + Mesh +
  • @@ -359,7 +365,7 @@

    Installation

    • Getting started

    -
    +
    import mikeio
 
 ds = mikeio.read("data/FakeLake.dfsu")
diff --git a/search.json b/search.json
index 26d299088..03d39bb7b 100644
--- a/search.json
+++ b/search.json
@@ -908,7 +908,7 @@
     "href": "examples/Generic.html#concatenation",
     "title": "Generic dfs processing",
     "section": "Concatenation",
-    "text": "Concatenation\nTake a look at these two files with overlapping timesteps.\n\nt1 = mikeio.read(\"../data/tide1.dfs1\")\nt1\n\n<mikeio.Dataset>\ndims: (time:97, x:10)\ntime: 2019-01-01 00:00:00 - 2019-01-03 00:00:00 (97 records)\ngeometry: Grid1D (n=10, dx=0.06667)\nitems:\n  0:  Level <Water Level> (meter)\n\n\n\nt2 = mikeio.read(\"../data/tide2.dfs1\")\nt2\n\n<mikeio.Dataset>\ndims: (time:97, x:10)\ntime: 2019-01-02 00:00:00 - 2019-01-04 00:00:00 (97 records)\ngeometry: Grid1D (n=10, dx=0.06667)\nitems:\n  0:  Level <Water Level> (meter)\n\n\nPlot one of the points along the line.\n\nplt.plot(t1.time,t1[0].isel(x=1).values, label=\"File 1\")\nplt.plot(t2.time,t2[0].isel(x=1).values,'k+', label=\"File 2\")\nplt.legend()\n\n\n\n\n\n\n\n\n\nmikeio.generic.concat(infilenames=[\"../data/tide1.dfs1\",\n                                   \"../data/tide2.dfs1\"],\n                     outfilename=\"concat.dfs1\")\n\n  0%|          | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 527.62it/s]\n\n\n\nc = mikeio.read(\"concat.dfs1\")\nc[0].isel(x=1).plot()\nc\n\n<mikeio.Dataset>\ndims: (time:145, x:10)\ntime: 2019-01-01 00:00:00 - 2019-01-04 00:00:00 (145 records)\ngeometry: Grid1D (n=10, dx=0.06667)\nitems:\n  0:  Level <Water Level> (meter)",
+    "text": "Concatenation\nTake a look at these two files with overlapping timesteps.\n\nt1 = mikeio.read(\"../data/tide1.dfs1\")\nt1\n\n<mikeio.Dataset>\ndims: (time:97, x:10)\ntime: 2019-01-01 00:00:00 - 2019-01-03 00:00:00 (97 records)\ngeometry: Grid1D (n=10, dx=0.06667)\nitems:\n  0:  Level <Water Level> (meter)\n\n\n\nt2 = mikeio.read(\"../data/tide2.dfs1\")\nt2\n\n<mikeio.Dataset>\ndims: (time:97, x:10)\ntime: 2019-01-02 00:00:00 - 2019-01-04 00:00:00 (97 records)\ngeometry: Grid1D (n=10, dx=0.06667)\nitems:\n  0:  Level <Water Level> (meter)\n\n\nPlot one of the points along the line.\n\nplt.plot(t1.time,t1[0].isel(x=1).values, label=\"File 1\")\nplt.plot(t2.time,t2[0].isel(x=1).values,'k+', label=\"File 2\")\nplt.legend()\n\n\n\n\n\n\n\n\n\nmikeio.generic.concat(infilenames=[\"../data/tide1.dfs1\",\n                                   \"../data/tide2.dfs1\"],\n                     outfilename=\"concat.dfs1\")\n\n  0%|          | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 582.99it/s]\n\n\n\nc = mikeio.read(\"concat.dfs1\")\nc[0].isel(x=1).plot()\nc\n\n<mikeio.Dataset>\ndims: (time:145, x:10)\ntime: 2019-01-01 00:00:00 - 2019-01-04 00:00:00 (145 records)\ngeometry: Grid1D (n=10, dx=0.06667)\nitems:\n  0:  Level <Water Level> (meter)",
     "crumbs": [
       "Home",
       "Examples",
@@ -920,7 +920,7 @@
     "href": "examples/Generic.html#difference-between-two-files",
     "title": "Generic dfs processing",
     "section": "Difference between two files",
-    "text": "Difference between two files\nTake difference between two dfs files with same structure - e.g. to see the difference in result between two calibration runs\n\nfn1 = \"../data/oresundHD_run1.dfsu\"\nfn2 = \"../data/oresundHD_run2.dfsu\"\nfn_diff = \"oresundHD_difference.dfsu\"\nmikeio.generic.diff(fn1, fn2, fn_diff)\n\n  0%|          | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2420.26it/s]\n\n\n\n_, ax = plt.subplots(1,3, sharey=True, figsize=(12,5))\nda = mikeio.read(fn1, time=-1)[0]\nda.plot(vmin=0.06, vmax=0.27, ax=ax[0], title='run 1')\nda = mikeio.read(fn2, time=-1)[0]\nda.plot(vmin=0.06, vmax=0.27, ax=ax[1], title='run 2')\nda = mikeio.read(fn_diff, time=-1)[0]\nda.plot(vmin=-0.1, vmax=0.1, cmap='coolwarm', ax=ax[2], title='difference');",
+    "text": "Difference between two files\nTake difference between two dfs files with same structure - e.g. to see the difference in result between two calibration runs\n\nfn1 = \"../data/oresundHD_run1.dfsu\"\nfn2 = \"../data/oresundHD_run2.dfsu\"\nfn_diff = \"oresundHD_difference.dfsu\"\nmikeio.generic.diff(fn1, fn2, fn_diff)\n\n  0%|          | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2686.93it/s]\n\n\n\n_, ax = plt.subplots(1,3, sharey=True, figsize=(12,5))\nda = mikeio.read(fn1, time=-1)[0]\nda.plot(vmin=0.06, vmax=0.27, ax=ax[0], title='run 1')\nda = mikeio.read(fn2, time=-1)[0]\nda.plot(vmin=0.06, vmax=0.27, ax=ax[1], title='run 2')\nda = mikeio.read(fn_diff, time=-1)[0]\nda.plot(vmin=-0.1, vmax=0.1, cmap='coolwarm', ax=ax[2], title='difference');",
     "crumbs": [
       "Home",
       "Examples",
@@ -944,7 +944,7 @@
     "href": "examples/Generic.html#scaling",
     "title": "Generic dfs processing",
     "section": "Scaling",
-    "text": "Scaling\nAdding a constant e.g to adjust datum\n\nds = mikeio.read(\"../data/gebco_sound.dfs2\")\nds.Elevation[0].plot();\n\n\n\n\n\n\n\n\n\nds['Elevation'][0,104,131].to_numpy()\n\n-1.0\n\n\nThis is the processing step.\n\nmikeio.generic.scale(\"../data/gebco_sound.dfs2\", \n                     \"gebco_sound_local_datum.dfs2\",\n                     offset=-2.1\n                     )\n\n  0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1586.95it/s]\n\n\n\nds2 = mikeio.read(\"gebco_sound_local_datum.dfs2\")\nds2['Elevation'][0].plot()\n\n\n\n\n\n\n\n\n\nds2['Elevation'][0,104,131].to_numpy()\n\n-3.1\n\n\n\nSpatially varying correction\n\nimport numpy as np\nfactor = np.ones_like(ds['Elevation'][0].to_numpy())\nfactor.shape\n\n(264, 216)\n\n\nAdd some spatially varying factors, exaggerated values for educational purpose.\n\nfactor[:,0:100] = 5.3\nfactor[0:40,] = 0.1\nfactor[150:,150:] = 10.7\nplt.imshow(factor)\nplt.colorbar();\n\n\n\n\n\n\n\n\nThe 2d array must first be flipped upside down and then converted to a 1d vector using numpy.ndarray.flatten to match how data is stored in dfs files.\n\nfactor_ud = np.flipud(factor)\nfactor_vec  = factor_ud.flatten()\nmikeio.generic.scale(\"../data/gebco_sound.dfs2\", \n                     \"gebco_sound_spatial.dfs2\",\n                     factor=factor_vec\n                     )\n\n  0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1259.55it/s]\n\n\n\nds3 = mikeio.read(\"gebco_sound_spatial.dfs2\")\nds3.Elevation[0].plot();",
+    "text": "Scaling\nAdding a constant e.g to adjust datum\n\nds = mikeio.read(\"../data/gebco_sound.dfs2\")\nds.Elevation[0].plot();\n\n\n\n\n\n\n\n\n\nds['Elevation'][0,104,131].to_numpy()\n\n-1.0\n\n\nThis is the processing step.\n\nmikeio.generic.scale(\"../data/gebco_sound.dfs2\", \n                     \"gebco_sound_local_datum.dfs2\",\n                     offset=-2.1\n                     )\n\n  0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1032.83it/s]\n\n\n\nds2 = mikeio.read(\"gebco_sound_local_datum.dfs2\")\nds2['Elevation'][0].plot()\n\n\n\n\n\n\n\n\n\nds2['Elevation'][0,104,131].to_numpy()\n\n-3.1\n\n\n\nSpatially varying correction\n\nimport numpy as np\nfactor = np.ones_like(ds['Elevation'][0].to_numpy())\nfactor.shape\n\n(264, 216)\n\n\nAdd some spatially varying factors, exaggerated values for educational purpose.\n\nfactor[:,0:100] = 5.3\nfactor[0:40,] = 0.1\nfactor[150:,150:] = 10.7\nplt.imshow(factor)\nplt.colorbar();\n\n\n\n\n\n\n\n\nThe 2d array must first be flipped upside down and then converted to a 1d vector using numpy.ndarray.flatten to match how data is stored in dfs files.\n\nfactor_ud = np.flipud(factor)\nfactor_vec  = factor_ud.flatten()\nmikeio.generic.scale(\"../data/gebco_sound.dfs2\", \n                     \"gebco_sound_spatial.dfs2\",\n                     factor=factor_vec\n                     )\n\n  0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1895.30it/s]\n\n\n\nds3 = mikeio.read(\"gebco_sound_spatial.dfs2\")\nds3.Elevation[0].plot();",
     "crumbs": [
       "Home",
       "Examples",
@@ -956,7 +956,7 @@
     "href": "examples/Generic.html#time-average",
     "title": "Generic dfs processing",
     "section": "Time average",
-    "text": "Time average\n\nfn = \"../data/NorthSea_HD_and_windspeed.dfsu\"\nfn_avg = \"Avg_NorthSea_HD_and_windspeed.dfsu\"\nmikeio.generic.avg_time(fn, fn_avg)\n\n  0%|          | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 16045.91it/s]\n\n\n\nds = mikeio.read(fn)\nds.mean(axis=0).describe()   # alternative way of getting the time average\n\n\n\n\n\n\n\n\n\nSurface elevation\nWind speed\n\n\n\n\ncount\n958.000000\n958.000000\n\n\nmean\n0.449857\n12.772706\n\n\nstd\n0.178127\n2.367667\n\n\nmin\n0.114355\n6.498364\n\n\n25%\n0.373691\n11.199439\n\n\n50%\n0.431747\n12.984060\n\n\n75%\n0.479224\n14.658077\n\n\nmax\n1.202888\n16.677952\n\n\n\n\n\n\n\n\n\nds_avg = mikeio.read(fn_avg)\nds_avg.describe()\n\n\n\n\n\n\n\n\n\nSurface elevation\nWind speed\n\n\n\n\ncount\n958.000000\n958.000000\n\n\nmean\n0.449857\n12.772706\n\n\nstd\n0.178127\n2.367667\n\n\nmin\n0.114355\n6.498364\n\n\n25%\n0.373691\n11.199439\n\n\n50%\n0.431747\n12.984060\n\n\n75%\n0.479224\n14.658077\n\n\nmax\n1.202888\n16.677952",
+    "text": "Time average\n\nfn = \"../data/NorthSea_HD_and_windspeed.dfsu\"\nfn_avg = \"Avg_NorthSea_HD_and_windspeed.dfsu\"\nmikeio.generic.avg_time(fn, fn_avg)\n\n  0%|          | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 16490.38it/s]\n\n\n\nds = mikeio.read(fn)\nds.mean(axis=0).describe()   # alternative way of getting the time average\n\n\n\n\n\n\n\n\n\nSurface elevation\nWind speed\n\n\n\n\ncount\n958.000000\n958.000000\n\n\nmean\n0.449857\n12.772706\n\n\nstd\n0.178127\n2.367667\n\n\nmin\n0.114355\n6.498364\n\n\n25%\n0.373691\n11.199439\n\n\n50%\n0.431747\n12.984060\n\n\n75%\n0.479224\n14.658077\n\n\nmax\n1.202888\n16.677952\n\n\n\n\n\n\n\n\n\nds_avg = mikeio.read(fn_avg)\nds_avg.describe()\n\n\n\n\n\n\n\n\n\nSurface elevation\nWind speed\n\n\n\n\ncount\n958.000000\n958.000000\n\n\nmean\n0.449857\n12.772706\n\n\nstd\n0.178127\n2.367667\n\n\nmin\n0.114355\n6.498364\n\n\n25%\n0.373691\n11.199439\n\n\n50%\n0.431747\n12.984060\n\n\n75%\n0.479224\n14.658077\n\n\nmax\n1.202888\n16.677952",
     "crumbs": [
       "Home",
       "Examples",
@@ -1086,7 +1086,72 @@
     "href": "user-guide/mesh.html",
     "title": "Mesh",
     "section": "",
-    "text": "read mesh file\nplot mesh\nconvert to shapely\ncheck if point is inside or outside mesh\nsubset mesh, plot subset\nchange z values\nchange boundary codes\n\n\nimport matplotlib.pyplot as plt\nimport mikeio\n\n\nmsh = mikeio.Mesh(\"../data/odense_rough.mesh\")\nmsh\n\n<Mesh>\nnumber of elements: 654\nnumber of nodes: 399\nprojection: UTM-33\n\n\n\nmsh.plot()\n\n\n\n\n\n\n\n\n\nmsh.plot.boundary_nodes(boundary_names=['Land','Open boundary']);\n\n\n\n\n\n\n\n\n\nConvert mesh to shapely\nConvert mesh to shapely MultiPolygon object, requires that the shapely library is installed.\n\nmp = msh.to_shapely()\nmp\n\n\n\n\n\n\n\n\nNow a lot of methods are available\n\nmp.area\n\n68931409.58160606\n\n\n\nmp.bounds\n\n(211068.501175313, 6153077.66681803, 224171.617336507, 6164499.42751662)\n\n\n\ndomain = mp.buffer(0)\ndomain\n\n\n\n\n\n\n\n\n\nopen_water = domain.buffer(-500)\n\ncoastalzone = domain - open_water\ncoastalzone\n\n\n\n\n\n\n\n\nFind if points are inside the domain\n\nfrom shapely.geometry import Point\n\np1 = Point(216000, 6162000)\np2 = Point(220000, 6156000)\nprint(mp.contains(p1))\nprint(mp.contains(p2))\n\nTrue\nFalse\n\n\nWe can get similar functionality from the .geometry attribute of the mesh object.\n\np1p2 = [[216000, 6162000], [220000, 6156000]]\nmsh.geometry.contains(p1p2)\n\narray([ True, False])\n\n\n\nax = msh.plot()\nax.scatter(p1.x, p1.y, marker=\"*\", s=200, c=\"red\", label=\"inside\")\nax.scatter(p2.x, p2.y, marker=\"+\", s=200, c=\"green\", label=\"outside\")\nax.legend();\n\n\n\n\n\n\n\n\n\n\nChange z values and boundary code\nAssume that we want to have a minimum depth of 2 meters and change the open boundary (code 2) to a closed one (code 1).\n\ng = msh.geometry\nprint(f'max z before: {g.node_coordinates[:,2].max()}')\nzc = g.node_coordinates[:,2]\nzc[zc>-2] = -2\ng.node_coordinates[:,2] = zc\nprint(f'max z after: {g.node_coordinates[:,2].max()}')\n\nmax z before: -0.200000002980232\nmax z after: -2.0\n\n\n\nc = g.codes\nc[c==2] = 1\ng.codes = c\n\nSave the modfied geometry to a new mesh file\n\ng.to_mesh(\"new_mesh.mesh\")\n\nCleanup\n\nimport os\n\nos.remove(\"new_mesh.mesh\")"
+    "text": "import matplotlib.pyplot as plt\nimport mikeio",
+    "crumbs": [
+      "Home",
+      "User Guide",
+      "Mesh"
+    ]
+  },
+  {
+    "objectID": "user-guide/mesh.html#read-mesh-file",
+    "href": "user-guide/mesh.html#read-mesh-file",
+    "title": "Mesh",
+    "section": "Read mesh file",
+    "text": "Read mesh file\n\nmsh = mikeio.Mesh(\"../data/odense_rough.mesh\")\nmsh\n\n<Mesh>\nnumber of elements: 654\nnumber of nodes: 399\nprojection: UTM-33",
+    "crumbs": [
+      "Home",
+      "User Guide",
+      "Mesh"
+    ]
+  },
+  {
+    "objectID": "user-guide/mesh.html#plot-mesh",
+    "href": "user-guide/mesh.html#plot-mesh",
+    "title": "Mesh",
+    "section": "Plot mesh",
+    "text": "Plot mesh\n\nmsh.plot()\n\n\n\n\n\n\n\n\n\nmsh.plot.boundary_nodes(boundary_names=['Land','Open boundary']);",
+    "crumbs": [
+      "Home",
+      "User Guide",
+      "Mesh"
+    ]
+  },
+  {
+    "objectID": "user-guide/mesh.html#check-if-points-are-inside-the-domain",
+    "href": "user-guide/mesh.html#check-if-points-are-inside-the-domain",
+    "title": "Mesh",
+    "section": "Check if points are inside the domain",
+    "text": "Check if points are inside the domain\n\nfrom shapely.geometry import Point\n\np1 = Point(216000, 6162000)\np2 = Point(220000, 6156000)\nprint(mp.contains(p1))\nprint(mp.contains(p2))\n\nTrue\nFalse\n\n\nWe can get similar functionality from the .geometry attribute of the mesh object.\n\np1p2 = [[216000, 6162000], [220000, 6156000]]\nmsh.geometry.contains(p1p2)\n\narray([ True, False])\n\n\n\nax = msh.plot()\nax.scatter(p1.x, p1.y, marker=\"*\", s=200, c=\"red\", label=\"inside\")\nax.scatter(p2.x, p2.y, marker=\"+\", s=200, c=\"green\", label=\"outside\")\nax.legend();",
+    "crumbs": [
+      "Home",
+      "User Guide",
+      "Mesh"
+    ]
+  },
+  {
+    "objectID": "user-guide/mesh.html#change-z-values-and-boundary-code",
+    "href": "user-guide/mesh.html#change-z-values-and-boundary-code",
+    "title": "Mesh",
+    "section": "Change z values and boundary code",
+    "text": "Change z values and boundary code\nAssume that we want to have a minimum depth of 2 meters and change the open boundary (code 2) to a closed one (code 1).\n\ng = msh.geometry\nprint(f'max z before: {g.node_coordinates[:,2].max()}')\nzc = g.node_coordinates[:,2]\nzc[zc>-2] = -2\ng.node_coordinates[:,2] = zc\nprint(f'max z after: {g.node_coordinates[:,2].max()}')\n\nmax z before: -0.200000002980232\nmax z after: -2.0\n\n\n\nc = g.codes\nc[c==2] = 1\ng.codes = c",
+    "crumbs": [
+      "Home",
+      "User Guide",
+      "Mesh"
+    ]
+  },
+  {
+    "objectID": "user-guide/mesh.html#save-the-modfied-geometry-to-a-new-mesh-file",
+    "href": "user-guide/mesh.html#save-the-modfied-geometry-to-a-new-mesh-file",
+    "title": "Mesh",
+    "section": "Save the modfied geometry to a new mesh file",
+    "text": "Save the modfied geometry to a new mesh file\n\ng.to_mesh(\"new_mesh.mesh\")\n\nCleanup\n\nimport os\n\nos.remove(\"new_mesh.mesh\")",
+    "crumbs": [
+      "Home",
+      "User Guide",
+      "Mesh"
+    ]
   },
   {
     "objectID": "user-guide/data-structures.html",
diff --git a/user-guide/data-structures.html b/user-guide/data-structures.html
index 91470e5d3..f19299b42 100644
--- a/user-guide/data-structures.html
+++ b/user-guide/data-structures.html
@@ -206,6 +206,12 @@ 
    
   
  Dfsu and Mesh Overview
    + +
  • +
    + + Mesh +
  • diff --git a/user-guide/dataarray.html b/user-guide/dataarray.html index ce9c44d27..ea29d9685 100644 --- a/user-guide/dataarray.html +++ b/user-guide/dataarray.html @@ -242,6 +242,12 @@

    DataArray

    Dfsu and Mesh Overview
    +
    • +
  • +
    + + Mesh +
  • @@ -359,7 +365,7 @@

    DataArray

  • values - a numpy.ndarray

    • Use DataArray’s string representation to get an overview of the DataArray

    -
    +
    import mikeio
 
 ds = mikeio.read("../data/HD2D.dfsu")
@@ -375,7 +381,7 @@ 
    DataArray

    Temporal selection

    -
    +
    da.sel(time="1985-08-06 12:00")
    <mikeio.DataArray>
@@ -386,7 +392,7 @@ 
    
+
    
 
    da["1985-8-7":]
    
 
    
 
    <mikeio.DataArray>
@@ -400,7 +406,7 @@ 
    
 
     Spatial selection
    
 
    The sel method finds the nearest element.
    
-
    
+
    
 
    da.sel(x=607002, y=6906734)
    
 
    
 
    <mikeio.DataArray>
@@ -415,17 +421,17 @@ 
    
 
    Modifying values
    
 
    You can modify the values of a DataArray by changing its values:
    
-
    
+
    
 
    da.values[0, 3] = 5.0
    
 
    
 
    If you wish to change values of a subset of the DataArray you should be aware of the difference between a view and a copy of the data. Similar to NumPy, MIKE IO selection method will return a view of the data when using single index and slices, but a copy of the data using fancy indexing (a list of indicies or boolean indexing). Note that prior to release 1.3, MIKE IO would always return a copy.
    
 
    It is recommended to change the values using values property directly on the original DataArray (like above), but it is also possible to change the values of the original DataArray by working on a subset DataArray if it is selected with single index or slice as explained above.
    
-
    
+
    
 
    da_sub = da.isel(time=0)
 da_sub.values[:] = 5.0    # will change da
    
 
    
 
    Fancy indexing will return a copy and therefore not change the original:
    
-
    
+
    
 
    da_sub = da.isel(time=[0,1,2])
 da_sub.values[:] = 5.0    # will NOT change da
    
 
    
@@ -433,7 +439,7 @@ 
    Modifying values
    
 
    
 
     Plotting
    
 
    The plotting of a DataArray is context-aware meaning that plotting behaviour depends on the geometry of the DataArray being plotted.
    
-
    
+
    
 
    da.plot()
    
 
    
 
    
@@ -443,7 +449,7 @@ 
    
 
    
 
    
-
    
+
    
 
    da.plot.contourf()
    
 
    
 
    
@@ -453,7 +459,7 @@ 
    
 
    
 
    
-
    
+
    
 
    da.plot.mesh()
    
 
    
 
    
diff --git a/user-guide/dataset.html b/user-guide/dataset.html
index c93c413ea..ca9d409b6 100644
--- a/user-guide/dataset.html
+++ b/user-guide/dataset.html
@@ -242,6 +242,12 @@ 
    
   
  Dfsu and Mesh Overview
   
    
+
+          
  • 
+  
     
+  
+ Mesh
+  
    
 
    • 
           
  • 
   
     
diff --git a/user-guide/dfs0.html b/user-guide/dfs0.html
index ad03ba210..6e3e6207a 100644
--- a/user-guide/dfs0.html
+++ b/user-guide/dfs0.html
@@ -243,6 +243,12 @@ 
    Dfs0
    
   
  Dfsu and Mesh Overview
   
    
+
    • 
+          
  • 
+  
     
+  
+ Mesh
+  
    
 
    • 
           
  • 
   
     
@@ -352,7 +358,7 @@ 
    Dfs0
    
 
 
    
 
    Read Dfs0 to Dataset
    
-
    
+
    
 
    import mikeio
 
 ds = mikeio.read("../data/da_diagnostic.dfs0")
@@ -372,7 +378,7 @@ 
    Read Dfs0 to Dataset<
 
    
 
    
 
    From Dfs0 to pandas DataFrame
    
-
    
+
    
 
    df = ds.to_dataframe()
 df.head()
    
 
    
@@ -449,7 +455,7 @@ 
    From pandas
 
    MIKE IO adds a new method to the DataFrame called to_dfs0. This method is used to save the DataFrame to a dfs0 file. (This method becomes available after importing the mikeio module.)
    
 
    
 
    
-
    
+
    
 
    import pandas as pd
 
 df = pd.read_csv(
diff --git a/user-guide/dfs1.html b/user-guide/dfs1.html
index f5c8a8151..6ce2f5794 100644
--- a/user-guide/dfs1.html
+++ b/user-guide/dfs1.html
@@ -240,6 +240,12 @@ 
    Dfs1
    
   
  Dfsu and Mesh Overview
   
    
+
    • 
+          
  • 
+  
     
+  
+ Mesh
+  
    
 
    • 
           
  • 
   
     
@@ -339,7 +345,7 @@ 
    Dfs1
    
 
 
 
    A dfs1 file contains node-based line series data. Dfs1 files do not contain enough metadata to determine their geographical position, but have a relative distance from the origo.
    
-
    
+
    
 
    import mikeio
 
 ds = mikeio.read("../data/tide1.dfs1")
@@ -356,7 +362,7 @@ 
    Dfs1
    
 
    
 
    Grid 1D
    
 
    The spatial information is available in the geometry attribute (accessible from Dfs1, Dataset, and DataArray), which in the case of a dfs1 file is a Grid1D geometry.
    
-
    
+
    
 
    ds.geometry
    
 
    
 
    <mikeio.Grid1D>
diff --git a/user-guide/dfs2.html b/user-guide/dfs2.html
index 005f391e5..c4f60dfe3 100644
--- a/user-guide/dfs2.html
+++ b/user-guide/dfs2.html
@@ -240,6 +240,12 @@ 
    Dfs2
    
   
  Dfsu and Mesh Overview
   
    
+
    • 
+          
  • 
+  
     
+  
+ Mesh
+  
    
 
    • 
           
  • 
   
     
@@ -341,7 +347,7 @@ 
    Dfs2
    
 
 
 
    A dfs2 file is also called a grid series file. Values in a dfs2 file are ‘element based’, i.e. values are defined in the centre of each grid cell.
    
-
    
+
    
 
    import mikeio
 ds = mikeio.read("../data/gebco_sound.dfs2")
 ds
    
@@ -357,7 +363,7 @@ 
    Dfs2
    
 
    
 
    Subset in space
    
 
    The most convenient way to subset in space is to use the sel method, which returns a new (smaller) dataset, which can be further processed or written to disk using the to_dfs method.
    
-
    
+
    
 
    ds.geometry
    
 
    
 
    <mikeio.Grid2D>
@@ -366,7 +372,7 @@ 
    Subset in space
    
 projection: LONG/LAT
    
 
    
 
    
-
    
+
    
 
    ds_aoi = ds.sel(x=slice(12.5, 13.0), y=slice(55.5, 56.0))
 ds_aoi.geometry
    
 
    
@@ -377,7 +383,7 @@ 
    Subset in space
    
 
    
 
    
 
    In order to specify an open-ended subset (i.e. where the end of the subset is the end of the domain), use None as the end of the slice.
    
-
    
+
    
 
    ds.sel(x=slice(None, 13.0))
    
 
    
 
    <mikeio.Dataset>
@@ -392,7 +398,7 @@ 
    Subset in space
    
 
    
 
    Grid2D
    
 
    The spatial information is available in the geometry attribute (accessible from Dfs2, Dataset, and DataArray), which in the case of a dfs2 file is a Grid2D geometry.
    
-
    
+
    
 
    ds.geometry
    
 
    
 
    <mikeio.Grid2D>
diff --git a/user-guide/dfsu.html b/user-guide/dfsu.html
index 348ce1329..7c4a56489 100644
--- a/user-guide/dfsu.html
+++ b/user-guide/dfsu.html
@@ -64,7 +64,7 @@
 
 
 
-
+
 
 
 
@@ -240,6 +240,12 @@ 
    Dfsu and Mesh Overview
    
   
  Dfsu and Mesh Overview
   
    
+
    • 
+          
  • 
+  
     
+  
+ Mesh
+  
    
 
    • 
           
  • 
   
     
@@ -488,7 +494,7 @@ 
    MIKE IO Fle
 
 
    These properties and methods are accessible from the geometry, but also from the Mesh/Dfsu object.
    
 
    If a .dfsu file is read with mikeio.read, the returned Dataset ds will contain a Flexible Mesh Geometry geometry. If a .dfsu or a .mesh file is opened with mikeio.open, the returned object will also contain a Flexible Mesh Geometry geometry.
    
-
    
+
    
 
    import mikeio
 
 ds = mikeio.read("../data/oresundHD_run1.dfsu")
@@ -500,7 +506,7 @@ 
    MIKE IO Fle
 projection: UTM-33
    
 
    
 
    
-
    
+
    
 
    dfs = mikeio.open("../data/oresundHD_run1.dfsu")
 dfs.geometry
    
 
    
@@ -932,8 +938,8 @@ 
    Dfsu types
    
                 
   
    
   
    
-      
-        EUM 
+      
+        Mesh 
       
   
    
 
diff --git a/user-guide/eum.html b/user-guide/eum.html
index 4bb315a7b..8b3f194ff 100644
--- a/user-guide/eum.html
+++ b/user-guide/eum.html
@@ -65,7 +65,7 @@
 
 
 
-
+
 
 
 
@@ -240,6 +240,12 @@ 
    EUM
    
   
  Dfsu and Mesh Overview
   
    
+
    • 
+          
  • 
+  
     
+  
+ Mesh
+  
    
 
    • 
           
  • 
   
     
@@ -345,7 +351,7 @@ 
    EUM
    
 
  • unit - an EUMUnit
    • 
 
 
    The ItemInfo class has some sensible defaults, thus you can specify only a name or a type. If you don’t specify a unit, the default unit for that type will be used.
    
-
    
+
    
 
    from mikeio import ItemInfo, EUMType, EUMUnit
 
 item = ItemInfo("Viken", EUMType.Water_Level)
@@ -354,47 +360,47 @@ 
    EUM
    
 
    Viken <Water Level> (meter)
    
 
    
 
    
-
    
+
    
 
    ItemInfo(EUMType.Wind_speed)
    
 
    
 
    Wind speed <Wind speed> (meter per sec)
    
 
    
 
    
-
    
+
    
 
    ItemInfo("Viken", EUMType.Water_Level, EUMUnit.feet)
    
 
    
 
    Viken <Water Level> (feet)
    
 
    
 
    
 
    Matching units for specific type:
    
-
    
+
    
 
    EUMType.Wind_speed.units
    
 
    
 
    [meter per sec, feet per sec, knot, km per hour, miles per hour]
    
 
    
 
    
 
    Default unit:
    
-
    
+
    
 
    EUMType.Precipitation_Rate.units[0]
    
 
    
 
    mm per day
    
 
    
 
    
-
    
+
    
 
    unit = EUMType.Precipitation_Rate.units[0]
 unit
    
 
    
 
    mm per day
    
 
    
 
    
-
    
+
    
 
    type(unit)
    
 
    
 
    <enum 'EUMUnit'>
    
 
    
 
    
 
    a [](mikeio.EUMUnit)` is encoded as integers, which you can utilize in some MIKE applications.
    
-
    
+
    
 
    int(unit)
 2004
    
 
    
@@ -404,7 +410,7 @@ 
    EUM
    
 
    
 
    
 
    
-
    
+
    +
    +

    Plot mesh

    +
    msh.plot()
    @@ -370,7 +380,7 @@

    Mesh

    -
    +
    msh.plot.boundary_nodes(boundary_names=['Land','Open boundary']);
    @@ -380,10 +390,11 @@

    Mesh

    +

    Convert mesh to shapely

    Convert mesh to shapely MultiPolygon object, requires that the shapely library is installed.

    -
    +
    mp = msh.to_shapely()
 mp
    @@ -395,19 +406,19 @@

    Convert mesh to shapely

    Now a lot of methods are available

    -
    +
    mp.area
    68931409.58160606
    -
    +
    mp.bounds
    (211068.501175313, 6153077.66681803, 224171.617336507, 6164499.42751662)
    -
    +
    domain = mp.buffer(0)
 domain
    @@ -418,7 +429,7 @@

    Convert mesh to shapely

    -
    +
    open_water = domain.buffer(-500)
 
 coastalzone = domain - open_water
@@ -431,8 +442,9 @@ 
    Convert mesh to shapely
    -

    Find if points are inside the domain

    -
    +
    +

    Check if points are inside the domain

    +
    from shapely.geometry import Point
 
 p1 = Point(216000, 6162000)
@@ -445,14 +457,14 @@ 
    Convert mesh to shapely

    We can get similar functionality from the .geometry attribute of the mesh object.

    -
    +
    p1p2 = [[216000, 6162000], [220000, 6156000]]
 msh.geometry.contains(p1p2)
    array([ True, False])
    -
    +
    ax = msh.plot()
 ax.scatter(p1.x, p1.y, marker="*", s=200, c="red", label="inside")
 ax.scatter(p2.x, p2.y, marker="+", s=200, c="green", label="outside")
@@ -466,10 +478,10 @@ 
    Convert mesh to shapely
    -
    -

    Change z values and boundary code

    +
    +

    Change z values and boundary code

    Assume that we want to have a minimum depth of 2 meters and change the open boundary (code 2) to a closed one (code 1).

    -
    +
    g = msh.geometry
 print(f'max z before: {g.node_coordinates[:,2].max()}')
 zc = g.node_coordinates[:,2]
@@ -481,23 +493,26 @@ 
    Change z values and boundary code
    
 max z after: -2.0
    -
    +
    c = g.codes
 c[c==2] = 1
 g.codes = c
    -

    Save the modfied geometry to a new mesh file

    -
    +
    +
    +

    Save the modfied geometry to a new mesh file

    +
    g.to_mesh("new_mesh.mesh")

    Cleanup

    -
    +
    import os
 
 os.remove("new_mesh.mesh")
    +
    @@ -888,6 +903,18 @@

    Change z values and boundary code

    } }); +