From e2498e6d162f586807ca1e43efc254886356fa63 Mon Sep 17 00:00:00 2001 From: ecomodeller Date: Wed, 13 Mar 2024 11:49:37 +0000 Subject: [PATCH] deploy: 6127a045164f3656ee82809b8815cd37d9626edf --- api/DataArray.html | 32 ++++----- api/Dataset.html | 2 +- api/Dfs0.html | 12 ++-- api/EUMType.html | 4 +- api/EUMUnit.html | 2 +- api/Grid1D.html | 10 +-- api/ItemInfo.html | 4 +- api/Mesh.html | 4 +- api/PfsDocument.html | 6 +- api/PfsSection.html | 6 +- ...ataset._data_plot._DataArrayPlotterFM.html | 12 ++-- ...et._data_plot._DataArrayPlotterGrid2D.html | 8 +-- examples/Dfsu-2D-interpolation.html | 58 ++++++++-------- examples/Generic.html | 66 +++++++++---------- examples/Time-interpolation.html | 28 ++++---- examples/dfs2/bathy.html | 44 ++++++------- examples/dfs2/gfs.html | 42 ++++++------ examples/index.html | 12 ++-- index.html | 2 +- search.json | 12 ++-- user-guide/dataarray.html | 20 +++--- user-guide/dfs0.html | 6 +- user-guide/dfs1.html | 4 +- user-guide/dfs2.html | 10 +-- user-guide/dfsu.html | 4 +- user-guide/eum.html | 18 ++--- user-guide/getting-started.html | 10 +-- user-guide/mesh.html | 32 ++++----- user-guide/pfs.html | 20 +++--- 29 files changed, 245 insertions(+), 245 deletions(-) diff --git a/api/DataArray.html b/api/DataArray.html index de9c19253..f11d41209 100644 --- a/api/DataArray.html +++ b/api/DataArray.html @@ -411,7 +411,7 @@

DataArray

Examples

-
+
import pandas as pd
 import mikeio
 
@@ -810,7 +810,7 @@ 
Returns

Examples

-
+
da1 = mikeio.read("../data/HD2D.dfsu", time=[0,1])[0]
 da2 = mikeio.read("../data/HD2D.dfsu", time=[2,3])[0]
 da1.time
@@ -818,7 +818,7 @@

Examples

DatetimeIndex(['1985-08-06 07:00:00', '1985-08-06 09:30:00'], dtype='datetime64[ns]', freq=None)
-
+
da3 = mikeio.DataArray.concat([da1,da2])
 da3
@@ -1116,7 +1116,7 @@

interp_na

Wrapper of xarray.DataArray.interpolate_na

Examples

-
+
import numpy as np
 import pandas as pd
 time = pd.date_range("2000", periods=3, freq="D")
@@ -1131,7 +1131,7 @@ 
Examples

 values: [0, nan, 2]
-
+
da.interp_na()
<mikeio.DataArray>
@@ -1314,7 +1314,7 @@ 
See Also

Examples

-
+
da = mikeio.read("../data/europe_wind_long_lat.dfs2")[0]
 da
@@ -1325,7 +1325,7 @@

Examples

geometry: Grid2D (ny=101, nx=221)
-
+
da.isel(time=-1)
<mikeio.DataArray>
@@ -1335,7 +1335,7 @@ 
Examples

 geometry: Grid2D (ny=101, nx=221)
-
+
da.isel(x=slice(10,20), y=slice(40,60))
<mikeio.DataArray>
@@ -1345,7 +1345,7 @@ 
Examples

 geometry: Grid2D (ny=20, nx=10)
-
+
da = mikeio.read("../data/oresund_sigma_z.dfsu").Temperature
 da.isel(element=range(200))
@@ -2022,7 +2022,7 @@

See Also

Examples

-
+
da = mikeio.read("../data/random.dfs1")[0]
 da
@@ -2033,7 +2033,7 @@

Examples

geometry: Grid1D (n=3, dx=100)
-
+
da.sel(time=slice(None, "2012-1-1 00:02"))
<mikeio.DataArray>
@@ -2043,7 +2043,7 @@ 
Examples

 geometry: Grid1D (n=3, dx=100)
-
+
da.sel(x=100)
<mikeio.DataArray>
@@ -2054,7 +2054,7 @@ 
Examples

 values: [0.3231, 0.6315, ..., 0.7506]
-
+
da = mikeio.read("../data/oresund_sigma_z.dfsu").Temperature
 da
@@ -2070,7 +2070,7 @@

Examples

projection: UTM-33
-
+
da.sel(time="1997-09-15")
<mikeio.DataArray>
@@ -2086,7 +2086,7 @@ 
Examples

 values: [16.31, 16.43, ..., 16.69]
-
+
da.sel(x=340000, y=6160000, z=-3)
<mikeio.DataArray>
@@ -2097,7 +2097,7 @@ 
Examples

 values: [17.54, 17.31, 17.08]
-
+
da.sel(layers="bottom")
<mikeio.DataArray>
diff --git a/api/Dataset.html b/api/Dataset.html
index ffe705714..ceb815f51 100644
--- a/api/Dataset.html
+++ b/api/Dataset.html
@@ -428,7 +428,7 @@ 
Selecting Items

Examples

-
+
import mikeio
 mikeio.read("../data/europe_wind_long_lat.dfs2")
diff --git a/api/Dfs0.html b/api/Dfs0.html index db396ab59..c259250e8 100644 --- a/api/Dfs0.html +++ b/api/Dfs0.html @@ -396,10 +396,10 @@

from_dataframe<

Parameters

----++++ @@ -437,7 +437,7 @@

Parameters

- + @@ -446,7 +446,7 @@

Parameters

read

-

Dfs0.read(items=None, time=None, keepdims=False)

+

Dfs0.read(items=None, time=None, **kwargs)

Read data from a dfs0 file.

Parameters

diff --git a/api/EUMType.html b/api/EUMType.html index 1dee8f93b..8a54cbf9e 100644 --- a/api/EUMType.html +++ b/api/EUMType.html @@ -370,14 +370,14 @@

EUMType

EUM type

Examples

-
+
import mikeio
 mikeio.EUMType.Temperature
Temperature
-
+
mikeio.EUMType.Temperature.units
[degree Celsius, degree Fahrenheit, degree Kelvin]
diff --git a/api/EUMUnit.html b/api/EUMUnit.html index ecb8de211..4064bda2c 100644 --- a/api/EUMUnit.html +++ b/api/EUMUnit.html @@ -370,7 +370,7 @@

EUMUnit

EUM unit

Examples

-
+
import mikeio
 mikeio.EUMUnit.degree_Kelvin
diff --git a/api/Grid1D.html b/api/Grid1D.html index 336a110ec..8f310bc10 100644 --- a/api/Grid1D.html +++ b/api/Grid1D.html @@ -451,7 +451,7 @@

Parameters

Examples

-
+
import mikeio
 mikeio.Grid1D(nx=3,dx=0.1)
@@ -459,7 +459,7 @@

Examples

x: [0, 0.1, 0.2] (nx=3, dx=0.1)
-
+
mikeio.Grid1D(x=[0.1, 0.5, 0.9])
<mikeio.Grid1D>
@@ -639,7 +639,7 @@ 
Returns

Examples

-
+
import mikeio
 g = mikeio.Grid1D(nx=3,dx=0.1)
 g
@@ -648,14 +648,14 @@

Examples

x: [0, 0.1, 0.2] (nx=3, dx=0.1)
-
+
g.isel([1,2])
<mikeio.Grid1D>
 x: [0.1, 0.2] (nx=2, dx=0.1)
-
+
g.isel(1)
GeometryUndefined()
diff --git a/api/ItemInfo.html b/api/ItemInfo.html index 1557f5cd5..8668aea12 100644 --- a/api/ItemInfo.html +++ b/api/ItemInfo.html @@ -419,14 +419,14 @@

Parameters

Examples

-
+
import mikeio
 mikeio.ItemInfo("Viken", mikeio.EUMType.Water_Level)
Viken <Water Level> (meter)
-
+
mikeio.ItemInfo(mikeio.EUMType.Wind_speed)
Wind speed <Wind speed> (meter per sec)
diff --git a/api/Mesh.html b/api/Mesh.html index 55b2b8d05..a245698aa 100644 --- a/api/Mesh.html +++ b/api/Mesh.html @@ -416,7 +416,7 @@

Attributes

Examples

-
+
import mikeio
 mikeio.Mesh("../data/odense_rough.mesh")
@@ -470,7 +470,7 @@

Returns

Examples

-
+
import mikeio
 msh = mikeio.open("../data/odense_rough.mesh")
 msh.to_shapely()
diff --git a/api/PfsDocument.html b/api/PfsDocument.html index 11eabea89..9e45dc65e 100644 --- a/api/PfsDocument.html +++ b/api/PfsDocument.html @@ -583,7 +583,7 @@

Parameters

Examples

-
+
import pandas as pd
 import mikeio
 df = pd.DataFrame(dict(station=["Foo", "Bar"],include=[0,1]), index=[1,2])
@@ -619,7 +619,7 @@ 
Examples
-
+
mikeio.PfsSection.from_dataframe(df,"STATION_")
[STATION_1]
@@ -787,7 +787,7 @@ 
Returns

Examples

-
+
pfs = mikeio.read_pfs("../data/pfs/lake.sw")
 pfs.SW.OUTPUTS.to_dataframe(prefix="OUTPUT_")
diff --git a/api/PfsSection.html b/api/PfsSection.html index 4f3cf5c4b..f32fe7335 100644 --- a/api/PfsSection.html +++ b/api/PfsSection.html @@ -502,7 +502,7 @@

Parameters

Examples

-
+
import pandas as pd
 import mikeio
 df = pd.DataFrame(dict(station=["Foo", "Bar"],include=[0,1]), index=[1,2])
@@ -538,7 +538,7 @@ 
Examples
-
+
mikeio.PfsSection.from_dataframe(df,"STATION_")
[STATION_1]
@@ -701,7 +701,7 @@ 
Returns

Examples

-
+
pfs = mikeio.read_pfs("../data/pfs/lake.sw")
 pfs.SW.OUTPUTS.to_dataframe(prefix="OUTPUT_")
diff --git a/api/dataset._data_plot._DataArrayPlotterFM.html b/api/dataset._data_plot._DataArrayPlotterFM.html index 97b8941a3..dc5b8a149 100644 --- a/api/dataset._data_plot._DataArrayPlotterFM.html +++ b/api/dataset._data_plot._DataArrayPlotterFM.html @@ -381,7 +381,7 @@

dataset._data_plot._DataArrayPlotterFM

If DataArray is 3D the surface layer will be plotted.

Examples

-
+
import mikeio
 da = mikeio.read("../data/HD2D.dfsu")["Surface elevation"]
 da.plot()
@@ -440,7 +440,7 @@

Examples

-
+
da = mikeio.read("../data/HD2D.dfsu")["Surface elevation"]
 da.plot.contour()
@@ -459,7 +459,7 @@

Examples

-
+
da = mikeio.read("../data/HD2D.dfsu")["Surface elevation"]
 da.plot.contourf()
@@ -554,7 +554,7 @@

Examples

-
+
da = mikeio.read("../data/HD2D.dfsu")["Surface elevation"]
 da.plot.mesh()
@@ -573,7 +573,7 @@

Examples

-
+
da = mikeio.read("../data/HD2D.dfsu")["Surface elevation"]
 da.plot.outline()
@@ -592,7 +592,7 @@

Examples

-
+
da = mikeio.read("../data/HD2D.dfsu")["Surface elevation"]
 da.plot.patch()
diff --git a/api/dataset._data_plot._DataArrayPlotterGrid2D.html b/api/dataset._data_plot._DataArrayPlotterGrid2D.html index 17816f11e..00873e09b 100644 --- a/api/dataset._data_plot._DataArrayPlotterGrid2D.html +++ b/api/dataset._data_plot._DataArrayPlotterGrid2D.html @@ -378,7 +378,7 @@

dataset._data_plot._DataArrayPlotterGrid2DIf DataArray has multiple time steps, the first step will be plotted.

Examples

-
+
import mikeio
 da = mikeio.read("../data/gebco_sound.dfs2")["Elevation"]
 da.plot()
@@ -429,7 +429,7 @@

Examples

-
+
da = mikeio.read("../data/gebco_sound.dfs2")["Elevation"]
 da.plot.contour()
@@ -448,7 +448,7 @@

Examples

-
+
da = mikeio.read("../data/gebco_sound.dfs2")["Elevation"]
 da.plot.contourf()
@@ -543,7 +543,7 @@

Examples

-
+
da = mikeio.read("../data/gebco_sound.dfs2")["Elevation"]
 da.plot.pcolormesh()
diff --git a/examples/Dfsu-2D-interpolation.html b/examples/Dfsu-2D-interpolation.html index dbe2be866..18a4a0122 100644 --- a/examples/Dfsu-2D-interpolation.html +++ b/examples/Dfsu-2D-interpolation.html @@ -383,10 +383,10 @@

Dfsu - 2D interpolation

-
+
import mikeio
-
+
ds = mikeio.read("../data/wind_north_sea.dfsu", items="Wind speed")
 ds
@@ -398,7 +398,7 @@

Dfsu - 2D interpolation

0: Wind speed <Wind speed> (meter per sec)
-
+
da = ds.Wind_speed
 da.plot();
@@ -416,7 +416,7 @@

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
    @@ -426,7 +426,7 @@

    Interpolate to grid

    -
    +
    da_grid = da.interp_like(g)
 da_grid
    @@ -437,7 +437,7 @@

    Interpolate to grid

    -
    +
    da_grid.plot();
    @@ -450,14 +450,14 @@

    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")
    @@ -478,7 +478,7 @@

    Save to GeoTiff

    This section requires the rasterio package.

    -
    +
    import numpy as np
 import rasterio
 from rasterio.transform import from_origin
@@ -502,7 +502,7 @@ 
    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
    
 
    
@@ -512,7 +512,7 @@ 
    Interpolate to other mesh
    
 projection: LONG/LAT
    -
    +
    dsi = da.interp_like(msh)
 dsi
    @@ -523,7 +523,7 @@

    Interpolate to other mesh

    geometry: Dfsu2D (2259 elements, 1296 nodes)
    -
    +
    da[0].plot(figsize=(9,7), show_mesh=True);
    @@ -533,7 +533,7 @@

    Interpolate to other mesh

    -
    +
    dsi[0].plot(figsize=(9,7), show_mesh=True);
    @@ -545,14 +545,14 @@

    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])
    @@ -561,10 +561,10 @@

    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
    @@ -577,14 +577,14 @@

    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();
    @@ -594,7 +594,7 @@

    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
    @@ -611,35 +611,35 @@

    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
    @@ -650,7 +650,7 @@

    Interpola geometry: Dfsu2D (958 elements, 570 nodes)

    -
    +
    da.plot(title="Interpolated scatter data");
    @@ -660,13 +660,13 @@

    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 771fad018..78ab802e9 100644
--- a/examples/Generic.html
+++ b/examples/Generic.html
@@ -394,7 +394,7 @@ 
    Generic dfs processing
    
 
  • quantile: Create temporal quantiles of dfs file
    • 
 
 
-
    
+
    
 
    import matplotlib.pyplot as plt
 import mikeio
 import mikeio.generic
    
@@ -402,7 +402,7 @@ 
    Generic dfs processing
    
 
    
 
    Concatenation
    
 
    Take a look at these two files with overlapping timesteps.
    
-
    
+
    
 
    t1 = mikeio.read("../data/tide1.dfs1")
 t1
    
 
    
@@ -414,7 +414,7 @@ 
    Concatenation
    
   0:  Level <Water Level> (meter)
    -
    +
    t2 = mikeio.read("../data/tide2.dfs1")
 t2
    @@ -427,7 +427,7 @@

    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()
    @@ -439,15 +439,15 @@

    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, 510.41it/s]
    +
      0%|          | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 516.32it/s]
    -
    +
    c = mikeio.read("concat.dfs1")
 c[0].isel(x=1).plot()
 c
    @@ -471,16 +471,16 @@

    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, 2158.23it/s]
    +
      0%|          | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2733.87it/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')
@@ -504,11 +504,11 @@ 
    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
    
 
    
@@ -520,11 +520,11 @@ 
    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
    @@ -545,7 +545,7 @@

    Extract time s

    Scaling

    Adding a constant e.g to adjust datum

    -
    +
    ds = mikeio.read("../data/gebco_sound.dfs2")
 ds.Elevation[0].plot();
    @@ -556,23 +556,23 @@

    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, 985.04it/s]
    +
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1135.74it/s]
    -
    +
    ds2 = mikeio.read("gebco_sound_local_datum.dfs2")
 ds2['Elevation'][0].plot()
    @@ -583,7 +583,7 @@

    Scaling

    -
    +
    ds2['Elevation'][0,104,131].to_numpy()
    -3.1
    @@ -591,7 +591,7 @@

    Scaling

    Spatially varying correction

    -
    +
    import numpy as np
 factor = np.ones_like(ds['Elevation'][0].to_numpy())
 factor.shape
    @@ -600,7 +600,7 @@

    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
@@ -615,7 +615,7 @@ 
    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", 
@@ -623,10 +623,10 @@ 
    Spatially var
                      factor=factor_vec
                      )
    -
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 945.73it/s]
    +
      0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1194.96it/s]
    -
    +
    ds3 = mikeio.read("gebco_sound_spatial.dfs2")
 ds3.Elevation[0].plot();
    @@ -641,15 +641,15 @@

    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, 15823.04it/s]
    +
      0%|          | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 16410.22it/s]
    -
    +
    ds = mikeio.read(fn)
 ds.mean(axis=0).describe()   # alternative way of getting the time average
    @@ -713,7 +713,7 @@

    Time average

    -
    +
    ds_avg = mikeio.read(fn_avg)
 ds_avg.describe()
    @@ -781,12 +781,12 @@

    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
    @@ -803,7 +803,7 @@

    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")
    @@ -817,7 +817,7 @@

    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 f830d1bf3..78f72f84e 100644
--- a/examples/Time-interpolation.html
+++ b/examples/Time-interpolation.html
@@ -376,11 +376,11 @@ 
    Time interpolation
    
 
 
 
-
    
+
    
 
    import numpy as np
 import mikeio
    
 
    
-
    
+
    
 
    ds = mikeio.read("../data/waves.dfs2")
 ds
    
 
    
@@ -397,7 +397,7 @@ 
    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
    
 
    
@@ -412,14 +412,14 @@ 
    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
    
 
    
@@ -434,10 +434,10 @@ 
    
+
    
 
    dsi = ds.interp_time(ts)
    
 
    
-
    
+
    
 
    dsi.time
    
 
    
 
    DatetimeIndex(['2004-01-01 01:00:00', '2004-01-01 02:00:00',
@@ -455,13 +455,13 @@ 
    
+
    
 
    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='+')
    
 
    
@@ -479,7 +479,7 @@ 
    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>
@@ -490,7 +490,7 @@ 
    Model validation
    
 values: [0.06521, 0.06771, ..., 0.0576]
    
 
    
 
    
-
    
+
    
 
    dsi["Sign. Wave Height"].sel(x=250, y=1200)
    
 
    
 
    <mikeio.DataArray>
@@ -501,7 +501,7 @@ 
    Model validation
    
 values: [0.0387, 0.03939, ..., nan]
    
 
    
 
    
-
    
+
    
 
    diff = (ts["Sign. Wave Height"]  - dsi["Sign. Wave Height"].sel(x=250, y=1200))
 diff.plot()
    
 
    
@@ -512,7 +512,7 @@ 
    Model validation
    
 
    
 
    
 
    
-
    
+
    
 
    mae = np.abs(diff).nanmean().to_numpy()
 mae
    
 
    
@@ -522,7 +522,7 @@ 
    Model validation
    
 
    
 
    
 
    Clean up
    
-
    
+
    
 
    import os
 os.remove("waves_3h.dfs2")
    
 
    
diff --git a/examples/dfs2/bathy.html b/examples/dfs2/bathy.html
index f92151565..01df6fc7c 100644
--- a/examples/dfs2/bathy.html
+++ b/examples/dfs2/bathy.html
@@ -377,11 +377,11 @@ 
    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
    
 
    
@@ -753,7 +753,7 @@ 
    Dfs2 - Bathymetric data
    
 Coordinates: (2)
 Data variables:
     elevation  (lat, lon) int16 114kB ...
-Attributes: (8)
  • 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();
    @@ -784,7 +784,7 @@

    Dfs2 - Bathymetric data

    -
    +
    ds.elevation.sel(lon=12.74792, lat=55.865, method="nearest")
    @@ -1153,17 +1153,17 @@

    Dfs2 - Bathymetric data

    <xarray.DataArray 'elevation' ()> Size: 2B
 [1 values with dtype=int16]
 Coordinates: (2)
-Attributes: (7)
    +Attributes: (7)

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

    -
    +
    ds.elevation.dims
    ('lat', 'lon')
    -
    +
    el = ds.elevation.values
 el.shape
    @@ -1171,37 +1171,37 @@

    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
    @@ -1211,7 +1211,7 @@

    Dfs2 - Bathymetric data

    projection: LONG/LAT
    -
    +
    da = mikeio.DataArray(data=el,
                item=mikeio.ItemInfo("Elevation", mikeio.EUMType.Total_Water_Depth),
                geometry=geometry,
@@ -1226,7 +1226,7 @@ 
    Dfs2 - Bathymetric data
    
 geometry: Grid2D (ny=264, nx=216)
    -
    +
    da.plot();
    @@ -1236,7 +1236,7 @@

    Dfs2 - Bathymetric data

    -
    +
    da.plot(cmap='coolwarm', vmin=-100, vmax=100);
    @@ -1246,10 +1246,10 @@

    Dfs2 - Bathymetric data

    -
    +
    da.to_dfs("gebco.dfs2")
    -
    +
    ds = mikeio.read("gebco.dfs2")
 ds.Elevation.plot()
    @@ -1262,7 +1262,7 @@

    Dfs2 - Bathymetric data

    Clean up

    -
    +
    import os
 
 os.remove("gebco.dfs2")
    diff --git a/examples/dfs2/gfs.html b/examples/dfs2/gfs.html index 367c90029..365130b80 100644 --- a/examples/dfs2/gfs.html +++ b/examples/dfs2/gfs.html @@ -380,13 +380,13 @@

    Dfs2 - Meteo data

    -
    +
    import xarray
 import pandas as pd
 import mikeio

    The file gfs_wind.nc contains a small sample of the GFS forecast data downloaded via their OpenDAP service

    -
    +
    ds = xarray.open_dataset('../../data/gfs_wind.nc')
 ds
    @@ -760,30 +760,30 @@

    Dfs2 - Meteo data

    msletmsl (time, lat, lon) float32 10kB ... ugrd10m (time, lat, lon) float32 10kB ... vgrd10m (time, lat, lon) float32 10kB ... -Attributes: (4)

    Running a Mike 21 HD model, needs at least three variables of meteorological forcing * Mean Sea Level Pressure * U 10m * V 10m

    Let’s take a look the U 10m

    -
    +
    ds.ugrd10m.isel(time=0).plot();
    @@ -797,7 +797,7 @@

    Dfs2 - Meteo data

    Convert to dfs2

    Time

    -
    +
    time = pd.DatetimeIndex(ds.time)
 time
    @@ -809,36 +809,36 @@

    Time

    Variable types

    -
    +
    mikeio.EUMType.Air_Pressure
    Air Pressure
    -
    +
    mikeio.EUMType.Air_Pressure.units
    [hectopascal, millibar]
    -
    +
    mikeio.EUMType.Wind_Velocity
    Wind Velocity
    -
    +
    mikeio.EUMType.Wind_Velocity.units
    [meter per sec, feet per sec, miles per hour, km per hour, knot]
    -
    +
    mslp = ds.msletmsl.values / 100 # conversion from Pa to hPa
 u = ds.ugrd10m.values
 v = ds.vgrd10m.values
    -
    +
    geometry = mikeio.Grid2D(x=ds.lon.values, y=ds.lat.values, projection="LONG/LAT")
 geometry
    @@ -848,14 +848,14 @@

    Variable types

    projection: LONG/LAT
    -
    +
    from mikeio import ItemInfo, EUMType, EUMUnit
 
 mslp_da = mikeio.DataArray(data=mslp,time=time, geometry=geometry, item=ItemInfo("Mean Sea Level Pressure", EUMType.Air_Pressure, EUMUnit.hectopascal))
 u_da = mikeio.DataArray(data=u,time=time, geometry=geometry, item=ItemInfo("Wind U", EUMType.Wind_Velocity, EUMUnit.meter_per_sec))
 v_da = mikeio.DataArray(data=v,time=time, geometry=geometry, item=ItemInfo("Wind V", EUMType.Wind_Velocity, EUMUnit.meter_per_sec))
    -
    +
    mds = mikeio.Dataset([mslp_da, u_da, v_da])
 mds
    @@ -869,11 +869,11 @@

    Variable types

    2: Wind V <Wind Velocity> (meter per sec)
    -
    +
    mds.to_dfs("gfs.dfs2")

    Clean up

    -
    +
    import os
 
 os.remove("gfs.dfs2")
    diff --git a/examples/index.html b/examples/index.html index d44ba7300..234df0782 100644 --- a/examples/index.html +++ b/examples/index.html @@ -456,7 +456,7 @@

    Examples

    - + @@ -464,7 +464,7 @@

    Examples

    Convert GEBCO 2020 NetCDF to dfs2     - + @@ -472,7 +472,7 @@

    Examples

    Conversion of NetCDF from Global Forecasting System to Dfs2     - + @@ -483,7 +483,7 @@

    Examples

     - + @@ -491,7 +491,7 @@

    Examples

    Interpolate dfsu data to a grid, save as dfs2 and geotiff. Interpolate dfsu data to another mesh.     - + @@ -499,7 +499,7 @@

    Examples

    Tools and methods that applies to any type of dfs files.     - + diff --git a/index.html b/index.html index 69f407871..2492a1d56 100644 --- a/index.html +++ b/index.html @@ -388,7 +388,7 @@

    Installation

    Getting started

    -
    +
    import mikeio
 
 ds = mikeio.read("data/FakeLake.dfsu")
diff --git a/search.json b/search.json
index 484a09565..7f10b86c2 100644
--- a/search.json
+++ b/search.json
@@ -1162,7 +1162,7 @@
     "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, 510.41it/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, 516.32it/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",
@@ -1174,7 +1174,7 @@
     "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, 2158.23it/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, 2733.87it/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",
@@ -1198,7 +1198,7 @@
     "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, 985.04it/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, 945.73it/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, 1135.74it/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, 1194.96it/s]\n\n\n\nds3 = mikeio.read(\"gebco_sound_spatial.dfs2\")\nds3.Elevation[0].plot();",
     "crumbs": [
       "Home",
       "Examples",
@@ -1210,7 +1210,7 @@
     "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, 15823.04it/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, 16410.22it/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",
@@ -1678,7 +1678,7 @@
     "href": "api/Dfs0.html",
     "title": "Dfs0",
     "section": "",
-    "text": "Dfs0(self, filename)\n\n\n\n\n\nName\nDescription\n\n\n\n\nitems\nList of items\n\n\nn_items\nNumber of items\n\n\nn_timesteps\nNumber of time steps\n\n\nstart_time\nFile start time\n\n\ntime\nFile all datetimes\n\n\ntimestep\nTime step size in seconds\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nfrom_dataframe\nCreate a dfs0 from a pandas Dataframe\n\n\nread\nRead data from a dfs0 file.\n\n\nto_dataframe\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\nDfs0.from_dataframe(df, filename, itemtype=None, unit=None, items=None)\nCreate a dfs0 from a pandas Dataframe\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nDataframe with data\nrequired\n\n\nfilename\nstr\nfilename to write output\nrequired\n\n\nitemtype\nmikeio.eum.EUMType | None\nSame type for all items\nNone\n\n\nunit\nmikeio.eum.EUMUnit | None\nSame unit for all items\nNone\n\n\nitems\ntyping.Sequence[mikeio.eum.ItemInfo] | None\nDifferent types, units for each items, similar to create\nNone\n\n\n\n\n\n\n\nDfs0.read(items=None, time=None, keepdims=False)\nRead data from a dfs0 file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nitems\nstr | int | typing.Sequence[str | int] | None\nRead only selected items, by number (0-based), or by name\nNone\n\n\ntime\nint | str | slice | None\nRead only selected time steps, by default None (=all)\nNone\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nmikeio.dataset.Dataset\nA Dataset with data dimensions [t]\n\n\n\n\n\n\n\nDfs0.to_dataframe(unit_in_name=False, round_time='ms')\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nunit_in_name\nbool\ninclude unit in column name, default False\nFalse\n\n\nround_time\nstr\nround time to avoid problem with floating point inaccurcy, set to False to avoid rounding\n'ms'\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame"
+    "text": "Dfs0(self, filename)\n\n\n\n\n\nName\nDescription\n\n\n\n\nitems\nList of items\n\n\nn_items\nNumber of items\n\n\nn_timesteps\nNumber of time steps\n\n\nstart_time\nFile start time\n\n\ntime\nFile all datetimes\n\n\ntimestep\nTime step size in seconds\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nfrom_dataframe\nCreate a dfs0 from a pandas Dataframe\n\n\nread\nRead data from a dfs0 file.\n\n\nto_dataframe\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\nDfs0.from_dataframe(df, filename, itemtype=None, unit=None, items=None)\nCreate a dfs0 from a pandas Dataframe\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nDataframe with data\nrequired\n\n\nfilename\nstr\nfilename to write output\nrequired\n\n\nitemtype\nmikeio.eum.EUMType | None\nSame type for all items\nNone\n\n\nunit\nmikeio.eum.EUMUnit | None\nSame unit for all items\nNone\n\n\nitems\ntyping.Sequence[mikeio.eum.ItemInfo] | None\nDifferent types, units for each items\nNone\n\n\n\n\n\n\n\nDfs0.read(items=None, time=None, **kwargs)\nRead data from a dfs0 file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nitems\nstr | int | typing.Sequence[str | int] | None\nRead only selected items, by number (0-based), or by name\nNone\n\n\ntime\nint | str | slice | None\nRead only selected time steps, by default None (=all)\nNone\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nmikeio.dataset.Dataset\nA Dataset with data dimensions [t]\n\n\n\n\n\n\n\nDfs0.to_dataframe(unit_in_name=False, round_time='ms')\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nunit_in_name\nbool\ninclude unit in column name, default False\nFalse\n\n\nround_time\nstr\nround time to avoid problem with floating point inaccurcy, set to False to avoid rounding\n'ms'\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame"
   },
   {
     "objectID": "api/Dfs0.html#attributes",
@@ -1692,7 +1692,7 @@
     "href": "api/Dfs0.html#methods",
     "title": "Dfs0",
     "section": "",
-    "text": "Name\nDescription\n\n\n\n\nfrom_dataframe\nCreate a dfs0 from a pandas Dataframe\n\n\nread\nRead data from a dfs0 file.\n\n\nto_dataframe\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\nDfs0.from_dataframe(df, filename, itemtype=None, unit=None, items=None)\nCreate a dfs0 from a pandas Dataframe\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nDataframe with data\nrequired\n\n\nfilename\nstr\nfilename to write output\nrequired\n\n\nitemtype\nmikeio.eum.EUMType | None\nSame type for all items\nNone\n\n\nunit\nmikeio.eum.EUMUnit | None\nSame unit for all items\nNone\n\n\nitems\ntyping.Sequence[mikeio.eum.ItemInfo] | None\nDifferent types, units for each items, similar to create\nNone\n\n\n\n\n\n\n\nDfs0.read(items=None, time=None, keepdims=False)\nRead data from a dfs0 file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nitems\nstr | int | typing.Sequence[str | int] | None\nRead only selected items, by number (0-based), or by name\nNone\n\n\ntime\nint | str | slice | None\nRead only selected time steps, by default None (=all)\nNone\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nmikeio.dataset.Dataset\nA Dataset with data dimensions [t]\n\n\n\n\n\n\n\nDfs0.to_dataframe(unit_in_name=False, round_time='ms')\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nunit_in_name\nbool\ninclude unit in column name, default False\nFalse\n\n\nround_time\nstr\nround time to avoid problem with floating point inaccurcy, set to False to avoid rounding\n'ms'\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame"
+    "text": "Name\nDescription\n\n\n\n\nfrom_dataframe\nCreate a dfs0 from a pandas Dataframe\n\n\nread\nRead data from a dfs0 file.\n\n\nto_dataframe\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\nDfs0.from_dataframe(df, filename, itemtype=None, unit=None, items=None)\nCreate a dfs0 from a pandas Dataframe\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nDataframe with data\nrequired\n\n\nfilename\nstr\nfilename to write output\nrequired\n\n\nitemtype\nmikeio.eum.EUMType | None\nSame type for all items\nNone\n\n\nunit\nmikeio.eum.EUMUnit | None\nSame unit for all items\nNone\n\n\nitems\ntyping.Sequence[mikeio.eum.ItemInfo] | None\nDifferent types, units for each items\nNone\n\n\n\n\n\n\n\nDfs0.read(items=None, time=None, **kwargs)\nRead data from a dfs0 file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nitems\nstr | int | typing.Sequence[str | int] | None\nRead only selected items, by number (0-based), or by name\nNone\n\n\ntime\nint | str | slice | None\nRead only selected time steps, by default None (=all)\nNone\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nmikeio.dataset.Dataset\nA Dataset with data dimensions [t]\n\n\n\n\n\n\n\nDfs0.to_dataframe(unit_in_name=False, round_time='ms')\nRead data from the dfs0 file and return a Pandas DataFrame.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nunit_in_name\nbool\ninclude unit in column name, default False\nFalse\n\n\nround_time\nstr\nround time to avoid problem with floating point inaccurcy, set to False to avoid rounding\n'ms'\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame"
   },
   {
     "objectID": "api/dataset._data_plot._DataArrayPlotterGrid1D.html",
diff --git a/user-guide/dataarray.html b/user-guide/dataarray.html
index 90230812e..45f426d20 100644
--- a/user-guide/dataarray.html
+++ b/user-guide/dataarray.html
@@ -388,7 +388,7 @@ 
    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")
@@ -404,7 +404,7 @@ 
    DataArray
    
 
    
 
    
 
     Temporal selection
    
-
    
+
    
 
    da.sel(time="1985-08-06 12:00")
    
 
    
 
    <mikeio.DataArray>
@@ -415,7 +415,7 @@ 
    
+
    
 
    da["1985-8-7":]
    
 
    
 
    <mikeio.DataArray>
@@ -429,7 +429,7 @@ 
    
 
     Spatial selection
    
 
    The sel method finds the nearest element.
    
-
    
+
    
 
    da.sel(x=607002, y=6906734)
    
 
    
 
    <mikeio.DataArray>
@@ -444,17 +444,17 @@ 
    
 
    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
    
 
    
@@ -462,7 +462,7 @@ 
    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()
    
 
    
 
    
@@ -472,7 +472,7 @@ 
    
 
    
 
    
-
    
+
    
 
    da.plot.contourf()
    
 
    
 
    
@@ -482,7 +482,7 @@ 
    
 
    
 
    
-
    
+
    
 
    da.plot.mesh()
    
 
    
 
    
diff --git a/user-guide/dfs0.html b/user-guide/dfs0.html
index 958c4c4a7..75a0be30b 100644
--- a/user-guide/dfs0.html
+++ b/user-guide/dfs0.html
@@ -381,7 +381,7 @@ 
    Dfs0
    
 
 
    
 
    Read Dfs0 to Dataset
    
-
    
+
    
 
    import mikeio
 
 ds = mikeio.read("../data/da_diagnostic.dfs0")
@@ -401,7 +401,7 @@ 
    Read Dfs0 to Dataset<
 
    
 
    
 
    From Dfs0 to pandas DataFrame
    
-
    
+
    
 
    df = ds.to_dataframe()
 df.head()
    
 
    
@@ -478,7 +478,7 @@ 
    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 571b26a0e..f692ea794 100644
--- a/user-guide/dfs1.html
+++ b/user-guide/dfs1.html
@@ -368,7 +368,7 @@ 
    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")
@@ -385,7 +385,7 @@ 
    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 2e9a5a230..57eaaec99 100644
--- a/user-guide/dfs2.html
+++ b/user-guide/dfs2.html
@@ -370,7 +370,7 @@ 
    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
    
@@ -386,7 +386,7 @@ 
    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>
@@ -395,7 +395,7 @@ 
    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
    
 
    
@@ -406,7 +406,7 @@ 
    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>
@@ -421,7 +421,7 @@ 
    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 0739ee49c..7ca4f6df8 100644
--- a/user-guide/dfsu.html
+++ b/user-guide/dfsu.html
@@ -517,7 +517,7 @@ 
    MIKE IO Fle
    items typing.Sequence[mikeio.eum.ItemInfo] | NoneDifferent types, units for each items, similar to createDifferent types, units for each items None
    Dfs2 - Bathymetric data
    Dfs2 - Meteo data
    Dfs2 examples
    Dfsu - 2D interpolation
    Generic dfs processing
    Time interpolation

    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")
@@ -529,7 +529,7 @@ 
    MIKE IO Fle
 projection: UTM-33
    -
    +
    dfs = mikeio.open("../data/oresundHD_run1.dfsu")
 dfs.geometry
    diff --git a/user-guide/eum.html b/user-guide/eum.html index 7990abd9b..e728ce6ac 100644 --- a/user-guide/eum.html +++ b/user-guide/eum.html @@ -374,7 +374,7 @@

    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)
@@ -383,47 +383,47 @@ 
    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
    @@ -433,7 +433,7 @@

    EUM