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 @@ The interpolated data is then saved to a dfs2 file.
-
Interpolate to gridThen interpolate all data to the new grid and plot.
+
-
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
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 bb440e963..325643b4d 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, 516.83it/s]
+ 0%| | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 576.22it/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, 2684.87it/s]
+ 0%| | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2018.43it/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, 1179.83it/s]
+ 0%| | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1193.26it/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, 1077.95it/s]
+ 0%| | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1230.00it/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, 17427.86it/s]
+ 0%| | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 17835.45it/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 da23a3ee0..1dbc3ad77 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 a54e186d4..ff141adca 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")
+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 b13730e37..283da989f 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 4cf7bf5fc..2a16d53c5 100644
--- a/examples/index.html
+++ b/examples/index.html
@@ -456,7 +456,7 @@ Examples
-
+
Dfs2 - Bathymetric data
@@ -464,7 +464,7 @@ Examples
Convert GEBCO 2020 NetCDF to dfs2
-
+
Dfs2 - Meteo data
@@ -472,7 +472,7 @@ Examples
Conversion of NetCDF from Global Forecasting System to Dfs2
-
+
Dfs2 examples
@@ -483,7 +483,7 @@ Examples
-
+
Dfsu - 2D interpolation
@@ -491,7 +491,7 @@ Examples
Interpolate dfsu data to a grid, save as dfs2 and geotiff. Interpolate dfsu data to another mesh.
-
+
Generic dfs processing
@@ -499,7 +499,7 @@ Examples
Tools and methods that applies to any type of dfs files.
-
+
Time interpolation
diff --git a/index.html b/index.html
index a944ada7a..636f24119 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 0ee8cb738..887272b79 100644
--- a/search.json
+++ b/search.json
@@ -1034,7 +1034,7 @@
"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, 516.83it/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, 576.22it/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",
@@ -1046,7 +1046,7 @@
"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, 2684.87it/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');",
+ "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, 2018.43it/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",
@@ -1070,7 +1070,7 @@
"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, 1179.83it/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, 1077.95it/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, 1193.26it/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, 1230.00it/s]\n\n\n\nds3 = mikeio.read(\"gebco_sound_spatial.dfs2\")\nds3.Elevation[0].plot();",
"crumbs": [
"Home",
"Examples",
@@ -1082,7 +1082,7 @@
"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, 17427.86it/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, 17835.45it/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",
diff --git a/user-guide/dataarray.html b/user-guide/dataarray.html
index ac8cf7ff0..53d7c7445 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 07da9aee1..4c8825f2a 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 09cc384f2..1e71c8a12 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 33a43333f..73f2fe89d 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 0dda26295..a27911a87 100644
--- a/user-guide/dfsu.html
+++ b/user-guide/dfsu.html
@@ -517,7 +517,7 @@ MIKE IO Fle
These properties and methods are accessible from the geometry, but also from the Mesh/Dfsu object.
Save to dfs2 file
-
+
-da_grid.to_dfs("wind_north_sea_interpolated.dfs2")
Save to NetCDF
-
+
@@ -478,7 +478,7 @@
xr_da = da_grid.to_xarray()
xr_da.to_netcdf("wind_north_sea_interpolated.nc")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 bb440e963..325643b4d 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, 516.83it/s]
+ 0%| | 0/2 [00:00<?, ?it/s]100%|██████████| 2/2 [00:00<00:00, 576.22it/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, 2684.87it/s]
+ 0%| | 0/5 [00:00<?, ?it/s]100%|██████████| 5/5 [00:00<00:00, 2018.43it/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, 1179.83it/s]
+ 0%| | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1193.26it/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, 1077.95it/s]
+ 0%| | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:00<00:00, 1230.00it/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, 17427.86it/s]
+ 0%| | 0/66 [00:00<?, ?it/s]100%|██████████| 66/66 [00:00<00:00, 17835.45it/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 da23a3ee0..1dbc3ad77 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 a54e186d4..ff141adca 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")
+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 b13730e37..283da989f 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 4cf7bf5fc..2a16d53c5 100644
--- a/examples/index.html
+++ b/examples/index.html
@@ -456,7 +456,7 @@ Examples
-
+
Dfs2 - Bathymetric data
@@ -464,7 +464,7 @@ Examples
Convert GEBCO 2020 NetCDF to dfs2
-
+
Dfs2 - Meteo data
@@ -472,7 +472,7 @@ Examples
Conversion of NetCDF from Global Forecasting System to Dfs2
-
+
Dfs2 examples
@@ -483,7 +483,7 @@ Examples
-
+
Dfsu - 2D interpolation
@@ -491,7 +491,7 @@ Examples
Interpolate dfsu data to a grid, save as dfs2 and geotiff. Interpolate dfsu data to another mesh.
-
+
Generic dfs processing
@@ -499,7 +499,7 @@ Examples
Tools and methods that applies to any type of dfs files.
-
+
Time interpolation
diff --git a/index.html b/index.html
index a944ada7a..636f24119 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 0ee8cb738..887272b79 100644
--- a/search.json
+++ b/search.json
@@ -1034,7 +1034,7 @@
"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, 516.83it/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, 576.22it/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",
@@ -1046,7 +1046,7 @@
"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, 2684.87it/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');",
+ "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, 2018.43it/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",
@@ -1070,7 +1070,7 @@
"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, 1179.83it/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, 1077.95it/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, 1193.26it/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, 1230.00it/s]\n\n\n\nds3 = mikeio.read(\"gebco_sound_spatial.dfs2\")\nds3.Elevation[0].plot();",
"crumbs": [
"Home",
"Examples",
@@ -1082,7 +1082,7 @@
"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, 17427.86it/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, 17835.45it/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",
diff --git a/user-guide/dataarray.html b/user-guide/dataarray.html
index ac8cf7ff0..53d7c7445 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 07da9aee1..4c8825f2a 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 09cc384f2..1e71c8a12 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 33a43333f..73f2fe89d 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 0dda26295..a27911a87 100644
--- a/user-guide/dfsu.html
+++ b/user-guide/dfsu.html
@@ -517,7 +517,7 @@ MIKE IO Fle
These properties and methods are accessible from the geometry, but also from the Mesh/Dfsu object.