Tools and methods that applies to any type of dfs files.
import matplotlib.pyplot as plt
import mikeio
import mikeio.generic
Take a look at these two files with overlapping timesteps.
t1 = mikeio.read("../tests/testdata/tide1.dfs1")
t1
<mikeio.Dataset> dims: (time:97, x:10) time: 2019-01-01 00:00:00 - 2019-01-03 00:00:00 (97 records) geometry: Grid1D (n=10, dx=0.06667) items: 0: Level <Water Level> (meter)
t2 = mikeio.read("../tests/testdata/tide2.dfs1")
t2
<mikeio.Dataset> dims: (time:97, x:10) time: 2019-01-02 00:00:00 - 2019-01-04 00:00:00 (97 records) geometry: Grid1D (n=10, dx=0.06667) items: 0: Level <Water Level> (meter)
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()
<matplotlib.legend.Legend at 0x127e378f730>
mikeio.generic.concat(infilenames=["../tests/testdata/tide1.dfs1",
"../tests/testdata/tide2.dfs1"],
outfilename="concat.dfs1")
c = mikeio.read("concat.dfs1")
c[0].isel(x=1).plot()
c
<mikeio.Dataset> dims: (time:145, x:10) time: 2019-01-01 00:00:00 - 2019-01-04 00:00:00 (145 records) geometry: Grid1D (n=10, dx=0.06667) items: 0: Level <Water Level> (meter)
Take difference between two dfs files with same structure - e.g. to see the difference in result between two calibration runs
fn1 = "../tests/testdata/oresundHD_run1.dfsu"
fn2 = "../tests/testdata/oresundHD_run2.dfsu"
fn_diff = "oresundHD_difference.dfsu"
mikeio.generic.diff(fn1, fn2, fn_diff)
100%|███████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 999.36it/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')
da = mikeio.read(fn2, time=-1)[0]
da.plot(vmin=0.06, vmax=0.27, ax=ax[1], title='run 2')
da = mikeio.read(fn_diff, time=-1)[0]
da.plot(vmin=-0.1, vmax=0.1, cmap='coolwarm', ax=ax[2], title='difference');
The extract() method can extract a part of a file:
infile = "../tests/testdata/tide1.dfs1"
mikeio.generic.extract(infile, "extracted.dfs1", start='2019-01-02')
e = mikeio.read("extracted.dfs1")
e
<mikeio.Dataset> dims: (time:49, x:10) time: 2019-01-02 00:00:00 - 2019-01-03 00:00:00 (49 records) geometry: Grid1D (n=10, dx=0.06667) items: 0: Level <Water Level> (meter)
infile = "../tests/testdata/oresund_vertical_slice.dfsu"
mikeio.generic.extract(infile, "extracted.dfsu", items='Salinity', end=-2)
e = mikeio.read("extracted.dfsu")
e
<mikeio.Dataset> dims: (time:2, element:441) time: 1997-09-15 21:00:00 - 1997-09-16 00:00:00 (2 records) geometry: DfsuVerticalProfileSigmaZ (441 elements, 4 sigma-layers, 5 z-layers) items: 0: Salinity <Salinity> (PSU)
Adding a constant e.g to adjust datum
ds = mikeio.read("../tests/testdata/gebco_sound.dfs2")
ds.Elevation[0].plot();
ds['Elevation'][0,104,131].to_numpy()
-1.0
This is the processing step.
mikeio.generic.scale("../tests/testdata/gebco_sound.dfs2",
"gebco_sound_local_datum.dfs2",
offset=-2.1
)
ds2 = mikeio.read("gebco_sound_local_datum.dfs2")
ds2['Elevation'][0].plot()
<AxesSubplot:xlabel='Longitude [degrees]', ylabel='Latitude [degrees]'>
ds2['Elevation'][0,104,131].to_numpy()
-3.1
import numpy as np
factor = np.ones_like(ds['Elevation'][0].to_numpy())
factor.shape
(264, 216)
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
plt.imshow(factor)
plt.colorbar();
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("../tests/testdata/gebco_sound.dfs2",
"gebco_sound_spatial.dfs2",
factor=factor_vec
)
ds3 = mikeio.read("gebco_sound_spatial.dfs2")
ds3.Elevation[0].plot();
fn = "../tests/testdata/NorthSea_HD_and_windspeed.dfsu"
fn_avg = "Avg_NorthSea_HD_and_windspeed.dfsu"
mikeio.generic.avg_time(fn, fn_avg)
ds = mikeio.read(fn)
ds.mean(axis=0).describe() # alternative way of getting the time average
Surface elevation | Wind speed | |
---|---|---|
count | 958.000000 | 958.000000 |
mean | 0.449857 | 12.772706 |
std | 0.178127 | 2.367667 |
min | 0.114355 | 6.498364 |
25% | 0.373691 | 11.199439 |
50% | 0.431747 | 12.984060 |
75% | 0.479224 | 14.658077 |
max | 1.202888 | 16.677952 |
ds_avg = mikeio.read(fn_avg)
ds_avg.describe()
Surface elevation | Wind speed | |
---|---|---|
count | 958.000000 | 958.000000 |
mean | 0.449857 | 12.772706 |
std | 0.178127 | 2.367667 |
min | 0.114355 | 6.498364 |
25% | 0.373691 | 11.199439 |
50% | 0.431747 | 12.984060 |
75% | 0.479224 | 14.658077 |
max | 1.202888 | 16.677952 |
Example that calculates the 25%, 50% and 75% percentile for all items in a dfsu file.
fn = "../tests/testdata/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
<mikeio.Dataset> dims: (time:1, element:958) time: 2017-10-27 00:00:00 (time-invariant) geometry: Dfsu2D (958 elements, 570 nodes) items: 0: Quantile 0.25, Surface elevation <Surface Elevation> (meter) 1: Quantile 0.5, Surface elevation <Surface Elevation> (meter) 2: Quantile 0.75, Surface elevation <Surface Elevation> (meter) 3: Quantile 0.25, Wind speed <Wind speed> (meter per sec) 4: Quantile 0.5, Wind speed <Wind speed> (meter per sec) 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");
import os
os.remove("concat.dfs1")
os.remove("oresundHD_difference.dfsu")
os.remove("extracted.dfs1")
os.remove("extracted.dfsu")
os.remove("gebco_sound_local_datum.dfs2")
os.remove("gebco_sound_spatial.dfs2")
os.remove("Avg_NorthSea_HD_and_windspeed.dfsu")
os.remove(fn_q)