mikeio: input/output of MIKE files in python

https://dhi.github.io/mikeio/

Facilitate creating, reading and writing dfs0, dfs2, dfs1 and dfs3, dfsu and mesh files. Reading Res1D data.

Requirements

Windows operating system
Python x64 3.6 or 3.7 (3.8 is not yet supprted by pythonnet)
VC++ redistributables

More info about dependancies

Installation

From PyPI:

pip install mikeio

Or development version:

pip install https://github.com/DHI/mikeio/archive/master.zip

Examples

Reading data from dfs0, dfs1, dfs2, dfsu

Generic read method to read values, if you need additional features such as coordinates, use specialised classes instead e.g. Dfsu

>>> import mikeio
>>> ds = mikeio.read("random.dfs0")
>>> ds
DataSet(data, time, items)
Number of items: 2
Shape: (1000,)
2017-01-01 00:00:00 - 2017-07-28 03:00:00

>>> ds = mikeio.read("random.dfs1")
>>> ds
DataSet(data, time, items)
Number of items: 1
Shape: (100, 3)
2012-01-01 00:00:00 - 2012-01-01 00:19:48

Reading dfs0 file into Pandas DataFrame

>>>  from mikeio import Dfs0
>>>  dfs = Dfs0()
>>>  ts = dfs.to_dataframe('simple.dfs0')

Create simple timeseries

>>>  from datetime import datetime, timedelta
>>>  import numpy as np
>>>  from mikeio import Dfs0
>>>  data = []
>>>  d = np.random.random([100])
>>>  data.append(d)
>>>  dfs = Dfs0()
>>>  dfs.create(filename='simple.dfs0',
>>>            data=data,
>>>            start_time=datetime(2017, 1, 1),
>>>            dt=60)

Create timeseries from dataframe

import pandas as pd
import mikeio
>>> df = pd.read_csv(
...         "tests/testdata/co2-mm-mlo.csv",
...         parse_dates=True,
...         index_col="Date",
...         na_values=-99.99,
...     )
>>> df.to_dfs0("mauna_loa.dfs0")

For more examples on timeseries data see this notebook

Read dfs2 data

>>>  from mikeio import Dfs2
>>> ds = dfs.read("tests/testdata/random.dfs2")
>>> ds
DataSet(data, time, items)
Number of items: 1
Shape: (3, 100, 2)
2012-01-01 00:00:00 - 2012-01-01 00:00:24
>>> ds.items
[testing water level <Water Level> (meter)]

Create dfs2

For a complete example of conversion from netcdf to dfs2 see this notebook.

Another example of downloading meteorlogical forecast from the Global Forecasting System and converting it to a dfs2 ready to be used by a MIKE 21 model.

Read Res1D file Return Pandas DataFrame

>>>  from mikeio import res1d
>>>  # Query the discharge time series at chainage 10.1 of branch1
>>>  q1 = res1d.QueryData('Discharge', 'branch1', 10.1)
>>>  # Query all the discharge time series of branch2
>>>  q2 = res1d.QueryData('Discharge', 'branch2')
>>>  # Query all the water level time series in the file
>>>  q3 = res1d.QueryData('WaterLevel')
>>>  # Combine the queries in a list
>>>  queries = [q1, q2, q3]
>>>  # The returned ts object is a pandas DataFrame
>>>  ts = r1d.read('res1dfile.res1d', queries)

Read dfsu files

>>>  import matplotlib.pyplot as plt
>>>  from mikeio import Dfsu
>>>  dfs = Dfsu()
>>>  filename = "HD.dfsu"
>>>  res = dfs.read(filename)
>>>  idx = dfs.find_closest_element_index(x=608000, y=6907000)
>>>  plt.plot(res.time, res.data[0][:,idx])

Items, units

Useful when creating a new dfs file

>>> from mikeio.eum import EUMType, EUMUnit
>>> EUMType.Temperature
<EUMType.Temperature: 100006>
>>> EUMType.Temperature.units
[degree Celsius, degree Fahrenheit, degree Kelvin]
>>> EUMUnit.degree_Kelvin
degree Kelvin

Aewaterhouse/mikeio