Dfs0 - Relative time axis

MIKE IO uses a pandas DatetimeIndex to represent the time dimension in dfs files. If the Dfs file has a relative time axis it will be converted to DatetimeIndex by using 1970-1-1 00:00:00 as start time.

import mikeio

ds = mikeio.read("../../data/eq_relative.dfs0")
ds

<mikeio.Dataset>
dims: (time:504)
time: 1970-01-01 00:00:00 - 1970-01-01 00:00:56.237000 (504 non-equidistant records)
geometry: GeometryUndefined()
items:
  0:  Item 1 <Undefined> (undefined)
  1:  Item 2 <Undefined> (undefined)
  2:  Item 3 <Undefined> (undefined)
  3:  Item 4 <Undefined> (undefined)
  4:  Item 5 <Undefined> (undefined)

df = ds.to_dataframe()
df.head()

	Item 1	Item 2	Item 3	Item 4	Item 5
1970-01-01 00:00:00.000	-0.006862	-0.000611	0.177047	32.484425	-304.720428
1970-01-01 00:00:00.112	-0.011746	-0.000611	0.189257	32.292774	-308.553406
1970-01-01 00:00:00.224	-0.006862	-0.000611	0.189257	32.292774	-308.553406
1970-01-01 00:00:00.335	-0.001978	0.004273	0.189257	32.292774	-300.887482
1970-01-01 00:00:00.447	0.002906	0.009157	0.177047	32.292774	-300.887482

Correcting the dataframe index by subtracting start time to get relative time axis.

df.index = (df.index - df.index[0]).total_seconds()
df.index.name = "Relative time (s)"

df.head()

	Item 1	Item 2	Item 3	Item 4	Item 5
Relative time (s)
0.000	-0.006862	-0.000611	0.177047	32.484425	-304.720428
0.112	-0.011746	-0.000611	0.189257	32.292774	-308.553406
0.224	-0.006862	-0.000611	0.189257	32.292774	-308.553406
0.335	-0.001978	0.004273	0.189257	32.292774	-300.887482
0.447	0.002906	0.009157	0.177047	32.292774	-300.887482

df['Item 5'].plot();

mikecore

An alternative is to use the underlying library mikecore to read the file.

from mikecore.DfsFileFactory import DfsFileFactory

dfs = DfsFileFactory.DfsGenericOpen("../../data/eq_relative.dfs0")

Using the ReadDfs0DataDouble method you get the data as a numpy array, with the time axis or other type of as the first column,

data = dfs.ReadDfs0DataDouble()

type(data)

numpy.ndarray

which can be converted to a pandas dataframe. First we extract the name of items (which in this example hapeens to be not very creative).

index_name = "time"
items = [i.Name for i in dfs.ItemInfo]
items = [index_name] + items
items

['time', 'Item 1', 'Item 2', 'Item 3', 'Item 4', 'Item 5']

import pandas as df

df = df.DataFrame(data, columns=items).set_index(index_name)
df.head()

	Item 1	Item 2	Item 3	Item 4	Item 5
time
0.000000	-0.006862	-0.000611	0.177047	32.484425	-304.720428
0.111803	-0.011746	-0.000611	0.189257	32.292774	-308.553406
0.223606	-0.006862	-0.000611	0.189257	32.292774	-308.553406
0.335409	-0.001978	0.004273	0.189257	32.292774	-300.887482
0.447212	0.002906	0.009157	0.177047	32.292774	-300.887482