ktk.TimeSeries.from_dataframe#

static TimeSeries.from_dataframe(dataframe, /, *, time_info={'Unit': 's'}, data_info={}, events=[])[source]#

Create a new TimeSeries from a Pandas Dataframe.

Data in column which names end with bracketed indices such as [0], [1], [0,0], [0,1], etc. are converted to multidimensional arrays. For example, if a DataFrame has these column names:

"Forces[0]", "Forces[1]", "Forces[2]", "Forces[3]"

then a single data key is created (“Forces”) and the shape of the data is Nx4.

Parameters
  • dataframe (DataFrame) – A Pandas DataFrame where the index corresponds to time, and where each column corresponds to a data key.

  • time_info (dict[str, Any]) – Optional. Will be copied to the TimeSeries’ time_info attribute.

  • data_info (dict[str, dict[str, Any]]) – Optional. Will be copied to the TimeSeries’ data_info attribute.

  • events (list[kineticstoolkit.timeseries.TimeSeriesEvent]) – Optional. Will be copied to the TimeSeries’ events attribute.

Returns

The converted TimeSeries.

Return type

TimeSeries

See also

ktk.TimeSeries.to_dataframe, ktk.TimeSeries.from_array, ktk.TimeSeries.to_array

Examples

Example with unidimensional data

Create a DataFrame with two series of 3 samples:

>>> import pandas as pd
>>> df = pd.DataFrame([[1., 2.], [3., 4.], [5., 6.]])
>>> df.columns = ["test1", "test2"]
>>> df
   test1  test2
0    1.0    2.0
1    3.0    4.0
2    5.0    6.0

Convert to a TimeSeries:

>>> ts = ktk.TimeSeries.from_dataframe(df)
>>> ts.data
{'test1': array([1., 3., 5.]), 'test2': array([2., 4., 6.])}

Example with multidimensional data

Create a DataFrame with one series of 3 samples of dimension 2:

>>> df = pd.DataFrame([[1., 2.], [3., 4.], [5., 6.]])
>>> df.columns = ["test[0]", "test[1]"]
>>> df
   test[0]  test[1]
0      1.0      2.0
1      3.0      4.0
2      5.0      6.0

Convert to a TimeSeries:

>>> ts = ktk.TimeSeries.from_dataframe(df)
>>> ts.data
{'test': array([[1., 2.], [3., 4.], [5., 6.]])}

Example with even more dimensions

Create a DataFrame with one series of 5 samples of dimension 2x2 (rot) and one series of 5 samples of dimension 2 (trans):

>>> df = pd.DataFrame()
>>> df.index = [0., 0.1, 0.2, 0.3, 0.4]  # Time in seconds
>>> df["rot[0,0]"] = np.cos([0., 0.1, 0.2, 0.3, 0.4])
>>> df["rot[0,1]"] = -np.sin([0., 0.1, 0.2, 0.3, 0.4])
>>> df["rot[1,0]"] = np.sin([0., 0.1, 0.2, 0.3, 0.4])
>>> df["rot[1,1]"] = np.cos([0., 0.1, 0.2, 0.3, 0.4])
>>> df["trans[0]"] = [0., 0.1, 0.2, 0.3, 0.4]
>>> df["trans[1]"] = [5., 6., 7., 8., 9.]
>>> df
     rot[0,0]  rot[0,1]  rot[1,0]  rot[1,1]  trans[0]  trans[1]
0.0  1.000000 -0.000000  0.000000  1.000000       0.0       5.0
0.1  0.995004 -0.099833  0.099833  0.995004       0.1       6.0
0.2  0.980067 -0.198669  0.198669  0.980067       0.2       7.0
0.3  0.955336 -0.295520  0.295520  0.955336       0.3       8.0
0.4  0.921061 -0.389418  0.389418  0.921061       0.4       9.0

Convert to a TimeSeries:

>>> ts = ktk.TimeSeries(df)
>>> ts.data
{'rot': array([[[ 1.        , -0.        ],
        [ 0.        ,  1.        ]],

       [[ 0.99500417, -0.09983342],
        [ 0.09983342,  0.99500417]],

       [[ 0.98006658, -0.19866933],
        [ 0.19866933,  0.98006658]],

       [[ 0.95533649, -0.29552021],
        [ 0.29552021,  0.95533649]],

       [[ 0.92106099, -0.38941834],
        [ 0.38941834,  0.92106099]]]), 'trans': array([[0. , 5. ],
       [0.1, 6. ],
       [0.2, 7. ],
       [0.3, 8. ],
       [0.4, 9. ]])}