Simulation and experimental results of the time inconsistent

[cui weiling]

hello everyone,
I have got static optimization results and wanted to compare with the experimental results, but the time of test and simulation inconsistent, how can I solve this problems？
thanks.

[Pierre Kibleur]

Hello,

I have this Python function that I use for such cases, it requires the Pandas library (

Code: Select all

import pandas as pd

). Well, there are probably neater ways of doing it, but maybe it can help

Best,
Pierre

EDIT: maybe it's less headache prone if you also know how I read OpenSim files into pandas:

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dfCMC_q = pd.read_csv(r"CMC\Kinematics_q.sto", header=8, sep="\t", index_col="time")

Code: Select all

def sync_dfs(df, df1, rolling_mean_size=3):
    """
    From two data frames sampled at slightly different times, return two dataframes 
    at synchronized time points. It uses linear interpolation to fill missing data, 
    and returns an averaged over some time points data.
    
    Example: 
        df has 10 entries, each at half a second (.5, 1.5, 2.5 ...)
        df1 has 30 entries, each at every third of a second (.33, .66, 1, 1.33, ...)
        rolling_mean_size = 3
        
        this function first computes a dataframe "whole" containing time points (.33, .5, .66, 1, 1.33, 1.5, ...)
        it fills the missing values by linearly interpolating between two know data points (e.g. between .5 and 1.5)
        it then averages over 3 time points, resulting in a "whole" data frame of size 40/3
        
        And it finally returns two dataframes, adaptations of the input ones, both of the same size and sampling times.
    """
    whole = pd.merge(df.reset_index(), df1.reset_index(), how='outer', on='time', suffixes=('_x', '_y')).set_index("time").sort_index().interpolate()
    whole = whole.rolling(rolling_mean_size).mean()
    df_ = whole.loc[:, whole.columns.str.endswith('x')].rename(columns = lambda x : str(x).rsplit('_', 1)[0])
    df1_ = whole.loc[:, whole.columns.str.endswith('y')].rename(columns = lambda x : str(x).rsplit('_', 1)[0])
    return df_, df1_