Find the closest index of the value before and after the specified index

Question

I have a dataframe - record of the stimulation

There i have a column where laser impulses are marked by '1's And a column where the ECG peaks marked by '1's

I am trying to come up with an efficient method to find two ecg peaks closest to the laser peak - one before and one after the laser peak

For me the simplest way seems to be a 'while' cycle, but maybe there are some pandas functions that can make it more efficiently?

Answer 1

Lets say you have the below dataframe to demonstrate:

df = pd.DataFrame({
    'ECG_peaks':[None, None, None, None, None, 1,    None, None, None, 1, None, None, None, None, 1, None, None, None],
    'Las_peaks':[None, None, None, None, None, None, None, 1,    None, None, None, None, 1, None, None, None, None, None]
})

print(df):

    ECG_peaks  Las_peaks
0         NaN        NaN
1         NaN        NaN
2         NaN        NaN
3         NaN        NaN
4         NaN        NaN
5         1.0        NaN
6         NaN        NaN
7         NaN        1.0
8         NaN        NaN
9         1.0        NaN
10        NaN        NaN
11        NaN        NaN
12        NaN        1.0
13        NaN        NaN
14        1.0        NaN
15        NaN        NaN
16        NaN        NaN
17        NaN        NaN

Now get the indexes of Las_peaks where value is 1 as:

las_peaks = df.loc[df.Las_peaks==1].index

Similarly for ecg_peaks:

ecg_peaks = df.loc[df.ECG_peaks==1].index

Now I use np.searchsorted to get the nearest index where each laser peak index can be inserted in ecg peak indexes.

searches = [np.searchsorted(ecg_peaks, idx) for idx in las_peaks]

Then the result of the ecg indexes with one nearest before and one nearest after can be found as:

[(ecg_peaks[max(s-1, 0)], ecg_peaks[s]) for s in searches]

For this example input, the output is:

[(5, 9), (9, 14)]

where 5 is the nearest-before index for laser peak at 7 and 9 is the nearest-after index for laser peak at 7.

Similarly 9, 14 for 12th laser index.

Answer 2

Here is an option using pd.IntervalIndex() and get_indexer()

ecg = df['ECG_peaks'].dropna().index
las = df['Las_peaks'].dropna().index

idx = pd.IntervalIndex.from_tuples(([(ecg[i],ecg[i+1]) for i in range(len(ecg)-1)]))

list(idx[idx.get_indexer(las)].to_tuples())