How to use the biosppy.ecg function in biosppy

bio["df"].plot()


#
pd.DataFrame(bio["ECG"]["Heart_Beats"]).T.plot(legend=False)  # Plot all the heart beats


df["ECG"].plot()

biosppy_ecg = dict(biosppy.ecg.ecg(df["ECG"]))
biosppy_ecg["templates"]


templates, r = biosppy.ecg._extract_heartbeats(signal=df["ECG"], rpeaks=nk.ecg_find_peaks(df["ECG"]), before=200, after=400)
pd.DataFrame(templates).T.plot(legend=False)
templates, r = biosppy.ecg.extract_heartbeats(signal=df["ECG_Filtered"], rpeaks=nk.ecg_find_peaks(df["ECG"]), sampling_rate=1000., before=0.2, after=0.4)




r, t = hamilton_segmenter(df["ECG"])




def hamilton_segmenter(signal=None, sampling_rate=1000.):
    """ECG R-peak segmentation algorithm.

    Follows the approach by Hamilton [Hami02]_.

    Parameters
    ----------

import pandas as pd
import biosppy
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
import sklearn.neural_network
#==============================================================================
# Create data
#==============================================================================
data = nk.read_nk_object("PTB-Diagnostic_database-ECG.nk")

All = []
for participant in data["Control"]:
    for signal_name in data["Control"][participant]["Signals"].columns:
        signal = data["Control"][participant]["Signals"][signal_name]
        CCs = dict(biosppy.ecg.ecg(signal, sampling_rate=data["Control"][participant]["sampling_rate"], show=False))["templates"]
        CCs = pd.DataFrame(CCs).T

        CCs.index = pd.date_range(pd.datetime.today(), periods=600, freq="ms")

        # 200 Hz
        CCs = CCs.rolling(20).mean().resample("3L").pad()
        CCs = CCs.reset_index(drop=True)[0:200]
        if list(CCs.index) == list(range(200)):
            if len(CCs) == 200:
                CCs = CCs.T
                CCs["Lead"] = signal_name
                All.append(CCs)

df = pd.concat(All, axis=0, ignore_index=True)
df.to_csv("cardiac_cycles.csv", index=False)
df = pd.DataFrame.from_csv("cardiac_cycles.csv")

fft_results['fft_peak'] = (ulf_peak, vlf_peak, lf_peak, hf_peak)

			Using VLF, LF and HF frequency bands:
				fft_results['fft_peak'] = (vlf_peak, lf_peak, hf_peak)

	..	If 'show_param' is true, the parameters (incl. frequency band limits) will be listed next to the graph and no
		legend with frequency band limits will be added to the plot graph itself, i.e. the effect of 'show_param'
		will be used over the 'legend' effect
	..	'fbands' and 'show' will be selected for all methods. Individually adding specific 'fbands' to 'kwargs_welch' or
		'kwargs_lomb' will have no effect
	.. 	Select the 'nfft' individually for each method using the kwargs dictionaries of the respective method(s)

	"""
	# Check input
	if signal is not None:
		rpeaks = biosppy.ecg.ecg(signal=signal, sampling_rate=sampling_rate, show=False)[2]
	elif nni is None and rpeaks is None:
		raise TypeError('No input data provided. Please specify input data.')

	# Get NNI series
	nn = tools.check_input(nni, rpeaks)

	# Check for kwargs for the 'welch_psd' function and compute the PSD
	if kwargs_welch is not None:
		if type(kwargs_welch) is not dict:
			raise TypeError("Expected , got %s: 'kwargs_welch' must be a dictionary containing "
							"parameters (keys) and values for the 'welch_psd' function." % type(kwargs_welch))

		# Supported kwargs
		available_kwargs = ['fbands', 'detrend', 'show', 'show_param', 'legend', 'window', 'nfft']

		# Unwrwap kwargs dictionary for Welch specific parameters

if filter_type in ["FIR", "butter", "cheby1", "cheby2", "ellip", "bessel"]:
        order = int(filter_order * sampling_rate)
        filtered, _, _ = biosppy.tools.filter_signal(signal=ecg,
                                          ftype=filter_type,
                                          band=filter_band,
                                          order=order,
                                          frequency=filter_frequency,
                                          sampling_rate=sampling_rate)
    else:
        filtered = ecg  # filtered is not-filtered

    # Segment
    if segmenter == "hamilton":
        rpeaks, = biosppy.ecg.hamilton_segmenter(signal=filtered, sampling_rate=sampling_rate)
    elif segmenter == "gamboa":
        rpeaks, = biosppy.ecg.gamboa_segmenter(signal=filtered, sampling_rate=sampling_rate, tol=0.002)
    elif segmenter == "engzee":
        rpeaks, = biosppy.ecg.engzee_segmenter(signal=filtered, sampling_rate=sampling_rate, threshold=0.48)
    elif segmenter == "christov":
        rpeaks, = biosppy.ecg.christov_segmenter(signal=filtered, sampling_rate=sampling_rate)
    elif segmenter == "ssf":
        rpeaks, = biosppy.ecg.ssf_segmenter(signal=filtered, sampling_rate=sampling_rate, threshold=20, before=0.03, after=0.01)
    elif segmenter == "pekkanen":
        rpeaks = segmenter_pekkanen(ecg=filtered, sampling_rate=sampling_rate, window_size=5.0, lfreq=5.0, hfreq=15.0)
    else:
        raise ValueError("Unknown segmenter: %s." % segmenter)


    # Correct R-peak locations
    rpeaks, = biosppy.ecg.correct_rpeaks(signal=filtered,
                             rpeaks=rpeaks,
                             sampling_rate=sampling_rate,

List of R-peak location indices.

    Example
    ----------
    >>> import neurokit as nk
    >>> Rpeaks = nk.eeg_find_Rpeaks(raw)

    Authors
    ----------
    Dominique Makowski, the biosppy dev team

    Dependencies
    ----------
    None
    """
    rpeaks, = biosppy.ecg.hamilton_segmenter(eeg_select_channels(raw, ecg_channel), sampling_rate=raw.info["sfreq"])
    return(rpeaks)

----------
    *Authors*

    - the bioSSPy dev team (https://github.com/PIA-Group/BioSPPy)

    *Dependencies*

    - biosppy

    *See Also*

    - BioSPPY: https://github.com/PIA-Group/BioSPPy

    """
    rpeaks, = biosppy.ecg.hamilton_segmenter(signal, sampling_rate=sampling_rate)
    rpeaks, = biosppy.ecg.correct_rpeaks(signal=signal, rpeaks=rpeaks, sampling_rate=sampling_rate, tol=0.05)
    return(rpeaks)

df = nk.create_epochs(df, events["onsets"], duration=events["durations"], onset=0)
df = df[0]  # Select the first element of that list.

bio = nk.bio_process(ecg=df["ECG"], rsp=df["RSP"], eda=df["EDA"], add=df["Photosensor"])

bio["df"].plot()


#
pd.DataFrame(bio["ECG"]["Heart_Beats"]).T.plot(legend=False)  # Plot all the heart beats


df["ECG"].plot()

biosppy_ecg = dict(biosppy.ecg.ecg(df["ECG"]))
biosppy_ecg["templates"]


templates, r = biosppy.ecg._extract_heartbeats(signal=df["ECG"], rpeaks=nk.ecg_find_peaks(df["ECG"]), before=200, after=400)
pd.DataFrame(templates).T.plot(legend=False)
templates, r = biosppy.ecg.extract_heartbeats(signal=df["ECG_Filtered"], rpeaks=nk.ecg_find_peaks(df["ECG"]), sampling_rate=1000., before=0.2, after=0.4)




r, t = hamilton_segmenter(df["ECG"])




def hamilton_segmenter(signal=None, sampling_rate=1000.):