"""
Peaks Widgets Gallery
=====================

Some widgets are useful before sorting and works with the "peaks" given by the detect_peaks()
function.

They are useful to check drift before running sorters.

"""

import matplotlib.pyplot as plt

import spikeinterface.full as si

##############################################################################
# First, let's generate a simulated dataset

recording, sorting = si.generate_ground_truth_recording()

##############################################################################
# Let's filter and detect peaks on it

from spikeinterface.sortingcomponents.peak_detection import detect_peaks

si.set_global_job_kwargs(chunk_memory="10M", n_jobs=1, progress_bar=True)
rec_filtered = si.bandpass_filter(recording=recording, freq_min=300.0, freq_max=6000.0, margin_ms=5.0)
print(rec_filtered)
peaks = detect_peaks(
    recording=rec_filtered,
    method="locally_exclusive",
    method_kwargs=dict(
        peak_sign="neg",
        detect_threshold=6,
        exclude_sweep_ms=0.3,
        radius_um=100,
    )
)


##############################################################################
# peaks is a numpy 1D array with structured dtype that contains several fields:

print(peaks.dtype)
print(peaks.shape)
print(peaks.dtype.fields.keys())

##############################################################################
# This "peaks" vector can be used in several widgets, for instance
# plot_peak_activity()

si.plot_peak_activity(recording=rec_filtered, peaks=peaks)

plt.show()

##############################################################################
# can be also animated with bin_duration_s=1. The animation only works if you
# run this code locally

si.plot_peak_activity(recording=rec_filtered, peaks=peaks, bin_duration_s=1.0)

plt.show()