added To Dos
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@ -86,16 +86,31 @@ def main(datapath: str):
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bh = Behavior(datapath)
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bh = Behavior(datapath)
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# chirps are not sorted in time (presumably due to prior groupings)
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# chirps are not sorted in time (presumably due to prior groupings)
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# sort chirps and corresponding fish_ids of the chirps
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# get and sort chirps and corresponding fish_ids of the chirps
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bh.chirps = bh.chirps[np.argsort(bh.chirps)]
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chirps = bh.chirps[np.argsort(bh.chirps)]
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bh.chirps_ids = bh.chirps_ids[np.argsort(bh.chirps)]
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chirps_ids = bh.chirps_ids[np.argsort(bh.chirps)]
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category = bh.behavior
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timestamps = bh.start_s
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# split categories
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chasing_onset = timestamps[category == 0]
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chasing_offset = timestamps[category == 1]
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physical_contact = timestamps[category == 2]
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# Physical contact-triggered chirps (PTC) mit Rasterplot
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# Wahrscheinlichkeit von Phys auf Ch und vice versa
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# Chasing-triggered chirps (CTC) mit Rasterplot
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# Wahrscheinlichkeit von Chase auf Ch und vice versa
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# First overview plot
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fig, ax = plt.subplots()
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fig, ax = plt.subplots()
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ax.plot(bh.chirps, np.ones_like(bh.chirps), marker='o')
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ax.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps')
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ax.plot(bh.start_s, np.ones_like(bh.start_s)*2, marker='*')
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ax.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset')
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ax.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset')
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ax.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact')
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plt.legend()
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plt.show()
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plt.show()
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embed()
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embed()
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exit()
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exit()
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