diff --git a/ethogram.py b/ethogram.py index 1745700..a437a88 100644 --- a/ethogram.py +++ b/ethogram.py @@ -126,6 +126,43 @@ def create_marcov_matrix(individual_event_times, individual_event_labels): marcov_matrix[4, 5] += np.sum(helper_mask) return marcov_matrix + +def fine_spec_plot(ax, example_1_path, trial_summary, example_ag_on_off): + + ex1_df_idx = trial_summary[trial_summary['recording'] == os.path.split(example_1_path)[-1]].index.to_numpy()[0] + lose_id = trial_summary.iloc[ex1_df_idx]['lose_ID'] + + fine_spec_shape = np.load(os.path.join(example_1_path, 'fill_spec_shape.npy')) + fine_spec = np.memmap(os.path.join(example_1_path, 'fill_spec.npy'), dtype='float', mode='r', shape=(fine_spec_shape[0], fine_spec_shape[1]), order='F') + fine_times = np.load(os.path.join(example_1_path, 'fill_times.npy')) + spec_freqs = np.load(os.path.join(example_1_path, 'fill_freqs.npy')) + + times = np.load(os.path.join(example_1_path, 'times.npy')) + fund_v = np.load(os.path.join(example_1_path, 'fund_v.npy')) + ident_v = np.load(os.path.join(example_1_path, 'ident_v.npy')) + idx_v = np.load(os.path.join(example_1_path, 'idx_v.npy')) + + # artificial_t_axis = np.linspace(times[0], times[-1], spec.shape[1]) + # artificial_f_axis = np.linspace(0, 2000, spec.shape[0]) + # plt.pcolormesh(artificial_t_axis, artificial_f_axis, decibel(spec), vmin=-100, vmax=-50) + lose_freq_in_snippet = fund_v[(ident_v == lose_id) & (times[idx_v] > example_ag_on_off[0][0]-5) & (times[idx_v] < example_ag_on_off[0][1]+5)] + max_f, min_f = np.max(lose_freq_in_snippet) + 25, np.min(lose_freq_in_snippet) - 25 + + f_idx0 = np.where(spec_freqs <= min_f)[0][-1] + f_idx1 = np.where(spec_freqs >= max_f)[0][0] + + t_idx0 = np.where(fine_times <= example_ag_on_off[0][0] - 5)[0][-1] + t_idx1 = np.where(fine_times >= example_ag_on_off[0][0] + 4)[0][0] + ax.pcolormesh(fine_times[t_idx0:t_idx1+1] - example_ag_on_off[0][0], spec_freqs[f_idx0:f_idx1+1], + decibel(fine_spec[f_idx0:f_idx1+1, t_idx0:t_idx1+1])) + + t_idx0 = np.where(fine_times <= example_ag_on_off[0][1] - 5)[0][-1] + t_idx1 = np.where(fine_times >= example_ag_on_off[0][1] + 5)[0][0] + ax.pcolormesh(fine_times[t_idx0:t_idx1+1] - example_ag_on_off[0][1] + 10, spec_freqs[f_idx0:f_idx1+1], + decibel(fine_spec[f_idx0:f_idx1+1, t_idx0:t_idx1+1])) + + ax.fill_between([4, 5], [spec_freqs[f_idx0], spec_freqs[f_idx0]], [spec_freqs[f_idx1], spec_freqs[f_idx1]], color='white') + def main(base_path): if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'markov')): os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'markov')) @@ -158,7 +195,7 @@ def main(base_path): example_chirp_times = [[], [], []] example_rise_times = [[], [], []] example_1_path = '' - example_skips = [3, 4, 3] + example_skips = [15, 4, 3] #3, 5, 9, 15, 19 for index, trial in trial_summary.iterrows(): trial_path = os.path.join(base_path, trial['recording']) @@ -222,10 +259,11 @@ def main(base_path): rise_before = True if np.any( ((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)): - # ToDo: check if I realy get all chirps... currently not the case + chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chase_dur) & ((chirp_times[1] - chase_off_time) < max_dt)] chirp_arround_end = True + # define agonistic categorie based on rise/chirp occurance if rise_before: if chirp_arround_end: @@ -273,7 +311,7 @@ def main(base_path): ### agonistic categorie example figure fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54)) - gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0) + gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.9, height_ratios=[1, 4], hspace=0) ax = fig.add_subplot(gs[1, 0]) ax_spec = fig.add_subplot(gs[0, 0], sharex=ax) plt.setp(ax_spec.get_xticklabels(), visible=False) @@ -300,34 +338,13 @@ def main(base_path): ax.text(15.2, enu + 1, f'{pct_each_categorie[enu] * 100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center') # plot correct spectrogram - ex1_df_idx = trial_summary[trial_summary['recording'] == os.path.split(example_1_path)[-1]].index.to_numpy()[0] - lose_id = trial_summary.iloc[ex1_df_idx]['lose_ID'] - # ToDo: use fill_spec - spec = np.load(os.path.join(example_1_path, 'spec.npy')) - times = np.load(os.path.join(example_1_path, 'times.npy')) - fund_v = np.load(os.path.join(example_1_path, 'fund_v.npy')) - ident_v = np.load(os.path.join(example_1_path, 'ident_v.npy')) - idx_v = np.load(os.path.join(example_1_path, 'idx_v.npy')) - - artificial_t_axis = np.linspace(times[0], times[-1], spec.shape[1]) - artificial_f_axis = np.linspace(0, 2000, spec.shape[0]) - # plt.pcolormesh(artificial_t_axis, artificial_f_axis, decibel(spec), vmin=-100, vmax=-50) - lose_freq_in_snippet = fund_v[(ident_v == lose_id) & (times[idx_v] > example_ag_on_off[0][0]-5) & (times[idx_v] < example_ag_on_off[0][1]+5)] - max_f, min_f = np.max(lose_freq_in_snippet) + 10, np.min(lose_freq_in_snippet) - 10 + fine_spec_plot(ax_spec, example_1_path, trial_summary, example_ag_on_off) - t_idx0 = np.where(artificial_t_axis >= example_ag_on_off[0][0] - 5)[0][0] - t_idx1 = np.where(artificial_t_axis <= example_ag_on_off[0][1] + 5)[0][-1] - f_idx0 = np.where(artificial_f_axis >= min_f)[0][0] - f_idx1 = np.where(artificial_f_axis <= max_f)[0][-1] + ########################################## - # ToDo this does not work. fix it tomorow - ax_spec.pcolormesh(artificial_t_axis[t_idx0:t_idx1+2] - example_ag_on_off[0][0], - artificial_f_axis[f_idx0:f_idx1+2], - decibel(spec[t_idx0:t_idx1+1, f_idx0:f_idx1+1]), vmin=-100, vmax=-50) - - ax.plot([0, 0], [0.8, 5], '--', color='k', lw=1) - ax.plot([10, 10], [0.8, 5], '--', color='k', lw=1) - ax.set_ylim(0.25, 4.5) + ax.plot([0, 0], [0.5, 5], '--', color='k', lw=1) + ax.plot([10, 10], [0.5, 5], '--', color='k', lw=1) + ax.set_ylim(0.5, 4.5) ax.set_xlim(-5, 15) ax.set_yticks([1, 2, 3, 4]) # ax.set_yticklabels([r'rise$_{pre}$ $&$ chirp$_{end}$', r'only rise$_{pre}$', r'only chirp$_{end}$', 'no communication']) @@ -343,11 +360,12 @@ def main(base_path): Patch(facecolor='tab:grey', edgecolor='w', label= 'chase event')] ax_spec.legend(handles=legend_elements, loc='lower right', ncol=3, bbox_to_anchor=(1, 1), frameon=False, fontsize=10, facecolor='white') + ax_spec.set_ylabel('EODf [Hz]', fontsize=12) ax.spines[['right', 'top']].set_visible(False) + + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'agonistic_categories' + '.png'), dpi=300) plt.show() - embed() - quit() ### bar plot - agonistic categories counts/pct #####################################################################