larger stimulus range, add didactic figure for
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@ -183,7 +183,7 @@ def simulate_responses(stimulus_params, model_params, repeats=10, deltaf=20):
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def main():
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models = load_models("models.csv")
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deltafs = [-200, -100, -20, 20, 100, 200] # Hz, difference frequency between self and other
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deltafs = [-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 200] # Hz, difference frequency between self and other
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stimulus_params = { "eodfs": {"self": 0.0, "other": 0.0}, # eod frequency in Hz, to be overwritten
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"contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
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"chirp_size": 100, # Hz, frequency excursion
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@ -210,7 +210,8 @@ def main():
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1./stimulus_params["chirp_frequency"])
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stimulus_params["chirp_times"] = chirp_times
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simulate_responses(stimulus_params, model_params, repeats=25, deltaf=deltaf)
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exit() # the first cell only for now!
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if cell_id == 9:
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exit() # the first 10 cell only for now!
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if __name__ == "__main__":
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@ -3,8 +3,10 @@ import glob
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import pandas as pd
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import nixio as nix
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import numpy as np
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import scipy.signal as sig
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import matplotlib.pyplot as plt
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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from matplotlib.patches import ConnectionPatch
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from sklearn.metrics import roc_curve, roc_auc_score
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from IPython import embed
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@ -320,7 +322,7 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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alone_chirping_snippets = np.zeros((len(chirp_times) * no_other_rates.shape[0], int(chirp_duration / dt)))
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self_snippets = np.zeros_like(alone_chirping_snippets)
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other_snippets = np.zeros_like(alone_chirping_snippets)
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baseline_snippets = np.zeros_like(alone_chirping_snippets)
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silence_snippets = np.zeros_like(alone_chirping_snippets)
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for i in range(no_other_rates.shape[0]):
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for j, chirp_time in enumerate(chirp_times):
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@ -330,9 +332,9 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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alone_chirping_snippets[index, :] = no_other_rates[i, start_index:end_index]
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self_snippets[index, :] = self_rates[i, start_index:end_index]
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other_snippets[index, :] = other_rates[i, start_index:end_index]
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baseline_start_index = int((chirp_time + 1.5 * chirp_duration)/dt)
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baseline_end_index = baseline_start_index + alone_chirping_snippets.shape[1]
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baseline_snippets[index, :] = no_other_rates[i, baseline_start_index:baseline_end_index]
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silence_start_index = int((chirp_time + 1.5 * chirp_duration)/dt)
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silence_end_index = silence_start_index + alone_chirping_snippets.shape[1]
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silence_snippets[index, :] = other_rates[i, silence_start_index:silence_end_index]
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# get the distances
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# 1. Soliloquy
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@ -340,9 +342,9 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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# 3. I chirp while the other is present compared to self chirping without the other one present
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# 4. the otherone chrips to me compared to baseline with anyone chirping
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alone_chirping_dist = within_group_distance(alone_chirping_snippets)
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baseline_dist = within_group_distance(baseline_snippets)
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silence_dist = within_group_distance(silence_snippets)
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self_vs_alone_dist = across_group_distance(alone_chirping_snippets, self_snippets)
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other_vs_baseline_dist = across_group_distance(baseline_snippets, other_snippets)
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other_vs_silence_dist = across_group_distance(silence_snippets, other_snippets)
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# sort and perfom ROC analysis for two comparisons
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# 1. soliloquy vs. self chirping in company
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@ -351,14 +353,14 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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valid_no_other_distances = alone_chirping_dist[triangle_indices]
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no_other_temp = np.zeros_like(valid_no_other_distances)
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valid_baseline_distances = baseline_dist[triangle_indices]
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baseline_temp = np.zeros_like(valid_baseline_distances)
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valid_silence_distances = silence_dist[triangle_indices]
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silence_temp = np.zeros_like(valid_silence_distances)
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valid_self_vs_alone_distances = self_vs_alone_dist.ravel()
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self_vs_alone_temp = np.ones_like(valid_self_vs_alone_distances)
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valid_other_vs_baseline_distances = other_vs_baseline_dist.ravel()
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other_vs_baseline_temp = np.ones_like(valid_other_vs_baseline_distances)
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valid_other_vs_silence_distances = other_vs_silence_dist.ravel()
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other_vs_silence_temp = np.ones_like(valid_other_vs_silence_distances)
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group = np.hstack((no_other_temp, self_vs_alone_temp))
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score = np.hstack((valid_no_other_distances, valid_self_vs_alone_distances))
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@ -368,8 +370,8 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
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else:
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detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
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group = np.hstack((baseline_temp, other_vs_baseline_temp))
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score = np.hstack((valid_baseline_distances, valid_other_vs_baseline_distances))
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group = np.hstack((silence_temp, other_vs_silence_temp))
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score = np.hstack((valid_silence_distances, valid_other_vs_silence_distances))
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fpr, tpr, _ = roc_curve(group, score, pos_label=1)
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auc = roc_auc_score(group, score)
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if store_roc:
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@ -394,14 +396,15 @@ def plot_detection_results(data_frame, df, kernel_width, cell, figure_name=None)
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contrasts = roc_data.contrast.unique()
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roc_ax = plt.subplot2grid(fig_grid, (i * 2 + i, 0), colspan=3, rowspan=2)
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roc_ax.set_title(c, fontsize=9, ha="left")
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roc_ax.set_title(c, fontsize=9, loc="left")
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auc_ax = plt.subplot2grid(fig_grid, (i * 2 + i, 4), colspan=3, rowspan=2)
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for c in contrasts:
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tpr = roc_data.true_positives[roc_data.contrast == c].values[0]
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fpr = roc_data.false_positives[roc_data.contrast == c].values[0]
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roc_ax.plot(fpr, tpr, label="%.3f" % c, zorder=2)
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roc_ax.legend(loc="best", fontsize=6, ncol=2, frameon=False)
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if i == 0:
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roc_ax.legend(loc="lower right", fontsize=6, ncol=2, frameon=False, handletextpad=0.4, columnspacing=1.0, labelspacing=0.25)
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roc_ax.plot([0., 1.],[0., 1.], color="k", lw=0.5, ls="--", zorder=0)
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roc_ax.set_xticks(np.arange(0.0, 1.01, 0.5))
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roc_ax.set_xticks(np.arange(0.0, 1.01, 0.25), minor=True)
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@ -418,23 +421,95 @@ def plot_detection_results(data_frame, df, kernel_width, cell, figure_name=None)
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aucs = np.asarray(condition_results.auc[condition_results.kernel_width == k])
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aucs_sorted = aucs[np.argsort(contrasts)]
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contrasts_sorted = np.sort(contrasts)
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auc_ax.plot(contrasts_sorted, aucs_sorted, marker=".", label=r"$\sigma$: %.4f" % k, zorder=1)
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auc_ax.plot(contrasts_sorted, aucs_sorted, marker=".", label=r"$\sigma$: %.2f ms" % (k * 1000), zorder=1)
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if i == len(conditions) - 1:
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auc_ax.set_xlabel("contrast [%]", fontsize=9)
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else:
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auc_ax.set_xticklabels("")
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auc_ax.set_ylim([0.25, 1.0])
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auc_ax.set_yticks(np.arange(0.25, 1.01, 0.25))
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auc_ax.set_yticklabels(np.arange(0.25, 1.01, 0.25), fontsize=8)
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auc_ax.set_ylabel("discriminability", fontsize=9)
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auc_ax.legend(ncol=2, fontsize=6, handletextpad=0.4, columnspacing=1.0, labelspacing=0.25)
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if i == 0:
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auc_ax.legend(ncol=2, fontsize=6, handletextpad=0.4, columnspacing=1.0, labelspacing=0.25, frameon=False, loc="lower center")
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auc_ax.plot([min(contrasts), max(contrasts)], [0.5, 0.5], lw=0.5, ls="--", zorder=0)
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name = figure_name if figure_name is not None else "foreign_fish_detection.pdf"
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name = (name + ".pdf") if ".pdf" not in name else name
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plt.savefig(os.path.join(figure_folder, name))
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fig.savefig(os.path.join(figure_folder, name))
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def plot_comparisons(block_map, all_dfs, all_contrasts, all_conditions, current_df):
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conditions = ["no-other", "self", "other"]
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condition_labels = ["soliloquy", "self chirping", "other chirping"]
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min_time = 0.5
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max_time = min_time + 0.5
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fig = plt.figure(figsize=(6.5, 2.))
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fig_grid = (3, len(all_conditions)*3+2)
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axes = []
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for i, condition in enumerate(conditions):
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# plot the signals
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block = block_map[(all_contrasts[0], current_df, condition)]
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signal, self_freq, other_freq, time = get_signals(block)
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self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
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other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"]
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# plot frequency traces
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ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=2, colspan=3, fig=fig)
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ax.plot(time[(time > min_time) & (time < max_time)], self_freq[(time > min_time) & (time < max_time)],
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color="#ff7f0e", label="%iHz" % self_eodf)
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ax.text(min_time-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9)
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if other_freq is not None:
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ax.plot(time[(time > min_time) & (time < max_time)], other_freq[(time > min_time) & (time < max_time)],
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color="#1f77b4", label="%iHz" % other_eodf)
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ax.text(min_time-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
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# ax.set_title(condition_labels[i])
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ax.set_ylim([735, 885])
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despine(ax, ["top", "bottom", "left", "right"], True)
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axes.append(ax)
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rects = []
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rect = Rectangle((0.675, 740), 0.098, 140)
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rects.append(rect)
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rect = Rectangle((0.57, 740), 0.098, 140)
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rects.append(rect)
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pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
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axes[0].add_collection(pc)
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rects = []
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rect = Rectangle((0.675, 740), 0.098, 140)
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rects.append(rect)
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rect = Rectangle((0.575, 740), 0.098, 140)
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rects.append(rect)
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pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
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axes[1].add_collection(pc)
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rects = []
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rect = Rectangle((0.57, 740), 0.098, 140)
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rects.append(rect)
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pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
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axes[2].add_collection(pc)
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con = ConnectionPatch(xyA=(0.625, 735), xyB=(0.625, 740), coordsA="data", coordsB="data",
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axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=.35")
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axes[1].add_artist(con)
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con = ConnectionPatch(xyA=(0.725, 885), xyB=(0.725, 880), coordsA="data", coordsB="data",
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axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=-.25")
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axes[1].add_artist(con)
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con = ConnectionPatch(xyA=(0.725, 735), xyB=(0.625, 740), coordsA="data", coordsB="data",
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axesA=axes[1], axesB=axes[2], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=.35")
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axes[1].add_artist(con)
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axes[0].text(1., 660, "2.")
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axes[1].text(1.05, 660, "3.")
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axes[0].text(1.1, 890, "1.")
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fig.subplots_adjust(bottom=0.1, top=0.8, left=0.1, right=0.9)
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fig.savefig(os.path.join(figure_folder, "comparisons.pdf"))
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plt.close()
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def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, cell_name=""):
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def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, cell_name="", store_roc=False):
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dfs = [current_df] if current_df is not None else all_dfs
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kernels = [0.00025, 0.0005, 0.001, 0.0025]
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result_dicts = []
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@ -442,15 +517,10 @@ def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, cu
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for kw in kernels:
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print("df: %i, kernel: %.4f" % (df, kw))
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print("Foreign fish detection during beat:")
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result_dicts.extend(foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kw, cell_name))
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result_dicts.extend(foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kw, cell_name, store_roc))
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print("Foreign fish detection during chirp:")
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result_dicts.extend(foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kw, cell_name))
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result_dicts.extend(foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kw, cell_name, store_roc))
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break
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embed()
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return result_dicts
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@ -466,21 +536,46 @@ def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
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baseline_spikes = read_baseline(block_map["baseline"])
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else:
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print("ERROR: no baseline data for file %s!" % filename)
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# fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
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# create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
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# fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
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# create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
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results = foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=20,
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cell_name=filename.split(os.path.sep)[-1].split(".nix")[0])
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results = foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None,
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cell_name=filename.split(os.path.sep)[-1].split(".nix")[0], store_roc=False)
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nf.close()
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return results
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def plot_examples(filename, dfs=[], contrasts=[], conditions=[]):
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nf = nix.File.open(filename, nix.FileMode.ReadOnly)
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block_map, all_contrasts, all_dfs, all_conditions = sort_blocks(nf)
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if "baseline" in block_map.keys():
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baseline_spikes = read_baseline(block_map["baseline"])
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else:
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print("ERROR: no baseline data for file %s!" % filename)
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nf.close()
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# plot the responses
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#fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
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#create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
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#fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
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#create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
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# sketch showing the comparisons
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# plot_comparisons(block_map, all_dfs, all_contrasts, all_conditions, 20)
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# plot the discrimination analyses
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#cell_name = filename.split(os.path.sep)[-1].split(".nix")[0]
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# results = foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=20,
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# cell_name=cell_name, store_roc=True)
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# pdf = pd.DataFrame(results)
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# plot_detection_results(pdf, 20, 0.001, cell_name)
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nf.close()
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def main():
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nix_files = sorted(glob.glob(os.path.join(data_folder, "cell*.nix")))
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for nix_file in nix_files:
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process_cell(nix_file, dfs=[20], contrasts=[20], conditions=["self"])
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#plot_examples(nix_file, dfs=[20], contrasts=[20], conditions=["self"])
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results = process_cell(nix_file, dfs=[], contrasts=[20], conditions=["self"])
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# break
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embed()
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if __name__ == "__main__":
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