224 lines
10 KiB
Python
224 lines
10 KiB
Python
import glob
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import os
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import nixio as nix
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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 nix_util import sort_blocks, read_baseline, get_signals
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from util import despine
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figure_folder = "figures"
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data_folder = "data"
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def plot_comparisons(current_df=20):
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files = sorted(glob.glob(os.path.join(data_folder, "*.nix")))
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if len(files) < 1:
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print("plot comparisons: no data found!")
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return
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filename = files[0]
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nf = nix.File.open(filename, nix.FileMode.ReadOnly)
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block_map, all_contrasts, _, all_conditions = sort_blocks(nf)
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conditions = ["no-other", "self", "other"]
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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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_, 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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nf.close()
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def create_response_plot(filename, current_df=20, figure_name=None):
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files = sorted(glob.glob(os.path.join(data_folder, "*.nix")))
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if len(files) < 1:
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print("plot comparisons: no data found!")
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return
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filename = files[0]
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nf = nix.File.open(filename, nix.FileMode.ReadOnly)
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block_map, all_contrasts, _, all_conditions = sort_blocks(nf)
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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, 5.5))
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fig_grid = (len(all_contrasts)*2 + 6, len(all_conditions)*3+2)
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all_contrasts = sorted(all_contrasts, reverse=True)
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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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am = extract_am(signal)
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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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despine(ax, ["top", "bottom", "left", "right"], True)
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# plot the am
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ax = plt.subplot2grid(fig_grid, (3, i * 3 + i), rowspan=2, colspan=3, fig=fig)
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ax.plot(time[(time > min_time) & (time < max_time)], signal[(time > min_time) & (time < max_time)],
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color="#2ca02c", label="signal")
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ax.plot(time[(time > min_time) & (time < max_time)], am[(time > min_time) & (time < max_time)],
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color="#d62728", label="am")
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despine(ax, ["top", "bottom", "left", "right"], True)
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ax.set_ylim([-1.25, 1.25])
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ax.legend(ncol=2, loc=(0.01, -0.5), fontsize=7, markerscale=0.5, frameon=False)
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# for each contrast plot the firing rate
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for j, contrast in enumerate(all_contrasts):
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t, rates, _ = get_firing_rate(block_map, current_df, contrast, condition)
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avg_resp = np.mean(rates, axis=0)
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error = np.std(rates, axis=0)
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ax = plt.subplot2grid(fig_grid, (j*2 + 6, i * 3 + i), rowspan=2, colspan=3)
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ax.plot(t[(t > min_time) & (t < max_time)], avg_resp[(t > min_time) & (t < max_time)], color="k", lw=0.5)
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ax.fill_between(t[(t > min_time) & (t < max_time)], (avg_resp - error)[(t > min_time) & (t < max_time)],
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(avg_resp + error)[(t > min_time) & (t < max_time)], color="k", lw=0.0, alpha=0.25)
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ax.set_ylim([0, 750])
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ax.set_xlabel("")
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ax.set_ylabel("")
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ax.set_xticks(np.arange(min_time, max_time+.01, 0.250))
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ax.set_xticklabels(map(int, (np.arange(min_time, max_time + .01, 0.250) - min_time) * 1000))
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ax.set_xticks(np.arange(min_time, max_time+.01, 0.125), minor=True)
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if j < len(all_contrasts) -1:
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ax.set_xticklabels([])
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ax.set_yticks(np.arange(0.0, 751., 500))
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ax.set_yticks(np.arange(0.0, 751., 125), minor=True)
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if i > 0:
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ax.set_yticklabels([])
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despine(ax, ["top", "right"], False)
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if i == 2:
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ax.text(max_time + 0.025*max_time, 350, "c=%.3f" % all_contrasts[j],
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color="#d62728", ha="left", fontsize=7)
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if i == 1:
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ax.set_xlabel("time [ms]")
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if i == 0:
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ax.set_ylabel("frequency [Hz]", va="center")
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ax.yaxis.set_label_coords(-0.45, 3.5)
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name = figure_name if figure_name is not None else "chirp_responses.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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plt.close()
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nf.close()
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def response_examples(*kwargs):
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if filename in kwargs:
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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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def main(task=None, parameter={}):
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plot_tasks = {"comparisons": plot_comparisons,
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"response_examples": create_response_plot}
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if task is not None and task in plot_tasks.keys():
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plot_tasks[task](*parameter)
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elif task is None:
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for t in plot_tasks.keys():
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plot_tasks[t](*parameter)
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if __name__ == "__main__":
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main("comparisons")
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def plot_examples(filename, dfs=[], contrasts=[], conditions=[]):
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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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pass |