import glob
import os
import nixio as nix
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
from matplotlib.patches import ConnectionPatch
from nix_util import sort_blocks, read_baseline, get_signals
from util import despine
figure_folder = "figures"
data_folder = "data"
def plot_comparisons(current_df=20):
files = sorted(glob.glob(os.path.join(data_folder, "*.nix")))
if len(files) < 1:
print("plot comparisons: no data found!")
return
filename = files[0]
nf = nix.File.open(filename, nix.FileMode.ReadOnly)
block_map, all_contrasts, _, all_conditions = sort_blocks(nf)
conditions = ["no-other", "self", "other"]
min_time = 0.5
max_time = min_time + 0.5
fig = plt.figure(figsize=(6.5, 2.))
fig_grid = (3, len(all_conditions)*3+2)
axes = []
for i, condition in enumerate(conditions):
# plot the signals
block = block_map[(all_contrasts[0], current_df, condition)]
_, self_freq, other_freq, time = get_signals(block)
self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"]
# plot frequency traces
ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=2, colspan=3, fig=fig)
ax.plot(time[(time > min_time) & (time < max_time)], self_freq[(time > min_time) & (time < max_time)],
color="#ff7f0e", label="%iHz" % self_eodf)
ax.text(min_time-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9)
if other_freq is not None:
ax.plot(time[(time > min_time) & (time < max_time)], other_freq[(time > min_time) & (time < max_time)],
color="#1f77b4", label="%iHz" % other_eodf)
ax.text(min_time-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
# ax.set_title(condition_labels[i])
ax.set_ylim([735, 885])
despine(ax, ["top", "bottom", "left", "right"], True)
axes.append(ax)
rects = []
rect = Rectangle((0.675, 740), 0.098, 140)
rects.append(rect)
rect = Rectangle((0.57, 740), 0.098, 140)
rects.append(rect)
pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
axes[0].add_collection(pc)
rects = []
rect = Rectangle((0.675, 740), 0.098, 140)
rects.append(rect)
rect = Rectangle((0.575, 740), 0.098, 140)
rects.append(rect)
pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
axes[1].add_collection(pc)
rects = []
rect = Rectangle((0.57, 740), 0.098, 140)
rects.append(rect)
pc = PatchCollection(rects, facecolor=None, alpha=0.15, edgecolor="k", ls="--")
axes[2].add_collection(pc)
con = ConnectionPatch(xyA=(0.625, 735), xyB=(0.625, 740), coordsA="data", coordsB="data",
axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=.35")
axes[1].add_artist(con)
con = ConnectionPatch(xyA=(0.725, 885), xyB=(0.725, 880), coordsA="data", coordsB="data",
axesA=axes[0], axesB=axes[1], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=-.25")
axes[1].add_artist(con)
con = ConnectionPatch(xyA=(0.725, 735), xyB=(0.625, 740), coordsA="data", coordsB="data",
axesA=axes[1], axesB=axes[2], arrowstyle="<->", shrinkB=5, connectionstyle="arc3,rad=.35")
axes[1].add_artist(con)
axes[0].text(1., 660, "2.")
axes[1].text(1.05, 660, "3.")
axes[0].text(1.1, 890, "1.")
fig.subplots_adjust(bottom=0.1, top=0.8, left=0.1, right=0.9)
fig.savefig(os.path.join(figure_folder, "comparisons.pdf"))
plt.close()
nf.close()
def create_response_plot(filename, current_df=20, figure_name=None):
files = sorted(glob.glob(os.path.join(data_folder, "*.nix")))
if len(files) < 1:
print("plot comparisons: no data found!")
return
filename = files[0]
nf = nix.File.open(filename, nix.FileMode.ReadOnly)
block_map, all_contrasts, _, all_conditions = sort_blocks(nf)
conditions = ["no-other", "self", "other"]
condition_labels = ["soliloquy", "self chirping", "other chirping"]
min_time = 0.5
max_time = min_time + 0.5
fig = plt.figure(figsize=(6.5, 5.5))
fig_grid = (len(all_contrasts)*2 + 6, len(all_conditions)*3+2)
all_contrasts = sorted(all_contrasts, reverse=True)
for i, condition in enumerate(conditions):
# plot the signals
block = block_map[(all_contrasts[0], current_df, condition)]
signal, self_freq, other_freq, time = get_signals(block)
am = extract_am(signal)
self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"]
# plot frequency traces
ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=2, colspan=3, fig=fig)
ax.plot(time[(time > min_time) & (time < max_time)], self_freq[(time > min_time) & (time < max_time)],
color="#ff7f0e", label="%iHz" % self_eodf)
ax.text(min_time-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9)
if other_freq is not None:
ax.plot(time[(time > min_time) & (time < max_time)], other_freq[(time > min_time) & (time < max_time)],
color="#1f77b4", label="%iHz" % other_eodf)
ax.text(min_time-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
ax.set_title(condition_labels[i])
despine(ax, ["top", "bottom", "left", "right"], True)
# plot the am
ax = plt.subplot2grid(fig_grid, (3, i * 3 + i), rowspan=2, colspan=3, fig=fig)
ax.plot(time[(time > min_time) & (time < max_time)], signal[(time > min_time) & (time < max_time)],
color="#2ca02c", label="signal")
ax.plot(time[(time > min_time) & (time < max_time)], am[(time > min_time) & (time < max_time)],
color="#d62728", label="am")
despine(ax, ["top", "bottom", "left", "right"], True)
ax.set_ylim([-1.25, 1.25])
ax.legend(ncol=2, loc=(0.01, -0.5), fontsize=7, markerscale=0.5, frameon=False)
# for each contrast plot the firing rate
for j, contrast in enumerate(all_contrasts):
t, rates, _ = get_firing_rate(block_map, current_df, contrast, condition)
avg_resp = np.mean(rates, axis=0)
error = np.std(rates, axis=0)
ax = plt.subplot2grid(fig_grid, (j*2 + 6, i * 3 + i), rowspan=2, colspan=3)
ax.plot(t[(t > min_time) & (t < max_time)], avg_resp[(t > min_time) & (t < max_time)], color="k", lw=0.5)
ax.fill_between(t[(t > min_time) & (t < max_time)], (avg_resp - error)[(t > min_time) & (t < max_time)],
(avg_resp + error)[(t > min_time) & (t < max_time)], color="k", lw=0.0, alpha=0.25)
ax.set_ylim([0, 750])
ax.set_xlabel("")
ax.set_ylabel("")
ax.set_xticks(np.arange(min_time, max_time+.01, 0.250))
ax.set_xticklabels(map(int, (np.arange(min_time, max_time + .01, 0.250) - min_time) * 1000))
ax.set_xticks(np.arange(min_time, max_time+.01, 0.125), minor=True)
if j < len(all_contrasts) -1:
ax.set_xticklabels([])
ax.set_yticks(np.arange(0.0, 751., 500))
ax.set_yticks(np.arange(0.0, 751., 125), minor=True)
if i > 0:
ax.set_yticklabels([])
despine(ax, ["top", "right"], False)
if i == 2:
ax.text(max_time + 0.025*max_time, 350, "c=%.3f" % all_contrasts[j],
color="#d62728", ha="left", fontsize=7)
if i == 1:
ax.set_xlabel("time [ms]")
if i == 0:
ax.set_ylabel("frequency [Hz]", va="center")
ax.yaxis.set_label_coords(-0.45, 3.5)
name = figure_name if figure_name is not None else "chirp_responses.pdf"
name = (name + ".pdf") if ".pdf" not in name else name
plt.savefig(os.path.join(figure_folder, name))
plt.close()
nf.close()
def response_examples(*kwargs):
if filename in kwargs:
#fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
#create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
#fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
#create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
def main(task=None, parameter={}):
plot_tasks = {"comparisons": plot_comparisons,
"response_examples": create_response_plot}
if task is not None and task in plot_tasks.keys():
plot_tasks[task](*parameter)
elif task is None:
for t in plot_tasks.keys():
plot_tasks[t](*parameter)
if __name__ == "__main__":
main("comparisons")
def plot_examples(filename, dfs=[], contrasts=[], conditions=[]):
# plot the responses
#fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
#create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
#fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
#create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
# sketch showing the comparisons
#plot_comparisons(block_map, all_dfs, all_contrasts, all_conditions, 20)
# plot the discrimination analyses
#cell_name = filename.split(os.path.sep)[-1].split(".nix")[0]
# results = foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=20,
# cell_name=cell_name, store_roc=True)
# pdf = pd.DataFrame(results)
# plot_detection_results(pdf, 20, 0.001, cell_name)
#nf.close()
pass