230 lines
9.7 KiB
Python
230 lines
9.7 KiB
Python
import os
|
|
import glob
|
|
import nixio as nix
|
|
import numpy as np
|
|
import scipy.signal as sig
|
|
import matplotlib.pyplot as plt
|
|
from IPython import embed
|
|
|
|
from util import firing_rate, despine
|
|
figure_folder = "figures"
|
|
data_folder = "data"
|
|
|
|
|
|
def read_baseline(block):
|
|
spikes = []
|
|
if "baseline" not in block.name:
|
|
print("Block %s does not appear to be a baseline block!" % block.name )
|
|
return spikes
|
|
spikes = block.data_arrays[0][:]
|
|
return spikes
|
|
|
|
|
|
def sort_blocks(nix_file):
|
|
block_map = {}
|
|
contrasts = []
|
|
deltafs = []
|
|
conditions = []
|
|
for b in nix_file.blocks:
|
|
if "baseline" not in b.name.lower():
|
|
name_parts = b.name.split("_")
|
|
cntrst = float(name_parts[1])
|
|
if cntrst not in contrasts:
|
|
contrasts.append(cntrst)
|
|
cndtn = name_parts[3]
|
|
if cndtn not in conditions:
|
|
conditions.append(cndtn)
|
|
dltf = float(name_parts[5])
|
|
if dltf not in deltafs:
|
|
deltafs.append(dltf)
|
|
block_map[(cntrst, dltf, cndtn)] = b
|
|
else:
|
|
block_map["baseline"] = b
|
|
return block_map, contrasts, deltafs, conditions
|
|
|
|
|
|
def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005):
|
|
block = block_map[(contrast, df, condition)]
|
|
response_map = {}
|
|
spikes = []
|
|
for da in block.data_arrays:
|
|
if "spike_times" in da.type and "response" in da.name:
|
|
resp_id = int(da.name.split("_")[-1])
|
|
response_map[resp_id] = da
|
|
duration = float(block.metadata["stimulus parameter"]["duration"])
|
|
dt = float(block.metadata["stimulus parameter"]["dt"])
|
|
time = np.arange(0.0, duration, dt)
|
|
rates = np.zeros((len(response_map.keys()), len(time)))
|
|
for i, k in enumerate(response_map.keys()):
|
|
spikes.append(response_map[k][:])
|
|
rates[i,:] = firing_rate(spikes[-1], duration, kernel_width, dt)
|
|
return time, rates, spikes
|
|
|
|
|
|
def get_signals(block):
|
|
self_freq = None
|
|
other_freq = None
|
|
signal = None
|
|
time = None
|
|
if "complete stimulus" not in block.data_arrays or "self frequency" not in block.data_arrays:
|
|
raise ValueError("Signals not stored in block!")
|
|
if "no-other" not in block.name and "other frequency" not in block.data_arrays:
|
|
raise ValueError("Signals not stored in block!")
|
|
|
|
signal = block.data_arrays["complete stimulus"][:]
|
|
time = np.asarray(block.data_arrays["complete stimulus"].dimensions[0].axis(len(signal)))
|
|
self_freq = block.data_arrays["self frequency"][:]
|
|
if "no-other" not in block.name:
|
|
other_freq = block.data_arrays["other frequency"][:]
|
|
return signal, self_freq, other_freq, time
|
|
|
|
|
|
def extract_am(signal):
|
|
# first add some padding
|
|
front_pad = np.flip(signal[:int(len(signal)/100)])
|
|
back_pad = np.flip(signal[-int(len(signal)/100):])
|
|
padded = np.hstack((front_pad, signal, back_pad))
|
|
# do the hilbert and take abs
|
|
am = np.abs(sig.hilbert(padded))
|
|
am = am[len(front_pad):-len(back_pad)]
|
|
return am
|
|
|
|
|
|
def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, current_df, figure_name=None):
|
|
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 the largest contrast plot the raster with psth, only a section of the data (e.g. 1s)
|
|
t, rates, spikes = get_firing_rate(block_map, current_df, all_contrasts[0], condition, kernel_width=0.001)
|
|
avg_resp = np.mean(rates, axis=0)
|
|
error = np.std(rates, axis=0)
|
|
|
|
ax = plt.subplot2grid(fig_grid, (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, 0.0625), minor=True)
|
|
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)
|
|
"""
|
|
# for all other contrast plot the firing rate alone
|
|
for j in range(0, len(all_contrasts)):
|
|
contrast = all_contrasts[j]
|
|
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()
|
|
|
|
|
|
def chrip_detection_soliloquy(spikes, chirp_times, kernel_width=0.0005):
|
|
#
|
|
pass
|
|
|
|
|
|
def chirp_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, current_condition=None):
|
|
|
|
pass
|
|
|
|
|
|
def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
|
|
nf = nix.File.open(filename, nix.FileMode.ReadOnly)
|
|
block_map, all_contrasts, all_dfs, all_conditions = sort_blocks(nf)
|
|
if "baseline" in block_map.keys():
|
|
baseline_spikes = read_baseline(block_map["baseline"])
|
|
else:
|
|
print("ERROR: no baseline data for file %s!" % filename)
|
|
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)
|
|
chirp_detection(block_map, all_dfs, all_contrasts, all_conditions)
|
|
|
|
|
|
nf.close()
|
|
|
|
|
|
def main():
|
|
nix_files = sorted(glob.glob(os.path.join(data_folder, "cell*.nix")))
|
|
for nix_file in nix_files:
|
|
process_cell(nix_file, dfs=[20], contrasts=[20], conditions=["self"])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |