jgrewe/chirp_probing
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finished response figure ...

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move data and figs to folders, ignore them in git
This commit is contained in:
Jan Grewe 2020-09-18 16:18:33 +02:00
parent dde8bd842d
commit 7e673267e5
3 changed files with 96 additions and 42 deletions
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2
.gitignore vendored
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@ -1,2 +1,4 @@
.vscode/.ropeproject/config.py
.vscode/.ropeproject/objectdb
figures
data

5
chirp_ams.py
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@ -1,3 +1,4 @@
import os
import numpy as np
import scipy.signal as sig
import matplotlib.pyplot as plt
@ -5,6 +6,8 @@ import matplotlib.pyplot as plt
from chirp_stimulation import create_chirp
from util import despine
figure_folder = "figures"
def get_signals(eodfs, condition, contrast, chirp_size, chirp_duration, chirp_amplitude_dip,
chirp_times, duration, dt):
@ -107,5 +110,5 @@ if __name__ == "__main__":
fig.subplots_adjust(left=0.1, bottom=0.1, top=0.99, right=0.99)
plt.savefig("Chirp_induced_ams.pdf")
plt.savefig(os.path.join(figure_folder, "Chirp_induced_AMs.pdf"))
plt.close()

129
response_discriminability.py
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@ -2,11 +2,14 @@ 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"
from IPython import embed
def read_baseline(block):
spikes = []
@ -42,7 +45,6 @@ def sort_blocks(nix_file):
def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005):
block = block_map[(contrast, df, condition)]
print(block.name)
response_map = {}
spikes = []
for da in block.data_arrays:
@ -60,7 +62,6 @@ def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005):
def get_signals(block):
print(block.name)
self_freq = None
other_freq = None
signal = None
@ -78,39 +79,79 @@ def get_signals(block):
return signal, self_freq, other_freq, time
def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, current_df):
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 = ["alone", "self", "other"]
max_time = 0.5
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) + 1, len(all_conditions)*3+2)
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)]
_, self_freq, other_freq, time = get_signals(block)
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"]
ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=1, colspan=3, fig=fig)
ax.plot(time[time < max_time], self_freq[time < max_time], color="#ff7f0e", label="%iHz" % self_eodf)
ax.text(-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9)
# 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 < max_time], other_freq[time < max_time], color="#1f77b4", label="%iHz" % other_eodf)
ax.text(-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
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, (1, i * 3 + i), rowspan=1, colspan=3)
ax.plot(t[t < max_time], avg_resp[t < max_time], color="k", lw=0.5)
ax.fill_between(t[t < max_time], (avg_resp - error)[t < max_time], (avg_resp + error)[t < max_time], color="k", lw=None, alpha=0.25)
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
@ -119,46 +160,54 @@ def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, curr
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+1, i * 3 + i), rowspan=1, colspan=3)
ax.plot(t[t < max_time], avg_resp[t < max_time], color="k", lw=0.5)
#ax.fill_between(t[t < max_time], (avg_resp - error)[t < max_time], (avg_resp + error)[t < max_time], color="k", lw=None, alpha=0.25)
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)
plt.savefig("chirp_responses.pdf")
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()
return
def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
nf = nix.File.open(filename, nix.FileMode.ReadOnly)
block_map, all_dfs, all_contrasts, all_conditions = sort_blocks(nf)
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(".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(".nix")[0] + "_df_-100Hz.pdf"
create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
create_response_plot(block_map, all_contrasts, all_dfs, all_conditions, 20)
"""
if len(dfs) == 0:
dfs = all_dfs
if len(contrasts) == 0:
contrasts = all_contrasts
if len(conditions) == 0:
conditions = all_conditions
for df in dfs:
for condition in conditions:
for contrast in contrasts:
time, rates = get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0025)
"""
nf.close()
def main():
nix_files = sorted(glob.glob("cell*.nix"))
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"])