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/config.py
.vscode/.ropeproject/objectdb .vscode/.ropeproject/objectdb
figures
data

5
chirp_ams.py
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@ -1,3 +1,4 @@
import os
import numpy as np import numpy as np
import scipy.signal as sig import scipy.signal as sig
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -5,6 +6,8 @@ import matplotlib.pyplot as plt
from chirp_stimulation import create_chirp from chirp_stimulation import create_chirp
from util import despine from util import despine
figure_folder = "figures"
def get_signals(eodfs, condition, contrast, chirp_size, chirp_duration, chirp_amplitude_dip, def get_signals(eodfs, condition, contrast, chirp_size, chirp_duration, chirp_amplitude_dip,
chirp_times, duration, dt): 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) 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() plt.close()

131
response_discriminability.py
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@ -2,11 +2,14 @@ import os
import glob import glob
import nixio as nix import nixio as nix
import numpy as np import numpy as np
import scipy.signal as sig
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from IPython import embed
from util import firing_rate, despine from util import firing_rate, despine
figure_folder = "figures"
data_folder = "data"
from IPython import embed
def read_baseline(block): def read_baseline(block):
spikes = [] spikes = []
@ -42,7 +45,6 @@ def sort_blocks(nix_file):
def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005): def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005):
block = block_map[(contrast, df, condition)] block = block_map[(contrast, df, condition)]
print(block.name)
response_map = {} response_map = {}
spikes = [] spikes = []
for da in block.data_arrays: 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): def get_signals(block):
print(block.name)
self_freq = None self_freq = None
other_freq = None other_freq = None
signal = None signal = None
@ -78,39 +79,79 @@ def get_signals(block):
return signal, self_freq, other_freq, time 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"] conditions = ["no-other", "self", "other"]
condition_labels = ["alone", "self", "other"] condition_labels = ["soliloquy", "self chirping", "other chirping"]
max_time = 0.5 min_time = 0.5
max_time = min_time + 0.5
fig = plt.figure(figsize=(6.5, 5.5)) 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) all_contrasts = sorted(all_contrasts, reverse=True)
for i, condition in enumerate(conditions): for i, condition in enumerate(conditions):
# plot the signals # plot the signals
block = block_map[(all_contrasts[0], current_df, condition)] 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"] self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"] other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"]
ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=1, colspan=3, fig=fig) # plot frequency traces
ax.plot(time[time < max_time], self_freq[time < max_time], color="#ff7f0e", label="%iHz" % self_eodf) ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=2, colspan=3, fig=fig)
ax.text(-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9) 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: if other_freq is not None:
ax.plot(time[time < max_time], other_freq[time < max_time], color="#1f77b4", label="%iHz" % other_eodf) ax.plot(time[(time > min_time) & (time < max_time)], other_freq[(time > min_time) & (time < max_time)],
ax.text(-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9) 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_title(condition_labels[i])
despine(ax, ["top", "bottom", "left", "right"], True) 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) # 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) 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) avg_resp = np.mean(rates, axis=0)
error = np.std(rates, axis=0) error = np.std(rates, axis=0)
ax = plt.subplot2grid(fig_grid, (1, i * 3 + i), rowspan=1, colspan=3) ax = plt.subplot2grid(fig_grid, (6, i * 3 + i), rowspan=2, colspan=3)
ax.plot(t[t < max_time], avg_resp[t < max_time], color="k", lw=0.5) ax.plot(t[(t > min_time) & (t < max_time)], avg_resp[(t > min_time) & (t < max_time)],
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) 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) despine(ax, ["top", "right"], False)
# for all other contrast plot the firing rate alone # 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) t, rates, _ = get_firing_rate(block_map, current_df, contrast, condition)
avg_resp = np.mean(rates, axis=0) avg_resp = np.mean(rates, axis=0)
error = np.std(rates, axis=0) error = np.std(rates, axis=0)
ax = plt.subplot2grid(fig_grid, (j+1, i * 3 + i), rowspan=1, colspan=3) ax = plt.subplot2grid(fig_grid, (j*2 + 6, i * 3 + i), rowspan=2, colspan=3)
ax.plot(t[t < max_time], avg_resp[t < max_time], color="k", lw=0.5) 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 < max_time], (avg_resp - error)[t < max_time], (avg_resp + error)[t < max_time], color="k", lw=None, alpha=0.25) 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) despine(ax, ["top", "right"], False)
if i == 1:
plt.savefig("chirp_responses.pdf") 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() plt.close()
return
def process_cell(filename, dfs=[], contrasts=[], conditions=[]): def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
nf = nix.File.open(filename, nix.FileMode.ReadOnly) 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(): if "baseline" in block_map.keys():
baseline_spikes = read_baseline(block_map["baseline"]) baseline_spikes = read_baseline(block_map["baseline"])
else: else:
print("ERROR: no baseline data for file %s!" % filename) print("ERROR: no baseline data for file %s!" % filename)
fig_name = filename.split(".nix")[0] + "_df_20Hz.pdf"
create_response_plot(block_map, all_contrasts, all_dfs, all_conditions, 20) 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"
if len(dfs) == 0: create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
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() nf.close()
def main(): 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: for nix_file in nix_files:
process_cell(nix_file, dfs=[20], contrasts=[20], conditions=["self"]) process_cell(nix_file, dfs=[20], contrasts=[20], conditions=["self"])