"""
========================================================
Population activity and membrane dynamics of 150 neurons
========================================================
The figure is invisibly generated and saved under the current working directory and
under the sub-directory `~/raster150_1s/`
.. code-block:: text
+---------------------+-----------+-----------+
| | | |
| subplot 1 | subplot 2 | subplot 3 |
| | | |
+---------------------+-----------+-----------+
| | |
| subplot 4 | subplot 5 |
| | |
+---------------------+-----------------------+
Figure contains five subplots such that for each disinhibition experiment it plots:
+-------+---------------------------------------+----------------------------------------------------------------------+
|Subplot| Content | Interpretation |
+=======+=======================================+======================================================================+
| 1 | raster of all the neurons | :func:`analyseur.cbgtc.visual.markerplot.plot_raster_in_ax` |
+-------+---------------------------------------+----------------------------------------------------------------------+
| 2 | CV distribution of all the neurons | :func:`analyseur.cbgtc.visual.variation.plotCV_in_ax` |
+-------+---------------------------------------+----------------------------------------------------------------------+
| 3 | mean rate of all the neurons | :func:`analyseur.cbgtc.visual.rate.plot_mean_rate_spikecounts_in_ax` |
+-------+---------------------------------------+----------------------------------------------------------------------+
| 4 | mean membrane voltage | :meth:`analyseur.cbgtc.visual.measurable.VoltageTrace.plot_in_ax` |
+-------+---------------------------------------+----------------------------------------------------------------------+
| 5 | pooled PSTH | :meth:`analyseur.cbgtc.visual.peristimulus.VizPSTH.plot_pool_in_ax` |
+-------+---------------------------------------+----------------------------------------------------------------------+
.. raw:: html
<hr style="border: 2px solid red; margin: 20px 0;">
"""
from pathlib import Path
import matplotlib
import matplotlib.pyplot as plt
from analyseur.cbgtc.loader import LoadSpikeTimes
from analyseur.cbgtc.visual.markerplot import plot_raster_in_ax
from analyseur.cbgtc.visual.variation import plotCV_in_ax
from analyseur.cbgtc.visual.rate import plot_mean_rate_isi_in_ax, plot_true_avg_inst_rate_in_ax, plot_mean_rate_spikecounts_in_ax
from analyseur.cbgtc.visual.peristimulus import VizPSTH
from analyseur.cbgtc.visual.measurable import VoltageTrace
from analyseur.cbgtc.parameters import SimulationParams, SignalAnalysisParams
siganal = SignalAnalysisParams()
decayfolderid = {
"0": 0, "1": 0.10, "2": 0.15, "3": 0.20, "4": 0.25, "5": 0.30,
"6": 0.35, "7": 0.40, "8": 0.45, "9": 0.50, "10": 0.55, "11": 0.60,
"12": 0.65, "13": 0.70, "14": 0.75, "15": 0.80, "16": 0.85, "17": 0.90,
"18": 0.95, "19": 1.0,
}
rootpath = "/home/lungsi/DockerShare/data/09Feb2026/"
regionfolders = ["CORTEX/", "CORTEX/", "CORTEX/",
"BG/", "BG/", "BG/",
"BG/", "BG/",
"THALAMUS/", "THALAMUS/"
]
filenames = ["/spikes_PTN.csv", "/spikes_CSN.csv", "/spikes_IN.csv",
"/spikes_MSN.csv", "/spikes_STN.csv", "/spikes_FSI.csv",
"/spikes_GPi.csv", "/spikes_GPe.csv",
"/spikes_TRN.csv", "/spikes_MD.csv",
]
# neurons = "all"
# window = None
# binsz = None
neurons = 150
window = (0,1)
binsz = siganal.binsz_10perbin # binsz_100perbin [default]
xlim_cv = {"PTN": 1.3, "CSN": 1.5, "IN": 1.5, "MSN": 0.3, "STN": 1.2, "FSI": 1.75,
"GPi": 0.5, "GPe": 0.4, "TRN": 0.225, "MD": 1.4}
# xlim_rate = {"PTN": 500, "CSN": 500, "IN": 1.5e8, "MSN": 250, "STN": 250, "FSI": 400,
# "GPi": 400, "GPe": 300, "TRN": 210, "MD": 210}
xlim_rate = {"PTN": 50, "CSN": 35, "IN": 1.5e8, "MSN": 8, "STN": 55, "FSI": 80,
"GPi": 160, "GPe": 130, "TRN": 75, "MD": 50}
[xlim_rate.update({e: 100}) for e in xlim_rate]
[docs]
def main():
for i, region in enumerate(regionfolders):
use_rootpath = rootpath + region
use_filename = filenames[i]
for folder_id in decayfolderid.keys():
fullfilepath = use_rootpath + folder_id + use_filename
loadST = LoadSpikeTimes(fullfilepath)
spiketimes_superset = loadST.get_spiketimes_superset()
spiketimes_set = LoadSpikeTimes.get_spiketimes_subset(spiketimes_superset, window=window, neurons=neurons)
nucleus = loadST.extract_nucleus_name(use_filename)
#plt.clf()
fig = plt.figure(figsize=(25,12))
gs = matplotlib.gridspec.GridSpec(2, 3, width_ratios=[3, 1, 1], height_ratios=[3,1])
ax1 = fig.add_subplot(gs[0, 0]) # row 0, col 0
ax2 = fig.add_subplot(gs[0, 1]) # row 0, col 1
ax3 = fig.add_subplot(gs[0, 2]) # row 0, col 2
ax4 = fig.add_subplot(gs[1, 0]) # row 1, col 0
ax5 = fig.add_subplot(gs[1, 1:]) # row 1, span cols 1 & 2
ax1 = plot_raster_in_ax(ax1, spiketimes_set, nucleus=nucleus, neurons=neurons, alpha=False)
ax2 = plotCV_in_ax(ax2, spiketimes_set, mode="portrait")
ax3 = plot_mean_rate_spikecounts_in_ax(ax3, spiketimes_set, window=window, binsz=binsz, nucleus=nucleus, mode="portrait")
# if neurons=="all":
# ax3 = plot_mean_rate_isi_in_ax(ax3, spiketimes_set, mode="portrait")
# else:
# ax3 = plot_true_avg_inst_rate_in_ax(ax3, spiketimes_set, mode="portrait")
try:
ax2.set_xlim(0, xlim_cv[nucleus])
ax3.set_xlim(0, xlim_rate[nucleus])
except:
pass
ax4 = VoltageTrace.plot_in_ax(ax4, use_rootpath + folder_id + "/",
nucleus, window=window)
ax5 = VizPSTH.plot_pool_in_ax(ax5, spiketimes_set, window=window, binsz=binsz,
nucleus=nucleus, neurons=neurons)
plt.tight_layout()
#
Path("raster150_1s").mkdir(parents=True, exist_ok=True)
#plt.savefig("raster150_1s_40pc/Raster_of_" + nucleus + "_" + f"{round(decayfolderid[folder_id]*100,2)}%" + ".png")
plt.savefig("raster150_1s/Raster_of_" + nucleus + "_" + f"{round(decayfolderid[folder_id]*100,2)}%" + ".png")
# plt.savefig("raster_all/Raster_of_" + nucleus + "_" + f"{round(decayfolderid[folder_id]*100,2)}%" + ".png")
#
plt.close()
# RUN THE SCRIPT
if __name__ == "__main__":
main()