MultiTimeFieldAnalysis/tcMultiTestMAT.py at main · BhallaLab/MultiTimeFieldAnalysis · GitHub

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"""

Test script for multi-peak detection on G394 .mat files.

Directory structure expected:

root/<date>/<sub>/*.mat

Reports per-session peak counts, a table of all cells with at least one

significant peak, and a combined heatmap (R2B | TI) across all sessions.

"""

import os

import glob

import sys

import numpy as np

import scipy.io

import natsort

import matplotlib.pyplot as plt

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

import pyBindMat as pb

ROOT = "/home/bhalla/homework/Hrishi/2025/DATA/G394_TenFiles_13Oct"

def load_params(data):

td = data["trialDetails"]

tcpy = data["ops"]["tcpy"]

sr = float(td["samplingRate"])

frameDt = 1.0 / sr

csOnset = int(round(float(td["preDuration"]) * sr))

usOnset = int(round(csOnset

+ float(td["csDuration"]) * sr

+ float(td["traceDuration"]) * sr))

circPad = int(tcpy["circPadFrames"])

binFrames = int(tcpy["binFrames"])

numShuffle = int(tcpy["numShuffle"])

r2bThresh = float(tcpy["r2bThresh"])

r2bPercentile = float(tcpy["r2bPercentile"])

transThresh = float(tcpy["transientThresh"])

tiPercentile = float(tcpy["tiPercentile"])

fracThresh = float(tcpy["fracTrialsFiredThresh"])

return (csOnset, usOnset, circPad, binFrames, numShuffle,

r2bThresh, r2bPercentile, transThresh, tiPercentile, fracThresh, frameDt)

def peak_times_s(indices, circPad, frameDt, binFrames=1):

return [(idx * binFrames - circPad) * frameDt for idx in indices]

def session_summary(results, method):

counts = {0: 0, 1: 0, 2: 0, 3: 0}

for r in results:

counts[min(len(r['allPkIndices']), 3)] += 1

print(f" {method}: 0 peaks={counts[0]} 1 peak={counts[1]} "

f"2 peaks={counts[2]} 3+ peaks={counts[3]}")

def _heatmap_ax(ax, traces, pk_indices, time_axis, method, us_time):

"""Render one normalised heatmap into ax. Returns False if no data."""

if not traces:

ax.text(0.5, 0.5, f'No significant\n{method} cells',

ha='center', va='center', transform=ax.transAxes, fontsize=11)

ax.set_title(method)

return False

order = np.argsort(pk_indices)

mat = np.array([traces[i] for i in order], dtype=float)

rmin = mat.min(axis=1, keepdims=True)

rmax = mat.max(axis=1, keepdims=True)

denom = rmax - rmin

denom[denom == 0] = 1.0

mat = (mat - rmin) / denom

ncells = mat.shape[0]

dt = time_axis[1] - time_axis[0]

im = ax.imshow(mat, aspect='auto', origin='upper', cmap='hot', vmin=0, vmax=1,

extent=[time_axis[0] - dt/2, time_axis[-1] + dt/2, ncells, 0])

ax.axvline(0.0, color='cyan', lw=1.0, ls='--', label='CS onset')

ax.axvline(us_time, color='lime', lw=1.0, ls='--', label='US onset')

ax.legend(loc='upper right', fontsize=7)

ax.set_xlabel('Time re CS onset (s)')

ax.set_ylabel(f'Cell (sorted by 1st peak) n={ncells}')

ax.set_title(method)

plt.colorbar(im, ax=ax, label='Norm. ΔF/F', fraction=0.03, pad=0.02)

return True

def plot_heatmaps(r2b_traces, r2b_pk, r2b_time,

ti_traces, ti_pk, ti_time,

r2b_traces_mp, r2b_pk_mp,

ti_traces_mp, ti_pk_mp,

us_time, title):

n_all = max(len(r2b_traces), len(ti_traces), 1)

n_mp = max(len(r2b_traces_mp), len(ti_traces_mp), 1)

fig, axes = plt.subplots(

2, 2, figsize=(14, n_all * 0.12 + n_mp * 0.12 + 3.0),

gridspec_kw={'height_ratios': [n_all, n_mp]})

fig.suptitle(title)

_heatmap_ax(axes[0, 0], r2b_traces, r2b_pk, r2b_time, 'R2B — all significant', us_time)

_heatmap_ax(axes[0, 1], ti_traces, ti_pk, ti_time, 'TI — all significant', us_time)

_heatmap_ax(axes[1, 0], r2b_traces_mp, r2b_pk_mp, r2b_time, 'R2B — 2+ peaks', us_time)

_heatmap_ax(axes[1, 1], ti_traces_mp, ti_pk_mp, ti_time, 'TI — 2+ peaks', us_time)

plt.tight_layout()

plt.show()

def main():

dates = natsort.natsorted(

d for d in os.listdir(ROOT)

if os.path.isdir(os.path.join(ROOT, d))

)

total_cells = 0

total_r2b = {0: 0, 1: 0, 2: 0, 3: 0}

total_ti = {0: 0, 1: 0, 2: 0, 3: 0}

table_rows = []

# Accumulated across all sessions for the combined heatmap

r2b_traces, r2b_pk, r2b_time = [], [], None

ti_traces, ti_pk, ti_time = [], [], None

r2b_traces_mp, r2b_pk_mp = [], [] # 2+ peaks only

ti_traces_mp, ti_pk_mp = [], []

us_time_s = None

for date in dates:

for sub in ["1", "2"]:

mat_path = os.path.join(ROOT, date, sub)

if not os.path.isdir(mat_path):

continue

matfiles = glob.glob(os.path.join(mat_path, "*.mat"))

if not matfiles:

continue

try:

data = scipy.io.loadmat(matfiles[0], simplify_cells=True)

except Exception as e:

print(f" Could not load {matfiles[0]}: {e}")

continue

dfbf = data["dfbf_clean"]

(csOnset, usOnset, circPad, binFrames, numShuffle,

r2bThresh, r2bPercentile, transThresh, tiPercentile,

fracThresh, frameDt) = load_params(data)

circShuff = dfbf.shape[2] - (csOnset - circPad)

print(f"\n{date}/{sub} [{dfbf.shape[0]} cells, {dfbf.shape[1]} trials, "

f"{dfbf.shape[2]} frames] csOnset={csOnset} usOnset={usOnset} "

f"frameDt={frameDt:.4f}")

r2b_res = pb.runR2Banalysis(dfbf, csOnset, usOnset, circPad, circShuff,

binFrames, numShuffle, r2bThresh, r2bPercentile,

frameDt=frameDt)

ti_res = pb.runTIanalysis(dfbf, csOnset, usOnset, circPad, circShuff,

binFrames, numShuffle, transThresh, tiPercentile,

fracThresh, frameDt)

session_summary(r2b_res, "R2B")

session_summary(ti_res, "TI ")

total_cells += dfbf.shape[0]

for r in r2b_res:

total_r2b[min(len(r['allPkIndices']), 3)] += 1

for r in ti_res:

total_ti[min(len(r['allPkIndices']), 3)] += 1

# Table rows

for cell_idx, (rr, tt) in enumerate(zip(r2b_res, ti_res)):

r2b_pks = list(zip(

peak_times_s(rr['allPkIndices'], circPad, frameDt, binFrames=1),

rr['allPkScores'], rr['allPkPvalues']))

ti_pks = list(zip(

peak_times_s(tt['allPkIndices'], circPad, frameDt, binFrames=binFrames),

tt['allPkScores'], tt['allPkPvalues']))

if r2b_pks or ti_pks:

table_rows.append((date, sub, cell_idx, r2b_pks, ti_pks))

# Accumulate traces for combined heatmap

this_r2b_time = np.array([(ff - circPad) * frameDt for ff in range(circShuff)])

numBins = circShuff // binFrames

this_ti_time = np.array([(bb * binFrames - circPad) * frameDt for bb in range(numBins)])

if r2b_time is None:

r2b_time = this_r2b_time

ti_time = this_ti_time

us_time_s = (usOnset - csOnset) * frameDt

for r in r2b_res:

n = len(r['allPkIndices'])

if n >= 1:

trace = np.array(r['meanTrace'])

if len(trace) == len(r2b_time):

r2b_traces.append(trace)

r2b_pk.append(r['allPkIndices'][0])

if n >= 2:

r2b_traces_mp.append(trace)

r2b_pk_mp.append(r['allPkIndices'][0])

for r in ti_res:

n = len(r['allPkIndices'])

if n >= 1:

trace = np.array(r['meanTrace'])

if len(trace) == len(ti_time):

ti_traces.append(trace)

ti_pk.append(r['allPkIndices'][0])

if n >= 2:

ti_traces_mp.append(trace)

ti_pk_mp.append(r['allPkIndices'][0])

# ── Summary ──────────────────────────────────────────────────────────────

print(f"\n=== Totals across all sessions ===")

print(f" Cells analysed : {total_cells}")

print(f" R2B — 0 peaks: {total_r2b[0]} 1 peak: {total_r2b[1]} "

f"2 peaks: {total_r2b[2]} 3+ peaks: {total_r2b[3]}")

print(f" TI — 0 peaks: {total_ti[0]} 1 peak: {total_ti[1]} "

f"2 peaks: {total_ti[2]} 3+ peaks: {total_ti[3]}")

# ── Per-cell table ────────────────────────────────────────────────────────

if not table_rows:

print("\nNo cells with significant peaks found.")

else:

hdr = (f"{'date':<10} {'sub':<4} {'cell':>4} "

f"{'method':<5} {'pk#':>3} {'time(s)':>8} {'score':>8} {'p-val':>7}")

print(f"\n=== Cells with significant peaks ===")

print(hdr)

print("-" * len(hdr))

for date, sub, cell_idx, r2b_pks, ti_pks in table_rows:

for method, pks in [("R2B", r2b_pks), ("TI", ti_pks)]:

for pk_num, (t, score, pval) in enumerate(pks, 1):

print(f"{date:<10} {sub:<4} {cell_idx:>4} "

f"{method:<5} {pk_num:>3} {t:>8.3f} {score:>8.4f} {pval:>7.4f}")

# ── Combined heatmap ─────────────────────────────────────────────────────

if r2b_time is not None:

plot_heatmaps(r2b_traces, r2b_pk, r2b_time,

ti_traces, ti_pk, ti_time,

r2b_traces_mp, r2b_pk_mp,

ti_traces_mp, ti_pk_mp,

us_time_s, "All sessions — G394")

if __name__ == "__main__":

main()

MultiTimeFieldAnalysis/tcMultiTestMAT.py at main · BhallaLab/MultiTimeFieldAnalysis · GitHub

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