multiple_task_analysis.py

# %%
import numpy as np  
from numpy.linalg import norm 
import sys 
import random 
from pathlib import Path
import json
import time 
import psutil
import copy
import pickle

import matplotlib.pyplot as plt 
import matplotlib.ticker as ticker
ticker.Locator.MAXTICKS = 10000 
import seaborn as sns 

from scipy.cluster.hierarchy import dendrogram, cophenet
from scipy.stats import spearmanr
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import cdist, pdist, squareform
from sklearn.decomposition import PCA

import torch
from torch.serialization import add_safe_globals

import scienceplots
plt.style.use('science')
plt.style.use(['no-latex'])

import helper           
import clustering
import clustering_metric
import color_func

import mpn 
import mpn_tasks

clean = True 
if clean: 
    dir_path = Path("./multiple_tasks/")

    for p in dir_path.glob("*.png"):
        if p.is_file() and not (p.name.startswith("loss") or p.name.startswith("lowD")):
            p.unlink()

c_vals = [
    "#e53e3e",  # red
    "#3182ce",  # blue
    "#38a169",  # green
    "#d69e2e",  # yellow-gold
    "#d53f8c",  # pink-magenta
    "#4c51bf",  # indigo
    "#dd6b20",  # orange
    "#0ea5e9",  # sky blue
    "#22c55e",  # bright green
    "#a855f7",  # purple
    "#f43f5e",  # red-pink
    "#0f766e",  # teal
    "#b83280",  # magenta-violet
    "#ca8a04",  # amber
    "#2b6cb0",  # deep blue
] * 10

c_vals_l = [
    "#feb2b2",  # light red
    "#90cdf4",  # light blue
    "#9ae6b4",  # light green
    "#faf089",  # light yellow-gold
    "#fbb6ce",  # light magenta
    "#c3dafe",  # light indigo
    "#fed7aa",  # light orange
    "#bae6fd",  # light sky blue
    "#bbf7d0",  # light bright green
    "#e9d5ff",  # light purple
    "#fecdd3",  # light red-pink
    "#a7f3d0",  # light teal
    "#f9a8d4",  # light violet-magenta
    "#fde68a",  # light amber
    "#bfdbfe",  # light deep blue
] * 10

c_vals_d = [
    "#9b2c2c",  # dark red
    "#2c5282",  # dark blue
    "#276749",  # dark green
    "#975a16",  # dark golden
    "#97266d",  # dark magenta
    "#4338ca",  # dark indigo
    "#7b341e",  # dark orange
    "#0369a1",  # dark sky blue
    "#15803d",  # dark bright green
    "#6b21a8",  # dark purple
    "#9f1239",  # dark red-pink
    "#0f4c3a",  # dark teal
    "#702459",  # dark violet-magenta
    "#854d0e",  # dark amber
    "#1e3a8a",  # dark deep blue
] * 10


l_vals = ['solid', 'dashed', 'dotted', 'dashdot', '-', '--', '-.', ':', (0, (3, 1, 1, 1)), (0, (5, 10))]
markers_vals = ['o', 'v', '*', '+', '>', '1', '2', '3', '4', 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd', '|', '_']
linestyles = ["-", "--", "-."]
cs = "coolwarm"

# %%
mem = psutil.virtual_memory()
print(f"Total: {mem.total / 1e9:.2f} GB")
print(f"Available: {mem.available / 1e9:.2f} GB")
print(f"Used: {mem.used / 1e9:.2f} GB")
print(f"Percentage: {mem.percent}%")

# %%
# load and unpack parameters
# make sure to change out_path and out_param_path simultaneously
seed = "749" 
task = "everything"
hidden = "300"
batch = "128"
feature = "L21e4" 
accfeature = "+angle" 

aname = f"{task}_seed{seed}_{feature}+hidden{hidden}+batch{batch}{accfeature}"
out_path_name = "multiple_tasks/" + f"param_{aname}_result.npz"
out_path = Path(out_path_name)

size_bytes = out_path.stat().st_size  
size_gb = size_bytes / 1024**3 
print(f"{out_path} = {size_gb:.3f} GiB")

with np.load(out_path_name, allow_pickle=True) as data:
    rules_epochs = data["rules_epochs"].item()
    hyp_dict = data["hyp_dict"].item()
    all_rules = data["all_rules"]
    test_task = data["test_task"]
    # Ms = data["Ms"]
    Ms_orig = data["Ms_orig"]
    hs = data["hs"]
    bs = data["bs"]
    xs = data["xs"]

print(f"Ms_orig: {Ms_orig.shape}")
print(f"hs: {hs.shape}")
print(f"xs: {xs.shape}")
print(f"bs: {bs.shape}")
print(f"test_task: {test_task.shape}")

print(f"all_rules: {all_rules}")

out_param_path = "multiple_tasks/" + f"param_{aname}_param.json"
out_param_path = Path(out_param_path)

with out_param_path.open() as f: 
    raw_cfg_param = json.load(f)

task_params, train_params, net_params = raw_cfg_param["task_params"], raw_cfg_param["train_params"], raw_cfg_param["net_params"]

savefigure_name = f"{hyp_dict['ruleset']}_seed{seed}_{hyp_dict['addon_name']}"

# %%
# 2025-11-19: make sure the bias is only cell-dependent but not time- or trail-dependent
ref = bs[0, 0, :]              
same_per_k = np.all(bs == ref, axis=(0, 1))  
all_k_constant = np.all(same_per_k)

# %%
add_safe_globals([np.core.multiarray._reconstruct])

netpathname = "multiple_tasks/" + f"savednet_{aname}.pt"
checkpoint = torch.load(netpathname, map_location="cpu")

state_dict = checkpoint["state_dict"]
print(state_dict.keys())
load_net_params = checkpoint["net_params"]
print(load_net_params)

# reload the network
model = mpn.DeepMultiPlasticNet(load_net_params, verbose=False, forzihan=True)

missing, unexpected = model.load_state_dict(checkpoint["state_dict"], strict=True)
print("missing:", missing)
print("unexpected:", unexpected)

model.eval()

task_params_c, train_params_c, net_params_c = mpn_tasks.convert_and_init_multitask_params(
    (task_params, train_params, net_params)
)

def shared_run(addtask): 
    """
    Trying to replicate Fig 3D-E in Driscoll etal 2024
    """
    assert addtask in ("dmcgo", "delaydm1", )
    
    task_params_dmcgo = copy.deepcopy(task_params_c)

    if addtask == "dmcgo":
        task_params_dmcgo["rules"] = ["dmcgo", "dmcnogo"]
    elif addtask == "delaydm1":
        task_params_dmcgo["rules"] = ["delaydm1", "delaydm2"]
        
    test_n_batch = 100

    task_params_dmcgo['hp']['batch_size_train'] = test_n_batch
    task_params_dmcgo["long_delay"] = "normal"
    
    test_data, test_trials_extra = mpn_tasks.generate_trials_wrap(task_params_dmcgo, 
                                                                test_n_batch, 
                                                                rules=task_params_dmcgo['rules'], 
                                                                mode_input="random", 
                                                                fix=True,
                                                                device="cpu",
                                                                verbose=True)
    test_input, test_output, _ = test_data

    print(f"test_input.shape: {test_input.shape}")

    test_task = helper.find_task(task_params_dmcgo, test_input.detach().cpu().numpy(), 0)
    test_task = [int(test_task_ - min(test_task)) for test_task_ in test_task]

    _, test_trials, test_rule_idxs = test_trials_extra

    stim1_choices = np.concatenate([test_trials[0].meta["stim1"], test_trials[1].meta["stim1"]])
    print(f"stim1_choices: {stim1_choices}")

    epochs = test_trials[0].epochs
    
    if addtask in ("dmcgo", "delaydm1", ):
        delay_period = epochs["delay1"]
    
    # is delay period start inclusive and end exclusive?
    print(f"delay_period: {delay_period}")

    assert len(test_task) == len(stim1_choices)

    net_out, _, db_test = model.iterate_sequence_batch(test_input, run_mode='track_states')
    
    if addtask in ("dmcgo", "delaydm1", ):
        hidden_test = db_test["hidden1"][:, delay_period[0]:delay_period[1]-1, :]
        em_test = (db_test["M1"][:, delay_period[0]:delay_period[1]-1, :, :] * state_dict["mp_layer1.W"]).cpu().numpy()
        m_test = db_test["M1"][:, delay_period[0]:delay_period[1]-1, :, :].cpu().numpy()

    print(f"hidden_test: {hidden_test.shape}; em_test: {em_test.shape}; m_test: {m_test.shape}")

    fig, axs = plt.subplots(5,2,figsize=(5*2,5*2))
    for i in range(5):
        for inp in range(test_input.shape[2]):
            axs[i,0].plot(test_input[i,:,inp].detach().cpu().numpy(), color=c_vals[inp], alpha=0.5)
        for inp in range(net_out.shape[2]):
            axs[i,1].plot(net_out[i,:,inp].detach().cpu().numpy(), color=c_vals[inp], alpha=0.5)
        for outp in range(test_output.shape[2]):
            axs[i,1].plot(test_output[i,:,outp].detach().cpu().numpy(), color=c_vals[outp], 
                        alpha=0.5, linestyle="--")
    fig.tight_layout()  
    fig.savefig(f"./multiple_tasks/{addtask}_{savefigure_name}.png", dpi=300)

    def combo_indices_16(A, B):
        A = np.asarray(A)
        B = np.asarray(B)
        if A.shape != B.shape:
            raise ValueError(f"Length mismatch: A{A.shape} vs B{B.shape}")

        out = {}
        for a in (0, 1):
            for b in range(8):
                out[(a, b)] = np.flatnonzero((A == a) & (B == b)).tolist()
        return out

    idx_map = combo_indices_16(test_task, stim1_choices)

    stacked_inputs = [[], []]
    stacked_inputs_labels = [[], []]
    for i in range(2):
        for b in range(8):
            # add multiple trials of the same kind of stimulus
            trial_num = 5
            for trial_idx in range(trial_num): 
                # for trials that are considered to have the same stim1 and same task
                # their input should be identical (quantified via sum) up to the end 
                # of the delay period; only check for dmcgo since for delaydm1, the magnitude
                # might be different across batches even the stim identity is the same 
                if addtask == "dmcgo":
                    input_check = []
                    for key in idx_map[(i,b)]:
                        input_check.append(torch.sum(test_input[key,:delay_period[1],:]).item())
                    input_check = np.array(input_check, dtype=float)
                    ok = np.all(np.isclose(input_check, input_check[0]))
                    print(f"input_check: {input_check}")
                    assert ok 
                
                idxs = idx_map[(i, b)][trial_idx]
                test_input_chosen = test_input[idxs]
                stacked_inputs[i].append(test_input_chosen)
                # add the stim1 label (for this trial) as well
                stacked_inputs_labels[i].append(b)
            
        xs = stacked_inputs[i]  
        stacked_inputs[i] = torch.stack(
            [x if isinstance(x, torch.Tensor) else torch.as_tensor(x) for x in xs],
            dim=0
        )
        
    assert stacked_inputs_labels[0] == stacked_inputs_labels[1], "The two conditions should have the same set of stimuli for interpolation to make sense"

    # common delay period PCA
    as_hidden_wantperiod = hidden_test.reshape(-1, hidden_test.shape[-1])
    pca_delay_h = PCA(n_components=3, random_state=np.random.randint(0, 10000), svd_solver="auto")
    pca_delay_h.fit(as_hidden_wantperiod)

    as_em_wantperiod = em_test.reshape(-1, em_test.shape[-1] * em_test.shape[-2])
    pca_delay_em = PCA(n_components=3, random_state=np.random.randint(0, 10000), svd_solver="auto")
    pca_delay_em.fit(as_em_wantperiod)

    as_m_wantperiod = m_test.reshape(-1, m_test.shape[-1] * m_test.shape[-2])
    pca_delay_m = PCA(n_components=3, random_state=np.random.randint(0, 10000), svd_solver="auto")
    pca_delay_m.fit(as_m_wantperiod)

    plot_all = [["hidden", pca_delay_h], ["e_modulation", pca_delay_em], ["m_modulation", pca_delay_m]]

    for plot_name, pca_delay in plot_all:
        # interpolate between the two conditions (stim1 and stim2) in the input space, 
        # and track how the fixed points evolve in the PCA space of the delay period activity
        alpha_lst = np.linspace(0,1,20)
        interpolate_inputs = [(1-a) * stacked_inputs[0] + a * stacked_inputs[1] for a in alpha_lst]
        fixed_points_all = []
        traj_all = []

        for idx, interpolate_input in enumerate(interpolate_inputs):
            _, _, db = model.iterate_sequence_batch(interpolate_input, run_mode="track_states", save_to_cpu=True, detach_saved=True)
            
            if plot_name == "hidden":
                data = db["hidden1"]
                as_flat = data.reshape(-1, data.shape[-1])
            elif plot_name == "e_modulation":
                data = (db["M1"] * state_dict["mp_layer1.W"]).cpu().numpy()    
                as_flat = data.reshape(-1, data.shape[-1] * data.shape[-2])
            elif plot_name == "m_modulation":
                data = db["M1"]
                as_flat = data.reshape(-1, data.shape[-1] * data.shape[-2])
                        
            hidden_tf = pca_delay.transform(as_flat)
            projected_data = hidden_tf.reshape(data.shape[0], data.shape[1], -1)
            
            if addtask in ("dmcgo", "delaydm1", ): 
                fixed_points_all.append(projected_data[:,delay_period[1]-1,:])
            
            if idx in (0, len(interpolate_inputs) - 1, ):
                if addtask in ("dmcgo", "delaydm1", ): 
                    traj_all.append(projected_data[:, delay_period[0]:delay_period[1], :])
            
        fixed_points_all_arr = np.stack(fixed_points_all, axis=0)  # (n_alpha, n_stim, n_pc)
        print(f"fixed_points_all_arr: {fixed_points_all_arr.shape}")
        n_alpha, n_stim, n_pc = fixed_points_all_arr.shape

        figmap, axsmap = plt.subplots(1,3,figsize=(5*3,5))
        pcs = [[0,1],[0,2],[1,2]]

        for pc_idx, (pc_x, pc_y) in enumerate(pcs):
            for stim in range(n_stim):
                traj_fp = fixed_points_all_arr[:, stim, :] 

                axsmap[pc_idx].plot(traj_fp[:, pc_x], traj_fp[:, pc_y], "-o", 
                                    color=c_vals[stacked_inputs_labels[0][stim]], linewidth=1.5, markersize=4, 
                                    alpha=0.5, 
                                    label=f"stim {(stim // trial_num + 1)}" if stim % trial_num == 0 else None)  

                axsmap[pc_idx].scatter(traj_fp[0, pc_x],  traj_fp[0, pc_y],  color=c_vals[stacked_inputs_labels[0][stim]], 
                                       marker="s", s=50, zorder=3)
                axsmap[pc_idx].scatter(traj_fp[-1, pc_x], traj_fp[-1, pc_y], color=c_vals[stacked_inputs_labels[0][stim]], 
                                       marker="^", s=50, zorder=3)

            # plot the trajectory during the delay period for the first and last alpha (i.e. the two original conditions)
            delay_traj = False
            if delay_traj: 
                for be in range(len(traj_all)):
                    proj = traj_all[be]
                    for stim in range(proj.shape[0]):
                        xs = proj[stim, :, pc_x]
                        ys = proj[stim, :, pc_y]
                        axsmap[pc_idx].plot(xs, ys, color=c_vals[stacked_inputs_labels[0][stim]], 
                                            alpha=0.3, linewidth=1.0, linestyle=l_vals[be+1])
                        axsmap[pc_idx].scatter(xs[0], ys[0], color=c_vals[stacked_inputs_labels[0][stim]], 
                                               marker="o", s=35, zorder=4, alpha=0.9)

            axsmap[pc_idx].set_xlabel(f"Memory State PC{pc_x+1}", fontsize=12)
            axsmap[pc_idx].set_ylabel(f"Memory State PC{pc_y+1}", fontsize=12)
            axsmap[pc_idx].legend(frameon=True)
            # axsmap[pc_idx].set_xscale("symlog", linthresh=1e-3)
            # axsmap[pc_idx].set_yscale("symlog", linthresh=1e-3)
            
        figmap.tight_layout()
        figmap.savefig(f"./multiple_tasks/{addtask}_fixed_points_{plot_name}_{savefigure_name}.png", dpi=300)

# shared_run("delaydm1") 
# shared_run("dmcgo")

# analyze the fitted weight matrices; we focus on the first layer of modulation and the output layer, since they are more interpretable than the hidden layer
output_W = state_dict["W_output"].cpu().numpy()
input_W = state_dict["W_initial_linear.weight"].cpu().numpy()
modulation_W = state_dict["mp_layer1.W"].cpu().numpy()

def heatmap_with_top_left_marginals(
    M,
    ax,
    cmap="coolwarm",
    center=0,
    vmin=None,
    vmax=None,
    xlabel="",
    ylabel="",
    label_fs=15,
    tick_fs=10,
    marginal_frac=0.18,   # thickness of top/left strips (in ax coords)
    marginal_pad=0.03,    # gap between heatmap and strips (in ax coords)
    marginal_lw=1.2,
    cbar=True,
    square=False
):
    """
    Heatmap on `ax` + marginals:
      - top inset:  col-wise sum(abs(M)) (length = n_cols)
      - left inset: row-wise sum(abs(M)) (length = n_rows), plotted vertically

    Layout: col-sum at top, row-sum at left. No child dirs, no extra axes elsewhere.
    """
    M = np.asarray(M)
    n_rows, n_cols = M.shape

    if vmin is None:
        vmin = np.nanmin(M)
    if vmax is None:
        vmax = np.nanmax(M)

    # ---- main heatmap
    sns.heatmap(
        M,
        ax=ax,
        cmap=cmap,
        vmin=vmin,
        vmax=vmax,
        center=center,
        cbar=cbar,
        square=square,
    )
    ax.set_xlabel(xlabel, fontsize=label_fs)
    ax.set_ylabel(ylabel, fontsize=label_fs)
    ax.tick_params(labelsize=tick_fs)

    # ---- marginals
    col_sum = np.nansum(np.abs(M), axis=0)  # (n_cols,)
    row_sum = np.nansum(np.abs(M), axis=1)  # (n_rows,)

    # inset axes positions are in the parent ax's coordinate system
    top_rect  = [0.0, 1.0 + marginal_pad, 1.0, marginal_frac]
    left_rect = [-(marginal_frac + marginal_pad), 0.0, marginal_frac, 1.0]

    ax_top  = ax.inset_axes(top_rect,  transform=ax.transAxes)
    ax_left = ax.inset_axes(left_rect, transform=ax.transAxes)

    # ---- top: col sums (aligned with heatmap columns)
    x = np.arange(n_cols)
    ax_top.plot(x, col_sum, lw=marginal_lw)
    ax_top.set_xlim(-0.5, n_cols - 0.5)
    ax_top.set_xticks([])
    ax_top.tick_params(axis="y", labelsize=tick_fs)
    ax_top.spines["top"].set_visible(False)
    ax_top.spines["right"].set_visible(False)

    # ---- left: row sums (vertical, aligned with heatmap rows)
    y = np.arange(n_rows)
    ax_left.plot(row_sum, y, lw=marginal_lw)  # x=row_sum, y=row index
    ax_left.set_ylim(n_rows - 0.5, -0.5)      # match heatmap's y-direction (top row at top)
    ax_left.set_yticks([])
    ax_left.tick_params(axis="x", labelsize=tick_fs)
    ax_left.spines["top"].set_visible(False)
    ax_left.spines["right"].set_visible(False)
    
    ax.set_xticks([]); ax.set_yticks([]) 

    return ax, ax_top, ax_left


def plot_weight_triplet_with_top_left_marginals(
    output_W,
    input_W,
    modulation_W,
    aname,
    cs="coolwarm",
    save_dir="./multiple_tasks",
):
    figoutput, axsoutput = plt.subplots(1,1,figsize=(10, 12))

    heatmap_with_top_left_marginals(
        output_W,
        ax=axsoutput,
        cmap=cs,
        center=0,
        vmin=np.nanmin(output_W),
        vmax=np.nanmax(output_W),
        xlabel="Hidden 2 Index",
        ylabel="Output Index",
        square=False
    )
    
    figinput, axsinput = plt.subplots(1,1,figsize=(10, 12))
    heatmap_with_top_left_marginals(
        input_W.T,
        ax=axsinput,
        cmap=cs,
        center=0,
        vmin=np.nanmin(input_W),
        vmax=np.nanmax(input_W),
        xlabel="Hidden 1 Index",
        ylabel="Input Index",
        square=False
    )
    
    figmodulation, axsmodulation = plt.subplots(1,1,figsize=(10, 12))
    heatmap_with_top_left_marginals(
        modulation_W.T,
        ax=axsmodulation,
        cmap=cs,
        center=0,
        vmin=np.nanmin(modulation_W),
        vmax=np.nanmax(modulation_W),
        xlabel="Hidden 1 Index",
        ylabel="Hidden 2 Index",
        square=False
    )

    figoutput.tight_layout()
    figoutput.savefig(f"{save_dir}/weight_matrices_{aname}.png", dpi=300)
    plt.close(figoutput)
    
    figinput.tight_layout()
    figinput.savefig(f"{save_dir}/weight_matrices_input_{aname}.png", dpi=300)
    plt.close(figinput)
    
    figmodulation.tight_layout()
    figmodulation.savefig(f"{save_dir}/weight_matrices_modulation_{aname}.png", dpi=300)
    plt.close(figmodulation)
    
    return

plot_weight_triplet_with_top_left_marginals(
    output_W=output_W,
    input_W=input_W,
    modulation_W=modulation_W,
    aname=aname,
    cs=cs,
    save_dir="./multiple_tasks",
)

all_input = ["Fix On", "Fix Off", "Stim 1 Cos", "Stim 1 Sin", "Stim 2 Cos", "Stim 2 Sin"] + task_params_c["rules"]
input_corr = np.corrcoef(input_W.T)
mask = np.triu(np.ones_like(input_corr, dtype=bool), k=1)
fig, ax = plt.subplots(1,1,figsize=(5,5))
sns.heatmap(input_corr, ax=ax, cmap=cs, center=0, mask=mask, vmin=-1, vmax=1, square=True, cbar_kws={"shrink": 0.5})
ax.set_xticks(np.arange(len(all_input)) + 0.5, labels=all_input, rotation=90)
ax.set_yticks(np.arange(len(all_input)) + 0.5, labels=all_input, rotation=0)
ax.set_xlabel("Input Index", fontsize=12)
ax.set_ylabel("Input Index", fontsize=12)
fig.tight_layout()
fig.savefig(f"./multiple_tasks/input_weight_correlation_{aname}.png", dpi=300)
plt.close(fig)

# should we re-evaluate the result? 
reevaluate = True 
if reevaluate:
    test_n_batch = 100
    task_params_c['hp']['batch_size_train'] = test_n_batch
        
    test_data, test_trials_extra = mpn_tasks.generate_trials_wrap(
        task_params_c, test_n_batch, rules=task_params_c['rules'],
        mode_input="random", fix=True, device="cpu", verbose=False
    )
    
    test_input, test_output, test_mask = test_data
    _, test_trials, test_rule_idxs = test_trials_extra
    
    with torch.no_grad():
        net_out, _, db_test = model.iterate_sequence_batch(test_input, run_mode='track_states')
        
    Ms_orig = db_test["M1"].cpu().numpy()
    xs = db_test["input1"].cpu().numpy()
    hs = db_test["hidden1"].cpu().numpy()
    
    print(f"Ms_orig: {Ms_orig.shape}; xs: {xs.shape}; hs: {hs.shape}")
    
    all_rules = np.array(task_params["rules"])
    
    def generate_response_stimulus(task_params, test_trials): 
        """
        """
        labels_resp, labels_stim1, labels_stim2 = [], [], []
        rules_epochs = {} 
        for rule_idx, rule in enumerate(task_params['rules']):
            rules_epochs[rule] = test_trials[rule_idx].epochs
            # print(test_trials[rule_idx].meta.keys())
            
            if rule in ('dmsgo','dmcgo','dmsnogo','dmcnogo',):
                labels_resp.append(test_trials[rule_idx].meta['matches'])                
            else:
                labels_resp.append(test_trials[rule_idx].meta['resp1'])           
                
            labels_stim1.append(test_trials[rule_idx].meta['stim1']) 
            
            try: 
                labels_stim2.append(test_trials[rule_idx].meta['stim2'])
            except KeyError:
                labels_stim2.append(np.full_like(test_trials[rule_idx].meta['stim1'], np.nan))

        
        labels_resp = np.concatenate(labels_resp, axis=0).reshape(-1,1)
        labels_stim1 = np.concatenate(labels_stim1, axis=0).reshape(-1,1)
        labels_stim2 = np.concatenate(labels_stim2, axis=0).reshape(-1,1)

        return labels_resp, labels_stim1, labels_stim2, rules_epochs
    
    labels_resp, labels_stim1, labels_stim2, rules_epochs = generate_response_stimulus(task_params, test_trials)
    
    # print(labels_stim1.shape)
    # print(labels_stim2.shape)
    
    test_task = helper.find_task(task_params, test_input.detach().cpu().numpy(), 0)
    test_task = np.array([int(c) for c in test_task]).flatten()


# %%
weighted_Ms_orig = Ms_orig * modulation_W
print(f"weighted_Ms_orig: {weighted_Ms_orig.shape}")

clustering_data_analysis = [xs, hs, Ms_orig]
clustering_data_analysis_names = ["input", "hidden", "modulation_all"]

clustering_data_hierarchy = {}
clustering_corr_info = []
col_clusters_all, row_clusters_all = [], []
row_cluster_breaker_all = []
input_hidden_comparison = []
base_data = []
metrics_all_all = []
rbreaks_all, cbreaks_all = [], []

selection_key = ["CH_blocks", "DB_blocks"]

upper_cluster = 300
lower_cluster = 5
silhouette_tol = 0.00

cluster_info_save = {}

for clustering_index in range(len(clustering_data_analysis)): 
    print("======================================================")
    clustering_data = clustering_data_analysis[clustering_index]
    clustering_name = clustering_data_analysis_names[clustering_index]
    print(f"clustering_name: {clustering_name}")
    print(f"clustering_data: {clustering_data.shape}")
    
    if hyp_dict['ruleset'] == "everything": 
        phase_to_indices = [
            ("stim1",  [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]),
            ("stim2",  [6, 7, 8, 9, 10]),
            ("delay1", [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]),
            ("delay2", [6, 7, 8, 9, 10]),
            ("go1",    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
        ]
    
    tb_break = [
        [idx, rules_epochs[all_rules[idx]][phase]]
        for phase, indices in phase_to_indices
        for idx in indices
    ]
    
    tb_break_name = [
        f"{all_rules[idx]}-{phase}"
        for phase, indices in phase_to_indices
        for idx in indices
    ]
    
    tb_break_name = np.array(tb_break_name)
    
    cell_vars_rules = [] 
    
    for el in range(len(tb_break)):
        n_rules = len(task_params['rules'])
        n_cells = clustering_data.shape[-1]
            
        rule_idx, period_time = tb_break[el][0], tb_break[el][1]
        
        # print('Rule {} (idx {}), {}'.format(all_rules[rule_idx], rule_idx, period_time))
        # print(f"tb_break_name[el]: {tb_break_name[el]}")
        
        labels_stim = labels_stim1
        labels_stim = labels_stim2 if ("stim2" in tb_break_name[el] or "delay2" in tb_break_name[el]) else labels_stim1
        
        if len(clustering_data.shape) == 3:
            # 2025-11-19: if period_time[1] is None, then go until the end along that axis
            rule_cluster = clustering_data[test_task == rule_idx, period_time[0]:period_time[1], :]
            # varval = np.var(rule_cluster, axis=(0, 1))   
            varval = helper.task_variance_period_numpy(rule_cluster, labels_stim[test_task == rule_idx].flatten())
            cell_vars_rules.append(varval) 
        else:
            clustering_data_old = clustering_data
            if "pre" in clustering_name: # outdated 
                rule_cluster = clustering_data[test_task == rule_idx, period_time[0]:period_time[1]]
                mean_var = np.var(rule_cluster, axis=(0, 1)).mean(axis=0)
                cell_vars_rules.append(mean_var)
                
            elif "post" in clustering_name: # outdated
                rule_cluster = clustering_data[test_task == rule_idx, period_time[0]:period_time[1]]
                mean_var = np.var(rule_cluster, axis=(0, 1)).mean(axis=1)
                cell_vars_rules.append(mean_var)
                
            elif "all" in clustering_name: 
                clustering_data = clustering_data.reshape(clustering_data.shape[0], clustering_data.shape[1], -1)
                rule_cluster = clustering_data[test_task == rule_idx, period_time[0]:period_time[1], :]
                # varval = np.var(rule_cluster, axis=(0, 1))
                varval = helper.task_variance_period_numpy(rule_cluster, labels_stim[test_task == rule_idx].flatten())
                cell_vars_rules.append(varval) 
                    
    cell_vars_rules = np.array(cell_vars_rules)    
    cell_vars_rules_norm = np.zeros_like(cell_vars_rules)

    print(f"cell_vars_rules.shape: {cell_vars_rules.shape}")
    
    # normalize
    cell_max_var = np.max(cell_vars_rules, axis=0) # Across rules
    print(f"cell_max_var.shape: {cell_max_var.shape}")

    for period_idx in range(len(tb_break)):
        cell_vars_rules_norm[period_idx] = np.where(
            cell_max_var > 0., cell_vars_rules[period_idx] / cell_max_var, 0.
        )

    # modulation only, reshape to (N, pre, post) shape after calculating the variance
    # N here as the number of sessions after breakdown
    if "all" in clustering_name: 
        N, MM = cell_vars_rules_norm.shape
        M = int(np.sqrt(MM))
        cell_vars_rules_norm_keepshape = cell_vars_rules_norm.reshape(N, M, M)
    
    # build rule-wise value lists and corresponding field names dynamically
    rule_vals  = [cell_vars_rules_norm[i].tolist() for i in range(n_rules)]
    # print(f"rule_vals: {rule_vals}")
    rule_names = [f"rule{i}" for i in range(n_rules)]
    
    # structured array whose fields are rule0, rule1, …, rule{n_rules-1}
    dtype = np.dtype([(name, float) for name in rule_names])
    rules_struct = np.array(list(zip(*rule_vals)), dtype=dtype)
    
    # sort_idxs = np.argsort(rules_struct, order=rule_names)[::-1] # descending lexicographic sort across all rule columns 
    sort_idxs = np.arange(rules_struct.shape[0], dtype=np.intp) # identity map for better interpretability

    # 2025-07-07: first sorting based on the normalized magnitude
    # all the following should be aligned with this change
    # 2025-08-22: sort based on the variance ordering OR using an identity map (i.e. do nothing)
    cell_vars_rules_sorted_norm = cell_vars_rules_norm[:, sort_idxs]
    base_data.append(cell_vars_rules_sorted_norm)
    print(f"cell_vars_rules_sorted_norm: {cell_vars_rules_sorted_norm.shape}")  

    # analyze input and hidden
    if not ("all" in clustering_name): 
        # clustering & grouping & re-ordering
        # first loop on input, second loop in hidden
        result = clustering.cluster_variance_matrix_repeat(cell_vars_rules_sorted_norm, 
                                                           k_min=lower_cluster, 
                                                           k_max=upper_cluster, 
                                                           metric="euclidean", 
                                                           method="ward", 
                                                           n_repeats=100, 
                                                           silhouette_tol=silhouette_tol)
        
        # sanity check to make sure no undesirable bug happens with in the clustering code
        assert sorted(result["row_order"]) == list(range(cell_vars_rules_sorted_norm.shape[0]))
        assert sorted(result["col_order"]) == list(range(cell_vars_rules_sorted_norm.shape[1]))

        assert len(result["row_labels"]) == cell_vars_rules_sorted_norm.shape[0]
        assert len(result["col_labels"]) == cell_vars_rules_sorted_norm.shape[1]

        assert np.unique(result["row_labels"]).size == result["row_k"]
        assert np.unique(result["col_labels"]).size == result["col_k"]
        
        eval_res = clustering_metric.evaluate_bicluster_clustering(
            cell_vars_rules_sorted_norm, row_labels=result["row_tol_labels"], col_labels=result["col_tol_labels"]
        )
        
        eval_metrics = eval_res["metrics"]
        eval_blocks = eval_res["blocks"]
        eval_stdmean = np.nanmedian(eval_blocks["std"] / eval_blocks["means"])

        eval_random_metrics_all = []
        eval_random_blocks_all = []
        for repeat in range(1000):
            rng = np.random.default_rng(seed=np.random.randint(0, 10000))
            row_arr = rng.permutation(result["row_tol_labels"])
            col_arr = rng.permutation(result["col_tol_labels"])
            eval_res_random = clustering_metric.evaluate_bicluster_clustering(
                cell_vars_rules_sorted_norm, row_labels=row_arr, col_labels=col_arr
            )
            eval_random_metrics_all.append(eval_res_random["metrics"])
            eval_random_blocks_all.append(eval_res_random["blocks"])

        eval_random_stdmean = [np.nanmedian(eval_random_blocks["std"] / eval_random_blocks["means"]) \
                                       for eval_random_blocks in eval_random_blocks_all]

        metrics_all = {}
        for metric_key in selection_key: 
            optimized_value = eval_metrics[metric_key]
            random_values = [eval_random_metrics[metric_key] for eval_random_metrics in eval_random_metrics_all]
            metrics_all[metric_key] = [optimized_value, np.mean(random_values), np.std(random_values, ddof=1)/np.sqrt(len(random_values))]

        metrics_all["std/mean"] = [eval_stdmean, np.mean(eval_random_stdmean), np.std(eval_random_stdmean, ddof=1)/np.sqrt(len(eval_random_stdmean))]
        
        # registeration
        metrics_all_all.append(metrics_all) 

        input_hidden_comparison.append([result, cell_vars_rules_sorted_norm])
        
        # reorder the original matrix based on the clustering result
        cell_vars_rules_sorted_norm_ordered = cell_vars_rules_sorted_norm[np.ix_(result["row_order"], result["col_order"])]
        
        rl = np.asarray(result["row_tol_labels"])[result["row_order"]]
        cl = np.asarray(result["col_tol_labels"])[result["col_order"]]
        rbreaks = clustering._breaks(rl)
        cbreaks = clustering._breaks(cl)
        rbreaks_all.append(rbreaks); cbreaks_all.append(cbreaks)

        best_k_row, best_k_col = result["row_k"], result["col_k"]
        best_alt_k_row, best_alt_k_col = result["row_tol_k"], result["col_tol_k"]
        print(f"best_k_row: {best_k_row}; best_k_col: {best_k_col}")
        print(f"best_alt_k_row: {best_alt_k_row}; best_alt_k_col: {best_alt_k_col}")
        
        row_t, col_t = result["row_cut_threshold"], result["col_cut_threshold"] 

        # extract the grouping information, i.e. which neuron belong to which cluster
        # instead of the view of dendrogram
        # 2025-10-20: we register the tolerant version of optimal cluster selection
        col_labels, col_k = result["col_tol_labels"], result["col_tol_k"]
        # group the neuron based on the labels
        col_clusters = {int(lab): np.where(col_labels == lab)[0] for lab in np.unique(col_labels)}
        # 2025-11-04: do similar things for row separation
        # we checked so that the cluster label (name) is monotonically increasing 
        # i.e. near by cluster (e.g. cluster 1 and cluster 2) should be more similiar in population 
        # as well, since increasing the cutoff threshold in dendrogram will "merge" these clusters
        row_labels, row_k = result["row_tol_labels"], result["row_tol_k"]
        row_clusters = {int(lab): np.where(row_labels == lab)[0] for lab in np.unique(row_labels)}
        # registeration
        col_clusters_all.append(col_clusters)
        row_clusters_all.append(row_clusters)
        
        cluster_info_save[clustering_name] = {
            "col_clusters": col_clusters,
            "row_clusters": row_clusters,
            "tb_break_name": tb_break_name,
        }
        
        # plot the optimization score as a function of number of clustering
        # also plot the indicator for the optimal number of cluster (and with tolerance version)
        figscore, axscore = plt.subplots(1,1,figsize=(5,3))
        axscore.plot(result["row_score_recording_mean"].keys(), 
                     result["row_score_recording_mean"].values(),
                     label="row", color=c_vals[0])
        axscore.axvline(best_k_row, color=c_vals[0], linestyle="-")
        axscore.axvline(best_alt_k_row, color=c_vals[0], linestyle="--")
        axscore.plot(result["col_score_recording_mean"].keys(), result["col_score_recording_mean"].values(), 
                     label="col", color=c_vals[1])
        axscore.axvline(best_k_col, color=c_vals[1], linestyle="-")
        axscore.axvline(best_alt_k_col, color=c_vals[1], linestyle="--")
        axscore.set_xlabel("Number of Cluster")
        axscore.set_ylabel("Silhouette Score")
        axscore.legend()
        axscore.set_title(f"{clustering_name}", fontsize=15)
        figscore.tight_layout()
        figscore.savefig(f"./multiple_tasks/{clustering_name}_variance_cluster_score_{savefigure_name}.png", dpi=300)
        plt.close(figscore)

        # 
        ordered_row_name = tb_break_name[result["row_order"]]
        row_breakers = [0]
        for row_group in row_clusters.values():
            row_breakers.append(row_breakers[-1] + len(row_group))
        row_breakers = row_breakers[1:]
        print(f"row_breakers: {row_breakers}")
        row_cluster_breaker_all.append(row_breakers)
        
        # pearson correlation matrix
        figcorr, axcorrs = plt.subplots(1,3,figsize=(8*3,8))
        metrics = ["Correlation", "Cosine Similarity", "L2 Distance"]
        cell_vars_rules_sorted_norm_ordered_measure = np.corrcoef(cell_vars_rules_sorted_norm_ordered, rowvar=True)
        cell_vars_rules_sorted_norm_ordered_measure_cos = cosine_similarity(cell_vars_rules_sorted_norm_ordered)
        cell_vars_rules_sorted_norm_ordered_measure_L2  = squareform(pdist(cell_vars_rules_sorted_norm_ordered, metric='euclidean'))

        # set uniform colorbar to cross-compare between analysis
        sns.heatmap(cell_vars_rules_sorted_norm_ordered_measure, cmap=cs, square=True, vmin=-0.5, vmax=1.0, ax=axcorrs[0])
        sns.heatmap(cell_vars_rules_sorted_norm_ordered_measure_cos, cmap=cs, square=True, vmin=-0.5, vmax=1.0, ax=axcorrs[1])
        sns.heatmap(cell_vars_rules_sorted_norm_ordered_measure_L2, cmap=cs, square=True, ax=axcorrs[2])

        for axcorr_index in range(len(axcorrs)): 
            axcorr = axcorrs[axcorr_index]
            # plot the group information (delimiter between different cluster)
            nn = cell_vars_rules_sorted_norm_ordered_measure.shape[0]
            boundaries = row_breakers
            for b in boundaries:
                axcorr.axvline(b, 0, 1, color="k", linewidth=1.2)
                axcorr.axhline(b, 0, 1, color="k", linewidth=1.2)
            
            axcorr.set_xticks(np.arange(len(tb_break_name)))
            axcorr.set_xticklabels(tb_break_name[result["row_order"]], rotation=45, ha='right', va='center', \
                                   rotation_mode='anchor', fontsize=9)    
            axcorr.set_yticks(np.arange(len(tb_break_name)))
            axcorr.set_yticklabels(tb_break_name[result["row_order"]], rotation=0, ha='right', va='center', \
                                   rotation_mode='anchor', fontsize=9) 
            axcorr.tick_params(axis="both", length=0)
            axcorr.set_title(f"{clustering_name}-{metrics[axcorr_index]}", fontsize=15)
        
        figcorr.tight_layout()
        figcorr.savefig(f"./multiple_tasks/{clustering_name}_variance_cluster_corr_{savefigure_name}.png", dpi=300)
        plt.close(figcorr)

        # register correlation information
        clustering_corr_info.append([
            cell_vars_rules_sorted_norm_ordered_measure, ordered_row_name, result["col_order"]
        ])

        # plot the effect of grouping & ordering through the feature axis
        fig, ax = plt.subplots(2,1,figsize=(24,8*2))
        sns.heatmap(cell_vars_rules_sorted_norm, ax=ax[0], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(cell_vars_rules_sorted_norm_ordered, ax=ax[1], cmap=cs, cbar=True, vmin=0, vmax=1)
        for rb in rbreaks:
            ax[1].axhline(rb, color="k", lw=0.6)
        for cb in cbreaks:
            ax[1].axvline(cb, color="k", lw=0.6)
        ax[0].set_title(f"Before Clustering; best k row: {best_alt_k_row}; best k col: {best_alt_k_col}", fontsize=15)
        ax[0].set_ylabel('Rule / Break-name', fontsize=12, labelpad=12)
        ax[0].set_yticks(np.arange(len(tb_break_name)))
        ax[0].set_yticklabels(tb_break_name, rotation=0, ha='right', va='center', fontsize=9)
        ax[1].set_title(f"After Clustering; best k row: {best_alt_k_row}; best k col: {best_alt_k_col}", fontsize=15)
        ax[1].set_ylabel('Rule / Break-name', fontsize=12, labelpad=12)
        ax[1].set_yticks(np.arange(len(tb_break_name)))
        ax[1].set_yticklabels(tb_break_name[result["row_order"]], rotation=0, ha='right', va='center', fontsize=9)    
        fig.savefig(f"./multiple_tasks/{clustering_name}_variance_cluster_{savefigure_name}.png", dpi=300)
        plt.close(fig)

        # 2025-11-04: for plotting purpose
        rbreaks_ = [0] + rbreaks + [cell_vars_rules_sorted_norm_ordered.shape[0]]
        cbreaks_ = [0] + cbreaks + [cell_vars_rules_sorted_norm_ordered.shape[1]]
        figvarmean, axsvarmean = plt.subplots(1,2,figsize=(4*2,4))
        varmean = np.zeros((len(rbreaks_) - 1, len(cbreaks_) - 1))
        for rr in range(len(rbreaks_) - 1):
            for cc in range(len(cbreaks_) - 1):
                # 2025-11-04: mean covariance in this bicluster
                varmean_ = np.mean(cell_vars_rules_sorted_norm_ordered[rbreaks_[rr]:rbreaks_[rr+1], cbreaks_[cc]:cbreaks_[cc+1]])
                varmean[rr,cc] = varmean_
        sns.heatmap(varmean, ax=axsvarmean[0], cmap=cs, cbar=True, vmin=0, vmax=1)
        axsvarmean[0].set_ylabel("Session Clusters", fontsize=15)
        axsvarmean[0].set_xlabel("Neuron Clusters", fontsize=15)
        # 2025-11-04: for sanity check; since the neuron clusters are ordered so that the adjacent ones 
        # are more similar to each other than the ones that are further, therefore the correlation matrix
        # should have larger value near the diagonal 
        varmeanC = np.corrcoef(varmean, rowvar=False) 
        sns.heatmap(varmeanC, ax=axsvarmean[1], cmap=cs, cbar=True, vmin=0, vmax=1)
        axsvarmean[1].set_xlabel("Neuron Clusters", fontsize=15)
        axsvarmean[1].set_ylabel("Neuron Clusters", fontsize=15)
        figvarmean.tight_layout()
        figvarmean.savefig(f"./multiple_tasks/{clustering_name}_variance_cluster_mean_{savefigure_name}.png", dpi=300)
        plt.close(figvarmean)

        # plot the norm of normalized variance of each session (across the neuron dimension)
        session_norm = cell_vars_rules_sorted_norm_ordered.sum(axis=1)
        norm_order = np.argsort(-session_norm)
        session_norm = session_norm[norm_order]
        session_norm_name = (tb_break_name[result["row_order"]])[norm_order]
        
        fig, ax = plt.subplots(1,1,figsize=(15,5))
        ax.plot([i for i in range(len(session_norm))], session_norm, "-o")
        ax.set_xticks([i for i in range(len(session_norm))])
        ax.set_xticklabels(session_norm_name, rotation=45, ha="right")
        ax.set_ylabel("Summation of Normalized Variance", fontsize=15)
        fig.tight_layout()
        fig.savefig(f"./multiple_tasks/{clustering_name}_variance_norm_{savefigure_name}.png", dpi=300)
        plt.close(fig)
        
        # # plot hierarchy of grouping 
        # fig, axs = plt.subplots(2,1,figsize=(25,4*2))
        # dendrogram(result["row_linkage"], ax=axs[0], labels=tb_break_name[result["row_order"]], leaf_rotation=45)
        # axs[0].axhline(row_t, linestyle="--", color="black")
        # axs[0].set_title(f"Row hierarchy (k = {result['row_k']})", fontsize=15)
        # dendrogram(result["col_linkage"], ax=axs[1], labels=np.array([i for i in range(cell_vars_rules_sorted_norm_ordered.shape[1])]), leaf_rotation=45)
        # axs[1].set_title(f"Col hierarchy (k = {result['col_k']})", fontsize=15)
        # axs[1].axhline(col_t, linestyle="--", color="black")
        # fig.suptitle(clustering_name, fontsize=15)
        # fig.tight_layout()
        # fig.savefig(f"./multiple_tasks/{clustering_name}_variance_hierarchy_{savefigure_name}.png", dpi=300)

        # register hierarchy clustering
        clustering_data_hierarchy[clustering_name] = result["col_linkage"]

    # align the correlation matrix for input and hidden based on an identical ordering 
    # this loop will be run during the hidden analysis iteration (not modulation iteration)
    # this IF condition will work when hidden is calculated but not yet move to the modulation iteration
    if (len(clustering_corr_info) == 2) and ("all" not in clustering_name):
        input_order_row, hidden_order_row = clustering_corr_info[0][1], clustering_corr_info[1][1]
        input_corr, hidden_corr = clustering_corr_info[0][0], clustering_corr_info[1][0]
        shuffle_hidden_to_input = helper.permutation_indices_b_to_a(input_order_row, hidden_order_row)
        # reordering
        hidden_corr_input = hidden_corr[np.ix_(shuffle_hidden_to_input, shuffle_hidden_to_input)]

        figinputhiddencorr, axinputhiddencorr = plt.subplots(1,2,figsize=(8*2,8))
        sns.heatmap(input_corr, ax=axinputhiddencorr[0], cmap=cs, square=True, vmin=-0.5, vmax=1.0)
        sns.heatmap(hidden_corr_input, ax=axinputhiddencorr[1], cmap=cs, square=True, vmin=-0.5, vmax=1.0)

        for ax in axinputhiddencorr:
            ax.set_xticks(np.arange(len(input_order_row)))
            ax.set_xticklabels(input_order_row, rotation=45, ha='right', va='center', \
                                   rotation_mode='anchor', fontsize=9)    
            ax.set_yticks(np.arange(len(input_order_row)))
            ax.set_yticklabels(input_order_row, rotation=0, ha='right', va='center', \
                                   rotation_mode='anchor', fontsize=9) 
            ax.tick_params(axis="both", length=0)

        axinputhiddencorr[0].set_title("Input Correlation", fontsize=15)
        axinputhiddencorr[1].set_title("Hidden Correlation (Reordered By Input Correlation)", fontsize=15)
        for ax in axinputhiddencorr:
            for b in row_cluster_breaker_all[0]:
                ax.axvline(b, 0, 1, color="k", linewidth=1.2)
                ax.axhline(b, 0, 1, color="k", linewidth=1.2)
            
        figinputhiddencorr.suptitle("Reorder Input & Hidden Correlation")
        figinputhiddencorr.savefig(f"./multiple_tasks/input2hidden_variance_hierarchy_{savefigure_name}.png", dpi=300)
        plt.close(figinputhiddencorr)
    
    # 2025-11-04: check the consistency of row clusters between input & hidden
    if (len(clustering_corr_info) == 2) and ("all" not in clustering_name):
        # save the clustering information for input & hidden
        with open(f"./multiple_tasks/cluster_info_{savefigure_name}.pkl", "wb") as f:
            pickle.dump(cluster_info_save, f)
        
        belonging = np.zeros((2, len(tb_break_name)))
        for ttind in range(len(tb_break_name)):
            for rowind in range(2):
                cluster_name = helper.find_key_by_membership(row_clusters_all[rowind], ttind)
                belonging[rowind, ttind] = cluster_name

        figbelonging, axbelonging = plt.subplots(1,1,figsize=(10,2))
        sns.heatmap(belonging, ax=axbelonging, cmap=cs, cbar=True, square=True)
        axbelonging.set_xticks([i for i in range(len(tb_break_name))])
        axbelonging.set_xticklabels(tb_break_name, rotation=45, ha='right', va='center', \
                                   rotation_mode='anchor', fontsize=9)
        figbelonging.tight_layout()
        figbelonging.savefig(f"./multiple_tasks/inputhidden_row_membership_{savefigure_name}.png", dpi=300)
        plt.close(figbelonging)

        [result_input, cell_vars_rules_sorted_norm_input] = input_hidden_comparison[0]
        [result_hidden, cell_vars_rules_sorted_norm_hidden] = input_hidden_comparison[1]
        cell_vars_rules_sorted_norm_ordered_input = cell_vars_rules_sorted_norm_input[np.ix_(result_input["row_order"], 
                                                                                             result_input["col_order"])]
        cell_vars_rules_sorted_norm_ordered_hidden = cell_vars_rules_sorted_norm_hidden[np.ix_(result_input["row_order"], 
                                                                                               result_hidden["col_order"])]
        
        figsame, axssame = plt.subplots(2,1,figsize=(24,8*2))
        sns.heatmap(cell_vars_rules_sorted_norm_ordered_input, ax=axssame[0], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(cell_vars_rules_sorted_norm_ordered_hidden, ax=axssame[1], cmap=cs, cbar=True, vmin=0, vmax=1)
        for tem in range(2):
            # 2025-11-05: input break
            for rb in rbreaks_all[0]:
                axssame[tem].axhline(rb, color="k", lw=0.6)
            for cb in cbreaks_all[tem]:
                axssame[tem].axvline(cb, color="k", lw=0.6)

        axssame[0].set_title("Input Using Input Session Break", fontsize=15)
        axssame[1].set_title("Hidden Using Input Session Break", fontsize=15)
        for axsind in range(2):
            axssame[axsind].set_xlabel("Hidden Neuron Index / Cluster", fontsize=15)
            axssame[axsind].set_ylabel("Input Neuron Index / Cluster", fontsize=15)
        figsame.tight_layout()
        figsame.savefig(f"./multiple_tasks/inputhidden_samerow_{savefigure_name}.png", dpi=300)
        plt.close(figsame)

        # 2025-11-18: whether to use the common session grouping based on input or hidden
        activecorr_lst = []
        for ih_index in range(2): 
            common_rbreaks_ = [0] + rbreaks_all[ih_index] + [cell_vars_rules_sorted_norm_input.shape[0]]
            cbreaks_input_ = [0] + cbreaks_all[0] + [cell_vars_rules_sorted_norm_input.shape[1]]
            cbreaks_hidden_ = [0] + cbreaks_all[1] + [cell_vars_rules_sorted_norm_hidden.shape[1]]
    
            varmeaninput = np.zeros((len(common_rbreaks_) - 1, len(cbreaks_input_) - 1))
            varmeanhidden = np.zeros((len(common_rbreaks_) - 1, len(cbreaks_hidden_) - 1))
            for rr in range(len(common_rbreaks_) - 1):
                for cc in range(len(cbreaks_input_) - 1):
                    varmeaninput[rr,cc] = np.mean(cell_vars_rules_sorted_norm_ordered_input[common_rbreaks_[rr]:common_rbreaks_[rr+1], 
                                                                                            cbreaks_input_[cc]:cbreaks_input_[cc+1]])
                for cc2 in range(len(cbreaks_hidden_) - 1):
                    varmeanhidden[rr,cc2] = np.mean(cell_vars_rules_sorted_norm_ordered_hidden[common_rbreaks_[rr]:common_rbreaks_[rr+1], 
                                                                                               cbreaks_hidden_[cc2]:cbreaks_hidden_[cc2+1]])
    
            figmeanact, axsmeanact = plt.subplots(1,1,figsize=(10,4))
            varmeanconcatenate = np.concatenate((varmeaninput, varmeanhidden), axis=1)
            sns.heatmap(varmeanconcatenate, ax=axsmeanact, cmap=cs, cbar=True, vmin=0, vmax=1, square=True)
            axsmeanact.axvline(len(cbreaks_input_), color="k", lw=0.6)
            axsmeanact.set_xlabel("Input / Hidden Cluster", fontsize=15)
            axsmeanact.set_ylabel("Common Session Cluster", fontsize=15)
            figmeanact.tight_layout()
            figmeanact.savefig(f"./multiple_tasks/inputhidden_aligned_activation_ih{ih_index}_{savefigure_name}.png", dpi=300)
            plt.close(figmeanact)

            figcoactive, axscoactive = plt.subplots(1,3,figsize=(4*3,4))
            s1, s2 = varmeaninput.shape[1], varmeanhidden.shape[1]
            activecorr, activeL1, activecosine = np.zeros((s1,s2)), np.zeros((s1,s2)), np.zeros((s1,s2))
            for inputind in range(varmeaninput.shape[1]):
                for hiddenind in range(varmeanhidden.shape[1]):
                    activecorr[inputind, hiddenind] = np.corrcoef(varmeaninput[:,inputind], varmeanhidden[:,hiddenind])[0,1]
                    activeL1[inputind, hiddenind] = np.sum(np.abs(varmeaninput[:,inputind] - varmeanhidden[:,hiddenind]))
                    activecosine[inputind, hiddenind] = np.dot(
                        varmeaninput[:, inputind],
                        varmeanhidden[:, hiddenind]
                    ) / (
                        norm(varmeaninput[:, inputind]) * norm(varmeanhidden[:, hiddenind])
                    )
                    
            sns.heatmap(activecorr, ax=axscoactive[0], cmap=cs, cbar=True, square=True)
            activecorr_lst.append(activecorr)
            sns.heatmap(activeL1, ax=axscoactive[1], cmap=cs, cbar=True, square=True)
            sns.heatmap(activecosine, ax=axscoactive[2], cmap=cs, cbar=True, square=True)
            
            for axsind in range(2):
                axscoactive[axsind].set_xlabel("Hidden Cluster", fontsize=15)
                axscoactive[axsind].set_ylabel("Input Cluster", fontsize=15)
                
            axscoactive[0].set_title("Pearson Correlation", fontsize=15)
            axscoactive[1].set_title("L1 Distance", fontsize=15)
            axscoactive[2].set_title("Cosine Similarity", fontsize=15)
            figcoactive.tight_layout()
            figcoactive.savefig(f"./multiple_tasks/inputhidden_coactivation_ih{ih_index}_{savefigure_name}.png", dpi=300)
            plt.close(figcoactive)

        # 2025-11-19: this result should be aligned by using / comparing the session clusters of 
        figcoactivecompare, axcoactivecompare = plt.subplots(1,1,figsize=(4,4))
        axcoactivecompare.scatter(activecorr_lst[0].flatten(), activecorr_lst[1].flatten(), c=c_vals[0], alpha=0.7)
        x_fit, y_fit, r_value, slope, _, p_val = helper.linear_regression(activecorr_lst[0].flatten(), 
                                                                          activecorr_lst[1].flatten(), 
                                                                          log=False, 
                                                                          through_origin=True)
        axcoactivecompare.plot(x_fit, y_fit, linestyle="--", label=f"R={r_value:.3f}; Slope: {slope:.3f}; p-value: {p_val:.3f}")
        axcoactivecompare.set_xlabel("Input Common Session Coactivation", fontsize=15)
        axcoactivecompare.set_ylabel("Hidden Common Session Coactivation", fontsize=15)
        axcoactivecompare.axhline(0, color="k", lw=0.6)
        axcoactivecompare.axvline(0, color="k", lw=0.6)
        axcoactivecompare.legend()
        figcoactivecompare.tight_layout()
        figcoactivecompare.savefig(f"./multiple_tasks/inputhidden_coactivation_ihcompare_{savefigure_name}.png", dpi=300)
        plt.close(figcoactivecompare)
        
    # use the clustering result for input and hidden to order the modulation information
    # and/or cross-compare the clustering result from input & hidden 
    # trying to observe consistency in between
    if (len(clustering_corr_info) == 2) and ("all" in clustering_name):
        input_order_col_ind, hidden_order_col_ind = clustering_corr_info[0][2], clustering_corr_info[1][2]
        # cell_vars_rules_norm_keepshape: 3D array
        # sort the modulation matrix based on the pre (input) and post (hidden) neuron ordering
        cell_vars_rules_norm_keepshape_ih = cell_vars_rules_norm_keepshape[:, input_order_col_ind, :]
        cell_vars_rules_norm_keepshape_ih = cell_vars_rules_norm_keepshape_ih[:, :, hidden_order_col_ind]

        flatten_by_pre = cell_vars_rules_norm_keepshape_ih.reshape(N, M*M)
        flatten_by_post = cell_vars_rules_norm_keepshape_ih.transpose(0,2,1).reshape(N, M*M)

        fig, axs = plt.subplots(2,1,figsize=(24,8*2))
        sns.heatmap(flatten_by_pre, ax=axs[0], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(flatten_by_post, ax=axs[1], cmap=cs, cbar=True, vmin=0, vmax=1)
        axs[0].set_title("Flatten By Pre (Input)", fontsize=15)
        axs[1].set_title("Flatten By Post (Hidden)", fontsize=15)
        for ax in axs: 
            ax.set_yticks(np.arange(len(tb_break_name)))
            ax.set_yticklabels(tb_break_name, rotation=0, ha='right', va='center', fontsize=9)
        fig.tight_layout()
        fig.savefig(f"./multiple_tasks/modulation_input2hidden_variance_hierarchy_{savefigure_name}.png", dpi=300)
        plt.close(fig)

        # 
        print(cell_vars_rules_sorted_norm.shape)
        cluster_input, cluster_hidden = col_clusters_all[0], col_clusters_all[1]
        cluster_combine = {}
        newc = 0
        for c1 in cluster_input.keys(): # input cluster name
            for c2 in cluster_hidden.keys(): # hidden cluster name
                newc += 1 # iterative counter of new cell type
                # different newc stands for a unique cell type 
                # (defined as a multiplication cluster of pre [input] and post [hidden])
                nc1_input, nc2_hidden = cluster_input[c1], cluster_hidden[c2]
                nc_combine = [(int(i),int(j)) for i in nc1_input for j in nc2_hidden]
                # transform to reshaped matrix index
                nc_combine = [cc[0] * M + cc[1] for cc in nc_combine]
                cluster_combine[newc] = nc_combine

        # Oct 8th: qualitatively, training MPN without regularization will cause more hidden cluster
        cluster_input_num, cluster_hidden_num = len(cluster_input), len(cluster_hidden)
        print(f"cluster_input_num: {cluster_input_num}; cluster_hidden_num: {cluster_hidden_num}")
        # by the setup, cluster_combine will be organized by shared pre
        # same pre neuron will be placed adjacently
        cluster_combine_pre = copy.deepcopy(cluster_combine)
        # create order of key to put post neuron together 
        post_order = []
        for c1 in range(cluster_input_num):
            for c2 in range(cluster_hidden_num):
                post_order.append(c1 + c2 * cluster_input_num + 1) 

        pre_order = list(cluster_combine.keys())

        pre_order_group, post_order_group = [], [] 
        for ind in range(cluster_hidden_num):
            pre_order_group.append([i+1 for i in range(ind * cluster_input_num, (ind+1) * cluster_input_num)])
        for ind in range(cluster_input_num):
            block = []
            for ind2 in range(cluster_hidden_num):
                block.append(ind + ind2 * cluster_input_num + 1)
            post_order_group.append(block)
        
        group_neurons_comb_pre, group_neurons_comb_post = [], [] 
        for preo in pre_order:
            group_neurons_comb_pre.extend(cluster_combine[preo])
        for posto in post_order:
            group_neurons_comb_post.extend(cluster_combine[posto])

        # 2025-09-02: we dont want random order of cell type
        # but based on certain pre or post neuron clustering 
        # random_celltype_orders = helper.concat_random_samples(cluster_combine, n_samples=1, seed=42)
        # random_celltype_orders = random_celltype_orders[0] # take the first sample
        # group_neurons_comb = random_celltype_orders[0] # concatenated list
        # assert len(group_neurons_comb) == len(set(group_neurons_comb)) # every element is unique
        # group_neurons = random_celltype_orders[1] # list of list, the order does not matter here

        group_neurons = [group for group in cluster_combine.values()]

        group_neurons_pre = []
        for pre_order_ in pre_order_group:
            block = []
            for ind in pre_order_:
                block.extend(group_neurons[ind-1])
            group_neurons_pre.append(block)

        group_neurons_post = []
        for post_order_ in post_order_group:
            block = []
            for ind in post_order_:
                block.extend(group_neurons[ind-1])
            group_neurons_post.append(block)

        # assert helper.all_leq(group_neurons_comb, cell_vars_rules_sorted_norm.shape[1])

        # cell_vars_rules_sorted_norm_r1 = cell_vars_rules_sorted_norm[:,group_neurons_comb]
        cell_vars_rules_sorted_norm_outer_bypre = cell_vars_rules_sorted_norm[:,group_neurons_comb_pre]
        cell_vars_rules_sorted_norm_outer_bypost = cell_vars_rules_sorted_norm[:,group_neurons_comb_post]
                
        fig, axs = plt.subplots(6,1,figsize=(24,8*6))
        sns.heatmap(cell_vars_rules_sorted_norm, ax=axs[0], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(cell_vars_rules_sorted_norm_outer_bypre, ax=axs[1], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(cell_vars_rules_sorted_norm_outer_bypost, ax=axs[2], cmap=cs, cbar=True, vmin=0, vmax=1)
        axs[0].set_title("Original", fontsize=15)
        axs[1].set_title(f"Ordering based on the Outer Product of Input & Hidden Clusters [Input Adjacent]", fontsize=15)
        axs[2].set_title(f"Ordering based on the Outer Product of Input & Hidden Clusters [Hidden Adjacent]", fontsize=15)
        for iii in range(3):
            axs[iii].set_yticks(np.arange(len(tb_break_name)))
            axs[iii].set_yticklabels(tb_break_name, rotation=0, ha='right', va='center', fontsize=9)

        group_all = [group_neurons, group_neurons_pre, group_neurons_post]
        group_all_names = ["Outer Product of Input & Hidden Clusters", "Shared Hidden", "Shared Input"]

        for group_neurons_index in range(len(group_all)):
            group_neurons_ = group_all[group_neurons_index]
            result_outer = clustering.cluster_variance_matrix_forgroup(cell_vars_rules_sorted_norm, row_groups=None, 
                                                                       col_groups=group_neurons_, k_min=int(lower_cluster-1), 
                                                                       k_max=upper_cluster, silhouette_tol=silhouette_tol)
            prior_cluster_num = len(group_neurons_)
            cell_vars_rules_sorted_norm_inputhidden = cell_vars_rules_sorted_norm[np.ix_(result_outer["row_order"], 
                                                                                         result_outer["col_order"])]
    
            sns.heatmap(cell_vars_rules_sorted_norm_inputhidden, ax=axs[3+group_neurons_index], 
                        cmap=cs, cbar=True, vmin=0, vmax=1)
            axs[3+group_neurons_index].set_yticks(np.arange(len(tb_break_name)))
            axs[3+group_neurons_index].set_yticklabels(tb_break_name[result_outer["row_order"]], 
                                                       rotation=0, ha='right', va='center', fontsize=9)
            axs[3+group_neurons_index].set_title(f"Ordering based on the {group_all_names[group_neurons_index]}, #{prior_cluster_num} [Post-Clustering]", fontsize=15)
            
        fig.tight_layout()
        fig.savefig(f"./multiple_tasks/modulation_input2hiddentogether_variance_hierarchy_{savefigure_name}.png", dpi=300)
        plt.close(fig)
            
    # plot the conditional grouping for modulation
    # currently grouped based on modulation-pre and modulation-post separately
    if ("all" in clustering_name): 
        assert len(clustering_data_old.shape) == 4
        pre_num, post_num = clustering_data_old.shape[2], clustering_data_old.shape[3]
        feature_group_post = [] 
        for i in range(post_num):
            feature_group_post.append(helper.basic_sort([i + j * post_num for j in range(pre_num)], sort_idxs))
        feature_group_pre = []
        for i in range(pre_num):
            feature_group_pre.append(helper.basic_sort([j for j in range(post_num * i, post_num * (i+1))], sort_idxs))

        result_pre = clustering.cluster_variance_matrix_forgroup(cell_vars_rules_sorted_norm, row_groups=None, 
                                                                 col_groups=feature_group_pre, k_min=lower_cluster, 
                                                                 k_max=upper_cluster, silhouette_tol=silhouette_tol)
        result_post = clustering.cluster_variance_matrix_forgroup(cell_vars_rules_sorted_norm, row_groups=None, 
                                                                  col_groups=feature_group_post, k_min=lower_cluster, 
                                                                  k_max=upper_cluster, silhouette_tol=silhouette_tol)

        # a arbitrarily chosen large cluster (in size, small in number)
        # to compare with the cutoff distance
        # higher the cutoff distance, fewer clusters will be formed effectively
        select_col_k = 10
        print(f"select_col_k: {select_col_k}")
        
        # Oct 6th: do not give any prior grouping prior to the modulation information
        # simply grouping and considering each individual column as separate
        # having smaller G, e.g. 200, will make the following calculation in determining pre- and post-
        # belonging identity more time costly
        G_lst = [100, 300, 1000]
        figcol, axscol = plt.subplots(1,len(G_lst),figsize=(4*len(G_lst),4))
        figppshare, axsppshare = plt.subplots(2,len(G_lst),figsize=(4*len(G_lst),4*2))

        result_all_lst = []
        result_all_name_lst = []

        # 2025-11-04: input cluster & hidden cluster along the neuron dimension (N)
        cluster_input, cluster_hidden = col_clusters_all[0], col_clusters_all[1]
        # sanity check: order it based on the key
        cluster_input = dict(sorted(cluster_input.items()))
        cluster_hidden = dict(sorted(cluster_hidden.items()))
        
        for G_idx in range(len(G_lst)): 
            G = G_lst[G_idx]
            col_groups_all, col_labels_all, centroids_all = clustering.make_col_groups_with_kmeans(cell_vars_rules_sorted_norm, 
                                                                                                   n_groups=G)
    
            # plot the statistics of grouping on all modulations without considering their pre/post identity 
            group_lengths = [len(sublist) for sublist in col_groups_all]
            
            axscol[G_idx].hist(group_lengths, bins=20, edgecolor='black', alpha=0.7)
            # if uniform grouping, what is the expected length for each group 
            axscol[G_idx].axvline(MM / G, linestyle="--", color="black")
            axscol[G_idx].set_xlabel('Length of Formed Group', fontsize=15)
            axscol[G_idx].set_ylabel('Frequency', fontsize=15)

            # modulation analysis: using different G will generate different dendogram effectively, since we use the mean/median 
            # of each group to calculate the distance
            result_all = clustering.cluster_variance_matrix_forgroup(cell_vars_rules_sorted_norm, 
                                                                     row_groups=None, 
                                                                     col_groups=col_groups_all, 
                                                                     k_min=lower_cluster, 
                                                                     k_max=G, 
                                                                     silhouette_tol=silhouette_tol)
    
            print(f"G = {G}; result_all['row_k']: {result_all['row_k']}; result_all['col_k']: {result_all['col_k']}")
            assert result_all["col_k"] < G
            axscol[G_idx].set_title(f"G={G}, Neuron Cluster={result_all['col_k']}", fontsize=15)

            result_all_lst.append(result_all)
            result_all_name_lst.append(f"G={G}")
    
            # 2025-10-21: after grouping and clustering the modulation, we ask for two modulation within the same cluster, 
            # how likely they share the same presynaptic neuron (or neuron cluster), 
            # postsynaptic neuron (or neuron cluster), or neither, or both for neuron cluster 
            # it is not possible for different modulation sharing the same pre and post neuron,
            # but they may still share the same neuron cluster
            # here we are curious about their collective behavior and calculate the total summation of count
            row_all, col_all = result_all["row_labels"], result_all["col_labels"]
            assert np.max(col_all) == result_all["col_k"]
            
            print(f"col_all: {col_all}")

            # some helper function
            def find_key_by_element(d, element):
                for key, values in d.items():
                    if element in values:
                        return key
                return None
                
            def same_pre(i, j, M):
                return (i // M) == (j // M)
    
            def same_post(i, j, M):
                return (i % M) == (j % M)

            def same_pre_cluster(i, j, M, pre_cluster):
                pre_i = i // M
                pre_j = j // M
                pre_i_belong = find_key_by_element(pre_cluster, pre_i)
                pre_j_belong = find_key_by_element(pre_cluster, pre_j)
                assert pre_i_belong is not None
                assert pre_j_belong is not None
                return pre_i_belong, pre_j_belong, pre_i_belong == pre_j_belong 

            def same_post_cluster(i, j, M, post_cluster):
                post_i = i % M
                post_j = j % M
                post_i_belong = find_key_by_element(post_cluster, post_i)
                post_j_belong = find_key_by_element(post_cluster, post_j)
                assert post_i_belong is not None
                assert post_j_belong is not None
                return post_i_belong, post_j_belong, post_i_belong == post_j_belong 
                
            # 2025-10-21: "both" for debugging purpose, effectively with "True" or "False" should obtain identical result
            # Use "True" for the actual implementation; 
            parallel_cal = "True" 
            if parallel_cal in ("False", "both", ): 
                # put here for future sanity check of consistency
                same_pre_all, same_post_all, no_same_pre_post_all = 0, 0, 0
                same_pre_cluster_all, same_post_cluster_all, same_pre_post_cluster_all, no_same_pre_post_cluster_all = 0, 0, 0, 0
                # looping through different identified cluster number
                for cluster_num in np.unique(col_all):
                    # find indices that match to the desired cluster
                    idx = np.where(col_all == cluster_num)[0]
                    # combinations of all indices pair by removing the permutation
                    idx_comb = [[idx[i], idx[j]] for i in range(len(idx)) for j in range(i + 1, len(idx))]
                    for idx_ in idx_comb: 
                        same_pre_check = same_pre(idx_[0], idx_[1], M)
                        same_post_check = same_post(idx_[0], idx_[1], M)
                        # at most one is true, since we exclude i == j case in idx_comb, it is 
                        # impossible to having two different modulation entry sharing the same
                        # presynaptic and postsynaptic neuron 
                        assert same_pre_check + same_post_check <= 1
                        non_check = 0 if (same_pre_check + same_post_check == 1) else 1
                        
                        same_pre_all += same_pre_check; same_post_all += same_post_check; no_same_pre_post_all += non_check 
                        
                        # 2025-10-20: check with pre/post [cluster] belonging
                        _, _, same_pre_cluster_check = same_pre_cluster(idx_[0], idx_[1], M, cluster_input)
                        _, _, same_post_cluster_check = same_post_cluster(idx_[0], idx_[1], M, cluster_hidden)
                        assert same_pre_cluster_check + same_post_cluster_check <= 2
                        non_cluster_check = 1 if (same_pre_cluster_check + same_post_cluster_check == 0) else 0
                        both_cluster_check = 1 if (same_pre_cluster_check + same_post_cluster_check == 2) else 0
                        same_pre_cluster_all += same_pre_cluster_check; same_post_cluster_all += same_post_cluster_check
                        same_pre_post_cluster_all += both_cluster_check; no_same_pre_post_cluster_all += non_cluster_check

                print(f"same_pre_all: {same_pre_all}; same_post_all: {same_post_all}; no_same_pre_post_all: {no_same_pre_post_all}")
                print(f"same_pre_cluster_all: {same_pre_cluster_all}; same_post_cluster_all: {same_post_cluster_all}")
                print(f"same_pre_post_cluster_all: {same_pre_post_cluster_all}; no_same_pre_post_cluster_all: {no_same_pre_post_cluster_all}")
            
            if parallel_cal in ("True", "both", ):
                # membership using the actual clustering information
                same_pre_all, same_post_all, no_same_pre_post_all, same_pre_cluster_all, same_post_cluster_all, \
                    same_pre_post_cluster_all, no_same_pre_post_cluster_all = clustering_metric.count_pairs_with_clusters(col_all, M, 
                                                                                                                          cluster_input, 
                                                                                                                          cluster_hidden)
                # (control) membership using the random clustering information 
                # 2026-03-03: increase the repeat times for a more reliable null distribution
                same_pre_all_c, same_post_all_c, no_same_pre_post_all_c, same_pre_cluster_all_c, same_post_cluster_all_c, \
                    same_pre_post_cluster_all_c, no_same_pre_post_cluster_all_c = clustering_metric.count_pairs_with_clusters_control(col_all, M, 
                                                                                                                                      cluster_input, 
                                                                                                                                      cluster_hidden,
                                                                                                                                      repeat=1000)
                
                print(f"same_pre_all: {same_pre_all}; same_post_all: {same_post_all}; no_same_pre_post_all: {no_same_pre_post_all}")
                print(f"same_pre_cluster_all: {same_pre_cluster_all}; same_post_cluster_all: {same_post_cluster_all}")
                print(f"same_pre_post_cluster_all: {same_pre_post_cluster_all}; no_same_pre_post_cluster_all: {no_same_pre_post_cluster_all}")

                print(f"same_pre_all_c: {same_pre_all_c}; same_post_all_c: {same_post_all_c}; no_same_pre_post_all_c: {no_same_pre_post_all_c}")
                print(f"same_pre_cluster_all_c: {same_pre_cluster_all_c}; same_post_cluster_all_c: {same_post_cluster_all_c}")
                print(f"same_pre_post_cluster_all_c: {same_pre_post_cluster_all_c}; no_same_pre_post_cluster_all_c: {no_same_pre_post_cluster_all_c}")
    
            bar_all_lst = [[same_pre_all, same_post_all, no_same_pre_post_all], 
                           [same_pre_cluster_all, same_post_cluster_all, same_pre_post_cluster_all, no_same_pre_post_cluster_all]]
            bar_all_ctrl_lst = [[same_pre_all_c, same_post_all_c, no_same_pre_post_all_c], 
                                [same_pre_cluster_all_c, same_post_cluster_all_c, same_pre_post_cluster_all_c, no_same_pre_post_cluster_all_c]]
            bar_name_lst = [["Share-Pre", "Share-Post", "Neither"], 
                           ["Share-Pre-Cluster", "Share-Post-Cluster", "Share-Both-Cluster", "Neither"]]

            N_cluster = np.max(col_all)
            
            for idx in range(len(bar_all_lst)):
                bar_all = np.array(bar_all_lst[idx])
                bar_all_c = np.array(bar_all_ctrl_lst[idx])

                over_membership = (bar_all - bar_all_c) / bar_all_c
                
                axsppshare[idx,G_idx].bar([i for i in range(len(over_membership))], over_membership)
                axsppshare[idx,G_idx].set_xticks([i for i in range(len(over_membership))])
                axsppshare[idx,G_idx].set_xticklabels(bar_name_lst[idx], rotation=45, ha="right")
                axsppshare[idx,G_idx].set_ylabel("Over-membership", fontsize=15)
                axsppshare[idx,G_idx].set_title(f"G = {G}; # Cluster = {N_cluster}")

            axsppshare[0,G_idx].set_title("Same Neuron Check")
            axsppshare[1,G_idx].set_title("Same Neuron Cluster Check")

            # Oct 21th: next analyze for each individual modulation cluster, the belonging to different individual 
            # pre and post cluster; only test in the minimal modulation cluster selection case
            def value_counts_desc(arr):
                """
                Return dict: {value: count}, sorted by count descending.
                """
                unique, counts = np.unique(arr, return_counts=True)
                sorted_idx = np.argsort(-counts)  # sort by count descending
                return {unique[i]: counts[i] for i in sorted_idx}

            # 2025-11-17: revise to plot for G=300 case
            if G_idx == 0: 
                print(f"Plot for G={G_lst[G_idx]} Case")
                # for plotting purpose
                mp = 5; cnt = 0
                # order the modulation cluster size from largest to smallest
                # select the ones with large cluster size for the following analysis
                all_choice_order_dict = value_counts_desc(col_all)
                # 2025-11-04: print the keys of modulation cluster name/labels after 
                # ordering based on the size from the largest to the smallest
                all_choice_order = list(all_choice_order_dict.keys())
                print(f"all_choice_order: {all_choice_order}")

                # 2025-11-04: calculate each cluster size and normalize it
                cluster_size_all = []
                for cluster_num in all_choice_order:
                    idx = np.where(col_all == cluster_num)[0]
                    cluster_size_all.append(len(idx))
                # 2025-11-04: normalize to percentage
                cluster_size_percent = [i / np.sum(cluster_size_all) for i in cluster_size_all]
                # 2025-11-17: the summation of percentage is trivially 1
                assert np.isclose(np.sum(cluster_size_percent), 1.0)
                
                for cluster_num in all_choice_order:
                    idx = np.where(col_all == cluster_num)[0]

                # size of cluster product by input and hidden 
                in_num = [len(cluster_input[key]) for key in cluster_input.keys()]
                hid_num = [len(cluster_hidden[key]) for key in cluster_hidden.keys()]
                all_num = np.zeros((len(cluster_input), len(cluster_hidden)))
                for ci in range(len(in_num)):
                    for ch in range(len(hid_num)):
                        all_num[ci,ch] = in_num[ci] * hid_num[ch]
                        
                figac, axac = plt.subplots(1,1,figsize=(4,4))
                sns.heatmap(all_num, ax=axac, cmap=cs)
                axac.set_xlabel("Hidden Cluster Index", fontsize=15)
                axac.set_ylabel("Input Cluster Index", fontsize=15)
                axac.set_title("Number of Total Modulation", fontsize=15)
                figac.tight_layout()
                figac.savefig(f"./multiple_tasks/{clustering_name}_inhidpair_num_{savefigure_name}.png", dpi=300) 
                plt.close(figac) 

                corr_lst, om_lst, num_lst = [], [], []
                over_membership_lst = []
                
                figsm, axsm = plt.subplots(2,mp,figsize=(4*mp,4*2))
                # 2025-10-27: loop through different modulation cluster
                for cidx, cluster_num in enumerate(all_choice_order):
                    Z_count = np.zeros((len(cluster_input), len(cluster_hidden)))
                    idx = np.where(col_all == cluster_num)[0]
                    # for modulation in this cluster, loop through index to check its belonging
                    for idx_ in idx: 
                        # 2025-10-27: slightly abuse the usage of same_pre_cluster; here only 
                        # extract the pre-cluster and post-cluster identity for idx_ 
                        pre_i, _, _ = same_pre_cluster(idx_, idx_, M, cluster_input)
                        post_i, _, _ = same_post_cluster(idx_, idx_, M, cluster_hidden)
                        # register of pre & post cluster belonging 
                        # -1 is for syntax consistency
                        Z_count[pre_i-1, post_i-1] += 1

                    # calculate Pearson correlation to the total membership count
                    # normalization issue is handled internally within the calculation
                    corr = np.corrcoef(all_num.flatten(), Z_count.flatten())[0,1]
                    corr_lst.append(corr)
                    # calculate over/under-membership, defined as the relative "exceeding" 
                    # compared to the membership by assuming uniform spliting on all_num
                    # to different modulation cluster
                    # this should captures the modulation-cluster-wise fluctuation 
                    # in input-hidden-bicluster attendence
                    # 2025-11-04: we should revise so that the average division is based on the 
                    # modulation cluster size (i.e. weighted), not purely uniform devision (1/N_cluster)
                    # all_num_avg = all_num / N_cluster
                    all_num_avg = all_num * cluster_size_percent[cidx]
                    # 2025-11-04: we dont take the absolute value so the over- and under-membership
                    # are separately treated
                    # 2025-11-17: revise the calculation (center at 1 instead)
                    over_membership = Z_count / all_num_avg
                    # 2025-11-04: the mean score is calculated through absolute value to prevent
                    # mutual cancellation
                    om = np.mean(np.abs(over_membership))
                    om_lst.append(om); num_lst.append(len(idx))
                    over_membership_lst.append(over_membership)
                    
                    if cnt < mp: 
                        sns.heatmap(Z_count, ax=axsm[0,cnt], cmap=cs)
                        sns.heatmap(over_membership, ax=axsm[1,cnt], cmap=cs, vmin=0, vmax=2)
                        for axindex in range(2): 
                            axsm[axindex,cnt].set_xlabel("Hidden Cluster Index", fontsize=15)
                            axsm[axindex,cnt].set_ylabel("Input Cluster Index", fontsize=15)
                        axsm[0,cnt].set_title(f"Modulation Cluster {cluster_num}; corr: {corr:.3f}", fontsize=12)
                        axsm[1,cnt].set_title(f"Modulation Cluster {cluster_num}; om: {om:.3f}", fontsize=12)
                        cnt += 1
                        
                figsm.tight_layout()
                figsm.savefig(f"./multiple_tasks/{clustering_name}_specific_case_{savefigure_name}.png", dpi=300)  
                plt.close(figsm)

                figomcluster, axsomcluster = plt.subplots(4,1,figsize=(10,4*4))
                
                over_membership_array_all = np.array([arr.flatten() for arr in over_membership_lst])
                # 2025-11-04: calculate the average over/under-membership for input & hidden cluster
                over_membership_array_input = np.array([np.mean(np.abs(arr), axis=1) for arr in over_membership_lst])
                over_membership_array_hidden = np.array([np.mean(np.abs(arr), axis=0) for arr in over_membership_lst])

                over_membership_array = [over_membership_array_all, over_membership_array_input, over_membership_array_hidden]

                for omindex, over_membership_array_ in enumerate(over_membership_array): 
                    cluster_colors = color_func.rainbow_generate(over_membership_array_.shape[1])
                    for cindex in range(over_membership_array_.shape[1]):
                        axsomcluster[omindex].plot(over_membership_array_[:,cindex], color=cluster_colors[cindex], alpha=0.5)
                    
                axsomcluster[0].set_ylabel("Overmembership at Bi-Cluster", fontsize=15)
                axsomcluster[1].set_ylabel("Mean Overmembership at Input-Cluster", fontsize=15)
                axsomcluster[2].set_ylabel("Mean Overmembership at Hidden-Cluster", fontsize=15)
                
                axsomcluster[3].plot(cluster_size_percent, "-o", linewidth=2, color=cluster_colors[0])
                axsomcluster[3].axhline(1 / len(cluster_size_percent), linestyle="--")
                axsomcluster[3].set_ylabel("Cluster Size (Normalized)", fontsize=15)
                for axomcluster in axsomcluster: 
                    axomcluster.set_xlabel("Modulation Cluster Order (Largest to Smallest)", fontsize=15)
                for indx in range(3):
                    axsomcluster[indx].set_xlim([0, 5])
                    axsomcluster[indx].set_ylim([0, 3])
                figomcluster.tight_layout()
                # figomcluster.savefig(f"./multiple_tasks/{clustering_name}_om_across_cluster_{savefigure_name}.png", dpi=300)  
                plt.close(figomcluster)
                
                figgroupcorr, axsgroupcorr = plt.subplots(1,5,figsize=(4*5,4))
                axsgroupcorr[0].hist(corr_lst, bins="auto")
                axsgroupcorr[0].set_xlabel("Correlation per Group", fontsize=15)
                axsgroupcorr[1].hist(om_lst, bins="auto")
                axsgroupcorr[1].set_xlabel("Mean Absolute Average OM", fontsize=15)
                for index in range(len(corr_lst)):
                    axsgroupcorr[2].scatter(corr_lst[index], om_lst[index], c=c_vals[0], alpha=0.7)
                    axsgroupcorr[3].scatter(num_lst[index], corr_lst[index], c=c_vals[0], alpha=0.7)
                    axsgroupcorr[4].scatter(num_lst[index], om_lst[index], c=c_vals[0], alpha=0.7)

                # 2025-11-04: do a linear regression
                x_fit, y_fit, _, _, _, _ = helper.linear_regression(num_lst, corr_lst, log=False, through_origin=False)
                axsgroupcorr[3].plot(x_fit, y_fit)
                x_fit2, y_fit2, _, _, _, _ = helper.linear_regression(num_lst, om_lst, log=False, through_origin=False)
                axsgroupcorr[4].plot(x_fit2, y_fit2)
                    
                axsgroupcorr[2].set_xlabel("Corr", fontsize=15)
                axsgroupcorr[2].set_ylabel("OM", fontsize=15)
                axsgroupcorr[3].set_xlabel("Modulation Cluster Size", fontsize=15)
                axsgroupcorr[3].set_ylabel("Corr", fontsize=15)
                axsgroupcorr[4].set_xlabel("Modulation Cluster Size", fontsize=15)
                axsgroupcorr[4].set_ylabel("OM", fontsize=15)
                
                figgroupcorr.tight_layout()
                figgroupcorr.savefig(f"./multiple_tasks/{clustering_name}_corr_allgroup_case_{savefigure_name}.png", dpi=300)  
                plt.close(figgroupcorr)

        figcol.tight_layout()
        figcol.savefig(f"./multiple_tasks/{clustering_name}_allneuron_grouplength_{savefigure_name}.png", dpi=300)  
        plt.close(figcol)
        figppshare.tight_layout()
        figppshare.savefig(f"./multiple_tasks/{clustering_name}_prepost_belonging_{savefigure_name}.png", dpi=300)  
        plt.close(figppshare)

        # all following analysis based on the maximal G (G_lst[-1])
        cell_vars_rules_sorted_norm_pre = cell_vars_rules_sorted_norm[np.ix_(result_pre["row_order"], result_pre["col_order"])]
        cell_vars_rules_sorted_norm_post = cell_vars_rules_sorted_norm[np.ix_(result_post["row_order"], result_post["col_order"])]

        # 2025-11-12: for all G results (after reordering)
        # up to here, the covariance matrix is determined. 
        cell_vars_rules_sorted_norm_all_lst = []
        for result_all_ in result_all_lst:
            cell_vars_rules_sorted_norm_all = cell_vars_rules_sorted_norm[np.ix_(result_all_["row_order"], result_all_["col_order"])]
            cell_vars_rules_sorted_norm_all_lst.append(cell_vars_rules_sorted_norm_all)

        clustering_data_hierarchy["modulation_all_pre"] = result_pre["col_linkage"]
        clustering_data_hierarchy["modulation_all_post"] = result_post["col_linkage"]

        # check if length is consistent
        assert len(G_lst) == len(result_all_lst)

        # plot original, pre, post, plus all results under different G
        tf = 3 + len(result_all_lst)
        figprepost, axprepost = plt.subplots(tf,1,figsize=(24,8*tf))
        sns.heatmap(cell_vars_rules_sorted_norm, ax=axprepost[0], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(cell_vars_rules_sorted_norm_pre, ax=axprepost[1], cmap=cs, cbar=True, vmin=0, vmax=1)
        sns.heatmap(cell_vars_rules_sorted_norm_post, ax=axprepost[2], cmap=cs, cbar=True, vmin=0, vmax=1)

        # plot the headmap for all G result
        for k in range(len(cell_vars_rules_sorted_norm_all_lst)):
            sns.heatmap(cell_vars_rules_sorted_norm_all_lst[k], ax=axprepost[3+k], cmap=cs, cbar=True, vmin=0, vmax=1)

        for ax in axprepost:
            ax.set_yticks(np.arange(len(tb_break_name)))
            
        axprepost[0].set_yticklabels(tb_break_name, rotation=0, ha='right', va='center', fontsize=9)
        axprepost[1].set_yticklabels(tb_break_name[result_pre["row_order"]], rotation=0, ha='right', va='center', fontsize=9)
        axprepost[2].set_yticklabels(tb_break_name[result_post["row_order"]], rotation=0, ha='right', va='center', fontsize=9)

        # plot the row
        # 2025-10-30: modulation, clustering by G groups from MinibatchKmeans, "border" between clusters
        modulation_cluster_norm = []
        for k in range(len(cell_vars_rules_sorted_norm_all_lst)):
            axprepost[3+k].set_yticklabels(tb_break_name[result_all_lst[k]["row_order"]], rotation=0, ha='right', va='center', fontsize=9)
            result_all = result_all_lst[k]
            rl_all = np.asarray(result_all["row_labels"])[result_all["row_order"]]
            cl_all = np.asarray(result_all["col_labels"])[result_all["col_order"]]
            rbreaks_all_ = clustering._breaks(rl_all)
            cbreaks_all_ = clustering._breaks(cl_all)
            for rb in rbreaks_all_:
                axprepost[3+k].axhline(rb, color="k", lw=0.6)
            for cb in cbreaks_all_:
                axprepost[3+k].axvline(cb, color="k", lw=0.6)

            # 2025-11-12: calculate the mean of the reordered covaraince matrix under different G value
            # notice G indicates the group size, not the actual 
            rbreaks_all_end = [0] + rbreaks_all_ + [cell_vars_rules_sorted_norm_all_lst[0].shape[0]]
            cbreaks_all_end = [0] + cbreaks_all_ + [cell_vars_rules_sorted_norm_all_lst[0].shape[1]]

            varmeanmod = np.zeros((len(rbreaks_all_end) - 1, len(cbreaks_all_end) - 1))
            for rr in range(len(rbreaks_all_end) - 1):
                for cc in range(len(cbreaks_all_end) - 1):
                    varmeanmod[rr,cc] = np.mean(cell_vars_rules_sorted_norm_all_lst[k][rbreaks_all_end[rr]:rbreaks_all_end[rr+1], 
                                                  cbreaks_all_end[cc]:cbreaks_all_end[cc+1]])

            modulation_cluster_norm.append(varmeanmod)            
            
        axprepost[0].set_title("Original", fontsize=15)
        axprepost[1].set_title(f"Group by Pre", fontsize=15)
        axprepost[2].set_title(f"Group by Post", fontsize=15)

        # 2025-10-30: modulation, clustering by pre/post, "border" between clusters
        rl_pre = np.asarray(result_pre["row_labels"])[result_pre["row_order"]]
        cl_pre = np.asarray(result_pre["col_labels"])[result_pre["col_order"]]
        rbreaks_pre = clustering._breaks(rl_pre)
        cbreaks_pre = clustering._breaks(cl_pre)
        for rb in rbreaks_pre:
            axprepost[1].axhline(rb, color="k", lw=0.6)
        for cb in cbreaks_pre:
            axprepost[1].axvline(cb, color="k", lw=0.6)

        rl_post = np.asarray(result_post["row_labels"])[result_post["row_order"]]
        cl_post = np.asarray(result_post["col_labels"])[result_post["col_order"]]
        rbreaks_post = clustering._breaks(rl_post)
        cbreaks_post = clustering._breaks(cl_post)
        for rb in rbreaks_post:
            axprepost[2].axhline(rb, color="k", lw=0.6)
        for cb in cbreaks_post:
            axprepost[2].axvline(cb, color="k", lw=0.6)
        
        for k in range(len(cell_vars_rules_sorted_norm_all_lst)):
            axprepost[3+k].set_title(f"Group by All, G={G_lst[k]}", fontsize=15)
            
        figprepost.tight_layout()
        figprepost.savefig(f"./multiple_tasks/{clustering_name}_bygroup_variance_{savefigure_name}.png", dpi=300)
        plt.close(figprepost)

        figmodnorm, axsmodnorm = plt.subplots(len(modulation_cluster_norm),1,figsize=(10,4*len(modulation_cluster_norm)))
        for idx in range(len(modulation_cluster_norm)):
            sns.heatmap(modulation_cluster_norm[idx], ax=axsmodnorm[idx], cmap=cs)
            axsmodnorm[idx].set_xlabel("Modulation Index", fontsize=15)
            axsmodnorm[idx].set_ylabel("Session Index", fontsize=15)

        figmodnorm.tight_layout()
        figmodnorm.savefig(f"./multiple_tasks/{clustering_name}_modulation_clusternorm_{savefigure_name}.png", dpi=300)
        plt.close(figmodnorm)

        # fig, axs = plt.subplots(tf,1,figsize=(25*1,10*tf))
        # # pre-neuron modulation clustering across session
        # dendrogram(result_pre["row_linkage"], ax=axs[0], labels=tb_break_name[result_pre["row_order"]], leaf_rotation=45)
        # axs[0].axhline(result_pre["row_best_cut_distance"], linestyle="--", color="black", linewidth=3)
        # axs[0].set_title(f"Modulation Pre Row hierarchy (k = {result_pre['row_k']})", fontsize=15)
        # # pre-neuron modulation clustering across neuron
        # dendrogram(result_pre["col_linkage"], ax=axs[1], no_labels=True, leaf_rotation=45)
        # axs[1].axhline(result_pre["col_best_cut_distance"], linestyle="--", color="black", linewidth=3)
        # axs[1].axhline(result_pre["col_cut_distance_by_k"][select_col_k], linestyle="--", color=c_vals[0], linewidth=3)
        # axs[1].set_title(f"Modulation Pre Col hierarchy (k = {result_pre['col_k']})", fontsize=15)
        # # post-neuron modulation clustering across neuron 
        # dendrogram(result_post["col_linkage"], ax=axs[2], no_labels=True, leaf_rotation=45)
        # axs[2].axhline(result_post["col_best_cut_distance"], linestyle="--", color="black", linewidth=3)
        # axs[2].axhline(result_post["col_cut_distance_by_k"][select_col_k], linestyle="--", color=c_vals[0], linewidth=3)
        # axs[2].set_title(f"Modulation Post Col hierarchy (k = {result_post['col_k']})", fontsize=15)
        # # all-neuron modulation clustering across neuron
        # for k in range(len(result_all_lst)):
        #     dendrogram(result_all_lst[k]["col_linkage"], ax=axs[3+k], no_labels=True, leaf_rotation=45)
        #     axs[3+k].axhline(result_all_lst[k]["col_best_cut_distance"], linestyle="--", color="black", linewidth=3)
        #     axs[3+k].axhline(result_all_lst[k]["col_cut_distance_by_k"][select_col_k], linestyle="--", color=c_vals[0], linewidth=3)
        #     axs[3+k].set_title(f"Modulation All Col hierarchy (k = {result_all_lst[k]['col_k']}); G={G_lst[k]}", fontsize=15)
        # fig.tight_layout()
        # fig.savefig(f"./multiple_tasks/{clustering_name}_bygroup_variance_hierarchy_{savefigure_name}.png", dpi=300) 

        figscore, axscore = plt.subplots(1,1,figsize=(10,3))
        # axscore.plot(result_pre["row_score_recording"].keys(), result_pre["row_score_recording"].values(), \
                     # label="row pre", color=c_vals[0])
        axscore.plot(result_pre["col_score_recording"].keys(), result_pre["col_score_recording"].values(), \
                     label="col pre", color=c_vals[1])
        # axscore.plot(result_post["row_score_recording"].keys(), result_post["row_score_recording"].values(), \
                     # label="row post", color=c_vals[2])
        axscore.plot(result_post["col_score_recording"].keys(), result_post["col_score_recording"].values(), \
                     label="col post", color=c_vals[3])
        for k in range(len(result_all_lst)): 
            # axscore.plot(result_all_lst[k]["row_score_recording"].keys(), result_all_lst[k]["row_score_recording"].values(), 
                         # label="row all", color=c_vals[4+k], linestyle="--")
            axscore.plot(result_all_lst[k]["col_score_recording"].keys(), result_all_lst[k]["col_score_recording"].values(), \
                         label=f"col all, G={G_lst[k]}", color=c_vals[5+k], linestyle="--")
        axscore.set_xlabel("Number of Cluster", fontsize=15)
        axscore.set_ylabel("Silhouette Score", fontsize=15)
        axscore.legend()
        axscore.set_xscale("log")
        figscore.tight_layout()
        figscore.savefig(f"./multiple_tasks/{clustering_name}_variance_cluster_score_{savefigure_name}.png", dpi=300)
        plt.close(figscore)

        input_info, hidden_info = base_data[0], base_data[1]

        # compare modulation grouping result
        # either using pre, post, or different level of K-means pre-clusters
        all_mod = [result_pre, result_post] + result_all_lst
        all_mod_name = ["Pre", "Post"] + result_all_name_lst

        all_mod_metric_allk = []
        # loop through downsampled clusters along the neuron dimension
        select_col_allk = np.arange(lower_cluster, 15)
        for select_col_k_ in select_col_allk:
            all_mod_metric = []
            for mod_ in all_mod: 
                eval_res_modulation = clustering_metric.evaluate_bicluster_clustering(
                    cell_vars_rules_sorted_norm, row_labels=mod_["row_labels_by_k"][select_col_k_], 
                    col_labels=mod_["col_labels_by_k"][select_col_k_]
                )
                
                all_mod_metric.append([
                    eval_res_modulation["metrics"]["CH_blocks"],
                    eval_res_modulation["metrics"]["DB_blocks"],
                    eval_res_modulation["metrics"]["XB_blocks"], 
                ])

            all_mod_metric_allk.append(all_mod_metric)

        all_mod_metric_allk = np.array(all_mod_metric_allk)
        print(all_mod_metric_allk.shape)
        
        # compare the effect by clustering based on presynaptic neuron, postsynaptic neuron, or all
        # hypothesis: all >≈ postsynaptic > presynaptic
        fig, ax = plt.subplots(1,all_mod_metric_allk.shape[-1], figsize=(4*all_mod_metric_allk.shape[-1], 4))
        for metric_index in range(all_mod_metric_allk.shape[2]):
            for model_index in range(all_mod_metric_allk.shape[1]):
                ax[metric_index].plot([j for j in range(len(select_col_allk))], all_mod_metric_allk[:,model_index,metric_index], \
                           "-o", label=all_mod_name[model_index], color=c_vals[model_index])
                ax[metric_index].set_xticks([j for j in range(len(select_col_allk))])
                ax[metric_index].set_xticklabels(select_col_allk)
                
        ax[0].set_ylabel("Calinski Harabasz", fontsize=15)
        ax[1].set_ylabel("Davies Bouldin", fontsize=15)
        ax[2].set_ylabel("Xie-Beni", fontsize=15)
        
        for ax_ in ax:
            ax_.legend()
            ax_.set_xlabel("# Cluster", fontsize=15)
        fig.tight_layout()
        fig.savefig(f"./multiple_tasks/between_modulation_{savefigure_name}.png", dpi=300)
        plt.close(fig)
        



# # %%
# rules_epochs

# # %%
# plot_num = 3
# modulation_norm = modulation_cluster_norm[2]
# print(f"modulation_norm: {modulation_norm.shape}")
# assert modulation_norm.shape[1] == max(all_choice_order)
# print(np.min(all_choice_order))

# fig, axs = plt.subplots(3,plot_num,figsize=(4*plot_num,4*3))
# for i in range(plot_num):
#     cluster_name = all_choice_order[i] - 1
#     modulation_norm_cl = modulation_norm[:,cluster_name].reshape(-1,1)
#     data = over_membership_lst[i]
#     hidden_mean = np.mean(data, axis=0)
#     input_mean = np.mean(data, axis=1)
#     mean, std = np.mean(hidden_mean), np.std(hidden_mean) / np.sqrt(len(hidden_mean))
#     idx = np.where(hidden_mean > 2)[0]
#     axs[0,i].plot(hidden_mean, "-o", color=c_vals[0])
#     # axs[0,i].axhline(mean, linestyle="--", linewidth=2)
#     # axs[0,i].axhline(2 * std, linestyle="--", linewidth=2)
#     axs[0,i].set_title(f"{idx + 1}")
#     # axs[0,i].set_title(f"Cluster Name: {cluster_name}")
#     idx = np.where(input_mean > 2)[0]
#     axs[1,i].plot(input_mean, "-o", color=c_vals[0])
#     axs[1,i].set_title(f"{idx + 1}")
#     sns.heatmap(modulation_norm_cl, ax=axs[2,i], cmap=cs, vmin=np.min(modulation_norm), vmax=np.max(modulation_norm))
    
# fig.tight_layout()

# # %%
# # 2025-11-20: filter out some small modulation cluster
# valid_mod_cluster = np.array(cluster_size_percent) > (1 / len(cluster_size_percent))

# # %%
# # 2025-11-20: filter out some small input * hidden bi-cluster
# inp_hidden_mask = all_num > (np.sum(all_num) / (all_num.shape[0] * all_num.shape[1]))

# # %%
# data_lst = []
# data_all = [[], []]
# fig, axs = plt.subplots(2,plot_num,figsize=(4*plot_num,4*2))
# for i in range(len(over_membership_lst)):
#     data = over_membership_lst[i]
#     xx, yy = activecorr.flatten(), data.flatten()
#     if valid_mod_cluster[i]: 
#         data_all[0].extend(list(activecorr[inp_hidden_mask].flatten())); 
#         data_all[1].extend(list(data[inp_hidden_mask].flatten()))
#     x_fit, y_fit, r_value, slope, _, p_val = helper.linear_regression(xx, yy, log=False, through_origin=False)
#     data_lst.append([slope, p_val])
#     if i < plot_num: 
#         sns.kdeplot(x=xx, y=yy, fill=True, cmap='coolwarm', ax=axs[0,i], levels=200, thresh=0, bw_adjust=0.5, gridsize=300)
#         axs[1,i].scatter(xx, yy, alpha=0.5, color=c_vals[0])
#         axs[1,i].plot(x_fit, y_fit, label=f"R={r_value:.3f}; Slope: {slope:.3f}; p-value: {p_val:.3f}")
#         axs[1,i].legend()
#         for j in range(2):
#             axs[j,i].set_xlabel("Co-activation Level", fontsize=15)
#             axs[j,i].set_ylabel("Overmembership Level", fontsize=15)
# fig.tight_layout()
# fig.savefig(f"./multiple_tasks/inputhiddencoactive_om_{savefigure_name}.png", dpi=300)

# # %%
# fig, ax = plt.subplots(1,1,figsize=(6,6))
# ax.scatter(data_all[0], data_all[1], alpha=0.5, s=10, color=c_vals[0])
# # ax.set_yscale("log")
# ax.set_ylim([0, 5])
# ax.set_xlabel("Coactivation Level", fontsize=15)
# ax.set_ylabel("Modulation-Cluster-Specific Overmembership", fontsize=15)
# ax.axhline(1, color=c_vals[1])

# unique_x = np.unique(data_all[0])
# median_y = [np.median(np.array(data_all[1])[data_all[0] == ux]) for ux in unique_x]
# ax.plot(unique_x, median_y, color=c_vals[2], linewidth=2, label="Median Y given X")
# ax.legend(loc="best", frameon=True, fontsize=15)

# # %%
# cutoff_lst = np.linspace(1,3,100)
# fig, axs = plt.subplots(1,4,figsize=(4*4,4))

# fits = []
# for cindex, cutoff in enumerate(cutoff_lst): 
#     prop_y_gt2 = []
    
#     for ux in unique_x:
#         y_vals = np.array(data_all[1])[np.array(data_all[0]) == ux]
#         prop_y_gt2.append(np.mean(y_vals > cutoff))  

#     x_fit, y_fit, r_value, slope, _, p_val = helper.linear_regression(unique_x, prop_y_gt2, 
#                                                                       log=False, through_origin=False)
#     fits.append([r_value, slope])

#     if cindex % 25 == 0: 
#         cindex = int(cindex/25)
#         axs[cindex].scatter(unique_x, prop_y_gt2, c=c_vals[0])
#         axs[cindex].set_xlabel("Coactivation Level", fontsize=15)
#         axs[cindex].set_ylabel("Percentage of Overmembership", fontsize=15)
#         axs[cindex].plot(x_fit, y_fit, linestyle="--", label=f"r_value: {r_value:.3f}; slope: {slope:.3f}; ")
#         axs[cindex].legend()
#         axs[cindex].set_title(f"cutoff: {cutoff}", fontsize=15)

# fig.tight_layout()
# fig.savefig(f"./multiple_tasks/coact_om_{savefigure_name}.png", dpi=300)

# fits = np.array(fits)
# figfit, axsfit = plt.subplots(1,2,figsize=(4*2,4))
# axsfit[0].plot(cutoff_lst, fits[:,0])
# axsfit[1].plot(cutoff_lst, fits[:,1])
# axsfit[0].set_xlabel("Threshold", fontsize=15)
# axsfit[1].set_xlabel("Threshold", fontsize=15)
# axsfit[0].set_ylabel("R-Value", fontsize=15)
# axsfit[1].set_ylabel("Slope", fontsize=15)
# figfit.tight_layout()
# figfit.savefig(f"./multiple_tasks/coact_om_threshold_{savefigure_name}.png", dpi=300)

# # %%
# phase_to_indices_fix = [
#     ("fix1",    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# ]

# tb_break_fix = [
#     [idx, rules_epochs[all_rules[idx]][phase]]
#     for phase, indices in phase_to_indices_fix
#     for idx in indices
# ]

# tb_break_fix_name = [
#     f"{all_rules[idx]}"
#     for phase, indices in phase_to_indices_fix
#     for idx in indices
# ]

# clustering_datas = [xs, hs, Ms_orig]
# clustering_data_names = ["input", "hidden", "modulation"]
# sindex = 1

# states = []
# for el in range(len(tb_break_fix)):        
#     rule_idx, period_time = tb_break[el][0], tb_break[el][1]
#     if sindex in (0, 1,):
#         rule_context_final_state = clustering_datas[sindex][test_task == rule_idx, period_time[1], :]
#     else:
#         rule_context_final_state = clustering_datas[sindex][test_task == rule_idx, period_time[1], :, :]
#         rule_context_final_state = rule_context_final_state.reshape(rule_context_final_state.shape[0], -1)
#     states.append(rule_context_final_state)

# print(tb_break_fix_name)

# # %%
# X_all = np.vstack(states)              
# N_per_task = [s.shape[0] for s in states]

# mean_vec = X_all.mean(axis=0, keepdims=True)
# X_centered = X_all - mean_vec      

# U, S, Vt = np.linalg.svd(X_centered, full_matrices=False)
# W = Vt[:2].T                         

# Z_all = X_centered @ W                 

# Z_tasks = []
# start = 0
# for n in N_per_task:
#     Z_tasks.append(Z_all[start:start+n])   
#     start += n

# print(len(Z_tasks))

# # %%
# from matplotlib.patches import Circle

# fig, ax = plt.subplots(1,1,figsize=(10,10))

# for k, Zk in enumerate(Z_tasks):
#     color = c_vals[k % len(c_vals)]
#     ax.scatter(Zk[:, 0], Zk[:, 1], s=20, alpha=0.5, color=color)

#     med = np.mean(Zk, axis=0)   
#     med_x, med_y = med[0], med[1]

#     ax.scatter(med_x, med_y, s=150, color=color, edgecolor="black",
#                marker="X", linewidth=1.5, zorder=5)

#     dists = np.linalg.norm(Zk - med, axis=1)
#     radius = dists.max()

#     circle = Circle(
#         (med_x, med_y),
#         radius,
#         edgecolor=color,
#         facecolor="none",
#         linestyle="--",
#         linewidth=2,
#         alpha=0.8
#     )
#     ax.add_patch(circle)

#     label = f"{tb_break_fix_name[k]} (med=({med_x:.2f}, {med_y:.2f}))"

#     ax.scatter([], [], color=color, marker="X", s=150, edgecolor="black",
#                label=label)

# ax.set_xlabel("Context endpt. state PC1", fontsize=15)
# ax.set_ylabel("Context endpt. state PC2", fontsize=15)
# ax.set_title(f"{clustering_data_names[sindex]}", fontsize=15)
# ax.legend(frameon=True, fontsize=12)
# fig.tight_layout()
# fig.show()

# # %%


# # %%