{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc77b8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import pickle\n",
    "import scipy\n",
    "import os\n",
    "from scipy.stats import zscore\n",
    "from scipy.sparse import csc_matrix, eye, diags\n",
    "from scipy.sparse.linalg import spsolve\n",
    "import difflib\n",
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af78e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from joblib import Parallel, delayed\n",
    "from scipy.signal import savgol_filter\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "def fill_missing(Y, kind='linear'):\n",
    "\n",
    "\tinitial_shape = Y.shape\n",
    "\tY = Y.reshape((initial_shape[0], -1))\n",
    "\tfor i in range(Y.shape[-1]):\n",
    "\t\ty = Y[:,i]\n",
    "\n",
    "\t\t# Build interpolant.\n",
    "\t\tx = np.flatnonzero(~np.isnan(y))\n",
    "\t\tf = interp1d(x, y[x], kind=kind, fill_value=np.nan, bounds_error=False)\n",
    "\n",
    "\t\t# Fill missing\n",
    "\t\txq = np.flatnonzero(np.isnan(y))\n",
    "\t\ty[xq] = f(xq)\n",
    "\n",
    "\t\t# Fill leading or trailing NaNs with the nearest non-NaN values\n",
    "\t\tmask = np.isnan(y)\n",
    "\t\ty[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), y[~mask])\n",
    "\n",
    "\t\t# Save slice\n",
    "\t\tY[:, i] = y\n",
    "\n",
    "\t# Restore to initial shape.\n",
    "\tY = Y.reshape(initial_shape)\n",
    "\n",
    "\treturn Y\n",
    "\n",
    "def WhittakerSmooth(x,w,lambda_,differences=1):\n",
    "\t'''\n",
    "\tPenalized least squares algorithm for background fitting\n",
    "\n",
    "\tinput\n",
    "\t\tx: input data (i.e. chromatogram of spectrum)\n",
    "\t\tw: binary masks (value of the mask is zero if a point belongs to peaks and one otherwise)\n",
    "\t\tlambda_: parameter that can be adjusted by user. The larger lambda is,  the smoother the resulting background\n",
    "\t\tdifferences: integer indicating the order of the difference of penalties\n",
    "\n",
    "\toutput\n",
    "\t\tthe fitted background vector\n",
    "\t'''\n",
    "\tX=np.matrix(x)\n",
    "\tm=X.size\n",
    "\ti=np.arange(0,m)\n",
    "\tE=eye(m,format='csc')\n",
    "\tD=E[1:]-E[:-1] # numpy.diff() does not work with sparse matrix. This is a workaround.\n",
    "\tW=diags(w,0,shape=(m,m))\n",
    "\tA=csc_matrix(W+(lambda_*D.T*D))\n",
    "\tB=csc_matrix(W*X.T)\n",
    "\tbackground=spsolve(A,B)\n",
    "\treturn np.array(background)\n",
    "\n",
    "def airPLS(x, lambda_=50000, porder=1, itermax=15):\n",
    "\t'''\n",
    "\tAdaptive iteratively reweighted penalized least squares for baseline fitting\n",
    "\n",
    "\tinput\n",
    "\t\tx: input data (i.e. chromatogram of spectrum)\n",
    "\t\tlambda_: parameter that can be adjusted by user. The larger lambda is,  the smoother the resulting background, z\n",
    "\t\tporder: adaptive iteratively reweighted penalized least squares for baseline fitting\n",
    "\n",
    "\toutput\n",
    "\t\tthe fitted background vector\n",
    "\t'''\n",
    "\tm=x.shape[0]\n",
    "\tw=np.ones(m)\n",
    "\tfor i in range(1,itermax+1):\n",
    "\t\tz=WhittakerSmooth(x,w,lambda_, porder)\n",
    "\t\td=x-z\n",
    "\t\tdssn=np.abs(d[d<0].sum())\n",
    "\t\tif(dssn<0.001*(abs(x)).sum() or i==itermax):\n",
    "\t\t\tbreak\n",
    "\t\tw[d>=0]=0 # d>0 means that this point is part of a peak, so its weight is set to 0 in order to ignore it\n",
    "\t\tw[d<0]=np.exp(i*np.abs(d[d<0])/dssn)\n",
    "\t\tw[0]=np.exp(i*(d[d<0]).max()/dssn) \n",
    "\t\tw[-1]=w[0]\n",
    "\n",
    "\treturn z\n",
    "\n",
    "def replace_below_median_with_nan(y, factor=1):\n",
    "\t# Compute the median and standard deviation of the time series\n",
    "\ty_median = np.median(y)\n",
    "\ty_std = np.std(y)\n",
    "\n",
    "\t# Identify the indices where the time series is 2 standard deviations below the median\n",
    "\tidx = np.where(y < y_median - factor * y_std)[0]\n",
    "\n",
    "\t# Replace these indices with NaN values\n",
    "\ty_with_nan = y.copy()\n",
    "\ty_with_nan[idx] = np.nan\n",
    "\n",
    "\treturn fill_missing(y_with_nan)\n",
    "\n",
    "def motion_artifact_correction(predictors, ncamp, column):\n",
    "\t'''  \n",
    "\tinits a support vector regressor using a radial basis function (nonlinear SVR) and \n",
    "\tperforms grid search over some params of interest, fitting the model that best fits the isosbestic \n",
    "\tto the photometry trace/ then once this trace is predicted, it is used to subtract from the real signal\n",
    "\t'''\n",
    "\t\n",
    "\tpredictors = predictors.to_numpy()\n",
    "\n",
    "\talphas = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
    "\t\n",
    "\t# initialize the best score and best model\n",
    "\tbest_score = float('inf')\n",
    "\tbest_model = None\n",
    "\t\n",
    "\tdef fit_model(a):\n",
    "\t\t\n",
    "\t\t# define the regression model\n",
    "\t\tregressor = Lasso(alpha=a, precompute=True, max_iter=4500, positive=True, \n",
    "\t\t\t\t\t\t  random_state=9999, selection='random')\n",
    "\n",
    "\t\t# fit the pipeline to the training data\n",
    "\t\tregressor.fit(predictors, ncamp)\n",
    "\n",
    "\t\t# evaluate the pipeline on the training data\n",
    "\t\ty_pred_train = regressor.predict(predictors)\n",
    "\t\tmse_train = mean_squared_error(ncamp, y_pred_train)\n",
    "\t\t\n",
    "\t\treturn a, mse_train, regressor\n",
    "\n",
    "\t# run loop in parallel\n",
    "\tresults = Parallel(n_jobs=-1)(delayed(fit_model)(a) for a in alphas)\n",
    "\n",
    "\t# update the best model if necessary\n",
    "\tbest_score = float('inf')\n",
    "\tbest_model = None\n",
    "\tfor a, mse_train, regressor in results:\n",
    "\t\tif mse_train < best_score:\n",
    "\t\t\tbest_score = mse_train\n",
    "\t\t\tbest_model = regressor\n",
    "\tprint('Best score: %f' % best_score)\n",
    "\tif best_score < 0.6:\n",
    "\t\tprint('Consider removing %s from your dataset' % column)\n",
    "\t\t\t\t\n",
    "\t# predict A from B using the best estimator\n",
    "\tpredicted_ncamp = best_model.predict(predictors)\n",
    "\n",
    "\t# subtract the predicted A from B to obtain the residual\n",
    "\tresidual = ncamp - predicted_ncamp\n",
    "\t\n",
    "\treturn residual\n",
    "\n",
    "def denoise(s):\n",
    "\ts = savgol_filter(s, 17, 3) # window size 17, polynomial order 3\n",
    "\treturn s\n",
    "\n",
    "def format_photometry(file):\n",
    "\t'''\n",
    "\timplements whole pre-processing pipeline for photometry\n",
    "\tloads data > performs baseline subtraction using airPLS algorithm > standardizes DFF > regresses out motion artifacts > smooths\n",
    "\t'''\n",
    "\t\n",
    "\t# load data\n",
    "\tdata = file.iloc[np.where(file['Input0'] == 1)[0]]\n",
    "\tgreen_isos = pd.DataFrame()\n",
    "\tred_isos = pd.DataFrame()\n",
    "\tgreen_dffs = pd.DataFrame()\n",
    "\tred_dffs = pd.DataFrame()\n",
    "\n",
    "\tfor col in [col for col in data.columns if 'Region' in col]:\n",
    "\n",
    "\t\tif 'R' in col and 'G' not in col:\n",
    "\n",
    "\t\t\tidx = 4\n",
    "\t\t\tisos = col\n",
    "\n",
    "\t\telif 'G' in col:\n",
    "\n",
    "\t\t\tidx = 2\n",
    "\t\t\tisos = col\n",
    "\t\t\n",
    "\t\t### init signal and isosbestic time series\n",
    "\t\tncamp = data[col].iloc[np.where(data['LedState']==idx)[0]].values # rcamp = 4; isosbestic in this channel = 1\n",
    "\t\tviolet = data[isos].iloc[np.where(data['LedState']==1)[0]].values\n",
    "\t\t\n",
    "\t\t###  baseline subtraction\n",
    "\t\n",
    "\t\t# procedure here involves doing bsc once, removing negative outliers that might affect baseline calculation, then\n",
    "\t\t# repeating this process to get a more accurate correction\n",
    "\t\t\n",
    "\t\tncamp_placeholder = np.zeros((len(ncamp)))\n",
    "\t\tviolet_placeholder = np.zeros((len(violet)))\n",
    "\t\t\n",
    "\t\tseg_start=2200\n",
    "\t\tdecay_remove = 600\n",
    "\n",
    "\t\t# separately do bsc for baseline and interaction\n",
    "\t\t\n",
    "\t\t# interaction ncamp\n",
    "\t\tncamp_placeholder[seg_start:] = ncamp[seg_start:] - airPLS(ncamp[seg_start:], lambda_=25000, porder=9)\n",
    "\t\tncamp_placeholder[seg_start:] = replace_below_median_with_nan(ncamp_placeholder[seg_start:], factor=3)\n",
    "\t\tncamp_placeholder[seg_start:] = ncamp_placeholder[seg_start:] - airPLS(ncamp_placeholder[seg_start:], lambda_=25000, porder=9)\n",
    "\n",
    "\t\t# baseline ncamp\n",
    "\t\tncamp_placeholder[:seg_start] = ncamp[:seg_start] - airPLS(ncamp[:seg_start], lambda_=250, porder=9)\n",
    "\t\tncamp_placeholder[:seg_start] = replace_below_median_with_nan(ncamp_placeholder[:seg_start], factor=1.5)\n",
    "\t\tncamp_placeholder[:decay_remove] = np.repeat(np.mean(ncamp_placeholder[decay_remove:decay_remove*2]), decay_remove) # remove large decay values from first frames\n",
    "\t\tncamp_placeholder[:seg_start] = ncamp_placeholder[:seg_start] - airPLS(ncamp_placeholder[:seg_start], lambda_=250, porder=9)\n",
    "\t\tncamp_placeholder[:decay_remove] = np.repeat(np.mean(ncamp_placeholder[decay_remove:decay_remove*2]), decay_remove) # remove large decay values from first frames\n",
    "\t\tncamp = ncamp_placeholder\n",
    "\n",
    "\t\tviolet = violet - airPLS(violet, lambda_=25000, porder=9)\n",
    "\t\tviolet = replace_below_median_with_nan(violet, factor=3)\n",
    "\t\tviolet[:decay_remove] = np.repeat(np.mean(violet[decay_remove:decay_remove*2]), decay_remove) # remove large decay values from first frames\n",
    "\t\tviolet = violet - airPLS(violet, lambda_=25000, porder=9) \n",
    "\n",
    "\t\t### DF/F calculation -- using mean of baseline period to normalize\n",
    "\t\tncamp = (ncamp - np.mean(ncamp[:2200])) / np.abs(np.mean(ncamp[:2200]))\n",
    "\t\tviolet = (violet - np.mean(violet[:2200])) / np.abs(np.mean(violet[:2200]))\n",
    "\n",
    "\t\t### z-score standardization\n",
    "\t\tviolet = (violet - np.median(violet)) / np.std(violet)\n",
    "\t\tncamp = (ncamp - np.median(ncamp)) / np.std(ncamp)\n",
    "\t\t\n",
    "\t\t### correct time series shapes\n",
    "\n",
    "\t\tif ncamp.shape[0] > violet.shape[0]:\n",
    "\n",
    "\t\t\tncamp = ncamp[:-1]\n",
    "\n",
    "\t\telif ncamp.shape[0] < violet.shape[0]:\n",
    "\n",
    "\t\t\tpad = np.zeros((violet.shape[0] - ncamp.shape[0]))\n",
    "\t\t\tpad[:] = np.nan\n",
    "\t\t\tncamp = np.concatenate([ncamp, pad])\n",
    "\t\t\tncamp = fill_missing(ncamp)\n",
    "\n",
    "\t\tif 'R' in col and 'G' not in col:\n",
    "\t\t\tred_dffs[col] = ncamp\n",
    "\t\t\tred_isos[col] = violet\n",
    "\t\telse:\n",
    "\t\t\tgreen_isos[col] = violet\n",
    "\t\t\tgreen_dffs[col] = ncamp\n",
    "\t\t\t\n",
    "\tall_predictors = pd.concat([green_isos, red_isos], axis=1)\n",
    "\tall_dffs = pd.concat([green_dffs, red_dffs], axis=1)\n",
    "\t\t\n",
    "\treturn all_dffs, all_predictors\n",
    "\t\t  \n",
    "def process_photometry(all_dffs, all_predictors, green_ROIs, red_ROIs):\n",
    "\t\n",
    "\trois = np.concatenate([green_ROIs, red_ROIs])\n",
    "\tproc_dffs = pd.DataFrame()\n",
    "\t\n",
    "\tfor col in all_dffs.columns: \n",
    "\t\t\n",
    "\t\t# regress out motion artifacts -- lasso regression trained on all isosbestic traces to predict signal of interest; does grid search for best regularization term\n",
    "\t\tcorrected_ncamp = motion_artifact_correction(all_predictors, all_dffs[col], col)\n",
    "\t\t# smooth\n",
    "\t\tcorrected_ncamp = denoise(corrected_ncamp)\n",
    "\t\t\n",
    "\t\t# add to final array\n",
    "\t\tproc_dffs[col] = corrected_ncamp\n",
    "\t\n",
    "\ttry:\n",
    "\t\tproc_dffs.columns = rois\n",
    "\texcept ValueError:\n",
    "\t\tproc_dffs = proc_dffs.iloc[:, :-1]\n",
    "\t\tproc_dffs.columns = rois\n",
    "\t\n",
    "\treturn proc_dffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2e128c",
   "metadata": {},
   "source": [
    "### Comparisons for JOVE preprint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba21e05",
   "metadata": {},
   "source": [
    "Making the case for regression-based corrections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9482430",
   "metadata": {},
   "source": [
    "Formatting and loading functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa03d66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from joblib import Parallel, delayed\n",
    "from scipy.signal import savgol_filter\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "def fill_missing(Y, kind='linear'):\n",
    "\n",
    "\tinitial_shape = Y.shape\n",
    "\tY = Y.reshape((initial_shape[0], -1))\n",
    "\tfor i in range(Y.shape[-1]):\n",
    "\t\ty = Y[:,i]\n",
    "\n",
    "\t\t# Build interpolant.\n",
    "\t\tx = np.flatnonzero(~np.isnan(y))\n",
    "\t\tf = interp1d(x, y[x], kind=kind, fill_value=np.nan, bounds_error=False)\n",
    "\n",
    "\t\t# Fill missing\n",
    "\t\txq = np.flatnonzero(np.isnan(y))\n",
    "\t\ty[xq] = f(xq)\n",
    "\n",
    "\t\t# Fill leading or trailing NaNs with the nearest non-NaN values\n",
    "\t\tmask = np.isnan(y)\n",
    "\t\ty[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), y[~mask])\n",
    "\n",
    "\t\t# Save slice\n",
    "\t\tY[:, i] = y\n",
    "\n",
    "\t# Restore to initial shape.\n",
    "\tY = Y.reshape(initial_shape)\n",
    "\n",
    "\treturn Y\n",
    "\n",
    "def WhittakerSmooth(x,w,lambda_,differences=1):\n",
    "\t'''\n",
    "\tPenalized least squares algorithm for background fitting\n",
    "\n",
    "\tinput\n",
    "\t\tx: input data (i.e. chromatogram of spectrum)\n",
    "\t\tw: binary masks (value of the mask is zero if a point belongs to peaks and one otherwise)\n",
    "\t\tlambda_: parameter that can be adjusted by user. The larger lambda is,  the smoother the resulting background\n",
    "\t\tdifferences: integer indicating the order of the difference of penalties\n",
    "\n",
    "\toutput\n",
    "\t\tthe fitted background vector\n",
    "\t'''\n",
    "\tX=np.matrix(x)\n",
    "\tm=X.size\n",
    "\ti=np.arange(0,m)\n",
    "\tE=eye(m,format='csc')\n",
    "\tD=E[1:]-E[:-1] # numpy.diff() does not work with sparse matrix. This is a workaround.\n",
    "\tW=diags(w,0,shape=(m,m))\n",
    "\tA=csc_matrix(W+(lambda_*D.T*D))\n",
    "\tB=csc_matrix(W*X.T)\n",
    "\tbackground=spsolve(A,B)\n",
    "\treturn np.array(background)\n",
    "\n",
    "def airPLS(x, lambda_=50000, porder=1, itermax=15):\n",
    "\t'''\n",
    "\tAdaptive iteratively reweighted penalized least squares for baseline fitting\n",
    "\n",
    "\tinput\n",
    "\t\tx: input data (i.e. chromatogram of spectrum)\n",
    "\t\tlambda_: parameter that can be adjusted by user. The larger lambda is,  the smoother the resulting background, z\n",
    "\t\tporder: adaptive iteratively reweighted penalized least squares for baseline fitting\n",
    "\n",
    "\toutput\n",
    "\t\tthe fitted background vector\n",
    "\t'''\n",
    "\tm=x.shape[0]\n",
    "\tw=np.ones(m)\n",
    "\tfor i in range(1,itermax+1):\n",
    "\t\tz=WhittakerSmooth(x,w,lambda_, porder)\n",
    "\t\td=x-z\n",
    "\t\tdssn=np.abs(d[d<0].sum())\n",
    "\t\tif(dssn<0.001*(abs(x)).sum() or i==itermax):\n",
    "\t\t\tbreak\n",
    "\t\tw[d>=0]=0 # d>0 means that this point is part of a peak, so its weight is set to 0 in order to ignore it\n",
    "\t\tw[d<0]=np.exp(i*np.abs(d[d<0])/dssn)\n",
    "\t\tw[0]=np.exp(i*(d[d<0]).max()/dssn) \n",
    "\t\tw[-1]=w[0]\n",
    "\n",
    "\treturn z\n",
    "\n",
    "def denoise(s):\n",
    "\ts = savgol_filter(s, 29, 3) # window size 35, polynomial order 3\n",
    "\treturn s\n",
    "\n",
    "def replace_below_median_with_nan(y, factor=1):\n",
    "\t# Compute the median and standard deviation of the time series\n",
    "\ty_median = np.median(y)\n",
    "\ty_std = np.std(y)\n",
    "\n",
    "\t# Identify the indices where the time series is 2 standard deviations below the median\n",
    "\tidx = np.where(y < y_median - factor * y_std)[0]\n",
    "\n",
    "\t# Replace these indices with NaN values\n",
    "\ty_with_nan = y.copy()\n",
    "\ty_with_nan[idx] = np.nan\n",
    "\n",
    "\treturn fill_missing(y_with_nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c586f9ec",
   "metadata": {},
   "source": [
    "Regressions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b2708e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def motion_artifact_correction(predictors, ncamp, column):\n",
    "\t'''  \n",
    "\tinits a support vector regressor using a radial basis function (nonlinear SVR) and \n",
    "\tperforms grid search over some params of interest, fitting the model that best fits the isosbestic \n",
    "\tto the photometry trace/ then once this trace is predicted, it is used to subtract from the real signal\n",
    "\t'''\n",
    "\t\n",
    "\tpredictors = predictors.to_numpy()\n",
    "\n",
    "\talphas = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
    "\t\n",
    "\t# initialize the best score and best model\n",
    "\tbest_score = float('inf')\n",
    "\tbest_model = None\n",
    "\t\n",
    "\tdef fit_model(a):\n",
    "\t\t\n",
    "\t\t# define the regression model\n",
    "\t\tregressor = Lasso(alpha=a, precompute=True, max_iter=4500, positive=True, \n",
    "\t\t\t\t\t\t  random_state=9999, selection='random')\n",
    "\n",
    "\t\t# fit the pipeline to the training data\n",
    "\t\tregressor.fit(predictors, ncamp)\n",
    "\n",
    "\t\t# evaluate the pipeline on the training data\n",
    "\t\ty_pred_train = regressor.predict(predictors)\n",
    "\t\tmse_train = mean_squared_error(ncamp, y_pred_train)\n",
    "\t\t\n",
    "\t\treturn a, mse_train, regressor\n",
    "\n",
    "\t# run loop in parallel\n",
    "\tresults = Parallel(n_jobs=-1)(delayed(fit_model)(a) for a in alphas)\n",
    "\n",
    "\t# update the best model if necessary\n",
    "\tbest_score = float('inf')\n",
    "\tbest_model = None\n",
    "\tfor a, mse_train, regressor in results:\n",
    "\t\tif mse_train < best_score:\n",
    "\t\t\tbest_score = mse_train\n",
    "\t\t\tbest_model = regressor\n",
    "\n",
    "\t\t\t\t\n",
    "\t# predict A from B using the best estimator\n",
    "\tpredicted_ncamp = best_model.predict(predictors)\n",
    "\n",
    "\t# subtract the predicted A from B to obtain the residual\n",
    "\tresidual = ncamp - predicted_ncamp\n",
    "\t\n",
    "\treturn residual\n",
    "\n",
    "def motion_artifact_correction_single(predictors, ncamp, column):\n",
    "\t'''  \n",
    "\tPerforms a simple Lasso regression to model motion artifacts in the isosbestic signal\n",
    "\tand subtracts the predicted noise from the real signal to obtain the residual.\n",
    "\t'''\n",
    "\t\n",
    "\t# define the regression model with a single alpha value\n",
    "\tregressor = Lasso(alpha=0.01, precompute=True, max_iter=4500, positive=True, \n",
    "\t\t\t\t\t  random_state=9999, selection='random')\n",
    "\n",
    "\t# fit the model to the data\n",
    "\tregressor.fit(predictors, ncamp)\n",
    "\n",
    "\t# predict the noise component\n",
    "\tpredicted_ncamp = regressor.predict(predictors)\n",
    "\n",
    "\t# subtract the predicted noise from the signal to obtain the residual\n",
    "\tresidual = ncamp - predicted_ncamp\n",
    "\t\n",
    "\treturn residual\n",
    "\n",
    "def format_photometry(file):\n",
    "\t'''\n",
    "\timplements whole pre-processing pipeline for photometry\n",
    "\tloads data > performs baseline subtraction using airPLS algorithm > standardizes DFF > regresses out motion artifacts > smooths\n",
    "\t'''\n",
    "\t\n",
    "\t# load data\n",
    "\tdata = file.iloc[np.where(file['Input0'] == 1)[0]]\n",
    "\tgreen_isos = pd.DataFrame()\n",
    "\tred_isos = pd.DataFrame()\n",
    "\tgreen_dffs = pd.DataFrame()\n",
    "\tred_dffs = pd.DataFrame()\n",
    "\n",
    "\tfor col in [col for col in data.columns if 'Region' in col]:\n",
    "\n",
    "\t\tif 'R' in col and 'G' not in col:\n",
    "\n",
    "\t\t\tidx = 4\n",
    "\t\t\tisos = col\n",
    "\n",
    "\t\telif 'G' in col:\n",
    "\n",
    "\t\t\tidx = 2\n",
    "\t\t\tisos = col\n",
    "\t\t\n",
    "\t\t### init signal and isosbestic time series\n",
    "\t\tncamp = data[col].iloc[np.where(data['LedState']==idx)[0]].values # rcamp = 4; isosbestic in this channel = 1\n",
    "\t\tviolet = data[isos].iloc[np.where(data['LedState']==1)[0]].values\n",
    "\t\t\n",
    "\t\t###  baseline subtraction\n",
    "\t\n",
    "\t\t# procedure here involves doing bsc once, removing negative outliers that might affect baseline calculation, then\n",
    "\t\t# repeating this process to get a more accurate correction\n",
    "\t\t\n",
    "\t\tncamp_placeholder = np.zeros((len(ncamp)))\n",
    "\t\tviolet_placeholder = np.zeros((len(violet)))\n",
    "\t\t\n",
    "\t\tseg_start=2200\n",
    "\t\tdecay_remove = 600\n",
    "\n",
    "\t\t# separately do bsc for baseline and interaction\n",
    "\t\t\n",
    "\t\t# interaction ncamp\n",
    "\t\tncamp_placeholder[seg_start:] = ncamp[seg_start:] - airPLS(ncamp[seg_start:], lambda_=25000, porder=9)\n",
    "\t\tncamp_placeholder[seg_start:] = replace_below_median_with_nan(ncamp_placeholder[seg_start:], factor=3)\n",
    "\t\tncamp_placeholder[seg_start:] = ncamp_placeholder[seg_start:] - airPLS(ncamp_placeholder[seg_start:], lambda_=25000, porder=9)\n",
    "\n",
    "\t\t# baseline ncamp\n",
    "\t\tncamp_placeholder[:seg_start] = ncamp[:seg_start] - airPLS(ncamp[:seg_start], lambda_=250, porder=9)\n",
    "\t\tncamp_placeholder[:seg_start] = replace_below_median_with_nan(ncamp_placeholder[:seg_start], factor=1.5)\n",
    "\t\tncamp_placeholder[:decay_remove] = np.repeat(np.mean(ncamp_placeholder[decay_remove:decay_remove*2]), decay_remove) # remove large decay values from first frames\n",
    "\t\tncamp_placeholder[:seg_start] = ncamp_placeholder[:seg_start] - airPLS(ncamp_placeholder[:seg_start], lambda_=250, porder=9)\n",
    "\t\tncamp_placeholder[:decay_remove] = np.repeat(np.mean(ncamp_placeholder[decay_remove:decay_remove*2]), decay_remove) # remove large decay values from first frames\n",
    "\t\tncamp = ncamp_placeholder\n",
    "\n",
    "\t\tviolet = violet - airPLS(violet, lambda_=25000, porder=9)\n",
    "\t\tviolet = replace_below_median_with_nan(violet, factor=3)\n",
    "\t\tviolet[:decay_remove] = np.repeat(np.mean(violet[decay_remove:decay_remove*2]), decay_remove) # remove large decay values from first frames\n",
    "\t\tviolet = violet - airPLS(violet, lambda_=25000, porder=9) \n",
    "\n",
    "\t\t### DF/F calculation -- using mean of baseline period to normalize\n",
    "\t\tncamp = (ncamp - np.mean(ncamp)) / np.abs(np.mean(ncamp))\n",
    "\t\tviolet = (violet - np.mean(violet)) / np.abs(np.mean(violet))\n",
    "\n",
    "\t\t### z-score standardization\n",
    "\t\tviolet = (violet - np.median(violet)) / np.std(violet)\n",
    "\t\tncamp = (ncamp - np.median(ncamp)) / np.std(ncamp)\n",
    "\t\t\n",
    "\t\t### correct time series shapes\n",
    "\n",
    "\t\tif ncamp.shape[0] > violet.shape[0]:\n",
    "\n",
    "\t\t\tncamp = ncamp[:-1]\n",
    "\n",
    "\t\telif ncamp.shape[0] < violet.shape[0]:\n",
    "\n",
    "\t\t\tpad = np.zeros((violet.shape[0] - ncamp.shape[0]))\n",
    "\t\t\tpad[:] = np.nan\n",
    "\t\t\tncamp = np.concatenate([ncamp, pad])\n",
    "\t\t\tncamp = fill_missing(ncamp)\n",
    "\n",
    "\t\tif 'R' in col and 'G' not in col:\n",
    "\t\t\tred_dffs[col] = ncamp\n",
    "\t\t\tred_isos[col] = violet\n",
    "\t\telse:\n",
    "\t\t\tgreen_isos[col] = violet\n",
    "\t\t\tgreen_dffs[col] = ncamp\n",
    "\t\t\t\n",
    "\tall_predictors = pd.concat([green_isos, red_isos], axis=1)\n",
    "\tall_dffs = pd.concat([green_dffs, red_dffs], axis=1)\n",
    "\t\t\n",
    "\treturn all_dffs, all_predictors\n",
    "\t\t  \n",
    "def process_photometry(all_dffs, all_predictors, green_ROIs, red_ROIs, multi=True):\n",
    "\t\n",
    "\trois = np.concatenate([green_ROIs, red_ROIs])\n",
    "\tproc_dffs = pd.DataFrame()\n",
    "\t\n",
    "\tfor col in all_dffs.columns: \n",
    "\t\t\n",
    "\t\tif multi == True:\n",
    "\t\t\t# regress out motion artifacts -- lasso regression trained on all isosbestic traces to predict signal of interest; does grid search for best regularization term\n",
    "\t\t\tcorrected_ncamp = motion_artifact_correction(all_predictors, all_dffs[col], col)\n",
    "\t\t\t# smooth\n",
    "\t\t\tcorrected_ncamp = denoise(corrected_ncamp)\n",
    "\t\t\t# add to final array\n",
    "\t\t\tproc_dffs[col] = corrected_ncamp\n",
    "\t\telse:\n",
    "\t\t\t# regress out motion artifacts -- lasso regression trained on isosbestic trace from same channel only\n",
    "\t\t\tcorrected_ncamp = motion_artifact_correction_single(all_predictors[col].values.reshape(-1,1), all_dffs[col].values, col)\n",
    "\t\t\t# smooth\n",
    "\t\t\tcorrected_ncamp = denoise(corrected_ncamp)\n",
    "\t\t\t# add to final array\n",
    "\t\t\tproc_dffs[col] = corrected_ncamp\n",
    "\t\n",
    "\ttry:\n",
    "\t\tproc_dffs.columns = rois\n",
    "\texcept ValueError:\n",
    "\t\tproc_dffs = proc_dffs.iloc[:, :-1]\n",
    "\t\tproc_dffs.columns = rois\n",
    "\t\n",
    "\treturn proc_dffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5601c685",
   "metadata": {},
   "source": [
    "Just smoothing (control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08a5650a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import medfilt\n",
    "\n",
    "def just_smooth(all_dffs, all_predictors, green_ROIs, red_ROIs):\n",
    "\t\n",
    "\trois = np.concatenate([green_ROIs, red_ROIs])\n",
    "\tproc_dffs = pd.DataFrame()\n",
    "\t\n",
    "\tfor col in all_dffs.columns: \n",
    "\t\t# smooth\n",
    "\t\tcorrected_ncamp = medfilt(all_dffs[col], kernel_size=11)\n",
    "\t\t\n",
    "\t\t# add to final array\n",
    "\t\tproc_dffs[col] = corrected_ncamp\n",
    "\t\n",
    "\ttry:\n",
    "\t\tproc_dffs.columns = rois\n",
    "\texcept ValueError:\n",
    "\t\tproc_dffs = proc_dffs.iloc[:, :-1]\n",
    "\t\tproc_dffs.columns = rois\n",
    "\t\n",
    "\treturn proc_dffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aafe5f61",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b2f64dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cohort 1\n",
    "green_ROIs0 = ['PrL (E)', 'BNST (E)', 'POA (E)', 'AH (E)', 'vLS (E)', 'VMH (E)', 'PAG (E)', 'MeA (E)', 'LHb (E)', \n",
    "\t\t\t  'PA (E)', 'NAc (DA)', 'PMv (E)']\n",
    "red_ROIs0 = ['PrL (I)', 'BNST (I)', 'POA (I)', 'AH (I)', 'vLS (I)', 'VMH (I)', 'PAG (I)', 'MeA (I)', 'LHb (I)', \n",
    "\t\t\t  'PA (I)', 'NAc (I)', 'PMv (I)']\n",
    "\n",
    "# cohort2\n",
    "green_ROIs1 = ['PrL (E)', 'BNST (E)', 'POA (E)', 'AH (E)', 'vLS (E)', 'VMH (E)', 'PAG (E)', 'MeA (E)', 'LHb (E)', \n",
    "\t\t\t  'PA (E)', 'PMv (E)', 'NAc (DA)']\n",
    "red_ROIs1 = ['PrL (I)', 'BNST (I)', 'POA (I)', 'AH (I)', 'vLS (I)', 'VMH (I)', 'PAG (I)', 'MeA (I)', 'LHb (I)', \n",
    "\t\t\t  'PA (I)', 'PMv (I)', 'NAc (I)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb774516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 3095_d2_balbc_t1_npm.csv...\n",
      "Processing 3095_d2_balbc_t2_npm.csv...\n",
      "Processing 3095_d2_balbc_t3_npm.csv...\n",
      "Processing 3095_d5_balbc_t1_npm.csv...\n",
      "Processing 3095_d5_balbc_t2_npm.csv...\n",
      "Processing 3095_d5_balbc_t3_npm.csv...\n",
      "Processing 3095_d7_balbc_t1_npm.csv...\n",
      "Processing 3095_d7_balbc_t2_npm.csv...\n",
      "Processing 3095_d7_balbc_t3_npm.csv...\n",
      "Processing 30L_d3_balbc_t1_npm.csv...\n",
      "Processing 30L_d3_balbc_t2_npm.csv...\n",
      "Processing 30L_d3_balbc_t3_npm.csv...\n",
      "Processing 91R2_d1_balbc_t1_npm.csv...\n",
      "Processing 91R2_d1_balbc_t2_npm.csv...\n",
      "Processing 91R2_d1_balbc_t3_npm.csv...\n",
      "Processing 91R2_d2_balbc_t1_npm.csv...\n",
      "Processing 91R2_d2_balbc_t2_npm.csv...\n",
      "Processing 91R2_d2_balbc_t3_npm.csv...\n"
     ]
    }
   ],
   "source": [
    "### Iterate through sample signals, save raw and corrected signals\n",
    "import time\n",
    "sample_signals = ['.\\\\sample_signals\\\\' + x for x in os.listdir('.\\\\sample_signals\\\\') if 'csv' in x]\n",
    "reg_raw_signals = {}\n",
    "reg_corrected_signals = {}\n",
    "sing_corrected_signals = {}\n",
    "reg_onlySmoothed_signals = {}\n",
    "isosbestics = {}\n",
    "processing_times = {}\n",
    "\n",
    "for file in sample_signals:\n",
    "\t\n",
    "\tif '3095' in file:\n",
    "\t\tgreen = green_ROIs0\n",
    "\t\tred = red_ROIs0\n",
    "\telse:\n",
    "\t\tgreen = green_ROIs1\n",
    "\t\tred = red_ROIs1\n",
    "\n",
    "\tnpm = file.split('\\\\')[-1]\n",
    "\tprint('Processing %s...' % npm)\n",
    "\t\n",
    "\t# Load data\n",
    "\ttrs = pd.read_csv(file)\n",
    "\n",
    "\t# Process with regressions (multi)\n",
    "\treg_dffs, isos = format_photometry(trs)\n",
    "\treg_signals = process_photometry(reg_dffs, isos, green, red)\n",
    "\n",
    "\t# Process with regressions (single)\n",
    "\tsingle_signals = process_photometry(reg_dffs, isos, green, red, multi=False)\n",
    "\n",
    "\t# Process with smoothing only\n",
    "\tonlySmoothed_signals = just_smooth(reg_dffs, isos, green, red)\n",
    "\n",
    "\t# Save to dictionaries\n",
    "\treg_raw_signals[npm] = reg_dffs\n",
    "\treg_corrected_signals[npm] = reg_signals\n",
    "\tsing_corrected_signals[npm] = single_signals\n",
    "\treg_onlySmoothed_signals[npm] = onlySmoothed_signals\n",
    "\tisosbestics[npm] = isos\n",
    "\n",
    "\n",
    "# Assign proper column names to all dataframes\n",
    "for key in reg_raw_signals.keys():\n",
    "\tdf1 = reg_raw_signals[key]\n",
    "\tdf2 = isosbestics[key]\n",
    "\tif  '3095' in key:\n",
    "\t\tdf1.columns = green_ROIs0 + red_ROIs0\n",
    "\t\tdf2.columns = green_ROIs0 + red_ROIs0\n",
    "\telse:\n",
    "\t\tdf1.columns = green_ROIs1 + red_ROIs1\n",
    "\t\tdf2.columns = green_ROIs1 + red_ROIs1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336528f5",
   "metadata": {},
   "source": [
    "Example of traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "066ac988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roi = 'POA (I)'\n",
    "reg_signal = reg_corrected_signals['3095_d2_balbc_t3_npm.csv'][roi]\n",
    "smoothed_signal = reg_onlySmoothed_signals['3095_d2_balbc_t3_npm.csv'][roi]\n",
    "single_site_signal = sing_corrected_signals['3095_d2_balbc_t3_npm.csv'][roi]\n",
    "isos_signal = isosbestics['3095_d2_balbc_t3_npm.csv'][roi]\n",
    "\n",
    "# Plot intruder entry period\n",
    "t1 = 20000\n",
    "t2 = -1\n",
    "\n",
    "# Create time axis in seconds (divide by 40 Hz sampling rate)\n",
    "time_frames = np.arange(len(reg_signal[t1:t2]))\n",
    "time_seconds = time_frames / 40\n",
    "\n",
    "fig, ax = plt.subplots(4, 1, figsize=(6, 10), sharex=True)\n",
    "fs = 20\n",
    "# Isosbestic Signal\n",
    "ax[0].plot(time_seconds, isos_signal[t1:t2].values, color='purple', linewidth=1.5)\n",
    "ax[0].set_title('Isosbestic Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[0].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "\n",
    "# Smoothed Only Signal\n",
    "ax[1].plot(time_seconds, smoothed_signal[t1:t2].values, color='green', linewidth=1.5)\n",
    "ax[1].set_title('Smoothed Only Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[1].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "ax[1].set_ylim([-3, 6])\n",
    "\n",
    "# Single-site Regression Corrected Signal\n",
    "ax[2].plot(time_seconds, single_site_signal[t1:t2].values, color='cyan', linewidth=1.5)\n",
    "ax[2].set_title('Single-site Regression Corrected Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[2].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "ax[2].set_ylim([-3, 6])\n",
    "\n",
    "# Multi-site Corrected Signal\n",
    "ax[3].plot(time_seconds, reg_signal[t1:t2].values, color='blue', linewidth=1.5)\n",
    "ax[3].set_title('Multi-site Corrected Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[3].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "ax[3].set_ylim([-3, 6])\n",
    "\n",
    "# Make axes thicker and set font\n",
    "for axis in ax:\n",
    "\tfor spine in axis.spines.values():\n",
    "\t\tspine.set_linewidth(2)\n",
    "\taxis.tick_params(width=2, labelsize=fs)\n",
    "\t\n",
    "\t# Set tick labels to Arial\n",
    "\tfor label in axis.get_xticklabels() + axis.get_yticklabels():\n",
    "\t\tlabel.set_fontfamily('Arial')\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig('./figures/JOVE/signal_comparison_example.svg', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b91b8f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roi = 'PrL (E)'\n",
    "session = '3095_d2_balbc_t3_npm.csv'\n",
    "reg_signal = reg_corrected_signals[session][roi]\n",
    "smoothed_signal = reg_onlySmoothed_signals[session][roi]\n",
    "single_site_signal = sing_corrected_signals[session][roi]\n",
    "isos_signal = isosbestics[session][roi]\n",
    "\n",
    "# Plot intruder entry period\n",
    "t1 = 20000\n",
    "t2 = -1\n",
    "\n",
    "# Create time axis in seconds (divide by 40 Hz sampling rate)\n",
    "time_frames = np.arange(len(reg_signal[t1:t2]))\n",
    "time_seconds = time_frames / 40\n",
    "\n",
    "fig, ax = plt.subplots(4, 1, figsize=(6, 10), sharex=True)\n",
    "fs = 20\n",
    "# Isosbestic Signal\n",
    "ax[0].plot(time_seconds, isos_signal[t1:t2].values, color='purple', linewidth=1.5)\n",
    "ax[0].set_title('Isosbestic Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[0].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "\n",
    "# Smoothed Only Signal\n",
    "ax[1].plot(time_seconds, smoothed_signal[t1:t2].values, color='green', linewidth=1.5)\n",
    "ax[1].set_title('Smoothed Only Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[1].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "ax[1].set_ylim([-3, 6])\n",
    "\n",
    "# Single-site Regression Corrected Signal\n",
    "ax[2].plot(time_seconds, single_site_signal[t1:t2].values, color='cyan', linewidth=1.5)\n",
    "ax[2].set_title('Single-site Regression Corrected Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[2].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "ax[2].set_ylim([-3, 6])\n",
    "\n",
    "# Regression Corrected Signal\n",
    "ax[3].plot(time_seconds, reg_signal[t1:t2].values, color='blue', linewidth=1.5)\n",
    "ax[3].set_title('Multi-site Corrected Signal', fontsize=fs, fontfamily='Arial')\n",
    "ax[3].set_ylabel('Signal (z-score)', fontsize=fs, fontfamily='Arial')\n",
    "ax[3].set_ylim([-3, 6])\n",
    "\n",
    "\n",
    "# Make axes thicker and set font\n",
    "for axis in ax:\n",
    "\tfor spine in axis.spines.values():\n",
    "\t\tspine.set_linewidth(2)\n",
    "\taxis.tick_params(width=2, labelsize=fs)\n",
    "\t\n",
    "\t# Set tick labels to Arial\n",
    "\tfor label in axis.get_xticklabels() + axis.get_yticklabels():\n",
    "\t\tlabel.set_fontfamily('Arial')\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig('./figures/JOVE/signal_comparison2_example.svg', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2a3eeb",
   "metadata": {},
   "source": [
    "Do TMAC and regressions reduce noise in signal? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f1ae45e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noise statistics computed with scale-invariant metrics\n"
     ]
    }
   ],
   "source": [
    "def compute_noise_stats(signal):\n",
    "\t\"\"\"\n",
    "\tCompute various noise metrics for a signal, focusing on high-frequency noise\n",
    "\t\"\"\"\n",
    "\t# Standard deviation (for reference)\n",
    "\tstd = np.std(signal)\n",
    "\t\n",
    "\t# Variance\n",
    "\tvar = np.var(signal)\n",
    "\t\n",
    "\t# High-frequency noise metrics\n",
    "\t\n",
    "\t# 1. Standard deviation of first derivative (high-frequency noise)\n",
    "\tdiff_std = np.std(np.diff(signal))\n",
    "\t\n",
    "\t# 2. Coefficient of variation of derivative (scale-invariant)\n",
    "\t# Measures noise relative to signal magnitude using peak-to-peak range\n",
    "\tsignal_range = np.ptp(signal)  # peak-to-peak range\n",
    "\tcv_diff = diff_std / signal_range if signal_range > 0 else 0\n",
    "\t\n",
    "\t# 3. Median absolute deviation of derivative (robust to outliers)\n",
    "\tdiff = np.diff(signal)\n",
    "\tmad_diff = np.median(np.abs(diff - np.median(diff)))\n",
    "\t\n",
    "\t# 4. Root mean square of second derivative (measures \"jaggedness\")\n",
    "\tsecond_diff = np.diff(signal, n=2)\n",
    "\trms_second_diff = np.sqrt(np.mean(second_diff**2))\n",
    "\t\n",
    "\t# 5. Signal-to-noise ratio using high-frequency content\n",
    "\t# Signal power = variance of smoothed signal\n",
    "\t# Noise power = variance of (signal - smoothed)\n",
    "\tfrom scipy.signal import savgol_filter\n",
    "\tsmoothed = savgol_filter(signal, window_length=51, polyorder=3)\n",
    "\tnoise = signal - smoothed\n",
    "\tsnr_hf = np.var(smoothed) / np.var(noise) if np.var(noise) > 0 else np.inf\n",
    "\t\n",
    "\t# 6. Normalized RMS of derivative (scale-invariant measure using peak-to-peak range)\n",
    "\trms_diff = np.sqrt(np.mean(diff**2))\n",
    "\tnormalized_rms_diff = rms_diff / signal_range if signal_range > 0 else rms_diff\n",
    "\t\n",
    "\treturn {\n",
    "\t\t'std': std,\n",
    "\t\t'variance': var,\n",
    "\t\t'diff_std': diff_std,\n",
    "\t\t'cv_diff': cv_diff,  # KEY METRIC: scale-invariant high-freq noise\n",
    "\t\t'mad_diff': mad_diff,\n",
    "\t\t'rms_second_diff': rms_second_diff,\n",
    "\t\t'snr_hf': snr_hf,\n",
    "\t\t'normalized_rms_diff': normalized_rms_diff\n",
    "\t}\n",
    "\n",
    "# Re-compute noise statistics with new metrics\n",
    "noise_stats = {}\n",
    "\n",
    "for key in reg_raw_signals.keys():\n",
    "\t\n",
    "\tnoise_stats[key] = {}\n",
    "\t\n",
    "\treg_raw_df = reg_raw_signals[key]\n",
    "\treg_corr_df = reg_corrected_signals[key]\n",
    "\tsmooth_only_df = reg_onlySmoothed_signals[key]\n",
    "\tsingle_corr_df = sing_corrected_signals[key]\n",
    "\t\n",
    "\tfor col in reg_raw_df.columns:\n",
    "\t\t\n",
    "\t\tnoise_stats[key][col] = {\n",
    "\t\t\t'reg_raw': compute_noise_stats(reg_raw_df[col].values),\n",
    "\t\t\t'reg_corrected': compute_noise_stats(reg_corr_df[col].values),\n",
    "\t\t\t'reg_smoothed_only': compute_noise_stats(smooth_only_df[col].values),\n",
    "\t\t\t'single_site_corrected': compute_noise_stats(single_corr_df[col].values)\n",
    "\t\t}\n",
    "\t\t\n",
    "\t\t# Compute noise reduction using scale-invariant metric\n",
    "\t\treg_noise_reduction = (1 - noise_stats[key][col]['reg_corrected']['cv_diff'] / \n",
    "\t\t\t\t\t\t\t   noise_stats[key][col]['reg_raw']['cv_diff']) * 100\n",
    "\t\tsmooth_only_reduction = (1 - noise_stats[key][col]['reg_smoothed_only']['cv_diff'] / \n",
    "\t\t\t\t\t\t\t\t   noise_stats[key][col]['reg_raw']['cv_diff']) * 100\n",
    "\t\tsingle_site_reduction = (1 - noise_stats[key][col]['single_site_corrected']['cv_diff'] / \n",
    "\t\t\t\t\t\t\t\t   noise_stats[key][col]['reg_raw']['cv_diff']) * 100\n",
    "\t\t\n",
    "\t\tnoise_stats[key][col]['reg_noise_reduction_pct'] = reg_noise_reduction\n",
    "\n",
    "print(\"Noise statistics computed with scale-invariant metrics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad361ce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== AGGREGATED COMPARISON SAMPLE SIZES (cv_diff) ===\n",
      "  Smoothed:    n = 414 (files × signals, after removing NaN/Inf)\n",
      "  Single-site: n = 414\n",
      "  Multi-site:  n = 414\n",
      "  (Raw shape: 18 files × 23 signals = 414 total)\n",
      "\n",
      "============================================================\n",
      "Statistical Analysis for cv_diff\n",
      "============================================================\n",
      "\n",
      "Per-Signal Comparison: Single-site vs Multi-site\n",
      "============================================================\n",
      "Signal 1 (PrL (E)): t-test p = 0.5500 ns | No difference\n",
      "  Single-site mean: 0.006224\n",
      "  Multi-site mean:  0.006546\n",
      "  Mean difference:  0.000322\n",
      "Signal 2 (BNST (E)): t-test p = 0.7514 ns | No difference\n",
      "  Single-site mean: 0.007170\n",
      "  Multi-site mean:  0.006974\n",
      "  Mean difference:  -0.000196\n",
      "Signal 3 (POA (E)): t-test p = 0.9521 ns | No difference\n",
      "  Single-site mean: 0.005941\n",
      "  Multi-site mean:  0.005981\n",
      "  Mean difference:  0.000039\n",
      "Signal 4 (AH (E)): t-test p = 0.8027 ns | No difference\n",
      "  Single-site mean: 0.006036\n",
      "  Multi-site mean:  0.006166\n",
      "  Mean difference:  0.000130\n",
      "Signal 5 (vLS (E)): t-test p = 0.8487 ns | No difference\n",
      "  Single-site mean: 0.007237\n",
      "  Multi-site mean:  0.007389\n",
      "  Mean difference:  0.000152\n",
      "Signal 6 (VMH (E)): Mann-Whitney U p = 0.9370 ns | No difference\n",
      "  Single-site mean: 0.004012\n",
      "  Multi-site mean:  0.004007\n",
      "  Mean difference:  -0.000005\n",
      "Signal 7 (PAG (E)): Mann-Whitney U p = 0.9370 ns | No difference\n",
      "  Single-site mean: 0.007259\n",
      "  Multi-site mean:  0.007295\n",
      "  Mean difference:  0.000036\n",
      "Signal 8 (MeA (E)): t-test p = 0.8031 ns | No difference\n",
      "  Single-site mean: 0.006226\n",
      "  Multi-site mean:  0.006342\n",
      "  Mean difference:  0.000115\n",
      "Signal 9 (LHb (E)): Mann-Whitney U p = 0.7160 ns | No difference\n",
      "  Single-site mean: 0.009692\n",
      "  Multi-site mean:  0.009235\n",
      "  Mean difference:  -0.000456\n",
      "Signal 10 (PA (E)): t-test p = 0.7668 ns | No difference\n",
      "  Single-site mean: 0.005403\n",
      "  Multi-site mean:  0.005540\n",
      "  Mean difference:  0.000137\n",
      "Signal 11 (NAc (DA)): t-test p = 0.8629 ns | No difference\n",
      "  Single-site mean: 0.007518\n",
      "  Multi-site mean:  0.007634\n",
      "  Mean difference:  0.000117\n",
      "Signal 12 (PMv (E)): t-test p = 0.9595 ns | No difference\n",
      "  Single-site mean: 0.004846\n",
      "  Multi-site mean:  0.004825\n",
      "  Mean difference:  -0.000021\n",
      "Signal 13 (PrL (I)): t-test p = 0.5325 ns | No difference\n",
      "  Single-site mean: 0.004811\n",
      "  Multi-site mean:  0.005060\n",
      "  Mean difference:  0.000249\n",
      "Signal 14 (BNST (I)): t-test p = 0.0000 * | Multi > Single\n",
      "  Single-site mean: 0.009473\n",
      "  Multi-site mean:  0.006045\n",
      "  Mean difference:  -0.003428\n",
      "Signal 15 (POA (I)): t-test p = 0.0112 * | Multi > Single\n",
      "  Single-site mean: 0.008430\n",
      "  Multi-site mean:  0.006292\n",
      "  Mean difference:  -0.002138\n",
      "Signal 16 (AH (I)): t-test p = 0.0117 * | Multi > Single\n",
      "  Single-site mean: 0.007307\n",
      "  Multi-site mean:  0.005735\n",
      "  Mean difference:  -0.001572\n",
      "Signal 17 (vLS (I)): t-test p = 0.6112 ns | No difference\n",
      "  Single-site mean: 0.004682\n",
      "  Multi-site mean:  0.004330\n",
      "  Mean difference:  -0.000353\n",
      "Signal 18 (VMH (I)): t-test p = 0.7535 ns | No difference\n",
      "  Single-site mean: 0.005343\n",
      "  Multi-site mean:  0.005168\n",
      "  Mean difference:  -0.000174\n",
      "Signal 19 (PAG (I)): Mann-Whitney U p = 0.0002 * | Multi > Single\n",
      "  Single-site mean: 0.011000\n",
      "  Multi-site mean:  0.007975\n",
      "  Mean difference:  -0.003025\n",
      "Signal 20 (MeA (I)): t-test p = 0.5241 ns | No difference\n",
      "  Single-site mean: 0.006368\n",
      "  Multi-site mean:  0.006076\n",
      "  Mean difference:  -0.000292\n",
      "Signal 21 (LHb (I)): t-test p = 0.2059 ns | No difference\n",
      "  Single-site mean: 0.009116\n",
      "  Multi-site mean:  0.008128\n",
      "  Mean difference:  -0.000987\n",
      "Signal 22 (PA (I)): t-test p = 0.4146 ns | No difference\n",
      "  Single-site mean: 0.007443\n",
      "  Multi-site mean:  0.006864\n",
      "  Mean difference:  -0.000579\n",
      "Signal 23 (PMv (I)): t-test p = 0.6387 ns | No difference\n",
      "  Single-site mean: 0.006344\n",
      "  Multi-site mean:  0.006102\n",
      "  Mean difference:  -0.000242\n",
      "\n",
      "============================================================\n",
      "Summary:\n",
      "  Multi-site significantly better: 4 signals\n",
      "    Signals: 14, 15, 16, 19\n",
      "  Single-site significantly better: 0 signals\n",
      "  No significant difference: 19 signals\n",
      "    Signals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 20, 21, 22, 23\n",
      "\n",
      "============================================================\n",
      "Overall Group Normality Tests (Shapiro-Wilk):\n",
      "  Smoothed: p = 0.0000\n",
      "  Multi-site: p = 0.0000\n",
      "  Single-site: p = 0.0000\n",
      "\n",
      "Kruskal-Wallis H-test: H = 501.9420, p = 0.0000\n",
      "\n",
      "Kruskal-Wallis is significant. Performing post-hoc tests...\n",
      "Bonferroni-corrected alpha: 0.0167\n",
      "\n",
      "Post-hoc pairwise comparisons:\n",
      "  Smoothed vs Single Site: Mann-Whitney U p = 0.0000 *\n",
      "  Smoothed vs Regression: Mann-Whitney U p = 0.0000 *\n",
      "  Single Site vs Regression: Mann-Whitney U p = 0.0073 *\n",
      "\n",
      "\n",
      "================================================================================\n",
      "PER-FIBER DIFFERENCES: Multi-site vs Single-site\n",
      "================================================================================\n",
      "\n",
      "=== AGGREGATED COMPARISON SAMPLE SIZES (diff_std) ===\n",
      "  Smoothed:    n = 414 (files × signals, after removing NaN/Inf)\n",
      "  Single-site: n = 414\n",
      "  Multi-site:  n = 414\n",
      "  (Raw shape: 18 files × 23 signals = 414 total)\n",
      "\n",
      "============================================================\n",
      "Statistical Analysis for diff_std\n",
      "============================================================\n",
      "\n",
      "Per-Signal Comparison: Single-site vs Multi-site\n",
      "============================================================\n",
      "Signal 1 (PrL (E)): Mann-Whitney U p = 0.6925 ns | No difference\n",
      "  Single-site mean: 0.047769\n",
      "  Multi-site mean:  0.048811\n",
      "  Mean difference:  0.001042\n",
      "Signal 2 (BNST (E)): t-test p = 0.5552 ns | No difference\n",
      "  Single-site mean: 0.046295\n",
      "  Multi-site mean:  0.044713\n",
      "  Mean difference:  -0.001582\n",
      "Signal 3 (POA (E)): Mann-Whitney U p = 0.9874 ns | No difference\n",
      "  Single-site mean: 0.049571\n",
      "  Multi-site mean:  0.049911\n",
      "  Mean difference:  0.000341\n",
      "Signal 4 (AH (E)): t-test p = 0.8031 ns | No difference\n",
      "  Single-site mean: 0.063774\n",
      "  Multi-site mean:  0.063121\n",
      "  Mean difference:  -0.000653\n",
      "Signal 5 (vLS (E)): t-test p = 0.6066 ns | No difference\n",
      "  Single-site mean: 0.059661\n",
      "  Multi-site mean:  0.058588\n",
      "  Mean difference:  -0.001073\n",
      "Signal 6 (VMH (E)): t-test p = 0.9135 ns | No difference\n",
      "  Single-site mean: 0.046030\n",
      "  Multi-site mean:  0.045832\n",
      "  Mean difference:  -0.000198\n",
      "Signal 7 (PAG (E)): t-test p = 0.9761 ns | No difference\n",
      "  Single-site mean: 0.058192\n",
      "  Multi-site mean:  0.058264\n",
      "  Mean difference:  0.000072\n",
      "Signal 8 (MeA (E)): t-test p = 0.9264 ns | No difference\n",
      "  Single-site mean: 0.051037\n",
      "  Multi-site mean:  0.050819\n",
      "  Mean difference:  -0.000218\n",
      "Signal 9 (LHb (E)): t-test p = 0.1550 ns | No difference\n",
      "  Single-site mean: 0.060878\n",
      "  Multi-site mean:  0.057139\n",
      "  Mean difference:  -0.003739\n",
      "Signal 10 (PA (E)): Mann-Whitney U p = 0.7637 ns | No difference\n",
      "  Single-site mean: 0.045190\n",
      "  Multi-site mean:  0.045890\n",
      "  Mean difference:  0.000701\n",
      "Signal 11 (NAc (DA)): t-test p = 0.9901 ns | No difference\n",
      "  Single-site mean: 0.053370\n",
      "  Multi-site mean:  0.053338\n",
      "  Mean difference:  -0.000032\n",
      "Signal 12 (PMv (E)): t-test p = 0.9910 ns | No difference\n",
      "  Single-site mean: 0.052448\n",
      "  Multi-site mean:  0.052475\n",
      "  Mean difference:  0.000028\n",
      "Signal 13 (PrL (I)): t-test p = 0.3564 ns | No difference\n",
      "  Single-site mean: 0.041926\n",
      "  Multi-site mean:  0.043573\n",
      "  Mean difference:  0.001647\n",
      "Signal 14 (BNST (I)): Mann-Whitney U p = 0.0001 * | Multi > Single\n",
      "  Single-site mean: 0.046713\n",
      "  Multi-site mean:  0.030363\n",
      "  Mean difference:  -0.016350\n",
      "Signal 15 (POA (I)): Mann-Whitney U p = 0.0000 * | Multi > Single\n",
      "  Single-site mean: 0.054086\n",
      "  Multi-site mean:  0.038706\n",
      "  Mean difference:  -0.015380\n",
      "Signal 16 (AH (I)): t-test p = 0.0000 * | Multi > Single\n",
      "  Single-site mean: 0.055102\n",
      "  Multi-site mean:  0.042636\n",
      "  Mean difference:  -0.012466\n",
      "Signal 17 (vLS (I)): t-test p = 0.4759 ns | No difference\n",
      "  Single-site mean: 0.046415\n",
      "  Multi-site mean:  0.043886\n",
      "  Mean difference:  -0.002529\n",
      "Signal 18 (VMH (I)): t-test p = 0.9307 ns | No difference\n",
      "  Single-site mean: 0.040495\n",
      "  Multi-site mean:  0.040307\n",
      "  Mean difference:  -0.000188\n",
      "Signal 19 (PAG (I)): t-test p = 0.0000 * | Multi > Single\n",
      "  Single-site mean: 0.067945\n",
      "  Multi-site mean:  0.043419\n",
      "  Mean difference:  -0.024526\n",
      "Signal 20 (MeA (I)): t-test p = 0.3888 ns | No difference\n",
      "  Single-site mean: 0.042456\n",
      "  Multi-site mean:  0.039845\n",
      "  Mean difference:  -0.002611\n",
      "Signal 21 (LHb (I)): t-test p = 0.0000 * | Multi > Single\n",
      "  Single-site mean: 0.059245\n",
      "  Multi-site mean:  0.048761\n",
      "  Mean difference:  -0.010484\n",
      "Signal 22 (PA (I)): Mann-Whitney U p = 0.2354 ns | No difference\n",
      "  Single-site mean: 0.040039\n",
      "  Multi-site mean:  0.037551\n",
      "  Mean difference:  -0.002487\n",
      "Signal 23 (PMv (I)): Mann-Whitney U p = 0.2892 ns | No difference\n",
      "  Single-site mean: 0.046043\n",
      "  Multi-site mean:  0.044490\n",
      "  Mean difference:  -0.001553\n",
      "\n",
      "============================================================\n",
      "Summary:\n",
      "  Multi-site significantly better: 5 signals\n",
      "    Signals: 14, 15, 16, 19, 21\n",
      "  Single-site significantly better: 0 signals\n",
      "  No significant difference: 18 signals\n",
      "    Signals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 20, 22, 23\n",
      "\n",
      "============================================================\n",
      "Overall Group Normality Tests (Shapiro-Wilk):\n",
      "  Smoothed: p = 0.0000\n",
      "  Multi-site: p = 0.0000\n",
      "  Single-site: p = 0.0000\n",
      "\n",
      "Kruskal-Wallis H-test: H = 495.6685, p = 0.0000\n",
      "\n",
      "Kruskal-Wallis is significant. Performing post-hoc tests...\n",
      "Bonferroni-corrected alpha: 0.0167\n",
      "\n",
      "Post-hoc pairwise comparisons:\n",
      "  Smoothed vs Single Site: Mann-Whitney U p = 0.0000 *\n",
      "  Smoothed vs Regression: Mann-Whitney U p = 0.0000 *\n",
      "  Single Site vs Regression: Mann-Whitney U p = 0.0006 *\n",
      "\n",
      "\n",
      "================================================================================\n",
      "PER-FIBER DIFFERENCES: Multi-site vs Single-site\n",
      "================================================================================\n",
      "\n",
      "CV_DIFF:\n",
      "--------------------------------------------------------------------------------\n",
      "Fiber                Single Mean     Multi Mean      Difference      P-value      Status\n",
      "--------------------------------------------------------------------------------\n",
      "PrL (E)              0.006224        0.006546        0.000322        ↑ 0.5500       ns    No difference\n",
      "BNST (E)             0.007170        0.006974        -0.000196       ↓ 0.7514       ns    No difference\n",
      "POA (E)              0.005941        0.005981        0.000039        ↑ 0.9521       ns    No difference\n",
      "AH (E)               0.006036        0.006166        0.000130        ↑ 0.8027       ns    No difference\n",
      "vLS (E)              0.007237        0.007389        0.000152        ↑ 0.8487       ns    No difference\n",
      "VMH (E)              0.004012        0.004007        -0.000005       ↓ 0.9370       ns    No difference\n",
      "PAG (E)              0.007259        0.007295        0.000036        ↑ 0.9370       ns    No difference\n",
      "MeA (E)              0.006226        0.006342        0.000115        ↑ 0.8031       ns    No difference\n",
      "LHb (E)              0.009692        0.009235        -0.000456       ↓ 0.7160       ns    No difference\n",
      "PA (E)               0.005403        0.005540        0.000137        ↑ 0.7668       ns    No difference\n",
      "NAc (DA)             0.007518        0.007634        0.000117        ↑ 0.8629       ns    No difference\n",
      "PMv (E)              0.004846        0.004825        -0.000021       ↓ 0.9595       ns    No difference\n",
      "PrL (I)              0.004811        0.005060        0.000249        ↑ 0.5325       ns    No difference\n",
      "BNST (I)             0.009473        0.006045        -0.003428       ↓ 0.0000       *     Multi > Single\n",
      "POA (I)              0.008430        0.006292        -0.002138       ↓ 0.0112       *     Multi > Single\n",
      "AH (I)               0.007307        0.005735        -0.001572       ↓ 0.0117       *     Multi > Single\n",
      "vLS (I)              0.004682        0.004330        -0.000353       ↓ 0.6112       ns    No difference\n",
      "VMH (I)              0.005343        0.005168        -0.000174       ↓ 0.7535       ns    No difference\n",
      "PAG (I)              0.011000        0.007975        -0.003025       ↓ 0.0002       *     Multi > Single\n",
      "MeA (I)              0.006368        0.006076        -0.000292       ↓ 0.5241       ns    No difference\n",
      "LHb (I)              0.009116        0.008128        -0.000987       ↓ 0.2059       ns    No difference\n",
      "PA (I)               0.007443        0.006864        -0.000579       ↓ 0.4146       ns    No difference\n",
      "PMv (I)              0.006344        0.006102        -0.000242       ↓ 0.6387       ns    No difference\n",
      "\n",
      "DIFF_STD:\n",
      "--------------------------------------------------------------------------------\n",
      "Fiber                Single Mean     Multi Mean      Difference      P-value      Status\n",
      "--------------------------------------------------------------------------------\n",
      "PrL (E)              0.047769        0.048811        0.001042        ↑ 0.6925       ns    No difference\n",
      "BNST (E)             0.046295        0.044713        -0.001582       ↓ 0.5552       ns    No difference\n",
      "POA (E)              0.049571        0.049911        0.000341        ↑ 0.9874       ns    No difference\n",
      "AH (E)               0.063774        0.063121        -0.000653       ↓ 0.8031       ns    No difference\n",
      "vLS (E)              0.059661        0.058588        -0.001073       ↓ 0.6066       ns    No difference\n",
      "VMH (E)              0.046030        0.045832        -0.000198       ↓ 0.9135       ns    No difference\n",
      "PAG (E)              0.058192        0.058264        0.000072        ↑ 0.9761       ns    No difference\n",
      "MeA (E)              0.051037        0.050819        -0.000218       ↓ 0.9264       ns    No difference\n",
      "LHb (E)              0.060878        0.057139        -0.003739       ↓ 0.1550       ns    No difference\n",
      "PA (E)               0.045190        0.045890        0.000701        ↑ 0.7637       ns    No difference\n",
      "NAc (DA)             0.053370        0.053338        -0.000032       ↓ 0.9901       ns    No difference\n",
      "PMv (E)              0.052448        0.052475        0.000028        ↑ 0.9910       ns    No difference\n",
      "PrL (I)              0.041926        0.043573        0.001647        ↑ 0.3564       ns    No difference\n",
      "BNST (I)             0.046713        0.030363        -0.016350       ↓ 0.0001       *     Multi > Single\n",
      "POA (I)              0.054086        0.038706        -0.015380       ↓ 0.0000       *     Multi > Single\n",
      "AH (I)               0.055102        0.042636        -0.012466       ↓ 0.0000       *     Multi > Single\n",
      "vLS (I)              0.046415        0.043886        -0.002529       ↓ 0.4759       ns    No difference\n",
      "VMH (I)              0.040495        0.040307        -0.000188       ↓ 0.9307       ns    No difference\n",
      "PAG (I)              0.067945        0.043419        -0.024526       ↓ 0.0000       *     Multi > Single\n",
      "MeA (I)              0.042456        0.039845        -0.002611       ↓ 0.3888       ns    No difference\n",
      "LHb (I)              0.059245        0.048761        -0.010484       ↓ 0.0000       *     Multi > Single\n",
      "PA (I)               0.040039        0.037551        -0.002487       ↓ 0.2354       ns    No difference\n",
      "PMv (I)              0.046043        0.044490        -0.001553       ↓ 0.2892       ns    No difference\n",
      "\n",
      "Saved per-fiber differences for cv_diff to './figures/JOVE/per_fiber_differences_cv_diff.csv'\n",
      "\n",
      "Saved per-fiber differences for diff_std to './figures/JOVE/per_fiber_differences_diff_std.csv'\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create comprehensive noise metric plots with noise reduction bars and statistics\n",
    "import matplotlib.gridspec as gridspec\n",
    "from scipy.stats import shapiro, levene, f_oneway, kruskal, ttest_ind, mannwhitneyu\n",
    "\n",
    "def add_significance_bracket(ax, x1, x2, y, p_value, height_offset=0.02):\n",
    "\t\"\"\"Add a significance bracket between two bars\"\"\"\n",
    "\t# Determine significance stars\n",
    "\tif p_value < 0.001:\n",
    "\t\tsig_text = '***'\n",
    "\telif p_value < 0.01:\n",
    "\t\tsig_text = '**'\n",
    "\telif p_value < 0.05:\n",
    "\t\tsig_text = '*'\n",
    "\telse:\n",
    "\t\treturn  # Don't plot if not significant\n",
    "\t\n",
    "\t# Get axis limits for relative positioning\n",
    "\tylim = ax.get_ylim()\n",
    "\ty_range = ylim[1] - ylim[0]\n",
    "\t\n",
    "\t# Draw bracket\n",
    "\tbracket_height = y + (y_range * height_offset)\n",
    "\tax.plot([x1, x1, x2, x2], \n",
    "\t\t\t[y, bracket_height, bracket_height, y], \n",
    "\t\t\t'k-', linewidth=1.5)\n",
    "\t\n",
    "\t# Add significance text\n",
    "\tax.text((x1 + x2) / 2, bracket_height, sig_text, \n",
    "\t\t\tha='center', va='bottom', fontsize=12, fontfamily='Arial')\n",
    "\n",
    "# Get signal names\n",
    "signal_names = list(noise_stats[list(noise_stats.keys())[0]].keys())\n",
    "signal_names = [x for x in signal_names if 'NAc (I)' not in x]  # Example exclusion\n",
    "n_signals = len(signal_names)\n",
    "\n",
    "# Updated metrics list\n",
    "metrics = ['cv_diff', 'diff_std']\n",
    "metric_titles = {\n",
    "\t'cv_diff': 'Frame-to-Frame Jitter Relative to Signal Range\\n(Lower = Smoother)',\n",
    "\t'diff_std': 'High-Frequency Noise Standard Deviation\\n(Raw frame-to-frame changes)'\n",
    "}\n",
    "\n",
    "# Dictionary to store per-fiber differences across metrics\n",
    "per_fiber_differences = {}\n",
    "\n",
    "# Create plots for each metric\n",
    "for metric in metrics:\n",
    "\t\n",
    "\t# Collect data for this metric (removed TMAC)\n",
    "\traw_noise_all = np.zeros((len(noise_stats), n_signals))\n",
    "\treg_corrected_noise_all = np.zeros((len(noise_stats), n_signals))\n",
    "\tsmooth_only_noise_all = np.zeros((len(noise_stats), n_signals))\n",
    "\tsingle_site_corrected_noise_all = np.zeros((len(noise_stats), n_signals))\n",
    "\t\n",
    "\tfor file_idx, key in enumerate(noise_stats.keys()):\n",
    "\t\tfor sig_idx, signal in enumerate(signal_names):\n",
    "\t\t\traw_noise_all[file_idx, sig_idx] = noise_stats[key][signal]['reg_raw'][metric]\n",
    "\t\t\treg_corrected_noise_all[file_idx, sig_idx] = noise_stats[key][signal]['reg_corrected'][metric]\n",
    "\t\t\tsmooth_only_noise_all[file_idx, sig_idx] = noise_stats[key][signal]['reg_smoothed_only'][metric]\n",
    "\t\t\tsingle_site_corrected_noise_all[file_idx, sig_idx] = noise_stats[key][signal]['single_site_corrected'][metric]\n",
    "\t\n",
    "\t# Compute means and SEMs\n",
    "\traw_means = np.nanmean(raw_noise_all, axis=0)\n",
    "\traw_sems = np.nanstd(raw_noise_all, axis=0) / np.sqrt(len(noise_stats))\n",
    "\t\n",
    "\treg_means = np.nanmean(reg_corrected_noise_all, axis=0)\n",
    "\treg_sems = np.nanstd(reg_corrected_noise_all, axis=0) / np.sqrt(len(noise_stats))\n",
    "\t\n",
    "\tsmooth_only_means = np.nanmean(smooth_only_noise_all, axis=0)\n",
    "\tsmooth_only_sems = np.nanstd(smooth_only_noise_all, axis=0) / np.sqrt(len(noise_stats))\n",
    "\t\n",
    "\tsingle_site_means = np.nanmean(single_site_corrected_noise_all, axis=0)\n",
    "\tsingle_site_sems = np.nanstd(single_site_corrected_noise_all, axis=0) / np.sqrt(len(noise_stats))\n",
    "\t\n",
    "\t# Calculate noise reduction percentages (removed TMAC)\n",
    "\tif metric == 'snr_hf':\n",
    "\t\t# For SNR: % improvement = ((corrected - raw) / raw) * 100\n",
    "\t\tsmooth_only_reduction_all = ((smooth_only_noise_all - raw_noise_all) / raw_noise_all) * 100\n",
    "\t\treg_noise_reduction_all = ((reg_corrected_noise_all - raw_noise_all) / raw_noise_all) * 100\n",
    "\t\tsingle_site_reduction_all = ((single_site_corrected_noise_all - raw_noise_all) / raw_noise_all) * 100\n",
    "\t\tylabel_text = 'SNR Improvement (%)'\n",
    "\telse:\n",
    "\t\t# For noise metrics: lower is better, so calculate reduction\n",
    "\t\tsmooth_only_reduction_all = (1 - smooth_only_noise_all / raw_noise_all) * 100\n",
    "\t\treg_noise_reduction_all = (1 - reg_corrected_noise_all / raw_noise_all) * 100\n",
    "\t\tsingle_site_reduction_all = (1 - single_site_corrected_noise_all / raw_noise_all) * 100\n",
    "\t\tylabel_text = 'Noise Reduction (%)'\n",
    "\t\n",
    "\t# Flatten for statistical tests (removed TMAC)\n",
    "\tsmooth_flat = smooth_only_reduction_all.flatten()\n",
    "\treg_flat = reg_noise_reduction_all.flatten()\n",
    "\tsingle_flat = single_site_reduction_all.flatten()\n",
    "\t\n",
    "\t# Remove NaNs and Infs\n",
    "\tsmooth_flat = smooth_flat[~np.isnan(smooth_flat) & ~np.isinf(smooth_flat)]\n",
    "\treg_flat = reg_flat[~np.isnan(reg_flat) & ~np.isinf(reg_flat)]\n",
    "\tsingle_flat = single_flat[~np.isnan(single_flat) & ~np.isinf(single_flat)]\n",
    "\t\n",
    "\t# Print sample sizes for aggregated comparisons (second subplot)\n",
    "\tprint(f\"\\n=== AGGREGATED COMPARISON SAMPLE SIZES ({metric}) ===\")\n",
    "\tprint(f\"  Smoothed:    n = {len(smooth_flat)} (files × signals, after removing NaN/Inf)\")\n",
    "\tprint(f\"  Single-site: n = {len(single_flat)}\")\n",
    "\tprint(f\"  Multi-site:  n = {len(reg_flat)}\")\n",
    "\tprint(f\"  (Raw shape: {smooth_only_reduction_all.shape[0]} files × {smooth_only_reduction_all.shape[1]} signals = {smooth_only_reduction_all.size} total)\")\n",
    "\t\n",
    "\tsmooth_only_reduction_mean = np.nanmean(smooth_only_reduction_all)\n",
    "\tsmooth_only_reduction_sem = np.nanstd(smooth_only_reduction_all.flatten()) / np.sqrt(smooth_only_reduction_all.size)\n",
    "\t\n",
    "\treg_noise_reduction_mean = np.nanmean(reg_noise_reduction_all)\n",
    "\treg_noise_reduction_sem = np.nanstd(reg_noise_reduction_all.flatten()) / np.sqrt(reg_noise_reduction_all.size)\n",
    "\t\n",
    "\tsingle_site_reduction_mean = np.nanmean(single_site_reduction_all)\n",
    "\tsingle_site_reduction_sem = np.nanstd(single_site_reduction_all.flatten()) / np.sqrt(single_site_reduction_all.size)\n",
    "\t\n",
    "\t# Statistical comparison for each signal: single-site vs multi-site\n",
    "\tmulti_better_sig = []  # Signals where multi-site is significantly better\n",
    "\tsingle_better_sig = []  # Signals where single-site is significantly better\n",
    "\tno_diff_sig = []  # Signals with no significant difference\n",
    "\t\n",
    "\tsignal_p_values = []\n",
    "\tsignal_comparisons = []\n",
    "\t\n",
    "\t# Initialize per-fiber difference storage for this metric\n",
    "\tper_fiber_differences[metric] = {}\n",
    "\t\n",
    "\tprint(f\"\\n{'='*60}\")\n",
    "\tprint(f\"Statistical Analysis for {metric}\")\n",
    "\tprint(f\"{'='*60}\")\n",
    "\tprint(f\"\\nPer-Signal Comparison: Single-site vs Multi-site\")\n",
    "\tprint(f\"{'='*60}\")\n",
    "\t\n",
    "\tfor sig_idx in range(n_signals):\n",
    "\t\t# Get data for this signal across all files\n",
    "\t\tsingle_data = single_site_corrected_noise_all[:, sig_idx]\n",
    "\t\tmulti_data = reg_corrected_noise_all[:, sig_idx]\n",
    "\t\t\n",
    "\t\t# Remove NaNs\n",
    "\t\tvalid_idx = ~(np.isnan(single_data) | np.isnan(multi_data))\n",
    "\t\tsingle_data = single_data[valid_idx]\n",
    "\t\tmulti_data = multi_data[valid_idx]\n",
    "\t\t\n",
    "\t\t# Calculate mean difference (multi - single; negative means multi is better for noise metrics)\n",
    "\t\tmean_diff = np.nanmean(multi_data - single_data)\n",
    "\t\t\n",
    "\t\t# Store per-fiber difference\n",
    "\t\tfiber_name = signal_names[sig_idx]\n",
    "\t\tper_fiber_differences[metric][fiber_name] = {\n",
    "\t\t\t'mean_difference': mean_diff,\n",
    "\t\t\t'single_mean': np.nanmean(single_data),\n",
    "\t\t\t'multi_mean': np.nanmean(multi_data),\n",
    "\t\t\t'single_std': np.nanstd(single_data),\n",
    "\t\t\t'multi_std': np.nanstd(multi_data)\n",
    "\t\t}\n",
    "\t\t\n",
    "\t\t# Test normality\n",
    "\t\tif len(single_data) >= 3:  # Need at least 3 samples for Shapiro-Wilk\n",
    "\t\t\t_, p_single_norm = shapiro(single_data)\n",
    "\t\t\t_, p_multi_norm = shapiro(multi_data)\n",
    "\t\t\tis_normal = (p_single_norm > 0.05) and (p_multi_norm > 0.05)\n",
    "\t\telse:\n",
    "\t\t\tis_normal = False\n",
    "\t\t\n",
    "\t\t# Perform appropriate test\n",
    "\t\tif is_normal:\n",
    "\t\t\tt_stat, p_val = ttest_ind(single_data, multi_data)\n",
    "\t\t\ttest_name = \"t-test\"\n",
    "\t\telse:\n",
    "\t\t\tu_stat, p_val = mannwhitneyu(single_data, multi_data, alternative='two-sided')\n",
    "\t\t\ttest_name = \"Mann-Whitney U\"\n",
    "\t\t\n",
    "\t\tsignal_p_values.append(p_val)\n",
    "\t\t\n",
    "\t\t# Determine which is better (lower noise is better)\n",
    "\t\tsingle_mean = np.nanmean(single_data)\n",
    "\t\tmulti_mean = np.nanmean(multi_data)\n",
    "\t\t\n",
    "\t\tif p_val < 0.05:\n",
    "\t\t\tif multi_mean < single_mean:\n",
    "\t\t\t\tmulti_better_sig.append(sig_idx + 1)  # 1-indexed\n",
    "\t\t\t\tcomparison = \"Multi > Single\"\n",
    "\t\t\t\tsignal_comparisons.append('multi')\n",
    "\t\t\telse:\n",
    "\t\t\t\tsingle_better_sig.append(sig_idx + 1)\n",
    "\t\t\t\tcomparison = \"Single > Multi\"\n",
    "\t\t\t\tsignal_comparisons.append('single')\n",
    "\t\t\tsig_marker = \"*\"\n",
    "\t\telse:\n",
    "\t\t\tno_diff_sig.append(sig_idx + 1)\n",
    "\t\t\tcomparison = \"No difference\"\n",
    "\t\t\tsignal_comparisons.append('no_diff')\n",
    "\t\t\tsig_marker = \"ns\"\n",
    "\t\t\n",
    "\t\tper_fiber_differences[metric][fiber_name]['p_value'] = p_val\n",
    "\t\tper_fiber_differences[metric][fiber_name]['significant'] = p_val < 0.05\n",
    "\t\tper_fiber_differences[metric][fiber_name]['comparison'] = comparison\n",
    "\t\t\n",
    "\t\tprint(f\"Signal {sig_idx + 1} ({fiber_name}): {test_name} p = {p_val:.4f} {sig_marker} | {comparison}\")\n",
    "\t\tprint(f\"  Single-site mean: {single_mean:.6f}\")\n",
    "\t\tprint(f\"  Multi-site mean:  {multi_mean:.6f}\")\n",
    "\t\tprint(f\"  Mean difference:  {mean_diff:.6f}\")\n",
    "\t\n",
    "\tprint(f\"\\n{'='*60}\")\n",
    "\tprint(f\"Summary:\")\n",
    "\tprint(f\"  Multi-site significantly better: {len(multi_better_sig)} signals\")\n",
    "\tif multi_better_sig:\n",
    "\t\tprint(f\"    Signals: {', '.join(map(str, multi_better_sig))}\")\n",
    "\tprint(f\"  Single-site significantly better: {len(single_better_sig)} signals\")\n",
    "\tif single_better_sig:\n",
    "\t\tprint(f\"    Signals: {', '.join(map(str, single_better_sig))}\")\n",
    "\tprint(f\"  No significant difference: {len(no_diff_sig)} signals\")\n",
    "\tif no_diff_sig:\n",
    "\t\tprint(f\"    Signals: {', '.join(map(str, no_diff_sig))}\")\n",
    "\t\n",
    "\t# Test normality for each group\n",
    "\t_, p_smooth_norm = shapiro(smooth_flat)\n",
    "\t_, p_reg_norm = shapiro(reg_flat)\n",
    "\t_, p_single_norm = shapiro(single_flat)\n",
    "\t\n",
    "\tprint(f\"\\n{'='*60}\")\n",
    "\tprint(f\"Overall Group Normality Tests (Shapiro-Wilk):\")\n",
    "\tprint(f\"  Smoothed: p = {p_smooth_norm:.4f}\")\n",
    "\tprint(f\"  Multi-site: p = {p_reg_norm:.4f}\")\n",
    "\tprint(f\"  Single-site: p = {p_single_norm:.4f}\")\n",
    "\t\n",
    "\t# Check if data is normal (all p > 0.05)\n",
    "\tis_normal = all([p_smooth_norm > 0.05, p_reg_norm > 0.05, p_single_norm > 0.05])\n",
    "\t\n",
    "\t# Omnibus test\n",
    "\tif is_normal:\n",
    "\t\t# Test homogeneity of variance\n",
    "\t\t_, p_levene = levene(smooth_flat, reg_flat, single_flat)\n",
    "\t\tprint(f\"\\nLevene's test for homogeneity of variance: p = {p_levene:.4f}\")\n",
    "\t\t\n",
    "\t\t# ANOVA\n",
    "\t\tf_stat, p_omnibus = f_oneway(smooth_flat, reg_flat, single_flat)\n",
    "\t\tprint(f\"\\nOne-way ANOVA: F = {f_stat:.4f}, p = {p_omnibus:.4f}\")\n",
    "\t\ttest_used = \"ANOVA\"\n",
    "\telse:\n",
    "\t\t# Kruskal-Wallis\n",
    "\t\th_stat, p_omnibus = kruskal(smooth_flat, reg_flat, single_flat)\n",
    "\t\tprint(f\"\\nKruskal-Wallis H-test: H = {h_stat:.4f}, p = {p_omnibus:.4f}\")\n",
    "\t\ttest_used = \"Kruskal-Wallis\"\n",
    "\t\n",
    "\t# Post-hoc tests if omnibus is significant\n",
    "\tposthoc_results = {}\n",
    "\tif p_omnibus < 0.05:\n",
    "\t\tprint(f\"\\n{test_used} is significant. Performing post-hoc tests...\")\n",
    "\t\t\n",
    "\t\t# Bonferroni correction: 3 comparisons\n",
    "\t\tbonferroni_alpha = 0.05 / 3\n",
    "\t\tprint(f\"Bonferroni-corrected alpha: {bonferroni_alpha:.4f}\")\n",
    "\t\t\n",
    "\t\tcomparisons = [\n",
    "\t\t\t('Smoothed', 'Single Site', smooth_flat, single_flat, 0, 1),\n",
    "\t\t\t('Smoothed', 'Regression', smooth_flat, reg_flat, 0, 2),\n",
    "\t\t\t('Single Site', 'Regression', single_flat, reg_flat, 1, 2)\n",
    "\t\t]\n",
    "\t\t\n",
    "\t\tprint(\"\\nPost-hoc pairwise comparisons:\")\n",
    "\t\tfor name1, name2, data1, data2, x1, x2 in comparisons:\n",
    "\t\t\tif is_normal:\n",
    "\t\t\t\t# Independent t-test\n",
    "\t\t\t\tt_stat, p_val = ttest_ind(data1, data2)\n",
    "\t\t\t\ttest_name = \"t-test\"\n",
    "\t\t\telse:\n",
    "\t\t\t\t# Mann-Whitney U test\n",
    "\t\t\t\tu_stat, p_val = mannwhitneyu(data1, data2, alternative='two-sided')\n",
    "\t\t\t\ttest_name = \"Mann-Whitney U\"\n",
    "\t\t\t\n",
    "\t\t\t# Check against Bonferroni-corrected alpha\n",
    "\t\t\tis_sig = p_val < bonferroni_alpha\n",
    "\t\t\tposthoc_results[(x1, x2)] = (p_val, is_sig)\n",
    "\t\t\t\n",
    "\t\t\tprint(f\"  {name1} vs {name2}: {test_name} p = {p_val:.4f} {'*' if is_sig else 'ns'}\")\n",
    "\telse:\n",
    "\t\tprint(f\"\\n{test_used} is not significant. Skipping post-hoc tests.\")\n",
    "\t\n",
    "\t# Create figure with gridspec - three panels\n",
    "\tfig = plt.figure(figsize=(10, 3))\n",
    "\tgs = gridspec.GridSpec(1, 3, width_ratios=[5, 1, 1], wspace=0.3)\n",
    "\t\n",
    "\t# Left plot: bar plot by signal (removed TMAC)\n",
    "\tax1 = plt.subplot(gs[0])\n",
    "\t\n",
    "\tx = np.arange(n_signals)\n",
    "\twidth = 0.2\n",
    "\t\n",
    "\tbars1 = ax1.bar(x - 1.5*width, raw_means, width, yerr=raw_sems, \n",
    "\t\t\t\t   label='Raw', color='gray', alpha=0.7, capsize=3)\n",
    "\tbars2 = ax1.bar(x - 0.5*width, smooth_only_means, width, yerr=smooth_only_sems, \n",
    "\t\t\t\t   label='Smoothed Only', color='green', alpha=0.7, capsize=3)\n",
    "\tbars3 = ax1.bar(x + 0.5*width, single_site_means, width, yerr=single_site_sems, \n",
    "\t\t\t\t   label='Single-site Corrected', color='cyan', alpha=0.7, capsize=3)\n",
    "\tbars4 = ax1.bar(x + 1.5*width, reg_means, width, yerr=reg_sems, \n",
    "\t\t\t\t   label='Multi-site Corrected', color='blue', alpha=0.7, capsize=3)\n",
    "\t\n",
    "\tax1.set_xlabel('Signal', fontsize=14, fontfamily='Arial')\n",
    "\tax1.set_ylabel(f'{metric.upper()}', fontsize=14, fontfamily='Arial')\n",
    "\tax1.set_title(metric_titles[metric], fontsize=16, fontfamily='Arial', pad=20)\n",
    "\tax1.set_xticks(x)\n",
    "\tax1.set_xticklabels([f'{i+1}' for i in range(n_signals)], \n",
    "\t\t\t\t\t\trotation=45, ha='right', fontfamily='Arial', fontsize=11)\n",
    "\t\n",
    "\t# Center the legend\n",
    "\tax1.legend(fontsize=12, loc='upper center', bbox_to_anchor=(0.5, -0.15), \n",
    "\t\t\t   ncol=4, frameon=False, prop={'family': 'Arial'})\n",
    "\t\n",
    "\t# Make axes thicker\n",
    "\tfor spine in ax1.spines.values():\n",
    "\t\tspine.set_linewidth(1.5)\n",
    "\tax1.tick_params(width=1.5, labelsize=12)\n",
    "\t\n",
    "\tfor label in ax1.get_yticklabels():\n",
    "\t\tlabel.set_fontfamily('Arial')\n",
    "\t\n",
    "\t# Middle plot: noise reduction percentage (removed TMAC)\n",
    "\tax2 = plt.subplot(gs[1])\n",
    "\t\n",
    "\tmethods = ['Smoothed', 'Single', 'Multi']\n",
    "\treduction_means = [smooth_only_reduction_mean, single_site_reduction_mean, reg_noise_reduction_mean]\n",
    "\treduction_sems = [smooth_only_reduction_sem, single_site_reduction_sem, reg_noise_reduction_sem]\n",
    "\t\n",
    "\tbars = ax2.bar(methods, reduction_means, yerr=reduction_sems, \n",
    "\t\t\t\t   color=['green', 'cyan', 'blue'], alpha=0.7, capsize=5)\n",
    "\t\n",
    "\tax2.set_ylabel(ylabel_text, fontsize=14, fontfamily='Arial')\n",
    "\tax2.set_title('Overall Noise\\nReduction', fontsize=14, fontfamily='Arial', pad=10)\n",
    "\t\n",
    "\t# Calculate max height for y-axis limit (accounting for significance brackets)\n",
    "\tmax_height = max([m + s for m, s in zip(reduction_means, reduction_sems)])\n",
    "\tmin_height = min([m - s for m, s in zip(reduction_means, reduction_sems)])\n",
    "\t\n",
    "\tif p_omnibus < 0.05:\n",
    "\t\t# Need extra space for brackets\n",
    "\t\tax2.set_ylim([min(0, min_height * 1.2), max_height * 1.5])\n",
    "\telse:\n",
    "\t\tax2.set_ylim([min(0, min_height * 1.2), max_height * 1.2])\n",
    "\t\n",
    "\tax2.axhline(y=0, color='black', linestyle='-', linewidth=1.5)\n",
    "\t\n",
    "\t# Add value labels on bars\n",
    "\tfor i, (bar, mean, sem) in enumerate(zip(bars, reduction_means, reduction_sems)):\n",
    "\t\theight = bar.get_height()\n",
    "\t\tax2.text(bar.get_x() + bar.get_width()/2., height + sem if height > 0 else height - sem,\n",
    "\t\t\t\tf'{mean:.1f}%',\n",
    "\t\t\t\tha='center', va='bottom' if height > 0 else 'top', \n",
    "\t\t\t\tfontsize=12, fontfamily='Arial')\n",
    "\t\n",
    "\t# Add significance brackets if omnibus test was significant\n",
    "\tif p_omnibus < 0.05 and posthoc_results:\n",
    "\t\t# Updated bracket order for 3 groups\n",
    "\t\tbracket_order = [(0, 1), (0, 2), (1, 2)]  # Smoothed-Single, Smoothed-Multi, Single-Multi\n",
    "\t\tbase_height = max([m + s for m, s in zip(reduction_means, reduction_sems)])\n",
    "\t\t\n",
    "\t\tfor bracket_idx, (x1, x2) in enumerate(bracket_order):\n",
    "\t\t\tif (x1, x2) in posthoc_results:\n",
    "\t\t\t\tp_val, is_sig = posthoc_results[(x1, x2)]\n",
    "\t\t\t\tif is_sig:\n",
    "\t\t\t\t\t# Stack brackets at different heights\n",
    "\t\t\t\t\ty_pos = base_height + (bracket_idx * max_height * 0.15)\n",
    "\t\t\t\t\tadd_significance_bracket(ax2, x1, x2, y_pos, p_val, height_offset=0.01)\n",
    "\t\n",
    "\t# Make axes thicker\n",
    "\tfor spine in ax2.spines.values():\n",
    "\t\tspine.set_linewidth(1.5)\n",
    "\tax2.tick_params(width=1.5, labelsize=12)\n",
    "\t\n",
    "\tfor label in ax2.get_xticklabels() + ax2.get_yticklabels():\n",
    "\t\tlabel.set_fontfamily('Arial')\n",
    "\t\n",
    "\t# Right plot: Stacked bar showing per-signal comparison results - SEPARATED BY SIGNAL TYPE\n",
    "\tax3 = plt.subplot(gs[2])\n",
    "\n",
    "\t# Separate signals into E/DA (first 12) and I (last 11)\n",
    "\te_da_signals = signal_names[:12]\n",
    "\ti_signals = signal_names[12:]\n",
    "\n",
    "\t# Count outcomes for E/DA signals (first 12)\n",
    "\tmulti_better_e = [sig for sig in multi_better_sig if sig <= 12]\n",
    "\tsingle_better_e = [sig for sig in single_better_sig if sig <= 12]\n",
    "\tno_diff_e = [sig for sig in no_diff_sig if sig <= 12]\n",
    "\n",
    "\tn_multi_better_e = len(multi_better_e)\n",
    "\tn_single_better_e = len(single_better_e)\n",
    "\tn_no_diff_e = len(no_diff_e)\n",
    "\n",
    "\tpct_multi_better_e = (n_multi_better_e / len(e_da_signals)) * 100\n",
    "\tpct_single_better_e = (n_single_better_e / len(e_da_signals)) * 100\n",
    "\tpct_no_diff_e = (n_no_diff_e / len(e_da_signals)) * 100\n",
    "\n",
    "\t# Count outcomes for I signals (last 11)\n",
    "\tmulti_better_i = [sig for sig in multi_better_sig if sig > 12]\n",
    "\tsingle_better_i = [sig for sig in single_better_sig if sig > 12]\n",
    "\tno_diff_i = [sig for sig in no_diff_sig if sig > 12]\n",
    "\n",
    "\tn_multi_better_i = len(multi_better_i)\n",
    "\tn_single_better_i = len(single_better_i)\n",
    "\tn_no_diff_i = len(no_diff_i)\n",
    "\n",
    "\tpct_multi_better_i = (n_multi_better_i / len(i_signals)) * 100\n",
    "\tpct_single_better_i = (n_single_better_i / len(i_signals)) * 100\n",
    "\tpct_no_diff_i = (n_no_diff_i / len(i_signals)) * 100\n",
    "\n",
    "\t# Create grouped bars\n",
    "\tcategories = ['E/DA\\nSignals', 'I\\nSignals']\n",
    "\tx_pos = np.arange(len(categories))\n",
    "\tbar_width = 0.6\n",
    "\n",
    "\t# Plot stacked bars for E/DA signals\n",
    "\tp1_e = ax3.bar(x_pos[0], pct_multi_better_e, bar_width,\n",
    "\t\t\t\tcolor='blue', alpha=0.7, label='Multi > Single*')\n",
    "\tp2_e = ax3.bar(x_pos[0], pct_no_diff_e, bar_width,\n",
    "\t\t\t\tbottom=pct_multi_better_e,\n",
    "\t\t\t\tcolor='gray', alpha=0.5, label='No Difference')\n",
    "\tp3_e = ax3.bar(x_pos[0], pct_single_better_e, bar_width,\n",
    "\t\t\t\tbottom=pct_multi_better_e + pct_no_diff_e,\n",
    "\t\t\t\tcolor='cyan', alpha=0.7, label='Single > Multi*')\n",
    "\n",
    "\t# Plot stacked bars for I signals\n",
    "\tp1_i = ax3.bar(x_pos[1], pct_multi_better_i, bar_width,\n",
    "\t\t\t\tcolor='blue', alpha=0.7)\n",
    "\tp2_i = ax3.bar(x_pos[1], pct_no_diff_i, bar_width,\n",
    "\t\t\t\tbottom=pct_multi_better_i,\n",
    "\t\t\t\tcolor='gray', alpha=0.5)\n",
    "\tp3_i = ax3.bar(x_pos[1], pct_single_better_i, bar_width,\n",
    "\t\t\t\tbottom=pct_multi_better_i + pct_no_diff_i,\n",
    "\t\t\t\tcolor='cyan', alpha=0.7)\n",
    "\n",
    "\tax3.set_xticks(x_pos)\n",
    "\tax3.set_xticklabels(categories, fontfamily='Arial', fontsize=12)\n",
    "\tax3.set_ylabel('Percentage of Signals (%)', fontsize=14, fontfamily='Arial')\n",
    "\tax3.set_title('Per-Signal\\nComparison', fontsize=14, fontfamily='Arial', pad=10)\n",
    "\tax3.set_ylim([0, 100])\n",
    "\n",
    "\t# Add percentage labels on E/DA bars\n",
    "\tif pct_multi_better_e > 5:\n",
    "\t\tax3.text(x_pos[0], pct_multi_better_e / 2,\n",
    "\t\t\t\tf'{pct_multi_better_e:.0f}%\\n(n={n_multi_better_e})',\n",
    "\t\t\t\tha='center', va='center', fontsize=10, fontfamily='Arial',\n",
    "\t\t\t\tcolor='white', fontweight='bold')\n",
    "\n",
    "\tif pct_no_diff_e > 5:\n",
    "\t\tax3.text(x_pos[0], pct_multi_better_e + pct_no_diff_e / 2,\n",
    "\t\t\t\tf'{pct_no_diff_e:.0f}%\\n(n={n_no_diff_e})',\n",
    "\t\t\t\tha='center', va='center', fontsize=10, fontfamily='Arial',\n",
    "\t\t\t\tcolor='black', fontweight='bold')\n",
    "\n",
    "\tif pct_single_better_e > 5:\n",
    "\t\tax3.text(x_pos[0], pct_multi_better_e + pct_no_diff_e + pct_single_better_e / 2,\n",
    "\t\t\t\tf'{pct_single_better_e:.0f}%\\n(n={n_single_better_e})',\n",
    "\t\t\t\tha='center', va='center', fontsize=10, fontfamily='Arial',\n",
    "\t\t\t\tcolor='white', fontweight='bold')\n",
    "\n",
    "\t# Add percentage labels on I bars\n",
    "\tif pct_multi_better_i > 5:\n",
    "\t\tax3.text(x_pos[1], pct_multi_better_i / 2,\n",
    "\t\t\t\tf'{pct_multi_better_i:.0f}%\\n(n={n_multi_better_i})',\n",
    "\t\t\t\tha='center', va='center', fontsize=10, fontfamily='Arial',\n",
    "\t\t\t\tcolor='white', fontweight='bold')\n",
    "\n",
    "\tif pct_no_diff_i > 5:\n",
    "\t\tax3.text(x_pos[1], pct_multi_better_i + pct_no_diff_i / 2,\n",
    "\t\t\t\tf'{pct_no_diff_i:.0f}%\\n(n={n_no_diff_i})',\n",
    "\t\t\t\tha='center', va='center', fontsize=10, fontfamily='Arial',\n",
    "\t\t\t\tcolor='black', fontweight='bold')\n",
    "\n",
    "\tif pct_single_better_i > 5:\n",
    "\t\tax3.text(x_pos[1], pct_multi_better_i + pct_no_diff_i + pct_single_better_i / 2,\n",
    "\t\t\t\tf'{pct_single_better_i:.0f}%\\n(n={n_single_better_i})',\n",
    "\t\t\t\tha='center', va='center', fontsize=10, fontfamily='Arial',\n",
    "\t\t\t\tcolor='white', fontweight='bold')\n",
    "\n",
    "\t# ax3.legend(fontsize=10, loc='upper right', frameon=False, prop={'family': 'Arial'})\n",
    "\n",
    "\t# Make axes thicker\n",
    "\tfor spine in ax3.spines.values():\n",
    "\t\tspine.set_linewidth(1.5)\n",
    "\tax3.tick_params(width=1.5, labelsize=12)\n",
    "\n",
    "\tfor label in ax3.get_xticklabels() + ax3.get_yticklabels():\n",
    "\t\tlabel.set_fontfamily('Arial')\n",
    "\t# Print per-fiber differences summary\n",
    "\tprint(f\"\\n\\n{'='*80}\")\n",
    "\tprint(\"PER-FIBER DIFFERENCES: Multi-site vs Single-site\")\n",
    "\tprint(f\"{'='*80}\")\n",
    "\n",
    "\tsns.despine()\n",
    "\n",
    "\tplt.savefig(f'./figures/JOVE/noise_metric_comparison_{metric}.svg', dpi=300, bbox_inches='tight')\n",
    "\n",
    "for metric in per_fiber_differences.keys():\n",
    "\tprint(f\"\\n{metric.upper()}:\")\n",
    "\tprint(f\"{'-'*80}\")\n",
    "\tprint(f\"{'Fiber':<20} {'Single Mean':<15} {'Multi Mean':<15} {'Difference':<15} {'P-value':<12} {'Status'}\")\n",
    "\tprint(f\"{'-'*80}\")\n",
    "\t\n",
    "\tfor fiber, stats in per_fiber_differences[metric].items():\n",
    "\t\tdiff_marker = \"↓\" if stats['mean_difference'] < 0 else \"↑\"  # ↓ means multi is better (lower noise)\n",
    "\t\tsig_marker = \"*\" if stats['significant'] else \"ns\"\n",
    "\t\t\n",
    "\t\tprint(f\"{fiber:<20} {stats['single_mean']:<15.6f} {stats['multi_mean']:<15.6f} \"\n",
    "\t\t\t  f\"{stats['mean_difference']:<15.6f} {diff_marker} {stats['p_value']:<12.4f} {sig_marker:<5} {stats['comparison']}\")\n",
    "\n",
    "# Save per-fiber differences to CSV\n",
    "for metric in per_fiber_differences.keys():\n",
    "\tdf = pd.DataFrame.from_dict(per_fiber_differences[metric], orient='index')\n",
    "\t# df.to_csv(f'./figures/JOVE/per_fiber_differences_{metric}.csv')\n",
    "\tprint(f\"\\nSaved per-fiber differences for {metric} to './figures/JOVE/per_fiber_differences_{metric}.csv'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2e9145f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved summary table: 23 regions × 16 columns\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jitter_test</th>\n",
       "      <th>Jitter_statistic</th>\n",
       "      <th>Jitter_p_value</th>\n",
       "      <th>Jitter_sig</th>\n",
       "      <th>Jitter_n</th>\n",
       "      <th>Jitter_single_mean ± std</th>\n",
       "      <th>Jitter_multi_mean ± std</th>\n",
       "      <th>Jitter_direction</th>\n",
       "      <th>High-frequency noise_test</th>\n",
       "      <th>High-frequency noise_statistic</th>\n",
       "      <th>High-frequency noise_p_value</th>\n",
       "      <th>High-frequency noise_sig</th>\n",
       "      <th>High-frequency noise_n</th>\n",
       "      <th>High-frequency noise_single_mean ± std</th>\n",
       "      <th>High-frequency noise_multi_mean ± std</th>\n",
       "      <th>High-frequency noise_direction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PrL (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>5.4999e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.006224 ± 0.001591</td>\n",
       "      <td>0.006546 ± 0.001517</td>\n",
       "      <td>No difference</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>149.00</td>\n",
       "      <td>6.9249e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.047769 ± 0.009866</td>\n",
       "      <td>0.048811 ± 0.008622</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNST (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.32</td>\n",
       "      <td>7.5139e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007170 ± 0.001829</td>\n",
       "      <td>0.006974 ± 0.001744</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5.5525e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.046295 ± 0.007997</td>\n",
       "      <td>0.044713 ± 0.007472</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POA (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>9.5206e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.005941 ± 0.001927</td>\n",
       "      <td>0.005981 ± 0.001871</td>\n",
       "      <td>No difference</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>161.00</td>\n",
       "      <td>9.8738e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.049571 ± 0.008903</td>\n",
       "      <td>0.049911 ± 0.008275</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AH (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>8.0271e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.006036 ± 0.001489</td>\n",
       "      <td>0.006166 ± 0.001517</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.25</td>\n",
       "      <td>8.0307e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.063774 ± 0.007774</td>\n",
       "      <td>0.063121 ± 0.007363</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vLS (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>8.4873e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007237 ± 0.002283</td>\n",
       "      <td>0.007389 ± 0.002328</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.0664e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.059661 ± 0.006111</td>\n",
       "      <td>0.058588 ± 0.005928</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VMH (E)</th>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>165.00</td>\n",
       "      <td>9.3696e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.004012 ± 0.001070</td>\n",
       "      <td>0.004007 ± 0.001008</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.1354e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.046030 ± 0.005627</td>\n",
       "      <td>0.045832 ± 0.004887</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAG (E)</th>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>159.00</td>\n",
       "      <td>9.3696e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007259 ± 0.001329</td>\n",
       "      <td>0.007295 ± 0.001412</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>9.7613e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.058192 ± 0.007164</td>\n",
       "      <td>0.058264 ± 0.006743</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeA (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>8.0307e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.006226 ± 0.001310</td>\n",
       "      <td>0.006342 ± 0.001365</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.09</td>\n",
       "      <td>9.2638e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.051037 ± 0.006835</td>\n",
       "      <td>0.050819 ± 0.006842</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LHb (E)</th>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>174.00</td>\n",
       "      <td>7.1597e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.009692 ± 0.002515</td>\n",
       "      <td>0.009235 ± 0.001978</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.5497e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.060878 ± 0.009112</td>\n",
       "      <td>0.057139 ± 0.005416</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>7.6678e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.005403 ± 0.001326</td>\n",
       "      <td>0.005540 ± 0.001350</td>\n",
       "      <td>No difference</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>152.00</td>\n",
       "      <td>7.6374e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.045190 ± 0.017702</td>\n",
       "      <td>0.045890 ± 0.017385</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAc (DA)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>8.6286e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007518 ± 0.001999</td>\n",
       "      <td>0.007634 ± 0.001913</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.01</td>\n",
       "      <td>9.9005e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.053370 ± 0.007558</td>\n",
       "      <td>0.053338 ± 0.007217</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMv (E)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9.5951e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.004846 ± 0.001239</td>\n",
       "      <td>0.004825 ± 0.001179</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>9.9095e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.052448 ± 0.007106</td>\n",
       "      <td>0.052475 ± 0.007009</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrL (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>5.3254e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.004811 ± 0.001171</td>\n",
       "      <td>0.005060 ± 0.001135</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>3.5641e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.041926 ± 0.005535</td>\n",
       "      <td>0.043573 ± 0.004702</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNST (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>5.42</td>\n",
       "      <td>4.8254e-06</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.009473 ± 0.002239</td>\n",
       "      <td>0.006045 ± 0.001333</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>285.00</td>\n",
       "      <td>1.0631e-04</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.046713 ± 0.010552</td>\n",
       "      <td>0.030363 ± 0.007694</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POA (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>2.68</td>\n",
       "      <td>1.1151e-02</td>\n",
       "      <td>*</td>\n",
       "      <td>18</td>\n",
       "      <td>0.008430 ± 0.002751</td>\n",
       "      <td>0.006292 ± 0.001792</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>292.00</td>\n",
       "      <td>4.1818e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.054086 ± 0.012592</td>\n",
       "      <td>0.038706 ± 0.004371</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AH (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>2.66</td>\n",
       "      <td>1.1723e-02</td>\n",
       "      <td>*</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007307 ± 0.001967</td>\n",
       "      <td>0.005735 ± 0.001432</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "      <td>t-test</td>\n",
       "      <td>7.10</td>\n",
       "      <td>3.3495e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.055102 ± 0.006205</td>\n",
       "      <td>0.042636 ± 0.003730</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vLS (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.1124e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.004682 ± 0.002204</td>\n",
       "      <td>0.004330 ± 0.001783</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.72</td>\n",
       "      <td>4.7586e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.046415 ± 0.011677</td>\n",
       "      <td>0.043886 ± 0.008538</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VMH (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.32</td>\n",
       "      <td>7.5354e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.005343 ± 0.001733</td>\n",
       "      <td>0.005168 ± 0.001470</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.09</td>\n",
       "      <td>9.3073e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.040495 ± 0.006862</td>\n",
       "      <td>0.040307 ± 0.005586</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAG (I)</th>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>279.00</td>\n",
       "      <td>2.2790e-04</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.011000 ± 0.002409</td>\n",
       "      <td>0.007975 ± 0.001730</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "      <td>t-test</td>\n",
       "      <td>13.72</td>\n",
       "      <td>2.0025e-15</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.067945 ± 0.006621</td>\n",
       "      <td>0.043419 ± 0.003233</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeA (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5.2409e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.006368 ± 0.001478</td>\n",
       "      <td>0.006076 ± 0.001145</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>0.87</td>\n",
       "      <td>3.8877e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.042456 ± 0.009497</td>\n",
       "      <td>0.039845 ± 0.007863</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LHb (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.0593e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.009116 ± 0.002324</td>\n",
       "      <td>0.008128 ± 0.002136</td>\n",
       "      <td>No difference</td>\n",
       "      <td>t-test</td>\n",
       "      <td>5.47</td>\n",
       "      <td>4.2671e-06</td>\n",
       "      <td>***</td>\n",
       "      <td>18</td>\n",
       "      <td>0.059245 ± 0.007302</td>\n",
       "      <td>0.048761 ± 0.003039</td>\n",
       "      <td>Multi &gt; Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.83</td>\n",
       "      <td>4.1457e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007443 ± 0.002381</td>\n",
       "      <td>0.006864 ± 0.001640</td>\n",
       "      <td>No difference</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2.3545e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.040039 ± 0.009869</td>\n",
       "      <td>0.037551 ± 0.006487</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMv (I)</th>\n",
       "      <td>t-test</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6.3868e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.006344 ± 0.001515</td>\n",
       "      <td>0.006102 ± 0.001460</td>\n",
       "      <td>No difference</td>\n",
       "      <td>Mann-Whitney U</td>\n",
       "      <td>196.00</td>\n",
       "      <td>2.8919e-01</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>18</td>\n",
       "      <td>0.046043 ± 0.008285</td>\n",
       "      <td>0.044490 ± 0.007188</td>\n",
       "      <td>No difference</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Jitter_test Jitter_statistic Jitter_p_value Jitter_sig  Jitter_n  \\\n",
       "Region                                                                          \n",
       "PrL (E)           t-test            -0.60     5.4999e-01       n.s.        18   \n",
       "BNST (E)          t-test             0.32     7.5139e-01       n.s.        18   \n",
       "POA (E)           t-test            -0.06     9.5206e-01       n.s.        18   \n",
       "AH (E)            t-test            -0.25     8.0271e-01       n.s.        18   \n",
       "vLS (E)           t-test            -0.19     8.4873e-01       n.s.        18   \n",
       "VMH (E)   Mann-Whitney U           165.00     9.3696e-01       n.s.        18   \n",
       "PAG (E)   Mann-Whitney U           159.00     9.3696e-01       n.s.        18   \n",
       "MeA (E)           t-test            -0.25     8.0307e-01       n.s.        18   \n",
       "LHb (E)   Mann-Whitney U           174.00     7.1597e-01       n.s.        18   \n",
       "PA (E)            t-test            -0.30     7.6678e-01       n.s.        18   \n",
       "NAc (DA)          t-test            -0.17     8.6286e-01       n.s.        18   \n",
       "PMv (E)           t-test             0.05     9.5951e-01       n.s.        18   \n",
       "PrL (I)           t-test            -0.63     5.3254e-01       n.s.        18   \n",
       "BNST (I)          t-test             5.42     4.8254e-06        ***        18   \n",
       "POA (I)           t-test             2.68     1.1151e-02          *        18   \n",
       "AH (I)            t-test             2.66     1.1723e-02          *        18   \n",
       "vLS (I)           t-test             0.51     6.1124e-01       n.s.        18   \n",
       "VMH (I)           t-test             0.32     7.5354e-01       n.s.        18   \n",
       "PAG (I)   Mann-Whitney U           279.00     2.2790e-04        ***        18   \n",
       "MeA (I)           t-test             0.64     5.2409e-01       n.s.        18   \n",
       "LHb (I)           t-test             1.29     2.0593e-01       n.s.        18   \n",
       "PA (I)            t-test             0.83     4.1457e-01       n.s.        18   \n",
       "PMv (I)           t-test             0.47     6.3868e-01       n.s.        18   \n",
       "\n",
       "         Jitter_single_mean ± std Jitter_multi_mean ± std Jitter_direction  \\\n",
       "Region                                                                       \n",
       "PrL (E)       0.006224 ± 0.001591     0.006546 ± 0.001517    No difference   \n",
       "BNST (E)      0.007170 ± 0.001829     0.006974 ± 0.001744    No difference   \n",
       "POA (E)       0.005941 ± 0.001927     0.005981 ± 0.001871    No difference   \n",
       "AH (E)        0.006036 ± 0.001489     0.006166 ± 0.001517    No difference   \n",
       "vLS (E)       0.007237 ± 0.002283     0.007389 ± 0.002328    No difference   \n",
       "VMH (E)       0.004012 ± 0.001070     0.004007 ± 0.001008    No difference   \n",
       "PAG (E)       0.007259 ± 0.001329     0.007295 ± 0.001412    No difference   \n",
       "MeA (E)       0.006226 ± 0.001310     0.006342 ± 0.001365    No difference   \n",
       "LHb (E)       0.009692 ± 0.002515     0.009235 ± 0.001978    No difference   \n",
       "PA (E)        0.005403 ± 0.001326     0.005540 ± 0.001350    No difference   \n",
       "NAc (DA)      0.007518 ± 0.001999     0.007634 ± 0.001913    No difference   \n",
       "PMv (E)       0.004846 ± 0.001239     0.004825 ± 0.001179    No difference   \n",
       "PrL (I)       0.004811 ± 0.001171     0.005060 ± 0.001135    No difference   \n",
       "BNST (I)      0.009473 ± 0.002239     0.006045 ± 0.001333   Multi > Single   \n",
       "POA (I)       0.008430 ± 0.002751     0.006292 ± 0.001792   Multi > Single   \n",
       "AH (I)        0.007307 ± 0.001967     0.005735 ± 0.001432   Multi > Single   \n",
       "vLS (I)       0.004682 ± 0.002204     0.004330 ± 0.001783    No difference   \n",
       "VMH (I)       0.005343 ± 0.001733     0.005168 ± 0.001470    No difference   \n",
       "PAG (I)       0.011000 ± 0.002409     0.007975 ± 0.001730   Multi > Single   \n",
       "MeA (I)       0.006368 ± 0.001478     0.006076 ± 0.001145    No difference   \n",
       "LHb (I)       0.009116 ± 0.002324     0.008128 ± 0.002136    No difference   \n",
       "PA (I)        0.007443 ± 0.002381     0.006864 ± 0.001640    No difference   \n",
       "PMv (I)       0.006344 ± 0.001515     0.006102 ± 0.001460    No difference   \n",
       "\n",
       "         High-frequency noise_test High-frequency noise_statistic  \\\n",
       "Region                                                              \n",
       "PrL (E)             Mann-Whitney U                         149.00   \n",
       "BNST (E)                    t-test                           0.60   \n",
       "POA (E)             Mann-Whitney U                         161.00   \n",
       "AH (E)                      t-test                           0.25   \n",
       "vLS (E)                     t-test                           0.52   \n",
       "VMH (E)                     t-test                           0.11   \n",
       "PAG (E)                     t-test                          -0.03   \n",
       "MeA (E)                     t-test                           0.09   \n",
       "LHb (E)                     t-test                           1.45   \n",
       "PA (E)              Mann-Whitney U                         152.00   \n",
       "NAc (DA)                    t-test                           0.01   \n",
       "PMv (E)                     t-test                          -0.01   \n",
       "PrL (I)                     t-test                          -0.93   \n",
       "BNST (I)            Mann-Whitney U                         285.00   \n",
       "POA (I)             Mann-Whitney U                         292.00   \n",
       "AH (I)                      t-test                           7.10   \n",
       "vLS (I)                     t-test                           0.72   \n",
       "VMH (I)                     t-test                           0.09   \n",
       "PAG (I)                     t-test                          13.72   \n",
       "MeA (I)                     t-test                           0.87   \n",
       "LHb (I)                     t-test                           5.47   \n",
       "PA (I)              Mann-Whitney U                         200.00   \n",
       "PMv (I)             Mann-Whitney U                         196.00   \n",
       "\n",
       "         High-frequency noise_p_value High-frequency noise_sig  \\\n",
       "Region                                                           \n",
       "PrL (E)                    6.9249e-01                     n.s.   \n",
       "BNST (E)                   5.5525e-01                     n.s.   \n",
       "POA (E)                    9.8738e-01                     n.s.   \n",
       "AH (E)                     8.0307e-01                     n.s.   \n",
       "vLS (E)                    6.0664e-01                     n.s.   \n",
       "VMH (E)                    9.1354e-01                     n.s.   \n",
       "PAG (E)                    9.7613e-01                     n.s.   \n",
       "MeA (E)                    9.2638e-01                     n.s.   \n",
       "LHb (E)                    1.5497e-01                     n.s.   \n",
       "PA (E)                     7.6374e-01                     n.s.   \n",
       "NAc (DA)                   9.9005e-01                     n.s.   \n",
       "PMv (E)                    9.9095e-01                     n.s.   \n",
       "PrL (I)                    3.5641e-01                     n.s.   \n",
       "BNST (I)                   1.0631e-04                      ***   \n",
       "POA (I)                    4.1818e-05                      ***   \n",
       "AH (I)                     3.3495e-08                      ***   \n",
       "vLS (I)                    4.7586e-01                     n.s.   \n",
       "VMH (I)                    9.3073e-01                     n.s.   \n",
       "PAG (I)                    2.0025e-15                      ***   \n",
       "MeA (I)                    3.8877e-01                     n.s.   \n",
       "LHb (I)                    4.2671e-06                      ***   \n",
       "PA (I)                     2.3545e-01                     n.s.   \n",
       "PMv (I)                    2.8919e-01                     n.s.   \n",
       "\n",
       "          High-frequency noise_n High-frequency noise_single_mean ± std  \\\n",
       "Region                                                                    \n",
       "PrL (E)                       18                    0.047769 ± 0.009866   \n",
       "BNST (E)                      18                    0.046295 ± 0.007997   \n",
       "POA (E)                       18                    0.049571 ± 0.008903   \n",
       "AH (E)                        18                    0.063774 ± 0.007774   \n",
       "vLS (E)                       18                    0.059661 ± 0.006111   \n",
       "VMH (E)                       18                    0.046030 ± 0.005627   \n",
       "PAG (E)                       18                    0.058192 ± 0.007164   \n",
       "MeA (E)                       18                    0.051037 ± 0.006835   \n",
       "LHb (E)                       18                    0.060878 ± 0.009112   \n",
       "PA (E)                        18                    0.045190 ± 0.017702   \n",
       "NAc (DA)                      18                    0.053370 ± 0.007558   \n",
       "PMv (E)                       18                    0.052448 ± 0.007106   \n",
       "PrL (I)                       18                    0.041926 ± 0.005535   \n",
       "BNST (I)                      18                    0.046713 ± 0.010552   \n",
       "POA (I)                       18                    0.054086 ± 0.012592   \n",
       "AH (I)                        18                    0.055102 ± 0.006205   \n",
       "vLS (I)                       18                    0.046415 ± 0.011677   \n",
       "VMH (I)                       18                    0.040495 ± 0.006862   \n",
       "PAG (I)                       18                    0.067945 ± 0.006621   \n",
       "MeA (I)                       18                    0.042456 ± 0.009497   \n",
       "LHb (I)                       18                    0.059245 ± 0.007302   \n",
       "PA (I)                        18                    0.040039 ± 0.009869   \n",
       "PMv (I)                       18                    0.046043 ± 0.008285   \n",
       "\n",
       "         High-frequency noise_multi_mean ± std High-frequency noise_direction  \n",
       "Region                                                                         \n",
       "PrL (E)                    0.048811 ± 0.008622                  No difference  \n",
       "BNST (E)                   0.044713 ± 0.007472                  No difference  \n",
       "POA (E)                    0.049911 ± 0.008275                  No difference  \n",
       "AH (E)                     0.063121 ± 0.007363                  No difference  \n",
       "vLS (E)                    0.058588 ± 0.005928                  No difference  \n",
       "VMH (E)                    0.045832 ± 0.004887                  No difference  \n",
       "PAG (E)                    0.058264 ± 0.006743                  No difference  \n",
       "MeA (E)                    0.050819 ± 0.006842                  No difference  \n",
       "LHb (E)                    0.057139 ± 0.005416                  No difference  \n",
       "PA (E)                     0.045890 ± 0.017385                  No difference  \n",
       "NAc (DA)                   0.053338 ± 0.007217                  No difference  \n",
       "PMv (E)                    0.052475 ± 0.007009                  No difference  \n",
       "PrL (I)                    0.043573 ± 0.004702                  No difference  \n",
       "BNST (I)                   0.030363 ± 0.007694                 Multi > Single  \n",
       "POA (I)                    0.038706 ± 0.004371                 Multi > Single  \n",
       "AH (I)                     0.042636 ± 0.003730                 Multi > Single  \n",
       "vLS (I)                    0.043886 ± 0.008538                  No difference  \n",
       "VMH (I)                    0.040307 ± 0.005586                  No difference  \n",
       "PAG (I)                    0.043419 ± 0.003233                 Multi > Single  \n",
       "MeA (I)                    0.039845 ± 0.007863                  No difference  \n",
       "LHb (I)                    0.048761 ± 0.003039                 Multi > Single  \n",
       "PA (I)                     0.037551 ± 0.006487                  No difference  \n",
       "PMv (I)                    0.044490 ± 0.007188                  No difference  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Metric display name mapping\n",
    "metric_display = {\n",
    "\t'cv_diff': 'Jitter',\n",
    "\t'diff_std': 'High-frequency noise'\n",
    "}\n",
    "\n",
    "# Build summary table\n",
    "summary_rows = []\n",
    "\n",
    "for fiber in per_fiber_differences[metrics[0]].keys():\n",
    "\trow = {'Region': fiber}\n",
    "\n",
    "\tfor metric in metrics:\n",
    "\t\tdisplay_name = metric_display[metric]\n",
    "\n",
    "\t\t# Extract data for this fiber across all files\n",
    "\t\tsingle_data_metric = []\n",
    "\t\tmulti_data_metric = []\n",
    "\n",
    "\t\tfor key in noise_stats.keys():\n",
    "\t\t\tif fiber in noise_stats[key]:\n",
    "\t\t\t\ts_val = noise_stats[key][fiber]['single_site_corrected'][metric]\n",
    "\t\t\t\tm_val = noise_stats[key][fiber]['reg_corrected'][metric]\n",
    "\t\t\t\tsingle_data_metric.append(s_val)\n",
    "\t\t\t\tmulti_data_metric.append(m_val)\n",
    "\n",
    "\t\tsingle_data_metric = np.array(single_data_metric, dtype=float)\n",
    "\t\tmulti_data_metric = np.array(multi_data_metric, dtype=float)\n",
    "\n",
    "\t\t# Filter out NaNs and Infs — require BOTH to be valid for paired comparison\n",
    "\t\tvalid_mask = (\n",
    "\t\t\t~np.isnan(single_data_metric) & ~np.isnan(multi_data_metric) &\n",
    "\t\t\t~np.isinf(single_data_metric) & ~np.isinf(multi_data_metric)\n",
    "\t\t)\n",
    "\t\tsingle_clean = single_data_metric[valid_mask]\n",
    "\t\tmulti_clean = multi_data_metric[valid_mask]\n",
    "\n",
    "\t\tn_valid = len(single_clean)\n",
    "\n",
    "\t\t# Check if we have enough data\n",
    "\t\tif n_valid < 3:\n",
    "\t\t\trow[f'{display_name}_test'] = 'insufficient data'\n",
    "\t\t\trow[f'{display_name}_statistic'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_p_value'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_sig'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_n'] = n_valid\n",
    "\t\t\trow[f'{display_name}_single_mean ± std'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_multi_mean ± std'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_direction'] = 'N/A'\n",
    "\t\t\tprint(f\"WARNING: {fiber} / {display_name} — only {n_valid} valid observations, skipping test\")\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\t# Check for zero variance\n",
    "\t\tif np.std(single_clean) == 0 and np.std(multi_clean) == 0:\n",
    "\t\t\trow[f'{display_name}_test'] = 'zero variance'\n",
    "\t\t\trow[f'{display_name}_statistic'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_p_value'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_sig'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_n'] = n_valid\n",
    "\t\t\trow[f'{display_name}_single_mean ± std'] = f\"{np.mean(single_clean):.6f} ± 0.000000\"\n",
    "\t\t\trow[f'{display_name}_multi_mean ± std'] = f\"{np.mean(multi_clean):.6f} ± 0.000000\"\n",
    "\t\t\trow[f'{display_name}_direction'] = 'No difference'\n",
    "\t\t\tprint(f\"WARNING: {fiber} / {display_name} — zero variance in both groups\")\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\t# Normality check\n",
    "\t\ttry:\n",
    "\t\t\t_, p_s_norm = shapiro(single_clean)\n",
    "\t\t\t_, p_m_norm = shapiro(multi_clean)\n",
    "\t\t\tis_normal = (p_s_norm > 0.05) and (p_m_norm > 0.05)\n",
    "\t\texcept Exception as e:\n",
    "\t\t\tprint(f\"WARNING: Shapiro failed for {fiber} / {display_name}: {e}\")\n",
    "\t\t\tis_normal = False\n",
    "\n",
    "\t\t# Run appropriate test\n",
    "\t\ttry:\n",
    "\t\t\tif is_normal:\n",
    "\t\t\t\ttest_stat, p_val = ttest_ind(single_clean, multi_clean)\n",
    "\t\t\t\ttest_kind = 't-test'\n",
    "\t\t\telse:\n",
    "\t\t\t\ttest_stat, p_val = mannwhitneyu(single_clean, multi_clean, alternative='two-sided')\n",
    "\t\t\t\ttest_kind = 'Mann-Whitney U'\n",
    "\t\texcept Exception as e:\n",
    "\t\t\tprint(f\"WARNING: Test failed for {fiber} / {display_name}: {e}\")\n",
    "\t\t\trow[f'{display_name}_test'] = 'test failed'\n",
    "\t\t\trow[f'{display_name}_statistic'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_p_value'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_sig'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_n'] = n_valid\n",
    "\t\t\trow[f'{display_name}_single_mean ± std'] = f\"{np.mean(single_clean):.6f} ± {np.std(single_clean):.6f}\"\n",
    "\t\t\trow[f'{display_name}_multi_mean ± std'] = f\"{np.mean(multi_clean):.6f} ± {np.std(multi_clean):.6f}\"\n",
    "\t\t\trow[f'{display_name}_direction'] = 'N/A'\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\t# Verify result is valid\n",
    "\t\tif np.isnan(test_stat) or np.isnan(p_val):\n",
    "\t\t\tprint(f\"WARNING: NaN result for {fiber} / {display_name}\")\n",
    "\t\t\trow[f'{display_name}_test'] = f'{test_kind} (NaN)'\n",
    "\t\t\trow[f'{display_name}_statistic'] = 'NaN'\n",
    "\t\t\trow[f'{display_name}_p_value'] = 'NaN'\n",
    "\t\t\trow[f'{display_name}_sig'] = 'N/A'\n",
    "\t\t\trow[f'{display_name}_n'] = n_valid\n",
    "\t\t\trow[f'{display_name}_single_mean ± std'] = f\"{np.mean(single_clean):.6f} ± {np.std(single_clean):.6f}\"\n",
    "\t\t\trow[f'{display_name}_multi_mean ± std'] = f\"{np.mean(multi_clean):.6f} ± {np.std(multi_clean):.6f}\"\n",
    "\t\t\trow[f'{display_name}_direction'] = 'N/A'\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\t# Significance stars\n",
    "\t\tif p_val < 0.001:\n",
    "\t\t\tsig_stars = '***'\n",
    "\t\telif p_val < 0.01:\n",
    "\t\t\tsig_stars = '**'\n",
    "\t\telif p_val < 0.05:\n",
    "\t\t\tsig_stars = '*'\n",
    "\t\telse:\n",
    "\t\t\tsig_stars = 'n.s.'\n",
    "\n",
    "\t\t# Direction\n",
    "\t\tsingle_mean = np.mean(single_clean)\n",
    "\t\tmulti_mean = np.mean(multi_clean)\n",
    "\t\tif p_val < 0.05:\n",
    "\t\t\tdirection = 'Multi > Single' if multi_mean < single_mean else 'Single > Multi'\n",
    "\t\telse:\n",
    "\t\t\tdirection = 'No difference'\n",
    "\n",
    "\t\trow[f'{display_name}_test'] = test_kind\n",
    "\t\trow[f'{display_name}_statistic'] = f\"{test_stat:.2f}\"\n",
    "\t\trow[f'{display_name}_p_value'] = f\"{p_val:.4e}\"\n",
    "\t\trow[f'{display_name}_sig'] = sig_stars\n",
    "\t\trow[f'{display_name}_n'] = n_valid\n",
    "\t\trow[f'{display_name}_single_mean ± std'] = f\"{single_mean:.6f} ± {np.std(single_clean):.6f}\"\n",
    "\t\trow[f'{display_name}_multi_mean ± std'] = f\"{multi_mean:.6f} ± {np.std(multi_clean):.6f}\"\n",
    "\t\trow[f'{display_name}_direction'] = direction\n",
    "\n",
    "\tsummary_rows.append(row)\n",
    "\n",
    "df_summary = pd.DataFrame(summary_rows)\n",
    "df_summary.set_index('Region', inplace=True)\n",
    "\n",
    "# Reorder columns: for each metric, enforce test -> statistic -> p_value -> sig -> n -> means -> direction\n",
    "ordered_cols = []\n",
    "for metric in metrics:\n",
    "\tdisplay_name = metric_display[metric]\n",
    "\tordered_cols.extend([\n",
    "\t\tf'{display_name}_test',\n",
    "\t\tf'{display_name}_statistic',\n",
    "\t\tf'{display_name}_p_value',\n",
    "\t\tf'{display_name}_sig',\n",
    "\t\tf'{display_name}_n',\n",
    "\t\tf'{display_name}_single_mean ± std',\n",
    "\t\tf'{display_name}_multi_mean ± std',\n",
    "\t\tf'{display_name}_direction',\n",
    "\t])\n",
    "# Only keep columns that exist (in case some were skipped)\n",
    "ordered_cols = [c for c in ordered_cols if c in df_summary.columns]\n",
    "df_summary = df_summary[ordered_cols]\n",
    "\n",
    "df_summary.to_csv('./figures/JOVE/per_fiber_summary_table.csv')\n",
    "print(f\"Saved summary table: {df_summary.shape[0]} regions × {df_summary.shape[1]} columns\\n\")\n",
    "display(df_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32f2d519",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "multifiber",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.23"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}