blp-analysis-diffusion-mri-tract-profiles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d000a44f",
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   "source": [
    "# Brainlife analyses using Jupyter Notebooks and Python3\n",
    "\n",
    "This example notebook will guide the user through grabbing data for their project from the secondary warehouse, compiling data across the entire project, and analyzing and visualizing the compiled results on brainlife.io via the 'Analysis' tab. This example is written for python3, and uses one of the Python3 notebook types.\n",
    "\n",
    "Within this notebook, I will guide the user through analyses comprising derivatives generated from one of the three main datatypes available on brainlife.io, specifically neuro/dwi. Specifically, I will guide the user through:\n",
    "        \n",
    "    1. Analyzing tract profilometry data and macrostructural statistics (length, streamline count, volume) using the 61 tract segmentation generated from the White Matter Anatomy Segmentation app available on brainlife.io\n",
    "        \n",
    "The functions provided in this notebook were designed for performing the many experiments provided in Hayashi & Caron et al (in prep), but should be a good starting point for you to get started on analyzing your project on brainlife.io!\n",
    "\n",
    "### Important Note regarding assumptions of data structures, both inputs and outputs\n",
    "\n",
    "Before we get started, it is important to note at the begging that the code in this notebook was designed mostly with a stereotypical data structure format in mind. For many of the datatypes, specifically the csv datatypes, follow the tidy format [https://www.jstatsoft.org/article/view/v059i10], where \"each column is a variable, every row is an observation, and every cell is a singular value\"[https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html]. There will be a few common columns, specifically 'subjectID' denoting the brainlife subject ID, 'sessionID' denoting the session ID on brainlife (will be 1 if there is no session ID data available), 'structureID' denoting the tissue structure of interest (ex. white matter tract, cortical parcel, eeg node), and 'nodeID' denoting the sample number of the structure. 'nodeID' is included across many .csv-based datatypes to conform with profilometry data, which has multiple samples or nodes per structure. For single measures of a given structure, the nodeID for all structures will be 1. \n",
    "\n",
    "However, this is not the case for network adjacency matrices. Specifically with the network.json.gz files that come within the generic/network datatype, the resulting compiled data structure will be compiled as a json structure with a numpy array of the matrix for each subject and session. \n",
    "\n",
    "I will provide examples of these resulting structures later on in the notebook.\n",
    "\n",
    "I bring this up now to note that if you are not a fan of the style of the resulting data structures, or your data doesn't confirm to this style, you are free to alter this code to your deepest desires! I've attempted to do my best to comment and explain everything as much as possible for each section, function, and analysis. If there are any points where something is not working or you are not sure what the code is doing, feel free to email me at [bacaron245@gmail.com!]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623ec1ae",
   "metadata": {},
   "source": [
    "# With that, let's get started!\n",
    "\n",
    "## Install packages\n",
    "The first things we need to do are to install some software packages using python's pip package manager, and then to load all of the necessary modules into our notebook.\n",
    "\n",
    "Specifically, we will install the python-import of the brain connectivity toolbox (BCT; ) known as bctpy [], jgf to load json packages (for our network matrices), and nilearn to make some pretty network plots!\n",
    "\n",
    "We will also be loading some other packages, including matplotlib, seaborn, and nilearn for plotting, numpy for numerical calculations and array manipulations, itertools and scipy for statistical testing, pandas, json, and jgf for loading various datat strucutres, and scikit-learn's mean_squared_error function to compute root-mean-square-error for some of the final analyses. "
   ]
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  {
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   "execution_count": 1,
   "id": "f2d27821",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bctpy in /home/brad/.local/lib/python3.8/site-packages (0.5.2)\n",
      "Requirement already satisfied: jgf in /home/brad/.local/lib/python3.8/site-packages (0.2.2)\n",
      "Requirement already satisfied: nilearn in /home/brad/.local/lib/python3.8/site-packages (0.6.2)\n",
      "Requirement already satisfied: scipy in /home/brad/.local/lib/python3.8/site-packages (from bctpy) (1.4.1)\n",
      "Requirement already satisfied: numpy in /home/brad/.local/lib/python3.8/site-packages (from bctpy) (1.21.5)\n",
      "Requirement already satisfied: sklearn in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (0.0)\n",
      "Requirement already satisfied: scikit-learn>=0.19 in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (0.22.2.post1)\n",
      "Requirement already satisfied: nibabel>=2.0.2 in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (3.0.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (1.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install bctpy jgf nilearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbb1577",
   "metadata": {},
   "source": [
    "## Importing packages\n",
    "The first things we need to do is to load python packages in order to perform actions on the processed data, including matplotlib and seaborn for plotting, numpy for numerical calculations and array manipulations, itertools and scipy for statistical testing, pandas, and scikit-learn's mean_squared_error function to compute root-mean-square-error for some of the final analyses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9951cac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,glob\n",
    "import requests\n",
    "from matplotlib import colors as mcolors\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import json\n",
    "import seaborn as sns\n",
    "from itertools import combinations\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy import stats\n",
    "import bct\n",
    "import jgf\n",
    "from nilearn import plotting\n",
    "from scipy.signal import resample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb690f4",
   "metadata": {},
   "source": [
    "## Let's define and load some useful functions!\n",
    "\n",
    "This next section loads many functions for loading, compiling, analyzing, and visualizing some common datatypes on brainlife.io. To make it easier to load, I've provided all the functions used throughout the rest of the notebook in a single cell. However, for each function, I've provided comments that attempt to explain what the function does and what the inputs and outputs are. I've sectioned off the code into multiple sections: 1) data loading, compilation, and storage, 2) data analysis, manipulation and statistical computations, and 3) visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2407a774",
   "metadata": {},
   "source": [
    "### Data loading, compilation, and storage\n",
    "The first section of functions deals with collecting data from secondary warehouse and compiling the data into a useful structures for further analysis. The main function the user will call from this section is the wrapper 'collect_data', which will identify the appropriate data from the secondary warehouse based on user input and compile that data into a structure the user can use for analysis. This function makes a couple of important assumptions. Specifically:\n",
    "\n",
    "    1. If there are duplicate datatypes that meet all of the specifications and filters, this function will only collect the data that was most recent to finish\n",
    "    \n",
    "    2. If the data can be formatted to a csv-file, it will be formatted to a csv-file. The only exception is network adjacency matrices.\n",
    "    \n",
    "    3. Users can control what data is grabbed and compiled by being specific with datatype_tags and tags.\n",
    "    \n",
    "The remaining functions perform other useful tasks, such as adding subjectID and sessionID data if missing, checking for duplicate entries of the same datatype (assumption 1), and append and compile data into the output data structure.\n",
    "\n",
    "As previously mentioned, the resulting csv-file structures will be in the tidy format, and network adjacency matricies will be stored as a json-structure with the matricies stored as numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc58a7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## this will add a subjectID and sessionID column to the output data\n",
    "def add_subjects_sessions(subject,session,path,data):\n",
    "    \n",
    "    if 'subjectID' not in data.keys():\n",
    "        data['subjectID'] = [ str(subject) for f in range(len(data)) ]\n",
    "    \n",
    "    if 'sessionID' not in data.keys():\n",
    "        data['sessionID'] = [ str(session) for f in range(len(data)) ]\n",
    "        \n",
    "    return data\n",
    "\n",
    "## this function calles check_for_duplicates and attempts to find duplicates. then uses that output, sets a dumby sessionID if not present,\n",
    "## and appends the object data\n",
    "def append_data(subjects,sessions,paths,finish_dates,obj,filename):\n",
    "        \n",
    "    # check for duplicates. if so, remove\n",
    "    finish_dates, subjects, sessions, paths = check_for_duplicates(obj,finish_dates,subjects,sessions,paths)\n",
    "\n",
    "    # append data to appropriate lists\n",
    "    subjects = np.append(subjects,str(obj['output']['meta']['subject']))\n",
    "    if 'session' in obj['output']['meta'].keys():\n",
    "        sessions = np.append(sessions,obj['output']['meta']['session'])\n",
    "    else:\n",
    "        sessions = np.append(sessions,'1')\n",
    "    paths = np.append(paths,\"input/\"+obj[\"path\"]+\"/\"+filename)\n",
    "    finish_dates = np.append(finish_dates,obj['finish_date'])\n",
    "    \n",
    "    return finish_dates, subjects, sessions, paths\n",
    "\n",
    "## this function will call add_subjects_sessions to add the appropriate columns and will append the object data to a study-wide dataframe\n",
    "def compile_data(paths,subjects,sessions,data):\n",
    "    # loops through all paths\n",
    "    for i in range(len(paths)):\n",
    "        # if network, load json. if not, load csv\n",
    "        if '.json.gz' in paths[i]:\n",
    "            tmpdata = pd.read_json(paths[i],orient='index').reset_index(drop=True)\n",
    "            tmpdata = add_subjects_sessions(subjects[i],sessions[i],paths[i],tmpdata)\n",
    "        else:\n",
    "            if '.tsv' in paths[i]:\n",
    "                sep = '\\t'\n",
    "            else:\n",
    "                sep = ','\n",
    "            tmpdata = pd.read_csv(paths[i],sep=sep)\n",
    "            tmpdata = add_subjects_sessions(subjects[i],sessions[i],paths[i],tmpdata)\n",
    "\n",
    "        data = data.append(tmpdata,ignore_index=True)\n",
    "\n",
    "    # replace empty spaces with nans\n",
    "    data = data.replace(r'^\\s+$', np.nan, regex=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "# this function will comile network adjacency matrices into a dictionary structure\n",
    "def compile_network_adjacency_matrices(paths,subjects,sessions,data):\n",
    "    \n",
    "    # loop through paths and append adjacency matrix to dictionary\n",
    "    for i in range(len(paths)):\n",
    "        data[subjects[i]+'_sess'+sessions[i]] = jgf.conmat.load(paths[i],compressed=True)[0]\n",
    "\n",
    "    return data\n",
    "\n",
    "### load data\n",
    "## this function is useful for identifying duplicate datatypes. if it finds one, it will update the data with the latest finishing dataset.\n",
    "def check_for_duplicates(obj,finish_dates,subjects,sessions,paths):\n",
    "    \n",
    "    # first checks if there is a session id available in the keys of the object. if finds one, then checks if the subject and session ID \n",
    "    # were already looped over. if so, will delete position in list and update with appropriate path. if it doesn't find a session ID, it\n",
    "    # just attempts to find if the subject has already been looped over\n",
    "    if 'session' in obj['output']['meta'].keys():\n",
    "        if (obj['output']['meta']['subject'] in subjects) and (obj['output']['meta']['session'] in sessions):\n",
    "            index = np.where(np.logical_and(subjects == obj['output']['meta']['subject'],sessions == obj['output']['meta']['session']))\n",
    "            if finish_dates[index] <= obj[\"finish_date\"]:\n",
    "                subjects = np.delete(subjects,index)\n",
    "                paths = np.delete(paths,index)\n",
    "                sessions = np.delete(sessions,index)\n",
    "                finish_dates = np.delete(finish_dates,index)\n",
    "    else:\n",
    "        if (obj['output']['meta']['subject'] in subjects):\n",
    "            index = np.where(subjects == obj['output']['meta']['subject'])\n",
    "            if finish_dates[index] <= obj[\"finish_date\"]:\n",
    "                subjects = np.delete(subjects,index)\n",
    "                paths = np.delete(paths,index)\n",
    "                sessions = np.delete(sessions,index)\n",
    "                finish_dates = np.delete(finish_dates,index)\n",
    "\n",
    "    return finish_dates, subjects, sessions, paths\n",
    "\n",
    "# this will check to see if the datatype tags or tags of the datatype object exists within the filtered ('!') tags\n",
    "def check_for_filter_tags(input_tags,obj,tagOrDatatypeTag):\n",
    "    \n",
    "    filter_checks = 0\n",
    "    for i in input_tags:\n",
    "        if i.replace('!','') not in obj['output'][tagOrDatatypeTag]:\n",
    "            filter_checks = filter_checks+1\n",
    "\n",
    "    return filter_checks\n",
    "\n",
    "## this function is the wrapper function that calls all the prevouis functions to generate a dataframe for the entire project of the appropriate datatype\n",
    "def collect_data(datatype,datatype_tags,tags,filename,outPath,net_adj):\n",
    "\n",
    "    # grab path and data objects\n",
    "    objects = requests.get('https://brainlife.io/api/warehouse/secondary/list/%s'%os.environ['PROJECT_ID']).json()\n",
    "\n",
    "    # subjects and paths\n",
    "    subjects = []\n",
    "    sessions = []\n",
    "    paths = []\n",
    "    finish_dates = []\n",
    "    obj_datatype_tags = []\n",
    "    obj_tags = []\n",
    "\n",
    "    # set up output\n",
    "    data = pd.DataFrame()\n",
    "\n",
    "    # loop through objects and find appropriate objects based on datatype, datatype_tags, and tags. can include drop tags ('!'). this logic could probably be simplified\n",
    "    for obj in objects:\n",
    "        if obj['datatype']['name'] == datatype:\n",
    "            # if datatype_tags is set, identify data using this info. if not, just use tag data. if no tags either, just append if meets datatype criteria. will check for filter with a not tag (!)\n",
    "            if datatype_tags:\n",
    "                # if the input datatype_tags are included in the object's datatype_tags, look for appropriate tags. if no tags, just append\n",
    "                if 'datatype_tags' in list(obj['output'].keys()) and len(obj['output']['datatype_tags']) != 0:\n",
    "                    if '!' in str(datatype_tags):\n",
    "                        datatype_tags_to_drop = [ f for f in datatype_tags if '!' in str(f) ]\n",
    "                        datatype_tag_keep = [ f for f in datatype_tags if f not in datatype_tags_to_drop ]\n",
    "\n",
    "                        if set(datatype_tag_keep).issubset(obj['output']['datatype_tags']):\n",
    "                            datatype_tag_checks = check_for_filter_tags(datatype_tags_to_drop,obj,'datatype_tags')\n",
    "                            if datatype_tag_checks == len(datatype_tags_to_drop):\n",
    "                                datatype_tag_filter = True\n",
    "                            else:\n",
    "                                datatype_tag_filter = False\n",
    "                        else:\n",
    "                            datatype_tag_filter = False\n",
    "                    else:\n",
    "                        if set(datatype_tags).issubset(obj['output']['datatype_tags']):\n",
    "                            datatype_tag_filter = True\n",
    "                        else:\n",
    "                            datatype_tag_filter = False\n",
    "                else:\n",
    "                    datatype_tag_filter = False\n",
    "            else:\n",
    "                datatype_tag_filter = True\n",
    "\n",
    "            if tags:\n",
    "                if 'tags' in list(obj['output'].keys()) and len(obj['output']['tags']) != 0:\n",
    "                    if '!' in str(tags):\n",
    "                        tags_drop = [ f for f in tags if '!' in str(f) ]\n",
    "                        tags_keep = [ f for f in tags if f not in tags_drop ]\n",
    "\n",
    "                        if set(tags_keep).issubset(obj['output']['tags']):\n",
    "                            tag_checks = checkcheck_for_filter_tagsForFilterTags(tags_drop,obj,'tags')\n",
    "                            if tag_checks == len(tags_drop):\n",
    "                                tag_filter = True\n",
    "                            else:\n",
    "                                tag_filter = False\n",
    "                        else:\n",
    "                            tag_filter = False\n",
    "                    else:\n",
    "                        if set(tags).issubset(obj['output']['tags']):\n",
    "                            tag_filter = True\n",
    "                        else:\n",
    "                            tag_filter = False\n",
    "                else:\n",
    "                    tag_filter = False\n",
    "            else:\n",
    "                tag_filter = True\n",
    "\n",
    "            if datatype_tag_filter == True & tag_filter == True:\n",
    "                obj_datatype_tags = np.append(obj_datatype_tags,obj['output']['datatype_tags'])\n",
    "                obj_tags = np.append(obj_tags,obj['output']['tags'])\n",
    "                finish_dates, subjects, sessions, paths = append_data(subjects,sessions,paths,finish_dates,obj,filename)\n",
    "\n",
    "    # shuffle data so subjects are in order\n",
    "    paths = [z for _,_,z in sorted(zip(subjects,sessions,paths))]\n",
    "    subjects = [x for x,_,_ in sorted(zip(subjects,sessions,paths))]\n",
    "    sessions = [y for _,y,_ in sorted(zip(subjects,sessions,paths))]\n",
    "\n",
    "    # check if tab separated or comma separated by looking at input filename\n",
    "    if '.tsv' in filename:\n",
    "        sep = '\\t'\n",
    "    else:\n",
    "        sep = ','\n",
    "\n",
    "    # compile data\n",
    "    if net_adj:\n",
    "        data = {}\n",
    "        data = compile_network_adjacency_matrices(paths,subjects,sessions,data)\n",
    "        if outPath:\n",
    "            np.save(outPath,data)\n",
    "    else:\n",
    "        data = compile_data(paths,subjects,sessions,data)\n",
    "\n",
    "    # output data structure for records and any further analyses\n",
    "    if outPath:\n",
    "        data.to_csv(outPath,sep=sep,index=False)\n",
    "\n",
    "    return data\n",
    "\n",
    "### subjects dataframe generation\n",
    "## this function will make a dataframe from a list of subjects and groups. will also add a color column for easy plotting\n",
    "def collect_subject_data(topPath,dataPath,groups,subjects,colors):\n",
    "\n",
    "    # set up variables\n",
    "    data_columns = ['subjectID','classID','colors']\n",
    "    data =  pd.DataFrame([],columns=data_columns)\n",
    "\n",
    "    # populate structure\n",
    "    data['subjectID'] = [ f for g in groups for f in subjects[g] ]\n",
    "    data['classID'] = [ g for g in groups for f in range(len(subjects[g]))]\n",
    "    data['colors'] = [ colors[c] for c in colors for f in subjects[c]]\n",
    "\n",
    "    # output data structure for records and any further analyses\n",
    "    if not os.path.exists(dataPath):\n",
    "        os.mkdir(dataPath)\n",
    "\n",
    "    data.to_csv(dataPath+'subjects.csv',index=False)\n",
    "\n",
    "    return data\n",
    "\n",
    "## this will create a subject-specific color for each subject in the subjects dataframe\n",
    "def create_color_dictionary(data,measure,colorPalette):\n",
    "\n",
    "    # Create subject keys and color values\n",
    "    keys = data[measure].unique()\n",
    "    values = sns.color_palette(colorPalette,len(keys))\n",
    "    values = values.as_hex()\n",
    "\n",
    "    # zip dictionary together\n",
    "    colors_dict = dict(zip(keys,values))\n",
    "\n",
    "    return colors_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27af75e",
   "metadata": {},
   "source": [
    "### Data analysis, manipulation and statistical computations\n",
    "This next section contains functions for manipulating, analysing, and computing statistics from the data collected using the functions previously described. This includes many different manipulations and computations, including trimming nodes off profilometry data, computing mean dataframes, computing outliers and generating reference datasets, and some simple network adjacency matrix manipulations including binarizing, thresholding, and computing the mean witihin-network functional connectivity. \n",
    "\n",
    "The bulk of these functions are devoted to identifying outliers and generating reference datasets. My approach for this included identifying outliers for each structure and measure across a given project  by computing the mean distribution and identifying those with distances from that point greater than a percentile threshold. I then removed those data points, and recomputed the reference dataset. The references generated have been built-into many of the verification steps following Apps. The resulting dataframes are formatted in both csv and json structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1767b9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "### dataframe manipulations\n",
    "## cut nodes for profilometry / timeseries data\n",
    "def cut_nodes(data,num_nodes,dataPath,foldername,savename):\n",
    "\n",
    "    # identify inner n nodes based on num_nodes input\n",
    "    total_nodes = len(data['nodeID'].unique())\n",
    "    cut_nodes = int((total_nodes - num_nodes) / 2)\n",
    "\n",
    "    # remove cut_nodes from dataframe\n",
    "    data = data[data['nodeID'].between((cut_nodes)+1,(num_nodes+cut_nodes))]\n",
    "\n",
    "    # replace empty spaces with nans\n",
    "    data = data.replace(r'^\\s+$', np.nan, regex=True)\n",
    "\n",
    "    # output data structure for records and any further analyses\n",
    "    if not os.path.exists(dataPath):\n",
    "        os.mkdir(dataPath)\n",
    "\n",
    "    data.to_csv(dataPath+'/'+foldername+'-'+savename+'.csv',index=False)\n",
    "\n",
    "    return data\n",
    "\n",
    "## will compute mean dataframe \n",
    "def compute_mean_data(dataPath,data,outname):\n",
    "\n",
    "    # make mean data frame\n",
    "    data_mean =  data.groupby(['subjectID','classID','structureID']).mean().reset_index()\n",
    "    data_mean['nodeID'] = [ 1 for f in range(len(data_mean['nodeID'])) ]\n",
    "\n",
    "    # output data structure for records and any further analyses\n",
    "    if not os.path.exists(dataPath):\n",
    "        os.mkdir(dataPath)\n",
    "\n",
    "    data_mean.to_csv(dataPath+outname+'.csv',index=False)\n",
    "\n",
    "    return data_mean\n",
    "\n",
    "### scripts related to outlier detection and reference dataframe generation\n",
    "## this function will compute distance measures from input data and reference data\n",
    "def compute_distance(data,references_data,measures,metric):\n",
    "\n",
    "    # imports important distance functions\n",
    "    from sklearn.metrics.pairwise import euclidean_distances\n",
    "    from scipy.stats import wasserstein_distance\n",
    "\n",
    "    # if distance metric desired is euclidean distance (i.e. for profiles), computes distance of profile from reference profile. else, just computes difference using emd\n",
    "    if metric == 'euclidean':\n",
    "        dist = data.groupby('subjectID',sort=False).apply(lambda x: euclidean_distances([x[measures].values.tolist(),references_data[measures].values.tolist()])[0][1]).values\n",
    "    else:\n",
    "        dist = data.groupby('subjectID',sort=False).apply(lambda x: wasserstein_distance(x[measures],[references_data[measures].values[0]]))\n",
    "\n",
    "    return dist\n",
    "\n",
    "## this function will compute simple average references for a given input data\n",
    "def compute_references(x,groupby_measures,index_measure,diff_measures):\n",
    "    \n",
    "    # computes mean and sd of the measures in a dataframe\n",
    "    references_mean = x.groupby(groupby_measures).mean().reset_index(index_measure)\n",
    "    references_sd = x.groupby(groupby_measures).std().reset_index(index_measure)\n",
    "    references_sd[diff_measures] = references_sd[diff_measures] * 2\n",
    "    \n",
    "    return references_mean, references_sd\n",
    "\n",
    "## this function calls compute_references and compute_distance to create a dataframe of distance measures\n",
    "def create_distance_dataframe(data,structures,groupby_measure,measures,dist_metric):\n",
    "    \n",
    "    # set up output lists that we will append to\n",
    "    dist = []\n",
    "    subj = []\n",
    "    meas = []\n",
    "    struc = []\n",
    "\n",
    "    # loop through appropriate structures\n",
    "    for i in structures:\n",
    "        print(i)\n",
    "        # set data for a given structure\n",
    "        subj_data = data.loc[data['structureID'] == i]\n",
    "        # compute reference for given structure\n",
    "        references_data = compute_references(subj_data,groupby_measure,groupby_measure,measures)\n",
    "        # loop through measures and compute distance from reference\n",
    "        for m in measures:\n",
    "            if dist_metric == 'euclidean':\n",
    "                dist = np.append(dist,compute_distance(subj_data,references_data[0],m,'euclidean'))\n",
    "            else:\n",
    "                dist = np.append(dist,compute_distance(subj_data,references_data[0],m,'emd'))\n",
    "            \n",
    "            # append data to appropriate lists\n",
    "            subj = np.append(subj,subj_data.subjectID.unique().tolist())\n",
    "            meas = np.append(meas,[ m for f in range(len(subj_data.subjectID.unique().tolist())) ])\n",
    "            struc = np.append(struc,[ i for f in range(len(subj_data.subjectID.unique().tolist())) ])\n",
    "\n",
    "    # create distance dataframe\n",
    "    dist_dataframe = pd.DataFrame()\n",
    "    dist_dataframe['subjectID'] = subj\n",
    "    dist_dataframe['structureID'] = struc\n",
    "    dist_dataframe['measures'] = meas\n",
    "    dist_dataframe['distance'] = dist\n",
    "    \n",
    "    return dist_dataframe\n",
    "\n",
    "## this function is useful for saving reference.jsons for a given structure\n",
    "def output_reference_json(ref_data,measures,profile,resample_points,sourceID,data_dir,filename):\n",
    "    \n",
    "    # loop through structures in dataframe\n",
    "    for st in ref_data.structureID.unique():\n",
    "        # set up important measures\n",
    "        reference_json = []\n",
    "        tmp = {}\n",
    "        tmp['structurename'] = st\n",
    "        tmp['source'] = sourceID\n",
    "        # loop through measures\n",
    "        for meas in measures:\n",
    "            # grab data\n",
    "            tmp[meas] = {}\n",
    "            if profile:\n",
    "                gb_frame = ref_data.loc[ref_data['structureID'] == st][['nodeID',meas]].dropna().groupby('nodeID')[meas]\n",
    "            else:\n",
    "                gb_frame = ref_data.loc[ref_data['structureID'] == st][[meas]].dropna()[meas]\n",
    "            \n",
    "            # if resample_points is a value, resamples references and computes multiple summary measures.\n",
    "            # else, will just output the entire data. this second option is really only useful for non-profile/series data\n",
    "            data_tmp = []\n",
    "            if resample_points:                \n",
    "                mean_tmp = resample(gb_frame.mean().values.tolist(),resample_points).tolist()\n",
    "                min_tmp = resample(gb_frame.min().values.tolist(),resample_points).tolist()\n",
    "                max_tmp = resample(gb_frame.max().values.tolist(),resample_points).tolist()\n",
    "                sd_tmp = resample(gb_frame.std().values.tolist(),resample_points).tolist()\n",
    "                five_tmp = resample(gb_frame.quantile(q=.05).values.tolist(),resample_points).tolist()\n",
    "                twofive_tmp = resample(gb_frame.quantile(q=.25).values.tolist(),resample_points).tolist()\n",
    "                sevenfive_tmp = resample(gb_frame.quantile(q=.75).values.tolist(),resample_points).tolist()\n",
    "                ninefive_tmp = resample(gb_frame.quantile(q=.95).values.tolist(),resample_points).tolist()\n",
    "                tmp[meas]['mean'] = mean_tmp\n",
    "                tmp[meas]['min'] = min_tmp\n",
    "                tmp[meas]['max'] = max_tmp\n",
    "                tmp[meas]['sd'] = sd_tmp\n",
    "                tmp[meas]['5_percentile'] = five_tmp\n",
    "                tmp[meas]['25_percentile'] = twofive_tmp\n",
    "                tmp[meas]['75_percentile'] = sevenfive_tmp\n",
    "                tmp[meas]['95_percentile'] = ninefive_tmp\n",
    "            else:\n",
    "                data_tmp = gb_frame.values.tolist()\n",
    "                tmp[meas]['data'] = data_tmp\n",
    "        reference_json.append(tmp)\n",
    "\n",
    "        with open(data_dir+'/'+filename+'_'+st+'.json','w') as ref_out_f:\n",
    "            json.dump(reference_json,ref_out_f)\n",
    "    \n",
    "    return reference_json\n",
    "\n",
    "## this function is used to build the reference dataset removing any subjects identified as outliers. the dataframe may or may not be useful\n",
    "def build_reference_data(data,outliers,profile,data_dir,filename):\n",
    "    \n",
    "    # set up dataframe\n",
    "    reference_data = pd.DataFrame()\n",
    "\n",
    "    # loop through structures and measures and set data\n",
    "    for s in outliers.structureID.unique():\n",
    "        for m in outliers.measures.unique():\n",
    "            if profile:\n",
    "                meas = ['structureID','subjectID','nodeID',m]\n",
    "            else:\n",
    "                meas = ['structureID','subjectID',m]\n",
    "            tmpdata = data[(data[\"structureID\"] == s) & (~data['subjectID'].isin(outliers.loc[outliers['structureID'] == s].loc[outliers['measures'] == m].subjectID.unique()))][meas].reset_index(drop=True)\n",
    "            reference_data = pd.concat([reference_data,tmpdata])\n",
    "    # if not profile, will compute average\n",
    "    if not profile:\n",
    "        reference_data = reference_data.groupby(['structureID','subjectID']).mean().reset_index()\n",
    "\n",
    "    reference_data.to_csv(data_dir+'/'+filename+'.csv',index=False)\n",
    "\n",
    "    return reference_data\n",
    "\n",
    "## this function will identify if a given subjects' data is an outlier based on distance from reference and a threshold percentage\n",
    "def compute_outliers(distances,threshold):\n",
    "    \n",
    "    # set up dataframe\n",
    "    outliers = pd.DataFrame()\n",
    "    \n",
    "    # loop through structureas and measures and identify outliers based on thereshold distance\n",
    "    for i in distances.structureID.unique():\n",
    "        for m in distances.measures.unique():\n",
    "            tmpdata = distances.loc[distances['structureID'] == i].loc[distances['measures'] == m]\n",
    "            outliers = pd.concat([outliers,tmpdata[tmpdata['distance'] > np.percentile(tmpdata['distance'],threshold)]])\n",
    "            \n",
    "    return outliers\n",
    "\n",
    "## this function calls compute_outliers, create_distance_dataframe, and build_reference_data and output_reference_json to actually generate the outliers\n",
    "## and final reference datasets\n",
    "def outlier_detection(data,structures,groupby_measure,measures,threshold,dist_metric,build_outliers,profile,resample_points,sourceID,data_dir,filename):\n",
    "    \n",
    "    # set up important lists\n",
    "    outliers_subjects = []\n",
    "    outliers_structures = []\n",
    "    outliers_measures = []\n",
    "    outliers_metrics = []\n",
    "\n",
    "    # compute distances and identify outliers\n",
    "    distances = create_distance_dataframe(data,structures,groupby_measure,measures,dist_metric)\n",
    "    outliers_dataframe = compute_outliers(distances,threshold)\n",
    "    \n",
    "    # if building references, build the reference data. otherwise, output a blank array\n",
    "    if build_outliers:\n",
    "        reference_dataframe = build_reference_data(data,outliers_dataframe,profile,data_dir,filename)\n",
    "        reference_json = output_reference_json(reference_dataframe,measures,profile,resample_points,sourceID,data_dir,filename)\n",
    "    else:\n",
    "        reference_dataframe = []\n",
    "        reference_json = []\n",
    "        \n",
    "    return distances, outliers_dataframe, reference_dataframe, reference_json\n",
    "\n",
    "## this function is useful in identifying subjects who may have had a flipped profile as compared to a reference profile.\n",
    "## calls compute_references, compute_distance.\n",
    "## logic: if the distance of a subject's tract profile from a reference profile is positive and greater than a threshold percentage, then it's likely\n",
    "## the data has been flipped. needs more work for all use cases. works well for easy examples like uncinate\n",
    "def profile_flip_check(data,subjects,structures,test_measure,flip_measures,dist_metric,threshold,outPath):\n",
    "    \n",
    "    # set up important lists\n",
    "    flipped_subjects = []\n",
    "    flipped_structures = []\n",
    "    distance = []\n",
    "    flipped_distance = []\n",
    "\n",
    "    # loop through structures\n",
    "    for i in structures:\n",
    "        print(i)\n",
    "        # set data frame for structure, including a copy that has the test_measure flipped\n",
    "        struc_data = data.loc[data['structureID'] == i]\n",
    "        flipped_struc_data = struc_data.copy()\n",
    "        flipped_struc_data[test_measure] = flipped_struc_data.groupby('subjectID',sort=False).apply(lambda x: np.flip(x['fa'])).tolist()\n",
    "\n",
    "        # build reference data\n",
    "        references_data = compute_references(struc_data,'nodeID','nodeID',flip_measures)\n",
    "\n",
    "        # compute distances for normal data and for flipped. then compute difference\n",
    "        dist = compute_distance(struc_data,references_data[0],test_measure,dist_metric)\n",
    "        dist_flipped = compute_distance(flipped_struc_data,references_data[0],test_measure,dist_metric)\n",
    "        differences = dist - dist_flipped\n",
    "\n",
    "        # identify threshold of distances based on percentile. identify those that have positive differences and are greater than the threshold.\n",
    "        # if so, append information\n",
    "        percentile_threshold = np.percentile(differences,threshold)\n",
    "        for m in range(len(differences)):\n",
    "            if differences[m] > 0 and differences[m] > percentile_threshold:\n",
    "                flipped_subjects = np.append(flipped_subjects,subjects[m])\n",
    "                flipped_structures = np.append(flipped_structures,i)\n",
    "                distance = np.append(distance,dist[m])\n",
    "                flipped_distance = np.append(flipped_distance,dist_flipped[m])\n",
    "\n",
    "    # generate ouput dataframe containing flipped subject data\n",
    "    output_summary = pd.DataFrame()\n",
    "    output_summary['flipped_subjects'] = flipped_subjects\n",
    "    output_summary['flipped_structures'] = flipped_structures\n",
    "    output_summary['distance'] = distance\n",
    "    output_summary['flipped_distance'] = flipped_distance\n",
    "\n",
    "    if outPath:\n",
    "        output_summary.to_csv(outPath+'_flipped_profiles.csv',index=False)\n",
    "        \n",
    "# this function will merge the structural and diffusion data for the reference datasets\n",
    "def merge_structural_diffusion_json(data,structuralPath,diffusionPath,outPath):\n",
    "    for i in data.structureID.unique():\n",
    "        print(i)\n",
    "        with open(structuralPath+'_'+i+'.json','r') as structural_f:\n",
    "            structural = json.load(structural_f)\n",
    "\n",
    "        with open(diffusionPath+'_'+i+'.json','r') as diffusion_f:\n",
    "            diffusion = json.load(diffusion_f)\n",
    "\n",
    "        merged = [{**structural[0],**diffusion[0]}]\n",
    "\n",
    "        with open(outPath+'_'+i+'.json','w') as out_f:\n",
    "            json.dump(merged,out_f)\n",
    "\n",
    "### adjacency-matrix related fuctions for computing network values locally\n",
    "# this function will binarize an adjacency matrix\n",
    "def binarize_matrices(data):\n",
    "    \n",
    "    # use brain connectivity toolbox to binarize data\n",
    "    bin_data = [ bct.utils.binarize(data[f]) for f in data.keys() ]\n",
    "    \n",
    "    return bin_data\n",
    "\n",
    "# this function will threshold an adjacency matrix based on a percentage threshold of subjects with a binary 1 in that node pair\n",
    "def threshold_matrices(data,bin_data,thresholdPercentageSubjects):\n",
    "    \n",
    "    # compute sum of matrices\n",
    "    sum_mat = np.zeros(np.shape(bin_data)[1:])\n",
    "    \n",
    "    for i in bin_data:\n",
    "        sum_mat = sum_mat + i\n",
    "        \n",
    "    # compute threshold value\n",
    "    thrs = thresholdPercentageSubjects*len(bin_data)\n",
    "    \n",
    "    # loop through data and make those nodes that dont meet threshold 0\n",
    "    for i in data.keys():\n",
    "        data[i][sum_mat < thrs] = 0\n",
    "        \n",
    "    return data\n",
    "\n",
    "# this function will compute the mean within-node functional connectivity\n",
    "def compute_mean_network_connectivity(data,networks,indices,out_path):\n",
    "\n",
    "    mean_data = []\n",
    "    subs = []\n",
    "    nets = []\n",
    "\n",
    "    for l in data.keys():\n",
    "        for n in networks:\n",
    "            tmpdata = []\n",
    "            subs = np.append(subs,l)\n",
    "            nets = np.append(nets,n)\n",
    "            for i in indices[n]:\n",
    "                for j in indices[n]:\n",
    "                    if i != j:\n",
    "                        tmpdata = np.append(tmpdata,data[l][int(i)][int(j)])\n",
    "            mean_data = np.append(mean_data,np.mean(tmpdata))\n",
    "\n",
    "    out_df = pd.DataFrame()\n",
    "    out_df['subjectID'] = [ f.split('_sess1')[0] for f in subs ]\n",
    "    out_df['FC'] = mean_data\n",
    "    out_df = pd.merge(out_df,subjects_data,on='subjectID')\n",
    "    out_df['structureID'] = nets\n",
    "\n",
    "    if out_path:\n",
    "        out_df.to_csv(out_path,index=False)\n",
    "        \n",
    "    return out_df\n",
    "\n",
    "# this function will compute the mean network matrix\n",
    "def compute_mean_network(data):\n",
    "    \n",
    "    mean_network = np.array(np.zeros(np.shape(data[list(data.keys())[0]])))\n",
    "    \n",
    "    for i in data.keys():\n",
    "        mean_network = mean_network + data[i]\n",
    "        \n",
    "    mean_network = mean_network / len(data.keys())\n",
    "    \n",
    "    return mean_network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5299fc5",
   "metadata": {},
   "source": [
    "### Visualizations\n",
    "The final set of functions deals with generating many useful visualizations and plots of the data, including scatterplots, violin plots, and tract profile plots. Within this are many useful wrapper functions that organize the data into useful structures for plotting. These scripts are very 'baked in', in that they are in a style that I like. However, many of these visualizations can be changed with simple alterations to the code. Many of the plots use packages like seaborn or matplotlib to make the visualizations, which both have extensive documentation to guide and help!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3104a191",
   "metadata": {},
   "outputs": [],
   "source": [
    "### visualization related scripts\n",
    "# groups data by input measure and computes mean for each value in that column. x_stat is a pd dataframe, with each row being a single value, and each column being a different ID value or measure\n",
    "def average_within_column(x_stat,y_stat,x_measure,y_measure,measure):\n",
    "\n",
    "    X = x_stat.groupby(measure).mean()[x_measure].tolist()\n",
    "    Y = y_stat.groupby(measure).mean()[y_measure].tolist()\n",
    "\n",
    "    return X,Y\n",
    "\n",
    "# groups data by input measure and creates an array by appending data into x and y arrays. x_stat and y_stat are pd dataframes, with each row being a single value, and each column being a different ID value or measure\n",
    "# designed for test retest. x_stat and y_stat should have the same number of rows. but more importantly, should correspond to the same source (i.e. subject)\n",
    "# can be same pd.dataframe, but indexing of specific subject groups\n",
    "def append_within_column(x_stat,y_stat,x_measure,y_measure,measure):\n",
    "\n",
    "    X,Y = [np.array([]),np.array([])]\n",
    "    for i in range(len(x_stat[measure].unique())):\n",
    "        x = x_stat[x_stat[measure] == x_stat[measure].unique()[i]][x_measure]\n",
    "        y = y_stat[y_stat[measure] == y_stat[measure].unique()[i]][y_measure]\n",
    "\n",
    "        if np.isnan(x).any() or np.isnan(y).any():\n",
    "            print(\"skipping %s due to nan\" %x_stat[measure].unique()[i])\n",
    "        else:\n",
    "            # checks to make sure the same data\n",
    "            if len(x) == len(y):\n",
    "                X = np.append(X,x)\n",
    "                Y = np.append(Y,y)\n",
    "\n",
    "    return X,Y\n",
    "\n",
    "# unravels networks. x_stat and y_stat should be S x M, where S is the number of subjects and M is the adjacency matrix for that subject\n",
    "def ravel_network(x_stat,y_stat):\n",
    "\n",
    "    import numpy as np\n",
    "\n",
    "    X = np.ravel(x_stat).tolist()\n",
    "    Y = np.ravel(y_stat).tolist()\n",
    "\n",
    "    return X,Y\n",
    "\n",
    "# unravels nonnetwork data. x_stat and y_stat should be pd dataframes. x_measure and y_measure are the measure to unrvavel. \n",
    "# designed for test retest. x_stat and y_stat should have the same number of rows. but more importantly, should correspond to the same source (i.e. subject)\n",
    "# can be same pd.dataframe, but indexing of specific subject groups\n",
    "def ravel_non_network(x_stat,y_stat,x_measure,y_measure):\n",
    "\n",
    "    X = x_stat[x_measure].to_list()\n",
    "    Y = y_stat[y_measure].to_list()\n",
    "\n",
    "    return X,Y\n",
    "\n",
    "# wrapper function to call either of the above scripts based on user input\n",
    "def setup_data(x_data,y_data,x_measure,y_measure,ravelAverageAppend,isnetwork,measure):\n",
    "\n",
    "    x_stat = x_data\n",
    "    y_stat = y_data\n",
    "\n",
    "    if ravelAverageAppend == 'average':\n",
    "        X,Y = average_within_column(x_stat,y_stat,x_measure,y_measure,measure)\n",
    "    elif ravelAverageAppend == 'append':\n",
    "        X,Y = append_within_column(x_stat,y_stat,x_measure,y_measure,measure)\n",
    "    elif ravelAverageAppend == 'ravel':\n",
    "        if isnetwork == True:\n",
    "            X,Y = ravel_network(x_stat,y_stat)\n",
    "        else:\n",
    "            X,Y = ravel_non_network(x_stat,y_stat,x_measure,y_measure)\n",
    "\n",
    "    return x_stat,y_stat,X,Y\n",
    "\n",
    "# function to shuffle data and colors\n",
    "def shuffle_data_alg(X,Y,hues):\n",
    "\n",
    "    from sklearn.utils import shuffle\n",
    "\n",
    "    X,Y,hues = shuffle(X,Y,hues)\n",
    "\n",
    "    return X,Y,hues\n",
    "\n",
    "# simple display or figure save function\n",
    "def save_or_show_img(dir_out,x_measure,y_measure,img_name):\n",
    "    import os,sys \n",
    "    import matplotlib.pyplot as plt\n",
    "    import warnings\n",
    "    \n",
    "    with warnings.catch_warnings():\n",
    "        # this will suppress all warnings in this block\n",
    "        warnings.simplefilter(\"ignore\")\n",
    " \n",
    "        # save or show plot\n",
    "        if dir_out:\n",
    "            if not os.path.exists(dir_out):\n",
    "                os.mkdir(dir_out)\n",
    "\n",
    "            if x_measure == y_measure:\n",
    "                img_name_eps = img_name+'_'+x_measure+'.eps'\n",
    "                img_name_png = img_name+'_'+x_measure+'.png'\n",
    "                img_name_svg = img_name+'_'+x_measure+'.svg'\n",
    "            else:\n",
    "                img_name_eps = img_name+'_'+x_measure+'_vs_'+y_measure+'.eps'\n",
    "                img_name_png = img_name+'_'+x_measure+'_vs_'+y_measure+'.png'\n",
    "                img_name_svg = img_name+'_'+x_measure+'_vs_'+y_measure+'.svg'\n",
    "\n",
    "            plt.savefig(os.path.join(dir_out, img_name_eps),transparent=True)\n",
    "            plt.savefig(os.path.join(dir_out, img_name_png))     \n",
    "        else:\n",
    "            plt.show()\n",
    "\n",
    "        # plt.close()\n",
    "    \n",
    "# uses seaborn's relplot function to plot data for each unique value in a column of a pandas dataframe (ex. subjects, structureID). useful for supplementary figures or sanity checking or preliminary results\n",
    "# column measure is the measure within which each unique value will have its own plot. hue_measure is the column to use for coloring the data. column_wrap is how many panels you want per row\n",
    "# trendline, depending on user input, can either be the linear regression between x_data[x_measure] and y_data[y_measure] or the line of equality\n",
    "# dir_out and img_name are the directory where the figures should be saved and the name for the image. will save .eps and .png\n",
    "# if want to view plot instead of save, set dir_out=\"\"\n",
    "def relplot_scatter(x_data,y_data,x_measure,y_measure,column_measure,hue_measure,column_wrap,trendline,dir_out,img_name):\n",
    "\n",
    "    import os,sys\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    import seaborn as sns\n",
    "    from sklearn.metrics import mean_squared_error\n",
    "\n",
    "    # grab data: CANNOT BE AVERAGE\n",
    "    [x_stat,y_stat,X,Y] = setup_data(x_data,y_data,x_measure,y_measure,'ravel',False,hue_measure)\n",
    "\n",
    "    p = sns.relplot(x=X,y=Y,col=x_stat[column_measure],hue=x_stat[hue_measure],kind=\"scatter\",s=100,col_wrap=column_wrap)\n",
    "\n",
    "    # setting counter. looping through axes to add important info and regression lines\n",
    "    i = 0\n",
    "    for ax in p.axes.flat:\n",
    "        x_lim,y_lim = [ax.get_xlim(),ax.get_ylim()]\n",
    "\n",
    "        if trendline == 'equality':\n",
    "            ax.plot(x_lim,y_lim,ls=\"--\",c='k')\n",
    "        elif trendline == 'linreg':\n",
    "            m,b = np.polyfit(p.data[p.data[column_measure] == x_stat[column_measure].unique()[i]]['x'],p.data[p.data[column_measure] == y_stat[column_measure].unique()[i]]['y'],1)\n",
    "            ax.plot(ax.get_xticks(),m*ax.get_xticks() + b)\n",
    "            plt.text(0.1,0.7,'y = %s x + %s' %(str(np.round(m,4)),str(np.round(b,4))),fontsize=12,verticalalignment=\"top\",horizontalalignment=\"left\",transform=ax.transAxes)\n",
    "\n",
    "        ax.set_xlim(x_lim)\n",
    "        ax.set_ylim(y_lim)\n",
    "        ax.set_xlabel(x_measure)\n",
    "        ax.set_ylabel(y_measure)\n",
    "\n",
    "        # compute correlation for each subject and add to plots\n",
    "        corr = np.corrcoef(p.data[p.data[column_measure] == x_stat[column_measure].unique()[i]]['x'],p.data[p.data[column_measure] == y_stat[column_measure].unique()[i]]['y'])[1][0]\n",
    "        plt.text(0.1,0.9,'r = %s' %str(np.round(corr,4)),fontsize=12,verticalalignment=\"top\",horizontalalignment=\"left\",transform=ax.transAxes)\n",
    "\n",
    "        # compute rmse for each subject and add to plots\n",
    "        rmse = np.sqrt(mean_squared_error(p.data[p.data[column_measure] == x_stat[column_measure].unique()[i]]['x'],p.data[p.data[column_measure] == y_stat[column_measure].unique()[i]]['y']))\n",
    "        plt.text(0.1,0.8,'rmse = %s' %str(np.round(rmse,4)),fontsize=12,verticalalignment=\"top\",horizontalalignment=\"left\",transform=ax.transAxes)\n",
    "\n",
    "        # update counter\n",
    "        i = i+1\n",
    "\n",
    "    # save image or show image\n",
    "    save_or_show_img(dir_out,x_measure,y_measure,img_name)\n",
    "\n",
    "# uses seaborn's scatter function to plot data from x_data[x_measure] and y_data[y_measure]. useful for publication worthy figure\n",
    "# column measure is the measure within which data will be summarized. hue_measure is the column to use for coloring the data. \n",
    "# ravelAverageAppend is a string value of either 'append' to use the append function, 'ravel' to use the ravel function, or 'average' to use the average function\n",
    "# trendline, depending on user input, can either be the linear regression between x_data[x_measure] and y_data[y_measure] or the line of equality\n",
    "# dir_out and img_name are the directory where the figures should be saved and the name for the image. will save .eps and .png\n",
    "# if want to view plot instead of save, set dir_out=\"\"\n",
    "def singleplot_scatter(colors_dict,x_data,y_data,x_measure,y_measure,logX,column_measure,hue_measure,ravelAverageAppend,trendline,shuffleData,colorDistance,perfectOrSlope,subsample_percentage,dir_out,img_name):\n",
    "\n",
    "    import os,sys\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    import seaborn as sns\n",
    "    from sklearn.metrics import mean_squared_error\n",
    "\n",
    "    # grab data\n",
    "    [x_stat,y_stat,X,Y] = setup_data(x_data,y_data,x_measure,y_measure,ravelAverageAppend,False,column_measure)\n",
    "    \n",
    "    # compute corr and rmse first in case data gets subsampled later\n",
    "    corr = np.corrcoef(X,Y)[1][0]\n",
    "    rmse = np.sqrt(mean_squared_error(X,Y))\n",
    "    \n",
    "    # map trendlines before subsampling\n",
    "    if trendline == 'linreg':\n",
    "        m,b = np.polyfit(X,Y,1)\n",
    "    elif trendline == 'groupreg':\n",
    "        for g in range(len(groups)):\n",
    "            if stat_name == 'volume':\n",
    "                slope, intercept, r_value, p_value, std_err = stats.linregress(np.log10(stat[['structureID',stat_name]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[stat_name]),stat[['structureID',dm]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[dm])\n",
    "            else:\n",
    "                slope, intercept, r_value, p_value, std_err = stats.linregress(stat[['structureID',stat_name]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[stat_name],stat[['structureID',dm]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[dm])\n",
    "\n",
    "    if subsample_percentage:\n",
    "        X,Y = subsample_data(X,Y,subsample_percentage)\n",
    "    \n",
    "    if colorDistance:\n",
    "        category = color_distance_scatter(X,Y,perfectOrSlope)\n",
    "    else:\n",
    "        colors = sns.color_palette('colorblind',len(x_stat[hue_measure]))\n",
    "\n",
    "    if ravelAverageAppend == 'average':\n",
    "        if isinstance(x_stat[hue_measure].unique()[0],str):\n",
    "            hues = x_stat[hue_measure].unique().tolist()\n",
    "        else:\n",
    "            hues = x_stat.groupby(column_measure).mean()[hue_measure].tolist()\n",
    "    else:\n",
    "        hues = list(x_stat[hue_measure])\n",
    "\n",
    "    if shuffleData == True:\n",
    "        X,Y,hues = shuffle_data_alg(X,Y,hues)\n",
    "\n",
    "    if logX == True:\n",
    "        X = np.log10(X)\n",
    "\n",
    "    if colors_dict:\n",
    "        p = sns.scatterplot(x=X,y=Y,hue=hues,s=100,palette=colors_dict,legend=False)\n",
    "    elif colorDistance:\n",
    "        p = sns.scatterplot(x=X,y=Y,hue=category,hue_order=['two-sd','one-sd','lt-one-sd'],palette=['red','green','blue'],s=100)\n",
    "    else:\n",
    "        p = sns.scatterplot(x=X,y=Y,hue=hues,s=100)\n",
    "\n",
    "    # set x and ylimits, plot line of equality, and legend\n",
    "    if x_measure == y_measure:\n",
    "        p.axes.axis('square')\n",
    "        y_ticks = p.axes.get_yticks()\n",
    "        p.axes.set_xticks(y_ticks)\n",
    "        p.axes.set_yticks(p.axes.get_xticks())\n",
    "        p.axes.set_ylim(p.axes.get_xlim())\n",
    "        p.axes.set_xlim(p.axes.get_xlim())\n",
    "\n",
    "    x_lim,y_lim = [p.axes.get_xlim(),p.axes.get_ylim()]\n",
    "\n",
    "    # trendline: either equality or linear regression\n",
    "    if trendline == 'equality':\n",
    "        p.plot(x_lim,y_lim,ls=\"--\",c='k')\n",
    "        ax = plt.gca()\n",
    "        ax.get_legend().remove()\n",
    "    elif trendline == 'linreg':\n",
    "        m,b = np.polyfit(X,Y,1)\n",
    "        p.plot(p.get_xticks(),m*p.get_xticks() + b,c='k')\n",
    "        plt.text(0.1,0.7,'y = %s x + %s' %(str(np.round(m,4)),str(np.round(b,4))),fontsize=16,verticalalignment=\"top\",horizontalalignment=\"left\",transform=p.axes.transAxes)\n",
    "        ax = plt.gca()\n",
    "        ax.get_legend().remove()\n",
    "\n",
    "    elif trendline == 'groupreg':\n",
    "        for g in range(len(groups)):\n",
    "            if stat_name == 'volume':\n",
    "                slope, intercept, r_value, p_value, std_err = stats.linregress(np.log10(stat[['structureID',stat_name]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[stat_name]),stat[['structureID',dm]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[dm])\n",
    "                ax = sns.regplot(x=np.log10(stat[['structureID',stat_name]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[stat_name]),y=stat[['structureID',dm]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[dm],color=colors[groups[g]],scatter=True,line_kws={'label':\"y={0:.5f}x+{1:.4f}\".format(slope,intercept)})\n",
    "\n",
    "            else:\n",
    "                slope, intercept, r_value, p_value, std_err = stats.linregress(stat[['structureID',stat_name]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[stat_name],stat[['structureID',dm]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[dm])\n",
    "                ax = sns.regplot(x=stat[['structureID',stat_name]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[stat_name],y=stat[['structureID',dm]][stat['subjectID'].str.contains('%s_' %str(g+1))].groupby('structureID',as_index=False).mean()[dm],color=colors[groups[g]],scatter=True,line_kws={'label':\"y={0:.5f}x+{1:.4f}\".format(slope,intercept)})\n",
    "\n",
    "            ax.legend()\n",
    "\n",
    "    # compute correlation for each subject and add to plots\n",
    "    plt.text(0.1,0.9,'r = %s' %str(np.round(corr,4)),fontsize=16,verticalalignment=\"top\",horizontalalignment=\"left\",transform=p.axes.transAxes)\n",
    "\n",
    "    # compute rmse for each subject and add to plots\n",
    "    plt.text(0.1,0.8,'rmse = %s' %str(np.round(rmse,4)),fontsize=16,verticalalignment=\"top\",horizontalalignment=\"left\",transform=p.axes.transAxes)\n",
    "\n",
    "    # set title and x and y labels\n",
    "    plt.title('%s vs %s' %(x_measure,y_measure),fontsize=20)\n",
    "    plt.xlabel(x_measure,fontsize=18)\n",
    "    plt.ylabel(y_measure,fontsize=18)\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "\n",
    "    # remove top and right spines from plot\n",
    "    p.axes.spines[\"top\"].set_visible(False)\n",
    "    p.axes.spines[\"right\"].set_visible(False)\n",
    "\n",
    "    # save image or show image\n",
    "    save_or_show_img(dir_out,x_measure,y_measure,img_name)\n",
    "    \n",
    "# this function will plot group summarized tract profile data, but can also be used with any timeseries-like data\n",
    "def plot_profiles(structures,stat,diffusion_measures,summary_method,error_method,dir_out,img_name):\n",
    "\n",
    "    # loop through all structures\n",
    "    for t in structures:\n",
    "        print(t)\n",
    "        # loop through all measures\n",
    "        for dm in diffusion_measures:\n",
    "            print(dm)\n",
    "\n",
    "            imgname=img_name+\"_\"+t+\"_\"+dm\n",
    "\n",
    "            # generate figures\n",
    "            fig = plt.figure(figsize=(15,15))\n",
    "\n",
    "            # fig.patch.set_visible(False)\n",
    "            p = plt.subplot()\n",
    "            \n",
    "            # set title and catch array for legend handle\n",
    "            plt.title(\"%s Profiles %s: %s\" %(summary_method,t,dm),fontsize=20)\n",
    "\n",
    "            # loop through groups and plot profile data\n",
    "            for g in range(len(stat.classID.unique())):\n",
    "                # x is nodes\n",
    "                x = stat['nodeID'].unique()\n",
    "\n",
    "                # y is summary (mean, median, max, main) profile data\n",
    "                if summary_method == 'mean':\n",
    "                    y = stat[stat['classID'] == stat.classID.unique()[g]].groupby(['structureID','nodeID']).mean()[dm][t]\n",
    "                elif summary_method == 'median':\n",
    "                    y = stat[stat['classID'] == stat.classID.unique()[g]].groupby(['structureID','nodeID']).median()[dm][t]\n",
    "                elif summary_method == 'max':\n",
    "                    y = stat[stat['classID'] == stat.classID.unique()[g]].groupby(['structureID','nodeID']).max()[dm][t]\n",
    "                elif summary_method == 'min':\n",
    "                    y = stat[stat['classID'] == stat.classID.unique()[g]].groupby(['structureID','nodeID']).min()[dm][t]\n",
    "\n",
    "                # error bar is either: standard error of mean (sem), standard deviation (std)\n",
    "                if error_method == 'sem':\n",
    "                    err = stat[stat['classID'] == stat.classID.unique()[g]].groupby(['structureID','nodeID']).std()[dm][t] / np.sqrt(len(stat[stat['classID'] == stat.classID.unique()[g]]['subjectID'].unique()))\n",
    "                else:\n",
    "                    err = stat[stat['classID'] == stat.classID.unique()[g]].groupby(['structureID','nodeID']).std()[dm][t]\n",
    "\n",
    "                # plot summary\n",
    "                plt.plot(x,y,color=stat[stat['classID'] == stat.classID.unique()[g]]['colors'].unique()[0],linewidth=5,label=stat.classID.unique()[g])\n",
    "\n",
    "                # plot shaded error\n",
    "                plt.fill_between(x,y-err,y+err,alpha=0.2,color=stat[stat['classID'] == stat.classID.unique()[g]]['colors'].unique()[0],label='1 %s %s' %(error_method,stat.classID.unique()[g]))\n",
    "\n",
    "            # set up labels and ticks\n",
    "            plt.xlabel('Location',fontsize=18)\n",
    "            plt.ylabel(dm,fontsize=18)\n",
    "            plt.xticks([x[0],x[-1]],['Begin','End'],fontsize=16)\n",
    "            plt.legend(fontsize=16)\n",
    "            y_lim = plt.ylim()\n",
    "            plt.yticks([np.round(y_lim[0],2),np.mean(y_lim),np.round(y_lim[1],2)],fontsize=16)\n",
    "\n",
    "            # remove top and right spines from plot\n",
    "            p.axes.spines[\"top\"].set_visible(False)\n",
    "            p.axes.spines[\"right\"].set_visible(False)\n",
    "\n",
    "            # save image or show image\n",
    "            save_or_show_img(dir_out,dm,dm,imgname)\n",
    "            \n",
    "# this function will identify data within 1 sd, between 1 and 2sd, and 2 sd or greater within a scatter distrbution. intended to be used when comparing a/b\n",
    "# like analyses (i.e. test retest, validity, etc)\n",
    "def color_distance_scatter(x,y,perfectOrSlope):\n",
    "    from scipy import stats\n",
    "    import numpy as np\n",
    "    import math\n",
    "\n",
    "    ### this process creates a list of sd categories by rotating the x,y distribution\n",
    "    ### by 45 degrees (to make the slope essentially zero) and computing the standard\n",
    "    ### deviation along the y-axis. then, it identifies whether the y-data falls either\n",
    "    ### within 1 sd, within 1-2 sd, and greater than 2 sds\n",
    "    \n",
    "    # if users want to compute distribution around perfect 45 deg equality line or around\n",
    "    # the actual data slope\n",
    "    if perfectOrSlope == True:\n",
    "        m = 1\n",
    "    else:\n",
    "        m,b = np.polyfit(x,y,1)\n",
    "    \n",
    "    # compute theta as the clockwise atan rotation along m\n",
    "    theta = -math.atan(m)\n",
    "    \n",
    "    # generate rotation matrix\n",
    "    r = np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]])\n",
    "    \n",
    "    # compute rotation\n",
    "    [x_dif,y_dif] = r.dot(np.array([x,y]))\n",
    "\n",
    "    # set output variable (category)\n",
    "    category = []\n",
    "    \n",
    "    # compute standard deviation thresholds for difference values\n",
    "    one_sd = np.std(np.abs(y_dif))\n",
    "    two_sd = one_sd * 2\n",
    "\n",
    "    # loop through each data point and determine category (one sd: within 1 and 2 sds, two-sd: greater or equal to 2 sds, lt-one-sd: within one sd)\n",
    "    category = [ 'one-sd' if one_sd <= f < two_sd else 'two-sd' if f >= two_sd else 'lt-one-sd' for f in np.abs(y_dif) ]\n",
    "    \n",
    "    return category\n",
    "\n",
    "# this function will randomly subsample the data to make lighter visualization images\n",
    "def subsample_data(x,y,percentage):\n",
    "    \n",
    "    data = np.array([x,y])\n",
    "    subsample = np.random.choice(data.shape[1],int(len(data[0])*(percentage/100)),replace=False)\n",
    "    \n",
    "    sub_x = data[0,subsample]\n",
    "    sub_y = data[1,subsample]\n",
    "    \n",
    "    return sub_x, sub_y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bc8a02",
   "metadata": {},
   "source": [
    "## Generating subject demographic dataframe\n",
    "Now that we've loaded all the functions needed, but before we get started collecting and analyzing our data, we first need to generate a subjects demographic database. This can include information stored in the participants.json file stored in the Project's Detail tab, but can also be user-generated. This is so we can then append that relevant information to each of the data structures we collect, compile, and generate for analysis. This makes it easier to build models and to perform statistical analyses with co-variates.\n",
    "\n",
    "In the case of Hayashi & Caron et al (in prep), since I was using data from multiple Big Data repositories including the Human Connectome Project, the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), and the Pediatric Imaging, Neurocognition, and Genetics (PING) Dataset  I uploaded the important subject identification and demographic data directly from each project's respective repository. I scrubbed the data of most of the relevant demographic information, as I was mostly only concerned with the participant's subject ID, age, and gender.\n",
    "\n",
    "In order to ensure participant anonymity, I've only included the subjectID in the example subjects_data.csv dataframe.\n",
    "\n",
    "Within this dataframe, I've also added a classID column corresponding to the project identification name (i.e. hcp, camcan, ping) and a colors column corresponding to the color I assigned to each project (hcp: green, camcan: orange, ping: purple).\n",
    "\n",
    "After this, I then created a color dictionary with a unique color for each participant in the project. This can make outlier detection a bit easier.\n",
    "\n",
    "In this example notebook, I will be using the HCP dataset!\n",
    "\n",
    "Finally, within this section we will also set up our data and image directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb3717c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting up variables\n",
      "setting up variables complete\n",
      "grabbing demographic data\n",
      "grabbing demographic data complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9328/473128509.py:28: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  subjects_data['subjectID'] = [ str(int(np.float(f))) for f in subjects_data.subjectID ]\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>subjectID</th>\n",
       "      <th>classID</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100004</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100206</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100307</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100408</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100610</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>992774</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>993675</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>994273</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>995174</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>996782</td>\n",
       "      <td>hcp</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1206 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     subjectID classID colors\n",
       "0       100004     hcp   blue\n",
       "1       100206     hcp   blue\n",
       "2       100307     hcp   blue\n",
       "3       100408     hcp   blue\n",
       "4       100610     hcp   blue\n",
       "...        ...     ...    ...\n",
       "1201    992774     hcp   blue\n",
       "1202    993675     hcp   blue\n",
       "1203    994273     hcp   blue\n",
       "1204    995174     hcp   blue\n",
       "1205    996782     hcp   blue\n",
       "\n",
       "[1206 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### setting up variables and adding paths\n",
    "print(\"setting up variables\")\n",
    "topPath = \"./\"\n",
    "os.chdir(topPath)\n",
    "data_dir = topPath+'/data/'\n",
    "if not os.path.exists(data_dir):\n",
    "    os.mkdir(data_dir)\n",
    "img_dir = topPath+'/img/'\n",
    "if not os.path.exists(img_dir):\n",
    "    os.mkdir(img_dir)\n",
    "\n",
    "# set up project identification (groups) and the project color (green)\n",
    "groups = ['hcp']\n",
    "colors_array = ['green']\n",
    "diff_micro_measures = ['ad','fa','md','rd','ndi','odi','isovf']\n",
    "print(\"setting up variables complete\")\n",
    "\n",
    "### grabbing subjects demographic data\n",
    "print(\"grabbing demographic data\")\n",
    "if os.path.isfile(data_dir+'/subjects_data.csv'):\n",
    "    subjects_data = pd.read_csv(data_dir+'/subjects_data.csv')\n",
    "else:\n",
    "    subjects_data = pd.read_json('input/participants.json')\n",
    "print(\"grabbing demographic data complete\")\n",
    "\n",
    "# hcp stored their subject IDs as integers instead of strings. changing that here\n",
    "if subjects_data['subjectID'].dtype == 'int':\n",
    "    subjects_data['subjectID'] = [ str(int(np.float(f))) for f in subjects_data.subjectID ]\n",
    "\n",
    "# create column for classID and colors\n",
    "subjects_data['classID'] = [ 'hcp' for f in subjects_data['subjectID'] ]\n",
    "subjects_data['colors'] = [ 'blue' for f in subjects_data['subjectID'] ]\n",
    "\n",
    "### generate colors dictionary with a unique color for each subject\n",
    "colors = {}\n",
    "subjects = {}\n",
    "\n",
    "# loop through groups and identify subjects and set color schema for each group\n",
    "for g in range(len(groups)):\n",
    "    # set subjects array\n",
    "    subjects[groups[g]] =  subjects_data['subjectID']\n",
    "    subjects[groups[g]].sort_values()\n",
    "    \n",
    "    # set colors array\n",
    "    colors_name = colors_array[g]\n",
    "    colors[groups[g]] = colors_array[g]\n",
    "\n",
    "# create subjects color dictionary\n",
    "colors_dict = create_color_dictionary(subjects_data,'subjectID','colorblind')\n",
    "\n",
    "## here's what the subjects_data dataframe looks like!\n",
    "subjects_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ce22bd",
   "metadata": {},
   "source": [
    "## Collecting tract profilometry data\n",
    "The next step of the analysis pipeline for me is to collect and analyze the tract macrostructural (length, streamline count, volume) and microstructural (derived from dMRI models; i.e. profilometry) properties of white matter tracts. For this, I tracked and segmented 61 major white matter tracts from each participant, computed the macrostructural statistics for each tract, and then mapped measures of diffusion microstructure along each tract using tract profilometry.\n",
    "\n",
    "For my analyses, I trim the first and last 10% of nodes from the profiles, as these nodes are typically the closest to gray matter and thus the partial voluming effects here are greatest. Following cutting, I then computed the tract average for each tract, structural property, and participant.\n",
    "\n",
    "For the sake of this example, I'll only be loading the tract profilometry data following cutting of the first and last nodes and computation of the mean value per tract. This is due to the file-size of the profilometry dataframe (>5GB). However, if you would like to investigate the data further, or have access to more RAM, I've included all of the data for this project in the data directory of the github repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9998dcbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing tractometry analyses\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",
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       "    }\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>subjectID</th>\n",
       "      <th>classID</th>\n",
       "      <th>structureID</th>\n",
       "      <th>nodeID</th>\n",
       "      <th>ad</th>\n",
       "      <th>fa</th>\n",
       "      <th>md</th>\n",
       "      <th>rd</th>\n",
       "      <th>ndi</th>\n",
       "      <th>isovf</th>\n",
       "      <th>...</th>\n",
       "      <th>AverageEndpointDistanceFromCentroid2</th>\n",
       "      <th>stdevOfEndpointDistanceFromCentroid1</th>\n",
       "      <th>stdevEndpointDistanceFromCentroid2</th>\n",
       "      <th>MidpointDensity</th>\n",
       "      <th>averageMidpointDistanceFromCentroid</th>\n",
       "      <th>stDevOfMidpointDistanceFromCentroid</th>\n",
       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100206</td>\n",
       "      <td>hcp</td>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>1</td>\n",
       "      <td>1.679560</td>\n",
       "      <td>0.620760</td>\n",
       "      <td>0.916050</td>\n",
       "      <td>0.534295</td>\n",
       "      <td>0.824575</td>\n",
       "      <td>0.173287</td>\n",
       "      <td>...</td>\n",
       "      <td>23.69213</td>\n",
       "      <td>7.137842</td>\n",
       "      <td>7.547169</td>\n",
       "      <td>2.695897</td>\n",
       "      <td>9.765391</td>\n",
       "      <td>4.774288</td>\n",
       "      <td>0.099990</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100206</td>\n",
       "      <td>hcp</td>\n",
       "      <td>forcepsMajor</td>\n",
       "      <td>1</td>\n",
       "      <td>1.834377</td>\n",
       "      <td>0.717358</td>\n",
       "      <td>0.899015</td>\n",
       "      <td>0.431334</td>\n",
       "      <td>0.785291</td>\n",
       "      <td>0.140918</td>\n",
       "      <td>...</td>\n",
       "      <td>20.99679</td>\n",
       "      <td>6.758827</td>\n",
       "      <td>7.489792</td>\n",
       "      <td>1.758294</td>\n",
       "      <td>11.214002</td>\n",
       "      <td>5.791409</td>\n",
       "      <td>0.077425</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100206</td>\n",
       "      <td>hcp</td>\n",
       "      <td>forcepsMinor</td>\n",
       "      <td>1</td>\n",
       "      <td>1.608018</td>\n",
       "      <td>0.603306</td>\n",
       "      <td>0.890990</td>\n",
       "      <td>0.532476</td>\n",
       "      <td>0.828563</td>\n",
       "      <td>0.169201</td>\n",
       "      <td>...</td>\n",
       "      <td>16.46617</td>\n",
       "      <td>5.437280</td>\n",
       "      <td>5.360826</td>\n",
       "      <td>4.108100</td>\n",
       "      <td>6.604532</td>\n",
       "      <td>3.283689</td>\n",
       "      <td>0.074511</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>0.007615</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100206</td>\n",
       "      <td>hcp</td>\n",
       "      <td>leftAnterioFrontoCerebellar</td>\n",
       "      <td>1</td>\n",
       "      <td>1.525586</td>\n",
       "      <td>0.501937</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.660607</td>\n",
       "      <td>0.816000</td>\n",
       "      <td>0.204673</td>\n",
       "      <td>...</td>\n",
       "      <td>15.85807</td>\n",
       "      <td>11.180350</td>\n",
       "      <td>7.009356</td>\n",
       "      <td>1.057971</td>\n",
       "      <td>10.032686</td>\n",
       "      <td>5.905239</td>\n",
       "      <td>0.031011</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subjectID classID                  structureID  nodeID        ad        fa  \\\n",
       "0     100206     hcp             anterioFrontalCC       1  1.679560  0.620760   \n",
       "1     100206     hcp                 forcepsMajor       1  1.834377  0.717358   \n",
       "2     100206     hcp                 forcepsMinor       1  1.608018  0.603306   \n",
       "3     100206     hcp  leftAnterioFrontoCerebellar       1  1.525586  0.501937   \n",
       "\n",
       "         md        rd       ndi     isovf  ...  \\\n",
       "0  0.916050  0.534295  0.824575  0.173287  ...   \n",
       "1  0.899015  0.431334  0.785291  0.140918  ...   \n",
       "2  0.890990  0.532476  0.828563  0.169201  ...   \n",
       "3  0.948934  0.660607  0.816000  0.204673  ...   \n",
       "\n",
       "   AverageEndpointDistanceFromCentroid2  stdevOfEndpointDistanceFromCentroid1  \\\n",
       "0                              23.69213                              7.137842   \n",
       "1                              20.99679                              6.758827   \n",
       "2                              16.46617                              5.437280   \n",
       "3                              15.85807                             11.180350   \n",
       "\n",
       "   stdevEndpointDistanceFromCentroid2  MidpointDensity  \\\n",
       "0                            7.547169         2.695897   \n",
       "1                            7.489792         1.758294   \n",
       "2                            5.360826         4.108100   \n",
       "3                            7.009356         1.057971   \n",
       "\n",
       "   averageMidpointDistanceFromCentroid  stDevOfMidpointDistanceFromCentroid  \\\n",
       "0                             9.765391                             4.774288   \n",
       "1                            11.214002                             5.791409   \n",
       "2                             6.604532                             3.283689   \n",
       "3                            10.032686                             5.905239   \n",
       "\n",
       "   TotalVolumeProportion  TotalCountProportion  TotalWiringProportion  colors  \n",
       "0               0.099990              0.004314               0.006784    blue  \n",
       "1               0.077425              0.001360               0.003135    blue  \n",
       "2               0.074511              0.004700               0.007615    blue  \n",
       "3               0.031011              0.000097               0.000225    blue  \n",
       "\n",
       "[4 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### structural and diffusion data in white matter tracts\n",
    "print(\"computing tractometry analyses\")\n",
    "\n",
    "# create data structures\n",
    "# tractmeasures data\n",
    "# if os.path.isfile(data_dir+'/tractmeasures-total_nodes.csv'):\n",
    "#     track_data = pd.read_csv(data_dir+'/tractmeasures-total_nodes.csv')\n",
    "# else:\n",
    "#     track_data = collect_data('neuro/tractmeasures',['macro_micro'],['full_macro_stats'],'tractmeasures.csv',data_dir+'/tractmeasures-total_nodes.csv',False)\n",
    "#     track_data['subjectID'] = track_data.subjectID.astype(str)\n",
    "#     track_data = track_data.drop_duplicates()\n",
    "#     track_data = track_data.loc[track_data['structureID'] != 'wbfg' ]\n",
    "#     track_data.to_csv(data_dir+'/tractmeasures-total_nodes.csv',index=False)\n",
    "    \n",
    "# # # remove subjects with missing data\n",
    "# subjs_with_nans = track_data[track_data.isna()['fa']].subjectID.unique()\n",
    "\n",
    "# # # check for missing individual tracts\n",
    "# subjs_missing_tracts = [ f for f in track_data.subjectID.unique().tolist() if len(track_data[track_data['subjectID'] == f].structureID.unique()) < 61 ]\n",
    "# subjs_missing_data = [*subjs_with_nans,*subjs_missing_tracts]\n",
    "\n",
    "# # # select only subjects that don't have missing data\n",
    "# track_data = track_data.loc[~track_data['subjectID'].isin(subjs_missing_data)]\n",
    "\n",
    "# # # # merge subjects_data\n",
    "# if os.path.isfile(data_dir+'/tractmeasures-total_nodes_merged.csv'):\n",
    "#     track_data = pd.read_csv(data_dir+'/tractmeasures-total_nodes_merged.csv')\n",
    "#     track_data = track_data.drop_duplicates()\n",
    "# else:\n",
    "#     track_data = pd.merge(track_data,subjects_data,on='subjectID')\n",
    "#     track_data.to_csv(data_dir+'/tractmeasures-total_nodes_merged.csv')\n",
    "\n",
    "# cut nodes\n",
    "if os.path.isfile(data_dir+'/tractmeasures-cut_nodes.csv'):\n",
    "    track_data_cut = pd.read_csv(data_dir+'/tractmeasures-cut_nodes.csv')\n",
    "else:\n",
    "    track_data_cut = cut_nodes(track_data,180,data_dir,'tractmeasures','cut_nodes')\n",
    "    track_data_cut = pd.merge(track_data_cut,subjects_data,on=['subjectID','classID','age'])\n",
    "    track_data_cut.to_csv(data_dir+'/tractmeasures-cut_nodes.csv',index=False)\n",
    "\n",
    "# # compute mean data\n",
    "if os.path.isfile(data_dir+'/tractmeasures-mean_nodes.csv'):\n",
    "    track_data_mean = pd.read_csv(data_dir+'/tractmeasures-mean_nodes.csv')\n",
    "else:\n",
    "    track_data_mean = compute_mean_data(data_dir,track_data_cut,'tractmeasures-mean_nodes')\n",
    "    track_data_mean = pd.merge(track_data_mean,subjects_data,on=['subjectID','classID'])\n",
    "    track_data_mean.to_csv(data_dir+'/tractmeasures-mean_nodes.csv',index=False)\n",
    "\n",
    "track_data_mean.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84185ac9",
   "metadata": {},
   "source": [
    "## Visualizing and summarizing tract profilometry data\n",
    "Now that we have our data and cleaned it, the next step of the analysis pipeline for me is to analyze the and vizualize the microstructural (derived from dMRI models; i.e. profilometry) properties of white matter tracts. For this, we will use the dataframe 'track_data_cut' as this contains the node-cleaned tract profilometry data. To show as an example, here I plot the average fractional anisotropy (FA) of the Forceps Major track from the HCP data. To visualize the variance of the data, I also plot one standard deviation as a shaded error bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bcded69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anterioFrontalCC\n",
      "fa\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute image of mean tract profile of forceps Major across all subjects\n",
    "structures = ['anterioFrontalCC'] # list of tracks in track_data_cut to make profile plots for\n",
    "stat = track_data_cut # the dataframe with the tract profile data\n",
    "diffusion_measures = ['fa'] # list of the measures to generate profiles for\n",
    "summary_method = 'mean' # summary method for plot ('mean','median','max','min')\n",
    "error_method = 'std' # error method for plot ('sem': standard error of mean, 'std': standard deviation)\n",
    "dir_out = img_dir # output directory for image to be saved to. if you do not want to save the image, set this to empty string\n",
    "img_name = 'hcp_full_dataset' # output image stem name for image to be saved to. if you don't want to save the image, set this to empty string\n",
    "\n",
    "plot_profiles(structures,stat,diffusion_measures,summary_method,error_method,dir_out,img_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a56a36b",
   "metadata": {},
   "source": [
    "## Removing outliers and creating reference datasets\n",
    "Once all of the appropriate data was collected, collated, and cleaned, I was now able to generate useful 'reference datasets' that can be used to identify potentially problematic participant data. I adopted a similar methodology as in the Pestilli et al (2014) LiFE paper which created \"reference datasets\", i.e. dataset of values of high-quality and high-diverse data, of properties derived from the LiFE model. The authors of this publication have released this data in the backend of the LiFE app here on brainlife.io, allowing users to directly compare their newly generated results with previously currated results.\n",
    "\n",
    "For this project, I took a similar approach across three large and diverse datasets: PING, HCP, and CAMCAN. For each data modality and structure, I computed the average value across many different models and measures including structural statistics derived from Freesurfer, diffusion microsturctural statistics derived from the DTI and NODDI models, and network measures such as max node degree from structural connectivity measures and average functional connectivity derived from fMRI. I then identified those data points that were outside the top and bottom p% of the distribution, removed them, and recomputed the distribution. This newly cleaned distribution is the 'reference dataset' for that datatype, measure, and dataset.\n",
    "\n",
    "Following iterating through many datatypes, measures, structures, and projects, I released the final reference datasets on github and we have incorporated the data into the validators for the apps that generate those particular datatypes! This way users can directly compare their newly generated data with those I processed, analyzed, and curated!!\n",
    "\n",
    "This code can also be used to remove outliers from individual study designs, as the methods for identifying outliers and removing them from the data involves simple distribution sampling and percentile thresholding.\n",
    "\n",
    "For this notebook, I will demonstrate how to create reference dataframes from the tractometry data using the HCP dataset using the right CST as an example track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "efcb193c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating references for tract macro and microstructural data\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureID</th>\n",
       "      <th>subjectID</th>\n",
       "      <th>StreamlineCount</th>\n",
       "      <th>volume</th>\n",
       "      <th>avgerageStreamlineLength</th>\n",
       "      <th>streamlineLengthStdev</th>\n",
       "      <th>averageFullDisplacement</th>\n",
       "      <th>fullDisplacementStdev</th>\n",
       "      <th>StreamlineLengthTotal</th>\n",
       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>nodeID</th>\n",
       "      <th>ad</th>\n",
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       "      <th>isovf</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anterioFrontalCC</td>\n",
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       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>11</td>\n",
       "      <td>1.342562</td>\n",
       "      <td>0.480046</td>\n",
       "      <td>0.866063</td>\n",
       "      <td>0.627813</td>\n",
       "      <td>0.714491</td>\n",
       "      <td>0.239300</td>\n",
       "      <td>0.138188</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>12</td>\n",
       "      <td>1.344758</td>\n",
       "      <td>0.484236</td>\n",
       "      <td>0.864075</td>\n",
       "      <td>0.623733</td>\n",
       "      <td>0.719778</td>\n",
       "      <td>0.235340</td>\n",
       "      <td>0.138758</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>13</td>\n",
       "      <td>1.347429</td>\n",
       "      <td>0.487675</td>\n",
       "      <td>0.862817</td>\n",
       "      <td>0.620511</td>\n",
       "      <td>0.724271</td>\n",
       "      <td>0.231224</td>\n",
       "      <td>0.139651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>14</td>\n",
       "      <td>1.350521</td>\n",
       "      <td>0.490307</td>\n",
       "      <td>0.862292</td>\n",
       "      <td>0.618178</td>\n",
       "      <td>0.728021</td>\n",
       "      <td>0.226604</td>\n",
       "      <td>0.140948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>15</td>\n",
       "      <td>1.353717</td>\n",
       "      <td>0.492584</td>\n",
       "      <td>0.861987</td>\n",
       "      <td>0.616122</td>\n",
       "      <td>0.731512</td>\n",
       "      <td>0.221933</td>\n",
       "      <td>0.142460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>16</td>\n",
       "      <td>1.357469</td>\n",
       "      <td>0.494849</td>\n",
       "      <td>0.861923</td>\n",
       "      <td>0.614150</td>\n",
       "      <td>0.734853</td>\n",
       "      <td>0.216781</td>\n",
       "      <td>0.143810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>17</td>\n",
       "      <td>1.360901</td>\n",
       "      <td>0.496967</td>\n",
       "      <td>0.861830</td>\n",
       "      <td>0.612295</td>\n",
       "      <td>0.737844</td>\n",
       "      <td>0.211844</td>\n",
       "      <td>0.144823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>18</td>\n",
       "      <td>1.364993</td>\n",
       "      <td>0.499044</td>\n",
       "      <td>0.862179</td>\n",
       "      <td>0.610771</td>\n",
       "      <td>0.740674</td>\n",
       "      <td>0.207087</td>\n",
       "      <td>0.145923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>19</td>\n",
       "      <td>1.369574</td>\n",
       "      <td>0.500669</td>\n",
       "      <td>0.862983</td>\n",
       "      <td>0.609688</td>\n",
       "      <td>0.743240</td>\n",
       "      <td>0.202371</td>\n",
       "      <td>0.147293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>100206</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>81067.0</td>\n",
       "      <td>102.59989</td>\n",
       "      <td>20.46052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.327950e+06</td>\n",
       "      <td>0.09999</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>20</td>\n",
       "      <td>1.374782</td>\n",
       "      <td>0.502116</td>\n",
       "      <td>0.864228</td>\n",
       "      <td>0.608951</td>\n",
       "      <td>0.745793</td>\n",
       "      <td>0.197843</td>\n",
       "      <td>0.148879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        structureID  subjectID  StreamlineCount   volume  \\\n",
       "0  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "1  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "2  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "3  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "4  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "5  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "6  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "7  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "8  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "9  anterioFrontalCC     100206          12943.0  81067.0   \n",
       "\n",
       "   avgerageStreamlineLength  streamlineLengthStdev  averageFullDisplacement  \\\n",
       "0                 102.59989               20.46052                      NaN   \n",
       "1                 102.59989               20.46052                      NaN   \n",
       "2                 102.59989               20.46052                      NaN   \n",
       "3                 102.59989               20.46052                      NaN   \n",
       "4                 102.59989               20.46052                      NaN   \n",
       "5                 102.59989               20.46052                      NaN   \n",
       "6                 102.59989               20.46052                      NaN   \n",
       "7                 102.59989               20.46052                      NaN   \n",
       "8                 102.59989               20.46052                      NaN   \n",
       "9                 102.59989               20.46052                      NaN   \n",
       "\n",
       "   fullDisplacementStdev  StreamlineLengthTotal  TotalVolumeProportion  \\\n",
       "0                    NaN           1.327950e+06                0.09999   \n",
       "1                    NaN           1.327950e+06                0.09999   \n",
       "2                    NaN           1.327950e+06                0.09999   \n",
       "3                    NaN           1.327950e+06                0.09999   \n",
       "4                    NaN           1.327950e+06                0.09999   \n",
       "5                    NaN           1.327950e+06                0.09999   \n",
       "6                    NaN           1.327950e+06                0.09999   \n",
       "7                    NaN           1.327950e+06                0.09999   \n",
       "8                    NaN           1.327950e+06                0.09999   \n",
       "9                    NaN           1.327950e+06                0.09999   \n",
       "\n",
       "   TotalCountProportion  TotalWiringProportion  nodeID        ad        fa  \\\n",
       "0              0.004314               0.006784      11  1.342562  0.480046   \n",
       "1              0.004314               0.006784      12  1.344758  0.484236   \n",
       "2              0.004314               0.006784      13  1.347429  0.487675   \n",
       "3              0.004314               0.006784      14  1.350521  0.490307   \n",
       "4              0.004314               0.006784      15  1.353717  0.492584   \n",
       "5              0.004314               0.006784      16  1.357469  0.494849   \n",
       "6              0.004314               0.006784      17  1.360901  0.496967   \n",
       "7              0.004314               0.006784      18  1.364993  0.499044   \n",
       "8              0.004314               0.006784      19  1.369574  0.500669   \n",
       "9              0.004314               0.006784      20  1.374782  0.502116   \n",
       "\n",
       "         md        rd       ndi       odi     isovf  \n",
       "0  0.866063  0.627813  0.714491  0.239300  0.138188  \n",
       "1  0.864075  0.623733  0.719778  0.235340  0.138758  \n",
       "2  0.862817  0.620511  0.724271  0.231224  0.139651  \n",
       "3  0.862292  0.618178  0.728021  0.226604  0.140948  \n",
       "4  0.861987  0.616122  0.731512  0.221933  0.142460  \n",
       "5  0.861923  0.614150  0.734853  0.216781  0.143810  \n",
       "6  0.861830  0.612295  0.737844  0.211844  0.144823  \n",
       "7  0.862179  0.610771  0.740674  0.207087  0.145923  \n",
       "8  0.862983  0.609688  0.743240  0.202371  0.147293  \n",
       "9  0.864228  0.608951  0.745793  0.197843  0.148879  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### creating reference datasets\n",
    "print(\"creating references for tract macro and microstructural data\")\n",
    "\n",
    "# if empty, create references directory\n",
    "if not os.path.isdir(data_dir+'/references'):\n",
    "    os.mkdir(data_dir+'/references')\n",
    "\n",
    "# set threshold\n",
    "thrs = 99\n",
    "\n",
    "# set track\n",
    "structure = ['anterioFrontalCC']\n",
    "\n",
    "# tractometry\n",
    "if os.path.isfile(data_dir+'/references/track_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0]+'.csv'):\n",
    "    track_reference_dataframe = pd.read_csv(data_dir+'/references/track_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0]+'.csv')\n",
    "else:\n",
    "    # compute reference dataset for tractmeasures  \n",
    "    # structural measures\n",
    "    print('macrostructural measures')\n",
    "    track_structural_measures = [ f for f in track_data_cut.keys() if f not in diff_micro_measures + subjects_data.keys().tolist() + ['structureID','sessionID','nodeID','ExponentialFitA','ExponentialFitB'] + track_data_cut.columns[track_data_cut.isna().any()].tolist() ]\n",
    "    track_structural_distances, track_structural_outliers, track_structural_reference_dataframe, track_structural_reference_json = outlier_detection(track_data_mean,structure,'structureID',track_structural_measures,thrs,'emd',True,False,\"\",groups[0],data_dir+'/references','tractmeasures_structural_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0])\n",
    "    \n",
    "    # dti measures\n",
    "    print('microstructural dti measures')\n",
    "    track_diffusion_measures = ['ad','fa','md','rd']\n",
    "    track_diffusion_dti_distances, track_diffusion_dti_outliers, track_diffusion_dti_reference_dataframe, track_diffusion_dti_reference_json = outlier_detection(track_data_cut,structure,'nodeID',track_diffusion_measures,thrs,'euclidean',True,True,50,groups[0],data_dir+'/references','tractmeasures_diffusion_reference_'+groups[0]+'_tensor_'+str(thrs)+'_'+structure[0])\n",
    "    track_diffusion_dti_reference_dataframe = track_diffusion_dti_reference_dataframe.groupby(['subjectID','structureID','nodeID'],as_index=False).mean().reset_index(drop=True)\n",
    "    \n",
    "    # noddi measures\n",
    "    print('microstructural noddi measures')\n",
    "    track_diffusion_measures = ['ndi','odi','isovf']\n",
    "    track_diffusion_noddi_distances, track_diffusion_noddi_outliers, track_diffusion_noddi_reference_dataframe, track_diffusion_noddi_reference_json = outlier_detection(track_data_cut,structure,'nodeID',track_diffusion_measures,thrs,'euclidean',True,True,50,groups[0],data_dir+'/references','tractmeasures_diffusion_reference_'+groups[0]+'_noddi_'+str(thrs)+'_'+structure[0])\n",
    "    track_diffusion_noddi_reference_dataframe = track_diffusion_noddi_reference_dataframe.groupby(['subjectID','structureID','nodeID'],as_index=False).mean().reset_index(drop=True)\n",
    "    \n",
    "    # merge reference datasets together for easier download and upload\n",
    "    print('merging track references')\n",
    "    merge_structural_diffusion_json(track_data_cut.loc[track_data_cut['structureID'] == structure[0]],data_dir+'/references/tractmeasures_diffusion_reference_'+groups[0]+'_tensor_'+str(thrs)+'_'+structure[0],data_dir+'/references/tractmeasures_diffusion_reference_'+groups[0]+'_noddi_'+str(thrs)+'_'+structure[0],data_dir+'/references/tractmeasures_diffusion_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0])\n",
    "    merge_structural_diffusion_json(track_data_cut.loc[track_data_cut['structureID'] == structure[0]],data_dir+'/references/tractmeasures_structural_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0],data_dir+'/references/tractmeasures_diffusion_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0],data_dir+'/references/tractmeasures_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0])\n",
    "    track_reference_diffusion_dataframe = pd.merge(track_diffusion_dti_reference_dataframe,track_diffusion_noddi_reference_dataframe,on=['subjectID','structureID','nodeID'])\n",
    "    track_reference_dataframe = pd.merge(track_structural_reference_dataframe,track_reference_diffusion_dataframe,on=['subjectID','structureID'])\n",
    "    \n",
    "    # save dataframe\n",
    "    track_reference_dataframe.to_csv(data_dir+'/references/track_reference_'+groups[0]+'_'+str(thrs)+'_'+structure[0]+'.csv',index=False)\n",
    "\n",
    "# show datatframe\n",
    "track_reference_dataframe.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc23ddc",
   "metadata": {},
   "source": [
    "### Generating visualizations and plots\n",
    "These are visualizations describing the reference dataframe generation. For the sake of this notebook, I will just show the figures for the tractometry reference dataframe results! Specifically, I will show the profile distribution of the FA in the Anterior Frontal Corpus Callosum Tract of the participants from the HCP dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9481ad57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anterioFrontalCC\n",
      "fa\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anterioFrontalCC\n",
      "fa\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### generate figures documenting outlier detection capabilities\n",
    "## track data\n",
    "track_diffusion_reference_dataframe = pd.read_csv(data_dir+'/references/tractmeasures_diffusion_reference_'+groups[0]+'_tensor_'+str(thrs)+'_'+structure[0]+'.csv')\n",
    "track_diffusion_reference_dataframe['classID'] = [ 'hcp' for f in track_diffusion_reference_dataframe['subjectID'] ]\n",
    "track_diffusion_reference_dataframe['colors'] = [ 'blue' for f in track_diffusion_reference_dataframe['subjectID'] ]\n",
    "\n",
    "# # generate plot pre-outlier computation\n",
    "plt.figure()\n",
    "fig = plot_profiles(structure,track_data_cut,['fa'],'mean','std',\"\",\"\")\n",
    "plt.savefig('./img/'+structure[0]+'_fa_pre_outliers_example.eps')\n",
    "plt.savefig('./img/'+structure[0]+'_fa_pre_outliers_example.png')\n",
    "\n",
    "# generate outliers and reference plot\n",
    "outlier_subjects = track_reference_dataframe.loc[track_reference_dataframe['structureID'] == structure[0]].loc[track_reference_dataframe['fa'].isna()].subjectID.unique().tolist()\n",
    "x = track_reference_dataframe.nodeID.unique().tolist()\n",
    "y = track_data_cut.loc[track_data_cut['structureID'] == structure[0]].groupby(['structureID','nodeID'],as_index=False).mean().reset_index(drop=True)['fa']\n",
    "err = track_diffusion_reference_dataframe.loc[track_diffusion_reference_dataframe['structureID'] == structure[0]].groupby(['structureID','nodeID'],as_index=False).std().reset_index(drop=True)['fa']\n",
    "plt.figure()\n",
    "sns.lineplot(x='nodeID',y='fa',data=track_diffusion_reference_dataframe.loc[track_diffusion_reference_dataframe['structureID'] == structure[0]].groupby(['structureID','nodeID'],as_index=False).mean().reset_index(drop=True),hue='structureID',legend=False,linewidth=5)\n",
    "plt.fill_between(x,y-err,y+err,alpha=0.2,color='blue')\n",
    "sns.lineplot(x='nodeID',y='fa',data=track_data_cut.loc[track_data_cut['structureID'] == structure[0]].loc[track_data_cut['subjectID'].isin(outlier_subjects)],hue='subjectID',legend=False,linewidth=5)\n",
    "plt.savefig('./img/'+structure[0]+'_cc_fa_outliers_example.eps')\n",
    "plt.savefig('./img/'+structure[0]+'_cc_fa_outliers_example.png')\n",
    "\n",
    "# # generate reference plot\n",
    "plt.figure()\n",
    "fig = plot_profiles(structure,track_diffusion_reference_dataframe.loc[track_diffusion_reference_dataframe['structureID'] == structure[0]].loc[~track_diffusion_reference_dataframe['fa'].isna()],['fa'],'mean','std',img_dir,structure[0])\n",
    "plt.savefig('./img/'+structure[0]+'_fa_reference_example.eps')\n",
    "plt.savefig('./img/'+structure[0]+'_fa_reference_example.png')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}