blp-analysis-diffusion-mri-structural-connectivity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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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. Analyze structural network matricies generated from dMRI using multiple the HCP-MMP atlas.\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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   "execution_count": 1,
   "id": "f2d27821",
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   "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: numpy in /home/brad/.local/lib/python3.8/site-packages (from bctpy) (1.21.5)\n",
      "Requirement already satisfied: scipy in /home/brad/.local/lib/python3.8/site-packages (from bctpy) (1.4.1)\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: joblib>=0.11 in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (1.1.0)\n",
      "Requirement already satisfied: sklearn in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (0.0)\n",
      "Requirement already satisfied: nibabel>=2.0.2 in /home/brad/.local/lib/python3.8/site-packages (from nilearn) (3.0.2)\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": 202,
   "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": 172,
   "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_12352/1637281729.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": 172,
     "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 structural network adjacency matrices\n",
    "The next step of the analysis pipeline for me is to collect and analyze the structural density matrices generated from diffusion tractography. To do this, I created matrices of the streamline density between parcels in the HCP-MMP atlas for each participant. I then removed all nodes that were not present in at least 50% of participants, following methods outlined in McPherson et al (2021)[https://www.nature.com/articles/s42003-021-02451-0]. Following this, I computed the network node degree for each node pair in the binarized and thresholded matrices, and computed the max node degree per participant. This data was then used in Figure XX to identify the relationship between max node degree and participant age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "9998dcbd",
   "metadata": {},
   "outputs": [
    {
     "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>degree</th>\n",
       "      <th>classID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100206</td>\n",
       "      <td>368.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100307</td>\n",
       "      <td>367.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100408</td>\n",
       "      <td>365.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100610</td>\n",
       "      <td>369.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101006</td>\n",
       "      <td>372.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>992774</td>\n",
       "      <td>370.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>993675</td>\n",
       "      <td>370.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>994273</td>\n",
       "      <td>370.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>995174</td>\n",
       "      <td>361.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>996782</td>\n",
       "      <td>368.0</td>\n",
       "      <td>hcp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1064 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subjectID  degree classID\n",
       "0        100206   368.0     hcp\n",
       "1        100307   367.0     hcp\n",
       "2        100408   365.0     hcp\n",
       "3        100610   369.0     hcp\n",
       "4        101006   372.0     hcp\n",
       "...         ...     ...     ...\n",
       "1059     992774   370.0     hcp\n",
       "1060     993675   370.0     hcp\n",
       "1061     994273   370.0     hcp\n",
       "1062     995174   361.0     hcp\n",
       "1063     996782   368.0     hcp\n",
       "\n",
       "[1064 rows x 3 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### dmri glasser structural (density) networks - max node degree vs age replication\n",
    "# load raw matrices\n",
    "if os.path.isfile(data_dir+'/'+groups[0]+'_dmri_glasser_preclean_adjacency.npy'):\n",
    "    dmri_glasser_networks_adjacency = np.load(data_dir+'/'+groups[0]+'_dmri_glasser_preclean_adjacency.npy',allow_pickle=True).item()\n",
    "else:\n",
    "    dmri_glasser_networks_adjacency = collect_data('generic/network',['!measurements','networkmatrices','preprocessed'],['bl_paper','ga','structural','density'],'network.json.gz',data_dir+'/'+groups[0]+'_dmri_glasser_preclean_adjacency',True)\n",
    "\n",
    "# cleaned matrices\n",
    "if os.path.isfile(data_dir+'/'+groups[0]+'_dmri_glasser_preclean_adjacency_cleaned.npy'):\n",
    "    dmri_glasser_networks_adjacency_cleaned = np.load(data_dir+'/'+groups[0]+'_dmri_glasser_preclean_adjacency_cleaned.npy',allow_pickle=True).item()\n",
    "else:\n",
    "    # find binarized matrices\n",
    "    bin_matrix = binarize_matrices(dmri_glasser_networks_adjacency)\n",
    "\n",
    "    # clean matrices\n",
    "    dmri_glasser_networks_adjacency_cleaned = threshold_matrices(dmri_glasser_networks_adjacency,bin_matrix,0.5)\n",
    "    \n",
    "    # save\n",
    "    np.save(data_dir+'/'+groups[0]+'_dmri_glasser_preclean_adjacency_cleaned.npy',dmri_glasser_networks_adjacency_cleaned)\n",
    "    \n",
    "# node degree\n",
    "if os.path.isfile(data_dir+'/'+groups[0]+'-mmp-strucural-cleaned-nodes-degree.csv'):\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max_degree = pd.read_csv(data_dir+'/'+groups[0]+'-mmp-strucural-cleaned-nodes-degree.csv')\n",
    "else:\n",
    "\n",
    "    # compute node degree for each subject\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree = pd.DataFrame()\n",
    "    subs = []\n",
    "    degree = []\n",
    "    nodes = []\n",
    "    for i in dmri_glasser_networks_adjacency_cleaned.keys():\n",
    "        subs = np.append(subs,[ str(i.split('_')[0]) for f in range(len(dmri_glasser_networks_adjacency_cleaned[i])) ])\n",
    "        nodes = np.append(nodes,[ str(f) for f in range(len(dmri_glasser_networks_adjacency_cleaned[i])) ])\n",
    "        degree = np.append(degree,bct.degrees_und(dmri_glasser_networks_adjacency_cleaned[i]))\n",
    "\n",
    "    # set up \n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree['subjectID'] = subs\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree['node_degree'] = degree\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree['node'] = nodes\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree = pd.merge(dmri_glasser_networks_adjacency_cleaned_nodes_degree,subjects_data.loc[subjects_data['subjectID'].isin(dmri_glasser_networks_adjacency_cleaned_nodes_degree['subjectID'].unique())][['subjectID']],on='subjectID')\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree['classID'] = [ groups[0] for f in range(len(dmri_glasser_networks_adjacency_cleaned_nodes_degree['subjectID'])) ]\n",
    "    \n",
    "    # save\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_degree.to_csv(data_dir+'/'+groups[0]+'-mmp-strucural-cleaned-nodes_degree.csv',index=False)\n",
    "\n",
    "# max node degree\n",
    "if os.path.isfile(data_dir+'/'+groups[0]+'-mmp-strucural-max-node-degree.csv'):\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max = pd.read_csv(data_dir+'/'+groups[0]+'-mmp-strucural-max-node-degree.csv')\n",
    "else:\n",
    "    # compute the max node degree per subject and save the max-node-degree dataframe\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max = dmri_glasser_networks_adjacency_cleaned_nodes_degree.groupby('subjectID').apply(lambda x: np.max(x['node_degree'])).reset_index()\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max.rename(columns={0: \"degree\"},inplace=True)\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max = pd.merge(dmri_glasser_networks_adjacency_cleaned_nodes_max,dmri_glasser_networks_adjacency_cleaned_nodes_degree[['subjectID']],on='subjectID')\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max['classID'] = [ groups[0] for f in range(len(dmri_glasser_networks_adjacency_cleaned_nodes_max)) ]\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max = dmri_glasser_networks_adjacency_cleaned_nodes_max.drop_duplicates()\n",
    "    \n",
    "    # save\n",
    "    dmri_glasser_networks_adjacency_cleaned_nodes_max.to_csv(data_dir+'/'+groups[0]+'-mmp-strucural-max-node-degree.csv',index=False)\n",
    "\n",
    "# show dataframe\n",
    "dmri_glasser_networks_adjacency_cleaned_nodes_max\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84185ac9",
   "metadata": {},
   "source": [
    "## Visualizing structural connectivity matrixes\n",
    "Now that the data is collected and cleaned, the next step for me is to visualize the connectivity matrices. To do this, I'll look at structural density adjacency matrix single subject (103818), and then average across all subjects. I log-scale the data in order to better visualize the magnitude of differences between node pairings in density values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "bcded69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12352/1130495777.py:3: RuntimeWarning: divide by zero encountered in log10\n",
      "  sns.heatmap(np.nan_to_num(np.log10(dmri_glasser_networks_adjacency_cleaned['103818_sess1']),neginf=-7),cmap='hot',vmin=-7,vmax=0)\n",
      "/tmp/ipykernel_12352/1130495777.py:10: RuntimeWarning: divide by zero encountered in log10\n",
      "  sns.heatmap(np.nan_to_num(np.log10(mean_network),neginf=-7),cmap='hot',vmin=-7,vmax=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate heatmap of single subject\n",
    "plt.figure()\n",
    "sns.heatmap(np.nan_to_num(np.log10(dmri_glasser_networks_adjacency_cleaned['103818_sess1']),neginf=-7),cmap='hot',vmin=-7,vmax=0)\n",
    "plt.savefig(img_dir+'/103818-'+groups[0]+'-network-density.png')\n",
    "plt.savefig(img_dir+'/103818-'+groups[0]+'-network-density.eps',transparent=True)\n",
    "\n",
    "# generate heatmap of average network\n",
    "plt.figure()\n",
    "mean_network = compute_mean_network(dmri_glasser_networks_adjacency_cleaned)\n",
    "sns.heatmap(np.nan_to_num(np.log10(mean_network),neginf=-7),cmap='hot',vmin=-7,vmax=0)\n",
    "plt.savefig(img_dir+'/'+groups[0]+'-mmp-mean-network-density.png')\n",
    "plt.savefig(img_dir+'/'+groups[0]+'-mmp-mean-network-density.eps',transparent=True)"
   ]
  },
  {
   "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, quality assurance statistics (SNR), 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 max node degree dataframe genereated from the cleaned structural density adjacency matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "efcb193c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating references for diffusion structural connectivity max node degree data\n",
      "1\n"
     ]
    },
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>101006</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>1</td>\n",
       "      <td>992774</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>1</td>\n",
       "      <td>993675</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>1</td>\n",
       "      <td>994273</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>1</td>\n",
       "      <td>995174</td>\n",
       "      <td>241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>1</td>\n",
       "      <td>996782</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1056 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     structureID subjectID  node_degree\n",
       "0              1    100206        252.0\n",
       "1              1    100307        239.0\n",
       "2              1    100408        247.0\n",
       "3              1    100610        262.0\n",
       "4              1    101006        262.0\n",
       "...          ...       ...          ...\n",
       "1051           1    992774        236.0\n",
       "1052           1    993675        237.0\n",
       "1053           1    994273        235.0\n",
       "1054           1    995174        241.0\n",
       "1055           1    996782        257.0\n",
       "\n",
       "[1056 rows x 3 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### creating reference datasets\n",
    "print(\"creating references for diffusion structural connectivity max node degree 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 structural connectivity measure and structure\n",
    "diffusion_structural_connectivity_measures = ['node_degree']\n",
    "structure = ['1']\n",
    "\n",
    "# rename node column to structure id for outlier code\n",
    "dmri_glasser_networks_adjacency_cleaned_nodes_degree.rename(columns={'node': 'structureID'},inplace=True)\n",
    "\n",
    "# max node degree\n",
    "if os.path.isfile(data_dir+'/references/diffusion_structural_connecity_node_degree_'+structure[0]+'_reference_'+groups[0]+'_'+str(thrs)+'.csv'):\n",
    "    diffusion_node_degree_reference_dataframe = pd.read_csv(data_dir+'/references/diffusion_structural_connecity_node_degree_'+structure[0]+'_reference_'+groups[0]+'_'+str(thrs)+'.csv')\n",
    "    diffusion_node_degree_reference_dataframe['subjectID'] = [ str(f) for f in diffusion_node_degree_reference_dataframe.subjectID ]\n",
    "else:\n",
    "    diffusion_node_degree_distances, diffusion_node_degree_outliers, diffusion_node_degree_reference_dataframe, diffusion_node_degree_reference_json = outlier_detection(dmri_glasser_networks_adjacency_cleaned_nodes_degree,structure,'structureID',diffusion_structural_connectivity_measures,thrs,'emd',True,False,'',groups[0],data_dir+'/references','diffusion_structural_connecity_node_degree_'+structure[0]+'_reference_'+groups[0]+'_'+str(thrs))\n",
    "    diffusion_node_degree_reference_dataframe['subjectID'] = [ str(f) for f in diffusion_node_degree_reference_dataframe.subjectID ]\n",
    "    diffusion_node_degree_reference_dataframe.to_csv(data_dir+'/references/diffusion_structural_connecity_node_degree_'+structure[0]+'_reference_'+groups[0]+'_'+str(thrs)+'.csv',index=False)\n",
    "    \n",
    "# show dataframe\n",
    "diffusion_node_degree_reference_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc23ddc",
   "metadata": {},
   "source": [
    "### Generating visualizations and plots of reference data\n",
    "These are visualizations describing the reference dataframe generation. For the sake of this notebook, I will just show the figures for node degree for the Left hemisphere V1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "9481ad57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brad/.local/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/brad/.local/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/brad/.local/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/brad/.local/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
      "image/png": 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H1kmTJtn169cnMI5I11JZWcmmTZsoKytj7969bNu2ncKthUTCYYwvlWCfEZjGGnyVxZ9/TzhzEL6aA2Dd9YFSj2Q81I2Zh6exhvR96yFYS3Z2HyZPnsTo0aPJy8sjJyeHMWPG4PN1/P2rMWaDtXbScZ93vEI2xlwDXAMwdOjQibt37+5wKJHu5re//e1XpnUMZ+bRmDeOSO9B4PGSsfGvmEgjBrCANV6MjWKwWODIiW7xfpzIdXfVMY+0XfPljYMn0jjo6xCN4K/YSerudzA2SnPXXHMN3/ve9+iothbycSvfWvtn4M/Q9A65w4lEuqG5c+eyd+9eqqurqa6poaa6Gl/1fvwNhwlmDSHcdxThrHz8Bz/9vAwivQbgqzmAjb1Dbv4/VbwfJ3Ld3WJM4yHceyAm1EDKZx+QWrEda6P4/X5y+/bF6/EwaNAgZsyYQTLoGLJIJ0yePJnJkycDTfNRlJWVsWnTJtatW8eat9+moXw7NpBDJKM/nobDhLPyCY48W8eQkz1mC8eQGweOw4Qa6LVjFSYSZvbsWcybN49x48Z16hBFR+kYskiCNDQ08Nprr7HkqafZu2c3NiOX+iGnEcl0/92Pewr/gU9I27OOUaNHc/uvfsXw4cMTMk7cjiE3p0IWab9oNMqrr77KAw/+mYrysqZT34ZM1qlvDvOV7yC96E3OOOMM7rjjDlJTUxM2VlsLWVfqiSSYx+Nhzpw5/PUvT/Cd73yHlPKtZG55AU9dhdPReixP3SECu99hwoQJ/PrXv05oGbeHClkkSdLT07nhhhv4z3vvJSfgo9eWF/GXbYV2/JYqcRCNEihaTe/evbjjjjtISUlxOtHnVMgiSXbKKafwyKJFnHrqKaTtepu0XWsgGnY6Vo/hL92CqTvIrT//OTk5OU7H+RIVsogDsrOzufuuu7jsssvwl2+nV+FLmIYqp2N1f+EG0ks+YOKkSZxxxhlOp/kKFbKIQ7xeL1dddRX//u//Ti+CZG55AV/FTqdjdWspBz7BhoNcf911GBfOPa1CFnHYtGnTWLToYU4aM5r0T1eTvuNVTGOd07G6n0iItNItnHHGGRQUFDid5phUyCIukJeXxx/+8F9cffXVpFV/Rubm5/Hv/wiimu8iXvxl27DhIJdeeqnTUVqkQhZxCZ/Px8KFC3nssUeZPPEU0va+R+bm5/GVbYNo9PgrkJZZS2r5VsaeeCInnnii02lapEIWcZn8/Hzuvusufve73zF62CDSd61pKuby7WBVzB3hrd6PqT/Mv3zrW05HaZXmshBxqUmTJjFx4kTWrl3LI488yo4db8H+TdQPOpVwn+G6IWo7+Mu3EcjIYObMmU5HaZUKWcTFjDFMmzaNqVOnsmbNGh56+GH27HydaK/+1A+ZQrRXf6cjul8kRMrhPcw6by5paWlOp2mVDlmIdAHGGGbMmMGjjzzCL37xC3L8YTK2vEha0VuYUIPT8VzNd3gPNhJi1qxZTkc5LhWySBfi9XqZN28ei5/8K5dccgmpBz9tdnxZl2Afi//gp+Tm9mX8+PFORzkuFbJIFxQIBPjxj3/MokUPc+KokaQXvUVg+ypMY63T0dwl3Iivah+zZn3D8RuYtoX7E4pIi0aMGMH999/HjTfeSHp9GZmfLMN3cJfTsVzDV7kXolHOPPNMp6O0iQpZpIvzeDxceOGFPPLIIk4YMYz0na+RuuefOkUO8B3aTXafPpx00klOR2kTFbJIN5Gfn89//+lPLFiwgJQDmwlsfwUiIadjOScaJqWqmDNnzOgShytAhSzSrfj9fm666SZuueUWfFX7yNi+sseWsreqBBsJu3JWt5aokEW6oQsuuIDf/OY3+GrLydi+qkfOt+w7vJfU1DQmTJjgdJQ2UyGLdFNnnXUWt99+O57q/aQVvdWzTouzltSqYqZMmeya2zO1hQpZpBubNWsW11xzDf6DRaTs3+R0nKTx1B3EBmuYNm2a01HaRYUs0s1997vf5eyzzyZ130a81fudjpMUvsq9AJx22mkOJ2kfFbJIN2eM4dZbbyUvL4/ArjU94kM+f2Uxo8eMcd09845HhSzSA2RkZPCr226DYDWpxeudjpNQJtSAp6aUaVOnOh2l3VTIIj3E+PHjWTB/PimlW/DUljsdJ2G8lcUAnH766Q4naT8VskgPcuWVV9I7K5v0Peu67VkXvsq99M7KZvTo0U5HaTcVskgPkpmZyY+uuRpPTSm+Q7ucjhN/NkpK1WdMm3p6l7k6r7mul1hEOmXu3LkMHTaM9M82drt79XlrSrHhIFO74PFjUCGL9Dher5cf/+hHUF+Jr2KH03Hiynt4L16vl0mTJjkdpUNUyCI90NSpUzlh1CjS92/qVrPCpVYVM278eDIyMpyO0iEqZJEeyBjDFZdfDg1V+Cp2Oh0nLkxDJdQdYsb06U5H6TAVskgPNW3aNIYPH0HagY+7xRkXvsN7ALrU7G5HUyGL9FDGGBYu/B6m7hDew3udjtNp/sN7GVFQQF5entNROkyFLNKDzZw5k/79B5B24COno3SKCdXjrTnAmTNmOB2lU1TIIj2Yz+fj4osvwlN9AE9NqdNxOsx3aDdYy1lnneV0lE5RIYv0cPPmzSOQkUHK/o+djtJh/kNFDM7PZ8SIEU5H6RQVskgPFwgEWDB/Pv5DuzANVU7HaTcTqsdbvZ9vzJyJMcbpOJ2iQhYRLrzwQrw+X5d8l+w7WATWMnPmTKejdJoKWUTIzc1l7rnnklqxAxOqczpOu6Qc3MmIggIKCgqcjtJpKmQRAZruLIKNkLJ/s9NR2sw0VOKpKePcc85xOkpcqJBFBID8/HxmzZpFalkhJtTgdJw28ZdvxxjDrFmznI4SFypkEfncZZddBtFw17ghqo2SenAnU047jX79+jmdJi5UyCLyuWHDhjFnzhxSS7dgGmudjtMqb2UxBGs5/5vfdDpK3KiQReRLrrjiCjwe4/p776WUbSUrO7vLzn18LCpkEfmSgQMHcvFFF+Gv2Imn+oDTcY7JBGvwVRZzwfnn4/P5nI4TNypkEfmKSy+9lJzcXAJ71rryriL+skIMcMEFFzgdJa5UyCLyFYFAgJt/9jNM3UH3fcAXjZBWsZ3TTz+dAQMGOJ0mrlTIInJM06dP5+yzzyat5EM8dQedjvM536Fd2MZ65s+f73SUuFMhi0iLfvrTn5KZmUlg11sQjTgdB4DUskIGDhzE5MmTnY4SdypkEWlRdnY2t/78FkxtBSklHzodB0/dQTzVB5g//1/weLpffXW/f5GIxNWMGTOYPXs2qSWb8NRVOJrFX7YVn8/P3LlzHc2RKCpkETmuG2+8kd69e5O++x3n7lIdCZF6cCczZ55NVlaWMxkSTIUsIsfVu3dvbrj+Ojw1ZfjLtjmSwXewCBtu7HanujWnQhaRNpkzZw7jxo8nveR9iISSPn5qxXYG5+czbty4pI+dLCpkEWkTYwzXX3cdtrGelJLknpvsqT+Mp/oAF5x/fpe/K0hrVMgi0mZjx45tOje59BMIJ2+KTl/5DjweD3PmzEnamE5QIYtIu3z/+9/HRkLJm8jeRkk79ClTpkwhNzc3OWM6RIUsIu1SUFDAjBkzSCvbApHGhI/nrd6PDdZ021PdmlMhi0i7LVy4EBtuTMoZF/7yHaQHAt1qms2WqJBFpN3Gjh3L18aNa3qXnMjzkiMhUg7v5hszZ5Kampq4cVxChSwiHXLJxRdDQzW+Q7sTNobv8B5sJNTtP8w7QoUsIh0ydepU+vXvT0rploSN4a/YSW7ffowfPz5hY7iJCllEOsTr9XLhggV4q/fjqTsU9/WbUD2+qn2cM2d2t5xI6Fh6xr9SRBLivPPOw+fz40/Au2TfwSKwtsccrgAVsoh0QlZWFjNnnk3qwZ1xv5w65eCnjCgooKCgIK7rdTMVsoh0yvz587GREP6KnXFbp2moxFNTyrnnnBO3dXYFKmQR6ZSTTjqJEQUFpJZvBWvjsk5/+Q6MMcyePTsu6+sqVMgi0inGGBbMn4+prcBTW9b5FVpL6sGdTJo0mb59+3Z+fV2ICllEOm327NmkpafH5RQ4b9VnEKxh3rzuf6n00VTIItJpgUCAueeeS8qhXZhQ52aB85dtJaNXJmeccUac0nUdKmQRiYv58+djoxH85R2f38KE6vEf3sM3z5vXIy6VPpoKWUTiYvjw4UyYMIHUssIOz2/hL9sGNsr5558f53RdgwpZROLm29/+NgRr8B3a0/5vjkZILS/k1FMnMnTo0PiH6wJUyCISN9OmTWNAXh6pBz5u9/f6DhZBsJaLL74oAcm6BhWyiMSN1+vl4osuwlNTiqf6QNu/0VrSDnzMkKHDmDJlSuICupwKWUTiat68eWRm9ia15MM2f4/vYBGm7iCX/+D73fompsejQhaRuEpPT+eSSy7GV1mMp6YNF4pEo6SVfMCQocM4++yzE57PzVTIIhJ3CxYsoFdmJmn71h/3cmp/6SeY+sP86Jqr8Xq9SUroTipkEYm7QCDAD6+6Cm9VSat3FDHBGtJLPmDKaaf1yAtBjqZCFpGEOP/88xk+ooDA3nWYUP1XnxCNEihaTYrPw0033tijjx0foUIWkYTw+Xz82+2/whsNkf7paoiGv/iijZK2+2081Qf4P7feyuDBg50L6iIqZBFJmBNOOIFbbrkZb9VnZGxb2XS7p+oDpG9fhb98O5dffjmzZs1yOqZr+JwOICLd27x580hJSeHue+7BU7gCgLT0dH50000sWLDA4XTuokIWkYSbNWsWU6dO5Z///Cc+n49x48aRnZ3tdCzXUSGLSFIEAgFmzpzpdAxX0zFkERGXUCGLiLiECllExCVUyCIiLqFCFhFxCRWyiIhLqJBFRFxChSwi4hIqZBERl1Ahi4i4hApZRMQlVMgiIi6hQhYRcQkVsoiIS6iQRURcQoUsIuISKmQREZdQIYuIuIQKWUTEJYy1tu1PNqYM2N2O9fcFytsbKgmUq/3cmk252ke52ideuYZZa/sd70ntKuT2Msast9ZOStgAHaRc7efWbMrVPsrVPsnOpUMWIiIuoUIWEXGJRBfynxO8/o5SrvZzazblah/lap+k5kroMWQREWk7HbIQEXGJuBWyMWaMMeaDZn+qjDE/Ncb8xhizr9ny8+I1ZitZHjHGlBpjPm62LMcYs8oYsz323z7NvnabMWaHMWarMebcJOe6xxhTaIzZZIxZaozJji0fboypb7bdHkhyrhZfN4e319PNMu0yxnwQW57M7TXEGPO6MWaLMWazMeam2HJH97FWcjm6j7WSyw37WEvZnNnPrLVx/wN4gf3AMOA3wM8TMU4r458JnAp83GzZ3cAvY49/CdwVe3wS8CGQCowAdgLeJOY6B/DFHt/VLNfw5s9zYHsd83Vzensd9fXfA3c4sL0GAqfGHmcC22LbxdF9rJVcju5jreRywz52zGxO7WeJOmQxC9hprW3PRSRxY619Ezh41OJ/AR6PPX4cmN9s+VPW2qC1tgjYAUxJVi5r7cvW2nDsr+uA/ESM3d5crXB0ex1hjDHARcCSRIzdGmttibV2Y+xxNbAFGIzD+1hLuZzex1rZXi1J5j7WarZk72eJKuRL+PI/4IbYr0uPNP81LskGWGtLoOlFAPrHlg8G9jZ7XjGt7yyJdCXw92Z/H2GMed8Ys9oYM8OBPMd63dyyvWYAB6y125stS/r2MsYMB04B/omL9rGjcjXn6D52jFyu2cda2GZJ3c/iXsjGmBTgW8AzsUX/A4wEJgAlNL39dxNzjGVJP/XEGHM7EAaejC0qAYZaa08BbgYWG2N6JzFSS6+bK7YX8F2+/EM/6dvLGNMLeA74qbW2qrWnHmNZwrZZS7mc3seOkcs1+1grr2VS97NEvEOeB2y01h4AsNYesNZGrLVR4CES9KtHGxwwxgwEiP23NLa8GBjS7Hn5wGfJDGaM+QFwPrDQxg5UxX5dq4g93kDTcbTRycrUyuvmhu3lAy4Enj6yLNnbyxjjp+l/4Cettc/HFju+j7WQy/F97Fi53LKPtbLNkr6fJaKQv/QT5cgOGrMA+Pgr35EcLwA/iD3+AbC82fJLjDGpxpgRwCjg3WSFMsbMBX4BfMtaW9dseT9jjDf2uCCW69N252SkAAAD+ElEQVQk5mrpdXN0e8XMBgqttcVHFiRze8WOKy4Ctlhr7232JUf3sZZyOb2PtZLL8X2sldcSnNjP4vyJZQCoALKaLfsL8BGwiaYNPTBRn1A2G3MJTb9ahGj6aXsVkAu8CmyP/Ten2fNvp+kn3VZgXpJz7aDpeNkHsT8PxJ77bWAzTZ82bwQuSHKuFl83J7dXbPljwI+Pem4yt9d0mn6F3tTsdTvP6X2slVyO7mOt5HLDPnbMbE7tZ7pST0TEJXSlnoiIS6iQRURcQoUsIuISKmQREZdQIYuIuIQKWUTEJVTI4rjYlIadvmDIGPOYMeY78cgk4gQVsshRYpfMiiSdClniIvYud4sx5qHYRN8vG2PSjTETjDHrzBeTo/eJPX+iMeZDY8xa4Ppm6/GapgnV34t9z49aGdMYY+43xnxijHmJL2ZXO7L+1caYDcaYlc3mmJgcW+/a2Dgfx5Zfbox5xhjzN+Dl2LJbm+X4v83Wfakx5l3TNEH5g0cupRXpLBWyxNMo4E/W2pOBwzRdZvoE8Atr7XiaLpP9dey5jwI3WmunHrWOq4BKa+1kYDJwdWw+g2NZAIwBxgFXA9Pg88li7gO+Y62dCDwC/L9m4/44Nm7kqPVNBX5grf2GMeac2L9nCk2zkU00xpxpjDkRuBg4w1o7IbaOhW3eQiKt0K9mEk9F1toPYo830DS1Yra1dnVs2ePAM8aYrKOW/4WmWQKh6e4W45sdC86iqRiLjjHemcASa20E+MwY81ps+Rjga8Cqprlj8AIlpunWRZnW2ndiz1tM0wxoR6yy1h6ZEP+c2J/3Y3/vFcsxHpgIvBdbdzpfzOom0ikqZImnYLPHESC7hecZWp7f1gA/sdaubOOYx1qPATYf/e7bHP/mCLVHreNOa+2DR63jJ8Dj1trb2phPpM10yEISqRI41OyuCpcBq621h4FKY8z02PLmv/KvBK6NHXbAGDPaGJPRwvrfpGmaRm/sGPHM2PKtQD9jzNTYOvzGmJOttYeAamPM6bHnXdJK9pXAlbGJyzHGDDbG9KdpFrfvxB4fubHpsLZsDJHj0TtkSbQfAA8YYwI0zRt7RWz5FcAjxpg6msrviIdpupHkxthctWV8cW+6oy0FvkHTseltwGoAa21j7JDHH2OHR3zAf9E0beJVwEPGmFrgDZp+aHyFtfbl2PHitbFDEzXApdbaT4wx/wa8bIzx0DQ16PWAI/ePlO5F029Kj2KM6WWtrYk9/iVNc/De5HAsEUDvkKXn+aYx5jaa9v3dwOXOxhH5gt4hi+sZY8bRdCZGc0Fr7WlO5BFJFBWyiIhL6CwLERGXUCGLiLiECllExCVUyCIiLqFCFhFxif8PuHTB/BCKCe0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### generate figures documenting outlier detection capabilities\n",
    "# ## track data\n",
    "diffusion_node_degree_reference_dataframe = pd.read_csv(data_dir+'/references/diffusion_structural_connecity_node_degree_'+structure[0]+'_reference_'+groups[0]+'_'+str(thrs)+'.csv')\n",
    "diffusion_node_degree_reference_dataframe['subjectID'] = [ str(f) for f in diffusion_node_degree_reference_dataframe.subjectID ]\n",
    "diffusion_node_degree_reference_dataframe['classID'] = [ 'hcp' for f in diffusion_node_degree_reference_dataframe['subjectID'] ]\n",
    "diffusion_node_degree_reference_dataframe['colors'] = [ 'blue' for f in diffusion_node_degree_reference_dataframe['subjectID'] ]\n",
    "\n",
    "# # generate plot pre-outlier computation\n",
    "plt.figure()\n",
    "sns.violinplot('node_degree',data = dmri_glasser_networks_adjacency_cleaned_nodes_degree.loc[dmri_glasser_networks_adjacency_cleaned_nodes_degree['structureID'] == structure[0]],inner='points',scale='count')\n",
    "axes = plt.gca()\n",
    "x_lim = axes.get_xlim()\n",
    "y_lim = axes.get_ylim()\n",
    "plt.savefig('./img/'+structure[0]+'_structural_connectivity_node_degree_pre_outliers_example.png')\n",
    "plt.savefig('./img/'+structure[0]+'_structural_connectivity_node_degree_pre_outliers_example.eps')\n",
    "\n",
    "# plot outliers on distribution\n",
    "outlier_subjects = dmri_glasser_networks_adjacency_cleaned_nodes_degree.loc[~dmri_glasser_networks_adjacency_cleaned_nodes_degree['subjectID'].isin(diffusion_node_degree_reference_dataframe.subjectID.unique().tolist())].subjectID.unique().tolist()\n",
    "\n",
    "plt.figure()\n",
    "sns.violinplot('node_degree',data = dmri_glasser_networks_adjacency_cleaned_nodes_degree.loc[dmri_glasser_networks_adjacency_cleaned_nodes_degree['structureID'] == structure[0]],inner='points',scale='count')\n",
    "sns.swarmplot('node_degree', data = dmri_glasser_networks_adjacency_cleaned_nodes_degree.loc[(dmri_glasser_networks_adjacency_cleaned_nodes_degree['structureID'] == structure[0]) & (dmri_glasser_networks_adjacency_cleaned_nodes_degree.subjectID.isin(outlier_subjects))],color='red')\n",
    "axes = plt.gca()\n",
    "axes.set_xlim(x_lim)\n",
    "axes.set_ylim(y_lim)\n",
    "plt.savefig('./img/'+structure[0]+'_structural_connectivity_node_degree_outliers_example.png')\n",
    "plt.savefig('./img/'+structure[0]+'_structural_connectivity_node_degree_outliers_example.eps')\n",
    "\n",
    "# # generate reference plot\n",
    "plt.figure()\n",
    "axes = plt.gca()\n",
    "axes.set_xlim(x_lim)\n",
    "axes.set_ylim(y_lim)\n",
    "sns.violinplot('node_degree', data = diffusion_node_degree_reference_dataframe,inner='points',scale='count')\n",
    "plt.savefig('./img/'+structure[0]+'_structural_connectivity_node_degree_reference_example.png')\n",
    "plt.savefig('./img/'+structure[0]+'_structural_connectivity_node_degree_reference_example.eps')"
   ]
  }
 ],
 "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
}