actual_interface.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3bc280bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.io as sc\n",
    "from scipy.io import wavfile\n",
    "import tkinter as tk\n",
    "from tkinter import *\n",
    "from tkinter import ttk as ttk\n",
    "from tkinter import filedialog as fd\n",
    "from tkinter.messagebox import showinfo\n",
    "import os as os\n",
    "from pydub import AudioSegment\n",
    "from pydub.playback import play\n",
    "import subprocess\n",
    "import math\n",
    "import random\n",
    "import datetime\n",
    "#from datetime import datetime\n",
    "from datetime import timedelta\n",
    "from pytz import timezone\n",
    "from PIL import ImageTk , Image\n",
    "\n",
    "# Dependency - QuickTimePlayer on mac\n",
    "# REMINDERS - change all paths and specific variables ; change try catch pattern of default players for different OS\n",
    "\n",
    "# GLOBAL VARIABLES\n",
    "ctc_timestamps_main = None\n",
    "chn_timestamps_main = None\n",
    "childid = None\n",
    "sorted_ctccount = None\n",
    "list_of_randoms = []\n",
    "list_of_randoms_2 = []\n",
    "rate = None # stores wavfile audio info\n",
    "wavdata = None # stores wavfile audio info\n",
    "checkbutton_value  = None\n",
    "long_CTCs_offsets = []\n",
    "all_avail_IDs = []\n",
    "LENA_start_time = None\n",
    "\n",
    "#GLOBAL CONSTANTS TO CHANGE:\n",
    "path_to_working_folder = '/Users/arjun/Box/box-group-lena-studies/Soundscape/ucsf_app_stuff'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "74cf2567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: pyobjc 10.0\r\n",
      "Uninstalling pyobjc-10.0:\r\n",
      "  Successfully uninstalled pyobjc-10.0\r\n"
     ]
    }
   ],
   "source": [
    "#!pip install playsound\n",
    "# !pip3 uninstall PyObjC -y\n",
    "# !pip install pydub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5eb0410a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "def quitme():\n",
    "    master.withdraw()\n",
    "    master.destroy()\n",
    "    \n",
    "def show_entry_fields():\n",
    "    entries = tk.Toplevel(master)\n",
    "    entries.title(\"Secondary Window\")\n",
    "    l1 = tk.Label(entries, text=\"First Name =  \"+e1.get())\n",
    "    l1.grid(row=0)\n",
    "    l2 = tk.Label(entries,text=\"Last Name = \"+e2.get())\n",
    "    l2.grid(row=1)\n",
    "    l3 = tk.Label(entries,text=\"Number you chose: =  \" + combo.get())\n",
    "    l3.grid(row=2, column=0) \n",
    "    button_close = ttk.Button(entries, text = \"Close\", command = lambda: [entries.withdraw(), entries.destroy()])\n",
    "    button_close.grid(row=2,column=1)\n",
    "\n",
    "valuegot=\"0\"    \n",
    "master = tk.Tk()\n",
    "l11 = tk.Label(master, text=\"First Name\")\n",
    "l11.grid(row=0)\n",
    "\n",
    "l12 = tk.Label(master, text=\"Last Name\")\n",
    "l12.grid(row=1)\n",
    "\n",
    "e1 = tk.Entry(master)\n",
    "e1.grid(row=0, column=1)\n",
    "\n",
    "e2 = tk.Entry(master)\n",
    "e2.grid(row=1, column=1)\n",
    "\n",
    "\n",
    "b1 = tk.Button(master, text='Quit', command= quitme)\n",
    "b1.grid(row=3, column=2, sticky=tk.W, pady=4)\n",
    "\n",
    "\n",
    "global valuegot \n",
    "\n",
    "combo = ttk.Combobox(master,state=\"readonly\", values=[\"1\", \"2\", \"3\", \"4\"])\n",
    "combo.grid(row=4, column=1)\n",
    "valuegot = combo.get()\n",
    "print(valuegot)\n",
    "\n",
    "b2 = tk.Button(master, text='Show', command=show_entry_fields)\n",
    "b2.grid(row=3, column=1, sticky=tk.W,pady=4)\n",
    "\n",
    "tk.mainloop()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some processing work before running actual tk loop\n",
    "path_of_childids = os.path.join(path_to_working_folder, 'avail_child_ID.txt')\n",
    "global all_avail_IDs\n",
    "with open(path_of_childids) as file:\n",
    "  for item in file:\n",
    "    all_avail_IDs.append(item.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d42c2017",
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "\n",
    "def getrealtime(i):\n",
    "\n",
    "    infocsvpath = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\" , childid + \"_speech_output\",  childid+\"_its_info.csv\")\n",
    "    \n",
    "    itsinfocsv = pd.read_csv(infocsvpath)\n",
    "    datetime_str = str(itsinfocsv['startClockTime'][0])\n",
    "    datetime_object = datetime.datetime.strptime(datetime_str, '%Y-%m-%dT%H:%M:%SZ')\n",
    "    datetime_object = datetime_object.replace(tzinfo=datetime.timezone.utc)\n",
    "\n",
    "    LENA_start_time = datetime_object\n",
    "    offset_amount = int(long_CTCs_offsets[i][0])\n",
    "    actual_time_of_clip_UTC = LENA_start_time + timedelta(seconds = offset_amount)\n",
    "    \n",
    "    if (childid.find('L')>=0 or childid.find('M')>=0  or childid.find('D')>=0  or childid.find('E')>=0):\n",
    "        actual_time_of_clip = actual_time_of_clip_UTC.astimezone(timezone('America/Chicago'))\n",
    "\n",
    "    if (childid.find('R')>=0 or childid.find('J')>=0):\n",
    "        actual_time_of_clip = actual_time_of_clip_UTC.astimezone(timezone('America/New_York'))\n",
    "    \n",
    "    return actual_time_of_clip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9eaccabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "which_child = None\n",
    "\n",
    "\n",
    "def select_file():\n",
    "    wantfiletypes = [ ('CSV files', '*.csv') ]\n",
    "    filepath = fd.askopenfilename( title='Open a file', initialdir='/', filetypes = wantfiletypes)\n",
    "    nameofcsv = os.path.basename(filepath)\n",
    "    underscore = nameofcsv.find('_')\n",
    "    global childid \n",
    "    childid = nameofcsv[:underscore]\n",
    "    message = \"You are now viewing child ID: \" + childid + \"!\"\n",
    "    showinfo(title='Selected File', message = message)\n",
    "    global rate \n",
    "    global wavdata\n",
    "    directory = \"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\"\n",
    "    wavpath = os.path.join(directory, childid+\".wav\")\n",
    "    rate, wavdata = wavfile.read(wavpath)\n",
    "    global ctc_timestamps_main \n",
    "    ctc_timestamps_main= pd.read_csv(filepath)\n",
    "    showinfo(title='Status Update', message = \"You can now use the interface.\")\n",
    "    if ctc_timestamps_main['segment_type'][0]!='CTC':\n",
    "        print(\"You have chosen the wrong file. Please close the window and try again.\")\n",
    "    \n",
    "    \n",
    "def prepare_CTCclips():\n",
    "    '''\n",
    "    This function prepares a dataframe with clips arrranged in descending order of CTC count\n",
    "    '''\n",
    "    global sorted_ctccount\n",
    "    sorted_ctccount = ctc_timestamps_main.sort_values(by=['convo_count'], ascending=0, ignore_index = 1) \n",
    "    \n",
    "    \n",
    "    \n",
    "def extract_clips_longCTC ():\n",
    "    '''\n",
    "    This function extracts and saves the clip with the longest CT measured by convo count, not duration.\n",
    "    It also extracts the top 15 clips based on CTC count length.\n",
    "    '''\n",
    "    directory = \"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\"\n",
    "    path = os.path.join(directory, childid+\".wav\")\n",
    "    \n",
    "    prepare_CTCclips()\n",
    "    upperbound = 15\n",
    "\n",
    "    global long_CTCs_offsets\n",
    "\n",
    "    if (15>=len(sorted_ctccount)):\n",
    "        upperbound = len(sorted_ctccount)\n",
    "        \n",
    "    for i in range(0 , upperbound):\n",
    "        start, ending = sorted_ctccount.at[i,'clip_onset'] , sorted_ctccount.at[i,'clip_offset']\n",
    "        split_at_frame = math.floor(rate * start)\n",
    "        stop_at_frame = math.ceil(rate * ending)\n",
    "        left_data  = wavdata[split_at_frame : stop_at_frame] \n",
    "        long_CTCs_offsets.append((start,ending))\n",
    "        # TODO : change this file path for outputs\n",
    "        newpath = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles/sample_expt_arjun\" , childid+ \"_longCTC\"+str(i)+\".wav\")\n",
    "        wavfile.write(newpath, rate, left_data)\n",
    "\n",
    "    showinfo(title='Status Update', message = \"File extraction process complete.\")\n",
    " \n",
    "    # idea : dropdown for all available options to play audio -- do this if lot of things to do\n",
    "    \n",
    "\n",
    "def play_longest_CTC():\n",
    "    '''\n",
    "    This function plays the clip with the longest CT measured by convo count which has been extracted\n",
    "    '''    \n",
    "\n",
    "    global LENA_start_time\n",
    "    filetoplay = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles/sample_expt_arjun\" , childid+ \"_longCTC0.wav\")\n",
    "    \n",
    "    '''infocsvpath = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\" , childid + \"_speech_output\",  childid+\"_its_info.csv\")\n",
    "    itsinfocsv = pd.read_csv(infocsvpath)\n",
    "    \n",
    "    datetime_str = str(itsinfocsv['startClockTime'][0])\n",
    "    datetime_object = datetime.datetime.strptime(datetime_str, '%Y-%m-%dT%H:%M:%SZ')\n",
    "    datetime_object = datetime_object.replace(tzinfo=datetime.timezone.utc)\n",
    "\n",
    "    LENA_start_time = datetime_object\n",
    "    offset_amount = int(long_CTCs_offsets[0][0])\n",
    "    actual_time_of_clip_UTC = LENA_start_time + timedelta(seconds = offset_amount)\n",
    "    \n",
    "    if (childid.find('L')>=0 or childid.find('M')>=0  or childid.find('D')>=0  or childid.find('E')>=0):\n",
    "        actual_time_of_clip = actual_time_of_clip_UTC.astimezone(timezone('America/Chicago'))\n",
    "\n",
    "    if (childid.find('R')>=0 or childid.find('J')>=0):\n",
    "        actual_time_of_clip = actual_time_of_clip_UTC.astimezone(timezone('America/New_York'))\n",
    "    '''\n",
    "\n",
    "    # for UCSF - pacific time\n",
    "    actual_time_of_clip = getrealtime(0)\n",
    "    time_message = str(actual_time_of_clip.strftime('This clip is from time %H:%M on date %d-%m-%Y.'))\n",
    "\n",
    "    if(checkbutton_value.get()):\n",
    "        showinfo(title=\"Time information!\", message=time_message)\n",
    "\n",
    "    subprocess.check_call(['open', '-a', 'Quicktime Player', filetoplay])\n",
    "    \n",
    "    \n",
    "def play_other_long_CTC():\n",
    "    '''\n",
    "    This function plays another clip with a long CT measured by convo count which has been extracted\n",
    "    '''\n",
    "    random_index = random.randint(1,14)\n",
    "    go_on = True\n",
    "    global list_of_randoms\n",
    "\n",
    "    if len(list_of_randoms)>=14:\n",
    "        showinfo(title='Status Update', message = \"All the extracted clips have been played. Resetting back to original state!\")\n",
    "        list_of_randoms = []\n",
    "\n",
    "    while (go_on):\n",
    "        if random_index not in list_of_randoms:\n",
    "            list_of_randoms.append(random_index)\n",
    "            go_on = False\n",
    "            filetoplay = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles/sample_expt_arjun\" , childid+ \"_longCTC\"+str(random_index)+\".wav\")\n",
    "        else:\n",
    "            random_index = random.randint(1,14)\n",
    "    \n",
    "    \n",
    "    \n",
    "    if(checkbutton_value.get()):\n",
    "        actual_time_of_clip = getrealtime(random_index)\n",
    "        time_message = str(actual_time_of_clip.strftime('This clip is from time %H:%M on date %d-%m-%Y.'))\n",
    "        showinfo(title=\"Time information!\", message=time_message)\n",
    "    \n",
    "    subprocess.check_call(['open', '-a', 'Quicktime Player', filetoplay])\n",
    "\n",
    "def extract_clips_longVoc ():\n",
    "    '''\n",
    "    This function extracts and saves the clips with the top 15 longest child utterances based on time duration.\n",
    "    \n",
    "    '''\n",
    "    filepath_of_csv = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\" , childid+ \"_voc_output\", childid+\"_CHN_timestamps.csv\")\n",
    " \n",
    "    chn_timestamps_main = pd.read_csv(filepath_of_csv)\n",
    "    utterances_df = chn_timestamps_main[chn_timestamps_main['childUttCnt']!=0]\n",
    "    sorted_chn_length  = utterances_df.sort_values(by=['duration'], ascending=0, ignore_index = 1)\n",
    "  \n",
    "   \n",
    "    upperbound = 15\n",
    "\n",
    "    if (15>=len(sorted_chn_length)):\n",
    "        upperbound = len(sorted_chn_length)\n",
    "        \n",
    "    for i in range(0 , upperbound):\n",
    "        start, ending = sorted_chn_length.at[i,'onset'] , sorted_chn_length.at[i,'offset']\n",
    "        split_at_frame = math.floor(rate * start)\n",
    "        stop_at_frame = math.ceil(rate * ending)\n",
    "        left_data  = wavdata[split_at_frame : stop_at_frame] \n",
    "        # TODO : change this file path for outputs\n",
    "        newpath = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles/sample_expt_arjun\" , childid+ \"_longVoc\"+str(i)+\".wav\")\n",
    "        wavfile.write(newpath, rate, left_data)\n",
    "\n",
    "    showinfo(title='Status Update', message = \"File extraction for vocalizations complete.\")\n",
    "\n",
    "def play_longest_CHN():\n",
    "    '''\n",
    "    This function plays the clip with the longest CHN vocalization measured by duration which has been extracted\n",
    "    '''    \n",
    "    filetoplay = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles/sample_expt_arjun\" , childid+ \"_longVoc0.wav\") \n",
    "    subprocess.check_call(['open', '-a', 'Quicktime Player', filetoplay])\n",
    "\n",
    "def play_other_long_CHN():\n",
    "    '''\n",
    "    This function plays another clip with a long vocalization measured by duration\n",
    "    '''\n",
    "    random_index = random.randint(1,14)\n",
    "    go_on = True\n",
    "    global list_of_randoms_2\n",
    "\n",
    "    if len(list_of_randoms_2)>=14:\n",
    "        showinfo(title='Status Update', message = \"All the extracted clips have been played. Resetting back to original state!\")\n",
    "        list_of_randoms_2 = []\n",
    "\n",
    "    while (go_on):\n",
    "        if random_index not in list_of_randoms_2:\n",
    "            list_of_randoms_2.append(random_index)\n",
    "            go_on = False\n",
    "            filetoplay = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles/sample_expt_arjun\" , childid+ \"_longVoc\"+str(random_index)+\".wav\")\n",
    "        else:\n",
    "            random_index = random.randint(1,14)\n",
    "     \n",
    "    subprocess.check_call(['open', '-a', 'Quicktime Player', filetoplay])\n",
    "\n",
    "\n",
    "def get_input():\n",
    "    global childid\n",
    "    global rate \n",
    "    global wavdata \n",
    "    global ctc_timestamps_main\n",
    "\n",
    "    value = my_text_box.get(\"1.0\",\"end-1c\")\n",
    "    my_text_box.delete(\"1.0\",\"end\")\n",
    "    if value in all_avail_IDs:\n",
    "        childid = value\n",
    "        message = \"You are now viewing child ID: \" + childid + \"!\"\n",
    "        showinfo(title='Selection', message = message)\n",
    "        directory = \"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\"\n",
    "        wavpath = os.path.join(directory, childid+\".wav\")\n",
    "        rate, wavdata = wavfile.read(wavpath)\n",
    "        ctc_timestamps_main= pd.read_csv(os.path.join(directory,childid+'_CTC_output', childid+'_CTC_timestamps.csv'))\n",
    "        showinfo(title='Status Update', message = \"You can now use the interface.\")\n",
    "\n",
    "        global which_child\n",
    "        which_child = Label(master, text=\"📍Current child: \"+childid, font=('Aerial 14'), bg='green', fg = 'white')\n",
    "        which_child.grid(row=1,column=2,sticky=W)\n",
    "    \n",
    "    else:\n",
    "       showinfo(title='Error', message = 'This child ID was not found in our database.')\n",
    "       \n",
    "\n",
    "def show_image(imagefile):\n",
    "    image = ImageTk.PhotoImage(file=imagefile)\n",
    "    imagebox.config(image=image)\n",
    "    imagebox.image = image # save a reference of the image to avoid garbage collection\n",
    "\n",
    "\n",
    "master = tk.Tk()\n",
    "master.title('SPOG x UCSF Interface')\n",
    "\n",
    "l2 = tk.Label(master,font=(\"Times New Roman\", 16), text= \"Start here: \")\n",
    "l2.grid(row=3,column=0,sticky=E)\n",
    "\n",
    "l3 = tk.Label(master,font=(\"Times New Roman\", 16), text= \"All about conversational turns (CTC): \")\n",
    "l3.grid(row=4,column=0,sticky=E)\n",
    "\n",
    "open_button = ttk.Button(master, text='Select CSV file of CTC timestamps', command=select_file)\n",
    "open_button.grid(row=3, column=1, sticky=W)\n",
    "\n",
    "longest_CTC_button = ttk.Button(master, text='Extract CTC clips', command=extract_clips_longCTC)\n",
    "longest_CTC_button.grid(row=4,column=1, sticky=W, pady=5)\n",
    "\n",
    "play_longest_CTC_button = ttk.Button(master, text='Play the longest CTC', command=play_longest_CTC)\n",
    "play_longest_CTC_button.grid(row=6,column=1, sticky=W)\n",
    "\n",
    "play_long_CTC_button = ttk.Button(master, text='Play another long CTC for us', command=play_other_long_CTC)\n",
    "play_long_CTC_button.grid(row=7,column=1, sticky=W)\n",
    "\n",
    "empty1 = tk.Label(master)\n",
    "empty1.grid(row=8)\n",
    "\n",
    "l4 = tk.Label(master,font=(\"Times New Roman\", 16), text= \"All about child vocalizations (CHN): \")\n",
    "l4.grid(row=10,column=0,sticky=E)\n",
    "\n",
    "longest_CHN_button = ttk.Button(master, text='Extract CHN vocalization clips', command=extract_clips_longVoc)\n",
    "longest_CHN_button.grid(row=10,column=1, sticky=W)\n",
    "\n",
    "play_longest_CHN_button = ttk.Button(master, text='Play the longest CHN voc', command=play_longest_CHN)\n",
    "play_longest_CHN_button.grid(row=11,column=1, sticky=W)\n",
    "\n",
    "play_long_CHN_button = ttk.Button(master, text='Play another long CHN for us', command=play_other_long_CHN)\n",
    "play_long_CHN_button.grid(row=12,column=1, sticky=W)\n",
    "\n",
    "\n",
    "#my_text_box=Text(master, height=3, width=20)\n",
    "#my_text_box.grid(row=13,column=1, sticky=E)\n",
    "\n",
    "inputdirections = tk.Label(master,font=(\"Times New Roman\", 16), text= \"Enter child ID for this session (case-sensitive, with no spaces) : \")\n",
    "inputdirections.grid(row=13, column=0)\n",
    "\n",
    "my_text_box=Text(master, height=3, width=20)\n",
    "my_text_box.grid(row=13,column=1, sticky=W)\n",
    "\n",
    "comment = tk.Button(master, height=2 , width=10, text=\"Done\", command=lambda: get_input(),bg='yellow', fg='blue')\n",
    "comment.grid(row=14,column=1, sticky=W)\n",
    "\n",
    "global checkbutton_value\n",
    "checkbutton_value = tk.BooleanVar()\n",
    "checkbutton = ttk.Checkbutton(text=\"Check this box before playing audio if you want time to be displayed\", variable=checkbutton_value)\n",
    "checkbutton.grid(row=5, column=1, pady=5)\n",
    "\n",
    "\n",
    "l1 = tk.Label(master, background= 'light blue',font=(\"SF Pro\", 26), text= \"Welcome to the interface!\")\n",
    "l1.grid(row=0,column=0, padx=10, pady=10,sticky=E)\n",
    "\n",
    "graph = tk.Button(master, text=\"Show graph\", command=lambda: show_image(\"output1.png\"))\n",
    "graph.grid(row=15,column=0)\n",
    "\n",
    "imagebox = tk.Label(master)\n",
    "imagebox.grid(row=15,column=1)\n",
    "\n",
    "# TO DO - CHANGE THIS PATH\n",
    "filepathlogo = '/Users/arjun/Documents/College/spoglabapp/spoglab_logo.png'\n",
    "img = ImageTk.PhotoImage(Image.open(filepathlogo).resize((380,110)))\n",
    "p1 = tk.Label(master,  image= img)\n",
    "p1.grid(row=1,column=0, sticky=E)       \n",
    " \n",
    "filepathlogo2 = '/Users/arjun/Documents/College/spoglabapp/ucsf_logo.jpeg'\n",
    "img2 = ImageTk.PhotoImage(Image.open(filepathlogo2).resize((150,110)))\n",
    "p2 = tk.Label(master,  image= img2)\n",
    "p2.grid(row=1,column=1,sticky=W)\n",
    "\n",
    "\n",
    "master.mainloop()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a578281",
   "metadata": {},
   "source": [
    "Questions:\n",
    "1. what is real time vs not?\n",
    "2. should extract button be replaced by just play?\n",
    "3. should preparation script be different? and actual interface just for playing things?\n",
    "4. box path location?\n",
    "5. error if play before extract? preprocess before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6b21a6",
   "metadata": {},
   "source": [
    "Notes:\n",
    "1. keeping box directory manual for now\n",
    "2. keeping extract and play as separate buttons\n",
    "3. design choice - integer values so floor and ceil\n",
    "4. debugging - file types, filepaths, integration\n",
    "5. play audio platform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe7676c",
   "metadata": {},
   "source": [
    "Notes from meeting:\n",
    "    1. have another play button that randomizes from top 20 longest CTC and that's the \"play another one button\" - random without replacement\n",
    "    2. create output directory on our own\n",
    "    3. extraction in separate script that is the pre-therapy script which populates the output directory\n",
    "    4. error handling for play button not found -- very clear\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "childid = '032LTP2'\n",
    "filepath_of_csv = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\" , childid+ \"_voc_output\", childid+\"_CHN_timestamps.csv\")\n",
    "chn_timestamps_main = pd.read_csv(filepath_of_csv)\n",
    "utterances_df = chn_timestamps_main[chn_timestamps_main['childUttCnt']!=0]\n",
    "sorted_chn_length  = utterances_df.sort_values(by=['duration'], ascending=0, ignore_index = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "datetime_str = '2014-06-12T13:11:29Z'\n",
    "datetime_object = datetime.strptime(datetime_str, '%Y-%m-%dT%H:%M:%SZ')\n",
    "print(datetime_object.strftime('This clip is from time %H:%M on date %d-%m-%Y.'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More notes to self:\n",
    "extraction order of CHN or CTC to save childId --> select file first?\n",
    "its extraction for initiation/ FAN MAN\n",
    "\n",
    "change all button names to more intuitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-27 11:48:47 CST-0600\n"
     ]
    }
   ],
   "source": [
    "# This clip is from time 14:48 on date 27-04-2014.\n",
    "from pytz import timezone\n",
    "fmt = \"%Y-%m-%d %H:%M:%S %Z%z\"\n",
    "if (childid.find('L')>=0):\n",
    "    now_time = datetime.now(timezone('America/Chicago'))\n",
    "    print (now_time.strftime(fmt))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acfbabd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10722.13, 10916.78),\n",
       " (11569.87, 11790.0),\n",
       " (37536.15, 37755.66),\n",
       " (18495.92, 18701.37),\n",
       " (38810.77, 39003.2),\n",
       " (37335.4, 37527.35),\n",
       " (38582.15, 38801.1),\n",
       " (40964.36, 41133.47),\n",
       " (38300.44, 38536.67),\n",
       " (12303.39, 12472.15),\n",
       " (6359.63, 6546.83),\n",
       " (10153.49, 10296.97),\n",
       " (37844.88, 37961.14),\n",
       " (9070.96, 9247.87),\n",
       " (37244.53, 37329.57)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_CTCs_offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5d7ac600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.dates as mdates\n",
    "'''for i in range(0,15):\n",
    "    time_message = str(getrealtime(i).strftime('This clip is from time %H:%M on date %d-%m-%Y.'))\n",
    "    print(time_message)'''\n",
    "\n",
    "xaxistimes = []\n",
    "for i in range(0,15):\n",
    "    xaxistimes.append((getrealtime(i) ))\n",
    "x = matplotlib.dates.date2num(xaxistimes) \n",
    "ax = plt.axes()\n",
    "yaxiscounts = sorted_ctccount['convo_count'][0:15]\n",
    "colors = np.random.rand(15)\n",
    "plt.figure(figsize=(20, 8)) \n",
    "plt.scatter (xaxistimes, yaxiscounts, c=colors)\n",
    "#fmt = xaxistimes.DateFormatter('%H:%M')\n",
    "ax.xaxis.set_major_formatter(fmt)\n",
    "ax.scatter(x , yaxiscounts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "259452bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.xaxis.set_major_formatter(fmt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dd0164e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09:48:47\n"
     ]
    }
   ],
   "source": [
    "#print (sorted_ctccount['convo_count'][0:15])\n",
    "print((getrealtime(0).time()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f97a445a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2c6c359a",
   "metadata": {},
   "source": [
    "## Experiments with types of visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc09c242",
   "metadata": {},
   "outputs": [],
   "source": [
    "childid = '032LTP2'\n",
    "filepath_of_csv = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\" , childid+ \"_speech_output\", childid+\"_MAN_timestamps.csv\")\n",
    "man_timestamps_main = pd.read_csv(filepath_of_csv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd64faf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "man_timestamps_main['words_per_sec'] = man_timestamps_main['wordCount']/man_timestamps_main['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0eef28df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "Name: words_per_sec, Length: 1517, dtype: float64"
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "man_timestamps_main['words_per_sec'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ea8eb10",
   "metadata": {},
   "outputs": [
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       "      Unnamed: 0  seg_id  clip_onset  clip_offset  duration  avg_dB  peak_dB  \\\n",
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       "\n",
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       "\n",
       "[1517 rows x 17 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man_timestamps_main "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f02b60",
   "metadata": {},
   "source": [
    "Alternate strategy: Aggregate the whole df into 20 bins (in chronological order) and then take talking speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d19487e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "man_timestamps1 = man_timestamps_main\n",
    "man_timestamps1['value_bin']=pd.cut(man_timestamps_main['clip_offset'], 20, labels=[x for x in range(1,21)])\n",
    "man_timestamps1['freq_bin']=pd.qcut(man_timestamps_main['clip_offset'], 20, labels=[x for x in range(1,21)])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6da7beb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "vbin_df = man_timestamps1.groupby('value_bin').agg('sum')\n",
    "fbin_df = man_timestamps1.groupby('freq_bin').agg('sum')\n",
    "vbin_df['wps'] = vbin_df['wordCount']/vbin_df['duration']\n",
    "fbin_df['wps'] = fbin_df['wordCount']/fbin_df['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1044ec8f",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27080</td>\n",
       "      <td>27160</td>\n",
       "      <td>881309.09</td>\n",
       "      <td>881431.68</td>\n",
       "      <td>122.59</td>\n",
       "      <td>-2154.64</td>\n",
       "      <td>-1526.68</td>\n",
       "      <td>485.23</td>\n",
       "      <td>0</td>\n",
       "      <td>883740.0</td>\n",
       "      <td>62911</td>\n",
       "      <td>312.633651</td>\n",
       "      <td>3.958153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86010</td>\n",
       "      <td>86193</td>\n",
       "      <td>2359213.46</td>\n",
       "      <td>2359504.49</td>\n",
       "      <td>291.03</td>\n",
       "      <td>-5058.00</td>\n",
       "      <td>-3552.07</td>\n",
       "      <td>1187.64</td>\n",
       "      <td>0</td>\n",
       "      <td>2364900.0</td>\n",
       "      <td>139358</td>\n",
       "      <td>757.986551</td>\n",
       "      <td>4.080816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>126994</td>\n",
       "      <td>127187</td>\n",
       "      <td>3052709.31</td>\n",
       "      <td>3053009.55</td>\n",
       "      <td>300.24</td>\n",
       "      <td>-5331.44</td>\n",
       "      <td>-3699.51</td>\n",
       "      <td>1196.90</td>\n",
       "      <td>0</td>\n",
       "      <td>3058800.0</td>\n",
       "      <td>142670</td>\n",
       "      <td>779.135098</td>\n",
       "      <td>3.986477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>116325</td>\n",
       "      <td>116466</td>\n",
       "      <td>2478866.35</td>\n",
       "      <td>2479058.70</td>\n",
       "      <td>192.35</td>\n",
       "      <td>-4329.29</td>\n",
       "      <td>-3063.08</td>\n",
       "      <td>759.98</td>\n",
       "      <td>0</td>\n",
       "      <td>2483460.0</td>\n",
       "      <td>101191</td>\n",
       "      <td>567.046234</td>\n",
       "      <td>3.951027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6293</td>\n",
       "      <td>6300</td>\n",
       "      <td>137109.84</td>\n",
       "      <td>137118.83</td>\n",
       "      <td>8.99</td>\n",
       "      <td>-227.35</td>\n",
       "      <td>-158.76</td>\n",
       "      <td>36.24</td>\n",
       "      <td>0</td>\n",
       "      <td>137280.0</td>\n",
       "      <td>7380</td>\n",
       "      <td>28.578216</td>\n",
       "      <td>4.031146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30327</td>\n",
       "      <td>30360</td>\n",
       "      <td>732410.42</td>\n",
       "      <td>732469.26</td>\n",
       "      <td>58.84</td>\n",
       "      <td>-1251.11</td>\n",
       "      <td>-983.09</td>\n",
       "      <td>245.62</td>\n",
       "      <td>0</td>\n",
       "      <td>733440.0</td>\n",
       "      <td>23179</td>\n",
       "      <td>144.752528</td>\n",
       "      <td>4.174371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20823</td>\n",
       "      <td>20845</td>\n",
       "      <td>606009.69</td>\n",
       "      <td>606038.31</td>\n",
       "      <td>28.62</td>\n",
       "      <td>-663.29</td>\n",
       "      <td>-483.94</td>\n",
       "      <td>147.69</td>\n",
       "      <td>0</td>\n",
       "      <td>606660.0</td>\n",
       "      <td>14956</td>\n",
       "      <td>117.535257</td>\n",
       "      <td>5.160377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>77727</td>\n",
       "      <td>77805</td>\n",
       "      <td>2323303.88</td>\n",
       "      <td>2323395.82</td>\n",
       "      <td>91.94</td>\n",
       "      <td>-2022.42</td>\n",
       "      <td>-1445.58</td>\n",
       "      <td>442.81</td>\n",
       "      <td>0</td>\n",
       "      <td>2326020.0</td>\n",
       "      <td>59795</td>\n",
       "      <td>378.773687</td>\n",
       "      <td>4.816293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68320</td>\n",
       "      <td>68384</td>\n",
       "      <td>2039925.10</td>\n",
       "      <td>2040009.42</td>\n",
       "      <td>84.32</td>\n",
       "      <td>-1448.59</td>\n",
       "      <td>-964.01</td>\n",
       "      <td>398.26</td>\n",
       "      <td>0</td>\n",
       "      <td>2042100.0</td>\n",
       "      <td>51454</td>\n",
       "      <td>307.403693</td>\n",
       "      <td>4.723197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>70091</td>\n",
       "      <td>70153</td>\n",
       "      <td>2137408.61</td>\n",
       "      <td>2137505.70</td>\n",
       "      <td>97.09</td>\n",
       "      <td>-1867.90</td>\n",
       "      <td>-1398.82</td>\n",
       "      <td>401.99</td>\n",
       "      <td>0</td>\n",
       "      <td>2139180.0</td>\n",
       "      <td>50920</td>\n",
       "      <td>259.754800</td>\n",
       "      <td>4.140385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30537</td>\n",
       "      <td>30563</td>\n",
       "      <td>974239.42</td>\n",
       "      <td>974273.52</td>\n",
       "      <td>34.10</td>\n",
       "      <td>-812.58</td>\n",
       "      <td>-587.60</td>\n",
       "      <td>142.50</td>\n",
       "      <td>0</td>\n",
       "      <td>975120.0</td>\n",
       "      <td>19937</td>\n",
       "      <td>108.888198</td>\n",
       "      <td>4.178886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>86833</td>\n",
       "      <td>86904</td>\n",
       "      <td>2760430.84</td>\n",
       "      <td>2760533.14</td>\n",
       "      <td>102.30</td>\n",
       "      <td>-2162.28</td>\n",
       "      <td>-1605.09</td>\n",
       "      <td>440.50</td>\n",
       "      <td>0</td>\n",
       "      <td>2762580.0</td>\n",
       "      <td>56737</td>\n",
       "      <td>313.352004</td>\n",
       "      <td>4.305963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140384</td>\n",
       "      <td>140491</td>\n",
       "      <td>4476186.77</td>\n",
       "      <td>4476335.16</td>\n",
       "      <td>148.39</td>\n",
       "      <td>-3927.20</td>\n",
       "      <td>-2931.74</td>\n",
       "      <td>644.52</td>\n",
       "      <td>0</td>\n",
       "      <td>4479720.0</td>\n",
       "      <td>83251</td>\n",
       "      <td>467.554700</td>\n",
       "      <td>4.343419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>208510</td>\n",
       "      <td>208655</td>\n",
       "      <td>6384624.49</td>\n",
       "      <td>6384824.38</td>\n",
       "      <td>199.89</td>\n",
       "      <td>-4111.30</td>\n",
       "      <td>-2874.52</td>\n",
       "      <td>847.87</td>\n",
       "      <td>0</td>\n",
       "      <td>6388920.0</td>\n",
       "      <td>104833</td>\n",
       "      <td>625.744428</td>\n",
       "      <td>4.241683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9081</td>\n",
       "      <td>9087</td>\n",
       "      <td>276474.68</td>\n",
       "      <td>276482.63</td>\n",
       "      <td>7.95</td>\n",
       "      <td>-215.02</td>\n",
       "      <td>-171.66</td>\n",
       "      <td>29.99</td>\n",
       "      <td>0</td>\n",
       "      <td>276600.0</td>\n",
       "      <td>6696</td>\n",
       "      <td>22.793005</td>\n",
       "      <td>3.772327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Unnamed: 0  seg_id  clip_onset  clip_offset  duration   avg_dB  \\\n",
       "value_bin                                                                   \n",
       "1                 703     741    59176.71     59227.59     50.88 -1260.77   \n",
       "2                 893     912    61640.68     61664.93     24.25  -622.79   \n",
       "3               13110   13225   737787.43    737976.59    189.16 -3851.75   \n",
       "4               29845   29972  1073965.92   1074153.93    188.01 -3518.53   \n",
       "5               27080   27160   881309.09    881431.68    122.59 -2154.64   \n",
       "6               86010   86193  2359213.46   2359504.49    291.03 -5058.00   \n",
       "7              126994  127187  3052709.31   3053009.55    300.24 -5331.44   \n",
       "8              116325  116466  2478866.35   2479058.70    192.35 -4329.29   \n",
       "9                6293    6300   137109.84    137118.83      8.99  -227.35   \n",
       "10              30327   30360   732410.42    732469.26     58.84 -1251.11   \n",
       "11                  0       0        0.00         0.00      0.00     0.00   \n",
       "12              20823   20845   606009.69    606038.31     28.62  -663.29   \n",
       "13              77727   77805  2323303.88   2323395.82     91.94 -2022.42   \n",
       "14              68320   68384  2039925.10   2040009.42     84.32 -1448.59   \n",
       "15              70091   70153  2137408.61   2137505.70     97.09 -1867.90   \n",
       "16              30537   30563   974239.42    974273.52     34.10  -812.58   \n",
       "17              86833   86904  2760430.84   2760533.14    102.30 -2162.28   \n",
       "18             140384  140491  4476186.77   4476335.16    148.39 -3927.20   \n",
       "19             208510  208655  6384624.49   6384824.38    199.89 -4111.30   \n",
       "20               9081    9087   276474.68    276482.63      7.95  -215.02   \n",
       "\n",
       "           peak_dB  wordCount  uttCnt    seconds  random_idx  words_per_sec  \\\n",
       "value_bin                                                                     \n",
       "1          -939.51     244.61       0    60360.0       26617     186.547506   \n",
       "2          -458.21     100.88       0    62160.0       15790      79.216842   \n",
       "3         -2789.62     776.58       0   741420.0       88610     499.182649   \n",
       "4         -2474.39     780.92       0  1078020.0       93601     546.625125   \n",
       "5         -1526.68     485.23       0   883740.0       62911     312.633651   \n",
       "6         -3552.07    1187.64       0  2364900.0      139358     757.986551   \n",
       "7         -3699.51    1196.90       0  3058800.0      142670     779.135098   \n",
       "8         -3063.08     759.98       0  2483460.0      101191     567.046234   \n",
       "9          -158.76      36.24       0   137280.0        7380      28.578216   \n",
       "10         -983.09     245.62       0   733440.0       23179     144.752528   \n",
       "11            0.00       0.00       0        0.0           0       0.000000   \n",
       "12         -483.94     147.69       0   606660.0       14956     117.535257   \n",
       "13        -1445.58     442.81       0  2326020.0       59795     378.773687   \n",
       "14         -964.01     398.26       0  2042100.0       51454     307.403693   \n",
       "15        -1398.82     401.99       0  2139180.0       50920     259.754800   \n",
       "16         -587.60     142.50       0   975120.0       19937     108.888198   \n",
       "17        -1605.09     440.50       0  2762580.0       56737     313.352004   \n",
       "18        -2931.74     644.52       0  4479720.0       83251     467.554700   \n",
       "19        -2874.52     847.87       0  6388920.0      104833     625.744428   \n",
       "20         -171.66      29.99       0   276600.0        6696      22.793005   \n",
       "\n",
       "                wps  \n",
       "value_bin            \n",
       "1          4.807586  \n",
       "2          4.160000  \n",
       "3          4.105413  \n",
       "4          4.153609  \n",
       "5          3.958153  \n",
       "6          4.080816  \n",
       "7          3.986477  \n",
       "8          3.951027  \n",
       "9          4.031146  \n",
       "10         4.174371  \n",
       "11              NaN  \n",
       "12         5.160377  \n",
       "13         4.816293  \n",
       "14         4.723197  \n",
       "15         4.140385  \n",
       "16         4.178886  \n",
       "17         4.305963  \n",
       "18         4.343419  \n",
       "19         4.241683  \n",
       "20         3.772327  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vbin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e255ee3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "man_vbin_offset_df = man_timestamps1.groupby('value_bin').agg({'duration': 'sum', 'wordCount': 'sum', 'clip_offset':'max'})\n",
    "man_vbin_offset_df['hourly_offset'] = man_vbin_offset_df['clip_offset']/3600\n",
    "man_vbin_offset_df['avg_speed_recorded'] = man_vbin_offset_df['wordCount']/man_vbin_offset_df['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2a87862d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "man_vbin_offset_df.dropna(inplace=True)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "truncated_x = ['{:.2f}'.format(t) for t in man_vbin_offset_df['hourly_offset']]\n",
    "truncated_y = [round(t,2) for t in man_vbin_offset_df['avg_speed_recorded']]\n",
    "ax = sns.barplot(x = man_vbin_offset_df['hourly_offset'], y = truncated_y, palette = \"ch:s=.25,rot=-.25\",edgecolor='black')\n",
    "ax.bar_label(ax.containers[0], fontsize=12)\n",
    "sns.set(rc={'figure.figsize':(12,8)})\n",
    "sns.set_theme(style='white')\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_xticklabels(truncated_x)\n",
    "plt.xlabel('No. of hours into the recording', fontsize=16)\n",
    "plt.ylabel('Average no. of words/sec', fontsize=16)\n",
    "plt.title(\"Average male speech rate (words/sec) around child\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e8cfc331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Refactoring code into function -- change variables when integrating into script\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def create_MAN_speed_graph(childid):\n",
    "    filepath_of_csv = os.path.join(\"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\" , childid+ \"_speech_output\", childid+\"_MAN_timestamps.csv\")\n",
    "    man_timestamps_main = pd.read_csv(filepath_of_csv)\n",
    "    man_timestamps1 = man_timestamps_main\n",
    "    man_timestamps1['value_bin']=pd.cut(man_timestamps_main['clip_offset'], 20, labels=[x for x in range(1,21)])\n",
    "    man_timestamps1['freq_bin']=pd.qcut(man_timestamps_main['clip_offset'], 20, labels=[x for x in range(1,21)])\n",
    "\n",
    "    '''\n",
    "    vbin_df = man_timestamps1.groupby('value_bin').agg('sum')\n",
    "    fbin_df = man_timestamps1.groupby('freq_bin').agg('sum')\n",
    "    vbin_df['wps'] = vbin_df['wordCount']/vbin_df['duration']\n",
    "    fbin_df['wps'] = fbin_df['wordCount']/fbin_df['duration']\n",
    "    '''\n",
    "\n",
    "    man_vbin_offset_df = man_timestamps1.groupby('value_bin').agg({'duration': 'sum', 'wordCount': 'sum', 'clip_offset':'max'})\n",
    "    man_vbin_offset_df['hourly_offset'] = man_vbin_offset_df['clip_offset']/3600\n",
    "    man_vbin_offset_df['avg_speed_recorded'] = man_vbin_offset_df['wordCount']/man_vbin_offset_df['duration']\n",
    "\n",
    "    man_vbin_offset_df.dropna(inplace=True)\n",
    "\n",
    "    truncated_x = ['{:.2f}'.format(t) for t in man_vbin_offset_df['hourly_offset']]\n",
    "    truncated_y = [round(t,2) for t in man_vbin_offset_df['avg_speed_recorded']]\n",
    "    ax = sns.barplot(x = man_vbin_offset_df['hourly_offset'], y = truncated_y, palette = \"ch:s=.25,rot=-.25\",edgecolor='black')\n",
    "    ax.bar_label(ax.containers[0], fontsize=12)\n",
    "    sns.set(rc={'figure.figsize':(12,8)})\n",
    "    sns.set_theme(style='white')\n",
    "    plt.xticks(rotation=0)\n",
    "    ax.set_xticklabels(truncated_x)\n",
    "    plt.xlabel('No. of hours into the recording', fontsize=16)\n",
    "    plt.ylabel('Average no. of words/sec', fontsize=16)\n",
    "    plt.title(\"Average male speech rate (words/sec) around child\", fontsize=20)\n",
    "    plt.show()\n",
    "\n",
    "create_MAN_speed_graph('001LTP1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e37531ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_FAN_speed_graph(id):\n",
    "\n",
    "        '''\n",
    "        This function creates a plot of male talking speed by mining MAN timestamps and saves it in the output directory. This has binning done w.r.t. values of offsets.\n",
    "        '''\n",
    "\n",
    "        filepath_of_csv = os.path.join( \"/Users/arjun/Box/box-group-lena-studies/Soundscape/wavFiles\",  id+ \"_speech_output\",  id+\"_FAN_timestamps.csv\")\n",
    "        man_timestamps_main = pd.read_csv(filepath_of_csv)\n",
    "        man_timestamps1 = man_timestamps_main\n",
    "        man_timestamps1['value_bin']=pd.cut(man_timestamps_main['clip_offset'], 20, labels=[x for x in range(1,21)])\n",
    "        man_timestamps1['freq_bin']=pd.qcut(man_timestamps_main['clip_offset'], 20, labels=[x for x in range(1,21)])\n",
    "        man_vbin_offset_df = man_timestamps1.groupby('value_bin').agg({'duration': 'sum', 'wordCount': 'sum', 'clip_offset':'max'})\n",
    "        man_vbin_offset_df['hourly_offset'] = man_vbin_offset_df['clip_offset']/3600\n",
    "        man_vbin_offset_df['avg_speed_recorded'] = man_vbin_offset_df['wordCount']/man_vbin_offset_df['duration']\n",
    "\n",
    "        man_vbin_offset_df.dropna(inplace=True)\n",
    "\n",
    "        truncated_x = ['{:.2f}'.format(t) for t in man_vbin_offset_df['hourly_offset']]\n",
    "        truncated_y = [round(t,2) for t in man_vbin_offset_df['avg_speed_recorded']]\n",
    "        ax = sns.barplot(x = man_vbin_offset_df['hourly_offset'], y = truncated_y, palette = \"ch:s=.25,rot=-.25\",edgecolor='black')\n",
    "        ax.bar_label(ax.containers[0], fontsize=11)\n",
    "        sns.set(rc={'figure.figsize':(12,8)})\n",
    "        sns.set_theme(style='white')\n",
    "        plt.xticks(rotation=30)\n",
    "        ax.set_xticklabels(truncated_x)\n",
    "        plt.xlabel('No. of hours into the recording', fontsize=16)\n",
    "        plt.ylabel('Average no. of words/sec', fontsize=16)\n",
    "        plt.title(\"Average female speech rate (words/sec) around child\", fontsize=20)\n",
    "        plt.show()\n",
    "\n",
    "create_FAN_speed_graph(\"001LTP1\")"
   ]
  }
 ],
 "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",
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