Sound lights with spotify, esp8266 and neopixel strip

app.py

from __future__ import annotations

import array
import asyncio
from bisect import bisect_left
from dataclasses import dataclass
import logging
import os
import socket
import time
from typing import Union, Dict, Any, NoReturn, AsyncIterable, List, Tuple, Callable
import aiohttp
from spotipy.util import prompt_for_user_token

# Shared communication
RawSpotifyResponse = Dict[str, Any]


@dataclass
class EventSongChanged:
    analysis: RawSpotifyResponse
    start_time: float


@dataclass
class EventAdjustStartTime:
    start_time: float


@dataclass
class EventStop:
    ...


Event = Union[EventSongChanged, EventAdjustStartTime, EventStop]

Colors = List[Tuple[int, int, int]]

# Event producer
API_CURRENT_PLAYING = 'https://api.spotify.com/v1/me/player/currently-playing'
API_AUDIO_ANALYSIS = 'https://api.spotify.com/v1/audio-analysis/'
SPOTIFY_SCOPE = 'user-read-currently-playing,user-read-playback-state'
SPOTIFY_CHANGES_LISTENER_DEALY = 1
SPOTIFY_CHANGES_LISTENER_FAILURE_DELAY = 1


async def _get_current_playing(session: aiohttp.ClientSession) -> RawSpotifyResponse:
    async with session.get(API_CURRENT_PLAYING) as response:
        return await response.json()


async def _get_audio_analysis(session: aiohttp.ClientSession, id: str) -> RawSpotifyResponse:
    async with session.get(API_AUDIO_ANALYSIS + id) as response:
        return await response.json()


def _get_start_time(current_playing: RawSpotifyResponse, request_time: float) -> float:
    # spotify timestamp appears to be incorrect https://github.com/spotify/web-api/issues/640
    return (request_time + time.time()) / 2 - current_playing['progress_ms'] / 1000


async def _listen_to_spotify_changes(session: aiohttp.ClientSession) -> AsyncIterable[Event]:
    current_id = None
    while True:
        request_time = time.time()
        current = await _get_current_playing(session)
        if not current['is_playing']:
            current_id = None
            yield EventStop()
        elif current['item']['id'] != current_id:
            current_id = current['item']['id']
            analysis = await _get_audio_analysis(session, current_id)
            yield EventSongChanged(analysis, _get_start_time(current, request_time))
        else:
            yield EventAdjustStartTime(_get_start_time(current, request_time))

        await asyncio.sleep(SPOTIFY_CHANGES_LISTENER_DEALY)


async def spotify_changes_listener(user_id: str,
                                   client_id: str,
                                   client_secret: str,
                                   events_queue: asyncio.Queue[Event]) -> NoReturn:
    while True:
        # I'm too lazy to make that async
        token = prompt_for_user_token(user_id, SPOTIFY_SCOPE,
                                      client_id=client_id,
                                      client_secret=client_secret,
                                      redirect_uri='http://localhost:8000/')
        headers = {'Authorization': f'Bearer {token}'}
        async with aiohttp.ClientSession(headers=headers) as session:
            try:
                async for event in _listen_to_spotify_changes(session):
                    await events_queue.put(event)
            except Exception:
                logging.exception('Something went wrong with spotify_changes_listener')

                await asyncio.sleep(SPOTIFY_CHANGES_LISTENER_FAILURE_DELAY)


# Communication with the device
class DeviceBus:
    def connection_made(self, _):
        logging.info('Connected')

    def error_received(self, exc):
        logging.exception('Error received', exc_info=exc)

    def connection_lost(self, exc):
        logging.exception('Connection closed', exc_info=exc)


async def make_send_to_device(ip: str, port: int) -> Callable[[Colors]]:
    loop = asyncio.get_event_loop()
    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
    transport, _ = await loop.create_datagram_endpoint(DeviceBus, sock=sock)

    def send_to_device(colors: Colors) -> None:
        line = array.array('B', [channel for color in colors
                                 for channel in color])
        transport.sendto(line.tobytes(), (ip, port))

    return send_to_device


# Light collors selector, spooky, more details in the notebook
SCALE = (50, 100, 150)
BASE_COLOR_MULTIPLIER = 100
LOUDNESS_MULTIPLIER = 1.5


def _normalize(pv: float) -> float:
    if pv < 0:
        return 0.
    elif pv > 255:
        return 255.
    else:
        return pv


_scale_pixel = lambda p: (int(_normalize(p[0]) * SCALE[0] / 255),
                          int(_normalize(p[1]) * SCALE[1] / 255),
                          int(_normalize(p[2]) * SCALE[2] / 255))


def make_get_current_colors(analysis: RawSpotifyResponse, leds: int) -> Callable[[float], Colors]:
    def make_get_current(name):
        keys = [x['start'] for x in analysis[name]]
        key_to_x = {x['start']: x for x in analysis[name]}
        return lambda t: key_to_x[keys[bisect_left(keys, t) - 1]]

    get_current_segmnet = make_get_current('segments')
    get_current_section = make_get_current('sections')
    get_current_beat = make_get_current('beats')

    def make_scale(name):
        xs = [x[name] for x in analysis['sections']]
        min_xs = min(xs)
        max_xs = max(xs)
        return lambda x: (x - min_xs) / (max_xs - min_xs)

    scale_loudness = make_scale('loudness')
    scale_tempo = make_scale('tempo')

    def get_current_colors(t):
        segment = get_current_segmnet(t)
        section = get_current_section(t)
        beat = get_current_beat(t)

        beat_color = BASE_COLOR_MULTIPLIER * (t - beat['start'] + beat['duration']) / beat['duration']
        tempo_color = BASE_COLOR_MULTIPLIER * scale_tempo(section['tempo'])
        pitch_colors = [BASE_COLOR_MULTIPLIER * p for p in segment['pitches']]

        loudness_multiplier = 1 + LOUDNESS_MULTIPLIER * scale_loudness(section['loudness'])

        colors = ((beat_color * loudness_multiplier,
                   tempo_color * loudness_multiplier,
                   pitch_colors[n // (leds // 12)] * loudness_multiplier)
                  for n in range(leds))

        if section['mode'] == 0:
            order = (0, 1, 2)
        elif section['mode'] == 1:
            order = (1, 2, 0)
        else:
            order = (2, 0, 1)

        ordered_colors = ((color[order[0]], color[order[1]], color[order[2]])
                          for color in colors)

        return [_scale_pixel(color) for color in ordered_colors]

    return get_current_colors


def get_empty_colors(leds: int) -> Colors:
    return [(0,) * 3] * leds


# Events listener, device controller
CONTROLLER_TICK = 0.01
CONTROLLER_ERROR_DELAY = 1


async def _events_to_colors(leds: int, events_queue: asyncio.Queue[Event]) -> AsyncIterable[Colors]:
    get_current_colors = None
    start_time = 0
    event = EventStop()
    while True:
        await asyncio.sleep(CONTROLLER_TICK)

        while not events_queue.empty():
            event = events_queue.get_nowait()

        if isinstance(event, EventSongChanged):
            start_time = event.start_time
            get_current_colors = make_get_current_colors(event.analysis, leds)
        elif isinstance(event, EventAdjustStartTime):
            start_time = event.start_time
        elif isinstance(event, EventStop):
            get_current_colors = None

        if get_current_colors is None:
            yield get_empty_colors(leds)
        else:
            yield get_current_colors(time.time() - start_time)


async def lights_controller(device_ip: str,
                            device_port: int,
                            leds: int,
                            events_queue: asyncio.Queue[Event]) -> NoReturn:
    while True:
        send_to_device = await make_send_to_device(device_ip, device_port)
        try:
            async for colors in _events_to_colors(leds, events_queue):
                send_to_device(colors)

        except Exception:
            logging.exception("Something went wrong with lights_controller")
            await asyncio.sleep(CONTROLLER_ERROR_DELAY)


# Glue
def main():
    device_ip = os.environ['DEVICE_IP']
    device_port = int(os.environ['DEVICE_PORT'])
    user_id = os.environ['USER_ID']
    client_id = os.environ['CLIENT_ID']
    client_secret = os.environ['CLIENT_SECRET']
    leds = int(os.environ['LEDS'])

    events_queue = asyncio.Queue()
    loop = asyncio.get_event_loop()
    loop.run_until_complete(asyncio.gather(
        spotify_changes_listener(user_id, client_id, client_secret, events_queue),
        lights_controller(device_ip, device_port, leds, events_queue),
    ))


if __name__ == '__main__':
    main()

esp.py

import socket
import machine
import neopixel


np = neopixel.NeoPixel(machine.Pin(5), 60)
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind(('', 42424))


while True:
    line, _ = sock.recvfrom(180)

    if len(line) < 180:
        continue

    for i in range(60):
        np[i] = (line[i * 3], line[i * 3 + 1], line[i * 3 + 2])

    np.write()

presearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config IPCompleter.greedy=True\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spotipy.util as util\n",
    "import spotipy\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import socket\n",
    "import array\n",
    "import time\n",
    "from bisect import bisect_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_id = ''\n",
    "client_id=''\n",
    "client_secret=''\n",
    "scope = 'user-read-currently-playing,user-read-playback-state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "token = util.prompt_for_user_token(user_id, scope,\n",
    "                                   client_id=client_id,\n",
    "                                   client_secret=client_secret,\n",
    "                                   redirect_uri='http://localhost:8000/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = spotipy.Spotify(auth=token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_playing = sp._get('me/player/currently-playing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('The Knife', 'Listen Now')"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_playing['item']['artists'][0]['name'], current_playing['item']['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = sp._get('audio-analysis/' + current_playing['item']['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'start': 0.06111, 'duration': 1.46978, 'confidence': 0.489},\n",
       " {'start': 1.53089, 'duration': 1.46458, 'confidence': 0.546}]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['bars'][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'start': 0.06111, 'duration': 0.36898, 'confidence': 1.0},\n",
       " {'start': 0.43009, 'duration': 0.36792, 'confidence': 0.826}]"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['beats'][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'start': 0.0,\n",
       "  'duration': 0.41791,\n",
       "  'confidence': 1.0,\n",
       "  'loudness_start': -60.0,\n",
       "  'loudness_max_time': 0.07244,\n",
       "  'loudness_max': -5.366,\n",
       "  'pitches': [0.417,\n",
       "   0.551,\n",
       "   0.538,\n",
       "   1.0,\n",
       "   0.772,\n",
       "   0.747,\n",
       "   0.536,\n",
       "   0.593,\n",
       "   0.32,\n",
       "   0.762,\n",
       "   0.577,\n",
       "   0.438],\n",
       "  'timbre': [32.278,\n",
       "   151.866,\n",
       "   80.945,\n",
       "   -367.861,\n",
       "   81.092,\n",
       "   256.167,\n",
       "   69.902,\n",
       "   22.089,\n",
       "   -31.833,\n",
       "   49.119,\n",
       "   -56.521,\n",
       "   11.736]},\n",
       " {'start': 0.41791,\n",
       "  'duration': 0.09297,\n",
       "  'confidence': 0.04,\n",
       "  'loudness_start': -13.175,\n",
       "  'loudness_max_time': 0.01334,\n",
       "  'loudness_max': -7.223,\n",
       "  'pitches': [0.239,\n",
       "   0.755,\n",
       "   1.0,\n",
       "   0.854,\n",
       "   0.454,\n",
       "   0.25,\n",
       "   0.155,\n",
       "   0.166,\n",
       "   0.179,\n",
       "   0.252,\n",
       "   0.187,\n",
       "   0.152],\n",
       "  'timbre': [49.846,\n",
       "   151.654,\n",
       "   -52.308,\n",
       "   -27.851,\n",
       "   11.71,\n",
       "   -62.051,\n",
       "   -25.347,\n",
       "   20.099,\n",
       "   -18.222,\n",
       "   -24.284,\n",
       "   9.733,\n",
       "   10.143]}]"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['segments'][:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'start': 0.0,\n",
       "  'duration': 23.8499,\n",
       "  'confidence': 1.0,\n",
       "  'loudness': -8.02,\n",
       "  'tempo': 163.99,\n",
       "  'tempo_confidence': 0.639,\n",
       "  'key': 8,\n",
       "  'key_confidence': 0.0,\n",
       "  'mode': 1,\n",
       "  'mode_confidence': 0.0,\n",
       "  'time_signature': 4,\n",
       "  'time_signature_confidence': 1.0},\n",
       " {'start': 23.8499,\n",
       "  'duration': 24.51498,\n",
       "  'confidence': 0.637,\n",
       "  'loudness': -7.619,\n",
       "  'tempo': 164.035,\n",
       "  'tempo_confidence': 0.511,\n",
       "  'key': 8,\n",
       "  'key_confidence': 0.349,\n",
       "  'mode': 1,\n",
       "  'mode_confidence': 0.403,\n",
       "  'time_signature': 4,\n",
       "  'time_signature_confidence': 1.0}]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['sections'][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'start': 0.06111, 'duration': 0.18449, 'confidence': 1.0},\n",
       " {'start': 0.2456, 'duration': 0.18449, 'confidence': 1.0}]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['tatums'][:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gantt of blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ('bars', 'beats', 'sections', 'segments', 'tatums')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 8)) \n",
    "ax.set_xlim(0, current_playing['item']['duration_ms'] / 1000)\n",
    "colors = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "plt.title('Available blocks\\n' + current_playing['item']['artists'][0]['name'] + ' - ' + current_playing['item']['name'])\n",
    "ax.grid(True)\n",
    "for n, name in enumerate(names):\n",
    "    ax.broken_barh(\n",
    "        [(x['start'], x['duration']) for x in analysis[name]],\n",
    "        (3 * n, 1), color=colors[n])\n",
    "    ax.broken_barh(\n",
    "        [(x['start'], 0.0001) for x in analysis[name]],\n",
    "        (3 * n, 1.5), color='black', alpha=0.4)\n",
    "\n",
    "ax.set_yticks(range(0, 3 * len(names), 3))\n",
    "ax.set_yticklabels(names)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# leds check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n",
    "sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n",
    "sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def send(pixels):\n",
    "    colors = [color for pixel in pixels for color in pixel]\n",
    "    line = array.array('B', colors).tostring()\n",
    "    sock.sendto(line, ('192.168.2.255', 42424))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nvbn/.virtualenvs/spotify-gerland/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "send([(50, 100, 150)] * 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "SCALE = (255, 255, 255)\n",
    "\n",
    "from typing import List, Tuple, Callable\n",
    "\n",
    "# Shared communication\n",
    "RawSpotifyResponse = Dict[str, Any]\n",
    "Colors = List[Tuple[int, int, int]]\n",
    "\n",
    "\n",
    "def _normalize(pv: float) -> float:\n",
    "    if pv < 0:\n",
    "        return 0.\n",
    "    elif pv > 255:\n",
    "        return 255.\n",
    "    else:\n",
    "        return pv\n",
    "\n",
    "\n",
    "_scale_pixel = lambda p: (int(_normalize(p[0]) * SCALE[0] / 255),\n",
    "                          int(_normalize(p[1]) * SCALE[1] / 255),\n",
    "                          int(_normalize(p[2]) * SCALE[2] / 255))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_get_current_colors(analysis: RawSpotifyResponse, leds: int) -> Callable[[float], Colors]:\n",
    "    def make_get_current(name):\n",
    "        keys = [x['start'] for x in analysis[name]]\n",
    "        key_to_x = {x['start']: x for x in analysis[name]}\n",
    "        return lambda t: key_to_x[keys[bisect_left(keys, t) - 1]]\n",
    "\n",
    "    get_current_segmnet = make_get_current('segments')\n",
    "    get_current_section = make_get_current('sections')\n",
    "    get_current_beat = make_get_current('beats')\n",
    "\n",
    "    def make_scale(name):\n",
    "        xs = [x[name] for x in analysis['sections']]\n",
    "        min_xs = min(xs)\n",
    "        max_xs = max(xs)\n",
    "        return lambda x: (x - min_xs) / (max_xs - min_xs)\n",
    "\n",
    "    scale_loudness = make_scale('loudness')\n",
    "    scale_tempo = make_scale('tempo')\n",
    "\n",
    "    def get_current_colors(t):\n",
    "        segment = get_current_segmnet(t)\n",
    "        section = get_current_section(t)\n",
    "        beat = get_current_beat(t)\n",
    "\n",
    "        beat_color = BASE_COLOR_MULTIPLIER * (t - beat['start'] + beat['duration']) / beat['duration']\n",
    "        tempo_color = BASE_COLOR_MULTIPLIER * scale_tempo(section['tempo'])\n",
    "        pitch_colors = [BASE_COLOR_MULTIPLIER * p for p in segment['pitches']]\n",
    "\n",
    "        loudness_multiplier = 1 + LOUDNESS_MULTIPLIER * scale_loudness(section['loudness'])\n",
    "\n",
    "        colors = ((beat_color * loudness_multiplier,\n",
    "                   tempo_color * loudness_multiplier,\n",
    "                   pitch_colors[n // (leds // 12)] * loudness_multiplier)\n",
    "                  for n in range(leds))\n",
    "\n",
    "        if section['mode'] == 0:\n",
    "            order = (0, 1, 2)\n",
    "        elif section['mode'] == 1:\n",
    "            order = (1, 2, 0)\n",
    "        else:\n",
    "            order = (2, 0, 1)\n",
    "\n",
    "        ordered_colors = ((color[order[0]], color[order[1]], color[order[2]])\n",
    "                          for color in colors)\n",
    "\n",
    "        return [_scale_pixel(color) for color in ordered_colors]\n",
    "\n",
    "    return get_current_colors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 8)) \n",
    "ax.set_xlim(0, current_playing['item']['duration_ms'] / 1000)\n",
    "get_current_colors = make_get_current_colors(analysis, 60)\n",
    "plt.title('Leds over time\\n' + current_playing['item']['artists'][0]['name'] + ' - ' + current_playing['item']['name'])\n",
    "for t in range(0, int(current_playing['item']['duration_ms'] / 1000)):\n",
    "    for n, led in enumerate(get_current_colors(t)):\n",
    "        plt.broken_barh([(t, 1)], (n, 1), color=(led[0] / 255, led[1] / 255, led[2] / 255))\n",
    "\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test():\n",
    "    current_playing = sp._get('me/player/currently-playing')\n",
    "    analysis = sp._get('audio-analysis/' + current_playing['item']['id'])\n",
    "    fig, ax = plt.subplots(figsize=(20, 8)) \n",
    "    ax.set_xlim(0, current_playing['item']['duration_ms'] / 1000)\n",
    "    get_current_colors = make_get_current_colors(analysis, 60)\n",
    "    plt.title('Leds over time\\n' + current_playing['item']['artists'][0]['name'] + ' - ' + current_playing['item']['name'])\n",
    "    for t in range(0, int(current_playing['item']['duration_ms'] / 1000)):\n",
    "        for n, led in enumerate(get_current_colors(t)):\n",
    "            plt.broken_barh([(t, 1)], (n, 1), color=(led[0] / 255, led[1] / 255, led[2] / 255))\n",
    "\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

requirements.txt

spotipy
aiohttp

run.sh

#!/bin/sh


# From https://developer.spotify.com/dashboard/applications
export USER_ID=''
export CLIENT_ID=''
export CLIENT_SECRET=''

export DEVICE_IP=''
export DEVICE_PORT='42424'
export LEDS=60

python researched_refactored.py