{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# requires python 3.6+ because I use format string literals\n",
    "\n",
    "# get this library here: https://github.com/ZhuangLab/storm-control\n",
    "from storm_control.sc_hardware.hamamatsu import hamamatsu_camera as hc\n",
    "\n",
    "# install with pip install pyqtgraph\n",
    "import pyqtgraph as pq\n",
    "from pyqtgraph.Qt import QtGui\n",
    "\n",
    "# stdlib\n",
    "import time\n",
    "from threading import Thread\n",
    "from queue import LifoQueue\n",
    "\n",
    "# this lets pyqtgraph without blocking execution in the notebook\n",
    "%gui qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are 2 camera classes, hc.HamamatsuCamera and hc.HamamatsuCameraMR, I'm not sure what the differences are yet \n",
    "# hc.HamamatsuCameraMR seems to have fewer ops? \n",
    "# I have 2 cameras, so I put them in a tuple\n",
    "cams = hc.HamamatsuCameraMR(camera_id=0), hc.HamamatsuCameraMR(camera_id=1)\n",
    "\n",
    "# Full frame at 100 FPS doesn't work, but that's a problem with pyqtgraph. 2x2 binning works.\n",
    "binning = 2\n",
    "\n",
    "# Set camera properties. I don't know what all of these mean yet. \n",
    "for cam in cams:\n",
    "    cam_x, cam_y = 2048, 2048\n",
    "    cam.setPropertyValue(\"defect_correct_mode\", \"OFF\")\n",
    "    cam.setPropertyValue(\"exposure_time\", 0.01)\n",
    "    cam.setPropertyValue(\"subarray_hsize\", cam_x)\n",
    "    cam.setPropertyValue(\"subarray_vsize\", cam_y)\n",
    "    cam.setPropertyValue(\"binning\", f\"{binning}x{binning}\")\n",
    "    cam.setPropertyValue(\"readout_speed\", 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create windows for displaying images using pyqtgraph \n",
    "disps = [pq.image() for cam in cams]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We store images in a last-in-first-out queue\n",
    "queues = [LifoQueue(maxsize=10 ** 5) for cam in cams]\n",
    "\n",
    "# Currently I start / stop acquisition based on the state of this boolean global. \n",
    "running = True\n",
    "\n",
    "# A function that takes frames from a camera and puts them in a queue\n",
    "def grabber(cam, queue):\n",
    "    global running\n",
    "    cam.startAcquisition()\n",
    "    while running:\n",
    "        frames, dims = cam.getFrames()\n",
    "        for f in frames:\n",
    "            queue.put(f.getData().reshape(*dims))        \n",
    "\n",
    "    cam.stopAcquisition()    \n",
    "\n",
    "# A function that takes frames from the queue and displays them    \n",
    "def drawer(disp, queue, sleep_time=.032):\n",
    "    global running\n",
    "    \n",
    "    while running:       \n",
    "        im = queue.get() // binning\n",
    "        # Optional: mess with im to reduce its size and reduce the load on pyqtgraph\n",
    "        disp.imageItem.updateImage(im)\n",
    "        QtGui.QApplication.processEvents()\n",
    "        # need to give pyqtgraph some time to process the image before we give it another one\n",
    "        time.sleep(sleep_time)\n",
    "        queue.task_done()\n",
    "\n",
    "\n",
    "# give our functions to threads with the right arguments \n",
    "frame_threads = [Thread(target=grabber, args=args) for args in zip(cams, queues)]\n",
    "draw_threads = [Thread(target=drawer, args=args) for args in zip(disps, queues)]\n",
    "\n",
    "# start the threads\n",
    "for t in frame_threads: t.start()\n",
    "for d in draw_threads: d.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run this cell to stop acquisition / display\n",
    "running = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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