C3_Swabian_FLIM_FR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "341475f0-3162-4685-9301-940e2c14a8dc",
   "metadata": {},
   "source": [
    "# env: swabian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4962e8a0-240f-498c-88b4-af4fa5596e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd6d44c0-dcf6-42d2-9d2b-5f2c71ad95a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import TimeTagger as TT\n",
    "from TimeTagger import Flim, TimeTagStream, createTimeTaggerVirtual, EventGenerator, Countrate,ConstantFractionDiscriminator, DelayedChannel\n",
    "#from intensity_renderer import IntensityRenderer\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib ipympl\n",
    "import ipywidgets as widgets\n",
    "from scipy.ndimage import maximum_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22cd6b36-86a6-4c1d-b7e7-b039166f11a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dump_file = r\"C:\\Users\\lociu\\Desktop\\PriyaM\\Data\\20230703_swab_tests\\TimeTags_2023-07-03_125136.ttbin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d7dc95f-ecda-40e2-b5d0-35ed50a170ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_file = r\"Z:\\PriyaM\\swabian_data\\20230712_tissue\\test_tissue_2023-07-12_154752.1.ttbin\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2bc33b-7b4b-4eca-a271-59a18b1b8251",
   "metadata": {},
   "source": [
    "# flim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59b9da73-bcf7-47ba-83f6-44e2dd68e25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "laser = 1\n",
    "click = laser\n",
    "frame = 3 \n",
    "frame_n = -3\n",
    "line = 2\n",
    "line_n = -2\n",
    "photon = -4\n",
    "\n",
    "n_bins = 256\n",
    "binwidth = 49 # 12.5ns/256\n",
    "n_pixel = 256\n",
    "\n",
    "laser_frequency = 80e6  # 1/12.5ns\n",
    "\n",
    "pixel_rate = 200e3\n",
    "pixel_time = 1 / pixel_rate\n",
    "integr_time = 3e9 # Integration time of 3 ms in picoseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2850c356-1f1e-41fc-bff8-8abeeca95267",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_tag_virt=TT.createTimeTaggerVirtual()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b55727e6-f95c-4bf9-9c71-7b13ab233caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_pattern_start = np.linspace(0, 256, num=n_pixel, endpoint=False, dtype=int) * (pixel_time * 1e12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9cbe3064-a076-45a2-b79b-d0c4457d4ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_pattern_end = pixel_pattern_start + integr_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe41d41b-c46d-4a12-b853-16e23cf204cd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# check pixel channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "cb4246ef-81ec-41e3-b0be-d0ba919b54b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EventGenerator(tagger, trigger_channel, pattern, trigger_divider, stop_channel)\n",
    "pixel_start_channel = EventGenerator(time_tag_virt, line, pixel_pattern_start)\n",
    "pixel_end_channel= EventGenerator(time_tag_virt, line, pixel_pattern_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "a5a3922e-dde0-4f14-b6c1-7ebe0b382304",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_start = pixel_start_channel.getChannel()\n",
    "pixel_end = pixel_end_channel.getChannel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "e0b3b87c-f692-47e0-a9f5-46b3da1a339c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Countrate(tagger, channels)\n",
    "rate = Countrate(time_tag_virt, [frame, line, pixel_start])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "4b725ff3-52a2-40f8-939b-2b708a25d0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_rates = Countrate(time_tag_virt, [1, 2, 3, 4, -1, -2, -3, -4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "117cc15e-2e0e-4891-9353-c83223190ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "intervals = TT.StartStop(time_tag_virt, -line, line, 0.1e-3/1e-12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "eea1f9e1-0977-4a7d-a339-3455a31f77d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 391 ms\n",
      "Wall time: 507 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "time_tag_virt.setReplaySpeed(-1)\n",
    "replay = time_tag_virt.replay(dump_file)\n",
    "time_tag_virt.waitForCompletion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "42ec154b-9e95-4d44-a857-9ee700cfc515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.23333333e+00, 3.21366667e+02, 8.22676333e+04])"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check that pixel clock is correct\n",
    "# Run Countrate for 1 second and print the result for all three channels\n",
    "#rate.startFor(int(1e12), clear=True)\n",
    "#rate.waitUntilFinished()\n",
    "comp_data = rate.getData()\n",
    "comp_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "a35acf4e-5a49-4e48-8913-f1976bddb596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines/frame =  260.56756756756755\n",
      "pixels/line =  255.9930505134322\n"
     ]
    }
   ],
   "source": [
    "print(\"lines/frame = \", comp_data[1]/comp_data[0])\n",
    "print(\"pixels/line = \", comp_data[2]/comp_data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "7e93d228-4a41-4453-889a-1d627e1de199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.37906200e+05, 3.21366667e+02, 1.23333333e+00, 1.38022100e+05,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.38028933e+05])"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rates.getData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "755fbed0-a582-41c7-a5c7-13bcbb67a04a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 2), dtype=int64)"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = intervals.getData()\n",
    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635dd4b1-ea26-4236-ba3e-34cb0ee5cfa8",
   "metadata": {
    "tags": []
   },
   "source": [
    "# flim class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3744d41b-00bc-4f8d-ab8d-c315d2ff25d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_tag_virt=TT.createTimeTaggerVirtual()\n",
    "time_tag_virt.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "191d6904-23d5-43ea-9411-4ff559cc1fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EventGenerator(tagger, trigger_channel, pattern, trigger_divider, stop_channel)\n",
    "pixel_start_channel = EventGenerator(time_tag_virt, line, pixel_pattern_start)\n",
    "pixel_start = pixel_start_channel.getChannel()\n",
    "\n",
    "pixel_end_channel= EventGenerator(time_tag_virt, line, pixel_pattern_end)\n",
    "pixel_end = pixel_end_channel.getChannel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c75f11a-6a1c-4b5d-b661-46099eaa18bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ConstantFractionDiscriminator(tagger, channels, search_window)\n",
    "photon_cfd_chan = ConstantFractionDiscriminator(time_tag_virt, (photon,), 10*1000)\n",
    "photon_cfd = photon_cfd_chan.getChannels()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff8a54a6-247e-4508-a443-64cd12eecb87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DelayedChannel(tagger, input_channel, delay)\n",
    "delayed_sync_chan = DelayedChannel(time_tag_virt, laser, 11.5e3)\n",
    "delayed_sync = delayed_sync_chan.getChannel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01366005-8277-4cb1-9b2c-1cd1752046df",
   "metadata": {},
   "outputs": [],
   "source": [
    "flim = Flim(time_tag_virt, start_channel=delayed_sync, click_channel=photon_cfd, pixel_begin_channel=pixel_start, n_pixels=n_pixel*n_pixel,\n",
    "             n_bins=n_bins, binwidth=binwidth, pixel_end_channel=pixel_end, frame_begin_channel=frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65bec6e2-fd84-4232-a882-3bfef52a1f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 2.09 s\n",
      "Wall time: 2.37 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "time_tag_virt.setReplaySpeed(-1)\n",
    "replay = time_tag_virt.replay(dump_file)\n",
    "time_tag_virt.waitForCompletion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b18b3ba4-4a22-4737-8283-587ac04c7016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#flim.waitUntilFinished()\n",
    "flim.getFramesAcquired()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f926f50-5737-44eb-8e22-9561fe331784",
   "metadata": {},
   "outputs": [],
   "source": [
    "flim_frames = flim.getSummedFrames()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bef74bd4-97ea-4505-9a8e-9f2147134c72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65536, 256)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flim_frames.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7afa164c-2b2c-4b9f-90db-8c3cf96fbb63",
   "metadata": {},
   "outputs": [],
   "source": [
    "flim_2d = flim_frames.reshape(256,256,256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a0e02e2-09f8-41be-ba49-e49c41c90e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flim_mean = flim_2d.sum(2)\n",
    "flim_mean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebd1d992-db24-4459-8535-fc50132477cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134667"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flim_2d.sum((0,1)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9807d1ac-243d-45ba-9e7d-29228771551a",
   "metadata": {},
   "outputs": [],
   "source": [
    "slider_vmax = widgets.IntSlider(\n",
    "    orientation='horizontal',\n",
    "    description='VMAX:',\n",
    "    step=.5,\n",
    "    min=0,\n",
    "    max=60\n",
    ")\n",
    "\n",
    "slider_vmax.layout.width = '60%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3d1851f-fcba-48f5-95e6-d91474fd7d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9b7e314019b43f5afc0767bd5e177c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='VMAX:', layout=Layout(width='60%'), max=60, step=0), Out…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_vmax(vmax)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def update_vmax(vmax):\n",
    "    close()\n",
    "    figure(figsize=[7,7])\n",
    "    imshow(flim_mean,vmax=vmax)\n",
    "    colorbar()\n",
    "    show()\n",
    "\n",
    "widgets.interact(update_vmax, vmax=slider_vmax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3a78484a-93fc-4bdd-82ef-4e0afe7891d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b80009190>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(flim_2d.sum((0,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d364fb07-f009-4128-9417-b861b52ce4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "maximas = (flim_mean == maximum_filter(flim_mean, footprint=np.ones((50,50))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2b1c2ba8-99d7-4d22-9614-1bab8d5d1696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x19b6bd9e7d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[7,7])\n",
    "imshow(maximas)\n",
    "colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9f7efc7-729b-4c0e-a4b3-348d5c40e805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "327.68"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check how many for percent\n",
    "dec = .005\n",
    "perc = 100- (dec*100)\n",
    "flim_mean.size *dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31b8d5f3-36c7-46b9-9479-fb98f7c04e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxima2 = (flim_mean==np.percentile(flim_mean.ravel(), perc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c0e1a04-4e40-4e32-a93f-e72a03855586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x19b6bbff7d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[7,7])\n",
    "imshow(maxima2,vmax=.5)\n",
    "colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da2a7cd-94c2-4580-b3d5-9150fad650ca",
   "metadata": {
    "tags": []
   },
   "source": [
    "# intensity renderer class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae7657f-97cb-4f0c-b8fc-6ab1e629bf89",
   "metadata": {},
   "source": [
    "#with the parameter 'divider', you can select how many input frames are integrated over to get one output frame\n",
    "intensity = IntensityRenderer(time_tag_virt, start_channel=photon, click_channel=laser, pixel_begin_channel=pixel, pixels_x=256, pixels_y=256,\n",
    "                              n_bins=bins, binwidth=binwidth, pixel_end_channel=pixelEnd, frame_begin_channel=frame, num_frames=0, divider=4, is_resonant=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8046d625-b1ad-4284-b127-0325a32282f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "# swab ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fed4ebeb-168a-4ef3-8a0b-68aba31ad05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The format for the table and the head of the table\n",
    "format_string = '{:>8} | {:>17} | {:>7} | {:>14} | {:>13}'\n",
    "#print(format_string.format('TAG #', 'EVENT TYPE', 'CHANNEL', 'TIMESTAMP (ps)', 'MISSED EVENTS'))\n",
    "#print('---------+-------------------+---------+----------------+--------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4fe329fe-a00b-429c-9a50-9f5c7cf312a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         |                   |         |                |              \n",
      "=============  Start of data chunk 1 with 100000 events  ==============\n",
      "         |                   |         |                |              \n",
      "       1 |       0 (TimeTag) |       1 | 279827555654787 |             0\n",
      "       2 |       0 (TimeTag) |      -1 | 279827555661643 |             0\n",
      "     ... |               ... |     ... |            ... |           ...\n",
      "  100000 |       0 (TimeTag) |       1 | 279828179922494 |             0\n",
      "         |                   |         |                |              \n",
      "=============  Start of data chunk 2 with 100000 events  ==============\n",
      "         |                   |         |                |              \n",
      "  100001 |       0 (TimeTag) |      -1 | 279828179929321 |             0\n",
      "  100002 |       0 (TimeTag) |       1 | 279828179934930 |             0\n",
      "     ... |               ... |     ... |            ... |           ...\n",
      "  200000 |       0 (TimeTag) |       1 | 279828804139874 |             0\n",
      "         |                   |         |                |              \n",
      "       . |                 . |       . |              . |             .\n",
      "       . |                 . |       . |              . |             .\n",
      "       . |                 . |       . |              . |             .\n",
      "         |                   |         |                |              \n",
      "============  Start of data chunk 18544 with 99131 events  ============\n",
      "         |                   |         |                |              \n",
      "1854300001 |       0 (TimeTag) |      -1 | 329627808907710 |             0\n",
      "1854300002 |       0 (TimeTag) |       1 | 329627808913386 |             0\n",
      "     ... |               ... |     ... |            ... |           ...\n",
      "1854399131 |       0 (TimeTag) |       1 | 329628425454596 |             0\n",
      "done\n",
      "CPU times: total: 44.5 s\n",
      "Wall time: 44.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_events = 100000  # Number of events to read at once\n",
    "event_name = ['0 (TimeTag)', '1 (Error)', '2 (OverflowBegin)', '3 (OverflowEnd)', '4 (MissedEvents)']\n",
    "i = 0\n",
    "\n",
    "t_events =0\n",
    "while fr.hasData():\n",
    "    # getData() does not return timestamps, but an instance of TimeTagStreamBuffer\n",
    "    # that contains more information than just the timestamp\n",
    "    data = fr.getData(n_events=n_events)\n",
    "\n",
    "    # With the following methods, we can retrieve a numpy array for the particular information:\n",
    "    channel = data.getChannels()            # The channel numbers\n",
    "    timestamps = data.getTimestamps()       # The timestamps in ps\n",
    "    overflow_types = data.getEventTypes()   # TimeTag = 0, Error = 1, OverflowBegin = 2, OverflowEnd = 3, MissedEvents = 4\n",
    "    missed_events = data.getMissedEvents()  # The numbers of missed events in case of overflow\n",
    "\n",
    "    # Output to table\n",
    "    if i < 2 or not fr.hasData():\n",
    "        print(format_string.format(*\" \"*5))\n",
    "        heading = ' Start of data chunk {} with {} events '.format(i+1, data.size)\n",
    "        extra_width = 69 - len(heading)\n",
    "        print('{} {} {}'.format(\"=\"*(extra_width//2), heading, \"=\"*(extra_width - extra_width//2)))\n",
    "        print(format_string.format(*\" \"*5))\n",
    "        print(format_string.format(i*n_events + 1, event_name[overflow_types[0]], channel[0], timestamps[0], missed_events[0]))\n",
    "        if data.size > 1:\n",
    "            print(format_string.format(i*n_events + 2, event_name[overflow_types[1]], channel[1], timestamps[1], missed_events[1]))\n",
    "        if data.size > 3:\n",
    "            print(format_string.format(*[\"...\"]*5))\n",
    "        if data.size > 2:\n",
    "            print(format_string.format(i*n_events + data.size, event_name[overflow_types[-1]], channel[-1], timestamps[-1], missed_events[-1]))\n",
    "    if i == 1:\n",
    "        print(format_string.format(*\" \"*5))\n",
    "        for j in range(3):\n",
    "            print(format_string.format(*\".\"*5))\n",
    "    i += 1\n",
    "    \n",
    "    t_events = t_events + len(channel)\n",
    "    \n",
    "\n",
    "#show that it's done\n",
    "print(\"done\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f90fdc6-1bae-4892-ab42-e99e50ad7eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.800869799809"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(329628425454596-279827555654787)/1e12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3390564-f615-4596-a15b-c37af9fa6037",
   "metadata": {},
   "source": [
    "#### length of events : 1854399131 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed923eac-3bda-4796-a70d-d002a22c82e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr.getData(1).getChannels()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15eb09cd-7519-4cf3-82d8-71d512fde815",
   "metadata": {
    "tags": []
   },
   "source": [
    "# my exp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8f9ace2e-fbec-4e51-85cf-9e0bbf1f1c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'channel number scheme': 2,\n",
       " 'configure version': 1,\n",
       " 'current time': '2022-06-10 16:33:51 -0500',\n",
       " 'devices': [{'PCB version': '1.6b (1)',\n",
       "   'model': 'Time Tagger Ultra',\n",
       "   'serial': '2143000XZI'}],\n",
       " 'firmware version': 'TT-Ultra, FW 5, TS 2022-02-02 13:09:16, OK 1.31',\n",
       " 'hardware buffer size': 67108864,\n",
       " 'inputs': [{'channel': [1, -1],\n",
       "   'conditional filter filtered': [False, False],\n",
       "   'conditional filter triggers': [False, False],\n",
       "   'deadtime': [2000, 2000],\n",
       "   'delay hardware': [0, 0],\n",
       "   'delay software': [0, 0],\n",
       "   'event divider': [0, 0],\n",
       "   'hardware delay compensation': [552, 552],\n",
       "   'input mux': 0,\n",
       "   'normalization': [True, True],\n",
       "   'resolution': 'Standard',\n",
       "   'resolution rms': 9.0,\n",
       "   'trigger level': -0.050011444266422345},\n",
       "  {'channel': [2, -2],\n",
       "   'conditional filter filtered': [False, False],\n",
       "   'conditional filter triggers': [False, False],\n",
       "   'deadtime': [2000, 2000],\n",
       "   'delay hardware': [0, 0],\n",
       "   'delay software': [0, 0],\n",
       "   'event divider': [0, 0],\n",
       "   'hardware delay compensation': [1210, 1210],\n",
       "   'input mux': 0,\n",
       "   'normalization': [True, True],\n",
       "   'resolution': 'Standard',\n",
       "   'resolution rms': 9.0,\n",
       "   'trigger level': 2.0000381475547417},\n",
       "  {'channel': [3, -3],\n",
       "   'conditional filter filtered': [False, False],\n",
       "   'conditional filter triggers': [False, False],\n",
       "   'deadtime': [2000, 2000],\n",
       "   'delay hardware': [0, 0],\n",
       "   'delay software': [0, 0],\n",
       "   'event divider': [0, 0],\n",
       "   'hardware delay compensation': [1458, 1458],\n",
       "   'input mux': 0,\n",
       "   'normalization': [True, True],\n",
       "   'resolution': 'Standard',\n",
       "   'resolution rms': 9.0,\n",
       "   'trigger level': 2.0000381475547417},\n",
       "  {'channel': [4, -4],\n",
       "   'conditional filter filtered': [False, False],\n",
       "   'conditional filter triggers': [False, False],\n",
       "   'deadtime': [2000, 2000],\n",
       "   'delay hardware': [0, 0],\n",
       "   'delay software': [0, 0],\n",
       "   'event divider': [0, 0],\n",
       "   'hardware delay compensation': [324, 324],\n",
       "   'input mux': 0,\n",
       "   'normalization': [True, True],\n",
       "   'resolution': 'Standard',\n",
       "   'resolution rms': 9.0,\n",
       "   'trigger level': -0.020027466239413982}],\n",
       " 'led bitmask': 0,\n",
       " 'measurements': [{'name': 'FileWriter',\n",
       "   'params': {'channels': [1, 2, 3, 4, -1, -2, -3, -4],\n",
       "    'filename': 'test_PMTON.ttbin'},\n",
       "   'registered channels': [-4, -3, -2, -1, 1, 2, 3, 4],\n",
       "   'virtual channels': []},\n",
       "  {'name': 'FileWriter',\n",
       "   'params': {'channels': [1, 2, 3, 4, -1, -2, -3, -4],\n",
       "    'filename': 'test_PMTON_2.ttbin'},\n",
       "   'registered channels': [-4, -3, -2, -1, 1, 2, 3, 4],\n",
       "   'virtual channels': []},\n",
       "  {'name': 'FileWriter',\n",
       "   'params': {'channels': [1, 2, 3, 4, -1, -2, -3, -4],\n",
       "    'filename': 'test.ttbin'},\n",
       "   'registered channels': [-4, -3, -2, -1, 1, 2, 3, 4],\n",
       "   'virtual channels': []}],\n",
       " 'network': {'client count': 0,\n",
       "  'protocol version major': 3,\n",
       "  'protocol version minor': 1,\n",
       "  'server running': False,\n",
       "  'server runs': 0},\n",
       " 'registered channels': [-4, -3, -2, -1, 1, 2, 3, 4],\n",
       " 'resolution': 'Standard',\n",
       " 'serial': '2143000XZI',\n",
       " 'software clock': None,\n",
       " 'software version': '2.11.0',\n",
       " 'stream block size events': 131072,\n",
       " 'stream block size latency': 20,\n",
       " 'test signal divider': 63}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## pretty print for json\n",
    "fr.getConfiguration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd4240d-37e8-44af-a525-b961395c0471",
   "metadata": {},
   "outputs": [],
   "source": [
    "buf = fr.getData(0)\n",
    "print(buf.tStart, buf.getTimestamps(), buf.getChannels())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c634da1-feb4-4d3a-9944-27f7e38d2e3e",
   "metadata": {},
   "source": [
    "#### last byte on the stream : 329628425460000 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "3ada6572-aca3-4c5b-bfe1-a9374be57a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr = TT.FileReader(r\"C:\\Users\\lociu\\Desktop\\PriyaM\\Data\\Swabian_tests\\test.ttbin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "adde294a-2926-45ba-aafa-fce9c3bc161a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, -1, -2, -3, -4)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr.getChannelList()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "2dca2263-bade-49fe-9097-db9fca9abd81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1 [] []\n"
     ]
    }
   ],
   "source": [
    "buf = fr.getData(0)\n",
    "print(buf.tStart, buf.getTimestamps(), buf.getChannels())"
   ]
  },
  {
   "cell_type": "raw",
   "id": "27d51cc8-c754-47bb-b763-a29882a638c8",
   "metadata": {},
   "source": [
    "for buf_event_ix in range(10):\n",
    "    buf = fr.getData(1)\n",
    "    print(buf_event_ix, buf.tStart, buf.getTimestamps(), buf.getChannels())"
   ]
  },
  {
   "cell_type": "raw",
   "id": "be62f559-aa40-47fb-90d0-8e0688dcfdd7",
   "metadata": {},
   "source": [
    "buf = fr.getData(1)\n",
    "print(buf.tStart, buf.getTimestamps(), buf.getChannels(),buf.getEventTypes(),buf.tGetData())"
   ]
  },
  {
   "cell_type": "raw",
   "id": "89743114-c66f-4376-8edd-8e43c014fd09",
   "metadata": {},
   "source": [
    "type(fr.getData(1))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7dbc429c-a0d2-4860-9fca-7819b66a26d9",
   "metadata": {},
   "source": [
    "n_events = int(1E5)\n",
    "\n",
    "while fr.hasData():\n",
    "    # getData() does not return timestamps, but an instance of TimeTagStreamBuffer\n",
    "    # that contains more information than just the timestamp\n",
    "    data = fr.getData(n_events=n_events)\n",
    "\n",
    "    # With the following methods, we can retrieve a numpy array for the particular information:\n",
    "    channel = data.getChannels()            # The channel numbers\n",
    "    timestamps = data.getTimestamps()       # The timestamps in ps\n",
    "    overflow_types = data.getEventTypes()   # TimeTag = 0, Error = 1, OverflowBegin = 2, OverflowEnd = 3, MissedEvents = 4\n",
    "    missed_events = data.getMissedEvents()  # The numbers of missed events in case of overflow\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "66effaff-18b7-47aa-9230-6ad23f2da258",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'total_events' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[238], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m total_events\n",
      "\u001b[1;31mNameError\u001b[0m: name 'total_events' is not defined"
     ]
    }
   ],
   "source": [
    "total_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "4d32cf48-7e55-4227-81f4-ff1521fe2713",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_events = int(1854399131.0)\n",
    "\n",
    "while fr.hasData():\n",
    "    data = fr.getData(n_events=n_events)\n",
    "\n",
    "# With the following methods, we can retrieve a numpy array for the particular information:\n",
    "channel = data.getChannels()            # The channel numbers\n",
    "timestamps = data.getTimestamps()       # The timestamps in ps\n",
    "event_types = data.getEventTypes()   # TimeTag = 0, Error = 1, OverflowBegin = 2, OverflowEnd = 3, MissedEvents = 4\n",
    "missed_events = data.getMissedEvents()  # The numbers of missed events in case of overflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "eba40262-7f26-4e34-bc16-65029e3a3d33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeTagger.TimeTagStreamBuffer"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "5d99f6e7-8d5c-40d7-a0a9-1b751478500f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1854399131, 1854399131)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(channel), len(event_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "ab7b0d7d-c229-43d7-8444-8755304e114e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "3060ee0c-d390-4fc0-9e41-94508947fc36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 11.1 s\n",
      "Wall time: 11.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "len(np.where(channel==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "728d0e4a-26ef-4ab7-b9b4-329a0c3bd582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4 (2377917,)\n",
      "-3 (10,)\n",
      "-2 (4821,)\n",
      "-1 (924769909,)\n",
      "0 (0,)\n",
      "1 (924769906,)\n",
      "2 (4812,)\n",
      "3 (20,)\n",
      "4 (2377130,)\n",
      "CPU times: total: 29.9 s\n",
      "Wall time: 29.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for ch in range(-4,5):\n",
    "    ch_1 = np.where(np.logical_and(channel==ch, event_types == 0))\n",
    "    print(ch, ch_1[0].shape)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "c1e1ce48-8b6b-44cf-ad9e-799d76967c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4 (271,)\n",
      "-3 (42,)\n",
      "-2 (42,)\n",
      "-1 (46906,)\n",
      "0 (0,)\n",
      "1 (46906,)\n",
      "2 (42,)\n",
      "3 (42,)\n",
      "4 (271,)\n",
      "CPU times: total: 23.7 s\n",
      "Wall time: 23.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for ch in range(-4,5):\n",
    "    ch_1 = np.where(np.logical_and(channel==ch, event_types == 4))\n",
    "    print(ch, ch_1[0].shape)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fd2ce6a-a78a-49a2-bc96-2aba17282965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0721806087658934e-05"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "46906/924769909"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adca03a6-800c-4dcb-8cfb-1ee5cd22c688",
   "metadata": {},
   "outputs": [],
   "source": [
    "1:laser/sync\n",
    "2: line clock\n",
    "3: frame clock\n",
    "4: photons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c358f456-e488-4042-b5f9-4a944c42407e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tests(n,m):\n",
    "    print(\"\\nSize:\")\n",
    "    for i in range(n,m):\n",
    "        x = fr.getData(i)\n",
    "        print(i,\":\", x.size)\n",
    "    \n",
    "    print(\"\\nChannels:\")\n",
    "    for i in range(n,m):\n",
    "        x = fr.getData(i)\n",
    "        print(i,\":\", x.getChannels())\n",
    "        \n",
    "    print(\"\\nEvent Types:\")    \n",
    "    for i in range(n,m):\n",
    "        x = fr.getData(i)\n",
    "        print(i,\":\", x.getEventTypes())\n",
    "        \n",
    "    print(\"\\nTime Stamps:\")\n",
    "    for i in range(n,m):\n",
    "        x = fr.getData(i)\n",
    "        print(i,\":\", x.getTimestamps())\n",
    "        \n",
    "    print(\"\\nGet Data:\")\n",
    "    for i in range(n,m):\n",
    "        x = fr.getData(i)\n",
    "        print(i,\":\", x.tGetData)\n",
    "        \n",
    "    print(\"\\nT Start:\")\n",
    "    for i in range(n,m):\n",
    "        x = fr.getData(i)\n",
    "        print(i,\":\", x.tStart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9197130-84c5-4e55-8ced-368c3fd4fe2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size:\n",
      "1 : 0\n",
      "2 : 0\n",
      "3 : 0\n",
      "4 : 0\n",
      "5 : 0\n",
      "6 : 0\n",
      "7 : 0\n",
      "8 : 0\n",
      "9 : 0\n",
      "\n",
      "Channels:\n",
      "1 : []\n",
      "2 : []\n",
      "3 : []\n",
      "4 : []\n",
      "5 : []\n",
      "6 : []\n",
      "7 : []\n",
      "8 : []\n",
      "9 : []\n",
      "\n",
      "Event Types:\n",
      "1 : []\n",
      "2 : []\n",
      "3 : []\n",
      "4 : []\n",
      "5 : []\n",
      "6 : []\n",
      "7 : []\n",
      "8 : []\n",
      "9 : []\n",
      "\n",
      "Time Stamps:\n",
      "1 : []\n",
      "2 : []\n",
      "3 : []\n",
      "4 : []\n",
      "5 : []\n",
      "6 : []\n",
      "7 : []\n",
      "8 : []\n",
      "9 : []\n",
      "\n",
      "Get Data:\n",
      "1 : 329628425460000\n",
      "2 : 329628425460000\n",
      "3 : 329628425460000\n",
      "4 : 329628425460000\n",
      "5 : 329628425460000\n",
      "6 : 329628425460000\n",
      "7 : 329628425460000\n",
      "8 : 329628425460000\n",
      "9 : 329628425460000\n",
      "\n",
      "T Start:\n",
      "1 : 329628425460000\n",
      "2 : 329628425460000\n",
      "3 : 329628425460000\n",
      "4 : 329628425460000\n",
      "5 : 329628425460000\n",
      "6 : 329628425460000\n",
      "7 : 329628425460000\n",
      "8 : 329628425460000\n",
      "9 : 329628425460000\n"
     ]
    }
   ],
   "source": [
    "tests(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "177ef442-459b-40cd-8c98-8660730a4e5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e12aeb7a-85e1-4db9-b2b1-8ee888703703",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ca1d437-2c92-4b0f-9efb-19fa84079301",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d7bb44e4-5612-40da-b181-243c847383c6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# server and client?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6ea8b29d-ea40-4332-b234-a0636a2773a5",
   "metadata": {},
   "source": [
    "import Pyro5.api\n",
    "import TimeTagger as TT"
   ]
  },
  {
   "cell_type": "raw",
   "id": "028ebd2f-f538-4864-86e7-184fad0624a0",
   "metadata": {},
   "source": [
    "@Pyro5.api.expose\n",
    "class TimeTaggerRPC:\n",
    "    \"\"\"Adapter for the Time Tagger Library\"\"\"\n",
    "\n",
    "    def scanTimeTagger(self):\n",
    "        \"\"\"This method will become available remotely.\"\"\"\n",
    "        return TT.scanTimeTagger()\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # Start server and expose the TimeTaggerRPC class\n",
    "    with Pyro5.api.Daemon(host='localhost', port=23000) as daemon:\n",
    "        # Register class with Pyro\n",
    "        uri = daemon.register(TimeTaggerRPC, 'TimeTagger')\n",
    "        # Print the URI of the published object\n",
    "        print(uri)\n",
    "        # Start the server event loop\n",
    "        daemon.requestLoop()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a4a35f06-6af5-4fdc-9d1e-7eae5b7a7e0f",
   "metadata": {},
   "source": [
    "# Connect to the TimeTaggerRPC object on the server\n",
    "# This line is all we need to establish remote communication\n",
    "TimeTagger = Pyro5.api.Proxy(\"PYRO:TimeTagger@localhost:23000\")\n",
    "\n",
    "# Now, we can call methods that will be executed on the server.\n",
    "# Lets check what Time Taggers are available at the server\n",
    "timetaggers = TimeTagger.scanTimeTagger()\n",
    "print(timetaggers)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "de8c7798-33ee-4d8e-847f-9e62b6413c82",
   "metadata": {},
   "source": [
    "@Pyro5.api.expose\n",
    "class TimeTagger:\n",
    "    \"\"\"Adapter for the Time Tagger object\"\"\"\n",
    "\n",
    "    def __init__(self, args, kwargs):\n",
    "        self._obj = TT.createTimeTagger(*args, **kwargs)\n",
    "\n",
    "    def setTestSignal(self, *args):\n",
    "        return self._obj.setTestSignal(*args)\n",
    "\n",
    "    def getSerial(self):\n",
    "        return self._obj.getSerial()\n",
    "\n",
    "    # ... Other methods of the TT.TimeTagger class are omitted here.\n",
    "\n",
    "\n",
    "@Pyro5.api.expose\n",
    "class TimeTaggerRPC:\n",
    "    \"\"\"Adapter for the Time Tagger Library\"\"\"\n",
    "\n",
    "    def scanTimeTagger(self):\n",
    "        \"\"\"Return the serial numbers of the available Time Taggers.\"\"\"\n",
    "        return TT.scanTimeTagger()\n",
    "\n",
    "    def createTimeTagger(self, *args, **kwargs):\n",
    "        \"\"\"Create the Time Tagger.\"\"\"\n",
    "        tagger = TimeTagger(args, kwargs)\n",
    "        self._pyroDaemon.register(tagger)\n",
    "        return tagger\n",
    "        # Pyro will automatically create and send a proxy object\n",
    "        # to the client.\n",
    "\n",
    "    def freeTimeTagger(self, tagger_proxy):\n",
    "        \"\"\"Free Time Tagger. \"\"\"\n",
    "        # Client only has a proxy object.\n",
    "        objectId = tagger_proxy._pyroUri.object\n",
    "        # Get adapter object from the server.\n",
    "        tagger = self._pyroDaemon.objectsById.get(objectId)\n",
    "        self._pyroDaemon.unregister(tagger)\n",
    "        return TT.freeTimeTagger(tagger._obj)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6fb4378f-3444-408b-9463-ad2caf961bc9",
   "metadata": {},
   "source": [
    "with client.createProxy(host='localhost', port=23000) as TT:\n",
    "    tagger = TT.createTimeTagger()\n",
    "    tagger.setTestSignal(1, True)\n",
    "    tagger.setTestSignal(2, True)\n",
    "\n",
    "    hist = TT.Correlation(tagger, 1, 2, binwidth=5, n_bins=2000)\n",
    "    hist.startFor(int(10e12), clear=True)\n",
    "\n",
    "    x = hist.getIndex()\n",
    "    while hist.isRunning():\n",
    "        plt.pause(0.1)\n",
    "        y = hist.getData()\n",
    "        plt.cla()\n",
    "        plt.plot(x, y)\n",
    "\n",
    "    TT.freeTimeTagger(tagger)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f320ef47-80f7-4282-8de5-acb46646d4df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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