From 8d14d2a47129bbe8ec1160917ef7804053b78a24 Mon Sep 17 00:00:00 2001
From: Steve Abreu <s.abreu@rug.nl>
Date: Mon, 2 Jan 2023 19:02:24 +0100
Subject: [PATCH 1/2] benchmarking: refactor + evt3, dat for metavision

---
 docs/benchmarking/script.ipynb | 321 +++++++++++++++++++++++++++------
 1 file changed, 261 insertions(+), 60 deletions(-)

diff --git a/docs/benchmarking/script.ipynb b/docs/benchmarking/script.ipynb
index f80199f..6cafd0a 100644
--- a/docs/benchmarking/script.ipynb
+++ b/docs/benchmarking/script.ipynb
@@ -13,7 +13,7 @@
    "id": "24a4b039",
    "metadata": {},
    "source": [
-    "In this notebook, a benchmark on three file types (DAT, EVT2 and EVT3) is run "
+    "In this notebook, a benchmark on three file types (DAT, EVT2 and EVT3) is run, comparing expelliarmus and metavision."
    ]
   },
   {
@@ -24,6 +24,8 @@
    "outputs": [],
    "source": [
     "from expelliarmus import Wizard\n",
+    "from metavision_core.event_io import EventsIterator, RawReader\n",
+    "from metavision_core.event_io.py_reader import EventDatReader\n",
     "import pathlib\n",
     "import h5py\n",
     "import numpy as np\n",
@@ -37,6 +39,14 @@
     "REPEAT = 10"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9355fcc5",
+   "metadata": {},
+   "source": [
+    "## setup"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -44,12 +54,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_diff_perc_str(ref, val):\n",
-    "    if (val > ref):\n",
-    "        return f\"+{round((val/ref-1)*100)}%\"\n",
-    "    else:\n",
-    "        return f\"-{round((1-val/ref)*100)}%\"\n",
-    "    \n",
     "get_fsize_MB = lambda fpath: round(fpath.stat().st_size/(1024*1024))"
    ]
   },
@@ -76,8 +80,17 @@
     "    print(\"Downloading EVT3 file... \", end=\"\")\n",
     "    if not pathlib.Path(get_fpath('evt3')).is_file():\n",
     "        r = requests.get(\"https://dataset.prophesee.ai/index.php/s/nVcLLdWAnNzrmII/download\", allow_redirects=True) # spinner.dat, DAT\n",
-    "        open(get_fpath('evt3'), 'wb').write(r.content)\n",
-    "    print(\"done!\")\n"
+    "        with open(get_fpath('evt3'), 'wb') as f:\n",
+    "            f.write(r.content)\n",
+    "    print(\"done!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37261eb9",
+   "metadata": {},
+   "source": [
+    "converting files"
    ]
   },
   {
@@ -114,21 +127,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "EMPTY_DICT = dict(fsize=0, full=0, windowed=0, chunked=0)\n",
     "data = dict(\n",
-    "    expelliarmus=dict(dat=dict(fsize=0, full=0, windowed=0, chunked=0), \n",
-    "                      evt2=dict(fsize=0, full=0, windowed=0, chunked=0), \n",
-    "                      evt3=dict(fsize=0, full=0, windowed=0, chunked=0),\n",
-    "                     ),\n",
-    "    hdf5=dict(fsize=0, full=0, windowed=0, chunked=0),\n",
-    "    hdf5_lzf=dict(fsize=0, full=0, windowed=0, chunked=0),\n",
-    "    hdf5_gzip=dict(fsize=0, full=0, windowed=0, chunked=0),\n",
-    "    numpy=dict(fsize=0, full=0),\n",
+    "    expelliarmus = dict(dat=EMPTY_DICT, evt2=EMPTY_DICT, evt3=EMPTY_DICT),\n",
+    "    metavision = dict(dat=EMPTY_DICT, evt2=EMPTY_DICT, evt3=EMPTY_DICT),\n",
+    "    hdf5 = EMPTY_DICT,\n",
+    "    hdf5_lzf = EMPTY_DICT,\n",
+    "    hdf5_gzip = EMPTY_DICT,\n",
+    "    numpy = dict(fsize=0, full=0),\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c1351099",
+   "metadata": {},
+   "source": [
+    "## run benchmarking"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22a41189",
+   "metadata": {},
+   "source": [
+    "### benchmarking for expelliarmus"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "f8f0e10d",
    "metadata": {},
    "outputs": [
@@ -136,24 +164,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Full file read\n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | Diff. DAT | Diff. EVT2 | Diff. EVT3\n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. DAT  | 851       | -0%       | +100%      | +143%      | 1.15    | -0%       | +43%       | -41%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT2 | 426       | -50%      | -0%        | +22%       | 0.80    | -30%      | -0%        | -59%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT3 | 350       | -59%      | -18%       | -0%        | 1.95    | +70%      | +144%      | -0%        \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5      | 1701      | +100%     | +299%      | +386%      | 0.73    | -36%      | -8%        | -62%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_lzf  | 746       | -12%      | +75%       | +113%      | 3.09    | +170%     | +287%      | +58%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_gzip | 419       | -51%      | -2%        | +20%       | 5.60    | +389%     | +600%      | +187%      \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "numpy     | 1701      | +100%     | +299%      | +386%      | 0.32    | -72%      | -60%       | -84%       \n",
-      "------------------------------------------------------------------------------------------------------------\n"
+      " dat: 0.90s\n",
+      "evt2: 0.86s\n",
+      "evt3: 1.83s\n"
      ]
     }
    ],
@@ -161,9 +174,7 @@
     "if LOAD_RESULTS:\n",
     "    data = pickle.load(open(\"./benchmark.pk\", \"rb\"))\n",
     "\n",
-    "print(\"Full file read\")\n",
     "for encoding in encodings:\n",
-    "\n",
     "    if not LOAD_RESULTS:  \n",
     "        fpath = pathlib.Path(get_fpath(encoding))\n",
     "        wizard.set_encoding(encoding)\n",
@@ -173,8 +184,121 @@
     "            \n",
     "        data[\"expelliarmus\"][encoding][\"fsize\"] = get_fsize_MB(fpath)       \n",
     "        data[\"expelliarmus\"][encoding][\"full\"] = sum(timeit.repeat(lambda: wizard.read(), number=1, repeat=REPEAT))/REPEAT\n",
-    "\n",
-    "# HDF5 formats.\n",
+    "        print(f'{encoding:>4}: {data[\"expelliarmus\"][encoding][\"full\"]:.2f}s')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2f7000",
+   "metadata": {},
+   "source": [
+    "### benchmarking for metavision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3605322f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def metavision_read_raw(rawreader, n_skip=10_000_000):\n",
+    "    rawreader.reset()\n",
+    "    while not rawreader.done:\n",
+    "        rawreader.seek_event(n_skip)\n",
+    "    n_events = rawreader.current_event_index()\n",
+    "    rawreader.reset()\n",
+    "    return rawreader.load_n_events(n_events)\n",
+    "\n",
+    "def metavision_read_dat(datreader):\n",
+    "    return datreader.load_n_events(datreader.event_count())\n",
+    "\n",
+    "def metavision_read_raw_iterator(eviterator, delta_t=10_000):\n",
+    "    return [ev for ev in eviterator]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "32c5982f",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[dat] duration (raw): 0.55s\n",
+      "[dat] duration (iterator): 7.15s\n",
+      "[evt3] duration (raw): 4.56s\n",
+      "[evt3] duration (iterator): 1.99s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# n_events: 111_484_516\n",
+    "# lower bound: 2 bytes per event\n",
+    "# upper bound: 9 bytes per event\n",
+    "REPEAT = 3\n",
+    "if not LOAD_RESULTS:\n",
+    "    for encoding in encodings:\n",
+    "        fpath = pathlib.Path(get_fpath(encoding))\n",
+    "#         print(encoding, fpath, '\\n', '-'*30, sep='', end='\\n\\n')\n",
+    "        \n",
+    "        data['metavision'][encoding]['fsize'] = get_fsize_MB(fpath)\n",
+    "\n",
+    "        if encoding == 'evt2':\n",
+    "            data['metavision'][encoding]['full'] = 100\n",
+    "            continue # TODO: skip for now (waiting for separate bugfix)\n",
+    "        elif 'evt' in encoding:\n",
+    "            N_SKIP = int(1e7)\n",
+    "            MAX_EVENTS = int(1e9)\n",
+    "            raw_reader = RawReader(str(fpath), max_events=MAX_EVENTS)\n",
+    "            raw_fn = lambda: metavision_read_raw(raw_reader, n_skip=N_SKIP)\n",
+    "        elif encoding == 'dat':\n",
+    "            dat_reader = EventDatReader(str(fpath))\n",
+    "            raw_fn = lambda: metavision_read_dat(dat_reader)\n",
+    "\n",
+    "        raw_duration = sum(timeit.repeat(raw_fn, number=1, repeat=REPEAT)) / REPEAT\n",
+    "        print(f'[{encoding}] duration (direct): {raw_duration:.2f}s')\n",
+    "\n",
+    "        DELTA_T = 10000\n",
+    "        evit_reader = EventsIterator(str(fpath), delta_t=DELTA_T)\n",
+    "        evit_fn = lambda: [ev for ev in evit_reader]\n",
+    "\n",
+    "        evit_duration = sum(timeit.repeat(evit_fn, number=1, repeat=REPEAT)) / REPEAT\n",
+    "        print(f'[{encoding}] duration (iterator): {evit_duration:.2f}s')\n",
+    "\n",
+    "        data['metavision'][encoding]['full'] = min(raw_duration, evit_duration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3055d47d",
+   "metadata": {},
+   "source": [
+    "### benchmarking for HDF5 and NPY formats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c4a002c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hdf5: 0.79s\n",
+      "hdf5_lzf: 2.94s\n",
+      "hdf5_gzip: 5.17s\n",
+      "numpy: 0.46s\n"
+     ]
+    }
+   ],
+   "source": [
     "if FIRST_RUN:\n",
     "    for sw in softwares[:-1]:\n",
     "        fpath = pathlib.Path(f\"ref_{sw}.hdf5\")\n",
@@ -192,36 +316,91 @@
     "        fp = h5py.File(fpath)\n",
     "        data[sw][\"full\"] = sum(timeit.repeat(lambda: fp[\"arr\"][:], number=1, repeat=REPEAT))/REPEAT\n",
     "        fp.close()\n",
+    "        print(f'{sw}: {data[sw][\"full\"]:.2f}s')\n",
     "\n",
     "    # NumPy\n",
     "    fpath = pathlib.Path(\"ref_np.npy\")\n",
     "    np.save(fpath, arr, allow_pickle=False)\n",
     "    data[\"numpy\"][\"fsize\"] = get_fsize_MB(fpath)\n",
     "    data[\"numpy\"][\"full\"] = sum(timeit.repeat(lambda: np.load(fpath), number=1, repeat=REPEAT))/REPEAT\n",
-    "    \n",
-    "# Printing results.\n",
-    "\n",
-    "def get_spacing (header, printed):\n",
-    "    if isinstance(printed, float):\n",
-    "        return \" \"*(len(header)+1 - len(f\"{printed:.3f}\"))\n",
+    "    print(f'numpy: {data[\"numpy\"][\"full\"]:.2f}s')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccb2664d",
+   "metadata": {},
+   "source": [
+    "## benchmarking results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e8db2ceb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_diff_perc_str(ref, val):\n",
+    "    if (val > ref):\n",
+    "        return f\"+{round((val/ref-1)*100):>3}%\"\n",
     "    else:\n",
-    "        return \" \"*(len(header)+1 - len(str(printed)))\n",
-    "    \n",
+    "        return f\"-{round((1-val/ref)*100):>3}%\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a78eca80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------------------------------------------------------------------------------------------------\n",
+      "Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | Diff. DAT | Diff. EVT2 | Diff. EVT3\n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "exp. DAT  | 851       | -  0%     | +100%      | +143%      | 0.90      | -  0%    | +  5%      | - 51%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "exp. EVT2 | 426       | - 50%     | -  0%      | + 22%      | 0.86      | -  5%    | -  0%      | - 53%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "exp. EVT3 | 350       | - 59%     | - 18%      | -  0%      | 1.83      | +104%    | +114%      | -  0%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "met. DAT  | 851       | -  0%     | +100%      | +143%      | 0.55      | - 39%    | - 36%      | - 70%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "met. EVT2 | 426       | - 50%     | -  0%      | + 22%      | 100.00    | +11000%  | +11572%    | +5354%  \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "met. EVT3 | 350       | - 59%     | - 18%      | -  0%      | 1.99      | +120%    | +132%      | +  8%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "hdf5      | 1701      | +100%     | +299%      | +386%      | 0.79      | - 12%    | -  7%      | - 57%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "hdf5_lzf  | 746       | - 12%     | + 75%      | +113%      | 2.94      | +226%    | +243%      | + 60%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "hdf5_gzip | 419       | - 51%     | -  2%      | + 20%      | 5.17      | +474%    | +504%      | +182%   \n",
+      "------------------------------------------------------------------------------------------------------------\n",
+      "numpy     | 1701      | +100%     | +299%      | +386%      | 0.46      | - 49%    | - 47%      | - 75%   \n",
+      "------------------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
     "def gen_row(sw_name, size_value, time_value, mode):\n",
     "    exp_dict = data[\"expelliarmus\"]\n",
     "    dat_fsize, evt2_fsize, evt3_fsize = exp_dict[\"dat\"][\"fsize\"], exp_dict[\"evt2\"][\"fsize\"], exp_dict[\"evt3\"][\"fsize\"]\n",
     "    dat_time, evt2_time, evt3_time = exp_dict[\"dat\"][mode], exp_dict[\"evt2\"][mode], exp_dict[\"evt3\"][mode]\n",
-    "    return f'{sw_name}{get_spacing(\"Software \", sw_name)}| \\\n",
-    "{size_value}{get_spacing(\"Size [MB]\", size_value)}| \\\n",
-    "{get_diff_perc_str(dat_fsize, size_value)}{get_spacing(\"Diff. DAT\", get_diff_perc_str(dat_fsize, size_value))}| \\\n",
-    "{get_diff_perc_str(evt2_fsize, size_value)}{get_spacing(\"Diff. EVT2\", get_diff_perc_str(evt2_fsize, size_value))}| \\\n",
-    "{get_diff_perc_str(evt3_fsize, size_value)}{get_spacing(\"Diff. EVT3\", get_diff_perc_str(evt3_fsize, size_value))}| \\\n",
-    "{time_value:.2f}{get_spacing(\"Time [s]\", time_value)}| \\\n",
-    "{get_diff_perc_str(dat_time, time_value)}{get_spacing(\"Diff. DAT\", get_diff_perc_str(dat_time, time_value))}| \\\n",
-    "{get_diff_perc_str(evt2_time, time_value)}{get_spacing(\"Diff. EVT2\", get_diff_perc_str(evt2_time, time_value))}| \\\n",
-    "{get_diff_perc_str(evt3_time, time_value)}{get_spacing(\"Diff. EVT3\", get_diff_perc_str(evt3_time, time_value))}'  \n",
-    "                                                                                  \n",
-    "header = f\"Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | Diff. DAT | Diff. EVT2 | Diff. EVT3\"\n",
+    "    return_str = f'{sw_name:<9} | {size_value:<9} | ' + \\\n",
+    "                 f'{get_diff_perc_str(dat_fsize, size_value):<9} | ' + \\\n",
+    "                 f'{get_diff_perc_str(evt2_fsize, size_value):<10} | ' + \\\n",
+    "                 f'{get_diff_perc_str(evt3_fsize, size_value):<10} | ' + \\\n",
+    "                 f'{time_value:<9.2f} | ' + \\\n",
+    "                 f'{get_diff_perc_str(dat_time, time_value):<8} | ' +\\\n",
+    "                 f'{get_diff_perc_str(evt2_time, time_value):<10} | ' + \\\n",
+    "                 f'{get_diff_perc_str(evt3_time, time_value):<8}'\n",
+    "    return return_str\n",
+    "\n",
+    "header = \"Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | \" +\\\n",
+    "         \"Diff. DAT | Diff. EVT2 | Diff. EVT3\"\n",
     "print(\"-\"*len(header))\n",
     "print(header)\n",
     "print(\"-\"*len(header))\n",
@@ -230,11 +409,33 @@
     "    print(gen_row(f\"exp. {encoding.upper()}\", data[\"expelliarmus\"][encoding][\"fsize\"], data[\"expelliarmus\"][encoding][\"full\"], \"full\"))\n",
     "    print(\"-\"*len(header))\n",
     "\n",
+    "for encoding in encodings:\n",
+    "    print(gen_row(f\"met. {encoding.upper()}\", data[\"metavision\"][encoding][\"fsize\"], data[\"metavision\"][encoding].get(\"full\", 100), \"full\"))\n",
+    "    print(\"-\"*len(header))\n",
+    "    \n",
     "for sw in softwares: \n",
     "    print(gen_row(sw, data[sw][\"fsize\"], data[sw][\"full\"], \"full\"))\n",
     "    print(\"-\"*len(header))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cbef7355",
+   "metadata": {},
+   "source": [
+    "***TODO: separate tables for metavision comparison and different file format comparison***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3239ea3",
+   "metadata": {},
+   "source": [
+    "## Plotting results\n",
+    "\n",
+    "***TODO: update with metavision results***"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -563,7 +764,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.8.10"
   },
   "vscode": {
    "interpreter": {

From 1a8f05e8ae2a495778f8f8149f4050c9bdab93d0 Mon Sep 17 00:00:00 2001
From: Steve Abreu <s.abreu@rug.nl>
Date: Mon, 2 Jan 2023 22:10:48 +0100
Subject: [PATCH 2/2] add evt2, new tables

---
 docs/benchmarking/script.ipynb | 235 ++++++++++-----------------------
 1 file changed, 69 insertions(+), 166 deletions(-)

diff --git a/docs/benchmarking/script.ipynb b/docs/benchmarking/script.ipynb
index 6cafd0a..42aaefe 100644
--- a/docs/benchmarking/script.ipynb
+++ b/docs/benchmarking/script.ipynb
@@ -127,13 +127,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "EMPTY_DICT = dict(fsize=0, full=0, windowed=0, chunked=0)\n",
+    "EMPTY_DICT = lambda: dict(fsize=0, full=0, windowed=0, chunked=0)\n",
     "data = dict(\n",
-    "    expelliarmus = dict(dat=EMPTY_DICT, evt2=EMPTY_DICT, evt3=EMPTY_DICT),\n",
-    "    metavision = dict(dat=EMPTY_DICT, evt2=EMPTY_DICT, evt3=EMPTY_DICT),\n",
-    "    hdf5 = EMPTY_DICT,\n",
-    "    hdf5_lzf = EMPTY_DICT,\n",
-    "    hdf5_gzip = EMPTY_DICT,\n",
+    "    expelliarmus = dict(dat=EMPTY_DICT(), evt2=EMPTY_DICT(), evt3=EMPTY_DICT()),\n",
+    "    metavision = dict(dat=EMPTY_DICT(), evt2=EMPTY_DICT(), evt3=EMPTY_DICT()),\n",
+    "    hdf5 = EMPTY_DICT(),\n",
+    "    hdf5_lzf = EMPTY_DICT(),\n",
+    "    hdf5_gzip = EMPTY_DICT(),\n",
     "    numpy = dict(fsize=0, full=0),\n",
     ")"
    ]
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "f8f0e10d",
    "metadata": {},
    "outputs": [
@@ -164,7 +164,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " dat: 0.90s\n",
+      " dat: 0.91s\n",
       "evt2: 0.86s\n",
       "evt3: 1.83s\n"
      ]
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "3605322f",
    "metadata": {},
    "outputs": [],
@@ -229,10 +229,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[dat] duration (raw): 0.55s\n",
-      "[dat] duration (iterator): 7.15s\n",
-      "[evt3] duration (raw): 4.56s\n",
-      "[evt3] duration (iterator): 1.99s\n"
+      "[dat] duration (direct): 0.48s\n",
+      "[dat] duration (iterator): 6.93s\n",
+      "[evt2] duration (direct): 3.38s\n",
+      "[evt2] duration (iterator): 1.31s\n",
+      "[evt3] duration (direct): 4.56s\n",
+      "[evt3] duration (iterator): 2.09s\n"
      ]
     }
    ],
@@ -244,14 +246,10 @@
     "if not LOAD_RESULTS:\n",
     "    for encoding in encodings:\n",
     "        fpath = pathlib.Path(get_fpath(encoding))\n",
-    "#         print(encoding, fpath, '\\n', '-'*30, sep='', end='\\n\\n')\n",
     "        \n",
     "        data['metavision'][encoding]['fsize'] = get_fsize_MB(fpath)\n",
     "\n",
-    "        if encoding == 'evt2':\n",
-    "            data['metavision'][encoding]['full'] = 100\n",
-    "            continue # TODO: skip for now (waiting for separate bugfix)\n",
-    "        elif 'evt' in encoding:\n",
+    "        if 'evt' in encoding:\n",
     "            N_SKIP = int(1e7)\n",
     "            MAX_EVENTS = int(1e9)\n",
     "            raw_reader = RawReader(str(fpath), max_events=MAX_EVENTS)\n",
@@ -291,9 +289,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "hdf5: 0.79s\n",
-      "hdf5_lzf: 2.94s\n",
-      "hdf5_gzip: 5.17s\n",
+      "hdf5: 0.77s\n",
+      "hdf5_lzf: 2.92s\n",
+      "hdf5_gzip: 5.14s\n",
       "numpy: 0.46s\n"
      ]
     }
@@ -350,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 79,
    "id": "a78eca80",
    "metadata": {},
    "outputs": [
@@ -361,25 +359,19 @@
       "------------------------------------------------------------------------------------------------------------\n",
       "Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | Diff. DAT | Diff. EVT2 | Diff. EVT3\n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "exp. DAT  | 851       | -  0%     | +100%      | +143%      | 0.90      | -  0%    | +  5%      | - 51%   \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT2 | 426       | - 50%     | -  0%      | + 22%      | 0.86      | -  5%    | -  0%      | - 53%   \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT3 | 350       | - 59%     | - 18%      | -  0%      | 1.83      | +104%    | +114%      | -  0%   \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "met. DAT  | 851       | -  0%     | +100%      | +143%      | 0.55      | - 39%    | - 36%      | - 70%   \n",
+      "exp. DAT  | 851       | -  0%     | +100%      | +143%      | 0.91      | -  0%    | +  7%      | - 50%   \n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "met. EVT2 | 426       | - 50%     | -  0%      | + 22%      | 100.00    | +11000%  | +11572%    | +5354%  \n",
+      "exp. EVT2 | 426       | - 50%     | -  0%      | + 22%      | 0.86      | -  6%    | -  0%      | - 53%   \n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "met. EVT3 | 350       | - 59%     | - 18%      | -  0%      | 1.99      | +120%    | +132%      | +  8%   \n",
+      "exp. EVT3 | 350       | - 59%     | - 18%      | -  0%      | 1.83      | +101%    | +114%      | -  0%   \n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5      | 1701      | +100%     | +299%      | +386%      | 0.79      | - 12%    | -  7%      | - 57%   \n",
+      "hdf5      | 1701      | +100%     | +299%      | +386%      | 0.77      | - 15%    | - 10%      | - 58%   \n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_lzf  | 746       | - 12%     | + 75%      | +113%      | 2.94      | +226%    | +243%      | + 60%   \n",
+      "hdf5_lzf  | 746       | - 12%     | + 75%      | +113%      | 2.92      | +220%    | +241%      | + 59%   \n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_gzip | 419       | - 51%     | -  2%      | + 20%      | 5.17      | +474%    | +504%      | +182%   \n",
+      "hdf5_gzip | 419       | - 51%     | -  2%      | + 20%      | 5.14      | +464%    | +501%      | +180%   \n",
       "------------------------------------------------------------------------------------------------------------\n",
-      "numpy     | 1701      | +100%     | +299%      | +386%      | 0.46      | - 49%    | - 47%      | - 75%   \n",
+      "numpy     | 1701      | +100%     | +299%      | +386%      | 0.46      | - 50%    | - 46%      | - 75%   \n",
       "------------------------------------------------------------------------------------------------------------\n"
      ]
     }
@@ -408,10 +400,6 @@
     "for encoding in encodings:\n",
     "    print(gen_row(f\"exp. {encoding.upper()}\", data[\"expelliarmus\"][encoding][\"fsize\"], data[\"expelliarmus\"][encoding][\"full\"], \"full\"))\n",
     "    print(\"-\"*len(header))\n",
-    "\n",
-    "for encoding in encodings:\n",
-    "    print(gen_row(f\"met. {encoding.upper()}\", data[\"metavision\"][encoding][\"fsize\"], data[\"metavision\"][encoding].get(\"full\", 100), \"full\"))\n",
-    "    print(\"-\"*len(header))\n",
     "    \n",
     "for sw in softwares: \n",
     "    print(gen_row(sw, data[sw][\"fsize\"], data[sw][\"full\"], \"full\"))\n",
@@ -419,11 +407,38 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "cbef7355",
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "473ae1be",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------------------------------------------------\n",
+      "Encoding | Metavision | Expelliarmus | Abs. diff. | % diff.\n",
+      "------------------------------------------------------------\n",
+      "DAT      |    0.48s   |     0.91s    |    0.43s   |   47% \n",
+      "------------------------------------------------------------\n",
+      "EVT2     |    1.31s   |     0.86s    |   -0.46s   |  -53% \n",
+      "------------------------------------------------------------\n",
+      "EVT3     |    2.09s   |     1.83s    |   -0.26s   |  -14% \n",
+      "------------------------------------------------------------\n",
+      "Negative numbers show that expelliarmus is faster than metavision.\n"
+     ]
+    }
+   ],
    "source": [
-    "***TODO: separate tables for metavision comparison and different file format comparison***"
+    "print('-'*60)\n",
+    "print(f'{\"Encoding\":<8} | {\"Metavision\":<10} | {\"Expelliarmus\":<12} | {\"Abs. diff.\":<9} | {\"% diff.\":<7}\\n' + '-'*60)\n",
+    "for encoding in encodings:\n",
+    "    exp = data[\"expelliarmus\"][encoding][\"full\"]\n",
+    "    met = data[\"metavision\"][encoding][\"full\"]\n",
+    "    rel_diff = -(met/exp - 1) if met > exp else 1 - met/exp\n",
+    "    print(f'{encoding.upper():<8} | {met:>7.2f}s   | {exp:>8.2f}s    | ' + \\\n",
+    "          f'{exp-met:>7.2f}s   | {rel_diff:>5.0%} \\n' + '-'*60)\n",
+    "print('Negative numbers show that expelliarmus is faster than metavision.')"
    ]
   },
   {
@@ -438,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ebc3f0ab",
    "metadata": {},
    "outputs": [],
@@ -451,32 +466,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "63fabd1f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x960 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "fig = plt.figure(figsize=(12, 8), dpi=120)\n",
@@ -503,33 +496,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "78854dde",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time windowing read\n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | Diff. DAT | Diff. EVT2 | Diff. EVT3\n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. DAT  | 851       | -0%       | +100%      | +143%      | 1.58    | -0%       | +4%        | -39%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT2 | 426       | -50%      | -0%        | +22%       | 1.51    | -4%       | -0%        | -42%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT3 | 350       | -59%      | -18%       | -0%        | 2.58    | +64%      | +71%       | -0%        \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5      | 1701      | +100%     | +299%      | +386%      | 1.02    | -35%      | -32%       | -60%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_lzf  | 746       | -12%      | +75%       | +113%      | 3.82    | +143%     | +153%      | +48%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_gzip | 419       | -51%      | -2%        | +20%       | 6.88    | +337%     | +355%      | +166%      \n",
-      "------------------------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "TIME_WINDOW = 20\n",
     "\n",
@@ -572,34 +542,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "892375ab",
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x960 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "fig = plt.figure(figsize=(12, 8), dpi=120)\n",
@@ -622,33 +570,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "2ca04d5d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chunk read\n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "Software  | Size [MB] | Diff. DAT | Diff. EVT2 | Diff. EVT3 | Time [s] | Diff. DAT | Diff. EVT2 | Diff. EVT3\n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. DAT  | 851       | -0%       | +100%      | +143%      | 1.64    | -0%       | +3%        | -22%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT2 | 426       | -50%      | -0%        | +22%       | 1.58    | -3%       | -0%        | -24%       \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "exp. EVT3 | 350       | -59%      | -18%       | -0%        | 2.09    | +28%      | +32%       | -0%        \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5      | 1701      | +100%     | +299%      | +386%      | 4.20    | +157%     | +166%      | +101%      \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_lzf  | 746       | -12%      | +75%       | +113%      | 10.36   | +534%     | +555%      | +395%      \n",
-      "------------------------------------------------------------------------------------------------------------\n",
-      "hdf5_gzip | 419       | -51%      | -2%        | +20%       | 17.23   | +954%     | +989%      | +724%      \n",
-      "------------------------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "CHUNK_LEN = 8192\n",
     "\n",
@@ -690,32 +615,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "dd436a2e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x960 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "fig = plt.figure(figsize=(12, 8), dpi=120)\n",
@@ -738,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "e8abe958",
    "metadata": {},
    "outputs": [],