diff --git a/PyFina/PyFina.py b/PyFina/PyFina.py
index 3f0243b..14d66d3 100644
--- a/PyFina/PyFina.py
+++ b/PyFina/PyFina.py
@@ -62,6 +62,9 @@ def getMeta(feed_id: int, data_dir: str) -> Literal[False] | dict[str, int]:
     npoints (4 bytes, Unsigned integer, Legacy : use instead filesize//4 )
     interval (4 bytes, Unsigned integer)
     start_time (4 bytes, Unsigned integer)
+    Returns:
+        dict with keys: interval, start_time, npoints, end_time
+        where end_time is the timestamp of the last data point
     """
     with open(f"{data_dir}/{feed_id}.meta", "rb") as f:
         f.seek(8, 0)
@@ -77,6 +80,7 @@ def getMeta(feed_id: int, data_dir: str) -> Literal[False] | dict[str, int]:
         "start_time": decoded[1],
         "npoints": os.path.getsize(f"{data_dir}/{feed_id}.dat") // 4,
     }
+    meta['end_time'] = meta['start_time'] + (meta['npoints'] * meta['interval']) - meta['interval']
     return meta
 
 class PyFina(np.ndarray):
@@ -106,14 +110,19 @@ def __new__(
         # pos = (time - meta["start_time"]) // meta["interval"]
         # Nota : if remove_nan is True and a NAN is detected, the algorithm takes previous value
         obj = super().__new__(cls, shape=(npts,))
-        #obj = np.zeros(npts).view(cls)
-        pyfina_logger.debug(obj)
+        obj.fill(np.nan)
         raw_obj = np.empty(npts)
-
+        raw_obj.fill(np.nan)
+        pyfina_logger.debug(obj)
         end = start + (npts - 1) * step
         time = start
         i = 0
         nb_nan = 0
+        # Avoid Reading file if time >= end_time
+        if time >= meta['end_time']:
+            raise ValueError("Error: invalid start value, start must be less than end time value "
+                             "defined by start_time + (npoints * interval) from meta."
+                            )
         with open(f"{data_dir}/{feed_id}.dat", "rb") as ts:
             while time < end:
                 time = start + step * i
@@ -139,18 +148,22 @@ def __new__(
                         else:
                             message = f"unpacking problem {i} len is {len(aa)} position is {pos}"
                             pyfina_logger.error(message)
+                # End reading loop if pos out of bounds
+                else:
+                    break
                 i += 1
         first_non_nan_value = -1
         first_non_nan_index = -1
         starting_by_nan = np.isnan(raw_obj[0])
         if nb_nan < npts:
             finiteness_obj = np.isfinite(raw_obj)
-            first_non_nan_index = np.where(finiteness_obj)[0][0]
-            first_non_nan_value = raw_obj[finiteness_obj][0]
-            if starting_by_nan and remove_nan:
-                obj[:first_non_nan_index] = (
-                    np.ones(first_non_nan_index) * first_non_nan_value
-                )
+            if finiteness_obj.sum() > 0:
+                first_non_nan_index = np.where(finiteness_obj)[0][0]
+                first_non_nan_value = raw_obj[finiteness_obj][0]
+                if starting_by_nan and remove_nan:
+                    obj[:first_non_nan_index] = (
+                        np.ones(first_non_nan_index) * first_non_nan_value
+                    )
         # storing the "signature" of the "sampled" feed
         obj.start = start
         obj.step = step
diff --git a/tests/PyFina.ipynb b/tests/PyFina.ipynb
new file mode 100644
index 0000000..50cb615
--- /dev/null
+++ b/tests/PyFina.ipynb
@@ -0,0 +1,1781 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyFina"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "import logging\n",
+    "import math\n",
+    "import os\n",
+    "import struct\n",
+    "\n",
+    "from typing import Literal\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "logging.basicConfig()\n",
+    "pyfina_logger = logging.getLogger(__name__)\n",
+    "\n",
+    "\n",
+    "def trim(feed_id: int, data_dir: str, limit: int = 100) -> None:\n",
+    "    \"\"\"\n",
+    "    checks and removes anomalies (values above a threshold limit, eg 100)\n",
+    "    feed_id: feed number\n",
+    "    data_dir: feed path (eg /var/opt/emoncms/phpfina)\n",
+    "    limit: threshold we don't want to exceed\n",
+    "    \"\"\"\n",
+    "    meta = getMeta(feed_id, data_dir)\n",
+    "    if not meta:\n",
+    "        return\n",
+    "    pos = 0\n",
+    "    i = 0\n",
+    "    nbn = 0\n",
+    "    with open(f\"{data_dir}/{feed_id}.dat\", \"rb+\") as ts:\n",
+    "        while pos <= meta[\"npoints\"]:\n",
+    "            ts.seek(pos * 4, 0)\n",
+    "            hexa = ts.read(4)\n",
+    "            aa = bytearray(hexa)\n",
+    "            if len(aa) == 4:\n",
+    "                value = struct.unpack(\"<f\", aa)[0]\n",
+    "                if math.isnan(value):\n",
+    "                    nbn += 1\n",
+    "                elif value > limit:\n",
+    "                    message = f\"anomaly detected at {pos} : {value}\"\n",
+    "                    pyfina_logger.debug(message)\n",
+    "                    i += 1\n",
+    "                    nv = struct.pack(\"<f\", float(\"nan\"))\n",
+    "                    try:\n",
+    "                        ts.seek(pos * 4, 0)\n",
+    "                        ts.write(nv)\n",
+    "                    except Exception as e:\n",
+    "                        pyfina_logger.error(e)\n",
+    "                    finally:\n",
+    "                        pyfina_logger.debug(\"4 bytes written\")\n",
+    "            pos += 1\n",
+    "        message = f\"{i} anomaly(ies)\"\n",
+    "        pyfina_logger.debug(message)\n",
+    "        message = f\"{nbn} nan\"\n",
+    "        pyfina_logger.debug(message)\n",
+    "\n",
+    "\n",
+    "def getMeta(feed_id: int, data_dir: str) -> Literal[False] | dict[str, int]:\n",
+    "    \"\"\"\n",
+    "    decoding the .meta file\n",
+    "    feed_id (4 bytes, Unsigned integer)\n",
+    "    npoints (4 bytes, Unsigned integer, Legacy : use instead filesize//4 )\n",
+    "    interval (4 bytes, Unsigned integer)\n",
+    "    start_time (4 bytes, Unsigned integer)\n",
+    "    \"\"\"\n",
+    "    with open(f\"{data_dir}/{feed_id}.meta\", \"rb\") as f:\n",
+    "        f.seek(8, 0)\n",
+    "        hexa = f.read(8)\n",
+    "        aa = bytearray(hexa)\n",
+    "        if len(aa) == 8:\n",
+    "            decoded = struct.unpack(\"<2I\", aa)\n",
+    "        else:\n",
+    "            pyfina_logger.error(\"corrupted meta - aborting\")\n",
+    "            return False\n",
+    "    meta = {\n",
+    "        \"interval\": decoded[0],\n",
+    "        \"start_time\": decoded[1],\n",
+    "        \"npoints\": os.path.getsize(f\"{data_dir}/{feed_id}.dat\") // 4,\n",
+    "    }\n",
+    "    meta['end_time'] = meta['start_time'] + (meta['npoints'] * meta['interval']) - meta['interval']\n",
+    "    return meta\n",
+    "\n",
+    "class PyFina(np.ndarray):\n",
+    "    \"\"\"pyfina class.\"\"\"\n",
+    "    start: int | None = None\n",
+    "    step: int | None = None\n",
+    "    nb_nan: int | None = None\n",
+    "    first_non_nan_value: float | None = None\n",
+    "    first_non_nan_index: int | None = None\n",
+    "    starting_by_nan : bool | None = None\n",
+    "\n",
+    "    def __new__(\n",
+    "        cls,\n",
+    "        feed_id: int,\n",
+    "        data_dir: str,\n",
+    "        start: int,\n",
+    "        step: int,\n",
+    "        npts: int,\n",
+    "        remove_nan: bool = True,\n",
+    "    ):\n",
+    "        meta = getMeta(feed_id, data_dir)\n",
+    "        if not meta:\n",
+    "            return None\n",
+    "        # decoding and sampling the .dat file\n",
+    "        # values are 32 bit floats, stored on 4 bytes\n",
+    "        # to estimate value(time), position in the dat file is calculated as follow :\n",
+    "        # pos = (time - meta[\"start_time\"]) // meta[\"interval\"]\n",
+    "        # Nota : if remove_nan is True and a NAN is detected, the algorithm takes previous value\n",
+    "        obj = super().__new__(cls, shape=(npts,))\n",
+    "        obj.fill(np.nan)\n",
+    "        pyfina_logger.debug(obj)\n",
+    "        raw_obj = np.empty(npts)\n",
+    "        raw_obj.fill(np.nan)\n",
+    "        end = start + (npts - 1) * step\n",
+    "        time = start\n",
+    "        if time >= meta['end_time']:\n",
+    "            raise ValueError(\"Error: invalid start value, \"\n",
+    "                             \"start must be upper than end time value \"\n",
+    "                             \"defined by start_time + (npoints * interval) from meta.\"\n",
+    "                            )\n",
+    "        i = 0\n",
+    "        nb_nan = 0\n",
+    "        with open(f\"{data_dir}/{feed_id}.dat\", \"rb\") as ts:\n",
+    "            while time < end:\n",
+    "                time = start + step * i\n",
+    "                pos = (time - meta[\"start_time\"]) // meta[\"interval\"]\n",
+    "                if 0 <= pos < meta[\"npoints\"]:\n",
+    "                    try:\n",
+    "                        # message = f\"trying to find point {i} going to index {pos}\"\n",
+    "                        # pyfina_logger.debug(message)\n",
+    "                        ts.seek(pos * 4, 0)\n",
+    "                        hexa = ts.read(4)\n",
+    "                        aa = bytearray(hexa)\n",
+    "                    except Exception as e:\n",
+    "                        message = f\"error during file operation {e}\"\n",
+    "                        pyfina_logger.error(message)\n",
+    "                    else:\n",
+    "                        if len(aa) == 4:\n",
+    "                            value = struct.unpack(\"<f\", aa)[0]\n",
+    "                            obj[i] = value\n",
+    "                            raw_obj[i] = value\n",
+    "                            if remove_nan and np.isnan(value):\n",
+    "                                nb_nan += 1\n",
+    "                                obj[i] = obj[i - 1]\n",
+    "                        else:\n",
+    "                            message = f\"unpacking problem {i} len is {len(aa)} position is {pos}\"\n",
+    "                            pyfina_logger.error(message)\n",
+    "                else:\n",
+    "                    time = end\n",
+    "                i += 1\n",
+    "        first_non_nan_value = -1\n",
+    "        first_non_nan_index = -1\n",
+    "        starting_by_nan = np.isnan(raw_obj[0])\n",
+    "        if nb_nan < npts:\n",
+    "            finiteness_obj = np.isfinite(raw_obj)\n",
+    "            if finiteness_obj.sum() > 0:\n",
+    "                first_non_nan_index = np.where(finiteness_obj)[0][0]\n",
+    "                first_non_nan_value = raw_obj[finiteness_obj][0]\n",
+    "                if starting_by_nan and remove_nan:\n",
+    "                    obj[:first_non_nan_index] = (\n",
+    "                        np.ones(first_non_nan_index) * first_non_nan_value\n",
+    "                    )\n",
+    "        # storing the \"signature\" of the \"sampled\" feed\n",
+    "        obj.start = start\n",
+    "        obj.step = step\n",
+    "        obj.nb_nan = nb_nan\n",
+    "        obj.first_non_nan_value = first_non_nan_value\n",
+    "        obj.first_non_nan_index = first_non_nan_index\n",
+    "        obj.starting_by_nan = starting_by_nan\n",
+    "        return obj\n",
+    "\n",
+    "    def timescale(self):\n",
+    "        \"\"\"\n",
+    "        return the time scale of the feed as a numpy array\n",
+    "        \"\"\"\n",
+    "        return np.arange(0, self.step * self.shape[0], self.step)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import display\n",
+    "from multigraph import check_starting_nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'interval': 10, 'start_time': 1575981140, 'npoints': 4551863}\n",
+      "température extérieure : 38 nan in the feed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mexse\\AppData\\Local\\Temp\\ipykernel_52284\\1106623605.py:20: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).\n",
+      "  utcstart = datetime.datetime.utcfromtimestamp(start)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "FEED_NB = 1\n",
+    "DATA_DIR = os.path.join(\n",
+    "    os.path.abspath(os.getcwd()),\n",
+    "    # \"tests\",\n",
+    "    \"datas\"\n",
+    ")\n",
+    "meta = getMeta(FEED_NB, DATA_DIR)\n",
+    "print(meta)\n",
+    "STEP = 3600\n",
+    "start = meta[\"start_time\"]\n",
+    "length = meta[\"npoints\"] * meta[\"interval\"]\n",
+    "WINDOW = min(8 * 24 * 3600, length)\n",
+    "\n",
+    "nbpts = WINDOW // STEP\n",
+    "temp_ext = PyFina(FEED_NB, DATA_DIR, start, STEP, nbpts)\n",
+    "\n",
+    "check_starting_nan(\"température extérieure\", temp_ext)\n",
+    "\n",
+    "localstart = datetime.datetime.fromtimestamp(start)\n",
+    "utcstart = datetime.datetime.utcfromtimestamp(start)\n",
+    "title = f\"starting on :\\nUTC {utcstart}\\n{time.tzname[0]} {localstart}\"\n",
+    "figure = plt.figure(figsize = (10, 10))\n",
+    "matplotlib.rc('font', size=8)\n",
+    "plt.subplot(111)\n",
+    "plt.title(title)\n",
+    "plt.ylabel(\"outdoor Temp °C\")\n",
+    "plt.xlabel(\"time in hours\")\n",
+    "plt.plot(temp_ext)\n",
+    "figure.savefig(f\"feed_{FEED_NB}.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test 2: empty array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'interval': 1, 'start_time': 1538492746, 'npoints': 5011599}\n",
+      "température extérieure : 0 nan in the feed\n",
+      "first non nan value -1 at index -1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "FEED_NB = 2\n",
+    "\n",
+    "meta = getMeta(FEED_NB, DATA_DIR)\n",
+    "print(meta)\n",
+    "STEP = 1\n",
+    "start = int(1540166400.0 - (3600 *24 * 1)) # + (8 * 3600) + (3600 / 2)) # 1542634200, 1542637800 1727276746\n",
+    "localstart = datetime.datetime.fromtimestamp(start)\n",
+    "utcstart = datetime.datetime.fromtimestamp(start, datetime.timezone.utc)\n",
+    "length = meta[\"npoints\"] * meta[\"interval\"]\n",
+    "WINDOW = min(24 * 3600, length)\n",
+    "\n",
+    "nbpts = WINDOW // STEP\n",
+    "temp_ext = PyFina(FEED_NB, DATA_DIR, start, STEP, nbpts, remove_nan=False)\n",
+    "\n",
+    "check_starting_nan(\"température extérieure\", temp_ext)\n",
+    "\n",
+    "title = f\"starting on :\\nUTC {utcstart}\\n{time.tzname[0]} {localstart}\"\n",
+    "figure = plt.figure(figsize = (10, 10))\n",
+    "matplotlib.rc('font', size=8)\n",
+    "plt.subplot(111)\n",
+    "plt.title(title)\n",
+    "plt.ylabel(\"outdoor Temp °C\")\n",
+    "plt.xlabel(\"time in hours\")\n",
+    "plt.plot(temp_ext)\n",
+    "figure.savefig(f\"feed_{FEED_NB}.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test 3: Scan file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feed Id: 2 file start at 2018-10-02 17:05:46 end at 2018-11-29 15:12:25+00:00\n",
+      "meta: {'interval': 1, 'start_time': 1538492746, 'npoints': 5011599, 'end_time': 1543504344}\n",
+      "Analysed 1460 Days in 3.2121125999838114s -- IO File Reads = 59\n",
+      "Global stats: \n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>perf</th>\n",
+       "      <th>lines</th>\n",
+       "      <th>nb_finite</th>\n",
+       "      <th>nb_nan</th>\n",
+       "      <th>nb_valid</th>\n",
+       "      <th>nb_inf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>5.900000e+01</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>59.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.540998e+09</td>\n",
+       "      <td>1.637403</td>\n",
+       "      <td>86400.0</td>\n",
+       "      <td>7715.694915</td>\n",
+       "      <td>78684.305085</td>\n",
+       "      <td>7715.694915</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.483969e+06</td>\n",
+       "      <td>0.934902</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11044.275798</td>\n",
+       "      <td>11044.275798</td>\n",
+       "      <td>11044.275798</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.538493e+09</td>\n",
+       "      <td>0.065007</td>\n",
+       "      <td>86400.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>50604.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.539746e+09</td>\n",
+       "      <td>0.852490</td>\n",
+       "      <td>86400.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>73140.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.540998e+09</td>\n",
+       "      <td>1.626940</td>\n",
+       "      <td>86400.0</td>\n",
+       "      <td>483.000000</td>\n",
+       "      <td>85917.000000</td>\n",
+       "      <td>483.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.542251e+09</td>\n",
+       "      <td>2.426568</td>\n",
+       "      <td>86400.0</td>\n",
+       "      <td>13259.500000</td>\n",
+       "      <td>86400.000000</td>\n",
+       "      <td>13259.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.543504e+09</td>\n",
+       "      <td>3.189124</td>\n",
+       "      <td>86400.0</td>\n",
+       "      <td>35796.000000</td>\n",
+       "      <td>86400.000000</td>\n",
+       "      <td>35796.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               time       perf    lines     nb_finite        nb_nan  \\\n",
+       "count  5.900000e+01  59.000000     59.0     59.000000     59.000000   \n",
+       "mean   1.540998e+09   1.637403  86400.0   7715.694915  78684.305085   \n",
+       "std    1.483969e+06   0.934902      0.0  11044.275798  11044.275798   \n",
+       "min    1.538493e+09   0.065007  86400.0      0.000000  50604.000000   \n",
+       "25%    1.539746e+09   0.852490  86400.0      0.000000  73140.500000   \n",
+       "50%    1.540998e+09   1.626940  86400.0    483.000000  85917.000000   \n",
+       "75%    1.542251e+09   2.426568  86400.0  13259.500000  86400.000000   \n",
+       "max    1.543504e+09   3.189124  86400.0  35796.000000  86400.000000   \n",
+       "\n",
+       "           nb_valid  nb_inf  \n",
+       "count     59.000000    59.0  \n",
+       "mean    7715.694915     0.0  \n",
+       "std    11044.275798     0.0  \n",
+       "min        0.000000     0.0  \n",
+       "25%        0.000000     0.0  \n",
+       "50%      483.000000     0.0  \n",
+       "75%    13259.500000     0.0  \n",
+       "max    35796.000000     0.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>perf</th>\n",
+       "      <th>lines</th>\n",
+       "      <th>nb_finite</th>\n",
+       "      <th>nb_nan</th>\n",
+       "      <th>nb_valid</th>\n",
+       "      <th>nb_inf</th>\n",
+       "      <th>nanmin</th>\n",
+       "      <th>nanmean</th>\n",
+       "      <th>nanmax</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-10-02 15:05:46+00:00</th>\n",
+       "      <td>1538492746</td>\n",
+       "      <td>0.065007</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>18354</td>\n",
+       "      <td>68046</td>\n",
+       "      <td>18354</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.26799964904785</td>\n",
+       "      <td>26.051037900001248</td>\n",
+       "      <td>28.304000854492188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-03 15:05:46+00:00</th>\n",
+       "      <td>1538579146</td>\n",
+       "      <td>0.119669</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>3964</td>\n",
+       "      <td>82436</td>\n",
+       "      <td>3964</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.628000259399414</td>\n",
+       "      <td>27.42328505655591</td>\n",
+       "      <td>28.520000457763672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-04 15:05:46+00:00</th>\n",
+       "      <td>1538665546</td>\n",
+       "      <td>0.173619</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>941</td>\n",
+       "      <td>85459</td>\n",
+       "      <td>941</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.582000732421875</td>\n",
+       "      <td>27.42080019992419</td>\n",
+       "      <td>28.44700050354004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-05 15:05:46+00:00</th>\n",
+       "      <td>1538751946</td>\n",
+       "      <td>0.229688</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-06 15:05:46+00:00</th>\n",
+       "      <td>1538838346</td>\n",
+       "      <td>0.282193</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>84033</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.676000595092773</td>\n",
+       "      <td>27.42406802223763</td>\n",
+       "      <td>27.94700050354004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-07 15:05:46+00:00</th>\n",
+       "      <td>1538924746</td>\n",
+       "      <td>0.337798</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>17645</td>\n",
+       "      <td>68755</td>\n",
+       "      <td>17645</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.58099937438965</td>\n",
+       "      <td>27.352939146846644</td>\n",
+       "      <td>28.628999710083008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-08 15:05:46+00:00</th>\n",
+       "      <td>1539011146</td>\n",
+       "      <td>0.391028</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>34870</td>\n",
+       "      <td>51530</td>\n",
+       "      <td>34870</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24.548999786376953</td>\n",
+       "      <td>27.32949967101272</td>\n",
+       "      <td>28.93400001525879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-09 15:05:46+00:00</th>\n",
+       "      <td>1539097546</td>\n",
+       "      <td>0.445812</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>33067</td>\n",
+       "      <td>53333</td>\n",
+       "      <td>33067</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.479000091552734</td>\n",
+       "      <td>27.209397055115176</td>\n",
+       "      <td>29.92300033569336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-10 15:05:46+00:00</th>\n",
+       "      <td>1539183946</td>\n",
+       "      <td>0.502006</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>13929</td>\n",
+       "      <td>72471</td>\n",
+       "      <td>13929</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.469999313354492</td>\n",
+       "      <td>26.32814978813781</td>\n",
+       "      <td>29.906999588012695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-11 15:05:46+00:00</th>\n",
+       "      <td>1539270346</td>\n",
+       "      <td>0.558134</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>10653</td>\n",
+       "      <td>75747</td>\n",
+       "      <td>10653</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.829999923706055</td>\n",
+       "      <td>27.057986568220677</td>\n",
+       "      <td>29.610000610351562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-12 15:05:46+00:00</th>\n",
+       "      <td>1539356746</td>\n",
+       "      <td>0.613385</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>9413</td>\n",
+       "      <td>76987</td>\n",
+       "      <td>9413</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.683000564575195</td>\n",
+       "      <td>27.090021389546155</td>\n",
+       "      <td>29.530000686645508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-13 15:05:46+00:00</th>\n",
+       "      <td>1539443146</td>\n",
+       "      <td>0.665588</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>16192</td>\n",
+       "      <td>70208</td>\n",
+       "      <td>16192</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.538000106811523</td>\n",
+       "      <td>27.125155579431254</td>\n",
+       "      <td>30.434999465942383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-14 15:05:46+00:00</th>\n",
+       "      <td>1539529546</td>\n",
+       "      <td>0.718233</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>12957</td>\n",
+       "      <td>73443</td>\n",
+       "      <td>12957</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.424999237060547</td>\n",
+       "      <td>26.34141606799502</td>\n",
+       "      <td>28.422000885009766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-15 15:05:46+00:00</th>\n",
+       "      <td>1539615946</td>\n",
+       "      <td>0.771334</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>19493</td>\n",
+       "      <td>66907</td>\n",
+       "      <td>19493</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.582000732421875</td>\n",
+       "      <td>27.3867852606255</td>\n",
+       "      <td>29.930999755859375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-16 15:05:46+00:00</th>\n",
+       "      <td>1539702346</td>\n",
+       "      <td>0.825193</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>35796</td>\n",
+       "      <td>50604</td>\n",
+       "      <td>35796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.410999298095703</td>\n",
+       "      <td>27.347879044529257</td>\n",
+       "      <td>29.549999237060547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-17 15:05:46+00:00</th>\n",
+       "      <td>1539788746</td>\n",
+       "      <td>0.879788</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>13939</td>\n",
+       "      <td>72461</td>\n",
+       "      <td>13939</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.391000747680664</td>\n",
+       "      <td>26.77908782335015</td>\n",
+       "      <td>29.547000885009766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-18 15:05:46+00:00</th>\n",
+       "      <td>1539875146</td>\n",
+       "      <td>0.931612</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>32509</td>\n",
+       "      <td>53891</td>\n",
+       "      <td>32509</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.135000228881836</td>\n",
+       "      <td>27.07881130703249</td>\n",
+       "      <td>29.847000122070312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-19 15:05:46+00:00</th>\n",
+       "      <td>1539961546</td>\n",
+       "      <td>0.983628</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>11639</td>\n",
+       "      <td>74761</td>\n",
+       "      <td>11639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.340999603271484</td>\n",
+       "      <td>26.54683056757698</td>\n",
+       "      <td>28.413000106811523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-20 15:05:46+00:00</th>\n",
+       "      <td>1540047946</td>\n",
+       "      <td>1.035788</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>583</td>\n",
+       "      <td>85817</td>\n",
+       "      <td>583</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.53499984741211</td>\n",
+       "      <td>25.546099500836068</td>\n",
+       "      <td>25.570999145507812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-21 15:05:46+00:00</th>\n",
+       "      <td>1540134346</td>\n",
+       "      <td>1.086550</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-22 15:05:46+00:00</th>\n",
+       "      <td>1540220746</td>\n",
+       "      <td>1.138297</td>\n",
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+       "      <td>86400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-23 15:05:46+00:00</th>\n",
+       "      <td>1540307146</td>\n",
+       "      <td>1.197227</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>483</td>\n",
+       "      <td>85917</td>\n",
+       "      <td>483</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.81399917602539</td>\n",
+       "      <td>27.427250500791562</td>\n",
+       "      <td>28.413000106811523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-24 15:05:46+00:00</th>\n",
+       "      <td>1540393546</td>\n",
+       "      <td>1.249731</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>35196</td>\n",
+       "      <td>51204</td>\n",
+       "      <td>35196</td>\n",
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+       "      <td>25.447999954223633</td>\n",
+       "      <td>27.31623176239149</td>\n",
+       "      <td>28.944000244140625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-25 15:05:46+00:00</th>\n",
+       "      <td>1540479946</td>\n",
+       "      <td>1.302276</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>13562</td>\n",
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+       "      <td>13562</td>\n",
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+       "      <td>25.45599937438965</td>\n",
+       "      <td>26.434626391390715</td>\n",
+       "      <td>27.82900047302246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-26 15:05:46+00:00</th>\n",
+       "      <td>1540566346</td>\n",
+       "      <td>1.355239</td>\n",
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+       "      <td>25.538000106811523</td>\n",
+       "      <td>26.67253906623529</td>\n",
+       "      <td>29.551000595092773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-27 15:05:46+00:00</th>\n",
+       "      <td>1540652746</td>\n",
+       "      <td>1.415010</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>7799</td>\n",
+       "      <td>78601</td>\n",
+       "      <td>7799</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.424999237060547</td>\n",
+       "      <td>26.592719177968043</td>\n",
+       "      <td>28.413999557495117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-28 15:05:46+00:00</th>\n",
+       "      <td>1540739146</td>\n",
+       "      <td>1.469298</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-29 15:05:46+00:00</th>\n",
+       "      <td>1540825546</td>\n",
+       "      <td>1.520873</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-30 15:05:46+00:00</th>\n",
+       "      <td>1540911946</td>\n",
+       "      <td>1.574885</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-31 15:05:46+00:00</th>\n",
+       "      <td>1540998346</td>\n",
+       "      <td>1.626940</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-01 15:05:46+00:00</th>\n",
+       "      <td>1541084746</td>\n",
+       "      <td>1.678789</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-02 15:05:46+00:00</th>\n",
+       "      <td>1541171146</td>\n",
+       "      <td>1.730694</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-03 15:05:46+00:00</th>\n",
+       "      <td>1541257546</td>\n",
+       "      <td>1.782092</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>1133</td>\n",
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+       "      <td>1133</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.517000198364258</td>\n",
+       "      <td>25.990633738114575</td>\n",
+       "      <td>28.468000411987305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-04 15:05:46+00:00</th>\n",
+       "      <td>1541343946</td>\n",
+       "      <td>1.834936</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>20526</td>\n",
+       "      <td>65874</td>\n",
+       "      <td>20526</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.784000396728516</td>\n",
+       "      <td>27.303087265068914</td>\n",
+       "      <td>30.798999786376953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-05 15:05:46+00:00</th>\n",
+       "      <td>1541430346</td>\n",
+       "      <td>1.887703</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>15738</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.361000061035156</td>\n",
+       "      <td>26.431978454158394</td>\n",
+       "      <td>31.12700080871582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-06 15:05:46+00:00</th>\n",
+       "      <td>1541516746</td>\n",
+       "      <td>1.940608</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-07 15:05:46+00:00</th>\n",
+       "      <td>1541603146</td>\n",
+       "      <td>2.011979</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-08 15:05:46+00:00</th>\n",
+       "      <td>1541689546</td>\n",
+       "      <td>2.069997</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-09 15:05:46+00:00</th>\n",
+       "      <td>1541775946</td>\n",
+       "      <td>2.121356</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-10 15:05:46+00:00</th>\n",
+       "      <td>1541862346</td>\n",
+       "      <td>2.178195</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-11 15:05:46+00:00</th>\n",
+       "      <td>1541948746</td>\n",
+       "      <td>2.239936</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-12 15:05:46+00:00</th>\n",
+       "      <td>1542035146</td>\n",
+       "      <td>2.295520</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>7801</td>\n",
+       "      <td>78599</td>\n",
+       "      <td>7801</td>\n",
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+       "      <td>25.92099952697754</td>\n",
+       "      <td>27.403101785995002</td>\n",
+       "      <td>28.670000076293945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-13 15:05:46+00:00</th>\n",
+       "      <td>1542121546</td>\n",
+       "      <td>2.347879</td>\n",
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+       "      <td>35043</td>\n",
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+       "      <td>25.565000534057617</td>\n",
+       "      <td>27.381561364858957</td>\n",
+       "      <td>29.235000610351562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-14 15:05:46+00:00</th>\n",
+       "      <td>1542207946</td>\n",
+       "      <td>2.400113</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>55</td>\n",
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+       "      <td>55</td>\n",
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+       "      <td>27.67300033569336</td>\n",
+       "      <td>28.228672686490146</td>\n",
+       "      <td>28.51300048828125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-15 15:05:46+00:00</th>\n",
+       "      <td>1542294346</td>\n",
+       "      <td>2.453023</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-16 15:05:46+00:00</th>\n",
+       "      <td>1542380746</td>\n",
+       "      <td>2.507303</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-17 15:05:46+00:00</th>\n",
+       "      <td>1542467146</td>\n",
+       "      <td>2.572663</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-18 15:05:46+00:00</th>\n",
+       "      <td>1542553546</td>\n",
+       "      <td>2.624449</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-19 15:05:46+00:00</th>\n",
+       "      <td>1542639946</td>\n",
+       "      <td>2.675888</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-20 15:05:46+00:00</th>\n",
+       "      <td>1542726346</td>\n",
+       "      <td>2.729842</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-21 15:05:46+00:00</th>\n",
+       "      <td>1542812746</td>\n",
+       "      <td>2.781364</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-22 15:05:46+00:00</th>\n",
+       "      <td>1542899146</td>\n",
+       "      <td>2.842387</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-23 15:05:46+00:00</th>\n",
+       "      <td>1542985546</td>\n",
+       "      <td>2.900620</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>8184</td>\n",
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+       "      <td>26.10300064086914</td>\n",
+       "      <td>27.14345785063616</td>\n",
+       "      <td>29.67799949645996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-24 15:05:46+00:00</th>\n",
+       "      <td>1543071946</td>\n",
+       "      <td>2.955630</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-25 15:05:46+00:00</th>\n",
+       "      <td>1543158346</td>\n",
+       "      <td>3.011035</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-26 15:05:46+00:00</th>\n",
+       "      <td>1543244746</td>\n",
+       "      <td>3.063060</td>\n",
+       "      <td>86400</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-27 15:05:46+00:00</th>\n",
+       "      <td>1543331146</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-28 15:05:46+00:00</th>\n",
+       "      <td>1543417546</td>\n",
+       "      <td>3.187849</td>\n",
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+       "      <td>26.2810001373291</td>\n",
+       "      <td>27.346280918220163</td>\n",
+       "      <td>28.632999420166016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-29 15:05:46+00:00</th>\n",
+       "      <td>1543503946</td>\n",
+       "      <td>3.189124</td>\n",
+       "      <td>86400</td>\n",
+       "      <td>399</td>\n",
+       "      <td>86001</td>\n",
+       "      <td>399</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.257999420166016</td>\n",
+       "      <td>27.394338363991643</td>\n",
+       "      <td>27.535999298095703</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 time      perf  lines  nb_finite  nb_nan  \\\n",
+       "index                                                                       \n",
+       "2018-10-02 15:05:46+00:00  1538492746  0.065007  86400      18354   68046   \n",
+       "2018-10-03 15:05:46+00:00  1538579146  0.119669  86400       3964   82436   \n",
+       "2018-10-04 15:05:46+00:00  1538665546  0.173619  86400        941   85459   \n",
+       "2018-10-05 15:05:46+00:00  1538751946  0.229688  86400          0   86400   \n",
+       "2018-10-06 15:05:46+00:00  1538838346  0.282193  86400       2367   84033   \n",
+       "2018-10-07 15:05:46+00:00  1538924746  0.337798  86400      17645   68755   \n",
+       "2018-10-08 15:05:46+00:00  1539011146  0.391028  86400      34870   51530   \n",
+       "2018-10-09 15:05:46+00:00  1539097546  0.445812  86400      33067   53333   \n",
+       "2018-10-10 15:05:46+00:00  1539183946  0.502006  86400      13929   72471   \n",
+       "2018-10-11 15:05:46+00:00  1539270346  0.558134  86400      10653   75747   \n",
+       "2018-10-12 15:05:46+00:00  1539356746  0.613385  86400       9413   76987   \n",
+       "2018-10-13 15:05:46+00:00  1539443146  0.665588  86400      16192   70208   \n",
+       "2018-10-14 15:05:46+00:00  1539529546  0.718233  86400      12957   73443   \n",
+       "2018-10-15 15:05:46+00:00  1539615946  0.771334  86400      19493   66907   \n",
+       "2018-10-16 15:05:46+00:00  1539702346  0.825193  86400      35796   50604   \n",
+       "2018-10-17 15:05:46+00:00  1539788746  0.879788  86400      13939   72461   \n",
+       "2018-10-18 15:05:46+00:00  1539875146  0.931612  86400      32509   53891   \n",
+       "2018-10-19 15:05:46+00:00  1539961546  0.983628  86400      11639   74761   \n",
+       "2018-10-20 15:05:46+00:00  1540047946  1.035788  86400        583   85817   \n",
+       "2018-10-21 15:05:46+00:00  1540134346  1.086550  86400          0   86400   \n",
+       "2018-10-22 15:05:46+00:00  1540220746  1.138297  86400          0   86400   \n",
+       "2018-10-23 15:05:46+00:00  1540307146  1.197227  86400        483   85917   \n",
+       "2018-10-24 15:05:46+00:00  1540393546  1.249731  86400      35196   51204   \n",
+       "2018-10-25 15:05:46+00:00  1540479946  1.302276  86400      13562   72838   \n",
+       "2018-10-26 15:05:46+00:00  1540566346  1.355239  86400       9416   76984   \n",
+       "2018-10-27 15:05:46+00:00  1540652746  1.415010  86400       7799   78601   \n",
+       "2018-10-28 15:05:46+00:00  1540739146  1.469298  86400          0   86400   \n",
+       "2018-10-29 15:05:46+00:00  1540825546  1.520873  86400          0   86400   \n",
+       "2018-10-30 15:05:46+00:00  1540911946  1.574885  86400          0   86400   \n",
+       "2018-10-31 15:05:46+00:00  1540998346  1.626940  86400          0   86400   \n",
+       "2018-11-01 15:05:46+00:00  1541084746  1.678789  86400          0   86400   \n",
+       "2018-11-02 15:05:46+00:00  1541171146  1.730694  86400          0   86400   \n",
+       "2018-11-03 15:05:46+00:00  1541257546  1.782092  86400       1133   85267   \n",
+       "2018-11-04 15:05:46+00:00  1541343946  1.834936  86400      20526   65874   \n",
+       "2018-11-05 15:05:46+00:00  1541430346  1.887703  86400      15738   70662   \n",
+       "2018-11-06 15:05:46+00:00  1541516746  1.940608  86400          0   86400   \n",
+       "2018-11-07 15:05:46+00:00  1541603146  2.011979  86400          0   86400   \n",
+       "2018-11-08 15:05:46+00:00  1541689546  2.069997  86400          0   86400   \n",
+       "2018-11-09 15:05:46+00:00  1541775946  2.121356  86400          0   86400   \n",
+       "2018-11-10 15:05:46+00:00  1541862346  2.178195  86400          0   86400   \n",
+       "2018-11-11 15:05:46+00:00  1541948746  2.239936  86400          0   86400   \n",
+       "2018-11-12 15:05:46+00:00  1542035146  2.295520  86400       7801   78599   \n",
+       "2018-11-13 15:05:46+00:00  1542121546  2.347879  86400      35043   51357   \n",
+       "2018-11-14 15:05:46+00:00  1542207946  2.400113  86400         55   86345   \n",
+       "2018-11-15 15:05:46+00:00  1542294346  2.453023  86400          0   86400   \n",
+       "2018-11-16 15:05:46+00:00  1542380746  2.507303  86400          0   86400   \n",
+       "2018-11-17 15:05:46+00:00  1542467146  2.572663  86400          0   86400   \n",
+       "2018-11-18 15:05:46+00:00  1542553546  2.624449  86400          0   86400   \n",
+       "2018-11-19 15:05:46+00:00  1542639946  2.675888  86400          0   86400   \n",
+       "2018-11-20 15:05:46+00:00  1542726346  2.729842  86400          0   86400   \n",
+       "2018-11-21 15:05:46+00:00  1542812746  2.781364  86400          0   86400   \n",
+       "2018-11-22 15:05:46+00:00  1542899146  2.842387  86400          0   86400   \n",
+       "2018-11-23 15:05:46+00:00  1542985546  2.900620  86400       8184   78216   \n",
+       "2018-11-24 15:05:46+00:00  1543071946  2.955630  86400          0   86400   \n",
+       "2018-11-25 15:05:46+00:00  1543158346  3.011035  86400          0   86400   \n",
+       "2018-11-26 15:05:46+00:00  1543244746  3.063060  86400          0   86400   \n",
+       "2018-11-27 15:05:46+00:00  1543331146  3.126908  86400          0   86400   \n",
+       "2018-11-28 15:05:46+00:00  1543417546  3.187849  86400      11580   74820   \n",
+       "2018-11-29 15:05:46+00:00  1543503946  3.189124  86400        399   86001   \n",
+       "\n",
+       "                           nb_valid  nb_inf              nanmin  \\\n",
+       "index                                                             \n",
+       "2018-10-02 15:05:46+00:00     18354       0   25.26799964904785   \n",
+       "2018-10-03 15:05:46+00:00      3964       0  26.628000259399414   \n",
+       "2018-10-04 15:05:46+00:00       941       0  26.582000732421875   \n",
+       "2018-10-05 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-10-06 15:05:46+00:00      2367       0  26.676000595092773   \n",
+       "2018-10-07 15:05:46+00:00     17645       0   25.58099937438965   \n",
+       "2018-10-08 15:05:46+00:00     34870       0  24.548999786376953   \n",
+       "2018-10-09 15:05:46+00:00     33067       0  25.479000091552734   \n",
+       "2018-10-10 15:05:46+00:00     13929       0  25.469999313354492   \n",
+       "2018-10-11 15:05:46+00:00     10653       0  25.829999923706055   \n",
+       "2018-10-12 15:05:46+00:00      9413       0  25.683000564575195   \n",
+       "2018-10-13 15:05:46+00:00     16192       0  23.538000106811523   \n",
+       "2018-10-14 15:05:46+00:00     12957       0  25.424999237060547   \n",
+       "2018-10-15 15:05:46+00:00     19493       0  23.582000732421875   \n",
+       "2018-10-16 15:05:46+00:00     35796       0  25.410999298095703   \n",
+       "2018-10-17 15:05:46+00:00     13939       0  25.391000747680664   \n",
+       "2018-10-18 15:05:46+00:00     32509       0  25.135000228881836   \n",
+       "2018-10-19 15:05:46+00:00     11639       0  25.340999603271484   \n",
+       "2018-10-20 15:05:46+00:00       583       0   25.53499984741211   \n",
+       "2018-10-21 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-10-22 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-10-23 15:05:46+00:00       483       0   26.81399917602539   \n",
+       "2018-10-24 15:05:46+00:00     35196       0  25.447999954223633   \n",
+       "2018-10-25 15:05:46+00:00     13562       0   25.45599937438965   \n",
+       "2018-10-26 15:05:46+00:00      9416       0  25.538000106811523   \n",
+       "2018-10-27 15:05:46+00:00      7799       0  25.424999237060547   \n",
+       "2018-10-28 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-10-29 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-10-30 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-10-31 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-01 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-02 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-03 15:05:46+00:00      1133       0  25.517000198364258   \n",
+       "2018-11-04 15:05:46+00:00     20526       0  23.784000396728516   \n",
+       "2018-11-05 15:05:46+00:00     15738       0  23.361000061035156   \n",
+       "2018-11-06 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-07 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-08 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-09 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-10 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-11 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-12 15:05:46+00:00      7801       0   25.92099952697754   \n",
+       "2018-11-13 15:05:46+00:00     35043       0  25.565000534057617   \n",
+       "2018-11-14 15:05:46+00:00        55       0   27.67300033569336   \n",
+       "2018-11-15 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-16 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-17 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-18 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-19 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-20 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-21 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-22 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-23 15:05:46+00:00      8184       0   26.10300064086914   \n",
+       "2018-11-24 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-25 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-26 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-27 15:05:46+00:00         0       0                 NaN   \n",
+       "2018-11-28 15:05:46+00:00     11580       0    26.2810001373291   \n",
+       "2018-11-29 15:05:46+00:00       399       0  27.257999420166016   \n",
+       "\n",
+       "                                      nanmean              nanmax  \n",
+       "index                                                              \n",
+       "2018-10-02 15:05:46+00:00  26.051037900001248  28.304000854492188  \n",
+       "2018-10-03 15:05:46+00:00   27.42328505655591  28.520000457763672  \n",
+       "2018-10-04 15:05:46+00:00   27.42080019992419   28.44700050354004  \n",
+       "2018-10-05 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-10-06 15:05:46+00:00   27.42406802223763   27.94700050354004  \n",
+       "2018-10-07 15:05:46+00:00  27.352939146846644  28.628999710083008  \n",
+       "2018-10-08 15:05:46+00:00   27.32949967101272   28.93400001525879  \n",
+       "2018-10-09 15:05:46+00:00  27.209397055115176   29.92300033569336  \n",
+       "2018-10-10 15:05:46+00:00   26.32814978813781  29.906999588012695  \n",
+       "2018-10-11 15:05:46+00:00  27.057986568220677  29.610000610351562  \n",
+       "2018-10-12 15:05:46+00:00  27.090021389546155  29.530000686645508  \n",
+       "2018-10-13 15:05:46+00:00  27.125155579431254  30.434999465942383  \n",
+       "2018-10-14 15:05:46+00:00   26.34141606799502  28.422000885009766  \n",
+       "2018-10-15 15:05:46+00:00    27.3867852606255  29.930999755859375  \n",
+       "2018-10-16 15:05:46+00:00  27.347879044529257  29.549999237060547  \n",
+       "2018-10-17 15:05:46+00:00   26.77908782335015  29.547000885009766  \n",
+       "2018-10-18 15:05:46+00:00   27.07881130703249  29.847000122070312  \n",
+       "2018-10-19 15:05:46+00:00   26.54683056757698  28.413000106811523  \n",
+       "2018-10-20 15:05:46+00:00  25.546099500836068  25.570999145507812  \n",
+       "2018-10-21 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-10-22 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-10-23 15:05:46+00:00  27.427250500791562  28.413000106811523  \n",
+       "2018-10-24 15:05:46+00:00   27.31623176239149  28.944000244140625  \n",
+       "2018-10-25 15:05:46+00:00  26.434626391390715   27.82900047302246  \n",
+       "2018-10-26 15:05:46+00:00   26.67253906623529  29.551000595092773  \n",
+       "2018-10-27 15:05:46+00:00  26.592719177968043  28.413999557495117  \n",
+       "2018-10-28 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-10-29 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-10-30 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-10-31 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-01 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-02 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-03 15:05:46+00:00  25.990633738114575  28.468000411987305  \n",
+       "2018-11-04 15:05:46+00:00  27.303087265068914  30.798999786376953  \n",
+       "2018-11-05 15:05:46+00:00  26.431978454158394   31.12700080871582  \n",
+       "2018-11-06 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-07 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-08 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-09 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-10 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-11 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-12 15:05:46+00:00  27.403101785995002  28.670000076293945  \n",
+       "2018-11-13 15:05:46+00:00  27.381561364858957  29.235000610351562  \n",
+       "2018-11-14 15:05:46+00:00  28.228672686490146   28.51300048828125  \n",
+       "2018-11-15 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-16 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-17 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-18 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-19 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-20 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-21 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-22 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-23 15:05:46+00:00   27.14345785063616   29.67799949645996  \n",
+       "2018-11-24 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-25 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-26 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-27 15:05:46+00:00                 NaN                 NaN  \n",
+       "2018-11-28 15:05:46+00:00  27.346280918220163  28.632999420166016  \n",
+       "2018-11-29 15:05:46+00:00  27.394338363991643  27.535999298095703  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "index\n",
+       "2018-10-05 15:05:46+00:00        0\n",
+       "2018-10-28 15:05:46+00:00        0\n",
+       "2018-10-31 15:05:46+00:00        0\n",
+       "2018-10-30 15:05:46+00:00        0\n",
+       "2018-11-01 15:05:46+00:00        0\n",
+       "2018-11-02 15:05:46+00:00        0\n",
+       "2018-10-22 15:05:46+00:00        0\n",
+       "2018-10-21 15:05:46+00:00        0\n",
+       "2018-10-29 15:05:46+00:00        0\n",
+       "2018-11-17 15:05:46+00:00        0\n",
+       "2018-11-16 15:05:46+00:00        0\n",
+       "2018-11-10 15:05:46+00:00        0\n",
+       "2018-11-07 15:05:46+00:00        0\n",
+       "2018-11-08 15:05:46+00:00        0\n",
+       "2018-11-27 15:05:46+00:00        0\n",
+       "2018-11-26 15:05:46+00:00        0\n",
+       "2018-11-19 15:05:46+00:00        0\n",
+       "2018-11-20 15:05:46+00:00        0\n",
+       "2018-11-21 15:05:46+00:00        0\n",
+       "2018-11-22 15:05:46+00:00        0\n",
+       "2018-11-24 15:05:46+00:00        0\n",
+       "2018-11-25 15:05:46+00:00        0\n",
+       "2018-11-15 15:05:46+00:00        0\n",
+       "2018-11-18 15:05:46+00:00        0\n",
+       "2018-11-11 15:05:46+00:00        0\n",
+       "2018-11-06 15:05:46+00:00        0\n",
+       "2018-11-09 15:05:46+00:00        0\n",
+       "2018-11-14 15:05:46+00:00       55\n",
+       "2018-11-29 15:05:46+00:00      399\n",
+       "2018-10-23 15:05:46+00:00      483\n",
+       "2018-10-20 15:05:46+00:00      583\n",
+       "2018-10-04 15:05:46+00:00      941\n",
+       "2018-11-03 15:05:46+00:00     1133\n",
+       "2018-10-06 15:05:46+00:00     2367\n",
+       "2018-10-03 15:05:46+00:00     3964\n",
+       "2018-10-27 15:05:46+00:00     7799\n",
+       "2018-11-12 15:05:46+00:00     7801\n",
+       "2018-11-23 15:05:46+00:00     8184\n",
+       "2018-10-12 15:05:46+00:00     9413\n",
+       "2018-10-26 15:05:46+00:00     9416\n",
+       "2018-10-11 15:05:46+00:00    10653\n",
+       "2018-11-28 15:05:46+00:00    11580\n",
+       "2018-10-19 15:05:46+00:00    11639\n",
+       "2018-10-14 15:05:46+00:00    12957\n",
+       "2018-10-25 15:05:46+00:00    13562\n",
+       "2018-10-10 15:05:46+00:00    13929\n",
+       "2018-10-17 15:05:46+00:00    13939\n",
+       "2018-11-05 15:05:46+00:00    15738\n",
+       "2018-10-13 15:05:46+00:00    16192\n",
+       "2018-10-07 15:05:46+00:00    17645\n",
+       "2018-10-02 15:05:46+00:00    18354\n",
+       "2018-10-15 15:05:46+00:00    19493\n",
+       "2018-11-04 15:05:46+00:00    20526\n",
+       "2018-10-18 15:05:46+00:00    32509\n",
+       "2018-10-09 15:05:46+00:00    33067\n",
+       "2018-10-08 15:05:46+00:00    34870\n",
+       "2018-11-13 15:05:46+00:00    35043\n",
+       "2018-10-24 15:05:46+00:00    35196\n",
+       "2018-10-16 15:05:46+00:00    35796\n",
+       "Name: nb_valid, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "nb_valid\n",
+       "0        27\n",
+       "55        1\n",
+       "399       1\n",
+       "483       1\n",
+       "583       1\n",
+       "941       1\n",
+       "1133      1\n",
+       "2367      1\n",
+       "3964      1\n",
+       "7799      1\n",
+       "7801      1\n",
+       "8184      1\n",
+       "9413      1\n",
+       "9416      1\n",
+       "10653     1\n",
+       "11580     1\n",
+       "11639     1\n",
+       "12957     1\n",
+       "13562     1\n",
+       "13929     1\n",
+       "13939     1\n",
+       "15738     1\n",
+       "16192     1\n",
+       "17645     1\n",
+       "18354     1\n",
+       "19493     1\n",
+       "20526     1\n",
+       "32509     1\n",
+       "33067     1\n",
+       "34870     1\n",
+       "35043     1\n",
+       "35196     1\n",
+       "35796     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "FEED_NB = 2\n",
+    "meta = getMeta(FEED_NB, DATA_DIR)\n",
+    "END_DATE = datetime.datetime.fromtimestamp(meta[\"start_time\"] + meta[\"npoints\"], tz=datetime.timezone.utc)\n",
+    "print(f\"Feed Id: {FEED_NB} file start at {datetime.datetime.fromtimestamp(meta[\"start_time\"])} end at {END_DATE}\")\n",
+    "print(f\"meta: {meta}\")\n",
+    "STEP = int(1)\n",
+    "day_1 = datetime.datetime.fromtimestamp(meta[\"start_time\"] + (24 * 3600), tz=datetime.timezone.utc)\n",
+    "# START_DAY = int(datetime.datetime(year=day_1.year, month=day_1.month, day=day_1.day, hour=0, minute=0, second=0, tzinfo=datetime.timezone.utc).timestamp())\n",
+    "# START_DAY = END_DATE.timestamp() + 3600 *24 * 31\n",
+    "START_DAY = meta[\"start_time\"]\n",
+    "NB_DAYS = int(365 * 4)\n",
+    "DAY_S = 3600 * 24\n",
+    "start = meta[\"start_time\"]\n",
+    "length = meta[\"npoints\"] * meta[\"interval\"]\n",
+    "WINDOW = min(DAY_S, length)\n",
+    "\n",
+    "nbpts = WINDOW // STEP\n",
+    "timer_start = time.perf_counter()\n",
+    "stats = []\n",
+    "nb_io_read = 0\n",
+    "for i in range(0, NB_DAYS + 1):\n",
+    "    start_i = START_DAY + (DAY_S * i)\n",
+    "    if start_i >= meta[\"end_time\"]:\n",
+    "        break\n",
+    "    # start_i = int(1727276746 - ((15 * 3600) + (3600 / 2)) + (NB_DAYS * 2))\n",
+    "    data = PyFina(FEED_NB, DATA_DIR, start_i, STEP, nbpts, remove_nan=False)\n",
+    "    # data = np.round(data, 3)\n",
+    "    nb_io_read += 1\n",
+    "    nb_valid = (20 < data) & (data < 40)\n",
+    "    nb_finite = int(np.isfinite(data).sum())\n",
+    "    nan_min, nan_mean, nan_max = np.nan, np.nan, np.nan\n",
+    "    if nb_finite > 0:\n",
+    "        nan_min = np.nanmin(data)\n",
+    "        nan_mean = np.nanmean(data)\n",
+    "        nan_max = np.nanmax(data)\n",
+    "    stats.append([\n",
+    "        start_i,\n",
+    "        time.perf_counter() - timer_start,\n",
+    "        data.shape[0],\n",
+    "        nb_finite,\n",
+    "        int(np.isnan(data).sum()),\n",
+    "        int(nb_valid.sum()),\n",
+    "        int(np.isinf(data).sum()),\n",
+    "        nan_min,\n",
+    "        nan_mean,\n",
+    "        nan_max\n",
+    "    ])\n",
+    "\n",
+    "def desc_resampler(arraylike):\n",
+    "    print(arraylike.describe())\n",
+    "    return arraylike\n",
+    "\n",
+    "if len(stats) > 0:\n",
+    "    df = pd.DataFrame(stats, columns=['time', 'perf', 'lines', 'nb_finite', 'nb_nan', 'nb_valid', 'nb_inf', 'nanmin', 'nanmean', 'nanmax'])\n",
+    "    df = df.set_index(pd.to_datetime(df['time'], unit='s', utc=True))\n",
+    "    df.index.name = 'index'\n",
+    "    print(f\"Analysed {NB_DAYS} Days in {time.perf_counter() - timer_start}s -- IO File Reads = {nb_io_read}\")\n",
+    "    print(\"Global stats: \")\n",
+    "    display(df.describe())\n",
+    "    display(df)\n",
+    "    display(df.sort_values('nb_valid')['nb_valid'])\n",
+    "    display(df.sort_values('nb_valid')['nb_valid'].value_counts())\n",
+    "    #print(\"Year stats: \")\n",
+    "    #print(df.resample('y').apply(desc_resampler))\n",
+    "    nb_valid_d = df['nb_valid'].resample('d').agg(['mean', 'std', 'min', 'max'])\n",
+    "    nb_finite_d = df['nb_finite'].resample('d').agg(['mean', 'std', 'min', 'max'])\n",
+    "    plt.figure(figsize = (18, 12))\n",
+    "    plt.subplot(1, 1, 1)\n",
+    "    plt.ylabel(\"Count\")\n",
+    "    plt.xlabel(\"time\")\n",
+    "    plt.fill_between(nb_valid_d.index, nb_valid_d['max'], nb_valid_d['min'], alpha=0.2, label='nb_valid min-max')\n",
+    "    plt.plot(nb_valid_d.index, nb_valid_d['mean'], label='nb_valid mean per month', lw=6)\n",
+    "    plt.fill_between(nb_finite_d.index, nb_finite_d['max'], nb_finite_d['min'], alpha=0.2, label='nb_finite min-max')\n",
+    "    plt.plot(nb_finite_d.index, nb_finite_d['mean'], label='nb_finite mean')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb valid = 466806 / 5011599\n",
+      "Nb finite = 466806 / 5011599\n",
+      "Nb NAN = 4556373 / 5011599\n",
+      "Total = 5023179 / 5011599\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Nb valid = {df['nb_valid'].sum()} / {meta['npoints']}\")\n",
+    "print(f\"Nb finite = {df['nb_finite'].sum()} / {meta['npoints']}\")\n",
+    "print(f\"Nb NAN = {df['nb_nan'].sum()} / {meta['npoints']}\")\n",
+    "print(f\"Total = {df['nb_nan'].sum() + df['nb_valid'].sum()} / {meta['npoints']}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyfina",
+   "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": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/datas/2.dat b/tests/datas/2.dat
new file mode 100644
index 0000000..29310c5
Binary files /dev/null and b/tests/datas/2.dat differ
diff --git a/tests/datas/2.meta b/tests/datas/2.meta
new file mode 100644
index 0000000..7b20e05
Binary files /dev/null and b/tests/datas/2.meta differ
diff --git a/tests/test.py b/tests/test.py
index f4d52db..ded8ac9 100644
--- a/tests/test.py
+++ b/tests/test.py
@@ -1,15 +1,27 @@
 """test : open feed and plot image."""
 
 import datetime
+import os
 import time
-
 import matplotlib
 import matplotlib.pyplot as plt
 from multigraph import check_starting_nan
 from PyFina import getMeta, PyFina
 
 FEED_NB = 1
-DATA_DIR = "./datas"
+DATA_DIR = os.path.join(
+    os.path.abspath(os.getcwd()),
+    "datas"
+)
+if not os.path.isdir(DATA_DIR):
+    DATA_DIR = os.path.join(
+        os.path.abspath(os.getcwd()),
+        "tests",
+        "datas"
+    )
+    if not os.path.isdir(DATA_DIR):
+        raise FileNotFoundError(f"Could not find data directory in any location: {DATA_DIR}")
+
 meta = getMeta(FEED_NB, DATA_DIR)
 print(meta)
 STEP = 3600
@@ -23,7 +35,7 @@
 check_starting_nan("température extérieure", temp_ext)
 
 localstart = datetime.datetime.fromtimestamp(start)
-utcstart = datetime.datetime.utcfromtimestamp(start)
+utcstart = datetime.datetime.fromtimestamp(start, datetime.timezone.utc)
 title = f"starting on :\nUTC {utcstart}\n{time.tzname[0]} {localstart}"
 figure = plt.figure(figsize = (10, 10))
 matplotlib.rc('font', size=8)
@@ -33,3 +45,5 @@
 plt.xlabel("time in hours")
 plt.plot(temp_ext)
 figure.savefig(f"feed_{FEED_NB}.png")
+if os.environ.get('DISPLAY_PLOTS', '').lower() == 'true':
+    plt.show()