From e4dac9b154b5c0dc7776b5b8a6977292e0c548b2 Mon Sep 17 00:00:00 2001
From: Marc Pound <22331890+mpound@users.noreply.github.com>
Date: Tue, 21 Nov 2023 13:54:40 -0500
Subject: [PATCH 1/2] Update subbeamnod.ipynb

typo in URL for example data.
---
 notebooks/examples/subbeamnod.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/examples/subbeamnod.ipynb b/notebooks/examples/subbeamnod.ipynb
index 47a747c0..d70e84c3 100644
--- a/notebooks/examples/subbeamnod.ipynb
+++ b/notebooks/examples/subbeamnod.ipynb
@@ -35,7 +35,7 @@
    "id": "a62b09df-8cdb-44c5-85d3-d93078e8603b",
    "metadata": {},
    "source": [
-    "Then load your SDFITS file containing SubBeamNod data. In this example, we use a GBT SDFITS file downloadable from GBO http://www.gb.nrao.edu/dysh/example_datasubbeamnod-Ka/data/TRCO_230413_Ka.raw.vegas/TRCO_230413_Ka.raw.vegas.A.fits"
+    "Then load your SDFITS file containing SubBeamNod data. In this example, we use a GBT SDFITS file downloadable from GBO http://www.gb.nrao.edu/dysh/example_data/subbeamnod-Ka/data/TRCO_230413_Ka.raw.vegas/TRCO_230413_Ka.raw.vegas.A.fits"
    ]
   },
   {

From 1811a4b15cc553470c7b72c0a1726f54963f496e Mon Sep 17 00:00:00 2001
From: Marc Pound <22331890+mpound@users.noreply.github.com>
Date: Tue, 21 Nov 2023 17:21:07 -0500
Subject: [PATCH 2/2] patching subbeamnod

---
 notebooks/examples/positionswitch.ipynb |  291 +-----
 notebooks/examples/subbeamnod.ipynb     | 1125 +++++++----------------
 src/dysh/spectra/core.py                |   15 +-
 src/dysh/spectra/scan.py                |   24 +-
 4 files changed, 396 insertions(+), 1059 deletions(-)

diff --git a/notebooks/examples/positionswitch.ipynb b/notebooks/examples/positionswitch.ipynb
index 2de2217c..8ab1e773 100644
--- a/notebooks/examples/positionswitch.ipynb
+++ b/notebooks/examples/positionswitch.ipynb
@@ -8,12 +8,13 @@
     "# Position-switched Data Reduction\n",
     "----------------------------------\n",
     "\n",
-    "This notebook shows how to use `dysh` to calibrate an OnOff observation."
+    "This notebook shows how to use `dysh` to calibrate an OnOff observation.\n",
+    "It retrieves and calibrates position-switch scans using `GBTFITSLoad.getps()`, which returns a `ScanBlock` object.  OffOn observations can be reduced the same way."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "b4967550-2ca1-4931-b53b-6f9868718490",
    "metadata": {
     "editable": true,
@@ -26,16 +27,23 @@
    },
    "outputs": [],
    "source": [
+    "import os\n",
     "import wget\n",
-    "\n",
     "import astropy.units as u\n",
-    "\n",
     "from dysh.fits.gbtfitsload import GBTFITSLoad"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "87669763-8d96-4521-9e81-f69d7213e133",
+   "metadata": {},
+   "source": [
+    "## First, we download the example SDFITS data, if necessary.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "6bc88bc5-986d-4eae-b1c7-6398cc9ddd5a",
    "metadata": {
     "editable": true,
@@ -51,65 +59,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 29% [....................                                                  ] 235528192 / 795479040"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The Jupyter server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--ServerApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "ServerApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 81% [........................................................              ] 645423104 / 795479040"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The Jupyter server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--ServerApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "ServerApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100% [......................................................................] 795479040 / 795479040TGBT21A_501_11.raw.vegas.fits\n"
+      "TGBT21A_501_11.raw.vegas.fits already downloaded\n"
      ]
     }
    ],
    "source": [
-    "url = \"http://www.gb.nrao.edu/dysh/example_data/onoff-L/data/TGBT21A_501_11.raw.vegas.fits\"\n",
-    "filename = wget.download(url)\n",
-    "print(filename)"
+    "filename = \"TGBT21A_501_11.raw.vegas.fits\"\n",
+    "if not os.path.isfile(filename):\n",
+    "    url = f\"http://www.gb.nrao.edu/dysh/example_data/onoff-L/data/{filename}\"\n",
+    "    print(f\"Downloading {filename}\")\n",
+    "    wget.download(url,out=filename)\n",
+    "    print(f\"\\nRetrieved {filename}\")\n",
+    "else:\n",
+    "    print(f\"{filename} already downloaded\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "93a62e3a-c95d-475b-8602-b5b8b7934733",
    "metadata": {
     "editable": true,
@@ -133,14 +100,13 @@
     }
    ],
    "source": [
-    "#f = '/data/gbt/examples/onoff-L/data/TGBT21A_501_11.raw.vegas.fits'\n",
     "sdfits = GBTFITSLoad(filename)\n",
     "sdfits.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "5578549d-7f9c-4abd-a1d3-c69f03187ec6",
    "metadata": {
     "editable": true,
@@ -153,100 +119,20 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SCAN</th>\n",
-       "      <th>OBJECT</th>\n",
-       "      <th>VELOCITY</th>\n",
-       "      <th>PROC</th>\n",
-       "      <th>PROCSEQN</th>\n",
-       "      <th>RESTFREQ</th>\n",
-       "      <th>DOPFREQ</th>\n",
-       "      <th># IF</th>\n",
-       "      <th># POL</th>\n",
-       "      <th># INT</th>\n",
-       "      <th># FEED</th>\n",
-       "      <th>AZIMUTH</th>\n",
-       "      <th>ELEVATIO</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>152</td>\n",
-       "      <td>NGC2415</td>\n",
-       "      <td>3784.0</td>\n",
-       "      <td>OnOff</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.617185</td>\n",
-       "      <td>1.420406</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>151</td>\n",
-       "      <td>1</td>\n",
-       "      <td>286.218008</td>\n",
-       "      <td>41.62843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>153</td>\n",
-       "      <td>NGC2415</td>\n",
-       "      <td>3784.0</td>\n",
-       "      <td>OnOff</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.617185</td>\n",
-       "      <td>1.420406</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>151</td>\n",
-       "      <td>1</td>\n",
-       "      <td>286.886521</td>\n",
-       "      <td>41.118134</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   SCAN   OBJECT VELOCITY   PROC  PROCSEQN  RESTFREQ   DOPFREQ # IF # POL  \\\n",
-       "0   152  NGC2415   3784.0  OnOff         1  1.617185  1.420406    5     2   \n",
-       "1   153  NGC2415   3784.0  OnOff         2  1.617185  1.420406    5     2   \n",
-       "\n",
-       "  # INT # FEED     AZIMUTH   ELEVATIO  \n",
-       "0   151      1  286.218008   41.62843  \n",
-       "1   151      1  286.886521  41.118134  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<pandas.io.formats.style.Styler object at 0x7f02424398d0>\n"
+     ]
     }
    ],
    "source": [
-    "sdfits.summary()"
+    "print(sdfits.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "684126da-97d4-4afb-8625-43160340cab1",
    "metadata": {
     "editable": true,
@@ -258,16 +144,7 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FILE  TGBT21A_501_11.raw.vegas.fits\n",
-      "FILE  TGBT21A_501_11.raw.vegas.fits\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "psscan = sdfits.getps(152, ifnum=0, plnum=0)\n",
     "psscan.calibrate()"
@@ -275,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "5deaae0c-e70b-4758-ae10-f11c6f66cc3b",
    "metadata": {
     "editable": true,
@@ -287,22 +164,14 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "T_sys = 17.17\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(f\"T_sys = {psscan[0].tsys.mean():.2f}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "e0a33c19-576b-46e8-9a57-f34b8f85f6aa",
    "metadata": {
     "editable": true,
@@ -314,18 +183,7 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ta = psscan.timeaverage(weights='tsys')"
    ]
@@ -351,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6c07cf19-3a5d-47a9-9a10-ccd9f305efb9",
    "metadata": {
     "editable": true,
@@ -363,25 +221,14 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ta[0].plot(xaxis_unit=\"km/s\",yaxis_unit=\"mK\",ymin=-100,ymax=500,xmin=3000,xmax=4500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "d4c4526b-2e78-4f0f-85ff-0a4e3694a1e5",
    "metadata": {
     "editable": true,
@@ -393,25 +240,7 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EXCLUDING [Spectral Region, 1 sub-regions:\n",
-      "  (1401242184.363393 Hz, 1403551474.1090915 Hz) \n",
-      "]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: The fit may be poorly conditioned\n",
-      " [astropy.modeling.fitting]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "kms = u.km/u.s\n",
     "ta[0].baseline(degree=2,exclude=[3600*kms,4100*kms],remove=True)"
@@ -419,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "527d868b-0ef7-40d3-9e80-dd5aea8f4405",
    "metadata": {
     "editable": true,
@@ -430,25 +259,14 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ta[0].plot(ymin=-250)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "219e21a8-91cb-405e-82fc-249143c219ae",
    "metadata": {
     "editable": true,
@@ -459,31 +277,14 @@
      "test"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: Polynomial1D\n",
-      "Inputs: ('x',)\n",
-      "Outputs: ('y',)\n",
-      "Model set size: 1\n",
-      "Degree: 2\n",
-      "Parameters:\n",
-      "             c0                   c1                    c2          \n",
-      "             K                  K / Hz               K / Hz2        \n",
-      "    ------------------- --------------------- ----------------------\n",
-      "    0.16984671749959526 6.155580315232285e-29 2.2305012033296116e-56\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(ta[0].baseline_model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "3a7e4887-ba55-447a-9274-8275e9896d39",
    "metadata": {},
    "outputs": [],
@@ -516,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.9"
   },
   "toc": {
    "base_numbering": 0
diff --git a/notebooks/examples/subbeamnod.ipynb b/notebooks/examples/subbeamnod.ipynb
index 47a747c0..622dd9e7 100644
--- a/notebooks/examples/subbeamnod.ipynb
+++ b/notebooks/examples/subbeamnod.ipynb
@@ -6,47 +6,71 @@
    "metadata": {},
    "source": [
     "# SubBeamNod Data Reduction\n",
-    "---------------------------"
+    "---------------------------\n",
+    "This notebook shows how to use `dysh` to calibrate an SubBeamNod observation via two different methods. It retrieves and calibrates SubBeamNod scans using `GBTFITSLoad.subbeamnod()` which returns a `ScanBlock` object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "71a0f9ba-9f38-408d-ba57-2630c1525a64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import wget\n",
+    "from dysh.fits.gbtfitsload import GBTFITSLoad"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "02ff283d-8772-43ee-88af-4e1c6363dcc8",
+   "id": "efc7bb5b-7979-4863-a4a2-7270760c2ee9",
    "metadata": {},
    "source": [
-    "SubBeamNod data, where a single pixel in a pixel \n",
-    "camera is used to do a beam nod, is retrieved \n",
-    "using `gbtfitsload.GBTFITSLoad.subbeamnod` which returns a `Spectrum` object. \n",
-    "First, import the relevant module:"
+    "## First, we download the example SDFITS data, if necessary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "71a0f9ba-9f38-408d-ba57-2630c1525a64",
+   "execution_count": 2,
+   "id": "f07cfb32-6864-4ac4-94f8-4c2b66b981c4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TRCO_230413_Ka.raw.vegas.A.fits already downloaded\n"
+     ]
+    }
+   ],
    "source": [
-    "from dysh.fits.gbtfitsload import GBTFITSLoad"
+    "filename=\"TRCO_230413_Ka.raw.vegas.A.fits\"\n",
+    "if not os.path.isfile(filename):\n",
+    "    url = f\"http://www.gb.nrao.edu/dysh/example_data/subbeamnod-Ka/data/TRCO_230413_Ka.raw.vegas/{filename}\"\n",
+    "    print(f\"Downloading {filename}\")\n",
+    "    wget.download(url,out=filename)\n",
+    "    print(f\"\\nRetrieved {filename}\")\n",
+    "else:\n",
+    "    print(f\"{filename} already downloaded\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a62b09df-8cdb-44c5-85d3-d93078e8603b",
+   "id": "cf925ad1-e149-46dd-9728-53ea2142fbf8",
    "metadata": {},
    "source": [
-    "Then load your SDFITS file containing SubBeamNod data. In this example, we use a GBT SDFITS file downloadable from GBO http://www.gb.nrao.edu/dysh/example_datasubbeamnod-Ka/data/TRCO_230413_Ka.raw.vegas/TRCO_230413_Ka.raw.vegas.A.fits"
+    "## Now, load the SDFITS file and examine it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "1b12df99-20b2-4f8b-95c3-802be4ffaf3c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "f = 'TRCO_230413_Ka.raw.vegas.A.fits'\n",
-    "sdfits = GBTFITSLoad(f)"
+    "sdfits = GBTFITSLoad(filename)"
    ]
   },
   {
@@ -60,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "259fec35-f69a-4061-ad37-c2235102af4c",
    "metadata": {},
    "outputs": [
@@ -77,382 +101,312 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
+       "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
+       "<table id=\"T_4d3ec\">\n",
        "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SCAN</th>\n",
-       "      <th>OBJECT</th>\n",
-       "      <th>VELOCITY</th>\n",
-       "      <th>PROC</th>\n",
-       "      <th>PROCSEQN</th>\n",
-       "      <th>RESTFREQ</th>\n",
-       "      <th>DOPFREQ</th>\n",
-       "      <th># IF</th>\n",
-       "      <th># POL</th>\n",
-       "      <th># INT</th>\n",
-       "      <th># FEED</th>\n",
-       "      <th>AZIMUTH</th>\n",
-       "      <th>ELEVATIO</th>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4d3ec_level0_col0\" class=\"col_heading level0 col0\" >SCAN</th>\n",
+       "      <th id=\"T_4d3ec_level0_col1\" class=\"col_heading level0 col1\" >OBJECT</th>\n",
+       "      <th id=\"T_4d3ec_level0_col2\" class=\"col_heading level0 col2\" >VELOCITY</th>\n",
+       "      <th id=\"T_4d3ec_level0_col3\" class=\"col_heading level0 col3\" >PROC</th>\n",
+       "      <th id=\"T_4d3ec_level0_col4\" class=\"col_heading level0 col4\" >PROCSEQN</th>\n",
+       "      <th id=\"T_4d3ec_level0_col5\" class=\"col_heading level0 col5\" >RESTFREQ</th>\n",
+       "      <th id=\"T_4d3ec_level0_col6\" class=\"col_heading level0 col6\" >DOPFREQ</th>\n",
+       "      <th id=\"T_4d3ec_level0_col7\" class=\"col_heading level0 col7\" ># IF</th>\n",
+       "      <th id=\"T_4d3ec_level0_col8\" class=\"col_heading level0 col8\" ># POL</th>\n",
+       "      <th id=\"T_4d3ec_level0_col9\" class=\"col_heading level0 col9\" ># INT</th>\n",
+       "      <th id=\"T_4d3ec_level0_col10\" class=\"col_heading level0 col10\" ># FEED</th>\n",
+       "      <th id=\"T_4d3ec_level0_col11\" class=\"col_heading level0 col11\" >AZIMUTH</th>\n",
+       "      <th id=\"T_4d3ec_level0_col12\" class=\"col_heading level0 col12\" >ELEVATIO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>26.5</td>\n",
-       "      <td>26.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>160.975324</td>\n",
-       "      <td>43.884984</td>\n",
+       "      <td id=\"T_4d3ec_row0_col0\" class=\"data row0 col0\" >32</td>\n",
+       "      <td id=\"T_4d3ec_row0_col1\" class=\"data row0 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row0_col3\" class=\"data row0 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row0_col4\" class=\"data row0 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row0_col5\" class=\"data row0 col5\" >26.500000</td>\n",
+       "      <td id=\"T_4d3ec_row0_col6\" class=\"data row0 col6\" >26.500000</td>\n",
+       "      <td id=\"T_4d3ec_row0_col7\" class=\"data row0 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row0_col8\" class=\"data row0 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row0_col9\" class=\"data row0 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row0_col10\" class=\"data row0 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row0_col11\" class=\"data row0 col11\" >160.975324</td>\n",
+       "      <td id=\"T_4d3ec_row0_col12\" class=\"data row0 col12\" >43.884984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>26.5</td>\n",
-       "      <td>26.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>161.174093</td>\n",
-       "      <td>43.928449</td>\n",
+       "      <td id=\"T_4d3ec_row1_col0\" class=\"data row1 col0\" >33</td>\n",
+       "      <td id=\"T_4d3ec_row1_col1\" class=\"data row1 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row1_col3\" class=\"data row1 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row1_col4\" class=\"data row1 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row1_col5\" class=\"data row1 col5\" >26.500000</td>\n",
+       "      <td id=\"T_4d3ec_row1_col6\" class=\"data row1 col6\" >26.500000</td>\n",
+       "      <td id=\"T_4d3ec_row1_col7\" class=\"data row1 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row1_col8\" class=\"data row1 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row1_col9\" class=\"data row1 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row1_col10\" class=\"data row1 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row1_col11\" class=\"data row1 col11\" >161.174093</td>\n",
+       "      <td id=\"T_4d3ec_row1_col12\" class=\"data row1 col12\" >43.928449</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>161.589629</td>\n",
-       "      <td>44.000491</td>\n",
+       "      <td id=\"T_4d3ec_row2_col0\" class=\"data row2 col0\" >34</td>\n",
+       "      <td id=\"T_4d3ec_row2_col1\" class=\"data row2 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row2_col3\" class=\"data row2 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row2_col4\" class=\"data row2 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row2_col5\" class=\"data row2 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row2_col6\" class=\"data row2 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row2_col7\" class=\"data row2 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row2_col8\" class=\"data row2 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row2_col9\" class=\"data row2 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row2_col10\" class=\"data row2 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row2_col11\" class=\"data row2 col11\" >161.589629</td>\n",
+       "      <td id=\"T_4d3ec_row2_col12\" class=\"data row2 col12\" >44.000491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>161.783395</td>\n",
-       "      <td>44.041622</td>\n",
+       "      <td id=\"T_4d3ec_row3_col0\" class=\"data row3 col0\" >35</td>\n",
+       "      <td id=\"T_4d3ec_row3_col1\" class=\"data row3 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row3_col3\" class=\"data row3 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row3_col4\" class=\"data row3 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row3_col5\" class=\"data row3 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row3_col6\" class=\"data row3 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row3_col7\" class=\"data row3 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row3_col8\" class=\"data row3 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row3_col9\" class=\"data row3 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row3_col10\" class=\"data row3 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row3_col11\" class=\"data row3 col11\" >161.783395</td>\n",
+       "      <td id=\"T_4d3ec_row3_col12\" class=\"data row3 col12\" >44.041622</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>120</td>\n",
-       "      <td>2</td>\n",
-       "      <td>162.124052</td>\n",
-       "      <td>44.100404</td>\n",
+       "      <td id=\"T_4d3ec_row4_col0\" class=\"data row4 col0\" >36</td>\n",
+       "      <td id=\"T_4d3ec_row4_col1\" class=\"data row4 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row4_col3\" class=\"data row4 col3\" >Unknown</td>\n",
+       "      <td id=\"T_4d3ec_row4_col4\" class=\"data row4 col4\" >0</td>\n",
+       "      <td id=\"T_4d3ec_row4_col5\" class=\"data row4 col5\" >0.750000</td>\n",
+       "      <td id=\"T_4d3ec_row4_col6\" class=\"data row4 col6\" >0.750000</td>\n",
+       "      <td id=\"T_4d3ec_row4_col7\" class=\"data row4 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row4_col8\" class=\"data row4 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row4_col9\" class=\"data row4 col9\" >120</td>\n",
+       "      <td id=\"T_4d3ec_row4_col10\" class=\"data row4 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row4_col11\" class=\"data row4 col11\" >162.124052</td>\n",
+       "      <td id=\"T_4d3ec_row4_col12\" class=\"data row4 col12\" >44.100404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>34.5</td>\n",
-       "      <td>34.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>162.611075</td>\n",
-       "      <td>44.183661</td>\n",
+       "      <td id=\"T_4d3ec_row5_col0\" class=\"data row5 col0\" >37</td>\n",
+       "      <td id=\"T_4d3ec_row5_col1\" class=\"data row5 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row5_col3\" class=\"data row5 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row5_col4\" class=\"data row5 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row5_col5\" class=\"data row5 col5\" >34.500000</td>\n",
+       "      <td id=\"T_4d3ec_row5_col6\" class=\"data row5 col6\" >34.500000</td>\n",
+       "      <td id=\"T_4d3ec_row5_col7\" class=\"data row5 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row5_col8\" class=\"data row5 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row5_col9\" class=\"data row5 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row5_col10\" class=\"data row5 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row5_col11\" class=\"data row5 col11\" >162.611075</td>\n",
+       "      <td id=\"T_4d3ec_row5_col12\" class=\"data row5 col12\" >44.183661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>34.5</td>\n",
-       "      <td>34.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>162.896506</td>\n",
-       "      <td>44.237997</td>\n",
+       "      <td id=\"T_4d3ec_row6_col0\" class=\"data row6 col0\" >38</td>\n",
+       "      <td id=\"T_4d3ec_row6_col1\" class=\"data row6 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row6_col3\" class=\"data row6 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row6_col4\" class=\"data row6 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row6_col5\" class=\"data row6 col5\" >34.500000</td>\n",
+       "      <td id=\"T_4d3ec_row6_col6\" class=\"data row6 col6\" >34.500000</td>\n",
+       "      <td id=\"T_4d3ec_row6_col7\" class=\"data row6 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row6_col8\" class=\"data row6 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row6_col9\" class=\"data row6 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row6_col10\" class=\"data row6 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row6_col11\" class=\"data row6 col11\" >162.896506</td>\n",
+       "      <td id=\"T_4d3ec_row6_col12\" class=\"data row6 col12\" >44.237997</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>39.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>163.333508</td>\n",
-       "      <td>44.306385</td>\n",
+       "      <td id=\"T_4d3ec_row7_col0\" class=\"data row7 col0\" >39</td>\n",
+       "      <td id=\"T_4d3ec_row7_col1\" class=\"data row7 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row7_col3\" class=\"data row7 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row7_col4\" class=\"data row7 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row7_col5\" class=\"data row7 col5\" >37.500000</td>\n",
+       "      <td id=\"T_4d3ec_row7_col6\" class=\"data row7 col6\" >37.500000</td>\n",
+       "      <td id=\"T_4d3ec_row7_col7\" class=\"data row7 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row7_col8\" class=\"data row7 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row7_col9\" class=\"data row7 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row7_col10\" class=\"data row7 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row7_col11\" class=\"data row7 col11\" >163.333508</td>\n",
+       "      <td id=\"T_4d3ec_row7_col12\" class=\"data row7 col12\" >44.306385</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>40.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>163.529285</td>\n",
-       "      <td>44.343704</td>\n",
+       "      <td id=\"T_4d3ec_row8_col0\" class=\"data row8 col0\" >40</td>\n",
+       "      <td id=\"T_4d3ec_row8_col1\" class=\"data row8 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row8_col3\" class=\"data row8 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row8_col4\" class=\"data row8 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row8_col5\" class=\"data row8 col5\" >37.500000</td>\n",
+       "      <td id=\"T_4d3ec_row8_col6\" class=\"data row8 col6\" >37.500000</td>\n",
+       "      <td id=\"T_4d3ec_row8_col7\" class=\"data row8 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row8_col8\" class=\"data row8 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row8_col9\" class=\"data row8 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row8_col10\" class=\"data row8 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row8_col11\" class=\"data row8 col11\" >163.529285</td>\n",
+       "      <td id=\"T_4d3ec_row8_col12\" class=\"data row8 col12\" >44.343704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>41.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>164.941425</td>\n",
-       "      <td>44.559629</td>\n",
+       "      <td id=\"T_4d3ec_row9_col0\" class=\"data row9 col0\" >41</td>\n",
+       "      <td id=\"T_4d3ec_row9_col1\" class=\"data row9 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row9_col3\" class=\"data row9 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row9_col4\" class=\"data row9 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row9_col5\" class=\"data row9 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row9_col6\" class=\"data row9 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row9_col7\" class=\"data row9 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row9_col8\" class=\"data row9 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row9_col9\" class=\"data row9 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row9_col10\" class=\"data row9 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row9_col11\" class=\"data row9 col11\" >164.941425</td>\n",
+       "      <td id=\"T_4d3ec_row9_col12\" class=\"data row9 col12\" >44.559629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>42.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>165.139436</td>\n",
-       "      <td>44.593378</td>\n",
+       "      <td id=\"T_4d3ec_row10_col0\" class=\"data row10 col0\" >42</td>\n",
+       "      <td id=\"T_4d3ec_row10_col1\" class=\"data row10 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row10_col3\" class=\"data row10 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row10_col4\" class=\"data row10 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row10_col5\" class=\"data row10 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row10_col6\" class=\"data row10 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row10_col7\" class=\"data row10 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row10_col8\" class=\"data row10 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row10_col9\" class=\"data row10 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row10_col10\" class=\"data row10 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row10_col11\" class=\"data row10 col11\" >165.139436</td>\n",
+       "      <td id=\"T_4d3ec_row10_col12\" class=\"data row10 col12\" >44.593378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>43.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SubBeamNod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>120</td>\n",
-       "      <td>2</td>\n",
-       "      <td>165.469522</td>\n",
-       "      <td>44.639023</td>\n",
+       "      <td id=\"T_4d3ec_row11_col0\" class=\"data row11 col0\" >43</td>\n",
+       "      <td id=\"T_4d3ec_row11_col1\" class=\"data row11 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row11_col3\" class=\"data row11 col3\" >SubBeamNod</td>\n",
+       "      <td id=\"T_4d3ec_row11_col4\" class=\"data row11 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row11_col5\" class=\"data row11 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row11_col6\" class=\"data row11 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row11_col7\" class=\"data row11 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row11_col8\" class=\"data row11 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row11_col9\" class=\"data row11 col9\" >120</td>\n",
+       "      <td id=\"T_4d3ec_row11_col10\" class=\"data row11 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row11_col11\" class=\"data row11 col11\" >165.469522</td>\n",
+       "      <td id=\"T_4d3ec_row11_col12\" class=\"data row11 col12\" >44.639023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>166.48287</td>\n",
-       "      <td>44.776997</td>\n",
+       "      <td id=\"T_4d3ec_row12_col0\" class=\"data row12 col0\" >44</td>\n",
+       "      <td id=\"T_4d3ec_row12_col1\" class=\"data row12 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row12_col2\" class=\"data row12 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row12_col3\" class=\"data row12 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row12_col4\" class=\"data row12 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row12_col5\" class=\"data row12 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row12_col6\" class=\"data row12 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row12_col7\" class=\"data row12 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row12_col8\" class=\"data row12 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row12_col9\" class=\"data row12 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row12_col10\" class=\"data row12 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row12_col11\" class=\"data row12 col11\" >166.482870</td>\n",
+       "      <td id=\"T_4d3ec_row12_col12\" class=\"data row12 col12\" >44.776997</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>45.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>166.688378</td>\n",
-       "      <td>44.808119</td>\n",
+       "      <td id=\"T_4d3ec_row13_col0\" class=\"data row13 col0\" >45</td>\n",
+       "      <td id=\"T_4d3ec_row13_col1\" class=\"data row13 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row13_col2\" class=\"data row13 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row13_col3\" class=\"data row13 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row13_col4\" class=\"data row13 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row13_col5\" class=\"data row13 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row13_col6\" class=\"data row13 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row13_col7\" class=\"data row13 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row13_col8\" class=\"data row13 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row13_col9\" class=\"data row13 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row13_col10\" class=\"data row13 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row13_col11\" class=\"data row13 col11\" >166.688378</td>\n",
+       "      <td id=\"T_4d3ec_row13_col12\" class=\"data row13 col12\" >44.808119</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>46.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SubBeamNod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>120</td>\n",
-       "      <td>2</td>\n",
-       "      <td>167.026583</td>\n",
-       "      <td>44.849753</td>\n",
+       "      <td id=\"T_4d3ec_row14_col0\" class=\"data row14 col0\" >46</td>\n",
+       "      <td id=\"T_4d3ec_row14_col1\" class=\"data row14 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row14_col2\" class=\"data row14 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row14_col3\" class=\"data row14 col3\" >SubBeamNod</td>\n",
+       "      <td id=\"T_4d3ec_row14_col4\" class=\"data row14 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row14_col5\" class=\"data row14 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row14_col6\" class=\"data row14 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row14_col7\" class=\"data row14 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row14_col8\" class=\"data row14 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row14_col9\" class=\"data row14 col9\" >120</td>\n",
+       "      <td id=\"T_4d3ec_row14_col10\" class=\"data row14 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row14_col11\" class=\"data row14 col11\" >167.026583</td>\n",
+       "      <td id=\"T_4d3ec_row14_col12\" class=\"data row14 col12\" >44.849753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>169.972904</td>\n",
-       "      <td>45.179358</td>\n",
+       "      <td id=\"T_4d3ec_row15_col0\" class=\"data row15 col0\" >52</td>\n",
+       "      <td id=\"T_4d3ec_row15_col1\" class=\"data row15 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row15_col2\" class=\"data row15 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row15_col3\" class=\"data row15 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row15_col4\" class=\"data row15 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row15_col5\" class=\"data row15 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row15_col6\" class=\"data row15 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row15_col7\" class=\"data row15 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row15_col8\" class=\"data row15 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row15_col9\" class=\"data row15 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row15_col10\" class=\"data row15 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row15_col11\" class=\"data row15 col11\" >169.972904</td>\n",
+       "      <td id=\"T_4d3ec_row15_col12\" class=\"data row15 col12\" >45.179358</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>53.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Nod</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>2</td>\n",
-       "      <td>170.175815</td>\n",
-       "      <td>45.201877</td>\n",
+       "      <td id=\"T_4d3ec_row16_col0\" class=\"data row16 col0\" >53</td>\n",
+       "      <td id=\"T_4d3ec_row16_col1\" class=\"data row16 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row16_col2\" class=\"data row16 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row16_col3\" class=\"data row16 col3\" >Nod</td>\n",
+       "      <td id=\"T_4d3ec_row16_col4\" class=\"data row16 col4\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row16_col5\" class=\"data row16 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row16_col6\" class=\"data row16 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row16_col7\" class=\"data row16 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row16_col8\" class=\"data row16 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row16_col9\" class=\"data row16 col9\" >60</td>\n",
+       "      <td id=\"T_4d3ec_row16_col10\" class=\"data row16 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row16_col11\" class=\"data row16 col11\" >170.175815</td>\n",
+       "      <td id=\"T_4d3ec_row16_col12\" class=\"data row16 col12\" >45.201877</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>54.0</td>\n",
-       "      <td>1256-0547</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SubBeamNod</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>120</td>\n",
-       "      <td>2</td>\n",
-       "      <td>170.518885</td>\n",
-       "      <td>45.232575</td>\n",
+       "      <td id=\"T_4d3ec_row17_col0\" class=\"data row17 col0\" >54</td>\n",
+       "      <td id=\"T_4d3ec_row17_col1\" class=\"data row17 col1\" >1256-0547</td>\n",
+       "      <td id=\"T_4d3ec_row17_col2\" class=\"data row17 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4d3ec_row17_col3\" class=\"data row17 col3\" >SubBeamNod</td>\n",
+       "      <td id=\"T_4d3ec_row17_col4\" class=\"data row17 col4\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row17_col5\" class=\"data row17 col5\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row17_col6\" class=\"data row17 col6\" >30.500000</td>\n",
+       "      <td id=\"T_4d3ec_row17_col7\" class=\"data row17 col7\" >1</td>\n",
+       "      <td id=\"T_4d3ec_row17_col8\" class=\"data row17 col8\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row17_col9\" class=\"data row17 col9\" >120</td>\n",
+       "      <td id=\"T_4d3ec_row17_col10\" class=\"data row17 col10\" >2</td>\n",
+       "      <td id=\"T_4d3ec_row17_col11\" class=\"data row17 col11\" >170.518885</td>\n",
+       "      <td id=\"T_4d3ec_row17_col12\" class=\"data row17 col12\" >45.232575</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+       "</table>\n"
       ],
       "text/plain": [
-       "    SCAN     OBJECT VELOCITY        PROC PROCSEQN RESTFREQ DOPFREQ # IF # POL  \\\n",
-       "0   32.0  1256-0547      0.0         Nod      1.0     26.5    26.5    1     2   \n",
-       "1   33.0  1256-0547      0.0         Nod      2.0     26.5    26.5    1     2   \n",
-       "2   34.0  1256-0547      0.0         Nod      1.0     30.5    30.5    1     2   \n",
-       "3   35.0  1256-0547      0.0         Nod      2.0     30.5    30.5    1     2   \n",
-       "4   36.0  1256-0547      0.0     Unknown      0.0     0.75    0.75    1     2   \n",
-       "5   37.0  1256-0547      0.0         Nod      1.0     34.5    34.5    1     2   \n",
-       "6   38.0  1256-0547      0.0         Nod      2.0     34.5    34.5    1     2   \n",
-       "7   39.0  1256-0547      0.0         Nod      1.0     37.5    37.5    1     2   \n",
-       "8   40.0  1256-0547      0.0         Nod      2.0     37.5    37.5    1     2   \n",
-       "9   41.0  1256-0547      0.0         Nod      1.0     30.5    30.5    1     2   \n",
-       "10  42.0  1256-0547      0.0         Nod      2.0     30.5    30.5    1     2   \n",
-       "11  43.0  1256-0547      0.0  SubBeamNod      1.0     30.5    30.5    1     2   \n",
-       "12  44.0  1256-0547      0.0         Nod      1.0     30.5    30.5    1     2   \n",
-       "13  45.0  1256-0547      0.0         Nod      2.0     30.5    30.5    1     2   \n",
-       "14  46.0  1256-0547      0.0  SubBeamNod      1.0     30.5    30.5    1     2   \n",
-       "15  52.0  1256-0547      0.0         Nod      1.0     30.5    30.5    1     2   \n",
-       "16  53.0  1256-0547      0.0         Nod      2.0     30.5    30.5    1     2   \n",
-       "17  54.0  1256-0547      0.0  SubBeamNod      1.0     30.5    30.5    1     2   \n",
-       "\n",
-       "   # INT # FEED     AZIMUTH   ELEVATIO  \n",
-       "0     60      2  160.975324  43.884984  \n",
-       "1     60      2  161.174093  43.928449  \n",
-       "2     60      2  161.589629  44.000491  \n",
-       "3     60      2  161.783395  44.041622  \n",
-       "4    120      2  162.124052  44.100404  \n",
-       "5     60      2  162.611075  44.183661  \n",
-       "6     60      2  162.896506  44.237997  \n",
-       "7     60      2  163.333508  44.306385  \n",
-       "8     60      2  163.529285  44.343704  \n",
-       "9     60      2  164.941425  44.559629  \n",
-       "10    60      2  165.139436  44.593378  \n",
-       "11   120      2  165.469522  44.639023  \n",
-       "12    60      2   166.48287  44.776997  \n",
-       "13    60      2  166.688378  44.808119  \n",
-       "14   120      2  167.026583  44.849753  \n",
-       "15    60      2  169.972904  45.179358  \n",
-       "16    60      2  170.175815  45.201877  \n",
-       "17   120      2  170.518885  45.232575  "
+       "<pandas.io.formats.style.Styler at 0x7fbeeb32c310>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "sdfits.info()\n",
-    "sdfits.summary()\n"
+    "sdfits.summary()"
    ]
   },
   {
@@ -460,343 +414,112 @@
    "id": "570e7d59-10ba-4826-9b98-bec3515cf783",
    "metadata": {},
    "source": [
-    "The SubBeamNod scans are 43, 46, and 54. Retrieve and calibrate a\n",
-    "SubBeamNod scan, then plot it:"
+    "## The SubBeamNod scans are 43, 46, and 54. \n",
+    "Retrieve and calibrate a SubBeamNod scan. There are two different methods for calibrating a SubBeamNod scan.  The first, with `method='cycle'` (the default) averages the data in each subreflector state for each cycle of integrations. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "d86e90f0-a285-4ce3-99bf-5aa48f524eeb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "w='tsys'"
+    "w='tsys'\n",
+    "sbnk = sdfits.subbeamnod(scan=43,fdnum=1,ifnum=0,weights=w, method='cycle')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ea4cc3f1-5cc7-4fd1-b8c4-570fc30090e8",
+   "cell_type": "markdown",
+   "id": "3d3b9fea-ac07-4415-bee2-41dd2e544e77",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GROUPS  [array([2883, 2887, 2891, 2895, 2899, 2903, 2907, 2911, 2915, 2919, 2923]), array([2991, 2995, 2999, 3003, 3007, 3011, 3015, 3019]), array([3083, 3087, 3091, 3095, 3099, 3103, 3107, 3111, 3115]), array([3179, 3183, 3187, 3191, 3195, 3199, 3203, 3207, 3211]), array([3275, 3279, 3283, 3287, 3291, 3295, 3299, 3303, 3307])] [array([2935, 2939, 2943, 2947, 2951, 2955, 2959, 2963, 2967, 2971]), array([3031, 3035, 3039, 3043, 3047, 3051, 3055, 3059, 3063, 3067]), array([3131, 3135, 3139, 3143, 3147, 3151, 3155, 3159, 3163]), array([3227, 3231, 3235, 3239, 3243, 3247, 3251, 3255, 3259]), array([3323, 3327, 3331, 3335, 3339, 3343, 3347, 3351, 3355, 3359])] [array([2882, 2886, 2890, 2894, 2898, 2902, 2906, 2910, 2914, 2918, 2922]), array([2990, 2994, 2998, 3002, 3006, 3010, 3014, 3018]), array([3082, 3086, 3090, 3094, 3098, 3102, 3106, 3110, 3114]), array([3178, 3182, 3186, 3190, 3194, 3198, 3202, 3206, 3210]), array([3274, 3278, 3282, 3286, 3290, 3294, 3298, 3302, 3306])] [array([2934, 2938, 2942, 2946, 2950, 2954, 2958, 2962, 2966, 2970]), array([3030, 3034, 3038, 3042, 3046, 3050, 3054, 3058, 3062, 3066]), array([3130, 3134, 3138, 3142, 3146, 3150, 3154, 3158, 3162]), array([3226, 3230, 3234, 3238, 3242, 3246, 3250, 3254, 3258]), array([3322, 3326, 3330, 3334, 3338, 3342, 3346, 3350, 3354, 3358])]\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "sbnk = sdfits.subbeamnod(scan=43,fdnum=1,ifnum=0,weights='tsys')\n",
-    "sbnk.plot()"
+    "####  Time average it, then plot it.  Here, we use system temperature weighting."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "d9de7a7a-ca5a-485b-88a7-fe532e961664",
+   "execution_count": 7,
+   "id": "fd2d612d-a8da-43ff-87d7-b5d6bb266dfd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GROUPS  [array([2883, 2887, 2891, 2895, 2899, 2903, 2907, 2911, 2915, 2919, 2923]), array([2991, 2995, 2999, 3003, 3007, 3011, 3015, 3019]), array([3083, 3087, 3091, 3095, 3099, 3103, 3107, 3111, 3115]), array([3179, 3183, 3187, 3191, 3195, 3199, 3203, 3207, 3211]), array([3275, 3279, 3283, 3287, 3291, 3295, 3299, 3303, 3307])] [array([2935, 2939, 2943, 2947, 2951, 2955, 2959, 2963, 2967, 2971]), array([3031, 3035, 3039, 3043, 3047, 3051, 3055, 3059, 3063, 3067]), array([3131, 3135, 3139, 3143, 3147, 3151, 3155, 3159, 3163]), array([3227, 3231, 3235, 3239, 3243, 3247, 3251, 3255, 3259]), array([3323, 3327, 3331, 3335, 3339, 3343, 3347, 3351, 3355, 3359])] [array([2882, 2886, 2890, 2894, 2898, 2902, 2906, 2910, 2914, 2918, 2922]), array([2990, 2994, 2998, 3002, 3006, 3010, 3014, 3018]), array([3082, 3086, 3090, 3094, 3098, 3102, 3106, 3110, 3114]), array([3178, 3182, 3186, 3190, 3194, 3198, 3202, 3206, 3210]), array([3274, 3278, 3282, 3286, 3290, 3294, 3298, 3302, 3306])] [array([2934, 2938, 2942, 2946, 2950, 2954, 2958, 2962, 2966, 2970]), array([3030, 3034, 3038, 3042, 3046, 3050, 3054, 3058, 3062, 3066]), array([3130, 3134, 3138, 3142, 3146, 3150, 3154, 3158, 3162]), array([3226, 3230, 3234, 3238, 3242, 3246, 3250, 3254, 3258]), array([3322, 3326, 3330, 3334, 3338, 3342, 3346, 3350, 3354, 3358])]\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sbn = sdfits.subbeamnod(scan=43,fdnum=1,ifnum=0,weights=w)\n",
-    "sbn.plot()"
+    "ta = sbnk.timeaverage(weights=w)\n",
+    "spec = ta[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "3c03a5b7-97d2-4655-8a7b-075069d28d53",
+   "execution_count": 8,
+   "id": "9d701828-c1d8-4e15-930a-710c09ec1cde",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "array([0., 0., 0., ..., 0., 0., 0.])"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "sbn.data - sbnk.data"
+    "spec.plot()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a4d049d1-e27d-4db6-83e4-ebb4c9c24733",
+   "cell_type": "markdown",
+   "id": "88080556-05cc-4bac-bbca-fc838a8e8352",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GROUPS  [array([2883, 2887, 2891, 2895, 2899, 2903, 2907, 2911, 2915, 2919, 2923]), array([2991, 2995, 2999, 3003, 3007, 3011, 3015, 3019]), array([3083, 3087, 3091, 3095, 3099, 3103, 3107, 3111, 3115]), array([3179, 3183, 3187, 3191, 3195, 3199, 3203, 3207, 3211]), array([3275, 3279, 3283, 3287, 3291, 3295, 3299, 3303, 3307])] [array([2935, 2939, 2943, 2947, 2951, 2955, 2959, 2963, 2967, 2971]), array([3031, 3035, 3039, 3043, 3047, 3051, 3055, 3059, 3063, 3067]), array([3131, 3135, 3139, 3143, 3147, 3151, 3155, 3159, 3163]), array([3227, 3231, 3235, 3239, 3243, 3247, 3251, 3255, 3259]), array([3323, 3327, 3331, 3335, 3339, 3343, 3347, 3351, 3355, 3359])] [array([2882, 2886, 2890, 2894, 2898, 2902, 2906, 2910, 2914, 2918, 2922]), array([2990, 2994, 2998, 3002, 3006, 3010, 3014, 3018]), array([3082, 3086, 3090, 3094, 3098, 3102, 3106, 3110, 3114]), array([3178, 3182, 3186, 3190, 3194, 3198, 3202, 3206, 3210]), array([3274, 3278, 3282, 3286, 3290, 3294, 3298, 3302, 3306])] [array([2934, 2938, 2942, 2946, 2950, 2954, 2958, 2962, 2966, 2970]), array([3030, 3034, 3038, 3042, 3046, 3050, 3054, 3058, 3062, 3066]), array([3130, 3134, 3138, 3142, 3146, 3150, 3154, 3158, 3162]), array([3226, 3230, 3234, 3238, 3242, 3246, 3250, 3254, 3258]), array([3322, 3326, 3330, 3334, 3338, 3342, 3346, 3350, 3354, 3358])]\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n",
-      "BINTABLE =  None\n"
-     ]
-    }
-   ],
    "source": [
-    "sbn2 = sdfits.subbeamnod2(scan=43,fdnum=1,ifnum=0,weights=w)"
+    "## Now the other method.\n",
+    "The second method (\"scan\") of calibrating SubBeamNod scans reproduces the method of GBTIDL's `snodka.pro`.  This method is less accurate because it treats the entire group of integrations as one cycle. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "4e9b8e9c-95c1-46bb-9ace-99503877b8d6",
+   "id": "3943d577-238e-4d61-99d0-43e8a6137683",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Spectrum1D(flux=<Quantity [ 1.29341831,  0.57197488,  0.54756602, ..., 20.17125875,\n",
-       "           19.92924671, 19.69146751] K>, spectral_axis=<SpectralAxis \n",
-       "   (observer to target:\n",
-       "      radial_velocity=0.0 km / s\n",
-       "      redshift=0.0\n",
-       "    doppler_rest=30500000000.0 Hz\n",
-       "    doppler_convention=radio)\n",
-       "  [2.97500000e+10, 2.97514648e+10, 2.97529297e+10, ..., 3.12456055e+10,\n",
-       "   3.12470703e+10, 3.12485352e+10] Hz>)>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sbn2[0].calibrated(0)"
+    "sbnk2 = sdfits.subbeamnod(scan=43,fdnum=1,ifnum=0,weights=w, method='scan')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "d683f168-44b0-4bfd-907b-685c5d7008cd",
+   "id": "2841bb86-3b6c-413b-83cf-303872829db8",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "NCHAN  1024\n"
+      "data:  [[-1.08554073  0.51817926  0.59441812 ... 21.04834646 20.80094823\n",
+      "  20.54906974]]\n",
+      "weights:  None\n",
+      "TIMEAVERAGED:  [-1.08554073  0.51817926  0.59441812 ... 21.04834646 20.80094823\n",
+      " 20.54906974]\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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XbC+QJKL6sU6uuBqff/55jc85Oztjx44dFmwNycnp6wZBjBmmVxsa0LqZ2HkeTb2N+yWQrTCVPef1Sxb8va91amFq4qC0w+/zBmH+hpP47VQGSssr8Mw3x7HrucHwcnG0dvOIyEokk4khaijjot66FYM1VI+W+roYwwyQ3N0pLMXPCdohGgelAgPa+Fq5RVUp7RR499Hu4sKDN/PV6P76LqzZf5n7LRE1UQxiSPbOVGZi3J3s0dLHxayfZThcZUubQf566joK1NrFAh/sGWy1gt57cVDaYcn9nYweW/r7OUz/4igqKiSzWgQRWQiDGJK17AI1MiprU8xZ1KvT3F0l1mb8lZ6Li1nyX4StqFSD93frZ/xMkdhQ0t06BHoYTb8GtEN7q+MuWalFRGQtDGJI1pIy88X7HQPNWw+jM6mHfhn+oynyn2q9Pzkb2QXa2V29W3mjixmLo01l+YNdkfzGWKMZVP/7Ixk/HEtjRoaoCWEQQ7J2yWA5+rb+lllBt3crfV2MLcyQ2ZqYId5/Nrq12bNZpuKgtMMHj/XA04O1KwqXlldg0cZTiFq+mzUyRE0EgxiStUsGwzmW2gagU5AnHJXaXx2572idV1KG7aczAQBeLg6SLOi9l+dGRmCgQbtv5Kkx6r04lGq4AzaRrWMQQ7J26WaheL91c8vsq+PkoESXYO2QS+qtItzMl++u6DtOZ0Jd2dlP7BYElb3Syi2qPycHJb5+so+YkQGAtNvF6LV0F05clX+mjIhqxiCGZE03nOTl4gAfV8utF2K4BYGcO8pjqfpM0viu8l3zRqFQ4N9j2mN8F/02JPklGkz+6CD2XbhpxZYRkTkxiCHZKlBrxJlJrZu7WbSWo6dBEHPksjyHlARBwLHK7RPs7RToGiz9gt7a2Nkp8OHUnvhoqvF+S49/cdSm1vQhIj0GMSRbKVYYStLpG+YDu8qYaX+yPP/SP3M9DynZ2p9hz5becHKQ31BSdcZ1CcSm2f2NHnvs08M2UYRNRMYYxJBsXc62fFGvjpeLI7qFeAEAkrMKcD2n2KKfbwqbTuh3rDacNm4Lerb0xtoZvcXjfLUGj35yCO/9cYFTsCupNeX4Ky2HBdAkawxiSLasMTPJ0OC2zcX7cqu70JRX4Je/tNsMOCrtjGpJbMXQ9n5IeGUk+oVrtynQVAh4749khL+4Fd8fu2rl1lnHlVuFGLxiL1q98Dva/Wc7Jn54ABH/2YadZzIZzJAsMYgh2TKameRn+SBmSDuDIEZmQ0p/pecgu0A7q2pYez94ujhYuUXm4eXiiLUz+lTZqPPlLWdsbhfy2pSUlWPlrgsY97/9uHq7qMrz//j6OKLf2ouLWfnVvJpIuhjEkGzpZiY5KBUI8Xa2+Od3C/aCp7O28/8zORuacvn8JXvokn7fp0ER8lsbpj6cHZV4/7EeiJncRXystLwCj39xBHcKS63YMsu4XViKB1cfxPu7k1FYyyKA13NL8M/1CSgq1ViwdUSNwyCGZKm8QsDlyqLUVs1cYa+0/H9lpZ1CXGQtr0SDv9LlMQOmpKwcaw+kisdy2GbAFB7r0xK/zxsI58oC5gs3CvDgxwdtOiNz8GI2ev53F85c1+/03tbPDe8+2g2Xlo3Dupl9MGdoazjaa39/zmXk4V8//AW1hisekzwwiCFZunanWBzDt0Y9jM6QCP2QUpxM6mIOXb6FW5UZiHb+7ugc1DSCGEC72vK2+YPQ3F0FALh8sxD//e2slVtlPu/+cUG8b6cAtswZgF0Lh+CBHsFQ2ikwJKI5/m90e2ye3R+6FQq2nc7Eop9OQRBYAE3SxyCGZMlwz6TWfpadXm3IcChGLsW9e89niffnj2gLOzt57JVkKq18XbF+Vj+4qewBAL8nZuCkjBcsrMnhy7fEdYAAYMn9ndC9ckbd3ToFeeIfg/QrHv+ccB0bjqWZu4lEjcYghmTJKIixYiYm0NMZEZUbT55Kz5F8jYUgCNhTGcQ4KBUY1Na262Fq0sbPDf8e0048fvabE0irpuBVrlKyC/Hkl8fE49nRrfF4VKtaX/PC2PaYN6yNeLw69hI30iTJYxBDsmQYxIRbMYgB9ENKFQLw58Vsq7blXi5mFSD9jnZNm96tfODuZJuzkurib31aom3lrLbMvBJM+vCA0f8ruSqvELDop7/EIt6uwZ6YaxCc1EShUGDhqHboG6adkn71dhG+OJBi1rYSNRaDGJKlS1n66dXhFl6t926DDepi9hgM1UiRYfuGtfezYkusz0Fph2+e6iuu9nyrsBSTPjyAkjJ5Zx/e350sDiP5ujni6yf7wsXRvs6vf31iZ3E16k/iLuG2xLOLlpJ+pwj7k28i/U4Rvj1yBTvPZFq7SQSg7v+ziSRE9xezn7sKHlbOJvQJ84G7kz3ySzTYeSYTak25ZHeDNgxihjbxIAYA/D2c8N2sfui7bDcA7aaRT399HJ8+3kuy/4a1KdVU4JvDV8TjF8d1EJcBqKt2Ae4Y3SkA205nIq9Eg2e+OY4Ns/o1udopQDuTb2tiBhb+8Fe1z6+a0gP3yXjjVFvATAzJzp3CUnF2jTXrYXRU9koMrwwICkvLcfpa3j1eYR15JWWIr9w/KLSZC8J9rZvBkgp/DycsN1hDJu7CTby46bQVW9Rwv/x1XfzdGNTWF5N7Bjfoff41KgLulYXPR1NuY7/Eh0nN4VpOMSZ9eKDGAAYA5n53Ev/4Kl722Ts5YxBDsmO0Z5IVZyYZ6l1ZRwAAx1Kluav1/gvZKK/cN2hoOz+L7votdX/r0xLrZ/UT15DZeCIdT62Ll9UChhdu5OM/WxLF49nR966DqUkbP3csMwjstpy8VsvZtuf4lTuYuOoAzmdWXcHY8a41qXaevYEhb+3F3iRpDyXbKgYxJDuG9TBSyMQAQL/wZuJ9qU613mEwht/U62GqE9W6GZbc31E8/uPcDby/56IVW1Q/H8ddQkmZNuga3t5P3DOqoUZ18oeHkzYbs/10JgrVTWMl3w/3XsSDqw+K23IA2uLolY90Q+ry8bjwxlj8+e+hGNnRX3z+Rp4aT6w9huXbzjMrY2EMYkh2pDK92lC4rytCfLRbHxxLvY0CiX3hZ+aW4PfEDACAp7MD+jayg7NVj0SG4G+9Q8Tj93cn47Vfz0h+5+u9SVniruRuKnusfKR7ozNtKnslxlfWexSXlWPnWdsvZF2x/Tze2pEkHvdp5YMTL4/EL3MHGg3NBXu74LPHI/H1k32MXv9x3CW8uf28xdpLDGJIhowXupNGEKNQKBAdoc1ulJULOCCxGoKdZzPFoaTHo0JlWbRqCQqFAssf7Ip/GkxJXnsgFVPXHEGZhIeWVmzXd7wPRwabbEPPyT1biPc3n7xukveUqrPX8/BR7CXx+L6ugfj6qT7wcXWs8TWD2jbH2ddHG/2c1h5Ixf/+SDZrW0mPQQzJjm7PJCcHOwR6OFm5NXqGWxAclFoQc+aGeH9s50ArtkQe5g1vi6cGhonHhy7fQv/leyS5OeL+5Js4l6EvJv/nsLYme+9eLb0RXLm56p/JN5GVb7v7TK3Zf1m8Pzu6NVZN6VmnYN/FUZv5Mvz/8u4fF/CvH/6SfAbPFjCIIVkpK6/A1VvalVVbNXOV1LTPvuE+UFa2Z/f5LMl8geUWl+HwZe2u1cHezugQ6G7lFkmfg9IO/7mvIx6N1A8t3cxXY8GGBOs1qhqXbxZg2udHxeOVj3SrNXNQX3Z2Ckzqrs0yVAjALwm2mY357shVbKosXnZ3ssfTQ1rX+z1eHNcB47vo/0DYeCId0744gus5xSZrJ1XFIIZkJf1OMTSVwYFU6mF03J0c0L+1tsA3/U4xTqblWLdBlXafuyH+zEZ29OespHpY/mAXPNRLXwux8+wNMSC0tpKycvx9zRHxuFeotxhwmNKkHvr33JJge7OUtiZm4MXN+lldi8fWf20dQBvwvfNIN0zopl835sDFWxj+TpwkM3i2gkEMyUqKwfTqMAmuc3JfV/1fYlKpi/l0nz5NPqZTgBVbIj8KhQJvP9wNL4xtLz725JfHkJVn/WGVn46n43quvh1LJ3U2S2ayjZ8bugZrdzo/fS0PyTeqTjuWq/IKAa//qt/FfGL3IKPC7vpyclDig8d64MVx+v8vxWXleOyzI5LJzNoaBjEkK5dv6qdXSzGI6d9av6HiwUvWD2IycovFtS66tPBEnzDOSmqImQPC0KOlFwDtgoaPf3HUqn9d3ypQY4XBLJiPpvZEh0APs33eAz0MC3xtJxuz+eQ1ZFYGpM1cHfHmg11NEgj+Y3BrrHk8Ujz+Ky0HizclQhAYyJiaZIKY1atXo2vXrvDw8ICHhweioqKwbds28XlBELBkyRIEBQXB2dkZ0dHROHPmjBVbTNaQkm0QxFh5z6TqhPi4oKWPCwDgxJUcq+8CfOiSfuhjaHsucNdQjvZ2eOfhbuJCZ+cz8/Hf385ZpS2CIODFzYnIK9EGUZGh3hjb2bwZtgndgsR6r58TrttEVkGtKcc7O/WzulY+2h1ODqabtTeioz9enaBfd+j7+DSsO5jKQMbEJBPEBAcHY/ny5YiPj0d8fDyGDRuGiRMnioHKihUrsHLlSqxatQrHjh1DQEAARo4cifx820lt0r2l3dEXyemCBanR1cWUllfgeOUy/9ZiGMREGSzIR/UX3twNG57uJ3bm649exe+nMizejvd3X8SOytlmSjsF3nyoq9mDU183FQa31WYZr+UUIyE9x6yfZwk/xKcjo3I4bnh7P6PZhabyxIAwrHiwq3i85NezWPB9gk0EgVIhmSBmwoQJGDduHCIiIhAREYE33ngDbm5uOHz4MARBwHvvvYeXXnoJkydPRufOnbFu3ToUFRXhu+++s3bTyYJ0lf5ODnZoZsJZGKbUv41+SOmAlYeUDlUWoTra24nDIdRwPVt6Y57BFObnvk9AggULuLPyS/DBHv0aJP83up3FCtzHGGR7/jh7o5Yzpe96TjHeMhiOWzAiwmyf9XBkMDq30A/1/ZxwHbO/PSGrLS2kTDJBjKHy8nJs2LABhYWFiIqKQkpKCjIzMzFq1CjxHJVKhSFDhuDgwYP3fL+8vDyjm1qtvudrSHoEQcC1ykxMkJezZIdGDDMe1lwv5mJWPtIrf169WnqbNFXelP1jcLj4b1xaXoFnvj6Om/nm/06pqBDw9o4kcaZZnzAf/GNQuNk/Vye6nZ+Yhfr68BVZL6//xZ8p4nDciA5+6FJZuGwOCoUCq6f2woA2+u+F7WcysXhTYi2vanrUanWVvrouJBXEJCYmws3NDSqVCs888ww2b96Mjh07IjNTu9y1v7+/0fn+/v7ic7UJCQmBp6eneIuJiTFL+8m8bhaoUVz5xdnCy9nKralZc3cV2vlr12JJvJZrtAeLJX24V7/66MC2vrWcSfXh7KjEupl90LuVNwAgM68Ej39xFDlFpWb7TEEQ8O+Np/BDfDoAQGVvh5WPdLPoOkn+Hk7i7Lv8Eo3Vh0obKiu/BF8fviIevzqhk9k/M8THBd8+1Q+P9WkpPvbj8XR8ZjBzsKmLiYkx6qdDQuo2S0xSQUy7du2QkJCAw4cP49lnn8X06dNx9qx++tvdf3kLglCnv8bT0tKQm5sr3hYvXmzytpP5nbmmj8zbB0h7wbZhHbRbEFQI2nVaLC2/pAxbK/dK8nJxwLSoUIu3wZY52tvhw6k94e+hAgCcy8hD99d3mW1F2w/3XsSPx7UBjJ0CeH1iJwR7W74mzHDj0P3J1p991xCbT1yDWqMdyhnRwQ8hFqyt++/ETkYZmZht53DKBuqLTGHx4sVG/XRaWlqdXiepIMbR0RFt2rRBZGQkYmJi0K1bN/zvf/9DQIB2LPburEtWVlaV7Ex1dDOedDeVSmWW9pN5nb6WK97v3MJ86V9TGG7wZX8s1fJ/sW47nSl+Ud/fLQgeTqbZS4f0/Nyd8Pn03kaPLfnF9JtFHryUjfcM9uJ55b6OeLR3y1peYT4DDOu9JLIOUn0UqDX4bH+KeLxwZDuLfr690g7fPtUPswZptyioEIB/fHUcdwrNl8WTC5VKVaWvrgtJBTF3EwQBarUaYWFhCAgIwK5du8TnSktLERcXh/79+1uxhWRJqZXbDQBAhL+0MzGdW3jCQanNEh5LvW3xaZWbT+jX8jBc44NMq3MLT6x8pJt4vDUxEy9tOW2Sf++KCgHrDqZi2udHxTqYGf1bYcaAsHu80nx83VTiejSnr+fKrvP9KT5NHN7t0sLTaltwzB8RIWaTM/NK8NqvXC6koSQTxLz44ovYv38/UlNTkZiYiJdeegmxsbGYOnUqFAoFFixYgGXLlmHz5s04ffo0ZsyYARcXF0yZMsXaTScLSbutD2IsmQJuCCcHJXq21NZMXLlVhBNXLZeNuZ5TjMMp2llJYb6u6B7iZbHPboom9wzGR1N7Qjeyvf7oVbywMRF5JWUNej9BEPDLX9cR/uJWvPrLGXH38chQb7w0voOpmt1ggyrrqwQB2CWjWUqCIBjVwsRM7mK1yQFuKnusm9kHXpW7jW9JuI7NJ9Ot0ha5k0wQc+PGDUybNg3t2rXD8OHDceTIEWzfvh0jR44EACxatAgLFizA7NmzERkZiWvXrmHnzp1wd5f2X+RkOlcrg5hmro5wU9lbuTX39ojB5oHrj9ZtfNcUtiRcgy4R8ECPFpKdxWVLxnUJxMpHuomBzPfxaei6ZCeW/HKmzlmZzNwSbDl5Df/64S/MW3/S6LluIV5YN7MPHJTW/8oe3Uk/hP/NkSu1nCkthy7fwqXKFb/7hPlYfUja38PJaLr+y1vO4LbMMltSoBBsePnAvLw8eHp6Ijc3t87jayRNJWXl6PDKdggC0D3EC1vmDLB2k+6puLQcfZb9gfwSDZwc7HD0pRFmr00RBAEj392Hi1naPab2Lxoq+ayVLdl8Mh3Pff+X0WPhzV3x69yBcK0l8D5x9Q4mf1T9chGdgjyw8dn+kpoiP/rdfUi6kQ87BfDXq6PgLoOaq5c2J+LbI1cBAO8/1gP3G2zUaC0VFQLmf5+AX//S7g7uqLTDroWDEdpMequRW1pd+2/rh/VEdXAtp1jMLsilU3Z2VIq7CpeUVVhkltLpa3liANOnlY9sfla24oEewVU2ELx8sxBdX9uJhT8k4FaBGrcLS1FRIaCkrBynr+Xi4Y8P1hjAfD49Er/PGySpAAYA+oZr9+CqECCbqda6dtopjAvvrcnOToHFY9vDufLft7S8Akt/t852FnIl/Zw8EfRDSQDQ0ke6a8TcbUK3IHEcfltiJh7oEWzWz/vuqD69/0BPFvRaQ8zkLpjevxW+OnQF649q//IvrxCw6cQ1bDIouLZTaIOA6nwxIxJD20l3r6s+YT746pD2/9qx1NuIbieNoKAm5zLyxI1QOwR61JoVs7QgL2d8Mq0XHv/iKABtndHRlNvcrLWOmIkhWUgzCmLkk13oFeoNXzftlP64CzdRqDbfzsd5JWXYclKblnZT2UsiXd4UKRQKdAj0QMzkLnj/sR4Y0KYZqluT7u4AZmrfltg8uz9OvzYaw9r7SzaAAbRZPp0DF2/VcqY0rI7VL/z4YE/z/iHREIMjmuONBzqLx0+tO4bzmXVbsbapYxBDsiCnmUmGlHYKsRBSralAbNJNs33WlpPXxBWNH+jRQlJ/bTZV93cLwrdP9cP3T0fBTWUvTrt3V9mjmasj2vm7Y0K3IGya3R9vPNAFPVp6y6Jo3c/DSVyV+q/0HFy5VXiPV1hPqaYCO89q1xjzcXU0WjVXSh6JDBGnXeeVaPCfzaet3CJ5kP5vCxHuHk6STxADAGM7B4oFhdtOZ2B85dLtpiQIAr4xmD46tZ80v6ibqt6tfPDXq6MgCALsJTDDyBTGdglA0o18CALw6b7LeOOBLtZuUrVOXr2DkjLtwo/R7ZrD2VFa9UU6Dko7rJ/VD5M+OoArt4oQf+UOpq45jG+e7CvprJy12cZvE9m8q7e1Gxna2ykQ6CmfmhhAWwSpWw9i7/kss2ycdyz1Di7c0Bb0RoZ6o30AZ+NJjdJOYTMBDAA8OTAMjvba6/nj3A2LL+hYVwcu6Ye7+reW9h5i3q6OmD9cP+36wMVbWPB9AopL5bvZprnZzm8U2SxBEMThpBbezuJOunLhoLTDyA7aIaXC0nKz7DljmIX5ez/uk0Tm5+7kgIGV2xDcyFPj9DXp1XCUaiqw6YR+Ebmo1s1qOVsaJnZvYTR76ueE63hj69laXtG0MYghybtTVIaCyoJYuQ0l6YztEiDe33jctCtzFqg12H5aP+Zv+FlE5jS8g76zjbuQZcWWVO/ApWyk39FmcfuF+6CFl/SzuEo7BT6f0RsfTukpPvbN4avYn2y+ejo5YxBDkifXol5DA9r4wr2yYHP7mUyjzSwba+/5LJSWa8f8x3UJgMpemmP+ZHuiwvWZjYS0HOs1pAZ7zukDqxn9W1mvIQ0wvmsgJhsskzDt86P41w9/mXyDUbljEEOSJ+eiXh2VvRIzBrQSj+MumOavKkEQ8HGcfvro6E7MwpDltGrmCg8nbXB+5PLtBu8XZQ7FpeXYfFK7Lo/K3g7920i7HqY6C0dGoGOgvr5t44l0rDuUar0GSRCDGJI8wyAmxFueQQwATDLYTfr3Uxkm+Yvq0OVbOHNdW4vQuYWHWKNAZAl2dgqM66KdbZev1mB7YqaVW6R34GK2OAw9oVuQ2bf8MIdgbxf8+EwU7A3qAF/79Sze2nFeUgGjNTGIIcmT60J3dwv3dUXr5to9Uc5m5DU6G1NeIWDF9iTx+B+DW3MqJlncw5H6xeP+vGj6ovWG+u3UdfG+nDOUrip77P/3UKPHPtx7CV2X7MTPCddqeFXTwXViSPLS7thGEKNQKLBoTHs8/fVxAMCGY1cxtBF7uBy8lC3WIbT0ccHYzvL9oib56hrsBVdHJQpLy3HwUjYEQbB6MK3WlGPHGe1eZZ7ODhjUVt4ZykBPZ5xaMgpvbjsvrjkFAPM3JGD+hgTMjm4NH1dHlJSVY1KPFmjh5YwbeWp8/udlfHP4KorLytEr1BsujkqUlJXD3ckBQ9s1x8ORIZLbl6u+GMSQ5OmGkzyc7OHpIr+UsKHh7f3g565CVr4au89lITO3BAGeTvV+n7LyCry/O1k8/r/R7eBgQ2uQkHw4KO3QL7wZdp/PQnZBKZJu5Ft9naITV3LE1atHdPCXfUcNAB5ODnjjgS4Y2dEf/1x/Evkl+i1MPjLYVuHtnRfg66ZCXnGZWPAPVN2oc8/5LPz6Vwa+ndVX1t8d8m05NQll5RW4nlMCAGjZTL5ZGB17pR0e6qVNv2sqBCzedKpB7/Nx7CUcS9V+KTVzdTSa6kpkaQMMarH+NMM6SPX150X9UK3cszB3i27nh2MvjcCsQWE1npNdoDYKYGpyNPW27Lc3YBBDkpaRU4LyygJYORf1GpraLxQulUuf7026iVPpOfV6fW5xGb4yWNxuxUNd4eLIpCpZj2EQY6qZd42x74I+kOrfRvoL3NWXk4MSL43viNOvjcbIjtqFNHWrJxsa0ykAXz7RGwmvjMT2BYPw+7yBeDa6NTyd9Rnt7+PT8FHsRYu13dT4zWcFak05UrILkVtUhl6h3ja1FLmpXc4uEO+HNnO1YktMp4WXMxaNboclv2pX4Vx7IBXvPtq9zq9/Z2cSbuarAQBDIppjeOVqwETWEuHvhmBvZ6TfKcahS7eQW1RmtaHflOxCJFauw9QpyAN+7vUfrpULN5U9Pns80uixm/lq7E++iRAfF/Q22G3cy8URANApyBOLRrfDU+visfu8dh2dt3ckYWznQIT5yu87lr2nhcUmZaF/zB6MeW8/Hv30MNq8tA2tXvgdrV74HRNX/YnYpCzJ7kFiDZdv6nfHDW8uv1+wmjzau6W4n9Kvf12v8y7Ap6/l4qtD+izMy/d1MEv7iOpDoVBgTOUMIE2FgD/O3bBaW3ae0U/zvr9bkNXaYS3N3VWY3DPYKIC5m0KhwJrpkXg0MgQAUCEAS387K8u+h0GMBSWm5+KpdfG4VVha7fN/pedixtpjCFu8FZM+PMB1AGCciWltQ0GMs6MSU/tqd5rWVAiY8tmRewYyRy7fwn0f/CkeD2rrizZ+7mZtJ1FdjTGYHbf9jPXWi9ltsEqvbqiFqlIoFHjpvg5wr1yscPf5LKMCYblgEGMhKdmFuP/DP6Gp4wJnCWk56B+zB+cypLepmiWlZOs79jBfNyu2xPSeGdJaTN9eyynGkLdixT2Q7nYjrwSzvz0hHns42eOTab0s0k6iuujZ0hvN3VUAgH0XbqJQrbnHK0wv/U4R4q/cBqBdlym8uW19Z5iah5MD5g3T75r91o4kZOQWW7FF9ccgxkKW/HIGukxdhL8bLiwdiwtLxyL2+Wj8d1JnPNwruMprCtQaPPbZYdyuIXPTFOiGk7xcHODj6mjl1piWu5MDvpjRW/ziB4BnvjmOVi/8jvkbTiIztwSCIOD3UxkYsHyPUQbv8xm9WcxLkmJnp8CoysyHWlNhlQLfNftToPs7cTTXTaqTxyozwjpfHki1TkMaiEGMBSRl5hv9Qq98pDsc7e3gaG+HVr6umNYvFG893A2py8cjJWYc/juxk3huTlEZnlh7FNdy5BUdm0JRqQYZudrp1eEyLDirizBfV6yd0bvK4z8nXEe/mN0IW7wVc747IWbw3FT2+P4f/Wod7yaylrGdA8X722rIKpqTbqdnpZ0CswaFW/zz5chNZY//jNfX1n1xIEWcOCAHDGIs4LP9l8X7r07oiM4tPGs8V6FQYFpUK/yxcIg4Ze6v9Fw88OEBpGbXrfjTVhgX9dpuWrhzC08ceGEYlHa1r3Ia4uOMPxYOQd9w25sySrahb7iPOH33j7M3xL2LLOHKrUJcqvzO6BbsaXOZW3N6alA4JnbXFkGXlQtWrWmqLwYxZpaZWyLub+Hp7IBHKqvB76WNnxt+eDoK3pUzWLLy1fj750cs+qVgbcb1MLaZidFp4eWMQy8Mw7R+odU+38bPDRuf6d+g1X2JLMVBaYf7umqzMcVl5fjjrOVmKW012HxylIz3SrKWfwzWZ65+++t6LWdKCwfVzezDvRdRVq4dCvh7v5ZwVdX9R949xAs7nhuMxz8/ivOZ+Ui/U4x//3QKq6b0sPreJJZgmImxpZlJNfHzcMJ/J3XG00PCcTNfje4hXvgrPReBnk7w92DwQvIwoVuQuL/PH+duGO3ebk4HL+kXuJPzho/W0jHQA2G+rkjJLkT8lTsoUGvgVo/+ylqYiTGjsvIKbKnMwrg6KvHEgJqXia6Jn7sTPpnWC6rKoaXfEzPw7h/J93iVbTCcXm3Lw0l3C/Z2QY+W3lAoFOge4sUAhmQlMtRbHFKKS7qJUs29l79vrAK1BkdTtLOSAj2d0MoGtiixNIVCgajW2qHq8goBCVdzrNugOmIQY0Ynr+aIm3SN7OgPXzfVPV5RvdBmrljxUFfxeNWeZByvnEZoy3TDSQqFvHevJmpK7JV2GFa5O3u+WoMjKbfM/pm7z92AujJYGt7Br0lkqs2hj8GEgaOp8uhjGMSY0enKpa8BNLoYc2L3Fnisj351xXnrE5BbbLuL4QmCIA4nBXs728QutERNxQiDrTBqWvvIlLYmZoj3x3UJrOVMqk3vMH0QcyyFQUyTZ7hQXcfAxm9N/+qETuge4gVAuzja3O9OQFOHnUrl6Ga+WixiDrexRe6IbN2Qds3h5KDtXn756zrUmnKzfVahWoPYJO3Ual83R/QN4+y9hmrh5YygyskDJ9PuoEwG/QuDGDM6WxnE2CmAdgGNXx7eyUGJDx7rIc5Y2p+cjf/+drbR7ytFl242nZlJRLbGTWUvFtfml2iMtgIwtd9PZYhDSaM7BdxzqQKqnS4bU1JWYTSaIFUMYsykrLwCyTe0hanhzd1MNhwS4uOCT6ZFwkGp/UVdd+iKUSrVVhhOr24KM5OIbE2UwRD6c98nmG1zwU0n08X7D1Wz8jnVT6RBXUx86h0rtqRuGMSYybmMPJRWpuJMMZRkqE+YD16+r6N4/MLGUza3fszlm01zZhKRrdAV9wLabQjOmmEfuNuFpeKspFbNXMThdmo4uRX3SiaIiYmJQe/eveHu7g4/Pz9MmjQJSUlJRufMmDEDCoXC6NavXz8rtbh2By7qK/INi6VMZVq/UAxq6wsAyCvRIGbrOZN/hjVdbkIL3RHZIj8PJ8wd2kY8NseQ0u5zN/R7JXUK4KwkE2jr5yZOkY9PvW22DJqpSCaIiYuLw5w5c3D48GHs2rULGo0Go0aNQmGh8VL7Y8aMQUZGhnjbunWrlVpcO8O/OvqaIYhRKBR4YWx78XjjiXTkFNnORpG6TIyzgxIBXCeFSJYMNxfcmphh8g5xxxn9isBcpdc07OwUiAz1BgDcKSoz+oNSiiQTxGzfvh0zZsxAp06d0K1bN6xduxZXr17F8ePHjc5TqVQICAgQbz4+0twIT9cJK+0UaNXMPJmETkGe+Hs/7ZdESVkF5m0w37izJZVqKpB2R7vhZZivK+xYqEckSy28nNGtcojnfGY+jl8xXY1FUalG3PCxubsKPTiUZDKGw3KJ6dIu7pVMEHO33FztD+7uICU2NhZ+fn6IiIjArFmzkJV17xRlXl6e0U2tNu8OnYIgiIWpId7O4kaO5jBnaBtxaeh9F25aZE0Gc7t6uwjllTnicBb1Esna9Cj9fmBrD6aa7H1/Trguzkoa2dGff+yYUJdg/SbFiRaaoaRWq6v01XUhySBGEAQsXLgQAwcOROfOncXHx44di2+//RZ79uzBO++8g2PHjmHYsGH3DEpCQkLg6ekp3mJiYsza/jtFZSgq1a6L0NJMWRidQE9nvP1wN/F4xY4klJSZb00GSzAq6mU9DJGsje8aCF837Y7S209nIjO3pNHvWVEh4P3d+u1X7uMCdybVpYXlg5iYmBijfjokpG6bJUsyiJk7dy5OnTqF9evXGz3+6KOPYvz48ejcuTMmTJiAbdu24cKFC/j9999rfb+0tDTk5uaKt8WLF5uz+Ua/pIEWqOcY3clfrChPyS7E5pPXzP6Z5mQ4vZozk4jkTWWvxGN9tMPe5RUCvjtypdHvefp6LjIqv2f7hvmIe/6QaTRzU4mL3p29noeKCvOXKSxevNion05LS6vT6yQXxPzzn//EL7/8gr179yI4uPY5/4GBgQgNDUVycu0bInp4eBjdVKqG7WFUV5l5xeL9AE/zBzEKhQL/HttOPF6z/7IsVlqsieHu1RxOIpK/qX1DxUXofjyeLg4XN9SmE/o/1O7vHsRZSWbQuTIbU6DWIOWW+Yt7VSpVlb66LiQTxAiCgLlz52LTpk3Ys2cPwsLuvePzrVu3kJaWhsBAaaUSM3P1w1uWCGIAoGdLb3GTxEs3C7FFxtkYw0xMKw4nEclegKcToiOaAwAycksaVbtXXFouZptV9na4r0uQSdpIxgyHlKS8cq9kgpg5c+bgm2++wXfffQd3d3dkZmYiMzMTxcXarEZBQQGef/55HDp0CKmpqYiNjcWECRPg6+uLBx54wMqtN5aVrx9O8vcwb9ZHR6FQ4LWJncTjd3ddkO2Ua92UPl83FTycHKzcGiIyhUd762sclm091+AhitVxl8TNb8d2DoCnC78jzKGzQRCTlJlvxZbUTjJBzOrVq5Gbm4vo6GgEBgaKt++//x4AoFQqkZiYiIkTJyIiIgLTp09HREQEDh06BHf3xu9LZErZBfpMjK+bZYIYAIiOaI4BbbRjw9dzS/DurgsW+2xTyS8pE39+LOolsh0jOvijZ0svANoNbPcm1X/xO015BX6K19ZK2NspMHdYW1M2kQwYLjJ65VaRFVtSO3trN0DnXuubODs7Y8eOHRZqTePcKtBnQCwZxCgUCrz9cDcMezsOxWXl+Ol4OhaP62CyfZssITVb/8vSytfFii0hIlOys1Ngat9QnLiaAwBYvCkR2xd4w8fVsc7v8e2Rq7heWdA7qK0v2vix8N9cWng7Q2mnQHmFgFQL1MQ0lGQyMbbEMBNTn19QUwj0dMZ9XbU1QoWl5VhnwnUZLMGwgCzMl19QRLZkYvcgcSZlVr4aS345U+fXFqg1WGmQXZ5jsKUBmZ6D0g4h3s4AgNTsQskupMogxgyyKzMx7k72VsmCjO2iX377w70XUaqRz0yllJvcM4nIVtkr7bD8wS7QrUu37XQGrtThr3xBELBi+3mxFmZS9yCj3ZbJPEIr1zkrLC0X+zWpYRBjBrpMjCWHkgwNbeeHDpU7Z+eVaHBMBjuR6qTeYhBDZMvCm7uJWZSycgF///wIsvJqXwDv3V0X8NUh7foyCgUwdxizMJbQqpl+SL8uwaY1MIgxsZKycuSXaAAAzSw8lKSjUCgwO7q1ePzar2ckmwq8m+H06tBmrIkhskX/GBwudpBpt4vxny2na52ttObPFPH+zAFhaOMnrckctspwiYtUiRb3MogxsduF1inqvdvgiOawr8zZXrhRgEOXblmtLfWhC2JaeDnLqiCZiOrO3ckB6//RDx5O2rklO8/ewOJNiSguNd4y5U5hKbq/vlPcxqV9gDv+M76DxdvbVIV46/+QTL/DIKZJMCzqbeZmnUwMAHg6O+D+bvpFoD7bf9lqbamrO4Wl4pg3ZyYR2bZAT2eseKgbdIvtfh+fhg6vbMfhy9o/uM5cz8Xo9/Yhp6hMfM20qFCuzmtBwT7O4v2028W1nGk9kplibSustUZMdZZN7oJ9yTeRXVCKvUk3cTErX9Jp2MuGK/WaeeNMIrK+MZ0DMG9YW/zPYDPHv316uNpzn41ujSmVezCRZQQzE9P0GP7V4G3llSSdHJR4cmC4eBx3IduKrbm31GwW9RI1NQtGtMWzBjV8d2vm6oiNz0bh32PaMwtjYW4qe7EfS78jzUwMgxgTK1RrxPtuElgyf1h7P/H+14dSLbIbaUMlZxWI97nxI1HToFAo8O8x7XH0peEYXLm/kk6nIA9sWzAIvUI5ndpaQir35MvILZbkxsIMYkys0KAwzdXR+oWp7QLcERWu3Yog9VaRON4sRecz88T77QPqtoMpEdkGP3cnfDmjN+YNb4sWXs54YWx7/DJ3IPzcLbOJLlUvuHLBuwoByMipfSq8NTCIMTHDTIyrSholR1P76ceRfzqebsWW1E63yZiHkz0CLbT7NxFJh52dAgtHRuDAC8PwzJDWUNpx+MjapD5DiUGMiRVIMIgZ0cFfnMq49XQG8kvK7vEKy8spKkVG5Z4o7QM8OPZNRCQBukwMAKQxiLF9RWr9cJKbRIIYJwclJnZvAQAoKavA1sQMK7eoqvMGW723D5TuDCoioqYk2McwEyO94l4GMSZWUKrPxLhIoCZG56FeweL9H+OlN6R0PoP1MEREUhNimIm5zUyMzTOanSSRTAwAdA32RNvKbevjr9wxWt5fCpJuMBNDRCQ1Lbz0mZg0ZmJsn+FwklRqYgDtNMaHI/XZmI0SK/A9l6EPYiL8GcQQEUmBs6NSXLiVhb1NgK6w11FpB0d7af14J/VoIVb7bzyRjnKJrBlTUSGIM5Na+rhIKoNFRNTU6Yp7b+SpUVJWfo+zLUtavawNKKysiXFRSaceRsfP3QnRlYtJZeSW4OAlaazge/V2EYrL9Bu8ERGRdBjOUNLNIpUKBjEmpquJcXWUZjbBsMBXKmvGGM1MYhBDRCQpQV6GQYy06mIYxJhYYWVNjFSHRIZ18INX5V4Y209nirtGW5PRSr2BnJlERCQlAR76xUczmYmxXeUVgjgs4irB4SQAUNkrMalyzRi1pgK/n7L+mjHnM5iJISKSKsMV1DmcZMMKS6W3Wm91jNaMOZ5mxZZo6TIxTg52CG3GjR+JiKTEcDhJajOUGMSYkNH0aonWxADanWF1GY+TV3Nw0WD3aEsrKtXgSuUCShH+7twrhYhIYsKa6/+4vJQlrTXGGMSYkBT3TaqOQqEwysZsPGG9At8LNwogVM705lASEZH0eDg5wM9du1bMxZvW+6O3OgxiTKjIaDhJmjUxOpN6tIB9ZdZjkxXXjOF2A0RE0temcsX324WluF1YauXW6DGIMaHiUv1wkrODtIMYXzcVhrb3A6BdwGh/8k2rtIMbPxIRSZ8uiAGASxLKxjCIMSG1pkK8r5J4EAMYF/j+ZqVZSueYiSEikrzWzfVBjDXrKO/GIMaEjIIYiW05UJ3ods3hqNS286fj6RZfTloQBDET4+eugo+ro0U/n4iI6sYwE8MgxkapNfogQA5BjMpeiS7BnuLx14euWPTzU7ILxcX2OgUxC0NEJFUMYpoAdZm8hpMA4BGDna0PWHgvpZNXc8T7ka18LPrZRERUd37uKrhXzrq1iSCmrKwMaWlpSEpKwu3bt03ZJtkqLTcIYpTyiA8fiQyBr5t2GOfI5du4VaC22GcnXssV73cL9rLY5xIRUf0oFAq0rszGXMspNprIYk316mkLCgrwySefIDo6Gp6enmjVqhU6duyI5s2bIzQ0FLNmzcKxY8fM1VbJUxvUlKgc5BHEKBQK3Nc1CABQXFaOj+MuWeyzz1zXBzGdW3A4iYhIygyLe6UyQ6nOPe27776LVq1a4bPPPsOwYcOwadMmJCQkICkpCYcOHcKrr74KjUaDkSNHYsyYMUhOTq5XQ2JiYtC7d2+4u7vDz88PkyZNQlJSktE5giBgyZIlCAoKgrOzM6Kjo3HmzJl6fY45ya2wV+fZ6NZiezccS7NIgW9FhYAz17Uzk4K9neHlwqJeIiIpa+uvD2IMl8ewpjr3tAcPHsTevXsRHx+PV155BWPGjEGXLl3Qpk0b9OnTBzNnzsTatWuRmZmJ+++/H3FxcfVqSFxcHObMmYPDhw9j165d0Gg0GDVqFAoL9Uscr1ixAitXrsSqVatw7NgxBAQEYOTIkcjPl8YP0ziIkUdNDAD4ezhhfNdAAEB+iQZ7zmeZ/TNTbhWiqDIdyaJeIiLp62owEeTk1TtWbIlendfG//HHH+t0XmlpKWbPnl3vhmzfvt3oeO3atfDz88Px48cxePBgCIKA9957Dy+99BImT54MAFi3bh38/f3x3Xff4emnn673Z5qa3GYnGXqwZzA2nbgGQLuC77gugWb9vBNX9L8AnYM8azmTiIikoGuwFxQKQBCMaxqtqV497dtvv13r83l5eRg1alSjGqSTm6v9Afn4aGetpKSkIDMz0+j9VSoVhgwZgoMHD96zXYY3tdo8xavGs5PkFcT0C2+GAA/tduuxSTdxPafYrJ936PIt8X7f8GZm/SwiImo8N5U9gr21O1pfyiqAIJhuuxq1Wl2lr66LevW0L7/8MtauXVvtcwUFBRg9enSdP7g2giBg4cKFGDhwIDp37gwAyMzMBAD4+/sbnevv7y8+V5OQkBB4enqKt5iYmEa3sTqGw0mOSvkMJwGA0k6/KaSmQsCXB1PN+nlHLmtntDk52KFbCDMxRERy0NZPuz1MYWk5MnJLTPa+MTExRv10SEhInV5XryDm66+/xuzZs7FlyxajxwsKCjBq1Cjcvn0be/furc9bVmvu3Lk4deoU1q9fX+U5hUJhdCwIQpXH7paWlobc3Fzxtnjx4ka3sTpGw0kyy8QAwBMDWkH3o9x3wXx7KaXdLsK1ykxPz5besqofIiJqysy16N3ixYuN+um0tLQ6va5ePe1DDz2EDz74AFOmTBGDlYKCAowZMwbZ2dmIjY2tkimpr3/+85/45ZdfsHfvXgQH6xdiCwgIAIAqWZesrKx7fqaHh4fRTaVSNaqNNSmV6ewknWZuKrHI9nxmPo5fMU/h1pEU/bpCfcM4lEREJBdtzLSHkkqlqtJX10W9e9qnnnoKS5YswaRJkxAbG4uxY8ciMzMTe/fuRWBgw4tBBUHA3LlzsWnTJuzZswdhYWFGz4eFhSEgIAC7du0SHystLUVcXBz69+/f4M81JbnOTjL0t94txftfHUo1y2ccMaqH4Uq9RERy0dogE5MsgZV76zw7ydCiRYtw584dDB8+HK1atUJcXBxatGjRqIbMmTMH3333HX7++We4u7uLGRdPT084OztDoVBgwYIFWLZsGdq2bYu2bdti2bJlcHFxwZQpUxr12aYi13ViDD0cGYy3dyYhp6gMO85kIq+kDB5ODib9DF0mxtHeDt1DvEz63kREZD4RBmvFnM1ofA1sY9UriNFNbdZxcHCAr68v5s2bZ/T4pk2b6t2Q1atXAwCio6ONHl+7di1mzJgBQBs8FRcXY/bs2bhz5w769u2LnTt3wt3dvd6fZw5yr4kBtBmk+7sF4atDV1BSVoGtpzLwtz4t7/3COrqeU4yrt4sAAN1DvOAkkz2miIgIcHdyQJivK1KyC3EuIw9l5RVwsOI2O/UKYjw9jWeRPPbYYyZrSF2maikUCixZsgRLliwx2eeakuEUa0eZ7J1UnYd6BeOryh2tN55IN2kQc/CSfigpilOriYhkp1OQB1KyC1GqqcDFrAJ0CLTegqX1CmJqml5NWrrhJKWdAvYyDmK6tPBEWz83JGcV4FjqHaRmF6KVr6tJ3vugwU7Z/VsziCEikpsuLTzx26kMANpF76wZxMi3p5Ug3XCSXOthdBQK/ZoxAPDtkSsmeV9BEHCoMhPj5GCH7i29TPK+RERkOV1a6Edl/krLsV5DUI8g5urVq/V642vXrtW7MXKnm2It9yAGAB7o0QL2dtpFY744kGqSFXxTbxWJiyP1buUj2xlcRERNWdcQL1R2D2ZbiqOu6tzb9u7dG7NmzcLRo0drPCc3NxefffYZOnfu3KDiXrlTi0GM/DtnPw8nPDlIO829vELAd0fqF8RWZ/e5G+L9KA4lERHJkpvKXhxCSrqRj9ziMqu1pc41MefOncOyZcswZswYODg4IDIyEkFBQXBycsKdO3dw9uxZnDlzBpGRkXjrrbcwduxYc7ZbksQgRqYzk+72eFQrfL4/BZoKAWsPpGD20NZwcWzQrHwAEDeYBIDRnQJM0UQiIrKCyFBvnLmeB0EAEtJyMCSiuVXaUefe1sfHB2+//TauX7+O1atXIyIiAtnZ2UhOTgYATJ06FcePH8eBAweaZAADAOoy26iJ0Wnh5YzJPbXr/xSWluPH+PQGv1dqdqG4pkC3YE+0Nlj1kYiI5KVnqLd4/3jq7VrONK96/1nt5OSEyZMnV1kzhmxrOEnnoV4h+KEyeHnj93OIat0MEf71X5dn22n9dhHjujR8ZWciIrK+yFY+6NnSC5GtfDDISlkYgLOTTEZTXgFNhXatG0cbycQAQJ8wH4zooN2bqrS8AhtP1D8bIwgCfk7QDyWN7cwghohIzlp4OWPT7AF4cVwH9G5lve1jbKe3tbLScvlvOVCT5Q92Ee9vPJ6OolJNvV5/5noezmfmAwB6tvRCy2YuJm0fERE1TbbV21qR4Wq9thbE+LqpcF9XbfYku6AU3x6u30ylLSf1WZjJPYNrOZOIiKjubKu3tSLjTIzt1MTozBveForKdQE+2XepztkYtaYcP/91HQDgoFSIwRAREVFjMYgxEaNMjI1MsTYU4e+O8V302ZiPYy/V6XU/xqfjZr4aADC8vT+8XBzN1kYiImpaGr7oh4Hbt2/j7Nmz4u3MmTM4d+4c0tMbPiVXbox2sLax4SSd+cPbYtvpTJRXCFi19yIGRzRHZC0FXRUVAv63O1k8nj20tSWaSURETUSjgpihQ4ciKSkJrq6u6NChA9q3b4/vvvsOW7ZsQdu2bU3VRlnQTa8GbHM4CQDa+rtj/vC2WLnrAioE4LkfErDruSFwcqj+et/cfl7Mwgxq64uuwV4WbC0REdm6RqUMvL29ERYWhrVr1+KXX37BihUr4Orqiv79+6N5c+vNG7cGw0yMLU2xvtucoW3QLcQLAJB2uxgPfHQQBeqq9TGJ6bn44kCKeDxzQJilmkhERE1Eo3rbTZs24cMPP8Sbb76J0aNH48iRI1Doqj+bGFuenWRIaafAf8Z3gINS++98LiMPD3x4QNwgUrdT9cx1x1BWrl0352+9QzC0vZ/V2kxERLapXsNJO3bswMiRI2Fnp++ku3fvjl9//RVHjhzBK6+8ghs3buDIkSPo27evyRsrZU1hOEmndysf/PB0FP6+5ggKS8uRnFWA/sv3VHtuZKg3ltzfycItJCKipqBeKYNx48YhOzu72uf69u2LHTt2YPv27Vi8eDFGjBhhkgbKhVEQY4Ozk+7Wo6U3Pvp7L3E79uq09HHBJ9N61VgzQ0RE1Bj1ysQIgnDPcwYNGoQ9e/Zgz57q/zK3VU1hdtLdhkQ0x7dP9cNPx9OxL/mmWMQLAM3dVfj2qb5o5qayYguJiMiWmWSKdXWGDRtmrreWpKY0nGQoqnUzRLVuhvIKAfklZVBrKuDp7MDsCxERmV29UwarVq3Cjh07ahxWaqoMgxhbnp1UE6WdAl4ujvD3cGIAQ0REFlHvTMzq1auxdOlSKBQKtGjRAj179kSvXr3Qs2dP9OzZE4GBTXNZeXVZ0xtOIiIisqZ6BzFnzpyBRqPByZMnceLECZw4cQJr1qxBWloaFAoF/P39cf36dXO0VdKMh5MYxBAREZlbvYIY3RowQUFBCAoKwvjx48Xnbt++jfj4eCQkJJi0gXJhPDuJwylERETmZrLZST4+Phg1ahRGjRrV6EbJUVOcnURERGRN9eptt23bBk9PT3O1RdbKNPoAz0HJIIaIiMjc6pWJGT16tLnaIXuaCv1wkm5JfiIiIjIfpgxMRLdPEADY2/HHSkREZG7sbU1EU85MDBERkSUxiDERTYVBJoY1MURERGbH3tZEygwyMfa17YpIREREJiGpIGbfvn2YMGECgoKCoFAosGXLFqPnZ8yYAYVCYXTr16+fdRp7F005ZycRERFZkqR628LCQnTr1g2rVq2q8ZwxY8YgIyNDvG3dutWCLayZ4ewke9bEEBERmZ3ZdrFuiLFjx2Ls2LG1nqNSqRAQEGChFtWd4ewkB85OIiIiMjtJBTF1ERsbCz8/P3h5eWHIkCF444034OfnV+tr8vLyjI5VKhVUKpVJ28VMDBERUcOo1Wqo1Wrx+O5+uyayShmMHTsW3377Lfbs2YN33nkHx44dw7Bhw4wuvDohISHw9PQUbzExMSZvm9E6MQxiiIiI6iwmJsaonw4JCanT62SViXn00UfF+507d0ZkZCRCQ0Px+++/Y/LkyTW+Li0tDR4eHuKxqbMwwF3rxHA4iYiIqM4WL16MhQsXisd5eXl1CmRkFcTcLTAwEKGhoUhOTq71PA8PD6Mgxhx068TYKQA7TrEmIiKqs4aWecg6ZXDr1i2kpaUhMDDQ2k0Rh5O40B0REZFlSCoTU1BQgIsXL4rHKSkpSEhIgI+PD3x8fLBkyRI8+OCDCAwMRGpqKl588UX4+vrigQcesGKrtXTDSQ7MwhAREVmEpIKY+Ph4DB06VDzWjY9Nnz4dq1evRmJiIr766ivk5OQgMDAQQ4cOxffffw93d3drNVmkG05iJoaIiMgyJBXEREdHQxCEGp/fsWOHBVtTP7ptB7j5IxERkWUwbWAium0H7DkziYiIyCLY45qIbrE7rhFDRERkGQxiTEQ3O4mbPxIREVkGe1wT0dXE2HN2EhERkUUwiDERDdeJISIisij2uCZSVsHZSURERJbEIMYEyisE6GaGcziJiIjIMhjEmECZweaPHE4iIiKyDPa4JqBbrRfgcBIREZGlMIgxAY1hJoaL3REREVkEe1wT0K0RAzATQ0REZCkMYkxAt1ovwEwMERGRpbDHNQGNQSaG2w4QERFZBoMYEzCcncRtB4iIiCyDPa4JGM5O4joxRERElsEgxgS4TgwREZHlscc1AQ1nJxEREVkcgxgT4OwkIiIiy2OPawJcJ4aIiMjyGMSYAKdYExERWR6DGBMo43ASERGRxbHHNQEW9hIREVkegxgT0HCKNRERkcWxxzWBMi52R0REZHEMYkxAw20HiIiILI49rglwdhIREZHlMYgxAcPZSQ6cnURERGQR7HFNgJkYIiIiy2MQYwLcAJKIiMjy2OOagMZgdpIDZycRERFZBIMYE+A6MURERJbHHtcEylgTQ0REZHGSCmL27duHCRMmICgoCAqFAlu2bDF6XhAELFmyBEFBQXB2dkZ0dDTOnDljncYa0HB2EhERkcVJqsctLCxEt27dsGrVqmqfX7FiBVauXIlVq1bh2LFjCAgIwMiRI5Gfn2/hlhrj7CQiIiLLs7d2AwyNHTsWY8eOrfY5QRDw3nvv4aWXXsLkyZMBAOvWrYO/vz++++47PP3005ZsqpEybgBJRERkcZLKxNQmJSUFmZmZGDVqlPiYSqXCkCFDcPDgwVpfm5eXZ3RTq9UmbZvhcJI9h5OIiIjqRa1WV+mr60I2PW5mZiYAwN/f3+hxf39/8bmahISEwNPTU7zFxMSYtG0s7CUiImq4mJgYo346JCSkTq+T1HBSXSgUxkGCIAhVHrtbWloaPDw8xGOVSmXSNnEDSCIiooZbvHgxFi5cKB7n5eXVKZCRTRATEBAAQJuRCQwMFB/Pysqqkp25m4eHh1EQY2qGi93Zc7E7IiKielGpVA1KMMgmbRAWFoaAgADs2rVLfKy0tBRxcXHo37+/FVtmvO0AMzFERESWIalMTEFBAS5evCgep6SkICEhAT4+PmjZsiUWLFiAZcuWoW3btmjbti2WLVsGFxcXTJkyxYqt5hRrIiIia5BUEBMfH4+hQ4eKx7rxsenTp+PLL7/EokWLUFxcjNmzZ+POnTvo27cvdu7cCXd3d2s1GQBnJxEREVmDpIKY6OhoCIJQ4/MKhQJLlizBkiVLLNeoOuA6MURERJbHtIEJGGViWBNDRERkEexxTcBonRjOTiIiIrIIBjEmwHViiIiILI89rgno1olRKAAlMzFEREQWwSDGBHTDSQ6cmURERGQx7HVNQDecxDViiIiILIdBjAnohpNY1EtERGQ5DGJMQLftAIt6iYiILIe9rgnoth1gEENERGQ57HVNQMzE2HM4iYiIyFIYxJgAh5OIiIgsj72uCeimWDsyiCEiIrIY9romUMYp1kRERBbHIKaRBEEQp1hzOImIiMhy2Os2kuHmjwxiiIiILIe9biOVGWz+yJoYIiIiy2Gv20iGQQxrYoiIiCyHQUwjlRoEMRxOIiIishz2uo2kMaiJ4XASERGR5bDXbaQyo0wMh5OIiIgshUFMIxnXxPDHSUREZCnsdRupVMMp1kRERNbAXreRNBWGU6w5nERERGQpDGIaicNJRERE1sFet5E4nERERGQd7HUbyXjFXg4nERERWQqDmEYyrIlhJoaIiMhy2Os2kuFwEmtiiIiILIe9biNxsTsiIiLrYBDTSEY1Mfb8cRIREVkKe91GMtw7iTUxRERElsNet5EMd7G2t+NwEhERkaXIKohZsmQJFAqF0S0gIMCqbeJwEhERkXXYW7sB9dWpUyf88ccf4rFSqbRia+4u7GUQQ0REZCmyC2Ls7e2tnn0xVMaaGCIiIquQXRCTnJyMoKAgqFQq9O3bF8uWLUN4eHitr8nLyzM6VqlUUKlUJmmP8d5JrIkhIiKqL7VaDbVaLR7f3W/XRFapg759++Krr77Cjh078NlnnyEzMxP9+/fHrVu3an1dSEgIPD09xVtMTIzJ2mS87YCsfpxERESSEBMTY9RPh4SE1Ol1CkEQhHufJk2FhYVo3bo1Fi1ahIULF1Z5Pi8vD56enkhLS4OHh4f4uCkzMcu2nsOn+y4DAH54Ogp9wnxM8r5ERERNRXWZmJCQEOTm5hr133eT3XCSIVdXV3Tp0gXJycm1nufh4VHrD6ExuGIvERFR4zQ0uSDr8Q+1Wo1z584hMDDQam3g7CQiIiLrkFWv+/zzzyMuLg4pKSk4cuQIHnroIeTl5WH69OlWa1OZhrOTiIiIrEFWw0np6el47LHHkJ2djebNm6Nfv344fPgwQkNDrdYmDicRERFZh6yCmA0bNli7CVWUVTATQ0REZA3sdRupTMOaGCIiImtgr9tIHE4iIiKyDgYxjWQ0nMQNIImIiCyGvW4jGQ0n2fHHSUREZCnsdRup1HDbAWZiiIiILIa9biOpNeUAAHs7BZR2rIkhIiKyFAYxjaQu02ZiVMzCEBERWRR73kZSV9bEqByUVm4JERFR08IgppF0w0nMxBAREVkWe95GEjMxDGKIiIgsij1vI+lrYjicREREZEkMYhpBEAT9cJIDf5RERESWxJ63ETQVAnQL9nI4iYiIyLLY8zaC2mC1Xg4nERERWRaDmEZQl5WL95mJISIisiz2vI1glIlhTQwREZFFsedthFIOJxEREVkNg5hGMMzEOCr5oyQiIrIk9ryNUGxQE+PE4SQiIiKLYs/bCEVqjXjfVWVvxZYQERE1PQxiGqGwVJ+JYRBDRERkWQxiGqGoVJ+JcXFkYS8REZElMYhphEK1QSbGkZkYIiIiS2IQ0whGmRgVMzFERESWxCCmEZiJISIish4GMY3AmhgiIiLrYRDTCIWlnGJNRERkLQxiGiG/hJkYIiIia2EQ0wiZuSXifX8PJyu2hIiIqOnhGEgDCIKAn46n40jKbQCAh5M9h5OIiIgsjD1vA6zZn4I3tp4TjwM9na3YGiIioqaJw0kNsPFEutFxsDeDGCIiIkuTZRDz0UcfISwsDE5OTujVqxf2799v0c83LOj1cXXE7KGtLfr5REREJMMg5vvvv8eCBQvw0ksv4eTJkxg0aBDGjh2Lq1evWqwNak0FAMDT2QGHFw9Hr1Afi302ERERackuiFm5ciWefPJJPPXUU+jQoQPee+89hISEYPXq1TW+Ji8vz+imVqsb1QZ1mXalXl83Rzjay+5HSEREJClqtbpKX10XsuqBS0tLcfz4cYwaNcro8VGjRuHgwYM1vi4kJASenp7iLSYmplHt0GViVPZcG4aIiKixYmJijPrpkJCQOr1OVrOTsrOzUV5eDn9/f6PH/f39kZmZWePr0tLS4OHhIR6rVKoGt6G8QkBpuTaIcXKQVQxIREQkSYsXL8bChQvF47y8vDoFMrIKYnQUCoXRsSAIVR4z5OHhYRTENEZpZRYGYCaGiIjIFFQqVYMSDLJKJfj6+kKpVFbJumRlZVXJzpiLWqPfuZqZGCIiIuuRVS/s6OiIXr16YdeuXUaP79q1C/3797dIG0rKmIkhIiKSAtkNJy1cuBDTpk1DZGQkoqKi8Omnn+Lq1at45plnLPL5zMQQERFJg+yCmEcffRS3bt3C66+/joyMDHTu3Blbt25FaGioRT6fmRgiIiJpkF0QAwCzZ8/G7NmzrfLZzMQQERFJA3vhejLKxDgwE0NERGQtDGLqySgTw9V6iYiIrIa9cD2pmYkhIiKSBFnWxFhT/zbNsPtfQ1BSVo7mbg1f+ZeIiIgah0FMPbk42qN1czdrN4OIiKjJ43ASERERyRKDGCIiIpIlBjFEREQkSwxiiIiISJYYxBAREZEsMYghIiIiWWIQQ0RERLLEIIaIiIhkiUEMERERyRKDGAtTq9VYsmQJ1Gq1tZtiEU3teoGmd828XtvG67Vtcr9ehSAIgrUbYS55eXnw9PREbm4uPDw8rN0cANJskzk1tesFmt4183ptG6/Xtkn1euvaLmZiiIiISJYYxBAREZEs2fQu1rqRsry8PCu3RE/XFim1yZya2vUCTe+aeb22jddr26R6vbr23KvixaZrYtLT0xESEmLtZhAREVEDpKWlITg4uMbnbTqIqaiowPXr1+Hu7g6FQmHt5hAREVEdCIKA/Px8BAUFwc6u5soXmw5iiIiIyHaxsJeIiIhkiUEMERERyRKDGCIiIpIlBjGNEBMTg969e8Pd3R1+fn6YNGkSkpKS7vm6Dz/8EB06dICzszPatWuHr776qso57733Htq1awdnZ2eEhITgueeeQ0lJiTkuo85Wr16Nrl27wsPDAx4eHoiKisK2bdtqfU1cXBx69eoFJycnhIeH4+OPP65yzsaNG9GxY0eoVCp07NgRmzdvNtcl1Is5rvezzz7DoEGD4O3tDW9vb4wYMQJHjx4152XUmbn+fXU2bNgAhUKBSZMmmbjlDWOu683JycGcOXMQGBgIJycndOjQAVu3bjXXZdSLua5Zit9XQP2vNyMjA1OmTEG7du1gZ2eHBQsWVHuerXxn1eV6pfydBQAQqMFGjx4trF27Vjh9+rSQkJAgjB8/XmjZsqVQUFBQ42s++ugjwd3dXdiwYYNw6dIlYf369YKbm5vwyy+/iOd88803gkqlEr799lshJSVF2LFjhxAYGCgsWLDAEpdVo19++UX4/fffhaSkJCEpKUl48cUXBQcHB+H06dPVnn/58mXBxcVFmD9/vnD27Fnhs88+ExwcHISffvpJPOfgwYOCUqkUli1bJpw7d05YtmyZYG9vLxw+fNhSl1Ujc1zvlClThA8//FA4efKkcO7cOeGJJ54QPD09hfT0dEtdVo3Mcb06qampQosWLYRBgwYJEydONPOV1I05rletVguRkZHCuHHjhD///FNITU0V9u/fLyQkJFjqsmpljmuW6veVINT/elNSUoR58+YJ69atE7p37y7Mnz+/yjm29J1Vl+uV8neWIAgCgxgTysrKEgAIcXFxNZ4TFRUlPP/880aPzZ8/XxgwYIB4PGfOHGHYsGFG5yxcuFAYOHCgaRtsAt7e3sKaNWuqfW7RokVC+/btjR57+umnhX79+onHjzzyiDBmzBijc0aPHi387W9/M31jTaCx13s3jUYjuLu7C+vWrTNpO03FFNer0WiEAQMGCGvWrBGmT58umSCmOo293tWrVwvh4eFCaWmpWdtpSo29Zjl9XwlC7ddraMiQIdV26rb0nWWopuu9m9S+szicZEK5ubkAAB8fnxrPUavVcHJyMnrM2dkZR48eRVlZGQBg4MCBOH78uJiyu3z5MrZu3Yrx48ebqeX1V15ejg0bNqCwsBBRUVHVnnPo0CGMGjXK6LHRo0cjPj5evNaazjl48KB5Gt5AprreuxUVFaGsrKzW/zPWYMrrff3119G8eXM8+eSTZm1zY5jqen/55RdERUVhzpw58Pf3R+fOnbFs2TKUl5eb/Rrqy1TXLIfvK6Bu11sXtvSd1RBS+86y6W0HLEkQBCxcuBADBw5E586dazxv9OjRWLNmDSZNmoSePXvi+PHj+OKLL1BWVobs7GwEBgbib3/7G27evImBAwdCEARoNBo8++yzeOGFFyx4RdVLTExEVFQUSkpK4Obmhs2bN6Njx47VnpuZmQl/f3+jx/z9/aHRaMRrremczMxMs11DfZj6eu/2wgsvoEWLFhgxYoRZ2l9fpr7eAwcO4PPPP0dCQoIFWl9/pr7ey5cvY8+ePZg6dSq2bt2K5ORkzJkzBxqNBq+88oolLumeTH3NUv6+Aup3vXVhS99ZDSG17ywGMSYyd+5cnDp1Cn/++Wet57388svIzMxEv379IAgC/P39MWPGDKxYsQJKpRIAEBsbizfeeAMfffQR+vbti4sXL2L+/PkIDAzEyy+/bInLqVG7du2QkJCAnJwcbNy4EdOnT0dcXFyNvyR3r5QsVK6taPh4dedIZYVlc1yvzooVK7B+/XrExsZWyc5ZiymvNz8/H3//+9/x2WefwdfX1+xtbwhT//tWVFTAz88Pn376KZRKJXr16oXr16/jrbfekkwQY+prlvL3FVD/660LW/rOqg8pfmexJsYE5s6dKwQHBwuXL1+u82tKS0uFtLQ0QaPRiMW+5eXlgiAIwsCBA6vUzXz99deCs7OzeI5UDB8+XPjHP/5R7XODBg0S5s2bZ/TYpk2bBHt7e7FmICQkRFi5cqXROStXrhRatmxpngY3UmOvV+ett94SPD09hWPHjpmtrabQmOs9efKkAEBQKpXiTaFQCAqFQlAqlcLFixctcQn10th/38GDBwvDhw83Omfr1q0CAEGtVpun0Y3U2GuW0/eVINR+vYZqqhGxpe8sQ/eqiZHqdxZrYhpBEATMnTsXmzZtwp49exAWFlbn1zo4OCA4OBhKpRIbNmzAfffdJ+4PUVRUVGWvCKVSCUFbiG3Sa2gsQRCgVqurfS4qKgq7du0yemznzp2IjIyEg4NDref079/fPA1upMZeLwC89dZb+O9//4vt27cjMjLSrO1trMZcb/v27ZGYmIiEhATxdv/992Po0KFISEiQ5Oasjf33HTBgAC5evIiKigrxnAsXLiAwMBCOjo7ma3gjNPaa5fR9BdR+vXVhS99ZdSXp7yzrxE624dlnnxU8PT2F2NhYISMjQ7wVFRWJ57zwwgvCtGnTxOOkpCTh66+/Fi5cuCAcOXJEePTRRwUfHx8hJSVFPOfVV18V3N3dhfXr1wuXL18Wdu7cKbRu3Vp45JFHLHl5VSxevFjYt2+fkJKSIpw6dUp48cUXBTs7O2Hnzp2CIFS9Vt30zOeee044e/as8Pnnn1eZnnngwAFBqVQKy5cvF86dOycsX75cMtMVzXG9b775puDo6Cj89NNPRv9n8vPzLX59dzPH9d5NSrOTzHG9V69eFdzc3IS5c+cKSUlJwm+//Sb4+fkJS5cutfj1Vccc1yzV7ytBqP/1CoIgnDx5Ujh58qTQq1cvYcqUKcLJkyeFM2fOiM/b0neWINz7eqX8nSUInGLdKACqva1du1Y8Z/r06cKQIUPE47Nnzwrdu3cXnJ2dBQ8PD2HixInC+fPnjd63rKxMWLJkidC6dWvByclJCAkJEWbPni3cuXPHMhdWg5kzZwqhoaGCo6Oj0Lx5c2H48OHiL4cgVL1WQRCE2NhYoUePHoKjo6PQqlUrYfXq1VXe98cffxTatWsnODg4CO3btxc2btxo7kupE3Ncb2hoaLX/Z1599VULXFHtzPXva0hKQYy5rvfgwYNC3759BZVKJYSHhwtvvPGGoNFozH05dWKOa5bq95UgNOx6q/v9DA0NNTrHlr6z7nW9Uv7OEgRB4C7WREREJEusiSEiIiJZYhBDREREssQghoiIiGSJQQwRERHJEoMYIiIikiUGMURERCRLDGKIiIhIlhjEEBERkWjfvn2YMGECgoKCoFAosGXLlnq9vqSkBDNmzECXLl1gb2+PSZMmVXteXFwcevXqBScnJ4SHh+Pjjz+ud1sZxBAR1aK0tBRt2rTBgQMHTPq+v/32G3r06GG0zxKRFBQWFqJbt25YtWpVg15fXl4OZ2dnzJs3DyNGjKj2nJSUFIwbNw6DBg3CyZMn8eKLL2LevHnYuHFjvT6LQQxREzJjxgwoFIoqt4sXL1q7aZL16aefIjQ0FAMGDBAfq+mv0xkzZtT4V+fd7rvvPigUCnz33XcmaimRaYwdOxZLly7F5MmTq32+tLQUixYtQosWLeDq6oq+ffsiNjZWfN7V1RWrV6/GrFmzEBAQUO17fPzxx2jZsiXee+89dOjQAU899RRmzpyJt99+u15tZRBD1MSMGTMGGRkZRrfqdmAvLS21Quuk54MPPsBTTz1llvd+4okn8MEHH5jlvYnM5YknnsCBAwewYcMGnDp1Cg8//DDGjBmD5OTkOr/HoUOHMGrUKKPHRo8ejfj4eJSVldX5fRjEEDUxKpUKAQEBRjelUono6GjMnTsXCxcuhK+vL0aOHAkAOHv2LMaNGwc3Nzf4+/tj2rRpyM7OFt+vsLAQjz/+ONzc3BAYGIh33nkH0dHRWLBggXhOdZkLLy8vfPnll+LxtWvX8Oijj8Lb2xvNmjXDxIkTkZqaKj6vy3K8/fbbCAwMRLNmzTBnzhyjLzy1Wo1FixYhJCQEKpUKbdu2xeeffw5BENCmTZsqf+WdPn0adnZ2uHTpUrU/qxMnTuDixYsYP358PX/KQGpqarVZr+joaPGc+++/H0ePHsXly5fr/f5E1nDp0iWsX78eP/74IwYNGoTWrVvj+eefx8CBA7F27do6v09mZib8/f2NHvP394dGozH6frkXBjFEJFq3bh3s7e1x4MABfPLJJ8jIyMCQIUPQvXt3xMfHY/v27bhx4wYeeeQR8TX/93//h71792Lz5s3YuXMnYmNjcfz48Xp9blFREYYOHQo3Nzfs27cPf/75J9zc3DBmzBijjNDevXtx6dIl7N27F+vWrcOXX35pFAg9/vjj2LBhA95//32cO3cOH3/8Mdzc3KBQKDBz5swqX7JffPGF+EVcnX379iEiIgIeHh71uh4ACAkJMcp2nTx5Es2aNcPgwYPFc0JDQ+Hn54f9+/fX+/2JrOHEiRMQBAERERFwc3MTb3FxcTX+MVAThUJhdKzbj/rux2tjX69PJCLZ++233+Dm5iYejx07Fj/++CMAoE2bNlixYoX43CuvvIKePXti2bJl4mNffPEFQkJCcOHCBQQFBeHzzz/HV199JWZu1q1bh+Dg4Hq1acOGDbCzs8OaNWvEL7C1a9fCy8sLsbGxYtrZ29sbq1atglKpRPv27TF+/Hjs3r0bs2bNwoULF/DDDz9g165dYjFheHi4+BlPPPEEXnnlFRw9ehR9+vRBWVkZvvnmG7z11ls1tis1NRVBQUHVPvfYY49BqVQaPaZWq8WsjVKpFOsBSkpKMGnSJERFRWHJkiVGr2nRooVRxolIyioqKqBUKnH8+PEq//8Nv1fuJSAgAJmZmUaPZWVlwd7eHs2aNavz+zCIIWpihg4ditWrV4vHrq6u4v3IyEijc48fP469e/dW++V06dIlFBcXo7S0FFFRUeLjPj4+aNeuXb3adPz4cVy8eBHu7u5Gj5eUlBj9ddepUyejL87AwEAkJiYCABISEqBUKjFkyJBqPyMwMBDjx4/HF198gT59+uC3335DSUkJHn744RrbVVxcDCcnp2qfe/fdd6vMvPj3v/+N8vLyKuc++eSTyM/Px65du2BnZ5wAd3Z2RlFRUY1tIJKSHj16oLy8HFlZWRg0aFCD3ycqKgq//vqr0WM7d+5EZGQkHBwc6vw+DGKImhhXV1e0adOmxucMVVRUYMKECXjzzTernBsYGFjnQj6FQiGminUMa1kqKirQq1cvfPvtt1Ve27x5c/H+3V9uCoVCnKLs7Ox8z3Y89dRTmDZtGt59912sXbsWjz76KFxcXGo839fXVwyS7hYQEFDl5+ju7o6cnByjx5YuXYrt27fj6NGjVYI0ALh9+7bRNRJZW0FBgdGMxZSUFCQkJMDHxwcRERGYOnUqHn/8cbzzzjvo0aMHsrOzsWfPHnTp0gXjxo0DoK2lKy0txe3bt5Gfn4+EhAQAQPfu3QEAzzzzDFatWoWFCxdi1qxZOHToED7//HOsX7++Xm1lEENENerZsyc2btyIVq1awd6+6tdFmzZt4ODggMOHD6Nly5YAgDt37uDChQtGGZHmzZsjIyNDPE5OTjbKPvTs2RPff/89/Pz8GlR/AgBdunRBRUUF4uLialybYty4ceL0z23btmHfvn21vmePHj2wevVqCIJQr3F6nY0bN+L111/Htm3bqq270WWaevToUe/3JjKX+Ph4DB06VDxeuHAhAGD69On48ssvsXbtWixduhT/+te/cO3aNTRr1gxRUVFiAANof9euXLkiHuv+j+v+mAkLC8PWrVvx3HPP4cMPP0RQUBDef/99PPjgg/VqKwt7iahGc+bMwe3bt/HYY4+Js2h27tyJmTNnory8HG5ubnjyySfxf//3f9i9ezdOnz6NGTNmVBkyGTZsGFatWoUTJ04gPj4ezzzzjFFWZerUqfD19cXEiROxf/9+pKSkIC4uDvPnz0d6enqd2tqqVStMnz4dM2fOxJYtW5CSkoLY2Fj88MMP4jlKpRIzZszA4sWL0aZNG6NhsOoMHToUhYWFOHPmTD1+alqnT5/G448/jn//+9/o1KkTMjMzkZmZidu3b4vnHD58GCqV6p7tILKk6OhoCIJQ5aYrondwcMBrr72GlJQUlJaWIiMjA5s2bUKXLl3E90hNTa32PQwNGTIEJ06cgFqtRkpKCp555pl6t5VBDBHVKCgoCAcOHEB5eTlGjx6Nzp07Y/78+fD09BQDlbfeeguDBw/G/fffjxEjRmDgwIHo1auX0fu88847CAkJweDBgzFlyhQ8//zzRsM4Li4u2LdvH1q2bInJkyejQ4cOmDlzJoqLi+uVmVm9ejUeeughzJ49G+3bt8esWbNQWFhodM6TTz6J0tJSzJw5857v16xZM0yePLnaYa57iY+PR1FREZYuXYrAwEDxZriA2Pr16zF16tRah7SIqGYK4e7QiIiokaKjo9G9e3e899571m5KFQcOHEB0dDTS09OrrFNRncTERIwYMaLawuPGuHnzJtq3b4/4+PhqFxskontjJoaImgS1Wo2LFy/i5ZdfxiOPPFKnAAbQ1tqsWLHC5NOgU1JS8NFHHzGAIWoEFvYSUZOwfv16PPnkk+jevTu+/vrrer12+vTpJm9Pnz590KdPH5O/L1FTwuEkIiIikiUOJxEREZEsMYghIiIiWWIQQ0RERLLEIIaIiIhkiUEMERERyRKDGCIiIpIlBjFEREQkSwxiiIiISJb+H+FnmXXdSZmLAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sbn2.timeaverage()[0].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9b5bd43f-d63a-429f-bea6-c9ff2cb19997",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NCHAN  1024\n",
-      "NCHAN  1024\n"
-     ]
-    }
-   ],
-   "source": [
-    "data2 = sbn2.timeaverage(weights='tsys')[0].data\n",
-    "data1 = sbn.data\n",
-    "data3 = sbn2.timeaverage()[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "231ca4df-9b31-4e7b-b711-a7491a4bb142",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 0., 0., ..., 0., 0., 0.])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data1 - data2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "45b71f69-05c2-4243-94db-fde5fe138f61",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.0972615 ,  0.46332444,  0.5356332 , ..., 18.97662869,\n",
-       "       18.75325767, 18.52688884])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0ddd7254-dc16-45c9-95b4-e826ba016a4d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.0972615 ,  0.46332444,  0.5356332 , ..., 18.97662869,\n",
-       "       18.75325767, 18.52688884])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "8e426ef5-a8d1-411d-9792-912cd7177bc5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8bf56739a0>]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "fig,ax = plt.subplots()\n",
-    "ax.plot(np.arange(len(data1)),data1 - data2)"
+    "ta2 = sbnk2.timeaverage(weights=None)\n",
+    "spec2 = ta2[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "efc93e67-901c-45ca-8fcd-5d936aa9b545",
+   "execution_count": null,
+   "id": "b7d01ea0-237e-40bd-984c-d0db942410dd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8bf56dab90>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -806,168 +529,26 @@
     }
    ],
    "source": [
-    "fig,ax = plt.subplots()\n",
-    "ax.plot(np.arange(len(data1)),data1 - data3)"
+    "spec2.plot()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "28bfd198-baa9-4c37-9589-b7cc93e4ed3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ True,  True,  True, ...,  True,  True,  True])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(data1 - data3) == (data1 - data2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "165368ba-2132-4e04-814c-c3f18d8b5698",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tadiff = sbn2[0]._timeaveraged._data - sbn2[0]._timeaveraged2._data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a694832e-da47-459e-b33a-b82d1818a9ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8bf5ed3730>]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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IyHsY4KjIeio4OxkTERH5BgMcFUmDGHNwY45xWGRMRETkPQxwVCSbQWX1PzM4RERE3sMAx0PMmRtzJocBDhERkfcwwFGR0nIMlgwOh6iIiIi8hgGOiuRDVLWhjYYZHCIiIq9jgOMpVkU4nCZORETkPQxwVKQUw7DImIiIyPsY4KhIWmdjLjKWdjMmIiIi72CAoyJZHxzz/xyiIiIi8joGOB5iafR39TbjGyIiIu/xaIBTWFiIzMxM6PV66PV6ZGZmoqioyOFjRFFEVlYWUlNTERUVhWHDhmHPnj2W+y9fvozHHnsMnTp1QnR0NFq1aoWpU6fCYDB48lupN3Og82XuCcVp5ERERKQ+jwY4EyZMQF5eHrKzs5GdnY28vDxkZmY6fMwbb7yBOXPmYN68edi8eTNSUlIwfPhwlJSUAADOnj2Ls2fP4p///Cd27dqFTz/9FNnZ2XjooYc8+a24xNEQ1UfrjuHX/QVePyYiIqJQFOapJ963bx+ys7ORm5uL9PR0AMD8+fORkZGBAwcOoFOnTjaPEUURc+fOxaxZs3DbbbcBAD777DMkJydj0aJFeOSRR9C9e3csXrzY8ph27drhlVdewb333ouamhqEhXnsW3JKqchYWmK871wxbuyS7N2DIiIiCkEey+Bs2LABer3eEtwAwKBBg6DX67F+/XrFxxw7dgz5+fkYMWKEZZtOp8PQoUPtPgYADAYD4uLifBrcWDM3+pOuKF5RbfLV4RAREYUUj0UE+fn5SEpKstmelJSE/Px8u48BgORkeZYjOTkZJ06cUHzMpUuX8NJLL+GRRx6xeyyVlZWorKy03C4uLnZ6/PWhVGKjkaRwyquNHnldIiIiknM7g5OVlQVBEBz+27JlCwB59sJMFEXF7VLW99t7THFxMUaPHo2uXbvi+eeft/t8s2fPthQ66/V6tGzZ0pVv1W2ypRqsFtsEgAoGOERERF7hdgZnypQpuPvuux3u06ZNG+zcuRPnz5+3ue/ChQs2GRqzlJQUALWZnGbNmlm2FxQU2DympKQEI0eORExMDJYsWYLw8HC7xzNz5kxMnz7dcru4uNgjQY6jxTYBDlERERF5i9sBTmJiIhITE53ul5GRAYPBgE2bNmHgwIEAgI0bN8JgMGDw4MGKj0lLS0NKSgpycnLQp08fAEBVVRXWrFmD119/3bJfcXExbr75Zuh0OixduhSRkZEOj0Wn00Gn07n6LaqiLoNTt40ZHCIiIu/wWJFxly5dMHLkSEyaNAm5ubnIzc3FpEmTMGbMGNkMqs6dO2PJkiUAaodzpk2bhldffRVLlizB7t27MXHiRERHR2PChAkAajM3I0aMwJUrV/Dxxx+juLgY+fn5yM/Ph9Ho2wBCqcsNh6iIiIi8z6PTjhYuXIipU6daZkWNHTsW8+bNk+1z4MABWZO+GTNmoLy8HI8++igKCwuRnp6O5cuXIzY2FgCwdetWbNy4EQDQvn172XMdO3YMbdq08eB35Ji8D468kzEAVNQwwCEiIvIGjwY4CQkJ+PLLLx3uY123IggCsrKykJWVpbj/sGHDAqIjsNIQVXkVAxwiIiJv4FpUalKcJl4X4VTWsMiYiIjIGxjgqEjWydjqfwAwmvw/80RERBQMGOCoSFaDI9h2MjYFwNAaERFRMGCA4yFKrQxrmMEhIiLyCgY4KlIKXzSSM8whKiIiIu9ggKMi6eyuutXE63I5DHCIiIi8gwGOiuRrUZlrcOq2McAhIiLyDgY4HiatxWENDhERkXcwwFGRs0lSJgY4REREXsEAR0XmPjjSYSlpSMMMDhERkXcwwFHT1fhFOiwlzeowg0NEROQdDHA8QNrcT9rd2MhGf0RERF7BAEdFSuGLNKYJtSGqIxdKMf+3o7h8pQqLt56Goaza14dEREQeJIoiXv5pLz5Zd8zXh+LZ1cRDjcghKpkb/7UGAPDKsn0AgGGdmuLTBwb68pCIiMiD9pwtxkdXg5sHr03z6bEwg+MBgtI6DQi9DI611Qcu+PoQiIjIg8qqjJavRR+XZTDAUZGoOEhltU+I1OGUVtb4+hCIiMjLNJIL/Moak+8OBAxwVFU3RCUpMrYKaIK5m/Hiraex+kABjCYR3Z//xdeHQ0REXiadZFNZ7dsAhzU4KrKELnaGqACg2igiTOuNo/GuYxev4P++3QEA+P2pGxT30WocnBgiIgp40ov6yhojgHCfHQszOB4gKzK2uq/a5NuI1lMulFRavr7mtV8V9wljgENEFNSqjHWfcRyiCiJK9TXWm6p9/AP3pRApPyIiClnVxro3+opqo4M9PY8BjoosNTiypRrkn+rSH36o6d48DkDoFFoTEYUa6UU8MzhBSF5kLL8vlLsZ15hEPPPDLlz7+ioYytn0j4go2FTLhqiYwQk69hbbBABjkGZwaozOI/WqGhO+zD2JM0XlWLrjrBeOioiIvElag1Ph41lUDHBUpJScsd5WE6RFxqeLyp3uUyVJV2qtuiHmGyrw/bbTsn2IiCiwVNX4TwaH08RVZK63cTRXKBj74Czfk48Z3+10ut+lK1WWryPC5LH1mHfW4mJpFc4UluOxGzuofoxEROR50jpTX/fBYQbHAwTB/kTxYFyu4dWra005I627qbYa0rpYWhv8rNxfoN6BERGRV0nf2ytYgxM8XBmiCsYMTlyU+42ctp0oxKvL9qGorMr5zkREFBCkQ1Q/78r36SLTHKJSkfnH6KjRXzBmcPT1CHC+3XoaAHC+uAJv3d1H7UMiIiIfkBYZ78svRnFFNeKjI3xyLMzgqEisW4zKLmMQFhnHRda/FfePeWdlM7CCL/wjIgod5iGq2MgwfP5gus+CG4ABjkdI4xuN1WyhmiCYJn7wfAn+K5nmHRnueHGtN+/q5fB+ThknIgoO5iGqP/VrgbTERj49Fg5RqUgpdAnXygOcYKjBGfHmbwBqa2+GdmzqdCrgH/u0QM7e81i2K1/x/iMXSlU/RiIi8j5zBidC6/v8ie+PIIgkREfgbzd3wlTJNOcwqwAnkGtwCoor8PmG45bbu04XAXDczGlCeisAwOzbetrdpyFDXERE5D/MGRzrViC+wAyOiho3isBfr28v2xaukf+QAzWDs+X4Zfzpgw2ybbvOGHC4oNThgmqv/rEHACA6wv4w1uyf99fdCOGlLIiIAp35gtdZ6YI3MMDxsGDJ4EyYv9Fm2y97zuPX/QXo0Vzv9PHhfpCuJCIizzL3vtH5QQbH90cQ5Kw/2AN1FlWVnbWmqo0izhZVePloiIjIHxWV1TZ09YcMDgMcDwuzCXB8dCAelF9cG+DMGtWlwc+147QBO6/W9hARUeD47eAFrDl4AQADnJAQrrEeogrCCOeqgWkJWDF9KBZNSkf7pBh8fH//ej3P2Hm/43RhmcpHR0REnvT3xXVrEkaG+z68YA2Oh1nX4ARqkbErYiLD0K5pDNonxWDF9KENeq4txwvRonG0SkdGRESeppVc0EeGMYMT9KxrcL7ZfMpHR1J/rq4lEqNzHC93S42T3W7b1H4TqJKKarv3ERGR/5EFOByiCn7WAc7GY5frlnQIEF+7GJQ5C3C+nZyB/0651nLbusuzVHFFjWsHR0REfkErSAMc34cXHKLysDCN7Yd4WZURjZwEA/7k6SW7XNrPUa+b2vvD0KOFHnf2b4H84krE6sJwuEC5i/HlK1xlnIgokDCDE2Ksa3AAoLQycLIT7gQagoOMjNQbf+qFzx8cCNHB0prbTha6/LpEROR78gDH9+GF748gyCk1uCsJoOGXQ+dLPPbcGe0S7d6341QRCpnFISIKCDtPF+Fiad17tj80d/X9EQS5MI3tKQ6kDM6VKs8d64SBrTDnTuWVxk0isO7wRY+9NhERqePg+RKMnfc7LpZWWrZpFcozvI0Bjofd0j3FZlupH2dwzhdXoKisLgovr7Lft+fzBwc26LW0GgG39W1h935zwygiIvJfW0/ISwraNW3kF20+GOB42I1dkvDd5AzZttJK/5wCXVxRjfRXV6L3izmWbeUOFtIc0rEphnVqCqBu1XA1bThySfXnJCIidVmvO/XGn3r66EjkGOB4mCAI6N8mQbbNX2twjl24Yvm62miCKIqYvWyfbJ8xPZvJbr8wthvm3tUbL47tVu/XnT68IwCgeXyUbPuZonJU1tgPsIiIyPesP9NiI8N9dCRygTNXOYgYyv0zgyOdBFVaUYMTl8twyarQN8KqcKx1k0Zo3cR+wz5XPHZDe9zRvwUSY3T4746zaB4fhbs+zAUA/PHd9Vj2+HUNen4iIvKcwjL550QzfaSPjkSOGRwfsP5l8BdVNXX1NqWVNcg9Kh8i+ubhQS5PBXeHIAhopo9CuFaD2/q2kAVMe88Vq/56RESkHvMK4mb+ksFhgOMlEwe3sXx9qdQ/ApwaowkVkhqbK1V1X9/57w147ef9ltvjeqcivW0TeKMwPlyhdxAREfmnIj+9aGeA4yWzRnfB7VdnDF30kwBnzDvr0POF5bhyddp6uWRK+DlDhWzfyura7I6j5RXUEuYH/ROIiMg1ZZKL46k3dvDhkcjxk8RLwrUaDO+aDAC4dKXSyd7q2H3GgJ92nrV7//78ElTVmLD9ZBEA+S+ptSrj1QDHC78x1nU+RETkny6UVGL53vMAgH/e0csyacQfsMjYi5rERACA1zr0jnlnHQCgmT4K/Vo3lt0nXfCzxlQbvFxxEOCYZzN5I4PDISoiosDw2FfbLF9H+cH6U1K8VPYic68AaTGvNxy9YLugpdFUF+BcqTQia+kerNx33u5zmFcK79+msd191GLdAXPZrnMef00iInJf7tHLlq/9Yf0pKY8eTWFhITIzM6HX66HX65GZmYmioiKHjxFFEVlZWUhNTUVUVBSGDRuGPXv2yPZ55JFH0K5dO0RFRaFp06YYN24c9u/fb+cZ/Yf5g7vGZH+RSU++rtmB/BK8tfKQ5faHa4/i0/XHsfqAcufgXi3j8czorgCAcb2a41939MLK/xvqseO1nqn16MJtdvYkIiJ/4Q8riEt5NMCZMGEC8vLykJ2djezsbOTl5SEzM9PhY9544w3MmTMH8+bNw+bNm5GSkoLhw4ejpKRu0cd+/fphwYIF2LdvH3755ReIoogRI0bAaPTvpnDmxceMPg5wbp77G9759bDl9jGrDM+k69LQOSUWAHB/Rmv8+Ndr0DKhtu22RiPg9n4t0K5pjIePmojIsZnf78Lt769HtdG7WXFS5m8ZHI/V4Ozbtw/Z2dnIzc1Feno6AGD+/PnIyMjAgQMH0KlTJ5vHiKKIuXPnYtasWbjtttsAAJ999hmSk5OxaNEiPPLIIwCAhx9+2PKYNm3a4OWXX0avXr1w/PhxtGvXzlPfUoOZAw1v/zFqBAG/H76INQcv4G83K5x3q9u6MC2+nZyBX/acx/VXl2IgIvI3X206CaB2WZchHfle5QtajWC5aA+ZDM6GDRug1+stwQ0ADBo0CHq9HuvXr1d8zLFjx5Cfn48RI0ZYtul0OgwdOtTuY65cuYIFCxYgLS0NLVu2VPebUFnY1QDHGxmcGkkQpdUIuOejjfjwt6P4YsMJm32tj0cXpkFsZDj+1K8FmsToPH6sRETukk6UMIrezYpTHemkkJAJcPLz85GUlGSzPSkpCfn5+XYfAwDJycmy7cnJyTaPee+99xATE4OYmBhkZ2cjJycHERERis9bWVmJ4uJi2T9fMPd3qfZCgFNtrHsN6cynoxdtC46tM0oRYf6VZiQislYpmazBeZe+Ey7pHRLwAU5WVhYEQXD4b8uWLQBsi0WB2qjbWbt/6/uVHnPPPfdg+/btWLNmDTp06IA777wTFRXy5nRms2fPthQ66/V6n2V6vJnBkc7Uktbg1BhtX7vaahsDHCLyN/mGCoybtw7fbT0NACiXtLXwxBIy5BqtNIPjZ58dbtfgTJkyBXfffbfDfdq0aYOdO3fi/HnbaccXLlywydCYpaSkAKjN5DRrVrdqdUFBgc1jzMFKhw4dMGjQIDRu3BhLlizB+PHjbZ535syZmD59uuV2cXGxT4IcrSTAcSXQa4gqo/LVjXUwo4QBDhH5i3WHLuKZH3ahotqE/OIK7Ph2B/7UrwU+23Dcsk+1l1tvUK2KaqNsHSp/y+C4HeAkJiYiMTHR6X4ZGRkwGAzYtGkTBg4cCADYuHEjDAYDBg8erPiYtLQ0pKSkICcnB3369AEAVFVVYc2aNXj99dcdvp4oiqisVO4QrNPpoNP5vpZEmsr7IvcE7sto47HXkgY4RoWmfo7owvzrlxRwLfNHRMHn3o83Km6fu6Ku1UVFjX/PoA1W7/x6SHbb3wIcj12qd+nSBSNHjsSkSZOQm5uL3NxcTJo0CWPGjJHNoOrcuTOWLFkCoDbNOG3aNLz66qtYsmQJdu/ejYkTJyI6OhoTJkwAABw9ehSzZ8/G1q1bcfLkSWzYsAF33nknoqKiMGrUKE99O6qQpvKe+3GPgz0bTnpF88GaI3XbXZjB5Y8ZnOKKGuc7EVFIqqhmBscXVu4rsHydGKOzaUniax5dqmHhwoWYOnWqZVbU2LFjMW/ePNk+Bw4cgMFgsNyeMWMGysvL8eijj6KwsBDp6elYvnw5YmNr+7JERkZi7dq1mDt3LgoLC5GcnIwhQ4Zg/fr1ikXN/iTMwz/8CyWVOGcoR88W8bIMjnmtKcDFISo/XAvq+n+uxviBLbH+yCV8NWmQ310pEJHvVFQzg+MLYZKL9rfu7u27A7HDowFOQkICvvzyS4f7iFbT+wRBQFZWFrKyshT3T01NxbJly9Q6RK/yZICz9cRl3P7+BgDAU7d0xkdrjynul7PX/nIMZjo/zOBcvlKFd1fVZqJ+2H4Gdw9s5eMjIiJfOVxQIrvNAMc3tJKyC3/M/PvfEQUxT6bvzMENALz2835cLK3/iuX+8IuanpZg9z6+mRGFtpvm/Ca7XckiY5+QXrT7Y+bf/44oiAVKkaw/ZHA+f2ggVkwfonifC6NsRBQEKl0sHuZFj29IL9rD/TDA8egQFQUmf8jg6MK0aJ8Ui/ZJMThcIG9OaPLyWl5E5FnWsySzd+dj8pdbXX58RbURF0sr0Tg6wu8KXYOZNIPjj9fvvv8kI7/QLTXO8rU/BDhm/87sZ7PNxLbsREFjw5FL6PNSDpbuOGvZ5k5wAwDbThah/8srMHHBJrUPjxyQdsmv8sNhQv/5JCOfSpSsORWm8Z9fC6VVy7nuDFHweODTTSgqq8bUr7bX+zm2nigEAKw9dFGtwyIXSIcQOzeL9eGRKPOfT7IQdOO/VuP3w/7xB9kkpm4dL28sJdEQjG+Igof1+01FtREtGkf56GjIHVcqawOcj+7r75cNYhng+NCRC1dwz0fKXTq9oV/rxpav4yLDLV+nxkf64nBcxhkTRMFDesFSUFKBof9YhdOF5b47IHJZ+dXi7riocCd7+gaLjP2IKIr4Ie8MeraIVxyaUfLe6sM4euGK26+lEYC5d/XGdW+sAgDowjXY+sxNqKwxITbSP39ZzThjgig4DXxlpa8PgdxQVlXbYT46wv+yNwADHL9gMonQaAT8tPMcnvhmBwDg+GujXXrsG9kH6vWagiDIugFrBQFNYny/XpcrPvztKG7ulox+re33yiEiIs8oKKnA8j3ncbG0CgAQxQCH7KkymhCp0cqWVPA0o0lEQqO6upviimoHe/uf5XvOM8AhCgIsqQs8kz7bgh2n65ZYahThn6EEa3D8QOXVheLc7ZPU0H4wWo2AD+7ti84psbjfgyube0JFtRGllVyAk4jkalxYUJgaRhrcAP6bwWGA4wfMU+00kqZJWUv34MQlx7U1VSr8IY/s3gzZ04agQ7L/TfEzm31bD5ttn204ge7P/2LTBJCIQhsnIXifv9bgMMDxA+Y/SGnTpE/XH8dd/84FYLsgqVl1iFypjB/YCiO7pSje99n64949GCJqMFEUkbV0Dz5ae9Tu+1t9cRKCd4VrBb9cpgFgDY5fMGdwrFcbzy+uwMzvd2LDkUv4aep1MIkiXv5pL8b1bo5r2if6ZedIT4mLUv5VZVd2osCz9tBFfHr14kTtv2FmcDyrxKpeMyrcP7M3ADM4Xrf4L4NttlVU22ZwzL7adArHL5VhyfYz+L//7MB/tpy29M5RY4gqUEwf3glpiY3QIUk+fT5QFjAlojq7ztTVcKjdV5QZHM+atWS37HYjnf/mSRjgeFm/1o3RNrGRbJvSEJW1feeKkbP3vOV2RbWxQRmcQIsLUvSRWPXkMPz1+vay7Y7OGRH5p1OXyzz23MzgeNZPO8/KbvtrgTHAISqfsP4DNA9RiQ4mTP6865zsdt+XctA4OsLO3s4FamCgs1oIlENURIGlqsaEA+dLPPb8FdVGlFRUY+b3uzC8azLG9W5uuW/lvvMQReCmrskee/1gF6bRBMzoATM4PmC9FII54HFUNFxulXYtqzLiTFH925knxwZGUz9runCrAIcRDlFAeeizzR7t+VVUXo1VBy7gp53n8PjXeTh0NZgqrazBQ59twZ8/32LpwEvus16LOdyPFme25r9HFsRev70nMto2sdyurDbBZBLx7qojdh9jrtNpiMV/GYzPHxyIbqlx+PC+/g1+Pl+wXtAtQBNRRCGrPit+92yhx4IHBri076XSKhReqbLcHv7mbyivMsqKY0NpgobatFZvutYXnf7Ef48siLVtGoOvHh6Ea9rXBjmVNUbkHrukynOnWdX3SCXGRGBIx6b439Tr0L25XpXX8zbrISoBjHCIgt3YXqm4vlOSzfbpwzvabLtYWglDuXymz5miMllQU6N2ZXMQOVtUjoc/34KNR5U/k7RWWfNIP1xF3Iw1OD5kzkZUVBst3YwbIilWh1VPDsPF0kqMfnstzhdXyu73114F7ohgDQ5RyFo65RqculyOM0VlaNE4Ghltm2BOzkHZPpdKK21mZomifJi/xsgAx54Z3+3EusMXsXzvecU1EcOsPkf8OYPDAMeHzP0DyquMNh/c9WG62jArMUaH3Jk3YvXBC3hgwWbL/cEQ4Fj3BAvUYmmiUNTQZRR6tohHzxbxlttKy9VcKKmE1qouRARwpbIuwAmVJqn14ay20/o9995BrT15OA0S+J94AayRrjbA+WbLafxrue2q4H1axbv1fNK/dUEQcH2nJHz5ULplW7g28IOBGpP8jcmkchfUQHGptBJZS/dgf36xrw+FyGUV9ax9sdfvSqMR8MRN8mGqg+dLYSivkm3LWroH5VWSDA6HqOyyd80oiiJ+2H4GF0vrRga+eGggRvjxjDRmcHzI3CBp3znlD6mmMe7NdFL6sJemD4Mhg9MtVV47FKpvVE99vws5e8/j0/XHFdPIRP6ovk34HF2amS8UzQ6cL7H5kF5/5BJiJA3pmMGxz15W/Nf9BZj2TZ5sW++W8X7dbDXwP/ECWIyTDpDuNlC6a0BLm23SmCcYApzIcC2mSJr9hepsiLxTRb4+BCKX/WfLKXyw5ohHmvBJ39f0UeEwmkTsOWt70bhc0iiVAY591rOkzPYqnNPoCP/Okfj30QU5Zy2uXY2Lh3Vqij/1a4GbutimCqVZnWAYogLkgV+ovlE1tJaByFtEUcSM73YCANo1jXGytzJHSQLprJ5RPVLw1aZTTp+PRcb22estZn3BvWDiAJsZVf4m8C/pA5ijACc9LcHl1F8zfRTG9ExFpMKiZ6JVXU6wWbjxJHaeLvL1YXgd36ApUEi73p51sTlp1h+64o0/9bTcdvTOJV2kuHUT+20ypEL1wsgV9mKWZVbd9BPdLKHwBQY4PhSjsw1I0tMS8PXDg/DR/a434rNehVwqoVH9l3PwV2N7pcpuP/ntDh8die+Eau0RBR5pca+02d51HRLtPmbiNWm4s3/dkLujizNpxiE+KtylY6pPs8FgdqGkEnNXHMQ5Q7msBufYxSsAgIPnS7DNqvt0RY3/L2rKAMeHlMYvSytrMKhtE8RGuvaHCgBhDoaeOqXEYtaoLnh7fJ96HaM/apkQjcdv7GC5fbG0ysHewcl6NhmRPyqvMmLriULL7U/XnwAAJMfpZGtEOePoQq2pZNmZ+GjX3jffWnnI5dcOBc/9uBtzVxzC+A9zZRmc+z7ZCAAosOqpBtR+tvg71uD4kF7hakPagdPVASVHGRwAmDSkrTuHFRCkMydaJkT78Eh8Q5rByVq6B1lju/nwaMibKqqNWHfoIjLaNXFax+dr4+fnygrizVOMzxdXIqGR82Bk7l29seXEZYzq0czuPsM6NsWD16Sha2oc4huwAHEo23y8Ngg9fqkMvVvGW7afulw7pGhdv/nyrd0R58ZFuK8wg+ND/Vo3ttkWJamjaeNg2QUpaeOrUCG9ogiS2mmHTly6glvf/d2yqry0turT9cd9c1DkE6/8bx/+/PkWPP71dl8filOOZvs1thOM9JAsI3Nrn+Z4+dYeDotZBUHAc3/oij/1a+FyBifYHbt4xa0p+a2b1F0kKp3rcKtGtPekt6r/wXkRAxwfCtdqbP4g37yrt+Xrh4e0RcuEKIfP8dK4bhjT0/7VTbAqKKkLcKQdSoPV7GX7kXeqCH9ZuE3x/opqI+77ZBMe/HQzxBBtfhgqvsitHeZZsa/Ax0dSf3f0ayEbdnrqls7Y8dwIbJh5Axb/ZXC9nzc+yvUMTrD+nWw6dhnX/3M1/vTBepcfkyQZ5rPup/bST3tRLZnef1OX5ICZsMIAx8eirWY+SRfBjAzX4vXbe1o/xEKrEZCZ0SZgftnUdLek509pZY0Pj8Q7Kp0U9C3Zfga/HbyAX/cXoLCs2uG+FDhKK2vwzA+7sPbQBWw7WRg07QEeGdoOjSUBTmxkGPTR4Wimj2rQsjXWTf8cqQ7SmYj/2VI7TX73Gde7nEs/QoxWExg+XndMVpQdSN3jGeD4WLSTMfRGCoXICY0iEBcZhs8eGOipw/J7g9sn4qP7amealVUFboAjiqLiejoA8Mr/9uK+TzahssaIJpIpmf/4Zb/NvrvPGCxfbz1RGLRXp4Hs513n8P2203bvN5psfxfe+fUQvsw9icyPN+G299Zj3qrDnj5Mr4jRhSFW8t4nnWnVEO40nqsKkmDRmnXz05KKavz5sy34385zMJlEzF1xECuuNj0sKK7AFasLRKUWFJfL6iZyWAdA/sy/K9RCQLSTbsVKVySjezTDi+O6hWTmRqpLahyAwB6iuu+TTSgorsT/pl6LMK0GK/aex3M/7sa0mzpi/tpjAIAftp+RFfS9u+qIzfMUV9S9SU36fAveHt/HZjo9+U6N0WQZXhzSsalND5Eaowm3vLUWsZFhWPyXwZa/7eNXp+mazV0RHLN/onVa2fuXWllYdxrPVdeYAP9v5eI2aYCz7tBFfLv1FFbsO48V+85j0Z/TLb9DXz88CHd/mItm+kj0ktRxKgUw0iCIGRxy2aC2TRzeb++KJNSDGwCIuXpuqowmpyvg+iNRFLH20EUcOF+C/fklAIAf8s7grKECMxbvtOy38dhlpz0nisvlw1KLNp5Q/4Cp3qTZAusrZgA4ebkMhwpKse1kESqq6/YN0zh+i/7btzuQs/c8DGXVyNl7PmAa2FkPzUunentLoJwrd0l/1+79eCN+zDtrub364AXL159dnZxwzlAhe4xSCwrpczDAIZc9cVNHy3TxiYPb2NyvNERFtaIl2a1rXvvVh0dSP9Kp3uYrT6VU/YWSSqcpfGkDNcD+gnnkG9KraqWfjb1shqMeVwDw7dbTmPT5FmR+shGTPt+Ceb/6zxCWvXohQQDCrq4f9dmDA/HQtWm4o5/tOnqe9nr2ARy4emERaKpq7F/UOVqf78SluoygdFVwabDnbAgqkIaoGOD4WFSEFnnPDceqJ4fhuTFdFe8nZYG+eKj0TcX8oadUYLn20EWsP+K486p0iApwvHYPeceRC6V4/OvtOFxQ4nRR2HxDheVrc4BjMomyK2dHdp6urcFasv1MPY9WffnFdd+TtN5GmgAY2rEpnh3TtUGFxfW1eNtp3Dz3N6+/rhr+9MF6XPPar9h+stDmPke/a9KgSNogVVpE7KxLeiD1GA3sT4ggIQgC0hIbKS5yFhGmwU1dkmTNl0QETgTtTfaKdf1VdU3d8ZoDkpOXyxT3Pa/QSVTKeohKgIBP1h3Dl7kcqvKVuz/MxY95Z/HAp5tlq2hLP0CqjSaMnPsbxs/PtWy7UlmDwwWl+FfOAbdf018C22qjCde9scpyO/fpG314NI4F4sw0c0D7x/fWY+W+87L7Kh18P2cK6wIcaVAt5Wzo7onhHV09TJ9jgBMAPrp/AJY8Wv/eEKHidGE53sw5GDD1ONazOP6z5RT2nHV9aqdUsdUQ1cXSSrz4014888Nup9kD8owLV3s1nbpcLvtZGyWXwHNyDlrqr8yKK6px05w1isXkzvhJfIPi8mpZpsafOy4//k2erw+hQf7ypbw3VqWDBn/SFhLldvYruzocrrSYZjN9JDLaOa4b9ScMcAIEi4qdG/KPVXhr5SF8fHX2kb+TFvMZTSLeyHb/it1MWpgKQPahyXWrPM9oErFo40mcspOBO1xQavna3H/l6IVSvL/aNoiZMH9jvY9DEATsPF2E+z7ZhP359QuW1aDUY2bK9e0BAG/d3durxxIb6Ti4Mk+flnp/9RE88sWWgMjuWF8oNXT6u7kD8rNjuuCxG9rL7vPnQFUJAxwKOhuPXfL1IbhEOkRlNInQeagO4d0g6Z3iz+auOIinl+zC/Qs2Kd7/yBdbLV+bizQ9VeB667u/47eDF5D5sfKxeIN0mOO+jNYAgKk3dsCavw1za5HN+ujevLZ9xNQb2uPAyyPx52udr8VXbXUR8Hr2fvyyp3Zqtb+TTo2vqDYqztJzhzk41QgCIq1muwVSgTHAACcgBdAsPZ9I8sGUU3eIoghRFK2GLUSbBe3U8u6qIxj99lrsOWvAvF8P2cy4oob7atNJAMDRC1ec7AkUXR0mqE9SVlqLp0QAYP4MulDiuG7LU65U1iBr6R4AtQsKvziuO4DaesLWTVxbX68hvnwoHR/c2xdTbugAXZgWvVrqnT7GXlfjyV9u8/ummeagI3v3OXR+NttpvZ6rwjSCzUVXoGWDGeAEoCYKY6NUR+ukd4gvmUwibn9/Pe77ZJPsKrfGJHp0Jsmes8UY/fY6/HP5Qbz0016PvU6oks5ic/aBeO/HG/HXRdswZZH7i2XGRTlZTNIqaJJ2uPaW537cg5X7a9fJ8sVMx/joCIzs3szy9zS0Y1O8O6Gvw8c8tXin3czHWTvFuP7kcEEJJlvV4jSUViNAZ53BCbDlLfz3k4BszJvQB6N6pOCRIc5TrqHMU5kQNRy/dAXbThZh7aGLsjdUo4cDHKmNxy575XVCSaJkXSVDebXTIOd/O885nY6rJCrc8e+I9W/+jO92Ku7nSf/dWTe1PcIP/hYFQcBoJwsS/7TznGI9FFAXsK45eAHrDjlu1+ArN81Rf7p7mFaA1irNWJ/fWV9igBNAxvRMxXv39Au4Qi9PeuIm2ymL/lyPLX1/uCJp3ldjMiHCS1e7fp5xDzifrT8uu8q/fKXKpujbW6wnI/iiZiIhui7Y86eFX0f3aIYWjaPs3n/CToG4IAgorazB/Z9swr0fb7QU4QY7rUaDDskxsm2B1MUYYIBDAW7qje1ttiktFucvpFf2svVdTMrp/MbRToYkAHS0ehNyJtDepPzd81frTcxMoghDuWc+2J0FLNaxvdEHP2vpa9qbiuwL8yb0wZq/XY9uV9ews+Yo2VQm+Vu1bsngCV9sOI6h/1iFk5eUgy5v0AoC+rdujKVTrrFsC7QV2BngUEBTmj7vr5X+r/28H8PfrEslL9+Tb/m62mTC1hO2XUljJFNc4yLD8Kd+LfDpAwOgjwpHZLgGU29oj36tE9w6DsY39ffe6sOY/fM+h/vUmEQUlVc53MdaRtsm6Nsq3ul+7g4R+OJvQa2VwdUmCAK0GgHv3aNcj6MRBJhMos3woiiKskaN1k01PeHZH/fgxKUyjH13HbJ35zt/gAdoNQIEQUBPJwtx+jMGOBR0qv30j/CDNfIx/h8kbfi/33ZG8cPr1OW6poXtkmLwzzt6YVinJGx/djj2v3QLpo/ohEgndRnWmMGpH1Gs7VX07zVHcbjA/hTvGqNomSnlqk4psbixS7LT/ZxmcKzifaX9l+/Jx8i5v2HfOfX75Iii6FdZGyXN9MrDVN9vP4Ob5/4mC2aA2gsC6fdkKFdn5XNrxy9ewbJd52QBVlFZNSZ/uVW2VIunZnX1a91YdltpZXYGOERe9uK4brLbxgCbyggA/93hfM0haY2OdFkPXZh765UxwKkf6Zu7dB0fpf3cDXCmj+joUh8kZz87Ac5rcB7+Yiv255fg8a/dn8XlyIlLV3CooNTvPwQdFfMfKijFnrPymWdGkyjLStUng+NKUDLsn6vx6MJtWLmvwOa+xVvPWDqSe+r8vn9PX7wwtu699FKp7XRzf//ZWmOAQwHvvow2stv+XIPTEIl2+vu42yDQJNYu5fDZ+uMw+FERqL+TZthKKuxfxdeYRBjcGKJ6bkxXxEWGu/RzdPa7rZTBEUURL/x3DxZulK9LVurge3BXVY0JQ/+xGiPeVH82j7dZ/2yNVlkpR/VVG45cwhvZ+2UtIKZ+tR2j3l7ndI0ns7xTRTbbFm87jd4vLseRC6Uem8mUFBeJ+we3sfQR66XQc4l9cIh8LNCuMqx1SJIXDc++rQc6p8Ri5i2dFfe37jbqjCiKmPT5Fjy/dA+e/G5HvY8z1Eh/r0or7X/IPfntDvx7zVGXnzfm6qxIVzJxf7263MHoHsrTnq3XtTKKIjYfL8SC349j1pLdsvu0Kk3hNppEfL/ttCrP5S13D2iJ+OhwxZYS54vlfW9qjPIAx1GR8fj5uXhv9REs2njSsm3pjrPYd64Ym11szzDPTufxsioj5uQc9PhU7V+fHIbfn7oBqfF1Q3kPXZsGAJh5SxePvrbaGOBQ0PHHGhxXr94A2xT6+IGtkD1tCFo0jlbcvz4ZnO0niwAAOXv9vxW9v5B+sDjKfhy7eAVHLzrvaGxmbvugs1NLJf19GNKxKTY+fSPeGd/Hsi3OwVpLRpNoNxiz7nFSX19vPomnvt+lynN5y2u398TmWTdBr9A48ZxVY79Rb69FhWSIylH2zuz4pdqfv3RoyrqmZcXe85j+nzyUVdW4PPX8bFE55uYcdGlfe968q5fD+2N0YWgeL69TmjWqC1Y/OQx/vi6tQa/tbQxwKOj4Yw2OO70zxg9s5dZzS69Ch3Zs6nR/1uDUjzSDU6zi8E60rjZzYy9QDbf6YEyOi4RGI+C6DokAgEnX2W/8WWM0QSMJZKTfg1IRqbuuVNbg512+meXTUOFajeK054PnrbJgJnkGx5UMsbkWSlqwHGbVBuLPn2/B99vO4IM1R11uK7D9ZBE+Wud8MeHRPZrh2OxRWPDAAIX7Ul16LSmNRkCbxEYBt+izRwOcwsJCZGZmQq/XQ6/XIzMzE0VFRQ4fI4oisrKykJqaiqioKAwbNgx79uyxu+8tt9wCQRDwww8/qP8NUMB4XzL1s6La5NP+EUrcmVkSHx1udwhCibSgtVNKrNP9Gd/Uj7T+oLSBCxpKuTNEJfXx/QOwdsb1mJBuPyA2iUCYZOkSaaAd1sAlTb7IPYFuz/+CdYf9s7uvKyprbP8ulykEbNZdx50xx46yAEcSUG4/WdcS4kxhOQrL3GsrIHV73xY223ThGgiCgOs7Jdms3m6dIY6NDMPEwW3q/fr+zKMBzoQJE5CXl4fs7GxkZ2cjLy8PmZmZDh/zxhtvYM6cOZg3bx42b96MlJQUDB8+HCUlttMy586dG3ARJXnGLT2a4fXbewAADheUYsg/VvlVW3V3eoOEaTTIGtsNN3ZOwicT+zvd/84BLdExOQYvjesmu1q3x+SHQ3j+qqyqBoVXaj98pB9s1TXqZQkbRZgDHPfejiPCNGiZEI0mMTo8N6ar4j5GkwhpHCMNcNzJ4FTWGG0W73z2h9129g4crnacvnyl7iLClQyo+c+wUnK+pX+bf3xvveVro8nUoGJ/paFN6YzLcb2b48bOSXYfv/3Z4cga283u/YHMYwHOvn37kJ2djY8++ggZGRnIyMjA/Pnz8dNPP+HAgQOKjxFFEXPnzsWsWbNw2223oXv37vjss89QVlaGRYsWyfbdsWMH5syZg08++cRT3wIFmLZN5cW5b61s2Fi1mtzJ4IRrBTSN1eHjiQNwQ2fnvVGS4yKx/ImhyMxog9v7Ngdg29NCikNUruv30gr0eSkHhvJq2Qwmd2qqnIlxUIMzsluKSxdx9lbMNppEWWBWIRsycT3AeWzRdgx4ZQV2nfb+4p3+4NKVuuDOOoNz+UoVXl22Tza0ZQ5mpBkce12la0wN6x2kFBhfsbqgmn17D9yT3grLpl5ns6/10Fkw8dh3tmHDBuj1eqSnp1u2DRo0CHq9HuvXr1d8zLFjx5Cfn48RI0ZYtul0OgwdOlT2mLKyMowfPx7z5s1DSkqK02OprKxEcXGx7B8FH+sr0stX6p/2VZu9DE6iwsrwDUmwdEiOxdZnbsI3Dw+yuw/DG9dIm9btPVssz+ComAUz1+BEhdcVC88a1QVv3d0b/7zTcUGo5TkilAuNjaIoC8akv4fuZHCWXy1Gn7lkJ7afLHQ5C/jqH3u4/Br+bK0kG7zlRCE+33DcUkD80k978eFvRzHm7XWWfQRLgOO8duennecaNOR5R7+WNtsuWmXbkmIj8cofe6CrnWUqgpXHApz8/HwkJdmmxZKSkpCfr1yUZt6enCy/ak1OTpY95oknnsDgwYMxbtw4l45l9uzZljogvV6Pli1tfyEo8IVb1RSoWQhaH3vPFuOLDcdhsmoUJpUaH2mzraHT3JvE6BCm1dgdVw/0afTe8n//qZtCbxJF7JV0/lVziMqcwWmRUDdzJUwrYFzv5pb7nEmy0yPJaBJlhbTS/jzbTxa53RV395li/PG99fhwreNp8MO7JmP/SyMd1gcFkmOSWXGbjl3Gcz/uwS97aoM+cz1NlSSQNCfdpENgjv7upiyqf9PFrqlxNpMLLio06QtFbgc4WVlZEATB4b8tW7YAUF4nSBRFpylX6/ulj1m6dCl+/fVXzJ071+VjnjlzJgwGg+XfqVOnXH4sBQ7rK1J3lzBQ26i31+LZH/fg++1n7KagryhcuakVgDz/h65Y/9QNaG/VV8e6Ff0xN6Y0h5Lvt5+xfL36QAEeXbjNcts8ZdyVDtRA7VCTPeY+RnGRdVOWpUt0uKKxZAVva9IMzlOL5dO5l+3KdxjkrD10AePe/d1m+2s/73d4POFawe3+TL4Q78JitvbsPlM7XKf0fZrfiWRDVB64sJh6YwcAQFOrANfegqKhxu1PgClTpmDfvn0O/3Xv3h0pKSk4f962x8aFCxdsMjRm5uEm6wxPQUGB5TG//vorjhw5gvj4eISFhSEsrPYK5/bbb8ewYcMUn1en0yEuLk72j4KPdU2BWn0+6uO3gxcsX+89W6wY4DSPj0KZQmZHrRWgBUFAanyU06v0YCgW9bRvt8ob2VUZTaioNuKxr1y78n5xXDcs+nO609XhzTNteruw8KaUxsFw07db6o79UEGp7L7/+zYPvV/Mwar9tssDAEDmx5uwQ6GzrjOBkiX8bnIGBrapW6x2+vCOLj/W/DetUwhwLDU4bk4vd5f5eKXPPXloOzxrp+g81Lgd4CQmJqJz584O/0VGRiIjIwMGgwGbNm2yPHbjxo0wGAwYPHiw4nOnpaUhJSUFOTk5lm1VVVVYs2aN5TFPPfUUdu7ciby8PMs/AHjzzTexYMECd78dCiLWGRxXZ0h4wn2f1P3eawTlGpxPHxhgJ4Oj7nHf7CB7APhXrZI/MC9vINtm1S+lusaExW507w3TajC4fSJ+/b9hDvf7bcb1eO+evrI2AQ0N09dIgm1rFdUmGMqr8f7qI3b3qY9ACXDaJ8XKZhCFu1Fwaw5wIhWKfMWrlW7SDI6ahelAbdNHM2kTyqdu6YwmCrV9ochjOfwuXbpg5MiRmDRpEnJzc5Gbm4tJkyZhzJgx6NSpk2W/zp07Y8mSJQBqrzinTZuGV199FUuWLMHu3bsxceJEREdHY8KECQBqszzdu3eX/QOAVq1aIS0tsLoskrqsa3Aqaox4I3s/PnJSL+BpGo1gk8F5cVw3dEiOVczgDOtof0pnfTx+UweM7WW/uRdnVdVZe+gCemT9gq83y4exS6wC0RqTaLP0gSPm4NvZsE1qfBRG9WhWryZ8303OwF+vb2d3yrgj5kJnqR/zzijs6ZpACXAA+VB2RJjG5en6jlo/mGfcSYuMH/h0c71XAl/yqDwp8M74Pvjk/roWEv7Y3NQfuFbBVk8LFy7E1KlTLbOixo4di3nz5sn2OXDgAAyGuqmHM2bMQHl5OR599FEUFhYiPT0dy5cvR2ys8wZmFNqs19YpKqvGe1evTP/soNur2qyzMhpBsHkzjLza1E165XXk1VGorDHanRFTX7owLSakt8JSO/UigfRh5GmPfbUdZVVGzHSy9IC7V+PmbtPu9rpxR/82CejfJgHr69F4b/WBCyivMiIqoi7QefzrvHofi6fXS1KTdIgpIkyDyHCtTZ2aEvPftNLSDeaCY+vnKa+u3993rxbxsttJsTrZ9O5gXWC4oTwa4CQkJODLL790uI91RCsIArKyspCVleXy69Q3KqbgEubgqteV4na1WH8waATbPjjmnieJMTpcLK1EdIQWWo2genBj1jJBeR0rgAGOVHS4FkVw3nTN3QDHnJGxrpX5apL96fz15Sz7c237RMXuw//45QCe+4M6tRuBlBWUDjGFawREhmtgcKHGu+zq37TS4puWDI7VMHlltQkO6sHtsv69se5GzL9hZcHb4YdCjqM3dm++AazYJy+u1yoMUZmv5D97cACGdWqKbydnePSYmsdH4YN7+yrep1ZRc6CrqDbaDEXZk3vUtZWhzZSWRZg8tB0y2jVx63lc4SyQt7fW2f921WX43L1oXDBRvuaRtoHLQHiTNIMjwvkwopm5K7RSBsccAFcZbTM4arAOcAIpY+ZNgfNbSOSEozcmX36ICwpDVOY31W6penz6wEB0S1XuRKumkd2b4aYutjMYefVX69Z3f3dppej6UAq+XQ4iVE48puh1eOIm29lC54srsWzXOQBQXITSkchwLdo2bWS5/ezoLg07SC+SZnBEsW742Cmx9mdYopDBuXylCr/syUdZlfz3yZ1Fd81+mTYEAPCfR+ougqyHOgMpY+ZNHh2iIvKmGF0Ynh3TFbowDZ6xmvrsyxo8pVlUnqzFcESp78fpwnLM/+0oHro2zeF042BUbTRh3aGL6N+mMfbn2653V19bnrkJTRpF4OkluxS7VQOe6yjtLHCKjghDVITy79+jC7fh+GujbTIPzkSGa/DFQ+lYvPU07h3UGgmN6jEO4yPSWhaTKLrcP0uEiCtVRsXO48v3nsfyvedtnqs+GRzzArqOuhCzBkcZAxwKKg9dWzuTzjrA8WUGRyPYH6Lytvgo5T4sryzbh2idFvekt/byEfnW2ysP4Z1fDyM9LcH5zm4wBzWzb+tpdx9fLXoaHaFFlINsZ+GVKreDr8hwLZrHR1kazwUqEcp9bRT3FaGYvZGyblVRUW3Cv9e4NiU/RheGWZJMmPxnJr8QYRZWGYeoKCT48g1g7oqD+GnnOautvsmUSDM4Y3o2k923cp+82ZtS8WSwMU8H33jMvZqahvhjn9oFUe/LaOO115SKitA6/BDv81IOisvd+9kHQtdiV4ii6PLFR7XR5PaQZklFNWY76AI9rFNdb5vv/pIhq5fSagTcO6gVRvVIQTvJcCAApLdNsOxDdZjBoZDgywBH6aWrVFzLyB3xkikc1quvS98b5+QcxNsrD2H+ff0xvKvzFc0DlZrdriPDNS41l5xzZy+8+scesinZjjRpFKFqbVCjiDCHGRwA+PvinW49p6+XRVFLuFaDYZ2SZItr2rPjtAEj3vzNreefuGCzw/ufHNEJqw/UNmZU+hm9fKvy4qV/vb49mjSKwPWd1e2hFeiC47eSyAl/S+F2SI5xvpMHSNcsSlCoxymvMuJ0YRneXnkIAPDcj8G9jIOaV7yu/o4JguBycAMA/87sj14t9Pj0gQHOd4bz2p6ocMdDVID7GS2XC3P91NQbO6BPq3jc2rs57s/w3TCtNMPq7GckFRmuxcRr0tC6SSPnO0N+MRPMmMGhoPTpAwNkV0v+Mstg7l290btlvN3CU09r3aSuH05jq0LQ8mojRr29Vrb4psaH63l5UmWNEbowLdSazdymSTROXi5T58msdEqJxY9TrlXt+TQadRbC1GoES1AX6ENU04d3lK1D1Tg6HIVl3h+ilbYTsJ4KriatRoApBAqTmcGhoGS9urK3MjjOig5bJkShTaJrV1meIH1t63V3fj98yWZl8WAc0/8x7wy6PfcLfsw7Y3eIKusPXdEp2fXu6R/dP0BxKNIXHM22MZPOonK2AKg9N0iGQ3xVNO8p3vxR9mpR2yJieNdkNI3VoXl8FFo0jpKtLq82wUc1gN4WXL+VRFdZf3h7K8BxNibv6yvdGF1d0raVg+7GZsEU4FTVmCCKIh7/Og81ptr/7WWotFoNpHf9+NdrHD53+6QYzL+vdm2g129XrpPwlrjIcOx4boTl9oT0VvhTvxayfaS/h/X9nZTWZgV7e4FYXRh6NPdMr6qP7h+A5//QFf/8Uy9oNQJWPTkMq54c5tFzOmlI7WzTUT0cL8Qb6DhERUEpIkz+5uCtIapzhgqH97szru4pPz12LY5fuoLuLrxhB+LnVlWNCZU1RsRKroAN5dW47vVfMdBqOvhRq4yVmfWyH71axtt9vYV/TgdQ+4F/8OVbPDq04Cp9dDheGtcN/91xDn8f2Rlf5p6Q3S8NasK09fsh92iux9vj+yChPmsPBJi850dg+Z58/GXhNpv7ujaLw95zxfV63p8euxZNY3V44Jq6haK98fvzxE0dMbRjEnq28HyDUV/y/V8ikQf4IoPz8y7rqeC23Cku9ZTuzfUY09P+6uJSgZjBueejXPTIWo4LJZWWbb/syUdxRQ1W7CtArM75dZ3S990yIQoA0Ewfia7N6oaBrmmfaPnaH4Ibs8yMNvjP5Azoo8LxwDVtcGPnJLzxp9q+PNJAO7yehUjx0eEY2ysV13ZIdL5zgNNqBLsZlbfH95HdfnJER5cbHfqqIWKYVoOBaQk+zyh7mv/8NRKpSCnAqTGasOu0wWPBjtLVnbXo8MBKmgZikfHm44UAgJ931wac2bvz8cm6Y5b7XfmWwjSCzZpOX00ahJu6JGHuXb39KpBxRXREGD6eOAB39m8JwCqTWM8fsd5O08hgoDQcpVSv9WFmP7RPks+InHJDB3z2wECXXsfRAsHUcIH1V0rkIpvVdkURzy/dgz/MW4c3cw766KiASDst8v2Vt1Zgb4hdpw3ImL0SP2w/I9t+pqgcu88YMPnLrbJlGIpd6Cmj1QjoafUh16JxND66fwDS2zYJuADHmvTKvb4Bvz8Mt3rKv+7sZbNNKasXbuf3oJFO+dzc3ldeCxXstUu+Fth/pUR2WGdwLpdWYeHGkwCAeasO++KQAAAR2sD6k5Me7qr9BXh6ya56LRjoSVO+2oZzhgpM+yZPtv1cUQXGvLOuXs8ZptHg6dFd8MiQtlg29Tqb+wN91pD0+Kvr2XQyEILf+kqKjbTZphSM2MvApMZHKW7/5x098WFmP6ePJ3UE9l8pkR3WgcSEjzb66EjkAu1DQZqWf+DTzVi08SQ+XX/cdwekQLqQqXR5ibNF5fV+Tq1GgD4qHDNHdVGcdm2eleTOVHJ/Iv2wrjaJ+HZyht19WzSOwuSh7WTbZozs5LFj8xdTrm8PAHj+D10BKC9ias7qmIt1B11dMsFebYsgCLJlMgKxxi2QBFZBAJGLwh3MDDEHP6Io4pyhwu7VlquuVNagkQuFq/7og3v7YeW+83hhXDd0fe4X2x0UArJ8JzPFvE36sTP9mx2Wr53NaHPE2ZX12F6paB4fhY4pgRngSNUYTRjQJgHx0eEoUmhu9+3kDDTTR+GDq4tEPjO6C/58XVtvH6bX/d+Ijrh3UGuk6GuzOUoTMc2ZsI/u64/vtp221Dg5Iv3dClOr0yQp4tmloOToysg8LfbVZfsw+LVf8YXVFFp3rD98Ed2e/wWvOVhAz5+N7J6Cf9zRy249hdKaWf6QVr9UWol/rzmCCyWVsg+eFfvOW74+05AMjpOp04IgoH+bBI82Y/MWR+uiPXRtGprpay8A0tMS0Dw+CncNcP4hHgwEQbAEN4C8Vmlc71SkpyWgd8vGAICkuEg8Oqy9rEP5e/f0VXxe6XsTMzieFZiXnUROCIKAPq3isf1kkc195jeV+WtrZ9a8/NNeZA6q3/ozL/x3LwBYrm4Dlb2hs0qFepswP6gjmvHdTqzcX4Blu/MVhw6c0YVpUOngg90fgjhvuVJlv6ZKGvgumjQIQOh+KBslv2dz7+rtdLj5lu7yJnp9WsUDkP9uheq59Bbfv1MRech3kwejTRPbbr3WBcgNmTQeYCU1blMqKPaHD/+V+wsAADtOFeHSlSq3H39b3xYY1qmp3fv5wWNLqxFC+rxIA2lXaumk+7RpEo2Prna6lp7DED6dXsEAh4KWViMoTue9fKUK3245pdprOPLwkMCuVVC6uveHDzlXmvU5fHxkGD510KskFGsjlH6qoldXZfJvDWmfdeeAlmhydfiqReO6i65Am3QQaELvr5hCir36gr99t7PuRgPeuJy9P/35ujTHO/i50soaiKIou3p1VMBdXFGN/2w5BUO5Z1dijmtgkzlzofnEwW0U7/eHIM7TrINEpT8Df1lA1B80pEFojWTl7qaxOnz/6GD8Mm2IGodFDjDAoaBW7kLPlvpepZ66XIbdZ+Rr0Fh3d7W3WnWgMJpElFcbUSN5c9dqNDh4vkRxNtX0b3Zgxnc7MfWr7R49rtjIhmVwzJm9rLHd8OZdrjV1CzY3XV0sM97BauJeWsItILRrGuN8Jzus1zLr26oxOgXBDDx/xyJjCmoV1c6bmNX3wuyGf6222bbggQFYtb8A7/xa20wwGD4oSyrk0+DzDeUY8eZvCNcKOPTKKNm+5llMaw5ecOs1KqqNLq2LYyirxv784gan9qVDl0rDUTXG+jW/CyQvjuuGVgnRGNu7dl0ypTPqD/VW/qJrahw+uq+/W20lVj85DPvzSzAkBNbr8kfM4FBQc6XrrtEk1msmTrXR9jGNIsKQ0a6J5XYwtGIvqaiRzaZad/giAOXvvz6e+3E3Oj+bjX0urMj8x/d+x10f5rq0ryPSRpBKH+KOZlgFi9jIcDwxvKNiZmLqDe3RKiEaD1zTxvsH5sdu6pqs2PjRnjaJjTCyewprbXyEAQ4FNVc/qJbtynfreaUdc6Ua6bSyYoZAH6ICgJKKatl5PHm5zPJ1fQJDa59vqO1D9M6vh5zue/TilQa/HiDP4Chl2UwhPjYzfUQn/DbjekthLFEgYoBDBODbre7NqrK3DECjCPmor/TDM66BdSO+UlJRIwtwpJmbKhWHcjy1yrsSWQZHUjTdq2U8rmnfBBltmyg9LKgxy0DBJjDfcYlU5u60YHu1PY10YbJiBq1GwEf39cdL/9uLOXf2bsAR+s7kL7fKOrpKVdaYoAtTZ1VpZ7FSZY1nFvnUSn72L47tZlMQGirUyMYR+RMGOERwPPVZ6lJpJbL+uxdtExspPod13x2tIOCmrsmWGSuBpGuzOOw9V4yyKiOOXlAeGqqsNgHKsY/b7H3AFldU4x/ZBzAgLUGdF4K8K620BkepbxIRBSb+NRPBdvmBrzedxL8Vll945X/78N8dZ/HWStt6EXNbe0GSwgmkImPrWpT+bRo7fYyaWRWjnQBn9rL9+CL3hKpTz42yae9137cuhAOcuXf3AQA8N6arj4+ESB3M4BABCJd8yImiiKe+3wUAuKV7M7SSLPdw2sECjoF+9b/6yWH4ftsZvLniIAAg3oVmej/vykdkhLbea3lJ2avB2XGqqMHPbc1kp3GhzoWp6sFqaMemOPDySNWGHIl8LbDfkYlUIr2KlxbRllTKZ0s5SsiY63gCtVazZUI07h9cF6i48mH/yrJ9ePaH3dh9xtDg17c3c8mVZo1uv5YkmNJIfmARfrCQqC8xuKFgEtp/zURXSYeoHM0M0jiIXsyzcVol2C7wGSikH3DuNCk8p9DV2F0mO6e9rKqmXs/3+1M3YOqNHdBNoW9J71Z1w2/SxJEunG+JRMGCQ1QUUrQaQXEoRFpoKl2/SppUqKoxYf2RS3af27xvanwUvn54kM2yDYFAF+a4AZ49Suf0lrfW4vMHByKhUQTezDmIAWkJGNrR/gre9mpwyhQW/HRF8/goTB/eEaUVNdhztrYx4E+PXYuSihr0lsyUkmaOQj2DQxRM+NdMQW1ktxTZ7S7NlNd/kX6WSwMc6RpMzy/d7fC1iiULTA5q2wRdmrne8dRfSIui3alHURpe2neuGO/8egg/5p3BvFWHcf8nmxw/h50anPJ6BjhKx9Y+KUbWaRqQL4QYykXGRMGGf80U1N4e3wf/m3qt5Xa4nSv0asmHqzTAMX99tqgcX21y3AywpLJ+Qyn+5sFr0nBj5ySkuzEtu8ZOcFJVY5J1PnbEXgbH3nMrUSr0HtqxKcK1Avq0ilcMYKQBEJvdEQUPDlFRUIsI06Bbqt5y216As2jjScy8pTNiI8NRZazLGJgDnAP5JZ49UD/y3B9qpwmfuOT6sghGOwU0Go3g8orUajQybhqjwxmrmW7Xd07CjudHIDJMqxjAdL2aaWNsQxRcGOBQSGlmpyMvAMxashtvj+8jW5bAnT4vr/yxe4OOzd+4M+1952kDTl22nUKvFQSXO+TaG6KyJzYyDCUVtVmzp0d1BgAUl9dg3qrDNvtGR9h/q2vcKAKbZt1o6WNERMGBAQ6FhLl39cbO0wb0a90YP+adVdxn6Y6zeGZMF8UhKji5un96VGfck97wXjD+xF62S8mC348rbtdqBFlmxmQSMfP7XeiYEouHrk2T7atUqOwo6EloFGEJcDIHtUFUhBYV1UZERWhxY5ckl48dAJJiVWrHTER+gwEOhYRb+zTHrX2aI2fveYf7nSuqkAU4Z4rK8dyPuy0rXlsL1woY1LYJ7stoo+bh+gU1GhdqBAGiZHn13KOX8M2W2lom6wBHqVDZHMDYe27L11cPNTJci79e374hh0xEQYIBDoUUZ2tOXS6rkk2Pfvl/+xzu/5dh7TF9eEdVjs3fqDFlWquR19aUSgqxjSZR1mtHKcAxlFfbbFPi7mKpRBT8+K5AIcXZsMsDCzbXLiDpomCeVuzOEJU91kXG0i7RL/9vLw6eryvePni+VJY9A4ALpfIGgn+7uRPeGd8Ha/42TFYUHEBLfhGRlzCDQyEl0oVC0jI3lgYI5gDHnU7Gdp9DkDdW/OuibZavF/x+3KZ2Z9vJQgxq2wSllTXQCgJuf3+D7H5dmAZ/6JUKQF4WxendRGSNAQ6FFH2U81/5IwWlLj9foC+w6WlajeBWH5srlTXYe7YYo95eq7jkRTAHlESkLr5bUEiJi3S+fMJbKw+5/Hxs7e+YRhDcmv5dUlGDh7/YAgCKDQKlw2bM2hCRI3x3ppASqxDgvHV373o/HxdndEyrEeBOd5tp3+ThdKFtPx0zZsyIyFV8t6CQEqkQkIzr3bzeRaoRWjaHc0QAXO5k7AppgMP8DRE5wgCHQor1sMYH9/YFUP9lAoI9o/DFQwMx85bO9X68URQVp3/XV3REXUCp4RAVETkQ3O/ORA48OqwdRnZv1qDnCPai1+s6NMUjQ9vV+/Emk3oBTv/WjXFdh6aW251SlFeGJyICGOBQCGoeHwUAuLFLcoOfK9gzOA2191yJ3S7Q7ujfujG++8tgWZHxtJs6AACubZ/Y4OcnouDDaeIUcn55YgjOFpWjY3JdBmBgWgI2Hbvs9nMxwHFsxT7HS2O4SikH1LZpDLY+cxMa6fg2RkS2+O5MISdGFyYLbgBg3vg+9XquYB+iMhuYlgAAmDi4jW8PxEqTGJ1LzRuJKPTw0ocIQFKcfDXpGzsnITOjNWIjw1FaWYP7P9mk+LhQCXA+fWAA9p0rRnJcJD5df7zBz/fiuG547sc9Lu8fG8m3KiJyT2i8OxO5SRCAYZ2S0K91Y8RH2W8OGCrTxKMjwtCvdQKiJNkS69XA3dE0Rmez7f17+iLGznDTC2O71fu1iCg0eTTAKSwsRGZmJvR6PfR6PTIzM1FUVOTwMaIoIisrC6mpqYiKisKwYcOwZ4/8Sm/YsGEQBEH27+677/bgd0Khp24KsqMhkFBr9Cetd5lyfXu8Y2doT6nfkFR8dITNtlt6NMPO50fYbB/ZLQWtmzRy80iJKNR59N15woQJyMvLQ3Z2NrKzs5GXl4fMzEyHj3njjTcwZ84czJs3D5s3b0ZKSgqGDx+OkpIS2X6TJk3CuXPnLP/+/e9/e/JboRAjbbEiHYb64qGB+ODefpbbobZUQ2S4Fp8/OBCfTOyPxo0iLAtfWptyfXuHz5PQyDbAAWpXH7cWHiLDgESkLo8NbO/btw/Z2dnIzc1Feno6AGD+/PnIyMjAgQMH0KlTJ5vHiKKIuXPnYtasWbjtttsAAJ999hmSk5OxaNEiPPLII5Z9o6OjkZKS4qnDpxAn/ZiVZnBS46Nkzea02tBrNjekY1On+ygFKlJxDhY9bZ8Ug8OSBU/DQ/AcE1HDeezSaMOGDdDr9ZbgBgAGDRoEvV6P9evXKz7m2LFjyM/Px4gRdWlqnU6HoUOH2jxm4cKFSExMRLdu3fDkk0/aZHikKisrUVxcLPtH5Ii9DE6EVoMmjerqRxpFsPhVSZiTACc6XH7eHh7S1vL10inX4IuHBlpuh1qWjIjU4bF35/z8fCQlJdlsT0pKQn5+vt3HAEBysrwBW3JyMk6cqGsWds899yAtLQ0pKSnYvXs3Zs6ciR07diAnJ0fxeWfPno0XXnihvt8KhSBBksORDpFoNAIiwjTY/cLN0Ai1i0mSLUfLKDw7piv00fLC7adHdbF8HR0Rhh7N9ZbbXDWciOrD7QAnKyvLabCwefNmAMpvTKIoOn3Dsr7f+jGTJk2yfN29e3d06NAB/fv3x7Zt29C3b1+b55s5cyamT59uuV1cXIyWLVs6PAYKbdJfwUYRWgzvmozKGhNS9bXTye3N9gl1w7sm44HBbXDgvHJGdcX0IWif5HyJBV1Y3TCg0WRS7fiIKHS4/S49ZcoUpzOW2rRpg507d+L8edsuphcuXLDJ0JiZa2ry8/PRrFndGkEFBQV2HwMAffv2RXh4OA4dOqQY4Oh0Ouh0ttNSieyRBjiCIGD+ff19dzABonF0uOU87T5rkN235NHBaJ8Ug9hI+1PupaSzsI5fLFPvIIkoZLgd4CQmJiIx0fnaLxkZGTAYDNi0aRMGDqwdT9+4cSMMBgMGDx6s+BjzsFNOTg769KmdflpVVYU1a9bg9ddft/tae/bsQXV1tSwoInLXoknpmDB/IwAgTMO6D3dJl1OoqJZnXaIitC4HN4A8i1tebWzooRFRCPLYu3iXLl0wcuRITJo0Cbm5ucjNzcWkSZMwZswY2Qyqzp07Y8mSJQBq39SmTZuGV199FUuWLMHu3bsxceJEREdHY8KECQCAI0eO4MUXX8SWLVtw/PhxLFu2DHfccQf69OmDa665xlPfDoWAwe0SMWNkJyTH6fC3m21n+ZGyPq3iAQC39m5u2VZhFZTUp1B4wcQBaNe0EbLGdm3Q8RFRaPJoIcHChQsxdepUy6yosWPHYt68ebJ9Dhw4AIOhLp09Y8YMlJeX49FHH0VhYSHS09OxfPlyxMbWjttHRERg5cqVeOutt1BaWoqWLVti9OjReP7556ENka6y5DmPDmuPvwxtx8JWNyyYOADrDl/ETZLV2a0zOI6Kju25vnMSru9sO1GBiMgVgiiKSgv1BrXi4mLo9XoYDAbExcX5+nCIgs7TS3Zh0caTlttrZ1yPlgnRsn3aPPU/y9fHXxvttWMjosDlzuc3Cw2ISHXSIar7MlrbBDcAEB/tek0OEZG7GOAQker6tIy3fP3iuO6K+3z+4ED0bhmPbx4e5KWjIqJQwmYeRKS68QNbQavRIL1tgt19eraIxw9/5cQAIvIMBjhEpLowrQYT0lv5+jCIKIRxiIqIiIiCDgMcIiIiCjoMcIiIiCjoMMAhIiKioMMAh4iIiIIOAxwiIiIKOgxwiIiIKOgwwCEiIqKgwwCHiIiIgg4DHCIiIgo6DHCIiIgo6DDAISIioqDDAIeIiIiCTkiuJi6KIgCguLjYx0dCRERErjJ/bps/xx0JyQCnpKQEANCyZUsfHwkRERG5q6SkBHq93uE+guhKGBRkTCYTzp49i9jYWAiCoOpzFxcXo2XLljh16hTi4uJUfW7i+fU0nl/P4vn1PJ5jz/L1+RVFESUlJUhNTYVG47jKJiQzOBqNBi1atPDoa8TFxfGPy4N4fj2L59ezeH49j+fYs3x5fp1lbsxYZExERERBhwEOERERBR0GOCrT6XR4/vnnodPpfH0oQYnn17N4fj2L59fzeI49K5DOb0gWGRMREVFwYwaHiIiIgg4DHCIiIgo6DHCIiIgo6DDAISIioqDDAEdF7733HtLS0hAZGYl+/fph7dq1vj6kgDB79mwMGDAAsbGxSEpKwq233ooDBw7I9hFFEVlZWUhNTUVUVBSGDRuGPXv2yPaprKzEY489hsTERDRq1Ahjx47F6dOnvfmtBITZs2dDEARMmzbNso3nt2HOnDmDe++9F02aNEF0dDR69+6NrVu3Wu7n+a2/mpoaPPPMM0hLS0NUVBTatm2LF198ESaTybIPz6/rfvvtN/zhD39AamoqBEHADz/8ILtfrXNZWFiIzMxM6PV66PV6ZGZmoqioyMPfnRWRVPH111+L4eHh4vz588W9e/eKjz/+uNioUSPxxIkTvj40v3fzzTeLCxYsEHfv3i3m5eWJo0ePFlu1aiWWlpZa9nnttdfE2NhYcfHixeKuXbvEu+66S2zWrJlYXFxs2Wfy5Mli8+bNxZycHHHbtm3i9ddfL/bq1UusqanxxbfllzZt2iS2adNG7Nmzp/j4449btvP81t/ly5fF1q1bixMnThQ3btwoHjt2TFyxYoV4+PBhyz48v/X38ssvi02aNBF/+ukn8dixY+K3334rxsTEiHPnzrXsw/PrumXLlomzZs0SFy9eLAIQlyxZIrtfrXM5cuRIsXv37uL69evF9evXi927dxfHjBnjrW9TFEVRZICjkoEDB4qTJ0+WbevcubP41FNP+eiIAldBQYEIQFyzZo0oiqJoMpnElJQU8bXXXrPsU1FRIer1evGDDz4QRVEUi4qKxPDwcPHrr7+27HPmzBlRo9GI2dnZ3v0G/FRJSYnYoUMHMScnRxw6dKglwOH5bZi///3v4rXXXmv3fp7fhhk9erT44IMPyrbddttt4r333iuKIs9vQ1gHOGqdy71794oAxNzcXMs+GzZsEAGI+/fv9/B3VYdDVCqoqqrC1q1bMWLECNn2ESNGYP369T46qsBlMBgAAAkJCQCAY8eOIT8/X3Z+dTodhg4dajm/W7duRXV1tWyf1NRUdO/enT+Dq/76179i9OjRuOmmm2TbeX4bZunSpejfvz/uuOMOJCUloU+fPpg/f77lfp7fhrn22muxcuVKHDx4EACwY8cOrFu3DqNGjQLA86smtc7lhg0boNfrkZ6ebtln0KBB0Ov1Xj3fIbnYptouXrwIo9GI5ORk2fbk5GTk5+f76KgCkyiKmD59Oq699lp0794dACznUOn8njhxwrJPREQEGjdubLMPfwbA119/jW3btmHz5s029/H8NszRo0fx/vvvY/r06Xj66aexadMmTJ06FTqdDvfddx/PbwP9/e9/h8FgQOfOnaHVamE0GvHKK69g/PjxAPj7qya1zmV+fj6SkpJsnj8pKcmr55sBjooEQZDdFkXRZhs5NmXKFOzcuRPr1q2zua8+55c/A+DUqVN4/PHHsXz5ckRGRtrdj+e3fkwmE/r3749XX30VANCnTx/s2bMH77//Pu677z7Lfjy/9fPNN9/gyy+/xKJFi9CtWzfk5eVh2rRpSE1Nxf3332/Zj+dXPWqcS6X9vX2+OUSlgsTERGi1WpvItKCgwCYSJvsee+wxLF26FKtWrUKLFi0s21NSUgDA4flNSUlBVVUVCgsL7e4TqrZu3YqCggL069cPYWFhCAsLw5o1a/D2228jLCzMcn54fuunWbNm6Nq1q2xbly5dcPLkSQD8/W2ov/3tb3jqqadw9913o0ePHsjMzMQTTzyB2bNnA+D5VZNa5zIlJQXnz5+3ef4LFy549XwzwFFBREQE+vXrh5ycHNn2nJwcDB482EdHFThEUcSUKVPw/fff49dff0VaWprs/rS0NKSkpMjOb1VVFdasWWM5v/369UN4eLhsn3PnzmH37t0h/zO48cYbsWvXLuTl5Vn+9e/fH/fccw/y8vLQtm1bnt8GuOaaa2zaGhw8eBCtW7cGwN/fhiorK4NGI/+o0mq1lmniPL/qUetcZmRkwGAwYNOmTZZ9Nm7cCIPB4N3z7bVy5iBnnib+8ccfi3v37hWnTZsmNmrUSDx+/LivD83v/eUvfxH1er24evVq8dy5c5Z/ZWVlln1ee+01Ua/Xi99//724a9cucfz48YpTF1u0aCGuWLFC3LZtm3jDDTeE5DRQV0hnUYkiz29DbNq0SQwLCxNfeeUV8dChQ+LChQvF6Oho8csvv7Tsw/Nbf/fff7/YvHlzyzTx77//XkxMTBRnzJhh2Yfn13UlJSXi9u3bxe3bt4sAxDlz5ojbt2+3tDRR61yOHDlS7Nmzp7hhwwZxw4YNYo8ePThNPJC9++67YuvWrcWIiAixb9++lmnO5BgAxX8LFiyw7GMymcTnn39eTElJEXU6nThkyBBx165dsucpLy8Xp0yZIiYkJIhRUVHimDFjxJMnT3r5uwkM1gEOz2/D/Pe//xW7d+8u6nQ6sXPnzuKHH34ou5/nt/6Ki4vFxx9/XGzVqpUYGRkptm3bVpw1a5ZYWVlp2Yfn13WrVq1SfL+9//77RVFU71xeunRJvOeee8TY2FgxNjZWvOeee8TCwkIvfZe1BFEURe/li4iIiIg8jzU4REREFHQY4BAREVHQYYBDREREQYcBDhEREQUdBjhEREQUdBjgEBERUdBhgENERERBhwEOERERBR0GOERERBR0GOAQERFR0GGAQ0REREGHAQ4REREFnf8HnRLrDlHZK/MAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots()\n",
-    "ax.plot(np.arange(len(tadiff)),tadiff)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "2bfc98ac-3e2c-49e2-b6d8-1d27abbdac76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.0972615 ,  0.46332444,  0.5356332 , ..., 18.97662869,\n",
-       "       18.75325767, 18.52688884], dtype=float128)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sbn2[0].tavg2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "6d826e7c-f702-4bc6-b410-028de582ff7f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.0972615 ,  0.46332444,  0.5356332 , ..., 18.97662869,\n",
-       "       18.75325767, 18.52688884])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sbn2[0]._timeaveraged.data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "2ef4905e-bbbe-4f9a-975b-e78529e5d768",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.07158761,  0.46514546,  0.53562311, ..., 18.99965839,\n",
-       "       18.7783763 , 18.55206856], dtype=float128)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sbn2[0]._timeaveraged2.data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c36bf91a-4c6f-447a-8278-f16188cb77ed",
+   "cell_type": "markdown",
+   "id": "c272edd5-cb66-44bf-b874-bb23c982f46e",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "tadiff=sbn2[0]._timeaveraged2.data-sbn2[0].tavg2"
+    "## The difference between the two methods can be see by subtracting the spectra and plotting.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "2f69ebbb-0f9c-4551-ab50-da367481220d",
+   "execution_count": 14,
+   "id": "0fa05f47-ec67-40ed-b1af-941af29fde21",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8bf60eca30>]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -977,37 +558,13 @@
     }
    ],
    "source": [
-    "fig,ax = plt.subplots()\n",
-    "ax.plot(np.arange(len(tadiff)),tadiff)\n",
-    "#ax.set_ylim(-.025,0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "4e5e3816-694e-48d5-8379-74b65b47856c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.56738901e-02,  1.82102124e-03, -1.00870067e-05, ...,\n",
-       "        2.30297070e-02,  2.51186280e-02,  2.51797155e-02], dtype=float128)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tadiff"
+    "(spec2-spec).plot()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eef0624b-e944-4856-bcc4-81c94b5cb2de",
+   "id": "c783a3d1-6885-4cfd-98ae-69004be21c7d",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1029,7 +586,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/src/dysh/spectra/core.py b/src/dysh/spectra/core.py
index 2070fe7b..b85088ea 100644
--- a/src/dysh/spectra/core.py
+++ b/src/dysh/spectra/core.py
@@ -1,31 +1,20 @@
 """
+Core functions for spectral data.
 """
 
 import warnings
 
 import astropy.units as u
-import matplotlib.pyplot as plt
 import numpy as np
 from astropy.io import fits
 from astropy.modeling.fitting import LevMarLSQFitter, LinearLSQFitter
 from astropy.modeling.polynomial import Chebyshev1D, Hermite1D, Legendre1D, Polynomial1D
 from astropy.wcs import WCS
-from specutils import SpectralRegion, Spectrum1D, SpectrumList
+from specutils import SpectralRegion, Spectrum1D
 from specutils.fitting import fit_continuum
 
 from ..util import uniq
 
-# def baseline_all(speclist,order,exclude=None,**kwargs):
-#    kwargs_opts = {
-#        'remove': False,
-#        'show': False,
-#        'model':'polynomial',
-#        'fitter':  LinearLSQFitter(calc_uncertainties=True),
-#    }
-#    kwargs_opts.update(kwargs)
-#    for p in speclist:
-#        p.baseline(order,exclude,**kwargs)
-
 
 def average(data, axis=0, weights=None):
     """Average a group of spectra or scans.
diff --git a/src/dysh/spectra/scan.py b/src/dysh/spectra/scan.py
index 095942e4..c835f2fc 100644
--- a/src/dysh/spectra/scan.py
+++ b/src/dysh/spectra/scan.py
@@ -718,7 +718,7 @@ class SubBeamNodScan(ScanMixin):  # SBNodScan?
 
     def __init__(self, sigtp, reftp, fulltp=None, method="cycle", calibrate=True, **kwargs):
         kwargs_opts = {
-            "timeaverage": True,
+            "timeaverage": False,
             "weights": "tsys",  # or None or ndarray
             "debug": False,
         }
@@ -732,6 +732,10 @@ def __init__(self, sigtp, reftp, fulltp=None, method="cycle", calibrate=True, **
         self._reftp = reftp
         self._fulltp = fulltp
         self._nchan = len(reftp[0]._data[0])
+        self._npol = 1
+        self._nint = 0
+        self._nif = 1
+        self._nfeed = 1
         self._method = method.lower()
         if self._method not in ["cycle", "scan"]:
             raise ValueError(f"Method {self._method} unrecognized. Must be one of 'cycle' or 'scan'")
@@ -865,7 +869,7 @@ def timeaverage(self, weights="tsys"):
             self._timeaveraged._data = average(data, axis=0, weights=w)
             self._timeaveraged.meta["MEANTSYS"] = np.mean(self._tsys)
             # data.meta["MEANTSYS"] = 0.5 * np.mean((on.meta["TSYS"] + off.meta["TSYS"]))
-            self._timeaveraged.meta["WTTSYS"] = fulltp.meta["TSYS"]
+            self._timeaveraged.meta["WTTSYS"] = self._timeaveraged.meta["WTTSYS"]
             self._timeaveraged.meta["TSYS"] = self._timeaveraged.meta["WTTSYS"]
             self._timeaveraged.meta["EXPOSURE"] = np.sum(self.exposure)
         if self._method == "cycle":  # or weights = "gbtidl"
@@ -881,8 +885,6 @@ def timeaverage(self, weights="tsys"):
                 ta_avg[:] += data[i] * self.tsys[i] ** -2.0
             wt1 = self.tsys**-2.0
             wt2 = tsys_weight(self.exposure, self.delta_freq, self.tsys)
-            average(data, 0, wt1)
-            average(self.tsys, 0, wt2)
             ta_avg /= wt_avg
             tsys_avg /= tsys_wt
             self._timeaveraged._data = ta_avg
@@ -890,17 +892,5 @@ def timeaverage(self, weights="tsys"):
             self._timeaveraged.meta["WTTSYS"] = tsys_avg  # sq_weighted_avg(self._tsys, axis=0, weights=w)
             self._timeaveraged.meta["TSYS"] = self._timeaveraged.meta["WTTSYS"]
             self._timeaveraged.meta["EXPOSURE"] = np.sum(self.exposure)
-            # return self._timeaveraged
-
-        # self.tavg2 = tavg2
-        # self.tsysavg2 = tsysavg2
-        # self.wt1 = wt1
-        # self.wt2 = wt2
-        # self._timeaveraged2 = deepcopy(self.calibrated(0))
-        ## print("DATA type ", type(data), "DATA SHAPE ", data.shape, "WEIGHTS SHAPE ", w.shape)
-        # self._timeaveraged2._data = average(data, axis=0, weights=w)
-        # self._timeaveraged2.meta["MEANTSYS"] = np.mean(self._tsys)
-        # self._timeaveraged2.meta["WTTSYS"] = sq_weighted_avg(self._tsys, axis=0, weights=w)
-        # self._timeaveraged2.meta["TSYS"] = self._timeaveraged.meta["WTTSYS"]
-        # self._timeaveraged2.meta["EXPOSURE"] = np.sum(self.exposure)
+
         return self._timeaveraged