diff --git a/ToolFunction/ToolFunction.py b/ToolFunction/ToolFunction.py
index 241148f..48e2fca 100644
--- a/ToolFunction/ToolFunction.py
+++ b/ToolFunction/ToolFunction.py
@@ -19,6 +19,15 @@ def auto_rechunk(dataSet):
     return dataSet.chunk(**kwargs)
 
 
+def copy_chunk(dataSet, dataChunk):
+    kwargs = {
+        key: dataChunk.chunksizes[key]
+        for key in dataChunk.chunksizes
+        if key in dataSet.dims
+    }
+    return dataSet.chunk(**kwargs)
+
+
 def get_h5_file_path(folderpath, maxFileNum=None, filename='*.h5',):
     filepath = np.sort(glob.glob(folderpath + filename))
     if maxFileNum is None:
@@ -31,6 +40,7 @@ def get_date():
     today = date.today()
     return today.strftime("%y/%m/%d")
 
+
 def resolve_fit_result(fitResult):
     
     
diff --git a/test.ipynb b/test.ipynb
index 69cdd30..30f2530 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -55,7 +55,7 @@
        "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-97bc1b1c-eb61-11ed-96e0-80e82ce2fa8e</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-93efe695-ebe5-11ed-a61c-9c7bef43b4fb</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "\n",
        "        <tr>\n",
@@ -86,7 +86,7 @@
        "    </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">09edd810</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">94c234d4</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
@@ -123,11 +123,11 @@
        "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
        "        <div style=\"margin-left: 48px;\">\n",
        "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-8491ba01-6207-4888-9e67-9507ebb1b358</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-77c49924-2581-4f0b-859a-8ae3d230ee64</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:63901\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:62867\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
        "                        <strong>Workers:</strong> 6\n",
@@ -169,7 +169,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:63937\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62896\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 10\n",
@@ -177,7 +177,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63939/status\" target=\"_blank\">http://127.0.0.1:63939/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62905/status\" target=\"_blank\">http://127.0.0.1:62905/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 9.31 GiB\n",
@@ -185,13 +185,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:63904\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62870\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-a3himnbj\n",
+       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-zu_ku8iu\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -214,7 +214,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:63931\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62899\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 10\n",
@@ -222,7 +222,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63932/status\" target=\"_blank\">http://127.0.0.1:63932/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62910/status\" target=\"_blank\">http://127.0.0.1:62910/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 9.31 GiB\n",
@@ -230,13 +230,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:63905\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62871\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-u5gnp3mc\n",
+       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-kqys5zrx\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -259,7 +259,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:63934\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62895\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 10\n",
@@ -267,7 +267,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63935/status\" target=\"_blank\">http://127.0.0.1:63935/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62902/status\" target=\"_blank\">http://127.0.0.1:62902/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 9.31 GiB\n",
@@ -275,13 +275,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:63906\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62872\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-vom2akqi\n",
+       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-jp4ehnch\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -304,7 +304,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:63938\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62894\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 10\n",
@@ -312,7 +312,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63940/status\" target=\"_blank\">http://127.0.0.1:63940/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62900/status\" target=\"_blank\">http://127.0.0.1:62900/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 9.31 GiB\n",
@@ -320,13 +320,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:63907\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62873\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-hxb9f3bt\n",
+       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-bwrlhm69\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -349,7 +349,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:63926\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62898\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 10\n",
@@ -357,7 +357,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63929/status\" target=\"_blank\">http://127.0.0.1:63929/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62908/status\" target=\"_blank\">http://127.0.0.1:62908/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 9.31 GiB\n",
@@ -365,13 +365,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:63908\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62874\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-h_t7j4hx\n",
+       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-qicelv9a\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -394,7 +394,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:63943\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62897\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 10\n",
@@ -402,7 +402,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63944/status\" target=\"_blank\">http://127.0.0.1:63944/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62904/status\" target=\"_blank\">http://127.0.0.1:62904/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 9.31 GiB\n",
@@ -410,13 +410,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:63909\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62875\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-dhchz4hh\n",
+       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-m2e77hzu\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -443,7 +443,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:63901' processes=6 threads=60, memory=55.88 GiB>"
+       "<Client: 'tcp://127.0.0.1:62867' processes=6 threads=60, memory=55.88 GiB>"
       ]
      },
      "execution_count": 2,
@@ -484,11 +484,11 @@
     "\n",
     "# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n",
     "\n",
-    "# filepath = r\"./testData/0000/*.h5\"\n",
+    "filepath = r\"./testData/0002/*.h5\"\n",
     "\n",
     "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n",
     "\n",
-    "filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\""
+    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\""
    ]
   },
   {
@@ -940,8 +940,8 @@
        "    dark        (runs, x, y) uint16 dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;\n",
        "    shotNum     (runs) &lt;U1 &#x27;0&#x27; &#x27;1&#x27; &#x27;2&#x27;\n",
        "    OD          (runs, x, y) float64 dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;\n",
-       "Attributes: (12/101)\n",
-       "    TOF_free:                          0.015\n",
+       "Attributes: (12/96)\n",
+       "    TOF_free:                          0.02\n",
        "    abs_img_freq:                      110.858\n",
        "    absorption_imaging_flag:           True\n",
        "    backup_data:                       True\n",
@@ -953,7 +953,7 @@
        "    z_offset_img:                      0.189\n",
        "    runs:                              [0. 1. 2.]\n",
        "    scanAxis:                          [&#x27;runs&#x27;]\n",
-       "    scanAxisLength:                    [3.]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-864fd1a7-f0c5-4830-beb1-cec965cef1ca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-864fd1a7-f0c5-4830-beb1-cec965cef1ca' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 3</li><li><span>x</span>: 1200</li><li><span>y</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b3b07cd3-6a88-43ac-8d8e-fdf87a3a4847' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3b07cd3-6a88-43ac-8d8e-fdf87a3a4847' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-d96b6d5f-2b13-4024-b468-a6df2f81f7ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d96b6d5f-2b13-4024-b468-a6df2f81f7ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a544a35a-8177-4c7a-93ad-22ae88b81763' class='xr-var-data-in' type='checkbox'><label for='data-a544a35a-8177-4c7a-93ad-22ae88b81763' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a48fe91-2668-45f5-8dde-32274fec992d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a48fe91-2668-45f5-8dde-32274fec992d' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-74809274-2030-412f-a01c-4e6353fadc86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74809274-2030-412f-a01c-4e6353fadc86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d706df-63e8-4d81-bd43-a810a0601642' class='xr-var-data-in' type='checkbox'><label for='data-a5d706df-63e8-4d81-bd43-a810a0601642' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    scanAxisLength:                    [3.]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a187193a-e3b4-4524-8414-12aa32a30386' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a187193a-e3b4-4524-8414-12aa32a30386' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 3</li><li><span>x</span>: 1200</li><li><span>y</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-be7308dc-b610-495f-9739-9ac7ff7c8c89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be7308dc-b610-495f-9739-9ac7ff7c8c89' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-d3f3150a-a55b-4cba-9b2a-075fc4c95e83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3f3150a-a55b-4cba-9b2a-075fc4c95e83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-960cb7aa-ca30-4ef0-821f-893eea9bdf7d' class='xr-var-data-in' type='checkbox'><label for='data-960cb7aa-ca30-4ef0-821f-893eea9bdf7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-481c6b35-95e8-49ee-b5c1-c4284419e452' class='xr-section-summary-in' type='checkbox'  checked><label for='section-481c6b35-95e8-49ee-b5c1-c4284419e452' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-cd21c711-c205-4459-8205-36b64bdbf31d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd21c711-c205-4459-8205-36b64bdbf31d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-851c0289-d29d-4a48-8374-3be1d4473025' class='xr-var-data-in' type='checkbox'><label for='data-851c0289-d29d-4a48-8374-3be1d4473025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1035,7 +1035,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-2384765b-33a5-4c7f-b716-3236436657ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2384765b-33a5-4c7f-b716-3236436657ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-925f278a-5703-4039-ac85-b2b517d8ec70' class='xr-var-data-in' type='checkbox'><label for='data-925f278a-5703-4039-ac85-b2b517d8ec70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-de3d3db3-84eb-4f04-99c6-c70ec524fa66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de3d3db3-84eb-4f04-99c6-c70ec524fa66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b842615-c9eb-43bb-a08a-9d036a46eab9' class='xr-var-data-in' type='checkbox'><label for='data-3b842615-c9eb-43bb-a08a-9d036a46eab9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1117,7 +1117,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-67f75caf-2965-4e01-9dd3-31e214364b6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f75caf-2965-4e01-9dd3-31e214364b6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db048b92-1c2c-4e0e-8475-9265c2fcedd4' class='xr-var-data-in' type='checkbox'><label for='data-db048b92-1c2c-4e0e-8475-9265c2fcedd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-956dda43-3591-45d6-b389-13c837760de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-956dda43-3591-45d6-b389-13c837760de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c46e3582-207f-44e6-9be9-0deae921ddf1' class='xr-var-data-in' type='checkbox'><label for='data-c46e3582-207f-44e6-9be9-0deae921ddf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1199,7 +1199,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27; &#x27;2&#x27;</div><input id='attrs-d7106b32-c4ee-48d1-a6df-7ccea32e3347' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7106b32-c4ee-48d1-a6df-7ccea32e3347' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faec6d1a-bc8c-413b-926b-03b6ec242669' class='xr-var-data-in' type='checkbox'><label for='data-faec6d1a-bc8c-413b-926b-03b6ec242669' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;, &#x27;2&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-a84862bc-7f7c-4f9c-a55c-f3d86f57b069' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a84862bc-7f7c-4f9c-a55c-f3d86f57b069' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9636ff42-7fff-431c-80c7-7107cf2f2880' class='xr-var-data-in' type='checkbox'><label for='data-9636ff42-7fff-431c-80c7-7107cf2f2880' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27; &#x27;2&#x27;</div><input id='attrs-a0ccce98-32ce-4b83-85ab-285ea3e55f92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0ccce98-32ce-4b83-85ab-285ea3e55f92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b6ec96-3698-4709-8996-3d975daf3ad0' class='xr-var-data-in' type='checkbox'><label for='data-44b6ec96-3698-4709-8996-3d975daf3ad0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;, &#x27;2&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-61327eb1-a1d4-41dd-ab20-4e728350639d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61327eb1-a1d4-41dd-ab20-4e728350639d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70d8f882-b659-4db6-8fc2-ed02c2440677' class='xr-var-data-in' type='checkbox'><label for='data-70d8f882-b659-4db6-8fc2-ed02c2440677' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1281,7 +1281,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e696e9f1-4baf-45c9-ab7c-458d3f0789f9' class='xr-section-summary-in' type='checkbox'  ><label for='section-e696e9f1-4baf-45c9-ab7c-458d3f0789f9' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-098e9109-f59c-4f3c-a636-dfe8213dd0f6' class='xr-index-data-in' type='checkbox'/><label for='index-098e9109-f59c-4f3c-a636-dfe8213dd0f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-205bc30d-356c-4ea8-add8-ae25484a1314' class='xr-section-summary-in' type='checkbox'  ><label for='section-205bc30d-356c-4ea8-add8-ae25484a1314' class='xr-section-summary' >Attributes: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.015</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.275</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>0.000105</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.0</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.0</dd><dt><span>mot_load_duration :</span></dt><dd>2</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>0.5</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.6</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>sin_mod_amplitude :</span></dt><dd>0.0405</dd><dt><span>sin_mod_dc_offset :</span></dt><dd>0.09</dd><dt><span>sin_mod_duration :</span></dt><dd>nan</dd><dt><span>sin_mod_freq :</span></dt><dd>nan</dd><dt><span>sin_mod_phase :</span></dt><dd>0.0</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>runs :</span></dt><dd>[0. 1. 2.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[3.]</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-63da2ff7-e031-4650-9f84-1b087f63a0ea' class='xr-section-summary-in' type='checkbox'  ><label for='section-63da2ff7-e031-4650-9f84-1b087f63a0ea' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4219d45-4c94-41b7-bc93-93a7ab6d76bd' class='xr-index-data-in' type='checkbox'/><label for='index-d4219d45-4c94-41b7-bc93-93a7ab6d76bd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-38582e53-cee5-45f9-811b-a2e9a3b08903' class='xr-section-summary-in' type='checkbox'  ><label for='section-38582e53-cee5-45f9-811b-a2e9a3b08903' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.002</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.283</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>runs :</span></dt><dd>[0. 1. 2.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[3.]</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1295,8 +1295,8 @@
        "    dark        (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
        "    shotNum     (runs) <U1 '0' '1' '2'\n",
        "    OD          (runs, x, y) float64 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
-       "Attributes: (12/101)\n",
-       "    TOF_free:                          0.015\n",
+       "Attributes: (12/96)\n",
+       "    TOF_free:                          0.02\n",
        "    abs_img_freq:                      110.858\n",
        "    absorption_imaging_flag:           True\n",
        "    backup_data:                       True\n",
@@ -1336,17 +1336,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "imageAnalyser.center = (529, 962)\n",
-    "imageAnalyser.span = (100,100)\n",
-    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "# imageAnalyser.center = (529, 962)\n",
+    "# imageAnalyser.span = (100,100)\n",
+    "# imageAnalyser.fraction = (0.1, 0.1)\n",
     "\n",
     "# imageAnalyser.center = (890, 1150)\n",
     "# imageAnalyser.span = (600,600)\n",
     "# imageAnalyser.fraction = (0.1, 0.1)\n",
     "\n",
-    "# imageAnalyser.center = (890, 950)\n",
-    "# imageAnalyser.span = (100,100)\n",
-    "# imageAnalyser.fraction = (0.1, 0.1)\n",
+    "imageAnalyser.center = (890, 950)\n",
+    "imageAnalyser.span = (100,100)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
     "\n",
     "dataSet_crop = imageAnalyser.crop_image(dataSet)"
    ]
@@ -1359,7 +1359,7 @@
     {
      "data": {
       "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x1f9211122b0>"
+       "<xarray.plot.facetgrid.FacetGrid at 0x1b2ff6dc580>"
       ]
      },
      "execution_count": 8,
@@ -1368,12 +1368,14 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x300 with 4 Axes>"
+       "<Figure size 720x216 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1433,7 +1435,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fitResult = fitAnalyser.fit(dataSet_crop.OD, params, dask=\"parallelized\")# .load()"
+    "fitResult = fitAnalyser.fit(dataSet_crop.OD, params, dask=\"parallelized\").load()"
    ]
   },
   {
@@ -1449,6 +1451,40 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x1b283209880>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fitCurve.isel(**{scanAxis[0]:np.arange(30), 'runs':range(dataSet_crop.OD['runs'].size)}).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
+    "\n",
+    "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1816,208 +1852,498 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;OD&#x27; (runs: 3, x: 100, y: 100)&gt;\n",
-       "array([[[5.86966574e-10, 7.65249266e-10, 9.91509448e-10, ...,\n",
-       "         2.46154913e-11, 1.75761294e-11, 1.24721796e-11],\n",
-       "        [9.42954541e-10, 1.22936348e-09, 1.59284766e-09, ...,\n",
-       "         3.95444823e-11, 2.82358344e-11, 2.00364022e-11],\n",
-       "        [1.50191506e-09, 1.95810025e-09, 2.53704902e-09, ...,\n",
-       "         6.29854895e-11, 4.49733502e-11, 3.19134941e-11],\n",
-       "        ...,\n",
-       "        [2.54388919e-07, 3.31655910e-07, 4.29716150e-07, ...,\n",
-       "         1.06682535e-08, 7.61742276e-09, 5.40539175e-09],\n",
-       "        [1.77937216e-07, 2.31983098e-07, 3.00573216e-07, ...,\n",
-       "         7.46211487e-09, 5.32815269e-09, 3.78090510e-09],\n",
-       "        [1.23399279e-07, 1.60880043e-07, 2.08447221e-07, ...,\n",
-       "         5.17496911e-09, 3.69506849e-09, 2.62205386e-09]],\n",
-       "\n",
-       "       [[9.02404312e-13, 1.36226913e-12, 2.03826777e-12, ...,\n",
-       "         2.07414123e-13, 1.32113406e-13, 8.34049874e-14],\n",
-       "        [1.66705507e-12, 2.51658556e-12, 3.76539048e-12, ...,\n",
-       "         3.83166126e-13, 2.44059476e-13, 1.54078061e-13],\n",
-       "        [3.04507755e-12, 4.59684767e-12, 6.87794078e-12, ...,\n",
-       "         6.99899236e-13, 4.45804128e-13, 2.81442200e-13],\n",
-       "...\n",
-       "        [9.87414581e-10, 1.49060060e-09, 2.23028114e-09, ...,\n",
-       "         2.26953403e-10, 1.44559043e-10, 9.12620869e-11],\n",
-       "        [6.10537300e-10, 9.21666828e-10, 1.37902544e-09, ...,\n",
-       "         1.40329625e-10, 8.93836181e-11, 5.64290919e-11],\n",
-       "        [3.73271211e-10, 5.63490048e-10, 8.43110640e-10, ...,\n",
-       "         8.57949366e-11, 5.46474905e-11, 3.44997029e-11]],\n",
-       "\n",
-       "       [[3.50772829e-03, 3.51239608e-03, 3.51706888e-03, ...,\n",
-       "         3.97151663e-03, 3.97641278e-03, 3.98131117e-03],\n",
-       "        [3.50458735e-03, 3.50925410e-03, 3.51392671e-03, ...,\n",
-       "         3.96794908e-03, 3.97284066e-03, 3.97773453e-03],\n",
-       "        [3.50066142e-03, 3.50532717e-03, 3.50999990e-03, ...,\n",
-       "         3.96348891e-03, 3.96837476e-03, 3.97326297e-03],\n",
-       "        ...,\n",
-       "        [1.12548401e-03, 1.12795860e-03, 1.13069623e-03, ...,\n",
-       "         1.27078253e-03, 1.27229756e-03, 1.27382711e-03],\n",
-       "        [1.09950837e-03, 1.10175364e-03, 1.10420980e-03, ...,\n",
-       "         1.24207255e-03, 1.24356247e-03, 1.24506415e-03],\n",
-       "        [1.07397978e-03, 1.07603161e-03, 1.07825160e-03, ...,\n",
-       "         1.21374189e-03, 1.21520532e-03, 1.21667822e-03]]])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;OD&#x27; (runs: 3)&gt;\n",
+       "array([&lt;lmfit.model.ModelResult object at 0x000001B28325EC40&gt;,\n",
+       "       &lt;lmfit.model.ModelResult object at 0x000001B28325EEB0&gt;,\n",
+       "       &lt;lmfit.model.ModelResult object at 0x000001B28320A0D0&gt;],\n",
+       "      dtype=object)\n",
        "Coordinates:\n",
-       "  * runs     (runs) float64 0.0 1.0 2.0\n",
-       "Dimensions without coordinates: x, y</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 3</li><li><span>x</span>: 100</li><li><span>y</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-eba7a8e2-c6af-44d9-acae-350fcf8f06c4' class='xr-array-in' type='checkbox' checked><label for='section-eba7a8e2-c6af-44d9-acae-350fcf8f06c4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.87e-10 7.652e-10 9.915e-10 1.277e-09 ... 0.001214 0.001215 0.001217</span></div><div class='xr-array-data'><pre>array([[[5.86966574e-10, 7.65249266e-10, 9.91509448e-10, ...,\n",
-       "         2.46154913e-11, 1.75761294e-11, 1.24721796e-11],\n",
-       "        [9.42954541e-10, 1.22936348e-09, 1.59284766e-09, ...,\n",
-       "         3.95444823e-11, 2.82358344e-11, 2.00364022e-11],\n",
-       "        [1.50191506e-09, 1.95810025e-09, 2.53704902e-09, ...,\n",
-       "         6.29854895e-11, 4.49733502e-11, 3.19134941e-11],\n",
-       "        ...,\n",
-       "        [2.54388919e-07, 3.31655910e-07, 4.29716150e-07, ...,\n",
-       "         1.06682535e-08, 7.61742276e-09, 5.40539175e-09],\n",
-       "        [1.77937216e-07, 2.31983098e-07, 3.00573216e-07, ...,\n",
-       "         7.46211487e-09, 5.32815269e-09, 3.78090510e-09],\n",
-       "        [1.23399279e-07, 1.60880043e-07, 2.08447221e-07, ...,\n",
-       "         5.17496911e-09, 3.69506849e-09, 2.62205386e-09]],\n",
-       "\n",
-       "       [[9.02404312e-13, 1.36226913e-12, 2.03826777e-12, ...,\n",
-       "         2.07414123e-13, 1.32113406e-13, 8.34049874e-14],\n",
-       "        [1.66705507e-12, 2.51658556e-12, 3.76539048e-12, ...,\n",
-       "         3.83166126e-13, 2.44059476e-13, 1.54078061e-13],\n",
-       "        [3.04507755e-12, 4.59684767e-12, 6.87794078e-12, ...,\n",
-       "         6.99899236e-13, 4.45804128e-13, 2.81442200e-13],\n",
-       "...\n",
-       "        [9.87414581e-10, 1.49060060e-09, 2.23028114e-09, ...,\n",
-       "         2.26953403e-10, 1.44559043e-10, 9.12620869e-11],\n",
-       "        [6.10537300e-10, 9.21666828e-10, 1.37902544e-09, ...,\n",
-       "         1.40329625e-10, 8.93836181e-11, 5.64290919e-11],\n",
-       "        [3.73271211e-10, 5.63490048e-10, 8.43110640e-10, ...,\n",
-       "         8.57949366e-11, 5.46474905e-11, 3.44997029e-11]],\n",
-       "\n",
-       "       [[3.50772829e-03, 3.51239608e-03, 3.51706888e-03, ...,\n",
-       "         3.97151663e-03, 3.97641278e-03, 3.98131117e-03],\n",
-       "        [3.50458735e-03, 3.50925410e-03, 3.51392671e-03, ...,\n",
-       "         3.96794908e-03, 3.97284066e-03, 3.97773453e-03],\n",
-       "        [3.50066142e-03, 3.50532717e-03, 3.50999990e-03, ...,\n",
-       "         3.96348891e-03, 3.96837476e-03, 3.97326297e-03],\n",
-       "        ...,\n",
-       "        [1.12548401e-03, 1.12795860e-03, 1.13069623e-03, ...,\n",
-       "         1.27078253e-03, 1.27229756e-03, 1.27382711e-03],\n",
-       "        [1.09950837e-03, 1.10175364e-03, 1.10420980e-03, ...,\n",
-       "         1.24207255e-03, 1.24356247e-03, 1.24506415e-03],\n",
-       "        [1.07397978e-03, 1.07603161e-03, 1.07825160e-03, ...,\n",
-       "         1.21374189e-03, 1.21520532e-03, 1.21667822e-03]]])</pre></div></div></li><li class='xr-section-item'><input id='section-41bfe7f0-209f-434d-b762-d2862454b3c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41bfe7f0-209f-434d-b762-d2862454b3c8' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-01b45c65-63bf-427f-a92b-bb35c69c87e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01b45c65-63bf-427f-a92b-bb35c69c87e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afcfb646-ffe7-4784-ac4e-f8dee4ea31f8' class='xr-var-data-in' type='checkbox'><label for='data-afcfb646-ffe7-4784-ac4e-f8dee4ea31f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db81fade-7d92-44aa-8e1c-ea17b06e3799' class='xr-section-summary-in' type='checkbox'  ><label for='section-db81fade-7d92-44aa-8e1c-ea17b06e3799' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf315149-6de3-48d8-8065-34c3d82bca69' class='xr-index-data-in' type='checkbox'/><label for='index-bf315149-6de3-48d8-8065-34c3d82bca69' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ed60a3a-817b-431d-ae92-be4cb8656f34' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ed60a3a-817b-431d-ae92-be4cb8656f34' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "  * runs     (runs) float64 0.0 1.0 2.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d812005-1457-408d-8681-193ea444a99b' class='xr-array-in' type='checkbox' checked><label for='section-7d812005-1457-408d-8681-193ea444a99b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>&lt;lmfit.model.ModelResult object at 0x000001B28325EC40&gt; ... &lt;lmfit.m...</span></div><div class='xr-array-data'><pre>array([&lt;lmfit.model.ModelResult object at 0x000001B28325EC40&gt;,\n",
+       "       &lt;lmfit.model.ModelResult object at 0x000001B28325EEB0&gt;,\n",
+       "       &lt;lmfit.model.ModelResult object at 0x000001B28320A0D0&gt;],\n",
+       "      dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-3a1eeb61-90b7-4c3e-877f-fbad29375c91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a1eeb61-90b7-4c3e-877f-fbad29375c91' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-2423a84e-e6d8-417c-bd94-a8ac683fdad8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2423a84e-e6d8-417c-bd94-a8ac683fdad8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7d8c4ef-7dc3-4056-94d5-0cb7a3e026ac' class='xr-var-data-in' type='checkbox'><label for='data-e7d8c4ef-7dc3-4056-94d5-0cb7a3e026ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1eba3687-0c97-41c6-b912-9bbfce14e949' class='xr-section-summary-in' type='checkbox'  ><label for='section-1eba3687-0c97-41c6-b912-9bbfce14e949' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63458bd1-6a22-46e2-a936-d2459b2d1e87' class='xr-index-data-in' type='checkbox'/><label for='index-63458bd1-6a22-46e2-a936-d2459b2d1e87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e16ca77-02c1-408a-a276-cedda56302f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e16ca77-02c1-408a-a276-cedda56302f4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'OD' (runs: 3, x: 100, y: 100)>\n",
-       "array([[[5.86966574e-10, 7.65249266e-10, 9.91509448e-10, ...,\n",
-       "         2.46154913e-11, 1.75761294e-11, 1.24721796e-11],\n",
-       "        [9.42954541e-10, 1.22936348e-09, 1.59284766e-09, ...,\n",
-       "         3.95444823e-11, 2.82358344e-11, 2.00364022e-11],\n",
-       "        [1.50191506e-09, 1.95810025e-09, 2.53704902e-09, ...,\n",
-       "         6.29854895e-11, 4.49733502e-11, 3.19134941e-11],\n",
-       "        ...,\n",
-       "        [2.54388919e-07, 3.31655910e-07, 4.29716150e-07, ...,\n",
-       "         1.06682535e-08, 7.61742276e-09, 5.40539175e-09],\n",
-       "        [1.77937216e-07, 2.31983098e-07, 3.00573216e-07, ...,\n",
-       "         7.46211487e-09, 5.32815269e-09, 3.78090510e-09],\n",
-       "        [1.23399279e-07, 1.60880043e-07, 2.08447221e-07, ...,\n",
-       "         5.17496911e-09, 3.69506849e-09, 2.62205386e-09]],\n",
-       "\n",
-       "       [[9.02404312e-13, 1.36226913e-12, 2.03826777e-12, ...,\n",
-       "         2.07414123e-13, 1.32113406e-13, 8.34049874e-14],\n",
-       "        [1.66705507e-12, 2.51658556e-12, 3.76539048e-12, ...,\n",
-       "         3.83166126e-13, 2.44059476e-13, 1.54078061e-13],\n",
-       "        [3.04507755e-12, 4.59684767e-12, 6.87794078e-12, ...,\n",
-       "         6.99899236e-13, 4.45804128e-13, 2.81442200e-13],\n",
-       "...\n",
-       "        [9.87414581e-10, 1.49060060e-09, 2.23028114e-09, ...,\n",
-       "         2.26953403e-10, 1.44559043e-10, 9.12620869e-11],\n",
-       "        [6.10537300e-10, 9.21666828e-10, 1.37902544e-09, ...,\n",
-       "         1.40329625e-10, 8.93836181e-11, 5.64290919e-11],\n",
-       "        [3.73271211e-10, 5.63490048e-10, 8.43110640e-10, ...,\n",
-       "         8.57949366e-11, 5.46474905e-11, 3.44997029e-11]],\n",
-       "\n",
-       "       [[3.50772829e-03, 3.51239608e-03, 3.51706888e-03, ...,\n",
-       "         3.97151663e-03, 3.97641278e-03, 3.98131117e-03],\n",
-       "        [3.50458735e-03, 3.50925410e-03, 3.51392671e-03, ...,\n",
-       "         3.96794908e-03, 3.97284066e-03, 3.97773453e-03],\n",
-       "        [3.50066142e-03, 3.50532717e-03, 3.50999990e-03, ...,\n",
-       "         3.96348891e-03, 3.96837476e-03, 3.97326297e-03],\n",
-       "        ...,\n",
-       "        [1.12548401e-03, 1.12795860e-03, 1.13069623e-03, ...,\n",
-       "         1.27078253e-03, 1.27229756e-03, 1.27382711e-03],\n",
-       "        [1.09950837e-03, 1.10175364e-03, 1.10420980e-03, ...,\n",
-       "         1.24207255e-03, 1.24356247e-03, 1.24506415e-03],\n",
-       "        [1.07397978e-03, 1.07603161e-03, 1.07825160e-03, ...,\n",
-       "         1.21374189e-03, 1.21520532e-03, 1.21667822e-03]]])\n",
+       "<xarray.DataArray 'OD' (runs: 3)>\n",
+       "array([<lmfit.model.ModelResult object at 0x000001B28325EC40>,\n",
+       "       <lmfit.model.ModelResult object at 0x000001B28325EEB0>,\n",
+       "       <lmfit.model.ModelResult object at 0x000001B28320A0D0>],\n",
+       "      dtype=object)\n",
        "Coordinates:\n",
-       "  * runs     (runs) float64 0.0 1.0 2.0\n",
-       "Dimensions without coordinates: x, y"
+       "  * runs     (runs) float64 0.0 1.0 2.0"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "fitCurve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fitResult = fitAnalyser.fit(dataSet_crop.OD, params, dask=\"parallelized\").load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auto_rechunk(fitResult)\n",
     "fitResult"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# fitCurve.isel(**{scanAxis[0]:np.arange(30), 'runs':range(dataSet_crop.OD['runs'].size)}).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
+    "def resolve_fit_result(fitResult):\n",
+    "    return\n",
     "\n",
-    "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fitResult.load()"
+    "def _get_best_value_sigle(fitResult, key):\n",
+    "    return fitResult.params[key].value\n",
+    "\n",
+    "def _get_best_value(fitResult, params):\n",
+    "    func = np.vectorize(_get_best_value_sigle)\n",
+    "    res = tuple(\n",
+    "            func(fitResult, key)\n",
+    "            for key in params\n",
+    "        )\n",
+    "\n",
+    "    return res\n",
+    "\n",
+    "def get_best_value(fitResult):\n",
+    "    firstIndex = {\n",
+    "        key: fitResult[key][0]\n",
+    "        for key in fitResult.dims\n",
+    "        }\n",
+    "    firstFitResult = fitResult.sel(firstIndex).item()\n",
+    "\n",
+    "    params = list(firstFitResult.params.keys())\n",
+    "\n",
+    "    output_core_dims=[ [] for _ in range(len(params))]\n",
+    "\n",
+    "    value = xr.apply_ufunc(_get_best_value, fitResult, dask=\"forbidden\", kwargs=dict(params=params), output_core_dims=output_core_dims)\n",
+    "\n",
+    "    value = xr.Dataset(\n",
+    "        data_vars={\n",
+    "            params[i]: value[i]\n",
+    "            for i in range(len(params))\n",
+    "        },\n",
+    "        attrs=fitResult.attrs\n",
+    "    )\n",
+    "\n",
+    "    return value\n",
+    "\n",
+    "# print(_get_best_value(fitResult.to_numpy()))\n",
+    "value = get_best_value(fitResult)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 139,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:      (runs: 3)\n",
+       "Coordinates:\n",
+       "  * runs         (runs) float64 0.0 1.0 2.0\n",
+       "Data variables:\n",
+       "    A_amplitude  (runs) float64 707.7 677.1 672.9\n",
+       "    A_centerx    (runs) float64 53.79 54.95 53.33\n",
+       "    A_centery    (runs) float64 41.15 45.33 42.93\n",
+       "    A_sigmax     (runs) float64 4.623 4.042 4.348\n",
+       "    A_sigmay     (runs) float64 11.14 11.08 10.93\n",
+       "    B_amplitude  (runs) float64 226.6 255.8 236.7\n",
+       "    B_centerx    (runs) float64 56.33 56.77 55.35\n",
+       "    B_centery    (runs) float64 40.97 45.56 43.29\n",
+       "    B_sigmax     (runs) float64 15.93 14.59 13.32\n",
+       "    B_sigmay     (runs) float64 11.67 11.32 10.98\n",
+       "    delta        (runs) float64 -11.31 -10.54 -8.974</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-22cde268-fa72-41a4-a98c-37a35b66c6d5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22cde268-fa72-41a4-a98c-37a35b66c6d5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-996ee5b7-e956-4188-982d-b95040b1a746' class='xr-section-summary-in' type='checkbox'  checked><label for='section-996ee5b7-e956-4188-982d-b95040b1a746' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-7afb9389-beb8-4b59-8457-2b4729e5db02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7afb9389-beb8-4b59-8457-2b4729e5db02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c3ac06d-97ec-41b0-91f4-3df87ef88588' class='xr-var-data-in' type='checkbox'><label for='data-3c3ac06d-97ec-41b0-91f4-3df87ef88588' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61965483-6ad8-4b6f-970b-9b6badb2ff54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61965483-6ad8-4b6f-970b-9b6badb2ff54' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>A_amplitude</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>707.7 677.1 672.9</div><input id='attrs-feaf83ac-6041-4d43-bac1-949fab4a332d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-feaf83ac-6041-4d43-bac1-949fab4a332d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1e5cc36-9efb-42b5-9a62-0a7f5a2f29ca' class='xr-var-data-in' type='checkbox'><label for='data-f1e5cc36-9efb-42b5-9a62-0a7f5a2f29ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([707.70718079, 677.12259023, 672.92256184])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>A_centerx</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.79 54.95 53.33</div><input id='attrs-97cb8567-4620-4592-8e34-233b88b24564' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97cb8567-4620-4592-8e34-233b88b24564' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76878f81-e28c-48fc-a709-be0b9252b777' class='xr-var-data-in' type='checkbox'><label for='data-76878f81-e28c-48fc-a709-be0b9252b777' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([53.78832066, 54.95499146, 53.32975848])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>A_centery</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>41.15 45.33 42.93</div><input id='attrs-53a1eea3-a7e0-4824-8a65-db173dc1b77e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53a1eea3-a7e0-4824-8a65-db173dc1b77e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9da89fa5-5fe2-43b8-a34b-b639555401ea' class='xr-var-data-in' type='checkbox'><label for='data-9da89fa5-5fe2-43b8-a34b-b639555401ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([41.15301238, 45.33254111, 42.92764426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>A_sigmax</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.623 4.042 4.348</div><input id='attrs-dc5ccf1c-aa1d-4b14-9209-fcd75dd5cfa4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc5ccf1c-aa1d-4b14-9209-fcd75dd5cfa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320675fd-d958-4ae3-b632-a17a3a281209' class='xr-var-data-in' type='checkbox'><label for='data-320675fd-d958-4ae3-b632-a17a3a281209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4.62256092, 4.04211467, 4.34793295])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>A_sigmay</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.14 11.08 10.93</div><input id='attrs-da7107be-7e95-4669-9e55-67c68367c4e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da7107be-7e95-4669-9e55-67c68367c4e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c6378e4-e2e9-40a2-bb6a-ae2b75d0df28' class='xr-var-data-in' type='checkbox'><label for='data-8c6378e4-e2e9-40a2-bb6a-ae2b75d0df28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([11.13994355, 11.08227865, 10.9281877 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_amplitude</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>226.6 255.8 236.7</div><input id='attrs-cf148a42-1f2f-4de5-8079-5ec0618c94f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf148a42-1f2f-4de5-8079-5ec0618c94f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-138bee21-cfbc-478d-9a62-bfc6945316ec' class='xr-var-data-in' type='checkbox'><label for='data-138bee21-cfbc-478d-9a62-bfc6945316ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([226.62313722, 255.80389409, 236.74703222])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_centerx</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>56.33 56.77 55.35</div><input id='attrs-90ddf4e8-9248-425f-9209-b83a09a37a7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90ddf4e8-9248-425f-9209-b83a09a37a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-425f3c34-aff9-4b4f-aa3a-caf279e8ef18' class='xr-var-data-in' type='checkbox'><label for='data-425f3c34-aff9-4b4f-aa3a-caf279e8ef18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([56.33373589, 56.77430551, 55.34814072])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_centery</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.97 45.56 43.29</div><input id='attrs-45219435-b5ef-484e-9f96-817935c09db0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45219435-b5ef-484e-9f96-817935c09db0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5aeedb1-eb28-487c-a3ea-93abe9acdef8' class='xr-var-data-in' type='checkbox'><label for='data-a5aeedb1-eb28-487c-a3ea-93abe9acdef8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([40.97192829, 45.56252676, 43.29367974])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_sigmax</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.93 14.59 13.32</div><input id='attrs-0336c1ec-07f8-40d0-8117-9b05692111ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0336c1ec-07f8-40d0-8117-9b05692111ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfe22eb7-bb8f-41d0-a8b5-34bd465a6b2c' class='xr-var-data-in' type='checkbox'><label for='data-cfe22eb7-bb8f-41d0-a8b5-34bd465a6b2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15.92936071, 14.58558209, 13.32154434])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_sigmay</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.67 11.32 10.98</div><input id='attrs-0c02e5f5-bb1d-43b5-b1b4-114d71e93163' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c02e5f5-bb1d-43b5-b1b4-114d71e93163' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f86a9ed-1500-4edd-9ec3-98c721f2f4e3' class='xr-var-data-in' type='checkbox'><label for='data-2f86a9ed-1500-4edd-9ec3-98c721f2f4e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([11.66564189, 11.32264141, 10.97731089])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-11.31 -10.54 -8.974</div><input id='attrs-5820ec35-9452-4b7e-b355-36be9eab49e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5820ec35-9452-4b7e-b355-36be9eab49e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b753a075-a834-4543-a616-e797a1797d1d' class='xr-var-data-in' type='checkbox'><label for='data-b753a075-a834-4543-a616-e797a1797d1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-11.30679978, -10.54346742,  -8.97361139])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a768751-747b-4e4b-a455-2827dea54421' class='xr-section-summary-in' type='checkbox'  ><label for='section-8a768751-747b-4e4b-a455-2827dea54421' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f0a5524-fb58-45e0-a0ab-57a36d7bfb1d' class='xr-index-data-in' type='checkbox'/><label for='index-6f0a5524-fb58-45e0-a0ab-57a36d7bfb1d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e3b1d7d-387a-47ac-8d4c-82d5d91be314' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1e3b1d7d-387a-47ac-8d4c-82d5d91be314' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:      (runs: 3)\n",
+       "Coordinates:\n",
+       "  * runs         (runs) float64 0.0 1.0 2.0\n",
+       "Data variables:\n",
+       "    A_amplitude  (runs) float64 707.7 677.1 672.9\n",
+       "    A_centerx    (runs) float64 53.79 54.95 53.33\n",
+       "    A_centery    (runs) float64 41.15 45.33 42.93\n",
+       "    A_sigmax     (runs) float64 4.623 4.042 4.348\n",
+       "    A_sigmay     (runs) float64 11.14 11.08 10.93\n",
+       "    B_amplitude  (runs) float64 226.6 255.8 236.7\n",
+       "    B_centerx    (runs) float64 56.33 56.77 55.35\n",
+       "    B_centery    (runs) float64 40.97 45.56 43.29\n",
+       "    B_sigmax     (runs) float64 15.93 14.59 13.32\n",
+       "    B_sigmay     (runs) float64 11.67 11.32 10.98\n",
+       "    delta        (runs) float64 -11.31 -10.54 -8.974"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "def resolve_fit_result(fitResult):\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    return"
+    "value"
    ]
   },
   {
@@ -2030,7 +2356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2039,33 +2365,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "No numeric data to plot.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32mf:\\Jianshun\\analyseScript\\test.ipynb Cell 33\u001b[0m in \u001b[0;36m5\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test.ipynb#X36sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m fig \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test.ipynb#X36sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m ax \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39mgca()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test.ipynb#X36sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m Ncount\u001b[39m.\u001b[39;49mplot(ax\u001b[39m=\u001b[39;49max)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\plot\\accessor.py:47\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(dataarray_plot\u001b[39m.\u001b[39mplot, assigned\u001b[39m=\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m__doc__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m__annotations__\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[0;32m     46\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m---> 47\u001b[0m     \u001b[39mreturn\u001b[39;00m dataarray_plot\u001b[39m.\u001b[39mplot(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_da, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\plot\\dataarray_plot.py:279\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[0;32m    276\u001b[0m plotfunc: Callable\n\u001b[0;32m    278\u001b[0m \u001b[39mif\u001b[39;00m ndims \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m darray\u001b[39m.\u001b[39msize \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m--> 279\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNo numeric data to plot.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    280\u001b[0m \u001b[39mif\u001b[39;00m ndims \u001b[39min\u001b[39;00m (\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m):\n\u001b[0;32m    281\u001b[0m     \u001b[39mif\u001b[39;00m row \u001b[39mor\u001b[39;00m col:\n",
-      "\u001b[1;31mTypeError\u001b[0m: No numeric data to plot."
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Ncount.load()\n",
     "\n",
@@ -2147,7 +2449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.8.10"
   },
   "orig_nbformat": 4,
   "vscode": {