diff --git a/docs/source/notebooks/TutorialNotebook.ipynb b/docs/source/notebooks/TutorialNotebook.ipynb
index 1cb3163..238726f 100644
--- a/docs/source/notebooks/TutorialNotebook.ipynb
+++ b/docs/source/notebooks/TutorialNotebook.ipynb
@@ -25,9 +25,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "# Add ldcpy root to system path\n",
     "import sys\n",
@@ -125,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -205,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +613,7 @@
        "    cell_measures:    area: cell_area\n",
        "    data_type:        cam-fv\n",
        "    file_size:        {'orig': 10622798, 'comp': 2994433}\n",
-       "    weighted:         True</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-3e88489e-ecdb-453c-a344-4204f2b40002' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e88489e-ecdb-453c-a344-4204f2b40002' 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'>collection</span>: 2</li><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>lev</span>: 30</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d6ff5649-ffe5-47f7-9d43-e7d41704d351' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6ff5649-ffe5-47f7-9d43-e7d41704d351' class='xr-section-summary' >Coordinates: <span>(6)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-2535e800-6812-4e80-ac57-1704795821ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2535e800-6812-4e80-ac57-1704795821ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6adc08ce-eb1c-45ca-a733-7aaf467d3e75' class='xr-var-data-in' type='checkbox'><label for='data-6adc08ce-eb1c-45ca-a733-7aaf467d3e75' 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>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "    weighted:         True</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-b3557513-2f88-4bd7-a5b1-a34ae005490a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3557513-2f88-4bd7-a5b1-a34ae005490a' 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'>collection</span>: 2</li><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>lev</span>: 30</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-914565d5-7c07-4bb9-a55b-7f60e5ab320a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-914565d5-7c07-4bb9-a55b-7f60e5ab320a' class='xr-section-summary' >Coordinates: <span>(6)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-b3986f73-a7d2-4a05-840c-b0ce5946580f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3986f73-a7d2-4a05-840c-b0ce5946580f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8076d9c2-e442-4194-bb71-a2f0044149ad' class='xr-var-data-in' type='checkbox'><label for='data-8076d9c2-e442-4194-bb71-a2f0044149ad' 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>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
        "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
        "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
        "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
@@ -635,14 +644,14 @@
        "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
        "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
        "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.643 7.595 14.36 ... 976.3 992.6</div><input id='attrs-d4ae76b7-772b-467b-9fd9-386adedea136' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4ae76b7-772b-467b-9fd9-386adedea136' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91a85bf-ec84-4a91-8b31-cf58112f1475' class='xr-var-data-in' type='checkbox'><label for='data-f91a85bf-ec84-4a91-8b31-cf58112f1475' 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>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([  3.643466,   7.59482 ,  14.356632,  24.61222 ,  38.2683  ,  54.59548 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.643 7.595 14.36 ... 976.3 992.6</div><input id='attrs-697645a8-2737-4cf1-9e8e-66b9411de053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-697645a8-2737-4cf1-9e8e-66b9411de053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ed9417f-3670-46e8-b6f4-647a8a99e9f1' class='xr-var-data-in' type='checkbox'><label for='data-1ed9417f-3670-46e8-b6f4-647a8a99e9f1' 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>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([  3.643466,   7.59482 ,  14.356632,  24.61222 ,  38.2683  ,  54.59548 ,\n",
        "        72.012451,  87.82123 , 103.317127, 121.547241, 142.994039, 168.22508 ,\n",
        "       197.908087, 232.828619, 273.910817, 322.241902, 379.100904, 445.992574,\n",
        "       524.687175, 609.778695, 691.38943 , 763.404481, 820.858369, 859.534767,\n",
-       "       887.020249, 912.644547, 936.198398, 957.48548 , 976.325407, 992.556095])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-17b8a61a-8771-43e6-9daa-40342ca304ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17b8a61a-8771-43e6-9daa-40342ca304ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e7d9a0a-b84d-4db1-8e66-5dfec4f63032' class='xr-var-data-in' type='checkbox'><label for='data-8e7d9a0a-b84d-4db1-8e66-5dfec4f63032' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-02-01 00:00:00 ... 1920-04-...</div><input id='attrs-e224871c-1c12-4b71-801a-985e620e87bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e224871c-1c12-4b71-801a-985e620e87bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adcd2bfd-ff3f-4830-8b43-3da3f349ae13' class='xr-var-data-in' type='checkbox'><label for='data-adcd2bfd-ff3f-4830-8b43-3da3f349ae13' 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>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       887.020249, 912.644547, 936.198398, 957.48548 , 976.325407, 992.556095])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-75b0b670-7428-4e69-b9b1-6ae09a8ac96b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75b0b670-7428-4e69-b9b1-6ae09a8ac96b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c97eb5b-6cae-423b-971c-8ece87e2b7ed' class='xr-var-data-in' type='checkbox'><label for='data-0c97eb5b-6cae-423b-971c-8ece87e2b7ed' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-02-01 00:00:00 ... 1920-04-...</div><input id='attrs-904d8484-0c3c-45d4-ba45-6f94eb26652a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-904d8484-0c3c-45d4-ba45-6f94eb26652a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ac9d5d0-0c59-43e2-9501-01704ab6dec2' class='xr-var-data-in' type='checkbox'><label for='data-2ac9d5d0-0c59-43e2-9501-01704ab6dec2' 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>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_area</span></div><div class='xr-var-dims'>(lat, collection, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 1, 288), meta=np.ndarray&gt;</div><input id='attrs-6a74179f-c612-4d2d-a49a-d1ce29372542' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a74179f-c612-4d2d-a49a-d1ce29372542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f72b5c76-f6fb-4193-bcbc-fc2709401355' class='xr-var-data-in' type='checkbox'><label for='data-f72b5c76-f6fb-4193-bcbc-fc2709401355' 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>long_name :</span></dt><dd>gauss weights</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_area</span></div><div class='xr-var-dims'>(lat, collection, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 1, 288), meta=np.ndarray&gt;</div><input id='attrs-2dee0a76-7d79-4b37-93e0-a1c418b76d49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dee0a76-7d79-4b37-93e0-a1c418b76d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8b83808-ec55-415b-bb5e-5503818ef138' class='xr-var-data-in' type='checkbox'><label for='data-d8b83808-ec55-415b-bb5e-5503818ef138' 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>long_name :</span></dt><dd>gauss weights</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -722,7 +731,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>collection</span></div><div class='xr-var-dims'>(collection)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;orig&#x27; &#x27;comp&#x27;</div><input id='attrs-f2ab814d-f239-43d2-8820-175ca1e16508' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2ab814d-f239-43d2-8820-175ca1e16508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b80bc0ba-2715-41b1-9ab9-77b893270a3d' class='xr-var-data-in' type='checkbox'><label for='data-b80bc0ba-2715-41b1-9ab9-77b893270a3d' 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;orig&#x27;, &#x27;comp&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90ffc429-f6f7-4c33-ae67-722df31007ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90ffc429-f6f7-4c33-ae67-722df31007ce' class='xr-section-summary' >Data variables: <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>T</span></div><div class='xr-var-dims'>(collection, time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 15, 96, 144), meta=np.ndarray&gt;</div><input id='attrs-bab89e9e-81d8-43fa-9c3b-3f0e6ecebbba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bab89e9e-81d8-43fa-9c3b-3f0e6ecebbba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da4b61de-3f81-4b4f-bea0-32f3855be968' class='xr-var-data-in' type='checkbox'><label for='data-da4b61de-3f81-4b4f-bea0-32f3855be968' 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>mdims :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>data_type :</span></dt><dd>cam-fv</dd><dt><span>set_name :</span></dt><dd>comp</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>collection</span></div><div class='xr-var-dims'>(collection)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;orig&#x27; &#x27;comp&#x27;</div><input id='attrs-33e405d5-ff89-4b8b-acf6-12e2d954b249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33e405d5-ff89-4b8b-acf6-12e2d954b249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5abf6314-754d-4b9e-9704-1a817d6bcf24' class='xr-var-data-in' type='checkbox'><label for='data-5abf6314-754d-4b9e-9704-1a817d6bcf24' 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;orig&#x27;, &#x27;comp&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f74961fb-96c5-48b4-9c6a-58e5532be5c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f74961fb-96c5-48b4-9c6a-58e5532be5c2' class='xr-section-summary' >Data variables: <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>T</span></div><div class='xr-var-dims'>(collection, time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 15, 96, 144), meta=np.ndarray&gt;</div><input id='attrs-9629104b-ef49-4424-b05d-fd48e289998c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9629104b-ef49-4424-b05d-fd48e289998c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3da63688-3d74-4af6-9dbd-f2001956d1da' class='xr-var-data-in' type='checkbox'><label for='data-3da63688-3d74-4af6-9dbd-f2001956d1da' 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>mdims :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>data_type :</span></dt><dd>cam-fv</dd><dt><span>set_name :</span></dt><dd>comp</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -825,7 +834,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2e94831-d6a1-4aa8-8501-bff5d8fce7a8' class='xr-section-summary-in' type='checkbox'  ><label for='section-d2e94831-d6a1-4aa8-8501-bff5d8fce7a8' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8c6b9fb-8b76-4a29-8866-efdd03adadc7' class='xr-index-data-in' type='checkbox'/><label for='index-b8c6b9fb-8b76-4a29-8866-efdd03adadc7' 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(Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-53d098e5-9c21-494e-929c-07b60725d451' class='xr-section-summary-in' type='checkbox'  ><label for='section-53d098e5-9c21-494e-929c-07b60725d451' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-623ccbda-92d6-48b2-b199-e6aa9b87569c' class='xr-index-data-in' type='checkbox'/><label for='index-623ccbda-92d6-48b2-b199-e6aa9b87569c' 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(Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
        "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
        "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
        "       -81.51832460732984,\n",
@@ -834,7 +843,7 @@
        "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
        "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
        "                     90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e5d8ef6-1e44-4bc8-8a6d-0ae6bef99583' class='xr-index-data-in' type='checkbox'/><label for='index-0e5d8ef6-1e44-4bc8-8a6d-0ae6bef99583' 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(Index([  3.64346569404006,  7.594819646328688, 14.356632251292467,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0f52c602-8b21-4000-9323-8ec57d180d3d' class='xr-index-data-in' type='checkbox'/><label for='index-0f52c602-8b21-4000-9323-8ec57d180d3d' 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(Index([  3.64346569404006,  7.594819646328688, 14.356632251292467,\n",
        "       24.612220004200935,  38.26829977333546,  54.59547974169254,\n",
        "        72.01245054602623,  87.82123029232025, 103.31712663173676,\n",
        "       121.54724076390266, 142.99403876066208, 168.22507977485657,\n",
@@ -844,13 +853,13 @@
        "         763.404481112957,  820.8583686500788,  859.5347665250301,\n",
        "        887.0202489197254,   912.644546944648,  936.1983984708786,\n",
        "         957.485479535535,   976.325407391414,   992.556095123291],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4643c10-ecac-46de-a805-43000e24b98c' class='xr-index-data-in' type='checkbox'/><label for='index-e4643c10-ecac-46de-a805-43000e24b98c' 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(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fbd87754-4471-4be3-8b22-a61580fe0f0a' class='xr-index-data-in' type='checkbox'/><label for='index-fbd87754-4471-4be3-8b22-a61580fe0f0a' 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(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
        "        11.25,\n",
        "       ...\n",
        "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
        "       358.75],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63ec0114-1ffd-425b-a707-09d90d2f5242' class='xr-index-data-in' type='checkbox'/><label for='index-63ec0114-1ffd-425b-a707-09d90d2f5242' 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(CFTimeIndex([1920-02-01 00:00:00, 1920-03-01 00:00:00, 1920-04-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=3, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>collection</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-261e3439-fce2-416d-9f85-80b745be8679' class='xr-index-data-in' type='checkbox'/><label for='index-261e3439-fce2-416d-9f85-80b745be8679' 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(Index([&#x27;orig&#x27;, &#x27;comp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;collection&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a400ad54-40f9-496f-bbd0-bdf00c426b43' class='xr-section-summary-in' type='checkbox'  ><label for='section-a400ad54-40f9-496f-bbd0-bdf00c426b43' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>case :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.031</dd><dt><span>title :</span></dt><dd>UNSET</dd><dt><span>logname :</span></dt><dd>mickelso</dd><dt><span>host :</span></dt><dd>ys0219</dd><dt><span>Version :</span></dt><dd>$Name$</dd><dt><span>revision_Id :</span></dt><dd>$Id$</dd><dt><span>initial_file :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc</dd><dt><span>topography_file :</span></dt><dd>/glade/p/cesmdata/cseg/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc</dd><dt><span>history :</span></dt><dd>Thu Jul  9 14:15:11 2020: ncks -d time,0,2,1 cam-fv.T.6months.nc cam-fv.T.3months.nc\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08c64bf9-3c5a-4fb9-94cc-ff818a77f223' class='xr-index-data-in' type='checkbox'/><label for='index-08c64bf9-3c5a-4fb9-94cc-ff818a77f223' 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(CFTimeIndex([1920-02-01 00:00:00, 1920-03-01 00:00:00, 1920-04-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=3, calendar=&#x27;noleap&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>collection</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd9a400e-e93b-4c00-beba-095f5a24d008' class='xr-index-data-in' type='checkbox'/><label for='index-fd9a400e-e93b-4c00-beba-095f5a24d008' 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(Index([&#x27;orig&#x27;, &#x27;comp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;collection&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d1443d6-ff89-40a9-ade9-500e6a3cbb79' class='xr-section-summary-in' type='checkbox'  ><label for='section-1d1443d6-ff89-40a9-ade9-500e6a3cbb79' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>case :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.031</dd><dt><span>title :</span></dt><dd>UNSET</dd><dt><span>logname :</span></dt><dd>mickelso</dd><dt><span>host :</span></dt><dd>ys0219</dd><dt><span>Version :</span></dt><dd>$Name$</dd><dt><span>revision_Id :</span></dt><dd>$Id$</dd><dt><span>initial_file :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc</dd><dt><span>topography_file :</span></dt><dd>/glade/p/cesmdata/cseg/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc</dd><dt><span>history :</span></dt><dd>Thu Jul  9 14:15:11 2020: ncks -d time,0,2,1 cam-fv.T.6months.nc cam-fv.T.3months.nc\n",
        "Fri Jun 12 14:17:32 2020: ncks -d time,0,5,1 b.e11.B20TRC5CNBDRD.f09_g16.031.cam.h0.T.192001-200512.nc cam-fv.T.6months.c</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>cell_measures :</span></dt><dd>area: cell_area</dd><dt><span>data_type :</span></dt><dd>cam-fv</dd><dt><span>file_size :</span></dt><dd>{&#x27;orig&#x27;: 10622798, &#x27;comp&#x27;: 2994433}</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -881,7 +890,7 @@
        "    weighted:         True"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -916,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1020,9 +1029,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99% real information cutoff bit</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>25</td>\n",
+       "      <td>29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>spatial autocorr - latitude</th>\n",
@@ -1056,7 +1065,7 @@
        "max value                         315.584    315.57   315.576\n",
        "probability positive                    1         1         1\n",
        "number of zeros                         0         0         0\n",
-       "99% real information cutoff bit         1         1         1\n",
+       "99% real information cutoff bit        18        25        29\n",
        "spatial autocorr - latitude      0.993918  0.993911  0.993918\n",
        "spatial autocorr - longitude     0.996801  0.996791  0.996801\n",
        "entropy estimate                 0.414723  0.247491  0.347534"
@@ -1216,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1320,9 +1329,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99% real information cutoff bit</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>25</td>\n",
+       "      <td>29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>spatial autocorr - latitude</th>\n",
@@ -1356,7 +1365,7 @@
        "max value                         315.584    315.57   315.576\n",
        "probability positive                    1         1         1\n",
        "number of zeros                         0         0         0\n",
-       "99% real information cutoff bit         1         1         1\n",
+       "99% real information cutoff bit        18        25        29\n",
        "spatial autocorr - latitude      0.993918  0.993911  0.993918\n",
        "spatial autocorr - longitude     0.996801  0.996791  0.996801\n",
        "entropy estimate                 0.414723  0.247491  0.347534"
diff --git a/ldcpy/calcs.py b/ldcpy/calcs.py
index e2c5254..9d37581 100644
--- a/ldcpy/calcs.py
+++ b/ldcpy/calcs.py
@@ -1048,34 +1048,41 @@ def dec_to_binary_3(self, num):
         print(b)
         return '{:032b}'.format(int32bits)
 
-    def get_adj_bit(self, bit_pos):
-        return [bit_pos[0] - 1, bit_pos[1]]
+    #    def get_adj_bit(self, bit_pos):
+    #        return [bit_pos[0] - 1, bit_pos[1]]
 
-    def get_dict_list(self, data_array, x_index):
+    def get_dict_list(self, data_array):
         dict_list_H = []
+        # To do: should make this work for 64 also
         N_BITS = 32
+
+        # sz = data_array.size
+        # Intialize
         for i in range(N_BITS - 1):
             new_dict = {'00': 0, '01': 0, '10': 0, '11': 0}
             dict_list_H.append(new_dict)
 
-        b = np.empty(data_array[x_index].size, dtype=object)
+        # convert data array values to bits (so len(b) = sz)
+        b = np.empty(data_array.size, dtype=object)
         i = 0
-        for y in np.nditer(np.asarray(data_array[x_index], dtype=np.float32).view(np.int32)):
+        for y in np.nditer(np.asarray(data_array, dtype=np.float32).view(np.int32)):
             b[i] = '{:032b}'.format(y)
             i += 1
 
+        # go through each bit position (i) (except the last)
         for i in range(N_BITS - 1):
             count = 0
-            for y in range(1, len(b) - 1):
-                if b[y][i] in ('0', '1') and b[y + 1][i] in ('0', '1'):
+            for y in range(1, len(b)):  # go through each data point
+
+                if b[y - 1][i] in ('0', '1') and b[y][i] in ('0', '1'):
                     count += 1
-                    p00 = p01 = p10 = p11 = 0
-                    dict_list_H[i][b[y][i] + b[y + 1][i]] += 1
-                    dict_list_H[i]['00'] += p00
-                    dict_list_H[i]['01'] += p01
-                    dict_list_H[i]['10'] += p10
-                    dict_list_H[i]['11'] += p11
+                    dict_list_H[i][b[y - 1][i] + b[y][i]] += 1
+                    # dict_list_H[i]['00']
+                    # dict_list_H[i]['01']
+                    # dict_list_H[i]['10']
+                    # dict_list_H[i]['11']
 
+            # turn to fractions (probabilities)
             if count == 0:
                 dict_list_H[i]['00'] = 0
                 dict_list_H[i]['01'] = 0
@@ -1086,6 +1093,7 @@ def get_dict_list(self, data_array, x_index):
                 dict_list_H[i]['01'] /= count
                 dict_list_H[i]['10'] /= count
                 dict_list_H[i]['11'] /= count
+
         return dict_list_H
 
     def get_mutual_info(self, p00, p01, p10, p11):
@@ -1107,15 +1115,17 @@ def get_mutual_info(self, p00, p01, p10, p11):
 
         return mutual_info
 
-    def get_real_info(self, x_index):
+    def get_real_info(self):
         # first, flatten the data array by stacking all the dimensions and removing the coordinates
         # like this:        flattened_ds = self._ds.stack(flattened=['lat', 'lon', 'time']).reset_index('flattened')
         # but over any number of dimensions
         flattened_ds = self._ds.stack(flattened=self._ds.dims).reset_index('flattened')
 
-        dict_list_H = self.get_dict_list(flattened_ds, x_index)
+        dict_list_H = self.get_dict_list(flattened_ds)
+        # print(dict_list_H)
 
         mutual_info_array = []
+        # here we go thru each bit position
         for bit_pos_dict in dict_list_H:
             p00 = bit_pos_dict['00']
             p01 = bit_pos_dict['01']
@@ -1127,14 +1137,15 @@ def get_real_info(self, x_index):
             mutual_info_array.append(mutual_info)
 
         mutual_info_array = np.array(mutual_info_array)
-        # print(mutual_info_array)
+        # print("MIA =", mutual_info_array)
+
         return xr.DataArray(mutual_info_array)
 
     @property
     def real_information(self) -> xr.DataArray:
         if not self._is_memoized('_real_information'):
-            # get the first binary digit
-            self._real_information = self.get_real_info(0)
+
+            self._real_information = self.get_real_info()
             if hasattr(self._ds, 'units'):
                 self._real_information.attrs['units'] = f'{self._ds.units}'
 
@@ -1146,9 +1157,8 @@ def real_information_cutoff(self) -> xr.DataArray:
             # get the first binary digit
             self._real_information_cutoff = 0
             self._captured_information = 0
-            # if self.real_information.sum() > 0:
-            normalized_information = self.real_information / self.real_information.sum()
             # print(self.real_information)
+            normalized_information = self.real_information / self.real_information.sum()
             while self._captured_information < 0.99:
                 self._captured_information += normalized_information[self._real_information_cutoff]
                 self._real_information_cutoff += 1