examples/csv_to_netcdf.ipynb

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   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import xarray as xr"
   ]
  },
  {
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   "id": "fa21c9cc-4fd9-498f-8ca9-8bdb6ced33c5",
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   "source": [
    "filename = Path('data', 'Heitronics-Apogee_Comparison_irt60s.dat')"
   ]
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   "source": [
    "### Here is the first 6 lines of the file:\n",
    "\n",
    "\"TOA5\",\"Heitronics-Apogee Comparison\",\"CR1000X\",\"9292\",\"CR1000X.Std.03.01\",\"CPU:Apogee-Heitronics-Analog.CR1X\",\"46927\",\"irt60s\"\n",
    "\n",
    "\"TIMESTAMP\",\"RECORD\",\"sfc_ir_temp_Avg\",\"sfc_ir_temp_Std\",\"sfc_a_ir_temp_Avg\",\"sfc_a_ir_temp_Std\",\"logger_temp\",\"logger_volt\",\"logger_LiBat\"\n",
    "\n",
    "\"TS\",\"RN\",\"Kelvin\",\"Kelvin\",\"Kelvin\",\"Kelvin\",\"Celsius\",\"Volt\",\"Volt\"\n",
    "\n",
    "\"\",\"\",\"Avg\",\"Std\",\"Avg\",\"Std\",\"Smp\",\"Smp\",\"Smp\"\n",
    "\n",
    "\"2022-07-15 18:57:00\",0,318.3,0.062,318.1,0.342,26.34,13.64,3.93\n",
    "\n",
    "\"2022-07-15 18:58:00\",1,318.1,0.347,317.7,1.452,26.38,13.64,3.931"
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    "We can read the file with no keywords and see what we get. It will do the best it can to read the file making some default assumptions. Not bad for knowing nothing about the data file. But there are some issues. So we need to understand what keywords we should use and the values for those keywords."
   ]
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       "      <th>TOA5</th>\n",
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       "      <th>TIMESTAMP</th>\n",
       "      <td>RECORD</td>\n",
       "      <td>sfc_ir_temp_Avg</td>\n",
       "      <td>sfc_ir_temp_Std</td>\n",
       "      <td>sfc_a_ir_temp_Avg</td>\n",
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       "      <th>2022-07-15 18:57:00</th>\n",
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       "      <td>13.64</td>\n",
       "      <td>3.93</td>\n",
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       "    <tr>\n",
       "      <th>2022-07-15 18:58:00</th>\n",
       "      <td>1</td>\n",
       "      <td>318.1</td>\n",
       "      <td>0.347</td>\n",
       "      <td>317.7</td>\n",
       "      <td>1.452</td>\n",
       "      <td>26.38</td>\n",
       "      <td>13.64</td>\n",
       "      <td>3.931</td>\n",
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       "                       TOA5 Heitronics-Apogee Comparison          CR1000X  \\\n",
       "TIMESTAMP            RECORD              sfc_ir_temp_Avg  sfc_ir_temp_Std   \n",
       "TS                       RN                       Kelvin           Kelvin   \n",
       "NaN                     NaN                          Avg              Std   \n",
       "2022-07-15 18:57:00       0                        318.3            0.062   \n",
       "2022-07-15 18:58:00       1                        318.1            0.347   \n",
       "\n",
       "                                  9292  CR1000X.Std.03.01  \\\n",
       "TIMESTAMP            sfc_a_ir_temp_Avg  sfc_a_ir_temp_Std   \n",
       "TS                              Kelvin             Kelvin   \n",
       "NaN                                Avg                Std   \n",
       "2022-07-15 18:57:00              318.1              0.342   \n",
       "2022-07-15 18:58:00              317.7              1.452   \n",
       "\n",
       "                    CPU:Apogee-Heitronics-Analog.CR1X        46927  \\\n",
       "TIMESTAMP                                 logger_temp  logger_volt   \n",
       "TS                                            Celsius         Volt   \n",
       "NaN                                               Smp          Smp   \n",
       "2022-07-15 18:57:00                             26.34        13.64   \n",
       "2022-07-15 18:58:00                             26.38        13.64   \n",
       "\n",
       "                           irt60s  \n",
       "TIMESTAMP            logger_LiBat  \n",
       "TS                           Volt  \n",
       "NaN                           Smp  \n",
       "2022-07-15 18:57:00          3.93  \n",
       "2022-07-15 18:58:00         3.931  "
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   "source": [
    "df = pd.read_csv(filename)\n",
    "df[:5]"
   ]
  },
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   "id": "2576787b-36e6-4d3b-acfe-0599293dfa79",
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   "source": [
    "- Even though the delimiter was assumed correctly to be a comma, we should be explicit.\n",
    "- We should indicate what rows should be skipped. There is some metadata about the values that we can skip over.\n",
    "- There is a column of dates and we can have Pandas do the work of parsing that for us.\n",
    "- We should index on time so we can do that during the read in process.\n",
    "- We can indicate what row contains the header names. Notice skiprows is applied first so the header is second row in the file, but we state it is the first row in the file after skiprows is applied."
   ]
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   "execution_count": 4,
   "id": "7d85c1f6-92f5-4669-8f72-7bfa76847d3b",
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       "      <th>2022-07-15 18:59:00</th>\n",
       "      <td>2</td>\n",
       "      <td>318.5</td>\n",
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       "      <td>318.1</td>\n",
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       "      <td>13.64</td>\n",
       "      <td>3.931</td>\n",
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       "    <tr>\n",
       "      <th>2022-07-15 19:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>318.2</td>\n",
       "      <td>0.214</td>\n",
       "      <td>319.2</td>\n",
       "      <td>0.875</td>\n",
       "      <td>26.42</td>\n",
       "      <td>13.64</td>\n",
       "      <td>3.931</td>\n",
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       "      <th>2022-07-15 19:01:00</th>\n",
       "      <td>4</td>\n",
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       "                     RECORD  sfc_ir_temp_Avg  sfc_ir_temp_Std  \\\n",
       "TIMESTAMP                                                       \n",
       "2022-07-15 18:57:00       0            318.3            0.062   \n",
       "2022-07-15 18:58:00       1            318.1            0.347   \n",
       "2022-07-15 18:59:00       2            318.5            0.201   \n",
       "2022-07-15 19:00:00       3            318.2            0.214   \n",
       "2022-07-15 19:01:00       4            317.6            0.322   \n",
       "\n",
       "                     sfc_a_ir_temp_Avg  sfc_a_ir_temp_Std  logger_temp  \\\n",
       "TIMESTAMP                                                                \n",
       "2022-07-15 18:57:00              318.1              0.342        26.34   \n",
       "2022-07-15 18:58:00              317.7              1.452        26.38   \n",
       "2022-07-15 18:59:00              318.1              0.587        26.40   \n",
       "2022-07-15 19:00:00              319.2              0.875        26.42   \n",
       "2022-07-15 19:01:00              317.4              1.086        26.43   \n",
       "\n",
       "                     logger_volt  logger_LiBat  \n",
       "TIMESTAMP                                       \n",
       "2022-07-15 18:57:00        13.64         3.930  \n",
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   "source": [
    "df = pd.read_csv(filename, delimiter=',', skiprows=[0, 2, 3],\n",
    "                 header=0, parse_dates=[0], index_col=[0])\n",
    "df[:5]"
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   "source": [
    "Since Pandas does not have a native place to hold the metadata, we can convert the Pandas Dataframe to an Xarray Dataset and add the metadata to the DataArrays.\n",
    "\n",
    "Before we do that we can clean up the index column name. CF convention indicates the dimension and coordinate name for time dimension should match. This allows Xarray tools to work correctly. Suggested name to use is 'time'."
   ]
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       "\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:            (time: 7426)\n",
       "Coordinates:\n",
       "  * time               (time) datetime64[ns] 2022-07-15T18:57:00 ... 2022-07-...\n",
       "Data variables:\n",
       "    RECORD             (time) int64 0 1 2 3 4 5 ... 7421 7422 7423 7424 7425\n",
       "    sfc_ir_temp_Avg    (time) float64 318.3 318.1 318.5 ... 308.6 308.6 308.7\n",
       "    sfc_ir_temp_Std    (time) float64 0.062 0.347 0.201 ... 0.065 0.053 0.047\n",
       "    sfc_a_ir_temp_Avg  (time) float64 318.1 317.7 318.1 ... 308.2 308.3 308.3\n",
       "    sfc_a_ir_temp_Std  (time) float64 0.342 1.452 0.587 ... 0.041 0.024 0.028\n",
       "    logger_temp        (time) float64 26.34 26.38 26.4 26.42 ... 24.3 24.3 24.3\n",
       "    logger_volt        (time) float64 13.64 13.64 13.64 ... 13.7 13.71 13.67\n",
       "    logger_LiBat       (time) float64 3.93 3.931 3.931 ... 3.932 3.939 3.937</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-746f3b54-118d-4f04-9c07-6a823e59a352' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-746f3b54-118d-4f04-9c07-6a823e59a352' 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'>time</span>: 7426</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4972bbf7-ca63-44fc-bd08-b8075a175a56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4972bbf7-ca63-44fc-bd08-b8075a175a56' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-07-15T18:57:00 ... 2022-07-...</div><input id='attrs-63960427-c803-4373-8685-83bc1f5526b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63960427-c803-4373-8685-83bc1f5526b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-925cf1d0-d60e-464d-a7a3-f929efd10d83' class='xr-var-data-in' type='checkbox'><label for='data-925cf1d0-d60e-464d-a7a3-f929efd10d83' 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;2022-07-15T18:57:00.000000000&#x27;, &#x27;2022-07-15T18:58:00.000000000&#x27;,\n",
       "       &#x27;2022-07-15T18:59:00.000000000&#x27;, ..., &#x27;2022-07-20T22:40:00.000000000&#x27;,\n",
       "       &#x27;2022-07-20T22:41:00.000000000&#x27;, &#x27;2022-07-20T22:42:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1743e940-5c91-4238-a5f7-ce8529a77bf0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1743e940-5c91-4238-a5f7-ce8529a77bf0' class='xr-section-summary' >Data variables: <span>(8)</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>RECORD</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 7422 7423 7424 7425</div><input id='attrs-7f76e119-f883-43ec-bbc7-a47d4577e3fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f76e119-f883-43ec-bbc7-a47d4577e3fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1afc517f-cf9a-4561-b9d5-3a5ce0f81494' class='xr-var-data-in' type='checkbox'><label for='data-1afc517f-cf9a-4561-b9d5-3a5ce0f81494' 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, ..., 7423, 7424, 7425])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_ir_temp_Avg</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>318.3 318.1 318.5 ... 308.6 308.7</div><input id='attrs-32948bca-f8ef-4daa-8f1b-9a72f2778c9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32948bca-f8ef-4daa-8f1b-9a72f2778c9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-029cbf37-5a1d-4a81-8d48-cccda0f94e9b' class='xr-var-data-in' type='checkbox'><label for='data-029cbf37-5a1d-4a81-8d48-cccda0f94e9b' 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([318.3, 318.1, 318.5, ..., 308.6, 308.6, 308.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_ir_temp_Std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.062 0.347 0.201 ... 0.053 0.047</div><input id='attrs-1646ccb6-8e74-4972-bc45-c6f52ea87bc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1646ccb6-8e74-4972-bc45-c6f52ea87bc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bf94fe0-7635-4f0c-a5ea-9b95207790f1' class='xr-var-data-in' type='checkbox'><label for='data-4bf94fe0-7635-4f0c-a5ea-9b95207790f1' 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([0.062, 0.347, 0.201, ..., 0.065, 0.053, 0.047])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_a_ir_temp_Avg</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>318.1 317.7 318.1 ... 308.3 308.3</div><input id='attrs-72701542-9fb5-4225-bafc-344dd7f62ac9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72701542-9fb5-4225-bafc-344dd7f62ac9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67796fae-4053-4bee-a5c5-934bad9c0180' class='xr-var-data-in' type='checkbox'><label for='data-67796fae-4053-4bee-a5c5-934bad9c0180' 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([318.1, 317.7, 318.1, ..., 308.2, 308.3, 308.3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_a_ir_temp_Std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.342 1.452 0.587 ... 0.024 0.028</div><input id='attrs-50e7f294-9283-4969-b6eb-092b3fc1be60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50e7f294-9283-4969-b6eb-092b3fc1be60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a9c1331-da68-434a-9ec9-ef209cab80f7' class='xr-var-data-in' type='checkbox'><label for='data-3a9c1331-da68-434a-9ec9-ef209cab80f7' 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([0.342, 1.452, 0.587, ..., 0.041, 0.024, 0.028])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.34 26.38 26.4 ... 24.3 24.3 24.3</div><input id='attrs-a2ff84a9-fd9b-4125-a192-d1cf93a0ab5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2ff84a9-fd9b-4125-a192-d1cf93a0ab5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16de615f-f7e7-48e8-8599-965885247373' class='xr-var-data-in' type='checkbox'><label for='data-16de615f-f7e7-48e8-8599-965885247373' 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([26.34, 26.38, 26.4 , ..., 24.3 , 24.3 , 24.3 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_volt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.64 13.64 13.64 ... 13.71 13.67</div><input id='attrs-b99e3a70-2bbe-453d-9790-3adb2e3d58f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b99e3a70-2bbe-453d-9790-3adb2e3d58f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da051f32-1f27-4eae-a306-292af44a2ecf' class='xr-var-data-in' type='checkbox'><label for='data-da051f32-1f27-4eae-a306-292af44a2ecf' 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([13.64, 13.64, 13.64, ..., 13.7 , 13.71, 13.67])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_LiBat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.93 3.931 3.931 ... 3.939 3.937</div><input id='attrs-0c4f292d-295c-4696-92d8-4f84944874a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c4f292d-295c-4696-92d8-4f84944874a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dc6d2fe-98e1-413b-8041-ff10249e7fc2' class='xr-var-data-in' type='checkbox'><label for='data-1dc6d2fe-98e1-413b-8041-ff10249e7fc2' 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([3.93 , 3.931, 3.931, ..., 3.932, 3.939, 3.937])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb30c761-81a1-41d8-8f10-620d574798ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb30c761-81a1-41d8-8f10-620d574798ba' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39eb43f6-1bc9-49c2-9a47-0bfa07d726b1' class='xr-index-data-in' type='checkbox'/><label for='index-39eb43f6-1bc9-49c2-9a47-0bfa07d726b1' 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(DatetimeIndex([&#x27;2022-07-15 18:57:00&#x27;, &#x27;2022-07-15 18:58:00&#x27;,\n",
       "               &#x27;2022-07-15 18:59:00&#x27;, &#x27;2022-07-15 19:00:00&#x27;,\n",
       "               &#x27;2022-07-15 19:01:00&#x27;, &#x27;2022-07-15 19:02:00&#x27;,\n",
       "               &#x27;2022-07-15 19:03:00&#x27;, &#x27;2022-07-15 19:04:00&#x27;,\n",
       "               &#x27;2022-07-15 19:05:00&#x27;, &#x27;2022-07-15 19:06:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-07-20 22:33:00&#x27;, &#x27;2022-07-20 22:34:00&#x27;,\n",
       "               &#x27;2022-07-20 22:35:00&#x27;, &#x27;2022-07-20 22:36:00&#x27;,\n",
       "               &#x27;2022-07-20 22:37:00&#x27;, &#x27;2022-07-20 22:38:00&#x27;,\n",
       "               &#x27;2022-07-20 22:39:00&#x27;, &#x27;2022-07-20 22:40:00&#x27;,\n",
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       "  * time               (time) datetime64[ns] 2022-07-15T18:57:00 ... 2022-07-...\n",
       "Data variables:\n",
       "    RECORD             (time) int64 0 1 2 3 4 5 ... 7421 7422 7423 7424 7425\n",
       "    sfc_ir_temp_Avg    (time) float64 318.3 318.1 318.5 ... 308.6 308.6 308.7\n",
       "    sfc_ir_temp_Std    (time) float64 0.062 0.347 0.201 ... 0.065 0.053 0.047\n",
       "    sfc_a_ir_temp_Avg  (time) float64 318.1 317.7 318.1 ... 308.2 308.3 308.3\n",
       "    sfc_a_ir_temp_Std  (time) float64 0.342 1.452 0.587 ... 0.041 0.024 0.028\n",
       "    logger_temp        (time) float64 26.34 26.38 26.4 26.42 ... 24.3 24.3 24.3\n",
       "    logger_volt        (time) float64 13.64 13.64 13.64 ... 13.7 13.71 13.67\n",
       "    logger_LiBat       (time) float64 3.93 3.931 3.931 ... 3.932 3.939 3.937"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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    "df.index.name = 'time'  # Set the index name to 'time'\n",
    "ds = df.to_xarray()  # Convert from Pandas Dataframe to Xarray Dataset\n",
    "ds"
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   "cell_type": "markdown",
   "id": "2a09f1c9-8b79-4656-9619-9fb2d2b06d9d",
   "metadata": {},
   "source": [
    "Since the units and long name are in the header of the .dat file we can read the .dat file as ASCII and apply those values to the DataArray attributes."
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   "cell_type": "code",
   "execution_count": 6,
   "id": "f8a84d55-a6e2-4fdc-8485-3f637bb39612",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long_name: ['TOA5', 'Heitronics-Apogee Comparison', 'CR1000X', '9292', 'CR1000X.Std.03.01', 'CPU:Apogee-Heitronics-Analog.CR1X', '46927', 'irt60s'] 8\n",
      "units: ['TS', 'RN', 'Kelvin', 'Kelvin', 'Kelvin', 'Kelvin', 'Celsius', 'Volt', 'Volt'] 9\n"
     ]
    }
   ],
   "source": [
    "# We will use the with: block controller to automatically close the file when we exit the loop\n",
    "with open(filename, 'r') as file_handle:  # Open the file and assign the file handle\n",
    "    for ii, line in enumerate(file_handle):  # Loop over and read each line of the file\n",
    "        line = line.rstrip()  # Strip off any extra white spaces or returns from the strings.\n",
    "        line = line.replace('\"', '')  # Remove quote character by replacing the quote with nothing.\n",
    "        if ii == 0:  # Read first line\n",
    "            long_name = line.split(',')  # Convert string into list of strings and assign to variable\n",
    "        elif ii == 2:  # Read the thrid line\n",
    "            units = line.split(',')  # Convert string into list of strings and assign to variable     \n",
    "        elif ii >= 4:  # After we get the header information exit loop.\n",
    "            break\n",
    "\n",
    "# Print the units and long_name lists so we can see what was read.\n",
    "print(\"long_name:\", long_name, len(long_name))\n",
    "print(\"units:\", units, len(units))"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d310abf-05a1-4818-8054-9917697ec8c8",
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    "tags": []
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   "source": [
    "# units has TIMESTAMP unit. But we made this the index\n",
    "# so we need to drop first value.\n",
    "units\n",
    "units = units[1: ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf909ac9-982c-48dd-82a1-9e9d39eaee7a",
   "metadata": {},
   "source": [
    "We can use the header information we read to populate the long_name and units attributes. We will do this by looping over the data variable names add applying the values we just read. We are making an assumption that everything is correctly applied, since the order of reading the data is the same as the order of reading the metadata."
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19d7ce4e-8ba8-49ac-8e66-4fbff865e97c",
   "metadata": {
    "tags": []
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   "source": [
    "# Loop over the variable names in the Dataset\n",
    "for ii, var_name in enumerate(ds.data_vars):\n",
    "    ds[var_name].attrs['long_name'] = long_name[ii]  # Set the long_name attribute\n",
    "    ds[var_name].attrs['units'] = units[ii]  # Set the units attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc762ec9-f08d-46c5-9094-8940a5db5fbf",
   "metadata": {
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       "Attributes:\n",
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       "    units:      Kelvin</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'>'sfc_ir_temp_Avg'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7426</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-da7d91d0-f510-4874-a746-7959783d845f' class='xr-array-in' type='checkbox' checked><label for='section-da7d91d0-f510-4874-a746-7959783d845f' 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>318.3 318.1 318.5 318.2 317.6 317.8 ... 308.5 308.5 308.6 308.6 308.7</span></div><div class='xr-array-data'><pre>array([318.3, 318.1, 318.5, ..., 308.6, 308.6, 308.7])</pre></div></div></li><li class='xr-section-item'><input id='section-24b71b02-4c98-4622-9b9c-488e503ff3a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-24b71b02-4c98-4622-9b9c-488e503ff3a5' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-07-15T18:57:00 ... 2022-07-...</div><input id='attrs-d591d2d4-3fbb-4a7b-b253-266f731efcf7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d591d2d4-3fbb-4a7b-b253-266f731efcf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f92d8558-b429-4dde-86c2-2c8aee43af46' class='xr-var-data-in' type='checkbox'><label for='data-f92d8558-b429-4dde-86c2-2c8aee43af46' 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;2022-07-15T18:57:00.000000000&#x27;, &#x27;2022-07-15T18:58:00.000000000&#x27;,\n",
       "       &#x27;2022-07-15T18:59:00.000000000&#x27;, ..., &#x27;2022-07-20T22:40:00.000000000&#x27;,\n",
       "       &#x27;2022-07-20T22:41:00.000000000&#x27;, &#x27;2022-07-20T22:42:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7de4e9a8-d202-455f-bab6-36d809fbc478' class='xr-section-summary-in' type='checkbox'  ><label for='section-7de4e9a8-d202-455f-bab6-36d809fbc478' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0782c21-3f52-4474-81fc-22a1f2a0667c' class='xr-index-data-in' type='checkbox'/><label for='index-c0782c21-3f52-4474-81fc-22a1f2a0667c' 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(DatetimeIndex([&#x27;2022-07-15 18:57:00&#x27;, &#x27;2022-07-15 18:58:00&#x27;,\n",
       "               &#x27;2022-07-15 18:59:00&#x27;, &#x27;2022-07-15 19:00:00&#x27;,\n",
       "               &#x27;2022-07-15 19:01:00&#x27;, &#x27;2022-07-15 19:02:00&#x27;,\n",
       "               &#x27;2022-07-15 19:03:00&#x27;, &#x27;2022-07-15 19:04:00&#x27;,\n",
       "               &#x27;2022-07-15 19:05:00&#x27;, &#x27;2022-07-15 19:06:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-07-20 22:33:00&#x27;, &#x27;2022-07-20 22:34:00&#x27;,\n",
       "               &#x27;2022-07-20 22:35:00&#x27;, &#x27;2022-07-20 22:36:00&#x27;,\n",
       "               &#x27;2022-07-20 22:37:00&#x27;, &#x27;2022-07-20 22:38:00&#x27;,\n",
       "               &#x27;2022-07-20 22:39:00&#x27;, &#x27;2022-07-20 22:40:00&#x27;,\n",
       "               &#x27;2022-07-20 22:41:00&#x27;, &#x27;2022-07-20 22:42:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=7426, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e01c4568-7a2d-4865-beab-a75a8efae328' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e01c4568-7a2d-4865-beab-a75a8efae328' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Heitronics-Apogee Comparison</dd><dt><span>units :</span></dt><dd>Kelvin</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'sfc_ir_temp_Avg' (time: 7426)>\n",
       "array([318.3, 318.1, 318.5, ..., 308.6, 308.6, 308.7])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-07-15T18:57:00 ... 2022-07-20T22:42:00\n",
       "Attributes:\n",
       "    long_name:  Heitronics-Apogee Comparison\n",
       "    units:      Kelvin"
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     },
     "execution_count": 9,
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    "The RECORD column is not needed so we can drop it from the Dataset. By waiting until the end of this process we do not need to accommodate the RECORD column in our loops. Often it is easier to do more work and then clean up at the end to ensure everyting lines up correctly. Now we have an Xarray Dataset with correctly formatted coordinates, variables, and metadata. We can go do great things now."
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       "    sfc_ir_temp_Avg    (time) float64 318.3 318.1 318.5 ... 308.6 308.6 308.7\n",
       "    sfc_ir_temp_Std    (time) float64 0.062 0.347 0.201 ... 0.065 0.053 0.047\n",
       "    sfc_a_ir_temp_Avg  (time) float64 318.1 317.7 318.1 ... 308.2 308.3 308.3\n",
       "    sfc_a_ir_temp_Std  (time) float64 0.342 1.452 0.587 ... 0.041 0.024 0.028\n",
       "    logger_temp        (time) float64 26.34 26.38 26.4 26.42 ... 24.3 24.3 24.3\n",
       "    logger_volt        (time) float64 13.64 13.64 13.64 ... 13.7 13.71 13.67\n",
       "    logger_LiBat       (time) float64 3.93 3.931 3.931 ... 3.932 3.939 3.937</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-67037905-5f09-4062-afa9-b9fccdd21edd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67037905-5f09-4062-afa9-b9fccdd21edd' 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'>time</span>: 7426</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-814888dd-2629-4d81-a058-39960219d382' class='xr-section-summary-in' type='checkbox'  checked><label for='section-814888dd-2629-4d81-a058-39960219d382' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-07-15T18:57:00 ... 2022-07-...</div><input id='attrs-dec9f7eb-aba6-4591-b672-abc82dcdfc4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dec9f7eb-aba6-4591-b672-abc82dcdfc4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-135e2977-9e37-4946-b02c-faed431050b5' class='xr-var-data-in' type='checkbox'><label for='data-135e2977-9e37-4946-b02c-faed431050b5' 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;2022-07-15T18:57:00.000000000&#x27;, &#x27;2022-07-15T18:58:00.000000000&#x27;,\n",
       "       &#x27;2022-07-15T18:59:00.000000000&#x27;, ..., &#x27;2022-07-20T22:40:00.000000000&#x27;,\n",
       "       &#x27;2022-07-20T22:41:00.000000000&#x27;, &#x27;2022-07-20T22:42:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6cafe16-e265-422c-ac2a-037b5a98fc03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6cafe16-e265-422c-ac2a-037b5a98fc03' class='xr-section-summary' >Data variables: <span>(7)</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>sfc_ir_temp_Avg</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>318.3 318.1 318.5 ... 308.6 308.7</div><input id='attrs-a35446d9-262e-4e80-9372-97f35728ecd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a35446d9-262e-4e80-9372-97f35728ecd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2276636a-31db-470e-9d44-2835cac573ce' class='xr-var-data-in' type='checkbox'><label for='data-2276636a-31db-470e-9d44-2835cac573ce' 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>Heitronics-Apogee Comparison</dd><dt><span>units :</span></dt><dd>Kelvin</dd></dl></div><div class='xr-var-data'><pre>array([318.3, 318.1, 318.5, ..., 308.6, 308.6, 308.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_ir_temp_Std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.062 0.347 0.201 ... 0.053 0.047</div><input id='attrs-c2243cf3-01fa-4720-9a8c-93ecc960ce64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2243cf3-01fa-4720-9a8c-93ecc960ce64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c43446d3-b5dd-4a52-8da8-f66d77dbdc7a' class='xr-var-data-in' type='checkbox'><label for='data-c43446d3-b5dd-4a52-8da8-f66d77dbdc7a' 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>CR1000X</dd><dt><span>units :</span></dt><dd>Kelvin</dd></dl></div><div class='xr-var-data'><pre>array([0.062, 0.347, 0.201, ..., 0.065, 0.053, 0.047])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_a_ir_temp_Avg</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>318.1 317.7 318.1 ... 308.3 308.3</div><input id='attrs-eabe190d-19d4-44f9-8f1a-522611c4caf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eabe190d-19d4-44f9-8f1a-522611c4caf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9b44601-2f97-4992-ab05-c44e6f58c616' class='xr-var-data-in' type='checkbox'><label for='data-a9b44601-2f97-4992-ab05-c44e6f58c616' 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>9292</dd><dt><span>units :</span></dt><dd>Kelvin</dd></dl></div><div class='xr-var-data'><pre>array([318.1, 317.7, 318.1, ..., 308.2, 308.3, 308.3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfc_a_ir_temp_Std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.342 1.452 0.587 ... 0.024 0.028</div><input id='attrs-c38f2fca-38e7-469b-b2e1-4c9562f3b794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c38f2fca-38e7-469b-b2e1-4c9562f3b794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd768046-ca39-4812-98e0-3c24a50cc722' class='xr-var-data-in' type='checkbox'><label for='data-cd768046-ca39-4812-98e0-3c24a50cc722' 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>CR1000X.Std.03.01</dd><dt><span>units :</span></dt><dd>Kelvin</dd></dl></div><div class='xr-var-data'><pre>array([0.342, 1.452, 0.587, ..., 0.041, 0.024, 0.028])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.34 26.38 26.4 ... 24.3 24.3 24.3</div><input id='attrs-7bf771b8-bbe0-49ce-9b6b-602e62a1f264' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bf771b8-bbe0-49ce-9b6b-602e62a1f264' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f0fe109-99f0-4640-aea0-033679abc6f8' class='xr-var-data-in' type='checkbox'><label for='data-8f0fe109-99f0-4640-aea0-033679abc6f8' 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>CPU:Apogee-Heitronics-Analog.CR1X</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>array([26.34, 26.38, 26.4 , ..., 24.3 , 24.3 , 24.3 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_volt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.64 13.64 13.64 ... 13.71 13.67</div><input id='attrs-c231609c-c729-47e3-b0e0-57bd3f3d9aa4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c231609c-c729-47e3-b0e0-57bd3f3d9aa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c67a830d-8e2a-4b8d-ac63-54a3d625ac2d' class='xr-var-data-in' type='checkbox'><label for='data-c67a830d-8e2a-4b8d-ac63-54a3d625ac2d' 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>46927</dd><dt><span>units :</span></dt><dd>Volt</dd></dl></div><div class='xr-var-data'><pre>array([13.64, 13.64, 13.64, ..., 13.7 , 13.71, 13.67])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_LiBat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.93 3.931 3.931 ... 3.939 3.937</div><input id='attrs-dbc04a32-fe04-48cb-b45d-46373e44945a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbc04a32-fe04-48cb-b45d-46373e44945a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb5bd5d6-b251-40f7-9edd-cee542174cb4' class='xr-var-data-in' type='checkbox'><label for='data-fb5bd5d6-b251-40f7-9edd-cee542174cb4' 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>irt60s</dd><dt><span>units :</span></dt><dd>Volt</dd></dl></div><div class='xr-var-data'><pre>array([3.93 , 3.931, 3.931, ..., 3.932, 3.939, 3.937])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1126f4d9-4e46-4126-99d1-441103609dd0' class='xr-section-summary-in' type='checkbox'  ><label for='section-1126f4d9-4e46-4126-99d1-441103609dd0' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6feb62ca-a0f6-48cd-9251-a97e0218e336' class='xr-index-data-in' type='checkbox'/><label for='index-6feb62ca-a0f6-48cd-9251-a97e0218e336' 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(DatetimeIndex([&#x27;2022-07-15 18:57:00&#x27;, &#x27;2022-07-15 18:58:00&#x27;,\n",
       "               &#x27;2022-07-15 18:59:00&#x27;, &#x27;2022-07-15 19:00:00&#x27;,\n",
       "               &#x27;2022-07-15 19:01:00&#x27;, &#x27;2022-07-15 19:02:00&#x27;,\n",
       "               &#x27;2022-07-15 19:03:00&#x27;, &#x27;2022-07-15 19:04:00&#x27;,\n",
       "               &#x27;2022-07-15 19:05:00&#x27;, &#x27;2022-07-15 19:06:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-07-20 22:33:00&#x27;, &#x27;2022-07-20 22:34:00&#x27;,\n",
       "               &#x27;2022-07-20 22:35:00&#x27;, &#x27;2022-07-20 22:36:00&#x27;,\n",
       "               &#x27;2022-07-20 22:37:00&#x27;, &#x27;2022-07-20 22:38:00&#x27;,\n",
       "               &#x27;2022-07-20 22:39:00&#x27;, &#x27;2022-07-20 22:40:00&#x27;,\n",
       "               &#x27;2022-07-20 22:41:00&#x27;, &#x27;2022-07-20 22:42:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=7426, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ad05416-549b-4a85-bef6-1f566b68f103' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6ad05416-549b-4a85-bef6-1f566b68f103' 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:            (time: 7426)\n",
       "Coordinates:\n",
       "  * time               (time) datetime64[ns] 2022-07-15T18:57:00 ... 2022-07-...\n",
       "Data variables:\n",
       "    sfc_ir_temp_Avg    (time) float64 318.3 318.1 318.5 ... 308.6 308.6 308.7\n",
       "    sfc_ir_temp_Std    (time) float64 0.062 0.347 0.201 ... 0.065 0.053 0.047\n",
       "    sfc_a_ir_temp_Avg  (time) float64 318.1 317.7 318.1 ... 308.2 308.3 308.3\n",
       "    sfc_a_ir_temp_Std  (time) float64 0.342 1.452 0.587 ... 0.041 0.024 0.028\n",
       "    logger_temp        (time) float64 26.34 26.38 26.4 26.42 ... 24.3 24.3 24.3\n",
       "    logger_volt        (time) float64 13.64 13.64 13.64 ... 13.7 13.71 13.67\n",
       "    logger_LiBat       (time) float64 3.93 3.931 3.931 ... 3.932 3.939 3.937"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del ds['RECORD']\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a7ee05-d924-4925-ac6c-9b2800e81942",
   "metadata": {},
   "source": [
    "We can also manually update the attributes to a better value if the one provided in the data file is not quite right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bae04979-1667-497d-9175-245aa910b8e0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds['sfc_ir_temp_Avg'].attrs['long_name'] = 'Heitronics Surface Temperature, 1 minute average'\n",
    "ds['sfc_ir_temp_Std'].attrs['long_name'] = 'Heitronics Surface Temperature, 1 minute standard deviation'\n",
    "ds['sfc_a_ir_temp_Avg'].attrs['long_name'] = 'Apogee Surface Temperature, 1 minute average'\n",
    "ds['sfc_a_ir_temp_Std'].attrs['long_name'] = 'Apogee Surface Temperature, 1 minute standard deviation'\n",
    "ds['logger_temp'].attrs['long_name'] = 'Logger Internal Temperature'\n",
    "ds['logger_volt'].attrs['long_name'] = 'Logger Internal Voltage'\n",
    "ds['logger_LiBat'].attrs['long_name'] = 'Logger Lithium Battery Voltage'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7556dc15-6316-4c01-a2f2-25915f25870f",
   "metadata": {},
   "source": [
    "We can also add metadata to the global attributes section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c75252fa-41aa-44b2-a139-dd4217f9b73e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds.attrs['Location'] = 'SGP E41'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634f6383-7666-480d-93a7-63a4b0f6c574",
   "metadata": {},
   "source": [
    "Since there is no location information in the .dat file we can add that. Notice we are creating Xarray DataArray and then assigning that DataArray to a variable in the Dataset. Since the values we are adding are scalars we only need to provide the value and attributes. Do not provide \"dims\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81ab9afd-061b-40d4-99bf-1255264b3330",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  --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.DataArray &#x27;lat&#x27; ()&gt;\n",
       "array(36.87956)\n",
       "Coordinates:\n",
       "    standard_name  &lt;U8 &#x27;latitude&#x27;</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'>'lat'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0675bded-39ed-4777-8a67-f56b7e6c3eb3' class='xr-array-in' type='checkbox' checked><label for='section-0675bded-39ed-4777-8a67-f56b7e6c3eb3' 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>36.88</span></div><div class='xr-array-data'><pre>array(36.87956)</pre></div></div></li><li class='xr-section-item'><input id='section-821c5398-80bd-4bf4-8f7b-989dfccc0ac9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-821c5398-80bd-4bf4-8f7b-989dfccc0ac9' 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>standard_name</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;latitude&#x27;</div><input id='attrs-7ed0bd45-8f64-4b2c-a5f9-ae0a2b3cd5f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ed0bd45-8f64-4b2c-a5f9-ae0a2b3cd5f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12cbbf74-9aa0-44e4-90a9-4ae4b91fb171' class='xr-var-data-in' type='checkbox'><label for='data-12cbbf74-9aa0-44e4-90a9-4ae4b91fb171' 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;latitude&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2326b46c-ba21-4f69-bd0a-799929309d1b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2326b46c-ba21-4f69-bd0a-799929309d1b' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ad307903-df55-444c-8564-57e8eec68c69' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad307903-df55-444c-8564-57e8eec68c69' 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 'lat' ()>\n",
       "array(36.87956)\n",
       "Coordinates:\n",
       "    standard_name  <U8 'latitude'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[\"lat\"] = xr.DataArray(36.87956, {'standard_name': 'latitude'})\n",
    "ds[\"lon\"] = xr.DataArray(-97.08645, {'standard_name': 'longitude'})\n",
    "ds[\"lat\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb8a03b-fa36-4630-8386-04dd837d89b2",
   "metadata": {},
   "source": [
    "If the units of the values are not how we want them, we can use Pint to perform the conversion. Just need to extract the values, perform conversion and put back, not forgetting to update the units attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e40aec1-b4de-447b-b4df-81749251ec27",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pint\n",
    "\n",
    "ureg = pint.UnitRegistry()  # Set up the regestry object.\n",
    "\n",
    "desired_unit = 'degC'\n",
    "var_names = ['sfc_ir_temp_Avg', 'sfc_a_ir_temp_Avg']  #, 'sfc_ir_temp_Std', 'sfc_a_ir_temp_Std']\n",
    "for var_name in var_names:\n",
    "    data = ds[var_name].values  # Get the data from Xarray object.\n",
    "    # Get the variable unit from the Dataset. Convert to lower case because Pint does not\n",
    "    # recognize \"Kelvin\" only \"kelvin\"\n",
    "    variable_units = ds[var_name].attrs['units'].lower()\n",
    "    # Convert the data to Pint object that contains the magnitude and unit\n",
    "    data = data * ureg.parse_expression(variable_units)  \n",
    "    # Convert from \"kelvin\" to \"degC\"\n",
    "    data = data.to(ureg.parse_expression(desired_unit))\n",
    "    ds[var_name].values = data  # Put data back into Dataset\n",
    "    ds[var_name].attrs['units'] = desired_unit  # Update units in Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12ceba4-1d72-478d-a7a6-7028b16b2b16",
   "metadata": {},
   "source": [
    "Let's make a plot of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "973d06a7-f1e8-4271-bb42-abd34400c26d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (15, 8))\n",
    "al, = ds['sfc_ir_temp_Std'].plot(ax = ax, linewidth=1.0, label='Apogee')\n",
    "hl, = ds['sfc_a_ir_temp_Std'].plot(ax = ax, linewidth=1.0, label='Heitronics')\n",
    "\n",
    "ax.set_title('Surf. Temperature, Standard Dev. of 1 min Data. Heitronics and Apogee', fontsize = 22)\n",
    "ax.legend(handles=[al, hl], fontsize = 15)\n",
    "ax.grid()\n",
    "plt.ylabel('Temperature Stand. Dev. (DegK)')\n",
    "\n",
    "# Set plot range limits\n",
    "begday = ds['time'].values[0].astype('datetime64[D]')\n",
    "endday = ds['time'].values[-1].astype('datetime64[D]') + 1\n",
    "plt.xlim(begday, endday)\n",
    "# plt.ylim(0, 3.5)\n",
    "\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "ax.xaxis.label.set_fontsize(10)\n",
    "ax.yaxis.label.set_fontsize(18)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}