DataQualityGuideNotebook.ipynb

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    "# Introduction and Overview\n",
    "\n",
    "* [OOI on Quality Control](https://ooi-website.whoi.edu/quality-control/)\n",
    "* [OOI on Data](https://oceanobservatories.org/tag/data/)\n",
    "* [OOI Data Explorer example](https://dataexplorer.oceanobservatories.org/#ooi/array/RS/platform_type/profiling-assets/annotations?instrument_type_id=FLUOR)\n",
    "* [Notebook reference on OOI QC flags (Peter Cable)](https://github.com/petercable/shared_notebooks/blob/master/qc_flags/qc_flags.ipynb)\n",
    "* [Notebook on OOI data exploration using Python (Chris Wingard)](https://github.com/oceanobservatories/ooi-data-explorations/tree/master/python)\n",
    "* [Notebook on Human In The Loop QC (Chris Wingard)](https://github.com/oceanobservatories/ooi-data-explorations/blob/master/python/examples/notebooks/dosta/ooiea_dosta_annotations_hitl_qc.ipynb)\n",
    "\n",
    "\n",
    "Temporary references\n",
    "\n",
    "\n",
    "* [chapter flowcharts](https://docs.google.com/presentation/d/1WuEsncfrxf_43osxAL-2g6dew1qh0wcXf2Lg5vNwCas/edit?pli=1#slide=id.g142d4c244fd_0_124)\n",
    "* [pseudo-code document](https://docs.google.com/document/d/1gQhFzkthh-S116F_p32hoF_uw22MB3mKF3DKyUtmIxw/edit#)\n",
    "* [flort flowchart](https://docs.google.com/drawings/d/1CYWFAGMNZQa8W3xFslrI9uXyHW78q24KhBOhiTMUz98/edit)\n",
    "\n",
    "\n",
    "This notebook follows the QC narrative for biooptics (fluorescence, specifically) to produce draft figures\n",
    "for the OOI QC Data Guide. A fluorometer triplet ('flort') produced the example dataset used here. It\n",
    "is deployed on the Regional Cabled Array \"Oregon Slope Base\" shallow profiler. \n",
    "The time frame is March 2021, later narrowed to one day, March 10, 2021. \n",
    "The latter 24-hour period includes nine profiles.\n",
    "\n",
    "\n",
    "Circa 2022 the data download link is\n",
    "[here (NetCDF file)](/thredds/fileServer/ooigoldcopy/public/RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record/deployment0008_RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record_20210123T120000.228181-20210414T115959.167518.nc).\n",
    "\n",
    "\n",
    "***NEEDED: Annotation files for HITL are available through the Data Explorer; specifics needed***\n",
    "\n",
    "\n",
    "For a more complete write-up on getting full resolution data see the \n",
    "**`Notebooks/Ocean 05 A Programming Resources.ipynb`** file within this repository. \n",
    "The full resolution source data file is over 500MB in size. As it does not fit in this repository,\n",
    "it is placed in a parallel folder with relative path `../data/rca/fluor/osb/2021`. \n",
    "\n",
    "\n",
    "This notebook uses some 'helper' Python found in the `DataQualityGuideModule.py` module in this folder.\n",
    "It is imported in the first Python cell below."
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      "OOI RCA Oregon Slope Base: Shallow Profiler status report\n",
      "=========================================================\n",
      "For March 2021:\n",
      "31 days, translates to 279 possible profiles\n",
      "Actual:\n",
      "262 profiles; 29 at local midnight and 30 at local noon\n",
      "\n",
      "Jupyter running Python 3\n"
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    "# Set up this notebook to use code from an accompanying Python module file\n",
    "from DataQualityGuideModule import *\n",
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    "# Preparation\n",
    "\n",
    "\n",
    "The data within file `flort_fullres.nc` are assigned to the XArray Dataset variable `D`. It is then\n",
    "slightly modified \n",
    "and the contents are examined. "
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     "text": [
      "Number of data values: after file is opened, before time range narrowed to March 2021:  5830998\n",
      "Number of data values: March 2021 only, compare to 'one per second' total of 2678400:  2231042\n",
      "D number of data values after dropping NaN values is the same:  2231042\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                          (time: 2231042)\n",
       "Coordinates:\n",
       "    obs                                              (time) int32 2627967 ......\n",
       "    lon                                              (time) float64 ...\n",
       "    lat                                              (time) float64 ...\n",
       "    depth                                            (time) float64 ...\n",
       "  * time                                             (time) datetime64[ns] 20...\n",
       "Data variables: (12/39)\n",
       "    practical_salinity                               (time) float64 33.87 ......\n",
       "    fluorometric_chlorophyll_a_qc_results            (time) uint8 12 12 ... 12\n",
       "    fdom                                             (time) float64 1.3 ... 1...\n",
       "    signal_3_offset                                  (time) float32 0.0 ... 0.0\n",
       "    fluorometric_cdom_qc_executed                    (time) uint8 29 29 ... 29\n",
       "    measurement_wavelength_cdom                      (time) float32 460.0 ......\n",
       "    ...                                               ...\n",
       "    total_volume_scattering_coefficient              (time) float64 9.269e-05...\n",
       "    signal_2_offset                                  (time) float32 0.0 ... 0.0\n",
       "    chlora                                           (time) float64 -0.0107 ....\n",
       "    raw_internal_temp                                (time) float32 549.0 ......\n",
       "    backscatter                                      (time) float64 0.0006074...\n",
       "    fluorometric_cdom_qc_results                     (time) uint8 29 29 ... 29\n",
       "Attributes: (12/68)\n",
       "    node:                               SF01A\n",
       "    comment:                            \n",
       "    publisher_email:                    \n",
       "    sourceUrl:                          http://oceanobservatories.org/\n",
       "    collection_method:                  streamed\n",
       "    stream:                             flort_d_data_record\n",
       "    ...                                 ...\n",
       "    geospatial_lon_max:                 -125.3896636\n",
       "    geospatial_lon_units:               degrees_east\n",
       "    geospatial_lon_resolution:          0.1\n",
       "    geospatial_vertical_units:          meters\n",
       "    geospatial_vertical_resolution:     0.1\n",
       "    geospatial_vertical_positive:       down</pre><div class='xr-wrap' hidden><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-69a39f1b-523d-4a84-91a8-886783264d3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-69a39f1b-523d-4a84-91a8-886783264d3d' 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>: 2231042</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b495b4c5-7bb2-400f-b387-f546e5ad32c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b495b4c5-7bb2-400f-b387-f546e5ad32c5' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>obs</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2627967 2627968 ... 4859007 4859008</div><input id='attrs-cdba08d3-392f-4b08-96a0-fb2254e0e626' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdba08d3-392f-4b08-96a0-fb2254e0e626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b1b370a-97eb-4b0b-af47-7743a9b77549' class='xr-var-data-in' type='checkbox'><label for='data-6b1b370a-97eb-4b0b-af47-7743a9b77549' 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([2627967, 2627968, 2627969, ..., 4859006, 4859007, 4859008], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e935e798-1138-49d8-a1e0-ea7f3796f780' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e935e798-1138-49d8-a1e0-ea7f3796f780' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e030e94c-b602-4412-a070-0decf7ee23e8' class='xr-var-data-in' type='checkbox'><label for='data-e030e94c-b602-4412-a070-0decf7ee23e8' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>[2231042 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0f94105-5a21-46c8-9f7d-c86246879bbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0f94105-5a21-46c8-9f7d-c86246879bbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3f64250-41a9-4198-98bf-d8d029b1c4f8' class='xr-var-data-in' type='checkbox'><label for='data-e3f64250-41a9-4198-98bf-d8d029b1c4f8' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>[2231042 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-467f30f3-a135-4935-b9cd-154626138c23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-467f30f3-a135-4935-b9cd-154626138c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e9e8d10-579f-4e7d-bd19-95e42c5e3a3d' class='xr-var-data-in' type='checkbox'><label for='data-0e9e8d10-579f-4e7d-bd19-95e42c5e3a3d' 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>comment :</span></dt><dd>Depth (m) calculated from pressure (dbar) and latitude.</dd><dt><span>long_name :</span></dt><dd>Depth calculated from pressure</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[2231042 values with dtype=float64]</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-01T00:00:00.728539648 .....</div><input id='attrs-dac904e0-cd71-4899-b62b-5e5f6b723be2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dac904e0-cd71-4899-b62b-5e5f6b723be2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e718e0e-b62d-4c6f-bafa-f0a398f9e125' class='xr-var-data-in' type='checkbox'><label for='data-3e718e0e-b62d-4c6f-bafa-f0a398f9e125' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-01T00:00:00.728539648&#x27;, &#x27;2021-03-01T00:00:01.856399360&#x27;,\n",
       "       &#x27;2021-03-01T00:00:02.984257536&#x27;, ..., &#x27;2021-03-31T23:59:56.694360064&#x27;,\n",
       "       &#x27;2021-03-31T23:59:57.823038976&#x27;, &#x27;2021-03-31T23:59:58.951681024&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e68de3db-9348-47ca-8d99-ab16aa0b94a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-e68de3db-9348-47ca-8d99-ab16aa0b94a2' class='xr-section-summary' >Data variables: <span>(39)</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>practical_salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>33.87 33.87 33.87 ... 33.96 33.96</div><input id='attrs-3ff15722-e597-4072-b1e1-47a178d455c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ff15722-e597-4072-b1e1-47a178d455c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dd27602-e217-4260-b876-361fb223ecab' class='xr-var-data-in' type='checkbox'><label for='data-9dd27602-e217-4260-b876-361fb223ecab' 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>comment :</span></dt><dd>Salinity is generally defined as the concentration of dissolved salt in a parcel of seawater. Practical Salinity is a more specific unitless quantity calculated from the conductivity of seawater and adjusted for temperature and pressure. It is approximately equivalent to Absolute Salinity (the mass fraction of dissolved salt in seawater) but they are not interchangeable.</dd><dt><span>data_product_identifier :</span></dt><dd>PRACSAL_L2</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>long_name :</span></dt><dd>Practical Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd></dl></div><div class='xr-var-data'><pre>array([33.870997, 33.871021, 33.871127, ..., 33.961597, 33.961569, 33.961613])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fluorometric_chlorophyll_a_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>12 12 12 12 12 ... 12 12 13 12 12</div><input id='attrs-14427406-6202-4860-bc12-888f34d6f6b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14427406-6202-4860-bc12-888f34d6f6b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e90b5706-892b-494a-aff5-06ac0cc51398' class='xr-var-data-in' type='checkbox'><label for='data-e90b5706-892b-494a-aff5-06ac0cc51398' 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([12, 12, 12, ..., 13, 12, 12], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fdom</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.3 1.227 1.3 ... 1.444 1.227 1.516</div><input id='attrs-7f099609-9910-4fd0-bdaf-d38471660ed4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f099609-9910-4fd0-bdaf-d38471660ed4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21b1259-5f74-4ffa-8908-029b6b1d82dc' class='xr-var-data-in' type='checkbox'><label for='data-f21b1259-5f74-4ffa-8908-029b6b1d82dc' 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>comment :</span></dt><dd>Fluorometric CDOM Concentration is a measure of how much light has been re-emitted (fluoresced) from colored organic compounds found in the colored dissolved organic matter (CDOM) in seawater. Examples of CDOM include tannins (polyphenols that bind to proteins and other large molecules) or lignins (polymers of phenolic acids) from decaying plant material and byproducts from the decomposition of animals. It accounts for the tea-like color in seawater. CDOM is not particulate, but seawater can contain both CDOM and turbidity.</dd><dt><span>long_name :</span></dt><dd>CDOM Concentration</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>data_product_identifier :</span></dt><dd>CDOMFLO_L1</dd><dt><span>units :</span></dt><dd>ppb</dd><dt><span>ancillary_variables :</span></dt><dd>raw_signal_cdom</dd></dl></div><div class='xr-var-data'><pre>array([1.2996, 1.2274, 1.2996, ..., 1.444 , 1.2274, 1.5162])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>signal_3_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f43b9ffd-73e2-41d2-8451-45f903b80b6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f43b9ffd-73e2-41d2-8451-45f903b80b6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41ae4af7-1f79-48ef-b6d3-b124fd4db00b' class='xr-var-data-in' type='checkbox'><label for='data-41ae4af7-1f79-48ef-b6d3-b124fd4db00b' 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>units :</span></dt><dd>counts</dd><dt><span>long_name :</span></dt><dd>Signal 3 (CDOM) Offset</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fluorometric_cdom_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>29 29 29 29 29 ... 29 29 29 29 29</div><input id='attrs-e833eb12-3845-4057-87b9-7ad1ddd8d128' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e833eb12-3845-4057-87b9-7ad1ddd8d128' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9339228e-55fb-4adb-8161-4ca2ab8a8197' class='xr-var-data-in' type='checkbox'><label for='data-9339228e-55fb-4adb-8161-4ca2ab8a8197' 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([29, 29, 29, ..., 29, 29, 29], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_wavelength_cdom</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>460.0 460.0 460.0 ... 460.0 460.0</div><input id='attrs-4a2dab88-d30f-4114-b219-62edf88a8082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a2dab88-d30f-4114-b219-62edf88a8082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcc97dc7-bc5a-46b1-a193-57d9b369e123' class='xr-var-data-in' type='checkbox'><label for='data-dcc97dc7-bc5a-46b1-a193-57d9b369e123' 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>comment :</span></dt><dd>Wavelength used by sensor for its cdom measurement.</dd><dt><span>units :</span></dt><dd>nm</dd><dt><span>long_name :</span></dt><dd>Measurement Wavelength, CDOM</dd></dl></div><div class='xr-var-data'><pre>array([460., 460., 460., ..., 460., 460., 460.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_volume_scattering_coefficient_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>29 29 29 29 29 ... 29 29 29 29 29</div><input id='attrs-29d09bc0-f24d-46a6-ac00-3e95373db957' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29d09bc0-f24d-46a6-ac00-3e95373db957' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a8fcdd7-6a93-4f3c-99a8-e1a54b6ab73a' class='xr-var-data-in' type='checkbox'><label for='data-8a8fcdd7-6a93-4f3c-99a8-e1a54b6ab73a' 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([29, 29, 29, ..., 29, 29, 29], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>driver_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-01T00:00:00.838394368 .....</div><input id='attrs-b894c307-3f5a-4249-af35-80651cc5ce84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b894c307-3f5a-4249-af35-80651cc5ce84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5554ea6c-17cc-4f69-962e-322c7976df02' class='xr-var-data-in' type='checkbox'><label for='data-5554ea6c-17cc-4f69-962e-322c7976df02' 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>comment :</span></dt><dd>Driver timestamp, UTC</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-01T00:00:00.838394368&#x27;, &#x27;2021-03-01T00:00:01.941340672&#x27;,\n",
       "       &#x27;2021-03-01T00:00:03.144965120&#x27;, ..., &#x27;2021-03-31T23:59:56.770181632&#x27;,\n",
       "       &#x27;2021-03-31T23:59:57.973696512&#x27;, &#x27;2021-03-31T23:59:59.077465088&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S36</div><div class='xr-var-preview xr-preview'>b&#x27;387277bb-b9f5-4e3c-8797-27cd9d...</div><input id='attrs-cdcafc8a-83e1-4d2b-b959-3ff55befd712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdcafc8a-83e1-4d2b-b959-3ff55befd712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-291e1ab3-dd80-44d8-803e-49bb9d688127' class='xr-var-data-in' type='checkbox'><label for='data-291e1ab3-dd80-44d8-803e-49bb9d688127' 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>name :</span></dt><dd>id</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;387277bb-b9f5-4e3c-8797-27cd9d07a805&#x27;,\n",
       "       b&#x27;362120c3-f83d-4429-b945-ec8d0ab4f3c5&#x27;,\n",
       "       b&#x27;e8833e54-ad26-4b8b-a49f-4288118de1ab&#x27;, ...,\n",
       "       b&#x27;2c3dcd4d-7937-41a3-b198-64b184befff4&#x27;,\n",
       "       b&#x27;753cb810-8635-4838-91aa-07e64e7b78bf&#x27;,\n",
       "       b&#x27;c8cdceac-7405-414f-846e-38ba6569ea16&#x27;], dtype=&#x27;|S36&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raw_signal_cdom</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>67.0 66.0 67.0 ... 69.0 66.0 70.0</div><input id='attrs-2dd497df-5703-4b14-a9ff-09a27a8d284b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dd497df-5703-4b14-a9ff-09a27a8d284b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d53d013-4a42-4f4a-98f0-192e86b23a49' class='xr-var-data-in' type='checkbox'><label for='data-4d53d013-4a42-4f4a-98f0-192e86b23a49' 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>comment :</span></dt><dd>Fluorometric CDOM unprocessed measurement output from the sensor.</dd><dt><span>long_name :</span></dt><dd>CDOM Measurement</dd><dt><span>data_product_identifier :</span></dt><dd>CDOMFLO_L0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>array([67., 66., 67., ..., 69., 66., 70.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>signal_2_scale_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6a07d0bb-8eeb-4139-8ac9-e2c56768cc45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a07d0bb-8eeb-4139-8ac9-e2c56768cc45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1c70799-d0d0-44b9-92ed-b60d83224aa0' class='xr-var-data-in' type='checkbox'><label for='data-f1c70799-d0d0-44b9-92ed-b60d83224aa0' 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>units :</span></dt><dd>ug L-1 counts-1</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>long_name :</span></dt><dd>Signal 2 (Chlorophyll) Scale Factor</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_wavelength_beta</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>700.0 700.0 700.0 ... 700.0 700.0</div><input id='attrs-52d47e39-5e37-46a8-a85d-3ec7682f2f93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d47e39-5e37-46a8-a85d-3ec7682f2f93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2a6a4e-1038-4bb5-bfd1-5be5bf426176' class='xr-var-data-in' type='checkbox'><label for='data-6f2a6a4e-1038-4bb5-bfd1-5be5bf426176' 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>comment :</span></dt><dd>Wavelength used by sensor for its scattering measurement.</dd><dt><span>units :</span></dt><dd>nm</dd><dt><span>long_name :</span></dt><dd>Measurement Wavelength, Scattering</dd></dl></div><div class='xr-var-data'><pre>array([700., 700., 700., ..., 700., 700., 700.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>provenance</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S36</div><div class='xr-var-preview xr-preview'>b&#x27;c4011cf6-bf45-458d-a9f1-4f5131...</div><input id='attrs-f45b0d08-b070-4314-b878-fceccb33d998' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f45b0d08-b070-4314-b878-fceccb33d998' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb6c73ab-1be7-446e-867b-135ca10a9962' class='xr-var-data-in' type='checkbox'><label for='data-cb6c73ab-1be7-446e-867b-135ca10a9962' 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>name :</span></dt><dd>provenance</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;c4011cf6-bf45-458d-a9f1-4f51313fb74d&#x27;,\n",
       "       b&#x27;c4011cf6-bf45-458d-a9f1-4f51313fb74d&#x27;,\n",
       "       b&#x27;c4011cf6-bf45-458d-a9f1-4f51313fb74d&#x27;, ...,\n",
       "       b&#x27;207246d8-3fda-40e5-9e82-978ac82be712&#x27;,\n",
       "       b&#x27;207246d8-3fda-40e5-9e82-978ac82be712&#x27;,\n",
       "       b&#x27;db049357-fc56-4370-9b4c-5fc3a55d95ca&#x27;], dtype=&#x27;|S36&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>signal_3_scale_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-8443895d-fc42-4250-a58a-c517640362cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8443895d-fc42-4250-a58a-c517640362cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-792aa19e-746a-4f23-8d03-3d055e5920d9' class='xr-var-data-in' type='checkbox'><label for='data-792aa19e-746a-4f23-8d03-3d055e5920d9' 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>units :</span></dt><dd>ppb counts-1</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>long_name :</span></dt><dd>Signal 3 (CDOM) Scale Factor</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_string</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>b&#x27;empty&#x27; b&#x27;empty&#x27; ... b&#x27;empty&#x27;</div><input id='attrs-0e7d4380-4650-43ff-93c9-d93df73e3ff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e7d4380-4650-43ff-93c9-d93df73e3ff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-962e70db-b214-4d30-b41f-830e386fba0c' class='xr-var-data-in' type='checkbox'><label for='data-962e70db-b214-4d30-b41f-830e386fba0c' 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>comment :</span></dt><dd>Measurement time in string format</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Measurement Time, UTC</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;empty&#x27;, b&#x27;empty&#x27;, b&#x27;empty&#x27;, ..., b&#x27;empty&#x27;, b&#x27;empty&#x27;, b&#x27;empty&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-02-28T23:56:18 ... 2021-03-...</div><input id='attrs-83f535f5-6c13-4f5a-8387-ab59c4448357' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83f535f5-6c13-4f5a-8387-ab59c4448357' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-341110ae-d3a9-46b2-9bce-eb57a1271884' class='xr-var-data-in' type='checkbox'><label for='data-341110ae-d3a9-46b2-9bce-eb57a1271884' 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>comment :</span></dt><dd>Internal timestamp, UTC</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-02-28T23:56:18.000000000&#x27;, &#x27;2021-02-28T23:56:19.000000000&#x27;,\n",
       "       &#x27;2021-02-28T23:56:20.000000000&#x27;, ..., &#x27;2021-03-31T23:55:42.000000000&#x27;,\n",
       "       &#x27;2021-03-31T23:55:43.000000000&#x27;, &#x27;2021-03-31T23:55:44.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_wavelength_chl</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>695.0 695.0 695.0 ... 695.0 695.0</div><input id='attrs-af16d43a-ef10-4d87-b8b3-964a7451a047' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af16d43a-ef10-4d87-b8b3-964a7451a047' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a001fb2-36d3-43f6-8b94-00d0e0f62b75' class='xr-var-data-in' type='checkbox'><label for='data-6a001fb2-36d3-43f6-8b94-00d0e0f62b75' 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>comment :</span></dt><dd>Wavelength used by sensor for its chlorophyll measurement .</dd><dt><span>units :</span></dt><dd>nm</dd><dt><span>long_name :</span></dt><dd>Measurement Wavelength, Chlorophyll</dd></dl></div><div class='xr-var-data'><pre>array([695., 695., 695., ..., 695., 695., 695.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>optical_backscatter_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>29 29 29 29 29 ... 29 29 29 29 29</div><input id='attrs-257798e9-f2b5-4e70-b9d4-ae15559750b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-257798e9-f2b5-4e70-b9d4-ae15559750b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-147467f3-10a0-4f3a-a8ff-510d7e543205' class='xr-var-data-in' type='checkbox'><label for='data-147467f3-10a0-4f3a-a8ff-510d7e543205' 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([29, 29, 29, ..., 29, 29, 29], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_string</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>b&#x27;empty&#x27; b&#x27;empty&#x27; ... b&#x27;empty&#x27;</div><input id='attrs-c38185d0-6773-40a2-be01-870c4f6816f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c38185d0-6773-40a2-be01-870c4f6816f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0f55d30-b676-4321-ad93-82a7d851ccc8' class='xr-var-data-in' type='checkbox'><label for='data-b0f55d30-b676-4321-ad93-82a7d851ccc8' 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>comment :</span></dt><dd>Measurement date in string format.</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Measurement Date, UTC</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;empty&#x27;, b&#x27;empty&#x27;, b&#x27;empty&#x27;, ..., b&#x27;empty&#x27;, b&#x27;empty&#x27;, b&#x27;empty&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fluorometric_chlorophyll_a_qc_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>29 29 29 29 29 ... 29 29 29 29 29</div><input id='attrs-0af8f0fd-7207-4133-bf12-f899415e7111' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0af8f0fd-7207-4133-bf12-f899415e7111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c9c6b1b-a1f8-47d9-8463-cb67ca3951e6' class='xr-var-data-in' type='checkbox'><label for='data-7c9c6b1b-a1f8-47d9-8463-cb67ca3951e6' 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([29, 29, 29, ..., 29, 29, 29], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seawater_scattering_coefficient</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0006395 0.0006395 ... 0.0006397</div><input id='attrs-24a11775-7492-4e62-a5bb-96139309fef1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24a11775-7492-4e62-a5bb-96139309fef1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08252c66-45bf-4e93-a4ab-3aa768478bd7' class='xr-var-data-in' type='checkbox'><label for='data-08252c66-45bf-4e93-a4ab-3aa768478bd7' 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>comment :</span></dt><dd>Total scattering coefficient of pure seawater.</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>long_name :</span></dt><dd>Total Scattering Coefficient of Pure Seawater</dd><dt><span>units :</span></dt><dd>m-1</dd><dt><span>ancillary_variables :</span></dt><dd>seawater_temperature practical_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0.000639, 0.000639, 0.000639, ..., 0.00064 , 0.00064 , 0.00064 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raw_signal_beta</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>91.0 97.0 106.0 ... 104.0 98.0</div><input id='attrs-b9b52831-93b5-479d-b214-9528236ba205' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9b52831-93b5-479d-b214-9528236ba205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74a6be4e-4bb1-4fab-bcfc-4662bce56b58' class='xr-var-data-in' type='checkbox'><label for='data-74a6be4e-4bb1-4fab-bcfc-4662bce56b58' 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>comment :</span></dt><dd>Optical backscatter unprocessed measurement output from the sensor.</dd><dt><span>long_name :</span></dt><dd>Optical Backscatter Measurement</dd><dt><span>data_product_identifier :</span></dt><dd>FLUBSCT_L0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>array([ 91.,  97., 106., ..., 109., 104.,  98.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>optical_backscatter_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>13 13 13 13 13 ... 13 13 13 13 13</div><input id='attrs-24d97525-f019-403c-8d6c-e940f19c2452' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24d97525-f019-403c-8d6c-e940f19c2452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df250a0-8d65-430a-8f1f-7fd8f61fa37f' class='xr-var-data-in' type='checkbox'><label for='data-8df250a0-8d65-430a-8f1f-7fd8f61fa37f' 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, 13, 13, ..., 13, 13, 13], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seawater_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.737 7.737 7.737 ... 7.768 7.768</div><input id='attrs-175f1267-83ce-4706-b461-89c6d7165049' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-175f1267-83ce-4706-b461-89c6d7165049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-948439c0-1a93-466b-8b22-f24ba2591dc4' class='xr-var-data-in' type='checkbox'><label for='data-948439c0-1a93-466b-8b22-f24ba2591dc4' 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>comment :</span></dt><dd>Seawater temperature near the sensor.</dd><dt><span>data_product_identifier :</span></dt><dd>TEMPWAT_L1</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>long_name :</span></dt><dd>Seawater Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>units :</span></dt><dd>ºC</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd></dl></div><div class='xr-var-data'><pre>array([7.736717, 7.736913, 7.736994, ..., 7.76834 , 7.768392, 7.76834 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingestion_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-01T00:00:01.614000128 .....</div><input id='attrs-89b02cf8-9b02-4271-8d1f-9452739eb779' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89b02cf8-9b02-4271-8d1f-9452739eb779' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-842137da-abda-4c44-b7b4-8b53ff688be2' class='xr-var-data-in' type='checkbox'><label for='data-842137da-abda-4c44-b7b4-8b53ff688be2' 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>comment :</span></dt><dd>The NTP Timestamp for when the granule was ingested</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-01T00:00:01.614000128&#x27;, &#x27;2021-03-01T00:00:07.203999744&#x27;,\n",
       "       &#x27;2021-03-01T00:00:07.203999744&#x27;, ..., &#x27;2021-03-31T23:59:58.588000256&#x27;,\n",
       "       &#x27;2021-03-31T23:59:58.588999680&#x27;, &#x27;2021-04-01T00:00:03.461000192&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>port_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-01T00:00:00.728539648 .....</div><input id='attrs-2fb562a6-f73f-44ba-933e-beaa16631fe0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fb562a6-f73f-44ba-933e-beaa16631fe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a62141b-3741-4602-b4b3-6ba8d7efdcf9' class='xr-var-data-in' type='checkbox'><label for='data-6a62141b-3741-4602-b4b3-6ba8d7efdcf9' 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>comment :</span></dt><dd>Port timestamp, UTC</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-01T00:00:00.728539648&#x27;, &#x27;2021-03-01T00:00:01.856399360&#x27;,\n",
       "       &#x27;2021-03-01T00:00:02.984257536&#x27;, ..., &#x27;2021-03-31T23:59:56.694360064&#x27;,\n",
       "       &#x27;2021-03-31T23:59:57.823038976&#x27;, &#x27;2021-03-31T23:59:58.951681024&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raw_signal_chl</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>53.0 52.0 50.0 ... 54.0 52.0 52.0</div><input id='attrs-f770e876-b1fb-4d48-a15f-7367d5b4e449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f770e876-b1fb-4d48-a15f-7367d5b4e449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec3b7650-c4fa-42eb-b01b-1efcf91e08cb' class='xr-var-data-in' type='checkbox'><label for='data-ec3b7650-c4fa-42eb-b01b-1efcf91e08cb' 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>comment :</span></dt><dd>Fluorometric chlorophyll-a unprocessed measurement output from the sensor.</dd><dt><span>long_name :</span></dt><dd>Chlorophyll-a Measurement</dd><dt><span>data_product_identifier :</span></dt><dd>CHLAFLO_L0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>array([53., 52., 50., ..., 54., 52., 52.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_volume_scattering_coefficient_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>13 13 13 13 13 ... 13 13 13 13 13</div><input id='attrs-56aa7bfd-af85-4693-a0a9-8bf8ff92b21b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56aa7bfd-af85-4693-a0a9-8bf8ff92b21b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e430a9-e236-4410-94e1-ce09a38938bd' class='xr-var-data-in' type='checkbox'><label for='data-99e430a9-e236-4410-94e1-ce09a38938bd' 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, 13, 13, ..., 13, 13, 13], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>signal_1_scale_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ddfad400-a5b4-41eb-978c-2aedd9c1d9d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddfad400-a5b4-41eb-978c-2aedd9c1d9d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eed8823-3991-42b0-ab69-7304add7fa13' class='xr-var-data-in' type='checkbox'><label for='data-0eed8823-3991-42b0-ab69-7304add7fa13' 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>units :</span></dt><dd>m-1 sr-1 counts-1</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>long_name :</span></dt><dd>Signal 1 (Scattering) Scale Factor</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deployment</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>8 8 8 8 8 8 8 8 ... 8 8 8 8 8 8 8 8</div><input id='attrs-c8f80132-5080-498a-b424-54008a4b8651' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8f80132-5080-498a-b424-54008a4b8651' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-912a0c27-a0c6-47c5-a1c9-4b87c2bb34e7' class='xr-var-data-in' type='checkbox'><label for='data-912a0c27-a0c6-47c5-a1c9-4b87c2bb34e7' 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>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><pre>array([8, 8, 8, ..., 8, 8, 8], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>signal_1_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-7f66cc9b-ccff-45a5-8734-789c761e9c77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f66cc9b-ccff-45a5-8734-789c761e9c77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3138b79-977d-4680-9ba2-1c45ca3503b7' class='xr-var-data-in' type='checkbox'><label for='data-a3138b79-977d-4680-9ba2-1c45ca3503b7' 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>units :</span></dt><dd>counts</dd><dt><span>long_name :</span></dt><dd>Signal 1 (Scattering) Offset</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>int_ctd_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>191.1 191.1 191.1 ... 191.8 191.8</div><input id='attrs-7e1816d4-64de-46f6-a6a7-1c551531b402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e1816d4-64de-46f6-a6a7-1c551531b402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8408d129-2af6-4d2a-9038-c954b2bead6e' class='xr-var-data-in' type='checkbox'><label for='data-8408d129-2af6-4d2a-9038-c954b2bead6e' 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>comment :</span></dt><dd>Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>dbar</dd></dl></div><div class='xr-var-data'><pre>array([191.051661, 191.052701, 191.056725, ..., 191.806332, 191.800285,\n",
       "       191.795122])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>preferred_timestamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>b&#x27;port_timestamp&#x27; ... b&#x27;port_tim...</div><input id='attrs-e53c8be2-d3e2-4545-89b5-6517e4a01087' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e53c8be2-d3e2-4545-89b5-6517e4a01087' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f4c0083-24ee-412a-8aad-2042b36512d8' class='xr-var-data-in' type='checkbox'><label for='data-5f4c0083-24ee-412a-8aad-2042b36512d8' 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>comment :</span></dt><dd>Timestamp preferred as official record.</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Preferred Timestamp</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;port_timestamp&#x27;, b&#x27;port_timestamp&#x27;, b&#x27;port_timestamp&#x27;, ...,\n",
       "       b&#x27;port_timestamp&#x27;, b&#x27;port_timestamp&#x27;, b&#x27;port_timestamp&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_volume_scattering_coefficient</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.269e-05 0.0001059 ... 0.0001081</div><input id='attrs-14902ac2-353d-47c3-b638-86e2d8498f87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14902ac2-353d-47c3-b638-86e2d8498f87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eee45f3-ff93-4e2b-a2d9-186ed8edff1f' class='xr-var-data-in' type='checkbox'><label for='data-5eee45f3-ff93-4e2b-a2d9-186ed8edff1f' 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>comment :</span></dt><dd>Total Volume Scattering Coefficient values represent the volume scattering from particles and the molecular scattering from water at a given wavelength of light and the default angle of 117 degrees for the ECO meter.</dd><dt><span>long_name :</span></dt><dd>Total Volume Scattering Coefficient</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>data_product_identifier :</span></dt><dd>FLUBSCT_L1</dd><dt><span>units :</span></dt><dd>m-1 sr-1</dd><dt><span>ancillary_variables :</span></dt><dd>raw_signal_beta</dd></dl></div><div class='xr-var-data'><pre>array([9.26940e-05, 1.05936e-04, 1.25799e-04, ..., 1.32420e-04, 1.21385e-04,\n",
       "       1.08143e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>signal_2_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-76d16043-0f03-4908-a772-d68c87d33df0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76d16043-0f03-4908-a772-d68c87d33df0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58420250-2d5a-40b2-982c-2e6b2249292c' class='xr-var-data-in' type='checkbox'><label for='data-58420250-2d5a-40b2-982c-2e6b2249292c' 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>units :</span></dt><dd>counts</dd><dt><span>long_name :</span></dt><dd>Signal 2 (Chlorophyll) Offset</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chlora</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0107 -0.0214 ... -0.0214 -0.0214</div><input id='attrs-1354359f-081e-47a3-9553-90589f967229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1354359f-081e-47a3-9553-90589f967229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edb954ad-92b3-4b3e-a43e-1614299e1fe3' class='xr-var-data-in' type='checkbox'><label for='data-edb954ad-92b3-4b3e-a43e-1614299e1fe3' 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>comment :</span></dt><dd>Fluorometric Chlorophyll-a Concentration is an estimate of phytoplankton biomass using fluorescence. The fluorometer emits light at a specific wavelength that is absorbed by chlorophyll and re-emitted as light at a different wavelength. By measuring the intensity of the re-emitted wavelength of light the chlorophyll-a concentration in the surrounding seawater can be estimated. Chlorophyll-a concentrations can be used as a proxy for phytoplankton biomass as it is a dominant photosynthetic pigment.</dd><dt><span>long_name :</span></dt><dd>Chlorophyll-a Concentration</dd><dt><span>precision :</span></dt><dd>6</dd><dt><span>data_product_identifier :</span></dt><dd>CHLAFLO_L1</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_a_in_sea_water</dd><dt><span>units :</span></dt><dd>µg L-1</dd><dt><span>ancillary_variables :</span></dt><dd>raw_signal_chl</dd></dl></div><div class='xr-var-data'><pre>array([-0.0107, -0.0214, -0.0428, ...,  0.    , -0.0214, -0.0214])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raw_internal_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>549.0 549.0 549.0 ... 548.0 548.0</div><input id='attrs-970d0367-6ab1-4e39-bbe8-e35bb7d5917a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-970d0367-6ab1-4e39-bbe8-e35bb7d5917a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7157ba45-3fce-4ed3-8031-fff94deea8dd' class='xr-var-data-in' type='checkbox'><label for='data-7157ba45-3fce-4ed3-8031-fff94deea8dd' 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>comment :</span></dt><dd>Raw internal instrument thermistor signal output.</dd><dt><span>units :</span></dt><dd>counts</dd><dt><span>long_name :</span></dt><dd>Raw Internal Temperature</dd></dl></div><div class='xr-var-data'><pre>array([549., 549., 549., ..., 548., 548., 548.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0006074 0.0006969 ... 0.0007118</div><input id='attrs-ce432555-7e08-418a-b69b-fff2375551d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce432555-7e08-418a-b69b-fff2375551d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c049dfaf-7822-474e-b5cd-3dba6037d3b0' class='xr-var-data-in' type='checkbox'><label for='data-c049dfaf-7822-474e-b5cd-3dba6037d3b0' 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>comment :</span></dt><dd>Optical Backscatter (Red Wavelengths) is a measure of the amount of red light (630-740 nm wavelengths) scattered in the backward direction due to suspended matter within seawater, providing a proxy for turbidity and suspended solids.</dd><dt><span>long_name :</span></dt><dd>Optical Backscatter</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>data_product_identifier :</span></dt><dd>FLUBSCT_L2</dd><dt><span>units :</span></dt><dd>m-1</dd><dt><span>ancillary_variables :</span></dt><dd>total_volume_scattering_coefficient seawater_temperature practical_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0.000607, 0.000697, 0.000831, ..., 0.000876, 0.000801, 0.000712])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fluorometric_cdom_qc_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>29 29 29 29 29 ... 29 29 29 29 29</div><input id='attrs-f3e6714d-9fda-468a-937d-e13e1d58870a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3e6714d-9fda-468a-937d-e13e1d58870a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c590ab40-a799-4296-98de-7d15250a89d5' class='xr-var-data-in' type='checkbox'><label for='data-c590ab40-a799-4296-98de-7d15250a89d5' 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([29, 29, 29, ..., 29, 29, 29], dtype=uint8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e47c47a4-d6d5-4aa9-a968-8d531a524689' class='xr-section-summary-in' type='checkbox'  ><label for='section-e47c47a4-d6d5-4aa9-a968-8d531a524689' class='xr-section-summary' >Attributes: <span>(68)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>comment :</span></dt><dd></dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>stream :</span></dt><dd>flort_d_data_record</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>creator_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>date_modified :</span></dt><dd>2021-07-26T16:57:38.049177</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>date_created :</span></dt><dd>2021-07-26T16:57:38.049175</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record</dd><dt><span>requestUUID :</span></dt><dd>6d5aedaa-2f70-48c6-a8f1-a5048804922f</dd><dt><span>contributor_role :</span></dt><dd></dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>feature_Type :</span></dt><dd>point</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>license :</span></dt><dd></dd><dt><span>contributor_name :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>6d5aedaa-2f70-48c6-a8f1-a5048804922f</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.18.0 for RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record</dd><dt><span>sensor :</span></dt><dd>3A-FLORTD101</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>acknowledgement :</span></dt><dd></dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>history :</span></dt><dd>2021-07-26T16:57:38.049133 generated from Stream Engine</dd><dt><span>Manufacturer :</span></dt><dd>WET Labs</dd><dt><span>ModelNumber :</span></dt><dd>ECO Triplet-w</dd><dt><span>SerialNumber :</span></dt><dd>1130</dd><dt><span>Description :</span></dt><dd>3-Wavelength Fluorometer: FLORT Series D</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-58322-00003</dd><dt><span>Notes :</span></dt><dd>Not specified.</dd><dt><span>Owner :</span></dt><dd>University of Washington</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2021-07-23T11:39:11.989000</dd><dt><span>time_coverage_start :</span></dt><dd>2021-01-23T12:00:00.228181</dd><dt><span>time_coverage_end :</span></dt><dd>2021-04-14T11:59:59.167518</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1.20S</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.52894899</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.52894899</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.3896636</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.3896636</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                          (time: 2231042)\n",
       "Coordinates:\n",
       "    obs                                              (time) int32 2627967 ......\n",
       "    lon                                              (time) float64 ...\n",
       "    lat                                              (time) float64 ...\n",
       "    depth                                            (time) float64 ...\n",
       "  * time                                             (time) datetime64[ns] 20...\n",
       "Data variables: (12/39)\n",
       "    practical_salinity                               (time) float64 33.87 ......\n",
       "    fluorometric_chlorophyll_a_qc_results            (time) uint8 12 12 ... 12\n",
       "    fdom                                             (time) float64 1.3 ... 1...\n",
       "    signal_3_offset                                  (time) float32 0.0 ... 0.0\n",
       "    fluorometric_cdom_qc_executed                    (time) uint8 29 29 ... 29\n",
       "    measurement_wavelength_cdom                      (time) float32 460.0 ......\n",
       "    ...                                               ...\n",
       "    total_volume_scattering_coefficient              (time) float64 9.269e-05...\n",
       "    signal_2_offset                                  (time) float32 0.0 ... 0.0\n",
       "    chlora                                           (time) float64 -0.0107 ....\n",
       "    raw_internal_temp                                (time) float32 549.0 ......\n",
       "    backscatter                                      (time) float64 0.0006074...\n",
       "    fluorometric_cdom_qc_results                     (time) uint8 29 29 ... 29\n",
       "Attributes: (12/68)\n",
       "    node:                               SF01A\n",
       "    comment:                            \n",
       "    publisher_email:                    \n",
       "    sourceUrl:                          http://oceanobservatories.org/\n",
       "    collection_method:                  streamed\n",
       "    stream:                             flort_d_data_record\n",
       "    ...                                 ...\n",
       "    geospatial_lon_max:                 -125.3896636\n",
       "    geospatial_lon_units:               degrees_east\n",
       "    geospatial_lon_resolution:          0.1\n",
       "    geospatial_vertical_units:          meters\n",
       "    geospatial_vertical_resolution:     0.1\n",
       "    geospatial_vertical_positive:       down"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open the dataset (chlor-a, fdom/cdom and backscatter) as variable 'D'\n",
    "D = xr.open_dataset('../data/rca/fluor/osb/2021/flort_fullres.nc')\n",
    "\n",
    "# substitute time for 'observation number' as the primary dimension\n",
    "D = D.swap_dims({'obs':'time'})\n",
    "\n",
    "# diagnostic: how many data values present\n",
    "print('Number of data values: after file is opened, before time range narrowed to March 2021: ', len(D.time))\n",
    "\n",
    "# narrow the time range to March 2021 (31 days, 86400 seconds per day: 2678400 samples\n",
    "D = D.sel(time=slice(dt64('2021-03-01'), dt64('2021-04-01')))\n",
    "\n",
    "# diagnostic: Number of data values after time window applied\n",
    "print(\"Number of data values: March 2021 only, compare to 'one per second' total of 2678400: \", len(D.time))\n",
    "\n",
    "# drop NaN values\n",
    "D = D.dropna('time')\n",
    "\n",
    "# diagnostic: Number of data values: after dropping NaNs\n",
    "print('D number of data values after dropping NaN values is the same: ', len(D.time))\n",
    "\n",
    "# rename the three sensor data streams; this makes them easier to read\n",
    "D = D.rename( {'fluorometric_cdom':          'fdom',\n",
    "               'fluorometric_chlorophyll_a': 'chlora',\n",
    "               'optical_backscatter':        'backscatter'})\n",
    "\n",
    "# The line of code below, just the letter 'D', is a reference to the full dataset. This simply produces a \n",
    "#   printout of its structure in terms of four components: Dimensions, Coordinates, Data Variables and Attributes.\n",
    "#       Dimensions:      1: time\n",
    "#       Coordinates:     5: time and depth (of the profiler) are the key coordinates \n",
    "#       Data Variables: 39: 3 fluorometer sensors (chlor-a, backscatter, fdom/cdom) plus 36 additional parameters:\n",
    "#                             Eight qc (quality control) flags:\n",
    "#                                 fluorometric_chlorophyll_a_qc_executed, ...qc_results\n",
    "#                                 fluorometric_cdom_qc_executed, ...qc_results\n",
    "#                                 optical_backscatter_qc_executed, ...qc_results\n",
    "#                                 total_volume_scattering_coefficient_qc_executed, ...qc_results\n",
    "#                             Temperature and Salinity: seawater_temperature, practical_salinity\n",
    "#                             For calculating particulate scattering: seawater_scattering_coefficient\n",
    "#                                 \n",
    "#       Attributes:     68: This is ancillary metadata such as fluormeter model and manufacturer\n",
    "\n",
    "D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25095b25-48ee-42c2-afea-c76bb021f797",
   "metadata": {},
   "source": [
    "# Figure 1: Examine data\n",
    "\n",
    "The starting point is to inspect data from the fluorometer triplet, in sequence chlorophyll-a, backscatter and fdom/cdom. \n",
    "The data time range has been narrowed to March 2021. A second narrowing is done to just March 10 2021.\n",
    "Some aberrant spikes are apparent in the FDOM, addressed subsequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff176e5a-9832-479f-ac09-0d38a086333a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(3, figsize=(12,12), tight_layout=True)\n",
    "\n",
    "axs[0].plot(D.time, D.chlora,       ms=2., marker='^', linestyle='None', color='g')\n",
    "axs[1].plot(D.time, D.backscatter,  ms=2., marker='^', linestyle='None', color='k')\n",
    "axs[2].plot(D.time, D.fdom,         ms=2., marker='^', linestyle='None', color='b')\n",
    "\n",
    "\n",
    "axs[0].set(ylim=(0.0, 2.0), title='Chlorophyll-A, March 2021, OSB Shallow Profiler')\n",
    "axs[1].set(ylim=(0.0, .02), title='Total Backscatter (700nm)')\n",
    "axs[2].set(ylim=(0.0, 3.0), title='FDOM (CDOM)')\n",
    "\n",
    "axs[2].text(dt64('2021-03-01'), 2.6, 'Quantization apparent as banding')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a1de9a2-5e12-45b4-a729-16d398e088eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(2021-03-10, 2.6, 'Quantization apparent as banding')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D1 = D.sel(time=slice(dt64('2021-03-10'), dt64('2021-03-11')))   \n",
    "\n",
    "fig,axs=plt.subplots(3, figsize=(12,12), tight_layout=True)\n",
    "\n",
    "axs[0].plot(D1.time, D1.chlora,       ms=2., marker='^', linestyle='None', color='g')\n",
    "axs[1].plot(D1.time, D1.backscatter,  ms=2., marker='^', linestyle='None', color='k')\n",
    "axs[2].plot(D1.time, D1.fdom,         ms=2., marker='^', linestyle='None', color='b')\n",
    "\n",
    "\n",
    "axs[0].set(ylim=(0.0, 1.0), title='Chlorophyll-A, March 2021, OSB Shallow Profiler')\n",
    "axs[1].set(ylim=(0.0, .01), title='Total Backscatter (700nm)')\n",
    "axs[2].set(ylim=(0.0, 3.0), title='FDOM (CDOM)')\n",
    "\n",
    "axs[0].text(dt64('2021-03-10'), 0.8, 'Structure due to 9 profiling runs per day')\n",
    "axs[2].text(dt64('2021-03-10'), 2.6, 'Quantization apparent as banding')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f17e39-ec4f-4447-b099-69480495273b",
   "metadata": {},
   "source": [
    "## Remark: QC flags\n",
    "\n",
    "OOI provides a [web guide to QC flags](https://oceanobservatories.org/knowledgebase/interpreting-qc-variables-and-results/)\n",
    "to help with their interpretation. Below we examine the first qc flag value for `fluorometric_cdom_qc_executed`\n",
    "This integer value (29) is interpreted as a series of binary flags (right to left) that tell which tests have been \n",
    "applied to the stream data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2ad241d-8df8-4a9d-9c27-b50b513737fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b11101'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The very first 'cdom_qc_executed' flag in the dataset time series, in binary\n",
    "bin(int(D.fluorometric_cdom_qc_executed[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1daa900a-46f7-4663-b044-4ecab1909e4c",
   "metadata": {},
   "source": [
    "Reading bits right to left: This indicates that tests 1, 3, 4 and 5 were completed. \n",
    "From the above web page these are respectively\n",
    "\n",
    "```\n",
    "1. global_range_test\n",
    "2. dataqc_localrangetest: Not completed\n",
    "3. dataqc_spiketest\n",
    "4. dataqc_polytrendtest\n",
    "5. dataqc_stuckvaluetest\n",
    "6. dataqc_gradienttest: Not completed\n",
    "```\n",
    "\n",
    "\n",
    "As we will see, there are two extraneous data spikes in the FDOM/CDOM signal during March 2021. We would need to \n",
    "examine `fluorometric_cdom_qc_results` to see if these were picked up by the above qc tests. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b077560-e621-4622-a4ff-5e07ce3734bf",
   "metadata": {},
   "source": [
    "## Remark: FDOM spikes\n",
    "\n",
    "\n",
    "The FDOM/CDOM spikes in the third chart are clearly out of range. We now use the qc_result Data Variable for FDOM/CDOM to remove those data.\n",
    "In particular, we will use the global_range_test qc flag (lowest order bit) as the discard criterion.\n",
    "For this task we also select out a single day (March 10) at full resolution. At 1.2 samples per second this comes\n",
    "to 72000 samples for one day. The `.sel` method is used to select out one day of data; and the `.where()` method\n",
    "is used to drop data values where the global_range_test result is 0. (Time is UTC.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f8bf26c-5e6e-4fbf-968e-cee297526634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "March 10 number of data values:  71998  compared to 72000 per day at 1.2 seconds per sample.\n"
     ]
    }
   ],
   "source": [
    "# Select out March 10 data to a new Dataset 'D1'\n",
    "print('March 10 number of data values: ', len(D1.time), ' compared to 72000 per day at 1.2 seconds per sample.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b060083c-0049-4101-b737-16cef4d6ed43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd9a9070f70>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D1.fdom.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f6394d-6da6-46e9-860a-21023832c09e",
   "metadata": {},
   "source": [
    "# Part 2: Drop bad data\n",
    "\n",
    "- global range test: implemented here\n",
    "- human in the loop: implemented but no impact on March 2021 dataset\n",
    "- quality assurance for real-time OOI data (QARTOD): Identified but not implemented\n",
    "\n",
    "Quality Control (QC) flags are built into the dataset on a sample-by-sample basis. These correspond to a sequence of qc tests, of which only the first \n",
    "is used to filter bad data. This qc filter is called the 'global range test'. \n",
    "\n",
    "\n",
    "Human In The Loop (HITL) data are available through an annotations file. <needed: directions to download as CSV>\n",
    "\n",
    "\n",
    "QA for Real Time OOI Data (QARTOD) are deferred at this time as the implementation within OOI is not completed (August 2022). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56fbcf80-07dc-47cd-91ce-4a18097715b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data sizes for source data and discards for chlora, backscatter and fdom: 71998 30445 71998 71987\n"
     ]
    }
   ],
   "source": [
    "# Some example code is given (commented out) to indicate related operations.\n",
    "#\n",
    "# Code to show the first five qc results flags for FDOM/CDOM: 29, as seen above as well. \n",
    "# D1.fluorometric_cdom_qc_results[0:5]\n",
    "# \n",
    "# Example of removing spike values using a threshold of 100; uses the .where() method.\n",
    "# Dprime = D1.where(D1.fdom > 100., drop=True)\n",
    "\n",
    "# Produce data subsets for March 10 that discard data where global_range_test is False. \n",
    "#   '% 2' is modulo 2, a simple way of selecting odd flag values: The low or 1-bit is True\n",
    "DA = D1.where(D1.fluorometric_chlorophyll_a_qc_results % 2, drop=True)\n",
    "DB = D1.where(D1.optical_backscatter_qc_results % 2,        drop=True)\n",
    "DC = D1.where(D1.fluorometric_cdom_qc_results % 2,          drop=True)\n",
    "print('Data sizes for source data and discards for chlora, backscatter and fdom:', len(D1.time), len(DA.time), len(DB.time), len(DC.time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2f29f1-619a-4477-80d1-0df3f0f53d7c",
   "metadata": {},
   "source": [
    "## Remark: Other qc flags\n",
    "\n",
    "The other qc tests (higher order bits in the `_qc_results` flag) point to data 'of interest'. They are not a basis for filtering out\n",
    "more data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ca487a-3c1d-4795-8e6d-29f70d5cacff",
   "metadata": {},
   "source": [
    "## Human In The Loop annotations (HITL)\n",
    "\n",
    "\n",
    "OOI provides annotation files through the Data Explorer. \n",
    "[Here is an example](https://dataexplorer.oceanobservatories.org/#metadata/104289/station/187/sensor/annotations).\n",
    "Annotations can be downloaded as a table (CSV file) and\n",
    "read into memory as shown below. \n",
    "\n",
    "\n",
    "The procedure given here reads the annotations file (staged with the data file) and scans the rows, \n",
    "specifically all of the annotation date ranges (one per row) to identify overlaps with the date range\n",
    "of interest, in this case March 1 - April 1 2021. Here there are no overlaps so the \n",
    "end result is 'nothing to be done from HITL'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf09de4d-7a67-4cee-aa3f-6f9ec77781ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "No overlap between annotation record and time range of interest.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "annot = pd.read_csv('../data/rca/fluor/osb/2021/annotations.csv')\n",
    "\n",
    "dt64_MAR_1_2021 = dt64('2021-03-01')\n",
    "dt64_APR_1_2021 = dt64('2021-04-01')\n",
    "\n",
    "found_overlap = False\n",
    "overlap_rows = []\n",
    "\n",
    "for i in range(annot.shape[0]):\n",
    "    if TimeOverlap(dt64_MAR_1_2021, dt64_APR_1_2021, \\\n",
    "                   dt64(annot['beginDate'][i]), dt64(annot['endDate'][i])):\n",
    "        found_overlap = True\n",
    "        overlap_rows.append(i)\n",
    "\n",
    "if found_overlap: \n",
    "    print()\n",
    "    print()\n",
    "    print(len(overlap_rows), \"time overlaps: annotations vs time range of interest. Rows:\")\n",
    "    print(overlap_rows)\n",
    "    print()\n",
    "    print()\n",
    "else: \n",
    "    print()\n",
    "    print()\n",
    "    print(\"No overlap between annotation record and time range of interest.\")\n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e76964-7f48-4dec-a1c8-be21f675b633",
   "metadata": {},
   "source": [
    "## Remark: Examining the 'annotations' data frame\n",
    "\n",
    "The dimensions of the data frame can be printed using `print(annot.shape)`. The cells below illustrate\n",
    "remarks from 'humans in the loop' over the course of operations for OOI arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d41a5f2-225f-47b6-bd11-dbeacf048bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>id</th>\n",
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       "      <th>source</th>\n",
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       "      <th>parameters</th>\n",
       "      <th>beginDate</th>\n",
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       "      <td>SF01A powered down on 3/21/2018 due to oil lea...</td>\n",
       "      <td>False</td>\n",
       "      <td>friedrich.knuth@rutgers.edu</td>\n",
       "      <td>not_operational</td>\n",
       "      <td>[]</td>\n",
       "      <td>2018-03-21T00:13:00</td>\n",
       "      <td>2018-06-24T10:40:02</td>\n",
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       "      <td>friedrich.knuth@rutgers.edu</td>\n",
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       "      <td>On Oct 31st the Southern Line was powered down...</td>\n",
       "      <td>False</td>\n",
       "      <td>m.vardaro@gmail.com</td>\n",
       "      <td>not_operational</td>\n",
       "      <td>[]</td>\n",
       "      <td>2020-11-02T00:11:32</td>\n",
       "      <td>2020-11-02T23:41:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1248</td>\n",
       "      <td>RS01SBPS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1417981500000</td>\n",
       "      <td>1418688000000</td>\n",
       "      <td>Note: PFE down. HVPS1 MOV explosion, 800A brea...</td>\n",
       "      <td>False</td>\n",
       "      <td>knuth@marine.rutgers.edu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>2014-12-07T19:45:00</td>\n",
       "      <td>2014-12-16T00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    id   subsite   node  sensor    method  stream        beginDT  \\\n",
       "0           0  1369  RS01SBPS  SF01A     NaN       NaN     NaN  1521591180000   \n",
       "1           1   319  RS01SBPS    NaN     NaN       NaN     NaN  1516330801000   \n",
       "2           2  2524  RS01SBPS    NaN     NaN  streamed     NaN  1604275892000   \n",
       "3           3  1248  RS01SBPS    NaN     NaN       NaN     NaN  1417981500000   \n",
       "\n",
       "           endDT                                         annotation  \\\n",
       "0  1529836802667  SF01A powered down on 3/21/2018 due to oil lea...   \n",
       "1  1516388401000  A brief violent storm caused loss of power to ...   \n",
       "2  1604360481000  On Oct 31st the Southern Line was powered down...   \n",
       "3  1418688000000  Note: PFE down. HVPS1 MOV explosion, 800A brea...   \n",
       "\n",
       "   exclusionFlag                       source           qcFlag parameters  \\\n",
       "0          False  friedrich.knuth@rutgers.edu  not_operational         []   \n",
       "1          False  friedrich.knuth@rutgers.edu  not_operational         []   \n",
       "2          False          m.vardaro@gmail.com  not_operational         []   \n",
       "3          False     knuth@marine.rutgers.edu              NaN         []   \n",
       "\n",
       "             beginDate              endDate  \n",
       "0  2018-03-21T00:13:00  2018-06-24T10:40:02  \n",
       "1  2018-01-19T03:00:01  2018-01-19T19:00:01  \n",
       "2  2020-11-02T00:11:32  2020-11-02T23:41:21  \n",
       "3  2014-12-07T19:45:00  2014-12-16T00:00:00  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the annotation data frame by referencing the first four rows, as:\n",
    "annot[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2dc6b86e-3933-48c3-9c75-9e0a69b052ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2018-03-21T00:13:00\n",
       "1    2018-01-19T03:00:01\n",
       "2    2020-11-02T00:11:32\n",
       "3    2014-12-07T19:45:00\n",
       "Name: beginDate, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine only a particular column of the data frame\n",
    "annot['beginDate'][0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc245007-092c-4f0f-b2f9-ad9a36cd4cf2",
   "metadata": {},
   "source": [
    "## Remark: sample timing\n",
    "\n",
    "The 'observation' coordinate is a simple counter. The 'time' coordinate consists of timestamps.\n",
    "The chart below shows sample time (vertical axis) versus observation number (horizontal axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc72050c-07ae-4859-b5a7-55317d08ac3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd9a73ad8e0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(5, 5), tight_layout=True)\n",
    "axs.plot(DA.obs, DA.time, color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "312adc8b-d6ea-408c-a76b-2211a3b4ea27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This code drops all 39 Data Variables except those listed ('chlora' and so on)\n",
    "DA = DA[['chlora']]\n",
    "DB = DB[['backscatter', 'seawater_scattering_coefficient']]\n",
    "DC = DC[['fdom']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b729e71-c688-4703-a6b4-4d7ba959c702",
   "metadata": {},
   "source": [
    "# Figure 3: Manual Intervention\n",
    "\n",
    "An example would be application of a median filter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090efc81-a253-431e-8071-866cf76a942b",
   "metadata": {},
   "source": [
    "# Figure 4: Particulate backscatter\n",
    "\n",
    "Calculated seawater scattering coefficient is available within the dataset. It is derived by OOI using \n",
    "from temperature and salinity. This is subtracted from total backscatter to arrive at particulate backscatter.\n",
    "\n",
    "Here is the verbatim pseudo-code: \n",
    "\n",
    "\n",
    "```\n",
    "Total optical backscatter ('bb', 700 nm): An OOI level 2 product\n",
    "Particulate backscatter 'bb_p' is calculated as follows:\n",
    "\n",
    "FOR i: over bb values of interest, e.g. a time series\n",
    "    find matching / minimal distance temperature and salinity values for sample i\n",
    "    calculate seawater backscatter 'bb_sw' as a function of this temperature and salinity per Zhang et al. (2009) \n",
    "    remove seawater backscatter from total backscatter to arrive at particulate backscatter\n",
    "    bb_p(i) = bb(i) - bb_sw\n",
    "```\n",
    "\n",
    "The OOI-provided full-resolution FLORT data includes a pre-calculated seawater scattering coefficient. Hence, \n",
    "here is the practical implementation:\n",
    "\n",
    "```\n",
    "FOR LOOP { total backscatter time series }\n",
    "    Establish bb and bb_sw(T, S) values\n",
    "    particulate backscatter bb_p = bb - bb_sw\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "17bba3ce-70de-4d51-b512-4e11fada0009",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                          (time: 71998)\n",
       "Coordinates:\n",
       "    obs                              (time) int32 3275959 3275960 ... 3347956\n",
       "    lon                              (time) float64 -125.4 -125.4 ... -125.4\n",
       "    lat                              (time) float64 44.53 44.53 ... 44.53 44.53\n",
       "    depth                            (time) float64 189.3 189.3 ... 189.3 189.3\n",
       "  * time                             (time) datetime64[ns] 2021-03-10T00:00:0...\n",
       "Data variables:\n",
       "    backscatter                      (time) float64 0.0007715 ... 0.0009207\n",
       "    seawater_scattering_coefficient  (time) float64 0.0006393 ... 0.0006392\n",
       "    particulate                      (time) float64 0.0001322 ... 0.0002815\n",
       "Attributes: (12/68)\n",
       "    node:                               SF01A\n",
       "    comment:                            \n",
       "    publisher_email:                    \n",
       "    sourceUrl:                          http://oceanobservatories.org/\n",
       "    collection_method:                  streamed\n",
       "    stream:                             flort_d_data_record\n",
       "    ...                                 ...\n",
       "    geospatial_lon_max:                 -125.3896636\n",
       "    geospatial_lon_units:               degrees_east\n",
       "    geospatial_lon_resolution:          0.1\n",
       "    geospatial_vertical_units:          meters\n",
       "    geospatial_vertical_resolution:     0.1\n",
       "    geospatial_vertical_positive:       down</pre><div class='xr-wrap' hidden><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-5cdf883a-e05e-4874-98ea-354ee034db80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5cdf883a-e05e-4874-98ea-354ee034db80' 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>: 71998</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-419d4aeb-fea1-435d-a699-77860659d399' class='xr-section-summary-in' type='checkbox'  checked><label for='section-419d4aeb-fea1-435d-a699-77860659d399' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>obs</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3275959 3275960 ... 3347955 3347956</div><input id='attrs-d1dff24a-1a55-43a4-942e-e7ba9f5d8012' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1dff24a-1a55-43a4-942e-e7ba9f5d8012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2239bdf-a71d-4ad8-88b5-751f2412e5d1' class='xr-var-data-in' type='checkbox'><label for='data-a2239bdf-a71d-4ad8-88b5-751f2412e5d1' 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([3275959, 3275960, 3275961, ..., 3347954, 3347955, 3347956],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-125.4 -125.4 ... -125.4 -125.4</div><input id='attrs-a7408bd7-0518-490e-abbe-722a0c061a02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7408bd7-0518-490e-abbe-722a0c061a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8abc033-6831-4288-9934-20873cd99ac6' class='xr-var-data-in' type='checkbox'><label for='data-c8abc033-6831-4288-9934-20873cd99ac6' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-125.3896636, -125.3896636, -125.3896636, ..., -125.3896636,\n",
       "       -125.3896636, -125.3896636])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.53 44.53 44.53 ... 44.53 44.53</div><input id='attrs-c899a4d6-2fb2-4754-ba0c-6c57dcd77f46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c899a4d6-2fb2-4754-ba0c-6c57dcd77f46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97fded60-9534-408a-803c-c8fa7bcd6ccc' class='xr-var-data-in' type='checkbox'><label for='data-97fded60-9534-408a-803c-c8fa7bcd6ccc' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.52894899, 44.52894899, 44.52894899, ..., 44.52894899,\n",
       "       44.52894899, 44.52894899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>189.3 189.3 189.3 ... 189.3 189.3</div><input id='attrs-9b2e76a3-622a-4957-9a80-6e4c9d68edf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b2e76a3-622a-4957-9a80-6e4c9d68edf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fb50c99-aee3-4406-9988-a614ba71c532' class='xr-var-data-in' type='checkbox'><label for='data-2fb50c99-aee3-4406-9988-a614ba71c532' 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>comment :</span></dt><dd>Depth (m) calculated from pressure (dbar) and latitude.</dd><dt><span>long_name :</span></dt><dd>Depth calculated from pressure</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([189.30768538, 189.31800719, 189.32793131, ..., 189.28052152,\n",
       "       189.27477021, 189.2711653 ])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-10T00:00:00.946225152 .....</div><input id='attrs-ae9a98d4-6135-416d-904c-7e20c37b9bb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae9a98d4-6135-416d-904c-7e20c37b9bb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eddb09b-3314-4881-811a-464c53a6c392' class='xr-var-data-in' type='checkbox'><label for='data-1eddb09b-3314-4881-811a-464c53a6c392' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-10T00:00:00.946225152&#x27;, &#x27;2021-03-10T00:00:02.074469376&#x27;,\n",
       "       &#x27;2021-03-10T00:00:03.202857472&#x27;, ..., &#x27;2021-03-10T23:59:56.876743680&#x27;,\n",
       "       &#x27;2021-03-10T23:59:58.005145088&#x27;, &#x27;2021-03-10T23:59:59.133545984&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57fc7b95-0129-4570-9c11-0d99bf77d42e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57fc7b95-0129-4570-9c11-0d99bf77d42e' class='xr-section-summary' >Data variables: <span>(3)</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>backscatter</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0007715 0.0008014 ... 0.0009207</div><input id='attrs-ed2ad981-a163-4990-b1ab-0e62ab3c8142' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2ad981-a163-4990-b1ab-0e62ab3c8142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f45f95a-b0ea-4cb8-ba25-8ead07334428' class='xr-var-data-in' type='checkbox'><label for='data-2f45f95a-b0ea-4cb8-ba25-8ead07334428' 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>comment :</span></dt><dd>Optical Backscatter (Red Wavelengths) is a measure of the amount of red light (630-740 nm wavelengths) scattered in the backward direction due to suspended matter within seawater, providing a proxy for turbidity and suspended solids.</dd><dt><span>long_name :</span></dt><dd>Optical Backscatter</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>data_product_identifier :</span></dt><dd>FLUBSCT_L2</dd><dt><span>units :</span></dt><dd>m-1</dd><dt><span>ancillary_variables :</span></dt><dd>total_volume_scattering_coefficient seawater_temperature practical_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0.00077153, 0.00080137, 0.00075661, ..., 0.00092074, 0.00108487,\n",
       "       0.00092074])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seawater_scattering_coefficient</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0006393 0.0006393 ... 0.0006392</div><input id='attrs-a9e485ea-d0bd-4167-9802-904f4a11154c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9e485ea-d0bd-4167-9802-904f4a11154c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccc44e14-cfa2-4c64-845f-c5af30de3143' class='xr-var-data-in' type='checkbox'><label for='data-ccc44e14-cfa2-4c64-845f-c5af30de3143' 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>comment :</span></dt><dd>Total scattering coefficient of pure seawater.</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>long_name :</span></dt><dd>Total Scattering Coefficient of Pure Seawater</dd><dt><span>units :</span></dt><dd>m-1</dd><dt><span>ancillary_variables :</span></dt><dd>seawater_temperature practical_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0.00063933, 0.00063933, 0.00063933, ..., 0.00063921, 0.00063921,\n",
       "       0.00063921])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>particulate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001322 0.000162 ... 0.0002815</div><input id='attrs-e2cb3f0e-2e6f-456a-83d6-6f347ad98997' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2cb3f0e-2e6f-456a-83d6-6f347ad98997' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37b2903e-f927-4b55-af29-38d3c9f4e27e' class='xr-var-data-in' type='checkbox'><label for='data-37b2903e-f927-4b55-af29-38d3c9f4e27e' 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([0.0001322 , 0.00016204, 0.00011728, ..., 0.00028153, 0.00044566,\n",
       "       0.00028153])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-660a03ab-e93d-45c7-b67a-99366e318339' class='xr-section-summary-in' type='checkbox'  ><label for='section-660a03ab-e93d-45c7-b67a-99366e318339' class='xr-section-summary' >Attributes: <span>(68)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>comment :</span></dt><dd></dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>stream :</span></dt><dd>flort_d_data_record</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>creator_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>date_modified :</span></dt><dd>2021-07-26T16:57:38.049177</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>date_created :</span></dt><dd>2021-07-26T16:57:38.049175</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record</dd><dt><span>requestUUID :</span></dt><dd>6d5aedaa-2f70-48c6-a8f1-a5048804922f</dd><dt><span>contributor_role :</span></dt><dd></dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>feature_Type :</span></dt><dd>point</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>license :</span></dt><dd></dd><dt><span>contributor_name :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>6d5aedaa-2f70-48c6-a8f1-a5048804922f</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.18.0 for RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record</dd><dt><span>sensor :</span></dt><dd>3A-FLORTD101</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>acknowledgement :</span></dt><dd></dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>history :</span></dt><dd>2021-07-26T16:57:38.049133 generated from Stream Engine</dd><dt><span>Manufacturer :</span></dt><dd>WET Labs</dd><dt><span>ModelNumber :</span></dt><dd>ECO Triplet-w</dd><dt><span>SerialNumber :</span></dt><dd>1130</dd><dt><span>Description :</span></dt><dd>3-Wavelength Fluorometer: FLORT Series D</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-58322-00003</dd><dt><span>Notes :</span></dt><dd>Not specified.</dd><dt><span>Owner :</span></dt><dd>University of Washington</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2021-07-23T11:39:11.989000</dd><dt><span>time_coverage_start :</span></dt><dd>2021-01-23T12:00:00.228181</dd><dt><span>time_coverage_end :</span></dt><dd>2021-04-14T11:59:59.167518</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1.20S</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.52894899</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.52894899</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.3896636</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.3896636</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                          (time: 71998)\n",
       "Coordinates:\n",
       "    obs                              (time) int32 3275959 3275960 ... 3347956\n",
       "    lon                              (time) float64 -125.4 -125.4 ... -125.4\n",
       "    lat                              (time) float64 44.53 44.53 ... 44.53 44.53\n",
       "    depth                            (time) float64 189.3 189.3 ... 189.3 189.3\n",
       "  * time                             (time) datetime64[ns] 2021-03-10T00:00:0...\n",
       "Data variables:\n",
       "    backscatter                      (time) float64 0.0007715 ... 0.0009207\n",
       "    seawater_scattering_coefficient  (time) float64 0.0006393 ... 0.0006392\n",
       "    particulate                      (time) float64 0.0001322 ... 0.0002815\n",
       "Attributes: (12/68)\n",
       "    node:                               SF01A\n",
       "    comment:                            \n",
       "    publisher_email:                    \n",
       "    sourceUrl:                          http://oceanobservatories.org/\n",
       "    collection_method:                  streamed\n",
       "    stream:                             flort_d_data_record\n",
       "    ...                                 ...\n",
       "    geospatial_lon_max:                 -125.3896636\n",
       "    geospatial_lon_units:               degrees_east\n",
       "    geospatial_lon_resolution:          0.1\n",
       "    geospatial_vertical_units:          meters\n",
       "    geospatial_vertical_resolution:     0.1\n",
       "    geospatial_vertical_positive:       down"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DB = DB.assign(particulate = lambda DB: DB.backscatter - DB.seawater_scattering_coefficient)\n",
    "DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a69da63-03fd-490b-be11-7ca879816f66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=DB.particulate.plot(ylim=(0.0, 0.02))[0]\n",
    "fig.set(color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce3cab82-f276-4a40-b01c-dfc10e5c2613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=DB.backscatter.plot(ylim=(0.0, 0.02))[0]\n",
    "fig.set(color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7bfcbee7-a81d-4336-858b-a55843a4ca28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=DB.seawater_scattering_coefficient.plot()[0]\n",
    "fig.set(color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5ec21205-6637-479f-bc8a-d518a313d09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BMfnYFAKxaWuziY3Edsl3WqXcevc2piiD+MEQGwMDg0AQYlNMio1OEPJNFrygm6LipNiUl3O6wjJFJRL2OzDExqAnMMTGwMAgEGQwLSZiUwiKjW6KipNiU1HBf2EpNqLWyP0NDLKFITYGBgaBUIzExig22aGzM3xiI/41gFFsDHoGQ2wMigqJBO81YxA+ipHYGMUmOwRRbDI1RRliYxAWDLExKCr8/OfAoEHA1q35TknxQYiMcR7OLeKo2ERtijLExqAnMMTGoKgwezYPUHfeme+UFB+M83B+4KbY5LvsoyA2umJjfGwMegJDbAyKCps38/GNN/KbjmKEMUXlHt3dnEanYpPvtOrEpquL0ykwpiiDfMMQG4OiwhVX8PH55/ObjmKEMUXlHjLAx1mxAZLLzpiiDPINQ2wMigrSIeqdpEE4kMG0vBwoKcn/4BoG4m6KchKbOCo28llgVkUZ5BuG2BgUFaSDNR1j+BAfm7Iy/isGYiP1hSj/ZMENQtD1vaKA/Jd9EGKTqSnKxLExCAuG2BgUFYTQGGITPmQwLSZiI2Smd+94Ehs/xUYp4Ne/Bk47DWhszG269MjD8llgFJvcY948YMqU3NeDuMIQG4OignSwxhQVPoqR2Oh7HsWR2PgpNqtWAf/zP8D8+cDMmblNlzFFxQuf/zywbFnu60FcYYiNQVHBKDbRoZiJTXV1PImNn2IjgSiVAhYsyG26nMRGb289dR5221jTwB/btvEx1/UgrjDExqCoYHxsooMQmdLS4iE2QmbiSmz8FJsDB/j/++8H6upymy59SwUgHB8bUWz69jXtN1NMnQocdlju60FcUZbvBBgYhAmzKio6FLPzcO/eyaaQuEDqsduWCkIU8pHuKE1RhthkjpYWU2Y6jGJjUFQwik10MKao3MPLFKUrNsVCbNraOIxATY1pv5nCEJtkGMXGoKhgfGyiQzESG90UFUe/Di9TVGenXcfjTmxKSoIrNlVVnNc4vos4Q4iNUhy64FCHUWwMigpmVVR0KEYfm7ivivJTbITQxJ3YZKLYCLEp1IlJRwdw8cXASSfldul1Swtva1EMbTIM5JzYENH5RPQuEa0joltdfici+qX1+3IiOiHdtUR0GRGtJKJuIprmuN9xRPS69fvbRFQZbQ4N8gmj2EQHp49NHIlApihU5+HOzviborJxHq6s5HsWavvdsAGYOxdYvDi3S69bWvhYqOUWNnJKbIioFMAsABcAmAzgCiKa7DjtAgDjrb9rADwQ4NoVAD4B4FXH88oA/BHAdUqpowGcCSCG3ZdBWDA+NtGhGE1RhazYxJ3YZOM8XOimKHknuVyCn0iYCZ0TuVZsTgKwTilVr5TqADAbwMWOcy4G8JhivAGgHxEN87tWKbVKKfWuy/M+AmC5UmqZdd4upVQimqwZxAFmVVR0KFZiU1bGA3QciY3fcu98m6LKy40pygl5Fz/9ae6WXotaAxRuuYWNXBObEQA2a5+3WN8FOSfItU4cBUAR0XNE9BYR3eJ2EhFdQ0SLiWjxjh07AmTDIK4wik10KEYfm85OHqDLy+NNbNwC9OVLsUkk2J8jCsWmsrI4iE0uJ1aG2KQi18TGzV/bWd29zglyrRNlAKYD+Ix1vJSIzkm5iVIPK6WmKaWmDR48OM0tDeIMI8lGh2JVbCoq4ktspB77BejLNbEREhOVKaqQfWzkneQy/YbYpCLXxGYLgFHa55EA3g94TpBr3Z73ilJqp1LqAIBnAZyQ5hqDAoYxRUWHYg3QJ8QmkUDawTfXkHosBCIOik1UxKatzVZsCtXHxig28UCuic0iAOOJ6AgiqgBwOYC5jnPmArjaWh11CoB9SqmGgNc68RyA44iot+VIfAaAd8LMkEF8oJQ96zYNPHwUo2Kjm6Lkc5zQ3s6DvcQmiYOPjZRRFKuiisXHxig2+UVOA/QppbqI6CYw4SgF8Dul1Eoius76/UGwqvJRAOsAHAAww+9aACCiSwH8CsBgAM8Q0VKl1HlKqT1E9HMwKVIAnlVKPZPDLBvkEHrnahp4+NCJTXl5codaqNAVG8DeAykuaGuzzVBAPJZ7Z6rYBA3QJ87DhWyKMopNPJDzyMNKqWfB5EX/7kHtfwXgxqDXWt/PATDH45o/gpd8GxQ59M7VmKLCR1jOw0uWAGedBbz2GnDcceGlLxsUimIjIOLyL1YfGzFFdXayg3JJgYWQzbePjen3GAVWbQwMvKF3JmbmEj7C8rH5zneApibg9tvDS1u2cFNs4gSnYgPYjs75MkXpxEbKLew4NkD83kUQGMUmHjDExqBoYExR0SIsH5uVK/n49tvhpKsnKARiU+mIlS5lHwfFhoiPYcexAQqzDRtiEw8YYmNQNNAbtZFkw0dYxOab3+TjXXeFk66eoBBMUV6KTRyIjRx7SmyUSt5SASjMQTrfpqhCLLMoYIiNQdHAKDbRIiwfm/37+djcHE66eoJCVmyE0HR02GbCXEDamZRZOmITxHlYfGp0xaYQl3wbxSYeMMTGoGhgfGyihRCZq6++HI8/XoZt21x9/NPCEJvgcDoPA6mKDZDbgTQKxUbSXyymKKPY5BeG2BgUDaRzLSkxpqgowKrAbjz55F+gVALNzQ9ldZ99+/gYB2ITd1OUm/Ow7mNTXc3f5dIcFZTYZBLHRtJfLKaoXCs2EueoEMssChhiY1A0kEbdp49p4FGgqwsoKRE2UoKqqmuzuo9RbILDS7Fpa+O0DhjA38WR2BjFJjdoaQH69cv9c+MMQ2wMigbSuRpiEw26uoCyMnbmmD795+jde1ZW94kTsSlUxUbKcOBAPuaS2OiRh+Wot7dsiI2u2Bgfm8zQ0mITXKNUMwyxMSga6IqNaeDhgxUbdrQh6uqx83BTU0gJ6wHirth4OQ87iU2cfWyCOA8LISh0xSZfq6L69GGn/iie+8gjfP+GhvDvHRUMsTEoGkijrq0tzE4x7kgkgNJSYTM9JzZxUGziTmy8lntLGRajKaqQfWzypdhUV0e3x9YDD/Ak5HvfC//eUcEQG4OigTFFRYuurnCIjXEeDo6gik2hExtjisoeURObLVv4uGBB+PeOCobYGBQNpFH37WtMUVEgLGITR8VGBum4ERsvxUbIYbEoNsYUlT2iJjaXXsrHOa67McYThtgYFA2kc62tZbNJLoOWHQpgHxsp1C4kEkgbJt8NcSM2hazY5JPYOAP0rVoFHHUUsGMHkKmPTS5XRW3YAIwfD2zdGv695T10deWu//EjNuvX8zvpiX+M5EnqXCHAEBuDooHuPKx/NggHTsUGyLzzbm+330sciE1nZ3x9bBIJLnMhNtdffz3KyspQX3/jwXKPkylqxgxg7Vrgxz8GemKKitrH5oYbgHXrgJtvDv/e+nvIVf/jR2yuu47fyde+lv39JU9xcPYPCkNsDIoGuo8NYMxRmeCdd4CTTwYaG73PcToPA5lvqyCzvpKS6InNvHnAaaf558npPJzOr2P+fGD6dP97hgUZpETBePjhh5FIJLB1qx0YMU7EZtUq/sybnPY8jk1UPjayCevSpeHeVyk2RdXU8Odc9T9+xObdd/n41lvZ398oNgYGeYRRbLLHJz8JvPkmMHOm9zn6cu+eEpuhQ6MlNp2dwFlnMRHxylN3N5O1TExR55zDTpR+5RQWZGAUxebUU08FAAwffvnBc+JiimpttaPfzpgBxNnH5n//l4+sLIUH2e+qf3/+nIv+p7vbjkBdWZlKpmTD2YeyCxIOwCg2BgZ5hVOxMcQmODZu5KPfygfdFKVUz4jN8OE8EEQ1K9+9Gwd9gLzypAeaC0JsEgmuU373DBNOxea8884DAEycePvBc/Kl2JSW8h/A5bdhg+3QvG0bkG0cm1yYovbu5WPYxFryIMQmF4qNPNNLsZE06PuKZfsMQ2wMDPKA9naeNcr+OcYUFQzd3VxupaX+knVXF0BkOw/Ld5lAiM2IEXyMSrXZvZuP11wD1NW5n6ObVIIQG9ls8LzzvO+ZDnv2MPE+7rj05iynYtNkjSylpXYi86XYCPkA+H+dtDiJTSamKH25d6ERGyEP6bY3uPpqYNAg7/f/4otsql20KP0zpU56ERupF2EQG2OKMjDIAzo6uHEX8nLRfGDLFu74Egn/ATKRsE1Rothk6myrKzZAdMRm167095e0BzVFySCyfXv26Vq/nme+K1akN2c5FZtmKzO2OZBXAJaX535LBSexAZis9uuXHbFpbeV8lJba94tKzduzh4/5Umyee47rp9f7/9GPmCjedVf6Z6YjNkaxMTAocLS3c6coM1xDbIJh9Wr7f7/OS/exydYUJeaKXCk2fvnJVLGRtPaE2MgsPYg5y0uxKSmxE1lZyX4p+VRshHiddhpw2GHZm6KqqmCdz++j0BQbJ7HxSr883+v9S3t8++30z8wlsTGKjYFBHuBUbIwpKhhkNQsQPbFxKjZRzQJFsQlCbLJRbLKJ3wPYxObTn05vzpJByklsiLjQq6rYhJhvYiP/T58ODBmSvSlKiI3cs9CJjVf/07u3v9n3K1/hozj++iEXpijJh1FsfEBE5xPRu0S0johudfmdiOiX1u/LieiEdNcS0WVEtJKIuolomss9RxNRMxF9PbqcGeQbotgYU1Rm0BUbv1mZHqCvp8QmasUmCLHJ1HlY0trZaStPmUKITZDl4jKgSH22fWy40Hv35u+zITaLFgFHHpld4DYnsZF2NmkSExseRDMjNrt2MeGQcunVKzpTVNQ+Nn7EprOTn59IeNdNqVtiMtPxox8B06bZ5WQUG3fklNgQUSmAWQAuADAZwBVENNlx2gUAxlt/1wB4IMC1KwB8AsCrHo++F8A/w8uJQRwhio0xRWWG1atZ/gfSKzaiFvSE2JSX26t5ojZF+d1fN0WVlPBfEMUGyN4clQ2xkfosPjZEnMieEJvrrgPeew+47bbMrgPsaM2CtWv5+Ne/MrFhKMsEFcwUtXgxt1fxO4lqewDAJjZhKxBBTFFSLwF34gLYBML5e1sbcOutwJIldjnpxKay0pvY6HU3Uxgfm/Q4CcA6pVS9UqoDwGwAFzvOuRjAY4rxBoB+RDTM71ql1Cql1LtuDySiSwDUA1gZTZYM4gKnYlOopqi//jV9YLkwsWoVcPTR/L/frEx3Hu7uzt7Hpk8fdnoF4qHYyCBdXh5MsQFyQ2yczsO2j41tipJjpsRm3To+LlwY/Jrnn+fw/HV17HAuedi8mY+vv55MbDJRbKQ8xe+kWE1RO3fa/+skR4e0QefvshklYJeTU7FxPrOnpqiuLruNG8XGGyMAbNY+b7G+C3JOkGuTQETVAL4J4M40511DRIuJaPEO3ujEoAAhGwYWsilq2zbgssv8A8uFiT17+JknncSfc6HY9OljR2fNJ7FxRtDNBbER08++fenJiJfzMNBzxUZCIvzmN8HO//e/gQsuYHVmzRoeKKV+1tWxz1FdnR+x8fdLGj4cuOIK2+8oKsWmuzu63eWDLPfWh5dMFZtNm/j4mc/Y5RS1KUqvV0ax8Qa5fOes7l7nBLnWiTsB3KuU8q3CSqmHlVLTlFLTBg8enOaWhyY6OuLP2IvBFCUDZpBVMz3x9RBIyHUhNul8bITYZKLYJBL2LDlXxEY3RXkNqJkSm7BMUWL2S6fapFvunS2x2bbNJlheg6uOBQuAiy+2y0nS41Y/3YiNmKS6u7276+3b9Wuj87HZv9+uD0Hq3oEDwcs2U8XGq+y9fGwkiKZeD92IjV7fwyQ2ce//deSa2GwBMEr7PBLA+wHPCXKtEycD+DERbQDwFQDfIqKbMk+2wa23chj8XJlHskExmKJEafjGN9KvmvnmN3l5bU927pUVUcEVm8ydh3/6Uw5IVl/PnWPfvrZiELViI/v3uCHXpiiluP0cdRR/ziRAn1IqRbHJ1hSl75GUjtgoBXzqU6x0LFzIn9vabIXGCT/FxovYtLVxvXASmygmJkKwgWB179JLgdGjg/V7QXxsghCbdIqNnm4nsVEquU321BQl19fUGMXGD4sAjCeiI4ioAsDlAOY6zpkL4GprddQpAPYppRoCXpsEpdRpSqnDlVKHA7gPwA+UUr8OOU+HBJ5+miv5nb5GvfyiGAL0yYAcRIl56inO4y23ZP+81XwAY0kAACAASURBVKuZDB5zDC8djsLH5o9/5GtvucVWbEpLeUCOWrEBvDvkbBWbfv2yIzbNzTzATJ3KnzNRbNra2pCwtvTuqWKjExsvPw9BYyOwdSunxbnf0DnnnIOSkhLceOONB7/zU2yUh3Qm5hmd2ETlYyPEpn//YHVv/nwmI0HMwkFWRfXEx0aIjVOxKSnx7vfCUmyGDOF2lG2Yg1wjp8RG8TTvJgDPAVgF4Aml1Eoiuo6IrrNOexbs7LsOwCMAbvC7FgCI6FIi2gLgVADPENFzOczWIQGOTQG89lp+0+GHYgjQJ52ZPrP0ggyM8+dn/7ylS4GyMu5wa2vTKzaylUIi0al95w/pkBcutJ2HAX5epsRm1ixWfOSeXti1i9UsID2xyUSx6d2blctsiI28r6DERldsmpIy0TMfm7o6YORI/j+dYiOqjJtp9OWXX4ZSCg9pjCcbxUbKMogpav9+dnSfOjU79Vja1ahR6etea6tNIoLsDRZUsZFo0WEpNtXVPCmJktgcdlj6yORxQs7j2CilnlVKHaWUGquUutv67kGl1IPW/0opdaP1+7FKqcV+11rfz1FKjVRK9VJKHaaUOs/luXcopX6aizwWI6Qznj07v+nwgyg2MgMvZFNUOsVG9ncCeO+ZbPHWW7YjaJ8+0fjYTJjAxwsvtBUbgOXtTInN/ffzPf77v73PaW3lv8MP589exEaPYwMEU2xqangA7gmxOfZYfneZEJvmpILq2aqopUs5FkrfvumJjag7e/emmp7Gjx8PALj22msPftevHxPlMIiNGzGoqwPeeQdYtiw753ohNiNH+vtfAbyxJ8D1wm8PNUFrK9clCR/g1v/s2AEMHszkJx2xaW5Oro9ePjZi1nUjNmGZouTdFIo5ykQeNggEaWzODn3VKmDYMHv5aD4hio1bSPa2NuCqq4BTT423n5AoNumITUOD3XFmuyEjkBzePRPFJhNis3Wrnc6eEhu514IFPIN0g5ThmDF89HpGNquiekJsxBdq5Ege3IKYooiYKISl2DQ386qmKVN4cE1nilq6lAP59e2b+tvUqVNRUVGBWbNmHfyupITzxnFsSqzv/E1RmRAbCSaZ7Q7rumKTSPhPftav52NnZ7AB/cABOxq029JrgBWbQYO8iY0E7pM4T5JepfwVG8BdqQ7TFAUUjgOxITYGgeBFbL72Ne6gv/a13KfJCVFsgNRgVcuWsa/HwoW5WUadLYIqNvX1fBw8ONlnIhN0d3OH+e1vM+kIptgkrGuDEZtEgusHEb+D9nZ7kMzGIfGYY+x3+/e/u58jZSjEJp1iE9QUJYNITxWboUP5L53Td1sb55XISWzcfWyC+D+8/TafN3Wqv2ogqKtjEuSGzs5OdHR0pBAWHgR7pth4+dgIsRk2LDtCrys2gD+xFmIDJPvGeKG11X4nbsHy5D6DBvGu7G6kUtIjdVfO2bHDDmeRiWITpikKMIqNQUC89BJ3HHFWEQB7oHV26CutsIdBNmyLGqLYAKkzPukgsp3p5QpBiY10updeyjO5dDNvr2clEjzIAukVm0TCNkUlEsGIzbZtTKBOOcV+Hz1RbLZvBy66CDjiCDZHuZEDKYt0pqieKDa7dmUew6exkdWXAQN4YE7X5nfv5jQ2NrorNropqrs72E7r//kPH4cP53QIsenuBu67Dzj9dDtdTU2sxIoZ2okOqwC7HAXBBKU7sPPw9u2cBxmgAW8fG1nF19Bg+/1lgr17mSgG2V1eJg9AcGIj7yRbxUYmFkJs5BxRayZMYDLT3c2fd+/mCNKNje6rQcM2RRnFxiAQZszI3l6cS0iFdnYmN9/Mx1tTdv3KPXTFxtmxCGG45ZaemW6iRlBTVH09y/4XW3G7s1FtdAUBYGKTTrHJ1BQlpqMLL7S/6ymxGTaM/5qb3bcEcCo2YZmidMUGCDbY6Whs5JlvSUmw0Anz5zOZnDnT3cdGV2yAYOaoxx/n4+9+lzy4zpsHfPWrvDhA+qJly/jopdgIselwMJBsFJshQ2yfMcDfFCX7jGVT5/fs4fondTCdYiMxh4K8azFFAekVm3TERki5nCP+NZOtTYTkXb/7LhPQmTNTFZvubruOZ7ulglFsDLKCEIU4qwjt7XYDcSo2MogECfQVNXTFxtmxSDqzDayWDgsWJM92s4Wks6XFnzSsX8/xNT7wAf6sd/Lvv8/OoelMHfK7EJs+fTJZFZUZsTnnHPvdZEts2tuZ8A0ZYqf9jTdSz4vKFKUrNkDmdamx0S5rITZ+5iPJ44IF6X1sgGDO8jLzf/31ZB8b2RZBVzSlTqVTbIISGz/FRjdDAe6mqAMHeID/1KeS05cJ9u5lB+cgASLr69n0CSRHDPZCOsXmwAH+E+dhN5VVJjRCbOQceW9CbCTdcv6CBanERo4VFZw2UXkygVFsDDKGUtyZEvEmcHGFXpmdnbk0vDgQG6di42aKioLYtLUxqdFnu9lCBmXAX7VZvx4YO5Y7yREjklWoG2/kjfIuuMB/4HRTbIIG6MuU2IwZYw8S2RIbPd6JKA8//3nqeU7n4TDj2OiKTaZ1qaGBlSaAy7yz07/dTJgAnHcev1ud2EhwRN0UBQRTbI44AvjYx/ieohooZb+nO++069LSpawuiNnGiXTEJnkTzPSKjQ43xUaiZJ96Kg/8URKb7m4mNiefzJ/D8LGRe4iPzb59qWTDzxRVXc2TGcBWYAYOBL7wBX5nTmIj9UEckbNZJWoUG4OMsXevveQwDsTAC37EJi6KjVK2cx3gbYqKgtjs3Mkmg5767yjFg7IQDT9iU1/Pq1UANhXonfySJXxMZ+J0EhtxHvYiQ7wKKXPFprSUBy4xaejOw+mIjVJ2enQnU0mzm0K2axcP9rW1EgPG/d7ZxLEJU7HxSr9AH/CDKDZBiM2mTfbgOGAAl0Frq01s9PSI47BuItLhRWxkVVRPFBs3HxtxHJ40idMVxKTsfKQQm3SbsL7/PvcnU6bYcZ7SwWmKchIJndj0789pc7ZxP2IzenQqIZP8AKnERp4vxCYbPxuj2BhkDLGbAsGkznxBGl/v3t6KTTbOq2HCGZPEOWOKUrHR8/7EE9nfR+JWCGHxIjZNTVxfxo7lz+PGAStW2PXpC1/gweiznwW+9z3guOPcB9DGRu4opbOsrfVfAtvVZasFmRCbYcOY3Eh6ZZl2bS2/Iy8y0dTETseHHcZpzYTYDBhgP8Nr8PKKY/Of//D/TjNXTxSbRILPl3SLcuNlLlQqecBvbm5GRUUFysvLD74DP2Jz9tk8OOrl09TEA6UQGwkkt3t3KrHp6uIFAatXe5MvL2LDy44VOjvTKzbOfAqE2OjEZNUq9nkZN47NY2vW+PuO3Horl5EQfYCJQP/+6RUbcRweN46JSDbOw36KjZS9c0IoxGHgQE6j7mMzerTtYN3SwnX1wAF7ouBc7i3tWNpCtsSGiNNbUmIUG4MA0KOnZuqImEtIYxs3Lr6KjfStXqYoXbEJOyy4bj7yWoIcBEKQ0hEb6XTlPFmRJo60GzfywPnII/x5xQp35UZXEADbROQ1K8vWx0acPTds4E7yD3/gzzK4uA1Ora3sGP3mm0ziZs5MJjaVlTxTdRt0d++2Z6l+5rWODk5PaSl/FmJzxx2cr+9+1z63u9sO0CdB6DIhNjt38j2CKjYtLVwGumJTW1uLsrIyKJW6KgqwiU1XF5OzTZuS37v0N6IG6IOrk9g0NHBZbN3qrfp5EZt58wBAYePG9IrN/v38Htx8bPje9nerV7MprbKSlRSl/Fdjzp7Ng/s559jvymmK8qobsurwyCNZgcrGFJVOsQFSJ4TS9vr0SfbDcVNs5FwhNl6mqJ4Sm8pKJjU1NUaxMQiAQiQ2TU3JM8O4EBvdUQ7wNkW1tYW/P5Hcu08fYM6cnt8nHbGRTlcUEHH8XLiQj9IJVlbKBoruJjInsRF53q2z7+6WuDdCaNyJjVJMsA4/nO///vs2sVm0KDkt0knv3Ml+H3qY/M9+Fnj5ZXsWOn9+arwTr5VFumLjFyuns9P2cQP4/44Oe0nxihX2uVLnJXz9wIHAT34CjB/Pwd7e99iO99VXuVw/9CH+fP/9yeX+ne+458GZ16amJtTU1KC8vBytrVzoMlA5iU19vU3e9fcu/Y1uigLciY2c62de9SI2XA8Vtm1LVmweeEAdvP/NN7MPkYSLcBIbac96H7l6NZuhANuseeaZfJ/Jk1PLURZm7NvHbeX994P72MiqwzFjgis2uinKT7EZPDi57HVIm6+psX2gDhxgcv/88/Y7bmmxz/UiNmGZoiRP6RYX6Pjyl/ld5SuMiSE2eUQhEhsg2WwWF+dhp2LjZoqSmXnY5igpgyuuYPNFtrttC7E54gg+BlVseIbMy3WBZD+K//ovdv5080doaAiu2Ij5KJ3z8J13Aj/8IatGt9+erNjU1SXvCi2Dy6xZwDPPsJ/QzJn8rP/3//i3D36Qj08+ye+tstK+zivIXSaKTUUFcOWVV6K0tBR1dTeis5MHS4CdrwUyAMqzx43jvDc0AFu2AOef764E3nwzX5tIMAlavdrevqKsjGOQuCkibsRGFJuNG1mxec7aEc9JbMQXpU+f5PfuJDaiGuzcaZdjQwPnQ8yaK1d6+7J4EZu6OmDQIIVPfYqs9PFxwwaFmTO53O6/n01JP/lJcj4Fsv+ZqGaJBJ8/cSJ/HjWKTazt7RxrZ9Wq1HLs1Qu46SZ+n83N/P/+/UxsJEKwF7GRVYfl5UxsMl0V5aXYlJTw8/1MUbW13FcJsZHy37QJePRR/r+52ZvYyHPDMkVJntItLhB0d3OblrqeDxhik0ds2mRHwCwEYmNtDXOw05U9eYBofGyUYh+RD34w+E7IXgH6du2yiVnYxEYIyRe+wGk++mjgssuA44/PbMYS1BS1fDkPitJxDRnCSsL69dyp6MRm6lSeqUqe772Xl4I3Nmam2Aix8VNs7r6biY3I8c8/z3VHiI0TQhIeeMD+bsECYO1a+3kyGNfXp8Y78VNsdGLjNXjt2cNlOHv2bHR3d2Pt2ofQ2WkrYnqANjGX6T4OgJ3Ot99mQqqnRylb/enXj6/t7uY86iYwN0XESWyam5tRW1uL8vJybN/Ohf7mm/ybF7HZvz8575s2cb0R/x4ZXN99l/Nx5JFcHvv3p5IgN3gRG8677TxcWirexwoLFtjPA2w/Jiex2bIl+fcNG7g9z51rR7IucYxeejnu28d/o0fb7fPVV/nYrx9fW13tXTdWr2Z1p7HRXbFZvJj7E/1966Yot+XeO3YwyRDSAribomSCIdGJZeKilB1bKF+KTRBT1KZN9vvNVxgTQ2zyiI0bWUatqSkM52EnMZBGOXw4V3ivvXuyxbp1wF13ceeWjvm7+dhIw+7s5PTJbC8KYlNVxYRh1CgeMP/6VyYgmcxYnIqN1w7fL73EhELuTcSDUn29HXpd/ChEsl+61N76YskSJox799qDHOCv2AiB8SI2SgH33MP/X3UVO/zKTDEdsWlvZ1NBnz6cNn258Suv8P/r16c6mboRG6WCm6JeeIHTP2jQCQCACRM+j85Om9DoIfWdio2oTxIfZNw4bs+33GJfs3Il18uHHuLznYrVWWcBJ57oroj4KTYXX9yZdB8nsREyBdgmJsCeSAmhkjISc9CJJ/KxsZHPHTDAzq8bOi3v63TERo59+yq89VbyCj6ZiDiJjYS/+NKX+ChmwbVr7Xov5fnNbzJhE6IH2ObZ0aO5HX74w3YfIauIvFbltbTwNXv38rMGDeK+Tu/fvvhFrh8SmLS7m+uxX4A+Cc4H+Cs20g5FsXntNe67dLOgn2Ljtdw7V4qN1L8xY/IXDNUQmzxCZtZBbbj5wv79LMmOGsWfpdOVgVgIj9dAnC2E7AVZRu1UbPSORToPsc9HYYoaOND2vQDswUMk9aD3AZgUVFV5KzZuQR2PPJI7WudMWyc2c+fa57/2Gh+DKjZOYpNIJAAoPPQQD4QbN9oqxsKF7Pgrs8t0xKZ3b+Daa7mebdjAaa2o4Pc1dCi/S12xEUj0YQmZ8Lvf8UShqwv4y184XV6dsb4SiOgcAMBpp/0Ye/Zwfenbl1UDqUNOxUYHkV3v/vlP+/s5c/i3j3/cPf+jRyebo3VIHeWl08k+Ns4tDGTguftuzvPq1bZvkk5sZGWNoLaWlQtxwHUSGz+1Bgiu2Mhx3z6FTZvsmCsf+ICdfxnw9bSNGGGrT0KG3PqCSZP4netEVExpQvAvvdSuB27EZu5c4LTTOO//+ley4jBoUGpIjrVr7d8Bm0T4maK2bmX1tLGRz6us5HuuWQOccAJ/v29fMrHZvZv7kOnT+TtdMQyq2IThPAz4KzZPP22Xn7yzdAEoo4QhNnlCRwfbswuF2PTpYwdp8iI2YZujpEwuvDA98/dbFSXpnDCBj1EoNtJ5yCxSzCt/+1tm96mp4UGyb193YtPUxJ34D36QXCZjx/LgLx267iA6ejQPDLpj87e/zUc3YuOn2AD2tLWkJIGNG3lWK3L5smWcrksvta/1IjYjR/LA39pqz7aXLuW/o4+2d2oX0uam2ABM9ObNY1OgDDgbNnC6vIjN00/zceFC4MorO6z8dB5cAn722fweN2zgz07FxomVK3nvqpYWm5DPmcPB5PQy1jF6NJ/rFn9m+3ZOuwyUYooqKytLITYyEIrCuXq1PRA6FRudrIi/hwxEOrFxkiA3ZEpsAHXw/R57LPt/AZwGIYY6Jk2y01ZXx+1XV6oEosTKuZJXwM6DbD0C2GqJEJvGRv5dAmz+/e88Qens5GcJuZT+6MABu78R9chJbNych1es4LYl14gi8/GP83O+8x3+XYhK//5MTvbuZdIA8ISpsjJZsRGiVlrKf17EJpttFYIoNrJ/2/z5nDd5DxIpPB8wxCZP2LqVG2lPiE1LS7I82tHhvnFcTyGNrbqaK7nTFCXEJp0DcabpkzLRZ2Je8FsVpZvM+vYNh9josx/dWVUgYej9oqM6OwndN8SL2OhBynQceWQyQdAHpalTudN58UWOZAuwMy7g7jwcxMcGAKqr+f/583lQ6NuXCQnAZhYhSl7EZuhQHuCU4llraaltstH3J0pHbBoakmOV9Oplz+zFx8Y5c/z737k+TJtmD8wlJXZAnQ9/mI9ilhJi46bYCL75Ta5z99zD5tO6OuCSS7zPFzXBTbVx5lVMUeXl5QdNQILqav5Tiv1I9u7lJc6ATWwSCVagnGSlf39uk6Wl7BMGcHlu2mSnzw1KqYPpaHfZFMmL2Mj7nTrVJr9OM5Rg4kSu70pxO/La2kEmLE5iU15u15HhwzkmEmATAakb77xjX/fKK0x6L7qIzVuArSZJf7RwoU305d0JsfFa7t3QYLcrUXn69+d8SUTlV15J9bERCFEFbN8gUcjlfHluVKYoL8VG2oi0Od0Umu1Cip7CEJs8QZdKsyE2zc3c4Y8da/sZfPKT3IDDXmIn8igRd0JOxUaWHacjNp/4BA9yQdMnM9/33ku/z4nfqihJ58CByenPFm+/zZ3iiSdyXnRCIjj6aB4svIjNj3/MREBfUqwTpH79/ImNzFIF8g5eftleKiqYMoU74M5OVmr697dNJjqxkaXMfoqNbH4JAD/5Cf//yCNMbD74QdsE16sXcO65PLj42eWXLbOdIidNAp59lt+7PoiNHcudZXu7O7FpbOTBctgwvldbmz2zr6nhuqOrIgcOsLnhkktYtZABurTUJgznnstHIdUy2/XzOZk4kVdS3XsvKzVA8oDkhJCMdMRGKXXQFOWm2ADA97/PR1kZd+KJXL+E2DQ0MLlxEhsZPIcO5X6ovJwH2v37/RUbnVwFVWzGjVP4xz+4nk+ZwosRJkxgxc2tT5g4kevOypXcX3ptxtmnD/cr+oAq/kS6g7G8UyG54n+lE6KBA5kw6IqjEBvpj157jdtJVZX97pxL73v14vKWVyWm34ULbcVpwAD2JZI0fuUrqT42APfp4ncn6RZTVO/eduRseW7YpiinYuOcJEgZ9OvHE4zVq+3+yCz3PsSgS6XZEJuHHuLOT0wBSrEz5K5d4S+x0xubTgwyVWxefpnzGTR9Uibt7d5xQgTSmHVTlHQsks4BA8IhNgsX8mBZV8d50U1Rgqoq7pjdTGgdHWwyUAr4+tft7/X7eCk2q1bxTFI6DoF8fustrlN6GHwhCUOGMPn40Ie4vISoCoi85Wanjw0AfOQjXSgtBX7zG571ilwu6Ns32ck5HaZM4fTL/4Ijj7Sf70VsvGb0bn5DEg9EBi8ZmIk6Dz5j3DgeNDJRbORaIh6MiIA//tH73KDEpr29HV1dXZ6KDWArQz/6ER8nTeLBXoiNMzifQAbPESM4vUOHuqt+TuhkJiixOeYYlfJ+p01LNs/oEFVy9mw+eik2gK3uCNxMadu3cx4ffpg/iylq1SquJxdfzIpX7962YgekKjbz5rHSOH68PUF187EB7H5p3jy+r54HKfvPfY7bfV2dO7GZPj25PYtis2+fbbYSuBGbfv34+jAUm0Qi1XQqdWvvXi6/HTvskAmG2BxikMowciTbcJua3Le5d0N7e/LmfwsW2HubAPbsICx4ERtZDSSb5Pn52OzbZzesoA61OtnTl966wbmhoe5Il6lic+ed/sqXdKAivbqZogDuxNwUm8cftxUA6eiB4KaoceOSZ2kAD1glJUy4nB26DCIlJdzpCAEZPNiW2wW1tfy+//IXLsMBA/gdL1okee7Szu3CGWcAv/0tf3aqE0uXZrZ/lk5mxCwCJJM4ndgMGsQK0YYNTKzcZvRuxOb//o+vExOG0xR15JH2SrNMFBvAzvPu3enzPmIEv5N0xKbZYlVePjYAB0ScMoV9jKqr+d46sXH6Xgl0YgOw6rV8ufu5OrIhNkcfrazPHIMGYDXGq5xElRRio9cJt3PFbAW4m9LefDM1QGRzM183caIdudvpuK8Tm64u3hl9+vRk52/p2773Pe43nI68r73GpjC93Qr5+fznua9YsoTrqZAVmeQsWZLcF+mKjRuxEUIjBKSykklVUGLz5S/zQpHGxmRiI6r5unXJ52/caCu1v/kNH886i4+G2Bxi2LSJO66qqtQZQTo89hgTGZlxzp2brAxIRxAW/IjNgAHeSxd1iB0ZYHNDEOzcaTfcdH42TsVGnzHt2sUDeG1tMGIzaxZL93fd5f67SN5f/CLbxbu6UhUbgAearVuTl/J3d/Os+vjjeVYjK80AHgx1xcZtlZl0wk5UVNj3cg5Io0ezSrNtG8+MhYC4ObVKdNFvf9smi21tLJMDgFK2U1dXVxcuuYRncRUVvMpFh3N5czrIbFaWfgu8iE1JCTu0v/gip8GP2IjisnkzO/UmEhxIELDNKkJs5HmyhF6/Pp1ioy8DT5f38nIm0PqecQDXkR07UjfAFGLjptgAdn8wcSKTBzfFRq9vQOqS/KFDbXUsKmIzfnzqsnm3cho2jN/f+vX8vyxecMOkSdxPNTRw+rduTU2/W4BIITaTJvFO3qedxu1QV5Cqqvi979zJJtPmZj5PJzbitL5yJV8r/Y/EBVq2LFXRVIrf05//zHVXVhFK3T/hBOCkk1KDOGai2JSXM+kISmw2bgR+9Sv2x5o5k+8hxEYWCNx5Z/I1mzZx+Q0fzmEuACZxvXoZYnPIYc0armgSAAoIRmy6u3lFTE0NM32AG6quDARxtvWDUlx5JUy57qnfuzeTqoYGW6no1Yu/9yM2bisW0mHnTh7sSkvT58mp2MisZdMmmzCI6UV243ZDZ6f9HiSGildeNm5MVoOc0JdaCx54gK+//nrg9NP5t6Ymfq979vgrNp2d3IG6ERvADuzn7NCJeIYns9UTT+R3tnlzasdTW8v5f+89/iyzPhkgdR8bITYAd+Q9Xe4vM/J9+5LTdfjhthTvdDQdOtQuXzdThXNPoJ/9LHW7AacpSspRVpopxeVXWWnPTMOC25Lv3bu5PjiJjddyb4G8C3mvojomEkzGS0tTV8Y4FRshu7rjrRuyITaTJ3PB79kTbMAjss1RfmYoIHll1PvvuyuXTtTUcF3bssW+XnxInAqSuAs89RR/Puoovv+ePXxNXR2nV67Vic3rr3N6nMRm3Tr7/ClTbEVEiE1pKfdrEtRRT7efYqMTG0lHUGLz058mtw9dsZEVghJjSCDq2PTp/OxevbjNegXQzAVyTmyI6HwiepeI1hHRrS6/ExH90vp9ORGdkO5aIrqMiFYSUTcRTdO+/zARLSGit63j2dHnMD06OriyNzfbAaCAYMSmoYErWEsLKzVE3LHX1dmDYjqzTTp8//u8EaCEKddjK6xYYfuG6KYTWbroBadjXxDs2MEztdGj0+fJqdj85z98vPPO5HQOGcIdhZfZ7N137YZ9772pv7e12YO+kCYgGLF59VWWeeW76dM5La+/zmXc3Z1MbFpbk3e+rq/n2ahzRZRAlAa31Sz6bLVXL+CYY+wAZDr69GHZvLublbXWVi4TIRZOYjNqFPsbNDX13Ldr4EAm004/scpKe+CVpbcCGXxramxCokM3Re3cyY7OV1+dPHO3FRAeoKUcx47lwWDbNm6r6cxQ2cCN2LgF5+O8+Cs2xx7LA7RsGjpihL2r+D/+wf8735EXsRk1KjWyr45siM2gQQoTJ2bmZyeEw8tx2Hne6tXeZjcnamrsti7XeylIgwZx+xWV77e/TfaRWrqUFR+9jQHcL82bxyTl5JOT76k/Syduulrplp6gio1OSoIQm+3b2ZQkquQLLyTfY/lynjhefnnydRJGQIjbhAmcX68tT3KBnBIbIioFMAvABQAmA7iCiCY7TrsAwHjr7xoADwS4dgWATwB41XGvnQAuUkodC+BzAP4v7Dxlg1desRUGCQAFBCM20miVYr+Ho46yY3/IEtueKDZ33837s8hA9tprnFZpbBIcbt68ZNOJvhOtG1avtjvPTBSbQYN4gJE8PfkkdxDOmYBzu4+gwwAAIABJREFUube+MaSeThksvMxRegeiq0yCNWu47EeM4HyIYuNmiho4kGfNM2cC//M/HBtFZvxvvMFybWkpl7Hc58EHOW/SYe3bx4PSSScxAQK8FRupR+nMJQAPcm4z09paJk+1tZxegOuYxBzZvDmZ2ACch0x8afxQUeF+rxEj+DnOOiYD8fHHuw/EOrH5wQ+4cxelUyAD84EDTBik7IUonX8+18Ug5Zopxozhuqqv+nPbTgHAQedhL8WGKHnTU2lv0laB1HKVdi6Dl5RnOlKQzaoopZTvpqxuEP+9ww9Pf151NfdfQXyEgGSi6jVZENTW8oRSfGQWLLAnEBs3cv+rky9dsXnxRS5fv813J0ywr3GSFbd0i2IjS9f152ar2Nx9N19z4438WSZwUjfKyuz9zgTNzVy3Ro+2TdwNDfZ2LYeKYnMSgHVKqXqlVAeA2QAudpxzMYDHFOMNAP2IaJjftUqpVUqpdx33gVKqTikl62lWAqgkol7RZC045szhRnjggGwYx98H2VZBSMGKFXa8j/nzeTY/dWqyw2M2kGWjsvngY4/xUYjNO+/wgH3uuclKyIAB6U1RJ5/MjdbpU+CGzk5uuIMH2yaBzZuBT3+anQCdMz7ncu+33+bZ/5QpqYoN4E1sli7lzqBvX3diI9+ddx7PZtas4c9uig1g+6z8+tdMJi6/3J6B1dZy+v7zH+Bb3+Lz16zhvEnntm0bL5NftMjeskCcXr3S9o9/uP+uw2tmKkTgox+1yxLgDS3ZMTGV2GTqS5NNuhIJd8VBtoTwMlVIfhoa2HcKAJ54IvkcGZiXLePBWoL3STkvX87ye1SKTUeHPWEAsldsgOTyE2Ij+a6vTy1XIcuSZynPoMH5nP8LvIhNurrS0dGBPVpHIr556RYcEPHk6v332dwYJA/yPt1WGTohZla9/cr9583jvkonNtJ29u/ntivqvBfKylhxA5IVG690B/WxCUpsEglWMwHbX0ichIXYAKmrz/TVvccey+kRRe5QIjYjAGzWPm+xvgtyTpBr/fBJAHVKqZS1R0R0DREtJqLFOyLetKm7m4ODnX++XWHE/yOIYuN0Apw61e4Ip0yxSUA6iO+Ajo4O2zdFKqQs/ZQGRMTMfN685OXJfqaozk5uJBMnukvvXV32pncCUTAGDWKytnMnrzhw+kcInIoNwNLo/Pn25nNAMGJz3HHJUU91rF7NZSAB0PSYFG6QjkWOMpsUSFk++STPOmU2KzOxp56ynTnXrLGDDLpBbOA9IRjSqepxPAC7TPTIw17KQRSQRznfuygMXqYKGbx+9atklVSHEIVt2/gowf6OOIIHHKWiU2zclnx7EZt0PjZOCLF59VVWtPRYKAKZBInjqpSnX3A+IHtikw5nnHEGBgwYgBst2UBUA0mfH6RdvPceTzTSvS8hvWPHpq4y9IKejmHDWEWUneh1ci3tfcGC4BtCSh1OR2zEFHXgQHBTlEykvfDGG7Y/nZBJN2IzaRLXGXnlehiB0lKuY9KHDR3K7caHh0eGXBMbcvnOWdu9zglyrftDiY4G8CMA17r9rpR6WCk1TSk1bbDTiB8yFi3i2aMekbSsjIlBUGLTr59d+fUOXRSb+nr/gHY/+AE35ilTkhm1zBoffth2nBVioze26dO50nd1JfvYeJmi1q/nc72Izb338vd6sDopCzFFAcDvf2+H2Xd2Ek7FRtK5fz/P4iSdEl/khhtSyZTMxKZM4Qas+wUJVq/mRqzb5AFvYpNulcw0yyPsa1+zo1HX1dkd1qOPctkvXcrPaG31ngWFoZzU1HD5nnBC6m91dcDEiV2osNhjLomNV97ER8Rrxi2DV309m2rd7iED8333JW8uScSxTQ4/nIldVIoNkEps9H3HMlFsdAwZYps+vSIgO8tVwgY8+aT/bDsqYrNw4UIAwEMPPeSaPj+sWMGkZuhQ7g/SqQXyPtOZobzSUVrK4TpWreIyO+YY+zfph/79bz5u25Y+D5KOdMWk+wZlotj4bakwZw6Tu3377HR6KTaJhP2bc+sKvZwkYGY+NnjONbHZAkBfcDgSgDP0mtc5Qa5NARGNBDAHwNVKqR6uF+o55sxhInPhhcnfBw3S5ww8JcRmyBBu0GPHcsX2ctr61a/sfYLefjtZHpWOYOhQJgA1NeyjAiQTG927388UJY1P3wbAjdj8/vepwep0YqM7hX73u0zaZOM+gTRmPS6LWzr79WMz2+7drLroBHDzZs7DlCncgBsbU1f6rFpl5wPgzrRPn+AzPicWLLD3S9IhHVZ9PdeV448HPvYx7njCDsCoY9cufhf33ef+e1dXFyqt3jKXxMYLEgVWtohworzcHmQkTokTMjC7EYbTTmMlbM2a6HxsgGTz7PbtdoweAHj88ccBALfffntGik1pqW1acipwXhg2jM29YhL1QlTEZoJl/7vmmmuCJdiBww9nNTyd6QewiY2Xz1oQyPubMMHeTgGwCcX8+fyb17YROsRJX/aZ84JeD/3i2AQ1RSnFVoRzzuG+rKKCXQC8iA2QvDJUr2c69ACauUauic0iAOOJ6AgiqgBwOYC5jnPmArjaWh11CoB9SqmGgNcmgYj6AXgGwG1KqQz2WY4G27YBf/gDNyhnML6gxMYZeOqww/ivs5PvLyTAzc/miSeAm2/mhl9SkurEpxMbIq7EK1fydzqxmTo12YwG8My5pcWWHTdsYEezESNs1WfCBCYEu3cnO9IJ0dGD1QnL1xWbgQPtiKDOwHcdHdyo9Qido0fzjEpPJ2DPXNasYcdYybfMVKZOtRuwHn+nu5s/T5zIaamq4vx6qTVB8Prr/B7E10Ggd1gyKC1fnrr0M2wsWuTv3NnV1YVeFlOIA7F54430zqi1tdzm9Bm1DiE0bsRGHCJ37IhGsenbl9P3jW9w3SXiqOItLbo5mBvQb37zm4wUG4D7hl69gg2sglWr0tezqIjNVMue8zNxlMkCQQNDShubMyf7wVcmOE4fL31VlN+2GjoWLgyWbr0ehrEqasUKHi+cG9e6ERvnvlybNvG5zkCfQPJebrlGTomN4rClNwF4DsAqAE8opVYS0XVEdJ112rMA6gGsA/AIgBv8rgUAIrqUiLYAOBXAM0T0nHWvmwCMA/AdIlpq/WXQxMPD7t08KO/Y4T7rHjQouPOw0ynulFPspbtCAtyIjQRZGz2aO/mLLkqWR3ViA7Ay4YytADCjlw3ldFMUwIpHQwM7F9fX2858I0ZwB+628Z/MdHTCpis2ffoAH/kIl+Gjj3Jjdsq67e2pOwQT2aqN7txbV8f5khVk8i6WLuVrjj3WloV1P5vNm7mzkABokl4vx+Eg8JLapcPq1YuJqN+5YSLdMxKJRKwUmyBlcvLJTBTEMd4JP8Xm+OPtgSQKxQbg4IlEPEuWY2urXS+PtwL8XHvttRkpNgDX0Y4O77y7IUiZRkVs2iy5wW1jzaAI2k6OPZbNk2vXZq+CSl/s9PESpQRIjV/jhaDpTqfYZOo8PGcO17uPf9z+bsQI29dLJzY1NezfKWZ6v81SDyXFBkqpZ5VSRymlxiql7ra+e1Ap9aD1v1JK3Wj9fqxSarHftdb3c5RSI5VSvZRShymlzrO+/75SqlopNUX7C2Fv58zQ1MQD/YoVrCC4sfLevbmy+FWC/fuZwDiJzcaN9j1Hj2Zp0OlAvGCBzZzffDN5CbVAfpcIn7pE62xAEkxNHONEtVi7liPqvvee7azY0WHPQJ0+Bbt2MaHr358btCg5QmyENGzfbisbU6akKja7d7v7n8hKAydkdgzY7+KNN7gjaGritJeXJ/vZSORNmf1KXnqi2HhBiGR1tb9tPNeImykqCMR3yWsm7KfYlJXZG1pGodgAXGeVsoPDOYPEnXbaaejXrx9mzZqVsWIjiwTCVvmiIjatlk3W7Z5hg4gnYj1RQaVfdPbJOrEJqtgEhZ9ik0hwn9rY6E5s3F7Bk0/avmiCEdqyHJ3YAMkro9wm2gIZRw4JYnMooqTEHqgvvNCdla9dy2aNO+7wvo/TUUugM/3ycv7dSVruuYdJQnMzn+fmZNzYyOeI8qETG6envqhLf/4zH0Wx+eQn7Yia55/PDe3EE22VyklsxNRz1VV87htv8GfZTkHS4gxmtXx5cvTgf//bfcNFWY793HNIwZe+xA1fImm+9po9U3aL2fDd7/LxmWf4KHnpiWLjhfJyduDdsydan5pMUYjEJsgSY8Cd2AD2jDsqxcbpYO50NG9vbz9Y5pkqNlGpfFJmFRUVsVNsMkFPy0cWPLz0UvL3YooaNsw9cGRPoNdDZxybpUu5X7zjjlRTVCKRukKpvZ3z4Ayw6UdsZMWorGb1IjaVlZw+Q2yKFNXVPPj5zZyEKLxqhRhcsYIrzFVXsdRXX++9Q68TI0dyXAqpUC+9xJ9nzLAbxdixzOj1SidBlQRijtEdMAXieyPOxUJstm1LVqVKSpKDwckSSXGWFEVkxgw+V1QRCc7nhilTePYh8RY6O221yVm+ouw4w4BL/traOC179thqkdxjzBgmRA0N3JBl6ancK0piAzDpjGK23RPoxCYT5SDOSEdsZMb9+OP56aTb2toO+jVlqthEBSmzmpoaT3WlJ4pNrohNTyGLGMSPUCBKifg+hgk/xUbMR6++mqrYAKnmKOlDnf2MH7EZNoz7yquvZnLz1796t4tBg3jvwly3G0NscoR0M4M//pGPEsb/+uvZp+NPf2JC86UveSs2TuzdywxcFIavfpWP+nJsNydjJ7EZO5ZJSJ8+yU65bvkZP54bGRGv3tF/088tK7Oj9gJMGHr1YpPR8cfbO5Pv2OFPbOS+ADsdK8WSqrN8/cpd9/AXAvSvf9nnNjVx53DRRbxxZVUVdxzye5SmqHRpzxfi5jwcBvxMUQD7kw0ezA7x+VDP2traslZsooIfsRECcygQG682WlXFphjn9iBhQFdsnEr673/Px1mzkjew9CI2oki/9VZyHvyIjfSVTzzBffe6dd55bG3NbAuNsGCITUwgREN8Y6TCiVKyeDErC2Vl/pvTAXblfeEFbnSiiuirjsTJWPfFcRKbigpWLfQVGl4YMCA5OJMfhg3j6LiNjZzPo45iAnXCCRyFd/Nmf8Vm0iRWkW65he8hZChTW7YbsdGdAEXBWbKEnZavuCJ5ryJRzvI1k88H4uY8HAbSKTZVVdzR50s9a29vj51iI2morq4OldiIKSoXPjZRoqTEjuMSdp3Rndmdq5F0U39ra3rFRsaZo45K/t6P2Ij7gCyy8Muj9Je5bjeG2MQEw4ZxJRQF5fjjeTVHaytw003sgLtqFZt50u0wvHo1m4ZOO419UTo7OVy2zshHj+bGJ89Tik0uTtJUXs7MPwjjDqow7N3LPjff+Q7nSQjGli1sfrnuOn9iU1HB+duyBbjrLjZfjRuXnvA5MXAgP0OIzbBhtsOb5Keri+8N2EEABRMmcBnW18fLDyZKFKKPjR+UUmkVGyC/6pmbYhOEJEQJo9ikR1R1RhQbtwjkEt5CNswNQmxGj071H9OJje4IDSTnK10e89VuDLGJCUpKWPEQBUUf8C+9lMnFM8+k968BmMVfdBGrIk88weahiy5KPqeiInnX7P37+RnOQEuiGIXJuMVh+Zln2G9FfHlE9XjlFX9io6fruedYscl25YFEGJaIw06UltozJD0yMsBlNXly/PxgokSxERs9D3FQQtzgVGwAoNsvtHgOYIhN/uBHbCoreYIniyacpqgtW5JdEiTgqBP9+9uExqnYFAIMsYkRZAm2bAMgxOb009nU09WV3r9GcOmlrIzcdx/HydCVCOfzgNQYNoJly8Jn3GvW8JLwvXuZ5Eg+ly4FfvELNn21tvoTm3Xr7NnJrl3BY0U4MXEiE5ZVq7w3UvSbdbz9dvz8YKKCUgrd3d1F5WOjD8pxJTZOxQbIf1qjIjbFYoqKEhUVrKR77Rk3erRtLnLuGH7hhaxUH300jzGrV7tHXiZi1aa0NPuo6vmEITYxgizB1rcgAGwFBghObD7yEWbaBw54h1PXdwL3IjZR4bbb7G0E9Ib1xS/ahMaP2FRU8BYMojhlq9hMnMgEq6vLeyNFA0bCWl9fTIpNIRAbfbm3KDb5LvuOjg4QEaqqqoxikwfU1PgTG1FshNicfDJw9tlMWEpLgXfeAW69lccHry0lhg8vTLUGMMQmVhg7lh1WZcmzXuFkE7snngjmqNq7N3DGGfz/hz7k/bwdO3j1T66JzYc/bAfP0xto797Al7/M/997r39ev/hFlkzLy1MDTAWFLsN6KTYGDBlMi4nY6GQmrsRGX+4dJ8WmoqICvXr1Co3YdHV1HSTPhtj4o6qKF4O49Y+jR9tBPYWYlJayCUp/BbLowmsT0IEDOc5NIS6MMMQmRpCVSs88wyqNHtjpox/lQHf6FgDpUFvLDP2xx9x/11di5ZrYELHzLRFvuaDjy1/mFVKrV/vntbqal+J2dWUWMl6HkMeamvADaRUbhMhUWb1lMRCbQlBsdFNUnBSbiooKVFRUpJCQbIlNq7YTrCE2/pg0yXsZte6HqTv+ikn92Wf5s4w3XopNSYl70NNCgCE2MYIMrPPmcVwY3bZZUcGB7jIJ/712rb9jq1TsK6/kc8vLo4vJ4gaJUOxMX20t5zNIXhsaeua8O3o0OyKXlNjBrQzcYRSb/EB3Ho6bYuMWeTgMYmN8bPyxa5d3v6e7KzhXNAHs39i3L0dr79fPe4PUqLbjyAUMsYkRZG+lri53Fp3p0rl050+YwGTmnXe4ksuu3rmCX/qC5tV53lVXXYXS0lLceOONgdJQWsoxHJwhxQ1SIUTGOA/nFnFXbMIiNuI4DBjFJh38+ked2Lj5yFRUsBMxYG/om+kz4g5DbGKEyko7foCXPBgmqqvtrRA2bMidGSpK/OlPf0J3dzceeuihwNeUlhbuzCSXMM7D+UFcFZvy8vJQfWyMKSocpFNsAHtByXvvFaYPTToYYhMziHnIy6ErbCxbZm91XwzEZqrlATxjxozA1xTyzCSXMKao/OBQUWwMsQkHAwfacWu8iM355/Nv27cXp1JtiE3MIH42uVBsACYzn/+8/X+h48wzzwQA3HPPPflNSBGiGImNPijH0a9DVgrFTbHp7Ow8SGy6u7sPqnlAOKaoOL6LQgGRrdp4LdeuqWFXhGJVqg2xiRlGjeKK6dyOPkp84xtsjnnhhcKXJWWmZ2Z84UOITEVFRdLnQkbcTVFSj+Os2MhngVFs8g8hNl6KDcABUYtVqTbEJmYQYnHffbl75hFHAJddxhunFbosKR2sPvszCAcymJaVlaGsrCzvg2sYiLspSuqxk9jkO62G2MQbsuTbj9gUMwyxiRkWLcqPPLh6dWZLyeMKo9hEBzE3FBOxkQG5tLQ072TBDVKPnaaofJd9FMTGmKLCg4TtaGrKbzryBUNsYoZ8ObIWiwOtdIiG2ISPYlRspL5UV1fHktj4KTbt7e2YPHkyysrKAoc3CAtGsYkXLrnkkqQwF42N7NLwi1/kOWF5giE2BkUF6RCNKSp8FCOxETLTu3fvWBIbP8XmnXfewapVq5BIJDIKbxAGolZsDLHJDHPnzk0KcyGbFxe6Ap8tyvKdAAODMGEUm+ggRKa0tLRoiE0hKzaicJSUlODaa6/NabqiVGxqa2tN+80QI0eOxObNmw/Wg0JX3nuKnCs2RHQ+Eb1LROuI6FaX34mIfmn9vpyITkh3LRFdRkQriaibiKY57nebdf67RHRetLkzyDeMj010KEYfm0JWbA4cOAAA+MUvfoFZs2blNF1REpt+/foZH5sMMWbMGAwZMiTn9SCuyCmxIaJSALMAXABgMoAriGiy47QLAIy3/q4B8ECAa1cA+ASAVx3PmwzgcgBHAzgfwP3WfQyKFGZVVHQoRlNUISs2Qmx035RcQSIPR2GK6tu3r5mYZIiWlhZTZhpyrdicBGCdUqpeKdUBYDaAix3nXAzgMcV4A0A/Ihrmd61SapVS6l2X510MYLZSql0p9R6AddZ9DIoURrGJDobY5B7OODZuik0+SHxUik1ZWRl69+5t2m+GMMQmGbkmNiMAbNY+b7G+C3JOkGuzeR6I6BoiWkxEi3fs2JHmlgZxhiE20aEYfWzibooS0iKmKDcfm3wpNlEQm6qqKlRUVJj2myFaWlrQ1taWtowPFeSa2LjtI+p8E17nBLk2m+dBKfWwUmqaUmra4MGD09zSIM4wpqjoYBSb3MNpinJTbPJBbPQtFYDwTFFVVVWuG2sWCpqamtC/f/+kpde5QEtLC4D8B26MC3JNbLYAGKV9Hgng/YDnBLk2m+cZFBGMYhMditF5OO7Exuk8rG+pkG8fmygUm8rKSvTq1atg2+/WrVuxd+/epKXXuYAQm0Itt7CRa2KzCMB4IjqCiCrAjr1zHefMBXC1tTrqFAD7lFINAa91Yi6Ay4moFxEdAXZIfjPMDBnEC2a5d3QoRsVGyExVVVUsiY2XYlOMpihdsSnU9puPJfidnZ0H626hllvYyGkcG6VUFxHdBOA5AKUAfqeUWklE11m/PwjgWQAfBTv6HgAww+9aACCiSwH8CsBgAM8Q0VKl1HnWvZ8A8A6ALgA3KqUSMChamAB90aEYiY2s7ikvL48lsYmjYqOUilSxqaioKFhTlLyTH/7wh/jGN76Rk2eKWgMYYiPIeYA+pdSzYPKif/eg9r8C4GqcdLvW+n4OgDke19wN4O4eJNmggGAUm+hQjM7DMkDHldj4KTb5IjaJRAJKqcich4tBscll+g2xSYXZUsGgqGB8bKJDMSo2uhNsHImNn2KTL1OUkJiKioqD6QpCbLq7u33vW0ymqFwqxobYpMIQG4OiglkVFR2K1XlYTFHd3d1pB99co62tDSUlJQcJTRwUG53YRGWKKtQBWt6JUWzyC0NsDIoGiUTi4OBrGnj4KGbFRicMcUJbWxsqKysPkoM4+NgIidEjD+vtLQxTVKH62ORbsTETOoYhNgZFA70zNMQmfIRJbDZt2hRWsnoE3ccGiB+xaW9vP2juAdi/CcjvqqioFJtiMkUZxSa/MMTGoGigN2ozcwkfYTkPX3bZZRgzZgwuv/zyMJOXFXRTFBA/YiOKjYCIDpZ9vhWbKOPYdHV1xc4sGAT52ObCEJtUGGJjUDQwik20cPrYZEsCnnrqKQDAk08+GVraskXcTVFOxQbAwRVc+SI2UkYVFRUoLS0FEYW+pQJQmG3YKDbxgCE2BkUDvVGbBh4+wjJFnXnmmQCAc889N6ykZY1CU2wAxEqxIaKUuDNOYlNSUpL0vRd0U5T+nEJCvn1sTL/HMMTGoGhgTFHRIixi8/GPfxwAYmOKKlTFJg4+NnJ0IzZCaIIoNkqpJFMUUJiDtDFFxQOG2BgUDYwpKlqE5WOzf/9+AEBzc3NoacsWTlNU3FSCIIpNV1dXTleoBSU2mZii5HpdsSnENmxMUfGAITYGRQNjiooWYSk2cSI2hWiKcvrYALlVbaIgNpJ+iWOjP6eQYExR8YAhNgZFA+kIKysrjSkqAojzsASMKxZiU2imKHHcbm1tRd++fQHEi9jIaqZsiI1RbDJHS0vLwTAApt9jGGJjUDSQzqRPnz4F2SnGHV1dXSgrK0tacpwN9u3bByAexCbuq6K8FJsDBw4gkUhg4MCBAPJDbKTMwlBsZEAudGKTLx+b/v37AyjMMosChtgYFA2kczXEJhoIsQFQVIpNnE1RXoqNlGE+iU3UpqhCbMP5UmwGDBgQ2XO7u7uTzJ6FAENsDIoGumJjJNnw0dXVdVDyDoPYNDU1hZa2bFGIio1ObGRAKxZiY5Z7Z46WlhbU1tairKwsEmJz9tlno7q6GjfccEPo944KhtgYFA2kI6ytrS3I2V7ckUgkjGKTY3gt9y42xabYTFG5Vmyqq6sj24ri1VdfBQA8/PDDod87KhhiY1A0MD420aJYTVGFrNgUC7HRTVGFTGzypdhESWyOOeYYAMBnP/vZ0O8dFQyxMSga6D42bW1taSOdGmSGsIiNcR4OjnSKTT5MUfqWCnIMU7ExPjaZIWpic8oppwAAbrvtttDvHRUMsTEoGuiKDRC/QarQ4fSxUUplvFGhUip2ik1cTVFKKU/FRvyT4qDY9OrVCx0dHbjyyitRUlKCmTNnAoivj80XvvAFlJSU4Jprrgn93mKKSiQSOQua6EdsZsyYgZKSElx//fVZ31/ejbTbQoAhNgZFA12xAQpzxhdnOBUb+S4THDhw4CAZiguxiatiIztcuy33FpIQJ+fh2bNnQyl1cJPTuJqiHn30USil8Nvf/jb0e+vvIVfmKCE2bvG7HnvsMSil8Mgjj2R9f8lTHJz9g8IQG4OigVOxMSujguOSSy5BaWkpbrzxRs9zdOdhIQKZEhuZ9VVVVUVObE4//XSUlZX55qmzszMjxebss89Oe8+wIPXZbbm3IA6KjRCbww47DABw6qmnAoivKUo2Yb3gggtCva/sdyVBE3M1sfJTbE4//XQA3L6zhVFsAoCIzieid4loHRHd6vI7EdEvrd+XE9EJ6a4logFE9AIRrbWO/a3vy4noD0T0NhGtIqLCMRIaZAx9VRRgFJtMMHfuXHR3d+Ohhx7yPCcMxUb8a4YPH47m5ubI/KAOHDiAefPmIZFIeOYpkUigu7s7I8Xm5Zdf9r1nmJDB3k2xEQixySWJdwvQt3//fuzevRsAMH36dOD/s3fm8VFX5/5/n5msEBJCErawI5AgakBQrKJWKz+lKirqS6t1Q4FrtLXW22vrta2tba22tl5FQawF61brvtZarxv2oqIElYLsJOyQBAiQfZ7fHzPn5Dsz3+/MZF8879drXsnMdzvnu53PeZ7nPIeu64o6++yzAbjyyivbdL+1tbWIiEmW1xHXpLGxkZqaGk9hM2vWLIBWDdW2Fps4KKX8wHzgLGA8cKlSanzEamcBY0KfOcBDCWx7K/C2iIwCD1m/AAAgAElEQVQB3g59B7gISBWRo4BjgblKqRHtUjlLpxNpsbHCJnG0GJw7d67nOm0hbHSvb/DgwaaH2x7oRlYp5Vknp+UhEWGj14+1z0SYNWtWQlafRCw2neWK0hmoIXj+tm3bZs7Prl27gK7riqqsrATa3hWq6xAvC/C1114b8/oXFxcnbBXUMT1ewkaXqTUJ9qzFJj7HAetFZKOI1AFPAzMj1pkJPCZBlgF9lVKD4mw7E1gS+n8JoO1uAvRWSiUB6UAd0H2ujqVZ1NbWkpSURHp6OmBdUYlSU1NjXvL333+/53qRwcP6t+bgFDbQfnE25eXlQDB4cv78+a7raBGTqCtKTzZ4yimneO4zHmVlZTz//PMJWX0Ssdh0lrDR7iIg7H+lVIuEjbOu7S1s9u3bB7S/sPF6/zz66KMxr/+CBQsStgrqe9JL2OgytIWwsRYbb/KBMsf3raHfElkn1rYDRGQHQOhv/9DvzwKHgB1AKfA7EalofTUsXRH9wu3OeTA6g/Xr1ycU0NuWFpv8/Py4x2sNWtjE2n9zLTZ6X7t3725xucrKgq+wRKw+iVhsevXqRVpaWpcQNoWFheTl5bXYYpOamopSylyL7iZstHjo27cv4F3+vLw8wNs6evrppwNw5plnxj1mRwoba7HxRrn8Fnm3e62TyLaRHAc0AoOBkcAPlVKjogql1Byl1HKl1PI9e/bE2aWlq6JzfugerhU2ibF69Wrzf6yXV2TmYWi9sGmvXqB2RcXavzMfS3MsNq0RNjt37gRg3rx5ca0+8Sw2Pp+PlJQU0tPTu4SwmTZtGv3792+xxUZbWpVSUblx2hItbNr63kvUYtO7d2/69u3ref11XMyll14a95jxhE1buqKsxcabrcBQx/chwPYE14m17a6Qu4rQX/3m+Q7wdxGpF5HdwIfA5MhCicjDIjJZRCZrNW3pftTV1ZGammp6uNYVlRhr1qwx/8d6ebV18DC0v8UmVn2cQbDNsdiUl5e3OEfJjh07wv7GwkvY6HPfq1cvlFJdRticdNJJ9O/f3wi/5lpstLAB2i3ZHHScK8qr/Hv27GH//v2eOaC0+NexQLGwrih3OlrYfAKMUUqNVEqlAJcAL0es8zJwRWh01FRgf8i9FGvblwEd4n4l8FLo/1LgtNC+egNTgaa3uKVHUVtba11RLcApbGJZbNoyxmbQoEFA1xA2zbXYiIjZf3PRFhv9NxZerihdVi0EWiJsLrjgghYnbosUNm+99Zb5279/f3MOmyNs3nvvPXbs2GECZrujsNHiIZbFRseziYgR+ZHEEjZHHXVUWFoGp7Bxy2NjXVEdgIg0ADcAbwKrgWdEZJVSap5Sal5otdeBjcB6YBFwfaxtQ9vcBZyhlFoHnBH6DsFRVBnAlwSF0Z9F5PP2raWls9AWG+uKah6rV682o6La22Jz4MAB0tPTzcu/vYSNdkXF2r8zeFgphd/vT8hiAy13RzVH2CRisYGWCZsXX3yxxYnb9DQUms8++wyAJ598kv79+5vfmyNs1q1bB2ACZlNSUrqdsEnEFeUUxF4WGS14IpcfPHiQL7/8MiwtQ3u7opwjF63FJgYi8rqIjBWR0SLyq9BvC0RkQeh/EZHi0PKjRGR5rG1Dv5eLyOkiMib0tyL0+0ERuUhEjhSR8SJyT0fX19JxRFpsuqMrqqGhgcLCwg5LAhcIBPjqq6+YPDnooe2IGJusrCwyMjKArmOxgaDAScRiA20jbOLl8IlnsWmNsBk4cCAAF154YcLbfPOb38Tn8/H888+zZs0ac3/OnTsXv9/P3LlzWyxs8vLywgKq9TQN7UFnuqL27t1r/tfiOxL9DEYudws8b29XVH19vXGZWYuNxdIJRMbYdEeLTWlpKWvWrOmwJHBbt27l8OHDHHfccUDHxNhkZma2u7BJJHjYLYNuR1lsampq4jYU8Sw22hXV3FFRgUDANIiJuqLuuece3n33XUTEXHN9f86fP5+Ghgbmz5/fYmGTnp7Od7/7XRNQ216uqIaGBnNPdIYryilsvCw2Xq6oLVu2AHDJJZeY89TewsZ5X1mLjaXHcdFFF8VNud/ZaItNd3ZFJZJYTnPZZZe1+proEVFa2MSLsWmJsLnqqqtMOQ8cONAhwkZbbKqrqz3L6HRF6b8dIWz0/RnPHdVeFpvNmzc3K0D1m9/8Jj/60Y/IzMzE7/czYcIEY6GJpCXCRkTYvXt32Lbt5YpyxrUkcu9997vfTdh6qq9BrOHezlG3zRU2paWlQLjl0E3YOM+zLpNzm+bgvK+sxcbS43juuefiptzvbHrCqCjdIH/ve9+LOxz4qaeeavU10YHDU6ZMAeJbbFoSPPzYY4+Zcmphk56ejlKq3YUNeL/U3VxRsdwfej9KqRYJm0AgwM6dOzn66KOB+MKmvWJsVqxYYf73coc4y/zuu+8Cwfo3NDTwxRdfGAtNJC0RNocOHaK6ujps2/ZyRWk3FCQmbB5//PGEraeJxNi0xhWlhY2z3JHCBsID4NvKYtO3b19rsbH0PEaNCqb/ueaaazq5JN70hFFRukH2GjHhRI8suuCCC1p8PB08etddd5GSktIurqhx48YBwUkHdYyNUoqMjIxmC5s5c+Yk1IOuqKgwZfSqU3NjbA4ePIjP52PAgAEtEjYVFRU0NDRQVFQExB/yrRulth4VVVJSYv6PZ7HZsGEDEMyZk8g0Ei0RNvpcRgobr+d33759LZ5jTAubAQMGJNRQa1GZSN2bG2PT3OBh7YqKtNgkJSV5vvfaStj079+fqqqqdpvbra2xwsaSEEcccQQAP/nJTzq5JN70hFFRupeWiLDR1pPWzFK8alVwYOHChQvp06dPuwQPDx8+HIATTzzRWGwgOD9Vc4XNI488ErcHLSJUVFQwdGgw7ZVXA9ZcV9ShQ4fIyMhosbDRFhotbBJ1RbWHxWb8+PH4/f64wkaLoE8++SShaSTaW9js3LmTnJycFgfXa2EzdOjQuPfegQMHqKmpoX///gnVPTLzsJfFJjs7m7S0tBa7oiItNr179wZwfe+1dlSU3n7AgAGISItdWh2NFTaWhNANrdsL3dkL6Uy0xUZP0Bf5YmlNLoeOIlGLTW1tLVu3bgXCXQvNJTs728Tz9OnTp10sNtu2bQOCjaQOHgZaZLEZO3YsgHHnuHHgwAEaGhoYMWIE0LYWm969e4cloWsOWsiMHz+e5OTkhF1RbR1jU1JSwqRJk+jbt29CwiYpKYnx4yPnKnYnIyPDNLCtETZeMTYrV64kEAi02AWrhc2QIUM4dOiQZ5I8gI0bNwLBZzLWeprq6mrS0tJITk7G7/d7Wmxyc3PJzs6OK2yqq6vD3mFeMTZa2LS3xcZZtq6OFTaWhNA3dOQL/eKLLyYvL4/vfOc7nVGsMLTFRikV1ePbunUrGRkZXT4AOlGLzebNm01D4XQttITrr7+e+fPnk5mZ2S4J+rSw+eyzz8IsNi0RNqNHjwaC81s54yWc6HOoLUVex2ipxaa1wmbw4MEMHDgwIYuN3+8PmxsKokdFNUfY7Nmzh23btlFUVER2dnbcGJsVK1ZQWFgYZTXyQillGsHWWmzcYmyc03+0ZIZ1p7CJN7u8dsM1NjZ63mtOnNmT3ZLlQVDY5OXleZ77hoYGDh8+bIbja/HT2NhoOjJeFhu32MK2FjbdJc7GChtLQngJm+eeew6Av/71rx1epkj0XFEQfLE4hc3GjRsRkS4fAK0tNvFepPqlW1RURElJSUI9ykhqa2uprKw0L9H2sNhUV1dTWVlJRkYG69atIxAIhAmb5r4od+/ezYgRI6iqquLBBx90XUefQy1suorFRsfUDBw4MCFhU1NT4yoovCw2icQ/aBE8ceLEmFYD5/oTJ06Mu18nLRU2zulsvFxROtg9KyuLBx54oFnlgiahMGTIECB2ALG22EBiVunDhw8bYeNV/j179pCbm0u/fv1cz71+z+p7V6+zY8cOGhoa6NOnT7MsNm3lirIWG0uzOP744/H5fF3aigDewubEE08EgkNCOxvtioLgQ+7suejGLpFh1J1Joq4o/dKdNWsWVVVVbNq0qdnH0pMVamGTiMWmucJm+/bgdG7Tp083v2VlZQEts9js2rWLU045hWHDhnHbbbcxb968qHV0T7itXVFOi83Bgweb3Vjs3LmTXr16kZGRkZCw+cc//sGhQ4ei3g1uMTaQWEzZz372MwCeeOKJsMa1qqoqKjHkrl272LFjh4kJSpSWCJvMzMwwEeflitLCZv/+/WzevLlZ5YJgh8Hn85l7Ptb9pzsPkJiwqa6uNtcklsUmlitKP3/63tXraDfU+PHjqa6uprGxEQhaQUtKSiguLm4XV5TefsCAAYC12FgS5OOPP0ZEurQVIRAImBs6UtjoBuvcc8/t8HJFol1REN1j0oKhuLg4oUDAzsLpiorVA9+wYQO9evXi//2//we0zB2lG9ZELTZuwcOxhAA0uaG+/e1vm99a6opy5jvRZvlHHnkkar1Ii01buaIOHjxohA2E5yRJhJ07dzJw4ECUUgwcODDuqChnYLcTt1FRQELuqGXLlgGwZMmSMHfIhx9+GJUY0mndaQ4tETZONxR4WzxWr17NhAkTwsrXHPbt20ffvn3NPRhP2Oj3SaLCxmmxiRQ2ItJqYVNYWAg0CRX9fC1cuDBK2DQ2Npr72bqiLB2KNiN2ZSuCnrQNooWNbkTi+eo7AqfFJtIVpcvX0sRqsRARpkyZ0ibTIOjzWV9fHzMPz8aNGxk1apSZFM8ZQHzdddfh8/m4+uqrYx5LCxs9bLw9LDb6xTt16lT69etnjgPNFzZVVVXU1tbSv39/MxJs5syZUeu1lytKm/31S76595IWNhA853v27Il5/vLz84Hod4OXxSYRYePcp7Nx1dfJOaxbC4djjjkmgdo10VbCJjLGpqKigt27d3PxxRfj8/laJWwSSRC5ceNGJk2aBDTfFRX5/tHHqqurM64ot3dmpCtKrxMpbHS5deqEuXPnRgkb/f7o1asXNTU1LXJXW1eUpdnU19ebG+eee7ruNFbOm9lL2CSSwbS9ibTYuLmimtvLToR9+/axfPnyNpkGoby83PTIY7mjNmzYwOjRo0lLS6OwsDDsJf/oo48iIixevDim4GyuxaYlwcO6wczPzzcujZYKG2eQ6Z133gnA5ZdfHrWerrOOo2jLGBunxaY1wmbgwIGISMz7MScnh3POOSfKwtgaYZORkcGsWbOYP38+/fr1Mzlh9HX6yU9+Yo63YsUKhg8fbvKyJEp7WWy++uorACZNmsS4ceNaNBowUtjESgWwZcsWk5E7kfeG0xXlZrHR4kgHD1dVVUU9P14xNlu2bKFv376mE6LjbNLS0rjuuuuYP39+1HBvffycnBxTvubiHO4N1mJjSYBt27YZFd1Vhky7EUvY6Eaks4VNIBCgoaEhLMamoyw2TvFw7bXXtng/evSFNkN7CRsRMRYbCDbQr732mrEW6bgnpVTMCTW1K0Q3KpmZmRw8eNCzZ9dSi02vXr3IzMw04lIHfeo8NrFcbt/5zndM+Z3CRgsEN3dOeXk5mZmZpKSkxAxQ9nJFFRcXu46eawuLjW6YdPljxdm4NfjO8sZyRV155ZVR111EKC0tZdiwYUBwqH9jYyNVVVVG2DjP55tvvklpaWmzrZBvvPEGAPfeey/QMmHjFmOjR0QVFBQwceLEuBabQ4cOmXpp9u3bR3Z2dlyLTVlZGY2NjRx11FGkpaU12xXlZrHR+9CuKF0eJ/qZ19fI6YoaNmxYVLn3799vYta8LDbaUtoSd5S12FiajTYvQvcQNoMHD+6yrijd+/YaFaXL2R7Cxpm+f9asWS3ej36JjRw5EvAWNjt37qS6ujps6DM0xWJMnTqV1NRUnnnmGXbv3u1pSdq5cye5ubmmoezTpw/g/bJvSYK+bdu2kZ+fj1KKL774AmgaSZeRkUFjY6Nn0Ounn37KU089ZcrvFDZ5eXn4fD5XYVBRUWF6qbGSAOp7JlLYLFiwgEAgwIIFC8LW1xYbPXqnOfdSbW0tFRUVYRYb8BY2gUCAPXv2uAqbRCw2jz32WNR1Ly8v5/Dhw8YaoBvXiooKE+TtnKRTW3Oaa4V85513AHjppZeA2MLGq56pqak0NDSEiew1a9aQkpLCiBEjKCoqoqysLOzZi+SEE05gyJAhYc9koq4oHTg8evRocnNzW+SKirTYaKuPU9hEvjf1u7Zfv35kZWWFuaKGDx9uQhcOHTpEXV0dNTU1nsJG3w9a2LQkuV51dTVKKbKysvD7/dZiY4lPdxM2Y8aMYc+ePWEvm67iitIPc7xRUXv37jUjCtoK58v1hRdeaPF+9EtMW2K8hnzrEVF6PR3Aff755wNNvbsLLriA5ORkz5FgTtcINAkbr15ZohabQCBgXqrbt283cR3z5s0LmzzR2bgcOHAg7L7697//bQKjIRgT4hQ2fr+fvLw8V2FQXl4eJmxiWWx8Pp9xr2lh841vfAOAU045JazutbW19O7d23w2bNhAWVlZXHFXXl7Ol19+CZCwsNm3bx8NDQ0xLTZewsbZM3ded/2+0dYA3eBVVlYay4Yujw7OTnQqBSeR1zmWsKmoqCAQCLgKGyAszmb16tWMHTsWv99vApqXL1/u6T7U5/z555831p1EhY1+xpojbCKDh2NZbJzn3ol+9jIzM8NioLZs2RJlsdEdn0hho997ka6ollps9Lxu8WLwuhJW2HQi3UXY6AfoiCOOoKGhIazB7SquqEiLjZcrSkRi9vJagt73hAkTeOmll1oUpAdNAkkLFi+LjbM3CfDb3/4WCM7ADk0vQZ/Px9SpUznhhBNcR4I5XSPQFPviJgQCgQAiElfYVFZWMmDAAHr16kVxcbGx2ADMnz8/bPJE/ZJevXo1OTk5JCcnG7fHKaecQnl5ucl4+5vf/CYq38mgQYM8hY1uOGK5ourq6owQhiZhowNGTzjhBLNM93Z1mZOSkli0aBHDhg0jOTmZ2bNnux7jnHPOITc3l8mTJwNBoVFcXGyEzezZs11dPW5J6zR6JNjDDz8MRAubtWvXAsH7yHndI4WNthq4CRu97j//+c9mjyKMvM5aOP7gBz8wdR06dCg+n4/vfe97rvV87bXXALjhhhvMb2vWrDHBszpe68wzzyQlJcXVdagb/KSkJCZOnMi8efOorKxM2GKTkpLC4MGDycvLa5Ph3m6uKDdho5Sid+/eRtjs27eP/fv389BDD3HfffcBwftRv4f1FA7t5YrS91e8GDwnRxxxRKcmQ7XCphPZsmWLuRnbI6i1rdAqXc8XpV+6gUDAPJidLWwiLTZurij9omtrd5QWJLNnz2b79u188sknrdpPPFfUxo0bUUqZWBwthLTgccZRTJw4kZUrV7paqZpjsdECJlbw8MGDB5kxY4Z5gS9cuDDMYhOJblzuuOMO43ZYuHAhhw4dMvvQeUs2btzI7t27ycrKMs+MVy6YSFdUc4WNPo/OBG3OWZR1XZ08+uijrudNN9A+nw+fz2fqqBsLL1dPLGHz6quvAk3WwUhho8/Ztm3bwqwkXsJm165d7N69G7/fz65duwgEAmbCRe22ag36udN1P3z4MFu3bkVETGLPyHr+3//9HwCLFy8Ggs/3xo0bKSgoAILiwOdrar4iE2+KCLW1tdx8882mo7Fo0SIOHz5M3759SUtLw+fzxRQ2I0eOxO/3t8gV5WWxSUpKIisry1PY6ClHlFJG2Hz88cemjtqNG8ti4+WKaq2wSdRio5+hzkyGaoVNJ1JaWsr48ePx+Xxd2mLjJWz2799PIBAgLS2NioqKNp/59cCBAwlPg+BmsdE9Fj0pon4ptrWwqaioQCnF5ZdfjlKKqVOnopRqduLFSFeUl7B56qmnEBF+8IMfAJiJGTdu3EhdXR07duwwjVdRURGHDh1iw4YNiAhDhw415zNS2MSy2GgBE8tiM2nSJJYtW2b2efTRR1NXVxdX2Lz99tvmt7lz55pYHJ/PZ+IjNmzYEBVk6pULxmmxiRVj89Zbb3HgwAFzjbSw0YLGmaBN70OXee7cufj9fiZMmGAa2L59+4Zd7/r6euMKnDdvXpSLJjc319NNGEvY6GPr7byEjY7r0WzZsoX09HRyc3OBJmHz73//G4AjjzyShoYGysvLKS0tRSnlee2ag3M/c+fONS4iwFjknFmHIThVCzQl/tRZq3/1q1+Zc6zPp27YneexvLyc6upqhg0bxnXXXQc0ibS+ffuilIp5b7z33nusXbuW4uJicnNzozqeesj59ddfb36LN6WCzmGjlDL3p1uMja6PHhL+wQcfAMFOxXe/+10gKLTjCZu2dEVB4hYbZ4egs9KYWGHTiZSWljJixAhycnK6hbDRrg/90nW6Turq6lo0nDAWmzdvNhPVxVP++mF2c0UdPnyY2tpaY8ZuD4tN3759zUtL09zAS30+R4wYgVLKU9hoV4Nz36NGjWLDhg2mJ+y02EBw6O6KFSvYunWrOZ81NTWuFhu3l5e2+HgJm/r6etatWwcErY8TJ040FoJ4wgbgvPPOIykpiXvvvdcM4924cSOLFi0y/7sJG21hcJZz37595mUeyxWlG3R9HpOTk6mrqzMv5lgWG+1u+eKLL2hsbMTn8yEiYQHH77//PnV1dTz//PPMnz8/ykVz0kknMX78eFdXTyxhE7kf3fDohsw5n5JzVJC25Ol7VDeuWmgce+yxQNCSV1paysCBA6Mm4GwpF198MWlpadx3330m3sXn85l7JLKeixcvxufzGXfg559/DoRbZvR5+OEPfwiEp8xwWqcWLFjAxRdfTFlZGdDkuvFKN7Bnzx7Ky8vN85ubm8v+/fvDYnmeffZZRMS4AxsaGqivr4853FtPpwDEdEXpDoa22CxdupRjjz2WhoYG7r//fiAxi01bu6IStdhoYZ2fn99pyVCtsOkknEMvEzV1dhb79+8nIyPDxGNEChttyWlrd5TuJSUyDUJkThKnK0qXs70sNk4Lge5F6h6oV+yF1358Pp/JjOombETENSB49OjRbNy40bzQde9UzyRdUlLCiy++aNa/8MILAVwtNrFcUZHC5vbbb6e4uJiVK1cCTcGm5513njnv8YSNUorDhw/T0NDAqlWrKCkpITs7m2HDhpGVlUVOTo6nxaa+vp7KykqqqqoYN24cycnJiAh33nknxcXFnr1MLQ6dwbHJycns3buX2tpaRo4cSXl5ubkGkRabSPQwf507B4KuovT09LDpJJwMGzaMLVu2uFo69T2qG8JY6Ibn2muvpbi4mDVr1hhh5yZsNL169SI5OdlYyHQc0M6dO02cVlsxceJEampqWLt2LStWrCArK4szzzzTxJTo8mpSU1MZNWqUEWklJSUm0DvyXaCfa31NgShX2vnnn2/uYS0qtOgVEU4++WQzPP6VV14BMMfS18AZm6fF0TnnnAM0WctiDfdetmwZX375JcXFxSQnJ9O7d28qKyv57ne/a6yokcKmoqKCjz76iJNOOgloChjvDFdULIvNt771LXP+tLCJ7HR0JFbYdBL79u3j4MGD3ULY6IdNP+D6pavNqO0lbPQ5OfHEE+Mq/1gWG13OMWPG4Pf728UVpV/Muhepe5XNETYVFRVkZ2fj8/nIyspyFTbbt2+nvr6eBx54IOycjBo1irKyMjP0WzdKKSkpjB8/npKSEl544QWTxEtnk03UYhMZY6PdL7pXq83lpaWlzJ8/34zQAm9hM3bsWJRSiIhxR5WUlFBSUkJRUZGxLIwaNcrVYqOF9s6dO3n77bdZu3atEQm6Z+/1MtbxKZs2bTLnUY82AjjjjDOAJquNFjbaYhPJwoUL+eUvf0lpaamZlPTFF1/kzDPPNI1RJMOHD+fgwYOuo992795NTk5O1MzebuiGWtf5q6++Mi6cWMJGx3HoOkZabNpS2OhgX+f11fdIbm6uua+cFBYWmkZSb+O0VGm0sNHrQnQ80VlnnWWub6TFZtOmTXzwwQdmePwLL7zA8OHDqa+vZ/78+aaTot9HFRUV5l13wQUXAO7CxmmxERETD6bfDVq4PPHEE+baRQobncR12rRpQPC569Wrl6sryufzkZyc3K6uKLdOT1lZGW+//bY5f1qMNjQ0dFoakA4XNkqpM5VSXyml1iulbnVZrpRS/xNa/rlSalK8bZVS/ZRSbyml1oX+ZjuWHa2U+j+l1Cql1BdKqejpcjsBZ4/CzYebCNOmTQuLPzn77LPbJRJdP2xJSUnk5OR4Wmzi3cTnnntus8qnXyTOWAcvYg331uXMzc0lLy+v1cKmvr4+rIfntNhonC9yN6655pqoc+EcppyVleXa4OmXt36Za0aPHk0gEDACw2k5mDhxIu+//z5ffvmlict59tlnAVxHRSVisQHMnD2zZ89m6dKljBw50oiYCRMmmP39+te/dj0HvXr14j/+4z/w+/3MmTOHPn36sHz5cj7//POwiRdHjx7N2rVr2bt3b5TFBoINsXZf6bgX3dvu06cPhw8fjgqefvHFF5k0aVJY4+0mbPS9Fzkqyo3i4mIzAmfgwIFs27YtpuleH9s5OlLjlZzPjZSUFBOXdckll1BbW8tpp50GNE1CWltby86dO6OCgfv164eIkJqaauJdduzYYfKmtBUFBQWkpqby6aefmuur55fbs2eP6zuhoKCAtWvX0tjYyIoVKzwn4xwzZgw+ny/MBVdaWkp6enrY86TvF50gUgsbpyCaNWsWb731Fueff74R1rpDp99HH374YdhxoEnYOF1RtbW1Rmjr+8hpIezXrx8ffPCBWefb3/52WMI95ztFW2wgKK6dFhv9nDmPC+3jinLrJDgnI507d27Y+Yw30Wt70aHCRinlB+YDZwHjgUuVUuMjVjsLGBP6zAEeSmDbW4G3RWQM8HboO0qpJOBxYJ6IHAmcCsSeta+DcPYoEh1O6OSTTz5h6dKlYT7n1ygJA+gAACAASURBVF57rV0i0Z29iP79+7fYYvPKK680q3z6nOzYsSPuQ+mVoM85vLtfv35h5W8p//jHP8J6eE5BohkxYgRZWVmead8XL14cdS6clh8vi40z+6oT3bC988479O/f37yMICiy9PmbM2cOY8aM4bPPPgPCLTapqakkJycnFGMD8Mc//hEIDrn94IMPTK8SgtYAvR8dJ+OGtnA9+OCDHHPMMTz33HPU1NSETbw4atQo47LxEjYlJSUUFhaauBfds9dCxJmcbPv27SxbtizMqgRNwsbn8xmLR6IWG2jK5AtNbtR3333Xc/22EjYQHFkGTXFwRx99NHl5ecZio+NLIq0w2tqTn59Pnz596N27N59//jm1tbVtarFJTk5mwoQJPPvssxw+fJiJEyfSv39/Ix7c3gkFBQXU1dWxdOlS9u7d6zkZZ1paGiNHjgxrULUrzRnzps/F3/72NyBa2CQnJ/Ppp59SW1sbdm9ECpulS5eSnJxM3759zbXTz5fTYqOzoQOmw/HFF18Yi1N2djYbNmww99TJJ58cZbGBoHDTUxrocmuLjR5coXF26LTYauvg4UjXqT4H6enp/OEPf2D16tUcddRRgHtm8I6goy02xwHrRWSjiNQBTwORs9jNBB6TIMuAvkqpQXG2nQksCf2/BDgv9P904HMRWQkgIuUi0rbZ2VqIU9hoV1RzRhX95je/Mf/PnTuXmpoa8xC3dSS6M1LfKQzKy8tRSpnhybGETSAQMA9gouVzir1NmzbFXNfNYgNB64oWYDk5OQkJm9mzZ8ec0FKLC937cgoSjVKKoqIiV4uNbmQAkwwOwmN1vITNmjVryMzMDLO0QFODVlZWFtUgORuEe+65xwiQlJQUY5bX6ABBbUkZNWoUfr+fn/zkJ0C4sDn55JPJzs7mnnvuYc+ePWG9SsDsI9HrXVRUZK5NpMVG4yZsduzY4dmjd3Ov6Yy4XsJm2LBh5OTkkJub2yyLDTTVuaCgIG7d21LYfPvb3yYpKckElxYUFJCfn28a80jXjEY3noMHDwaC5/Sjjz5yXbe1FBUVmXLoazVnzhzP86SD/Z9++umwbdwoKCiIckVFWpzcEkQePHiQ1atXk5eXxzXXXMOGDRvIzc0105JAk7DRYvWDDz5g8uTJjB49Ospi4xzuDU3vpaVLl9KvX7+wDok+9zfeeCP5+fmsWLHCVdhEPldOi03k8+tmscnMzMTv9ycsbHTOKl0vp8VGRKIyGDvPweuvv86BAwdMx+BrYbEB8oEyx/etod8SWSfWtgNEZAdA6K9+I4wFRCn1plLqM6XUj9wKpZSao5RarpRa3lH5ZEpLS0lNTSUvL4/c3FwaGxtjTnroZPXq1bzwwgtkZ2fj9/v53e9+x6pVq8zN+NOf/rRNy6pzK0C0sNGjgSC2K6q0tNT0ZhOd8NMpbJwjVNxwG+4NwYe7uRabP//5zzEntNQv0FtuuYX77ruP/fv3R7miIPgi/vzzz6PcIL///e/x+/3069fPNCgQ7YryEjYFBQVhPVEINkj6BRT5QnfOzrxw4ULzohw4cGDUfnSv7OGHHyYQCLBp0yYCgQBPPPEEEC5skpOTOfvss03OEafFBqJH78RDN1ypqalhDYC2RkG4sOnTpw/p6emsWrWKsrKyhIXNL37xCwAefPDBsHW1sNFCSsf2QGIWG2iq8+rVq+PWvX///qSmphq3tJPmCpvs7GxOPfVUKisrycvLIycnp1nCRrsQBw4cGBWn1Vbo65OcnGxEy4IFCzzP07hx44AmC0usWcYLCwv56quvzLPmFiPkliBSW2wKCgr40Y+CzUN5eblJHAhNFo+9e/dSXV3N8uXLmTZtGsOHDzfXTt9fs2bNori42MSyaXHxwQcfcNJJJ4Xl3tHBzjt37mTixIksX76cw4cPm3etttIsWbIkrJOlLTb79u0zHU6Nm7BJS0szcTmJMG7cOOMmdwobnZMpssNXWlpq6quTR5566qmmbp1BRwsb5fJbpJnCa51Eto0kCTgJuCz093yl1OlROxF5WEQmi8jkyHwK7YV+8Hw+X5SpMx5333036enp3HXXXTQ2NvLll1+GWQYSiUmJx6FDh8wDEssVlZOTYxJKxbLYOHtTTotFLPbs2cPYsWOB+HVyS9Cnf6+oqKB3796kpqYmJGx0r9xrQkttsSktLTV1jrTYQJMLSA+DhuA1XrRoEZdddhnTp08P87E7LT99+/b1dEVFuqEgaCHSAiDyhZ6VlcUVV1xheqtagDjdUBodIKifA22N07EDkUGezgBQ3RC1FG1ZmjBhQli8i5fFRinFoEGDePPNN8O2j6wPNAmTpUuXRgVxavQx9XkcPXp0mMVGKRXm4mstPp+PoUOHRlls6urqqKysbJawgaZroe+PSGGjlAqLvYKm+AstbJyWwLaMsYHw6+tMjOiF7oiUl5dzxBFHmGvpRkFBAbW1tWzZsoWamhp27doVV5g5hU1hYSGjRo0yw/ad90ZKSgqZmZns3buXjz/+mPr6ek466SSGDRtGaWkpImLeCdq97LTY7Nq1i3Xr1kVZXvRs5X/5y18oKioy37VY0TmSIl3WTotNLGFTXV1NamqqCThOxGKzcuVK1q9fb86BU9hod9rjjz8ets2WLVsoLCxk9OjRZgLUKVOm0KtXr6+NsNkKDHV8HwJsT3CdWNvuCrmrCP3VLddW4D0R2Ssih4HXgUl0Af75z3+ybt06kwAKEhM2hw4dYsmSJdTU1JgbraSkJCyWo7XCRuewSE9PjxqC+Mknn1BZWcmcOXOMhcHn84XNa+KGM7DPrYfqxt69exk3bhyZmZnNttjoDJ0333xzmCWkf//+VFVVeebc2bdvn+l9ubminC+xLVu2hFmDItEvci06A4EAJ554IocPH6auro5p06axfft2Nm/eTF1dHQcPHoxyRTndkwcOHGD79u2mtxuJl7CBYK9P91ZHjx5Neno6n3zySVQdMzMzWbduHbt37+aOO+5g48aN/P73vzfCIHKUzvTp0/H7/ZSXl4elv28JRx55JEopPvvss7By5efnm4YwsrF3Julz69Frkaqv6W9+8xvS0tJc3R+RFhvtaqivr+fgwYP06tUrrMfdFujG0Yl+DzRX2MycGfTML126lOLiYvLz89mzZw+1tbU8+eSTiAg333xz2DZuFhvApPRvS44++mgg+DwkOoBA3+te8TUa58gor3iiSLSI37t3r9k+0l2l0XGQN954IxB0Zw4bNoxDhw5RWVnJihUrzNQOc+fODbPYLF26FIi2aDoTLTqtjfpdqxM7RpbHGWMTKWycw8xrampMORIVNnfddZf5f86cOWHC5vLLL3eth3b7TZs2jUAgQEZGBvn5+Z6ZwTuCjhY2nwBjlFIjlVIpwCXAyxHrvAxcERodNRXYH3Ivxdr2ZeDK0P9XAi+F/n8TOFop1SsUSHwK8O/2qlyibN261TSIOgEUJDatgg6iFBGeeuopE6C6YsUKpkyZglIqrgiIxa5du/jWt77FwYMHjWqvqqoyD9Dy5csB+NOf/kRFRYVpiOMJmzVr1pjevltMgRt79+4lLy/PJJ+LRaTF5r333gPgySefDItd0Y2F17l2Wr6cYsxZpsrKSpRSlJaWmuvoZrEpLCwkOTmZFStWICJ8//vfN+bnZ555xvTgli5dGrWfrKwsGhoawgSY7tG5WWygqUGO90JXSpnA6kirRZ8+ffj3v/+NiBgLwJw5c8z5ixQ2vXv3Nvdja4PWU1NTzfBv5758Ph8jR47E5/NFCUjdEA8ePNhVCDhdUStXruT111/ntttuc3V/RFpsRo0aRWNjI6WlpRw6dChufE1LGD58eNTzoO/N5gqb/Pz8MIuDFivbt283907kNfISNpGBt21Bnz59XC0isdD3eqz4Gud6a9as8XS7ReK8nnp7L/dpbm4ur732msn5s3jx4rAYqZKSEk488USzrdNivHTpUtLT080cZBrnsZzCzTnKya082tIUz2LTXGGzfv16nnnmGeMCu/3226mtrTXC5s9//jO9evUKK6uImEBt/T7TrnKvudw6gg4VNiLSANxAUHCsBp4RkVVKqXlKqXmh1V4HNgLrgUXA9bG2DW1zF3CGUmodcEboOyJSCdxLUBSVAJ+JyGvtXtE46ERpWolH5kmIhX5odeBqUVERn332GStXruT4449nyJAhrbLYTJo0iXXr1pmH/oorrkBEzMN29dVXA8GcF05LiM7J4MWaNWuYPHlyWLbRWIiISUGuXQIi4jm5WqTFZs6cOUAwQZ3TxaMbCy93lFPYON1nGi12jjvuOLZv386uXbsAd2GjTdh33303KSkpPPDAA+Tk5JjrPmHCBLKysvjggw+Mu+qGG26guLjYvLD27dvHxIkT8fv9/PjHPwa8hc2nn34KBKdciIdXz1Rf59GjR5vh3BkZGaaneuGFF0ade699tQSvfVVXVxMIBEw5NLoh9urRO4WNTkroHJ7qRMdy6L9aKI4dO5Z33nmnXYTNsGHD2L59e9gs1rGyDsfDef60WNHB0m4zdet5p/ScTU5h0x40917RWZH/9a9/xVwvJyeHtLQ0brnlFpNeIJ4rzXk9vaygmp07d7J//3769Oljyq/P0caNG6NSFDhj/J544gmqq6tNqgU3RowYYZ49p7Bxo3fv3p4Wm0hXlBYliQibmTNnEggEjEtZtyNaHPl8vqgg7f3795t8bNqS8+mnn5qJXjtrVJTpbdlP8HPsscdKe3P66adLQUGB+V5VVSWA3H333XG3XbBggQBSWloqIiLf//73RSklgDzyyCNy6qmnyoknntiicgUCASEYtyQ+n08Aef311wWQhx9+2Kx39NFHyxlnnCGZmZnyve99T0REpk+fLscff7znvvPy8uTaa6+VIUOGyJVXXhm3LM5z8p//+Z+SkpIir7zyiimf3+8PW//ee+8VQCorK81vp59+uhQVFcm4cePk4osvFhGRZcuWCSCvvfaa63GvuOIKGTRokIwYMUIuvfTSqOULFy4UQH76058KID/72c8EkA0bNrjuT59Hr3LPmDFDhg4dKgMHDgxb58knnxRA/vnPf4Ztn5SUJHV1da7H8vv9rsdoDrNnzxZAfvjDH4b9XllZae6z1uy/pejzGHnsX/ziFwLIbbfd5rrdtm3bBJBbbrnF8xpoIs/f3r17zTZKKTn66KPbtlIi8qc//UkA2bhxo/nt8ccfF0C++uqrVu37888/F0COOOII8fv9Ul5eHrVOZJ1fffVVAWTOnDmtOnZb0Zx7Wt+f+nrV1NTEXH/x4sUCSFpamjQ2NsZc1+3+27VrlwAyb948AeSxxx4zy958800BzN9E6jBt2jQB5KOPPoq53s033yy9e/eW1NRU+dGPfhS27IwzzpATTjhBREQuvvhi086ccsopcvLJJ3vus6amJurd/+c//1kA+eMf/2jW+853viPDhw8330tKSgSQZ555RgKBQNg7ori4WLKzs2PWpTUAy8WjHbeZhzuYiooK3n333bChpjqwNVGLjd/vN6NpioqKTBzGxIkTE3LblJWVUVhYGDWkWQerOicf1DPLOnsR06ZN41//+hcHDhwIs9h4uaLKy8vZs2cPBQUFYSMJNJdddlnUhHL6XOTl5TF69Gjq6urC4gMie3yRFhtdzpUrV7J169YoV9Q555zj6ufXGU4LCwtdXVFr1qwhPT3dDAfVFh43iw009VCdSeOcVFRUUFZWRmVlJZdeeqlZR/fEHnvssbD1GxoauOmmm1yPFTk5YkvQrsZId2bfvn2bPXy7LfHq6f/v//4v0FTuSHSv/MEHH/RMya+JPH85OTlmwsT8/Px2s9hAuHu2NRYbJ9pis379ek455RTXOLDIOuvg4UWLFrV5os+W0Jx7WltpAdd4okj09Rw3blzc2Cm3+y8vL4/U1FRefjkYEeFmsdF5jNysZZHohJw6P5QX2mJTW1vrarHRo6Ga44p65513TDn1RJu6HXEGzBcUFLBlyxazL+cULkqpsHfEwIEDqaysjJpaokPwUjxf1097W2yWLFniqsrz8/Plmmuuibv95Zdf7qqYk5KSpKamRn71q18JIIcOHXLdfteuXTJ27FjXXsTq1asFkCeeeEIOHTokgMyYMcNYbjRPPfWU2f6BBx4QEZF58+ZJXl6e6zE//PBDYyW59NJLZdSoUWHL3SwBH3/8sQDyyiuvyFtvvWWON2HCBAFk27ZtYfu44447BJCGhgbzm9PaoXv0dXV1nj2ompoaSUpKkh//+Mfygx/8QNLT06N6cmeddZYUFRXJV199JYAMGzZM/H6/BAIB17rHw6tHunTpUgGkd+/eMnbsWKmtre0Qi4mXZaSrEq9H39jYaK733Llzm73/OXPmSFZWlkyZMkXOOOOM1hY3irVr1wogS5YsMb/deuutkpyc3OJ7ShMIBCQtLU0Auf/++xPaZt++fQlbGLoi2tKbSPn//ve/CyCXXHJJi483ZswYASQ1NTXMkqotw1OmTBGfzyf79++Pu69En73f/va3po7z588PWzZr1iwZP368iIRbb5y/uzFnzhzJyMiQ6upqqa+vF5/PJ5dcckmUJepvf/ubALJixQoREXnggQcEkB07dkTtc9GiRQLIli1b4ta9JWAtNl2Hn//850Aw+MxJotMqROZnKCwsRClFQ0MDN998s4kLcAsgvu666xgwYIBR4pETKepAr0GDBtGrVy+GDx9uknU5ewbOYYu6F9ivXz8qKiqM9QhgxowZJCUl8d///d9AUO0PGzaMsrKysMnR9JBiZ7I6bbHJzc0Ny2Oi141MfFdXV4ff7w8bjjx16lTzXZczOTmZyy67zNTfabVatWoVDQ0NTJw4kYKCAqqrq6OGpq9evZrCwkKGDg0O0CstLaVfv34tDrT06pHq833o0CHOO+88UlJSOsRi0pbxMh1BvB69z+czAc9uMznH46STTmL//v18/vnn7WKx0cOvr7rqKkaOHInf7+eBBx6gvr6+1aPMtKUJgrOnJ0JWVhbXX399t7oHnGRkZCT8nOjr6RWzlgj6XRyZokBbbD799FOKiorixs1A4s+eM5dSWwQPNzY28tJLLzFjxgzS0tJISkoKy2cUabGBpvjD0tJSUlJSXK2LzrncOhorbDqQRYsWmQy6erp7TaITYUbOuuvMB7Fw4UIjAtzcUX/605+AoJVuyJAhXHHFFWHR9jrQSwcQFhQUmNE6zgdzyJAhjBgxAiDMFdXY2Ggaj3vvvZc33niDxsZG3n33XVJTUxk+fDjDhg2jvr7eBN1C04PjzKviFDbO+r7++usAUVMV1NbWhrmhIPgC0CMRnK6ixx9/3KTddybi02KpqKjIdWK9w4cPs2XLFgoKCkhPTzdB315uqETwGoXhzCiq3ZbNTXjXluXpqiRSXp3WXmewbQ46ILK2tjZucr6WoO99EWHz5s0EAgHzDLXF1Cj19cEZZJyZyuPR3e6BSB588MGEyq9H//ziF79osdtNByhHjtrSgiIQCETlr/Ei0fPuFNhtIWw++ugjdu3aFSZ+8/PzXYVN5LxcW7ZsYejQoa6uPOeUJx2NFTYdxPTp05kzZ46Z28MrT0IsGhsb2bp1a1S0v7OH4mWxqaqqIjk52eRGcCYf0+gbUN+QzpECkT0O/cJ3DveG4LQKixYt4oc//KGxaoiIyWapRYqOs2loaDAjgpYuXWosPk5hk5SUFFVHN4uNW9IvXc5I8TFhwgTy8vLCrFYrVqwgIyOD0aNHm7o742zWrVuHiBjRo6+DW+xCa9EvrEGDBnHccce1+f6/Tlx77bUttkAMHz7cWD3aw2IDcOWVV4YNadeWm7awmOgEfW09f1xPQI+wbM38evp9FilsImP92pJYFhtnHhvnqKjevXt7CpsXXniB5ORkZsyYYX7Lz883MT9OYZOamsqoUaPCLDZeI+issOnh7Ny5k7feegsI3mxuqnzVqlUmYZ8XO3bsoLGxMWaq8OzsbLKysqJEy8KFC6mrq2PZsmXMnz8/LF28s5zO+YOcJtpIYaNnDdYz5erG/aGHHmLOnDkopZgxYwaffPIJycnJfPHFFxQXFxsxoIPOdHK6448/nh07dpgy7d27F7/fbx5cZy9s4sSJURabN998k3379kWdP2d2z0imT5/O0KFDzbV49tlnOXjwIDfeeCO5ubn069cvzGKj06y/8MILQNNLrTUWGy8yMjJISkpi586dUUOcLc1j0aJFLbZAKKVMw9QeFhsIuqUbGxvZuHGj+dtWFpO2CCjvybT2/Lz//vtAUxC7RltKIHqup9YSy2Lz4YcfmtnSIy02hw4dCgsV0OjnQ88HB4RN9RKZbds55DvWLPDaPdUpQ769gm++rp/2Ch4+99xzxe/3y/XXX++63C1wrL6+Xmpra6W+vl5EmoJwnYG8bkyaNEnOOuss872mpkYGDRokp512mvntzjvvFEAOHz5sfrviiitk2LBh5vu7775rgtScQbki0UGb77zzTtiwZOcy57o6OPGee+4REZGXX35ZABNotnjxYhEJBrMNGDDAtX667M6AvMhjepXTbT9VVVVhQaZ63W984xtyyimniIhIQ0ND1PKbbrpJALnqqqtcy9la2mL4tqX16ADJn/3sZ51dFEsXw+sZraioMEPt25r333/fvIs2b94ctsw5wGDIkCFmQMqvf/1rAaS6ujpsfa9gaz0IBUegsOaWW26R1NRUqa6uFqWU/PSnP/Usa25ursybN6+1VXYFGzzc+bz00ksxe2HTp08HmuIpzjvvPJKTk0lNTSU5OZlLLrnEuG/iJc/av38/b7zxhrFezJgxgx07doQpfR2L47Ta7Ny5MyzORbtjtPvMSWRPR2dP1ts5lznXzcrKIjMz01hstPK/4IILyM7ONtNE7NmzB695u7TZd+XKlWZdiA6GdiunE22RWrt2rTkPzmGZlZWVvPfee1x99dVmigbncn0d2sMVFa/slo5DW2x++ctfdokh0Jaug9czql1RGzZsaPN7JpbF5vTTg1MhXnzxxVEJ+oAod5TOhB45HF27XyHaYvPpp59SW1vLqFGjEBHuvPNOzzpqN1+HPzdeiufr+umIBH1u6GRaTz/9tIiEJ5silHDqrrvuEkAOHDgQc1+R1h+3YcIfffSRAPLyyy+b34466iiZOXOm+R4IBCQ7O1sGDx4ct/yBQCDh4YoTJkwwx7nmmmuMZebss8+WsWPHikgwWdWpp57qur1OuvY///M/IiLywgsvCCBLly6NW04nX375pRnerocxLl++3Cx3Jtc76qijZOzYsWGWq+eee04A+dWvftWs41q6F42NjZ2anNDS/YhMVteW6FQTuFjStTXnzTfflN69e5tEmw8//LAAUlZWFrb+E088IYCsWrUq7Hdnio3I4draSuX8eNWxPZ8brMWm66OHMWvLgQ4C1mnts7KyKC0tJTs7O+Yst9A0WdmUKVOoqqpCKRVlzXAbPRVpsVFKkZKSwvbt2+Mqbq8J29yoqqripZdeori4OGy26r1797J27VquuuoqM52CG4MGDSItLY3vf//7FBcXs3TpUlJTU5k8eXLM40ZyxBFH4PP5WLNmDStWrMDv93PkkUea5fPmzTPR/l988QX5+flhlittsbn99tttT74H4/P5OjU5oaX7EZmsri3RFpvMzMwoS7oz4WNkjA1EW2xWr16N3+837Y0mlsVGW6muv/76uKkBOu258VI8X9dPZ1lsREQGDBggs2fPFhGRoqIiEydz//33CyBjx46VY445JqF9TZ48WY4//nj561//KoC89957YcsDgYBkZmbKjTfeKCLBxHVKqag4gvZI2OZU8dnZ2cYHq3sCSinJy8uL6Zt17uO4446TadOmtagsY8aMkQsvvFBmzJghRx11lOs6XudA+5jb+vxYLBaLFzpOcejQoVHLdHK9W2+9VQC58847RUTk+eefd42XufDCC2XMmDFR+9m/f7+xxlRVVbVPRVoJ1mLTPdAjlQKBAF999ZWxZOj8AmvXrk14crrzzz+fjz76iAcffJDc3FwzBYBGKRU2/cKePXsQkTCLDbRPwrZrrrkGCOaRqKysNPWcO3cuSimSk5MpLy/3tNgAXHTRRQAcc8wxfPbZZy0eeaAj/FesWOE5g7DXOUhLS7M9eYvF0qHo0XmR8TUASUlJ5Ofnm9GgkRabY489llGjRpnEpGvWrHFNUJiZmWksQ5EWm+6AFTZdCJ1bprS0lOrqahO8O2TIEKZMmQIkPuuuFkPvvfce5557bpTJUh9Pu74ic9ho2iNZ1yOPPMJ//dd/mSHj+sGaP38+//rXv6irqyMQCMQUNn/961+ZMWMGJSUlNDQ0tDhXREFBAatXr2bHjh2ewibWOejuycwsFkv3IikpidTUVFdhA8E2QgcFa2Gjh14HAgE2bdpkEpOuXbvWc2bz/Px8kpOTXduOro4VNl2I0aNHU1ZWZkb7OJW0Hi310EMPJRTPUVhYaG58rwRJo0ePZtOmTQQCgaisw+3NTTfdZOJXnnjiCfP71KlTOfXUUwG4+eabY9b1xz/+sZmaQY9aai6FhYU0NjYCwUlELRaLpauTkZERU9jopKfa2lJUVMRFF12Ez+cziVovueQS6urqPKeUyM/P75bWGrDCpkuhh8/9/e9/B8KFzYUXXgiQcJZMpRQHDhwAgsnrvI5XW1vLtm3bwuaJ6ggGDhxI0E0KTz75ZNiy22+/HYhfV6f7KXLurURxnuNjjjmmRfuwWCyWjqS+vj4spYeT4cOHU1dXBzRZbJRSPPPMMzQ2NnLTTTfh9/s588wzAe+5srZt28aBAwe65cAIK2y6EDoy/bXXXiMnJycsj8uYMWOanRo+XnyMc/oFLWz0/CkdgVf5TjvtNK6++uqE6tqadPkA48aNA4K9nPbKR2OxWCxtycGDBxER146fM1zBmQFZc/7559PQ0MDvf/97jE8wLwAAIABJREFUwFvY6LmiuuV0HF5RxV/XT2eOitq+fbuJRD/xxBPb/XibNm0SQHw+n0yYMEH69u3b7sfsivTq1UuUUp5ZoS0Wi6Urcf3113tmsn/11VdNO+KWpb6xsVEGDRokgGd293jH6ApgR0V1DwYOHGh8ml4qui3Rc3wEAgFWrVrVYfE1XY2amhrP3o/FYrF0NWINWohnsfH5fMycOROI3c5054ERVth0IfQQbMAzUr2tj3fWWWeZ719XYdMeQ9otFoulM3BOSukmbKBp1Oz777/fLWNo4mGFTRdDx710hMUG4NVXX+XII490zWHzdaE790wsFovFSWZmphkx5TWq6Zvf/CZAj7VUW2HTxdi8eTMATz/9dIccz+fzceuttwLB3DA9Ub1bLBbL1wntjvKy2KSkpMSdDqE7oyQ05LbDDqjUmcB9gB94RETuiliuQstnAIeBq0Tks1jbKqX6AX8FRgCbgYtFpNKxz2HAv4Gfi8jvYpVv8uTJsnz58tZXtIX4/X4CgQB+v5+GhoYOOWZDQwMpKSmISIce12KxWCxtz9lnn81rr73Gpk2bGDFiRGcXp11QSn0qIq4TBHaoxUYp5QfmA2cB44FLlVLjI1Y7CxgT+swBHkpg21uBt0VkDPB26LuTPwBvtHmF2oHOiPdISkqyUwNYLBZLD0HH2XTXBHutpaNdUccB60Vko4jUAU8DMyPWmQk8FhrRtQzoq5QaFGfbmcCS0P9LgPP0zpRS5wEbgVXtVam2pLPiPWycicVisfQMdPZ6nez060ZHC5t8oMzxfWvot0TWibXtABHZARD62x9AKdUb+C/gjliFUkrNUUotV0ot37NnT7MqZLFYLBZLV2LZsmUAPProo51cks6ho4WNcvktMsjHa51Eto3kDuAPInIw1koi8rCITBaRyc5svxaLxWKxdDfmzp37tQ4tSOrg420Fhjq+DwG2J7hOSoxtdymlBonIjpDbanfo9+OBC5VSdwN9gYBSqkZEHmiT2lgsFovF0sWYP3/+1zqsoKMtNp8AY5RSI5VSKcAlwMsR67wMXKGCTAX2h9xLsbZ9Gbgy9P+VwEsAIjJNREaIyAjgj8CvraixWCwWi6Xn0qEWGxFpUErdALxJcMj2oyKySik1L7R8AfA6waHe6wkO97461rahXd8FPKOUmg2UAhd1YLUsFovFYrF0ETo8j01Xp7Pz2FgsFovFYolNl8ljY7FYLBaLxdKeWGFjsVgsFoulx2CFjcVisVgslh6DFTYWi8VisVh6DFbYWCwWi8Vi6TFYYWOxWCwWi6XHYIWNxWKxWCyWHoMVNhaLxWKxWHoMVthYLBaLxWLpMVhhY7FYLBaLpcdghY3FYrFYLJYegxU2FovFYrFYegxW2FgsFovFYukxWGFjsVgsFoulx2CFjcVisVgslh6DFTYWi8VisVh6DFbYWCwWi8Vi6TFYYWOxWCwWi6XHYIWNxWKxWCyWHoMVNhaLxWKxWHoMVthYLBaLxWLpMXS4sFFKnamU+koptV4pdavLcqWU+p/Q8s+VUpPibauU6qeUeksptS70Nzv0+xlKqU+VUl+E/p7WMbW0WCwWi8XSGXSosFFK+YH5wFnAeOBSpdT4iNXOAsaEPnOAhxLY9lbgbREZA7wd+g6wFzhHRI4CrgT+0k5Vs1gsFovF0gXoaIvNccB6EdkoInXA08DMiHVmAo9JkGVAX6XUoDjbzgSWhP5fApwHICIrRGR76PdVQJpSKrW9KmexWCwWi6VzSerg4+UDZY7vW4HjE1gnP862A0RkB4CI7FBK9Xc59ixghYjURi5QSs0haB0COKiU+iqx6iRMLkHrUU/F1q97Y+vXvbH1697Y+rWM4V4LOlrYKJffJMF1EtnW/aBKHQn8FpjutlxEHgYeTmRfLUEptVxEJrfX/jsbW7/uja1f98bWr3tj69f2dLQraisw1PF9CLA9wXVibbsr5K4i9He3XkkpNQR4AbhCRDa0QR0sFovFYrF0UTpa2HwCjFFKjVRKpQCXAC9HrPMycEVodNRUYH/IzRRr25cJBgcT+vsSgFKqL/Aa8GMR+bA9K2axWCwWi6Xz6VBXlIg0KKVuAN4E/MCjIrJKKTUvtHwB8DowA1gPHAaujrVtaNd3Ac8opWYDpcBFod9vAI4AbldK3R76bbqIGItOB9Fubq4ugq1f98bWr3tj69e9sfVrY5RIQmEqFovFYrFYLF0em3nYYrFYLBZLj8EKG4vFYrFYLD0GK2xciDF1w42h31cppe722PZRpdRupdSXEb/fo5RaE5om4oVQYHNzju06bUR3qp9SKk0p9bFSamXoGHd0o/r9MlS3EqXUP5RSg5t57G5fv+58/RzLb1FKiVIqt5nH7vb1687XTyn1c6XUttD9WaKUmtHMY3f7+nXE9WuvezPB7dvu2omI/Tg+BAOTNwCjgBRgJcEpHL4J/BNIDa3X32P7k4FJwJcRv08HkkL//xb4baLHDi27G7g19P+tbtt3g/opICP0fzLwETC1m9Qv0/H/94AFPez6JVK/bnv9QsuGEhx8sAXI7UnXL8H6ddvrB/wcuKUlx+5B9WvX69eOdYu7fVtfO2uxicZr6ob/AO6SUOZi8RhZJSLvAxUuv/9DRBpCX5cRzMOT6LHBY9qIFtBp9ZMgB0Nfk0MfHb3e1et3wPG1N+7JIbvz9Ytbv+58/UL8AfgR3ok9u+31CxGzfj3g+rX02NAD6tcB16+96pbI9m167aywicZrSoexwDSl1EdKqfeUUlMAlFKDlVKvN/MY1wBvuGzvdWyImDYCcJs2IhE6s34opfxKqRKCSRTfEpGPQou6fP2UUr9SSpUBlwE/ddm+W1+/BOrXba+fUupcYJuIrIz4vUdcvwTr122vX4gbVNBd+qh2R/SU6xciXv3a+/q1V90S2b5Nr50VNtF4Td2QBGQDU4H/JJg3R4nIdhFx9fe67lyp24AG4AmAiO1bPG1EM+jM+iEijSJSRNCic5xSakLLq+JeBJff2qR+InKbiAwlWLcbQr/1mOuXQP265fVTSvUCbiMk1sJ23AOuXzPq1y2vX4iHgNFAEbAD+D30jOsXIpH6tff1a6+6JbJ9m147K2yiiTWlw/Mhc+DHQIDg5F4Jo5S6EjgbuExE3C5ai6aNaCadWT+DiOwD3gXODP3U5evn4EmCk6omemzoGfUzdLPrNxoYCaxUSm0O7fMzpdTABI8NPaN+hm52/RCRXaFGPQAsIui6SPTY0DPq51y/Pa5fe71bEtm+Ta+dFTbReE3d8CJwGoBSaizBAKeEZyxVSp0J/BdwrogcbuaxwWPaiBbQafVTSuWp0GgppVQ68C1gTWhxV6/fGMfXcx3lTuTY0APq112vn4h8ISL9RWSEiIwg+BKdJCI7Ezx2j6hfd71+oW0GOb6eD7iNCuuW1y+0Tdz6dcD1a5e6Jbh92147aUFkeE//EJzSYS3BKO3bQr+lAI8TvOE+A04L/T4YeN2x7VMETYn1BF8ws0O/ryfoQywJfRZ4bB917NDvOcDbwLrQ337drX7A0cAK4PPQcX7ajer3XGjbz4FXgPwedv3i1q87X7+I/W8mNGqop1y/ROrXna8f8Bfgi1DZXwYG9aTrl0j9OuL6tVPdEt2+za6dnVLBYrFYLBZLj8G6oiwWi8VisfQYrLCxWCwWi8XSY7DCxmKxWCwWS4/BChuLxWKxWCw9BitsLBaLxWKx9BissLFYLBaLxdJjsMLGYrFYLBZLj8EKG4vFYrFYLD0GK2wsFovFYrH0GKywsVgsFovF0mOwwsZisVgsFkuPwQobi8VisVgsPQYrbCwWiwVQSr2hlLqys8vRXJRS/6GU2qWUOqiUylFKnaiUWhf6fl6i9VJKrVJKndoBRbZY2hU7u7fF0kEopU4C7gaOBBqB1cBNIvJJpxYMUEqNADYBySLS0Ib7nQ38J5APHAaWA5eISFUr9rkY2Coi/+34bTNwrYj8s1UFbieUUpnAL4ALgH7ATuBV4E4R2duK/SYDB4CpIrIy9NvbwMsicl+rC96yMv0cOEJELu+M41ss1mJjsXQAoYbtVeB+gg1bPnAHUNuZ5WorlFJJLr+dAvwauFRE+gCFwDMdXbZ4qCDt9i5USqUAbxMUtGcCmcA3gHLguFbufgCQBqxy/DY84rvF8vVCROzHfuynnT/AZGBfnHWuIWjFqQTeBIY7lt0HlBHsnX8KTAv9ngZUA7mh7/8NNACZoe93An8M/f9tYEVoH2XAzx37LwUEOBj6nJBAmQQoBtYBm1zqcwvwYoz6pgO/B7YA+4GlQHpo2d8IWjX2A+8DR4Z+nwPUA3Whcr4C/AUIhM7DQeBHoXWnAv8C9gErgVMdx34X+BXwYWi7I0K/XRtaflWoPL8L1X0TcJZj+5GhclUB/wTmA4971PNaYBeQEeNcFIaOv4+gKDnXsSw1VI7S0H4WhM7dWOCQ47r9L7Ah4lykOusV2t91oWtaBfwbmBT6fTPwrdD/PuDW0P7KCQrSfqFlI0LHvDJUpr3AbaFlZ4auTX3o+Cs7+9mzn6/fp9MLYD/283X4EOyllwNLgLOA7Ijl5wHrQw1cEkGB8i/H8suBnNCyH4Ya/bTQsveBWaH//xFqjM5yLDs/9P+pwFGhRuvoUCN5XmiZbqySmlEmAd4iaIFKd6nztFADewdwIpAasXx+qNHNB/wErRipoWXXAH1CDfMfgRLHdosJunCc+zKNcuh7fuh8zwjV94zQ97zQ8ndDjfKRobolEy1s6gmKAD/wH8B2mtz3/0dQbKQAJxEUi17C5mlgSYx7Izl0nn8S2t9pBEXHuNDyPwIvh85zH4Ji7jcxrlvkuXDW6yJgGzAFUAQF3fDI7YCbgGXAkNA1WAg8FXHMRQQF1jEELY+FoeU/9zoX9mM/HfHp9ALYj/18XT4EBcJiYCtBq8rLwIDQsjeA2Y51fQRjUoZ77KsSOCb0/y+B/wk10DuB7wN3EWHNcdnHH4E/hP53ayBjlim0/mlx6nxWqCHeR7AHf29IKPhCZTsmgfPWN3SsrND3xcQXNv8F/CVinTeBK0P/vwv8ImK5UwBcBax3LOsVKsNAYFjo+vVyLH/cqzEnKP7uilG/aaHr5nP89lRIICiCVpnRjmUnELKQeVy3yHPhrNebwPc9ymG2I2jROd2xbBBBoZfkOOYQx/KPCcZOgRU29tPJHxtjY7F0ECKyWkSuEpEhwARgMEFxAcG4iPuUUvuUUvuACoKNWj6AUuqHSqnVSqn9oeVZQG5o2/cIWmMmAV8QbEhPIeiKWS+h4FSl1PFKqXeUUnuUUvuBeY59uBGzTCHK4tT5DZH/396dh0dV3v0ff39JCgqigCxFwaKAVcEIgoC2VbGPoFhAbN26iLV9rK20tb/HBW3rWhU3qIqVuoOKYLViqghSEERZA7KIC1KlGkUggKAgsn1/f9znJDOTSTJgIJnh87quXDNzzrnPnDMTycd79b6E2ob+hMDwy+h99yHULiUxszwzG2Jm/zGzDYQ/uFRxremu/ez42qPr/y7hD3RG104IG/F9bIqe7kf43tYmbKvqXGtS3jfVQcBH7r4jYdt/CZ9zM0KompdwHxOi7buiNWk+8zS+BTyX8J5vEzq8t0g45tOE55sIn41IjVOwEakB7v4OoeahY7TpI+BX7t4o4Wdfd59hZt8j1ECcQ2jCakToe2JR2RnAt4EBwDR3f4tQq3AGIfTERhNqiVq7+wGEvhrxOdINj6zwmhJvJcP73eHukwn9QDoS+mVsBtqmOfzHhBD0P4QA1ybaXtm1pm77iFBjk3jtDdx9yM5eexorgCZmVj9hW+tKjv830NvMGlSw/xOgdUoH5kMITUYlhJqtDgn3cYC772qI+Ij0n3m6405P+fz2cfePMyirobZSoxRsRPYAMzsiqnVpFb1uDZxP6McAIWRcbWYdov0HmNnZ0b6GhKaP1UC+mV1L6LMDlNYmzCN05I2DzAzgVyQHm4aEmobNZtaNECBiqwmdTg9L2FbZNWVyz/3N7DwzaxyNPOpGqEmaFdVOPAIMNbODolqa482sXnSdXxFqOuoTRlYlWplynem2PQH0NbPe0bn3MbOT48//63D3/xKGrV9vZnXN7HigbyVFHicEhWej34M60Xwz15hZH2A2obnpSjP7RjSXTF9gTPQ5PQgMM7PmAGZ2sJn13sXLfwi43My6RN9JOzP7VprjRgA3x/vMrJmZ9c/wPVYCbXbnSDORyugXT2TP+BzoDsw2s42EQPMmoSMw7v4ccBswJmp+eZPQPwVCv4iXgKWEJorNlG/6mEbohDon4XVDQufh2G+AG83sc+BaEoZeR+HoZuD1qPmhRxXXlIl1hM637xF1rgXucPcno/2XE5rO5hKauW4j/Js0KrrPjwmjdmYln5aHgaOi6xwXbbsV+FO07XJ3/4hQ63MNIbR9RJhPp7r+zfsJoa/LGsLIs7FUMHTf3b8i1D69Q2gm3ED4npoCs919C9CP8NmWAH8DLohq9SDU1i0DZkXfw78JNXQ7zd3/QfieRxN+J8cRmglT3U2o3Xs5+n2ZRfj9zcQ/osc1ZjZ/V65T5OvQBH0iIl+TmY0F3nH362r6WkT2dqqxERHZSWZ2nJm1jZqVTiPUDo2rqpyI7H4ZBRszO83M3jWzZWY2OM1+M7N7ov2LzOzYqsqaWRMzm2RhTZNJZtY4YV+Bmc20sHbJYjPbJ9o+wcwWRttHmFleQplzzOytaN/ohO0Do/d4z7JwHRgRqZW+SRhG/QVhqP2v3f2NGr0iEQEyaIqKwsNSwgRXxYT28POjkRfxMX2A3xImw+oO3O3u3Ssra2a3EzoyDokCT2N3v8rC1OzzgZ+5+0IzO5AwY+t2M9vf3TeYmQHPAP9w9zFm1p7QX+AUd19nZs3dfZWZNSF08utK6Kk/D+ji7uuq5dMTERGRWiWTGptuhLkw3o86uY0hVLsm6g+M8mAW0MjMWlZRtj9hFlaixzOj572ARR4t6Obua9x9e/R8Q3RMPmGGzjiV/S9wXxxY3H1VtL03MMnd10b7JhGm/BYREZEcVG7hujQOJnkERjHle8enO+bgKsq2cPcVAO6+Ih7KSFj/xM1sImESqjHufnt8gmh7N8IokWcSymBmrxNmNb3e3SdUcl1JzOxiwho0NGjQoMsRRxyR9oMQERGRmjdv3rwSd087UWUmwcbSbEttv6romEzKprum7xLWMtkETDazedHkXrh776jPzZOENVUmRWXaE2ZfbQVMN7OOmb6/uz8APADQtWtXLyoqquISRUREpKaY2X8r2pdJU1QxybNqtiLMlJnJMZWVXRk1VxE9xs1HxYTZU0uiuTXGE6aKL+XumwlzLPRPKPO8u2919w+AdwlBJ5NrFxERkRyRSbCZC7Q3s0PNrC5wHiFUJCoELohGR/UA1kfNTJWVLSQse0/0+Hz0fCJQYGb1o47EJwFvmdl+CUEon9BROZ7AahzQM9rXlNA09X50rl4WZj5tTOi/MzGjT0ZERESyTpVNUe6+zcwGEQJBHvCIuy8xs0ui/SMItSp9CLNjbgJ+XlnZ6NRDgKfN7BfAh8DZUZl1ZjaUEIocGO/uL5pZC6DQwpTreYQ1Z0ZE54oDzFuEhdqucPc1AGZ2U3QuCKv5rt2VD0pERERqP808nEJ9bERERGq3qO9t13T7NPOwiIiI5AwFGxEREckZCjYiIiKSMxRsREREJGco2IiIiEjOULARERGRnKFgIyIiIjlDwUZERERyhoKNiIiI5AwFGxEREckZCjYiIiKSMxRsREREJGco2IiIiEjOULARERGRnKFgIyIiIjlDwUZERERyhoKNiIiI5AwFGxEREckZCjYiIiKSMxRsREREJGco2IiIiEjOULARERGRnKFgIyIiIjlDwUZERERyhoKNiIiI5AwFGxEREckZCjYiIiKSMxRs9hAz42c/+1np623bttGsWTN+8IMfVMv599tvvyqPeeeddzj++OOpV68ed955Z9K+CRMm8O1vf5t27doxZMiQKs/15JNPUlBQQEFBASeccAILFy6s8lxXXHEFRxxxBAUFBQwYMIDPPvsMgEmTJtGlSxeOPvpounTpwpQpU9K+5wcffED37t1p37495557Llu2bCndN3XqVDp16kSHDh046aST0pYfPnw47dq1w8woKSkpt3/u3Lnk5eXxzDPPpC2f+hk/9thjDBo0iJtvvplOnTrRqVMn8vLySp/fc889LF26lD59+tCuXTuOPPJIzjnnHFauXFnBp1pm+/btdO7c+Wv/fvTp06f0c95ZCxYsYPz48V/r/S+88MIKP8+dtXz5ckaPHl36uqioiN/97nfVcm4RySHurp+Eny5duvju0KBBA+/UqZNv2rTJ3d3Hjx/vxxxzjJ9xxhkZn2Pr1q2Vnr8qK1eu9Dlz5vg111zjd9xxR+n2bdu2+WGHHeb/+c9//KuvvvKCggJfsmRJped6/fXXfe3ate4e7qVbt25VnmvixIml93DllVf6lVde6e7u8+fP948//tjd3RcvXuwHHXRQ2vc8++yz/amnnnJ391/96lf+t7/9zd3d161b50ceeaT/97//Lb3PdObPn+8ffPCBf+tb3/LVq1cn7du2bZv37NnTTz/9dP/HP/6RtnzqZ/zoo4/6pZdeWuExX375pbdr184LCwtLt02ZMsUXL16c9vyJ7rrrLj///PN36vejuqW7v501cODACj/PdCr7HX/llVdq9PMQkdoDKPIK/o6rxmYPOv3003nxxRcBeOqppzj//PNL982ZM4cTTjiBzp07c8IJJ/Duu+8CoVbg7LPPpm/fvvTq1YsvvviCn//85xx99NEUFBTw7LPPlp7jj3/8I8cccww9evRIWyvQvHlzjjvuOL7xjW8kbZ8zZw7t2rXjsMMOo27dupx33nk8//zzld7LCSecQOPGjQHo0aMHxcXFVZ6rV69e5OfnlyvTuXNnDjroIAA6dOjA5s2b+eqrr5Lez92ZMmUKP/rRjwAYOHAg48aNA2D06NGcddZZHHLIIaX3mU7nzp1p06ZN2n333nsvP/zhDyssuytGjx7N8ccfT9++fUu39ezZk44dO1Zarri4mBdffJFf/vKXFR5z4YUX8utf/5qePXty2GGHMW3aNC666CKOPPJILrzwwtLj2rRpQ0lJCcuXL+fII4/kf//3f+nQoQO9evXiyy+/BODkk0+mqKgIgJKSEtq0acOWLVu49tprGTt2LJ06dWLs2LFs3LiRiy66iOOOO47OnTun/R1xdwYNGsRRRx3FGWecwapVq8pdC4TalpNPPhmA66+/nosvvphevXpxwQUXsHz5cr73ve9x7LHHcuyxxzJjxgwABg8ezPTp0+nUqRPDhg1j6tSppTVaa9eu5cwzz6SgoIAePXqwaNGi0nNfdNFFnHzyyRx22GHcc889lX72IpL9FGz2oPPOO48xY8awefNmFi1aRPfu3Uv3HXHEEbz66qu88cYb3HjjjVxzzTWl+2bOnMnIkSOZMmUKN910EwcccACLFy9m0aJFnHLKKQBs3LiRHj16sHDhQk488UQefPDBjK/r448/pnXr1qWvW7Vqxccff5xx+YcffpjTTz99p871yCOPlJZJ9Oyzz9K5c2fq1auXtH3NmjU0atSoNBglnnfp0qWsW7eOk08+mS5dujBq1KjScn369OGTTz6p9Po//vhjnnvuOS655JIM7zgzb775Jl26dNnpcpdddhm33347depU/p/nunXrmDJlCsOGDaNv37784Q9/YMmSJSxevJgFCxaUO/69997j0ksvZcmSJTRq1CgpFKeqW7cuN954I+eeey4LFizg3HPP5eabb+aUU05h7ty5vPLKK1xxxRVs3Lgxqdxzzz3Hu+++y+LFi3nwwQdLQ0lV5s2bx/PPP8/o0aNp3rw5kyZNYv78+YwdO7a0uWnIkCF873vfY8GCBfzhD39IKn/dddfRuXNnFi1axC233MIFF1xQuu+dd95h4sSJzJkzhxtuuIGtW7dmdE0ikp3yMznIzE4D7gbygIfcfUjKfov29wE2ARe6+/zKyppZE2As0AZYDpzj7uuifQXA34H9gR3Ace6+2cwmAC2j654OXOru283sQuAOIP4LOtzdH4rOtR1YHG3/0N37ZfrhVLeCggKWL1/OU089RZ8+fZL2rV+/noEDB/Lee+9hZkn/+J566qk0adIEgH//+9+MGTOmdF9ca1K3bt3S/3vt0qULkyZNyvi6Qq1esvCVVu2VV17h4Ycf5rXXXsv4XDfffDP5+fn85Cc/Sdq+ZMkSrrrqKl5++eWdusZt27Yxb948Jk+ezJdffsnxxx9Pjx49OPzwwzPqI3LZZZdx2223kZeXV+WxFV1DdXnhhRdo3rw5Xbp0YerUqZUe27dvX8yMo48+mhYtWnD00UcDodZr+fLldOrUKen4Qw89tHRbly5dWL58+U5d28svv0xhYWFp/6zNmzfz4YcfcuSRR5Ye8+qrr3L++eeTl5fHQQcdVBq8q9KvXz/23XdfALZu3cqgQYNYsGABeXl5LF26tMryr732WmlQO+WUU1izZg3r168H4IwzzqBevXrUq1eP5s2bs3LlSlq1arVT9y4i2aPKYGNmecB9wKlAMTDXzArd/a2Ew04H2kc/3YH7ge5VlB0MTHb3IWY2OHp9lZnlA08AP3P3hWZ2IBD/lT/H3TdEQeoZ4Gwg/is/1t0HpbmFL929U5rtNaJfv35cfvnlTJ06lTVr1pRu//Of/0zPnj157rnnWL58eWk1PUCDBg1Kn7t72j+m3/jGN0q35+XlsW3btoyvqVWrVnz00Uelr4uLi0ubhiqzaNEifvnLX/LSSy9x4IEHZnSukSNH8sILLzB58uSk+yguLmbAgAGMGjWKtm3blnuvpk2b8tlnn7Ft2zby8/OTztuqVSuaNm1KgwYNaNCgASeeeCILFy5W5OoaAAAgAElEQVTk8MMPz+j+i4qKOO+884DQFDN+/Hjy8/M588wzk47bd9992bJlC3Xr1gVC80fTpk0rPG+HDh2YNm1aRtcQe/311yksLGT8+PFs3ryZDRs28NOf/pQnnnii3LFxrVadOnWSarjq1KmT9vtPPCYvL6+0KSo/P58dO3YAIaxUxN159tln+fa3v13pPVQU9ip7n8Tf8WHDhtGiRQsWLlzIjh072GeffSp9v/jaKrqO1Pvemf82RCT7ZNIU1Q1Y5u7vu/sWQpDon3JMf2BU1KdnFtDIzFpWUbY/MDJ6PhKI/4r0Aha5+0IAd1/j7tuj5xuiY/KBukD5f81quYsuuohrr7229P+uY+vXr+fggw8GQr+aivTq1Yvhw4eXvl63bt3XvqbjjjuO9957jw8++IAtW7YwZswY+vULFVvDhw9Per/Yhx9+yFlnncXjjz+eFCAqO9eECRO47bbbKCwspH79+qVlPvvsM8444wxuvfVWvvOd76S9RjOjZ8+epSNsRo4cSf/+4Vepf//+TJ8+nW3btrFp0yZmz56dVItQlQ8++IDly5ezfPlyfvSjH/G3v/2tXKgBOOmkk0oDxpdffsnTTz9Nz549Kzzvj3/8Y2bMmFHaryr+DBYvXszHH3/M97///XJlbr31VoqLi1m+fDljxozhlFNOSRtqqlObNm2YN28eQNIIpoYNG/L555+Xvu7duzf33ntvaYh44403yp3rxBNPZMyYMWzfvp0VK1bwyiuvpH2fyprB1q9fT8uWLalTpw6PP/4427dvT3s9qe/75JNPAmGEXNOmTdl///0zun8RyS2ZBJuDgY8SXhdH2zI5prKyLdx9BUD0GPfaPBxwM5toZvPN7MrENzKzicAq4HNCrU3sh2a2yMyeMbPWCdv3MbMiM5tlZuX/WoVzXhwdU7R69ep0h1SbVq1a8fvf/77c9iuvvJKrr76a73znO6X/kKfzpz/9iXXr1tGxY0eOOeaYpD8cVfn0009p1aoVQ4cO5S9/+QutWrViw4YN5OfnM3z4cHr37l06JLlDhw5A6J8Q18YkuvHGG1mzZg2/+c1v6NSpE127dgWo9FyDBg3i888/59RTT6VTp06lfVqGDx/OsmXLuOmmm0qHSid2Oo3ddtttDB06lHbt2rFmzRp+8YtfAHDkkUdy2mmnUVBQQLdu3fjlL39Z2kE3sY/NPffcQ6tWrSguLqagoKDSzrnp3H333fzzn/+kU6dO9OjRg7PPPpsTTzyxwuP33XdfXnjhBe69917at2/PUUcdxWOPPUbz5s1ZsWJFaX+hmnb55Zdz//33c8IJJyQNg+/ZsydvvfVWaefhP//5z2zdupWCggI6duzIn//853LnGjBgAO3bt+foo4/m17/+ddLQ++uuu47f//73fO9736u02e83v/kNI0eOpEePHixdurS0NqegoID8/HyOOeYYhg0bllTm+uuvp6ioiIKCAgYPHszIkSPTnVpE9gKWrgo36QCzs4He7v7L6PXPgG7u/tuEY14EbnX316LXk4ErgcMqKmtmn7l7o4RzrHP3xmZ2OXApcByhv85k4E/uPjnh2H2AJ4ER7j4paq76wt2/MrNLCE1Wp0THHuTun5jZYcAU4Pvu/p+K7rdr164ejxAR+MEPfsA///nP0uYXqR7Dhw/nkEMOKa3NEhHJKSUlkFjbP2gQVNJ0v7PMbJ67d023L5P/ZSwGEmtAWgGpw0wqOqZuJWVXmllLd18RNVvF/4teDExz95Lo4scDxxICDgBRR+JCQnPWJHcv66wCDwK3JRz7SfT4vplNBToDFQYbSfbCCy/U9CXkpEGD0nUHExHJUnGQ2bQJZs+GffeFiRPDvjp1oEcPOO20PXIpmQSbuUB7MzuUMOroPODHKccUAoPMbAyh8/D6KLCsrqRsITAQGBI9xpNiTASuNLP6wBbgJGCYme0HNIzOm08YgTUdIA5IUfl+wNvR9sbApqgmpynwHeD2DD8bERGRvVdiWJkzBzp2hDffhG7dyo5xh2nTYOtWSDPNBABt20Lv3nvmmskg2Lj7NjMbRAgcecAj7r4kavLB3UcA4wlBYxmh+ejnlZWNTj0EeNrMfgF8SBjhhLuvM7OhhEDlwHh3f9HMWgCFZlYvOtcUYER0rt+ZWT9gG7AWuDDafiTwdzPbQehPNCRlNJeIiMjeLTXAdOgAb7wB9evD5Mllx8WjPKsa7VmnDuzYEZqeSkrgzjuhmqfGqEyVfWz2NupjIyIiOa2yZqNM1a0L+fnhHHl5sH07tGwJK1bANdfALbfAuHHQogV0717twebr9rERERGRbBcHmrfegn/8I/0xZqF5KbXW5Q9/gGHDykLLP/4RjjvzTLjqqrDt/vtDkOnWDfr23S2BJhMKNiIiIrkmcVTSpk2wdm1oXpo/P/m4OMg0awarV8PVV4eQMnhweHzoobKwcs45yaEFYObM9EGmR489d68p1BSVQk1RIiKSteJA8957MHp0xccdeCCsWVO+2ahbt9DPJn6soVqXqqgpSkREJJfFgWbRInjuueR9++wD++0Xjon7wTz8cMXNRnFtSw3WunwdCjYiIiLZKg40b7wBhYXJ+xo1gs8+g7Fjy/rDxP1gakmz0e6gYCMiIpJt4kAzbx6kTqTavDmsWgWPPVYWYiD0h6mlTUvVScFGREQkW8SBZu5cGD8+eV8caB54IP0w6xyrmamIgo2IiEhtFweaOXPgpZeS97VoAStXVhxo9jIKNiIiIrVB6hDt2IwZsGVLqKVJFAeav/9dgSaBgo2IiEhNSF3KoGlTePbZqssp0FRKwUZERGR3Sg0wRx0VOv3m54famIrk58O2bWWzAH/zm/Dppwo0VVCwERERqQ6pAebb34aiorCWUmIzUrpFJOMZgA84ANavD0O069ULQ7TjWYBHjFCgyYCCjYiIyM5K7A+zcSOsWwdvv51cA1NZgIkXjoybleKlDEaOLD9Eu4bXXso2CjYiIiJVSayNmTkzNA+9+mrFx6cGmLgZKQ4w8cKRcbNSReEly2cBrgkKNiIiIqniILNxI0yfHvq4pI5KijVsGJYtWL264gATNyPFAaaWLRyZSxRsREREEmtkpk+HrVtDB9904s68cTPSE0+ULVmwMwFGQWa3ULAREZG9Vxxoli6Fp55Kf0zqqKS4M2/i6CQov2SBAkyNULAREZG9TxxoFi+Gf/4zeV9FQSa1NmYvXbKgtlOwERGR3JbYzDR7NrRuDUuWwIIFycc1bhxGN2USZBRiai0FGxERyU1xoHn7bXj66fL74xFLzZqFjr+PPqogkwMUbEREJLfEgeaNN6CwMHlfPAz70ENh6FAYMAAefLD8xHcKMllLwUZERHJDHGgWLoRx45L3NWkCa9eWDcP+619DrUxqh1/Jego2IiKSnVInzdu+PTwmat4cVq2CRx5J38ykmpmco2AjIiLZJQ40c+fC+PHpj4kDzQMPqJlpL6NgIyIi2eVf/4IbbkjeljpEO12gkb1CnZq+ABERkYyUlMDAgfDrX5dta9EiPA4eHB5HjAjNUf36hZoZhZq9jmpsRESkdispgXvvDc1ORUXJ+6paRFL2Ogo2IiJSu73+Otx4Y/K2du1g1KjkWhn1nREUbEREpDaKOwj/97/w/PNl2+M+NHfdBccfX3PXJ7WWgo2IiNS8xKHbc+ZAgwbJI55++tOwina8zEG88KRIioyCjZmdBtwN5AEPufuQlP0W7e8DbAIudPf5lZU1sybAWKANsBw4x93XRfsKgL8D+wM7gOPcfbOZTQBaRtc9HbjU3beb2YXAHcDH0SUNd/eHonMNBP4Ubf+Lu4/M9MMREZHdJHX9pn33hYkT0x/bti2MHAmXXqo+NFKlKoONmeUB9wGnAsXAXDMrdPe3Eg47HWgf/XQH7ge6V1F2MDDZ3YeY2eDo9VVmlg88AfzM3Rea2YHA1uh9znH3DVGQegY4GxgT7Rvr7oNSrr0JcB3QFXBgXvT+63bmQxIRkWoSB5p33oGxYys/Np6LZujQMJxbfWgkA5nU2HQDlrn7+wBmNgboDyQGm/7AKHd3YJaZNTKzloTamIrK9gdOjsqPBKYCVwG9gEXuvhDA3dfEb+LuGxKuuy4hrFSmNzDJ3ddG7z8JOA14KoP7FhGRryuxZmb6dNiyBebPTz4mXr+padNw/DXXhGUPEueiEclQJsHmYOCjhNfFhFqZqo45uIqyLdx9BYC7rzCz5tH2wwE3s4lAM2CMu98enyDa3g14iVBrE/uhmZ0ILAX+4O4fVXJdIiKyO8WBZskSeOaZ9Mc0bgzr1pWt3/TQQxq6LV9bJsEm3W9Vak1JRcdkUjbdNX0XOI7QX2eymc1z98kA7t7bzPYBngROASYB/wKecvevzOwSQg3QKZm+v5ldDFwMcMghh1RxeSIikiS14+/RR4f5ZmbNSj4udXbgRx+tOMio2Ul2USbBphhonfC6FfBJhsfUraTsSjNrGdXWtARWJZxrmruXAJjZeOBYYHJ8kqgjcSGhOWtSYnMV8CBwW8K5Tk55/6mpN+juDwAPAHTt2rWq4CUiIlAWaJYtgyefLNs+bVp4zMsLC1PGTUyDB4eamcSRTQoyUs0yCTZzgfZmdihh1NF5wI9TjikEBkV9aLoD66PAsrqSsoXAQGBI9BhPVDARuNLM6gNbgJOAYWa2H9AwOm8+YQTWdIA4IEXl+wFvJ5zrFjNrHL3uBVydwT2LiEiq1JqZAw6AwsL0x7ZuDXffDWedpSYm2aOqDDbuvs3MBhFCQh7wiLsviZp8cPcRwHhC0FhGaD76eWVlo1MPAZ42s18AHxJGOOHu68xsKCFQOTDe3V80sxZAoZnVi841BRgRnet3ZtYP2AasBS6MzrXWzG6KzgVwY9yRWEREKpAaYDp2hMWLw9wyL72UvkzDhvD552Udf4cPDyFm5kzVzMgeZWEgk8S6du3qRalrkYiI7A3iQPPuuzBmTNXHQ1kz07hxZbUyc+aoVkZ2q6jvbdd0+zTzsIjI3q6kJCxRMG1aqGFJJ+74e+CBsGZNWc1M3MykWhmpJRRsRET2JonNTDNnhhqX+fPhww+Tj0scin3rrWUdfx9+WP1lpFZTsBERyWWpSxfk58OUKeWPq18/HJNuKHa/fhqSLVlDwUZEJBfFgebtt+Hpp9MfEzcvtWsHd9wBAwZUPhRbQUaygIKNiEguiQPNG2+UH4odL10Qr8EUNy/ddVf6EUwiWUjBRkQkF1S2hEHc4TdeuiBegym1eUk1MpIDFGxERHLB3Llw442hRiYW18xU1uFXYUZyjIKNiEg2KymBO++Ef/2rLNTEc8skro6tICN7CQUbEZFsFDc9zZgBkyYl70s3t4zIXkLBRkQkG82eXb7pqV07GDUq1Moo0MheSsFGRCRbxLU0n30WOgjHoSaee+auu+D442v2GkVqmIKNiEhtFweaZcvgySfLtg8YAM89lzz3jMheTsFGRKS2KyqCm24Kk+nF2rQJtTZacFIkiYKNiEhtFdfUFBeXbdtvP/jiC7j77jBzsEY5iSRRsBERqW0qWw7hiSfU7CRSCQUbEZHaJt1ke4ceGvrXaMSTSKXq1PQFiIhIpKQEfvMbuOSSslBz4IHh8a9/DSOeFGpEKqUaGxGRmhbPHjxuHLz7bvK+eDkENT2JZETBRkSkppSUwL33wssvw6xZyfs02Z7ILlGwERHZ0+JA8+qrMHVq8r6WLWHFCk22J7KLFGxERPaUeLTTrFkwcWLyvnj24PvvV9OTyNegYCMisrvFgWb+/LAKd6I40CTOHqymJ5FdpmAjIrI7xGHGPazA/e9/J+9XoBHZLRRsRER2h6Ki5LlozMLz5s1h1SoFGpHdRMFGRKQ6xTU1H3xQFmqaNw+dhc89Fx54QIFGZDdSsBERqU6pNTUADz4IffvCIYco0IjsZgo2IiLVIa6peeedslCTugyCFqwU2e0UbEREqkO6mpp4GQQR2WMUbEREvo6SErjnHpg8OX1NjYjsUQo2IiK7Im56mjcPXngheZ9qakRqjIKNiMjOiAPN0qXw1FPJ+xLXdxKRGlEnk4PM7DQze9fMlpnZ4DT7zczuifYvMrNjqyprZk3MbJKZvRc9Nk7YV2BmM81siZktNrN9ou0TzGxhtH2EmeWlXMePzMzNrGvCtu1mtiD6Kdy5j0dEJEXclyYx1DRvHh7j9Z006kmkxlRZYxOFh/uAU4FiYK6ZFbr7WwmHnQ60j366A/cD3asoOxiY7O5DosAzGLjKzPKBJ4CfuftCMzsQ2Bq9zznuvsHMDHgGOBsYE11nQ+B3wOyUW/jS3Tvt3MciIpIiXrhy5syyvjSNG8O6dclz04hIjcqkKaobsMzd3wcwszFAfyAx2PQHRrm7A7PMrJGZtQTaVFK2P3ByVH4kMBW4CugFLHL3hQDuviZ+E3ffkHDddYGE4QfcBNwOXJ7BPYmI7Jxp00JNTaJHH9VkeyK1TCZNUQcDHyW8Lo62ZXJMZWVbuPsKgOgxqsvlcMDNbKKZzTezKxPfyMwmAquAzwm1NphZZ6C1u6f04ANgHzMrMrNZZnZmuhs0s4ujY4pWr16d7hAR2VuVlMDvfge/+lXZtrZtw/pP/fqVzVEjIrVCJsEm3X+xnuExmZRNlQ98F/hJ9DjAzL5fWti9N9ASqAecYmZ1gGHA/1VwvkPcvSvwY+CvZta23AW5P+DuXd29a7Nmzaq4PBHZqzzySGiCWrOmbNvQoepLI1JLZdIUVQy0TnjdCvgkw2PqVlJ2pZm1dPcVUbPVqoRzTXP3EgAzGw8cC0yOT+Lum6OOwP2BOUBHYGroesM3gUIz6+fuRe7+SVTmfTObCnQG/pPBfYtIrotHOG3aBHPmQMeO8Oab0K0b7NgBixbBpElQp054rVFPIrVeJsFmLtDezA4FPgbOI9R+JCoEBkV9aLoD66PAsrqSsoXAQGBI9Ph8tH0icKWZ1Qe2ACcBw8xsP6BhdN58oA8w3d3XA03jC4nCy+XuXhSNtNrk7l+ZWVPgO4R+OCKyN4sDzbJlYSK92LRpyY+xuLNwPOpJRGqtKoONu28zs0GEwJEHPOLuS8zskmj/CGA8IWgsAzYBP6+sbHTqIcDTZvYL4EPCCCfcfZ2ZDSUEKgfGu/uLZtaCUBNTLzrXFGBEFZd/JPB3M9tBaHYbkjKaS0T2JnGgefNNePbZyo9t0AAaNoRPPw19alRTI5IVzL2qLi97l65du3pRUVFNX4aIVKc40CxcCOPGJe9r2BA+/zx0Dv773+HKK+H22+H550NNzZlnhuf9+tXMtYtIOWY2L+o/W45mHhaR3FXZsgfNmsHq1fD442HIdrducOGF4XHAgLI5aWbO1Pw0IllEwUZEck8caBYtgueeS97XvDmsWgUPPlh+Dpq4qSmxyUnNTyJZRcFGRLJTHF4gjGqKzZ4N++4LEycmHx8HmsRZgjVcWyTnKNiISHZJHNE0enTZiKWKxE1OCjQiewUFGxHJLkVFcNNNYV6ZRPvsA5s3h9DiXhZo0jU5iUjOUrARkewQ19QkzgAch5lnnoH8/DCC6eqr4ZZbFGhE9lIKNiKSHYqKwiKUiU1PY8Ykr6o9c2YY1dS3rwKNyF5KwUZEaoeKljfo2DGMbtq8uSzUHHJICDWpC1CmG9UkInsVBRsRqR3mzEmukaloeQMIi1JqaQMRSUPBRkRqRmINzbRpUFxc8QinuEOwFqEUkSoo2IjI7pcYYmbPDmsvffQRTJ6cPsxcc03oADx4MAwZUtYhWItQikgVFGxE5OtL1z9m8WI44gh4+23Yf3948cWy4199Nbl8nTph+HZcI9O9e+gA3K0b9O+f3CFYRKQSWgQzhRbBFMlAYpB59VXIy4MZMzIrGzcrHXwwXHEFXHZZWQ2NFpsUkQxoEUwRqR6Js/4++WTlx8a1MPFj06ahfNys9Le/ldXCqEZGRKpJnZq+ABHJInPnhpFLiaGmTvTPSPPm4fGaa8Lj4MHJjw89FOaZuemm8Ni3b6i96dEjnCN16LaIyC5QjY2IVK2kBP76V3jhhbLOvvvvDxs2hOByyy1lazHFtS+pj+lW0RYRqWYKNiKSLLUjcNeuYdbf1PlkRo1KDjLpgosmzBORPUzBRkSSpS5dEAea1JFLiU1HCi4iUkuoj42IBCUlcN11cMcdZaEmDi4tW8LYseF5PJeM+sOISC2kYCMiQVFR6Ng7ZUrZtquvDo8jRsAPf1jW6VdEpJZSU5RIrov7zMQGDQpDr1P3v/lmWU1N27bw+ONlE+XF/WfU5CQitZyCjUg2q2hF7OOOC9s2boQVK+Dll8vKvPlmKBcf26wZPPNM8nmHDi1bukBhRkSyiGYeTqGZh6VWS11zad99YeLE6jt/mzYwerTmlBGRWk0zD4tkuzjQvPsujBlT+bHxkgVNm0J+Pnz6KTRpAmvXwn77wRdflC8Tz0lz991aZFJEspo6D4vUZiUlcP318Nvfwg03JIeauEYl7i8Tz/Abd/h9+OHQ6RfgkUdCx9/HHw+v49mB48dRo9QxWERygmpsRGqz1DlloKz2JV5z6aGHyibKS7cS9syZZZ1/3cPrymYFFhHJYupjk0J9bKRWmTOnLKA0bgzr1sG4cWVBJt6vUCIiexH1sRHJNiUloTbmwQehfv3QWfjRR0Og0ZpLIiIVUrARqY1mz4Zhw8peDx0K/fqpZkZEpArqPCxSG33ySdnz9u3hsssUakREMqBgI1LbbNwY1mz69rfD6zvvVKgREclQRsHGzE4zs3fNbJmZDU6z38zsnmj/IjM7tqqyZtbEzCaZ2XvRY+OEfQVmNtPMlpjZYjPbJ9o+wcwWRttHmFleynX8yMzczLombBsYvcd7ZjZw5z4ekT2spCSMUlqxAk44AV56SUOwRUR2QpXBJgoP9wGnA0cB55vZUSmHnQ60j34uBu7PoOxgYLK7twcmR68xs3zgCeASd+8AnAxsjcqc4+7HAB2BZsDZCdfZEPgdMDthWxPgOqA70A24LjFAidQ6U6bAK6+E5yNHhkfV1oiIZCyTGptuwDJ3f9/dtwBjgP4px/QHRnkwC2hkZi2rKNsfiP7lZiRwZvS8F7DI3RcCuPsad98ePd8QHZMP1AUSx6rfBNwObE7Y1huY5O5r3X0dMAk4LYN7FqkZb71V9rxtW+jdu+auRUQkC2USbA4GPkp4XRxty+SYysq2cPcVANFj82j74YCb2UQzm29mVya+kZlNBFYBnwPPRNs6A63d/YVduHbM7GIzKzKzotWrV6fuFtkzNm4MyyYcd1x4rb41IiI7LZNgk+5f1tRZ/So6JpOyqfKB7wI/iR4HmNn3Swu79wZaAvWAU8ysDjAM+L9dvHbc/QF37+ruXZs1a1bF5YnsBiUl8KMfwZo10KGD+taIiOyiTIJNMdA64XUr4JMMj6ms7MqouYrocVXCuaa5e4m7bwLGA8cmnAN33wwUEpqzGhL63Ew1s+VAD6Aw6kCcybWL1LxZs2DChPB81KjwqNoaEZGdlkmwmQu0N7NDzawucB4hVCQqBC6IRkf1ANZHzUuVlS0E4lFKA4Hno+cTgQIzqx91JD4JeMvM9ksIQvlAH+Add1/v7k3dvY27twFmAf3cvSg6Vy8zaxx1Gu4VbROpXT5KaDFV3xoRkV1W5czD7r7NzAYRAkEe8Ii7LzGzS6L9Iwi1Kn2AZcAm4OeVlY1OPQR42sx+AXxINMLJ3deZ2VBCKHJgvLu/aGYtCDUx9aJzTQFGVHHta83spuhcADe6+9pMPhiRPWbDhjBvzVFHhc7D6lsjIrLLtAhmCi2CKXtMSUnoLDx5Mrz2Grz8MjRsqEUtRUSqoEUwRWqjoiK48UaI/+di+3Ytaiki8jUp2IjsaXFNzaZNoWbGHdq0Ub8aEZFqoGAjsqcVFcFNN8GOHWXb7r5bzU8iItVAwUZkT4lraoqLy7Y1bw7jxqkJSkSkmijYiGQiDiUQmpAA6tcPj4MGQdOmlZe9916YMQP+/e/kfQ8+CMcfX/3XKyKyl1KwEUknsR/MzJmh2WjGjLL9cd8YgGXLoF27soCTWPa11+CLL2Dx4uTzH3ooPPmkampERKqZgo1IojiUvP02PP10xcclTpPw5JPh8c03YfVqaNAgLImQzje/CZ9+Cn/9q2pqRER2AwUb2bsl1q7Mnh22vfpq8jFx7UwcSsaOhXffhWuvTT7u2WfLn79OnVDb07IlrFgBI0ZAixZhrhoREal2Cjayd4oDzdKl8NRT6Y9p3BjWrYOrr4ZbbikfSk49FZYvh/PPD7U0GzeWBZm4SWrw4FD2/vvLymr0k4jIbqNgI3unOXOSJ8eD8jUzjz4awki3bmGl7dRQ0qNH2NamTTh+wICyIPPQQ5WXFRGR3ULBRvYuJSVwzz3w/PNloaZRI/jss/Q1M3EYqaiTr1nY5x46GacLMuogLCKyxyjYyN5lzpwwOV6ixx77+rUrccABBRkRkRqkYCN7l4ULy563awejRoUgotoVEZGcUKemL0BkjygpgQsugGuugdatw7a77gpDrtX3RUQkZ6jGRrJf4pDtOXOgY8cwp0z82K0bvPFG2ay/xcUwdGhodhIRkZxinjgqROjatasXFRXV9GVIJuJA8847YW6ZquTlwfbt0L59mIdGNTUiIlnJzOa5e9d0+9QUJdmrqCgM2U4MNXWiX+nU0LL//vDEE+H5nXcq1IiI5CgFG8k+JSXwpz/BDTeUDdlu0iQ8Dh4cHq++OjxecUV4fPxxOPfcMCRbTVAiIjlLfWwk+7zyCtx8c/K2Rx5JHrKd+HjWWWVDuDXqSUQkpynYSPYoKYHbbw9DtGOVDdnWvDIiInsdBRvJHhMnwh13JG+Lh2yLiIigPjaSDUpKYNAg+M1vyra1awczZqi/jIiIJFGNjdR+//oX3Hdf8jbV1IiISBoKNlJ7lZSEAPPgg2XbEvvUiIiIpFCwkdprzhwYMuMI0RUAAA9xSURBVCR5m2pqRESkEupjI7XXjh1lz9WnRkREMqBgI7XXPfeUTbynBStFRCQDaoqS2umtt2DSJPjLX+D73w8T7ImIiFRBNTZS+5SUwAUXhEUr168PzVCqqRERkQwo2Ejt88orMG9eWIn7rrvCYpciIiIZULCR2mfu3LLnbdtC7941dy0iIpJVMgo2Znaamb1rZsvMbHCa/WZm90T7F5nZsVWVNbMmZjbJzN6LHhsn7Csws5lmtsTMFpvZPtH2CWa2MNo+wszyou2XRMctMLPXzOyohHNtj7YvMLPCXfuYZI/58MPQafjkk8PrO+9UM5SIiGSsymAThYf7gNOBo4DzE4ND5HSgffRzMXB/BmUHA5PdvT0wOXqNmeUDTwCXuHsH4GRga1TmHHc/BugINAPOjraPdvej3b0TcDswNOHavnT3TtFPv6o/EqkRJSVw/fVhOPe2bTB0KMycqeHdIiKyUzKpsekGLHP39919CzAG6J9yTH9glAezgEZm1rKKsv2BkdHzkcCZ0fNewCJ3Xwjg7mvcfXv0fEN0TD5QF/CU7QAN4u2SRYqK4MYbYdGiMH/NypXJK3aLiIhkIJNgczDwUcLr4mhbJsdUVraFu68AiB6bR9sPB9zMJprZfDO7MvGNzGwisAr4HHgmYfulZvYfQo3N7xKK7GNmRWY2y8zOJA0zuzg6pmj16tVpPwTZTeKamr/9DTzKo4ceqn41IiKySzKZxybd/zKn1ohUdEwmZdNd03eB44BNwGQzm+fukwHcvXfU5+ZJ4BRgUrT9PuA+M/sx8CdgYHS+Q9z9EzM7DJhiZovd/T9JF+T+APAAQNeuXVXbszuVlMDdd4fHhQshPx+mT08+Ztgw1dSIiMguySTYFAOtE163Aj7J8Ji6lZRdaWYt3X1F1Gy1KuFc09y9BMDMxgPHEvrhAODum6OOwP2Jgk2CMUR9fKJjP4ke3zezqUBn4D/InlFSAsOHw6ZNoc/MF1/AggXpj23bFh5/XAtciojILssk2MwF2pvZocDHwHnAj1OOKQQGmdkYoDuwPgosqyspW0ioVRkSPT4fbZ8IXGlm9YEtwEnAMDPbD2gYnTcf6ANMBzCz9u7+XlT+DOC9aHtjYJO7f2VmTYHvEJqqpDrEoQVg48bQ6Tc/PwzX7tgx9JfJy4OpU9OXr1Mn9Kf55jfh009Dh2EtcCkiIl9DlcHG3beZ2SBC4MgDHnH3JWZ2SbR/BDCeEDSWEZqPfl5Z2ejUQ4CnzewXwIdEI5zcfZ2ZDSUEKgfGu/uLZtYCKDSzetG5pgAjonMNMrP/IYyeWkdZM9SRwN/NbAehP9EQd39rVz4oSRAHmjfegMIKRtBPm1Z+WxxkWraEFStg8GC45RYYMQJatNCyCSIi8rWZu7qUJOratasXaabb9OJAs2QJPPNM8r4DDgi1NWvWhP4x7mVBpkWLMMrpmmtCkBk3Lmzr1g3mzAmBRn1qREQkQ1Hf267p9mkRzNoksWkHYNAgaNq05q4nVTwkOzEMN28Oq1bBqFFh+5lnwtVXhwAT18j8/e9lQaZv3+Qgo/40IiJSjRRsaoM40CxdCk89FbbVqRP+6J92Ws1eG4Tru+eesIZTHGqaNg3bH3gguRlp5syyAKMgIyIie5iCTU2KA83s2TBhQvI+Mxg7FmbNqrmam8qanh56qCzQJDYjxcEl9VFERGQPULCpCSUlcO+98NprMGVK8r5GjeCzz8LK1o89FkJDTdXcpGt6atcuNDtpVmAREamFFGxqwrRpITAkioc8P/ZY6Lfy05/C+++HUPHXv+7Zmpu4pmbhwvJNT3fdpSHZIiJSaynY7EklJTBkSJiELhYHmsQhz2ZhTpczoxUgJk6El1/eczU3s2bBDTckb0tsehIREamlFGz2pAkTQo1HotRAE+vXD2bMKKu5adBg19ZPSpz5d86c0KG3fv1Q+wPJ+zp2hPnz4b//LSufOBuwmp5ERKSWU7DZE0pK4NZb4dFHy7ZV1VfFLDT5xDU3mzfD2rVw4IGZv+e994bal5dfLtseT5w3fXqYKfjVV8vvS6TZgEVEJIso2OwJkyeHgJAo074q/fqFGpOf/Sw8XnZZ5cfHgeaVV8ovLhlPmAflOy2nHpMYvERERLJEnZq+gL3CWWfB/vuH5+3ahSamvn0zK2sWmqM6dw6T3V13HVx/fQgw6cyZEzomJ4aab34zPA4eHB6feiosawChGQzCrMCJx8TBS81PIiKSRVRjsyd84xuh9uPMM3d9VNGJJ8Ldd4fQkm7yvrgvzaSExc5TOyYnTpi3777helJnBU48RkREJMtoragUu22tKPcwEd+urov0+efQpEnoF3PYYbBsWfJ5JkyAPn2S55yJ12RK955f93pERERqSGVrRakpak+JJ9rb1RDRsGHogAzw5ZehOSqxSaqgoOzYtm1Dc1e/fpV3TtZIJxERyTFqisom//d/IbA891z5JqnLL4e8vFCjo5FMIiKyl1KNTTYxC2s27bdfeH3QQdClCwwcGDoEH388vPRS5h2TRUREcoyCTbapUyd0+IXQDPXd74aOyQCvvx4e1bwkIiJ7KQWbbHT++fD//l+YtG/p0rLtbdvu2uzEIiIiOULBJhuZwZ13wre+FV7Hc9Lceadqa0REZK+mYJOtzMK8NgD33w8zZ6pvjYiI7PU0Kiqb9esXAo3mohEREQEUbLJbPBeNiIiIAGqKEhERkRyiYCMiIiI5Q8FGREREcoaCjYiIiOQMBRsRERHJGQo2IiIikjMUbERERCRnKNiIiIhIzlCwERERkZyhYCMiIiI5I6NgY2anmdm7ZrbMzAan2W9mdk+0f5GZHVtVWTNrYmaTzOy96LFxwr4CM5tpZkvMbLGZ7RNtn2BmC6PtI8wsL9p+SXTcAjN7zcyOSjjXwOg93jOzgbv2MYmIiEg2qDLYROHhPuB04Cjg/MTgEDkdaB/9XAzcn0HZwcBkd28PTI5eY2b5wBPAJe7eATgZ2BqVOcfdjwE6As2As6Pto939aHfvBNwODI3O1QS4DugOdAOuSwxQIiIiklsyqbHpBixz9/fdfQswBuifckx/YJQHs4BGZtayirL9gZHR85HAmdHzXsAid18I4O5r3H179HxDdEw+UBfwlO0ADeLtQG9gkruvdfd1wCTgtAzuWURERLJQJqt7Hwx8lPC6mFADUtUxB1dRtoW7rwBw9xVm1jzafjjgZjaRUCszxt1vj08Qbe8GvAQ8k7D9UuD/EQLPKVVcVxIzu5hQ0wTwhZm9m3rM19QUKKnmc9Ymur/spvvLbrq/7Kb72zXfqmhHJsHG0mzzDI/JpGy6a/oucBywCZhsZvPcfTKAu/eO+tw8SQgwk6Lt9wH3mdmPgT8BAzN9f3d/AHigiuvaZWZW5O5dd9f5a5ruL7vp/rKb7i+76f6qXyZNUcVA64TXrYBPMjymsrIro+YqosdVCeea5u4l7r4JGA8cm3AO3H0zUEj5JjEIzV1xs1Ym1y4iIiI5IpNgMxdob2aHmlld4DxCqEhUCFwQjY7qAayPmpkqK1tIqFUhenw+ej4RKDCz+lFH4pOAt8xsv4QglA/0Ad6JXrdPuJYzgPcSztXLzBpHnYZ7RdtEREQkB1XZFOXu28xsECEQ5AGPuPsSM7sk2j+CUKvSB1hGaD76eWVlo1MPAZ42s18AHxKNcHL3dWY2lBCKHBjv7i+aWQug0MzqReeaAoyIzjXIzP6HMHpqHVFgcve1ZnZTdC6AG9197a58UF/TbmvmqiV0f9lN95fddH/ZTfdXzcy9qi4vIiIiItlBMw+LiIhIzlCwERERkZyhYJNGJctA/DbavsTMbq+g7CNmtsrM3kzZfoeZvRMtOfGcmTXayfeucAmKbLk/M9vHzOYkLItxQxbd303RvS0ws5fN7KCdfO+sv79s/v4S9l9uZm5mTXfyvbP+/rL5+zOz683s4+j3c4GZ9dnJ9876+9sT39/u+t3MsHz1fXfurp+EH0LH5P8AhxEm+1tIWA6iJ/BvoF50XPMKyp9IGJ7+Zsr2XkB+9Pw24LZM3zvadzswOHo+OF35LLg/A/aLnn8DmA30yJL72z/h+e+AETn2/WVyf1n7/UX7WhMGMvwXaJpL31+G95e13x9wPXD5rrx3Dt3fbv3+duO9VVm+ur871diUV9EyEL8Ghrj7VwDuvipdYXd/FSg38srdX3b3bdHLWYQ5dTJ9b6h4CYqdVWP358EX0ctvRD9x7/Xafn8VLduRyXtDDtxfNn9/kWHAlaT/7ip7b8iB+8uB729X3xty4P72wPe3u+4tk/LV+t0p2JRX0TIMhwPfM7PZZjbNzI4DMLODzGz8Tr7HRYQlIVLLV7YERNISFEBzdk1N3h9mlmdmCwgTMk5y99nRrlp/f2Z2s5l9BPwEuDZN+az+/jK4v6z9/sysH/CxR2vQJWzPie8vw/vL2u8vMshCc+kjcXNErnx/karub3d/f7vr3jIpX63fnYJNeRUtw5APNAZ6AFcQ5uAxd//E3dO296Y9udkfgW2EJSFIKb8rS1DsrJq8P9x9u4dV2FsB3cys467fSvpLSLOtWu7P3f/o7q0J9zYo2pYz318G95eV35+Z1Qf+SBTWkk6cA9/fTtxfVn5/kfuBtkAnYAVwF+TG9xfJ5P529/e3u+4tk/LV+t0p2JRX2fIQ/4yqA+cAOwiLe2XMzAYCPwB+4u7pvrRdWYJiZ9Xk/ZVy98+AqZSttl7r7y/BaOCHO/HekBv3VyrLvr+2wKHAQjNbHp1zvpl9M8P3hty4v1JZ9v3h7iujP+o7gAcJTReZvjfkxv0lHr87vr/d9W9LJuWr9btTsCmvomUgxhGtGm5mhxM6OGW8YqmZnQZcBfTzsAbWzrw3VLwExc6qsfszs2YWjZYys32B/yFaFoPaf3+Jy3b0S7juTN4bcuD+svX7c/fF7t7c3du4exvCP6LHuvunGb53Ttxftn5/UZmWCS8HAOlGhWXl9xeVqfL+9sD3t1vuLcPy1fvd+S70DM/1H8LyEEsJvbT/GG2rCzxB+IWbD5wSbT+IsOxDXPYpQlXiVsI/ML+Iti8jtCEuiH5GVFC+3HtH2w8EJhPWwZoMNMm2+wMKgDeARdH7XJtF9/dsVHYR8C/g4Bz7/qq8v2z+/lLOv5xo1FCufH+Z3F82f3/A48Di6NoLgZa59P1lcn974vvbTfeWaflq++60pIKIiIjkDDVFiYiISM5QsBEREZGcoWAjIiIiOUPBRkRERHKGgo2IiIjkDAUbERERyRkKNiIiIpIz/j9Wl6FxGKrMowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(2, figsize=(8,8), tight_layout=True)\n",
    "dsmall = DB.sel(time=slice(dt64('2021-03-10 06:12'), dt64('2021-03-10 06:16')))\n",
    "\n",
    "axs[0].plot(dsmall.time, dsmall.backscatter,                     ms=2., marker='^', linestyle='-', color='b')\n",
    "axs[0].plot(dsmall.time, dsmall.particulate,                     ms=2., marker='o', linestyle='-', color='k')\n",
    "axs[0].set(ylim=(0.0002,     0.0018  ), title='Total (blue) and Particulate (black) Backscatter')\n",
    "\n",
    "axs[1].plot(dsmall.time, dsmall.seawater_scattering_coefficient, ms=3., marker='v', linestyle='-', color='r')\n",
    "axs[1].set(ylim=(0.000633,  0.000636 ), title='Seawater Scattering Coefficient')\n",
    "axs[1].text(dt64('2021-03-10 06:12'), .0006355, 'March 10, 2022 06:14 UTC, 4 minute duration')\n",
    "\n",
    "fig.show()\n",
    "\n",
    "#    ds_1day = ds.sel(time=slice(dt64(t0str), dt64(t1str)))   \n",
    "#    fig, axs = plt.subplots(figsize=(12,4), tight_layout=True)   \n",
    "#    axs.plot(ds_1day.time, ds_1day.z, marker='.', ms=9., color='k', mfc='r')   \n",
    "#    axs.set(ylim = (-200., 0.), title=title)\n",
    "#    axs.text(dt64('2021-02-28 23:15'), -184, 'AT')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab73e758-7223-4d85-b7a0-a826fe29a7c6",
   "metadata": {},
   "source": [
    "# Figure 5: Profiler Chlor-A dark correction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a58221-50db-45ff-90fb-b869c86b0a32",
   "metadata": {},
   "source": [
    "# Figure 6: Discrete data comparative \n",
    "\n",
    "\n",
    "Source: [This notebook](https://github.com/robfatland/ocean/blob/2e4019439f9b0148457c20107b11514a0f0b916b/Notebooks/Ocean%2001%20B2%20Discrete%20Comparison.ipynb)\n",
    "\n",
    "\n",
    "## Remark on discrete (cast/bottle) data\n",
    "\n",
    "\n",
    "There is a slight misnomer here in that Niskin bottles on cruise casts (Regional Cabled Array)\n",
    "produce discrete samples; but continuous sensors are also bolted to the rosette frame. These\n",
    "produce data at some high rate during the cast. This is effectively an independent\n",
    "profile that can be compared to the shallow profiler data.\n",
    "\n",
    "\n",
    "**Discrete Summary** data files are discretized by niskin bottle samples. The data files\n",
    "are CSV tables available from a dedicated Server called 'Alfresco'. They\n",
    "include fluorescence data that seems to be discrete; but in fact it is excerpted\n",
    "from continuous data. \n",
    "\n",
    "\n",
    "* Cruise data are available from the OOI 'Alfresco' server\n",
    "    * [Alfresco browse site](https://alfresco.oceanobservatories.org/alfresco/faces/jsp/browse/browse.jsp)\n",
    "    * Navigate: Cabled array > Cruise data > (year of choice) > Ship Data > Water Sampling\n",
    "    * Example: (year of choice) = 2021 is folder **Cabled-12_TN393_2021-07-30**\n",
    "        * TN refers to the ship: Thompson. Atlantis = AT; Revelle = RR; Sikuliaq = SKQ (2016 cruise)\n",
    "        * 393 refers to the cruise number\n",
    "            * Cabled-12 indicates the OOI Regional Cabled Array; where 12 is simply an index number (by year)\n",
    "            * For the OOI cabled array the annual cruise uses Newport OR as the base of operations\n",
    "            * The cruise consists of a series of sorties or 'legs', each typically 5-10 days\n",
    "            * The provided date 2021-07-30 indicates the year and approximate date of the cruise start\n",
    "    * In the Water Sampling folder there are two files of interest\n",
    "        * **Cabled-12_TN393_Discrete_Summary-README.pdf** is a guide to deciphering the data, particularly ephemeris flags\n",
    "        * **Cabled-12_TN393_Discrete_Summary.csv** is a tabular data file \n",
    "            * This file is the **Discrete Summary** of interest\n",
    "            * It summarizes cast sample data and synchronous continuous sensor data; see below\n",
    "\n",
    "\n",
    "* **Discrete Summary** data are associated with Niskin bottle trigger events\n",
    "    * A Niskin bottle is deployed in 'open' state on a submerged framework\n",
    "        * Most commonly this framework is a [rosette](https://www.whoi.edu/what-we-do/explore/instruments/instruments-sensors-samplers/rosette-sampler/)\n",
    "        * A Remotely Operated Vehicle (ROV) can also be used as a Niskin bottle framework\n",
    "        * For our purposes here we just use the term 'rosette' to refer to the sample bottle framework\n",
    "    * Submerged rosettes capture water samples in Niskin bottles\n",
    "        * Rosettes also carry sensors that continuously record data, typically at one sample per second\n",
    "            * Examples of continuous sensor data includes seawater conductivity, temperature, depth, and fluorescence\n",
    "    * Niskin bottles are lengths of pipe with two end caps\n",
    "        * They are deployed in an open state so seawater flows through them\n",
    "        * When they are triggered: Caps under tension are released so as to seal the ends of the pipe\n",
    "        * As a results a seawater sample from some depth is captured for later (shipboard laboratory) analysis\n",
    "    \n",
    "    \n",
    "The 2021 VISIONS cruise cast for Oregon Slope Base began August 6, 2021: OSB SP, CTD-005, \n",
    "2021-08-06T01:43:57.000Z, 500m SW of the shallow profiler location.\n",
    "\n",
    "\n",
    "- **`SP`** is Shallow Profiler station. Cast location is stated relative; but the cast might go much deeper than 200 meters.\n",
    "- **`CTD-005`** is a CTD cast with index (skips are common)\n",
    "- **`500m SW`** indicates an offset from the station position\n",
    "- The repo folder **`DiscreteSummaries`** contains:\n",
    "    - Tabular data (**`.csv`**) for 2015 - 2021\n",
    "    - README metadata (**`.pdf`**) for 2015 - 2021\n",
    "    - Renamed for simplicity: **`RCA2021_TN_DiscSumm.csv`** and **`README_RCA2021.pdf`** etcetera\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9053c054-9c5a-456d-9958-1af8d0da3f93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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