diff --git a/book/chapters/data/rca/sensors/osb b/book/chapters/data/rca/sensors/osb index f0f3689..ca64769 120000 --- a/book/chapters/data/rca/sensors/osb +++ b/book/chapters/data/rca/sensors/osb @@ -1 +1 @@ -/home/rob/osb \ No newline at end of file +/home/kilroy/osb \ No newline at end of file diff --git a/book/chapters/dataloader.ipynb b/book/chapters/dataloader.ipynb index a227b11..aad134f 100644 --- a/book/chapters/dataloader.ipynb +++ b/book/chapters/dataloader.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 1, "id": "e3021b6b-d5b8-4c06-9b2d-05e1ccceb286", "metadata": { "tags": [] @@ -75,6 +75,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "Jupyter Notebook running Python 3\n", "List Oregon Slope Base Profiler streams:\n", "\n", "ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n", @@ -133,7 +135,7 @@ "from shallowprofiler import *\n", "from charts import *\n", "\n", - "doIngest = False\n", + "doIngest = True\n", "\n", "fs = s3fs.S3FileSystem(anon=True)\n", "\n", @@ -272,7 +274,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found CTD: ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n" + "Found CTD: ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n", + "<xarray.DataArray 'time' ()>\n", + "array('2022-01-01T00:00:00.097717760', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-01-01T00:00:00.097717760\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time <xarray.DataArray 'time' ()>\n", + "array('2022-12-31T23:59:59.150689280', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-12-31T23:59:59.150689280\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time\n" ] } ], @@ -366,7 +383,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 600x400 with 2 Axes>" ] @@ -399,7 +416,30 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found this instrument stream: ooi-data/RS01SBPS-SF01A-2D-PHSENA101-streamed-phsen_data_record\n", + "<xarray.DataArray 'time' ()>\n", + "array('2022-01-01T02:12:11.470588928', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-01-01T02:12:11.470588928\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time <xarray.DataArray 'time' ()>\n", + "array('2022-12-06T19:55:50.204456448', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-12-06T19:55:50.204456448\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time\n" + ] + } + ], "source": [ "if doIngest:\n", " instrument_key = 'phsen'\n", @@ -435,7 +475,22 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "looks ok\n", + "<xarray.Dataset>\n", + "Dimensions: (time: 1003)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-01-01T02:12:11.470588928 ... 2022-01-...\n", + "Data variables:\n", + " depth (time) float64 ...\n", + " ph (time) float64 ...\n" + ] + } + ], "source": [ "if doIngest:\n", " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", @@ -464,7 +519,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 600x1600 with 4 Axes>" ] @@ -495,7 +550,30 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found this instrument stream: ooi-data/RS01SBPS-SF01A-3A-FLORTD101-streamed-flort_d_data_record\n", + "<xarray.DataArray 'time' ()>\n", + "array('2022-01-01T00:00:00.702079488', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-01-01T00:00:00.702079488\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time <xarray.DataArray 'time' ()>\n", + "array('2022-12-31T23:58:28.044592128', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-12-31T23:58:28.044592128\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time\n" + ] + } + ], "source": [ "if doIngest:\n", " instrument_key = 'flort'\n", @@ -571,7 +649,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 600x400 with 1 Axes>" ] @@ -581,7 +659,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "<Figure size 600x400 with 1 Axes>" ] @@ -591,7 +669,7 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 600x400 with 1 Axes>" ] @@ -755,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "id": "8344cfa7-362c-4522-818c-788e7a639d48", "metadata": { "tags": [] @@ -782,7892 +860,865 @@ " long_name: time\n", " standard_name: time\n" ] + } + ], + "source": [ + "if doIngest: \n", + " instrument_key = 'nutnr_a_dark_sample'\n", + " for s in osb_profiler_streams: \n", + " if instrument_key in s: \n", + " print('Found this instrument stream:', s)\n", + " instrument_stream = s\n", + " break\n", + "\n", + " ds = loadData(instrument_stream) # lazy load\n", + " t0, t1 = '2022-01-01T00', '2022-12-31T23' # January 2022\n", + " ds = ds.sel(time=slice(t0, t1)) # Subset the full time range to one month\n", + " print(ds.time[0], ' ', ds.time[-1]) # verify selected one month time range\n", + " ds # get a 'data variable' list of sensors/metadata for this instrument" + ] + }, + { + "cell_type": "markdown", + "id": "1c12fc1c-a16c-40fc-8c6c-38fc9c72e10e", + "metadata": {}, + "source": [ + "**The dark sample nitrate is on hold pending an explanation of what is going on.** \n", + "Presuming it was business as usual, maybe: `nutnr_nitrogen_in_nitrate` becomes `nitrate_dark` and `int_ctd_pressure` becomes depth." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0097acb9-a10b-409b-9c28-c1713fbb7d12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attempting 1 charts\n", + "\n" + ] }, { "data": { - "text/html": [ - "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", - "<defs>\n", - "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", - "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", - "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", - "<path d=\"M16 26c-8.837 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var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-sections {\n", - " padding-left: 0 !important;\n", - " display: grid;\n", - " grid-template-columns: 150px auto auto 1fr 20px 20px;\n", - "}\n", - "\n", - ".xr-section-item {\n", - " display: contents;\n", - "}\n", - "\n", - ".xr-section-item input {\n", - " display: none;\n", - "}\n", - "\n", - ".xr-section-item input + label {\n", - " color: var(--xr-disabled-color);\n", - "}\n", - "\n", - ".xr-section-item input:enabled + label {\n", - " cursor: pointer;\n", - " color: var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-section-item input:enabled + label:hover {\n", - " color: var(--xr-font-color0);\n", - "}\n", - "\n", - ".xr-section-summary {\n", - " grid-column: 1;\n", - " color: var(--xr-font-color2);\n", - " font-weight: 500;\n", - "}\n", - "\n", - ".xr-section-summary > span {\n", - " display: inline-block;\n", - " padding-left: 0.5em;\n", - "}\n", - "\n", - ".xr-section-summary-in:disabled + label {\n", - " color: var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-section-summary-in + label:before {\n", - " display: inline-block;\n", - " content: 'â–º';\n", - " font-size: 11px;\n", - " width: 15px;\n", - " text-align: center;\n", - "}\n", - "\n", - ".xr-section-summary-in:disabled + label:before {\n", - " color: var(--xr-disabled-color);\n", - "}\n", - "\n", - ".xr-section-summary-in:checked + label:before {\n", - " content: 'â–¼';\n", - "}\n", - "\n", - ".xr-section-summary-in:checked + label > span {\n", - " display: none;\n", - "}\n", - "\n", - ".xr-section-summary,\n", - ".xr-section-inline-details {\n", - " padding-top: 4px;\n", - " padding-bottom: 4px;\n", - "}\n", - "\n", - ".xr-section-inline-details {\n", - " grid-column: 2 / -1;\n", - "}\n", - "\n", - ".xr-section-details {\n", - " display: none;\n", - " grid-column: 1 / -1;\n", - " margin-bottom: 5px;\n", - "}\n", - "\n", - ".xr-section-summary-in:checked ~ .xr-section-details {\n", - " display: contents;\n", - "}\n", - "\n", - ".xr-array-wrap {\n", - " grid-column: 1 / -1;\n", - " display: grid;\n", - " grid-template-columns: 20px auto;\n", - "}\n", - "\n", - ".xr-array-wrap > label {\n", - " grid-column: 1;\n", - " vertical-align: top;\n", - "}\n", - "\n", - ".xr-preview {\n", - " color: var(--xr-font-color3);\n", - "}\n", - "\n", - ".xr-array-preview,\n", - ".xr-array-data {\n", - " padding: 0 5px !important;\n", - " grid-column: 2;\n", - "}\n", - "\n", - ".xr-array-data,\n", - ".xr-array-in:checked ~ .xr-array-preview {\n", - " display: none;\n", - "}\n", - "\n", - ".xr-array-in:checked ~ .xr-array-data,\n", - ".xr-array-preview {\n", - " display: inline-block;\n", - "}\n", - "\n", - ".xr-dim-list {\n", - " display: inline-block !important;\n", - " list-style: none;\n", - " padding: 0 !important;\n", - " margin: 0;\n", - "}\n", - "\n", - ".xr-dim-list li {\n", - " display: inline-block;\n", - " padding: 0;\n", - " margin: 0;\n", - "}\n", - "\n", - ".xr-dim-list:before {\n", - " content: '(';\n", - "}\n", - "\n", - ".xr-dim-list:after {\n", - " content: ')';\n", - "}\n", - "\n", - ".xr-dim-list li:not(:last-child):after {\n", - " content: ',';\n", - " padding-right: 5px;\n", - "}\n", - "\n", - ".xr-has-index {\n", - " font-weight: bold;\n", - "}\n", - "\n", - ".xr-var-list,\n", - ".xr-var-item {\n", - " display: contents;\n", - "}\n", - "\n", - ".xr-var-item > div,\n", - ".xr-var-item label,\n", - ".xr-var-item > .xr-var-name span {\n", - " background-color: var(--xr-background-color-row-even);\n", - " margin-bottom: 0;\n", - "}\n", - "\n", - ".xr-var-item > .xr-var-name:hover span {\n", - " padding-right: 5px;\n", - "}\n", - "\n", - ".xr-var-list > li:nth-child(odd) > div,\n", - ".xr-var-list > li:nth-child(odd) > label,\n", - ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", - " background-color: var(--xr-background-color-row-odd);\n", - "}\n", - "\n", - ".xr-var-name {\n", - " grid-column: 1;\n", - "}\n", - "\n", - ".xr-var-dims {\n", - " grid-column: 2;\n", - "}\n", - "\n", - ".xr-var-dtype {\n", - " grid-column: 3;\n", - " text-align: right;\n", - " color: var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-var-preview {\n", - " grid-column: 4;\n", - "}\n", - "\n", - ".xr-index-preview {\n", - " grid-column: 2 / 5;\n", - " color: var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-var-name,\n", - ".xr-var-dims,\n", - ".xr-var-dtype,\n", - ".xr-preview,\n", - ".xr-attrs dt {\n", - " white-space: nowrap;\n", - " overflow: hidden;\n", - " text-overflow: ellipsis;\n", - " padding-right: 10px;\n", - "}\n", - "\n", - ".xr-var-name:hover,\n", - ".xr-var-dims:hover,\n", - ".xr-var-dtype:hover,\n", - ".xr-attrs dt:hover {\n", - " overflow: visible;\n", - " width: auto;\n", - " z-index: 1;\n", - "}\n", - "\n", - ".xr-var-attrs,\n", - ".xr-var-data,\n", - ".xr-index-data {\n", - " display: none;\n", - " background-color: var(--xr-background-color) !important;\n", - " padding-bottom: 5px !important;\n", - "}\n", - "\n", - ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data,\n", - ".xr-index-data-in:checked ~ .xr-index-data {\n", - " display: block;\n", - "}\n", - "\n", - ".xr-var-data > table {\n", - " float: right;\n", - "}\n", - "\n", - ".xr-var-name span,\n", - ".xr-var-data,\n", - ".xr-index-name div,\n", - ".xr-index-data,\n", - ".xr-attrs {\n", - " padding-left: 25px !important;\n", - "}\n", - "\n", - ".xr-attrs,\n", - ".xr-var-attrs,\n", - ".xr-var-data,\n", - ".xr-index-data {\n", - " grid-column: 1 / -1;\n", - "}\n", - "\n", - "dl.xr-attrs {\n", - " padding: 0;\n", - " margin: 0;\n", - " display: grid;\n", - " grid-template-columns: 125px auto;\n", - "}\n", - "\n", - ".xr-attrs dt,\n", - ".xr-attrs dd {\n", - " padding: 0;\n", - " margin: 0;\n", - " float: left;\n", - " padding-right: 10px;\n", - " width: auto;\n", - "}\n", - "\n", - ".xr-attrs dt {\n", - " font-weight: normal;\n", - " grid-column: 1;\n", - "}\n", - "\n", - ".xr-attrs dt:hover span {\n", - " display: inline-block;\n", - " background: var(--xr-background-color);\n", - " padding-right: 10px;\n", - "}\n", - "\n", - ".xr-attrs dd {\n", - " grid-column: 2;\n", - " white-space: pre-wrap;\n", - " word-break: break-all;\n", - "}\n", - "\n", - ".xr-icon-database,\n", - ".xr-icon-file-text2,\n", - ".xr-no-icon {\n", - " display: inline-block;\n", - " vertical-align: middle;\n", - " width: 1em;\n", - " height: 1.5em !important;\n", - " stroke-width: 0;\n", - " stroke: currentColor;\n", - " fill: currentColor;\n", - "}\n", - "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", - "Dimensions: (time: 1650111, wavelength: 256)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2022-01-01T0...\n", - " * wavelength (wavelength) int32 0 1 2 ... 254 255\n", - "Data variables: (12/36)\n", - " aux_fitting_1 (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " aux_fitting_2 (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " checksum (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " date_of_sample (time) float64 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " deployment (time) int32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " driver_timestamp (time) datetime64[ns] dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " ... ...\n", - " temp_interior (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " temp_lamp (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " temp_spectrometer (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " time_of_sample (time) float64 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " voltage_lamp (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " voltage_main (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - "Attributes: (12/62)\n", - " AssetManagementRecordLastModified: 2024-07-04T16:24:15.338000\n", - " AssetUniqueID: ATOSU-68020-00005\n", - " Conventions: CF-1.6\n", - " Description: Nitrate: NUTNR Series A\n", - " FirmwareVersion: Not specified.\n", - " Manufacturer: Satlantic\n", - " ... ...\n", - " stream: nutnr_a_dark_sample\n", - " subsite: RS01SBPS\n", - " summary: Dataset Generated by Stream Engine fr...\n", - " time_coverage_end: 2024-07-08T11:15:25.654908928\n", - " time_coverage_start: 2014-10-06T22:38:13.329600000\n", - " title: Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dd29ce62-4fbd-4d90-aafa-628bebb1a6f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd29ce62-4fbd-4d90-aafa-628bebb1a6f2' 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>: 1650111</li><li><span class='xr-has-index'>wavelength</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e415140a-ef41-4ec4-910e-05df136a3a70' class='xr-section-summary-in' type='checkbox' checked><label for='section-e415140a-ef41-4ec4-910e-05df136a3a70' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T07:22:05.457853440 .....</div><input id='attrs-8a8f5f3e-36f1-4da1-b088-8cf43bd33f37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a8f5f3e-36f1-4da1-b088-8cf43bd33f37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8db7fa8-c756-4246-9539-60318266248b' class='xr-var-data-in' type='checkbox'><label for='data-a8db7fa8-c756-4246-9539-60318266248b' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2022-01-01T07:22:05.457853440', '2022-01-01T07:22:10.089222656',\n", - " '2022-01-01T07:22:11.168378368', ..., '2022-12-31T21:48:33.536158208',\n", - " '2022-12-31T21:48:34.606148608', '2022-12-31T21:48:35.696192512'],\n", - " dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 251 252 253 254 255</div><input id='attrs-1cdd957e-3e74-46cd-ac50-58ac724e9859' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cdd957e-3e74-46cd-ac50-58ac724e9859' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e926e4fe-1b31-4aff-888a-f6187e78ee46' class='xr-var-data-in' type='checkbox'><label for='data-e926e4fe-1b31-4aff-888a-f6187e78ee46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 253, 254, 255], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd49525f-e262-4632-b7ab-1ef10c875597' class='xr-section-summary-in' type='checkbox' ><label for='section-cd49525f-e262-4632-b7ab-1ef10c875597' class='xr-section-summary' >Data variables: <span>(36)</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>aux_fitting_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-4f4b635a-d3f8-4847-877e-27604f31488f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f4b635a-d3f8-4847-877e-27604f31488f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fddab749-8979-4fff-a4ea-427e4e50cb35' class='xr-var-data-in' type='checkbox'><label for='data-fddab749-8979-4fff-a4ea-427e4e50cb35' 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>Aux Fitting 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 1</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aux_fitting_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-c288f1e7-0e87-4263-beea-4f9740af3b89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c288f1e7-0e87-4263-beea-4f9740af3b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec08fda3-7ec8-4979-8116-1481ff28e0ff' class='xr-var-data-in' type='checkbox'><label for='data-ec08fda3-7ec8-4979-8116-1481ff28e0ff' 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>Aux Fitting 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 2</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-b02b0fb1-30a6-465d-89da-4f208d713f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b02b0fb1-30a6-465d-89da-4f208d713f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f34fc21b-44db-4348-9c31-8e0e4a8f8d63' class='xr-var-data-in' type='checkbox'><label for='data-f34fc21b-44db-4348-9c31-8e0e4a8f8d63' 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>Data checksum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Data Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-eec1ec6a-38af-489a-8de6-0b5e8ccede43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eec1ec6a-38af-489a-8de6-0b5e8ccede43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-594f6ce6-40d6-4fc4-9e9d-a114c5e10b98' class='xr-var-data-in' type='checkbox'><label for='data-594f6ce6-40d6-4fc4-9e9d-a114c5e10b98' 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>Date of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Date of Sample</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-e9ff8908-7905-489f-93a6-6aaa1c2cb519' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9ff8908-7905-489f-93a6-6aaa1c2cb519' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efd53ee9-e124-493b-9b61-021726eb3bdd' class='xr-var-data-in' type='checkbox'><label for='data-efd53ee9-e124-493b-9b61-021726eb3bdd' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> int32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-a0184496-a32f-47bc-9c35-cd6a5e8b97aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0184496-a32f-47bc-9c35-cd6a5e8b97aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-078462b8-69e6-4697-bd31-ba22a2fc055f' class='xr-var-data-in' type='checkbox'><label for='data-078462b8-69e6-4697-bd31-ba22a2fc055f' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>humidity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-c696ed61-83d1-45b2-b83d-b118cbfd3395' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c696ed61-83d1-45b2-b83d-b118cbfd3395' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12b0fdd4-d188-4e7f-802f-5d2eaffc1930' class='xr-var-data-in' type='checkbox'><label for='data-12b0fdd4-d188-4e7f-802f-5d2eaffc1930' 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>Humidity</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Humidity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-f5ef80e6-afca-410d-a533-18996530646c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ef80e6-afca-410d-a533-18996530646c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17a949df-21d3-4dab-9326-d9cb11c96fa5' class='xr-var-data-in' type='checkbox'><label for='data-17a949df-21d3-4dab-9326-d9cb11c96fa5' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-2b8a5981-9604-4f16-ad29-45c37c90a547' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b8a5981-9604-4f16-ad29-45c37c90a547' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-354416f7-af37-44bf-868d-e1315ef4a326' class='xr-var-data-in' type='checkbox'><label for='data-354416f7-af37-44bf-868d-e1315ef4a326' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</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'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-db4a5ec1-7b98-4afb-85c3-c703568fb539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db4a5ec1-7b98-4afb-85c3-c703568fb539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28542e61-0070-4725-bd50-b193edbe1e14' class='xr-var-data-in' type='checkbox'><label for='data-28542e61-0070-4725-bd50-b193edbe1e14' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lamp_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-98ddbc37-b7df-43e7-919e-6b1a059bdc97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98ddbc37-b7df-43e7-919e-6b1a059bdc97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f579a6f8-bcae-43b8-9348-ba9af4f501da' class='xr-var-data-in' type='checkbox'><label for='data-f579a6f8-bcae-43b8-9348-ba9af4f501da' 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>Elapsed time lamp has been on in seconds</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Time</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-5fbb390e-91d1-4d5f-99b3-cfd86362f506' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fbb390e-91d1-4d5f-99b3-cfd86362f506' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cebd21cf-46aa-4edb-b816-254a9e86609c' class='xr-var-data-in' type='checkbox'><label for='data-cebd21cf-46aa-4edb-b816-254a9e86609c' 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>Dissolved Nitrate Concentration, uncorrected for temperature and salinity effects.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITRDIS_L1</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration</dd><dt><span>units :</span></dt><dd>umol L-1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-d862c375-680d-4602-bb6d-83396671d495' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d862c375-680d-4602-bb6d-83396671d495' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7b77894-94dc-4312-9a08-acab26707f64' class='xr-var-data-in' type='checkbox'><label for='data-d7b77894-94dc-4312-9a08-acab26707f64' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 1.57 MiB </td>\n", - " <td> 1.57 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-e15e86b9-ff8d-42e0-81d9-307400d5270b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e15e86b9-ff8d-42e0-81d9-307400d5270b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87f9436f-139c-43db-b5b7-8427bef89d62' class='xr-var-data-in' type='checkbox'><label for='data-87f9436f-139c-43db-b5b7-8427bef89d62' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 1.57 MiB </td>\n", - " <td> 1.57 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_254_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-fec8a7cf-9fae-4d09-95e2-ab38dea58aab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fec8a7cf-9fae-4d09-95e2-ab38dea58aab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f23505e9-966a-44d8-ba97-383e396375dd' class='xr-var-data-in' type='checkbox'><label for='data-f23505e9-966a-44d8-ba97-383e396375dd' 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>Absorbance at 254 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 254 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_350_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-ccc64f27-cbc9-4b3e-b35b-21c62b561f2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccc64f27-cbc9-4b3e-b35b-21c62b561f2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c20750b9-7323-4467-b319-70acd88ae48f' class='xr-var-data-in' type='checkbox'><label for='data-c20750b9-7323-4467-b319-70acd88ae48f' 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>Absorbance at 350 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 350 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_bromide_trace</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-f113b5bd-dcab-45e6-b996-b8e88c667052' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f113b5bd-dcab-45e6-b996-b8e88c667052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c97e6507-07a8-4ce3-bb5d-553c1538f15e' class='xr-var-data-in' type='checkbox'><label for='data-c97e6507-07a8-4ce3-bb5d-553c1538f15e' 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>Bromide Trace</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Bromide Trace</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_current_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-cac535e8-1c2c-4301-a158-6143ee9f693f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cac535e8-1c2c-4301-a158-6143ee9f693f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9dd2dfc-f4fb-4772-b85c-b0b4d787b5db' class='xr-var-data-in' type='checkbox'><label for='data-c9dd2dfc-f4fb-4772-b85c-b0b4d787b5db' 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>Main Current</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Current</dd><dt><span>units :</span></dt><dd>mA</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_dark_value_used_for_fit</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-c8a1aab5-69b0-4efb-ade0-f05ef869e946' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8a1aab5-69b0-4efb-ade0-f05ef869e946' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c6a14e8-28d9-4199-8384-48e0691d8bcf' class='xr-var-data-in' type='checkbox'><label for='data-6c6a14e8-28d9-4199-8384-48e0691d8bcf' 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>Dark Value Used for Fit</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dark Value Used for Fit</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-fe6447d1-4ab2-41e1-a828-7f0d7583b9e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe6447d1-4ab2-41e1-a828-7f0d7583b9e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c80127a6-a2e9-4a29-9ef4-be7013361e5c' class='xr-var-data-in' type='checkbox'><label for='data-c80127a6-a2e9-4a29-9ef4-be7013361e5c' 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>Fit Base 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 1</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-2d66ef26-e029-4ab5-b48a-af7b4fe1baec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d66ef26-e029-4ab5-b48a-af7b4fe1baec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cc5479-f93b-404a-bb3f-b82e1a883c42' class='xr-var-data-in' type='checkbox'><label for='data-c3cc5479-f93b-404a-bb3f-b82e1a883c42' 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>Fit Base 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 2</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_rmse</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-3a9f3fc9-f894-4948-9574-97bc78f6a1a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a9f3fc9-f894-4948-9574-97bc78f6a1a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a17824df-f762-4ad8-a068-cb77e5b1ac38' class='xr-var-data-in' type='checkbox'><label for='data-a17824df-f762-4ad8-a068-cb77e5b1ac38' 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>Fit RMSE</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit RMSE</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_integration_time_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-22168739-c7b5-4295-85bf-10c10b93bd1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22168739-c7b5-4295-85bf-10c10b93bd1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-984a0853-c5e2-4cc2-bf33-c76f9cd82159' class='xr-var-data-in' type='checkbox'><label for='data-984a0853-c5e2-4cc2-bf33-c76f9cd82159' 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>Integration Time Factor</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Integration Time Factor</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-7d2df3cf-0179-4b25-8a35-64203b626d2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d2df3cf-0179-4b25-8a35-64203b626d2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f880d6f-a06e-4aac-a5ef-4121d35ea4f6' class='xr-var-data-in' type='checkbox'><label for='data-1f880d6f-a06e-4aac-a5ef-4121d35ea4f6' 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>Nitrogen in Nitrate</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Nitrogen in Nitrate</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-107c80f5-154c-439c-93b8-720873548b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-107c80f5-154c-439c-93b8-720873548b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cd6e9c7-ca53-42cb-9876-31c7357891e0' class='xr-var-data-in' type='checkbox'><label for='data-5cd6e9c7-ca53-42cb-9876-31c7357891e0' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 1.57 MiB </td>\n", - " <td> 1.57 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-8c978722-0778-4d50-adf0-57204a773a4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c978722-0778-4d50-adf0-57204a773a4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54ea9252-ff08-4a36-bca5-2b2132f5ae35' class='xr-var-data-in' type='checkbox'><label for='data-54ea9252-ff08-4a36-bca5-2b2132f5ae35' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 1.57 MiB </td>\n", - " <td> 1.57 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_spectrum_average</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-efafc6e7-b5ea-4bd4-bf80-f1accb7e87e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efafc6e7-b5ea-4bd4-bf80-f1accb7e87e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bae11de-7990-4f92-9efe-2ebc310ad51b' class='xr-var-data-in' type='checkbox'><label for='data-5bae11de-7990-4f92-9efe-2ebc310ad51b' 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>Spectrum Average</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrum Average</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_voltage_int</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-5dd1a76f-0551-4095-824b-e33ac15da2c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dd1a76f-0551-4095-824b-e33ac15da2c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-027ab412-cebe-4f01-8f73-01882054ce65' class='xr-var-data-in' type='checkbox'><label for='data-027ab412-cebe-4f01-8f73-01882054ce65' 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 Power Supply Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Power Supply Voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-7c1cf563-1338-4489-9005-645e9518849d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c1cf563-1338-4489-9005-645e9518849d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-477159f9-39c6-4ed9-ad7f-2a3bf1764a2e' class='xr-var-data-in' type='checkbox'><label for='data-477159f9-39c6-4ed9-ad7f-2a3bf1764a2e' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectral_channels</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(96286, 256), meta=np.ndarray></div><input id='attrs-c017173c-d470-48b8-88bb-f59a9194683d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c017173c-d470-48b8-88bb-f59a9194683d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d4b9828-8f85-4f57-a058-ad7e0f490702' class='xr-var-data-in' type='checkbox'><label for='data-8d4b9828-8f85-4f57-a058-ad7e0f490702' 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>UV Absorption Spectra is an array of values produced by the NUTNR instrument class and is based on the ultraviolet (UV) absorption characteristics of the components of seawater, including dissolved nitrate.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITROPT_L0</dd><dt><span>long_name :</span></dt><dd>UV Absorption Spectra</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 1.57 GiB </td>\n", - " <td> 94.73 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111, 256) </td>\n", - " <td> (97000, 256) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 18 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"7\" x2=\"25\" y2=\"7\" />\n", - " <line x1=\"0\" y1=\"14\" x2=\"25\" y2=\"14\" />\n", - " <line x1=\"0\" y1=\"21\" x2=\"25\" y2=\"21\" />\n", - " <line x1=\"0\" y1=\"28\" x2=\"25\" y2=\"28\" />\n", - " <line x1=\"0\" y1=\"35\" x2=\"25\" y2=\"35\" />\n", - " <line x1=\"0\" y1=\"42\" x2=\"25\" y2=\"42\" />\n", - " <line x1=\"0\" y1=\"49\" x2=\"25\" y2=\"49\" />\n", - " <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n", - " <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n", - " <line x1=\"0\" y1=\"70\" x2=\"25\" y2=\"70\" />\n", - " <line x1=\"0\" y1=\"77\" x2=\"25\" y2=\"77\" />\n", - " <line x1=\"0\" y1=\"84\" x2=\"25\" y2=\"84\" />\n", - " <line x1=\"0\" y1=\"91\" x2=\"25\" y2=\"91\" />\n", - " <line x1=\"0\" y1=\"98\" x2=\"25\" y2=\"98\" />\n", - " <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n", - " <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n", - " <line x1=\"0\" y1=\"119\" x2=\"25\" y2=\"119\" />\n", - " <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", - " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >256</text>\n", - " <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1650111</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_interior</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-75ffea8a-5f5f-4174-ad35-95fbcb6a05e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75ffea8a-5f5f-4174-ad35-95fbcb6a05e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd2fe3f-67e1-482e-9223-bfc0b56d56f0' class='xr-var-data-in' type='checkbox'><label for='data-bfd2fe3f-67e1-482e-9223-bfc0b56d56f0' 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>Interior Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Interior Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-4930e4aa-8428-4474-8365-ef5847b1ab08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4930e4aa-8428-4474-8365-ef5847b1ab08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dee75a4-1e40-4206-b516-cb9b22744071' class='xr-var-data-in' type='checkbox'><label for='data-4dee75a4-1e40-4206-b516-cb9b22744071' 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>Lamp Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_spectrometer</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-81108e6c-5771-48d3-9de4-a0d1ccde25c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81108e6c-5771-48d3-9de4-a0d1ccde25c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a71d2b-de6d-498d-b1ac-b00f77bc71bb' class='xr-var-data-in' type='checkbox'><label for='data-86a71d2b-de6d-498d-b1ac-b00f77bc71bb' 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>Spectrometer Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrometer Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-34b88c0a-ea46-4249-b380-891c051e731f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34b88c0a-ea46-4249-b380-891c051e731f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e3b9dd6-6cb4-4cf0-a670-1637689cba9b' class='xr-var-data-in' type='checkbox'><label for='data-9e3b9dd6-6cb4-4cf0-a670-1637689cba9b' 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>Time of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Time of Sample</dd><dt><span>units :</span></dt><dd>h</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 12.59 MiB </td>\n", - " <td> 12.59 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-f6d98df1-4ba9-4f9a-b03a-c3fe13994cf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6d98df1-4ba9-4f9a-b03a-c3fe13994cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d797a79-564e-4688-89a7-8c0a573a4cc8' class='xr-var-data-in' type='checkbox'><label for='data-7d797a79-564e-4688-89a7-8c0a573a4cc8' 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>Lamp Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1650111</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1650111,), meta=np.ndarray></div><input id='attrs-2d3ac2bb-fac5-45b4-9c8e-61a0a0b8c269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d3ac2bb-fac5-45b4-9c8e-61a0a0b8c269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9917142d-4717-4e61-bc9a-3066851ec9e7' class='xr-var-data-in' type='checkbox'><label for='data-9917142d-4717-4e61-bc9a-3066851ec9e7' 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>Main Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 6.29 MiB </td>\n", - " <td> 6.29 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (1650111,) </td>\n", - " <td> (1650111,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" 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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-69d9fe7e-0e9f-4a87-90bb-c1f48b4157ec' class='xr-section-summary-in' type='checkbox' ><label for='section-69d9fe7e-0e9f-4a87-90bb-c1f48b4157ec' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16b222f1-158f-48ef-98e4-187125b3bdd6' class='xr-index-data-in' type='checkbox'/><label for='index-16b222f1-158f-48ef-98e4-187125b3bdd6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-01-01 07:22:05.457853440',\n", - " '2022-01-01 07:22:10.089222656',\n", - " '2022-01-01 07:22:11.168378368',\n", - " '2022-01-01 07:22:12.265265152',\n", - " '2022-01-01 07:22:13.336309248',\n", - " '2022-01-01 07:22:14.425379328',\n", - " '2022-01-01 07:22:15.531576320',\n", - " '2022-01-01 07:22:16.613574656',\n", - " '2022-01-01 07:22:17.711744512',\n", - " '2022-01-01 07:22:18.782683648',\n", - " ...\n", - " '2022-12-31 21:48:25.919754240',\n", - " '2022-12-31 21:48:27.003810816',\n", - " '2022-12-31 21:48:28.101828608',\n", - " '2022-12-31 21:48:29.176862208',\n", - " '2022-12-31 21:48:30.267947008',\n", - " '2022-12-31 21:48:31.334020608',\n", - " '2022-12-31 21:48:32.436094464',\n", - " '2022-12-31 21:48:33.536158208',\n", - " '2022-12-31 21:48:34.606148608',\n", - " '2022-12-31 21:48:35.696192512'],\n", - " dtype='datetime64[ns]', name='time', length=1650111, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7101e010-b94d-47b6-96ec-49877c363d53' class='xr-index-data-in' type='checkbox'/><label for='index-7101e010-b94d-47b6-96ec-49877c363d53' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", - " ...\n", - " 246, 247, 248, 249, 250, 251, 252, 253, 254, 255],\n", - " dtype='int32', name='wavelength', length=256))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8e5182e-fa19-42a5-a6f9-7757ec1ddf5e' class='xr-section-summary-in' type='checkbox' ><label for='section-f8e5182e-fa19-42a5-a6f9-7757ec1ddf5e' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:15.338000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATOSU-68020-00005</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Nitrate: NUTNR Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Satlantic</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>Deep SUNA</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>344</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:16:04.174989</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:15:27.957591</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:16:04.174991</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:20:35.646657</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:16:04.174961 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4A-NUTNRA101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>nutnr_a_dark_sample</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:15:25.654908928</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T22:38:13.329600000</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_dark_sample</dd></dl></div></li></ul></div></div>" - ], - "text/plain": [ - "<xarray.Dataset>\n", - "Dimensions: (time: 1650111, wavelength: 256)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2022-01-01T0...\n", - " * wavelength (wavelength) int32 0 1 2 ... 254 255\n", - "Data variables: (12/36)\n", - " aux_fitting_1 (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " aux_fitting_2 (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " checksum (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " date_of_sample (time) float64 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " deployment (time) int32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " driver_timestamp (time) datetime64[ns] dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " ... ...\n", - " temp_interior (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " temp_lamp (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " temp_spectrometer (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " time_of_sample (time) float64 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " voltage_lamp (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - " voltage_main (time) float32 dask.array<chunksize=(1650111,), meta=np.ndarray>\n", - "Attributes: (12/62)\n", - " AssetManagementRecordLastModified: 2024-07-04T16:24:15.338000\n", - " AssetUniqueID: ATOSU-68020-00005\n", - " Conventions: CF-1.6\n", - " Description: Nitrate: NUTNR Series A\n", - " FirmwareVersion: Not specified.\n", - " Manufacturer: Satlantic\n", - " ... ...\n", - " stream: nutnr_a_dark_sample\n", - " subsite: RS01SBPS\n", - " summary: Dataset Generated by Stream Engine fr...\n", - " time_coverage_end: 2024-07-08T11:15:25.654908928\n", - " time_coverage_start: 2014-10-06T22:38:13.329600000\n", - " title: Data produced by Stream Engine versio..." - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "if doIngest: \n", - " instrument_key = 'nutnr_a_dark_sample'\n", - " for s in osb_profiler_streams: \n", - " if instrument_key in s: \n", - " print('Found this instrument stream:', s)\n", - " instrument_stream = s\n", - " break\n", - "\n", - " ds = loadData(instrument_stream) # lazy load\n", - " t0, t1 = '2022-01-01T00', '2022-12-31T23' # January 2022\n", - " ds = ds.sel(time=slice(t0, t1)) # Subset the full time range to one month\n", - " print(ds.time[0], ' ', ds.time[-1]) # verify selected one month time range\n", - " ds # get a 'data variable' list of sensors/metadata for this instrument" - ] - }, - { - "cell_type": "markdown", - "id": "1c12fc1c-a16c-40fc-8c6c-38fc9c72e10e", - "metadata": {}, - "source": [ - "**The dark sample nitrate is on hold pending an explanation of what is going on.** \n", - "Presuming it was business as usual, maybe: `nutnr_nitrogen_in_nitrate` becomes `nitrate_dark` and `int_ctd_pressure` becomes depth." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0097acb9-a10b-409b-9c28-c1713fbb7d12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if doIngest:\n", - " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", - " ds_nitrate_dark, reply = ShallowProfilerDataReduce(ds, t0, t1, ['nutnr_nitrogen_in_nitrate', 'int_ctd_pressure'], ['nitrate_dark', 'depth'])\n", - " ds_nitrate_dark.to_netcdf('./data/rca/sensors/osb/nitrate_dark_jan_2022.nc')\n", - "\n", - "ds_nitrate_dark = xr.open_dataset('./data/rca/sensors/osb/nitrate_dark_jan_2022.nc')\n", - "fig, axes = ChartSensor(profiles, [0, 1], [3], ds_nitrate_dark.nitrate_dark, -ds_nitrate_dark.depth, 'nitrate (dark)', 'black', 'ascent', 6, 4)" - ] - }, - { - "cell_type": "markdown", - "id": "98f8cabc-dcfb-434a-be5d-a1bd263ee05a", - "metadata": {}, - "source": [ - "#### 8 of 10: **nutnr_a_sample** i.e. nitrate" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "69f59bc3-04c9-4dff-9950-b10a3bffd5fa", - "metadata": { - "tags": [] - 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".xr-dim-list li {\n", - " display: inline-block;\n", - " padding: 0;\n", - " margin: 0;\n", - "}\n", - "\n", - ".xr-dim-list:before {\n", - " content: '(';\n", - "}\n", - "\n", - ".xr-dim-list:after {\n", - " content: ')';\n", - "}\n", - "\n", - ".xr-dim-list li:not(:last-child):after {\n", - " content: ',';\n", - " padding-right: 5px;\n", - "}\n", - "\n", - ".xr-has-index {\n", - " font-weight: bold;\n", - "}\n", - "\n", - ".xr-var-list,\n", - ".xr-var-item {\n", - " display: contents;\n", - "}\n", - "\n", - ".xr-var-item > div,\n", - ".xr-var-item label,\n", - ".xr-var-item > .xr-var-name span {\n", - " background-color: var(--xr-background-color-row-even);\n", - " margin-bottom: 0;\n", - "}\n", - "\n", - ".xr-var-item > .xr-var-name:hover span {\n", - " padding-right: 5px;\n", - "}\n", - "\n", - ".xr-var-list > li:nth-child(odd) > div,\n", - ".xr-var-list > li:nth-child(odd) > label,\n", - ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", - " background-color: var(--xr-background-color-row-odd);\n", - "}\n", - "\n", - ".xr-var-name {\n", - " grid-column: 1;\n", - "}\n", - "\n", - ".xr-var-dims {\n", - " grid-column: 2;\n", - "}\n", - "\n", - ".xr-var-dtype {\n", - " grid-column: 3;\n", - " text-align: right;\n", - " color: var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-var-preview {\n", - " grid-column: 4;\n", - "}\n", - "\n", - ".xr-index-preview {\n", - " grid-column: 2 / 5;\n", - " color: var(--xr-font-color2);\n", - "}\n", - "\n", - ".xr-var-name,\n", - ".xr-var-dims,\n", - ".xr-var-dtype,\n", - ".xr-preview,\n", - ".xr-attrs dt {\n", - " white-space: nowrap;\n", - " overflow: hidden;\n", - " text-overflow: ellipsis;\n", - " padding-right: 10px;\n", - "}\n", - "\n", - ".xr-var-name:hover,\n", - ".xr-var-dims:hover,\n", - ".xr-var-dtype:hover,\n", - ".xr-attrs dt:hover {\n", - " overflow: visible;\n", - " width: auto;\n", - " z-index: 1;\n", - "}\n", - "\n", - ".xr-var-attrs,\n", - ".xr-var-data,\n", - ".xr-index-data {\n", - " display: none;\n", - " background-color: var(--xr-background-color) !important;\n", - " padding-bottom: 5px !important;\n", - "}\n", - "\n", - ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data,\n", - ".xr-index-data-in:checked ~ .xr-index-data {\n", - " display: block;\n", - "}\n", - "\n", - ".xr-var-data > table {\n", - " float: right;\n", - "}\n", - "\n", - ".xr-var-name span,\n", - ".xr-var-data,\n", - ".xr-index-name div,\n", - ".xr-index-data,\n", - ".xr-attrs {\n", - " padding-left: 25px !important;\n", - "}\n", - "\n", - ".xr-attrs,\n", - ".xr-var-attrs,\n", - ".xr-var-data,\n", - ".xr-index-data {\n", - " grid-column: 1 / -1;\n", - "}\n", - "\n", - "dl.xr-attrs {\n", - " padding: 0;\n", - " margin: 0;\n", - " display: grid;\n", - " grid-template-columns: 125px auto;\n", - "}\n", - "\n", - ".xr-attrs dt,\n", - ".xr-attrs dd {\n", - " padding: 0;\n", - " margin: 0;\n", - " float: left;\n", - " padding-right: 10px;\n", - " width: auto;\n", - "}\n", - "\n", - ".xr-attrs dt {\n", - " font-weight: normal;\n", - " grid-column: 1;\n", - "}\n", - "\n", - ".xr-attrs dt:hover span {\n", - " display: inline-block;\n", - " background: var(--xr-background-color);\n", - " padding-right: 10px;\n", - "}\n", - "\n", - ".xr-attrs dd {\n", - " grid-column: 2;\n", - " white-space: pre-wrap;\n", - " word-break: break-all;\n", - "}\n", - "\n", - ".xr-icon-database,\n", - ".xr-icon-file-text2,\n", - ".xr-no-icon {\n", - " display: inline-block;\n", - " vertical-align: middle;\n", - " width: 1em;\n", - " height: 1.5em !important;\n", - " stroke-width: 0;\n", - " stroke: currentColor;\n", - " fill: currentColor;\n", - "}\n", - "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", - "Dimensions: (time: 91592, wavelength: 256)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2022-01...\n", - " * wavelength (wavelength) int32 0 1 ... 255\n", - "Data variables: (12/45)\n", - " aux_fitting_1 (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " aux_fitting_2 (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " checksum (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " date_of_sample (time) float64 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " deployment (time) int32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " driver_timestamp (time) datetime64[ns] dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " ... ...\n", - " temp_interior (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " temp_lamp (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " temp_spectrometer (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " time_of_sample (time) float64 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " voltage_lamp (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " voltage_main (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - "Attributes: (12/62)\n", - " AssetManagementRecordLastModified: 2024-07-04T16:24:15.338000\n", - " AssetUniqueID: ATOSU-68020-00005\n", - " Conventions: CF-1.6\n", - " Description: Nitrate: NUTNR Series A\n", - " FirmwareVersion: Not specified.\n", - " Manufacturer: Satlantic\n", - " ... ...\n", - " stream: nutnr_a_sample\n", - " subsite: RS01SBPS\n", - " summary: Dataset Generated by Stream Engine fr...\n", - " time_coverage_end: 2024-07-08T11:13:21.608314368\n", - " time_coverage_start: 2014-10-06T22:38:15.226800128\n", - " title: Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c3d113ce-eef9-43f9-b7da-744b9eea5869' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c3d113ce-eef9-43f9-b7da-744b9eea5869' 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>: 91592</li><li><span class='xr-has-index'>wavelength</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-018af22e-b0df-4c23-9d33-70a4b4dee8d8' class='xr-section-summary-in' type='checkbox' checked><label for='section-018af22e-b0df-4c23-9d33-70a4b4dee8d8' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T07:22:07.611640320 .....</div><input id='attrs-8bc189d0-724f-4b1c-8309-6aa704762f3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bc189d0-724f-4b1c-8309-6aa704762f3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bff4c458-ccbe-4c4a-a47b-4f6f9881a9eb' class='xr-var-data-in' type='checkbox'><label for='data-bff4c458-ccbe-4c4a-a47b-4f6f9881a9eb' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2022-01-01T07:22:07.611640320', '2022-01-01T07:22:08.877734912',\n", - " '2022-01-01T07:22:48.130280960', ..., '2022-12-31T21:47:22.890671616',\n", - " '2022-12-31T21:48:01.261864960', '2022-12-31T21:48:02.217944576'],\n", - " dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 251 252 253 254 255</div><input id='attrs-a4f0e49b-71db-447b-aef5-d93f76efd74c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4f0e49b-71db-447b-aef5-d93f76efd74c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f022915-6248-4308-b894-d50b1f0ad533' class='xr-var-data-in' type='checkbox'><label for='data-9f022915-6248-4308-b894-d50b1f0ad533' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 253, 254, 255], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8c9336c-d0ed-471a-8f91-dbe2a0b5a68a' class='xr-section-summary-in' type='checkbox' ><label for='section-f8c9336c-d0ed-471a-8f91-dbe2a0b5a68a' class='xr-section-summary' >Data variables: <span>(45)</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>aux_fitting_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-97710b9a-8d15-4943-b153-0127e8bcc7cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97710b9a-8d15-4943-b153-0127e8bcc7cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce269614-d71f-4765-9cd0-fe753995b51c' class='xr-var-data-in' type='checkbox'><label for='data-ce269614-d71f-4765-9cd0-fe753995b51c' 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>Aux Fitting 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 1</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aux_fitting_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-40f3af0f-3454-4f22-8cc5-6c9fe3df9f47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40f3af0f-3454-4f22-8cc5-6c9fe3df9f47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a1b7145-5dd9-45f3-bba0-77977778f146' class='xr-var-data-in' type='checkbox'><label for='data-2a1b7145-5dd9-45f3-bba0-77977778f146' 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>Aux Fitting 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Aux Fitting 2</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-25d01741-b161-4599-972a-84dd7e19535c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25d01741-b161-4599-972a-84dd7e19535c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d489d99d-6508-4add-8ed4-768075c4749e' class='xr-var-data-in' type='checkbox'><label for='data-d489d99d-6508-4add-8ed4-768075c4749e' 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>Data checksum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Data Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-f7d100c7-277d-46b6-aa54-8b3d2f259904' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7d100c7-277d-46b6-aa54-8b3d2f259904' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a0c6abe-8479-43ff-8616-666147e1cb45' class='xr-var-data-in' type='checkbox'><label for='data-2a0c6abe-8479-43ff-8616-666147e1cb45' 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>Date of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Date of Sample</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-ca62f091-31c1-4e03-ae4c-4d6da4b56212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca62f091-31c1-4e03-ae4c-4d6da4b56212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8494cc3a-fa24-4715-9d36-b02c2dcc3fad' class='xr-var-data-in' type='checkbox'><label for='data-8494cc3a-fa24-4715-9d36-b02c2dcc3fad' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> int32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-a1330912-a922-4ba9-ae8d-24fb2b2a7293' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1330912-a922-4ba9-ae8d-24fb2b2a7293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb41357-c8dd-4cfb-95dd-0667fb7e51f1' class='xr-var-data-in' type='checkbox'><label for='data-3eb41357-c8dd-4cfb-95dd-0667fb7e51f1' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>humidity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-9bbf005d-4939-48ee-9f46-2fb602797358' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bbf005d-4939-48ee-9f46-2fb602797358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5aeda007-6a7c-45e2-b004-4ca648c2467e' class='xr-var-data-in' type='checkbox'><label for='data-5aeda007-6a7c-45e2-b004-4ca648c2467e' 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>Humidity</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Humidity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-47cd48d6-3ba7-4c32-8253-aed38e96737c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47cd48d6-3ba7-4c32-8253-aed38e96737c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c2a538-65a3-4768-aa44-ba45ecaceab9' class='xr-var-data-in' type='checkbox'><label for='data-b1c2a538-65a3-4768-aa44-ba45ecaceab9' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-a599011b-45cb-49dd-887e-d4dc14fa0ef7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a599011b-45cb-49dd-887e-d4dc14fa0ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6c4957c-e8f5-4bdb-a95f-a3825b98dd82' class='xr-var-data-in' type='checkbox'><label for='data-c6c4957c-e8f5-4bdb-a95f-a3825b98dd82' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</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'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-60e59476-d486-4db4-afef-94836c044d71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60e59476-d486-4db4-afef-94836c044d71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28bdb1a2-dc84-42d3-8030-33cade9d519f' class='xr-var-data-in' type='checkbox'><label for='data-28bdb1a2-dc84-42d3-8030-33cade9d519f' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lamp_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-e0cf356c-3c19-4dfb-b9be-3d044f89ee9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0cf356c-3c19-4dfb-b9be-3d044f89ee9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-135c063c-58b7-43c5-9012-115ec811cad2' class='xr-var-data-in' type='checkbox'><label for='data-135c063c-58b7-43c5-9012-115ec811cad2' 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>Elapsed time lamp has been on in seconds</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Time</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-63b046c0-f1a5-4ea2-a45f-e108d7b66b22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63b046c0-f1a5-4ea2-a45f-e108d7b66b22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4f37668-c2b3-48ca-b63a-27368824ec99' class='xr-var-data-in' type='checkbox'><label for='data-f4f37668-c2b3-48ca-b63a-27368824ec99' 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>ancillary_variables :</span></dt><dd>nitrate_concentration_qartod_results nitrate_concentration_qartod_executed</dd><dt><span>comment :</span></dt><dd>Dissolved Nitrate Concentration, uncorrected for temperature and salinity effects.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITRDIS_L1</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration</dd><dt><span>units :</span></dt><dd>umol L-1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-e573d57a-f23a-4ec8-ba72-8f7e7842c28a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e573d57a-f23a-4ec8-ba72-8f7e7842c28a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8669fd7-efd5-4e81-96e6-a9c96c5f5344' class='xr-var-data-in' type='checkbox'><label for='data-d8669fd7-efd5-4e81-96e6-a9c96c5f5344' 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>Individual QARTOD test flags. For each datum, flags are listed in a string matching the order of the tests_executed attribute. Flags should be interpreted using the standard QARTOD mapping: [1: pass, 2: not_evaluated, 3: suspect_or_of_high_interest, 4: fail, 9: missing_data].</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration Individual QARTOD Flags</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>nitrate_concentration status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test, climatology_test</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> <U2 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_qartod_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-596f2ecd-bd94-4c96-a483-6d9493b1ee28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-596f2ecd-bd94-4c96-a483-6d9493b1ee28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d23b9c4-4c79-4d00-bb8f-ba27f000df07' class='xr-var-data-in' type='checkbox'><label for='data-0d23b9c4-4c79-4d00-bb8f-ba27f000df07' 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>Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>flag_meanings :</span></dt><dd>pass not_evaluated suspect_or_of_high_interest fail missing_data</dd><dt><span>flag_values :</span></dt><dd>1,2,3,4,9</dd><dt><span>long_name :</span></dt><dd>Dissolved Nitrate Concentration QARTOD Summary Flag</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>nitrate_concentration status_flag</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-f4a8d24f-7922-4a85-aefd-87bef057b6b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4a8d24f-7922-4a85-aefd-87bef057b6b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b885f2b1-93c9-4c82-b603-395899576707' class='xr-var-data-in' type='checkbox'><label for='data-b885f2b1-93c9-4c82-b603-395899576707' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nitrate_concentration_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-6fa3b597-60d2-4923-82e4-360c6e828005' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fa3b597-60d2-4923-82e4-360c6e828005' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdc562c8-e8fe-4e74-b102-8e61ed285880' class='xr-var-data-in' type='checkbox'><label for='data-cdc562c8-e8fe-4e74-b102-8e61ed285880' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_254_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-2e6d9616-f8fa-4ae6-82cd-078b090207c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e6d9616-f8fa-4ae6-82cd-078b090207c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41e24872-5021-4244-adaa-d51edad1415f' class='xr-var-data-in' type='checkbox'><label for='data-41e24872-5021-4244-adaa-d51edad1415f' 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>Absorbance at 254 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 254 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_absorbance_at_350_nm</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-84affea3-cd3f-4812-bf1f-63bcf9d7290f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84affea3-cd3f-4812-bf1f-63bcf9d7290f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f121f0-3728-4b36-8c4d-e24e1f9bac9e' class='xr-var-data-in' type='checkbox'><label for='data-42f121f0-3728-4b36-8c4d-e24e1f9bac9e' 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>Absorbance at 350 nm</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Absorbance at 350 Nm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_bromide_trace</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-0b5fd583-b401-4618-a3ef-7ee702f7cd91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b5fd583-b401-4618-a3ef-7ee702f7cd91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54a25142-95ef-4bf3-8b74-f1d21fbd9870' class='xr-var-data-in' type='checkbox'><label for='data-54a25142-95ef-4bf3-8b74-f1d21fbd9870' 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>Bromide Trace</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Bromide Trace</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_current_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-de19558d-ddf1-43eb-8902-73a321471d4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de19558d-ddf1-43eb-8902-73a321471d4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3dc8c74-1208-4455-94fb-54935fab8270' class='xr-var-data-in' type='checkbox'><label for='data-e3dc8c74-1208-4455-94fb-54935fab8270' 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>Main Current</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Current</dd><dt><span>units :</span></dt><dd>mA</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_dark_value_used_for_fit</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-854080a7-043c-4d05-b358-3b889754194d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-854080a7-043c-4d05-b358-3b889754194d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27a7d891-512e-43d6-921d-23c7b234b580' class='xr-var-data-in' type='checkbox'><label for='data-27a7d891-512e-43d6-921d-23c7b234b580' 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>Dark Value Used for Fit</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Dark Value Used for Fit</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-c8acd9ea-734f-41e2-8ff2-ec14762aefb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8acd9ea-734f-41e2-8ff2-ec14762aefb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c64de076-77cb-4452-b085-748817f244e1' class='xr-var-data-in' type='checkbox'><label for='data-c64de076-77cb-4452-b085-748817f244e1' 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>Fit Base 1</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 1</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_base_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-d2fa3a35-43a6-4518-9732-2e41204618dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2fa3a35-43a6-4518-9732-2e41204618dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f08c4ca7-b16d-4e5e-b862-f620083633a0' class='xr-var-data-in' type='checkbox'><label for='data-f08c4ca7-b16d-4e5e-b862-f620083633a0' 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>Fit Base 2</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit Base 2</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_fit_rmse</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-dd3e6e88-bdc0-4e33-89b5-d1116e54f9e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd3e6e88-bdc0-4e33-89b5-d1116e54f9e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49398c5f-d9dc-43ca-bfe9-71c024a628fd' class='xr-var-data-in' type='checkbox'><label for='data-49398c5f-d9dc-43ca-bfe9-71c024a628fd' 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>Fit RMSE</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Fit RMSE</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_integration_time_factor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-f08dfbee-32a9-41c2-b14e-99e8b896942e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f08dfbee-32a9-41c2-b14e-99e8b896942e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c96b3756-641d-48fb-b250-85c950ebb28a' class='xr-var-data-in' type='checkbox'><label for='data-c96b3756-641d-48fb-b250-85c950ebb28a' 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>Integration Time Factor</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Integration Time Factor</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-106609d4-8bb3-4650-83e1-dec5067fbd21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-106609d4-8bb3-4650-83e1-dec5067fbd21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09ed4592-831d-4d67-be4c-3c29ac759f32' class='xr-var-data-in' type='checkbox'><label for='data-09ed4592-831d-4d67-be4c-3c29ac759f32' 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>Nitrogen in Nitrate</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Nitrogen in Nitrate</dd><dt><span>units :</span></dt><dd>mg/l</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-4b5874b5-f1e5-410a-9039-d7d402ecc343' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b5874b5-f1e5-410a-9039-d7d402ecc343' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe78b394-98b8-42ea-ab86-03a9bff0430f' class='xr-var-data-in' type='checkbox'><label for='data-fe78b394-98b8-42ea-ab86-03a9bff0430f' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_nitrogen_in_nitrate_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-32a6949d-955c-4a2c-a93e-ed691dea8cce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32a6949d-955c-4a2c-a93e-ed691dea8cce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2ceaa79-2bc7-4efe-908e-630e01407a97' class='xr-var-data-in' type='checkbox'><label for='data-c2ceaa79-2bc7-4efe-908e-630e01407a97' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_spectrum_average</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-969b02f8-3077-488f-b7df-b326f25080a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-969b02f8-3077-488f-b7df-b326f25080a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49bd0f79-32e6-4f45-942e-40485f293883' class='xr-var-data-in' type='checkbox'><label for='data-49bd0f79-32e6-4f45-942e-40485f293883' 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>Spectrum Average</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrum Average</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nutnr_voltage_int</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-1f375d1a-4c1f-472a-9622-a9939ea77dc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f375d1a-4c1f-472a-9622-a9939ea77dc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cfd7c3c-6b9c-4f65-8fa5-764a2324d03d' class='xr-var-data-in' type='checkbox'><label for='data-5cfd7c3c-6b9c-4f65-8fa5-764a2324d03d' 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 Power Supply Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Power Supply Voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-cb2e7cec-314b-4423-8805-aa0e72da722c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb2e7cec-314b-4423-8805-aa0e72da722c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28f2a01c-c55f-4135-b91a-182d4cdca8aa' class='xr-var-data-in' type='checkbox'><label for='data-28f2a01c-c55f-4135-b91a-182d4cdca8aa' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-373499f9-f67c-48b5-a73d-21cf03dbd948' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-373499f9-f67c-48b5-a73d-21cf03dbd948' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f295dd5-537a-4a09-8aa8-b0534752c35e' class='xr-var-data-in' type='checkbox'><label for='data-2f295dd5-537a-4a09-8aa8-b0534752c35e' 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>ancillary_variables :</span></dt><dd>salinity_corrected_nitrate_qartod_results salinity_corrected_nitrate_qartod_executed sea_water_temperature frame_type spectral_channels sea_water_practical_salinity nutnr_dark_value_used_for_fit</dd><dt><span>comment :</span></dt><dd>Temperature and salinity corrected dissolved nitrate concentration.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITRTSC_L2</dd><dt><span>long_name :</span></dt><dd>Nitrate Concentration - Temp and Sal Corrected</dd><dt><span>units :</span></dt><dd>umol L-1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-47cab74d-6493-444a-8c32-5a54d0670ccb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47cab74d-6493-444a-8c32-5a54d0670ccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9c23363-9dd8-4c26-b62e-b53c94cb2154' class='xr-var-data-in' type='checkbox'><label for='data-f9c23363-9dd8-4c26-b62e-b53c94cb2154' 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>Individual QARTOD test flags. For each datum, flags are listed in a string matching the order of the tests_executed attribute. Flags should be interpreted using the standard QARTOD mapping: [1: pass, 2: not_evaluated, 3: suspect_or_of_high_interest, 4: fail, 9: missing_data].</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Nitrate Concentration - Temp and Sal Corrected Individual QARTOD Flags</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>salinity_corrected_nitrate status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test, climatology_test</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> <U2 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_qartod_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-77075564-18bd-448e-9ee7-6acf7ccebb87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77075564-18bd-448e-9ee7-6acf7ccebb87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3405cdae-3dcb-40e4-a70f-2b3179d78d63' class='xr-var-data-in' type='checkbox'><label for='data-3405cdae-3dcb-40e4-a70f-2b3179d78d63' 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>Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>flag_meanings :</span></dt><dd>pass not_evaluated suspect_or_of_high_interest fail missing_data</dd><dt><span>flag_values :</span></dt><dd>1,2,3,4,9</dd><dt><span>long_name :</span></dt><dd>Nitrate Concentration - Temp and Sal Corrected QARTOD Summary Flag</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>salinity_corrected_nitrate status_flag</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-22359c4b-aa6b-4265-88a1-e2233e754f7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22359c4b-aa6b-4265-88a1-e2233e754f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6220cf00-83b2-4c81-8560-d7977c84e279' class='xr-var-data-in' type='checkbox'><label for='data-6220cf00-83b2-4c81-8560-d7977c84e279' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_corrected_nitrate_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-bce70f88-97bd-4d07-8b90-3c8e178cc4bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bce70f88-97bd-4d07-8b90-3c8e178cc4bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cabb502-8650-45c9-8c04-228588727c0b' class='xr-var-data-in' type='checkbox'><label for='data-5cabb502-8650-45c9-8c04-228588727c0b' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 kiB </td>\n", - " <td> 89.45 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_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'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-d6407a2d-6b8e-49b2-b3bb-cff3dbe7687f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6407a2d-6b8e-49b2-b3bb-cff3dbe7687f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8300296-2160-42c2-b336-8589702a8b56' class='xr-var-data-in' type='checkbox'><label for='data-e8300296-2160-42c2-b336-8589702a8b56' 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>alternate_parameter_name :</span></dt><dd>ctdpf_sbe43_sample-practical_salinity</dd><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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRACSAL_L2</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Practical Salinity</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-df16e8b9-0f83-4f88-9fd4-c98c71c7a08a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df16e8b9-0f83-4f88-9fd4-c98c71c7a08a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1178bcc7-82cd-4890-84df-7fee6e310c7b' class='xr-var-data-in' type='checkbox'><label for='data-1178bcc7-82cd-4890-84df-7fee6e310c7b' 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>alternate_parameter_name :</span></dt><dd>ctdpf_sbe43_sample-seawater_temperature</dd><dt><span>comment :</span></dt><dd>Seawater temperature near the sensor.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>TEMPWAT_L1</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Seawater Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectral_channels</span></div><div class='xr-var-dims'>(time, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(39491, 256), meta=np.ndarray></div><input id='attrs-5a593e30-b856-47c5-a049-859d4547ab4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a593e30-b856-47c5-a049-859d4547ab4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fc347bd-192c-4e23-bb72-9c552472cb48' class='xr-var-data-in' type='checkbox'><label for='data-7fc347bd-192c-4e23-bb72-9c552472cb48' 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>UV Absorption Spectra is an array of values produced by the NUTNR instrument class and is based on the ultraviolet (UV) absorption characteristics of the components of seawater, including dissolved nitrate.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>NITROPT_L0</dd><dt><span>long_name :</span></dt><dd>UV Absorption Spectra</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 89.45 MiB </td>\n", - " <td> 50.88 MiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592, 256) </td>\n", - " <td> (52101, 256) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"51\" x2=\"25\" y2=\"51\" />\n", - " <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", - " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >256</text>\n", - " <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">91592</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_interior</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-81a73bcc-552e-469f-b85f-0992069790d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81a73bcc-552e-469f-b85f-0992069790d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5241835-3c71-4b7a-9f13-475ba3215a5f' class='xr-var-data-in' type='checkbox'><label for='data-d5241835-3c71-4b7a-9f13-475ba3215a5f' 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>Interior Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Interior Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-498adaf5-b0dc-446f-a820-915240ad2598' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-498adaf5-b0dc-446f-a820-915240ad2598' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09d109e1-338e-4bd8-9848-e173f2105b37' class='xr-var-data-in' type='checkbox'><label for='data-09d109e1-338e-4bd8-9848-e173f2105b37' 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>Lamp Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_spectrometer</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-a2cc56f6-9b80-4c53-b242-275b88992e99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2cc56f6-9b80-4c53-b242-275b88992e99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1693718-63fc-4608-bdfb-8c4bc5b110ae' class='xr-var-data-in' type='checkbox'><label for='data-e1693718-63fc-4608-bdfb-8c4bc5b110ae' 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>Spectrometer Temperature</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Spectrometer Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_of_sample</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-3fe0e15c-71d0-4b58-9c04-8947b4e92a69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fe0e15c-71d0-4b58-9c04-8947b4e92a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58ff92db-4e22-44f6-85ec-dc9aaeac7e1a' class='xr-var-data-in' type='checkbox'><label for='data-58ff92db-4e22-44f6-85ec-dc9aaeac7e1a' 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>Time of Sample</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Time of Sample</dd><dt><span>units :</span></dt><dd>h</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 715.56 kiB </td>\n", - " <td> 715.56 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_lamp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-8380ca6a-63ab-446d-9db2-71ba03a773c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8380ca6a-63ab-446d-9db2-71ba03a773c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b4d44f-869e-4b3c-9f7a-767bd54b7f2b' class='xr-var-data-in' type='checkbox'><label for='data-c2b4d44f-869e-4b3c-9f7a-767bd54b7f2b' 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>Lamp Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Lamp Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_main</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(91592,), meta=np.ndarray></div><input id='attrs-47f0e623-e284-4fa8-9eb7-ee3b60de40d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47f0e623-e284-4fa8-9eb7-ee3b60de40d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2140849c-c6dd-49f3-986d-3b845256c983' class='xr-var-data-in' type='checkbox'><label for='data-2140849c-c6dd-49f3-986d-3b845256c983' 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>Main Voltage</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Main Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 357.78 kiB </td>\n", - " <td> 357.78 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (91592,) </td>\n", - " <td> (91592,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91592</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-297e9400-999c-4b32-9a8c-5732d17ceb09' class='xr-section-summary-in' type='checkbox' ><label for='section-297e9400-999c-4b32-9a8c-5732d17ceb09' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-caf47a7f-b0b3-4417-a025-6c2985f91353' class='xr-index-data-in' type='checkbox'/><label for='index-caf47a7f-b0b3-4417-a025-6c2985f91353' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-01-01 07:22:07.611640320',\n", - " '2022-01-01 07:22:08.877734912',\n", - " '2022-01-01 07:22:48.130280960',\n", - " '2022-01-01 07:23:27.352933888',\n", - " '2022-01-01 07:23:28.655081984',\n", - " '2022-01-01 07:24:07.928694272',\n", - " '2022-01-01 07:24:09.222790656',\n", - " '2022-01-01 07:24:48.597636608',\n", - " '2022-01-01 07:24:49.888985600',\n", - " '2022-01-01 07:25:29.212340736',\n", - " ...\n", - " '2022-12-31 21:45:21.636364800',\n", - " '2022-12-31 21:45:22.591453696',\n", - " '2022-12-31 21:46:01.992573952',\n", - " '2022-12-31 21:46:02.951598080',\n", - " '2022-12-31 21:46:41.406718976',\n", - " '2022-12-31 21:46:42.353713664',\n", - " '2022-12-31 21:47:21.945701888',\n", - " '2022-12-31 21:47:22.890671616',\n", - " '2022-12-31 21:48:01.261864960',\n", - " '2022-12-31 21:48:02.217944576'],\n", - " dtype='datetime64[ns]', name='time', length=91592, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-25b0ee0d-c9a4-47ab-bbd7-f28bc394dd16' class='xr-index-data-in' type='checkbox'/><label for='index-25b0ee0d-c9a4-47ab-bbd7-f28bc394dd16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", - " ...\n", - " 246, 247, 248, 249, 250, 251, 252, 253, 254, 255],\n", - " dtype='int32', name='wavelength', length=256))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b097aa63-10b1-482c-8b2a-aa0d492e16d4' class='xr-section-summary-in' type='checkbox' ><label for='section-b097aa63-10b1-482c-8b2a-aa0d492e16d4' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:15.338000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATOSU-68020-00005</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Nitrate: NUTNR Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Satlantic</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>Deep SUNA</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>344</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:16:01.225709</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:15:44.922199</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:16:01.225711</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:20:52.479634</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:16:01.225682 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4A-NUTNRA101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>nutnr_a_sample</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:13:21.608314368</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T22:38:15.226800128</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample</dd></dl></div></li></ul></div></div>" - ], - "text/plain": [ - "<xarray.Dataset>\n", - "Dimensions: (time: 91592, wavelength: 256)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2022-01...\n", - " * wavelength (wavelength) int32 0 1 ... 255\n", - "Data variables: (12/45)\n", - " aux_fitting_1 (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " aux_fitting_2 (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " checksum (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " date_of_sample (time) float64 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " deployment (time) int32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " driver_timestamp (time) datetime64[ns] dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " ... ...\n", - " temp_interior (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " temp_lamp (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " temp_spectrometer (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " time_of_sample (time) float64 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " voltage_lamp (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - " voltage_main (time) float32 dask.array<chunksize=(91592,), meta=np.ndarray>\n", - "Attributes: (12/62)\n", - " AssetManagementRecordLastModified: 2024-07-04T16:24:15.338000\n", - " AssetUniqueID: ATOSU-68020-00005\n", - " Conventions: CF-1.6\n", - " Description: Nitrate: NUTNR Series A\n", - " FirmwareVersion: Not specified.\n", - " Manufacturer: Satlantic\n", - " ... ...\n", - " stream: nutnr_a_sample\n", - " subsite: RS01SBPS\n", - " summary: Dataset Generated by Stream Engine fr...\n", - " time_coverage_end: 2024-07-08T11:13:21.608314368\n", - " time_coverage_start: 2014-10-06T22:38:15.226800128\n", - " title: Data produced by Stream Engine versio..." - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "if doIngest:\n", - " instrument_key = 'nutnr_a_sample'\n", - " for s in osb_profiler_streams: \n", - " if instrument_key in s: \n", - " print('Found this instrument stream:', s)\n", - " instrument_stream = s\n", - " break\n", - "\n", - " ds = loadData(instrument_stream) # lazy load\n", - " t0, t1 = '2022-01-01T00', '2022-12-31T23' # January 2022\n", - " ds = ds.sel(time=slice(t0, t1)) # Subset the full time range to one month\n", - " print(ds.time[0], ' ', ds.time[-1]) # verify selected one month time range\n", - " ds # get a 'data variable' list of sensors/metadata for this instrument" - ] - }, - { - "cell_type": "markdown", - "id": "b2f399c7-339d-4536-9740-7e609f2e6e3a", - "metadata": {}, - "source": [ - "`salinity_corrected_nitrate` > `nitrate` and `int_ctd_pressure` > `depth`" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5d3aa1c9-d4ae-45de-a79c-94d56195c008", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if True:\n", - " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", - " ds_nitrate, reply = ShallowProfilerDataReduce(ds, t0, t1, ['salinity_corrected_nitrate', 'int_ctd_pressure'], ['nitrate', 'depth'])\n", - " ds_nitrate.to_netcdf('./data/rca/sensors/osb/nitrate_jan_2022.nc')\n", - "\n", - "ds_nitrate = xr.open_dataset('./data/rca/sensors/osb/nitrate_jan_2022.nc')\n", - "fig, axes = ChartSensor(profiles, ranges['nitrate'], [3], ds_nitrate.nitrate, -ds_nitrate.depth, 'nitrate (dark)', 'black', 'ascent', 6, 4)\n" - ] - }, - { - "cell_type": "markdown", - "id": "037fc2c3-ab7b-4003-9ee9-8636daafe528", - "metadata": {}, - "source": [ - "#### 9 of 10: **velpt** i.e. current velocity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b764c28-ae36-4066-9d3e-64873c13962a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "instrument_key = 'velpt'\n", - "for s in osb_profiler_streams: \n", - " if instrument_key in s: \n", - " print('Found this instrument stream:', s)\n", - " instrument_stream = s\n", - " break\n", - " \n", - "ds = loadData(instrument_stream) # lazy load\n", - "t0, t1 = '2022-01-01T00', '2022-12-31T23' # January 2022\n", - "ds = ds.sel(time=slice(t0, t1)) # Subset the full time range to one month\n", - "print(ds.time[0], ' ', ds.time[-1]) # verify selected one month time range\n", - "ds # get a 'data variable' list of sensors/metadata for this instrument" - ] - }, - { - "cell_type": "markdown", - "id": "79f86ba8-a9f0-4eef-a8b9-78014e01bb1a", - "metadata": {}, - "source": [ - "For the current sensor: \n", - "depth: `int_ctd_pressure`. Velocities: `velpt_d_upward_velocity`, `velpt_d_northward_velocity`, `velpt_d_eastward_velocity`.\n", - "Respectively `depth`, `up`, `north`, `east`" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "065f320a-a80c-420b-8440-cfef842a599a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n", - "Attempting 1 charts\n", - "\n", - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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ZmDBhAho2bAi5XA4/Pz/07dsX8fHx6jYymQxbt241yfFSUlIgk8lw8uRJve3i4uIgk8mQk5NTZlvbtm0RFRWlbqNviYmJAQD88ssv6NmzJzw8PODu7o7WrVtj3rx5yM7ONjj75s2b0bt3b/j4+KBmzZoIDg7Gn3/+WabdL7/8ghYtWkAul6NFixbYsmWLxvaFCxeiU6dOqFGjBurUqYNnn30W//77r3q7QqHAO++8g1atWsHNzQ3+/v4YM2YMbt68WW7GO3fuYPTo0fDw8ICHhwdGjx5d5nv4xhtvoEOHDpDL5Wjbtq3B/d+/fz86dOgAZ2dnNGrUCCtXrtTYfvbsWTz//PMIDAyETCbDZ599ZtB+T58+jZCQELi4uKBevXqYN28eSv9/srxjl5adnY3XX38dzZo1g6urKxo2bIipU6ciNzdX3SYlJQXjxo1DUFAQXFxc8MgjjyAyMhKFhYXlZi7vZwzw8VNEpsaiiST3/PPP49SpU1izZg3Onz+P2NhY9OzZ06hiwlCGfBgZo2vXrkhLS1Mvw4YNQ1hYmMa64cOH491338Xw4cPRqVMnbN++HWfOnMEnn3yCU6dOYd26dQYf76+//kLv3r3xxx9/4Pjx4wgNDcWAAQOQlJSkbhMfH4/hw4dj9OjROHXqFEaPHo1hw4bh6NGj6jb79+/H5MmTceTIEezatQtFRUXo06cP7t27BwC4f/8+Tpw4gffeew8nTpzA5s2bcf78eQwcOLDcjKNGjcLJkyexY8cO7NixAydPnsTo0aM12ggh8PLLL2P48OEG9/3KlSvo168fevTogaSkJMyePRtTp07FL7/8om5z//59NGrUCB999BH8/PwM2m9eXh569+4Nf39/JCYmYtmyZViyZAk+/fRTo45d2s2bN3Hz5k0sWbIEp0+fRkxMDHbs2IFx48ap2yQnJ0OlUmHVqlU4e/Ysli5dipUrV2L27Nl6MxvyM+bjp4jMQFQDubm5AoDIzc2VOgqVcufOHQFAxMXF6WwTEBAgAKiXgIAAIYQQFy9eFAMHDhR16tQRbm5uomPHjmLXrl1l3jt//nwxduxYUbNmTTFmzBiNfQEQISEhWo+7b98+AUDcuXOnzLY2bdqIyMjIMuvHjh0rBg0apLHu6NGjAoD47LPPdH4PKqNFixZi7ty56tfDhg0TYWFhGm369u0rRowYoXMfmZmZAoDYv3+/zjYJCQkCgLh69arONufOnRMAxJEjR9Tr4uPjBQCRnJxcpn1kZKRo06aNzv2V9Pbbb4vmzZtrrJswYYLo0qWL1vYBAQFi6dKl5e53+fLlwsPDQ+Tn56vXLVy4UPj7+wuVSlWhY+vy448/CicnJ6FQKHS2WbRokQgKCtK7H0N+xp07dxYTJ07UaNO8eXMxa9YsozIT2SpzfPbzTBNJyt3dHe7u7ti6davGo29KSkxMBABER0cjLS1N/fru3bvo168fdu/ejaSkJPTt2xcDBgwo8z/pxYsXo2XLljh+/Djee+89JCQkAAB2796NtLQ0bN682Yw9BNavXw93d3dMmjRJ63ZPT08A/7tsGBcXZ/C+VSoV/vvvP9SuXVu9Lj4+Hn369NFo17dvX52PHQKgvmRUcj/a2shkMnVebeLj4+Hh4YHHH39cva5Lly7w8PDQe3xD6OrXsWPHoFAoKrXfkJAQjbnd+vbti5s3byIlJcXgYxdfqi1+jzbFYyscHHTfg5Obm1vm5xAYGIioqCiNzPp+xsWPnyrdRt/jp4iofCyaSFIODg6IiYnBmjVr4OnpiW7dumH27Nn4+++/1W18fHwAPCwu/Pz81K/btGmDCRMmoFWrVmjSpAk++OADNGrUCLGxsRrH6NWrF9566y00btwYjRs3Vr/fy8sLfn5+egsFU7hw4QIaNWoER0dHve0cHR3V418M9cknn+DevXsYNmyYel16erpRjx0SQmD69Ono3r07WrZsqbVNfn4+Zs2ahVGjRukdUJmeno46deqUWV+nTh2dxzeUrn4VFRUhKyvL5Pst3mbosV1dXdGsWTOdP+fbt29j/vz5mDBhgs4sly5dwrJly8pMp/LII4/A29u73MzFeSvy+CkiKh+LJpLc888/j5s3byI2NhZ9+/ZFXFwc2rdvrx5Arcu9e/fw9ttvo0WLFvD09IS7uzuSk5PLnGnq2LGjGdOXT+h55E9J9erVQ3JyMjp37mzQfjds2ICoqChs2rSpTKFizGOHpkyZgr///hsbNmzQul2hUGDEiBFQqVRYvny5ev3EiRPVZwrd3d11Hru842tTcr8lCwht/dJ1TGMYst/y2nTu3BnJycmoV69emf3n5eWhf//+aNGiBSIjI7VmuHnzJsLCwjB06FCMHz9eY9uePXswZcqUcjOXXmfM3wMiKp9NzNNEVZ+zszN69+6N3r174/3338f48eMRGRmJ8PBwne+ZOXMm/vzzTyxZsgSNGzeGi4sLhgwZUmawt5ubW4UyFZ9Ryc3NLXNJKicnBx4eHgbtp2nTpjh48CAUCkW5Z5sMtWnTJowbNw4//fQTnnrqKY1tfn5+Bj926PXXX0dsbCz++usvrQ+0VCgUGDZsGK5cuYK9e/dqnGWaN28e3nrrrTLHzsjIKLOfW7du6XzskTYl72wsPqaufjk4OMDLy8vgfZema7/A/844VebY//33H8LCwuDu7o4tW7Zo/Ttw8+ZNhIaGqifvrWjm4rwVefwUEZWPZ5rIKrVo0UJ9Jxfw8NKVUqnUaHPgwAGEh4dj8ODBaNWqFfz8/PSOJynm5OQEAGX2V1qTJk1gZ2enHkNVLC0tDTdu3ECzZs0M6suoUaNw9+5djbM0JWmb0kCfDRs2IDw8HD/88AP69+9fZntwcHCZxw7t3LlT47FDQghMmTIFmzdvxt69exEUFFRmP8UF04ULF7B79+4yxUGdOnXUlzwbN26sPnZubq563BgAHD16FLm5uUY99qjkfovPounqV8eOHStVjAYHB+Ovv/7SKLZ37twJf39/BAYGVurYeXl56NOnD5ycnBAbGwtnZ+cybW7cuIGePXuiffv2iI6Ohp1d+b+Wy/sZ8/FTRGZisiHlVox3z1mvrKwsERoaKtatWydOnTolLl++LH788Ufh6+srXn75ZXW7Jk2aiNdee02kpaWJ7OxsIYQQzz77rGjbtq1ISkoSJ0+eFAMGDBA1atQQb7zxhvp92u6gUigUwsXFRXzwwQciPT1d5OTk6Mz32muviYYNG4otW7aIy5cvi4MHD4qQkBDRqlUrrXdAabt7ToiHd1/Z29uLmTNnisOHD4uUlBSxe/duMWTIEPVdddevXxfNmjUTR48e1Znnhx9+EA4ODuKrr74SaWlp6qVkHw4dOiTs7e3FRx99JP755x/x0UcfCQcHB4072l577TXh4eEh4uLiNPZz//599fdo4MCBon79+uLkyZMabQoKCnTmE0KIsLAw0bp1axEfHy/i4+NFq1atxDPPPKPR5sKFCyIpKUlMmDBBNG3aVCQlJYmkpCS9+758+bJwdXUVb775pjh37pxYvXq1cHR0FD///LO6TUFBgXpfdevWFW+99ZZISkoSFy5c0LnfnJwc4evrK0aOHClOnz4tNm/eLGrWrCmWLFli1LGPHj0qmjVrJq5fvy6EECIvL088/vjjolWrVuLixYsa38OioiIhhBA3btwQjRs3Fr169RLXr1/XaFNSr169xLJly9SvDfkZb9y4UTg6OorVq1eLc+fOiWnTpgk3NzeRkpKi83tBVJWY47OfRRNJKj8/X8yaNUu0b99eeHh4CFdXV9GsWTMxZ84c9Qe4EELExsaKxo0bCwcHB/WUA1euXBGhoaHCxcVFNGjQQHz55ZciJCSk3KJJCCG++eYb0aBBA2FnZ6dzyoHifPPmzROPPvqocHFxEQEBASI8PLzMh1oxXUWTEEJs2rRJPPHEE6JGjRrCzc1NtG7dWsybN0895cCVK1cEALFv3z6deUJCQspMmQBAjB07VqPdTz/9JJo1ayYcHR1F8+bNxS+//KKxXds+AIjo6GiNLNoWffmEEOL27dvihRdeEDVq1BA1atQQL7zwQplpFXT148qVK3r3HRcXJ9q1ayecnJxEYGCgWLFihcZ2Xbn1/YyFEOLvv/8WPXr0EHK5XPj5+YmoqCj1dAOGHrt4ioriPhS/1tfP6OhonW1KCggIKDPFRXk/YyGE+Oqrr0RAQIBwcnIS7du31zulBFFVY47Pfj5GhYiIiKocPkaFiIiISCIsmoiIiIgMwKKJiIiIyAAsmoiIiIgMwKKJiIiIyAAsmoiIiIgMwKKJiIiIyAAsmoiIiIgMwKKJiIiIyAAsmoiIiIgMwKKJiIiIyAAsmoiIiIgMwKKJiIiIyAAOUgcgIiL9kpOTMWXKFCgUCri5uakXV1dX9aJSqaBUKqFUKpGZmYl9+/bB29sbSqUSACCEgBACeXl5uHr1qqT9mTNnDubPny9pBqKKkAkhhNQhzC0vLw8eHh7Izc1FzZo1pY5DRDYsMTERnTt3ljpGlfDqq68iNDQUMpkMLVq0QKtWraSORFWIOT77eaaJiMgIK1askDpClfH111/j66+/Vr/+/fff0a9fPwkTEenHoomIyAgLFixAeno6kpOTYWdnB5lMpv6z9NfFr7Vt1/UeBwcHODo6wsnJCY6Ojrh37x7+/PNPqbttEcuWLcOhQ4dgb28PBweHSv/p4eGBzp07QyaTSd01qiJ4eY6IyIpt2rQJI0aMkDqGzZo4cSLPDlZTvDxHRFTNDBgwAJMmTcIPP/wAZ2dnpKenSx3JpqxcuRKOjo4QQkClUsHb2xszZ86Eu7u71NHIBkl2piklJQXz58/H3r17kZ6eDn9/f7z44ot499134eTk9L+AWk6rrlixAhMnTjT4WDzTRETWrrCwEN9++y3S0tLg4OBg0CKEwJEjR9SXo4qV/LWuVCrxxRdfSNElq1YNLrJUe1XqTFNycjJUKhVWrVqFxo0b48yZM3jllVdw7949LFmyRKNtdHQ0wsLC1K89PDwsHZeIyGyOHj2KLl26SB2jWhFCcKwTGU2yoiksLEyjEGrUqBH+/fdfrFixokzR5OnpCT8/P0tHJCKyiGHDhkkdoVqQyWSwt7fHzp07WTBRhVjVjOC5ubmoXbt2mfVTpkyBt7c3OnXqhJUrV0KlUundT0FBAfLy8jQWIiJr9fzzz0sdwWpt2LABycnJuHnzJpRKpXqSzoosKpUKCoUCoaGhUneLbJTVDAS/dOkSli1bhk8++URj/fz58/Hkk0/CxcUFe/bswYwZM5CVlYU5c+bo3NfChQsxd+5cc0cmIjKJ3NxcqSNYrZEjR6q/DggIwMWLFzXGbxFZkskHgkdFRZVbsCQmJqJjx47q1zdv3kRISAhCQkLw7bff6n3vJ598gnnz5un9JVNQUICCggL167y8PDRo0IADwYnIKt2+fRvTpk3DhQsXAPxvkHLJX88nT56EQqGQJJ+1aNSoEf755x+Nm4WIdDHHQHCTF01ZWVnIysrS2yYwMBDOzs4AHhZMoaGhePzxxxETEwM7O/1XDA8dOoTu3bsjPT0dvr6+BmXi3XNEVFWoVCrMmDEDn332mdRRJHPp0iU0atRI6hhk5Wzi7jlvb294e3sb1PbGjRsIDQ1Fhw4dEB0dXW7BBABJSUlwdnaGp6dnJZMSEVmX2NhYvPbaaxrjcIqVXuft7a2x7s6dO1LFtriMjAwWTSQJyS4M37x5Ez179kTDhg2xZMkS3Lp1S72t+E65bdu2IT09HcHBwXBxccG+ffvw7rvv4tVXX4VcLpcqOhGRyd29exeDBg2SOoZVmjp1Krp16wYAaNOmDZo1ayZxIqquJJvcMiYmBi+99JLWbcWRduzYgYiICFy8eBEqlQqNGjXC+PHjMXnyZKMGAvLyHBHZgsjISMybN0/qGFbp2rVrqF+/vtQxyIbYxJgma8SiiYiqunv37kGhUEAIgYYNG+Lu3btSRzKprKwseHl5SR2DbIg5Pvutap4mIiKqGDc3N3h6eqJWrVrYsmWL1HFMKjk5mQUTWQUWTURENk4IAaVSiaKiIhQUFKBjx45o2LCh1LGMMn78eJ2TUnIME1kLzhBGRGSF9u7diyeffFLqGBbj5uYmdQSicrFoIiKyQr/99pvUEUzG2dkZr7/+OiZMmABnZ2c4ODjA0dERDg4OcHBwgL29Pe+IJpvAgeBERFaooKAAn3/+Oc6dO4ddu3ahqKgIAJCZmSlxsopr3bq10c+LAzTnqOrXrx8WL17MM1NULt49V0EsmoioKsjMzDT4SQhV2YoVKzBx4kSpY5CV491zRETVmJeXF8LCwqSOIbnXXnsNMplM7zJmzBgUFhZKHZWqGJ5pIiKSmFKphEql0nlZ6vLly1i6dCmKior0LsDDAeT0UFhYGHr06AEAkMlk6vU9evRA9+7dpYpFFsLLcxXEoomIrNXcuXMRFRUldYxqJy4uDiEhIVLHIDPi5TkioiomLy9P6gjVEh/4SxXBM01ERBJLSUlBYWGhxpgcAOqv79y5g1GjRuHff/+VOKlteOKJJ7B79244OjpKHYUkxMtzFcSiiYiqs927d6N3795Sx7Coq1ev2tys6GRa5vjs5+SWRERW7tixYxg4cCDu3bunMaC5WMl1xWembNXgwYPVX5c841aavm0TJkxgwURmwTNNRERWrmnTprhw4YLUMayCTCbD5cuXERgYKHUUsnIcCE5EVA0tXrxY6ghWQwiBuLg4qWNQNcWiiYjIyg0aNAgbNmyQOobVeOmll9SD5DkvFVkSiyYiIhuQkJAgdQSr9Pvvv0sdgaoRDgQnIrIyJWcEL/7z448/Rrdu3XDr1i3Y2dnBzs4OH330ES5duiRlVIubNGmSeqb0Ro0aYerUqVJHomqERRMRkZVYsWIFJk2aJHUMqzV+/Hh89dVXUsegaoxFExGRlcjJyZE6gtVwcXHBY489BiEEioqK4OPjgw8//FDqWFTNsWgiIpKAEAIzZszA0qVLpY5ilR48eIBjx45prBs+fDgHfpOkOBCciEgCOTk5LJiMNGbMGKhUKqljUDXGyS2JiCTy66+/YubMmZDJZLCzs1PfRq/ta13b4+Pjpe6G1fL09ERqaipq1KghdRSSAB+jQkRUhQwaNAiDBg2q8Pujo6NZNOmRk5ODjIwMFk1kMiyaiIhsFAdGl/X5559DqVRCqVQiLCwMjRs3ljoSVSEsmoiIbMSxY8fQqVMnqWNYpWHDhmHBggWoXbs2AKgvXwohtD7Ul6giOBCciMhGnDt3TuoIVuvHH39EkyZN4OXlBS8vL9SuXRu1atVC69atOXicTIZFExGRjRgzZgz27NmDd955R+ooNqOwsBDV4H4nshBeniMikogQAosWLcKsWbOkjmLVJk6cqDF+q/gxKiUfMyOEUJ9RKrm9bt26sLPj+QEyDUn/JgUGBqqvOxcvpX95pKamYsCAAXBzc4O3tzemTp2KwsJCiRITEZnOxYsXWTAZ4MSJE6hVq5Z6qV27Nry8vODt7Q1vb2/4+PigTp068PPzg5+fH+rWrQt/f3/Uq1ePBROZlORnmubNm4dXXnlF/drd3V39tVKpRP/+/eHj44ODBw/i9u3bGDt2LIQQWLZsmRRxiYhM5pFHHsHrr7/O32dahIeHo2PHjnBycsKLL74odRwiABJPbhkYGIhp06Zh2rRpWrdv374dzzzzDK5duwZ/f38AwMaNGxEeHo7MzEyDJ6vi5JZEVFWEhYXhzz//lDqGxcXHx6NLly5SxyAbYo7PfsmLpoKCAhQWFqJBgwYYOnQoZs6cCScnJwDA+++/j19//RWnTp1Sv+fOnTuoXbs29u7di9DQUIOOw6KJiKqK+/fvY/ny5bh7926Z4Q3FS0JCAn799Vepo5rcU089BTs7O/XSt29fTJo0CQ4Okl80IStU5WYEf+ONN9C+fXvUqlULCQkJiIiIwJUrV/Dtt98CANLT0+Hr66vxnlq1asHJyQnp6ek691tQUICCggL167y8PPN0gIjIwlxdXfHWW2/pbXPu3LkqWTTt3r1b4/Uff/wBHx8fjBw5UqJEVN2YvGiKiorC3Llz9bZJTExEx44d8eabb6rXtW7dGrVq1cKQIUPw8ccfw8vLCwC0TkpW3mRlCxcuLDcDEVFVcODAAfX8TcUXDnJyciRMZDnOzs7o3r271DGoGjF50TRlyhSMGDFCb5vAwECt64uvV1+8eBFeXl7w8/PD0aNHNdrcuXMHCoWizBmokiIiIjB9+nT167y8PDRo0MDAHhARSevUqVPqZ8qVHEFR+uv169dXm2fPJSQkcDZ0kpzJi6biW0ArIikpCQBQt25dAEBwcDAWLFiAtLQ09bqdO3dCLpejQ4cOOvcjl8shl8srlIGISEpHjhxBcHCw1DGszpEjR1g0keQkG9MUHx+PI0eOIDQ0FB4eHkhMTMSbb76JgQMHomHDhgCAPn36oEWLFhg9ejQWL16M7OxsvPXWW3jllVc4oJuIqqT69etLHUEy48ePh5ubm3qiSiEEnJ2d0alTJwwdOlTqeETS3T134sQJTJo0CcnJySgoKEBAQABGjBiBt99+G66urup2qampmDRpEvbu3QsXFxeMGjUKS5YsMepMEu+eI6KqRKFQwNPTE/fv35c6isVkZGSgTp06UscgG1LlphywFBZNRFSVCCHw3HPPYevWrVJHkYSDgwOaNGminmIBeHjTkKenJ2JiYvDII49InJCsAYumCmLRRETVzZ07d5CZmQkAyM/Px+OPP64xFUtV1alTJyQkJEgdg6xAlZuniYiITOv06dNo3bq11DEk884770gdgaowPsmQiMhKzJkzR+cs34Yu1blgcnBwQIsWLaSOQVUYiyYiIitQUFCABQsWSB3DphUVFWH16tVSx6AqjJfniIisgFwux+HDh9WPSCk5yLn4dck/AeDChQtIS0uzbFArJJfLUaNGDXTu3Blvv/221HGoCmPRRERkJYKDg3Ho0CGD2u7YsQNPP/20mRNZl969e2Pnzp1Sx6BqjEUTEZENunr1qtQRLMrT0xMRERFIT0+Hvpu+tT12xs3NDZ6enuaOSNUApxwgIrJB9+/fR9++fXHw4EGpo9iEtWvXYvTo0VLHIAsyx2c/B4ITEdkgV1dXHDhwAEKIMguf0VaWvb291BGoCuDlOSKiKuSPP/5AYmKi1DEkt2LFCowYMQIymQyOjo4aj+ciqigWTUREJpKWlgZ/f3+pYxAeXprhOCYyNRZNREQmkp2dLXWEKu2ll17CoEGD1K9VKhVUKhWcnJzg7OysXu/t7Y127dpJEZGqOA4EJyIyoatXr+LEiRPqO7e0jTlSqVTqrwHgxRdflDIylXL58mUEBQVJHYMqic+eIyKycgEBAQgICDDqPSNGjEBSUhIcHBzg6Oio/vPWrVuYOHEiLl26pDGpZUnF63NzcyudnR5KSUlh0URa8UwTEZENysvLw507dzSKpgkTJiA+Pl7iZOYVHR2tcZauWMkze4au17atU6dO6Nixo8lzk+XxTBMRURV18eJFNGnSROoYVqFFixbw9fXVeHSMq6srli5dioYNG6rX6fuz9NdEpsAzTUREJrJy5Uq89tprUsegSrh06RIaNWokdQwyAU5uSURkxc6cOSN1BKqku3fvSh2BrBjPNBERmYgQAnFxccjLy9P6DDRtv261bVOpVJg5cyZSU1PNnLh6i4uLg5ubm/p7HxgYCB8fH4lTkalwTBMRkRWTyWQIDQ2t9H7OnTvHgslIe/bsQa9evaSOQVUcL88REVmZ5s2bSx3B5nDQN1kCiyYiIitjZ2eH/v37Sx3DZnTr1s0kZ/iIysPLc0REVig2Nhbx8fFQKBQa69euXYvo6GiJUlmnWbNmSR2BqgkOBCcismGc38k4fn5++Pfff/lZUA1wygEiIsKDBw/w4osvQiaTsWAyUnp6Oi5fvix1DLJRvDxHRGRjdu/ejfXr10sdw6r17NkTLVq0KLN+8ODBaNu2reUDUZXAoomIyEakpaVhxIgR+Ouvv6SOYvW6d++O+fPnSx2DqhgWTURENuKjjz5iwaTDY489BuDh1AOdOnXCW2+9JXEiqopYNBERWbnU1FQEBARIHcMqNGvWDDKZTGNeJjs7O42Z1Y8ePYquXbtqfX959z6V3i6TyTTWubq6Yu3ateoijaoXFk1ERFbuypUrUkewGv/++6/UEdCyZUt8+eWXGoXagAEDEBgYKG0wMjvJphyIi4vTORlZQkICOnXqBED7LK8rVqzAxIkTDT4WpxwgIlt35MgRxMfHq1/r+tW9ePFipKenWyoWlZCZmcln11kRc3z2S1Y0FRYWIjs7W2Pde++9h927d+Py5cvqYkkmkyE6OhphYWHqdh4eHnBxcTH4WCyaiKg6uX79Oho0aCB1jGolKCgIycnJcHJykjoK/b8q9cBeJycn+Pn5qV8rFArExsZiypQpZc4ueXp6arQlIrImmzZtwogRI6SOQUbif6TJWFYzI/gvv/yCYcOGISUlReN/SDKZDPXq1UN+fj6CgoIwbtw4vPrqq7CzM3xeTp5pIqraYmNj8dVXX5VZr21Qr7b15SlvP7t37zZqf2QdpkyZgmXLlkkdg8ykSp1pKm316tXo27dvmVPK8+fPx5NPPgkXFxfs2bMHM2bMQFZWFubMmaNzXwUFBSgoKFC/zsvLM1tuIpLWpUuXMGjQIKljkA0aOXKk1BHI1ggTi4yMFAD0LomJiRrvuXbtmrCzsxM///xzuftfsmSJqFmzZoUy5ObmVqpvRGR9CgsLRc+ePcv9vcOFi7alV69eQqVSSf3XmMwgNzdXAKb97Df55bmsrCxkZWXpbRMYGAhnZ2f16/nz52PZsmW4ceMGHB0d9b730KFD6N69O9LT0+Hr66u1jbYzTQ0aNODlOSIy2PLlyzF58mSpY5AF3Lt3D66urlLHIBOzictz3t7e8Pb2Nri9EALR0dEYM2ZMuQUTACQlJcHZ2Rmenp4628jlcsjlcoMzEBGVNnDgQMydOxeZmZlSRyEzc3NzkzqCTg0bNkR8fDz8/f2ljkIADB9NbSZ79+7FlStXMG7cuDLbtm3bhm+++QZnzpzBpUuX8O233+Ldd9/Fq6++yqKIiMyqfv36yMjIgBCiQkt+fj4nO6RKS01NRWxsrNQx6P9JfvfcqFGjcPXqVRw6dKjMth07diAiIgIXL16ESqVCo0aNMH78eEyePBkODoafJOPdc0RkaUePHkWXLl2kjkFVgJOTE9zc3LRO9myMdu3aYd26dahbt66Jklm3KjW5pSWxaCIiS1OpVPj444/xxRdfAABn6SarMHnyZHz55ZdSx7AImxjTRERkqK1bt2Lw4MFSxyCqFmQymVGPIKOyWDQRkWRSU1OljkBkEa6urjh37pzeR4Dpu/xW+qKQMZfqSre9deuW0cfX17a8C1aG7lcmkxk88awh+zTHHI28PEdEkjp58iQyMjIAlP3lW/K1rq/L22bse/S1M2R/Fy5cKPOhVLrtZ599pnWfRGR6vDxHRFVG27ZtpY5gMsuXL8fs2bOljkFEZsKiiYhIi507d6Jv375SxyAbtHjxYowdO9aoy02GtKvIsxN1tS19eauy+9R1uawi/S/v0puh+8zLy0Pz5s0NamsoFk1EVO38/PPPGDp0qNQxyAp9/fXXqFWrlsa6kh/ixV+rVCoUFRVBqVRCqVSqv27SpAl69Oih9T2lvybzMsekpRzTRETVTmBgIK5evSp1DCKbl5KSgoCAAKljaMUpB4iITOCnn37CoEGDUFBQUOZ//qVfl/csTaLqLDMz02qLJnNg0URE1U6nTp1w8+bNSu3D0dERRUVFJkpEVYm7uzuefPJJqFQqKJVKCCGgUqnUi1Kp1Piz9HZ7e3tMnz5dfZmw+IKQtrs8tV0sKr2tou8rb19t27ZFkyZNjPre2DpeniMik0hMTETnzp2ljkFUJTz66KM4d+6c1DFsmjk++yV/YC8RVQ26JswjIuONHDlS6gikBS/PEZFJ9OvXD5cuXcLVq1cNOr1v6CUAKd9f8s/i5cUXXzTo+0GWsWjRIoSFhaFVq1ZSR6FqgJfniKjKKSgogL+/P7Kzs6WOQjbmww8/REREhNQxyAR4eY6IyAAKhYIFE1XI7NmzoVAopI5BVoqX54ioynF3d8fdu3dx5swZvP/++9i5c6fUkchGREZGIi4uTn1ptiKTURr74Ftjj2VnZwd7e3vY2dlpfG1vbw93d3c0bdrU6MxkGF6eIyKrUlhYiFatWuH8+fNSRyGi/zd27FhER0fb1IzmvDxHRFVednY2CyYiK7NmzRrcvXtX6hiS4+U5IrIqfn5+OHfuHN577z388ssvUschMkrDhg3RuHFjAA8vt5V+7py2MzUl1xd/XfKSna596LusV16b8s4Yld4eERGBGjVq6H1PdcDLc0REAPLz8/HFF1/g2rVrZbbp+oAxZn3JdZ999lnFQhJZgTfeeMMm/g7z2XNERDrcvn0b3t7eUscgqvL27NkjdQTJsGgioirh9u3bUkcgMli9evUwc+ZMODo6aky0WvpSnK5t2ujbXpn3ltxuZ2dXrWcrZ9FERFVC06ZNcenSJcTHx6vX6Rt9UHKsR+l2+rZpa1fettL7KT3ORN9+lEolTp8+jV27duGff/7R2Y5sy40bN+Dn54fhw4dLHYWMwDFNRGSQkydPol27dlLHIKq2du7cid69e0sdw2ZwygEikszXX38tdQSiam3mzJlSR6j2eHmOiAwSFRWF69ev4/Tp00a/15IT4lV0BmdtJ90vX75sikhEai4uLhqvDfn7KoSAh4cHVq9eba5YZCAWTURkkDp16iA2NlbqGISHg95fffVVJCQkaN1eugBUKBTIzMy0RDTS4/LlywgKCpI6BlUCiyYiIhNJTU1FZGSk3oHdhijv/WvXrq3U/kkajRo1AgDExcUhJCRE4jRUESyaiIhMQAiBjh074tatW1JHISvXs2fPSu+jVatWaN26tbrA1nUHqL29PT788EM0bNiw0sckFk1ERCYhk8nw2muvYd68eVJHoWrg9OnTBo8vjIuLQ2pqqs7HuJDhePccEVE5cnNz0ahRI/WHjq6FBRNZoxs3bsDe3h52dnbl/h02ZPnyyy+l7pJkzFo0LViwAF27doWrqys8PT21tklNTcWAAQPg5uYGb29vTJ06FYWFhRptTp8+jZCQELi4uKBevXqYN29epccMEBEZ6ty5c7hy5YrUMYiswuuvv661mJo+fToUCoXU8czKrJfnCgsLMXToUAQHB2u9VVKpVKJ///7w8fHBwYMHcfv2bYwdOxZCCCxbtgzAw8mpevfujdDQUCQmJuL8+fMIDw+Hm5sbZsyYYc74REQAgC5dumDTpk3YtGlTmW2l/wNXepbvLVu2mD8gkRVYunQpli5dapZ9//777+jXr59Z9m0Mi8wIHhMTg2nTpiEnJ0dj/fbt2/HMM8/g2rVr8Pf3BwBs3LgR4eHhyMzMRM2aNbFixQpEREQgIyMDcrkcAPDRRx9h2bJluH79ukHXZzkjOBEZQqlU4rfffsP9+/cBlH8XW0nFbU+cOIFPP/3ULPmIqisvLy/cunXLqDFZ5vjsl3QgeHx8PFq2bKkumACgb9++KCgowPHjxxEaGor4+HiEhISoC6biNhEREUhJSdE650VBQQEKCgrUr/Py8szbESIyqxs3bqBt27bIysqSOgoRSSAmJsYqBrFLOhA8PT0dvr6+Gutq1aoFJycnpKen62xT/Lq4TWkLFy6Eh4eHemnQoIEZ0hORpezatYsFE1E1NmDAAMhkMowfPx4qlUqyHEafaYqKisLcuXP1tklMTETHjh0N2p+2ylEIobG+dJvSTwgvLSIiAtOnT1e/zsvLY+FEZMNGjhyJixcvYvv27RV6vyn/h6rtkSvl7f/WrVu4evWqyTIQVVerV6/G7Nmz1ROFWprRRdOUKVMwYsQIvW0CAwMN2pefnx+OHj2qse7OnTtQKBTqs0l+fn5lzigVPw6g9BmoYnK5XONyHhHZNrlcjg8++AAffPCB1FEqZPny5Zg8ebLUMcjKBQYGljkpULJA17et9HZzbDMmT2X6oe34xaKioiQrmIAKFE3e3t7w9vY2ycGDg4OxYMECpKWloW7dugCAnTt3Qi6Xo0OHDuo2s2fPRmFhIZycnNRt/P39DS7OiMg0CgoKsGfPHq2/5PSdAdb1S7LkL+nSv7BNsc1a8AYUKs+KFSswceJEqWNQOcw6EDw1NRXZ2dlITU2FUqnEyZMnAQCNGzeGu7s7+vTpgxYtWmD06NFYvHgxsrOz8dZbb+GVV15R/5IZNWoU5s6di/DwcMyePRsXLlzAhx9+iPfff98qBoURVRdCCLRv3x7nzp2TOgqRZDp27IgmTZqUmaMIgHrySADlThBZsk3dunXh5+eHrVu36hyaYsjXFX2foV+3b98etWrV0vv9qfKEGY0dO1YAKLPs27dP3ebq1auif//+wsXFRdSuXVtMmTJF5Ofna+zn77//Fj169BByuVz4+fmJqKgooVKpDM6Rm5srAIjc3FxTdY2oWho5cqTWf9NcuHCpHktWVpbUv4YMZo7PfovM0yQ1ztNERJUhtIzH0LVt1apV+PDDDwGUPQug6+x4ee20vRY6LocWv75x40aZpysQVYZcLkd2djZcXV2ljmIQc3z2s2gioiotPz8f//zzj/q1rl95Qs+4KKFj/JS+fRi6rXh9VlYWBgwYYHXjscj2ubm5wd7eHiqVCo0bN0aHDh3Uf8+EEOo71ufOnVul7jSvcpNbEhGZU35+Pjw8PHjGhaq1YcOGaRTjRUVFZdqoVCr88ccfGD9+POzt7S0Zz6bwTBMRVVkKhQJBQUG4ceOG1FGIqpwtW7bg2WeflTqGTjzTRERkBEdHR1y/ft0k+zLksl5F1iuVSixatAhfffWVep2uy4AKhQLZ2dmGRiYyq++//96qiyZzYNFEVM2dOHECI0aMgEKhKLNN1+Rzxkz3oW0fUrzfkH3oKla07aOiGXTtw8PDw6AMPj4++Pfffw0+NlVvLVu2RJs2bQDoH3NX3vbS23x9fREVFVXpfLaGRRNRNTdgwADcvHlT6hhEVEEvv/wyVq9eLXWMaoFjmoiquXXr1mHMmDFSxyAiKxUQEKDxBI7Sk17KZDJMnToVgwYNkiCdbpxyoIJYNBGZ35YtW9TFV8nbmUvTN8+RMev0tTHlPonIMHK5HEFBQVq3rVq1Ck888YRF87BoqiAWTUSWtWjRIrzzzjtSxyAiK1JQUKB+hqwlmOOz384keyEiKuHChQtSRyAiK/Lhhx9atGAyF55pIiKTE0LgyJEjuH//fpn1Jem6O8+Q/Zd8v6596JvBW9ddcNoyVjSfIRlLO3ToEObPn2/U8ah6ad++PZo1a6aezbt4AaDxWqVSQQiB5s2b47333oObm5vEyS2L8zQRkU2QyWQIDg6WOoZN6tmzJy5cuICNGzdKHYWsVNu2bfHtt98aNe0FmQYvzxERWRG5XI4NGzaozxZcvnwZPXv2hIMD/49LD3333Xdo06YNVCqV1FGqHf4rJCKyEtu3b0e/fv2kjkE24PTp03juued0PidO11kofWen9L2nvEldy9tv9+7dMWHCBJsv/m07PRFRFcIB9GSMX3/9VeoIBtu4cSNcXFzw8ssvSx2lUlg0ERHpsWLFCkyaNEnqGEQ2r0ePHlJHqDQWTUQkmYKCAqxduxZ3794ts83Q570Z207XHXwRERFl7vYjIuNt2rQJw4YNkzqGWbBoIiLJPPvss9ixY4fUMYhsyrJlyzBlyhSpY1RLvHuOiCTTu3dvqSMQ2ZyvvvpK6gjVFie3JCKygICAAKSmpkodg6qAunXrokuXLmXWCyHg4OAAFxcXhISE4KWXXoKdXfU9N8Jnz1UQiyYiktq6devUDzQmklLJMX+lv27VqhW2bNmCwMBACZKZFp89R0Rko0aPHl3msRfmXI4fPy51l8lKlX7USvGiVCpx8uRJREZGSh3RavFMExGRicTHx2Pr1q1atxk6QWB5bXS1L/36o48+MiY6UZXFZ88REVmZf/75B127dpU6BhGZEYsmIiITqF+/Pnx9fZGRkSF1FCKr0rBhQ8yaNUv9WtcFrpLrjWlT+oxr8dcPHjxARERExYNrwctzRETVnEqlwvbt25GTk2P0e4UQ2LJlCw4cOABA/yDjkuNoSo6nEUJAqVQiPz8fSqWy0v0h6zJz5kwsWrTI4sc1x2c/zzQREVVzr732Gr7++mupY5AV+Pjjj9G5c2fIZDK9i52dnUHbXVxc8Mgjj0jdLZNh0UREVM21b99e6ghkInK5HHv37uX4OjPhlANERNXchAkT9E5dUJ0nSLQ1BQUF6Natm8ZZn9OnT0sdq8rgvwQiItKpffv2UCqV2LVrl9RRqIJat25d7uU2a1p69eoFlUol9bdNK7NenluwYAF+//13nDx5Ek5OTmUGGZ46dQofffQRDh48iKysLAQGBmLixIl444031G1SUlIQFBRUZt/bt29HWFiYOeMT2YTExESt41FKDsLVN/dP8brS8/1oa1f6vhFD5xIyhLHtTJFXV5udO3fi/PnzBuUhItPat28frly5Uu7g7fJ+F/z3338mz2bWoqmwsBBDhw5FcHAwVq9eXWb78ePH4ePjg++//x4NGjTA4cOH8eqrr8Le3r7ME5x3796Nxx57TP26du3a5oxOZBMyMjLQuXNnqWMQEZlU48aNpY6glVmLprlz5wIAYmJitG5/+eWXNV43atQI8fHx2Lx5c5miycvLC35+fmbJSWSrvL29ERISgv3790sdhYioyrO6u+dyc3O1nkUaOHAg8vPz0aRJE7z55psYMmSIzn0UFBSgoKBA/TovL88sWYn0UalUSExMRFFRkdHv1XbZSJcFCxao31OSIe/Xd0lM3/tlMhnef/997Nmzx6CMRERVgVUVTfHx8fjxxx/x+++/q9e5u7vj008/Rbdu3WBnZ4fY2FgMHz4ca9aswYsvvqh1PwsXLlSf5SKSSv/+/bFjxw6pYxARkakII0VGRgoAepfExESN90RHRwsPDw+9+z1z5ozw8fER8+fPLzfDlClTRKtWrXRuz8/PF7m5uerl2rVrAoDIzc01qI9EpjBx4sRy/61w4cKFCxfzLqb87Df6MSpZWVnIysrS2yYwMBDOzs7q1zExMZg2bZrOKfrPnTuH0NBQjB8/Xn2pQZ/169dj/PjxePDggUGZ+RgVIjI1I3916nzPnj17sHfvXvVrQ+8irEh7fXdPFufLy8vD5cuX1a9Ltrl37x7i4uKMykckNUkfo+Lt7Q1vb2+THBwAzp49i169emHs2LEGFUwAkJSUhLp165osAxGRsYwtbrS9Z9++fejTp4+pIhFZzC+//ILnnntO6hh6FZ8wMSWzjmlKTU1FdnY2UlNToVQqcfLkSQAPbyV0d3fH2bNnERoaij59+mD69OlIT08HANjb28PHxwcAsGbNGjg6OqJdu3aws7PDtm3b8MUXX+Djjz82Z3QiIrNr2rSp1BGIKuT55583uO3XX3+NV155xYxpLMhkF/q0GDt2rNbri/v27RNC6B4fFRAQoN5HTEyMePTRR4Wrq6uoUaOG6NChg1i3bp1ROXJzc01+XZOIyFLWr18v+bgQLlzMvRw6dMik/27M8dlv9JgmW8QxTURky4KCgpCSkiJ1DCKL8vPzw86dO9XDcbTN6F9ayXV5eXkIDAw06Wc/iyYiIit36tQpDB8+XD3/nKG/tnW1E6UeO1GRdtevXzcoA5HUJB0ITkREltWmTRskJyebbH8KhQIrV67ExYsXAZT/P3hdg9537tyJs2fPmiwXkbVj0URENm3z5s1GDUolIuvj6+uLxMREndtFqekvSp8d1XbGNC8vD61atTJpThZNRGSzlEolXnjhBaljEFElffvtt2jQoIFJ92mOR6jZmXyPREQWYm9vjx9++EHqGERkIEdHRygUCgghNJZnnnlG6mgG4ZkmIrJpgwcPrtDs3FWROSbzIzKladOmwcHBdksPnmkiIqoiatasiRUrVkgdg6zYokWLoFKpypzpsdSyaNEiqb8FlcIpB4iIyOT27t2LJ598UuoYVMKYMWPQu3dvndvLezRQycHWhrQ1VEUfSeTr64uQkBCd7zfHZz+LJiIiqhSFQoHY2FiNeaSWL1+Ow4cPS5yMqrrIyEhERUVp3WaOz37bvbBIRERWoW/fvti3b5/UMagaatGihUWPxzFNRERUKY8//rjUEaiE6dOnSzZmydLLsGHDLPq95ZkmIiLS68aNG5g4caLeR6e0bdtW57bSY2GKv87Ly8Ply5dNmpWA0aNHSx2hymLRREREek2aNAm//fab1DHIQPXr15c6QpXFoomIiPSaMmUKYmNjpY5BBvLx8an0PqZOnYq2bdtqfTyJm5sbnnvuOZueb6miePccERFViBAC27dvR3x8PJycnJCRkYG8vDz06NEDXl5esLOzg0wmK7MUFhYiIiIC//77r9RdoAp69tlnsWXLFqlj6MW754iIyOSys7MRFRWF27dvA0ClH02zbt06U8QiK9a1a1epI0iCRRMRmY0hJ7It2cYaM1lD7kGDBuHgwYMG5SDbFxQUhIsXL8LOjjfQG4tFExFpePnllxEdHS11DCIyk4CAABZMFcTvGhFpyMjIkDoCEZmJvb29zT//TUocCE5EGoQQOHPmDFQqlcWOqe0OHW1tAMMvQRUvut5Tcpu+toZs19VW27EMbXfs2DHMmTOn3L4SkX4cCE5EZiOTydCqVSupY5Rr69atGDx4sNQxiKgaYdFERDYpNzdX6ghEVUbz5s3x2GOPqc/oGqP0mWJtZ4VLr6vocYzZb2FhIbZu3Wr0cfRm4OU5IrJV165dQ3Z2NoD//bIs/cu4vPWGtiu5/ujRoxgzZkxl4xNZjV9++QXPPfec1DFMyhyf/SyaiIgqKDY2FoMGDZI6BpHZPPPMM/jss8/KrDfmjJGXlxc8PT1NnKx8LJoqiEUTEZlLcnIyrly5orGu9ANqdTFkW0X3U1RUhB9//FF9Jq50u+LZuYuPUfJ16falt2lrl5qaivv376vXqVQqnD59Wmdmql7279+PJ554wqLH5IzgRERWpnnz5mjevLnUMcoYN24cYmJipI5BBABwcnKSOoJJsGgiIqqCevToge+++07qGFQBXbp0wezZs9Wv9Y3H03aWseS6xo0bw8/Pz+j96NpmyOW40mdH7e3tq8zDfXl5johIYiqVCgqFAnZ2dmXmdCoqKsK2bduQk5ODwsJCKBQKFBUVQalUQqVSQaVSaf1a27xSpeeJKnkZUdefxV/b29ujRYsW6veUbqdQKNTri7MUf33jxg188cUXZvv+kW157rnn8PPPP1foDjpj8PIcEVEVc/fuXdSoUUPqGEQWs3nzZuTm5koyOLyyzPoYlQULFqBr165wdXXV+c0pHmBYclm5cqVGm9OnTyMkJAQuLi6oV68e5s2bZ/BDLomIrJklZ14nksrAgQMxatQojBo1CgkJCTZZMAFmPtNUWFiIoUOHIjg4GKtXr9bZLjo6GmFhYerXHh4e6q/z8vLQu3dvhIaGIjExEefPn0d4eDjc3NwwY8YMc8YnIjK7mjVr4sGDB0hNTUVRUREUCgVWrVqFFStWSB2tUgYMGAA/Pz+tl/12796N1NRUKeORhc2cORPdu3eXOkalmbVomjt3LgCUeweHp6eneqBaaevXr0d+fj5iYmIgl8vRsmVLnD9/Hp9++immT59u9muiRETm5uzsjOvXr+P3338HALi4uKj/U6htygFdUxpUdqoDfdMcmOqYaWlpLJiqmS+//BJdu3aVOoZJWMWYpilTpmD8+PEICgrCuHHj8Oqrr8LO7uGVw/j4eISEhEAul6vb9+3bFxEREUhJSUFQUJBUsYmIynX79m1kZWUB0P2w4aNHjyI8PNyCqYgsY8WKFZg4caLUMUxG8qJp/vz5ePLJJ+Hi4oI9e/ZgxowZyMrKUj/dOz09HYGBgRrv8fX1VW/TVjQVFBSgoKBA/TovL898HSDSIjs7G++++y5u3rwJoOK3+mp7XXJdebcbV6Zt6bMHJdv++++/SExMLLM/IrKMuLg42Nvbq1+X/Peq7Y5JbXdKlmaK9SXX+fr6omXLlvq6YXOMLpqioqLUl910SUxMRMeOHQ3aX3FxBABt27YFAMybN09jfelf7OWdCl64cGG5GYnM6ZVXXsHmzZuljkFEldC4cWPUqFEDMpkMdnZ2sLOzU39d8ualkttKb3d0dMTMmTPh4+NTZpuu/ZX+j0p564rpm9Vd17ry/kNXs2ZNDoMpweiiacqUKRgxYoTeNqXPDBmjS5cuyMvLQ0ZGBnx9feHn54f09HSNNpmZmQD+d8aptIiICEyfPl39Oi8vDw0aNKhwJiJjhYeHs2gisnEXL140yX6Kx6rZos6dO+PIkSMsnP6f0UWTt7c3vL29zZEFAJCUlARnZ2f17YjBwcGYPXs2CgsL1dOw79y5E/7+/jqLM7lcrjEGisjSBgwYwGkxyGzKm5Cy9OWZXr164dChQxZOSVVBQkKCzc6pZA5mnacpNTUVJ0+eRGpqKpRKJU6ePImTJ0/i7t27AIBt27bhm2++wZkzZ3Dp0iV8++23ePfdd/Hqq6+qi55Ro0ZBLpcjPDwcZ86cwZYtW/Dhhx/yzjkiqrZKXxayt7dXP6rCwcEBjo6O6sXJyQmDBw+WOjLZsFq1auHGjRtSx7AKZn2MSnh4ONasWVNm/b59+9CzZ0/s2LEDERERuHjxIlQqFRo1aoTx48dj8uTJGs+pOX36NCZPnoyEhATUqlULEydOxPvvv29w0cTHqBARaYqOjsasWbM01hX/Ts3IyJAiElmxW7dumfUqkzmY47Ofz54jIqpmbty4gfr160sdg6xYp06dtA4Yd3FxwapVq9C0aVOpohmMz54jIqJK8/f3x6RJk7B8+XKpo5CV0jelSP/+/XHhwgULprEeZh3TRERE1kcmk+Grr76CEEK91K1bV+pYZCMiIiKkjiAZnmkiIqrmPvjgA6SlpUkdg8zo6aefVn9dXCiXfA0AU6dORf/+/S2ezZawaCIiquby8/OljkBmtn379nLb7Ny5E1evXkXDhg0tkMg2cSA4EREhLS0NhYWFAMrOEp2bm4vWrVtLEYuo0jgQnIiITErfmKZjx45ZMAmR9WLRRERk4x48eIDU1FSt41TKW6erTcmvHRwcsGDBAvzyyy8ag8dLtiu579Kv//nnH5P0k0hqLJqIiGxYdnY2vLy8pI5BVVjdunVx48YNm3sKR/HQHFPilANERDas5NMTiMzhnXfesbmCyVz4r42IyIbVrFkTKpUKKpUKwMNB3KtWrcK7776rfg3871Jb6dd37tyxdGQyoSZNmiApKQlubm5SR6kWWDQREdmwffv2oVevXlLHIAvq3Lkzjh49KnWMaomX54iIbNi4ceOkjkAWlpCQAJlMpnU5ceKE1PGqNBZNRERWas2aNTo/HIuXK1euSB2TrMjdu3eljlCl8fIcEZGVioyMlDoCSWDcuHGoU6eOxuDr0gOxZTIZhBDq4hl4+CDm7OxsFBQUQC6XWzRzdcEZwYmIrNSZM2cwduxYFBYW4syZM1LHIRvx2GOP4fTp09X+jjdzfPbzTBMR0f9btmwZpk6dKnUMokrp2LFjtS+YzIVjmoiI/t+FCxekjkBUrlmzZmnMvF56iYmJkTpilcUzTURE/+/zzz/HiBEj8N9//1n82Nu3b8fnn39u8eOS7Rk8eLDUEaotjmkiIrICycnJePTRR6WOQRLy8PCAk5OTxkSkpT+iS67Ttt1YoaGhWLVqFWrVqlWp/VgjjmkiIqqimjdvXukPQF0uXLiA+/fvA/jfTODr16/HkiVLzHI8qpjc3FyLH/Onn35C27ZtMXv2bIsf2xaxaCIisnIli6nir+/evYsJEyZg48aNUsWiKsDb2xsjR46UOobNYNFERGRlwsPDsWbNGqljUAVERESgc+fOAFDuPEvaaFtfsmju3r17lbyUZitYNBERWZlbt25JHYEqaOHChVJHqJKWLl2KadOmSR2DRRMRkbX57bffkJycDJVKVe44J13bK7P+0qVLGDJkiIFpiczvyJEjUkcAwKKJiMjqyGQySe+ka9u2rc7i6umnn8aOHTssnIiqk8WLF8PHx0f92t3dHc8++6x0gUpg0URERAZbunQpLl68iMuXL0OlUkkdhyTy/fff44UXXpA6hsWxaCIiquYSEhLw+OOPSx2DJDR79mz069cPwP8Go+sayO7r64ugoCDLBrQSLJqIiKq5LVu2SB2BKqBnz546twkhYGdnp77Mqu/PIUOGYMqUKbC3tzdv4CqARRMRUTX33nvvQSaT4ezZs+p1165dQ1JSkoSpqqfGjRvDwUH/R7ObmxvWrVvHGeQlwMeoEBFRhWVkZCAnJ6fM+u+++w6LFi2yfCAym5deegnffPONzZyRMsdnv51J9qLDggUL0LVrV7i6usLT07PM9piYGMhkMq1LZmYmACAlJUXrdt69QUQkPV9fXzRr1qzM8tJLL0kdjUwsOjoaDg4OOj+3tS0vv/yy1LFNyqxnmiIjI+Hp6Ynr169j9erVZf438uDBgzLP2gkPD0d+fj7i4uIAPCyagoKCsHv3bjz22GPqdrVr14aTk5NBOXimiYjIdvz8888YOnSo1DHIRB48eABnZ2eLH9fmHtg7d+5cAA/PKGnj4uICFxcX9etbt25h7969WL16dZm2Xl5e8PPzM0tOIiIyPyEEnnvuOWzdulXqKFSOiRMnqr8uPrcik8nKzN+lbxsATJ8+XZKCyVysaiD42rVr4erqqnUm2oEDByI/Px9NmjTBm2++ydlqiYhKEEJg69atSE1NVb+uyD7M1V4IAZVKxYLJCrVr1w7t27cHAAQGBmLGjBkaJzTof6yqaPruu+8watQojR+Wu7s7Pv30U3Tr1g12dnaIjY3F8OHDsWbNGrz44ota91NQUICCggL167y8PLNnJyKS0vz58xEZGSl1DLIx06dPx6JFi2xmcLfUjC6aoqKi1JfddElMTETHjh2N2m98fDzOnTuHtWvXaqz39vbGm2++qX7dsWNH3LlzB4sWLdJZNC1cuLDcjEREVYmxv3OpamjatCl27tyJgIAAqaNUC0YPBM/KykJWVpbeNoGBgRrXMGNiYjBt2jStt6UWGzduHE6cOGHQvCDr16/H+PHj8eDBA63btZ1patCgAQeCExFZifv370OhUAAALly4gHXr1qGwsBArV66UOJltc3BwwKFDh9C5c2epo0jOKgaCe3t7w9vb2yQHL3b37l38+OOPWLhwoUHtk5KSULduXZ3b5XI55HK5qeIREVU5SqUS3377LbZv3w7g4ZgjfbNGFy+3b99GYmKiNKGpXEVFRZg2bRoOHz4sdZQqyaxjmlJTU5GdnY3U1FQolUqcPHkSwMMZT93d3dXtNm3ahKKiIq0P/1uzZg0cHR3Rrl072NnZYdu2bfjiiy/w8ccfmzM6EVGV9uuvv2rcIUVVR3x8vMaz4rSRyWTo3r27+uuSnn/+eUyePJnjnLQwa9H0/vvvY82aNerX7dq1AwDs27dP45k5q1evxnPPPYdatWpp3c8HH3yAq1evwt7eHk2bNsV3332nczwTEVFlJCcn8/EUVOUJIXDgwAGt2/766y888sgj6N+/v4VTWT8+RoWIqIS1a9di7NixUscgkkyDBg1w4sQJkw/FsTSrGNNERFSVjR49Gg4ODjh69KjG+tKT9+m7/GHIpRFj91VyEsHy9m2pfQkhcO/ePZw9e1Y95knX2QsynbVr18LHx0fjcSV2dnY6H2VSvA0A7OzsUKdOHTRq1EjiXtgmnmkiIiKT4eVN29CnTx9MmzYNffv2hZ2dWR9DKxlzfPazaCIiIslVtNhq3bo1nJ2dIZPJUFRUhNu3b6NNmzYoKCjgg92tmFwux/HjxzWeKWtqLJoqiEUTEZF0MjIycOrUqTLrdT3HTNe68raXfp2WloZNmzap15eeVkHf17qmXUhISCint2SoPn364M8//zTb/jmmiYiIzEalUmHbtm1lJiLWNrZJ13in0uuvXr2KOXPmmCwjVR19+vSROoLRWDQREREA4NVXX8Xq1auljkESCQoKgqOjo87tJQtiXRepSraxt7dH165dtY6Z6tGjB0aNGlWJtNJg0URERACATp06sWiqxq5cuaLxury7K4u1aNECsbGx1eKOPI5pIiIim5SXl4evvvoKhYWFADTHI5VeDH1MF1XMyJEj8cMPP0gdQwMHglcQiyYiourr1q1bqFOnjtQxyEhPPPEE9u3bV+EpETgQnIiIJFd8az+g/462kpN4alun7T3l7Uffe27fvo2nnnpKnY1s219//YW7d+9a1ckOFk1ERGSwe/fuaTxwnaiyxowZg0ceeUQ9gznwsFAeNWqUVRVMAIsmIiIiklBcXBzWrFkjdQyDsGgiIiKDubm5QaFQ4M6dO+p1FXmGnUwmw6VLl9C5c2eTZyTbMn36dKkjGIxFExERGcXBwQE+Pj6V3s/Zs2dNkIbMaf/+/XjiiSekjmE1WDQREZFOly9fxiOPPCJ1DLKw2rVrY86cOejWrZvUUawKiyYiIitw9uxZtGzZUuoYRHBycsLVq1c54F+Lik1+QEREJqXtgbZEunh7e+PKlSt6J/Ss6FJQUMCCSQeeaSIisgIjR45EjRo1cPz4cQD/m926WOn5jkq2Ka/dokWLzJqdLC8rKwtBQUGws7NT/x3QNuC+5FxXvr6+GmPRSs5/VfK9kZGReO6558zcA9vEGcGJiKq4+Ph4dO3aVeoYZEPS0tLg5+cndYxKMcdnPy/PERFVccHBwSa/hNOrVy+pu0VmMmbMGPj6+kodwyrx8hwRERnt559/Ru3ataWOQeXo3Lkzjh49KnWMKoNFE1ElKBQKjB8/HmvXrpU6ChFRGQkJCeVOPkqG4+U5okpISkpiwUREVE2waCKqhA4dOuCdd96ROgYREVkA754jIiKiKod3zxERERFJhEUTERERkQFYNBEREREZgEUTERERkQFYNBEREREZwGxFU0pKCsaNG4egoCC4uLjgkUceQWRkJAoLCzXapaamYsCAAXBzc4O3tzemTp1aps3p06cREhICFxcX1KtXD/PmzUM1uOmPiIiIrIjZZgRPTk6GSqXCqlWr0LhxY5w5cwavvPIK7t27hyVLlgAAlEol+vfvDx8fHxw8eBC3b9/G2LFjIYTAsmXLADy8ZbB3794IDQ1FYmIizp8/j/DwcLi5uWHGjBnmik9ERESkwaLzNC1evBgrVqzA5cuXAQDbt2/HM888g2vXrsHf3x8AsHHjRoSHhyMzMxM1a9bEihUrEBERgYyMDMjlcgDARx99hGXLluH69esGTQ/PeZqIiIiqF3N89lv02XO5ubkaD3iMj49Hy5Yt1QUTAPTt2xcFBQU4fvw4QkNDER8fj5CQEHXBVNwmIiICKSkpCAoKKnOcgoICFBQUaBwXePgNJCIioqqv+DPflOeGLFY0Xbp0CcuWLcMnn3yiXpeeng5fX1+NdrVq1YKTkxPS09PVbQIDAzXaFL8nPT1da9G0cOFCzJ07t8z6Bg0aVLYbREREZENu374NDw8Pk+zL6KIpKipKa0FSUmJiIjp27Kh+ffPmTYSFhWHo0KEYP368Rlttl9eEEBrrS7cprhp1XZqLiIjA9OnT1a9zcnIQEBCA1NRUk33jbEleXh4aNGiAa9euVcvLk+x/9e1/de47wP6z/9W7/7m5uWjYsKHGFa7KMrpomjJlCkaMGKG3TckzQzdv3kRoaCiCg4Px9ddfa7Tz8/PD0aNHNdbduXMHCoVCfTbJz89PfdapWGZmJgCUOUtVTC6Xa1zOK+bh4VEt/+IUq1mzJvvP/ksdQxLVue8A+8/+V+/+29mZbqIAo4smb29veHt7G9T2xo0bCA0NRYcOHRAdHV0meHBwMBYsWIC0tDTUrVsXALBz507I5XJ06NBB3Wb27NkoLCyEk5OTuo2/v3+Zy3ZERERE5mK2eZpu3ryJnj17okGDBliyZAlu3bqF9PR0jbNGffr0QYsWLTB69GgkJSVhz549eOutt/DKK6+oq+JRo0ZBLpcjPDwcZ86cwZYtW/Dhhx9i+vTpBt05R0RERGQKZhsIvnPnTly8eBEXL15E/fr1NbYVj0myt7fH77//jkmTJqFbt25wcXHBqFGj1PM4AQ8vqe3atQuTJ09Gx44dUatWLUyfPl1jzFJ55HI5IiMjtV6yqw7Yf/a/uva/OvcdYP/Zf/bf1P236DxNRERERLaKz54jIiIiMgCLJiIiIiIDsGgiIiIiMgCLJiIiIiIDVNmi6c6dOxg9ejQ8PDzg4eGB0aNHIycnx+D3T5gwATKZDJ999pnZMppLRfoeFRWF5s2bw83NDbVq1cJTTz1VZuJRW2Fs/xUKBd555x20atUKbm5u8Pf3x5gxY3Dz5k3LhTahivz8N2/ejL59+8Lb2xsymQwnT560SFZTWL58OYKCguDs7IwOHTrgwIEDetvv378fHTp0gLOzMxo1aoSVK1daKKl5GNP/tLQ0jBo1Cs2aNYOdnR2mTZtmuaBmYkz/N2/ejN69e8PHxwc1a9ZEcHAw/vzzTwumNT1j+n/w4EF069YNXl5ecHFxQfPmzbF06VILpjU9Y//9Fzt06BAcHBzQtm1b4w4oqqiwsDDRsmVLcfjwYXH48GHRsmVL8cwzzxj03i1btog2bdoIf39/sXTpUvMGNYOK9H39+vVi165d4tKlS+LMmTNi3LhxombNmiIzM9NCqU3H2P7n5OSIp556SmzatEkkJyeL+Ph48fjjj4sOHTpYMLXpVOTnv3btWjF37lzxzTffCAAiKSnJMmEraePGjcLR0VF888034ty5c+KNN94Qbm5u4urVq1rbX758Wbi6uoo33nhDnDt3TnzzzTfC0dFR/PzzzxZObhrG9v/KlSti6tSpYs2aNaJt27bijTfesGxgEzO2/2+88Yb4+OOPRUJCgjh//ryIiIgQjo6O4sSJExZObhrG9v/EiRPihx9+EGfOnBFXrlwR69atE66urmLVqlUWTm4axva/WE5OjmjUqJHo06ePaNOmjVHHrJJF07lz5wQAceTIEfW6+Ph4AUAkJyfrfe/169dFvXr1xJkzZ0RAQIDNFU2V6XtJubm5AoDYvXu3OWKajan6n5CQIACU+4/P2lS2/1euXLGpoqlz585i4sSJGuuaN28uZs2apbX922+/LZo3b66xbsKECaJLly5my2hOxva/pJCQEJsvmirT/2ItWrQQc+fONXU0izBF/wcPHixefPFFU0eziIr2f/jw4WLOnDkiMjLS6KKpSl6ei4+Ph4eHBx5//HH1ui5dusDDwwOHDx/W+T6VSoXRo0dj5syZeOyxxywR1eQq2veSCgsL8fXXX8PDwwNt2rQxV1SzMEX/gYcPepTJZPD09DRDSvMxVf9tQWFhIY4fP44+ffporO/Tp4/OvsbHx5dp37dvXxw7dgwKhcJsWc2hIv2vSkzRf5VKhf/++8+kD3S1FFP0PykpCYcPH0ZISIg5IppVRfsfHR2NS5cuITIyskLHNduM4FJKT09HnTp1yqyvU6dOmYf/lvTxxx/DwcEBU6dONWc8s6po3wHgt99+w4gRI3D//n3UrVsXu3btMvg5g9aiMv0vlp+fj1mzZmHUqFE295BLU/TfVmRlZUGpVJZ5cLevr6/Ovqanp2ttX1RUhKysLPUzMG1BRfpflZii/5988gnu3buHYcOGmSOiWVWm//Xr18etW7dQVFSEqKgojB8/3pxRzaIi/b9w4QJmzZqFAwcOwMGhYuWPTZ1pioqKgkwm07scO3YMALQ+l04IofN5dcePH8fnn3+OmJgYq3ymnTn7Xiw0NBQnT57E4cOHERYWhmHDhiEzM9Ms/TGWJfoPPBwUPmLECKhUKixfvtzk/agoS/XfFpXuV3l91dZe23pbYWz/q5qK9n/Dhg2IiorCpk2btP5Hw1ZUpP8HDhzAsWPHsHLlSnz22WfYsGGDOSOalaH9VyqVGDVqFObOnYumTZtW+Hg2daZpypQpGDFihN42gYGB+Pvvv5GRkVFm261bt8pUpcUOHDiAzMxMNGzYUL1OqVRixowZ+Oyzz5CSklKp7JVlzr4Xc3NzQ+PGjdG4cWN06dIFTZo0werVqxEREVGp7KZgif4rFAoMGzYMV65cwd69e63qLJMl+m9rvL29YW9vX+Z/lZmZmTr76ufnp7W9g4MDvLy8zJbVHCrS/6qkMv3ftGkTxo0bh59++glPPfWUOWOaTWX6HxQUBABo1aoVMjIyEBUVhZEjR5otqzkY2////vsPx44dQ1JSEqZMmQLg4eVZIQQcHBywc+dO9OrVq9zj2lTR5O3tbdDlouDgYOTm5iIhIQGdO3cGABw9ehS5ubno2rWr1veMHj26zD+evn37YvTo0XjppZcqH76SzNl3XYQQKCgoqFBeUzN3/4sLpgsXLmDfvn1W9wEqxc/f2jk5OaFDhw7YtWsXBg8erF6/a9cuDBo0SOt7goODsW3bNo11O3fuRMeOHeHo6GjWvKZWkf5XJRXt/4YNG/Dyyy9jw4YN6N+/vyWimoWpfv7W9HveGMb2v2bNmjh9+rTGuuXLl2Pv3r34+eef1YVkuYwaNm5DwsLCROvWrUV8fLyIj48XrVq1KnPbdbNmzcTmzZt17sMW754Twvi+3717V0RERIj4+HiRkpIijh8/LsaNGyfkcrk4c+aMFF2oFGP7r1AoxMCBA0X9+vXFyZMnRVpamnopKCiQoguVUpG/+7dv3xZJSUni999/FwDExo0bRVJSkkhLS7N0fKMU33K8evVqce7cOTFt2jTh5uYmUlJShBBCzJo1S4wePVrdvnjKgTfffFOcO3dOrF69ukpMOWBo/4UQIikpSSQlJYkOHTqIUaNGiaSkJHH27Fkp4leasf3/4YcfhIODg/jqq680/p3n5ORI1YVKMbb/X375pYiNjRXnz58X58+fF999952oWbOmePfdd6XqQqVU5O9/SRW5e67KFk23b98WL7zwgqhRo4aoUaOGeOGFF8SdO3c02gAQ0dHROvdhq0WTsX1/8OCBGDx4sPD39xdOTk6ibt26YuDAgSIhIcHy4U3A2P4X32avbdm3b5/F81dWRf7uR0dHa+1/ZGSkRbNXxFdffSUCAgKEk5OTaN++vdi/f79629ixY0VISIhG+7i4ONGuXTvh5OQkAgMDxYoVKyyc2LSM7b+2n3NAQIBlQ5uQMf0PCQnR2v+xY8daPriJGNP/L774Qjz22GPC1dVV1KxZU7Rr104sX75cKJVKCZKbhrF//0uqSNEkE+L/R0ESERERkU42dfccERERkVRYNBEREREZgEUTERERkQFYNBEREREZgEUTERERkQFYNBEREREZgEUTERERkQFYNBEREREZgEUTERERkQFYNBEREREZgEUTERERkQFYNBEREREZ4P8Ajue0w3718S8AAAAASUVORK5CYII=", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if doIngest:\n", - " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", - " ds_up, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_upward_velocity', 'int_ctd_pressure'], ['up', 'depth'])\n", - " ds_north, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_northward_velocity', 'int_ctd_pressure'], ['north', 'depth'])\n", - " ds_east, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_eastward_velocity', 'int_ctd_pressure'], ['east', 'depth'])\n", - "\n", - " ds_up.to_netcdf('./data/rca/sensors/osb/up_jan_2022.nc')\n", - " ds_north.to_netcdf('./data/rca/sensors/osb/north_jan_2022.nc')\n", - " ds_east.to_netcdf('./data/rca/sensors/osb/east_jan_2022.nc')\n", - " \n", - " \n", - "ds_up = xr.open_dataset('./data/rca/sensors/osb/up_jan_2022.nc')\n", - "ds_north = xr.open_dataset('./data/rca/sensors/osb/north_jan_2022.nc')\n", - "ds_east = xr.open_dataset('./data/rca/sensors/osb/east_jan_2022.nc')\n", - "\n", - "fig, axes = ChartSensor(profiles, ranges['up'], [0], ds_up.up, -ds_up.depth, 'current up', 'black', 'ascent', 6, 4)\n", - "fig, axes = ChartSensor(profiles, ranges['north'], [0], ds_north.north, -ds_north.depth, 'current north', 'black', 'ascent', 6, 4)\n", - "fig, axes = ChartSensor(profiles, ranges['east'], [0], ds_east.east, -ds_east.depth, 'current east', 'black', 'ascent', 6, 4)" - ] - }, - { - "cell_type": "markdown", - "id": "10d0a25c-1a19-4a1f-b4db-aaed671d5c28", - "metadata": {}, - "source": [ - "#### 10 of 10: **pco2w** i.e. pCO2" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "129d7553-1fbc-4bd3-aed5-a21583f02e29", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record\n", - "<xarray.DataArray 'time' ()>\n", - "array('2022-01-01T02:16:20.634201088', dtype='datetime64[ns]')\n", - "Coordinates:\n", - " time datetime64[ns] 2022-01-01T02:16:20.634201088\n", - "Attributes:\n", - " axis: T\n", - " long_name: time\n", - " standard_name: time <xarray.DataArray 'time' ()>\n", - "array('2022-12-31T23:26:46.404636160', dtype='datetime64[ns]')\n", - "Coordinates:\n", - " time datetime64[ns] 2022-12-31T23:26:46.404636160\n", - "Attributes:\n", - " axis: T\n", - " long_name: time\n", - " standard_name: time\n" - ] - }, - { - "data": { - "text/html": [ - "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", - "<defs>\n", - "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", - "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", - "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", - "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 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~ .xr-index-data {\n", - " display: block;\n", - "}\n", - "\n", - ".xr-var-data > table {\n", - " float: right;\n", - "}\n", - "\n", - ".xr-var-name span,\n", - ".xr-var-data,\n", - ".xr-index-name div,\n", - ".xr-index-data,\n", - ".xr-attrs {\n", - " padding-left: 25px !important;\n", - "}\n", - "\n", - ".xr-attrs,\n", - ".xr-var-attrs,\n", - ".xr-var-data,\n", - ".xr-index-data {\n", - " grid-column: 1 / -1;\n", - "}\n", - "\n", - "dl.xr-attrs {\n", - " padding: 0;\n", - " margin: 0;\n", - " display: grid;\n", - " grid-template-columns: 125px auto;\n", - "}\n", - "\n", - ".xr-attrs dt,\n", - ".xr-attrs dd {\n", - " padding: 0;\n", - " margin: 0;\n", - " float: left;\n", - " padding-right: 10px;\n", - " width: auto;\n", - "}\n", - "\n", - ".xr-attrs dt {\n", - " font-weight: normal;\n", - " grid-column: 1;\n", - "}\n", - "\n", - ".xr-attrs dt:hover span {\n", - " display: inline-block;\n", - " background: var(--xr-background-color);\n", - " padding-right: 10px;\n", - "}\n", - "\n", - ".xr-attrs dd {\n", - " grid-column: 2;\n", - " white-space: pre-wrap;\n", - " word-break: break-all;\n", - "}\n", - "\n", - ".xr-icon-database,\n", - ".xr-icon-file-text2,\n", - ".xr-no-icon {\n", - " display: inline-block;\n", - " vertical-align: middle;\n", - " width: 1em;\n", - " height: 1.5em !important;\n", - " stroke-width: 0;\n", - " stroke: currentColor;\n", - " fill: currentColor;\n", - "}\n", - "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", - "Dimensions: (time: 5190, spectrum: 14)\n", - "Coordinates:\n", - " * spectrum (spectrum) int32 0 1 2 ... 12 13\n", - " * time (time) datetime64[ns] 2022-01-0...\n", - "Data variables: (12/31)\n", - " absorbance_ratio_434 (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_434_qc_executed (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_434_qc_results (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_620 (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_620_qc_executed (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_620_qc_results (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " ... ...\n", - " record_length (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " record_time (time) datetime64[ns] dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " record_type (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " thermistor_raw (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " unique_id (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " voltage_battery (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - "Attributes: (12/62)\n", - " AssetManagementRecordLastModified: 2024-07-04T16:24:20.856000\n", - " AssetUniqueID: ATAPL-58336-00003\n", - " Conventions: CF-1.6\n", - " Description: pCO2 Water: PCO2W Series A\n", - " FirmwareVersion: Not specified.\n", - " Manufacturer: Sunburst Sensors\n", - " ... ...\n", - " stream: pco2w_a_sami_data_record\n", - " subsite: RS01SBPS\n", - " summary: Dataset Generated by Stream Engine fr...\n", - " time_coverage_end: 2024-07-08T11:05:11.220713472\n", - " time_coverage_start: 2014-10-07T01:05:10.333669376\n", - " title: Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9129cc63-2c4c-4124-9465-9cf81ae71a3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9129cc63-2c4c-4124-9465-9cf81ae71a3d' 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>: 5190</li><li><span class='xr-has-index'>spectrum</span>: 14</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-47fea935-abe1-40e8-87e7-0beadd1fc849' class='xr-section-summary-in' type='checkbox' checked><label for='section-47fea935-abe1-40e8-87e7-0beadd1fc849' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spectrum</span></div><div class='xr-var-dims'>(spectrum)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12 13</div><input id='attrs-d8ea4f9a-366a-48a3-acfc-1e7066a5ad8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8ea4f9a-366a-48a3-acfc-1e7066a5ad8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4df6e3a-480e-4052-830e-5bcb2d043f04' class='xr-var-data-in' type='checkbox'><label for='data-f4df6e3a-480e-4052-830e-5bcb2d043f04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype=int32)</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'>2022-01-01T02:16:20.634201088 .....</div><input id='attrs-4672847b-c960-46ef-a7f9-16712a861bb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4672847b-c960-46ef-a7f9-16712a861bb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76398741-77a2-44ea-b358-364250c57d8d' class='xr-var-data-in' type='checkbox'><label for='data-76398741-77a2-44ea-b358-364250c57d8d' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2022-01-01T02:16:20.634201088', '2022-01-01T03:43:32.948877312',\n", - " '2022-01-01T06:41:21.954166272', ..., '2022-12-31T23:07:29.245393920',\n", - " '2022-12-31T23:16:27.824401408', '2022-12-31T23:26:46.404636160'],\n", - " dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4bffbe35-b1da-4297-b6bf-43e2ba9c53da' class='xr-section-summary-in' type='checkbox' ><label for='section-4bffbe35-b1da-4297-b6bf-43e2ba9c53da' class='xr-section-summary' >Data variables: <span>(31)</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>absorbance_ratio_434</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-258f8ca6-345e-47ae-bfcd-05d59e12efb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-258f8ca6-345e-47ae-bfcd-05d59e12efb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6631648d-623d-4273-b550-79c5c5a5fe3c' class='xr-var-data-in' type='checkbox'><label for='data-6631648d-623d-4273-b550-79c5c5a5fe3c' 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>ancillary_variables :</span></dt><dd>light_measurements</dd><dt><span>comment :</span></dt><dd>The Optical Absorbance ratio at 434 nm collected during the measurement cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS1-SAMP_L0</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 434 Nm</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_434_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-f148bb9b-c58c-4e22-a801-0f7a33ef7dd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f148bb9b-c58c-4e22-a801-0f7a33ef7dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37de4d59-8ced-4a7e-a30a-d3876c4df190' class='xr-var-data-in' type='checkbox'><label for='data-37de4d59-8ced-4a7e-a30a-d3876c4df190' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_434_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-ca84d57a-6493-4407-8bcf-62ac480124b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca84d57a-6493-4407-8bcf-62ac480124b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7412d026-8df8-4e21-a46b-5475dc18bf35' class='xr-var-data-in' type='checkbox'><label for='data-7412d026-8df8-4e21-a46b-5475dc18bf35' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_620</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-0ded7665-7d7f-4383-8523-784a7966b6f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ded7665-7d7f-4383-8523-784a7966b6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49c5376-91f2-4485-8a03-151f9c90a9d9' class='xr-var-data-in' type='checkbox'><label for='data-f49c5376-91f2-4485-8a03-151f9c90a9d9' 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>ancillary_variables :</span></dt><dd>light_measurements</dd><dt><span>comment :</span></dt><dd>The Optical Absorbance ratio at 620 nm collected during the measurement cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS2-SAMP_L0</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 620 Nm</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_620_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-83f4fbdf-1b39-46d1-8aa4-7f49179eacf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83f4fbdf-1b39-46d1-8aa4-7f49179eacf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-762894a9-87d9-4c90-9377-1cdfac1277de' class='xr-var-data-in' type='checkbox'><label for='data-762894a9-87d9-4c90-9377-1cdfac1277de' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absorbance_ratio_620_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-5ecd4b9b-24eb-4f21-8a8d-81995929b3bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ecd4b9b-24eb-4f21-8a8d-81995929b3bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1750bc0-4c15-4108-bfc7-01d811af8359' class='xr-var-data-in' type='checkbox'><label for='data-c1750bc0-4c15-4108-bfc7-01d811af8359' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>checksum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-9e6f7b40-1f2a-4260-96d9-ed0de62d5f75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e6f7b40-1f2a-4260-96d9-ed0de62d5f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c38619bb-8014-4a05-8be3-32dc5cefa435' class='xr-var-data-in' type='checkbox'><label for='data-c38619bb-8014-4a05-8be3-32dc5cefa435' 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>Data checksum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Data Checksum</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-4149d031-b81e-431a-8738-0017cce49e3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4149d031-b81e-431a-8738-0017cce49e3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-742845ae-7d5b-4894-b952-9d52a91b278c' class='xr-var-data-in' type='checkbox'><label for='data-742845ae-7d5b-4894-b952-9d52a91b278c' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> int32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-f57d3450-cd81-4a84-a3b2-661f4d3fc9bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f57d3450-cd81-4a84-a3b2-661f4d3fc9bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a4b727d-31c8-4c62-bafe-6fd8658df1c3' class='xr-var-data-in' type='checkbox'><label for='data-4a4b727d-31c8-4c62-bafe-6fd8658df1c3' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-55f167ca-208c-4966-9ee7-5118eaa16764' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55f167ca-208c-4966-9ee7-5118eaa16764' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72843f1e-0745-49ae-a8f8-dc2dafe593d4' class='xr-var-data-in' type='checkbox'><label for='data-72843f1e-0745-49ae-a8f8-dc2dafe593d4' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-96c6faef-3f84-44df-b242-e5bb1c2a9ce3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96c6faef-3f84-44df-b242-e5bb1c2a9ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50dffc15-7d61-46c8-b293-c606eb692518' class='xr-var-data-in' type='checkbox'><label for='data-50dffc15-7d61-46c8-b293-c606eb692518' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</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'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-c7017c77-b9e1-4d8f-9a6e-c540503410e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7017c77-b9e1-4d8f-9a6e-c540503410e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3765e2f-07cf-4004-b45d-22b3e783aee0' class='xr-var-data-in' type='checkbox'><label for='data-d3765e2f-07cf-4004-b45d-22b3e783aee0' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>light_measurements</span></div><div class='xr-var-dims'>(time, spectrum)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190, 14), meta=np.ndarray></div><input id='attrs-21852b0c-53b1-4983-b4b2-ef9a5d302c4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21852b0c-53b1-4983-b4b2-ef9a5d302c4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a43c66f7-e706-484a-b220-babcad9263e5' class='xr-var-data-in' type='checkbox'><label for='data-a43c66f7-e706-484a-b220-babcad9263e5' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Light Measurements</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 283.83 kiB </td>\n", - " <td> 283.83 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190, 14) </td>\n", - " <td> (5190, 14) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", - " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >14</text>\n", - " <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">5190</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_battery_volts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-31c5a600-6731-4a1e-9b26-2a346f4a418e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31c5a600-6731-4a1e-9b26-2a346f4a418e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f27f4864-7db9-4f99-9d4a-2e644c17c83a' class='xr-var-data-in' type='checkbox'><label for='data-f27f4864-7db9-4f99-9d4a-2e644c17c83a' 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>ancillary_variables :</span></dt><dd>voltage_battery</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-b2db068c-259e-4e4f-838f-6ae79cf163b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2db068c-259e-4e4f-838f-6ae79cf163b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22f6a10e-f3ae-49fb-af90-933d733a323a' class='xr-var-data-in' type='checkbox'><label for='data-22f6a10e-f3ae-49fb-af90-933d733a323a' 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>ancillary_variables :</span></dt><dd>pco2_seawater_qartod_results pco2_seawater_qartod_executed pco2w_a_absorbance_blank_434 pco2w_thermistor_temperature light_measurements record_type pco2w_a_absorbance_blank_620</dd><dt><span>comment :</span></dt><dd>Partial Pressure of CO2 in Seawater provides a measure of the amount of CO2 and HCO3 in seawater. Specifically, it refers to the pressure that would be exerted by CO2 if all other gases were removed. Partial pressure of a gas dissolved in seawater is understood as the partial pressure in air that the gas would exert in a hypothetical air volume in equilibrium with that seawater. NOTE: the following calibration coefficients in the parameterfunctionmap are deprecated: "ea434", "eb434", "ea620", "eb620". Those arguments are still present in the function declaration, but they are completely unused and can be supplied with an arbitrary fill value.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PCO2WAT_L1</dd><dt><span>long_name :</span></dt><dd>pCO2 Seawater</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>standard_name :</span></dt><dd>partial_pressure_of_carbon_dioxide_in_sea_water</dd><dt><span>units :</span></dt><dd>uatm</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", - "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_qartod_executed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-3009d1c6-c6a5-4fc6-9b4a-92d63a83f7a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3009d1c6-c6a5-4fc6-9b4a-92d63a83f7a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-465838fd-519b-43d7-9e34-53832088d61d' class='xr-var-data-in' type='checkbox'><label for='data-465838fd-519b-43d7-9e34-53832088d61d' 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>Individual QARTOD test flags. For each datum, flags are listed in a string matching the order of the tests_executed attribute. Flags should be interpreted using the standard QARTOD mapping: [1: pass, 2: not_evaluated, 3: suspect_or_of_high_interest, 4: fail, 9: missing_data].</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>pCO2 Seawater Individual QARTOD Flags</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>partial_pressure_of_carbon_dioxide_in_sea_water status_flag</dd><dt><span>tests_executed :</span></dt><dd>gross_range_test, climatology_test</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> <U2 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", - "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", - "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if doIngest:\n", + " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", + " ds_nitrate_dark, reply = ShallowProfilerDataReduce(ds, t0, t1, ['nitrate_concentration', 'int_ctd_pressure'], ['nitrate_dark', 'depth'])\n", + " ds_nitrate_dark.to_netcdf('./data/rca/sensors/osb/nitrate_dark_jan_2022.nc')\n", + "\n", + "ds_nitrate_dark = xr.open_dataset('./data/rca/sensors/osb/nitrate_dark_jan_2022.nc')\n", + "fig, axes = ChartSensor(profiles, [0, 1], [3], ds_nitrate_dark.nitrate_dark, -ds_nitrate_dark.depth, 'nitrate (dark)', 'black', 'ascent', 6, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "98f8cabc-dcfb-434a-be5d-a1bd263ee05a", + "metadata": {}, + "source": [ + "#### 8 of 10: **nutnr_a_sample** i.e. nitrate" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6476db17-0835-498c-865e-07641dbe09e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f54177b4590>]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds.nitrate_concentration.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8aefd0e3-2738-4e9b-9f64-9864f9223710", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f54142cc0e0>]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_nitrate_dark.nitrate_dark.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33a82c38-ddb1-411d-beca-d1dc900905c4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "69f59bc3-04c9-4dff-9950-b10a3bffd5fa", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4A-NUTNRA101-streamed-nutnr_a_sample\n", + "<xarray.DataArray 'time' ()>\n", + "array('2022-01-01T07:22:07.611640320', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-01-01T07:22:07.611640320\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time <xarray.DataArray 'time' ()>\n", + "array('2022-12-31T21:48:02.217944576', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-12-31T21:48:02.217944576\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time\n" + ] + } + ], + "source": [ + "if doIngest:\n", + " instrument_key = 'nutnr_a_sample'\n", + " for s in osb_profiler_streams: \n", + " if instrument_key in s: \n", + " print('Found this instrument stream:', s)\n", + " instrument_stream = s\n", + " break\n", + "\n", + " ds = loadData(instrument_stream) # lazy load\n", + " t0, t1 = '2022-01-01T00', '2022-12-31T23' # January 2022\n", + " ds = ds.sel(time=slice(t0, t1)) # Subset the full time range to one month\n", + " print(ds.time[0], ' ', ds.time[-1]) # verify selected one month time range\n", + " ds # get a 'data variable' list of sensors/metadata for this instrument" + ] + }, + { + "cell_type": "markdown", + "id": "b2f399c7-339d-4536-9740-7e609f2e6e3a", + "metadata": {}, + "source": [ + "`salinity_corrected_nitrate` > `nitrate` and `int_ctd_pressure` > `depth`" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5d3aa1c9-d4ae-45de-a79c-94d56195c008", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Dataset' object has no attribute 'nitrate'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m ds_nitrate\u001b[38;5;241m.\u001b[39mto_netcdf(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data/rca/sensors/osb/nitrate_jan_2022.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 6\u001b[0m ds_nitrate \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data/rca/sensors/osb/nitrate_jan_2022.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m ChartSensor(profiles, ranges[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnitrate\u001b[39m\u001b[38;5;124m'\u001b[39m], [\u001b[38;5;241m3\u001b[39m], ds_nitrate\u001b[38;5;241m.\u001b[39mnitrate, \u001b[38;5;241m-\u001b[39mds_nitrate\u001b[38;5;241m.\u001b[39mdepth, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnitrate \u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mascent\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m4\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/core/common.py:278\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 279\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m )\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'nitrate'" + ] + } + ], + "source": [ + "if doIngest:\n", + " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", + " ds_nitrate, reply = ShallowProfilerDataReduce(ds, t0, t1, ['salinity_corrected_nitrate', 'int_ctd_pressure'], ['nitrate', 'depth'])\n", + " ds_nitrate.to_netcdf('./data/rca/sensors/osb/nitrate_jan_2022.nc')\n", + "\n", + "ds_nitrate = xr.open_dataset('./data/rca/sensors/osb/nitrate_jan_2022.nc')\n", + "fig, axes = ChartSensor(profiles, ranges['nitrate'], [3], ds_nitrate.nitrate, -ds_nitrate.depth, 'nitrate ', 'black', 'ascent', 6, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "037fc2c3-ab7b-4003-9ee9-8636daafe528", + "metadata": {}, + "source": [ + "#### 9 of 10: **velpt** i.e. current velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5b764c28-ae36-4066-9d3e-64873c13962a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data\n", + "<xarray.DataArray 'time' ()>\n", + "array('2022-01-01T00:00:00.527429120', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-01-01T00:00:00.527429120\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time <xarray.DataArray 'time' ()>\n", + "array('2022-12-31T23:59:59.845964288', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-12-31T23:59:59.845964288\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time\n" + ] + }, + { + "data": { + "text/html": [ + "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", + "<defs>\n", + "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", + "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", + "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", + "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", + "</symbol>\n", + "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", + "<path d=\"M28.681 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class='xr-var-name'><span>pco2_seawater_qartod_results</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-86a8abf4-dcef-4b57-abe9-bd7476ff2477' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86a8abf4-dcef-4b57-abe9-bd7476ff2477' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3ea6fe-9863-4626-9229-91ac89d3415e' class='xr-var-data-in' type='checkbox'><label for='data-5c3ea6fe-9863-4626-9229-91ac89d3415e' 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>Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>flag_meanings :</span></dt><dd>pass not_evaluated suspect_or_of_high_interest fail missing_data</dd><dt><span>flag_values :</span></dt><dd>1,2,3,4,9</dd><dt><span>long_name :</span></dt><dd>pCO2 Seawater QARTOD Summary Flag</dd><dt><span>references :</span></dt><dd>https://ioos.noaa.gov/project/qartod https://github.com/ioos/ioos_qc</dd><dt><span>standard_name :</span></dt><dd>partial_pressure_of_carbon_dioxide_in_sea_water status_flag</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - 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" <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + "html[theme=dark],\n", + "body[data-theme=dark],\n", + "body.vscode-dark {\n", + " --xr-font-color0: rgba(255, 255, 255, 1);\n", + " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", + " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", + " --xr-border-color: #1F1F1F;\n", + " --xr-disabled-color: #515151;\n", + " --xr-background-color: #111111;\n", + " --xr-background-color-row-even: #111111;\n", + " --xr-background-color-row-odd: #313131;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-c42e3be4-def8-41c2-b1aa-e35118485a60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c42e3be4-def8-41c2-b1aa-e35118485a60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd9f735-44b3-4692-b3a9-653ff08f756d' class='xr-var-data-in' type='checkbox'><label for='data-cbd9f735-44b3-4692-b3a9-653ff08f756d' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-wrap {\n", + " display: block !important;\n", + " min-width: 300px;\n", + " max-width: 700px;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-text-repr-fallback {\n", + " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", + " display: none;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-header {\n", + " padding-top: 6px;\n", + " padding-bottom: 6px;\n", + " margin-bottom: 4px;\n", + " border-bottom: solid 1px var(--xr-border-color);\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-header > div,\n", + ".xr-header > ul {\n", + " display: inline;\n", + " margin-top: 0;\n", + " margin-bottom: 0;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_seawater_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-19dd20d3-b9c3-4e76-9edc-f385ef81ac07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19dd20d3-b9c3-4e76-9edc-f385ef81ac07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b228ab85-102e-47d6-84a7-d9b36113d575' class='xr-var-data-in' type='checkbox'><label for='data-b228ab85-102e-47d6-84a7-d9b36113d575' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-obj-type,\n", + ".xr-array-name {\n", + " margin-left: 2px;\n", + " margin-right: 10px;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-obj-type {\n", + " color: var(--xr-font-color2);\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-sections {\n", + " padding-left: 0 !important;\n", + " display: grid;\n", + " grid-template-columns: 150px auto auto 1fr 20px 20px;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-section-item {\n", + " display: contents;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_a_absorbance_blank_434</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-0e3aec01-f1fe-4552-a13d-ad3490b69317' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e3aec01-f1fe-4552-a13d-ad3490b69317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ced92249-c46d-46b0-b8d8-9cdf4e98c99a' class='xr-var-data-in' type='checkbox'><label for='data-ced92249-c46d-46b0-b8d8-9cdf4e98c99a' 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 Optical Absorbance ratio at 434 nm collected during the blank cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS1-BLNK_L0</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 434 Nm - Blank</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>stream :</span></dt><dd>pco2w_a_sami_data_record_cal</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-section-item input {\n", + " display: none;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-item input + label {\n", + " color: var(--xr-disabled-color);\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-item input:enabled + label {\n", + " cursor: pointer;\n", + " color: var(--xr-font-color2);\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-section-item input:enabled + label:hover {\n", + " color: var(--xr-font-color0);\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_a_absorbance_blank_620</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-bb4f99c5-6275-4e6b-a856-d2df40e8f95f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb4f99c5-6275-4e6b-a856-d2df40e8f95f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523c1b73-b730-4b73-b1ae-0fdd925f9391' class='xr-var-data-in' type='checkbox'><label for='data-523c1b73-b730-4b73-b1ae-0fdd925f9391' 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 Optical Absorbance ratio at 620 nm collected during the blank cycle and used to calculate the PCO2WAT data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2ABS2-BLNK_L0</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101</dd><dt><span>long_name :</span></dt><dd>Optical Absorbance Ratio at 620 Nm - Blank</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>stream :</span></dt><dd>pco2w_a_sami_data_record_cal</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-section-summary {\n", + " grid-column: 1;\n", + " color: var(--xr-font-color2);\n", + " font-weight: 500;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-summary > span {\n", + " display: inline-block;\n", + " padding-left: 0.5em;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-summary-in:disabled + label {\n", + " color: var(--xr-font-color2);\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-section-summary-in + label:before {\n", + " display: inline-block;\n", + " content: 'â–º';\n", + " font-size: 11px;\n", + " width: 15px;\n", + " text-align: center;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_thermistor_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-ec3d5c0c-e509-49f0-b08a-f6f3b5556d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec3d5c0c-e509-49f0-b08a-f6f3b5556d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ebe30d-d7c3-43d5-9a03-fc51920b47d9' class='xr-var-data-in' type='checkbox'><label for='data-f5ebe30d-d7c3-43d5-9a03-fc51920b47d9' 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>ancillary_variables :</span></dt><dd>thermistor_raw</dd><dt><span>comment :</span></dt><dd>This is the Thermistor Temperature measurement, used to calculate the L1 PCO2WAT Partial Pressure of CO2 in Seawater data product.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2THRM_L1</dd><dt><span>long_name :</span></dt><dd>Thermistor Temperature</dd><dt><span>precision :</span></dt><dd>4</dd><dt><span>units :</span></dt><dd>degrees_Celsius</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-section-summary-in:disabled + label:before {\n", + " color: var(--xr-disabled-color);\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-summary-in:checked + label:before {\n", + " content: 'â–¼';\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-summary-in:checked + label > span {\n", + " display: none;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-section-summary,\n", + ".xr-section-inline-details {\n", + " padding-top: 4px;\n", + " padding-bottom: 4px;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_thermistor_temperature_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-0d09a9fa-876e-4449-8216-21764dbe5380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d09a9fa-876e-4449-8216-21764dbe5380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6409140a-d6b3-44b5-812d-8406e8ba4023' class='xr-var-data-in' type='checkbox'><label for='data-6409140a-d6b3-44b5-812d-8406e8ba4023' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-section-inline-details {\n", + " grid-column: 2 / -1;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-details {\n", + " display: none;\n", + " grid-column: 1 / -1;\n", + " margin-bottom: 5px;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-section-summary-in:checked ~ .xr-section-details {\n", + " display: contents;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-array-wrap {\n", + " grid-column: 1 / -1;\n", + " display: grid;\n", + " grid-template-columns: 20px auto;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2w_thermistor_temperature_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-b1595ee1-fb3e-455b-9e00-0f3d5cd6e40a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1595ee1-fb3e-455b-9e00-0f3d5cd6e40a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1a4097e-a84b-42c8-9b36-f48a9bc0c583' class='xr-var-data-in' type='checkbox'><label for='data-b1a4097e-a84b-42c8-9b36-f48a9bc0c583' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 5.07 kiB </td>\n", - " <td> 5.07 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> uint8 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-array-wrap > label {\n", + " grid-column: 1;\n", + " vertical-align: top;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-preview {\n", + " color: var(--xr-font-color3);\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-array-preview,\n", + ".xr-array-data {\n", + " padding: 0 5px !important;\n", + " grid-column: 2;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-array-data,\n", + ".xr-array-in:checked ~ .xr-array-preview {\n", + " display: none;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-48c29f2f-35a3-4411-9e2c-10ae80c3caf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48c29f2f-35a3-4411-9e2c-10ae80c3caf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7d922a7-9ce8-4ef0-81cc-d58f409e5ef6' class='xr-var-data-in' type='checkbox'><label for='data-c7d922a7-9ce8-4ef0-81cc-d58f409e5ef6' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-array-in:checked ~ .xr-array-data,\n", + ".xr-array-preview {\n", + " display: inline-block;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-dim-list {\n", + " display: inline-block !important;\n", + " list-style: none;\n", + " padding: 0 !important;\n", + " margin: 0;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-dim-list li {\n", + " display: inline-block;\n", + " padding: 0;\n", + " margin: 0;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-dim-list:before {\n", + " content: '(';\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_length</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-555558f3-ed81-4a54-b919-b472d4df1f6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-555558f3-ed81-4a54-b919-b472d4df1f6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8716be4-1ce0-4790-8422-444895e7b6df' class='xr-var-data-in' type='checkbox'><label for='data-b8716be4-1ce0-4790-8422-444895e7b6df' 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>A 1 byte value indicating the length of the record. Records can be up to 255 bytes long</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Length</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-dim-list:after {\n", + " content: ')';\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-dim-list li:not(:last-child):after {\n", + " content: ',';\n", + " padding-right: 5px;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-has-index {\n", + " font-weight: bold;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-var-list,\n", + ".xr-var-item {\n", + " display: contents;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_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'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-ea1e18ee-7dfd-4b8a-b430-8e04a547b4fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea1e18ee-7dfd-4b8a-b430-8e04a547b4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e849bb87-f598-45d3-8fd1-d784e0315999' class='xr-var-data-in' type='checkbox'><label for='data-e849bb87-f598-45d3-8fd1-d784e0315999' 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>Time of control record in seconds since January 1, 1904.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Time, UTC</dd><dt><span>precision :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 40.55 kiB </td>\n", - " <td> 40.55 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-var-item > div,\n", + ".xr-var-item label,\n", + ".xr-var-item > .xr-var-name span {\n", + " background-color: var(--xr-background-color-row-even);\n", + " margin-bottom: 0;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-item > .xr-var-name:hover span {\n", + " padding-right: 5px;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-list > li:nth-child(odd) > div,\n", + ".xr-var-list > li:nth-child(odd) > label,\n", + ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", + " background-color: var(--xr-background-color-row-odd);\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-var-name {\n", + " grid-column: 1;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>record_type</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-ea772fdf-2971-4624-a07c-ddf890f1aa55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea772fdf-2971-4624-a07c-ddf890f1aa55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e74b254f-31c8-47f4-b272-24746eedf382' class='xr-var-data-in' type='checkbox'><label for='data-e74b254f-31c8-47f4-b272-24746eedf382' 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>Record type</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Record Type</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-var-dims {\n", + " grid-column: 2;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-dtype {\n", + " grid-column: 3;\n", + " text-align: right;\n", + " color: var(--xr-font-color2);\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-preview {\n", + " grid-column: 4;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermistor_raw</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-96997f0c-779e-4372-b0e8-022e847562d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96997f0c-779e-4372-b0e8-022e847562d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f735b233-8d3a-48c6-99e6-7ea0200bc484' class='xr-var-data-in' type='checkbox'><label for='data-f735b233-8d3a-48c6-99e6-7ea0200bc484' 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>This is the raw measurement, used to calculate the Thermistor Temperature.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>CO2THRM_L0</dd><dt><span>long_name :</span></dt><dd>Thermistor Temperature Raw</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-var-name,\n", + ".xr-var-dims,\n", + ".xr-var-dtype,\n", + ".xr-preview,\n", + ".xr-attrs dt {\n", + " white-space: nowrap;\n", + " overflow: hidden;\n", + " text-overflow: ellipsis;\n", + " padding-right: 10px;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-name:hover,\n", + ".xr-var-dims:hover,\n", + ".xr-var-dtype:hover,\n", + ".xr-attrs dt:hover {\n", + " overflow: visible;\n", + " width: auto;\n", + " z-index: 1;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-attrs,\n", + ".xr-var-data,\n", + ".xr-index-data {\n", + " display: none;\n", + " background-color: var(--xr-background-color) !important;\n", + " padding-bottom: 5px !important;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", + " display: block;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-06889171-3cfd-4c5f-9008-90623eb444a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06889171-3cfd-4c5f-9008-90623eb444a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c6c87c-0448-4b78-8833-ae01a1bb57f6' class='xr-var-data-in' type='checkbox'><label for='data-a4c6c87c-0448-4b78-8833-ae01a1bb57f6' 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>A 1 byte hash of the SAMI name and calibration.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Instrument Unique ID</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-var-data > table {\n", + " float: right;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-var-name span,\n", + ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", + ".xr-attrs {\n", + " padding-left: 25px !important;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-attrs,\n", + ".xr-var-attrs,\n", + ".xr-var-data,\n", + ".xr-index-data {\n", + " grid-column: 1 / -1;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + "dl.xr-attrs {\n", + " padding: 0;\n", + " margin: 0;\n", + " display: grid;\n", + " grid-template-columns: 125px auto;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>voltage_battery</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(5190,), meta=np.ndarray></div><input id='attrs-42ebb50a-5822-4cf1-a472-21c1fc20bef1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42ebb50a-5822-4cf1-a472-21c1fc20bef1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed1785ab-c317-43e4-b341-4f3a35d513e1' class='xr-var-data-in' type='checkbox'><label for='data-ed1785ab-c317-43e4-b341-4f3a35d513e1' 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>Battery voltage in counts.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Voltage Battery</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><table>\n", - " <tr>\n", - " <td>\n", - " <table style=\"border-collapse: collapse;\">\n", - " <thead>\n", - " <tr>\n", - " <td> </td>\n", - " <th> Array </th>\n", - " <th> Chunk </th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " <th> Bytes </th>\n", - " <td> 20.27 kiB </td>\n", - " <td> 20.27 kiB </td>\n", - " </tr>\n", - " \n", - " <tr>\n", - " <th> Shape </th>\n", - " <td> (5190,) </td>\n", - " <td> (5190,) </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Dask graph </th>\n", - " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", - " </tr>\n", - " <tr>\n", - " <th> Data type </th>\n", - " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", - " </tr>\n", - " </tbody>\n", - " </table>\n", - " </td>\n", - " <td>\n", - " <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", + " padding: 0;\n", + " margin: 0;\n", + " float: left;\n", + " padding-right: 10px;\n", + " width: auto;\n", + "}\n", "\n", - " <!-- Horizontal lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", - " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-attrs dt {\n", + " font-weight: normal;\n", + " grid-column: 1;\n", + "}\n", "\n", - " <!-- Vertical lines -->\n", - " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", - " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", + ".xr-attrs dt:hover span {\n", + " display: inline-block;\n", + " background: var(--xr-background-color);\n", + " padding-right: 10px;\n", + "}\n", "\n", - " <!-- Colored Rectangle -->\n", - " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", + ".xr-attrs dd {\n", + " grid-column: 2;\n", + " white-space: pre-wrap;\n", + " word-break: break-all;\n", + "}\n", "\n", - " <!-- Text -->\n", - " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5190</text>\n", - " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", - "</svg>\n", - " </td>\n", - " </tr>\n", - "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b930ffb-1857-4726-b622-274b994f151d' class='xr-section-summary-in' type='checkbox' ><label for='section-2b930ffb-1857-4726-b622-274b994f151d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>spectrum</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-91261e01-9702-4ea2-939f-a07107e3177d' class='xr-index-data-in' type='checkbox'/><label for='index-91261e01-9702-4ea2-939f-a07107e3177d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int32', name='spectrum'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-49823820-3ae4-4a4a-83ca-37e6042f52bf' class='xr-index-data-in' type='checkbox'/><label for='index-49823820-3ae4-4a4a-83ca-37e6042f52bf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-01-01 02:16:20.634201088',\n", - " '2022-01-01 03:43:32.948877312',\n", - " '2022-01-01 06:41:21.954166272',\n", - " '2022-01-01 08:38:47.687980032',\n", - " '2022-01-01 08:45:48.267300864',\n", - " '2022-01-01 08:52:51.156151296',\n", - " '2022-01-01 08:59:51.750470656',\n", - " '2022-01-01 09:06:52.408842240',\n", - " '2022-01-01 09:13:53.112488960',\n", - " '2022-01-01 09:20:54.940146176',\n", + ".xr-icon-database,\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", + " display: inline-block;\n", + " vertical-align: middle;\n", + " width: 1em;\n", + " height: 1.5em !important;\n", + " stroke-width: 0;\n", + " stroke: currentColor;\n", + " fill: currentColor;\n", + "}\n", + "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", + "Dimensions: (time: 29325960)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-01-01T...\n", + "Data variables: (12/40)\n", + " amplitude_beam1 (time) float32 ...\n", + " amplitude_beam2 (time) float32 ...\n", + " amplitude_beam3 (time) float32 ...\n", + " analog1 (time) float32 ...\n", + " battery_voltage_dv (time) float32 ...\n", + " ctdpf_sbe43_sample-depth (time) float64 ...\n", + " ... ...\n", + " velpt_d_northward_velocity (time) float64 ...\n", + " velpt_d_northward_velocity_qc_executed (time) uint8 ...\n", + " velpt_d_northward_velocity_qc_results (time) uint8 ...\n", + " velpt_d_upward_velocity (time) float64 ...\n", + " velpt_d_upward_velocity_qc_executed (time) uint8 ...\n", + " velpt_d_upward_velocity_qc_results (time) uint8 ...\n", + "Attributes: (12/62)\n", + " AssetManagementRecordLastModified: 2024-07-04T16:24:19.204000\n", + " AssetUniqueID: ATAPL-70114-00004\n", + " Conventions: CF-1.6\n", + " Description: Single Point Velocity Meter: VELPT Se...\n", + " FirmwareVersion: Not specified.\n", + " Manufacturer: Nortek\n", + " ... ...\n", + " stream: velpt_velocity_data\n", + " subsite: RS01SBPS\n", + " summary: Dataset Generated by Stream Engine fr...\n", + " time_coverage_end: 2024-07-09T11:15:53.441897472\n", + " time_coverage_start: 2014-10-06T23:32:22.570285568\n", + " title: Data produced by Stream Engine versio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2a043ca0-6fd1-4a5f-8e7c-4d7a886fa7fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a043ca0-6fd1-4a5f-8e7c-4d7a886fa7fc' 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>: 29325960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d63374c7-8f2d-4052-8d61-49475035ce8f' class='xr-section-summary-in' type='checkbox' checked><label for='section-d63374c7-8f2d-4052-8d61-49475035ce8f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T00:00:00.527429120 .....</div><input id='attrs-404d12bd-12fc-4f2a-9d9e-aed208d668fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-404d12bd-12fc-4f2a-9d9e-aed208d668fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8eb67db7-5dc2-4a5c-bbfc-08f0f4ba2ebd' class='xr-var-data-in' type='checkbox'><label for='data-8eb67db7-5dc2-4a5c-bbfc-08f0f4ba2ebd' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2022-01-01T00:00:00.527429120', '2022-01-01T00:00:01.527426048',\n", + " '2022-01-01T00:00:02.527426048', ..., '2022-12-31T23:59:57.845967360',\n", + " '2022-12-31T23:59:58.845957120', '2022-12-31T23:59:59.845964288'],\n", + " dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5b30253-c6bb-45d2-abd3-eb3648c9089f' class='xr-section-summary-in' type='checkbox' ><label for='section-b5b30253-c6bb-45d2-abd3-eb3648c9089f' class='xr-section-summary' >Data variables: <span>(40)</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>amplitude_beam1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-410e3a4b-e483-41bb-8c50-38b17c49dff7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-410e3a4b-e483-41bb-8c50-38b17c49dff7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c94cfe17-6633-479e-8ec5-6bf0bddf272c' class='xr-var-data-in' type='checkbox'><label for='data-c94cfe17-6633-479e-8ec5-6bf0bddf272c' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Amplitude, Beam 1</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amplitude_beam2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92b35cd0-6ca5-40b0-b382-3e6ab327c6ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92b35cd0-6ca5-40b0-b382-3e6ab327c6ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fed7494d-ae11-414b-bbb2-c906ddd92291' class='xr-var-data-in' type='checkbox'><label for='data-fed7494d-ae11-414b-bbb2-c906ddd92291' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Amplitude, Beam 2</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amplitude_beam3</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-775a9eae-4893-4aa2-959a-6d2df7317621' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-775a9eae-4893-4aa2-959a-6d2df7317621' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84432129-6515-4d55-bab5-892d8927d11a' class='xr-var-data-in' type='checkbox'><label for='data-84432129-6515-4d55-bab5-892d8927d11a' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Amplitude, Beam 3</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analog1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40ef83ff-be52-4c8a-957e-66540d505cb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40ef83ff-be52-4c8a-957e-66540d505cb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68a75258-1862-4f8c-bbb3-78c2c5a16790' class='xr-var-data-in' type='checkbox'><label for='data-68a75258-1862-4f8c-bbb3-78c2c5a16790' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Analog 1</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>battery_voltage_dv</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88c6ed87-9933-439a-aa14-5afa52048215' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88c6ed87-9933-439a-aa14-5afa52048215' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35371542-8efd-4572-95b9-c38e50e6ac5c' class='xr-var-data-in' type='checkbox'><label for='data-35371542-8efd-4572-95b9-c38e50e6ac5c' 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>Instrument battery voltage reported in decivolts.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>units :</span></dt><dd>dV</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctdpf_sbe43_sample-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-d1432516-9ecc-4709-825f-742aa9bf9fd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1432516-9ecc-4709-825f-742aa9bf9fd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84e3bb05-ca05-43fd-ae16-9bf49a81b3fb' class='xr-var-data-in' type='checkbox'><label for='data-84e3bb05-ca05-43fd-ae16-9bf49a81b3fb' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>instrument :</span></dt><dd>RS01SBPS-SF01A-2A-CTDPFA102</dd><dt><span>long_name :</span></dt><dd>Depth calculated from pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>stream :</span></dt><dd>ctdpf_sbe43_sample</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float64]</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'>...</div><input id='attrs-01926d70-5d1a-459f-bfc9-e9befd9e14a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01926d70-5d1a-459f-bfc9-e9befd9e14a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6255be2d-ba57-4f5c-8e1c-fc48e752eb59' class='xr-var-data-in' type='checkbox'><label for='data-6255be2d-ba57-4f5c-8e1c-fc48e752eb59' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Deployment Number</dd><dt><span>name :</span></dt><dd>deployment</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=int32]</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'>...</div><input id='attrs-a2f426b0-b3a6-44d0-b318-a0251f1854ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2f426b0-b3a6-44d0-b318-a0251f1854ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ff93255-67f9-4d3f-8f3f-b583c77479c2' class='xr-var-data-in' type='checkbox'><label for='data-2ff93255-67f9-4d3f-8f3f-b583c77479c2' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Driver Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_code</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1d102bd-e9f1-44a5-96d6-74011e0c1f84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1d102bd-e9f1-44a5-96d6-74011e0c1f84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-856de54a-8fb2-474e-b845-777917b4ea66' class='xr-var-data-in' type='checkbox'><label for='data-856de54a-8fb2-474e-b845-777917b4ea66' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Error Code</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_decidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1785671c-7a6d-4027-b99a-541d466910d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1785671c-7a6d-4027-b99a-541d466910d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f07cf6ea-efc4-4d76-9241-4431a3a90de6' class='xr-var-data-in' type='checkbox'><label for='data-f07cf6ea-efc4-4d76-9241-4431a3a90de6' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Compass Heading</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</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'>...</div><input id='attrs-c3aabdc2-2510-489a-9dc9-0447308d398a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3aabdc2-2510-489a-9dc9-0447308d398a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a04bcf07-45fc-4119-b7ff-460a90b1ce6a' class='xr-var-data-in' type='checkbox'><label for='data-a04bcf07-45fc-4119-b7ff-460a90b1ce6a' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Ingestion Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=datetime64[ns]]</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'>...</div><input id='attrs-cc4a4992-78c5-46e5-b1e7-df714f7765f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc4a4992-78c5-46e5-b1e7-df714f7765f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6336333c-6a35-4adc-acef-2d7c0f876ec5' class='xr-var-data-in' type='checkbox'><label for='data-6336333c-6a35-4adc-acef-2d7c0f876ec5' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>PRESWAT_L1</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</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>[29325960 values with dtype=float64]</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'>...</div><input id='attrs-0113ca89-e18e-4b73-ba03-591e2a2337c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0113ca89-e18e-4b73-ba03-591e2a2337c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b73c105-9540-45dc-9dde-3b3ba01fe1c1' class='xr-var-data-in' type='checkbox'><label for='data-2b73c105-9540-45dc-9dde-3b3ba01fe1c1' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Internal Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_decidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e5dbde3-c54e-4867-be48-ba0fa6739e9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e5dbde3-c54e-4867-be48-ba0fa6739e9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc833524-a31c-461e-94e0-c3ab3e31b3d3' class='xr-var-data-in' type='checkbox'><label for='data-dc833524-a31c-461e-94e0-c3ab3e31b3d3' 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 rotated angle about the pitch-axis relative to the horizontal plane. Rotation follows the right hand rule designation; i.e. with the r.h. thumb pointing in the rotation axis direction, positive rotation is in the direction of the curled fingers.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Compass Pitch</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>platform_pitch_angle</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</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'>...</div><input id='attrs-cd75d830-d0e6-4080-b8ce-b71249d9d006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd75d830-d0e6-4080-b8ce-b71249d9d006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24827665-02d8-49b9-8289-5504a80c2878' class='xr-var-data-in' type='checkbox'><label for='data-24827665-02d8-49b9-8289-5504a80c2878' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Port Timestamp, UTC</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_decidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29d54187-8615-45ea-89bb-4324cf493949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29d54187-8615-45ea-89bb-4324cf493949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7a57af0-5816-4501-8d40-c71aeac740c2' class='xr-var-data-in' type='checkbox'><label for='data-f7a57af0-5816-4501-8d40-c71aeac740c2' 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 rotated angle about the roll-axis relative to the horizontal plane. Rotation follows the right hand rule designation; i.e. with the r.h. thumb pointing in the rotation axis direction, positive rotation is in the direction of the curled fingers.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Compass Roll</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>platform_roll_angle</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_mbar</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-7d0eb968-9ae4-4380-bafa-e1f5bdc8abea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d0eb968-9ae4-4380-bafa-e1f5bdc8abea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca63da81-021d-46f7-8aa2-124a876b215a' class='xr-var-data-in' type='checkbox'><label for='data-ca63da81-021d-46f7-8aa2-124a876b215a' 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>alternate_parameter_name :</span></dt><dd>pressure_mbar</dd><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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Seawater Pressure</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_mbar_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'>...</div><input id='attrs-1cba7ef9-6959-4575-bdd4-13a148bb3ca2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cba7ef9-6959-4575-bdd4-13a148bb3ca2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a72e9a7-d8c1-46b0-9dd3-740b5fc58735' class='xr-var-data-in' type='checkbox'><label for='data-2a72e9a7-d8c1-46b0-9dd3-740b5fc58735' 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>alternate_parameter_name :</span></dt><dd>pressure_mbar_qc_executed</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_water_pressure_mbar_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'>...</div><input id='attrs-05871e35-25bd-4862-a854-fabd204772a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05871e35-25bd-4862-a854-fabd204772a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-522d471d-6f71-40a4-b6a3-9fbf59ef6078' class='xr-var-data-in' type='checkbox'><label for='data-522d471d-6f71-40a4-b6a3-9fbf59ef6078' 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>alternate_parameter_name :</span></dt><dd>pressure_mbar_qc_results</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sound_speed_dms</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3c2b68cc-05fb-4718-80a1-26cad03c0196' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c2b68cc-05fb-4718-80a1-26cad03c0196' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09af08ca-0107-4973-9fa9-28586bb51c71' class='xr-var-data-in' type='checkbox'><label for='data-09af08ca-0107-4973-9fa9-28586bb51c71' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>units :</span></dt><dd>dm s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b129ce7-fca1-4538-93b2-f4ba7d3cf747' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b129ce7-fca1-4538-93b2-f4ba7d3cf747' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96a5fa88-ed12-4edf-aa45-2c9ab6f793e1' class='xr-var-data-in' type='checkbox'><label for='data-96a5fa88-ed12-4edf-aa45-2c9ab6f793e1' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Status</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_centidegree</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9697b40-06a2-41a0-9a93-50f7357d92cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9697b40-06a2-41a0-9a93-50f7357d92cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99cda662-4383-4ccf-97ec-a31bdbc893e6' class='xr-var-data-in' type='checkbox'><label for='data-99cda662-4383-4ccf-97ec-a31bdbc893e6' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>Seawater Temperature</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>units :</span></dt><dd>cdegrees_Celsius</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-47d09c0c-e610-49db-b3bb-b94322668c9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47d09c0c-e610-49db-b3bb-b94322668c9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33056f2d-2e99-4330-abac-2d2e9c686bb6' class='xr-var-data-in' type='checkbox'><label for='data-33056f2d-2e99-4330-abac-2d2e9c686bb6' 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>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single "point" of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the eastward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLE_L0</dd><dt><span>long_name :</span></dt><dd>Eastward Mean Point Seawater Velocity</dd><dt><span>units :</span></dt><dd>mm s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam1_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'>...</div><input id='attrs-1911fe1c-cb1d-4294-8e1b-4743e82f037d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1911fe1c-cb1d-4294-8e1b-4743e82f037d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d1123e-8dc4-4240-967e-8d03b10c0ca9' class='xr-var-data-in' type='checkbox'><label for='data-99d1123e-8dc4-4240-967e-8d03b10c0ca9' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam1_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'>...</div><input id='attrs-ac209a48-64c0-4240-bbc5-03791029c4e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac209a48-64c0-4240-bbc5-03791029c4e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94060305-3ad3-445c-9cdc-10e111100ba4' class='xr-var-data-in' type='checkbox'><label for='data-94060305-3ad3-445c-9cdc-10e111100ba4' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1a51060-10ef-4031-bfa3-c3072f564196' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1a51060-10ef-4031-bfa3-c3072f564196' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d4e24f8-d3eb-4cdc-88ca-7bb73965bc2e' class='xr-var-data-in' type='checkbox'><label for='data-3d4e24f8-d3eb-4cdc-88ca-7bb73965bc2e' 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>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single "point" of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the northward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLN_L0</dd><dt><span>long_name :</span></dt><dd>Northward Mean Point Seawater Velocity</dd><dt><span>units :</span></dt><dd>mm s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam2_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'>...</div><input id='attrs-1ca41af5-cc53-4aad-98fc-9fe0267fa7ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ca41af5-cc53-4aad-98fc-9fe0267fa7ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-765fe610-7aa6-4cd9-a850-2a8ca115aee1' class='xr-var-data-in' type='checkbox'><label for='data-765fe610-7aa6-4cd9-a850-2a8ca115aee1' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam2_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'>...</div><input id='attrs-6b048260-3e6d-4f82-bc8a-bd2b085db4f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b048260-3e6d-4f82-bc8a-bd2b085db4f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bf9e680-0817-4c64-a56e-6e77ea5a9cad' class='xr-var-data-in' type='checkbox'><label for='data-7bf9e680-0817-4c64-a56e-6e77ea5a9cad' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam3</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11006da4-7238-44b3-ab15-00724bdb3a99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11006da4-7238-44b3-ab15-00724bdb3a99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-817c80d6-ba0c-4b50-a763-92f64c3e87fd' class='xr-var-data-in' type='checkbox'><label for='data-817c80d6-ba0c-4b50-a763-92f64c3e87fd' 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>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single "point" of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the upward component of mean point seawater velocity.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLU_L0</dd><dt><span>long_name :</span></dt><dd>Upward Mean Point Seawater Velocity</dd><dt><span>units :</span></dt><dd>mm s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam3_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'>...</div><input id='attrs-02223b2f-e89b-4ce6-a92e-cc554f1dfdbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02223b2f-e89b-4ce6-a92e-cc554f1dfdbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4024878-41b6-41fa-b09f-fd098b3d1999' class='xr-var-data-in' type='checkbox'><label for='data-b4024878-41b6-41fa-b09f-fd098b3d1999' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velocity_beam3_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'>...</div><input id='attrs-87a74f08-e206-4cc4-82f9-78dd6676732a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87a74f08-e206-4cc4-82f9-78dd6676732a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02c84fc8-3697-486e-960a-5c7572f41167' class='xr-var-data-in' type='checkbox'><label for='data-02c84fc8-3697-486e-960a-5c7572f41167' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_eastward_velocity</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-a29533d4-f5b6-4d0c-ae5b-84637bd8e4e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a29533d4-f5b6-4d0c-ae5b-84637bd8e4e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45472aa2-8497-4565-85b6-80a560e8c275' class='xr-var-data-in' type='checkbox'><label for='data-45472aa2-8497-4565-85b6-80a560e8c275' 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>ancillary_variables :</span></dt><dd>velocity_beam1 time velocity_beam2</dd><dt><span>comment :</span></dt><dd>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single "point" of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the eastward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLE_L1</dd><dt><span>long_name :</span></dt><dd>Eastward Mean Point Seawater Velocity</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_eastward_velocity_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'>...</div><input id='attrs-a944e6a1-4e5e-4477-8fac-778ec26818e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a944e6a1-4e5e-4477-8fac-778ec26818e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8405f6b-2e45-4c1a-9d84-ddceb9661cfb' class='xr-var-data-in' type='checkbox'><label for='data-f8405f6b-2e45-4c1a-9d84-ddceb9661cfb' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_eastward_velocity_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'>...</div><input id='attrs-673e79f1-41e7-45be-a799-3749d257ec65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-673e79f1-41e7-45be-a799-3749d257ec65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11f1b43c-067f-4036-9f07-14d3d365b838' class='xr-var-data-in' type='checkbox'><label for='data-11f1b43c-067f-4036-9f07-14d3d365b838' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_northward_velocity</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-08a41551-643e-4c8e-95a4-347c78fbdcf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08a41551-643e-4c8e-95a4-347c78fbdcf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f338807-6af1-4fe6-8e2a-791f956d0349' class='xr-var-data-in' type='checkbox'><label for='data-7f338807-6af1-4fe6-8e2a-791f956d0349' 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>ancillary_variables :</span></dt><dd>velocity_beam1 time velocity_beam2</dd><dt><span>comment :</span></dt><dd>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single "point" of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the northward component of mean point seawater velocity in earth coordinates relative to true north (accounted for magnetic variation).</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLN_L1</dd><dt><span>long_name :</span></dt><dd>Northward Mean Point Seawater Velocity</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_northward_velocity_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'>...</div><input id='attrs-08ce20bd-cbc7-4a2a-a1b7-0a14b1e55abe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08ce20bd-cbc7-4a2a-a1b7-0a14b1e55abe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5308c8f-f586-4d81-9994-e50c9a30c7e3' class='xr-var-data-in' type='checkbox'><label for='data-e5308c8f-f586-4d81-9994-e50c9a30c7e3' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_northward_velocity_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'>...</div><input id='attrs-638de0f5-eba1-43e3-983c-6172e613d9ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-638de0f5-eba1-43e3-983c-6172e613d9ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95feb209-4d85-4c5c-8248-6bb9c048f7ec' class='xr-var-data-in' type='checkbox'><label for='data-95feb209-4d85-4c5c-8248-6bb9c048f7ec' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_upward_velocity</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-91012a46-0a07-46f6-bf5f-012a59733d23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91012a46-0a07-46f6-bf5f-012a59733d23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eab3010a-5d49-4b9b-939b-711eebffa262' class='xr-var-data-in' type='checkbox'><label for='data-eab3010a-5d49-4b9b-939b-711eebffa262' 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>ancillary_variables :</span></dt><dd>velocity_beam3</dd><dt><span>comment :</span></dt><dd>Mean Point Seawater Velocity refers to the velocity (speed and direction) of a single "point" of water (in this case, a volume of several square meters) averaged over time-scales associated with surface gravity waves. This instrument estimates water velocity by measuring the Doppler shift of acoustic signals reflected by particles suspended in the water. This data product is the upward component of mean point seawater velocity.</dd><dt><span>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>data_product_identifier :</span></dt><dd>VELPTMN-VLU_L1</dd><dt><span>long_name :</span></dt><dd>Upward Mean Point Seawater Velocity</dd><dt><span>precision :</span></dt><dd>3</dd><dt><span>standard_name :</span></dt><dd>upward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_upward_velocity_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'>...</div><input id='attrs-df94933e-578a-41a0-9ef7-a5de9b5e2d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df94933e-578a-41a0-9ef7-a5de9b5e2d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cd72333-b892-4b4f-8ed0-81ebada5cf93' class='xr-var-data-in' type='checkbox'><label for='data-0cd72333-b892-4b4f-8ed0-81ebada5cf93' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Executed</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>velpt_d_upward_velocity_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'>...</div><input id='attrs-c104b484-b940-4b81-8bcc-dcc081284502' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c104b484-b940-4b81-8bcc-dcc081284502' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e4d9cf-d24d-4cf5-b2cf-a22a67dc5253' class='xr-var-data-in' type='checkbox'><label for='data-27e4d9cf-d24d-4cf5-b2cf-a22a67dc5253' 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>coordinates :</span></dt><dd>lat depth lon time</dd><dt><span>long_name :</span></dt><dd>QC Checks Results</dd></dl></div><div class='xr-var-data'><pre>[29325960 values with dtype=uint8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3ac9df3-21d5-49e3-b67a-dd3af52bc423' class='xr-section-summary-in' type='checkbox' ><label for='section-e3ac9df3-21d5-49e3-b67a-dd3af52bc423' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4a911957-2851-42b2-ab8a-4a5ece243667' class='xr-index-data-in' type='checkbox'/><label for='index-4a911957-2851-42b2-ab8a-4a5ece243667' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-01-01 00:00:00.527429120',\n", + " '2022-01-01 00:00:01.527426048',\n", + " '2022-01-01 00:00:02.527426048',\n", + " '2022-01-01 00:00:03.527422976',\n", + " '2022-01-01 00:00:04.527421952',\n", + " '2022-01-01 00:00:05.527422976',\n", + " '2022-01-01 00:00:06.527418368',\n", + " '2022-01-01 00:00:07.527422976',\n", + " '2022-01-01 00:00:08.527418368',\n", + " '2022-01-01 00:00:09.527418368',\n", " ...\n", - " '2022-12-31 22:16:21.463670784',\n", - " '2022-12-31 22:23:22.184012288',\n", - " '2022-12-31 22:30:23.808183296',\n", - " '2022-12-31 22:37:25.636162560',\n", - " '2022-12-31 22:44:26.278152704',\n", - " '2022-12-31 22:51:27.935206912',\n", - " '2022-12-31 22:58:29.559876608',\n", - " '2022-12-31 23:07:29.245393920',\n", - " '2022-12-31 23:16:27.824401408',\n", - " '2022-12-31 23:26:46.404636160'],\n", - " dtype='datetime64[ns]', name='time', length=5190, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d35e45ca-1804-4510-bc48-be62d0c43236' class='xr-section-summary-in' type='checkbox' ><label for='section-d35e45ca-1804-4510-bc48-be62d0c43236' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:20.856000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-58336-00003</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>pCO2 Water: PCO2W Series A</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Sunburst Sensors</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>SAMI-pCO2</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>C0076</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-08T11:17:01.393782</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-08T11:16:37.513113</dd><dt><span>date_modified :</span></dt><dd>2024-07-08T11:17:01.393784</dd><dt><span>date_processed :</span></dt><dd>2024-07-08T11:21:44.776799</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-08T11:17:01.393758 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4F-PCO2WA101</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>pco2w_a_sami_data_record</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-08T11:05:11.220713472</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-07T01:05:10.333669376</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record</dd></dl></div></li></ul></div></div>" + " '2022-12-31 23:59:50.845976576',\n", + " '2022-12-31 23:59:51.845972480',\n", + " '2022-12-31 23:59:52.845967360',\n", + " '2022-12-31 23:59:53.845965312',\n", + " '2022-12-31 23:59:54.845969920',\n", + " '2022-12-31 23:59:55.845967360',\n", + " '2022-12-31 23:59:56.845964288',\n", + " '2022-12-31 23:59:57.845967360',\n", + " '2022-12-31 23:59:58.845957120',\n", + " '2022-12-31 23:59:59.845964288'],\n", + " dtype='datetime64[ns]', name='time', length=29325960, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03780237-f4e4-4c92-b8dc-a8a063c6566d' class='xr-section-summary-in' type='checkbox' ><label for='section-03780237-f4e4-4c92-b8dc-a8a063c6566d' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AssetManagementRecordLastModified :</span></dt><dd>2024-07-04T16:24:19.204000</dd><dt><span>AssetUniqueID :</span></dt><dd>ATAPL-70114-00004</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Description :</span></dt><dd>Single Point Velocity Meter: VELPT Series D</dd><dt><span>FirmwareVersion :</span></dt><dd>Not specified.</dd><dt><span>Manufacturer :</span></dt><dd>Nortek</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Mobile :</span></dt><dd>False</dd><dt><span>ModelNumber :</span></dt><dd>Aquadopp</dd><dt><span>Notes :</span></dt><dd>This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.</dd><dt><span>Owner :</span></dt><dd>University of Washington Cabled Array Value Add Team.</dd><dt><span>RemoteResources :</span></dt><dd>[]</dd><dt><span>SerialNumber :</span></dt><dd>AQS-6802/AQD 11930</dd><dt><span>ShelfLifeExpirationDate :</span></dt><dd>Not specified.</dd><dt><span>SoftwareVersion :</span></dt><dd>Not specified.</dd><dt><span>cdm_data_type :</span></dt><dd>Point</dd><dt><span>collection_method :</span></dt><dd>streamed</dd><dt><span>comment :</span></dt><dd>Some of the metadata of this dataset has been modified to be CF-1.6 compliant.</dd><dt><span>creator_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>creator_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>date_created :</span></dt><dd>2024-07-09T11:16:06.299233</dd><dt><span>date_downloaded :</span></dt><dd>2024-07-09T11:15:57.016603</dd><dt><span>date_modified :</span></dt><dd>2024-07-09T11:16:06.299236</dd><dt><span>date_processed :</span></dt><dd>2024-07-09T11:21:04.391531</dd><dt><span>featureType :</span></dt><dd>point</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.515161</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_min :</span></dt><dd>-125.389899</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meters</dd><dt><span>history :</span></dt><dd>2024-07-09T11:16:06.299198 generated from Stream Engine</dd><dt><span>id :</span></dt><dd>RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data</dd><dt><span>infoUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>institution :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>keywords :</span></dt><dd></dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd></dd><dt><span>naming_authority :</span></dt><dd>org.oceanobservatories</dd><dt><span>nodc_template_version :</span></dt><dd>NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1</dd><dt><span>node :</span></dt><dd>SF01A</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>project :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd>Ocean Observatories Initiative</dd><dt><span>publisher_url :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>references :</span></dt><dd>More information can be found at http://oceanobservatories.org/</dd><dt><span>sensor :</span></dt><dd>4B-VELPTD102</dd><dt><span>source :</span></dt><dd>RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data</dd><dt><span>sourceUrl :</span></dt><dd>http://oceanobservatories.org/</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29</dd><dt><span>stream :</span></dt><dd>velpt_velocity_data</dd><dt><span>subsite :</span></dt><dd>RS01SBPS</dd><dt><span>summary :</span></dt><dd>Dataset Generated by Stream Engine from Ocean Observatories Initiative</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-09T11:15:53.441897472</dd><dt><span>time_coverage_start :</span></dt><dd>2014-10-06T23:32:22.570285568</dd><dt><span>title :</span></dt><dd>Data produced by Stream Engine version 1.20.8 for RS01SBPS-SF01A-4B-VELPTD102-streamed-velpt_velocity_data</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset>\n", - "Dimensions: (time: 5190, spectrum: 14)\n", + "Dimensions: (time: 29325960)\n", "Coordinates:\n", - " * spectrum (spectrum) int32 0 1 2 ... 12 13\n", - " * time (time) datetime64[ns] 2022-01-0...\n", - "Data variables: (12/31)\n", - " absorbance_ratio_434 (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_434_qc_executed (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_434_qc_results (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_620 (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_620_qc_executed (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " absorbance_ratio_620_qc_results (time) uint8 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " ... ...\n", - " record_length (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " record_time (time) datetime64[ns] dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " record_type (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " thermistor_raw (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " unique_id (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", - " voltage_battery (time) float32 dask.array<chunksize=(5190,), meta=np.ndarray>\n", + " * time (time) datetime64[ns] 2022-01-01T...\n", + "Data variables: (12/40)\n", + " amplitude_beam1 (time) float32 ...\n", + " amplitude_beam2 (time) float32 ...\n", + " amplitude_beam3 (time) float32 ...\n", + " analog1 (time) float32 ...\n", + " battery_voltage_dv (time) float32 ...\n", + " ctdpf_sbe43_sample-depth (time) float64 ...\n", + " ... ...\n", + " velpt_d_northward_velocity (time) float64 ...\n", + " velpt_d_northward_velocity_qc_executed (time) uint8 ...\n", + " velpt_d_northward_velocity_qc_results (time) uint8 ...\n", + " velpt_d_upward_velocity (time) float64 ...\n", + " velpt_d_upward_velocity_qc_executed (time) uint8 ...\n", + " velpt_d_upward_velocity_qc_results (time) uint8 ...\n", "Attributes: (12/62)\n", - " AssetManagementRecordLastModified: 2024-07-04T16:24:20.856000\n", - " AssetUniqueID: ATAPL-58336-00003\n", + " AssetManagementRecordLastModified: 2024-07-04T16:24:19.204000\n", + " AssetUniqueID: ATAPL-70114-00004\n", " Conventions: CF-1.6\n", - " Description: pCO2 Water: PCO2W Series A\n", + " Description: Single Point Velocity Meter: VELPT Se...\n", " FirmwareVersion: Not specified.\n", - " Manufacturer: Sunburst Sensors\n", + " Manufacturer: Nortek\n", " ... ...\n", - " stream: pco2w_a_sami_data_record\n", + " stream: velpt_velocity_data\n", " subsite: RS01SBPS\n", " summary: Dataset Generated by Stream Engine fr...\n", - " time_coverage_end: 2024-07-08T11:05:11.220713472\n", - " time_coverage_start: 2014-10-07T01:05:10.333669376\n", + " time_coverage_end: 2024-07-09T11:15:53.441897472\n", + " time_coverage_start: 2014-10-06T23:32:22.570285568\n", " title: Data produced by Stream Engine versio..." ] }, - "execution_count": 26, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "instrument_key = 'velpt'\n", + "for s in osb_profiler_streams: \n", + " if instrument_key in s: \n", + " print('Found this instrument stream:', s)\n", + " instrument_stream = s\n", + " break\n", + " \n", + "ds = loadData(instrument_stream) # lazy load\n", + "t0, t1 = '2022-01-01T00', '2022-12-31T23' # January 2022\n", + "ds = ds.sel(time=slice(t0, t1)) # Subset the full time range to one month\n", + "print(ds.time[0], ' ', ds.time[-1]) # verify selected one month time range\n", + "ds # get a 'data variable' list of sensors/metadata for this instrument" + ] + }, + { + "cell_type": "markdown", + "id": "79f86ba8-a9f0-4eef-a8b9-78014e01bb1a", + "metadata": {}, + "source": [ + "For the current sensor: \n", + "depth: `int_ctd_pressure`. Velocities: `velpt_d_upward_velocity`, `velpt_d_northward_velocity`, `velpt_d_eastward_velocity`.\n", + "Respectively `depth`, `up`, `north`, `east`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "065f320a-a80c-420b-8440-cfef842a599a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attempting 1 charts\n", + "\n", + "Attempting 1 charts\n", + "\n", + "Attempting 1 charts\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if doIngest:\n", + " t0, t1 = '2022-01-01T00', '2022-01-31T23'\n", + " ds_up, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_upward_velocity', 'int_ctd_pressure'], ['up', 'depth'])\n", + " ds_north, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_northward_velocity', 'int_ctd_pressure'], ['north', 'depth'])\n", + " ds_east, reply = ShallowProfilerDataReduce(ds, t0, t1, ['velpt_d_eastward_velocity', 'int_ctd_pressure'], ['east', 'depth'])\n", + "\n", + " ds_up.to_netcdf('./data/rca/sensors/osb/up_jan_2022.nc')\n", + " ds_north.to_netcdf('./data/rca/sensors/osb/north_jan_2022.nc')\n", + " ds_east.to_netcdf('./data/rca/sensors/osb/east_jan_2022.nc')\n", + " \n", + " \n", + "ds_up = xr.open_dataset('./data/rca/sensors/osb/up_jan_2022.nc')\n", + "ds_north = xr.open_dataset('./data/rca/sensors/osb/north_jan_2022.nc')\n", + "ds_east = xr.open_dataset('./data/rca/sensors/osb/east_jan_2022.nc')\n", + "\n", + "fig, axes = ChartSensor(profiles, ranges['up'], [0], ds_up.up, -ds_up.depth, 'current up', 'black', 'ascent', 6, 4)\n", + "fig, axes = ChartSensor(profiles, ranges['north'], [0], ds_north.north, -ds_north.depth, 'current north', 'black', 'ascent', 6, 4)\n", + "fig, axes = ChartSensor(profiles, ranges['east'], [0], ds_east.east, -ds_east.depth, 'current east', 'black', 'ascent', 6, 4)" + ] + }, + { + "cell_type": "markdown", + "id": "10d0a25c-1a19-4a1f-b4db-aaed671d5c28", + "metadata": {}, + "source": [ + "#### 10 of 10: **pco2w** i.e. pCO2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "129d7553-1fbc-4bd3-aed5-a21583f02e29", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found this instrument stream: ooi-data/RS01SBPS-SF01A-4F-PCO2WA101-streamed-pco2w_a_sami_data_record\n", + "<xarray.DataArray 'time' ()>\n", + "array('2022-01-01T02:16:20.634201088', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-01-01T02:16:20.634201088\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time <xarray.DataArray 'time' ()>\n", + "array('2022-12-31T23:26:46.404636160', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 2022-12-31T23:26:46.404636160\n", + "Attributes:\n", + " axis: T\n", + " long_name: time\n", + " standard_name: time\n" + ] + } + ], "source": [ "if doIngest: \n", " instrument_key = 'pco2w'\n", @@ -8694,7 +1745,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 18, "id": "b0346a35-7345-4da8-822a-fe1bcdbb6f76", "metadata": {}, "outputs": [ @@ -8708,7 +1759,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 600x1600 with 4 Axes>" ] @@ -8723,7 +1774,7 @@ " ds_pco2, reply = ShallowProfilerDataReduce(ds, t0, t1, ['pco2_seawater', 'int_ctd_pressure'], ['pco2', 'depth'])\n", " ds_pco2.to_netcdf('./data/rca/sensors/osb/pco2_jan_2022.nc')\n", "\n", - "ds_pco2 = xr.open_dataset('./data/rca/sensors/osb/pco2_jan_2022.nc')\n", + "ds_pco2 = xr.open_dataset('./data/rca/sensors/osb/pco2_jan_2022.nc')\n", "fig, axes = ChartSensor(profiles, ranges['pco2'], [3, 8, 12, 17], ds_pco2.pco2, -ds_pco2.depth, 'pco2', 'black', 'descent', 6, 4)" ] }, @@ -8752,7 +1803,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/book/chapters/epipelargosy.ipynb b/book/chapters/epipelargosy.ipynb index bf817f6..e972813 100644 --- a/book/chapters/epipelargosy.ipynb +++ b/book/chapters/epipelargosy.ipynb @@ -348,6 +348,14 @@ " d['conductivity'][0], -d['conductivity'][1], 'Conductivity', 'blue', 'ascent', 6, 6)" ] }, + { + "cell_type": "markdown", + "id": "f461af19-2e98-4417-b5ad-68cc6041e679", + "metadata": {}, + "source": [ + "Interpretation: ...hmmmm... compared to the one prior: It seems like conductivity and salinity are not 'pretty much the same thing'..." + ] + }, { "cell_type": "code", "execution_count": 13, @@ -812,7 +820,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/environment.yml b/environment.yml index a245f1e..c0b94b9 100644 --- a/environment.yml +++ b/environment.yml @@ -13,6 +13,7 @@ dependencies: - ipywidgets - netcdf4 - s3fs + - zarr