continuum_imaging_example.ipynb

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        "<a href=\"https://colab.research.google.com/github/Jan-Willem/casacore/blob/master/continuum_imaging_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Continuum Imaging \n",
        "\n",
        "This notebook will demonstrate how to create a continuum dirty image with natural weighting using ngCASA. The resulting image will be compared with an image created by CASA. \n",
        "\n",
        "For this demonstration data from the ALMA First Look at Imaging CASAguide (https://casaguides.nrao.edu/index.php/First_Look_at_Imaging) will be used. The measurement set has been converted to vis.zarr (using convert_ms in cngi.conversion)\n",
        "\n",
        "This walkthrough is designed to be run in a Jupyter notebook on Google Colaboratory. To open the notebook in colab, go [here](https://colab.research.google.com/github/casangi/cngi_prototype/blob/master/docs/imaging/continuum_imaging_example.ipynb)."
      ]
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      "source": [
        "## Installation and Dataset Download\n"
      ]
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        "outputId": "3cce4a75-f248-4767-e068-44ac04bf52c6"
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      "source": [
        "import os\n",
        "os.system(\"pip install --extra-index-url https://test.pypi.org/simple/ cngi-prototype==0.0.68\")\n",
        "\n",
        "#sis14_twhya_field_5_lsrk_pol_xx.vis.zarr.zip https://drive.google.com/file/d/1PNL0ANqnN7eyYOpQ_vMslQlorUhtrUmE/view?usp=sharing\n",
        "id = '1PNL0ANqnN7eyYOpQ_vMslQlorUhtrUmE'\n",
        "os.system('curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=%s\"' % id)\n",
        "os.system('curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk \\'/download/ {print $NF}\\' ./cookie`&id=%s\" -o vis.zarr.zip' % id)\n",
        "os.system('unzip vis.zarr.zip')\n",
        "\n",
        "\n",
        "#casa_twhya_standard_gridder_lsrk_mfs_natural.img.zarr.zip https://drive.google.com/file/d/19VlXp9Qyx60ld0rBSAYMVFTq5Y1KuxO8/view?usp=sharing\n",
        "id = '19VlXp9Qyx60ld0rBSAYMVFTq5Y1KuxO8'\n",
        "os.system('curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=%s\"' % id)\n",
        "os.system('curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk \\'/download/ {print $NF}\\' ./cookie`&id=%s\" -o vis.zarr.zip' % id)\n",
        "os.system('unzip vis.zarr.zip')\n",
        "\n",
        "print('complete')"
      ],
      "execution_count": 5,
      "outputs": [
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          "output_type": "stream",
          "text": [
            "complete\n"
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          "name": "stdout"
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      "source": [
        "## Load Dataset\n",
        "\n",
        "Two datasets are are needed for this notebook sis14_twhya_field_5_lsrk_pol_xx.vis.zarr and casa_twhya_standard_gridder_lsrk_mfs_natural.img.zarr (for more information about the img.zarr format go [here](https://cngi-prototype.readthedocs.io/en/latest/visibilities.html) and for the vis.zarr format go [here](https://cngi-prototype.readthedocs.io/en/latest/images.html)). \n",
        "\n",
        "The sis14_twhya_field_5_lsrk_pol_xx.vis.zarr dataset is used to create a continuum image. The dataset was created by using the ```mstransform``` command in CASA\n",
        "\n",
        "```python\n",
        "mstransform('sis14_twhya_calibrated_flagged.ms',\n",
        "            outputvis='sis14_twhya_field_5_lsrk_pol_xx.ms', \n",
        "            regridms=True, outframe='LSRK', datacolumn='data',\n",
        "            correlation='XX',field='5')\n",
        "```\n",
        "\n",
        "and then convert_ms in cngi.conversion\n",
        "\n",
        "```python\n",
        "infile = 'sis14_twhya_field_5_lsrk_pol_xx.ms'\n",
        "outfile = 'sis14_twhya_field_5_lsrk_pol_xx.vis.zarr'\n",
        "chunk_shape=(270, 210, 12, 1)\n",
        "convert_ms(infile, outfile=outfile , chunk_shape=chunk_shape)\n",
        "```\n",
        "\n",
        "The conversion to 'LSRK' is necessary because cngi does not currently have an implementation and tclean does a conversion to 'LSRK' before imaging.\n",
        "\n",
        "To check the ngcasa imaging results the casa_twhya_standard_gridder_lsrk_mfs_natural.img.zarr dataset is used. This dataset was generated by running ```tclean``` in CASA\n",
        "\n",
        "```python\n",
        "tclean(vis='sis14_twhya_field_5_lsrk_pol_xx.ms',\n",
        "       imagename='twhya_standard_gridder_lsrk_mfs_natural',\n",
        "       specmode='mfs',\n",
        "       deconvolver='hogbom',\n",
        "       imsize=[200,400],\n",
        "       cell=['0.08arcsec'],\n",
        "       weighting='natural',\n",
        "       threshold='0mJy',\n",
        "       niter=0,stokes='XX')\n",
        "```\n",
        "\n",
        "and then ```image_ms``` in cngi.conversion\n",
        "\n",
        "```python\n",
        "infile = 'twhya_standard_gridder_lsrk_mfs_natural.image'\n",
        "outfile = 'casa_twhya_standard_gridder_lsrk_mfs_natural.img.zarr'\n",
        "convert_image(infile=infile,outfile=outfile)\n",
        "```"
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      "source": [
        "import xarray as xr\n",
        "from cngi.dio import read_vis, read_image\n",
        "\n",
        "xr.set_options(display_style=\"html\")\n",
        "\n",
        "mxds = read_vis(\"sis14_twhya_field_5_lsrk_pol_xx.vis.zarr\",chunks={'chan':192})\n",
        "mxds.xds0"
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              "</style><div class='xr-wrap'><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-fb7734b2-4ed2-495b-aa40-d091f3dace80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb7734b2-4ed2-495b-aa40-d091f3dace80' 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'>baseline</span>: 210</li><li><span class='xr-has-index'>chan</span>: 384</li><li><span class='xr-has-index'>pol</span>: 1</li><li><span class='xr-has-index'>pol_id</span>: 1</li><li><span class='xr-has-index'>spw_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 270</li><li><span>uvw_index</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8d4ecd99-22a9-4f20-8038-4e69a2b4a005' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d4ecd99-22a9-4f20-8038-4e69a2b4a005' class='xr-section-summary' >Coordinates: <span>(9)</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'>baseline</span></div><div class='xr-var-dims'>(baseline)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 205 206 207 208 209</div><input id='attrs-ca009dd1-aabe-4e84-a32d-58995ee3cc30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca009dd1-aabe-4e84-a32d-58995ee3cc30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2eb4a4a-0ecd-4236-86c4-61b167b603ad' class='xr-var-data-in' type='checkbox'><label for='data-e2eb4a4a-0ecd-4236-86c4-61b167b603ad' title='Show/Hide data 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><pre class='xr-var-data'>array([  0,   1,   2, ..., 207, 208, 209])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chan</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.725e+11 3.725e+11 ... 3.728e+11</div><input id='attrs-19ae78c1-e3bc-48ca-b325-2a82b125f1b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19ae78c1-e3bc-48ca-b325-2a82b125f1b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee260825-a7e0-4a65-bd20-090b3cf92a1b' class='xr-var-data-in' type='checkbox'><label for='data-ee260825-a7e0-4a65-bd20-090b3cf92a1b' title='Show/Hide data 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><pre class='xr-var-data'>array([3.725200e+11, 3.725206e+11, 3.725212e+11, ..., 3.727526e+11,\n",
              "       3.727532e+11, 3.727538e+11])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>chan_width</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192,), meta=np.ndarray&gt;</div><input id='attrs-a7cd7727-fd6b-444a-91a4-33659943b302' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7cd7727-fd6b-444a-91a4-33659943b302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97e74ae2-a351-493a-9563-c13ba5675775' class='xr-var-data-in' type='checkbox'><label for='data-97e74ae2-a351-493a-9563-c13ba5675775' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 3.07 kB </td> <td> 1.54 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (384,) </td> <td> (192,) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\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\" >384</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></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_bw</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192,), meta=np.ndarray&gt;</div><input id='attrs-ef0993c9-970a-4f46-9e3d-d403a0b2c35c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef0993c9-970a-4f46-9e3d-d403a0b2c35c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c06759-659b-4082-8da6-cd3c27e194e9' class='xr-var-data-in' type='checkbox'><label for='data-b1c06759-659b-4082-8da6-cd3c27e194e9' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 3.07 kB </td> <td> 1.54 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (384,) </td> <td> (192,) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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",
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              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
              "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
              "\n",
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              "  <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",
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              "  <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></pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pol</span></div><div class='xr-var-dims'>(pol)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>9</div><input id='attrs-ba41d79a-255b-475a-be85-437856c2eb33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba41d79a-255b-475a-be85-437856c2eb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-184a9a0a-8049-4ab8-b375-6d6e59c83c3a' class='xr-var-data-in' type='checkbox'><label for='data-184a9a0a-8049-4ab8-b375-6d6e59c83c3a' title='Show/Hide data 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><pre class='xr-var-data'>array([9], dtype=int32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pol_id</span></div><div class='xr-var-dims'>(pol_id)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2293875a-4057-4027-b465-1735e02fa6c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2293875a-4057-4027-b465-1735e02fa6c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b291afe7-ffee-4e73-9f40-cf3b656a81e3' class='xr-var-data-in' type='checkbox'><label for='data-b291afe7-ffee-4e73-9f40-cf3b656a81e3' title='Show/Hide data 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><pre class='xr-var-data'>array([0], dtype=int32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>resolution</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192,), meta=np.ndarray&gt;</div><input id='attrs-e82d8098-40d8-4cd6-b69e-89e4fbaa8def' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e82d8098-40d8-4cd6-b69e-89e4fbaa8def' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81410d53-abff-412b-9068-9720e2288c8a' class='xr-var-data-in' type='checkbox'><label for='data-81410d53-abff-412b-9068-9720e2288c8a' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 3.07 kB </td> <td> 1.54 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (384,) </td> <td> (192,) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\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\" >384</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></pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spw_id</span></div><div class='xr-var-dims'>(spw_id)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-e4e750aa-8123-4560-9c2a-f47a677d6b85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4e750aa-8123-4560-9c2a-f47a677d6b85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b894db69-270f-41cd-88b0-30d73a367b19' class='xr-var-data-in' type='checkbox'><label for='data-b894db69-270f-41cd-88b0-30d73a367b19' title='Show/Hide data 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><pre class='xr-var-data'>array([0], dtype=int32)</pre></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'>2012-11-19T07:56:26.544000626 ... 2012-11-19T09:07:28.607999802</div><input id='attrs-698492d2-653e-4c8c-9fac-60c8c0f5716c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-698492d2-653e-4c8c-9fac-60c8c0f5716c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bd2a78f-bc84-4130-8c2a-f9916bd0446f' class='xr-var-data-in' type='checkbox'><label for='data-9bd2a78f-bc84-4130-8c2a-f9916bd0446f' title='Show/Hide data 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><pre class='xr-var-data'>array([&#x27;2012-11-19T07:56:26.544000626&#x27;, &#x27;2012-11-19T07:56:32.592000008&#x27;,\n",
              "       &#x27;2012-11-19T07:56:38.640000343&#x27;, ..., &#x27;2012-11-19T09:07:16.512000082&#x27;,\n",
              "       &#x27;2012-11-19T09:07:22.560000420&#x27;, &#x27;2012-11-19T09:07:28.607999802&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-87e49dc9-f5d4-467e-9b7a-0833d2598d4a' class='xr-section-summary-in' type='checkbox'  ><label for='section-87e49dc9-f5d4-467e-9b7a-0833d2598d4a' class='xr-section-summary' >Data variables: <span>(17)</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>ANTENNA1</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-c737a2d2-09eb-4387-ac8d-1df5f88a3f79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c737a2d2-09eb-4387-ac8d-1df5f88a3f79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da8ffac0-f918-4b90-828a-db99083199d6' class='xr-var-data-in' type='checkbox'><label for='data-da8ffac0-f918-4b90-828a-db99083199d6' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</td>\n",
              "<td>\n",
              "<svg width=\"143\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
              "\n",
              "  <!-- Horizontal lines -->\n",
              "  <line x1=\"0\" y1=\"0\" x2=\"93\" y2=\"0\" style=\"stroke-width:2\" />\n",
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              "\n",
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              "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"120\" style=\"stroke-width:2\" />\n",
              "\n",
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              "\n",
              "  <!-- Text -->\n",
              "  <text x=\"46.666667\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >210</text>\n",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>ANTENNA2</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-bb35ab7f-2531-4554-814f-cbe4fe181d74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb35ab7f-2531-4554-814f-cbe4fe181d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-070005b0-8683-4233-a1b5-30ddc9e57884' class='xr-var-data-in' type='checkbox'><label for='data-070005b0-8683-4233-a1b5-30ddc9e57884' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</td>\n",
              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>ARRAY_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-51ac1130-fb1e-4f19-be4d-80a0cfbd4427' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51ac1130-fb1e-4f19-be4d-80a0cfbd4427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9c6e5ca-f3c5-4840-afcc-9f51596882cf' class='xr-var-data-in' type='checkbox'><label for='data-d9c6e5ca-f3c5-4840-afcc-9f51596882cf' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>DATA</span></div><div class='xr-var-dims'>(time, baseline, chan, pol)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 192, 1), meta=np.ndarray&gt;</div><input id='attrs-5b99c46d-9ba3-40b1-9d52-d4211881034d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b99c46d-9ba3-40b1-9d52-d4211881034d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10f7eaf7-8682-4b55-ac3b-5552673fd292' class='xr-var-data-in' type='checkbox'><label for='data-10f7eaf7-8682-4b55-ac3b-5552673fd292' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 348.36 MB </td> <td> 174.18 MB </td></tr>\n",
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              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> complex128 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>EXPOSURE</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-5bbae791-74a5-4995-bf4b-78da4bbd6cab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bbae791-74a5-4995-bf4b-78da4bbd6cab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2f8879f-3bc2-46c2-b534-e1965260bc2d' class='xr-var-data-in' type='checkbox'><label for='data-f2f8879f-3bc2-46c2-b534-e1965260bc2d' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 453.60 kB </td> <td> 453.60 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FEED1</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-d783aa34-079b-46ef-8e94-2b2edb42c89c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d783aa34-079b-46ef-8e94-2b2edb42c89c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82f9b7f2-c2d2-4af9-bf3e-7a4479ed8323' class='xr-var-data-in' type='checkbox'><label for='data-82f9b7f2-c2d2-4af9-bf3e-7a4479ed8323' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
              "<svg width=\"143\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FEED2</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-e96fd564-0159-4b23-b526-4f3dc13059a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e96fd564-0159-4b23-b526-4f3dc13059a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aff0649-9d14-4166-86aa-d9f86139cda1' class='xr-var-data-in' type='checkbox'><label for='data-9aff0649-9d14-4166-86aa-d9f86139cda1' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
              "  <thead>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "<td>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FIELD_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-bd314011-1dd0-488a-984a-1c629da1d12e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd314011-1dd0-488a-984a-1c629da1d12e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2abecd7c-a301-4696-9385-fd46f8b4add6' class='xr-var-data-in' type='checkbox'><label for='data-2abecd7c-a301-4696-9385-fd46f8b4add6' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
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              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FLAG</span></div><div class='xr-var-dims'>(time, baseline, chan, pol)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 192, 1), meta=np.ndarray&gt;</div><input id='attrs-887ede24-c3f8-45bb-a7a1-1209480fe3a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-887ede24-c3f8-45bb-a7a1-1209480fe3a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87c9b1ea-5739-464d-9477-edc157379137' class='xr-var-data-in' type='checkbox'><label for='data-87c9b1ea-5739-464d-9477-edc157379137' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
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              "  <thead>\n",
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              "    <tr><th> Type </th><td> bool </td><td> numpy.ndarray </td></tr>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>INTERVAL</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-6897a521-ac9c-47e5-94a5-cc6e4e3edc8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6897a521-ac9c-47e5-94a5-cc6e4e3edc8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83b4a694-f3a0-48a1-a630-e1a9d61d2b2a' class='xr-var-data-in' type='checkbox'><label for='data-83b4a694-f3a0-48a1-a630-e1a9d61d2b2a' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
              "  <thead>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>OBSERVATION_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-56961b88-2033-47f2-ad65-8f5fbdc5abf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56961b88-2033-47f2-ad65-8f5fbdc5abf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a2dc257-7678-4a9e-9179-32198ce57763' class='xr-var-data-in' type='checkbox'><label for='data-3a2dc257-7678-4a9e-9179-32198ce57763' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>PROCESSOR_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-08d8b163-72b5-4213-8d1f-19f2dac2a47c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08d8b163-72b5-4213-8d1f-19f2dac2a47c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad98f30b-124a-40d6-a071-ec5371bc3b55' class='xr-var-data-in' type='checkbox'><label for='data-ad98f30b-124a-40d6-a071-ec5371bc3b55' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>SCAN_NUMBER</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-27f1ad7a-2d34-499c-86fc-b65778972065' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27f1ad7a-2d34-499c-86fc-b65778972065' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a857f2a-abcc-4baa-85a9-97c55fb072e3' class='xr-var-data-in' type='checkbox'><label for='data-4a857f2a-abcc-4baa-85a9-97c55fb072e3' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "<td>\n",
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              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>STATE_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-1084a4ce-7677-49e7-9b2a-a72df36098ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1084a4ce-7677-49e7-9b2a-a72df36098ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85a1d73e-9d0b-4ce2-81be-f80ee4969940' class='xr-var-data-in' type='checkbox'><label for='data-85a1d73e-9d0b-4ce2-81be-f80ee4969940' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>TIME_CENTROID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-59518c6b-caf1-4022-b083-61184d5621ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59518c6b-caf1-4022-b083-61184d5621ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f27ea3c8-497b-473e-a48c-924065e6689a' class='xr-var-data-in' type='checkbox'><label for='data-f27ea3c8-497b-473e-a48c-924065e6689a' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>UVW</span></div><div class='xr-var-dims'>(time, baseline, uvw_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 3), meta=np.ndarray&gt;</div><input id='attrs-3015f122-29fa-4762-8f54-197b40d4df5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3015f122-29fa-4762-8f54-197b40d4df5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b3356e-80b7-4d67-9460-f316c2161d43' class='xr-var-data-in' type='checkbox'><label for='data-e9b3356e-80b7-4d67-9460-f316c2161d43' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>WEIGHT</span></div><div class='xr-var-dims'>(time, baseline, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 192, 1), meta=np.ndarray&gt;</div><input id='attrs-89fd5195-73dd-4ca6-bb5b-c5ecb77c342f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89fd5195-73dd-4ca6-bb5b-c5ecb77c342f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dd370fb-f920-46ae-b97a-e5ac79e26d10' class='xr-var-data-in' type='checkbox'><label for='data-9dd370fb-f920-46ae-b97a-e5ac79e26d10' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
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            ],
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:         (baseline: 210, chan: 384, pol: 1, pol_id: 1, spw_id: 1, time: 270, uvw_index: 3)\n",
              "Coordinates:\n",
              "  * baseline        (baseline) int64 0 1 2 3 4 5 6 ... 204 205 206 207 208 209\n",
              "  * chan            (chan) float64 3.725e+11 3.725e+11 ... 3.728e+11 3.728e+11\n",
              "    chan_width      (chan) float64 dask.array<chunksize=(192,), meta=np.ndarray>\n",
              "    effective_bw    (chan) float64 dask.array<chunksize=(192,), meta=np.ndarray>\n",
              "  * pol             (pol) int32 9\n",
              "  * pol_id          (pol_id) int32 0\n",
              "    resolution      (chan) float64 dask.array<chunksize=(192,), meta=np.ndarray>\n",
              "  * spw_id          (spw_id) int32 0\n",
              "  * time            (time) datetime64[ns] 2012-11-19T07:56:26.544000626 ... 2012-11-19T09:07:28.607999802\n",
              "Dimensions without coordinates: uvw_index\n",
              "Data variables:\n",
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              "    ANTENNA2        (time, baseline) int32 dask.array<chunksize=(270, 210), meta=np.ndarray>\n",
              "    ARRAY_ID        (time, baseline) int32 dask.array<chunksize=(270, 210), meta=np.ndarray>\n",
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              "    EXPOSURE        (time, baseline) float64 dask.array<chunksize=(270, 210), meta=np.ndarray>\n",
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              "    WEIGHT          (time, baseline, chan, pol) float64 dask.array<chunksize=(270, 210, 192, 1), meta=np.ndarray>\n",
              "Attributes:\n",
              "    bbc_no:           2\n",
              "    corr_product:     [[0, 0]]\n",
              "    data_groups:      [{'1': {'data': 'DATA', 'flag': 'FLAG', 'id': '1', 'uvw...\n",
              "    freq_group:       0\n",
              "    freq_group_name:  \n",
              "    if_conv_chain:    0\n",
              "    meas_freq_ref:    1\n",
              "    name:             ALMA_RB_07#BB_2#SW-01#FULL_RES\n",
              "    net_sideband:     2\n",
              "    num_chan:         384\n",
              "    num_corr:         1\n",
              "    ref_frequency:    372520022603.63745\n",
              "    total_bandwidth:  234366781.0546875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUScKAZTsAo0"
      },
      "source": [
        "Note that the chunks parameter in cngi and ngcasa functions specifies the size of a chunk and not the number of chunks (in CASA ```tclean``` chanchunks refers to the number of channel chunks).\n",
        "\n",
        "The dimensionality of the sis14_twhya_field_5_lsrk_pol_xx.vis.zarr dataset is (time:270,baseline:210,chan:384,pol:1) and a zarr chunk size of (time:270,baseline:210,chan:12,pol:1) was chosen. With the ```cngi.dio.read_vis``` function the dask chunk size was set to (time:270,baseline:210,chan:192,pol:1). For more information concerning chunking go [here](https://cngi-prototype.readthedocs.io/en/latest/development.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SML0aGlkesmv"
      },
      "source": [
        "## Flag Data and Create Imaging Weights \n",
        "The ```apply_flags``` cngi.vis function sets all values that should be flagged to nan. The ngcasa.imaging code does not internally apply flags but does ignore nan values. [apply_flags documentation](https://cngi-prototype.readthedocs.io/en/latest/_api/autoapi/cngi/vis/apply_flags/index.html#cngi.vis.apply_flags.apply_flags)\n",
        "\n",
        "The ```make_imaging_weight``` cngi.imaging function takes the WEIGHT data variables and creates IMAGING_WEIGHT data variable that has dimensions time x baseline x chan x pol (matches the visibility DATA variable). Weighting schemes that are supported include natural, uniform, briggs, briggs_abs. [make_imaging_weight documentation](https://cngi-prototype.readthedocs.io/en/latest/_api/autoapi/ngcasa/imaging/make_imaging_weight/index.html#ngcasa.imaging.make_imaging_weight.make_imaging_weight)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "u235e4Bvesmv",
        "outputId": "134f21bf-4768-4c2b-f183-ed7e23c8c770"
      },
      "source": [
        "from cngi.vis import apply_flags\n",
        "from ngcasa.imaging import make_imaging_weight\n",
        "\n",
        "mxds = apply_flags(mxds, 'xds0', flags='FLAG')\n",
        "\n",
        "imaging_weights_parms = {}\n",
        "imaging_weights_parms['weighting'] = 'natural'\n",
        "\n",
        "sel_parms = {}\n",
        "sel_parms['xds'] = 'xds0'\n",
        "sel_parms['data_group_in_id'] = 1 \n",
        "\n",
        "grid_parms = {}\n",
        "grid_parms['chan_mode'] = 'continuum'\n",
        "grid_parms['image_size'] = [200,400]\n",
        "grid_parms['cell_size'] = [0.08,0.08]\n",
        "\n",
        "mxds = make_imaging_weight(mxds, imaging_weights_parms, grid_parms, sel_parms)\n",
        "\n",
        "mxds.xds0"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "######################### Start make_imaging_weights #########################\n",
            "Setting data_group_in  to  {'data': 'DATA', 'flag': 'FLAG', 'id': '1', 'uvw': 'UVW', 'weight': 'WEIGHT'}\n",
            "Setting default data_group_out  to  {'data': 'DATA', 'flag': 'FLAG', 'id': '1', 'uvw': 'UVW', 'weight': 'WEIGHT', 'imaging_weight': 'IMAGING_WEIGHT'}\n",
            "Since weighting is natural input weight will be reused as imaging weight.\n",
            "######################### Created graph for make_imaging_weight #########################\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "</style><div class='xr-wrap'><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-f7246baa-449d-4f29-8cfa-12dab3b749a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7246baa-449d-4f29-8cfa-12dab3b749a0' 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'>baseline</span>: 210</li><li><span class='xr-has-index'>chan</span>: 384</li><li><span class='xr-has-index'>pol</span>: 1</li><li><span class='xr-has-index'>pol_id</span>: 1</li><li><span class='xr-has-index'>spw_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 270</li><li><span>uvw_index</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a2266cbc-5af1-4256-aee7-19a55a2bd6d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2266cbc-5af1-4256-aee7-19a55a2bd6d3' class='xr-section-summary' >Coordinates: <span>(9)</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'>baseline</span></div><div class='xr-var-dims'>(baseline)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 205 206 207 208 209</div><input id='attrs-a455c96c-d42b-43f8-a37e-5cb5f7285a61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a455c96c-d42b-43f8-a37e-5cb5f7285a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f418de1-754e-4b85-a445-d95ee9b4ef28' class='xr-var-data-in' type='checkbox'><label for='data-3f418de1-754e-4b85-a445-d95ee9b4ef28' title='Show/Hide data 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><pre class='xr-var-data'>array([  0,   1,   2, ..., 207, 208, 209])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chan</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.725e+11 3.725e+11 ... 3.728e+11</div><input id='attrs-3c43e0f8-6a69-4a47-8c9e-7692d3783d3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c43e0f8-6a69-4a47-8c9e-7692d3783d3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a1fffe9-2c77-4462-b0b5-bf1dc9175614' class='xr-var-data-in' type='checkbox'><label for='data-7a1fffe9-2c77-4462-b0b5-bf1dc9175614' title='Show/Hide data 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><pre class='xr-var-data'>array([3.725200e+11, 3.725206e+11, 3.725212e+11, ..., 3.727526e+11,\n",
              "       3.727532e+11, 3.727538e+11])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>chan_width</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192,), meta=np.ndarray&gt;</div><input id='attrs-b71c73bb-a80c-429c-a812-93da37085435' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b71c73bb-a80c-429c-a812-93da37085435' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7a1c8c-d62a-455c-98a8-96a8c45b532d' class='xr-var-data-in' type='checkbox'><label for='data-1a7a1c8c-d62a-455c-98a8-96a8c45b532d' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
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              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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              "    <tr><th> Shape </th><td> (384,) </td> <td> (192,) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_bw</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192,), meta=np.ndarray&gt;</div><input id='attrs-938f43f1-39a1-40d6-8f35-9562a5ae2fe6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-938f43f1-39a1-40d6-8f35-9562a5ae2fe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0856eb55-cee1-4a59-9979-24b34442362d' class='xr-var-data-in' type='checkbox'><label for='data-0856eb55-cee1-4a59-9979-24b34442362d' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 3.07 kB </td> <td> 1.54 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (384,) </td> <td> (192,) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pol</span></div><div class='xr-var-dims'>(pol)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>9</div><input id='attrs-7f9860b2-0978-4ea3-b984-e7e3f6330bf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f9860b2-0978-4ea3-b984-e7e3f6330bf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-715c18cc-67e8-4264-ace4-eae3d9b5806f' class='xr-var-data-in' type='checkbox'><label for='data-715c18cc-67e8-4264-ace4-eae3d9b5806f' title='Show/Hide data 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><pre class='xr-var-data'>array([9], dtype=int32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pol_id</span></div><div class='xr-var-dims'>(pol_id)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f03a5a44-2640-4fbb-86cf-411f78186694' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f03a5a44-2640-4fbb-86cf-411f78186694' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae65e636-148f-4576-b74b-9cbcc43bf5e9' class='xr-var-data-in' type='checkbox'><label for='data-ae65e636-148f-4576-b74b-9cbcc43bf5e9' title='Show/Hide data 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><pre class='xr-var-data'>array([0], dtype=int32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>resolution</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192,), meta=np.ndarray&gt;</div><input id='attrs-a31b42e5-e25c-4cf2-9612-d164a144768d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a31b42e5-e25c-4cf2-9612-d164a144768d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-259644b8-1d32-462c-affa-68cc3f67f05d' class='xr-var-data-in' type='checkbox'><label for='data-259644b8-1d32-462c-affa-68cc3f67f05d' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 3.07 kB </td> <td> 1.54 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (384,) </td> <td> (192,) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "<td>\n",
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              "</td>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spw_id</span></div><div class='xr-var-dims'>(spw_id)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4a7f39a0-4eaa-4ba8-806b-a986dc2a3256' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a7f39a0-4eaa-4ba8-806b-a986dc2a3256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e42ba341-2a1c-4c3f-9f3b-40e3c766bb89' class='xr-var-data-in' type='checkbox'><label for='data-e42ba341-2a1c-4c3f-9f3b-40e3c766bb89' title='Show/Hide data 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><pre class='xr-var-data'>array([0], dtype=int32)</pre></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'>2012-11-19T07:56:26.544000626 ... 2012-11-19T09:07:28.607999802</div><input id='attrs-ce53d506-bd29-4fe8-8ebf-a168561705ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce53d506-bd29-4fe8-8ebf-a168561705ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2fb58cd-fe4a-49c2-911e-f4fe40be2efe' class='xr-var-data-in' type='checkbox'><label for='data-e2fb58cd-fe4a-49c2-911e-f4fe40be2efe' title='Show/Hide data 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><pre class='xr-var-data'>array([&#x27;2012-11-19T07:56:26.544000626&#x27;, &#x27;2012-11-19T07:56:32.592000008&#x27;,\n",
              "       &#x27;2012-11-19T07:56:38.640000343&#x27;, ..., &#x27;2012-11-19T09:07:16.512000082&#x27;,\n",
              "       &#x27;2012-11-19T09:07:22.560000420&#x27;, &#x27;2012-11-19T09:07:28.607999802&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-9fdd0f03-202a-4fb5-af4b-5a11f8d612d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-9fdd0f03-202a-4fb5-af4b-5a11f8d612d6' class='xr-section-summary' >Data variables: <span>(17)</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>ANTENNA1</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-2200c80b-29b0-4246-acf0-0e1f11dadf18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2200c80b-29b0-4246-acf0-0e1f11dadf18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f10d1ddd-fd6a-45ab-b1b1-ecf83bb44d11' class='xr-var-data-in' type='checkbox'><label for='data-f10d1ddd-fd6a-45ab-b1b1-ecf83bb44d11' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "<td>\n",
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              "  <thead>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>DATA</span></div><div class='xr-var-dims'>(time, baseline, chan, pol)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 192, 1), meta=np.ndarray&gt;</div><input id='attrs-f2362b84-8408-4d3d-8a9c-b0882a91fb91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2362b84-8408-4d3d-8a9c-b0882a91fb91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-394a7a89-65e9-4604-a343-0777b0a89e9b' class='xr-var-data-in' type='checkbox'><label for='data-394a7a89-65e9-4604-a343-0777b0a89e9b' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 348.36 MB </td> <td> 174.18 MB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210, 384, 1) </td> <td> (270, 210, 192, 1) </td></tr>\n",
              "    <tr><th> Count </th><td> 10 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> complex128 </td><td> numpy.ndarray </td></tr>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>EXPOSURE</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-09124bcf-89bd-491a-8d64-24b5301d33bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09124bcf-89bd-491a-8d64-24b5301d33bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e625a4-0337-4fd4-a294-7fc2c3dbfd4e' class='xr-var-data-in' type='checkbox'><label for='data-65e625a4-0337-4fd4-a294-7fc2c3dbfd4e' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 453.60 kB </td> <td> 453.60 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FEED1</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-41cabf9c-fe7d-4bcc-9210-d347e40e76f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41cabf9c-fe7d-4bcc-9210-d347e40e76f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-928d7c1b-d861-4db2-b2a0-f7edb7c2b82b' class='xr-var-data-in' type='checkbox'><label for='data-928d7c1b-d861-4db2-b2a0-f7edb7c2b82b' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FEED2</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-bd104b2f-e9b9-4a68-9637-5e8ade607c8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd104b2f-e9b9-4a68-9637-5e8ade607c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61898aaf-2508-425c-a481-c922f4d1ffad' class='xr-var-data-in' type='checkbox'><label for='data-61898aaf-2508-425c-a481-c922f4d1ffad' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FIELD_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-2d92ffaf-a6ca-4516-9c2a-33dece73c99b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d92ffaf-a6ca-4516-9c2a-33dece73c99b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ded38aff-62f9-4d20-a6e1-993875c35d81' class='xr-var-data-in' type='checkbox'><label for='data-ded38aff-62f9-4d20-a6e1-993875c35d81' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
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              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "<td>\n",
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              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FLAG</span></div><div class='xr-var-dims'>(time, baseline, chan, pol)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 192, 1), meta=np.ndarray&gt;</div><input id='attrs-697e8d44-9044-42c4-b6a4-f2699715e0e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-697e8d44-9044-42c4-b6a4-f2699715e0e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37fe3ece-e88f-4c87-b366-4c353687f2ef' class='xr-var-data-in' type='checkbox'><label for='data-37fe3ece-e88f-4c87-b366-4c353687f2ef' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
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              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> bool </td><td> numpy.ndarray </td></tr>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>INTERVAL</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-fa952324-e424-49cc-8b83-2da2191fe640' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa952324-e424-49cc-8b83-2da2191fe640' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b08aac4-f8bd-4110-b761-bc995181cd9f' class='xr-var-data-in' type='checkbox'><label for='data-7b08aac4-f8bd-4110-b761-bc995181cd9f' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
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              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>OBSERVATION_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-36933b4a-d4da-46a4-8a0e-f1b924436b53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36933b4a-d4da-46a4-8a0e-f1b924436b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2268c233-9386-4f21-9c32-4cb059bb49b8' class='xr-var-data-in' type='checkbox'><label for='data-2268c233-9386-4f21-9c32-4cb059bb49b8' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>PROCESSOR_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-4ee2c10d-e19d-4223-8129-445730061350' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ee2c10d-e19d-4223-8129-445730061350' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f3dcbd1-508c-4553-b6a8-461fa467a096' class='xr-var-data-in' type='checkbox'><label for='data-7f3dcbd1-508c-4553-b6a8-461fa467a096' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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              "</table>\n",
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              "<td>\n",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>SCAN_NUMBER</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-af108227-210d-4deb-9122-6e26897e21d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af108227-210d-4deb-9122-6e26897e21d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f633788-7013-484a-b1e5-0262767bcfbd' class='xr-var-data-in' type='checkbox'><label for='data-4f633788-7013-484a-b1e5-0262767bcfbd' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "<td>\n",
              "<svg width=\"143\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>STATE_ID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-7e3500c2-eb85-4ca5-923c-1e78175e5ef6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e3500c2-eb85-4ca5-923c-1e78175e5ef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce43ece5-64ef-4dbf-9754-482d20343c74' class='xr-var-data-in' type='checkbox'><label for='data-ce43ece5-64ef-4dbf-9754-482d20343c74' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 226.80 kB </td> <td> 226.80 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</td>\n",
              "<td>\n",
              "<svg width=\"143\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
              "\n",
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              "</svg>\n",
              "</td>\n",
              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>TIME_CENTROID</span></div><div class='xr-var-dims'>(time, baseline)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210), meta=np.ndarray&gt;</div><input id='attrs-d04dc73f-25d1-4a52-944a-295963c8a89f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d04dc73f-25d1-4a52-944a-295963c8a89f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb59f52-73a5-4639-a818-3bc0c2b2c902' class='xr-var-data-in' type='checkbox'><label for='data-4bb59f52-73a5-4639-a818-3bc0c2b2c902' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 453.60 kB </td> <td> 453.60 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (270, 210) </td> <td> (270, 210) </td></tr>\n",
              "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "<td>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>UVW</span></div><div class='xr-var-dims'>(time, baseline, uvw_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(270, 210, 3), meta=np.ndarray&gt;</div><input id='attrs-f40c8de1-aefc-48c0-bc3b-83c25eedcd4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f40c8de1-aefc-48c0-bc3b-83c25eedcd4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb201ee4-8667-4956-bd59-e63fd0cf1436' class='xr-var-data-in' type='checkbox'><label for='data-fb201ee4-8667-4956-bd59-e63fd0cf1436' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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            ],
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:         (baseline: 210, chan: 384, pol: 1, pol_id: 1, spw_id: 1, time: 270, uvw_index: 3)\n",
              "Coordinates:\n",
              "  * baseline        (baseline) int64 0 1 2 3 4 5 6 ... 204 205 206 207 208 209\n",
              "  * chan            (chan) float64 3.725e+11 3.725e+11 ... 3.728e+11 3.728e+11\n",
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              "    effective_bw    (chan) float64 dask.array<chunksize=(192,), meta=np.ndarray>\n",
              "  * pol             (pol) int32 9\n",
              "  * pol_id          (pol_id) int32 0\n",
              "    resolution      (chan) float64 dask.array<chunksize=(192,), meta=np.ndarray>\n",
              "  * spw_id          (spw_id) int32 0\n",
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              "Dimensions without coordinates: uvw_index\n",
              "Data variables:\n",
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              "    ANTENNA2        (time, baseline) int32 dask.array<chunksize=(270, 210), meta=np.ndarray>\n",
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              "    UVW             (time, baseline, uvw_index) float64 dask.array<chunksize=(270, 210, 3), meta=np.ndarray>\n",
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              "Attributes:\n",
              "    bbc_no:           2\n",
              "    corr_product:     [[0, 0]]\n",
              "    data_groups:      [{'1': {'data': 'DATA', 'flag': 'FLAG', 'id': '1', 'uvw...\n",
              "    freq_group:       0\n",
              "    freq_group_name:  \n",
              "    if_conv_chain:    0\n",
              "    meas_freq_ref:    1\n",
              "    name:             ALMA_RB_07#BB_2#SW-01#FULL_RES\n",
              "    net_sideband:     2\n",
              "    num_chan:         384\n",
              "    num_corr:         1\n",
              "    ref_frequency:    372520022603.63745\n",
              "    total_bandwidth:  234366781.0546875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLeHWWXGesmz"
      },
      "source": [
        "## Create Dirty Continuum Image and Primary Beam\n",
        "The ```make_image``` cngi.imaging function grids the data (using the prolate spheroidal function as an anti-aliasing filter), fast Fourier transform the gridded data to an image and normalizes the image. The ```make_pb``` function currently supports rotationally symmetric airy disk primary beams. The ```write_zarr``` function is now used to trigger a compute (which includes applying the flags, creating the imaging weights and making the image).\n",
        "\n",
        "[make_pb documentation](https://cngi-prototype.readthedocs.io/en/latest/_api/autoapi/ngcasa/imaging/make_mosaic_pb/index.html#ngcasa.imaging.make_mosaic_pb.make_mosaic_pb)\n",
        "\n",
        "[make_image documentation](https://cngi-prototype.readthedocs.io/en/latest/_api/autoapi/ngcasa/imaging/make_image/index.html#ngcasa.imaging.make_image.make_imagel)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jOzH8uBesm0",
        "outputId": "fb049367-d40e-4ffc-a643-7d715ec3f46b"
      },
      "source": [
        "from ngcasa.imaging import make_image\n",
        "from ngcasa.imaging import make_pb\n",
        "from cngi.dio import write_zarr\n",
        "import dask\n",
        "\n",
        "grid_parms = {}\n",
        "grid_parms['chan_mode'] = 'continuum'\n",
        "grid_parms['image_size'] = [200,400]\n",
        "grid_parms['cell_size'] = [0.08,0.08]\n",
        "grid_parms['phase_center'] = mxds.FIELD.PHASE_DIR[0,0,:].data.compute()\n",
        "\n",
        "vis_sel_parms = {}\n",
        "vis_sel_parms['xds'] = 'xds0'\n",
        "vis_sel_parms['data_group_in_id'] = 1\n",
        "\n",
        "img_sel_parms = {}\n",
        "img_sel_parms['data_group_out_id'] = 1\n",
        "\n",
        "img_xds = xr.Dataset() #empty dataset\n",
        "\n",
        "img_xds = make_image(mxds, img_xds, grid_parms, vis_sel_parms, img_sel_parms)\n",
        "\n",
        "\n",
        "make_pb_parms = {}\n",
        "make_pb_parms['function'] = 'alma_airy'\n",
        "make_pb_parms['list_dish_diameters'] = [10.7]\n",
        "make_pb_parms['list_blockage_diameters'] = [0.75]\n",
        "\n",
        "sel_parms = {}\n",
        "sel_parms['img_description_in_indx'] = 1\n",
        "\n",
        "\n",
        "img_xds = make_pb(img_xds,make_pb_parms, grid_parms, sel_parms)\n",
        "\n",
        "\n",
        "img_xds = write_zarr(img_xds, 'twhya_standard_gridder_lsrk_mfs_natural.img.zarr')\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "######################### Start make_image #########################\n",
            "Setting default image_center  to  [100 200]\n",
            "Setting default fft_padding  to  1.2\n",
            "Setting data_group_in  to  {'data': 'DATA', 'flag': 'FLAG', 'id': '1', 'uvw': 'UVW', 'weight': 'WEIGHT', 'imaging_weight': 'WEIGHT'}\n",
            "Setting default data_group_out  to  {'data': 'DATA', 'flag': 'FLAG', 'id': '2', 'uvw': 'UVW', 'weight': 'WEIGHT', 'imaging_weight': 'WEIGHT'}\n",
            "Setting default data_group_in  to  {'id': '1'}\n",
            "Setting default data_group_out [' sum_weight ']  to  SUM_WEIGHT\n",
            "Setting default data_group_out [' image ']  to  IMAGE\n",
            "######################### Created graph for make_image #########################\n",
            "######################### Start make_pb #########################\n",
            "Setting default data_group_in  to  {'id': '1', 'sum_weight': 'SUM_WEIGHT', 'image': 'IMAGE'}\n",
            "Setting default data_group_out  to  {'id': '1', 'sum_weight': 'SUM_WEIGHT', 'image': 'IMAGE', 'pb': 'PB'}\n",
            "Setting default image_center  to  [100 200]\n",
            "Setting default fft_padding  to  1.2\n",
            "######################### Created graph for make_pb #########################\n",
            "Time to store and execute graph  write_zarr 6.771796226501465\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2pZlsP4BmRw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "6789cf2f-abc9-42bd-bdc7-f7781367039b"
      },
      "source": [
        "casa_image_dataset = read_image(\"casa_twhya_standard_gridder_lsrk_mfs_natural.img.zarr\")\n",
        "casa_image_dataset"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style><div class='xr-wrap'><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-bfaa5150-aa2e-4d7a-9557-9272e4ea1c79' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bfaa5150-aa2e-4d7a-9557-9272e4ea1c79' 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'>chan</span>: 1</li><li><span class='xr-has-index'>l</span>: 200</li><li><span class='xr-has-index'>m</span>: 400</li><li><span class='xr-has-index'>pol</span>: 1</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-27de03d3-6f09-4ac0-840b-f703c4fa8e48' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27de03d3-6f09-4ac0-840b-f703c4fa8e48' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chan</span></div><div class='xr-var-dims'>(chan)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.726e+11</div><input id='attrs-215e7ac3-77d2-4be9-ad2c-d74dae8641bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-215e7ac3-77d2-4be9-ad2c-d74dae8641bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-465758aa-f513-4283-9be7-c62345562472' class='xr-var-data-in' type='checkbox'><label for='data-465758aa-f513-4283-9be7-c62345562472' title='Show/Hide data 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><pre class='xr-var-data'>array([3.726369e+11])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>declination</span></div><div class='xr-var-dims'>(l, m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400), meta=np.ndarray&gt;</div><input id='attrs-1b3ee6be-3fd2-413c-983b-00e581dd5f69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b3ee6be-3fd2-413c-983b-00e581dd5f69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-003721fd-3803-496d-a247-73125d2d3912' class='xr-var-data-in' type='checkbox'><label for='data-003721fd-3803-496d-a247-73125d2d3912' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><th> Bytes </th><td> 640.00 kB </td> <td> 640.00 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (200, 400) </td> <td> (200, 400) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
              "  </tbody>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>l</span></div><div class='xr-var-dims'>(l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.879e-05 3.84e-05 ... -3.84e-05</div><input id='attrs-6f547280-833b-40ad-ac42-ef7b65a0abc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f547280-833b-40ad-ac42-ef7b65a0abc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-043b7ca1-a7e6-45ba-8355-74916cebdfde' class='xr-var-data-in' type='checkbox'><label for='data-043b7ca1-a7e6-45ba-8355-74916cebdfde' title='Show/Hide data 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><pre class='xr-var-data'>array([ 3.878509e-05,  3.839724e-05,  3.800939e-05,  3.762154e-05,\n",
              "        3.723369e-05,  3.684584e-05,  3.645799e-05,  3.607014e-05,\n",
              "        3.568229e-05,  3.529444e-05,  3.490659e-05,  3.451873e-05,\n",
              "        3.413088e-05,  3.374303e-05,  3.335518e-05,  3.296733e-05,\n",
              "        3.257948e-05,  3.219163e-05,  3.180378e-05,  3.141593e-05,\n",
              "        3.102808e-05,  3.064022e-05,  3.025237e-05,  2.986452e-05,\n",
              "        2.947667e-05,  2.908882e-05,  2.870097e-05,  2.831312e-05,\n",
              "        2.792527e-05,  2.753742e-05,  2.714957e-05,  2.676172e-05,\n",
              "        2.637386e-05,  2.598601e-05,  2.559816e-05,  2.521031e-05,\n",
              "        2.482246e-05,  2.443461e-05,  2.404676e-05,  2.365891e-05,\n",
              "        2.327106e-05,  2.288321e-05,  2.249535e-05,  2.210750e-05,\n",
              "        2.171965e-05,  2.133180e-05,  2.094395e-05,  2.055610e-05,\n",
              "        2.016825e-05,  1.978040e-05,  1.939255e-05,  1.900470e-05,\n",
              "        1.861685e-05,  1.822899e-05,  1.784114e-05,  1.745329e-05,\n",
              "        1.706544e-05,  1.667759e-05,  1.628974e-05,  1.590189e-05,\n",
              "        1.551404e-05,  1.512619e-05,  1.473834e-05,  1.435048e-05,\n",
              "        1.396263e-05,  1.357478e-05,  1.318693e-05,  1.279908e-05,\n",
              "        1.241123e-05,  1.202338e-05,  1.163553e-05,  1.124768e-05,\n",
              "        1.085983e-05,  1.047198e-05,  1.008412e-05,  9.696274e-06,\n",
              "        9.308423e-06,  8.920572e-06,  8.532721e-06,  8.144870e-06,\n",
              "        7.757019e-06,  7.369168e-06,  6.981317e-06,  6.593466e-06,\n",
              "        6.205615e-06,  5.817764e-06,  5.429913e-06,  5.042062e-06,\n",
              "        4.654211e-06,  4.266360e-06,  3.878509e-06,  3.490659e-06,\n",
              "        3.102808e-06,  2.714957e-06,  2.327106e-06,  1.939255e-06,\n",
              "        1.551404e-06,  1.163553e-06,  7.757019e-07,  3.878509e-07,\n",
              "       -0.000000e+00, -3.878509e-07, -7.757019e-07, -1.163553e-06,\n",
              "       -1.551404e-06, -1.939255e-06, -2.327106e-06, -2.714957e-06,\n",
              "       -3.102808e-06, -3.490659e-06, -3.878509e-06, -4.266360e-06,\n",
              "       -4.654211e-06, -5.042062e-06, -5.429913e-06, -5.817764e-06,\n",
              "       -6.205615e-06, -6.593466e-06, -6.981317e-06, -7.369168e-06,\n",
              "       -7.757019e-06, -8.144870e-06, -8.532721e-06, -8.920572e-06,\n",
              "       -9.308423e-06, -9.696274e-06, -1.008412e-05, -1.047198e-05,\n",
              "       -1.085983e-05, -1.124768e-05, -1.163553e-05, -1.202338e-05,\n",
              "       -1.241123e-05, -1.279908e-05, -1.318693e-05, -1.357478e-05,\n",
              "       -1.396263e-05, -1.435048e-05, -1.473834e-05, -1.512619e-05,\n",
              "       -1.551404e-05, -1.590189e-05, -1.628974e-05, -1.667759e-05,\n",
              "       -1.706544e-05, -1.745329e-05, -1.784114e-05, -1.822899e-05,\n",
              "       -1.861685e-05, -1.900470e-05, -1.939255e-05, -1.978040e-05,\n",
              "       -2.016825e-05, -2.055610e-05, -2.094395e-05, -2.133180e-05,\n",
              "       -2.171965e-05, -2.210750e-05, -2.249535e-05, -2.288321e-05,\n",
              "       -2.327106e-05, -2.365891e-05, -2.404676e-05, -2.443461e-05,\n",
              "       -2.482246e-05, -2.521031e-05, -2.559816e-05, -2.598601e-05,\n",
              "       -2.637386e-05, -2.676172e-05, -2.714957e-05, -2.753742e-05,\n",
              "       -2.792527e-05, -2.831312e-05, -2.870097e-05, -2.908882e-05,\n",
              "       -2.947667e-05, -2.986452e-05, -3.025237e-05, -3.064022e-05,\n",
              "       -3.102808e-05, -3.141593e-05, -3.180378e-05, -3.219163e-05,\n",
              "       -3.257948e-05, -3.296733e-05, -3.335518e-05, -3.374303e-05,\n",
              "       -3.413088e-05, -3.451873e-05, -3.490659e-05, -3.529444e-05,\n",
              "       -3.568229e-05, -3.607014e-05, -3.645799e-05, -3.684584e-05,\n",
              "       -3.723369e-05, -3.762154e-05, -3.800939e-05, -3.839724e-05])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>m</span></div><div class='xr-var-dims'>(m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.757e-05 -7.718e-05 ... 7.718e-05</div><input id='attrs-07c9bafd-5816-4635-94f3-3e4abf683ed5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07c9bafd-5816-4635-94f3-3e4abf683ed5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f480b3f-3944-4bde-b085-645ae1286e74' class='xr-var-data-in' type='checkbox'><label for='data-5f480b3f-3944-4bde-b085-645ae1286e74' title='Show/Hide data 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><pre class='xr-var-data'>array([-7.757019e-05, -7.718234e-05, -7.679449e-05, ...,  7.640664e-05,\n",
              "        7.679449e-05,  7.718234e-05])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pol</span></div><div class='xr-var-dims'>(pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.0</div><input id='attrs-434c37f7-ed36-4b82-bc2e-3e0935f23568' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-434c37f7-ed36-4b82-bc2e-3e0935f23568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da001d0-f4e2-4956-85a2-156900cd6dbd' class='xr-var-data-in' type='checkbox'><label for='data-0da001d0-f4e2-4956-85a2-156900cd6dbd' title='Show/Hide data 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><pre class='xr-var-data'>array([9.])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>right_ascension</span></div><div class='xr-var-dims'>(l, m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400), meta=np.ndarray&gt;</div><input id='attrs-2e676853-f4ac-4243-9ab0-549743f404cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e676853-f4ac-4243-9ab0-549743f404cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56a8e834-1b87-44e9-b895-b6e5adb67220' class='xr-var-data-in' type='checkbox'><label for='data-56a8e834-1b87-44e9-b895-b6e5adb67220' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
              "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
              "  </thead>\n",
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              "    <tr><th> Bytes </th><td> 640.00 kB </td> <td> 640.00 kB </td></tr>\n",
              "    <tr><th> Shape </th><td> (200, 400) </td> <td> (200, 400) </td></tr>\n",
              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</table></pre></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'>2012-11-19T07:56:26.544000626</div><input id='attrs-00bc3c97-22fd-4998-918a-fcdd4d0b2b46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00bc3c97-22fd-4998-918a-fcdd4d0b2b46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0d8dbd5-0c50-47be-80be-43b65de15566' class='xr-var-data-in' type='checkbox'><label for='data-c0d8dbd5-0c50-47be-80be-43b65de15566' title='Show/Hide data 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><pre class='xr-var-data'>array([&#x27;2012-11-19T07:56:26.544000626&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-30d42130-c336-4b80-9005-a53234913674' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30d42130-c336-4b80-9005-a53234913674' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>IMAGE</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-be59d925-910f-4e30-8e70-34a693d72477' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be59d925-910f-4e30-8e70-34a693d72477' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-906d2215-1d25-4257-94ac-b2dc8976f6d8' class='xr-var-data-in' type='checkbox'><label for='data-906d2215-1d25-4257-94ac-b2dc8976f6d8' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
              "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>IMAGE_MASK0</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-1e698fba-d766-4b89-9c71-6b9f57fafd6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e698fba-d766-4b89-9c71-6b9f57fafd6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db43b652-3890-4dbf-b81d-1bf8fa02b0f1' class='xr-var-data-in' type='checkbox'><label for='data-db43b652-3890-4dbf-b81d-1bf8fa02b0f1' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "</tr>\n",
              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>IMAGE_PBCOR</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-29152f31-a06b-4c4e-9eb9-302c0651c712' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29152f31-a06b-4c4e-9eb9-302c0651c712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ed8e538-2b81-42ce-99d5-22fa8d3fcde8' class='xr-var-data-in' type='checkbox'><label for='data-5ed8e538-2b81-42ce-99d5-22fa8d3fcde8' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>IMAGE_PBCOR_MASK0</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-f4e25d25-5836-4fda-9212-24ba0c240132' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4e25d25-5836-4fda-9212-24ba0c240132' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae0b2aac-dd72-4d69-afa6-bb231f473bdd' class='xr-var-data-in' type='checkbox'><label for='data-ae0b2aac-dd72-4d69-afa6-bb231f473bdd' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>MODEL</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-9b5b6db3-a33e-4357-81b8-6415dc041ef6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b5b6db3-a33e-4357-81b8-6415dc041ef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26a9cb6f-5ded-42fc-aa58-a688620c9634' class='xr-var-data-in' type='checkbox'><label for='data-26a9cb6f-5ded-42fc-aa58-a688620c9634' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "<td>\n",
              "<table>\n",
              "  <thead>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>PB</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-c1d49ecc-1920-43f2-b55f-2d987881f94e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1d49ecc-1920-43f2-b55f-2d987881f94e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13e735c3-042b-40f3-8ec5-f58ae149abb8' class='xr-var-data-in' type='checkbox'><label for='data-13e735c3-042b-40f3-8ec5-f58ae149abb8' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>PB_MASK0</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-3122d0b1-fb31-4c56-8001-1956ff2c0e88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3122d0b1-fb31-4c56-8001-1956ff2c0e88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb08f3a-f753-4fb4-bc64-0416a7f17548' class='xr-var-data-in' type='checkbox'><label for='data-ccb08f3a-f753-4fb4-bc64-0416a7f17548' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
              "<table>\n",
              "  <thead>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>PSF</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-cf93b6cf-eab1-4b52-b535-bd50beeb13db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf93b6cf-eab1-4b52-b535-bd50beeb13db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e6189b9-a4d6-452b-831e-e65f48ea75b7' class='xr-var-data-in' type='checkbox'><label for='data-7e6189b9-a4d6-452b-831e-e65f48ea75b7' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
              "<tr>\n",
              "<td>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>RESIDUAL</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-9df4c427-4f48-4f52-b556-32651db9bcf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9df4c427-4f48-4f52-b556-32651db9bcf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f29a3f24-49c0-4e58-99b0-3c97017487f8' class='xr-var-data-in' type='checkbox'><label for='data-f29a3f24-49c0-4e58-99b0-3c97017487f8' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>RESIDUAL_MASK0</span></div><div class='xr-var-dims'>(l, m, time, chan, pol)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 400, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-79a33785-4d40-4bbf-b6d4-c4fed55c084c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79a33785-4d40-4bbf-b6d4-c4fed55c084c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49af380a-e9ca-49ad-a8b3-c0171ffe46cd' class='xr-var-data-in' type='checkbox'><label for='data-49af380a-e9ca-49ad-a8b3-c0171ffe46cd' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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              "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>SUMWT</span></div><div class='xr-var-dims'>(time, chan, pol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-469e45c3-0ce7-43be-91b5-5e0326b989b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-469e45c3-0ce7-43be-91b5-5e0326b989b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6ea9861-5450-4e1f-a0c7-21ef3244d1f5' class='xr-var-data-in' type='checkbox'><label for='data-e6ea9861-5450-4e1f-a0c7-21ef3244d1f5' title='Show/Hide data 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><pre class='xr-var-data'><table>\n",
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              "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-919ccc53-26e9-453e-a2a1-c8fc8492ab05' class='xr-section-summary-in' type='checkbox'  ><label for='section-919ccc53-26e9-453e-a2a1-c8fc8492ab05' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axisnames :</span></dt><dd>[&#x27;Right Ascension&#x27;, &#x27;Declination&#x27;, &#x27;Time&#x27;, &#x27;Frequency&#x27;, &#x27;Stokes&#x27;]</dd><dt><span>axisunits :</span></dt><dd>[&#x27;rad&#x27;, &#x27;rad&#x27;, &#x27;datetime64[ns]&#x27;, &#x27;Hz&#x27;, &#x27;&#x27;]</dd><dt><span>commonbeam :</span></dt><dd>[0.6637904644012451, 0.5050938129425049, -65.90008544921875]</dd><dt><span>commonbeam_units :</span></dt><dd>[&#x27;arcsec&#x27;, &#x27;arcsec&#x27;, &#x27;deg&#x27;]</dd><dt><span>date_observation :</span></dt><dd>2012/11/19/07</dd><dt><span>direction_reference :</span></dt><dd>j2000</dd><dt><span>imagetype :</span></dt><dd>Intensity</dd><dt><span>incr :</span></dt><dd>[-3.878509448876288e-07, 3.878509448876288e-07, 1.0, 234366781.0546875]</dd><dt><span>object_name :</span></dt><dd>tw hya</dd><dt><span>observer :</span></dt><dd>cqi</dd><dt><span>pointing_center :</span></dt><dd>11</dd><dt><span>rest_frequency :</span></dt><dd>3.72637e+11 hz</dd><dt><span>restoring_beam :</span></dt><dd>0.66379 arcsec, 0.505094 arcsec, -65.9001 deg</dd><dt><span>restoringbeam :</span></dt><dd>[0.6637904644012451, 0.5050938129425049, -65.90008544921875]</dd><dt><span>spectral__reference :</span></dt><dd>lsrk</dd><dt><span>telescope :</span></dt><dd>alma</dd><dt><span>telescope_position :</span></dt><dd>[2.22514e+06m, -5.44031e+06m, -2.48103e+06m] (itrf)</dd><dt><span>unit :</span></dt><dd>Jy/beam</dd><dt><span>velocity__type :</span></dt><dd>radio</dd></dl></div></li></ul></div></div>"
            ],
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:            (chan: 1, l: 200, m: 400, pol: 1, time: 1)\n",
              "Coordinates:\n",
              "  * chan               (chan) float64 3.726e+11\n",
              "    declination        (l, m) float64 dask.array<chunksize=(200, 400), meta=np.ndarray>\n",
              "  * l                  (l) float64 3.879e-05 3.84e-05 ... -3.801e-05 -3.84e-05\n",
              "  * m                  (m) float64 -7.757e-05 -7.718e-05 ... 7.679e-05 7.718e-05\n",
              "  * pol                (pol) float64 9.0\n",
              "    right_ascension    (l, m) float64 dask.array<chunksize=(200, 400), meta=np.ndarray>\n",
              "  * time               (time) datetime64[ns] 2012-11-19T07:56:26.544000626\n",
              "Data variables:\n",
              "    IMAGE              (l, m, time, chan, pol) float64 dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    IMAGE_MASK0        (l, m, time, chan, pol) bool dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    IMAGE_PBCOR        (l, m, time, chan, pol) float64 dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    IMAGE_PBCOR_MASK0  (l, m, time, chan, pol) bool dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    MODEL              (l, m, time, chan, pol) float64 dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    PB                 (l, m, time, chan, pol) float64 dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    PB_MASK0           (l, m, time, chan, pol) bool dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    PSF                (l, m, time, chan, pol) float64 dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    RESIDUAL           (l, m, time, chan, pol) float64 dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    RESIDUAL_MASK0     (l, m, time, chan, pol) bool dask.array<chunksize=(200, 400, 1, 1, 1), meta=np.ndarray>\n",
              "    SUMWT              (time, chan, pol) float64 dask.array<chunksize=(1, 1, 1), meta=np.ndarray>\n",
              "Attributes:\n",
              "    axisnames:            ['Right Ascension', 'Declination', 'Time', 'Frequen...\n",
              "    axisunits:            ['rad', 'rad', 'datetime64[ns]', 'Hz', '']\n",
              "    commonbeam:           [0.6637904644012451, 0.5050938129425049, -65.900085...\n",
              "    commonbeam_units:     ['arcsec', 'arcsec', 'deg']\n",
              "    date_observation:     2012/11/19/07\n",
              "    direction_reference:  j2000\n",
              "    imagetype:            Intensity\n",
              "    incr:                 [-3.878509448876288e-07, 3.878509448876288e-07, 1.0...\n",
              "    object_name:          tw hya\n",
              "    observer:             cqi\n",
              "    pointing_center:      11\n",
              "    rest_frequency:       3.72637e+11 hz\n",
              "    restoring_beam:       0.66379 arcsec, 0.505094 arcsec, -65.9001 deg\n",
              "    restoringbeam:        [0.6637904644012451, 0.5050938129425049, -65.900085...\n",
              "    spectral__reference:  lsrk\n",
              "    telescope:            alma\n",
              "    telescope_position:   [2.22514e+06m, -5.44031e+06m, -2.48103e+06m] (itrf)\n",
              "    unit:                 Jy/beam\n",
              "    velocity__type:       radio"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4mILsznesm3"
      },
      "source": [
        "## Plot and Compare With CASA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bltOMTEHsApC",
        "outputId": "a00a8967-e165-43b2-9d58-289a616dfee4"
      },
      "source": [
        "import matplotlib.pylab as plt\n",
        "import numpy as np\n",
        "import xarray as xr\n",
        "import dask.array as da\n",
        "import dask\n",
        "from cngi.dio import read_image\n",
        "\n",
        "image_dataset = read_image(\"twhya_standard_gridder_lsrk_mfs_natural.img.zarr\")\n",
        "casa_image_dataset = read_image(\"casa_twhya_standard_gridder_lsrk_mfs_natural.img.zarr\")\n",
        "\n",
        "#### Primary Beam Corrected Images ####\n",
        "pb_limit = 0.2\n",
        "primary_beam = image_dataset.PB[100,:,0,0,0,0].where(image_dataset.PB[100,:,0,0,0,0] > pb_limit,other=0.0)\n",
        "dirty_image_pb_cor = image_dataset.IMAGE[:,:,0,0,0]/image_dataset.PB[:,:,0,0,0,0]\n",
        "dirty_image_pb_cor = dirty_image_pb_cor.where(image_dataset.PB[:,:,0,0,0,0] > pb_limit,other=np.nan)\n",
        "\n",
        "#print(casa_image_dataset)\n",
        "casa_primary_beam = casa_image_dataset['PB'][100, :,0, 0, 0].data #Primary beam created by CASA\n",
        "casa_dirty_image_pb_cor = (casa_image_dataset['IMAGE_PBCOR'][:, :,0, 0, 0]).where(casa_image_dataset['PB'][:, :,0, 0, 0] > pb_limit,other=np.nan).data #Image created by CASA\n",
        "\n",
        "#Plot Primary Beams\n",
        "fig0, ax0, = plt.subplots(1, 2, sharey=True,figsize=(10, 5))\n",
        "im0 = ax0[0].plot(casa_primary_beam)\n",
        "im1 = ax0[1].plot(primary_beam)\n",
        "ax0[0].title.set_text('CASA Primary Beam Cross Section')\n",
        "ax0[1].title.set_text('ngCASA Primary Beam Cross Section')\n",
        "plt.show()\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(casa_primary_beam-primary_beam)\n",
        "plt.title('Difference Primary Beam')\n",
        "plt.show()\n",
        "\n",
        "#Plotting Images\n",
        "fig0, ax0 = plt.subplots(1, 2, sharey=True,figsize=(10, 5))\n",
        "im0 = ax0[0].imshow(casa_dirty_image_pb_cor)\n",
        "im1 = ax0[1].imshow(dirty_image_pb_cor)\n",
        "ax0[0].title.set_text('CASA PB Corrected Dirty Image')\n",
        "ax0[1].title.set_text('ngCASA PB Corrected Dirty Image')\n",
        "fig0.colorbar(im0, ax=ax0[0], fraction=0.046, pad=0.04)\n",
        "fig0.colorbar(im1, ax=ax0[1], fraction=0.046, pad=0.04)\n",
        "plt.show()\n",
        " \n",
        "plt.figure()\n",
        "plt.imshow(casa_dirty_image_pb_cor - dirty_image_pb_cor)\n",
        "plt.title('Difference Dirty Image')\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "\n",
        "dirty_image_pb_cor = dirty_image_pb_cor / np.nanmax(np.abs(dirty_image_pb_cor))\n",
        "casa_dirty_image_pb_cor = casa_dirty_image_pb_cor / np.nanmax(np.abs(casa_dirty_image_pb_cor))\n",
        "norm_diff_image_pb_cor = dirty_image_pb_cor - casa_dirty_image_pb_cor\n",
        "\n",
        "# Calculate max error\n",
        "max_error_dirty_image = np.nanmax(np.abs(norm_diff_image_pb_cor))\n",
        "print('Max Normalized Error',max_error_dirty_image)\n",
        "\n",
        "# Calculate root mean square error\n",
        "rms_error_dirty_image = np.sqrt(np.nansum(np.square(norm_diff_image_pb_cor)))\n",
        "print('RMS Normalized Error',rms_error_dirty_image)\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/WGe5QazQEJE0cDNwGjAdOE9EphvVLgI2K6WmADcCN+hjpwOzgAOAU4FbRCQd0+a9wP7AgcAA4GLPeR5TSh2i/66t5gv3R7bu6mR3hzNia8/l2dmRZ6MOT9y22/EfbNGhsG9s2MnVv3sJ8EShGJPwzFBGr6bRmXdGdwWfpuEe459FHmVOsDQ2pg/D9Fd4y4LqmPeSe6uayTi95ijlyYf2jd8sZe2W3UDpGXF9bht2tLN1d2cxkmtne46tu60/LommcQTQqpRaqZTqAGYDM406M4G79ec5wEniGMJnArOVUu1KqdeBVt1eaJtKqXlKAywExnftK/Z/LrxrIdf/6WWg9LB1mCGH+kH7wr3PFH0KPjtviBbhdYSDEw5pOsuDtAif38OwJ1ssoUkzy5IPBmsjzvLDwabO0LkchfJ79I8vvs3X5ryg65iad7l28q3fv8Slv1hS3RftR2QS1BkHeBPcrwGODKujlMqJyFZghC5/2jh2nP4c2aY2S30W+DdP8dEi8jzwFvAVpdRLZmdF5BLgEoB99903wderfzbsaGe9MfEpzvTkljkpRYxjir4NHT0l5WLjC/cuYf5L75a1n8QRXgjQRiyNjS+wwrhXIEgbcf5/9o6FnPa+sWXtuabU4nyign/gEjZoMYWTub1uezsbd3RU/iX7GbXsCL8F+IdS6jG9/QwwQSl1MPBj4LdBBymlbldKzVBKzRg1alQvdbVvyecVHbkQp7Zh000ZAuCXC1dx6S+eKTvWfabCNA1XYHjrmCPFl9/exvV/eqW8ToLUI5bGwjSJuvfRt/+wjNZ1zkTRsHsM4E9L3ylrT0yfhnHPXXT3IuYsWR18jM95XvBtR6050ygkERprgX082+N1WWAdEckAQ4GNEcdGtiki3wRGAf/hlimltimldujP84CsiIxM0P9+T2dBBdzg5Y6+Ti1UUsYv/o3fLC1mBA1acwDKfRom/lxUTluzbn+avywztBEjZt5iCXNY//HFt7n47kXOvpDkg0FISPRUcZnijjz/93cvBR5jmlpLmnfpGbL+uGRCYxEwVUQmiUgTjmN7rlFnLnCB/nwO8JD2ScwFZunoqknAVBw/RWibInIxcApwnlKqeHeIyFjtJ0FEjtB931jNl+5veNfGMG/40voB7kS9cAngX93MFRrhx4StyVEe/RKcesRiCTM9OWXO/7AZ4EG4d6p/Rni4oHEjrsxnxvSvdOQL1h9HAp+G9lFcDswH0sCdSqmXRORaYLFSai5wB3CPiLQCm3CEALreA8AyIAdcppTKAwS1qU95G/Am8JR+Wf1aR0qdA1wqIjlgNzBL2Vg4wDFFBanS4NcaUhFaQ1hOn4hDQudrRLVrR2sWl7Bw2uA65dtBiCEAkgRflPwgfiFR3rf4tT4agSSOcNccNM8ou9rzuQ04N+TY64DrkrSpywP7pJS6CbgpSX8bjXxBlaKlCuWmJlOIRNmacgXFvQve5EE9x8M91vSDmMeAP7TRK2lM7cNqGhaXqEAK97bzzQ1KIADcY2YvWsXglkwi7aQzV26OCvJx2Hu3th3hloTkCqp4w4dFT5UEQEQ7+QLf+M1SVurJTa5ZKcqnkfPN7fBrJ/6HPtqZ2LpuOzvac5F1LLXP1t2drFi/I7KOP2S7dG+U5lyYg44on4bbrlP31Xd38NU5L0RmISj6NArBA62iTyOvsAuKWaHRL/D6NIopQEzVOhdvajJNA0lU8bCQyfJ8VcECbEd7jlseafWd52O3PMndT74Re25LbfOTf6zk0z95uqysM1/g5odbaet0JqNGmTdLWkPy1SJdn535ck9i0jInBHYUTb566dic1TTACo26x53d2mnc8P6VypKYmsoftM68XwCYhEVGeTWaMGfn46+t57t/Xs6r724v+z7b23Jss5lw655tbZ1sbyvXGF9Ys5XvzV/O0yudGJbIcGxXa/BF6CXwaRh1ojQNM/WI6Qj3zmOyPo2EPg1L7eLew6YPo8MXCRJvajIfiKSO8PsWrmLei2+Xna9M0whxZHYatmPv97FRKvVPZ8DqeP40567pyU1AWKprRkL97rm3GLZHU6J7wzSBJtFOOlyfRtEv6Df5Wk3DCo26p6g6B0R6QKXhs8YDXlDsbM9Fpo/uLBS46tcv+voTtQaHf41nf74qO6Krf/KFQlnotVNmROh5NA0zLNacc/Hi2q185VfPM3JQc+g5C8q5Z/0DoAjnuba3mP65TkPjcHwa9r605qk6x5cnxxi9uxOjOhL4NMzRWS6vOOCb83nlne0hR/gn6gUlOcwXytOVhGU2hWBBYqlPcgVVlkMKgtZsKTnCw5IPmv6JKGf0E60bOeCb830DoKh5GqWFm8r7VIym8mjv7r3cyFhNo84pqs65ME3DrRecRiSorbDtJMeUhEj5eb7/l1e56eFW5xjffJBSGzapYf8hl1fFdbjF59R26rgv98/dtYgvnLBf2fHm/IliuwnuS1MARb3nzYgrMxIwpwWFN7gkk44afvVvrKZR53jDAcFrriofNSVJCeJ/OONH+z4zQICmARQFhveYoBm+Nj9V/yHI1OhPx1/ad8sjK8qON3NCmW1EUclCX2YaEXOuk5uC3RU8jX5vWk2jzvE6vh9YtJorHtRpnvMF7nride54/HWg9ABEaRqV2IFdzIfTbSNyPkjI3A7At06HpX7xjtQzaafM9GtVpc1WoWlEUYqecvp06yMrSKdSRZPuJ//3KW74xIFVtd0fsZpGHaOUYuvuUqpmV2CA8zK/5vfLeHdbe3EbojUNk3a9FGcUfk0jPsdV1CxgM+zRUr+YGiX4I+mi/BOmSavURrwWYYbcRlGcEa4HSW9tbePrv3mx7Dxfe7AU7LFld2dD+zWs0KhjfvbEG5z8g38E7vNHQhU4/YeP8dhrGxK372a/jcIfUpncDOY66b19Ndc/sNQv5noUUHrhmznRgijOCDfusSTyoJKZ27997i0+ceuTvnsuKA8WwDHXP8SvdKqdRsQKjTrmL8veCd1nmo0684plb2+rqP2OKoSG+4JIlORQlW87ZVbT6C+Yi29BtE/DpBgKW0VQhLsUQFKWvLnZH3EVMXB5ZPm6ivvUX7BCo46JMgGZN3w1I/ckmka4Izy+b0G5hOw8jf6DmWof/L9vlBmpGApbxb3QUcX97h9oxYfpNiJWaNQx5oJKXszRWZiqHUUSTcN33kKBnz3xOmu37A6tE+XTcOVHJdEvltokl/cPCvxrY4T/zsvf3c4vnn6zqiSB1d27yQNBKvEN9jes0KhjoiKhzJFWNS/hJI5w33lzim/9fllknajJfUnMFpb6IGq97yTzcTpyBf7zt0ur0jSquXd92nnEedNR4YH9HCs06phKJupVJTQ6qxjhJTiPuapamU+jglBMS21japQQlFKme8NnXaq5dyvRxqOevf6OFRp1TNRgx3zQkvgnTKo5piPBCM90hrr+lp/8Y2UxGsy7euD1f3qFzTs7Alqy1BLrtrfxvfmv+BbbOub6h7jn6TedMiMbcyXp9yuhmnu3Er9fA8sMKzTqmUpGO7s7qzA1VaGdJLElm5qG+3K5bt7LxTruA7xi/U5ue3QFj7WWhwp/5w/L+NkTr1fcP0v3cNujK/jun18pK3tk+XpufngFazY7/izvS/i/5y93yox5Gkk0yqi8UWFUc+9Wot1aTcNSl0RFKJlUo65XQ7K5HQV+8NdXmaNj3QPXFTciq0xn6COvrufJFRu72l1LlTzRusE358d0fEct3frLhW9y88P+BbiC6K2giEqc5w3s0rBpROqZSm7cahyD1ZB0bseP/v5a2baJGeMftKqgnQDYdwStLVGcuBcx18YtW71pN9+bv5zRg8PTnLv03oAn+TNiNQ1LXVLJjdvWSw9eErOAaaMOGm2a64GYdfIF/wI/lt4jX1C+tTJK2Yvd7dK9EJ7mPP43jLunuuv9XYlwqkTL729YoVHHRM3TMGkL8Gn0hIpdiU/DJSj1SNhiPd79Nn1639GZ96+VEfWbdSXNedw9la3kQXCPCUht3laRplHxKfsNVmjUMZWMdoIezky6+3/+qvJVJVjtL2j5TjuXo+8Iuv6dhi8jyKeRRMs0ibunqpkz0eKm3fVgQ26TYYVGHdPVGzdbxcOWiTkmmaYRPInKKwTNF4/PPBWwPKil9+jUq9h5yZuh1GX73Yy1wQOGKOLuqbh7MojmbNdefVbTsNQlXb1xq9E04lYs664kh+6oz3WqBjvCrabRV+QCNY1w7bCoaVTj04gTGlWsopftopZtfRoxiMipIrJcRFpF5MqA/c0icr/ev0BEJnr2XaXLl4vIKXFtisi9unypiNwpIlldLiLyI13/BRE5rCtfvD/QVU2jmhFaJsZ+XI0jPNinUT4zPMiBah3hfUeQeSofET0lnuO8JDEJxd1T6Sp8Gl1NA1Kw62mEIyJp4GbgNGA6cJ6ITDeqXQRsVkpNAW4EbtDHTgdmAQcApwK3iEg6ps17gf2BA4EBwMW6/DRgqv67BLi1mi/cn+jqQjDVjNC6I+dO2MvGmzm0lLcoStOw5qm+IjDk1p2nEbCORphPozuoZvDTVU2jkQcsSa7cEUCrUmqlUqoDmA3MNOrMBO7Wn+cAJ4mjv80EZiul2pVSrwOtur3QNpVS85QGWAiM95zj53rX08AwEdmryu/dL+jq8xenNQQRFHVSKaaJopge29O0+eKxIbe1RS6vfKNtbwZbpZShaVSf5jyOagY/1QgaL408YEny1hgHrPZsr9FlgXWUUjlgKzAi4tjYNrVZ6rPAnyvoR0NRTcpoL9UIgO7QNMz3hutA9bZsJrsLDLm1jvA+I8in4RXw5r6ST6M2NI2uRg428oCllh3htwD/UEo9VslBInKJiCwWkcXr16/voa7VBl1V9atyhFehncThPoBeH42ZisIXcqusI7wvyRX80VPFCZkBWmAqZL3v7qCagUxXNeZGDvdO8gZYC+zj2R6vywLriEgGGApsjDg2sk0R+SYwCviPCvuBUup2pdQMpdSMUaNGJfh69UtXb9zqRmjdHzVS9Gl4mi4ox58RNFFMKWXNU31MPl+ZpuHSE4K+Gv9El81TDXzvJbnai4CpIjJJRJpwHNtzjTpzgQv053OAh7RPYi4wS0dXTcJxYi+MalNELgZOAc5TShWMc5yvo6iOArYqpd6u4jv3G6KWykxCpQ/b5JEDe2TxmVxBsasjh+nX/8/fLeVf73tW13FuhWVvbeOU/3HSpzeyXbmv6SwU2N2Z5yM3Pkrruh1OmRYIF/98Md/548u+Y3Z15HpkhJ5OCWOGxOew8tJV81S+gbXc2CunfRSXA/OBl4EHlFIvici1IvJRXe0OYISItOJoB1fqY18CHgCW4fgmLlNK5cPa1G3dBowBnhKR50Tkal0+D1iJ40z/CfCFrn31+ser6leibn9w2iju+/xRFWkNXzxpKg995YQuj9CC2NWRZ/rV833p23+5YFXxs/uy+d78V3j1Xecl5R21tq7bwe6O3knK2IjsbM+xcv2O4rZ77V99dwe3PboCKL8f71u4quz4tVt2M/3q+T3kCE+x4Osn89mjJiQ+JpsWfnHRkRy677CKjnFpZE0jUZZbpdQ8nJe2t+xqz+c24NyQY68DrkvSpi4P7JPWXC5L0t9GwfvSbMmm6cznEh03ZfQgjt5vRGU5e7Qa0BM+jR3t8f3uLM7l8D+4+YLirB8/zpc/Mo2Lj5vc7f2zwF1PvsHt/1jJ89/8SEBklEMSzbcn0py7A5lK5k5kUimOnTqSB59Zw7OrtiQ6xvuM9YRvpl6oZUe4JQavqt+S9efSCcN9uCrRNNwz9YxPI/5hzxed5aUy12TVmXdMJdt2d3Z73ywOW3Z1sK3Nub7m71V0cieZqBczu7saRdY1mUad3dTE3e1KBM0AzzPWyJqGFRp1jPfGbc4k/ynd56QSu657TE/4NJKMPkt1xFPmzAcophpp4Ae5p3GutROcYL4ww1KEBBE3u7saX4MrAKLe/6aG7G5XMj92QJNHaFifhqUe8Y74mioSAM5x3oSFk0YOjPRXFIrHdP8tU0k6dTNzSkF5VvmzjvEeoxgCraKERjfkkapiUFJKIxJ+/kxKGDmo5CzPdFHTsCG3lrrEO0KvJB4TQb4AACAASURBVBKqUNQ0nAfnvz52IH/+0nGRWoT7iFSjaXRHZtxiJlyjvDNf8K01bul+vDP0/cI5OINtEHG/dVfur0hNIy08/rUPcfWZTrYi93mpRNPwavONPLHUCo06xvuQZjPJH7aC4dQe1JKhOZOOFDzV+EFc4l4EidbgyPvncoAjKEqahhUaPYU36MDMA+b+JknMjHG/dXVJNOO1hkw6RUs2zcBmR1tIJzjG9IM0eYSG1TQsdUmZ0DBe+FEJcE2ndlpXjhQIrnbSA5pGJQs3iaFr5PPKo2k07uivp8l5lt/1pQjR/7tnQaXq0/VH+zScOq7TPokfxHymvNuNrNVaoVHH5CKERpTvQRmahjvqSuLTcB/qkYOai6p+HHHOzfYEy2y6WoT5tToLpWyrlay8ZqmM4loZeeXTKNwXcZLrH/dbJ51v9M2zpjN8YBNQuiej3uOuYCn+Lx4TpWmECw2raVjqkihHeNTD5z4nbp2S0IgSNOg6rh/kfXz26GSTqbpntb8QTcObasQ6wnsM99oGJSp0tdre8mlkUsLnjpnEN8/S/oliyG2EAND3dsrQqisL0y09Hz0x36ResEKjjskVCgzbI8vlH5riu8GjRvemf8Kt6jVPXfiBiWXH+ExaKSmateKIexEkWbipuLSr0ZR32dFGNhn0NK6mV1D+7MLFyX0JfsfYkNskQiNdbmpKhzjCvfewW8f9X3KEJ9c0mjLCJcdPZlBzxmoalvokl1ecedBefOWU9xRv8HMOH8/8Lx0f6dQuaQ2uecr57x7zl38/ns8YKRlMTSOVEucv4hk/9/Dx/PlLx8W+CJJEsJQm95W3lctbR3hvkPMIZr8jPHn0VNxvnU4Jf/zisZxxYPhSOT6zaogAuOT4yfzusmPK6mQMU2xcxNW8Lx7HzEP2Bpzn4+unv5ePHDCmoQcoVmjUMbmCKj5AWR3ZcfiEPXnP2ME0RZinCqZ5yhixpVPi01xc1d8VMCXnefgtNGX0IPYfO4R0dyzcFLDmBjjXoLS8aOOaDHoa99oWAsxTLt0x+s6kUhyw91DGDx8QWidtOLWLAsDXlng0C+OYdDKfxvS9h3Dw+GHFbbddq2lY6pJ8QRUfCtenYY6+Rgxs4lsfPaDsuKIjXNdxXRnuw5cW8ZmUwv0g8XM7uiNfVa5QoHXddjbubC8r397WyZsbdwHWEd6TuNf29Q072d5Wnits/Y52Vq7fUTIhdoHifRcZ1eQf5IBfa8ikU7771DVtuX4Q893/7bPfx7A9skDpmSoeky5p5o2saSRKWGipTXKFQvFhyKbLHw53+2efez9jh7TwzbkvFY8rze4ud4BnPYLHNG+pYvRUsD05CNOk1RVyBcXJP/iHr/zTP1lQzI5rNY2ew3WEn3/nQoa0lL82/vjC2/zxhbfZe2hLl8+TTeCgLgmL6GPSHk0j43OEB2saZx+yN1NHD2LW7U+X+e+AovZuNQ1L3eLVNLKGpuFupyRAAOj/meIx7nbJX2EKg4IhAMzzBFEyaXWfecrEm07d+jR6Du/IeltbcFbi7sj9VdIaolKClAuAdEj4bDYtvmgp01xlkvVoJ1nDD+J9xho5Us8KjTrG8WmYN3i51pBJi2/SXjplPkjlD0daxDfPwxUApqBJpGl0g08jyciukU0GPU0S0193+TQges6FaZYqarLKX88015rHmIOeMj+IGSiSKT0njXyvWaFRpxQKTtZR94Z2UxyYAiFjmJo+e9QE/vMMN77dcGp7olJM5/VxU0cV23PqlAuaIJQxIbArJAnn9I7+Vm/aFTlatUSjlGL1pl3F7STrR3TH3IUw/4QXU2twt0967+iyetlUyhctZQZwXP/xA/nUDGcVaRGnzaJAyQQLmHTaCg1LHeI6HTOGL8O8wVMiZS/2az56QHEmbckcRdl22YOTFpZdewqnHDC2uA/8EVf7jx3M10/fv6yP3enTSDKKdUfDb2zYyXHffZjFb27u8nkblcde28AHv/cw72xtA5KZ/rpF0yj6J8rb+s8z3svU0YOcOqYA0NsfP2w8L1zzEcARAG5YeNkxhnlq9JAW/vPM9zplqRQi4kk1Yph8PRqI9WlY6g73pg0aBYEn8iPl2Gjd6Q3e93fGMGl5H0Zvrp49mjK+Y1xB4573pxfM4IhJI8r6WIqe6rrQSDKKda/J5l0dAGzc0R5V3RLBhh3tFBRs1QtbJYmM6g6fUtj8ieOnjeLWzxwGeMK+Da0XYJC+V00t2hQA3oi+4v0fkmqkpHmU2sgXVMNqslZo1CmuelyM7MiU3+BFrSEtiPZROMKj9AIvjb70tkcgmHZj/zH+/6Zw6G2fhvtic+vaENzqcQWAG5GWRCB0R/RamCkznRLfvIyUoTV4y0wt2ucHKTuGsnYzxWcqOOjD3d+o2oYNua1T3KU1zRDbsBs8kxYwnumwzJ8ZraZn0/5UIaZ5qkw7iZkQ2BWS2JBLLzo72a+rVCOAu+MdGpbmPJtKeYIxgk2kLkGhtmbqkSBNo+QXTAUe2+QZiIEOREm+ynK/wWoadUpR00gbcywMAeAdnZkC4KjJI/j4oeMYv+ceuk656SntsQm7FNv1jej8moYZpvv54ybx4KUf6NL3jayTt5pGd2EK4CSO8Gr5/eXHcoFOfum+kE3LTzrt1zTMyKhiXU+YefF50Df1lNGDmHnI3syYuGexvnvbZkJMWqaPo9E1DSs06pQwn4Y5e7Wkaqd8L/V9hu/BDz51iM+05X3QzGPMNCLe7SjTAsAHp41m6phBlX9Zkpqnyl90jZyJtKt0GgK4J+fAvHevwRwzZSQQ7v/yhsKGpRFx8WoaZpBHSzbND2cdypghpYmIojMgZAxNoqkYPWVOfnX+N2oElTVP1SnuQ+1PI1KuWqc9JqfOVPRNboYyptN+TcM3yvPYl30PvFJGne5xioeRN0bFdrJf9ZhJILsjRUgY6QCBYEZPeU1QZsLCIPNUNuQ+De2DRzsp+TaM6CljcNWoE/ysplGn+DQNI6a86MTzzIiNe3Ay6RQpKWUtzaRSPpNWxjBPlTnCfT6NUrtuX7pjdngYpdFx+balcjr1uhfFZJA9JIDd4IyiUzvlZqwtr5dNpQJmd+t9xn2XktJgKcyEFdQP06Rrau9NRpvWPGWpKwpGLqimtOORM8MFvY5xU2swyabEGNH5X/KHT9iTk/YfzahBzbrdknDypx4pF2xOhFXP3XLui62oaTToQ90deE19SqmqrmWSAYJv3k/adYQb9dLi0xrGDduDD04bVcxC623TN3E1JoIv47k3w/K5mRNoG/X2skKjTnFvWFcrOH7aSK449T1MGz0YCIieCjIfGaQ9ozlwHjTzHb//2CHcceH7fZEmjunJTHLotlvSTnpQ0SiFh4as5HfhzxZy419f7bkO1Cn/b97L/Ms9S8rKSkEFhaqFb9JV+Lx1S/eo8tUzo6UGNKW5+/8cweRR5X6yIHNX3L2f8mgaacMveOC4oXz1lPdw9GTH7+I2FZVWvT+TSGiIyKkislxEWkXkyoD9zSJyv96/QEQmevZdpcuXi8gpcW2KyOW6TInISE/5CSKyVUSe039XV/ul+wPuxCL3URjckuULJ0wpqfkBMeZxD/Fx00Zy7ozxxe2giCuTqHTqE0bs4asjEi+8gKqES2feGRWHRU+99u4OVqzfUXnD/ZzWdf7r4vVpVGuGySb4EYPm+wDsFyAISvMpol9bKZGiOUpE+OSM8Ryz38jIY7zpdvzBJSku+9AUBjQ52ry75HCjCo1YR7iIpIGbgQ8Da4BFIjJXKbXMU+0iYLNSaoqIzAJuAD4lItOBWcABwN7A30Rkmj4mrM0ngD8AjwR05zGl1JlVfM9+h3u7mivZuYwc2MywPbJFTSSbjvcnfOg9o/nQe0r5ezLpVOys16DJfYObM/zkghkcOWm4r477P270mkmlEi0Da/K9+cv55cJVgH+eRq5QsH6OADryfm3CvU5X/fpFztW5mSqlK5rG/zlmEgeOG8o//XRBMTFn0MS8sDa9fo7vnnNwbD9SHp9cNp1iUHOG4YOaAuu6j1yDyoxE0VNHAK1KqZUAIjIbmAl4hcZM4Br9eQ5wkzhvq5nAbKVUO/C6iLTq9ghrUyn1rC7ryvfq97ijnLDn8rNHT+DMg0tLZmYSaBommZTE2m2zaSdFiUh5yOJRk0d46vjj3dtz0S/vdEogH1klkFseWVH8bDpv8wFLlVocAeETsPo6bdzZwW2Prgg6LJY457O3jrnORSolHDl5hJOmH8pyQsXdx0Gp/WP74dE0sukU8//9+KLfzte+7kejCo0k5qlxwGrP9hpdFlhHKZUDtgIjIo5N0mYQR4vI8yLyJxE5IKiCiFwiIotFZPH69esTNFmfuM94mHBtyabZa2hpycwkpiaTjGdCVRhpT4SVOTmqVCfYBBHEmQftxf2XHNUtqUdMAeGsb201DZPOvCpmGHDpjtn0mZRw78VHcrKRfdasA+G+B6/ZM3H4bMqf2j8OM7HnuGEDin47X11d3KjmqXpyhD8DTFBKHQz8GPhtUCWl1O1KqRlKqRmjRo3q1Q72JnGahklTpnJNI52KPyaTKoVLulXD53aUbwdx5OQRHDl5RI8kOczlrdAIIpcv+BZQ6g6NLJMSjpky0hfd5CUsa7J3vznpNC4tTTWh3Zm0f7GyMNyBlBUa4awFvEbN8bossI6IZIChwMaIY5O0WYZSaptSaof+PA/Ieh3ljYYyoqfiaMmmac5WlijHDMENwqvBuE7uME0jiXnB3dU9+aqCfBqN+aBH0RHg7O6OiWvFlCARdbwTP73bLikpj76D8FX3iscEzBmKoyWTLjq645Ci0KjoFP2GJD6NRcBUEZmE82KfBXzaqDMXuAB4CjgHeEgppURkLvBLEfkBjiN8KrAQJ+gnrs0yRGQs8K5u9wgcgbcx2dfsf7gzZpMOqL526nvY3VHZiyCdElKFGKFhONjNbQg3QVxw9ASm7z2Erz34YrFuJWtwNGVSdET4RnzmKatpBNKZ9wcIxC3dGnftoWSuNAfk3z/3YBa/uZn7Fq4KMF0aeaRS5ZmZ0wkGMmlJFqHn5YZzDmLogGyiuqWWG1NqxAoNpVRORC4H5gNp4E6l1Esici2wWCk1F7gDuEc7ujfhCAF0vQdwnOY54DKlVB6c0FqzTV3+ReAKYCzwgojMU0pdjCOMLhWRHLAbmKUaNaE9pVFOnM/BZYqev1EJ+w7fo2wN7iDSxtyLTCrlm9tRnMthTOD6t5On8co728rqlmaRx3+v5pgXlztaLhQUm3Z1kCuo2BddI5LLF8gXFOu3tzNyUBMi8Wtgx117KAl+MyXI5FEDOXLycO5buMrnBwvKdea9xYNS8JvsM3wA44YNiKxjcsg+4SY0k5TVNOLR5qB5RtnVns9twLkhx14HXJekTV3+I+BHAeU3ATcl6W8j4NpTezLI7IZPHBRb54wD9y5zuKcDzFO+5Tk9LwpfzL0xizyKphgbtDtavvXRFXxv/nLAzhIPojOv2NWR5/3X/Y1rZx7A+UdPjE0b0pxJs51cZJ2wpVszHl+ZaXryr2dfPjD6wgn7cdzUaKv07Z+d0aPPRaNP7rMJC+sUVXSE99zTEZd2BODo/UZw9H6l8FqvY9xbBkFJDv3+DfednijGP0YbcUfLD7+yrlhmzVN+vPNhHnttA+cfPTF2jkxzSGSRl+LSrcbL1Tuw8KahgeDoKe/99KWTpxFHkvu2KxR9Gg16K9VT9JTFQymNSN/2wyQTsXCT6QgPXu3P+WJJIlniZga7o2VvdzqtecqHV5C6lypW08hW//tkPNmT43waqVTyqKbeotE1jdr6NSyJKRR6XtOohkxAmO6wPZpoSqeK0SneVdTCtIXu0DRc85R4XJcdNnrKh1dAuLdT3DyNONMgeH0a/nIz/fjA5gzplDBsj3JndBLHd2/jahoNKjOseapece/XGpMZeNdydvnowXtzyD7DGNSc0XVK2UJ9SQ71/yQ+jbg6rnmqTNOw5ikfXlOU+9vFhSYnGf1H+TRMTWP4wCb+/G/HMWnkwPI2qoiE6mnc7pgO/kbBCo06pdALPo1qCEqn3pRJMWX0oLI6EDwJqxhy20PmqUZdOCcMpcrDkJNqGklG/65g8S2o5DFherXFqWP8EX7VpATpaRo9esqap+oU9+Vaa0IjaF3xoDoiYeuKl68TEkWcecodQXvNU3ZyXzn5girTBNxrFefTiJtgB+HrTgQt3RraRg1qGu4jZ30alrqiN0JuqyGTThH3Psmm/Wsj+NqJeFFMGzOIB/756FhtxB0te69RR74Qm7m3kfCFIOtrFWfGy6RS3Pf5oxjrWWvbXyfYPOU1YcamOU9JIq2zNyklLGzM+6i2fg1LYkqT+/q2HyZB5imTtMem7VsiNiDk1mxu72EDOGLS8AQ+jWATXq6gKBQU/3zPYha9sSmyjf7IE60buPyXz6CU8oXWutcqNnV9Wjh6vxGMGRKcCRY8Po2gBZWM8OvwNpJpnb1JSdPo2370FVZo1CnFRZhqTNUIcoSbePNT+R3h5SG33/3EQfz+X48tr5Mw1UhnvsBLb21lw472svJcXrGjI8f8l95l4euNJzSeXrmRP7zwtpP11whBXretjVfe2RZrngqLjPr7lz/It2c6CaiLznLTPKXXoodSdoAwatE81eip0a0jvE6pVZ/GyEFNoSmlXdIRI03Xpu7unzJmECMGlo9mi2uPx03uKyjO+NHjvvIOnTYDGjOaytUunFxc5W++Ba9v4tT/eYzRg8M1CChFwJl2/ZGDmovLr5qzvV0yOp9USuIF//CBTQxsrq3XVKP7NGrr17AkptLU6L3F9889BInRXzOefFXuSHNANs05h4/nM0dNKNYBPdIMEQ5Jo6dMOvOF4jyXhhQaWrvojFjJMM48lU0Hj7a9K0S6v+EXTtiPHW057lu4ilxBlTnB40xP//OpQ2Pvp97Gpka31CXFGeHUltQYukeWIS3R2UKDNI2hA7J8++z3eSYAeudyBPs94iJ4wl6InZ7lTRsxgaF7XfIRWX9jHeFuOG2Ak9tclnVwi/PbuhqDa5o0Fz4KIsn91NtY85SlLqnV6KkkBIVchqVTd2aNB/s94kapYaPlXL4UZtqIIbidOa1lRawvUq1PI5tKedLF+NOcp4SyiX3dsW5Kb2PNU5a6pFZ9GklwHKFOv91lOf3p1Euj1TBNIy4UM2y07I0YikvM1x8p92lUqWkUw2nLU5CkArRIF3MAkBZJNN+j1ijOCG9MmWGFRr1SzHJbfwM1ZkzYsyT09EQ/0z+RTYebMIrmqQTRU2HlrlmvEc1TSYRGfMit/8ZzBwAZwzzlkk6VC5IPvmcUh+27Z/KO1wjS4D4NKzTqlEoXYaolZh2xL7OO2Le47XWMu0Q5S0vmqWiJGSYQ3tiwi1WbdgKN6Qh3w2wfeuVdhg+KjpIKI2jinpnNOCjNufe3vOnTh1V17r7G+jQsdUnRp9HH/egOggSDd6Em0S8bN0w2qSM8bLD8L79YUvzciELD1TSu+f2yqtsorpXh8WqY66b4fBoBpsZ6xKZGt9QlhRqd3FcNGY/z1KUU4+/WKe0PmjVeLY1onqpGUJove3fbK5hLKzQGH+NoGvX/yrEJCy11TT8YuAVrGunyGeNlQsOYNd4VGnF9jWoEpemfKIXclq5fKeV9KvCYoASV9YzNPWWpK2o1NXo1eCeEuWRMTcMjIMw0IsdOGcltnzm8qhdSRy5fRY/rm2oEZTaV4sfnHcoRk4brbX/IbZKlW+Nm8dcDVtOw1CXucgf9QWgE5atKe3waUP4CmqgX6nFnk5996DhOfd/Yql5IjTlPozpN46yD9+asg/YCSr/PlFGldVJMk2LQPI3+oGm4X8tqGpa6op4n95kELRGbNZyqrkC46dOH8u2Z79N1XHNIqZ1Kce3767e3c8aPHmPtlt2Vf4Ea5/UNOznzx4+xZVcHUN3clHRxPk357/H9Tx7M9889GCiZC93fzAxUSAUsulWPWE3DUpe4g5z+IDTSnqy3xbK0ITT0S+vk947xrTUeZkNPgmvfX7F+By+9tY1X39lexTeobZa9tY2la7exatMuoDpHeDZdrvFlPSlCPrT/aMA/u98UEEHL+9YjNnrKUpe4zuD+YJ7KpMQ3SXHq6MFMHjmQlqwpIKTsOCA0zXoS3FG3+yJt74fRVB15x2/jfsdqzVNAYIoQcwb4kJYs+wwfwH4e0xXU5tKt1RGcQqVRsPM06pR6ntxnEhQ99eHpY/jw9DFldYAyjSQTYw5JQqdndjT0z7Qixay2XfiOWUOb815r04TYkk3z2BUn+tpIC6h+4Qh3/lufRgQicqqILBeRVhG5MmB/s4jcr/cvEJGJnn1X6fLlInJKXJsicrkuUyIy0lMuIvIjve8FEanP6aTdRK2mRq+GTDoVG79fiqYK0DRC0lYkofRCLZRt9yfc71QUjN2iaXiERkiCQl8bqVT/cIQ3eBqRWKEhImngZuA0YDpwnohMN6pdBGxWSk0BbgRu0MdOB2YBBwCnAreISDqmzSeAk4E3jXOcBkzVf5cAt1b2VfsXRSdc/T+DDG7OMFD7KcIIcpZnDLNIdY5w50L251Tp7Z71M6BrjnD3Gmc919r9GJcLbGBzuuYWVKqGotDof7dKIpL8gkcArUqplQAiMhuYCXhzEMwErtGf5wA3iTNVeSYwWynVDrwuIq26PcLaVEo9q8vMfswEfq4cnfBpERkmInsppd6u5Av3F1Q/mqfx3XMOil3tLxM0lyNtRPRUMYrt9Gka5fM2Fr2xibFDWthn+B4Vt90XvL5hJ5t2dnD4hFIiQFcwdhpmqkrImrO9016Nr9xMGMa1Ouqt3mn01OhJhmbjgNWe7TW6LLCOUioHbAVGRBybpM1q+oGIXCIii0Vk8fr162OarF/qOTW6ycSRA9l72IDIOt51xb1lZf+rmCHeno+2939p9nPc8siKitvtK374t1e5Ys7zZWVF81RBkdd/lVIyBfoFhPsxbob+PsP3qBvhG4V7GzamyOiH0VNKqduVUjOUUjNGjRrV193pMfqTTyMJgeYpV9Nw1+ZI4NMwZWxnvsAzqzaz5M1NgN88tasjx66OXLXd7nV2duRp6yz/Dm701IKVG3lm1WbfMXFaHniDDpxtr4Bw1/vuH5FR8ZSy3Dam2EhinloL7OPZHq/LguqsEZEMMBTYGHNsXJvV9KNhKC732g80jSQEmaeKcwfSyc1T2XSqTDAoBR+/5cnitik0OvOK9s76MV635wo+bcn9Tnc/9SZ3P2W6Cp312eN8OVlzTkxAVuJ6XFCpGuzkvngWAVNFZJKINOE4tucadeYCF+jP5wAPad/DXGCWjq6ahOPEXpiwTZO5wPk6iuooYGuj+jOgNMppEJkRGJbrX78h/nZuijGhtJsv3HyB9jrKT9XemfdN3osTCEk0DV9aF18ywkbSNJz/jerTiNU0lFI5EbkcmA+kgTuVUi+JyLXAYqXUXOAO4B7t6N6EIwTQ9R7AcZrngMuUUnlwQmvNNnX5F4ErgLHACyIyTyl1MTAPOB1oBXYBn+uui1CP9KeEhUnIpv3p080MuN4XWUqCR4Jxo+FyLcRZ2a6eJvy15wq+yXtx0VJxUU/eOuYMfZd0wFru/RVpcE0jUfybUmoezkvbW3a153MbcG7IsdcB1yVpU5f/CPhRQLkCLkvS30ag5Ajv2370FkHJ7qaNGcTkUQMZv6fjRHdfWj+cdQjj99yDT9z6pK+dpOuK5/IFVm3ahVL1NUu8PVegM694c+NOxu+5B+mUxPY/G6Jp/OFfj2XZW9u44sEXigJ50siBTB45kMmjBpbV7S/JCJNQ/JoNqmk0xtCgH9KfZoQnIRNgnpo8ahAPffkERuglS93R8P5jh7DnHtnAduLMU66m8V/zXuHE7z8KUF/mqVyejnyBD37vEX7499eAePNUWNTTiEFNTB3jpAJxhe3YoS089JUTGL9neRRUkPmwv9LomoYVGnVKf8pym4RMOuXLT+WvU5pLEPQiFElunnq8tRSuXU+OcK+AWLByo68siDChkU2nPJP5oq9bOiVlE/76M43u02iMX7kf0p8m9yXhrIP24jNHToisUwoLTflehGcctBev/78zYucSuPZ/73WtN/OUi/sdYn0aaeGN68/gqMnDy8tTqcBFsIK48AMTOe3AsVX0uP5odE2j/uf0NyjFkNu+7Uav8ZED4l9I3sl+vjxU7mp/Cc1TUiY06sg81Vnqq/vCT6ppmAPnTFp8wQZhXH7i1Ap7Wr/YhIWWuqQ/zQjvLtwXXCrAVOKmkm+KMU+5I3XvO7Ke8lEFahoJ52CYr0Dv3Jj+sExrd1Ga3NfHHekjrNCoUxrNp5EE70JB5kvOfcBjzVNFTaNUVi/mKaVUYF/jzVPu+qVGuWcWfn9YPKm7sLmnLHWJUgqRxpkRngTvQk1VC4069mmYwiG5pqHNUx6pkRJHYzNX6rPYGeFWaNQpBWVNUyZe+3uYeSrOzNKRK3Dlgy/wwpqtxbJ8QZGrg8WZTOH26Kvr+ebvliZyhEO5ucVc4KpRJu4lwWoalrqkoFTDOMGT4l2oKZWSMr+E+3wnmacxe9FqX3k9aBtBGsXdT71ZgabhKfPNALd3m0ujJyy0QqNOsZqGH3c0HJQqvVChecqkHoRGWB+TCg3vyNmnaVifRhFrnrLUJQplneAG2XT56Lh8QprzhIelzHAJe8G6YbfPr95CW2fthODu6sixdK1jSmsP6Vc15qmskTnYRk+VaPAsIlZo1CvKaho+WrJpJ52F+DWNoiM8xsyysz147Yz2zgLb2jr5+K1P8ttnaycj/+yFq/n4LU/S1pnvsqbhfQcWZ4LrfS3Z6OV4Gwnr07DUJYWCaphkhUn55Ix9+Mn5hxeFhTfi5/PHT9Zl0bf8ro7g0Xp7rsDO9hz5gmLzrs5u6nHX2bKrg458yiRm1gAAFC9JREFUIVJoxCYs1NfkSyeXJui5msXA5gy3f/ZwPn5o3MKajYOIIGJ9GpY6o6BsuK3JqMHNnLj/mOJ2Np1CBJ0iY4RTlom+ZrkQQ/WO9k42bO8AqCnzVJsWCOu3t7OjLVhLilve1RWuH3rPaF759qlAecDARw4Yy54Dm7qju/2GlEjD+jRsGpE6paCsTyMOJw2GudpfdeOkT9z6VPFzLQmN3Voz+vCN/6ha8/ReE+vDSIazXktjSg2radQx1qcRTTZgXXH3Bfn10/fn6atOqqrdWhIa3r4kHfk2pVMs+PpJfPnD04ByoWGjpZIhIr60K42CvTPqlIKyPo04vAn3XFxTzL7DBzJ2aEtVa0DsriGhUU1fmjIpxgxpYZ/hzpoYXt+PiJNvys4Aj0awmoalznCEhn2wo8hEaBruS7Gal2NbDa2vUU1fiuG0xWtgLN2aapylW6slJWJDbi31heMI7+te1DbZCJ9GKcKq8kegljSNakxlTZnySZDmNcg00NKt1ZISJ4KxEbFCo05xEhbaBzuKTDpI0yiftNYcM9kviFr1aSSlKDjduRgZv6ZRbcBAo9DI0VP2zqhTnMl9fd2L2iZoxJxNR4+yk1BLQqMqn0ZR2wqaOR+yiJWlDJHyrMCNhBUadYr1acSTTadIp8PMU10RGrXk0+iKeSrYRJdOpWz0VAxifRqWesMmLIwnKHqqyTDNNFVhnqotn0a5AEsSDeYTnBm/T8NGT0Vj52lY6o5GvWErISh6KhMTORSEKZtryTxl9iWJj8YVlNkQ85SNnorH8Wk05jNoZ4TXKUqBtSBEc8SkPRk7tLmsbI8m55YfoBPwxa0ZDs6L2DuiryWhYWo9zZlUaP4sF1dYuEkIBzSVJyP8wH4jeN+4od3Yy/6HWEd4NCJyqogsF5FWEbkyYH+ziNyv9y8QkYmefVfp8uUickpcmyIySbfRqtts0uUXish6EXlO/13clS9e7yjr04jlkuP34ztnH1hWduL+o/nfzx7O5FGDgHLz1LhhAwLbac6Uv1RrxaehlArQNIKz0Y4d0lL87GpX0/cawq3/dBjHThlZVvd75x7MBR+Y2L2d7WekxKZGD0VE0sDNwGnAdOA8EZluVLsI2KyUmgLcCNygj50OzAIOAE4FbhGRdEybNwA36rY267Zd7ldKHaL/flrVN+4nWJ9GdTRlUpxywNjitvsCvf+So7jlnw4LPcbL7s58TWQ47cgXfKPdMB/Nzy86grs+936gZMISEU47cC9riqoCm+U2miOAVqXUSqVUBzAbmGnUmQncrT/PAU4SZxLBTGC2UqpdKfU60KrbC2xTH3OibgPd5tnVf73+i01Y2D24QmOvoQMY2FxurT1u6kjmXn6Mz0+QLyg6833/wmjr8Gs8zZkUv/nCB3jfuCFl5YOaM4wd6mgbdg5G12lkn0aSu2cc4F00eY0uC6yjlMoBW4EREceGlY8Atug2gs71CRF5QUTmiMg+Cfreb1EKu0Z4N1AMP02LTziM33MAB40fFuhcbsv1vV8jqA/N2RSH7rsnew8tN7U1ZVJdihizlGMn99UHvwcmKqUOAv5KSbMpQ0QuEZHFIrJ4/fr1vdrB3sTO0+gevBPdzJdpQQ/kg/wE29tydOYL7AhZ6a8n2dHunHt7wPoZbl+DzFbZLsxNsZQjDRxymyR6ai3gHdWP12VBddaISAYYCmyMOTaofCMwTEQyWtso1ldKbfTU/ynw3aDOKqVuB24HmDFjRr/9Ve1yr91DKezU/yJ1Z/w2Z/37jrn+oeLn+y85iiP1Ik89zcOvrONzdy0K3e9qRaa9vSmd8iRrtEKjq6ScKeENSZK7ZxEwVUc1NeE4tucadeYCF+jP5wAPKeeunQvM0tFVk4CpwMKwNvUxD+s20G3+DkBE9vKc76PAy5V91f6F9Wl0D17zlKlpuO/duLkPi97Y1CN9C+Kx1zZE7i8KDaO8KZ0qzk2pJt+WpRyraUSglMqJyOXAfCAN3KmUeklErgUWK6XmAncA94hIK7AJRwig6z0ALANywGVKqTxAUJv6lF8DZovId4BnddsAXxSRj+p2NgEXdvnb1zE2eqp78I6+zYmAhaLQCA5jNev1BnEvqpJ5qlQvmxZSKSlqU3a2d9dpZJ9Gosl9Sql5wDyj7GrP5zbg3JBjrwOuS9KmLl+JE11lll8FXJWkv42AsppGt+BNE55OiQ6ldPa5Jp64kXktDThdU5q3T74EhdY81WUaWdOwd0+dYh3h3UNTOoUIWmBI8QULMHSPrFMnRmj05ssj7lxu/4cPbCqWZTPlvgwbPdV17CJMlrpDYVOjdweHTdiTk/YfXVybxAlNFa45azpXnLI/EG+e6s13R9yLytU0vjXzAK45y5kv6wqSpnSK46aO5JB9hvVoHxuBVAOnRre5p+oUZ+U+KzW6yikHjC2bId6cSZEvKC48ZlKpLCB6yktHrvfSiiT1aQxpyXLBBybyrT8sK2oWqZRwz0VH9ngfG4GUSDEku9Gwmkad4uSe6ute9D+a0infwk1en8ae2mTlZVdH783VCBJQQ1pKYz9vX0Uc57c1R/UM1qdhqSsKdrnXHqEp43/JuqP3n5w/g7/8+wd9x/TmBL+dAQLqyatO4kfnHQr4TWnZdLmfxtI9NHL0lL2b6pRCwfo0egJvug0Xd/Q+qDnD4Ba/RXdXe++lFNlpnEsEBjalGazzZpmmtEw6Zedl9ACplE1YaKkzFFbT6AmaMimyGcM8lS1FHQW9gING/z3FTkOrGZBNO1Fful9m/7Jpa57qCVIiDeoGt0KjbnEm9/V1L/ofTemUL6WIa/JxwnOFFmM035vmKfNc7kJKbjhtoHnKCo1uR2yWW0u9YRdh6hmaMqV0G94y73931T+X3jRPmavyFVcgNProkrE+jR5B6N1MALWEvZvqFCfktq970f9oyqR9Po0W44XcYgiNXnWEG+dyTWfN1jzVq6QaeBEmO0+jTrEzwnuGCz8wgR2G5nDSe8fw5Q9PY8LwPYAATaM3fRodfp8GwLQxg/nSyVM5ftqosv2Xf2gKowaXr5Nu6TqNvAiTFRp1ip3c1zOcuP8YX9nwgU3860lTi9vNhtDY2Z7ntkdXcOyUkbxv3NAe6dczqzaz5I3NvvXJXa0nnRK+dPI033EfP2x8j/Sn0WnkyX1WaNQrdnJfnzHAcIR35Atc/6dXAHjj+jN65Jwfv+XJkL5Epzix9AzSwGlErLGzTrGp0fsOr0/DnCF+4DXzae/GpWB3tueYfvWfy8q85zQjuSy9g5Pltq970TfYO65OKShl1wjvI9zR/eRRAzl2arkPYXtbjnXb2rvtXK9v2OmLmDp+2igmjHD8K6ZT3tI7OFluG1NqWKFRp1ifRt/R0uS8qOdefiwfPXhv3/5129sodMMwtFBQbNnV6Sv/5Ix9mPMvHwCseaqvsGlELHWHTVjYd7ToCXQtmVRZskCXT9z6FF+Z83yXz3PpvUv4zB0LfOWDWzJFs5TVNPoGuwiTpe5Q1qfRZwxocjLhZtIpBrf4s94C/PqZtV0+z/yX3g0sH9ySLQqLAU1WaPQFdhEmS91RUIqU/fX6hAnDB7KvnrMRlMCwpxnckiGbTrH30Bb20f2w9C52cp+l7nAc4VbT6AsuOnYSF3xgIuAsdtQTRL2QXEH1yFc/5Fv7w9I7iPVpWOoNZdOI9BmpVCkJ4KAITcNM+VEJG3d2BJY3Z1KlBIqZFCkrNPqElPVpWOoNm0akNkinhEHNwYLjrS27q2537ebgY8N8KJbexWoalrpDYVOj1wqmX8ONqFrTBaHhCpxhxuTBoGgtS+9jfRqWusNqGrXD4JYMzZk9+NLJ0zjjoL3YsquT91/3N1rf3cHRk0dUHBbb1pnntXU7AHjsig/RlEnx56Xv8F/zXu4Tx7vFj42estQdhQJYP3htMLgly5ABWc4+dBzZdIqRg5oYPrCJ6+a9HJozKgylFKf/8DF+8NdXGTdsAINbsjRn0sw8ZByDW7LWPFUjNPI8DTtsqVPsIky1w/lHTyCXL71ARIRpYwbx9MpNLHt7G79csIqPHzYuVuPY1ZHj/kWrWblhJwD7jx1ctv+iYycxdIAVGrWAXbkvBhE5VUSWi0iriFwZsL9ZRO7X+xeIyETPvqt0+XIROSWuTRGZpNto1W02xZ2jEbHLvdYOMw8ZxycOD09B/vXfvMi3fr8stp0rHyyvZwqZ847Yl9MP3Kv6jlq6DWueikBE0sDNwGnAdOA8EZluVLsI2KyUmgLcCNygj50OzAIOAE4FbhGRdEybNwA36rY267ZDz9GoKKymUcucYbzcf/fcWpau3cr2Nn8uqa27O3lu9Rb+tPTtsvKT3ju6R/toqZ6U0KCJ0ZOZp44AWpVSKwFEZDYwE/AOnWYC1+jPc4CbxMmmNxOYrZRqB14XkVbdHkFtisjLwInAp3Wdu3W7t4adQ/VACMOjr67nO3+IHxn2JRt2dNh5GjXMZ46awEcPGUdbZ563tuzmMz9dwJk/fpyWbIrRg1vK6r67rY32XIEhLRl+felRjBnaTHMmbSOlapiUCGs27+LDP3i0r7sSyqfevw8XHze529tNcleOA1Z7ttcAR4bVUUrlRGQrMEKXP20cO05/DmpzBLBFKZULqB92jg3ejojIJcAlAPvuu2+Cr+dnUHOGqWMGVXVsbzFtzGDOPmRcfEVLnyAiDB2QZeiALGOGtHDfJUfx2GsbWLetjW1t5ZP+hg7IMnJQEyfuP4bpew/pox5bKuETh43v1nVTeoKRg3pmmd9+N5RRSt0O3A4wY8aMqrSQwyfsyeETDu/Wflkam4PGD+Og8cP6uhuWbuLYqSM5durIvu5Gn5DEEb4W2MezPV6XBdYRkQwwFNgYcWxY+UZgmG7DPFfYOSwWi8XSSyQRGouAqTqqqQnHsT3XqDMXuEB/Pgd4SPsa5gKzdOTTJGAqsDCsTX3Mw7oNdJu/izmHxWKxWHqJWPOU9h9cDswH0sCdSqmXRORaYLFSai5wB3CPdnRvwhEC6HoP4DjNc8BlSqk8QFCb+pRfA2aLyHeAZ3XbhJ3DYrFYLL2H9OfB+owZM9TixYv7uhsWi8VSV4jIEqXUjKB9No2IxWKxWBJjhYbFYrFYEmOFhsVisVgSY4WGxWKxWBLTrx3hIrIeeLPKw0dizDavIWq1b7ZflWH7VRm2X5VTbd8mKKVGBe3o10KjK4jI4rDogb6mVvtm+1UZtl+VYftVOT3RN2ueslgsFktirNCwWCwWS2Ks0Ajn9r7uQAS12jfbr8qw/aoM26/K6fa+WZ+GxWKxWBJjNQ2LxWKxJMYKDYvFYrEkxgqNAETkVBFZLiKtInJlH/flDRF5UUSeE5HFumy4iPxVRF7T//fshX7cKSLrRGSppyywH+LwI339XhCRw3q5X9eIyFp9zZ4TkdM9+67S/VouIqf0YL/2EZGHRWSZiLwkIv+my/v0mkX0qxauWYuILBSR53XfvqXLJ4nIAt2H+/VyCuglF+7X5QtEZGIv9+suEXndc80O0eW9dv/r86VF5FkR+YPe7tnrpZSyf54/nFTtK4DJQBPwPDC9D/vzBjDSKPsucKX+fCVwQy/043jgMGBpXD+A04E/AQIcBSzo5X5dA3wloO50/Xs2A5P075zuoX7tBRymPw8GXtXn79NrFtGvWrhmAgzSn7PAAn0tHgBm6fLbgEv15y8At+nPs4D7e7lfdwHnBNTvtftfn+8/gF8Cf9DbPXq9rKbh5wigVSm1UinVAcwGZvZxn0xmAnfrz3cDZ/f0CZVS/8BZxyRJP2YCP1cOT+OsxrhXL/YrjJnAbKVUu1LqdaAV5/fuiX69rZR6Rn/eDryMs859n16ziH6F0ZvXTCmldujNrP5TwInAHF1uXjP3Ws4BThIR6cV+hdFr97+IjAfOAH6qt4Uevl5WaPgZB6z2bK8h+qHqaRTwFxFZIiKX6LIxSqm39ed3gDF907XQftTCNbxcmwbu9Jjv+qRf2gxwKM4ItWaumdEvqIFrpk0tzwHrgL/iaDZblFK5gPMX+6b3bwVG9Ea/lFLuNbtOX7MbRaTZ7FdAn7ub/wGuAAp6ewQ9fL2s0Kh9jlVKHQacBlwmIsd7dypH1+zzuOla6YfmVmA/4BDgbeD7fdURERkEPAh8SSm1zbuvL69ZQL9q4poppfJKqUOA8Tgazf590Q8Ts18i8j7gKpz+vR8YjrPqaK8hImcC65RSS3rzvFZo+FkL7OPZHq/L+gSl1Fr9fx3wG5wH6V1X3dX/1/VR98L60afXUCn1rn7IC8BPKJlTerVfIpLFeTHfq5T6tS7u82sW1K9auWYuSqktwMPA0TjmHXdpau/5i33T+4cCG3upX6dqU59SSrUDP6P3r9kxwEdF5A0cM/qJwA/p4etlhYafRcBUHYHQhOMwmtsXHRGRgSIy2P0MfARYqvtzga52AfC7vuhfRD/mAufrKJKjgK0ek0yPY9iPP4Zzzdx+zdJRJJOAqcDCHuqD4Kxr/7JS6geeXX16zcL6VSPXbJSIDNOfBwAfxvG5PAyco6uZ18y9lucAD2ntrTf69YpH+AuO38B7zXr8t1RKXaWUGq+UmojznnpIKfVP9PT16k4vfn/5w4l+eBXHnvqNPuzHZJzIleeBl9y+4Ngh/w68BvwNGN4LfbkPx2zRiWMnvSisHzhRIzfr6/ciMKOX+3WPPu8L+kHZy1P/G7pfy4HTerBfx+KYnl4AntN/p/f1NYvoVy1cs4OAZ3UflgJXe56DhThO+F8Bzbq8RW+36v2Te7lfD+lrthT4BaUIq167/z19PIFS9FSPXi+bRsRisVgsibHmKYvFYrEkxgoNi8VisSTGCg2LxWKxJMYKDYvFYrEkxgoNi8VisSTGCg2LxWKxJMYKDYvFYrEk5v8DfWah8LwjZCAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Max Normalized Error 0.0005571487508716499\n",
            "RMS Normalized Error 0.01138875657799844\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7yAyh9psApE"
      },
      "source": [
        "The difference in primary beam is due to CASA using a sampled 1D function while ngCASA calculates the PB for each pixel. If it is found that PB creation becomes a bottleneck for ngCASA the implementation will be changed to match CASA."
      ]
    }
  ]
}