06-Tables/Advanced_Tables.ipynb

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   "source": [
    "# Advanced Tables for JWST\n"
   ]
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    "# Table design goals and requirements\n",
    "\n",
    "- Easily mutable container of heterogeneous tabular data\n",
    "- Relatively lightweight yet powerful enough for most needs\n",
    "- Responsive to astronomy community needs\n",
    "  - For JWST community - if something is missing, broken, needs improvement then ASK!\n",
    "- Deep integration with Astropy (I/O, units, quantity)\n",
    "- Persistent metadata (column units, table header keywords, formatting)\n",
    "- Support missing data"
   ]
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   "source": [
    "# Why doesn't Astropy use Pandas DataFrame?\n",
    "\n",
    "- Easily mutable container of heterogeneous tabular data **(only scalar data)**\n",
    "- **Relatively lightweight** yet powerful enough for most needs\n",
    "- Responsive to **astronomy community** needs\n",
    "- **Deep integration with Astropy (I/O, units, quantity)**\n",
    "- **Persistent metadata (column units, table header keywords, formatting)**\n",
    "- Support missing data: **Pandas will cast ``int`` types to ``float64`` to use ``NaN``**\n",
    "  - Large ``int64`` values lose precision\n",
    "  - Short int (e.g. ``uint8``) values take 4 times as much memory as ``MaskedColumn``\n",
    "\n",
    "### Nevertheless...\n",
    "\n",
    "We recognize Pandas is very fast, powerful and widely used.\n",
    "\n",
    "*Astropy Project recommendation is to use `astropy.Table` where possible.  This especially applies to community packages.*\n"
   ]
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    "# Example: multiband photometry of a field\n",
    "\n",
    "### Observations in 5 bands of a single field with 5 \"galaxy-like\" sources\n",
    "\n",
    "- Assumes basic image reduction and source detection is done.\n",
    "- Could be similar to JWST post-image processing workflow.\n",
    "\n",
    "### Key Table concepts to be covered\n",
    "\n",
    "- Basic table structure (dict of independent column objects)\n",
    "- Base column class properties and attributes (flexibility in data elements)\n",
    "- Table mutability and formatting\n",
    "- Database operations: join, grouping, binning, stacking, indexing\n",
    "- Missing data\n",
    "- Mixin columns (Quantity, Time, Coordinates, QTable vs. Table)"
   ]
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    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import astropy\n",
    "from astropy import table\n",
    "from astropy.table import Column, Table, QTable\n",
    "from astropy.time import Time\n",
    "import astropy.units as u\n",
    "import photutils.datasets\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning)"
   ]
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    "astropy.__version__"
   ]
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   "source": [
    "### Make table of  5 random gaussians corresponding to fake elliptical galaxy-like sources\n",
    "\n",
    "Use a photutils utility function [make_random_gaussians_table](https://photutils.readthedocs.io/en/stable/api/photutils.datasets.make_random_gaussians_table.html)."
   ]
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    "n_sources = 5\n",
    "param_ranges = dict([('flux', [1000, 10000]),\n",
    "                ('x_mean', [10, 190]),\n",
    "                ('y_mean', [10, 190]),\n",
    "                ('x_stddev', [2, 5]), \n",
    "                ('y_stddev', [2, 5]),\n",
    "                ('theta', [0, np.pi])])\n",
    "sources = photutils.datasets.make_random_gaussians_table(n_sources, param_ranges, random_state = 1)"
   ]
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    "sources"
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    "### Digression: learn a little about the Table and Column objects\n",
    "\n",
    "- `Table` is a **container class** where `Table.columns` is the main table data structure\n",
    "- `Table.columns` is an OrderedDict of columns (`Column`, `MaskedColumn`, or mixin-column)\n",
    "- `Column` class inherits from `np.ndarray`\n",
    "- `MaskedColumn` class inherits from `np.ma.MaskedArray`"
   ]
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   "source": [
    "type(sources.columns).__mro__"
   ]
  },
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   "source": [
    "type(sources.columns['flux']).__mro__"
   ]
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   "source": [
    "sources.columns['flux'] is sources['flux']"
   ]
  },
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   "source": [
    "### Make a synthetic image for cutouts"
   ]
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   "source": [
    "img = photutils.datasets.make_gaussian_sources_image(shape=(200, 200), \n",
    "                                               source_table=sources)\n",
    "plt.imshow(img);  # Trick: trailing semicolon to suppress output"
   ]
  },
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   "source": [
    "### Make postage-stamp cutouts for each source"
   ]
  },
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    "# Make integer columns with rounded representation of source mean position \n",
    "\n",
    "sources['x0'] = np.round(sources['x_mean']).astype(int)\n",
    "sources['y0'] = np.round(sources['y_mean']).astype(int)"
   ]
  },
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   "source": [
    "# Generate list of cutout images around each source\n",
    "\n",
    "npix = 10\n",
    "cutouts = [img[y0-npix:y0+npix, x0-npix:x0+npix] for x0, y0 in sources['x0', 'y0']]\n",
    "\n",
    "# `cutouts` is a list of 2-d ndarrays"
   ]
  },
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    "# Add the cutouts into table: each element is a 2-d image\n",
    "# This shows:\n",
    "#  - Table mutability and independent columns\n",
    "#  - Storage of ndarray in each table cell\n",
    "\n",
    "sources['cutout'] = cutouts"
   ]
  },
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   "source": [
    "plt.imshow(sources['cutout'][3], interpolation='nearest');"
   ]
  },
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   "metadata": {},
   "source": [
    "### Add a source identifier to the table"
   ]
  },
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   "source": [
    "sources['id'] = ['jwst-{}-{}'.format(x0, y0) for x0, y0 in sources['x0', 'y0']]\n",
    "sources"
   ]
  },
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   "source": [
    "### Formatting: let's be a little fussy about the Table:\n",
    "- Put the 'id' column first\n",
    "- Make the precision of table outputs more reasonable (and beautiful!)\n",
    "- Add units to `flux` and `theta` columns\n",
    "\n"
   ]
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    "# Move the `id` column to be the first column \n",
    "# (Should Table get a method `move_column` to make this easier?)\n",
    "sources_id = sources['id']\n",
    "del sources['id']\n",
    "sources.add_column(sources_id, index=0)\n",
    "\n",
    "# Set the output formatting for particular columns\n",
    "for name in ('flux', 'x_mean', 'y_mean', 'x_stddev', 'y_stddev', 'theta'):\n",
    "    sources[name].format = '.3f'  # Could also use '%.3f' or '{:.3f}'\n",
    "sources['cutout'].format = '.3g'\n",
    "\n",
    "# Set the unit for flux and theta\n",
    "sources['flux'].unit = u.electron\n",
    "sources['theta'].unit = u.rad\n",
    "\n",
    "sources"
   ]
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   "source": [
    "### Digression: table and column summary information"
   ]
  },
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   "outputs": [],
   "source": [
    "sources.info"
   ]
  },
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   "source": [
    "sources.info('stats')\n",
    "# You can write your own info specifications!"
   ]
  },
  {
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   "source": [
    "# You can roll your own custom info!\n",
    "\n",
    "from astropy.utils.data_info import data_info_factory\n",
    "mystats = data_info_factory(names=['my_min', 'my_median', 'my_max'],\n",
    "                            funcs=[np.min, np.median, np.max])\n",
    "sources.info(mystats)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column info: name, dtype, unit, format, description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sources['theta'].info.description = 'Elliptical gaussian rotation angle'\n",
    "sources['theta'].info"
   ]
  },
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   "source": [
    "### Make fake observations of these sources in 5 bands 'u', 'b', 'v', 'r', 'k'"
   ]
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    "def make_observation(sources, band, flux_mult):\n",
    "    \"\"\"\n",
    "    Make fake observation of ``sources`` in a field in ``band``.  Apply\n",
    "    ``flux_mult`` flux multiplier and some gaussian noise on parameters.\n",
    "    \"\"\"\n",
    "    n = len(sources)\n",
    "    out = sources.copy()\n",
    "\n",
    "    # Multiply flux by randomized version of flux_mult\n",
    "    out['flux'] *= flux_mult * np.random.normal(loc=1, scale=0.1, size=n)\n",
    "\n",
    "    # Add 1.0 pixel of position and stddev noise\n",
    "    for name in ('x_mean', 'y_mean'):\n",
    "        out[name] += np.random.normal(loc=0, scale=1.0, size=n)\n",
    "  \n",
    "    # Add 0.1 pixel noise to stddev\n",
    "    for name in ('x_stddev', 'y_stddev'):\n",
    "        out[name] += np.random.normal(loc=0, scale=0.1, size=n)\n",
    "        \n",
    "    # Add a list that repeats the ``band`` as the second column\n",
    "    out.add_column(Column([band] * n, name='band'), index=1)\n",
    "    \n",
    "    # Make integer columns with rounded representation of source mean position  \n",
    "    sources['x0'] = np.round(sources['x_mean']).astype(int)\n",
    "    sources['y0'] = np.round(sources['y_mean']).astype(int)\n",
    "    \n",
    "    # Generate list of cutout images around each source\n",
    "    img = photutils.datasets.make_gaussian_sources_image(shape=(200, 200), source_table=out)\n",
    "    npix = 10\n",
    "    cutouts = [img[y0-npix:y0+npix, x0-npix:x0+npix] for x0, y0 in sources['x0', 'y0']]\n",
    "    sources['cutout'] = cutouts\n",
    "\n",
    "    return out"
   ]
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    "sources_list = []\n",
    "for band, flux_mult in [('u', 0.1), \n",
    "                        ('b', 0.2), \n",
    "                        ('v', 0.5), \n",
    "                        ('r', 1.0), \n",
    "                        ('k', 1.5)]:\n",
    "    sources_list.append(make_observation(sources, band, flux_mult))\n",
    "    \n",
    "sources_list[0]\n",
    "\n",
    "# Notice that the formatting and units got inherited into our new tables"
   ]
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   "source": [
    "# Pretend that there are non-detections in some bands\n",
    "\n",
    "sources_list[0].remove_rows([1,2,3,4])\n",
    "sources_list[1].remove_row(1)\n",
    "sources_list[3].remove_row(0)"
   ]
  },
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   "source": [
    "## Database-like features for more power: vstack, indexing, group and join\n",
    "\n",
    "Our list of source tables **`sources_list`** has the raw data we need for analysis but is inconvenient.  Things we'd like to do:\n",
    "\n",
    "- Find all observations of a particular source\n",
    "- List all sources in a particular band\n",
    "- Compute statistics for a particular source (mean centroid, mean image cutout)\n",
    "- Make a single wide table organized by source\n",
    "\n",
    "See [Table high-level operations](http://docs.astropy.org/en/stable/table/operations.html) for all the details."
   ]
  },
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   "source": [
    "### Stacking"
   ]
  },
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   "source": [
    "# Stack the list of tables to create a single table (database) of every source observation.\n",
    "\n",
    "srcs = table.vstack(sources_list)\n",
    "\n",
    "srcs"
   ]
  },
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   "source": [
    "### Indexing\n",
    "\n",
    "- Indexing means that supplemental information (an index) is added to the table that allows access to particular elements in `time << O(N)`.  \n",
    "- In the case of astropy Table it uses a binary search of an ordered index table `O(log(N))`."
   ]
  },
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    "# Now add a database index on the `id` column.  This becomes the 'primary key'.\n",
    "# In this case it does not need to be unique, though one can declare that an\n",
    "# index must be unique.\n",
    "\n",
    "srcs.add_index('id')"
   ]
  },
  {
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   "source": [
    "# Now access elements with id == 'jwst-27-85'.  This should be familiar to Pandas users.\n",
    "# This returns another Table.\n",
    "\n",
    "srcs.loc['jwst-27-85']"
   ]
  },
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   "source": [
    "# Let's make a secondary index to allow slicing the table by band\n",
    "srcs.add_index('band')"
   ]
  },
  {
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   "source": [
    "# Get a table of all 'b' band source detections\n",
    "\n",
    "srcs.loc['band', 'b']"
   ]
  },
  {
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   "source": [
    "# A special case is if only one table row is selected, in which case\n",
    "# a Row object is returned.  This is convenient for the common use case\n",
    "# of a table with unique keys.\n",
    "\n",
    "srcs.loc['band', 'u']"
   ]
  },
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   "source": [
    "### Digression: difference between Row and length=1 Table\n",
    "\n",
    "- Indexing a single element of a table returns a `Row` object which can be used to set or access a column value.  This always returns a scalar value.\n",
    "- Indexing a single row slice of a table returns a Table, so accessing a column returns a `Column` object (an array) with a length of 1.\n",
    "- This is consistent with numpy structured arrays and Pandas (`df.ix[0]` vs. `df[0:1]`)."
   ]
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   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "67a60374-bbad-4e5e-b67a-3fc487e7678d"
    }
   },
   "outputs": [],
   "source": [
    "srcs[0]['band']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "101395fe-facb-4c06-855f-883483d44872"
    }
   },
   "outputs": [],
   "source": [
    "srcs[0:1]['band']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "548cb8e9-2d36-46bf-a99f-14d474465cd1"
    }
   },
   "source": [
    "### Digression: table access performance\n",
    "\n",
    "**``srcs[0:1]['band']``** and **``srcs['band'][0:1]``** give the same output, but performance is very different!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "be14a180-2c14-4285-8033-84cd5cf4d918"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# This creates an entire new Table object (slow) and then selects one column (fast)\n",
    "%timeit srcs[0:1]['band']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "e2827346-e5f4-47fc-a07d-2e995d101e50"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# This selects a column (fast) and then slices it (fast-ish, creates new Column)\n",
    "%timeit srcs['band'][0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b777e5f7-4554-4c08-b1fe-3ec2868aa0a8"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# For the most performance, drop the `Column` machinery (no metadata) and use a straight numpy array\n",
    "\n",
    "%timeit np.array(srcs['band'], copy=False)[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "82cd29e1-8d82-44f5-8f69-c358daf8df76"
    }
   },
   "source": [
    "### Grouping\n",
    "\n",
    "Astropy `Table` supports the powerful concept of grouping which lets you group the rows into sub-tables which you can then:\n",
    "\n",
    "- [Examine](http://docs.astropy.org/en/stable/table/operations.html#manipulating-groups): select and loop over groups\n",
    "- [Aggregate](http://docs.astropy.org/en/stable/table/operations.html#aggregation): apply a reduction function like np.mean to each group\n",
    "- [Filter](http://docs.astropy.org/en/stable/table/operations.html#filtering): select groups by means a selection function\n",
    "\n",
    "This is a close cousin to indexing, and if a table is already indexed then creating the grouped version is faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "3c3ba47a-841e-4638-b91e-452cd02f111a"
    }
   },
   "outputs": [],
   "source": [
    "srcs_grouped = srcs.group_by('id')\n",
    "\n",
    "# srcs_grouped has all the same rows but now ordered by ``id``\n",
    "srcs_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "eccb5d7c-f84b-4401-898e-6c3fc2934d30"
    }
   },
   "outputs": [],
   "source": [
    "for src in srcs_grouped.groups:\n",
    "    print(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "c3b3c5fd-14a2-410e-934c-577f2197355e"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Now let's make a new table where each row is the mean of all rows in the group\n",
    "mean_srcs = srcs_grouped.groups.aggregate(np.mean)\n",
    "mean_srcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "f33d2a7c-7088-4f9a-a3a6-3026e90cb6a0"
    }
   },
   "outputs": [],
   "source": [
    "# We can define custom behavior depending on column type or even name\n",
    "\n",
    "def sources_mean(arr):\n",
    "    if arr.dtype.kind in ('S', 'U'):\n",
    "        out= ', '.join(arr)\n",
    "    elif arr.info.name == 'flux':\n",
    "        # Take the log mean\n",
    "        out = np.exp(np.mean(np.log(arr)))\n",
    "    else:\n",
    "        out = np.mean(arr, axis=0)\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "6dd83292-ad2f-4d69-a496-9bd984824fff"
    }
   },
   "outputs": [],
   "source": [
    "mean_srcs = srcs_grouped.groups.aggregate(sources_mean)\n",
    "mean_srcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "ad224e3e-7125-4d3d-aa82-8ca642683a08"
    }
   },
   "outputs": [],
   "source": [
    "plt.imshow(mean_srcs['cutout'][3], interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6fede881-f2c1-4dbb-bc5b-7b4083a41319"
    }
   },
   "source": [
    "### Digression: Binning\n",
    "\n",
    "A common tool in analysis is to **bin** a table based on some reference value. Examples:\n",
    "\n",
    "- Photometry of a binary star in several bands taken over a span of time which should be binned by orbital phase.\n",
    "- Reducing the sampling density for a table by combining 100 rows at a time.\n",
    "- Unevenly sampled historical data which should binned to four points per year.\n",
    "\n",
    "The common theme in all these cases is to convert the key value array into a \n",
    "new `float`- or `int`-valued array whose values are identical for rows in the same output bin. As an example, generate a fake light curve:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "75749b2c-6ab7-42a7-ae2c-3815076115a7"
    }
   },
   "outputs": [],
   "source": [
    "year = np.linspace(2000.0, 2010.0, 200)  # 200 observations over 10 years\n",
    "period = 1.811\n",
    "y0 = 2005.2\n",
    "mag = 14.0 + 1.2 * np.sin(2 * np.pi * (year - y0) / period) + np.random.normal(scale=0.1, size=200)\n",
    "phase = ((year - y0) / period) % 1.0\n",
    "dat = Table([year, phase, mag], names=['year', 'phase', 'mag'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "2431bbb4-5212-4705-994a-d14d05f02993"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 2))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(dat['year'], dat['mag'], '.')\n",
    "plt.xlabel('year')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.xlabel('phase')\n",
    "plt.plot(dat['phase'], dat['mag'], '.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b0f69602-a1a2-4491-88f6-58141daed464"
    }
   },
   "outputs": [],
   "source": [
    "phase_bin = np.trunc(phase / 0.1)\n",
    "phase_bin[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "2f85f058-5271-40f4-befb-4cb102a1d395"
    }
   },
   "outputs": [],
   "source": [
    "dat_grouped = dat.group_by(phase_bin)\n",
    "dat_mean = dat_grouped.groups.aggregate(np.mean)\n",
    "dat_std = dat_grouped.groups.aggregate(np.std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b1509784-99b7-4f8a-a21e-390b7ddf46e0"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(4, 2))\n",
    "plt.xlabel('phase')\n",
    "plt.errorbar(x=dat_mean['phase'], xerr=0.05, y=dat_mean['mag'], yerr=dat_std['mag'], fmt='.');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join tables to make a single wide table by source `id`\n",
    "\n",
    "- Have one row corresponding to each of the 5 sources\n",
    "- Each row has columns with the 5 band u, b, v, r, k properties\n",
    "- De-duplicate column names by labeling as `{colname}_{band}`\n",
    "\n",
    "Because there are non-detections for some bands / sources, the result is a **Masked Table**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sources_id = None\n",
    "for left, right in zip(sources_list[:-1], sources_list[1:]):\n",
    "    sources_id = table.join(left=sources_id or left,\n",
    "                            right=right,\n",
    "                            keys='id',\n",
    "                            join_type='outer',\n",
    "                            table_names=[left['band'][0], right['band'][0]])\n",
    "sources_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspect a masked element\n",
    "\n",
    "sources_id.add_index('id')\n",
    "sources_id.loc['jwst-44-133']['flux_u'] is np.ma.masked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4da39228-2386-45a3-9a5a-73176774f28b"
    }
   },
   "source": [
    "## Astropy integration: quantities, units, mixin columns, QTable and all that\n",
    "\n",
    "A major feature of astropy `Table` is integrated support for:\n",
    "\n",
    "- ``Quantity`` columns that have meaningful units\n",
    "- ``Time`` and ``Coordinate`` columns\n",
    "- Other \"mixin columns\"\n",
    "\n",
    "Mixin columns are object types that adhere to the mixin protocol and are\n",
    "stored and manipulated **natively** in the table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0fa3c312-747b-447f-8dbe-9182338d58ad"
    }
   },
   "source": [
    "### Example: store a Time object in a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "dcab0899-6050-4b51-b218-c48aa335cbee"
    }
   },
   "outputs": [],
   "source": [
    "t = Table()\n",
    "t['index'] = [1, 2]\n",
    "t['time'] = Time(['2001-01-02T12:34:56', '2001-02-03T00:01:02'])\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "2a8ce3d6-9a10-413d-b442-f71a342312cb"
    }
   },
   "outputs": [],
   "source": [
    "# The time column is a bona-fide Time object\n",
    "t['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "bf20c29a-a34b-4e66-8e48-ebd0a2a8dd2d"
    }
   },
   "outputs": [],
   "source": [
    "# In case you don't believe me\n",
    "t['time'].mjd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8dce3059-e0fe-4f6f-b1d2-3106e8d7285c"
    }
   },
   "source": [
    "### Quantity: doesn't Table already support units?\n",
    "\n",
    "We saw in the `sources` table that we can define unit.  Aren't we good to go?  **No**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "df40f679-3d17-41b7-9832-b30932ac73c8"
    }
   },
   "outputs": [],
   "source": [
    "type(sources['theta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "46442fe3-3ff6-4008-a6c5-884ac0034739"
    }
   },
   "outputs": [],
   "source": [
    "sources['theta'].unit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "37777a69-8759-4e9a-bc49-d47b41d972c7"
    }
   },
   "source": [
    "**Normal table `Column` class is just carrying `unit` as an attribute.**\n",
    "\n",
    "It is no more special than `description` or `format`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "22974075-0940-4ad6-9ec1-1afe11ee7e1a"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "t2 = sources['theta'] ** 2\n",
    "t2.unit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "89a3f266-89ae-4b1c-9072-54a4e4b57d93"
    }
   },
   "source": [
    "### QTable to the rescue\n",
    "\n",
    "Astropy has a `QTable` class for tables that use `Quantity` objects for columns with units.\n",
    "\n",
    "See the [Quantity and QTable](http://docs.astropy.org/en/stable/table/mixin_columns.html#quantity-and-qtable) section for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "6b2c13c5-f982-443f-b750-acbe3ec626aa"
    }
   },
   "outputs": [],
   "source": [
    "# Let's make `flux` and `theta` be real Quantity objects!\n",
    "qsources = QTable(sources)\n",
    "qsources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2ca67d27-df29-41d6-95e7-e50909841b75"
    }
   },
   "source": [
    "*The repeated presence of `electron` and `rad` in each Quantity value is a problem that is fixed in 1.3-dev*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b56371c9-fc65-488d-bcfd-9860900d9116"
    }
   },
   "outputs": [],
   "source": [
    "type(qsources['theta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "a551ad97-8da0-49e7-9b74-82c6c56077f6"
    }
   },
   "outputs": [],
   "source": [
    "qt2 = qsources['theta'] ** 2\n",
    "qt2.unit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "15e41f96-b13b-4001-beb9-d353a92dbc5c"
    }
   },
   "source": [
    "### Summary of `Table` and `QTable`\n",
    "\n",
    "In short, `Table` and `QTable` are **identical in every way except for handling columns with units**:\n",
    "- `Table` uses `Column` for any columns with units (with informational-only unit attribute)\n",
    "- `QTable` uses `Quantity` for any columns with units (with meaningful unit attribute)\n",
    "\n",
    "Use `QTable` in general if you are fully on-board with using `Quantity` and do not deal with much legacy code.\n",
    "\n",
    "Use `Table` if you are using code that is not `Quantity`-aware, OR if you need full missing data support.  `Quantity` does not support missing (masked) data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a03b7659-82e3-4b87-a072-c60cfa58d0c2"
    }
   },
   "source": [
    "### Digression: storing a Pandas Series within Astropy Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b4010a3d-ccb2-4266-b3fb-874a1c4df56f"
    }
   },
   "outputs": [],
   "source": [
    "from astropy.utils.data_info import ParentDtypeInfo\n",
    "import pandas as pd\n",
    "\n",
    "class SeriesMixin(pd.Series):\n",
    "    info = ParentDtypeInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "be1c29e8-7230-4c9a-90c2-16e187bfeb96"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "s = SeriesMixin((np.arange(5)-2)**2)\n",
    "pt = Table([s], names=['s'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b568145e-9eb8-4a7a-b707-69298de87da8"
    }
   },
   "outputs": [],
   "source": [
    "pt['s'].info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "769f3ac3-424d-42b7-a357-3cd70630973e"
    }
   },
   "outputs": [],
   "source": [
    "isinstance(pt['s'], pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "102ec9ba-389c-4aec-9b37-12ef2481f372"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pt['s'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But wait, there's more... for another day\n",
    "\n",
    "- Transform to/from other table-like objects (Pandas DataFrame, numpy structured array, LSST)\n",
    "- Subclass for custom applications (documentation example of params)\n",
    "- Unified interface for I/O (ASCII, FITS, votable)"
   ]
  }
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