astro-otter 0.0.2

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OTTER API

Open mulTiwavelength Transient Event Repository

A Python API for the OTTER.

Installation

To install the OTTER API use

git clone https://github.com/astro-otter/otter.git
cd otter
python -m pip install .

This will be changed into the more convenient python -m pip install astro-otter at a later date!

For developers, please also enable the pre-commit hooks using

pre-commit install

Tutorial

Connecting to the OTTER

# import the API
from otter import Otter, Transient

# connect to the database
# this username and password is just for now and will be updated later!
db = Otter(username='user@otter', password='insecure')

A typical workflow

First use Otter.getMeta to query

# can query by ANY name associated with an object
db.getMeta(names=['ASASSN-15oi', 'AT2020opy'])

[{'name': {'default_name': 'ASASSN-15oi', 'alias': [{'value': 'ASASSN-15oi', 'reference': 'ASASSN'}]}, 'coordinate': {'equitorial': [{'ra': '20 39 09.096', 'dec': '-30 45 20.71', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2021NatAs...5..491H'], 'computed': False, 'default': True, 'uuid': 'a06be641-1601-4737-9a1a-bd25c5dd61e6'}], 'galactic': [{'l': 13.01154485751856, 'b': -35.41877256185317, 'l_units': 'deg', 'b_units': 'deg', 'reference': 'a06be641-1601-4737-9a1a-bd25c5dd61e6', 'computed': True}]}, 'epoch': {'date_discovery': [{'value': 57248.2, 'date_format': 'MJD', 'reference': ['2021NatAs...5..491H'], 'computed': False}]}, 'distance': {'redshift': [{'value': '0.0484', 'reference': ['2021NatAs...5..491H'], 'computed': False, 'default': True}]}, 'classification': [{'object_class': 'TDE', 'confidence': 1, 'reference': ['2021NatAs...5..491H'], 'default': True}]},
 {'name': {'default_name': 'AT2020opy', 'alias': [{'value': 'AT2020opy', 'reference': 'TNS'}]}, 'coordinate': {'equitorial': [{'ra': '15 56 25.728', 'dec': '+23 22 21.15', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2023MNRAS.518..847G'], 'computed': False, 'default': True, 'uuid': '4f414ede-e0f0-4423-b2cc-f3ad309f0936'}], 'galactic': [{'l': 38.411569358993255, 'b': 48.23253616380999, 'l_units': 'deg', 'b_units': 'deg', 'reference': '4f414ede-e0f0-4423-b2cc-f3ad309f0936', 'computed': True}]}, 'epoch': {'date_discovery': [{'value': 59038.23, 'date_format': 'MJD', 'reference': ['2023MNRAS.518..847G'], 'computed': False}]}, 'distance': {'redshift': [{'value': '0.159', 'reference': ['2023MNRAS.518..847G'], 'computed': False, 'default': True}]}, 'classification': [{'object_class': 'TDE', 'confidence': 1, 'reference': ['2023MNRAS.518..847G'], 'default': True}]}]

We can also do a cone search

from astropy.coordinates import SkyCoord
import astropy.units as u
coord = SkyCoord(239, 23, unit=('deg', 'deg'))
rad = (1*u.deg).to(u.arcsec).value
db.getMeta(coords=coord, radius=rad)

[{'name': {'default_name': 'AT2020opy', 'alias': [{'value': 'AT2020opy', 'reference': 'TNS'}]}, 'coordinate': {'equitorial': [{'ra': '15 56 25.728', 'dec': '+23 22 21.15', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2023MNRAS.518..847G'], 'computed': False, 'default': True, 'uuid': '4f414ede-e0f0-4423-b2cc-f3ad309f0936'}], 'galactic': [{'l': 38.411569358993255, 'b': 48.23253616380999, 'l_units': 'deg', 'b_units': 'deg', 'reference': '4f414ede-e0f0-4423-b2cc-f3ad309f0936', 'computed': True}]}, 'epoch': {'date_discovery': [{'value': 59038.23, 'date_format': 'MJD', 'reference': ['2023MNRAS.518..847G'], 'computed': False}]}, 'distance': {'redshift': [{'value': '0.159', 'reference': ['2023MNRAS.518..847G'], 'computed': False, 'default': True}]}, 'classification': [{'object_class': 'TDE', 'confidence': 1, 'reference': ['2023MNRAS.518..847G'], 'default': True}]}]

Or search within a redshift range (or just a maximum or minimum)

# can search a redshift range
db.getMeta(minZ=0.5, maxZ=0.9)

[{'name': {'default_name': 'Sw J1112-82', 'alias': [{'value': 'Sw J1112-82', 'reference': 'Swift'}]}, 'coordinate': {'equitorial': [{'ra': '11 11 47.6', 'dec': '-82 38 44.44', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2017MNRAS.472.4469B'], 'computed': False, 'default': True, 'uuid': '3bb02aa4-eb60-4f09-acdf-d1431a6addc4'}], 'galactic': [{'l': 299.6337165869647, 'b': -20.420594756871665, 'l_units': 'deg', 'b_units': 'deg', 'reference': '3bb02aa4-eb60-4f09-acdf-d1431a6addc4', 'computed': True}]}, 'epoch': {'date_discovery': [{'value': '55729.5', 'date_format': 'MJD', 'reference': ['2017MNRAS.472.4469B'], 'computed': False}]}, 'distance': {'redshift': [{'value': '0.89', 'reference': ['2017MNRAS.472.4469B'], 'computed': False, 'default': True}]}, 'classification': [{'object_class': 'TDE', 'confidence': 1, 'reference': ['2017MNRAS.472.4469B'], 'default': True}]}]

We can even get all objects that have spectra associated with them

# just get objects that have spectra associated with them
db.getMeta(hasSpec=True)

[]

This should be empty because at the time of developing this tutorial there were no spectra in OTTER. Similarly, we can get all objects that have photometry associated with them with db.getMeta(hasPhot=True).

These outputs may appear like dictionaries but they're actually customized!

Besides the typical dictionary methods the following methods are also implemented for Transient objects.

help(Transient)

Help on class Transient in module otter.transient:

class Transient(collections.abc.MutableMapping)
 |  Transient(d={}, name=None)
 |
 |  Method resolution order:
 |      Transient
 |      collections.abc.MutableMapping
 |      collections.abc.Mapping
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __add__(self, other, strict_merge=True)
 |      Merge this transient object with another transient object
 |
 |      Args:
 |          other [Transient]: A Transient object to merge with
 |          strict_merge [bool]: If True it won't let you merge objects that
 |                               intuitively shouldn't be merged (ie. different
 |                               transient events).
 |
 |  __delitem__(self, keys)
 |
 |  __getitem__(self, keys)
 |
 |  __init__(self, d={}, name=None)
 |      Overwrite the dictionary init
 |
 |      Args:
 |          d [dict]: A transient dictionary
 |
 |  __iter__(self)
 |
 |  __len__(self)
 |
 |  __repr__(self, html=False)
 |      Return repr(self).
 |
 |  __setitem__(self, key, value)
 |
 |  cleanPhotometry(self, flux_unit='mag(AB)', date_unit='MJD')
 |      Ensure the photometry associated with this transient is all in the same units/system/etc
 |
 |  getMeta(self, keys=None)
 |      Get the metadata (no photometry or spectra)
 |
 |      This essentially just wraps on __getitem__ but with some checks
 |
 |      Args:
 |          keys [list[str]] : list of keys
 |
 |  getSkyCoord(self, coord_type='equitorial', idx=0)
 |      Convert the coordinates to an astropy SkyCoord
 |
 |  keys(self)
 |      D.keys() -> a set-like object providing a view on D's keys
 |
 |  plotPhotometry(self, flux_unit='mag(AB)', date_unit='datetime', **kwargs)
 |      Plot the photometry associated with this transient (if any)
 |
 |      Args:
 |          flux_unit [str]: Valid astropy unit string for the flux (y-axis) units.
 |                           Default: 'ABmag'
 |          date_unit [str]: Valid astropy unit string for the date (x-axis) units.
 |                           Default: 'MJD'

Some other advantages of the Transient objects are

t = db.getMeta(minZ=0.5, maxZ=0.9)[0]
print(type(t))
print()
# say you want to get the equitorial coordinates
# you can do it classically
print(t['coordinate']['equitorial'])

# or you can use the hdf5 style
print(t['coordinate/equitorial'])

<class 'otter.transient.Transient'>

[{'ra': '11 11 47.6', 'dec': '-82 38 44.44', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2017MNRAS.472.4469B'], 'computed': False, 'default': True, 'uuid': '3bb02aa4-eb60-4f09-acdf-d1431a6addc4'}]
[{'ra': '11 11 47.6', 'dec': '-82 38 44.44', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2017MNRAS.472.4469B'], 'computed': False, 'default': True, 'uuid': '3bb02aa4-eb60-4f09-acdf-d1431a6addc4'}]

You can even get multiple fields at once like with astropy Tables or pandas DataFrames

# You can also get multiple fields at once
t[['name/default_name', 'coordinate/equitorial', 'distance']]

{'name/default_name': 'Sw J1112-82', 'coordinate/equitorial': [{'ra': '11 11 47.6', 'dec': '-82 38 44.44', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2017MNRAS.472.4469B'], 'computed': False, 'default': True, 'uuid': '3bb02aa4-eb60-4f09-acdf-d1431a6addc4'}], 'distance': {'redshift': [{'value': '0.89', 'reference': ['2017MNRAS.472.4469B'], 'computed': False, 'default': True}]}}

You can also add two Transient objects to merge them

t1, t2 = db.query(names=['ASASSN-15oi', 'AT2020opy'])

try:
    t1 + t2
except ValueError as ve:
    print('The following error is actually expected!')
    print('We dont want you to be able to combine any old transients!')
    print()
    print('Error Message:')
    print(ve)

The following error is actually expected!
We dont want you to be able to combine any old transients!

Error Message:
These two transients are not within 5 arcseconds! They probably do not belong together! If they do You can set strict_merge=False to override the check

This error message is expected! If you want to override it then you can do

t2['photometry'][0]['reference'] = '2021NatAs...5..491H'

t3 = t1.__add__(t2, strict_merge=False)

Obviously, this result doesn't makes sense! This has the data from two completely different transients in it. So, be careful using strict_merge=False!

Can then get photometry

This does the conversion for you!!!

db.getPhot?

Get the photometry of the objects matching the arguments. This will do the
unit conversion for you!

Args:
    flux_units [astropy.unit.Unit]: Either a valid string to convert
                                    or an astropy.unit.Unit
    date_units [astropy.unit.Unit]: Either a valid string to convert to a date
                                    or an astropy.unit.Unit
    return_type [str]: Either 'astropy' or 'pandas'. If astropy, returns an
                       astropy Table. If pandas, returns a pandas DataFrame.
                       Default is 'astropy'.

    **kwargs : Arguments to pass to Otter.query(). Can be:
               names [list[str]]: A list of names to get the metadata for
               coords [SkyCoord]: An astropy SkyCoord object with coordinates to match to
               radius [float]: The radius in arcseconds for a cone search, default is 0.05"
               minZ [float]: The minimum redshift to search for
               maxZ [float]: The maximum redshift to search for
               refs [list[str]]: A list of ads bibcodes to match to. Will only return
                      metadata for transients that have this as a reference.
               hasSpec [bool]: if True, only return transients that have spectra.

Return:
   The photometry for the requested transients that match the arguments.
   Will be an astropy Table sorted by transient default name.

This means you can easily grab photometry in consistent units to plot!

import matplotlib.pyplot as plt
flux_unit = u.ABmag #u.erg/u.s/u.cm**2/u.Hz #'erg/s/cm^2/Hz'
tab = db.getPhot(flux_unit=flux_unit, date_unit='datetime', names=['ASASSN-15oi', 'ASASSN-14li'], return_type='pandas')

tab

	reference	date	filter_key	computed	obs_type	upperlimit	freq_eff	freq_units	human_readable_refs	converted_flux	converted_date	name
0	2016ApJ...819L..25A	57124.871000	5.0GHz	False	radio	False	5.0	GHz	Alexander et al. (2016)	15.697417	2015-04-12 20:54:14.400000	ASASSN-14li
1	2016ApJ...819L..25A	57190.830000	5.0GHz	False	radio	False	5.0	GHz	Alexander et al. (2016)	15.797567	2015-06-17 19:55:12.000000	ASASSN-14li
2	2016ApJ...819L..25A	57229.750000	5.0GHz	False	radio	False	5.0	GHz	Alexander et al. (2016)	15.915797	2015-07-26 18:00:00.000000	ASASSN-14li
3	2016ApJ...819L..25A	57286.514583	5.0GHz	False	radio	False	5.0	GHz	Alexander et al. (2016)	16.152094	2015-09-21 12:20:59.997120	ASASSN-14li
4	2016ApJ...819L..25A	57362.700000	5.0GHz	False	radio	False	5.0	GHz	Alexander et al. (2016)	16.547278	2015-12-06 16:48:00.000000	ASASSN-14li
0	2021NatAs...5..491H	57256.200000	6.1GHz	False	radio	True	6.1	GHz	Horesh et al. (2021)	20.103715	2015-08-22 04:48:00.000000	ASASSN-15oi
1	2021NatAs...5..491H	57271.200000	6.1GHz	False	radio	True	6.1	GHz	Horesh et al. (2021)	20.009244	2015-09-06 04:48:00.000000	ASASSN-15oi
2	2021NatAs...5..491H	57338.200000	6.1GHz	False	radio	True	6.1	GHz	Horesh et al. (2021)	19.454622	2015-11-12 04:48:00.000000	ASASSN-15oi
3	2021NatAs...5..491H	57430.200000	4.8GHz	False	radio	False	4.8	GHz	Horesh et al. (2021)	16.282787	2016-02-12 04:48:00.000000	ASASSN-15oi
4	2021NatAs...5..491H	57438.200000	4.8GHz	False	radio	False	4.8	GHz	Horesh et al. (2021)	16.515601	2016-02-20 04:48:00.000000	ASASSN-15oi
5	2021NatAs...5..491H	57445.200000	5.5GHz	False	radio	False	5.5	GHz	Horesh et al. (2021)	16.537560	2016-02-27 04:48:00.000000	ASASSN-15oi
6	2021NatAs...5..491H	57481.200000	5.5GHz	False	radio	False	5.5	GHz	Horesh et al. (2021)	16.610182	2016-04-03 04:48:00.000000	ASASSN-15oi
7	2021NatAs...5..491H	57531.200000	5.0GHz	False	radio	False	5.0	GHz	Horesh et al. (2021)	16.571355	2016-05-23 04:48:00.000000	ASASSN-15oi
8	2021NatAs...5..491H	57617.200000	5.0GHz	False	radio	False	5.0	GHz	Horesh et al. (2021)	16.924287	2016-08-17 04:48:00.000000	ASASSN-15oi
9	2021NatAs...5..491H	57824.200000	5.0GHz	False	radio	False	5.0	GHz	Horesh et al. (2021)	17.854247	2017-03-12 04:48:00.000000	ASASSN-15oi
10	2021NatAs...5..491H	58665.200000	3.0GHz	False	radio	False	3.0	GHz	Horesh et al. (2021)	14.142275	2019-07-01 04:48:00.000000	ASASSN-15oi

fig, ax = plt.subplots()
for key, table in tab.groupby('name'):
    ax.plot(table['converted_date'], table['converted_flux'], label=key, marker='o', linestyle='none')

ax.set_ylabel(f'Flux Density [{flux_unit}]')
ax.set_xlabel('Date')
ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45)
ax.legend();

General Queries (shouldn't be used unless you know what you're doing!)

The structure of a more generalized query to OTTER is

# General queries
Otter.query?

Wraps on the super.AQLQuery and queries the OTTER database more intuitively.

WARNING! This does not do any conversions for you!
This is how it differs from the `getMeta` method. Users should prefer to use
`getMeta`, `getPhot`, and `getSpec` independently because it is a better
workflow and can return the data in an astropy table with everything in the
same units.

Args:
    names [list[str]]: A list of names to get the metadata for
    coords [SkyCoord]: An astropy SkyCoord object with coordinates to match to
    radius [float]: The radius in arcseconds for a cone search, default is 0.05"
    minZ [float]: The minimum redshift to search for
    maxZ [float]: The maximum redshift to search for
    refs [list[str]]: A list of ads bibcodes to match to. Will only return
                      metadata for transients that have this as a reference.
    hasPhot [bool]: if True, only returns transients which have photometry.
    hasSpec [bool]: if True, only return transients that have spectra.

Return:
   Get all of the raw (unconverted!) json data for objects that match the criteria.

An example of this is

res = db.query(names=['ASASSN-15oi', 'AT2020opy'])
print(len(res))
print(type(res[0]))

2
<class 'otter.transient.Transient'>

res[0].keys()

dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'coordinate', 'distance', 'classification', 'reference_alias', 'epoch', 'photometry', 'filter_alias'])

However, Notice how this is simply the raw results!!! This means you need to be very careful with the results you get from these queries because no conversion is done!

res[0]

{'_key': '3776907', '_id': 'tdes/3776907', '_rev': '_hD8NTy2---', 'schema_version': {'value': '0', 'comment': 'Original Dataset'}, 'name': {'default_name': 'ASASSN-15oi', 'alias': [{'value': 'ASASSN-15oi', 'reference': 'ASASSN'}]}, 'coordinate': {'equitorial': [{'ra': '20 39 09.096', 'dec': '-30 45 20.71', 'epoch': 'J2000', 'system': 'ICRS', 'ra_units': 'hourangle', 'dec_units': 'deg', 'reference': ['2021NatAs...5..491H'], 'computed': False, 'default': True, 'uuid': 'a06be641-1601-4737-9a1a-bd25c5dd61e6'}], 'galactic': [{'l': 13.01154485751856, 'b': -35.41877256185317, 'l_units': 'deg', 'b_units': 'deg', 'reference': 'a06be641-1601-4737-9a1a-bd25c5dd61e6', 'computed': True}]}, 'distance': {'redshift': [{'value': '0.0484', 'reference': ['2021NatAs...5..491H'], 'computed': False, 'default': True}]}, 'classification': [{'object_class': 'TDE', 'confidence': 1, 'reference': ['2021NatAs...5..491H'], 'default': True}], 'reference_alias': [{'name': '2021NatAs...5..491H', 'human_readable_name': 'Horesh et al. (2021)'}], 'epoch': {'date_discovery': [{'value': 57248.2, 'date_format': 'MJD', 'reference': ['2021NatAs...5..491H'], 'computed': False}]}, 'photometry': [{'reference': '2021NatAs...5..491H', 'raw': [0.033, 0.036, 0.06, 1.114, 0.899, 0.881, 0.824, 0.854, 0.617, 0.262, 8], 'raw_units': ['mJy', 'mJy', 'mJy', 'mJy', 'mJy', 'mJy', 'mJy', 'mJy', 'mJy', 'mJy', 'mJy'], 'date': [57256.2, 57271.2, 57338.2, 57430.2, 57438.2, 57445.2, 57481.2, 57531.2, 57617.2, 57824.2, 58665.2], 'date_format': ['MJD', 'MJD', 'MJD', 'MJD', 'MJD', 'MJD', 'MJD', 'MJD', 'MJD', 'MJD', 'MJD'], 'filter_key': ['6.1GHz', '6.1GHz', '6.1GHz', '4.8GHz', '4.8GHz', '5.5GHz', '5.5GHz', '5.0GHz', '5.0GHz', '5.0GHz', '3.0GHz'], 'computed': [False, False, False, False, False, False, False, False, False, False, False], 'obs_type': ['radio', 'radio', 'radio', 'radio', 'radio', 'radio', 'radio', 'radio', 'radio', 'radio', 'radio'], 'upperlimit': [True, True, True, False, False, False, False, False, False, False, False]}], 'filter_alias': [{'filter_key': '6.1GHz', 'freq_eff': 6.1, 'freq_units': 'GHz'}, {'filter_key': '4.8GHz', 'freq_eff': 4.8, 'freq_units': 'GHz'}, {'filter_key': '5.5GHz', 'freq_eff': 5.5, 'freq_units': 'GHz'}, {'filter_key': '5.0GHz', 'freq_eff': 5, 'freq_units': 'GHz'}, {'filter_key': '3.0GHz', 'freq_eff': 3, 'freq_units': 'GHz'}]}

Some helpful methods

To get the Astropy SkyCoord object for a specific transient you can use

skycoord = t1.getSkyCoord()
skycoord

<SkyCoord (ICRS): (ra, dec) in deg
    (309.7879, -30.75575278)>

Or, to get the html code for plotting the photometry for a transient

html = res[0].plotPhotometry()
html[:100] # only show the first part of it

'<div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: \'l'

Uploading/Editing Data

If you have admin access to OTTER you can also upload new data! An example of this is that we first need to create a new Transient object.

from otter import Otter, Transient
from copy import deepcopy
from collections import Counter
import awkward as ak
import warnings
import numpy as np
import re
from astropy.coordinates import SkyCoord
import json

# generate some test cases
db = Otter()
t1 = db.query(names='2022xkq')[0] #
t2 = deepcopy(t1)
print(t1.keys())

# change t2 for testing
t2['name'] = {'default_name':'2022xkq',
             'alias': [{'value':'foo', 'reference': 'x'},
                      {'value': '2022xkq', 'reference': 'x'}]}
t2['reference_alias'].append({'name': 'x',
   'human_readable_name': 'test, name (year)'}) # add an extra value
del t2['photometry']
t2['for_test'] = {'test': 'bar'} # add a test key that isn't in t1
t2['coordinate/equitorial'][0]['reference'] = 'noah'
t2['filter_alias'].append({'filter_key': 'foo'})
t2['schema_version/value'] = 100
t2['epoch'] = {'date_peak': [{'value': 56983,
    'date_format': 'MJD',
    'reference': ['2016ApJ...819L..25A',
     '2016Sci...351...62V',
     '2016ApJ...832L..10R',
     '2018MNRAS.475.4011B'],
    'computed': False}],

               'date_discovery': [{'value': 56983,
    'date_format': 'MJD',
    'reference': ['2016ApJ...819L..25A',
     '2016Sci...351...62V',
     '2016ApJ...832L..10R',
     '2018MNRAS.475.4011B'],
    'computed': False}],


              'date_discovery': [{'value': 56984,
    'date_format': 'MJD',
    'reference': ['2016ApJ...819L..25A',
     '2016Sci...351...62V',
     '2016ApJ...832L..10R',
     '2018MNRAS.475.4011B'],
    'computed': False}]
              }

t2['distance'] = {
    "redshift": [
      {
        "value": "0.0207",
        "reference": [
          "Noah"
        ],
        "computed": False
      },
        {
        "value": "0.02",
        "reference": [
          "Noah"
        ],
        "computed": False
      }
    ],

    "dispersion_measure": [
      {
        "value": "0.0206",
        "reference": [
          "Noah"
        ],
        "computed": False
      }
    ]
  }

t2['classification'] = [{'object_class':'SN',
                        'confidence': 1,
                         'reference': 'Noah'
                        }]

t2['photometry'] = {'phot_0': {'telescope': 'Noahs Telescope',
                               'reference': 'Noah',
                               'flux': [{'filter': 'z',
                                 'telescope': 'Noahs Telescope',
                                 'upperlimit': True,
                                 'date': 59864.4914116667,
                                 'date_format': 'MJD',
                                 'raw': 20.01,
                                 'raw_units': 'mag(AB)',
                                 'filter_key': 'NoahsTelescope.z',
                                 'obs_type': 'uvoir'}]},
                    'phot_1': {'telescope': 'CAHA',
                               'reference': 'Noah',
                               'flux': [{'filter': 'H',
                                 'telescope': 'CAHA',
                                 'upperlimit': False,
                                 'date': 59898.12077,
                                 'date_format': 'MJD',
                                 'raw': 14.87048,
                                 'raw_err': 0.0187,
                                 'raw_units': 'mag(AB)',
                                 'filter_key': 'CAHA.H',
                                 'obs_type': 'uvoir'}]}

                               }

Now we need to connect to OTTER with admin access and upload the new transient object.

db = Otter(username='admin@otter', password='insecure')
#db.upload(t2)

Repo Organization

Directory	Contents
py/otter	A pip installable API for interfacing with the ArangoDB database