Supplemental Modules

These modules are not strictly necessary for the host association, but provide useful ancillary data about the associated hosts.

photoz_helper

astro_ghost.photoz_helper.build_sfd_dir(fname='./sfddata-master.tar.gz')[source]

Downloads directory of Galactic dust maps for extinction correction.

Parameters:

fname (str) – Filename for dustmaps.

astro_ghost.photoz_helper.calc_photoz(hosts)[source]
PhotoZ beta: not tested for missing objids.

photo-z uses a artificial neural network to estimate P(Z) in range Z = (0 - 1) range_z is the value of z posterior is an estimate PDF of the probability of z point estimate uses the mean to find a single value estimate error is an array that uses sampling from the posterior to estimate a std dev. Relies upon the sfdmap package, (which is compatible with both unix and windows), found at https://github.com/kbarbary/sfdmap.

Parameters:

hosts (pandas DataFrame) – The matched hosts from GHOST.

Returns:

The matched hosts from GHOST, with photo-z point estimates and uncertainties.

Return type:

pandas DataFrame

astro_ghost.photoz_helper.evaluate(X, mymodel, range_z)[source]

Evaluate the MLP for a set of PS1 inputs, and return predictions.

Parameters:

  • X (array-like) – PS1 properties of associated hosts.

  • mymodel (tensorflow keras Model) – MLP model for photo-z estimation.

  • range_z (array-like) – Grid over which to evaluate the posterior distribution of photo-zs.

Returns:

Posterior distributions for the grid of redshifts defined as `np.linspace(0, 1, n)`

Return type:

numpy ndarray shape of (df.shape[0], n)

Returns:

Means

Return type:

numpy ndarray shape of (df.shape[0],)

Returns:

Standard deviations

Return type:

numpy ndarray shape of (df.shape[0],)

astro_ghost.photoz_helper.fetch_information_serially(url, data, verbose=False, format='csv')[source]

A helper function called by serial_objID_search– Queries PanStarrs API for data.

Parameters:

  • url (str) – Remote PS1 url.

  • data (list) – List of objIDs requesting

  • verbose (bool, optional) – If True,

  • format (str) – Can be 'csv', 'json', or 'votable'.

Returns:

Return type:

str in format given by ‘format’.

astro_ghost.photoz_helper.get_common_constraints_columns()[source]

Helper function that returns a dictionary of constraints used for the matching objects in PS1 archive, and the columns of data we requre.

Returns:

dictionary with our constaint that we must have more than one detection

Return type:

dict

Returns:

List of PS1 fields required for matching and NN inputs

Return type:

list

astro_ghost.photoz_helper.get_photoz(df)[source]

Evaluate photo-z model for Pan-STARRS forced photometry.

Parameters:

df (pandas DataFrame) – Pan-STARRS forced mean photometry data, you can get it using `ps1objIDsearch` from this module, Pan-STARRS web-portal or via astroquery i.e., `astroquery.mast.Catalogs.query_{criteria,region}(…, catalog='Panstarrs',table='forced_mean')`

Returns:

Posterior distributions for the grid of redshifts defined as `np.linspace(0, 1, n)`

Return type:

numpy ndarray shape of (df.shape[0], n)

Returns:

Means

Return type:

numpy ndarray shape of (df.shape[0],)

Returns:

Standard deviations

Return type:

numpy ndarray shape of (df.shape[0],)

astro_ghost.photoz_helper.get_photoz_weights(fname='./MLP_lupton.hdf5')[source]

Get weights for MLP photo-z model.

Parameters:

fname (str) – Filename of saved MLP weights.

astro_ghost.photoz_helper.load_lupton_model(model_path)[source]

Helper function that defines and loads the weights of our NN model and the output space of the NN.

Parameters:

model_path (str) – path to the model weights.

Returns:

Trained photo-z MLP.

Return type:

tensorflow keras Model

Returns:

Array of binned redshift space corresponding to the output space of the NN

Return type:

numpy ndarray

astro_ghost.photoz_helper.post_url_parallel(results, YSE_ID)[source]

TODO: unused function. The querying of PS servers is the slowest part of the script. How to parallelize?

Parameters:

  • results (type) – Description of param.

  • YSE_ID (type) – Description of param.

Returns:

Return type:

astro_ghost.photoz_helper.post_url_serial(results, YSE_ID)[source]

A helper function called by serial_objID_search. Post-processes the data retrieved from PS1 Servers into a pandas.DataFrame object.

Parameters:

  • results (str) – The string resulting from PS1 query.

  • YSE_ID (int) – local integer used for as an index tracking user objects vs retrived objects.

Returns:

DataFrame object of the retrieved data from PS1 servers

Return type:

pandas.DataFrame

astro_ghost.photoz_helper.preprocess(DF, PATH='../DATA/sfddata-master/', ebv=True)[source]

Preprocesses the data inside pandas.DataFrame object returned by serial_objID_search to the space of Inputs of our Neural Network.

Parameters:

  • DF (pandas DataFrame) – Dataframe object containing the data for each matched objID

  • PATH (str) – string path to extinction maps data

  • ebv (False) – boolean for lookup of extinction data. If False, all extinctions set to 0.

Returns:

Preprocessed inputs ready to be used as input to NN

Return type:

numpy ndarray

astro_ghost.photoz_helper.ps1objIDsearch(objID, table='mean', release='dr1', format='csv', columns=None, baseurl='https://catalogs.mast.stsci.edu/api/v0.1/panstarrs', verbose=False, **kw)[source]

Do an object lookup by objID.

Parameters:

  • objID (List of objIDs) – list of objIDs (or dictionary?)

  • table (str) – Can be 'mean', 'stack', or 'detection'.

  • release (str) – Can be ‘dr1’ or ‘dr2’.

  • format (str) – Can be ‘csv’, ‘votable’, or ‘json’

  • columns (arrray-like) – list of column names to include (None means use defaults)

  • baseurl (str) – base URL for the request

  • verbose (bool,optional) – print info about request

  • **kw (dictionary) – other parameters (e.g., ‘nDetections.min’:2)

Given a list of ObjIDs, queries the PS1 server these object’s Forced Mean Photometry, then returns matches as a pandas.DataFrame.

Parameters:

  • objIDs (list) – list of PS1 objIDs for objects user would like to query

  • table (str) – Which table to perform the query on. Default ‘forced_mean’

  • release (str) – Which release to perform the query on. Default ‘dr2’

  • columns (list or None) – list of what data fields to include; None means use default columns. Default None

  • verbose (bool) – boolean setting level of feedback user received. default False

  • **constraints (dict) – Keyword dictionary with an additional constraints for the PS1 query

Returns:

list of pd.DataFrame objects. If a match was found, then the Dataframe contains data, else it only contains a local integer.

Return type:

pd.DataFrame