py4py.reverb.output

Reverberation mapping output module

This contains helper functions that bundle up the production of multiple transfer functions or the creation of response functions.

TransferFunction

Used to create, store and query emissivity and response functions

`do_tf_plots`(tf_list_inp[, dynamic_range, keplerian, ...])	Produces plots of the transfer functions for a list of provided TFs,
`do_rf_plots`(tf_min, tf_mid, tf_max[, keplerian, name, ...])	Do response plot for a transfer function, optionally with keplerian disk lines on it.

Module Contents

class py4py.reverb.output.TransferFunction(database: sqlalchemy.engine.Connection, filename: str, continuum: float, wave_bins: int = None, delay_bins: int = None, template: TransferFunction = None, template_different_line: bool = False, template_different_spectrum: bool = False)

Used to create, store and query emissivity and response functions

Initialises the TF, optionally by templating off another TF.

Sets up all the basic properties of the TF that are required to create it. It must be .run() to query the DB before it can itself be queried. If templating, it applies all the same filters that were applied to the template TF, unless explicitly told not to. Filters don’t overwrite! They stack. So you can’t simply call .line() to change the line the TF corresponds to if its template was a different line, unless you specify that the template was of a different line.

Parameters:

database (sqlalchemy.engine.Connection) – The database to be queried for this TF.
filename (string) – The root filename for plots created for this TF.
continuum (float) – The continuum value associated with this TF. Central source + disk luminosity.
wave_bins (int) – Number of wavelength/velocity bins.
delay_bins (int) – Number of delay time bins.
template (TransferFunction) – Other TF to copy all filter settings from. Will match delay, wave and velocity bins exactly.
template_different_line (bool) – Is this TF going to share delay & velocity bins but have different wavelength bins?
template_different_spectrum (bool) – Is this TF going to share all specified bins but be taken on photons from a different observer.

Todo

Consider making it impossible to apply filters after calling run().

__getstate__() → dict

Removes invalid data before saving to disk.

Returns:

Updated internal dict, with references to external,: session-specific database things, removed.

Return type:

dict

__setstate__(state: dict)

Restores the data from disk, and sets a flag to show this is a frozen TF.

Parameters:: state (dict) – The unpickled object dict..

spectrum(number: int) → TransferFunction

Constrain the TF to photons from a specific observer

Parameters:: number (int) – Observer number from Python run
Returns:: Self, so filters can be stacked
Return type:: TransferFunction

line(number: int, wavelength: float) → TransferFunction

Constrain the TF to only photons last interacting with a given line

This includes being emitted in the specified line, or scattered off it

Parameters:

number (int) – Python line number. Will vary based on data file!
wavelength (float) – Wavelength of the line in angstroms

Returns:

Self, so filters can be stacked

Return type:

TransferFunction

velocities(velocity: float) → TransferFunction

Constrain the TF to only photons with a range of Doppler shifts

Parameters:: velocity (float) – Maximum doppler shift velocity in m/s. Applies to both positive and negative Doppler shift
Returns:: Self, so filters can be stacked
Return type:: TransferFunction

wavelengths(wave_min: float, wave_max: float) → TransferFunction

Constrain the TF to only photons with a range of wavelengths

Parameters:

wave_min (float) – Minimum wavelength in angstroms
wave_max (float) – Maximum wavelength in angstroms

Returns:

Self, so filters can be stacked

Return type:

TransferFunction

wavelength_bins(wave_range: numpy.ndarray) → TransferFunction

Constrain the TF to only photons with a range of wavelengths, and to a specific set of bins

Parameters:: wave_range (np.ndarray) – Array of bins to use
Returns:: Self, so filters can be stacked
Return type:: TransferFunction

lines(line_list: List[int]) → TransferFunction

Constrain the TF to only photons with a specific internal line number. This list number will be specific to the python atomic data file!

Parameters:: line_list (List[int]) – List of lines
Returns:: Self, so filters can be stacked
Return type:: TransferFunction

delays(delay_min: float, delay_max: float, days: bool = True) → TransferFunction

The delay range that should be considered when producing the TF.

Parameters:

delay_min (float) – Minimum delay time (in seconds or days)
delay_max (float) – Maximum delay time (in seconds or days)
days (bool) – Whether or not the delay range has been provided in days

Returns:

Self, so filters can be stacked

Return type:

TransferFunction

delay_dynamic_range(delay_dynamic_range: float) → TransferFunction

If set, the TF will generate delay bins to cover this dynamic range of responses, i.e. (1 - 10^-ddr) of the delays. So a ddr of 1 will generate photons with delays up to 1 - (1/10) = the 90th percentile of delays. ddr=2 will give up to the 99th percentile, 3=99.9th percentile, etc.

Arguably this is a bit of an ambiguous name

Parameters:: delay_dynamic_range (float) – The dynamic range to be used when
Returns:: Self, so filters can be stacked
Return type:: TransferFunction

cont_scatters(scat_min: int, scat_max: int | None = None) → TransferFunction

Constrain the TF to only photons that have scattered min-max times via a continuum scattering process (e.g. electron scattering).

Parameters:

scat_min (int) – Minimum number of continuum scatters
scat_max (Optional[int]) – Maximum number of continuum scatters, if desired

Returns:

Self, so filters can be stacked

Return type:

TransferFunction

res_scatters(scat_min: int, scat_max: int | None = None) → TransferFunction

Constrain the TF to only photons that have scattered min-max times via a resonant scattering process (e.g. line scattering).

Parameters:

scat_min (int) – Minimum number of resonant scatters
scat_max (Optional[int]) – Maximum number of resonant scatters, if desired

Returns:

Self, so filters can be stacked

Return type:

TransferFunction

filter(*args) → TransferFunction

Apply a SQLalchemy filter directly to the content.

Parameters:: args – The list of filter arguments
Returns:: Self, so filters can be stacked
Return type:: TransferFunction

response_map_by_tf(low_state: TransferFunction, high_state: TransferFunction, cf_low: float = 1.0, cf_high: float = 1.0) → TransferFunction

Creates a response function for this transfer function by subtracting two transfer functions bracketing it. Requires two other completed transfer functions, bracketing this one in luminosity, all with matching wavelength/velocity and delay bins.

Correction factors are there to account for things like runs that have been terminated early, e.g. if you request 100 spectrum cycles and stop (or Python dies) after 80, the total photon luminosity will only be 80/100. A correction factor allows you to bump this up. Arguably correction factors should be applied during the ‘run()’ method.

Parameters:

low_state (TransferFunction) – A full, processed transfer function for a lower-luminosity system.
high_state (TransferFunction) – A full, processed transfer function for a higher-luminosity system.
cf_low (float) – Correction factor for low state. Multiplier to the whole transfer function.
cf_high (float) – Correction factor for high state. Multiplier to the whole transfer function.

Returns:

Self, so plotting can be chained on.

Return type:

TransferFunction

fwhm(response: bool = False, velocity: bool = True)

Calculates the full width half maximum of the delay-summed transfer function, roughly analogous to the line profile. Possibly meaningless for the response function?

Parameters:

response (bool) – Whether to calculate the FWHM of the transfer or response function
velocity (bool) – Whether to return the FWHM in wavelength or velocity-space

Returns:

Full width at half maximum for the function.: If the function is a doublet, this will not work properly.

Return type:

float

Todo

Catch doublets.

delay(response: bool = False, threshold: float = 0, bounds: float = None, days: bool = False) → float | Tuple[float, float, float]

Calculates the centroid delay for the current data

Parameters:

response (bool) – Whether or not to calculate the delay from the response
threshold (float) – Exclude all bins with value < the threshold fraction of the peak value. Standard value used in the reverb papers was 0.8.
bounds (float) – Return the fractional bounds (i.e. bounds=0.25, the function will return [0.5, 0.25, 0.75]). Not implemented.
days (bool) – Whether to return the delay in days or seconds

Returns:

Centroid delay, and lower and upper fractional bounds if bounds keyword provided

Return type:

Union[float, Tuple[float, float, float]]

Todo

Implement fractional bounds. Should just be able to call the centroid_delay function!

delay_peak(response: bool = False, days: bool = False) → float

Calculates the peak delay for the transfer or response function, i.e. the delay at which the response is strongest.

Parameters:

response (bool) – Whether or not to calculate the peak transfer or response function.
days (bool) – Whether to return the value in seconds or days.

Returns:

The peak delay.

Return type:

float

run(scaling_factor: float = 1.0, limit: int = None, verbose: bool = False) → TransferFunction

Performs a query on the photon DB and bins it.

A TF must be run after all filters are applied and before any attempts to retrieve or process data from it. This can be a time-consuming call, on the order of 1 minute per GB of input file.

Parameters:

scaling_factor (float) – 1/Number of cycles in the spectra file
limit (int) – Number of photons to limit the TF to, for testing. Recommend testing filters on a small number of photons to begin with.
verbose (bool) – Whether to output exactly what the query is.

Returns:

Self, for chaining commands

Return type:

TransferFunction

Internal function used by response(), emissivity() and count()

Parameters:

array (np.ndarray) – Array to return value from
delay (Optional[float]) – Delay to return value for. Must provide this or delay_index.
delay_index (Optional[int]) – Delay index to return value for. Must provide this or delay.
wave (Optional[float]) – Wavelength to return value for

Returns:

Either a subset of the array if only delay is provided,: or the value of a single array element if delay and wavelength provided.

Return type:

Union[np.ndarray, float]

Todo

Allow for only wavelength to be provided?

response_total() → float

Returns the total response.

Returns:: Total response.
Return type:: float

delay_bins() → numpy.ndarray

Returns the range of delays covered by this TF.

Returns:: Array of the bin boundaries.
Return type:: np.ndarray

response(delay: float | None = None, wave: float | None = None, delay_index: int | None = None) → float | numpy.ndarray

Returns the responsivity in either one specific wavelength/delay bin, or all wavelength bins for a given delay.

Parameters:

delay (Optional[float]) – Delay to return value for. Must provide this or delay_index.
delay_index (Optional[int]) – Delay index to return value for. Must provide this or delay.
wave (Optional[float]) – Wavelength to return value for.

Returns:

Either the responsivity in one specific bin, or if wave is not specified: the counts in each wavelength bin at this delay

Return type:

Union[int, np.ndarray]

Todo

Allow for only wavelength to be provided?

emissivity(delay: float | None = None, wave: float | None = None, delay_index: int | None = None) → float | numpy.ndarray

Returns the emissivity in either one specific wavelength/delay bin, or all wavelength bins for a given delay.

Parameters:

delay (Optional[float]) – Delay to return value for. Must provide this or delay_index.
delay_index (Optional[int]) – Delay index to return value for. Must provide this or delay.
wave (Optional[float]) – Wavelength to return value for.

Returns:

Either the emissivity in one specific bin, or if wave is not specified: the counts in each wavelengthin bin at this delay

Return type:

Union[int, np.ndarray]

Todo

Allow for only wavelength to be provided?

count(delay: float | None = None, wave: float | None = None, delay_index: int | None = None) → int | numpy.ndarray

Returns the photon count in either one specific wavelength/delay bin, or all wavelength bins for a given delay.

Parameters:

delay (Optional[float]) – Delay to return value for. Must provide this or delay_index.
delay_index (Optional[int]) – Delay index to return value for. Must provide this or delay.
wave (Optional[float]) – Wavelength to return value for

Returns:

Either the count in one specific bin, or if wave is not specified: the counts in each wavelength bin at this delay

Return type:

Union[int, np.ndarray]

Todo

Allow for only wavelength to be provided?

transfer_function_1d(response: bool = False, days: bool = True) → numpy.ndarray

Collapses the 2-d transfer/response function into a 1-d response function, and returns the bin midpoints and values in each bin for plotting.

Parameters:

response (bool) – Whether or not to return the response function data
days (bool) – Whether the bin midpoints should be in seconds or days

Returns:

A [bins, 2]-d array containing the midpoints of the delay bins,: and the value of the 1-d transfer or response function in each bin.

Return type:

np.ndarray

plot(log: bool = False, normalised: bool = False, rescaled: bool = False, velocity: bool = False, name: str = None, days: bool = True, response_map=False, keplerian: dict = None, dynamic_range: int = None, rms: bool = False, show: bool = False, max_delay: float | None = None, format: str = '.eps', return_figure: bool = False) → TransferFunction | matplotlib.figure.Figure

Takes the data gathered by calling ‘run’ and outputs a plot

Parameters:

log (bool) – Whether the plot should be linear or logarithmic.
normalised (bool) – Whether or not to rescale the plot such that the total emissivity = 1.
rescaled (bool) – Whether or not to rescale the plot such that the maximum emissivity = 1.
velocity (bool) – Whether the plot X-axis should be velocity (true) or wavelength (false).
name (Optional[str]) – The file will be output to ‘tf_filename.eps’. May add the ‘name’ component to modify it to ‘tf_filename_name.eps’. Useful for adding e.g. ‘c4’ or ‘log’.
days (bool) – Whether the plot Y-axis should be in days (true) or seconds (false).
response_map (bool) – Whether to plot the transfer function map or the response function.
keplerian (Optional[dict]) – A dictionary describing the profile of a keplerian disk, the bounds of which will be overlaid on the plot. Arguments include angle (float) - Angle of disk to the observer, mass (float) - Mass of the central object in M_sol, radius (Tuple(float, float)) - Inner and outer disk radii, in $r_{ISCO}$. include_minimum_velocity - Whether or not to include the outer disk velocity profile (default no).
dynamic_range (Optional[int]) – If the plot is logarithmic, the dynamic range the colour bar should show. If not provided, will attempt to use the base dynamic range property, otherwise will default to showing 99.9% of all emissivity.
max_delay (Optional[float]) – The optional maximum delay to plot out to.
rms (bool) – Whether or not the line profile panel should show the root mean squared line profile.
show (bool) – Whether or not to display the plot to screen.
format (str) – The output file format. .eps by default.
return_figure (bool) – If true, return the figure instead of platting it.

Returns:

Self, for chaining outputs

Return type:

TransferFunction

py4py.reverb.output.do_tf_plots(tf_list_inp: List[py4py.reverb.TransferFunction], dynamic_range: float | None = None, keplerian: dict | None = None, name: str | None = None, file: str | None = None, threshold: float = 0.8)

Produces plots of the transfer functions for a list of provided TFs, with matching plotted dynamic ranges and name structures. Also optionally generates a file containing the centroid delays for each transfer function.

Parameters:

tf_list_inp (List[TransferFunction]) – The transfer functions to plot.
dynamic_range (Optional[float]) – The dynamic range to plot them all across (see TransferFunction.plot).
keplerian (Optional[dict]) – The keplerian orbit parameters to overplot on all of them.
name (Optional[str]) – The name component to be added to the output plot filenames, e.g. [tf.name]_[name].eps. Useful for ending up with e.g. c4_with_keplerian.eps.
file (Optional[str]) – The output filename for the list of centroid delays.
threshold (float) – The peak flux threshold to use for calculating the centroid (see TransferFunction.delay).

Outputs:

` {file}_tf_delay.txt` [Optional]: {tf.filename}_name.eps [for each tf in tf_list_inp]

py4py.reverb.output.do_rf_plots(tf_min: py4py.reverb.TransferFunction, tf_mid: py4py.reverb.TransferFunction, tf_max: py4py.reverb.TransferFunction, keplerian: dict | None = None, name: str | None = None, file: str | None = None)

Do response plot for a transfer function, optionally with keplerian disk lines on it. This will generate not just a response function from the two bracketing TFs, but one from the midpoint TF and the bracketing ones (e.g. Min-Max, Min-Mid, Mid-Max). Ideally, all three should be similar- if they are not, it suggests that the change in luminosity from min-max covers a point of inflection in d[Ionisation state]dL.

Parameters:

tf_min (TransferFunction) – The low-state TF
tf_mid (TransferFunction) – The TF bracketed by low and high to produce the RF for.
tf_max (TransferFunction) – The high-state TF
keplerian (Optional[dict]) – The keplerian orbit parameters to overplot on all of them.
name (Optional[str]) – The name component to be added to the output plot filenames, e.g. [tf.name]_[name].eps. Useful for ending up with e.g. c4_with_keplerian.eps.
file (Optional[str]) – The output filename for the list of centroid delays.

Outputs:: {tf_mid.name}_resp_mid.eps {tf_mid.name}_resp_low.eps {tf_mid.name}_resp_high.eps {file}_rf_delay.txt [Optional]