py4py.reverb.timeseries.output
Timeseries output module
This module contains the output code for the timeseries analysis. This is intended to output to the formats the CARAMEL and MEMEcho team analyse & plot.
Used to create, store and query emissivity and response functions |
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Converts passed line and velocity into red/blue-shifted wavelength |
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Given a lightcurve, series of spectra and time outputs to CARAMEL format, and then |
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Given a lightcurve, series of spectra and time outputs to MEMECHO format, outputs them |
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Generate a trailed spectrogram of both the time series of spectra and difference relative to the mean, |
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Given a lightcurve and table containing a time series of spectra, |
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Outputs response functions for rescaled versions of the input. |
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Given a list of timeseries of spectra (full and continuum subtracted), produce a trailed |
Module Contents
- py4py.reverb.timeseries.output.doppler_shift_wave(line: float, vel: float) float
Converts passed line and velocity into red/blue-shifted wavelength
- Parameters:
line (float) – Line wavelength (any length unit)
vel (float) – Doppler shift velocity (m/s)
- Returns:
Doppler shifted line wavelength (as above)
- Return type:
float
- class py4py.reverb.timeseries.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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- 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:
- _return_array(array: numpy.ndarray, delay: float | None = None, wave: float | None = None, delay_index: int | None = None) int | float | numpy.ndarray
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:
- py4py.reverb.timeseries.output.write_caramel_data(lightcurve: astropy.table.Table, spectra: astropy.table.Table, spectra_times: astropy.table.Table, suffix: str, rescale: bool | None = True)
Given a lightcurve, series of spectra and time outputs to CARAMEL format, and then compresses the output into a ZIP file.
- Parameters:
lightcurve (Table) – Continuum values and times, in seconds and real units.
spectra (Table) – Table of wavelengths and spectra. Continuum-subtracted.
spectra_times (Table) – Table of spectrum times.
suffix (str) – Suffix appended to filename. Intended to sort outputs as e.g. caramel/qso/caramel_lightcurve_qso.
rescale (bool) – Whether or not the spectra should be rescaled to 1-100 range, defaults to yes.
- Outputs:
caramel/{suffix}/caramel_lightcurve_{suffix}.txt: Continuum lightcurve. caramel/{suffix}/caramel_spectra_{suffix}.txt: Values of the mock observations. caramel/{suffix}/caramel_spectra_times_{suffix}.txt: Times of the mock observations. caramel/caramel_{suffix}.zip: Zip of all the outputs.
- py4py.reverb.timeseries.output.write_memecho_data(lightcurve: astropy.table.Table, spectra: astropy.table.Table, spectra_times: astropy.table.Table, suffix: str)
Given a lightcurve, series of spectra and time outputs to MEMECHO format, outputs them to files and zips them up for distribution.
- Parameters:
lightcurve (Table) – Continuum values and times, in seconds and real units.
spectra (Table) – Table of wavelengths and spectra. Not continuum-subtracted.
spectra_times (Table) – Table of spectrum times.
suffix (str) – Suffix appended to file name.
- Outputs:
memecho/{suffix}/prepspec_{suffix}_{timestep}.txt: The mock observations for each timestep memecho/{suffix}/prepspec_times.txt: The times of each mock observation, and spectra file associated with each. memecho/{suffix}/memecho_lightcurve.txt: The driving continuum lightcurve. memecho/memecho_{suffix}.zip: Zip of all the outputs
- py4py.reverb.timeseries.output.trailed_spectrogram(spectra: astropy.table.Table, lightcurve: astropy.table.Table, spectra_times: astropy.table.Table, filename: str, line_wavelength: float = None, wavelength_range: Tuple[float, float] | None = None)
Generate a trailed spectrogram of both the time series of spectra and difference relative to the mean, with the continuum as an adjacent line plot.
- Parameters:
spectra (Table) – Spectra (starting at column 3).
spectra_times (Table) – Times to plot the TS for.
lightcurve (Table) – The continuum lightcurve.
filename (String) – File to write to.
line_wavelength (float) – The wavelength of the line being plotted.
wavelength_range ([float, float]) – The wavelength range to plot.
- Outputs:
{filename}.eps: Time series output.
- py4py.reverb.timeseries.output.next_column = None
- py4py.reverb.timeseries.output.write_animation(spectra: astropy.table.Table, lightcurve: astropy.table.Table, spectra_times: astropy.table.Table, times: astropy.table.Table, filename: str, is_reversed: bool = False)
Given a lightcurve and table containing a time series of spectra, generate an animation that shows how the output spectrum changes over time.
- Parameters:
spectra (Table) – Spectra (starting at column 3).
spectra_times (Table) – Times to plot the spectra values for
times (Table) – High time-resolution interpolated continuum lightcurve.
lightcurve (Table) – The continuum lightcurve.
filename (str) – File to write to
is_reversed (bool) – Whether newer points should be overlaid by older ones
- Outputs:
{filename}.mp4
- py4py.reverb.timeseries.output.rescaled_rfs(tfs: List[py4py.reverb.TransferFunction], rescale_max_time: astropy.units.Quantity, figure_max_time: astropy.units.Quantity, keplerian: dict = None)
Outputs response functions for rescaled versions of the input. Different mass SMBHs scale straightforwardly; accretion disks are generated at locations with the same doppler shifts, so the only thing you need to do to scale the mass is to rescale the time delays.
- Parameters:
tfs (List[TransferFunction]) – The transfer functions to plot the response functions for.
rescale_max_time (Quantity) – The new ‘maximum time’ the TF should extend to.
figure_max_time (Quantity) – The maximum time that should be shown on the plot (may be lower than rescale_max_time).
keplerian (dict) – Dictionary containing Keplerian disk profile as used by plot.
- Outputs:
{tf.name}_resp.eps
- py4py.reverb.timeseries.output.plot_spectra_rms(spectra: List[Tuple[astropy.table.Table, astropy.table.Table]], filenames: List[str])
Given a list of timeseries of spectra (full and continuum subtracted), produce a trailed spectrogram of each, plus the RMS spectra.
- Parameters:
spectra (List[(Table, Table)]) – Pairs of tables containing full and continuum subtracted (in that order) timeseries of spectra
filenames (List[str]) – Filenems List of filenames for each of the pairs.
- Outputs:
{filename}.eps for filename in filenames