pynrc.reduce.calib.nircam_dark

class pynrc.reduce.calib.nircam_dark(scaid, datadir, outdir, lindir=None, DMS=False, same_scan_direction=False, reverse_scan_direction=False)

Bases: object

__init__(scaid, datadir, outdir, lindir=None, DMS=False, same_scan_direction=False, reverse_scan_direction=False)

Methods

__init__(scaid, datadir, outdir[, lindir, ...])
calc_cds_noise([cds_type, temperature, temp_key])	Return CDS Noise components for each channel
deconvolve_supers()	Deconvolve the super dark and super bias images
get_cds_dict([force])	Calculate CDS noise for all files
get_column_variations([force])	Get column offset variations
get_dark_slope_image([deg, force])	Calculate dark slope image
get_effective_noise([ideal_Poisson, force])	Calculate effective noise curves for each readout pattern
get_ipc([calc_ppc])	Calculate IPC (and PPC) kernels
get_ktc_noise(**kwargs)	Calculate and store kTC (Reset) Noise
get_linear_coeffs([deg, use_legendre, ...])	Determine linearity coefficents
get_nonlinear_coeffs([deg, use_legendre, ...])	Determine non-linear coefficents
get_pixel_slope_averages([force])	Get average pixel ramp
get_power_spectrum([include_oh, calc_cds, ...])	keyword include_oh Zero-pad the data to insert line and frame overhead pixels?
get_ref_pixel_noise([force])	Generate Dictionary of Reference Pixel behavior info
get_super_bias_init([deg, nsplit, force])
get_super_bias_update([force])
get_super_dark_ramp([force])	Create or read super dark ramp and update super bias
get_super_flats([split_low_high, smth_sig, ...])	Get flat field information
plot_bias_darks([save, return_figax, deconvolve])
plot_cds_noise([tkey, save, return_figax, xlim])
plot_dark_distribution([save, xlim, ...])	Plot histogram of dark slope
plot_dark_overview([save, xlim_hist, ...])	Plot Overview of Dark Current Characteristics
plot_dark_ramps([save, time_cut, return_figax])	Plot average dark current ramps
plot_dark_ramps_ch([save, time_cut, ...])	Plot fits to each channel dark current ramp
plot_eff_noise([ideal_Poisson, save, ...])	Plot effective noise of slope fits
plot_eff_noise_patterns([ideal_Poisson, ...])	Plot effective noise of slope fits for variety of read patterns
plot_ipc_ppc([k_ipc, k_ppc, save, return_figax])
plot_power_spectrum([save, cds, return_figax])
plot_reset_overview([save, binsize, ...])	Overview Plots of Bias and kTC Noise

Attributes

allfiles
cds_act_dict
cds_ref_dict
chsize	Width of output channel
column_prob_bad
column_variations
dark_ramp_dict
dark_shape	Shape of dark ramps
datadir
eff_noise_dict
ipc_alpha_frac	Fractional IPC value (alpha)
kernel_ipc
kernel_ppc
ktc_noise
lindir
linfiles
mask_act
mask_channels
mask_ref
nchan	Number of output channels
nchans	Number of output channels
outdir
pow_spec_dict
ppc_frac	Fractional PPC value
ref_pixel_dict
super_bias
super_bias_deconv
super_dark
super_dark_deconv
super_dark_ramp
temperature_dict
time_arr

calc_cds_noise(cds_type='spatial', temperature=None, temp_key='T_FPA1')

Return CDS Noise components for each channel

Parameters

• cds_type (str) – Return ‘spatial’, ‘temporal’, or ‘average’ noise values?

• temperature (float or None) – Option to supply temperature at which to interpolate. If None is provided, then returns the median of all noise values.

• temp_key (str) – Temperature key from self.temperature_dict to interpolate over. Generally, either ‘T_FPA1’ or ‘T_FPA2’ as those most closely represent the detector operating temperatures.

property chsize

Width of output channel

property dark_shape

Shape of dark ramps

deconvolve_supers()

Deconvolve the super dark and super bias images

get_cds_dict(force=False)

Calculate CDS noise for all files

Creates a dictionary of CDS noise components, including total noise, amplifier 1/f noise, correlated 1/f noise, white noise, and reference pixel ratios. Two different methods are used to calculate CDS per pixels: temporal and spatial.

Creates dictionary attributes self.cds_act_dict and self.cds_ref_dict.

get_column_variations(force=False, **kwargs)

Get column offset variations

Create a series of column offset models. These are likely FETS in the ASIC preamp or ADC causing entire columns within a ramp to jump around.

get_dark_slope_image(deg=1, force=False)

Calculate dark slope image

get_effective_noise(ideal_Poisson=False, force=False)

Calculate effective noise curves for each readout pattern

get_ipc(calc_ppc=False)

Calculate IPC (and PPC) kernels

get_ktc_noise(**kwargs)

Calculate and store kTC (Reset) Noise

Keyword Arguments

• bias_sigma_arr (ndarray) – Image of the pixel uncertainties.

• binsize (float) – Size of the histogram bins.

• return_std (bool) – Also return the standard deviation of the distribution?

get_linear_coeffs(deg=8, use_legendre=True, lxmap=[0, 100000.0], counts_cut=None, sat_calc=0.98, force=False, DMS=None, super_bias=None, **kwargs)

Determine linearity coefficents

These coefficients allow us to convert from an observed ramp (DN) to an idealized linear ramp (in e-). Values are stored in the dictionary self.linear_dict.

Parameters

• force (bool) – Force calculation of coefficients.

• DMS (None or bool) – Option to specifiy if linearity files are DMS format. If set to None, then uses self.DMS.

• super_bias (None or ndarray) – Option to specify an input super bias image. If not specified, then defaults to self.super_bias.

• counts_cut (None or float) – Option to fit two sets of polynomial coefficients to lower and uppper values. ‘counts_cut’ specifies the division in values of electrons. Useful for pixels with different non-linear behavior at low flux levels. Recommended values of 15000 e-.

• deg (int) – Degree of polynomial to fit. Default=8.

• use_legendre (bool) – Fit with Legendre polynomial, an orthonormal basis set. Default=True.

• lxmap (ndarray or None) – Legendre polynomials are normaly mapped to xvals of [-1,+1]. lxmap gives the option to supply the values for xval that should get mapped to [-1,+1]. If set to None, then assumes [xvals.min(),xvals.max()].

get_nonlinear_coeffs(deg=8, use_legendre=True, lxmap=[0, 100000.0], counts_cut=15000, sat_calc=0.998, force=False, DMS=None, super_bias=None, **kwargs)

Determine non-linear coefficents

These coefficients allow us to go from an ideal linear ramp to some observed (simulated) non-linear ramp. Value are store in the self.nonlinear_dict dictionary.

Parameters

• force (bool) – Force calculation of coefficients.

• DMS (None or bool) – Option to specifiy if linearity files are DMS format. If set to None, then uses self.DMS.

• super_bias (None or ndarray) – Option to specify an input super bias image. If not specified, then defaults to self.super_bias.

• counts_cut (None or float) – Option to fit two sets of polynomial coefficients to lower and uppper values. ‘counts_cut’ specifies the division in values of electrons. Useful for pixels with different non-linear behavior at low flux levels. Recommended values of 15000 e-.

• deg (int) – Degree of polynomial to fit. Default=8.

• use_legendre (bool) – Fit with Legendre polynomial, an orthonormal basis set. Default=True.

• lxmap (ndarray or None) – Legendre polynomials are normaly mapped to xvals of [-1,+1]. lxmap gives the option to supply the values for xval that should get mapped to [-1,+1]. If set to None, then assumes [xvals.min(),xvals.max()].

get_pixel_slope_averages(force=False)

Get average pixel ramp

get_power_spectrum(include_oh=False, calc_cds=True, per_pixel=False, return_corr=False, return_ucorr=False, mn_func=<function mean>, force=False, save=True)

Keyword Arguments

• include_oh (bool) – Zero-pad the data to insert line and frame overhead pixels?

• calc_cds (bool) – Power spectrum of CDS pairs or individual frames?

• return_corr (bool) – Return power spectrum of channel correlated 1/f noise?

• return_ucorr (bool) – Return power spectra of channel-dependent (uncorrelated) 1/f noise?

• per_pixel (bool) – Calculate average power spectrum of each pixel along ramp (frame timescales)? If False, samples pixels within a frame (pixel read timescales).

get_ref_pixel_noise(force=False, **kwargs)

Generate Dictionary of Reference Pixel behavior info

get_super_dark_ramp(force=False, **kwargs)

Create or read super dark ramp and update super bias

get_super_flats(split_low_high=True, smth_sig=10, force=False, **kwargs)

Get flat field information

Splits flat field into to lflats and pflats (low and high frequency).

property ipc_alpha_frac

Fractional IPC value (alpha)

property nchan

Number of output channels

property nchans

Number of output channels

plot_bias_darks(save=False, return_figax=False, deconvolve=False)

plot_dark_distribution(save=False, xlim=None, return_figax=False)

Plot histogram of dark slope

plot_dark_overview(save=False, xlim_hist=None, return_figax=False)

Plot Overview of Dark Current Characteristics

plot_dark_ramps(save=True, time_cut=None, return_figax=False)

Plot average dark current ramps

time_cutfloat

Some darks show distinct slopes before and after a characteristic time. Setting this keyword will fit separate slopes before and after the specified time. A time of 200 sec is used for SCA 485.

plot_dark_ramps_ch(save=True, time_cut=None, return_figax=False)

Plot fits to each channel dark current ramp

time_cutfloat

Some darks show distinct slopes before and after a characteristic time. Setting this keyword will fit separate slopes before and after the specified time. A time of 200 sec is used for SCA 485.

plot_eff_noise(ideal_Poisson=False, save=False, return_figax=False)

Plot effective noise of slope fits

plot_eff_noise_patterns(ideal_Poisson=False, save=False, ylim=None, return_figax=False)

Plot effective noise of slope fits for variety of read patterns

plot_reset_overview(save=False, binsize=0.25, xlim_hist=None, return_figax=False)

Overview Plots of Bias and kTC Noise

property ppc_frac

Fractional PPC value