pynrc.reduce.calib

Functions

apply_linearity(cube, det, coeff_dict)	Apply pixel linearity corrections to ramp
apply_nonlin(cube, det, coeff_dict[, ...])	Apply pixel non-linearity to ideal ramp
broken_pink_powspec(freq, scales[, fcut1, ...])
calc_cdsnoise(data[, temporal, spatial, ...])	Calculate CDS noise from input image cube
calc_eff_noise(allfiles[, superbias, ...])	Determine Effective Noise
calc_ktc(bias_sigma_arr[, binsize, return_std])	Calculate kTC (Reset) Noise
calc_linearity_coeff(data, sat_vals, well_depth)	counts_cut : None or float
calc_nonlin_coeff(data, sat_vals, well_depth)	counts_cut : None or float
chisqr_red(yvals[, yfit, err, dof, err_func])	Calculate reduced chi square metric
cube_fit(tarr, data[, bias, sat_vals, ...])
deconv_single_image(im, kfft)	Image deconvolution for a kernel
find_group_sat(file[, DMS, bias, sat_vals, ...])	Group at which 98% of pixels are saturated
find_sat(data[, bias, ref_info, bit_depth])	Given a data cube, find the values in ADU in which data reaches hard saturation.
fit_corr_powspec(freq, ps[, flim1, flim2, alpha])	Fit Correlated Noise Power Spectrum
fit_func_var_ex(params, det, patterns, ...)	Function for lsq fit to get excess variance
gen_cds_dict(allfiles[, DMS, superbias, ...])	Generate dictionary of CDS noise info
gen_col_variations(allfiles[, super_bias, ...])	Create a series of column offset models
gen_ref_dict(allfiles, super_bias[, ...])	Generate Reference Pixel Behavior Dictionary
gen_super_bias(allfiles[, DMS, mn_func, ...])	Generate a Super Bias Image
gen_super_dark(allfiles[, super_bias, DMS])	Average together all dark ramps to create a super dark ramp.
gen_super_ramp(allfiles[, super_bias, DMS, ...])	Average together all linearity ramps to create a super ramp.
get_bias_offsets(data[, nchan, ref_bot, ...])	Get Reference Bias Characteristics
get_fits_data(fits_file[, return_header, ...])	Read in FITS file data
get_flat_fields(im_slope[, split_low_high, ...])	Calculate QE variations in flat field
get_freq_array(pow_spec[, dt, nozero, npix_odd])	Return frequencies associated with power spectrum
get_ipc_kernel(imdark[, tint, boxsize, ...])	Derive IPC/PPC Convolution Kernels
get_linear_coeffs(allfiles[, super_bias, ...])
get_oddeven_offsets(data[, nchan, ref_bot, ...])	Even/Odd Column Offsets
get_power_spec(data[, nchan, calc_cds, ...])	Calculate the power spectrum of an input data ramp in a variety of ways.
get_power_spec_all(allfiles[, super_bias, ...])	Return the average power spectra (white, 1/f noise correlated and uncorrelated) of all FITS files.
get_ref_instability(data[, nchan, ref_bot, ...])	Reference Pixel Instability
ipc_deconvolve(imarr, kernel[, kfft])	Simple IPC image deconvolution
pixel_linearity_gains(frame, coeff_arr[, ...])	Given some image data and coefficient
plot_dark_histogram(im, ax[, binsize, ...])
plot_kernel(kern[, ax, return_figax])	Plot image of IPC or PPC kernel
pow_spec_ramp(data, nchan[, nroh, nfoh, ...])	Get power spectrum within frames of input ramp
pow_spec_ramp_pix(data, nchan[, ...])	Get power spectrum of pixels within ramp
ppc_deconvolve(im, kernel[, kfft, nchans, ...])	PPC image deconvolution
ramp_derivative(y[, dx, fit0, deg, ifit])	Get the frame-by-frame derivative of a ramp.
ramp_resample(data, det_new[, return_zero_frame])	Resample a RAPID dataset into new detector format
time_to_sat(data, sat_vals[, dt, sat_calc, ...])	Determine time of saturation

Classes

nircam_cal(scaid[, same_scan_direction, ...])	NIRCam Calibration class
nircam_dark(scaid, datadir, outdir[, ...])