Reference/API

threadcount.procedures

threadcount.procedures.open_cube_and_deredshift.run(...)	Open a fits data cube and de-redshift it, as well as any continuum cube.
threadcount.procedures.fit_lines.run(...)
threadcount.procedures.analyze_outflow_extent.run(...)
threadcount.procedures.set_rcParams.set_params([...])
threadcount.procedures.explore_results.run(...)	This funtion put everything together.

threadcount.models Module

Custom Models that should function just like lmfit’s models.

Functions

flux_expr(model)	Return constraint expression for line flux.
gaussianH(x[, height, center, sigma])	Return a 1-dimensional Gaussian function.
guess_from_peak(y, x[, negative])	Estimate starting values from 1D peak data and return (height,center,sigma).
log10_sum(arr1, arr2)	Add the 2 arguments and return the log10 of result.
mean_edges(y[, x, edge_fraction])	Compute the mean of the outer points of y.
set_common_limits(params, x, data)

Classes

Const_1GaussModel([independent_vars, ...])	Constant + 1 Gaussian Model.
Const_2GaussModel([independent_vars, ...])	Constant + 2 Gaussians Model.
Const_3GaussModel([independent_vars, ...])	Constant + 3 Gaussians Model.
GaussianModelH([independent_vars, prefix, ...])	A model heavily based on lmfit's GaussianModel, fitting height instead of amplitude.
Log10_DoubleExponentialModel([...])	Log10 of double exponential decay Model.
Quadratic_1GaussModel([independent_vars, ...])	Quadratic + 1 Gaussian Model.
Quadratic_2GaussModel([independent_vars, ...])	Quadratic + 2 Gaussians Model.
Quadratic_3GaussModel([independent_vars, ...])	Quadratic + 3 Gaussians Model.

Variables

min_sigma	int([x]) -> integer int(x, base=10) -> integer
tiny	Convert a string or number to a floating point number, if possible.

threadcount.lines Module

Library of line wavelengths.

Classes

Line(center[, plus, minus, label, save_str])

Line object containing center and wavelength range.

Variables

Hb4861	= 4861.333 # Hβ
Hdelta	= 4101.742 # Hδ
Hgamma	= 4340.471 # Hγ
L_Hb4861	Line instance, Hb4861 +/- 15
L_Hdelta	Line instance, Hdelta +/- 15
L_Hgamma	Line instance, Hgamma +/- 15
L_NeIII	Line instance, NeIII +/- 15
L_OII3727d	Line instance, (OII3726 + OII3729)/2 +/- 16
L_OIII4363	Line instance, OIII4363 +/- 15
L_OIII4959	Line instance, OIII4959 +/- 15
L_OIII5007	Line instance, OIII5007 +/- 15
NeIII	= 3868.760 # [Ne III] 3869
OII3726	= 3726.032 # [O II] 3727 doublet, line 1
OII3729	= 3728.815 # [O II] 3727 doublet, line 2
OIII4363	= 4363.210 # [O III] 4363
OIII4959	= 4958.911 # [O III] 4959
OIII5007	= 5006.843 # [O III] 5007

threadcount.fit Module

main threadcount module.

Functions

choose_model_aic(model_list[, d_aic])	Broadcast choose_model_aic_single() over array.
choose_model_aic_single(model_list[, d_aic])	Determine best modelresult in a list, chosen by computing \({\Delta}aic\).
collect_stats(model_result[, model_keys, ...])	Collect the information from one model_result.
de_redshift(wavecoord[, z, z_initial])	De-redshift the WaveCoord in-place.
extract_spaxel_info(fit_results[, fit_info, ...])	Extract ModelResult attributes into numpy array.
extract_spaxel_info_mc(mc_fits, fit_info, ...)	Compute the average and standard deviation of the information requested.
fit_baseline(spectrum, this_baseline_range, ...)
get_SNR_map(cube[, signal_idx, ...])	Create Image of signal to noise ratio in a given bandwidth.
get_SignalBW_idx(cube[, nsigma, plot])	Determine the wavelength indices containing signal.
get_aic(model[, error])	Return the aic_real of a successful fit.
get_gcomponent_comparison(fit)	Determine component comparison to this highest flux gaussian.
get_header_stats([model_keys, fit_info])	Collect list of column names, suitable for a header row.
get_index(array, value)	Determine the index of 'array' which is closest to value.
get_model_keys(model_results[, ignore])	Retrieve the names of parameters from model results.
get_ngaussians(fit)	Determine the number of gaussians in a lmfit.model.Model.
get_param_values(params, param_name[, ...])	Retrieve parameter value by name from lmfit objects.
get_reg_image(region)	Create kernel image from list of pixels.
get_region(rx[, ry])	Select pixels in ellipse of radius rx, ry from (0,0).
interactive_lower_threshold(image[, title])	Create plot and interact with user to determine the lower threshold for valid data.
interactive_user_choice(fits, choices, ...)	Choose best model from a display of all model fits to a pixel.
iter_spaxel(image[, index])	Create an iterator over the spaxels of successive image pixels in a 2d numpy array.
marginal_fits(fit_list, choices[, flux, dmu])	Determine which fits should be inspected by hand.
open_fits_cube(data_filename[, ...])	Load a fits file using mpdaf.obj.Cube, and handle variance in separate file.
plot_ModelResults_pixel(fitList[, title, ...])	Create a multipanel figure comparing fits to the same data.
plot_baseline(fitresult)
process_settings(default_settings[, ...])	Combine the default settings with any user settings.
process_settings_dict(default_settings[, ...])	Combine the default settings with any user settings.
remove_baseline(cube, subcube_av, ...)
save_choice_fit_stats(filename, fit_results, ...)	Compile fit choice and parameter summary, and save to file.
save_fit_stats([filename, dataset, ...])	Compile fit parameter summary and save to file.
save_pdf_plots(filename, fitList[, ...])	Save plots of each spaxel fit as separate page in 1 pdf file.
save_to_file(filename, result_list)	Convience wrapper of csv.writer.
spatial_average(cube, kernel_image, **kwargs)	Apply kernel image smoothing on every spatial image in a cube.
tweak_redshift(cube, z_gal[, ...])	Interactively choose a new redshift.

Classes

RecursiveArray([array])	Subclass of list which distributes attribute and function calls recursively.
ResultDict([data_array, names, data_dict, ...])	Container for ordered dict of numpy ndarrays with save/load functionality.

Variables

DEFAULT_FIT_INFO	Define typical ModelResult information we might want.
FLAM16	A header["BUNIT"] value we have.
FLOAT_FMT	Default formatting for floats in output files.
vget_aic	Vectorized get_aic().
vget_param_values	Retrieve parameter value by name from lmfit objects.

threadcount.mpdaf_ext Module

Functions to extend Spectrum, Image, and Cube from package mpdaf.

Functions

correlate2d_norm(self, other[, interp])	Return the cross-correlation of the image with an array.
lmfit(self, model, **kwargs)	Fit model to Spectrum using lmfit.

threadcount.lmfit_ext Module

Functions to extend classes Model, ModelResult, Parameters from package lmfit.

Functions

aic_real(self)	Chisqr + 2 * nvarys.
bic_real(self)	Chisqr + np.log(ndata) * nvarys.
extend_lmfit(lmfit)
mc_iter(self[, n_mc_iterations, distribution])	ModelResult extension to change the data related to sigma and refit.
order_gauss(self[, delta_x])
plot2(self[, datafmt, fitfmt, initfmt, ...])	Plot the fit results and residuals using matplotlib.
plot_components(self[, ax, show_combined, ...])
set_param_hints_endswith(self, name, **kwargs)	Set param hints for all model params names ending in name.
stderrsdict(self)	Return an ordered dictionary of parameter stderrs.
summary_array(self[, fit_info, param_info])
valerrsdict(self)	Return a dictionary of parameter [value, stderr].