This documentation is for astroML version 0.2

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11. Class reference

This is a list of modules, classes, and functions available in astroML. For more details, please refer to the user guide or the text book. Examples of the use of astroML can also be found in the code examples, the text book figures and the paper figures.

11.1. Plotting Functions: astroML.plotting

11.1.1. Functions

plotting.hist(x[, bins, range]) Enhanced histogram
plotting.scatter_contour(x, y[, levels, ...]) Scatter plot with contour over dense regions

11.1.2. Classes

plotting.MultiAxes(ndim[, inner_labels, ...]) Visualize Multiple-dimensional data

11.2. Density Estimation & Histograms: astroML.density_estimation

11.2.1. Histogram Tools

density_estimation.histogram(a[, bins, range]) Enhanced histogram
density_estimation.bayesian_blocks(t[, x, ...]) Bayesian Blocks Implementation
density_estimation.knuth_bin_width(data[, ...]) Return the optimal histogram bin width using Knuth’s rule [R2]
density_estimation.scotts_bin_width(data[, ...]) Return the optimal histogram bin width using Scott’s rule:
density_estimation.freedman_bin_width(data) Return the optimal histogram bin width using the Freedman-Diaconis rule

11.2.2. Density Estimation

density_estimation.XDGMM(n_components[, ...]) Extreme Deconvolution
density_estimation.KDE([metric, h]) Kernel Density Estimate
density_estimation.KNeighborsDensity([...]) K-neighbors density estimation
density_estimation.EmpiricalDistribution(data) Empirically learn a distribution from one-dimensional data
density_estimation.FunctionDistribution(...) Generate random variables distributed according to an arbitrary function

11.3. Linear Regression & Fitting: astroML.linear_model

11.3.1. Linear Regression

linear_model.LinearRegression([fit_intercept]) Simple Linear Regression with errors in y
linear_model.PolynomialRegression([degree, ...]) Polynomial Regression with errors in y
linear_model.BasisFunctionRegression([...]) Basis Function with errors in y
linear_model.NadarayaWatson([kernel, h]) Nadaraya-Watson Kernel Regression

11.3.2. Functions

linear_model.TLS_logL(v, X, dX) Compute the total least squares log-likelihood

11.4. Loading of Datasets: astroML.datasets

11.4.1. Astronomy Datasets

datasets.fetch_sdss_spectrum(plate, mjd, fiber) Fetch an SDSS spectrum from the Data Archive Server
datasets.fetch_sdss_corrected_spectra([...]) Loader for Iterative PCA pre-processed galaxy spectra
datasets.fetch_sdss_S82standards([...]) Loader for SDSS stripe82 standard star catalog
datasets.fetch_dr7_quasar([data_home, ...]) Loader for SDSS DR7 quasar catalog
datasets.fetch_moving_objects([data_home, ...]) Loader for SDSS moving objects datasets
datasets.fetch_sdss_galaxy_colors([...]) Loader for SDSS galaxy colors.
datasets.fetch_nasa_atlas([data_home, ...]) Loader for NASA galaxy atlas data
datasets.fetch_sdss_sspp([data_home, ...]) Loader for SDSS SEGUE Stellar Parameter Pipeline data
datasets.fetch_sdss_specgals([data_home, ...]) Loader for SDSS Galaxies with spectral information
datasets.fetch_great_wall([data_home, ...]) Get the 2D SDSS “Great Wall” distribution, following Cowan et al 2008
datasets.fetch_imaging_sample([data_home, ...]) Loader for SDSS Imaging sample data
datasets.fetch_wmap_temperatures([masked, ...]) Loader for WMAP temperature map data
datasets.fetch_rrlyrae_mags([data_home, ...]) Loader for RR-Lyrae data
datasets.fetch_rrlyrae_combined([data_home, ...]) Loader for RR-Lyrae combined data
datasets.fetch_LINEAR_sample([data_home, ...]) Loader for LINEAR data sample
datasets.fetch_LINEAR_geneva([data_home, ...]) Loader for LINEAR geneva data.
datasets.fetch_LIGO_bigdog([data_home, ...]) Loader for LIGO bigdog event
datasets.fetch_LIGO_large([data_home, ...]) Loader for LIGO large dataset
datasets.fetch_hogg2010test([structured]) Fetch the Hogg et al 2010 test data
datasets.fetch_rrlyrae_templates([...]) Loader for RR-Lyrae template data
datasets.fetch_sdss_filter(fname[, ...]) Loader for SDSS Filter profiles
datasets.fetch_vega_spectrum([data_home, ...]) Loader for Vega reference spectrum
datasets.generate_mu_z([size, z0, dmu_0, ...]) Generate a dataset of distance modulus vs redshift.

11.5. Time Series Analysis: astroML.time_series

11.5.1. Periodic Time Series

time_series.lomb_scargle (Generalized) Lomb-Scargle Periodogram with Floating Mean
time_series.lomb_scargle_bootstrap(t, y, dy, ...) Use a bootstrap analysis to compute Lomb-Scargle significance
time_series.multiterm_periodogram(t, y, dy, ...) Perform a multiterm periodogram at each omega
time_series.search_frequencies(t, y, dy[, ...]) Utility Routine to find the best frequencies
time_series.MultiTermFit(omega, n_terms) Multi-term Fourier fit to a light curve

11.5.2. Aperiodic Time Series

time_series.ACF_scargle(t, y, dy[, n_omega, ...]) Compute the Auto-correlation function via Scargle’s method
time_series.ACF_EK(t, y, dy[, bins]) Auto-correlation function via the Edelson-Krolik method
time_series.generate_power_law(N, dt, beta) Generate a power-law light curve
time_series.generate_damped_RW(t_rest[, ...]) Generate a damped random walk light curve

11.6. Statistical Functions: astroML.stats

stats.binned_statistic(x, values[, ...]) Compute a binned statistic for a set of data.
stats.binned_statistic_2d(x, y, values[, ...]) Compute a bidimensional binned statistic for a set of data.
stats.binned_statistic_dd(sample, values[, ...]) Compute a multidimensional binned statistic for a set of data.
stats.sigmaG(a[, axis, overwrite_input, ...]) Compute the rank-based estimate of the standard deviation
stats.median_sigmaG(a[, axis, ...]) Compute median and rank-based estimate of the standard deviation
stats.mean_sigma(a[, axis, dtype, ddof, ...]) Compute mean and standard deviation for an array
stats.fit_bivariate_normal(x, y[, robust]) Fit bivariate normal parameters to a 2D distribution of points
stats.bivariate_normal([mu, sigma_1, ...]) Sample points from a 2D normal distribution
stats.trunc_exp A truncated positive exponential continuous random variable.
stats.linear A truncated positive exponential continuous random variable.

11.7. Dimensionality Reduction: astroML.dimensionality

dimensionality.iterative_pca(X, M[, n_ev, ...])
Parameters:

11.8. Correlation Functions: astroML.correlation

Tools for computing two-point correlation functions.

correlation.two_point(data, bins[, method, ...]) Two-point correlation function
correlation.two_point_angular(ra, dec, bins) Angular two-point correlation function
correlation.bootstrap_two_point(data, bins) Bootstrapped two-point correlation function
correlation.bootstrap_two_point_angular(ra, ...) Angular two-point correlation function

11.9. Filters: astroML.filters

filters.savitzky_golay(y, window_size, order) Smooth (and optionally differentiate) data with a Savitzky-Golay filter
filters.wiener_filter(t, h[, signal, noise, ...]) Compute a Wiener-filtered time-series
filters.min_component_filter(x, y, feature_mask) Minimum component filtering

11.10. Fourier and Wavelet Transforms: astroML.fourier

fourier.FT_continuous(t, h[, axis, method]) Approximate a continuous 1D Fourier Transform with sampled data.
fourier.IFT_continuous(f, H[, axis, method]) Approximate a continuous 1D Inverse Fourier Transform with sampled data.
fourier.PSD_continuous(t, h[, axis, method]) Approximate a continuous 1D Power Spectral Density of sampled data.
fourier.wavelet_PSD(t, h, f0[, Q]) Compute the wavelet PSD as a function of f0 and t
fourier.sinegauss(t, t0, f0, Q) Sine-gaussian wavelet
fourier.sinegauss_FT(f, t0, f0, Q) Fourier transform of the sine-gaussian wavelet.
fourier.sinegauss_PSD(f, t0, f0, Q) Compute the PSD of the sine-gaussian function at frequency f

11.11. Luminosity Functions: astroML.lumfunc

lumfunc.Cminus(x, y, xmax, ymax) Lynden-Bell’s C-minus method
lumfunc.binned_Cminus(x, y, xmax, ymax, ...) Compute the binned distributions using the Cminus method
lumfunc.bootstrap_Cminus(x, y, xmax, ymax, ...) Compute the binned distributions using the Cminus method, with

11.12. Classification: astroML.classification

classification.GMMBayes([n_components]) GMM Bayes Classifier

11.13. Resampling: astroML.resample

resample.bootstrap(data, n_bootstraps, ...) Compute bootstraped statistics of a dataset.
resample.jackknife(data, user_statistic[, ...]) Compute first-order jackknife statistics of the data.

..automodule:: astroML_addons

11.14. Addon Functions astroML_addons

These functions should not be called directly: when they are installed, they are used automatically by astroML.

periodogram.lomb_scargle (Generalized) Lomb-Scargle Periodogram with Floating Mean