sciapy.regress

SCIAMACHY regression modelling

This module contains regression models and methods to analyse SCIAMACHY level 2 number density data (only NO for now).

sciapy.regress.load_data

SCIAMACHY data interface

Data loading and selection routines for SCIAMACHY data regression fits. Includes reading the solar and geomagnetic index files used as proxies.

sciapy.regress.load_data.load_scia_dzm(filename, alt, lat, tfmt='jyear', scale=1, subsample_factor=1, subsample_method='greedy', akd_threshold=0.002, cnt_threshold=0, center=False, season=None, SPEs=False)[source]

Load SCIAMACHY daily zonal mean data

Interface function for SCIAMACHY daily zonal mean data files version 6.x. Uses xarray [1] to load and select the data. Possible selections are by hemispheric summer (NH summer ~ SH winter and SH summer ~ NH winter) and exclusion of strong solar proton events (SPE).

[1]https://xarray.pydata.org
Parameters:
  • filename (str) – The input filename
  • alt (float) – The altitude
  • lat (float) – The longitude
  • tfmt (string, optional) – The astropy.time “Time Format” for the time units, for example, “jyear”, “decimalyear”, “jd”, “mjd”, etc. See: http://docs.astropy.org/en/stable/time/index.html#time-format Default: “jyear”
  • scale (float, optional) – Scale factor of the data (default: 1)
  • subsample_factor (int, optional) – Factor to subsample the data by (see subsample_method) (default: 1 (no subsampling))
  • subsample_method (“equal”, “greedy”, or “random”, optional) – Method for subsampling the data (see subsample_factor). “equal” for equally spaced subsampling, “greedy” for selecting the data based on their uncertainty, and “random” for uniform random subsampling. (default: “greedy”)
  • center (bool, optional) – Center the data by subtracting the global mean. (default: False)
  • season (“summerNH”, “summerSH”, or None, optional) – Select the named season or None for all data (default: None)
  • SPEs (bool, optional) – Set to True to exclude strong SPE events (default: False)
Returns:

(times, dens, errs) – The measurement times according to the tfmt keyword, the number densities, and their uncertainties.
Return type:

tuple of (N,) array_like
sciapy.regress.load_data.load_solar_gm_table(filename, cols, names, sep='\t', tfmt='jyear')[source]

Load proxy tables from ascii files

This function wraps numpy.genfromtxt() [2] with pre-defined settings to match the file format. It explicitly returns the times as UTC decimal years or julian epochs.

[2]https://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html
Parameters:
  • filename (str) – The file to read
  • cols (sequence) – The columns in the file to use, passed to numpy.genfromtxt’s usecols keyword. Should be at least of length 2, indicating time values in the first column, e.g., cols=[0, 1].
  • names (sequence) – The column names, passed as names to numpy.genfromtxt(). Should be at least of length 2, naming the proxy values in the second column, e.g., names=[“time”, “proxy”].
  • sep (str, optional) – The column separator character, passed as sep. Default: ` `
  • tfmt (str, optional) – The astropy.time “Time Format” for the time units, for example, “jyear”, “decimalyear”, “jd”, “mjd”, etc. See: http://docs.astropy.org/en/stable/time/index.html#time-format Default: “jyear”
Returns:

  • times (array_like) – The proxy times according to the tfmt keyword (UTC) as numpy.ndarray from the first column of “cols”.
  • values (tuple of array_like) – The proxy values combined into a structured array (numpy.recarray) with fields set according to “names[1:]” and “cols[1:]” passed above.

See also

astropy.time.Time, numpy.recarray

sciapy.regress.mcmc

SCIAMACHY data regression MCMC sampler

Markov Chain Monte Carlo (MCMC) routines to sample the posterior probability of SCIAMACHY data regression fits. Uses the emcee.EnsembleSampler [3] do do the real work.

[3]https://emcee.readthedocs.io
sciapy.regress.mcmc.mcmc_sample_model(model, y, beta=1.0, nwalkers=100, nburnin=200, nprod=800, nthreads=1, optimized=False, bounds=None, return_logpost=False, show_progress=False, progress_mod=10)[source]

Markov Chain Monte Carlo sampling interface

MCMC sampling interface to sample posterior probabilities using the emcee.EnsembleSampler [4].

[4]https://emcee.readthedocs.io
Arguments:
  • model (celerite.GP, george.GP, or sciapy.regress.RegressionModel instance) – The model to draw posterior samples from. It should provide either log_likelihood() and log_prior() functions or be directly callable via __call__().
  • y ((N,) array_like) – The data to condition the probabilities on.
  • beta (float, optional) – Tempering factor for the probability, default: 1.
  • nwalkers (int, optional) – The number of MCMC walkers (default: 100). If this number is smaller than 4 times the number of parameters, it is multiplied by the number of parameters. Otherwise it specifies the number of parameters directly.
  • nburnin (int, optional) – The number of burn-in samples to draw, default: 200.
  • nprod (int, optional) – The number of production samples to draw, default: 800.
  • nthreads (int, optional) – The number of threads to use with a multiprocessing.Pool, used as pool for emcee.EnsembleSampler. Default: 1.
  • optimized (bool, optional) – Indicate whether the actual (starting) position was determined with an optimization algorithm beforehand. If False (the default), a pre-burn-in run optimizes the starting position. Sampling continues from there with the normal burn-in and production runs. In that case, latin hypercube sampling is used to distribute the walker starting positions equally in parameter space.
  • bounds (iterable, optional) – The parameter bounds as a list of (min, max) entries. Default: None
  • return_logpost (bool, optional) – Indicate whether or not to return the sampled log probabilities as well. Default: False
  • show_progress (bool, optional) – Print the percentage of samples every progress_mod samples. Default: False
  • progress_mod (int, optional) – Interval in samples to print the percentage of samples. Default: 10
Returns:

samples or (samples, logpost) – (nwalkers * nprod, ndim) array of the sampled parameters from the production run if return_logpost is False. A tuple of an (nwalkers * nprod, ndim) array (the same as above) and an (nwalkers,) array with the second entry containing the log posterior probabilities if return_logpost is True.
Return type:

array_like or tuple