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# -*- coding: utf-8 -*- |
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# vim:fileencoding=utf-8 |
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# |
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# Copyright (c) 2017-2018 Stefan Bender |
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# |
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# This module is part of sciapy. |
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# sciapy is free software: you can redistribute it or modify |
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# it under the terms of the GNU General Public License as published |
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# by the Free Software Foundation, version 2. |
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# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html. |
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"""SCIAMACHY data regression command line interface |
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Command line main program for regression analysis of SCIAMACHY |
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daily zonal mean time series (NO for now). |
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""" |
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import ctypes |
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import logging |
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import numpy as np |
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import scipy.optimize as op |
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from scipy.interpolate import interp1d |
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import george |
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import celerite |
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import matplotlib as mpl |
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# switch off X11 rendering |
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mpl.use("Agg") |
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from .load_data import load_solar_gm_table, load_scia_dzm |
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from .models_cel import trace_gas_model |
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from .mcmc import mcmc_sample_model |
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from .statistics import mcmc_statistics |
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from ._gpkernels import (george_solvers, |
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setup_george_kernel, setup_celerite_terms) |
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from ._plot import (plot_single_sample_and_residuals, |
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plot_residual, plot_random_samples) |
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from ._options import parser |
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View Code Duplication |
def save_samples_netcdf(filename, model, alt, lat, samples, |
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scale=1e-6, |
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lnpost=None, compressed=False): |
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from xarray import Dataset |
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smpl_ds = Dataset(dict([(pname, (["lat", "alt", "sample"], |
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samples[..., i].reshape(1, 1, -1))) |
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for i, pname in enumerate(model.get_parameter_names())] |
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# + [("lpost", (["lat", "alt", "sample"], lnp.reshape(1, 1, -1)))] |
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), |
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coords={"lat": [lat], "alt": [alt]}) |
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for modn in model.mean.models: |
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modl = model.mean.models[modn] |
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if hasattr(modl, "mean"): |
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smpl_ds.attrs[modn + ":mean"] = modl.mean |
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units = {"kernel": { |
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"log": "log(10$^{{{0:.0f}}}$ cm$^{{-3}}$)" |
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.format(-np.log10(scale))}, |
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"mean": { |
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"log": "log(10$^{{{0:.0f}}}$ cm$^{{-3}}$)" |
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.format(-np.log10(scale)), |
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"cos": "10$^{{{0:.0f}}}$ cm$^{{-3}}$".format(-np.log10(scale)), |
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"sin": "10$^{{{0:.0f}}}$ cm$^{{-3}}$".format(-np.log10(scale)), |
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"val": "10$^{{{0:.0f}}}$ cm$^{{-3}}$".format(-np.log10(scale)), |
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"amp": "10$^{{{0:.0f}}}$ cm$^{{-3}}$".format(-np.log10(scale)), |
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"tau": "d"}} |
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for pname in smpl_ds.data_vars: |
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_pp = pname.split(':') |
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for _n, _u in units[_pp[0]].items(): |
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if _pp[-1].startswith(_n): |
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logging.debug("units for %s: %s", pname, _u) |
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smpl_ds[pname].attrs["units"] = _u |
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smpl_ds["alt"].attrs = {"long_name": "altitude", "units": "km"} |
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smpl_ds["lat"].attrs = {"long_name": "latitude", "units": "degrees_north"} |
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_encoding = None |
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if compressed: |
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_encoding = {var: {"zlib": True, "complevel": 1} |
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for var in smpl_ds.data_vars} |
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smpl_ds.to_netcdf(filename, encoding=_encoding) |
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smpl_ds.close() |
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View Code Duplication |
def _train_test_split(times, data, errs, train_frac, |
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test_frac, randomize): |
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# split the data into training and test subsets according to the |
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# fraction given (default is 1, i.e. no splitting) |
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ndata = len(times) |
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train_size = int(ndata * train_frac) |
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test_size = min(ndata - train_size, int(ndata * test_frac)) |
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# randomize if requested |
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if randomize: |
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permut_idx = np.random.permutation(np.arange(ndata)) |
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else: |
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permut_idx = np.arange(ndata) |
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train_idx = np.sort(permut_idx[:train_size]) |
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test_idx = np.sort(permut_idx[train_size:train_size + test_size]) |
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times_train = times[train_idx] |
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data_train = data[train_idx] |
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errs_train = errs[train_idx] |
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if test_size > 0: |
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times_test = times[test_idx] |
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data_test = data[test_idx] |
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errs_test = errs[test_idx] |
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else: |
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times_test = times |
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data_test = data |
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errs_test = errs |
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logging.info("using %s of %s samples for training.", len(times_train), ndata) |
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logging.info("using %s of %s samples for testing.", len(times_test), ndata) |
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return (times_train, data_train, errs_train, |
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times_test, data_test, errs_test) |
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View Code Duplication |
def _r_sun_earth(time, tfmt="jyear"): |
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"""First order approximation of the Sun-Earth distance |
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The Sun-to-Earth distance can be used to (un-)normalize proxies |
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to the actual distance to the Sun instead of 1 AU. |
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Parameters |
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---------- |
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time : float |
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Time value in the units given by 'tfmt'. |
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tfmt : str, optional |
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The units of 'time' as supported by the |
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astropy.time time formats. Default: 'jyear'. |
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Returns |
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------- |
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dist : float |
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The Sun-Earth distance at the given day of year in AU. |
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""" |
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from astropy.time import Time |
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tdoy = Time(time, format=tfmt) |
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tdoy.format = "yday" |
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doy = int(tdoy.value.split(':')[1]) |
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return 1 - 0.01672 * np.cos(2 * np.pi / 365.256363 * (doy - 4)) |
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View Code Duplication |
def main(): |
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logging.basicConfig(level=logging.WARNING, |
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format="[%(levelname)-8s] (%(asctime)s) " |
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"%(filename)s:%(lineno)d %(message)s", |
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datefmt="%Y-%m-%d %H:%M:%S %z") |
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args = parser.parse_args() |
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logging.info("command line arguments: %s", args) |
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if args.quiet: |
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logging.getLogger().setLevel(logging.ERROR) |
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elif args.verbose: |
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logging.getLogger().setLevel(logging.INFO) |
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else: |
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logging.getLogger().setLevel(args.loglevel) |
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from numpy.distutils.system_info import get_info |
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for oblas_path in get_info("openblas")["library_dirs"]: |
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oblas_name = "{0}/libopenblas.so".format(oblas_path) |
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logging.info("Trying %s", oblas_name) |
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try: |
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oblas_lib = ctypes.cdll.LoadLibrary(oblas_name) |
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oblas_cores = oblas_lib.openblas_get_num_threads() |
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oblas_lib.openblas_set_num_threads(args.openblas_threads) |
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logging.info("Using %s/%s Openblas thread(s).", |
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oblas_lib.openblas_get_num_threads(), oblas_cores) |
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except: |
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logging.info("Setting number of openblas threads failed.") |
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if args.random_seed is not None: |
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np.random.seed(args.random_seed) |
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if args.proxies: |
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proxies = args.proxies.split(',') |
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proxy_dict = dict(_p.split(':') for _p in proxies) |
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else: |
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proxy_dict = {} |
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lag_dict = {pn: 0 for pn in proxy_dict.keys()} |
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# Post-processing of arguments... |
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# List of proxy lag fits from csv |
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fit_lags = args.fit_lags.split(',') |
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# List of proxy lifetime fits from csv |
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fit_lifetimes = args.fit_lifetimes.split(',') |
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fit_annlifetimes = args.fit_annlifetimes.split(',') |
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# List of proxy lag times from csv |
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lag_dict.update(dict(_ls.split(':') for _ls in args.lag_times.split(','))) |
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# List of cycles (frequencies in 1/year) from argument list (csv) |
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try: |
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freqs = list(map(float, args.freqs.split(','))) |
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except ValueError: |
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freqs = [] |
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args.freqs = freqs |
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# List of initial parameter values |
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initial = None |
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if args.initial is not None: |
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try: |
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initial = list(map(float, args.initial.split(','))) |
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except ValueError: |
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pass |
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# List of GP kernels from argument list (csv) |
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kernls = args.kernels.split(',') |
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lat = args.latitude |
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alt = args.altitude |
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logging.info("location: %.0f°N %.0f km", lat, alt) |
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no_ys, no_dens, no_errs, no_szas = load_scia_dzm(args.file, alt, lat, |
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tfmt=args.time_format, |
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scale=args.scale, |
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#subsample_factor=args.random_subsample, |
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#subsample_method="random", |
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akd_threshold=args.akd_threshold, |
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cnt_threshold=args.cnt_threshold, |
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center=args.center_data, |
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season=args.season, |
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SPEs=args.exclude_spe) |
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(no_ys_train, no_dens_train, no_errs_train, |
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no_ys_test, no_dens_test, no_errs_test) = _train_test_split( |
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no_ys, no_dens, no_errs, args.train_fraction, |
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args.test_fraction, args.random_train_test) |
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sza_intp = interp1d(no_ys, no_szas, bounds_error=False) |
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max_amp = 1e10 * args.scale |
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max_days = 100 |
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proxy_config = {} |
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for pn, pf in proxy_dict.items(): |
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pt, pp = load_solar_gm_table(pf, cols=[0, 1], names=["time", pn], tfmt=args.time_format) |
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# use log of proxy values |
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pv = np.log(pp[pn]) if pn in args.log_proxies.split(',') else pp[pn] |
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# normalize to sun--earth distance squared |
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if pn in args.norm_proxies_distSEsq.split(','): |
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rad_sun_earth = np.vectorize(_r_sun_earth)(pt, tfmt=args.time_format) |
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pv /= rad_sun_earth**2 |
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# normalize by cos(SZA) |
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if pn in args.norm_proxies_SZA.split(',') and sza_intp is not None: |
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pv *= np.cos(np.radians(sza_intp(pt))) |
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proxy_config.update({pn: |
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dict(times=pt, values=pv, |
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center=pn in args.center_proxies.split(','), |
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positive=pn in args.positive_proxies.split(','), |
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lag=float(lag_dict[pn]), |
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max_amp=max_amp, max_days=max_days, |
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sza_intp=sza_intp if args.use_sza else None, |
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)} |
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) |
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model = trace_gas_model(constant=args.fit_offset, |
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proxy_config=proxy_config, **vars(args)) |
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logging.debug("model dict: %s", model.get_parameter_dict()) |
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model.freeze_all_parameters() |
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# thaw parameters according to requested fits |
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for pn in proxy_dict.keys(): |
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model.thaw_parameter("{0}:amp".format(pn)) |
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if pn in fit_lags: |
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model.thaw_parameter("{0}:lag".format(pn)) |
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if pn in fit_lifetimes: |
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model.set_parameter("{0}:tau0".format(pn), 1e-3) |
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model.thaw_parameter("{0}:tau0".format(pn)) |
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if pn in fit_annlifetimes: |
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model.thaw_parameter("{0}:taucos1".format(pn)) |
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model.thaw_parameter("{0}:tausin1".format(pn)) |
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else: |
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model.set_parameter("{0}:ltscan".format(pn), 0) |
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for freq in freqs: |
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if not args.fit_phase: |
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model.thaw_parameter("f{0:.0f}:cos".format(freq)) |
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model.thaw_parameter("f{0:.0f}:sin".format(freq)) |
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else: |
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model.thaw_parameter("f{0:.0f}:amp".format(freq)) |
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model.thaw_parameter("f{0:.0f}:phase".format(freq)) |
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if args.fit_offset: |
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#model.set_parameter("offset:value", -100.) |
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#model.set_parameter("offset:value", 0) |
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model.thaw_parameter("offset:value") |
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285
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if initial is not None: |
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model.set_parameter_vector(initial) |
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# model.thaw_parameter("GM:ltscan") |
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288
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logging.debug("params: %s", model.get_parameter_dict()) |
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289
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logging.debug("param names: %s", model.get_parameter_names()) |
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290
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logging.debug("param vector: %s", model.get_parameter_vector()) |
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logging.debug("param bounds: %s", model.get_parameter_bounds()) |
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#logging.debug("model value: %s", model.get_value(no_ys)) |
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#logging.debug("default log likelihood: %s", model.log_likelihood(model.vector)) |
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295
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# setup the Gaussian Process kernel |
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kernel_base = (1e7 * args.scale)**2 |
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ksub = args.name_suffix |
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299
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solver = "basic" |
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300
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skwargs = {} |
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301
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if args.HODLR_Solver: |
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solver = "HODLR" |
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303
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#skwargs = {"tol": 1e-3} |
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304
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305
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if args.george: |
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306
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gpname, kernel = setup_george_kernel(kernls, |
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307
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kernel_base=kernel_base, fit_bias=args.fit_bias) |
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308
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gpmodel = george.GP(kernel, mean=model, |
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309
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white_noise=1.e-25, fit_white_noise=args.fit_white, |
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310
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solver=george_solvers[solver], **skwargs) |
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311
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# the george interface does not allow setting the bounds in |
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312
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# the kernel initialization so we prepare simple default bounds |
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313
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kernel_bounds = [(-0.3 * max_amp, 0.3 * max_amp) |
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314
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for _ in gpmodel.kernel.get_parameter_names()] |
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315
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bounds = gpmodel.mean.get_parameter_bounds() + kernel_bounds |
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316
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else: |
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317
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gpname, cel_terms = setup_celerite_terms(kernls, |
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318
|
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fit_bias=args.fit_bias, fit_white=args.fit_white) |
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319
|
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gpmodel = celerite.GP(cel_terms, mean=model, |
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320
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fit_white_noise=args.fit_white, |
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321
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fit_mean=True) |
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322
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bounds = gpmodel.get_parameter_bounds() |
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323
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gpmodel.compute(no_ys_train, no_errs_train) |
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324
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logging.debug("gpmodel params: %s", gpmodel.get_parameter_dict()) |
|
325
|
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logging.debug("gpmodel bounds: %s", bounds) |
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326
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logging.debug("initial log likelihood: %s", gpmodel.log_likelihood(no_dens_train)) |
|
327
|
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if isinstance(gpmodel, celerite.GP): |
|
328
|
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|
logging.info("(GP) jitter: %s", gpmodel.kernel.jitter) |
|
329
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model_name = "_".join(gpmodel.mean.get_parameter_names()).replace(':', '') |
|
330
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|
gpmodel_name = model_name + gpname |
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331
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|
logging.info("GP model name: %s", gpmodel_name) |
|
332
|
|
|
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|
333
|
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|
pre_opt = False |
|
334
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if args.optimize > 0: |
|
335
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|
def gpmodel_mean(x, *p): |
|
336
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|
|
gpmodel.set_parameter_vector(p) |
|
337
|
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|
return gpmodel.mean.get_value(x) |
|
338
|
|
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|
|
339
|
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|
def gpmodel_res(x, *p): |
|
340
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|
gpmodel.set_parameter_vector(p) |
|
341
|
|
|
return (gpmodel.mean.get_value(x) - no_dens_train) / no_errs_train |
|
342
|
|
|
|
|
343
|
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|
def lpost(p, y, gp): |
|
344
|
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|
gp.set_parameter_vector(p) |
|
345
|
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|
return gp.log_likelihood(y, quiet=True) + gp.log_prior() |
|
346
|
|
|
|
|
347
|
|
|
def nlpost(p, y, gp): |
|
348
|
|
|
lp = lpost(p, y, gp) |
|
|
|
|
|
|
349
|
|
|
return -lp if np.isfinite(lp) else 1e25 |
|
350
|
|
|
|
|
351
|
|
|
def grad_nlpost(p, y, gp): |
|
352
|
|
|
gp.set_parameter_vector(p) |
|
353
|
|
|
grad_ll = gp.grad_log_likelihood(y) |
|
354
|
|
|
if isinstance(grad_ll, tuple): |
|
355
|
|
|
# celerite |
|
356
|
|
|
return -grad_ll[1] |
|
357
|
|
|
# george |
|
358
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|
return -grad_ll |
|
359
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|
|
360
|
|
|
if args.optimize == 1: |
|
361
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|
resop_gp = op.minimize( |
|
362
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|
|
nlpost, |
|
363
|
|
|
gpmodel.get_parameter_vector(), |
|
364
|
|
|
args=(no_dens_train, gpmodel), |
|
365
|
|
|
bounds=bounds, |
|
366
|
|
|
# method="l-bfgs-b", options=dict(disp=True, maxcor=100, eps=1e-9, ftol=2e-15, gtol=1e-8)) |
|
367
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|
|
method="l-bfgs-b", jac=grad_nlpost) |
|
368
|
|
|
# method="tnc", options=dict(disp=True, maxiter=500, xtol=1e-12)) |
|
369
|
|
|
# method="nelder-mead", options=dict(disp=True, maxfev=100000, fatol=1.49012e-8, xatol=1.49012e-8)) |
|
370
|
|
|
# method="Powell", options=dict(ftol=1.49012e-08, xtol=1.49012e-08)) |
|
371
|
|
|
if args.optimize == 2: |
|
372
|
|
|
resop_gp = op.differential_evolution( |
|
373
|
|
|
nlpost, |
|
374
|
|
|
bounds=bounds, |
|
375
|
|
|
args=(no_dens_train, gpmodel), |
|
376
|
|
|
popsize=2 * args.walkers, tol=0.01) |
|
377
|
|
|
if args.optimize == 3: |
|
378
|
|
|
resop_bh = op.basinhopping( |
|
379
|
|
|
nlpost, |
|
380
|
|
|
gpmodel.get_parameter_vector(), |
|
381
|
|
|
niter=200, |
|
382
|
|
|
minimizer_kwargs=dict( |
|
383
|
|
|
args=(no_dens_train, gpmodel), |
|
384
|
|
|
bounds=bounds, |
|
385
|
|
|
# method="tnc")) |
|
386
|
|
|
# method="l-bfgs-b", options=dict(maxcor=100))) |
|
387
|
|
|
method="l-bfgs-b", jac=grad_nlpost)) |
|
388
|
|
|
# method="Nelder-Mead")) |
|
389
|
|
|
# method="BFGS")) |
|
390
|
|
|
# method="Powell", options=dict(ftol=1.49012e-08, xtol=1.49012e-08))) |
|
391
|
|
|
logging.debug("optimization result: %s", resop_bh) |
|
392
|
|
|
resop_gp = resop_bh.lowest_optimization_result |
|
393
|
|
|
if args.optimize == 4: |
|
394
|
|
|
resop_gp, cov_gp = op.curve_fit( |
|
395
|
|
|
gpmodel_mean, |
|
396
|
|
|
no_ys_train, no_dens_train, gpmodel.get_parameter_vector(), |
|
397
|
|
|
bounds=tuple(np.array(bounds).T), |
|
398
|
|
|
# method='lm', |
|
399
|
|
|
# absolute_sigma=True, |
|
400
|
|
|
sigma=no_errs_train) |
|
401
|
|
|
print(resop_gp, np.sqrt(np.diag(cov_gp))) |
|
402
|
|
|
logging.info("%s", resop_gp.message) |
|
|
|
|
|
|
403
|
|
|
logging.debug("optimization result: %s", resop_gp) |
|
404
|
|
|
logging.info("gpmodel dict: %s", gpmodel.get_parameter_dict()) |
|
405
|
|
|
logging.info("log posterior trained: %s", lpost(gpmodel.get_parameter_vector(), no_dens_train, gpmodel)) |
|
406
|
|
|
gpmodel.compute(no_ys_test, no_errs_test) |
|
407
|
|
|
logging.info("log posterior test: %s", lpost(gpmodel.get_parameter_vector(), no_dens_test, gpmodel)) |
|
408
|
|
|
gpmodel.compute(no_ys, no_errs) |
|
409
|
|
|
logging.info("log posterior all: %s", lpost(gpmodel.get_parameter_vector(), no_dens, gpmodel)) |
|
410
|
|
|
# cross check to make sure that the gpmodel parameter vector is really |
|
411
|
|
|
# set to the fitted parameters |
|
412
|
|
|
logging.info("opt. model vector: %s", resop_gp.x) |
|
413
|
|
|
gpmodel.compute(no_ys_train, no_errs_train) |
|
414
|
|
|
logging.debug("opt. log posterior trained 1: %s", lpost(resop_gp.x, no_dens_train, gpmodel)) |
|
415
|
|
|
gpmodel.compute(no_ys_test, no_errs_test) |
|
416
|
|
|
logging.debug("opt. log posterior test 1: %s", lpost(resop_gp.x, no_dens_test, gpmodel)) |
|
417
|
|
|
gpmodel.compute(no_ys, no_errs) |
|
418
|
|
|
logging.debug("opt. log posterior all 1: %s", lpost(resop_gp.x, no_dens, gpmodel)) |
|
419
|
|
|
logging.debug("opt. model vector: %s", gpmodel.get_parameter_vector()) |
|
420
|
|
|
gpmodel.compute(no_ys_train, no_errs_train) |
|
421
|
|
|
logging.debug("opt. log posterior trained 2: %s", lpost(gpmodel.get_parameter_vector(), no_dens_train, gpmodel)) |
|
422
|
|
|
gpmodel.compute(no_ys_test, no_errs_test) |
|
423
|
|
|
logging.debug("opt. log posterior test 2: %s", lpost(gpmodel.get_parameter_vector(), no_dens_test, gpmodel)) |
|
424
|
|
|
gpmodel.compute(no_ys, no_errs) |
|
425
|
|
|
logging.debug("opt. log posterior all 2: %s", lpost(gpmodel.get_parameter_vector(), no_dens, gpmodel)) |
|
426
|
|
|
pre_opt = resop_gp.success |
|
427
|
|
|
try: |
|
428
|
|
|
logging.info("GM lt: %s", gpmodel.get_parameter("mean:GM:tau0")) |
|
429
|
|
|
except ValueError: |
|
430
|
|
|
pass |
|
431
|
|
|
logging.info("(GP) model: %s", gpmodel.kernel) |
|
432
|
|
|
if isinstance(gpmodel, celerite.GP): |
|
433
|
|
|
logging.info("(GP) jitter: %s", gpmodel.kernel.jitter) |
|
434
|
|
|
|
|
435
|
|
|
bestfit = gpmodel.get_parameter_vector() |
|
436
|
|
|
filename_base = ("NO_regress_fit_{0}_{1:.0f}_{2:.0f}_{{0}}_{3}" |
|
437
|
|
|
.format(gpmodel_name, lat * 10, alt, ksub)) |
|
438
|
|
|
|
|
439
|
|
|
if args.mcmc: |
|
440
|
|
|
gpmodel.compute(no_ys_train, no_errs_train) |
|
441
|
|
|
samples, lnp = mcmc_sample_model(gpmodel, |
|
442
|
|
|
no_dens_train, |
|
443
|
|
|
beta=1.0, |
|
444
|
|
|
nwalkers=args.walkers, nburnin=args.burn_in, |
|
445
|
|
|
nprod=args.production, nthreads=args.threads, |
|
446
|
|
|
show_progress=args.progress, |
|
447
|
|
|
optimized=pre_opt, bounds=bounds, return_logpost=True) |
|
448
|
|
|
|
|
449
|
|
|
if args.train_fraction < 1. or args.test_fraction < 1.: |
|
450
|
|
|
logging.info("Statistics for the test samples") |
|
451
|
|
|
mcmc_statistics(gpmodel, |
|
452
|
|
|
no_ys_test, no_dens_test, no_errs_test, |
|
453
|
|
|
no_ys_train, no_dens_train, no_errs_train, |
|
454
|
|
|
samples, lnp, |
|
455
|
|
|
) |
|
456
|
|
|
logging.info("Statistics for all samples") |
|
457
|
|
|
mcmc_statistics(gpmodel, |
|
458
|
|
|
no_ys, no_dens, no_errs, |
|
459
|
|
|
no_ys_train, no_dens_train, no_errs_train, |
|
460
|
|
|
samples, lnp, |
|
461
|
|
|
) |
|
462
|
|
|
|
|
463
|
|
|
sampl_percs = np.percentile(samples, [2.5, 50, 97.5], axis=0) |
|
464
|
|
|
if args.plot_corner: |
|
465
|
|
|
import corner |
|
466
|
|
|
# Corner plot of the sampled parameters |
|
467
|
|
|
fig = corner.corner(samples, |
|
468
|
|
|
quantiles=[0.025, 0.5, 0.975], |
|
469
|
|
|
show_titles=True, |
|
470
|
|
|
labels=gpmodel.get_parameter_names(), |
|
471
|
|
|
truths=bestfit, |
|
472
|
|
|
hist_args=dict(normed=True)) |
|
473
|
|
|
fig.savefig(filename_base.format("corner") + ".pdf", transparent=True) |
|
474
|
|
|
|
|
475
|
|
|
if args.save_samples: |
|
476
|
|
|
if args.samples_format in ["npz"]: |
|
477
|
|
|
# save the samples compressed to save space. |
|
478
|
|
|
np.savez_compressed(filename_base.format("sampls") + ".npz", |
|
479
|
|
|
samples=samples) |
|
480
|
|
|
if args.samples_format in ["nc", "netcdf4"]: |
|
481
|
|
|
save_samples_netcdf(filename_base.format("sampls") + ".nc", |
|
482
|
|
|
gpmodel, alt, lat, samples, scale=args.scale, compressed=True) |
|
483
|
|
|
if args.samples_format in ["h5", "hdf5"]: |
|
484
|
|
|
save_samples_netcdf(filename_base.format("sampls") + ".h5", |
|
485
|
|
|
gpmodel, alt, lat, samples, scale=args.scale, compressed=True) |
|
486
|
|
|
# MCMC finished here |
|
487
|
|
|
|
|
488
|
|
|
# set the model times and errors to use the full data set for plotting |
|
489
|
|
|
gpmodel.compute(no_ys, no_errs) |
|
490
|
|
|
if args.save_model: |
|
491
|
|
|
try: |
|
492
|
|
|
# python 2 |
|
493
|
|
|
import cPickle as pickle |
|
494
|
|
|
except ImportError: |
|
495
|
|
|
# python 3 |
|
496
|
|
|
import pickle |
|
497
|
|
|
# pickle and save the model |
|
498
|
|
|
with open(filename_base.format("model") + ".pkl", "wb") as f: |
|
499
|
|
|
pickle.dump((gpmodel), f, -1) |
|
500
|
|
|
|
|
501
|
|
|
if args.plot_samples and args.mcmc: |
|
502
|
|
|
plot_random_samples(gpmodel, no_ys, no_dens, no_errs, |
|
503
|
|
|
samples, args.scale, |
|
|
|
|
|
|
504
|
|
|
filename_base.format("sampls") + ".pdf", |
|
505
|
|
|
size=4, extra_years=[4, 2]) |
|
506
|
|
|
|
|
507
|
|
|
if args.plot_median: |
|
508
|
|
|
plot_single_sample_and_residuals(gpmodel, no_ys, no_dens, no_errs, |
|
509
|
|
|
sampl_percs[1], |
|
|
|
|
|
|
510
|
|
|
filename_base.format("median") + ".pdf") |
|
511
|
|
|
if args.plot_residuals: |
|
512
|
|
|
plot_residual(gpmodel, no_ys, no_dens, no_errs, |
|
513
|
|
|
sampl_percs[1], args.scale, |
|
514
|
|
|
filename_base.format("medres") + ".pdf") |
|
515
|
|
|
if args.plot_maxlnp: |
|
516
|
|
|
plot_single_sample_and_residuals(gpmodel, no_ys, no_dens, no_errs, |
|
517
|
|
|
samples[np.argmax(lnp)], |
|
|
|
|
|
|
518
|
|
|
filename_base.format("maxlnp") + ".pdf") |
|
519
|
|
|
if args.plot_maxlnpres: |
|
520
|
|
|
plot_residual(gpmodel, no_ys, no_dens, no_errs, |
|
521
|
|
|
samples[np.argmax(lnp)], args.scale, |
|
522
|
|
|
filename_base.format("mlpres") + ".pdf") |
|
523
|
|
|
|
|
524
|
|
|
labels = gpmodel.get_parameter_names() |
|
525
|
|
|
logging.info("param percentiles [2.5, 50, 97.5]:") |
|
526
|
|
|
for pc, label in zip(sampl_percs.T, labels): |
|
527
|
|
|
median = pc[1] |
|
528
|
|
|
pc_minus = median - pc[0] |
|
529
|
|
|
pc_plus = pc[2] - median |
|
530
|
|
|
logging.debug("%s: %s", label, pc) |
|
531
|
|
|
logging.info("%s: %.6f (- %.6f) (+ %.6f)", label, |
|
532
|
|
|
median, pc_minus, pc_plus) |
|
533
|
|
|
|
|
534
|
|
|
logging.info("Finished successfully.") |
|
535
|
|
|
|
|
536
|
|
|
|
|
537
|
1 |
|
if __name__ == "__main__": |
|
538
|
|
|
main() |
|
539
|
|
|
|
st-bender /
sciapy
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